80d4660db0d1b8b5310c2a50b0b3935a16934719This paper analyses the impact of the COVID-19 pandemic crisis on professional regulatory change in two Canadian provinces, drawing on ecological theory. The dataset, constructed using web-scraping techniques, includes all laws and by-law modifications concerning regulated professions enacted during the first 18 months of the pandemic in Quebec and British Columbia. Data show that the crisis prompted regulatory changes but that the impact and nature of these changes varied depending on the structure of the ecology of professional regulation in each province. Furthermore, crisis-related concerns were more likely to induce or accelerate durable changes if they intersected with pre-existing, ongoing professional projects. Our findings have implications for theorizing crisis-related regulatory change and demonstrate the value of a comparative approach to studying professional ecologies and state-profession interfaces.  Professions and Professionalism270Professions and Professionalism["Julien Prud'homme", 'Tracey L. Adams', 'Jean-Luc Bédard']2024-05-20T00:00:00https://www.semanticscholar.org/paper/80d4660db0d1b8b5310c2a50b0b3935a16934719 84b890e02c0dc428ced9882b8b637adc68da54fcThis article analyzes Internet intermediaries’ regulation in the Latin American Governance Forums between 2018-2021 with the approach of public policy in communications. The topics, actors and their position on human rights and freedom of expression are examined based on the review of videos and reports of the forums. The article concludes that the human rights approach shifts from freedom of expression to privacy. The paper aims to make a contribution to the understanding of the emerging debates on Internet regulation in the region.Comunicación y Sociedad00Comunicación y Sociedad['A. Bizberge', 'Guillermo Mastrini']2024-05-20T00:00:00https://www.semanticscholar.org/paper/84b890e02c0dc428ced9882b8b637adc68da54fc e68a15b356c3ae91607cfa417f0c5be85716b918Open source developers have emerged as key actors in the political economy of artificial intelligence (AI), with open model development being recognised as an alternative to closed-source AI development. However, we still have a limited understanding of collaborative practices in open source AI. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. First, we find that various types of activity across 348,181 model, 65,761 dataset, and 156,642 space repositories exhibit right-skewed distributions. Activity is extremely imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. Second, we analyse a snapshot of the social network structure of collaboration on models, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing isolates, collaboration is characterised by high reciprocity regardless of developers' network positions. Third, we examine model adoption through the lens of model usage in spaces, finding that a minority of models, developed by a handful of companies, are widely used on the HF Hub. Overall, we find that various types of activity on the HF Hub are characterised by Pareto distributions, congruent with prior observations about OSS development patterns on platforms like GitHub. We conclude with a discussion of the implications of the findings and recommendations for (open source) AI researchers, developers, and policymakers.12117A three-part quantitative analysis of development activity on the Hugging Face (HF) Hub finds that various types of activity on the HF Hub are characterised by Pareto distributions, congruent with prior observations about OSS development patterns on platforms like GitHub.['Cailean Osborne', 'Jennifer Ding', 'Hannah Rose Kirk']2024-05-20T00:00:00https://www.semanticscholar.org/paper/e68a15b356c3ae91607cfa417f0c5be85716b918 b0c530df4c93d4c7ac54cb17ae8bf266d7306639Human trafficking thrives in the shadows, and the rise of social media has provided traffickers with a powerful and unregulated tool. This paper delves into how these criminals exploit online platforms to target and manipulate vulnerable populations. A thematic analysis of existing research explores the tactics used by traffickers on social media, revealing how algorithms can be manipulated to facilitate exploitation. Furthermore, the paper examines the limitations of current regulations in tackling this online threat. The research underscores the urgent need for collaboration between governments and researchers to combat algorithmic exploitation. By harnessing data analysis and machine learning, proactive strategies can be developed to disrupt trafficking networks and protect those most at risk.Laws240The research underscores the urgent need for collaboration between governments and researchers to combat algorithmic exploitation and explores the tactics used by traffickers on social media, revealing how algorithms can be manipulated to facilitate exploitation.Laws['Derek M. Moore']2024-05-20T00:00:00https://www.semanticscholar.org/paper/b0c530df4c93d4c7ac54cb17ae8bf266d7306639 0e86fd68050eab7b1a2d2726b8dfcb3ea6d18f34REC: interventional cardiology (English Edition)00REC: interventional cardiology (English Edition)['Gloria Hernández Hernández']2024-05-20T00:00:00https://www.semanticscholar.org/paper/0e86fd68050eab7b1a2d2726b8dfcb3ea6d18f34 9cd9170c983bdd120ec3b6e80e8fce22bf7eb07bLarge Language Models (LLMs) lack robust metacognitive learning abilities and depend on human-provided algorithms and prompts for learning and output generation. Metacognition involves processes that monitor and enhance cognition. Learning how to learn - metacognitive learning - is crucial for adapting and optimizing learning strategies over time. Although LLMs possess limited metacognitive abilities, they cannot autonomously refine or optimize these strategies. Humans possess innate mechanisms for metacognitive learning that enable at least two unique abilities: discerning which metacognitive strategies are best and automatizing learning strategies. These processes have been effectively modeled in the ACT-R cognitive architecture, providing insights on a path toward greater learning autonomy in AI. Incorporating human-like metacognitive learning abilities into AI could potentially lead to the development of more autonomous and versatile learning mechanisms, as well as improved problem-solving capabilities and performance across diverse tasks.Proceedings of the AAAI Symposium Series31Large Language Models lack robust metacognitive learning abilities and depend on human-provided algorithms and prompts for learning and output generation, which could potentially lead to the development of more autonomous and versatile learning mechanisms, as well as improved problem-solving capabilities and performance across diverse tasks.Proceedings of the AAAI Symposium Series['Brendan Conway-Smith', 'Robert L. West']2024-05-20T00:00:00https://www.semanticscholar.org/paper/9cd9170c983bdd120ec3b6e80e8fce22bf7eb07b 0997938daa47c779bd328c21df1f5e2fc285824dArtificial Intelligence (AI) has become pervasive in modern lives, with AI generative tools driving further transformation. However, a notable issue persists: the underrepresentation of females and individuals from ethnic and racial minorities in the tech industry. Despite generally positive attitudes toward technology among young students, this enthusiasm often does not extend to aspirations for careers in the field. To address this disparity, many schools in the United States are now offering computer science and AI courses at the high school level. Nevertheless, students from underrepresented groups often feel disconnected from these subjects, leading to low enrollment rates. Research underscores that students' career aspirations are solidified between the ages of 10-14 yrs, highlighting the importance of engaging them with computer science and computing skills during this formative period. Leveraging the Bourdieusian concept of social capital, this paper proposes educational interventions tailored for elementary schools. By nurturing students' technical social capital, these interventions aim to foster an inclusive ecosystem from an early age, when aspirations are taking shape. Ultimately, the goal is to enhance the accessibility of computer science education and related skills, empowering young students from underrepresented groups to pursue higher studies and careers in computer science and AI fields.Proceedings of the AAAI Symposium Series410Proceedings of the AAAI Symposium Series['Swati Mehrotra', 'Neelu Sinha']2024-05-20T00:00:00https://www.semanticscholar.org/paper/0997938daa47c779bd328c21df1f5e2fc285824d fd86c2ba4e22d902c0187ea3913809b7e5b0f17fA rapidly developing threat to societal well-being is from misinformation widely spread on social media. Even more concerning is ”mal-info” (malicious) which is amplified on certain social networks. Now there is an additional dimension to that threat, which is the use of Generative AI to deliberately augment the mis-info and mal-info. This paper highlights some of the ”fringe” social media channels which have a high level of mal-info as characterized by our AI/ML algorithms. We discuss various channels and focus on one in particular, ”GAB”, as representative of the potential negative impacts. We outline some of the current mal-info as an example. We capture elements, and observe the trends in time. We provide a set of AI/ML modes which can characterize the mal-info and allow for capture, tracking, and potentially for responding or for mitigation. We highlight the concern about malicious agents using GenAI for deliberate mal-info messaging specifically to disrupt societal well being. We suggest the characterizations presented as a methodology for initiating a more deliberate and quantitative approach to address these harmful aspects of social media which would adversely impact societal well being. The article highlights the potential for ”mal-info,” including disinfo, cyberbullying, and hate speech, to disrupt segments of society. The amplification of mal-info can result in serious real-world consequences such as mass shootings. Despite attempts to introduce moderation on major platforms like Facebook and to some extent on X/Twitter, there are now growing social networks such as Gab, Gettr, and Bitchute that offer completely unmoderated spaces. This paper presents an introduction to these platforms and the initial results of a semiquantitative analysis of Gab’s posts. The paper examines several characterization modes using text analysis. The paper emphasizes the developing dangerous use of generative AI algorithms by Gab and other fringe platforms, highlighting the risks to societal well being. This article aims to lay the foundation for capturing, monitoring, and mitigating these risks.Proceedings of the AAAI Symposium Series80The paper presents an introduction to these platforms and the initial results of a semiquantitative analysis of Gab’s posts, and provides a set of AI/ML modes which can characterize the mal-info and allow for capture, tracking, and potentially for responding or for mitigation.Proceedings of the AAAI Symposium Series['Andy Skumanich', 'Han Kyul Kim']2024-05-20T00:00:00https://www.semanticscholar.org/paper/fd86c2ba4e22d902c0187ea3913809b7e5b0f17f 18ca9f9bb073a09e4a61fbd77cfb3e7b78d8cb7cWe explore the foresighted concerns that Norbert Wiener voiced in 1960 about the potential of machines to learn and create strategies that could not be anticipated, drawing parallels to the fable "The Sorcerer's Apprentice" by Goethe. The progress in artificial intelligence (AI) has brought these worries back to the forefront, as shown by a survey AI Impacts conducted in 2022 with more than 700 machine learning researchers. This survey found a five percentage probability that advanced AI might cause "extremely adverse" outcomes, including the possibility of human extinction. Importantly, the introduction of OpenAI's ChatGPT, powered by GPT-4, has led to a surge in entrepreneurial activities, highlighting the ease of use of large language models (LLMs).AI's potential for adverse outcomes, such as military control and unregulated AI races, is explored alongside concerns about AI's role in governance, healthcare, media portrayal, and surpassing human intelligence. Given their transformative impact on content creation, the prominence of generative AI tools such as ChatGPT is noted. The societal assessment of Artificial Intelligence (AI) has grown increasingly intricate and pressing in tandem with the rapid evolution of this technology. As AI continues to advance at a swift pace, the need to comprehensively evaluate its societal implications has become more complex and urgent, necessitating a thorough examination of its potential impact on various domains such as governance, healthcare, media portrayal, and surpassing human intelligence. This assessment is crucial in addressing ethical concerns related to bias, data misuse, technical limitations, and transparency gaps, and in integrating ethical and legal principles throughout AI algorithm lifecycles to ensure alignment with societal well-being. Furthermore, the urgency of addressing the societal implications of AI is underscored by the need for healthcare workforce upskilling and ethical considerations in the era of AI-assisted medicine, emphasizing the critical importance of integrating societal well-being into the development and deployment of AI technologies. Our study entails an examination of the ethical quandaries and obstacles presented when developing methods to evaluate and predict the broader societal impacts of AI on decision-making processes involving the generating of images, videos, and textual content.Proceedings of the AAAI Symposium Series400This study entails an examination of the ethical quandaries and obstacles presented when developing methods to evaluate and predict the broader societal impacts of AI on decision-making processes involving the generating of images, videos, and textual content.Proceedings of the AAAI Symposium Series['Yohn Jairo Parra Bautista', 'Carlos Theran', 'Richard A. Aló']2024-05-20T00:00:00https://www.semanticscholar.org/paper/18ca9f9bb073a09e4a61fbd77cfb3e7b78d8cb7c d3b1fd3348f040814effaada60c1b6761ef0170fIntegrating generative AI (GAI) into higher education is crucial for preparing a future generation of GAI-literate students. Yet a thorough understanding of the global institutional adoption policy remains absent, with most of the prior studies focused on the Global North and the promises and challenges of GAI, lacking a theoretical lens. This study utilizes the Diffusion of Innovations Theory to examine GAI adoption strategies in higher education across 40 universities from six global regions. It explores the characteristics of GAI innovation, including compatibility, trialability, and observability, and analyses the communication channels and roles and responsibilities outlined in university policies and guidelines. The findings reveal a proactive approach by universities towards GAI integration, emphasizing academic integrity, teaching and learning enhancement, and equity. Despite a cautious yet optimistic stance, a comprehensive policy framework is needed to evaluate the impacts of GAI integration and establish effective communication strategies that foster broader stakeholder engagement. The study highlights the importance of clear roles and responsibilities among faculty, students, and administrators for successful GAI integration, supporting a collaborative model for navigating the complexities of GAI in education. This study contributes insights for policymakers in crafting detailed strategies for its integration.330The study highlights the importance of clear roles and responsibilities among faculty, students, and administrators for successful GAI integration, supporting a collaborative model for navigating the complexities of GAI in education and contributes insights for policymakers in crafting detailed strategies for its integration.['Yueqiao Jin', 'Lixiang Yan', 'Vanessa Echeverría', "Dragan Gavsevi'c", 'Roberto Martínez-Maldonado']2024-05-20T00:00:00https://www.semanticscholar.org/paper/d3b1fd3348f040814effaada60c1b6761ef0170f 135d8f70761c24ce63787c5ebaf8fd05dbb7d32eWhile students are often passionate about their chosen fields, they often have limited awareness of the profound impact of AI technologies on their professions. In order to advance efforts in building subject-relevant AI literacy among undergraduate students studying Computer Science and non-Computer Science (Criminal Justice and Forensic Science) it is imperative to engage in rigorous efforts to develop and study curricular infusion of Artificial Intelligence topics. Using a Design-Based Research model, the project team and the external evaluators studied the first iteration of the module development and implementation. Using data collected through surveys, focus groups, critical review, and reflection exercises the external evaluation team produced findings that informed the project team in revising and improving their materials and approach for the second iteration. These efforts can help educators and the AI module developers tailor their AI curriculum to address these specific areas, ensuring that students develop a more accurate understanding of applications of AI in their future career field.Proceedings of the AAAI Symposium Series10Using a Design-Based Research model, the project team and the external evaluators studied the first iteration of the module development and implementation and produced findings that informed the project team in revising and improving their materials and approach for the second iteration.Proceedings of the AAAI Symposium Series['Sambit Bhattacharya', 'Bogdan Czejdo', 'Rebecca Zulli', 'Adrienne A. Smith']2024-05-20T00:00:00https://www.semanticscholar.org/paper/135d8f70761c24ce63787c5ebaf8fd05dbb7d32e 0d5c20b98a39a72de003cd8c7ce132d2aa7d14a2Machine learning and artificial intelligence (AI/ML) models in healthcare may exacerbate health biases. Regulatory oversight is critical in evaluating the safety and effectiveness of AI/ML devices in clinical settings. We conducted a scoping review on the 692 FDA 510k-approved AI/ML-enabled medical devices to examine transparency, safety reporting, and sociodemographic representation. Only 3.6% of approvals reported race/ethnicity, 99.1% provided no socioeconomic data. 81.6% did not report the age of study subjects. Only 46.1% provided comprehensive detailed results of performance studies; only 1.9% included a link to a scientific publication with safety and efficacy data. Only 9.0% contained a prospective study for post-market surveillance. Despite the growing number of market-approved medical devices, our data shows that FDA reporting data remains inconsistent. Demographic and socioeconomic characteristics are underreported, exacerbating the risk of algorithmic bias and health disparity.medRxiv470A scoping review on the 692 FDA 510k-approved AI/ML-enabled medical devices to examine transparency, safety reporting, and sociodemographic representation shows that FDA reporting data remains inconsistent.['V. Muralidharan', 'Boluwatife Adeleye', 'Caroline J Huang', 'Mfon Thelma Nta', 'Peter Oluwaduyilemi Ademiju', 'Pirunthan Pathmarajah', 'Man Kien Hang', 'O. Adesanya', 'R. O. Abdullateef', 'A. Babatunde', 'Abdulquddus Ajibade', 'Sonia Onyeka', 'Zhou Ran Cai', 'Roxana Daneshjou', 'Tobi Olatunji']2024-05-20T00:00:00https://www.semanticscholar.org/paper/0d5c20b98a39a72de003cd8c7ce132d2aa7d14a2 99fcee5d06cba3a3104152c30287e128e7ed9322Health Agents are introduced as the concept of a personalized AI health advisor overlay for continuous health monitoring (e.g. 1000x/minute) medical-grade smartwatches and wearables for “healthcare by app” instead of “sickcare by appointment.” Individuals can customize the level of detail in the information they view. Health Agents “speak” natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their reinforcement learning agent behavior. As an AI health interface, the agent facilitates the management of precision medicine as a service. Healthy longevity is a high-profile area characterized by the increasing acceptance of medical intervention, longevity biotech venture capital investment, and global priority as 2 billion people will be over 65 in 2050. Aging hallmarks, biomarkers, and clocks provide a quantitative measure for intervention. Some of the leading interventions include metformin, rapamycin, spermidine, NAD+/sirtuins, alpha-ketoglutarate, and taurine. AI-driven digital biology, longevity medicine, and Web3 personalized healthcare come together in the idea of Health Agents. This Web3 genAI tool for automated health management, specifically via digital-biological twins and pathway2vec approaches, demonstrates human-AI intelligence amplification and works towards healthy longevity for global well-being.Proceedings of the AAAI Symposium Series00This Web3 genAI tool for automated health management, specifically via digital-biological twins and pathway2vec approaches, demonstrates human-AI intelligence amplification and works towards healthy longevity for global well-being.Proceedings of the AAAI Symposium Series['Melanie Swan', 'Takashi Kido', 'Eric Roland', 'Renato P. Dos Santos']2024-05-20T00:00:00https://www.semanticscholar.org/paper/99fcee5d06cba3a3104152c30287e128e7ed9322 327764eacf5d7f8402411303ad0632485e3883abArtificial intelligence (AI) technologies have been increasingly integrated into human workflows. For example, the usage of AI-based decision aids in human decision-making processes has resulted in a new paradigm of AI-assisted decision making---that is, the AI-based decision aid provides a decision recommendation to the human decision makers, while humans make the final decision. The increasing prevalence of human-AI collaborative decision making highlights the need to understand how humans engage with the AI-based decision aid in these decision-making processes, and how to promote the effectiveness of the human-AI team in decision making. In this talk, I'll discuss a few examples illustrating that when AI is used to assist humans---both an individual decision maker or a group of decision makers---in decision making, people's engagement with the AI assistance is largely subject to their heuristics and biases, rather than careful deliberation of the respective strengths and limitations of AI and themselves. I'll then describe how to enhance AI-assisted decision making by accounting for human engagement behavior in the designs of AI-based decision aids. For example, AI recommendations can be presented to decision makers in a way that promotes their appropriate trust and reliance on AI by leveraging or mitigating human biases, informed by the analysis of human competence in decision making. Alternatively, AI-assisted decision making can be improved by developing AI models that can anticipate and adapt to the engagement behavior of human decision makers.Proceedings of the AAAI Symposium Series110This talk will discuss a few examples illustrating that when AI is used to assist humans---both an individual decision maker or a group of decision makers---in decision making, people's engagement with the AI assistance is largely subject to their heuristics and biases, rather than careful deliberation of the respective strengths and limitations of AI and themselves.Proceedings of the AAAI Symposium Series['Ming Yin']2024-05-20T00:00:00https://www.semanticscholar.org/paper/327764eacf5d7f8402411303ad0632485e3883ab ac2bc54bb4654f7a179c6ec83fb868c37cbca224In this ongoing narrative review, I summarize the existing body of literature on the role of artificial intelligence in mediating human communication, focusing on how it is currently transforming our communication patterns. Moreover, this re-view uniquely contributes by critically analyzing potential future shifts in these patterns, particularly in light of the advancing capabilities of artificial intelligence. Special emphasis is placed on the implications of emerging generative AI technologies, projecting how they might redefine the landscape of human interaction.Proceedings of the AAAI Symposium Series60This re-view uniquely contributes by critically analyzing potential future shifts in these patterns, particularly in light of the advancing capabilities of artificial intelligence.Proceedings of the AAAI Symposium Series['Kevin Vo']2024-05-20T00:00:00https://www.semanticscholar.org/paper/ac2bc54bb4654f7a179c6ec83fb868c37cbca224 f9c90115ba8e28c9d21fc76dcfa5473c7ecc534ePurpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.International Journal of Management, Technology, and Social Sciences450The paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies.International Journal of Management, Technology, and Social Sciences['Swathi Kumari H.', 'K. T. Veeramanju']2024-05-20T00:00:00https://www.semanticscholar.org/paper/f9c90115ba8e28c9d21fc76dcfa5473c7ecc534e 6dfe5d2776030b3dbf1053ef35619eb41629b94dThe transformative potential of AI presents remarkable opportunities, but also significant risks, underscoring the importance of responsible AI development and deployment. Despite a growing emphasis on this area, there is limited understanding of industry's engagement in responsible AI research, i.e., the critical examination of AI's ethical, social, and legal dimensions. To address this gap, we analyzed over 6 million peer-reviewed articles and 32 million patent citations using multiple methods across five distinct datasets to quantify industry's engagement. Our findings reveal that the majority of AI firms show limited or no engagement in this critical subfield of AI. We show a stark disparity between industry's dominant presence in conventional AI research and its limited engagement in responsible AI. Leading AI firms exhibit significantly lower output in responsible AI research compared to their conventional AI research and the contributions of leading academic institutions. Our linguistic analysis documents a narrower scope of responsible AI research within industry, with a lack of diversity in key topics addressed. Our large-scale patent citation analysis uncovers a pronounced disconnect between responsible AI research and the commercialization of AI technologies, suggesting that industry patents rarely build upon insights generated by the responsible AI literature. This gap highlights the potential for AI development to diverge from a socially optimal path, risking unintended consequences due to insufficient consideration of ethical and societal implications. Our results highlight the urgent need for industry to publicly engage in responsible AI research to absorb academic knowledge, cultivate public trust, and proactively mitigate AI-induced societal harms.00A large-scale patent citation analysis uncovers a pronounced disconnect between responsible AI research and the commercialization of AI technologies, suggesting that industry patents rarely build upon insights generated by the responsible AI literature.['Nur Ahmed', 'Amit Das', 'Kirsten Martin', 'Kawshik Banerjee']2024-05-20T00:00:00https://www.semanticscholar.org/paper/6dfe5d2776030b3dbf1053ef35619eb41629b94d 951027fc8848a7d585125a658b1ac0ac89d9db01International Journal for Educational Integrity310It is demonstrated that specific detectors and experienced reviewers can accurately identify articles generated by Large Language Models, even after paraphrasing, and may be incorporated as an additional screening tool in the peer-review process of academic journals.International Journal for Educational Integrity['Jae Q. J. Liu', 'Kelvin T. K. Hui', 'Fadi Al Zoubi', 'Zing Z. X. Zhou', 'Dino Samartzis', 'Curtis C. H. Yu', 'J. R. Chang', 'Arnold Y. L. Wong']2024-05-20T00:00:00https://www.semanticscholar.org/paper/951027fc8848a7d585125a658b1ac0ac89d9db01 8f72ddc05c855e2e758b9b0226988d687721d98eAs a first step towards realizing an AI sleep counselor capable of generating personalized advice, this paper proposes a method for monitoring daily sleep conditions with a mattress sensor. To improve the accuracy of sleep stage estimation and to get accurate sleep structure, this paper introduced sleep domain knowledge to machine learning for improving the accuracy of sleep stage estimation. Concretely, the proposed method estimates ultradian rhythm based on the body movement density, updates prediction probabilities of each sleep stage by ML model and applies WAKE/NR3 detection based on the large/small body movement. Through the human subject experiment, the following implications have been revealed: (1) the proposed method improved the percentage of Accuracy by 65.0% from 61.5% and the QWK score by 0.196 from 0.297 by the conventional machine learning method; (2) the proposed method prevents over-NR12 estimating and is useful for understanding sleep structure by estimating REM sleep and NR3 sleep correctly. (3) the correct estimation of ultradian rhythms significantly improved the sleep stage estimation, with an Accuracy of 77.6% and a QWK score of 0.52 when all subjects' ultradian rhythms were estimated correctly.Proceedings of the AAAI Symposium Series00A method for monitoring daily sleep conditions with a mattress sensor that prevents over-NR12 estimating and is useful for understanding sleep structure by estimating REM sleep and NR3 sleep correctly is proposed.Proceedings of the AAAI Symposium Series['Iko Nakari', 'K. Takadama']2024-05-20T00:00:00https://www.semanticscholar.org/paper/8f72ddc05c855e2e758b9b0226988d687721d98e 69c9bf0ec29877c84a7909212cbbc5b16780cdbaThis research aims to explore consumer attitudes toward the incorporation of Artificial Intelligence (AI) in physical retail settings, specifically examining how prior AI experiences, perceived risks, consumer self-efficacy in AI usage, and gender differences influence their readiness to embrace AI technologies in retail environments. Employing a quantitative cross-sectional survey methodology, the study gathered data from 243 consumers knowledgeable about AI who have engaged in shopping activities within physical stores over the past year. Through descriptive statistics, Pearson's correlation, and t-tests, the analysis reveals a direct positive correlation between consumers' previous AI interactions and their openness to AI in retail. Conversely, perceived risks are found to affect their willingness to engage with AI technologies negatively. The research is geographically limited to Slovenia, which may restrict the applicability of its findings to other contexts. The study emphasizes the potential for increasing consumer acceptance of AI in retail through the introduction of strategic technology and the emphasis on security features. Contributing original insights into the dynamics of consumer perceptions of AI within the physical retail sector, this work offers valuable implications for retailers aiming to optimize AI integration strategies to mitigate consumer apprehensions and accommodate diverse demographic preferences.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development170Analysis of consumer attitudes toward the incorporation of Artificial Intelligence in physical retail settings reveals a direct positive correlation between consumers' previous AI interactions and their openness to AI in retail, whereas perceived risks are found to affect their willingness to engage with AI technologies negatively.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development['Nina Kolar']2024-05-20T00:00:00https://www.semanticscholar.org/paper/69c9bf0ec29877c84a7909212cbbc5b16780cdba cb92eb7dbd0da11fb12f7e2b13dfa9f9af0d2f0eEuropean Radiology Experimental310The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema, while FPs/scan for HR were higher than AI for 30–100 mm3 nodules in non-emphysema, a difference not observed for HR.European Radiology Experimental['N. Sourlos', 'G. Pelgrim', 'H. J. Wisselink', 'Xiaofei Yang', 'G. D. de Jonge', 'M. Rook', 'Mathias Prokop', 'G. Sidorenkov', 'Marcel van Tuinen', 'R. Vliegenthart', 'Peter M A van Ooijen']2024-05-20T00:00:00https://www.semanticscholar.org/paper/cb92eb7dbd0da11fb12f7e2b13dfa9f9af0d2f0e 1ad9c76927c07591c9fe8d33f7361f31e3694dbaThe majority of current research on the application of artificial intelligence (AI) and machine learning (ML) in science, technology, engineering, and mathematics (STEM) education relies on centralized model training architectures. Typically, this involves pooling data at a centralized location alongside an ML model training module, such as a cloud server. However, this approach necessitates transferring student data across the network, leading to privacy concerns. In this paper, we explore the application of federated learning (FL), a highly recognized distributed ML technique, within the educational ecosystem. We highlight the potential benefits FL offers to students, classrooms, and institutions. Also, we identify a range of technical, logistical, and ethical challenges that impede the sustainable implementation of FL in the education sector. Finally, we discuss a series of open research directions, focusing on nuanced aspects of FL implementation in educational contexts. These directions aim to explore and address the complexities of applying FL in varied educational settings, ensuring its deployment is technologically sound, beneficial, and equitable for all stakeholders involved.Proceedings of the AAAI Symposium Series00This paper explores the application of federated learning (FL), a highly recognized distributed ML technique, within the educational ecosystem, and identifies a range of technical, logistical, and ethical challenges that impede the sustainable implementation of FL in the education sector.Proceedings of the AAAI Symposium Series['Anurata Prabha Hridi', 'R. Sahay', 'Seyyedali Hosseinalipour', 'Bita Akram']2024-05-20T00:00:00https://www.semanticscholar.org/paper/1ad9c76927c07591c9fe8d33f7361f31e3694dba e2ab4b0a920913b4f8b37df654a8dd0ebd237fd0In this paper, the main aim concerns the impact of AI as a driving force behind sustainable business strategies analysed after the 2008 financial crisis. Although there are some existing empirical studies regarding the impact of AI on the sustainability and growth of enterprises during financial crises, there is still room for further research and empirical contributions. Thus, by using a systematic review of literature and appropriate case studies, this research seeks to analyse how AI-driven approaches have been incorporated into operations for improved sustainability and efficiency in the post-crisis era. Methodologically, this study adopts a mixed method approach whereby case studies’ qualitative analyses and quantitative data evaluation stand alongside one another to grasp the big picture of AI’s effect. Finally, the results show that AI has provided sufficient support to sustainable business, which promotes economic revival and the formation of resistant future-focussed strategies. Thus, this paper provides contribution towards understanding technology-sustainability linkage with economic recovery for policymakers, practitioners, and researchers.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development60The results show that AI has provided sufficient support to sustainable business, which promotes economic revival and the formation of resistant future-focussed strategies in the post-crisis era.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development['Ahmet Lokce', 'Liza Alili Sulejmani']2024-05-20T00:00:00https://www.semanticscholar.org/paper/e2ab4b0a920913b4f8b37df654a8dd0ebd237fd0 e8ec8ab34a6a40f4138a17c282f3846d260aa756The field of Artificial Intelligence (AI) is rapidly advancing, with significant potential to transform society. However, it faces a notable challenge: lack of diversity, a longstanding issue in STEM fields. In this context, this position paper examines the intersection of AI and identity as a pathway to understanding biases, inequalities, and ethical considerations in AI development and deployment. We present a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts. Understanding AI's identity involves analyzing the diverse individuals involved in AI's development, the technologies produced, and the social, ethical, and psychological implications. After exploring the AI identity ecosystem and its societal dynamics, We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity. This paper presents a research framework for examining the implications and changes needed to foster a more inclusive and responsible AI ecosystem through the lens of identity.Proceedings of the AAAI Symposium Series410A framework is proposed that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity, and presents a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts.Proceedings of the AAAI Symposium Series['Sri Yash Tadimalla', 'Mary Lou Maher']2024-05-20T00:00:00https://www.semanticscholar.org/paper/e8ec8ab34a6a40f4138a17c282f3846d260aa756 77d364e405372f628cf96b6895717d95b730e810In schools, teachers play a multitude of roles, serving as educators, counselors, decision-makers, and members of the school community. With recent advances in artificial intelligence (AI), there is increasing discussion about how AI can assist, complement, and collaborate with teachers. To pave the way for better teacher-AI complementary relationships in schools, our study aims to expand the discourse on teacher-AI complementarity by seeking educators' perspectives on the potential strengths and limitations of AI across a spectrum of responsibilities. Through a mixed method using a survey with 100 elementary school teachers in South Korea and in-depth interviews with 12 teachers, our findings indicate that teachers anticipate AI's potential to complement human teachers by automating administrative tasks and enhancing personalized learning through advanced intelligence. Interestingly, the deficit of AI's socio-emotional capabilities has been perceived as both challenges and opportunities. Overall, our study demonstrates the nuanced perception of teachers and different levels of expectations over their roles, challenging the need for decisions about AI adoption tailored to educators' preferences and concerns.100Overall, the study demonstrates the nuanced perception of teachers and different levels of expectations over their roles, challenging the need for decisions about AI adoption tailored to educators' preferences and concerns.['Soon-young Oh', 'Yongsu Ahn']2024-05-20T00:00:00https://www.semanticscholar.org/paper/77d364e405372f628cf96b6895717d95b730e810 571d416b612f2814e38103b4d7160aa3c86dc684The integration of artificial intelligence (AI) into military capabilities has become a norm for major military power across the globe. Understanding how these AI models operate is essential for maintaining strategic advantages and ensuring security. This paper demonstrates an open-source methodology for analyzing military AI models through a detailed examination of the Zhousidun dataset, a Chinese-originated dataset that exhaustively labels critical components on American and Allied destroyers. By demonstrating the replication of a state-of-the-art computer vision model on this dataset, we illustrate how open-source tools can be leveraged to assess and understand key military AI capabilities. This methodology offers a robust framework for evaluating the performance and potential of AI-enabled military capabilities, thus enhancing the accuracy and reliability of strategic assessments.890An open-source methodology for analyzing military AI models through a detailed examination of the Zhousidun dataset, a Chinese-originated dataset that exhaustively labels critical components on American and Allied destroyers.['Ritwik Gupta', 'Leah Walker', 'Eli Glickman', 'Raine Koizumi', 'Sarthak Bhatnagar', 'A. Reddie']2024-05-20T00:00:00https://www.semanticscholar.org/paper/571d416b612f2814e38103b4d7160aa3c86dc684 8afaf886464369e08bd8d79c3d490f6dca09021cAdvances in AI have led to new types of technical debt in software engineering projects. AI-based competition platforms face challenges due to rapid prototyping and a lack of adherence to software engineering principles by participants, resulting in technical debt. Additionally, organizers often lack methods to evaluate platform quality, impacting sustainability and maintainability. In this research, we identify and categorize types of technical debt in AI systems through a scoping review. We develop a questionnaire for assessing technical debt in AI competition platforms, categorizing debt into various types, such as algorithm, architectural, code, configuration, data etc. We introduce Accessibility Debt, specific to AI competition platforms, highlighting challenges participants face due to inadequate platform usability. Our framework for managing technical debt aims to improve the sustainability and effectiveness of these platforms, providing tools for researchers, organizers, and participants.480A questionnaire for assessing technical debt in AI competition platforms is developed, categorizing debt into various types, such as algorithm, architectural, code, configuration, data etc, and a framework for managing technical debt is introduced.['Dionysios Sklavenitis', 'Dimitris Kalles']2024-05-20T00:00:00https://www.semanticscholar.org/paper/8afaf886464369e08bd8d79c3d490f6dca09021c 0e70205930de660a2552cf0bb0a4d9b49a062a0aWith generative AI disrupting human monopoly of creativity, there is an urgent need to freshly rearticulate cultural labour as a marker of human creativity. I suggest we critically revisit the existing perspectives of cultural labour in cultural policy discussion (unproductive, creative and precarious labour) to reflect on their limitations and implications for our understanding of AI’s challenges. Based on this, I argue that we should expand the discussion of precarious labour to elaborate the emerging ‘creative precarity’. In particular, I will explore its key dimensions – the increasing uncertainty in terms of cultural workers’ creative roles, rights and identity, and audience responses – and their policy implications. At the core of potential policy response to and our research into creative precarity, there are fundamental questions of how we redefine cultural work in the time of AI, what new meanings we can attach to cultural labour, what constitutes the human-ness in human creativity and why it crucially matters.Media, Culture &amp; Society220It is argued that the discussion of precarious labour should expand to elaborate the emerging ‘creative precarity’ and its key dimensions – the increasing uncertainty in terms of cultural workers’ creative roles, rights and identity, and audience responses – and their policy implications.Media, Culture &amp; Society['Hye-Kyung Lee']2024-05-20T00:00:00https://www.semanticscholar.org/paper/0e70205930de660a2552cf0bb0a4d9b49a062a0a 87474585e23f2a580dc935ff8e0aa1f73fb3e37bThis paper addresses the ethics of inclusion in artificial in-telligence in the context of African fashion. Despite the proliferation of fashion-related AI applications and da-tasets global diversity remains limited, and African fash-ion is significantly underrepresented. This paper docu-ments two use-cases that enhance AI's inclusivity by in-corporating sub-Saharan fashion elements. The first case details the creation of a Senegalese fashion dataset and a model for classifying traditional apparel using transfer learning. The second case investigates African wax textile patterns generated through generative adversarial net-works (GANs), specifically StyleGAN architectures, and machine learning diffusion models. Alongside the practi-cal, technological advances, theoretical ethical progress is made in two directions. First, the cases are used to elabo-rate and define the ethics of inclusion, while also contrib-uting to current debates about how inclusion differs from ethical fairness. Second, the cases engage with the ethical debate on whether AI innovation should be slowed to prevent ethical imbalances or accelerated to solve them.Proceedings of the AAAI Symposium Series290Two use-cases arements that enhance AI's inclusivity by in-corporating sub-Saharan fashion elements and engage with the ethical debate on whether AI innovation should be slowed to prevent ethical imbalances or accelerated to solve them.Proceedings of the AAAI Symposium Series['Christelle Scharff', 'James Brusseau', 'K. Bathula', 'Kaleemunnisa Fnu', 'Samyak Rakesh Meshram', 'Om Gaikhe']2024-05-20T00:00:00https://www.semanticscholar.org/paper/87474585e23f2a580dc935ff8e0aa1f73fb3e37b 5d242f9b8a8cfc3fb6f82fb85a4256b47291756cCurrent ethical debates on the use of artificial intelligence (AI) in healthcare approach AI technology in three primary ways. First, they assess the risks and potential benefits of current AI-enabled products using ethical checklists. Second, they propose ex ante lists of ethical values relevant to the design and development of assistive technologies. Third, they advocate for incorporating moral reasoning into AI's automation processes. These three perspectives dominate the discourse, as evidenced by a brief literature summary. We propose a fourth approach: viewing AI as a methodological tool to aid ethical reflection. This involves an AI simulation concept informed by three elements: 1) stochastic human behavior models based on behavioral data for simulating realistic scenarios, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components to illustrate the impact of variable changes. This approach aims to inform an interdisciplinary field about anticipated ethical challenges or trade-offs in specific settings, prompting a re-evaluation of design and implementation plans. This is particularly useful for applications involving complex values and behaviors or limited communication resources, such as dementia care or care for individuals with cognitive impairments. While simulation does not replace ethical reflection, it allows for detailed, context-sensitive analysis during the design process and before implementation.Finally, we discuss the quantitative analysis methods enabled by stochastic simulations and the potential for these simulations to enhance traditional thought experiments and future-oriented technology assessments.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300Simulation does not replace ethical reflection, but it allows for detailed, context-sensitive analysis during the design process and before implementation, and the quantitative analysis methods enabled by stochastic simulations are discussed and the potential for these simulations to enhance traditional thought experiments and future-oriented technology assessments is discussed.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Evin Miser', 'Orcun Sarioguz']2024-05-20T00:00:00https://www.semanticscholar.org/paper/5d242f9b8a8cfc3fb6f82fb85a4256b47291756c e40e7e0e222dc8e9da8cc397c717e2ff99761198This project aims to broaden AI education by developing and studying the efficacy of innovative learning practices and resources for AI education for social good. We have developed three AI learning modules for students to: 1) identify social issues that align with the SDGs in their community (e.g., poverty, hunger, quality education); 2) learn AI through hands-on labs and business applications; and 3) create AI-powered solutions in teams to address social is-sues they have identified. Student teams are expected to situate AI learning in their communities and contribute to their communities. Students then use the modules to en-gage in an interdisciplinary approach, facilitating AI learn-ing for social good in informational sciences and technology, geography, and computer science at three CSU HSIs (San Jose State University, Cal Poly Pomona and CSU San Bernardino). Finally, we aim to evaluate the efficacy and impact of the proposed AI teaching methods and activities in terms of learning outcomes, student experience, student engagement, and equity.Proceedings of the AAAI Symposium Series00Three AI learning modules are developed for students to identify social issues that align with the SDGs in their community and create AI-powered solutions in teams to address social is-sues they have identified.Proceedings of the AAAI Symposium Series['Yu Chen', 'Gabriel Granco', 'Yunfei Hou', 'Heather Macias', 'Frank A. Gomez']2024-05-20T00:00:00https://www.semanticscholar.org/paper/e40e7e0e222dc8e9da8cc397c717e2ff99761198 12f9098542fe17732c08a2f61c5a16535a6cb7c7For decades, the route to familiarity in AI was through technical studies such as computer science. Yet AI has infiltrated many areas of our society. Many fields are rightfully now demanding at least a passing familiarity with machine learning: understanding the standard architectures, knowledge on how to use them, and addressing common concerns. A few such fields look at the standard ethical issues such as fairness, accountability, and transparency. Very few fields situate AI technologies in sociotechnical system analysis, nor give a rigorous foundation in ethical analysis applied to the design, development, and use of the technologies. We have proposed an undergraduate certificate in AI that gives equal weight to social and ethical issues and to technical matters of AI system design and use, aimed at students outside of the traditional AI-related disciplines. By including social and ethical issues in our AI certificate requirements, we expect to attract a broader population of students. By creating an accessible AI certification, we create an opportunity for individuals from diverse experiences to contribute to the discussion of what AI is, what its impact is, and where it should go in the future.Proceedings of the AAAI Symposium Series290An undergraduate certificate in AI is proposed that gives equal weight to social and ethical issues and to technical matters of AI system design and use, aimed at students outside of the traditional AI-related disciplines.Proceedings of the AAAI Symposium Series['Kathleen Timmerman', 'Judy Goldsmith', 'Brent Harrison', 'Zongming Fei']2024-05-20T00:00:00https://www.semanticscholar.org/paper/12f9098542fe17732c08a2f61c5a16535a6cb7c7 a719d3cb3c257684b000a8acef6c175171abcb40Degree completion rates for Hispanic students lag far be-hind their white non-Hispanic peers. To close this gap and accelerate degree completion for Hispanic students at Hispanic-Serving Institutions (HSIs), we offer a pedagogical framework to incorporate AI Literacy into existing programs and encourage faculty-mentored undergraduate research initiatives to solve real-world problems using AI. Using a holistic perspective that includes experience, perception, cognition, and behavior, we describe the ideal process of learning based on a four-step cycle of experience, reflecting, thinking, and acting. Additionally, we emphasize the role of social interaction and community in developing mental abilities and understand how cognitive development is influenced by cultural and social factors. Tailoring the content to be culturally relevant, accessible, and engaging to our Hispanic students, and employing projects-based learning, we offer hands-on activities based on social justice, inclusion, and equity to incorporate AI Literacy. Furthermore, combining the pedagogical framework along with faculty-mentored undergraduate research (the significance of which has been shown to have numerous benefits) will enable our Hispanic students develop competencies to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool anywhere; preparing them for the future and encouraging them to use AI ethically.Proceedings of the AAAI Symposium Series160A pedagogical framework to incorporate AI Literacy into existing programs and encourage faculty-mentored undergraduate research initiatives to solve real-world problems using AI will enable Hispanic students to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI as a tool anywhere.Proceedings of the AAAI Symposium Series['Neelu Sinha', 'Rama Madhavarao', 'Robert Freeman', 'Irene Oujo', 'Janet Boyd']2024-05-20T00:00:00https://www.semanticscholar.org/paper/a719d3cb3c257684b000a8acef6c175171abcb40 326b2859c196a101080a4df33deb7d8df50a773fAI &amp; SOCIETY330This paper identifies two issues with Personal AI: first, like other AI companions, it is deceptive about the presence of their emotions, which undermines the moral value of companionship, and second, Personal AI leads to a distinctly new form of deception concerning the origins of its emotions.AI &amp; SOCIETY['Philip Maxwell Thingbø Mlonyeni']2024-05-20T00:00:00https://www.semanticscholar.org/paper/326b2859c196a101080a4df33deb7d8df50a773f 9985bbb4c83d9b593071df7a90a2a7e767bb7e97PurposeThe objective of this research is to examine the association of artificial intelligence (AI) awareness on workers' retention and the boundary conditions in the context of project organizations.Design/methodology/approachWe collected time-lagged data from project organizations in China.FindingsThe results showed that AI awareness predicted workers' turnover intention. Moreover, this association was moderated via supervisor support.Practical implicationsThis research provides several practical implications aimed at timely communication, training and automation guide for helping firms to foster healthy workplace climate, support and workers’ retention.Originality/valueThe rise of AI and its potential impact on manpower is a popular topic. Yet, the evidence of project workers’ awareness of such potential effects on their retention is scant. Therefore, this study broadens our understanding of the association of AI awareness on turnover intention and boundary conditions in the context of project organizations.International Journal of Managing Projects in Business550This study broadens the understanding of the association of AI awareness on turnover intention and boundary conditions in the context of project organizations to help firms to foster healthy workplace climate, support and workers’ retention.International Journal of Managing Projects in Business['M. Moin', 'J. Zhang']2024-05-20T00:00:00https://www.semanticscholar.org/paper/9985bbb4c83d9b593071df7a90a2a7e767bb7e97 d4f8fff7a4f7837a7018066f21852622ca2a3118AI is rapidly emerging as a tool that can be used by everyone, increasing its impact on our lives, society, and the economy. There is a need to develop educational programs and curricula that can increase capacity and diversity in AI as well as awareness of the implications of using AI-driven technologies. This paper reports on a workshop whose goals include developing guidelines for ensuring that we expand the diversity of people engaged in AI while expanding the capacity for AI curricula with a scope of content that will reflect the competencies and needs of the workforce. The scope for AI education included K-Gray and considered AI knowledge and competencies as well as AI literacy (including responsible use and ethical issues). Participants discussed recommendations for metrics measuring capacity and diversity as well as strategies for increasing capacity and diversity at different level of education: K-12, undergraduate and graduate Computer Science (CS) majors and non-CS majors, the workforce, and the public.Proceedings of the AAAI Symposium Series10Recommendations for metrics measuring capacity and diversity as well as strategies for increasing capacity and diversity at different level of education: K-12, undergraduate and graduate Computer Science majors and non-CS majors, the workforce, and the public.Proceedings of the AAAI Symposium Series['Mary Lou Maher', 'Sri Yash Tadimalla']2024-05-20T00:00:00https://www.semanticscholar.org/paper/d4f8fff7a4f7837a7018066f21852622ca2a3118 73cdf664efb56c904d02cd32bb368e7c2392722eJournal of Artificial Intelligence and Big Data00Journal of Artificial Intelligence and Big Data['Chirag Shah', 'Venkat Rama Reddy Sabbella', 'Hussain Vali Buvvaji']2024-05-20T00:00:00https://www.semanticscholar.org/paper/73cdf664efb56c904d02cd32bb368e7c2392722e 6907de99f81d7bff76571968d44ea7349684aa38Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed deployment use case is psychotherapy, where a LLM-powered chatbot can treat a patient undergoing a mental health crisis. Deployment of LLMs for mental health response could hypothetically broaden access to psychotherapy and provide new possibilities for personalizing care. However, recent high-profile failures, like damaging dieting advice offered by the Tessa chatbot to patients with eating disorders, have led to doubt about their reliability in high-stakes and safety-critical settings. In this work, we develop an evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment. Using human evaluation with trained clinicians and automatic quality-of-care metrics grounded in psychology research, we compare the responses provided by peer-to-peer responders to those provided by a state-of-the-art LLM. We show that LLMs like GPT-4 use implicit and explicit cues to infer patient demographics like race. We then show that there are statistically significant discrepancies between patient subgroups: Responses to Black posters consistently have lower empathy than for any other demographic group (2%-13% lower than the control group). Promisingly, we do find that the manner in which responses are generated significantly impacts the quality of the response. We conclude by proposing safety guidelines for the potential deployment of LLMs for mental health response.550An evaluation framework for determining whether LLM response is a viable and ethical path forward for the automation of mental health treatment is developed and safety guidelines for the potential deployment of LLMs for mental health response are proposed.['Saadia Gabriel', 'Isha Puri', 'Xuhai Xu', 'Matteo Malgaroli', 'Marzyeh Ghassemi']2024-05-20T00:00:00https://www.semanticscholar.org/paper/6907de99f81d7bff76571968d44ea7349684aa38 ff5e32a17367187d8de5a4776f82dddb1949c810International Journal of Human-Computer Interaction500International Journal of Human–Computer Interaction['Mirae Kim', 'Jaedong Oh', 'Doha Kim', 'Jungwoo Shin', 'Daeho Lee']2024-05-20T00:00:00https://www.semanticscholar.org/paper/ff5e32a17367187d8de5a4776f82dddb1949c810 acb03971505be3f31477ebc25b4789de0784d9f2This study focuses on the primary stakeholders of higher education institutions, students, with a particular emphasis on first-year students. The aim of the study is to segment students by institutional choice and to characterize them by demographics and by level and field of education. An online questionnaire (2,330 students) was used to investigate the factors that influence the outcome of the decision-making process at the time of application. Based on the factors (education and reputation; dormitory and services; opinion of others; city), four groups of students could be distinguished: Uninterested Students; Conscious Students; Ambitious Students; and City Lovers. The focal points (avatar, headline, visual content and textual content) of a communication campaign were identified with the help of marketing master students (12) using the Design Thinking method.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development60Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development['Adrienn Dernóczi-Polyák', 'Veronika Keller']2024-05-20T00:00:00https://www.semanticscholar.org/paper/acb03971505be3f31477ebc25b4789de0784d9f2 19714db7be74a06fc138e2f1a41a980a20363794Virtual assistance has emerged as a transformative technology in the realm of banking, offering an efficient and user-friendly means of engaging with financial services. This abstract explores the concept of a virtual assistant for the banking system, a sophisticated digital entity designed to facilitate and enhance customer interactions within the financial sector. A groundbreaking solution to empower blind individuals in navigating the complex landscape of banking operations. Rooted in the ethos of inclusivity, the project merges cutting-edge technologies with a user-friendly interface, ensuring a seamless and independent banking experience for visually impaired users. The frontend of the application is developed using Flutter, offering a responsive and visually appealing cross-platform interface that caters to a diverse range of user preferences. Meanwhile, the Python backend orchestrates the intricate ballet of banking operations, communicating securely with banking APIs to perform tasks such as balance inquiries, fund transfers, and transaction history retrieval. Central to the project's innovation is the integration of speech-to-text and text-to-speech functionalities. Users can interact with the application through spoken commands, which are accurately transcribed into text, and receive information through synthesized speech, providing a natural and conversational experience. A sophisticated chat box further facilitates communication, enabling users to seek assistance and guidance effortlessly. The text-based conversation within the chat box becomes a crucial element in ensuring a user-friendly and accessible interface. Security is paramount, with robust measures in place to safeguard sensitive information, employing encryption for data such as login credentials and transaction detailsInternational Journal of Advanced Research in Science, Communication and Technology30The concept of a virtual assistant for the banking system is explored, a sophisticated digital entity designed to facilitate and enhance customer interactions within the financial sector and a groundbreaking solution to empower blind individuals in navigating the complex landscape of banking operations.International Journal of Advanced Research in Science, Communication and Technology['Sayali More', 'Vanshika Kotasthane', 'Mrugnayani Ahire', 'Sakshi Shelke']2024-05-20T00:00:00https://www.semanticscholar.org/paper/19714db7be74a06fc138e2f1a41a980a20363794 09a4bb06186d4af42ae99468f9562390d0f1a72cIt is a widely-accepted fact that the processing of very large amounts of data with state-of-the-art Natural Language Processing (NLP) practices (i.e. Machine Learning –ML, language agnostic approaches) has resulted to a dramatic improvement in the speed and efficiency of systems and applications. However, these developments are accompanied with several challenges and difficulties that have been voiced within the last years. Specifically, in regard to NLP, evident improvement in the speed and efficiency of systems and applications with GenAI also entails some aspects that may be problematic, especially when particular text types, languages and/or user groups are concerned. State-of-the-art NLP approaches with automated processing of vast amounts of data in GenAI are related to observed problematic Aspects 1-7, namely: (1) Underrepresentation, (2) Standardization. These result to (3) Barriers in Text Understanding, (4) Discouragement of HCI Usage for Special Text Types and/or User Groups, (5) Barriers in Accessing Information, (6) Likelihood of Errors and False Assumptions and (7) Difficulties in Error Detection and Recovery. An additional problem are typical cases, such as less-resourced languages (A), less experienced users (B) and less agile users (C). A hybrid approach involving the re-introduction and integration of traditional concepts in state-of-the-art processing approaches, whether they are automatic or interactive, concerns the following targets: i), (ii) and (iii): Making more types of information accessible to more types of recipients and user groups (i), Making more types of services accessible and user-friendly to more types of user groups (ii), Making more types of feelings, opinions, voices and reactions visible from more types of user groups (iii) Specifically, in the above-presented cases traditional and classical theories, principles and models are re-introduced and can be integrated into state-of-the art data-driven approaches involving Machine Learning and neural networks, functioning as training data and seed data in Natural Language Processing applications where user requirements and customization are of particular interest and importance. A hybrid approach may be considered a compromise between speed and correctness / userfriendliness in (types of) NLP applications where the achievement of this balance plays a crucial role. In other words, a hybrid approach and the examples presented here target to prevent mechanisms from adopting human biases, ensuring fairness and socially responsible outcome and responsible Social Media. A hybrid approach and the examples presented here also target to customizing content to different linguistic and cultural groups, ensuring equitable information distribution. Here, we present characteristic examples with cases employing the re-introduction of four typical types of traditional concepts concerning classical theories, principles and models. These four typical classical theories, principles and models are also not considered to be flawless, however they can be transformed into practical strategies that can be integrated into evaluation modules, neural networks and training data (including knowledge graphs) and dialogue design. The proposed and discussed re-introduction of traditional concepts is not limited only to the particular models, principles and theories presented here. The first example concerns the application of a classic principle from Theoretical Linguistics. The concept employed in the second example concerns a model from the field of Linguistics and Translation. The third and the fourth examples demonstrate the interdisciplinary application of models and theoretical frameworks from the fields of Linguistics-Cognitive Science and Linguistics-Psychology respectively.Proceedings of the AAAI Symposium Series00A hybrid approach may be considered a compromise between speed and correctness / userfriendliness in (types of) NLP applications where the achievement of this balance plays a crucial role.Proceedings of the AAAI Symposium Series['Christina Alexandris']2024-05-20T00:00:00https://www.semanticscholar.org/paper/09a4bb06186d4af42ae99468f9562390d0f1a72c ca3ee9f84b8b1be0a6029bcf9f511a8d2453bd1bFacing the current debate on whether Large Language Models (LLMs) attain near-human intelligence levels (Mitchell&Krakauer, 2023; Bubeck et al., 2023; Kosinski, 2023; Shiffrin&Mitchell, 2023; Ullman, 2023), the current study introduces a benchmark for evaluating social intelligence, one of the most distinctive aspects of human cognition. We developed a comprehensive theoretical framework for social dynamics and introduced two evaluation tasks: Inverse Reasoning (IR) and Inverse Inverse Planning (IIP). Our approach also encompassed a computational model based on recursive Bayesian inference, adept at elucidating diverse human behavioral patterns. Extensive experiments and detailed analyses revealed that humans surpassed the latest GPT models in overall performance, zero-shot learning, one-shot generalization, and adaptability to multi-modalities. Notably, GPT models demonstrated social intelligence only at the most basic order (order = 0), in stark contrast to human social intelligence (order>= 2). Further examination indicated a propensity of LLMs to rely on pattern recognition for shortcuts, casting doubt on their possession of authentic human-level social intelligence. Our codes, dataset, appendix and human data are released at https://github.com/bigai-ai/Evaluate-n-Model-Social-Intelligence.490Humans surpassed the latest GPT models in overall performance, zero-shot learning, one-shot generalization, and adaptability to multi-modalities and indicated a propensity of LLMs to rely on pattern recognition for shortcuts, casting doubt on their possession of authentic human-level social intelligence.['Junqi Wang', 'Chunhui Zhang', 'Jiapeng Li', 'Yuxi Ma', 'Lixing Niu', 'Jiaheng Han', 'Yujia Peng', 'Yixin Zhu', 'Lifeng Fan']2024-05-20T00:00:00https://www.semanticscholar.org/paper/ca3ee9f84b8b1be0a6029bcf9f511a8d2453bd1b 33cc5164c32b2534cdd26ccec6c6716efa7d9678Information &amp; Communications Technology Law00Information &amp; Communications Technology Law['Noëlle Gaumann', 'Michael Veale']2024-05-20T00:00:00https://www.semanticscholar.org/paper/33cc5164c32b2534cdd26ccec6c6716efa7d9678 984b52988ef80a613a7cebf8ab677ecf38f5ae8eInternational Journal of Human-Computer Interaction530International Journal of Human–Computer Interaction['Avital Shulner-Tal', 'T. Kuflik', 'D. Kliger', 'Azzurra Mancini']2024-05-20T00:00:00https://www.semanticscholar.org/paper/984b52988ef80a613a7cebf8ab677ecf38f5ae8e 2f717b918983d6164f2df1ea67f11cb4e95f26edWe present a new technique that explains the output of a CNN-based model using a combination of GradCAM and LRP methods. Both of these methods produce visual explanations by highlighting input regions that are important for predictions. In the new method, the explanation produced by GradCAM is first processed to remove noises. The processed output is then multiplied elementwise with the output of LRP. Finally, a Gaussian blur is applied on the product. We compared the proposed method with GradCAM and LRP on the metrics of Faithfulness, Robustness, Complexity, Localisation and Randomisation. It was observed that this method performs better on Complexity than both GradCAM and LRP and is better than atleast one of them in the other metrics.210A new technique that explains the output of a CNN-based model using a combination of GradCAM and LRP methods that performs better on Complexity than both GradCAM and LRP and is better than atleast one of them in the other metrics.['Vaibhav Dhore', 'Achintya Bhat', 'Viraj Nerlekar', 'Kashyap Chavhan', 'Aniket Umare']2024-05-20T00:00:00https://www.semanticscholar.org/paper/2f717b918983d6164f2df1ea67f11cb4e95f26ed fda23fa8adf30005c1406792e4691f5290665a01The trends in the digitalization of marketing require the expansion of marketing management tools, which is primarily associated with the capabilities of artificial intelligence. The purpose of the paper is to study the modern capabilities of artificial intelligence tools for managing consumer behaviour. The methodological basis of the research is general (such as generalization, analysis and synthesis) and special (system and structural analysis) methods. System analysis identifies the features of artificial intelligence tools for consumer behaviour management, and structural analysis summarizes the functions of artificial intelligence tools for consumer behaviour management. In the paper, the artificial intelligence tools are structured according to the possibilities of their use in the process of consumer analysis, promotion, development and implementation of consumer behaviour management strategies. The result of the study is a grouping of artificial intelligence tools for managing consumer behaviour and the formation of models of interaction between objects and subjects of consumer behaviour management. The originality and value of the study lies in providing recommendations for the use of artificial intelligence tools to manage consumer behaviour, which will allow businesses to increase profits.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development40The result of the study is a grouping of artificial intelligence tools for managing consumer behaviour and the formation of models of interaction between objects and subjects of consumer behaviour management.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development['Nataliia Parkhomenko']2024-05-20T00:00:00https://www.semanticscholar.org/paper/fda23fa8adf30005c1406792e4691f5290665a01 0f65b75a6741761a15e29ea4bb68cbf846fcbb39The rapid development of artificial intelligence (AI) is bringing major changes to various aspects of life, including the world of work. AI-driven automation and robotization is predicted to replace many jobs currently performed by humans raising concerns about the future of work, especially for the younger generation. This research aims to understand how AI will influence the world of work in the future and how character education and religious moderation can help the younger generation in facing these changes. This study used qualitative research methods. The data collection technique in this research is literature study. The data that has been collected is then analyzed in three stages, namely data reduction, data presentation and drawing conclusions. . The results show the ethical, social, and educational challenges that come with the development of AI, as well as the educational strategies and policies needed to prepare the younger generation. Cross-sector collaboration is key in meeting these challenges, with governments, educational institutions, and the private sector working together to ensure that future generations have a strong foundation to face the AI era with confidence and success. Thus, the importance of concerted efforts in preparing the younger generation for an AI-influenced future in Multi Racial PD.Lentera150The results show the ethical, social, and educational challenges that come with the development of AI, as well as the educational strategies and policies needed to prepare the younger generation for an AI-influenced future.Lentera: Multidisciplinary Studies['Nur Kumala Dewi', 'Abdul Jamil', 'Fisa Wisnu Wijaya']2024-05-20T00:00:00https://www.semanticscholar.org/paper/0f65b75a6741761a15e29ea4bb68cbf846fcbb39 1368682dab47f702f1a2eec6831d0b2cc9aa8ac9In recent years, artificial intelligence (AI) has seen significant advances that have in fact exceeded even optimistic prognoses. Using data-driven AI, namely deep learning techniques, it has been demonstrated that computers may now be equipped with abilities of remarkable scope and quality, such as solving image and text processing tasks at human level. Large language models, in particular, have sparked debates regarding opportunities and challenges of this rapidly developing area. Will remaining fundamental challenges of data-driven AI, such as factual or logical mistakes, be overcome for good if complemented and hybridized with symbolic AI techniques, such as knowledge representation and reasoning? Will systems of artificial general intelligence (AGI) emerge from this, possessing common sense and in fact completing the decades-old quest for AI that motivated the raise of the field in the 1950s? In the light of these questions, we review the likewise, decades-old philosophical debate about capabilities and limitations of computers from a hybrid AI point of view. Here, we discuss how hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do. At the same time, we shed light on a lesser discussed challenge for hybrid AI: the possibility that its developers might be its biggest limiters.Proceedings of the AAAI Symposium Series320How hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do is discussed and the possibility that its developers might be its biggest limiters is shed on.Proceedings of the AAAI Symposium Series['Ben Schuering', 'Thomas Schmid']2024-05-20T00:00:00https://www.semanticscholar.org/paper/1368682dab47f702f1a2eec6831d0b2cc9aa8ac9 0a431305e31ce6ca601f480948cf1088dbcce9dfUnder the background of new engineering, artificial intelligence courses in applied undergraduate colleges and universities face great challenges in course teaching and students’ learning effect due to the comprehensive factors such as content, class hours, and students’ knowledge structure. In order to improve the level and quality of talent training, this paper explores and practices the problems existing in the teaching of artificial intelligence courses in applied undergraduate colleges from the aspects of curriculum teaching objectives, contents, methods, teacher construction, and experimental teaching, promoting learning through competition and teaching assessment, and insists on taking “applied and innovative” learning training as the center. It is necessary to constantly optimize the ideas and methods of artificial intelligence course construction, and promote the development and innovation of computer education in application-oriented colleges under the background of new engineering.Education Reform and Development140This paper explores and practices the problems existing in the teaching of artificial intelligence courses in applied undergraduate colleges from the aspects of curriculum teaching objectives, contents, methods, teacher construction, and experimental teaching, promoting learning through competition and teaching assessment, and insists on taking “applied and innovative" learning training as the center.Education Reform and Development['Huiying Zhang', 'Yingquan Mu', 'Jihuan Xi', 'Yuancheng Gu']2024-05-20T00:00:00https://www.semanticscholar.org/paper/0a431305e31ce6ca601f480948cf1088dbcce9df f0e2237ccb88c452cf7f811c0f8d3003018670c5OBJECTIVES Surface the urgent dilemma that healthcare delivery organizations (HDOs) face navigating the US Food and Drug Administration (FDA) final guidance on the use of clinical decision support (CDS) software. MATERIALS AND METHODS We use sepsis as a case study to highlight the patient safety and regulatory compliance tradeoffs that 6129 hospitals in the United States must navigate. RESULTS Sepsis CDS remains in broad, routine use. There is no commercially available sepsis CDS system that is FDA cleared as a medical device. There is no public disclosure of an HDO turning off sepsis CDS due to regulatory compliance concerns. And there is no public disclosure of FDA enforcement action against an HDO for using sepsis CDS that is not cleared as a medical device. DISCUSSION AND CONCLUSION We present multiple policy interventions that would relieve the current tension to enable HDOs to utilize artificial intelligence to improve patient care while also addressing FDA concerns about product safety, efficacy, and equity.JAMIA Journal of the American Medical Informatics Association260Multiple policy interventions are presented that would relieve the current tension to enable HDOs to utilize artificial intelligence to improve patient care while also addressing FDA concerns about product safety, efficacy, and equity.Journal of the American Medical Informatics Association : JAMIA['M. Sendak', 'Vincent X Liu', 'Ashley Beecy', 'David E Vidal', 'Keo Shaw', 'Mark A Lifson', 'Danny Tobey', 'Alexandra Valladares', 'Brenna Loufek', 'Murtaza Mogri', 'Suresh Balu']2024-05-20T00:00:00https://www.semanticscholar.org/paper/f0e2237ccb88c452cf7f811c0f8d3003018670c5 3f600dff6823636904fb06805c64d7205dd67b55Self-organizing ensembles of software and hardware agents with artificial intelligence model the intellectual abilities of a person's natural intelligence. The Creator endowed man with various types of intellectual abilities: generation of meanings, perception of meanings, meaningful actions and behavior, sensory reaction to meanings, emotional reaction to meanings. Based on the synergy of various intellectual abilities, a person carries out life activities. For example, Dialogue is conducted on the basis of two intellectual abilities: the generation and perception of meanings. A multimodal self-organizing ensemble of intelligent software and hardware agents with artificial intelligence, based on existing knowledge and skills, is able to write poetry, draw pictures, give recommendations and solutions to specialists, manage production and systems in various sectors of the economy, and take part in scientific research. Multimodal ensembles of intelligent agents, modeling the functions of natural intelligence, contain a functional control structure. To ensure the safe and reliable use of multimodal ensembles of intelligent agents, they are being standardized internationally under the guidance of ISO. International standardization of multimodal ensembles of intelligent agents expands the market and reduces the risks of their use.Research on Intelligent Manufacturing and Assembly250Multimodal ensembles of intelligent agents, modeling the functions of natural intelligence, contain a functional control structure and are being standardized internationally under the guidance of ISO to ensure the safe and reliable use.Research on Intelligent Manufacturing and Assembly['E. Bryndin']2024-05-20T00:00:00https://www.semanticscholar.org/paper/3f600dff6823636904fb06805c64d7205dd67b55 ac53eb577883f12599438ee41d5051bde7d5e381This study experimentally investigates initial trust formation in the organizational context of an artificial intelligence (AI) system in human resource management (HRM). Drawing on social exchange theory and leader‐member exchange theory, we identify factors that contribute to initial trust in AI through cognitive and affective processing from the perspective of employees in the Chinese context. An online survey (N = 426) was conducted with a 2 (explanation of AI: without vs with) × 2 (trust in leaders: low vs high) design. Our findings demonstrate that initial trust plays a crucial role in AI adoption, and a trustworthy leader increases employees' AI trust and intention to adopt. Providing AI's benefits and risks moderates initial trust and the pathway to adoption. Moreover, familiarity with AI's application in HRM and organizational collectivism is also beneficial. Our findings suggest that organizations should prioritize cultivating initial trust in AI with employee‐oriented strategies, including trusted leadership and supportive training resources.Social Science Research Network840It is demonstrated that initial trust plays a crucial role in AI adoption, and a trustworthy leader increases employees' AI trust and intention to adopt, and organizations should prioritize cultivating initial trust in AI with employee‐oriented strategies, including trusted leadership and supportive training resources.SSRN Electronic Journal['Yi Xu', 'Yijie Huang', 'Jiahe Wang', 'D. Zhou']2024-05-20T00:00:00https://www.semanticscholar.org/paper/ac53eb577883f12599438ee41d5051bde7d5e381 cc99e011406594161fec50ec618a88a7bf91d958Generation Z’s attitude towards ever-developing technology and related AI reflects the interweavement of curiosity, fear, and cautious optimism. Since AI is constantly developing, it certainly changes the labour market, organisation processes, different human resource processes, as well as the training and development of employees. The main purpose of the research reported in this paper is to examine the attitudes of Generation Z regarding the use of artificial intelligence in the context of employee training and development. Empirical research was conducted on a sample of 129 respondents from Slovenia, and hypotheses were tested by descriptive statistics and T-test. The research results confirm the positive attitudes of Generation Z members towards contemporary training models, regardless of their sociodemographic characteristics. This aligns with the finding that Generation Z shows a strong interest in AI, with many actively seeking out information on the topic and learning about it, either formally or informally. This paper contributes to the human resource management literature because it brings new insights into Generation Z, whose participation in the active workforce will significantly increase in the coming years.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development110The research results confirm the positive attitudes of Generation Z members towards contemporary training models, regardless of their sociodemographic characteristics, which aligns with the finding that Generation Z shows a strong interest in AI.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development['Branka Zolak Poljašević', 'Simona Šarotar Žižek', 'Ana Marija Gričnik']2024-05-20T00:00:00https://www.semanticscholar.org/paper/cc99e011406594161fec50ec618a88a7bf91d958 6c8d02ab79aee42639935cd77d6e070a236a3d36It is the contention of this paper that ethics of work ought to be anthropological, and artificial intelligence (AI) research and development, which is the focus of work today, should be anthropological, that is, human-centered. This paper discusses the philosophical and theological implications of the development of AI research on the intrinsic nature of work and the nature of the human person. AI research and the implications of its development and advancement, being a relatively new phenomenon, have not been comprehensively interrogated in the social and ethical teachings of the Catholic Church. This paper, therefore, proposes a path for this interrogation by expounding a discourse which is believed to be epistemically helpful in the developing discourse of AI in the ethical and social teachings of the Church. The advancement in the research on AI is not only redefining the meaning of work, but, even more so, it is questioning the metaphysical notion of the human person and the theological notion of work as an intrinsic part in the selfhood and dignity of the human person.Religions70Religions['Justin Nnaemeka Onyeukaziri']2024-05-20T00:00:00https://www.semanticscholar.org/paper/6c8d02ab79aee42639935cd77d6e070a236a3d36 6c80fe5c53dba89b8a95334362566194e39a230eBACKGROUND Large language models (LLMs) offer significant potential in medical information extraction but carry risks of generating incorrect information. This study aims to develop and validate a retriever-augmented generation (RAG) model that provides accurate medical knowledge about diabetes and diabetic foot care to laypersons with an eighth-grade literacy level. Improving health literacy through patient education is paramount to addressing the problem of limb loss in the diabetic population. In addition to affecting patient well-being through improved outcomes, improved physician well-being is an important outcome of a self-management model for patient health education. METHODS We used an RAG architecture and built a question-and-answer artificial intelligence (AI) model to extract knowledge in response to questions pertaining to diabetes and diabetic foot care. We utilized GPT-4 by OpenAI, with Pinecone as a vector database. The NIH National Standards for Diabetes Self-Management Education served as the basis for our knowledge base. The model's outputs were validated through expert review against established guidelines and literature. Fifty-eight keywords were used to select 295 articles and the model was tested against 175 questions across topics. RESULTS The study demonstrated that with appropriate content volume and few-shot learning prompts, the RAG model achieved 98% accuracy, confirming its capability to offer user-friendly and comprehensible medical information. CONCLUSION The RAG model represents a promising tool for delivering reliable medical knowledge to the public which can be used for self-education and self-management for diabetes, highlighting the importance of content validation and innovative prompt engineering in AI applications.Journal of Diabetes Science and Technology50The RAG model represents a promising tool for delivering reliable medical knowledge to the public which can be used for self-education and self-management for diabetes, highlighting the importance of content validation and innovative prompt engineering in AI applications.Journal of diabetes science and technology['Shayan Mashatian', 'David G Armstrong', 'Aaron Ritter', 'Jeffery Robbins', 'Shereen Aziz', 'Ilia Alenabi', 'Michelle Huo', 'Taneeka Anand', 'K. Tavakolian']2024-05-20T00:00:00https://www.semanticscholar.org/paper/6c80fe5c53dba89b8a95334362566194e39a230e 440cb3d6e09eb6eb0b40dbb5069207adf4a7d853The development of science and technology has ushered in the era of artificial intelligence. This development affects school education in terms of information technology curriculum. The improvement of China’s national strength has led to the widespread of information technology education in primary and secondary schools. In this context, this paper studies artificial intelligence education in terms of its curriculum in primary and secondary schools.Education Reform and Development110This paper studies artificial intelligence education in terms of its curriculum in primary and secondary schools in China in terms of information technology curriculum.Education Reform and Development['Dawei Zhao', 'Xinlei Sun']2024-05-20T00:00:00https://www.semanticscholar.org/paper/440cb3d6e09eb6eb0b40dbb5069207adf4a7d853 0c2e9008fc770c9804aa2dcf52c95c2acb37c2a0Artificial Intelligence (AI) adoption has seen substantial growth across industries. This paper explores the escalating use of AI within the United States Department of Defense (DoD) and the implications that diversity, equity, and inclusion (DEI) have on Service members and Civilians across the Department. More specifically, this paper explores the DEI considerations within AI technologies on individual, team, and Department readiness. The DoD's AI usage spans various strategic and operational capabilities, however this paper explores two critical domains: healthcare and recruitment. In healthcare, AI offers the promise of early disease detection, enhanced diagnostic capabilities, and streamlined administrative processes. However, potential biases stemming from homogenous training data threaten the accuracy and reliability of these systems, jeopardizing Service member health and eroding trust in AI-assisted medical decision-making and potentially the DoD at large. In recruitment, while AI promises efficiency in identifying ideal candidates, its deployment can perpetuate biases, especially when the training data used is not representative of all demographics. Despite efforts to design "unbiased" systems by excluding demographic data, such strategies may inadvertently overlook the unique challenges faced by marginalized communities, further entrenching existing disparities. Both case studies underscore the importance of considering DEI in the development and deployment of AI systems. As the DoD continues to integrate AI into its operations, this paper’s recommendations stress the necessity of continuous DEI assessment to ensure that AI serves as an asset rather than a liability. The authors recommend the following: 1. Data diversity & review 2. Continuous monitoring and calibration 3. Stakeholder engagement 4. Adoption of DEI requirements within Ethical AI Frameworks 5. Further researchProceedings of the AAAI Symposium Series00As the DoD continues to integrate AI into its operations, this paper’s recommendations stress the necessity of continuous DEI assessment to ensure that AI serves as an asset rather than a liability.Proceedings of the AAAI Symposium Series['Sara Darwish', 'Alison Bragaw-Butler', 'Paul Marcelli', 'Kaylee Gassner']2024-05-20T00:00:00https://www.semanticscholar.org/paper/0c2e9008fc770c9804aa2dcf52c95c2acb37c2a0 d942e215e0cd872b4bc1513c214bdfe835268badBACKGROUND AND AIM Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. METHODS A still image-driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real-world applicability, the model was validated using 113 10-s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2-3)" or "inadequate (BBPS 0-1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter-rater variability was analyzed between the AI model and endoscopists. RESULTS The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. CONCLUSIONS The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.Journal of Gastroenterology and Hepatology180The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists and further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.Journal of gastroenterology and hepatology['Ji Young Lee', 'Jooyoung Park', 'H. Lee', 'Hana Park', 'E. Jin', 'Kanggil Park', 'Ji Eun Baek', 'Dong-Hoon Yang', 'Seung Wook Hong', 'Namkug Kim', 'J. Byeon']2024-05-20T00:00:00https://www.semanticscholar.org/paper/d942e215e0cd872b4bc1513c214bdfe835268bad 44a0e5e8edeb1a57546cdb7b50a580881002c787The rapid progress in the development of automation and artificial intelligence (AI) technologies, such as ChatGPT, represents a step-wise change in human's interactions with technology as part of a broader complex, sociotechnical system. Based on historical parallels to the present moment, such changes are likely to bring forth structural shifts to the nature of work, where near and future technologies will occupy key roles as workers or assistants in sports science and sports medicine multidisciplinary teams (MDTs). This envisioned future may bring enormous benefits, as well as a raft of potential challenges. These challenges include the potential to remove many human roles and allocate them to semi- or fully-autonomous AI. Removing such roles and tasks from humans will make many current jobs and careers untenable, leaving a set of difficult and unrewarding tasks for the humans that remain. Paradoxically, replacing humans with technology increases system complexity and makes them more prone to failure. The automation and AI boom also brings substantial opportunities. Among them are automated sentiment analysis and Digital Twin technologies which may reveal novel insights into athlete health and wellbeing and team tactical patterns, respectively. However, without due consideration of the interactions between humans and technology in the broader system of sport, adverse impacts are likely to be felt. Human and AI teamwork may require new ways of thinking.Frontiers in Sports and Active Living400Frontiers in Sports and Active Living['Mitchell Naughton', 'Paul M. Salmon', 'Heidi R. Compton', 'S. McLean']2024-05-20T00:00:00https://www.semanticscholar.org/paper/44a0e5e8edeb1a57546cdb7b50a580881002c787 152770486ccffa17d21d3107fb6da0087da894a8The significance of technology integration including artificial intelligence (AI)-mediated tools has established a notable presence in the academic spectrum. Despite the abundance of studies available on AI-mediated technology integration in writing instructions in diverse settings, there remains an apparent gap in exploring teachers’ insights, particularly within the context of Arab universities. Therefore, the current study explores the employment of AI-driven tools in student-centered writing instructions from English as a Foreign Language (EFL) teachers’ perspectives. Using a qualitative research methodology, this study collected data through semi-structured interviews with a sample of (N = 16) teachers from four different universities. The content analysis indicates that teachers strongly perceive a positive impact of AI writing assistants on both student involvement and the role of teachers. Additionally, they underscore the significance of professional development and the role of AI in facilitating student-centered approach for effective writing instructions. While acknowledging the efficiency, customization, and time-saving aspects of AI tools, they also expressed reservations about potential issues such as overreliance, bias, digital divide, and concerns regarding accuracy. Furthermore, the participants observed the ways to address issues and concerns associated with the integration of AI-mediated tools include, but not limited to, clear communication, ethical considerations, academic integrity, teacher roles, ongoing and latest AI updates, student-centered learning, and professional development. Finally, the study offers limitations and recommendations for future research.International Journal of English Linguistics590The content analysis indicates that teachers strongly perceive a positive impact of AI writing assistants on both student involvement and the role of teachers, and the ways to address issues and concerns associated with the integration of AI-mediated tools include clear communication, ethical considerations, academic integrity, teacher roles, ongoing and latest AI updates, student-centered learning, and professional development.International Journal of English Linguistics['Mohd Nazim']2024-05-20T00:00:00https://www.semanticscholar.org/paper/152770486ccffa17d21d3107fb6da0087da894a8 84ca88c53b8dc984c028087dabed9ffd77307776Machine learning and artificial intelligence produce algorithms that appear to be able to make "intelligent" decisions similar to those of humans but function differently from human thinking. To make decisions based on machine suggestions, humans should be able to understand the background of these suggestions. However, since humans are oriented to understand human intelligence, it is not yet fully clear whether humans can truly understand the "thinking" generated by machine learning, or whether they merely transfer human-like cognitive processes to machines. In addition, media representations of artificial intelligence show higher capabilities and greater human likeness than they currently have. In our daily lives, we increasingly encounter assistance systems that are designed to facilitate human tasks and decisions based on intelligent algorithms. These algorithms are predominantly based on machine learning technologies, which make it possible to discover previously unknown correlations and patterns by analyzing large amounts of data. One example is the machine analysis of thousands of X-ray images of sick and healthy people. This requires identifying the patterns by which images labeled as "healthy" can be distinguished from those labeled as "sick" and to find an algorithm that identifies the latter. In the meantime, "trained" algorithms created in this way are used in various fields of application, not only for medical diagnoses but also in the pre-selection of applicants for a job advertisement or in communication with the help of voice assistants. These voice assistants are enabled by intelligent algorithms to offer internet services through short commands. Harald Lesch, referring to his book Unpredictable, written together with Thomas Schwarz, says the development of artificial intelligence can be compared to bringing aliens to Earth. With machine learning, a previously unknown form of non-human intelligence has been created. This chapter discusses whether forms of artificial intelligence, as they are currently being publicly discussed, differ substantially from human thinking. Furthermore, it will be discussed to what extent humans can comprehend the functioning of artificial intelligence that has been created through machine learning when interacting with them. Finally, the risks and opportunities will be weighed and discussed..The Turkish Online Journal of Design Art and Communication00This chapter discusses whether forms of artificial intelligence, as they are currently being publicly discussed, differ substantially from human thinking and to what extent humans can comprehend the functioning of artificial intelligence that has been created through machine learning when interacting with them.Turkish Online Journal of Design Art and Communication['Büşra Sarıkaya']2024-05-20T00:00:00https://www.semanticscholar.org/paper/84ca88c53b8dc984c028087dabed9ffd77307776 282b5408b96f63ce2c16ced271b8787ea10a1db6The recent paper highlights the impact of artificial intelligence on Machine Translation without the interaction of Humans. The use of Google Translator, Bing, Microsoft Translator, Systran Translate and Amazon Translate has become widely spread (CAT Tools). This study aims to reveal the contrast between Artificial Intelligence and Human Translation in the legal field. A hypothesis of the difference between Artificial Translation and Human Translation was raised. The concerns about the lack of a translator increased, and machine translation was selected as the most selected option. Local and foreign contracts were selected and subjected to Human and Machine translation. Strengths and weaknesses points were selected and analyzed. The previous studies in the legal translation field were considered. The results revealed the gap between human translation and machine translation, and human translation is dominant in the light of accuracy and the existence of legal language. The findings also focused on the Translators' experience and knowledge in the translation field.International Journal of Linguistics Literature & Translation220The gap between human translation and machine translation is revealed, and human translation is dominant in the light of accuracy and the existence of legal language.International Journal of Linguistics, Literature and Translation['T. Al-Romany', 'Maryam Jawad Kadhim']2024-05-20T00:00:00https://www.semanticscholar.org/paper/282b5408b96f63ce2c16ced271b8787ea10a1db6 1953bebb1c3125449e803b058fc48ef42d5128a5Education and Information Technologies : Official Journal of the IFIP technical committee on Education330There is a significant positive relation between all three variables and that KAI is a robust and substantial predictor of TAI, providing practical implications for policy, teacher preparation and professional development in the rapidly evolving landscape of AI integration in education.Education and Information Technologies['Margarida Lucas', 'Yidi Zhang', 'P. Bem-haja', 'Paulo Nuno Vicente']2024-05-20T00:00:00https://www.semanticscholar.org/paper/1953bebb1c3125449e803b058fc48ef42d5128a5 c019a85681e24002719f557ac1e5f4d198376f4bCausal inference is the idea of cause-and-effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why” whereas the effect describes the “what”. The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning (ML) and artificial intelligence (AI) systems, have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. We include a detailed taxonomy of causal inference frameworks, methods, and evaluation. An overview of causality for security is also provided. Open challenges are detailed, and approaches for evaluating the robustness of causal inference methods are described. This paper aims to provide a comprehensive survey on such studies of causality. We provide an in-depth review of causality frameworks, and describe the different methods.ACM Computing Surveys520An in-depth review of causality frameworks, methods, and evaluation is provided, which includes a detailed taxonomy of causal inference frameworks, methods, and evaluation.ACM Computing Surveys['A. Rawal', 'Adrienne Raglin', 'Danda B. Rawat', 'Brian M. Sadler', 'J. McCoy']2024-05-20T00:00:00https://www.semanticscholar.org/paper/c019a85681e24002719f557ac1e5f4d198376f4b 84386a152b32370c8adc0c80cac08ffb7419c48eElectronic warfare is a critical component of modern military operations and has undergone significant advances in recent years. This book provides an overview of electronic warfare, its historical development, key components, and its role in contemporary conflict scenarios. It also discusses emerging trends and challenges in electronic warfare and its contemporary relevance in an era of advanced technology and cyber threats, emphasizing the need for continued research and development in this area. The book explores the burgeoning intersection of artificial intelligence and electronic warfare, highlighting the evolving landscape of modern conflicts and the implications of integrating advanced technologies. The multifaceted roles of artificial intelligence in electronic warfare are highlighted, examining its potential advantages, ethical considerations, and challenges associated with its integration.00The multifaceted roles of artificial intelligence in electronic warfare are highlighted, examining its potential advantages, ethical considerations, and challenges associated with its integration.['Nicolae Sfetcu']2024-05-20T00:00:00https://www.semanticscholar.org/paper/84386a152b32370c8adc0c80cac08ffb7419c48e 818069b9f7ec0b405db63706f59f31eeedeecb3fModern Artificial Intelligence (AI) technologies have a rapidly growing impact on a wide range of human activities. AI methods are being used in varied domains such as healthcare, material science, infrastructure engineering, social media, surveillance technologies, and even artistic expression. They have been used for the purposes of drug discovery via protein folding prediction, power usage optimization through reinforcement learning, and facial recognition by means of image segmentation. Their effectiveness and wide-scale, unregulated deployment within our societies pose significant risks to our fundamental rights. Multiple existing AI methods have the potential to profoundly undermine our ability to safeguard our privacy. The societal impact of such AI models can be investigated through six concentric Heuristic Zones of privacy. These AI models can perform inferences regarding highly sensitive, personal information such as race, gender, and intelligence from seemingly innocuous data sources beyond the capabilities of human experts. They are capable of generating increasingly accurate text and image recreations of our thoughts from non-invasive brain activity recordings such as magnetoencephalography and functional magnetic resonance imaging. Furthermore, prospective AI technologies pose concerns about the existential risk to our civilization which extend beyond the erosion of privacy and other fundamental human rights.Privacy Studies Journal1030Concerns about the existential risk to the authors' civilization which extend beyond the erosion of privacy and other fundamental human rights extend beyond the erosion of privacy and other fundamental human rights.Privacy Studies Journal['Mateusz Jurewicz', 'Natacha Klein Kafer', 'Esben Kran']2024-05-20T00:00:00https://www.semanticscholar.org/paper/818069b9f7ec0b405db63706f59f31eeedeecb3f cc10f449a9ef1f9ff4b45f84823acacec6bbdcb4This paper deals with the current state and research trends of artificial intelligence in manufacturing companies. The main objective of the paper is to determine the adoption of specific artificial intelligence software in manufacturing. The results are based on a subsample of 141 manufacturing companies that are located in Slovenia. The data were gathered, obtained through the 2022 European Manufacturing Survey research project. The results show that the use of artificial intelligence differs heavily in specific manufacturing areas. The paper also presents the plans of Slovenian manufacturing companies in terms of introducing artificial intelligence software solutions by the end of the year 2025.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development100The results show that the use of artificial intelligence differs heavily in specific manufacturing areas and the plans of Slovenian manufacturing companies in terms of introducing artificial intelligence software solutions by the end of the year 2025 are presented.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development['I. Palčič', 'Klemen Kovic']2024-05-20T00:00:00https://www.semanticscholar.org/paper/cc10f449a9ef1f9ff4b45f84823acacec6bbdcb4 8be0bb88ba00f2f6ac49e98176d96846870f0acaGenerative Artificial Intelligence (AI) was incorporated into a competitive programming event that targeted undergraduate students, including those with little programming experience. The competition incorporated a range of challenge design approaches that promoted meaningful interaction with generative AI system, even while keeping the challenge difficulty level to an appropriate level. An analysis of survey responses and competition data showed that this format lowered barriers to participation, successfully engaged students throughout the competition, and increased the likelihood that they would participate in a similar event. In an extension of this work, a professional development workshop for high school teachers is being developed, along with a contest for high school students. Participant surveys and logs of interaction with the contest and generative AI systems will be analyzed to measure the effect of generative AI on student self-efficacy and suggest ways to integrate generative AI instruction into computer science curriculum.Proceedings of the AAAI Symposium Series80Generative Artificial Intelligence was incorporated into a competitive programming event that targeted undergraduate students, including those with little programming experience, and showed that this format lowered barriers to participation, successfully engaged students throughout the competition, and increased the likelihood that they would participate in a similar event.Proceedings of the AAAI Symposium Series['Devang Jayachandran', 'P. Maldikar', 'Tyler S. Love', 'Jeremy Blum']2024-05-20T00:00:00https://www.semanticscholar.org/paper/8be0bb88ba00f2f6ac49e98176d96846870f0aca a00a212c5f4ad0d929b6225f8da88e60cd2e5939This white paper delves into the transformative potential of Artificial Intelligence (AI) in revolutionizing payment systems through personalized payment recommendations. It explores how AI technologies can be leveraged to analyze consumer behavior and customize payment options, thereby enhancing user engagement and security in digital transactions. Stakeholders, including financial institutions, e-commerce platforms, payment service providers, and technology developers, will find in-depth analysis and actionable insights on integrating AI to optimize payment experiences. This document outlines the benefits, challenges, and practical implementations of AI in payment systems, offering stakeholders a comprehensive guide to harnessing AI for improved consumer satisfaction and transaction efficiency. Through this exploration, stakeholders can anticipate gaining a clear understanding of how AI-driven personalization can be strategically implemented to drive business innovation and maintain competitive advantage in the rapidly evolving digital marketplace.International Journal of Finance80International Journal of Finance['Kalyanasundharam Ramachandran']2024-05-20T00:00:00https://www.semanticscholar.org/paper/a00a212c5f4ad0d929b6225f8da88e60cd2e5939 a9b7fa875bdfc4d95f160376db0b01c28eea99ddInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-05-20T00:00:00https://www.semanticscholar.org/paper/a9b7fa875bdfc4d95f160376db0b01c28eea99dd f61990dfecc068ab4f41fa154865766456abf89bBrazilian Symposium on Information Systems270{'pages': '65:1-65:9'}['Patricia de Oliveira Santos', 'Allan Chamon Figueiredo', 'Pedro Nuno Moura', 'Bruna Diirr', 'Adriana C. F. Alvim', 'Rodrigo Pereira dos Santos']2024-05-20T00:00:00https://www.semanticscholar.org/paper/f61990dfecc068ab4f41fa154865766456abf89b e335358fc2e2d2469109363f3264398707229239Annals of Medicine &amp; Surgery00Annals of Medicine &amp; Surgery['Mishal Abid', 'Maham Asif', 'Zoya Khemane', 'Afia Jawaid', 'Aimen Waqar Khan', 'Hufsa Naveed', 'Tooba Naveed', 'A. A. Farah', 'M. Siddiq']2024-05-20T00:00:00https://www.semanticscholar.org/paper/e335358fc2e2d2469109363f3264398707229239 d636a515e8c63c1b7a77224fe236b95fa8661311Brazilian Symposium on Information Systems170{'pages': '56:1-56:9'}['Patricia de Oliveira Santos', 'Allan Chamon Figueiredo', 'Pedro Nuno Moura', 'Bruna Diirr', 'Adriana C. F. Alvim', 'Rodrigo Pereira dos Santos']2024-05-20T00:00:00https://www.semanticscholar.org/paper/d636a515e8c63c1b7a77224fe236b95fa8661311 8e45c143e92c58d98c8fea097145cfbc6c9a6c06Medicine International00Medicine International['Adham Al‑Rahbi', 'Omar Al-Mahrouqi', 'Tariq Al-Saadi']2024-05-20T00:00:00https://www.semanticscholar.org/paper/8e45c143e92c58d98c8fea097145cfbc6c9a6c06 2e9e9c660e6c56ffc12b9ef1e9dfd411d6693180AI systems are breaking into new domains and applications, and it is pivotal to center humans in contemporary AI systems and contemplate what this means. This discussion considers three perspectives or human roles in AI as users, contributors, and researchers-in-training, to illustrate this notion.Proceedings of the AAAI Symposium Series190This discussion considers three perspectives or human roles in AI as users, contributors, and researchers-in-training, to illustrate this notion of center humans in contemporary AI systems.Proceedings of the AAAI Symposium Series['Cecilia O. Alm']2024-05-20T00:00:00https://www.semanticscholar.org/paper/2e9e9c660e6c56ffc12b9ef1e9dfd411d6693180 5eb1930014595eb81a452e808d161a04ca026cbdThis short paper is the status report of a project in progress. We aim to model human-like agents' decision-making behaviors under risks with neural-symbolic approach. Our model integrates the learning, reasoning, and emotional aspects of an agent and takes the dual process thinking into consideration when the agent is making a decision. The model construction is based on real behavioral and brain imaging data collected in a lottery gambling experiment. We present the model architecture including its main modules and the interactions between them.Proceedings of the AAAI Symposium Series30This work aims to model human-like agents' decision-making behaviors under risks with neural-symbolic approach and takes the dual process thinking into consideration when the agent is making a decision.Proceedings of the AAAI Symposium Series['Jonathan C.H. Tong', 'Yung-Fong Hsu', 'C. Liau']2024-05-20T00:00:00https://www.semanticscholar.org/paper/5eb1930014595eb81a452e808d161a04ca026cbd b94713084432ac3175e06de00fc9773d98296299Coexistence of technology and business dates back to the late 18th century when the first ever use of a computer was for recording the census by the US government in 1890. The use of technology in business can be ascribed to different organisations in the different nations on the parallel lines of time. It includes invention of cash machines and their use by Barclays in England in the early 1960s, the induction of telephone-based modems for order management by Baxter Pharmaceuticals and use of small desktop computing device called Minitel for processing customer orders in France were the other notable developments in the history of coexistence of technology and business. Increasing operations in the business functions have created an urge for the technological innovation in the industry to handle the operations electronically. Figueiredo and Cohen (2019) say that technology has become an indispensable component of every business function by delivering ease in operations and productivity. The end of the 20th century had witnessed the leaping progress in computing in the form of artificial intelligence (AI) performing the tasks that were unimaginable to comprehend a decade back in time. Developments in the technological research and development prove that organisations have started inducting AI into as many fields as possible at a considerable pace. As a part of the shifting technological dynamics in the industry HR function has also transformed digitally. Tools like enterprise applications have forayed intensely into the operations of human resources management (HRM). These enterprise resource planning (ERP) tools remain to primarily serve the integration of HRM to the other functions. However, enterprise tools could not serve the purpose of supporting decisiveness in the areas of HR planning, workforce design and performance management at large. However, Tuck (2019) argues that AI is assuming increased responsibilities in the different sections of the society and business including the HRM function. At present, the amount of knowledge on the status quo of the role of AI in the HRM functions is scarcely available. Literature related to this disruptive technology in the HR function is still at the nascent stage. This study will examine the role of AI as a key component in the HRM function, which is regarded to be highly human-driven.IMIB Journal of Innovation and Management00The role of AI is examined as a key component in the HRM function, which is regarded to be highly human-driven, which is regarded to be highly human-driven.IMIB Journal of Innovation and Management['Mohsin Khan', 'V. V. K. Reddy']2024-05-20T00:00:00https://www.semanticscholar.org/paper/b94713084432ac3175e06de00fc9773d98296299 6d4966df3afe6822341a1cdd594e36b24ef43144Hypertension Research10Hypertension research : official journal of the Japanese Society of Hypertension['Z. Karbasi', 'Michaeel Motaghi Niko', 'M. Zahmatkeshan']2024-05-20T00:00:00https://www.semanticscholar.org/paper/6d4966df3afe6822341a1cdd594e36b24ef43144 211453fea080cd167ec715d8ed6ef04f6ddcdee8Hybrid intelligence systems aim to leverage synergies in closely collaborating teams of humans and artificial intelligence (AI). To guide the realization of such teams, recent research proposed design patterns that capture role-based knowledge on human-AI collaborations. Building on these patterns requires hybrid intelligence systems to provide mechanisms that orchestrate human and AI contributions accordingly. So far, it is unclear if such mechanisms can be provided based on shared representations of the required knowledge. In this regard, we expect ontology-based data-centric process modeling to be a promising direction for hybrid intelligence systems that aim to support knowledge-intensive processes (KiPs). We illustrate this through exemplary process models (realized with our ontology- and data-driven business process model -- ODD-BP) that reflect the team design patterns for hybrid intelligence systems. We point out that relying on such process models enables multiple actors to fulfill roles jointly and allows them to address individual shortcomings. This is examined by discussing integrating large language models (LLMs) into the process models and describing how complementary AI actors could help to empower LLMs to fulfill their role in human-AI collaboration more comprehensively. Future work will extend the provided concepts while their evaluation initially focuses on the KiP of medical emergency call handling.Proceedings of the AAAI Symposium Series00Proceedings of the AAAI Symposium Series['Carsten Maletzki', 'E. Rietzke', 'Ralph Bergmann']2024-05-20T00:00:00https://www.semanticscholar.org/paper/211453fea080cd167ec715d8ed6ef04f6ddcdee8 25017d5311816d17ee5a1bc7f033a5fe6f809fbeDigital transformation and “artificial intelligence (AI)”—which can more adequately be called “data-based systems (DS)”—comprise ethical opportunities and risks. Therefore, it is necessary to identify precisely ethical opportunities and risks in order to be able to benefit sustainably from the opportunities and to master the risks. The UN General Assembly has recently adopted a resolution aiming for ‘safe, secure and trustworthy artificial intelligence systems’. It is now urgent to implement and build on the UN General Assembly Resolution. Allowing humans and the planet to flourish sustainably in peace and guaranteeing globally that human dignity is respected not only offline but also online, in the digital sphere, and in the domain of DS, requires two policy measures: (1) human rights-based data-based systems (HRBDS): HRBDS means that human rights serve as the basis of digital transformation and DS. (2) International Data-Based Systems Agency (IDA): IDA should be established at the UN as a platform for cooperation in the field of digital transformation and DS, fostering human rights, security, and peaceful uses of DS, as well as a global supervisory institution and regulatory authority in digital transformation and DS. The establishment of IDA is realistic because humanity has already shown in its past that we are able to not always “blindly” pursue the technically possible but also to limit ourselves to what is technically feasible when humanity and the planet are at stake. For instance, humans researched the field of nuclear technology, developed the atomic bomb, and detonated it several times. Nonetheless, the same humans limited research and development in the field of nuclear technology to prevent even worse consequences by establishing the International Atomic Energy Agency (IAEA) at the UN.Philosophies250It is necessary to identify precisely ethical opportunities and risks in order to be able to benefit sustainably from the opportunities and to master the risks of digital transformation and “artificial intelligence”.Philosophies['P. G. Kirchschlaeger']2024-05-20T00:00:00https://www.semanticscholar.org/paper/25017d5311816d17ee5a1bc7f033a5fe6f809fbe 8675da29283f1917ed2265a0607a16a8494642b5 As governments increasingly adopt algorithms and artificial intelligence (AAI), we still know comparatively little about citizens’ support for algorithmic government. In this paper, we analyze how many and what kind of reasons for government use of AAI citizens support. We use a sample of 17,000 respondents from 16 OECD countries and find that opinions on algorithmic government are divided. A narrow majority of people (55.6%) support a majority of reasons for using algorithmic government, and this is relatively consistent across countries. Results from multilevel models suggest that most of the cross-country variation is explained by individual-level characteristics, including age, education, gender, and income. Older and more educated respondents are more accepting of algorithmic government, while female and low-income respondents are less supportive. Finally, we classify the reasons for using algorithmic government into two types, “fairness” and “efficiency,” and find that support for them varies based on individuals’ political attitudes.Journal of Public Policy250This paper analyzes how many and what kind of reasons for government use of AAI citizens support, and classify the reasons for using algorithmic government into two types, “fairness” and “efficiency,” and finds that support for them varies based on individuals’ political attitudes.Journal of Public Policy['Dario Sidhu', 'Beatrice Magistro', 'Benjamin Allen Stevens', 'P. Loewen']2024-05-20T00:00:00https://www.semanticscholar.org/paper/8675da29283f1917ed2265a0607a16a8494642b5 d8dc3c46747b3d4551784d48484df13910a2a182The last few decades have been characterized by the most active development of medicine and genetics, leading to the emergence of new knowledge about the functioning of the human body. The identification of a genetic predisposition to the development of certain diseases requires a rethink of traditional approaches to the use of employee personal data, labor protection, and the balance of public and private interests in labor legislation. This article analyzes approaches to determining the legal nature of genetic information, identifies possible areas of restriction of workers’ rights depending on the identification of hereditary predisposition to certain diseases, draws a line between discrimination based on genetic characteristics and differentiation. In the context of the active digitalization of personnel processes, the risks of possible digital discrimination in the selection and evaluation of employees using artificial intelligence technologies are noted.Courier of the Kutafin Moscow State Law University40Courier of Kutafin Moscow State Law University (MSAL))['N. V. Chernyh']2024-05-20T00:00:00https://www.semanticscholar.org/paper/d8dc3c46747b3d4551784d48484df13910a2a182 18cfa66603f09627527fad6b2dbd7a4dba022e22The challenge of understanding how alterations in non-coding DNA regulate gene expression is substantial, with far-reaching consequences for the advancement of human genetics and disease research. Accurately predicting the up- and down-regulation of gene expression quantitative trait loci (eQTLs) offers a potential avenue to accelerate the identification of associations between non-coding variants and phenotypic traits. However, current methods for predicting the impact of non-coding mutations on gene expression changes fail to predict the sign of eQTLs accurately. Additionally, the requirement for tissue-specific training models within these methods restricts their applicability, especially when extending predictive abilities to single-cell resolution. In this study, we present EMO, an innovative transformer-based pre-trained method, designed to predict the up- and down-regulation of gene expression caused by single non-coding mutations using DNA sequences and ATAC-seq data. EMO extends the effective prediction range up to 1Mbp between the non-coding mutation and the transcription start site (TSS) of the target gene. It demonstrates competitive prediction performance across various variant TSS distances and surpasses the state-of-the-art structure. To assess its effectiveness, EMO was fine-tuned using eQTLs from two brain tissues for external validation. We also evaluated EMO’s transferability to single-cell resolution by fine-tuning it on eQTLs from six types of immune cells, achieving satisfactory results in each cell type (AUC > 0.860). Furthermore, EMO displayed promising potential in analyzing disease-associated eQTLs.bioRxiv320EMO, an innovative transformer-based pre-trained method, designed to predict the up- and down-regulation of gene expression caused by single non-coding mutations using DNA sequences and ATAC-seq data, demonstrates competitive prediction performance across various variant TSS distances and surpasses the state-of-the-art structure.bioRxiv['Zhe Liu', 'Yihang Bao', 'Weichen Song', 'G. Lin']2024-05-19T00:00:00https://www.semanticscholar.org/paper/18cfa66603f09627527fad6b2dbd7a4dba022e22 6c1343ac615b7d7307ac4fc1ad5759bdf6e931f8This research presents the development and implementation of a novel healthcare ecosystem leveraging Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning and Quantum Magnetic Resonance Analysis (QMRA) technology. The aim is to streamline medical diagnostics and treatment processes by integrating a hardware device capable of conducting comprehensive body scans in minutes, coupled with an Android application for data collection and presentation. The hardware device utilizes Quantum Magnetic Resonance Analyzer for rapid and accurate body scanning, enabling users to record symptoms through voice clips and capture images of wounds or other concerns. The collected data is securely transmitted to a backend database hosted on Firebase for storage and retrieval. The Android app facilitates seamless access to patient profiles by registered doctors, empowering them to analyze scan reports, review recorded symptoms, and view images for informed diagnosis and treatment planning. By combining IoT, AI, and Quantum Magnetic Resonance Analysis, this healthcare ecosystem offers a transformative approach to medical care, improving efficiency, accessibility, and accuracy in diagnosis and treatment.International Journal For Multidisciplinary Research50This research presents the development and implementation of a novel healthcare ecosystem leveraging Internet of Things (IoT), Artificial Intelligence (AI), Deep Learning and Quantum Magnetic Resonance Analysis (QMRA) technology to streamline medical diagnostics and treatment processes.International Journal For Multidisciplinary Research['Soyel Akter Habib', 'Md Maruf Billah', 'Marrimanu Hasif basha', 'Vipul Kumar']2024-05-19T00:00:00https://www.semanticscholar.org/paper/6c1343ac615b7d7307ac4fc1ad5759bdf6e931f8 89a6f47f45ebae467542a4ec7533742bc6bc88f5Purpose: The purpose of this study is to explore how artificial intelligence (AI) becomes a part of data visualization. Thus, data from complex datasets are transformed into dynamic, interactive, and personalized visual experiences that will help in deeper insights and actionable knowledge. The research is supposed to design a holistic system and rules for using AI to make data visualization more effective and super interactive for the users. Methodology: The methodology involves the in-depth examination of artificial intelligence-based data visualization tools and platforms by using case studies. The study analyses the impact of AI technologies such as machine learning, natural language processing, and augmented and virtual reality on the scalability, interactivity, and personalization of data visualizations. The sentence also talks about the analysis of the moral factors that are part of the process of introducing AI in data visualization. Findings: The findings indicate that AI greatly improves the process and the quality of data visualization, thus, it makes possible the management of big, complicated, multi-dimensional datasets in a more efficient and precise way. The AI-driven tools give the users the opportunity to see the actions that are happening in real-time, predict the results, and personalize the tools according to their individual needs, thereby increasing the decision-making processes. Furthermore, ethical issues like data privacy, bias, and transparency must be well managed. This research has the distinctive feature of providing a theoretical framework that emphasizes the importance of AI in the development of data visualization technologies. Unique contribution to theory, policy and practice: In practice, it gives the rules for the implementation of AI tools to achieve more effective and user-focused visualizations. The policy focuses on the necessity of ethical standards in AI deployments, which means the data visualization practices should be transparent, accountable, and bias-free, thus creating trust and reliability in the AI applications.Journal of Technology and Systems260It is indicated that AI greatly improves the process and the quality of data visualization, thus, it makes possible the management of big, complicated, multi-dimensional datasets in a more efficient and precise way.Journal of Technology and Systems['Siva Karthik Devineni']2024-05-19T00:00:00https://www.semanticscholar.org/paper/89a6f47f45ebae467542a4ec7533742bc6bc88f5 d5e6b953b3b3d2d588e46c9055094c70803397ae. The advent of artificial intelligence (AI) in academic settings has presented educators with unprecedented challenges in maintaining academic integrity. This study examines the efficacy of three AI-powered text-checking services — Quillbot, HiveModeration, and ZeroGPT — in detecting AI-generated content and alterations in academic texts. The investigation employs five text variants: text generated by ChatGPT, original pre-AI era text, AI-rephrased text (Quillbot), Quillbot-rephrased original text, and edited (minor edits) original text by DeepL and Grammarly services. All three services exhibit 100% accuracy in detecting AI-generated text while failing to identify AI signs in original English text.Наукові інновації та передові технології00This study examines the efficacy of three AI-powered text-checking services — Quillbot, HiveModeration, and ZeroGPT — in detecting AI-generated content and alterations in academic texts.Наукові інновації та передові технології['Валентина Лук’яненко', 'Ірина Шастко', 'Oksana Korbut']2024-05-19T00:00:00https://www.semanticscholar.org/paper/d5e6b953b3b3d2d588e46c9055094c70803397ae 66d5a27cf45b5355ac3b1608826dfb083c8d4516Explainable Artificial Intelligence (XAI) has emerged as a critical aspect of machine learning models, particularly in domains where transparency and interpretability are paramount. In this study, we present an enhanced deep learning framework leveraging XAI techniques for improved model interpretability and decision understanding. Our methodology encompasses preprocessing steps such as image conversion to numpy arrays, visualization of grey scale histograms, data augmentation, and image enhancement through contrast stretching and histogram equalization. Additionally, we integrate Explainable AI methods including LIME, SHAP, RISE, MFPP, and LRP to provide insights into the model's decision-making process. Through these techniques, we aim to elucidate the underlying factors influencing model predictions, thereby fostering trust and facilitating domain expert understanding. Experimental results demonstrate the efficacy of our approach in enhancing model interpretability while maintaining high predictive performance. This research contributes to the advancement of XAI methodologies, offering a transparent and interpretable framework applicable across various domainsInternational Journal of Advanced Research in Science, Communication and Technology170This study presents an enhanced deep learning framework leveraging XAI techniques for improved model interpretability and decision understanding, and integrates Explainable AI methods including LIME, SHAP, RISE, MFPP, and LRP to provide insights into the model's decision-making process.International Journal of Advanced Research in Science, Communication and Technology['Siddhi Kore', 'Prasad Nakhate', 'Yash Rajput', 'Sanket Zambare']2024-05-19T00:00:00https://www.semanticscholar.org/paper/66d5a27cf45b5355ac3b1608826dfb083c8d4516 0fcac3c045e7bbf2640e093f311698fc185ba5a4Environment and Planning B Urban Analytics and City Science50Environment and Planning B: Urban Analytics and City Science['Anthony Gar-On Yeh']2024-05-19T00:00:00https://www.semanticscholar.org/paper/0fcac3c045e7bbf2640e093f311698fc185ba5a4 0cb5d2b065dbe103596af6a6e80c5904910200b6enadakultura00enadakultura['Andreea-Roxana Morar']2024-05-19T00:00:00https://www.semanticscholar.org/paper/0cb5d2b065dbe103596af6a6e80c5904910200b6 6a11a2f2e2c08ded3f42d5f0f5187b7bbe10168dSustainable development goals challenge the carbon lock-in dilemma of the industrial economy, and identifying the motivation and mechanism behind carbon unlocking has become an urgent priority. With its inclusive and precise advantages, digital finance (DF) provides a new impetus for the economy’s low-carbon transformation, while reasonable environmental regulation (ER) acts as an important guiding constraint. We focus on the carbon unlocking performance of DF under ER constraints. After constructing and calculating the industrial carbon unlocking efficiency (ICUE), we observe the trends of ICUE fluctuating positively, clustering towards the eastern region, and polarization. Subsequently, based on theoretical analyses, we explore the marginal and configuration effects of DF and ER in improving ICUE using panel data from 30 Chinese provinces between 2011 and 2021 and adopt a mixed research method with regression analysis (Tobit hierarchical regression and quantile regression for panel data (QRPD)) and dynamic fuzzy-set qualitative comparative analysis (fsQCA). The regression analysis results show that DF can notably enhance China’s provincial ICUE, with ER generally serving as a positive moderator; however, the unlocking potential of informal environmental regulations needs further exploration. As ICUE improves in a specific location or time, the positive contribution of DF to ICUE also increases, whereas the moderating effect of ER exhibits an optimal range and follows an inverted U-shape. The dynamic fsQCA results support the findings of the regression analysis and further emphasize that effective cooperation between DF and ER is crucial for high ICUE, while inadequate DF support and the absence of formal environmental regulations remain bottlenecks in industrial carbon lock-in. Moreover, configuration paths demonstrate clear path dependency in both time and space, indicating a prolonged unlocking endeavor.Sustainability920Sustainability['Weicheng Xu', 'Hanxia Li']2024-05-19T00:00:00https://www.semanticscholar.org/paper/6a11a2f2e2c08ded3f42d5f0f5187b7bbe10168d 6e90e89fe6d0f80bcb87e19c3a1e22a556852d16Robust access to trustworthy information is a critical need for society with implications for knowledge production, public health education, and promoting informed citizenry in democratic societies. Generative AI technologies may enable new ways to access information and improve effectiveness of existing information retrieval systems but we are only starting to understand and grapple with their long-term social implications. In this chapter, we present an overview of some of the systemic consequences and risks of employing generative AI in the context of information access. We also provide recommendations for evaluation and mitigation, and discuss challenges for future research.2140This chapter presents an overview of some of the systemic consequences and risks of employing generative AI in the context of information access and provides recommendations for evaluation and mitigation, and discusses challenges for future research.['Bhaskar Mitra', 'Henriette Cramer', 'Olya Gurevich']2024-05-19T00:00:00https://www.semanticscholar.org/paper/6e90e89fe6d0f80bcb87e19c3a1e22a556852d16 f19eaf11d301183dd3fe6bed0b62a5797f528bcePurpose: This study aims to examine the threats of Artificial Intelligence with special reference to Education sector, and further navigate the challenges and understand how to convert these threats into potential opportunities - particularly in India. Design/methodology/approach: This is a conceptual study involving in person interview with a few AI- experts from the industry and academicians. Findings: The findings of this research call for a nuanced and context-specific approach, recognizing that the impact of AI in India may differ from global trends. By fostering a collaborative and forward-thinking mindset, India can position itself to leverage the transformative power of AI in education while minimizing its potential threats. Research limitations/implications: This research paper predominantly focuses on the threats and potential opportunities of AI in the education sector, wherein other sectors are not focused. There is ample scope to perform empirical study on this topic. Practical implications: The findings and inferences of this paper can be utilized by Education institutions for restructuring their policies and for further efficient administration. Originality/value: The value of the study is to educational institutions and related organizations seeking for the role of artificial intelligence in education. Keywords: (AI) Artificial Intelligence, Threats, Challenges, Opportunities. Education sectorINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The threats of Artificial Intelligence with special reference to Education sector is examined to navigate the challenges and understand how to convert these threats into potential opportunities - particularly in India.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Mr. Kiran Suraj S']2024-05-19T00:00:00https://www.semanticscholar.org/paper/f19eaf11d301183dd3fe6bed0b62a5797f528bce 7012976d3a98df8358df3bb56db76fabc64e2f13مجلة کلیة التربیة00مجلة کلية التربية (أسيوط)['ِِِAshwag Almethen']2024-05-19T00:00:00https://www.semanticscholar.org/paper/7012976d3a98df8358df3bb56db76fabc64e2f13 0d3502383638848451662ddf1623e2e0cc4050c8مجلة کلیة التربیة00مجلة کلية التربية (أسيوط)['Maryam Alomair']2024-05-19T00:00:00https://www.semanticscholar.org/paper/0d3502383638848451662ddf1623e2e0cc4050c8 7949b536042a64b6bfce07b675a47a5fc4a8d493AI &amp; SOCIETY380AI &amp; SOCIETY['José Luis Guerrero Quiñones']2024-05-19T00:00:00https://www.semanticscholar.org/paper/7949b536042a64b6bfce07b675a47a5fc4a8d493 dc000aab7c46587489b7c77173cf8619da5dac1eenadakultura00enadakultura['Nino Tskhakaia']2024-05-19T00:00:00https://www.semanticscholar.org/paper/dc000aab7c46587489b7c77173cf8619da5dac1e ca8d30c4c7dd1e23448b8d9dcd953f19a0574054ACS Synthetic Biology10ACS synthetic biology['Arik Shams', 'Alexandria Fischer', 'Anastasia Bodnar', 'Melinda Kliegman']2024-05-18T00:00:00https://www.semanticscholar.org/paper/ca8d30c4c7dd1e23448b8d9dcd953f19a0574054 b834a17acc9f84e22d7e57662d35dcb188b339b6Environment, Development and Sustainability180Environment, Development and Sustainability['Jie Wei', 'Yining Li', 'Yushun Liu']2024-05-18T00:00:00https://www.semanticscholar.org/paper/b834a17acc9f84e22d7e57662d35dcb188b339b6 d4953a63a3c14d968e9692940be6397199b382d0Despite the growing concerns surrounding algorithmic biases in generative AI (artificial intelligence), there is a noticeable lack of research on how to facilitate children and young people’s awareness and understanding of them. This study aimed to address this gap by conducting hands-on workshops with fourth- and seventh-grade students in Finland, and by focusing on students’ ( N = 209) evolving explanations of the potential causes of algorithmic biases within text-to-image generative models. Statistically significant progress in children’s data-driven explanations was observed on a written reasoning test, which was administered prior to and after the intervention, as well as in their responses to the worksheets they filled out during a lesson that focused on algorithmic biases. The article concludes with a discussion on the development and facilitation of children’s understanding of algorithmic biases.New Media &amp; Society291Hand-on workshops with fourth- and seventh-grade students in Finland focused on students’ evolving explanations of the potential causes of algorithmic biases within text-to-image generative models, resulting in statistically significant progress in children’s data-driven explanations.New Media &amp; Society['Henriikka Vartiainen', 'J. Kahila', 'M. Tedre', 'Sonsoles López-Pernas', 'Nicolas Pope']2024-05-18T00:00:00https://www.semanticscholar.org/paper/d4953a63a3c14d968e9692940be6397199b382d0 2a3badba902e89effce702d94ea287e14c44af4fIntegrating artificial intelligence (AI) in the construction industry could revolutionise workplace safety and efficiency. However, this integration also carries complex socio-legal implications that require further investigation. Presently, there is a research gap in the socio-legal dimensions of AI use to enhance health and safety regulations and protocols for the construction sector in the United Kingdom, particularly in understanding how the existing legal frameworks can adapt to AI integration effectively. Comprehensive research is indispensable to identify where the existing regulations may fall short or require more specificity in addressing the unique implications introduced by AI technologies. This article aims to address the pressing socio-legal challenges surrounding the integration of AI in the UK construction industry, specifically in enhancing health and safety protocols on construction sites, through a systematic review encompassing the PRISMA protocol. The review has identified that the existing legal and regulatory framework provides a strong foundation for risk management. Still, it needs to sufficiently account for the socio-legal dimensions introduced by AI deployment and how AI may evolve in the future. The Health and Safety Executive (HSE) will require standardised authorities to effectively oversee the use of AI in the UK construction industry. This will enable the HSE to collect data related to AI processes and carry out technical, empirical, and governance audits. The provision of sufficient resources and the empowerment of the HSE within the context of the construction industry are critical factors that must be taken into consideration to ensure effective oversight of AI implementation.Buildings110Buildings['A. Agapiou']2024-05-18T00:00:00https://www.semanticscholar.org/paper/2a3badba902e89effce702d94ea287e14c44af4f 7bc65f73c44da3930995d103608c0492f47512d0Multi-omic data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data. Graph neural network (GNN) AI models have been widely used to analyze graph-structure datasets and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data by node and edge ranking analysis for signaling flow/cascade inference. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert them into graph-structure data for individual samples, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), a novel computational tool that generates multi-omics signaling graphs of individual samples by mapping the multi-omics data onto a biologically meaningful multi-level background signaling network. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. We evaluated the mosGraphGen using both multi-omics datasets of cancer and Alzheimer’s disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/Multi-OmicGraphBuilder/mosGraphGenbioRxiv440mosGraphGen (multi-omics signaling graph generator), a novel computational tool that generates multi-omics signaling graphs of individual samples by mapping the multi-omics data onto a biologically meaningful multi-level background signaling network, is developed.bioRxiv['Heming Zhang', 'Dekang Cao', 'Zirui Chen', 'Xiuyuan Zhang', 'Yixin Chen', 'Cole Sessions', 'Carlos Cruchaga', 'Philip Payne', 'Guangfu Li', 'Michael Province', 'Fuhai Li']2024-05-18T00:00:00https://www.semanticscholar.org/paper/7bc65f73c44da3930995d103608c0492f47512d0 a72ffa3ab19c1b1cb8c98009839418e02b5b393bReview of Ethan Mollick's 2024 book 'Co-Intelligence: Living and Working with AI'.Canadian journal of information and library science00The Canadian Journal of Information and Library Science['Michael Ridley']2024-05-18T00:00:00https://www.semanticscholar.org/paper/a72ffa3ab19c1b1cb8c98009839418e02b5b393b f45178f66ff48aaff6aebfd83bcbc58742d3ea18The journal of the International Association of Medical Science Educators : JIAMSE10A targeted approach to learning incorporating Adult Learning Theory can foster student development of critical thinking for translation to appropriate use in clinical practice.Medical Science Educator['Stacia Hall Thompson', 'Patricia Fecher']2024-05-18T00:00:00https://www.semanticscholar.org/paper/f45178f66ff48aaff6aebfd83bcbc58742d3ea18 ec683de114ea87a99a830a575e3f49bff85b22b3The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.Electronics00This survey reviews related works in this field of network softwarization, with a particular focus on the specific role of network softwarization, finding a lack of integrated practical implementations and a potential desynchronization with current standards.Electronics['Elisa Rojas', 'David Carrascal', 'Diego Lopez-Pajares', 'Joaquin Alvarez-Horcajo', 'J. A. Carral', 'J. M. Arco', 'I. Martínez-Yelmo']2024-05-18T00:00:00https://www.semanticscholar.org/paper/ec683de114ea87a99a830a575e3f49bff85b22b3 816c1e6f59437cd24b97fa9a6c26054c1c8e9d85This survey delves into the transformative role of Artificial Intelligence (AI) in healthcare, examining its multifaceted impacts on patient care, operational efficiency, and medical research. Through a comprehensive analysis of existing literature and empirical data, the abstract elucidates AI's potential to revolutionize diagnostics, treatment planning, and disease management. Additionally, it explores the ethical and regulatory challenges surrounding AI integration in healthcare systems, highlighting the imperative for responsible implementation to ensure equitable access and patient privacy. Overall, this survey offers valuable insights into the evolving landscape of AI-driven healthcare and underscores the necessity for ongoing research and collaboration to maximize its benefits while mitigating potential risksInternational Journal of Advanced Research in Science, Communication and Technology170This survey delves into the transformative role of Artificial Intelligence in healthcare, examining its multifaceted impacts on patient care, operational efficiency, and medical research and explores the ethical and regulatory challenges surrounding AI integration in healthcare systems.International Journal of Advanced Research in Science, Communication and Technology['Mr. Thitme Vijay Vitthal']2024-05-18T00:00:00https://www.semanticscholar.org/paper/816c1e6f59437cd24b97fa9a6c26054c1c8e9d85 d9eeed0c7e4781cbf2fddae1a9cd51c05b0769d8Cognition, Technology &amp; Work650Several design recommendations were developed for the HCI community to guide how AI teammates should share decision information with their human counterparts considering the careful balance between trust and competence in human-AI teams.Cognition, Technology &amp; Work['Allyson I. Hauptman', 'Beau G. Schelble', 'Wen Duan', 'Christopher Flathmann', 'Nathan J. Mcneese']2024-05-18T00:00:00https://www.semanticscholar.org/paper/d9eeed0c7e4781cbf2fddae1a9cd51c05b0769d8 07d1c1fd13061c54efdc8abdc2a2c57e7a841cb6In the realm of law enforcement, the accurate and timely filing of First Information Reports (FIRs) stands as a crucial step in the initiation of criminal investigations. However, this process is often fraught with challenges, ranging from the complexity of legal frameworks to the potential for inaccuracies in manual documentation. In recent years, the modernization of law enforcement practices has been propelled by advancements in technology, offering promising solutions to address these challenges. This survey paper aims to provide a comprehensive overview of the landscape of technological solutions designed to enhance FIR filing processes, with a specific focus on their application in law enforcement contexts.International Journal of Innovative Science and Research Technology261This survey paper aims to provide a comprehensive overview of the landscape of technological solutions designed to enhance FIR filing processes, with a specific focus on their application in law enforcement contexts.International Journal of Innovative Science and Research Technology (IJISRT)['Aniket Chaudhari', 'Bhavesh Amborkar', 'Om Deshmukh', 'Ashwini Bhide', 'Asmita Kamble']2024-05-18T00:00:00https://www.semanticscholar.org/paper/07d1c1fd13061c54efdc8abdc2a2c57e7a841cb6 a449d9f218d93c16dbedfc2756782ce87efa82b7Environmental Modeling &amp; Assessment660Environmental Modeling &amp; Assessment['R. Pushpalatha', 'T. Roshni', 'Byju Gangadharan', 'Govindan Kutty']2024-05-18T00:00:00https://www.semanticscholar.org/paper/a449d9f218d93c16dbedfc2756782ce87efa82b7 c6d0e12582094621847d3ec5cadb4ee538684573Radiography180ATC and SVO enable the radiographer to save time during chest or stitched examinations and reduce machine interactions during chest examinations, as well as support the radiographer during the image acquisition by providing a more efficient workflow.Radiography['A. Rasche', 'P. Brader', 'J. Borggrefe', 'H. Seuss', 'Z. Carr', 'A. Hebecker', 'G. ten Cate']2024-05-18T00:00:00https://www.semanticscholar.org/paper/c6d0e12582094621847d3ec5cadb4ee538684573 026271eba6cac6a805875aaacf5987bfecd3b93dPurpose: While organisations struggle to improve efficiency and productivity, the interaction between AI, organisational behaviour, and communication dynamics tends to be complex in the rapidly evolving modern workplace. Moreover, understanding the implications of this intersection is necessary to encourage a balanced and effective workplace. Hence, employing AI technologies in organisational structures introduces challenges such as workforce adaptation, psychological impact, and communication patterns. This research explored how AI influences organisational behaviour and communication dynamics in the modern workplace. Study design/methodology/approach: This study used a mixed method to explore organisational behaviour and communication dynamics across diverse industries comprehensively. This questionnaire aimed to gather respondents’ experiences, focusing on organisational behaviour and communication dynamics. Furthermore, in-depth interviews were conducted with key stakeholders (n = 13) to obtain additional insights into goodness and trustworthiness. These interviews offered more depth and richness, providing insights into organisational settings. This mixed methodology ensured a complex understanding of the research problem, leveraging quantitative and qualitative data to enhance the analysis. Findings: The research findings revealed the pathways to how AI influences organisational behaviour and communication dynamics. Initial analysis of survey data and interviews offered insights into employee perceptions, identifying potential challenges and opportunities. One major trait emerging from data analysis is employees’ unstable levels of apprehension and enthusiasm regarding AI integration. While many view it as a mechanism for efficiency, productivity, and innovation, others indicate distress about job shifts and loss of autonomy. Understanding and adopting these opposing insights are fundamental for encouraging a positive organisational ecosystem for AI readiness and adoption. Originality/value: By examining the impact of AI on employees’ attitudes, collaboration, and communication patterns, this research contributes viable insights for organisations navigating the integration of AI into their structures.International Journal of Management, Knowledge and Learning00By examining the impact of AI on employees’ attitudes, collaboration, and communication patterns, this research contributes viable insights for organisations navigating the integration of AI into their structures.International Journal of Management, Knowledge and Learning['Ioseb Gabelaia', 'Ramunė Bagočiūnaitė', 'Viktorija Navickienė']2024-05-18T00:00:00https://www.semanticscholar.org/paper/026271eba6cac6a805875aaacf5987bfecd3b93d 25e7c9665395862ca3537e987f335c4523867d14Purpose: The purpose of the research was to identify a feasible way to support the ethical use of AI in universities based on identifying how the AI software application ChatGPT is used in education at higher education institutions and with students in different majors. Study design/methodology/approach: The research methodology consisted of the following stages: (1) a survey based on a questionnaire designed considering the Technology Acceptance Model (TAM) framework to collect students’ opinions on the use of AI ChatGPT; (2) The results of the survey were used to design the materials of the AI Transmedia Campaign, but also to identify the best distribution channels of them; (3) After implementing the AI Transmedia Campaign, a feedback survey was developed, ascertaining the effectiveness of the approach to create an ethical behaviour towards the AI software application in general, and on AI ChatGPT, in particular. Findings: Research shows a gap in regulating the ethical use of AI applications in higher education. Thus, the AI Transmedia Campaign has been well received and appreciated by all categories of students. Originality/value: The research is international and was carried out in higher education institutions in Romania, Greece, and Slovenia (the size of the research sample, 2942, proves the scope of the study). The research results have demonstrated and characterised the students’ behaviour (the cognitive response and the intention to use) towards the use of AI ChatGPT and the utility of the AI Transmedia Campaign realised in the context of the implementation of the RespectNET project (2021-1-IT02-KA220-HED-000027578, https://respectnet.eu/).International Journal of Management, Knowledge and Learning00The research results have demonstrated and characterised the students’ behaviour (the cognitive response and the intention to use) towards the use of AI ChatGPT and the utility of the AI Transmedia Campaign realised in the context of the implementation of the RespectNET project.International Journal of Management, Knowledge and Learning['A. Draghici', 'C. Luminosu', 'Angela Repanovici', 'Manolis Koukourakis', 'Valerij Dermol', 'I. Taucean']2024-05-18T00:00:00https://www.semanticscholar.org/paper/25e7c9665395862ca3537e987f335c4523867d14 2f98aa97e08ef0b4a6304523dad496f254d6834eThe advancement of Artificial Intelligence (AI) poses challenges in the field of bioethics, especially concerning issues related to life and death. AI has permeated areas such as health and research, generating ethical dilemmas and questions about privacy, decision-making, and access to technology. Life and death have been recurring human concerns, particularly in connection with depression. AI has created systems like Thanabots or Deadbots, which digitally recreate deceased individuals and allow interactions with them. These systems rely on information generated by AI users during their lifetime, raising ethical and emotional questions about the authenticity and purpose of these recreations. AI acts as a mediator between life, death, and the human being, enabling a new form of communication with the deceased. However, this raises ethical issues such as informed consent from users and the limits of digital recreation. Companies offer services like the Digital Resurrection of deceased individuals and the generation of hyper-realistic avatars. Still, concerns arise about the authenticity of these representations and their long-term emotional impact. Interaction with Thanabots may alter perceptions of death and finitude, leading to a potential “postmortal society” where death is no longer viewed as a definitive end. Nevertheless, this raises questions about the value of life and the authenticity of human experiences. AI becomes a bridge between the living and the dead, partially replacing rituals and mystical beliefs. As technology advances, there will be a need for greater transparency in interacting with AI systems and ethical reflections on the role of these technologies in shaping perceptions of life and death. Ultimately, the question arises of whether we should allow the dead to rest in peace and how to balance the pursuit of emotional relief with authenticity and respect for the memory of the deceased. A deeper ethical consideration is needed on how AI alters traditional notions of life, death, and communication in contemporary society. In this research, an interdisciplinary approach was utilized to conduct a comprehensive systematic review of the recent academic literature, followed by a detailed analysis of two key texts. Central ideas were extracted, and recurring themes were identified. Finally, a reflective analysis of the findings was conducted, yielding significant conclusions and recommendations for future research.Philosophies190A deeper ethical consideration is needed on how AI alters traditional notions of life, death, and communication in contemporary society on how AI becomes a bridge between the living and the dead.Philosophies['Hugo Rodríguez Reséndíz', 'Juvenal Rodríguez Reséndiz']2024-05-18T00:00:00https://www.semanticscholar.org/paper/2f98aa97e08ef0b4a6304523dad496f254d6834e 0bdce47b73e2fc28df0e9890ccab7f7c1c71518aGenerative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective interaction with Generative and conventional AI technologies are common challenges across all domains. Tertiary education institutions are uniquely positioned to address this void. In this article, we present the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting. In comparison to related work in AI literacy, our design is inclusive due to the collaborative approach between multiple stakeholder groups and is comprehensive given the descriptive formulation of the primary constructs of this module with exemplars of how they activate core operational competence across the four groups.Machine Learning and Knowledge Extraction370This article presents the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting.Machine Learning and Knowledge Extraction['Daswin de Silva', 'Shalinka Jayatilleke', 'Mona El-Ayoubi', 'Zafar Issadeen', 'Harsha Moraliyage', 'Nishan Mills']2024-05-18T00:00:00https://www.semanticscholar.org/paper/0bdce47b73e2fc28df0e9890ccab7f7c1c71518a 933f1889dc27ee3a103bc6a6ac9e4152d8c0cba6The study aims to find how much cost effectiveness is achieved when e-commerce supply chain operations use automation and AI technologies. therefore,ing technology of AI and its models have essentially proved to be impactful in making data driven decisions by the organisations gaining leverage in productivity and profit sustainability in a way that the companies achieve competitive advantage therefore creating sustainable value propositions from business and economics perspective. Aim/Purpose The aim of the paper is to explore the positive relationship between AI technologies and productivity levels in the e-commerce supply chain sector through achieving cost effectiveness and economic growth. Methodology/Approach The study focussed on reviewing literature on how Artificial Intelligence driven technology can optimise various supply chain functions within e-commerce sector. Solow-Swan growth model has been applied to investigate the value AI creates in utilising capital, labour input to achieve output growth. Positivist approach that allowed for objective observation and independent conclusions has been adopted. For primary data, quantitative methodology is used through survey questionnaires to gather data from a sample of 206 employees, managers, data analysts in e-commerce supply chain sectors. Findings The findings from the secondary sources inform that the use of AI and automation in the e-commerce industry leads to a high rate of productivity in terms of reducing costs and promoting economic growth. The primary research methods, through survey questionnaires collects real-time data that helps achieve quantifiable and measurable values to conclude that AI-led technology can increase productivity and competitive advantage as it saves cost while increasing productivity and overall economic input. Descriptive statistics of measures of central tendency were used to present findings in simpler, presentable way followed by interpretations of the data in percentages. Practical implications Managers and decision-making directorial board members have important lessons to learn from these findings as the quantifiable values may give them an insight into how much capital investment should be allocated for AI technologies and how predictive analytics and data analysis can accelerate their service towards becoming customer centric. Significant strategic planning and implementation of resource management can lead to higher rates of productivity profitability and eventually higher economic growth.The Business and Management Review00The study aims to find how much cost effectiveness is achieved when e-commerce supply chain operations use automation and AI technologies to conclude that AI-led technology can increase productivity and competitive advantage as it saves cost while increasing productivity and overall economic input.The Business and Management Review['Kripalini Saginala', 'Flomny Menon']2024-05-18T00:00:00https://www.semanticscholar.org/paper/933f1889dc27ee3a103bc6a6ac9e4152d8c0cba6 beaded025f297f4e8ad0fddf1d9c39c88bd49d2cScientific Reports380The CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), and effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis.Scientific Reports['Qingjiang Li', 'Guilin Zou', 'Wenlong Zeng', 'Jie Gao', 'Feipeng He', 'Yujun Zhang']2024-05-18T00:00:00https://www.semanticscholar.org/paper/beaded025f297f4e8ad0fddf1d9c39c88bd49d2c 271379a3315afddb105d5ce1a02b77a7e382b89eAbstract: This paper focuses on the how Game Playing is an important domain of Artificial intelligence .AI is also used in game playing. Various researchers have extensively contributed for computer game –playing methods. Games don’t require much knowledge: the only knowledge we need to provide is the rules, legal moves and the condition of winning or losing Game . Both players try to win the game . so both of them try to make the best possible move at each turn. Shannon’s paper on chess-playing and Samuel’s checkers are considered as land marks in the area of computer game playing. We also explain in this paper major components of game playing program , static evaluation function generator, game playing strategies: i) minimax strategy ii) minimax strategy with alpha – beta cutoffs.International Journal For Multidisciplinary Research00Major components of game playing program, static evaluation function generator, game playing strategies: i) minimax strategy ii) minimax strategy with alpha – beta cutoffs and ii) minimax strategy with alpha – beta cutoffs are explained.International Journal For Multidisciplinary Research['Vanita']2024-05-18T00:00:00https://www.semanticscholar.org/paper/271379a3315afddb105d5ce1a02b77a7e382b89e 5b08b24ec11e6a1da95039b6ac6831eb30218140Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.Cardiology in Review1400This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts, and provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research.Cardiology in review['Muhammad Umer Riaz Gondal', 'Hassan Atta Mehdi', 'R. R. Khenhrani', 'Neha Kumari', 'Muhammad Faizan Ali', 'Sooraj Kumar', 'Maria Faraz', 'Jahanzeb Malik']2024-05-18T00:00:00https://www.semanticscholar.org/paper/5b08b24ec11e6a1da95039b6ac6831eb30218140 2633a948f06a02417a39c9ff4e9c948bbad460d7Reflections on Artificial Intelligence for Humanity570This paper discusses how this change is happening and explores the reasons behind this change and discusses whether artificial intelligence and automation will completely replace workers in some industries or are they a mean to enhance workers’ productivity.{'pages': '53-67'}['Yuko Harayama', 'Michela Milano', 'Richard Baldwin', 'Céline Antonin', 'Janine Berg', 'Anousheh Karvar', 'Andrew Wyckoff']2024-05-18T00:00:00https://www.semanticscholar.org/paper/2633a948f06a02417a39c9ff4e9c948bbad460d7 34c89161a86ca0e8c97063c97aa68f1f6902814cAI &amp; SOCIETY140AI &amp; SOCIETY['Harry Collins']2024-05-18T00:00:00https://www.semanticscholar.org/paper/34c89161a86ca0e8c97063c97aa68f1f6902814c 59958a3be33e81f7e9343d72f89c70c1419a86c1AI &amp; SOCIETY260The notion of the dividual is presented as the logic that characterises the human–machine relationship in the case of artificial intelligence and as the horizon of what Felix Guattari called “machinic capitalism”.AI &amp; SOCIETY['Joan Rovira Martorell', 'Francisco Tirado', 'José Luís Blasco', 'Ana Gálvez']2024-05-18T00:00:00https://www.semanticscholar.org/paper/59958a3be33e81f7e9343d72f89c70c1419a86c1 da42a7a02977da7dd98177c9dc21a7289d1e97c3Education and Information Technologies : Official Journal of the IFIP technical committee on Education560Education and Information Technologies['Dongkuk Lee', 'Hyuksoo Kwon']2024-05-18T00:00:00https://www.semanticscholar.org/paper/da42a7a02977da7dd98177c9dc21a7289d1e97c3 5304d1caed2b9f5c50cdda13faabf2694d63040aUnited European Gastroenterology journal70United European gastroenterology journal['M. Iacucci', 'Yasuharu Maeda', 'Subrata Ghosh']2024-05-18T00:00:00https://www.semanticscholar.org/paper/5304d1caed2b9f5c50cdda13faabf2694d63040a a5064d09c862082bfbd8be05facfeeebf4ec39efNature Network Boston00Nature medicine['Brian Anderson']2024-05-17T00:00:00https://www.semanticscholar.org/paper/a5064d09c862082bfbd8be05facfeeebf4ec39ef b3155f0f2a133beb41f5f4ece9d089bfbc485679Purpose This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies. Design/methodology/approach This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI. Findings By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI. Originality/value Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm. Transforming Government: People, Process and Policy440The role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm are underscored.Transforming Government: People, Process and Policy['Yucong Lao', 'Yukun You']2024-05-17T00:00:00https://www.semanticscholar.org/paper/b3155f0f2a133beb41f5f4ece9d089bfbc485679 22db1da2e295f4f513cc1e08f659055f79ab435dThis paper examines the use of training compute thresholds as a tool for governing artificial intelligence (AI) systems. We argue that compute thresholds serve as a valuable trigger for further evaluation of AI models, rather than being the sole determinant of the regulation. Key advantages of compute thresholds include their correlation with model capabilities and risks, quantifiability, ease of measurement, robustness to circumvention, knowability before model development and deployment, potential for external verification, and targeted scope. Compute thresholds provide a practical starting point for identifying potentially high-risk models and can be used as an initial filter in AI governance frameworks alongside other sector-specific regulations and broader governance measures.50It is argued that compute thresholds serve as a valuable trigger for further evaluation of AI models, rather than being the sole determinant of the regulation.['Lennart Heim']2024-05-17T00:00:00https://www.semanticscholar.org/paper/22db1da2e295f4f513cc1e08f659055f79ab435d 2c93af417fb75f28b209604b90a54f75fb4209daAI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In this position paper we explore how contestability can be achieved computationally in and for AI. We argue that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can (i) interact with humans and/or other machines to progressively explain their outputs and/or their reasoning as well as assess grounds for contestation provided by these humans and/or other machines, and (ii) revise their decision-making processes to redress any issues successfully raised during contestation. Given that much of the current AI landscape is tailored to static AIs, the need to accommodate contestability will require a radical rethinking, that, we argue, computational argumentation is ideally suited to support.1200It is argued that contestable AI requires dynamic (human-machine and/or machine-machine) explainability and decision-making processes, whereby machines can interact with humans and/or other machines to progressively explain their outputs and/or their reasoning and revise their decision-making processes to redress any issues successfully raised during contestation.['Francesco Leofante', 'Hamed Ayoobi', 'Adam Dejl', 'Gabriel Freedman', 'Deniz Gorur', 'Junqi Jiang', 'Guilherme Paulino-Passos', 'Antonio Rago', 'Anna Rapberger', 'Fabrizio Russo', 'Xiang Yin', 'Dekai Zhang', 'Francesca Toni']2024-05-17T00:00:00https://www.semanticscholar.org/paper/2c93af417fb75f28b209604b90a54f75fb4209da a2a54dfa877512091b02c449bb1ec619503b24bfRegulation by design (RBD) is a growing research field that explores, develops, and criticises the regulative function of design. In this article, we provide a qualitative thematic synthesis of the existing literature. The aim is to explore and analyse RBD’s core features, practices, limitations, and related governance implications. To fulfil this aim, we examine the extant literature on RBD in the context of digital technologies. We start by identifying and structuring the core features of RBD, namely the goals, regulators, regulatees, methods, and technologies. Building on that structure, we distinguish among three types of RBD practices: compliance by design, value creation by design, and optimisation by design. We then explore the challenges and limitations of RBD practices, which stem from risks associated with compliance by design, contextual limitations, or methodological uncertainty. Finally, we examine the governance implications of RBD and outline possible future directions of the research field and its practices.Social Science Research Network751This article provides a qualitative thematic synthesis of the existing literature on Regulation by design (RBD) and distinguishes among three types of RBD practices: compliance by design, value creation by design, and optimisation by design.SSRN Electronic Journal['Kostina Prifti', 'Jessica Morley', 'Claudio Novelli', 'Luciano Floridi']2024-05-17T00:00:00https://www.semanticscholar.org/paper/a2a54dfa877512091b02c449bb1ec619503b24bf c4a03bda060daf9d0990260c1524379b04971b94Introduction. The article examines the specifics and role of genetic information for insurance risk assessment in the life insurance market in Ukraine. Problem Statement. The insurance market developments of different countries are increasingly characterized by the adoption of specific regulations regarding the features and conditions of use of genetic information. Therefore, the issue of regulating the rights and obligations of all participants in insurance relations regarding the use of such information of future owners of insurance policies for underwriting in insurance requires a comprehensive solution taking into account the interests of all parties. The purpose of the research is to evaluate the necessity of application of specific regulations on the insurance market, specifically in the field of using genetic data for insurance purposes. Methods. The sources of materials were scientific publications, analytical studies, as well as legislation in the field of regulation of the use of genetic information for the assessment of insurance risks. The research paper used the following empirical methods, such as analysis, synthesis, grouping, description, comparison, theoretical generalization. Results. The results show that currently some risks exist in Ukraine: firstly, the genetic discrimination, since most life insurance companies are interested in the genetic information of policyholders, and can request it from any third parties: therefore, there is a possibility of using it to assess insurance risk; secondly, an information asymmetry, which is a consequence of greater awareness of insurance companies about the insurance risks than that of the policyholders. After all, policyholders may not inform the insurance company about all the genetic data (for example, the results of genetic studies) that describe their genetic predisposition to future changes in health. Conclusions. This study substantiated factors which confirm the relevance of introducing legislative regulation regarding the use of genetic information (including the results of genetic analyzes) for underwriting in insurance.Fìnansi Ukraïni210Fìnansi Ukraïni['M. Arych', 'Khrystyna Shchubelka', 'Walter W. Wolfsberger', 'Taras K. Oleksyk']2024-05-17T00:00:00https://www.semanticscholar.org/paper/c4a03bda060daf9d0990260c1524379b04971b94 5fd0f3a3aefe7f6a965d30bd87bf65167854be10OECD Competition Law and Policy Working Papers00OECD Competition Law and Policy Working Papers[]2024-05-17T00:00:00https://www.semanticscholar.org/paper/5fd0f3a3aefe7f6a965d30bd87bf65167854be10 e803a573935df386557c4dc8c12cd7f63fff5bb5OECD Public Governance Reviews00OECD Public Governance Reviews[]2024-05-17T00:00:00https://www.semanticscholar.org/paper/e803a573935df386557c4dc8c12cd7f63fff5bb5 a0382fee84da6d81365e8752a99469bec194b45cThis protocol paper outlines an innovative multimodal and multilevel approach to studying the emergence and evolution of how children build social bonds with their peers, and its potential application to improving social artificial intelligence (AI). We detail a unique hyperscanning experimental framework utilizing functional near-infrared spectroscopy (fNIRS) to observe inter-brain synchrony in child dyads during collaborative tasks and social interactions. Our proposed longitudinal study spans middle childhood, aiming to capture the dynamic development of social connections and cognitive engagement in naturalistic settings. To do so we bring together four kinds of data: the multimodal conversational behaviors that dyads of children engage in, evidence of their state of interpersonal rapport, collaborative performance on educational tasks, and inter-brain synchrony. Preliminary pilot data provide foundational support for our approach, indicating promising directions for identifying neural patterns associated with productive social interactions. The planned research will explore the neural correlates of social bond formation, informing the creation of a virtual peer learning partner in the field of Social Neuroergonomics. This protocol promises significant contributions to understanding the neural basis of social connectivity in children, while also offering a blueprint for designing empathetic and effective social AI tools, particularly for educational contexts.Frontiers in Neuroergonomics1380An innovative multimodal and multilevel approach to studying the emergence and evolution of how children build social bonds with their peers, and its potential application to improving social artificial intelligence (AI).Frontiers in Neuroergonomics['Julie Bonnaire', 'Guillaume Dumas', 'Justine Cassell']2024-05-17T00:00:00https://www.semanticscholar.org/paper/a0382fee84da6d81365e8752a99469bec194b45c ce087d052b3f7ef6b71b1a0fb6337d00b14cccbbEmotion AI, also known as sentiment analysis or affective computing, refers to the ability of AI systems to recognize, analyze, and interpret human emotions through various inputs, such as text, speech, facial expressions, and physiological signals. With the recent advancements in artificial intelligence (AI) and machine learning, emotion analysis has witnessed significant progress in terms of accuracy, efficiency, and scalability. This paper provides an overview of the emotion analysis through AI, explores its applications and challenges that researchers and developers face in this domain, and showcases the potential applications of this technology.International journal of engineering science & humanities00An overview of the emotion analysis through AI is provided, its applications and challenges that researchers and developers face are explored, and the potential applications of this technology are showcased.International Journal of Engineering Science and Humanities['Amit Kapoor', 'Vishal Verma']2024-05-17T00:00:00https://www.semanticscholar.org/paper/ce087d052b3f7ef6b71b1a0fb6337d00b14cccbb 8b3860107e96ef145bf782f915b34018720f24e2This study evaluates the effectiveness of Artificial Intelligence (AI) in mitigating medical overtreatment, a significant issue characterized by unnecessary interventions that inflate healthcare costs and pose risks to patients. We conducted a lab-in-the-field experiment at a medical school, utilizing a novel medical prescription task, manipulating monetary incentives and the availability of AI assistance among medical students using a three-by-two factorial design. We tested three incentive schemes: Flat (constant pay regardless of treatment quantity), Progressive (pay increases with the number of treatments), and Regressive (penalties for overtreatment) to assess their influence on the adoption and effectiveness of AI assistance. Our findings demonstrate that AI significantly reduced overtreatment rates by up to 62% in the Regressive incentive conditions where (prospective) physician and patient interests were most aligned. Diagnostic accuracy improved by 17% to 37%, depending on the incentive scheme. Adoption of AI advice was high, with approximately half of the participants modifying their decisions based on AI input across all settings. For policy implications, we quantified the monetary (57%) and non-monetary (43%) incentives of overtreatment and highlighted AI's potential to mitigate non-monetary incentives and enhance social welfare. Our results provide valuable insights for healthcare administrators considering AI integration into healthcare systems.Social Science Research Network680It is demonstrated that AI significantly reduced overtreatment rates by up to 62% in the Regressive incentive conditions where (prospective) physician and patient interests were most aligned, and provided valuable insights for healthcare administrators considering AI integration into healthcare systems.SSRN Electronic Journal['Ziyi Wang', 'Lijia Wei', 'Lian Xue']2024-05-17T00:00:00https://www.semanticscholar.org/paper/8b3860107e96ef145bf782f915b34018720f24e2 b28f9a7f407fdf5ca3309ab92920e24be44bd059International Journal of Educational Technology in Higher Education610Analysis of data from 300 university students revealed that the relationship between academic self-efficacy and AI dependency was mediated by academic stress and performance expectations, and the top five negative effects of AI dependency include increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking.International Journal of Educational Technology in Higher Education['Shunan Zhang', 'Xiangying Zhao', 'Tong Zhou', 'Jang Hyun Kim']2024-05-17T00:00:00https://www.semanticscholar.org/paper/b28f9a7f407fdf5ca3309ab92920e24be44bd059 af4332e22cd38d8936ad3e0e21502dd3707e560fThis research paper delves into the transformative potential of artificial intelligence (AI) within transportation tracking systems, spotlighting its profound impact on the industry. With the transportation landscape continually evolving, the integration of AI-powered automation in tracking processes heralds a paradigm shift in how vehicles are monitored and managed. Through a meticulous examination of cutting-edge techniques, persistent challenges, and notable benefits, this paper unveils the pivotal role of AI in optimizing operational efficiency, elevating tracking accuracy, and fortifying safety standards across transportation networks. Real-world case studies and technological breakthroughs are scrutinized, offering concrete illustrations of AI's capacity to reshape tracking systems. These examples underscore the diverse possibilities AI presents, from predictive analytics to dynamic routing algorithms. As AI's capabilities are harnessed, the transportation sector stands poised for a future defined by seamless, effective, and intelligent tracking solutions, fostering enhanced reliability and agility in the face of evolving demands.International Journal of Innovative Research in Science Engineering and Technology00The pivotal role of AI in optimizing operational efficiency, elevating tracking accuracy, and fortifying safety standards across transportation networks is unveiled, fostering enhanced reliability and agility in the face of evolving demands.International Journal of Innovative Research in Science,Engineering and Technology['Jotiba A Patil', 'Srikanth V']2024-05-17T00:00:00https://www.semanticscholar.org/paper/af4332e22cd38d8936ad3e0e21502dd3707e560f 80750ee48ac22f41af80e5e4feba5dad74c56a07This study aims to explore potential mechanisms of ambidextrous leadership (AL) in product innovativeness from the perspective of organizational agility (OA) and entrepreneurial orientation (EO) in firms operating in the artificial intelligence (AI) industry. A quantitative research method was used with 405 questionnaires, and the respondents were randomly selected from reputable databases. Structural equation modeling was employed to evaluate the model fit and conduct hypothesis testing. The findings suggest that ambidextrous leadership demonstrates a significant positive influence on product innovativeness and OA; also, through the mediating role of OA, it is possible to analyze both the direct and indirect relationships among the factors. Additionally, the moderating effect of EO on the intercorrelations among these factors was explored. This study enhances existing knowledge on leadership dynamics in the context of new product development, highlights the importance of adaptability in leadership, and sheds light on the interplay between OA, EO, and new product innovation. This study highlights the role of product innovativeness in sustainable AI product development. Enhanced product innovativeness not only sustains AI product development but also promotes environmental sustainability. This is achieved through the minimization of energy use, reduction in material requirements, and prevention of pollution. Firms are using these insights to develop sustainable and eco-friendly products, as well as create new market opportunities while reducing environmental impact. This research underscores the interconnectedness of factors in this study and sustainability, providing a new perspective on sustainable AI product development.Sustainability00The findings suggest that ambidextrous leadership demonstrates a significant positive influence on product innovativeness and OA; also, through the mediating role of OA, it is possible to analyze both the direct and indirect relationships among the factors.Sustainability['Shuxin Zhang', 'Sid Suntrayuth']2024-05-17T00:00:00https://www.semanticscholar.org/paper/80750ee48ac22f41af80e5e4feba5dad74c56a07 9b2f3b1b7ed3b95aa0a7fa32c2b28a51b65ae2aaThe escalating volumes of textile waste globally necessitate innovative waste management solutions to mitigate the environmental impact and promote sustainability in the fashion industry. This paper addresses the inefficiencies of traditional textile sorting methods by introducing an autonomous textile analysis pipeline. Utilising robotics, spectral imaging, and AI-driven classification, our system enhances the accuracy, efficiency, and scalability of textile sorting processes, contributing to a more sustainable and circular approach to waste management. The integration of a Digital Twin system further allows critical evaluation of technical and economic feasibility, providing valuable insights into the sorting system's accuracy and reliability. The proposed framework, inspired by Industry 4.0 principles, comprises five interconnected layers facilitating seamless data exchange and coordination within the system. Preliminary results highlight the potential of our holistic approach to mitigate environmental impact and foster a positive shift towards recycling in the textile industry.280['Yannis Spyridis', 'Vasileios Argyriou', 'Antonios Sarigiannidis', 'Panagiotis Radoglou', 'Panagiotis Sarigiannidis']2024-05-17T00:00:00https://www.semanticscholar.org/paper/9b2f3b1b7ed3b95aa0a7fa32c2b28a51b65ae2aa 7860c29b72af646d2e6f8001e2ee3c9b13538e88Artificial intelligence (AI) has emerged as a transformative force in healthcare, offering numerous benefits such as improved diagnostics, personalized treatments, and enhanced patient care. However, its integration into medical personnel training comes with both opportunities and challenges. This research article explores the benefits of AI in healthcare training for doctors and other healthcare providers across various hospital departments, while also addressing concerns and challenges associated with its implementation. Recommendations and implications for optimizing AI integration in medical training are discussed.Arts &amp; Humanities Open Access Journal40The benefits of AI in healthcare training for doctors and other healthcare providers across various hospital departments are explored, while also addressing concerns and challenges associated with its implementation.Arts &amp; Humanities Open Access Journal['Astha Puri', 'Rohan Mathur', 'Nehal Sindhu']2024-05-17T00:00:00https://www.semanticscholar.org/paper/7860c29b72af646d2e6f8001e2ee3c9b13538e88 4436ec21a97162e3cf81396a3687c2337dc5f4a7Model evaluations are central to understanding the safety, risks, and societal impacts of AI systems. While most real-world AI applications involve human-AI interaction, most current evaluations (e.g., common benchmarks) of AI models do not. Instead, they incorporate human factors in limited ways, assessing the safety of models in isolation, thereby falling short of capturing the complexity of human-model interactions. In this paper, we discuss and operationalize a definition of an emerging category of evaluations --"human interaction evaluations"(HIEs) -- which focus on the assessment of human-model interactions or the process and the outcomes of humans using models. First, we argue that HIEs can be used to increase the validity of safety evaluations, assess direct human impact and interaction-specific harms, and guide future assessments of models' societal impact. Second, we propose a safety-focused HIE design framework -- containing a human-LLM interaction taxonomy -- with three stages: (1) identifying the risk or harm area, (2) characterizing the use context, and (3) choosing the evaluation parameters. Third, we apply our framework to two potential evaluations for overreliance and persuasion risks. Finally, we conclude with tangible recommendations for addressing concerns over costs, replicability, and unrepresentativeness of HIEs.860This paper argues that HIEs can be used to increase the validity of safety evaluations, assess direct human impact and interaction-specific harms, and guide future assessments of models' societal impact and proposes a safety-focused HIE design framework.['Lujain Ibrahim', 'Saffron Huang', 'Lama Ahmad', 'Markus Anderljung']2024-05-17T00:00:00https://www.semanticscholar.org/paper/4436ec21a97162e3cf81396a3687c2337dc5f4a7 c55faef9a4c7c1c3fb3886fd47b7624acbb3dda5As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.BioMedInformatics00The critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation is underscored, as key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare are identified.BioMedInformatics['Ankush U. Patel', 'Qiangqiang Gu', 'Ronda N. Esper', 'Danielle Maeser', 'Nicole Maeser']2024-05-17T00:00:00https://www.semanticscholar.org/paper/c55faef9a4c7c1c3fb3886fd47b7624acbb3dda5 65a695cfc23a79d7b4e27cc5abd724fe1c2942d9Recent improvements in artificial intelligence (AI) and machine learning (ML) have significantly impacted a number of sectors, such as digital marketing. The influence of AI and ML on digital marketing strategies and implications for businesses are examined in this study. To do this, this paper first highlights the abilities and prospective uses of AI and ML by providing a brief description thereof. The upcoming discussion will outline how AI and ML have transformed important aspects of digital marketing such as customer segmentation, personalized targeting, content creation, customer experience optimization and any other relevant topic under study. The paper also explores the advantages as well as the disadvantages that come with incorporating AI and ML methods in digital marketing strategies. Moreover, some ethical issues coupled with possible prejudices related to using AI and ML. Using computers and robots to help perform Human tasks in the present-day era is referred to as Artificial intelligence. There are numerous everyday obligations that are easily carried out by computers and robots instead of people. For efficient use of green computer systems, Artificial intelligence makes it less complicated to carry out tasks that need human brains.International Journal of Multidisciplinary Research in Science, Engineering and Technology00The influence of AI and ML on digital marketing strategies and implications for businesses are examined in this study and some ethical issues coupled with possible prejudices related to using AI and ML are explored.International Journal of Multidisciplinary Research in Science, Engineering and Technology['Ashish Bhati', 'Radhakrishna M']2024-05-17T00:00:00https://www.semanticscholar.org/paper/65a695cfc23a79d7b4e27cc5abd724fe1c2942d9 34a2aedce65d05083aab3e649d4f17c6779dcd91In recent years, the integration of machine learning (ML) and artificial intelligence (AI) technologies has revolutionized the landscape of digital banking. This review paper synthesizes the current state of research and practical implementations regarding the utilization of ML and AI in digital banking services. Leveraging a comprehensive review of scholarly articles, industry reports, and case studies, this paper examines key applications of ML and AI in enhancing various facets of digital banking, including fraud detection, customer service, risk assessment, personalization, and predictive analytics. Additionally, it explores the challenges and opportunities associated with the adoption of ML and AI in the banking sector, such as data privacy concerns, regulatory compliance, algorithmic biases, and the evolving role of human interaction. By critically analysing existing literature and real-world examples, this paper aims to provide insights into the transformative potential of ML and AI technologies in reshaping the future of digital banking, while also highlighting areas for further research and development.International Journal of Innovative Research in Computer and Communication Engineering00This review paper synthesizes the current state of research and practical implementations regarding the utilization of ML and AI in digital banking services to provide insights into the transformative potential of ML and AI technologies in reshaping the future of digital banking.International Journal of Innovative Research in Computer and Communication Engineering['Swaraj Kumar', 'K. R']2024-05-17T00:00:00https://www.semanticscholar.org/paper/34a2aedce65d05083aab3e649d4f17c6779dcd91 b74d13130b80b9ee306683f9e347d0be14230877 Artificial intelligence (AI) applications are rapidly evolving, disruptive technologies that recently attracted tremendous public awareness including euphoria, bull runs at stock exchanges but also criticism and skepticism. Pathology, a crucial clinical diagnostic discipline, faces significant challenges despite its critical role and broad scope. A notable issue is the declining interest among medical graduates in pursuing further training in Surgical Pathology and Molecular Pathology. This disinterest is compounded by a trend towards part-time work and the aging demographic in Western industrial nations, which increases the demand for precise cancer diagnoses, molecular pathology and advanced oncological treatments. As the need for skilled pathologists grows, the availability of these professionals is steadily decreasing. Furthermore, political policies in heavily regulated healthcare systems (e.g., Germany) are exacerbating the shortage by limiting residency and specialist training positions despite the growing need. Adding to the reluctance among medical graduates to consider pathology as discipline is the looming “threat”” of replacing the field with Artificial Intelligence (AI) technologies. However, historical precedents suggest that necessity often spurs significant innovation. AI's role in pathology is expanding, reshaping the field with new technological advancements. AI, broadly encompassing various computational algorithms designed to perform tasks typically requiring human intelligence such as learning, reasoning, and pattern recognition, is now being applied extensively across different domains including medical diagnostics. Recent implementations in pathology include AI-driven algorithms that automate processes such as the Gleason grading of prostate biopsies, lymph node metastasis detection, detection of molecular bladder cancer subtypes or grading of kidney cancer showing high accuracy levels, speed and reproducibility in delivering results. These tools, while they do not replace pathologists, significantly reduce their workload by automating routine tasks, thus freeing up time for more complex diagnostic activities or simply dealing with the increasing workload. Despite the exciting possibilities, the use of AI in predicting molecular alterations, such as FGFR3 mutations in bladder cancer, faces limitations due to insufficient sensitivity and specificity, indicating that such technologies are not yet suitable for screening purposes. In conclusion, AI is increasingly vital in addressing the challenges of modern pathology, particularly in compensating for the shortage of pathologists. As technology advances, its integration into clinical practice offers promising solutions to enhance diagnostic accuracy and efficiency in uropathology, paving the way for more personalized and effective patient care. Citation Format: Markus Eckstein. AI in bladder cancer pathology and research: What can we expect and what not? [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA022.Clinical Cancer Research00In conclusion, AI is increasingly vital in addressing the challenges of modern pathology, particularly in compensating for the shortage of pathologists.Clinical Cancer Research['Markus Eckstein']2024-05-17T00:00:00https://www.semanticscholar.org/paper/b74d13130b80b9ee306683f9e347d0be14230877 38a184c70cd9003734f0cf2e1252bafdb394c259Objective: To explore the perception of university students on the use of Artificial Intelligence (AI) tools for the development of autonomous learning.   Theoretical Framework: The research is based on Technological Acceptance Theory and Constructivism, focusing on the perception of AI in autonomous learning of university students.   Method: Quantitative approach with a descriptive scope, the sample consisted of 665 students enrolled in the Faculty of Education Sciences and Languages (FCEI) of the Peninsula de Santa Elena State University (UPSE)-Ecuador; in the collection of information, the Questionnaire of Perception on the Use of Artificial Intelligence for Autonomous Learning was designed based on 4 dimensions of both variables, and the statistical program SPSS version 29 was used for data processing.   Results and Discussion: The results indicate that students show a favorable perception towards the use of AI tools for the autonomous learning process, however, although AI is recognized as a potential tool in university environments, there are still challenges to be overcome.   Research Implications: The study has practical implications for strengthening in students the digital competencies needed to effectively use AI tools in their autonomous learning.   Originality/Value: The research provides data on the perception of AI tools among university students, offering a starting point for future technology integration strategies in universities.Revista de Gestão Social e Ambiental450The results indicate that students show a favorable perception towards the use of AI tools for the autonomous learning process, however, although AI is recognized as a potential tool in university environments, there are still challenges to be overcome.Revista de Gestão Social e Ambiental['Gertrudis Amarilis Laínez Quinde', 'Mónica Yiomar Tumbaco Muñoz', 'Jessenia Margarita Ricardo Suárez', 'Ruth Esther Peñafiel Villarreal', 'Wilson Alexander Zambrano Vélez', 'Andrea Annabella Del Pezo Laínez']2024-05-17T00:00:00https://www.semanticscholar.org/paper/38a184c70cd9003734f0cf2e1252bafdb394c259 bc1689dc043579cf6b90ae869ded72516459e9e7This research aimed to develop and validate a rubric to assess Artificial Intelligence (AI) chatbots' effectiveness in accomplishing tasks, particularly within educational contexts. Given the rapidly growing integration of AI in various sectors, including education, a systematic and robust tool for evaluating AI chatbot performance is essential. This investigation involved a rigorous process including expert involvement to ensure content validity, as well as the application of statistical tests for assessing internal consistency and reliability. Factor analysis also revealed two significant domains, "Quality of Content" and "Quality of Expression", which further enhanced the construct validity of the evaluation scale. The results from this investigation robustly affirm the reliability and validity of the developed rubric, thus marking a significant advancement in the sphere of AI chatbot performance evaluation within educational contexts. Nonetheless, the study simultaneously emphasizes the requirement for additional validation research, specifically those entailing a variety of tasks and diverse AI chatbots, to further corroborate these findings. The ramifications of this research are profound, offering both researchers and practitioners engaged in chatbot development and evaluation a comprehensive and validated framework for the assessment of chatbot performance.Electronic Journal of e-Learning840This research aimed to develop and validate a rubric to assess Artificial Intelligence (AI) chatbots' effectiveness in accomplishing tasks, particularly within educational contexts, and robustly affirm the reliability and validity of the developed rubric.Electronic Journal of e-Learning['Mohammad Hmoud', 'Hadeel Swaity', 'Eman Anjass', 'E. Aguaded-Ramírez']2024-05-17T00:00:00https://www.semanticscholar.org/paper/bc1689dc043579cf6b90ae869ded72516459e9e7 a17a8cc42e84b33ce34853d39a7e9121d0811dffWith the increasing adoption of Artificial Intelligence (AI) systems in high-stake domains, such as healthcare, effective collaboration between domain experts and AI is imperative. To facilitate effective collaboration between domain experts and AI systems, we introduce an Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge. The system includes an explanation dashboard that combines different types of data-centric and model-centric explanations and allows prediction models to be steered through manual and automated data configuration approaches. It allows domain experts to apply their prior knowledge for configuring the underlying training data and refining prediction models. Additionally, our model steering system has been evaluated for a healthcare-focused scenario with 174 healthcare experts through three extensive user studies. Our findings highlight the importance of involving domain experts during model steering, ultimately leading to improved human-AI collaboration.300An Explanatory Model Steering system that allows domain experts to steer prediction models using their domain knowledge and allows prediction models to be steered through manual and automated data configuration approaches, ultimately leading to improved human-AI collaboration.['Aditya Bhattacharya', 'Simone Stumpf', 'K. Verbert']2024-05-17T00:00:00https://www.semanticscholar.org/paper/a17a8cc42e84b33ce34853d39a7e9121d0811dff c1d5f5bb145d937dafe4689ec9882c53dcbeb472The primary objective of this study is to contribute to the existing literature concerning the utilization of AI in education. It achieves this by introducing four distinct research clusters that offer directions for scholars to expand their investigations into AI-based learning. The study's focus centers on student-centric literature related to AI-based learning from 2000 onwards, employing established bibliometric techniques to yield comprehensive insights. Through the application of these techniques, a comprehensive analysis was conducted on 722 articles published within the last decade This assessment encompassed the identification of key research domains, influential authors, countries contributing significantly, influential journals, and prominent organizations involved in AI-based learning research. Additionally, a scrutiny of the most influential studies was conducted based on their citation counts. The study acknowledges the transformation of AI-based learning from an emerging field to a robust tool for both teaching and research. Given this progression, the investigation is designed to explore and delineate the evolving trends in the application of AI within the realms of learning and research. By conducting a meticulous examination of relevant literature and employing bibliometric methodologies the study provides a valuable resource for researchers, educators, and stakeholders keen on understanding and contributing to the dynamic field of AI-based learning.International Journal of Religion460Four distinct research clusters are introduced by the study by introducing four distinct research clusters that offer directions for scholars to expand their investigations into AI-based learning by employing established bibliometric techniques to yield comprehensive insights.International Journal of Religion['Anuj Verma', 'Srikanth Reddy Dhupam', 'S. Bansod', 'Rahul R']2024-05-17T00:00:00https://www.semanticscholar.org/paper/c1d5f5bb145d937dafe4689ec9882c53dcbeb472 da274d7181a5d06b76e3d971bc5a2d97e289006bWild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.Big Data and Cognitive Computing290This work presents both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation and shows that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.Big Data and Cognitive Computing['Alexander Lehnert', 'Falko Gawantka', 'Jonas During', 'Franz Just', 'Marc Reichenbach']2024-05-17T00:00:00https://www.semanticscholar.org/paper/da274d7181a5d06b76e3d971bc5a2d97e289006b 6b5944ca4f42fb0bc72bdbcc05f3ceb55fd2f782The rise of artificial intelligence (AI) and natural language processing (NLP) has revolutionized many aspects of daily life, particularly in the field of development of medical research articles. the use of AI in scientific writing has both advantages and disadvantages. As AI tools gain in popularity and their application becomes more ubiquitous, it's essential to consider how they may affect the future of medical literature. This work aims to describe a number of IT-based tools that contribute to scientific research and writing as ChatGPT, Gemini, Elicit, SCISPACE... Each tool has its own advantages and applications, not to mention shortcomings that can affect the quality of medical research. To conclude artificial intelligence tools have emerged as catalysts for innovation in healthcare research, providing motivation and driving progress even amidst challenges. Therefore, it's crucial to confront the obstacles related to AI and to tackle ethical and regulatory issues to enhance research quality and scientific output.Data and Metadata80Artificial intelligence tools have emerged as catalysts for innovation in healthcare research, providing motivation and driving progress even amidst challenges, and it's crucial to confront the obstacles related to AI and to tackle ethical and regulatory issues to enhance research quality and scientific output.Data and Metadata['Hind Berrami', 'Manar Jallal', 'Z. Serhier', 'M. Bennani Othmani']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6b5944ca4f42fb0bc72bdbcc05f3ceb55fd2f782 fbe0659dfb2af237a65de5b1c3b052bc136c865dArtificial Intelligence (AI) integration into healthcare has the potential to transform patient care, diagnostics, and treatment. This paper provides a detailed overview of AI support in healthcare, focusing on the intersection of (NLP) Natural Language Processing, (ML) Machine Learning, and (DS) Data Science. By employing the power of these slice- edge technologies, AI can offer intelligent, data- driven results to ameliorate healthcare delivery. Natural Language Processing( NLP) is employed to prize precious perceptivity from medical textbooks, clinical notes, and case records. This enables healthcare providers to more understand patient histories and make informed opinions. Machine literacy ways are abused to prognosticate complaint issues, identify anomalies, and epitomize treatment plans. also, data wisdom plays a vital part in aggregating and analysing large healthcare datasets, icing data security, and maintaining compliance with nonsupervisory norms. The paper explores colorful AI operations in healthcare, similar as automated opinion and triage, medical image analysis, medicine discovery, and patient monitoring. These operations have the eventuality to enhance clinical decision- timber, reduce medical crimes, and ameliorate patientoutcomes.AI backing in healthcare isn't without its challenges, including data sequestration enterprises, the need for robust model interpretability, and ethical considerations. The paper discusses these issues and presents strategies to address them. In conclusion, the integration of AI, NLP, Machine literacy, and Data Science in healthcare has the implicit to marshal in a new period of perfection drug and case- centred care. Then we used SVM Algorithm for training the data set and the delicacy position is 97 percent. This technology confluence is poised to revise healthcare by perfecting opinion delicacy, treatment efficacy-ity, and patient issues while icing data security and ethical use.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The integration of AI, NLP, Machine literacy, and Data Science in healthcare has the implicit to marshal in a new period of perfection drug and case- centred care.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['D. N. S. Divya']2024-05-17T00:00:00https://www.semanticscholar.org/paper/fbe0659dfb2af237a65de5b1c3b052bc136c865d 048f2774f4f0c491b0cff7e7e7d8fb2380ba5cc7OBJECTIVE Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer. METHOD This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT. RESULTS While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs. CONCLUSION Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.JAMIA Journal of the American Medical Informatics Association210Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.Journal of the American Medical Informatics Association : JAMIA['F. C. Dos Santos', 'Lisa G Johnson', 'Olatunde O. Madandola', 'Karen J B Priola', 'Yingwei Yao', 'Tamara G. R. Macieira', 'Gail M. Keenan']2024-05-17T00:00:00https://www.semanticscholar.org/paper/048f2774f4f0c491b0cff7e7e7d8fb2380ba5cc7 7bb509491976c81b823f2ed6c7291a38bece01a7AI &amp; SOCIETY1470AI &amp; SOCIETY['Muhammad Farhan Jalil', 'Patrick Lynch', 'D. A. A. Marikan', 'Abu Hassan Bin Md Isa']2024-05-17T00:00:00https://www.semanticscholar.org/paper/7bb509491976c81b823f2ed6c7291a38bece01a7 1797000c70c1318befa7d644efe7a785b504915cSN Computer Science330SN Computer Science['Maryam Mohseni', 'Mary Lou Maher', 'Kazjon Grace', 'Safat Siddiqui', 'Nadia Najjar']2024-05-17T00:00:00https://www.semanticscholar.org/paper/1797000c70c1318befa7d644efe7a785b504915c 73d5e8222198680eda3e21b72c22e709ad00fa4dNeural computing & applications (Print)400Bi-xBcNet-96 is presented, a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96 that seeks to attain high accuracy at the lowest computing cost.Neural Computing and Applications['Nesma Abd El-Mawla', 'M. Berbar', 'Nawal A. El-Fishawy', 'Mohamed A. El-Rashidy']2024-05-17T00:00:00https://www.semanticscholar.org/paper/73d5e8222198680eda3e21b72c22e709ad00fa4d cc814268815576130a0082a3ec4bee1568d853ddHypertension Research10Hypertension research : official journal of the Japanese Society of Hypertension['Z. Karbasi', 'Michaeel Motaghi Niko', 'Maryam Kazemi', 'M. Zahmatkeshan']2024-05-17T00:00:00https://www.semanticscholar.org/paper/cc814268815576130a0082a3ec4bee1568d853dd c87f252154c2cd1297cdb69f6f768ff1ca3a9d3fJournal of Emergency Management and Disaster Communications00Journal of Emergency Management and Disaster Communications['Jaideep Visave', 'Abby Cameron']2024-05-17T00:00:00https://www.semanticscholar.org/paper/c87f252154c2cd1297cdb69f6f768ff1ca3a9d3f a1e1a4c421ee0533188e9f165502fccdcb6cc00b00['Seth Dobrin']2024-05-17T00:00:00https://www.semanticscholar.org/paper/a1e1a4c421ee0533188e9f165502fccdcb6cc00b 0cee230c3af7775c7f40ab10ec58a524b85e54e1Philosophy &amp; Technology160Philosophy &amp; Technology['Floridi Luciano']2024-05-17T00:00:00https://www.semanticscholar.org/paper/0cee230c3af7775c7f40ab10ec58a524b85e54e1 f30ad5845c60259bb251a60d2e72a6a050e419b1Berkeley scientific journal00Berkeley Scientific Journal['Lara Potgieter', 'Ann Palayur', 'Andrew Delaney']2024-05-17T00:00:00https://www.semanticscholar.org/paper/f30ad5845c60259bb251a60d2e72a6a050e419b1 9be2253cd4780045263546ce52dae4d255766d05BMC Medical Research Methodology390This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients’ smoking status and providing explanations for the decisions, and achieves a high level of classification performance.BMC Medical Research Methodology['Ali Ebrahimi', 'M. B. Henriksen', 'C.L. Brasen', 'O. Hilberg', 'T. Hansen', 'L.H. Jensen', 'A. Peimankar', 'U. Wiil']2024-05-17T00:00:00https://www.semanticscholar.org/paper/9be2253cd4780045263546ce52dae4d255766d05 1a2771d003577ce074500fa742501434b099bb6aEuropean Journal of Therapeutics20European Journal of Therapeutics['Abdullah Yıldız']2024-05-17T00:00:00https://www.semanticscholar.org/paper/1a2771d003577ce074500fa742501434b099bb6a 75652aedbb664ef75e631b40a8b91026af4c2909We present a novel explainable artificial intelligence (XAI) method to assess the associations between the temporal patterns in the patient trajectories recorded in longitudinal clinical data and the adverse outcome risks, through explanations for a type of deep neural network model called Hybrid Value-Aware Transformer (HVAT) model. The HVAT models can learn jointly from longitudinal and non-longitudinal clinical data, and in particular can leverage the time-varying numerical values associated with the clinical codes or concepts within the longitudinal data for outcome prediction. The key component of the XAI method is the definitions of two derived variables, the temporal mean and the temporal slope, which are defined for the clinical concepts with associated time-varying numerical values. The two variables represent the overall level and the rate of change over time, respectively, in the trajectory formed by the values associated with the clinical concept. Two operations on the original values are designed for changing the values of the two derived variables separately. The effects of the two variables on the outcome risks learned by the HVAT model are calculated in terms of impact scores and impacts. Interpretations of the impact scores and impacts as being similar to those of odds ratios are also provided. We applied the XAI method to the study of cardiorespiratory fitness (CRF) as a risk factor of Alzheimer's disease and related dementias (ADRD). Using a retrospective case-control study design, we found that each one-unit increase in the overall CRF level is associated with a 5% reduction in ADRD risk, while each one-unit increase in the changing rate of CRF over time is associated with a 1% reduction. A closer investigation revealed that the association between the changing rate of CRF level and the ADRD risk is nonlinear, or more specifically, approximately piecewise linear along the axis of the changing rate on two pieces: the piece of negative changing rates and the piece of positive changing rates.medRxiv30medRxiv : the preprint server for health sciences['Yijun Shao', 'Edward Y Zamrini', 'Ali Ahmed', 'Yan Cheng', 'Stuart J Nelson', 'Peter Kokkinos', 'Qing Zeng-Treitler']2024-05-17T00:00:00https://www.semanticscholar.org/paper/75652aedbb664ef75e631b40a8b91026af4c2909 0eca9badb3479a23989ad205023d11ccb8a98592Self-driving cars hold immense potential for revolutionizing transportation. However, public acceptance hinges on trust in the car's ability to navigate safely and make critical decisions. This trust deficit stems from the "black box" nature of traditional machine learning models used in self-driving cars. Passengers are left in the dark about the car's perception of the environment and the reasoning behind its actions. This research proposes leveraging Explainable Artificial Intelligence (XAI) techniques to enhance passenger trust in self-driving cars. By incorporating explainability into the perception and prediction modules of the car's decision- making system, we aim to provide passengers with real-time insights into how the car perceives its surroundings and translates those perceptions into driving decisions. This paper explores various XAI methods suitable for self-driving car applications. We discuss the integration of these techniques into the perception and prediction pipelines, enabling the car to explain its reasoning behind lane changes, obstacle avoidance maneuvers, and other critical actions. We evaluate the effectiveness of the proposed approach through user studies, assessing how explainability can improve passenger trust and comfort in self-driving vehicles. The ultimate goal of this research is to foster greater transparency and trust in self-driving car technology, paving the way for wider public adoption and a future of safe and reliable autonomous transportation.International Journal of Innovative Research in Computer and Communication Engineering00This paper explores various XAI methods suitable for self-driving car applications and discusses the integration of these techniques into the perception and prediction pipelines, enabling the car to explain its reasoning behind lane changes, obstacle avoidance maneuvers, and other critical actions.International Journal of Innovative Research in Computer and Communication Engineering['Jamuna Purushotham', 'Srikanth V']2024-05-17T00:00:00https://www.semanticscholar.org/paper/0eca9badb3479a23989ad205023d11ccb8a98592 eb23a4d14704a11f5ccf6ede915849713fc1db2dInternational Journal of Technology00International Journal of Technology['Mohammadmahdi Naghipour', 'Lew Sook Ling', 'T. Connie']2024-05-17T00:00:00https://www.semanticscholar.org/paper/eb23a4d14704a11f5ccf6ede915849713fc1db2d 6d523974a3f3234dfd1325af29256bd439fdeb65 Bladder cancer is the most recurrent form of cancer, requiring frequent screening and surveillance, which contributes to its status as the costliest per capita. While urine cytology serves as a crucial adjunct to invasive cystoscopy, it is high volume, labor-intensive, and relatively inconsistent. Despite the implementation of The Paris System, which introduced updated quantitative guidelines, implementation of digital tools for urine cytology still trails the technological advancements seen in semi-automated Pap smear screenings. This lag can be attributed to the diverse cytomorphology of urine specimens and variation in specimen preparation and screening. Our team has developed AutoParis-X (APX), a state-of-the-art technology leveraging deep learning algorithms to extract objective (e.g., nucleus-to-cytoplasm ratio) and subjective (e.g., hyperchromasia) features from whole slide images (WSI). APX transforms these features into the Atypia Burden Score (ABS), offering a quantitative assessment of malignancy and recurrence risks on a scale from 0 to 1. Initially validated with 1300 samples (ThinPrep; Leica Aperio GT450) at Dartmouth Hitchcock, achieving an AUROC of 0.9, APX has evolved into APX-WEB, a user-friendly, web-based tool for cytopathologists. Notable challenges persist, including: 1) validating across nationally representative cohorts with varied specimen preparations and imaging, 2) addressing the intricacies of z-stacking for imaging non-monolayer preparations, and 3) integration of genomics to heighten the sensitivity of imaging assays. This study aimed to establish a performance baseline for APX's adaptability to these challenges without algorithmic adjustments. APX was applied across three distinct cohorts: 1) a preliminary national validation on 500 ThinPrep WSI in collaboration with Hologic; 2) assessment on non-monolayer specimen preparations (e.g., SurePath), using z-stacking, conducted with Johns Hopkins (n=100 WSI); and 3) a pilot study at Dartmouth (n=19) for integrating APX's imaging features with whole exomic sequencing (WES). Preliminary findings indicate: 1) Promising performance on a held-out Hologic national cohort (sensitivity=0.87, specificity=0.73; original APX study: sensitivity=0.88, specificity=0.83). 2) In SurePath z-stack WSI, the number of detected urothelial cells per cluster decreased with distance from the focus plane (rho =-0.13, p<0.001). 3) Of eight cases deemed negative or atypical by cytology, three exhibited TERT mutations, suggesting subclinical recurrence, in contrast to the consistent findings in all nine suspicious or positive cases. This highlights the potential of integrating genomics with image-based classifiers to enhance diagnostic precision. The findings underscore the need for future multicenter work aimed at refining machine learning models to achieve enhanced predictive performance across various operational parameters and settings, facilitating their implementation. Citation Format: Joshua Levy, Keluo Yao, Jonathan Marotti, Darcy Kerr, Edward Gutmann, Sam Harvey, Yoseph Sayegh, Chris VandenBussche, Michael Quick, Peter Costa, Lauren Wainman, Donald Green, Parth Shah, Xiaoying Liu, Louis Vaickus. AI-powered tool for rapid & reliable bladder cancer screening and surveillance: multicenter validation efforts [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA020.Clinical Cancer Research00A performance baseline is established for AutoParis-X's adaptability to these challenges without algorithmic adjustments, highlighting the need for future multicenter work aimed at refining machine learning models to achieve enhanced predictive performance across various operational parameters and settings, facilitating their implementation.Clinical Cancer Research['Joshua Levy', 'Keluo Yao', 'Jonathan Marotti', 'Darcy Kerr', 'Edward Gutmann', 'Sam Harvey', 'Yoseph Sayegh', 'Chris VandenBussche', 'Michael Quick', 'Peter Costa', 'Lauren Wainman', 'Donald Green', 'Parth Shah', 'Xiaoying Liu', 'Louis Vaickus']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6d523974a3f3234dfd1325af29256bd439fdeb65 099f769c1a442ffa08cf194d0d1e7bdb46397098The purpose of this study is to develop a multimodal model based on artificial intelligence to assist clinical doctors in the early diagnosis of necrotizing enterocolitis in newborns.This study is a retrospective study that collected the initial laboratory test results and abdominal x-ray image data of newborns (non-NEC, NEC) admitted to our hospital from January 2022 to January 2024.A multimodal model was developed to differentiate multimodal data, trained on the training dataset, and evaluated on the validation dataset. The interpretability was enhanced by incorporating the Gradient-weighted Class Activation Mapping (GradCAM) analysis to analyze the attention mechanism of the multimodal model, and finally compared and evaluated with clinical doctors on external datasets.The dataset constructed in this study included 11,016 laboratory examination data from 408 children and 408 image data. When applied to the validation dataset, the area under the curve was 0.91, and the accuracy was 0.94. The GradCAM analysis shows that the model's attention is focused on the fixed dilatation of the intestinal folds, intestinal wall edema, interintestinal gas, and portal venous gas. External validation demonstrated that the multimodal model had comparable accuracy to pediatric doctors with ten years of clinical experience in identification.The multimodal model we developed can assist doctors in early and accurate diagnosis of NEC, providing a new approach for assisting diagnosis in underdeveloped medical areas.Frontiers in Pediatrics210The multimodal model developed can assist doctors in early and accurate diagnosis of NEC, providing a new approach for assisting diagnosis in underdeveloped medical areas.Frontiers in Pediatrics['Kaijie Cui', 'Changrong Shao', 'Maomin Yu', 'Zhang Hui', 'Xiuxiang Liu']2024-05-17T00:00:00https://www.semanticscholar.org/paper/099f769c1a442ffa08cf194d0d1e7bdb46397098 b2f35a83077d39c05c9f1ce07d11321bf0e63646This research paper explores the potential of artificial intelligence (AI) and automation to reduce the digital divide in developing countries. The study investigates how these technologies can enhance accessibility and efficiency in critical sectors such as education, healthcare, agriculture, and economic development, thereby contributing to social and economic progress. Key findings indicate that AI and automation can significantly improve educational outcomes by providing personalized learning experiences, enhancing healthcare delivery through better diagnostics and patient care, increasing agricultural productivity with precision farming, and stimulating economic growth by creating new job opportunities and improving market access. However, deploying these technologies faces substantial challenges, including inadequate infrastructure, significant skills gaps, cultural resistance, and policy constraints. The research adopts a mixed-methods approach, combining quantitative data analysis with qualitative case studies to comprehensively understand the impacts and challenges associated with implementing AI and automation in less developed regions. The scope of the study spans several developing countries, offering insights relevant to policymakers, NGOs, and the private sector engaged in technology deployment for development. The paper underscores the necessity for a collaborative approach to overcome the identified barriers and suggests strategic recommendations for stakeholders to leverage AI and automation as practical tools for closing the digital divide. This study contributes to the broader discourse on technology’s role in global development, highlighting both the transformative potential of AI and automation and the critical need for inclusive and context-sensitive technology governance.Journal of Engineering Research and Reports00Key findings indicate that AI and automation can significantly improve educational outcomes by providing personalized learning experiences, enhancing healthcare delivery through better diagnostics and patient care, increasing agricultural productivity with precision farming, and stimulating economic growth by creating new job opportunities and improving market access.Journal of Engineering Research and Reports['Amaka Debie Samuel-Okon', 'Oluwatotan Olaperi Abejide']2024-05-17T00:00:00https://www.semanticscholar.org/paper/b2f35a83077d39c05c9f1ce07d11321bf0e63646 a64c66cf10e7d355f24971f0452c4c3a31609caaHuman moral reactions to artificial intelligence (AI) agents' behavior constitute an important aspect of modern-day human-AI relationships. Although previous studies have mainly focused on autonomy ethics, this study investigates how individuals judge AI agents' violations of community ethics (including betrayals and subversions) compared with human violations. Participants' behavioral responses, event-related potentials (ERPs), and individual differences were assessed. Behavioral findings reveal that participants rated AI agents' community-violating actions less morally negative than human transgressions, possibly because AI agents are commonly perceived as having less agency than human adults. The ERP N1 component showed the same pattern with moral rating scores, indicating the modulation effect of human-AI differences on initial moral intuitions. Moreover, the level of social withdrawal correlated with a smaller N1 in the human condition but not in the AI condition. The N2 and P2 components were sensitive to the difference between the loyalty/betrayal and authority/subversion domains but not human/AI differences. Individual levels of moral sense and autistic traits also influenced behavioral data, especially on the loyalty/betrayal domain. In our opinion, these findings offer insights for predicting moral responses to AI agents and guiding ethical AI development aligned with human moral values.Cyberpsychology, Behavior, and Social Networking480Behavioral findings reveal that participants rated AI agents' community-violating actions less morally negative than human transgressions, possibly because AI agents are commonly perceived as having less agency than human adults.Cyberpsychology, behavior and social networking['Yue He', 'Ruolei Gu', 'Guangzhi Deng', 'Yongling Lin', 'Tian Gan', 'Fang Cui', 'Chao Liu', 'Yue-jia Luo']2024-05-17T00:00:00https://www.semanticscholar.org/paper/a64c66cf10e7d355f24971f0452c4c3a31609caa 3e119681c7beaf0c49808728281bea51a40b36d9The study’s goal was to demonstrate the modifying role of supply chain agility in the impact of artificial intelligence applications on organizational excellence in Jordanian e-commerce companies. The analytical and descriptive approach was adopted. The study population consisted of 160 companies operating in the e-commerce sector in Jordan. The sample comprised 400 respondents working in senior and middle management positions. The questionnaire was utilized to collect the data. The results showed an impact of artificial intelligence applications in all dimensions (expert systems and neural networks) on the organizational excellence of e-commerce companies in Jordan. The value of the adjusted coefficient of determination (Adj. R2) was .265%. Based on the model’s F value (4.1190) and its level of significance (P; 0.00), the impact of these techniques on organizational excellence is statistically significant. Additionally, the results displayed an impact of supply chain agility on improving the impact of artificial intelligence applications on organizational excellence. The value of the degree of influence ß after introducing the modified variable supply chain agility and the value of R Square increased by .11 at the significance level (Sig). = 0.000. This study recommended training workers to stay up to date with developments in artificial intelligence, expert systems, and neural networks in their operations, control of searching for this evidence within databases, and knowledge representation.Problems and Perspectives in Management210This study recommended training workers to stay up to date with developments in artificial intelligence, expert systems, and neural networks in their operations, control of searching for this evidence within databases, and knowledge representation.Problems and Perspectives in Management['Mohammad Alnadi', 'Shadi Altahat']2024-05-17T00:00:00https://www.semanticscholar.org/paper/3e119681c7beaf0c49808728281bea51a40b36d9 b16dc08d696dd999bbcd4b3b46f15fc35a7c0f78In 2023, global temperatures witnessed an alarming escalation, reaching an unprecedented 1.46°C above preindustrial levels, marking it as the hottest year on record. Simultaneously, atmospheric carbon dioxide surpassed 420 ppm, exceeding a stability maintained for over 6000 years by more than double. This troubling surge in CO2 intensifies global warming, leading to an increased frequency of extreme weather events and contributing to 24% of global deaths attributed to environmental concerns. These alarming environmental challenges demand urgent attention and the implementation of innovative policies. Responding to this imperative, the study examines the impact of artificial intelligence‐based industrial robotics (AIIR) and other control variables such as green energy, green finance, and green energy investment on CO2 emissions in economies supporting green initiatives, including Canada, Denmark, China, Japan, New Zealand, Norway, Sweden, and Switzerland. Using monthly data from 2008 to 2021 and a novel nonlinear autoregressive distributed lag approach, the results indicate that AIIR significantly reduces CO2 emissions in the sample economies. Additionally, green energy, green finance, and green energy investment also significantly decrease CO2 emissions. The study's outcomes bear policy implications for decision‐makers in the sampled economies, offering tangible insights for effective environmental management.Natural resources forum (Print)690Natural Resources Forum['Lingli Qing', 'Muhammad Shahbaz', 'Muhammad Saeed Meo', 'Yasir Jamshed', 'Likun Li']2024-05-17T00:00:00https://www.semanticscholar.org/paper/b16dc08d696dd999bbcd4b3b46f15fc35a7c0f78 6a5a3b2f97240204e9895f8b9ccf5192b4558acePurpose: The purpose of the presented study was to develop a set of recommendations for decision-makers (policymakers and public managers) and public employees to enhance the effectiveness and efficiency of organisational elements in the adoption of artificial intelligence (AI) in public institutions. Design/methodology/approach: Utilising a systematic literature review following the PRISMA protocol, the study examines the organisational enablers of AI adoption in public institutions. Comprehensive search queries in the Scopus database identified relevant literature focusing on the intersection of AI technologies and various organisational elements. The analysis was facilitated by NVivo 12, enabling a structured examination of key organisational facets for people, culture, structure, processes, and technology within public institutions. Findings: Previous studies on AI adoption in public institutions identified numerous enablers of AI adoption associated with organisational elements like people/employees, structure, culture, technology, and processes. Several surveys and case studies stress the importance of concentrating on the introduction or transformation of these organisational elements prior to or concurrently with the adoption of AI. Academic contribution to the field: By applying a systematic literature review protocol, the study represents the first holistic and systematic review of specific organisational elements that can serve as enablers of AI adoption in public institutions. Research limitations/implications: This systematic literature review was subject to several limitations. Firstly, the division of AI literature between natural and social sciences, with the former focusing on technical aspects and the latter on broader organisational themes, may have resulted in an incomplete depiction of the intersection of AI and organisational change. Secondly, despite the broad search queries, inherent limitations of keyword-based searches may have excluded some relevant studies. Thirdly, considering the rapid evolution of AI technology, our review may not fully encapsulate the very latest developments in the field as it covers literature published until May 2023. Finally, the interpretation and coding of literature, despite the use of NVivo 12, involved subjective elements that could affect the study’s outcomes. Practical implications: Drawing from experiences in the private sector, public institutions are increasingly adopting AI technologies across various subsectors such as public finance (taxation), research, healthcare, law enforcement, defence, education. This requires a transformation in both hard (structure, processes etc.) and soft aspects (people, organisational culture etc.). Therefore, the enablers identified in the study can serve as guidelines for decision-makers and implementers of AI at all levels of public institutions. Social implications: If adopted effectively and efficiently and used professionally and ethically, the use of AI in public institutions can bring many benefits to society, such as transparency, justice, cost and time efficiency, high quality services, and improved collaboration between different stakeholders in society. Originality/significance/value: Our study makes a distinct contribution by shifting the focus from technological barriers to organisational enablers of AI adoption in public institutions. It bridges a critical gap in the literature by integrating both technical and social science perspectives, providing valuable insights for theory and practice in the fields of organisation and management.Central European Public Administration Review1010The enablers identified in the study can serve as guidelines for decision-makers and implementers of AI at all levels of public institutions and are the first holistic and systematic review of specific organisational elements that can serve as enablers of AI adoption in public institutions.Central European Public Administration Review['Nina Tomaževič', 'E. Murko', 'Aleksander Aristovnik']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6a5a3b2f97240204e9895f8b9ccf5192b4558ace cc9553161c30bfc3ef86633e2dcf3c94ae7fdd5cBlockchain and artificial intelligence have now become the emerging technologies of our time. However, both strategies induce a change in the market but differ in terms of how creative and complex they are. While block chain is decentralized; distributed ledger technology that the one stores information in multiple locations with no centralized monitoring system, on the other hand, Artificial Intelligence (AI) emulates human problem-solving ability as well as decision-making abilities using software, data or even robots. These two technologies combined together to bring about new opportunities such as productivity gain from both advantages of blockchain and AI security and transparency. Businesses can transform significantly through integration of AI with blockchain technology for data protection, clear data trails and general efficiency. Cyber security is one of the most important concepts linked to these two technologies. The possibility to combine AI’s capabilities with blockchain’s reliable and decentralized nature can be used to manage resources as well as decision making at educational institutes or within healthcare agencies; it may also be employed by society to influence agriculture outputs, planning urban dwellings among others. Artificial intelligence (AI) can help identify risks at an early stage whereas information security and integrity might vanish.International Journal of Innovative Research in Computer and Communication Engineering00AI’s capabilities with blockchain’s reliable and decentralized nature can be used to manage resources as well as decision making at educational institutes or within healthcare agencies; it may also be employed by society to influence agriculture outputs, planning urban dwellings among others.International Journal of Innovative Research in Computer and Communication Engineering['Madhusudhana Vc', 'Srikanth']2024-05-17T00:00:00https://www.semanticscholar.org/paper/cc9553161c30bfc3ef86633e2dcf3c94ae7fdd5c 6068c6c003186efeefe0cdbfbad33957a116aa5eArtificial Intelligence (AI) has emerged as a disruptive force in pharmaceutical healthcare, offering innovative solutions to complex challenges and driving transformative advancements across various domains. This comprehensive review explores the multifaceted applications of AI in pharmaceutical healthcare as well as research, drawing insights from a diverse range of literature sources. By synthesizing findings from relevant studies and research articles, this review elucidates AI's pivotal role in drug discovery, disease diagnosis, personalized treatment, clinical trials, healthcare management, and patient care. Through proper referencing from reputable sources, including PubMed, Science Direct, Google Scholar, and others, this review provides a thorough examination of AI's impact, challenges, and future directions in shaping the future of pharmaceutical healthcare.Scholars Academic Journal of Pharmacy00This review elucidates AI's pivotal role in drug discovery, disease diagnosis, personalized treatment, clinical trials, healthcare management, and patient care by synthesizing findings from relevant studies and research articles.Scholars Academic Journal of Pharmacy['Prashant Singh', 'A. Singh', 'N. verma', 'Abhay Kumar', 'Zahra Chegini', 'Ankita Malviya']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6068c6c003186efeefe0cdbfbad33957a116aa5e 6a88611d0002947ede685a3639d49ee7edf86fde Acute kidney injury (AKI) is associated with increased morbidity/mortality. With artificial intelligence (AI), more dynamic models for mortality prediction in AKI patients have been developed using machine learning (ML) algorithms. The performance of various ML models were reviewed in their ability to predict in-hospital mortality (IHM) for AKI patients. A literature search was conducted through Pubmed, Embase, and Web of Science Databases. Included studies contained variables regarding the efficacy of the AI model (AUC, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV)). Only original studies which consisted of cross-sectional studies, prospective and retrospective studies were included, while reviews and self-reported outcomes were excluded. There was no restriction on time and geographic location. 8 studies with 37 032 AKI patients were included with a mean age of 65.3 years. The IHM was 18.0% in the derivation and 15.8% in the validation cohorts. The pooled (95% CI) AUC was observed to be highest for Broad Learning System (BLS) Models: [0.852 (0.820–0.883) and Elastic Net Final Model (ENF) [0.852 (0.813–0.891)] and lowest for proposed clinical model (PCM) [0.765 (0.716–0.814)]. The pooled (95% CI) AUC of BLS and ENF did not differ significantly from other models except PCM [Delong's test P = 0.022]. PCM exhibited the highest NPV, which supports this model's use as a possible rule out tool. Our results show that BLS & ENF Models are equally effective as other ML models, in predicting in-hospital mortality with variability across all models. Additional studies are needed. Clinical Kidney Journal00BLS & ENF Models are equally effective as other ML models, in predicting in-hospital mortality with variability across all models, and PCM exhibited the highest NPV, which supports this model's use as a possible rule out tool.Clinical Kidney Journal['Rupesh Raina', 'Raghav Shah', 'P. Nemer', 'Jared Fehlmen', 'Lena Nemer', 'Ali Murra', 'A. Tibrewal', 'S. Sethi', 'Javier A. Neyra', 'J. Koyner']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6a88611d0002947ede685a3639d49ee7edf86fde b18084ea0588d96c305bea16132dbf0ce974faa6The integration of Artificial Intelligence (AI) in Energy Storage Systems (ESS) for Electric Vehicles (EVs) has emerged as a pivotal solution to address the challenges of energy efficiency, battery degradation, and optimal power management. The capability of such systems to differ from theoretical modeling enhances their applicability across various domains. The vast amount of data available today has enabled AI to be trained and to predict the behavior of complex systems with a high degree of accuracy. As we move towards a more sustainable future, the electrification of vehicles and integrating electric systems for energy storage are becoming increasingly important and need to be addressed. The synergy of AI and ESS enhances the overall efficiency of electric vehicles and plays a crucial role in shaping a sustainable and intelligent energy ecosystem. To the best of the authors’ knowledge, AI applications in energy storage systems for the integration of electric vehicles have not been explicitly reviewed. The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge (SOC) and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles. This study examined all topics to identify the most commonly used methods, which were analyzed based on their characteristics and potential. Future trends were identified by exploring emerging techniques introduced in recent literature contributions published since 2017.Electronics00The research investigates the importance of AI advancements in energy storage systems for electric vehicles, specifically focusing on Battery Management Systems (BMS), Power Quality (PQ) issues, predicting battery State-of-Charge and State-of-Health (SOH), and exploring the potential for integrating Renewable Energy Sources with EV charging needs and optimizing charging cycles.Electronics['S. Miraftabzadeh', 'Michela Longo', 'Andrea Di Martino', 'Alessandro Saldarini', 'R. Faranda']2024-05-17T00:00:00https://www.semanticscholar.org/paper/b18084ea0588d96c305bea16132dbf0ce974faa6 41331cef3b4a3c0469c76047ae716bfe8f34ab0eAim: This paper aims to examine the bias and fairness issues accorded with artificial intelligence (AI)-driven cybersecurity.                                                               Problem Statement: The evolving global dependence on cybersecurity has exposed organizations, individuals, and nations to different vulnerabilities and security threats. However, merging of cyberspace with AI technologies has the potential to transform multiple domains but the implementation of AI is faced with bias problems limiting its application. Significance of Study: Artificial intelligence and cybersecurity have been identified as two transformative and interconnected entities with great potential to revolutionize numerous areas of human life. However, it is imperative to critically look at the bias and fairness accorded with the implication of artificial intelligence-driven cybersecurity which are keywords limiting the usage and efficiency of the approach.                               Discussion: The concept of artificial intelligence and cybersecurity was discussed together with their interconnectivity which enhances the application in tackling cyber threats. Various areas of artificial intelligence deployment in cyberspace were presented. The sources and solutions to bias and fairness in artificial intelligence-driven cybersecurity were also discussed. This paper has critically discussed various ways via which AI biases influence cyber security. Nonetheless, ways by which this problem can be tackled were presented.    Conclusion: Artificial intelligence-driven cybersecurity has found wide industrial applications in different areas. However, there is a need to critically address the issues of bias and fairness attached to it to improve its efficiency. The use of the teams; AI model; and Corporate governance and leadership should be adopted to find lasting solutions to the problem of biases in AI-driven cyber security.Current Journal of Applied Science and Technology00There is a need to critically address the issues of bias and fairness attached to AI-driven cybersecurity to improve its efficiency and there is a need to critically address the issues of bias and fairness attached to it to improve its efficiency.Current Journal of Applied Science and Technology['Ugochukwu Mmaduekwe']2024-05-17T00:00:00https://www.semanticscholar.org/paper/41331cef3b4a3c0469c76047ae716bfe8f34ab0e c0b987b9f45fb806c02ca1af55f2043204bf9870Despite the increasing popularity and research focus on the application of artificial intelligence (AI) in entrepreneurship, no comprehensive bibliometric analysis of relevant papers has been conducted. Our review examines 127 Scopus-indexed publications from 2007 to October 2023. The inquiry explores thematic progression, geographical production trends, and seminal works shaping the discourse. Notably, while authors from Asia, mainly China, lead in publication volume, influential works from the USA, the UK, and Australia attract significant scholarly attention. The analysis reveals a prominent trend: highly cited papers often lack theoretical frameworks, with literature reviews and conceptual analyses prevailing over empirical investigations. When theoretical foundations are present, diverse theories are often amalgamated. In conclusion, our review highlights numerous avenues for future academic exploration in this domain. Pragmatically, our effort, the first of its kind, consolidates fragmented insights, providing a cohesive understanding of AI's pivotal role in entrepreneurship.International Journal of E-Entrepreneurship and Innovation490A comprehensive bibliometric analysis of relevant papers from 2007 to October 2023 reveals a prominent trend: highly cited papers often lack theoretical frameworks, with literature reviews and conceptual analyses prevailing over empirical investigations.International Journal of E-Entrepreneurship and Innovation['S. Boateng', 'O. Penu', 'Joseph Budu', 'Richard Boateng', 'J. Marfo', 'T. Anning-Dorson', 'F. Broni']2024-05-17T00:00:00https://www.semanticscholar.org/paper/c0b987b9f45fb806c02ca1af55f2043204bf9870 6015a210c51dd26db1b24d87dd8a25cd0ed6996eThe rapid advancement of techniques and technologies leads to the emergence of innovative solutions that facilitate, enhance, and shorten the time required for work. Companies are searching for innovative strategies that will help them more easily contend with growing competition and reach target desired consumers more precisely. Opportunities for business development increase through the use of artificial intelligence in digital marketing strategies. Considering that digital marketing requires the existence of marketing content, the success of a company's online communication largely depends on the quality of its content. Artificial intelligence transforms marketing by enabling companies to connect with the right audience in real-time through personalized product recommendations and dynamic content creation. The aim of this work is to present the benefits of artificial intelligence in the function of content creation in digital marketing. Secondary data, including the results of previous research, relevant industry reports, expert articles, and relevant statistical data, have been used in further research.Proceedings of the 29th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management00The aim of this work is to present the benefits of artificial intelligence in the function of content creation in digital marketing, and to highlight the opportunities for business development increase through the use of artificial intelligence in digital marketing strategies.Proceedings of the 29th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management['Jelena Šiđanski']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6015a210c51dd26db1b24d87dd8a25cd0ed6996e 323dea37ba319fc6b8ff1652973a8b8c92500c70This study is intended to find a relationship between Artificial Intelligence and Business School Students’ Performance. Data was collected from 150 Business School students at KIMEP University using questionnaires in Spring semester of 2024. Questionnaires were distributed two times: at the early beginning of the semester and at the end of the semester. During the semester, students thoroughly studied Artificial Intelligence and its functions and capacity. Additional classes on Artificial Intelligence tools were offered to students. Based on findings, the study proposes to address some positive implications of Artificial Intelligence integration in university teaching and learning globally. Artificial Intelligence has positive impact on students’ learning process. However, special ethical rules and standards must be developed and introduced due to the fact that there are increasing concerns over the risks associated with Artificial Intelligence usage and how it might affect human being further. International Journal of Religion320It is proposed to address some positive implications of Artificial Intelligence integration in university teaching and learning globally to address increasing concerns over the risks associated with Artificial Intelligence usage and how it might affect human being further.International Journal of Religion['Maya Katenova', 'Karlygash Turmaganbetova']2024-05-17T00:00:00https://www.semanticscholar.org/paper/323dea37ba319fc6b8ff1652973a8b8c92500c70 4b5090be2d304bee7794ae9c2d50ab47c1550753Explainable artificial intelligence (XAI) is increasingly crucial due to its extremely important role in modern marketing, as it advances predictive analytics of consumer behavior and analysis of the purchase decision-making process. This paper examines the importance of XAI in marketing, emphasizing its role in improving the effectiveness and efficiency of marketing strategies. By examining the evolution of AI in marketing and the challenges posed by opaque algorithms, this study highlights the transformative potential of XAI in bridging the gap between marketers and consumers. In addition, ethical issues related to the application of XAI are discussed, emphasizing the imperative of conscientious application in order to maintain privacy and consumer trust. Furthermore, possible directions for the use of XAI are explored, with the aim of driving marketing practices in a data-dominated era. This paper highlights the key role of XAI in shaping future trends in marketing research and its implications for businesses operating in a dynamic market environment.Proceedings of the 29th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management00The key role of XAI in shaping future trends in marketing research and its implications for businesses operating in a dynamic market environment are highlighted, with the aim of driving marketing practices in a data-dominated era.Proceedings of the 29th International Scientific Conference Strategic Management and Decision Support Systems in Strategic Management['Zvjezdana Krstić', 'Mirjana Maksimović']2024-05-17T00:00:00https://www.semanticscholar.org/paper/4b5090be2d304bee7794ae9c2d50ab47c1550753 51508319c003c5aedabfdb1285ef692949114167This paper sheds light on how Artificial Intelligence (AI) is reshaping the landscape of crime detection and prevention, bringing about a significant change in traditional law enforcement methods. With society increasingly embracing AI-driven solutions, it's crucial to understand how they contribute to public safety. We'll dive into various aspects of AI, such as predictive policing, video surveillance analysis, forensic science, and criminal profiling, to see how they enhance law enforcement capabilities and streamline crime resolution processes.Starting with predictive policing, AI algorithms sift through massive datasets to identify patterns and predict future criminal activity, empowering law enforcement to take proactive measures. Similarly, AI-powered video surveillance systems enhance real-time monitoring and anomaly detection, allowing for quick identification of suspicious behavior in urban environments.Moving on, we'll explore AI's impact on forensic analysis, where it expedites processes like DNA sequencing, fingerprint recognition, and ballistics analysis, speeding up the resolution of cold cases. Additionally, we'll delve into AI's role in criminal profiling, highlighting its ability to decipher behavioral patterns and predict criminal motivations, though ethical considerations remain paramount.We'll also address ethical concerns surrounding AI deployment in law enforcement, including privacy issues and algorithmic biases. Emphasizing the need for transparency and accountability, we advocate for responsible use of AI-powered crime detection systems to uphold ethical standards and protect individual rights.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper sheds light on how Artificial Intelligence is reshaping the landscape of crime detection and prevention, bringing about a significant change in traditional law enforcement methods, and advocates for responsible use of AI-powered crime detection systems to uphold ethical standards and protect individual rights.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Louis Zvikomborero Kahla']2024-05-17T00:00:00https://www.semanticscholar.org/paper/51508319c003c5aedabfdb1285ef692949114167 d9bb21615062eb677976a112af38474fc30cacfeRecommender systems anticipate customers' present preferences for certain items based on their past behavior, offering individualized service support to users. The evolution of recommender systems has naturally included artificial intelligence (AI), notably computational intelligence and machine learning techniques and algorithms to enhance prediction precision and resolve issues with cold start and data sparsity. This paper methodically examines the fundamental approaches and current practices in recommender systems.International journal of engineering science & humanities00The fundamental approaches and current practices in recommender systems are examined, notably computational intelligence and machine learning techniques and algorithms to enhance prediction precision and resolve issues with cold start and data sparsity.International Journal of Engineering Science and Humanities['Nancy Sharma']2024-05-17T00:00:00https://www.semanticscholar.org/paper/d9bb21615062eb677976a112af38474fc30cacfe 35c8d684112aab59667a6d68e4b2bb9c30993a1dEuropean heart journal. Acute cardiovascular care00European heart journal. Acute cardiovascular care['J. Jentzer', 'Xavier Rossello']2024-05-17T00:00:00https://www.semanticscholar.org/paper/35c8d684112aab59667a6d68e4b2bb9c30993a1d 182ada3b2d9832544fc5d2410e3cb931bdba40d3ABSTRACT The Emergency Severity Index (ESI) is the most popular tool used to triage patients in the US and abroad. Evidence has shown that ESI has its limitations in correctly assigning acuity. To address this, AI can be incorporated into the triage process, decreasing the likelihood of assigning an incorrect ESI level.Nursing160Artificial intelligence can be incorporated into the triage process, decreasing the likelihood of assigning an incorrect ESI level, in order to address the limitations of ESI.Nursing['Melinda Regan']2024-05-17T00:00:00https://www.semanticscholar.org/paper/182ada3b2d9832544fc5d2410e3cb931bdba40d3 b9e9f0bd27013185332bf8b4bdeb3acb57b757d7Nature Mental Health80Nature Mental Health['A. J. Thirunavukarasu', 'Jessica O’Logbon']2024-05-17T00:00:00https://www.semanticscholar.org/paper/b9e9f0bd27013185332bf8b4bdeb3acb57b757d7 56da7650964db01bd851ea1ef5763fc1cceaa016International Journal of Academic Research in Accounting, Finance and Management Sciences00International Journal of Academic Research in Accounting, Finance and Management Sciences['Zahir Osman', 'Malik Yatam']2024-05-17T00:00:00https://www.semanticscholar.org/paper/56da7650964db01bd851ea1ef5763fc1cceaa016 6fea340eb9a8fa9f63db19121827560ef2ae534b00['J. Johannessen']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6fea340eb9a8fa9f63db19121827560ef2ae534b cf44535e3097296ac237e2270ba704d10fb0c64bBMJ Quality & Safety210BMJ quality & safety['T. Soukup', 'Bryony Dean Franklin']2024-05-17T00:00:00https://www.semanticscholar.org/paper/cf44535e3097296ac237e2270ba704d10fb0c64b 0272efc782267afff420d28d5e042b8ab61e7302Artificial Intelligence and Law200Artificial Intelligence and Law['Yulin Zhou', 'Yongbin Qin', 'Ruizhang Huang', 'Yanping Chen', 'Chuan Lin', 'Yuan Zhou']2024-05-17T00:00:00https://www.semanticscholar.org/paper/0272efc782267afff420d28d5e042b8ab61e7302 e92d2812680cdfa9efb581a98fc5d366e7b7b3f9Study objective This study aimed to prospectively validate the performance of an artificially augmented home sleep apnea testing device (WVU-device) and its patented technology. Methodology The WVU-device, utilizing patent pending (US 20210001122A) technology and an algorithm derived from cardio-pulmonary physiological parameters, comorbidities, and anthropological information was prospectively compared with a commercially available and Center for Medicare and Medicaid Services (CMS) approved home sleep apnea testing (HSAT) device. The WVU-device and the HSAT device were applied on separate hands of the patient during a single night study. The oxygen desaturation index (ODI) obtained from the WVU-device was compared to the respiratory event index (REI) derived from the HSAT device. Results A total of 78 consecutive patients were included in the prospective study. Of the 78 patients, 38 (48%) were women and 9 (12%) had a Fitzpatrick score of 3 or higher. The ODI obtained from the WVU-device corelated well with the HSAT device, and no significant bias was observed in the Bland-Altman curve. The accuracy for ODI > = 5 and REI > = 5 was 87%, for ODI> = 15 and REI > = 15 was 89% and for ODI> = 30 and REI of > = 30 was 95%. The sensitivity and specificity for these ODI /REI cut-offs were 0.92 and 0.78, 0.91 and 0.86, and 0.94 and 0.95, respectively. Conclusion The WVU-device demonstrated good accuracy in predicting REI when compared to an approved HSAT device, even in patients with darker skin tones.PLoS ONE180The WVU-device demonstrated good accuracy in predicting REI when compared to an approved HSAT device, even in patients with darker skin tones.PLOS ONE['Sunil Sharma', 'Kassandra Olgers', 'S. Knollinger', 'Saivenkat Somisetty', 'Calvin Seol', 'Naveena Yanamala']2024-05-17T00:00:00https://www.semanticscholar.org/paper/e92d2812680cdfa9efb581a98fc5d366e7b7b3f9 dfaa0c48c7f2b2aae792d62a1c6e27b8b67648e3Este estudo tem como objetivo é salientar a crescente importância da inteligência artificial (IA) no âmbito farmacêutico e de saúde, destacando sua capacidade de impulsionar transformações significativas em diversas esferas, abrangendo desde a pesquisa e fabricação de medicamentos até o diagnóstico e o envolvimento com os pacientes. Ademais, pretende-se explorar as considerações éticas e os obstáculos relacionados à integração da IA na indústria farmacêutica, enquanto ressalta os benefícios em potencial dessa tecnologia, como a melhoria dos procedimentos e a adaptação da terapia medicamentosa. Além disso, a IA tem sido aplicada no varejo farmacêutico para otimizar a gestão de inventário e antecipar as necessidades dos pacientes. No entanto, sua implementação enfrenta desafios, como a falta de estudos abrangentes sobre sua eficácia e a dificuldade de acesso a algoritmos atualizados. Esta pesquisa revisa a literatura sobre o emprego da inteligência artificial em farmacoterapias e processos farmacêuticos nos últimos cinco anos. Utilizando abordagem qualitativa, foram pesquisados artigos nas plataformas PubMed e BVS, com descritores específicos como Artificial Intelligence, Pharmaceutical, Chatbot in Pharmacy e Hospital Pharmacy. Após a exclusão de artigos fora do escopo temático, foram selecionadas 10 publicações relevantes.Revista contemporânea70Revista Contemporânea['Gabriela Silva Soares', 'Yasmim Cristini Ribeiro dos Santos', 'João Gomes Pontes Neto']2024-05-17T00:00:00https://www.semanticscholar.org/paper/dfaa0c48c7f2b2aae792d62a1c6e27b8b67648e3 087c723dc172d907392ceacbb9f5b3b258bc4b21The fourth industrial revolution is bringing artificial intelligence (AI) into various workplaces, and many businesses worldwide are already capitalizing on AI assistants. Trust is essential for the successful integration of AI into organizations. We hypothesized that people have higher trust in human assistants than AI assistants and that people trust AI assistants more if they have more control over their activities. To test our hypotheses, we utilized a survey experiment with 828 participants from Finland. Results showed that participants would rather entrust their schedule to a person than to an AI assistant. Having control increased trust in both human and AI assistants. The results of this study imply that people in Finland still have higher trust in traditional workplaces where people, rather than smart machines, perform assisting work. The findings are of relevance for designing trustworthy AI assistants, and they should be considered when integrating AI technology into organizations.Human Behavior and Emerging Technologies820The results of this study imply that people in Finland still have higher trust in traditional workplaces where people, rather than smart machines, perform assisting work.Human Behavior and Emerging Technologies['Anica Cvetkovic', 'N. Savela', 'Rita Latikka', 'Atte Oksanen']2024-05-17T00:00:00https://www.semanticscholar.org/paper/087c723dc172d907392ceacbb9f5b3b258bc4b21 6cb92e9ec0df03c84e1fb72aceec2fadd75c2f38Society's capacity for algorithmic problem-solving has never been greater. Artificial Intelligence is now applied across more domains than ever, a consequence of powerful abstractions, abundant data, and accessible software. As capabilities have expanded, so have risks, with models often deployed without fully understanding their potential impacts. Interpretable and interactive machine learning aims to make complex models more transparent and controllable, enhancing user agency. This review synthesizes key principles from the growing literature in this field. We first introduce precise vocabulary for discussing interpretability, like the distinction between glass box and explainable algorithms. We then explore connections to classical statistical and design principles, like parsimony and the gulfs of interaction. Basic explainability techniques -- including learned embeddings, integrated gradients, and concept bottlenecks -- are illustrated with a simple case study. We also review criteria for objectively evaluating interpretability approaches. Throughout, we underscore the importance of considering audience goals when designing interactive algorithmic systems. Finally, we outline open challenges and discuss the potential role of data science in addressing them. Code to reproduce all examples can be found at https://go.wisc.edu/3k1ewe.750This review synthesizes key principles from the growing literature in interpretability and interactive machine learning, and explores connections to classical statistical and design principles, like parsimony and the gulfs of interaction.['Kris Sankaran']2024-05-17T00:00:00https://www.semanticscholar.org/paper/6cb92e9ec0df03c84e1fb72aceec2fadd75c2f38 3245d1e53c6b0af8bdef0e651ebefbfdf7107bdeThe article reveals certain types of non-contractual obligations (NCO) in the field of private international law (PIL). The common features and differences between certain types of NCO in Ukraine and other countries of the Romano-Germanic legal family through the usage of the comparative legal method are described. The article reveals such types of obligations in PIL as: tort/delict obligations; obligations arising out of unjust enrichment; obligations arising out of damage caused by a product, work, service; culpa in contrahendo; negotiorum gestio. The article substantiates that NCO arise, first of all, between persons who are not in a contractual relationship, or between persons who are bound by a contract, but the damage is not caused in connection with a violation of contractual obligations. The article reveals the main provisions of conflict regulation of NCO. The issue of refusal to use the general collision binding of the place of harm (lex loci delicti commissi) is being studied. Nowadays, instead of that collision binding, which was originally used for each type of non-contractual obligation, several alternative collision bindings are used. In addition to the law of the place where the tort was committed, the article also reveals the features of using such collision bindings as the citizenship of the parties or the place of residence of the parties of the legal relationship, the place of release of the goods, the place of registration of the vehicle, etc. The article highlights some aspects of the recent reform of civil legislation and PIL, in particular. The root cause of the reform and renewal of domestic legislation in various areas, which is caused by the European integration processes taking place in Ukraine in recent years, is revealed. The article substantiates the active recodification of civil legislation introduced by the Government of Ukraine, aimed at eliminating certain shortcomings and contradictions in national civil legislation and harmonizing it with the legislation of the European Union. The article places special emphasis on the need to update national legislation and bring it into line with European standards for democratization and liberalization of all spheres of life.Uzhhorod National University Herald. Series: Law20Uzhhorod National University Herald. Series: Law['O. V. Rudenko', 'O. R. Vaitsekhovska']2024-05-16T00:00:00https://www.semanticscholar.org/paper/3245d1e53c6b0af8bdef0e651ebefbfdf7107bde 2f04ed5adc6ff51ce61c2f942895f8996e210cd9Journal of Science in Sport and Exercise190Journal of Science in Sport and Exercise['Faten Sahli', 'Manar Boujabli', 'H. Sahli', 'N. Jebabli', 'Hatem Ghouili', 'Khaled Trabelsi', 'Mohamed Mansour Bouzouraa', 'N. Guelmami', 'Mohamed Ben Aissa', 'A. Ammar', 'Ismail Dergaa', 'Makram Zghibi']2024-05-16T00:00:00https://www.semanticscholar.org/paper/2f04ed5adc6ff51ce61c2f942895f8996e210cd9 904c8daffa3d8a321467225110e758083ed8ff3dBMC Medical Ethics420The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI, and collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.BMC Medical Ethics['Hisham E Hasan', 'Deema Jaber', 'O. Khabour', 'K. Alzoubi']2024-05-16T00:00:00https://www.semanticscholar.org/paper/904c8daffa3d8a321467225110e758083ed8ff3d 53a9668635e5c166f2f6f3236c2caf2c2954bc0fArtificial Intelligence and Law530This Presidential Address will demonstrate how the Netherlands National Police Lab AI are developing responsible AI by combining insights from different disciplines, and how this connects to the future of the field.Artificial Intelligence and Law['Floris Bex']2024-05-16T00:00:00https://www.semanticscholar.org/paper/53a9668635e5c166f2f6f3236c2caf2c2954bc0f a83519538a6cd6357b948e20eac4ba2dfc2c4afeThis article argues that through the EU's technology regulation, technological concepts permeate legal language. Such concepts may function as transplants, even irritants, causing tensions and uncertainties. As technology regulation is increasingly horizontal, i.e. obligating private and public actors alike, these newfound legal concepts remain disconnected from established public law vocabulary and the power constellations it represents and embeds. We approach this evolution of legal language from public law perspective and concentrate on the concepts of ‘user’ and ‘deployer’ in the EU's upcoming Artificial Intelligence Act. We discuss these emerging legal concepts in relation to the rich theorizing on the concepts in human–computer interaction research. Our analysis demonstrates a discrepancy between legal and technology-oriented conceptualizations of the ‘user-deployer’. We draw three conclusions. First, the digital revolution is taking place in conceptual-linguistic practices of law, and not only when translating law into code. Second, when external concepts are appropriated into law, they are uprooted from their established habitat, which may result in unpredictability in future legal interpretation. Third, in public law, adopting the ‘user-deployer’ may have some additional challenges, as it introduces a new agent into the relationship between public authority and private entities. Simultaneously, citizens seem to be mainly excluded from the legal conceptualizing, which risks blurring traditional power constellations.Maastricht Journal of European and Comparative Law00It is argued that through the EU's technology regulation, technological concepts permeate legal language and may function as transplants, even irritants, causing tensions and uncertainties in future legal interpretation.Maastricht Journal of European and Comparative Law['Ida Koivisto', 'Riikka Koulu', 'Stefan Larsson']2024-05-16T00:00:00https://www.semanticscholar.org/paper/a83519538a6cd6357b948e20eac4ba2dfc2c4afe 204b4ed437d5c00157c74cf5416498c66f59415a Purpose The purpose of this study is to comprehensively analyse and compare equity crowdfunding (ECF) regulations across 26 countries, shedding light on the diverse regulatory frameworks, investor and issuer limits and the evolution of ECF globally. By addressing this research gap and providing consolidated insights, the study aims to inform policymakers, researchers and entrepreneurs about the regulatory landscape of ECF, fostering a deeper understanding of its potential and challenges in various economies. Ultimately, the study contributes to the advancement of ECF as an alternative financing method for small and medium enterprises (SMEs) and startups, empowering them to access much-needed capital for growth. Design/methodology/approach The study used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model for a systematic literature review on global ECF regulations. Starting with 74 initial articles from Web of Sciences and Scopus databases, duplicates were removed and language criteria applied, leaving 42 articles. After a thorough full-text screening, 20 articles were excluded, resulting in the review of 22 papers from 2016 to 2022. PRISMA’s structured framework enhances the quality of systematic reviews, ensuring transparency and accessibility of findings for various stakeholders, including researchers, practitioners and policymakers, in the field of ECF regulations. Findings This study examines ECF regulations across various countries. Notably, the UK has advanced regulations, while the USA adopted them later through the Jumpstart Our Business Startups Act. Canada regulates at the provincial level. Malaysia and China were early adopters in Asia, but Hong Kong, Japan, Israel and India have bans. Turkey introduced regulations in 2019. New Zealand and Australia enacted laws, with Australia referring to it as “crowd-sourced equity funding”. Italy, Austria, France, Germany and Belgium have established regulations in Europe. These regulations vary in investor and issuer limits, disclosure requirements and anti-corruption measures, impacting the growth of ECF markets. Research limitations/implications This study’s findings underscore the diverse regulatory landscape governing ECF worldwide. It reveals that regulatory approaches vary from liberal to protectionist, reflecting each country’s unique economic and political context. The implications of this research highlight the need for cross-country analysis to inform practical implementation and the effectiveness of emerging ECF ecosystems. This knowledge can inspire regulatory adjustments, support startups and foster entrepreneurial growth in emerging economies, ultimately reshaping early-stage funding for new-age startups and SMEs on a global scale. Originality/value This study’s originality lies in its comprehensive analysis of ECF regulations across 26 diverse countries, shedding light on the intricate interplay between regulatory frameworks and a nation’s political-economic landscape. By delving into the nuanced variations in investor limits, investment types and regulatory strategies, it unveils the multifaceted nature of ECF regulation globally. Furthermore, this research adds value by comparing divergent perspectives on investment constraints and offering an understanding of their impact on ECF efficacy. Ultimately, the study’s unique contribution lies in its potential to inform practical implementation, shape legislative frameworks and catalyse entrepreneurial ecosystems in emerging economies, propelling the evolution of early-stage funding practices. Journal of Financial Regulation and Compliance660Journal of Financial Regulation and Compliance['Prateek Gupta', 'Shivansh Singh', 'Renu Ghosh', 'Sanjeev Kumar', 'Chirag Jain']2024-05-16T00:00:00https://www.semanticscholar.org/paper/204b4ed437d5c00157c74cf5416498c66f59415a 3fd7f4ba4ccb84fefe44aa6c73861c117349e422The scientific article examines issues related to the determination of the essence, role and meaning of definitions in the regulatory and legal regulation of economic security. The uniqueness of the phenomenon of economic security is emphasized, which is the guarantor of ensuring the national interests of the state, society, and the individual in the economic sphere and the basis for the unhindered implementation of the state’s strategic national priorities. Ensuring these interests and implementing strategic priorities is possible through the creation of effective legal regulation, which includes various legal means, in particular, regulatory prescriptions. It is pointed out the important role of definitions in normative and legal regulation, which contain definitions of concepts as integral elements of the legal basis for ensuring economic security. It is noted that the legal definitions of the concepts have a universally binding nature and contribute to the formation of a single legal space. The functions of legal definitions in the normative and legal regulation of ensuring economic security are revealed. An attempt was made to evaluate the conceptual and categorical apparatus in the analyzed field from the point of view of the universality of the relevant definitions, the completeness of their textual expression, as well as state policy. It is noted that the current security legislation does not contain a legislative definition of key concepts in the field of ensuring economic security. In this regard, the problem of unification of the conceptual and categorical apparatus in the field of ensuring economic security by adopting the main strategic planning documents is raised. In the opinion of the author, taking into account the main approaches of the legislation to the definition of national interests, the legal concept of “economic security”, similar to the definition of “state security”, should reflect a certain state of the state, under which conditions are created to ensure the appropriate state of security of the economic system of Ukraine. The expediency of developing a new Strategy for the sustainable development of the economy of Ukraine for the post-war period was emphasized, which would not only determine the ways to achieve goals and implement priorities of national interests in the field of ensuring economic security and foresee the main challenges and threats, but also contain a legal definition of “economic security” and such its components, such as “financial security”, “foreign economic security”, “investment and innovation security”, etc.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['V. V. Zub']2024-05-16T00:00:00https://www.semanticscholar.org/paper/3fd7f4ba4ccb84fefe44aa6c73861c117349e422 03e3c74c4bd7849a6218d7e8e84fb5142a36998dThe analysis of scientific sources provides grounds to assert that legal regulation is the regulation of social relations carried out through law and the entire set of legal means. The concept of «regulation» (from Latin regulo - rule) implies organization, adjustment, and bringing something into conformity with something else. In our view, to regulate means to define the behavior of individuals and their collectives, to direct their functioning and development, to provide certain limits, and to organize them purposefully. Alongside this, some scholars relate the term «regulation» solely to law as a system of norms and some other specific legal phenomena (legal relations, acts of law implementation). They disagree with the existing understanding of the regulation of social relations as the rigid and authoritative norming by the state and law, as, in their opinion, the category of «regulation» is not synonymous with coercion, rigid, and authoritative prescription. The legal norm establishes only a model of relations in which social interests must be correlated with the interests of society members, and alongside this, law widely uses such means of influencing people’s behavior as stimulation, encouragement, granting rights, etc. It is argued that to transition to the definition of legal regulation, it is necessary to refer to the theory of law, which provides explanations for the concepts of «legal influence» and «legal regulation». Legal influence is considered a broader concept, as it includes the normative-organizational influence on social relations not only through a system of special legal means (those that directly regulate these relations - legal norms, legal relations, acts of implementation and application of law), but also through other legal phenomena – legal consciousness, legal culture, legal principles, law-making process, etc. A proposed definition states that legal regulation is the authoritative influence on social relations carried out by the state through all legal means for the purpose of their organization, establishment, protection, and development. Besides such (regulatory) influence, law also exerts a spiritual-ideological influence on individual and social consciousness (both in the process of legal regulation and beyond). «Restrictions» and «prohibitions» as legal categories have been analyzed. The etymology of the words «restriction» and «prohibition,» their relationship to each other and to adjacent and synonymous concepts, have been explored, and an original interpretation of the content and essence of these concepts has been proposed. A number of features characterizing restrictions and prohibitions as legal categories have been identified (defined in normative legal acts; established to prevent potential abuses of law; associated with a «narrowing» of an individual’s legal status; presuppose a specific model of behavior, specifically restrictions entail active behavior, meaning to do only what is defined within limits; prohibitions entail passive behavior, meaning to refrain from doing prohibited actions; they perform a protective function in social relations; non-compliance with them is accompanied by a negative response from the state. The concept, characteristics, classification, and a systematic analysis of restrictions and prohibitions as means of legal regulation have been defined. Based on the analysis of dictionary, reference, encyclopedic literature, as well as specialized legal sources, the article formulates original definitions of «restriction». The specificity of these particular restrictions and prohibitions lies in their special area of application (they apply to individuals when exercising their powers within the civil service); they apply to specific subjects (directly to individuals who have the legal status of civil servants); their application is determined by a special purpose; they are characterized by specific, comprehensive normative legal regulation; their application is ensured by state coercion. Distinctive features inherent to restrictions and prohibitions in the field of legal regulation have been identified: individual character; preventive nature; limiting aspect; coercive nature; the presence of a special subject; connection to professional activity; relation to delict norms, and their essence has been explained. The normative basis for defining and applying restrictions and prohibitions as means of legal regulation has been characterized (substantive legislation, procedural legislation, sub-legislative normative legal acts). A classification of restrictions and prohibitions has been conducted, and it is proposed to conditionally divide them into three groups (personal, property, and mixed).Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['M. Kelman', 'R. Kelman']2024-05-16T00:00:00https://www.semanticscholar.org/paper/03e3c74c4bd7849a6218d7e8e84fb5142a36998d 440a65c097a98ab2dc95d8ceec8c0ad92cec2e48Social Science Research Network540SSRN Electronic Journal['J. P. Bastos', 'J. Pavlik']2024-05-16T00:00:00https://www.semanticscholar.org/paper/440a65c097a98ab2dc95d8ceec8c0ad92cec2e48 f7f7f9651a48fff9c8b43fe82c66da7d4aad18acThe presented article is devoted to the analysis of the norms of the Salic Law which regulated civil legal relations of the Frankish society. The article notes that the Salic Law is a collection that was primarily devoted to the rules of criminal law and criminal procedure. Less attention was paid to the norms of civil law. This was due to the fact that the agricultural economy was not organized on a contractual basis. However, despite this, it contained rules that regulated property rights, obligations, marriage and family law, and inheritance law. The author has established that the Salic Law does not have a clear definition of property. The Franks retained communal ownership of land for a long time, as the rural neighborhood community, the marka, continued to exist despite the formation of the state. It is noted that at the end of the sixth century, the Franks formed a «full allod» – freely alienable land property, which was allowed to be freely given, transferred and bequeathed, including to women. A movable property was owned by families or individuals and could also be freely alienated and inherited. Based on the analysis of the norms of the Salic Law, the author found that the law of obligations was the least developed. There were such types of contracts as sale and purchase, exchange, gift, and loan. For a contract to be valid, in addition to the consent of the parties, it was necessary to observe various rites. As for marriage and family law, the article states that a legal marriage could be concluded between an adult free man and an adult free woman (no minimum age was set), subject to the consent of their relatives and provided that both parties were not bound by prohibited ties of kinship. Marriage was concluded through the purchase of a wife, i.e. power over her, from the person who had previously exercised this power over her. The grounds for invalidating a marriage were also defined. The author also analyzes the order of inheritance according to the Salic Law. It is established that the heirs in descending order were: children (and their descendants); mother and father; brothers and sisters; father’s sisters; mother’s sisters; father’s relatives (up to the sixth generation). Allodial land was а family land, it could not be inherited by females.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['B. Hutiv']2024-05-16T00:00:00https://www.semanticscholar.org/paper/f7f7f9651a48fff9c8b43fe82c66da7d4aad18ac d10504e04ddd8f45495c79fa687365533bcc5e38Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such as resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain.Social Science Research Network651The article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation and provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain.SSRN Electronic Journal['Christopher Thomas', 'Huw Roberts', 'Jakob Mökander', 'Andreas Tsamados', 'M. Taddeo', 'Luciano Floridi']2024-05-16T00:00:00https://www.semanticscholar.org/paper/d10504e04ddd8f45495c79fa687365533bcc5e38 37d1c61e413239c676d7a9df94dda3a1eebd6058Driven by the algorithmic advancements in reinforcement learning and the increasing number of implementations of human-AI collaboration, Collaborative Reinforcement Learning (CRL) has been receiving growing attention. Despite this recent upsurge, this area is still rarely systematically studied. In this paper, we provide an extensive survey, investigating CRL methods based on both interactive reinforcement learning algorithms and human-AI collaborative frameworks that were proposed in the past decade. We elucidate and discuss via synergistic analysis methods both the growth of the field and the state-of-the-art; we conceptualise the existing frameworks from the perspectives of design patterns, collaborative levels, parties and capabilities, and review interactive methods and algorithmic models. Specifically, we create a new Human-AI CRL Design Trajectory Map, as a systematic modelling tool for the selection of existing CRL frameworks, as well as a method of designing new CRL systems, and finally of improving future CRL designs. Furthermore, we elaborate generic Human-AI CRL challenges, providing the research community with a guide towards novel research directions. The aim of this paper is to empower researchers with a systematic framework for the design of efficient and 'natural' human-AI collaborative methods, making it possible to work on maximised realisation of humans' and AI's potentials.1450The aim of this paper is to empower researchers with a systematic framework for the design of efficient and 'natural' human-AI collaborative methods, making it possible to work on maximised realisation of humans' and AI's potentials.['Zhaoxing Li']2024-05-16T00:00:00https://www.semanticscholar.org/paper/37d1c61e413239c676d7a9df94dda3a1eebd6058 4e40248cae18f7a6020b568e874c6402801fd445Existing strategies for managing risks from advanced AI systems often focus on affecting what AI systems are developed and how they diffuse. However, this approach becomes less feasible as the number of developers of advanced AI grows, and impedes beneficial use-cases as well as harmful ones. In response, we urge a complementary approach: increasing societal adaptation to advanced AI, that is, reducing the expected negative impacts from a given level of diffusion of a given AI capability. We introduce a conceptual framework which helps identify adaptive interventions that avoid, defend against and remedy potentially harmful uses of AI systems, illustrated with examples in election manipulation, cyberterrorism, and loss of control to AI decision-makers. We discuss a three-step cycle that society can implement to adapt to AI. Increasing society's ability to implement this cycle builds its resilience to advanced AI. We conclude with concrete recommendations for governments, industry, and third-parties.750A conceptual framework is introduced which helps identify adaptive interventions that avoid, defend against and remedy potentially harmful uses of AI systems, illustrated with examples in election manipulation, cyberterrorism, and loss of control to AI decision-makers.['Jamie Bernardi', 'Gabriel Mukobi', 'Hilary Greaves', 'Lennart Heim', 'Markus Anderljung']2024-05-16T00:00:00https://www.semanticscholar.org/paper/4e40248cae18f7a6020b568e874c6402801fd445 59123c3b02a1e952aa5f6d7eb3cd09f473a6ac17This study aimed to assess the influence of artificial intelligence (AI) voice assistant (VA) on customer experience, resulting in the continuous use of mobile brands. Specifically, this article assesses the role of hedonic, utilitarian, and social benefits provided by AIVA on customer experience and continuous usage of a mobile phone brand. Using a primary data collection instrument, the quantitative approach was adopted to examine the study’s variables. Data from 348 valid responses were used for the analysis based on structural equation modeling (SEM) with AMOS version 23. Three main factors were identified to influence customer experience, which results in continuous usage of a mobile phone brand. These factors are social benefits, hedonic benefits, and utilitarian benefits. In conclusion, a significant and positive relationship exists between AI-enabled customer experience and brand continuous usage. It recommended that mobile brands consider and research their prospects’ and customers’ social, hedonic, and utilitarian needs to provide them with desired products and experiences.Journal of Applied Business and Economics840A significant and positive relationship exists between AI-enabled customer experience and brand continuous usage and it is recommended that mobile brands consider and research their prospects’ and customers’ social, hedonic, and utilitarian needs to provide them with desired products and experiences.Journal of Applied Business and Economics['G. Agbemabiese', 'M. Nyamekye', 'J. Andoh', 'Mohammed Muniru Husseini']2024-05-16T00:00:00https://www.semanticscholar.org/paper/59123c3b02a1e952aa5f6d7eb3cd09f473a6ac17 28b760fa5f9ec49bff2df0c5045f8ac917be0c42AI Collaborator, powered by OpenAI's GPT-4, is a groundbreaking tool designed for human-AI collaboration research. Its standout feature is the ability for researchers to create customized AI personas for diverse experimental setups using a user-friendly interface. This functionality is essential for simulating various interpersonal dynamics in team settings. AI Collaborator excels in mimicking different team behaviors, enabled by its advanced memory system and a sophisticated personality framework. Researchers can tailor AI personas along a spectrum from dominant to cooperative, enhancing the study of their impact on team processes. The tool's modular design facilitates integration with digital platforms like Slack, making it versatile for various research scenarios. AI Collaborator is thus a crucial resource for exploring human-AI team dynamics more profoundly.170The tool's modular design facilitates integration with digital platforms like Slack, making it versatile for various research scenarios, and is a crucial resource for exploring human-AI team dynamics more profoundly.['M. Samadi', 'Spencer Jaquay', 'Jing Gu', 'Nia Nixon']2024-05-16T00:00:00https://www.semanticscholar.org/paper/28b760fa5f9ec49bff2df0c5045f8ac917be0c42 b69f7c7796bf4246c76d61376e374daea13f4287Energy assets demand periodic regular maintenance to ensure safe, reliable and efficient operations. The dynamic nature of operating conditions and evolving best practices necessitate frequent optimisation of maintenance programs. Traditionally, these programs were labour-intensive and costly to deploy, often yielding unclear outcomes due to subjective decision-making. The advent of artificial intelligence (AI) presents an opportunity to challenge traditional approaches. This paper presents a case study where asset knowledge, data, and AI capabilities are leveraged to streamline maintenance optimisation using our ‘maintAI’ approach. The program addresses maintenance strategy, backlog, spares, and predictive maintenance optimisation with a focus on value creation, data-driven decisions, and consistent recommendations. A systematic methodology employs AI to sift through and eliminate non-value-adding tasks, enabling prioritisation of work and enhancing reliability and productivity throughout the production facility lifecycle. AI, including Natural Language Processing and Generative AI algorithms, enhances the speed and accuracy of failure mode classification from operational maintenance data. Reliability modelling techniques provide insights into equipment reliability. Recommendations undergo expert review before integration into a Computerised Maintenance Management System. Implementation of this data-driven approach demonstrates rapid deployment and sustained efficiency, yielding substantial gains in production uptime, cost reduction, and safety. The user-centric design ensures agility and ease of configuration. A recent project, which took only 6 weeks to deliver, led to a ~28% reduction in maintenance backlog, freeing capacity for critical focus areas. The maintAI approach proves a meaningful change for energy producers, offering a new solution in maintenance optimisation for enhanced reliability and productivity.Australian Energy Producers Journal10The maintAI approach proves a meaningful change for energy producers, offering a new solution in maintenance optimisation for enhanced reliability and productivity.Australian Energy Producers Journal['Gordon Buchan', 'Muhammad Abdullah']2024-05-16T00:00:00https://www.semanticscholar.org/paper/b69f7c7796bf4246c76d61376e374daea13f4287 9bd78fe9f831c65f9852fedfa15120a32c585bebAs the significance of artificial intelligence (AI) continues to increase, there is a need for effective scaffolding and support for novice learners. Educators have encountered challenges in effectively scaffolding novice learners AI concepts, and providing appropriate motivational support. Research evidence has shown the potential of game‐based approaches to fostering secondary school students' AI literacy and motivation to learn AI.This study developed an online platform TreasureIsland to gamify ebooks and investigated whether and how students playing with it can effectively enhance their AI literacy. This study aims to contribute an empirical and theoretical basis for AI literacy education and promote the use of gamification that would be broadly applied in other schools.A quasi‐experiment was conducted to evaluate the effects of the proposed gamified approach, which included a control group using an ebook with playful resources. To triangulate the quantitative results obtained from the pre and post‐test, focus group interviews were also conducted.The platform was effective in improving students' motivation, self‐efficacy, career interest, and understanding of AI concepts and ethics, but did not enhance their confidence of using AI, and high cognition of applying, evaluating and creating AI. TreasureIsland players demonstrated significant improvement in all affective and cognitive domains, except for the ability to apply, evaluate, and create AI. Interviews revealed that the gamified approach could promote students' AI literacy by adhering to guidelines, including (1) creating a competitive and motivating learning environment through game mechanics, (2) providing scaffolding modules and feedback, and (3) visualising complex AI concepts via simulations. Feedback collected from the study suggested adding pedagogical elements such as flipped classrooms and project‐based learning in future research to improve the instructional design, and enable students to reach a higher level of cognition.This study concludes that the use of gamification can provide affective and cognitive support and an enjoyable experience for fostering learners' AI literacy. It helps instructional designers and teachers enrich the pedagogical knowledge related to gamified platform and AI literacy.Journal of Computer Assisted Learning620It is concluded that the use of gamification can provide affective and cognitive support and an enjoyable experience for fostering learners' AI literacy and is promoted to contribute an empirical and theoretical basis for AI literacy education.Journal of Computer Assisted Learning['D. Ng', 'Chen Xinyu', 'J. Leung', 'S. K. Chu']2024-05-16T00:00:00https://www.semanticscholar.org/paper/9bd78fe9f831c65f9852fedfa15120a32c585beb 819c26b9cbcdf960aaa9dd81bdfafbeab0e628c5Generative artificial intelligence (AI) is interacting with people at an unprecedented scale, offering new avenues for immense positive impact, but also raising widespread concerns around the potential for individual and societal harm. Today, the predominant paradigm for human-AI safety focuses on fine-tuning the generative model's outputs to better agree with human-provided examples or feedback. In reality, however, the consequences of an AI model's outputs cannot be determined in an isolated context: they are tightly entangled with the responses and behavior of human users over time. In this position paper, we argue that meaningful safety assurances for these AI technologies can only be achieved by reasoning about how the feedback loop formed by the AI's outputs and human behavior may drive the interaction towards different outcomes. To this end, we envision a high-value window of opportunity to bridge the rapidly growing capabilities of generative AI and the dynamical safety frameworks from control theory, laying a new foundation for human-centered AI safety in the coming decades.990A high-value window of opportunity is envisioned to bridge the rapidly growing capabilities of generative AI and the dynamical safety frameworks from control theory, laying a new foundation for human-centered AI safety in the coming decades.['Andrea V. Bajcsy', 'J. Fisac']2024-05-16T00:00:00https://www.semanticscholar.org/paper/819c26b9cbcdf960aaa9dd81bdfafbeab0e628c5 be17e54f25316073f7d059c9df44775328ba9c51The emergence of large language models (LLMs) capable of generating realistic texts and images has sparked ethical concerns across various sectors. In response, researchers in academia and industry are actively exploring methods to distinguish AI-generated content from human-authored material. However, a crucial question remains: What are the unique characteristics of AI-generated text? Addressing this gap, this study proposes StyloAI, a data-driven model that uses 31 stylometric features to identify AI-generated texts by applying a Random Forest classifier on two multi-domain datasets. StyloAI achieves accuracy rates of 81% and 98% on the test set of the AuTextification dataset and the Education dataset, respectively. This approach surpasses the performance of existing state-of-the-art models and provides valuable insights into the differences between AI-generated and human-authored texts.100StyloAI, a data-driven model that uses 31 stylometric features to identify AI-generated texts by applying a Random Forest classifier on two multi-domain datasets, exceeds the performance of existing state-of-the-art models and provides valuable insights into the differences between AI-generated and human-authored texts.['Chidimma Opara']2024-05-16T00:00:00https://www.semanticscholar.org/paper/be17e54f25316073f7d059c9df44775328ba9c51 da64b0d7f7be46414623e3441c49f247b898b12cThis project aims to develop a personalized and interactive healthcare chatbot leveraging natural language processing and machine learning. It offers tailored advice based on user symptoms, medical history, and preferences. Integrated with healthcare databases, it provides reliable information and services like symptom analysis, triage recommendations, medication details, and personalized health tips. Seamlessly accessing patient records and appointment schedules within existing healthcare systems ensures a cohesive user experience. The AI healthcare chatbot optimizes services by reducing communication burdens, improving information accessibility, and enhancing patient engagement. Preliminary evaluations demonstrate promising results in user satisfaction and healthcare administration efficiency gains. Keywords: Healthcare chatbot, Symptom analysis, Disease Prediction, Medication information and Machine learning.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Prahlad Kr']2024-05-16T00:00:00https://www.semanticscholar.org/paper/da64b0d7f7be46414623e3441c49f247b898b12c 344639ff0b82aa39a3362a87bab42893b72c9fc8Global Surgical Education - Journal of the Association for Surgical Education30This study provides a preliminary basis of cross-industrial themes to guide best practices and governance standards for integration of generative AI into surgical education and outlines a framework to guide ethical consideration when integrating generative AI into surgical education.Global Surgical Education - Journal of the Association for Surgical Education['Hillary Lia', 'Angela G. Atkinson', 'Sergio M. Navarro']2024-05-16T00:00:00https://www.semanticscholar.org/paper/344639ff0b82aa39a3362a87bab42893b72c9fc8 864ff100a16dcc5f97f5153c104b1eff24d20b05The Generative AI, also known as GenAI. It is a subset of artificial intelligence (AI) that is reshaping the collaboration with technology by the minute. GenAI possesses the unique ability to create the information. GenAI has been used for many applications across various areas, demonstrating its powerful capabilities for generating creative and even life-like content. As an innovation in GenAI image style transfer tools such as DeepArt and DeepDream have signifies the potential of generative AI tools by making users capable of creating amazing artworks out of ordinary images. Latest, the Generative Pre-trained Transformer (GPT) series, particularly ChatGPT-3 has taken the world by an electrifying storm for its remarkable ability to generate human-like text from simple prompts, igniting global imagination about the creative potential of AI. Gen AI tools uses in financial services for fraud detection, cybersecurity, Chabot’s, risk management, Alpha sense, and sentiment analysis. This research paper describes GenAI tools supports to financial services and some specific applications in both the financial and non-financial sector and also this research paper will examine the difficulties that lie ahead and the business opportunities for this fundamental technology that is going to transform our digital world. Also by addressing both the opportunities and challenges, this paper aims to provide a holistic perspective on the role of GenAI in the evolving Financial services. Keywords: GenAI, Tools, Financial and non-financial sector, TechnologyINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00GenAI tools supports to financial services and some specific applications in both the financial and non-financial sector are described and the difficulties that lie ahead and the business opportunities for this fundamental technology that is going to transform the authors' digital world are examined.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['G. R.']2024-05-16T00:00:00https://www.semanticscholar.org/paper/864ff100a16dcc5f97f5153c104b1eff24d20b05 adaf14032a9ea3f7d5c133d0a46f5150e4d471eeAI and Ethics70This paper replaces the attempts to provide a corrigible utility function with the proposed corrigible software architecture; this takes the agency off the RL agent – which now becomes an RL solver – and grants it to the system as a whole.AI and Ethics['Erez Firt']2024-05-16T00:00:00https://www.semanticscholar.org/paper/adaf14032a9ea3f7d5c133d0a46f5150e4d471ee 7ac69b129841ce2cc623cc1dcfbc32d65f99d6b3AI &amp; SOCIETY140This paper stresses the potential benefits of automation as a mechanism for lightening work and aims to advance critical debates focused on creating a future in which AI works for people not just for profits.AI &amp; SOCIETY['David A. Spencer']2024-05-16T00:00:00https://www.semanticscholar.org/paper/7ac69b129841ce2cc623cc1dcfbc32d65f99d6b3 ba5be61daa5b3813246d3f9618fbc916b225f035Scientific Reports630A state-of-the-art framework, incorporating virtual reality (VR) and artificial intelligence (AI) to metamorphose the pedagogy of advanced manufacturing systems is introduced, preparing entities to navigate Mexico’s manufacturing sector’s vibrant and competitive nearshoring landscape.Scientific Reports['Pedro Ponce', 'Brian Anthony', 'Russel Bradley', 'Javier Maldonado-Romo', 'Juana Isabel Méndez', 'Luis Montesinos', 'Arturo Molina']2024-05-16T00:00:00https://www.semanticscholar.org/paper/ba5be61daa5b3813246d3f9618fbc916b225f035 421e0ccbb4395be8340fb5370453507417033a70The most popular types of artificial intelligence are inspired by different biological processes, such as the functioning of nervous systems or adaptive immunity. Here, I make a short dive into the history of AI to track how bio-inspired models of AI replace other models and discuss which model could be the next one.The biochemist00A short dive into the history of AI is made to track how bio-inspired models of AI replace other models and discuss which model could be the next one.The Biochemist['Georgy Kurakin']2024-05-16T00:00:00https://www.semanticscholar.org/paper/421e0ccbb4395be8340fb5370453507417033a70 219add0ef5c6e6a7073e17df3b210e9c6d594f44In 2022, OpenAI’s unveiling of generative AI Large Language Models (LLMs)- ChatGPT, heralded a significant leap forward in human-machine interaction through cutting-edge AI technologies. With its surging popularity, scholars across various fields have begun to delve into the myriad applications of ChatGPT. While existing literature reviews on LLMs like ChatGPT are available, there is a notable absence of systematic literature reviews (SLRs) and bibliometric analyses assessing the research’s multidisciplinary and geographical breadth. This study aims to bridge this gap by synthesizing and evaluating how ChatGPT has been integrated into diverse research areas, focusing on its scope and the geographical distribution of studies. Through a systematic review of scholarly articles, we chart the global utilization of ChatGPT across various scientific domains, exploring its contribution to advancing research paradigms and its adoption trends among di ff erent disciplines. Our findings reveal a widespread endorsement of ChatGPT across multiple fields, with significant implementations in healthcare (38.6%), computer science / IT (18.6%), and education / research (17.3%). Moreover, our demographic analysis underscores ChatGPT’s global reach and accessibility, indicating participation from 80 unique countries in ChatGPT-related research, with the most frequent countries keyword occurrence, USA (719), China (181), and India (157) leading in contributions. Additionally, our study highlights the leading roles of institutions such as King Saud University, the All India Institute of Medical Sciences, and Taipei Medical University in pioneering ChatGPT research in our dataset. This research not only sheds light on the vast opportunities and challenges posed by ChatGPT in scholarly pursuits but also acts as a pivotal resource for future inquiries. It emphasizes that the generative AI (LLM) role is revolutionizing every field. The insights provided in this paper are particularly valuable for academics, researchers, and practitioners across various disciplines, as well as policymakers looking to grasp the extensive reach and impact of generative AI technologies like ChatGPT in the global research community.Connection science1290This study synthesizes and evaluates how ChatGPT has been integrated into diverse research areas, focusing on its scope and the geographical distribution of studies, and charts the global utilization of ChatGPT across various scientific domains.Connection Science['Nauman Khan', 'Zahid Khan', 'Anis Koubaa', 'Muhammad Khurram Khan', 'Rosli bin Salleh']2024-05-16T00:00:00https://www.semanticscholar.org/paper/219add0ef5c6e6a7073e17df3b210e9c6d594f44 868f8d956d00d314886a189fb84609a2e859af77Neuroradiology290The AP of CerebralDoc presents clear advantages over MP and holds significant clinical value, however, further optimization is required in the image quality of the V4 and M1 segments on VR as well as the C4 segment on MIP.Neuroradiology['Kunhua Li', 'Yang Yang', 'Shengwen Niu', 'Yongwei Yang', 'Bitong Tian', 'Xinyue Huan', 'Dajing Guo']2024-05-16T00:00:00https://www.semanticscholar.org/paper/868f8d956d00d314886a189fb84609a2e859af77 1b7c0c3008fd50151933bb3d16dc5db18539c396Research in Equitable and Sustained Participation in Engineering, Computing, and Technology40{'pages': '81-85'}['Shana V. White', 'Joshua Childs', 'Sonia Koshy', 'Allison Scott']2024-05-16T00:00:00https://www.semanticscholar.org/paper/1b7c0c3008fd50151933bb3d16dc5db18539c396 6a9856676f7d68135aeb947b343b6e6053432913Research in Equitable and Sustained Participation in Engineering, Computing, and Technology130{'pages': '94-99'}['Sukanya Kannan Moudgalya', 'Sai Swaminathan']2024-05-16T00:00:00https://www.semanticscholar.org/paper/6a9856676f7d68135aeb947b343b6e6053432913 50768b22c19facca8bd38399767f179368359a47npj Biodiversity100It is found the LLM was able to extract relevant data over 50 times faster than the reviewer and had very high accuracy in extracting discrete and categorical data, but it performed poorly when extracting certain quantitative data.npj Biodiversity['A. Gougherty', 'Hannah L. Clipp']2024-05-16T00:00:00https://www.semanticscholar.org/paper/50768b22c19facca8bd38399767f179368359a47 9469a5374392471238f4d7d43740c1d44f005497Research in Equitable and Sustained Participation in Engineering, Computing, and Technology120{'pages': '86-93'}['Chinasa T. Okolo']2024-05-16T00:00:00https://www.semanticscholar.org/paper/9469a5374392471238f4d7d43740c1d44f005497 825508ee16bae9599f3e672b2c6708ce94210103Journal of Electronic Resources in Medical Libraries30Journal of Electronic Resources in Medical Libraries['Gary S. Atwood']2024-05-16T00:00:00https://www.semanticscholar.org/paper/825508ee16bae9599f3e672b2c6708ce94210103 546d61cee3e754000f24bb73dfe199f020587a24Journal of the American Medical Association (JAMA)30JAMA['Katherine E Goodman', 'Daniel J Morgan']2024-05-16T00:00:00https://www.semanticscholar.org/paper/546d61cee3e754000f24bb73dfe199f020587a24 d74038e9c95efde1f7f2581a1fa80c048546990aResearch in Equitable and Sustained Participation in Engineering, Computing, and Technology90{'pages': '75-80'}['Julie M. Smith']2024-05-16T00:00:00https://www.semanticscholar.org/paper/d74038e9c95efde1f7f2581a1fa80c048546990a 2d1e404c47c0e85b9b0b04836c0e12781130ca73Scalable and accessible professional development programs have the potential to address the opportunity gap many teachers experience. Yet many asynchronous online programs lack interaction with and timely feedback to teachers. We addressed this problem by developing a virtual, interactive program that uses intelligent tutoring systems to provide just‐in‐time feedback to teachers. We conducted a randomized controlled trial with teachers across the United States in which teachers were assigned to either this program or no additional training. We found that teachers who completed our program ( N = 29) used mathematically richer tasks and created a more coherent, connected learning environment for students to build conceptual understandings than did teachers who were in the business‐as‐usual condition ( N = 23).The American Educational Research Journal840This work develops a virtual, interactive program that uses intelligent tutoring systems to provide just‐in‐time feedback to teachers and finds that teachers who completed this program used mathematically richer tasks and created a more coherent, connected learning environment for students to build conceptual understandings.American Educational Research Journal['Yasemin Copur-Gencturk', 'Jingxian Li', 'Sebnem Atabas']2024-05-16T00:00:00https://www.semanticscholar.org/paper/2d1e404c47c0e85b9b0b04836c0e12781130ca73 2d3a26dd80e4494db7a221b7779287c02587f1b6Cureus180Cureus['Denver S Pinto', 'Sharon M Noronha', 'Gaurav Saigal', 'R. Quencer']2024-05-16T00:00:00https://www.semanticscholar.org/paper/2d3a26dd80e4494db7a221b7779287c02587f1b6 d21bf0dffcae2385deca6f6aa29434a4c72cdbf4In a world increasingly driven by technology, the field of creative content production stands on the initiative of a revolution. This paper explores the cutting-edge advancements in Generative Artificial Intelligence (GAI) & its profound impact on creative content creation. By leveraging the power of neural networks, GAI has the potential to transform how we generate, consume, and interact with creative works across various mediums. The paper begins by explaining the core principles of GAI, simplifying complex concepts for readers. It explores the fascinating world of neural networks, demonstrating how these intricate systems learn and replicate creative patterns and styles similar to human thinking. But the fascination doesn't end there. As the story progresses, the paper reveals the diverse benefits of GAI for creative professionals and enthusiasts alike. From transforming content creation processes to enabling personalized experiences and simplifying automation, the possibilities seem limitless. However, every technological wonder comes with its own set of challenges and considerations. This paper navigates through the ethical maze surrounding GAI, addressing concerns such as prejudices, copyright issues, and the intricate balance between human and artificial creativity. It advocates for responsible and moral practices in the use of GAI, ensuring that this potent tool is employed for the betterment of society. Yet, the most compelling feature lies in the introduction of a groundbreaking innovation: the "Investigate" algorithm. This algorithm, a culmination of advanced techniques including reinforcement learning and evolutionary optimization, promises to enhance GAI's impact by expanding the boundaries of neural network potential even further. It intrigues readers with the promising potential of a future where AI continuously improves itself, leading to the creation of increasingly sophisticated and innovative creative content. In conclusion, this paper calls upon readers to embark on a journey of discovery and innovation, inviting them to explore further into the realm of GAI's impact on creative content production. Through the examination of neural network potential and the moral considerations that come with it, we stand on the threshold of a new era in creativity and innovation.Journal of Informatics Education and Research20This paper navigates through the ethical maze surrounding GAI, addressing concerns such as prejudices, copyright issues, and the intricate balance between human and artificial creativity, and advocates for responsible and moral practices in the use of GAI, ensuring that this potent tool is employed for the betterment of society.Journal of Informatics Education and Research['Dr. Rajkumar Garg, Ms. Meenakshi Verma', 'Upasana Singh, Sushant Joshi']2024-05-16T00:00:00https://www.semanticscholar.org/paper/d21bf0dffcae2385deca6f6aa29434a4c72cdbf4 5b39d90b83be425cb0e84fe5d3a75d02a6822ecaSoftware vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage.Machine Learning and Knowledge Extraction530An XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types and observes that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings.Machine Learning and Knowledge Extraction['Ding Li', 'Yan Liu', 'Jun Huang']2024-05-16T00:00:00https://www.semanticscholar.org/paper/5b39d90b83be425cb0e84fe5d3a75d02a6822eca 1d96860619a4dff0f8112c0d6e6c6247ed284edcThis paper investigates the application of the transformer architecture in protein folding, as exemplified by DeepMind's AlphaFold project, and its implications for the understanding of large language models as models of language. The prevailing discourse often assumes a ready-made analogy between proteins -- encoded as sequences of amino acids -- and natural language -- encoded as sequences of discrete symbols. Instead of assuming as given the linguistic structure of proteins, we critically evaluate this analogy to assess the kind of knowledge-making afforded by the transformer architecture. We first trace the analogy's emergence and historical development, carving out the influence of structural linguistics on structural biology beginning in the mid-20th century. We then examine three often overlooked pre-processing steps essential to the transformer architecture, including subword tokenization, word embedding, and positional encoding, to demonstrate its regime of representation based on continuous, high-dimensional vector spaces, which departs from the discrete, semantically demarcated symbols of language. The successful deployment of transformers in protein folding, we argue, discloses what we consider a non-linguistic approach to token processing intrinsic to the architecture. We contend that through this non-linguistic processing, the transformer architecture carves out unique epistemological territory and produces a new class of knowledge, distinct from established domains. We contend that our search for intelligent machines has to begin with the shape, rather than the place, of intelligence. Consequently, the emerging field of critical AI studies should take methodological inspiration from the history of science in its quest to conceptualize the contributions of artificial intelligence to knowledge-making, within and beyond the domain-specific sciences.480This paper investigates the application of the transformer architecture in protein folding, as exemplified by DeepMind's AlphaFold project, and its implications for the understanding of large language models as models of language, and examines three often overlooked pre-processing steps essential to the transformer architecture.['Fabian Offert', 'Paul Kim', 'Qiaoyu Cai']2024-05-16T00:00:00https://www.semanticscholar.org/paper/1d96860619a4dff0f8112c0d6e6c6247ed284edc 1eace509dc5081c37eb51a0e719fe5cef6dfb3ed Some regulations do not only reduce human deaths, injuries, and illnesses; they also protect nonhuman animals. Regulatory Impact Analyses, required by prevailing executive orders, usually do not disclose or explore benefits or costs with respect to nonhuman animals, even when those benefits or costs are significant. This is an inexcusable gap. If a regulation prevents dogs, horses, or cats from being killed or hurt, the benefits should be specified and quantified. This proposition holds even if those benefits are in some sense incidental to the main goal of the regulation. At the same time, turning the relevant benefits into monetary equivalents raises serious challenges, akin to those raised by the valuation of statistical children.Social Science Research Network261SSRN Electronic Journal['C. Sunstein']2024-05-16T00:00:00https://www.semanticscholar.org/paper/1eace509dc5081c37eb51a0e719fe5cef6dfb3ed 3a10804da7b5e98fe7a7ff7fcc8b2e7a47e661d6This study provides a comprehensive review of the impact of artificial intelligence (AI) on marketing practices across various industries. The rapid advancements in AI technologies have revolutionized how marketers understand consumer behavior, personalize marketing strategies, and automate repetitive tasks. Through an extensive review of existing literature, this paper synthesizes the key findings and identifies the major ways in which AI is transforming marketing practices. This study is an overview of how AI technologies are reshaping marketing practices, offering valuable insights for academics, practitioners, and policymakers alike. Keywords : Marketing, Artificial Intelligence, Marketing EfficiencyINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This study provides a comprehensive review of the impact of artificial intelligence on marketing practices across various industries and identifies the major ways in which AI is transforming marketing practices.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT[]2024-05-16T00:00:00https://www.semanticscholar.org/paper/3a10804da7b5e98fe7a7ff7fcc8b2e7a47e661d6 9ae8e7926beced350607eda6dae0fc20fac34f27Discover Artificial Intelligence2200This review article provides a comprehensive analysis of the most recent research published in the field of AI, specifically related to communication, to explain a complex phenomenon and to create a conceptual framework that is appropriate for this goal.Discov. Artif. Intell.['Muhammad Asif', 'Gouqing Zhou']2024-05-16T00:00:00https://www.semanticscholar.org/paper/9ae8e7926beced350607eda6dae0fc20fac34f27 0efd92bfea11737a3dd3670a77b832f3e3c6648dCurrent Cardiology Reports530Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation and exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality.Current cardiology reports['Benjamin Ose', 'Zeeshan Sattar', 'Amulya Gupta', 'C. Toquica', 'Chris Harvey', 'Amit Noheria']2024-05-16T00:00:00https://www.semanticscholar.org/paper/0efd92bfea11737a3dd3670a77b832f3e3c6648d 782be3f57a5d8d0e64c790297fe133eb53d02facArtificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.Journal of Scientific Research and Reports00It is concluded that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.Journal of Scientific Research and Reports['Ashoka, P', 'B. R. Devi', 'Nilesh Sharma', 'Madhumita Behera', 'Abhishek Gautam', 'Ayushi Jha', 'Gayatri Sinha']2024-05-16T00:00:00https://www.semanticscholar.org/paper/782be3f57a5d8d0e64c790297fe133eb53d02fac 14cd77f8e02356de8df992e7e9e0d643db342d52The effectiveness of innovative scientific and educational activities largely relies on the technologies of the library industry, including modern information and library systems. Academic and research libraries introduce new technologies in an effort to improve the quality of their services. Artificial intelligence is one of the key technologies for digital transformation of the library industry. The study is aimed at identifying trends in the use of artificial intelligence in information and library service technologies. The author applied the general dialectical method, theoretical analysis and generalization of the content of special, scientific and technical literature, regulatory documents in the field of digitalization of librarianship and artificial intelligence. The results of the study have revealed the role of the integration of artificial intelligence technologies into information and library systems, which lies in the fact that they contribute to the formation of national intellectual libraries, computerization of the routine library services, and structuring innovative academic digital space for the users. Using the example of integration of context awareness technology into some information and library systems, the ability of a digital system to make “reasonable” decisions to extract relevant information for learning and self-education is demonstrated, which also allows to find educational materials on the topic being studied, and to build explicit educational route for a user.Scientific and Technical Libraries120The role of the integration of artificial intelligence technologies into information and library systems lies in the fact that they contribute to the formation of national intellectual libraries, computerization of the routine library services, and structuring innovative academic digital space for the users.Scientific and Technical Libraries['N. A. Moiseeva']2024-05-16T00:00:00https://www.semanticscholar.org/paper/14cd77f8e02356de8df992e7e9e0d643db342d52 06867aff1dbd27e046e1fa31497378c333cdf440PurposeThe current research study aims to examine the application feasibility and impact of artificial intelligence (AI) among higher educational institutions (HEIs) in talent acquisitions (TA).Design/methodology/approachA systematic sampling method was adopted to collect the responses from the 385 staff working across the various levels of management in HEIs in metropolitan cities in India. JAMOVI & SmartPLS 4 were applied to validate the hypothesis by performing the simple percentage analysis and structural equation modelling. The demographic and construct variables considered were adoption, actual usage, perceived usefulness, perceived ease of use and talent management.FindingsThe key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs. These are the most influential items impacting the application of AI in TA.Originality/valueAI has the potential to revolutionize TA in HEIs in the form of enhanced efficiency, improved candidate experience, more objective hiring decisions, talent analytics and risk automation. However, they facilitate resume screening, candidate sourcing, applicant tracking, interviewing and predictive analytics for attrition.The international journal of information and learning technology570The key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs.The International Journal of Information and Learning Technology['V. R.', 'Hariharan R.', 'Sudha E.', 'Divyashree V.']2024-05-16T00:00:00https://www.semanticscholar.org/paper/06867aff1dbd27e046e1fa31497378c333cdf440 77d972eb347141ebb4e250a3c605bc3e3ac61d67This paper introduces Fusion Intelligence (FI), a bio-inspired intelligent system, where the innate sensing, intelligence and unique actuation abilities of biological organisms such as bees and ants are integrated with the computational power of Artificial Intelligence (AI). This interdisciplinary field seeks to create systems that are not only smart but also adaptive and responsive in ways that mimic the nature. As FI evolves, it holds the promise of revolutionizing the way we approach complex problems, leveraging the best of both biological and digital worlds to create solutions that are more effective, sustainable, and harmonious with the environment. We demonstrate FI's potential to enhance agricultural IoT system performance through a simulated case study on improving insect pollination efficacy (entomophily).350Fusion Intelligence (FI), a bio-inspired intelligent system, where the innate sensing, intelligence and unique actuation abilities of biological organisms are integrated with the computational power of Artificial Intelligence (AI), is introduced.['Rohan Reddy Kalavakonda', 'Junjun Huan', 'Peyman Dehghanzadeh', 'Archit Jaiswal', 'Soumyajit Mandal', 'Swarup Bhunia Department of Electrical', 'Computer Engineering', 'U. Florida', 'Gainesville', 'Fl', 'Instrumentation Department', 'Brookhaven National Laboratory', 'Upton', 'Ny']2024-05-16T00:00:00https://www.semanticscholar.org/paper/77d972eb347141ebb4e250a3c605bc3e3ac61d67 81c3b66aea88c0dd18a0b1f62464ab633297199fNature Reviews. Cancer1310This article provides a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them and conveys general principles of AI for applications in image analysis, natural language processing and drug discovery.Nature reviews. Cancer['R. Perez-Lopez', 'N. Ghaffari Laleh', 'Faisal Mahmood', 'J. N. Kather']2024-05-16T00:00:00https://www.semanticscholar.org/paper/81c3b66aea88c0dd18a0b1f62464ab633297199f 8f3b4f8793ee0083f5398b56455c4a5f5bfb17a0Artificial intelligence (AI) has been widely adopted in the service sector to enhance the customer experience and gain a competitive advantage. However, there are a limited number of papers that focus on the relationship between AI and customer experience, and there is no clear framework to reveal how AI influences the customer experience. Therefore, this paper will address how AI affects the customer experience and develop a conceptual framework of AI applications in customer experience along the customer journey. A two-step research design is adopted in this paper. The first phase aims to identify a framework through an extensive systematic literature review of the relevant databases. The findings cover three main themes: AI experience, AI functions, and AI services. A research framework is created on the basis of the findings. This paper contributes to consumer behavior and services by integrating AI with customer experience and providing a comprehensive framework for guiding future research. The study also offers practical implications for practitioners to enhance customer experience.Australasian Marketing Journal790This paper will address how AI affects the customer experience and develop a conceptual framework of AI applications in customer experience along the customer journey, providing a comprehensive framework for guiding future research.Australasian Marketing Journal['Ying Chen', 'Catherine Prentice']2024-05-16T00:00:00https://www.semanticscholar.org/paper/8f3b4f8793ee0083f5398b56455c4a5f5bfb17a0 5f32b2c73e6eba0defcdd807166c23b7c4f5f8c7AI &amp; SOCIETY850A clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value is offered to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value.AI &amp; SOCIETY['Fabio Fossa']2024-05-16T00:00:00https://www.semanticscholar.org/paper/5f32b2c73e6eba0defcdd807166c23b7c4f5f8c7 4c32519bcc38325ab71ee30bd052e47c92add069The article is devoted to the civil law aspects of the problems of artificial intelligence and its place in the structure of civil law relations, as well as questions regarding the legal regime of objects generated by artificial intelligence. The approaches expressed in the domestic legal doctrine regarding the understanding of artificial intelligence as an object and as a subject of civil law relations, as well as a point of view regarding the possibility of simultaneously considering artificial intelligence as an object and as a subject of such legal relations, are analyzed. On the basis of approaches to the definition of the concept of «artificial intelligence», which are beginning to be formed at the level of legal acts of the United Nations and the European Union, as well as enshrined in the legislation of Ukraine, it is concluded that artificial intelligence must be considered as an object of civil law relations. In this regard, a critical position has been expressed regarding attempts made at the theoretical level to interpret artificial intelligence or robots endowed with artificial intelligence as subjects equivalent to natural persons, as quasi-legal entities. The main provisions of the Law of Ukraine «On Copyright and Related Rights» on the sui generis right on a non-original object generated by a computer program, which can be considered corresponding concept of «object generated by artificial intelligence», have been analyzed. The features of a non-original object generated by a computer program are singled out as a special type of an object of sui generis right. A position has been expressed regarding the expediency of clarifying the provisions of the Law of Ukraine «On Copyright and Related Rights» in the part of defining the subject of a sui generis right to a non-original object generated by a computer program. It was concluded that objects generated by artificial intelligence can be classified as objects of intellectual property law, within the framework of which their legal protection should be ensured by a special kind of right – sui generis right. The expediency of enshrining the main provisions regarding the sui generis right to an object generated by artificial intelligence in Book 4 of the Civil Code of Ukraine is emphasized.Uzhhorod National University Herald. Series: Law00It was concluded that objects generated by artificial intelligence can be classified as objects of intellectual property law, within the framework of which their legal protection should be ensured by a special kind of right – sui generis right.Uzhhorod National University Herald. Series: Law['I. Yakubivskyi']2024-05-16T00:00:00https://www.semanticscholar.org/paper/4c32519bcc38325ab71ee30bd052e47c92add069 b7740350aca0904e55a7ffc6dbbe99bc06ce07e4Journal of the Knowledge Economy530It is indicated that the integration of modern AI can significantly enhance green technology sectors in mid-to-low-income countries, providing vital insights for policymakers striving to foster a sustainable and technologically advanced future.Journal of the Knowledge Economy['Youngsam Chun', 'Junseok Hwang']2024-05-16T00:00:00https://www.semanticscholar.org/paper/b7740350aca0904e55a7ffc6dbbe99bc06ce07e4 ab66661b77b6ff74b57ec2ef65d9423a3b10feb6Laboratory Animal Research220While some LLM consistently structure their outputs effectively, all models exhibit notable variations in assigning numerical values to symptoms and defining intervention thresholds accurately, highlighting the dual nature of AI performance in this field—its potential to create useful foundational drafts and the critical need for professional review to ensure precision and reliability.Laboratory Animal Research['Cristian Berce']2024-05-16T00:00:00https://www.semanticscholar.org/paper/ab66661b77b6ff74b57ec2ef65d9423a3b10feb6 de4a6d4b7c24ae4c9edea2e77efb129acaaa3537 Recent years have seen significant advances in artificial intelligence (AI) technology. This advancement has enabled the development of decision support systems that support farmers with herbivorous pest identification and pest monitoring. In these systems, the AI supports farmers through the detection, classification and quantification of herbivorous pests. However, many of the systems under development fall short of meeting the demands of the end user, with these shortfalls acting as obstacles that impede the integration of these systems into integrated pest management (IPM) practices. There are four common obstacles that restrict the uptake of these AI‐driven decision support systems. Namely: AI technology effectiveness, functionality under field conditions, the level of computational expertise and power required to use and run the system and system mobility. We propose four criteria that AI‐driven systems need to meet in order to overcome these challenges: (i) The system should be based on effective and efficient AI; (ii) The system should be adaptable and capable of handling ‘real‐world’ image data collected from the field; (iii) Systems should be user‐friendly, device‐driven and low‐cost; (iv) Systems should be mobile and deployable under multiple weather and climate conditions. Systems that meet these criteria are likely to represent innovative and transformative systems that successfully integrate AI technology with IPM principles into tools that can support farmers. Agricultural and Forest Entomology500Four criteria that AI‐driven systems need to meet in order to overcome challenges are proposed and are likely to represent innovative and transformative systems that successfully integrate AI technology with IPM principles into tools that can support farmers.Agricultural and Forest Entomology['Daniel J. Leybourne', 'Nasamu Musa', 'Po Yang']2024-05-16T00:00:00https://www.semanticscholar.org/paper/de4a6d4b7c24ae4c9edea2e77efb129acaaa3537 6b511cfe262c2465a10b47f413cba82d4c2dbec2Trials470This pragmatic clinical trial is unique in directly comparing a fully automated AI-powered digital DPP with a standard of care human coach-based DPP without direct human coach interaction, and could be a scalable, cost-effective strategy.Trials['Mohammed S. Abusamaan', 'Jeromie Ballreich', 'Adrian Dobs', 'Brian Kane', 'Nisa Maruthur', 'John McGready', 'Kristin Riekert', 'Amal A. Wanigatunga', 'M. Alderfer', 'Defne Alver', 'Benjamin Lalani', 'Benjamin Ringham', 'Fatmata Vandi', 'Daniel Zade', 'N. Mathioudakis']2024-05-16T00:00:00https://www.semanticscholar.org/paper/6b511cfe262c2465a10b47f413cba82d4c2dbec2 39525dccb966e3281ab40912d1c5ab68735ad999PRCIS In this meta-analysis of 6 studies and 5,269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed-angle compared to gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively. PURPOSE This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle-closure in patients with glaucoma. METHODS We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity. RESULTS The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5,269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI 83.8%-97.9%), whereas the pooled specificity was 93.6% (95% CI 85.7%-97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1% to 95.1%. Similarly, specificity results ranged from 90.3 to 94.5% with the removal of each individual study and recalculation of pooled specificity. CONCLUSION DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.Journal of glaucoma00DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure and may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.Journal of glaucoma['Marco Antonio Castro Olyntho', 'Carlos Alberto Campello Jorge', 'Everton Castanha', 'Andreia Nunes Gonçalves', 'Barbara Lins Silva', 'Bernardo Vieira Nogueira', 'Geovana Maciel Lima', 'Carolina P B Gracitelli', 'Andrew J Tatham']2024-05-16T00:00:00https://www.semanticscholar.org/paper/39525dccb966e3281ab40912d1c5ab68735ad999 ea39a498747abae46ed1e063b65cfcdccae5b4a5Journal of social service research550Journal of Social Service Research['Buğra Tulğan', 'Merve Deniz Pak Güre']2024-05-16T00:00:00https://www.semanticscholar.org/paper/ea39a498747abae46ed1e063b65cfcdccae5b4a5 54927758eeed972289e2d694ba0fc51f08aec3a4AI &amp; SOCIETY290AI &amp; SOCIETY['E. Balay-odao', 'Dinara Omirzakova', 'S. Bolla', 'J. Almazan', 'Jonas Preposi Cruz']2024-05-16T00:00:00https://www.semanticscholar.org/paper/54927758eeed972289e2d694ba0fc51f08aec3a4 c31694e602f3686cf34eb18b4cc4d742bc1a4e9100['L. Ikelle']2024-05-16T00:00:00https://www.semanticscholar.org/paper/c31694e602f3686cf34eb18b4cc4d742bc1a4e91 4bea33c1bc8f9d096362605d23cff21f0192c4adScience of the Total Environment310In this discussion text the author comments on mistakes that should be avoided when trying to use artificial intelligence (AI) in research, with special focus on soil science and environmental sciences.The Science of the total environment['Avelino Núñez-Delgado']2024-05-16T00:00:00https://www.semanticscholar.org/paper/4bea33c1bc8f9d096362605d23cff21f0192c4ad 0e3c977ab7835a9cf8f782ea1dd5708db8c19d6bHydrological Sciences Journal660Hydrological Sciences Journal['Gülhan Özdoğan-Sarıkoç']2024-05-16T00:00:00https://www.semanticscholar.org/paper/0e3c977ab7835a9cf8f782ea1dd5708db8c19d6b fd777a179734435a38c224b939859c92e551edb0Journal of Internet Commerce490Journal of Internet Commerce['Khouloud Oueslati', 'Salma Ayari']2024-05-16T00:00:00https://www.semanticscholar.org/paper/fd777a179734435a38c224b939859c92e551edb0 f19f7921952612f2f895734c650f92c00675d194Open Source Intelligence (OSINT) has emerged as a powerful tool in the information age, offering valuable insights to individuals, organizations, and governments. This paper explores the significance of OSINT in contemporary decision-making processes, highlighting its role in providing timely, relevant, and actionable information from publicly available sources. The first section elucidates the concept of OSINT, delineating its scope, sources, and methodologies. OSINT encompasses a wide array of publicly accessible information, including social media posts, news articles, government reports, and academic publications. Leveraging advanced data mining, web scraping, and analytical techniques, OSINT practitioners sift through this vast trove of data to extract pertinent insights. The subsequent section delves into the multifaceted applications of OSINT across various domains. In the realm of national security, OSINT aids in threat assessment, geopolitical analysis, and monitoring of adversarial activities. Law enforcement agencies utilize OSINT for criminal investigations, intelligence gathering, and identifying emerging trends. Moreover, businesses employ OSINT for competitive intelligence, market research, and brand monitoring, gaining a competitive edge in dynamic markets. The paper also examines the ethical and privacy implications inherent in OSINT practices. While OSINT offers unprecedented access to information, it raises concerns regarding privacy infringement, misinformation propagation, and algorithmic biases. Safeguarding individual privacy rights and ensuring data accuracy are imperative considerations in the ethical utilization of OSINT. Furthermore, the paper discusses the evolving landscape of OSINT technologies and methodologies. Advancements in artificial intelligence, natural language processing, and machine learning have revolutionized OSINT capabilities, enabling automated data collection, sentiment analysis, and predictive modeling. However, these technological advancements also pose challenges in terms of information overload, data veracity, and algorithmic transparency.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The significance of OSINT in contemporary decision-making processes is explored, highlighting its role in providing timely, relevant, and actionable information from publicly available sources and the evolving landscape of OSINT technologies and methodologies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Nitish Kumar']2024-05-16T00:00:00https://www.semanticscholar.org/paper/f19f7921952612f2f895734c650f92c00675d194 ec89a2a8d4bf60be57ba346fb6331c5027de4df4The accelerated progress of artificial intelligence (AI) has popularized deep learning models across domains, yet their inherent opacity poses challenges, notably in critical fields like healthcare, medicine and the geosciences. Explainable AI (XAI) has emerged to shed light on these"black box"models, helping decipher their decision making process. Nevertheless, different XAI methods yield highly different explanations. This inter-method variability increases uncertainty and lowers trust in deep networks' predictions. In this study, for the first time, we propose a novel framework designed to enhance the explainability of deep networks, by maximizing both the accuracy and the comprehensibility of the explanations. Our framework integrates various explanations from established XAI methods and employs a non-linear"explanation optimizer"to construct a unique and optimal explanation. Through experiments on multi-class and binary classification tasks in 2D object and 3D neuroscience imaging, we validate the efficacy of our approach. Our explanation optimizer achieved superior faithfulness scores, averaging 155% and 63% higher than the best performing XAI method in the 3D and 2D applications, respectively. Additionally, our approach yielded lower complexity, increasing comprehensibility. Our results suggest that optimal explanations based on specific criteria are derivable and address the issue of inter-method variability in the current XAI literature.420This study proposes a novel framework designed to enhance the explainability of deep networks, by maximizing both the accuracy and the comprehensibility of the explanations, and employs a non-linear explanation optimizer to construct a unique and optimal explanation.['Michail Mamalakis', 'Antonios Mamalakis', 'Ingrid Agartz', 'L. Mørch-Johnsen', 'Graham K Murray', 'J. Suckling', 'Pietro Lio']2024-05-16T00:00:00https://www.semanticscholar.org/paper/ec89a2a8d4bf60be57ba346fb6331c5027de4df4 06f661d8bf26b046831dbf8f763b29c757aee44bScience and Engineering Ethics370Science and Engineering Ethics['Stephen Rainey']2024-05-15T00:00:00https://www.semanticscholar.org/paper/06f661d8bf26b046831dbf8f763b29c757aee44b ac90aaa39709c0d3e3e36fefe1045ddca4300c3bThe Bulletin of Technology &amp; Public Life00The Bulletin of Technology &amp; Public Life['Scott Timcke']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ac90aaa39709c0d3e3e36fefe1045ddca4300c3b 43d21071c7b36f89727534b075ec38c6ff3c6221The purpose of the study is to disclose the concept of "artificial intelligence", to outline the possibilities of legal regulation of AI. The article considers the studies of various authors, among which it is necessary to name P.M. Morhat, A. Romaglio, V.L. Entin. The scientific novelty of the work lies in the fact that the category of "artificial intelligence" is considered from the position of the legislation of the Russian Federation, as well as based on the opinion of domestic and foreign scientists. As a result, based on the comparative analysis of information contained in the works of various authors, regulatory legal acts of the Russian Federation, foreign judicial practice, it is concluded that representatives of the legal and scientific communities demonstrate the lack of consensus on the question of whether the units of artificial intelligence and neural networks can be subjects of copyright. A. Ramalho is convinced that AI units cannot be subjects of copyright, which is confirmed by the analysis of the following court decisions: Decision of the Court of Appeal of the Federal Court of Australia dated 13.04.2022, Decision of Shenzhen Nanshan District People's Court dated 24 December 2019. The opposing view is reflected in the Japanese "Intellectual Property Strategy Program 2016", which uses the following argument: it seems impossible to distinguish the product of AI units from human intellectual products. It is impossible to agree with this, as the images generated by neural networks often cause the "uncanny valley" effect in people; no neural network has been able to pass the Turing test so far.Bulletin of Science and Practice10It is concluded that representatives of the legal and scientific communities demonstrate the lack of consensus on the question of whether the units of artificial intelligence and neural networks can be subjects of copyright.Bulletin of Science and Practice['N. Chernykh']2024-05-15T00:00:00https://www.semanticscholar.org/paper/43d21071c7b36f89727534b075ec38c6ff3c6221 6ff3a1c3b2659361870062e45d8eebab2249d567The rapid advancement of AI technologies yields numerous future impacts on individuals and society. Policy-makers are therefore tasked to react quickly and establish policies that mitigate those impacts. However, anticipating the effectiveness of policies is a difficult task, as some impacts might only be observable in the future and respective policies might not be applicable to the future development of AI. In this work we develop a method for using large language models (LLMs) to evaluate the efficacy of a given piece of policy at mitigating specified negative impacts. We do so by using GPT-4 to generate scenarios both pre- and post-introduction of policy and translating these vivid stories into metrics based on human perceptions of impacts. We leverage an already established taxonomy of impacts of generative AI in the media environment to generate a set of scenario pairs both mitigated and non-mitigated by the transparency legislation of Article 50 of the EU AI Act. We then run a user study (n=234) to evaluate these scenarios across four risk-assessment dimensions: severity, plausibility, magnitude, and specificity to vulnerable populations. We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security. Through this case study on generative AI harms we demonstrate the efficacy of our method as a tool to iterate on the effectiveness of policy on mitigating various negative impacts. We expect this method to be useful to researchers or other stakeholders who want to brainstorm the potential utility of different pieces of policy or other mitigation strategies.500This work develops a method for using large language models to evaluate the efficacy of a given piece of policy at mitigating specified negative impacts by using GPT-4 to generate scenarios both pre- and post-introduction of policy and translating these vivid stories into metrics based on human perceptions of impacts.['Julia Barnett', 'Kimon Kieslich', 'Nicholas Diakopoulos']2024-05-15T00:00:00https://www.semanticscholar.org/paper/6ff3a1c3b2659361870062e45d8eebab2249d567 786259f5a1d86007a09ade317ba7197a93007574The emerging European Health Data Space (EHDS) Regulation opens new prospects for large-scale sharing and re-use of health data. Yet, the proposed regulation suffers from two important limitations: it is designed to benefit the whole population with limited consideration for individuals, and the generation of secondary datasets from heterogeneous, unlinked patient data will remain burdensome. AIDAVA, a Horizon Europe project that started in September 2022, proposes to address both shortcomings by providing patients with an AI-based virtual assistant that maximises automation in the integration and transformation of their health data into an interoperable, longitudinal health record. This personal record can then be used to inform patient-related decisions at the point of care, whether this is the usual point of care or a possible cross-border point of care. The personal record can also be used to generate population datasets for research and policymaking. The proposed solution will enable a much-needed paradigm shift in health data management, implementing a ‘curate once at patient level, use many times’ approach, primarily for the benefit of patients and their care providers, but also for more efficient generation of high-quality secondary datasets. After 15 months, the project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described. Additionally, the conceptualization phase of the project identified a set of recommendations for the development of a patient-centric EHDS, significantly facilitating the generation of data for secondary use.Frontiers in Medicine140The AIDAVA project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described.Frontiers in Medicine['Isabelle de Zegher', 'Kerli Norak', 'Dominik Steiger', 'Heimo Müller', 'Dipak Kalra', 'Bart Scheenstra', 'Isabella Cina', 'Stefan Shulz', 'Kanimozhi Uma', 'Petros Kalendralis', 'Eno-Martin Lotmam', 'Martin Benedikt', 'Michel Dumontier', 'Remzi Celebi']2024-05-15T00:00:00https://www.semanticscholar.org/paper/786259f5a1d86007a09ade317ba7197a93007574 d59c75bba7a057eab1844bb7871d05ca0b46981dINTRODUCTION. In modern international and domestic maritime law, regional fragmentation of legal regulation is becoming more and more noticeable, which, in turn, objectifies and actualizes the formation of complex arrays of legal norms, united by the consistency of the political and legal positions of the contracting states that have national interests in the relevant water area, primarily-coastal states extending their state sovereignty to certain areas of maritime space. In this context, the Greater Mediterranean region should be considered as one of the most important in the world merchant shipping and naval support of international peace and security, as a basin that optimally connects the Atlantic and Indian Oceans from the point of view of logistics, which requires the formation of an appropriate scientific and methodological basis for full implementation of the fundamental principle of international cooperation in the maritime policy of the states of the region.MATERIALS AND METHODS. To substantiate the expediency of singling out the Greater Mediterranean as an independent object of legal regulation, general and special international legal treaties, the domestic legislation of the Mediterranean states, as well as political and legal documents indicating the existence of certain disputes and situations around certain zones of the Mediterranean water area, primarily – in the Eastern Mediterranean region. To obtain reliable and substantiated results of the study, methods of scientific knowledge were used: formal-legal, logical, historical-legal, system-structural analysis. Thus, the formal legal method made it possible to clarify the content and meaning of international legal treaties concluded at different times and aimed at regulating public relations in the maritime sphere. The logical method made it possible to substantiate the need for comprehensive international cooperation of the coastal states of the Greater Mediterranean. With the help of the historical and legal method, an overview was made of both the world, Soviet and Russian practice of applying the norms of domestic and international law on issues related to ensuring international law and order in the Greater Mediterranean region. The logical method made it possible to build the necessary connections and patterns of development of international legal regulation in the Greater Mediterranean region in the general context of ongoing universal and regional political and legal processes and transformations. Using the method of system-structural analysis, it was possible to display a holistic picture of law-making and law enforcement of the Mediterranean states, aimed at the formation of unified principles and norms for the exercise of the sovereign rights of coastal states.RESEARCH RESULTS. International maritime merchant shipping seems to be a very complex area of public relations with a large number of entities that have different legal status and, accordingly, are related to each other in a very different way.DISCUSSION AND CONCLUSIONS. This work is devoted to the study of the main trends in the development of the Greater Mediterranean region in terms of formulating key international legal guidelines and rules of conduct for its constituent states. The object of the study is the legal relations carried out in the maritime spaces of the Greater Mediterranean as one of the key regions, which, along with its economic and political significance, is an integral zone for the implementation of the national interests of the Russian Federation, extending to the entire World Ocean.Moscow Journal of International Law70Moscow Journal of International Law['V. N. Koval', 'S. A. Vasiliev', 'E. Godovanik', 'A. V. Polischuk']2024-05-15T00:00:00https://www.semanticscholar.org/paper/d59c75bba7a057eab1844bb7871d05ca0b46981d 2f3f2e985944f2d823801a52731fc36b325bc1c6Review of World Economics440Review of World Economics['Shuang Zheng', 'Xiaohui Liu', 'Yihe Zuo']2024-05-15T00:00:00https://www.semanticscholar.org/paper/2f3f2e985944f2d823801a52731fc36b325bc1c6 2b18056a217496af9bb909538ba8be41c2728d56The STEM Fellowship High School Big Data Challenge provides students with the unique opportunity of Open Data inquiry into one of the UN Sustainable Development Goals and experiential learning of fundamentals of data analysis – an essential skill set for a young researcher in the digital age. This year, students explore Generative AI and Data Cybersecurity to Conserve and Foster Local Biodiversity and to suggest their own evidence-based solutions following the principles of Open Science. They investigated different topics, ranging from Enhancing Forest Fire Predictions with Sequential Models for Ecosystem Preservation and Public Safety to Leveraging Semantic Segmentation to Perform Wildfire Prediction. We designed an interdisciplinary and agile educational environment, and in-depth learning modules for students as a means of bridging the gap between traditional high school courseware and digital reality and computational science. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, CISCO Networking Academy, Canadian Science Publishing, Schulich Foundation, SciNet at University of Toronto, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. We were privileged to witness first-hand the analytical capabilities of the data-native generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers.STEM Fellowship Journal00Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages, and are confident they will demonstrate excellence throughout their academic and professional careers.STEM Fellowship Journal[]2024-05-15T00:00:00https://www.semanticscholar.org/paper/2b18056a217496af9bb909538ba8be41c2728d56 1190cce2c1f2d3dc2eb1dc2e06ac828a79edecd2The application of artificial intelligence (AI) in programming assistance has garnered researchers’ attention for its potential to reduce learning costs for users, increase work efficiency, and decrease repetitive coding tasks. However, given the novelty of AI Coding Assistant Tools (AICATs), user acceptance is currently limited, and the factors influencing this phenomenon are unclear. This study proposes an expanded model based on the Technology Acceptance Model (TAM) that incorporates the characteristics of AICAT users to explore the key factors affecting college students’ willingness to use AICATs. Utilizing a survey methodology, 303 Chinese participants completed the questionnaire. Factor analysis and Structural Equation Modeling (SEM) results indicate that users’ dependence worry (DW) about AICATs positively affects perceived risk (PR), which in turn negatively impacts perceived usefulness (PU) and perceived ease of use (PEOU), thus reducing user willingness to use. Dependence concerns also negatively impact perceived trust (PT), while PT positively affects PU and PEOU, thereby enhancing willingness to use. Additionally, a user’s self-efficacy (SE) negatively impacts DW and positively affects PEOU. This study discusses the potential significance of these findings and offers suggestions for AICAT developers to foster and promote widespread use.Systems660An expanded model based on the Technology Acceptance Model (TAM) that incorporates the characteristics of AICAT users is proposed to explore the key factors affecting college students’ willingness to use AICATs and offers suggestions for AICAT developers to foster and promote widespread use.Systems['Zelin Pan', 'Zhendong Xie', 'Tingting Liu', 'Tiansheng Xia']2024-05-15T00:00:00https://www.semanticscholar.org/paper/1190cce2c1f2d3dc2eb1dc2e06ac828a79edecd2 ba9009af2eb3c6b632d209a8c9618fba2684e43fThe social sciences have long relied on comparative work as the foundation upon which we understand the complexities of human behavior and society. However, as we go deeper into the era of artificial intelligence (AI), it becomes imperative to move beyond mere comparison (e.g., how AI compares to humans across a range of tasks) to establish a visionary agenda for AI as collaborative partners in the pursuit of knowledge and scientific inquiry. This paper articulates an agenda that envisions AI models as the preeminent scientific collaborators. We advocate for the profound notion that our thinking should evolve to anticipate, and include, AI models as one of the most impactful tools in the social scientist's toolbox, offering assistance and collaboration with low-level tasks (e.g., analysis and interpretation of research findings) and high-level tasks (e.g., the discovery of new academic frontiers) alike. This transformation requires us to imagine AI's possible/probable roles in the research process. We defend the inevitable benefits of AI as knowledge generators and research collaborators—agents who facilitate the scientific journey, aiming to make complex human issues more tractable and comprehensible. We foresee AI tools acting as co-researchers, contributing to research proposals and driving breakthrough discoveries. Ethical considerations are paramount, encompassing democratizing access to AI tools, fostering interdisciplinary collaborations, ensuring transparency, fairness, and privacy in AI-driven research, and addressing limitations and biases in large language models. Embracing AI as collaborative partners will revolutionize the landscape of social sciences, enabling innovative, inclusive, and ethically sound research practices.Frontiers in Social Psychology630An agenda that envisions AI models as the preeminent scientific collaborators, and defends the inevitable benefits of AI as knowledge generators and research collaborators—agents who facilitate the scientific journey, aiming to make complex human issues more tractable and comprehensible.Frontiers in Social Psychology['David M. Markowitz', 'Ryan L. Boyd', 'Kate G Blackburn']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ba9009af2eb3c6b632d209a8c9618fba2684e43f 51723d8e058903aea953c89215552f4d9442a570Robustness is a key requirement for any method in medicine, especially when the method in question is being used as part of a diagnostic process. This is particularly true for artificial intelligence-based decision support systems, which, although being used as a supportive tool, will ultimately influence diagnostic assessments. In pathology, attaining clinical robustness in AI methods poses a particularly challenging task, primarily due to the extensive diversity of digital images, which humans can adapt to far more easily. This paper presents factors that contribute to this challenge, but also identifies and evaluates common solutions to counteract domain shift, which is known to deteriorate the performance of artificial intelligence models.Annual Edition 202410Annual Edition 2024['Marc Aubreville']2024-05-15T00:00:00https://www.semanticscholar.org/paper/51723d8e058903aea953c89215552f4d9442a570 a85b1c3044a986d5ecbef22baaa30e123394e465This article explores the evaluation method for the language communication proficiency of AI-driven robots engaging in interactive communication with children. The utilization of AI-driven robots in children's everyday communication is swiftly advancing, underscoring the importance of evaluating these robots'language communication skills. Based on 11 Chinese families' interviews and thematic analysis of the comment text from shopping websites, a framework is introduced in the article to assess five key dimensions of child-robot language communication: interactivity, specificity, development, sociality, and safety. We draw on the concept of"children's agency", viewing children as active participants in shaping society and cultural life alongside adults. Therefore, this article places particular emphasis on collecting data related to children. Whether through survey interviews or direct interactive experiments, we treat children as an independent object for data collection. The study involved empirical research following the mentioned framework, which involved capturing interaction videos in natural conversation settings among children from 6 families. Analysis was performed on quantitative data obtained from video recordings, alongside questionnaires and interviews carried out by parents acting as participants or observers. We found that the presence or absence of parents during children's interactions with robots can impact the evaluation of robots'language communication abilities. Ultimately, this article proposes an enhanced comprehensive evaluation framework incorporating insights from parents and children, supported by empirical evidence and inter-rater consistency assessments, showcasing the scheme's efficacy.120It is found that the presence or absence of parents during children's interactions with robots can impact the evaluation of robots' language communication abilities, leading to an enhanced comprehensive evaluation framework incorporating insights from parents and children being proposed.['Siqi Xie', 'Jiantao Li']2024-05-15T00:00:00https://www.semanticscholar.org/paper/a85b1c3044a986d5ecbef22baaa30e123394e465 3c7fa190e6c7ad311f6b90bbade038f4729c76a6This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outline the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.Applied and Computational Engineering00An integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model, achieving a 100% accuracy rate and eliminating operational risks is proposed.Applied and Computational Engineering['Binbin Wu', 'Jingyu Xu', 'Yifan Zhang', 'Bo Liu', 'Yulu Gong', 'Jiaxin Huang']2024-05-15T00:00:00https://www.semanticscholar.org/paper/3c7fa190e6c7ad311f6b90bbade038f4729c76a6 930b9d09657fc5bc7306c8057db36a9554bdabd0NLP has opened a new level for artificial intelligence: the truth. But AI text generators do not tell the truth reliably. That creates the contradiction of a truth-seeking authority saying something untrue. The paradigm of truth seems to be under attack. The threat of accepting AI as having the ability to speak truth pushes a model to a stereotype of truth that thwarts the possibility of becoming aware of further levels of truth. It will be asked what dynamics occur when we encounter the truth. From a phenomenological perspective, truth will be outlined as a dynamic experiencing-experience construct.Filozofia180From a phenomenological perspective, truth will be outlined as a dynamic experiencing-experience construct and it will be asked what dynamics occur when the authors encounter the truth.Filozofia['Kathrin Burghardt']2024-05-15T00:00:00https://www.semanticscholar.org/paper/930b9d09657fc5bc7306c8057db36a9554bdabd0 de2aa4738f82a059fb96d24113a8e62cd67fa8caThis study explores the intersection of international business and artificial intelligence (AI), focusing on how new ventures navigate environmental challenges for international expansion within Africa's transportation sector. Despite a wealth of literature on AI in developed countries, a notable gap exists in the understanding of the challenges emerging economies face in implementing AI practices, particularly in the context of Africa‐to‐Africa internationalization. The current study delved into the transformative potential of AI, identifying institutional voids as opportunities for innovation on the continent. Employing the Technology Organization and Environment framework, the study investigated the adoption of AI technology in the African business environment. Qualitative data gathered through interviews with transport tech startup founders across Africa provided insights into technological innovation, institutional dynamics, and market peculiarities. The founders recognized hurdles such as data scarcity, human resource constraints, and regulatory obstacles amid institutional voids. The study underscores the importance of understanding expectations, balancing possibilities and realities, and fostering collaboration. It offers valuable insights into the complexities faced by and opportunities for new ventures leveraging AI in internationalization, with practical implications for strategic AI implementation, policy development, market expansion, technology solutions, and cross‐border transportation within Africa's unique business landscape.Thunderbird International Business Review640Thunderbird International Business Review['Moayad Moharrak', 'N. P. Nguyen', 'E. Mogaji']2024-05-15T00:00:00https://www.semanticscholar.org/paper/de2aa4738f82a059fb96d24113a8e62cd67fa8ca 94a49139e1b996d2532b8cb21d706e943b8a9c64Artificial Intelligence (AI) holds immense potential to revolutionize education globally. This research paper investigates how undergraduate college teachers in India perceive AI’s role in education and examines the key dimensions such as familiarity, usability, concerns, and challenges. To address this complex issue, researcher adopted a mixed-methods research design, combining both quantitative and qualitative research approaches. Data was collected through a structured online google form survey questionnaire that was administered to the randomly selected 441 sample of undergraduate college teachers in India by stratified random sampling technique from the five different states of the country (West Bengal, Bihar, Jharkhand, Gujrat, & Tripura). Here researcher used the basic descriptive statistics such as mean, median and standard deviations to summarize survey responses. On the other side, inferential statistics, such as ‘Confirmatory Factor Analysis’ and chi-square were used. This mixed approach-based investigation revealed distinct patterns in familiarity, usability, concerns, and challenges among the undergraduate college teachers. Notably, male teachers from private institutions exhibited higher familiarity with AI. On the other side, female teachers and private undergraduate college teachers demonstrated more favourable perceptions of AI’s usability in education. But concerns, especially regarding privacy and security, were more pronounced among female teachers. Challenges were also highlighted, with a shared dissatisfaction among undergraduate college teachers concerning institutional support, while technical support and infrastructure issues loomed large. Confirmatory Factor Analysis (CFA) validated positive relationships between familiarity and both usability and concerns, emphasizing the vital role of enhancing AI knowledge to shape perceptions positively and reduce concerns.Scholars Journal of Arts Humanities and Social Sciences00This mixed approach-based investigation revealed distinct patterns in familiarity, usability, concerns, and challenges among the undergraduate college teachers in India, with male teachers from private institutions and female teachers and private undergraduate college teachers demonstrating more favourable perceptions of AI’s usability in education.Scholars Journal of Arts, Humanities and Social Sciences['Dr. Sahin Sahari']2024-05-15T00:00:00https://www.semanticscholar.org/paper/94a49139e1b996d2532b8cb21d706e943b8a9c64 c03322cb104f8591904c2b1f6248e28664ae61e2A concern about cutting-edge or"frontier"AI foundation models is that an adversary may use the models for preparing chemical, biological, radiological, nuclear, (CBRN), cyber, or other attacks. At least two methods can identify foundation models with potential dual-use capability; each has advantages and disadvantages: A. Open benchmarks (based on openly available questions and answers), which are low-cost but accuracy-limited by the need to omit security-sensitive details; and B. Closed red team evaluations (based on private evaluation by CBRN and cyber experts), which are higher-cost but can achieve higher accuracy by incorporating sensitive details. We propose a research and risk-management approach using a combination of methods including both open benchmarks and closed red team evaluations, in a way that leverages advantages of both methods. We recommend that one or more groups of researchers with sufficient resources and access to a range of near-frontier and frontier foundation models run a set of foundation models through dual-use capability evaluation benchmarks and red team evaluations, then analyze the resulting sets of models' scores on benchmark and red team evaluations to see how correlated those are. If, as we expect, there is substantial correlation between the dual-use potential benchmark scores and the red team evaluation scores, then implications include the following: The open benchmarks should be used frequently during foundation model development as a quick, low-cost measure of a model's dual-use potential; and if a particular model gets a high score on the dual-use potential benchmark, then more in-depth red team assessments of that model's dual-use capability should be performed. We also discuss limitations and mitigations for our approach, e.g., if model developers try to game benchmarks by including a version of benchmark test data in a model's training data.00A research and risk-management approach using a combination of methods including both open benchmarks and closed red team evaluations, in a way that leverages advantages of both methods to identify foundation models with potential dual-use capability.['Anthony M. Barrett', 'Krystal Jackson', 'Evan R. Murphy', 'Nada Madkour', 'Jessica Newman']2024-05-15T00:00:00https://www.semanticscholar.org/paper/c03322cb104f8591904c2b1f6248e28664ae61e2 d4279a8dc7f519574259df7e4af640f5fa59bb8cDeterministic response is the core foundation for the safe operation of industrial production systems. However, with the increasing demand for intelligence, flexibility, and agility, ensuring the deterministic response of computing and control tasks while meeting new demands has become the primary issue that manufacturers urgently need to address. In response to this issue, this article focuses on AI-driven cyber–physical production systems (AI-CPPSs) and conducts research on the adaptive interaction mechanism of network, computing, and manufacturing resources with guaranteed performance. The efficient adaptive configuration of network, computing, and manufacturing resources is used to meet the response requirements of dynamic tasks. To achieve on-demand configuration of multidimensional resources for tasks, we first propose an AI-CPPS-oriented modeling method named the hourglass method, which redefines task models and multidimensional resources with resources as the core. Furthermore, through the proposed method of computing power quantification and a heterogeneous frame structure, we achieve the unified arrangement of network, computing, and manufacturing resources in the time dimension. Finally, to ensure the security and reliability of resource interaction, a multidimensional resource interaction mechanism is proposed for network computing control, namely, the quicksand mechanism. The experimental results indicate that the proposed quicksand mechanism can optimize resource utilization based on ensuring a deterministic task response.IEEE Internet of Things Journal840An AI-CPPS-oriented modeling method named the hourglass method is proposed, which redefines task models and multidimensional resources with resources as the core, and the experimental results indicate that the proposed quicksand mechanism can optimize resource utilization based on ensuring a deterministic task response.IEEE Internet of Things Journal['Changqing Xia', 'Renjun Wang', 'Xi Jin', 'Chi Xu', 'Dong Li', 'Peng Zeng']2024-05-15T00:00:00https://www.semanticscholar.org/paper/d4279a8dc7f519574259df7e4af640f5fa59bb8c 952a2323e0db6c5d1302fbe083e8843934803bb8This study developed an explainable AI for ship collision avoidance. Initially, a critic network composed of sub-task critic networks was proposed to individually evaluate each sub-task in collision avoidance to clarify the AI decision-making processes involved. Additionally, an attempt was made to discern behavioral intentions through a Q-value analysis and an Attention mechanism. The former focused on interpreting intentions by examining the increment of the Q-value resulting from AI actions, while the latter incorporated the significance of other ships in the decision-making process for collision avoidance into the learning objective. AI's behavioral intentions in collision avoidance were visualized by combining the perceived collision danger with the degree of attention to other ships. The proposed method was evaluated through a numerical experiment. The developed AI was confirmed to be able to safely avoid collisions under various congestion levels, and AI's decision-making process was rendered comprehensible to humans. The proposed method not only facilitates the understanding of DRL-based controllers/systems in the ship collision avoidance task but also extends to any task comprising sub-tasks.160An explainable AI for ship collision avoidance was developed, confirmed to be able to safely avoid collisions under various congestion levels, and AI's decision-making process was rendered comprehensible to humans.['Hitoshi Yoshioka', 'Hirotada Hashimoto']2024-05-15T00:00:00https://www.semanticscholar.org/paper/952a2323e0db6c5d1302fbe083e8843934803bb8 7405c1ff6ecc8bae9a7df693eb5cad9eb3a48381As more pathology laboratories are transitioning to a digital workflow, the availability of commercial Artificial Intelligence assistance systems is also increasing. Today nearly 40 such products approved for diagnostic use are available. This article provides an overview of the most widely addressed use cases, including Immunohistochemistry scoring for breast cancer and non-small-cell lung cancer, Gleason grading for prostate cancer, or metastasis detection in lymph nodes. While automation alone already promises an increase in efficiency that may help to bridge the growing gap between supply (pathology work force) and demand (histological testing), this article introduces another category of Artificial Intelligence products that go beyond just mimicking today’s established score. Various Artificial Intelligence are being introduced that detect genetic alterations or stratify risk, directly from the standard hematoxylin & eosin staining. Finally, a brief outlook explains how basic AI models are currently finding their way into computational pathology and promise to further accelerate product developments by decreasing the time-to-model.Annual Edition 202400An overview of the most widely addressed use cases, including Immunohistochemistry scoring for breast cancer and non-small-cell lung cancer, Gleason grading for prostate cancer, or metastasis detection in lymph nodes is provided.Annual Edition 2024['Volker Bruns', 'Cleo-Aron Weis']2024-05-15T00:00:00https://www.semanticscholar.org/paper/7405c1ff6ecc8bae9a7df693eb5cad9eb3a48381 dc82606b67a65c7ba3583e5adafdb93144963192Reproductive Sciences240A.I. shows promise in significantly improving the ovarian cancer screening processes, increasing accuracy, efficiency, and resource allocation, but data quality and bias issues pose considerable challenges, potentially leading to healthcare disparities.Reproductive sciences['Sierra Silverwood', 'Anna Jeter', 'Margo Harrison']2024-05-15T00:00:00https://www.semanticscholar.org/paper/dc82606b67a65c7ba3583e5adafdb93144963192 ad366edb20d031c4944c14c149be6b3f67bf5cadThe paper makes the case for establishing ethically substantiated foundations for using AI tools before turning to more detailed issues. First, I point out AI tools’ potential benefits in academia. Second, I discuss the risks of missing out on building ethically substantiated foundations by shifting the focus too much on specific questions. To illustrate this risk, I examine three kinds of issues: problematic outputs of AI tools; the amplification of warped incentives already present in academia; and the risk that universities, other educational institutions, and their members potentially jeopardize their digital autonomy. Third, I present starting points for a discussion on building ethical foundations for using AI tools in academia: we should concentrate on the questions of how to dismantle general warped incentives, what needs to be done to empower institutions in academia to create independent AI infrastructures collaboratively, and how to secure a responsible and productive use of AI technologies.Filozofia170The paper points out AI tools’ potential benefits in academia and discusses the risks of missing out on building ethically substantiated foundations by shifting the focus too much on specific questions.Filozofia['Amrei Bahr']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ad366edb20d031c4944c14c149be6b3f67bf5cad 0781ef33be078e6a45d2bbc09b26812c5b73ae16Asian Bioethics Review310An AI model based on convolutional neural networks developed by a team of researchers at NUH/NUS medical school in Singapore for automated detection and classification of the lumbar spinal canal, lateral recess, and neural foraminal narrowing in an MRI scan of the spine to diagnose LSS is used.Asian Bioethics Review['Kathryn Muyskens', 'Yonghui Ma', 'Jerry Menikoff', 'James Hallinan', 'Julian Savulescu']2024-05-15T00:00:00https://www.semanticscholar.org/paper/0781ef33be078e6a45d2bbc09b26812c5b73ae16 6067ff4d1d7161de28784745c28598403e807e55In the quest for accurate and interpretable AI models, eXplainable AI (XAI) has become crucial. Fuzzy Cognitive Maps (FCMs) stand out as an advanced XAI method because of their ability to synergistically combine and exploit both expert knowledge and data-driven insights, providing transparency and intrinsic interpretability. This letter introduces and investigates the"Total Causal Effect Calculation for FCMs"(TCEC-FCM) algorithm, an innovative approach that, for the first time, enables the efficient calculation of total causal effects among concepts in large-scale FCMs by leveraging binary search and graph traversal techniques, thereby overcoming the challenge of exhaustive causal path exploration that hinder existing methods. We evaluate the proposed method across various synthetic FCMs that demonstrate TCEC-FCM's superior performance over exhaustive methods, marking a significant advancement in causal effect analysis within FCMs, thus broadening their usability for modern complex XAI applications.160The proposed TCEC-FCM algorithm is investigated, an innovative approach that enables the efficient calculation of total causal effects among concepts in large-scale FCMs by leveraging binary search and graph traversal techniques, thereby overcoming the challenge of exhaustive causal path exploration that hinder existing methods.['Marios Tyrovolas', 'Nikolaos D. Kallimanis', 'Chrysostomos Stylios']2024-05-15T00:00:00https://www.semanticscholar.org/paper/6067ff4d1d7161de28784745c28598403e807e55 bec2b1a4af641d40310ba9e5cc5335df880665b3AI and Ethics180It is suggested that one can communicate knowledge of an AI-enabled system’s safety by structuring their exchange according to Paul Grice’s Cooperative Principle which can be achieved via adherence to the Gricean maxims of communication.AI and Ethics['Marten H. L. Kaas', 'I. Habli']2024-05-15T00:00:00https://www.semanticscholar.org/paper/bec2b1a4af641d40310ba9e5cc5335df880665b3 ebb852745c75a6a7fb8c7160214352114641b617The expansive development of AI technologies challenges our conventional understanding of agency, which has traditionally been anchored in the human capacity for autonomous action and decision-making. As human-AI interactions become increasingly complex, the boundary between human and machine agency is continuously breached, prompting a reconsideration of the concept of agency as potentially no longer a human proprium. This paper offers preliminary reflections on the prerequisites and conditions for a post-anthropocentric theory of agency in the age of AI, beginning with a historical reconstruction and conceptual validation of the evolving notion of agency.Filozofia260Reflections on the prerequisites and conditions for a post-anthropocentric theory of agency in the age of AI are offered, beginning with a historical reconstruction and conceptual validation of the evolving notion of agency.Filozofia['Gerhard Schreiber']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ebb852745c75a6a7fb8c7160214352114641b617 ef527f45f74229505ca5abdcbce508e603668a8dGenerative artificial intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech, and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimize training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. However, not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimize outcomes. Currently, the previously well-controlled integration of real and synthetic data is becoming uncontrollable. The widespread and unregulated dissemination of synthetic data online leads to the contamination of datasets traditionally compiled through web scraping, now mixed with unlabeled synthetic data. This trend portends a future where generative AI systems may increasingly rely blindly on consuming self-generated data, raising concerns about model performance and ethical issues. What will happen if generative AI continuously consumes itself without discernment? What measures can we take to mitigate the potential adverse effects? There is a significant gap in the scientific literature regarding the impact of synthetic data use in generative AI, particularly in terms of the fusion of multimodal information. To address this research gap, this review investigates the consequences of integrating synthetic data blindly on training generative AI on both image and text modalities and explores strategies to mitigate these effects. The goal is to offer a comprehensive view of synthetic data's role, advocating for a balanced approach to its use and exploring practices that promote the sustainable development of generative AI technologies in the era of large models.00A comprehensive view of synthetic data's role is offered, advocating for a balanced approach to its use and exploring practices that promote the sustainable development of generative AI technologies in the era of large models.['Xiaodan Xing', 'Fadong Shi', 'Jiahao Huang', 'Yinzhe Wu', 'Yang Nan', 'Sheng Zhang', 'Yingying Fang', 'Mike Roberts', 'C. Schonlieb', 'J. Ser', 'Guang Yang']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ef527f45f74229505ca5abdcbce508e603668a8d ad071a06d1b5895815771088b94f9b63fbbad9a1This paper explores the potential of artificial intelligence (AI) in facilitating human learning and promoting behaviour change. By employing machine learning algorithms, natural language processing, and data analysis, AI systems can provide personalized learning experiences, identify learning gaps, and adapt to individual learning styles. Furthermore, AI can be utilized to create nudges and interventions that encourage positive behaviour change, offering promising applications in fields such as health, finance, and environmental conservation. The paper also discusses ethical considerations and challenges, emphasizing the importance of transparency, fairness, and privacy in AI-driven learning and behaviour change systems.International Journal of Advanced Science and Computer Applications00Artificial intelligence can be utilized to create nudges and interventions that encourage positive behaviour change, offering promising applications in fields such as health, finance, and environmental conservation.International Journal of Advanced Science and Computer Applications['Divya Divya']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ad071a06d1b5895815771088b94f9b63fbbad9a1 d2e351f7b83d686f8e863d377825c278528a2f79Automated chatbots powered by artificial intelligence (AI) can act as a ubiquitous point of contact, improving access to healthcare and empowering users to make effective decisions. However, despite the potential benefits, emerging literature suggests that apprehensions linked to the distinctive features of AI technology and the specific context of use (healthcare) could undermine consumer trust and hinder widespread adoption. Although the role of trust is considered pivotal to the acceptance of healthcare technologies, a dearth of research exists that focuses on the contextual factors that drive trust in such AI-based Chatbots for Self-Diagnosis (AICSD). Accordingly, a contextual model based on the trust-in-technology framework was developed to understand the determinants of consumers’ trust in AICSD and its behavioral consequences. It was validated using a free simulation experiment study in India (N = 202). Perceived anthropomorphism, perceived information quality, perceived explainability, disposition to trust technology, and perceived service quality influence consumers’ trust in AICSD. In turn, trust, privacy risk, health risk, and gender determine the intention to use. The research contributes by developing and validating a context-specific model for explaining trust in AICSD that could aid developers and marketers in enhancing consumers’ trust in and adoption of AICSD.Australasian Journal of Information Systems00A contextual model based on the trust-in-technology framework was developed to understand the determinants of consumers’ trust in AICSD and its behavioral consequences and could aid developers and marketers in enhancing consumers’ trust in and adoption of AICSD.Australasian Journal of Information Systems['A. V. Prakash', 'Saini Das']2024-05-15T00:00:00https://www.semanticscholar.org/paper/d2e351f7b83d686f8e863d377825c278528a2f79 7110c47858f106094c6da25944e15910b7b3081cFood safety remains one of the major concerns in ASEAN, with many of the recent developmental plans and published policies in the region being focused on the topic. Most recent WHO data indicate that over 90% of the food safety burden in ASEAN is due to microbial foodborne diseases. However, conventional systems for controlling FBDs are resource‐intensive and require significant infrastructure which may not yet be present in ASEAN. Prior work on the use of Artificial intelligence (AI) in food safety application has shown its potential to reduce costs and increase efficiency. However, there remains a paucity in such research specific for the ASEAN region. In this review, the state of microbial food safety and the unique challenges in the ASEAN region are presented. The global state‐of‐the‐art of microbial food safety applications of AI are presented and possible steps for its adaptation to the ASEAN context are then discussed.International Journal of Food Science &amp; Technology640The global state‐of‐the‐art of microbial food safety applications of AI are presented and possible steps for its adaptation to the ASEAN context are discussed.International Journal of Food Science &amp; Technology['Dominic Panaligan', 'Isaac Cornelius Bensley Sy', 'Riann Martin Sarza']2024-05-15T00:00:00https://www.semanticscholar.org/paper/7110c47858f106094c6da25944e15910b7b3081c 3019016ea3f5a4fcdf3f574fd7d0dc5ce84e36de To compare and evaluate the performance of artificial intelligence (AI) against physicians in classifying benign and malignant pulmonary nodules from computerized tomography (CT) images. A total of 506 CT images with pulmonary nodules were retrospectively collected. The AI was trained using in-house software. For comparing the diagnostic performance of artificial intelligence and different groups of physicians in pulmonary nodules, statistical methods of receiver operating characteristic (ROC) curve and area under the curve (AUC) were analyzed. The nodules in CT images were analyzed in a case-by-case manner. The diagnostic accuracy of AI surpassed that of all groups of physicians, exhibiting an AUC of 0.88 alongside a sensitivity of 0.80, specificity of 0.84, and accuracy of 0.83. The area under the curve (AUC) of seven groups of physicians varies between 0.63 and 0.84. The sensitivity of the physicians within these groups varies between 0.4 and 0.76. The specificity of different groups ranges from 0.8 to 0.85. Furthermore, the accuracy of the seven groups ranges from 0.7 to 0.82. The professional insights for enhancing deep learning models were obtained through an examination conducted on a per-case basis. AI demonstrated great potential in the benign–malignant classification of pulmonary nodules with higher accuracy. More accurate information will be provided by AI when making clinical decisions. ONCOLOGIE280AI demonstrated great potential in the benign–malignant classification of pulmonary nodules with higher accuracy, and more accurate information will be provided by AI when making clinical decisions.Oncologie['Weiguo Hu', 'Jie Zhang', 'Dingyi Zhou', 'Shu Xia', 'Xingxiang Pu', 'Jianzhong Cao', 'Mingzhu Zou', 'Zhangfan Mao', 'Qibin Song', 'Xiaodong Zhang']2024-05-15T00:00:00https://www.semanticscholar.org/paper/3019016ea3f5a4fcdf3f574fd7d0dc5ce84e36de 8f852495e57469d427ece5c394da41bc416b9a88In the industrial age of 5.0, technology can be used to make it easier for humans to do their everyday work. The development of information technology today is proven by the emergence of artificial intelligence (AI). Research related to AI in the field of education to this day still continues to be studied and researched. Therefore, there is a need for a literary study to find out the trends of research on artificial intelligence in the field of education in order to facilitate further researchers in determining the themes of their research. This research aims to collect, identify, evaluate, analyze, interpret, and conclude similar research that deals with artificial intelligence in education. The research method uses library study with bibliometric analysis and SLR techniques. The results of this research are divided into four sections according to the research questions studied in 98 articles relevant to research topics. The four components are the benefits of AI, the application of artificial intelligence, the positive and negative impacts of AI, and the challenges to be faced with AI.International Education Trend Issues610This research aims to collect, identify, evaluate, analyze, interpret, interpret, and conclude similar research that deals with artificial intelligence in education that deals with artificial intelligence in education.International Education Trend Issues['Ristyana Suryanti', 'Jaja Jahidin', 'Muhammad Fadlil']2024-05-15T00:00:00https://www.semanticscholar.org/paper/8f852495e57469d427ece5c394da41bc416b9a88 b5f846e1af4935b2d924d0e197a126f8e0f71547This study examines the attitudes and emotional responses towards artificial intelligence (AI) adoption among a sample of 40 respondents. The demographic distribution shows equal representation between genders, with women and men each constituting 50% of the sample. Regarding trust and acceptance of AI, 42% of respondents were willing to trust AI, 32% held ambivalent attitudes, and 22% were unwilling to trust. Emotionally, the majority of respondents expressed moderate to high levels of optimism (63%), excitement (58%), and relaxation (55%). However, a considerable proportion reported feelings of worry (45%) and fear (42%) towards AI, while outrage was less prevalent (20%). These findings shed light on the complex interplay between demographic factors and emotional responses in shaping attitudes towards AI adoption.International Journal For Multidisciplinary Research130Findings shed light on the complex interplay between demographic factors and emotional responses in shaping attitudes towards AI adoption.International Journal For Multidisciplinary Research['Taleb Hammad']2024-05-15T00:00:00https://www.semanticscholar.org/paper/b5f846e1af4935b2d924d0e197a126f8e0f71547 f1697d606238d59a699333adcef37f092f06a729The growth of artificial intelligence (AI) has allowed industries to automate and improve their efficiency in operations. Especially in process industries, AI helps to develop intelligent models and tools to proactively monitor and predict equipment or system failures, minimize downtime, and optimize maintenance schedules. With the advancements in AI and its ability to perform tasks, there is a growing belief that AI may eventually replace humans. However, the absence of human involvement in operations in the process industry raises safety concerns. Therefore, AI should collaborate with humans rather than replace them in processing facility operations. This technology is referred to as intelligence augmentation (IA). This article (i) presents a detailed comparison between AI and IA's potential in process systems, (ii) identifies the feasibility of using AI and IA in process safety, and (iii) identifies the risk associated with the implementation of AI or IA in process industries.AIChE Journal110A detailed comparison between AI and IA's potential in process systems is presented, the feasibility of using AI and IA in process safety is identified, and the risk associated with the implementation of AI or IA in process industries is identified.AIChE Journal['Rajeevan Arunthavanathan', 'Zaman Sajid', 'Md. Tanjin Amin', 'Yuhe Tian', 'Faisal Khan', 'E. Pistikopoulos']2024-05-15T00:00:00https://www.semanticscholar.org/paper/f1697d606238d59a699333adcef37f092f06a729 7270624683fe2ca018632a860459e06f2bb6a092Objectives The purpose of this study is to empirically verify the relationship between technology utilization ability and awareness of artificial intelligence and coding education for pre-service early childhood teachers. Methods For this purpose, a survey was conducted on 180 pre-service early childhood teachers majoring in child studies and early childhood education at a university located in J city, Gyeongsangnam-do, and the collected data were analyzed using SPSS program to verify reliability, mean comparison, and correlation analysis. Results As for the difference in perception of research variables according to the demographic characteristics of pre-service early childhood teachers, pre-service early childhood teachers who completed information literacy education and had experience in artificial intelligence and coding education showed high level of perception of technology utilization ability and artificial intelligence and coding education. In addition, there was a significant positive correlation between technology utilization ability and the perception of artificial intelligence and coding education. Conclusions In the era of the Fourth Industrial Revolution, early childhood teachers should cultivate information- related skills. Therefore, educational institutions that train early childhood teachers should actively review ways to strengthen education related to technology utilization education, artificial intelligence education, and coding education.Korean Association For Learner-Centered Curriculum And Instruction00Pre-service early childhood teachers who completed information literacy education and had experience in artificial intelligence and coding education showed high level of perception of technology utilization ability and artificial intelligence and coding education.Korean Association For Learner-Centered Curriculum And Instruction['JeongLee Go']2024-05-15T00:00:00https://www.semanticscholar.org/paper/7270624683fe2ca018632a860459e06f2bb6a092 7f020015123d6d86ce67a26c2069e2eb4088e253The purpose of this research study was to examine the overall effect of adaptive learning systems deployed using artificial intelligence technology across a range of relevant variables (e.g., duration, student level, etc.). Following a systematic procedure, this meta-analysis examined literature from 18 academic databases and identified N = 45 independent studies utilizing AI-enabled adaptive learning. This meta-analysis examined the overall effect of AI-enabled adaptive learning systems on students’ cognitive learning outcomes when compared with non-adaptive learning interventions and found that they have a medium to large positive effect size ( g = 0.70). The effect was significantly moderated by publication type, origin of study, student classification level, student discipline, duration, and research design. In addition, all three adaptive sources (cognitive, affective, and behavioral) and adaptive targets (navigation and assessment) were significant moderators. The type of AI used in the adaptive engine did not moderate the effects. Implications for both practice and research are provided.Journal of educational computing research550This meta-analysis examined the overall effect of AI-enabled adaptive learning systems on students’ cognitive learning outcomes when compared with non-adaptive learning interventions and found that they have a medium to large positive effect size.Journal of Educational Computing Research['Xiaoman Wang', 'Rui “Tammy” Huang', 'Max Sommer', 'Bo Pei', 'Poorya Shidfar', 'Muhammad Shahroze Rehman', 'Albert D. Ritzhaupt', 'Florence Martin']2024-05-15T00:00:00https://www.semanticscholar.org/paper/7f020015123d6d86ce67a26c2069e2eb4088e253 21f080f3d1e29af1775edd4fe685095b055cbbf6This book explores the use of artificial intelligence by intelligence services around the world and its critical role in intelligence analysis, defense, and national security. Intelligence services play a crucial role in national security, and the adoption of artificial intelligence technologies has had a significant impact on their operations. It also examines the various applications of artificial intelligence in intelligence services, the implications, challenges and ethical considerations associated with its use. The book emphasizes the need for continued research and development in the field of artificial intelligence to ensure that intelligence services. and overall national defense and security can effectively adapt to emerging threats.00The need for continued research and development in the field of artificial intelligence is emphasized to ensure that intelligence services.['Nicolae Sfetcu']2024-05-15T00:00:00https://www.semanticscholar.org/paper/21f080f3d1e29af1775edd4fe685095b055cbbf6 ce9e7c74a9c6ab0526bf63846932e4283e4e0c89Artificial intelligence (AI), machine learning (ML) and deep learning (DL) have had a profound impact on various sectors including Banking (Fin Tech), Health (HealthTech) and Charitable Fundraising (Charity Tech). The ‘natural’ ability of an AI system to independently perform and, often, outthink its human-counter parts by developing ‘intelligence’(simulating human intelligence) through its own experiences and processing deep layers of information i.e., complex representations of data, and learn has resulted in astounding improvements in the completion of tasks that are complex and technical, time-consuming.AI, with the ease of working with the most granular level of detail, can identify people and objects, recognise voices, uncover patterns and, in advance, screen for problems. Yet, RegTech (or LawTech/LegalTech) has not seen the same level of advancement. AI can provide solutions and enormous economic, political, and social benefits – in terms of public service administration. The purpose of this article is to explore advents in AI (ML and DL) and whether the criminal justice system, in the United Kingdom (UK), which is heavily overburdened, could benefit from some of the advances that have taken place in other sectors and jurisdictions, and whether automation and algorithmic decision making could be used to modernise it. This research draws on domestic and international published law, regulation, and literature, and isset out in six parts, the first partre views the position of the criminal justice system i.e., issues, part two then looks at relative technological advancements in AI, and the Metaverse. Part three explores current advents in AI relating to RegTech (LawTech/LegalTech) and how, if at all, the CJS can use this technology. Part four explores what aspects of the U.K.’s CJS would be fit for automation. Part five focuses on those matters pertaining to AI that pose problems in relation to matters in part 4 i.e., AI discrimination and bias, and explores safeguarding and mitigation including the requirement for explanation as set out in the GDPR. Part six concludes the discussion with some recommendations, as at, January 2024. It is suggested that AI and algorithmic decision making, with the correct legal framework and safeguards in place, could assist in modernising the CJS focussed legal functions, services in law firms, innovating for the next decade. This work is original and timely given the increased debate relating to how AI can assist in modernising the U.K.’s CJS, the global criminal justice challenges, solutions, and what, if any, role the Metaverse can play.Frontiers in Law310It is suggested that AI and algorithmic decision making, with the correct legal framework and safeguards in place, could assist in modernising the CJS focussed legal functions, services in law firms, innovating for the next decade.Frontiers in Law['C. Singh']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ce9e7c74a9c6ab0526bf63846932e4283e4e0c89 a8ea3249e5b9f2d8d4912ce34b74935189a6f816This study delves into the ethical considerations of artificial intelligence (AI) use in higher education, focusing on counselling and psychotherapy students' perspectives. Amidst growing interest in AI across educational sectors, this research aimed to highlight student views on the benefits, risks and ethical challenges posed by AI tools in their training.Employing a qualitative approach, this scoping study gathered data from seven counselling and psychotherapy students through an online survey, which were analysed using reflexive thematic analysis.Four main themes were constructed: (1) guidelines, (2) concerns about the use of AI with highly sensitive information, (3) acceptable and unacceptable uses, and (4) risk of AI compromising the quality of knowledge and practice.This research underscores the necessity for collaborative guideline development that addresses ethical AI use, the protection of sensitive information, and the delineation of AI's appropriate roles in education and practice. It advocates for ongoing discussion amongst educational institutions, professional bodies and students to create dynamic, ethical standards that evolve with AI advancements, ensuring technology enhances learning outcomes, upholds integrity and respects privacy.Counselling and Psychotherapy Research130This research underscores the necessity for collaborative guideline development that addresses ethical AI use, the protection of sensitive information, and the delineation of AI's appropriate roles in education and practice.Counselling and Psychotherapy Research['Stuart Gore', 'Emily Dove']2024-05-15T00:00:00https://www.semanticscholar.org/paper/a8ea3249e5b9f2d8d4912ce34b74935189a6f816 f9a5259ffe180118d152b8c0759e82a3e8d5bc13The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.International Journal of Educational Methodology530An assessment instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.International Journal of Educational Methodology['Andie Tangonan Capinding']2024-05-15T00:00:00https://www.semanticscholar.org/paper/f9a5259ffe180118d152b8c0759e82a3e8d5bc13 4cd59fef3aa2e25e6d2bd0c93be39df17b050ea1This study aimed to create a foundation for the integration of AI in technology education. The framework of this study was based on the important problem-solving learning process in technology education. The developed framework structures the problem-solving steps of such types of problems into problem solving, design, development, and evaluation. Each technological problem and problem-solving step area can be implemented using Intelligent Tutoring System (ITS), Dialogue-Based Tutoring System (DBTS), and Exploratory Learning Environment (ELE) of AI convergence education. Technology education supports learners develop Technological knowledge and develop critical and creative thinking when attempting to resolve real-life technological glitches. Since the process of solving technological problems is complex in itself, it was necessary to categorize technological problems in real life and organize the procedure for resolving technical issues into a series for formal education in schools. In this process of technological problem solving learning, explicit knowledge may be necessary, and interpretive or procedural knowledge may be necessary. Methods of utilizing AI convergence education vary depending on the target knowledge. Therefore, structuring and presenting a plan to apply various AI convergence educations to the process of solving highly complex technological problems is meaningful in that it suggests the basis for educational direction. Based on the framework developed in this study, expected that AI convergence education methods appropriate for each problem-solving process of various technological problems will be systematically researched and implemented in the future.Tehnički glasnik00It is expected that AI convergence education methods appropriate for each problem-solving process of various technological problems will be systematically researched and implemented in the future.Tehnički glasnik['Mika Lim']2024-05-15T00:00:00https://www.semanticscholar.org/paper/4cd59fef3aa2e25e6d2bd0c93be39df17b050ea1 41597f316e8775e97fcf3f52471ac1fbb5624d8e لقد شهدت فترة ما بعد الحداثة نشوء أساليب فنية متطورة وذلك بفعل العديد من المتغيرات الجمالية والفنية والتي شكلت الأساس في تمظهر مفاهيم جديدة ومبتكرة في مجال التصميم التي تأثرت بهذه المتغيرات سيّما ظهور مدرسة الواقعية المفرطة التي تميل الى إبهار المتلقي بالقدرة على صياغة عمل فني ينقل أدق التفاصيل المماثلة للواقع عبر محاكاة إبداعية لها سماتها التي تتميز بالفرادة التي أخذت تنتشر ملامحها الشكلية واتجاهاتها الفنية في مزيج تصميمي مفرط بواقعيته يبعث على الانبهار لتلك الفنون التي منحت المصمم أسلوباً جديداً في طرح الأفكار وبرؤية مغايرة للتصميم، وفي عصر باتت الآلات هي الرائدة في تحقيق ما يعجز عنه البشر بذات المستوى من الدقة ظهرت تقنيات الذكاء الاصطناعي ذات فاعلية كبيرة في المجال الفني والابداعي والاهتمام بالجودة العالية للمنجز التصميمي وتفاصيله الدقيقة من خلال إيجاد العديد من البدائل المبتكرة، إذ وفرّ الذكاء الاصطناعي الكثير من الوقت والجهد المبذول بفضل امكانياته في اختيار الأفكار وبكل سهولة ودقة عالية من خلال اعتماد الذكاء الاصطناعي الذي يستمد مقوماته من حاجة المجتمع كونه يعد فناً إبداعياً يحمل رسالة فنية هادفة في جذب انتباه المتلقي، وقد تضمن الفصل الثاني الإطار النظري على مبحثين المبحث الأول: نشأة ومفهوم الواقعية المفرطة، الإبداع الفكري للواقعية المفرطة، الأبعاد الجمالية للواقعية المفرطة، اما المبحث الثاني: فقد شمل نشأة الذكاء الاصطناعي، تقنية الذكاء الاصطناعي للواقعية المفرطة، تطبيقات سكامبر في الواقعية المفرطة، وتضمن الفصل الثالث إجراءات البحث الذي أعتمد فيه المنهج الوصفي لتحليل الملصقات الإعلانية لأفلام شركة والت ديزني الأمريكية للفترة (2014-2019)، أما الفصل الرابع فلقد تضمن النتائج والاستنتاجات والمقترحات والتوصيات.Al-Academy120Al-Academy['Raghad Munther Ahmed', 'Shaima Kamel Dakhel']2024-05-15T00:00:00https://www.semanticscholar.org/paper/41597f316e8775e97fcf3f52471ac1fbb5624d8e a9872eeaa61543d5b9ded7c826f9fc1bb504820c تعتبر صناعة الأفلام واحدة من العديد من الصناعات التي لا مفر منها في استخدام الذكاء الاصطناعي فيها بسبب التقدم السريع في التكنولوجيا. يفيد الذكاء الاصطناعي الصناعة في كل مرحلة من مراحل دورة الإنتاج. يركز هذا البحث على خمس مراحل: كتابة السيناريو في الذكاء الاصطناعي، توليد الصور في الذكاء الاصطناعي، تسجيلات الصوت في الذكاء الاصطناعي، صنع الرسوم المتحركة في الذكاء الاصطناعي، تحرير الأفلام، مع عرض أدوات الذكاء الاصطناعي. يسلط هذا البحث الضوء على أن تكنولوجيا الذكاء الاصطناعي (AI) والإنتاج الفني البشري أساسيان للنمو القوي لصناعة الأفلام فقط عندما يكمل كل منهما الآخر من خلال فحص المزايا والمخاطر المحتملة. يشجع البحث على التوجه الجديد نحو استخدام الذكاء الاصطناعي في إنتاج الأفلام القصيرة لطلاب الدراسات العليا والجامعية والباحثين والمهتمين، والاستفادة من الميزات المجانية المتاحة على مواقع الذكاء الاصطناعي التي لا تتطلب شراءات مكلفة للحصول على الفوائد.Al-Academy50Al-Academy['Aya Khalid Naji']2024-05-15T00:00:00https://www.semanticscholar.org/paper/a9872eeaa61543d5b9ded7c826f9fc1bb504820c e00e6f87920cb447b5589c846a2fe146d7731be5        لقد دخل الذكاء الاصطناعي في كثير من جوانب حياتنا، والتصميم ليس استثناءً بأي حال من الأحوال. وفي العامين الماضيين، شهدنا تطوراً سريعاً في هذا القطاع. ظهرت كثير من الحلول الجديدة، مما أدى إلى إغراق سوق البرمجيات وإتاحة الفرص للمصممين لتغيير طريقة إبداعهم وتعاونهم. أذ نقدم في هذه المقالة نظرة عامة على الاستخدام الحالي للذكاء الاصطناعي في التصميم الجرافيكي، ونناقش الدور الذي قد يلعبه في عملية التصميم. فقد كان الهدف الأساسي لأدوات الذكاء الاصطناعي في التصميم الجرافيكي هو التحسين والسرعة - ليحل محل المصممين في القيام بمهام متكررة أو تحليل الكم الهائل من بيانات المستخدم لإنشاء حلول أفضل. واليوم لا يقتصر دور الذكاء الاصطناعي على تسريع العمليات فحسب، مما يسمح للمصممين بالتركيز على الجزء الإبداعي من عملهم، بل يقوم أيضًا بإنشاء تصميمات من الصفر من خلال متابعة مدخلات المستخدمين. دُمِج الذكاء الاصطناعي اليوم في مجموعة متنوعة من الاقتصادات، وصناعة التصميم ليست استثناءً: حيث يتم تطبيق الذكاء الاصطناعي على نحو متزايد في تطوير منتجات وخدمات التصميم. ومع ذلك، مع تحول الاختراقات التكنولوجية بسرعة الحدود بين مهام العمل التي يؤديها البشر، وتلك التي تؤديها الآلات والخوارزميات، تشهد أسواق العمل العالمية تحولات كبيرة. وهذا يثير السؤال: كيف تؤثر هذه التغييرات على عمل المصممين، وستستمر في التأثير فيه في المستقبل؟ ما هي مجموعات المهارات اللازمة للمصممين لبدء أو مواصلة العمل في هذه الصناعة؟ تهدف المقالة إلى إجراء تحليل تلوي، يلخص البحث حول تأثير الذكاء الاصطناعي على النشاط المهني للمصمم واختبار قدرات ونتائج حلول التصميم القائمة على الذكاء الاصطناعي. طرق البحث – النظرية – البحث وتحليل الأدبيات وموارد الإنترنت؛ التجريبية – دراسة حالة لتحليل إمكانيات ونتائج حلول التصميم القائمة على الذكاء الاصطناعيAl-Academy280Al-Academy['Esam Ibrahim Mohammed Al-Kubisy']2024-05-15T00:00:00https://www.semanticscholar.org/paper/e00e6f87920cb447b5589c846a2fe146d7731be5 4e370d091c3e2a0ab491ba72c1e0ea23b2fc0223To evaluate the accuracy of AI chatbots in staging pressure injuries according to the National Pressure Injury Advisory Panel (NPIAP) Staging through clinical image interpretation, a cross-sectional design was conducted to assess five leading publicly available AI chatbots. As a result, three chatbots were unable to interpret the clinical images, whereas GPT-4 Turbo achieved a high accuracy rate (83.0%) in staging pressure injuries, notably outperforming BingAI Creative mode (24.0%) with statistical significance (p < 0.001). GPT-4 Turbo accurately identified Stages 1 (p < 0.001), 3 (p = 0.001), and 4 (p < 0.001) pressure injuries, and suspected deep tissue injuries (p < 0.001), while BingAI demonstrated significantly lower accuracy across all stages. The findings highlight the potential of AI chatbots, especially GPT-4 Turbo, in accurately diagnosing images and aiding the subsequent management of pressure injuries.Wound Repair and Regeneration50GPT-4 Turbo achieved a high accuracy rate (83.0%) in staging pressure injuries, notably outperforming BingAI Creative mode (24.0%) with statistical significance with statistical significance, and highlights the potential of AI chatbots, especially GPT-4 Turbo, in accurately diagnosing images and aiding the subsequent management of pressure injuries.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society['Makoto Shiraishi', 'Koji Kanayama', 'Daichi Kurita', 'Yuta Moriwaki', 'Mutsumi Okazaki']2024-05-15T00:00:00https://www.semanticscholar.org/paper/4e370d091c3e2a0ab491ba72c1e0ea23b2fc0223 367eacf7977567bccc210f516fd1c04995641c22Cardiovascular and Interventional Radiology30Cardiovascular and interventional radiology['E. Barabino', 'Michele Tosques', 'Giuseppe Cittadini']2024-05-15T00:00:00https://www.semanticscholar.org/paper/367eacf7977567bccc210f516fd1c04995641c22 b21713ed44baad01837763999998a443017fb51aEnterprise Information Systems420Enterprise Information Systems['Dervis Ozay', 'Mohammad Jahanbakht', 'Atefeh Shoomal', 'Shouyi Wang']2024-05-15T00:00:00https://www.semanticscholar.org/paper/b21713ed44baad01837763999998a443017fb51a ae528580427759a0cfce23e67bb2e4ab253e9801International Journal of Research Studies in Education640International Journal of Research Studies in Education['Lorna Uden', 'G. S. Ching']2024-05-15T00:00:00https://www.semanticscholar.org/paper/ae528580427759a0cfce23e67bb2e4ab253e9801 d9195c8c3daea2288262fde02321afd61b7f7b13International Journal of Research Studies in Education170International Journal of Research Studies in Education['Andrea P Adigue']2024-05-15T00:00:00https://www.semanticscholar.org/paper/d9195c8c3daea2288262fde02321afd61b7f7b13 16b87317ecd3d2e2fb7110e6acbaba89a42144cdAnaesthesia180Anaesthesia['A. Shah', 'P. Dhiman']2024-05-15T00:00:00https://www.semanticscholar.org/paper/16b87317ecd3d2e2fb7110e6acbaba89a42144cd 07d237a8d8a2fffb731956d1c66754fadb12101bJournal of Hospital Medicine300Journal of hospital medicine['Stephen P Ma', 'N. Rohatgi', 'Jonathan H Chen']2024-05-15T00:00:00https://www.semanticscholar.org/paper/07d237a8d8a2fffb731956d1c66754fadb12101b 18040fd517628d28f0e3b348e6bbca3a1d6512b2International Journal of Lifelong Education150International Journal of Lifelong Education['Anke Grotlüschen', 'Gregor Dutz', 'Kristin Skowranek']2024-05-15T00:00:00https://www.semanticscholar.org/paper/18040fd517628d28f0e3b348e6bbca3a1d6512b2 8a899db7a5d78bc9c0abdefe74f0b2a0aedea619Journal AWWA00Journal AWWA['Allan Luk']2024-05-15T00:00:00https://www.semanticscholar.org/paper/8a899db7a5d78bc9c0abdefe74f0b2a0aedea619 48edd8b2c63999baba1b3635b3371c1393df99dd Recent robotics and artificial intelligence advancements have exacerbated fears of technology-driven unemployment and inequality. However, the relationship between automation risks and regulatory policy support remains inconclusive. Moreover, the role of institutional safety net in shaping this connection, and factors influencing preference shifts regarding automation, remain understudied. This study conducts an online survey experiment in the UK and Sweden to address these gaps. First, we find subjective concern, and occupational risks combined with perceived weaker labor market safeguards, lead to calls for automation restriction and job loss compensation. These trends are particularly pronounced in the UK, where institutional protection for workers is less robust. Second, people support accelerating technology-driven change when they see its benefits shared widely, but this shift is mainly observed among individuals relatively safer from automation risks. Our findings suggest strengthening the institutional safety net and envisioning equitable benefit-sharing are crucial for moderating public anxiety toward technology-driven economic change.Socio-Economic Review630The findings suggest strengthening the institutional safety net and envisioning equitable benefit-sharing are crucial for moderating public anxiety toward technology-driven economic change.Socio-Economic Review['Jaewook Lee']2024-05-15T00:00:00https://www.semanticscholar.org/paper/48edd8b2c63999baba1b3635b3371c1393df99dd 307ff4e6803a859e7dd5571576ca5a973fb8c5bcThe article discusses the legal and economic aspects of the development of the digital economy, highlighting the main elements of the digital economy, which become fundamental in the presence of digital platforms and the digital environment. The active implementation of information and communication technologies creates the basis for digital platforms for the introduction of new technologies and information relations based on the use of artificial intelligence, global industrial Internet networks of things and services, and big data technology. The development of services for the provision of online services, the development of online stores in new forms, the improvement of information sites, online communities and other forms of digital transformation of the economy, make it possible to extract more and more financial income by processing and providing more information through the digitalization of goods and services produced. The experience of introducing the digital agenda in the EAEU, including in trade relations, was studied, directions for digital transformation in the formation and development of the digital economy of the EAEU, aimed at creating a common information environment, including in the Internet space, were noted and proposed.Bulletin of Science and Practice10Bulletin of Science and Practice['N. Semenov', 'S. Semenov']2024-05-15T00:00:00https://www.semanticscholar.org/paper/307ff4e6803a859e7dd5571576ca5a973fb8c5bc d2fc98c85ba3adb7e3a67dd173cc572db9b572cfCloud automation is the key to realization a fully-optimized performance of modern cloud platforms while cloud resources utilization. Resource allocation efficiency is valuable. We are however faced with increasing pressure for computational resources. The Long Short-Term Memory (LSTM) algorithms have found a great use case in the dynamic resource allocation problem when the problem is solved by the proactive provisioning of resources based on historical usage patterns taking advantage of recurrent neural networks. Furthermore, the concern over quality-of-service delivery (QoS) and energy efficiency is now almost as challenging for cloud providers when implementing the utilization of cloud resources, especially in a dynamic setting. Deep Reinforcement Learning (DRL) allows to pursue that end by developing agents, which might guide the work of optimizing resource allocation and reducing energy expenses at the same time. It improves the result and adaptability of the applications running on clouds. Artificial intelligence, in its diverse form, for the instance machine learning and optimization algorithms, brings in a great influence in the areas of cloud operations, resource management, and security. Furthermore, the 3rd generation of FPGAs (Reconfigurable Digital Computing-In-Memory or ReDCIM processors) and the bitwise parallelism via-memory core multipliers also improve the efficiency of computing in cloud environments. Adopting these inputting methodologies is a consequence of cloud systems achieving top performance, high scalability, and low costing. Keywords - Cloud Automation, Long Short-Term Memory (LSTM) algorithms, Deep Reinforcement Learning (DRL), Reconfigurable Digital Computing-In-Memory (ReDCIM) processors, Resource sAllocation, Energy Efficiency.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Deep Reinforcement Learning (DRL) allows to pursue that end by developing agents, which might guide the work of optimizing resource allocation and reducing energy expenses at the same time, as a consequence of cloud systems achieving top performance, high scalability, and low costing.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Anurag J']2024-05-15T00:00:00https://www.semanticscholar.org/paper/d2fc98c85ba3adb7e3a67dd173cc572db9b572cf 56416ed64a8c3fdf9307eff3b1651f1dfa0e5832Sustaining a healthy lifestyle has grown more crucial in the fast-paced world of today. But not everybody has access to a personal trainer for fitness. This is where technology can become extremely important in increasing the accessibility and appeal of fitness. A project called "Fit- Sense AI” is an AI-powered fitness Trainer that uses OpenCV for computer vision, MediaPipe for position estimation, and artificial intelligence (AI) to build a virtual fitness trainer that you can use on your computer. The goal of this project is to offer people a customized, interactive fitness experience. It can follow the user's motions in real time and provide feedback and instructions for different exercises by using computer vision techniques. The AI fitness trainer will assess your technique while you're doing strength training or yoga poses and provide suggestions for improvementInternational Journal of Advanced Research in Science, Communication and Technology20An AI-powered fitness Trainer that uses OpenCV for computer vision, MediaPipe for position estimation, and artificial intelligence (AI) to build a virtual fitness trainer that you can use on your computer.International Journal of Advanced Research in Science, Communication and Technology['Dr. Sharon Christa', 'Jayant Sanjay Ghadge', 'Jay Sanjay Kothale', 'Prathmesh Prashant Buradkar', 'Prajyot Santosh Chavan']2024-05-15T00:00:00https://www.semanticscholar.org/paper/56416ed64a8c3fdf9307eff3b1651f1dfa0e5832 b53edda4a3d8870e7308cca2bb5e9ce813b62a97The past decades have seen unbridled growth in the economic, social and political influence of large technology corporations (Big Tech) in the United States. The rising popularity of Generative Artificial Intelligence (GenAI) is likely to further consolidate the power of these companies. The rapid expansion of Big Tech in various domains has triggered a wide range of economic, ethical, and political concerns. However, the US Government is also engaged in a growing technology and AI race with China. As a result, the US government now faces the challenges of balancing the external goal of winning the AI race through close collaboration with the Big Tech and the internal objective of regulating the Big Tech. In this paper, we argue that this intersection of interest has been the primary motivator of US policy on the governance of Big Tech. By exploring the evolution of AI policy in the US, we highlight the role internal and external pressures have played in its approach to AI governance.Digital Government: Research and Practice150By exploring the evolution of AI policy in the US, this paper highlights the role internal and external pressures have played in the role internal and external pressures have played in its approach to AI governance.Digital Government: Research and Practice['Hongzhou Zhang', 'Shaleen Khanal', 'Araz Taeihagh']2024-05-14T00:00:00https://www.semanticscholar.org/paper/b53edda4a3d8870e7308cca2bb5e9ce813b62a97 a2fe1cb6c4ac946953996d732cda13dd8a850c9cThe pre-eminence of the European Union (EU) General Data Protection Regulation (GDPR) in regulating the business of data collection, processing and transfer cannot be understated. It has come to serve as the model for laws in several non-EU jurisdictions who share in the EU’s concerns about citizens’ data being harnessed by Big Tech in particular. This article explores the GDPR’s outsized impact in the sphere of data regulation, the conflict between the contrasting models of regulation adopted by the EU and the United States and comments on the possibility of the AI regulations becoming what the GDPR is for data. It further delves into the possibility of this approach being followed by countries like India, where government efforts to integrate AI-based innovation and entrepreneurship have led to the possibility of newer regulatory approaches being adopted.Journal of Development Policy and Practice110The GDPR’s outsized impact in the sphere of data regulation, the conflict between the contrasting models of regulation adopted by the EU and the United States and the possibility of the AI regulations becoming what the GDPR is for data are explored.Journal of Development Policy and Practice['Agnidipto Tarafder', 'Aniruddh Vadlamani']2024-05-14T00:00:00https://www.semanticscholar.org/paper/a2fe1cb6c4ac946953996d732cda13dd8a850c9c 80fbe6ce7155dfd61b0333129c90793259f9001fApplications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science&medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source generative AI. Using a three-stage framework for Gen AI development (near, mid and long-term), we analyze the risks and opportunities of open-source generative AI models with similar capabilities to the ones currently available (near to mid-term) and with greater capabilities (long-term). We argue that, overall, the benefits of open-source Gen AI outweigh its risks. As such, we encourage the open sourcing of models, training and evaluation data, and provide a set of recommendations and best practices for managing risks associated with open-source generative AI.2030It is argued that the benefits of open-source Gen AI outweighs its risks, and the open sourcing of models, training and evaluation data is encouraged, and a set of recommendations and best practices for managing risks associated with open-source generative AI are provided.['Francisco Eiras', 'Aleksander Petrov', 'Bertie Vidgen', 'Christian Schroeder', 'Fabio Pizzati', 'Katherine Elkins', 'Supratik Mukhopadhyay', 'Adel Bibi', 'Aaron Purewal', 'Csaba Botos', 'Fabro Steibel', 'Fazel Keshtkar', 'Fazl Barez', 'Genevieve Smith', 'G. Guadagni', 'Jon Chun', 'Jordi Cabot', 'Joseph Marvin Imperial', 'J. A. Nolazco', 'Lori Landay', 'Matthew Jackson', 'Phillip H. S. Torr', 'Trevor Darrell', 'Y. Lee', 'Jakob Foerster']2024-05-14T00:00:00https://www.semanticscholar.org/paper/80fbe6ce7155dfd61b0333129c90793259f9001f 9dc760044af4cb745b582c4ae221b1a69fee6c69The article is devoted to the phenomenon of trust in criminal proceedings. By analyzing the criminal procedure form and its current state, the authors conclude that the problems inherent in it at the present stage in the form of increasing transformation, formalization, overload of rules, conditions and grounds can be overcome by referring to the genetic Russian code, which is based on ethical and moral values, a worthy place among which is occupied by trust. The paper reveals the essence of two areas of trust: vertical trust as an indicator of the legitimacy of government and state institutions and emphasizes that currently the court and the police are institutions with a negative level of trust in Russian society. In relation to horizontal trust as trust in another subject, participant in public relations or to an object, the work analyzes coercive measures, the admissibility of evidence, challenges, as well as the institution of representation.Courier of the Kutafin Moscow State Law University00Courier of Kutafin Moscow State Law University (MSAL))['I. G. Smirnova', 'N. V. Sofijchuk']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9dc760044af4cb745b582c4ae221b1a69fee6c69 b9891a37ebf8b4b9ecd2339da808a51347db2e7aJournal of Environmental Planning and Management330Journal of Environmental Planning and Management['Yilin Zhao', 'Juan Shang', 'Xiaodong Yang', 'Guanqing Shi']2024-05-14T00:00:00https://www.semanticscholar.org/paper/b9891a37ebf8b4b9ecd2339da808a51347db2e7a 579fe3408f2249dcd25da13d13f9cf5f7a9a3a13Discover Education160It is found that acceptance of and attitudes about artificial intelligence vary across academic disciplines, and gender differences in the responses are smaller the closer the student’s major is to informatics (computer science).Discover Education['F. Balabdaoui', 'Nora Dittmann-Domenichini', 'Henry Grosse', 'Claudia Schlienger', 'Gerd Kortemeyer']2024-05-14T00:00:00https://www.semanticscholar.org/paper/579fe3408f2249dcd25da13d13f9cf5f7a9a3a13 e0dcfda16382f90ff5ba073f3b11a2e8deb2b230Since ChatGPT's debut, generative AI technologies have surged in popularity within the AI community. Recognized for their cutting-edge language processing capabilities, these excel in generating human-like conversations, enabling open-ended dialogues with end-users. We consider that the future adoption of generative AI for critical public domain applications transforms the accountability relationship. Previously characterized by the relationship between an actor and a forum, the introduction of generative systems complicates accountability dynamics as the initial interaction shifts from the actor to an advanced generative system. We conceptualise a dual-phase accountability relationship involving the actor, the forum, and the generative AI as a foundational approach to understanding public sector accountability in the context of these technologies. Focusing on integrating generative AI for assisting healthcare triaging, we identify potential challenges introduced for maintaining effective accountability relationships, highlighting concerns that these technologies relegate actors to a secondary phase of accountability and creates a disconnect between government actors and citizens. We suggest recommendations aimed at disentangling the complexities generative systems bring to the accountability relationship. As we speculate on the technologies disruptive impact on accountability, we urge public servants, policymakers, and system designers to deliberate on the potential accountability impact generative systems produce prior to their deployment.Digital Government: Research and Practice330Focusing on integrating generative AI for assisting healthcare triaging, potential challenges introduced for maintaining effective accountability relationships are identified and recommendations aimed at disentangling the complexities generative systems bring to the accountability relationship are suggested.Digital Government: Research and Practice['Marc T.J Elliott', 'D. P.', 'Muiris Maccarthaigh']2024-05-14T00:00:00https://www.semanticscholar.org/paper/e0dcfda16382f90ff5ba073f3b11a2e8deb2b230 fb27412fd4eff203eef3e634fa3ba3eb51882e92The need for privacy in elderly care is crucial, especially where constant monitoring can intrude on personal dignity. This research introduces the development of a unique camera-based monitoring system designed to address the dual objectives of elderly care: privacy and safety. At its core, the system employs an AI-driven technique for real-time subject anonymization. Unlike traditional methods such as pixelization or blurring, our proposed approach effectively removes the subject under monitoring from the scene, replacing them with a two-dimensional avatar. This is achieved through the use of YOLOv8, which facilitates accurate real-time person detection and pose estimation. Furthermore, the proposed system incorporates a fall detection algorithm that utilizes a residual causal convolutional network together with motion features of persons to identify emergency situations and promptly notify caregivers in the event of a fall. The effectiveness of the system is evaluated to emphasize its advanced privacy protection technique and fall detection capabilities using several metrics. This evaluation demonstrates the system’s proficiency in real-world applications and its potential to enhance both safety and privacy in elderly care environments.Applied Sciences640This research introduces the development of a unique camera-based monitoring system designed to address the dual objectives of elderly care: privacy and safety, which employs an AI-driven technique for real-time subject anonymization and a fall detection algorithm that utilizes a residual causal convolutional network.Applied Sciences['C. Wang', 'Fang-Suey Lin']2024-05-14T00:00:00https://www.semanticscholar.org/paper/fb27412fd4eff203eef3e634fa3ba3eb51882e92 7306673752ca7b3f97bf50de9c7724f0249515b4TechTrends260Strategies for using generative AI for improving UDL to benefit ALL learners brainstormed by the teachers are discussed and practical implications for multilingual learners are discussed.TechTrends['A. Evmenova', 'J. Borup', 'Joan Kang Shin']2024-05-14T00:00:00https://www.semanticscholar.org/paper/7306673752ca7b3f97bf50de9c7724f0249515b4 74abf6fda81ce257b44777106328b370cbdbd13aIn the world of fast-paced lifestyles and dynamic technical events, the organization and management of such an event is one of the complex tasks. This project proposes new way in the form of AI- powered event organization and recommendation system which normalizes the task of evet management. This project uses Artificial Intelligence, Machine learning algorithms to recommend the targeted users only for whom we have to recommend the events only. This works by analyzing the uses the previous history of attended events, registered events and also the events which are attended by users with similar interest of the targeted users. It will work the both the content based as well as collaborative filtering for the event recommendation. The project architecture integrates the organization functionalities with the help of finding the targeted uses from the taken interest and also the interest generated due to friends for them according to the system. Through the evaluation process, including user feedback, eighter he likes or dislikes or his friends likes or registered accordingly the events are recommended. In this way the efficiency of the proposed system is enhanced with the help of suitable algorithms for the management of events in this digital era. Keywords: Artificial Intelligence, recommendation, event management, interests, training.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This project proposes new way in the form of AI- powered event organization and recommendation system which normalizes the task of evet management and uses Artificial Intelligence, Machine learning algorithms to recommend the targeted users only for whom the authors have to recommend the events only.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ishwar Sanap,']2024-05-14T00:00:00https://www.semanticscholar.org/paper/74abf6fda81ce257b44777106328b370cbdbd13a 308152c61c50e250abd9da8c15cf8b4db2abe2f4The rapid advancement of Artificial Intelligence (AI) in China has led to the implementation of significant regulatory efforts, including regulations on Recommendation Algorithms, Deep Synthesis, and Generative AI. These regulations aim to address issues, such as discrimination, transparency in content generation, and accuracy in AI outputs. China’s approach to AI regulations is guided by the New Generational AI Development Plan of 2017, emphasizing technology-driven innovation, socialist principles, market dominance, and open-source collaboration. The country’s strategic roadmap focuses on achieving leadership in AI development, aligning with global trends, and fostering innovation through policy reforms and ethical frameworks. In comparison, India’s fragmented frameworks illustrate a lack of an organized governance system. By adopting principles from China’s regulations, such as developing comprehensive legislations, establishing centralized authorities, and mandating transparency and user control measures, India can ensure the responsible advancement of AI technologies and address the emerging ethical, legal, and social challenges.International Journal For Multidisciplinary Research190By adopting principles from China’s regulations, such as developing comprehensive legislations, establishing centralized authorities, and mandating transparency and user control measures, India can ensure the responsible advancement of AI technologies and address the emerging ethical, legal, and social challenges.International Journal For Multidisciplinary Research['Tanima Bhatia']2024-05-14T00:00:00https://www.semanticscholar.org/paper/308152c61c50e250abd9da8c15cf8b4db2abe2f4 a9f8709ebb906c5d7e0e2f9dba6ed3a99279c702Commercial automatic cleaning robots for homes are quite common these days. However, a robot that can clean and mop while being autonomous and remotely controlled is quite expensive. Recently, there has been a growing interest in using artificial intelligence (AI) and the Internet of Things (IoT) to improve various aspects of daily life. One such area is home computerization, particularly in the realm of cleaning tasks. This task proposes the development of a simulated intelligence-based cleaning robot equipped with ultrasonic sensors and controlled by NodeMCU, an IoT platform, and AI calculations. The robot navigates indoor spaces autonomously, detects harmful gases with gas sensors, and cleans using AI and machine learning algorithms. The NodeMCU IoT platform allows users to remotely monitor air quality and control the robot's operations. The combination of gas sensing, AI, machine learning, and Internet of Things capabilities provides a proactive solution for indoor air pollution management, resulting in healthier and safer indoor environments. Additionally, the integration with cloud platforms such as ThingSpeak allows for remote monitoring and predictive maintenance. Following the implementation and testing of this prototype, it was observed that the robot works as programmed, and is equipped with the majority of the functionalities of a household commercial state-of-the-art cleaning robot.International Journal For Multidisciplinary Research200Following the implementation and testing of this prototype, it was observed that the robot works as programmed, and is equipped with the majority of the functionalities of a household commercial state-of-the-art cleaning robot.International Journal For Multidisciplinary Research['Tigulla Hruthika Goud', 'Bodula Sujitha', 'Kanithi Ravi Kishore', 'M. Rudra Kumar']2024-05-14T00:00:00https://www.semanticscholar.org/paper/a9f8709ebb906c5d7e0e2f9dba6ed3a99279c702 25d5f9ef2e479ea3ad7f0a97bb0c9e21f5e88110AI is powerful, but it can make choices that result in objective errors, contextually inappropriate outputs, and disliked options. We need AI-resilient interfaces that help people be resilient to the AI choices that are not right, or not right for them. To support this goal, interfaces need to help users notice and have the context to appropriately judge those AI choices. Existing human-AI interaction guidelines recommend efficient user dismissal, modification, or otherwise efficient recovery from AI choices that a user does not like. However, in order to recover from AI choices, the user must notice them first. This can be difficult. For example, when generating summaries of long documents, a system's exclusion of a detail that is critically important to the user is hard for the user to notice. That detail can be hiding in a wall of text in the original document, and the existence of a summary may tempt the user not to read the original document as carefully. Once noticed, judging AI choices well can also be challenging. The interface may provide very little information that contextualizes the choices, and the user may fall back on assumptions when deciding whether to dismiss, modify, or otherwise recover from an AI choice. Building on prior work, this paper defines key aspects of AI-resilient interfaces, illustrated with examples. Designing interfaces for increased AI-resilience of users will improve AI safety, usability, and utility. This is especially critical where AI-powered systems are used for context- and preference-dominated open-ended AI-assisted tasks, like ideating, summarizing, searching, sensemaking, and the reading and writing of text or code.280Key aspects of AI-resilient interfaces are defined, which will improve AI safety, usability, and utility and help people be resilient to the AI choices that are not right, or not right for them.['Elena L. Glassman', 'Ziwei Gu', 'Jonathan K. Kummerfeld']2024-05-14T00:00:00https://www.semanticscholar.org/paper/25d5f9ef2e479ea3ad7f0a97bb0c9e21f5e88110 9c02d6444a08c005026503a68f87df22905f9a05npj Digital Medicine230An open source, bias-corrected external accuracy estimate, PEst, is proposed that better estimates external accuracy to within 4% on average by measuring and calibrating for DAB-induced shortcut learning.NPJ Digital Medicine['Cathy Ong Ly', 'Balagopal Unnikrishnan', 'Tony Tadic', 'Tirth Patel', 'Joe Duhamel', 'Sonja Kandel', 'Yasbanoo Moayedi', 'Michael Brudno', 'Andrew J Hope', 'Heather Ross', 'Chris McIntosh']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9c02d6444a08c005026503a68f87df22905f9a05 2c69be5dc6e87da3e51093d9a836daa1b68c8f60Artificial Intelligence is one of the current generation’s inventions that has been widely used. It has helped make lives easier, especially regarding appliances and business, and it has also altered various industries, including the arts. However, this development has sparked different perspectives among artists and non-artists. This study aims to evaluate how students, both artists, and non-artists, perceive the differences between real art and artificial intelligence-generated art. For this study, a total of fifty (50) students will be gathered, consisting of twenty-five (25) non-artist students from different courses and twenty-five (25) artist students from the IARFA Institute of Far Eastern University. The participants will receive the questionnaire via Messenger. The results of this study showed that the participants could differentiate actual art from AI-generated art based on their knowledge when evaluating artworks. Additionally, it demonstrates that artist students showed more confidence in determining actual artworks from AI-generated artworks due to their knowledge. While non-artists remain skeptical in determining artworks as they base their perception on how they see art.International Journal of Current Science Research and Review190The results of this study showed that the participants could differentiate actual art from AI-generated art based on their knowledge when evaluating artworks, and showed that artist students showed more confidence in determining actual artworks from AI-generated artworks due to their knowledge.International Journal of Current Science Research and Review['Jazmine Kelly R. Ayuman', 'Marianne C. Lacorte', 'Mc Rollyn D. Vallespin']2024-05-14T00:00:00https://www.semanticscholar.org/paper/2c69be5dc6e87da3e51093d9a836daa1b68c8f60 cd6422baf28d7849c695c6cd290561be8c4bbfd1This paper explores the role of Artificial Intelligence (AI) in language learning, examining its impact on language skills. The study synthesises research findings from diverse sources, including academic journals, conference proceedings, and scholarly books, to provide a comprehensive overview of the current state of AI integration in language learning. Key themes addressed include the effectiveness of AI-driven language learning platforms, the role of AI in learning experiences, the potential of AI technologies in language learning. Additionally, the paper discusses challenges and future directions in the field, offering insights for researchers, educators, and practitioners interested in harnessing the potential of AI to enhance language learning outcomes.International Journal of Educational Sciences and Arts00The study synthesises research findings from diverse sources to provide a comprehensive overview of the current state of AI integration in language learning, offering insights for researchers, educators, and practitioners interested in harnessing the potential of AI to enhance language learning outcomes.International Journal of Educational Sciences and Arts['Omar Alsaif']2024-05-14T00:00:00https://www.semanticscholar.org/paper/cd6422baf28d7849c695c6cd290561be8c4bbfd1 449b5ac5ad668b2f49f803e7b4a40bfebf43286aThis review delves into the dynamic intersection of artificial intelligence (AI) and the pharmaceutical industry, exploring a wide spectrum of clinical and commercial applications, challenges and risks, potential solutions, and future outlooks as these domains converge. With the rapid advancement of AI, this review addresses the profound implications of AI in the life sciences sector, emphasizing its potential to revolutionize drug discovery, clinical trials, personalized medicine, pharmacovigilance, sales, and marketing. While lauding the paradigm-shifting prospects, this paper confronts the ethical, privacy, and bias risks entwined with AI development and deployment. Forward-looking solutions, including fortified data governance frameworks, transparent AI algorithms, and interdisciplinary alliances, stand as bulwarks against these impediments. Furthermore, it considers the possibilities afforded to AI by emergent technologies, such as quantum cloud computing and low-code solutions. In conclusion, this review envisions a future where AI, in collaboration with innovative technologies, reshapes the pharmaceutical landscape. By promoting informed discussions and collaboration, this review seeks to empower the industry to harness the transformative potential of AI in an ethical manner.Artificial Intelligence in Health00This review addresses the profound implications of AI in the life sciences sector, emphasizing its potential to revolutionize drug discovery, clinical trials, personalized medicine, pharmacovigilance, sales, and marketing and considers the possibilities afforded to AI by emergent technologies, such as quantum cloud computing and low-code solutions.Artificial Intelligence in Health['Harjeevan Singh Kang']2024-05-14T00:00:00https://www.semanticscholar.org/paper/449b5ac5ad668b2f49f803e7b4a40bfebf43286a cd47990716fa0215f40fc219b485d81e879df87cThe next generation of AI systems requires strong safety guarantees. This report looks at the software implementation of neural networks and related memory safety properties, including NULL pointer deference, out-of-bound access, double-free, and memory leaks. Our goal is to detect these vulnerabilities, and automatically repair them with the help of large language models. To this end, we first expand the size of NeuroCodeBench, an existing dataset of neural network code, to about 81k programs via an automated process of program mutation. Then, we verify the memory safety of the mutated neural network implementations with ESBMC, a state-of-the-art software verifier. Whenever ESBMC spots a vulnerability, we invoke a large language model to repair the source code. For the latest task, we compare the performance of various state-of-the-art prompt engineering techniques, and an iterative approach that repeatedly calls the large language model.350This report looks at the software implementation of neural networks and related memory safety properties, including NULL pointer deference, out-of-bound access, double-free, and memory leaks, and compares the performance of various state-of-the-art prompt engineering techniques, and an iterative approach that repeatedly calls the large language model.['Yiannis Charalambous', 'Edoardo Manino', 'Lucas C. Cordeiro']2024-05-14T00:00:00https://www.semanticscholar.org/paper/cd47990716fa0215f40fc219b485d81e879df87c cfef633f7266a8ba0fd9a6f823af2d604954a549This research aims to develop a prototype of a smart assistant system for independent learning activities based on Artificial Intelligence. Web-based instructional design (WBID) approach is used in research to develop intelligent assistant systems. Artificial Intelligence-Learning Analytics helps students make important decisions and creates an effective and efficient learning environment while still building learning independence. The method used is a research and development (R&D) approach. The findings of this research are an intelligent assistant system in independent learning activities assisted by artificial intelligence in the form of the AILA ChatBot application. In the end, this research concludes that the ChatBOT application developed in this research is used to provide recommendations for using AI applications to students, which are then implemented and integrated into SPOC/MOOC.Psychology, Evaluation, and Technology in Educational Research00The ChatBOT application developed in this research is used to provide recommendations for using AI applications to students, which are then implemented and integrated into SPOC/MOOC.Psychology, Evaluation, and Technology in Educational Research['Henry Praherdhiono', 'Jamaludin Jamaludin', 'Citra Kurniawan', 'Afriani Afriani', 'Bachriah Fatwa Dhini', 'Alim Sumarno', 'Andi Kristanto', 'Aryo Pinandito']2024-05-14T00:00:00https://www.semanticscholar.org/paper/cfef633f7266a8ba0fd9a6f823af2d604954a549 87ca95589b53754f27671531d7bc255177232eb9The agricultural industry is witnessing a significant shift due to the emergence of Artificial Intelligence driven Internet of Things (AIIoT), which is providing farmers with unparalleled automation capabilities and insights. The goal of this paper is to present a thorough review of AIIoT in smart agriculture, emphasising its uses, advantages, and consequences for decision-making. Smart agriculture decision-making is a game-changing technology that gives farmers the ability to maximise their operations and make well-informed judgements. Through the utilisation of advanced analytics, real-time data, and decision support systems, farmers may optimise crop yields, minimise expenses, and minimise risks. The agricultural industry will become more sustainable and productive as a result of farmers' increased ability to make decisions as the field of smart agriculture develops. The application of AI and IoT in smart agriculture is a game-changing technology that might completely alter how we grow and prepare food. Farmers can increase crop yields and quality, streamline operations, and contribute to a more effective and sustainable food supply chain by utilising AI and IoT. The advantages of AIIoT in smart agriculture are evident, despite the fact that there are still certain obstacles to be addressed, and its use is anticipated to increase in the years to come.International Journal of Advanced Research in Science, Communication and Technology150A thorough review of AIIoT in smart agriculture is presented, emphasising its uses, advantages, and consequences for decision-making, and its use is anticipated to increase in the years to come.International Journal of Advanced Research in Science, Communication and Technology['Arati Amol Kale', 'Swati Arvind Ghadge', 'Shefali Ajay Gaddam']2024-05-14T00:00:00https://www.semanticscholar.org/paper/87ca95589b53754f27671531d7bc255177232eb9 1b9e629acb96c288e9a785c0f756b83d13f03ffeThe Journal of craniofacial surgery (Print)00The Journal of craniofacial surgery[]2024-05-14T00:00:00https://www.semanticscholar.org/paper/1b9e629acb96c288e9a785c0f756b83d13f03ffe ab95933a2e7dc3d9d6942a1e1220c5c7fa57dd60Nature00Nature['Matthew Hutson']2024-05-14T00:00:00https://www.semanticscholar.org/paper/ab95933a2e7dc3d9d6942a1e1220c5c7fa57dd60 9f26adeca737f3655f2630f9b28126efe3044cf7Large-scale Vision-Language Models (VLMs) have demonstrated exceptional performance in natural vision tasks, motivating researchers across domains to explore domain-specific VLMs. However, the construction of powerful domain-specific VLMs demands vast amounts of annotated data, substantial electrical energy, and computing resources, primarily accessible to industry, yet hindering VLM research in academia. To address this challenge and foster sustainable and equitable VLM research, we present the Generalized Domain Prompt Learning (GDPL) framework. GDPL facilitates the transfer of VLMs' robust recognition capabilities from natural vision to specialized domains, without the need for extensive data or resources. By leveraging small-scale domain-specific foundation models and minimal prompt samples, GDPL empowers the language branch with domain knowledge through quaternion networks, uncovering cross-modal relationships between domain-specific vision features and natural vision-based contextual embeddings. Simultaneously, GDPL guides the vision branch into specific domains through hierarchical propagation of generated vision prompt features, grounded in well-matched vision-language relations. Furthermore, to fully harness the domain adaptation potential of VLMs, we introduce a novel low-rank adaptation approach. Extensive experiments across diverse domains like remote sensing, medical imaging, geology, Synthetic Aperture Radar, and fluid dynamics, validate the efficacy of GDPL, demonstrating its ability to achieve state-of-the-art domain recognition performance in a prompt learning paradigm. Our framework paves the way for sustainable and inclusive VLM research, transcending the barriers between academia and industry.450This framework facilitates the transfer of VLMs' robust recognition capabilities from natural vision to specialized domains, without the need for extensive data or resources, and introduces a novel low-rank adaptation approach.['Qinglong Cao', 'Yuntian Chen', 'Lu Lu', 'Hao Sun', 'Zhenzhong Zeng', 'Xiaokang Yang', 'Dong-juan Zhang']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9f26adeca737f3655f2630f9b28126efe3044cf7 b4caa859f3c36422f51a30a2319330ead1695d0bStudents learning to code for the first time face several challenges. For instance, they struggle to interpret the error messages they see when their code fails to run. Since teaching standards in coding are focused primarily on whether a student’s code runs successfully, students are often penalized in their grades not for the effort they put into their work but for the code they turn in. Automated grading tools deployed in educational institutions, unfortunately, make the issue worse. In this work, we discuss a novel approach to motivate students to learn coding by shifting the focus of both educators and students from outcomes to the process behind the outcomes. In this study, educators and students are introduced to a new tool, Process Feedback (PF), which shows each student’s work as a visual journey. After using both qualitative and quantitative data collection and analysis, the paper discusses how using PF can help students code insightfully and help educators grade quickly and thoroughly. Our findings reveal that tools such as PF can be beneficial in addressing challenges in learning to code and encouraging students to be original in the age of AI. The results also imply that the incorporation of process-oriented learning tools can make coding education more effective and that the process-centric approach can play a key role in the development and effectiveness of educational tools for students.  Edukasiana: Jurnal Inovasi Pendidikan380This work discusses a novel approach to motivate students to learn coding by shifting the focus of both educators and students from outcomes to the process behind the outcomes, and introduces a new tool, Process Feedback, which shows each student’s work as a visual journey.Edukasiana: Jurnal Inovasi Pendidikan['Kate Arendes', 'Shea Kerkhoff', 'Badri Adhikari']2024-05-14T00:00:00https://www.semanticscholar.org/paper/b4caa859f3c36422f51a30a2319330ead1695d0b e08ed38d0bfe75792f163390146351c18cfe2c14This project introduces a smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture. The system employs a series of IoT sensors distributed across the field to consistently monitor key environmental factors such as soil moisture levels, temperature, humidity, and others. The collected data is analysed using machine learning Cat Boosting algorithm. This algorithm analyze the data to determine the optimal irrigation schedule based on crop water requirements, soil conditions, weather forecasts, and historical data. The system controls irrigation equipment such as pumps, valves, and sprinklers to deliver precise amounts of water to crops at the right time. Continuous feedback from the system allows for refinement of irrigation schedules, leading to improved water conservation, increased crop yield, and cost savings for farmers. This project discusses the benefits of such a system, including water conservation, increased crop yield, cost savings, environmental sustainability, and remote monitoring and control capabilities. Overall, the integration of IoT and machine learning technologies offers a powerful solution for sustainable agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity. Keywords— Smart irrigation system, Internet of Things(IOT)technology, Water conservation, Environmental parameters, Soil moisture monitoring, Increased crop yield, Machine learning algorithmsINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity is introduced.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Bhuvana T V']2024-05-14T00:00:00https://www.semanticscholar.org/paper/e08ed38d0bfe75792f163390146351c18cfe2c14 5d3b05bee5a1b17bc4c13cdb6264f48b10eedd75Behaviour &amp; Information Technology900Behaviour &amp; Information Technology['Eva Durall Gazulla', 'Noora Hirvonen', 'Sumita Sharma', 'Heidi Hartikainen', 'Ville Jylhä', 'N. Iivari', 'Marianne Kinnula', 'Aizhan Baizhanova']2024-05-14T00:00:00https://www.semanticscholar.org/paper/5d3b05bee5a1b17bc4c13cdb6264f48b10eedd75 9e51103c1447fbc0a0959ba0180e93c558ab8c97Artificial intelligence increasingly integrates our daily lives with the creation and analysis of intelligent software and hardware, called intelligent agents. Intelligent agents can do a variety of tasks ranging from labor work to sophisticated operations. A Chatbot is a typical example of an artificial intelligence system. For developing artificial intelligence Chatbot, we have implemented encoder-decoder attention mechanism architecture memory cells, TFIDF (term frequency-inverse document frequency) algorithm and LLM (Large Language Module). To conduct this research, mixed methods research could involve combining both qualitative and quantitative methods to provide a more comprehensive understanding of the research topic. Survey utilized as the main methods for collecting the primary data and reviewed the existing literature for collecting the secondary data in this research. The data was collected from 18 participants, including medical doctors, patients, lecturers and university students in Afghanistan. Additionally, secondary data was obtained through a review of literature from other countries that have faced similar situations. Moreover, the analysis reveals a widespread belief among respondents that the implementation of AI based Chatbot for Afghanistan healthcare service in mental health disorder will contribute to reduce illness in Afghanistan.International journal of multidisciplinary research and analysis90The analysis reveals a widespread belief among respondents that the implementation of AI based Chatbot for Afghanistan healthcare service in mental health disorder will contribute to reduce illness in Afghanistan.INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS['Esmatullah Sabet', 'Sayed Shafiullah Sadat', 'Hasib Ahmad Khaliqi']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9e51103c1447fbc0a0959ba0180e93c558ab8c97 620e8d904af2eff9c1698e29d05b8d64901cc8abAir-writing refers to virtually writing linguistic characters through hand gestures in three dimensional space with six degrees of freedom. In this paper a generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed. Gestures are performed using a marker of fixed color in front of a generic video camera followed by color based segmentation to identify the marker and track the trajectory of marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies greatly on the illumi nation condition due to color based segmentation. In a less fluctuating illumination condition the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework achieved 97.7recognition rate in person inde pendent evaluation over English, Bengali and Devanagari numerals, respectively. Object tracking is considered as an important task within the field of Computer Vision. The invention of faster computers, availability of inexpensive and good quality video cameras and demands of automated video analysis has given popularity to object tracking techniques. Generally, video analysis procedure has three major steps: firstly, detecting of the object, secondly tracking its movement from frame to frame and lastly analysing the behaviour of that object. For object tracking, four different issues are taken into account; selection of suitable object representation, feature selection for tracking, object detection and object tracking. In real world, Object tracking algorithms are the primarily part of different applications such as: automatic surveillance, video indexing and vehicle navigation etc. The generated text can also be used for various purposes, such as sending messages, emails, etc. It will be a powerful means of communication for the deaf. It is an effective communication method that reduces mobile and laptop usage by eliminating the need to write. Key Words: Air Writing, Character Recognition, Object Detection, Real-Time Gesture Control System, Computer Vision , Hand tracking.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A generic video camera dependent convolutional neural network (CNN) based air-writing framework has been proposed and the proposed framework achieved 97.7 recognition rate in person evaluation over English, Bengali and Devanagari numerals, respectively.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Prof.Shital Patil']2024-05-14T00:00:00https://www.semanticscholar.org/paper/620e8d904af2eff9c1698e29d05b8d64901cc8ab e0fb03c94ff49f2ef90746377da01b7589b49743This paper proposed a straightforward and efficient current control solution for induction machines employing deep symbolic regression (DSR). The proposed DSR-based control design offers a simple yet highly effective approach by creating an optimal control model through training and fitting, resulting in an analytical dynamic numerical expression that characterizes the data. Notably, this approach not only produces an understandable model but also demonstrates the capacity to extrapolate and estimate data points outside its training dataset, showcasing its adaptability and resilience. In contrast to conventional state-of-the-art proportional-integral (PI) current controllers, which heavily rely on specific system models, the proposed DSR-based approach stands out for its model independence. Simulation and experimental tests validate its effectiveness, highlighting its superior extrapolation capabilities compared to conventional methods. These findings pave the way for the integration of deep learning methods in power conversion applications, promising improved performance and adaptability in the control of induction machines. The simulation and experimental test results are provided with a 3.7 kw induction machine to verify the efficacy of the proposed control solution.100The proposed DSR-based control design offers a simple yet highly effective approach by creating an optimal control model through training and fitting, resulting in an analytical dynamic numerical expression that characterizes the data.['M. Usama', 'Yunkyung Hwang', 'Jaehong Kim']2024-05-14T00:00:00https://www.semanticscholar.org/paper/e0fb03c94ff49f2ef90746377da01b7589b49743 b9eaa37036837fba277c40517a2be7671dbfa768In recent years, several industrial solutions for the problem of multi-token code completion have appeared, each making a great advance in the area but mostly focusing on cloud-based runtime and avoiding working on the end user's device. In this work, we describe our approach for building a multi-token code completion feature for the JetBrains' IntelliJ Platform, which we call Full Line Code Completion. The feature suggests only syntactically correct code and works fully locally, i.e., data querying and the generation of suggestions happens on the end user's machine. We share important time and memory-consumption restrictions, as well as design principles that a code completion engine should satisfy. Working entirely on the end user's device, our code completion engine enriches user experience while being not only fast and compact but also secure. We share a number of useful techniques to meet the stated development constraints and also describe offline and online evaluation pipelines that allowed us to make better decisions. Our online evaluation shows that the usage of the tool leads to 1.5 times more code in the IDE being produced by code completion. The described solution was initially started with the help of researchers and was bundled into two JetBrains' IDEs - PyCharm Pro and DataSpell - at the end of 2023, so we believe that this work is useful for bridging academia and industry, providing researchers with the knowledge of what happens when complex research-based solutions are integrated into real products.510This work describes the approach for building a multi-token code completion feature for the JetBrains' IntelliJ Platform, which it is called Full Line Code Completion, and shares a number of useful techniques to meet the stated development constraints and also describes offline and online evaluation pipelines that allowed us to make better decisions.['Anton Semenkin', 'Vitaliy Bibaev', 'Yaroslav Sokolov', 'Kirill Krylov', 'Alexey Kalina', 'Anna Khannanova', 'Danila Savenkov', 'Darya Rovdo', 'Igor Davidenko', 'Kirill Karnaukhov', 'Maxim Vakhrushev', 'Mikhail Kostyukov', 'Mikhail Podvitskii', 'Petr Surkov', 'Yaroslav Golubev', 'Nikita Povarov', 'T. Bryksin']2024-05-14T00:00:00https://www.semanticscholar.org/paper/b9eaa37036837fba277c40517a2be7671dbfa768 bb0cce82286e3dd70b88119a58f3629be59bdc74Nature00Nature['Katrina Krämer']2024-05-14T00:00:00https://www.semanticscholar.org/paper/bb0cce82286e3dd70b88119a58f3629be59bdc74 53a8d1a6e5ef7e8795f143111a48f43172d34534Artificial intelligence (AI) is reshaping the field of neurology, enhancing diagnosis, treatment, and management of neurological disorders. This article explores AI's role in neurology, highlighting its ability to process vast amounts of data to improve diagnostic accuracy and personalize treatments. AI applications, from neuroimaging to clinical decision support, have shown promising results in enhancing patient care. However, challenges such as data security, ethical concerns, and the need for stringent regulatory frameworks remain significant. The potential of AI in neurology continues to grow, promising revolutionary changes in patient outcomes and healthcare practices, provided these challenges are effectively managed.International Journal of Innovative Science and Research Technology291The potential of AI in neurology continues to grow, promising revolutionary changes in patient outcomes and healthcare practices, provided these challenges are effectively managed.International Journal of Innovative Science and Research Technology (IJISRT)['Taoufik Boubga', 'Amine Bentaher', 'Abdellah Taous', 'Maha Ait Berri', 'Tarik Boulahri']2024-05-14T00:00:00https://www.semanticscholar.org/paper/53a8d1a6e5ef7e8795f143111a48f43172d34534 01b63c95fe93678b45f71c68e1cc3d097c763ec5This research analyses managers’ perceptions of the multiple types of artificial intelligence (AI) required at each stage of the business‐to‐business (B2B) service recovery journey for successful human–AI collaboration in this context. Study 1 is an exploratory study that identifies managers’ perceptions of the main stages of a B2B service recovery journey based on human–AI collaboration and the corresponding roles of the human–AI collaboration at each stage. Study 2 provides an empirical examination of the proposed theoretical framework to identify the specific types of intelligence required by AI to enhance performance in each stage of B2B service recovery, based on managers’ perceptions. Our findings show that the prediction stage benefits from collaborations involving processing‐speed and visual‐spatial AI. The detection stage requires logic‐mathematical, social and processing‐speed AI. The recovery stage requires logic‐mathematical, social, verbal‐linguistic and processing‐speed AI. The post‐recovery stage calls for logic‐mathematical, social, verbal‐linguistic and processing‐speed AI.British Journal of Management500Analysis of managers’ perceptions of the multiple types of artificial intelligence required at each stage of the business‐to‐business (B2B) service recovery journey for successful human–AI collaboration shows that the prediction stage benefits from collaborations involving processing‐speed and visual‐spatial AI.British Journal of Management['Nisreen Ameen', 'Margherita Pagani', 'Eleonora Pantano', 'J. Cheah', 'S. Tarba', 'Senmao Xia']2024-05-14T00:00:00https://www.semanticscholar.org/paper/01b63c95fe93678b45f71c68e1cc3d097c763ec5 9960b5cc9e8f2e7b700bb99741896a74b48880e0Although many scholars have evaluated how Hispanic-serving institutions (HSIs) serve and can better serve Latinx students and their communities, scant research has integrated artificial intelligence (AI) technology within this evaluation of diversity and servingness. With institutions of higher education continuing to explore ways to integrate AI into their everyday operations, it is critical to understand whether HSIs are leveraging AI chatbot technology, given this specific technology’s ubiquity in modern society. This study aimed to evaluate whether HSIs employ AI chatbot technology on their websites and the usability of this technology in accessing admissions information in English and Spanish. As a result, this study analyzes all 558 Hispanic-serving institution (.edu) websites and interacts with embedded AI chatbots to evaluate whether HSIs utilize chatbots and if these chatbots are engineered or staffed to communicate with Spanish-speaking audiences. This study employed a mixed methods approach, utilizing qualitative and quantitative data collection and analysis methods. Given the study’s emphasis on human–computer interaction, the researchers also engaged with interactive research methods to perform artificial intelligence testing. Findings suggest roughly 20% of HSIs employ AI chatbots, but far fewer use bilingual English/Spanish chatbots (12%). However, HSIs are equally as likely to staff English (20%) live agents as Spanish (21%) live agents when users are elevated from AI. Our findings demonstrate the importance of having Spanish and bilingual information embedded into HSIs to truly serve Latinx students and their families (Garcia, 2019). More specifically, Spanish should be incorporated within technological spaces where Latinx students and their communities may seek HSI information to pursue higher education such as how to apply for admission and financial aid. Ultimately, HSIs must consider how technology services like AI chatbots provide resources and answer questions in Spanish to ensure equitable access to information for Latinx students, their families, and their communities. Moreover, HSI leadership must continue to explore how HSIs may be transformed through technological innovation, such as artificial intelligence integration within digital spaces, including the HSI’s website.Teachers College Record280The findings demonstrate the importance of having Spanish and bilingual information embedded into HSIs to truly serve Latinx students and their families and highlight how technology services like AI chatbots provide resources and answer questions in Spanish to ensure equitable access to information for Latinx students, their families, and their communities.Teachers College Record: The Voice of Scholarship in Education['Z. Taylor', 'Guillermo Ortega', 'Susana H. Hernández']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9960b5cc9e8f2e7b700bb99741896a74b48880e0 9e5c5bd09139a3ba45925074c73c86749e7b9a7bnpj Digital Medicine830Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.NPJ Digital Medicine['Maria Paz Salinas', 'Javiera Sepulveda', 'Leonel Hidalgo', 'Dominga Peirano', 'Macarena Morel', 'Pablo Uribe', 'V. Rotemberg', 'Juan Briones', 'Domingo Mery', 'C. Navarrete-Dechent']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9e5c5bd09139a3ba45925074c73c86749e7b9a7b 11abf1c82e0b7ec29e0753b0e67327621e7580c4The present letter to the editor is related to the study with the title “Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models”. Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians’ tasks. The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.World Journal of Gastroenterology60The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.World Journal of Gastroenterology['S. Cocca', 'G. Pontillo', 'Giuseppe Grande', 'Rita Conigliaro']2024-05-14T00:00:00https://www.semanticscholar.org/paper/11abf1c82e0b7ec29e0753b0e67327621e7580c4 ff0a7d6d25c585c114ddc5d924536e81a1970ac4Artificial Intelligence (AI) has emerged as a prominent area of investigation in the field of education. Also, perceptions, challenges and threats of AI among university professors show notable variations. This study explores university professors’ perspectives regarding AI, including their familiarity with AI, its educational impacts, challenges associated with its implementation, and perceived threats. To achieve this, a survey was administered to 65 university professors from diverse Egyptian institutions, both state and private. Subsequent statistical analyses were conducted to treat the collected data. The outcomes of these analyses reveal that university professors possess varying degrees of familiarity with AI. Despite this, they view AI as a valuable educational tool. The study identifies several challenges hindering AI adoption, encompassing difficulties in comprehending and interpreting AI algorithmic outcomes, the intricate autonomy of AI systems, financial implications of implementation, and concerns regarding data privacy. Additionally, the study identifies apprehensions pertaining to AI’’s influence on professors’ skills, potential dehumanization of pedagogy, adverse effects on students, and the potential obsolescence of professors. These findings bear implications for the integration of AI in educational contexts, highlighting the necessity for further exploration in this evolving field.Australian Journal of Applied Linguistics430The study identifies several challenges hindering AI adoption, encompassing difficulties in comprehending and interpreting AI algorithmic outcomes, the intricate autonomy of AI systems, financial implications of implementation, and concerns regarding data privacy.Australian Journal of Applied Linguistics['N. Abdelaal', 'Islam Al Sawy']2024-05-14T00:00:00https://www.semanticscholar.org/paper/ff0a7d6d25c585c114ddc5d924536e81a1970ac4 6c9e74de76d15f7d3ad18eeaa31ebcad31da5751The exponential growth of network intrusions necessitates the development of advanced artificial intelligence (AI) techniques for intrusion detection systems (IDSs). However, the reliance on AI for IDSs presents several challenges, including the performance variability of different AI models and the opacity of their decision-making processes, hindering comprehension by human security analysts. In response, we propose an end-to-end explainable AI (XAI) framework tailored to enhance the interpretability of AI models in network intrusion detection tasks. Our framework commences with benchmarking seven black-box AI models across three real-world network intrusion datasets, each characterized by distinct features and challenges. Subsequently, we leverage various XAI models to generate both local and global explanations, shedding light on the underlying rationale behind the AI models’ decisions. Furthermore, we employ feature extraction techniques to discern crucial model-specific and intrusion-specific features, aiding in understanding the discriminative factors influencing the detection outcomes. Additionally, our framework identifies overlapping and significant features that impact multiple AI models, providing insights into common patterns across different detection approaches. Notably, we demonstrate that the computational overhead incurred by generating XAI explanations is minimal for most AI models, ensuring practical applicability in real-time scenarios. By offering multi-faceted explanations, our framework equips security analysts with actionable insights to make informed decisions for threat detection and mitigation. To facilitate widespread adoption and further research, we have made our source code publicly available, serving as a foundational XAI framework for IDSs within the research community.Applied Sciences510This framework leverages various XAI models to generate both local and global explanations, shedding light on the underlying rationale behind the AI models’ decisions, and demonstrates that the computational overhead incurred by generating XAI explanations is minimal for most AI models, ensuring practical applicability in real-time scenarios.Applied Sciences['Osvaldo Arreche', 'Tanish R. Guntur', 'Mustafa Abdallah']2024-05-14T00:00:00https://www.semanticscholar.org/paper/6c9e74de76d15f7d3ad18eeaa31ebcad31da5751 052bf71ff588f383d532a35a1914d1637118c5f3This research contributes to the overarching objectives of achieving carbon neutrality and enhancing environmental governance by examining the role of artificial intelligence-enhanced multi-energy optimization in rural energy planning within the broader context of a sustainable energy economy. By proposing an innovative planning framework that accounts for geographical and economic disparities across rural regions, this study specifically targets the optimization of energy systems in X County of Yantai City, Y County of Luoyang City, and Z County of Lanzhou City. Furthermore, it establishes a foundation for integrating these localized approaches into broader national carbon-neutral efforts and assessments of green total factor productivity. The comparative analysis of energy demand, conservation, efficiency, and economic metrics among these counties underscores the potential of tailored solutions to significantly advance low-carbon practices in agriculture, urban development, and industry. Additionally, the insights derived from this study offer a deeper understanding of the dynamics between government and enterprise in environmental governance, empirically supporting the Porter hypothesis, which postulates that stringent environmental policies can foster innovation and competitiveness. The rural coal-coupled biomass power generation model introduced in this work represents the convergence of green economy principles and financial systems, serving as a valuable guide for decision-making in decisions aimed at sustainable consumption and production. Moreover, this research underscores the importance of resilient and adaptable energy systems, proposing a pathway for evaluating emission trading markets and promoting sustainable economic recovery strategies that align with environmental sustainability goals.Sustainability490Sustainability['Xiaoyan Peng', 'Xin Guan', 'Yanzhao Zeng', 'Jiali Zhang']2024-05-14T00:00:00https://www.semanticscholar.org/paper/052bf71ff588f383d532a35a1914d1637118c5f3 30c96ec77f014878b3e50ff80b33c13f18f63c0dBACKGROUND Evaluation of standard echocardiographic examination with artificial intelligence may help in the diagnosis of myocardial viability and function recovery after acute coronary syndrome. METHODS Sixty-one consecutive patients with acute coronary syndrome were enrolled in the present study (43 men, mean age 61 ± 9 years). All patients underwent percutaneous coronary intervention (PCI). 533 segments of the heart echo images were used. After 12 ± 1 months of follow-up, patients had an echocardiographic evaluation. After PCI each patient underwent cardiac magnetic resonance (CMR) with late enhancement and low-dose dobutamine echocardiographic examination. For texture analysis, custom software was used (MaZda 5.20, Institute of Electronics).Linear and non-linear (neural network) discriminative analyses were performed to identify the optimal analytic method correlating with CMR regarding the necrosis extent and viability prediction after follow-up. Texture parameters were analyzed using machine learning techniques: Artificial Neural Networks, Namely Multilayer Perceptron, Nonlinear Discriminant Analysis, Support Vector Machine, and Adaboost algorithm. RESULTS The mean concordance between the CMR definition of viability and three classification models in Artificial Neural Networks varied from 42% to 76%. Echo-based detection of non-viable tissue was more sensitive in the segments with the highest relative transmural scar thickness: 51-75% and 76-99%. The best results have been obtained for images with contrast for red and grey components (74% of proper classification). In dobutamine echocardiography, the results of appropriate prediction were 67% for monochromatic images. CONCLUSIONS Detection and semi-quantification of scar transmurality are feasible in echocardiographic images analyzed with artificial intelligence. Selected analytic methods yielded similar accuracy, and contrast enhancement contributed to the prediction accuracy of myocardial viability after myocardial infarction in 12 months of follow-up.Cardiology Journal00Cardiology journal['B. Michalski', 'S. Skonieczka', 'Michał Strzelecki', 'M. Simiera', 'K. Kupczyńska', 'E. Szymczyk', 'P. Wejner-Mik', 'P. Lipiec', 'J. Kasprzak']2024-05-14T00:00:00https://www.semanticscholar.org/paper/30c96ec77f014878b3e50ff80b33c13f18f63c0d ba92f3b97241e195d32861bc6b4c9c9e78f471c8Introduction: In order to have an idea about the use of artificial intelligence in the field of nursing and to determine the developments in applications and research in this field, it is necessary to investigate the characteristics of relevant publications. Aim: The aimed was to examine the characteristics of the current knowledge structure and development process in the field of the use of artificial intelligence in nursing. Methods: In the descriptive and evaluative bibliometric analysis study, data were obtained from Web of Science. All relevant studies conducted between 2004 and 2023 were included in the study. Data analysis was performed using R Biblioshniy software. Two hundred seventy-three studies were included in the study. Results: The most publications (n=86, 31.502%) were made in this field in 2022. The most productive author in the field of nursing and artificial intelligence was Topaz, Maxim. The prominent topics in the studies were "virtual reality, artificial intelligence, nursing, machine learning, simulation, nursing education, education, pain, nursing students, natural language processing, nurses, robotics, deep learning and mental health". Conclusion: There has been a significant increase in the number of studies on the use of artificial intelligence in nursing and this area offers an active field of study for nursing researchers.Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi00There has been a significant increase in the number of studies on the use of artificial intelligence in nursing and this area offers an active field of study for nursing researchers.Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi['F. Azizoğlu', 'Banu Terzi']2024-05-14T00:00:00https://www.semanticscholar.org/paper/ba92f3b97241e195d32861bc6b4c9c9e78f471c8 228cc5c6db97e14fa27d71d9ae4fce1e66cde24bBACKGROUND This study assessed the consistency and accuracy of responses provided by two artificial intelligence (AI) applications, ChatGPT and Google Bard (Gemini), to questions related to dental trauma. MATERIALS AND METHODS Based on the International Association of Dental Traumatology guidelines, 25 dichotomous (yes/no) questions were posed to ChatGPT and Google Bard over 10 days. The responses were recorded and compared with the correct answers. Statistical analyses, including Fleiss kappa, were conducted to determine the agreement and consistency of the responses. RESULTS Analysis of 4500 responses revealed that both applications provided correct answers to 57.5% of the questions. Google Bard demonstrated a moderate level of agreement, with varying rates of incorrect answers and referrals to physicians. CONCLUSIONS Although ChatGPT and Google Bard are potential knowledge resources, their consistency and accuracy in responding to dental trauma queries remain limited. Further research involving specially trained AI models in endodontics is warranted to assess their suitability for clinical use.Dental Traumatology470Although ChatGPT and Google Bard are potential knowledge resources, their consistency and accuracy in responding to dental trauma queries remain limited and further research involving specially trained AI models in endodontics is warranted to assess their suitability for clinical use.Dental traumatology : official publication of International Association for Dental Traumatology['Idil Ozden', 'Merve Gokyar', 'Mustafa Enes Ozden', 'Hesna Sazak Ovecoglu']2024-05-14T00:00:00https://www.semanticscholar.org/paper/228cc5c6db97e14fa27d71d9ae4fce1e66cde24b c7a4405b3ded63a28af1f650aea6af4a0a6aba9bArtificial Intelligence (AI) is revolutionizing the healthcare sector by offering innovative solutions to various challenges. This review explores the applications and benefits of AI in healthcare including AI techniques, machine learning, natural language processing, and computer vision, which are being utilized to enhance medical diagnostics, treatment planning, patient care, and administrative processes. One significant application of AI in healthcare is medical imaging analysis. Machine learning algorithms can analyze medical images such as X-rays, MRIs, and CT scans with high accuracy, aiding in early detection and diagnosis of diseases like cancer and neurological disorders. Additionally, AI-powered predictive analytics enable healthcare providers to forecast patient outcomes and identify individuals at risk of developing certain conditions, allowing for proactive intervention and personalized treatment plans. Furthermore, AI-driven virtual health assistants and chabot’s provide patients with instant access to medical information, advice, and support, improving healthcare accessibility and patient engagement. Natural language processing algorithms enable these systems to understand and respond to patients' queries and concerns effectively. In clinical decision support systems, AI algorithms analyze vast amounts of patient data, including medical records, genetic information, and real-time physiological data, to assist healthcare professionals in making informed decisions about diagnosis and treatment strategies. Moreover, AI-driven robotic surgery systems enhance surgical precision, reduce errors, and shorten recovery times. Despite the numerous benefits, challenges such as data privacy concerns, regulatory compliance, and the need for interdisciplinary collaboration remain. However, with ongoing advancements in AI technology and increased adoption by healthcare organizations, the potential for AI to transform healthcare delivery, improve patient outcomes, and reduce costs is substantial. Collaborative efforts between AI developers, healthcare providers, policymakers, and regulators are essential to harnessing the full potential of AI in healthcare while ensuring ethical and responsible use.International Journal For Multidisciplinary Research00The applications and benefits of AI in healthcare including AI techniques, machine learning, natural language processing, and computer vision, which are being utilized to enhance medical diagnostics, treatment planning, patient care, and administrative processes are explored.International Journal For Multidisciplinary Research['Indranil Chatterjee', 'Rajkumar Ghosh', 'Suchetan Sarkar', 'Krishna Das', 'Monojit Kundu']2024-05-14T00:00:00https://www.semanticscholar.org/paper/c7a4405b3ded63a28af1f650aea6af4a0a6aba9b 3a2d0d8428271c0a353c5f7aa5c15d704e50857cIn recent years, the integration of artificial intelligence (AI) in healthcare has led to numerous groundbreaking applications that have transformed various aspects of medical practice. One of the primary areas where AI has made substantial contributions is in medical imaging analysis. By leveraging machine learning algorithms, AI systems can assist radiologists in interpreting medical images with greater accuracy and efficiency. AI-driven tools can detect subtle abnormalities, aid in early disease detection, and facilitate more precise diagnosis and treatment planning. Predictive analytics is another key application of AI in healthcare, wherein algorithms analyze vast amounts of patient data to forecast potential health outcomes and identify individuals at high risk of developing certain conditions. Additionally, the rise of virtual health assistants powered by AI has revolutionized patient care delivery by providing personalized and accessible healthcare services. These virtual assistants, often in the form of chatbots or voice-enabled interfaces, can interact with patients, answer medical queries, schedule appointments, and even provide medication reminders. Overall, the various applications of AI in healthcare, including medical imaging analysis, predictive analytics, personalized medicine, and virtual health assistants, have demonstrated significant potential in improving diagnostic accuracy, optimizing treatment plans, and enhancing patient care delivery. As these technologies continue to evolve and mature, they have the potential to revolutionize healthcare delivery and contribute to better health outcomes for individuals worldwide. This research paper contributes to the ongoing discourse surrounding the integration of AI in healthcare by providing a comprehensive overview of its advancements, challenges, and ethical considerations.International Research Journal on Advanced Engineering and Management (IRJAEM)140The various applications of AI in healthcare, including medical imaging analysis, predictive analytics, personalized medicine, and virtual health assistants, have demonstrated significant potential in improving diagnostic accuracy, optimizing treatment plans, and enhancing patient care delivery.International Research Journal on Advanced Engineering and Management (IRJAEM)['Ruta Vaidya', 'Snehal H. Kulkarni', 'Trupti Gaikwad', 'Snehal Jadhav']2024-05-14T00:00:00https://www.semanticscholar.org/paper/3a2d0d8428271c0a353c5f7aa5c15d704e50857c dbbc69f4e35a71c0cbf8fa2183fee2ce34dac11fArtificial intelligence represents one of the tools/means of transformation that has affected the contents of contemporary power, and which has begun to spread greatly as a result of technological-informational progress, specifically in the military-security aspect, and since power has undergone a transition from one type to another type that is completely different from the previous one, it is self-evident That this transformation leads to changes in the military-security field of wars and global security, as it represents a four-way equation of composition and interconnection between the variables of power, security, wars, and conflicts. If the tools of power change, it is reflected in the nature and contents of wars. International unity today no longer faces traditional-classical armies. As it was during the past years, today we find that the tools/methods of war have differed, and obviously we cannot ignore the role of artificial intelligence technology in the strategic-military field, which represents one of the tactical and technical strategies that are used whether by the international unit as the main actor in international relations or by Before the new non-state actors in global politics, the tools and types of power have become available and not limited to the state that possesses them, due to the effects of artificial intelligence on all political, economic, military, security and cultural levels. However, I focus in this research on the military-security aspect as it is one of the aspects that... It is of great importance at the present time, especially since sovereignty is linked to this important strategic aspect.International Journal of Educational Sciences and Arts00This research focuses on the military-security aspect as it is one of the aspects that is of great importance at the present time, especially since sovereignty is linked to this important strategic aspect.International Journal of Educational Sciences and Arts['Riyadh Al-Zubaidi', 'Rasha Zeidan']2024-05-14T00:00:00https://www.semanticscholar.org/paper/dbbc69f4e35a71c0cbf8fa2183fee2ce34dac11f aab964a5014c89075c0e410451fdc729669b0f3eThe construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.Journal of Economics Management and Trade00The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.Journal of Economics, Management and Trade['Roozbeh Shakibaei']2024-05-14T00:00:00https://www.semanticscholar.org/paper/aab964a5014c89075c0e410451fdc729669b0f3e 9c2a2473eeff183a10f2cbee97071773a849e537This paper explores the evolving relationship between humans and technology, particularly focusing on the integration of artificial intelligence (AI) into daily life and its implications for human creativity. It discusses the challenges posed by sedentary lifestyles resulting from the automation of physical tasks and emphasizes the importance of maintaining reasoning abilities amidst technological advancements. Categorizing AI into machines that mimic human thought processes and those that operate rationally, the study examines the potential impact of AI on creativity and decision-making. The text highlights the increasing use of AI in wearable technology and textile design, pointing out the shift towards garments capable of autonomous decision-making. However, it raises concerns about the potential consequences of overreliance on AI, including loss of human reasoning abilities and ethical implications such as social manipulation and invasion of privacy. Drawing from various scholarly perspectives, the paper discusses the potential risks and benefits of AI, including its role in augmenting human creativity. While some researchers argue that AI can enhance creativity by providing innovative tools and workflows, others caution against the homogenization of designs and the need for regulatory frameworks to ensure responsible AI use. Ultimately, the paper concludes that AI should be viewed as a tool to augment human creativity rather than replace it entirely. It advocates for collaborative approaches where AI assists human designers in exploring new creative possibilities while upholding ethical standards and individual expression.Journal of Textile Engineering &amp; Fashion Technology60Artificial intelligence should be viewed as a tool to augment human creativity rather than replace it entirely, and advocates for collaborative approaches where AI assists human designers in exploring new creative possibilities while upholding ethical standards and individual expression.Journal of Textile Engineering &amp; Fashion Technology['Züleyha Değirmenci']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9c2a2473eeff183a10f2cbee97071773a849e537 bc31f0b3ea4ac0cebcc2e0314218e36dd65081fc00[]2024-05-14T00:00:00https://www.semanticscholar.org/paper/bc31f0b3ea4ac0cebcc2e0314218e36dd65081fc ae12aa4d73f94bb7c4a9d7e0987282d15152b01cFrontiers in Medicine00Frontiers in Medicine['Jason C. Hsu', 'Christine Y. Lu', 'Min-Huei Hsu']2024-05-14T00:00:00https://www.semanticscholar.org/paper/ae12aa4d73f94bb7c4a9d7e0987282d15152b01c 9e183fd6969ae8cb9f9aefe75cf7c31af24f0098Nature Reviews Bioengineering80Nature Reviews Bioengineering['Y. Mekki', 'S. Zughaier']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9e183fd6969ae8cb9f9aefe75cf7c31af24f0098 58034a324148766e80be0e3330fa33c4715c689cGeological Quarterly00Geological Quarterly['Julia Piotrowska', 'Dominika Dąbrowska']2024-05-14T00:00:00https://www.semanticscholar.org/paper/58034a324148766e80be0e3330fa33c4715c689c 5dc1a0f293057e2845f5147079bb5818a561b49fArchitectural Science Review830Architectural Science Review['Darya Gachkar', 'Sadaf Gachkar', 'Antonio García Martínez', 'Cecilio Angulo', 'Soheila Aghlmand', 'Javad Ahmadi']2024-05-14T00:00:00https://www.semanticscholar.org/paper/5dc1a0f293057e2845f5147079bb5818a561b49f 15e3b331831671f43f7474c515a0026cdbc8294dИскусственный интеллект (ИИ) сегодня используется во многих целях и присутствует практически в каждом доме, и мы постепенно становимся поколением автоматизированного ИИ. Как отмечается в статье, ИИ в слуховых аппаратах может значительно улучшить качество прослушивания для людей с потерей слуха. Автоматизация слуховых аппаратов совершает скачок, и чтобы слуховые аппараты были успешными, они должны хорошо адаптироваться к потребностям слуха пользо-вателя, а также решать такие проблемы, как фоновый шум. Автоматизированные функции слуховых аппаратов действительно помогли владельцам получить доступ к лучшему звуку. Слуховые аппараты с возможностями ИИ могут анализировать и адаптироваться к среде прослушивания пользователя в режиме реального времени, автоматически регулируя громкость и частоту звука для оптимизации восприятия звука. Это может быть особенно полезно в шумной обстановке, где традиционные слуховые аппараты могут с трудом различать важные звуки и фоновый шум. В некоторых слуховых аппаратах используются датчики для сбора данных о привычках пользователя в окружающей среде, которые могут быть проанализированы алгоритмами ИИ для выявления закономерностей и тенденций. Эту информацию можно использовать для оптимизации настроек слухового аппарата для пользователя или оповещения пользователя и его поставщика медицинских услуг о любых изменениях в состоянии его слуха. Таким образом, слуховые аппараты с ИИ открывают путь к улучшению качества слуха и, возможно, к другим революционным прорывам, поскольку они перенимают процессы существующих потребительских технологий. Несомненно, что в конечном счете, решение об использовании слухового аппарата с технологией искусственного интеллекта должно основываться на индивидуальных потребностях и предпочтениях. Արհեստական բանականությունը(ԱԲ) այսօր օգտագործվում է տարբեր նպատակներով և առկա է գրեթե յուրաքանչյուրի տանը, և մենք աստիճա-նաբար դառնում ենք ավտոմատացված ԱԲ-ի սերունդ: Ինչպես նշվում է հոդվածում, ԱԲ-ն լսողական սարքերում կարող է զգալիորեն բարելավվել լսողության ընկալումը լսողության կորուստ ունեցող մարդկանց համար: Լսողական ապարատի ավտոմատացումը թռիչք է կատարում, և որպեսզի լսողական սարքերն արդյունավետ լինեն, դրանք պետք է լավ հարմարվեն կրողի լսողության կարիքներին, ինչպես նաև լուծեն այնպիսի խնդիրներ, ինչպիսին է ֆոնային աղմուկը: Լսողական սարքերի ավտոմատացված գործառույթներն իսկապես օգնել են կրողներին ավելի լավ ձայն ընկալել: ԱԲ-ի հնարավորություններով լսողական սարքերը կարող են իրատեսական ժամանակում վերլուծել և հարմարվել օգտատիրոջ լսողական միջավայրին՝ ավտոմատ կարգավորելով ձայնը և հաճախականությունը՝ լսելու փորձն օպտիմալացնելու համար: Սա կարող է հատկապես օգտակար լինել աղմկոտ միջավայրերում, որտեղ ավանդական լսողական սարքերը կարող են դժվարությամբ տարբերել կարևոր ձայները ֆոնային աղմուկից:Medical Science of Armenia260Medical Science of Armenia['M. Shukuryan', 'H. M. А. Diab', 'L. A. Shukuryan', 'S. V. Levin', 'A. K. Shukuryan']2024-05-14T00:00:00https://www.semanticscholar.org/paper/15e3b331831671f43f7474c515a0026cdbc8294d a15e91b627c119145a3eb074a813388ca7dde48aThe Transnational Human Rights Review00The Transnational Human Rights Review['J. O. Effoduh']2024-05-14T00:00:00https://www.semanticscholar.org/paper/a15e91b627c119145a3eb074a813388ca7dde48a 6176f375ed96540fb24da439449e57bc44337763BMC Medical Ethics10BMC Medical Ethics['Menno T. Maris', 'Ayca Koçar', 'Dick L. Willems', 'Jeannette Pols', 'Hanno L. Tan', 'Georg L. Lindinger', 'M. Bak']2024-05-14T00:00:00https://www.semanticscholar.org/paper/6176f375ed96540fb24da439449e57bc44337763 9dd420da1af3addd0bc72a7eb10cebe0529338efThe uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-makers. Nevertheless, some uses of ML models involve high-stakes and safety-critical applications. Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding the operation of such complex ML models, thus eliciting trust in their operation. Unfortunately, the majority of past XAI work is based on informal approaches, that offer no guarantees of rigor. Unsurprisingly, there exists comprehensive experimental and theoretical evidence confirming that informal methods of XAI can provide human-decision makers with erroneous information. Logic-based XAI represents a rigorous approach to explainability; it is model-based and offers the strongest guarantees of rigor of computed explanations. However, a well-known drawback of logic-based XAI is the complexity of logic reasoning, especially for highly complex ML models. Recent work proposed distance-restricted explanations, i.e. explanations that are rigorous provided the distance to a given input is small enough. Distance-restricted explainability is tightly related with adversarial robustness, and it has been shown to scale for moderately complex ML models, but the number of inputs still represents a key limiting factor. This paper investigates novel algorithms for scaling up the performance of logic-based explainers when computing and enumerating ML model explanations with a large number of inputs.00Novel algorithms for scaling up the performance of logic-based explainers when computing and enumerating ML model explanations with a large number of inputs are investigated.['Yacine Izza', 'Xuanxiang Huang', 'A. Morgado', 'Jordi Planes', 'Alexey Ignatiev', 'João Marques-Silva']2024-05-14T00:00:00https://www.semanticscholar.org/paper/9dd420da1af3addd0bc72a7eb10cebe0529338ef 69ae784e9608eba303aa272309f44005a14be2ecThe increasing complexity of Artificial Intelligence models poses challenges to interpretability, particularly in the healthcare sector. This study investigates the impact of deep learning model complexity and Explainable AI (XAI) efficacy, utilizing four ResNet architectures (ResNet-18, 34, 50, 101). Through methodical experimentation on 4,369 lung X-ray images of COVID-19-infected and healthy patients, the research evaluates models' classification performance and the relevance of corresponding XAI explanations with respect to the ground-truth disease masks. Results indicate that the increase in model complexity is associated with a decrease in classification accuracy and AUC-ROC scores (ResNet-18: 98.4%, 0.997; ResNet-101: 95.9%, 0.988). Notably, in eleven out of twelve statistical tests performed, no statistically significant differences occurred between XAI quantitative metrics - Relevance Rank Accuracy and the proposed Positive Attribution Ratio - across trained models. These results suggest that increased model complexity does not consistently lead to higher performance or relevance of explanations for models' decision-making processes.650The research evaluates models' classification performance and the relevance of corresponding XAI explanations with respect to the ground-truth disease masks and suggests that increased model complexity does not consistently lead to higher performance or relevance of explanations for models' decision-making processes.['Mateusz Cedro', 'Marcin Chlebus']2024-05-14T00:00:00https://www.semanticscholar.org/paper/69ae784e9608eba303aa272309f44005a14be2ec f41106b9bfc4e1d8d3d9dce6facd3b3fc40cf5deIn this article, we present a systematic review of the literature that explores the impact of Machine Learning as a teaching strategy in the educational field. Machine Learning, a branch of artificial intelligence, has gained relevance in teaching and learning due to its ability to personalize education and improve instructional effectiveness. The systematic review focuses on identifying studies investigating how Machine Learning has been used in educational settings. Through a thorough analysis, its impact on various areas related to teaching and learning, including student performance, knowledge retention, and curricular adaptability, is examined. The findings of this review indicate that Machine Learning has proven to be an effective strategy for tailoring instruction to individual student needs. As a result, engagement and academic performance are significantly improved. Furthermore, the review underscores the importance of future research. This future research will enable a deeper understanding of how Machine Learning can optimize education and address current challenges and emerging opportunities in this evolving field. This systematic review provides valuable information for educators, curriculum designers, and educational policymakers. It also emphasizes the continuing need to explore the potential of Machine Learning to enhance teaching and learning in the digital age of the 21st century. ICST Transactions on Scalable Information Systems540The findings of this review indicate that Machine Learning has proven to be an effective strategy for tailoring instruction to individual student needs and, as a result, engagement and academic performance are significantly improved.ICST Transactions on Scalable Information Systems['Deixy Ximena Ramos Rivadeneira', 'Javier Alejando Jiménez Toledo']2024-05-14T00:00:00https://www.semanticscholar.org/paper/f41106b9bfc4e1d8d3d9dce6facd3b3fc40cf5de 3e4d9f7d0bd5bd2f23e736fd13e866aa8c83d30fArtificial intelligence (AI)-based applications contribute to monitoring financial transactions and detect fraudulent activity in real-time by analyzing transaction patterns, consumer behavior, and other statistics, making them essential for quickly addressing potential threats in the fight against financial crime dynamics. Leveraging financial crime systems with intelligent supervised neuro-structures exploiting nonlinear autoregressive exogenous networks integrating damped least square (NARX-DLS) optimization methods to achieve an appropriate degree of accuracy and adaptability for the estimation of complex nonlinear financial crime differential systems (NFCDSs). The representative NFCDS for financial crime indicators is expressed as susceptible individuals, financial criminals, individuals under prosecution, imprisoned individuals, and honest individuals. The Adams numerical solver accomplishes the acquisition of synthetic data for the layer structure NARX-DLS algorithm execution to solve NFCDSs for various financial crime parameters, such as recruitment rate, influence rate, conversion rate to honest people, financial criminal prosecution rate per capita, discharge and acquittal rate from prosecutions, percentage of discharge rate from prosecution, transition rate to prison, and freedom rate. A sturdy overlap between the solutions of NARX-DLSs and the reference numerical results of NFCDSs implies that the error value is close to a desirable value of zero. The effectiveness of the NARX-DLSs is evidenced by including a variety of assessment metrics that carefully examine the model’s correctness and efficacy, including mean square error-based convergence arches, adaptive regulating parameters, error distribution, and input-error/cross-correlation analyses.Modern physics letters B700The effectiveness of the NARX-DLSs is evidenced by including a variety of assessment metrics that carefully examine the model’s correctness and efficacy, including mean square error-based convergence arches, adaptive regulating parameters, error distribution, and input-error/cross-correlation analyses.Modern Physics Letters B['Farwah Ali Syed', 'Kwo-Ting Fang', 'A. Kiani', 'Dong-Her Shih', 'Muhammad Shoaib', 'M. A. Zahoor Raja']2024-05-14T00:00:00https://www.semanticscholar.org/paper/3e4d9f7d0bd5bd2f23e736fd13e866aa8c83d30f 7d1502011b10f4530370c94058c910c24aa426f9 This article undertakes a comparative ethical analysis of the types of public expectations and concerns related to the development of two technologies: the telegraph and artificial intelligence. For each technology I provide a historical survey of public ethical expectations and concerns followed by a survey of the outcome or results of those expectations. Expectations and concerns of the telegraph era public are drawn together from popular and public literature and regulation of the period, whereas the expectations and concerns of our contemporary public AI engagement are drawn both from popular literature and public surveys, and supported by a manual search and ranking of a number of ethics related terms found in the raw feedback of the Stakeholder Consultation on the EU Commission High Level Expert Group Guidelines for Trustworthy AI. I then go on to compare those results, highlighting the similarities and differences between the two technologies, in particular the positive economic and socially responsible use expectations outcomes and the negative concerns regarding monopoly, regulation, and control. Finally, I argue that, taking the telegraph outcome as a guide, an ethical focus on accentuating positive expectations toward AI is more likely to produce definite results than concentrating upon prohibitory and negative approaches. Studia Philosophica Wratislaviensia850It is argued that, taking the telegraph outcome as a guide, an ethical focus on accentuating positive expectations toward AI is more likely to produce definite results than concentrating upon prohibitory and negative approaches.Studia Philosophica Wratislaviensia['Marc M. Anderson']2024-05-13T00:00:00https://www.semanticscholar.org/paper/7d1502011b10f4530370c94058c910c24aa426f9 fd76f33ef5c13cda3b7ab2aa28cb8169d208ded5This Report delves into the financial aspect of online campaigning, depicting a landscape characterized by lax regulation, the proliferation of malicious actors, and limited oversight capabilities to keep pace with rapid changes. In this sense, the challenges facing Albania are no different from those being tackled across much of the democratic world. While this Report focuses on untracked financing in Albania, it acknowledges the broader concerns, such as opaque communication practices and the unchecked exploitation of personal data for voter targeting. To date, there has been minimal in-depth research into the specific problems that Albania should address and the effectiveness of existing legislation. This Report seeks to fill this gap by examining current regulatory practices in Albania, analysing activities during the 2021 parliamentary election and 2023 local government election. It also explores broader challenges encountered when regulating online campaign expenditure and examines international developments and regulatory trends in other countries in this realm. The paper concludes with recommendations for reforming Albania's political finance regulations.00['Samuel Power']2024-05-13T00:00:00https://www.semanticscholar.org/paper/fd76f33ef5c13cda3b7ab2aa28cb8169d208ded5 6452171a0a805aa75af726119d5b5ec617f762deERA Forum10ERA Forum['Fernando Gascón\xa0Inchausti']2024-05-13T00:00:00https://www.semanticscholar.org/paper/6452171a0a805aa75af726119d5b5ec617f762de 1b86609c328b9793f8c667dc18bcdcaccaa90f3cLegislation produced under the EU Commission's Better Regulation Agenda sometimes fails to achieve the Treaty obligation to ensure a high level of health protection in all EU policies and activities. Public health advocates have raised the question of whether EU law can be employed to challenge this apparent breach of Treaty obligations at the proposal preparation stage, compelling the Commission to amend prospective EU legislation so that it better protects health. This article will demonstrate that unfortunately this is not possible due to the justiciability of both Article 168 TFEU and the Better Regulation Agenda. However, this awareness can help public health advocates to re-focus their efforts on strategies that will likely have a greater impact in swaying the direction of EU health policy.European Journal of Health Law00European journal of health law['Ollie Bartlett']2024-05-13T00:00:00https://www.semanticscholar.org/paper/1b86609c328b9793f8c667dc18bcdcaccaa90f3c af929244628e5444cd567e07a77a7a88de43162900['Alberto Brown']2024-05-13T00:00:00https://www.semanticscholar.org/paper/af929244628e5444cd567e07a77a7a88de431629 9554968f6190056d5c1b7c9bf9b7efd573716cf6Данная статья посвящена вопросам отнесения онлайн-игр к азартным, владения игровым иму- ществом и уголовно-правовой охраны игрового имущества. С этой целью автором проанализированы поло- жения законодательства Российской Федерации, а также судебная практика Российской Федерации и ино- странных государств. В статье делается вывод о том, для обеспечения охраны игровых предметов их следует относить к иному имуществу по смыслу ст. 128 ГК РФ. The given article is devoted to consideration of questions of attribution of online games to gambling, possession of game property and criminal legal protection of game property. For this purpose, the author analyzed the provisions of the legislation of the Russian Federation judicial practice of the Russian Federation and foreign countries. The article concludes that in order to ensure the protection of game objects they should be referred to other property in the sense of Art. 128 of the Civil Code of the Russian Federation.Eurasian Advocacy (Evraziiskaya Advokatura)00Eurasian Advocacy (Evraziiskaya Advokatura)['Н.Н. Бруньковский']2024-05-13T00:00:00https://www.semanticscholar.org/paper/9554968f6190056d5c1b7c9bf9b7efd573716cf6 3a20971a78166f1e0e995f750d8f2ecaf7e576e8 Trust between constituent actors within the European Union (EU)’s multilevel regulatory regimes is decisive for regulatory success. Trust drives information flows, increases compliance, and improves cooperation within these regimes. Despite its importance, systematic knowledge regarding the drivers of trust within regulatory regimes is limited. This paper inquires whether trust in regulatory agencies is influenced by their affiliation with the national or EU governmental level, as well as by their performance. While existing literature predominantly focuses on why citizens place their trust in governments or regulatory agencies, this paper presents original insights regarding the formation of trust among elites within the regulatory regime, including politicians, ministerial officials, agency officials, interest groups, and regulated entities. We employ data obtained from a large-scale vignette experiment conducted in six countries involving 752 decision-makers from relevant organizations. The experimental results suggest that both public and private elite actors’ trust assessment of regulatory agencies does not hinge on cues associated with the governmental level, but rather depends on agency performance. Accordingly, belonging to the national or EU governmental level does not create a difference in trust assessment of regulatory agencies in itself. It, however, shows that particularly elite actors are rather sensitive in terms of the performance of a regulatory agency.European Political Science Review570European Political Science Review['Moritz Kappler', 'Koen Verhoest', 'Tobias Bach', 'Libby Maman', 'Rahel M. Schomaker']2024-05-13T00:00:00https://www.semanticscholar.org/paper/3a20971a78166f1e0e995f750d8f2ecaf7e576e8 50ea365443f44d31b5efc67139733593c2e208e8The article analyzes the use of artificial intelligence in the field of criminal proceedings. The existing discourse between legal and technical knowledge and the increasingly growing imbalance between legal and technical approaches in justifying the use of professional artificial intelligence are discussed. The use of facial recognition technologies in criminal proceedings, the introduction of promising systems for monitoring and analyzing big data obtained on the Internet, and the use of ChatGPT in criminal proceedings create significant risks in achieving the purpose of criminal proceedings. The experience of introducing artificial intelligence into the field of criminal justice in the People’s Republic of China as one of the leading states in this field seems interesting and noteworthy. A range of problems are outlined that the Russian Federation also needs to solve, related to the incorrect interpretation of court decisions by artificial intelligence, the inability to make value judgments, possible bias of algorithms, selectivity of data, the procedural form of sentencing, a decrease in the level of public confidence in the system of making court decisions made with the help of artificial intelligence.Courier of the Kutafin Moscow State Law University40A range of problems are outlined that the Russian Federation needs to solve, related to the incorrect interpretation of court decisions by artificial intelligence, the inability to make value judgments, possible bias of algorithms, selectivity of data, the procedural form of sentencing, a decrease in the level of public confidence in the system of making court decisions made with the help of artificial intelligence.Courier of Kutafin Moscow State Law University (MSAL))['A. A. Sobenin']2024-05-13T00:00:00https://www.semanticscholar.org/paper/50ea365443f44d31b5efc67139733593c2e208e8 dbe4a859c76dcc58339a7dce3600f956117a898dEighth International Conference on Energy System, Electricity, and Power (ESEP 2023)00Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)['Ying Huang', 'Chengsheng Zhang', 'DI Ruan', 'Yijie Yu', 'Hongyang Li']2024-05-13T00:00:00https://www.semanticscholar.org/paper/dbe4a859c76dcc58339a7dce3600f956117a898d 3bb611b376fc699cace2c59e61bb8c21c80afc51Journal of IT Cases and Applications04Journal of Information Technology Case and Application Research['Vivek Kumar Singh']2024-05-13T00:00:00https://www.semanticscholar.org/paper/3bb611b376fc699cace2c59e61bb8c21c80afc51 f1d39175484e58db627c0dcdbe96ebdfa6d22da1Monolithic large language models (LLMs) like GPT-4 have paved the way for modern generative AI applications. Training, serving, and maintaining monolithic LLMs at scale, however, remains prohibitively expensive and challenging. The disproportionate increase in compute-to-memory ratio of modern AI accelerators have created a memory wall, necessitating new methods to deploy AI. Composition of Experts (CoE) is an alternative modular approach that lowers the cost and complexity of training and serving. However, this approach presents two key challenges when using conventional hardware: (1) without fused operations, smaller models have lower operational intensity, which makes high utilization more challenging to achieve; and (2) hosting a large number of models can be either prohibitively expensive or slow when dynamically switching between them. In this paper, we describe how combining CoE, streaming dataflow, and a three-tier memory system scales the AI memory wall. We describe Samba-CoE, a CoE system with 150 experts and a trillion total parameters. We deploy Samba-CoE on the SambaNova SN40L Reconfigurable Dataflow Unit (RDU) - a commercial dataflow accelerator architecture that has been co-designed for enterprise inference and training applications. The chip introduces a new three-tier memory system with on-chip distributed SRAM, on-package HBM, and off-package DDR DRAM. A dedicated inter-RDU network enables scaling up and out over multiple sockets. We demonstrate speedups ranging from 2x to 13x on various benchmarks running on eight RDU sockets compared with an unfused baseline. We show that for CoE inference deployments, the 8-socket RDU Node reduces machine footprint by up to 19x, speeds up model switching time by 15x to 31x, and achieves an overall speedup of 3.7x over a DGX H100 and 6.6x over a DGX A100.710Samba-CoE, a CoE system with 150 experts and a trillion total parameters is described and deployed on the SambaNova SN40L Reconfigurable Dataflow Unit (RDU) - a commercial dataflow accelerator architecture that has been co-designed for enterprise inference and training applications.['R. Prabhakar', 'R. Sivaramakrishnan', 'Darshan Gandhi', 'Yun Du', 'Mingran Wang', 'Xiangyu Song', 'Kejie Zhang', 'Tianren Gao', 'Angela Wang', 'Karen Li', 'Yongning Sheng', 'Joshua Brot', 'Denis Sokolov', 'Apurv Vivek', 'Calvin Leung', 'Arjun Sabnis', 'Jiayu Bai', 'Tuowen Zhao', 'Mark Gottscho', 'David Jackson', 'Mark Luttrell', 'Manish Shah', 'Edison Chen', 'Kaizhao Liang', 'Swayambhoo Jain', 'Urmish Thakker', 'Dawei Huang', 'Sumti Jairath', 'Kevin J. Brown', 'K. Olukotun']2024-05-13T00:00:00https://www.semanticscholar.org/paper/f1d39175484e58db627c0dcdbe96ebdfa6d22da1 ec42d0f4d454689f446849fbd28354928d3a087aCyberharassment is a critical, socially relevant cybersecurity problem because of the adverse effects it can have on targeted groups or individuals. While progress has been made in understanding cyber-harassment, its detection, attacks on artificial intelligence (AI) based cyberharassment systems, and the social problems in cyberharassment detectors, little has been done in designing experiential learning educational materials that engage students in this emerging social cybersecurity in the era of AI. Experiential learning opportunities are usually provided through capstone projects and engineering design courses in STEM programs such as computer science. While capstone projects are an excellent example of experiential learning, given the interdisciplinary nature of this emerging social cybersecurity problem, it can be challenging to use them to engage non-computing students without prior knowledge of AI. Because of this, we were motivated to develop a hands-on lab platform that provided experiential learning experiences to non-computing students with little or no background knowledge in AI and discussed the lessons learned in developing this lab. In this lab used by social science students at North Carolina A&T State University across two semesters (spring and fall) in 2022, students are given a detailed lab manual and are to complete a set of well-detailed tasks. Through this process, students learn AI concepts and the application of AI for cyberharassment detection. Using pre- and post-surveys, we asked students to rate their knowledge or skills in AI and their understanding of the concepts learned. The results revealed that the students moderately understood the concepts of AI and cyberharassment.250A hands-on lab platform that provided experiential learning experiences to non-computing students with little or no background knowledge in AI and revealed that the students moderately understood the concepts of AI and cyberharassment.['Ebuka Okpala', 'Nishant Vishwamitra', 'Keyan Guo', 'Song Liao', 'Long Cheng', 'Hongxin Hu', 'Yongkai Wu', 'Xiaohong Yuan', 'Jeannette Wade', 'S. Khorsandroo']2024-05-13T00:00:00https://www.semanticscholar.org/paper/ec42d0f4d454689f446849fbd28354928d3a087a e0059bef949b3a4956b7372944bc1ad770a303f4This paper verifies the capacity of generative AI (ChatGPT in particular) to design a marketing strategy for a hotel by using the example of a 5-star property in the centre of Lisbon. The indicative solutions and reflexive tactics suggested by Dwivedi et al. (2023) were used to develop the prompts based on which ChatGPT designed the hotel's marketing strategy. The proposed approach incorporates the marketing mix elements, including digital marketing tips, the competitive set definition, a name for a marketing campaign, a logo, identification of an appropriate partner and suggested cross-selling actions. This is one of the first attempts to utilise generative AI to design a marketing strategy for a product/service. The findings of this research verify the previous assumption that ChatGPT can write it!Tourism00This paper verifies the capacity of generative AI (ChatGPT in particular) to design a marketing strategy for a hotel by using the example of a 5-star property in the centre of Lisbon to verify the previous assumption that ChatGPT can write it.Tourism['S. Almeida', 'Stanislav Ivanov']2024-05-13T00:00:00https://www.semanticscholar.org/paper/e0059bef949b3a4956b7372944bc1ad770a303f4 6a753ae62473bf93f93d2b1f6c30716508212459Proponents of generative AI tools claim they will supplement, even replace, the work of cultural production. This raises questions about the politics of visibility: what kinds of stories do these tools tend to generate, and what do they generally not? Do these tools match the kind of diversity of representation that marginalized populations and non-normative communities have fought to secure in publishing and broadcast media? I tested three widely available generative AI tools with prompts designed to reveal these normative assumptions; I prompted the tools multiple times with each, to track the diversity of the outputs to the same query. I demonstrate that, as currently designed and trained, generative AI tools tend to reproduce normative identities and narratives, rarely representing less common arrangements and perspectives. When they do generate variety, it is often narrow, maintaining deeper normative assumptions in what remains absent.Big Data &amp; Society200It is demonstrated that, as currently designed and trained, generative AI tools tend to reproduce normative identities and narratives, rarely representing less common arrangements and perspectives.Big Data &amp; Society['Tarleton Gillespie']2024-05-13T00:00:00https://www.semanticscholar.org/paper/6a753ae62473bf93f93d2b1f6c30716508212459 0f4340cbab38474b941cd6989d5fd2e56525f2d4With the gradual maturity of artificial intelligence technology, artificial intelligence products continue to emerge in the market, which poses new challenges to the current copyright law system. In particular, whether artificial intelligence products are copyrightable is a hot and difficult issue in the current theoretical and practical circles. Based on this, there are two mainstream viewpoints in the current theoretical circle, that is, the viewpoint that supports copyright protection for artificial intelligence products from the perspective of "reader-centered", and the viewpoint that opposes copyright protection for artificial intelligence products from the perspective of author-centered". However, the question of whether artificial intelligence products can be protected by copyright involves verifying the reasonableness and forward-looking nature of the "open" regulation of copyright objects in China on one hand. On the other hand, it is also relates to determining the future legislative direction of high-level artificial intelligence. Therefore, by comparing the similarities and differences in originality between the copyright law system and the copyright law system, the conclusion is drawn that artificial intelligence products should be protected by copyright. The evaluation criteria of subject-object separation established by "reader-centered doctrine" should be adopted.Lecture Notes in Education Psychology and Public Media00By comparing the similarities and differences in originality between the copyright law system and the copyright law system, the conclusion is drawn that artificial intelligence products should be protected by copyright and the evaluation criteria of subject-object separation established by "reader-centered doctrine" should be adopted.Lecture Notes in Education Psychology and Public Media['Meilin Wen']2024-05-13T00:00:00https://www.semanticscholar.org/paper/0f4340cbab38474b941cd6989d5fd2e56525f2d4 b276dda4debe537633452f3f9bb3a3dbfe97e7bcProfessional and technological education - EPT, in Brazil, has a history marked by advances and setbacks, reflecting the different political, economic and social situations in the country over the decades, as well as the teaching of Libras and the regulation of the profession of translator and Libras interpreter.V Seven International Multidisciplinary Congress00V Seven International Multidisciplinary Congress['Valteides V', 'Júlio César Neves dos Santos']2024-05-13T00:00:00https://www.semanticscholar.org/paper/b276dda4debe537633452f3f9bb3a3dbfe97e7bc e54f1c61df59f6825d9edf6511b3fb54d64e2f0aBackground: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully.BioMedInformatics960A review focusing on studies that applied artificial intelligence techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC) highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments.BioMedInformatics['Serin Moideen Sheriff', 'Aaftab Sethi', 'Divyanshi Sood', 'Sourav Bansal', 'Aastha Goudel', 'Manish Murlidhar', 'D. Damani', 'Kanchan Kulkarni', 'S. Arunachalam']2024-05-13T00:00:00https://www.semanticscholar.org/paper/e54f1c61df59f6825d9edf6511b3fb54d64e2f0a adfd785534e2b3888a4d4aac6c536471b47e813dArtificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.Multiple Sclerosis360This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.Multiple sclerosis['S. Collorone', 'L. Coll', 'Marco Lorenzi', 'Xavier Lladó', 'J. Sastre-Garriga', 'M. Tintoré', 'Xavier Montalban', 'Àlex Rovira', 'D. Pareto', 'Carmen Tur']2024-05-13T00:00:00https://www.semanticscholar.org/paper/adfd785534e2b3888a4d4aac6c536471b47e813d 8dae1c895da73fc17e77dae91a845a409f02b1deThis study investigated Artificial Intelligence (AI) and the future of secondary education in Delta State, with a particular focus on its implications for educational administration. The purpose of this study was to assess the benefits and challenges of AI in future of secondary education in Delta State. To address this, three research questions and hypotheses were raised and formulated. Using the quantitative research approach of the ex-post-facto research design. Stratified sampling method was used to sample 191 school administrators, representing 40% of the population of 477 government schools in Delta State. A carefully designed, 24-item questionnaire was used to collect information from respondents. Mean, standard deviation, graphical representation, and t-test were employed to answer research questions and formulate hypotheses at a significance level of 0.05. Findings reveal that implementing AI in education offers prospective advantages. Ethical considerations arising from AI integration encompass potential biases in decision-making, among others. We also discovered that effective AI implementation is associated with critical issues. The findings shed light on the perceptions and beliefs of school administrators regarding the integration of Artificial Intelligence (AI) in education. There is an urgent need to develop and implement a comprehensive state-wide plan for the integration of AI in education.Journal of Asian Scientific Research00Investigation of Artificial Intelligence and the future of secondary education in Delta State finds that effective AI implementation is associated with critical issues and reveals that implementing AI in education offers prospective advantages.Journal of Asian Scientific Research['Nkedishu Victor Chukwubueze', 'Okonta Vinella']2024-05-13T00:00:00https://www.semanticscholar.org/paper/8dae1c895da73fc17e77dae91a845a409f02b1de b2d1ae821f8aee9d49b15b07525f3157722ba14cArtificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. It is a critical branch of cognitive science, and its influence is increasingly being felt in other areas, including the humanities. Intelligence might be defined as the ability to learn and perform suitable techniques to solve problems and achieve goals, appropriate to the context in an uncertain, ever-varying world. A fully pre-programmed factory robot is flexible, accurate, and consistent but not intelligent. Artificial Intelligence (AI), a term coined by emeritus Stanford Professor John McCarthy in 1955, was define as “the science and engineering of making intelligent machines”. Much research has humans program machines to behave in a clever way, like playing chess, but, today, people emphasize machines that can learn, at least somewhat like human beings do.  Autonomous systems can independently plan and decide sequences of steps to achieve a specified goal without micro-management. A hospital delivery robot must autonomously navigate busy corridors to succeed in its task. In AI, autonomy doesn’t have the sense of being self-governing common in politics or biology.International Research Journal on Advanced Engineering and Management (IRJAEM)110Artificial intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms, and its influence is increasingly being felt in other areas, including the humanities.International Research Journal on Advanced Engineering and Management (IRJAEM)['Dr. Amtul Wahab']2024-05-13T00:00:00https://www.semanticscholar.org/paper/b2d1ae821f8aee9d49b15b07525f3157722ba14c b47ff5951f1b01818b2fa0b9fc1e2c5462ecdaa5OBJECTIVE Survey the current literature on artificial intelligence (AI) applications for detecting and classifying vocal pathology using voice recordings, and identify challenges and opportunities for advancing the field forward. DATA SOURCES PubMed, EMBASE, CINAHL, and Scopus databases. REVIEW METHODS A comprehensive literature search was performed following the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews guidelines. Peer-reviewed journal articles in the English language were included if they used an AI approach to detect or classify pathological voices using voice recordings from patients diagnosed with vocal pathologies. RESULTS Eighty-two studies were included in the review between the years 2000 and 2023, with an increase in publication rate from one study per year in 2012 to 10 per year in 2022. Seventy-two studies (88%) were aimed at detecting the presence of voice pathology, 24 (29%) at classifying the type of voice pathology present, and 4 (5%) at assessing pathological voice using the Grade, Roughness, Breathiness, Asthenia, and Strain scale. Thirty-six databases were used to collect and analyze speech samples. Fourteen articles (17%) did not provide information about their AI model validation methodology. Zero studies moved beyond the preclinical and offline AI model development stages. Zero studies specified following a reporting guideline for AI research. CONCLUSION There is rising interest in the potential of AI technology to aid the detection and classification of voice pathology. Three challenges-and areas of opportunities-for advancing this research are heterogeneity of databases, lack of clinical validation studies, and inconsistent reporting.Otolaryngology Head & Neck Surgery290There is rising interest in the potential of AI technology to aid the detection and classification of voice pathology and three challenges-and areas of opportunities-for advancing this research are heterogeneity of databases, lack of clinical validation studies, and inconsistent reporting.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery['George S Liu', 'Nedeljko Jovanovic', 'C. K. Sung', 'Philip C. Doyle']2024-05-13T00:00:00https://www.semanticscholar.org/paper/b47ff5951f1b01818b2fa0b9fc1e2c5462ecdaa5 857a7c05e06fc414c7a2ead56d5bf534196e31dfBACKGROUND Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. RESULTS A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). CONCLUSIONS The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.Journal of Medical Internet Research890A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.Journal of medical Internet research['L. Farah', 'I. Borget', 'Nicolas Martelli', 'Alexandre Vallée']2024-05-13T00:00:00https://www.semanticscholar.org/paper/857a7c05e06fc414c7a2ead56d5bf534196e31df c7ad771ffefb9a07cf865702fe80b0e131b7cc79Doing business in the conditions of globalization and rapid changes requires from entrepreneurs constant development and adaptation, search for new ideas and use of advanced technologies. Active competition and functioning in conditions of uncertainty require the formation of new competitive advantages, effective management of business processes and digital awareness. The aim of the paper is the systematization of the key approaches to implementation of artificial intelligence in business processes of the company and assessment of its influence on business results Empirical research uses quantitative methodology. Secondary data is collected from surveys and reports of leading world consulting companies. Within the framework of the research, the essence and key directions of artificial intelligence development were studied. Analysis of the use of artificial intelligence in company’s business processes was conducted. The positive cases of artificial intelligence implementation were considered, the influence of AI solutions on the development of modern organizations was determined. Advantages and disadvantages of AI solutions for business were considered.Facta Universitatis. Series: Economics and Organization210The aim of the paper is the systematization of the key approaches to implementation of artificial intelligence in business processes of the company and assessment of its influence on business results.Facta Universitatis, Series: Economics and Organization['A. Kvitka', 'Dmytro Sosnin', 'Yuliia Kvitka', 'Kateryna Andreieva']2024-05-13T00:00:00https://www.semanticscholar.org/paper/c7ad771ffefb9a07cf865702fe80b0e131b7cc79 a10da1ac609b04e576ef1c0842dddffa295d1749Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.Frontiers in Transplantation510More models need to be created and analyzed, in order to fully support the results of this review, which reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022.Frontiers in Transplantation['Aiste Gulla', 'I. Jakiūnaitė', 'I. Juchneviciute', 'G. Dzemyda']2024-05-13T00:00:00https://www.semanticscholar.org/paper/a10da1ac609b04e576ef1c0842dddffa295d1749 7e2155d1bd6c6204463de90aedc443b66ab98a4eThis in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.AAPS PharmSciTech730This in-depth study looks into how artificial intelligence could be used to make formulation development easier in fluidized bed processes (FBP), and case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up.AAPS PharmSciTech['Aachal A Gosavi', 'T. Nandgude', 'Rakesh K Mishra', 'Dhiraj B. Puri']2024-05-13T00:00:00https://www.semanticscholar.org/paper/7e2155d1bd6c6204463de90aedc443b66ab98a4e 239009b567a67abcc24219b1912e4d280be4eab3Microbial resistance has long been linked to antibiotic resistance, a serious worldwide health issue. But as technology advances quickly in this day and age, a related phenomenon in the field of artificial intelligence [AI] is beginning to take shape. In order to better understand the idea of "antibiotic resistance" in the context of artificial intelligence [AI], this study will compare and contrast the evolution of bacterial resistance with potential obstacles in the design and implementation of intelligent systems. The increasing prevalence of AI systems across several industries highlights the striking similarities between their capacity to adapt and withstand hostile attacks and changing surroundings, and the biological resistance mechanisms seen in bacteria. This study explores the causes behind AI resistance, looking at how data drift, adversarial manipulations, and changing user behavior might cause machine learning systems to lose their effectiveness over time. The paper also examines the ethical ramifications of AI resistance, addressing issues with biases, unforeseen outcomes, and the influence of intelligent systems on society that are resistant to change or intervention. The area of antibiotic stewardship in medicine serves as an inspiration for the paper's discussion of potential mitigation techniques for AI resistance. Through the identification of parallels between AI resistance and antibiotic resistance in bacteria, this study adds to a better comprehension of the difficulties pertaining to the long-term viability and efficiency of intelligent systems. Since AI will continue to be a major influence on the future, it is critical to address the problem of "antibiotic resistance" in this context in order to ensure that AI is developed responsibly and ethically.Uttar Pradesh Journal of Zoology00This study explores the causes behind AI resistance, looking at how data drift, adversarial manipulations, and changing user behavior might cause machine learning systems to lose their effectiveness over time.UTTAR PRADESH JOURNAL OF ZOOLOGY['D. Y. Kalyani', 'Rompicharla Narasimha Sai', 'K. S. Reddy', 'L. S. Jyotika', 'Bhupalam Pradeep Kumar']2024-05-13T00:00:00https://www.semanticscholar.org/paper/239009b567a67abcc24219b1912e4d280be4eab3 33e168f34878ffc27a493ac46e87b09c9733fa01Smart signal processing approaches using Artificial Intelligence are gaining momentum in NMR applications. In this study, we demonstrate that AI offers new opportunities beyond tasks addressed by traditional techniques. We developed and trained several artificial neural networks in our new toolbox Magnetic Resonance with Artificial intelligence (MR-Ai) to solve three"impossible"problems: quadrature detection using only Echo (or Anti-Echo) modulation from the traditional Echo/Anti-Echo scheme; accessing uncertainty of signal intensity at each point in a spectrum processed by any given method; and defining a reference-free score for quantitative access of NMR spectrum quality. Our findings highlight the potential of AI techniques to revolutionize NMR processing and analysis.440This study developed and trained several artificial neural networks in the new toolbox Magnetic Resonance with Artificial intelligence (MR-Ai) to solve three "impossible" problems: quadrature detection using only Echo (or Anti-Echo) modulation from the traditional Echo/Anti-Echo scheme.['Amir Jahangiri', 'Vladislav Orekhov']2024-05-13T00:00:00https://www.semanticscholar.org/paper/33e168f34878ffc27a493ac46e87b09c9733fa01 bd7fa343edffdde472a1f502004f9eed6bce5102People management is a fundamental area for the success of an organization, and artificial intelligence is playing an increasingly important role in this process. Through the use of technologies such as data analytics, machine learning and automation, companies can improve talent selection and recruitment, as well as performance management and employee skills development. Artificial intelligence can also be used to predict employee behavior and identify potential problems before they occur. This can help increase productivity and talent retention, as well as reduce training and turnover costs . However, the implementation of artificial intelligence in people management also brings challenges and concerns, such as information privacy and the possibility of algorithmic discrimination. It is important that companies have clear and transparent policies for use in people management, in order to ensure that it is used ethically and responsibly.V Seven International Multidisciplinary Congress00It is important that companies have clear and transparent policies for use of artificial intelligence in people management, in order to ensure that it is used ethically and responsibly.V Seven International Multidisciplinary Congress['Gustavo JF do Nascimento', 'Leo Leo Couto', 'Neide Aparecida Peres']2024-05-13T00:00:00https://www.semanticscholar.org/paper/bd7fa343edffdde472a1f502004f9eed6bce5102 1e087c417d15c73430da9bb67de4a5bc2a1cd08eHuman-Centric Intelligent Systems840It is opine that AI has great potential to elicit sustainable outcomes in food security, social innovation and environmental stewardship, albeit such potential is more likely to be realised through concurrent development of appropriate ethical, moral and legal dimensions.Human-Centric Intelligent Systems['Hanyu Wei', 'Wen Xu', 'Byeong Kang', 'R. Eisner', 'Albert Muleke', 'Daniel Rodriguez', 'P. deVoil', 'Victor Sadras', 'Marta Monjardino', 'M. T. Harrison']2024-05-13T00:00:00https://www.semanticscholar.org/paper/1e087c417d15c73430da9bb67de4a5bc2a1cd08e 52d6f4f3bf2fc86fabed8bfd4d7bf544cf0efe87Nature Network Boston1130This work discusses how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care, and outlines a vision for future advances through multimodal foundation models.Nature medicine['Chris Varghese', 'Ewen M Harrison', 'Greg O’Grady', 'E. Topol']2024-05-13T00:00:00https://www.semanticscholar.org/paper/52d6f4f3bf2fc86fabed8bfd4d7bf544cf0efe87 c0aff049979b7e0ada22ac6bfb271f604f33bdb4The World Health Organization (WHO) states that millions of people worldwide suffer from severe health conditions like diabetes, cardiovascular diseases, stroke, autism, and epilepsy. Some of these conditions, like diabetes, have been on the rise in low-and middle-income countries (LMICs) recently. These conditions have a significant impact on mortality, disability, economic losses, and physical and emotional suffering. However, with more accurate diagnosis, early detection, and prediction of occurrence, these conditions can be treated and managed more effectively, and in some cases, even prevented. This paper presents a comprehensive healthcare system that utilizes artificial intelligence (AI), including large language models (LLMs)–such as Bard and GPT-4 (and their improved future variants), deep learning neural networks, and machine learning platforms such as TensorFlow, electronic health records (EHR), as well as conventional and innovative three-dimensional multilayer EEG systems. The system permits the incorporation of genetic, lifestyle, and environmental information that provides more accurate representations of the participant’s environment and leads to improved health outcomes. This will provide actionable insights for clinical decision support in the early detection, diagnosis, treatment, management, prediction, and prevention of various conditions, including diabetes, cardiovascular diseases, stroke, autism, and epilepsy-saving lives and improving living conditions by reducing the economic, social, psychological and physical burden of the conditions so predicted and possibly prevented, detected early, diagnosed, treated and managed more efficiently. Additionally, the system aims to facilitate practical human-machine interfaces (HMIs) such as brain computer interfaces (BCIs) and progress towards computer-mediated brain-to-brain communication. It also seeks to enhance our understanding of the human brain’s functioning in both normal and diseased states, which can be used for the rehabilitation of individuals with neurological conditions and to create innovative ways for healthy individuals to interact with their environment and improve their lives.European Journal of Electrical Engineering and Computer Science490This paper presents a comprehensive healthcare system that utilizes artificial intelligence (AI), including large language models (LLMs), deep learning neural networks, and machine learning platforms such as TensorFlow, electronic health records (EHR), as well as conventional and innovative three-dimensional multilayer EEG systems.European Journal of Electrical Engineering and Computer Science['F. Ekpar']2024-05-13T00:00:00https://www.semanticscholar.org/paper/c0aff049979b7e0ada22ac6bfb271f604f33bdb4 344d9f23e248e1f8890fbb2277303e3e063e5612It is now a necessity of survival rather than choice for businesses to accept and keep technology separate from them. Businesses are now integrating technology with their objectives to guarantee their existence, expansion, and continuity. The company has completely changed due to technological improvements, which have made its commercial processes more efficient and well connected than before. Business technology innovations involve bringing people and machines closer together and investigating methods to use technology to increase productivity, simplicity of use, and efficiency. One such transformation in business is the application of artificial intelligence (AI). In today's cutthroat corporate world, artificial intelligence (AI) is critical to human resource management (HRM). AI has the potential to completely transform HRM procedures by automating mundane jobs, expediting workflows, and offering HR managers specialized solutions. AI can also enhance hiring, training, performance evaluation, and pay administration. HR departments may manage their workforce more effectively and efficiently by utilizing AI technologies. HR organizations may optimize expenses and deliver superior employee service by utilizing AI-powered solutions. AI is playing a bigger role in HRM now that it can help businesses become more efficient while simultaneously raising employee satisfaction. By analyzing employee feedback, HR departments can better understand how employees view their work environment and chances for advancement. This helps HR departments get insights into employee engagement and productivity. It makes it possible for HR departments to automate the hiring process, which facilitates the process of finding qualified applicants. This study can offer insights into AI potential in HRM and assist clarify the existing state of AI in HRM.International journal of engineering science & humanities20In today's cutthroat corporate world, artificial intelligence (AI) is critical to human resource management (HRM) and has the potential to completely transform HRM procedures by automating mundane jobs, expediting workflows, and offering HR managers specialized solutions.International Journal of Engineering Science and Humanities['Sapna Gupta', 'Dr. Anuj Bishnoi']2024-05-13T00:00:00https://www.semanticscholar.org/paper/344d9f23e248e1f8890fbb2277303e3e063e5612 43fe46ee89605c9ac82df26eaefd4d2d22a140a1Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60Bangladesh Journal of Medicine370In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life and paving the way for a more individualized, accurate, predictive, and portable future.Bangladesh Journal of Medicine['Prof. Dr. Md. Azizul Haque', 'Md Azizul Haque', 'Quazi Tarikul Islam']2024-05-13T00:00:00https://www.semanticscholar.org/paper/43fe46ee89605c9ac82df26eaefd4d2d22a140a1 2a535b62ef3945ecef9659c8c74ecee129fd51b3Air traffic flow managers are continually faced with the decision of when and how to respond to predictions of future constraints. The promise of artificial intelligence, and specifically reinforcement learning, to provide decision support in this domain stems from the ability to systematically evaluate a sequence of potential actions, or strategy, across a range of uncertain futures. As decision support for human traffic managers, the generated recommendations must embody characteristics of a good management strategy; doing so requires introducing such notions to the algorithm. This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time, where two such constraint sets are considered. The paper further evaluates the impact of adding a performance improvement threshold that must be overcome to accept a new strategy recommendation. The combination of search constraints and threshold values is evaluated against the agent’s reward function in addition to measures proposed to capture the stability of the strategy. The results show that the more restrictive set of constraints yields the best performance in terms of strategy stability and is more likely to reduce the delay where implementation of the threshold has a minor impact on overall performance. However, for the highest impact day of 8 June 2018, applying the threshold reverses the performance gains in delay but dramatically improves the stability of the resulting traffic flow management strategy from a flight level perspective, implying a potential tradeoff between delay optimization and flight predictability.Journal of Air Transportation50This paper proposes inducing stability into the strategy by dynamically constraining the design space based on upstream design decisions to promote consistency in the recommendations over time to promote consistency in the recommendations over time, where two such constraint sets are considered.Journal of Air Transportation['Christine Taylor', 'Erik Vargo', 'Tyler Manderfield', 'Simon Heitin']2024-05-13T00:00:00https://www.semanticscholar.org/paper/2a535b62ef3945ecef9659c8c74ecee129fd51b3 c22dd72b4acf93d1b3304f12d2cdcb6cb0cbc163The computational significance of consciousness is an important and potentially more tractable research theme than the hard problem of consciousness, as one could look at the correlation of consciousness and computational capacities through, e.g., algorithmic or complexity analyses. In the literature, consciousness is defined as what it is like to be an agent (i.e., a human or a bat), with phenomenal properties, such as qualia, intentionality, and self-awareness. The absence of these properties would be termed “unconscious.” The recent success of large language models (LLMs), such as ChatGPT, has raised new questions about the computational significance of human conscious processing. Although instances from biological systems would typically suggest a robust correlation between intelligence and consciousness, certain states of consciousness seem to exist without manifest existence of intelligence. On the other hand, AI systems seem to exhibit intelligence without consciousness. These instances seem to suggest possible dissociations between consciousness and intelligence in natural and artificial systems. Here, I review some salient ideas about the computational significance of human conscious processes and identify several cognitive domains potentially unique to consciousness, such as flexible attention modulation, robust handling of new contexts, choice and decision making, cognition reflecting a wide spectrum of sensory information in an integrated manner, and finally embodied cognition, which might involve unconscious processes as well. Compared to such cognitive tasks, characterized by flexible and ad hoc judgments and choices, adequately acquired knowledge and skills are typically processed unconsciously in humans, consistent with the view that computation exhibited by LLMs, which are pretrained on a large dataset, could in principle be processed without consciousness, although conversations in humans are typically done consciously, with awareness of auditory qualia as well as the semantics of what are being said. I discuss the theoretically and practically important issue of separating computations, which need to be conducted consciously from those which could be done unconsciously, in areas, such as perception, language, and driving. I propose conscious supremacy as a concept analogous to quantum supremacy, which would help identify computations possibly unique to consciousness in biologically practical time and resource limits. I explore possible mechanisms supporting the hypothetical conscious supremacy. Finally, I discuss the relevance of issues covered here for AI alignment, where computations of AI and humans need to be aligned.Frontiers in Psychology790Several cognitive domains potentially unique to consciousness are identified, such as flexible attention modulation, robust handling of new contexts, choice and decision making, cognition reflecting a wide spectrum of sensory information in an integrated manner, and finally embodied cognition, which might involve unconscious processes as well.Frontiers in Psychology['Ken Mogi']2024-05-13T00:00:00https://www.semanticscholar.org/paper/c22dd72b4acf93d1b3304f12d2cdcb6cb0cbc163 928c3d9883fe9732616d1d029df3f810f4ba664bHomeschooling, or homeschooling, is an alternative form of education that has grown in popularity in several countries around the world. Homeschooling is characterized by the absence of a formal educational institution, such as schools or daycare centers, and by the fact that learning takes place within the family environment.V Seven International Multidisciplinary Congress00Homeschooling is characterized by the absence of a formal educational institution, such as schools or daycare centers, and by the fact that learning takes place within the family environment.V Seven International Multidisciplinary Congress['Vinicius Iuri de Menezes', 'Eliana Marques Zanata', 'Eder Pires de Camargo']2024-05-13T00:00:00https://www.semanticscholar.org/paper/928c3d9883fe9732616d1d029df3f810f4ba664b 40d6827ebc082ce33f9e0796e12a7f420d6fa513The Web Conference180{'pages': '1115-1118'}['E. Haq', 'Yiming Zhu', 'Pan Hui', 'Gareth Tyson']2024-05-13T00:00:00https://www.semanticscholar.org/paper/40d6827ebc082ce33f9e0796e12a7f420d6fa513 a71fb6c9a9c95510c61089f02f0f39b68a00518aJournalism &amp; Communication Monographs00Journalism &amp; Communication Monographs['Errol Salamon']2024-05-13T00:00:00https://www.semanticscholar.org/paper/a71fb6c9a9c95510c61089f02f0f39b68a00518a a8b55fded71ce8a817d4175b174c739b4bce3649ASPET 2024 Annual Meeting Abstract - Pharmacology Education00ASPET 2024 Annual Meeting Abstract - Pharmacology Education['Jorge Rios Duarte', 'Ricardo A. Pena Silva']2024-05-13T00:00:00https://www.semanticscholar.org/paper/a8b55fded71ce8a817d4175b174c739b4bce3649 2befc0bca84b540065bbbbfb2ef76375ef5cf01aHuman Resource Development International370Human Resource Development International['Jessica Li', 'Roland K. Yeo']2024-05-13T00:00:00https://www.semanticscholar.org/paper/2befc0bca84b540065bbbbfb2ef76375ef5cf01a 95fbbb7d755312fe4a301d7d3b80cf4803fa98afASPET 2024 Annual Meeting Abstract - Cancer Pharmacology00ASPET 2024 Annual Meeting Abstract - Cancer Pharmacology['Tudor Oprea', 'Mohammed Quazi', 'Suman Sirimulla', 'Alexei Pushechnikov', 'Nikolay Savchuk']2024-05-13T00:00:00https://www.semanticscholar.org/paper/95fbbb7d755312fe4a301d7d3b80cf4803fa98af 9351419f048eb231e32f0c9e24c1cb9c3deb381500['Tony J Prescott']2024-05-13T00:00:00https://www.semanticscholar.org/paper/9351419f048eb231e32f0c9e24c1cb9c3deb3815 f3e551ecaba4a6a18933aa38d12d28197f825bf1Postdigital Science and Education210Postdigital Science and Education['G. Ritzer', 'J. M. Ryan', 'Sarah Hayes', 'Mark Elliot', 'P. Jandrić']2024-05-13T00:00:00https://www.semanticscholar.org/paper/f3e551ecaba4a6a18933aa38d12d28197f825bf1 b4e0dc2a58cd37d95c6681b76777cf38b89c7a63The Web Conference30{'pages': '1482-1485'}['Wei Jin', 'Haohan Wang', 'D. Zha', 'Qiaoyu Tan', 'Yao Ma', 'Sharon Li', 'Su-In Lee']2024-05-13T00:00:00https://www.semanticscholar.org/paper/b4e0dc2a58cd37d95c6681b76777cf38b89c7a63 32c90a1831b18c194b368a9de05de88719489a20International Journal of Human-Computer Interaction720International Journal of Human–Computer Interaction['Vito Tassiello', 'Cesare Amatulli', 'Jack S. Tillotson', 'Benjamin Laker']2024-05-13T00:00:00https://www.semanticscholar.org/paper/32c90a1831b18c194b368a9de05de88719489a20 53a964fd4f4d2e25469342d24914cbf5bd78dc08Artificial Intelligence playing an important role in IT recruitment. In recent days most of the IT companies giving importance to AI to ensure and effective recruitment process to select an efficient and qualified people. The present paper analyses the role of AI i.e., Artificial Intelligence in IT companies. The present analysis selected three top IT companies in India and examined the role of AI in their recruitment process. The papers examined the different factors which are linking to both recruitment process and IT industry. The present paper collected the opinions of employees working in three different companies and evaluated the importance of AI in recruitment process in IT industry.International Journal of Multidisciplinary Research in Science, Engineering and Technology00The present analysis selected three top IT companies in India and examined the role of AI in their recruitment process and collected the opinions of employees working in three different companies and evaluated the importance of AI in recruitment process in IT industry.International Journal of Multidisciplinary Research in Science, Engineering and Technology['Muskan Yadav', 'Radhakrishna M']2024-05-13T00:00:00https://www.semanticscholar.org/paper/53a964fd4f4d2e25469342d24914cbf5bd78dc08 1b6e94fe0af021a6152ff519dd1806e42df92f56In recent years, Artificial Intelligence (AI) has completely transformed the world of digital marketing. It's brought about a whole new era of innovation and efficiency. This abstract will dive into the many ways AI has impacted digital marketing strategies, highlighting its game-changing rise and what it means for businesses. AI has given marketers access to an incredible amount of data, allowing them to make smarter decisions using advanced analytics and machine learning algorithms. By digging deep into consumer behavior, preferences, and trends, AI helps create highly targeted and personalized marketing campaigns that really engage customers and drive up conversion rates. AI-powered platforms take care of all the repetitive tasks like ad placement, audience segmentation, and campaign optimization. This automation not only saves time and resources, but it also lets marketers focus on the big picture and drive real results. So, in a nutshell, AI has completely revolutionized the world of digital marketing. It's given marketers a wealth of opportunities to understand their customers better, create amazing content, and streamline their campaigns. But it's not without its challenges. As AI continues to advance, it's important for marketers to stay on top of the game and navigate the ethical considerations that come with it.International Journal of Multidisciplinary Research in Science, Engineering and Technology00This abstract will dive into the many ways AI has impacted digital marketing strategies, highlighting its game-changing rise and what it means for businesses.International Journal of Multidisciplinary Research in Science, Engineering and Technology['Pushpendra Singh Tanwar', 'S.Maria Antonyraj', 'Rishav Shrivastav']2024-05-13T00:00:00https://www.semanticscholar.org/paper/1b6e94fe0af021a6152ff519dd1806e42df92f56 4e148f1328853a4dad11c600e2553a27d472101eIn the dynamic landscape of modern education, characterized by an increasingly active involvement of IT technologies in learning, the imperative to transfer to university students the skills necessary to integrate Artificial Intelligence (AI) into the process represents an important goal. This paper presents a novel framework for knowledge transfer, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology. This framework proposes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge. Through a structured combination of theoretical instruction and hands-on experimentation, students develop proficiency in understanding and harnessing AI capabilities. Emphasizing critical thinking, problem-solving, and creativity, this approach equips students with the tools to navigate the complexities of real-world AI applications effectively. By leveraging PSoC 6 as an educational tool, a new generation of individuals is efficiently cultivated with essential AI skills. These individuals are adept at leveraging AI technologies to address societal challenges and drive innovation, thereby contributing to long-term sustainability initiatives. Specific strategies for experiential learning, curriculum recommendations, and the results of knowledge application are presented, aimed at preparing university students to excel in a future where AI will be omnipresent and indispensable.Sustainability200A novel framework for knowledge transfer is presented, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology, and proposes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge.Sustainability['H. Modran', 'D. Ursuțiu', 'C. Samoilă']2024-05-13T00:00:00https://www.semanticscholar.org/paper/4e148f1328853a4dad11c600e2553a27d472101e 2eebee74ef2f8f1f7d5fe48c1712762d2d99f1f9Artificial Intelligence (AI) is increasingly used as a helper to develop computing programs. While it can boost software development and improve coding proficiency, this practice offers no guarantee of security. On the contrary, recent research shows that some AI models produce software with vulnerabilities. This situation leads to the question: How serious and widespread are the security flaws in code generated using AI models?Through a systematic literature review, this work reviews the state of the art on how AI models impact software security. It systematizes the knowledge about the risks of using AI in coding security-critical software.It reviews what security flaws of well-known vulnerabilities (e.g., the MITRE CWE Top 25 Most Dangerous Software Weaknesses) are commonly hidden in AI-generated code. It also reviews works that discuss how vulnerabilities in AI-generated code can be exploited to compromise security and lists the attempts to improve the security of such AI-generated code.Overall, this work provides a comprehensive and systematic overview of the impact of AI in secure coding. This topic has sparked interest and concern within the software security engineering community. It highlights the importance of setting up security measures and processes, such as code verification, and that such practices could be customized for AI-aided code production.Frontiers in Big Data360This work highlights the importance of setting up security measures and processes, such as code verification, and that such practices could be customized for AI-aided code production, and that such practices could be customized for AI-aided code production.Frontiers in Big Data['Claudia Negri-Ribalta', 'Rémi Geraud-Stewart', 'Anastasia Sergeeva', 'Gabriele Lenzini']2024-05-13T00:00:00https://www.semanticscholar.org/paper/2eebee74ef2f8f1f7d5fe48c1712762d2d99f1f9 cab60f5020681336ec2f99fd7528fc3471b8ecedThe emergence of ChatGPT and other related artificial intelligence systems has posed many questions upon the impact that such tools could have on some jobs, including media workers. Serious legal concerns have arisen regarding the learning practices of AI-related companies such as OpenAI and Google. These concerns involve crawling and extracting presumably unauthorized copyright works from news repositories, whose rightholders are often media companies. In this article, we aim to categorize the newsroom practices and routines affected by artificial intelligence. We also explore copyright-law related issues, including AI-assisted reporting, its impact on journalists and the media workforce, SEO and commercial strategies, as well as training and blocking AI engines. The legal solutions applied to solve those questions are also addressed, including technical solutions, fair use guidelines and legal solutions (litigation, legislative reform, and negotiation). Our conclusion is twofold: first, in the unequal fight against artificial intelligence systems, a utilitarian and entrepreneurial conception of intellectual property is enforced; and second, the position of journalists as authors is weakening.  Tripodos00In the unequal fight against artificial intelligence systems, a utilitarian and entrepreneurial conception of intellectual property is enforced and the position of journalists as authors is weakening, according to the newsroom practices and routines affected by artificial intelligence.Tripodos['J. Díaz-Noci', 'Simón Peña-Fernández', 'Koldobica Meso-Ayerdi', 'A. Larrondo-Ureta']2024-05-13T00:00:00https://www.semanticscholar.org/paper/cab60f5020681336ec2f99fd7528fc3471b8eced c2d95f0295c1bb8288de6cbd84ca9cfa36c3d263The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.Lecture Notes in Education Psychology and Public Media00This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works.Lecture Notes in Education Psychology and Public Media['Miao Yan', 'Longfei Wang']2024-05-13T00:00:00https://www.semanticscholar.org/paper/c2d95f0295c1bb8288de6cbd84ca9cfa36c3d263 11c93f0f94452ef82c3d229ab9d8766a7e87551cDiscover Artificial Intelligence590This paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being.Discov. Artif. Intell.['Ali Fenwick', 'Gábor Molnár', 'Piper Frangos']2024-05-13T00:00:00https://www.semanticscholar.org/paper/11c93f0f94452ef82c3d229ab9d8766a7e87551c 1bd873e05fe242e98fe4cae01e90f476e68ceaf1Artificial Intelligence (AI) emerged as a game-changer with transformative potential across global sectors. In the unique context of rural India, where socio-economic dynamics diverge significantly from urban centers, AI's impact is poised to be profound, presenting both unprecedented opportunities and daunting challenges. This research paper seeks to conduct a comprehensive analysis of the landscape surrounding AI adoption in rural India. Through an extensive review of existing literature, insightful case studies, and meticulous data analysis, this paper endeavors to delve deeply into the potential benefits AI offers, while concurrently scrutinizing the formidable obstacles it confronts. By meticulously examining how AI can be strategically harnessed, this paper aims to shed light on its capacity to address pivotal rural development issues, all while navigating the complex web of concerns, ranging from accessibility and infrastructural limitations to intricate socio-cultural factors.International journal of engineering science & humanities00By meticulously examining how AI can be strategically harnessed, this paper aims to shed light on its capacity to address pivotal rural development issues, all while navigating the complex web of concerns, ranging from accessibility and infrastructural limitations to intricate socio-cultural factors.International Journal of Engineering Science and Humanities['R. Goel', 'Sandhya Rani Professor', 'M.M.E.C. Mullana']2024-05-13T00:00:00https://www.semanticscholar.org/paper/1bd873e05fe242e98fe4cae01e90f476e68ceaf1 6a37b0f4c36ed89c5cdda35be46b39c3f326c957Computational analysis techniques are transforming empirical legal scholarship. Two paradigms have emerged: law-as-code, which seeks to represent legal rules in a logical, executable format; and law-as-data, which leverages quantitative analysis of legal texts to reveal patterns and insights. This article surveys these approaches, emphasizing recent developments in large language models and generative artificial intelligence (AI). Law-as-code systems have enabled applications from tax preparation software to smart contracts, but realizing the vision of fully computational law has proven challenging. Law-as-data techniques like natural language processing and machine learning have charted the semantic relationship between courts and illuminated changes in judicial culture. Generative models showcase AI's explosive progress, with impressive feats like passing the U.S. bar example, but they also highlight limitations like factual inaccuracy and interpretability issues. Hybrid approaches integrating computational law, data science, and AI offer a promising research direction. As these tools spread, legal scholars can analyze more legal data than ever before, but they must remain cognizant of challenges like biased or low-quality data and linguistic/cultural limitations. Used judiciously alongside traditional methods, computational analysis has the potential to revolutionize empirical legal studies.European Journal of Empirical Legal Studies920European Journal of Empirical Legal Studies['Bao Chau', 'Michael Livermore']2024-05-13T00:00:00https://www.semanticscholar.org/paper/6a37b0f4c36ed89c5cdda35be46b39c3f326c957 601901be7d9bc0a3e460eee0ef9ab953b60cf715With the increasing demand for efficient and responsive customer service in the banking sector, artificial intelligence offers a promising solution. This paper presents a comparative analysis of artificial intelligence methodologies applied to intent classification within the banking sector customer service domain. Utilizing a comprehensive dataset of banking service inquiries, we evaluate several machine learning approaches, including Naive Bayes, Logistic Regression, Support Vector Machine with Linear Kernel, Random Forest, XGBoost, and the transformer-based DistilBERT model. The models are assessed based on their accuracy, precision, recall, and F1 score metrics. Our findings indicate that DistilBERT, with its distilled architecture, not only outstrips traditional models but also demonstrates exceptional performance with an accuracy and F1 score exceeding 92%. The paper delves into the advantages of employing such an efficient and powerful model in real-time customer service settings, suggesting that DistilBERT offers a substantial enhancement over conventional methods. By providing detailed insights into the model’s capabilities, we underscore the transformative impact of employing advanced AI in the financial industry to elevate customer service standards, streamline operational efficiency, and harness the power of state-of-the-art technology for improved client interactions. The results showcased in this study are indicative of the strides being made in AI applications for financial services and set a benchmark for future exploratory and practical endeavors in the field.International Journal of Current Science Research and Review350A comparative analysis of artificial intelligence methodologies applied to intent classification within the banking sector customer service domain indicates that DistilBERT, with its distilled architecture, not only outstrips traditional models but also demonstrates exceptional performance.International Journal of Current Science Research and Review['Saurabh Kumar', 'Suman Deep', 'Pourush Kalra']2024-05-13T00:00:00https://www.semanticscholar.org/paper/601901be7d9bc0a3e460eee0ef9ab953b60cf715 274c5e690df6ca44c62430a39f3f8e4a6d9e5b79Diagnosing and managing a patient is a complex, sequential decision making process that requires physicians to obtain information -- such as which tests to perform -- and to act upon it. Recent advances in artificial intelligence (AI) and large language models (LLMs) promise to profoundly impact clinical care. However, current evaluation schemes overrely on static medical question-answering benchmarks, falling short on interactive decision-making that is required in real-life clinical work. Here, we present AgentClinic: a multimodal benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments. In our benchmark, the doctor agent must uncover the patient's diagnosis through dialogue and active data collection. We present two open medical agent benchmarks: a multimodal image and dialogue environment, AgentClinic-NEJM, and a dialogue-only environment, AgentClinic-MedQA. We embed cognitive and implicit biases both in patient and doctor agents to emulate realistic interactions between biased agents. We find that introducing bias leads to large reductions in diagnostic accuracy of the doctor agents, as well as reduced compliance, confidence, and follow-up consultation willingness in patient agents. Evaluating a suite of state-of-the-art LLMs, we find that several models that excel in benchmarks like MedQA are performing poorly in AgentClinic-MedQA. We find that the LLM used in the patient agent is an important factor for performance in the AgentClinic benchmark. We show that both having limited interactions as well as too many interaction reduces diagnostic accuracy in doctor agents. The code and data for this work is publicly available at https://AgentClinic.github.io.470This work presents AgentClinic: a multimodal benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments, and finds that the LLM used in the patient agent is an important factor for performance in the AgentClinic benchmark.['Samuel Schmidgall', 'Rojin Ziaei', 'Carl Harris', 'Eduardo Reis', 'Jeffrey Jopling', 'Michael Moor']2024-05-13T00:00:00https://www.semanticscholar.org/paper/274c5e690df6ca44c62430a39f3f8e4a6d9e5b79 8a025b839b3623e566cfd57bff251e798b46d397 This Viewpoint provides recommendations and stakeholder actions to support safe and equitable use of artificial intelligence (AI) in pediatric clinical settings. JAMA pediatrics80JAMA pediatrics['Jessica L. Handley', 'Christoph U. Lehmann', 'Raj M. Ratwani']2024-05-13T00:00:00https://www.semanticscholar.org/paper/8a025b839b3623e566cfd57bff251e798b46d397 45e5e2b588b66c608246922d73ff23fac01aad22This mixed methods study was conducted to investigate mathematics preservice teachers’ (PTs) promotion of self-regulated learning (PSRL) with respect to time through participation in a self-regulated learning (SRL) enriched seminar course. PTs’ self-efficacy beliefs for promotion of self-regulation (SE-PSRL) over time was also investigated. Forty-four PTs participated in the study. They were divided into two sections and the SRL enriched seminar course was implemented with the experimental group for one academic term. The control group followed a parallel course without a particular focus on SRL. Participants were administered two different scales that measured their SE-PSRL and PSRL four times during the semester. Qualitative data were also gathered through semi-structured interviews with 9 participants. Mixed design analysis of variance (ANOVA) were conducted separately for SE-PSRL and PSRL scores to investigate the differences between the groups with respect to time. Results of the study indicated that while participants’ SE-PSRL scores differed statistically (F(3,126) = 9.13, p = .00, η2= 0.18), PSRL scores did not differ according to time and group (F(3,126) = 0.20, p = .90, η2= 0.01). The results from the quantitative analyses did not exactly conform with the hypotheses and interview data pointed towards various reasons for such unanticipated findings.               International Journal of Research in Education and Science00International Journal of Research in Education and Science['Meryem Cihangir', 'Engin Ader']2024-05-12T00:00:00https://www.semanticscholar.org/paper/45e5e2b588b66c608246922d73ff23fac01aad22 10e805fe6961b5beacfa41d6a83b652657415e3aEssay writing is a fundamental part of higher education. Students’ use of self-regulatory skills, such as time management and planning and writing strategies, while writing essays predicts better writing quality. Current characterisations of the relationship between self-regulation and essay writing are limited by the difficulty of assessing self-regulation in real-life essay writing contexts. This paper reports on a novel approach to examine students’ use of self-regulation strategies in a real-life setting, using learning analytics. Four case studies are presented to illustrate similarities and differences in students’ use of time management, planning and writing strategies. Participants managed their time in very different ways to complete the assignment. They were active over a different number of days, engaged in sessions of different durations, and at different times of the day. The participants used a variety of approaches to their writing: one participant started early and allowed editing time, another typed gradually over a number of days, and two participants waited until the due date to complete the essay, with varying amounts of editing. Findings from this research contribute to a novel detailed empirical evidence of different essay preparation behaviour in real-life settings. After further studies with a variety of essay types and student samples, there may be significant value in using the approached outlined in this paper as the basis of tools they provide students with advice and support in their essay preparation.ASCILITE Publications120A novel approach is reported on to examine students’ use of self-regulation strategies in a real-life setting, using learning analytics to contribute to a novel detailed empirical evidence of different essay preparation behaviour in real-life settings.ASCILITE Publications['Kelly Tresize', 'Paula De Barba', 'D. Jennens', 'Alexander Zarebski', 'Robert Russo', 'Gregor Kennedy']2024-05-12T00:00:00https://www.semanticscholar.org/paper/10e805fe6961b5beacfa41d6a83b652657415e3a 71b419cc6cc76317304c43acf202765c58ab0de3ChatGPT, the AI-powered chatbot with a massive user base of hundreds of millions, has become a global phenomenon. However, the use of Conversational AI Systems (CAISs) like ChatGPT for research in the field of Social Simulation is still limited. Specifically, there is no evidence of its usage in Agent-Based Social Simulation (ABSS) model design. While scepticism towards anything new is inherent to human nature, we firmly believe it is imperative to initiate the use of this innovative technology to support ABSS model design. This paper presents a proof-of-concept that demonstrates how CAISs can facilitate the development of innovative conceptual ABSS models in a concise timeframe and with minimal required upfront case-based knowledge. By employing advanced prompt engineering techniques and adhering to the Engineering ABSS framework, we have constructed a comprehensive prompt script that enables the design of ABSS models with or by the CAIS. The effectiveness of the script is demonstrated through an illustrative case study concerning the use of adaptive architecture in museums. Despite occasional inaccuracies and divergences in conversation, the CAIS proved to be a valuable companion for ABSS modellers.490This paper presents a proof-of-concept that demonstrates how CAISs can facilitate the development of innovative conceptual ABSS models in a concise timeframe and with minimal required upfront case-based knowledge.['P. Siebers']2024-05-12T00:00:00https://www.semanticscholar.org/paper/71b419cc6cc76317304c43acf202765c58ab0de3 93693c2edf90e9d844d134bdf5addd05b335e3b1This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords:  Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.International Journal of Management &amp; Entrepreneurship Research00The convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.International Journal of Management &amp; Entrepreneurship Research['Olubunmi Adeolu Adenekan', 'Nko Okina Solomon', 'Peter Simpa', 'Scholar Chinenye Obasi']2024-05-12T00:00:00https://www.semanticscholar.org/paper/93693c2edf90e9d844d134bdf5addd05b335e3b1 001c20688bd5868cd41e0f5d52c799536c592981Artificial Intelligence Generated Content (AIGC) has grown rapidly in recent years, among which AI-based image generation has gained widespread attention due to its efficient and imaginative image creation ability. However, AI-generated Images (AIGIs) may not satisfy human preferences due to their unique distortions, which highlights the necessity to understand and evaluate human preferences for AIGIs. To this end, in this paper, we first establish a novel Image Quality Assessment (IQA) database for AIGIs, termed AIGCIQA2023+, which provides human visual preference scores and detailed preference explanations from three perspectives including quality, authenticity, and correspondence. Then, based on the constructed AIGCIQA2023+ database, this paper presents a MINT-IQA model to evaluate and explain human preferences for AIGIs from Multi-perspectives with INstruction Tuning. Specifically, the MINT-IQA model first learn and evaluate human preferences for AI-generated Images from multi-perspectives, then via the vision-language instruction tuning strategy, MINT-IQA attains powerful understanding and explanation ability for human visual preference on AIGIs, which can be used for feedback to further improve the assessment capabilities. Extensive experimental results demonstrate that the proposed MINT-IQA model achieves state-of-the-art performance in understanding and evaluating human visual preferences for AIGIs, and the proposed model also achieves competing results on traditional IQA tasks compared with state-of-the-art IQA models. The AIGCIQA2023+ database and MINT-IQA model will be released to facilitate future research.810The proposed MINT-IQA model achieves state-of-the-art performance in understanding and evaluating human visual preferences for AIGIs, and the proposed model also achieves competing results on traditional IQA tasks compared with state-of-the-art IQA models.['Jiarui Wang', 'Huiyu Duan', 'Guangtao Zhai', 'Xiongkuo Min']2024-05-12T00:00:00https://www.semanticscholar.org/paper/001c20688bd5868cd41e0f5d52c799536c592981 cb8720de249a9f64bf39dac6c99104b70bcd8a04The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.ChemBioChem00By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.Chembiochem : a European journal of chemical biology['Mohamed Abbas', 'Abrar Rassam', 'Rehab Abunora', 'Fatima Karamshahi', 'Maha Abouseada']2024-05-12T00:00:00https://www.semanticscholar.org/paper/cb8720de249a9f64bf39dac6c99104b70bcd8a04 926e3538b1a8419671e8f1d43ac123a2315ab326rtificial Intelligence (AI) has become a transformative force in the modern workplace, reshaping job roles across various industries. This comprehensive review explores the multifaceted impact of AI on employment, delving into the opportunities it presents for innovation and efficiency, as well as the challenges it poses in terms of job displacement and skills evolution. Through an in-depth analysis of automation, augmentation, and the emergence of new job roles, this paper aims to provide insights into how individuals and organizations can navigate the evolving job landscape shaped by AI technologies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Through an in-depth analysis of automation, augmentation, and the emergence of new job roles, this paper aims to provide insights into how individuals and organizations can navigate the evolving job landscape shaped by AI technologies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Harsh Raj']2024-05-12T00:00:00https://www.semanticscholar.org/paper/926e3538b1a8419671e8f1d43ac123a2315ab326 acf79b17769d07bebd49fe94aa322cdedb5b2b3fThis article is about how artificial intelligence is bringing transformation in processes of HR and how inculcating AI has made easy the appointment of employees. Traditionally recruitment of employees was a very time consuming and difficult process. In old times recruitment was a subjective process and it usually takes a very long time to hire any employee in an organization but now AI has turned the tables by automating resume screening, identifying potential candidates with relevant skills and experience and conduct first round interviews with the help of chatbots. The involvement of AI in HR has reduced a lot of burden of HR professionals and helped them to focus on deeper engagement with promising candidates and making an informed hiring decision. This article is centered around how HR processes has undergone a profound transformation after involvement of AI in their processes. HR is embracing the power of artificial intelligence to become data-driven powerhouse, enhancing the entire employee experience and making hiring of employees a easy task from dull, monotonous and burdenized work.This article has also mention about how we can continuously evaluate the performance of employees and can make strategies for their development and betterment. This article has focused how AI has revolutionized human resources from inside out and also includes some practical examples.International Journal of Advanced Research in Science, Communication and Technology00How AI has revolutionized human resources from inside out is focused on and also includes some practical examples of how HR processes has undergone a profound transformation after involvement of AI in their processes.International Journal of Advanced Research in Science, Communication and Technology['Dr. Pushpa Rani', 'Ms. Chahat Malhotra']2024-05-12T00:00:00https://www.semanticscholar.org/paper/acf79b17769d07bebd49fe94aa322cdedb5b2b3f 937be2fd39c0b157ccb70dacc2c3c421697ffe98   An analytical review of the models and risks in the researcher’s reproduction system in the scientific specialty “1.2.1. Artificial Intelligence and Machine Learning” is presented. The issues of graduate school management and regulatory barriers in the training of young scientists are considered. Successful practices for defending a PhD thesis at leading national research universities have been identified and categorized. The justifications for the need to protect a PhD thesis by machine learning engineers are given. Proposals for changes to the scientific model of postgraduate studies and for AI augmentation of scientific research have been summarized, which help overcome risks in assigning qualification based on the textual results of scientific work.Vysshee Obrazovanie v Rossii = Higher Education in Russia00Proposals for changes to the scientific model of postgraduate studies and for AI augmentation of scientific research have been summarized, which help overcome risks in assigning qualification based on the textual results of scientific work.Vysshee Obrazovanie v Rossii = Higher Education in Russia['A. N. Alfimtsev', 'N. Bagdasaryan', 'S. Sakulin']2024-05-12T00:00:00https://www.semanticscholar.org/paper/937be2fd39c0b157ccb70dacc2c3c421697ffe98 cfa09f501c5b5bcb118a9bd7936454ae5a564ee6IJGIS May 202400IJGIS May 2024['Madhavi Najana']2024-05-12T00:00:00https://www.semanticscholar.org/paper/cfa09f501c5b5bcb118a9bd7936454ae5a564ee6 10c8d1a8d069a004fa0ded77ec668397a4946f83The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first competency-based applied artificial intelligence curriculum at the higher-education institution level. The development was based on advanced system research on existing artificial intelligence-related resources and surveying target groups of teachers, information technology students, and employers, which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data were collected with the help of surveying by identifying job offers, existing artificial intelligence training courses, scientific projects, and real cases. A synthetic analysis of the textual information from the studies was conducted using the word clouds technique. A tensor-based approach was used for the presentation of the competency-based course. The specific numerical requirements for the course in the form of priorities followed from the solution to decision-making problems using the analytic hierarchy process technique. Based on a comprehensive study of surveys, educational experience, scientific projects, and business requirements, and a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to content and educational modules.Applied Sciences240Applied Sciences['V. Martsenyuk', 'G. Dimitrov', 'D. Rancic', 'I. D. Luptáková', 'Igor Jovančević', 'Marcin Bernas', 'A. Kłos-Witkowska', 'Tomasz Gancarczyk', 'I. Kostadinova', 'Elizabet Mihaylova', 'Dragan Stojanovic', 'Marko Milojkovic', 'J. Pospíchal', 'Aleksandar Plamenac']2024-05-12T00:00:00https://www.semanticscholar.org/paper/10c8d1a8d069a004fa0ded77ec668397a4946f83 74e65b253d136c14c40ffd1d3bdfb145d5d0cb57   The breakneck expansion of artificial intelligence (AI) technologies in recent years has attracted the attention of higher education researchers. However, their curiosity often comes down to using specific AI tools, such as text and image generators, translators, and personal assistants in the educational process. This paper considers a broader question: what fundamental problems and challenges does the penetration of AI technologies into human lives originate for higher education? The authors offer a working definition of what it means to give/receive higher education in the age of AI. This definition identifies five critical characteristics of higher education: 1) the professor and the student continue to be in a subject-subject relationship and learn from each other; 2) higher education should prepare for life in conditions of “human-machine” interdependence; 3) these conditions imply choice in situations of uncertainty; 4) the spread of AI technologies brings enormous opportunities and 5) almost unpredictable dangers, risks and threats to humans. The authors consider general principles and specific problems associated with entering AI tools into society and higher education. They discuss the inevitability of the evolvement of “machine-machine interdependence” related to the development of autonomous agents. In conclusion, several theses and counter-theses summarize the article’s argumentation.Vysshee Obrazovanie v Rossii = Higher Education in Russia111A working definition of what it means to give/receive higher education in the age of AI is offered and the inevitability of the evolvement of “machine-machine interdependence” related to the development of autonomous agents is discussed.Vysshee Obrazovanie v Rossii = Higher Education in Russia['A. Rezaev', 'A. M. Stepanov', 'N. Tregubova']2024-05-12T00:00:00https://www.semanticscholar.org/paper/74e65b253d136c14c40ffd1d3bdfb145d5d0cb57 f879c9557fa7d418d268fde24c1572fd1687ece8In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed to augment these algorithms with explainable AI (XAI) features to enhance transparency. A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models. Where discrepancies were noted, training data were refined to align a white-box model’s decisions closer to its black-box counterpart. Testing this methodology on three distinct medical datasets revealed consistent correlations between the adapted white-box models and their black-box analogs. Notably, integrating this strategy with established methods like local interpretable model-agnostic explanations (LIMEs) and SHapley Additive exPlanations (SHAPs) further enhanced transparency, underscoring the potential value of decision trees as a favored white-box algorithm in medicine due to its inherent explanatory capabilities. The findings highlight a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains.Electronics370A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models, highlighting a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains.Electronics['Bojan Žlahtič', 'J. Završnik', 'Helena Blažun Vošner', 'Peter Kokol']2024-05-12T00:00:00https://www.semanticscholar.org/paper/f879c9557fa7d418d268fde24c1572fd1687ece8 283d0db719be9b942b0b538346731757b08e5626   As artificial intelligence (AI) becomes an integral part of our daily lives, the concern of the teaching community about the illegal use of these technologies in the educational process is increasing. In order to adapt the education system and teaching practices to new technological challenges, it is necessary to analyze the opinions of all the parties concerned.   The purpose of this study is to identify the attitude of students of Kazan Federal University to the use of artificial intelligence technologies in the educational process and the practice of their application in foreign languages learning process.   To achieve this goal, an online survey of students of Kazan Federal University was conducted. The survey touched upon the practical aspects of the use of artificial intelligence in language teaching, the advantages and disadvantages of AI tools from the students’ point of view, as well as their opinion regarding the prospects of AI in education. As a result of the study, we came to the conclusion that at the moment AI tools are not widespread enough in teaching foreign languages. Only one-fifth of the respondents use these tools, but the respondents’ comments suggest that the number of users will grow. The attitude of students towards the use of AI is ambiguous, with responses ranging from enthusiastic to skeptical. Students’ positive impressions are mainly related to saving time and effort, as well as to the ability of AI to present complex materials with understandable language. Among the main disadvantages, the respondents noted unreliability of data and fake content. Despite the fact that students are generally positive about the use of AI, a significant part of respondents do not trust software products such as ChatGPT, since, in their opinion, it provides answers of average quality that need to be corrected. Based on the data obtained, the authors attempted to formulate recommendations on improving the methods of teaching and control in the process of teaching foreign languages at universities.Vysshee Obrazovanie v Rossii = Higher Education in Russia220It is concluded that at the moment AI tools are not widespread enough in teaching foreign languages at universities.Vysshee Obrazovanie v Rossii = Higher Education in Russia['N. Tikhonova', 'G. Ilduganova']2024-05-12T00:00:00https://www.semanticscholar.org/paper/283d0db719be9b942b0b538346731757b08e5626 dae787f796e4e1093c4915f0c0329ffa8e931252As the future of teaching, learning, and workplace environment changes due to the evolution and advancement of artificial intelligence (AI) and interconnected technologies, it is required that human resource development (HRD) scholars and practitioners intervenes for a successful transition. In deploying the use of narrative review technique, which led to the evaluation of several related literatures published between 1988 and 2024 in various academic and professional platforms and connected to the study’s objective, this article brings forth a new perspective which was able to interconnect the fields of HRD with that of adult education (AE) and AI. In spite of global affirmation which acknowledges HRD as a field and practice whose expertise is responsible for the development of new knowledge and reskilling of adult educators and learners, in addition to the workforce, HRD is yet to contribute to the scholarly discourse linking AE to AI in the age of digitalising learning and education, which is because of the need to adapt to new technologies through continuous enhancement of learning and development. As its intervention will help in the transformation of adult pedagogy to be in tune with today’s and tomorrow’s learning and work environments. With the expertise of HRD, the capabilities of adult educators and the knowledge of learners will be developed, with the intention that theoretical and practical knowledge and competencies on AI technologies will be acquired by all stakeholders in the AE domain. As a proactive discipline with a multidisciplinary outlook which transcends the social and management sciences, it is necessary to utilise its know-how for promoting awareness and developing new competencies on AI technologies among adult educators and learners, as this is required for seamless transition on integrating AI into AE teaching and learning curriculum. Through this method, AE educators and learners will have theirInternational Journal of Academic Research in Business and Social Sciences470This article brings forth a new perspective which was able to interconnect the fields of HRD with that of adult education (AE) and AI, as this is required for seamless transition on integrating AI into AE teaching and learning curriculum.International Journal of Academic Research in Business and Social Sciences['E. H. Osolase', 'Roziah Mohd Rasdi', 'Z. Mansor', 'Kou Qi']2024-05-12T00:00:00https://www.semanticscholar.org/paper/dae787f796e4e1093c4915f0c0329ffa8e931252 83966695259b92aef85933b630173a7ee278de57This paper explores the seamless integration of Generative AI (GenAI) and Evolutionary Algorithms (EAs) within the domain of large-scale multi-objective optimization. Focusing on the transformative role of Large Language Models (LLMs), our study investigates the potential of LLM-Assisted Inference to automate and enhance decision-making processes. Specifically, we highlight its effectiveness in illuminating key decision variables in evolutionarily optimized solutions while articulating contextual trade-offs. Tailored to address the challenges inherent in inferring complex multi-objective optimization solutions at scale, our approach emphasizes the adaptive nature of LLMs, allowing them to provide nuanced explanations and align their language with diverse stakeholder expertise levels and domain preferences. Empirical studies underscore the practical applicability and impact of LLM-Assisted Inference in real-world decision-making scenarios.260This paper explores the seamless integration of Generative AI and Evolutionary Algorithms within the domain of large-scale multi-objective optimization and investigates the potential of LLM-Assisted Inference to automate and enhance decision-making processes.['Gaurav Singh', 'K. Bali']2024-05-12T00:00:00https://www.semanticscholar.org/paper/83966695259b92aef85933b630173a7ee278de57 423a169a2c119c4ce8904f2bfadb989489ec2839Transhumanism, which has become a trendy topic today, means ensuring human psychological and physiological transformation by using the opportunities brought by science. In addition, developments in artificial intelligence technologies have accelerated this transformation, and the survival of societies and moving into the future depends on keeping up with these transformations and adapting to digital transformations. This study examines the effects of transhumanism, artificial intelligence, and digital society on social values. For this purpose, a traditional literature review, also known as a narrative literature review, was adopted, and the results were synthesized by establishing a relationship between the relevant research topic and the literature. Accordingly, in the first stage of the study, the main goals and benefits of transhumanism, from the ideas of development and liberation, were mentioned in terms of social values. In the second stage, digital society was criticized in terms of social value and technological determinism about the Society 5.0 philosophy that artificial intelligence has brought about through social transformation. In this respect, the effects that will develop on social values in the context of transhumanize philosophy and artificial intelligence have been critically examined. Various suggestions have been developed in line with the literature review conducted within the scope of this study, the findings obtained from previous research, and the synthesis resulting from the study. Accordingly, while discussing the effects of technological developments on society, it is emphasized that observing social and ethical values is important in maintaining social harmony and balance.Journal of Interdisciplinary Education: Theory and Practice00The effects that will develop on social values in the context of transhumanize philosophy and artificial intelligence have been critically examined.Journal of Interdisciplinary Education: Theory and Practice['Hakan Öngören']2024-05-12T00:00:00https://www.semanticscholar.org/paper/423a169a2c119c4ce8904f2bfadb989489ec2839 16fb5211eaded25ab65404efbaa74c4659133cd2Cureus250Cureus['Fizza Khalid', 'Lara Alsadoun', 'Faria Khilji', 'Maham Mushtaq', 'Anthony Eze-odurukwe', 'Muhammad Muaz Mushtaq', 'Husnain Ali', 'Rana Omer Farman', 'Syed Momin Ali', 'Rida Fatima', 'S. F. H. Bokhari']2024-05-12T00:00:00https://www.semanticscholar.org/paper/16fb5211eaded25ab65404efbaa74c4659133cd2 c349fcd2a9ea68443d6a7ad13025be05bc10d5b0The hypothesis of conscious machines has been debated since the invention of the notion of artificial intelligence, powered by the assumption that the computational intelligence achieved by a system is the cause of the emergence of phenomenal consciousness in that system as an epiphenomenon or as a consequence of the behavioral or internal complexity of the system surpassing some threshold. As a consequence, a huge amount of literature exploring the possibility of machine consciousness and how to implement it on a computer has been published. Moreover, common folk psychology and transhumanism literature has fed this hypothesis with the popularity of science fiction literature, where intelligent robots are usually antropomorphized and hence given phenomenal consciousness. However, in this work, we argue how these literature lacks scientific rigour, being impossible to falsify the opposite hypothesis, and illustrate a list of arguments that show how every approach that the machine consciousness literature has published depends on philosophical assumptions that cannot be proven by the scientific method. Concretely, we also show how phenomenal consciousness is not computable, independently on the complexity of the algorithm or model, cannot be objectively measured nor quantitatively defined and it is basically a phenomenon that is subjective and internal to the observer. Given all those arguments we end the work arguing why the idea of conscious machines is nowadays a myth of transhumanism and science fiction culture.510This work argues why the idea of conscious machines is nowadays a myth of transhumanism and science fiction culture, and illustrates a list of arguments that show how every approach that the machine consciousness literature has published depends on philosophical assumptions that cannot be proven by the scientific method.["Eduardo C. Garrido-Merch'an"]2024-05-12T00:00:00https://www.semanticscholar.org/paper/c349fcd2a9ea68443d6a7ad13025be05bc10d5b0 b96d7fbd5d2646163964d8bf4e0c2a6dadeda404Efforts by states to regulate the development of technology become a unique problem when it comes to artificial intelligence (AI). It is impossible to predict all possible consequences of its use in the military sphere, making a choice in favor of its advantages. The world has realized the fact that the use of weapons controlled by AI requires not only legal regulation, but also control of compliance with international legal norms, revision of methods of warfare in accordance with the new reality. The definition of what constitutes an "weapon with artificial intelligence”, "artificial intelligence” itself, and their legal status - remain open to interpretation in technical, military and legal circles. The article analyzes separate issues of regulating the development, distribution and use of weapons with artificial intelligence (hereinafter - AI). It is emphasized that although AI systems are a fundamentally new way of waging war, controlling their use and imposing restrictions does not constitute a completely new task for international humanitarian law (hereinafter - IHL). It is still based on the established principles that were used to regulate existing types of weapons and should be extended to the use of anti-aircraft weapons: the principle of distinguishing targets, the principle of proportionality, the principle of using precautionary measures during an attack. At the same time, AI has a number of characteristics that make it difficult to control. As a general-purpose technology, AI has many non­military and defense applications. Unlike military technology, it is developed primarily in the civilian sector. And although the widespread use of AI calls into question a complete ban on its military use, the international community should work together to regulate or ban certain types of military AI use. The optimal solution to the problem could be the adoption of a corresponding international codified act, which would define the concept, regulate the creation and application of autonomous systems of the ZHI, as well as contain mechanisms of control and responsibility for violations of these norms.Analytical and Comparative Jurisprudence00Although the widespread use of AI calls into question a complete ban on its military use, the international community should work together to regulate or ban certain types of military AI use.Analytical and Comparative Jurisprudence['T. Fedchuk']2024-05-11T00:00:00https://www.semanticscholar.org/paper/b96d7fbd5d2646163964d8bf4e0c2a6dadeda404 4a11827a918666107a87be2d2a77da1aebf9483aThis study delves into the effects of agricultural technological innovation on agricultural green total factor productivity (AGTFP) and the intermediating role of environmental regulation (ER) in 30 Chinese provinces from 2010 to 2021. Employing mediation analysis methods such as the three-step approach, Sobel–Goodman test, and Bootstrap methods, the findings are robust: technological innovation significantly enhances AGTFP, as evidenced by a 1% level significant coefficient of 0.030. Additionally, ER acts as a potent mediator, where its inclusion as an independent variable alongside agricultural technological innovation (AST) boosts the coefficient to 0.031, further confirming its synergistic effect on AGTFP. These data points underline the importance of innovation in agricultural sustainability and the reinforcing role of environmental regulation. Consequently, this study advocates for intensified agricultural innovation support, tailored environmental regulation policies, augmented environmental education, and a meticulous evaluation system for environmental legislation to foster sustainable agricultural practices.Sustainability220Sustainability['Lihuan Huang', 'Ying Ping']2024-05-11T00:00:00https://www.semanticscholar.org/paper/4a11827a918666107a87be2d2a77da1aebf9483a e4aa48eb17a2d35f1beccac116b64cd5d0fc5932The article examines the problems of legal regulation of nuclear and radiation safety that arise during the operation of nuclear power plants. On the basis of the legal definition of such legal categories as radiation and nuclear safety, radiation protection, which must be guaranteed by the state to human life and health, the main attention is focused on the analysis of the legal regime of lands as the basis of human life activities that have undergone radioactive contamination, which are legally classified as man-made polluted. The genesis of the legislation that secures the definition of these lands is analyzed, it is emphasized that these lands are the result of human economic activity, that is, it is about the consequences of anthropogenic activity, when highlighting the peculiarities of the legal regime of man-made contaminated lands, their division into radioactively dangerous and radioactively contaminated is taken as a basis. The mentioned lands, being in state ownership, cannot be transferred to private ownership, are included in the exclusion zone and the zone of mandatory (unconditional) resettlement of the territory that has undergone radioactive contamination as a result of the Chernobyl disaster. Residence and permanent stay of people in the territory of these zones is prohibited by law. Land plots located in the zone of guaranteed voluntary resettlement are also radioactively contaminated and are used in accordance with the procedure determined by the Cabinet of Ministers of Ukraine. The amendments to the current legislation are analyzed separately, which relate to the inclusion of radioactively contaminated lands in the ecological network for the purpose of restoration and are related to the creation of the Chernobyl radiation- ecological biosphere reserve. It is emphasized that part of the territory of the reserve, created at the expense of the zone of exclusion and the zone of unconditional (mandatory) resettlement, being on the border with the Republic of Belarus, causes additional restrictions of its legal regime. In this case, we are talking about changing the purpose of a part of the relevant lands, which have been transferred to defense lands. The conclusions emphasize that lands that have undergone radioactive contamination need priority restoration, which today is carried out naturally. The legal mechanism for restoring the state of such lands is imperfect and needs significant changes. It is time to develop and approve the Strategy for returning radioactively contaminated land to economic use.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['M. Shulga']2024-05-11T00:00:00https://www.semanticscholar.org/paper/e4aa48eb17a2d35f1beccac116b64cd5d0fc5932 057cd5e05c522ca58987d8b0ba61fb1d64b37e40In the context of the European integration of Ukraine, the evolution of human rights, the development of civil society, environmental problems in Ukraine, including those caused by the military actions of the aggressor, the study of the problems of legal regulation of the ecological network in Ukraine is of important practical and theoretical importance. Researching the issues of legal regulation of the ecological network in Ukraine is important for the further improvement of environmental legislation, as well as for the improvement of law enforcement practice in this area. As a result of this research, the problems of legal regulation of the ecological network in Ukraine and its elements are important for the development of the sciences of constitutional, administrative and environmental law. The article is devoted to the problems of legal regulation of the ecological network in Ukraine. In this article legal regulation of the ecological network in Ukraine is characterized. In the context of the modern European integration of Ukraine, the evolution of human rights, the development of civil society, environmental problems in Ukraine, including those caused by the military actions of the aggressor, the study of the legal regulation of the ecological network in Ukraine is of great practical and theoretical importance. Ensuring the effective functioning of the ecological network of Ukraine, including the protection of the environment, overcoming the negative consequences of damage to the environment caused by hostilities, prompt demining of de-occupied territories, is an important element for the effective functioning of the system of natural human rights in Ukraine, among which the right for a safe environment for life and health in Ukraine. The concept of "legal regulation of the ecological network in Ukraine” can be defined as a system of norms and principles of law aimed at regulating social relations by legal norms in the field of formation, preservation and rational, tireless use of the ecological network in Ukraine as one of the main prerequisites for ensuring the sustainable development of Ukraine, protection of the environment, protection of flora and fauna, nature conservation areas, satisfaction of ecological, economic, social and other interests of individual citizens, society, territorial communities and the state. The elements of the ecological network of Ukraine includes: key, connecting, buffer and renewable territories.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['L. Vasylchuk', 'R.M. Fridmanskyy']2024-05-11T00:00:00https://www.semanticscholar.org/paper/057cd5e05c522ca58987d8b0ba61fb1d64b37e40 303b872857bdbda6b81f8490cc097ccf4c67b41cThe article examines the impact of digital transformation on the development of legal regulation of labor relations. The author analyzes current novelties of legal regulation of labor relations through the prism of the development of digital technologies. The opinions of leading scientists in the field of labor and other fields of law are given regarding the impact of digitalization on the development of labor relations, and law enforcement practice is analyzed. The potential areas of application of artificial intelligence are considered, as well as the possibilities of automating many production processes with the help of artificial intelligence, which is expected to lead to an increase in product quality and production speed. These issues become especially relevant in the period of growing needs of the state's import substitution policy. The article analyzes the experience of the use of artificial intelligence by European countries, therefore a comparative analysis of the current legislation on the use of artificial intelligence in Ukraine and foreign countries is carried out. Several possible levels of involvement of AI in the field of work are distinguished: work facilitation; labor automation; employee empowerment; full replacement of the employee. At the same time, recruiters note the effectiveness of individual use of ChatGPT in their work, in particular: creation of attractive job descriptions; market research: competitors, salaries, demand, etc.; preparation of interview questions; communication with candidates: correspondence, working with objections, sensitive feedback; search for candidates: advice on resources, creation of Boolean queries (search for web pages using special operators), selection of keywords; resume analysis: keyword recognition to assess the candidate's skills; presentation of the candidate to the hiring manager. The reasons for the «fear of acceptance» of artificial intelligence by modern society are highlighted. The importance of further theoretical studies of this issue, which will contribute to the effective protection and protection of the rights and freedoms of citizens, is emphasized. It was concluded that before the widespread introduction of artificial intelligence, it is worth conducting a study at the state level on unemployment and the socio-economic consequences of the use of artificial intelligence in the field of work, thereby reducing millions of jobs across the country. Will the implementation of AI cost a stable social situation in the country? Labor relations are not only inextricably linked with the individual, but also with the employee's ability to provide for his family members. In the case of replacing workers with robots controlled by artificial intelligence, unemployment can reach huge proportions, due to which crime in the country will inevitably increase, as a person will be in the stage of finding satisfaction of his natural needs for food and shelter. Therefore, it is important that the state has a clear position on the formation of legal, economic and social strategies to solve problems related to «technological unemployment». So now it is necessary to create a list of professions and positions that cannot be replaced by artificial intelligence, to define clear rules (order) of interaction between robots and people, in particular in the case of freelance situations, and in some enterprises, it is possible to set quotas for places occupied by people.Analytical and Comparative Jurisprudence00It was concluded that before the widespread introduction of artificial intelligence, it is worth conducting a study at the state level on unemployment and the socio-economic consequences of the use of artificial intelligence in the field of work, thereby reducing millions of jobs across the country.Analytical and Comparative Jurisprudence['R.Ya. Butynska']2024-05-11T00:00:00https://www.semanticscholar.org/paper/303b872857bdbda6b81f8490cc097ccf4c67b41c 624a467a9cfe761f03b0be3fd49f19940bd18251International Conference on Human Factors in Computing Systems550{'pages': '363:1-363:13'}['Karla Waldenmeier', 'Susanne Poeller', 'M. Dechant', 'Nicola Baumann', 'R. Mandryk']2024-05-11T00:00:00https://www.semanticscholar.org/paper/624a467a9cfe761f03b0be3fd49f19940bd18251 b4db5c8d8f3f0cb3e37305314fb7edbabaca43f1Developing novel research questions (RQs) often requires extensive literature reviews, especially for interdisciplinary fields. Leveraging Large Language Models (LLMs), we built an LLM-based agent sys-tem, called CoQuest , supporting RQ development throughhuman-AI co-creation. We conducted an experimental design with 20 participants to examine the effect of two interaction designs: breadth-first and depth-first RQ generation. The results showed that participants found the breadth-first approach more creative and trustworthy upon task completion. However, during the task, they rated the RQs generated through the depth-first approach as more creative. We also discovered that AI processing delays allowed users to contem-plate multiple RQs simultaneously, resulting in more generated RQs and an increased sense of perceived control. Our work makes both theoretical and practical contributions by proposing and assessing a mental model for human-AI co-creation RQs.International Conference on Human Factors in Computing Systems903This work proposes and assesses a mental model for human-AI co-creation RQs, and discovered that AI processing delays allowed users to contem-plate multiple RQs simultaneously, resulting in more generated RQs and an increased sense of perceived control.{'pages': '17:1-17:25'}['Yiren Liu', 'Si Chen', 'Haocong Cheng', 'Mengxia Yu', 'Xiao Ran', 'Andrew Mo', 'Yiliu Tang', 'Yun Huang']2024-05-11T00:00:00https://www.semanticscholar.org/paper/b4db5c8d8f3f0cb3e37305314fb7edbabaca43f1 b510ed0ffa417cb1695c0c76eb7203756cfdb578While effective for recording and sharing experiences, traditional in-context writing tools are relatively passive and unintelligent, serving more like instruments rather than companions. This reduces primary task (e.g., travel) enjoyment and hinders high-quality writing. Through formative study and iterative development, we introduce PANDALens , a P roactive A I N arrative D ocumentation A ssistant built on an Optical See-Through Head Mounted Display that supports personalized documentation in everyday activities. PANDALens observes multimodal contextual information from user behaviors and environment to confirm interests and elicit contem-plation, and employs Large Language Models to transform such multimodal information into coherent narratives with significantly reduced user effort. A real-world travel scenario comparing PAN-DALens with a smartphone alternative confirmed its effectiveness in improving writing quality and travel enjoyment while minimizing user effort. Accordingly, we propose design guidelines for AI-assisted in-context writing, highlighting the potential of transforming them from tools to intelligent companions.International Conference on Human Factors in Computing Systems1011This work introduces PANDALens, a PANDALens built on an Optical See-Through Head Mounted Display that supports personalized documentation in everyday activities and proposes design guidelines for AI-assisted in-context writing, highlighting the potential of transforming them from tools to intelligent companions.{'pages': '1053:1-1053:24'}['Runze Cai', 'Nuwan Janaka', 'Yang Chen', 'Lucia J. Wang', 'Shengdong Zhao', 'Can Liu']2024-05-11T00:00:00https://www.semanticscholar.org/paper/b510ed0ffa417cb1695c0c76eb7203756cfdb578 0d27aae74c2e87920b1a4fad41d83908e02870efIn the era of technological ascendancy, "Rise of the Algorithm: A Journey into the World of AI" embarks on a compelling exploration of the dynamic and ever-evolving domain of artificial intelligence. This journey unfolds through the lens of algorithms, the bedrock of AI, guiding readers through the intricate landscapes of machine learning, neural networks, and the convergence of data-driven intelligence. The abstract navigates the transformative impact of AI on diverse sectors, spanning from healthcare to finance, and delves into the ethical considerations inherent in the rise of intelligent algorithms. As we traverse this captivating odyssey, we discover the profound implications, challenges, and potential of AI, illuminating the path forward into a future shaped by the relentless rise of the algorithm.International Journal of Innovative Science and Research Technology171The abstract navigates the transformative impact of AI on diverse sectors, spanning from healthcare to finance, and delves into the ethical considerations inherent in the rise of intelligent algorithms.International Journal of Innovative Science and Research Technology (IJISRT)['Kanduri Abhinay']2024-05-11T00:00:00https://www.semanticscholar.org/paper/0d27aae74c2e87920b1a4fad41d83908e02870ef 3b178d2c71b8eaeca4ccbbc5f6dd0108b903b841This paper explores how the combination of artificial intelligence (AI) can enhance governance in cryptocurrency communities and Decentralized Autonomous Organizations (DAOs). Using insights from blockchain, machine studying, and social computing, we examine moral concerns and dangers While addressing them, we discuss the potential of AI to improve efficiency, transparency and inclusion in phrases of governance shape. Through case studies, we demonstrate sensible packages of AI, consisting of social media sentiment analysis, algorithmic trading, and decentralized forecasting markets. We explore the impact of AI on governance token systems, selection- making processes and community-pushed governance models. Challenges along with algorithmic bias, records privacy, and the need for human oversight are discussed in conjunction with suggested studies suggestions and great practices for implementing responsible AI. This paper explores how the integration of synthetic intelligence (AI) can enhance governance in cryptocurrency communities and decentralized c Decentralized Autonomous Organizations (DAOs). Using insights from blockchain, gadget learning, and social computing, we examine moral worries and dangers While addressing them, we talk the potential of AI to enhance performance, transparency and inclusion in terms of governance structure. Through case research, we display realistic programs of AI, which include social media sentiment evaluation, algorithmic trading, and decentralized forecasting markets. We discover the effect of AI on governance token systems, decision-making methods and network-pushed governance models. Challenges including algorithmic bias, records privateness, and the need for human oversight are mentioned in conjunction with suggested research tips and exceptional practices for the responsible use of AI By clarifying the ability of AI in cryptocurrency governance, we help bridge the space among AI and decentralized selection-making.International Journal of Innovative Science and Research Technology251By clarifying the ability of AI in cryptocurrency governance, this paper helps bridge the space among AI and decentralized selection-making.International Journal of Innovative Science and Research Technology (IJISRT)['T. A. Victorie', 'M. Vasuki', 'Sakthi Ganapathy S']2024-05-11T00:00:00https://www.semanticscholar.org/paper/3b178d2c71b8eaeca4ccbbc5f6dd0108b903b841 53cbaf8d71d1c09ab562766c8295756c1d2810f3Time series prediction is a fundamental problem in scientific exploration and artificial intelligence (AI) technologies have substantially bolstered its efficiency and accuracy. A well-established paradigm in AI-driven time series prediction is injecting physical knowledge into neural networks through signal decomposition methods, and sustaining progress in numerous scenarios has been reported. However, we uncover non-negligible evidence that challenges the effectiveness of signal decomposition in AI-based time series prediction. We confirm that improper dataset processing with subtle future label leakage is unfortunately widely adopted, possibly yielding abnormally superior but misleading results. By processing data in a strictly causal way without any future information, the effectiveness of additional decomposed signals diminishes. Our work probably identifies an ingrained and universal error in time series modeling, and the de facto progress in relevant areas is expected to be revisited and calibrated to prevent future scientific detours and minimize practical losses.100This work probably identifies an ingrained and universal error in time series modeling, and the de facto progress in relevant areas is expected to be revisited and calibrated to prevent future scientific detours and minimize practical losses.['Kexin Jiang', 'Chuhan Wu', 'Yaoran Chen']2024-05-11T00:00:00https://www.semanticscholar.org/paper/53cbaf8d71d1c09ab562766c8295756c1d2810f3 eef2fb5dc309e007557f5d493e782676041b3bd8In today's research and development, artificial intelligence (AI) ethics are a complex and urgent issue. Concerns about artificial intelligence (AI) systems' possible effects on people, communities, and the larger global environment are raised as these systems are incorporated into more and more facets of society. This study examines the ethical implications of artificial intelligence (AI), looking at topics including privacy, fairness, accountability, transparency, and the possibility of prejudice and discrimination in AI algorithms and decision-making processes. The study endeavours to contribute to the establishment of frameworks and rules that encourage the responsible and ethical use of AI technologies, guaranteeing their conformity with society values and the preservation of human rights, by critically assessing these ethical issues. Keywords:-AI ethics , artificial intelligence, ethics, machine ethics, robotics, challenges.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This study examines the ethical implications of artificial intelligence (AI), looking at topics including privacy, fairness, accountability, transparency, and the possibility of prejudice and discrimination in AI algorithms and decision-making processes.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Pragya K. K']2024-05-11T00:00:00https://www.semanticscholar.org/paper/eef2fb5dc309e007557f5d493e782676041b3bd8 eadef648203458a4a435e67cd24f313b39712958Recently, Artificial intelligence (AI) has significantly influenced academic writing. We aimed to investigate the sensitivity of the free versions of popular AI-detection software programs in detecting AI-generated text. We searched for AI-content-detection software on Google and selected the first 10 free versions that allowed a minimum of 500 words for text analysis. Then, we gave ChatGPT 3.5 version a command to generate a scientific article on the “Role of Electroconvulsive Therapy (ECT) in Treatment-resistant Depression” under 500 words. After generating the primary text, we rephrased it using three different software tools. We then used AI-detection software to analyse the original and paraphrase texts. 10 AI-detector tools were tested on their ability to detect AI-generated text. The sensitivity ranged from 0% to 100%. 5 out of 10 tools detected AI-generated content with 100% accuracy. For paraphrased texts, Sapling and Undetectable AI detected all three software-generated contents with 100% accuracy. Meanwhile, Copyleaks, QuillBot, and Wordtune identified content generated by two software programs with 100% accuracy. The integration of AI technology in academic writing is becoming more prevalent. Nonetheless, relying solely on AI-generated content can diminish the author’s credibility, leading most academic journals to suggest limiting its use. AI-content-detection software programs have been developed to detect AI-generated or AI-assisted texts. Currently, some of the platforms are equally sensitive. However, future upgrades may enhance their ability to detect AI-generated text more accurately.Indian Journal of Psychological Medicine30The sensitivity of the free versions of popular AI-detection software programs in detecting AI-generated text was investigated to determine whether future upgrades may enhance their ability to detect AI-generated text more accurately.Indian Journal of Psychological Medicine['S. Kar', 'Teena Bansal', 'Sumit Modi', 'Amit Singh']2024-05-11T00:00:00https://www.semanticscholar.org/paper/eadef648203458a4a435e67cd24f313b39712958 40d74f6ff1621acff786ae00894647bc8438e747This article examines aspects of the intersection of artificial intelligence (AI) and religion, challenging Western Christian perspectives that warn against playing God and ascribing human and God-like characteristics to AI. Instead of a theistic emphasis, East Asian religious perspectives emphasize concern for the potential implications of AI on communities and relationships. This article argues for the inclusion of perspectives from Chinese and Korean traditions in the growing discourse on AI and religion to adequately address the potential social impacts of AI technologies. First, we describe some of the questions and concerns being posed regarding AI and consider how certain normative interpretations of Western Christianity may influence some of these issues. Second, we discuss the contributions of Asian philosophies and religious traditions, which emphasize relationality and fluidity, to provide alternative approaches to AI. Third, we outline the discussion of AI from Confucian, Daoist, and Buddhist traditions, which see the cosmos as an interwoven whole and both humans and the cosmos as evolving. Lastly, we introduce the example of digital resurrection (e.g., deadbots) and consider how the philosophical and theological Korean concept of Jeong might refocus our understanding of the potential impacts of this AI technology.Religions190It is argued for the inclusion of perspectives from Chinese and Korean traditions in the growing discourse on AI and religion to adequately address the potential social impacts of AI technologies.Religions['Tracy J. Trothen', 'Pui Lan Kwok', 'Boyung Lee']2024-05-11T00:00:00https://www.semanticscholar.org/paper/40d74f6ff1621acff786ae00894647bc8438e747 9b3471314b67c68151a51a78b2e542f9665e4213This study investigates the prevalent use of artificial intelligence (AI) among college students at Far Eastern University. Despite the risks, it aims to understand the primary reason behind the continuous use of AI. According to academics, the benefits of implementing AI in higher education include better inclusion, increased efficiency in administrative costs, and improvements in the learning-teaching process (Pisica et al., 2023). However, the extreme utilization of AI among students has led to cheating and plagiarism for many reasons that impact their personal lives and mental health. The researcher identified potential gaps while assessing possible solutions to lessen the student using Artificial Intelligence (AI) excessively. Moreover, the researcher used a quantitative method analysis involving 40 college students from 1st-year level to 4th-year level to explore the impacts of Artificial Intelligence on their academic tasks and learning styles. Thus, examining the utilization of AI from different year levels of Far Eastern University college students revealed that the researcher provides valuable insights into addressing the challenges posed by excessive dependency on AI while maintaining academic integrity and the need for the students to develop their critical thinking skills. After the researcher analyzed the collected data, it showed that the utilization of Artificial Intelligence (AI) for academic workloads varies among participants covering different college students, showing that first-year students rely on AI due to peer pressure. In contrast, the second-year students use it to improve their academic standing. Third-year students depend on AI because of time constraints, while fourth-year students use AI to minimize the possibility of human errors. The study conveys no significant differences in the probability of using AI for academic purposes, and it does not prevent the students from using AI regardless of their year level. Therefore, the researcher recommends proposing stricter AI checkers and educating the students on responsible Artificial Intelligence (AI) usage to mitigate academic misconduct.International Journal of Current Science Research and Review140Examining the utilization of AI from different year levels of Far Eastern University college students revealed that the researcher provides valuable insights into addressing the challenges posed by excessive dependency on AI while maintaining academic integrity and the need for the students to develop their critical thinking skills.International Journal of Current Science Research and Review['Abhira Charmit Dela Rosa', 'Arianne Kaye C. Dacuma', 'Carmencita Angelez B. Ang', 'Cristine Joy R. Nudalo', 'Leshamei J. Cruz', 'Mc Rollyn D. Vallespin']2024-05-11T00:00:00https://www.semanticscholar.org/paper/9b3471314b67c68151a51a78b2e542f9665e4213 33cf37705ba20a78a32fbbb8a2cd080a08d58b25Foundational multi-modal models have democratized AI access, yet the construction of complex, customizable machine learning pipelines by novice users remains a grand challenge. This paper demonstrates a visual programming system that allows novices to rapidly prototype multimodal AI pipelines. We first conducted a formative study with 58 contributors and collected 236 proposals of multimodal AI pipelines that served various practical needs. We then distilled our findings into a design matrix of primitive nodes for prototyping multimodal AI visual programming pipelines, and implemented a system with 65 nodes. To support users’ rapid prototyping experience, we built InstructPipe, an AI assistant based on large language models (LLMs) that allows users to generate a pipeline by writing text-based instructions. We believe InstructPipe enhances novice users onboarding experience of visual programming and the controllability of LLMs by offering non-experts a platform to easily update the generation.CHI Extended Abstracts330{'pages': '402:1-402:5'}['Zhongyi Zhou', 'Jing Jin', 'Vrushank Phadnis', 'Xiuxiu Yuan', 'Jun Jiang', 'Xun Qian', 'Jingtao Zhou', 'Yiyi Huang', 'Zheng Xu', 'Yinda Zhang', 'Kristen Wright', 'Jason Mayes', 'Mark Sherwood', 'Johnny Lee', 'A. Olwal', 'David Kim', 'Ram Iyengar', 'Na Li', 'Ruofei Du']2024-05-11T00:00:00https://www.semanticscholar.org/paper/33cf37705ba20a78a32fbbb8a2cd080a08d58b25 89047dea49ed8078b4aaec1343936117e84797b4AI-generated images are proliferating as a new visual medium. However, state-of-the-art image generation models do not output alternative (alt) text with their images, rendering them largely inaccessible to screen reader users (SRUs). Moreover, less is known about what information would be most desirable to SRUs in this new medium. To address this, we invited AI image creators and SRUs to evaluate alt text prepared from various sources and write their own alt text for AI images. Our mixed-methods analysis makes three contributions. First, we highlight creators’ perspectives on alt text, as creators are well-positioned to write descriptions of their images. Second, we illustrate SRUs’ alt text needs particular to the emerging medium of AI images. Finally, we discuss the promises and pitfalls of utilizing text prompts written as input for AI models in alt text generation, and areas where broader digital accessibility guidelines could expand to account for AI images.International Conference on Human Factors in Computing Systems810This work invited AI image creators and SRUs to evaluate alt text prepared from various sources and write their own alt text for AI images, and highlights creators’ perspectives on alt text, as creators are well-positioned to write descriptions of their images.{'pages': '900:1-900:21'}['Maitraye Das', 'Alexander J. Fiannaca', 'Meredith Ringel Morris', 'Shaun Kane', 'Cynthia L. Bennett']2024-05-11T00:00:00https://www.semanticscholar.org/paper/89047dea49ed8078b4aaec1343936117e84797b4 595de1049db498ac5cbe9d63fe70a045a96e4cc5An emerging area of research explores the impact of artificial intelligence (AI) on workers' emotional intelligence (EQ) in the workplace. EQ encompasses the ability to perceive, process, manage, and effectively utilize one's own and others' emotional states, crucial for deep interpersonal understanding. Leadership, teamwork, conflict resolution, and employee well-being are domains significantly influenced by EQ in the workplace. The utilization of AI tools such as chatbots, emotion analysis, and sentiment detection in the workplace is an evolving topic. Emotion AI is gaining traction in organizational settings and holds great potential for enhancing productivity. However, there is limited understanding of employees' perceptions and experiences regarding its implementation. To address this gap, we conducted interviews with 80 IT professionals in Pune. Our findings reveal that (1) participants perceive emotion AI as a potential intrusion into the privacy of their emotional data, (2) it may compel adherence to personal work expectations, and (3) employees may engage in personal work to safeguard their emotional privacy. These results underscore the importance of research and policy considerations concerning the preservation of personal privacy in the workplace, as well as the recognition and delineation of an individual's right to what we term emotional autonomy.International Journal For Multidisciplinary Research90The findings reveal that participants perceive emotion AI as a potential intrusion into the privacy of their emotional data, it may compel adherence to personal work expectations, and employees may engage in personal work to safeguard their emotional privacy.International Journal For Multidisciplinary Research['Taleb Hammad']2024-05-11T00:00:00https://www.semanticscholar.org/paper/595de1049db498ac5cbe9d63fe70a045a96e4cc5 084b252378da7ee0c4ad82aeb10a46661b050a82CHI Extended Abstracts101{'pages': '468:1-468:4'}['Steeven Villa', 'Robin Welsch', 'Alena Denisova', 'Thomas Kosch']2024-05-11T00:00:00https://www.semanticscholar.org/paper/084b252378da7ee0c4ad82aeb10a46661b050a82 2084dffe05ce8bfe5b6b21b8824dca8742c2ff2aInternational Conference on Human Factors in Computing Systems741{'pages': '104:1-104:30'}['V. D. Swain', 'Lan Gao', 'Abhirup Mondal', 'G. Abowd', 'Munmun De Choudhury']2024-05-11T00:00:00https://www.semanticscholar.org/paper/2084dffe05ce8bfe5b6b21b8824dca8742c2ff2a 303f78dd766f95adbedb1eb6b07a8f2ed8492e0bCHI Extended Abstracts71{'pages': '584:1-584:5'}['Passant Elagroudy', 'Jie Li', 'Kaisa Väänänen', 'Paul Lukowicz', 'Hiroshi Ishii', 'Wendy E. Mackay', 'Elizabeth F Churchill', 'Anicia Peters', 'A. Oulasvirta', 'Rui Prada', 'Alexandra Diening', 'G. Barbareschi', 'Agnes Gruenerbl', 'Midori Kawaguchi', 'Abdallah El Ali', 'Fiona Draxler', 'Robin Welsch', 'Albrecht Schmidt']2024-05-11T00:00:00https://www.semanticscholar.org/paper/303f78dd766f95adbedb1eb6b07a8f2ed8492e0b 58c21b7488ec9d5cdf51507c740277acacf10f3dInternational Conference on Human Factors in Computing Systems641{'pages': '678:1-678:17'}['Ariel Han', 'Xiaofei Zhou', 'Zhenyao Cai', 'Shenshen Han', 'Richard Ko', 'Seth Corrigan', 'Kylie A Peppler']2024-05-11T00:00:00https://www.semanticscholar.org/paper/58c21b7488ec9d5cdf51507c740277acacf10f3d 43b27fc46290a5b4255bb1ed1c44d0bf2271a295International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-05-11T00:00:00https://www.semanticscholar.org/paper/43b27fc46290a5b4255bb1ed1c44d0bf2271a295 0cd7b836b44ee982136a5d6dc3fa65f9d9f89a5bCHI Extended Abstracts130{'pages': '5:1-5:6'}['Panagiotis Germanakos']2024-05-11T00:00:00https://www.semanticscholar.org/paper/0cd7b836b44ee982136a5d6dc3fa65f9d9f89a5b 5c829a8562a3acac28d13fa38af59bd2f7e5bb98International Conference on Human Factors in Computing Systems450{'pages': '435:1-435:16'}['A. Hwang', 'Daniel A. Adler', 'Meir Friedenberg', 'Qian Yang']2024-05-11T00:00:00https://www.semanticscholar.org/paper/5c829a8562a3acac28d13fa38af59bd2f7e5bb98 e064b3e88963326a633db3a5685dc13a65450bddCHI Extended Abstracts60{'pages': '458:1-458:5'}['Jed R. Brubaker', 'Meredith Ringel Morris', 'Dylan Thomas Doyle', 'Casey Fiesler', 'Martin Gibbs', 'Joanna Mcgrenere']2024-05-11T00:00:00https://www.semanticscholar.org/paper/e064b3e88963326a633db3a5685dc13a65450bdd 35fe300d2e45723d99ceb6c475dfd890318d4f05International Conference on Human Factors in Computing Systems690{'pages': '399:1-399:21'}['Ian René Solano-Kamaiko', 'Dibyendu Mishra', 'Nicola Dell', 'Aditya Vashistha']2024-05-11T00:00:00https://www.semanticscholar.org/paper/35fe300d2e45723d99ceb6c475dfd890318d4f05 a9bfe48b9b11b020ab891c5c0a73625d5015959cCHI Extended Abstracts140{'pages': '501:1-501:7'}['Yuwen Lu', 'Tiffany Knearem', 'Shona Dutta', 'Jamie Blass', 'Clara E Kliman-Silver', 'Frank Bentley']2024-05-11T00:00:00https://www.semanticscholar.org/paper/a9bfe48b9b11b020ab891c5c0a73625d5015959c b0ef0eb1953402f0ead0e223266f11c89c699cf2International Conference on Human Factors in Computing Systems870{'pages': '437:1-437:20'}['Yuexing Hao', 'Zeyu Liu', 'Robert N. Riter', 'Saleh Kalantari']2024-05-11T00:00:00https://www.semanticscholar.org/paper/b0ef0eb1953402f0ead0e223266f11c89c699cf2 636ba964e7957ddf991a61b2518e05d959f9f3d9CHI Extended Abstracts00{'pages': '597:1-597:3'}['Jonathan Grudin', 'Donald Brinkman']2024-05-11T00:00:00https://www.semanticscholar.org/paper/636ba964e7957ddf991a61b2518e05d959f9f3d9 48ff320d751efdeef78357a8aaaaac43b8c395b6CHI Extended Abstracts60{'pages': '586:1-586:5'}['Joseph Kaye', 'Jaime Teevan', 'Victoria Bellotti', 'Lauren Wilcox']2024-05-11T00:00:00https://www.semanticscholar.org/paper/48ff320d751efdeef78357a8aaaaac43b8c395b6 4e656182cb95218dcd8750dd500071bb5321381eCHI Extended Abstracts220{'pages': '493:1-493:6'}['Qiaosi Wang', 'Sarah Walsh', 'Mei Si', 'Jeffrey Kephart', 'Justin D. Weisz', 'Ashok K. Goel']2024-05-11T00:00:00https://www.semanticscholar.org/paper/4e656182cb95218dcd8750dd500071bb5321381e 2aa31889f5e390c781c42b2446115fda24c15855CHI Extended Abstracts160{'pages': '49:1-49:6'}['Charlotte Bird']2024-05-11T00:00:00https://www.semanticscholar.org/paper/2aa31889f5e390c781c42b2446115fda24c15855 1a825db0f31ef7f3128d1c4be35e2b633e8b3cf7CHI Extended Abstracts100{'pages': '571:1-571:9'}['Gregg C Vanderheiden', 'Crystal Yvette Marte']2024-05-11T00:00:00https://www.semanticscholar.org/paper/1a825db0f31ef7f3128d1c4be35e2b633e8b3cf7 8f041c03023f46a74cc32cc408c89897b7e2059bUsers ask large language models (LLMs) to help with their home-work, for lifestyle advice, or for support in making challenging decisions. Yet LLMs are often unable to fulfil these requests, either as a result of their technical inabilities or policies restricting their responses. To investigate the effect of LLMs denying user requests, we evaluate participants’ perceptions of different denial styles. We compare specific denial styles (baseline, factual, diverting, and opinionated) across two studies, respectively focusing on LLM’s technical limitations and their social policy restrictions. Our results indicate significant differences in users’ perceptions of the denials between the denial styles. The baseline denial, which provided participants with brief denials without any motivation, was rated significantly higher on frustration and significantly lower on usefulness, appropriateness, and relevance. In contrast, we found that participants generally appreciated the diverting denial style. We provide design recommendations for LLM denials that better meet peoples’ denial expectations.International Conference on Human Factors in Computing Systems480Design recommendations for LLM denials that better meet peoples’ denial expectations are provided, and it is found that participants generally appreciated the diverting denial style.{'pages': '979:1-979:14'}['Joel Wester', 'Tim Schrills', 'Henning Pohl', 'Niels van Berkel']2024-05-11T00:00:00https://www.semanticscholar.org/paper/8f041c03023f46a74cc32cc408c89897b7e2059b dc1340d59b3c96239ed299206c3eff5a2826ae0bHumanities and Social Sciences Communications1190This paper delves into the complexities of authorship, consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life.Humanities and Social Sciences Communications['Cédric Sueur', 'Jessica Lombard', 'Olivier Capra', 'Benjamin Beltzung', 'Marie Pelé']2024-05-11T00:00:00https://www.semanticscholar.org/paper/dc1340d59b3c96239ed299206c3eff5a2826ae0b a304ecb1c2fcf1348c7b0471b896f68d2727076aCHI Extended Abstracts10{'pages': '600:1-600:3'}['Daniel M. Russell', 'Chinmay Kulkarni', 'Elena L. Glassman', 'Hariharan Subramonyam', 'Nikolas Martelaro']2024-05-11T00:00:00https://www.semanticscholar.org/paper/a304ecb1c2fcf1348c7b0471b896f68d2727076a c2f3bce5267389c03c817e3b13cb84bc9de2c1a7CHI Extended Abstracts120{'pages': '105:1-105:8'}['Anders Gammelgård-Larsen', 'Niels van Berkel', 'M. Skov', 'J. Kjeldskov']2024-05-11T00:00:00https://www.semanticscholar.org/paper/c2f3bce5267389c03c817e3b13cb84bc9de2c1a7 d707bd05bd69885569b2fc78f966c3c2f1ecdef1CHI Extended Abstracts120{'pages': '570:1-570:9'}['Petra Jääskeläinen', 'Cecilia Åsberg']2024-05-11T00:00:00https://www.semanticscholar.org/paper/d707bd05bd69885569b2fc78f966c3c2f1ecdef1 847a6b5933af6e2577c11f9f2011e13e887b040dCHI Extended Abstracts100{'pages': '83:1-83:6'}['B. Chaudhry']2024-05-11T00:00:00https://www.semanticscholar.org/paper/847a6b5933af6e2577c11f9f2011e13e887b040d 88632c77ad0d3c94bf2dc73460c1addb62fbacd7CHI Extended Abstracts70{'pages': '502:1-502:8'}['S. Mozgai', 'Cari Kaurloto', 'Jade G Winn', 'Andrew Leeds', 'Sarah Beland', 'Arman Sookiassian', 'Arno Hartholt']2024-05-11T00:00:00https://www.semanticscholar.org/paper/88632c77ad0d3c94bf2dc73460c1addb62fbacd7 4b09a3507b1282d2110e7d4e5973bbd5f5c03e59International Conference on Human Factors in Computing Systems590{'pages': '480:1-480:21'}['Astrid Bertrand', 'James R. Eagan', 'Winston Maxwell', 'Joshua Brand']2024-05-11T00:00:00https://www.semanticscholar.org/paper/4b09a3507b1282d2110e7d4e5973bbd5f5c03e59 0903b92dcb32ca785a26d6e509adc3fb26ce8022International Conference on Human Factors in Computing Systems420{'pages': '96:1-96:15'}['Anna Xygkou', 'Chee Siang Ang', 'Panote Siriaraya', 'Jonasz Piotr Kopecki', 'A. Covaci', 'E. Kanjo', 'W. She']2024-05-11T00:00:00https://www.semanticscholar.org/paper/0903b92dcb32ca785a26d6e509adc3fb26ce8022 b759ecf94aa119cadbe5405d399183c9a11b1689International Conference on Human Factors in Computing Systems650{'pages': '782:1-782:21'}['Sumit Asthana', 'Jane Im', 'Zhe Chen', 'Nikola Banovic']2024-05-11T00:00:00https://www.semanticscholar.org/paper/b759ecf94aa119cadbe5405d399183c9a11b1689 65bb5331cb4d87324fd7a5fa5579d3d45fd7773bCHI Extended Abstracts120{'pages': '403:1-403:6'}['L. Wen', 'C. Morrison', 'Martin Grayson', 'R. Marques', 'Daniela Massiceti', 'Camilla Longden', 'Edward Cutrell']2024-05-11T00:00:00https://www.semanticscholar.org/paper/65bb5331cb4d87324fd7a5fa5579d3d45fd7773b 360a8fa00bad3d0c054d7c38ccfa1ff910e5059cInternational Conference on Human Factors in Computing Systems1220{'pages': '25:1-25:17'}['Sara Salimzadeh', 'Gaole He', 'U. Gadiraju']2024-05-11T00:00:00https://www.semanticscholar.org/paper/360a8fa00bad3d0c054d7c38ccfa1ff910e5059c 62cf17b8fd821d4a2635a30ac9049e7a78c7c28bInternational Conference on Human Factors in Computing Systems240{'pages': '745:1-745:14'}['Katharina Weitz', 'Ruben Schlagowski', 'Elisabeth André', 'Maris Männiste', 'Ceenu George']2024-05-11T00:00:00https://www.semanticscholar.org/paper/62cf17b8fd821d4a2635a30ac9049e7a78c7c28b bb03bf66edc989059bd216643c246567cbeb9b61International Conference on Human Factors in Computing Systems480{'pages': '268:1-268:16'}['M. H. Kaspersen', 'Line Have Musaeus', 'K. Bilstrup', 'Marianne Graves Petersen', 'O. Iversen', 'Christian Dindler', 'Peter Dalsgaard']2024-05-11T00:00:00https://www.semanticscholar.org/paper/bb03bf66edc989059bd216643c246567cbeb9b61 a8e40f6071d5eb67159c4d11a84c60a600244059CHI Extended Abstracts190{'pages': '1:1-1:7'}['Ruchi Panchanadikar', 'Guo Freeman', 'Lingyuan Li', 'Kelsea Schulenberg', 'Yang Hu']2024-05-11T00:00:00https://www.semanticscholar.org/paper/a8e40f6071d5eb67159c4d11a84c60a600244059 da5d8dd25898bbdcd3d212e015cd4730bbd3d3d2International Conference on Human Factors in Computing Systems350{'pages': '776:1-776:17'}['Saniat Javid Sohrawardi', 'Y. K. Wu', 'Andrea Hickerson', 'Matthew Wright']2024-05-11T00:00:00https://www.semanticscholar.org/paper/da5d8dd25898bbdcd3d212e015cd4730bbd3d3d2 b0017f8ec1ecbf72a9c08de5b1fa66ad8dfc6977International Conference on Human Factors in Computing Systems340{'pages': '439:1-439:17'}['Dajung Kim', 'N.J.H. Vegt', 'V. Visch', 'Marina Bos-De Vos']2024-05-11T00:00:00https://www.semanticscholar.org/paper/b0017f8ec1ecbf72a9c08de5b1fa66ad8dfc6977 1f332772568cb1334451da8e4c7e4ba252c9a09aSince their inception, programming languages have trended towards greater readability and lower barriers for programmers. Following this trend, natural language can be a promising type of programming language that provides great flexibility and usability and helps towards the democracy of programming. However, the inherent vagueness, ambiguity, and verbosity of natural language pose significant challenges in developing an interpreter that can accurately understand the programming logic and execute instructions written in natural language. Fortunately, recent advancements in Large Language Models (LLMs) have demonstrated remarkable proficiency in interpreting complex natural language. Inspired by this, we develop a novel system for Code Representation and Execution (CoRE), which employs LLM as interpreter to interpret and execute natural language instructions. The proposed system unifies natural language programming, pseudo-code programming, and flow programming under the same representation for constructing language agents, while LLM serves as the interpreter to interpret and execute the agent programs. In this paper, we begin with defining the programming syntax that structures natural language instructions logically. During the execution, we incorporate external memory to minimize redundancy. Furthermore, we equip the designed interpreter with the capability to invoke external tools, compensating for the limitations of LLM in specialized domains or when accessing real-time information. This work is open-source at https://github.com/agiresearch/CoRE, https://github.com/agiresearch/OpenAGI, and https://github.com/agiresearch/AIOS.500This work develops a novel system for Code Representation and Execution (CoRE), which employs LLM as interpreter to interpret and execute natural language instructions, and unifies natural language programming, pseudo-code programming, and flow programming under the same representation for constructing language agents.['Shuyuan Xu', 'Zelong Li', 'Kai Mei', 'Yongfeng Zhang']2024-05-11T00:00:00https://www.semanticscholar.org/paper/1f332772568cb1334451da8e4c7e4ba252c9a09a 21115266c11c5c890dc356e25f4a18eafb7ecabfInternational Conference on Human Factors in Computing Systems380{'pages': '436:1-436:16'}['Paul Calle', 'Ruosi Shao', 'Yunlong Liu', 'Emily T Hébert', 'Darla E. Kendzor', 'JM Neil', 'Michael S. Businelle', 'Chongle Pan']2024-05-11T00:00:00https://www.semanticscholar.org/paper/21115266c11c5c890dc356e25f4a18eafb7ecabf cd6e4a48dd1b333127561d85136f7194ff0dbdb7International Conference on Human Factors in Computing Systems830{'pages': '468:1-468:20'}['Sheree May Saßmannshausen', 'Nazmun Nisat Ontika', 'Aparecido Fabiano Pinatti de Carvalho', 'M. Rouncefield', 'V. Pipek']2024-05-11T00:00:00https://www.semanticscholar.org/paper/cd6e4a48dd1b333127561d85136f7194ff0dbdb7 e467b6788e95bb7775b8fd43655ea13f13b06218CHI Extended Abstracts140{'pages': '88:1-88:10'}['Brenna Li', 'Amy Wang', 'Patricia Strachan', 'Julie Anne Séguin', 'Sami Lachgar', 'Karyn C Schroeder', 'Mathias Fleck', 'Renee Wong', 'A. Karthikesalingam', 'Vivek Natarajan', 'Yossi Matias', 'G. Corrado', 'D. Webster', 'Yun Liu', 'N. Hammel', 'R. Sayres', 'Christopher Semturs', 'M. Schaekermann']2024-05-11T00:00:00https://www.semanticscholar.org/paper/e467b6788e95bb7775b8fd43655ea13f13b06218 d1a7922c0f4fd612b74a10d0fc762028fb23ac77CHI Extended Abstracts160{'pages': '119:1-119:7'}['Manoj Deshpande', 'Brian Magerko']2024-05-11T00:00:00https://www.semanticscholar.org/paper/d1a7922c0f4fd612b74a10d0fc762028fb23ac77 562d393bf7677f65a61ca652569859fa46cca0d3Understanding how communities experience algorithms is necessary to mitigate potential harmful impacts. This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning systems. This research draws on data collected from a workshop with 15 artists from 10 countries who incorporate T2I models in their creative practice. Through reflexive thematic analysis of work-shop data, we highlight artist folk theories of T2I use, harm, and harm reduction. Folk theories of use envision T2I models as an artistic medium, a mundane tool, and locate true creativity as rising above model affordances. Theories of harm articulate T2I models as harmed by engineering efforts to eliminate glitches and product policy efforts to limit functionality. Theories of harm-reduction orient towards protecting T2I models for creative practice through transparency and distributed governance. We examine how these theories relate, and conclude by discussing how folk theorization informs responsible AI efforts.International Conference on Human Factors in Computing Systems1290This paper presents folk theories of text-to-image (T2I) models to enrich understanding of how artist communities experience creative machine learning systems and examines how folk theorization informs responsible AI efforts.{'pages': '32:1-32:17'}['Renee Shelby', 'Shalaleh Rismani', 'Negar Rostamzadeh']2024-05-11T00:00:00https://www.semanticscholar.org/paper/562d393bf7677f65a61ca652569859fa46cca0d3 08b11beee8946e4bf2e29917f31671e2d4344018BMC Medical Imaging350This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection, demonstrating a significant improvement in model performance.BMC Medical Imaging['Mohamed Musthafa M', 'M. T R', 'V. V', 'Suresh Guluwadi']2024-05-11T00:00:00https://www.semanticscholar.org/paper/08b11beee8946e4bf2e29917f31671e2d4344018 c3bd44be5f7dee8a66582375d1e1d620e68227b0Technology is transforming service delivery and practice in many regulated professions, altering required skills, scopes of practice, and the organization of professional work. Professional regulators face considerable pressure to facilitate technology-enabled work while adapting to digital changes in their practices and procedures. However, our understanding of how regulators are responding to technology-driven risks and the impact of technology on regulatory policy is limited. To examine the impact of technology and digitalization on regulation, we conducted an exploratory case study of the regulatory bodies for nursing, law, and social work in Ontario, Canada. Data were collected over two phases. First, we collected documents from the regulators’ websites and regulatory consortiums. Second, we conducted key informant interviews with two representatives from each regulator. Data were thematically analyzed to explore the impact of technological change on regulatory activities and policies and to compare how regulatory structure and field shape this impact. Five themes were identified in our analysis: balancing efficiency potential with risks of certain technological advances; the potential for improving regulation through data analytics; considering how to regulate a technologically competent workforce; recalibrating pandemic emergency measures involving technology; and contemplating the future of technology on regulatory policy and practice. Regulators face ongoing challenges with providing equity-based approaches to regulating virtual practice, ensuring practitioners are technologically competent, and leveraging regulatory data to inform decision-making. Policymakers and regulators across Canada and internationally should prioritize risk-balanced policies, guidelines, and practice standards to support professional practice in the digital era.PLoS ONE720PLOS ONE['Kathleen Leslie', 'Sophia Myles', 'Abeer A Alraja', 'Patrick Chiu', 'Catharine J. Schiller', 'Sioban Nelson', 'Tracey L Adams']2024-05-10T00:00:00https://www.semanticscholar.org/paper/c3bd44be5f7dee8a66582375d1e1d620e68227b0 cb9d655e857d6cdd755c46f5fbfbbbb85e241f74Exploring the degree to which phenotypic variation, influenced by intrinsic nonlinear biological mechanisms, can be accurately captured using statistical methods is essential for advancing our comprehension of complex biological systems and predicting their functionality. Here, we examine this issue by combining a computational model of gene regulation networks with a linear additive prediction model, akin to polygenic scores utilized in genetic analyses. Inspired by the variational framework of quantitative genetics, we create a population of individual networks possessing identical topology yet showcasing diversity in regulatory strengths. By discerning which regulatory connections determine the prediction of phenotypes, we contextualize our findings within the framework of core and peripheral causal determinants, as proposed by the omnigenic model of complex traits. We establish connections between our results and concepts such as global sensitivity and local stability in dynamical systems, alongside the notion of sloppy parameters in biological models. Furthermore, we explore the implications of our investigation for the broader discourse surrounding the role of epistatic interactions in the prediction of complex phenotypes.bioRxiv430A computational model of gene regulation networks with a linear additive prediction model, akin to polygenic scores utilized in genetic analyses is examined, inspired by the variational framework of quantitative genetics, to create a population of individual networks possessing identical topology yet showcasing diversity in regulatory strengths.bioRxiv['J. F. Poyatos']2024-05-10T00:00:00https://www.semanticscholar.org/paper/cb9d655e857d6cdd755c46f5fbfbbbb85e241f74 e3c060961f204ce1c812fdb16f4bb73775f83f78The relevance of the research topic is determined by the current state of social relations. It is notedthat the analysis of modern social relations cannot be carried out outside the boundaries of significantchallenges that require not only their understanding, but also consideration in the relevant reform measures.The most important challenges today are the following: Russia's war with Ukraine; European integrationprocesses that characterize modern trends in Ukrainian development; digitization of all aspects of public life.Given the problems associated with this, the article defines both objective reasons that affect tax relations, aswell as requirements that are subjective in nature.The purpose of the article is to highlight problematic aspects of the state of modern legal regulation.Attention is focused on the fact that war, European integration and digitalization are among the mostinfluential factors in this aspect. It is clear that they cannot fail to influence the traditional legal means thatguarantee the effectiveness of tax legislation. These aspects are the subject of analysis.Traditional methods of scientific knowledge are used in the research, thanks to which a systematicidea is formed about the reasons for adjusting legal means of influence on tax relations. The characteristicsof the influence of martial law on tax regulation are carried out depending on the stages. The beginning ofthe first is associated with 2014, while the second - with 2022. Despite common features (narrowing of theterritories where Ukrainian jurisdiction exists, reduction of the tax base and tax-paying taxpayers, etc.),differences in the content of these stages have been singled out. European integration processes, whichreflect the movement of Ukraine towards the European community, provide for the adjustment of the natureand content of tax legislation in the following directions: a) adaptation of the current tax legislation ofUkraine to European requirements; b) consideration of European prescriptions at the stage of developmentof zocono projects; c) achieving a balance of acts of the national legislation system.Three areas of relations, which most fundamentally affect the legal status of tax regulation, havebeen studied. The prospects of tax changes depending on the state of war, the prospects of the impact ofharmonization of EU legislation and national legislation, the need to take into account the processes ofdigitalization of tax relations are considered.On the basis of the conducted research, conclusions were made and recommendations were maderegarding the harmonization of the prescriptions of both exclusively tax norms and tax norms on the borderwith other industry regulations. Adjustment of the current set of legislative norms determines the importanceof a systematic approach to the turnover of virtual assets. The formation of a generalized, systematicapproach to these relations is fundamentally important. In order to achieve such a state, it is very importantto supplement the private law means of regulating the circulation of virtual assets with effective tools ofpublic and legal influence (taxation, supervision, public control). The study analyzes the substantive factorsof the regulation of such relations proposed by the two draft laws.Problems of Legality00Problems of legality['Olha Lohvinova']2024-05-10T00:00:00https://www.semanticscholar.org/paper/e3c060961f204ce1c812fdb16f4bb73775f83f78 ec48192df913401f6342a4b5f6b5df86b876fd71A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.Journal of Computational Methods in Sciences and Engineering380Journal of Computational Methods in Sciences and Engineering['Z. Dou', 'Chunyan Zhang', 'Chuanxu Duan', 'Xuan Wen', 'Chen Sun']2024-05-10T00:00:00https://www.semanticscholar.org/paper/ec48192df913401f6342a4b5f6b5df86b876fd71 1e6a04d2d9708c028b9bc9b0688f9fde80aca5f100['Pierre de Gioia Carabellese', 'Camilla Della Giustina']2024-05-10T00:00:00https://www.semanticscholar.org/paper/1e6a04d2d9708c028b9bc9b0688f9fde80aca5f1 d81e8d1d3c8acbcf9755ef470098b9c02adf5963Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.1651This paper introduces and defines a family of approaches to AI safety, which it will refer to as guaranteed safe (GS) AI, which aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees.['DaviddavidadDalrymple', 'Joar Skalse', 'Y. Bengio', 'Stuart Russell', 'Max Tegmark', 'S. Seshia', 'Steve Omohundro', 'Christian Szegedy', 'Ben Goldhaber', 'Nora Ammann', 'Alessandro Abate', 'Joe Halpern', 'Clark Barrett', 'Ding Zhao', 'Zhi-Xuan Tan', 'Jeannette Wing', 'Joshua B. Tenenbaum']2024-05-10T00:00:00https://www.semanticscholar.org/paper/d81e8d1d3c8acbcf9755ef470098b9c02adf5963 7b393e689c128caf069ac551a35157a00894665aThis paper is the introduction to the Artificial Intelligence (AI) landscape in the present day and its future properties. AI is now being used in numerous fields, such as technology, finance and banking, and healthcare to name a few. The current use of AI is in automation, to perform tasks at the same level or even better than humans can in terms of efficiency, accuracy and speed. This is why AI is being used in more businesses, and why understanding AI has become more important than ever before. The majority of people don't truly understand what AI even is, and assume that it’s a dangerous field filled with killer robots, however that couldn’t be farther than the truth. The following chapters in this paper will explain what AI actually is as well as comparing it to the public’s perception. Once a proper understanding of AI is established, the paper aims to show how AI is being applied into industries, and how they can skyrocket the success of all of them in vast ways that were never possible before the AI revolution.International Journal of Innovative Science and Research Technology321This paper is the introduction to the Artificial Intelligence landscape in the present day and its future properties, and aims to show how AI is being applied into industries, and how they can skyrocket the success of all of them in vast ways that were never possible before the AI revolution.International Journal of Innovative Science and Research Technology (IJISRT)['Vansh Soni – Silva']2024-05-10T00:00:00https://www.semanticscholar.org/paper/7b393e689c128caf069ac551a35157a00894665a 6b5d005ab61182b4f2460f613a4ce4d99d7b331aNew research from Google.org shows that a healthy portion of nonprofit organizations are already making use of artificial intelligence, and in particular, what's known as generative AI.Nonprofit Business Advisor00Nonprofit Business Advisor[]2024-05-10T00:00:00https://www.semanticscholar.org/paper/6b5d005ab61182b4f2460f613a4ce4d99d7b331a b661081f8f83cd0f69a545b5a0e794e8df5f0d5aBusinesses must ask themselves this question as we approach a revolutionary era: how will artificial intelligence (AI) change the B2B and B2C customer journey landscapes? This capstone study explores and covers the journey of artificial intelligence (AI), it’s impact on the business, the new opportunities along with challenges and the future predictions following the literature reviews, case studies, and expert insights. The research unveils a wide variety of possibilities waiting to be unlocked. This research focuses on the transformative potential of artificial intelligence (AI) for businesses and customers alike. It serves as a valuable resource for business leaders, marketing and sales professionals, customer service teams to managing artificial intelligence (AI) adoption plans and optimizing customer journey opportunities. Using AI-powered tools for personalized engagement and data-driven decisions. Using AI to improve productivity and customize assistance. Includes key Findings like Personalized Experiences, Data-Driven Decisions Automation & Efficiency. AI-powered chatbots offer seamless B2B customer service, while B2C brands use recommendation engines to provide hyper-personalized product suggestions. AI streamlines B2B workflows, boosting productivity. Beyond these, the research identifies: Opportunities: How AI increases customer engagement to a greater extent, unlocking brand loyalty and advocacy. Areas to integration of AI in business regular process. Challenges: What could be the Ethical considerations and potential job displacement carefully.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This capstone study explores and covers the journey of artificial intelligence (AI), it’s impact on the business, the new opportunities along with challenges and the future predictions following the literature reviews, case studies, and expert insights.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Kishan Kumar']2024-05-10T00:00:00https://www.semanticscholar.org/paper/b661081f8f83cd0f69a545b5a0e794e8df5f0d5a c02bb0391e4d8942762a0510b80f8f502db9ed8cThe development of hydroelectric power is essential to the production of sustainable energy. Continuous monitoring and maintenance are necessary to guarantee the safe and effective functioning of hydroelectric plants. A revolutionary method for improving the security, effectiveness, and monitoring capacities of hydroelectric power plants is presented in the project "Monitoring through AI-Based Remote Access Vehicle in Hydro Power Plant."This creative project uses cutting-edge technologies, such as Bluetooth connectivity, artificial intelligence (AI), Raspberry Pi, and sensor systems, to develop a remotely controlled car that is specifically designed to meet the demands of hydroelectric power plant conditions. Real-time object detection and obstacle avoidance are made possible by this configuration, guaranteeing safe passage across the complex industrial infrastructure.Incorporating a temperature sensor also makes it easier to identify fire situations early on, while a water-detecting sensor protects against water leaks. By utilizing AI algorithms for object recognition, vehicles can detect and react to impediments in a proactive manner, hence decreasing the likelihood of accidents. As sentinels, the temperature and water leakage sensors keep an eye on things constantly and send out alarms right once if anything seems out of the ordinary. Reduced reliance on in-person inspections due to remote monitoring capabilities results in lower costs and more dependability. To sum up, this initiative tackles important issues pertaining to hydro power plant maintenance, efficiency, and safetyInternational Journal of Advanced Research in Science, Communication and Technology120This creative project uses cutting-edge technologies, such as Bluetooth connectivity, artificial intelligence (AI), Raspberry Pi, and sensor systems, to develop a remotely controlled car that is specifically designed to meet the demands of hydroelectric power plant conditions.International Journal of Advanced Research in Science, Communication and Technology['Chitra Shree', 'K. T. Tanuja', 'Prof. Aravinda Thejas', 'Chandra', 'U. G. Students']2024-05-10T00:00:00https://www.semanticscholar.org/paper/c02bb0391e4d8942762a0510b80f8f502db9ed8c c97ec3a82b1b909a63315cbf47faa90da7c85902International Journal of Information Security210The integration of AI-driven sectorial threat intelligence and forecasting to identify emerging and relevant threats and anticipate their impact in different industries and enhances the effectiveness of cyber security exercises by tailoring the scenarios to reflect the threats that are more relevant and imminent to the sector of the targeted organisation, thereby enhancing its preparedness for cyber attacks.International Journal of Information Security['A. Zacharis', 'Vasilios Katos', 'Constantinos Patsakis']2024-05-10T00:00:00https://www.semanticscholar.org/paper/c97ec3a82b1b909a63315cbf47faa90da7c85902 8b63d50ff1ab34a47293e786e68d4964836b14cfRecent advancements in technology have led to the emergence of Cyber-Physical Systems (CPS), which seamlessly integrate the cyber and physical domains in various sectors such as agriculture, autonomous systems, and healthcare. This integration presents opportunities for enhanced efficiency and automation through the utilization of artificial intelligence (AI) and machine learning (ML). However, the complexity of CPS brings forth challenges related to transparency, bias, and trust in AI-enabled decision-making processes. This research explores the significance of AI and ML in enabling CPS in these domains and addresses the challenges associated with interpreting and trusting AI systems within CPS. Specifically, the role of explainable AI (XAI) in enhancing trustworthiness and reliability in AI-enabled decision-making processes is discussed. Key challenges such as transparency, security, and privacy are identified, along with the necessity of building trust through transparency, accountability, and ethical considerations.440The role of explainable AI (XAI) in enhancing trustworthiness and reliability in AI-enabled decision-making processes is discussed and key challenges such as transparency, security, and privacy are identified.['R. Mhapsekar', 'Muhammad Iftikhar Umrani', 'Malik Faizan', 'Omer Ali', 'Lizy Abraham']2024-05-10T00:00:00https://www.semanticscholar.org/paper/8b63d50ff1ab34a47293e786e68d4964836b14cf 080460076f8ef0f82c6668f3b782e210c72b06bdThe rapid advancements in generative artificial intelligence have opened up new avenues for enhancing various aspects of research, including the design and evaluation of survey questionnaires. However, the recent pioneering applications have not considered questionnaire pretesting. This article explores the use of GPT models as a useful tool for pretesting survey questionnaires, particularly in the early stages of survey design. Illustrated with two applications, the article suggests incorporating GPT feedback as an additional stage before human pretesting, potentially reducing successive iterations. The article also emphasizes the indispensable role of researchers' judgment in interpreting and implementing AI-generated feedback.80This article explores the use of GPT models as a useful tool for pretesting survey questionnaires, particularly in the early stages of survey design, and suggests incorporating GPT feedback as an additional stage before human pretesting, potentially reducing successive iterations.['Francisco Olivos', 'Minhui Liu']2024-05-10T00:00:00https://www.semanticscholar.org/paper/080460076f8ef0f82c6668f3b782e210c72b06bd 665e301cd9051a2268765db72c400ba0bea2d814The role of artificial intelligence (AI-based) English games in enhancing English communication skills among Indonesian Generation Z (Gen-Z) for whom English is a second language (L2), is the main focus of this study. Questionnaires to collect data consisted of both open and closed-ended questions. Seventy-eight Indonesian Gen- Z from different parts of Indonesia participated, and provides a strong representation of the country. This study is aimed to address a gap in the present literature by directly engaging with game players and examining their perspectives and experiences with actual games that they play for enjoyment rather than games designed for educational purposes. The study’s findings show that English AI-based games significantly help respondents develop their vocabulary, improve comprehension, and acquire confidence in English speaking. The findings also indicate that English AI-based game learning is favoured over traditional methods. The respondents noted gaming's immersive and interactive aspect as beneficial to language acquisition and skill development. These findings emphasise the transformative potential of English AI-based games in language learning and advocate for their integration into educational frameworks to better fulfil the needs of L2 learners, particularly Gen-Z.Journal of global research in education and social science00The study’s findings show that English AI-based games significantly help respondents develop their vocabulary, improve comprehension, and acquire confidence in English speaking, and indicate that English AI-based game learning is favoured over traditional methods.Journal of Global Research in Education and Social Science['Clara Evi C. Citraningtyas', 'Wiputra Cendana']2024-05-10T00:00:00https://www.semanticscholar.org/paper/665e301cd9051a2268765db72c400ba0bea2d814 b50a0752e812f75cec35225ffa7649356094e5b9In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardized consistent documentation. Our work addresses the information incompleteness in current human-generated model and data cards. We propose an automated generation approach using Large Language Models (LLMs). Our key contributions include the establishment of CardBench, a comprehensive dataset aggregated from over 4.8k model cards and 1.4k data cards, coupled with the development of the CardGen pipeline comprising a two-step retrieval process. Our approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability.450This work proposes an automated generation approach using Large Language Models (LLMs) that exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability.['Jiarui Liu', 'Wenkai Li', 'Zhijing Jin', 'Mona Diab']2024-05-10T00:00:00https://www.semanticscholar.org/paper/b50a0752e812f75cec35225ffa7649356094e5b9 154ce43147da7d1ee26a7824c6c3eb36a74b709fIn recent years, machine learning approaches have made dramatic advances, reaching superhuman performance in Go, Atari, and poker variants. These games, and others before them, have served not only as a testbed but have also helped to push the boundaries of AI research. Continuing this tradition, we examine the tabletop game Dominion and discuss the properties that make it well-suited to serve as a benchmark for the next generation of reinforcement learning (RL) algorithms. We also present the Dominion Online Dataset, a collection of over 2,000,000 games of Dominion played by experienced players on the Dominion Online webserver. Finally, we introduce an RL baseline bot that uses existing techniques to beat common heuristic-based bots, and shows competitive performance against the previously strongest bot, Provincial.110An RL baseline bot is introduced that uses existing techniques to beat common heuristic-based bots, and shows competitive performance against the previously strongest bot, Provincial.['Danny Halawi', 'Aron Sarmasi', 'Siena Saltzen', 'Joshua McCoy']2024-05-10T00:00:00https://www.semanticscholar.org/paper/154ce43147da7d1ee26a7824c6c3eb36a74b709f 99ebb6d1d12b08683e506a86e26334a74f41971fThe purpose of this effort is to promote equitable environments and allow people with hearing disabilities to communicate in their native language by proposing an AI-based sign language translator. We used a transformer neural network, which can analyze over 500 data points from a person's face and gestures, to translate sign language into text. The translator can expand, produce new datasets, and build models for sign language recognition thanks to our machine learning process. As a proof of concept, we developed an interpreter for emergency calls using more than 200 sign language words. The main goal is to empower people who are deaf to participate in social, political, economic, and cultural spheres of life. We see a lot of people with illnesses, including blindness, deafness, and dumbness, every day. They have trouble interacting with other people. The suggested method can translate sign language into text and voice since this study describes two-way communication between deaf, dumb, and normal individuals. Key Words: Sign Language, Inclusion, Social Development, Artificial Intelligence, Machine Learning.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The purpose of this effort is to promote equitable environments and allow people with hearing disabilities to communicate in their native language by proposing an AI-based sign language translator using a transformer neural network to translate sign language into text.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Prof. Aradhana Pawar']2024-05-10T00:00:00https://www.semanticscholar.org/paper/99ebb6d1d12b08683e506a86e26334a74f41971f cd27a792a536f8997fdd5641f3d0394795a542bcWith the rapid development of artificial intelligence technology, technology is empowering transform teaching in the field of education. as smart education models become widespread, the concept of smart ecological education is receiving increased attention. This paper explores how AI is transforming the education ecosystem from the perspectives of student learning, teacher research, school management, home-school collaboration, and teaching evaluation. By examining AI's impact on education from before class, in-class, and post-class perspectives, we reflect on how to construct a smart ecological system.Frontiers in Computing and Intelligent Systems80By examining AI's impact on education from before class, in-class, and post-class perspectives, this paper reflects on how to construct a smart ecological system.Frontiers in Computing and Intelligent Systems['Zhenjing Zhou']2024-05-10T00:00:00https://www.semanticscholar.org/paper/cd27a792a536f8997fdd5641f3d0394795a542bc 767e710dd7036a4b01a9a2156b006b66bc4b2250As Artificial Intelligence (AI) continues its rapid evolution, its profound influence on cybersecurity becomes increasingly evident. This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks. However, alongside its promise, recognition of AI's susceptibility to exploitation in sophisticated cyber-attacks exists, underscoring the imperative for continual advancements in AI-driven security solutions. This research offers a nuanced perspective on AI's impact on cybersecurity, advocating for the proactive integration of AI strategies, sustained research efforts, and formulating ethical guidelines. Adopting supervised machine learning (ML) algorithms like decision trees, support vector machines, and neural networks aims to harness AI's potential to bolster cybersecurity while concurrently addressing associated risks, paving the way for a secure digital landscape. Regarding accuracy, the neural network outperforms other models by 98%.Journal of Computer Science and Technology Studies310This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks.Journal of Computer Science and Technology Studies['Atia Shahana', 'Rakibul Hasan', 'Sayeda Farjana Farabi', 'Jahanara Akter', 'Md Abdullah al Mahmud', 'F. Johora', 'Gurkan Suzer']2024-05-10T00:00:00https://www.semanticscholar.org/paper/767e710dd7036a4b01a9a2156b006b66bc4b2250 a78b0eb79ddc4b17ac16b4b599e7ee8aca12036dFollowing the recent release of AI assistants, such as OpenAI's ChatGPT and GitHub Copilot, the software industry quickly utilized these tools for software development tasks, e.g., generating code or consulting AI for advice. While recent research has demonstrated that AI-generated code can contain security issues, how software professionals balance AI assistant usage and security remains unclear. This paper investigates how software professionals use AI assistants in secure software development, what security implications and considerations arise, and what impact they foresee on secure software development. We conducted 27 semi-structured interviews with software professionals, including software engineers, team leads, and security testers. We also reviewed 190 relevant Reddit posts and comments to gain insights into the current discourse surrounding AI assistants for software development. Our analysis of the interviews and Reddit posts finds that despite many security and quality concerns, participants widely use AI assistants for security-critical tasks, e.g., code generation, threat modeling, and vulnerability detection. Their overall mistrust leads to checking AI suggestions in similar ways to human code, although they expect improvements and, therefore, a heavier use for security tasks in the future. We conclude with recommendations for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development.580Recommendations are made for software professionals to critically check AI suggestions, AI creators to improve suggestion security and capabilities for ethical security tasks, and academic researchers to consider general-purpose AI in software development.['J. Klemmer', 'Stefan Albert Horstmann', 'Nikhil Patnaik', 'Cordelia Ludden', 'Cordell Burton', 'Carson Powers', 'Fabio Massacci', 'Akond Rahman', 'Daniel Votipka', 'H. Lipford', 'Awais Rashid', 'Alena Naiakshina', 'Sascha Fahl Cispa Helmholtz Center for Information Security', 'R. Bochum', 'U. Bristol', 'Tufts University', 'V. U. Amsterdam', 'U. Trento', 'Auburn University', 'University of North Carolina at Charlotte']2024-05-10T00:00:00https://www.semanticscholar.org/paper/a78b0eb79ddc4b17ac16b4b599e7ee8aca12036d ec6847577bf128d88064b057880a67cfd6c4bed8International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-05-10T00:00:00https://www.semanticscholar.org/paper/ec6847577bf128d88064b057880a67cfd6c4bed8 80f14008e8718803864bd4c4fcd36ea52e510e3bSN Computer Science470SN Computer Science['Manoj Kumar Mishra', 'Akanksha Upadhyaya']2024-05-10T00:00:00https://www.semanticscholar.org/paper/80f14008e8718803864bd4c4fcd36ea52e510e3b 8fb1db36e2de742fa83e0d4b5a5fa8819a6655af00['Brandon Biggs', 'Kaylee Dalton']2024-05-10T00:00:00https://www.semanticscholar.org/paper/8fb1db36e2de742fa83e0d4b5a5fa8819a6655af 8cc0b07060ec31eb490c298bd779ac36173918ecJournal of Marketing Analytics20Journal of Marketing Analytics['R. O. Walton', 'D. V. Watkins']2024-05-10T00:00:00https://www.semanticscholar.org/paper/8cc0b07060ec31eb490c298bd779ac36173918ec 1ae35e59546a68fbf63cfd4ff0282e6f30996084International Journal of Surgery00International journal of surgery['Anqi Lin', 'Lingxuan Zhu', 'Weiming Mou', 'Zizhi Yuan', 'Quan Cheng', 'Aimin Jiang', 'Peng Luo']2024-05-10T00:00:00https://www.semanticscholar.org/paper/1ae35e59546a68fbf63cfd4ff0282e6f30996084 607a212137b49f4c6f853c690c97b1efdf246bb5Drugs, Habits and Social Policy230Drugs, Habits and Social Policy['Marta Rychert', 'Aysel Sultan', 'Mélissa Mialon']2024-05-10T00:00:00https://www.semanticscholar.org/paper/607a212137b49f4c6f853c690c97b1efdf246bb5 718f6674caffdf6093d65d21dbf04bab419c8aa900['Lakshmi D', 'R. Tiwari', 'Rajesh Kumar Dhanaraj', 'Seifedine Kadry']2024-05-10T00:00:00https://www.semanticscholar.org/paper/718f6674caffdf6093d65d21dbf04bab419c8aa9 55a2a90746dffb50c9101eebfbfed495809db3d2ACM inroads80ACM Inroads['Sanaz Zamani', 'Roopak Sinha']2024-05-10T00:00:00https://www.semanticscholar.org/paper/55a2a90746dffb50c9101eebfbfed495809db3d2 7fdf77e97bb8f54ed33bbc4bff0e0765c56c4468Clincal and Experimental Dermatology00Clinical and experimental dermatology['R. Matin']2024-05-10T00:00:00https://www.semanticscholar.org/paper/7fdf77e97bb8f54ed33bbc4bff0e0765c56c4468 38298e21678cbdf1b5e8ba4b93ae3e9e51d5c313ACS Energy Letters30ACS Energy Letters['Prashant V. Kamat']2024-05-10T00:00:00https://www.semanticscholar.org/paper/38298e21678cbdf1b5e8ba4b93ae3e9e51d5c313 b7974286750cf28d51a14c0c63dc11cb8c921e63Educational Administration Theory and Practices00Educational Administration Theory and Practices['Siddhant Mishra']2024-05-10T00:00:00https://www.semanticscholar.org/paper/b7974286750cf28d51a14c0c63dc11cb8c921e63 7c31ac872606e8536d80331274ecad8c1d84661cAsia-Pacific Psychiatry20Asia-Pacific psychiatry : official journal of the Pacific Rim College of Psychiatrists['Aaradhana Rukadikar', 'Komal Khandelwal']2024-05-10T00:00:00https://www.semanticscholar.org/paper/7c31ac872606e8536d80331274ecad8c1d84661c 941b58a02308662c4ed7ea83173bc6d0d2194c8eGoats and sheep are extremely fertile animals! The ewe and doe have very high reproductive potential. However, it must be remembered that “sex is a luxury” metabolically speaking. The ewe or doe must be healthy and on a good plane of nutrition be­fore maximum fertility is attained.American Association of Bovine Practitioners Conference Proceedings00American Association of Bovine Practitioners Conference Proceedings['Clare Scully']2024-05-10T00:00:00https://www.semanticscholar.org/paper/941b58a02308662c4ed7ea83173bc6d0d2194c8e 8dab631128bda282a0eed81a7ecfc816c746a68bGenerative artificial intelligence (GAI) is a technology based on algorithms, models, etc., that creates content such as text, audio, images, videos, and code. GAI is deeply integrated into journalism as tools, platforms and systems. However, GAI’s role in journalism dilutes the power of media professionals, changes traditional news production and poses ethical questions. This study attempts to systematically answer these ethical questions in specific journalistic practices from the perspectives of journalistic professionalism and epistemology. Building on the review of GAI’s development and application, this study identifies the responsibilities of news organizations, journalists and audiences, ensuring that they realize the potential of GAI while adhering to journalism professionalism and universal human values to avoid negative technological effects.Journalism and Media540This study identifies the responsibilities of news organizations, journalists and audiences, ensuring that they realize the potential of GAI while adhering to journalism professionalism and universal human values to avoid negative technological effects.Journalism and Media['Yi Shi', 'Lin Sun']2024-05-10T00:00:00https://www.semanticscholar.org/paper/8dab631128bda282a0eed81a7ecfc816c746a68b f01653555d6071086e8d94b37b4cac84c9b02e68Industri media menghadapi berbagai tantangan, perkembangan teknologi dan disrupsi pasar menyebabkan perusahaan media tradisional mengalami penurunan pendapatan iklan dan melakukan pemutusan hubungan kerja massal. Dalam upaya untuk tetap kompetitif dan mengatasi tantangan ini, perusahaan media mulai beradaptasi dengan berbagai strategi, termasuk penggunaan kecerdasan artifisial.             Sekarang kecerdasan artisial mulai digunakan dalam berbagai aspek pembuatan konten video, seperti untuk video editing dengan nama Autopod, dengan pemanfaatan kecerdasan artifisial yang maksimal, perusahaan media dapat mengefisiensi waktu dan biaya biaya operasional.             Metode penelitian yang digunakan adalah kualitatif dengan pendekatan pastisipatif, pendekatan ini bertujuan untuk mengembangkan pemahaman yang lebih dalam dan holistik tentang pengalaman dan pandangan peneliti yang berfokus membahas proses adaptasi dan pengaplikasian kecerdasan artifisial  dalam proses video editing dan kebutuhan estetika visualnya.Citradirga - Jurnal Desain Komunikasi Visual dan Intermedia00Citradirga : Jurnal Desain Komunikasi Visual dan Intermedia['Subadi Subadi']2024-05-10T00:00:00https://www.semanticscholar.org/paper/f01653555d6071086e8d94b37b4cac84c9b02e68 77acea5d8f238f00e9747bff280ab746da19c813 Water is one of the most critical resources for maintaining life. Although it makes upto 70% of the earth’s surface but only a small amount of it is usable. Since water is used for a variety of functions, its quality must be determined before usage. The rapid increase of the world’s population has also had a significant influence on the environment, particularly on water quality. The quality of water has been deteriorating in recent years due to various pollutants. To control the water pollution, modeling and predicting the water quality has become a crucial need. In this work, we propose a machine learning (ML)-based model to predict and classify the water quality. The results from six different ML models are analyzed for accuracy, precision, recall, and F1 score as performance measures. The proposed approach is validated using benchmark dataset. The results show that Decision Tree ML model has a distinct superiority on other classifiers in terms of performance indicators like accuracy of 97.53%, precision of 87.66%, recall of 74.59%, and F1-score of 80.60%. This will help the aquatic system for better water quality analysis.Water Practice &amp; Technology00This work proposes a machine learning (ML)-based model to predict and classify the water quality and shows that Decision Tree ML model has a distinct superiority on other classifiers in terms of performance indicators like accuracy, precision, recall, and F1 score.Water Practice &amp; Technology['Ahmad Talha Ansari', 'Natasha Nigar', 'Hafiz Muhammad Faisal', 'Muhammad Kashif Shahzad']2024-05-10T00:00:00https://www.semanticscholar.org/paper/77acea5d8f238f00e9747bff280ab746da19c813 edd6ef8b90d23e7b13943e582ba55e8aa22ef615ACM inroads150ACM Inroads['Daniel Gooch', 'Kevin Waugh', 'Mike Richards', 'Mark Slaymaker', 'John Woodthorpe']2024-05-10T00:00:00https://www.semanticscholar.org/paper/edd6ef8b90d23e7b13943e582ba55e8aa22ef615 4bff4a8482959c247f29ed7825acc5257586224b To evaluate an artificial intelligence (AI) model in predicting soft tissue and alveolar bone changes following orthodontic treatment and compare the predictive performance of the AI model with conventional prediction models. A total of 1774 lateral cephalograms of 887 adult patients who had undergone orthodontic treatment were collected. Patients who had orthognathic surgery were excluded. On each cephalogram, 78 landmarks were detected using PIPNet-based AI. Prediction models consisted of 132 predictor variables and 88 outcome variables. Predictor variables were demographics (age, sex), clinical (treatment time, premolar extraction), and Cartesian coordinates of the 64 anatomic landmarks. Outcome variables were Cartesian coordinates of the 22 soft tissue and 22 hard tissue landmarks after orthodontic treatment. The AI prediction model was based on the TabNet deep neural network. Two conventional statistical methods, multivariate multiple linear regression (MMLR) and partial least squares regression (PLSR), were each implemented for comparison. Prediction accuracy among the methods was compared. Overall, MMLR demonstrated the most accurate results, while AI was least accurate. AI showed superior predictions in only 5 of the 44 anatomic landmarks, all of which were soft tissue landmarks inferior to menton to the terminal point of the neck. When predicting changes following orthodontic treatment, AI was not as effective as conventional statistical methods. However, AI had an outstanding advantage in predicting soft tissue landmarks with substantial variability. Overall, results may indicate the need for a hybrid prediction model that combines conventional and AI methods. Angle Orthodontist01Results may indicate the need for a hybrid prediction model that combines conventional and AI methods in predicting soft tissue and alveolar bone changes following orthodontic treatment, as AI was not as effective as conventional statistical methods.The Angle Orthodontist['Sung Joo Cho', 'Jun-Ho Moon', 'Dong-Yub Ko', 'Ju-Myung Lee', 'Ji-Ae Park', 'R. E. Donatelli', 'Shin-Jae Lee']2024-05-10T00:00:00https://www.semanticscholar.org/paper/4bff4a8482959c247f29ed7825acc5257586224b d6cb209f1aecb7c56a9da70e461a88294bd0b9be To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes. Angle Orthodontist01An artificial intelligence model based on the TabNet deep neural network showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area, and conventional prediction methods did not always outperform AI predictions.The Angle Orthodontist['Ji-Ae Park', 'Jun-Ho Moon', 'Ju-Myung Lee', 'Sung Joo Cho', 'Byoung-Moo Seo', 'R. E. Donatelli', 'Shin-Jae Lee']2024-05-10T00:00:00https://www.semanticscholar.org/paper/d6cb209f1aecb7c56a9da70e461a88294bd0b9be e923e5513b71a53d5fb29a2d117f350e55a1df31Abstract In recent years, artificial intelligence has found wide application in labor law, including in hiring processes. Artificial intelligence algorithms are used to automate recruiting, skills assessment and decision making. Although this can provide certain advantages and efficiency in the selection of candidates, the use of artificial intelligence algorithms in hiring also creates new legal problems and challenges, in particular in the context of the protection of labor rights in hiring: discrimination, transparency of artificial intelligence algorithms, protection of personal data. The problems caused by the use of artificial intelligence in labor law create challenges for lawyers in the context of creating ethical criteria and legal frameworks that regulate the use of artificial intelligence in the hiring process. The purpose of this article is to outline the main legal issues related to the violation and protection of labor rights in the case of the use of artificial intelligence algorithms in hiring. To achieve the goal of the research, methods of analysis, generalization, formal-logical, comparison, forecasting, dialectical and others were used. The current state of Ukrainian legislation and the experience of foreign countries are considered. The signs by which the artificial intelligence system can be classified as high-risk are highlighted. The problems of personal data protection during recruitment using artificial intelligence algorithms are analyzed. The definition of discrimination contained in international legal acts has been studied. Insufficient legal regulation of discrimination with the use of artificial intelligence algorithms has been established, which in turn creates problems in law enforcement. The criteria necessary to prevent manifestations of discrimination during recruitment with the use of artificial intelligence algorithms are highlighted. On the basis of the conducted research, a conclusion was made about the insufficient legal regulation of the use of artificial intelligence algorithms in domestic legislation, criteria that should become key for the protection of labor rights during employment with the use of artificial intelligence algorithms.Problems of Legality110The main legal issues related to the violation and protection of labor rights in the case of the use of artificial intelligence algorithms in hiring are outlined and a conclusion was made about the insufficient legal regulation of the use of artificial intelligence algorithms in domestic legislation.Problems of legality['S. Vavzhenchuk', 'Vladyslav Zhmaka']2024-05-10T00:00:00https://www.semanticscholar.org/paper/e923e5513b71a53d5fb29a2d117f350e55a1df31 15403a5b0e4ab739d31176eab4ea770b1daa697dArtificial intelligence introduces a fresh research perspective to digital image processing. However, its integration into the curriculum of colleges and universities for teaching digital image processing remains scarce. This lack of incorporation results in outdated course content, reliance on singular teaching methods, and simplistic course experiments, consequently impeding effective teaching and hindering the development of well-rounded and innovative individuals. Digital image processing technology expands the horizons of communication engineering, facilitating more convenient modes of communication for people. For instance, video calls and photo transmissions diversify everyday communication methods, transcending the constraints of time and space by enabling online meetings and fostering enhanced communication possibilities. Despite these advancements, numerous challenges and methodologies merit thorough exploration. Therefore, this paper aims to comprehensively grasp both traditional and deep learning approaches to digital image processing, enhancing practical project proficiency and fostering scientific research exploration capabilities, thus serving as a valuable reference for similar research endeavors.Frontiers in Computing and Intelligent Systems140This paper aims to comprehensively grasp both traditional and deep learning approaches to digital image processing, enhancing practical project proficiency and fostering scientific research exploration capabilities, thus serving as a valuable reference for similar research endeavors.Frontiers in Computing and Intelligent Systems['Chen Li', 'Zengyi Huang']2024-05-10T00:00:00https://www.semanticscholar.org/paper/15403a5b0e4ab739d31176eab4ea770b1daa697d c5d56badb4bdf001e5606eac134cffc729a2c60fOne of the fundamental problems in the field of technological studies is to clarify the drivers and dynamics of technological evolution for sustaining industrial and economic change. This study confronts the problem by analyzing the converging technologies to explain effects on the evolutionary dynamics over time. This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents). Findings show that quantum technology has a growth rate of 1.07, artificial intelligence technology has a rate of growth of 1.37, whereas the technological interaction of converging quantum and artificial intelligence technologies has an accelerated rate of growth of 1.58, higher than trends of these technologies taken individually. These findings suggest that technological interaction is one of the fundamental determinants in the rapid evolution of path-breaking technologies and disruptive innovations. The deductive implications of results about the effects of converging technologies are: (a) accelerated evolutionary growth; (b) a disproportionate (allometric) growth of patents driven by publications supporting a fast technological evolution. Our results support policy and managerial implications for the decision making of policymakers, technology analysts, and R&D managers that can direct R&D investments towards fruitful inter-relationships between radical technologies to foster scientific and technological change with positive societal and economic impcats.Technologies880This paper focuses on technological interaction between artificial intelligence and quantum technologies using a technometric model of technological evolution based on scientific and technological information (publications and patents).Technologies['Mario Coccia']2024-05-10T00:00:00https://www.semanticscholar.org/paper/c5d56badb4bdf001e5606eac134cffc729a2c60f d14afef79a97e3b1a9286d33b7ab24d361112d3bWith the rapid development of Artificial Intelligence (AI) and Big Data (BD), they provide people with new ways and tools in information acquisition, processing and dissemination. The purpose of this study is to deeply discuss the influence of AI and BD technologies on the development of Information Security (InfoSec) in media economy, and analyze the opportunities and challenges it brings. Firstly, the evaluation system of the influence of BD technology on the development of InfoSec, a media enterprise, is constructed. Then, this study discusses the development mode of the multilateral platform of media economy supported by BD technology. Finally, taking S company and X platform as examples, this study analyses the influence of BD technology on the development of InfoSec and the development mode of media economy supported by BD technology. The research results show that with the support of BD technology, the proportion of InfoSec products in enterprises has reached 0.2426, and the InfoSec score of S company has reached 77.74, reaching a high level. The profit margin of digital network media is three times that of traditional media. The number of users of multilateral digital media economic platform X has increased by 21%, and its turnover has increased by 54%. AI and BD technologies have brought opportunities and challenges to the InfoSec development of media economy. While applying these technologies, it is necessary to take effective measures to protect users’ privacy and strengthen the information review and verification mechanism to ensure the sustainable development of media economy. The uniqueness of this study lies in the construction of a comprehensive BD technology impact assessment system and the discussion of the development model of multilateral media economic platform, which provides a powerful reference and guidance for media enterprises. In addition, this study also reveals the positive influence of BD technology on InfoSec and the development of media economy, which provides beneficial enlightenment for the future development of media industry. In response to the challenges of privacy and InfoSec, this study puts forward important policy and practical suggestions to ensure the sustainable growth of media economy and the protection of users’ rights and interests. Therefore, this study has important contribution and innovative value in both theory and practice.Journal of Computational Methods in Sciences and Engineering150The positive influence of BD technology on InfoSec and the development of media economy, which provides beneficial enlightenment for the future development of media industry is revealed.Journal of Computational Methods in Sciences and Engineering['Yan Meng', 'Kim Jungjin', 'Lee Hak-Chun', 'Cho Dong Je', 'Peiyun Cheng']2024-05-10T00:00:00https://www.semanticscholar.org/paper/d14afef79a97e3b1a9286d33b7ab24d361112d3b de6f19b9a216264f06c0c9f93aec18073affa647Head and Neck Pathology500ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology, but further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.Head and neck pathology['Shahd A. Alajaji', 'Zaid H Khoury', 'Maryam Jessri', 'James J Sciubba', 'Ahmed S Sultan']2024-05-10T00:00:00https://www.semanticscholar.org/paper/de6f19b9a216264f06c0c9f93aec18073affa647 65f4cd9ae394d41f2d5c6f3bd115feee9152077aThis research article explores the potential of Artificial Intelligence (AI) in empowering women in various aspects of life, particularly in management and work-life balance. AI offers a plethora of opportunities to support women's empowerment, and this study highlights several ways in which AI can contribute to their advancement. By offering flexible and affordable learning opportunities, AI can empower women to acquire new skills and knowledge, enabling them to pursue fulfilling careers and leadership roles. In the healthcare sector, AI-powered solutions can improve access to healthcare for women in remote or disadvantaged regions. AI-driven diagnostics and virtual health aides enable early detection of health issues, facilitating prompt and effective treatment. In the workplace, AI can help identify trends in gender inequality, wage discrepancies, and other injustices. Armed with these insights, employers can take specific actions to promote a diverse and inclusive workplace, offering women equal opportunities to advance in their careers. Furthermore, AI-powered personal assistants can assist women in managing their daily responsibilities and schedules effectively, facilitating a better work-life balance. Additionally, AI-powered security and surveillance systems can enhance workplace and public safety, contributing to a safer environment for women to commute, work, and interact. AI can also play a vital role in promoting social change and advocacy for women's rights and gender equality. In times of crisis and humanitarian contexts, AI can offer support in disaster response, resource allocation, and medical aid, catering to the unique challenges women may face in such situations. This research article emphasizes the importance of women's empowerment in society and how AI can act as a catalyst for positive change. By leveraging AI's capabilities while prioritizing the well-being and equality of women, this study seeks to create a more inclusive and empowering environment for women in management and beyondInternational Journal of Advanced Research in Science, Communication and Technology220This research article explores the potential of Artificial Intelligence in empowering women in various aspects of life, particularly in management and work-life balance and highlights several ways in which AI can contribute to their advancement.International Journal of Advanced Research in Science, Communication and Technology['Dr. V. Mahalakshmi', 'A. Jayanthiladevi']2024-05-10T00:00:00https://www.semanticscholar.org/paper/65f4cd9ae394d41f2d5c6f3bd115feee9152077a 221410221c8460d50174268a9807dc256096f254The integration of artificial intelligence into legal systems has engendered a paradigm shift in the legal landscape, presenting a complex interplay of challenges and opportunities for the legal profession and the justice system. This Comprehensive research delves into the multifaceted impact of artificial intelligence on legal systems, focusing on its transformative potential and implications. Through an extensive analysis of the integration of artificial intelligence technologies, including natural language processing, machine learning, and predictive analytics, the study illuminates the profound improvements in legal research, decision-making processes, and case management, emphasizing the unprecedented efficiency and accessibility that artificial intelligence offers within the legal domain. Furthermore, the research critically examines the ethical and societal challenges stemming from artificial intelligence integration, including concerns related to data privacy, algorithmic bias, and the accountability of artificial intelligence-driven legal solutions. By scrutinizing the existing regulatory frameworks governing artificial intelligence implementation, the study underscores the necessity of responsible and ethical artificial intelligence integration, advocating for transparency, fairness, and equitable practices in the legal profession. The findings contribute to the ongoing discourse on the ethical implications and effective management of artificial intelligence integration in legal systems, providing valuable insights and recommendations for stakeholders and policymakers to navigate the complexities and ensure the responsible adoption of artificial intelligence technologies within the legal sphereProblems of Legality80This Comprehensive research delves into the multifaceted impact of artificial intelligence on legal systems, focusing on its transformative potential and implications, and critically examines the ethical and societal challenges stemming from artificial intelligence integration.Problems of legality['Nadjia Madaoui']2024-05-10T00:00:00https://www.semanticscholar.org/paper/221410221c8460d50174268a9807dc256096f254 8ad4cf0866cf90338526d6063f21907dfe3778baHumanities and Social Sciences Communications860The relationship between artificial intelligence and religious freedom is intricate and shaped by a variety of socioeconomic factors, sheds light on key factors that affect religious freedom, uncovering a positive correlation with elements such as economic growth, political stability, and education levels.Humanities and Social Sciences Communications['Yugang He']2024-05-10T00:00:00https://www.semanticscholar.org/paper/8ad4cf0866cf90338526d6063f21907dfe3778ba f74ac01006bd019dabbb3dbfd1da0da5d615eea1Generative artificial intelligence tools have been shown to substantially increase productivity in a range of different contexts. I discuss the potential and limitations of the current models, and the evidence on how economic researchers can best make use of generative artificial intelligence in their work. To illustrate these points, I show how the data analysis tools of ChatGPT can be used to address a specific question: the accuracy of agricultural forecasts—and discuss the strengths and weaknesses of artificial intelligence in data cleaning, data analysis and producing graphs and illustrations.Australian Journal of Agricultural and Resource Economics20It is shown how the data analysis tools of ChatGPT can be used to address a specific question: the accuracy of agricultural forecasts—and the strengths and weaknesses of artificial intelligence in data cleaning, data analysis and producing graphs and illustrations are discussed.Australian Journal of Agricultural and Resource Economics['Andrew Leigh']2024-05-10T00:00:00https://www.semanticscholar.org/paper/f74ac01006bd019dabbb3dbfd1da0da5d615eea1 a5807456366fa8099c2500ad29c7b306a90ec3eeEmployee onboarding is one of the most important phases of an employee’s life cycle. Human resources (HR) is vital in setting employees up for success, particularly during this employment stage. Some organizations lose employees before they have had a chance to learn about and acclimate to the organization. An ineffective onboarding program may contribute to high turnover costs and low employee retention. This article discusses emerging trends in the role of artificial intelligence (AI) in human resource development (HRD) and how AI is being used to develop more effective and efficient processes and programs. Recommendations for practitioners will include how AI may create an informative and engaging onboarding program that may increase employee retention. Organizational development (OD) and learning and development (LD) professionals who want a more effective employee onboarding program that will provide the knowledge employees need while creating an engaging learning experience.Advances in Developing Human Resources60Recommendations for practitioners will include how AI may create an informative and engaging onboarding program that may increase employee retention and how AI is being used to develop more effective and efficient processes and programs.Advances in Developing Human Resources['Julie G. Brown']2024-05-10T00:00:00https://www.semanticscholar.org/paper/a5807456366fa8099c2500ad29c7b306a90ec3ee d333d3026747371fc60e5f380e1439e6af627395Background: Technological advancements, particularly in Artificial Intelligence (AI), are revolutionizing operations management, especially in the domain of supply chain management. This paper delves into the application of AI in demand planning processes within the supply chain context. Drawing upon a comprehensive review of the existing literature, the main objective of this study is to analyze how AI is being applied and adopted in the demand planning process, identifying the resources needed to build the capacity of AI in the demand process, as well as the mechanisms and practices contributing to AI capability’s advancement and formation. Methodology: The approach was qualitative, and case studies of three different companies were conducted. Results: This study identified crucial resources necessary for fostering AI capabilities in demand planning. Our study extends the literature on AI capability in several ways. First, we identify the resources that are important in the formation of the capacity to implement AI in the context of demand planning. Conclusions: This study’s practical contributions underscore the multifaceted nature of AI implementation for demand planning, emphasizing the importance of resource allocation, human capital development, collaborative relationships, organizational alignment, and relational capital and AI.Logistics620This study identifies crucial resources necessary for fostering AI capabilities in demand planning, and extends the literature on AI capability in several ways, emphasizing the importance of resource allocation, human capital development, collaborative relationships, organizational alignment, and relational capital and AI.Logistics['C. D. de Mattos', 'Fernanda Caveiro Correia', 'K. Kissimoto']2024-05-10T00:00:00https://www.semanticscholar.org/paper/d333d3026747371fc60e5f380e1439e6af627395 ca78e38ff71e5ad3e688f2002610394df044673dIn today’s era Artificial Intelligence (AI) has become an increasingly prominent field of research and development over the past few decades. In recent years, the development of AI has been accelerating, with more and more resources being devoted to the creation of increasingly sophisticated and capable AI systems. With more and more countries being in an AI arm race, companies competing and racing to be the one with the next most disruptive and transformative AI system. Artificial Intelligence (AI) is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to limited itself to procedures that are biologically observable. While no exact definition of Artificial Intelligence (AI) exists, AI is broadly characterized as the study of computations that allow for reason, perception and action. Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. This paper focuses features of artificial Intelligence, introduction, history, applications, growth and achievements. This paper also analyzes the current state of artificial intelligence progresses, and how the current AI race with recent trends.International Journal of Advanced Research in Science, Communication and Technology20The current state of artificial intelligence progresses, and how the current AI race with recent trends is analyzed is analyzed.International Journal of Advanced Research in Science, Communication and Technology['Dr. Quazi Farheen']2024-05-10T00:00:00https://www.semanticscholar.org/paper/ca78e38ff71e5ad3e688f2002610394df044673d 9e6e275d391e2271e8d2a6435ff13ad11e6bcd44Global Surgical Education - Journal of the Association for Surgical Education450This paper recommends incorporating AI-based video labeling into robotic surgical education where available and recommends combining supervised AI-generated, APM-based feedback with expert-based feedback to allow surgeons and trainees to reflect on metrics like bimanual dexterity and efficiency.Global Surgical Education - Journal of the Association for Surgical Education['Riley Brian', 'Alyssa Murillo', 'Camilla Gomes', 'Adnan Alseidi']2024-05-10T00:00:00https://www.semanticscholar.org/paper/9e6e275d391e2271e8d2a6435ff13ad11e6bcd44 92573e77e5081f2952524af09d1dd4e4460e8688Now a day it is very common human collaboration with machine. Artificial Intelligence, Internet of Things and Business Analytics are the important terms in technology. Top business companies through the world have started concentrating on Artificial Intelligence and machines. In present era, machines have started to act like human. Artificial intelligence has been adopted in various sectors since this technology, improves productivity, decreases costs, and quickly resolves challenging issues. It has been predicted that one of the most promising technologies of the future will be artificial intelligence. As one of the latest modern technologies, AI is influencing developments throughout a number of industries and societal safety norms. In this paper has an attempt to know women employee safety level through Artificial Intelligence and to identify the factors which are influencing for women empowerment safety through AI. An online survey was conducted and circulated through emails to collected the data, to study the status and level of empowerment among women safety through Artificial Intelligence. The status of women empowerment was evaluated using questionnaire. The information like age, educational qualification, occupation, marital status, work experience, city of residence was collected and analysed with five-point Likert scale. The questionnaire was administrated and circulated on a sample of 120 women associated with various email groups. The questionnaire was response rate was 42%, as 50 women respondents retuned fully filled, usable questionnaire. Data collected for this study were evaluated using Multiple Regression and ANOVA in SPSS statistical package. Empowering women employees’ safety through AI involved a combination of technology, regulations, policies and a supportive working environment. It is very essential to implement AI systems that respect privacy and maintain ethical standards while enhancing safetyInternational Journal of Advanced Research in Science, Communication and Technology90This paper has an attempt to know women employee safety level through Artificial Intelligence and to identify the factors which are influencing for women empowerment safety through AI.International Journal of Advanced Research in Science, Communication and Technology['Dr. V. Mahalakshmi', 'A. Jayanthiladevi']2024-05-10T00:00:00https://www.semanticscholar.org/paper/92573e77e5081f2952524af09d1dd4e4460e8688 39d958eca50521946e4f1da756b4541b6fbdea0bPurpose: The aim of this study was to investigate the medical accuracy of responses produced by Chat Generative Pretrained Transformer 4 (Chat GPT-4) and DALLE-2 in relation to common questions encountered during oculoplastic consultations. Methods: The 5 most frequently discussed oculoplastic procedures on social media were selected for evaluation using Chat GPT-4 and DALLE-2. Questions were formulated from common patient concerns and inputted into Chat GPT-4, and responses were assessed on a 3-point scale. For procedure imagery, descriptions were submitted to DALLE-2, and the resulted images were graded for anatomical and surgical accuracy. Grading was completed by 5 oculoplastic surgeons through a 110-question survey. Results: Overall, 87.3% of Chat GPT-4’s responses achieved a score of 2 or 3 points, denoting a good to high level of accuracy. Across all procedures, questions about pain, bruising, procedure risk, and adverse events garnered high scores. Conversely, responses regarding specific case scenarios, procedure longevity, and proceduredefinitions were less accurate. Images produced by DALLE-2-were notably subpar, often failing to accurately depict surgical outcomes and realistic details. Conclusions: Chat GPT-4 demonstrated a creditable level of accuracy in addressing common oculoplastic procedure concerns. However, its limitations in handling case-based scenarios suggests that it is best suited as a supplementary source of information rather than a primary diagnostic or consultative tool. The current state of medical imagery generated by means of artificial intelligence lacks anatomical accuracy. Significant technological advancements are necessary before such imagery can complement oculoplastic consultations effectively.Modeling and Artificial Intelligence in Ophthalmology170Chat GPT-4 demonstrated a creditable level of accuracy in addressing common oculoplastic procedure concerns, however, its limitations in handling case-based scenarios suggests that it is best suited as a supplementary source of information rather than a primary diagnostic or consultative tool.Modeling and Artificial Intelligence in Ophthalmology['Adam J. Neuhouser', 'A. Kamboj', 'A. Mokhtarzadeh', 'Andrew R. Harrison']2024-05-10T00:00:00https://www.semanticscholar.org/paper/39d958eca50521946e4f1da756b4541b6fbdea0b 7a985eb2fd10978535413eedd90272537ed0da96The threat landscape of biological hazards with the evolution of AI presents challenges. While AI promises innovative solutions, concerns arise about its misuse in the creation of biological weapons. The convergence of AI and genetic editing raises questions about biosecurity, potentially accelerating the development of dangerous pathogens. The mapping conducted highlights the critical intersection between AI and biological threats, underscoring emerging risks in the criminal manipulation of pathogens. Technological advancement in biology requires preventative and regulatory measures. Expert recommendations emphasize the need for solid regulations and responsibility of creators, demanding a proactive, ethical approach and governance to ensure global safety.Frontiers in Artificial Intelligence150The mapping conducted highlights the critical intersection between AI and biological threats, underscoring emerging risks in the criminal manipulation of pathogens.Frontiers in Artificial Intelligence['Renan Chaves de Lima', 'Lucas Sinclair', 'Ricardo Megger', 'Magno Alessandro Guedes Maciel', 'Pedro Fernando da Costa Vasconcelos', 'J. Quaresma']2024-05-10T00:00:00https://www.semanticscholar.org/paper/7a985eb2fd10978535413eedd90272537ed0da96 5c5f87d9fbf611b47d5bff0404ef4e4da27faa27International Journal of Dermatology20International journal of dermatology['Mohamad Goldust', 'J. Grant-Kels']2024-05-10T00:00:00https://www.semanticscholar.org/paper/5c5f87d9fbf611b47d5bff0404ef4e4da27faa27 e0f75810709d093652c89d93476cc1df36949de8International Journal of Surgery00International journal of surgery['Jie Wang', 'Ying Li', 'Jing Liu']2024-05-10T00:00:00https://www.semanticscholar.org/paper/e0f75810709d093652c89d93476cc1df36949de8 3637e226e49267f1005629108eba8566eb2c6265Respirology (Carlton South. Print)40Respirology['Hong Luo', 'Jisong Yan', 'Xia Zhou']2024-05-10T00:00:00https://www.semanticscholar.org/paper/3637e226e49267f1005629108eba8566eb2c6265 3df422a2e08fad7e0792fcfd1fa705d653589888European journal of dental education90European journal of dental education : official journal of the Association for Dental Education in Europe['David C Johnsen', 'L. Marchini']2024-05-10T00:00:00https://www.semanticscholar.org/paper/3df422a2e08fad7e0792fcfd1fa705d653589888 10938542ba595c733554f437424ca480f8dc55e8Political Research Exchange80Political Research Exchange['Ottorino Cappelli', 'Marco Aliberti', 'Rodrigo Praino']2024-05-10T00:00:00https://www.semanticscholar.org/paper/10938542ba595c733554f437424ca480f8dc55e8 15f71aa618854007d08144f1da1da79d7c12da06Education and Information Technologies : Official Journal of the IFIP technical committee on Education280Education and Information Technologies['Duncan Nyale', 'Simon M. Karume', 'Andrew Kipkebut']2024-05-10T00:00:00https://www.semanticscholar.org/paper/15f71aa618854007d08144f1da1da79d7c12da06 9e0e253a7f71f6702f40b083080232989c482464Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative AI. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.Proceedings of the National Academy of Sciences of the United States of America4117It is argued that social scientists can address many of these limitations of Generative AI by creating open-source infrastructure for research on human behavior, not only to ensure broad access to high-quality research tools, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.Proceedings of the National Academy of Sciences of the United States of America['Christopher A Bail']2024-05-09T00:00:00https://www.semanticscholar.org/paper/9e0e253a7f71f6702f40b083080232989c482464 a75b45457f9a7cef46d10344b325372abbd167efInternational Review of Law, Computers &amp; Technology150International Review of Law, Computers &amp; Technology['Beatriz Kira']2024-05-09T00:00:00https://www.semanticscholar.org/paper/a75b45457f9a7cef46d10344b325372abbd167ef 1090cd654963e80843395046363c533eac1b9e28School psychology review480School Psychology Review['Timothy Cleary', 'Rui Zhang', 'Michelle R. Russo', 'Jacqueline Slemp']2024-05-09T00:00:00https://www.semanticscholar.org/paper/1090cd654963e80843395046363c533eac1b9e28 d910685cc0d9a4be690b774155ef040a3a470efeS\'ami, an indigenous language group comprising multiple languages, faces digital marginalization due to the limited availability of data and sophisticated language models designed for its linguistic intricacies. This work focuses on increasing technological participation for the S\'ami language. We draw the attention of the ML community towards the language modeling problem of Ultra Low Resource (ULR) languages. ULR languages are those for which the amount of available textual resources is very low, and the speaker count for them is also very low. ULRLs are also not supported by mainstream Large Language Models (LLMs) like ChatGPT, due to which gathering artificial training data for them becomes even more challenging. Mainstream AI foundational model development has given less attention to this category of languages. Generally, these languages have very few speakers, making it hard to find them. However, it is important to develop foundational models for these ULR languages to promote inclusion and the tangible abilities and impact of LLMs. To this end, we have compiled the available S\'ami language resources from the web to create a clean dataset for training language models. In order to study the behavior of modern LLM models with ULR languages (S\'ami), we have experimented with different kinds of LLMs, mainly at the order of $\sim$ seven billion parameters. We have also explored the effect of multilingual LLM training for ULRLs. We found that the decoder-only models under a sequential multilingual training scenario perform better than joint multilingual training, whereas multilingual training with high semantic overlap, in general, performs better than training from scratch.This is the first study on the S\'ami language for adapting non-statistical language models that use the latest developments in the field of natural language processing (NLP).260This is the first study on the Sami language for adapting non-statistical language models that use the latest developments in the field of natural language processing (NLP).['Ronny Paul', 'Himanshu Buckchash', 'Shantipriya Parida', 'Dilip K. Prasad']2024-05-09T00:00:00https://www.semanticscholar.org/paper/d910685cc0d9a4be690b774155ef040a3a470efe 7d28a84ce4f264afac2c7525ab36df8b74f7aebaSignificant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.Annual review of genomics and human genetics (Print)00While AI has the potential to aid in leveraging patient data to make clinical care decisions, it also risk exacerbating existing disparities and organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.Annual review of genomics and human genetics['Jenna Wiens', 'Kayte Spector-Bagdady', 'Bhramar Mukherjee']2024-05-09T00:00:00https://www.semanticscholar.org/paper/7d28a84ce4f264afac2c7525ab36df8b74f7aeba d3fb929a8e4ef9660ee21a03584274d686ec5289Communications Medicine230A comprehensive digital pathology workflow for AI-assisted Gleason grading that incorporates A!MagQC, A!HistoClouds, Pathologist-AI Interaction, and A!HistoClouds for continuous model improvement and achieves outstanding performance across diverse scanners.Communications Medicine['Xinmi Huo', 'K. Ong', 'Kah Weng Lau', 'Laurent Gole', 'David M. Young', 'C. Tan', 'Xiaohui Zhu', 'Chongchong Zhang', 'Yonghui Zhang', 'Longjie Li', 'Hao Han', 'Haoda Lu', 'Jing Zhang', 'Jun Hou', 'Huanfen Zhao', 'Hualei Gan', 'Lijuan Yin', 'Xingxing Wang', 'Xiaoyue Chen', 'Hong Lv', 'Haotian Cao', 'Xiaozhen Yu', 'Yabin Shi', 'Ziling Huang', 'G. Marini', 'Jun Xu', 'Bingxian Liu', 'Bingxian Chen', 'Qiang Wang', 'Kun Gui', 'Wenzhao Shi', 'Yingying Sun', 'Wanyuan Chen', 'Dalong Cao', 'Stephan J. Sanders', 'Hwee Kuan Lee', 'S. Hue', 'Weimiao Yu', 'S. Tan']2024-05-09T00:00:00https://www.semanticscholar.org/paper/d3fb929a8e4ef9660ee21a03584274d686ec5289 abeef5dba55fbd85487a53bffccadc3b06c07f3dThis article utilizes speculative and visual storytelling alongside interdisciplinary research on artificial intelligence (AI) and algorithmic oppression to engage in a thought experiment on how literacy studies might refuse the oppressionist logics currently undermining the possibilities of AI in literacy education. As technological advancements in education will only continue to increase and as society is yet to ascertain the parameters of an ethical AI system, it is paramount to analyze the past and present and contemplate potential futures, especially those that do not result in violence against Black and Brown peoples. To engage in speculation, we employ Endarkened Storywork (Toliver, 2022) to present an empirically driven, futuristic, science fiction narrative from two perspectives: (1) a US, Black girl who is forced to participate in AI‐structured secondary schooling and (2) a Black girl in Haiti who is forced to live in a country polluted by technological byproduct. This narrative, which is grounded in academic research and news editorials, is accompanied by comic art and followed by a companion analysis detailing the theoretical backdrop of the story. By utilizing multiple methods of scholarly distribution, we provide multiple entry points for readers to engage with this work. We offer a means for readers to see—via story, art, and scholarship—the potential impacts of AI on Black people globally. Additionally, by situating this article in the creative and scholarly realms, we strategically deconstruct traditional forms and methods of knowledge production that have constrained academic research and rendered invisible alternative forms of data representation.Reading Research Quarterly320Reading Research Quarterly['Mia S. Shaw', 'S. Toliver', 'Tiera Tanksley']2024-05-09T00:00:00https://www.semanticscholar.org/paper/abeef5dba55fbd85487a53bffccadc3b06c07f3d c570f0fce54ffcf666a60b23203e64e5d5d6f1e7npj Digital Medicine340It is concluded that effective tools exist for detecting clinically relevant distribution shifts and classifier-based tests can be easily implemented components in the post-market surveillance strategy of medical device manufacturers.NPJ Digital Medicine['Lisa M. Koch', 'Christian F Baumgartner', 'Philipp Berens']2024-05-09T00:00:00https://www.semanticscholar.org/paper/c570f0fce54ffcf666a60b23203e64e5d5d6f1e7 c094d9c52bfe78efb72433b90dddc249b5901f50Inspired by the increasing use of AI to augment humans, researchers have studied human-AI systems involving different tasks, systems, and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here, we addressed this question by conducting a meta-analysis of over 100 recent experimental studies reporting over 300 effect sizes. First, we found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone. Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when the AI outperformed humans alone we found losses. These findings highlight the heterogeneity of the effects of human-AI collaboration and point to promising avenues for improving human-AI systems.780A meta-analysis of over 100 recent experimental studies found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone and found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content.['Michelle Vaccaro', 'Abdullah Almaatouq', 'Thomas Malone']2024-05-09T00:00:00https://www.semanticscholar.org/paper/c094d9c52bfe78efb72433b90dddc249b5901f50 5208c755d4a865d195e267ed219a6381cb699700This paper addresses the critical gaps in existing AI risk management frameworks, emphasizing the neglect of human factors and the absence of metrics for socially related or human threats. Drawing from insights provided by NIST AI RFM and ENISA, the research underscores the need for understanding the limitations of human-AI interaction and the development of ethical and social measurements. The paper explores various dimensions of trustworthiness, covering legislation, AI cyber threat intelligence, and characteristics of AI adversaries. It delves into technical threats and vulnerabilities, including data access, poisoning, and backdoors, highlighting the importance of collaboration between cybersecurity engineers, AI experts, and social-psychology-behavior-ethics professionals. Furthermore, the socio-psychological threats associated with AI integration into society are examined, addressing issues such as bias, misinformation, and privacy erosion. The manuscript proposes a comprehensive approach to AI trustworthiness, combining technical and social mitigation measures, standards, and ongoing research initiatives. Additionally, it introduces innovative defense strategies, such as cyber-social exercises, digital clones, and conversational agents, to enhance understanding of adversary profiles and fortify AI security. The paper concludes with a call for interdisciplinary collaboration, awareness campaigns, and continuous research efforts to create a robust and resilient AI ecosystem aligned with ethical standards and societal expectations.Frontiers in Big Data320The manuscript proposes a comprehensive approach to AI trustworthiness, combining technical and social mitigation measures, standards, and ongoing research initiatives, and introduces innovative defense strategies to enhance understanding of adversary profiles and fortify AI security.Frontiers in Big Data['Nineta Polemi', 'Isabel Praça', 'K. Kioskli', 'Adrien Bécue']2024-05-09T00:00:00https://www.semanticscholar.org/paper/5208c755d4a865d195e267ed219a6381cb699700 9ca8f5908af610057a0ba7b845ec7b3065f27955AI intent alignment, ensuring that AI produces outcomes as intended by users, is a critical challenge in human-AI interaction. The emergence of generative AI, including LLMs, has intensified the significance of this problem, as interactions increasingly involve users specifying desired results for AI systems. In order to support better AI intent alignment, we aim to explore human strategies for intent specification in human-human communication. By studying and comparing human-human and human-LLM communication, we identify key strategies that can be applied to the design of AI systems that are more effective at understanding and aligning with user intent. This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems.170This study aims to advance toward a human-centered AI system by bringing together human communication strategies for the design of AI systems by studying and comparing human-human and human-LLM communication.['Yoonsu Kim', 'Kihoon Son', 'Seoyoung Kim', 'Juho Kim']2024-05-09T00:00:00https://www.semanticscholar.org/paper/9ca8f5908af610057a0ba7b845ec7b3065f27955 e09501b14db36e10a35f2c5e53cd75245d555691Philosophy &amp; Technology390This paper presents three speculative design scenarios for AI-enabled simulation of the deceased, and draws on the scenarios to map out several key ethical concerns posed by ‘re-creation services’ and put forward recommendations on the ethical development of AI systems in this specific area of application.Philosophy &amp; Technology['Tomasz Hollanek', 'Katarzyna Nowaczyk-Basińska']2024-05-09T00:00:00https://www.semanticscholar.org/paper/e09501b14db36e10a35f2c5e53cd75245d555691 209d2b2e5083f49dbb3f08d3844aa02b8b05ec60BMC Health Services Research540Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment while acknowledging concerns about privacy and unemployment.BMC Health Services Research['Muhammad Daniyal', 'Moiz Qureshi', 'R. Marzo', 'Mohammed Aljuaid', 'Duaa Shahid']2024-05-09T00:00:00https://www.semanticscholar.org/paper/209d2b2e5083f49dbb3f08d3844aa02b8b05ec60 0987b5d7c3b02e6356a2c63804bd6d9f5f910988As Large Language Models (LLMs) are nondeterministic, the same input can generate different outputs, some of which may be incorrect or hallucinated. If run again, the LLM may correct itself and produce the correct answer. Unfortunately, most LLM-powered systems resort to single results which, correct or not, users accept. Having the LLM produce multiple outputs may help identify disagreements or alternatives. However, it is not obvious how the user will interpret conflicts or inconsistencies. To this end, we investigate how users perceive the AI model and comprehend the generated information when they receive multiple, potentially inconsistent, outputs. Through a preliminary study, we identified five types of output inconsistencies. Based on these categories, we conducted a study (N=252) in which participants were given one or more LLM-generated passages to an information-seeking question. We found that inconsistency within multiple LLM-generated outputs lowered the participants' perceived AI capacity, while also increasing their comprehension of the given information. Specifically, we observed that this positive effect of inconsistencies was most significant for participants who read two passages, compared to those who read three. Based on these findings, we present design implications that, instead of regarding LLM output inconsistencies as a drawback, we can reveal the potential inconsistencies to transparently indicate the limitations of these models and promote critical LLM usage.750Investigating how users perceive the AI model and comprehend the generated information when they receive multiple, potentially inconsistent, outputs found that inconsistency within multiple LLM-generated outputs lowered the participants' perceived AI capacity, while also increasing their comprehension of the given information.['Yoonjoo Lee', 'Kihoon Son', 'Tae Soo Kim', 'Jisu Kim', 'John Joon Young Chung', 'Eytan Adar', 'Juho Kim']2024-05-09T00:00:00https://www.semanticscholar.org/paper/0987b5d7c3b02e6356a2c63804bd6d9f5f910988 533cc39b54e24a83f3105c775a7716858dfdb6c8The use of and research on the large language model (LLM) Generative Pretrained Transformer (GPT) is growing steadily, especially in mathematics education. As students and teachers worldwide increasingly use this AI model for teaching and learning mathematics, the question of the quality of the generated output becomes important. Consequently, this study evaluates AI-supported mathematical problem solving with different GPT versions when the LLM is subjected to prompt techniques. To assess the mathematics educational quality (content related and process related) of the LLM’s output, we facilitated four prompt techniques and investigated their effects in model validations (N = 1,080) using three mathematical problem-based tasks. Subsequently, human raters scored the mathematics educational quality of AI output. The results showed that the content-related quality of AI-supported problem solving was not significantly affected by using various prompt techniques across GPT versions. However, certain prompt techniques, particular Chain-of-Thought and Ask-me-Anything, notably improved process-related quality.Frontiers in Education440The results showed that the content-related quality of AI-supported problem solving was not significantly affected by using various prompt techniques across GPT versions, however, certain prompt techniques, particular Chain-of-Thought and Ask-me-Anything, notably improved process-related quality.Frontiers in Education['Sebastian Schorcht', 'Nils Buchholtz', 'Lukas Baumanns']2024-05-09T00:00:00https://www.semanticscholar.org/paper/533cc39b54e24a83f3105c775a7716858dfdb6c8 227526bed44c51c6e0da8f19f4f7618f39e5a9f8Sam Foster, Executive Director of Professional Practice, Nursing and Midwifery Council, considers the issues raised for regulators and assessors by the availability of tools such as ChatGPT.British Journal of Nursing01British journal of nursing['Sam Foster']2024-05-09T00:00:00https://www.semanticscholar.org/paper/227526bed44c51c6e0da8f19f4f7618f39e5a9f8 978b6af6a9a782af9785d009c2702dd4340efb9fJournal of Children and Media130Journal of Children and Media['Amy Jordan', 'Nikhila Natarajan']2024-05-09T00:00:00https://www.semanticscholar.org/paper/978b6af6a9a782af9785d009c2702dd4340efb9f 915c39949316c803ce02b2de39a241991e21393eJournal of Research on Technology in Education600Journal of Research on Technology in Education['John J. H. Lin']2024-05-09T00:00:00https://www.semanticscholar.org/paper/915c39949316c803ce02b2de39a241991e21393e 761bc77e9e207775832ff01b2d12310f198a8ccaNature00Nature['Nicola Jones']2024-05-09T00:00:00https://www.semanticscholar.org/paper/761bc77e9e207775832ff01b2d12310f198a8cca 2e16808ea3e149aaefef23cb4b2f95939fbc173dMedical Teacher20Medical teacher['Chinthaka Balasooriya']2024-05-09T00:00:00https://www.semanticscholar.org/paper/2e16808ea3e149aaefef23cb4b2f95939fbc173d cd4243fdf4ca8a61db4890c8e9cd437776be4709European Radiology110European radiology['Frederick J A Meijer']2024-05-09T00:00:00https://www.semanticscholar.org/paper/cd4243fdf4ca8a61db4890c8e9cd437776be4709 998343916104f07c5b7a42f4391331eea14503a4Journal of Risk Research200Journal of Risk Research['S. Thekdi', 'T. Aven']2024-05-09T00:00:00https://www.semanticscholar.org/paper/998343916104f07c5b7a42f4391331eea14503a4 81cb2114f35a348022f67aec921c303581febb69Rhetoric Society Quarterly30Rhetoric Society Quarterly['Christa Teston']2024-05-09T00:00:00https://www.semanticscholar.org/paper/81cb2114f35a348022f67aec921c303581febb69 4daf7f5405a96386bbef51308e3dca4e5678c424Cureus900Cureus['Diny Dixon', 'Hina Sattar', 'Natalia Moros', 'Srija Reddy Kesireddy', 'Huma Ahsan', 'Mohit Lakkimsetti', 'Madiha Fatima', 'Dhruvi Doshi', 'Kanwarpreet Sadhu', 'Muhammad Junaid Hassan']2024-05-09T00:00:00https://www.semanticscholar.org/paper/4daf7f5405a96386bbef51308e3dca4e5678c424 f08b63afa793f506c87cc791147bf7332a2b30a4GI-Fachtagung CAD00CAD'24['Dipo Olaosun', 'E. Unver', 'J. Binder']2024-05-09T00:00:00https://www.semanticscholar.org/paper/f08b63afa793f506c87cc791147bf7332a2b30a4 0ce92dd88091ff6f1db3a25ab00bfdf551ad6ca8Information &amp; Communications Technology Law00Information &amp; Communications Technology Law['Lena Enqvist']2024-05-09T00:00:00https://www.semanticscholar.org/paper/0ce92dd88091ff6f1db3a25ab00bfdf551ad6ca8 505ca513ea70dc2d9688719795d9254cc0eeed9dISCDISD 202310ISCDISD 2023['Christoforos Ar. Pasialakos']2024-05-09T00:00:00https://www.semanticscholar.org/paper/505ca513ea70dc2d9688719795d9254cc0eeed9d c61b091b0bba923c5f5bd7d50d9b0fd6f92fe556Higher education (HE) is changing. Students are crossing boundaries, such as physical (those of countries) or digital (through distance learning). During COVID-19, the concept of a learning space was redefined, for many studied at home. As the student experience changes, so does the use of learning spaces. This article focuses upon ‘post-digital’ learning spaces and goes on to frame a narrative about how our HE institutional environments need to sharpen the now much hazier boundaries between the physical, digital, spatial and temporal realms; by drawing upon research with 103 Chinese postgraduates in a Sino-British university, it demonstrates piracy of ebooks as one indicator – and disruptor – of a shift in post-digital lived experience (analysis shows how students turn to online ‘shadow libraries’, to save not just money, but time and space too, redefining universities, reading and information retrieval practices); it concludes by discussing how institutional repositories need to be transformed into multifunctional spaces where students can access resources in various ways, not just in hard copies of books. In consequence, it positions the need for a future ‘post-digital library’ in universities as a place of collaboration, creativity, enterprise and critical thinking, not as one of stacked shelves.Compass: Journal of Learning and Teaching00‘post-digital’ learning spaces are focused upon and the need for a future ‘post-digital library’ in universities is positioned as a place of collaboration, creativity, enterprise and critical thinking, not as one of stacked shelves.Compass: Journal of Learning and Teaching['Michael Day']2024-05-09T00:00:00https://www.semanticscholar.org/paper/c61b091b0bba923c5f5bd7d50d9b0fd6f92fe556 af31cec7816c7600eb20a657f10aa91a4783dcffIn the digital era, with escalating privacy concerns, it's imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual information. This research embarks on a comprehensive examination of text anonymisation methods, focusing on Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and the transformative capabilities of the Transformers architecture. Each model presents unique strengths since LSTM is modeling long-term dependencies, CRF captures dependencies among word sequences, ELMo delivers contextual word representations using deep bidirectional language models and Transformers introduce self-attention mechanisms that provide enhanced scalability. Our study is positioned as a comparative analysis of these models, emphasising their synergistic potential in addressing text anonymisation challenges. Preliminary results indicate that CRF, LSTM, and ELMo individually outperform traditional methods. The inclusion of Transformers, when compared alongside with the other models, offers a broader perspective on achieving optimal text anonymisation in contemporary settings.180A comparative analysis of text anonymisation methods, focusing on Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and the transformative capabilities of the Transformers architecture, emphasising their synergistic potential in addressing text anonymisation challenges.['Dimitris Asimopoulos', 'Ilias Siniosoglou', 'Vasileios Argyriou', 'Sotirios K Goudos', 'Konstantinos E. Psannis', 'Nikoleta Karditsioti', 'Theocharis Saoulidis', 'Panagiotis Sarigiannidis']2024-05-09T00:00:00https://www.semanticscholar.org/paper/af31cec7816c7600eb20a657f10aa91a4783dcff 267aae046413009d64b8d4df21be29fd0cf6cdefSensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 202400Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024['Yuntae Jeon', 'Byoungjoon Yu', 'Dongyoung Ko', 'D. Tran', 'Seunghee Park']2024-05-09T00:00:00https://www.semanticscholar.org/paper/267aae046413009d64b8d4df21be29fd0cf6cdef 9406fed4c75693fe0696ff00e775f7fff7082711The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. Artificial Intelligence (AI) has emerged as a transformative force in education, promising to revolutionize traditional teaching and learning methods. One critical aspect of this transformation is AI's potential to enhance equity and inclusion in educational settings. This paper explores the current state, challenges, and opportunities regarding AI's role in promoting equity and inclusion in education. The historical evolution of AI in education is examined, tracing its roots from early intelligent tutoring systems to contemporary adaptive learning platforms and virtual tutoring systems. Advances in machine learning, natural language processing, and data analytics have expanded AI's capabilities, enabling personalized learning experiences tailored to individual student needs. However, the widespread implementation of AI in education faces several challenges, including concerns about data privacy, algorithmic bias, and the digital divide. It is crucial to address these challenges through responsible and ethical AI deployment, ensuring that AI interventions prioritize equity, inclusivity, and transparency. Further research is needed to explore the effectiveness of AI interventions in different educational contexts and to develop strategies for mitigating potential risks and maximizing benefits. By leveraging AI technologies thoughtfully and ethically, educators and policymakers can work towards building a more equitable and inclusive education system that empowers all learners to reach their full potential.International Journal of Innovative Science and Research Technology221The current state, challenges, and opportunities regarding AI's role in promoting equity and inclusion in education are explored, and its roots from early intelligent tutoring systems to contemporary adaptive learning platforms and virtual tutoring systems are examined.International Journal of Innovative Science and Research Technology (IJISRT)['Gitanjali Pawar', 'Jaydip Khose']2024-05-09T00:00:00https://www.semanticscholar.org/paper/9406fed4c75693fe0696ff00e775f7fff7082711 b5d293297fbbf5e345a73571c272ca142dadf3beBACKGROUND Observation of child behaviour provides valuable clinical information but often requires rigorous, tedious, repetitive and time expensive protocols. For this reason, tests requiring significant time for administration and rating are rarely used in clinical practice, however useful and effective they are. This article shows that Artificial Intelligence (AI), designed to capture and store the human ability to perform standardised tasks consistently, can alleviate this problem. CASE STUDY We demonstrate how an AI-powered version of the Manchester Child Attachment Story Task can identify, with over 80% concordance, children with insecure attachment aged between 5 and 9 years. DISCUSSION We discuss ethical issues to be considered if AI technology is to become a useful part of child mental health assessment and recommend practical next steps for the field.Child and Adolescent Mental Health200An AI-powered version of the Manchester Child Attachment Story Task can identify, with over 80% concordance, children with insecure attachment aged between 5 and 9 years.Child and adolescent mental health['Helen Minnis', 'Alessandro Vinciarelli', 'Huda Alsofyani']2024-05-09T00:00:00https://www.semanticscholar.org/paper/b5d293297fbbf5e345a73571c272ca142dadf3be fc25872313e1bd52bc5acfcccaacd3704bea8787Medicine, Health care and Philosophy640This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges.Medicine, health care, and philosophy['O. Rennie']2024-05-09T00:00:00https://www.semanticscholar.org/paper/fc25872313e1bd52bc5acfcccaacd3704bea8787 549b4aa860b608107821a0ff1b811921b8d362b6With the objective to predict and pre-empt the emergence of political violence, the US Department of Defence (DoD) has devoted increasing attention to the intersection between neurobiology and artificial intelligence. Concepts such as ‘cognitive biotechnologies’, ‘digital biosecurity’ and large-scale collection of ‘neurodata’ herald a future in which neurobiological intervention on a global scale is believed to come of age. This article analyses how the relationship between neurobiology and AI – between the human and the machine – is conceived, made possible, and acted upon within the SMA programme, an interdisciplinary research programme sponsored by the DoD. By showcasing the close intersection between the computer sciences and the neurosciences within the US military, the article questions descriptions of algorithmic governmentality as decentring the human, and as juxtaposed to biopolitical techniques to regulate processes of subjectivity. The article shows that within US military discourse, new biotechnologies are seen to engender algorithmic governmentality a biopolitical dimension, capable of monitoring and regulating emotions, thoughts, beliefs, and subjectivity on population level, particularly targeting the minds and brains of ‘vulnerable’ populations in the global South.Security Dialogue220The article shows that within US military discourse, new biotechnologies are seen to engender algorithmic governmentality a biopolitical dimension, capable of monitoring and regulating emotions, thoughts, beliefs, and subjectivity on population level, particularly targeting the minds and brains of ‘vulnerable’ populations in the global South.Security Dialogue['Claes Tängh Wrangel']2024-05-09T00:00:00https://www.semanticscholar.org/paper/549b4aa860b608107821a0ff1b811921b8d362b6 5ff50f63dfa82244348bf09be0aacf08c70584bdArtificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry.Information00This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration.Information['Dhanasak Bhumichai', 'Christos Smiliotopoulos', 'Ryan Benton', 'Georgios Kambourakis', 'D. Damopoulos']2024-05-09T00:00:00https://www.semanticscholar.org/paper/5ff50f63dfa82244348bf09be0aacf08c70584bd 2a7a494e12eadec7409a5a07b1718756fd949b61Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks. Methods: Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line CT or MR neuroimaging. A bivariate random-effects model was used for meta-analysis where appropriate. PROSPERO: CRD42021269563. Results: Only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies. 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial haemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% CI 0.85 - 0.94) and 0.90 (95% CI 0.83 - 0.95) respectively. Other AI studies using CT and MRI detected target conditions other than haemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers. Conclusion: The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.00The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts, and the few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.['Siddharth Agarwal', 'D. Wood', 'M. Grzeda', 'Chandhini Suresh', 'Munaib Din', 'James Cole', 'Marc Modat', 'Thomas C Booth']2024-05-09T00:00:00https://www.semanticscholar.org/paper/2a7a494e12eadec7409a5a07b1718756fd949b61 eeb39a54d6cc41268bbcd45f2226a03094be5102Surgeons are skilled at making complex decisions over invasive procedures that can save lives and alleviate pain and avoid complications in patients. The knowledge to make these decisions is accumulated over years of schooling and practice. Their experience is in turn shared with others, also via peer-reviewed articles, which get published in larger and larger amounts every year. In this work, we review the literature related to the use of Artificial Intelligence (AI) in surgery. We focus on what is currently available and what is likely to come in the near future in both clinical care and research. We show that AI has the potential to be a key tool to elevate the effectiveness of training and decision-making in surgery and the discovery of relevant and valid scientific knowledge in the surgical domain. We also address concerns about AI technology, including the inability for users to interpret algorithms as well as incorrect predictions. A better understanding of AI will allow surgeons to use new tools wisely for the benefit of their patients.Frontiers in Surgery800It is shown that AI has the potential to be a key tool to elevate the effectiveness of training and decision-making in surgery and the discovery of relevant and valid scientific knowledge in the surgical domain.Frontiers in Surgery['Miranda X. Morris', 'Davide Fiocco', 'Tommaso Caneva', 'Paris Yiapanis', 'Dennis P. Orgill']2024-05-09T00:00:00https://www.semanticscholar.org/paper/eeb39a54d6cc41268bbcd45f2226a03094be5102 03469b4f9d0d3c9aef6e2105e7168c8cc1fcc933Environmental, social, and governance (ESG) reporting is crucial for conveying a company's sustainability performance. However, challenges related to standardization, consistency, and data quality persist. This study explores the potential of artificial intelligence (AI) and digital technology to enhance ESG reporting by addressing these challenges. AI and digital technology utilize advanced tools like natural language processing, machine learning, data analytics, and blockchain to streamline data collection, improve quality, and facilitate communication. Regression analysis using A-share listed companies' data from 2012 to 2021 examines the relationship between digital transformation and corporate ESG performance. The research emphasizes the implications of ESG reporting, AI, and digital technology for corporate management, risk assessment, shareholder value, and social responsibility. AI and digital technology are vital for growth, innovation, efficiency, and competitiveness. ESG reporting significantly impacts a company's risk profile, reputation, performance, and overall value, contributing to sustainable development. Through a comprehensive review, methodology exploration, and in-depth analysis, this study provides valuable insights and recommendations for leveraging AI and digital technology in advancing ESG reporting. It concludes that implementing AI and digital technology enhances the comprehensiveness and assurance level of ESG reporting.International Journal of Global Economics and Management150Implementing AI and digital technology enhances the comprehensiveness and assurance level of ESG reporting, and provides valuable insights and recommendations for leveraging AI and digital technology in advancing ESG reporting.International Journal of Global Economics and Management['Shuyue Chen']2024-05-09T00:00:00https://www.semanticscholar.org/paper/03469b4f9d0d3c9aef6e2105e7168c8cc1fcc933 265d200676743daab66c99ae707add0930819b3a The utilization of novel technologies in contemporary times not only reduces the cost associated with healthcare but also improves the efficiency of hospital resources and elevates the standard of medical assistance. One of the new technologies used in the field of health is Artificial Intelligence (AI). The purpose of this study is to investigate the application of AI in the field of nursing. The present investigation was conducted in the year 2023 utilizing a review methodology and an innovative scientific inquiry. Comprehensive research was performed in reliable databases such as PubMed, Scopus, Google Scholar, Science Direct, and Springer, using the keywords AI, smart hospital, nursing, and health care to accomplish the intended objectives. From 2016 to 2023,120 articles were chosen as the initial selection. Studies that were not related to the subject matter were excluded from the analysis afterward. After searching and eliminating duplicate articles through objective screening, a total of 98 articles were reviewed, with 63 ultimately selected for the study. Within the realm of nursing care, research has been conducted in various areas, such as electronic health records (13 studies), health information collection and analysis (27 studies), healthcare cost analysis (16 studies), and the implementation of smart technology and hospitals (7 studies). The integration of AI technology has shown promise in enhancing nursing care by reducing diagnostic errors, improving emergency response times, improving patient care quality and psychological support, and enabling remote care for elderly patients through the use of smart technology. AI is a significant technological advancement that can directly impact the operational effectiveness of healthcare organizations. This is achieved through the optimization of healthcare business processes and the enhancement of patient safety. However, limited research has been conducted regarding the affordability and economic aspects of AI implementation. Therefore, it is advisable for healthcare policymakers to establish the necessary infrastructure to leverage this technology, which will facilitate a more effective therapeutic and economic approach. The Open Public Health Journal550Investigation of the application of AI in the field of nursing shows promise in enhancing nursing care by reducing diagnostic errors, improving emergency response times, improving patient care quality and psychological support, and enabling remote care for elderly patients through the use of smart technology.The Open Public Health Journal['Hassan Mahmoudi', 'Mohammad Hesam Moradi']2024-05-09T00:00:00https://www.semanticscholar.org/paper/265d200676743daab66c99ae707add0930819b3a 8f0a03bdcc4f05de77aaefdf3d8eb15b1496665dBACKGROUND Limited access to dermatologic care may pose an obstacle to the early detection and intervention of cutaneous malignancies. The role of artificial intelligence (AI) in skin cancer diagnosis may alleviate potential care gaps. OBJECTIVE The aim of this systematic review was to offer an in-depth exploration of published AI algorithms trained on dermoscopic and macroscopic clinical images for the diagnosis of melanoma, basal cell carcinoma, and cutaneous squamous cell carcinoma (cSCC). METHODS Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, a systematic review was conducted on peer-reviewed articles published between January 1, 2000, and January 26, 2023. RESULTS AND DISCUSSION Among the 232 studies in this review, the overall accuracy, sensitivity, and specificity of AI for tumor detection averaged 90%, 87%, and 91%, respectively. Model performance improved with time. Despite seemingly impressive performance, the paucity of external validation and limited representation of cSCC and skin of color in the data sets limits the generalizability of the current models. In addition, dermatologists coauthored only 12.9% of all studies included in the review. Moving forward, it is imperative to prioritize robustness in data reporting, inclusivity in data collection, and interdisciplinary collaboration to ensure the development of equitable and effective AI tools.Dermatologic Surgery240A systematic review of published AI algorithms trained on dermoscopic and macroscopic clinical images for the diagnosis of melanoma, basal cell carcinoma, and cutaneous squamous cell carcinoma found that the overall accuracy, sensitivity, and specificity of AI for tumor detection averaged 90%, 87%, and 91%, respectively.Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]['Neil K Jairath', 'Vartan Pahalyants', 'Rohan Shah', 'Jason Weed', 'John A Carucci', 'M. Criscito']2024-05-09T00:00:00https://www.semanticscholar.org/paper/8f0a03bdcc4f05de77aaefdf3d8eb15b1496665d af5b29343cee9ad1b45c166f45136c26de76ac4cINTRODUCTION Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events. METHODS Patients who underwent EVAR at a tertiary hospital from January 2017 to March 2020 with at least 2 follow-up computed tomography angiography (CTA) were analyzed using PRAEVAorta 2 (Nurea). The software was compared to the ground truth provided by human experts using Sensitivity (Se), Specificity (Sp), Negative Predictive Value (NPV), and Positive Predictive Value (PPV). Endovascular aortic repair-related adverse events were defined as aneurysm-related death, rupture, endoleak, limb occlusion, and EVAR-related re-interventions. RESULTS Fifty-six patients were included with a median imaging follow-up of 27 months (interquartile range [IQR]: 20-40). There were no significant differences overtime in the evolution of maximum aneurysm diameters (55.62 mm [IQR: 52.33-59.25] vs 54.34 mm [IQR: 46.13-59.47]; p=0.2162) or volumes (130.4 cm3 [IQR: 113.8-171.7] vs 125.4 cm3 [IQR: 96.3-169.1]; p=0.1131) despite a -13.47% decrease in the volume of thrombus (p=0.0216). PRAEVAorta achieved a Se of 89.47% (95% confidence interval [CI]: 80.58 to 94.57), a Sp of 91.25% (95% CI: 83.02 to 95.70), a PPV of 90.67% (95% CI: 81.97 to 95.41), and an NPV of 90.12% (95% CI: 81.70 to 94.91) in detecting endoleaks. Endovascular aortic repair-related adverse events were associated with global volume modifications with an area under the curve (AUC) of 0.7806 vs 0.7277 for maximum diameter. The same trend was observed for endoleaks (AUC of 0.7086 vs 0.6711). CONCLUSIONS The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up. The association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter. CLINICAL IMPACT The integration of PRAEVAorta AI software into clinical practice promises a transformative shift in post-EVAR surveillance. By offering precise and rapid detection of endoleaks and comprehensive anatomic assessments, clinicians can expect enhanced diagnostic accuracy and streamlined patient management. This innovation reduces reliance on manual measurements, potentially reducing interpretation errors and shortening evaluation times. Ultimately, PRAEVAorta's capabilities hold the potential to optimize patient care, leading to more timely interventions and improved outcomes in endovascular aortic repair.Journal of Endovascular Therapy280The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up, including the association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter.Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists['Quentin Coatsaliou', 'F. Lareyre', 'J. Raffort', 'Claire Webster', 'Colin Bicknell', 'A. Pouncey', 'Éric Ducasse', 'C. Caradu']2024-05-09T00:00:00https://www.semanticscholar.org/paper/af5b29343cee9ad1b45c166f45136c26de76ac4c 38a8c83d831d44c23de7987680e609a0b29e4cc7Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.BioMedInformatics1290Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.BioMedInformatics['Dimitrios Rallis', 'Maria S Baltogianni', 'K. Kapetaniou', 'V. Giapros']2024-05-09T00:00:00https://www.semanticscholar.org/paper/38a8c83d831d44c23de7987680e609a0b29e4cc7 b53ceaff3443758cf36539704c60f2fdbf15e0e7The rapid advancement of Artificial Intelligence (AI) technologies presents both opportunities and challenges for labor markets globally. By adopting a qualitative methodology, this study analyses the impact of AI integration in jobs on the labor market and the subsequent policy responses by governments. The study specifies the industries and jobs that are highly susceptible to AI-driven automation, particularly those characterized by routine and repetitive tasks. The study also acknowledges the potential for AI to create new job categories. Key findings suggest that while AI poses a significant risk of job displacement in several sectors, it also offers opportunities for economic growth, opening of new jobs and innovation. Moreover, the study highlights various government strategies employed globally, including up-skilling and re-skilling initiatives, strengthening social protection systems, and fostering AI-human collaboration. These strategies aim to mitigate the adverse impacts of AI on employment and ensure a balanced transition to AI-integrated economies. Based on these strategies, the paper suggests some recommendations for governments to address AI-driven job losses, emphasizing the importance of multidisciplinary education, ethical AI use, inclusive growth strategies, and international cooperation.Technium Social Sciences Journal00Analysis of the impact of AI integration in jobs on the labor market and the subsequent policy responses by governments suggests that while AI poses a significant risk of job displacement in several sectors, it also offers opportunities for economic growth, opening of new jobs and innovation.Technium Social Sciences Journal['Mohamad Hassan Soueidan', 'Rodwan Shoghari']2024-05-09T00:00:00https://www.semanticscholar.org/paper/b53ceaff3443758cf36539704c60f2fdbf15e0e7 c8c2c8e3107499d1cce11229d2b6e076c0dccf17Empirica730It is highlighted that workers’ ER probabilities may either increase or decrease in response to the AI revolution, depending on their education level and the characteristics of their occupations in terms of AI advances and AI exposure.Empirica['Pablo Casas', 'Concepción Román']2024-05-09T00:00:00https://www.semanticscholar.org/paper/c8c2c8e3107499d1cce11229d2b6e076c0dccf17 0ce1bdae34cd3802049760fafa5015fa8220ec93Rapid changes in artificial intelligence (AI) have already impacted the medical field. While the use of AI to assist medical diagnosis has been documented, AI is continually expanding within medical applications. AI applications in sonography and their effect on ultrasound examinations and sonographers are still indeterminate. Six papers were reviewed to investigate AI applications and effects within the sonography field. These papers provided results on a range of ultrasound applications including breast, obstetric, skin lesions, carotid, blood flow and cardiac ultrasound imaging when combined with AI. In this narrative review, the application of AI demonstrated that accuracy and speed of clinical diagnosis can be improved. These six aspects of ultrasound imaging combined with AI demonstrated the potential to assist the operator and clinicians with a diagnosis in various applications and settings. Additionally, AI can be beneficial to telehealth applications for rural and remote areas where healthcare access can be limited. These changes are opportunities to assist with medical care to provide benefits to patients, sonographers and clinicians as AI transitions to a positive integration within many aspects of clinical care.Sonography220The application of AI demonstrated that accuracy and speed of clinical diagnosis can be improved and has the potential to assist the operator and clinicians with a diagnosis in various applications and settings.Sonography['R. Boman', 'S. Penkala', 'R. H. M. Chan', 'F. Joshua', 'R. Cheung']2024-05-09T00:00:00https://www.semanticscholar.org/paper/0ce1bdae34cd3802049760fafa5015fa8220ec93 238204610a13a21c35de6519acb7de01836070aeI review opportunities and threats that widely accessible Artificial Intelligence (AI)-powered services present for educational statistics and measurement. Algorithmic and computational advances continue to improve approaches to item generation, scale maintenance, test security, test scoring, and score reporting. Predictable misuses of AI for these purposes will result in biased scores, construct underrepresentation, and differential impact over time. Recent efforts to develop standards for AI use in testing like those of Burstein are promising. I argue that similar efforts to develop AI standards for educational measurement will benefit from increased attention to the context of test use and explicit commitment to ongoing monitoring of bias and scale drift over time.Journal of educational and behavioral statistics90It is argued that similar efforts to develop AI standards for educational measurement will benefit from increased attention to the context of test use and explicit commitment to ongoing monitoring of bias and scale drift over time.Journal of Educational and Behavioral Statistics['Andrew D. Ho']2024-05-09T00:00:00https://www.semanticscholar.org/paper/238204610a13a21c35de6519acb7de01836070ae 8033644314a332a48b7fe541e54f15228fbcde4dInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-05-09T00:00:00https://www.semanticscholar.org/paper/8033644314a332a48b7fe541e54f15228fbcde4d 5211d1919019491b7693665aa24e1db5af534b20This paper uses eye-tracking research to examine the cognitive load and translation accuracy of simultaneous interpretering with and without technology-assisted in the direction of Chinese-to-English translation and to explore the moderating role of simultaneous interpreting ability. It was found that the new type of simultaneous interpreting assisted by speech recognition technology and machine translation technology can significantly reduce the cognitive load of student interpreters and improve the accuracy of translation. However, the advantages of technology-assisted simultaneous interpreting are not significant enough for professional interpreters compared with student interpreters. This paper explains the results of the study from the perspectives of memory pressure, bilingual switching pressure, and the “ceiling effect”.English Language Teaching and Linguistics Studies00It was found that the new type of simultaneous interpreting assisted by speech recognition technology and machine translation technology can significantly reduce the cognitive load of student interpreters and improve the accuracy of translation.English Language Teaching and Linguistics Studies['Mengxi Li']2024-05-09T00:00:00https://www.semanticscholar.org/paper/5211d1919019491b7693665aa24e1db5af534b20 8bb28e2b12c1701ac7a13e0cc6f6ad1fe5b41246This article touches on the issue of understanding and approach to the use of AI by the British army from the perspective of a representative of the British armed forces. The article will address the issues of tripartite division as to the essence of the problem today. This article was an excellent part of the author’s speech delivered during the international conference 2023 Warsaw Cyber Summit.Cybersecurity and Law10The article will address the issues of tripartite division as to the essence of the problem today and understanding and approach to the use of AI by the British army from the perspective of a representative of the British armed forces.Cybersecurity and Law['Keith Eble']2024-05-09T00:00:00https://www.semanticscholar.org/paper/8bb28e2b12c1701ac7a13e0cc6f6ad1fe5b41246 22da95b7a8fbebdb9452004617f891b6dbe58d21,Cybersecurity and Law100Cybersecurity and Law['Paweł Pelc']2024-05-09T00:00:00https://www.semanticscholar.org/paper/22da95b7a8fbebdb9452004617f891b6dbe58d21 5c8249aa0c6640ac6d5902cdc4dc7a9922d6b26eJournal of Hospital Medicine80Journal of hospital medicine['A. Rodman', 'Zahir Kanjee']2024-05-09T00:00:00https://www.semanticscholar.org/paper/5c8249aa0c6640ac6d5902cdc4dc7a9922d6b26e 0325f84801133ca427ec514869ed3f405c175399Indian Journal of Surgery150Indian Journal of Surgery['Kaushik Bhattacharya', 'N. Bhattacharya', 'Sandeep Kumar', 'Vipul D Yagnik', 'P. Garg', 'P. Choudhary']2024-05-09T00:00:00https://www.semanticscholar.org/paper/0325f84801133ca427ec514869ed3f405c175399 bcda8c38f5d2b72c933f1741d397f22754ffe769International Journal of Surgery00International journal of surgery['Hongda Li', 'Jianhua Li']2024-05-09T00:00:00https://www.semanticscholar.org/paper/bcda8c38f5d2b72c933f1741d397f22754ffe769 55f5615f3610115694fc6adb27221023596477d5This article contributes to the dynamic debate surrounding the intersection of artificial intelligence (AI) and copyright law, offering a fresh perspective that builds upon interdisciplinary analyses. Focusing on the cognitive processes underpinning creativity in both human and AI contexts, the study draws a detailed parallel between Vincent Van Gogh's iconic “Starry Night” and its AI‐generated counterpart generated through DeepDream technology. Central to the investigation is the application of psychological and neuroscientific theories to understand and compare the creative processes in humans and AI. Based on such exercise, the article first examines whether art generated with AI, devoid of human emotions and motivations yet capable of mimicking human creative cognitive processes, qualifies for copyright protection. The analysis suggests that the similarities between human and AI creativity, particularly in their cognitive structuring, could render the work “original” according to different jurisdictional standards and interpretation of copyright law. Second, the article investigates whether AI infringes copyright if protected material is used for its training and processing. This question becomes particularly relevant in light of recent legal actions against AI‐artwork generators in California, which raise issues of potential infringement by AI using latent diffusion techniques on existing artworks. The discussion provides an original perspective that can advance the ongoing debate on the use of copyrighted material for AI training. The paper aims to contribute to the ongoing debate about AI and copyright by challenging the traditional human‐centric view of authorship in copyright law. The article argues for a nuanced understanding that acknowledges the complex nature of creativity, transcending the binary division between human and artificial sources. This approach is critical in redefining legal frameworks, ensuring they are adaptive to the evolving landscape of AI capabilities. At the same time, the article addresses the implications of AI drawing inspiration from existing art, recognizing the need to balance different stakeholders' interests when drawing policy considerations. Ultimately, the goal is to provide a layered perspective that not only deepens the legal discourse but also respects and fosters the coexistence and mutual advancement of both human and artificial creativity in the digital age, in line with the purpose of copyright.Journal of World Intellectual Property00The paper draws a detailed parallel between Vincent Van Gogh's iconic “Starry Night” and its AI‐generated counterpart generated through DeepDream technology, and investigates whether AI infringes copyright if protected material is used for its training and processing.The Journal of World Intellectual Property['Francesca Mazzi', 'Salvatore Fasciana']2024-05-09T00:00:00https://www.semanticscholar.org/paper/55f5615f3610115694fc6adb27221023596477d5 5149cc0e85f78345ebf965ad4b90cf7abc8f14ceMinds Mach.430It is argued that transnational fairness offers a perspective on global injustices in relation to AI development and application that has the potential to substantiate discussions by identifying gaps in data and technology.Minds Mach.['Cem Kozcuer', 'Anne Mollen', 'Felix Bießmann']2024-05-09T00:00:00https://www.semanticscholar.org/paper/5149cc0e85f78345ebf965ad4b90cf7abc8f14ce bbcb9ac7f910e1458a6f67e77e1ddb4578dfbe39The purpose of this study was to improve students’ problem-solving ability and metacognitive self-regulation (MSR) by applying science technology society (STS) learning model integrated with metacognitive self-regulation. The participants were 13 students from Department of Biology Education who took microbiology courses. A quasi-experimental method with one-group pre and posttest design was used in this study. Data were collected through pre and posttest with eight open ended questions to measure students’ problem-solving ability on four microbiology topics (food, pathogen, waste and water microbiology). These topics were studied sequentially in two months. The instrument used to measure MSR is a questionnaire with open-ended questions. This questionnaire was developed based on three aspects of MSR namely, planning, monitoring and evaluation. MSR questionnaire was administered to students at the end of each topic. The N-gain test was used to analyze the improvement of students' problem-solving ability on each topic. The average score of all aspects in MSR questionnaire was used to explore students’ metacognitive self-regulation. The correlation between MSR and problem-solving ability was analyzed using Pearson correlation. The results revealed that the N gain score of problem-solving ability was increased from 0.56 in the first topic to 0.7 in the next three topics. The average of MSR score also increased from 66.15 in the first topic to 87.23 in fourth topic. There was a positive correlation between students’ MSR and problem-solving ability. These results indicated that application of Science Technology Society integrated with MSR is an effective strategy in improving students’ problem-solving ability and MSR. The implementation of this research is to develop a lecture program as an effort to improve the learning processBiosfer00The results indicated that application of Science Technology Society integrated with MSR is an effective strategy in improving students’ problem-solving ability and MSR.Biosfer['Euis Erlin', 'Adi Rahmat', 'Widi Purwianingsih']2024-05-07T00:00:00https://www.semanticscholar.org/paper/bbcb9ac7f910e1458a6f67e77e1ddb4578dfbe39 83e86299d2daff6189ddf865cd5b15c04c57486b Machines powered by artificial intelligence (AI) are increasingly taking over tasks previously performed by humans alone. In accomplishing such tasks, they may intentionally commit ‘AI crimes’, ie engage in behaviour which would be considered a crime if it were accomplished by humans. For instance, an advanced AI trading agent may—despite its designer’s best efforts—autonomously manipulate markets while lacking the properties for being held criminally responsible. In such cases (hard AI crimes) a criminal responsibility gap emerges since no agent (human or artificial) can be legitimately punished for this outcome. We aim to shift the ‘hard AI crime’ discussion from blame to deterrence and design an ‘AI deterrence paradigm’, separate from criminal law and inspired by the economic theory of crime. The homo economicus has come to life as a machina economica, which, even if cannot be meaningfully blamed, can nevertheless be effectively deterred since it internalises criminal sanctions as costs.Oxford Journal of Legal Studies00This work aims to shift the ‘hard AI crime’ discussion from blame to deterrence and design an ‘AI deterrence paradigm’, separate from criminal law and inspired by the economic theory of crime.Oxford Journal of Legal Studies['Elina Nerantzi', 'Giovanni Sartor']2024-05-07T00:00:00https://www.semanticscholar.org/paper/83e86299d2daff6189ddf865cd5b15c04c57486b 2c9d8a0babd69ee215f386dc1fb071e8f492f96fArtificial Intelligence and Law750This paper analyses whether current explainable AI techniques can help to address taxpayer concerns about the use of AI in taxation and suggests technical and legal approaches for designing explanation mechanisms that meet the needs of legal explanation in the tax domain.Artificial Intelligence and Law['Łukasz Górski', 'Błażej Kuźniacki', 'Marco Almada', 'K. Tylinski', 'Madalena Calvo', 'Pablo Matias Asnaghi', 'Luciano Almada', 'Hilario Iñiguez', 'Fernando Rubianes', 'Octavio Pera', 'Juan Ignacio Nigrelli']2024-05-07T00:00:00https://www.semanticscholar.org/paper/2c9d8a0babd69ee215f386dc1fb071e8f492f96f 12388d0800771258de145014c2d0425b6b582212Artificial Intelligence (AI) has facilitated significant breakthroughs in drug discovery, the design of materials, and organic synthesis. The advancements in the latter group are especially remarkable due to the abilities of the latest computational methods (molecular design algorithms) that enable the exploration of extensive chemical spaces and enhance research in fields such as predicting molecule properties, designing molecules, retrosynthesis, predicting reaction conditions, and predicting reaction outcomes. A literary review was conducted following PRISMA guidelines. This study aimed to review existing data on the application of AI in separation chromatography. The evolution and utilization of AI in the pharmaceutical industry and its future aspects were articulated in this study. The utilization of AI can completely transform the field of chromatography analysis by facilitating expedited, more precise, and more effective data processing. By automating chromatography analysis, AI can enhance efficiency and minimize the potential for human mistakes. This advancement enables scientists to dedicate their efforts towards addressing intricate and demanding analytical issues. With the evolution of technology and the increasing adoption, we can anticipate more progress in chromatography analysis and analytical chemistry.International Journal of Applied Pharmaceutics590The evolution and utilization of AI in the pharmaceutical industry and its future aspects were articulated in this study and the utilization of AI can completely transform the field of chromatography analysis by facilitating expedited, more precise, and more effective data processing.International Journal of Applied Pharmaceutics['Divekar Kalpana', 'Shishir Kumar Prasad']2024-05-07T00:00:00https://www.semanticscholar.org/paper/12388d0800771258de145014c2d0425b6b582212 06ba6c393d1a625da5e1e3521e0ab25b85bd9cb0Despite the impressive performance of Large Language Models (LLMs) in software development activities, recent studies show the concern of introducing vulnerabilities into software codebase by AI programming assistants (e.g., Copilot, CodeWhisperer). In this work, we present Codexity, a security-focused code generation framework integrated with five LLMs. Codexity leverages the feedback of static analysis tools such as Infer and CppCheck to mitigate security vulnerabilities in LLM-generated programs. Our evaluation in a real-world benchmark with 751 automatically generated vulnerable subjects demonstrates Codexity can prevent 60% of the vulnerabilities being exposed to the software developer.210This work presents Codexity, a security-focused code generation framework integrated with five LLMs that leverages the feedback of static analysis tools such as Infer and CppCheck to mitigate security vulnerabilities in LLM-generated programs.['Sung Yong Kim', 'Zhiyu Fan', 'Yannic Noller', 'Abhik Roychoudhury']2024-05-07T00:00:00https://www.semanticscholar.org/paper/06ba6c393d1a625da5e1e3521e0ab25b85bd9cb0 b6b5be200abd1221ff0abadd1c546811ad15ad76This conceptual article provides a comprehensive overview of the current status of Artificial Intelligence (AI) integration and its influence on adult education. It discusses generative AI technologies and their potential applications in adult education settings, examines the opportunities and ethical challenges associated with integrating AI, and provides insights into emerging trends. The article consists of five sections. The introduction provides a rationale as to why AI should be integrated into adult education. Second, it describes evolving AI technologies such as Large Language Models (LLM) for personalized learning, Machine Learning Algorithms for adaptive learning systems, Virtual Reality (VR) and Augmented Reality (AR) for immersive learning experiences, Chatbots and virtual assistants for learner support and guidance, and Data Learning Analytics (DLA) for tracking learner progress and performance into adult education. Section three explores the ethical implications of AI in adult education, including academic honesty and integrity, data privacy, and algorithmic bias. In section four, emerging trends and future directions are discussed. The final section considers policy implications and makes recommendations for adult educators working to develop AI-enriched adult education.International Journal of Adult Education and Technology290International Journal of Adult Education and Technology['Valerie A. Storey', 'Amiee Wagner']2024-05-07T00:00:00https://www.semanticscholar.org/paper/b6b5be200abd1221ff0abadd1c546811ad15ad76 957bd82ad095a20d8d5e872e75b966d97e42a2a5The generative artificial intelligence (AI) model ChatGPT holds transformative prospects in medicine. The development of such models has signaled the beginning of a new era where complex biological data can be made more accessible and interpretable. ChatGPT is a natural language processing tool that can process, interpret, and summarize vast data sets. It can serve as a digital assistant for physicians and researchers, aiding in integrating medical imaging data with other multiomics data and facilitating the understanding of complex biological systems. The physician’s and AI’s viewpoints emphasize the value of such AI models in medicine, providing tangible examples of how this could enhance patient care. The editorial also discusses the rise of generative AI, highlighting its substantial impact in democratizing AI applications for modern medicine. While AI may not supersede health care professionals, practitioners incorporating AI into their practices could potentially have a competitive edge.JMIR Bioinformatics and Biotechnology240The editorial discusses the rise of generative AI, highlighting its substantial impact in democratizing AI applications for modern medicine and practitioners incorporating AI into their practices could potentially have a competitive edge.JMIR Bioinformatics and Biotechnology['Sean Hacking']2024-05-07T00:00:00https://www.semanticscholar.org/paper/957bd82ad095a20d8d5e872e75b966d97e42a2a5 3dd2f74daf02a05f5fd3feb755c339024f7ae91eRetaining learner engagement is a major challenge in online learning environments, which is even more intensified with learning spaces increasingly built by combining resources from multiple independent sources. Narrative-centric learning experience has been found to improve learner engagement by several researchers. Towards this end, we propose an AI-based approach that generates auxiliary learning content called narrative fragments which are interspersed into the learning pathways to create interactive learning narratives. The proposed approach consists of the automatic generation of two types of narrative fragments– overviews of the learning pathway segments and reflection quizzes or formative assessments from learning resources in any format including open educational resources. The pipeline for the generation of the narrative fragments consists of various components based on different semantic models and a natural language generation (NLG) component based on a pre- trained language model GPT-2 (Generative Pre-trained Transformer 2). Automation enables the generation of narrative fragments on the fly whenever there are changes in the learning pathway due to the need for reiteration of concepts, pre-requisite knowledge acquisition, etc., enabling adaptability in the learning pathways. The proposed approach is domain agnostic which makes it easily adaptable to different domains. The NLG model is evaluated using ROUGE scores against several baselines. Automatically generated narrative fragments are evaluated by human evaluators. We obtained encouraging results in both casesInternational Journal of Advanced Research in Science, Communication and Technology40An AI-based approach that generates auxiliary learning content called narrative fragments which are interspersed into the learning pathways to create interactive learning narratives is proposed, enabling adaptability in the learning pathways.International Journal of Advanced Research in Science, Communication and Technology['Mr. Anand Tilagul', 'Ms. Rafiya Firdouse', 'Ms. Sirisha R', 'Ms. Swathi M', 'Ms. Thanu Gowda N M']2024-05-07T00:00:00https://www.semanticscholar.org/paper/3dd2f74daf02a05f5fd3feb755c339024f7ae91e 88ecae6f9b8046281a609c9da20d42f0822ad9abThis paper describes a website based chatbot. This chatbot can make it easier to interact with the website. The bot understands and converses with the user in Simple Language. This chatbot is linked to an e-commerce website. This website has a variety of products with different features. The chatbot helps you to make a decision which product is suitable for you. This is especially helpful when you have not narrowed down the criteria for the product. Its functions basically like an online automated assistant. This paper presents a novel approach to e-commerce tailored specifically for artisans and craftspeople, integrating advanced artificial intelligence (AI) capabilities to create a seamless, personalized, and efficient marketplace. Our AI-infused e-commerce platform aims to bridge the gap between traditional craftsmanship and modern digital commerce, offering artisans a unique space to showcase their work and connect with a global audience. Key features of the platform include intelligent product recommendations, personalized marketing, automated inventory management, and AI-assisted customer support. By leveraging machine learning algorithms, we enable a dynamic user experience where customers receive tailored product suggestions based on their browsing and purchase history, leading to increased engagement and sales. The platform's AI-powered marketing tools help artisans optimize their reach through targeted campaigns and personalized content.International Journal of Advanced Research in Science, Communication and Technology60A novel approach to e-commerce tailored specifically for artisans and craftspeople is presented, integrating advanced artificial intelligence (AI) capabilities to create a seamless, personalized, and efficient marketplace.International Journal of Advanced Research in Science, Communication and Technology['Mrs. G. Jeyasri', 'Nisanthan. S', 'Salman Farcy K A', 'Shreeharan A', 'Vignesh M']2024-05-07T00:00:00https://www.semanticscholar.org/paper/88ecae6f9b8046281a609c9da20d42f0822ad9ab c2d2296fdf6f9c17f6e9e8dbc933090cf5f43f5eThe landscape of bioscience education has changed dramatically over the last few years, first due to the COVID pandemic and now with the explosion of artificial intelligence. David Smith, Professor of Bioscience Education at Sheffield Hallam University, discusses some of the bigger questions in relation to the use of AI in bioscience education and assessment.The biochemist00Professor David Smith, Professor of Bioscience Education at Sheffield Hallam University, discusses some of the bigger questions in relation to the use of AI in bioscience education and assessment.The Biochemist['David Smith']2024-05-07T00:00:00https://www.semanticscholar.org/paper/c2d2296fdf6f9c17f6e9e8dbc933090cf5f43f5e 4fb48d09fdc111d19d7ae6ea4b5e4335d6007bc9 Purpose The widespread integration of artificial intelligence (AI)-enabled personalization has sparked a need for a deeper understanding of its transformative potential. To address this, this study aims to investigate the mental models held by consumers from diverse cultures regarding the impact and role of AI-enabled personalization in their lives (i.e. individual well-being) and in society (i.e. societal well-being). Design/methodology/approach This paper uses the theories-in-use approach, collecting qualitative data via the critical incident technique. This data encompasses 487 narratives from 176 consumers in two culturally distinct countries, Belgium and Pakistan. Additionally, it includes insights from a focus group of six experts in the field. Findings This research reveals that consumers view AI-enabled personalization as a dual-edged sword: it may both extend and restrict the self and also contribute to an affluent society as well as an ailing society. The particular aspects of the extended/restricted self and the affluent/ailing society that emerge differ across respondents from different cultural contexts. Originality/value This cross-cultural research contributes to the personalization and well-being literature by providing detailed insight into the transformative potential of AI-enabled personalization while also having important managerial and policy implications. Journal of Services Marketing1110It is revealed that consumers view AI-enabled personalization as a dual-edged sword: it may both extend and restrict the self and also contribute to an affluent society as well as an ailing society.Journal of Services Marketing['Khalid Mehmood', 'K. Verleye', 'Arne De Keyser', 'Bart Larivière']2024-05-07T00:00:00https://www.semanticscholar.org/paper/4fb48d09fdc111d19d7ae6ea4b5e4335d6007bc9 71bd14e56919872770f66eeff7ffe29e76d28292Since its public debut in late 2022, ChatGPT has sparked growing interest in AI (Artificial Intelligence) within the Process Safety Management (PSM) community, serving as a tool for data capture and industry‐wide knowledge utilization. While machine learning (ML) had previously been explored in process safety, the emergence of large language models with chat‐style interfaces has made AI more accessible to non‐experts. Over the past year, I have managed the “AI‐PSM” LinkedIn group, facilitating discussions among PSM practitioners on the AI applications in PSM. These discussions have been analyzed using Prof. Thomas Malone's “4 Roles of AI” framework. This paper explores prevailing sentiments among AI‐PSM group members using Malone's model, addressing challenges such as mathematical problem‐solving, errors, and benchmarking.Process safety progress30Process Safety Progress['Rainer Hoff']2024-05-07T00:00:00https://www.semanticscholar.org/paper/71bd14e56919872770f66eeff7ffe29e76d28292 3ef3af120cdc09211f47d0e677ebc057a81cefd1Purpose Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context. Design/methodology/approach The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed. Findings The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention. Practical implications The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption. Originality/value As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy. Journal of Entrepreneurship in Emerging Economies1400The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier and the need to address perceived risks and enhance facilitating conditions is highlighted.Journal of Entrepreneurship in Emerging Economies['Samer Abaddi']2024-05-07T00:00:00https://www.semanticscholar.org/paper/3ef3af120cdc09211f47d0e677ebc057a81cefd1 cad968c43f538b9cd72d1d5f79dc1ba6ca93a350This web application is proposed to generate images that is given in a prompt. This can generate imaginary pictures. For the conversion, we need DALL E & Open Ai. It will be fun creating the artistic, realistic images from the prompt. This is a Web Application project developed using A& OpenAI, we used Natural language description prompt for our project. It creates images through prompts. This will be advantage for executing different ideas, thoughts into textual presentation. DALLE can be used for advertising, printing, selling etc. Using this web application project, we can enhance our imaginative ideas into a realistic one. It is a friendly web where we won't face any issues in pictures. And We can't find this imaginary picture generator in any search engine. This Android application project will give you a picture with whatever size you want. And it won't reduce the quality of a picture. The quality size of a picture will be 256 × 256, 512 × 512 and 1024 × 1024.We can choose a quality size based on our network quality. Before using this web application, we have to make sure the network facilities. Keywords – Web application, user login, user registration, prompt field, search field, download, image assessment.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This web application is proposed to generate images that is given in a prompt, which can generate imaginary pictures, and this is a Web Application project developed using A& OpenAI, it creates images through prompts.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Deepa Athawale']2024-05-07T00:00:00https://www.semanticscholar.org/paper/cad968c43f538b9cd72d1d5f79dc1ba6ca93a350 3c29def9f21714f7c518f6671ae05951a7f0910bGenerative AI technologies demand new practical and critical competencies, which call on design to respond to and foster these. We present an exploratory study guided by Research-through-Design, in which we partnered with a primary school to develop a constructionist curriculum centered on students interacting with a generative AI technology. We provide a detailed account of the design of and outputs from the curriculum and learning materials, finding centrally that the reflexive and prolonged `hands-on' approach led to a co-development of students' practical and critical competencies. From the study, we contribute guidance for designing constructionist approaches to generative AI technology education; further arguing to do so with `critical responsivity.' We then discuss how HCI researchers may leverage constructionist strategies in designing interactions with generative AI technologies; and suggest that Research-through-Design can play an important role as a `rapid response methodology' capable of reacting to fast-evolving, disruptive technologies such as generative AI.970How HCI researchers may leverage constructionist strategies in designing interactions with generative AI technologies is discussed; and it is suggested that Research-through-Design can play an important role as a `rapid response methodology' capable of reacting to fast-evolving, disruptive technologies such as generative AI.['Jesse Josua Benjamin', 'Joseph Lindley', 'Elizabeth Edwards', 'Elisa Rubegni', 'Tim Korjakow', 'David Grist', 'Rhiannon Sharkey']2024-05-07T00:00:00https://www.semanticscholar.org/paper/3c29def9f21714f7c518f6671ae05951a7f0910b 34a93ce642e40890f4d6cddecb00597d0e051791Human-AI co-creation aims to combine human and AI strengths for artistic results exceeding individual capabilities. Frameworks exist for painting, music, and poetry, but choreography's embodied nature demands a dedicated approach. This paper explores AI-assisted choreography techniques (e.g., generative ideation, embodied improvisation) and analyzes interaction design -- how humans and AI collaborate and communicate -- to inform the design considerations of future human-AI choreography co-creation systems.250This paper explores AI-assisted choreography techniques and analyzes interaction design -- how humans and AI collaborate and communicate -- to inform the design considerations of future human-AI choreography co-creation systems.['Yimeng Liu']2024-05-07T00:00:00https://www.semanticscholar.org/paper/34a93ce642e40890f4d6cddecb00597d0e051791 73f03854b34b4ca0e5a82705bc1efb3055702df1PurposeThis paper examines and empirically validates the artificial intelligence-enabled human resource management (AI-enabled HRM) dimensions and sustainable organisational performance (SOP) relationship. It also examines the mediation and moderation of employee engagement (EE) and fusion skills (FS).Design/methodology/approachThe indirect effects of AI-enabled HRM dimensions on SOP were found using structural equation modelling (SEM), bootstrapping and FS’s moderation effect by AMOS 22.FindingsResults showed that AI-enabled HRM dimensions indirectly affected SOP through EE as a full and partial mediator with no moderation effects of FS.Originality/valueThis is the first study to link AI-enabled HRM dimensions, EE and SOP and determine how FS moderates EE and SOP.Evidence-based HRM: a Global Forum for Empirical Scholarship750This is the first study to link AI-enabled HRM dimensions, EE and SOP and determine how FS moderates EE and SOP and showed that AI-enabled HRM dimensions indirectly affected SOP through EE as a full and partial mediator with no moderation effects of FS.Evidence-based HRM: a Global Forum for Empirical Scholarship['Uttara Jangbahadur', 'Sakshi Ahlawat', 'Prinkle Rozera', 'Neha Gupta']2024-05-07T00:00:00https://www.semanticscholar.org/paper/73f03854b34b4ca0e5a82705bc1efb3055702df1 d376586fe3e67dd44051740819c5c1022e1e132cThe conversational, social and intelligent capabilities of Artificial Intelligence (AI) enabled voice assistants (VAs) to assist humans have grown. However, their use still remains limited for complex tasks such as shopping. While some studies find that consumers are willing to use them, others report negative reactions that lead to rejection, especially for complex activities. This study aims to bridge the gap between the dichotomous streams of literature by investigating the overall attitude towards the use of AI VAs for a multi‐step task that is, shopping. We first identify 27 attitudinal criteria through a comprehensive literature review in light of the Uncanny Valley Theory and the Self Determination Theory. The criteria are organized using the cognitive, affective and conative framework of attitude, and ranked using the Fuzzy Analytical Hierarchy Process. Sensitivity analysis is done to affirm the robustness of the framework. Findings reveal that the behavioral intentions to use (conative criteria) are the strongest, followed by feelings evoked during use (affective criteria), followed by beliefs people hold regarding AI VAs (cognitive criteria). Intention to use AI voice assistants during all stages of the purchase journey takes precedence over negative feelings such as loss of control, unfulfillment and vulnerability. This study reconciles the existing stream of literature on conversational AI and provides managerial implications based on the dimensions of attitude.Journal of Consumer Behaviour760This study reconciles the existing stream of literature on conversational AI and provides managerial implications based on the dimensions of attitude and intention to use AI voice assistants during all stages of the purchase journey takes precedence over negative feelings.Journal of Consumer Behaviour['Sana Zehra Kamoonpuri', 'Anita Sengar']2024-05-07T00:00:00https://www.semanticscholar.org/paper/d376586fe3e67dd44051740819c5c1022e1e132c a1972dd257a30391d393ec94ddac8f1184a08bf3Since late 2022, generative AI has taken the world by storm, with widespread use of tools including ChatGPT, Gemini, and Claude. Generative AI and large language model (LLM) applications are transforming how individuals find and access data and knowledge. However, the intricate relationship between open data and generative AI, and the vast potential it holds for driving innovation in this field remain underexplored areas. This white paper seeks to unpack the relationship between open data and generative AI and explore possible components of a new Fourth Wave of Open Data: Is open data becoming AI ready? Is open data moving towards a data commons approach? Is generative AI making open data more conversational? Will generative AI improve open data quality and provenance? Towards this end, we provide a new Spectrum of Scenarios framework. This framework outlines a range of scenarios in which open data and generative AI could intersect and what is required from a data quality and provenance perspective to make open data ready for those specific scenarios. These scenarios include: pertaining, adaptation, inference and insight generation, data augmentation, and open-ended exploration. Through this process, we found that in order for data holders to embrace generative AI to improve open data access and develop greater insights from open data, they first must make progress around five key areas: enhance transparency and documentation, uphold quality and integrity, promote interoperability and standards, improve accessibility and useability, and address ethical considerations.00In order for data holders to embrace generative AI to improve open data access and develop greater insights from open data, they first must make progress around five key areas: enhance transparency and documentation, uphold quality and integrity, promote interoperability and standards, improve accessibility and useability, and address ethical considerations.['Hannah Chafetz', 'Sampriti Saxena', 'S. Verhulst']2024-05-07T00:00:00https://www.semanticscholar.org/paper/a1972dd257a30391d393ec94ddac8f1184a08bf3 0814ca754712a3a836997c215655f7d5edfbe8acBACKGROUND: Lung cancer's high mortality rate can be mitigated by early detection, which is increasingly reliant on artificial intelligence (AI) for diagnostic imaging. However, the performance of AI models is contingent upon the datasets used for their training and validation. METHODS: This study developed and validated the DLCSD-mD and LUNA16-mD models utilizing the Duke Lung Cancer Screening Dataset (DLCSD), encompassing over 2,000 CT scans with more than 3,000 annotations. These models were rigorously evaluated against the internal DLCSD and external LUNA16 and NLST datasets, aiming to establish a benchmark for imaging-based performance. The assessment focused on creating a standardized evaluation framework to facilitate consistent comparison with widely utilized datasets, ensuring a comprehensive validation of the model's efficacy. Diagnostic accuracy was assessed using free-response receiver operating characteristic (FROC) and area under the curve (AUC) analyses. RESULTS: On the internal DLCSD set, the DLCSD-mD model achieved an AUC of 0.93 (95% CI:0.91-0.94), demonstrating high accuracy. Its performance was sustained on the external datasets, with AUCs of 0.97 (95% CI: 0.96-0.98) on LUNA16 and 0.75 (95% CI: 0.73-0.76) on NLST. Similarly, the LUNA16-mD model recorded an AUC of 0.96 (95% CI: 0.95-0.97) on its native dataset and showed transferable diagnostic performance with AUCs of 0.91 (95% CI: 0.89-0.93) on DLCSD and 0.71 (95% CI: 0.70-0.72) on NLST. CONCLUSION: The DLCSD-mD model exhibits reliable performance across different datasets, establishing the DLCSD as a robust benchmark for lung cancer detection and diagnosis. Through the provision of our models and code to the public domain, we aim to accelerate the development of AI-based diagnostic tools and encourage reproducibility and collaborative advancements within the medical machine-learning (ML) field.00The DLCSD-mD model exhibits reliable performance across different datasets, establishing the DLCSD as a robust benchmark for lung cancer detection and diagnosis and accelerating the development of AI-based diagnostic tools.['F. I. Tushar', 'Avivah Wang', 'Lavsen Dahal', 'Michael R. Harowicz', 'Kyle J. Lafata', 'Tina D. Tailor', 'Joseph Y. Lo']2024-05-07T00:00:00https://www.semanticscholar.org/paper/0814ca754712a3a836997c215655f7d5edfbe8ac 4fc53bbcd907405f1d3b370ecbb6904341698c96Climate change, primarily caused by human activities, poses a significant global challenge. Countries worldwide are integrating efforts to combat climate change through initiatives such as the Paris Agreement and setting targets to reach net-zero emissions by 2050. This paper explores the potential of artificial intelligence (AI) as a promising solution to address climate change, particularly through the analysis of mass data. AI can aid in environmental decision-making processes, optimize renewable energy use, and accelerate the global transition to a low-carbon economy. Using public data from the OECD, the study investigates the effectiveness of AI in promoting a low-carbon economy by examining its impact on greenhouse gas emissions, carbon footprint, investment in research and development, renewable energy production, and recycling rates. The findings suggest that AI has been considerably effective in supporting the growth of renewable energy and recycling while restraining gas emissions and carbon footprint. However, the study also identifies potential limitations, such as the carbon release from AI itself, and suggests further improvements to AI models.Journal of Economics and Management Sciences240The study investigates the effectiveness of AI in promoting a low-carbon economy by examining its impact on greenhouse gas emissions, carbon footprint, investment in research and development, renewable energy production, and recycling rates and identifies potential limitations.Journal of Economics and Management Sciences['Xinyi Huang']2024-05-07T00:00:00https://www.semanticscholar.org/paper/4fc53bbcd907405f1d3b370ecbb6904341698c96 d5dfea804c5d318b3c175b8dc7d4d6e7f40b7fb2 The objective of this study is to develop an explainable data-driven method using five different methods, namely: Recurrent Neural Network (RNN), XGBoost, GMDH, CatBoost and GP to create a model using a multi-dimensional dataset with over 700 rows of data for predicting MMP. In this work, we applied various AI methods (three black box algorithms and two White-box algorithms) to train a model using a multi-dimensional dataset with over 700 rows of data. Moreover, two robust correlations will be developed that can be used for a wide range of parameters. The dataset has 20 variables, and five subsets (labelled SET1 to SET5) were used as input parameters to develop models. The subsets were selected using a feature importance analysis (similar to Gray’s theorem). Among the multiple inputs tested, the model trained with SET1 and SET5 input parameters (including mole fraction of different hydrocarbon and nonhydrocarbon components and reservoir temperature) resulted in the most accurate estimations of MMP (R2 = 0.99). To further improve the explainability of the model, sensitivity and shapely values analyses were conducted on the developed models, and the impact of each individual feature on the output (MMP) was explained. Temperature, volatile/intermediate, and nonhydrocarbon components are the most influential parameters depending on the subset of parameters chosen; moreover, the models developed in this work performed considerably better (25-40% more accurately) compared with three well-known empirical models from the literature. The result of the current study is repeatable; the developed correlations can be readily applied in other scenarios within the scope of the parameters used to develop the models. The vast range of features in the dataset makes it suitable to study the effects of different parameters on MMP in conditions representative of CO2-EOR and CCUS.Day 1 Tue, May 07, 2024430Day 1 Tue, May 07, 2024['Fahimeh Hadavimoghaddam', 'P. Pourafshary', 'Alexei Rozhenko', 'Erfan Mohammadian', 'Jianguang Wei']2024-05-07T00:00:00https://www.semanticscholar.org/paper/d5dfea804c5d318b3c175b8dc7d4d6e7f40b7fb2 4bce8b60e97007387a943700a15544a023849338Purpose: The aim of this research was to discuss the use of artificial intelligence (AI), machine learning (ML), and big data analytics as fundamental pillars of strategic supply chain management, for better decision-making, more precise forecasting, and higher supply chain agility. Methodology: The paper reviewed existing literature and industry reports to get an in-depth insight into the modern supply chain planning environment, the problems that it faces, and the efficiency of traditional techniques. It then analyzed the opportunities of utilization of AI, ML and big data analytics as well as the certain technologies or techniques that could be utilized, such as the predictive/prescriptive analytics, digital twins and blockchain. Findings: The study concluded that the traditional supply chain planning processes are becoming more and more out of style and inefficient, taking into account the business environment that are constantly changing, global supply chains, and technological advancements. It emphasized the risks to long-term performance associated to relying too much on the past practices and a call for action for progressive modernization of supply chain planning mechanisms. Unique Contribution to Theory, Practice and Policy: The report pointed to innovative ways such as AI, ML, and big data analytics for the integration into the supply chain operations for increasing the productivity, resilience and competitiveness. Moreover, it promoted the increase of budgeting on the talent side in order to obtain an appropriate use of technology and to explore new paths in the market.International journal of supply chain management00The traditional supply chain planning processes are becoming more and more out of style and inefficient, taking into account the business environment that are constantly changing, global supply chains, and technological advancements.International Journal of Supply Chain Management['Jayapal Vummadi', 'Krishna Hajarath']2024-05-07T00:00:00https://www.semanticscholar.org/paper/4bce8b60e97007387a943700a15544a023849338 0cda4bc0594664f4dad314829406e78cae0e389bIntroduction Online mental healthcare has gained significant attention due to its effectiveness, accessibility, and scalability in the management of mental health symptoms. Despite these advantages over traditional in-person formats, including higher availability and accessibility, issues with low treatment adherence and high dropout rates persist. Artificial intelligence (AI) technologies could help address these issues, through powerful predictive models, language analysis, and intelligent dialogue with users, however the study of these applications remains underexplored. The following mixed methods review aimed to supplement this gap by synthesizing the available evidence on the applications of AI in online mental healthcare. Method We searched the following databases: MEDLINE, CINAHL, PsycINFO, EMBASE, and Cochrane. This review included peer-reviewed randomized controlled trials, observational studies, non-randomized experimental studies, and case studies that were selected using the PRISMA guidelines. Data regarding pre and post-intervention outcomes and AI applications were extracted and analyzed. A mixed-methods approach encompassing meta-analysis and network meta-analysis was used to analyze pre and post-intervention outcomes, including main effects, depression, anxiety, and study dropouts. We applied the Cochrane risk of bias tool and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) to assess the quality of the evidence. Results Twenty-nine studies were included revealing a variety of AI applications including triage, psychotherapy delivery, treatment monitoring, therapy engagement support, identification of effective therapy features, and prediction of treatment response, dropout, and adherence. AI-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions. The quality of the data was low to very low. Discussion The review supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare. Nevertheless, given the low quality of the available evidence, this study highlighted the need for additional robust and high-powered studies in this emerging field. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=443575, identifier CRD42023443575.Frontiers in Psychiatry1040Artificial intelligence-delivered self-guided interventions demonstrated medium to large effects on managing mental health symptoms, with dropout rates comparable to non-AI interventions, which supported the use of AI in enhancing treatment response, adherence, and improvements in online mental healthcare.Frontiers in Psychiatry['Gilmar Gutiérrez', 'C. Stephenson', 'J. Eadie', 'Kimia Asadpour', 'N. Alavi']2024-05-07T00:00:00https://www.semanticscholar.org/paper/0cda4bc0594664f4dad314829406e78cae0e389b 348a65c3015b0878240b6e5deff510e9effe9027This article presents a comprehensive analysis of the application and effect of ChatGPT, an advanced AI chatbot model, on global information management across various industries such as healthcare, industry, education, and more. Leveraging a dataset obtained from the Scopus database encompassing research papers from 2022 to 2023, this study investigates the influence of ChatGPT by examining publisher impact, authorship patterns based on Lotka's Law, country-specific scientific production, and keyword distribution. The analysis sheds light on prominent publishers, prolific authors, geographic distribution of research contributions, and prevailing research themes. By understanding the impact of ChatGPT in these sectors, this research contributes to the advancement of knowledge and facilitates informed decision-making regarding the responsible and effective utilization of artificial intelligence (AI) chatbot technologies in global information management.International Journal on Semantic Web and Information Systems (IJSWIS)350The influence of ChatGPT is investigated by examining publisher impact, authorship patterns based on Lotka's Law, country-specific scientific production, and keyword distribution by examining dataset obtained from the Scopus database from 2022 to 2023.International Journal on Semantic Web and Information Systems['Wang Xian', 'Guo-Wei Chen', 'Varsha Arya', 'Kwok Tai Chui']2024-05-07T00:00:00https://www.semanticscholar.org/paper/348a65c3015b0878240b6e5deff510e9effe9027 05ec73040d2a23567ed8e0853339538a72ca54ecEnvironmental science and pollution research international90The present research will increase recycling and reproduction with a balance of cost, efficiency, and human resources consumption in agro-industrial waste management to maintain a balance between socioeconomic structures.Environmental science and pollution research international['Amrita Rai', 'Krishanu Kundu']2024-05-07T00:00:00https://www.semanticscholar.org/paper/05ec73040d2a23567ed8e0853339538a72ca54ec bb070c88dd99361f3d1f651f75557c4f0e4384d6Negative performance feedback is vital for stimulating employees to enhance their performance despite resulting in stress and adverse work outcomes. Fortunately, artificial intelligence (AI)‐enabled automated agents have gradually assumed certain functions led by human leaders, such as providing feedback. Drawing from regulatory focus theory, we propose that AI‐based feedback systems can serve as a “remediation” tool, effectively mitigating employees' apprehensions about receiving negative feedback. In two studies, we found that for employees who fear losing face, AI‐based negative feedback motivates promotion‐focused cognition—motivation to learn—representing a learning mechanism to promote job performance and impedes their prevention‐focused cognition—interpersonal rumination—reducing the depletion needed for job performance. These findings present novel perspectives on using AI in performance feedback.Human Resource Management660It is found that for employees who fear losing face, AI‐based negative feedback motivates promotion‐focused cognition—motivation to learn—representing a learning mechanism to promote job performance and impedes their prevention‐focused cognition—interpersonal rumination—reducing the depletion needed for job performance.Human Resource Management['Jialiang Pei', 'Hongli Wang', 'Qiuping Peng', 'Shanshi Liu']2024-05-07T00:00:00https://www.semanticscholar.org/paper/bb070c88dd99361f3d1f651f75557c4f0e4384d6 568083b9732d688a332333bb7537edf2c9076f88Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.Frontiers in Public Health460An approach based on explainable artificial intelligence (XAI) based on remote sensing data is proposed to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data.Frontiers in Public Health['Donato Romano', 'Pierfrancesco Novielli', 'Roberto Cilli', 'N. Amoroso', 'A. Monaco', 'Roberto Bellotti', 'S. Tangaro']2024-05-07T00:00:00https://www.semanticscholar.org/paper/568083b9732d688a332333bb7537edf2c9076f88 69289cf1f997a6f16eff7fa912f866b5c7fd585fThis systematic literature review (SLR) explores current state-of-the-art artificial intelligence (AI) methods for forecasting hotel demand. Since revenue management (RM) is crucial for business success in the hotel industry, this study aims to identify state-of-the-art effective AI-based solutions for hotel demand forecasting, including machine learning (ML), deep learning (DP), and artificial neural networks (ANNs). The study conducted an SLR using the PRISMA model and identified 20 papers indexed in Scopus and the Web of Science. It addresses the gaps in the literature on AI-based demand forecasting, highlighting the need for clarity in model specification, understanding the impact of AI on pricing accuracy and financial performance, and the challenges of available data quality and computational expertise. The review concludes that AI technology can significantly improve forecasting accuracy and empower data-driven decisions in hotel management. Additionally, this study discusses the limitations of AI-based demand forecasting, such as the need for high-quality data. It also suggests future research directions for further enhancing AI forecasting techniques in the hospitality industry.Tourism &amp; Management Studies00The review concludes that AI technology can significantly improve forecasting accuracy and empower data-driven decisions in hotel management and suggests future research directions for further enhancing AI forecasting techniques in the hospitality industry.Tourism &amp; Management Studies['Henrique Henriques', 'Luis Nobre Pereira']2024-05-07T00:00:00https://www.semanticscholar.org/paper/69289cf1f997a6f16eff7fa912f866b5c7fd585f ad4b43568b682aeb9fd62a04501a02f24441c62bBACKGROUND Artificial intelligence (AI) large language models (LLMs), such as Chat Generative Pre-trained Transformer (ChatGPT), have gained traction as both augmentative tools in patient care but also as powerful synthesizing machines. The use of ChatGPT in orthopaedic foot and ankle surgery, particularly as an informative resource for patients, has not been described to date. The purpose of this study was to assess the quality of information provided by ChatGPT in response to commonly asked questions about total ankle replacement (TAR). METHODS ChatGPT was asked 10 frequently asked questions about TAR in a conversational thread. Responses were recorded without follow-up, and subsequently graded A, B, C, or F, corresponding with "excellent response," "adequate response needing mild clarification," "inadequate response needing moderate clarification," and "poor response needing severe clarification." RESULTS Of the 10 responses, 2 were grade "A," 6 were grade "B," 2 were grade "C," and none were grade "F." Overall, the LLM provided good-quality responses to the posed prompts. Conclusion. Overall, the provided responses were understandable and representative of the current literature surrounding TAR. This study highlights the potential role LLMs in augmenting patient understanding of foot and ankle operative procedures. LEVELS OF EVIDENCE IV.Foot & Ankle Specialist340The potential role LLMs in augmenting patient understanding of foot and ankle operative procedures is highlighted as well as the quality of information provided by ChatGPT in response to commonly asked questions about total ankle replacement (TAR).Foot & ankle specialist['A. P. Samsonov', 'Akram Habibi', 'J. Butler', 'Raymond J Walls', 'John G. Kennedy']2024-05-07T00:00:00https://www.semanticscholar.org/paper/ad4b43568b682aeb9fd62a04501a02f24441c62b a10b7197e689d1c34b7377549aea04b8f10348caIn an increasingly digital and interconnected world, the integration of advanced technologies such as robotics, artificial intelligence (AI), and service automation has become pivotal for shaping the future of various industries, including tourism. The paper investigates complex relationship between three independent variables: (robotic adoption, AI adoption, and service automation adoption) and three dependent variables: (social sustainability, economic sustainability, and environmental sustainability). Employing a quantitative research approach, the study gathered data from 608 randomly selected tourism supply chain stakeholders using the Krejcie and Morgan table to determine the sample size. Data collection was facilitated through Google Forms questionnaires, and the analysis relied on structural equation modeling. The statistical findings highlight positive direct significant relationships among these variables, as evidenced by t‐statistic values surpassing the threshold of 1.96. These values ranged from a minimum of 2.156 to a maximum of 10.083. These results suggest that by strategically integrating these technologies, tourism businesses and policymakers in Zimbabwe can enhance tourist experience, the industry's long‐term viability and its positive impact on society, the economy, and the environment. This study's outcomes provide a compelling foundation for informed decision‐making and the development of targeted strategies aimed at advancing sustainability objectives within the Zimbabwean tourism landscape.Business Strategy &amp; Development1120It is suggested that by strategically integrating these technologies, tourism businesses and policymakers in Zimbabwe can enhance tourist experience, the industry's long‐term viability and its positive impact on society, the economy, and the environment.Business Strategy &amp; Development['Option Takunda Chiwaridzo', 'Shingirirai Chiwaridzo']2024-05-07T00:00:00https://www.semanticscholar.org/paper/a10b7197e689d1c34b7377549aea04b8f10348ca 1945bfa6a2989235cd9aa7ead469299f7cc9ad8eBMC Medical Education690Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine, according to undergraduate medical students’ attitudes toward AI in medicine.BMC Medical Education['Kamel Jebreen', 'Eqbal Radwan', 'Wafa Kammoun-Rebai', 'Etimad Alattar', 'Afnan Radwan', 'Walaa Safi', 'Walaa Radwan', 'Mohammed Alajez']2024-05-07T00:00:00https://www.semanticscholar.org/paper/1945bfa6a2989235cd9aa7ead469299f7cc9ad8e 668af550565d78a7f57035ee62c05812238a579cEffective patient education and communication are integral components of quality dental care, contributing to informed decision-making, treatment compliance, and positive clinical outcomes. However, traditional methods face challenges such as language barriers, anxiety, and information retention issues. Artificial intelligence (AI) presents innovative solutions to enhance patient engagement and communication in dentistry. This review explores the transformative role of AI in redefining patient education and communication strategies, focusing on applications, benefits, challenges, and future directions. A literature search identified articles from 2018 to 2024, encompassing empirical evidence and conceptual frameworks related to AI in dental patient engagement and communication. Key findings reveal AI's potential to offer personalized educational materials, virtual consultations, language translation tools, and virtual reality simulations, improving patient understanding and experience. Despite advancements, concerns about overreliance, accuracy, implementation costs, patient acceptance, privacy, and regulatory compliance persist. Future implications suggest AI's ability to track patient progress, analyze feedback, streamline administrative processes, and provide ongoing support, enhancing oral health outcomes. However, ethical, regulatory, and equity considerations require attention for responsible AI deployment and widespread adoption. Overall, AI holds promise for revolutionizing dental patient education, communication, and care delivery, emphasizing the need for comprehensive strategies to address emerging challenges and maximize benefits.Cureus280Key findings reveal AI's potential to offer personalized educational materials, virtual consultations, language translation tools, and virtual reality simulations, improving patient understanding and experience, and future implications suggest AI's ability to track patient progress, analyze feedback, streamline administrative processes, and provide ongoing support, enhancing oral health outcomes.Cureus['Vinayak Thorat', 'Prajakta Rao', 'Nilesh Joshi', 'Prakash Talreja', 'Anupa R Shetty']2024-05-07T00:00:00https://www.semanticscholar.org/paper/668af550565d78a7f57035ee62c05812238a579c 363cc534cd98457cd1bef7fd1bc84c3862d2a8dfThis study advances knowledge in the AI field. It provides deep insight into current industry generative AI inclusion systems. It shows both literature and practical leading industry operations can link, overlap, and complement each other when it comes to AI and understanding its complexities. It shows how to structurally model and link AI inclusions towards delivering a suitable sustainability positioning. It shows approaches to integrate external AI contributions from one firm into another firm’s intelligences developments. It shows how to track, and maybe benchmark, the progress of such AI inclusions from either an external or an integrated internal software developer perspective. It shows how to understand and create a more sustainable, AI-integrated business positioning. This study considers firm artificial intelligence (AI) and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement. Several substantive industrial warehousing throughput areas are discussed. Amazon’s ‘smart dynamic warehousing’ necessitates both digital and generative ongoing AI system prowess. Amazon and other substantive, digitally focused industry warehousing operations also likely benefit from astute ongoing external software developer firm inclusions. This study causally, and stagewise, models significant global software development firms involved in generative AI systems developments—specifically ones designed to beneficially enhance both warehouse operational productivity and its ongoing sustainability. A structural equation model (SEM) approach offers unique perspectives through which substantive firms already using AI can now model and track/benchmark the relevance of their prospective or existing external software developer firms, and so create rapid internal ‘net-AI’ competencies incorporations and AI capabilities developments through to sustainable operational and performance outcomes solutions.Sustainability300This study considers firm artificial intelligence and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement and shows how to understand and create a more sustainable, AI-integrated business positioning.Sustainability['John R. Hamilton', 'Stephen J. Maxwell', 'Syeda Arfa Ali', 'Singwhat Tee']2024-05-07T00:00:00https://www.semanticscholar.org/paper/363cc534cd98457cd1bef7fd1bc84c3862d2a8df 832ca804ef57c878f1d599ff894a0671ba30a99eGlobal Surgical Education - Journal of the Association for Surgical Education210Artificial intelligence and machine learning technologies can promote responsible and personalized opioid prescribing practices among healthcare providers, while achieving adequate post-surgical pain control and curbing opioid overprescription.Global Surgical Education - Journal of the Association for Surgical Education['Joyce E. Wang', 'B. Beaulieu-Jones', 'G. Brat', 'J. Marwaha']2024-05-07T00:00:00https://www.semanticscholar.org/paper/832ca804ef57c878f1d599ff894a0671ba30a99e 4fae9195a0f815f1f06a9662299d71a4752fc236This review is structured as a systematic analysis of the literature to evaluate the impacts of artificial intelligence (AI) on the field of plastic surgery, with a focus on breast augmentation and aesthetic surgical procedures. Key areas of exploration include advancements in machine-learning techniques relevant to plastic surgery, the integration of AI into preoperative planning processes, and the historical evolution of AI in aesthetic surgery. The review also systematically assesses current AI tools specifically developed for breast augmentation, such as 3D imaging and predictive analytics, to understand their efficacy and role in clinical practice. This article explores the current state and future prospects of AI in plastic surgery, with a focus on breast augmentation and aesthetic procedures. Emphasis is placed on the benefits, challenges, and the imperative for collaborative efforts in the integration of AI technologies. Advancements in machine-learning algorithms and AI technologies are examined for their potential in automating the assessment and enhancement of surgical skills. The role of AI in facilitating objective evaluations in aesthetic surgery is discussed, addressing challenges such as the lack of standardized training datasets and integration issues. The importance of mitigating potential biases introduced by AI to ensure objectivity in patient assessments is highlighted. The article discusses the historical evolution of AI, from Alan Turing’s conceptualization to contemporary applications in aesthetic surgery. Artificial intelligence’s ability to analyze vast patient datasets is explored, showcasing its potential for offering personalized treatment recommendations and improving accuracy over time. Specific AI tools for breast augmentation, including Canfield Mirror, QuantifiCare LifeViz Infinity Pro, Crisalix, BreastGAN, Arbrea Breast Software (ABS), and Deep Surface AI, are examined in detail, emphasizing their advantages and drawbacks. Evaluation of clinical photography techniques, relying on specific hardware, is presented, with consideration given to the potential of AI-based illumination systems to enhance consistency in preoperative images. The review concludes by envisioning the transformative future of AI in aesthetic surgery, considering its untapped potential in diagnostic imaging, personalized treatment plans, and enhanced surgical precision through integration with virtual and augmented reality. Despite challenges, the promises of AI in personalized treatments, precise patient care, and improved surgical assistance suggest a transformative future for plastic surgery, contingent on addressing current concerns. Collaborative efforts are deemed essential for the successful implementation of AI technologies in the field.The American Journal of Cosmetic Surgery170The promises of AI in personalized treatments, precise patient care, and improved surgical assistance suggest a transformative future for plastic surgery, contingent on addressing current concerns.The American Journal of Cosmetic Surgery['Senthilvasan Supramaniam']2024-05-07T00:00:00https://www.semanticscholar.org/paper/4fae9195a0f815f1f06a9662299d71a4752fc236 2f36553700574f31dd65c0e3a9f5ca38f5227303Artificial intelligence (AI) can create works deceptively resembling paintings, graphics, or photographs. This article examines how to treat these works, and under what circumstances, if any, they should be understood as art. The focus is placed on the work itself in the l’art-pour-l’art-tradition, on the reception, on the skills involved in the creation, and on the authors themselves. Besides looking at literary sources touching on the aforementioned aspects, the evaluation considers the perspective of people with an affinity for art through in-depth interviews. Most interviewees revised their initial reaction after learning that the works were AI generated, being more skeptical about their status as art. It then becomes obvious that the role of the artist is undergoing change. The confrontation with the artificial brings the human creator into the foreground and makes them inseparable from the work. The new technical-cultural situation leads to a new, more contextual evaluation of art.International journal of cultural studies220The evaluation considers the perspective of people with an affinity for art through in-depth interviews, and the confrontation with the artificial brings the human creator into the foreground and makes them inseparable from the work.International Journal of Cultural Studies['Maja Jerrentrup']2024-05-07T00:00:00https://www.semanticscholar.org/paper/2f36553700574f31dd65c0e3a9f5ca38f5227303 11d2e89590d1e1b0f9ea4b78f4586a696fe9959aPurposeThe aim of this paper is to explore how multi-national corporations (MNCs) can effectively adopt artificial intelligence (AI) into their talent acquisition (TA) practices. While the potential of AI to address emerging challenges, such as talent shortages and applicant surges in specific regions, has been anecdotally highlighted, there is limited empirical evidence regarding its effective deployment and adoption in TA. As a result, this paper endeavors to develop a theoretical model that delineates the motives, barriers, procedural steps and critical factors that can aid in the effective adoption of AI in TA within MNCs.Design/methodology/approachGiven the scant empirical literature on our research objective, we utilized a qualitative methodology, encompassing a multiple-case study (consisting of 19 cases across seven industries) and a grounded theory approach.FindingsOur proposed framework, termed the Framework on Effective Adoption of AI in TA, contextualizes the motives, barriers, procedural steps and critical success factors essential for the effective adoption of AI in TA.Research limitations/ implicationsThis paper contributes to literature on effective adoption of AI in TA and adoption theory.Practical implicationsAdditionally, it provides guidance to TA managers seeking effective AI implementation and adoption strategies, especially in the face of emerging challenges.Originality/valueTo the best of the authors' knowledge, this study is unparalleled, being both grounded in theory and based on an expansive dataset that spans firms from various regions and industries. The research delves deeply into corporations' underlying motives and processes concerning the effective adoption of AI in TA.Management Decision600A theoretical model is developed that contextualizes the motives, barriers, procedural steps and critical success factors essential for the effective adoption of AI in TA within MNCs and provides guidance to TA managers seeking effective AI implementation and adoption strategies, especially in the face of emerging challenges.Management Decision['J. S. Roppelt', 'N. S. Greimel', 'Dominik K. Kanbach', 'Stephan Stubner', 'Thomas K. Maran']2024-05-07T00:00:00https://www.semanticscholar.org/paper/11d2e89590d1e1b0f9ea4b78f4586a696fe9959a 4f72653d005a681b916bded718395ef00a46f994Human Immunodeficiency Virus (HIV) is a stigmatizing disease that disproportionately affects African Americans and Latinos among people living with HIV (PLWH). Researchers are increasingly utilizing artificial intelligence (AI) to analyze large amounts of data such as social media data and electronic health records (EHR) for various HIV-related tasks, from prevention and surveillance to treatment and counseling. This paper explores the ethical considerations surrounding the use of AI for HIV with a focus on acceptability, trust, fairness, and transparency. To improve acceptability and trust towards AI systems for HIV, informed consent and a Federated Learning (FL) approach are suggested. In regard to unfairness, stakeholders should be wary of AI systems for HIV further stigmatizing or even being used as grounds to criminalize PLWH. To prevent criminalization, in particular, the application of differential privacy on HIV data generated by data linkage should be studied. Participatory design is crucial in designing the AI systems for HIV to be more transparent and inclusive. To this end, the formation of a data ethics committee and the construction of relevant frameworks and principles may need to be concurrently implemented. Lastly, the question of whether the amount of transparency beyond a certain threshold may overwhelm patients, thereby unexpectedly triggering negative consequences, is posed.Applied Informatics260To improve acceptability and trust towards AI systems for HIV, informed consent and a Federated Learning approach are suggested and the question of whether the amount of transparency beyond a certain threshold may overwhelm patients, thereby unexpectedly triggering negative consequences, is posed.AI['Renee Garett', 'Seungjun Kim', 'Sean D. Young']2024-05-07T00:00:00https://www.semanticscholar.org/paper/4f72653d005a681b916bded718395ef00a46f994 efe584ee73fda5453add4373621d824304902415Artificial intelligence (AI) has become an increasingly important research subject in various fields, including education. In the educational context, the use of AI promises a fundamental transformation in the way we teach and learn. However, despite AI's enormous potential to improve the efficiency and effectiveness of education, there are a number of challenges that need to be overcome. The purpose of this research is to provide readers with the view that apart from the many positive sides of AI, there are also negative sides. This research focuses on the negative side of using AI in the education sector. The method used is library research. However, if managed wisely, the use of AI in education can provide significant benefits. For example, AI can be used to personalize learning, enabling teaching tailored to each student's individual needs. Additionally, AI can help in automated grading, freeing up teachers' time to focus on direct interactions with students. By considering existing challenges and exploiting the opportunities offered by AI, education can make significant progress in improving its quality and accessibility. Therefore, further research and development is needed to overcome technical and ethical obstacles to the application of AI in education, while ensuring that its benefits are widespread and non-discriminatory.Al-DYAS130The purpose of this research is to provide readers with the view that apart from the many positive sides of AI, there are also negative sides and if managed wisely, the use of AI in education can provide significant benefits.Al-DYAS['Slamet Budiyono', 'P. Azhari', 'Maulana Al Bana Pamungkas']2024-05-07T00:00:00https://www.semanticscholar.org/paper/efe584ee73fda5453add4373621d824304902415 0490dc8f2ee87418aa0a9d9f198a12895af15b98Critical Care40The potential of generative AI in natural language processing of specialized scientific texts is highlighted, however, careful consideration is warranted in balancing the roles of AI tools and human experts to ensure fairness and reliability in the peer review process.Critical Care['Kunming Cheng', 'Zaijie Sun', 'Xiaojun Liu', 'Haiyang Wu', 'Cheng Li']2024-05-07T00:00:00https://www.semanticscholar.org/paper/0490dc8f2ee87418aa0a9d9f198a12895af15b98 32ba945e11edbde0628e7125d995c73bc5a180a7A revolutionary transformation and overhaul of our approach to how we instruct and interact with educational content is being heralded by the simultaneous development of artificial intelligence and education. Businesses dealing with educational technology are greatly engaging and spending exponentially over technologies delivering education by artificial intelligence. Such technologies generate highly interactive and deeply engaging educational experiences for the learners creating an everlasting impact. Some of the cutting-edge technologies that improve student engagement and encourage deeper connections with the educational content or the conversational assistants that interact verbally and virtually. Virtual reality (VR), Augmented reality (AR), holographic simulations, etc. are some more examples of AI generated applications which are now being used to create immersive learning experiences. This paper explores the role of AI in higher education, it’s benefits for both learners & educators, it’s application in H.E. classrooms, utilisation of AI-detection technology, challenges and strategies to implement AI seamlessly in educational settings. It is an effort made in order to discuss this emerging topic and to offer an insight into the constantly evolving relationship between AI an education.International Journal For Multidisciplinary Research50The role of AI in higher education, it’s benefits for both learners & educators, it’s application in H.E. classrooms, utilisation of AI-detection technology, challenges and strategies to implement AI seamlessly in educational settings are explored.International Journal For Multidisciplinary Research['Vidushi Joshi', 'Santosh Kumar Tripathi']2024-05-07T00:00:00https://www.semanticscholar.org/paper/32ba945e11edbde0628e7125d995c73bc5a180a7 8fa78f3c28d7a5c0bb644920901551b1562ac516PurposeRecently there has been a surge in interest about the use of artificial intelligence in organisations with art galleries introducing new technological innovations that coincide with the digitalisation revolution. Virtual and immersive environments that are supported by social media and digital platforms are significantly changing customer experiences at art galleries. This is internationalising and making art gallery experiences more accessible thereby fostering the competitive advantage of art galleries.Design/methodology/approachArt gallery customers, stakeholders and managers are appreciating the use of artificial intelligence with resulting higher satisfaction rates. Building on competency and transformational entrepreneurship theory international art gallery managers were interviewed to understand the role of artificial intelligence in their organisations and the impact of internationalisation.FindingsThe data analysis revealed that the internationalisation of art galleries enabled artificial intelligence to transform in person and online visitor experience, work and marketing, and future art gallery development ideas. Results show that artificial intelligence is opening up new transformations derived from entrepreneurial behaviours.Originality/valueKey managerial implications are that art gallery managers need to utilise their international networks in order to learn about artificial intelligence and other new technological innovation. Theoretical implications are that existing theory can be adapted to an art gallery and artificial intelligence context. Limitations and future research suggestions focus on the need to focus more on art galleries as cultural entities that are more likely to utilise new technology innovation such as artificial intelligence.International journal of sociology and social policy590The data analysis revealed that the internationalisation of art galleries enabled artificial intelligence to transform in person and online visitor experience, work and marketing, and future art gallery development ideas.International Journal of Sociology and Social Policy['Vanessa Ratten']2024-05-07T00:00:00https://www.semanticscholar.org/paper/8fa78f3c28d7a5c0bb644920901551b1562ac516 1b3034c5d9fe216e06a94948b2a0d6ac081bf7daAbstract Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI‐based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self‐supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI‐driven orthopaedic research. Level of Evidence Level IV.Journal of Experimental Orthopaedics1130This second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI‐driven orthopaedic research.Journal of Experimental Orthopaedics['Bálint Zsidai', 'Janina Kaarre', 'E. Narup', 'Eric Hamrin Senorski', 'Ayoosh Pareek', 'Alberto Grassi', 'Christophe Ley', 'U. Longo', 'E. Herbst', 'Michael T Hirschmann', 'Sebastian Kopf', 'R. Seil', 'Thomas Tischer', 'Kristian Samuelsson', 'R. Feldt']2024-05-07T00:00:00https://www.semanticscholar.org/paper/1b3034c5d9fe216e06a94948b2a0d6ac081bf7da 972c28c52373be6d2e3f07e217763194346b052dJournal of Educational and Human Sciences00Journal of Educational and Human Sciences[]2024-05-07T00:00:00https://www.semanticscholar.org/paper/972c28c52373be6d2e3f07e217763194346b052d 23b90718d55027df6d0a0a2ca2eb0f8a32c37fa3 Can drillers extract insights from successful and challenging cases by writing one sentence? Today, the drillers either dig, for days or weeks, the mixed-structured data of the Daily Drilling Report (DDR), the structured drilling data, or both to extract knowledge about successful cases (e.g., a record rate of penetration) and challenging cases (e.g., stuck pipe and Non-Productive Time (NPT)). The objective is to have the drilling operations insights extracted with no time from the current and historical data reports. We propose a more efficient knowledge extraction of drilling operations in seconds or minutes by writing one sentence using the latest artificial intelligent Chat Generative Pretrained Transformer algorithm (ChatGPT). Therefore, we launched the first drilling dedicated ChatGPT pilot. ChatGPT has pretrained models; however, in this pilot, we enable ChatGPT to learn from our drilling data to provide specific answers to our challenges accurately and efficiently. The implementation method of ChatGPT requires multiple stages: (1) Data Loading/Downloading and Document Scanning, (3) Data Indexing, (4) ChatGPT Training, and (5) ChatGPT extraction of knowledge. Our drilling data is available in structured (tabulated), unstructured, and mix-structure formats; therefore, understanding the behavior of ChatGPT in these different formats and other training indexing and cognitive capabilities are some of the pilot targeted objectives. This novel pilot is the first in the oil industry to use ChatGPT, particularly in drilling. Its outcome determines ChatGPT's ability to ease drilling operations by providing insight and learning from historical success and challenging cases. This paper reveals the methods and tools to quickly deliver efficient and quality answers to drilling operations to the drilling engineers.Day 2 Wed, May 08, 2024130This novel pilot is the first in the oil industry to use ChatGPT, particularly in drilling, and its outcome determines ChatGPT's ability to ease drilling operations by providing insight and learning from historical success and challenging cases.Day 2 Wed, May 08, 2024['O. Alfarisi', 'R. Singh', 'R. Singhal', 'R. M. Alzarooni', 'S. Fernandes', 'Y. Ayvaz', 'M. Vijayan', 'J. Mohamed']2024-05-07T00:00:00https://www.semanticscholar.org/paper/23b90718d55027df6d0a0a2ca2eb0f8a32c37fa3 e2920e91537389a39ba4a5cb608729e63ba64cedFirstly, this paper analyzes the role of AI in the reading management of English language and literature, establishes the implicit knowledge base of neural network, designs the auxiliary reading system for learning English language and literature, and optimizes the English language and literature management model of AI. The experimental results show that its reading efficiency is increased by 0.48%, and the performance of the credibility model is improved by 0.53% compared with the original system, which greatly optimizes the running time of the system. To some extent, it helps users to manage their time in English language and literature reading, and greatly improves users' reading efficiency and quality. Based on this advantage of AI algorithm, this paper introduces that the algorithm optimizes the reading management model and the training process of neural grid, and constructs a model of English language and literature assisted reading system based on AI. The system can better meet the needs of users in English language and literature reading management.International Journal of Information and Communication Technology Education180The algorithm optimizes the reading management model and the training process of neural grid, and constructs a model of English language and literature assisted reading system based on AI that can better meet the needs of users in English language and literature reading management.International Journal of Information and Communication Technology Education['Xisheng Chen']2024-05-07T00:00:00https://www.semanticscholar.org/paper/e2920e91537389a39ba4a5cb608729e63ba64ced a4814f967f6f56e89ea13a17ac4bcb1f6954e13dOBJECTIVES Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. MATERIALS AND METHODS A total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors. RESULTS Increasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99. CONCLUSION The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.Orthodontics & craniofacial research160The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.Orthodontics & craniofacial research['Ju-Myung Lee', 'Jun-Ho Moon', 'Ji-Ae Park', 'Jong-Hak Kim', 'Shin-Jae Lee']2024-05-07T00:00:00https://www.semanticscholar.org/paper/a4814f967f6f56e89ea13a17ac4bcb1f6954e13d 7b4a717511311f0fadd1a6097393fe5998cf6313Nature00Nature['D. Byrne']2024-05-07T00:00:00https://www.semanticscholar.org/paper/7b4a717511311f0fadd1a6097393fe5998cf6313 858b7c3907374941ebf8f9369c563c6ed132c45cMedical Journal of Australia200The Medical journal of Australia['I. Olver']2024-05-07T00:00:00https://www.semanticscholar.org/paper/858b7c3907374941ebf8f9369c563c6ed132c45c b1531d05e5d84485ac2236d5d25c7e198e639414International Conference on Performance Engineering00{'pages': '93'}['Emilio Incerto', 'Marin Litoiu', 'Daniele Masti']2024-05-07T00:00:00https://www.semanticscholar.org/paper/b1531d05e5d84485ac2236d5d25c7e198e639414 50993f7524aec534efb486d80f6e6eccd0c1ee72EMJ Rheumatology40EMJ Rheumatology['Emj']2024-05-07T00:00:00https://www.semanticscholar.org/paper/50993f7524aec534efb486d80f6e6eccd0c1ee72 6037d11649eefd597baa52a9e3c9f2afd24373e3Cureus580Cureus['Vinay Suresh', 'Kaushal K Singh', 'Esha Vaish', 'Mohan Gurjar', 'Anubuvanan Am', 'Yashita Khulbe', 'Syed Muzaffar']2024-05-07T00:00:00https://www.semanticscholar.org/paper/6037d11649eefd597baa52a9e3c9f2afd24373e3 f81a291e80e68a682d369dfd8ac06f62411672b0Journal of Community Hospital Internal Medicine Perspectives00Journal of Community Hospital Internal Medicine Perspectives['N. Javed', 'Haider Ghazanfar', 'Bhavna Balar', 'H. Patel']2024-05-07T00:00:00https://www.semanticscholar.org/paper/f81a291e80e68a682d369dfd8ac06f62411672b0 b591fe7f6ede0bbd9e67f5725186e4c6514752f5Policy Design and Practice330Policy Design and Practice['Ruby O’Connor', 'M. Bolton', 'Alexander K. Saeri', 'Tom Chan', 'Ross Pearson']2024-05-07T00:00:00https://www.semanticscholar.org/paper/b591fe7f6ede0bbd9e67f5725186e4c6514752f5 ccc548c2e437663590bf4b9837c5985ae5cd7c21Discover Artificial Intelligence130The underlying idea of ASI is based on an environment that consists only of text and it is shown that this avoids the problem of embodiment of an agent and leads to a system with restricted capabilities compared to AGI.Discov. Artif. Intell.['Frank Emmert-Streib']2024-05-07T00:00:00https://www.semanticscholar.org/paper/ccc548c2e437663590bf4b9837c5985ae5cd7c21 c880a2fc1317edac878ce36a0432433d9f5042deThe Causal Cognitive Architecture is a brain-inspired cognitive architecture developed from the hypothesis that the navigation circuits in the ancestors of mammals duplicated to eventually form the neocortex. Thus, millions of neocortical minicolumns are functionally modeled in the architecture as millions of “navigation maps.” An investigation of a cognitive architecture based on these navigation maps has previously shown that modest changes in the architecture allow the ready emergence of human cognitive abilities such as grounded, full causal decision-making, full analogical reasoning, and near-full compositional language abilities. In this study, additional biologically plausible modest changes to the architecture are considered and show the emergence of super-human planning abilities. The architecture should be considered as a viable alternative pathway toward the development of more advanced artificial intelligence, as well as to give insight into the emergence of natural human intelligence.Frontiers in Computational Neuroscience780The architecture should be considered as a viable alternative pathway toward the development of more advanced artificial intelligence, as well as to give insight into the emergence of natural human intelligence.Frontiers in Computational Neuroscience['Howard Schneider']2024-05-07T00:00:00https://www.semanticscholar.org/paper/c880a2fc1317edac878ce36a0432433d9f5042de ae2899cf300f1d780f3c61951abd168ff8b63474Artificial neural networks (ANNs) perform extraordinarily on numerous tasks including classification or prediction, e.g., speech processing and image classification. These new functions are based on a computational model that is enabled to select freely all necessary internal model parameters as long as it eventually delivers the functionality it is supposed to exhibit. Here, we review the connection between the model parameter selection in machine learning (ML) algorithms running on ANNs and the epistemological theory of neopragmatism focusing on the theory's utility and anti-representationalist aspects. To understand the consequences of the model parameter selection of an ANN, we suggest using neopragmatist theories whose implications are well studied. Incidentally, neopragmatism's notion of optimization is also based on utility considerations. This means that applying this approach elegantly reveals the inherent connections between optimization in ML, using a numerical method during the learning phase, and optimization in the ethical theory of consequentialism, where it occurs as a maxim of action. We suggest that these connections originate from the way relevance is calculated in ML systems. This could ultimately reveal a tendency for specific actions in ML systems.460The connection between the model parameter selection in machine learning (ML) algorithms running on ANNs and the epistemological theory of neopragmatism and the way relevance is calculated in ML systems is reviewed to reveal a tendency for specific actions in ML systems.["Antonio Biki'c", 'Sayan Mukherjee']2024-05-07T00:00:00https://www.semanticscholar.org/paper/ae2899cf300f1d780f3c61951abd168ff8b63474 df57085b824f491c36aca6f321c9d3f227918803With the increasing adoption of artificial intelligence (AI) technologies in the news industry, media organizations have begun publishing guidelines that aim to promote the responsible, ethical, and unbiased implementation of AI-based technologies. These guidelines are expected to serve journalists and media workers by establishing best practices and a framework that helps them navigate ever-evolving AI tools. Drawing on institutional theory and digital inequality concepts, this study analyzes 37 AI guidelines for media purposes in 17 countries. Our analysis reveals key thematic areas, such as transparency, accountability, fairness, privacy, and the preservation of journalistic values. Results highlight shared principles and best practices that emerge from these guidelines, including the importance of human oversight, explainability of AI systems, disclosure of automated content, and protection of user data. However, the geographical distribution of these guidelines, highlighting the dominance of Western nations, particularly North America and Europe, can further ongoing concerns about power asymmetries in AI adoption and consequently isomorphism outside these regions. Our results may serve as a resource for news organizations, policymakers, and stakeholders looking to navigate the complex AI development toward creating a more inclusive and equitable digital future for the media industry worldwide.620Analysis of 37 AI guidelines for media purposes in 17 countries reveals key thematic areas, such as transparency, accountability, fairness, privacy, and the preservation of journalistic values, which may serve as a resource for news organizations, policymakers, and stakeholders looking to navigate the complex AI development.['Mathias-Felipe de-Lima-Santos · Wang', 'Ngai Yeung', 'Tom´as Dodds', 'Wang Ngai', 'Yeung']2024-05-07T00:00:00https://www.semanticscholar.org/paper/df57085b824f491c36aca6f321c9d3f227918803 239cc19b329a91a5f3a130d767c5a416b3703a4cOrganizations around the world are on a verge of embracing a major technological shift. The increasing use of artificially intelligent (AI) driven tools and solutions by organizations to bring improvements in existing HR processes and willingness to even replace traditional HR processes is undeniable (Bersin & Litai, 2020; Deloitte, 2023). We can see the traditional methods struggling to keep pace with the ever-evolving needs of the diverse workforce, technological advancement, and the progressively competitive global talent pool (McKinsey Global Institute, 2021). During this transition, Artificial Intelligence seems to emerge as probable solution that has a potential to reshape how organizations across the globe attract, manage, and develop their talent. However, attention needs to be paid when synergizing collaboration of Artificial Intelligence (AI) with human insight as it remains the cornerstone of effective HR while AI's true potential lies in its ability to supplement, not replace, human decision-making. This synergy between AI and HR also comes with a set of challenges to be dealt with. One of the key concerns surrounding utilization and incorporation of AI in human resources practices is ensuring ethical and unbiased use of AI. Due to the use of algorithms involved in the AI processes, there seems to be a possible risk of reinforcing existing biases in the data to be used by AI models, potentially leading to discriminatory outcomes in HR processes. Therefore, it is imperative for the HR professionals to navigate these ethical considerations and ensure responsible usage of AI in the workplace (Tian, 2021).Day 2 Wed, May 08, 202400Attention needs to be paid when synergizing collaboration of Artificial Intelligence (AI) with human insight as it remains the cornerstone of effective HR while AI's true potential lies in its ability to supplement, not replace, human decision-making.Day 2 Wed, May 08, 2024['S. U. Haque']2024-05-07T00:00:00https://www.semanticscholar.org/paper/239cc19b329a91a5f3a130d767c5a416b3703a4c 6b5c951f8ce329caa236480427fe97d09e5e6094This paper discusses the dispute resolution procedure that innovative digital commerce has adopted for the future for sustainable business. As digital trade becomes increasingly important for economic growth, trade-related disputes must be settled in both business and consumer situations. This study examines the advantages of using digital technology to resolve disputes involving digital trade and discusses how digital technology is changing traditional dispute resolution procedures. Conventional trade disputes differ from their digital counterparts because the digital sphere gives rise to more complex trade conflicts that require stronger regulatory resources. The utilization of digital technologies such as blockchain, artificial intelligence, innovation-based models, digital strategies, and others can enhance the efficacy of conflict resolution. Digital technology can assist in resolving disputes with digital trade, even though procedural fairness issues including prejudice and algorithmic opacity may also arise. The research highlights the importance of developing innovative techniques to set up trade dispute resolution procedures and building legal frameworks for jurisdiction, trial, and enforcement procedures in addition to stressing the usage of digital technology.Sustainability540Sustainability['Shumin Wang', 'Yincheng Li', 'M. Khaskheli']2024-05-07T00:00:00https://www.semanticscholar.org/paper/6b5c951f8ce329caa236480427fe97d09e5e6094 5abe1154e17f5ec28b734828e5bcb6ec6609b36fArtificial intelligence (AI) is revolutionizing the way customers interact with organizations and companies. There is a lack of research into AI-enabled customer experiences. Hence, this study aims to use the relevant literature to propose a conceptual framework for how the integration of AI in customer service can lead to an improved AI-enabled customer experience. Five propositions drawn from the reviewed literature present the main factors needed to ensure end users' acceptance of AI customer service in the United Arab Emirates (UAE). Our theoretical model extends the trust-commitment theory and service quality model, and incorporates perceived problem-solving ability, to address these factors and thereby guide the successful implementation of AI based customer service projects. The paper will help in understanding the key issues surrounding AI customer service applications that may support successful operations.Journal of Global Information Management620A conceptual framework for how the integration of AI in customer service can lead to an improved AI-enabled customer experience is proposed, which extends the trust-commitment theory and service quality model and incorporates perceived problem-solving ability to address these factors and thereby guide the successful implementation of AI based customer service projects.Journal of Global Information Management['N. Almuraqab', 'S. Jasimuddin', 'F. Saci']2024-05-07T00:00:00https://www.semanticscholar.org/paper/5abe1154e17f5ec28b734828e5bcb6ec6609b36f db7d39f8d3b5ab2360a6f819e8f1b43a56d25aa9Mounting ecological and socio-economic challenges demand a paradigm shift to sustainability. Artificial intelligence (AI) becomes a powerful means to pursue this objective; it establishes a “Symbiotic Synergy” with nature’s complex networks. This study attempts to investigate the SDGs using AI as a transformative tool, the sustainability of AI and its socio-economic impact. The research applies a multimethod approach, using surveys, case studies and categorizations to determine how AI is helping achieve the SGDs in harmony with nature. Key applications of AI in important domains are covered including agriculture and sanitation, renewable energy sector, hydrogen energy sector and the impact that AI is having leading to the future sustainable cities. Case studies and real-world examples demonstrate the effectiveness of AI technologies in forecasting energy demand, improving power dispatchment, and boosting performance within renewable resources. This study is a guide to the harnessing of AI potential for meeting SDGs, which leads us into an era where humans will attain great heights with cooperation between technology and nature.International Journal For Multidisciplinary Research20International Journal For Multidisciplinary Research['Rehmah Ahmed Batki', 'Hasan Phudinawala']2024-05-07T00:00:00https://www.semanticscholar.org/paper/db7d39f8d3b5ab2360a6f819e8f1b43a56d25aa9 3abf90f42cdea96b14397e9862999b9311fd5b66Artificial intelligence use in prostate cancer encompasses 4 main areas including diagnostic imaging, prediction of outcomes, histopathology, and treatment planning.Oncology00Oncology['James B Yu Md Mhs Fastro', 'Julian C Hong']2024-05-07T00:00:00https://www.semanticscholar.org/paper/3abf90f42cdea96b14397e9862999b9311fd5b66 c7d5d27e2010d6ff1ca506293f1f23f16bf3df42AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model's applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework's effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security.160An MoE-enabled GAI framework for network optimization problems for communication security is proposed and results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security.['Changyuan Zhao', 'Hongyang Du', 'D. Niyato', 'Jiawen Kang', 'Zehui Xiong', 'Dong In Kim', 'X. Shen', 'K. B. Letaief']2024-05-07T00:00:00https://www.semanticscholar.org/paper/c7d5d27e2010d6ff1ca506293f1f23f16bf3df42 862aac2322118d0818500b7956eeeb9cc49c5792A cautious interpretation of AI regulations and policy in the EU and the USA place explainability as a central deliverable of compliant AI systems. However, from a technical perspective, explainable AI (XAI) remains an elusive and complex target where even state of the art methods often reach erroneous, misleading, and incomplete explanations."Explainability"has multiple meanings which are often used interchangeably, and there are an even greater number of XAI methods - none of which presents a clear edge. Indeed, there are multiple failure modes for each XAI method, which require application-specific development and continuous evaluation. In this paper, we analyze legislative and policy developments in the United States and the European Union, such as the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, the AI Act, the AI Liability Directive, and the General Data Protection Regulation (GDPR) from a right to explanation perspective. We argue that these AI regulations and current market conditions threaten effective AI governance and safety because the objective of trustworthy, accountable, and transparent AI is intrinsically linked to the questionable ability of AI operators to provide meaningful explanations. Unless governments explicitly tackle the issue of explainability through clear legislative and policy statements that take into account technical realities, AI governance risks becoming a vacuous"box-ticking"exercise where scientific standards are replaced with legalistic thresholds, providing only a false sense of security in XAI.400This paper analyze legislative and policy developments in the United States and the European Union, such as the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, the AI Act, the AI Liability Directive, and the General Data Protection Regulation from a right to explanation perspective from a right to explanation perspective to argue that these AI regulations and current market conditions threaten effective AI governance and safety.['N. C. Chung', 'Hongkyou Chung', 'Hearim Lee', 'Hongbeom Chung', 'L. Brocki', 'George C. Dyer']2024-05-06T00:00:00https://www.semanticscholar.org/paper/862aac2322118d0818500b7956eeeb9cc49c5792 4e7ea99d8d9e6b9183c999bb1e3ab4ae430bfa0bThis paper investigates the effect of internal control quality and mandatory internal control disclosure regulation on executive perks in China. Based on a sample of Chinese firms between 2008 and 2019, this study finds that internal control quality and the mandatory disclosure regulation limit both under‐consumption and over‐consumption of executive perks. Furthermore, the mandatory regulation strengthens the relationship between internal control quality and the levels of executive perks. The results are consistent across alternative measures of key variables and are robust to endogeneity analyses. The findings of this study have important implications for policymakers, managers, and investors seeking to understand the determinants of the abnormal consumption of executive perks and the associated economic consequences of the internal control system.International Journal of Auditing690International Journal of Auditing['M. Fonseka', 'Omar Al Farooque', 'Xing Yang', 'Wu Qilin']2024-05-06T00:00:00https://www.semanticscholar.org/paper/4e7ea99d8d9e6b9183c999bb1e3ab4ae430bfa0b df4083bcc15a1d03a99b4f9b3e62bde4ddc543dfThe protection of public order is one of the most important functions of the state, with proper implementation of which the normal progressive development of society is possible. The participation of citizens in the protection of public order is often unreasonably overlooked by scientific research, since attention is paid only to the activities of law enforcement agencies in this area. We believe that the potential of citizens' participation in the protection of public order is underestimated and needs additional scientific analysis and improvement of regulatory regulation of such activities. The article examines the essence of the concept of administrative and legal regulation of citizens' participation in the protection of public order by studying its content as a set of legal categories "participation of citizens in the protection of public order" and "mechanism of administrative and legal regulation". The analysis made it possible to formulate the author's definitions of the concepts "participation of citizens in the protection of public order" and "administrative and legal regulation of citizens' participation in the protection of public order".Vestnik of Polotsk State University. Part D. Economic and legal sciences00Vestnik of Polotsk State University Part D Economic and legal sciences['S. Koliosko']2024-05-06T00:00:00https://www.semanticscholar.org/paper/df4083bcc15a1d03a99b4f9b3e62bde4ddc543df 659658c8f04789168f5013a4594a8eb237c959d2Purpose This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings. Design/methodology/approach The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis. Findings This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations. Research limitations/implications This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality. Originality/value To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality. Journal of Hospitality and Tourism Technology470This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, and a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots.Journal of Hospitality and Tourism Technology['Yue (Darcy) Lu', 'Yifeng Liang', 'Yao-Chin Wang']2024-05-06T00:00:00https://www.semanticscholar.org/paper/659658c8f04789168f5013a4594a8eb237c959d2 74973ae3984385f2808b1d0d46a778e92b11722dJournal of Big Data3160The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data.Journal of Big Data['Shailesh Tripathi', 'Nadine Bachmann', 'Manuel Brunner', 'Ziad Rizk', 'H. Jodlbauer']2024-05-06T00:00:00https://www.semanticscholar.org/paper/74973ae3984385f2808b1d0d46a778e92b11722d 352abb76670a1aa3cb32be121452cdd5874c5fb9AI and Ethics350The article contends that AI hype has successfully persisted because now, more than ever, contemporary promotional culture strategically deploys emotions as part of affective capitalism, and the affective nature of a digital media infrastructure controlled by the tech sector.AI and Ethics['Clea Bourne']2024-05-06T00:00:00https://www.semanticscholar.org/paper/352abb76670a1aa3cb32be121452cdd5874c5fb9 00e59288f93174b6bbf3d252da1e7ced3370a82cThe integration of Artificial Intelligence (AI) into the realm of education has emerged as a transformative force with the potential to reshape teaching practices and enhance learning outcomes. This conceptual paper delves into the multifaceted factors that influence the attitudes and intentions of pre-service teachers regarding the incorporation of AI technology in their future teaching endeavors. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technological Pedagogical Content Knowledge (TPACK) framework, this paper discusses the key components that shape pre-service teachers' perspectives on AI integration. By synthesizing these theoretical foundations, the paper aims to develop an integrated model, thereby offering a holistic perspective on the factors that influence pre-service teachers' attitudes and intentions in employing AI in their teaching practices. The insights and implications derived from this conceptual paper have the potential to inform teacher education programs and educational policymakers, ensuring the effective preparation of future educators to navigate AI-enhanced teaching environments. The study underscores the importance of addressing pre-service teachers' perspectives and concerns, ultimately fostering a seamless and productive integration of AI within the educational landscape.International Journal of Academic Research in Business and Social Sciences180This conceptual paper delves into the multifaceted factors that influence the attitudes and intentions of pre-service teachers regarding the incorporation of AI technology in their future teaching endeavors, offering a holistic perspective on the factors that influence pre-service teachers' attitudes and intentions in employing AI in their teaching practices.International Journal of Academic Research in Business and Social Sciences['Nur Yasmin Khairani Zakaria', 'Harwati Hashim']2024-05-06T00:00:00https://www.semanticscholar.org/paper/00e59288f93174b6bbf3d252da1e7ced3370a82c 5ca860fa5d0d4ec5a58fc5857a25d68ab53c2453Recent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity. Both humans and AI detection technologies have difficulty distinguishing between AI-generated vs. human-authored text. This weakness raises a significant possibility of false positive errors: human-authored writing incorrectly judged as AI-generated. AI detection methodology, whether machine or human-based, is based on writing style characteristics. Empirical evidence demonstrates that AI detection technologies are more sensitive to AI-generated text than human judges, yet a positive finding from these technologies cannot provide absolute certainty of AI plagiarism. Given the uncertainty of detecting AI, a forgiving, pro-growth response to AI academic integrity cases is recommended, such as revise and resubmit decisions. Faculty should cautiously embrace the use of AI detection technologies with the understanding that false positive errors will occasionally occur. This use is ethical provided that the responses to problematic cases are approached with the goal of educational growth rather than punishment.Teaching of psychology120Given the uncertainty of detecting AI, a forgiving, pro-growth response to AI academic integrity cases is recommended, such as revise and resubmit decisions.Teaching of Psychology['G. Fisk']2024-05-06T00:00:00https://www.semanticscholar.org/paper/5ca860fa5d0d4ec5a58fc5857a25d68ab53c2453 fc15dd222e75743b909ea376e317e5fa2e22e6abImage safety classifiers play an important role in identifying and mitigating the spread of unsafe images online (e.g., images including violence, hateful rhetoric, etc.). At the same time, with the advent of text-to-image models and increasing concerns about the safety of AI models, developers are increasingly relying on image safety classifiers to safeguard their models. Yet, the performance of current image safety classifiers remains unknown for real-world and AI-generated images. To bridge this research gap, in this work, we propose UnsafeBench, a benchmarking framework that evaluates the effectiveness and robustness of image safety classifiers. First, we curate a large dataset of 10K real-world and AI-generated images that are annotated as safe or unsafe based on a set of 11 unsafe categories of images (sexual, violent, hateful, etc.). Then, we evaluate the effectiveness and robustness of five popular image safety classifiers, as well as three classifiers that are powered by general-purpose visual language models. Our assessment indicates that existing image safety classifiers are not comprehensive and effective enough in mitigating the multifaceted problem of unsafe images. Also, we find that classifiers trained only on real-world images tend to have degraded performance when applied to AI-generated images. Motivated by these findings, we design and implement a comprehensive image moderation tool called PerspectiveVision, which effectively identifies 11 categories of real-world and AI-generated unsafe images. The best PerspectiveVision model achieves an overall F1-Score of 0.810 on six evaluation datasets, which is comparable with closed-source and expensive state-of-the-art models like GPT-4V. UnsafeBench and PerspectiveVision can aid the research community in better understanding the landscape of image safety classification in the era of generative AI.590This work proposes UnsafeBench, a benchmarking framework that evaluates the effectiveness and robustness of image safety classifiers, and designs and implements a comprehensive image moderation tool called PerspectiveVision, which effectively identifies 11 categories of real-world and AI-generated unsafe images.['Y. Qu', 'Xinyue Shen', 'Yixin Wu', 'Michael Backes', 'Savvas Zannettou', 'Yang Zhang']2024-05-06T00:00:00https://www.semanticscholar.org/paper/fc15dd222e75743b909ea376e317e5fa2e22e6ab 0e38d9bdbe41e1bc1123d82c2d4c07d2f57099e5In recent years, Large Language Models (LLMs) have become integrated into our daily lives, serving as invaluable assistants in completing tasks. Widely embraced by users, the abuse of LLMs is inevitable, particularly in using them to generate text content for various purposes, leading to difficulties in distinguishing between text generated by LLMs and that written by humans. In this study, we present a dataset named ViDetect, comprising 6.800 samples of Vietnamese essay, with 3.400 samples authored by humans and the remainder generated by LLMs, serving the purpose of detecting text generated by AI. We conducted evaluations using state-of-the-art methods, including ViT5, BartPho, PhoBERT, mDeberta V3, and mBERT. These results contribute not only to the growing body of research on detecting text generated by AI but also demonstrate the adaptability and effectiveness of different methods in the Vietnamese language context. This research lays the foundation for future advancements in AI-generated text detection and provides valuable insights for researchers in the field of natural language processing.250A dataset named ViDetect is presented, comprising 6.800 samples of Vietnamese essay, with 3.400 samples authored by humans and the remainder generated by LLMs, serving the purpose of detecting text generated by AI.['Quang-Dan Tran', 'Van-Quan Nguyen', 'Quang-Huy Pham', 'K. B. T. Nguyen', 'Trong-Hop Do']2024-05-06T00:00:00https://www.semanticscholar.org/paper/0e38d9bdbe41e1bc1123d82c2d4c07d2f57099e5 44ac2391cbc6dfdc02e428c68e0ea6cf70145c42This paper shows how to construct genre patterns, whose purpose is to guide interactive story composition in a way that enforces thematic consistency. To start the discussion we argue, based on previous seminal works, for the existence of five fundamental genres, namely comedy, romance - in the sense of epic plots, flourishing since the twelfth century -, tragedy, satire, and mystery. To construct the patterns, a simple two-phase process is employed: first retrieving examples that match our genre characterizations, and then applying a form of most specific generalization to the groups of examples in order to find their commonalities. In both phases, AI agents are instrumental, with our PatternTeller prototype being called to operate the story composition process, offering the opportunity to generate stories from a given premise of the user, to be developed under the guidance of the chosen pattern and trying to accommodate the user's suggestions along the composition stages.720This paper shows how to construct genre patterns, whose purpose is to guide interactive story composition in a way that enforces thematic consistency, using the PatternTeller prototype to operate the story composition process.['E. S. D. Lima', 'Margot M. E. Neggers', 'Antonio L. Furtado']2024-05-06T00:00:00https://www.semanticscholar.org/paper/44ac2391cbc6dfdc02e428c68e0ea6cf70145c42 dbbe08e09c430bf0b7b011b0454ec1e51d427afdBMC Neuroscience150This Editorial invites contributions for a BMC Neuroscience Collection on “AI and Cognitive Neuroscience”, which holds promise for significant breakthroughs in the authors' ability to probe the intrinsic mechanisms of the brain.BMC Neuroscience['Zhiyi Chen', 'Ali Yadollahpour']2024-05-06T00:00:00https://www.semanticscholar.org/paper/dbbe08e09c430bf0b7b011b0454ec1e51d427afd adb8a0b36d1d8293b73daedb33fa45a46c425c86Global Surgical Education - Journal of the Association for Surgical Education390Though early in its development, artificial Intelligence-enabled clinical decision support (CDS) systems show promise in improving surgical decision-making, these new tools require validation in real-world clinical settings, adherence to standardized reporting guidelines, and a comprehensive evaluation of both performance and fairness metrics.Global Surgical Education - Journal of the Association for Surgical Education['Jeremy A. Balch', 'B. Shickel', 'A. Bihorac', 'Gilbert R. Upchurch', 'Tyler J. Loftus']2024-05-06T00:00:00https://www.semanticscholar.org/paper/adb8a0b36d1d8293b73daedb33fa45a46c425c86 91845ef3e471cc7b32a0f86aa2f15f0c11b09936AI and Ethics160This paper provides a systematic overview on risk aggregation schemes used in existing AI risk assessment frameworks, focusing on the question how potential trade-offs among the risk dimensions are incorporated.AI and Ethics['Anna Schmitz', 'Michael Mock', 'Rebekka Görge', 'Armin B. Cremers', 'Maximilian Poretschkin']2024-05-06T00:00:00https://www.semanticscholar.org/paper/91845ef3e471cc7b32a0f86aa2f15f0c11b09936 25b5801e19286e06e05324faa248194effe3f640This study analyses the dimensions of trust in artificial intelligence (AI), focusing on why a significant portion of the UK population demonstrates a higher level of trust in AI compared to humans. Conducted through a mixed-methods approach, this study gathered 894 responses, with 451 meeting the criteria for analysis. It utilised a combination of a six-step Likert-scale survey and open-ended questions to explore the psychological, sociocultural, and technological facets of trust. The analysis was underpinned by structural equation modelling (SEM) and correlation techniques. The results unveil a strong predilection for trusting AI, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems. This preference starkly contrasts with the scepticism towards human reliability, which is influenced by the perception of inherent self-interest and dishonesty in humans, further exacerbated by a general distrust in media narratives. Additionally, this study highlights a significant correlation between distrust in AI and an unwavering confidence in human judgment, illustrating a dichotomy in trust orientations. This investigation illuminates the complex dynamics of trust in the era of digital technology, making a significant contribution to the ongoing discourse on AI’s societal integration and underscoring vital considerations for future AI development and policymaking.The social science510A strong predilection for trusting AI is revealed, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems, underscoring vital considerations for future AI development and policymaking.Social Sciences['Michael Gerlich']2024-05-06T00:00:00https://www.semanticscholar.org/paper/25b5801e19286e06e05324faa248194effe3f640 f5c4fcc7fe92c4516ad62a5b0c9536bc51d8340aWe investigate how generative Artificial Intelligence (AI) can be used to optimize resources in Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks. In particular, generative AI models for real-time decision-making have been used in public safety scenarios. This work describes how generative AI models can improve resource management within UAV-assisted networks. Furthermore, this work presents generative AI in UAV-assisted networks to demonstrate its practical applications and highlight its broader capabilities. We demonstrate a real-life case study for public safety, demonstrating how generative AI can enhance real-time decision-making and improve training datasets. By leveraging generative AI in UAV- assisted networks, we can design more intelligent, adaptive, and efficient ecosystems to meet the evolving demands of wireless networks and diverse applications. Finally, we discuss challenges and future research directions associated with generative AI for resource optimization in UAV-assisted networks.190By leveraging generative AI in UAV- assisted networks, the authors can design more intelligent, adaptive, and efficient ecosystems to meet the evolving demands of wireless networks and diverse applications.['Sana Sharif', 'S. Zeadally', 'Waleed Ejaz']2024-05-06T00:00:00https://www.semanticscholar.org/paper/f5c4fcc7fe92c4516ad62a5b0c9536bc51d8340a f63096156d320d3081efc4e41f7f228ca0998b27Eye and Vision780A narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography and highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases.Eye and Vision['Yong Yu Tan', 'Hyun Goo Kang', 'C. J. Lee', 'Sung Soo Kim', 'Sungha Park', 'Sahil Thakur', 'Zhi Da Soh', 'Yunnie Cho', 'Qingsheng Peng', 'Y. Tham', 'T. H. Rim', 'Ching-Yu Cheng']2024-05-06T00:00:00https://www.semanticscholar.org/paper/f63096156d320d3081efc4e41f7f228ca0998b27 82bcdbd077edaaf6a8f7c9ae49d87072ff9e68a6Spisak conducts a variety of activities and areas of research, including being a research associate at the National Preparedness Leadership Initiative (Harvard T.H. Chan School of Public Health, Harvard University). His major concept, computational leadership, is described as a “leadership process that seamlessly integrates high‐quality data and state‐of‐the‐art technology with practical insights and validated social science.” In discussion of his creation of the concept, he writes that “science provides a clear plan of action, data informs this plan, tech scales it, and experience makes it human,” and in the intersection of artificial intelligence/AI and leadership, he notes his personal principle and motto: “Leadership First, Tech Last.” He writes of the dangers and potential pitfalls of organizational use of chatbots, and of the dangers of “blind adoption of technology without a robust plan in place.” For the application of computational leadership, five steps are outlined, which in his words are (1) Define Ambitious Goals Guided by Science (2) Leverage Data for Strategic Decision‐Making (3) Embrace Technology with Purpose (4) Combine Experience with Innovation (5) Communicate Your Vision Clearly. Responsible AI is outlined in his words in four imperatives: Informed participation; transparent objectives; Employee and Stakeholder Well‐Being; and Data Management.Leader to Leader00His major concept, computational leadership, is described as a “leadership process that seamlessly integrates high‐quality data and state‐of‐the‐art technology with practical insights and validated social science.”Leader to Leader['B. Spisak']2024-05-06T00:00:00https://www.semanticscholar.org/paper/82bcdbd077edaaf6a8f7c9ae49d87072ff9e68a6 fab237188e9b61638b44e4b124c95b4cf438fe62Many patients miss their appointments all around the world and many of them don't even cancel at all or don't do so in time due to several reasons. In order to address the widespread issue of medical no-shows, this paper proposes a solution that involves building a machine learning model utilizing patient datasets that are already available. This model will identify patterns and links between various patient factors and the patients' propensity to miss appointments. As a result, based on their information, it is possible to anticipate the chance of a patient appearing. Based on the Support Vector Machines classification technique, the machine learning model created the solution predictive model. Effective healthcare services are vital in today's fast-paced environment. This strategy aims to reduce the distance between patients and medical professionals by offering a workable and friendly solution. For certain medical institutions, such as clinics and hospitals, this initiative makes it easier for patients and customers to schedule doctor appointments online. Using this technology, patients may easily browse a database of doctors' biographies, specializations, and availability. Even the day and time of their choosing can be chosen for appointments. Each patient's appointment request will be scheduled by this doctor's appointment system and forwarded to the physician. The system administrator will update the list of doctors, including their specialties, personal information, and system access credentials. Patients will look for a physician who specializes in their requirements by exploring the doctor's appointment system online. Before making their request, the patient can browse the doctor's weekly schedule to choose a day and time that works best for them. Following that, the physicians have access to all of their appointments as well as the patients' appointment requests, which are prioritized according to their availability. It gives medical professionals a strong tool for successfully managing the schedules, which reduces administrative strain and ensures a positive patient experience.International Journal of Innovative Science and Research Technology221A machine learning model will identify patterns and links between various patient factors and the patients' propensity to miss appointments, which gives medical professionals a strong tool for successfully managing the schedules, which reduces administrative strain and ensures a positive patient experience.International Journal of Innovative Science and Research Technology (IJISRT)['Pramodd Komarneni', 'Toshan Kumar Kalakoti', 'Pavan Kumar Narla', 'Sai Pujitha Alla', 'Richitha Bomma']2024-05-06T00:00:00https://www.semanticscholar.org/paper/fab237188e9b61638b44e4b124c95b4cf438fe62 472f0d4a8467f8022e94b7f50d164b503f88f9351001[]2024-05-06T00:00:00https://www.semanticscholar.org/paper/472f0d4a8467f8022e94b7f50d164b503f88f935 bc1f4745fc22b0c24c11173a0bfe73340b0d1819OECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2024-05-06T00:00:00https://www.semanticscholar.org/paper/bc1f4745fc22b0c24c11173a0bfe73340b0d1819 9b8d33bac3505553ac176fc891c8f6f7828bbdbbTCC Africa00TCC Africa['Joy Owango', 'N. Outa', 'Emma Warren-Jones', 'Wilson de Souza']2024-05-06T00:00:00https://www.semanticscholar.org/paper/9b8d33bac3505553ac176fc891c8f6f7828bbdbb 014d47a42d377dfd64ed3852d5d35b84491b9cd2Qualities of organizations constitute dimensions of trustworthiness. Guided by the integrative model of organizational trust, we developed dimensions of trustworthiness of policymakers, technology developers, and media organizations that are involved in introducing artificial intelligence for autonomous vehicles. We collected data through six focus group discussions with the public in Singapore. In addition to the core dimensions of trustworthiness, the public would consider acclaim, collaboration, public communication, and affiliation. Further, we identified all the dimensions of trustworthiness as either ability-, recognition-, relation-, or principle-based. These findings carry important implications for the development of the model and stakeholders’ communication about science and technology.Science communication580Science Communication['Tong Jee Goh', 'Shirley S. Ho']2024-05-06T00:00:00https://www.semanticscholar.org/paper/014d47a42d377dfd64ed3852d5d35b84491b9cd2 408e36ae718420c1ef03c5aa1894589b87c7fae3Background EUCAST recommends a two-step process for beta-lactamases in Gram-negative bacteria. Screening with minimal inhibitory concentrations (MICs) or inhibition zone diameters for potential extended spectrum beta-lactamase (ESBL), plasmid-mediated AmpC beta-lactamase, or carbapenemase production is followed by confirmatory tests. GPT-4 and its newly released customized GPT-agent may support the initial EUCAST-screening process. We aimed to validate a customized GPT-agent to identify potential resistance mechanisms. Methods We used 225 Gram-negative isolates. Based on phenotypic resistances against beta-lactam antibiotics, we formed four categories: “none”, “ESBL”, “AmpC”, or “carbapenemase”. We included 862 phenotypic categories. Next, we customized a GPT-agent with EUCAST-guidelines, expert rules, and EUCAST-breakpoint table (v13.1). We compared routine diagnostic outputs (reference) to (i) EUCAST-GPT-expert, (ii) medical microbiologists, and (iii) GPT-4 without customization. We determined performance as sensitivities and specificities to flag suspect resistance mechanisms. Results Three human readers showed concordance in 814/862 (94.4%) phenotypic categories and used in median eight words (IQR 4-11) for reasoning. Median sensitivity and specificity for ESBL, AmpC, and carbapenemase were 98%/99.1%, 96.8%/97.1%, and 95.5%/98.5%, respectively. Three independent prompting rounds of the GPT-agent showed concordance in 706/862 (81.9%) categories but used in median 158 words (IQR 140-174) for reasoning,. Median sensitivity and specificity for ESBL, AmpC, and carbapenemase prediction were 95.4%/69.23%, 96.9%/86.3%, and 100%/98.8%, respectively. In the non-customized GPT-4, 169/862 (19.6%) categories could be interpreted. Of these 137/169 (81.1%) categories agreed with routine diagnostic. The non-customized GPT-4 used in median 85 words (IQR 72-105) for reasoning. Conclusion Human experts showed higher concordance and shorter argumentations compared to GPT-agents. Human experts showed comparable median sensitivities and higher specificities compared to GPT-agents. GPT-agents showed more unspecific flagging of ESBL and AmpC, potentially, resulting in additional testing, diagnostic delays, and higher costs. GPT-4 and GPT-agents are not IVDR/FDA-approved, but validation of LLMs is critical and datasets for benchmarking are needed.bioRxiv20GPT-4 and its newly released customized GPT-agent may support the initial EUCAST-screening process and validate potential resistance mechanisms, and human experts showed higher concordance and shorter argumentations compared to GPT-agents.bioRxiv['C. Giske', 'Michelle Bressan', 'Farah Fiechter', 'V. Hinić', 'Stefano Mancini', 'Oliver Nolte', 'Adrian Egli']2024-05-06T00:00:00https://www.semanticscholar.org/paper/408e36ae718420c1ef03c5aa1894589b87c7fae3 0c180c90c7bd2b9e4a3dca5919aed799daf7abaeEducational Administration Theory and Practices00Educational Administration Theory and Practices['Vishwanadham Mandala', 'Srinivas Naveen Reddy Dolu Surabhi', 'Phani Durga Nanda Kishore Kommisetty', 'Bala Maruthi Subba Rao Kuppala', 'Roopak Ingole']2024-05-06T00:00:00https://www.semanticscholar.org/paper/0c180c90c7bd2b9e4a3dca5919aed799daf7abae eec7a1941c78a62eee23634dc79800ed38784638Uncertainty has long been a critical area of study in robotics, particularly when robots are equipped with analytical models. As we move towards the widespread use of deep neural networks in robots, which have demonstrated remarkable performance in research settings, understanding the nuances of uncertainty becomes crucial for their real-world deployment. This guide offers an overview of the importance of uncertainty and provides methods to quantify and evaluate it from an applications perspective.1570This guide offers an overview of the importance of uncertainty and provides methods to quantify and evaluate it from an applications perspective.['Ransalu Senanayake']2024-05-06T00:00:00https://www.semanticscholar.org/paper/eec7a1941c78a62eee23634dc79800ed38784638 bbd8d2ce09d11e740a83c1766b753deee437b20aResponsible artificial intelligence (RAI) is increasingly recognized as a critical concern. However, the level of corporate RAI prioritization has not kept pace. In this work, we conduct 16 semi-structured interviews with practitioners to investigate what has historically motivated companies to increase the prioritization of RAI. What emerges is a complex story of conflicting and varied factors, but we bring structure to the narrative by highlighting the different strategies available to employ, and point to the actors with access to each. While there are no guaranteed steps for increasing RAI prioritization, we paint the current landscape of motivators so that practitioners can learn from each other, and put forth our own selection of promising directions forward.640This work conducts 16 semi-structured interviews with practitioners to investigate what has historically motivated companies to increase the prioritization of RAI, and paints the current landscape of motivators so that practitioners can learn from each other, and put forth their own selection of promising directions forward.['Angelina Wang', 'Teresa Datta', 'John P. Dickerson']2024-05-06T00:00:00https://www.semanticscholar.org/paper/bbd8d2ce09d11e740a83c1766b753deee437b20a e552e02da6ddd40281841550e93684078c334a4eJournal of Medical Economics160Journal of medical economics['J. Carapinha', 'Danélia Botes', 'René Carapinha']2024-05-06T00:00:00https://www.semanticscholar.org/paper/e552e02da6ddd40281841550e93684078c334a4e 5ec5c7c0407d5c74d3fb8ebf005be3132c26b785Emerging Media00Emerging Media['Liangwen Kuo']2024-05-06T00:00:00https://www.semanticscholar.org/paper/5ec5c7c0407d5c74d3fb8ebf005be3132c26b785 af9b783c28bfacb1ad88790b46acdaade69bfe8dJournal of Special Topics in Information Technology and Management470Information Technology and Management['Huishuang Su', 'Lingxia Li', 'Shuo Tian', 'Zhongwei Cao', 'Qiang Ma']2024-05-06T00:00:00https://www.semanticscholar.org/paper/af9b783c28bfacb1ad88790b46acdaade69bfe8d 964ffecbae63437eeb5cf304f14a9438fccfc014C&amp;EN Global Enterprise00C&amp;EN Global Enterprise['Aayushi Pratap']2024-05-06T00:00:00https://www.semanticscholar.org/paper/964ffecbae63437eeb5cf304f14a9438fccfc014 86181d9299eca8ea8af3d00eb6cc01182f2a3847Endocrine Abstracts00Endocrine Abstracts['Cardenas Saturnino Dominguez']2024-05-06T00:00:00https://www.semanticscholar.org/paper/86181d9299eca8ea8af3d00eb6cc01182f2a3847 8713379d71fb6606af0de0ba5820e064dc7f136fThe article addresses hardware-software and other key aspects of the artificial intelligence development strategy for mobile technologies. The proposed components of the strategy include a series of approaches to address issues related to the development and deployment of large language models on mobile devices, as well as suggestions for improving connectivity, memory management, and data security.Journal of Mobile Multimedia380The article addresses hardware-software and other key aspects of the artificial intelligence development strategy for mobile technologies to address issues related to the development and deployment of large language models on mobile devices, as well as suggestions for improving connectivity, memory management, and data security.Journal of Mobile Multimedia['V. Slyusar', 'Yuriy P. Kondratenko', 'Anatolii Shevchenko', 'Tetiana Yeroshenko']2024-05-06T00:00:00https://www.semanticscholar.org/paper/8713379d71fb6606af0de0ba5820e064dc7f136f 42b7a95c2ae89ebf50afe6610439f825e2f32ec2Background: Young mortality is prevalent among patients with cardiovascular disease (CVD). To develop prediction models for CVD mortality in young adults, it is crucial to assess CVD risks. Early detection of cardiac disorders using machine learning algorithms, a branch of artificial intelligence (AI) is crucial for preventing more damage to coronary arteries and saving lives. Aims: To predict mortality versus a life outcome among young adults (18-45 years) with CVD using AI technique known as Chi-squared automatic interaction detector (CHAID) algorithms. Methods: A large-scale dataset was extracted from the electronic health records of 809 young adult patients diagnosed with CVD using a retrospective design. Information was retrieved regarding young adults from Jordan who were admitted to public health institutions between 2015 and the end of 2021. Results: CHAID algorithms were chosen among seven prediction models based on accuracy and area under curve to predict mortality vs life in young individuals (18-45 years old) with CVD. The mortality prediction algorithms started with pulse pressure, then diastolic blood pressure, then ischemic heart disease, and last geographical location. Conclusions: CHAID model used in our study indicated how the death rate was classified and distributed among a variety of parameters. As a result, we may argue that AI model could provide additional information on how many aspects are articulated in connection to CVD patient fatality situations.Electronic Journal of General Medicine290CHAID model used in this study indicated how the death rate was classified and distributed among a variety of parameters, and may argue that AI model could provide additional information on how many aspects are articulated in connection to CVD patient fatality situations.Electronic Journal of General Medicine['Muayyad Ahmad', 'Salam H. Bani Hani', 'Mhmoud A Abu-Abeeleh', 'Ibrahim Aljarah']2024-05-06T00:00:00https://www.semanticscholar.org/paper/42b7a95c2ae89ebf50afe6610439f825e2f32ec2 0d6d2ef8801424d297fd5358013d66ee1af09d7aThe U.S. Bureau of Labor Statistics projects that by the year 2029, the United States will lose a million jobs in the office and administrative support occupations because technology, automation, and artificial intelligence (AI) have the potential to substitute or replace the office and administrative functions performed by office workers. Despite the potential impact AI will have on office work and the important role office workers play in the American economy, we have limited knowledge of the state of the art research in office work at the intersection of emerging artificial intelligence technologies. In this study, we conducted a bibliometric analysis of the scholarly literature at the intersection of office work and artificial intelligence. We extracted literature sources from Compendex and Scopus databases and used VOSviewer for visualizing and quantifying our bibliometric analyses. Our findings from keywords analysis indicate that office automation, humans, human-computer interaction, and artificial intelligence occurred more frequently in the scholarly literature and had high link strengths. Keyword clusters from co-occurrence analysis indicate that intelligent buildings, robotics, and the internet of things are emerging topics in the office work domain. The two clusters related to ergonomics, worker characteristics, human performance, and safety indicate the types of human factors concerns that are more widely studied in office work settings. In summary, our findings on the state-of-the-art research in office work indicate that more studies have been conducted on smart buildings, robotics, and technology development for office work, compared to studies on office workers and their professional development.700A bibliometric analysis of the scholarly literature at the intersection of office work and artificial intelligence indicates that more studies have been conducted on smart buildings, robotics, and technology development for office work, compared to studies on office workers and their professional development.['Priyadarshini Pennathur', 'Valerie Boksa', 'A. Pennathur', 'Andrew Kusiak', 'Beth Livingston']2024-05-06T00:00:00https://www.semanticscholar.org/paper/0d6d2ef8801424d297fd5358013d66ee1af09d7a 42002f970ffa75b0c24b1b079d4668924b4761fcWith the development of generative artificial intelligence, text and data mining has been put into more applications. However, as a technology for collecting and processing a large number of other works, it faces copyright infringement under the current legal system. As a step in the operation of artificial intelligence, text and data mining is to collect and process existing works and information to realize the behavior of knowledge "reproduction", thus promoting the utilization of knowledge and social innovation. Meanwhile, copyright exemption should be provided for its fair use system. The fair use system in the United States can give full play to the autonomy of judges in specific cases and flexibly identify fair use exemptions, while the copyright exception in Europe clarifies that text and data mining activities should be exempted under specific circumstances. Learning from both, China can not only break the closed fair use system, but also build an evaluation system of artificial intelligence infringement and benefit-sharing mechanism, so as to provide scientific norms for fair use and improve the development of artificial intelligence and big data industry in China.Transactions on Social Science, Education and Humanities Research320China can not only break the closed fair use system, but also build an evaluation system of artificial intelligence infringement and benefit-sharing mechanism, so as to provide scientific norms for fair use and improve the development of artificial intelligence and big data industry in China.Transactions on Social Science, Education and Humanities Research['Zhenyi Gao']2024-05-06T00:00:00https://www.semanticscholar.org/paper/42002f970ffa75b0c24b1b079d4668924b4761fc c10a3e3ad857f6d1adc7e659a08ed632834a9297This study, which is anchored to Resource‐Based View (RBV) theory, explores the relationship between artificial intelligence (AI), process optimization, organizational flexibility, and sustainability performance in organizational settings. Leveraging the RBV framework's focus on internal resources as sources of competitive advantage, this research seeks to clarify how AI adoption, process optimization, and organizational flexibility foster sustainable growth. SPSS PROCESS Macro was used to analyze the data from 288 organizations. The findings derived from the empirical data, this study verify the positive relationships between AI and process optimization as well as AI and sustainability performance, revealing the strategic nature of AI as a resource to improve operational effectiveness and environmental responsibility. Further, our results indicate the mediating role of process optimization and the moderating effect of organizational flexibility in determining the link between AI and sustainability outcomes. Moreover, results confirmed the indirect effects of AI on sustainability performance via process optimization under the boundary conditions of the organizational flexibility. These findings add to the developing literature of sustainable operational practices and provide practical suggestions that might be useful for the practitioners aiming at using AI driven by organizational capabilities to deliver sustainability performance.Corporate Social Responsibility and Environmental Management440The results indicate the mediating role of process optimization and the moderating effect of organizational flexibility in determining the link between AI and sustainability outcomes and confirmed the indirect effects of AI on sustainability performance via process optimization under the boundary conditions of the organizational flexibility.Corporate Social Responsibility and Environmental Management['Ali Nawaz Khan', 'Khalid Mehmood', 'Ahsan Ali']2024-05-06T00:00:00https://www.semanticscholar.org/paper/c10a3e3ad857f6d1adc7e659a08ed632834a9297 c59f7bb37643bc803d5b66240e976f6fffe3c90aIntroduction. Artificial intelligence (AI) is a technology that simulates human brain data processing, its intellectual behavior and critical thinking. Sophisticated AI models can potentially improve patient management by speeding up processes and increasing their accuracy and efficiency at a lower cost of human resources. Compared to other specialties, use of AI in gynecology remains in its infancy. It is important to understand that the available methods for clinical imaging have certain limitations, namely clinician’s workload and data variably interpreted by different doctors. AI, in turn, has the potential to overcome these limitations while increasing diagnostic accuracy.Aim: to structure and analyze current published data on AI use in gynecology.Materials and Methods. A search for primary sources was carried out in the electronic databases PubMed, eLibrary and Google Scholar. The search queries included the following keywords "artificial intelligence", "gynecology", "endometrial cancer", "endometriosis", "ovarian cancer", "diagnostics", "oncogynecology" retrieved from February 2014 to February 2024. Articles were assessed according to PRISMA guidelines. After identification, before the screening stage, duplicates were excluded. At the screening stage, the titles and annotations of the identified articles were analyzed for eligibility to the review topic as well as for available full-text versions; abstracts and letters to the editorial board in scientific journals were excluded at this stage. 685 full-text articles were evaluated for eligibility, the inclusion criteria were as follows: publication in Russian or English; the study describes use of AI technologies in diagnostics or treatment of gynecological diseases. All disagreements between authors were resolved by consensus. Ultimately, 80 primary sources were included in this review.Results. AI-based systems have succeeded in image analyzing and interpreting and over the past decade have become powerful tools that have revolutionized the field of gynecological imaging. In the studies analyzed, AI was able to provide faster and more accurate forecasts and diagnostics, increasing the overall effectiveness of gynecological care. It is important to note that AI cannot fully replace doctors, but it can perfectly integrate into clinical practice, helping in the decision-making process and reducing errors in differential diagnosis and variability of interaction between different specialists. In the field of oncogynecology, undoubtedly one of the most promising aspects is the possibility of better and especially early diagnostics and, ultimately, improved patient survival.Conclusion. A great success has been achieved so far, and AI use is expected to extend in the next few years. In fact, it will take a very long way to go before AI-based technologies are fully integrated into clinical practice.Obstetrics Gynecology and Reproduction710In the studies analyzed, AI was able to provide faster and more accurate forecasts and diagnostics, increasing the overall effectiveness of gynecological care and, ultimately, improved patient survival.Obstetrics, Gynecology and Reproduction['Sh. L. Shailieva', 'D. K. Mamchueva', 'A. P. Vishnevskaya', 'Kh. Sh. Dzhalaeva', 'E. G. Ramazanova', 'Y. R. Kokaeva', 'Z. M. Eloeva', 'D. R. Aisanova', 'A. S. Vinogradova', 'R. R. Tuko', 'A. V. Sineva', 'L. A. Valiullina', 'A. A. Kutseva']2024-05-06T00:00:00https://www.semanticscholar.org/paper/c59f7bb37643bc803d5b66240e976f6fffe3c90a 81e18867ea61a35a259e184dd9c24f2a2af8c396Artificial Intelligence (AI) is the subset of Advanced Analytics (AA) and involves automating steps that normally humans would take to complete an exhaustive analysis. Artificial Intelligence is a multi disciplinary field whose goal is to automate activities that presently require human intelligence. The aim of this research paper is to define the incidence of BA and BI in business activities and analyse scientific activity and advances of BA and BI to define new research horizons in this field. For this purpose, an analysis is required allowing to highlight the findings that provide recognition and comparison of the results. This will make possible the understanding of the current dynamics, its importance for organizations and its efficacy in the face of the new challenges generated by the requirements of world trade. The paper investigates the wide range of implications of Artificial Intelligence (AI) in BA and BI. This paper investigates the overall impact of AI from research and innovation to deployment in BA and BI. The conjecture procured from the research will provide an improved understanding of the innovations and the efficacy of AI on businesses and society in general. It will also give a better understanding of how AI can transform the business operations. Keywords: Artificial Intelligence, Advanced Analytics, Business Analytics and Business IntelligenceINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The overall impact of AI from research and innovation to deployment in BA and BI and the conjecture procured from the research will provide an improved understanding of the innovations and the efficacy of AI on businesses and society in general.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['MD Ezharul Ahamad']2024-05-06T00:00:00https://www.semanticscholar.org/paper/81e18867ea61a35a259e184dd9c24f2a2af8c396 18d24d72830e75a464cf8eff4b447584e8e2ca1cThe use of artificial intelligence in pharmaceutical technology has grown over time. This is because technology may be used to save costs and time, as well as to better comprehend the interactions between various formulations and process parameters. A subfield of computer science called artificial intelligence studies problem-solving with the use of symbolic programming. It has significantly advanced into a science of problem-solving with numerous applications in engineering, business, and healthcare. Artificial intelligence has enormous potential for solving health-related issues First of all. Artificial intelligence (AI) approaches have reached a degree of maturity where they can be used to support human decision-makers in real-world scenarios. Artificial Intelligence (AI) holds promise for revolutionising clinical trial design, from study planning to trial execution, with the goal of increasing trial success rates and reducing pharmaceutical R&D costs. The present study explain various pharmaceutical areas in AI plays an important role for development and growth of pharmaceutical industryInternational Journal of Advanced Research in Science, Communication and Technology390Various pharmaceutical areas in AI plays an important role for development and growth of pharmaceutical industry.International Journal of Advanced Research in Science, Communication and Technology['Ms. S. S. Satkar', 'Ms. P. A. Jadhav', 'Mr. T. A. Randhe']2024-05-06T00:00:00https://www.semanticscholar.org/paper/18d24d72830e75a464cf8eff4b447584e8e2ca1c 4ace26697454c9158a3276b58c9a4b325fa1b2a0How to participate in artificial intelligence otherwise? Put simply, when it comes to technological developments, participation is either understood as public debates with non-expert voices to anticipate risks and potential harms, or as a way to better design technical systems by involving diverse stakeholders in the design process. We advocate for a third path that considers participation as crucial to problematise what is at stake and to get a grip on the situated developments of artificial intelligence technologies. This study addresses how the production of accounts shape problems that arise with artificial intelligence technologies. Taking France as a field of study, we first inspected how media narratives account for the entities and issues of artificial intelligence, as reported by the national press over the last decade. From this inspection, we identified four genres and described their performative effects. We then conducted a participatory inquiry with 25 French artificial intelligence practitioners’ to ground artificial intelligence in situated experiences and trajectories. These experiential accounts enabled a plural problematisation of artificial intelligence, playing with the geometries of artificial intelligence and its constituencies, while diversifying and thickening its problems. To conclude, we discuss how participatory inquiries, through experiential and plural accounts offer a refreshing weaving of artificial intelligence problems into the fabric of its deployments. Our participatory approach seeks to re-politicise artificial intelligence from practitioners’ situated experiences, by making the ongoing relationships between past trajectories, current frictions and future developments tangible and contestable, opening avenues to contribute otherwise.Big Data &amp; Society310This study addresses how the production of accounts shape problems that arise with artificial intelligence technologies, and seeks to re-politicise artificial intelligence from practitioners’ situated experiences by making the ongoing relationships between past trajectories, current frictions and future developments tangible and contestable.Big Data &amp; Society['Pauline Gourlet', 'Donato Ricci', 'Maxime Crépel']2024-05-06T00:00:00https://www.semanticscholar.org/paper/4ace26697454c9158a3276b58c9a4b325fa1b2a0 51a41e774f9e5b3a8bacea3a37ea4b87cd25f041This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)--driven models. Utilizing advanced machine learning techniques, including Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine (SVM), the research evaluates the predictive capabilities of these models through comprehensive assessments based on key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Error. A particular focus is placed on ensemble learning, exemplified by the Random Forest model, which demonstrates superior accuracy in capturing intricate patterns within gold price data. These findings contribute valuable insights to the field of financial forecasting, emphasizing the potential of AI-driven models to inform stakeholders in gold investment and financial markets. The study concludes by advocating for ongoing research and continuous model refinement to adapt to dynamic market conditions and enhance the precision of gold price predictions. Keywords: gold price prediction artificial intelligence, MSE.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT110This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)--driven models, focusing on ensemble learning, exemplified by the Random Forest model, which demonstrates superior accuracy in capturing intricate patterns within gold price data.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Neha Singh']2024-05-06T00:00:00https://www.semanticscholar.org/paper/51a41e774f9e5b3a8bacea3a37ea4b87cd25f041 5bc0b1ed7a8887af99fc66dc2a8730a74a5e4dffArtificial intelligence (AI), which provides unmatched efficiency and customization, is changing the marketing environment. But these advantages come with serious ethical ramifications that call for consideration. To fill in the gaps in the existing literature, this study explores the ethical implications of artificial intelligence in marketing. It seeks to create a cohesive ethical framework, comprehend consumer views, foresee long-term societal effects, investigate the interdependence of ethical concerns, and look at cross- cultural differences through a thorough examination. This work aims to close these gaps by offering insights that can direct researchers, policymakers, and marketers toward ethical AI- driven marketing strategies. Keywords: Artificial intelligence, Marketing, Ethical Framework, Consumer perceptionINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This study explores the ethical implications of artificial intelligence in marketing, seeks to create a cohesive ethical framework, comprehend consumer views, foresee long-term societal effects, investigate the interdependence of ethical concerns, and look at cross- cultural differences through a thorough examination.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Nandyala Lokeswar']2024-05-06T00:00:00https://www.semanticscholar.org/paper/5bc0b1ed7a8887af99fc66dc2a8730a74a5e4dff 37f1fd1de1761433a296e148592795b75d102dd7International Journal of Artificial Intelligence in Education310International Journal of Artificial Intelligence in Education['Can Tatar', 'Shiyan Jiang', 'Carolyn P. Rosé', 'Jie Chao']2024-05-06T00:00:00https://www.semanticscholar.org/paper/37f1fd1de1761433a296e148592795b75d102dd7 53e230785ae1bfd42f802174beaf3121b874c4e1The paper discusses an innovative approach to monitoring grain ecosystems, based on the use of neural network technologies. A classification of the tasks of monitoring the phytosanitary condition of crops in a grain field has been carried out, and the corresponding intellectualization tools have been identified. The main attention is paid to the problems of detection, classification and development of diseases in crops, for the effective solution of which it is proposed to use computer vision methods, including a complex of convolutional architectures GoogleNet, DenseNet, U-Net, which have shown high performance in classification and segmentation problems on test samples of images of wheat diseases, obtained as a result of three years of field experiments in the Krasnodar Region. The results of the study show that the use of neural network methods in the process of monitoring grain ecosystems contributes to the effective solution of complex problems associated with diagnostic procedures, allowing to reduce the level of uncertainty in the decision-making process, which is especially important under the influence of environmental factors with a high level of randomness and variability. The main barrier to the intellectualization of production processes is the lack of methodology for working with artificial intelligence, big data and other digital technologies at different levels of management in the agricultural sector of the economy, which affects not only issues of technical implementation and implementation of artificial intelligence, but also organizational aspects, including work with data, staffing the intellectualization process, information infrastructure, defining the roles and responsibilities of participants in the process, as well as the integration of intelligent solutions with the agricultural solutions module of the national platform “Digital Agriculture”.Socio-economic and humanitarian magazine20The results of the study show that the use of neural network methods in the process of monitoring grain ecosystems contributes to the effective solution of complex problems associated with diagnostic procedures, allowing to reduce the level of uncertainty in the decision-making process.Socio-economic and humanitarian magazine['Igor Arinichev']2024-05-06T00:00:00https://www.semanticscholar.org/paper/53e230785ae1bfd42f802174beaf3121b874c4e1 ffe0b3f18735ce738b3a8d0b3acee22077cbcfdbObjectives: Suicide risk is elevated in lesbian, gay, bisexual, and transgender (LGBT) individuals. Limited data on LGBT status in healthcare systems hinder our understanding of this risk. This study used natural language processing to extract LGBT status and a deep neural network (DNN) to examine suicidal death risk factors among US Veterans. Methods: Data on 8.8 million veterans with visits between 2010 and 2017 was used. A case-control study was performed, and suicide death risk was analyzed by a DNN. Feature impacts and interactions on the outcome were evaluated. Results: The crude suicide mortality rate was higher in LGBT patients. However, after adjusting for over 200 risk and protective factors, known LGBT status was associated with reduced risk compared to LGBT-Unknown status. Among LGBT patients, black, female, married, and older Veterans have a higher risk, while Veterans of various religions have a lower risk. Conclusion: Our results suggest that disclosed LGBT status is not directly associated with an increase suicide death risk, however, other factors (e.g., depression and anxiety caused by stigma) are associated with suicide death risks.International Journal of Public Health580The results suggest that disclosed LGBT status is not directly associated with an increase suicide death risk, however, other factors (e.g., depression and anxiety caused by stigma) are associated with suicide death risks.International Journal of Public Health['Ying Yin', 'T. E. Workman', 'J. Blosnich', 'Cynthia A Brandt', 'M. Skanderson', 'Y. Shao', 'Joseph L Goulet', 'Qing Zeng-Treitler']2024-05-06T00:00:00https://www.semanticscholar.org/paper/ffe0b3f18735ce738b3a8d0b3acee22077cbcfdb 3af3842c92361c440f63da7d17d0d9fe51d4bfcdPURPOSE To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements. METHODS Patients presenting to the ophthalmology clinic were prospectively enrolled and had their images taken using a standardised protocol. Facial landmarks were segmented on the images to enable calculation of marginal reflex distance (MRD) 1 and 2, palpebral fissure height (PFH), inner intercanthal distance (IICD), outer intercanthal distance (OICD), interpupillary distance (IPD) and horizontal palpebral aperture (HPA). These manual segmentations were used to train a machine learning algorithm to automatically detect facial landmarks and calculate these measurements. The main outcomes were the mean absolute error and intraclass correlation coefficient. RESULTS A total of 958 eyes from 479 participants were included. The testing set consisted of 290 eyes from 145 patients. The AI algorithm demonstrated close agreement with human measurements, with mean absolute errors ranging from 0.22 mm for IPD to 0.88 mm for IICD. The intraclass correlation coefficients indicated excellent reliability (ICC > 0.90) for MRD1, MRD2, PFH, OICD, IICD, and IPD, while HPA showed good reliability (ICC 0.84). The landmark detection model was highly accurate and achieved a mean error rate of 0.51% and failure rate at 0.1 of 0%. CONCLUSION The automated facial landmark detection network provided accurate and reliable periocular measurements. This may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.European Journal of Ophthalmology30The automated facial landmark detection network provided accurate and reliable periocular measurements and may help increase the objectivity of periocular measurements in the clinic and may facilitate remote assessment of patients with tele-health.European journal of ophthalmology['Khizar Rana', 'Mark B Beecher', 'Carmelo Caltabiano', 'C. Macri', 'Yang Zhao', 'Johan Verjans', 'Dinesh Selva']2024-05-06T00:00:00https://www.semanticscholar.org/paper/3af3842c92361c440f63da7d17d0d9fe51d4bfcd cb6a30ecd652cc39fb670fd135d794e179da17e0This paper presents multi- and interdisciplinary approaches for finding the appropriate AI technologies for research information. Professional research information management (RIM) is becoming increasingly important as an expressly data-driven tool for researchers. It is not only the basis of scientific knowledge processes, but also related to other data. A concept and a process model of the elementary phases from the start of the project to the ongoing operation of the AI methods in the RIM is presented, portraying the implementation of an AI project, meant to enable universities and research institutions to support their researchers in dealing with incorrect and incomplete research information, while it is being stored in their RIMs. Our aim is to show how research information harmonizes with the challenges of data literacy and data quality issues, related to AI, also wanting to underline that any project can be successful if the research institutions and various departments of universities, involved work together and appropriate support is offered to improve research information and data management.370It is shown how research information harmonizes with the challenges of data literacy and data quality issues, related to AI, to underline that any project can be successful if the research institutions and various departments of universities, involved work together and appropriate support is offered to improve research information and data management.['Otmane Azeroual', 'Tibor Koltay']2024-05-06T00:00:00https://www.semanticscholar.org/paper/cb6a30ecd652cc39fb670fd135d794e179da17e0 5fc59cdda1c0a3bbc783cd8c7201aff37a2cf645The article outlines the theoretical aspects and directions of AI development in the modern world, presents a classification, and spheres of its application in real economic conditions. Based on the research, the following are compiled: a list of top professions at the global and national levels; the structure of vacancies and resumes on the national market; a comparison of trends in the labor market outlined by international and national expert organizations in the field of the labor market with trends identified using AI; an assessment of the opinion of AI (chatbots ChatGPT and Gemini) on the issues of evaluating and forecasting the development directions of the labor market, demand and supply for various professions in the Republic of Belarus.Vestnik of Polotsk State University. Part D. Economic and legal sciences00The article outlines the theoretical aspects and directions of AI development in the modern world, presents a classification, and spheres of its application in real economic conditions, and an assessment of the opinion of AI (chatbots ChatGPT and Gemini) on the issues of evaluating and forecasting the development directions of the labor market, demand and supply for various professions in the Republic of Belarus.Vestnik of Polotsk State University Part D Economic and legal sciences['K. Krayenkova', 'K. Mitskevich']2024-05-06T00:00:00https://www.semanticscholar.org/paper/5fc59cdda1c0a3bbc783cd8c7201aff37a2cf645 7521b0cb308011b2db39b16b4e742ef84e2df24bWith the development of technology, people expect real-time communication with computers. Wearable devices, such as those for monitoring physiological signals, have rapidly developed and are now being applied in college and university evaluation. Due to the non-standard and unscientific practices in teaching, teachers may experience psychological obstacles when evaluating students. To ensure successful evaluation, we must motivate teachers to correctly understand and actively participate in the evaluation process, thus facilitating communication between people and computers. Emotion recognition based on multi-physiological signals, such as ECG, pulse, electromyography, electrodermal, and respiratory signals, is an effective method for achieving this. This dissertation conducts in-depth research on the methods for emotion recognition based on multi-physiological signals. It explores feature extraction methods, feature selection, and fusion to provide objective assessments of physiological and psychological activity states, which are used as a basis for accurate emotional judgments.International Journal of Information and Communication Technology Education240This dissertation conducts in-depth research on the methods for emotion recognition based on multi-physiological signals to provide objective assessments of physiological and psychological activity states, which are used as a basis for accurate emotional judgments.International Journal of Information and Communication Technology Education['Yang Qin']2024-05-06T00:00:00https://www.semanticscholar.org/paper/7521b0cb308011b2db39b16b4e742ef84e2df24b 400c1d987c1942436ffb3f3797533c0e115cccf9Cureus1210Cureus['Allan Hamilton']2024-05-06T00:00:00https://www.semanticscholar.org/paper/400c1d987c1942436ffb3f3797533c0e115cccf9 f6796461ff9c4e87dfadf31cea39836662203572Yemen Journal of Medicine00Yemen Journal of Medicine['Maher Najm', 'Moustafa Mohamad Najm']2024-05-06T00:00:00https://www.semanticscholar.org/paper/f6796461ff9c4e87dfadf31cea39836662203572 b774d92fc004c3d1ad465b7f5352c9f1c0bb2ff5Endocrine Abstracts00Endocrine Abstracts['C. Sulu', 'S. Şahin', 'H. Ozkaya', 'Pınar Kadıoğlu']2024-05-06T00:00:00https://www.semanticscholar.org/paper/b774d92fc004c3d1ad465b7f5352c9f1c0bb2ff5 9c218064cab662ec72bc75b9ce64aab71c2353afHuman Resource Development International310Human Resource Development International['Jia Wang', 'Roya Pashmforoosh']2024-05-06T00:00:00https://www.semanticscholar.org/paper/9c218064cab662ec72bc75b9ce64aab71c2353af 7497cd2907a0fc3d139730b8a35f7900eae72545Ultrasound Quarterly370Ultrasound Quarterly['Jie Zhang', 'Adrian Dawkins']2024-05-06T00:00:00https://www.semanticscholar.org/paper/7497cd2907a0fc3d139730b8a35f7900eae72545 829a216f22af7d34938c6400c339674d9e009ff2Cureus580Cureus['Natasha Hastings', 'Dany Samuel', 'Aariz N Ansari', 'Purvi Kaurani', 'Jenkin Winston J', 'Vaibhav S Bhandary', 'Prabin Gautam', 'Afsal Latheef Tayyil Purayil', 'Taimur Hassan', 'Mummareddi Dinesh Eshwar', 'Bala Sai Teja Nuthalapati', 'Jeevan Kumar Pothuri', 'Noor Ali']2024-05-06T00:00:00https://www.semanticscholar.org/paper/829a216f22af7d34938c6400c339674d9e009ff2 306c260f361ae288769ddd74e85236d470d9173bEndocrine Abstracts00Endocrine Abstracts['R. Penso', 'Natalia Perez', 'Pilar Matias', 'Antonio Torres', 'A. Sánchez-Pernaute', 'Candela Garcia', 'Irene Crespo', 'L. Herráiz', 'Mendoza Maria Elena', 'Rubio Herrera Miguel Angel']2024-05-06T00:00:00https://www.semanticscholar.org/paper/306c260f361ae288769ddd74e85236d470d9173b f72fda21feb03104b04c6ab161c03ec2ecdb1b84Endocrine Abstracts00Endocrine Abstracts['Şebnem Burhan', 'Yılmaz Mehmet Zahit', 'Benginur Sokmen', 'Uzman Dilara Tekin', 'Zeynep Karaali', 'M. Niyazoğlu', 'Esra Hatipoğlu']2024-05-06T00:00:00https://www.semanticscholar.org/paper/f72fda21feb03104b04c6ab161c03ec2ecdb1b84 7535411a43c08560e6f99b51561d04b24ddba780 This article identifies a new legal stakeholder in the data economy: the data crowd. A data crowd is a collective that: (i) is unorganised, non-deliberate and unable to form an agenda; (ii) relies on productive aggregation that creates an interdependency among participants; and (iii) is subjected to an external authority. Notable examples of crowds include users of a social network, users of a search engine and users of artificial intelligence-based applications. The law currently only protects users in the data economy as individuals, and in certain cases may address broad public concerns. However, it does not recognise the collective interests of the crowd of users and its unique vulnerability to platform power. The article presents and defends the crowd’s legal interests in a stable infrastructure for participation. It therefore reveals the need for a new approach to consumers’ rights in the data economy.Oxford Journal of Legal Studies00The article presents and defends the crowd’s legal interests in a stable infrastructure for participation and reveals the need for a new approach to consumers’ rights in the data economy.Oxford Journal of Legal Studies['Shelly Kreiczer-Levy']2024-05-06T00:00:00https://www.semanticscholar.org/paper/7535411a43c08560e6f99b51561d04b24ddba780 9c36ef621a9576c123194ffb8aa7c136f22f1c39Social media platforms such as Twitter, Reddit, and Sina Weibo play a crucial role in global communication but often encounter strict regulations in geopolitically sensitive regions. This situation has prompted users to ingeniously modify their way of communicating, frequently resorting to coded language in these regulated social media environments. This shift in communication is not merely a strategy to counteract regulation, but a vivid manifestation of language evolution, demonstrating how language naturally evolves under societal and technological pressures. Studying the evolution of language in regulated social media contexts is of significant importance for ensuring freedom of speech, optimizing content moderation, and advancing linguistic research. This paper proposes a multi-agent simulation framework using Large Language Models (LLMs) to explore the evolution of user language in regulated social media environments. The framework employs LLM-driven agents: supervisory agent who enforce dialogue supervision and participant agents who evolve their language strategies while engaging in conversation, simulating the evolution of communication styles under strict regulations aimed at evading social media regulation. The study evaluates the framework's effectiveness through a range of scenarios from abstract scenarios to real-world situations. Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses. Furthermore, it was found that LLM agents adopt different strategies for different scenarios.440Key findings indicate that LLMs are capable of simulating nuanced language dynamics and interactions in constrained settings, showing improvement in both evading supervision and information accuracy as evolution progresses.['Jinyu Cai', 'Jialong Li', 'Mingyue Zhang', 'Munan Li', 'Chen-Shu Wang', 'Kenji Tei']2024-05-05T00:00:00https://www.semanticscholar.org/paper/9c36ef621a9576c123194ffb8aa7c136f22f1c39 01ba9751006fe3e23f3ffebb271a6a306b7169bcGiven a time-series <inline-formula> <tex-math notation="LaTeX">$\{\textrm {z}^{k}\}_{\textrm {k=1}}^{\textrm {N}}$ </tex-math></inline-formula> of noisy measured outputs along a single trajectory of a dynamical system, the Identifying Regulation with Adversarial Surrogates (IRAS) algorithm aims to find a non-trivial first integral of the system, that is, a scalar function g such that <inline-formula> <tex-math notation="LaTeX">$ g\text {(}z^{i}\text {)} \approx g\text {(}z^{j}\text {)}$ </tex-math></inline-formula>, for all i, j. IRAS has been suggested recently and was used successfully in several learning tasks in models from biology and physics. Here, we give the first rigorous analysis of this algorithm in a specific setting. We assume that the observations admit a linear first integral and that they are contaminated by Gaussian noise. We show that in this case the IRAS iterations are closely related to the self-consistent-field (SCF) iterations for solving a generalized Rayleigh quotient minimization problem. Using this approach, we derive several sufficient conditions guaranteeing local convergence of IRAS to the linear first integral.IEEE Control Systems Letters240The first rigorous analysis of the IRAS algorithm is given, showing that in this case the IRAS iterations are closely related to the self-consistent-field (SCF) iterations for solving a generalized Rayleigh quotient minimization problem.IEEE Control Systems Letters['Ron Teichner', 'Ron Meir', 'Michael Margaliot']2024-05-05T00:00:00https://www.semanticscholar.org/paper/01ba9751006fe3e23f3ffebb271a6a306b7169bc be0406dbe78db43ff843ffdf7c151fbcb518f9f2In the contemporary landscape of online educa- tion, the proliferation of educational content on platforms like YouTube has opened new avenues for learners. However, the sheer volume of videos presents a challenge, requiring innovative solutions. This project takes a pioneering approach by developing a specialized AI assistant for educational YouTube lectures, lever- aging advanced language models such as Generative Pretrained Transformers (GPT). This project focuses on the development of an AI assistant tailored specifically for educational YouTube lectures and courses. The primary goal is to enhance the learning experience by leveraging the capabilities of GPT-based models to provide personalised and contextually relevant interactions for learners. By harnessing the power of natural language understanding, generation, and dialog systems, the AI assistant aims to assist learners in navigating, understanding, and engaging with educa- tional content more effectively. The ultimate goal is to bridge the gap between the abundance of online educational content and learners’ comprehension, thereby revolutionizing the educational landscape in the digital age. By providing a sophisticated AI-driven companion, this project aspires to empower learners, making the educational journey more accessible, engaging, and conducive to deeper learning experiences. Through the integration of cutting-edge technologies, it envisions a future where learners can unlock the full potential of online education with confidence and easeINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This project focuses on the development of an AI assistant tailored specifically for educational YouTube lectures and courses to enhance the learning experience by leveraging the capabilities of GPT-based models to provide personalised and contextually relevant interactions for learners.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Prof. Ajinkya Valanjoo']2024-05-05T00:00:00https://www.semanticscholar.org/paper/be0406dbe78db43ff843ffdf7c151fbcb518f9f2 2de1529358a065b9166c2857b25d01d0907bdbc2The project work is a practical experience of the knowledge one has. The documentation leads a way to the concept to present the thinking and the upgradation of various techniques into the project. This project entitled “Mental Health Analysis AI Chatbot” is a practical project based on some trends of computer science. Every day the world is searching new techniques in the field of computer science to upgrade human limitations into machines to get more and more accurate and meaningful data. More and more mental health issues such as depression are getting known and recognized by our society today. However, not all of them can receive appropriate treatment. There are many of us still facing the problem of getting the appropriate mental health services every day. We cannot deny the fact that not everyone can get mental healthcare services as they might face some difficulties such as financial problems. Therefore, we may look for new solutions to fix this mental health issue. This demand for solving this issue has led to the proposal of technology as a solution. Chatbot, also known as a conversational agent which can participate in the conversation might be considered one of the solutions too. By mimicking the conversation between human counselor and patient, it can provide counselor service to the patient at some point. However, to further improve the quality of the counselor service, the improvement of the chatbot has to be carried out. By using deep learning, this proposed chatbot can recognize the meaning of the conversation and give a relevant response.International Journal of Advanced Research in Science, Communication and Technology50This proposed chatbot can recognize the meaning of the conversation and give a relevant response and by using deep learning, can recognize the meaning of the conversation and give a relevant response.International Journal of Advanced Research in Science, Communication and Technology['Farzana Khan', 'Hassan Ansari', 'Khan Khalid', 'Singh Omkant']2024-05-05T00:00:00https://www.semanticscholar.org/paper/2de1529358a065b9166c2857b25d01d0907bdbc2 8451f3760a9ae914be644e09dbd03b6236ab0c36Artificial intelligence emerges as a powerful catalyst poised to reshape the global sustainability landscape by facilitating the attainment of Sustainable Development Goals (SDGs). This comprehensive Delphi study meticulously probes the insights of domain experts, shedding light on the strategic utilization of AI to advance these critical sustainability objectives. Employing rigorous statistical techniques, encompassing measures of central tendency and interquartile deviation, this research scrutinizes consensus dynamics among experts and elucidates potential variations in their viewpoints. The findings resoundingly convey experts’ collective positive perspective regarding AI’s pivotal role in propelling the SDGs forward. Through two iterative rounds of extensive discussions, a compelling consensus crystallizes—AI indeed exerts an overall positive impact, exemplified by a robust mean score of 78.8%. Intriguingly, distinct SDGs manifest varied propensities toward AI intervention, with Goals 6, 7, 8, 9, 11, 13, 14, and 15 basking in the radiance of highly positive impacts. Goals 1, 2, 3, 4, 5, 10, and 12 exhibit positive impact scores, indicating a juncture ripe for positive advancements. Meanwhile, Goal 16 and Goal 17 languish with neutral scores, signifying a juncture demanding nuanced deliberations about AI’s impact on peace, justice, and strong institutions as well as on partnerships for the goals, respectively. This paper underscores AI as a formidable instrument poised to address humanity’s most pressing challenges while harmonizing seamlessly with the overarching SDG objectives. It gracefully dovetails into established practices across pivotal domains such as health, education, and resilient infrastructures, amplifying the collective global endeavor to navigate the path toward a more sustainable future.Sustainability630This paper underscores AI as a formidable instrument poised to address humanity’s most pressing challenges while harmonizing seamlessly with the overarching SDG objectives, leveraging established practices across pivotal domains such as health, education, and resilient infrastructures.Sustainability['S. O. Ametepey', 'C. Aigbavboa', 'W. Thwala', 'Hutton Addy']2024-05-05T00:00:00https://www.semanticscholar.org/paper/8451f3760a9ae914be644e09dbd03b6236ab0c36 67ed26924013540d3a11da2d81478bf5b16f2ec7Increasingly artificial intelligence (AI) is employed by artists for creative purposes. At the same time, AI causes significant concerns among creative professionals in terms of copyright violations and possible job loss. To understand how it may be possible to (co)create with AI this article will enter into conversation with Indian artist Harshit Agrawal who is both a designer with Adobe and an artist who works with AI for creative purposes. Introducing the concept of the sociocultural archive comprising AI imaginations as they have featured in popular culture, this article suggests that when we seek to understand how we now live, work and create with AI in the ‘present’, we must also interrogate how this was once envisioned in the ‘past’. This will facilitate a more productive approach to AI for the ‘future’.European Journal of Cultural Studies00Introducing the concept of the sociocultural archive comprising AI imaginations as they have featured in popular culture, this article suggests that when the authors seek to understand how they now live, work and create with AI in the ‘present’, they must also interrogate how this was once envisioned in the ‘past’ to facilitate a more productive approach to AI for the ‘future’.European Journal of Cultural Studies['M. Baas']2024-05-05T00:00:00https://www.semanticscholar.org/paper/67ed26924013540d3a11da2d81478bf5b16f2ec7 df8bb43acbf82f63802f7cb2acc5ebc49e8e64baThe oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.Computer Science &amp; IT Research Journal00The role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector is explored and the principles and benefits of AI-driven predictive maintenance are examined, showcasing real-world examples of its successful implementation.Computer Science &amp; IT Research Journal['Dazok Donald Jambol', 'Oludayo Olatoye Sofoluwe', 'Ayemere Ukato', 'Obinna Joshua Ochulor']2024-05-05T00:00:00https://www.semanticscholar.org/paper/df8bb43acbf82f63802f7cb2acc5ebc49e8e64ba e9a6aacfe0a83319c5b53e5bad7a91be3f490facIn the contemporary landscape of cardiac healthcare, the integration of Artificial Intelligence (AI) into monitoring devices represents a groundbreaking shift towards enhancing patient outcomes and streamlining the management of heart health. This review paper delves into the burgeoning domain of AI-based devices designed for the meticulous monitoring of heart patients, elucidating the methodologies behind their operation, the breadth of their applications, and the palpable impact they have had on both clinical practices and patient experiences. Through a meticulous selection and analysis of current studies, we uncover the transformative capabilities of AI technologies in detecting and predicting heart-related anomalies, offering a personalized approach to cardiac care. The findings underscore the paramount importance of AI in facilitating early intervention, reducing the incidence of heart-related morbacies, and ultimately, charting a course towards a more responsive and adaptive healthcare ecosystem. The significance of this review lies not only in its comprehensive synthesis of existing research but also in its exploration of future potentials, highlighting the ongoing evolution of AI as a pivotal ally in the quest for improved heart health monitoring. This paper aims to contribute to the academic and medical discourse surrounding AI applications in healthcare, offering insights that may guide future innovations and research directions. Keywords--- AI-driven Cardiac Monitoring, Wearable Health Technology, Remote Heart Health Monitoring, Smart Health Devices, Cardiovascular Disease Management, Machine Learning in, Cardiology, Digital Health Solutions, Real-time Heart Health Analytics, Sensor Technology in Cardiac Care, Patient-Centric Heart Monitoring, AI-based Cardiac Diagnostics, Continuous Heart Rate Monitoring, Mobile Health Applications, Personalized Cardiac Care, Telemedicine in Cardiology.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This review paper delves into the burgeoning domain of AI-based devices designed for the meticulous monitoring of heart patients, elucidating the methodologies behind their operation, the breadth of their applications, and the palpable impact they have had on both clinical practices and patient experiences.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Naitik Anand']2024-05-05T00:00:00https://www.semanticscholar.org/paper/e9a6aacfe0a83319c5b53e5bad7a91be3f490fac 9bad3d0cbc43b6dd52087d3a6994d2733901fc96This research paper explores the impact of AI-powered predictive maintenance on industrial equipment. Predictive maintenance is crucial for minimizing downtime and optimizing asset performance. AI has made maintenance more proactive, efficient, and cost-effective. The paper reviews maintenance practices, the evolution of predictive techniques, and AI integration. Through literature review and case studies, it examines efficacy, challenges, and implications of AI-powered maintenance. Findings highlight benefits such as enhanced efficiency, cost reduction, and extended equipment lifespan. Challenges include data quality, model accuracy, and organizational readiness. The paper concludes with recommendations for practitioners and policymakers, emphasizing AI's potential to revolutionize maintenance and foster a sustainable future. Keywords: Predictive maintenance, AI-powered maintenance, Industrial equipment, Maintenance efficiency, Asset performance optimization, Data qualityINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Aman Mani']2024-05-05T00:00:00https://www.semanticscholar.org/paper/9bad3d0cbc43b6dd52087d3a6994d2733901fc96 19e5ca8f1e025e91f21c34bc77da7d6d532563ebShifting the focus from principles to practical implementation, responsible artificial intelligence (AI) has garnered considerable attention across academia, industry, and society at large. Despite being in its nascent stages, this emerging field grapples with nebulous concepts and intricate knowledge frameworks. By analyzing three prevailing concepts - explainable AI, trustworthy AI, and ethical AI, this study defined responsible AI and identified its core principles. Methodologically, this study successfully demonstrated the implementation of leveraging AI's capabilities into bibliometrics for enhanced knowledge discovery and the cross-validation of experimentally examined models with domain insights. Empirically, this study investigated 17,799 research articles contributed by the AI community since 2015. This involves recognizing key technological players and their relationships, unveiling the topical landscape and hierarchy of responsible AI, charting its evolution, and elucidating the interplay between the responsibility principles and primary AI techniques. An analysis of a core cohort comprising 380 articles from multiple disciplines captures the most recent advancements in responsible AI. As one of the pioneering bibliometric studies dedicated to exploring responsible AI, this study will provide comprehensive macro-level insights, enhancing the understanding of responsible AI while furnishing valuable knowledge support for AI regulation and governance initiatives.530This study defined responsible AI and identified its core principles, and successfully demonstrated the implementation of leveraging AI's capabilities into bibliometrics for enhanced knowledge discovery and the cross-validation of experimentally examined models with domain insights.['Yi Zhang', 'Mengjia Wu', 'Guangquan Zhang', 'Jie Lu']2024-05-05T00:00:00https://www.semanticscholar.org/paper/19e5ca8f1e025e91f21c34bc77da7d6d532563eb 5952127d603be982b06a1491a19cb210aaaf30e1International journal of simulation: systems, science &amp; technology00International journal of simulation: systems, science &amp; technology['Qiang Shen']2024-05-05T00:00:00https://www.semanticscholar.org/paper/5952127d603be982b06a1491a19cb210aaaf30e1 3fd82afd1d6558a5d490ed450359100d9b319c82International journal of simulation: systems, science &amp; technology00International journal of simulation: systems, science &amp; technology['Tim Bashford']2024-05-05T00:00:00https://www.semanticscholar.org/paper/3fd82afd1d6558a5d490ed450359100d9b319c82 54ec2e8443c32081450d68c372aa74a993cd793dInternational journal of simulation: systems, science &amp; technology00International journal of simulation: systems, science &amp; technology['Simon Thorne']2024-05-05T00:00:00https://www.semanticscholar.org/paper/54ec2e8443c32081450d68c372aa74a993cd793d 9ac3cbf5b900c4c305f27b4f4e68c2394f9ac111International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Archana Balkrishna Yadav']2024-05-05T00:00:00https://www.semanticscholar.org/paper/9ac3cbf5b900c4c305f27b4f4e68c2394f9ac111 a4bc056fb00731fb39806795bab474c23484c1a5In today's digital landscape, fortifying cyber security is of utmost importance. This research introduces an innovative strategy that relies on transfer learning within deep neural networks to combat evolving threats, with a specific focus on phishing URLs and Malicious links, a major vector for cyber-attacks. We meticulously curate a diverse dataset of phishing and legitimate URLs, subjecting it to rigorous pre-processing. Departing from traditional methods, we leverage transfer learning to extract intricate patterns within URLs and their content. Our unique approach integrates transfer learning into a hybrid model, combining deep learning techniques with the power of transfer learning. This hybrid model employs soft and hard voting to optimize phishing threat detection accuracy and efficiency. We fine-tune our models with advanced feature selection and hyper parameter optimization, using rigorous evaluation metrics to assess performanceInternational Journal For Multidisciplinary Research110This research introduces an innovative strategy that relies on transfer learning within deep neural networks to combat evolving threats, with a specific focus on phishing URLs and Malicious links, a major vector for cyber-attacks.International Journal For Multidisciplinary Research['E. Satya Vinayak', 'Mr. K Anbuthiruvarangan', 'Kudupudi Chakradhar', 'Anbudoss P']2024-05-05T00:00:00https://www.semanticscholar.org/paper/a4bc056fb00731fb39806795bab474c23484c1a5 eb9b1072e229675f328860a181fc4073e638a75cThe decentralized anonymous cryptocurrency is a new kind of technology that can be used for many purposes such as transferring money and investing. However, they do not have a legal entity that is in charge monitoring its uses. Its extraordinary rise raises critical questions such as, should we regulate it or ban it? Since its purposes have been converted from an anonymous payment system to a tool that is used in illegal actions and undermining financial standards. This paper seeks CC regulation options. Plus, it attempts to lay out the various risks they pose and benefits they bring with the technology they use (blockchain). The objective is to investigate which approach will be more reasonable for the country’s conditions. The regulators will try to convince CC service providers to obey rules and operate under official standards, while banners restrict the new instrument’s integration with the financial system. The study relied on the descriptive approach to achieve its objectives. The recent literature and publications of the most important related bodies around the world were reviewed. Findings reveal that it is too early for CCs to be considered legal tender. Moreover, both approaches could be adopted according to the country’s conditions. Plus, alternatives may have their say. Some suggestions are made for local agencies and investors.Humanities Journal of University of Zakho550Humanities Journal of University of Zakho['Omar Ibrahim']2024-05-05T00:00:00https://www.semanticscholar.org/paper/eb9b1072e229675f328860a181fc4073e638a75c 703d9b8f607b3e65f5862d4820c9eb0e5ce895aaThis paper discusses three statements: Enterprise AI strategy must be linked with the existing business strategy of the organization, Enterprise AI must be a virtual strategist of the organization, and Enterprise AI must be a virtual strategy manager for organizational performance. Based on the research on these statements, this paper will introduce the conceptual hierarchical framework titled Strategic Enterprise Artificial Intelligence (SEAI) consisting of four levels: Disconnected Enterprise AI, Linked Enterprise AI, Strategist Enterprise AI, and Integrative Enterprise AI. Disconnected Enterprise AI represents the lowest level, while Integrative Enterprise AI is the highest. This framework, with its title, can be introduced as a new field of study. If the corporate sector uses this framework tactfully, it will help the organizations to strategize Enterprise AI to achieve business results.International Journal of Business &amp; Management Studies00The conceptual hierarchical framework titled Strategic Enterprise Artificial Intelligence (SEAI) consisting of four levels: Disconnected Enterprise AI, Linked Enterprise AI, Strategist Enterprise AI, and Integrative Enterprise AI is introduced.International Journal of Business &amp; Management Studies['Jibran Bashir']2024-05-05T00:00:00https://www.semanticscholar.org/paper/703d9b8f607b3e65f5862d4820c9eb0e5ce895aa 1a0296133b2e1320e93eb06b3ea5fc12caf93bb8The contemporary healthcare landscape faces unprecedented challenges, ranging from data fragmentation within health information systems to the need for timely and accurate diagnostics. This research explores the transformative potential of Artificial Intelligence (AI) in enhancing the Kosovo Health Information System (HIS). By leveraging the capabilities of AI, we aim to address existing limitations in data interoperability, predictive analytics, and personalized healthcare. The study incorporates a comprehensive literature review, methodological data collection, and analysis of the current state of the Kosovo HIS. Drawing inspiration from successful global implementations, we delve into the possibilities of AI applications in diagnosis, treatment personalization, and population health management. The research also examines ongoing initiatives and collaborations aimed at integrating AI into the Kosovo HIS. Through a critical assessment of technical challenges and ethical considerations, the paper provides insights into the opportunities and hurdles associated with the implementation of AI in healthcare. Ultimately, this research contributes to the discourse on the future prospects of healthcare in Kosovo, highlighting the potential long-term impacts of AI integration and offering recommendations for advancing the country's health information infrastructure.   Received: 5 February 2024 / Accepted: 23 April 2024 / Published: 5 May 2024Academic Journal of Interdisciplinary Studies230This research explores the transformative potential of Artificial Intelligence in enhancing the Kosovo Health Information System (HIS) by leveraging the capabilities of AI to address existing limitations in data interoperability, predictive analytics, and personalized healthcare.Academic Journal of Interdisciplinary Studies['A. Loku', 'Enver Malsia']2024-05-05T00:00:00https://www.semanticscholar.org/paper/1a0296133b2e1320e93eb06b3ea5fc12caf93bb8 3e9004633bca0f97d0b77df88f34eda0fa658ba7Financial education is considered an essential skill that enables students to effectively manage their economic resources. However, it is still at an embryonic stage in several countries, and the ability of young people to apply financial education in life contexts has not substantially improved. To address these challenges, this study proposes and evaluates a novel approach to improve financial education among high school students by using generative artificial intelligence tools. Following a quasi-experimental design, we randomly assigned a total of 110 high school students to two conditions: an experimental group that participated in learning experiences under the financial education approach using artificial intelligence tools such as ChatGPT and a control group that engaged in the same learning activities following the traditional teaching approach. The results of the Mann-Whitney U test indicate that there are significant differences between the scores of the experimental group and the control group (p=1.64E-19<0.05,  group experimental=82.92>  group control=28.08), demonstrating the effectiveness of the generative artificial intelligence approach in enhancing financial education compared to the traditional approach. Furthermore, the Kruskal-Wallis test revealed a significance p-value of less than 0.05 (p=0.000935<0.05), indicating that the use of AI significantly improves the five indicators of financial education according to the post-test evaluation phase of the experimental group. On the other hand, Dunn's test for multiple comparisons reveals a significantly greater influence of the innovative approach using artificial intelligence in the following dimensions: Financial Planning Actions, Financial Analysis Actions, Financial Behavior, and Strategic Expense Management; however, the Investment Initiative dimension shows a significantly lesser influence ( =103.46). The implementation of artificial intelligence in the classroom to promote student learning is favored by innovative approaches to pedagogical action. In this way, from the classroom, we can address the lack of skills and financial education in our students.     Received: 11 December 2023 / Accepted: 19 March 2024 / Published: 5 May 2024Academic Journal of Interdisciplinary Studies440A novel approach to improve financial education among high school students by using generative artificial intelligence tools using ChatGPT and Dunn's test for multiple comparisons reveals a significantly greater influence of the innovative approach using artificial intelligence in the following dimensions.Academic Journal of Interdisciplinary Studies['Luigi Italo Villena Zapata', 'Benicio Gonzalo Acosta Enriquez', 'Jose Carlos Montes Ninaquispe', 'Jonathan Alexander Ruiz Carrillo', 'Lorena Stefany Villarreal Gonzales', 'Jenny Alva Morales', 'Manuel Amadeo Sevilla Angelaths']2024-05-05T00:00:00https://www.semanticscholar.org/paper/3e9004633bca0f97d0b77df88f34eda0fa658ba7 2ea82de6b7b7822e3cb8896c0f4c7774e10028a7This work explores the integration and effectiveness of artificial intelligence in improving the security of critical energy infrastructure, highlighting its potential to transform cybersecurity practices in the sector. The ability of artificial intelligence solutions to detect and respond to cyber threats in critical energy infrastructure environments was evaluated through a methodology that combines empirical analysis and artificial intelligence modeling. The results indicate a significant increase in the threat detection rate, reaching 98%, and a reduction in incident response time by more than 70%, demonstrating the effectiveness of artificial intelligence in identifying and mitigating cyber risks quickly and accurately. In addition, implementing machine learning algorithms has allowed for the early prediction of failures and cyber-attacks, significantly improving proactivity and security management in energy infrastructure. This study highlights the importance of integrating artificial intelligence into energy infrastructure security strategies, proposing a paradigmatic change in cybersecurity management that increases operational efficiency and strengthens the resilience and sustainability of the energy sector against cyber threats.Systems450A significant increase in the threat detection rate, reaching 98%, and a reduction in incident response time by more than 70%, demonstrate the effectiveness of artificial intelligence in identifying and mitigating cyber risks quickly and accurately.Systems['Jaime Govea', 'Walter Gaibor-Naranjo', 'W. Villegas-Ch.']2024-05-05T00:00:00https://www.semanticscholar.org/paper/2ea82de6b7b7822e3cb8896c0f4c7774e10028a7 fd9e8250b01146200eb26c75232f53ed6ba4151eWith the rapid development of social economy and information technology, safety protection in daily life has become more and more important. Although the awareness of safety has increased, the children's safety is still not paid enough attention. Children still may suffer accidental injuries, especially in developing countries. Children spend most of time at school in a day. Thus, it has become an emergent challenge to guarantee children's safety at school. In order handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) safety protection system for preschool education. The AIoT safety protection system consists of three parts: camera, Raspberry Pi, and monitoring computer. The camera captures the images of classroom scene during preschool education. The Raspberry Pi analyzes the images from camera to determine the unsafe behaviors of children, in which a YOLOv8 model is deployed. The monitoring computer receives the alarms from Raspberry Pi. The camera, Raspberry Pi, and monitoring computer are connected using wireless sensor network. The experiments show the behavior recognition model can correctly identify most of dangerous behaviors of children in classroom. The simulation result demonstrates the AIoT safety protection system can find the dangerous behaviors in time.Internet Technology Letters110The experiments show the behavior recognition model can correctly identify most of dangerous behaviors of children in classroom and the simulation result demonstrates the AIoT safety protection system can find the dangerous behaviors in time.Internet Technology Letters['Yun Tan', 'Shuangyuan Mo']2024-05-05T00:00:00https://www.semanticscholar.org/paper/fd9e8250b01146200eb26c75232f53ed6ba4151e 2ce54ef200d816c1c776b0bdde2ce91414a524e7International Journal of Artificial Intelligence in Education400International Journal of Artificial Intelligence in Education['Helen Zhang', 'Anthony Perry', 'Irene Lee']2024-05-05T00:00:00https://www.semanticscholar.org/paper/2ce54ef200d816c1c776b0bdde2ce91414a524e7 5077cafda7edcb67b9f71f636f909dae23354644Educational Administration Theory and Practices00Educational Administration Theory and Practices['Dr. Ziaul Islam', 'Dr. Ashfaque Ahmed', 'Dr. Mohammed H Alfify', 'Nida Riyaz']2024-05-05T00:00:00https://www.semanticscholar.org/paper/5077cafda7edcb67b9f71f636f909dae23354644 5a7e03f6c1e089d3549b80317098d5ec3a8fb62cCureus1980Cureus['Diptiman Medhi', 'Sushmitha Reddy Kamidi', 'Kannuru Paparaju Mamatha Sree', 'Shifa Shaikh', 'Shanida Rasheed', 'Abdul Hakeem Thengu Murichathil', 'Zahra Nazir']2024-05-05T00:00:00https://www.semanticscholar.org/paper/5a7e03f6c1e089d3549b80317098d5ec3a8fb62c 3b220476ca814e33af0fd2b982276c870c26db61AbstractCureus260Cureus['Selkin Yilmaz Muluk']2024-05-05T00:00:00https://www.semanticscholar.org/paper/3b220476ca814e33af0fd2b982276c870c26db61 82e5bfdc5ba146a46211e8cf82737516d9e345baNeural computing & applications (Print)560Neural Computing and Applications['S. K. Bhagat', 'Tiyasha Tiyasha', 'A. H. Shather', 'M. Jamei', 'Adarsh Kumar', 'Zainab Al-Khafaji', 'L. Goliatt', 'Shafik S. Shafik', 'O. A. Alawi', 'ZaherMundher Yaseen']2024-05-05T00:00:00https://www.semanticscholar.org/paper/82e5bfdc5ba146a46211e8cf82737516d9e345ba 05721b104f7649aa6ccfd7aa51bb5bcc1d0bea03This research article investigates challenges and innovative procurement approaches within the Service Sector. Specifically, through the lens of ten diverse global companies within the service sector, where traditional processes are being transformed through procurement software, yet complexity in implementing procurement software is ever on the rise with the need for customization, integration, user adoption and compliance with regulation and security. Add to this, the cost implications and constant improvements in technology and a picture of the challenges faced is painted. This study examines the multifaceted challenges faced by companies operating in the service sector and how they implement innovative strategies to overcome challenges. The study looks at the challenges, solutions that have been designed and implemented and how successful they have been. A detailed analysis of ten individual cases, utilities transformers to entertainment, and impact of collaborative strategies and organizational culture on how successful these have been presented. The results provide for a richer understanding of procurement challenges and innovative approaches that can be applied and provide insight into success and failure and reasons why, with technology integration, strategy collaboration and organizational cultures providing keys to success. Additionally, gaps in the literature that lead to areas of further study are presented. Journal of Electrical Systems510This study examines the multifaceted challenges faced by companies operating in the service sector and how they implement innovative strategies to overcome challenges, with technology integration, strategy collaboration and organizational cultures providing keys to success.Journal of Electrical Systems['Tara Prasad Tripathy Prof', 'Dr. J. K Tandon']2024-05-04T00:00:00https://www.semanticscholar.org/paper/05721b104f7649aa6ccfd7aa51bb5bcc1d0bea03 434b32b18d074f1e13539762c7b388cdbd2122fdHealth technology70Health and Technology['Leandro Pecchia', 'A. Maccaro', 'M. A. G. Matarrese', 'F. Folkvord', 'G. Fico']2024-05-04T00:00:00https://www.semanticscholar.org/paper/434b32b18d074f1e13539762c7b388cdbd2122fd 5652e8541d9c1e6eee5acfa36664452ab9447880The article presents the results of the analysis of trends in global agricultural production, which determine the importance and necessity of state regulation of agricultural development at the national level (the need to overcome the impact of external challenges to the food and economic security of the country; the emergence of new trade barriers; the importance of sustainable development of the agroindustrial complex and agriculture for social well-being and preservation of the employment potential of the rural population in the agrarian sphere; the paramount scientific and technological development of the agricultural sector). It has been established that agriculture in Belarus is one of the national priorities and is one of the main components of the strategy of sustainable socio-economic development of the Belarusian state. The directions of improving the approaches of state regulation and support are outlined, which include: economic stimulation of efficiency improvement; regulation of food markets, support of export-oriented industries; targeted programs to promote products with high added value; fair pricing of agricultural products and means of production; development of market infrastructure; scientific and information support of agricultural producers’ activities.AGRARIAN ECONOMY50Agrarian Economics['Светлана Кондратенко', 'Надежда Котковец', 'S. Kondratenko', 'N. Kotkovets']2024-05-04T00:00:00https://www.semanticscholar.org/paper/5652e8541d9c1e6eee5acfa36664452ab9447880 1e54904d03912b6d669f9070d73e83d9e99b28a5 The challenge of sustainability lies in achieving a balance between satisfying present needs and protecting resources for future generations with an emphasis on its three pillars—environmental, social and governance. This study explored sustainable development encompassing environmental, social and governance aspects along with sustainability reporting through various sustainability frameworks. A systematic review of literature for the period 2010–23 on major worldwide sustainability frameworks was conducted, by offering insights into enhancing reporting mechanisms for a sustainable future. Secondary data related to sustainability reports were obtained from the Sustainability Accounting Standards Board and International Integrated Reporting Council, which helped in examining sector and year variations across countries. The results reflected that mandatory sustainability disclosures help to meet the United Nations Sustainable Development Goals and global sustainability frameworks help to set standards, disseminate information and promote transparency. Collaboration of investment, company action and sustainability organizations can lead to a sustainable global economy. The adoption of sustainability reporting can help organizations by fostering a proper understanding of sustainability practices, improving transparency and identifying potential business opportunities in sectors with lower sustainability. The paper provided insights into sustainability reporting published across various countries in both advanced as well as emerging and developing economies. The analysis showed which sectors and time periods have had the most sustainability reports and which areas needed to be targeted for action to advance sustainable development.Clean Energy550Clean Energy['Jovita Shanta Job', 'Shivi Khanna']2024-05-04T00:00:00https://www.semanticscholar.org/paper/1e54904d03912b6d669f9070d73e83d9e99b28a5 a80af29efe303f72149f277c660907300959584cNeuroscience and Biobehavioral Reviews3640Neuroscience & Biobehavioral Reviews['Kimberly S. Bress', 'C. Cascio']2024-05-04T00:00:00https://www.semanticscholar.org/paper/a80af29efe303f72149f277c660907300959584c 7582b204fdb51451e407a8e8b1866be1dd4fcf05The objective of this paper is to provide an evaluation of project management skills and competence in AI-driven environments as an essential scope due to their pivotal role in producing effective outcomes in the fast-paced world of rapidly evolving technology. This study employs a cross-sectional research design and qualitative survey methodology to examine project management in the context of AI integration. The study involved a broad base, which includes players from various sectors like technology, finance, health, and manufacturing, so its findings and recommendations are all-encompassing. This study achieves this by utilizing datasets obtained from industry-leading AI companies, academic research institutions, and governmental agencies. These datasets comprise project management metrics, AI implementation case studies, and surveys conducted among project managers and stakeholders in AI-driven industries in addition to literature sources, using sophisticated statistical techniques. The findings show and present the changes that the practice of project management faces in the era of AI and provide the most helpful guidance for project managers, stakeholders, and organisations that strive to cope with this dynamic and changing environment. The research result shows that by emphasising skill-based development programmes, cultivating an innovation- friendly culture, and adopting AI-driven technologies, organisations can be at the forefront of technological growth, gaining a competitive advantage in the highly dynamic business environment. The research shows that in the future, the enhanced use of AI technologies will keep changing the project management landscape. This research strengthens the theoretical underpinnings of project management in AI-powered projects and ensures the enhancement of project management's actual efficacy in response to technological advancements. This will aid project managers deliver the most important skills necessary for effective project management in AI-driven environments.International Journal of Innovative Science and Research Technology401The research shows that by emphasising skill-based development programmes, cultivating an innovation- friendly culture, and adopting AI-driven technologies, organisations can be at the forefront of technological growth, gaining a competitive advantage in the highly dynamic business environment.International Journal of Innovative Science and Research Technology (IJISRT)['David Oyekunle', 'Joseph Asante Darkwah', 'Lateef Damilare Olusesi']2024-05-04T00:00:00https://www.semanticscholar.org/paper/7582b204fdb51451e407a8e8b1866be1dd4fcf05 8987d63531b57b039018876538f98781e610bfbfEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery301The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms, and might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery['L. Vaira', 'J. Lechien', 'V. Abbate', 'Fabiana Allevi', 'Giovanni Audino', 'G. Beltramini', 'Michela Bergonzani', 'Paolo Boscolo-Rizzo', 'G. Califano', 'Giovanni Cammaroto', 'C. Chiesa-Estomba', 'U. Committeri', 'Salvatore Crimi', 'Nicholas R Curran', 'Francesco Di Bello', 'Arianna di Stadio', 'Andrea Frosolini', 'Guido Gabriele', 'Isabelle M. Gengler', 'Fabio Lonardi', 'F. Maglitto', 'M. Mayo-Yáñez', 'M. Petrocelli', 'Resi Pucci', 'A. Saibene', 'G. Saponaro', 'A. Tel', 'Franco Trabalzini', 'E. Trecca', 'V. Vellone', 'G. Salzano', 'G. De Riu']2024-05-04T00:00:00https://www.semanticscholar.org/paper/8987d63531b57b039018876538f98781e610bfbf e872b2470089e9149ac93bac7e8cca170bdfcaa0Minds Mach.730It is argued that looking beyond bias and analyzing AI tools for online discourses through a sociotechnical frame reveals how they interact with social hierarchies and inequalities, reproducing patterns of exclusion.Minds Mach.['Jonas Aaron Carstens', 'Dennis Friess']2024-05-04T00:00:00https://www.semanticscholar.org/paper/e872b2470089e9149ac93bac7e8cca170bdfcaa0 3cbd0a0dc711afb7944b146ecb10435e9662705cTo accomplish a sustainable energy future, this article researches the consolidation of computerized reasoning (AI) into request reaction components inside smart grids. While considering the joining of sustainable power sources and expanding energy utilization, current energy frameworks depend altogether on load the executives and continuous energy request reaction. Conventional methodologies battle with the intricacies of dynamic energy situations. Results from the examination feature the worth of AI and ML for enhancing energy use. Versatile learning for energy proficiency, exact interest determining, continuous observing, and sustainable power source mix are completely made conceivable by these innovations. An intensive vision for the Smart Lattice is introduced, underscoring financial matters, proficiency, wellbeing, ecological obligation, security, and reliability. We check out at the benefits and downsides of incorporating energy stockpiling gadgets and the job of circulated framework insight. The determination gives a careful future vision to AI-enhanced request reaction in smart grids by featuring research issues connected with self-learning frameworks, complete robotization, self-recuperating grids, fitting and-play advances, network safety, and labor force improvement.Journal of Electrical Systems300The determination gives a careful future vision to AI-enhanced request reaction in smart grids by featuring research issues connected with self-learning frameworks, complete robotization, self-recuperating grids, fitting and-play advances, network safety, and labor force improvement.Journal of Electrical Systems['Hassan Hadi', 'M. A. Al-Fatlawi', 'Qaeser Mohsen Khayoon', 'Ali Jasim Albhadly', 'Ahmed Kamal']2024-05-04T00:00:00https://www.semanticscholar.org/paper/3cbd0a0dc711afb7944b146ecb10435e9662705c 65cb00f3974a80b31107b3bbec5ea1ca90a31172Storytelling has always been a powerful tool to convey information, connect with people, and make an impact. The aim of our project is to generate story by using the concept of AI and Machine learning .AI generated storytelling has apotential to transform a variety of industries, for marketing and education to entertainment and beyond. By utilizing the capabilities of ML and NLP, stories generated by artificial intelligence can be tailored to meet the specific need of each industry like personalized narratives, interactive and dynamic storytelling, multimodal storytelling, cross domain storytelling. The introduction of GEN-AI can lead to both opportunities and potential challenges among different creator communities which required collaboration from both academic and industry. This topic gathers and analyse vast amount of data from various sources including literature, movies, and social media we are using natural language processing which understand and process human language enabling them to communicate with the user effectively. To use the facial recognition technology, we are using DALL-E, OpenCV which uses model like SVM, decision tree, and knn etc. The objective of the research is to investigate the power of AI software and automating the process of story generation. This exploration involves utilizing various AI models, such as natural language generation (NLG) algorithms, deep learning architectures, and reinforcement learning frameworks, to create compelling and coherent stories across different genres and themes. We're showcasing an innovative system that empowers users to effortlessly create short stories accompanied by relevant images with minimal input. Our final project is accessible through a user-friendly web page interface, allowing individuals to craft their narratives seamlessly. Key Words: Storytelling, Artificial intelligence, Natural language processing, Ai system, Narratives, Automatic, DALL -E, OpenCV.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This exploration involves utilizing various AI models, such as natural language generation (NLG) algorithms, deep learning architectures, and reinforcement learning frameworks, to create compelling and coherent stories across different genres and themes.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Mr. Anurag Golwalker,']2024-05-04T00:00:00https://www.semanticscholar.org/paper/65cb00f3974a80b31107b3bbec5ea1ca90a31172 9b119d28aa671c6c38191cae245d4c507a21211aPurpose – Artificial intelligence (AI) adoption is critical in the age of digital technology. This review article aims to evaluate the literature on AI in the hospitality industry. Method – A narrative synthesis was used in this review article. Moreover, the literature was reviewed systematically to explore AI in the hospitality industry. The literature and information were obtained from various books and research articles on EBSCO, Google Scholar, Scopus, Web of Science, and Science Direct. The inclusion criteria were studies that clearly defined AI in all aspects of the hospitality industry, were published and written in English and were peer-reviewed. Content analysis was employed. Results – The use of AI is a strategic and critical factor in economic development. Furthermore, AI technologies are increasingly being used as digital assistants. They help businesses in the hospitality industry in a variety of ways, including improving customer service, expanding operational capability, and lowering costs. However, there are some risks associated with AI advancements, such as job loss in low-tech sectors, loss of control due to robot autonomy, and safety, security, and privacy concerns. Conclusion – AI technologies have both positive and negative effects on the workforce and job employment in the hospitality industry. Recommendations – The recommendation is to consider a quantitative study regarding AI adoption in the hospitality industry or other sectors. Also, a qualitative approach could give a clear view of insight results for further study. Research Implications – This review article contributed to the existing literature on AI adoption in the hospitality industry. Hence, it could be used to guide future research on AI adoption in the hospitality industry. It may also aid academics in broadening their research by incorporating more potential elements. Practical Implications – This review article could lead to a better understanding of AI adoption in the hospitality industry. Moreover, it may assist business owners, managers, and marketers in the hospitality industry or any sector to achieve and enhance high business performance by implementing appropriate strategies to meet the needs and expectations of both customers and employees through the use of AI.International Journal For Multidisciplinary Research390Artificial intelligence technologies have both positive and negative effects on the workforce and job employment in the hospitality industry, and may assist business owners, managers, and marketers in the hospitality industry or any sector to achieve and enhance high business performance.International Journal For Multidisciplinary Research['Akash Indora']2024-05-04T00:00:00https://www.semanticscholar.org/paper/9b119d28aa671c6c38191cae245d4c507a21211a 7065e7645dab54e89b85058d7811e12aaf5ec5e5This research paper investigates the integration of artificial intelligence (AI) into web development for online pharmacy stores using the Flask framework with Python programming language. Authors Yogesh Raikwar and Gaurav Rajak explore the potential benefits and challenges associated with incorporating AI technologies in the context of pharmacy e-commerce platforms. The study aims to develop a prototype system capable of providing intelligent recommendations, medication reminders, and virtual consultations to enhance user experience and streamline operations in online pharmacy services. Key Words: AI, E-COMMERCE, HIPAA, GDPRINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper investigates the integration of artificial intelligence into web development for online pharmacy stores using the Flask framework with Python programming language and aims to develop a prototype system capable of providing intelligent recommendations, medication reminders, and virtual consultations.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Sadhana Pandey,']2024-05-04T00:00:00https://www.semanticscholar.org/paper/7065e7645dab54e89b85058d7811e12aaf5ec5e5 ea04cc420b1cc2daf06f77c60f11f15d5f3dc058Artificial intelligence (AI) is expected to take a large part in the domain of software development, bringing a lot of innovative devices and methods that can possibly change how applications are made and distributed. However, various tools like machine learning, natural language processing, and computer vision help AI to be convincingly integrated into all phases of the software development life cycle, opening up new development opportunities for designers to improve solutions, streamline processes, and enhance movement. In software development AI is helping out with a lot of significant tasks, including code generation and bug revealing, automatic testing and optimization of the performance. AI controls instruments to examine huge data sets containing patterns and provide the engineers with intelligent advice to help them make more informed decisions. On the other hand, this information helps engineers with the development process with higher efficiency and accuracy.International Journal on Recent and Innovation Trends in Computing and Communication00In software development AI is helping out with a lot of significant tasks, including code generation and bug revealing, automatic testing and optimization of the performance, and helps engineers with the development process with higher efficiency and accuracy.International Journal on Recent and Innovation Trends in Computing and Communication['Nilesh Charankar']2024-05-04T00:00:00https://www.semanticscholar.org/paper/ea04cc420b1cc2daf06f77c60f11f15d5f3dc058 1275b560c927583c3e2c6c4c4e8e3bbb6a4c3f6fCycling is ubiquitous as a means of transportation and a common hobby to get active outdoors. However, the recent uprise in cycling accidents has led cyclists to feel unsafe on the roads. Copilot by Velo.ai is an AI-based bicycle technology that addresses this problem by collecting spatial and audio-visual data and using AI, preventing accidents before they occur by predicting oncoming vehicles or pedestrians. Copilot is novel in adapting sensory, photography, and AI technology from autonomous vehicles to the cycling industry. Although Copilot is cutting-edge and ground-breaking for cycling safety, it is not accessible to everyone. Barriers to Copilot include disability, unemployment/job security, and employment/working conditions. These social determinants of health (SDoH) interact with each other, as well as intersectional factors, to influence individual access to Copilot, which notably differs depending on the status of one’s country of residence (i.e., developed versus developing). Recent peer-reviewed research confirms the efficacy of the technology used in Copilot and Copilot itself to prevent cycling accidents resulting in injury/death. As technology evolves and machine learning is used to address current limitations, the future of Copilot is promising.Aging and (Geron) Technology00Copilot is an AI-based bicycle technology that addresses this problem by collecting spatial and audio-visual data and using AI, preventing accidents before they occur by predicting oncoming vehicles or pedestrians.Aging and (Geron) Technology['Omar Hamed', 'Elizabeth Clarke', 'Emily Sunn', 'Alex Lu-Sullivan']2024-05-04T00:00:00https://www.semanticscholar.org/paper/1275b560c927583c3e2c6c4c4e8e3bbb6a4c3f6f cb5c3c28599a00fb1c72ee47bded1baa1338e19aThis review paper examines the pivotal role of AI-driven predictive analytics in optimizing supply chain operations within the IT industry. By leveraging machine learning, deep learning, and neural networks, predictive analytics can significantly enhance demand forecasting, inventory management, supplier selection, and risk management. Despite its potential to revolutionize supply chains, the integration of AI faces challenges, including data quality, the need for skilled personnel, and organizational resistance. Strategic implementation approaches are discussed, emphasizing robust data infrastructure, stakeholder engagement, and continuous innovation. This paper contributes to the academic discourse by highlighting AI's economic and social implications in supply chains and suggesting directions for future research. It is a comprehensive guide for practitioners and academics navigating the complexities of AI-driven predictive analytics in supply chain optimization. Keywords:  AI-driven Predictive Analytics, Supply Chain Optimization, IT Industry, Machine Learning, Strategic Implementation.International Journal of Management &amp; Entrepreneurship Research00This review paper examines the pivotal role of AI-driven predictive analytics in optimizing supply chain operations within the IT industry and offers a comprehensive guide for practitioners and academics navigating the complexities of AI-driven predictive analytics in supply chain optimization.International Journal of Management &amp; Entrepreneurship Research['Godwin Nzeako', 'Michael Oladipo Akinsanya', 'Oladapo Adeboye Popoola', 'Excel G Chukwurah', 'Chukwuekem David Okeke']2024-05-04T00:00:00https://www.semanticscholar.org/paper/cb5c3c28599a00fb1c72ee47bded1baa1338e19a 66d9197a932b99bcd31e00d871abe35a77b898ccThis integrative literature review analyzes how AI, specifically Machine Learning (ML) and Large Language Models (LLMs), is used in Moroccan education. The study emphasizes the significance of modernizing traditional educational techniques. It investigates how artificial intelligence may personalize learning experiences, minimize educational disparities, and foster a culturally diverse and technologically sophisticated learning environment. The ILR uses a conceptual framework that highlights customized learning, equitable education, technological innovation, and a systematic methodology that incorporates extensive literature synthesis. The research process thoroughly examines multiple academic sources, including peer-reviewed articles, books, conference papers, and reports. The ILR identifies relevant patterns, barriers, and opportunities associated with integrating AI in Moroccan education by carefully studying and synthesizing data. The findings demonstrate the enormous potential of ML and LLMs in revolutionizing teaching methods, encouraging active student participation, and closing educational inequalities across Morocco. The paper finishes by underlining the need to incorporate AI into educational practice and identifying areas for future research. It emphasizes investigating how AI may help Morocco and other countries modernize their education systems.International Journal For Multidisciplinary Research840The findings demonstrate the enormous potential of ML and LLMs in revolutionizing teaching methods, encouraging active student participation, and closing educational inequalities across Morocco.International Journal For Multidisciplinary Research['Rachid Ejjami']2024-05-04T00:00:00https://www.semanticscholar.org/paper/66d9197a932b99bcd31e00d871abe35a77b898cc d60dd1b470aeee1f728c271f42148dc6754dd710New energy vehicles (NEVs) are becoming increasingly popular in the automotive industry as a sustainable solution to reduce carbon emissions. In order to attract and retain customers, NEVS needs to have visually appealing exterior designs and interior color schemes. The traditional process of selecting art design and interior colors for vehicles can take time and effort. To address this challenge, our team has developed a sustainable AI algorithm for art design and interior color selection for NEVs. This algorithm utilizes machine learning techniques to analyze market trends, customer preferences, and sustainable color choices. The algorithm uses a database of color options and design elements to generate customizable options for NEV manufacturers. It reduces the need for physical prototypes and manual color matching, leading to cost and time savings. The algorithm also takes into consideration the sustainable aspect of NEVs by suggesting color options that are environmentally friendly and promote eco-consciousness.Journal of Electrical Systems300Journal of Electrical Systems['Jia Yong']2024-05-04T00:00:00https://www.semanticscholar.org/paper/d60dd1b470aeee1f728c271f42148dc6754dd710 c257ed2559a4c7bf952213ec60fcab81881c701dGlobal Surgical Education - Journal of the Association for Surgical Education280Understanding the impact of AI in surgery residency is important to ensure the safe, beneficial, and optimal implementation of this technology in advancing the training of surgical residents and their transition into practice.Global Surgical Education - Journal of the Association for Surgical Education['Ruchi Thanawala']2024-05-04T00:00:00https://www.semanticscholar.org/paper/c257ed2559a4c7bf952213ec60fcab81881c701d 86910fe7d5424af4c5b9b06e9c75121b00ecfca7Generative Artificial Intelligence (AI) is integrated into everyday technology, including news, education, and social media. AI has further pervaded private conversations as conversational partners, auto-completion, and response suggestions. As social media becomes young people's main method of peer support exchange, we need to understand when and how AI can facilitate and assist in such exchanges in a beneficial, safe, and socially appropriate way. We asked 622 young people to complete an online survey and evaluate blinded human- and AI-generated responses to help-seeking messages. We found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health. However, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response. We also discuss the role of training in online peer support exchange and its implications for supporting young people's well-being. Disclaimer: This paper includes sensitive topics, including suicide ideation. Reader discretion is advised.International Conference on Human Factors in Computing Systems960It was found that participants preferred the AI-generated response to situations about relationships, self-expression, and physical health, however, when addressing a sensitive topic, like suicidal thoughts, young people preferred the human response.{'pages': '1006:1-1006:18'}['Jordyn Young', 'Laala M Jawara', 'Diep N Nguyen', 'Brian Daly', 'Jina Huh-Yoo', 'Afsaneh Razi']2024-05-04T00:00:00https://www.semanticscholar.org/paper/86910fe7d5424af4c5b9b06e9c75121b00ecfca7 ced25dcf34f24b521cdb45a62292f92a54ca27caArtificial intelligence (AI) has brought tremendous prospects and breakthroughs to a number of areas, including education. With an emphasis on the use of chatbots, analytics, generative AI, and personalized learning experiences, this research study offers a thorough analysis of the effects of AI on education. In order to shed light on the ethical implications, cultural considerations, language competence issues, and privacy concerns related to the use of AI in education, it explores the related limitations, obstacles, and concerns. Artificial Intelligence (AI) has the potential to completely transform higher education by promoting efficiency, creativity, customization, and engagement. Higher education could undergo a significant transition with the use of Generative Artificial Intelligence (GAI) tools as ChatGPT, Google BARD, and Bing Chat. But this integration also presents problems for avoiding plagiarism and upholding academic integrity. Within this In this work, we explore and evaluate useful strategies for effectively utilizing GAI's potential while also guaranteeing assignment integrity. We present the PAIGE (Promoting Assignment Integrity using Generative AI in Education) conceptual framework as a viable means of addressing these issues. This concept places a focus on the moral. The inclusion of GAI, encourages student engagement, and fosters chances for collaborative learning. Institutions of higher learning can efficiently use the promise of GAI while maintaining assignment integrity by utilizing the PAIGE framework. A responsible and prosperous future in education powered by generative AI is made possible by this strategy. The research report also explores the roles that parents, legislators, and educators play in minimizing the risks and optimizing the advantages of implementing AI in the classroom. Challenges with linguistic ability, privacy, and other factors related to using AI in education. Key Words: Generative AI, analytics, learning experiences, cognitive achievement, AI in the classroom, customized feedbackINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This work explores and evaluates useful strategies for effectively utilizing GAI's potential while also guaranteeing assignment integrity and presents the PAIGE (Promoting Assignment Integrity using Generative AI in Education) conceptual framework as a viable means of addressing these issues.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Prof, Shweta A. Solanke']2024-05-04T00:00:00https://www.semanticscholar.org/paper/ced25dcf34f24b521cdb45a62292f92a54ca27ca bbec09f8bee49c257a74e0de34359914f0e78e53This paper offers a comprehensive review of the evolving landscape at the intersection of quantum mechanics and artificial intelligence (AI). Focusing on current trends and advancements, it explores the profound impact of quantum computing on AI methodologies and applications.International Journal For Multidisciplinary Research00Focusing on current trends and advancements, this paper explores the profound impact of quantum computing on AI methodologies and applications.International Journal For Multidisciplinary Research['Raveesh Gupta']2024-05-04T00:00:00https://www.semanticscholar.org/paper/bbec09f8bee49c257a74e0de34359914f0e78e53 9e954a1d539502025891f26e05dd0557062c1e0cInternational Journal of Dermatology40International journal of dermatology['Mohamad Goldust', 'J. Grant-Kels']2024-05-04T00:00:00https://www.semanticscholar.org/paper/9e954a1d539502025891f26e05dd0557062c1e0c e228fbca670fc53268c3ca851cd8f0781923ebe2npj Digital Medicine1200This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease, including over 152,000 whole slide images (WSIs) representing many diseases.NPJ Digital Medicine['Clare McGenity', 'Emily L. Clarke', 'Charlotte Jennings', 'Gillian Matthews', 'C. Cartlidge', 'Henschel Freduah-Agyemang', 'Deborah D Stocken', 'Darren Treanor']2024-05-04T00:00:00https://www.semanticscholar.org/paper/e228fbca670fc53268c3ca851cd8f0781923ebe2 53455edfe8d862cb8e99c082a1188a6aa9d760e3The application of artificial intelligence (AI) in accounting has transformed conventional methods and changed how financial management and decision-making are conducted. This essay examines the complex effects of artificial intelligence (AI) on accounting, focusing on how these effects may affect organizational efficiency, accuracy, and strategic decision-making. This study investigates the use of artificial intelligence (AI) technologies, including as machine learning, natural language processing, and robotic process automation, in accounting operations, from data input and analysis to risk assessment and forecasting, through a thorough evaluation of the body of existing literature. It also looks at the potential and problems that come with implementing AI, such as labor reskilling, ethical issues, and regulatory compliance. This research gives insightful information about the transformative potential of AI in accounting by combining findings from academic studies and industry sources. It also includes tips for practitioners and policymakers navigating this ever-changing field. The economy, science, and technology are developing at a rapid pace, ushering in the age of artificial intelligence, which has had a profound impact on every part of life. Whether or not there is widespread worry about the fate of accountants facing elimination. This essay will examine how artificial intelligence will affect accounting staff and how to prevent accounting fraud while also producing positive effects on the quality of accounting information. Since machines cannot make decisions, artificial intelligence won't result in widespread unemployment. The article's conclusion will emphasize that, in the grand scheme of artificial intelligence, accounting staff members should develop their own seven skill areas and become fully qualified professionals.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The article's conclusion will emphasize that, in the grand scheme of artificial intelligence, accounting staff members should develop their own seven skill areas and become fully qualified professionals.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Muhammad Arquam']2024-05-04T00:00:00https://www.semanticscholar.org/paper/53455edfe8d862cb8e99c082a1188a6aa9d760e3 bbdf526001c151c67507de963a0c683064b1f630Widespread deployment of societal-scale machine learning systems necessitates a thorough understanding of the resulting long-term effects these systems have on their environment, including loss of trustworthiness, bias amplification, and violation of AI safety requirements. We introduce a repeated learning process to jointly describe several phenomena attributed to unintended hidden feedback loops, such as error amplification, induced concept drift, echo chambers and others. The process comprises the entire cycle of obtaining the data, training the predictive model, and delivering predictions to end-users within a single mathematical model. A distinctive feature of such repeated learning setting is that the state of the environment becomes causally dependent on the learner itself over time, thus violating the usual assumptions about the data distribution. We present a novel dynamical systems model of the repeated learning process and prove the limiting set of probability distributions for positive and negative feedback loop modes of the system operation. We conduct a series of computational experiments using an exemplary supervised learning problem on two synthetic data sets. The results of the experiments correspond to the theoretical predictions derived from the dynamical model. Our results demonstrate the feasibility of the proposed approach for studying the repeated learning processes in machine learning systems and open a range of opportunities for further research in the area.400A novel dynamical systems model of the repeated learning process is presented and the limiting set of probability distributions for positive and negative feedback loop modes of the system operation are proved.['Andrey Veprikov', 'Alexander Afanasiev', 'Anton Khritankov']2024-05-04T00:00:00https://www.semanticscholar.org/paper/bbdf526001c151c67507de963a0c683064b1f630 03bef4b052a9ce350683f98742a7e82ea84a7a20BACKGROUND: In recent years, Despite the proven economic growth brought by AI technology globally, the adoption of AI in the construction industry faces obstacles. To better promote the adoption of AI technology in the construction domain, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, delves into the key factors influencing the adoption of AI technology in the construction industry. By introducing personal-level influencing factors such as AI anxiety and personal innovativeness, the UTAUT model is extended to comprehensively understand users’ attitudes and adoption behaviors towards AI technology. METHODOLOGY: The research framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with the added constructs of artificial intelligence anxiety and individual Innovativeness. These data were collected through a combination of online and offline surveys, with a total of 258 valid data collected and analyzed using structural equation modeling. RESULTS: The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors. Specifically, Performance Expectancy (PE) (β= 0.266, 95%), Effort Expectancy (EE) (β= 0.262, 95%), and Social Influence (SI) (β= 0.131, 95%) were identified as significant predictors of UB. Additionally, Facilitating Conditions (FC) (β= 0.168, 95%) also positively influenced UB.Moreover, the study explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology. PRACTICAL IMPLICATIONS: This study lie in informing industry stakeholders about the multifaceted dynamics influencing AI adoption. Armed with this knowledge, organizations can make informed decisions, implement effective interventions, and navigate the challenges associated with integrating AI technology into the construction sector.Journal of Intelligent &amp; Fuzzy Systems510The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors, and explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology.Journal of Intelligent &amp; Fuzzy Systems['Xiongyu Wu', 'Yixuan Yan', 'Wenxi Zhu', 'Nina Yang']2024-05-04T00:00:00https://www.semanticscholar.org/paper/03bef4b052a9ce350683f98742a7e82ea84a7a20 a3fe6145cee61b1d0c1c6b3143d40298524cf2a8Artificial intelligence (AI) has become an omnipresent area in modern culture. Every industry in the world has been greatly impacted by the development of technology, which has improved people’s quality of life. With the advent of AI, even 10 years old can now use smartphones to conquer the world by simplifying complex jobs. AI has made a substantial contribution to the health-care industry as well, sparking debates about whether robots may or may not eventually replace doctors in the medical field. Interestingly, AI additionally has made important advances in the field of dermatology. Through its discovery of applications that can predict a person’s skin type and the products they should use to achieve “perfect skin,” AI has greatly targeted its audience in the esthetics space, where people are most concerned with the health of their bodies and hair. AI has also developed strong relationships with these people and provided excellent advice for skin-related concerns. However, the question of whether individuals are mistreating their skin or relying too much on AI to address their skin troubles remains. Certain applications use the beauty calculator based on face symmetry, which can have a drastic impact on one’s self-confidence. These commercials may also instill false hope, and they may even be an advertising strategy used by the gods of the metaverse. Applications that give predictions regarding skin health can also create a state of anxiety in people who use them. This article examines whether AI has had a discernible effect on skin health, how it may influence cosmetic dermatology in the future, how accurate AI is in diagnosing conditions and recommending treatments, and whether we should rely on AI in the future for dermatological issues.Cosmoderma260Whether AI has had a discernible effect on skin health, how it may influence cosmetic dermatology in the future, how accurate AI is in diagnosing conditions and recommending treatments, and whether the authors should rely on AI in the future for dermatological issues are examined.Cosmoderma['Jijo Joseph', 'Thejalakshmi Chettyparambil Lalchand']2024-05-04T00:00:00https://www.semanticscholar.org/paper/a3fe6145cee61b1d0c1c6b3143d40298524cf2a8 372d7488db2a9ced6203506ca69c3f8ce6fcd496Cardiology and Therapy540The impact of AI and ML in echocardiography is assessed by integrating novel approaches such as speckle tracking or tissue Doppler echocardiography or vector flow mapping, improved phenotyping, distinguishing between cardiac conditions, and incorporating information from mobile health and genomics.Cardiology and Therapy['Karthik Seetharam', 'H. Thyagaturu', 'Gabriel Lora Ferreira', 'Aditya Patel', 'Chinmay Patel', 'Asim Elahi', 'Roman Pachulski', 'Jilan Shah', 'P. Mir', 'Arunita Thodimela', 'Manya Pala', 'Z. Thet', 'Yasmin Hamirani']2024-05-04T00:00:00https://www.semanticscholar.org/paper/372d7488db2a9ced6203506ca69c3f8ce6fcd496 a56792de1a2c0b8b894358d32e82d4842ffb31a0Penelitian ini bertujuan untuk memaparkan tanda-tanda adanya pengaruh teknologi artificial intelligence pada layanan chatbot Shopee terhadap kepuasan pelanggan. Dengan melibatkan 111 responden yang tersebar di wilayah Bandung Raya, penelitian ini fokus pada mereka yang pernah atau sering menggunakan layanan chatbot Shopee (Choki). Metode analisis yang digunakan mencakup pendekatan deskriptif, dengan penggunaan korelasi dan regresi berganda sebagai alat bantu. Hasil penelitian menunjukkan bahwa teknologi artificial intelligence dalam layanan chatbot Shopee memiliki pengaruh signifikan sebesar 48.2%, sementara 51.8% sisanya dipengaruhi oleh faktor-faktor lain.International Journal Administration, Business &amp; Organization00International Journal Administration, Business &amp; Organization['Aflah Malik Alghaniy']2024-05-04T00:00:00https://www.semanticscholar.org/paper/a56792de1a2c0b8b894358d32e82d4842ffb31a0 2b66c3ab162c71dd360cc1e3894fa01295745aa7International Journal of Dermatology50International journal of dermatology['Clay J. Cockerell', 'Mohamad Goldust']2024-05-04T00:00:00https://www.semanticscholar.org/paper/2b66c3ab162c71dd360cc1e3894fa01295745aa7 7520edb547241915e12b80b24223c1f847731404Education and Information Technologies : Official Journal of the IFIP technical committee on Education280Education and Information Technologies['Jiahong Su']2024-05-04T00:00:00https://www.semanticscholar.org/paper/7520edb547241915e12b80b24223c1f847731404 f50ce5575c2681b7a9181c358f55c9071a1ed031Applied Economics510Applied Economics['Hongshan Ai', 'Xiaoqing Tan', 'Tenglong Zhong', 'Yuhan Zhou']2024-05-03T00:00:00https://www.semanticscholar.org/paper/f50ce5575c2681b7a9181c358f55c9071a1ed031 1da8bdea32744b12d489bcdadd560e588534a7e5This paper presented the views of Malaysians regarding their perception of cryptocurrency. Data were collected through interviews with 59 existing and potential users of cryptocurrency. Text analytics through keyword extraction and content analysis on the responses from the survey were then performed. Keywords on the question related to the regulation of cryptocurrency included “Government should”, “Should regulate”, and “Money laundering”. Keywords such as “Government should”, “Should issue”, and “Regulatory regime” appeared for the question related to whether the government should develop a cryptocurrency regulatory regime. On issuing their own cryptocurrency, 49% of the respondents supported it, while 42% were against such a move. 88% of the respondents felt that the government should not ban cryptocurrency transactions. For the question related to suggestions to overcome issues associated with cryptocurrency, keywords such as “financial system”, “privacy seriously”, and “enhance financial” were reported. This paper adds to the body of knowledge on cryptocurrency by shedding light on the regulation landscape in an emerging market. In view of the increased usage of cryptocurrency, the public should be educated, and relevant bodies should establish regulations to minimise possible related illegal activities while developing a cryptocurrency-friendly regulatory regime. Keywords: cryptocurrency, text analytics, regulation, Malaysia, fintechKnE Social Sciences360KnE Social Sciences['Yip Chiann Huey', 'Yap Kiew Heong Angeline', 'Y. Teng', 'Teoh Teng-Tenk, Melissa', 'Wong Siew Chin', 'Zakiah Saleh']2024-05-03T00:00:00https://www.semanticscholar.org/paper/1da8bdea32744b12d489bcdadd560e588534a7e5 303d27555f2aefd2faf1e61a7e98bf2c3c348be6Journal of Transportation Safety &amp; Security290Journal of Transportation Safety &amp; Security['Nuri Park', 'S. Lee', 'Juneyoung Park', 'Ling Wang']2024-05-03T00:00:00https://www.semanticscholar.org/paper/303d27555f2aefd2faf1e61a7e98bf2c3c348be6 b8058243d5a9df91fb1a0542606a6d8f2b490c14Ultrasound in Obstetrics and Gynecology00Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology['A. Kotlarz', 'W. Froyman', 'L. Valentin', 'A. Testa', 'M. Van Hove', 'B. Van calster', 'T. Bourne', 'D. Timmerman']2024-05-03T00:00:00https://www.semanticscholar.org/paper/b8058243d5a9df91fb1a0542606a6d8f2b490c14 1dc5833be1435323df4dbe0751daea028f53918cArtificial intelligence (AI) technologies, and in particular, generative AI, have the potential to significantly disrupt the research environment. Given the changes that could be brought on by more accessible AI technologies, there is an immediate need for members of the research environment to be proactive and plan for the possible futures that AI will continue to bring to their communities and their workplaces. To address this, ARL and CNI jointly charged a task force to conduct scenario planning for an AI-influenced future. The scenarios were developed through a highly consultative process leveraging the expertise of the ARL/CNI Joint Task Force on Scenario Planning for AI/ML Futures. The strategic focus and critical uncertainties highlighted in the scenarios were identified through extensive stakeholder engagement with the ARL and CNI membership during the winter of 2023 and spring of 2024 and involved over 300 people. Input was provided through focus groups, workshops, and one-on-one interviews.10A task force was charged by ARL and CNI to conduct scenario planning for an AI-influenced future and the strategic focus and critical uncertainties highlighted in the scenarios were identified through extensive stakeholder engagement.[]2024-05-03T00:00:00https://www.semanticscholar.org/paper/1dc5833be1435323df4dbe0751daea028f53918c 33c4792298e8d0b456deed6fef15abcad37270b8This paper explores the evolving role of Generative AI (GenAI) and Large Language Models (LLMs) in cybersecurity. The motivation behind this research is the rapid advancement of GenAI technologies and their potential implications for cybersecurity professionals. This work focuses on assessing how GenAI and LLMs influence cybersecurity practices, including both the opportunities and risks they present. It specifically examines the use of GenAI in cybersecurity, its functions and industries, and the potential impact on the profession. The methodology involves conducting semi-structured interviews with eight cybersecurity professionals to gather insights on their experiences and perspectives regarding GenAI and LLMs. This qualitative approach allows for a deep exploration of the subjective experiences of these professionals in their work environments. The results indicate a cautious approach towards the adoption of GenAI in cybersecurity. While some professionals have begun to utilize these technologies, there are concerns regarding ethical and safety considerations, information security, and the potential for GenAI to influence the nature of cyber threats. The findings highlight the need for a balanced approach that recognizes the potential of GenAI while addressing the associated risks.Systems and Information Engineering Design Symposium150Assessing how GenAI and LLMs influence cybersecurity practices, including both the opportunities and risks they present, highlights the need for a balanced approach that recognizes the potential of GenAI while addressing the associated risks.2024 Systems and Information Engineering Design Symposium (SIEDS)['Noelle Capodieci', 'Christopher Sanchez-Adames', 'Joesph Harris', 'Unal Tatar']2024-05-03T00:00:00https://www.semanticscholar.org/paper/33c4792298e8d0b456deed6fef15abcad37270b8 91da6e8d6ed13737684f0d7f4ed035e052b096c1Algorithmic technologies and (large) data infrastructures, often referred to as Artificial Intelligence (AI), have received increasing attention from gerontological research in the last decade. While there is much literature that dissects and explores the development, application, and evaluation of AI relevant for gerontology, this article makes a novel contribution by critically engaging with the theorizing in this growing field of research. We observe that gerontology's engagement with AI is shaped by an interventionist logic that situates AI as a black box for gerontological research. We demonstrate how this black box logic has neglected many aspects of AI as a research topic for gerontology and discuss three classical concepts in gerontology to show how they can be used to open various black boxes of aging and AI in the areas: a) the datafication of aging, b) the political economy of AI and aging, and c) everyday engagements and embodiments of AI in later life. In the final chapter, we propose a model of the co-constitution of aging and AI that makes theoretical propositions to study the relational terrain between aging and AI and hence aims to open the black box of AI in gerontology beyond an interventionist logic.The gerontologist00A model of the co-constitution of aging and AI is proposed that makes theoretical propositions to study the relational terrain between aging and AI and hence aims to open the black box of AI in gerontology beyond an interventionist logic.The Gerontologist['Vera Gallistl', 'M. U. Banday', 'C. Berridge', 'Alisa Grigorovich', 'Juliane Jarke', 'I. Mannheim', 'Barbara Marshall', 'Wendy Martin', 'Tiago Moreira', 'Catharina Margaretha Van Leersum', 'Alexander Peine']2024-05-03T00:00:00https://www.semanticscholar.org/paper/91da6e8d6ed13737684f0d7f4ed035e052b096c1 31c10fdf47b9cd507913b75d2ed9aa290f7c54baThe recent embrace of machine learning (ML) in the development of autonomous weapons systems (AWS) creates serious risks to geopolitical stability and the free exchange of ideas in AI research. This topic has received comparatively little attention of late compared to risks stemming from superintelligent artificial general intelligence (AGI), but requires fewer assumptions about the course of technological development and is thus a nearer-future issue. ML is already enabling the substitution of AWS for human soldiers in many battlefield roles, reducing the upfront human cost, and thus political cost, of waging offensive war. In the case of peer adversaries, this increases the likelihood of"low intensity"conflicts which risk escalation to broader warfare. In the case of non-peer adversaries, it reduces the domestic blowback to wars of aggression. This effect can occur regardless of other ethical issues around the use of military AI such as the risk of civilian casualties, and does not require any superhuman AI capabilities. Further, the military value of AWS raises the specter of an AI-powered arms race and the misguided imposition of national security restrictions on AI research. Our goal in this paper is to raise awareness among the public and ML researchers on the near-future risks posed by full or near-full autonomy in military technology, and we provide regulatory suggestions to mitigate these risks. We call upon AI policy experts and the defense AI community in particular to embrace transparency and caution in their development and deployment of AWS to avoid the negative effects on global stability and AI research that we highlight here.1300The goal in this paper is to raise awareness among the public and ML researchers on the near-future risks posed by full or near-full autonomy in military technology, and to provide regulatory suggestions to mitigate these risks.['Riley Simmons-Edler', 'R. Badman', 'Shayne Longpre', 'K. Rajan']2024-05-03T00:00:00https://www.semanticscholar.org/paper/31c10fdf47b9cd507913b75d2ed9aa290f7c54ba 33031358a802294e49b7946ca23e1c40b83574dfIn the ever‐evolving AI‐driven education, integrating AI technologies into teaching practices has become increasingly imperative for aspiring STEM educators. Yet, there remains a dearth of studies exploring pre‐service STEM teachers' readiness to incorporate AI into their teaching practices. This study examined the factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre‐service STEM teachers' attitudes towards the application of AI in teaching. In the study, a comprehensive survey was conducted among 239 pre‐service STEM teachers, examining the influences and interconnectedness of Technological Pedagogical Content Knowledge (TPACK), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Self‐Efficacy (SE) on WIAI. Structural Equation Modeling (SEM) was employed for data analysis. The findings illuminated direct influences of TPACK, PU, PE, and SE on WIAI. TPACK was found to directly affect PE, PU, and SE, while PE and PU also directly influenced SE. Further analysis revealed significant mediating roles of PE, PU, and SE in the relationship between TPACK and WIAI, highlighting the presence of a chain mediation effect. In light of these insights, the study offers several recommendations on promoting pre‐service STEM teachers' willingness to integrate AI into their teaching practices. What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. British Journal of Educational Technology580The factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre‐service STEM teachers' attitudes towards the application of AI in teaching, are examined.British Journal of Educational Technology['Fengyao Sun', 'Peiyao Tian', 'Daner Sun', 'Yanhua Fan', 'Yuqin Yang']2024-05-03T00:00:00https://www.semanticscholar.org/paper/33031358a802294e49b7946ca23e1c40b83574df 2ce6dedcd1270aeb5071171a0b190cf72a6b32b8Artificial intelligence (AI) is playing an increasingly important role in medicine. We examine the current trends in the evolution of this role and attempt to understand how it may develop over the next two hundred years, as the very nature of human existence may be altered. We are concerned with weaknesses in the AI development and deployment processes, in particular with lapses in the use of AI evaluation methodology and in the tendency of both humans and AI systems to accept unquestioningly the conclusions reached by such systems. We posit the need for a “humble” AI aware of its own limitations and of the limitations of its ambit. Without serious attention to these aspects of AI, it may come to represent an existential threat to our species. We reference examples from popular culture to illustrate these concerns.MOJ Applied Bionics and Biomechanics50This work is concerned with weaknesses in the AI development and deployment processes, in particular with lapses in the use of AI evaluation methodology and in the tendency of both humans and AI systems to accept unquestioningly the conclusions reached by such systems.MOJ Applied Bionics and Biomechanics['David G Brown', 'Frank W Samuelson']2024-05-03T00:00:00https://www.semanticscholar.org/paper/2ce6dedcd1270aeb5071171a0b190cf72a6b32b8 31d8be8bde2bf592b79481b21b955bddeb4b2a8fJournals and conferences worry that peer reviews assisted by artificial intelligence (AI), in particular, large language models (LLMs), may negatively influence the validity and fairness of the peer-review system, a cornerstone of modern science. In this work, we address this concern with a quasi-experimental study of the prevalence and impact of AI-assisted peer reviews in the context of the 2024 International Conference on Learning Representations (ICLR), a large and prestigious machine-learning conference. Our contributions are threefold. Firstly, we obtain a lower bound for the prevalence of AI-assisted reviews at ICLR 2024 using the GPTZero LLM detector, estimating that at least $15.8\%$ of reviews were written with AI assistance. Secondly, we estimate the impact of AI-assisted reviews on submission scores. Considering pairs of reviews with different scores assigned to the same paper, we find that in $53.4\%$ of pairs the AI-assisted review scores higher than the human review ($p = 0.002$; relative difference in probability of scoring higher: $+14.4\%$ in favor of AI-assisted reviews). Thirdly, we assess the impact of receiving an AI-assisted peer review on submission acceptance. In a matched study, submissions near the acceptance threshold that received an AI-assisted peer review were $4.9$ percentage points ($p = 0.024$) more likely to be accepted than submissions that did not. Overall, we show that AI-assisted reviews are consequential to the peer-review process and offer a discussion on future implications of current trends460It is shown that AI-assisted reviews are consequential to the peer-review process and a discussion on future implications of current trends is offered.['Giuseppe Russo Latona', 'Manoel Horta Ribeiro', 'Tim R. Davidson', 'V. Veselovsky', 'Robert West']2024-05-03T00:00:00https://www.semanticscholar.org/paper/31d8be8bde2bf592b79481b21b955bddeb4b2a8f ed51f62e623cbd99ac77cc77a3074b5ddd7ed68b We report here the processes involved in creating our entry in the 2020 AI Song Contest, “Beautiful the World”; the technical innovations from the project; and the decision-making that divided tasks between human and machine in a way that ensured that the final creation was AI inspired but human created, starting from generated melodies, lyrics, and timbres. Key innovations include the use of lyric stress patterns as queries to a stress-based melody index to a database of generated melodies, and the creation of a novel instrument timbre with differential digital signal processing, trained on Australian animal calls. We reflect on how human–AI cocreativity occurred during the process and how it may develop in the future.Computer Music Journal00The processes involved in creating the AI Song Contest entry, “Beautiful the World”, are reported; the technical innovations from the project are reported; and the decision-making that divided tasks between human and machine in a way that ensured that the final creation was AI inspired but human created.Computer Music Journal['A. Uitdenbogerd', 'Oliver Bown', 'Charlton Hill', 'Caroline Pegram', 'Justin Shave', 'Brendan Wright']2024-05-03T00:00:00https://www.semanticscholar.org/paper/ed51f62e623cbd99ac77cc77a3074b5ddd7ed68b 1cb5e67616cecb31f42fb54d624f4a47266369b0Philosophy &amp; Technology660Drawing on recent empirical findings that suggest providing reliability scores to human decision-makers improves calibration in the AI system, it is argued that reliability scores provide a good index of competence and enable humans to determine how much they wish to rely on the system.Philosophy &amp; Technology['John Dorsch', 'Ophélia Deroy']2024-05-03T00:00:00https://www.semanticscholar.org/paper/1cb5e67616cecb31f42fb54d624f4a47266369b0 84d5c230d0ba933b87767466879e63a179efefb3Purpose: This paper investigates the phenomenon of "AI hallucinations" in healthcare and life sciences, where large language models (LLMs) produce outputs that, while coherent, are factually incorrect, irrelevant, or misleading. Understanding and mitigating such errors is critical given the high stakes of accurate and reliable information in healthcare and life sciences. We classify hallucinations into three types input-conflicting, context-conflicting, and fact-conflicting and examine their implications through real-world cases. Methodology: Our methodology combines the Fact Score, Med-HALT, and adversarial testing to evaluate the fidelity of AI outputs. We propose several mitigation strategies, including Retrieval-Augmented Generation (RAG), Chain-of-Verification (CoVe), and Human-in-the-Loop (HITL) systems, to enhance model reliability. Findings: As artificial intelligence continues to permeate various sectors of society, the issue of hallucinations in AI-generated text poses significant challenges, especially in contexts where precision and reliability are paramount. This paper has delineated the types of hallucinations commonly observed in AI systems input-conflicting, context-conflicting, and fact-conflicting and highlighted their potential to undermine trust and efficacy in critical domains such as healthcare and legal proceedings. Unique contribution to theory, policy and practice: This study's unique contribution lies in its comprehensive analysis of AI hallucinations' types and impacts and the development of robust controls that advance theoretical understanding, practical application, and policy formulation in AI deployment. These efforts aim to foster safer, more effective AI integration across healthcare and life sciences sectorsInternational Journal of Health Sciences50The types of hallucinations commonly observed in AI systems input-conflicting, context-conflicting, and fact-conflicting are delineated and their potential to undermine trust and efficacy in critical domains such as healthcare and legal proceedings is highlighted.International Journal of Health Sciences['Gadiko Aditya']2024-05-03T00:00:00https://www.semanticscholar.org/paper/84d5c230d0ba933b87767466879e63a179efefb3 58197bb0443c59b82996914276060135ea7efc86International Journal of Educational Technology in Higher Education590This study concludes that consistent with human instructors, self-disclosure by AI instructors led to higher emotional attachment, learning interest, and knowledge gain and emotional attachment played an important mediating role in AI instructor self-disclosure and students’ learning interest and knowledge gain.International Journal of Educational Technology in Higher Education['Shunan Zhang', 'Xiangying Zhao', 'Dongyan Nan', 'Jang Hyun Kim']2024-05-03T00:00:00https://www.semanticscholar.org/paper/58197bb0443c59b82996914276060135ea7efc86 534aaa3ecc8dc82db5f1f2c379c2672b14f1267fHumanities and Social Sciences Communications510This study represents one of the earliest attempts to empirically examine the power of AI literacy and explore the determinants of behavioral intentions to learn AI among K-12 teachers.Humanities and Social Sciences Communications['Hua Du', 'Yanchao Sun', 'Haozhe Jiang', 'A. Y. M. A. Islam', 'Xiaoqing Gu']2024-05-03T00:00:00https://www.semanticscholar.org/paper/534aaa3ecc8dc82db5f1f2c379c2672b14f1267f 001720a782840652b573bb4794774aee826510caInteractive software tools employing generative artificial intelligence (AI) that help users formulate custom system queries are increasingly needed with growth in data quantities, relationships, and complexity. The need to afford such interactions is not new. Indeed, chatbots have long sought to bridge gaps between an individual’s intent and the system’s response. However, generative AI chatbots – in contrast to traditional chatbots that navigate pre-defined, rules-based decision trees – are unique in their promise to accept and respond to highly customized queries. At present though, most still rely upon the precise articulation of a structured prompt. The work herein develops and evaluates design features to facilitate AI-assistive user interactions in query formulation. The design features attempt to balance functional needs of users to make specific, goal-oriented, customized queries, with minimal constraints on exactly articulating pre-defined prompts. In a case study, we wireframe user interface prototypes in the domain of data log management, for evaluation with expert and novice users. Key elements of the design features revolve around the 1) refinement of search categories, 2) context-aware prompt recommendations, and 3) customization of query input per a user’s technical ability.Systems and Information Engineering Design Symposium90The work herein develops and evaluates design features to facilitate AI-assistive user interactions in query formulation, which attempt to balance functional needs of users to make specific, goal-oriented, customized queries, with minimal constraints on exactly articulating pre-defined prompts.2024 Systems and Information Engineering Design Symposium (SIEDS)['S. Meng', 'R. Dollahite', 'A. Kaur', 'P. Schell', 'A. Sharma', 'G. Ventre', 'I. Kranz', 'G. Nudelman', 'G. J. Gerling']2024-05-03T00:00:00https://www.semanticscholar.org/paper/001720a782840652b573bb4794774aee826510ca c2af89a1956ffcde3a141a46e8c4386ce97535d7We conducted an in-person ethnography in India and the US to investigate how young people (18-24) trusted online content, with a focus on generative AI (GenAI). We had four key findings about how young people use GenAI and determine what to trust online. First, when online, we found participants fluidly shifted between mindsets and emotional states, which we term"information modes."Second, these information modes shaped how and why participants trust GenAI and how they applied literacy skills. In the modes where they spent most of their time, they eschewed literacy skills. Third, with the advent of GenAI, participants imported existing trust heuristics from familiar online contexts into their interactions with GenAI. Fourth, although study participants had reservations about GenAI, they saw it as a requisite tool to adopt to keep up with the times. Participants valued efficiency above all else, and used GenAI to further their goals quickly at the expense of accuracy. Our findings suggest that young people spend the majority of their time online not concerned with truth because they are seeking only to pass the time. As a result, literacy interventions should be designed to intervene at the right time, to match users' distinct information modes, and to work with their existing fact-checking practices.690It is suggested that young people spend the majority of their time online not concerned with truth because they are seeking only to pass the time, and literacy interventions should be designed to intervene at the right time, to match users' distinct information modes, and to work with their existing fact-checking practices.['Rachel Xu', 'Nhu Le', 'Rebekah Park', 'Laura Murray', 'Vishnupriya Das', 'Devika Kumar', 'Beth Goldberg']2024-05-03T00:00:00https://www.semanticscholar.org/paper/c2af89a1956ffcde3a141a46e8c4386ce97535d7 5fb6667bc75ca84ff152fedcbf88114de2f82958Reaching a consensus on the team plans is vital to human-AI coordination. Although previous studies provide approaches through communications in various ways, it could still be hard to coordinate when the AI has no explainable plan to communicate. To cover this gap, we suggest incorporating external models to assist humans in understanding the intentions of AI agents. In this paper, we propose a two-stage paradigm that first trains a Theory of Mind (ToM) model from collected offline trajectories of the target agent, and utilizes the model in the process of human-AI collaboration by real-timely displaying the future action predictions of the target agent. Such a paradigm leaves the AI agent as a black box and thus is available for improving any agents. To test our paradigm, we further implement a transformer-based predictor as the ToM model and develop an extended online human-AI collaboration platform for experiments. The comprehensive experimental results verify that human-AI teams can achieve better performance with the help of our model. A user assessment attached to the experiment further demonstrates that our paradigm can significantly enhance the situational awareness of humans. Our study presents the potential to augment the ability of humans via external assistance in human-AI collaboration, which may further inspire future research.Adaptive Agents and Multi-Agent Systems490A two-stage paradigm that first trains a Theory of Mind (ToM) model from collected offline trajectories of the target agent, and utilizes the model in the process of human-AI collaboration by real-timely displaying the future action predictions of the target agent is proposed.{'pages': '2546-2548'}['Chenxu Wang', 'Zilong Chen', 'Huaping Liu']2024-05-03T00:00:00https://www.semanticscholar.org/paper/5fb6667bc75ca84ff152fedcbf88114de2f82958 54f0346a52622ba525d48dc102a6da5d8d3ea241Artificial Intelligence (AI) has grown dramatically over the past few decades and has much greater influence today on human performance in every walk of life. AI is great for accelerating our work, but there are also downsides to it. One such downside is the application of Facial Recognition Technologies (FRT) available today, which has adverse consequences like racial discrimination or wrongful judgement by commercial firms and even law enforcement organizations. The landmark ‘Gender Shades’ project [1] in 2018 showed a highly skewed error bar between facial recognition accuracies of subjects who are black females compared to white males using an intersectional comparison approach between facial recognition software by IBM, Microsoft and Face++. This supplemented previous studies which highlighted issues with gender classification or on the whole misidentification issues posed by facial recognition technologies (FRT) [2]. This algorithm bias towards misgendering or misclassification based on skin color could be attributed to a highly unbalanced training datasets that lack diversity as well as a lack of understanding of the black box machine learning (ML) models that are used to build the FRTs. From a Code of Ethics perspective, such algorithms do not conform to the fundamental canons [3] such as prioritizing the safety, and welfare of the public. Furthermore, they cannot be used honorably, responsibly and ethically to enhance the reputation, and usefulness of the companies promoting these technologies because they are unable to provide truthful and objective information regarding facial recognition. A possible remedy could be to curate an exhaustively diverse dataset w.r.t both color and gender. However, the task of building such a dataset would require access to a diverse population, which is not always possible in a multi-racial and multi-ethnic society. We thus propose an exhaustive review-based study of existing work on the introduction of explainable AI [4],[5] (XAI) techniques in such ML models to understand how the model itself learns. This study would explore what constraints to put on the learning method of the model, which can enforce reduction in misclassification errors from gender and skin color thus enforcing various ethical aspects like taking care of algorithm bias, introducing transparency and accountability. It would also explore and underline the overall social impact improved FRTs might have from a code of ethics perspective.Systems and Information Engineering Design Symposium170An exhaustive review-based study of existing work on the introduction of explainable AI (XAI) techniques in such ML models to understand how the model itself learns and what constraints to put on the learning method, which can enforce reduction in misclassification errors from gender and skin color.2024 Systems and Information Engineering Design Symposium (SIEDS)['Ankita B', 'ChienChen H', 'Lauren P', 'Lydia O']2024-05-03T00:00:00https://www.semanticscholar.org/paper/54f0346a52622ba525d48dc102a6da5d8d3ea241 147269eb03ef634381b5e881d99a785541d3e62bGenerative AI (GAI) tools have recently triggered an unprecedented disruption in the industry and education sectors, with both positive and negative effects requiring investigation. Several studies have already observed how the advanced language and dialogue capabilities (GPT3/4, LLaMA, Bard etc.), visual creativity (MidJourney), and GAI’s ability to adapt to different scenarios is already impacting work processes within fields like customer care, marketing, and software development. This constructivist grounded theory study uses IT vocational education and industry as an example to understand the impact, concerns, and current practices of GAI, leading to propositions for correct and effective use of GAI tools for learning. A group of students were assigned a Python project and were encouraged to use Chat-GPT 3.5 to assist them. Using these student-AI dialogues as sample cases, primary data was then collected through interviews with education/industry experts and students engaged in work-based learning. Interviews were progressively coded and analysed, with emerging theory suggesting that GAI tools are indeed revolutionising IT education and industry. Acting as advanced assistants, these tools facilitate lexically flexible conversations across diverse linguistic styles, igniting concerns regarding potential misuse. While these tools usher in enhanced accessibility and a personalized learning trajectory, it is suggested that adapting curriculum and assessment policies, fostering self-discipline, and nurturing maturity are pivotal to steering their appropriate usage. Ensuring fruitful and correct use still necessitates human intelligence with expert validation, especially in tackling large projects and addressing specialized industry-specific challenges.MCAST Journal of Applied Research &amp; Practice00This constructivist grounded theory study uses IT vocational education and industry as an example to understand the impact, concerns, and current practices of GAI, leading to propositions for correct and e-ective use of GAI tools for learning.MCAST Journal of Applied Research &amp; Practice['Daren Scerri']2024-05-03T00:00:00https://www.semanticscholar.org/paper/147269eb03ef634381b5e881d99a785541d3e62b 65e5e477a4c56b56dc0cc0b69e0ae0add9fe3e23The lack of people who know about the use of AI in Islamic boarding schools means that students do not have the instinct to explore the vast digital world, which is very beneficial for life. This research aims to analyze the role of training and mentoring programs on digital skills and the introduction of AI for students at Islamic boarding schools. This research uses the PAR (Participatory Action Research) method through collaborative steps involving discussion, dialogue, and active participation from all related parties. The subjects involved in the research were Islamic boarding school students. Data collection methods were carried out by observation and interviews. Data analysis was carried out descriptively. This research shows that the empowerment program through training effectively improves digital skills and introduces AI to Islamic boarding school students. Through this digital skills empowerment and development program, they provide knowledge and skills to see various learning opportunities around them and be creative in utilizing everything in the digital world to make everything easier.International Journal of Community Service Learning210This research shows that the empowerment program through training effectively improves digital skills and introduces AI to Islamic boarding school students.International Journal of Community Service Learning['Siti Mutmainah', 'Khurin’In Ratnasari', 'Dukan Jauhari F Ratnasari']2024-05-03T00:00:00https://www.semanticscholar.org/paper/65e5e477a4c56b56dc0cc0b69e0ae0add9fe3e23 c735069e4c7d23aa863ce0ba47fa680a776aa793Purpose: The general objective of this study was to explore moral agency and responsibility in AI systems. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to moral agency and responsibility in AI systems. Preliminary empirical review revealed that AI systems possess a form of moral agency, albeit different from human agents, and promoting transparency and accountability was deemed crucial in ensuring ethical decision-making. Interdisciplinary collaboration and stakeholder engagement were emphasized for addressing ethical challenges. Ultimately, the study highlighted the importance of upholding ethical principles to ensure that AI systems contribute positively to society. Unique Contribution to Theory, Practice and Policy: Utilitarianism, Kantianism and Aristotelian Virtue Ethics may be used to anchor future studies on the moral agency and responsibility in AI systems. The study provided a nuanced analysis of moral agency in AI systems, offering practical recommendations for developers, policymakers, and stakeholders. The study emphasized the importance of integrating ethical considerations into AI development and deployment, advocating for transparency, accountability, and regulatory frameworks to address ethical challenges. Its insights informed interdisciplinary collaboration and ethical reflection, shaping the discourse on responsible AI innovation and governance. Keywords: Moral Agency, Responsibility, AI Systems, Ethics, Decision-Making, Framework, Analysis, Regulation, Governance, Transparency, Accountability, Interdisciplinary, Innovation, Deployment, StakeholdersInternational Journal of Philosophy190The study provided a nuanced analysis of moral agency in AI systems, offering practical recommendations for developers, policymakers, and stakeholders, and emphasized the importance of integrating ethical considerations into AI development and deployment, advocating for transparency, accountability, and regulatory frameworks to address ethical challenges.International Journal of Philosophy['Luiz Saraiva']2024-05-03T00:00:00https://www.semanticscholar.org/paper/c735069e4c7d23aa863ce0ba47fa680a776aa793 b77f21e163f2af441518bf6bd8974111e38f5d95International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-05-03T00:00:00https://www.semanticscholar.org/paper/b77f21e163f2af441518bf6bd8974111e38f5d95 728b70ba941f52091f9c8dd16bb4bcee35963af4An output of the Open Development & Education, https://opendeved.net/00['Christopher Klune', 'Bethany Huntington', 'Adnane Touiyate', 'Jonathan Kay', 'Mohammad Zaman', 'Hassan Mansour', 'Björn Haßer']2024-05-03T00:00:00https://www.semanticscholar.org/paper/728b70ba941f52091f9c8dd16bb4bcee35963af4 fcad5386004ddad0c9bd5d8a66e8e29db574ec74 This Medical News article is an interview with psychiatrist Vikram Patel, chair of the Department of Global Health and Social Medicine at Harvard Medical School. Journal of the American Medical Association (JAMA)00JAMA['Jennifer Abbasi', 'Y. Hswen']2024-05-03T00:00:00https://www.semanticscholar.org/paper/fcad5386004ddad0c9bd5d8a66e8e29db574ec74 80a73db0bf70177e30822c01f90caa7214b8b03aThis pioneering research introduces a novel approach for decision-makers in the heavy machinery industry, specifically focusing on production management. The study integrates machine learning techniques like Ridge Regression, Markov chain analysis, and radar charts to optimize North American Crawler Cranes market production processes. Ridge Regression enables growth pattern identification and performance assessment, facilitating comparisons and addressing industry challenges. Markov chain analysis evaluates risk factors, aiding in informed decision-making and risk management. Radar charts simulate benchmark product designs, enabling data-driven decisions for production optimization. This interdisciplinary approach equips decision-makers with transformative insights, enhancing competitiveness in the heavy machinery industry and beyond. By leveraging these techniques, companies can revolutionize their production management strategies, driving success in diverse markets.450['Tian Tian', 'Jiahao Deng']2024-05-03T00:00:00https://www.semanticscholar.org/paper/80a73db0bf70177e30822c01f90caa7214b8b03a be41d822082fb4e42dbdbfaa37903f3d5173d8ffBackground: Healthcare big data has become an important strategic resource, and the study of applying it to the rational application of antimicrobial drugs is of great significance in improving and optimizing the various tasks of medicine and health and promoting the development of social health. Based on its own characteristics, the issue of data quality has important research value in promoting data output and application. Objective: To optimize the status quo of antimicrobial drug use in medical institutions, to explore the value of healthcare big data, and to explore the data governance methods in the era of digital intelligence in order to build a good ecosystem for the application of healthcare big data. Methods: This paper constructs a set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality, adopts PDCA cycle management mode, analyzes the problems in the process of applying healthcare big data and gives the corresponding solutions, authorizes according to the personnel's duties, and guarantees the data security. Results: Since the platform went online in January 2021, the antimicrobial drug use intensity indicator of a hospital has decreased year by year from 38 to 34.4 as of June 2023, effectively standardizing the use of antimicrobial drugs. The time consumed for the statistics of antimicrobial use intensity related data has decreased from an average of 30min to 2min, which has significantly improved the work efficiency.Healthcare professionals are able to monitor indicator data in real time, with or without abnormal cases, and fully grasp the trend of indicators. At the same time, the patient's medication can be viewed at any time, and the condition can be grasped in a timely manner. Conclusions: The platform not only effectively solves the problem of information barriers, but also accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.The practical experience of platform construction has important reference significance for the governance and application of healthcare big data, which provides data support and technical guarantee for the scientific, standardized and refined management of hospitals.Journal of Electrical Systems410A set of intelligent full-closed-loop antimicrobial rational application management platform integrated with data quality that accurately determines the data source and statistical logic of indicator collection, reflecting the actual clinical diagnosis and treatment, and at the same time greatly reduces labor costs and optimizes the workflow.Journal of Electrical Systems['Juan Li']2024-05-03T00:00:00https://www.semanticscholar.org/paper/be41d822082fb4e42dbdbfaa37903f3d5173d8ff 566d77d461f3d9e15c15757bd71df962235caad8Driving under the influence remains a pressing issue in the United States, contributing to approximately one-third of all fatal car crashes and claiming 11,000 lives annually. This research project aims to progress public safety by developing a technological solution aligned with the Department of Transportation’s specifications. Our system administers an automated Horizontal Gaze Nystagmus (HGN) test that exposes and detects a driver’s physiological impairment in order to prevent them from driving while intoxicated. The novelty in our system lies in the personalized machine-learning algorithm that ensures a precise and confidential assessment of a user’s sobriety. Our algorithm is backed by significant amounts of data collected from controlled IRB testing that establishes a statistical baseline and is personalized through the performance of each user during their one-time calibration phase. Once calibrated, our system can determine if a user is unfit to operate their vehicle and will consequently immobilize their automobile’s transmission. Throughout the entire process the user’s data such as their identity, scores, or personalized scoring model, remains confidential through advanced encryption techniques. In all, these technological advancements within our system address the needs determined by the Department of Transportation in a personalized, robust, and confidential manner.Systems and Information Engineering Design Symposium220This research project aims to progress public safety by developing a technological solution aligned with the Department of Transportation’s specifications by administers an automated Horizontal Gaze Nystagmus test that exposes and detects a driver’s physiological impairment in order to prevent them from driving while intoxicated.2024 Systems and Information Engineering Design Symposium (SIEDS)['Chase Coleman', 'James Coulthard', 'Patrick Dodds', 'Ahmad Salman', 'Rod MacDonald']2024-05-03T00:00:00https://www.semanticscholar.org/paper/566d77d461f3d9e15c15757bd71df962235caad8 a7f1b7ef11e70dd8a1acb13b1221a44104278f10Adversarial attacks are more prevalent in Consumer Internet of Things (CIoT) devices (i.e., smart home devices, cameras, actuators, sensors, and micro-controllers) because of their growing integration into daily activities, which brings attention to their possible shortcomings and usefulness. Keeping protection in the CIoT and countering emerging risks require constant updates and monitoring of these devices. Machine learning (ML), in combination with Explainable Artificial Intelligence (XAI), has become an essential component of the CIoT ecosystem due to its rapid advancement and impressive results across several application domains for attack detection, prevention, mitigation, and providing explanations of such decisions. These attacks exploit and steal sensitive data, disrupt the devices’ functionality, or gain unauthorized access to connected networks. This research generates a novel dataset by injecting adversarial attacks into the CICIoT2023 dataset. It presents an adversarial attack detection approach named TXAI-ADV that utilizes deep learning (Mutli-Layer Perceptron (MLP) and Deep Neural Network (DNN)) and machine learning classifiers (K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Gaussian Naive Bayes (GNB), ensemble voting, and Meta Classifier) to detect attacks and avert such situations rapidly in a CIoT. This study utilized Shapley Additive Explanations (SHAP) techniques, an XAI technique, to analyze the average impact of each class feature on the proposed models and select optimal features for the adversarial attacks dataset. The results revealed that, with a 96% accuracy rate, the proposed approach effectively detects adversarial attacks in a CIoT.Electronics320An adversarial attack detection approach named TXAI-ADV is presented that utilizes deep learning and machine learning classifiers and machine learning classifiers to detect attacks and avert such situations rapidly in a CIoT with a 96% accuracy rate.Electronics['Stephn Ojo', 'M. Krichen', 'Meznah A. Alamro', 'Alaeddine Mihoub']2024-05-03T00:00:00https://www.semanticscholar.org/paper/a7f1b7ef11e70dd8a1acb13b1221a44104278f10 064f208cd58815a8e5939da51e4b01e5ace1be5aSystems Science &amp; Control Engineering430Systems Science &amp; Control Engineering['Harsh Nagar', 'Rajendra Machavaram', 'Pranav Kulkarni', 'Peeyush Soni']2024-05-03T00:00:00https://www.semanticscholar.org/paper/064f208cd58815a8e5939da51e4b01e5ace1be5a f917792dcc3715c19319acc614e1e4eeb81b89f8Acquiring a traditional service dog for visually impaired persons is expensive with long waitlist times, complicated training programs, and the responsibility of caring for a living creature. A robotic service dog alternative could be a significantly more accessible and practical option for many members of the visually impaired community. Another benefit of a robotic service dog is the ability to imbue the dog with the ability to converse with their companion in human language. This will allow human users to gather more information about their environment and make specific commands to the service dog. The focus of this project is to create a voice module that takes commands and gives verbal replies by incorporating hardware and software components as a module. This module will ultimately be installed on a Unitree Go1 robotic dog using a Raspberry Pi, microphone, speaker, and several python libraries such as PyAudio and SpeechRecognition for voice recognition and pyttsx3 for speech synthesis. These libraries rely on the Hidden Markov Model, voice activity detectors, and preinstalled TTS engines. A working prototype will be demonstrated with functional speech and audio input capabilities in English to report on the environment and to receive voice commands. Testing and validation results will also include metrics like speaking range, rate of speaking, and volume of speech synthesis. The outcomes of our research contribute to the advancement of assistive technologies for visually impaired individuals, ultimately improving their quality of life and independence.Systems and Information Engineering Design Symposium190This project is to create a voice module that takes commands and gives verbal replies by incorporating hardware and software components as a module and will ultimately be installed on a Unitree Go1 robotic dog.2024 Systems and Information Engineering Design Symposium (SIEDS)['Amalie J. Keefe', 'Blake Hament']2024-05-03T00:00:00https://www.semanticscholar.org/paper/f917792dcc3715c19319acc614e1e4eeb81b89f8 67fad09fa04bdc2a09924e70f1e9fa09ad1535e7GenAI has gained the attention of a myriad of users in almost every profession. Its advancement has had an intense impact on education, significantly disrupting the assessment design and evaluation methodologies. Despite the potential benefits and possibilities of GenAI in the education sector, there are several concerns primarily centred around academic integrity, authenticity, equity of access, assessment evaluation methodology, and feedback. Consequently, academia is encountering challenges in assessment design that are essential to retaining academic integrity in the age of GenAI. In this article, we discuss the challenges, and opportunities that need to be addressed for the assessment design and evaluation. The article also highlights the importance of clear policy about the usage of GenAI in completing assessment tasks, and also in design approaches to ensure academic integrity and subject learning. Additionally, this article also provides assessment categorisation based on the use of GenAI to cultivate knowledge among students and academic professionals. It also provides information on the skills necessary to formulate and articulate problems and evaluate the task, enabling students and academics to effectively utilise GenAI tools.150The article highlights the importance of clear policy about the usage of GenAI in completing assessment tasks, and also in design approaches to ensure academic integrity and subject learning, and provides assessment categorisation based on the use of GenAI to cultivate knowledge among students and academic professionals.['Rajan Kadel', 'Bhupesh Kumar Mishra', 'S. Shailendra', 'Samia Abid', 'Maneeha Rani', 'Shiva Prasad Mahato']2024-05-03T00:00:00https://www.semanticscholar.org/paper/67fad09fa04bdc2a09924e70f1e9fa09ad1535e7 da6781b041356de396ab4519bd6e8b7f10ecc2f9Linköping Studies in Science and Technology. Licentiate Thesis00Linköping Studies in Science and Technology. Licentiate Thesis['Elinor Särner']2024-05-03T00:00:00https://www.semanticscholar.org/paper/da6781b041356de396ab4519bd6e8b7f10ecc2f9 54bdda4081e2d3a64bc94300566442644ac1179700['S. Benraouane']2024-05-03T00:00:00https://www.semanticscholar.org/paper/54bdda4081e2d3a64bc94300566442644ac11797 1555d50f4f0c32dc3fc0df860488810009fd9185AI &amp; SOCIETY290AI &amp; SOCIETY['Sungjin Park']2024-05-03T00:00:00https://www.semanticscholar.org/paper/1555d50f4f0c32dc3fc0df860488810009fd9185 4f69cd182a338fe2f88ef4f1ae8c172812145b4aInternational Journal of Surgery00International Journal of Surgery['R. Cahill']2024-05-03T00:00:00https://www.semanticscholar.org/paper/4f69cd182a338fe2f88ef4f1ae8c172812145b4a e51a32691b1c9b49201cc890cab8724a2ae21528Nature00Nature["Jonathan O'Callaghan"]2024-05-03T00:00:00https://www.semanticscholar.org/paper/e51a32691b1c9b49201cc890cab8724a2ae21528 578c6c9f80a902a60d1c50a014dc287e797e8cfdInternational Journal of Technology and Design Education190Insight is provided into primary school students perceptions and use of AI, serving as a foundation for further exploration of AI literacy in education contexts and considerations for policy makers to take into account, listening to children’s voices.International Journal of Technology and Design Education['Susanne Walan']2024-05-03T00:00:00https://www.semanticscholar.org/paper/578c6c9f80a902a60d1c50a014dc287e797e8cfd 1d2234fec158fd0717d88e761145b5e5b3d31b5bIntroduction Generative Artificial Intelligence has made significant impacts in many fields, including computational cognitive modeling of decision making, although these applications have not yet been theoretically related to each other. This work introduces a categorization of applications of Generative Artificial Intelligence to cognitive models of decision making. Methods This categorization is used to compare the existing literature and to provide insight into the design of an ablation study to evaluate our proposed model in three experimental paradigms. These experiments used for model comparison involve modeling human learning and decision making based on both visual information and natural language, in tasks that vary in realism and complexity. This comparison of applications takes as its basis Instance-Based Learning Theory, a theory of experiential decision making from which many models have emerged and been applied to a variety of domains and applications. Results The best performing model from the ablation we performed used a generative model to both create memory representations as well as predict participant actions. The results of this comparison demonstrates the importance of generative models in both forming memories and predicting actions in decision-modeling research. Discussion In this work, we present a model that integrates generative and cognitive models, using a variety of stimuli, applications, and training methods. These results can provide guidelines for cognitive modelers and decision making researchers interested in integrating Generative AI into their methods.Frontiers in Psychology680A model that integrates generative and cognitive models, using a variety of stimuli, applications, and training methods is presented, which can provide guidelines for cognitive modelers and decision making researchers interested in integrating Generative AI into their methods.Frontiers in Psychology['Tyler Malloy', 'Cleotilde Gonzalez']2024-05-03T00:00:00https://www.semanticscholar.org/paper/1d2234fec158fd0717d88e761145b5e5b3d31b5b 7ad23a8abaa5a1d04ebb567bfa424ba4ef611b31Artificial Intelligence (AI) has become a disruptive element in modern engineering and consequently led to the reshaping of industry demands and educational frameworks worldwide. Recognizing the potential impact of AI on the engineering sector, there is indeed a growing need to evaluate how well current educational programs are integrating these advancements. This study therefore focused on assessing the extent of AI integration within the curricula of engineering programs offered by Philippine State Universities and Colleges (SUCs) with the objective to identify their current capabilities and gaps on AI integration. To achieve this goal, the methodology employed three phases: Data Collection, Data Analysis, and Insight Generation. Initially, the Data Collection phase involved the creation of a tabular data from 78 publicly accessible SUC websites which contained the engineering program descriptions, objectives, courses and their respective descriptions. When specific course descriptions were unavailable, standard definitions provided by the Commission on Higher Education (CHED) were utilized. The Data Analysis phase involved the normalization and preprocessing of text data to establish two distinct corpora based on constructed dataset and were subsequently analyzed for AI-related keywords using 1-gram and 2-gram models. Implementation of the last phase revealed a significant but partial integration of AI competencies in the engineering curricula. Keywords such as "learn", "teamwork", "ICT", "R", "data", "statistic", "software", and "communication" suggested foundational AI skills are being incorporated within the engineering curricula. However, the absence of advanced AI terms like "machine learning" and "neural networks" highlighted a gap in preparing students for more sophisticated AI roles. This analysis indicated that while some foundational aspects of AI are introduced, a more comprehensive approach is needed for full integration. To address this, this work proposes to: (i) extend engineering programs to include core AI courses, (ii) integrate AI-focused courses into existing tracks, or (iii) offer AI courses as summer modules. Collectively, the findings of this study underlined the need for curriculum adjustments to better align engineering programs with the evolving demands of the current technology landscape, thereby enhancing the global competitiveness of Filipino engineering graduates.Systems and Information Engineering Design Symposium180Assessment of the extent of AI integration within the curricula of engineering programs offered by Philippine State Universities and Colleges revealed a significant but partial integration of AI competencies in the engineering curricula, underlining the need for curriculum adjustments to better align engineering programs with the evolving demands of the current technology landscape.2024 Systems and Information Engineering Design Symposium (SIEDS)['John Raymond B. Barajas', 'Maria Jihan G. Sangil', 'Nico O. Aspra', 'Pee Jay N. Gealone', 'Arpon T. Lucero', 'Marben Ramos', 'Oliver M. Padua', 'Ronell Oropesa']2024-05-03T00:00:00https://www.semanticscholar.org/paper/7ad23a8abaa5a1d04ebb567bfa424ba4ef611b31 0cc3fca5e84c7449998893216df1ab5aa927c931This scientific article is devoted to the study and analysis of legal relations between the Republic of Kazakhstan and the BRICS countries in the field of the production and operation of medical electric vehicles with artificial intelligence technologies. Particular attention is paid to legislative measures that promote the formation and development of a new industrial sector, such as the production of medical electric vehicles with artificial intelligence technologies. The research uses a number of methods, including studying empirical data, comparative legal analysis, synthesis, generalization, and scientific forecasting. The article proposes legislative measures to solve the problems facing the medical electrical machinebuilding industry and the unmanned medical electric vehicle industry, as well as the difficulties of integrating automation and digitalization into the production process of transportation plants in Kazakhstan and the BRICS countries. In the order of forecasting, the authors propose the adoption of several laws that are relevant to the issue under consideration. These proposals include the signing of new international cooperation agreements between Kazakhstan and the BRICS countries aimed at the introduction of digitalization at machine-building plants in Kazakhstan for the production of medical electric vehicles equipped with artificial intelligence technologies.BRICS Law Journal50BRICS Law Journal['А. Y. Yelegen', 'М. Sarsembayev']2024-05-03T00:00:00https://www.semanticscholar.org/paper/0cc3fca5e84c7449998893216df1ab5aa927c931 46705f681a8b6994f770c7a8bc863964990587f8Objective: This scoping review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in health care, including clinical decision support, health care quality, and patient safety. We focused on the integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application in health care. Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this interdisciplinary research field. Results: Fifty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. Conclusion: The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support, health care quality, and patient safety.medRxiv1180AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals.['J. Ye', 'J. Hai', 'J. Song', 'Z. Wang']2024-05-03T00:00:00https://www.semanticscholar.org/paper/46705f681a8b6994f770c7a8bc863964990587f8 0b93953aac10b67204fd2f973fa2712c774590f8The article presents a structured bibliometric study examining the impact of artificial intelligence (AI) in higher education across four thematic axes: AI in higher education, AI in education review, AI in the teaching-learning process, and AI tools applied to higher education. Research productivity and impact indicators are analyzed using data from major databases like Scopus, Web of Science, and ScienceDirect. Results reveal a significant increase in AI-related research output, particularly in machine learning, data mining, and learning analytics. The study highlights China and the United States as leading contributors to AI research in higher education. The findings highlight AI's evolving role in transforming higher education and the need for multidisciplinary research approaches to address emerging challenges and opportunities. However, limitations include the reliance on quantitative measures, the narrow temporal scope, and the limited focus on high-production countries. Future research should incorporate qualitative methods to explore practical applications and social impacts more comprehensively, consider a broader range of geographic contexts, and discuss ethical considerations around integrating AI into higher education.Sapienza: International Journal of Interdisciplinary Studies300A structured bibliometric study examining the impact of artificial intelligence (AI) in higher education across four thematic axes reveals a significant increase in AI-related research output, particularly in machine learning, data mining, and learning analytics.Sapienza: International Journal of Interdisciplinary Studies['Bimba Katiuska Carrión Montalván', 'Carlos German Pillajo Angos', 'Jaime Aurelio Castellanos Fonseca', 'Raúl Vega Aguaiza']2024-05-03T00:00:00https://www.semanticscholar.org/paper/0b93953aac10b67204fd2f973fa2712c774590f8 2c4d02f380a2a4e97ebd7be2fa4c2f5769dd4393The integration of Artificial Intelligence (AI) in marketing strategies is pivotal in the era of digital transformation, especially in automation, personalization, and forecasting. This research investigates the evolutionary role of AI in transitioning from traditional marketing frameworks to data-driven methodologies, thereby enhancing marketing efficiency and customer engagement. The increasing reliance on AI for strategic decision-making in marketing underscores the significance of this study. Employing a systematic literature review and thematic analysis, this research synthesizes data from an array of studies to thoroughly understand the impact of AI on marketing. The findings reveal that AI significantly streamlines marketing operations, fosters highly personalized marketing strategies, and enhances the accuracy of forecasting market trends and consumer behavior. However, this study also sheds light on the ethical and privacy concerns associated with the use of AI in marketing. Results point towards a significant transformation in marketing practices propelled by AI, marked by improvements in operational efficiency and customer interaction. Nevertheless, the study advocates the importance of addressing ethical considerations and privacy issues, emphasizing responsible AI deployment. The study offers a comprehensive perspective on the integration of AI in marketing and suggests insights into prospective trends. It recommends a balanced approach to leveraging AI’s capabilities while upholding ethical standards. The research’s practical implications aim to guide marketers and researchers towards responsible and effective AI adoption in marketing strategies, paving the way for a future where technology enhances marketing endevaours without compromising ethical integrity.Journal of Management World550The research investigates the evolutionary role of AI in transitioning from traditional marketing frameworks to data-driven methodologies, thereby enhancing marketing efficiency and customer engagement and recommends a balanced approach to leveraging AI’s capabilities while upholding ethical standards.Journal of Management World['Maciej Potwora', 'Olha Vdovichena', 'Dmytrii Semchuk', 'Liubov Lipych', 'Volodymyr Saienko']2024-05-03T00:00:00https://www.semanticscholar.org/paper/2c4d02f380a2a4e97ebd7be2fa4c2f5769dd4393 6dada369e2020171d3945d9cb638dc8c09fbea9fGenerative artificial intelligence (genAI) technology is used among students, yet it remains unclear how public health students and instructors perceive it to be effective in a learning environment. We described how and why public health students and instructors are using genAI technology along with their perceived benefits and limitations of using genAI, noting where perceptions overlap. We surveyed public health students and instructors at a higher education institution in the United States. Student survey questions covered which genAI technologies they used, which activities they used genAI for, and perceived benefits and limitations of using genAI. Questions for instructors covered which genAI technology they used, course activities genAI was integrated, and perceived benefits and limitations of using genAI. Student respondents ( n = 300) indicated using genAI technology for writing or clarifying concepts. Students and instructors ( n = 62) agreed genAI technology could save time on tedious tasks and will be part of our future workforce. They agreed that appropriate use in the classroom will better prepare future professionals. Alternatively, students and instructors indicated genAI may impede learning, produce inaccurate information, and pose opportunities for unethical behavior. While students and instructors agree on many aspects of genAI technology, instructors should be explicit about their expectations and rationale for use of genAI technology in classrooms.Pedagogy in health promotion60While students and instructors agree on many aspects of genAI technology, instructors should be explicit about their expectations and rationale for use of genAI technology in classrooms.Pedagogy in Health Promotion['Olivia S. Anderson', 'Frederique A. Laubepin', 'Ella T. August']2024-05-03T00:00:00https://www.semanticscholar.org/paper/6dada369e2020171d3945d9cb638dc8c09fbea9f 21d9b4c4d1b30f9abde1a6124551a763c8f68fa9The International Journal of Cardiovascular Imaging930The focus of the review will be detailing the evidence, strengths, and limitations of end-to-end CACS algorithms in electrocardiography-gated and non-gated scans, as well as the current role of deep-learning image reconstructions, segmentation techniques, and combined applications such as simultaneous coronary artery calcium and pulmonary nodule segmentation.The international journal of cardiovascular imaging['A. Gennari', 'Alexia Rossi', 'Carlo N. De Cecco', 'M. van Assen', 'T. Sartoretti', 'Andreas A. Giannopoulos', 'M. Schwyzer', 'M. Huellner', 'M. Messerli']2024-05-03T00:00:00https://www.semanticscholar.org/paper/21d9b4c4d1b30f9abde1a6124551a763c8f68fa9 04e2c3f40a9c708f4ba97ddf1c5f53986fb15b97In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception. This article is protected by copyright. All rights reserved.Advances in Materials00The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability.Advanced materials['Atanu Bag', 'Gargi Ghosh', 'M. J. Sultan', 'Hamna Haq Chouhdry', 'Seok Ju Hong', 'Tran Quang Trung', 'Geun-Young Kang', 'Nae‐Eung Lee']2024-05-03T00:00:00https://www.semanticscholar.org/paper/04e2c3f40a9c708f4ba97ddf1c5f53986fb15b97 45cc0a45bc6411fb0752aa46a03f1f5b2f73799fTechnology, Knowledge and Learning330A systematic review of the literature on digital ecosystems and artificial intelligence around the personalization of learning highlights the current focus of research, which relates digital ecosystems and artificial intelligence around the personalization of learning.Technology, Knowledge and Learning['Milena Patricia Rojas', 'Andrés Chiappe']2024-05-03T00:00:00https://www.semanticscholar.org/paper/45cc0a45bc6411fb0752aa46a03f1f5b2f73799f 4c2d7ba374ce237f370cd8b40ed07f506854e89eThe LifeGuardAI project is a groundbreaking initiative that aims to utilize artificial intelligence to predict mortality rates associated with sepsis. The project utilizes Multilayer Perceptron (MLP) models and collaborative AI development techniques to provide healthcare professionals with advanced, AI-driven insights for preemptive intervention, ultimately enhancing patient- centered care. The project framework involves a comprehensive approach that begins with defining the problem statement focused on leveraging AI to improve sepsis-related outcomes. The dataset for this project is sourced from the Kaggle Prediction of Sepsis dataset, which contains crucial information related to patient health, such as vital signs, laboratory values, and demographic informationInternational Journal of Advanced Research in Science, Communication and Technology50International Journal of Advanced Research in Science, Communication and Technology['Kashish Joshi', 'K. Janardhan', 'M. Manohar', 'L. Harshavardhan', 'Mrs. D. Hima Bindu']2024-05-03T00:00:00https://www.semanticscholar.org/paper/4c2d7ba374ce237f370cd8b40ed07f506854e89e f02ae471c22d2ec3ca122ef035e2a9ee32edb7d9BMC Methods140The BMC Methods Collection "Artificial intelligence for omics data analysis" will feature novel artificial intelligence approaches leveraging multi-omics data to accelerate discoveries in personalized medicine, disease diagnostics, drug development, and biological pathway elucidation.BMC Methods['Zeeshan Ahmed', 'Shibiao Wan', 'Fan Zhang', 'Wen Zhong']2024-05-03T00:00:00https://www.semanticscholar.org/paper/f02ae471c22d2ec3ca122ef035e2a9ee32edb7d9 5281c00c3331706cd00ac8c8192bfba34df265f1In the dynamic landscape of digital marketing, businesses are constantly challenged to engage their target audience effectively amidst a plethora of information and choices. Artificial Intelligence (AI) emerges as a pivotal solution in addressing these challenges, revolutionizing traditional marketing approaches through its diverse applications and capabilities. This paper explores the multifaceted role of AI in digital marketing, drawing upon insights from existing literature and practical examples. AI empowers marketers with enhanced customer insights, predictive analytics, and automated campaign optimization, enabling them to tailor marketing strategies to individual preferences and behaviors. Moreover, AI-driven tools such as chatbots and virtual assistants streamline customer interactions, while also offering cost efficiencies and improved return on investment (ROI). Despite the undeniable benefits, the integration of AI in digital marketing raises concerns regarding data privacy, algorithmic bias, and ethical considerations. Nevertheless, by navigating these challenges responsibly and adopting ethical frameworks, businesses can leverage AI to unlock new opportunities for growth, differentiation, and sustainable success in the digital eraInternational Journal of Advanced Research in Science, Communication and Technology20The multifaceted role of AI in digital marketing is explored, drawing upon insights from existing literature and practical examples, to unlock new opportunities for growth, differentiation, and sustainable success in the digital era.International Journal of Advanced Research in Science, Communication and Technology['Prof. Kothiram N. Girsawale', 'Rushabh Vijay Mandavgade', 'Vibhusha Sonkusare', 'Mr. Murlidhar K. Jambhulkar']2024-05-03T00:00:00https://www.semanticscholar.org/paper/5281c00c3331706cd00ac8c8192bfba34df265f1 e15367410295b7bb737197608bab3ee1e6d47fe1PurposeThis paper reviews recent research on the expected economic effects of developing artificial intelligence (AI) through a survey of the latest publications, in particular papers and reports issued by academics, consulting companies and think tanks.Design/methodology/approachOur paper represents a point of view on AI and its impact on the global economy. It represents a descriptive analysis of the AI phenomenon.FindingsAI represents a driver of productivity and economic growth. It can increase efficiency and significantly improve the decision-making process by analyzing large amounts of data, yet at the same time it creates equally serious risks of job market polarization, rising inequality, structural unemployment and the emergence of new undesirable industrial structures.Practical implicationsThis paper presents itself as a building block for further research by introducing the two main factors in the production function (Cobb-Douglas): labor and capital. Indeed, Zeira (1998) and Aghion, Jones and Jones (2017) suggested that AI can stimulate growth by replacing labor, which is a limited resource, with capital, an unlimited resource, both for the production of goods, services and ideas.Originality/valueOur study contributes to the previous literature and presents a descriptive analysis of the impact of AI on technological development, economic growth and employment.Journal of Electronic Business &amp; Digital Economics220A descriptive analysis of the impact of AI on technological development, economic growth and employment and introduces the two main factors in the production function (Cobb-Douglas): labor and capital.Journal of Electronic Business &amp; Digital Economics['Mohamed Ali Trabelsi']2024-05-03T00:00:00https://www.semanticscholar.org/paper/e15367410295b7bb737197608bab3ee1e6d47fe1 e7e2363807f58490f314f1d661746a6d1404db60A mamografia de rastreio é uma ferramenta vital na detecção precoce do câncer de mama, comprovadamente reduzindo a mortalidade associada a essa doença. No entanto, enfrenta desafios, como limitada sensibilidade em casos de tecido mamário denso e erros interpretativos. A integração da inteligência artificial (IA) na interpretação mamográfica surge como uma solução promissora para superar essas limitações, com estudos recentes demonstrando desempenho equiparável ou superior ao dos radiologistas. Essa abordagem tem o potencial de aprimorar a detecção precoce, aumentando a confiabilidade diagnóstica. O câncer de mama é uma das principais causas de mortalidade feminina nos Estados Unidos, exigindo melhores métodos de detecção. A integração da IA na radiologia mamária é fundamental para melhorar o diagnóstico e tratamento. Essa revisão aborda aplicações, benefícios e desafios da IA na prática clínica, enfatizando sua importância como complemento à mamografia. Com o desenvolvimento contínuo, espera-se um impacto significativo na saúde das mulheres, salvando vidas e promovendo diagnósticos mais precisos.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação['Aline Angélica Peres Guerreiro', 'Kathlen Oliveira Martins Tiede', 'Flávia Larisse Rabelo', 'Márcio José Rosa Requeijo']2024-05-03T00:00:00https://www.semanticscholar.org/paper/e7e2363807f58490f314f1d661746a6d1404db60 7f9fe812010d5ed17c48c79ae28c9e7e39ed6dc1The current research aims to identify the effects of physical exercises accompanied by a diet on some health-related body morphology variables and weight loss among obese persons. The researcher used the experimental approach (one-group design) with pre- and post-measurements. The researcher purposefully chose (16) obese persons, (12) of them were recruited as a main sample while (4) were recruited as a pilot sample. Results indicated that: The recommended exercise program with diet had positive effects on health-related physical fitness variables (speed – endurance – agility – power – strength – cardiorespiratory strength) among obese persons. The recommended exercise program with diet had positive effects on weight reduction variables (body weight – BMI – Fat percentage) among obese persons. Exercise with diet helped decrease weight among obese persons. There are statistically significant differences between the pre-and post-measurements of health-related physical fitness variables (among obese persons in favor of post-measurements. There are statistically significant differences between the pre-and post-measurements of weight decrease among obese persons in favor of post-measurements. Journal of Electrical Systems230The current research aims to identify the effects of physical exercises accompanied by a diet on some health-related body morphology variables and weight loss among obese persons and indicates that exercise with diet helped decrease weight among obese persons.Journal of Electrical Systems['Ahmed Mohamed', 'Ahmed Elmaghraby']2024-05-03T00:00:00https://www.semanticscholar.org/paper/7f9fe812010d5ed17c48c79ae28c9e7e39ed6dc1 73907f516324c2f858ea7f5f09ba6156f639e372Global challenges presented by multidrug-resistant Acinetobacter baumannii infections have stimulated the development of new treatment strategies. We reported that outer membrane protein W (OmpW) is a potential therapeutic target in A. baumannii. Here, a library of 11,648 natural compounds was subjected to a primary screening using quantitative structure-activity relationship (QSAR) models generated from a ChEMBL data set with >7,000 compounds with their reported minimal inhibitory concentration (MIC) values against A. baumannii followed by a structure-based virtual screening against OmpW. In silico pharmacokinetic evaluation was conducted to assess the drug-likeness of these compounds. The ten highest-ranking compounds were found to bind with an energy score ranging from -7.8 to -7.0 kcal/mol where most of them belonged to curcuminoids. To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethoxycurcumin, was tested against a panel of A. baumannii strains to determine its antibacterial activity using microdilution and time-kill curve assays. To validate whether the compound binds to the selected target, an OmpW-deficient mutant was studied and compared with the wild type. Our results demonstrate that demethoxycurcumin in monotherapy and in combination with colistin is active against all A. baumannii strains. Finally, the compound was found to significantly reduce the A. baumannii interaction with host cells, suggesting its anti-virulence properties. Collectively, this study demonstrates machine learning as a promising strategy for the discovery of curcuminoids as antimicrobial agents for combating A. baumannii infections. IMPORTANCE Acinetobacter baumannii presents a severe global health threat, with alarming levels of antimicrobial resistance rates resulting in significant morbidity and mortality in the USA, ranging from 26% to 68%, as reported by the Centers for Disease Control and Prevention (CDC). To address this threat, novel strategies beyond traditional antibiotics are imperative. Computational approaches, such as QSAR models leverage molecular structures to predict biological effects, expediting drug discovery. We identified OmpW as a potential therapeutic target in A. baumannii and screened 11,648 natural compounds. We employed QSAR models from a ChEMBL bioactivity data set and conducted structure-based virtual screening against OmpW. Demethoxycurcumin, a lead compound, exhibited promising antibacterial activity against A. baumannii, including multidrug-resistant strains. Additionally, demethoxycurcumin demonstrated anti-virulence properties by reducing A. baumannii interaction with host cells. The findings highlight the potential of artificial intelligence in discovering curcuminoids as effective antimicrobial agents against A. baumannii infections, offering a promising strategy to address antibiotic resistance.mSystems480mSystems['Yassir Boulaamane', 'Irene Molina Panadero', 'A. Hmadcha', 'Celia Atalaya Rey', 'Soukayna Baammi', 'Achraf El Allali', 'Amal Maurady', 'Y. Smani']2024-05-03T00:00:00https://www.semanticscholar.org/paper/73907f516324c2f858ea7f5f09ba6156f639e372 233c543a9dda51b90864df11d2ba5eb6be217becForestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.Applied Sciences00Applied Sciences['Ferdinand Hoenigsberger', 'Anna Saranti', 'Anahid Jalali', 'K. Stampfer', 'Andreas Holzinger']2024-05-03T00:00:00https://www.semanticscholar.org/paper/233c543a9dda51b90864df11d2ba5eb6be217bec 56a6b4acee364f0db45784bc6e21aa2c81232666Acta Orthopaedica et Traumatologica Turcica00Acta Orthopaedica et Traumatologica Turcica['Tej D Azad', 'Vikas N. Vattipally', 'Christopher P. Ames']2024-05-03T00:00:00https://www.semanticscholar.org/paper/56a6b4acee364f0db45784bc6e21aa2c81232666 9ca61b2b0073bcba2284896faed2448fb37b5d89ANZ journal of surgery10ANZ journal of surgery['H. Daungsupawong', 'V. Wiwanitkit']2024-05-03T00:00:00https://www.semanticscholar.org/paper/9ca61b2b0073bcba2284896faed2448fb37b5d89 170886b8fa2ab25f8497070f07e00458b683d404Electrical Engineering280Electrical Engineering['Gulcihan Ozdemir']2024-05-03T00:00:00https://www.semanticscholar.org/paper/170886b8fa2ab25f8497070f07e00458b683d404 543fdaa92bcdb211ae5965227714f238a3162f1bJournal of Data, Information and Management250Journal of Data, Information and Management['Rebaka Sultana', 'M. Faruk']2024-05-03T00:00:00https://www.semanticscholar.org/paper/543fdaa92bcdb211ae5965227714f238a3162f1b 8e16eb5a9f4860b1b84c467cc95bacc569518c22BMJ Quality & Safety170BMJ quality & safety['Gurpreet Dhaliwal']2024-05-03T00:00:00https://www.semanticscholar.org/paper/8e16eb5a9f4860b1b84c467cc95bacc569518c22 e2b22b4fa804cdafc200dd1c707d2f3c27802ad7Educational Administration Theory and Practices00Educational Administration Theory and Practices['Cherry Kushwaha']2024-05-03T00:00:00https://www.semanticscholar.org/paper/e2b22b4fa804cdafc200dd1c707d2f3c27802ad7 d6eb732e5cb2a2093c295e7af942a6339add4ffcArtificial intelligence systems exhibit many useful capabilities, but they appear to lack understanding. This essay describes how we could go about constructing a machine capable of understanding. As John Locke (1689) pointed out words are signs for ideas, which we can paraphrase as thoughts and concepts. To understand a word is to know and be able to work with the underlying concepts for which it is an indicator. Understanding between a speaker and a listener occurs when the speaker casts his or her concepts into words and the listener recovers approximately those same concepts. Current models rely on the listener to construct any potential meaning. The diminution of behaviorism as a psychological paradigm and the rise of cognitivism provide examples of many experimental methods that can be used to determine whether and to what extent a machine might understand and to make suggestions about how that understanding might be instantiated.20This essay describes how to go about constructing a machine capable of understanding, and examples of many experimental methods that can be used to determine whether and to what extent a machine might understand and to make suggestions about how that understanding might be instantiated.['Herbert L. Roitblat']2024-05-03T00:00:00https://www.semanticscholar.org/paper/d6eb732e5cb2a2093c295e7af942a6339add4ffc 6309edb794f4b769bf6dd1fbd1412d425a984224AI and Ethics320A novel, collaborative initiative in Japan between researchers in the humanities and social sciences, and industry actors to co-design organizational AI ethics principles is reported on, finding that participants were not just concerned with trade-offs between ethical concerns and organizational benefits or technological development, but also between competing, ethically-oriented considerations.AI and Ethics['Amelia Katirai', 'Yusuke Nagato']2024-05-02T00:00:00https://www.semanticscholar.org/paper/6309edb794f4b769bf6dd1fbd1412d425a984224 125d309bb480c353099ae3bf235bda0d72e38367This article analyzes the impact of artificial intelligence (AI) on contemporary society and the importance of adopting an ethical approach to its development and implementation within organizations. It examines the technocritical perspective of some philosophers and researchers, who warn of the risks of excessive technologization that could undermine human autonomy. However, the article also acknowledges the active role that various actors, such as governments, academics, and civil society, can play in shaping the development of AI aligned with human and social values. A multidimensional approach is proposed that combines ethics with regulation, innovation, and education. It highlights the importance of developing detailed ethical frameworks, incorporating ethics into the training of professionals, conducting ethical impact audits, and encouraging the participation of stakeholders in the design of AI. In addition, four fundamental pillars are presented for the ethical implementation of AI in organizations: 1) Integrated values, 2) Trust and transparency, 3) Empowering human growth, and 4) Identifying strategic factors. These pillars encompass aspects such as alignment with the company's ethical identity, governance and accountability, human-centered design, continuous training, and adaptability to technological and market changes. The conclusion emphasizes that ethics must be the cornerstone of any organization's strategy that seeks to incorporate AI, establishing a solid framework that ensures that technology is developed and used in a way that respects and promotes human values.40It is emphasized that ethics must be the cornerstone of any organization's strategy that seeks to incorporate AI, establishing a solid framework that ensures that technology is developed and used in a way that respects and promotes human values.["Ernesto Giralt Hern'andez"]2024-05-02T00:00:00https://www.semanticscholar.org/paper/125d309bb480c353099ae3bf235bda0d72e38367 cf8f3d14fb027c590d9148978ac3789932ce4e6bMore attention has been paid to environmental regulation of greenhouse gas emissions in the energy industry under the transformation of industrial structure. This paper takes microdata of Chinese energy enterprises from 1998 to 2012 as a sample to build a duty-sharing model, analyzes the effect of environmental regulations on the industrial chain, and explains the “double growth” phenomenon that occurred in China, which is nothing short of miraculous in terms of the environment and economy. In the industrial chain, the environmental obligations and responsibilities will be shared between upstream and downstream enterprises due to trade linkages. This paper finds that environmental responsibilities will move forward through the industrial chain when environmental regulations are strengthened. Downstream companies will loosen “relative” control constraints, thereby expanding output but increasing demand for upstream products. Different from the existing research, we claim that, since environmental regulation has a differential effect on the industrial chain, it will promote the growth of output in the entire chain, in contrast to the theory of “cost compliance”, which claims that environmental regulation will inevitably lead to the output. Based on this research, this paper puts forward some suggestions and insights on how the government implements environmental regulations.Frontiers in Environmental Science400Frontiers in Environmental Science['Su Zhang', 'Qingyu Yan', 'Xin Huang', 'Bin Yan']2024-05-02T00:00:00https://www.semanticscholar.org/paper/cf8f3d14fb027c590d9148978ac3789932ce4e6b 2f86e5345947c0f30a1e623b3d286959af7f8852This study provides a thorough examination of the potential and problems associated with integrating artificial intelligence (AI) into the circular economy (CE) framework within Sri Lanka’s construction industry. The study uses approach that combines primary data obtained through a questionnaire survey involving several stakeholders with secondary data analysis from academic sources. The data were interpreted using descriptive and statistical analysis, such as Kendall’s Tau correlation and Pearson’s correlation. There is an optimistic view about AI’s potential advantages, including resource and energy conservation, even if the technology is still in its early integration phases. Nevertheless, there are still significant barriers to adoption, such as a lack of knowledge and reluctance to change. The study offers a conceptual framework for combining AI with CE principles, including IoT, computer vision, and machine learning technologies to enhance the Reduce, Reuse, and Recycle (3R) CE principles. This framework supports cooperative efforts, skill development, and policy development to support sustainable building practices in Sri Lanka.Mokslas: Lietuvos Ateitis270The study offers a conceptual framework for combining AI with CE principles, including IoT, computer vision, and machine learning technologies to enhance the Reduce, Reuse, and Recycle (3R) CE principles.Mokslas - Lietuvos ateitis['Thilina Ganganath Weerakoon', 'J. Sliogeriene', 'Zenonas Turskis']2024-05-02T00:00:00https://www.semanticscholar.org/paper/2f86e5345947c0f30a1e623b3d286959af7f8852 e1b96c878b1ff6eb2dc9fd1159a1ae8c1a4e089aAI Foundation models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, leaving their generalisability and biases unexplored. We present RayDINO, a large visual encoder trained by self-supervision on 873k chest X-rays. We compare RayDINO to previous state-of-the-art models across nine radiology tasks, from classification and dense segmentation to text generation, and provide an in depth analysis of population, age and sex biases of our model. Our findings suggest that self-supervision allows patient-centric AI proving useful in clinical workflows and interpreting X-rays holistically. With RayDINO and small task-specific adapters, we reach state-of-the-art results and improve generalization to unseen populations while mitigating bias, illustrating the true promise of foundation models: versatility and robustness.60RayDINO, a large visual encoder trained by self-supervision on 873k chest X-rays, is presented, suggesting that self-supervision allows patient-centric AI proving useful in clinical workflows and interpreting X-rays holistically.['T. Moutakanni', 'Piotr Bojanowski', 'G. Chassagnon', "C'eline Hudelot", 'Armand Joulin', 'Yann LeCun', 'Matthew Muckley', 'Maxime Oquab', 'M. Revel', 'M. Vakalopoulou']2024-05-02T00:00:00https://www.semanticscholar.org/paper/e1b96c878b1ff6eb2dc9fd1159a1ae8c1a4e089a 8b315e94a8abe3a08e18dd557e4b6bc8fc84b5b8 BACKGROUND: Navya-AI is a validated online cancer informatics solution that combines artificial intelligence based analysis of the guidelines and evidence, along with asynchronous expert review. Navya-AI releases preliminary system-generated opinions for patients whose treatment plans fit high confidence based on NCCN Guidelines and prior expert reviews. Prior research (SABCS 2014-2018 and ASCO 2021 showed: 1) 97% concordance of Navya-AI predictions with an academic medical center in India and in the US 2) 80% of patients implement treatment concordant with Navya-AI recommendations on the ground 3) Navya’s NCCN and evidence-based treatment plans reduce the patient waiting times for an expert opinion by an average by 3.5 days. Next generation sequencing (NGS) is an expensive but necessary method for identifying patients for risk reduction and therapy selection in breast cancer (BC). NGS testing can be over-used or under-used, and compliance with NCCN guidelines on patient selection is especially important in resource-constrained settings and Low Middle Income Countries (LMIC) such as India. The aim of this study is to find the physician compliance to NCCN guidelines on NGS testing in India, and how Navya-AI can identify the opportunity for improved care, and improve compliance with guidelines for optimal care. METHODS: From January 2022 to May 2023, Breast Cancer patients receiving Navya-AI treatment plan (based on guidelines and live expert-review) were analyzed. Patients who could afford NGS testing and targeted therapies, and who met the Enhanced tier of NCCN resource stratified guidelines for their care centers were identified. Their records were screened by Navya-AI to assess if they met NCCN criteria for NGS testing for risk reduction or for treatment selection (young age, positive family history, triple negative breast cancer etc.). Treating physicians were then analyzed if they had ordered NGS appropriately, or had missed an opportunity for NGS (undertreatment), or had over-ordered NGS (overtreatment).RESULTS: 521 BC patients who could afford NGS tests received a Navya-AI plan during this period. Of these, 85% were Indian patients (415/521) and were analyzed. Patients were diverse with respect to age,cancer stage, family history of cancer and histology: Age < 35: 6.7%, 35-50: 33%, 51-65: 43.76%, >65: 16.51%; early stage: 21.5%, locally advanced stage: 29.56%,metastatic stage: 47.79% and benign disease: 1%; positive family history of cancer: 37.35%, triple negative BC: 21.9%. Of 415 indian breast cancer patients who could afford testing, NGS testing was indicated in 69.64% (289/415) as per NCCN guidelines. The treating physician was compliant with NCCN guidelines in only 47.95% (199/415) of the cases: 19.2% (80/415) of the time, NGS was indicated and the treating physician ordered the same; 28.67% (119/415) of the time, NGS was not indicated, and the treating physician did not order NGS. The remaining 52% were not compliant with NCCN. Under-treatment was present in 50.36% (209/415) of cases, and was the vast majority of fallouts. Over-treatment was only present in 1.69% (7/415). In all 50.63% (209/415) cases, where NGS was not ordered by the treating oncologist, but was indicated, and affordable to the patient, Navya requested the patient to discuss the risks/benefits of NGS testing with their treating oncologist. CONCLUSION: NGS testing in BC patients has significant impact on risk-reduction, genetic counseling, choice of surgery, and treatment options for adjuvant therapy. Missed opportunities for NGS testing in more than 50% of the patients who can afford the testing and resultant therapies, points to a signficant area where compliance with guidelines and expert opinions can impact outcomes. A 'Technological Earthshot" that significantly increases adoption of guideline-based care is the first and an easy step towards 'Cancer Moonshots'. Citation Format: RA Badwe, Premal Thaker, Farzana Begum, Mayukh Acherjee, Gitika Srivastava, Naresh Ramarajan, Shona Nag, Sudeep Gupta, Seema Gulia, Jaya Ghosh, Bhawna Sirohi. NAVYA-AI enabled intervention to increase real-world guideline compliant care: Improving NGS testing in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-10-10.Cancer Research00This study aims to find the physician compliance to NCCN guidelines on NGS testing in India, and how Navya-AI can identify the opportunity for improved care, and improve compliance with guidelines for optimal care.Cancer Research['R. Badwe', 'Premal Thaker', 'F. Begum', 'Mayukh Acherjee', 'Gitika Srivastava', 'Naresh Ramarajan', 'S. Nag', 'Sudeep Gupta', 'S. Gulia', 'J. Ghosh', 'Bhawna Sirohi']2024-05-02T00:00:00https://www.semanticscholar.org/paper/8b315e94a8abe3a08e18dd557e4b6bc8fc84b5b8 30e387aa91f1cd8710b3dce0a42dd223866bc682 Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation in Black women of a mammography-driven AI breast cancer risk model (Mirai) originally developed on screening cohorts primarily consisting of White women. In this institutional review board-approved, Health Insurance Portability and Protection Act (HIPAA)-compliant study under a waiver of consent, we retrospectively analyzed a case–cohort sample nested within the core academic breast cancer screening practice of BJC Healthcare, the hospital partner of Washington University in St. Louis. For the purposes of this validation study, relying on 2D digital mammography (DM) images, we focused on Black women presenting for annual DM screening (Selenia or Selenia Dimensions; Hologic) between 2008 and 2018. Eligible breast cancer cases were derived from all women with a breast cancer diagnosis (with associated biopsy-confirmed tumor pathology via institutional cancer registry) after negative (BI-RADS 1 or 2) DM screening 1 to 5 years prior to cancer diagnosis. We also identified a random sample of controls, defined as women who had negative (BI-RADS 1 or 2) DM screening, with 1 to 5 years of screening follow-up without a cancer diagnosis. Risk scores for all DM exams were calculated via the Mirai model. Performance was evaluated using concordance-index (C) analyses and associated 95% confidence intervals (CIs) for the entire cohort, as well as for study subgroups of invasive versus in-situ cancer and cancer molecular subtypes. We analyzed 1368 DM screening exams, including 672 DM exams from 391 women diagnosed with breast cancer (mean age, 58 years; standard deviation, 10 years) and 696 DM exams from 406 controls (mean age, 55 years; standard deviation, 10 years). The overall C-index was 0.62 [95% CIs 0.60–0.64] for all Black women, which was lower compared to previously reported validation results for Mirai in studies of similar design on predominantly White and racially diverse screening cohorts (C-index = 0.67-0.78). There was no evidence of a significant difference between invasive and in-situ cancer (C-index = 0.64 [95% CIs 0.61–0.66] vs. 0.64 [95% CIs 0.61–0.67]). Compared to other cancer molecular subtypes, performance was significantly higher among triple-negative (C-index = 0.67 [95% CIs 0.62–0.71]) and estrogen receptor (ER) negative cancer (C-index = 0.66 [95% CIs 0.61–0.70]). A previously developed mammography-driven AI model showed overall good performance in long-term breast cancer risk assessment in a dataset of Black women only, particularly for triple-negative and ER- cancer types. However, performance was lower compared to previously reported validation results from similar studies on predominantly White and racially diverse screening cohorts. Our results suggest that further refinements are needed towards more accurate breast cancer risk assessment in Black women. Long-term breast cancer risk prediction performance (C-indices) of Mirai in the full cohort of Black women and in study subgroups. Citation Format: Juanita Hernandez Lopez, Zhixin Sun, Shirin Shoushtari, Ulugbek Kamilov, Debbie Bennett, Aimilia Gastounioti. Long-term Breast Cancer Risk Prediction in Black Women: External Validation of a Mammography-driven AI Model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-09.Cancer Research00A previously developed mammography-driven AI model showed overall good performance in long-term breast cancer risk assessment in a dataset of Black women only, however, performance was lower compared to previously reported validation results from similar studies on predominantly White and racially diverse screening cohorts.Cancer Research['Juanita Hernandez Lopez', 'Zhixin Sun', 'S. Shoushtari', 'U. Kamilov', 'Debbie L Bennett', 'A. Gastounioti']2024-05-02T00:00:00https://www.semanticscholar.org/paper/30e387aa91f1cd8710b3dce0a42dd223866bc682 10afc18d272c80ced30a62e17efef8981e8d7f76 Background: Image-derived AI risk models for breast cancer have shown high discriminatory performances compared with clinical risk models based on family history and lifestyle factors. However, little is known about their generalizability across different screening settings and clinical feasibility. Purpose: To investigate the predictive performance of a clinically used image-derived AI-based breast cancer risk model in multiple European screening populations. Methods: Four European mammographic screening populations in three countries screened between 2009-2020 for women aged 45-69 was used to perform a nested case-control study. In total, 739 women with incident breast cancers were included together with 7,812 controls matched to cases on year of study-entry. Mammographic features (density, microcalcifications, masses, left-right breast asymmetries of these features) for risk assessment were extracted using AI from full-field digital mammograms. Breast cancer occurrence was assessed after two years of follow-up. Absolute risks of breast cancer were predicted using the risk model from negative mammograms at study-entry. Adjusted Area Under the receiver operating characteristic Curves (aAUC) estimated discriminatory performance and, adjusted risk-ratios estimated the stratification performance of women at high/general risk per the clinical guidelines. Results: The overall aAUC of the AI risk model was 0.72 (95%CI 0.70-0.75), range 0.71 (95%CI 0.67-0.75) to 0.74 (95%CI 0.69-0.78) for breast cancers developed in four screening populations. In the 4.6% of women classified at high risk using the NICE guidelines thresholds, cancers were more likely diagnosed after 2 years follow-up, risk-ratio (RR) 6.7 (95%CI 5.6-8.0), compared with the 71% of women classified at general risk by the model. Similar risk-ratios were observed across tertiles of mammographic density. In the high-risk group, 22% of the 2-year future cancers were diagnosed, and 29% of stage 2 and higher cancers, p< 0.01. Conclusions: The AI risk model showed generalizable discriminatory performances across European populations and, captured ~30% of clinically relevant stage 2 and higher breast cancers in ~5% of high-risk women who were sent home with a negative mammogram. Similar results were seen in fatty and dense breasts. An image-derived AI model is feasible for personalized screening to improve screening outcomes. Citation Format: Mikael Eriksson, Marta Román, Axel Gräwingholt, Sylvia Heywang-Köbrunner, Paolo Rossi, Per Hall. Validation of a clinical image-based AI-risk model for individualized breast cancer screening in a multi-national setting [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-08.Cancer Research00The AI risk model showed generalizable discriminatory performances across European populations and, captured ~30% of clinically relevant stage 2 and higher breast cancers in ~5% of high-risk women who were sent home with a negative mammogram.Cancer Research['Mikael Eriksson', 'Marta Román', 'Axel Gräwingholt', 'Sylvia H. Heywang-Köbrunner', 'Paolo G. Rossi', 'P. Hall']2024-05-02T00:00:00https://www.semanticscholar.org/paper/10afc18d272c80ced30a62e17efef8981e8d7f76 a8e8f7d1fe0a0b728d732ef64108aea0b3fc83aaThe rapid emergence of generative Language Models (LMs) has led to growing concern about the impacts that their unexamined adoption may have on the social well-being of diverse user groups. Meanwhile, LMs are increasingly being adopted in K-20 schools and one-on-one student settings with minimal investigation of potential harms associated with their deployment. Motivated in part by real-world/everyday use cases (e.g., an AI writing assistant) this paper explores the potential psychosocial harms of stories generated by five leading LMs in response to open-ended prompting. We extend findings of stereotyping harms analyzing a total of 150K 100-word stories related to student classroom interactions. Examining patterns in LM-generated character demographics and representational harms (i.e., erasure, subordination, and stereotyping) we highlight particularly egregious vignettes, illustrating the ways LM-generated outputs may influence the experiences of users with marginalized and minoritized identities, and emphasizing the need for a critical understanding of the psychosocial impacts of generative AI tools when deployed and utilized in diverse social contexts.Proceedings of the AAAI Symposium Series290The potential psychosocial harms of stories generated by five leading LMs in response to open-ended prompting are explored and particularly egregious vignettes are highlighted, illustrating the ways LM-generated outputs may influence the experiences of users with marginalized and minoritized identities.Proceedings of the AAAI Symposium Series['Faye-Marie Vassel', 'Evan Shieh', 'Cassidy R. Sugimoto', 'T. Monroe-White']2024-05-02T00:00:00https://www.semanticscholar.org/paper/a8e8f7d1fe0a0b728d732ef64108aea0b3fc83aa 06e1f7e52a90366fc23aa1c372a55ca9cbef7b41This paper presents a set of intersectional feminist principles for conducting equitable, ethical, and sustainable AI research. In Data Feminism (2020), we offered seven principles for examining and challenging unequal power in data science. Here, we present a rationale for why feminism remains deeply relevant for AI research, rearticulate the original principles of data feminism with respect to AI, and introduce two potential new principles related to environmental impact and consent. Together, these principles help to 1) account for the unequal, undemocratic, extractive, and exclusionary forces at work in AI research, development, and deployment; 2) identify and mitigate predictable harms in advance of unsafe, discriminatory, or otherwise oppressive systems being released into the world; and 3) inspire creative, joyful, and collective ways to work towards a more equitable, sustainable world in which all of us can thrive.1250A rationale for why feminism remains deeply relevant for AI research is presented, the original principles of data feminism with respect to AI are rearticulate, and two potential new principles related to environmental impact and consent are introduced.['Lauren Klein', 'C. D’Ignazio']2024-05-02T00:00:00https://www.semanticscholar.org/paper/06e1f7e52a90366fc23aa1c372a55ca9cbef7b41 0ba11a3fe8600ca555a9505a9f5bc93a9867147cAI &amp; SOCIETY50This Open Forum paper argues for global governance of AI for moral reasons but also outlines the governance challenges that this project raises.AI &amp; SOCIETY['Mark Coeckelbergh']2024-05-02T00:00:00https://www.semanticscholar.org/paper/0ba11a3fe8600ca555a9505a9f5bc93a9867147c 482bc5a37eabcfc20934e33eb7e2036d9f80f0ffAI and Ethics210A proposal consisting in a semi-automated software model to verify the ethical compliance of an AI system’s code both at design-time (ethics-by-design perspective) and afterwards on the resulting software.AI and Ethics['Maria Assunta Cappelli', 'G. Di Marzo Serugendo']2024-05-02T00:00:00https://www.semanticscholar.org/paper/482bc5a37eabcfc20934e33eb7e2036d9f80f0ff 05b45fcd739d471ae6946b872ca276e772c31fefThis article sheds light on the profound impact of technology on consumer behavior, specifically focusing on the rise of generative AI tools. It highlights how these advancements have revolutionized consumer engagement, purchase decision‐making, and technology interaction. The article underscores the transformative potential of generative AI in shaping consumer behavior through personalized recommendations and interactive shopping experiences. It emphasizes the need for continued research and exploration to comprehend and effectively navigate the ever‐evolving landscape of consumer behavior influenced by generative AI. Additionally, the article identifies implications for research and practice, offers valuable strategies for brands, and presents a comprehensive research agenda to delve deeper into this field. Ultimately, it provides valuable insights into the challenges and opportunities presented by generative AI in consumer behavior, serving as a guiding resource for advancing theory, practice, and policy in this domain.Journal of Consumer Behaviour150The article underscores the transformative potential of generative AI in shaping consumer behavior through personalized recommendations and interactive shopping experiences and emphasizes the need for continued research and exploration to comprehend and effectively navigate the ever‐evolving landscape of consumer behavior influenced by generative AI.Journal of Consumer Behaviour['E. Mogaji', 'Varsha Jain']2024-05-02T00:00:00https://www.semanticscholar.org/paper/05b45fcd739d471ae6946b872ca276e772c31fef 56dc2e03a575764e6c00cae2a9c4c7f4c2b289e4Bangladesh's low-lying geography and climatic sensitivity are making Sylhet in particular more vulnerable to flooding. The Surma and Kushiara rivers spilling their banks is the primary source of the floods in Sylhet. These rivers flow into Bangladesh with more monsoon water from their source in northeastern India. A detailed study of upstream causes is necessary to develop effective mitigation techniques, particularly about the rainfall patterns in India's northeastern regions. To address this issue, we provide an approach that investigates the intricate link between rainfall patterns in Northeastern India and flood dynamics in Sylhet by combining Explainable AI (SHAP) with cutting-edge deep learning models. An important addition is the comparison of Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) and Feedforward Neural Networks (FNN), which are critical for interpreting time-series data in flood preparedness. LSTM-RNN shows strong temporal sensitivity with an R-squared of 0.94; FNN, on the other hand, is more precise with an enhanced R-squared of 0.96, a bigger Root Mean Squared Error of 0.112, and a smaller Mean Squared Error of 0.02.International Conference on Electrical Engineering and Information Communication Technology320An approach is provided that investigates the intricate link between rainfall patterns in Northeastern India and flood dynamics in Sylhet by combining Explainable AI (SHAP) with cutting-edge deep learning models and an important addition is the comparison of Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) and Feedforward Neural Networks (FNN), which are critical for interpreting time-series data in flood preparedness.2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)['Shahariar Hossain Mahir', 'MD Tanjum AN TASHRIF', 'Md. Amir Hamza', 'Tawfiqul Haq Tamim', 'Dipanjali Kundu', 'Anichur Rahman']2024-05-02T00:00:00https://www.semanticscholar.org/paper/56dc2e03a575764e6c00cae2a9c4c7f4c2b289e4 f81748bd941789f4b7b42b3c1f7e128032d9d0fcAs the pace of artificial intelligence (AI) evolution accelerates, the line separating authentic from AI-produced imagery becomes increasingly indistinct. This shift carries profound consequences for sectors such as content verification and digital investigation, underscoring the need for proficient AI-generated image identification systems. Our study utilizes established architectures like AlexNet, Convolutional Neural Networks (CNNs), and VGG16 to explore and evaluate the effectiveness of models based on transfer learning for spotting AI-crafted images. Transfer learning, which applies models pre-trained on large datasets, has proven beneficial in numerous computer vision tasks. In this research, we modify the intricate patterns recognized by AlexNet, CNNs, and VGG16 from extensive datasets to specifically target the detection of AI-generated content. We introduce models that are trained, validated, and tested on a comprehensive dataset that includes both real and AI-generated images. Our experimental findings demonstrate the utility of transfer learning methods in discerning between real and synthetic visuals. By conducting a comparative analysis, we highlight the comparative advantages and limitations of each model in terms of metrics such as precision, recall, accuracy, and the F1-score. Further, we investigate the distinct features identified by each model to elucidate their contribution to accurate classification.Journal of Electrical Systems260The utility of transfer learning methods in discerning between real and synthetic visuals is demonstrated and the comparative advantages and limitations of each model are highlighted in terms of metrics such as precision, recall, accuracy, and the F1-score.Journal of Electrical Systems['Ans Ibrahim Mahameed', 'Ali Awad Kadhim', 'Hussein Ali Aiiedane']2024-05-02T00:00:00https://www.semanticscholar.org/paper/f81748bd941789f4b7b42b3c1f7e128032d9d0fc 67ea59cd93506b090b7f6973d36fc0e61cfa830bGenerative artificial intelligence (GAI) is rapidly becoming ubiquitous in many contexts. There is limited scholarship, however, in the fields of Developmental Psychology and Early Childhood Education exploring the implications of generative AI for babies and young children. In this Perspectives piece, we discuss potential use cases, opportunities, and risks for the application of AI in early childhood. Our insights are informed by extensive discussion with stakeholders and by desk research carried out in our roles as academics and analysts in a social innovation foundation. Our aim is to stimulate nuanced and informed discourse on the topic of generative AI in early childhood that can inform innovation in both research and practice.Infant and Child Development240Potential use cases, opportunities, and risks for the application of AI in early childhood, as well as nuanced and informed discourse on the topic of generative AI in early childhood are discussed.Infant and Child Development['Karlis Kanders', 'Louis Stupple‐Harris', 'Laurie Smith', 'J. Gibson']2024-05-02T00:00:00https://www.semanticscholar.org/paper/67ea59cd93506b090b7f6973d36fc0e61cfa830b 1ce7cc7fead67e1fc44d1a29c2aca487ff331d35This study investigated the determinants of public sector managers' intentions to adopt artificial intelligence (AI) systems within their organizations. An extended technology acceptance model (TAM) was developed, incorporating additional constructs including fairness, humanity, reliability, safety, transparency, accountability, privacy, security, trust, social norms, tolerance, impact, and isomorphic pressure. A survey was conducted among 330 public sector managers, and the data were analyzed using linear regression tests to evaluate the model. The results showed significant positive influences of both perceived usefulness and perceived impact on managers' attitudes and behavioral intentions toward AI adoption. Isomorphic pressure was also a significant determinant of managers' behavioral intentions toward adopting AI systems. Our findings also indicated that perceptions related to AI ethical principles, such as transparency, privacy, and security, influenced managers' trust in AI systems.International Journal of Public Administration in the Digital Age720Perceptions related to AI ethical principles, such as transparency, privacy, and security, influenced managers' trust in AI systems, and their attitudes and behavioral intentions toward AI adoption.International Journal of Public Administration in the Digital Age['Khalid Majrashi']2024-05-02T00:00:00https://www.semanticscholar.org/paper/1ce7cc7fead67e1fc44d1a29c2aca487ff331d35 6ae07a6c857408012f6beffb0a29d3f9fd9c90fcObjective In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Methods Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5th edition of the Breast Imaging Reporting and Data System (BI-RADS), with categories 3 to 5 included in this study. The examinations were conducted at two municipal health centers from May 2021 to May 2023.The final pathological results from surgical resection or biopsy served as the gold standard for comparison. Ultrasonographic images were obtained in longitudinal and transverse sections, and two junior radiologists and one senior radiologist independently assessed the images without knowing the pathological findings. The BI-RADS classification was adjusted following AI assistance, and diagnostic performance was compared using receiver operating characteristic curves. Results A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, p < 0.05). The AI software improved the accuracy, sensitivity, and negative predictive value of the adjusted BI-RADS classification for the junior radiologists’ group (p< 0.05), while no difference was observed in the senior radiologist group. Furthermore, AI increased the negative predictive value for BI-RADS 4a masses and the positive predictive value for 4b masses among radiologists (p < 0.05). AI enhances the sensitivity of invasive breast cancer detection more effectively than ductal carcinoma in situ and rare subtypes of breast cancer. Conclusions The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.Frontiers in Oncology220The off-the-shelf artificial intelligence software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses.Frontiers in Oncology['Sai Ma', 'Yanfang Li', 'Jun Yin', 'Qinghua Niu', 'Zichen An', 'Lianfang Du', 'Fan Li', 'Ji-ying Gu']2024-05-02T00:00:00https://www.semanticscholar.org/paper/6ae07a6c857408012f6beffb0a29d3f9fd9c90fc 6a2c9e05e190bc2a7e084336a5cf0b1ab0799f5bThe exponential advancement of artificial intelligence (AI) techniques like generative adversarial networks has fueled the proliferation of synthesized media challenging reliable discrimination. As AI frameworks now conjure stunningly realistic imagery, developing enhanced systems to authenticate media provenance and expose forgeries is vital for sectors across the board. This research proposes a new convolutional neural network architecture to reliably distinguish genuine photographic images from AI-fabricated fakes. By analyzing underlying spatial correlations, noise patterns, and scene coherence contradictions among images, we compute intrinsic fingerprints exposing synthetic imagery provenance. The proposed architecture achieves 94.44% binary accuracy, 0.9863 AUC, 94.53% precision, and 95.61 % recall in discriminating real pictures from AI fakes, significantly outperforming the previous approaches. The proposed methodology hence delivers a breakthrough solution directly combating growing threats around misinformation and deception enabled by synthetic media. However, this research also exposes the deeper imperative for trustworthy AI design that perpetually outpaces the exponential curve of generative progress to enact techno-social guardrails before threats violate ethics, safety, or justice.International Conference on Electrical Engineering and Information Communication Technology220A new convolutional neural network architecture to reliably distinguish genuine photographic images from AI-fabricated fakes, and exposes the deeper imperative for trustworthy AI design that perpetually outpaces the exponential curve of generative progress to enact techno-social guardrails before threats violate ethics, safety, or justice.2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)['Abu Bakar Siddik', 'Sree Suvro Kumar Biswas', 'Afroza Islam', 'Md Saziduzzaman']2024-05-02T00:00:00https://www.semanticscholar.org/paper/6a2c9e05e190bc2a7e084336a5cf0b1ab0799f5b 9bfc6820e48ff0250e0f0accbb4a33a8572b83afArtificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.00This review focuses on the new applications of AI in manufacturing and healthcare, including physics integrated machine learning, model explainability, security, and governance during model deployment.['Jihua Chen', 'Yue Yuan', 'Amir Koushyar Ziabari', 'Xuan Xu', 'Honghai Zhang', 'Panagiotis Christakopoulos', 'P. Bonnesen', 'Ilia N. Ivanov', 'Panchapakesan Ganesh', 'Chen Wang', 'Karen Patino Jaimes', 'Guang Yang', 'Rajeev Kumar', 'B. Sumpter', 'R. Advíncula']2024-05-02T00:00:00https://www.semanticscholar.org/paper/9bfc6820e48ff0250e0f0accbb4a33a8572b83af e088a86a2ca2e58107b8f1b49dfe42b1baa55fc9PurposeAn AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer’s needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey.Design/methodology/approachThis article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics.FindingsThis article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer’s experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.Practical implicationsThis paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey.Originality/valueThis article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges’ fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain.Journal of Service Management720The article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.Journal of Service Management['Stephanie Q. Liu', 'K. Vakeel', 'Nicholas. A. Smith', 'Roya Sadat Alavipour', 'C. Wei', 'Jochen Wirtz']2024-05-02T00:00:00https://www.semanticscholar.org/paper/e088a86a2ca2e58107b8f1b49dfe42b1baa55fc9 db59a8e358d2eaf61747bc6cf7eca524f6863debArtificial intelligence (AI), specifically machine learning (ML), is adept at identifying patterns and insights from vast amounts of data from routine outcome monitoring (ROM) and clinical feedback during treatment. When applied to patient feedback data, AI/ML models can assist clinicians in predicting treatment outcomes. Common reasons for clinician resistance to integrating data‐driven decision‐support tools in clinical practice include concerns about the reliability, relevance and usefulness of the technology coupled with perceived conflicts between data‐driven recommendations and clinical judgement. While AI/ML‐based tools might be precise in guiding treatment decisions, it might not be possible to realise their potential at present, due to implementation, acceptability and ethical concerns. In this article, we will outline the concept of eXplainable AI (XAI), a potential solution to these concerns. XAI refers to a form of AI designed to articulate its purpose, rationale and decision‐making process in a manner that is comprehensible to humans. The key to this approach is that end‐users see a clear and understandable pathway from input data to recommendations. We use real Norse Feedback data to present an AI/ML example demonstrating one use case for XAI. Furthermore, we discuss key learning points that we will employ in future XAI implementations.Counselling and Psychotherapy Research220The concept of eXplainable AI (XAI), a potential solution to clinician resistance to integrating data‐driven decision‐support tools in clinical practice, is outlined and a key to this approach is that end‐users see a clear and understandable pathway from input data to recommendations.Counselling and Psychotherapy Research['Hans Jacob Westbye', 'Christian Moltu', 'A. McAleavey']2024-05-02T00:00:00https://www.semanticscholar.org/paper/db59a8e358d2eaf61747bc6cf7eca524f6863deb 35a04bdfd1c9284259eb6af194be2d074bf17005Attribution methods compute importance scores for input features to explain the output predictions of deep models. However, accurate assessment of attribution methods is challenged by the lack of benchmark fidelity for attributing model predictions. Moreover, other confounding factors in attribution estimation, including the setup choices of post-processing techniques and explained model predictions, further compromise the reliability of the evaluation. In this work, we first identify a set of fidelity criteria that reliable benchmarks for attribution methods are expected to fulfill, thereby facilitating a systematic assessment of attribution benchmarks. Next, we introduce a Backdoor-based eXplainable AI benchmark (BackX) that adheres to the desired fidelity criteria. We theoretically establish the superiority of our approach over the existing benchmarks for well-founded attribution evaluation. With extensive analysis, we also identify a setup for a consistent and fair benchmarking of attribution methods across different underlying methodologies. This setup is ultimately employed for a comprehensive comparison of existing methods using our BackX benchmark. Finally, our analysis also provides guidance for defending against backdoor attacks with the help of attribution methods.630This work identifies a set of fidelity criteria that reliable benchmarks for attribution methods are expected to fulfill, thereby facilitating a systematic assessment of attribution benchmarks and introduces a Backdoor-based eXplainable AI benchmark (BackX) that adheres to the desired fidelity criteria.['Peiyu Yang', 'Naveed Akhtar', 'Jiantong Jiang', 'Ajmal Mian']2024-05-02T00:00:00https://www.semanticscholar.org/paper/35a04bdfd1c9284259eb6af194be2d074bf17005 e496eba892d0b9744e528876a632608e8ebc1cd1Background: The use of ChatGPT in the learning process is becoming a common practice. Researchers identify opportunities to improve the learning process using AI tools. At the same time, there are many unresolved problems and threats from the use of ChatGPT. These include unreliable information, false information, lack of references to primary sources, lack of intellectual property protection, and especially the problem of plagiarism in academic texts. Objectives: The purpose of the study is to summarise the results of published research on the benefits and threats of using ChatGPT in higher education and to analyse the experience of using AI to write academic assignments by university students in compliance with the requirements of academic integrity. Methods: A survey was conducted among Kyiv National Economic University named after Vadym Hetman (KNEU) students about their experience of using ChatGPT in performing academic tasks and the degree of satisfaction with this tool. Results: The survey involved 58 KNEU students. We have analysed how satisfied students are with using ChatGPT for different learning purposes. Students are most satisfied with using ChatGPT to quickly find information and translate texts. The majority of respondents said that ChatGPT does not always provide accurate and reliable information. Students also pointed to the problem of violating academic integrity when using ChatGPT to complete their assignments. Conclusions: The study shows the general advantages and disadvantages of using ChatGPT in higher education. Particular attention should be paid to the level of borrowing in academic texts prepared with the help of ChatGPT.Review of Artificial Intelligence in Education130The study shows the general advantages and disadvantages of using ChatGPT in higher education and to analyse the experience of using AI to write academic assignments by university students in compliance with the requirements of academic integrity.Review of Artificial Intelligence in Education['Yeliena Prokhorova', 'Rashmi Gujrati', 'Hayri Uygun']2024-05-02T00:00:00https://www.semanticscholar.org/paper/e496eba892d0b9744e528876a632608e8ebc1cd1 8020018ddfa2e70729f4566c87d8cafb1729544eNuclear medicine and molecular imaging221BTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences and the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation is suggested.Nuclear Medicine and Molecular Imaging['S. Kang', 'Mina Heo', 'Ji Yeon Chung', 'Daewoon Kim', 'Seong A Shin', 'Hongyoon Choi', 'Ari Chung', 'Jung-Min Ha', 'Hoowon Kim', 'J. S. Lee']2024-05-02T00:00:00https://www.semanticscholar.org/paper/8020018ddfa2e70729f4566c87d8cafb1729544e 973d02a33e1c8fd4a771426766b7b7e5544cbe1bThis study investigates the underrepresentation of women in Artificial Intelligence (AI), Big Data, and Cloud Infrastructures, exploring the barriers and challenges they face and assessing the effectiveness of current policies and initiatives to promote gender diversity within the tech industry. Employing quantitative research methods, the study used a survey distributed to 572 female professionals in tech-related roles across various industries, achieving a 67.9% response rate. Multiple regression analysis was utilized to test four main hypotheses concerning barriers to entry and advancement, the inclusivity of educational programs, the impact of diverse teams on innovation and performance, and the effectiveness of gender-inclusive policies. Key findings indicate that the type of organization and specific tech sectors significantly influence the barriers experienced by women. Notably, gender diversity within teams correlates strongly with improved innovation and performance. However, educational and training programs often fail to be sufficiently inclusive, underscoring the need for programs better tailored to women's needs in tech fields. Moreover, the study confirms that implementing gender-inclusive policies substantially increases women's participation in tech roles, especially when these policies are applied long-term. Based on the findings, recommendations are made for adopting comprehensive, inclusive practices at organizational and educational levels, promoting diversity in team composition and leadership, committing long-term to effective policy implementation, and developing supportive networks through mentorship and sponsorship programs. These measures are aimed at reducing gender disparities and enhancing the integration of women into the high-tech economy. The study underscores the critical role that strategic policy-making and organizational change play in fostering an inclusive tech environment that not only addresses gender disparities but also enhances overall industry innovation and performance.Journal of Engineering Research and Reports00Recommendations are made for adopting comprehensive, inclusive practices at organizational and educational levels, promoting diversity in team composition and leadership, committing long-term to effective policy implementation, and developing supportive networks through mentorship and sponsorship programs.Journal of Engineering Research and Reports['Favour Amarachi Ezeugwa', 'O. O. Olaniyi', 'Jennifer Chinelo Ugonnia', 'Abayomi Shamsudeen Arigbabu', 'Princess Chimmy Joeaneke']2024-05-02T00:00:00https://www.semanticscholar.org/paper/973d02a33e1c8fd4a771426766b7b7e5544cbe1b d6728e53e085da407d0da7fad84fcae5775f3bc5 Background. Breast cancer treatment has improved overall survival rates, with different locoregional approaches offering patients similar locoregional control but variable aesthetic outcomes that may lead to disappointment and poor quality of life (QoL). There are no standardized methods for informing patients of the different therapies prior to intervention, nor validated tools for evaluation of aesthetics and patients' expectations. The CINDERELLA Project is based on years of research and developments of new healthcare technologies by various partners, aimed to provide an artificial intelligence (AI) tool to aid shared decision-making by showing breast cancer patients the predicted aesthetic outcomes of their locoregional treatment. The clinical trial will evaluate the use of this tool within an AI cloud-based platform approach (CINDERELLA App) versus a standard approach. We anticipate that the CINDERELLA App will lead to improved satisfaction, psychosocial well-being and health-related QoL while maintaining the quality of care and providing environmental and economic benefits. Trial design. CINDERELLA is an international multicentric interventional randomized controlled open-label clinical trial. Using the CINDERELLA App, the AI and Digital Health arm will provide patients with complete information about the proposed types of locoregional treatments and photographs of similar patients previously treated with the same techniques. The Control arm will follow the standard approach of each clinical site. Randomization will be conducted online using the digital health platform CANKADO, ensuring a balanced distribution of participants between the two groups. CANKADO is the underlying platform through which physicians control the patients' app content and conduct all data collection. Privacy, data protection and ethical principles in AI usage were taken into account. Eligibility criteria. Patients diagnosed with primary breast cancer without evidence of systemic disease. All patients must sign an informed consent and be able to use a web-based app autonomously or with home-based support. Specific aims. Primary objective: to assess the levels of agreement among patients' expectations regarding the aesthetic outcome before and 12 months after locoregional treatment. The trial will also evaluate the aesthetic outcome level of agreement between the AI evaluation tool and self-evaluation. Secondary objectives: health-related QoL (EQ-5D-5L and BREAST-Q ICHOM questionnaires) and resource consumption (e.g., time spent in the hospital, out-of-pocket expenses). The questionnaires and photographs will be applied prior to any treatment, at wound healing, at 6 and 12 months following the completion of locoregional therapy. Statistical methods. Wilcoxon signed rank test will be used to assess the intervention's impact on the agreement level between expectations and obtained results. Weighted Cohen's kappa will be calculated to measure the improvement in classifying aesthetic results with intervention. Statistical tests and/or bootstrap techniques will compare results between arms. A similarity measure will be calculated between self-evaluation and outcome obtained with the AI tool for each participant, and a beta regression model will be used to analyze the intervention's effect. Secondary objectives will be evaluated by scoring questionnaires based on provided guidelines. Target accrual. The clinical trial, led by Champalimaud Clinical Centre, will enroll a minimum of 515 patients in each arm between July 2023 and January 2025. Recruitment is currently open at five study sites in Germany, Israel, Italy, Poland and Portugal. The clinical trial is still open for further international study sites. Funding. European Union grant HORIZON-HLTH-2021-DISEASE-04-04 Agreement No. 101057389. Citation Format: Eduard-Alexandru Bonci, Orit Kaidar-Person, Marília Antunes, Oriana Ciani, Helena Cruz, Rosa Di Micco, Oreste Davide Gentilini, Nicole Rotmensz, Pedro Gouveia, Jörg Heil, Pawel Kabata, Nuno Freitas, Tiago Gonçalves, Miguel Romariz, Helena Montenegro, Hélder P. Oliveira, Jaime S. Cardoso, Henrique Martins, Daniela Lopes, Marta Martinho, Ludovica Borsoi, Elisabetta Listorti, Carlos Mavioso, Martin Mika, André Pfob, Timo Schinköthe, Giovani Silva, Maria-Joao Cardoso. CINDERELLA Clinical Trial (NCT05196269): using artificial intelligence-driven healthcare to enhance breast cancer locoregional treatment decisions [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-19-11.Cancer Research00The CINDERELLA App will lead to improved satisfaction, psychosocial well-being and health-related QoL while maintaining the quality of care and providing environmental and economic benefits, and the aesthetic outcome level of agreement between the AI evaluation tool and self-evaluation will be evaluated.Cancer Research['E. Bonci', 'O. Kaidar-Person', 'Marília Antunes', 'O. Ciani', 'Helena Cruz', 'R. D. Micco', 'Oreste D Gentilini', 'N. Rotmensz', 'Pedro Gouveia', 'Jörg Heil', 'P. Kabata', 'Nuno Freitas', 'Tiago Gonçalves', 'M. Romariz', 'Helena Montenegro', 'Hélder P. Oliveira', 'Jaime S. Cardoso', 'Henrique Martins', 'Daniela Lopes', 'M. Martinho', 'Ludovica Borsoi', 'E. Listorti', 'C. Mavioso', 'Martin Mika', 'André Pfob', 'T. Schinköthe', 'Giovani Silva', 'Maria-João Cardoso']2024-05-02T00:00:00https://www.semanticscholar.org/paper/d6728e53e085da407d0da7fad84fcae5775f3bc5 634421b117ea46472479e9757a4b2db4c293669fEmerging digital technologies promise to improve breast cancer care, however lack of awareness among clinicians often prevents timely adoption. This study aims to investigate current awareness and intention-to-use of three technologies among breast cancer healthcare professionals (HCP): (1) digital health applications (DHA), (2) artificial intelligence (AI), and (3) blockchain technology (BC). A 22-item questionnaire was designed and administered before and after a 30 min educational presentation highlighting technology implementation examples. Technology awareness and intention-to-use were measured using 7-point Likert scales. Correlations between demographics, technology awareness, intention-to-use, and eHealth literacy (GR-eHEALS scale) were analyzed. 45 HCP completed the questionnaire, of whom 26 (57.8%) were female. Age ranged from 24 to 67 {mean age (SD): 44.93 ± 12.62}. Awareness was highest for DHA (68.9%) followed by AI (66.7%) and BC (24.4%). The presentation led to a non-significant increase of intention-to-use AI {5.37 (±1.81) to 5.83 (±1.64)}. HCPs´ intention-to-use BC after the presentation increased significantly {4.30 (±2.04) to 5.90 (±1.67), p < 0.01}. Mean accumulated score for GR-eHEALS averaged 33.04 (± 6.61). HCPs´ intended use of AI significantly correlated with eHealth literacy (ρ = 0.383; p < 0.01), intention-to-use BC (ρ = 0.591; p < 0.01) and participants´ age (ρ = −0.438; p < 0.01). This study demonstrates the effect that even a short practical presentation can have on HCPs´ intention-to-use emerging digital technologies. Training potential professional users should be addressed alongside the development of new information technologies and is crucial to increase HCPs´ corresponding awareness and intended use.Frontiers in Medicine330It is demonstrated that even a short practical presentation can have on HCPs´ intention-to-use emerging digital technologies and is crucial to increase HCPs´ corresponding awareness and intended use.Frontiers in Medicine['Sebastian Griewing', 'Johannes Knitza', 'N. Gremke', 'M. Wallwiener', 'Uwe Wagner', 'Michael Lingenfelder', 'Sebastian Kuhn']2024-05-02T00:00:00https://www.semanticscholar.org/paper/634421b117ea46472479e9757a4b2db4c293669f 7e1aa9eb7e3e10978b3c7e91d455ac86b2aa6544 Background: Trastuzumab deruxtecan has substantially changed the treatment of HER2-low breast cancer, emphasizing the need for accurate differentiation between HER2 immunohistochemistry (IHC) scores of 0 and 1+. However, the current accuracy of HER2 IHC 0 and 1+ scoring in real-world is inadequate. Artificial intelligence (AI) has emerged as a potential solution to improve interpretation accuracy. We developed an AI algorithm based on whole slide images (WSI) to quantitatively assess HER2 expression and its role in interpreting HER2 IHC scores of 0 and 1+. Methods: Our three-phase AI analysis framework involved segmenting tumor areas (excluding ductal carcinoma in situ), detecting and classifying tumor cells based on membrane staining patterns, and grading HER2 IHC scores according to the 2018 ASCO/CAP HER2 guideline. The AI tool was trained using 6012 patches annotated by experienced pathologists. Performance evaluation was conducted using a test dataset comprising 265 slides. A total of 141 HER2 IHC slides (27 IHC 0 and 114 IHC 1+) from patients diagnosed with invasive breast cancer at Fudan University Shanghai Cancer Center in 2021 were included. Two pretrained expert pathologists independently rescored the HER2 slides, followed by analysis using the AI tool. All inconsistent cases were also reviewed by a third senior pathologist. Interobserver agreement between the pathologists and concordance between the pathologists and the AI interpretation results were assessed. We also explored potential ranges where HER2 interpretation by pathologists exhibited inconsistency or inaccuracy. Results: The HER2 AI algorithm showed high performance in interpreting all levels of HER2 expression, with an overall kappa value of 0.85. For HER2 IHC 0 and 1+ cases, the AI model demonstrated high sensitivities (0.839 and 0.914) and specificities (0.983 and 0.890) (Table 1). After the rescoring process, 51 cases were reclassified as IHC 0 and 90 cases as IHC 1+. The overall agreement rate between the historical and re-scoring results was 73.05% (103/141). The agreement rate between the AI and re-scoring results was 91.49% (129/141) (Table 2), indicating comparable interpretation capabilities between the AI model and well-trained pathologists. The interobserver agreement between the two pathologists was 83.69% (118/141), with agreement rates of 88.23% (45/51) for IHC 0 and 81.11% (73/90) for IHC 1+. Analyzing the inconsistent cases (n=12) between IHC 0 and IHC 1+, we examined the percentage of weak and incomplete expression provided by the AI. In 9 out of 12 cases, the AI provided the same results as the third senior pathologist, suggesting that AI could assist in interpretation within this range. We identified a range of 2.83% to 24.98% as a "grey-zone," where even well-trained pathologists provided inconsistent results. Conclusion: Our study demonstrates the excellent performance of an AI-based tool for scoring HER2 IHC 0 and 1+. The AI model exhibits comparable capabilities to well-trained pathologists in interpreting HER2 expression. Additionally, we identified a "grey-zone" among pathologists, highlighting the limitations of manual subjective interpretation. AI assistance within this zone may help mitigate subjective variations. Table 1. Correlation between pathologist read and AI read of HER2 immunohistochemistry. Table 2. Correlation between pathologist rescoring and AI read of HER2 immunohistochemistry. Citation Format: Ming Li, Hong Lv, Yizhi Zhao, Chenglu Zhu, Hansheng Li, Mingzhen Lin, Wen-Tao Yang. The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-26-09.Cancer Research00The HER2 AI algorithm showed high performance in interpreting all levels of HER2 expression, with an overall kappa value of 0.85, indicating comparable interpretation capabilities between the AI model and well-trained pathologists.Cancer Research['Ming Li', 'Hong Lv', 'Yizhi Zhao', 'Chenglu Zhu', 'Hansheng Li', 'Mingzhen Lin', 'Wen-tao Yang']2024-05-02T00:00:00https://www.semanticscholar.org/paper/7e1aa9eb7e3e10978b3c7e91d455ac86b2aa6544 451f87249e1a6dd21c76b45b7cffda0a464127abA growing number of organizations have used artificial intelligence (AI) to make decisions to replace human resource (HR) workers; yet, the fairness perceptions of the people affected by the decision are still unclear. Given that an organization’s sustainability is significantly influenced by individuals’ perceptions of fairness, this study takes a resume-screening scenario as an example to explore the impact of AI replacing humans on applicants’ perceptions of fairness. This study adopts the method of the online scenario experiment and uses SPSS to analyze the experimental data: 189 and 214 people, respectively, participated in two online scenarios, with two independent variables of decision makers (AI and humans), two dependent variables of procedural and distributive fairness, and two moderating variables of outcome favorability and the expertise of AI. The results show that the applicants tend to view AI screening resumes as less fair than humans. Furthermore, moderating effects exist between the outcome favorability and the expertise of AI. This study reveals the impact of AI substituting for humans in decision-making on fairness. The proposed model can help organizations use AI to screen resumes more effectively. And future research can explore the collaboration between humans and AI to make human resource management decisions.Sustainability00The results show that the applicants tend to view AI screening resumes as less fair than humans, and moderating effects exist between the outcome favorability and the expertise of AI.Sustainability['Fei Cai', 'Jiashu Zhang', 'Lei Zhang']2024-05-02T00:00:00https://www.semanticscholar.org/paper/451f87249e1a6dd21c76b45b7cffda0a464127ab 6d43458c4df05111b94101f60230e9238d75430fObjectivesThis study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection.MethodsSixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons.ResultsFifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001).ConclusionThe majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike.Australian Health Review00The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms, and considered AI as an optimal second-reader mode being the most ideal use in a screening program.Australian health review : a publication of the Australian Hospital Association['P. Trieu', 'M. Barron', 'Zhengqiang Jiang', 'S. Tavakoli Taba', 'Z. Gandomkar', 'S.J. Lewis']2024-05-02T00:00:00https://www.semanticscholar.org/paper/6d43458c4df05111b94101f60230e9238d75430f bfea2b9779b08bcefcdf3f580541ae833bf678adPurposeThe paper aims to reveal the intellectual structure of studies on artificial intelligence (AI) in the fields of tourism and hospitality. Evaluations regarding the intellectual structure have been made based on co-author, co-word and citation.Design/methodology/approachThe study is exploratory in nature. The study, using bibliometric analysis, provides a Web of Sciences (WOS) overview. The data has been obtained from the WOS database by coding as “artificial intelligence” and “tourism” and “hospitality.” VOSviewer program has been used to obtain and analyze the data.FindingsThe findings of the research show that studies on the use of AI in tourism and hospitality have become very popular, especially in the last 4 years. The authors of the study are working in the tourism and hospitality fields and have a high h-index. Generally, in current AI studies in tourism, topics such as robot, automation, ChatGPT, technology adoption and mechanical learning are studied. It has also been determined that topics related to the future of destinations and literature reviews are also discussed.Research limitations/implicationsAlthough this paper examines all studies identified as a result of filtering, the analysis is limited to 195 studies. However, due to the widespread use of AI in tourism-related studies recently, bibliometric analysis has been made with extensive filtering. As studies on the subject become more widespread in the coming years, it would be useful to repeat similar studies by filtering with more specific quotas.Originality/valueThere are a few similar studies on the subject in the field. However, these studies need to be repeated at certain periods. This paper contributes to monitoring the literature of AI studies, which are new to use in tourism and hospitality, and to the formation of a theoretical framework on the subject.Worldwide Hospitality and Tourism Themes410The intellectual structure of studies on artificial intelligence (AI) in the fields of tourism and hospitality is revealed to reveal and the literature of AI studies, which are new to use in tourism and hospitality, is monitored to form a theoretical framework on the subject.Worldwide Hospitality and Tourism Themes['Ümit Şengel', 'Merve Işkın']2024-05-02T00:00:00https://www.semanticscholar.org/paper/bfea2b9779b08bcefcdf3f580541ae833bf678ad a2c2b079781787ed8ada4e8fb2946f692650297dThis study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were "colonoscopy" (title) and "deep learning" (abstract). The eligibility criteria were: (1) the dependent variable of gastrointestinal disease; (2) the interventions of deep learning for classification, detection and/or segmentation for colonoscopy; (3) the outcomes of accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1, intersection of union (IOU), Dice and/or inference frames per second (FPS); (3) the publication year of 2021 or later; (4) the publication language of English. Based on the results of this study, different deep learning methods would be appropriate for different tasks for colonoscopy, e.g., Efficientnet with neural architecture search (AUC 99.8%) in the case of classification, You Only Look Once with the instance tracking head (F1 96.3%) in the case of detection, and Unet with dense-dilation-residual blocks (Dice 97.3%) in the case of segmentation. Their performance measures reported varied within 74.0-95.0% for accuracy, 60.0-93.0% for sensitivity, 60.0-100.0% for specificity, 71.0-99.8% for the AUC, 70.1-93.3% for precision, 81.0-96.3% for F1, 57.2-89.5% for the IOU, 75.1-97.3% for Dice and 66-182 for FPS. In conclusion, artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis.The Korean Journal of Internal Medicine00Artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis from detection to diagnosis.The Korean journal of internal medicine['Eun Sun Kim', 'Kwang-Sig Lee']2024-05-02T00:00:00https://www.semanticscholar.org/paper/a2c2b079781787ed8ada4e8fb2946f692650297d e5af96e37fc68ecca57f3412ceb6e0ea16a4272fEuropean Radiology Experimental690AI adoption in IR is more complex compared to diagnostic radiology, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.European Radiology Experimental['Pierluigi Glielmo', 'Stefano Fusco', 'S. Gitto', 'Giulia Zantonelli', 'Domenico Albano', 'Carmelo Messina', 'L. Sconfienza', 'Giovanni Mauri']2024-05-02T00:00:00https://www.semanticscholar.org/paper/e5af96e37fc68ecca57f3412ceb6e0ea16a4272f 0ecd61580b8485c9ca6b8b06cc424618e2bd58cfAs health systems organize to deliver the highest quality stroke care to their patients, there is increasing emphasis being placed on prehospital stroke recognition, accurate diagnosis, and efficient triage to improve outcomes after stroke. Emergency medical services (EMS) personnel currently rely heavily on dispatch accuracy, stroke screening tools, bypass protocols and prehospital notification to care for patients with suspected stroke, but novel tools including mobile stroke units and telemedicine-enabled ambulances are already changing the landscape of prehospital stroke care. Herein, the authors provide our perspective on the current state of prehospital stroke diagnosis and triage including several of these emerging trends. Then, we provide commentary to highlight potential artificial intelligence (AI) applications to improve stroke detection, improve accurate and timely dispatch, enhance EMS training and performance, and develop novel stroke diagnostic tools for prehospital use.Frontiers in Neurology450Commentary is provided to highlight potential artificial intelligence (AI) applications to improve stroke detection, improve accurate and timely dispatch, enhance EMS training and performance, and develop novel stroke diagnostic tools for prehospital use.Frontiers in Neurology['Zoe C. Wolcott', 'Stephen W. English']2024-05-02T00:00:00https://www.semanticscholar.org/paper/0ecd61580b8485c9ca6b8b06cc424618e2bd58cf 785f782d56a25abb213e2d6f1604a578b55490d0BMC Biomedical Engineering840GAI could represent the means by which BCI would be transformed into a prevalent assistive technology, thereby improving the quality of life of people with disabilities, and helping in adopting BCI as an emerging human-computer interaction technology for general use.BMC Biomedical Engineering['Seif Eldawlatly']2024-05-02T00:00:00https://www.semanticscholar.org/paper/785f782d56a25abb213e2d6f1604a578b55490d0 629d28742a733ff79f634d230e4290216f769e52In the realm of finance, decision-making processes have always been pivotal in determining the success or failure of individuals, businesses, and economies at large. With the advent of Artificial Intelligence (AI), this landscape is undergoing a profound transformation. The integration of AI technologies in financial decision-making is not merely an augmentation of existing processes but a fundamental shift in how decisions are conceived, analysed, and executed. This introduction seeks to delve into the evolving role of AI in financial decision-making, particularly within the context of the Indian financial sector. The rapid pace of technological advancement has propelled AI into the forefront of various industries, and finance is no exception. AI encompasses a spectrum of technologies, including machine learning, natural language processing, deep learning, and predictive analytics, among others. These tools offer the ability to process vast amounts of data, identify patterns, and generate insights at speeds and scales that were previously unimaginable. In the context of financial decision-making, this translates into enhanced capabilities for risk assessment, portfolio optimization, fraud detection, and customer service, among others. India, with its burgeoning economy and vibrant financial markets, stands at the cusp of this AI-driven revolution. The country's financial ecosystem, characterized by a diverse array of institutions ranging from traditional banks to fintech startups, is ripe for AI-led innovation. The adoption of AI in Indian finance is driven by several factors, including the proliferation of digital technologies, the increasing availability of data, and the imperative for efficiency and competitiveness in a globalized economy. Furthermore, initiatives such as the government's Digital India campaign and regulatory efforts to promote fintech innovation have created a conducive environment for AI adoption in the financial sector.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This introduction seeks to delve into the evolving role of AI in financial decision-making, particularly within the context of the Indian financial sector, with implications for efficiency and competitiveness in a globalized economy.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Utsav Goswami,']2024-05-02T00:00:00https://www.semanticscholar.org/paper/629d28742a733ff79f634d230e4290216f769e52 96db072ada9fa8d64f92419394e1120fc3957e22This paper presents the development of a novel anti-violence device titled “no pAIn” (an acronym for Never Oppressed Protected by Artificial Intelligence Nonviolence system), which harnesses the power of artificial intelligence (AI). Primarily designed to combat violence against women, the device offers personal safety benefits for individuals across diverse demographics. Operating autonomously, it necessitates no user interaction post-activation. The AI engine conducts real-time speech recognition and effectively discerns genuine instances of aggression from non-violent disputes or conversations. Facilitated by its Internet connectivity, in the event of detected aggression, the device promptly issues assistance requests with real-time precise geolocation tracking to predetermined recipients for immediate assistance. Its compact size enables discreet concealment within commonplace items like candy wrappers, purpose-built casings, or wearable accessories. The device is battery-operated. The prototype was developed using a microcontroller board (Arduino Nano RP2040 Connect), incorporating an omnidirectional microphone and Wi-Fi module, all at a remarkably low cost. Subsequent functionality testing, performed in debug mode using the Arduino IDE serial monitor, yielded successful results. The AI engine exhibited exceptional accuracy in word recognition, complemented by a robust logic implementation, rendering the device highly reliable in discerning genuine instances of aggression from non-violent scenarios.Electronics00Electronics['Agostino Giorgio']2024-05-02T00:00:00https://www.semanticscholar.org/paper/96db072ada9fa8d64f92419394e1120fc3957e22 2fd1a8afe147f3e00dfd04f2c6de46d5c475a249 The determination of the diagnosis of inflammatory periodontitis is generally based on clinical examination, which is then strengthened by radiographic examination. Still, the inequality of assessment of clinical conditions, along with limitations of radiographic interpretation, makes determining the diagnosis of the periodontal disease difficult. The use of artificial intelligence as a digital system approach is believed to reduce costs, time, the need for medical services, and medical errors that may occur due to human factors. This systematic review study is to analyze the use of dental and panoramic radiographs combined with the use of artificial intelligence in establishing the diagnosis of periodontitis based on the parameters of periodontal disease severity according to the 2017 American Academy of Periodontology/European Federation of Periodontology Workshop (pocket depth, clinical attachment loss (CAL) and the pattern and level of alveolar bone damage that occurs). Journal searches for articles published in English were carried out through the PubMed and Scopus databases in the 2011-2021 period, using the search terms periodontitis, periodontal disease, food impaction, trauma occlusion, periapical radiograph, panoramic, machine learning, artificial intelligence, and periodontal bone loss, after going through article selection, two suitable articles were obtained. Two studies fell into the analyzed category. Both list periodontal bone loss as a parameter that marks periodontitis, and the use of panoramic photos in detecting this parameter assisted by Convolutional Neural Networks as artificial intelligence. The use of panoramic radiographs and Convolutional Neural Networks as artificial intelligence that serves as a tool to detect periodontal bone damage has almost the same results as experienced clinicians In order for this method to be developed in the future to help clinicians establish the diagnosis, more clinical and image data will be required. Open Dentistry Journal440The use of dental and panoramic radiographs combined with the use of artificial intelligence in establishing the diagnosis of periodontitis based on the parameters of periodontal disease severity according to the 2017 American Academy of Periodontology/European Federation of Periodontology Workshop is analyzed.The Open Dentistry Journal['D. Fidyawati', 'S. L. Masulili', 'Hanna Bachtiar Iskandar', 'Heru Suhartanto', 'Y. Soeroso']2024-05-02T00:00:00https://www.semanticscholar.org/paper/2fd1a8afe147f3e00dfd04f2c6de46d5c475a249 235eafc2fccadf3ba07101ee1569f19929d05d3fDi era digital ini, kita perlu meningkatkan produktivitas kerja kita dengan perkembangan teknologi. Artificial intelligence tanpa ragu telah berkembang pesat di perusahaan-perusahaan untuk membantu manusia menjadi lebih efisien terutama sekretaris. Seorang sekretaris memainkan peran penting dalam memfasilitasi fungsi administratif, manajerial, dan operasional sebuah perusahaan. Mereka dipercayakan oleh pimpinan untuk mengelola jadwal, berkoordinasi antara departemen, mengatur pertemuan, menangani laporan, dan menjalankan tugas administratif lainnya. Penelitian ini membahas penggunaan personal assistant berbasis artificial intelligence untuk mendukung produktivitas sekretaris di perusahaan. Personal assistant berbasis artificial intelligence, seperti Google Assistant, memiliki dampak positif dalam meningkatkan efisiensi dengan mengotomatisasi tugas administratif dan menyederhanakan komunikasi. Jadi, sekretaris akan memiliki lebih banyak waktu untuk melakukan tugas lain dalam keseharian sekretaris. Penelitian ini mengidentifikasi manfaat penggunaan Google Assistant dalam mengelola jadwal, pesan, pencarian informasi, dan terjemahan bahasa. Teknologi asisten pribadi berbasis kecerdasan buatan telah terbukti memiliki potensi besar dalam meningkatkan produktivitas sekretaris di era digital. Penelitian ini juga membahas kelemahan Google Assistant, meskipun menyoroti keunggulan teknologi tersebut.Jurnal Bisnis Mahasiswa80Jurnal Bisnis Mahasiswa['Frinki', 'S. Hati', 'Fuad Arif', 'Rahman', 'Info Artikel', 'Kata Kunci']2024-05-02T00:00:00https://www.semanticscholar.org/paper/235eafc2fccadf3ba07101ee1569f19929d05d3f 6ca77635e7ab060a81a0c0c1e6b607bb75833a5dBridge2AI Voice: Voice Artificial Intelligence Symposium00Bridge2AI Voice: Voice Artificial Intelligence Symposium[]2024-05-02T00:00:00https://www.semanticscholar.org/paper/6ca77635e7ab060a81a0c0c1e6b607bb75833a5d 0451dd99b654aca67202d7d27317d3a1969d1c08Cancer Investigation141Cancer investigation['G. Lyman', 'N. Kuderer']2024-05-02T00:00:00https://www.semanticscholar.org/paper/0451dd99b654aca67202d7d27317d3a1969d1c08 84fc59d6e4490e4638d80aa0c327277c541a4cc8What are the effects of AI for decision making, workplace rights, transparency, surveillance, civil liberties and intellectual property?00['Ansh Bhatnagar', 'Devyani Gajjar']2024-05-02T00:00:00https://www.semanticscholar.org/paper/84fc59d6e4490e4638d80aa0c327277c541a4cc8 eef81f629485562511bc189ca18ccc39ae84252600['Yang He', 'Ning Liu', 'Jie Yang', 'Zhongheng Zhang']2024-05-02T00:00:00https://www.semanticscholar.org/paper/eef81f629485562511bc189ca18ccc39ae842526 02714f40324328c701226ea31519453a8a732fb1Background: Surgical site complications (SSCs) are common, yet preventable hospital-acquired conditions. Single-use negative pressure wound therapy (sNPWT) has been shown to be effective in reducing rates of these complications. In the era of value-based care, strategic allocation of sNPWT is needed to optimize both clinical and financial outcomes. Materials and Methods: We conducted a retrospective analysis using data from the Premier Healthcare Database (2017-2021) for 10 representative open procedures in orthopedic, abdominal, cardiovascular, cesarean delivery, and breast surgery. After separating data into training and validation sets, various machine learning algorithms were used to develop pre-operative SSC risk prediction models. Model performance was assessed using standard metrics and predictors of SSCs were identified through feature importance evaluation. Highest-performing models were used to simulate the cost-effectiveness of sNPWT at both the patient and population level. Results: The prediction models demonstrated good performance, with an average area under the curve of 76%. Prominent predictors across subspecialities included age, obesity, and the level of procedure urgency. Prediction models enabled a simulation analysis to assess the population-level cost-effectiveness of sNPWT, incorporating patient and surgery-specific factors, along with the established efficacy of sNPWT for each surgical procedure. The simulation models uncovered significant variability in sNPWT's cost-effectiveness across different procedural categories. Conclusions: This study demonstrates that machine learning models can effectively predict a patient's risk of SSC and guide strategic utilization of sNPWT. This data-driven approach allows for optimization of clinical and financial outcomes by strategically allocating sNPWT based on personalized risk assessments.Surgical Infections150This study demonstrates that machine learning models can effectively predict a patient's risk of SSC and guide strategic utilization of Single-use negative pressure wound therapy (sNPWT) based on personalized risk assessments.Surgical infections['Barrett J Larson', 'Ashley Roakes', 'Steve Yurick', 'Nathan A Netravali']2024-05-02T00:00:00https://www.semanticscholar.org/paper/02714f40324328c701226ea31519453a8a732fb1 bfe96ec5aa1522411a36cc8209be1a1457a63148 Background: Traditionally, multi-focal breast cancer results in mastectomy. The Alliance Trial offers a paradigm shift in surgical options available for multi-focal breast cancer patients in the context of adjuvant chemotherapy. In the trial, patients with multi-focal disease (< 3 tumors) who underwent breast conservation surgery (BCS) were found to have similar outcomes to patients undergoing mastectomy. BCS for large volume tumors ( >30%) has been cited as having a high potential for cosmetic defect, and hence represents a typical upper limit for potential tissue removal in BCS. Here, we evaluated a patient cohort to better understand the economic impact of the Alliance trial and further categorize patients that would most benefit without suffering cosmetic impact. We employed a novel computational technology to quantify the ratio of tumor size to breast tissue volume. Methods: Using a publicly available, single site cohort (n=243, DUMC) of breast cancer patients that underwent mastectomy, we segmented the tumors using our TumorSight Viz software platform. This platform uses artificial intelligence to segment the tumor and surrounding tissues and allows for a volumetric and morphologic assessment in 3D space. We then applied relevant inclusion/exclusion criteria from the Alliance Trial to the cohort (Saha et al, 2018). In trial eligible patients, we used TumorSight Viz to create a convex hull (CH) around the multi-focal disease using dilations of 1 cm and 2 cm. The volume of the CH, corresponding to proposed surgical extirpation, and the overall breast volume (BV) were then computationally assessed in 3D. The ratio of CH to BV (CH:BV) was calculated and a cutoff of 30% (high potential for cosmetic deformity) was applied. A cost analysis was then carried out. We determined the aggregate per annum savings that could potentially be realized by transforming a subset of mastectomies to BCS by tabulating total costs of mastectomy+reconstruction vs. BCS+WBI (whole-breast irradiation), as well as adjusted for relative rates of adjuvant therapy (~80%) across the nationwide patient population. Results: We found that 19.3% of adjuvant mastectomy patients were eligible for BCS based on Alliance Trial criteria. Of those, 68% had tumor CH:BV < 30% when using a 1 cm dilation around the tumor. When using a 2 cm dilation, 56% had tumor CH:BV < 30%. Together, these results indicate that of all adjuvant mastectomy patients, an estimated 10.8-13.1% are eligible for BCS based on volumetric measures of cosmetically acceptable breast tissue removal. Our economic analysis of BCS vs. mastectomy revealed an estimated $28,500 cost savings for patients with private insurance, suggesting that both decreased costs and improved quality-of-life (QOL) can be mutually aligned. By assessing the nationwide number of patients receiving adjuvant therapy for breast cancer, alongside the percentage potentially eligible for BCS using the above cosmetic defect analysis, we estimate that BCS conversion from mastectomy offers to provide a net savings of $300-350 million annually. Conclusion: The Alliance Trial guidelines unveiled the potential option of BCS in ~20% of patients with multi-focal disease in our cohort, demonstrating considerable cost-savings. Computational tools can further differentiate individuals who may not be best candidates for BCS in this setting, ensuring high QOL and informed decision-making. Citation Format: John Pfeiffer, Matthew Biancalana, Dorys Lopez-Ramos, Bradley Feiger, Anuja Antony. Applying the Alliance Trial Guidelines in Multi-focal Breast Disease Using an Artificial Intelligence Computational Platform: Economic Analysis and Cosmetic Sensitivity [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-04.Cancer Research00A patient cohort of breast cancer patients that underwent mastectomy was evaluated to better understand the economic impact of the Alliance trial and further categorize patients that would most benefit without suffering cosmetic impact, and a novel computational technology was employed to quantify the ratio of tumor size to breast tissue volume.Cancer Research['J. Pfeiffer', 'Matthew Biancalana', 'D. Lopez-Ramos', 'Bradley Feiger', 'A. Antony']2024-05-02T00:00:00https://www.semanticscholar.org/paper/bfe96ec5aa1522411a36cc8209be1a1457a63148 6fda53f8297d5dc623f26927802853b795b53f2fIn the rapidly evolving landscape of computing disciplines, substantial efforts are being dedicated to unraveling the sociotechnical implications of generative AI (Gen AI). While existing research has manifested in various forms, there remains a notable gap concerning the direct engagement of knowledge workers in academia with Gen AI. We interviewed 18 knowledge workers, including faculty and students, to investigate the social and technical dimensions of Gen AI from their perspective. Our participants raised concerns about the opacity of the data used to train Gen AI. This lack of transparency makes it difficult to identify and address inaccurate, biased, and potentially harmful, information generated by these models. Knowledge workers also expressed worries about Gen AI undermining trust in the relationship between instructor and student and discussed potential solutions, such as pedagogy readiness, to mitigate them. Additionally, participants recognized Gen AI's potential to democratize knowledge by accelerating the learning process and act as an accessible research assistant. However, there were also concerns about potential social and power imbalances stemming from unequal access to such technologies. Our study offers insights into the concerns and hopes of knowledge workers about the ethical use of Gen AI in educational settings and beyond, with implications for navigating this new landscape.00Insight is offered into the concerns and hopes of knowledge workers about the ethical use of Gen AI in educational settings and beyond, with implications for navigating this new landscape.['Afsaneh Razi', 'Layla Bouzoubaa', 'Aria Pessianzadeh', 'John S. Seberger', 'R. Rezapour']2024-05-02T00:00:00https://www.semanticscholar.org/paper/6fda53f8297d5dc623f26927802853b795b53f2f c76a853520d4a31b1b7ee3c0c59122a1c80b1e57Wireless personal communications220Wireless Personal Communications['Ke Tao', 'Jincan Lei', 'Jing Huang']2024-05-02T00:00:00https://www.semanticscholar.org/paper/c76a853520d4a31b1b7ee3c0c59122a1c80b1e57 4d4a4dedb3345c73e888946d86902aad3727e0fdJournal of Nuclear Medicine10Journal of nuclear medicine : official publication, Society of Nuclear Medicine['Bradley J Beattie']2024-05-02T00:00:00https://www.semanticscholar.org/paper/4d4a4dedb3345c73e888946d86902aad3727e0fd bcdd4e6dce9cfd543093f40305d0598e38be8418Intensive Care Medicine40Intensive care medicine['Lluis Blanch', 'Verónica Santos-Pulpón', 'Oriol Roca', 'L. Sarlabous', 'C. de Haro']2024-05-02T00:00:00https://www.semanticscholar.org/paper/bcdd4e6dce9cfd543093f40305d0598e38be8418 882bba603361fbed206ccb45e068854cf2388d86International Journal of Educational Technology in Higher Education20International Journal of Educational Technology in Higher Education['Olaf Zawacki-Richter', 'J. Bai', 'Kyungmee Lee', 'Patricia J. Slagter van Tryon', 'Paul Prinsloo']2024-05-02T00:00:00https://www.semanticscholar.org/paper/882bba603361fbed206ccb45e068854cf2388d86 d11053c80d7532239dfc3c29a0b9b88f737c0ecbEducation and Information Technologies : Official Journal of the IFIP technical committee on Education290Education and Information Technologies['B. Memarian', 'Tenzin Doleck']2024-05-02T00:00:00https://www.semanticscholar.org/paper/d11053c80d7532239dfc3c29a0b9b88f737c0ecb 8f619f2d292ee173a47d27dd03b7b1a154670187Cogent Business &amp; Management350Cogent Business &amp; Management['Ebtisam Labib']2024-05-02T00:00:00https://www.semanticscholar.org/paper/8f619f2d292ee173a47d27dd03b7b1a154670187 219da30f488158a98ce56354caad8471c54d5d37Journal of Nuclear Medicine60Journal of nuclear medicine : official publication, Society of Nuclear Medicine['J. Dutta', 'Vibha Balaji', 'Tzu-An Song']2024-05-02T00:00:00https://www.semanticscholar.org/paper/219da30f488158a98ce56354caad8471c54d5d37 d3637823c33d37656705a557faf6351622ba1a0aBMC Health Services Research340A stakeholder analysis was conducted to identify relevant stakeholders, explore their views on the proposed reform and develop strategies for managing ‘important’ stakeholders, and five strategies were developed to maintain and improve the support of ‘important’ stakeholders.BMC Health Services Research['R. Newlands', 'H. Bruhn', 'Magdalena Rzewuska Díaz', 'Gerald Lip', 'Lesley A Anderson', 'Craig Ramsay']2024-05-02T00:00:00https://www.semanticscholar.org/paper/d3637823c33d37656705a557faf6351622ba1a0a 836e887a8cf5389124ed5d989a2dfefe6d8c293aArtificial intelligence (AI)-based generative imaging systems such as DALL·E, Midjourney, Stable Diffusion, and Adobe Firefly, which work by transforming natural language descriptions into images, are revolutionizing computer vision. In this exploratory and qualitative research, we have replicated requests for images of women in different professions by comparing these representations in previous studies with DALL·E, observing that this model continues to provide in its last version, DALL·E 3, inequitable results in terms of gender. In addition, Bing Image Creator, Microsoft’s free tool that is widely used among the population and runs under DALL·E, has been tested for the first time. It also presents a sexualization of women and stereotypical children’s representations. The results reveal the following: 1. A slight improvement in terms of the presence of women in professions previously shown only with men. 2. They continue to offer biased results in terms of the objectification of women by showing sexualized women. 3. The representation of children highlights another level of gender bias, reinforcing traditional stereotypes associated with gender roles from childhood, which can impact future decisions regarding studies and occupations.The social science140Replicating requests for images of women in different professions by comparing these representations in previous studies with DALL·E shows that this model continues to provide inequitable results in terms of gender, and Bing Image Creator, Microsoft’s free tool, presents a sexualization of women and stereotypical children’s representations.Social Sciences['Teresa Sandoval-Martin', 'Ester Martínez-Sanzo']2024-05-02T00:00:00https://www.semanticscholar.org/paper/836e887a8cf5389124ed5d989a2dfefe6d8c293a a1d439faae928e71baf7807ad5e40a3ef4d7832aThis paper provides evidence of student perspectives of Artificial Intelligence (AI) in Higher Education (HE). In contrast to the extant literature that uses large-scale survey data to gather the student voice, research methods that prioritise qualitative data collection are presented. The data responds to a gap in the empirical evidence, redressing the lack of qualitative data on students’ thoughts and feelings in response to AI within a UK context. The paper also compares categorisations of concern relating to AI in HE between that published by academics and that generated by students using their own frames of reference. Recommendations for HE institutions are provided in response to issues identified in the literature and the research data.Research and Practice in Technology Enhanced Learning610Evidence of student perspectives of Artificial Intelligence (AI) in Higher Education (HE) is provided and research methods that prioritise qualitative data collection are presented, responding to a gap in the empirical evidence.Research and Practice in Technology Enhanced Learning['Aisling Tierney', 'Peter Peasey', 'Joe Gould']2024-05-02T00:00:00https://www.semanticscholar.org/paper/a1d439faae928e71baf7807ad5e40a3ef4d7832a efdf2b2aae13196a9526d8e7b7cea4b0a892371cWe calculate the social return on algorithmic interventions (specifically, their marginal value of public funds (MVPF)) across multiple domains of interest to economists—regulation, criminal justice, medicine, and education. Though these algorithms are different, the results are similar and striking. Each one has an MVPF of infinity: not only does it produce large benefits, it provides a “free lunch.” We do not take these numbers to mean these interventions ought to be necessarily scaled but rather that much more research and development should be devoted to developing and carefully evaluating algorithmic solutions to policy problems.Social Science Research Network100SSRN Electronic Journal['Jens O. Ludwig', 'S. Mullainathan', 'Ashesh Rambachan']2024-05-01T00:00:00https://www.semanticscholar.org/paper/efdf2b2aae13196a9526d8e7b7cea4b0a892371c 075fc2ad488714071bf284072d705701589477f1Mayo Clinic Proceedings: Digital Health00Mayo Clinic Proceedings: Digital Health['Hannah van Kolfschooten', 'Astrid Pilottin']2024-05-01T00:00:00https://www.semanticscholar.org/paper/075fc2ad488714071bf284072d705701589477f1 276a8079c94b558250c7a95608d5d00c3bd5393dThe journal of academic librarianship500The Journal of Academic Librarianship['Yung-Hsiang Hu', 'Chieh-Lun Hsieh', 'Ellen San Nicolas Salac']2024-05-01T00:00:00https://www.semanticscholar.org/paper/276a8079c94b558250c7a95608d5d00c3bd5393d c26814c259a3cad4e30828fe6f89d088af115670Environmental regulation, as a tool for the government and society to reduce environmental pollution and achieve social civilization and sustainable economic development, has long been a key factor in economic development, and it is of great significance to explore the impact of environmental regulation on the competitiveness of the pharmaceutical manufacturing industry on the development of China's manufacturing industry. Based on China's provincial panel data from 2011-2021, an evaluation system is constructed from four dimensions: scale, efficiency, innovation and input, the entropy method is applied to measure the competitiveness indicators of China's pharmaceutical manufacturing industry, and a benchmark regression is constructed through a two-way fixed-effects model, and heterogeneity test, robustness test and mediation test are conducted. It is found that environmental regulation has a significant positive effect on the competitiveness of China's pharmaceutical manufacturing industry, and there are differences in the impact on each region. In addition to direct empowerment, environmental regulation also enhances the competitiveness of pharmaceutical manufacturing industry through technological innovation, and its mediation effect is significant. Finally, based on the above findings, it is recommended that the intensity of environmental regulations and subsidies should be increased, differentiated environmental regulation policies should be formulated, and milder market interventions should be used to create a healthy and positive market environment, and the introduction of scientific and technological talents should be increased in order to enhance the scientific research strength of China's pharmaceutical manufacturing industry.Asian Journal of Economics Business and Accounting01It is found that environmental regulation has a significant positive effect on the competitiveness of China's pharmaceutical manufacturing industry, and there are differences in the impact on each region.Asian Journal of Economics, Business and Accounting['Yuqin Wang', 'Jun Sun']2024-05-01T00:00:00https://www.semanticscholar.org/paper/c26814c259a3cad4e30828fe6f89d088af115670 e303633673653de44814631febb44f2695cdb88eThe extreme complexity of many real-world tasks poses considerable challenges to agents' decision making. Most existing models only rely on task-related data for knowledge learning, while ignoring the important influence of potential task-agnostic factors. Effective learning coupled with both task-related and task-agnostic data can strongly enrich the agent's knowledge and improve its decision making performance. Furthermore, many existing learning models simply leverage data to learn knowledge but fail to express the thought process of decision making as humans do, which significantly limits their explanatory capability. To this end, we propose a novel knowledge flow empowered cognitive framework for real-world tasks. To obtain more reliable and trustworthy knowledge, a bottom-up knowledge learning model is developed, which incorporates both task-related data and task-agnostic data for comprehensive knowledge accumulation and value assessment of influencing factors. To demonstrate the thought process of decision making, a top-down knowledge expression model is proposed to coordinate different influencing factors by a knowledge flow structure. Two real-world case studies, including traffic anomaly detection and vehicle following anomaly detection, are introduced, where task-agnostic data are presented for the first time in both tasks. Experimental evaluation demonstrates the strong necessity of incorporating task-agnostic data in knowledge accumulation for real-world tasks and the effectiveness of our cognitive framework.IEEE Transactions on Artificial Intelligence500A bottom-up knowledge learning model is developed, which incorporates both task-related data and task-agnostic data for comprehensive knowledge accumulation and value assessment of influencing factors and to demonstrate the thought process of decision making.IEEE Transactions on Artificial Intelligence['Liming Huang', 'Yulei Wu', 'Niccoló Tempini']2024-05-01T00:00:00https://www.semanticscholar.org/paper/e303633673653de44814631febb44f2695cdb88e ec5d4a7e51e958b55b12ee7aa81fbe285066d6f4In the context of sustainable development and the increasing shift to non-fossil alternative energy sources, solar energy offers countless advantages for its conversion into electricity. Modern technologies offer great opportunities for the introduction of artificial intelligence in the process of predicting the production of solar energy. However, this topic is still quite unexplored in the international scientific community, which makes this study relevant. The purpose of this study is to analyze the impact of artificial intelligence on solar energy production forecasting. To achieve the purpose of the study, a systematic literature analysis method was applied. As a result of the study, it was possible to establish that artificial intelligence has great potential for its implementation in the process of forecasting solar energy production. The study was able to establish random prediction models and machine learning models based on artificial intelligence for their cost effectiveness and risk in the process of forecasting solar energy production. During the literature review, it became clear that the following four models are the most effective in the work: the RFR, LIME, ELI5 and SHAP. Each model has its own advantages and disadvantages. These are manifested in production management, forecasting with high speed, flexibility, and explanation, reducing the risk of variability. However, the cost-effectiveness of implementing artificial intelligence in the process of forecasting solar energy production has much more economic efficiency than the risk aspects.Futurity Economics&amp;Law00Futurity Economics&amp;Law[]2024-05-01T00:00:00https://www.semanticscholar.org/paper/ec5d4a7e51e958b55b12ee7aa81fbe285066d6f4 dcd6e19ac1544c58dc33f7fdc5ca96822e232081Artificial intelligence (AI) has come to improve digital marketing. With the help of AI, marketers can produce better products, make delivery faster, attract customers more effectively, and understand customer behavior. The company is using AI in marketing to create a more personalized experience across all channels. It will help marketers to better understand their audience interaction with their brand and better understand how their audience interacts with it and how they can attach to it. To create a balance between using AI and remaining transparent with customers, as more companies are funding AI-based products, customers want more transparency around how these technologies work—and what they mean for privacy. AI can be used in many ways, from improving website navigation to using computer vision to build more targeted ads. With the help of AI, many digital marketing tools are being used by marketers in daily transactions. It can help them in many ways, like social media management, graphic design, content creation, and research. It can also help them to provide better services to their customers. Without any human intervention, AI might be able to give a simple question’s answer about a company's products or services. With the help of AI, marketers' lives can be easier in the future by automating much of their present activity and providing information about consumers. In this study, conceptual methodology is used as the basis to collect secondary data and achieve the study objectives.International journal of engineering science & humanities01Artificial intelligence (AI) can help marketers to better understand their audience interaction with their brand and better understand how their audience interacts with it and how they can attach to it.International Journal of Engineering Science and Humanities['Mrs. Santosh']2024-05-01T00:00:00https://www.semanticscholar.org/paper/dcd6e19ac1544c58dc33f7fdc5ca96822e232081 91107e7387860f8cab63f42ec0eb347f36a2c3ddArtificial intelligence (AI) is experiencing advances and integration in all medical specializations, and this creates excitement but also concerns. This narrative review aims to critically assess the state of the art of AI in the field of endometriosis and adenomyosis. By enabling automation, AI may speed up some routine tasks, decreasing gynecologists’ risk of burnout, as well as enabling them to spend more time interacting with their patients, increasing their efficiency and patients’ perception of being taken care of. Surgery may also benefit from AI, especially through its integration with robotic surgery systems. This may improve the detection of anatomical structures and enhance surgical outcomes by combining intra-operative findings with pre-operative imaging. Not only that, but AI promises to improve the quality of care by facilitating clinical research. Through the introduction of decision-support tools, it can enhance diagnostic assessment; it can also predict treatment effectiveness and side effects, as well as reproductive prognosis and cancer risk. However, concerns exist regarding the fact that good quality data used in tool development and compliance with data sharing guidelines are crucial. Also, professionals are worried AI may render certain specialists obsolete. This said, AI is more likely to become a well-liked team member rather than a usurper.Journal of Clinical Medicine1100This narrative review aims to critically assess the state of the art of AI in the field of endometriosis and adenomyosis to ensure that AI is more likely to become a well-liked team member rather than a usurper.Journal of Clinical Medicine['G. Cetera', 'A. E. Tozzi', 'Valentina Chiappa', 'Isabella Castiglioni', 'C. Merli', 'P. Vercellini']2024-05-01T00:00:00https://www.semanticscholar.org/paper/91107e7387860f8cab63f42ec0eb347f36a2c3dd f3f2a38d41b098f93f7fc6a25067f1b44ebe4a59Breast cancer represents a significant health concern, particularly in Saudi Arabia, where it ranks as the most prevalent cancer type among women. This study focuses on leveraging eXplainable Artificial Intelligence (XAI) techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients. Six distinct models were trained and evaluated based on common performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC score. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were applied. The analysis identified the Random Forest model as the top performer, achieving an accuracy of 0.72, along with robust precision, recall, F1 score, and AUC-ROC score values. Conversely, the Support Vector Machine model exhibited the poorest performance metrics, indicating its limited predictive capability. Notably, the XAI approaches unveiled variations in the feature importance rankings across models, underscoring the need for further investigation. These findings offer valuable insights into breast cancer diagnosis and machine learning interpretation, aiding healthcare providers in understanding and potentially integrating such technologies into clinical practices.Healthcare700This study focuses on leveraging eXplainable Artificial Intelligence techniques to predict benign and malignant breast cancer cases using various clinical and pathological features specific to Saudi Arabian patients, and identifies the Random Forest model as the top performer.Healthcare['Turki Alelyani', 'Maha M. Alshammari', 'Afnan Almuhanna', 'Onur Asan']2024-05-01T00:00:00https://www.semanticscholar.org/paper/f3f2a38d41b098f93f7fc6a25067f1b44ebe4a59 09f319f51c022aaa71a1a2928f153e618f8f293aThe circular economy (CE) presents a contemporary approach to integrating economic activity and environmental well‐being sustainably, opposing the linear open‐ended system. However, implementing this paradigm poses challenges, needing a fundamental shift in companies' strategies and business models. Emerging technology can support companies in transition toward CE and in particular artificial intelligence (AI) can play a crucial role in facilitating this transition by supporting companies not only in specific CE activities but also in strategically redefining their entire business models. Recognizing the increasing importance of both AI and CE in management literature, our paper seeks to integrate these two distinct streams of literature. Starting from existing studies, it tries to provide a framework able to understand the role of AI technology into enhancing CE. Through a systematic literature review using the Preferred Reporting Items for Systematic Review and Meta‐Analysis protocol, using keywords research on three databases, we identified 63 articles that concurrently address both CE and AI topics. Subsequent co‐occurrence and content analyses revealed how AI is utilized to bolster CE efforts leveraging the ReSOLVE framework (from the acronymous of Regenerate, Share, Optimise, Loop, Virtualise, and Exchange)—an operational tool for CE. The last step consists into the development of a conceptual framework outlining four stages of AI's engagement with the CE: namely System optimization, System redesign, Business Model redesign, and Ecosystem innovation. From our study emerges that while AI is already recognized for enhancing specific activities within CE, its potential as a strategic planning tool for business model redesign and ecosystem innovation remains largely unexplored.Corporate Social Responsibility and Environmental Management490From this study, it emerges that while AI is already recognized for enhancing specific activities within CE, its potential as a strategic planning tool for business model redesign and ecosystem innovation remains largely unexplored.Corporate Social Responsibility and Environmental Management['Ilaria Tutore', 'A. Parmentola', 'Michele Costagliola di Fiore', 'F. Calza']2024-05-01T00:00:00https://www.semanticscholar.org/paper/09f319f51c022aaa71a1a2928f153e618f8f293a 03e44b87cafc556bcf356fb035192942e7d409dfThe integration of AI into HRM practices will mark a turning point in the history of organizational dynamics, bringing with it improved productivity and fresh perspectives on long-term planning. Because of this integration, many difficult questions and ethical dilemmas emerge. The hazy waters of artificial intelligence (AI) in human resource management are explored in this study, which examines its effects on training, engagement, performance reviews, and recruiting. Examining the AI-HRM nexus, this study draws on recent literature and statistics to highlight key developments, motivating factors, and obstacles. In addition, it explores the ways AI is changing HRM practices, illuminating the potential for innovation as well as concerns about prejudice and privacy invasion. We are examining the openness, responsibility, and fairness of AI-driven HRM systems since ethical concerns are at the heart of this discussion. For companies looking to apply AI to HRM and use an integrated framework, this report provides strategic insights to help them manage the hurdles of AI adoption. Helping companies make the most of technology while protecting and valuing their personnel is the main goal. Keywords: Artificial Intelligence, Human Resource Management, AI Adoption, Recruitment, Talent Management, Employee Engagement, Performance Evaluation, Ethical Implications, Privacy Concerns, Workforce Upskilling.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The hazy waters of artificial intelligence in human resource management are explored in this study, which examines its effects on training, engagement, performance reviews, and recruiting and the openness, responsibility, and fairness of AI-driven HRM systems.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Anurag Kumar']2024-05-01T00:00:00https://www.semanticscholar.org/paper/03e44b87cafc556bcf356fb035192942e7d409df a8d26211f22ba2be792eb5eda8d647d1f872219dThe role of Artificial Intelligence (AI) in design is clearly growing. One of the tenets of the paper is that stimulation could be among the design processes mostly benefitting from the introduction of AI. Available contributions have been reviewed to understand the current support AI can give in design inspiration and ideation. We also reflected on what AI should and ahould not do in the future: a framework is proposed. Based on the reviewed contributions, in no case, AI is seen as a substitute of designers. Most contributions originate from the IT domain and have a demonstrative purpose.Proceedings of the Design Society00It is suggested that stimulation could be among the design processes mostly benefitting from the introduction of AI, and no case is seen as a substitute of designers.Proceedings of the Design Society['A. Berni', 'Y. Borgianni', 'F. Rotini', 'Milene Gonçalves', 'Katja Thoring']2024-05-01T00:00:00https://www.semanticscholar.org/paper/a8d26211f22ba2be792eb5eda8d647d1f872219d 6602575d66e1f315857ca0b0736ef8cd4f86c265 The primary surgical approach for removing adrenal masses is minimally invasive adrenalectomy. Recognition of anatomical landmarks during surgery is critical for minimizing complications. Artificial intelligence-based tools can be utilized to create real-time surgical navigation systems. In this experimental feasibility study, intraoperative videos of 20 patients who underwent minimally invasive right adrenalectomy in a tertiary care center between 2011 and 2023 were analyzed and used to develop an artificial intelligence-based anatomical landmark recognition system. Semantic segmentation of the liver, the inferior vena cava (IVC), and the right adrenal gland was performed. Fifty random images per patient during the dissection phase were extracted. The experiments on the annotated images were performed on two state-of-the-art segmentation models named U-Net and SwinUNETR. The dataset was split into training and validation subsets with an 80:20 distribution. The models were trained with the Dice-Cross Entropy loss function, and the dice similarity coefficient (DSC) was calculated for the predictions on the validation subset. Out of 20 videos, 1000 images were extracted, and anatomical landmarks (liver, IVC, and right adrenal gland) were annotated. Randomly distributed 800 and 200 images were selected for the training and the validation subsets, respectively. Our benchmark results show that the transformer-based SwinUNETR model achieved 78.37%, and the U-Net model with the EfficientNet-0 backbone achieved 77.95% DSC scores on a three-region prediction task. Artificial intelligence-based systems can predict anatomical landmarks with high performance in minimally invasive right adrenalectomy. Such tools can be used to create real-time navigation systems during surgery in the near future. British Journal of Surgery00In this experimental feasibility study, intraoperative videos of 20 patients who underwent minimally invasive right adrenalectomy in a tertiary care center between 2011 and 2023 were analyzed and used to develop an artificial intelligence-based anatomical landmark recognition system.British Journal of Surgery['B. Sengun', 'Y. Iscan', 'Z. A. Yazici', 'I. Cem Sormaz', 'N. Aksakal', 'F. Tunca', 'H. K. Ekenel', 'Y. Senyurek']2024-05-01T00:00:00https://www.semanticscholar.org/paper/6602575d66e1f315857ca0b0736ef8cd4f86c265 662d78e8c6067004e494f565134a679c7fe353b8Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. These machines are designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI technologies include machine learning, which enables computers to learn from experience and improve their performance over time without being explicitly programmed, and deep learning, which uses neural networks to process complex patterns and data. AI has applications in various fields, including healthcare, defence, transportation, education, and labour market, and its development has raised ethical, social, educational, and economic implications. The main objective of the study is to explore the literature on the artificial intelligence, its impact on society as well as challenges and risks posed by AI. Recently AI has been utilized in various fields. To cover various societal aspects, literature has been reviewed from various disciplines where AI is applied. These areas include healthcare, automobiles, education, labour market, defence, entertainment, computation, and security. These articles are retrieved from peer-reviewed sources based on the keywords suggesting the role of AI, forecasting & assessment of impact, behavioural & ecological aspects of AI, and AI's relation to employment. In this study it has been concluded that application of Artificial Intelligence has transformed the conventional ways of almost every area of modern society by bringing the significant changes in the industry, commerce, defence, education, labour market and healthcare services. But it has raised certain concerns also such as automation leads to job displacement and economic inequality concentrating revenue among fewer individuals. AI supported Autonomous Weapons to kill could cause mass casualties if misused. Hence the idea is both devastating and exciting and the application of AI should be carefully monitored. International journal of engineering science & humanities00Application of Artificial Intelligence has transformed the conventional ways of almost every area of modern society by bringing the significant changes in the industry, commerce, defence, education, labour market and healthcare services but it has raised certain concerns also.International Journal of Engineering Science and Humanities['Subhash Chander']2024-05-01T00:00:00https://www.semanticscholar.org/paper/662d78e8c6067004e494f565134a679c7fe353b8 bf691ca245d30066bfe65e2ba126ec021b69c7a7Introduction: Regional economic growth can be predicted to make more effective countermeasures and promote the development of local regions. However, the existing regional economic growth forecasting models have the problems that the forecasting speed is too slow and the forecasting results are inaccurate, which greatly hinders people's understanding of economic growth. Methods: Based on artificial intelligence and computer vision model, this paper designed a regional economic growth forecast model and predicted the economic growth of different regions. Through testing different areas, it was found that: The prediction risk index of the economic growth prediction model based on artificial intelligence and computer vision model was lower. Results: Among them, the accuracy rate was increased by 6.9%, and the prediction speed was improved, as well as the user satisfaction rate was increased by 9.16%. Conclusion: Therefore, artificial intelligence and computer vision technology could optimize the regional economic growth forecast model.Salud, Ciencia y Tecnología130A regional economic growth forecast model based on artificial intelligence and computer vision model and predicted the economic growth of different regions found that it could optimize the regional economic growth forecast model.Salud, Ciencia y Tecnología - Serie de Conferencias['Yong Yin', 'Dongyu Zhang', 'Yueran Xu', 'Xiaomeng Zhang', 'Yonghong Wang']2024-05-01T00:00:00https://www.semanticscholar.org/paper/bf691ca245d30066bfe65e2ba126ec021b69c7a7 c340421c3a8b8c1ca13ef1f2dae20ef4fd88c6baArtificial intelligence (AI) is currently becoming a leading field in data processing [...].Cancers400Cancers['R. Obuchowicz', 'Michał Strzelecki', 'A. Piórkowski']2024-05-01T00:00:00https://www.semanticscholar.org/paper/c340421c3a8b8c1ca13ef1f2dae20ef4fd88c6ba 8d9534b3c07a41d14a4e22c3920b30fe6ede4827International Journal of Networked and Distributed Computing (IJNDC)100Understanding the dynamics of job displacement resulting from artificial intelligence (AI) using a sophisticated non-linear dynamical system modeled through the Lotka-Volterra equations is crucial for policymakers to navigate the complexities of AI-induced job displacement and ensure equitable societal outcomes.International Journal of Networked and Distributed Computing['M. J. Idrisi', 'Delelegn Geteye', 'P. Shanmugasundaram']2024-05-01T00:00:00https://www.semanticscholar.org/paper/8d9534b3c07a41d14a4e22c3920b30fe6ede4827 78f919580d3af30f2bc5f9be8fdcc9e664ab4f35The influence of behavioral science on various organizations has been experiencing remarkable growth worldwide. With the integration of recent technological advancements, behavioral science's impact has expanded into diverse fields such as finance and policy. The term "artificial intelligence" (AI) has become increasingly prevalent, but it is essential to provide clarity before proceeding. AI pertains to the theory and creation of systems capable of executing tasks that typically necessitate human intelligence. Integrating artificial intelligence (AI) in pediatric dentistry has emerged as a promising avenue to enhance patient care, improve diagnostic accuracy, streamline treatment planning, and augment patient engagement. AI-driven tools such as image analysis, natural language processing, and machine learning algorithms assist in early caries detection, orthodontic treatment planning, behavior management, and personalized oral hygiene education for pediatric patients. This paper presents an overview of AI's applications in pediatric dentistry, particularly behavior management, highlighting its potential to revolutionize traditional pediatric dental practices.Journal of Clinical Pediatric Dentistry510An overview of AI's applications in pediatric dentistry, particularly behavior management, highlighting its potential to revolutionize traditional pediatric dental practices is presented.The Journal of clinical pediatric dentistry['Sonu Acharya', 'B. Godhi', 'Vrinda Saxena', 'A. Assiry', 'Noura Alessa', 'A. Dawasaz', 'Abdullah Alqarni', 'M. Karobari']2024-05-01T00:00:00https://www.semanticscholar.org/paper/78f919580d3af30f2bc5f9be8fdcc9e664ab4f35 748ae7c026b22435923a99ed4b2eac263de8a65cBackground and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.Diagnostics520Its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise, and AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis.Diagnostics['P. Uchikov', 'Usman Khalid', 'Nikola Vankov', 'M. Kraeva', 'K. Kraev', 'B. Hristov', 'Milena Sandeva', 'Snezhanka Dragusheva', 'D. Chakarov', 'Petko Petrov', 'B. Dobreva-Yatseva', 'Ivan Novakov']2024-05-01T00:00:00https://www.semanticscholar.org/paper/748ae7c026b22435923a99ed4b2eac263de8a65c 8b12501d6e2339b3e1adcdba067bed00c111f2ecThe combination of Big Data and Artificial Intelligence (AI) has become a revolutionary force in the era of unparalleled data creation. The volume, pace, and variety of data have grown exponentially, pushing the boundaries of standard analytic techniques. This paper investigates the ways in which artificial intelligence (AI) is transforming big data analysis and enabling its application in a variety of sectors. We examine the particular contributions made by AI approaches and explore the advantages, difficulties, and potential future directions of this revolutionary partnership. In-depth tables complement this analysis to give a more thorough grasp of AI's effects.International journal of engineering science & humanities00This paper investigates the ways in which artificial intelligence is transforming big data analysis and enabling its application in a variety of sectors and examines the particular contributions made by AI approaches.International Journal of Engineering Science and Humanities['Dr. Pankaj Kumar', 'Mr. Lakhvinder Singh']2024-05-01T00:00:00https://www.semanticscholar.org/paper/8b12501d6e2339b3e1adcdba067bed00c111f2ec 25ae383270e9ac7f12e00e184106614e26103f7b Expansion of functional capabilities of artificial intelligence systems actualizes the question of the possibility of their autonomous activity aimed at cognition and creation of the world. A significant factor determining the properties of artificial intelligence is its subjectiveness. If the world is deterministic, its subjectiveness is not necessary to ensure the functionality of artificial intelligence. Subjectiveness, however, is necessary for the initialization of cognitive or creative activity. A reasonable question arises regarding the ability of non-subjective artificial intelligence to act as a subject of cognition. Studies show that two main forms of participation of non-subject artificial intelligence in the system of subject-object relations are possible: as part of an integrated subject of cognition, in which a human being-operator plays the leading role, or fully autonomous functioning of artificial intelligence initialized (formalized or non-formalized) by the tasks of satisfying human being's needs. The analysis of the properties of cognition by means of artificial intelligence and other cognitive systems shows that the ability to act as a subject of cognition is formed not at the formation of subjectness, but at a lower level - the level of formation of self-consciousness, available to relatively uncomplicated cognitive systems, both natural and artificial. The conclusion about the direct connection between self-consciousness and the ability to cognize follows from the definition of cognition in the logic of subject-object opposition, corresponding to the epistemological interpretation of subject-object relations. However, an alternative ontological interpretation is also possible, which identifies being and consciousness. Such an idealistic understanding of the world corresponds to the philosophy of irrationalism, in the framework of which a reliable answer to the question of the necessity of subjectiveness for solving intellectual problems cannot be obtained. Философская мысль70The analysis of the properties of cognition by means of artificial intelligence and other cognitive systems shows that the ability to act as a subject of cognition is formed not at the formation of subjectness, but at a lower level - the level of formation of self-consciousness, available to relatively uncomplicated cognitive systems, both natural and artificial.Философская мысль['Andrei Armovich Gribkov']2024-05-01T00:00:00https://www.semanticscholar.org/paper/25ae383270e9ac7f12e00e184106614e26103f7b bca6ff537b0a996a9da11ff7bb2f7f6c9322bb39Applications of deep neural networks (DNNs) are booming in more and more fields but lack transparency due to their black-box nature. Explainable artificial intelligence (XAI) is, therefore, of paramount importance, where strategies are proposed to understand how these black-box models function. The research so far mainly focuses on producing, for example, class-wise saliency maps, highlighting parts of a given image that affect the prediction the most. However, this method does not fully represent the way humans explain their reasoning, and awkwardly, validating these maps is quite complex and generally requires subjective interpretation. In this article, we conduct XAI differently by proposing a new XAI methodology in a multilevel (i.e., visual and linguistic) manner. By leveraging the interplay between the learned representations, i.e., image features and linguistic attributes, the proposed approach can provide salient attributes and attribute-wise saliency maps, which are far more intuitive than the class-wise maps, without requiring per-image ground-truth human explanations. It introduces self-interpretable attributes to overcome the current limitations in XAI and bring the XAI closer to a human-like explanation. The proposed architecture is simple in use and can reach surprisingly good performance in both prediction and explainability for deep neural networks thanks to the low-cost per-class attributes.IEEE Transactions on Artificial Intelligence420This article proposes a new XAI methodology in a multilevel manner that can provide salient attributes and attribute-wise saliency maps, which are far more intuitive than the class-wise maps, without requiring per-image ground-truth human explanations.IEEE Transactions on Artificial Intelligence['Halil Ibrahim Aysel', 'Xiaohao Cai', 'A. Prugel-Bennett']2024-05-01T00:00:00https://www.semanticscholar.org/paper/bca6ff537b0a996a9da11ff7bb2f7f6c9322bb39 b917690adadffcb43e265957d1e9d158aed7c51bIn this editorial, Professor Roger Kirby, Editor‐in‐Chief of Trends in Urology and Men's Health, considers the impact of artificial intelligence (AI) on health care – a change warranting both optimism and caution.Trends in Urology &amp; Men's Health00The impact of artificial intelligence (AI) on health care – a change warranting both optimism and caution – is considered.Trends in Urology &amp; Men's Health['Roger Kirby']2024-05-01T00:00:00https://www.semanticscholar.org/paper/b917690adadffcb43e265957d1e9d158aed7c51b b7e186db7cd0d8a962d570fcaab43dd894bef7ecWhy Is This an issue? Inefficient patient flow contributes to the overcrowding of health care settings and negative clinical outcomes and patient experiences downstream. Patient flow management aims to achieve seamless patient movement through the health care system and between acute and long-term settings, ensuring timely access to quality care. What Is the Technology? Artificial intelligence (AI)-based patient flow management tools are interventions designed to forecast and monitor patient movement from admission to discharge as they progress through different care settings. AI-driven tools can leverage big data and digital information systems (e.g., electronic health records) to facilitate effective patient flow. AI-based patient appointment scheduling tools, which can help improve patient flow, are created to automate appointment scheduling and optimize it by minimizing wait times and matching the demand for health services and hospital capacity. What Is the Potential Impact? AI tools for patient flow management can support volume forecasting of patients with various conditions, especially those experiencing chronic conditions that require different types of treatment or care in different settings over a long period of time. These AI tools can predict admissions, patient movement from the emergency department to inpatient beds, discharge, and transfers to different health care settings. Evidence for their effectiveness in patients with emergency admissions and those transferred to tertiary and quaternary care, as well as inpatients from the general, cardiology, and mental health departments, was reported. In addition, evidence suggested that AI tools can optimize appointment scheduling in general outpatient settings and operating rooms. In health care systems in Canada, AI tools are being used or investigated to enhance patient flow by predicting emergency admissions, transfers to alternate levels of care, and general inpatient discharges, as well as optimizing capacity planning for patients receiving oncology care. AI appointment scheduling tools are currently being used in some oncology care settings and operating rooms across Canada. The implementation of AI systems generally requires an upfront investment of time and other resources in addition to the financial cost of the system itself for set-up, integration, and staff training. The goal of these systems is to improve efficiency and save money, time, and human resources in the long run. What Else Do We Need to Know? Patient privacy and data security issues are concerns regarding widespread implementation of AI tools trained on electronic health records systems and patient datasets. AI algorithms trained on datasets lacking adequate representation of all relevant patients may not predict their flow accurately. Training datasets with sufficient data from all relevant patient groups can ensure the inputs and outputs of the algorithms accurately reflect patient care needs and mitigate potential bias. To accurately predict the care needs of local patients, AI algorithms, once deployed, should be retrained on site-specific datasets containing data for the patient populations that are representative of the hospital or health system in which they are being used. Not all institutions have the hardware or computing power available to adequately or efficiently process the large amounts of data that are required by these AI systems, or the infrastructure needed to use big data (e.g., from electronic health records), and may require additional resources for implementation, as well as for ongoing maintenance and updating of the systems. Canadian Journal of Health Technologies260In health care systems in Canada, AI tools are being used or investigated to enhance patient flow by predicting emergency admissions, transfers to alternate levels of care, and general inpatient discharges, as well as optimizing capacity planning for patients receiving oncology care.Canadian Journal of Health Technologies['Cadth Horizon', 'Scan']2024-05-01T00:00:00https://www.semanticscholar.org/paper/b7e186db7cd0d8a962d570fcaab43dd894bef7ec 36e9348603eb3e80550370d9a6dec2c6e5b95e7dIn today's digitally driven marketplace, personalized marketing has emerged as a pivotal strategy for businesses aiming to engage customers on a deeper level and foster long-term relationships. With the advent of Artificial Intelligence (AI), personalized marketing has reached unprecedented levels of customization and effectiveness. This master's thesis delves into the intricate relationship between AI and personalized marketing strategies, focusing on how AI-powered techniques can revolutionize customer engagement and satisfaction. The research explores various facets of personalized marketing, including data collection, analysis, and implementation of tailored campaigns. Through a comprehensive literature review, this thesis examines the theoretical underpinnings of personalized marketing and the evolution of AI technologies in this domain. Moreover, empirical studies and case analyses are conducted to illustrate real-world applications and outcomes of AI-driven personalized marketing strategies across diverse industries. Key themes investigated include the role of machine learning algorithms in deciphering consumer behavior patterns, the ethical considerations surrounding data privacy and consent, and the implications of AI-generated content on brand-consumer interactions.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This master's thesis delves into the intricate relationship between AI and personalized marketing strategies, focusing on how AI-powered techniques can revolutionize customer engagement and satisfaction.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Anchal Jaiswal']2024-05-01T00:00:00https://www.semanticscholar.org/paper/36e9348603eb3e80550370d9a6dec2c6e5b95e7d f0bac80cfe0c92c59edabb6f3f9d02cda2e8cb85With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.Tomography1160relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality are introduced.Tomography['Dhurgham Al-karawi', 'S. Al-Zaidi', 'Khaled Ahmad Helael', 'Naser Obeidat', 'Abdulmajeed Mounzer Mouhsen', 'Tarek Ajam', 'Bashar A. Alshalabi', 'Mohamed Salman', 'Mohammed H. Ahmed']2024-05-01T00:00:00https://www.semanticscholar.org/paper/f0bac80cfe0c92c59edabb6f3f9d02cda2e8cb85 fc4e15deeb913d5ac0bc3e73b731937ca6b70857"Your body has a mind of its own of which your mind has no knowledge" John H. Pflaum, Delightism, 1972 (Prentice Hall) The term emotional intelligence was first used in 1985 by Wayne Payne. In 1930 the psychologist Edward Thorndike used the concept of social intelligence, that means the ability of individuals to get familiar with society. Emotional Intelligence also known as EI, is the state of being able to recognize and act upon behavioral traits of oneself as well as others. The first purpose is to recognize, understand and manage ones own emotions. The second purpose is to recognize, understand and influence the emotions of others.  Artificial Intelligence is the process of exhibiting human-like roles into machines or computers. In other words, the science and engineering of creating machines which portrays the basic fundamentals of human beings is called Artificial Intelligence. Both emotional intelligence and artificial intelligence have been popular over last two decades. In this paper we will be talking about comparison between both emotional and artificial intelligence.International journal of engineering science & humanities40In this paper, the science and engineering of creating machines which portrays the basic fundamentals of human beings is called Artificial Intelligence.International Journal of Engineering Science and Humanities['Capt. Dr. Shweta Sharma']2024-05-01T00:00:00https://www.semanticscholar.org/paper/fc4e15deeb913d5ac0bc3e73b731937ca6b70857 0da62586c414d94addab8117cbe36de559ebabd9Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.Radiographics430The authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development.Radiographics : a review publication of the Radiological Society of North America, Inc['Ali S. Tejani', 'Yee Seng Ng', 'Y. Xi', 'Jesse C Rayan']2024-05-01T00:00:00https://www.semanticscholar.org/paper/0da62586c414d94addab8117cbe36de559ebabd9 2bc45bafaa1a5efaa839ce21414478e5ae22331fThe integration of artificial intelligence (AI) into business and industry is catalyzing a paradigm shift in how organizations operate, innovate, and interact with stakeholders. This abstract explores the multifaceted implications of AI across various domains, highlighting its role in automation, predictive analytics, personalized customer experiences, supply chain optimization, enhanced decision-making, natural language processing, product innovation, risk management, fraud detection, healthcare advancements, and workforce augmentation. By leveraging AI technologies, businesses can automate repetitive tasks, anticipate trends, tailor experiences, optimize operations, mitigate risks, and foster innovation. However, the widespread adoption of AI also poses ethical and societal challenges, including concerns about job displacement, data privacy, and algorithmic bias. Therefore, a holistic approach that balances technological advancement with ethical considerations is essential to harness the full potential of AI while ensuring its responsible and equitable deployment in business and industry.Journal of Informatics Education and Research360This abstract explores the multifaceted implications of AI across various domains, highlighting its role in automation, predictive analytics, personalized customer experiences, supply chain optimization, enhanced decision-making, natural language processing, product innovation, risk management, fraud detection, healthcare advancements, and workforce augmentation.Journal of Informatics Education and Research['Dr Subhadra P.S, Dr. A. Kalaivani, Dr. Rohit Markan', 'Ramesh Kumar, Dr Sundarapandiyan Natarajan, M. Rajalakshmi']2024-05-01T00:00:00https://www.semanticscholar.org/paper/2bc45bafaa1a5efaa839ce21414478e5ae22331f 1c1e80285e63c34cd69c6b34b12820243c010bfeArtificial intelligence (AI) is becoming a significant part of healthcare, with the potential to alter the landscape of medicine as we know it. The primary objective of this review is to provide definitions of crucial AI terminology and explore how AI is revolutionizing aspects of healthcare that encompass diagnosis, clinical operations, and treatment. The authors describe a framework to help leaders facilitate the selection and deployment of AI in healthcare. They also discuss the potential challenges of AI, including the regulatory angle, data bias, data accuracy, cost of AI, and how AI can affect healthcare jobs in the future.Physician leadership journal00A framework to help leaders facilitate the selection and deployment of AI in healthcare and the potential challenges of AI are discussed, including the regulatory angle, data bias, data accuracy, cost of AI, and how AI can affect healthcare jobs in the future.Physician Leadership Journal['Steven Brass', 'Donald Larsen', 'Deeksha Panuganti', 'Noorafsha Khan', 'Vijay Khatri']2024-05-01T00:00:00https://www.semanticscholar.org/paper/1c1e80285e63c34cd69c6b34b12820243c010bfe 4a5d9116c65e6fd737a35ca20e92cf2e6c85c177IMPORTANT ROLE OF ICT AND ARTIFICIAL INTELLIGENCE IN SPORTS MANAGEMENTInternational journal of engineering science & humanities00International Journal of Engineering Science and Humanities['Dr. Daisy Rani']2024-05-01T00:00:00https://www.semanticscholar.org/paper/4a5d9116c65e6fd737a35ca20e92cf2e6c85c177 db7138f68ae94123e042843200168c11e4d248fcThis study aims to investigate the role of artificial intelligence applications in improving the transparency of financial reports. The main results of the study show that the application of artificial intelligence systems contributes to improving the reliability and transparency of financial reports, which enhances investor confidence and improves corporate governance. Therefore, companies should keep pace with developments and use technology, especially artificial intelligence technologies and systems, to improve and enhance the transparency of financial reports. Artificial intelligence can help detect financial fraud patterns in financial data, which leads to protecting investors from financial losses and potential risks. It also leads to improving investment and risk management decisions. Artificial intelligence can also help in creating interactive financial reports that users can customize according to their needs, which can lead to increased transparency and improved understanding of financial informationRIMAK International Journal of Humanities and Social Sciences00The main results of the study show that the application of artificial intelligence systems contributes to improving the reliability and transparency of financial reports, which enhances investor confidence and improves corporate governance.RIMAK International Journal of Humanities and Social Sciences['Dr. Soumia Salaa']2024-05-01T00:00:00https://www.semanticscholar.org/paper/db7138f68ae94123e042843200168c11e4d248fc 9cdbf127b52dd1292c2e356c94ea67dd6d8bdf9dSimple Summary Robot-assisted surgery facilitates the examination and improvement of artificial intelligence (AI) integration in surgical processes through the provision of comprehensive telemetry data and an advanced viewing interface. Machine learning (ML) techniques enhance the feedback on the development of surgical abilities, the efficacy of the surgical operation, surgical guiding, and predicted results. By incorporating tension sensors on the robotic arms and employing augmented reality techniques, the surgical experience can be greatly improved. This enables the continuous monitoring of organ movements in real time, resulting in enhanced precision and accuracy. The integration of artificial intelligence (AI) into robotic surgery is anticipated to have a substantial influence on the education of upcoming surgeons and improve the entire surgical process. Both endeavours strive for ultimate accuracy in order to enhance the quality of surgical care. Abstract With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included “urologic surgery”, “artificial intelligence”, “machine learning”, “neural network”, “automation”, and “robotic surgery”. Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ‘’master–slave’’ robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.Cancers440Robot-assisted surgery facilitates the examination and improvement of artificial intelligence (AI) integration in surgical processes through the provision of comprehensive telemetry data and an advanced viewing interface and tension sensors on robotic arms and augmented reality techniques.Cancers['T. Bellos', 'I. Manolitsis', 'Stamatios Katsimperis', 'P. Juliebø-Jones', 'G. Feretzakis', 'I. Mitsogiannis', 'I. Varkarakis', 'BK Somani', 'L. Tzelves']2024-05-01T00:00:00https://www.semanticscholar.org/paper/9cdbf127b52dd1292c2e356c94ea67dd6d8bdf9d 2f2a37516738764375905879b109a78fe3640f66In today's digitally driven marketplace, personalized marketing has emerged as a pivotal strategy for businesses aiming to engage customers on a deeper level and foster long-term relationships. With the advent of Artificial Intelligence (AI), personalized marketing has reached unprecedented levels of customization and effectiveness. This master's thesis delves into the intricate relationship between AI and personalized marketing strategies, focusing on how AI-powered techniques can revolutionize customer engagement and satisfaction. The research explores various facets of personalized marketing, including data collection, analysis, and implementation of tailored campaigns. Through a comprehensive literature review, this thesis examines the theoretical underpinnings of personalized marketing and the evolution of AI technologies in this domain. Moreover, empirical studies and case analyses are conducted to illustrate real-world applications and outcomes of AI-driven personalized marketing strategies across diverse industries. Key themes investigated include the role of machine learning algorithms in deciphering consumer behavior patterns, the ethical considerations surrounding data privacy and consent, and the implications of AI-generated content on brand-consumer interactions. Additionally, the study investigates the impact of personalized recommendations, dynamic pricing, and predictive analytics on enhancing customer satisfaction and loyalty. Furthermore, this research highlights the challenges and opportunities associated with integrating AI into personalized marketing initiatives, such as algorithm bias, algorithmic transparency, and the need for continuous adaptation to evolving consumer preferences. Insights gathered from interviews with industry experts and surveys of consumers contribute to a comprehensive understanding of the practical implications and future directions of AI-enabled personalized marketingINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This master's thesis delves into the intricate relationship between AI and personalized marketing strategies, focusing on how AI-powered techniques can revolutionize customer engagement and satisfaction.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['P. Singh']2024-05-01T00:00:00https://www.semanticscholar.org/paper/2f2a37516738764375905879b109a78fe3640f66 e7a7cace68d2232d4ecbc9994d60ac645ecd0702Abstract Conducting high-quality peer review of scientific manuscripts has become increasingly challenging. The substantial increase in the number of manuscripts, lack of a sufficient number of peer-reviewers, and questions related to effectiveness, fairness, and efficiency, require a different approach. Large-language models, 1 form of artificial intelligence (AI), have emerged as a new approach to help resolve many of the issues facing contemporary medicine and science. We believe AI should be used to assist in the triaging of manuscripts submitted for peer-review publication.Health affairs scholar100AI should be used to assist in the triaging of manuscripts submitted for peer-review publication, according to large-language models, which have emerged as a new approach to help resolve many of the issues facing contemporary medicine and science.Health Affairs Scholar['Howard Bauchner', 'F. Rivara']2024-05-01T00:00:00https://www.semanticscholar.org/paper/e7a7cace68d2232d4ecbc9994d60ac645ecd0702 74ac66cf95bfd411ff36ac476d894d13b8702ca4 Artificial intelligence (AI) has been widely introduced to dentistry in the past decade. Its application in endodontics is limited to different areas such as working length determination, morphological assessment, detection of vertical root fracture, and the detection of periapical lesion. Therefore, this study aims to highlight the available evidence for the uses of AI in endodontics. It also presents the current status as well as the future perspectives on the uses of AI and its potential application in everyday practice. A literature search was conducted from January 2000 to January 2023 using PubMed and Google Scholar for the terms AI and endodontics. Thirty-one studies were evaluated and summarized, highlighting the potential use of different AI models in endodontics. The evaluation of the studies indicated that the use of AI is promising and could aid in tailored endodontics therapy. It would help the clinician in the detection of periapical radiolucency, root fractures, and determination of working length. However, well-designed, high-quality research is required to assess the possible implementation of AI into day-to-day practice in endodontics.Saudi Endodontic Journal690The evaluation of the studies indicated that the use of AI is promising and could aid in tailored endodontics therapy and would help the clinician in the detection of periapical radiolucency, root fractures, and determination of working length.Saudi Endodontic Journal['Abdulaziz A. Bakhsh']2024-05-01T00:00:00https://www.semanticscholar.org/paper/74ac66cf95bfd411ff36ac476d894d13b8702ca4 66884636ece9875235654d490185e4719a65d877Artificial Intelligence has been the talk of the town these days. It is predominant in every aspect of life. Every nick and corner of the world is touched with technology. It has been proven a great help for mankind. But as AI development advances, people are talking about the negative and positive aspect of it. Human beings have mixed opinion regarding AI. This article is an attempt to review the positive and negative aspect of AI technology. This article concludes that AI has both negative and positive impact on the society. Also, there must be regulations on the development and use of AI.International journal of engineering science & humanities00It is concluded that AI has both negative and positive impact on the society and there must be regulations on the development and use of AI.International Journal of Engineering Science and Humanities['Manisha Ranga']2024-05-01T00:00:00https://www.semanticscholar.org/paper/66884636ece9875235654d490185e4719a65d877 06007c9c45df176d8b5710cc5fe9fd4cce781310 The CDC standardizes grading of postoperative complications. However, consistent, and precise application in dynamic clinical settings is challenging. AI offers a potential solution for efficient automated grading. To assess ChatGPT’s capability of grading postoperative complications using the Clavien-Dindo classification (CDC) via Artificial Intelligence (AI) with Natural Language Processing (NLP). ChatGPT's accuracy in defining the CDC, generating clinical examples, grading complications from existing scenarios, and interpreting complications from fictional clinical summaries, was tested. ChatGPT 4 precisely mirrored the CDC, outperforming version 3.5. In generating clinical examples, ChatGPT 4 showcased 99% agreement with minor errors in urinary catheterization. For single complications, it achieved 97% accuracy. ChatGPT was able to accurately extract, grade, and analyze complications from free text fictional discharge summaries. ChatGPT 4 demonstrates promising proficiency and accuracy in applying the CDC. In the future, AI has the potential to become the mainstay tool to accurately capture, extract, and analyze CDC data from clinical datasets. British Journal of Surgery00ChatGPT’s capability of grading postoperative complications using the Clavien-Dindo classification via Artificial Intelligence (AI) with Natural Language Processing (NLP) via Artificial Intelligence (AI) with Natural Language Processing (NLP) is assessed.British Journal of Surgery['S. Staubli', 'H. L. Walker', 'F. Saner', 'C. H. Salinas', 'D. C. Broering', 'M. Malago', 'M. Spiro', 'D. A. Raptis']2024-05-01T00:00:00https://www.semanticscholar.org/paper/06007c9c45df176d8b5710cc5fe9fd4cce781310 6a1e6dc5828a7fc2027e89cb201219f064a1e794 In hemodialysis (HD) patients, arteriovenous (AV) access aneurysms may lead to severe and potentially life-threatening consequences, such as rupture. To address this issue, we developed an artificial intelligence-based application that utilizes images of the AV access to classify AV aneurysms [1]. We conducted a blinded, multicenter, prospective pilot study to assess the correlation between the classification results generated by the aneurysm classification application and the independent clinical examination conducted by physicians specializing in vascular access care. AV accesses were photographed using tablets. These images were then uploaded to the cloud, where they were classified as either "Advanced" or "Not Advanced" by a convolutional neural network algorithm [1] (Fig. 1A). The study compared AV aneurysm classifications generated by our application to those made by vascular access physicians blinded to the app results. The physicians’ classifications served as the ground truth for our analysis. The study was conducted at two vascular access care centers in New York, NY, USA. A total of 121 patients were included in the analysis (Table 1). The physicians’ assessment identified a 21% prevalence of advanced aneurysms. The application accurately classified 84 out of 95 aneurysm images as “Not Advanced” and 20 out of 26 as “Advanced,” resulting in an accuracy of 86.0% (95% CI: 78.5% to 91.6%), a sensitivity of 76.9% (95% CI: 56.4% to 91.0%), a specificity of 88.4% (95% CI: 80.2% to 94.1%), and an area under the receiver operating characteristics curve (AUROC) of 0.87 (95% CI: 0.78 to 0.94) (Fig. 1B). Our results demonstrate that an AI-powered application exhibits actionable accuracy in classifying AV aneurysms within a demographically diverse HD population. These findings align with Zhang et al., where a team of vascular access experts classified 1,093 out of 1,341 (81.5%) images as “Not Advanced” aneurysms and 248 (18.5%) as “Advanced,” resulting in an AUROC of 0.96 in the validation set (n = 402) [1]. The difference in AUROCs can be attributed to the fact that, in Zhang et al., vascular access experts evaluated only AV access images, whereas in the present study, they performed a physical examination of the patient. Our tool may have the potential to support aneurysm monitoring processes, enabling timely detection, decision-making, and interventions. Nephrology, Dialysis and Transplantation10An artificial intelligence-based application that utilizes images of the AV access to classify AV aneurysms demonstrates actionable accuracy in classifying AV aneurysms within a demographically diverse HD population.Nephrology Dialysis Transplantation['Zijun Dong', 'Hanjie Zhang', 'Lin-Chun Wang', 'Sarah Ren', 'Lela Tisdale', 'Maggie Han', 'Valeria G Bittencourt', 'Laura Rosales Merlo', 'Sindhuri Prakash-Polet', 'Denzil Douglas', 'Piotr Starakiewicz', 'Norbert Shtaynberg', 'Nicholas Fuca', 'Andrzej Kozyra', 'S. Thijssen', 'Dean C. Preddie', 'Peter Kotanko']2024-05-01T00:00:00https://www.semanticscholar.org/paper/6a1e6dc5828a7fc2027e89cb201219f064a1e794 f192b130b92028c65f35acfb2c6662848136ea3cThis paper argues that Paul Tillich's theology of art is an effective approach to assessing images generated by artificial intelligence (AI). Tillich's theology of art and concept of participation demonstrate its limits and provide a helpful supplement to the dominant approach of focusing on AI creativity and consciousness, particularly through the framework of philosopher Margaret Boden. In Tillich's theology of art, there is an existential experience of being grasped into participation in the ground of being through the artwork that comes through participation in the art. In participating in the art, one also participates in that artist's contextual answer to the question of ultimate meaning. This article finds that AI-generated images, on their own, lack intentionality and desire to express participation in the spiritual presence and so do not provide this "religious style." Rather, the participation of a human artist crafting text prompts and curating the produced images is necessary along with the AI software.Toronto Journal of Theology10It is found that AI-generated images, on their own, lack intentionality and desire to express participation in the spiritual presence and so do not provide this "religious style," so the participation of a human artist crafting text prompts and curating the produced images is necessary along with the AI software.Toronto Journal of Theology['Eric Trozzo']2024-05-01T00:00:00https://www.semanticscholar.org/paper/f192b130b92028c65f35acfb2c6662848136ea3c 652875d91af806d1efe977c6394683eb089bb3a1Abstract Background Chat-based artificial intelligence (AI) web interfaces that aim to mimic human conversation have increasing utilization in healthcare to help with simple tasks such as scheduling appointments, and even more complex tasks such as providing patient educational responses to COVID-19 questions as done by the World Health Organization.1 Chat-based AI has also been shown to provide accurate responses to cardiovascular disease prevention questions.2 Its ability to provide patient education for more complex treatments like atrial fibrillation (AF) ablation has not been explored. Purpose To evaluate the quality of a popular chat-based AI program’s answers to patient questions about AF ablation. Methods Twenty commonly asked questions ("prompts") regarding AF ablation were entered into ChatGPT (Chat Generative Pre-trained Transformer), a large language model-based AI program (Fig. 1). Prompts were written in plain language; technical terms were avoided except for "radiofrequency", "cryoablation" and "pulsed field ablation" (PFA). SMOG readability calculator was used to assess responses for difficulty and grade-level, as healthcare organizations recommend ≤ 8th-grade level complexity for patient information. Response content was graded by 3 experienced cardiac electrophysiologists as "reasonable", "missing important elements/some inaccuracies" or "misleading/inappropriate". Responses are presented in mean +/- standard deviation and percentages. Results Responses averaged 118±67 words (Fig. 1). Of 20 responses, 17 (85%) were deemed reasonable, 3 (15%) missing important elements/some inaccuracies and none inappropriate or misleading; 16 (80%) emphasized discussion of issues with the healthcare team (Fig. 2). Responses missing important elements/some inaccuracies were those about risks/complications of ablation [missing phrenic nerve palsy, atrioesophageal fistula (AEF), potential need for emergent cardiac surgery or pacemaker, death], concerning symptoms post-procedure (missing symptoms of hematoma, AEF, stroke), and that PFA is not yet approved for use in all regions. Average reading grade level of responses was 13.8 (college level or "professional"): 17 (85%) responses were 12th grade level, 11 (55%) were college-level or higher, and 6 (30%) were college-graduate level ("extremely difficult"). None were ≤ 8th grade level (Fig. 2). Conclusions A majority of ChatGPT responses to common patient questions about AF ablation had reasonable content quality that frequently emphasized the importance of discussion with the healthcare team. However, responses to more difficult questions regarding risks, symptoms of potential complications, or newer technology missed important details; more than half of responses required college-level reading skills. While use of Chat-AI for patient education on EP topics appears promising, patients should be advised to use caution. Further AI training to improve content and readability should be explored.Europace00A majority of ChatGPT responses to common patient questions about AF ablation had reasonable content quality that frequently emphasized the importance of discussion with the healthcare team, however, responses to more difficult questions regarding risks, symptoms of potential complications, or newer technology missed important details.Europace['J. Han', 'T. Baykaner', 'S. Mittal']2024-05-01T00:00:00https://www.semanticscholar.org/paper/652875d91af806d1efe977c6394683eb089bb3a1 7459250d0e4e921a9117e6ca9823e98e9fda2b0dAbstract Background Artificial intelligence-enhanced electrocardiogram (AI-ECG) algorithms have primarily been created using digitised signal data, owing to a relative absence of publicly available image-based datasets. ECGs are often scanned or photographed into electronic health records. For maximum clinical utility, AI-ECG algorithms should be applicable to these data. Synthetic data could expedite the creation of extensive, fully anonymised image-based ECG datasets to permit training image-based AI algorithms, but it is essential that such datasets contain the artefacts encountered in clinical practice. We investigated whether iterative clinical Turing tests with user certainty analysis could be used to develop and validate synthetic ECG data. Purpose To create synthetic ECG images containing the artefacts typically encountered in clinical practice, and to validate the images through iterative Turing testing and user certainty analysis. Methods Synthetic ECG images containing artefacts were created using the PTB-XL dataset (a publicly available signal-based dataset comprising 21799 ECGs) as source data. Iterative clinical Turing tests were conducted where healthcare professionals completed an online survey comprising 60 real and synthetic ECGs. Participants were asked to select whether they thought ECGs were real or synthetic. For user certainty analysis, participants were asked to rate their confidence in their answers using a five-point Likert scale (Figure 1). Likert scale responses were converted into a signed ordinal scale representing user certainty in the identification of real or synthetic data. This scale was used to perform Receiver Operating Characteristic (ROC) analysis. Following quantitative survey completion, qualitative feedback was sought and used to iteratively improve the realism of the synthetic images. Results A total of 26 healthcare professionals completed the clinical Turing tests over three rounds. Qualitative feedback was used to improve the fidelity of the synthetic ECG images between rounds (Table 1). During iterative testing, the proportion of synthetic ECGs correctly identified fell from 61.5% to 53.7%, and the proportion of real-world ECGs correctly identified fell from 66.3% to 53.0% (Figure 1). Following the final Turing test, ROC analysis revealed no discriminative ability for identifying synthetic data (C-statistic 0.480, 95% confidence interval 0.432-0.529). Conclusion Iterative Turing testing with user certainty analysis and qualitative user feedback may be used to create synthetic ECG images containing the artefacts typically encountered in clinical practice. Iterative Turing testing improved the images’ realism confirming their potential to augment image-based AI algorithm development. The presented methodology establishes a framework to develop high fidelity, synthetic patient datasets presenting a significant opportunity to enhance the uptake of AI within electrophysiology, cardiology, and medicine.Europace00Europace['N. Bodagh', 'K. S. Tun', 'A. Barton', 'M. Javidi', 'I. Kotadia', 'M. Klis', 'A. Gharaviri', 'V. Vigneswaran', 'S. Niederer', "M. O'neill", 'M. O. Bernabeu', 'S. Williams']2024-05-01T00:00:00https://www.semanticscholar.org/paper/7459250d0e4e921a9117e6ca9823e98e9fda2b0d 9dd7cf8c182fa955ee1ce8724b307ada3f2356d5The use of artificial intelligence (AI) is growing across disciplines and becoming increasingly discussed in neurorehabilitation. To capture the latest developments in order to understand which, if any, solutions are sufficiently developed for use in practice, we conducted a very rapid literature review, systematically searching the Embase and MEDLINE databases. The five publications that met the criteria for review point to most recent developments in improving diagnosis and prognostication using AI, with no studies examining AI-based rehabilitation interventions directly. However, there was a theoretical ambition of ingraining this technology in rehabilitation programmes themselves in the future. AI has demonstrated superior predictive power compared to traditional approaches when built on large subsets of patient outcome data and was revealed beneficial in estimating the location and extent of brain damage using brain scans. Nevertheless, the quality of the current evidence is limited by lack of follow-up studies of and lack of variability within the study samples, which reduces generalisation to certain groups, such as those with complex needs.The Neuropsychologist170The quality of the current evidence is limited by lack of follow-up studies of and lack of variability within the study samples, which reduces generalisation to certain groups, such as those with complex needs.The Neuropsychologist['Natalia Masztalerz', 'Sara da Silva Ramos']2024-05-01T00:00:00https://www.semanticscholar.org/paper/9dd7cf8c182fa955ee1ce8724b307ada3f2356d5 8aa6bf45ce42b018b8432123bfcd47c2dfcf3068 The emergence of artificial intelligence (AI) chatbots has created new opportunities. This study aims to assess how well online AI chatbots, capable to interact in multiple languages, could respond in accordance with the 2023 ESH Guidelines. We structured 20 questions, both in English and Greek, covering issues that were included in the 2023 ESH Guidelines recommendations. The questions were fed to four free online chatbots that can interrogate questions in multiple languages. The responses were recorded and evaluated by three experienced cardiologists with special interest in hypertension. To assess consistency, each question was asked three times, though only the first response was included in the accuracy analysis. All questions were preceded by ’According to the 2023 ESH Guidelines for the management of arterial hypertension’. A response was considered ’accurate’ if it included all essential information, ’inaccurate’ if it was not in accordance with the guidelines and ’incomplete’ if any essential information was missing. In total there were 160 responses recorded (80 in Greek and 80 in English). A total of 62 (38.8%) responses were deemed accurate with significant difference between the languages (30% for Greek vs 47.5% for English responses), ranging from only 2 out of 20 (10% for YOU.COM in Greek) to 13 out of 20 (65% for BARD in English). Eighty-five (53.1%) of the responses were judged as inaccurate and 13 (8.1%) as incomplete. There were two questions that got no accurate responses from any chatbot (in either language). Moreover, 138 out of the 160 regenerated responses were consistent with the initial answer (86.3%). No chatbot would have replied accurately to every question even if the regenerated responses were to be considered. The study resulted in a variation of accuracy of the responses generated by four popular AI chatbots when asked about issues covered in the 2023 ESH Guidelines. The observed accuracy is lower for Greek. While the use of chat-based AI in medicine is still in its early stages and current models are not intended for medical use, the potential for such technology is significant. Journal of Hypertension00Assessment of how well online AI chatbots, capable to interact in multiple languages, could respond in accordance with the 2023 ESH Guidelines resulted in a variation of accuracy of the responses generated by four popular AI chatbots when asked about issues covered in the 2023 ESH Guidelines.Journal of Hypertension['Antonios Ioannidis', 'Dimitrios Tsounis', 'Georgios Bouras', 'Despoina Komninou', 'A. Pechlevanis', 'Eleni Zalokosta', 'Eirini Mylona', 'Christina Sidera', 'Efthymia Markidou', 'Theodora Kafkia']2024-05-01T00:00:00https://www.semanticscholar.org/paper/8aa6bf45ce42b018b8432123bfcd47c2dfcf3068 5f6c172eaff3000f80eecfaf5299b14b1f3c4e43Abstract Chronic Obstructive Pulmonary Disease (COPD), as one of the major global health threat diseases, particularly in China, presents a high prevalence and mortality rate. Early diagnosis is crucial for controlling disease progression and improving patient prognosis. However, due to the lack of significant early symptoms, the awareness and diagnosis rates of COPD remain low. Against this background, primary healthcare institutions play a key role in identifying high-risk groups and early diagnosis. With the development of Artificial Intelligence (AI) technology, its potential in enhancing the efficiency and accuracy of COPD screening is evident. This paper discusses the characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening. It also highlights challenges in AI application, such as data privacy, algorithm accuracy, and interpretability. Suggestions for improvement, such as enhancing AI technology dissemination, improving data quality, promoting interdisciplinary cooperation, and strengthening policy and financial support, aim to further enhance the effectiveness and prospects of AI technology in COPD screening at primary healthcare institutions in China.International Journal of COPD310The characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening are discussed and challenges in AI application are highlighted.International Journal of Chronic Obstructive Pulmonary Disease['Xu Yang']2024-05-01T00:00:00https://www.semanticscholar.org/paper/5f6c172eaff3000f80eecfaf5299b14b1f3c4e43 c4af02fa1deb9a1608b6f37b67b8709aa114b3d1Abstract Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators (“AND” and “OR”) were the following: “Artificial intelligence”, “Machine learning”, Deep learning”, “Early diagnosis”, “Treatment”, “interventions”, “ethical consideration”, and “mental Healthcare”. Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual’s response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.Risk Management and Healthcare Policy790The literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual’s response to various interventions, and aligns with the move toward individualized and preventive mental healthcare.Risk Management and Healthcare Policy['A. Alhuwaydi']2024-05-01T00:00:00https://www.semanticscholar.org/paper/c4af02fa1deb9a1608b6f37b67b8709aa114b3d1 01313cf67e73e07498e98d6070b1cb55fb544a90This study examines the impact of Artificial Intelligence (AI) and Machine Learning (ML) on inventory management within global industrial manufacturing and supply chains, particularly in the context of Industry 4.0. Through a comparative analysis across several countries, the research analyzes quantitative and qualitative data to assess the adoption and integration of these technologies and their implications for supply chain optimization. The research methodology includes a comprehensive literature review using multiple databases and expert interviews conducted within a specific timeframe. The study identifies a significant favorable influence of AI and ML on enhancing efficiency, reducing costs, and improving real-time data analytics and predictive maintenance. It highlights the evolution from theoretical potential to practical applications, with an increased focus on regulatory compliance and data integrity, reflecting the industry's maturation in digital integration. Furthermore, the study explores the strategic role of AI and ML in process design and the holistic adoption of Industry 4.0 principles across the supply chain. The findings contribute to the academic literature by detailing the benefits and challenges of AI and ML implementation, offering insights for future research and practical applications in the supply chain sector. The conclusion emphasizes the transformative potential of AI and ML, advocating for their strategic implementation to foster resilience and adaptability in supply chain networks.GLOBAL MAINSTREAM JOURNAL00The study identifies a significant favorable influence of AI and ML on enhancing efficiency, reducing costs, and improving real-time data analytics and predictive maintenance within global industrial manufacturing and supply chains, particularly in the context of Industry 4.0.GLOBAL MAINSTREAM JOURNAL['Md Khyrul Islam, Hasib Ahmed, Mahboob Al Bashar Md Abu Taher']2024-05-01T00:00:00https://www.semanticscholar.org/paper/01313cf67e73e07498e98d6070b1cb55fb544a90 dc2549193ad630dff51d8b9db2a503af8783d3dfInterest in the role played by artificial intelligence has increased in various fields, including the field of management in general and the service sector in particular, as most institutions seek to achieve first ranks in the level of competition. The research aims to identify the impact of knowledge management in light of artificial intelligence on improving the performance of the hotel organization. The case of the Rotana Babel five-star hotel in Baghdad was studied and a questionnaire was used to collect data for the research, which included 50 workers in the hotel at all job levels. The results showed the clear impact of knowledge management and artificial intelligence in improving Hotel performance: The study recommended paying attention to the applications of artificial intelligence and its use in knowledge management in the hotel.International Journal of Tourism and Hospitality Management00The research identified the clear impact of knowledge management and artificial intelligence in improving Hotel performance and recommended paying attention to the applications of artificial intelligence and its use in knowledge management in the hotel.International journal of tourism and hospitality management['Muhammad Mohsen Ibrahim Al-Obaidi']2024-05-01T00:00:00https://www.semanticscholar.org/paper/dc2549193ad630dff51d8b9db2a503af8783d3df 2586b647911a18da4b18be8d70fc4efde1e03847ABSTRACT Artificial intelligence (AI) has led to the development of various opportunities during the COVID-19 pandemic. An abundant number of applications have surfaced responding to the pandemic, while some other applications were futile. The present study aimed to assess the perception and opportunities of AI used during the COVID-19 pandemic and to explore the perception of medical data analysts about the inclusion of AI in medical education. This study adopted a mixed-method research design conducted among medical doctors for the quantitative part while including medical data analysts for the qualitative interview. The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others (P < 0.05). The learning barrier like engaging in new skills and working under a non-medical hierarchy led to dissatisfaction among medical data analysts. There was widespread recognition of their work after the COVID-19 pandemic. Notwithstanding that the majority of professionals are aware that public health emergency creates a significant strain on doctors, the majority still have to work in extremely high case load setting to demand solutions. AI applications are still not being integrated into medicine as fast as technology has been advancing. Sensitization workshops can be conducted among specialists to develop interest which will encourage them to identify problem statements in their fields, and along with AI experts, they can create AI-enabled algorithms to address the problems. A lack of educational opportunities about AI in formal medical curriculum was identified. Journal of Family Medicine and Primary Care90The study reveals that nearly 64.8% of professionals were working in high COVID-19 patient-load settings and had significantly more acceptance of AI tools compared to others, and a lack of educational opportunities about AI in formal medical curriculum was identified.Journal of Family Medicine and Primary Care['Ashwini S. Dongre', 'Sandeep D. More', 'Vidhya Wilson', 'R. J. Singh']2024-05-01T00:00:00https://www.semanticscholar.org/paper/2586b647911a18da4b18be8d70fc4efde1e03847 9f463ed2b3332602d27d529c00d841df9e2ff022The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.Environmental Science and Technology600This Perspective aims to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI in toxicology studies using zebrafish and suggests that AI-based toxicity analysis is in its infancy.Environmental science & technology['Nan Wang', 'Gongqing Dong', 'Ruxia Qiao', 'Xiang Yin', 'Sijie Lin']2024-05-01T00:00:00https://www.semanticscholar.org/paper/9f463ed2b3332602d27d529c00d841df9e2ff022 1824ec32c8bbd56e23f64670fe1ee8efd5efede0This article delves into the intricate process of artificial intelligence-generated content detection, shedding light on automated detectors’ challenges and revealing human detection biases, strengths, and weaknesses.IEEE Security and Privacy150IEEE Security & Privacy['Luca Maiano', 'Alexandra Benova', 'Lorenzo Papa', 'Mara Stockner', 'M. Marchetti', 'Gianmarco Convertino', 'Giuliana Mazzoni', 'Irene Amerini']2024-05-01T00:00:00https://www.semanticscholar.org/paper/1824ec32c8bbd56e23f64670fe1ee8efd5efede0 09716493bcf7dc63a531574aff6c84b05ef76b41: Pandemics have existed since the existence of life and will continue as life continues. Throughout many of the previous pandemics, what played a major role in decreasing their severity is how we mitigated and controlled them. The main reason for this is the time it takes for treatments and vaccinations to be developed, which usually takes a long time. Therefore, the techniques used to control a pandemic rapidly change over the course of the pandemic until a cure or vaccine comes to light. At present, advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), fifth generation networks, and big data can without a doubt play major roles in controlling upcoming pandemics including COVID-19. This paper provides a comprehensive survey of current technologies that use AI and big data analytics to take part in the fight against the current pandemic (COVID-19), including their objectives, strengths, weaknesses, and challenges. This paper also studies existing telemedicine technologies and contact tracing tools used in various countries, which governments have adapted to fight against the current COVID-19 pandemic. This work concludes by suggesting a novel strategic model for controlling and mitigating pandemic crises (e.g., COVID-19). This model represents a guided solution for identifying pandemics and for controlling them using advanced digital solutions from the early stages.Journal of Computer Science1720This work concludes by suggesting a novel strategic model for controlling and mitigating pandemic crises (e.g., COVID-19), which represents a guided solution for identifying pandemics and for controlling them using advanced digital solutions from the early stages.Journal of Computer Science['Bandar M. Alshammari']2024-05-01T00:00:00https://www.semanticscholar.org/paper/09716493bcf7dc63a531574aff6c84b05ef76b41 2c5b602ca6a0fb81cf9dc263f45de410bf311d5f Current guidelines recommend home blood pressure (HBP) monitoring in patients diagnosis with hypertension and those with white coat hypertension or masked hypertension. Delicate classification of these patients are important because the intensification of antihypertensive drug therapy for patients with white coat uncontrolled hypertension or white coat hypertension may not be needed because cardiovascular risk seems not to be greater. Therefore, its detection is important to avoid overtreatment. Meanwhile patients with masked hypertension or masked uncontrolled hypertension and uncontrolled hypertension need further intensification of antihypertensive treatment. Dr. Answer project consists of 21 artificial intelligence (AI) medical software that cover eight major diseases including hypertension. The main object of Dr. Answer hypertension project is to predict the patients next visit office BP according to the home BP data and provide effective and precise treatment plan. 2,057 patients and 156,117 pure HBP data were collected in the Dr. Answer hypertension project. Patients were enrolled in 1 tertiary center (Chonnam National University Hospital) and other 21 non-tertiary centers from January 2022 to September 2022. 1,447 patients and 153,712 were excluded due to insufficient HBP data and follow up loss. Finally 610 patients and 2,405 HBP data were evaluated in the AI program model. Patients were 66.7 ± 14.4 years and proportion of women was 24.1%. and patients enrolled in tertiary center was 73.4%. Mean HBP 8weeks of systolic blood pressure and 4 weeks of diastolic blood pressure data were evaluated in the AI program model. Recurrent Neural Network, Long Short-term Memory, Attention algorism were selected in AI model program. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and Accuracy data were used as the AI model parameter. MAE of systolic BP was 3.96 and MAE of diastolic BP was 2.1. Meanwhile, RMSE of systolic BP was 5.81 and RSME of diastolic BP was 2.88. Accuracy data showed 74.1% and 91.4% respectively. Dr. Answer hypertension AI medical software showed promising results for predicting future blood pressure data in hypertension patients. Further advancement in the AI program model would be needed. Journal of Hypertension00AI medical software showed promising results for predicting future blood pressure data in hypertension patients and further advancement in the AI program model would be needed.Journal of Hypertension['Joon Ho Ahn', 'Seok Oh', 'Ki Hong Lee', 'Ju Han Kim']2024-05-01T00:00:00https://www.semanticscholar.org/paper/2c5b602ca6a0fb81cf9dc263f45de410bf311d5f b5ff8f5ab79dbb71671555275018b0fcf97f27c3In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.European Journal of Investigation in Health, Psychology and Education490It is suggested that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs and while ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions.European Journal of Investigation in Health, Psychology and Education['Sahar Borna', 'Cesar A Gomez-Cabello', 'Sophia M Pressman', 'S. A. Haider', 'Ajai Sehgal', 'Bradley C. Leibovich', 'Dave Cole', 'AJ Forte']2024-05-01T00:00:00https://www.semanticscholar.org/paper/b5ff8f5ab79dbb71671555275018b0fcf97f27c3 b894e2f30c681c1ef6d6ecdb0d6981c5ffb1ae96Artificial Intelligence-based (AI) Health Service is a solution to the limited number and distribution of doctors in Indonesia. However, the use of AI technology also presents several challenges. This research used a normative juridical approach to analyse protection regarding the development of AI-based technology-based health services in Indonesia. The analysis shows that challenges to utilizing AI in healthcare include bias, protection of personal data and risk of malpractice. As a system that has no moral values, doctors is expected to be wiser in operating it. To ensure the delivery of quality AI-based health services, patients, doctors, service providers and the government need to understand and carry out their respective roles collaboratively.Jurnal Globalisasi Hukum140The analysis shows that challenges to utilizing AI in healthcare include bias, protection of personal data and risk of malpractice, and patients, doctors, service providers and the government need to understand and carry out their respective roles collaboratively.Jurnal Globalisasi Hukum['Sigit Primasatya']2024-05-01T00:00:00https://www.semanticscholar.org/paper/b894e2f30c681c1ef6d6ecdb0d6981c5ffb1ae96 ea1de3ed8a758e2da2eecdb3ddd749eb86402ce9The role of artificial intelligence (AI) in cancer care has evolved in the face of ageing population, workforce shortages and technological advancement. Despite recent uptake in AI research and adoption, the extent to which it improves quality, efficiency and equity of care beyond cancer diagnostics is uncertain to date. Henceforth, the objective of our systematic review is to assess the clinical readiness and deployability of AI through evaluation of prospective studies of AI in cancer care following diagnosis.We undertook a systematic review to determine the types of AI involved and their respective outcomes. A PubMed and Web of Science search between 1 January 2013 and 1 May 2023 identified 15 articles detailing prospective evaluation of AI in postdiagnostic cancer pathway. We appraised all studies using Risk of Bias Assessment of Randomised Controlled Trials and Risk of Bias In Non-randomised Studies-of Interventions quality assessment tools, as well as implementational analysis concerning time, cost and resource, to ascertain the quality of clinical evidence and real-world feasibility of AI.The results revealed that the majority of AI oncological research remained experimental without prospective clinical validation or deployment. Most studies failed to establish clinical validity and to translate measured AI efficacy into beneficial clinical outcomes. AI research are limited by lack of research standardisation and health system interoperability. Furthermore, implementational analysis and equity considerations of AI were largely missing.To overcome the triad of low-level clinical evidence, efficacy-outcome gap and incompatible research ecosystem for AI, future work should focus on multicollaborative AI implementation research designed and conducted in accordance with up-to-date research standards and local health systems.BMJ Oncology430A systematic review of prospective studies of AI in cancer care following diagnosis revealed that the majority of AI oncological research remained experimental without prospective clinical validation or deployment and most studies failed to establish clinical validity and to translate measured AI efficacy into beneficial clinical outcomes.BMJ Oncology['Sheba Macheka', 'Peng Yun Ng', 'Ophira Ginsburg', 'Andrew Hope', 'Richard Sullivan', 'Ajay Aggarwal']2024-05-01T00:00:00https://www.semanticscholar.org/paper/ea1de3ed8a758e2da2eecdb3ddd749eb86402ce9 649ae493991062ba26ed54edd085fb77a769fda4Abstract Background/Introduction Early diagnosis of atrial fibrillation (AF) presents a challenging yet critical task for appropriate interventions aimed at reducing disease-related burden. In this context, strategies employing classical artificial intelligence (CAI) and deep learning (DL) have emerged as promising approaches to optimize cardiac disorder screening and detection. Purpose This study aimed to compare a CAI model and a DL model for the detection of AF in patients undergoing electrocardiographic (ECG) examinations in tertiary healthcare centers. Methods Between December 2022 and November 2023, a total of 135,476 ECGs were performed, comprising 5,067 with AF and 130,409 without AF. The ECGs were analyzed using both artificial intelligence models. The obtained results were then compared to the gold standard (cardiologist's report). In the CAI model, signals were extracted from ECG images, analyzing five key parameters: cardiac rhythm, atrial depolarization, atrioventricular conduction, ventricular depolarization, and ventricular repolarization (figure 1A). These parameters were benchmarked against the standard values from the Brazilian Society of Cardiology guidelines for detecting cardiac anomalies. Conversely, the DL model utilized a one-dimensional ResNet-based Convolutional Neural Network (CNN). This model was trained using ADAM optimization and binary cross-entropy loss, enabling the learning of complex patterns in the data (figure 1B). Results The mean age was 54.6 years (71.9 years with AF and 53.9 without AF). In the AF population, 52.2% were male (46% were male in the overall sample). In the analysis conducted, the CAI model showed a sensitivity and specificity of 90% and 62%, respectively, while the DL model had 90% and 69%, respectively. ROC curves were generated for both models, demonstrating the superior performance of the DL model (figure 2A). Conclusions Although the sensitivity remained similar between the models, the DL model distinguished itself with higher specificity. These results suggest that artificial intelligence, particularly the deep learning approach, holds promise as a supportive resource in AF diagnosis. However, further studies are needed to evaluate the models more thoroughly and determine their clinical applicability in a broader context. Artificial intelligence models ROC curvesEuropace00The results suggest that artificial intelligence, particularly the deep learning approach, holds promise as a supportive resource in AF diagnosis, however, further studies are needed to evaluate the models more thoroughly and determine their clinical applicability in a broader context.Europace['D. Mota', 'F. N. B. Filho', 'E. B. Sousa', 'R. A. Chagas', 'E. M. Sassaki', 'M. Candoti', 'G. Kuster', 'J. H. Lopes']2024-05-01T00:00:00https://www.semanticscholar.org/paper/649ae493991062ba26ed54edd085fb77a769fda4 5b3e7511ca04f9a394807cf858e00d29c18ef347Introduction Emerging developments in applications of artificial intelligence (AI) in healthcare offer the opportunity to improve diagnostic capabilities in obstetrics and gynaecology (O&G), ensuring early detection of pathology, optimal management and improving survival. Consensus on a robust AI healthcare framework is crucial for standardising protocols that promote data privacy and transparency, minimise bias, and ensure patient safety. Here, we describe the study protocol for a systematic review and meta-analysis to evaluate current applications of AI in O&G diagnostics with consideration of reporting standards used and their ethical implications. This protocol is written following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 checklist. Methods and analysis The study objective is to explore the current application of AI in O&G diagnostics and assess the reporting standards used in these studies. Electronic bibliographic databases MEDLINE, EMBASE and Cochrane will be searched. Study selection, data extraction and subsequent narrative synthesis and meta-analyses will be carried out following the PRISMA-P guidelines. Included papers will be English-language full-text articles from May 2015 to March 2024, which provide original data, as AI has been redefined in recent literature. Papers must use AI as the predictive method, focusing on improving O&G diagnostic outcomes. We will evaluate the reporting standards including the risk of bias, lack of transparency and consider the ethical implications and potential harm to patients. Outcome measures will involve assessing the included studies against gold-standard criteria for robustness of model development (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis, model predictive performance, model risk of bias and applicability (Prediction model Risk Of Bias Assessment Tool and study reporting (Consolidated Standards of Reporting Trials-AI) guidance. Ethics and dissemination Ethical approval is not required for this systematic review. Findings will be shared through peer-reviewed publications. There will be no patient or public involvement in this study. PROSPERO registration number CRD42022357024 .BMJ Open210The study objective is to explore the current application of AI in O&G diagnostics and assess the reporting standards used and assess the reporting standards used in these studies, as AI has been redefined in recent literature.BMJ Open['Anjalee Chaurasia', 'Georgia Curry', 'Yi Zhao', 'Fatema Dawoodbhoy', 'Jennifer Green', 'Matilde Vaninetti', 'N. Shah', 'Orene Greer']2024-05-01T00:00:00https://www.semanticscholar.org/paper/5b3e7511ca04f9a394807cf858e00d29c18ef347 6ffa4839eb7958644a10d98bc0abadf66a6e6a84Abstract The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.International Journal of Women's Health730This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health, and examines potential future directions and challenges associated with these applications.International Journal of Women's Health['Ishfaq Yaseen', 'Riyaz Rather']2024-05-01T00:00:00https://www.semanticscholar.org/paper/6ffa4839eb7958644a10d98bc0abadf66a6e6a84 ffd7002fc70a82a038cc90c3d78065b59ba5043dImprovements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.Journal of Clinical Medicine810This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.Journal of Clinical Medicine['A. Pozza', 'Luca Zanella', 'B. Castaldi', 'G. di Salvo']2024-05-01T00:00:00https://www.semanticscholar.org/paper/ffd7002fc70a82a038cc90c3d78065b59ba5043d 272220c351c699bdfc161b9e7879c6c2d3614a0c The widespread influence of Artificial Intelligence (AI) is evident across various sectors, including the medical field. Hospitals and healthcare systems are increasingly embracing its capabilities in order to improve clinic workflow, to save time and simplify procedures with some degree of complexity. One of those tasks is the referral process to Nephrology appointments by the Primary Care (PC) physicians. As they may face challenges in determining which patients meet the referral criteria, it would be of great help to create a tool that could assist the process. Our work aimed to understand if AI can be useful in the referral to Nephrology appointments in Portugal's National Healthcare System. A cross-sectional study was preformed using appointment requisitions made by PC Physicians to a single center Nephrology Department throughout 2023. An AI bot, named RefNef, was developed, incorporating referral criteria from our center, the Portuguese National Healthcare Authority, and the Kidney Disease Improving Global Outcome (KDIGO) guidelines. Stripping personal data, those requisitions were then submitted to the created bot, and the answers were analyzed. Statistical analyses, including T-student, ANOVA, and Chi-squared tests, were employed to compare different groups. A total of 408 referrals were analyzed (52% of them males), with a mean age of 74 ± 15 years old. Mean creatinine at referral was 1.87 ± 0.72 mg/dl, with a mean estimated Glomerular Filtration Rate using the Crockford-Gault equation of 33.23 ± 15.12 mL/min. 5.6% of them had hematuria and 15.2% had proteinuria at referral. These four criteria were lacking in the referral information in 19.37%, 56.86%, 92.9% and 61.5% of the requests, respectively, but their absence did not appear to affect the nephrologist's or the bot's rate of acceptance. 86.3% of the referrals were approved by the nephrologists, and the bot accepted 97.1% of them. The rate of acceptance was not related (χ2 (1, N = 208) = 5.038, p = 0.082). The time between the referral and the appointment differed from the PC's requirements, the bot's perspective and the actual time the appointment was scheduled. In 68.6% of the times, the bot provided the user with some useful information, as management tips or other details that should be investigated. There was a need to ask for further information to the bot in 3.18% of the cases. While not yet poised to function as a triage system, as the information wasn't consistent with the decision of the nephrologist, the AI tool demonstrated utility in potentially aiding PC physicians in decision-making by providing useful information. Additionally, it exhibited user-friendliness, as it answers with all the needed details at the first question. Further refinement of the AI tool could enhance its alignment with nephrologist decisions, contributing to improved efficiency in the referral process. It is to be noted that the study also highlights the frequent omission of key criteria in Nephrology referrals, emphasizing the need for educational reinforcement in this aspect. Nephrology, Dialysis and Transplantation00While not yet poised to function as a triage system, the AI tool demonstrated utility in potentially aiding PC physicians in decision-making by providing useful information, as it exhibited user-friendliness.Nephrology Dialysis Transplantation['Filipa Trigo', 'Ana Rita Ramos', 'R. Alves', 'Karina Lopes', 'Hernâni Gonçalves', 'Paulo Santos']2024-05-01T00:00:00https://www.semanticscholar.org/paper/272220c351c699bdfc161b9e7879c6c2d3614a0c 8092f9abe2ce00a5fae5e810d379932b0746eb6eThis paper highlights the significance, potential benefits, challenges, and proposed solutions associated with integrating AI-driven tools and platforms into SEL initiatives. The importance of integrating AI with SEL in primary education lies in its ability to foster the holistic development of students. By equipping students with the tools to navigate academic challenges, interpersonal relationships, and emotional regulation, schools can create dynamic learning environments that prioritize the whole child. The potential benefits of integrating AI with SEL are manifold. AI-powered adaptive learning platforms can personalize instruction, provide targeted support, and promote the development of emotional intelligence and social skills among students. Additionally, AI-driven tools and platforms can facilitate collaborative learning experiences, promote active engagement, and provide real-time feedback to students and educators. However, the integration of AI with SEL also presents various challenges that must be addressed. Ethical considerations, such as data privacy, algorithmic bias, and the digital divide, require careful attention to ensure equitable access and outcomes for all students. Additionally, educators may lack the necessary knowledge and skills to effectively utilize AI tools and platforms for SEL purposes, highlighting the need for training and professional development programs. To address these challenges, collaborative efforts among educators, policymakers, technologists, and researchers are essential. By working together, stakeholders can develop evidence-based practices and solutions that align with the goals and values of primary education. Training and professional development programs for educators, robust policies and safeguards for the ethical use of AI technologies, and equitable access to technology for all students are critical components of successful integration.Open Access Research Journal of Multidisciplinary Studies00This paper highlights the significance, potential benefits, challenges, and proposed solutions associated with integrating AI-driven tools and platforms into SEL initiatives and suggests collaborative efforts among educators, policymakers, technologists, and researchers are essential to address these challenges.Open Access Research Journal of Multidisciplinary Studies['Olateju Temitope Akintayo', 'Chima Abimbola Eden', 'Oyebola Olusola Ayeni', 'Nneamaka Chisom Onyebuchi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/8092f9abe2ce00a5fae5e810d379932b0746eb6e 23803dcde7dbd4bdb8d89095c739de36bbc1fce4The development of information technology at this time has created many changes in life in society, the presence of artificial intelligence (A.I) in the midst of human life activities has provided many benefits both in aspects, so that today's society is very dependent on A.I which is considered very helpful in its work. Of course it can be seen that A.I also does the same thing as humans and has a positive impact and a negative impact on human life, where every aspect has a great impact on human life. The formulation of the problem in this study is the regulation of the use of artificial intelligence in Indonesia at this time and the Urgency of Criminal Responsibility of Artificial Intelligence as Perpetrators of Cybercrime. This research belongs to the normative legal research type. and the nature of this research is descriptive analysis. The results of the discussion in this study found that the A.I Regulation at this time is not specifically regulated by the law on A.I, but is regulated in the ITE law, namely in article 1 number 8 concerning electronic agents, where electronic agents are interpreted as A.I by analogy with the meaning of the word "automatic", then the next discussion is about the criminal responsibility of artificial intelligence as cyber criminals is a very important study for The lack of discussion on the use of A.I in Indonesian state regulations raises concerns in the public about the increasing potential for violations of the law and crime by these entitiesInternational Journal of Scientific Multidisciplinary Research210The results of the discussion found that the A.I Regulation at this time is not specifically regulated by the law on A.I, but is regulated in the ITE law, namely in article 1 number 8 concerning electronic agents, where electronic agents are interpreted as A.I.International Journal of Scientific Multidisciplinary Research['Cheny Berlian', 'Helmi', 'Universitas Jambi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/23803dcde7dbd4bdb8d89095c739de36bbc1fce4 051224ead49c8fbf9cc363d314c0d925191c1143The article analyzes the impact of artificial intelligence (AI) on contemporary society and the importance of adopting an ethical approach to its development and implementation within organizations. It examines the critical perspective of French philosopher \'Eric Sadin and others, who warn of the risks of unbridled technologization that can erode human autonomy. However, the article also recognizes the active role that various actors, such as governments, academics and civil society, can play in shaping the development of AI aligned with human and social values. A multidimensional approach is proposed that combines ethics with regulation, innovation and education. It highlights the importance of developing detailed ethical frameworks, incorporating ethics in the training of professionals, conducting ethical impact audits, and encouraging stakeholder participation in AI design. In addition, four fundamental pillars for the ethical implementation of AI in organizations are presented: 1) Integrated values, 2) Trust and transparency, 3) Empowering human growth, and 4) Identifying strategic factors. These pillars cover aspects such as alignment with the company's ethical identity, governance and accountability, human-centered design, continuous training and adaptability in the face of technological and market changes. It concludes by emphasizing that ethics must be the cornerstone of the strategy of any organization that aspires to incorporate AI, establishing a solid framework to ensure that the technology is developed and used in a way that respects and promotes human values.40It is emphasized that ethics must be the cornerstone of the strategy of any organization that aspires to incorporate AI, establishing a solid framework to ensure that the technology is developed and used in a way that respects and promotes human values.["Ernesto Giralt Hern'andez"]2024-04-30T00:00:00https://www.semanticscholar.org/paper/051224ead49c8fbf9cc363d314c0d925191c1143 96f785a9a73cd4fba9bed2d435561011ef8cf060Green buildings play a crucial role in sustainable development by reducing energy consumption, minimizing environmental impact, and enhancing occupant health and well-being. This review explores the role of policy and regulation in promoting green buildings, highlighting the importance of government intervention in driving sustainable building practices. Government policies and regulations play a pivotal role in shaping the adoption of green building practices. Through a combination of mandates, incentives, and standards, governments can encourage the construction and renovation of buildings that prioritize energy efficiency, water conservation, and environmental sustainability. Mandatory building codes and standards are among the most effective tools governments use to promote green buildings. These codes set minimum requirements for energy performance, water efficiency, and indoor environmental quality, ensuring that new constructions and major renovations meet established sustainability criteria. In addition to mandatory standards, governments also use financial incentives to encourage green building practices. These incentives may include tax credits, grants, or subsidies for building owners and developers who incorporate sustainable design features or achieve green building certifications. Furthermore, governments can influence the market through procurement policies that prioritize green buildings for public projects. By leading by example, governments can create a ripple effect in the private sector, encouraging more developers and building owners to embrace sustainable building practices. Overall, the role of policy and regulation in promoting green buildings is essential for advancing sustainable development goals. By setting clear standards, providing incentives, and leading by example, governments can create an environment where green buildings are the norm rather than the exception, leading to a more sustainable built environment for future generations.World Journal of Advanced Research and Reviews930World Journal of Advanced Research and Reviews['Deborah Aanuoluwa Soyombo', 'Azubuike Chukwudi Okwandu', 'Adeola Ona-Olapo', 'Esho', 'Tosin Daniel Iluyomade', 'Tosin Michael Olatunde']2024-04-30T00:00:00https://www.semanticscholar.org/paper/96f785a9a73cd4fba9bed2d435561011ef8cf060 4edce0635922c0c2794ad57f88f6f9ef88c4906dCorporation as a subject of criminal law that can be held criminally responsible is not known in the old Criminal Code. This is because the old Criminal Code is a legacy of the Dutch colonial government whose legal system adheres to the Continental European legal system (civil law). Countries that adhere to the civil law legal system are a little behind in terms of regulating corporations as subjects of criminal law, in contrast to countries that adhere to the common law legal system, which has regulated corporate liability and this has started since the industrial revolution. In Indonesia itself, the regulation on corporation as a subject of criminal law is regulated in the Law outside the Criminal Code. Meanwhile, the new Criminal Code has regulated corporations as legal subjects that can be held criminally liable. As regulated in Article 45 to Article 50, Article 56, and Articles 118 to 124 of Law No. 1 of 2023 on the Criminal Code. Although prior to the enactment of Law No. 1 of 2023 on the Criminal Code there was already Perma No. 13 of 2016 concerning Procedures for Handling Criminal Cases by Corporations and Regulation of the Attorney General of the Republic of Indonesia Number PER-28/A/JA/10/2014 concerning Guidelines for Handling Criminal Cases with Corporate Legal Subjects. Prior to the issuance of the regulation, the Attorney General's Office had first issued Circular Letter of the Attorney General of the Republic of Indonesia Number B-036/A/FT.1/06/2009 regarding Corporations as Suspects/Defendants in Corruption Crimes addressed to the Head of High Prosecutors throughout Indonesia. Thus, Corporations as a subject of criminal law can already be held criminally liable with the strength and legal certainty stipulated in the New Criminal Code.KRTHA BHAYANGKARA00KRTHA BHAYANGKARA['Joko Sriwidodo', 'M.S. Tumanggor']2024-04-30T00:00:00https://www.semanticscholar.org/paper/4edce0635922c0c2794ad57f88f6f9ef88c4906d b6662a71e0f74521245867528948ef9521207f44The paper defines the concept of "education", elucidates and synthesizes scientific approaches regarding the interpretation of "education". Based on the analysis of scholarly views and current legislation in Ukraine, the authors propose their own understanding of the concept of "education". It has been clarified that the term "education" is a multifaceted phenomenon used in various sciences, including sociology, philosophy, pedagogy, psychology, jurisprudence, economics, cybernetics, and others. It is generalized that education is a comprehensive system that integrates learning, socialization, and development, aimed at individual acquisition of key aspects of global experience. The essence of administrative-legal regulation is examined, and the significance of administrative-legal regulation in the sphere of education is determined. It is emphasized that effective administrative-legal regulation in the educational sphere is important for ensuring quality education, safeguarding the rights and interests of participants in the educational process, addressing issues of accessibility in education, as well as for ensuring compliance of educational legislation with international standards. It is concluded that administrative-legal regulation in the field of education is necessary to ensure accessibility of quality education for all segments of the population, address issues of inequality in the educational process, ensure standards and quality of educational programs and services, as well as safeguard the rights and interests of participants in the educational process, including students, teachers, and parents.Economics. Finances. Law00Economics. Finances. Law['Iryna Novitska']2024-04-30T00:00:00https://www.semanticscholar.org/paper/b6662a71e0f74521245867528948ef9521207f44 56ca323c254098bee33016345dd940eb0f2693b4Purpose: This study was conducted to provide a comprehensive understanding of online platform regulation in South Korea. It aims to illuminate key issues related to online platform regulation from 2017 to 2021, exploring the current state and future prospects. The primary goal is to foster social discourse on online platform regulation and to offer valuable insights for policymakers and academics. Design/methodology/approach: The methodology involved collecting articles from a large news database using keywords associated with online platform regulation. This data was analyzed using Latent Dirichlet Allocation (LDA) topic modeling. This technique is instrumental in identifying themes and patterns within large volumes of text data, facilitating the identification of significant themes and trends related to online platform regulation. Findings: The study unearthed a variety of themes. Among the most significant are the impact of the COVID-19 pandemic and its ramifications, political aspects, the roles of major Korean corporations (e.g., Naver and Kakao), the influence of global tech giants, the impact of news and online communities, technology and innovation, and social issues. These themes indicate that online platform regulation in Korea is influenced by a multitude of factors. Research limitations/implications: While this study offers diverse perspectives on online platform regulation within Korea, it has limitations. Reliance on data from a single news source may restrict the study's diversity and depth. Future research should employ a variety of news sources to provide a broader perspective on online platform regulation. Originality/value: This research makes a significant contribution to the academic study and policy formulation in the field by analyzing the key themes and trends related to online platform regulation over time. By exploring the complex aspects of online platform regulation, the study provides new insights for policymakers, researchers, and the general public.GLOBAL BUSINESS FINANCE REVIEW00Key issues related to online platform regulation from 2017 to 2021 are illuminated, exploring the current state and future prospects and providing new insights for policymakers, researchers, and the general public.GLOBAL BUSINESS FINANCE REVIEW['F. f']2024-04-30T00:00:00https://www.semanticscholar.org/paper/56ca323c254098bee33016345dd940eb0f2693b4 6a4ed0485cb959a54febc91a602a60acdcdc0cfdThis study builds on Burnette’s SOMA model. The research was conducted to test the premise that a growth mindset of work predicts living a calling and to explore the mediating role of self-regulation in the influence of a growth mindset of work on living a calling. The process in Burnett’s SOMA model includes goal setting, goal operation, goal monitoring, and goal achievement. In this theoretical model, a growth mindset as a motivational construct predicts self-regulation. Data analysis uses the PLS method with the help of SmartPLS software. The research was conducted at PT United Tractors Tbk, located at Jl.Raya Bekasi KM 22 Jakarta, for three months. Research results: A growth mindset of work has a positive but insignificant effect on living a calling, with PValues of 0.244. A growth mindset of work positively and significantly affects self-regulation, PValues of 0.000. Self-regulation has a positive and significant effect on living a calling, with PValues of 0.002. It can mediate (full mediation) a growth mindset of work on living a calling on Employees of PT United Tractors Tbk Jakarta.West Science Information System and Technology110West Science Information System and Technology['Lisa Sarinah', 'Rizki Nurul Nugraha']2024-04-30T00:00:00https://www.semanticscholar.org/paper/6a4ed0485cb959a54febc91a602a60acdcdc0cfd c7434c63e115995f845246109e225f6b4eab0cc1As the so-called 4th Industrial Revolution is progressing, regulatory innovation is emphasized. In this context, negative regulation is proposed as a preferred form of regulation instead of positive regulation. However, contrary to these requests, it is not easy to apply the form of negative regulation to the entire regulatory system. There is a gap between theoretical claims and actual regulatory reality. Various reasons can be considered for this. One of them is that there are limits to solving many regulatory problems that arise in modern society through negative regulations alone. This shows that there are reasons why many positive regulations could be found in the regulatory area. In this context, this article examines the meaning of positive and negative regulation and then handles the theoretical and historical backgrounds of these forms of regulation. This article argues that there are many reasons why the form of positive regulation is expanding in place of negative regulation today. Accordingly, this article argues that it is not valid to understand positive and negative regulation as a dichotomy of ‘regulation <-> innovation’. This article argues that what we need is to explore regulatory measures that can support social innovation on the one hand while taking modern society’s complexity into account on the other hand.The Legal Studies Institute of Chosun University00The Legal Studies Institute of Chosun University['Chun-Soo Yang']2024-04-30T00:00:00https://www.semanticscholar.org/paper/c7434c63e115995f845246109e225f6b4eab0cc1 f6fb730e1c74314a0ae471c29a92bd54419be360The paper shows that under the conditions of Ukraine's association with the European Union, the following institutions of financial law are subject to significant transformations: budget law - the development of the medium-term budget planning system, the refusal of the annual adoption of the Laws of Ukraine "On the State Budget", tax law - the common VAT system; gradual approximation of excise tax rates on tobacco products to the corresponding EU rates; bringing the classification of alcoholic beverages and the list of excise goods into compliance with EU requirements through the inclusion of electricity and natural gas, coal and coke (for heating and electricity generation) in the list of goods; legal regulation of financial control - implementation of standards and methods of the International Organization of Higher Financial Control Bodies INTOSAI, harmonization of state internal control with international standards of the Institute of Internal Auditors, the International Federation of Accountants, etc.; the legal basis of public expenditures and budget financing - the spread of program-targeted approaches in the budget process and the analysis of the efficiency and effectiveness of the implementation of budget programs. It has been proven that for the implementation of European legislation, Ukraine needs to carry out a number of reforms, including civil service reform; reform of anti-corruption legislation; deregulation reform; budget and tax reform, introduction of electronic tax administration. Important positive consequences should be expected from joint measures in the field of combating tax evasion, tax fraud, as well as the use of new methods of investigation of tax crimes, etc.Economics. Finances. Law30Economics. Finances. Law['Hanna Solomina', 'Mariia Rozhenko', 'Anastasiia Vovchenko']2024-04-30T00:00:00https://www.semanticscholar.org/paper/f6fb730e1c74314a0ae471c29a92bd54419be360 9f02799b9736dc5abc89256b9c426578b26c38e4The relationship between new technologies and security is well established in the fields of defence, law enforcement, communications and public health. This has been highlighted by recent public debate about the security implications of data held by companies operating in social media and information technology (such as TikTok and Huawei). While genomic technology had been less high profile in the context of security, this changed following the COVID-19 pandemic, which focused attention on the significant implications of this form of data. This article discusses commercial genomic technology, related government interests and the growing implications for data security and regulation, such as through the example of the Beijing Genomics Institute, a large company providing genomic testing services to consumers worldwide. We suggest that commercial genomic data has growing implications for countries such as the United States and Australia and argue for greater attention to be directed to this form of technology and associated data security and regulation, including security assessment to address the risks associated with international transfer via corporate entities.Law, Technology and Humans00Law, Technology and Humans['Marcus Smith', 'Ausma Bernot']2024-04-30T00:00:00https://www.semanticscholar.org/paper/9f02799b9736dc5abc89256b9c426578b26c38e4 f5fec56d24f01f9d554e14961bfdc7cdef7ac273The paper examines the administrative and legal basis of the functioning of the National Commission, which carries out state regulation in the spheres of electronic communications, radio frequency spectrum and postal communications in Ukraine. The main tasks, functions and powers of the National Commission are summarized, the main purpose of its creation and functioning is indicated - it is the regulation of the communication industries, the electromagnetic spectrum and the postal industry in Ukraine. It was determined that the National Commission aims to ensure equal conditions for all participants of these markets. The regulatory framework regulating the Commission's activities, including regulatory acts, was analyzed. Problems related to the procedure for appointing the chairman and members of the commission, which may affect their independence and effectiveness of regulation by the regulatory and legal framework, are highlighted. Important aspects of providing anti-crisis management and protection of electronic communication infrastructure in the conditions of military conflict are outlined and highlighted. In the work of the National Commission, inconsistencies and contradictions in the legislative framework regulating the Commission's activities were identified and the need to improve the administrative and legal framework was emphasized to ensure the effective functioning of the body in the context of modern challenges and integration with the European Union, also military risks should be taken into account, which should be reflected in the normative legal acts regulating the activity of the commission, in particular, the use of the radio frequency spectrum should take into account not only the economic component, competitiveness and impartiality, but also the responsibility for the security of the state.Economics. Finances. Law00Economics. Finances. Law['Yurii Chubatyi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/f5fec56d24f01f9d554e14961bfdc7cdef7ac273 a03407fca879b2cb342fc5ef498cd436a24775bbThe current study presents preliminary results from an extensive study called "Potential to be well", conducted with different quotas taking in account gender, people with or without psy­chiatric diagnosis and cohorts of different ages. The aim of the re­search is to test some scales for self-assessment developed by positive psychology, implementing the multi-method ap­proach, but also to analyze the data for existing patterns’ differences in complex affective func­tions and dysfunctions. The “resilience”, “subjective life-satisfaction”, “emotional expressive­ness”, “expressive ambivalence”, “suppression or reappraisal in emotion regulation” invento­ries are reliable and valid measures, which reflect complex subjective functions from the emo­tional sphere. It is noted that these indices follow a bi-directional logic – some patterns express negative trends in experiences, and others reflect a positive psychological transformation of primary functions helping to overcome emotional difficulties and turning adversity into ad­vantage. It was also found that three scripts of complex mediatory sequential models are demonstrated, which combined, predict how two intuitive routes sustain and effect deterioration or improvement in the context of an individual case interpretation.Psychological Thought00Psychological Thought['K. Ferdinandov']2024-04-30T00:00:00https://www.semanticscholar.org/paper/a03407fca879b2cb342fc5ef498cd436a24775bb d438f69c5a7e100e2c1680867afb77211cab1086One argument that is at times adduced against proposals for legal change, such as granting personhood to autonomous agents, is that the change in question will be inefficacious if it takes the law too far from the folk world of people. By contrast, in this article we argue that legal concepts and folk concepts are more malleable than we tend to assume. We turn to (legal) history to demonstrate that the relationship between legal concepts and folk concepts is not one-directional, which means that changes in the law can and have influenced folk psychology as well as vice versa. This has implications for debates around the regulation of new technologies: the ‘lack of efficacy’ argument is not a strong one and mere reference to current folk concepts cannot suffice in such debates. Moreover, the efficacy argument does not, and cannot, replace normative arguments in this respect, so the malleability of folk concepts needs to be considered by legal decision-makers. Current conceptual apparatuses (legal or folk) are not immutable, and reification of the current status quo should not be presupposed.Law, Technology and Humans490Law, Technology and Humans['Mariken Lenaerts', 'Antonia M Waltermann']2024-04-30T00:00:00https://www.semanticscholar.org/paper/d438f69c5a7e100e2c1680867afb77211cab1086 61269a342a16d6b37d546c7db5e98846e12051d9Journal of social sciences and humanities00Journal of Social Science and Humanities[]2024-04-30T00:00:00https://www.semanticscholar.org/paper/61269a342a16d6b37d546c7db5e98846e12051d9 15d8dbfea866d98c5697473d6a9057d0407a2637The object of this study is the existing regulatory approaches to determining the forms of technology transfer in the legal systems of economically developed countries of the world, international treaties, and agreements. During the research and generalization of existing concepts, it was established that they are not unified and differ significantly from each other. It has been proven that this does not meet the needs of technology transfer participants and significantly destabilizes the technology transfer process. The expediency of improving the existing normative concept of determining the forms of technology transfer by fixing their single list has been substantiated. Recommendations on the list of the main forms of technology transfer have been formed based on a systematic analysis of legal acts that determine the peculiarities of the essence of technology. A classification of the main forms of technology transfer was proposed. Four main forms of technology transfer were identified as the transfer of rights to technology during its creation, within the framework of joint cooperation, within the framework of cooperation based on corporate and/or proprietary commercial principles. The expediency of dividing each form of transfer into separate subtypes was also substantiated. The need to make changes to the provisions of such international treaties and agreements as the World Trade Organization Agreements, the Recommendations of the World Organization for the Protection of Intellectual Property, the Oslo Guidelines, the UNCTAD Recommendations, and the framework program "Horizon Europe" has been proven. The study is aimed at forming general theoretical foundations for improving the essence of regulatory techniques for identifying forms of technology transfer. The research results could be used in the formation of international normative acts, recommendations of international institutions, acts of national legislation, and serve as a basis for further scientific research into these issuesEastern-European Journal of Enterprise Technologies00Eastern-European Journal of Enterprise Technologies['O. Davydiuk', 'T. Shvydka', 'Bohdan Hnatkivskyi', 'H. Ivanova', 'Rehina Vaksman']2024-04-30T00:00:00https://www.semanticscholar.org/paper/15d8dbfea866d98c5697473d6a9057d0407a2637 6d07cdccc79fe4db463f76eb910ae3ff0e9f3dceArtificial intelligence has significant potentials in revolutionizing education by enhancing teaching methods, personalizing learning experiences, and improving educational outcomes. The aim of the research is to specify the role of AI in creation of future education and to outline the potentials and challenges artificial intelligence can bring within the educational process. To answer the research questions the case study on the role of AI in the creation of future education was conducted. The methods included structured interviews among institutions administrators, faculty members, students, and AI developers; survey among students and faculty to collect quantitative data on their use of and attitudes towards AI technologies; classroom observations. Over 50 recent scientific works were selected to evaluate the role of artificial intelligence in education and to conduct the comparative educational analysis. The research was conducted among 56 participants, in particularly 9 institutions administrators, 19 faculty members, 24 students, and 4 artificial intelligence developers. The results showed that artificial intelligence integrated in the educational process are used to design simulation-based activities, organize personalized learning, form specific skills among students, and automate the administrative process. artificial intelligence leads to enhancement of learning outcomes among students, increased efficiency, and greater accessibility of education, during war in particularly when the educational process can be disrupted. Using artificial intelligence in education presents several challenges that need to be addressed for its effective implementation. Also, it was found that efficient using artificial intelligence tools within the educational process will lead to the creation of positive artificial intelligence based learning landscape at the institutions of higher education which is characterized by efficiency, interactivity, inclusivity, innovation, and accessibility. The results of research can be implied by educational institutions to improve the educational process and by EdTech companies to develop educational tools and platforms.Futurity Education00It was found that efficient using artificial intelligence tools within the educational process will lead to the creation of positive artificial intelligence based learning landscape at the institutions of higher education which is characterized by efficiency, interactivity, inclusivity, innovation, and accessibility.Futurity Education[]2024-04-30T00:00:00https://www.semanticscholar.org/paper/6d07cdccc79fe4db463f76eb910ae3ff0e9f3dce 068186a0da96ce2c1437a971d08817dd54980116Explainable Artificial Intelligence (XAI) is increasingly recognized as a vital component in the deployment of AI systems within healthcare settings. This abstract synthesizes findings from fifteen research papers investigating the prevalence, detection methods, and implications of XAI in healthcare. The review highlights the growing interest in XAI applications among healthcare professionals, emphasizing the importance of interpretability in medical decision-making. Various detection methods, including rule-based approaches and machine learning interpretability techniques, are explored, illustrating the diversity of strategies employed to enhance AI transparency. Furthermore, the review examines the ethical implications of XAI in healthcare, addressing concerns surrounding accountability, bias mitigation, and patient privacy. By synthesizing findings from multiple studies, this abstract provides insights into the integration of XAI technologies in healthcare, contributing to the ongoing discourse on ensuring transparency, trust, and ethical considerations in AI-driven medical practices.International Journal For Multidisciplinary Research00Insight is provided into the integration of XAI technologies in healthcare, contributing to the ongoing discourse on ensuring transparency, trust, and ethical considerations in AI-driven medical practices.International Journal For Multidisciplinary Research[]2024-04-30T00:00:00https://www.semanticscholar.org/paper/068186a0da96ce2c1437a971d08817dd54980116 22ae98a6b27518c70c5a9a91f53fdebe713429a6Human rights and artificial intelligence are two important concepts that interact with each other in today's rapidly developing digital age. How will artificial intelligence technology affect human rights that are spreading throughout people's lives? This question has become an increasing focus of attention. Artificial intelligence is the field that enables computer systems to simulate human-like thinking and decision-making capabilities. However, some of the problems that arise from artificial intelligence may have a direct impact on human rights. The interaction between human rights and artificial intelligence is a complex problem. While artificial intelligence technology has the potential to harm people and human rights, it also has great potential to be used to protect and promote human rights. Therefore, artificial intelligence technology must be developed and used while observing human rights through an approach supported by an ethical and legal framework. In this paper, after reviewing human rights issues such as artificial intelligence and freedom of expression, artificial intelligence and freedom of privacy, artificial intelligence and bias, hate, discrimination, artificial intelligence and defamation, artificial intelligence and copyright, we will discuss the issue of legal response to artificial intelligence. Regarding the relationship between the state and science and technology, the Constitution stipulates that “the state shall endeavor to develop the national economy through innovation of science and technology and the development of information and manpower (Article 127), and the rights of authors, inventors, and scientists are protected by law (Article 22).” It declares that the state will actively plan and form for the promotion of science and technology and lead in a certain direction. In relation to these constitutional provisions, the establishment and implementation of science and technology policies can be seen as an important duty given to the Republic of Korea as a democratic welfare state. It can be said that the main legislative duty is to achieve a balance between the value of promoting the development and utilization of artificial intelligence systems and the appropriate response to the risks that artificial intelligence can pose. To achieve a balance between artificial intelligence and human rights, it is necessary to comply with ethical values, use transparent and responsible systems, update legal regulations, and adopt an approach that guarantees stakeholder participation. By doing so, we will be able to build a future where human rights are protected while taking advantage of the potential benefits of artificial intelligence.The Legal Studies Institute of Chosun University00The main legislative duty is to achieve a balance between the value of promoting the development and utilization of artificial intelligence systems and the appropriate response to the risks that artificial intelligence can pose.The Legal Studies Institute of Chosun University['Byeongrok Kim']2024-04-30T00:00:00https://www.semanticscholar.org/paper/22ae98a6b27518c70c5a9a91f53fdebe713429a6 c01e6b0721ec0a261d84459da31342b9470162ceSophia, a humanoid robot in the form of a woman who is equipped with artificial intelligence Sophia is good at numbers, performing repetitive tasks, interacting, communicating, and displaying attractive facial expressions. numbers, performing repetitive tasks, interacting and communicating in an interesting way as well as displaying facial expressions with various emotions. facial expressions with various emotions. AI in the context of sociology has unwittingly changed social life. social life. The existence of AI in the implementation of e-Government changes the pattern of communication in public services, affecting the socio-cultural ties that exist in the society. services, affecting previously strong socio-cultural ties and forming new communities. This phenomenon reflects the parameters of modernity, indicating changes in the order of the social system due to technological interference. This qualitative research aims to find out how the form of Social Interaction in Artificial Intelligence-Based Public Services (AI)-based Public Services is formed. Social Interaction of Artificial Intelligence-Based Public Services (AI) from the perspective of the sociology of government? The results The results concluded that AI is able to complete work without direct human interaction, or directly with humans. The use of AI technology in public services has potential benefits, such as increasing efficiency, service quality, and benefits for the community. Through the system Automated Customer service, Big Data Analysis for Decision Making, Security Detection Kiiminal Detection, Efficient Transportation Administration, Better Health Care and Diagnosis Society is increasingly responsive to the utilization of AI in public services, this is driven by increasing public awareness of the benefits of AI and the risks associated with AI utilization, such as loss of workers.KRTHA BHAYANGKARA110The results concluded that AI is able to complete work without direct human interaction, or directly with humans, as well as increasing efficiency, service quality, and benefits for the community.KRTHA BHAYANGKARA['Ratna Indriasari', 'Amalia Syauket']2024-04-30T00:00:00https://www.semanticscholar.org/paper/c01e6b0721ec0a261d84459da31342b9470162ce 78daf7c4ceacebe62b1e7684f3ae2b54480afdf8With the continuous and deep integration of artificial intelligence technology and school education, students' artificial intelligence educational literacy has become an important part of students' development, and the cultivation and improvement of students' artificial intelligence educational literacy has attracted extensive attention from the government and society. In order to accurately grasp the development status of artificial intelligence literacy of primary and secondary school students in China, and to provide decision-making basis for comprehensively and efficiently improving teachers' intelligent education literacy, this study builds a framework of intelligent education literacy of primary and secondary school teachers with intelligent education awareness, knowledge, skills and ethics based on in-depth comparative analysis of relevant standards, frameworks and existing achievements at home and abroad. The questionnaire of teachers' intelligent educational literacy was compiled to carry out empirical research, and the intelligent educational literacy of primary and secondary school teachers was measured. The study found that students have a high level of artificial intelligence awareness, artificial intelligence affection and ethics, but they still need to improve their artificial intelligence ability and knowledge.International Journal of Social Science and Research00The study found that students have a high level of artificial intelligence awareness, artificial intelligence affection and ethics, but they still need to improve their artificial intelligence ability and knowledge.International Journal of Social Science and Research['Yu Yang', 'Xiaoshuang Xu']2024-04-30T00:00:00https://www.semanticscholar.org/paper/78daf7c4ceacebe62b1e7684f3ae2b54480afdf8 33002c0396b92539a43deb6bc140f65141ed89fdAbstract: As the digital landscape continues to evolve, ensuring web accessibility for all users, including those with disabilities, becomes increasingly imperative. Artificial Intelligence (AI) presents a promising avenue to address accessibility challenges by providing innovative solutions to improve user experiences. This research paper examines the utilization of AI in enhancing web accessibility, exploring its applications, benefits, challenges, and future prospects. Through an extensive review of literature and case studies, this paper demonstrates the significant impact of AI in fostering inclusivity and facilitating seamless access to online content for individuals with disabilitiesInternational Journal for Research in Applied Science and Engineering Technology00An extensive review of literature and case studies demonstrates the significant impact of AI in fostering inclusivity and facilitating seamless access to online content for individuals with disabilities.International Journal for Research in Applied Science and Engineering Technology['Sayyed Abrar Hussain']2024-04-30T00:00:00https://www.semanticscholar.org/paper/33002c0396b92539a43deb6bc140f65141ed89fd e6ae3f395b3a173dc8f115630cf58b7b2a00aa54В статье исследуются возможности искусственного интеллекта и его влияние на некоторые сферы экономики. В современных реалиях искусственный интеллект занимает центральное положение и является одной из самых быстроразвивающихся областей. Он внедряется почти во все сферы общественной жизни: образование, предпринимательство, медицина, экономика и т.д. Дано понятие искусственного интеллекта, выявлены пути его влияния на экономическое развитие, а также произведен анализ полученных результатов и их оценка. The article explores the possibilities of artificial intelligence and its impact on some areas of the economy. In modern realities, artificial intelligence occupies a central position and is one of the fastest growing areas. Artificial intelligence is being introduced into almost all spheres of public life: education, entrepreneurship, medicine, economics, etc. In the course of the study, the concept of artificial intelligence was given, the ways of its influence on economic development were identified, and the results were analyzed and evaluated.Journal of Applied Research00Journal of Applied Research['В.А. Мирончук', 'А.Л. Золкин', 'И.А. Поскряков', 'А.Д. Маринов']2024-04-30T00:00:00https://www.semanticscholar.org/paper/e6ae3f395b3a173dc8f115630cf58b7b2a00aa54 64490795b64d128bd440345cb373f0f65ae0a757As a second international language, Arabic has an existence to be learned by the general public, especially in formal and non-formal educational institutions. The use of Artificial Intelligence (AI) technology in various aspects of life has brought significant impacts, including in the field of education and learning. One increasingly popular application of AI is Text-to-Speech (TTS), which allows computers to convert written text into audible voice. This study aims to help users understand Arabic text reading, especially for users who cannot read Arabic script. And can be one of the tools or alternatives in the process of learning Arabic to improve reading skills. AI text-to-speech (TTS) can be very useful in improving reading skills. One way that AI TTS can help is in Clear Pronunciation: AI TTS can produce clear and accurate sounds, helping listeners understand words correctly and mimic proper pronunciation.Jurnal Naskhi Jurnal Kajian Pendidikan dan Bahasa Arab140This study aims to help users understand Arabic text reading, especially for users who cannot read Arabic script, and can be one of the tools or alternatives in the process of learning Arabic to improve reading skills.Jurnal Naskhi Jurnal Kajian Pendidikan dan Bahasa Arab['Suharia Sarif', 'Amran Ar']2024-04-30T00:00:00https://www.semanticscholar.org/paper/64490795b64d128bd440345cb373f0f65ae0a757 786f24da66a3c00a0c3150bd939992d81de24e8dCardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009–2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.Applied Bionics and Biomechanics1880Applied Bionics and Biomechanics['Oliwia Kolaszyńska', 'Jacek Lorkowski']2024-04-30T00:00:00https://www.semanticscholar.org/paper/786f24da66a3c00a0c3150bd939992d81de24e8d c872a8aa4f19deec0435c4ac486433b86da26541In past few years’ drastic evolutions in technology has occurred. In almost all sectors artificial intelligence has significantly used. Using AI was little difficult in past but technological advancements have made it possible and feasible. Many people even have a thought that AI may replace the humans in many applications. AI has been used in education, medical, defense, corporate etc. for specific tasks. However, depending on the application, it has its own importance. This paper is an attempt to present a study on role of AI in education sectors and its impact on the performance. Introduction In recent few years’ artificial intelligence has become a part of most of the fields due to its features that has improved the performance. AI has revolutionized the industrial sector, education, agriculture, healthcare and many people think that if it will continue, it will have a direct impact on jobs. There is a threat to jobs, many people may lose their jobs and such various views are a part of common discussion in society. Various researchers have shared their research work elaborating the aspects that have made AI a compulsory component in most of the design and development of systems. No doubt AI has both positive and negative sides. It is necessary to review what makes AI so powerful and in which areas it can replace humans and what are various cautions to be understood while using AI in development of a system. This paper basically discusses various aspects of AI that can be introduced in education to improve the performance. Education system needs to be improved and has been facing challenges at various levels and due to various reasons. As per the reports from various agencies like KPMG students graduated do not have skills as per the requirements of industries. Every education system claims that students graduating from their institute have all the skills required but in practice reality is something different. Hence the government of India has framed a new education policy that will provide students with all the types of skills. In this contest the author of this paper has studied the education system and expressed various aspects of AI which can be utilized to improve the performance of the whole education system. In schools AI can be used to identify the cognitive level of students at entry level only that will be helpful to the concerned student to choose a career in the field of his interest or as per the strength of the concerned student. In the current scenario parents decide the field of education for their wards ignoring the parameter like wards interest, his strengths etc. The impact of such types of trends has resulted in drop out of students from education at various stages.International Research Journal of Computer Science00This paper is an attempt to present a study on role of AI in education sectors and its impact on the performance, and various aspects of AI that can be introduced in education to improve the performance.International Research Journal of Computer Science['Gunjankumar Nitin']2024-04-30T00:00:00https://www.semanticscholar.org/paper/c872a8aa4f19deec0435c4ac486433b86da26541 7e70574b909cb83f4f2f70c2cf67070dbd539094Abstract: The paper presents an apt process for Machine Learning and Artificial Intelligence. To solve the real-world problems with Machine Learning (ML) and Artificial Intelligence (AI) a definite process is required which would emphasis on complete solution rather than approaching as randomly. Machine Learning and Artificial Intelligence are not just mere algorithms which you can put anywhere and start getting fantabulous results. ML and AI are processes which starts with defining the data and completes with the model with defined level of accuracy. In this paper an apt process is proposed to solve the real-world problems with Machine Learning and Artificial IntelligenceInternational Journal for Research in Applied Science and Engineering Technology00To solve the real-world problems with Machine Learning (ML) and Artificial Intelligence (AI) a definite process is required which would emphasis on complete solution rather than approaching as randomly.International Journal for Research in Applied Science and Engineering Technology['Dr P Vijaya Vardhan Reddy']2024-04-30T00:00:00https://www.semanticscholar.org/paper/7e70574b909cb83f4f2f70c2cf67070dbd539094 65d94b705d85169b37491f103664b0080c773c3aJournal of Translational Medicine1290This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.Journal of Translational Medicine['Claudio Carini', 'A. Seyhan']2024-04-30T00:00:00https://www.semanticscholar.org/paper/65d94b705d85169b37491f103664b0080c773c3a a666a355b750e90bc3b9a03e8d24d08a678a5cb6 This review critically analyzes the incorporation of artificial intelligence (AI) in surface chemistry and catalysis to emphasize the revolutionary impact of AI techniques in this field. The current review examines various studies that using AI techniques, including machine learning (ML), deep learning (DL), and neural networks (NNs), in surface chemistry and catalysis. It reviews the literature on the application of AI models in predicting adsorption behaviours, analyzing spectroscopic data, and improving catalyst screening processes. It combines both theoretical and empirical studies to provide a comprehensive synthesis of the findings. It demonstrates that AI applications have made remarkable progress in predicting the properties of nanostructured catalysts, discovering new materials for energy conversion, and developing efficient bimetallic catalysts for CO2 reduction. AI-based analyses, particularly using advanced NNs, have provided significant insights into the mechanisms and dynamics of catalytic reactions. It will be shown that AI plays a crucial role in surface chemistry and catalysis by significantly accelerating discovery and enhancing process optimization, resulting in enhanced efficiency and selectivity. This mini-review highlights the challenges of data quality, model interpretability, scalability, and ethical, and environmental concerns in AI-driven research. It highlights the importance of continued methodological advancements and responsible implementation of artificial intelligence in catalysis research.Tenside Surfactants Detergents780It will be shown that AI plays a crucial role in surface chemistry and catalysis by significantly accelerating discovery and enhancing process optimization, resulting in enhanced efficiency and selectivity.Tenside Surfactants Detergents['F. Al-Akayleh', 'Ahmed S. A. Ali Agha', 'Rami A. Abdel Rahem', 'M. Al-Remawi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/a666a355b750e90bc3b9a03e8d24d08a678a5cb6 dc0019c2f1fc75ad59d075c0d985c7cfc4844f55This study examines the duality of immersive artificial intelligence AI in the luxury hospitality sector,focusing on how immersive AI influences consumer distinction and adds value to luxury experiences.Through a comprehensive review of literature, key findings and insights are synthesized to provide a holistic understanding of the opportunities and challenges associated with the integration of immersive AI technologies, including virtual reality VR, augmented reality AR, and personalized AI assistants, in luxury hospitality. The analysis reveals that immersive AI holds significant promise for luxury brands to enhance guest engagement, satisfaction, and loyalty by offering personalized, memorable experiences that resonate with discerning consumers. However, the adoption and implementation of immersive AI also present challenges, including concerns around authenticity, human connection, data privacy, security, and ethical implications. By prioritizing authenticity, transparency, innovation, sustainability, diversity, data driven optimization, and customer centricity, luxury brands can unlock the full potential of immersive AI to create truly differentiated and memorable experiences for discerning consumers. This study highlights the need for further research and practice to address gaps and limitations in understanding the long term impacts of immersive AI, factors influencing guest acceptance and adoption, emerging trends and challenges, ethical considerations, economic viability, operational challenges, and the impact on the employee experience. By addressing these gaps, researchers and practitioners can advance knowledge and inform strategies for effective and responsible implementation of immersive AI in luxury hospitality, thereby shaping the future of luxury experiences in the digital age.International Journal for Multidimensional Research Perspectives150There is a need for further research and practice to address gaps and limitations in understanding the long term impacts of immersive AI, factors influencing guest acceptance and adoption, emerging trends and challenges, ethical considerations, economic viability, operational challenges, and the impact on the employee experience.International Journal for Multidimensional Research Perspectives['Jenifer v']2024-04-30T00:00:00https://www.semanticscholar.org/paper/dc0019c2f1fc75ad59d075c0d985c7cfc4844f55 28e322baa45342c57f8ce500737e5a0f0aee2ec1The purpose of this study is to reveal the results of cases where artificial intelligence descriptive evaluation and feedback systems were applied in large-scale online liberal arts lectures at universities. For this purpose, we investigated and analyzed the results and satisfaction of descriptive evaluations conducted by students in the online liberal arts course ‘Understanding Sustainable Development’ taken by 2,600 students at University A. As a research method, frequency analysis and trajectory analysis were conducted to confirm changes in student participation rates, and factor analysis was used to cluster and compare students' narrative evaluation response trends. As a result of the analysis, first, more than 60% of the students who took the ‘Understanding Sustainable Development’ course participated even after the 14th week, and more than 70% of the students participated more than 10 times. Second, the students' evaluation scores showed a similar score range in the first week, but the range of scores increased as the 14th week progressed. In addition, there was a group whose score difference reversed around the 7th week, the midpoint of a semester's lectures, so it was possible to identify both the period and the group in which the students' learning needs to be carefully managed. Third, when looking at students' satisfaction with AI quizzes, 86.6% responded positively regarding their active participation in AI-based quizzes, and 83.1% responded positively regarding the usefulness of AI-based quizzes. Meanwhile, the degree of motivation and expansion of AI-based quizzes into other liberal arts classes was relatively low at positive rates of 52.6% and 61.5%, respectively, but was much higher than the negative rates of 19.6% and 11.8%. Through these results, this study confirmed that the artificial intelligence-based narrative evaluation feedback system that provides feedback to individual students has implications in terms of student learning management and quality management of liberal arts courses. In order to improve learners' learning fidelity and increase their academic retention rate in large-scale online liberal arts lectures in the future, interest in and improvement of the evaluation feedback system is essential, and its use can be expanded to other classes that require descriptive evaluation.The Korean Association of General Education110Investigation and analysis of descriptive evaluations conducted by students in the online liberal arts course ‘Understanding Sustainable Development’ confirmed that the artificial intelligence-based narrative evaluation feedback system that provides feedback to individual students has implications in terms of student learning management and quality management of liberal arts courses.The Korean Association of General Education['Minsu Ha', 'Cholkyun Shin']2024-04-30T00:00:00https://www.semanticscholar.org/paper/28e322baa45342c57f8ce500737e5a0f0aee2ec1 ef74ed035e05b75a23170aac52f9d56ccffef6aeThis study systematically reviews the transformative role of Artificial Intelligence (AI) in enhancing agricultural productivity and sustainability in the United States. With the aim of understanding how AI technologies can be effectively integrated into farming practices, this research employs a systematic literature review methodology, focusing on peer-reviewed journal articles, conference proceedings, and reputable reports from 2010 to 2024. The methodology includes a structured search strategy, defined inclusion and exclusion criteria, and thematic analysis to categorize findings into relevant themes. Key findings reveal that AI technologies, such as machine learning models, predictive analytics, and robotics, are revolutionizing U.S. agriculture by optimizing resource use, improving crop health monitoring, and enhancing decision-making processes. Despite the promising potential of AI to address challenges like food security and environmental sustainability, the adoption of AI in agriculture faces barriers including technological adoption, data privacy concerns, and the need for significant investment in digital infrastructure. The study concludes that leveraging AI for sustainable agriculture requires collaborative efforts among stakeholders, including investment in digital literacy, development of regulatory frameworks, and fostering public-private partnerships. Future research directions emphasize the socio-economic impacts of AI adoption, ethical considerations, and the development of scalable AI solutions. This study underscores AI's pivotal role in ensuring a sustainable, productive, and resilient agricultural sector.Open Access Research Journal of Engineering and Technology320The study concludes that leveraging AI for sustainable agriculture requires collaborative efforts among stakeholders, including investment in digital literacy, development of regulatory frameworks, and fostering public-private partnerships.Open Access Research Journal of Engineering and Technology['Olabimpe Banke Akintuyi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/ef74ed035e05b75a23170aac52f9d56ccffef6ae 5cbed6bc69da11e8fd264121dd95740e0d0ad0b9This study compared the level and relationship in attitudes and ethical awareness toward Artificial Intelligence (AI) between elementary general and science-gifted students. For this purpose, 90 elementary general students in grades5-6 and 87 elementary science-gifted students in grades 5-6 were selected, and their attitudes toward AI and ethical awareness toward AI were tested. The results of the independent samples t-test showed that the means of the science-gifted students were statistically significantly higher than those of the general students in the overall and all sub-scales of attitudes toward AI. In addition, the means of the science-gifted students were statistically significantly higher than those of the general students in the overall and three sub-domains (‘stability and reliability’, ‘transparency and explanability’, and ‘robot rights’) of ethical awareness toward AI. However, the mean differences between the two groups were not statistically significant in two sub-domains (‘no discrimination’ and ‘employment’) of ethical awareness toward AI. The correlation analysis showed statistically significant correlations between attitudes and ethical awareness toward AI in both elementary general and science-gifted students. In particular, five sub-domains of attitudes toward AI were significantly correlated with the overall of ethical awareness toward AI and three or four in the five sub-domains of ethical awareness toward AI with high reliability. The correlation coefficients between ethical awareness and attitudes toward AI, especially the two sub-domains of ‘emotional interaction with AI’ and ‘social influence of AI’, were statistically significantly larger for the science-gifted students than for the general students. Educational implications of these findings are discussed.Korean Science Education Society for the Gifted00The correlation analysis showed statistically significant correlations between attitudes and ethical awareness toward AI in both elementary general and science-gifted students.Korean Science Education Society for the Gifted['Hyunjung Cha', 'Hunsik Kang']2024-04-30T00:00:00https://www.semanticscholar.org/paper/5cbed6bc69da11e8fd264121dd95740e0d0ad0b9 5f98a6b15b852ed95b0ec5612e09929716e47089Abstract: Across the globe, artificial intelligence (AI) is revolutionizing the financial industry, and India is no exception. Artificial intelligence (AI) technologies are transforming a number of banking services, including customer care, risk management, fraud detection, and personalized financial advice. These innovations are attributed to the development of sophisticated machine learning algorithms, natural language processing, and data analytics. This study offers a thorough analysis of how artificial intelligence (AI) is enhancing banking services in India, looking at the prospects, obstacles, and state of the industry now.We highlight the most important AI applications in Indian banking, including chatbots for customer service, predictive analytics for credit scoring, and algorithmic trading for investment management, through a review of recent research articles, industry reports, and case studies. We also go over the ethical issues, legal framework, and possible societal repercussions of AI use in the Indian banking industry. In conclusion, we provide perspectives on forthcoming patterns and avenues for investigation to fully leverage artificial intelligence in augmenting banking amenities and promoting financial inclusivity in India.International Journal for Research in Applied Science and Engineering Technology00A thorough analysis of how artificial intelligence (AI) is enhancing banking services in India, looking at the prospects, obstacles, and state of the industry now is offered.International Journal for Research in Applied Science and Engineering Technology['Mragank Shakyawar', 'Kanchan Shakya']2024-04-30T00:00:00https://www.semanticscholar.org/paper/5f98a6b15b852ed95b0ec5612e09929716e47089 db86344c75b8ef8336a6dfeaef60c31dee9a0947The integration of artificial intelligence (AI) into health care offers the potential to enhance patient care, improve diagnostic precision, and broaden access to health-care services. Nurses, positioned at the forefront of patient care, play a pivotal role in utilizing AI to foster a more efficient and equitable health-care system. However, to fulfil this role, nurses will require education that prepares them with the necessary skills and knowledge for the effective and ethical application of AI. This article proposes a framework for nurses which includes AI principles, skills, competencies, and curriculum development focused on the practical use of AI, with an emphasis on care that aims to achieve health equity. By adopting this educational framework, nurses will be prepared to make substantial contributions to reducing health disparities and fostering a health-care system that is more efficient and equitable.Creative Nursing260A framework for nurses is proposed which includes AI principles, skills, competencies, and curriculum development focused on the practical use of AI, with an emphasis on care that aims to achieve health equity.Creative nursing['Michael P Cary', 'J. C. De Gagne', 'Elaine D Kauschinger', 'Brigit M. Carter']2024-04-30T00:00:00https://www.semanticscholar.org/paper/db86344c75b8ef8336a6dfeaef60c31dee9a0947 9255f46f392779bac8d52198c4b19ad46056426bArtificial Intelligence (AI) plays a crucial role in enhancing operational efficiency in manufacturing medical devices by revolutionizing various aspects of the production process. The integration of Artificial Intelligence (AI) technologies within the manufacturing processes of medical devices has significantly transformed operational efficiency. This abstract delves into the pivotal role of AI in optimizing various aspects of manufacturing medical devices, ranging from design and production to quality control and maintenance. AI-driven design methodologies enable the creation of complex medical devices with enhanced functionality and precision. Machine learning algorithms assist in analysing vast datasets related to materials, performance metrics, and user feedback, facilitating the development of innovative device prototypes. In manufacturing, AI optimizes production workflows by predicting demand, managing inventory, and automating assembly processes. Predictive maintenance powered by AI ensures the continuous functionality of manufacturing equipment, reducing downtime and operational costs. Quality control is strengthened through AI-enabled inspection systems that can detect microscopic defects and ensure compliance with stringent regulatory standards. Real-time monitoring of manufacturing processes using AI-driven analytics enhances product consistency and minimizes errors. Furthermore, AI enhances supply chain management by optimizing logistics, procurement, and supplier selection processes, ensuring timely delivery of components and materials essential for medical device manufacturing.AI-driven technologies revolutionize medical device manufacturing through enhanced quality control, predictive maintenance, process optimization, supply chain efficiency, regulatory compliance, customization, cost reduction, and data-driven decision support, optimizing reliability and efficiency while ensuring regulatory standards.Journal of Multidisciplinary Research00AI-driven technologies revolutionize medical device manufacturing through enhanced quality control, predictive maintenance, process optimization, supply chain efficiency, regulatory compliance, customization, cost reduction, and data-driven decision support, optimizing reliability and efficiency while ensuring regulatory standards.The Journal of Multidisciplinary Research['Rishabh Roy', 'Alpana Srivastava']2024-04-30T00:00:00https://www.semanticscholar.org/paper/9255f46f392779bac8d52198c4b19ad46056426b a070a118fe9c84bbaa6c6d21e48840cb4eabfad1Abstract: Motorcycle safety is a critical concern in traffic systems worldwide. This paper introduces a novel approach to enhancing rider safety through the integration of airless tires and advanced airbag systems, underpinned by artificial intelligence (AI) and control sensors. Airless tires contribute to vehicle stability by eliminating the risk of sudden deflation, while AI-driven airbag systems promise dynamic protection for riders during collisions. The study begins with an analysis of airless tire technology, emphasizing its impact on motorcycle stability and safety. It then transitions to the development of motorcyclespecific airbag systems, which utilize AI to process sensor data and make real-time decisions regarding airbag deployment. The effectiveness of these systems is validated through crash simulation tests and impact force measurements, demonstrating a substantial reduction in injury severity. Challenges such as cost, user acceptance, and technical constraints are thoroughly examined. The paper concludes with a discussion on future trends, including the potential for AI to predict and prevent accidents before they occur, thereby setting a new standard for motorcycle safety.International Journal for Research in Applied Science and Engineering Technology00A novel approach to enhancing rider safety through the integration of airless tires and advanced airbag systems, underpinned by artificial intelligence (AI) and control sensors is introduced, demonstrating a substantial reduction in injury severity.International Journal for Research in Applied Science and Engineering Technology['Shivam Rupnawar', 'Puja Deokate', 'Mohit Bhandari']2024-04-30T00:00:00https://www.semanticscholar.org/paper/a070a118fe9c84bbaa6c6d21e48840cb4eabfad1 c43a2ee2376daa7b44c9a0328718949bc91e47f8Purpose This study aims to examine the impact of artificial intelligence (AI) adoption on job insecurity and its subsequent effect on turnover intentions within the hotel industry. It investigated how AI-induced job insecurity affects the likelihood of employees considering leaving their current hotel jobs for other hotels or for opportunities outside the hotel sector, mediated by feelings of job stress and insecurity. Design/methodology/approach Quantitative data analysis used 259 responses from frontline hotel employees. Confirmatory factor analysis was used to explore the factor structure and assess model fit indices. Structural equation modeling was then applied to test the hypotheses. Findings Findings reveal that AI awareness has a positive impact on job stress and insecurity. Moreover, job insecurity is found to positively affect turnover intentions, with a notably stronger effect observed for turnover intentions toward non-hotel companies. Additionally, the influence of social capital as a moderator on the relationship between job insecurity and turnover intention varies depending on the specific dimensions of turnover intention. Research limitations/implications This study contributes to enhancing both theoretical frameworks and empirical insights into turnover dynamics within the hotel sector. However, future research should take into account employees’ positions, roles, organizations and career levels by examining these factors in relation to technology awareness, job stress, job insecurity and turnover intention. Originality/value This study initially focuses on the phenomenon of dynamic turnover issues within the hospitality sector, offering empirical and practical perspectives on effectively integrating new technologies and managing human resources amidst the automation and AI era. Journal of Hospitality and Tourism Technology440Findings reveal that AI awareness has a positive impact on job stress and insecurity, and job insecurity is found to positively affect turnover intentions, with a notably stronger effect observed for turnover intentions toward non-hotel companies.Journal of Hospitality and Tourism Technology['Juhyun Kang', 'Hakseung Shin', 'Changseong Kang']2024-04-30T00:00:00https://www.semanticscholar.org/paper/c43a2ee2376daa7b44c9a0328718949bc91e47f8 ec35cef24ab0db45616b62ac9b5f82ccc98a64efObjective: This research aims to investigate the degree of attitudes of Auditors toward employing Artificial intelligence in auditing process. Also, it aims to investigate if there any statistically differences in a degree of auditor's attitude toward employing artificial intelligence in auditing process distribute to the gender and experience. Methodology: The study used a descriptive analytical approach. The data were collected from (94) auditors from four accounting company in Amman ، Jordan who were chosen randomly. using a questionnaire developed for this purpose. Findings: The findings of the study shows that the degree of attitudes of auditors in accounting companies in Jordan toward employing artificial intelligence in auditing process is high with mean (3.69) and Std. Dev (0.91)، Also ، the finding of the study shows that there no statistically differences at the level (a= 0.05) in degree of attitude of Jordanian auditors toward distributes to gender and experience year.International Journal of Science and Research Archive110The degree of attitudes of auditors in accounting companies in Jordan toward employing artificial intelligence in auditing process is high and there are no statistically differences at the level of degree of attitude in degree of attitude of Jordanian auditors toward distributes to gender and experience year.International Journal of Science and Research Archive['Zain Mohammad', 'Ali Al-dahabi', 'R. Y. Hajjaj', 'Fatima Ali Algazo']2024-04-30T00:00:00https://www.semanticscholar.org/paper/ec35cef24ab0db45616b62ac9b5f82ccc98a64ef 07462a4dd2ebb2a8d837f2768b02c4a46298704dThe increasing availability of Artificial Intelligence and related tools and technologies has made it possible to use AI in various domains of life. The field of education is also witnessing an increasing use of AI; however, its scope and related challenges remain unclear. In the current paper, we, by using previous empirical and theoretical studies, provided an overview of the use of AI in the education field, its related challenges, and future opportunities. Accordingly, we noted that AI is being used in the Education field as evidenced by global initiatives such as UNESCO. We also noted that AI applications in the education field include enabling the preparation of content, assignments, automated grading, and assistance to students. The benefits of AI in education include greater flexibility in terms of time and space and a changing role of the tutor as a facilitator. We also discussed key challenges including ethical issues such as data privacy, lack of inclusion and equity for students of all backgrounds, and lack of human touch. Based on these themes we concluded that AI's role in education will increase in the future, but the challenges also need to be addressed to fully unlock the benefits of AI in the education field.World Journal of Advanced Research and Reviews420It is concluded that AI's role in education will increase in the future, but the challenges also need to be addressed to fully unlock the benefits of AI in the education field.World Journal of Advanced Research and Reviews['F. Deeba', 'Farha Muhammad Tahir', 'Deeba Hassan', 'Mudasir Rahim']2024-04-30T00:00:00https://www.semanticscholar.org/paper/07462a4dd2ebb2a8d837f2768b02c4a46298704d 6d4e5c8512ddbcb3641a1499d364b2502ae99ffdPurposeThe existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI) by integrating the feedback and sequential updating mechanisms. This study challenged the existing models and constructed an integrated longitudinal model. Using a territory-wide two-wave survey of a representative sample, this new model examined the effects of hedonic motivation, social motivation, perceived ease of use, and perceived usefulness on continued trust, intended use, and actual use of conversational AI.Design/methodology/approachAn autoregressive cross-lagged model was adopted to test the structural associations of the seven repeatedly measured constructs.FindingsThe results revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI. While the original technology acceptance model was unable to explain the continued acceptance of conversational AI, the findings showed positive feedback effects of actual use on continued intended use. Except for trust, the sequential updating effects of all the measured factors were significant.Originality/valueThis study intended to contribute to the technology acceptance and human–AI interaction paradigms by developing a longitudinal model of continued acceptance of conversational AI. This new model adds to the literature by considering the feedback and sequential updating mechanisms in understanding continued conversational AI acceptance.Information Technology &amp; People550An integrated longitudinal model of continued acceptance of conversational AI revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI.Information Technology &amp; People['Yu-Leung Ng']2024-04-30T00:00:00https://www.semanticscholar.org/paper/6d4e5c8512ddbcb3641a1499d364b2502ae99ffd 33f1927e845e82715ca14bd2e23915bb9069003eThe article discusses the role of images produced by artificial intelligence (AI) in visual anthropology,  highlighting their ability to represent identity and  experience. It also addresses the technical and  ethical challenges of AI data classification and its  interaction with socio-technical networks, questioning technological neutrality. Technical implications include  data categorization that can perpetuate biases and  power relations. The simplification and distortion of representations by AI is highlighted, requiring a critical  analysis of the stories embedded in the categorizations. It is proposed that anthropologists examine the relationship between image, label, and referent, recognizing differences and similarities in their roles and those of AI labelers in knowledge production. In addition, it discusses how AI images can influence anthropological interpretation and analysis by blending reality and emotion. It is argued that a critical engagement with the ethical and technical implications of the generation and use of these images is necessary. Desde el Sur00How AI images can influence anthropological interpretation and analysis by blending reality and emotion is discussed, and it is argued that a critical engagement with the ethical and technical implications of the generation and use of these images is necessary.Desde el Sur['Juliane Cristina Helanski Cardoso']2024-04-30T00:00:00https://www.semanticscholar.org/paper/33f1927e845e82715ca14bd2e23915bb9069003e 79137a765ae27b696f6de39ba8cd5b59c8b4129dAbstract: Artificial intelligence (AI) has become increasingly prevalent in various industries, revolutionizing traditional practices and introducing novel approaches. One such domain significantly influenced by AI is marketing, particularly in the realm of personalized marketing. Personalized marketing aims to tailor promotional efforts to individual consumers based on their preferences, behaviours, and demographics. The integration of AI technologies in personalized marketing strategies has promised to enhance targeting accuracy, improve customer engagement, and ultimately drive higher conversion rates Consequently, investigating the impact of AI on personalized marketing holds substantial significance in understanding the evolving dynamics of consumer-brand interactions in the digital age.International Journal for Research in Applied Science and Engineering Technology00Investigating the impact of AI on personalized marketing holds substantial significance in understanding the evolving dynamics of consumer-brand interactions in the digital age.International Journal for Research in Applied Science and Engineering Technology['Gautham S', 'Dr. Shalini Rao']2024-04-30T00:00:00https://www.semanticscholar.org/paper/79137a765ae27b696f6de39ba8cd5b59c8b4129d 93ac3813f67abd6d706340628f3a1820943a9d6aAbstract: Building intelligent computers that can carry out tasks that traditionally require human intelligence is the goal of artificial intelligence (AI), a broad field of computer science. While there are many different approaches to AI, it is an interdisciplinary discipline, and recent developments in machine learning and deep learning in particular are causing a paradigm change in almost every area of the tech industry. Machines equipped with artificial intelligence are able to mimic or even outperform human brain functions. And as generative AI tools like ChatGPT and Google's Bard proliferate and selfdriving car technology advances, AI is quickly becoming a part of daily life and a field that businesses in every sector are investing in.International Journal for Research in Applied Science and Engineering Technology00International Journal for Research in Applied Science and Engineering Technology['Ms. Shilpa Sandhu']2024-04-30T00:00:00https://www.semanticscholar.org/paper/93ac3813f67abd6d706340628f3a1820943a9d6a ae43cd4da221be25a0cfa328d90b56dad23fa3b4Artificial intelligence (AI) is a technology that has the potential to significantly impact organizational decision-making. In supply chain management (SCM), data analysis and organizational decision-mak- ing play a crucial role in providing insights into various aspects of the supply chain, such as relevant goods, inventory levels, order processing times, delivery times, and transportation costs. By analyzing data, companies can identify inefficiencies in their supply chain and take corrective actions to improve performance. The SC system is a customer-oriented and integrated system that determines planning, administration, and management processes for internal and external material and related flows, for- mulating the optimal factor of added value. In this study, we aim to identify hazardous products by analyzing product identification information at customs checkpoints and locations where vendible items are delivered to customers. We have identified several commodity groups and substances that are potentially harmful and dangerous to human health.Journal of Technical Science and Technologies00This study aims to identify hazardous products by analyzing product identification information at customs checkpoints and locations where vendible items are delivered to customers.Journal of Technical Science and Technologies['L. Petriashvili', 'Tinatin Kaishauri', 'N. Otkhozoria']2024-04-30T00:00:00https://www.semanticscholar.org/paper/ae43cd4da221be25a0cfa328d90b56dad23fa3b4 d58ece4bda1b6a438d69c06e2d4d2ef7bf4b269bAbstract: Artificial intelligence, also known as AI, is a technology that enables computers and machines to emulate human intelligence and problem-solving abilities. Computers can perform a wide range of advanced functions thanks to the use of artificial intelligence (AI) technologies. These functions include the ability of machines to see, comprehend, and translate spoken and written language, analyzing data, making recommendations, and more. Artificial intelligence is considered a field of computer science that encompasses other areas like machine learning and deep learning, data analytics, linguistics, software engineering, and so on. These disciplines often involve the development of AI algorithms that are based on the decision-making processes of the human brain, that has the ability to learn and memorize from existing data and allows more precise classifications or predictions over a period of time. AI is the foundation for innovation in modern computing, discovering the value for both individuals and businesses. For illustration, AI is used to extricate content and information from pictures and documents, turns unstructured content into business-ready structured data, and unlocks valuable insights. AI has the ability to perform tasks that would otherwise require human intelligence or intervention, when combined with other technologies such as sensors, and robotics. It is used in many areas of life, including education, finance, healthcare, and manufacturing. Here are some examples of AI in different areas: Facial detection and recognition, Text editors, Digital assistants, Self-driving cars, and many more.International Journal for Research in Applied Science and Engineering Technology00Artificial intelligence has the ability to perform tasks that would otherwise require human intelligence or intervention, when combined with other technologies such as sensors, and robotics.International Journal for Research in Applied Science and Engineering Technology['Dr. Anurag Shrivastava']2024-04-30T00:00:00https://www.semanticscholar.org/paper/d58ece4bda1b6a438d69c06e2d4d2ef7bf4b269b 3a9dbf1921a56b7c3ca454d491f5dfdad76307b5This study provides a comprehensive review of the integration of Artificial Intelligence (AI) into Supply Chain Management (SCM), focusing on its impact on operational efficiency, strategic innovation, and sustainability. Employing a systematic literature review and content analysis methodology, the research synthesizes findings from peer-reviewed articles and conference papers published between 2013 and 2023. The study identifies key advancements in AI technologies, such as machine learning, natural language processing, and robotics, and their applications across various supply chain processes including demand forecasting, inventory management, and logistics optimization. Key findings reveal that AI significantly enhances supply chain efficiency by improving decision-making, reducing costs, and optimizing resource allocation. However, challenges such as data privacy concerns, ethical considerations, and the need for skilled personnel emerge as critical factors influencing AI adoption in SCM. The future outlook for AI-enhanced supply chains is promising, with potential for further innovation and resilience, albeit contingent upon addressing existing challenges. The study concludes with strategic recommendations for practitioners and policymakers, emphasizing the importance of fostering a culture of innovation, developing digital competencies, and creating supportive regulatory frameworks for AI integration. Directions for future research include exploring the long-term impacts of AI on supply chain sustainability, ethical implications of autonomous systems, and the interplay between AI and emerging technologies. This research contributes to the academic discourse on AI in SCM, offering insights for enhancing supply chain operations in the digital age.Open Access Research Journal of Multidisciplinary Studies00Key findings reveal that AI significantly enhances supply chain efficiency by improving decision-making, reducing costs, and optimizing resource allocation, however, challenges such as data privacy concerns, ethical considerations, and the need for skilled personnel emerge as critical factors influencing AI adoption in SCM.Open Access Research Journal of Multidisciplinary Studies['Nsisong Louis Eyo-Udo']2024-04-30T00:00:00https://www.semanticscholar.org/paper/3a9dbf1921a56b7c3ca454d491f5dfdad76307b5 0ae25197d5449c7c0fac02ea8964a1eeb12ffd9aAbstract: This survey paper explores various research studies and papers related to Emotional Artificial Intelligence (Emotion AI). Emotion AI has gained significant attention in recent years due to its potential applications in diverse fields, such as mental health, workplace surveillance, education, and music generation. The paper provides an overview of the methodologies employed in these studies, along with the benefits and drawbacks associated with each approach. By examining the literature, this paper aims to shed light on the current state of Emotional AI and its implications for different domainsInternational Journal for Research in Applied Science and Engineering Technology00An overview of the methodologies employed in research studies related to Emotional Artificial Intelligence, along with the benefits and drawbacks associated with each approach are provided.International Journal for Research in Applied Science and Engineering Technology['Nidhishree M S']2024-04-30T00:00:00https://www.semanticscholar.org/paper/0ae25197d5449c7c0fac02ea8964a1eeb12ffd9a 846dbc1f7c9e8ec2a9a4fbe342f87ed3d63983b5Abstract: Climate change and digital modifications are the two most dominant trends of our century and have become a global concern. The strategy in which humans can manage them, and their enlarged interactions will play a key role in mankind’s future. Primarily we have to make pathways to combine the climate and digital transformations in a way that validates our social and democratic values. In the present situation, climate change is the major concern that leads to the depletion of various environmental resources which is an alarming situation for living beings. Technology has changed a lot in the past few decades, changing our lives and us along with it. Artificial Intelligence is already playing a key role in Education, Health, and various other industries. Artificial intelligence (AI) is bringing about many changes in our perception of technology and lifestyle. Advancements in AI and Machine Learning (ML) are promising, and their applications are continually redefining the way we interact with our devices, people, and the planet. Various initiatives of Artificial Intelligence for tackling climate change have started, like the UN Climate Change Initiative on Artificial Intelligence for Climate Action. These investigate how AI might be used as a potent instrument to advance and intensify climate action that is revolutionary in poor nations. This paper mainly identifies the effective impacts of Artificial Intelligence on climate change and its sustainability.International Journal for Research in Applied Science and Engineering Technology00How AI might be used as a potent instrument to advance and intensify climate action that is revolutionary in poor nations is investigated.International Journal for Research in Applied Science and Engineering Technology['Nikita Saklani', 'Kanchan Bade']2024-04-30T00:00:00https://www.semanticscholar.org/paper/846dbc1f7c9e8ec2a9a4fbe342f87ed3d63983b5 e20f2223963796cdb1b4418d296bac966e9c9f8bExplainable Artificial Intelligence (XAI) is increasingly recognized as a vital component in the deployment of AI systems within healthcare settings. This abstract synthesizes findings from fifteen research papers investigating the prevalence, detection methods, and implications of XAI in healthcare. The review highlights the growing interest in XAI applications among healthcare professionals, emphasizing the importance of interpretability in medical decision-making. Various detection methods, including rule-based approaches and machine learning interpretability techniques, are explored, illustrating the diversity of strategies employed to enhance AI transparency. Furthermore, the review examines the ethical implications of XAI in healthcare, addressing concerns surrounding accountability, bias mitigation, and patient privacy. By synthesizing findings from multiple studies, this abstract provides insights into the integration of XAI technologies in healthcare, contributing to the ongoing discourse on ensuring transparency, trust, and ethical considerations in AI-driven medical practices.International Journal For Multidisciplinary Research180Insight is provided into the integration of XAI technologies in healthcare, contributing to the ongoing discourse on ensuring transparency, trust, and ethical considerations in AI-driven medical practices.International Journal For Multidisciplinary Research['Aniket Kumar', 'Eshan Jaiswal', 'Kanishk Gupta', 'Kartik Chaudhary', 'Pratyush Rai', 'Er. Radha']2024-04-30T00:00:00https://www.semanticscholar.org/paper/e20f2223963796cdb1b4418d296bac966e9c9f8b a2627b5fde0e4d0dde3802d858929fdb5f7dccebArtificial Intelligence is now widely used by humans. The use of this technology is based on the view that Artificial Intelligence can make their lives easier. Many sectors have utilized this technology, including government, private, social, health, to education. Even though Artificial Intelligence is felt to have many benefits, there are perceived threats so that appropriate policies are needed. Thus, the aim of this research is to find out policies that can be recommended for the use of Artificial Intelligence that focus on humanitarian aspects. This research uses a qualitative approach to deepen the literature review that has been carried out. The results of this research show that the presence of Artificial Intelligence provides quite large benefits, especially as a technology for predicting the future. However, to regulate the use of this technology, appropriate policies are needed to avoid increasingly widespread digital crimes. In formulating Artificial Intelligence policies, humanitarian aspects need to be considered to provide appropriate protection. Especially for vulnerable groups who have low access to the use of Artificial Intelligence.The Eastasouth Journal of Information System and Computer Science200The results of this research show that the presence of Artificial Intelligence provides quite large benefits, especially as a technology for predicting the future, however, to regulate the use of this technology, appropriate policies are needed to avoid increasingly widespread digital crimes.The Eastasouth Journal of Information System and Computer Science['Khairul Syafuddin']2024-04-30T00:00:00https://www.semanticscholar.org/paper/a2627b5fde0e4d0dde3802d858929fdb5f7dcceb 8f53b866c1e14f4e8cc6b889e8d513bb74d51354The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.Artificial Intelligence in Gastroenterology340In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising, and AI is useful in colonoscopy, particularly in detecting smaller colonic polyps.Artificial Intelligence in Gastroenterology['G. Sridhar', 'Atmakuri V Siva Prasad', 'G. Lakshmi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/8f53b866c1e14f4e8cc6b889e8d513bb74d51354 cbef8c0ae42e96d9e540509014c6b1a139d52ddfBackground The integration of artificial intelligence (AI) into pharmacy education and practice holds the potential to advance learning experiences and prepare future pharmacists for evolving healthcare practice. However, it also raises ethical considerations that need to be addressed carefully. This study aimed to explore pharmacy students’ attitudes regarding AI integration into pharmacy education and practice. Methods A cross-sectional design was employed, utilizing a validated online questionnaire administered to 702 pharmacy students from diverse demographic backgrounds. The questionnaire gathered data on participants’ attitudes and concerns regarding AI integration, as well as demographic information and factors influencing their attitudes. Results Most participants were female students (72.8%), from public universities (55.6%) and not working (64.2%). Participants expressed a generally negative attitude toward AI integration, citing concerns and barriers such as patient data privacy (62.0%), susceptibility to hacking (56.2%), potential job displacement (69.3%), cost limitations (66.8%), access (69.1%) and the absence of regulations (48.1% agree), training (70.4%), physicians’ reluctance (65.1%) and patient apprehension (70.8%). Factors including country of residence, academic year, cumulative GPA, work status, technology literacy, and AI understanding significantly influenced participants’ attitudes (p < 0.05). Conclusion The study highlights the need for comprehensive AI education in pharmacy curricula including related ethical concerns. Addressing students’ concerns is crucial to ensuring ethical, equitable, and beneficial AI integration in pharmacy education and practice.Research Square300The study highlights the need for comprehensive AI education in pharmacy curricula including related ethical concerns and addressing students’ concerns is crucial to ensuring ethical, equitable, and beneficial AI integration in pharmacy education and practice.Research Square['Hisham E Hasan', 'Deema Jaber', 'O. Khabour', 'K. Alzoubi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/cbef8c0ae42e96d9e540509014c6b1a139d52ddf 19940359f59ab2336780e5bd3b8e0cfa4da6f50fThe integration of artificial intelligence (AI) into the financial sector has raised ethical concerns that need to be addressed. This paper analyzes the ethical implications of using AI in financial decision-making and emphasizes the importance of an ethical framework to ensure its fair and trustworthy deployment. The study explores various ethical considerations, including the need to address algorithmic bias, promote transparency and explainability in AI systems, and adhere to regulations that protect equity, accountability, and public trust. By synthesizing research and empirical evidence, the paper highlights the complex relationship between AI innovation and ethical integrity in finance. To tackle this issue, the paper proposes a comprehensive and actionable ethical framework that advocates for clear guidelines, governance structures, regular audits, and collaboration among stakeholders. This framework aims to maximize the potential of AI while minimizing negative impacts and unintended consequences. The study serves as a valuable resource for policymakers, industry professionals, researchers, and other stakeholders, facilitating informed discussions, evidence-based decision-making, and the development of best practices for responsible AI integration in the financial sector. The ultimate goal is to ensure fairness, transparency, and accountability while reaping the benefits of AI for both the financial sector and society.Computer and Information Science170The paper proposes a comprehensive and actionable ethical framework that advocates for clear guidelines, governance structures, regular audits, and collaboration among stakeholders that aims to maximize the potential of AI while minimizing negative impacts and unintended consequences.Computer and Information Science['O. Owolabi', 'Prince C. Uche', 'Nathaniel T. Adeniken', 'Christopher Ihejirika', 'Riyad Bin Islam', 'B. Chhetri']2024-04-30T00:00:00https://www.semanticscholar.org/paper/19940359f59ab2336780e5bd3b8e0cfa4da6f50f 7e2c6a8f15bcfce92d46f5a0d400236d43c48035Artificial intelligence has become a revolutionary power in the fashion industry by reshaping the brand Artificial intelligence (AI) has emerged as a transformative force within the fashion industry, reshaping brand-consumer interactions, optimizing operations, and driving innovative product offerings. This research endeavours to explore the multifaceted role of Al in the fashion sector, delving into its meaning, diverse applications, benefits, drawbacks, and ethical considerations. The involvement of Al is manifested through a comprehensive analysis, encompassing virtual try-ons, customer service chatbots, predictive analysis, design assistance tools, and gauging customer satisfaction and market response based on these features. By leveraging Al technologies, fashion brands can enhance customer engagement, streamline operations, and deliver personalized experiences tailored to individual preferences. Moreover, the research underscores the advantages of Al adoption, including improved decision-making, expedited results, and precise design assistance, which collectively contribute to enhanced efficiency and competitiveness within the industry. However, it also highlights legitimate concerns surrounding data privacy, algorithmic bias, and the potential for job displacement resulting from increased automation. Ethical considerations play a pivotal role in the discourse surrounding Al deployment in the fashion industry. The study emphasizes the significance of addressing labour displacement issues, advocating for accountable, transparent, and fair Al practices, particularly in data collection for market response and consumer preferences. Striking a balance between technological advancement and ethical responsibility is imperative to ensure the equitable and sustainable interest of Al within the fashion landscape. Looking ahead, the research outlines future trends and advancements in Al-driven personalized styling recommendations, sustainable practices, and creativity in fashion. It advocates for a responsible Al deployment framework, which prioritizes the harmonious collaboration of humanity and technology, thereby fostering a balanced and inclusive fashion ecosystem that embraces innovation while safeguarding ethical principles. By embracing responsible Al practices, the fashion industry can harness the transformative potential of Al to drive positive change, foster creativity, and meet evolving consumer demands in an ethically conscious manner.International Journal For Multidisciplinary Research430The study emphasizes the significance of addressing labour displacement issues, advocating for accountable, transparent, and fair Al practices, particularly in data collection for market response and consumer preferences, and highlighting legitimate concerns surrounding data privacy, algorithmic bias, and the potential for job displacement resulting from increased automation.International Journal For Multidisciplinary Research['Adeena Khan', 'Armaiti Shukla']2024-04-30T00:00:00https://www.semanticscholar.org/paper/7e2c6a8f15bcfce92d46f5a0d400236d43c48035 eaedd1d8d5dc8744695614c41156fc8f721f6692Artificial Intelligence (AI), a subset of Advanced Analytics (AA), involves the automation of steps that would typically require human intervention to complete a comprehensive analysis. AI is a multidisciplinary field with the objective of automating tasks that currently necessitate human intelligence. The purpose of this research paper is to explore the impact of Business Analytics (BA) and Business Intelligence (BI) on business activities, and to analyze the scientific advancements in BA and BI to identify new research directions in this area. An analysis is needed to highlight findings that offer recognition and comparison of results, enabling an understanding of the current dynamics, its significance for organizations, and its effectiveness in addressing the new challenges posed by global trade requirements. This paper examines the broad implications of AI in BA and BI, investigating the overall influence of AI from research and innovation to implementation in BA and BI. The hypotheses derived from the research will provide a better understanding of the innovations and effectiveness of AI on businesses and society at large. It will also offer insights into how AI can transform business operations. Keywords: Artificial Intelligence, Advanced Analytics, Business Analytics, Business Intelligence.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The broad implications of AI in BA and BI are examined, investigating the overall influence of AI from research and innovation to implementation in BA and BI and the hypotheses derived from the research will provide a better understanding of the innovations and effectiveness of AI on businesses and society at large.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Anchal Sachan']2024-04-30T00:00:00https://www.semanticscholar.org/paper/eaedd1d8d5dc8744695614c41156fc8f721f6692 09a63290d85569edf93a9ffb3dd7aa5772027190The purpose of this study is to derive implications for application in technology subjects by synthesizing the results of several previous studies related to artificial intelligence education programs conducted in secondary school subject classes and analyzing their effectiveness. To this end, the effect size of the education program according to the variables (program form, measurement variable, continuous variable) was analyzed by reviewing the literature related to the artificial intelligence education program in secondary school subjects. The conclusions drawn through the research results are as follows. First, the effect of secondary artificial intelligence education showed a moderate effect size. Second, there was a significant difference in the effectiveness of the education program according to the educational tool and activity type. Third, a large effect was observed in the variable related to literacy in the cognitive domain and the variable related to the ego in the affective domain. This study provides the following implications for technical education. Since artificial intelligence education in secondary school varies depending on educational tools and types of activities, it is necessary to design an educational program suitable for artificial intelligence education by reflecting the perspective of the technology subject, and it is necessary to discuss the content system or instructional design plan for convergence centered on technology subjects.Korean Technology Education Association00The effect size of the education program according to the variables was analyzed by reviewing the literature related to the artificial intelligence education program in secondary school subjects and a large effect was observed in the variable related to literacy in the cognitive domain and the variable related to the ego in the affective domain.Korean Technology Education Association['Hangil Oh', 'Hye Yeon Huh', 'Ki-Soo Kim']2024-04-30T00:00:00https://www.semanticscholar.org/paper/09a63290d85569edf93a9ffb3dd7aa5772027190 9a23f3656dfa86072083b907f01aeefc56952eceAbstract: Artificial intelligence (AI) is changing many sectors by upending traditional methods of task execution. This wave of technical innovation is not exclusive to the legal profession. Even while AI isn't quite ready to take the position of human attorneys, it is already revolutionizing the legal profession. This essay will examine the advantages, difficulties, and possible ramifications of artificial intelligence for the legal profession.International Journal for Research in Applied Science and Engineering Technology00This essay will examine the advantages, difficulties, and possible ramifications of artificial intelligence for the legal profession.International Journal for Research in Applied Science and Engineering Technology['Khushi Jaiswal']2024-04-30T00:00:00https://www.semanticscholar.org/paper/9a23f3656dfa86072083b907f01aeefc56952ece 7d168d790307d9650d7c6786a7c6f719192c1924В статье проанализирована важность использования искусственного интеллекта в процессе анализа и управления рисками в информационно-технологических проектах. Сформулировано определение рисков в ИТ-проектах и приведены их типы. Рассмотрены этапы управления рисками и способы возможного применения в них одной из наиболее востребованной на сегодняшний день технологии искусственного интеллекта. Описаны основные проблемы, связанные с управлением рисками в ИТ-проектах, и предложены рекомендации по их устранению. Рассматриваются примеры положительного отечественного и зарубежного опыта применения технологии искусственного интеллекта для управления рисками. Завершается статья обсуждением будущего управления рисками с использованием технологий искусственного интеллекта. This article analyzes the importance of using artificial intelligence (AI) in risk analysis and management in information technology (IT) projects. The definition of risks in IT projects and their types are formulated. The stages of risk management and the scope of possible application of artificial intelligence are considered. The main problems related to risk management in IT projects are described and recommendations for their elimination are proposed. The article concludes with a discussion of the future of risk management using artificial intelligence technologies.Journal of Applied Research00Journal of Applied Research['Н.А. Стефанова', 'Д.А. Тюрина']2024-04-30T00:00:00https://www.semanticscholar.org/paper/7d168d790307d9650d7c6786a7c6f719192c1924 86bbddfae9cc48929e63bc81d5a47c85c4a15a8dAbstract: The fusion of artificial intelligence (AI) and cybersecurity signifies a profound turning point in our perpetual struggle against cyber threats. With the expansion of digital landscapes and the escalating sophistication of cyber adversaries, harnessing the capabilities of AI to bolster our defences has become not only advisable but imperative. This paper embarks on a deep exploration of the intricate relationship between AI and cybersecurity, uncovering the rich tapestry of synergies that underlie their integration while deftly navigating the labyrinth of complex challenges and ethical quandaries inherent in this convergence. Through a meticulous examination of advanced threat detection methodologies, adaptive defence mechanisms, and the unfolding panorama of emerging trends, this paper endeavours to lay the groundwork for a seismic shift in cybersecurity paradigms. It is a journey through the corridors of innovation, where AI-driven insights illuminate the path forward and interdisciplinary collaboration serves as the compass guiding us toward a future fortified by AI innovation and fortified by collective wisdom.International Journal for Research in Applied Science and Engineering Technology00A deep exploration of the intricate relationship between AI and cybersecurity is embarks on, uncovering the rich tapestry of synergies that underlie their integration while deftly navigating the labyrinth of complex challenges and ethical quandaries inherent in this convergence.International Journal for Research in Applied Science and Engineering Technology['Aditya Banyal']2024-04-30T00:00:00https://www.semanticscholar.org/paper/86bbddfae9cc48929e63bc81d5a47c85c4a15a8d 37e90292234ef08cb6c81e5561b5e23edcc420e3The integration of artificial intelligence (AI) into global trade agreements presents a transformative opportunity to enhance efficiency, competitiveness, and innovation in international commerce. This review outlines policy recommendations aimed at facilitating the seamless incorporation of AI technologies into the framework of global trade agreements. As AI technologies continue to proliferate across various sectors, including manufacturing, logistics, and services, understanding the implications and challenges of their integration into trade agreements becomes paramount. This review provides insights into the current landscape of AI in global trade, highlighting both the existing challenges and opportunities for leveraging AI to drive economic growth and sustainable development. Key policy considerations include the harmonization of AI standards, addressing intellectual property rights and data ownership, facilitating cross-border data flows, ensuring transparency and accountability in AI decision-making, mitigating potential job displacement and inequality, and promoting ethical AI practices. Drawing on case studies and best practices from regional trade agreements and industry-specific implementations, this review offers actionable recommendations for policymakers, businesses, and international organizations. These recommendations emphasize the importance of collaborative efforts, capacity building, and monitoring mechanisms to effectively harness the benefits of AI while mitigating potential risks. By embracing AI as a driver of innovation and efficiency in global trade, stakeholders can foster a more inclusive and resilient trading environment that maximizes the benefits of technological advancements for all participants.International Journal of Engineering Research Updates460This review outlines policy recommendations aimed at facilitating the seamless incorporation of AI technologies into the framework of global trade agreements, highlighting both the existing challenges and opportunities for leveraging AI to drive economic growth and sustainable development.International Journal of Engineering Research Updates['Etinosa Igbinenikaro', 'Adefolake Olachi Adewusi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/37e90292234ef08cb6c81e5561b5e23edcc420e3 61b3316cf0e490079f9e407233a010b6549458daThe proliferation of Artificial Intelligence (AI) in various sectors raises significant privacy concerns, demanding a nuanced understanding and strategic approach to privacy protection. This article delves into the intricate challenges of safeguarding privacy in the age of AI, exploring the dynamic interplay between technological advancements and privacy norms. With AI's capacity for extensive data collection and analysis, privacy risks escalate, highlighting the necessity for transparent and ethical data practices. Through examining case studies and regulatory responses, the article underscores the critical role of decentralized AI platforms and robust legal frameworks in ensuring privacy. It advocates for a collaborative effort among stakeholders to balance AI's benefits against privacy rights, aiming for a future where AI technologies are developed and deployed responsibly, with a steadfast commitment to upholding individual privacy and dignity.International journal of law and policy280The intricate challenges of safeguarding privacy in the age of AI are delved into, exploring the dynamic interplay between technological advancements and privacy norms and underscores the critical role of decentralized AI platforms and robust legal frameworks in ensuring privacy.International Journal of Law and Policy['Navmi Joshi']2024-04-30T00:00:00https://www.semanticscholar.org/paper/61b3316cf0e490079f9e407233a010b6549458da 68c55e44ca2bcff3adf15f00f3c86ef05b2d62adThe journals of gerontology. Series A, Biological sciences and medical sciences00The journals of gerontology. Series A, Biological sciences and medical sciences[]2024-04-30T00:00:00https://www.semanticscholar.org/paper/68c55e44ca2bcff3adf15f00f3c86ef05b2d62ad 3a50772d9c465745085a4a7cfd3a921589418856OBJETIVO. La inteligencia artificial (IA) es una disciplina de la informática que se encarga de crear sistemas capaces de realizar tareas que se atribuyen a la inteligencia humana. El objetivo principal de este estudio ha sido evaluar las respuestas de algunas IA a preguntas del campo de la toxicología clínica (TC). MATERIAL Y MÉTODOS. Se han valorado cuatro aplicaciones de IA: ChatGPT, Bing, LuzIA y Bard. Para evaluar sus conocimientos en TC se les formularon 30 preguntas sobre diversos aspectos de la TC. Cada pregunta ofrecía cinco opciones de respuesta, de las cuales sólo una era correcta. Se evaluó el acierto/error en la respuesta, así como si había apoyo bibliográfico. Si se detectaban respuestas erróneas, se reformuló la misma pregunta, pero utilizando otra forma de lenguaje para evaluar de nuevo la respuesta y ver si la misma era sensible a la calidad de la pregunta. Los datos se introdujeron en una base SPSS para su análisis estadístico. Se consideró significativo un valor de p < 0,05. RESULTADOS. Los porcentajes de respuestas acertadas fueron del 70% (Bing), 67% (ChatGPT y LuzIA) y 57% (Bard), sin diferencias estadísticamente significativas. Al reformular las preguntas en los casos en los que la respuesta de la IA había sido errónea, los porcentajes de aciertos subieron en los cuatro sistemas, pero sin diferencias significativas. En sus respuestas, Bing ofreció el acceso directo a tres citas bibliográficas y Bard a cuatro, pero su presencia en PubMed era muy baja (7,2% y 0,85% respectivamente). CONCLUSIONES. Los cuatro sistemas de IA han mostrado una capacidad de acierto en más del 50% de las preguntas formuladas de TC. No obstante, el soporte bibliográfico que proporcionan es escaso y de muy baja calidad.Revista Española de Urgencias y Emergencias00Revista Española de Urgencias y Emergencias['Santiago Nogué-Xarau', 'M. Amigó-Tadín', 'José Ríos-Guillermo']2024-04-30T00:00:00https://www.semanticscholar.org/paper/3a50772d9c465745085a4a7cfd3a921589418856 1af0c9d3b0afc45d0df0c537fd640ad4295f139cThe study investigates the impact of Circular 200 on the credit rating model for small, medium, and large enterprises in Vietnam during the period 2008-2018, using Artificial Neural Network (ANN) methodology. The research compares credit rating models before and after the implementation of Circular 200 between 2008-2014 and 2015-2018, using data from 39,162 businesses in Vietnam obtained from the Orbis database. Results indicate that NITA, ROE, liquidity, and current payment ratio are important independent variables with significant differences in credit rating models before and after Circular 200. The research method proves useful for investors in analyzing investment risks for informed investment decisions. Vietnamese financial institutions can apply the model to identify specific credit rating issues for borrowers, enabling them to formulate appropriate credit policies and set different interest rates for varying risk levels. Future research directions include (1) enhancing data and variables, (2) improving data analysis methods, and (3) enhancing performance metrics.Journal of Development and Integration00Results indicate that NITA, ROE, liquidity, and current payment ratio are important independent variables with significant differences in credit rating models before and after Circular 200.Journal of Development and Integration['Hai Quoc Pham', 'Ha My Tang', 'Tung Anh Tran']2024-04-30T00:00:00https://www.semanticscholar.org/paper/1af0c9d3b0afc45d0df0c537fd640ad4295f139c 91d70b34b2769087be5167f3ef67d1633242ad0d01['Marcus Hutter', 'David Quarel', 'Elliot Catt']2024-04-30T00:00:00https://www.semanticscholar.org/paper/91d70b34b2769087be5167f3ef67d1633242ad0d 27a5d2e5b3ed6cb3e11663d3dc561c5c253fbe74Brain tumors are debilitating, and can cause a shorter life in case not analyzed early adequately. Fake bits of knowledge (AI) can offer help to overcome the issue of bring and time in diagnosing brain tumors. There are two sorts of Brain tumor classification: pituitary and glioma The proposed models are associated with a dataset of 1,800 MRI pictures comprising two classes of investigation; glioma tumors and pituitary tumors. To realize a reasonable treatment course of action, classification of brain tumors is an incredibly fundamental step after detection. A dataset comprising 1,800 MRI pictures comprising two classes of investigation, pituitary tumor, and glioma tumors, was utilized to classify brain tumors: pituitary tumor and glioma tumor. It is essential to classify brain tumors after area in arrange to be able to characterize a successful treatment arrangement. This term paper focuses on amplifying the level and viability of utilizing AI Algorithms. In afterward a long time, the utilization of fake experiences (AI) is surging through all circles of science, and no address, it is revolutionizing the field of neurology. The application of AI in helpful science has made brain disease estimates and areas more exact and correct.International Journal of Science and Research Archive00This term paper focuses on amplifying the level and viability of utilizing AI Algorithms by utilizing models associated with a dataset of 1,800 MRI pictures comprising two classes of investigation; glioma tumors and pituitary tumors.International Journal of Science and Research Archive['N. Vaishnavi', 'R. Haripriya', 'K. Mahasuwetha', 'B. Kabilesh', 'D. Sajith Ramana', 'N. Manikandan', 'M. Gokul', 'J. Benito Sam Kumar', 'D. Iswariya', 'R. Sowmiya']2024-04-30T00:00:00https://www.semanticscholar.org/paper/27a5d2e5b3ed6cb3e11663d3dc561c5c253fbe74 4e59fd1fed9390fcb37b054a27846445d880370ePesatnya perkembangan teknologi seperti saat ini sangat mendukung inovasi di berbagai sektor. Termasuk dalam bidang pendidikan, teknologi menjadi salah satu aspek penting untuk menunjang efektivitas proses pembelajaran. Terjadi perubahan dalam dunia pendidikan saat ini akibat adanya integrasi teknologi kecerdasan buatan (AI) yang memberikan pengaruh terhadap proses pembelajaran. Proses pembelajaran yang semula dilakukan secara manual melalui membaca buku, kini semakin canggih dengan bantuan kecerdasan buatan. Jurnal ini menggunakan metode penelitian kualitatif dimana sumber informasi diperoleh dari analisis literatur, studi kasus dan sumber terkait. Sehingga dihasilkan informasi berupa deskripsi dan narasi mengenai perubahan modernitas alat pendidikan akibat kemajuan zaman pendidikan.Pedagogi: Jurnal Ilmu Pendidikan200Pedagogi: Jurnal Ilmu Pendidikan['Nabilatul Muthmainnah', 'Vitha Azalia Rahmayanti', 'M. Faizin']2024-04-30T00:00:00https://www.semanticscholar.org/paper/4e59fd1fed9390fcb37b054a27846445d880370e ced5366ceae880c8b68afcb8d9b617b756cd074aThe transition to renewable energy (RE) sources is critical for addressing global energy demands and environmental concerns. This review paper focuses on the pivotal role of Machine Learning (ML) and Deep Learning (DL) in optimizing and predicting the performance of RE systems, particularly solar and wind power. We explore various applications of these advanced technologies in forecasting energy demand and consumption, predicting the output power of renewable systems, and optimizing the operation and maintenance of these systems. The paper also delves into the significance of Explainable AI (XAI) in enhancing the transparency and understandability of AI models in energy applications. Our comprehensive analysis reveals that while ML and DL offer transformative potential in the RE sector, challenges such as data complexity, system integration, and model interpretability remain. Concluding, this work aims to provide a foundation for future research and development in this rapidly evolving field, asserting that the continued advancement and integration of AI technologies in RE systems is essential for achieving a sustainable and efficient energy future.Journal of Electrical Systems1490This comprehensive analysis reveals that while ML and DL offer transformative potential in the RE sector, challenges such as data complexity, system integration, and model interpretability remain.Journal of Electrical Systems['Faisal Ghazi Bishaw', 'Mohamad Khairi Ishak', 'T. Atyia']2024-04-30T00:00:00https://www.semanticscholar.org/paper/ced5366ceae880c8b68afcb8d9b617b756cd074a d3c64a9f83621e55021f18c92a8bbd599c8d8248B61. THE NEED FOR SPEED: ARTIFICIAL INTELLIGENCE IN PVD00B61. THE NEED FOR SPEED: ARTIFICIAL INTELLIGENCE IN PVD['A. Talon', 'C. Puri', 'D.L. Mccreary', 'D. Windschill', 'W. Bowker', 'Y.A. Gao', 'M. Mathew', 'S. Uppalapu']2024-04-30T00:00:00https://www.semanticscholar.org/paper/d3c64a9f83621e55021f18c92a8bbd599c8d8248 124c1f2d7ffe9b9413e9ef09fc977af8c055f0afB61. THE NEED FOR SPEED: ARTIFICIAL INTELLIGENCE IN PVD00B61. THE NEED FOR SPEED: ARTIFICIAL INTELLIGENCE IN PVD['S. Pettigrew', 'J. Nathan', 'K. Maruti', 'G. Cohen', 'G.J. Criner', 'P. Rali']2024-04-30T00:00:00https://www.semanticscholar.org/paper/124c1f2d7ffe9b9413e9ef09fc977af8c055f0af 168f9e50a4bd5409cd6915e383be432253352199B61. THE NEED FOR SPEED: ARTIFICIAL INTELLIGENCE IN PVD00B61. THE NEED FOR SPEED: ARTIFICIAL INTELLIGENCE IN PVD['E. Garces', 'F. Rahaghi', 'A. Ahmad', 'R. San José Estépar', 'G.R. Washko', 'P. Nardelli']2024-04-30T00:00:00https://www.semanticscholar.org/paper/168f9e50a4bd5409cd6915e383be432253352199 a75fb5b9b1f1e1239fe270eac03c8f4df67b18b3C22. ARTIFICIAL INTELLIGENCE IN THE ICU: THE MACHINE WILL SEE YOU NOW00C22. ARTIFICIAL INTELLIGENCE IN THE ICU: THE MACHINE WILL SEE YOU NOW['P. Nauka', 'J. Kennedy', 'J.M. Kahn', 'B.J. Mcverry', 'C. Seymour']2024-04-30T00:00:00https://www.semanticscholar.org/paper/a75fb5b9b1f1e1239fe270eac03c8f4df67b18b3 e6a17141fc9223e5b02cc43d080b807bba161d98C22. ARTIFICIAL INTELLIGENCE IN THE ICU: THE MACHINE WILL SEE YOU NOW00C22. ARTIFICIAL INTELLIGENCE IN THE ICU: THE MACHINE WILL SEE YOU NOW['A. Ayobi', 'J. Schlossman', 'S. Salehi', 'A. Franciosini', 'M. Scudeler', 'S. Quenet', 'Y. Chaibi', 'D. Chow', 'P. Chang', 'B. Bista', 'A. Imanzadeh']2024-04-30T00:00:00https://www.semanticscholar.org/paper/e6a17141fc9223e5b02cc43d080b807bba161d98 4d5297027f7ad3f133695daf6dd2ab36128c219eKeterampilan menulis sangat diperlukan dalam pengembangan ilmu pengetahuan, khususnya bagi tenaga pendidik. Guru di SMAN 11 Pangkep menghadapi beberapa kendala pada proses penulisan artikel ilmiah, seperti kurangnya motivasi dari dalam diri mereka dan kurang memiliki keterampilan dalam menulis artikel ilmiah seperti ketidakpastian tata bahasa serta kurangnya pengetahuan teknis. Pemanfaatan kecerdasan buatan (AI) merupakan solusi yang menjanjikan, memungkinkan guru-guru untuk mengatasi kendala teknis dan meningkatkan kualitas penulisan. Pelatihan dilakukan dengan memberikan pemaparan materi terkait pentingnya menulis artikel ilmiah bagi guru untuk meningkatkan motivasi dalam diri guru untuk menulis. Selain itu, tim pengabdi juga memaparkan materi berupa beberapa aplikasi AI yang dapat digunakan untuk membantu penulisan tata bahasa Inggris, mempermudah parafrase, serta cek plagiasi melalui penggunaan Grammarly, Quillbot, serta Turnitin. Dengan demikian, pelatihan ini diharapkan para peserta dapat menerapkan ilmu yang didapatkannya dalam penulisan artikel ilmiah dengan benar dan dipublikasikan pada jurnal nasional bereputasi.SMART: Jurnal Pengabdian Kepada Masyarakat120SMART: Jurnal Pengabdian Kepada Masyarakat['Sumiati Side', 'Suriati Eka Putri', 'Sakinah Zubair', 'Nita Magfirah Ilyas']2024-04-30T00:00:00https://www.semanticscholar.org/paper/4d5297027f7ad3f133695daf6dd2ab36128c219e f32635ff5211bd2c8d8a14b29ac3765e6c04caccRecent AI advancements offer transformative potential for global education, yet their application often overlooks Africa's unique educational landscape. AfricAIED 2024 will address this gap, spotlighting efforts to develop AI in Education (AIED) systems tailored to Africa's needs. Building on the success of the inaugural workshop, AfricAIED 2024 will feature an online AI Hackathon focused on democratizing preparation for Ghana's National Science&Maths Quiz (NSMQ). Participants will create open-source AI tools leveraging resources from the Brilla AI project to level the academic playing field and enhance science and math education across Africa. The workshop will showcase top competitors' solutions, invite discussions on AIED opportunities and challenges in Africa, and highlight the latest advancements in AI education integration. AfricAIED 2024 aims to foster collaboration and innovation, amplifying African voices in the AIED community and driving positive change in African education through AI.90The inaugural AfricAIED 2024 will feature an online AI Hackathon focused on democratizing preparation for Ghana's National Science&Maths Quiz (NSMQ) and create open-source AI tools leveraging resources from the Brilla AI project to level the academic playing field and enhance science and math education across Africa.['George Boateng', 'V. Kumbol']2024-04-30T00:00:00https://www.semanticscholar.org/paper/f32635ff5211bd2c8d8a14b29ac3765e6c04cacc 7a617896645ded985ca20350572eadc1734632afThe study aimed to investigate the perception of government services employees regarding the future perspective of AI in government services and to compare the perspectives between federal and local level employees. Research adopted objectivity methods to explore the result. Data collected from federal and local government employees in Ramechhap District, Bagmati Province of Nepal. The study revealed insights into the perception of government services employees regarding AI in government services, indicating a moderate belief in AI's potential to enhance job efficiency and a cautious optimism towards AI integration within organizations. Respondents recognized the importance of investing in AI infrastructure and training, foreseeing changes in daily tasks and increased AI usage in service tasks. Anticipation of new HR roles and a demand for flexible virtual work setups was also noted. The ANOVA results comparing federal and local level employees' perspectives on AI in government services showed no significant difference between the two groups, suggesting that the variation in perspectives was not statistically significant. This research provides valuable insights into the perceptions and expectations of government services employees regarding AI adoption and its potential impact on future work environmentsJurnal Multidisiplin Madani160Insight is revealed into the perception of government services employees regarding AI in government services, indicating a moderate belief in AI's potential to enhance job efficiency and a cautious optimism towards AI integration within organizations.Jurnal Multidisiplin Madani['Dipak Mahat']2024-04-30T00:00:00https://www.semanticscholar.org/paper/7a617896645ded985ca20350572eadc1734632af 19c1f3b55b7dca7925168c7f3b1b2513c0fc7ab2Journal of engineering and applied sciences220The outcomes of the research improved the system’s security and dependability, showed the worth of the overall framework design, and significantly decreased the number of resources needed for system operation and maintenance.Journal of Engineering and Applied Science['Guoming Jiang']2024-04-30T00:00:00https://www.semanticscholar.org/paper/19c1f3b55b7dca7925168c7f3b1b2513c0fc7ab2 3f376b20cb267df4f732f5dcd09a1bbdb7348cbdAbstract: Water is necessary for humans to survive, and everyone's health depends on maintaining the quality of the resource. Drinking polluted water can put one's health at risk, raising the chances of contracting diseases like cholera and other waterborne infections. By predicting the water's quality, ‘machine learning algorithms’ have developed into beneficial tools for quickly and reliably monitoring water supplies. Many forecasting techniques are the main subject of this study. This project aims to estimate water potability using various algorithms by forecasting the physicochemical characteristics of water samples taken from the Drinking Water dataset on Kaggle. To find the potability of drinking water, we use a variety of methods, including 'random forest', 'logistic regression', 'decision tree', 'SVM', 'AdaBoost', and 'KNN'. There is hence a strong chance that the investigation will yield precise data regarding the quality of the waterInternational Journal for Research in Applied Science and Engineering Technology00This project aims to estimate water potability using various algorithms by forecasting the physicochemical characteristics of water samples taken from the Drinking Water dataset on Kaggle using a variety of methods, including 'random forest', 'logistic regression', 'decision tree', 'SVM', 'AdaBoost', and 'KNN'.International Journal for Research in Applied Science and Engineering Technology['Vijendra S N', 'Prashant', 'Jayprakash M', 'Ananya R', 'Shivashankar N']2024-04-30T00:00:00https://www.semanticscholar.org/paper/3f376b20cb267df4f732f5dcd09a1bbdb7348cbd 4a4a44cde47943cfb5cb845937ac8630172918cdIntroduction. First aid is a special type of assistance, is the primary measure in the chain of survival of the victim in emergency situations. The scope of the first aid in this edition of regulatory legal acts is not sufficient in situations where the victim is in a remote, inaccessible area and without the possibility of providing emergency medical care, which significantly reduces the chance of survival. The purpose of the work is to determine the list of measures for the provision of the extended first aid and develop proposals for improving the regulation of relations in the field of the first aid in an expanded volume between the participants of these relations to protect their legitimate rights and interests. Materials and methods. Research material: regulatory legal acts regulating relations in the field of the first aid and determining its scope. Research methods: analytical, logical, and information modelling. Results. The analysis of the studied normative legal acts showed the presence of a legal gap in the field of regulation of first aid including the uncertainty of the volume of the extended first aid. To improve the regulatory and legal regulation of first aid activities and contribute to the current legislation, the author has developed and proposed a draft volume of the extended first aid for discussion. Research limitations. In the study of issues related to the provision of the extended first aid, 18 regulatory legal acts were selected and analyzed: the Constitution of the Russian Federation, 7 federal laws, 10 orders of relevant ministries; as well as 2 technical acts (GOST). Conclusion. The developed project of the expanded first aid is proposed for consultations and discussions with state structures and officials defining health policy, as well as representatives of scientific institutions of the medical community to develop management decisions aimed at achieving a high level of preservation of human and citizen life and health in emergency situations.Health Care of the Russian Federation00HEALTH CARE OF THE RUSSIAN FEDERATION['M.G. Kolomeitsev']2024-04-29T00:00:00https://www.semanticscholar.org/paper/4a4a44cde47943cfb5cb845937ac8630172918cd 5dcf91479814a63fc7381e5ea4858606290d70a7In the modern era, large-scale renewable energy systems are integrated with advanced power systems and provide efficient operations. Also, optimized power systems require accurate energy generation and effective control systems to manage and ensure a stable power supply. Nevertheless, uncertainties are the intermittent balance of supply and high electricity demand. In addition, conventional power sources are not applicable for this challenging task and increase electricity costs. Therefore, an efficient Neuro Fuzzy Single phase Unified Power Quality Conditioner with Maximum Power Point Tracking (NF-SP UPQC -MPPT) strategy is developed for enhancing the grid-connected power systems. Here, Neuro-fuzzy logic is used as dynamic reactive power compensation in the grid. Also, this logic can efficiently handle the Energy storage system (ESS). Then SP UPQC was used to enhance power quality in electrical distribution systems. It combines both series and shunt compensators to mitigate various power quality issues such as voltage sags, harmonics, and unbalance. After that MPPT was utilized to extract the maximum power from the grid system. Model Predictive Control (MPC) controller to determine the overall stability and performance of the system. Moreover, the developed model was implemented on the MATLAB platform and performance is analyzed in terms of voltage deviation, grid current, reactive power fluctuations and Total Harmonic Distortion (THD).    Journal of Electrical Systems230An efficient Neuro Fuzzy Single phase Unified Power Quality Conditioner with Maximum Power Point Tracking (NF-SP UPQC -MPPT) strategy is developed for enhancing the grid-connected power systems.Journal of Electrical Systems['Kun Zhang', 'Xiaogang Wu', 'Zhizhong Li', 'Yaotang Lv', 'Shiqi Liu']2024-04-29T00:00:00https://www.semanticscholar.org/paper/5dcf91479814a63fc7381e5ea4858606290d70a7 07f9841f3ea0e1db0c5211a5c89ef41ae5d7f159The widespread use of artificial intelligence (AI) systems across various domains is increasingly highlighting issues related to algorithmic fairness, especially in high-stakes scenarios. Thus, critical considerations of how fairness in AI systems might be improved, and what measures are available to aid this process, are overdue. Many researchers and policymakers see explainable AI (XAI) as a promising way to increase fairness in AI systems. However, there is a wide variety of XAI methods and fairness conceptions expressing different desiderata, and the precise connections between XAI and fairness remain largely nebulous. Besides, different measures to increase algorithmic fairness might be applicable at different points throughout an AI system's lifecycle. Yet, there currently is no coherent mapping of fairness desiderata along the AI lifecycle. In this paper, we set out to bridge both these gaps: We distill eight fairness desiderata, map them along the AI lifecycle, and discuss how XAI could help address each of them. We hope to provide orientation for practical applications and to inspire XAI research specifically focused on these fairness desiderata.1371This paper distill eight fairness desiderata, map them along the AI lifecycle, and discusses how XAI could help address each of them, in order to provide orientation for practical applications and to inspire XAI research specifically focused on these fairness desiderata.['Luca Deck', 'Astrid Schomacker', 'Timo Speith', 'Jakob Schoffer', 'Lena Kastner', 'Niklas Kuhl']2024-04-29T00:00:00https://www.semanticscholar.org/paper/07f9841f3ea0e1db0c5211a5c89ef41ae5d7f159 185fb11b5d100569bbf8f31bb28c2bc9ed1c6fdeEthics and Information Technology1751A concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners.Ethics Inf. Technol.['Jose M. Alvarez', 'A. Colmenarejo', 'Alaa Elobaid', 'Simone Fabbrizzi', 'Miriam Fahimi', 'Antonio Ferrara', 'Siamak Ghodsi', 'Carlos Mougan', 'Ioanna Papageorgiou', 'Paula Reyero Lobo', 'Mayra Russo', 'Kristen M. Scott', 'Laura State', 'Xuan Zhao', 'Salvatore Ruggieri']2024-04-29T00:00:00https://www.semanticscholar.org/paper/185fb11b5d100569bbf8f31bb28c2bc9ed1c6fde e9b3534ba1295b7dc5f2d5992c3ac236296651d4Our cutting-edge Generative AI project assistant serves as a versatile tool aiding users in generating data across various applications. Harnessing the latest technologies such as Langchain, OpenAI, and Google Colab Face, it ensures superior performance. We're elevating its capabilities by integrating voice assistance, enabling seamless interaction in both text and voice formats. This endeavor involves leveraging a suite of tools tailored for the purpose. The Generative AI Project Assistant represents a groundbreaking approach to project management, integrating generative AI to streamline processes. By employing sophisticated algorithms, it automates tasks, fosters collaboration, and provides valuable insights. Utilizing natural language processing and machine learning, it delivers intelligent suggestions, anticipates challenges, and facilitates the creation of customized solutions. In essence, this assistant redefines project management, offering an intuitive, adaptable, and efficient platform for teams to achieve their objectives with Chat GPT's prowess at its core.International Journal of Innovative Science and Research Technology21The Generative AI Project Assistant redefines project management, offering an intuitive, adaptable, and efficient platform for teams to achieve their objectives with Chat GPT's prowess at its core.International Journal of Innovative Science and Research Technology (IJISRT)['V. Manikandan', 'Giridhar Reddy G', 'Nishitha V', 'Ganga Hemalatha K', 'K. C.']2024-04-29T00:00:00https://www.semanticscholar.org/paper/e9b3534ba1295b7dc5f2d5992c3ac236296651d4 9538df9a77549ca019b522eedfd5b367310293f7This article acknowledges the profound transformations that Artificial Intelligence imposes on society. A descriptive-exploratory study aims to discuss algorithmic biases and understand their impacts on society. The article starts from the understanding of human intelligence and learning from a pluralistic perspective, based on the analysis of literary works and scientific articles. This approach provides a context in which AI and machine learning can be conceived from an innovation perspective for the common good. The critical analysis emphasizes the need for ethical approaches in the development of these systems. The topics discussed highlight the importance of a multidimensional approach in mitigating algorithmic biases. From data selection to audits and accountability, diversity of perspectives, both in datasets and development teams, is crucial. The implementation of continuous training and human supervision reflects a continuous commitment to transparency and fairness in artificial intelligence. These integrated strategies are essential for the ethical, transparent, and equitable development of AI. This holistic approach, involving diverse skills and people, continuous training, and vigilant oversight, is vital to ensure the ethical use of AI for the collective well-being.Journal on Innovation and Sustainability RISUS00A descriptive-exploratory study aims to discuss algorithmic biases and understand their impacts on society to provide a context in which AI and machine learning can be conceived from an innovation perspective for the common good.Journal on Innovation and Sustainability RISUS['Erika Ribeiro Fernandes', 'Marcelo Augusto Vieira Graglia']2024-04-29T00:00:00https://www.semanticscholar.org/paper/9538df9a77549ca019b522eedfd5b367310293f7 cde7d3af27476dcbae839283fe0edf94ab81a2c0With the continuous evolution of AI technology, the demand for talents is constantly increasing, and the demand for high-quality talents is becoming increasingly urgent. Campus recruitment, as an important way of talent introduction, provides AI companies with rich talent selection space. However, people from different eras have completely different cognitive and behavioral preferences towards campus recruitment. The generation that grew up before and after the reform may not have established a sense of independent career choice when they first entered society at a young age; Some of the post-90s generation, who were initially labeled as "rebellious," have now entered their thirties and are increasingly learning to seek their professional value under the premise of seeking stability in reality. In the post-95s and post-00s generation who have been "rectifying the workplace", they have learned to make two-way choices and can even use various information channels to conduct "reverse interviews" with enterprises to ensure suitable employment opportunities. At present, the campus recruitment group has gradually entered the "post-2000" era. While AI companies need to face fierce market competition, how to attract and retain excellent young talents has become an important challenge. The article analyzes and summarizes the current situation of campus recruitment in contemporary AI enterprises, and provides suggestions for optimization solutions. It is hoped that this can solve the practical problems that contemporary AI companies face in campus recruitment and provide reference for the implementation of campus recruitment plans in related AI enterprises in the future. Science and innovation40The article analyzes and summarizes the current situation of campus recruitment in contemporary AI enterprises, and provides suggestions for optimization solutions, it is hoped that this can solve the practical problems that contemporary AI companies face in campus recruitment.Science Innovation['Yuwen Xinghang']2024-04-29T00:00:00https://www.semanticscholar.org/paper/cde7d3af27476dcbae839283fe0edf94ab81a2c0 a5d4d9cacd4a16cd202f31dd8cf0b281490491baThe convergence of artificial intelligence (AI) and synthetic biology is rapidly accelerating the pace of biological discovery and engineering. AI techniques, such as large language models and biological design tools, are enabling the automated design, build, test, and learning cycles for engineered biological systems. This convergence promises to democratize synthetic biology and unlock novel applications across domains from medicine to environmental sustainability. However, it also poses significant risks around reliability, dual use, and governance. The opacity of AI models, the deskilling of workforces, and the outdated nature of current regulatory frameworks present challenges in ensuring responsible development. Urgent attention is needed to update governance structures, integrate human oversight into increasingly automated workflows, and foster a culture of responsibility among the growing community of bioengineers. Only by proactively addressing these issues can we realize the transformative potential of AI-driven synthetic biology while mitigating its risks.550Urgent attention is needed to update governance structures, integrate human oversight into increasingly automated workflows, and foster a culture of responsibility among the growing community of bioengineers to realize the transformative potential of AI-driven synthetic biology while mitigating its risks.['Cindy Vindman', 'Benjamin D. Trump', 'Christopher Cummings', 'Madison Smith', 'Alexander J. Titus', 'Ken Oye', 'Valentina Prado', 'E. Turmus', 'Igor Linkov']2024-04-29T00:00:00https://www.semanticscholar.org/paper/a5d4d9cacd4a16cd202f31dd8cf0b281490491ba 574c225b7082c45b4ce358ec665c7dfb12ae0194Nature Network Boston170The regulatory landscape and potential health risks of AI-enabled wellness apps are discussed, the problems that arise when AI-based wellness apps cross into medical territory and the implications for app developers and regulatory bodies are discussed.Nature medicine['Julian De Freitas', 'I. G. Cohen']2024-04-29T00:00:00https://www.semanticscholar.org/paper/574c225b7082c45b4ce358ec665c7dfb12ae0194 003d4de52213cb05e7a67f7d56905d54246826cbDiscover Artificial Intelligence200In clinical decision-making, integrating a retrospectively analyzed and prospectively validated AI system, along with post hoc explanations, can facilitate the explanatory needs of physicians and patients in the context of medical decision-making supported by AI.Discov. Artif. Intell.['Anne Gerdes']2024-04-29T00:00:00https://www.semanticscholar.org/paper/003d4de52213cb05e7a67f7d56905d54246826cb 93a5adb6f15a976a6a71b232ffc0a19c0deefebfConsidering the vast challenges Indian Farmers undergo, a project based on AI-powered farming has been developed. The project offers an all-in-one solution for Indian Farmers. It can quickly detect and diagnose potential crop threats, fertilizer usage and provide accurate weather forecasts. Our crop recommendation system suggests suitable crops based on soil type and climate conditions, helping farmers maximize their profits and come out of Debt traps (in India, the farmers’ suicide rate is very high due to debt challenges). We integrate these four essential features with machine learning to revolution- ize farming practices and achieving sustainable growth. Keyword: AI, agriculture, farming, crop recommendation, weather forecast.Indian Journal of Computer Science and Technology00The crop recommendation system suggests suitable crops based on soil type and climate conditions, helping farmers maximize their profits and come out of Debt traps, helping farmers maximize their profits and come out of Debt traps.Indian Journal of Computer Science and Technology['Vatsal Shah', 'Devyani Bhakare', 'Tanishka Wani', 'Vaishali Shirsath']2024-04-29T00:00:00https://www.semanticscholar.org/paper/93a5adb6f15a976a6a71b232ffc0a19c0deefebf 8d2fa8aeb0c9dc0f7c64da07ac99579eba13f39eAs we are witnessing one of the most interesting shifts in technology every day we can find cases where algorithms and data have improved people’s lives as well as examples where they have perpetuated bias and abuse. In light of the expanding influence of algorithms within governance structures and the imperative to provide elucidation on the utilization of artificial intelligence (AI) within the public sector, this paper endeavors to scrutinize the potential for reinventing citizen engagement and participation through the integration of AI within online mass participatory platforms.Logos Universality Mentality Education Novelty: Law190The potential for reinventing citizen engagement and participation through the integration of AI within online mass participatory platforms is scrutinized.Logos Universality Mentality Education Novelty: Law['Raluca Onufreiciuc']2024-04-29T00:00:00https://www.semanticscholar.org/paper/8d2fa8aeb0c9dc0f7c64da07ac99579eba13f39e 32adb5cb8544df9ff0f6f1fb825ed1ded21e92d6This inquiry explores the impact of automation and artificial intelligence (AI) on leadership and the workforce, as well as strategies for leaders to effectively navigate the transition towards a technology-focused workplace. The aim of this research is to expand current understanding by providing valuable insights into how AI and automation affect leadership and the workforce, alongside practical suggestions for managing this transformation. It is essential to recognize the potential benefits of AI and automation, such as improved efficiency and decision-making abilities, while also acknowledging concerns about potential job displacement and ethical considerations. Through a thorough examination of these issues, this study aims to equip organizations and leaders with the necessary resources to prepare for the future of work and ensure they are well-positioned for success in an increasingly technology-driven environment.Indonesian Journal of Banking and Financial Technology250This study aims to equip organizations and leaders with the necessary resources to prepare for the future of work and ensure they are well-positioned for success in an increasingly technology-driven environment.Indonesian Journal of Banking and Financial Technology['Ram Paudel']2024-04-29T00:00:00https://www.semanticscholar.org/paper/32adb5cb8544df9ff0f6f1fb825ed1ded21e92d6 568f096965bc563ab21c775b793d9216e25898a3Purpose-Explore the impact of AI and ChatGPT on library information services; Design/methodology/approach-A sample of twenty-two reference questions are fed to ChatGPT and the answers are evaluated for quality and accuracy; Findings-ChatGPT are excellent in information retrieval in some areas, but it is not comparable to a reference librarian in others; Research limitations/implications-The findings may not be conclusive due to small sample size; Practical implications-Understand AI and ChatGPT and their behavior; Social implications -The knowledge from the study can assist librarians to adjust their services to better serve users; Originality/value-No research has been done in this area.International Journal of Librarianship00ChatGPT are excellent in information retrieval in some areas, but it is not comparable to a reference librarian in others.International Journal of Librarianship['Sharon Q. Yang']2024-04-29T00:00:00https://www.semanticscholar.org/paper/568f096965bc563ab21c775b793d9216e25898a3 db56fcaccb77418cae5ee2c439f8821a99a64815In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.260The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.['Zeeshan Rasheed', 'Malik Abdul Sami', 'Muhammad Waseem', 'Kai-Kristian Kemell', 'Xiaofeng Wang', 'Anh Nguyen', 'Kari Systa', 'Pekka Abrahamsson']2024-04-29T00:00:00https://www.semanticscholar.org/paper/db56fcaccb77418cae5ee2c439f8821a99a64815 98179a3ee909e106cd9df634ea7c94ceb8a4163aTo better understand the current challenges surrounding the use of artificial intelligence (AI) in K-12 schools, a team of researchers (Raffaella Borasi, David E. Miller, Patricia Vaughan-Brogan, Karen DeAngelis, Yu Jung Han, and Sharon Mason) interviewed 36 western New York school leaders in late 2023. Their concerns moved beyond potential cheating, as they instead identified four main priorities: receiving guidance to inform their decisions about AI, empowering all stakeholders to better understand AI and its implications, capitalizing on AI to support the work of teachers and staff, and enabling better technology solutions. These should inform future interventions aiming at leveraging AI in K-12 education.Phi Delta Kappan40Concerns moved beyond potential cheating, as school leaders identified four main priorities: receiving guidance to inform their decisions about AI, empowering all stakeholders to better understand AI and its implications, capitalizing on AI to support the work of teachers and staff, and enabling better technology solutions.Phi Delta Kappan['Raffaella Borasi', 'David E. Miller', 'Patricia Vaughan-Brogan', 'Karen DeAngelis', 'Yu Jung Han', 'Sharon Mason']2024-04-29T00:00:00https://www.semanticscholar.org/paper/98179a3ee909e106cd9df634ea7c94ceb8a4163a 19c6ea73bb9778255174e0ce6fc41bf6e0b3f9beAs Christian List has recently argued, the increasing arrival of powerful AI systems that operate autonomously in high-stakes contexts creates a need for “future-proofing” our regulatory frameworks, i.e., for reassessing them in the face of these developments. One core part of our regulatory frameworks that dominates our everyday moral interactions is blame. Therefore, “future-proofing” our extant regulatory frameworks in the face of the increasing arrival of powerful AI systems requires, among other things, that we ask whether it makes sense to extend our blaming practices to these systems. In this paper, we argue for the admittedly surprising thesis that this question should be answered in the affirmative: contrary to what one might initially think, it can make a lot of sense to blame AI systems since, as we furthermore argue, many of the important functions that are fulfilled by blaming humans can also be served by blaming AI systems. The paper concludes that this result gives us a good pro tanto reason to extend our blame practices to AI systems.Journal of Ethics and Social Philosophy710It can make a lot of sense to blame AI systems since, contrary to what one might initially think, many of the important functions that are fulfilled by blaming humans can also be served by blaming AI systems.Journal of Ethics and Social Philosophy['Hannah Altehenger', 'Leonhard Menges']2024-04-29T00:00:00https://www.semanticscholar.org/paper/19c6ea73bb9778255174e0ce6fc41bf6e0b3f9be 7cbccab45dc4d4b0d60dbe413d933c1d9c5e7ed5 This paper explores the utilization of video-based artificial intelligence (AI) tools for enhancing safety measures on the drill floor in the oil and gas industry. It delves into the application of AI-powered systems in monitoring and analyzing critical activities, identifying potential risks, and preventing hazardous incidents. The study showcases the development and implementation of advanced AI algorithms integrated with video monitoring technology, highlighting their effectiveness in real-time risk detection and mitigation. Results demonstrate significant improvements in safety protocols and incident prevention, thereby emphasizing the pivotal role of video-based AI tools in ensuring a safer working environment on the drill floor. The implementation of AI solutions has a profound impact on safety Key Performance Indicators (KPIs). These solutions act as safety tools, capable of accelerating drill performance and fostering a secure work environment on the rig floor. They directly influence the efficiency of drill floor operators, including drillers, assistant drillers, tool pushers, and rig men, while simultaneously reducing the potential for incidents. Based on field data, we can imagine three major challenges during drilling operations: The Lack of Personal Protective Equipment (PPE), Red Zone Management and Latch Monitoring. To address these challenges, high-resolution cameras deliver intricate images of operations on the drill floor. This rich dataset is curated to distinguish between various elements, including various machinery on the drill floor, personnel, and Personal Protective Equipment (PPE). These annotations play a pivotal role in training deep learning algorithms. Incorporating real-world operational data enables the algorithms to grasp the context of each task, bolstering their robustness and accuracy. The techniques prominently applied include segmentation, classification, object detection, pose estimation, and tracking. Owing to the multifaceted image analyses required in real-time, we employ servers equipped with powerful GPUs. All software functionalities run on the edge/rigs, eliminating the need for an internet connection. The real time derived insights are showcased on a web platform, presented as alerts, reports, and dashboards. For swift responses in critical scenarios audible alarms are activated. This ensures immediate interventions to circumvent potential mishaps. Periodic reports are also available, aiding in the refinement of procedures and enhancing the training regimen of the operational teams. Implementation of these solutions yielded key findings: Hazard Detection, Improved Safety, Cost Savings, and Future Potential. This study establishes that Video-Based AI Tools for Safety Enhancement on the Drill Floor represents a paradigm shift in safety management within the oil and gas industry. The findings underscore the significant contribution of these tools in preventing accidents, protecting personnel, and optimizing operational performance. As the industry continues to evolve, the integration of advanced AI technologies stands out as a pivotal strategy to ensure a safer and more sustainable future for drill floor operations.Day 4 Thu, May 09, 202450This paper explores the utilization of video-based artificial intelligence (AI) tools for enhancing safety measures on the drill floor in the oil and gas industry and underscores the significant contribution of these tools in preventing accidents, protecting personnel, and optimizing operational performance.Day 4 Thu, May 09, 2024['Bruno Henrique Veneziani Pianissola', 'Guilherme Mendes Cicarini Hott', 'Leonardo Mendes Nogueira', 'Raphael Migoto Campos de Paula']2024-04-29T00:00:00https://www.semanticscholar.org/paper/7cbccab45dc4d4b0d60dbe413d933c1d9c5e7ed5 b00705a77830f806a8ac7c287586298ed3b14028The increasing frequency and rapidity of criminal activities require faster digital forensic (DF) investigations. Currently, most DF phases involve manual procedures, requiring significant human effort and time, often facing evolving requirements. This paper proposes an integrated framework employing AutoGen Artificial Intelligence (AI) agents and Large Language Models (LLMs) such as LLAMA, and StarCoder. The suggested framework utilizes AI agents and LLMs to perform tasks articulated in natural language by a human agent. The proposed architecture presents a significant advantage by alleviating the investigative workload and shortening the learning curve for investigators. However, it is still combined with risks such as information accuracy, hallucination impact, and legal barriers. Although, this research contributes to the ongoing discourse on optimizing DF processes in response to the evolving landscape of criminal activities and the corresponding demands placed on investigative resources.International Symposium on Digital Forensics and Security300This paper proposes an integrated framework employing AutoGen Artificial Intelligence agents and Large Language Models (LLMs) such as LLAMA, and StarCoder to perform tasks articulated in natural language by a human agent.2024 12th International Symposium on Digital Forensics and Security (ISDFS)['Akila Wickramasekara', 'M. Scanlon']2024-04-29T00:00:00https://www.semanticscholar.org/paper/b00705a77830f806a8ac7c287586298ed3b14028 07354c02acc80ba3ad9141afe05ed15eb1043721 Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.BJR|Artificial Intelligence00This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging and underscores the need for comprehensive attention to the underlying and often subtle factors.BJR|Artificial Intelligence['Ravi K. Samala', 'Karen Drukker', 'A. Shukla-Dave', 'Heang-Ping Chan', 'B. Sahiner', 'N. Petrick', 'H. Greenspan', 'Usman Mahmood', 'Ronald M Summers', 'Georgia Tourassi', 'T. Deserno', 'Daniele Regge', 'J. Näppi', 'Hiroyuki Yoshida', 'Zhimin Huo', 'Quan Chen', 'Daniel Vergara', 'Kenny H. Cha', 'Richard Mazurchuk', 'Kevin T Grizzard', 'Henkjan Huisman', 'Lia Morra', 'Kenji Suzuki', 'Samuel G Armato', 'Lubomir Hadjiiski']2024-04-29T00:00:00https://www.semanticscholar.org/paper/07354c02acc80ba3ad9141afe05ed15eb1043721 e865494118c7d75186e2fc46cf0a8df5250e8205This study investigates the impact of AI technology adoption, innovation readiness, and digital entrepreneurship on competitive advantage in startup enterprises within Jakarta's entrepreneurial ecosystem. A quantitative approach employing Structural Equation Modeling (SEM) with Partial Least Squares (PLS) analysis was utilized to analyze data collected from 229 startup founders and executives. The findings reveal significant positive relationships between AI technology adoption, innovation readiness, digital entrepreneurship, and competitive advantage. Specifically, startups that strategically embrace AI technologies, foster innovation readiness, and leverage digital entrepreneurship practices demonstrate higher levels of competitive advantage. The study contributes to theoretical understanding by extending literature on technology-driven entrepreneurship and provides practical insights for startup stakeholders and policymakers aiming to enhance the competitiveness of Jakarta's startup ecosystem.West Science Interdisciplinary Studies300The findings reveal significant positive relationships between AI technology adoption, innovation readiness, digital entrepreneurship, and competitive advantage in startup enterprises within Jakarta's entrepreneurial ecosystem.West Science Interdisciplinary Studies['Bakri Bakri', 'A. Zm', 'Siska Yulia Defitri', 'Halek Mu’min']2024-04-29T00:00:00https://www.semanticscholar.org/paper/e865494118c7d75186e2fc46cf0a8df5250e8205 853f5efeab3a31c2b0f7864dfcae8786059edf54Penelitian ini bertujuan untuk memahami fenomena perilaku kecurangan akademik berbasis Artificial Intelligence (AI) yang dilakukan mahasiswa akuntansi dalam perspektif fraud triangle theory. Metode yang digunakan adalah metode kuantitatif dengan teknik pengumpulan data berupa kuesioner yang didistribusikan melalui Microsoft Form. Data dari 330 mahasiswa Akuntansi Fakultas Ekonomi dan Bisnis Universitas Kristen Artha Wacana Kupang, menunjukkan bahwa mahasiswa yang merasa tertekan atau memiliki alasan untuk membenarkan perilaku tidak jujur mereka cenderung lebih sering menggunakan AI untuk melakukan kecurangan akademik, seperti menyalin tugas, mencontek ujian, atau memalsukan data. Sedangkan mahasiswa yang memiliki kesempatan untuk berlaku tidak jujur, seperti tidak ada pengawasan, tidak ada sanksi, atau tidak ada deteksi, tidak memiliki pengaruh terhadap kecurangan akademik berbasis AI. Penelitian ini memberikan pemahaman tentang motivasi ketidakjujuran akademik dan menyarankan langkah-langkah untuk mencegah kecurangan akademik yang didukung Artificial Intelligence.Jurnal Akuntansi Manado (JAIM)00Jurnal Akuntansi Manado (JAIM)['Lispridona Magdalena Saduk', 'Anis Chariri']2024-04-29T00:00:00https://www.semanticscholar.org/paper/853f5efeab3a31c2b0f7864dfcae8786059edf54 0c6d15bbf9bbed31d4acc7c868c5e374588c1da9Nature Network Boston240Results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality, in a multisite randomized controlled trial involving 39 physicians and 15,965 patients.Nature medicine['Chin Lin', 'Wei-Ting Liu', 'Dung-Jang Tsai', 'Yu-Sheng Lou', 'Chiao-Hsiang Chang', 'Chiao-Chin Lee', 'Wen-Hui Fang', 'Chih-Chia Wang', 'Yen-Yuan Chen', 'Wei-Shiang Lin', 'Cheng-Chung Cheng', 'Chia-Cheng Lee', 'Chih-Hung Wang', 'Chien-Sung Tsai', 'Shih-Hua Lin', 'Chin Lin']2024-04-29T00:00:00https://www.semanticscholar.org/paper/0c6d15bbf9bbed31d4acc7c868c5e374588c1da9 636f4e2acdb9fdc6533b5c4aeef15db69daa9c1f This survey study assesses the ability of health care professionals to discern whether abstracts were written by investigators or by an artificial intelligence (AI) chatbot. JAMA pediatrics50JAMA pediatrics['Dennis Ren', 'Andrew James Tagg', 'Helena Wilcox', 'Damian Roland']2024-04-29T00:00:00https://www.semanticscholar.org/paper/636f4e2acdb9fdc6533b5c4aeef15db69daa9c1f a1366e818bc064cf104bbe9490b21e0954cd8f2fThe advents of Artificial Intelligence (AI)-driven models marks a paradigm shift in risk management strategies for meteorological hazards. This study specifically employs tropical cyclones (TCs) as a focal example. We engineer a perturbation-based method to produce ensemble forecasts using the advanced Pangu AI weather model. Unlike traditional approaches that often generate fewer than 20 scenarios from Weather Research and Forecasting (WRF) simulations for one event, our method facilitates the rapid nature of AI-driven model to create thousands of scenarios. We offer open-source access to our model and evaluate its effectiveness through retrospective case studies of significant TC events: Hurricane Irma (2017), Typhoon Mangkhut (2018), and TC Debbie (2017), affecting regions across North America, East Asia, and Australia. Our findings indicate that the AI-generated ensemble forecasts align closely with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble predictions up to seven days prior to landfall. This approach could substantially enhance the effectiveness of weather forecast-driven risk analysis and management, providing unprecedented operational speed, user-friendliness, and global applicability.250A perturbation-based method to produce ensemble forecasts using the advanced Pangu AI weather model, which indicates that the AI-generated ensemble forecasts align closely with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble predictions up to seven days prior to landfall.['Kairui Feng', 'Dazhi Xi', 'Wei Ma', 'Cao Wang', 'Yuanlong Li', 'Xuanhong Chen']2024-04-29T00:00:00https://www.semanticscholar.org/paper/a1366e818bc064cf104bbe9490b21e0954cd8f2f 2485a5ef120ef32f4335e6ee65f76360ff90ffe6This study examines the extent to which U.S. federal agencies responded to and implemented the principles outlined in the White House's October 2022"Blueprint for an AI Bill of Rights."The Blueprint provided a framework for the ethical governance of artificial intelligence systems, organized around five core principles: safety and effectiveness, protection against algorithmic discrimination, data privacy, notice and explanation about AI systems, and human alternatives and fallback. Through an analysis of publicly available records across 15 federal departments, the authors found limited evidence that the Blueprint directly influenced agency actions after its release. Only five departments explicitly mentioned the Blueprint, while 12 took steps aligned with one or more of its principles. However, much of this work appeared to have precedents predating the Blueprint or motivations disconnected from it, such as compliance with prior executive orders on trustworthy AI. Departments' activities often emphasized priorities like safety, accountability and transparency that overlapped with Blueprint principles, but did not necessarily stem from it. The authors conclude that the non-binding Blueprint seems to have had minimal impact on shaping the U.S. government's approach to ethical AI governance in its first year. Factors like public concerns after high-profile AI releases and obligations to follow direct executive orders likely carried more influence over federal agencies. More rigorous study would be needed to definitively assess the Blueprint's effects within the federal bureaucracy and broader society.20It is concluded that the non-binding Blueprint seems to have had minimal impact on shaping the U.S. government's approach to ethical AI governance in its first year.['Darren Lage', 'Riley Pruitt', 'Jason Ross Arnold']2024-04-29T00:00:00https://www.semanticscholar.org/paper/2485a5ef120ef32f4335e6ee65f76360ff90ffe6 129bfd6a3683d19e6b42f3a9bc209c0292e8f672Artificial intelligence (AI) models can produce output that closely mimics human-generated content. We examined individual differences in the human ability to differentiate human- from AI-generated texts, exploring relationships with fluid intelligence, executive functioning, empathy, and digital habits. Overall, participants exhibited better than chance text discrimination, with substantial variation across individuals. Fluid intelligence strongly predicted differences in the ability to distinguish human from AI, but executive functioning and empathy did not. Meanwhile, heavier smartphone and social media use predicted misattribution of AI content (mistaking it for human). Determinations about the origin of encountered content also affected sharing preferences, with those who were better able to distinguish human from AI indicating a lower likelihood of sharing AI content online. Word-level differences in linguistic composition of the texts did not meaningfully influence participants’ judgements. These findings inform our understanding of how individual difference factors may shape the course of human interactions with AI-generated information.Research Square570Individual differences in the human ability to differentiate human- from AI-generated texts are examined, exploring relationships with fluid intelligence, executive functioning, empathy, and digital habits to inform understanding of how individual difference factors may shape the course of human interactions with AI-generated information.Research Square['Jason Chein', 'Steven Martinez', 'Alexander Barone']2024-04-29T00:00:00https://www.semanticscholar.org/paper/129bfd6a3683d19e6b42f3a9bc209c0292e8f672 f8cc1bf479f39bc802dafa1656d6ce12b9d638f9 In the last five years, several issues related to the development of artificial intelligence have been tackled by various countries and international organisations and the legal definition is one of them. Fundamentally to understand what Artificial Intelligence is, and when to apply the related rules, the definition issue is addressed in this essay considering various documents adopted by several countries and international organizations, such as the European Union, United Kingdom, United States of America, China and the Organization for Economic Co-operation and Development (oecd). Moreover, the paper also considers the intended meaning of “trustworthy ai” in considered countries and supranational organizations and eventually, aims to give a comprehensive definition of Artificial Intelligence starting from what is exposed and trying to merge the considered solutions.European Journal of Comparative Law and Governance01The definition issue is addressed in this essay considering various documents adopted by several countries and international organizations, such as the European Union, United Kingdom, United States of America, China and the Organization for Economic Co-operation and Development (oecd).European Journal of Comparative Law and Governance['Daniele Chiappini']2024-04-29T00:00:00https://www.semanticscholar.org/paper/f8cc1bf479f39bc802dafa1656d6ce12b9d638f9 9dff21adbfc58a2b071b57a8d64ce83d947be3baThe rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care.Diagnostic and Interventional Radiology00This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists, and focuses on essential factors radiology departments must consider when choosing AI solutions.Diagnostic and interventional radiology['D. Alis', 'Toygar Tanyel', 'Emine Meltem', 'Mustafa Ege Seker', 'Delal Seker', 'H. Karakaş', 'E. Karaarslan', 'İlkay Öksüz']2024-04-29T00:00:00https://www.semanticscholar.org/paper/9dff21adbfc58a2b071b57a8d64ce83d947be3ba ac0f9e8ea11864a1a93ba2c6e14de270f03dbcbcIn recent years, with the rapid development and application of artificial intelligence technology, image technology based on artificial intelligence has gradually become a research hotspot in the field of cultural relic restoration. This article will explore the application of artificial intelligence based image technology in the field of cultural relic and museum restoration. Firstly, the importance of restoring cultural relics and artifacts was introduced, as well as the limitations and shortcomings of traditional restoration methods. Secondly, elaborate on the development and application of artificial intelligence technology, as well as its potential and advantages in the field of cultural relic restoration. Next, we will introduce the application of artificial intelligence based image technology in cultural relic restoration, including image enhancement, segmentation, recognition, and reconstruction. Finally, the application prospects and challenges of artificial intelligence based image technology in the field of cultural relic restoration were summarized, and future research directions and suggestions were proposed.Theoretical and Natural Science00The importance of restoring cultural relics and artifacts was introduced, as well as the limitations and shortcomings of traditional restoration methods, and future research directions and suggestions were proposed.Theoretical and Natural Science['Wei Sun']2024-04-29T00:00:00https://www.semanticscholar.org/paper/ac0f9e8ea11864a1a93ba2c6e14de270f03dbcbc c2c7e6c00c5f901f56c16e9d4736135da5318951In the present era of innovation, Artificial Intelligence (AI) has opened up tremendous opportunities in the workplace through robotics innovation, which envelops AI. Precision, Efficiency, and Flexibility are considered the potential benefits of Industry. The implementation of Industry requires a lot of changes, including the Human Resource (HR) function. In Industry, the HR capability is more critical and gives an upper hand to the organization. The HR capability should be more cautious and adaptable to adjust to the difficulties and requirements. This thesis study the contributions of AI in HR digitalization and practices in Industry. HR experts working in Information Technology (IT), Manufacturing, and administration are selected to participate in this review focusing on five AI applications in HR capability and three elements of HR readiness. The information collected was examined utilizing the different Microsoft word tools, excel formulas, and other mathematical formulas. The results uncovered that hierarchical organization examination is a fundamental part of acquiring sustainable development. Adaptability and human asset capability are upheld by each of the five components of AI application areas of HR. Well-being and Safety improvement were viewed as vital components under the AI application in HR. Detailed analysis of the need of artificial intelligence in human resource. The various AI tools to be used by this sector to enhance the organization, and finally check the improvement and growth in the organization with the introduction of AI in human resource management. Keywords- artificial intelligence, human resource management, healthcare industryINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The results uncovered that hierarchical organization examination is a fundamental part of acquiring sustainable development and improvement and growth in the organization with the introduction of AI in human resource management.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Yashika Yadav']2024-04-29T00:00:00https://www.semanticscholar.org/paper/c2c7e6c00c5f901f56c16e9d4736135da5318951 328df4230564a47e714d0f159aee585acc3640eb Subsea tiebacks are a principal building block of deepwater developments. Operators seek to continuously enhance operational efficiencies and tieback distances while reducing capital, operating expenditures, and emissions. Rapid advances have been made in remote operations, subsea equipment capabilities and standardization. However, significant additional improvements in subsea tieback operational efficiencies are achievable by leveraging the steep decline in data storage and processing costs, the massive increase in processing power and high-speed internet along with the availability of proven Artificial Intelligence (AI) and Machine Learning (ML) tools. Subsea tieback operational efficiency improvements are bottlenecked by human operator ability to process and respond in a timely manner to the overwhelming quantity of data collected by modern subsea monitoring and sensor technologies. This paper will address specific areas where the proven ability of AI and ML tools to assimilate large quantities of data, together with bespoke algorithms, can provide real-time, targeted recommendations to unlock the following improvements in subsea tieback operational efficiencies: Enhanced Oil Recovery: With real-time detection and analyses of changes and anomalies in production flow, AI can use Model Predictive Control (MPC) or Hybrid AI-Physics Models to optimize production rates, riser base gas lift, and gas or water injection systems. Predictive Maintenance: By ingesting and analyzing real-time sensor and Autonomous Underwater Vehicle (AUV)/Remote Operated Vehicle (ROV) data, AI algorithms can:Improve performance and reliability of subsea boosting systems (multiphase pumps, power generation and conditioning).Increase operational uptime and service life by anticipating potential failures in subsea infrastructure. Operational Efficiencies: AI can process vast amounts of data from disparate sources to support decision-making related to:Monitoring and predicting flow assurance challenges while suggesting targeted mitigations.Maintaining optimum production rates and flow conditions from wellhead to production manifold.Providing a holistic view of complex multi-tieback systems to facilitate decision-making targeting total asset performance. By combining data gathered from multiple sources (SCADA, PLC, Camera DVR, etc.) with bespoke algorithms, AI can provide diagnostic, prescriptive, intelligent insights; accelerate positive interventions; increase ultimate recovery while reducing downtime, power requirements, and emissions. The offshore oil and gas industry is embracing AI to make operations safer and more efficient. This paper will show how an integrated, holistic and targeted approach of incorporating AI into subsea tiebacks will enable the industry to immediately and inexpensively attain new levels of operational excellence, cost-effectiveness, and environmental sustainability.Day 4 Thu, May 09, 202420This paper will show how an integrated, holistic and targeted approach of incorporating AI into subsea tiebacks will enable the industry to immediately and inexpensively attain new levels of operational excellence, cost-effectiveness, and environmental sustainability.Day 4 Thu, May 09, 2024['B. Jarrell', 'R. D’Souza', 'N. Chauhan']2024-04-29T00:00:00https://www.semanticscholar.org/paper/328df4230564a47e714d0f159aee585acc3640eb 28577f953ca35c892cce950624a72754acae724fAt present, it is impossible to deny the existence of artificial intelligence in various areas of social life, understood as the simulation of expert human intelligence from computer processes that involve learning, reasoning, and self-correction, its benefits to the medical field, in particular, are innumerable, but their incorporation into health systems has been gradual for many reasons. According to the above, this research analyzed artificial intelligence based on resilient leadership in the health sector, for which qualitative research was carried out with a documentary-bibliographic design with printed and electronic documentary sources with theoretical contributions from Ávila, Mayer, and Quesada, Morgan, Villa, and Finol, among others. It is highlighted that resilient leadership has become a strategic factor in all organizations, since times of uncertainty and changes lead institutions to properly manage the incorporation of technologies specifically AI, achieving in this way that the centers and professionals in the field of health assume the needs of the contexts and the innovations of the same. It is concluded that resilient leadership will allow artificial intelligence in the health sector to generate higher levels of learning and adaptability to the transformations that are necessary, whose resistance would make its application difficult and in the long run it will leave behind professionals who refuse to assume the contributions of these innovative techniques in medical practice.Proceedings of Business and Economic Studies180It is concluded that resilient leadership will allow artificial intelligence in the health sector to generate higher levels of learning and adaptability to the transformations that are necessary, whose resistance would make its application difficult and in the long run it will leave behind professionals who refuse to assume the contributions of these innovative techniques in medical practice.Proceedings of Business and Economic Studies['Elaine Bastidas Tapia']2024-04-29T00:00:00https://www.semanticscholar.org/paper/28577f953ca35c892cce950624a72754acae724f b2549db310d926effeadbf2604c889111b07ee46This study is aimed at exploring the impact of artificial intelligence (AI) on academic research by conducting a focus group research strategy. The focus group consists of individuals who are actively involved in academic research and have experience working with AI technologies. The purpose of the focus group is to gather in-depth insights into how AI has influenced research methodologies, findings, and overall knowledge creation. The study will begin by identifying seven participants through purposive sampling, with an aim of recruiting a diverse group of individuals from various academic disciplines. Purposive sampling, also known as selective sampling, enhances the study’s validity by ensuring that the sample consists of individuals with a high level of expertise in the subject matter. Seven is large enough to generate a diverse range of perspectives and experiences and small enough to ensure that every participating academic researcher has a chance to contribute to the conversation. The focus group is conducted using a Zoom video conferencing to gather academics from different institutions across the world. It also eliminates distance issue required for conducting an in-person session. This provides opportunity to cover a wide array research specialization representation. Data analysis is conducted using a thematic analysis approach, with a focus on identifying key themes and patterns that emerge from the data. The findings of this study contribute to a better understanding of the impact of AI on academic research and provide insights into the potential future direction of AI in academic research. While the study is aimed at providing practical recommendations for researchers who are interested in incorporating AI into their research practices, it also ignites the conversation on future incorporation of technologies into academic research activity.Human Behavior and Emerging Technologies680The purpose of the focus group is to gather in-depth insights into how AI has influenced research methodologies, findings, and overall knowledge creation to contribute to a better understanding of the impact of AI on academic research.Human Behavior and Emerging Technologies['Tosin Ekundayo', 'Zafarullah Khan', 'Sabiha Nuzhat']2024-04-29T00:00:00https://www.semanticscholar.org/paper/b2549db310d926effeadbf2604c889111b07ee46 4b28f5f3fb6e3359ce6e96eb1f638bb6839c6fdeThe ongoing technological transformation, driven by rapid advancements in machine learning (ML), holds the potential to profoundly impact our lives and redefine the essence of humanity. Recent years have witnessed significant progress in ML, fuelled by the widespread adoption of artificial intelligence (AI) due to machines' capacity for large-scale data processing and management. The intersection of ML and healthcare encompasses both computer science along with medical science. Within medical science, ML techniques have enabled the examination of complex medical data, marking a significant advancement. In the healthcare industry, ML functions as the cognitive and knowledge counterpart of healthcare professionals. This paper explores the benefits of ML-based solutions and their applications in healthcare, emphasizing their role in automating data analysis for patients' health records along with making predictions per the extracted insights. The integration of ML in healthcare promises transformative outcomes, shaping the future of medical diagnostics and decision-making.   Journal of Electrical Systems130The benefits of ML-based solutions and their applications in healthcare are explored, emphasizing their role in automating data analysis for patients' health records along with making predictions per the extracted insights.Journal of Electrical Systems['Dr Kannan Vishwanatth', 'Dr. Savitha Satish', 'Prasad Enagandula', 'Anindita Khade', 'Pratibh', 'Dr. Zainab Mirza']2024-04-29T00:00:00https://www.semanticscholar.org/paper/4b28f5f3fb6e3359ce6e96eb1f638bb6839c6fde a7c9c36ea05c18bfec05af1314de08dc180c4468As the global population ages, nurses with a positive attitude toward caring for older adults is crucial. However, studies indicate that nursing students often exhibit negative attitudes toward older adults. This study aimed to determine if a three-phased educational intervention significantly improved nursing students’ attitudes toward older adults. A pre/post-test study design was used to measure the change in nursing students’ attitudes toward older adults, as measured by the UCLA Geriatrics Attitudes Survey, after participating in an Artificial Intelligence in Education learning event (n = 151). Results indicate that post-intervention scores (M = 35.07, SD = 5.34) increased from pre-intervention scores (M = 34.50, SD = 4.86). This difference was statistically significant at the 0.10 significance level (t = 1.88, p = 0.06). Incorporating artificial intelligence technology in a learning event is an effective educational strategy due to its convenience, repetition, and measurable learning outcomes. Improved attitudes toward older adults are foundational for delivering competent care to a rapidly growing aging population. This study was prospectively registered with the university’s Institutional Review Board (IRB) on 30 July 2021 with the registration number IRB-FY22-3.Nursing Reports180Results indicate that post-intervention scores in nursing students’ attitudes toward older adults increased from pre-intervention scores, and incorporating artificial intelligence technology in a learning event is an effective educational strategy due to its convenience, repetition, and measurable learning outcomes.Nursing Reports['Anne White', 'M. Maguire', 'Austin Brown', 'Diane Keen']2024-04-29T00:00:00https://www.semanticscholar.org/paper/a7c9c36ea05c18bfec05af1314de08dc180c4468 58113f4717d4893fb6550527290ceba583a1bd60Integrasi teknologi Artificial Intelligence (AI) telah memberikan dampak positif yang signifikan pada berbagai bidang, termasuk pemanfaatannya di praktik hukum. Pemanfaatan AI dalam praktik hukum, dan pertimbangan mengenai potensi penggantian peran hakim oleh AI dalam membuat keputusan pemidanaan masih menjadi polemik didunia hukum. Metode penelitian yang digunakan pada penelitian ini adalah penelitian hukum yuridis normatif dengan pendekatan perundang-undangan dan konseptual. Hasilnya menunjukkan bahwa saat ini integrasi AI dalam hukum positif masih terbatas, pemanfaatan AI telah dilakukan oleh sebagian penegak hukum, namun AI belum dapat menggantikan hakim dalam membuat keputusan pemidanaan karena beberapa pertimbangan hukum sebab AI tidak dapat dianggap sebagai subjek hukum serta adanya sistem pembuktian negatif yang diatur KUHAP yang mensyaratkan adanya ‘keyakinan hakim’ yang turut mendegradasi AI dalam membuat putusan pidana. oleh karena itu perlu adanya regulasi khusus tentang penggunaan AI yang berbasis nilai-nilai Pancasila dan UUD 1945, serta perlunya kajian lebih lanjut mengenai potensi AI dalam kasus-kasus tertentu.Locus Journal of Academic Literature Review190Locus Journal of Academic Literature Review['Ekinia Karolin Sebayang', 'Mahmud Mulyadi', 'M. Ekaputra']2024-04-29T00:00:00https://www.semanticscholar.org/paper/58113f4717d4893fb6550527290ceba583a1bd60 f32c2c727f5366b3f1c816b7075caf52a8b900bcPurpose Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation. Design/methodology/approach The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model. Findings There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2). Originality/value This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry. International Journal of Law and Management380The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry.International Journal of Law and Management['K. Bansal', 'Aseem Chandra Paliwal', 'Arun Kumar Singh']2024-04-29T00:00:00https://www.semanticscholar.org/paper/f32c2c727f5366b3f1c816b7075caf52a8b900bc 593c6514fb8857747a9429e78981062e225c9879This study assesses the knowledge of artificial intelligence (AI) for teaching and research among lecturers in the faculties of arts in Nigeria. Despite the growing recognition of AI's potential to enhance educational practices, there are significant gaps in educators' AI literacy. The research adopts a quantitative approach, surveying lecturers across Nigerian universities to gauge their awareness, engagement, and perceptions of AI integration. Results reveal a moderate level of AI awareness among respondents, with a notable interest in further training tailored to arts disciplines. Challenges such as technical barriers and limited resources hinder seamless AI integration, highlighting the need for targeted interventions and support mechanisms. Recommendations include enhanced training programs, infrastructure improvement, and ethical guidelines to facilitate responsible AI utilization in arts education. Addressing these challenges and fostering AI literacy among educators can create an enabling environment for leveraging AI to enhance teaching and research outcomes in Nigerian arts faculties.Journal of global research in education and social science00A moderate level of AI awareness among respondents is revealed, with a notable interest in further training tailored to arts disciplines, and recommendations include enhanced training programs, infrastructure improvement, and ethical guidelines to facilitate responsible AI utilization in arts education.Journal of Global Research in Education and Social Science['Abdulrahman Burour Ibrahim']2024-04-29T00:00:00https://www.semanticscholar.org/paper/593c6514fb8857747a9429e78981062e225c9879 04a6ba5d7a7f6a0cbd89931695705f97a6e94b47Radiotherapy remains a cornerstone in the treatment and management of cancer, however, current developments in artificial intelligence (AI) have shown promising opportunities in this field. Hence, the objective of this paper is to assess the need for integrating artificial intelligence to enhance cancer radiotherapy in hospitals and healthcare centers in Nigeria, using the twelve radiotherapy centers across the country. The article highlights the need for Nigerian hospitals and healthcare centers to start working towards embracing and integrating AI techniques into her radiotherapy (RT) procedures for optimized cancer treatment. Also, important groundwork required to ease the integration process is discussed. To highlight the need for Nigerian hospitals and healthcare centers to embrace and integrate AI into their radiotherapy procedures, a state-of-the-art review of accessible literatures from Scopus, PubMed, and Google Scholar was carried out. Finally, several applications of AI (machine/deep learning) techniques in radiotherapy were identified. Also, the current status of radiotherapy services in Nigeria and factors hindering its marriage with AI has been highlighted. Necessary groundwork required for a seamless AI integration was equally highlighted.Journal of Applied Sciences and Environmental Management00The article highlights the need for Nigerian hospitals and healthcare centers to start working towards embracing and integrating AI techniques into her radiotherapy (RT) procedures for optimized cancer treatment.Journal of Applied Sciences and Environmental Management['H. A. Momoh', 'H. A. Ibrahim', 'M. O. Abdulmalik', 'A. I. Bello']2024-04-29T00:00:00https://www.semanticscholar.org/paper/04a6ba5d7a7f6a0cbd89931695705f97a6e94b47 c67febc975c97e8fb0644c10af2fa6ea12555c43This research investigates utilizing Machine Learning (ML) and Artificial Intelligence (AI) within academic settings. Drawing upon scholarly sources, we explore the strategic deployment of ML algorithms for tasks such as detecting AI-generated content, evaluating students' graduation potential, and enhancing personalized learning experiences. Our methodology encompasses several key stages: gathering and understanding ML, selecting appropriate models, collecting and prepossessing data, model training, evaluation, testing, and comparative analysis. Through rigorous evaluation using diverse datasets, we assess the performance of Decision Trees, Multinomial Naive Bayes, and Neural Network models in accurately classifying text samples. The findings from this study provide valuable insights into the efficacy of ML algorithms in academic contexts and offer practical implications for their implementation.International Symposium on Digital Forensics and Security170This research investigates utilizing Machine Learning (ML) and Artificial Intelligence (AI) within academic settings for tasks such as detecting AI-generated content, evaluating students' graduation potential, and enhancing personalized learning experiences.2024 12th International Symposium on Digital Forensics and Security (ISDFS)['Hala Strohmier', 'Vincent Langner', 'Fardeen Mohamed', 'Ethan Wood']2024-04-29T00:00:00https://www.semanticscholar.org/paper/c67febc975c97e8fb0644c10af2fa6ea12555c43 a50815f6bc0bb561caa30338c02d1f47833d13edBackground Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI. Conclusions The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssvJournal of Medical Internet Research00The use of AI proves to be a valuable tool for creating predictive models of fall risk as it enables the development of low-cost predictive models with a high level of accuracy.Journal of Medical Internet Research['Ana González-Castro', 'Raquel Leirós-Rodríguez', 'C. Prada-García', 'J. Benítez-Andrades']2024-04-29T00:00:00https://www.semanticscholar.org/paper/a50815f6bc0bb561caa30338c02d1f47833d13ed 93499e0623edf8b8f1f7fd2074a8d01373fb4da2As artificial intelligence (AI), including its potential role in influencing elections, has become an increasingly important topic, electoral management bodies have to develop plans to respond to and, in some cases, use AI to maintain free, fair and secure elections. AI is a rapidly evolving category of technologies that are largely unregulated, and very little research has been conducted so far concerning its potential impact on elections. This Report is aimed at supporting electoral management bodies and other relevant parties in developing a broad understanding of the opportunities, challenges and legal implications of the use of AI for elections.00This Report is aimed at supporting electoral management bodies and other relevant parties in developing a broad understanding of the opportunities, challenges and legal implications of the use of AI for elections.['Prathm Juneja']2024-04-29T00:00:00https://www.semanticscholar.org/paper/93499e0623edf8b8f1f7fd2074a8d01373fb4da2 aa89cc672aac4091da7b509cd3277b9326b82212With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist’s task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.Journal of dentistry research590Artificial intelligence (AI) has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data, to help orthodontists with decision support for treatment decisions.Journal of Dental Research['N. F. Nordblom', 'M. Büttner', 'F. Schwendicke']2024-04-29T00:00:00https://www.semanticscholar.org/paper/aa89cc672aac4091da7b509cd3277b9326b82212 091ba40aded3687237396feb815754a19b08d6ba In recent years, artificial intelligence (AI) has been changing the way the industry operates, especially the oil industry. In the oil exploration and production activity, specifically in the Reservoirs and Wells areas, the use of AI has been growing exponentially, with applications ranging from reservoir drainage plan evaluation (CARDOSO et al., 2017) to predicting well instability issues during drilling (LENWOUE et al., 2023). The Selection of Alternatives via Artificial Intelligence – SAVIA, arises from the Internal Startups Program, an initiative of Petrobras' Digital Transformation that aims to enable innovations conceived by the employees themselves for value generation, maximizing results and reducing costs for the Company. Centro de Estudos Avançados do Recife (CESAR), as a digital innovation center, partners with Petrobras in the development of these projects to make the ideas feasible. SAVIA emerges from the need to streamline and innovate the well conception process, known as SELEPOÇO, by improving the technological incorporation stage aligned with cost optimization expectations. AI proposes to well Designers alternatives of well configuration for a given investment project. The Minimum Viable Product (MVP) of SAVIA was developed through the construction of a decision tree algorithm based on the accumulated knowledge of Petrobras' technicians and engineers, in the form of business rules implemented in Python, and the construction of a machine learning model based on historical data from built wells. Data from lithology and other information from about 1600 offshore wells built by Petrobras in the last two decades were used to train and assess SAVIA's performance.Day 4 Thu, May 09, 202400AVIA emerges from the need to streamline and innovate the well conception process, known as SELEPOÇO, by improving the technological incorporation stage aligned with cost optimization expectations, and proposes to well Designers alternatives of well configuration for a given investment project.Day 4 Thu, May 09, 2024['Antonio Carlos Ramos Junior', 'Carolina Bertholdo da Cunha', 'Cristiano de Souza Santos', 'Jorel Lopes Rodrigues Dos Anjos', 'M. Marques', 'Marcio Fernandes Leal', 'Paulo Roberto Santos Pinto da Fonseca', 'Rafael Gustavo da Cunha Pereira Pinto', 'Francisco Bráulio Oliveira']2024-04-29T00:00:00https://www.semanticscholar.org/paper/091ba40aded3687237396feb815754a19b08d6ba 900b2ffcdab1446c24b2459389df50db72d0d87c This JAMA Pediatrics Patient Page describes what artificial intelligence chatbots are and how they may influence learning among children. JAMA pediatrics00JAMA pediatrics['Arezoo Movaghar', 'Lindsay A Thompson']2024-04-29T00:00:00https://www.semanticscholar.org/paper/900b2ffcdab1446c24b2459389df50db72d0d87c b08ffcb81e9491c784fefd8b118d9c91eaff0e2bIn today's landscape, the widespread integration of artificial intelligence (AI) solutions across diverse domains has become commonplace. Yet, despite its omnipresence, AI applications, often lack adequate protection, leaving them vulnerable to various threats. Consequently, businesses find themselves in need of clear guidance to navigate these risks effectively. This study aims to address this gap by shedding light on attacker activities targeting AI applications, offering robust defense mechanisms, and creating a comprehensive checklist for evaluating current processes. By analyzing attack and defense strategies, it is clear that although these methods are similar to those used in traditional information systems, their implementation in AI contexts differs significantly. This study provides detailed implementation insights and a security checklist to help organizations assess process maturity quickly. By identifying and addressing security gaps promptly, organizations can enhance the resilience of their AI infrastructure.International Symposium on Digital Forensics and Security290This study provides detailed implementation insights and a security checklist to help organizations assess process maturity quickly and identify and address security gaps promptly so that organizations can enhance the resilience of their AI infrastructure.2024 12th International Symposium on Digital Forensics and Security (ISDFS)['İrem Zehra Altun', 'Abdurrahman Emre Özkök']2024-04-29T00:00:00https://www.semanticscholar.org/paper/b08ffcb81e9491c784fefd8b118d9c91eaff0e2b 26bb35a8cd6b5b0f630bebab0a3eb9f746423a11The position of the fetal head during engagement and progression in the birth canal is the primary cause of dystocic labor and arrest of progression, often due to malposition and malrotation. The authors performed an investigation on pregnant women in labor, who all underwent vaginal digital examination by obstetricians and midwives as well as intrapartum ultrasonography to collect four “geometric parameters”, measured in all the women. All parameters were measured using artificial intelligence and machine learning algorithms, called AIDA (artificial intelligence dystocia algorithm), which incorporates a human-in-the-loop approach, that is, to use AI (artificial intelligence) algorithms that prioritize the physician’s decision and explainable artificial intelligence (XAI). The AIDA was structured into five classes. After a number of “geometric parameters” were collected, the data obtained from the AIDA analysis were entered into a red, yellow, or green zone, linked to the analysis of the progress of labor. Using the AIDA analysis, we were able to identify five reference classes for patients in labor, each of which had a certain sort of birth outcome. A 100% cesarean birth prediction was made in two of these five classes. The use of artificial intelligence, through the evaluation of certain obstetric parameters in specific decision-making algorithms, allows physicians to systematically understand how the results of the algorithms can be explained. This approach can be useful in evaluating the progress of labor and predicting the labor outcome, including spontaneous, whether operative VD (vaginal delivery) should be attempted, or if ICD (intrapartum cesarean delivery) is preferable or necessary.Journal of Imaging220An investigation on pregnant women in labor, who all underwent vaginal digital examination by obstetricians and midwives as well as intrapartum ultrasonography to collect four “geometric parameters”, which were measured using artificial intelligence and machine learning algorithms.Journal of Imaging['Antonio Malvasi', 'Lorenzo E. Malgieri', 'Ettore Cicinelli', 'Antonella Vimercati', 'Antonio D’Amato', 'M. Dellino', 'Giuseppe Trojano', 'Tommaso Difonzo', 'R. Beck', 'Andrea Tinelli']2024-04-29T00:00:00https://www.semanticscholar.org/paper/26bb35a8cd6b5b0f630bebab0a3eb9f746423a11 09eb89dbc3e69a57a5bc17481fbc6e070ae6dd70The aim of this study was to investigate the impact of artificial intelligence (AI) with its dimensions (expert systems, automatic machine learning, usability) on organizational performance, mediated by intellectual capital, in commercial banks operating in the Kingdom of Saudi Arabia. The study population comprised all employees in commercial banks in Saudi Arabia, and the study relied on a proportionate stratified random sample of administrators in these banks (managers, department heads, supervisors, administrators), totaling 338 administrators. The study utilized a descriptive-analytical methodology, developing a questionnaire consisting of 38 items. Statistical Package for the Social Sciences (SPSS) was used for data analysis and hypothesis testing. The study yielded several key findings: There is a significant impact of artificial intelligence, with its dimensions (expert systems, automatic machine learning, usability), on organizational performance. Additionally, there is a significant impact of artificial intelligence dimensions on intellectual capital, and intellectual capital significantly impacts organizational performance. Moreover, there is a significant indirect impact of artificial intelligence dimensions on organizational performance through intellectual capital in commercial banks. The study recommended that bank management focus on expert systems provided by artificial intelligence to acquire knowledge from stored databases for problem-solving and application through organizational performance and intellectual capital. It also suggested developing systems to logically and programmatically address accounting errors to enhance the ability to automatically monitor and reevaluate internal processes continuously in line with changes in the external work environment. This can be achieved through the use of modern technology, knowledge transfer, research, and development.International Journal for Scientific Research00The study recommended that bank management focus on expert systems provided by artificial intelligence to acquire knowledge from stored databases for problem-solving and application through organizational performance and intellectual capital.International Journal for Scientific Research['Mai Abu Al-Khair', 'Shahd Al-Sayegh']2024-04-29T00:00:00https://www.semanticscholar.org/paper/09eb89dbc3e69a57a5bc17481fbc6e070ae6dd70 bf8d97b838c6dbd1d00e18cad48e88425248b3f6Jurnal ini dilatarbelakangi dengan penelitian kuantitatif mengenai penggunaan media sosial di masa sekarang dengan memanfaatkan teknologi artificial intelligence dan penerapan teknologi tersebut dalam permainan akun alter. Penelitian ini didasarkan pada survei dalam bentuk Google Form dan menargetkan remaja usia 17 tahun hingga dewasa usia 30 tahun. Tujuan diadakannya penelitian ini antara lain : (1) Mengetahui persentase masyarakat yang menggunakan media sosial tanpa menunjukkan profilnya, (2) Menjadikan hasil penelitian sebagai aware kepada masyarakat dalam menggunakan teknologi dan media sosial. Dari hasil penelitian yang telah dilakukan, mayoritas masyarakat menggunakan media sosial sebagai sarana komunikasi dan banyak yang memiliki lebih dari satu akun yang tidak menunjukkan identitas asli. Kemajuan dalam kecerdasan buatan membawa banyak perubahan signifikan dalam proses komunikasi dan interaksi. Oleh karena itu, penting untuk memanfaatkan teknologi ini dengan bijak. Pada saat menggunakan platform  media sosial, disarankan agar pengguna tetap mempertahankan kerahasiaan identitas dan menghindari menyebarkan informasi palsu yang dapat merugikan orang lain.Deliberatio: Jurnal Mahasiswa Komunikasi40Deliberatio: Jurnal Mahasiswa Komunikasi['Ismaya Nurafifah', 'Salsa Billa', 'Rahma Dewi', 'Kaisa Lovina Aprilianti']2024-04-29T00:00:00https://www.semanticscholar.org/paper/bf8d97b838c6dbd1d00e18cad48e88425248b3f6 25d03a8952eb02b5ddc9501a5df420ac3317b9c6The International Journal of Advanced Manufacturing Technology1590This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks, Fuzzy Logic, Fuzzy Logic, Machine Learning, Meta-Heuristic Algorithms, and Hybrid Methods as applied to Friction Stir Welding, Ultrasonic Welding, and Diffusion Bonding.The International Journal of Advanced Manufacturing Technology['Sambath Yaknesh', 'Natarajan Rajamurugu', 'Prakash K. Babu', 'Saravanakumar Subramaniyan', 'Sher Afghan Khan', 'C. A. Saleel', 'Mohammad Nur-E-Alam', 'M. Soudagar']2024-04-29T00:00:00https://www.semanticscholar.org/paper/25d03a8952eb02b5ddc9501a5df420ac3317b9c6 dd7937999dcdb7f2ae095eaf712f3b735a3341f4Minia Scientific Nursing Journal00Minia Scientific Nursing Journal[]2024-04-29T00:00:00https://www.semanticscholar.org/paper/dd7937999dcdb7f2ae095eaf712f3b735a3341f4 797850a8324f5b2699a84d71af4d4aa448cd24fdMind Genomics Studies in Psychology &amp; Experience00Mind Genomics Studies in Psychology &amp; Experience[]2024-04-29T00:00:00https://www.semanticscholar.org/paper/797850a8324f5b2699a84d71af4d4aa448cd24fd 523bc226be61f864eb2fc1aeb442b45f85820b4aEmotional research in film and animation has long been implemented in the West. This is because Elements of emotion are essential in an animated film to generate an affective impression and emotions in the audience. Malaysia's animation industry has increased recently, and Malaysian animators have produced many animation products. However, previous studies stated that the current situation of local animated films needed a more potent storytelling technique. There is a West scholar who argues that good storytelling can evoke the emotions of the audience. Applying emotions in animation through computer technology is complicated compared to live-action films that can control emotions through the actors' acting techniques. Thus, this study identified the elements of emotion in developing the case study of storytelling Agent Ali in the movie. The mixed method was used to identify aspects of emotion in Agent Ali the Movie, such as Freytag's Pyramid model and Hume AI. As a result, this study found that aspects of emotion exist in the story development process in Agent Ali the Movie, such as happiness, sadness, and anger in every three acts of structure (exposition, conflict, and resolution). The existence of emotion has proved that animated films in Malaysia need to be focused to overcome the weak storytelling technique. At the end of the discussion, this study also found that Artificial intelligence (AI) technology like Hume AI could speed up the animation production process, especially in identifying the facial expressions and body language of characters in the process of storytelling development.PaperASIA360It is found that aspects of emotion exist in the story development process in Agent Ali the Movie, such as happiness, sadness, and anger in every three acts of structure (exposition, conflict, and resolution).PaperASIA['R. Zainal', 'Mohd Asyiek Mat Desa']2024-04-29T00:00:00https://www.semanticscholar.org/paper/523bc226be61f864eb2fc1aeb442b45f85820b4a d8d4eedd7d1b7ca2e852c018c11fe7805ad47174Building multisensory AI systems that learn from multiple sensory inputs such as text, speech, video, real-world sensors, wearable devices, and medical data holds great promise for impact in many scientific areas with practical benefits, such as in supporting human health and well-being, enabling multimedia content processing, and enhancing real-world autonomous agents. By synthesizing a range of theoretical frameworks and application domains, this thesis aims to advance the machine learning foundations of multisensory AI. In the first part, we present a theoretical framework formalizing how modalities interact with each other to give rise to new information for a task. These interactions are the basic building blocks in all multimodal problems, and their quantification enables users to understand their multimodal datasets, design principled approaches to learn these interactions, and analyze whether their model has succeeded in learning. In the second part, we study the design of practical multimodal foundation models that generalize over many modalities and tasks, which presents a step toward grounding large language models to real-world sensory modalities. We introduce MultiBench, a unified large-scale benchmark across a wide range of modalities, tasks, and research areas, followed by the cross-modal attention and multimodal transformer architectures that now underpin many of today's multimodal foundation models. Scaling these architectures on MultiBench enables the creation of general-purpose multisensory AI systems, and we discuss our collaborative efforts in applying these models for real-world impact in affective computing, mental health, cancer prognosis, and robotics. Finally, we conclude this thesis by discussing how future work can leverage these ideas toward more general, interactive, and safe multisensory AI.00A theoretical framework formalizing how modalities interact with each other to give rise to new information for a task is presented, and how future work can leverage these ideas toward more general, interactive, and safe multisensory AI is discussed.['Paul Pu Liang']2024-04-29T00:00:00https://www.semanticscholar.org/paper/d8d4eedd7d1b7ca2e852c018c11fe7805ad47174 7b970d01fe4bbc936efb2a7842d2e85690e3cd3dEthics &amp; Behavior800Ethics &amp; Behavior['Hyemin Han']2024-04-29T00:00:00https://www.semanticscholar.org/paper/7b970d01fe4bbc936efb2a7842d2e85690e3cd3d 3f8954a738f5f73f75d7be1ed201978109788a02Journal of Biomed Research00Journal of Biomed Research['T. Addissouky', 'Ibrahim El Tantawy El Sayed', 'Majeed M. A. Ali', 'Mahmood Hasen shuhata Alubiady', 'Yuliang Wang']2024-04-29T00:00:00https://www.semanticscholar.org/paper/3f8954a738f5f73f75d7be1ed201978109788a02 e0f7713ad9b6b25b6c4ff4d0e95fbbec5ffff3e4Issues in Mental Health Nursing220Issues in mental health nursing['Michelle Cleary', 'R. Kornhaber', 'Danielle Le Lagadec', 'Robert Stanton', 'C. Hungerford']2024-04-29T00:00:00https://www.semanticscholar.org/paper/e0f7713ad9b6b25b6c4ff4d0e95fbbec5ffff3e4 86c9e7ae930835a4565b8cab94a733c39ec8ef56The complex faults, especially mid-deep faults, in the Laoyemiao area of the Nanpu Sag, the Bohai Bay Basin, are unclearly understood for their characteristics, constraining the structural and geological delineation of the area. The hydrocarbon enrichment in the Laoyemiao area is closely related to the faults, and thus the precise identification of mid-deep faults is of great significance for understanding the structural system and reservoir distribution in the area. In the past twenty years, artificial intelligence (AI) scholars developed new technologies and methods to solve engineering problems. Typically, the AI seismic data interpretation technology plays a critical role in improving the accuracy and efficiency of fault interpretation. In order to define the structural characteristics of the Laoyemiao area, the "2W1H" seismic data were processed by fault-constrained structure-oriented filtering, and then interpreted using the EasyTrack module of GeoEast independently developed by BGP. It is found that the imaging quality and accuracy of mid-deep faults are improved effectively. On this basis, the SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood. The results are of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area. Earth Sciences40The SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood, of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area.Earth Sciences['Zeng Cheng', 'Sun Lizhi', 'Yongbin Bi', 'Xu Bo', 'Duan Jian', 'Yingxin Xu', 'Liping Qian', 'Zhang Wanfu', 'Zhang Hao', 'Zijuan Ying']2024-04-29T00:00:00https://www.semanticscholar.org/paper/86c9e7ae930835a4565b8cab94a733c39ec8ef56 2ee8ca424d383260e764f3877e29c9018ec24b30Today artificial intelligence (AI) has become an indispensable assistance for human traders. AI systems provide human traders with numerous advantages, such as the capability to conduct genuine high-frequency trading (HFT), which capitalizes on price discrepancies and market anomalies, and to analyze a massive data set from multiple sources in a fraction of a second. The main goal of this research is to examine the role that AI plays in the financial markets, with an emphasis on how it affects trading, portfolio management, and price prediction. In this study, quantitative research methodology was utilised. Primary and secondary sources of data were used in the investigation. An online questionnaire was used to collect the primary data, and finance databases, pertinent industry bulletins, and already published literature were used to collect the secondary data. It was found that there is an increasing incorporation of AI and machine learning technologies into financial institutions. Many of the participants revealed that these technologies are used moderately to significantly in their organizations. The most prominent AI and machine learning applications are “algorithmic trading, risk management, fraud detection, credit scoring, and customer service”. Journal of Electrical Systems140The main goal of this research is to examine the role that AI plays in the financial markets, with an emphasis on how it affects trading, portfolio management, and price prediction.Journal of Electrical Systems['Sunil Kumar Das', 'Dr Shaista Anwar', 'Urvee Tulsyan', 'Yash Gupta', 'Rahul Vudatta', 'Dr Syed', 'Hassan Imam Gardezi']2024-04-29T00:00:00https://www.semanticscholar.org/paper/2ee8ca424d383260e764f3877e29c9018ec24b30 f09512e6c29e63ab72dcd3882fd3e2a1e752901aThe study of machine consciousness has a wide range of potential and problems as it sits at the intersection of ethics, technology, and philosophy. This work explores the deep issues related to the effort to comprehend and maybe induce awareness in machines. Technically, developments in artificial intelligence, neurology, and cognitive science are required to bring about machine awareness. True awareness is still a difficult to achieve objective, despite significant progress being made in creating AI systems that are capable of learning and solving problems. The implications of machine awareness are profound in terms of ethics. Determining a machine's moral standing and rights would be crucial if it were to become sentient. It is necessary to give careful attention to the ethical issues raised by the development of sentient beings, the abuse of sentient machines, and the moral ramifications of turning off sentient technologies. Philosophically, the presence of machine consciousness may cast doubt on our conceptions of identity, consciousness, and the essence of life. It could cause us to reevaluate how we view mankind and our role in the cosmos. It is imperative that machine awareness grow responsibly in light of these challenges. The purpose of this study is to provide light on the present status of research, draw attention to possible hazards and ethical issues, and offer recommendations for safely navigating this emerging subject. We want to steer the evolution of machine consciousness in a way that is both morally just and technologically inventive by promoting an educated and transparent discourse.The Asian Bulletin of Big Data Management00The purpose of this study is to provide light on the present status of research, draw attention to possible hazards and ethical issues, and offer recommendations for safely navigating this emerging subject.The Asian Bulletin of Big Data Management['Tanveer Rafiq', 'Muhammad Azam', 'Maher u Nisa', 'Mian Mohsin Sattar', 'Sana Zafar', 'Hiba Inam']2024-04-29T00:00:00https://www.semanticscholar.org/paper/f09512e6c29e63ab72dcd3882fd3e2a1e752901a 2d071f275c229cd2779603fbafa3927ffcd9f9bfTraditional demand planning methods often struggle to keep pace with the complexity, volatility, and vast datasets inherent in modern supply chains. Artificial intelligence (AI) offers a transformative solution, revolutionizing demand planning with its ability to analyze vast amount of data, identify complex patterns, and generate highly accurate forecasts. This paper explores the latest advancements in AI for demand planning, encompassing machine learning, deep learning, and natural language processing (NLP). The focus is on how these techniques enhance demand sensing capabilities, incorporating real-time market signals, external data sources, and unstructured text information. Furthermore, the potential of AI to optimize inventory management, enable scenario planning, and increase supply chain resilience in response to unexpected disruptions are examined. The paper also addresses practical challenges in implementing AI-powered demand planning solutions, and outlines areas for future research. Most importantly, the paper provides the robust methologies to integrate the emerging AI developments in demand planning process.International journal of supply chain management310The latest advancements in AI for demand planning, encompassing machine learning, deep learning, and natural language processing, are explored, focusing on how these techniques enhance demand sensing capabilities, incorporating real-time market signals, external data sources, and unstructured text information.International Journal of Supply Chain Management['Antara Khastgir', 'Adesh Kumar']2024-04-29T00:00:00https://www.semanticscholar.org/paper/2d071f275c229cd2779603fbafa3927ffcd9f9bf 37407b690bd5175e6e29a3a27aebd62424bbb514Artificial intelligence (AI), the most widespread technological term, has become an integral part of human daily life. People are becoming increasingly dependent on AI-powered devices; thus, it is now essential to have a legal framework to oversee artificial intelligence's various uses and legal peculiarities. As artificial intelligence has entered all areas of life and into various sectors, which requires examining in detail the legal nature of artificial intelligence, determining the laws that must be applied to it, and identifying the people or entities responsible for it to determine the scope of their responsibilities for the resulting damages that may be caused to others as a result of the use of intelligence. Artificial, this research aimed to examine the adequacy of the legal rules in Jordanian legislation regulating the provisions of artificial intelligence in light of the diversity of its applications and its different legal nature. The research is divided into two chapters. The first chapter presents the concept of artificial intelligence, and the second chapter discusses the legal liability of artificial intelligence. The results of the research revealed that the Jordanian legislator did not specify the legal nature of the artificial intelligence and contented himself with stipulating it in separate texts, Legal liability resulting from utilizing artificial intelligence systems can be challenged under regulations of the defect in manufacturing, responsibility for guarding things, and the distinction between responsibilities due to the degree of independence and intelligence of artificial intelligence, In determining the liability of artificial intelligence, the legislator should consider the types of artificial intelligence systems, their different capabilities, and their independence from humans, The process of enacting special laws regulating all aspects of artificial intelligence must be expedited. This law should be characterized by flexibility that enables it to keep pace with and rapid development witnessed in this field.F1000Research90The results of the research revealed that the Jordanian legislator did not specify the legal nature of the artificial intelligence and contented himself with stipulating it in separate texts.F1000Research['Hashim Balas', 'Reem Shatnawi']2024-04-29T00:00:00https://www.semanticscholar.org/paper/37407b690bd5175e6e29a3a27aebd62424bbb514 bde6cac9badbf5c86454bdd136ab833913e61e66Due to China’s rapid economic development, there has been an increased demand for Chinese language education in Malaysia. The utilization of appropriate language learning strategies plays a crucial role in ensuring successful learning outcomes. Therefore, the objective of this research is to investigate learners’ perception regarding their use of learning strategies in acquiring Mandarin as a foreign language. A quantitative survey was conducted among 148 students enrolled in an Introductory Mandarin course in a public university. The survey employed a 5 Likert-scale and consisted of four sessions which are demographic profile, cognitive components, metacognitive self-regulation and resource management. The collected data were analyzed using SPSS. The findings indicate that the resource management components “environment management” and “help-seeking” received a highest mean scores (M=4.14, 4.15), followed by the cognitive component “rehearsal” (M=4). Conversely, metacognitive self-regulation strategies had the lowest mean score (M=3.78). Furthermore, the research also reveals a strong positive correlation between metacognitive self-regulation, cognitive strategies and resource management strategies in foreign language learning. It is hoped that this study can provide insights for instructors to enhance their teaching approach to facilitate students' learning.Quantum Journal of Social Sciences and Humanities280Quantum Journal of Social Sciences and Humanities['Yee Feng Neo', 'Jie Yan Chan', 'Chin Shuang Goh', 'Ayisha Huiqin Zhang']2024-04-28T00:00:00https://www.semanticscholar.org/paper/bde6cac9badbf5c86454bdd136ab833913e61e66 6c9477a2539468aa276fc8c7e76a5619e0cb6d54Scientific Reports361The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it, and the lack of reduction in participants' overreliance on inaccurate advice when the systems' predictions were made more explainable highlights the complexity of human-AI interaction.Scientific reports['Julia Cecil', 'E. Lermer', 'Matthias F. C. Hudecek', 'Jan Sauer', 'S. Gaube']2024-04-28T00:00:00https://www.semanticscholar.org/paper/6c9477a2539468aa276fc8c7e76a5619e0cb6d54 3481c47de4b3a82531f33a5545825b888bac5e30Artificial intelligence (AI) is rapidly transforming the business landscape and the global economy. This paper explores the multifaceted impact of AI on existing businesses, focusing on both opportunities and challenges. It analyzes how AI is enhancing productivity, optimizing decision-making, and creating new business models. The paper also examines the potential for job displacement and the need for workforce retraining. Furthermore, it discusses the broader economic implications of AI, including its contribution to economic growth, potential for inequality, and the importance of ethical considerations.Global Mainstream Journal00How AI is enhancing productivity, optimizing decision-making, and creating new business models is analyzed, including its contribution to economic growth, potential for inequality, and the importance of ethical considerations.Global Mainstream Journal[]2024-04-28T00:00:00https://www.semanticscholar.org/paper/3481c47de4b3a82531f33a5545825b888bac5e30 520577ad0d14c6d3911f1a5510c8c3531a7ed7beSam Freed’s AI and Human Thought and Emotion is a pioneering venture into the possibility of programming an anthropic model of artificial intelligence grounded on the author’s philosophical reflection on the accuracy-oriented and optimisation-driven making of AI. In critique of the rationalist tradition of AI development and expurgation of introspection from cognitive science, this book draws heavily upon phenomenology to argue for the necessity of incorporating human non-linear thinking processes into the technical design of AI. Freed’s conceptual revolution enters a substantive dialogue with Hubert Dreyfus’s What Computers Can’t Do to offer an answer to the impossibility of formalising human introspection, which is conventionally deemed unprogrammable under the ostracism of human subjectivity from the science discipline and anti-scientism that pervades the intellectual circle of the humanities.Journal of Posthumanism50Journal of Posthumanism['Amanda Hsu Yuk-kwan']2024-04-28T00:00:00https://www.semanticscholar.org/paper/520577ad0d14c6d3911f1a5510c8c3531a7ed7be 12cd25755b7647187be1b06d9cc77c85f222032aIt is everywhere, in the industrial world, in the arts, in movies, in administrations, in security institutions, in politics, and in science publishing: Artificial Intelligence AI...Music and Medicine00Music and Medicine['R. Spintge', 'J. Loewy']2024-04-28T00:00:00https://www.semanticscholar.org/paper/12cd25755b7647187be1b06d9cc77c85f222032a 69cc5b107f27cb9c76852945a2f64eb4a2aac441Amyotrophic Lateral Sclerosis (ALS) is an incurable deadly motor neuron disease that causes the gradual deterioration of nerve cells in the spinal cord and brain. It impacts voluntary limb control and can result in breathing impairment. ALS diagnosis is often challenging due to its symptoms overlapping with other medical conditions and many tests must be performed to rule out other conditions, as easily identifiable biomarkers are still lacking. In this study, we explore T1-weighted (T1w) brain Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging approach which has shown to be a reliable biomarker in many medical fields. Nonetheless, current literature on ALS diagnosis fails to retrieve evidence on how to identify biomarkers from T1w MRI. In this paper, we leverage Artificial Intelligence (AI) methods to unveil the unexplored potential of T1w brain MRI for distinguishing ALS patients from those who have similar symptoms but different diseases (mimicking). We consider a retrospective single-center dataset of brain T1-weighted MRIs collected from 2010 to 2018 recruited from the Piemonte and Valle d'Aosta ALS register (PARALS). The collection includes 548 patients with ALS and 106 with mimicking diseases. Our goal is to develop and validate a ML diagnostic model based exclusively on T1w MRI distinguishing the two classes. First, we extract a set of radiomic features and two sets of Deep Learning (DL)-based features from MRI scans. Then, using each representation, we train 8 binary classifiers. The best results were obtained by combining DL-based features with SVM classifier, reaching an F1-score of 0.91, and a Precision of 0.88, a Recall of 0.94, and an AUC of 0.7 considering the ALS group as the positive class in the testing set.medRxiv230AI methods are leveraged to unveil the unexplored potential of T1w brain MRI for distinguishing ALS patients from those who have similar symptoms but different diseases (mimicking), and develop and validate a ML diagnostic model based exclusively on T1w MRI distinguishing the two classes.['Rosanna Turrisi', 'Federica Forzanini c', 'Mario Stanziano', 'Anna Nigri', 'Davide Fedeli', 'Carrara Giovanna', 'Lequio Laura', 'U. Manera', 'C. Moglia', 'M. C. Valentini', 'Andrea Calvo', 'A. Chiò', 'Annalisa Barla', 'Als Centre']2024-04-28T00:00:00https://www.semanticscholar.org/paper/69cc5b107f27cb9c76852945a2f64eb4a2aac441 398c84dccf67188fcd6b94c9dda84727917a2d54Predictive maintenance is a critical aspect of industrial operations, enabling proactive identification and mitigation of potential failures in machinery and equipment. However, the widespread adoption of AI-driven predictive maintenance solutions has been hindered by the opaque nature of many machines learning models, raising concerns about transparency, accountability, and trust. This research aims to address these challenges by developing explainable AI techniques for predictive maintenance in industrial systems. By integrating interpretability methods with advanced predictive models, we seek to enhance the transparency and interpretability of AI-driven maintenance decisions. Our proposed methodology combines state-of-the-art machine learning algorithms with local and global explainability techniques, such as LIME, SHAP, and feature importance analysis. Through extensive experiments on real-world industrial data, we evaluate the performance of our explainable AI models and demonstrate their ability to provide insightful explanations, enabling domain experts to understand the underlying reasoning and critical factors contributing to maintenance predictions. Furthermore, we explore the impact of explainable AI on improving trust, accountability, and adoption of AI systems in industrial predictive maintenance scenarios. Keywords— Predictive Maintenance, Explainable AI (XAI), Machine Learning, Interpretability, LIME, SHAP, Feature Importance, Industrial Systems, Trust in AI, Accountability.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research develops explainable AI techniques for predictive maintenance in industrial systems by integrating interpretability methods with advanced predictive models, and explores the impact of explainable AI on improving trust, accountability, and adoption of AI systems in industrial predictive maintenance scenarios.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Rajat soni,']2024-04-28T00:00:00https://www.semanticscholar.org/paper/398c84dccf67188fcd6b94c9dda84727917a2d54 caa9bcc446e695ba22564a04edf8ccee2b255419Offline licensing is a technical mechanism for compute governance that could be used to prevent unregulated training of potentially dangerous frontier AI models. The mechanism works by disabling AI chips unless they have an up-to-date license from a regulator. In this report, we present a technical design for a minimal version of offline licensing that could be delivered via a firmware update. Existing AI chips could potentially support offline licensing within a year if they have the following (relatively common) hardware security features: firmware verification, firmware rollback protection, and secure non-volatile memory. Public documentation suggests that NVIDIA's H100 AI chip already has these security features. Without additional hardware modifications, the system is susceptible to physical hardware attacks. However, these attacks might require expensive equipment and could be difficult to reliably apply to thousands of AI chips. A firmware-based offline licensing design shares the same legal requirements and license approval mechanism as a hardware-based solution. Implementing a firmware-based solution now could accelerate the eventual deployment of a more secure hardware-based solution in the future. For AI chip manufacturers, implementing this security mechanism might allow chips to be sold to customers that would otherwise be prohibited by export restrictions. For governments, it may be important to be able to prevent unsafe or malicious actors from training frontier AI models in the next few years. Based on this initial analysis, firmware-based offline licensing could partially solve urgent security and trade problems and is technically feasible for AI chips that have common hardware security features.80Firmware-based offline licensing could partially solve urgent security and trade problems and is technically feasible for AI chips that have common hardware security features.['James Petrie']2024-04-28T00:00:00https://www.semanticscholar.org/paper/caa9bcc446e695ba22564a04edf8ccee2b255419 039aab783df3cf7520e8557d4ff5c60d4b171242The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AIbyAI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AIbyAI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.Electronics00The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AIbyAI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK.Electronics['Emilio Isaac Baungarten-Leon', 'Susana Ortega-Cisneros', 'Mohamed Abdelmoneum', 'Ruth Yadira Vidana Vidana Morales', 'German Pinedo-Diaz']2024-04-28T00:00:00https://www.semanticscholar.org/paper/039aab783df3cf7520e8557d4ff5c60d4b171242 0e6220089b2fe51183780ed9a1954cd1a2747501The integration of artificial intelligence (AI) into health care has seen remarkable advancements, with applications extending to animal health. This article explores the potential benefits and challenges associated with employing AI chatbots as tools for pet health care. Focusing on ChatGPT, a prominent language model, the authors elucidate its capabilities and its potential impact on pet owners' decision-making processes. AI chatbots offer pet owners access to extensive information on animal health, research studies and diagnostic options, providing a cost-effective and convenient alternative to traditional veterinary consultations. The fate of a case involving a Border Collie named Sassy demonstrates the potential benefits of AI in veterinary medicine. In this instance, ChatGPT played a pivotal role in suggesting a diagnosis that led to successful treatment, showcasing the potential of AI chatbots as valuable tools in complex cases. However, concerns arise regarding pet owners relying solely on AI chatbots for medical advice, potentially resulting in misdiagnosis, inappropriate treatment and delayed professional intervention. We emphasize the need for a balanced approach, positioning AI chatbots as supplementary tools rather than substitutes for licensed veterinarians. To mitigate risks, the article proposes strategies such as educating pet owners on AI chatbots' limitations, implementing regulations to guide AI chatbot companies and fostering collaboration between AI chatbots and veterinarians. The intricate web of responsibilities in this dynamic landscape underscores the importance of government regulations, the educational role of AI chatbots and the symbiotic relationship between AI technology and veterinary expertise. In conclusion, while AI chatbots hold immense promise in transforming pet health care, cautious and informed usage is crucial. By promoting awareness, establishing regulations and fostering collaboration, the article advocates for a responsible integration of AI chatbots to ensure optimal care for pets.Veterinary Medicine and Science100The article proposes strategies such as educating pet owners on AI chatbots' limitations, implementing regulations to guide AI chatbot companies and fostering collaboration between AI chatbots and veterinarians to ensure optimal care for pets.Veterinary medicine and science['M. Jokar', 'A. Abdous', 'Vahid Rahmanian']2024-04-28T00:00:00https://www.semanticscholar.org/paper/0e6220089b2fe51183780ed9a1954cd1a2747501 d3bf56597736d14b3921bd2f97af7905397bfcd4This study explores the use of artificial intelligence (AI) to improve motor skills learning outcomes for elementary school teacher education (PGSD) students. The research investigates how AI can provide timely feedback and personalized instruction in teaching motor skills. Data analysis methods include surveys, observations, and evaluations of learning outcomes. The research findings reveal the following: 1) Female students' motor skills are classified as moderate, with an average score of 200. The classification criteria for female students' motor skills are excellent (5.67%), good (24.33%), moderate (30.33%), poor (32.67%), and very poor (3%). 2) Male students' motor skills are also categorized as moderate, with an average score of 200. The percentage breakdown for male students' motor skills is as follows: excellent (7.32%), good (25.61%), moderate (37.80%), poor (29.39%), and very poor (4.88%). The results indicate that integrating AI into motor skills learning significantly enhances the academic performance of PGSD students. These findings highlight the importance of incorporating AI technology in teacher education, particularly to enhance the motor skills development of PGSD students. The research supports the concept that AI can effectively support motor skills learning at this educational level.DIAJAR Jurnal Pendidikan dan Pembelajaran80The research supports the concept that AI can effectively support motor skills learning at this educational level and highlights the importance of incorporating AI technology in teacher education, particularly to enhance the motor skills development of PGSD students.DIAJAR: Jurnal Pendidikan dan Pembelajaran['Khaerul Anam', 'Muhamad Sadli', 'Hadi Wijaya', 'Informasi Artikel']2024-04-28T00:00:00https://www.semanticscholar.org/paper/d3bf56597736d14b3921bd2f97af7905397bfcd4 68ccdd96b39cfe6c05d34e90705ee9f381dd75b2Mild Cognitive Impairment (MCI) is a cognitive state frequently observed in older adults, characterized by significant alterations in memory, thinking, and reasoning abilities that extend beyond typical cognitive decline. It is worth noting that around 10–15% of individuals with MCI are projected to develop Alzheimer’s disease, effectively positioning MCI as an early stage of Alzheimer’s. In this study, a novel approach is presented involving the utilization of eXtreme Gradient Boosting to predict the onset of Alzheimer’s disease during the MCI stage. The methodology entails utilizing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through the analysis of longitudinal data, spanning from the baseline visit to the 12-month follow-up, a predictive model was constructed. The proposed model calculates, over a 36-month period, the likelihood of progression from MCI to Alzheimer’s disease, achieving an accuracy rate of 85%. To further enhance the precision of the model, this study implements feature selection using the Recursive Feature Elimination technique. Additionally, the Shapley method is employed to provide insights into the model’s decision-making process, thereby augmenting the transparency and interpretability of the predictions.Information240A novel approach is presented involving the utilization of eXtreme Gradient Boosting to predict the onset of Alzheimer’s disease during the MCI stage using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).Information['Gerasimos Grammenos', 'Aristidis G. Vrahatis', 'Vlamos', 'Dean Palejev', 'T. Exarchos']2024-04-28T00:00:00https://www.semanticscholar.org/paper/68ccdd96b39cfe6c05d34e90705ee9f381dd75b2 e09ea9d34bc71de51781e81c95c9ca32a07ffc10International Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Ajaykumar Bhupatiaju', 'Claire L. Grantier']2024-04-28T00:00:00https://www.semanticscholar.org/paper/e09ea9d34bc71de51781e81c95c9ca32a07ffc10 368fc86bcc79d483f57fa38167eb9016a72bbd31Journal Europeen des Systemes Automatises00Journal Européen des Systèmes Automatisés​['Maira Uaissova', 'Bakhtiyar Zharlykassov']2024-04-28T00:00:00https://www.semanticscholar.org/paper/368fc86bcc79d483f57fa38167eb9016a72bbd31 54026d2b3de3236ec6786d5a4b8027103224dcecUC Merced Undergraduate Research Journal00UC Merced Undergraduate Research Journal['Emily Yu']2024-04-28T00:00:00https://www.semanticscholar.org/paper/54026d2b3de3236ec6786d5a4b8027103224dcec 40e2d42f2485798f0765237b72342a6f66697dc3With the continuous development of artificial intelligence technology, using machine learning technology to predict market trends may no longer be out of reach. In recent years, artificial intelligence has become a research hotspot in the academic circle,and it has been widely used in image recognition, natural language processing and other fields, and also has a huge impact on the field of quantitative investment. As an investment method to obtain stable returns through data analysis, model construction and program trading, quantitative investment is deeply loved by financial institutions and investors. At the same time, as an important application field of quantitative investment, the quantitative investment strategy based on artificial intelligence technology arises at the historic moment.How to apply artificial intelligence to quantitative investment, so as to better achieve profit and risk control, has also become the focus and difficulty of the research. From a global perspective, inflation in the US and the Federal Reserve are the concerns of investors, which to some extent affects the direction of global assets, including the Chinese stock market. This paper studies the application of AI technology, quantitative investment, and AI technology in quantitative investment, aiming to provide investors with auxiliary decision-making, reduce the difficulty of investment analysis, and help them to obtain higher returns.Information Systems and Economics50This paper studies the application of AI technology, quantitative investment, and AI technology in quantitative investment, aiming to provide investors with auxiliary decision-making, reduce the difficulty of investment analysis, and help them to obtain higher returns.Information Systems and Economics['Shuochen Bi', 'Wenqing Bao', 'Jue Xiao', 'Jiangshan Wang', 'Tingting Deng']2024-04-28T00:00:00https://www.semanticscholar.org/paper/40e2d42f2485798f0765237b72342a6f66697dc3 472f39b821e11f0a6a385a01d979a5e080d2884fUC Merced Undergraduate Research Journal00UC Merced Undergraduate Research Journal['Gabriela Rabago']2024-04-28T00:00:00https://www.semanticscholar.org/paper/472f39b821e11f0a6a385a01d979a5e080d2884f 46069e1ebe609967d65ce36699ddb379ab13ccf6This article aims to explore the potential challenges that may arise when employing generative AI models in the process of Shi’i ijtihad. By drawing upon academic literature and relevant primary sources, the essay surveys the most critical AI-related hurdles in this field, including issues of accessibility, privacy concerns, the problem of “AI hallucination” and the generative nature of AI models, biases in AI systems, the lack of transparency and inexplicability, the intricacies of interpreting and understanding sensitive topics, accountability, authority, trust and acceptance among lay believers. Using discourse and content analysis as method, the article concludes that, given these challenges, generative AI models are not yet suitable for utilization in this process. However, the rapid progress in AI may eventually make it an effective tool for this purpose.Religions110The article concludes that, given these challenges, generative AI models are not yet suitable for utilization in this process of Shi’i ijtihad, but the rapid progress in AI may eventually make it an effective tool for this purpose.Religions['Hasan Latifi']2024-04-28T00:00:00https://www.semanticscholar.org/paper/46069e1ebe609967d65ce36699ddb379ab13ccf6 9ee56f6219c9db667c561608c09ec381b068e28aThe emergence of security vulnerabilities and risks in software development assisted by self-generated tools, particularly with regard to the generation of code that lacks due consideration of security measures, could have significant consequences for industry and its organizations. This manuscript aims to demonstrate how such self-generative vulnerabilities manifest in software programming, through a case study. To this end, this work undertakes a methodology that illustrates a practical example of vulnerability existing in the code generated using an AI model such as ChatGPT, showcasing the creation of a web application database, SQL queries, and PHP server-side. At the same time, the experimentation details a step-by-step SQL injection attack process, highlighting the hacker’s actions to exploit the vulnerability in the website’s database structure, through iterative testing and executing SQL commands to gain access to sensitive data. Recommendations on effective prevention strategies include training programs, error analysis, responsible attitude, integration of tools and audits in software development, and collaboration with third parties. As a result, this manuscript discusses compliance with regulatory frameworks such as GDPR and HIPAA, along with the adoption of standards such as ISO/IEC 27002 or ISA/IEC 62443, for industrial applications. Such measures lead to the conclusion that incorporating secure coding standards and guideline—from organizations such as OWASP and CERT training programs—further strengthens defenses against vulnerabilities introduced by AI-generated code and novice programming errors, ultimately improving overall security and regulatory compliance.Applied Sciences00It is concluded that incorporating secure coding standards and guideline—from organizations such as OWASP and CERT training programs—further strengthens defenses against vulnerabilities introduced by AI-generated code and novice programming errors, ultimately improving overall security and regulatory compliance.Applied Sciences['T. J. M. Mateo Sanguino']2024-04-28T00:00:00https://www.semanticscholar.org/paper/9ee56f6219c9db667c561608c09ec381b068e28a 7d5de08d16a6a6c4fdeb10004f04b31a9129a8f1The article is devoted to the relevant issue of modern science and ethical support for scientific research using Artificial Intelligence (AI). Despite a significant number of foreign and domestic publications about AI, the conceptual framework for the ethics of scientific research using AI remains undeveloped. Based on the international recommendations and articles, as well as own research experience and membership of research ethical committees, the authors define and analyze the basic ethical principles for the scientific research using AI. The proposed principles are considered in the context of their practical application in the field of biomedicine, which are connected with protection of the mankind and nature, maintaining the confidentiality of participants‘ data, preventing discrimination, protecting against errors of AI, respecting informed consent, as well as observing the norms of “open science”, mutual trust from developers and users, etc. The application of the proposed principles orients the scientists, the developers of artificial intelligence, ethical committees, realizing review process, all society, to the priority of the humanization of science, respect for man and nature, as well as education of society regarding to AI, the creation of a regulatory framework, ethical recommendations and codes of ethics for the using of AI in scientific research.Proceedings of the National Academy of Sciences of Belarus Humanitarian Series40The article defines and analyzes the basic ethical principles for the scientific research using AI, and orients the scientists, the developers of artificial intelligence, ethical committees, realizing review process, all society, to the priority of the humanization of science.Proceedings of the National Academy of Sciences of Belarus, Humanitarian Series['V. Sokolchik', 'A. I. Razuvanau']2024-04-28T00:00:00https://www.semanticscholar.org/paper/7d5de08d16a6a6c4fdeb10004f04b31a9129a8f1 d5265b4d5769402874658d1f2ae26ad9e42d4122A growing number of individuals are coming to the realization that the incorporation of Artificial Intelligence (AI) into educational frameworks has a significant impact on the learning environment of this notion. Explores the many uses and possible advantages of AI in the educational setting. The ability of AI to personalize the way information is presented to learners is one of its strongest points. Artificial intelligence (AI) encourages a personalized learning strategy that takes into consideration different learning styles and paces via the use of data analytics and adaptive algorithms. As a result, student engagement and academic achievement are enhanced. As a cognitive enhancement tool, artificial intelligence (AI) can also assist educators in creating and disseminating interesting information for diverse student populations. The use of AI in the classroom has the potential to usher in a new era of student engagement and knowledge exchange through the development of dynamic, collaborative learning spaces. With the support of AI-driven tools, educators have the potential to cultivate classrooms that are more interactive and cooperative. Traditional boundaries of the classroom. It is unclear how AI systems will affect cultural norms, standards, and the student-teacher interaction, despite the fact that AI has many positive uses. Students' happiness and success are greatly affected by online learning. In the learner-instructor relationship, which is defined by engagement, presence, support, and communication. If we want to know what kinds of problems are holding AI systems back from reaching their full potential and putting kids' and instructors' safety at risk, we need to know how people see the effects of these systems on their interactions. Having said that, we must carefully examine the ethical issues that emerge from the widespread use of AI in the classroom. Concerns about privacy, algorithmic biases, and the digital divide need our full attention. While AI is changing the educational landscape in profound ways, this abstract offers a more nuanced view of the subject. Since AI is a catalyst for innovation, researchers, teachers, and policymakers should carefully consider how to incorporate it to ensure future inclusive, equitable, and morally acceptable education. Keywords:- AI , Education,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00It is unclear how AI systems will affect cultural norms, standards, and the student-teacher interaction, despite the fact that AI has many positive uses.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Nitesh Nagar']2024-04-28T00:00:00https://www.semanticscholar.org/paper/d5265b4d5769402874658d1f2ae26ad9e42d4122 dfe74e8171d9f15a402068716972c2330ce15083With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.European Journal of Investigation in Health, Psychology and Education650A scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains found six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics.European Journal of Investigation in Health, Psychology and Education['Sahar Borna', 'Barbara A. Barry', 'Svetlana Makarova', 'Yogesh Parte', 'Clifton R. Haider', 'Ajai Sehgal', 'Bradley C. Leibovich', 'AJ Forte']2024-04-28T00:00:00https://www.semanticscholar.org/paper/dfe74e8171d9f15a402068716972c2330ce15083 5b65ae532370b3c68f67343b5829f5260e7890a7Due to the advancement of natural language generation, a subset of artificial intelligence, digital tools that can generate human-like text have arisen. Such tools can play a promising role in facilitating students’ writing growth. Among them, one recent noteworthy tool is Sudowrite, which is designed for creative story writing. This technology review provides an overview of its affordances and how the tool can be utilized for improving students’ writing. We hope that teachers can apply this tool in practice and help students effectively and critically use it for writing.RELC Journal : A Journal of Language Teaching and Research in Southeast Asia60Sudowrite, a digital tool designed for creative story writing, is reviewed to provide an overview of its affordances and how the tool can be utilized for improving students’ writing.RELC Journal['Xiaoxuan Fang', 'Kaiyang Guo', 'D. Ng']2024-04-28T00:00:00https://www.semanticscholar.org/paper/5b65ae532370b3c68f67343b5829f5260e7890a7 c83cb3c63c8ea4622f7f9c92caeeb86d30f3344fThe Interpreter and Translator Trainer190The Interpreter and Translator Trainer['F. Prieto Ramos']2024-04-28T00:00:00https://www.semanticscholar.org/paper/c83cb3c63c8ea4622f7f9c92caeeb86d30f3344f 7f96090941bdc8900e443fc046683021b7ff2745International Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Chhaya Bardia', 'D. Itam']2024-04-28T00:00:00https://www.semanticscholar.org/paper/7f96090941bdc8900e443fc046683021b7ff2745 7f2f3e69861ab8484e63afc40064809d9cccfbcb This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek’s theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek’s theory on the role of a lawyer and the precision of neural networks.Studia Humana70The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms, and highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law.Studia Humana['Vadim Verenich']2024-04-28T00:00:00https://www.semanticscholar.org/paper/7f2f3e69861ab8484e63afc40064809d9cccfbcb ad952eb6a1e3f8a8ad0e4103fb9458e4d72d8811On November 22, 2022, the State Administration of Market Supervision and Administration drafted the "Anti-Unfair Competition Law of the People's Republic of China (Revised Draft for Comments)" (hereinafter referred to as the Revised Draft) to solicit opinions from the public. This is another revision since the 'Anti-Unfair Competition Law' came into effect in 2017, and it is also a step forward in the context of the traffic era. The provisions of the 'Internet special provisions' are a major step forward in China's legislation to comply with social development. However, in today's traffic era, new types of unfair competition behaviors emerge in an endless stream. The law itself has a lag, and relying solely on 'Internet special provisions' cannot solve all unfair competition behaviors in the Internet field. Unfair competition in the Internet field has the dilemma of legal application. From the perspective of perfecting the special provisions of the Internet, standardizing the application of the general provisions and the special provisions of the Internet, and giving play to the role of the principle of multi-interest balance, we will promote the legal regulation of unfair competition in the era of traffic.Academic Journal of Management and Social Sciences70Academic Journal of Management and Social Sciences['Zhaonan Gong']2024-04-27T00:00:00https://www.semanticscholar.org/paper/ad952eb6a1e3f8a8ad0e4103fb9458e4d72d8811 ea16a41765f14959f7103855acceebc21ec9f727Web 3.0 and artificial intelligence (AI) have presented unprecedent impact on the food sector. However, there is no clear scientific description yet related to their influences on food safety, quality and traceability across the meat supply chain. This study systematically reviews the available data pertaining to Web 3.0 and related novel technologies, their possible use in the meat supply chain and their confluent effect on meat safety. This systematic review followed the PRISMA methodology. The articles selected were identified by searching three databases: Scopus, Web of Science and PubMed. The search results showed that the meat industry and the meat supply chain have their share of positive implications instigated by Web 3.0 technologies. Web 3.0 technologies are shown to be effective for the food safety of meat from farm to fork, particularly in inspection and quality assessment with blockchain integration enhancing transparency and traceability with innovative approaches promising to improve meat safety, increase profitability, efficiency, scalability and modularisation of meat manufacturing in addition to better adherence to animal welfare increasing thus consumer's confidence. Further research is still, needed to examine the role of such technologies at the level of other parts of the supply chain including the pre‐processing step, processing and packaging.International Journal of Food Science &amp; Technology681International Journal of Food Science &amp; Technology['Aline Issa', 'Alexandria Nivella Mekanna', 'Jacqueline Doumit', 'Christelle Bou-Mitri']2024-04-27T00:00:00https://www.semanticscholar.org/paper/ea16a41765f14959f7103855acceebc21ec9f727 b36459704a3f7be7ba3046bab3de6918390af0f9Accurate representation of medical information is crucial for patient safety, yet artificial intelligence (AI) systems, such as Large Language Models (LLMs), encounter challenges in error-free clinical text interpretation. This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben Abacha et al., 2024a), focusing on the automatic correction of single-word errors in clinical notes. Unlike LLMs that rely on extensive generic data, our method emphasizes extracting contextually relevant information from available clinical text data. Leveraging an ensemble of extractive and abstractive question-answering approaches, we construct a supervised learning framework with domain-specific feature engineering. Our methodology incorporates domain expertise to enhance error correction accuracy. By integrating domain expertise and prioritizing meaningful information extraction, our approach underscores the significance of a human-centric strategy in adapting AI for healthcare.00This paper presents a novel approach submitted to the MEDIQA-CORR 2024 shared task, focusing on the automatic correction of single-word errors in clinical notes, and incorporates domain expertise to enhance error correction accuracy.['Nadia Saeed']2024-04-27T00:00:00https://www.semanticscholar.org/paper/b36459704a3f7be7ba3046bab3de6918390af0f9 2087cf47a6552e9d21287f2308913966cd846f1cGiven that AI is becoming an increasingly active participant in work teams, this study explores how team trust emerges in human–AI teams compared to human–human teams. Adopting a multi‐level approach, we conducted two experimental studies (NStudy1 = 247 two‐member teams and NStudy2 = 106 three‐member teams, 828 individuals overall) and investigated how team composition (with AI or human team members) impacts interpersonal trust (affective and cognitive) and thus team trust. In two‐member teams, interpersonal trust via perceived trustworthiness and not via perceived similarity was lower in human–AI teams compared to human–human teams. Exploratory findings showed that team identification and cognitive interpersonal trust were also lower in two‐member human–AI teams than in human–human teams. However, in three‐member teams, we found no differences in team trust via interpersonal trust between the two team types. Instead, our findings revealed that perceived trustworthiness and perceived similarity increased interpersonal trust and, in turn, team trust for both team types. With this research, we showed that underlying theories and evidence of team trust in human‐only teams can enhance understanding of human–AI teams, though the results indicated certain differences that call for further investigation.Journal of Occupational and Organizational Psychology860It is shown that underlying theories and evidence of team trust in human‐only teams can enhance understanding of human–AI teams, though the results indicated certain differences that call for further investigation.Journal of Occupational and Organizational Psychology['Eleni Georganta', 'Anna-Sophie Ulfert']2024-04-27T00:00:00https://www.semanticscholar.org/paper/2087cf47a6552e9d21287f2308913966cd846f1c c8f79d9a94090462e7bd9d59480217f761a97cafThis paper explores the transformative impact of Artificial Intelligence (AI) on modern marketing practices. It examines how AI applications are revolutionizing various aspects of marketing, including customer experience, campaign personalization, content creation, and market research. The paper analyzes real- world examples and data to showcase the effectiveness of AI implementation. It also discusses potential challenges and ethical considerations surrounding AI in marketing. Finally, the paper explores the future potential of AI and its role in shaping the marketing landscape of tomorrow.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00It is examined how AI applications are revolutionizing various aspects of marketing, including customer experience, campaign personalization, content creation, and market research, to showcase the effectiveness of AI implementation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Abhinav SINGH,']2024-04-27T00:00:00https://www.semanticscholar.org/paper/c8f79d9a94090462e7bd9d59480217f761a97caf ec7d83dcda0516cff772bf01c1f8a65039b9630bAims: The objective of Veritas AI is to revolutionize the domain of lie detection through the deployment of a cutting-edge algorithm within the realms of computational linguistics and artificial intelligence. Study Design: Veritas AI is conceptualized as a groundbreaking framework that integrates advanced syntactic and semantic analysis, leveraging generative pre-trained transformers to identify linguistic cues indicative of deception. Place and Duration of Study: The research underpinning Veritas AI’s algorithm was meticulously executed at the Abacus CSE Lab over a period from December 2022 to March 2024, ensuring a robust empirical foundation for the system’s validation and optimization. Methodology: Employing a deep learning neural network at its core, Veritas AI is trained on a diverse dataset comprising both truthful and deceptive dialogues. This training is complemented by multimodal biometric interrogation techniques and sophisticated natural language processing algorithms. Results: The empirical results underscore Veritas AI’s unparalleled accuracy in discerning truth, marked by its ability to provide real-time adaptive feedback and maintain robust performance across various communication scenarios. Conclusion: In conclusion, Veritas AI stands as a testament to the symbiotic potential of human ingenuity and machine learning. Its precision-engineered algorithm, underpinned by empirical validation, heralds a transformative leap in the field of automated veracity assessment, setting a new benchmark for truth analysis in the digital age.Asian Journal of Research in Computer Science00The empirical results underscore Veritas AI’s unparalleled accuracy in discerning truth, marked by its ability to provide real-time adaptive feedback and maintain robust performance across various communication scenarios.Asian Journal of Research in Computer Science['Anshit Mukherjee']2024-04-27T00:00:00https://www.semanticscholar.org/paper/ec7d83dcda0516cff772bf01c1f8a65039b9630b 8621caacb15166297f90e36af26ec61556c26edb The objectives of this research/study were (1) to investigate the ethical challenges AI has brought to the news production of Chengdu Radio and television station, (2) to explore how the station balances these challenges and innovation needs, and (3) to understand the regulatory needs and challenges of AI in the media. This quantitative research employed surveys distributed to journalists and other broadcast staff as the research tools. The method of collecting data involved the distribution of surveys to gather views and attitudes towards AI in news production. Survey data were analyzed using a statistics package of social science to identify any patterns or trends. Major Findings/Results: (1) The ethical challenges AI has brought to the news production at Chengdu Radio and television station include issues related to accuracy, bias, and control over content, (2) The station balances these challenges and innovation needs by implementing ethical guidelines, training, and human oversight in AI-driven processes, and (3) Understanding the regulatory needs and challenges of AI in the media is crucial for developing appropriate policies and guidelines for AI integration in newsrooms.Academic Journal of Science and Technology380The ethical challenges AI has brought to the news production at Chengdu Radio and television station include issues related to accuracy, bias, and control over content, and the station balances these challenges and innovation needs by implementing ethical guidelines, training, and human oversight in AI-driven processes.Academic Journal of Science and Technology['Jingyang Zhao', 'Nutteera Phakdeephirot']2024-04-27T00:00:00https://www.semanticscholar.org/paper/8621caacb15166297f90e36af26ec61556c26edb d0e5debbdab6b68aa314942e678f967c1d2e6be5The integration of artificial intelligence (AI), specifically large language models (LLMs), into healthcare continues to accelerate, necessitating thoughtful evaluation and oversight to ensure safe, ethical, and effective deployment. This editorial summarizes key perspectives from a recent panel conversation among AI experts regarding central issues around implementing LLMs for clinical applications. Key topics covered include: the potential of explainable AI to facilitate transparency and trust; challenges in aligning AI with variable global healthcare protocols; the importance of evaluation via translational and governance frameworks tailored to healthcare contexts; scepticism around overly expansive uses of LLMs for conversational interfaces; and the need to judiciously validate LLMs, considering risk levels. The discussion highlights explainability, evaluation and careful deliberation with healthcare professionals as pivotal to realizing benefits while proactively addressing risks of larger AI adoption in medicine.Telehealth and Medicine Today00This editorial summarizes key perspectives from a recent panel conversation among AI experts regarding central issues around implementing LLMs for clinical applications, highlighting explainability, evaluation and careful deliberation with healthcare professionals as pivotal to realizing benefits while proactively addressing risks of larger AI adoption in medicine.Telehealth and Medicine Today['Sandeep Reddy', 'Alexandre Lebrun', 'Adam Chee', 'Dimitros Kalogeropoulos']2024-04-27T00:00:00https://www.semanticscholar.org/paper/d0e5debbdab6b68aa314942e678f967c1d2e6be5 95bc72a8e9e2af4b71accd06f7d22567a2b7376aPesatnya perkembangan teknologi Artificial Intelligence (AI) dalam dekade terakhir ini berhasil menjadi pusat perhatian para akademisi dan ilmuan. Tidak hanya akademisi dan ilmuan, tetapi AI juga berhasil merambat ke dalam dunia kesehatan dan kedoketaran. AI in precision medicine menjadi topik yang sangat viral diperbincangkan dalam berbagai forum ilmiah di belahan dunia. Akurasi dan ketapatan AI dalam membantu dokter melakukan diagnosis menjadi suatu topik penelitain yang sedang hangat diperbincangkan karena menyangkut etika dan regulasi dari AI itu sendiri. Maraknya penelitian yang mengkaji tentang regulasi dan etika dari AI dalam kedokteran presisi (precision-medicine) menjadi landasan penelitian ini utnuk meninjau ulang dan melakukan studi literatur yang bersumber pada database jurnal internasional bereputasi yaitu Scopus-database. Dalam studi literatur penelitian ini, kami menemukan beberapa aspek yang perlu diregulasikan dan ditinjau ulang kembali dari segi etika dari AI in precision medicine. Aspek yang ditemukan setelah melakukan review secara comprehensive seperti aspek transparansi dan penjelasan, privasi dan perlindungan data, aspek bias dan fairness, keselamatan dan keamanan, akuntabilitas dan tanggung jawab, serta kolaborasi dan standar global. Beberapa urgensi pentingnya etika AI dalam precision medicine juga dibahas dalam paper penelitian in, seperti kesetaraan dalam akses dan keterjangkauan, keselamatan pasien dan kualitas pelayanan, pengawasan peraturan dan kerangka hukum, efek jangka panjang dan konsekuensi, pendidikan dan kesadaran masyarakat. Selain daripada itu, dalam paper penelitian ini penulis juga memberikan pemaparan terkait trend-research dari AI in precision medicine yang diulas secara detail dan komprehensif.JURNAL FASILKOM170JURNAL FASILKOM['Faisal Asadi']2024-04-27T00:00:00https://www.semanticscholar.org/paper/95bc72a8e9e2af4b71accd06f7d22567a2b7376a c77b8b738aaae5fb32b97598d5c6f1b7850bbb17People with visual impairment may face struggles in their daily activities, as these may affect them socially, physically, and psychologically. This study aims to address this problem by utilizing quantitative experimental research to come up with Object Recognition Eyeglasses out of ImageAI. This device aims to assist the visually impaired person by recognizing the object in front of them and giving an audio output of the name of the object. Throughout the testing process, the Object Recognition Eyeglasses showed accuracy in recognizing different objects and their different varieties. It also showed its capability to recognize objects from far distances, with a maximum distance of 45 meters, and its efficiency in delivering a timely recognition and audio output with an average time interval of 1.61 and 0.63 seconds respectively. Based on these results, the Object Recognition Eyeglasses stands as an accurate, efficient, and capable assistive device that can help visually impaired people in their day-to-day lives. However, this device still needs improvement in terms of convenience by using a phone instead and modifying it to not require any internet connection.International Journal of Innovative Science and Research Technology131The Object Recognition Eyeglasses stands as an accurate, efficient, and capable assistive device that can help visually impaired people in their day-to-day lives, however, this device still needs improvement in terms of convenience by using a phone instead and modifying it to not require any internet connection.International Journal of Innovative Science and Research Technology (IJISRT)['Julie Ann B. Real', 'Kal-el Gabriel C. Ceradoy', 'RJ Leandrei J. Fortuna', 'Jeileen Roze N. Gallarte', 'Kyla Nezel S. Soriano', 'Akirah Faith A. Emperio', 'Nicole Margareth I. Carlos', 'Dyanna Chrisleen V. Camia']2024-04-27T00:00:00https://www.semanticscholar.org/paper/c77b8b738aaae5fb32b97598d5c6f1b7850bbb17 b8c3ac54f2a7efd3670beb06a9e21d48e6207130Dynamic inventory management revolves around the practice of progressively modifying inventory degrees to adapt to fluctuations in client demand, production, and supply chain dynamics. At the center, inventory management focuses on upholding enhanced levels of stock to balance consumer service via availability with the costs related to holding excess inventory. This research paper aimed to explore the dynamic inventory management activities employed by organizations in the USA, shedding light on the machine learning strategies that can be deployed and their implications. The performance of the algorithms was empirically evaluated in a Python program experiment utilizing real-world data. To facilitate the data for input into the Neural Network, feature engineering, and selection were imposed to affirm its suitability. This study proposes the Sequence-to-Sequence (Seq2Quant) algorithm, a neural network-powered technique for demand prediction in inventory management.  The current experiment compared and contrasted the performance of the Neural Networks against the following baselines, most notably, Naïve Seasonal Forecast, Moving Average Forecast, ARIMA, Naïve Seasonal Forecast with Averaging over four periods, SARIMAX. From the experiment, it was evident that the Seq2Seq had the lowest MAE (17.44) and the lowest SMAPE (66.91), suggesting that it was the best-performing algorithm overall. Besides, SARIMAX and ARIMAX also performed well, with MAE values of 18.33 and 18.09, respectively.Journal of Economics, Finance and Accounting Studies130This study proposes the Sequence-to-Sequence (Seq2Quant) algorithm, a neural network-powered technique for demand prediction in inventory management, a neural network-powered technique for demand prediction in inventory management.Journal of Economics, Finance and Accounting Studies['Md zahidul Islam', 'N. Gurung', 'Md Sumon Gazi', 'Md Rokibul Hasan']2024-04-27T00:00:00https://www.semanticscholar.org/paper/b8c3ac54f2a7efd3670beb06a9e21d48e6207130 078dfe6421109faa5cf7154c80f10241f22c7624This project introduces an integrated system for smart agriculture, employing Internet of Things (IoT) technology for soil type analysis and deep learning methodologies for pest detection. The proposed system leverages a specialized NPK sensor for real-time measurement of soil nutrient levels, facilitating precision agriculture practices. Additionally, a Convolutional Neural Network (CNN) algorithm is employed to detect pests in crops, enhancing crop management efficiency and yield optimization. The IoT-based NPK sensor enables farmers to monitor essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) levels remotely and in real-time. This data empowers farmers to make informed decisions regarding fertilization strategies, ensuring optimal nutrient balance for healthy plant growth while minimizing resource wastage and environmental impact. To the deep learning framework, specifically CNN, is utilized for pest detection in crops. By analyzing images captured from smart agricultural cameras, the CNN model can identify and classify various pests and diseases affecting crops. This enables early detection and intervention, thereby mitigating potential crop damage and yield losses. The integration of CNN-based pest detection with IoT infrastructure enables timely and targeted pest management actions, reducing reliance on chemical pesticides and promoting sustainable agricultural practices.International Journal For Multidisciplinary Research110An integrated system for smart agriculture, employing Internet of Things (IoT) technology for soil type analysis and deep learning methodologies for pest detection, leverages a specialized NPK sensor for real-time measurement of soil nutrient levels, facilitating precision agriculture practices.International Journal For Multidisciplinary Research['M.Gomathi', 'G.A.Monika', 'M.Nachammai', 'A.Saranya', 'R.Shyni Deva Priya']2024-04-27T00:00:00https://www.semanticscholar.org/paper/078dfe6421109faa5cf7154c80f10241f22c7624 c2ae1f80fcf95d787ca1a16ed2e96a9e75cc4cdcsafeguarding international borders against illegal entries, a task fraught with risk. This paper suggests replacingInternational Research Journal on Advanced Science Hub170International Research Journal on Advanced Science Hub['Subhashini E', 'Swathi M', 'Vijayalakshmi V', 'Ms. Pavaiyarkarasi R']2024-04-27T00:00:00https://www.semanticscholar.org/paper/c2ae1f80fcf95d787ca1a16ed2e96a9e75cc4cdc 233ec684a7016dbb9551dbf5b34d35eab643bcdbVěda a perspektivy00Věda a perspektivy['Daniella Mushka']2024-04-27T00:00:00https://www.semanticscholar.org/paper/233ec684a7016dbb9551dbf5b34d35eab643bcdb e4cbd7e0c4f499fab8ec155eb09c8de42c290f17AI &amp; SOCIETY270AI &amp; SOCIETY['B. Gulumbe', 'Shuaibu Muhammad Audu', 'Abu Hashim']2024-04-27T00:00:00https://www.semanticscholar.org/paper/e4cbd7e0c4f499fab8ec155eb09c8de42c290f17 019f980aa2a18bb47d240ab00c298428139ca251Věda a perspektivy00Věda a perspektivy['Illia Yermolenko']2024-04-27T00:00:00https://www.semanticscholar.org/paper/019f980aa2a18bb47d240ab00c298428139ca251 2b0883d5639f1896c7814fabbab1ee7910c61c9fThis article explores the intricate relationship between the development of Artificial Intelligence (AI) and the ethical considerations it necessitates. AI, as a transformative technology, holds immense potential for societal benefit in fields ranging from healthcare and environmental conservation to finance and education. However, this potential comes intertwined with significant ethical challenges, including algorithmic bias, privacy concerns, and impacts on employment and societal structures. The article delves into case studies that illustrate these dual facets of AI - its benefits and the ethical dilemmas encountered. Furthermore, it discusses the responsibilities of developers, corporations, and governments in ensuring ethical AI deployment, emphasizing the need for ongoing multi-disciplinary dialogue and international cooperation. Looking to the future, the article speculates on the evolution of AI ethics, advocating for a proactive approach to ensure that AI developments are aligned with societal values and benefit humanity as a whole.International Journal of Material and Mathematical Sciences70The article delves into case studies that illustrate these dual facets of AI - its benefits and the ethical dilemmas encountered, advocating for a proactive approach to ensure that AI developments are aligned with societal values and benefit humanity as a whole.International Journal of Material and Mathematical Sciences[]2024-04-27T00:00:00https://www.semanticscholar.org/paper/2b0883d5639f1896c7814fabbab1ee7910c61c9f 6adfcf2a5b8ec4f977eb6deeb80bdd5da62a7d55Aim: The first half of the two-part study is on the emerging role of artificial intelligence in the forensic sciences. After clarifying the basic concepts and a brief historical overview, the possibilities of using AI in various forensic fields are discussed: genetics, pattern recognition, chemistry, toxicology, anthropology, forensic medicine, and scene reconstruction. Methodology: The study synthesises several recently published international papers. Findings: The penetration of the application of artificial intelligence into some fields of science is undoubtedly an ongoing process. Most of the varied forensic fields also cannot avoid this development. Analysing large databases unmanageable with traditional methods, pattern recognition, and machine learning can all be important tools for forensic science. However, an important conclusion is that AI is a supporter of human expert work, not a substitute. Value: In the field of forensic sciences, no such detailed summary article has been published in Hungarian so far.Belügyi Szemle240An important conclusion is that AI is a supporter of human expert work, not a substitute for forensic science, which is undoubtedly an ongoing process.Belügyi Szemle['Márton Lontai', 'Horolma Pamzsav', 'Dávid Petrétei']2024-04-27T00:00:00https://www.semanticscholar.org/paper/6adfcf2a5b8ec4f977eb6deeb80bdd5da62a7d55 8d4e64530fabc70f394a9af6f6ca63e653aa6f94The implementation of artificial intelligence (AI) involves the utilization of various methodologies, including machine learning, deep learning, natural language processing, and robotics. These methodologies enable computers to perform tasks that traditionally require human intelligence, such as recognizing patterns in data, understanding natural language, and making decisions. AI's future potential is vast and spans across numerous industries and applications. In healthcare, AI can assist in disease diagnosis, personalized treatment plans, and drug discovery. In finance, AI algorithms can analyze market trends, detect fraud, and optimize investment portfolios. In transportation, AI powers autonomous vehicles, optimizing routes and reducing accidents. In entertainment, AI-driven recommendation systems personalize content for users. However, realizing this potential requires addressing several challenges. Ethical considerations are paramount, including ensuring fairness, transparency, accountability, and privacy in AI systems. Bias in algorithms must be mitigated to prevent discrimination, and AI decisions must be interpretable and explainable to users. Research efforts are underway to advance AI capabilities while addressing these challenges. This includes developing more robust and efficient algorithms, improving AI safety and security, and exploring applications of AI for social good, such as addressing climate change and promoting inclusivity. Collaboration between academia, industry, and government is crucial for driving AI research forward and ensuring its responsible integration into society. This collaboration facilitates knowledge sharing, resource allocation, and the development of standards and regulations to guide the ethical and equitable deployment of AI technologies. Ultimately, by harnessing the power of AI responsibly, society can benefit from its transformative potential while mitigating potential risks.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT430By harnessing the power of AI responsibly, society can benefit from its transformative potential while mitigating potential risks, and society can benefit from its transformative potential while mitigating potential risks.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Neetu Dubey']2024-04-27T00:00:00https://www.semanticscholar.org/paper/8d4e64530fabc70f394a9af6f6ca63e653aa6f94 1b1885e60e9e59eb6347b0bc57ffb480540bd1caThis paper explores the application of Swarm-Structured Multi-Agent Systems (MAS) to establish medical necessity, a process that involves a systematic review of patient-specific medical structured and unstructured data against clinical guidelines. We addressed this complex task by decomposing it into smaller, more manageable sub-tasks. Each sub-task is handled by a specialized AI agent. We conduct a systematic study of the impact of various prompting strategies on these agents and benchmark different Large Language Models (LLMs) to determine their accuracy in completing these tasks. Additionally, we investigate how these agents can provide explainability, thereby enhancing trust and transparency within the system.60['Himanshu Pandey', 'Akhil Amod', 'Shivang']2024-04-27T00:00:00https://www.semanticscholar.org/paper/1b1885e60e9e59eb6347b0bc57ffb480540bd1ca 70caf6a95b030c37e2299a285e168dda57f87a96A primary challenge associated with the implementation of educational support systems is the establishment of student trust in the systems themselves. Trust is a critical factor in the acceptance and use of AI-enabled systems, as it reduces uncertainty and the perception of risk associated with new technology adoption. A literature review of existing studies on trust in AI-based systems is needed to provide a solid foundation for future studies. This research aims to identify gaps in the literature regarding the establishment of user trust in AI-based educational systems by exploring the criteria of trust and the challenges of building trust in AI systems. A narrative review of the literature is conducted to synthesize the findings of selected articles, covering (1) fundamental principles of trust and the process of establishing trust in non-human entities; (2) technical issues relating to explainable AI; (3) the utilization of explainable AI to facilitate decision-making; and (4) the use of AI systems in facilitating educational activities and its influence. This article summarizes trust criteria, including reliance, transparency, affectiveness, integrity, consistency, fairness, accountability, security, and usability. Building trust in AI systems involves addressing technical, ethical, and societal challenges to ensure the responsible and beneficial use of AI for individuals and society.Jurnal Sosioteknologi370This research aims to identify gaps in the literature regarding the establishment of user trust in AI-based educational systems by exploring the criteria of trust and the challenges of building trust in AI systems.Jurnal Sosioteknologi['Anisa Herdiani', 'Dimitri Mahayana', 'Y. Rosmansyah']2024-04-27T00:00:00https://www.semanticscholar.org/paper/70caf6a95b030c37e2299a285e168dda57f87a96 6ca3021b3eaebe1542b3a036540190a6491a8e94High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system’s core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchOr boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorRT-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals.Italian National Conference on Sensors260An end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time and develops a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time is proposed.Sensors (Basel, Switzerland)['Umair Iqbal', 'Johan Barthélemy', 'Guillaume Michal']2024-04-27T00:00:00https://www.semanticscholar.org/paper/6ca3021b3eaebe1542b3a036540190a6491a8e94 d0fdcdf60f4990010fc3007f9ae915b20916484dArtificial Intelligence (AI) and management have many benefits and relevance that are very important to be applied in various entities both large, medium, to small including Eduwisata Kelompok Tani Elok Mekar Sari Surabaya. By implementing AI and management within entities, these organizations can harness the potential of technology to improve their performance, innovation, and competitiveness in an increasingly competitive market. The purpose of this study is to examine the impact of several factors, such as AI and management on the effectiveness of accounting systems in Lestari educational tours located in Surabaya. Researchers use a mix method, namely primary data and secondary data for data collection methods. The results of the analysis show that the effectiveness of accounting information systems is positively influenced by artificial intelligence and management. This is evidenced by the results of the F test, where the calculated F value (139.983) is greater than the table F value (3.940), with a significance level of 0.000 which is less than 0.05. In addition, a qualitative approach is also needed to understand the phenomena that occur in the field. Therefore, it can be concluded that artificial intelligence and management variables have a positive effect on the effectiveness of accounting information systems. The conclusions of this study are consistent with previous findings that have been done by other researchers in the past. The difference is that this study uses qualitative and quantitative approaches.DiE: Jurnal Ilmu Ekonomi dan Manajemen00It can be concluded that artificial intelligence and management variables have a positive effect on the effectiveness of accounting information systems.DiE: Jurnal Ilmu Ekonomi dan Manajemen['Irda Agustin Kustiwi']2024-04-27T00:00:00https://www.semanticscholar.org/paper/d0fdcdf60f4990010fc3007f9ae915b20916484d 966b1322651bcba1c5b38b6ff6de463d2486475eThe demand for sustainable farming techniques is growing as the world's population rises. In light of this requirement, enlisting young people in agriculture becomes an essential tactic for guaranteeing food security, economic growth, and rural rejuvenation. Using artificial intelligence (AI) offers a viable way to empower and draw young people to the agriculture industry. This study explores the possibilities for Nigeria while providing a summary of international approaches to using AI to encourage youth involvement in agriculture. By utilizing AI technologies like machine learning, remote sensing, and data analytics, youth can access valuable insights, optimize resource utilization, and mitigate risks associated with agricultural production. In conclusion, this paper emphasizes how AI might inspire young people to pursue careers in agriculture and emphasizes the necessity of developing methods specifically designed to maximize AI's potential in Nigeria. Nigeria can set the stage for a sustainable and inclusive agricultural future by encouraging stakeholder engagement, investing in digital infrastructure, and advancing creative policies. This would enable the country's young to spearhead agricultural transformation and economic success.International Journal of Advance Social Sciences and Education (IJASSE)420Nigeria can set the stage for a sustainable and inclusive agricultural future by encouraging stakeholder engagement, investing in digital infrastructure, and advancing creative policies, which would enable the country's young to spearhead agricultural transformation and economic success.International Journal of Advance Social Sciences and Education (IJASSE)['Oluwatoyin Olatunde Olagunju']2024-04-27T00:00:00https://www.semanticscholar.org/paper/966b1322651bcba1c5b38b6ff6de463d2486475e 62dff5512851997d44affe91a99f06bfd0ff5a93Morphological changes in canals are greatly influenced by sediment load dynamics, whose estimation is a challenging task because of the non-linear behavior of the sediment concentration variables. This study aims to compare different techniques including Artificial Intelligence Models (AIM) and empirical equations for estimating sediment load in Upper Chenab Canal based on 10 years of sediment data from 2012 to 2022. The methodology involves utilization of a newly developed empirical equation, the Ackers and White formula and AIM including 20 neural networks with 10 training functions for both Double and Triple Layers, two Artificial Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization, and Ensemble Learning Random Forest models. Sensitivity analysis of sediment concentration variables has also been performed using various scenarios of input combinations in AIM. A state-of-the-art optimization technique has been used to identify the parameters of the empirical equation, and its performance is tested against AIM and the Ackers and White equation. To compare the performance of various models, four types of errors—correlation coefficient (R), T-Test, Analysis of Variance (ANOVA), and Taylor’s Diagram—have been used. The results of the study show successful application of Artificial Intelligence (AI) and empirical equations to capture the non-linear behavior of sediment concentration variables and indicate that, among all models, the ANFIS outperformed in simulating the total sediment load with a high R-value of 0.958. The performance of various models in simulating sediment concentration was assessed, with notable accuracy achieved by models AIM11 and AIM21. Moreover, the newly developed equation performed better (R = 0.92) compared to the Ackers and White formula (R = 0.88). In conclusion, the study provides valuable insights into sediment concentration dynamics in canals, highlighting the effectiveness of AI models and optimization techniques. It is suggested to incorporate other AI techniques and use multiple canals data in modeling for the future.Hydrology580Hydrology['Muhammad Ashraf Khalid', 'A. Ghumman', 'Ghufran Ahmed Pasha']2024-04-27T00:00:00https://www.semanticscholar.org/paper/62dff5512851997d44affe91a99f06bfd0ff5a93 c3c4c8f878566814484a206c1c484b8c21ff579eThe importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection.Electronics480A bibliometric analysis of the involvement of artificial intelligence in the banking sector is conducted to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field.Electronics['A. Manta', 'R. Bădîrcea', 'Nicoleta Mihaela Doran', 'Gabriela Badareu', 'Claudia Gherțescu', 'Jenica Popescu']2024-04-27T00:00:00https://www.semanticscholar.org/paper/c3c4c8f878566814484a206c1c484b8c21ff579e ce26e06d0eb126adf590dfb4bd284adb7c2f167aThis analysis delves into the evolving landscape of artificial intelligence (AI) adoption within the financial services industry, juxtaposed against broader market trends. Drawing insights from industry experts and research findings, it examines key challenges and opportunities faced by financial institutions in leveraging AI technologies to drive innovation and competitive advantage. The study highlights the critical importance of data management strategies, cultural transformation, and talent development in facilitating successful AI implementation. It underscores the significance of striking a balance between centralization and federation in data management approaches, alongside the imperative of strengthening ethics and bias management practices. Furthermore, the analysis delves into the pivotal role of multidisciplinary AI teams, emphasizing the necessity of integrating diverse skill sets, including data scientists, business experts, and senior executives, to maximize the efficacy of AI initiatives. It also sheds light on regulatory developments, such as the Canadian government's Algorithmic Impact Assessment (AIA), aimed at fostering transparency and accountability in automated decision-making systems. Overall, this study provides valuable insights into the challenges and opportunities inherent in AI adoption within the financial services sector, offering recommendations to guide firms towards sustainable AI-driven growth and innovation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The study highlights the critical importance of data management strategies, cultural transformation, and talent development in facilitating successful AI implementation, and underscores the significance of striking a balance between centralization and federation in data management approaches, alongside the imperative of strengthening ethics and bias management practices.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Aman Kumar']2024-04-27T00:00:00https://www.semanticscholar.org/paper/ce26e06d0eb126adf590dfb4bd284adb7c2f167a b985d135204cd432273546ffa4fede2d14529fd3 In the era of intelligent media, artificial intelligence as a new social object, its interactive characteristics are worthy of in-depth study. In this paper, based on the theory of media equivalence and dramaturgical theory, the current research focus on human self-disclosure will be changed to explore whether there is self-disclosure and its characteristics in human-computer interaction of artificial intelligence represented by chatbots. It is found that although AI lacks emotional and consciousness-based self-disclosure, they exhibit behaviors that can be regarded as self-disclosure in the sense of performance (user perception level). At the same time, at the technical level, algorithmic disclosure and transparency can be considered self-disclosure in the true sense.Academic Journal of Science and Technology140It is found that although AI lacks emotional and consciousness-based self-disclosure, they exhibit behaviors that can be regarded as self-disclosure in the sense of performance (user perception level) and algorithmic disclosure and transparency can be considered self-disclosure in the true sense.Academic Journal of Science and Technology['Rui Wu']2024-04-27T00:00:00https://www.semanticscholar.org/paper/b985d135204cd432273546ffa4fede2d14529fd3 37a8f88eecdd1560e56c9de9ff9359608b0cd567Strategic Journal of Business &amp; Change Management00Strategic Journal of Business &amp; Change Management['Maxwell Okeyo', 'Justice MUTUA, PhD']2024-04-27T00:00:00https://www.semanticscholar.org/paper/37a8f88eecdd1560e56c9de9ff9359608b0cd567 71d7187f865d961bc1c3454fd733b1ef600a0a7aCureus360Cureus['Suhit Naseri', 'Samarth Shukla', 'K.M. Hiwale', 'M. Jagtap', 'Pravin Gadkari', 'Kartik Gupta', 'Mamta Deshmukh', 'Shakti Sagar']2024-04-27T00:00:00https://www.semanticscholar.org/paper/71d7187f865d961bc1c3454fd733b1ef600a0a7a 88c6c953def525b0a4900989689eb77443384b01Journal of Environmental Management960This research delves into the transformative roles of FinTech and AI in broadening financial access, fostering green financing initiatives, and aligning financial practices with environmentally conscious objectives, and reveals significant volatility connectivity within these intergroups.Journal of environmental management['K. Si Mohammed', 'Vanessa Serret', 'Sami Ben Jabeur', 'Haitham Nobanee']2024-04-27T00:00:00https://www.semanticscholar.org/paper/88c6c953def525b0a4900989689eb77443384b01 e3bf7828195316276fe3457ecb9ca3c01a12281fThe Lancet Digital Health510The Lancet. Digital health['Ryan Han', 'J. N. Acosta', 'Zahra Shakeri', 'J. P. Ioannidis', 'E. Topol', 'P. Rajpurkar']2024-04-27T00:00:00https://www.semanticscholar.org/paper/e3bf7828195316276fe3457ecb9ca3c01a12281f 3fd081bad173b3684fa58fea5852bf07b5c4de10International Conference on Architectural Support for Programming Languages and Operating Systems00{'pages': '1'}['Amin Vahdat']2024-04-27T00:00:00https://www.semanticscholar.org/paper/3fd081bad173b3684fa58fea5852bf07b5c4de10 c1e72a8c3dd420684475363ee6643099b7054d73This research explores the application of Large Language Models (LLMs) for automating the extraction of requirement-related legal content in the food safety domain and checking legal compliance of regulatory artifacts. With Industry 4.0 revolutionizing the food industry and with the General Data Protection Regulation (GDPR) reshaping privacy policies and data processing agreements, there is a growing gap between regulatory analysis and recent technological advancements. This study aims to bridge this gap by leveraging LLMs, namely BERT and GPT models, to accurately classify legal provisions and automate compliance checks. Our findings demonstrate promising results, indicating LLMs' significant potential to enhance legal compliance and regulatory analysis efficiency, notably by reducing manual workload and improving accuracy within reasonable time and financial constraints.410This research explores the application of Large Language Models for automating the extraction of requirement-related legal content in the food safety domain and checking legal compliance of regulatory artifacts, indicating LLMs' significant potential to enhance legal compliance and regulatory analysis efficiency.['Shabnam Hassani']2024-04-26T00:00:00https://www.semanticscholar.org/paper/c1e72a8c3dd420684475363ee6643099b7054d73 70a1f5857ccbdc7bcbc2f88cea8dee90fc7cdf06International Journal for the Semiotics of Law220International Journal for the Semiotics of Law - Revue internationale de Sémiotique juridique['Daniel Green']2024-04-26T00:00:00https://www.semanticscholar.org/paper/70a1f5857ccbdc7bcbc2f88cea8dee90fc7cdf06 9b2b8b01a914ef20275389af0cb8fd34b711c785This study aims to find out the development of business competition law in Indonesia as stipulated in Law Number 5 of 1999 on the Prohibition of Monopolistic Practices and Unfair Business Competition and its impact on the dynamics of business competition in Indonesia after the enactment of Law Number 6 of 2023 on Stipulation of Government Regulation in Lieu of Law (Government Regulation in Lieu of Law) Number 2 of 2022 concerning Job Creation into Law. This study uses a normative method with a statute approach and a library research approach. The formulation of the problem in this study is the development of business competition law in Indonesia after the enactment of the Government Regulation in Lieu of Law on Job Creation into Law and the impact of the enactment of Law Number 6 of 2023 on the dynamics of business competition in Indonesia. The results showed that the Business Competition Law in Indonesia experienced the first change in terms of substance after the enactment of Law Number 6 of 2023 on the Stipulation of Government Regulation in Lieu of Law Number 2 of 2022 concerning Job Creation into Law. These changes then led to a shift in the dynamics of the Competition Procedure Law, which was considered regressed. Keywords : Business Competition Law, Business Actors, and Unfair CompetitionJUSTISI10JUSTISI['Gusliadi Gusliadi', 'Nur Aziziyah Purnama', 'Rizaldi Tri Pamungkas', 'Muhammad Azimuddin']2024-04-26T00:00:00https://www.semanticscholar.org/paper/9b2b8b01a914ef20275389af0cb8fd34b711c785 9475def30d38f83a07b276b8c6583794f3c37353Purpose: This research aims to examine the regulations governing the establishment of Legal Aid Institutions (LAIs/LAOs) in Indonesia, with a focus on achieving necessary legal clarity to ensure their effective functioning.   Method: This research employs a prescriptive research method, involving the description of primary and secondary data findings related to the regulations governing LAIs/LAOs establishment in Indonesia. The data are processed and analyzed to derive insights into the current legal framework.   Result and Discussion: The findings indicate that the existing regulations concerning the establishment of LAIs/LAOs in Indonesia lack clarity, particularly regarding the legal entity status utilized. While foundation status predominates, its alignment with Indonesian legal principles requires further consistency. Therefore, this research underscores the need for more transparent and comprehensive reformulation of these regulations.   Implication of the Research: Reforming the regulations governing the establishment of LAIs/LAOs holds significant implications for improving their efficiency and effectiveness. Such reforms can enhance access to justice, protect human rights, and ensure the proper functioning of LAIs/LAOs, thereby contributing to greater legal certainty and benefiting Indonesians in need of legal aid.   Originality/Value: This research contributes to the existing literature by providing insights into the regulatory framework surrounding the establishment of LAIs/LAOs in Indonesia. By highlighting the need for reform and recommending clearer regulations, this study offers practical guidance for policymakers and stakeholders involved in legal aid provision, ultimately advancing the efficacy of legal aid services in Indonesia.Revista de Gestão Social e Ambiental360Revista de Gestão Social e Ambiental['S. Prasetyorini', 'Edy Lisdiyono', 'Sri Mulyani', 'Annisa Ghina Savira']2024-04-26T00:00:00https://www.semanticscholar.org/paper/9475def30d38f83a07b276b8c6583794f3c37353 93104c2a9d2d6f14bf9b4963f0e22e9bd4e428b5Genetically engineered microorganisms (GEMs) represent a new paradigm in our ability to address the needs of a growing, changing world. GEMs are being used in agriculture, food production and additives, manufacturing, commodity and noncommodity products, environmental remediation, etc., with even more applications in the pipeline. Along with modern advances in genome-manipulating technologies, new manufacturing processes, markets, and attitudes are driving a boom in more products that contain or are derived from GEMs. Consequentially, researchers and developers are poised to interact with biotechnology regulatory policies that have been in effect for decades, but which are out of pace with rapidly changing scientific advances and knowledge. In the United States, biotechnology is regulated by multiple agencies with overlapping responsibilities. This poses a challenge for both developers and regulators to simultaneously allow new innovation and products into the market while also ensuring their safety and efficacy for the public and environment. This article attempts to highlight the various factors that interact between regulatory policy and development of GEMs in the United States, with perspectives from both regulators and developers. We present insights from a 2022 workshop hosted at the University of California, Berkeley that convened regulators from U.S. regulatory agencies and industry developers of various GEMs and GEM-derived products. We highlight several new biotechnologies and applications that are driving innovation in this space, and how regulatory agencies evaluate and assess these products according to current policies. Additionally, we describe recent updates to regulations that incorporate new technology and knowledge and how they can adapt further to effectively continue regulating for the future.ACS Synthetic Biology460This article highlights several new biotechnologies and applications that are driving innovation in this space, and how regulatory agencies evaluate and assess these products according to current policies, and describes recent updates to regulations that incorporate new technology and knowledge.ACS Synthetic Biology['Arik Shams', 'Alexandria Fischer', 'Anastasia Bodnar', 'Melinda Kliegman']2024-04-26T00:00:00https://www.semanticscholar.org/paper/93104c2a9d2d6f14bf9b4963f0e22e9bd4e428b5 3aae0afd06fb11da3028ed21d598c1bb97a793acWe propose a new model for multi-lane traffic with moving bottlenecks, e.g., autonomous vehicles (AV). It consists of a system of balance laws for traffic in each lane, coupled in the source terms for lane changing, and fully coupled to ODEs for the AVs' trajectories.More precisely, each AV solves a controlled equation depending on the traffic density, while the PDE on the corresponding lane has a flux constraint at the AV's location. We prove existence of entropy weak solutions, and we characterize the limiting behavior for the source term converging to zero (without AVs), corresponding to a scalar conservation law for the total density.The convergence in the presence of AVs is more delicate and we show that the limit does not satisfy an entropic equation for the total density as in the original coupled ODE-PDE model. Finally, we illustrate our results via numerical simulations.420It is proved existence of entropy weak solutions, and the limiting behavior for the source term converging to zero (without AVs) is characterized, corresponding to a scalar conservation law for the total density.['Paola Goatin', 'Benedetto Piccoli']2024-04-26T00:00:00https://www.semanticscholar.org/paper/3aae0afd06fb11da3028ed21d598c1bb97a793ac b63bb8221117a6dde146cccdb1eb70a6c542cf4bDiscover Education821Investigating the trust dynamics of in-service STEAM teachers in Nigeria reveals that anxiety, preferred methods to increase trust, and perceived benefits significantly influence teachers' trust in AI-based edtech, thus fostering trust in the transformative potentials of AI in STEAM education.Discover Education['M. A. Ayanwale', 'Owolabi Paul Adelana', 'Tolulope Timothy Odufuwa']2024-04-26T00:00:00https://www.semanticscholar.org/paper/b63bb8221117a6dde146cccdb1eb70a6c542cf4b f49f1eadda109b03d4e79e9b13a712e66959514dThis paper delves into the advancements in AI-driven seismic imaging and data analysis techniques aimed at augmenting the efficiency of oil and gas exploration. We explore various AI algorithms and machine learning models that have been deployed to interpret seismic data, predict subsurface structures, and identify potential hydrocarbon reservoirs with unprecedented precision. Furthermore, we discuss the integration of big data analytics and high-performance computing in handling vast volumes of seismic data, thereby facilitating rapid decision-making in exploration projects. Through case studies and empirical evidence, we highlight the tangible benefits and potential challenges associated with the adoption of AI-driven seismic imaging and data analysis in the oil and gas industry. Ultimately, this paper underscores the transformative impact of AI technologies in optimizing exploration workflows and maximizing resource discovery while mitigating risks and reducing operational costs. In the pursuit of optimizing oil and gas exploration, the integration of artificial intelligence (AI) methodologies has emerged as a transformative force. This paper examines the evolving landscape of AI-driven seismic imaging and data analysis techniques, aimed at enhancing efficiency within the exploration domain. By harnessing AI algorithms and machine learning models, seismic data interpretation is propelled to unprecedented levels of accuracy, enabling the prediction of subsurface structures and the identification of potential hydrocarbon reservoirs with enhanced precision. Moreover, the synergistic fusion of big data analytics and high-performance computing facilitates the processing of vast seismic datasets, expediting decision-making processes in exploration endeavors. Through a synthesis of case studies and empirical evidence, this paper elucidates the tangible benefits and potential challenges associated with AI adoption in the oil and gas sector. By amplifying exploration workflows, mitigating risks, and curbing operational costs, AI-driven seismic imaging and data analysis stand poised to revolutionize the landscape of oil and gas exploration, catalyzing sustainable resource discovery in an evolving energy paradigm. Keywords: AI-Driven Seismic Imaging, Oil And Gas Exploration, Data Analysis, Efficiency Optimization, Geophysical Insights, Seismic Interpretation.Engineering Science &amp; Technology Journal00Engineering Science &amp; Technology Journal['Gideon Oluseyi Daramola', 'Boma Sonimiteim Jacks', 'Olakunle Abayomi Ajala', 'Abiodun Emmanuel Akinoso']2024-04-26T00:00:00https://www.semanticscholar.org/paper/f49f1eadda109b03d4e79e9b13a712e66959514d 957b3700e0f72d8daff8f48522d7b44a054be69eThe launch of ChatGPT in November 2023 will perhaps be come to be one of the defining moments in our relationship with technology. The rapid pace in which generative artificial intelligence (GAI) is developing and the rate in which it is being adopted, is transforming how we interact with technology, and poses new risks and challenges. As GAI tools such as ChatGPT are used by non-lawyers, this article explores the implications of generative AI in the provision of legal advice. This research examines the performance of GAI tools in providing legal information and advice in response to commonly experienced legal problems and finds there are significant errors and mistakes with the responses it produces. There is a critical need to improve access to justice and this article explores the implications for non-lawyers in using GAI tools and considers the risks of reliance on GAI advice. The article goes on to examine the utility of generative AI in clinical legal education to consider whether there is a role for responsible use of GAI in law clinics. It suggests the adoption of GAI tools has the potential to increase the capacity of law clinics, and enhance employability skills, but law schools need to be cognisant of the risks of GAI.  International Journal of Clinical Legal Education00The performance of GAI tools in providing legal information and advice in response to commonly experienced legal problems is examined and finds there are significant errors and mistakes with the responses it produces.International Journal of Clinical Legal Education['Francine Ryan', 'Liz Hardie']2024-04-26T00:00:00https://www.semanticscholar.org/paper/957b3700e0f72d8daff8f48522d7b44a054be69e 0b815324647015fb5778f4252b6a7e2d9a66cd0fA new era of artificial intelligence (AI) has begun, which can radically alter how humans interact with and profit from technology. The confluence of chat interfaces with large language models lets humans write a natural language inquiry and receive a natural language response from a machine. This experimental design study tests the capabilities of three popular AI chatbot services referred to as my AI students: Microsoft Bing, Google Bard, and OpenAI ChatGPT on completeness and accuracy. A Likert scale was used to rate completeness and accuracy, respectively, a three-point and five-point. Descriptive statistics and non-parametric tests were used to compare marks and scale ratings. The results show that AI chatbots were awarded a score of 80.0% overall. However, they struggled with answering questions from the higher Bloom’s taxonomic levels. The median completeness was 3.00 with a mean of 2.75 and the median accuracy was 5.00 with a mean of 4.48 across all Bloom’s taxonomy questions (n=128). Overall, the completeness of the solution was rated mostly incomplete due to limited response (76.2%), while accuracy was rated mostly correct (83.3%). In some cases, generative text was found to be verbose and disembodied, lacking perspective and coherency. Microsoft Bing ranked first among the three AI text generative tools in providing correct answers (92.0%). The Kruskal-Wallis test revealed a significant difference in completeness (asymp. sig.=0.037, p<0.05) and accuracy (asymp. sig.=0.006, p<0.05) among the three AI chatbots. A series of Mann and Whitney tests were carried out showing no significance between AI chatbots for completeness (all p-values>0.015 and 0<r<0.2), while a significant difference was found for accuracy between Google Bard and Microsoft Bing (asymp. sig.=0.002, p<0.05, r=0.3 medium effect). The findings suggest that while AI chatbots can generate comprehensive and correct responses, they may have limits when dealing with more complicated cognitive tasks.Contemporary Educational Technology280The findings suggest that while AI chatbots can generate comprehensive and correct responses, they may have limits when dealing with more complicated cognitive tasks.Contemporary Educational Technology['Reginald Gerald Govender']2024-04-26T00:00:00https://www.semanticscholar.org/paper/0b815324647015fb5778f4252b6a7e2d9a66cd0f 551e6464e6d9ce7128ff7cd5585bc7410921e5dbThis research focusses on the potential application of artificial intelligence (AI) techniques in the analysis of behavioural addictions, specifically addressing problematic Internet use among adolescents. Using tabular data from a representative sample from Serbian high schools, the authors investigated the feasibility of employing eXplainable AI (XAI) techniques, placing special emphasis on feature selection and feature importance methods. The results indicate a successful application to tabular data, with global interpretations that effectively describe predictive models. These findings align with previous research, which confirms both relevance and accuracy. Interpretations of individual predictions reveal the impact of features, especially in cases of misclassified instances, underscoring the significance of XAI techniques in error analysis and resolution. Although AI’s influence on the medical domain is substantial, the current state of XAI techniques, although useful, is not yet advanced enough for the reliable interpretation of predictions. Nevertheless, XAI techniques play a crucial role in problem identification and the validation of AI models.Elektronika ir Elektrotechnika460Using tabular data from a representative sample from Serbian high schools, the authors investigated the feasibility of employing eXplainable AI techniques, placing special emphasis on feature selection and feature importance methods.Elektronika ir Elektrotechnika['Aleksandar S. Stanimirovic', 'Mina S. Nikolic', 'Jelena J. Jovic', 'D. Ignjatovic Ristic', 'A. Corac', 'Leonid Stoimenov', 'Zoran H. Peric']2024-04-26T00:00:00https://www.semanticscholar.org/paper/551e6464e6d9ce7128ff7cd5585bc7410921e5db b40f8ada04396acfa8b1e289b2db9488e7b945c4We use the process and findings from a case study of design educators' practices of assessment and feedback to fuel theorizing about how to make AI useful in service of human experience. We build on Suchman's theory of situated actions. We perform a qualitative study of 11 educators in 5 fields, who teach design processes situated in project-based learning contexts. Through qualitative data gathering and analysis, we derive codes: design process; assessment and feedback challenges; and computational support. We twice invoke creative cognition's family resemblance principle. First, to explain how design instructors already use assessment rubrics and second, to explain the analogous role for design creativity analytics: no particular trait is necessary or sufficient; each only tends to indicate good design work. Human teachers remain essential. We develop a set of situated design creativity analytics--Fluency, Flexibility, Visual Consistency, Multiscale Organization, and Legible Contrast--to support instructors' efforts, by providing on-demand, learning objectives-based assessment and feedback to students. We theorize a methodology, which we call situating analytics, firstly because making AI support living human activity depends on aligning what analytics measure with situated practices. Further, we realize that analytics can become most significant to users by situating them through interfaces that integrate them into the material contexts of their use. Here, this means situating design creativity analytics into actual design environments. Through the case study, we identify situating analytics as a methodology for explaining analytics to users, because the iterative process of alignment with practice has the potential to enable data scientists to derive analytics that make sense as part of and support situated human experiences.1580A set of situated design creativity analytics is developed--Fluency, Flexibility, Visual Consistency, Multiscale Organization, and Legible Contrast--to support instructors' efforts, by providing on-demand, learning objectives-based assessment and feedback to students.['Ajit Jain', 'Andruid Kerne', 'Hannah Fowler', 'Jinsil Seo', 'Galen Newman', 'Nic Lupfer', 'Aaron Perrine']2024-04-26T00:00:00https://www.semanticscholar.org/paper/b40f8ada04396acfa8b1e289b2db9488e7b945c4 ee8a32a290641a9832c38009bd15fa1f663c40c3The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator perceptions. It then examines the repercussions of AI-enabled content proliferation, referencing the limitation of three books self-published per day in September 2023 by Amazon due to a suspected influx of AI-generated material. The discourse extends to the potential of AI in accelerating research akin to the contributions of digital humanities and computational linguistics, highlighting its accessibility to the general public. The article further delves into the implications of AI on pedagogical approaches to research and writing, contemplating its impact on communication and critical thinking skills, while also considering its role in bridging the digital divide and socio-economic disparities. Finally, it proposes revisions to writing curricula, adapting to the transformative influence of AI in academic contexts. Journal of Intelligent Communication710The evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing, is explored, contemplating its impact on communication and critical thinking skills, while also considering its role in bridging the digital divide and socio-economic disparities.Journal of Intelligent Communication['James Hutson']2024-04-26T00:00:00https://www.semanticscholar.org/paper/ee8a32a290641a9832c38009bd15fa1f663c40c3 1457a32a52ac2cb95a553ca1ae09c5ecec551b60The year 2023 is the first year of the outbreak of generative artificial intelligence applications, and digital transformation has also become the development trend of international trade today. Digital transformation, driven by the rapid development and adoption of generative AI technologies, has also become the dominant trend of international trade in the contemporary world. Generative AI, exemplified by ChatGPT, a powerful natural language generation model, has attracted global attention and sparked heated discussions about its implications and possibilities in different fields. Generative AI is a subset of artificial intelligence, which create new content from existing data by learning the underlying patterns and structures of the data. International trade, as an important part of the global economy, is inevitably influenced by the emergence and advancement of generative artificial intelligence. This paper aims to explore the current and potential impacts of generative artificial intelligence on international trade, focusing on three main aspects: creating new value from data, data analysis and preparation. The paper argues that generative artificial intelligence offers unprecedented opportunities for the international trade industry, as it help sellers in international trade to create more diverse and customized products and services, to optimize their production and marketing strategies, and to increase their competitiveness and profitability. The foreign trade enterprises should seize the opportunity and adopt proactive and innovative approaches to leverage the benefits of generative artificial intelligence, while also being aware of and prepared for the potential risks and challenges it poses to international trade.Advances in Economics, Management and Political Sciences00It is argued that generative artificial intelligence offers unprecedented opportunities for the international trade industry, as it help sellers in international trade to create more diverse and customized products and services, to optimize their production and marketing strategies, and to increase their competitiveness and profitability.Advances in Economics, Management and Political Sciences['Yuexi Liu', 'Zhaokai Liang', 'Jiangang Zhang']2024-04-26T00:00:00https://www.semanticscholar.org/paper/1457a32a52ac2cb95a553ca1ae09c5ecec551b60 94534d7e9b37bdca7c3747fde42d6b500ce88ca8Artificial intelligence (AI) has emerged as one of the central topics of 2023 with extensive media coverage of the most relevant technologies and issues associated with this subject. In a highly competitive digital media landscape, search engine optimization (SEO) has become cybermedia’s primary strategy to increase visibility and attract more readers. The objective of this paper is to analyze the visibility of content published by the media relating to artificial intelligence, focusing on a selection of related keywords. The research also aims to investigate how this visibility has impacted both the technologies themselves and the analyzed media outlets. A total of 69 media outlets from 12 European Union countries, the United States, and the United Kingdom were examined. The results reveal a pronounced dominance of U.S. media, closely followed by Spanish media. There is an uneven distribution of media outlets across most of the countries analyzed, with two or three most of the of visibility. The search queries that contribute the most visibility to the analyzed media align with an informational intent, are of the long-tail type, and are associated with OpenAI technologies, particularly ChatGPT. Moreover, these queries are primarily found in news sections dedicated to science and technology. The findings underscore both the increasing interest in the subject and the effective SEO practices of certain media outlets.Communication &amp; Society430The search queries that contribute the most visibility to the analyzed media align with an informational intent, are of the long-tail type, and are associated with OpenAI technologies, particularly ChatGPT.Communication &amp; Society['Rubén Alcaraz-Martínez', 'Mari Vállez', 'Carlos Lopezosa']2024-04-26T00:00:00https://www.semanticscholar.org/paper/94534d7e9b37bdca7c3747fde42d6b500ce88ca8 f59993c036b2f5b231c87715ab496a2337cff034As organizations navigate the complexities of the digital age, the role of Human Resources (HR) is evolving to meet the demands of a digital workforce. This review explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in HR practices to enhance efficiency, effectiveness, and employee satisfaction in the digital era. AI and ML technologies offer HR departments the opportunity to streamline operations, improve decision-making processes, and enhance employee experiences. By leveraging AI and ML, HR professionals can automate routine tasks such as recruitment, onboarding, training, and performance evaluation, allowing them to focus on more strategic initiatives that drive organizational success. One of the key advantages of integrating AI and ML in HR practices is the ability to personalize employee experiences. These technologies can analyze large volumes of data to identify patterns and trends, enabling HR professionals to tailor programs and policies to meet the unique needs of individual employees. This personalization can lead to higher levels of employee engagement, satisfaction, and retention. Furthermore, AI and ML can help HR departments make more informed decisions by providing data-driven insights. These technologies can analyze employee data to identify areas for improvement, predict future trends, and develop strategies to address challenges proactively. By leveraging these insights, HR professionals can make strategic decisions that align with the organization's goals and objectives. However, integrating AI and ML in HR practices also presents challenges, such as data privacy concerns, ethical considerations, and the need for upskilling HR professionals to use these technologies effectively. Organizations must address these challenges to realize the full potential of AI and ML in HR practices. In conclusion, integrating AI and ML in HR practices offers organizations the opportunity to enhance efficiency, effectiveness, and employee satisfaction in the digital age. By leveraging these technologies, HR departments can streamline operations, personalize employee experiences, and make more informed decisions that drive organizational success. As organizations increasingly turn to digital solutions, the role of artificial intelligence (AI) and machine learning (ML) in Human Resources becomes pivotal. This paper will focus on how AI and ML are being integrated into HR functions such as recruitment, onboarding, and employee engagement. It will also discuss the ethical implications and the challenges of maintaining human touch in an increasingly automated workplace. Case studies of companies leading in digital HR practices will be highlighted to provide real-world insights. Keywords: Digital Force, HR Practices, AI, Machine Learning, Future.Computer Science &amp; IT Research Journal00This paper will focus on how AI and ML are being integrated into HR functions such as recruitment, onboarding, and employee engagement, and the ethical implications and the challenges of maintaining human touch in an increasingly automated workplace.Computer Science &amp; IT Research Journal['Chinenye Gbemisola Okatta', 'Funmilayo Aribidesi Ajayi', 'Olufunke Olawale']2024-04-26T00:00:00https://www.semanticscholar.org/paper/f59993c036b2f5b231c87715ab496a2337cff034 4a9826c093c4127975dac78a4ce329f36a0126e1Medicine is entering a new era in which artificial intelligence (AI) and deep learning have a measurable impact on patient care. This impact is especially evident in cardiovascular medicine. While the purpose of this short opinion paper is not to provide an in-depth review of the many applications of AI in cardiovascular medicine, we summarize some of the important advances that have taken place in this domain.Frontiers in Cardiovascular Medicine160This short opinion paper is not to provide an in-depth review of the many applications of AI in cardiovascular medicine, but to summarize some of the important advances that have taken place in this domain.Frontiers in Cardiovascular Medicine['Paul Cerrato', 'John D. Halamka']2024-04-26T00:00:00https://www.semanticscholar.org/paper/4a9826c093c4127975dac78a4ce329f36a0126e1 1c7f13033138082e92fc964647df0a1dca2aa55dThis study explores the impact of artificial intelligence (AI) on customer perceptions and behavior in restaurants, airline companies, and hotel sectors within the hospitality industry of Iran. The primary objective is to analyze how AI affects customer trust, brand engagement, electronic word-of-mouth (eWOM), and tourists’ readiness to use AI technologies. Using a comparative analysis approach and surveys, this research tests hypotheses about the effects of artificial intelligence on various dimensions of customer interaction. The findings highlight significant relationships between the quality of artificial intelligence and customer engagement metrics, such as trust and brand loyalty, which are crucial for understanding and predicting customer behavior in response to technological advancements. This study lays the groundwork for theoretical assumptions about sustainability in these sectors in developing countries, providing a basis for future empirical research that could validate these assumptions and explore broader implications of AI integration in enhancing sustainable practices within the hospitality industry.Sustainability00This study lays the groundwork for theoretical assumptions about sustainability in these sectors in developing countries, providing a basis for future empirical research that could validate these assumptions and explore broader implications of AI integration in enhancing sustainable practices within the hospitality industry.Sustainability['Tamara Gajić', 'Alireza Ranjbaran', 'Dragan Vukolić', 'Jovan Bugarčić', 'Ana Spasojević', 'Jelena Đorđević Boljanović', 'Duško Vujačić', 'Marija Mandarić', 'Marija Kostić', 'D. Sekulić', 'Marina Bugarčić', 'Bojana D. Drašković', 'Sandra R. Rakić']2024-04-26T00:00:00https://www.semanticscholar.org/paper/1c7f13033138082e92fc964647df0a1dca2aa55d bbac00c19902555026ab360321158dbb3fca61baThe concept paper explores the potential of artificial intelligence (AI) in improving case management for vulnerable migrants. With the increasing challenges faced by migrants, including displacement, exploitation, and lack of access to services, there is a growing need for innovative solutions to support their well-being and resilience. The paper begins by providing an overview of the current landscape of migration and the challenges faced by vulnerable migrants. It highlights the limitations of traditional case management approaches, including resource constraints, lack of data-driven decision-making, and difficulty in tracking and monitoring cases effectively. The paper then delves into the potential of AI in transforming case management for vulnerable migrants. AI technologies, such as natural language processing (NLP), machine learning (ML), and data analytics, can enable more efficient and effective case management processes. AI can help in automating routine tasks, such as data entry and documentation, allowing case workers to focus more on providing personalized support to migrants. Furthermore, AI can assist in identifying patterns and trends in migration flows and service utilization, enabling more proactive and targeted interventions. By leveraging AI, case workers can make more informed decisions, improve service delivery, and enhance the overall resilience of vulnerable migrants. However, the paper also acknowledges the challenges and ethical considerations associated with the use of AI in case management. These include concerns about data privacy and security, algorithmic bias, and the potential for AI to replace human decision-making entirely. Addressing these challenges will be crucial in ensuring that AI is used responsibly and ethically in supporting vulnerable migrants. In conclusion, the concept paper emphasizes the importance of leveraging AI in case management for vulnerable migrants as a means to enhance resilience and improve outcomes. It calls for a collaborative approach involving policymakers, practitioners, and technology developers to harness the full potential of AI in supporting vulnerable migrants and building more inclusive and resilient communities. Keywords: AI, Case Management, Migrants.Computer Science &amp; IT Research Journal00The concept paper explores the potential of artificial intelligence (AI) in improving case management for vulnerable migrants and calls for a collaborative approach involving policymakers, practitioners, and technology developers to harness the full potential of AI in supporting vulnerable migrants and building more inclusive and resilient communities.Computer Science &amp; IT Research Journal['Ayo Amen Ediae', 'Chidinma Favour Chikwe', 'Kevin Namiiro Kuteesa']2024-04-26T00:00:00https://www.semanticscholar.org/paper/bbac00c19902555026ab360321158dbb3fca61ba 7c3c1d0654951ccbdc815c468c9d34530505fe01In Autonomous Driving (AD) transparency and safety are paramount, as mistakes are costly. However, neural networks used in AD systems are generally considered black boxes. As a countermeasure, we have methods of explainable AI (XAI), such as feature relevance estimation and dimensionality reduction. Coarse graining techniques can also help reduce dimensionality and find interpretable global patterns. A specific coarse graining method is Renormalization Groups from statistical physics. It has previously been applied to Restricted Boltzmann Machines (RBMs) to interpret unsupervised learning. We refine this technique by building a transparent backbone model for convolutional variational autoencoders (VAE) that allows mapping latent values to input features and has performance comparable to trained black box VAEs. Moreover, we propose a custom feature map visualization technique to analyze the internal convolutional layers in the VAE to explain internal causes of poor reconstruction that may lead to dangerous traffic scenarios in AD applications. In a second key contribution, we propose explanation and evaluation techniques for the internal dynamics and feature relevance of prediction networks. We test a long short-term memory (LSTM) network in the computer vision domain to evaluate the predictability and in future applications potentially safety of prediction models. We showcase our methods by analyzing a VAE-LSTM world model that predicts pedestrian perception in an urban traffic situation.350A transparent backbone model for convolutional variational autoencoders (VAE) that allows mapping latent values to input features and has performance comparable to trained black box VAEs is built and a custom feature map visualization technique is proposed to analyze the internal convolutional layers in the VAE to explain internal causes of poor reconstruction that may lead to dangerous traffic scenarios in AD applications.['Mohamed Roshdi', 'Julian Petzold', 'Mostafa Wahby', 'Hussein Ebrahim', 'Mladen Berekovic', 'Heiko Hamann']2024-04-26T00:00:00https://www.semanticscholar.org/paper/7c3c1d0654951ccbdc815c468c9d34530505fe01 a99a48afbcbc9081f3a61f35923a02912cd26e07AI presents immense potential in addressing the complex challenges of developmental economics, particularly in the realms of financial inclusion and poverty alleviation. This abstract explores the opportunities and challenges associated with integrating AI solutions in these critical areas. Financial inclusion, essential for sustainable development, remains hampered by barriers such as limited access to banking services, socioeconomic disparities, and regulatory constraints. AI offers innovative approaches through data analytics and prediction models, enabling tailored financial services, risk assessment, and personalized interventions. However, the implementation of AI solutions poses significant challenges, including concerns regarding data privacy, ethical implications such as algorithmic bias, and accessibility issues in underserved regions. Through case studies and best practices, lessons can be gleaned to inform future initiatives, emphasizing the importance of adaptable policy frameworks, collaboration, and impact assessment. Looking ahead, emerging AI technologies like blockchain and enhanced regulatory measures hold promise, necessitating cross-sector partnerships and a concerted effort to harness AI's transformative potential for sustainable development and inclusive growth. Keywords: Developmental Economics, Financial Inclusion, Poverty Alleviation, AI Solutions, Challenges, Opportunities.International journal of advanced economics00This abstract explores the opportunities and challenges associated with integrating AI solutions in these critical areas of financial inclusion and poverty alleviation, emphasizing the importance of adaptable policy frameworks, collaboration, and impact assessment.International Journal of Advanced Economics['Temitayo Oluwaseun Jejeniwa', 'Noluthando Zamanjomane Mhlongo', 'Titilola Olaide Jejeniwa']2024-04-26T00:00:00https://www.semanticscholar.org/paper/a99a48afbcbc9081f3a61f35923a02912cd26e07 52a504714e68cfe02efe839fc2724e74a25ae22aThis paper explores various AI applications in reservoir management, highlighting how machine learning, data analytics, and advanced algorithms contribute to optimizing production and recovery in oil and gas fields. From predictive modeling to real-time monitoring and control, AI offers promising solutions to address the industry's evolving demands and enhance operational performance Reservoir management in oil and gas fields has historically presented challenges demanding continuous monitoring, analysis, and optimization for maximizing production and recovery. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. This paper delves into diverse AI applications within reservoir management, emphasizing the pivotal roles played by machine learning, data analytics, and advanced algorithms in optimizing production and recovery processes in oil and gas fields. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. Keywords: Reservoir Management, Oil And Gas Fields, Optimization, Recovery, Machine Learning, Data Analytics, Predictive Modeling.Computer Science &amp; IT Research Journal00Computer Science &amp; IT Research Journal['Gideon Oluseyi Daramola', 'Boma Sonimiteim Jacks', 'Olakunle Abayomi Ajala', 'Abiodun Emmanuel Akinoso']2024-04-26T00:00:00https://www.semanticscholar.org/paper/52a504714e68cfe02efe839fc2724e74a25ae22a 994e18482d4db3b21a8e13923aa038851464e3b0In general, the integration of Artificial Intelligence into personalized marketing has revolutionized the mode in which companies engage with their consumers, enabling them to deliver tailor-made experiences and targeted ads dependent on consumers’ individual preferences and activities. The above analysis gets driven by the fact that the utility of AI in personalized marketing enhances customer satisfaction, increases sales, and improves the overall efficiency of marketing. However, the vast application of Artificial Intelligence in personalized marketing usage has raised significant privacy concerns centring on the aspect of data collection, profiling, as well as the use of targeted ad measures for strategies. For this reason, it is imperative that while the benefits of personalized marketing via AI are maximized, privacy considerations should also be taken into account to build consumers’ trust and compliance with relevant laws.International Journal of Advanced Research in Science, Communication and Technology650It is imperative that while the benefits of personalized marketing via AI are maximized, privacy considerations should also be taken into account to build consumers’ trust and compliance with relevant laws.International Journal of Advanced Research in Science, Communication and Technology['Mandeep Yadav', 'Amitesh Kumar', 'Rachit Jha']2024-04-26T00:00:00https://www.semanticscholar.org/paper/994e18482d4db3b21a8e13923aa038851464e3b0 ce9613477dc565d57a6c8398548230152e03635fThere is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components' relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case. Our two use cases cover a broad perspective within sustainable finance. The Brazilian cattle farming use case is an example of greening finance - integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations. The deployment of nature-based solutions in the UK water utility use case is an example of financing green - driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance. This report is primarily aimed at financial institutions but is also of interest to ESG data providers, TNFD, systems modellers, and, of course, AI practitioners.00This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance.['Steven Reece', 'Emma O ’ Donnell', 'Felicia Liu', 'Joanna Wolstenholme', 'Frida Arriaga', 'Giacomo Ascenzi', 'R. Pywell']2024-04-26T00:00:00https://www.semanticscholar.org/paper/ce9613477dc565d57a6c8398548230152e03635f ed7da79be14fed5e08004d6540ce661cb11678deSCAS (Socrates as a Service), incorporating ChatGPT 3.5, was instructed to create ten visions for the future waiting room of the doctor, based upon positioning the waiting room as part of the continuum of healthcare, rather than just a room housing people before they are admitted to see the medical professionals. SCAS ‘fleshed out’ each of the 10 visions in paragraph form, and then generated 15 questions about aspects of implementing these visions. In the subsequent iteration, SCAS was given the 15 questions it had previously created, and instructed to provide answers to each question, as well as estimate the difficulty involved in achieving what the answer specified. At the end of the process, about 15 minutes later, the program returns with a detailed Microsoft Excel file, the Idea Book, showing each iteration, and providing additional insights generated by the AI-based ‘Summarizer.’ The process presented here shows the power of AI to help create the future by allowing easy-to-create, quick-to-run queries, and providing detailed answers and additional subsequent analysis. With the turn-around time in seconds, and with a system which is iterative, the user can explore a topic in depth, generating a strong educational experience for any topic that one can imagine. The strength of the approach is the ability to help one think and envision in a way which is absorbing, user-driven, and often filled with surprises.Internal Medicine Research – Open Journal90The process presented here shows the power of AI to help create the future by allowing easy-to-create, quick-to-run queries, and providing detailed answers and additional subsequent analysis.Internal Medicine Research – Open Journal['Howard R. Moskowitz', 'Stephen Rappaport', 'Rubin Cooper', 'Angelica Dilorenzo', 'Sunaina Saharan', 'Maryam Ahmed', 'Taylor Mulvey', 'Martin Mulvey']2024-04-26T00:00:00https://www.semanticscholar.org/paper/ed7da79be14fed5e08004d6540ce661cb11678de eb5022b83e0ab12f5650f6d9552cddd1d2d96a70This article explores the rapidly developing field of Critical AI Studies and its relation to issues of class and capitalism through a hybrid approach based on distant reading of a newly collected corpus of 300 full-text scientific articles, the creation of which is itself a first attempt at properly delineating the field. We find that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up their own distinct subfield of Critical AI Studies, in contrast to, e.g., issues of race and gender, which are more broadly present in the corpus.tripleC: Communication, Capitalism &amp; Critique. Open Access Journal for a Global Sustainable Information Society00It is found that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up their own distinct subfield of Critical AI Studies, in contrast to, e.g., issues of race and gender, which are more broadly present in the corpus.tripleC: Communication, Capitalism &amp; Critique. Open Access Journal for a Global Sustainable Information Society['Petter Ericson', 'Roel Dobbe', 'Simon Lindgren']2024-04-26T00:00:00https://www.semanticscholar.org/paper/eb5022b83e0ab12f5650f6d9552cddd1d2d96a70 324b7942c3b970896d102ba6fbbf4284ded18691This study scrutinizes free AI tools tailored for supporting literature review and analysis in academic research, emphasizing their response to direct inquiries. Through a targeted keyword search, we cataloged relevant AI tools and evaluated their output variation and source validity. Our results reveal a spectrum of response qualities, with some tools integrating non-academic sources and others depending on outdated information. Notably, most tools showed a lack of transparency in source selection. Our study highlights two key limitations: the exclusion of commercial AI tools and the focus solely on tools that accept direct research queries. This raises questions about the potential capabilities of paid tools and the efficacy of combining various AI tools for enhanced research outcomes. Future research should explore the integration of diverse AI tools, assess the impact of commercial tools, and investigate the algorithms behind response variability. This study contributes to a better understanding of AI's role in academic research, emphasizing the importance of careful selection and critical evaluation of these tools in academic endeavors.dHealth00This study scrutinizes free AI tools tailored for supporting literature review and analysis in academic research, emphasizing their response to direct inquiries and reveals a spectrum of response qualities, with some tools integrating non-academic sources and others depending on outdated information.Studies in health technology and informatics['Martin Danler', 'W. Hackl', 'Sabrina B. Neururer', 'Bernhard Pfeifer']2024-04-26T00:00:00https://www.semanticscholar.org/paper/324b7942c3b970896d102ba6fbbf4284ded18691 6ac847a24ff46ee5ebf5039aea714a50a48807caGlobal warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.Sustainability1050This paper systematically reviews recent studies on AI and machine learning for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained and reveals a need for more systematically summarized research.Sustainability['James Ogundiran', 'Ehsan Asadi', 'Manuel Carlos Gameiro da Silva']2024-04-26T00:00:00https://www.semanticscholar.org/paper/6ac847a24ff46ee5ebf5039aea714a50a48807ca e9c6279f0d85a73560d22de24956ce2d71129303Frontiers in Artificial Intelligence10Frontiers in Artificial Intelligence['Nicola Lettieri', 'Alessandro Pluchino']2024-04-26T00:00:00https://www.semanticscholar.org/paper/e9c6279f0d85a73560d22de24956ce2d71129303 0160ff11b6969a22265648d95b71dcabe88cce60Multimedia tools and applications00Multimedia Tools and Applications['Kamlesh Mani', 'Kamlesh Kumar Singh', 'R. Litoriya']2024-04-26T00:00:00https://www.semanticscholar.org/paper/0160ff11b6969a22265648d95b71dcabe88cce60 5b8765aa9f119b19568c33696d36a621b67b9752ATZ worldwide00ATZ worldwide['Frank Jung']2024-04-26T00:00:00https://www.semanticscholar.org/paper/5b8765aa9f119b19568c33696d36a621b67b9752 4e4f09134eebca370df8947097b9035531401e75Türk Patoloji Dergisi00Turk patoloji dergisi['K. Yorukoglu']2024-04-26T00:00:00https://www.semanticscholar.org/paper/4e4f09134eebca370df8947097b9035531401e75 b42239ffb81077486f054d6eac31b88a91a8c839In this paper, we map out the landscape of options available to visual artists for creating personal artworks, including crafting, adapting and navigating deep generative models. Following that, we argue for revisiting model crafting, defined as the design and manipulation of generative models for creative goals, and motivate studying and designing for model crafting as a creative activity in its own right.480It is argued for revisiting model crafting, defined as the design and manipulation of generative models for creative goals, to motivate studying and designing for model crafting as a creative activity in its own right.['Ahmed M. Abuzuraiq', 'Philippe Pasquier']2024-04-26T00:00:00https://www.semanticscholar.org/paper/b42239ffb81077486f054d6eac31b88a91a8c839 642ac97e14c55baf29cbc24355ac57f2a176792a00['M. Yee-King']2024-04-26T00:00:00https://www.semanticscholar.org/paper/642ac97e14c55baf29cbc24355ac57f2a176792a d8eb3b159edb37df7a7615b484b36b338a2ebbb0Nurse Educator00Nurse educator['Coleen E. Toronto', 'Maureen Hillier']2024-04-26T00:00:00https://www.semanticscholar.org/paper/d8eb3b159edb37df7a7615b484b36b338a2ebbb0 e99d899043f141298c20d9d2ba590f621750997cVarious industrial applications deal with high-frequency data. Traditionally, these systems have analyzed high-frequency data directly on the data source or at the commanding PLC. However, currently, Industry 4.0 technologies support new monitoring scenarios for high-frequency data monitoring where raw data is transmitted in soft-real time to an Edge/Fog or Cloud node for processing, enabling centralized computing. This demands efficient communication protocols capable of handling high-frequency, high-throughput data. This paper focuses on analyzing the performance of key IIoT (Industrial Internet of Things) messaging protocols—AMQP, MQTT, KAFKA, ZeroMQ, and OPCUA—to evaluate their suitability, in terms of latency and jitter, for transmitting high-frequency data within these new scenarios. The analysis reveals MQTT, AMQP, and ZeroMQ as top performers in Edge/Fog computing, while ZeroMQ exhibits the lowest latency and jitter in Cloud computing. Finally, a guideline for protocol selection is proposed, aiding industrial enterprises in protocol selection for specific AI use cases.International Journal of Cloud Applications and Computing130This paper focuses on analyzing the performance of key IIoT messaging protocols—AMQP, MQTT, KAFKA, ZeroMQ, and OPCUA—to evaluate their suitability, in terms of latency and jitter, for transmitting high-frequency data within these new scenarios.International Journal of Cloud Applications and Computing['Telmo Fernández de Barrena Sarasola', 'Ander García', 'Juan Luís Ferrando']2024-04-26T00:00:00https://www.semanticscholar.org/paper/e99d899043f141298c20d9d2ba590f621750997c b7104e1bbeb0dbc9f00cc8fd704495cae85d11e0In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science&medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation, in particular from some of the major tech companies who are leading in AI development. This regulation is likely to put at risk the budding field of open-source Generative AI. We argue for the responsible open sourcing of generative AI models in the near and medium term. To set the stage, we first introduce an AI openness taxonomy system and apply it to 40 current large language models. We then outline differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions. We hope that this report will add a much needed missing voice to the current public discourse on near to mid-term AI safety and other societal impact.1441It is argued for the responsible open sourcing of generative AI models in the near and medium term and differential benefits and risks of open versus closed source AI and present potential risk mitigation, ranging from best practices to calls for technical and scientific contributions.['Francisco Eiras', 'Aleksandar Petrov', 'Bertie Vidgen', 'C. S. D. Witt', 'Fabio Pizzati', 'Katherine Elkins', 'Supratik Mukhopadhyay', 'Adel Bibi', 'Botos Csaba', 'Fabro Steibel', 'Fazl Barez', 'Genevieve Smith', 'G. Guadagni', 'Jon Chun', 'Jordi Cabot', 'Joseph Marvin Imperial', 'J. Nolazco-Flores', 'Lori Landay', 'Matthew Jackson', 'Paul Rottger', 'P. Torr', 'Trevor Darrell', 'Y. Lee', 'Jakob Foerster']2024-04-25T00:00:00https://www.semanticscholar.org/paper/b7104e1bbeb0dbc9f00cc8fd704495cae85d11e0 10416503e32c89d9b629c34c45fa0ac39a79fbe1This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.1209This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC).['Xiaohong Liu', 'Xiongkuo Min', 'Guangtao Zhai', 'Chunyi Li', 'Tengchuan Kou', 'Wei Sun', 'Haoning Wu', 'Yixuan Gao', 'Y. Cao', 'Zicheng Zhang', 'Xiele Wu', 'R. Timofte', 'Sandeep Mishra']2024-04-25T00:00:00https://www.semanticscholar.org/paper/10416503e32c89d9b629c34c45fa0ac39a79fbe1 6753bcbc0ff6164cdd56c7072587d7a7eac3d619Cryptocurrency has sparked expressions of concern from regulators – though sometimes coupled with expressions of interest in state-backed alternatives. This paradoxical situation neatly encapsulates the conundrum confronting regulators as they seek to come to terms with the new world opened up by blockchain and leading ultimately perhaps to decentralised finance. How do we best understand this confusing situation? This paper looks for answers by attempting to conceptualise the phenomenon of decentralised finance in autopoietic systems terms. Insofar as a plausible argument can be made for the proposition that finance represents an example of the internal differentiation of the economy, does decentralised finance in some sense constitute an intensified internal differentiation? Alternatively, and paradoxically, insofar as what we are concerned with is decentralised finance, does it instead in some sense represent an example of dedifferentiation? Answers to these questions will have relevance for efforts to regulate this emerging phenomenon. They will also help to shed light on whether state and central bank experiments in this space will produce positive effects or bring their own challenges. La criptomoneda ha suscitado la preocupación de los reguladores, aunque a veces ha ido acompañada del interés expresado sobre algunas alternativas respaldadas por el Estado. Esta paradójica situación resume a la perfección el enigma al que se enfrentan los reguladores cuando tratan de aceptar el nuevo mundo abierto por la cadena de bloques y que, en última instancia, quizá conduzca a unas finanzas descentralizadas. ¿Cuál es la mejor manera de entender esta confusa situación? Este artículo busca respuestas intentando conceptualizar el fenómeno de las finanzas descentralizadas en términos de sistemas autopoiéticos. En la medida en que se puede argumentar de forma plausible que las finanzas representan un ejemplo de la diferenciación interna de la economía, ¿constituyen las finanzas descentralizadas, en cierto sentido, una diferenciación interna intensificada? Por otra parte, y paradójicamente, en la medida en que tratamos sobre finanzas descentralizadas, ¿representan en cierto sentido un ejemplo de desdiferenciación? Las respuestas a estas preguntas serán relevantes para los esfuerzos por regular este fenómeno emergente. También ayudarán a arrojar luz sobre si los experimentos del Estado y los bancos centrales en este espacio producirán efectos positivos o nuevos desafíos.Oñati Socio-Legal Series300Oñati Socio-Legal Series['John Paterson']2024-04-25T00:00:00https://www.semanticscholar.org/paper/6753bcbc0ff6164cdd56c7072587d7a7eac3d619 2611f9a7f11e26c1a9f1b84ff00092159021e84fChina's commitment to achieving carbon neutrality by 2060 has sparked scholars' interest in examining the environmental ramifications of green technologies in the digital era. While plenty of them provide eco-efficiency policy such as increasing R&D investment or stimulating green exports, little attention has been paid to the firm-level technological management and recombination strategies such as differentiation/specialization of green portfolios along with AI integration, which can significantly impact the pace of net-zero transitions. To address these gaps, this study investigates the moderating effect of technological specialization on levels of AI integration into green technologies estimated by green-AI technological distance and enterprises' innovation performance in Chinese contemporary contexts. Regression results of fixed-effect model in Chinese patent data (2011–2020) indicate that enterprises' green innovation performance is significantly improved as AI integrates more into the green technologies due to the legitimacy and the inability to appropriate more green values. Interestingly, specialized green-technological enterprises demonstrate superior performance in integrating distant AI technologies. This occurrence could potentially be driven by the governments' incentives and the organization's risk attitudes, shaping green innovation outcomes. Hence, the study underscores the importance of considering both the AI integration and green specialization in shaping innovation outcomes amidst green transitions.Frontiers in Artificial Intelligence690Investigating the moderating effect of technological specialization on levels of AI integration into green technologies estimated by green-AI technological distance and enterprises' innovation performance in Chinese contemporary contexts indicates that enterprises' green innovation performance is significantly improved as AI integrates more into the green technologies due to the legitimacy and the inability to appropriate more green values.Frontiers in Artificial Intelligence['Sirinant Khunakornbodintr']2024-04-25T00:00:00https://www.semanticscholar.org/paper/2611f9a7f11e26c1a9f1b84ff00092159021e84f 93d6b0a8a5348847c9db012693fa7133321bda5eThe pervasive integration of Artificial Intelligence (AI) has introduced complex challenges in the responsibility and accountability in the event of incidents involving AI-enabled systems. The interconnectivity of these systems, ethical concerns of AI-induced incidents, coupled with uncertainties in AI technology and the absence of corresponding regulations, have made traditional responsibility attribution challenging. To this end, this work proposes a Computational Reflective Equilibrium (CRE) approach to establish a coherent and ethically acceptable responsibility attribution framework for all stakeholders. The computational approach provides a structured analysis that overcomes the limitations of conceptual approaches in dealing with dynamic and multifaceted scenarios, showcasing the framework's explainability, coherence, and adaptivity properties in the responsibility attribution process. We examine the pivotal role of the initial activation level associated with claims in equilibrium computation. Using an AI-assisted medical decision-support system as a case study, we illustrate how different initializations lead to diverse responsibility distributions. The framework offers valuable insights into accountability in AI-induced incidents, facilitating the development of a sustainable and resilient system through continuous monitoring, revision, and reflection.290A Computational Reflective Equilibrium (CRE) approach is proposed to establish a coherent and ethically acceptable responsibility attribution framework for all stakeholders, and the pivotal role of the initial activation level associated with claims in equilibrium computation is examined.['Yunfei Ge', 'Quanyan Zhu']2024-04-25T00:00:00https://www.semanticscholar.org/paper/93d6b0a8a5348847c9db012693fa7133321bda5e 1f1ec9b55f2eb7cb9c3eac3a2c8ae7f7375a7093PurposeThis study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.Design/methodology/approachThe study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.FindingsThe findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.Originality/valueThis research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.Management Decision750The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM, and the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks is emphasized.Management Decision['Mojtaba Rezaei', 'Marco Pironti', 'Roberto Quaglia']2024-04-25T00:00:00https://www.semanticscholar.org/paper/1f1ec9b55f2eb7cb9c3eac3a2c8ae7f7375a7093 d51b9422d9fc8b1397291aef51127f768b817bfbFood and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030—Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.Frontiers in Artificial Intelligence1300The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies.Frontiers in Artificial Intelligence['Ali Ahmad', 'Anderson X. W. Liew', 'Francesca Venturini', 'Athanasios P. Kalogeras', 'Alessandro Candiani', 'Giacomo di Benedetto', 'Segun Ajibola', 'Pedro Cartujo', 'Pablo Romero', 'Aspasia Lykoudi', 'Michelangelo Mastrorocco De Grandis', 'Christos Xouris', 'Riccardo Lo Bianco', 'Irawan Doddy', 'Isa Elegbede', "Giuseppe Falvo D'Urso Labate", 'Luis F. García del Moral', 'Vanessa M. Martos']2024-04-25T00:00:00https://www.semanticscholar.org/paper/d51b9422d9fc8b1397291aef51127f768b817bfb 60394d4f7d8c4ae290d94f6dc74f355c04c534e8Besides humans and machines, Artificial Intelligence (AI) models have emerged to be another important audience of programming languages, as we come to the era of large language models (LLMs). LLMs can now excel at coding competitions and even program like developers to address various tasks, such as math calculation. Yet, the grammar and layout of existing programs are designed for humans. Particularly, abundant grammar tokens and formatting tokens are included to make the code more readable to humans. While beneficial, such a human-centric design imposes an unnecessary computational burden on LLMs where each token, either consumed or generated, consumes computational resources. To improve inference efficiency and reduce computational costs, we propose the concept of AI-oriented grammar, which aims to represent the code in a way that better suits the working mechanism of AI models. Code written with AI-oriented grammar discards formats and uses a minimum number of tokens to convey code semantics effectively. To demonstrate the feasibility of this concept, we explore and implement the first AI-oriented grammar for Python, named Simple Python (SimPy). SimPy is crafted by revising the original Python grammar through a series of heuristic rules. Programs written in SimPy maintain identical Abstract Syntax Tree (AST) structures to those in standard Python, allowing execution via a modified AST parser. In addition, we explore methods to enable existing LLMs to proficiently understand and use SimPy, and ensure the changes remain imperceptible for human developers. Compared with the original Python, SimPy not only reduces token usage by 13.5% and 10.4% for CodeLlama and GPT-4, but can also achieve equivalent, even improved, performance over the models trained on Python code.430The concept of AI-oriented grammar, which aims to represent the code in a way that better suits the working mechanism of AI models, is proposed and the first AI-oriented grammar for Python is implemented, named Simple Python (SimPy).['Zhensu Sun', 'Xiaoning Du', 'Zhou Yang', 'Li Li', 'David Lo']2024-04-25T00:00:00https://www.semanticscholar.org/paper/60394d4f7d8c4ae290d94f6dc74f355c04c534e8 e997f19a3b2a8d4556bdc3df5551387bdcd2ef75In the context of the rule of law in the new era of network, the protection of personal information rights and interests is an important content of great concern. Through empirical research on the use of AI face-swapping technology, the legal issues in AI face-swapping can be categorized from civil infringement and criminal offense respectively. On this basis, the five aspects of regulating network technology, regulating network platforms, clarifying the main responsibility, improving Blockchain technology, and enhancing citizens' network rule of law literacy can be taken as the "entry point" to regulate the use of AI face-swapping technology, so as to safeguard network security and create a clear cyberspace environment.Journal of Theory and Practice of Social Science80The five aspects of regulating network technology, regulating network platforms, clarifying the main responsibility, improving Blockchain technology, and enhancing citizens' network rule of law literacy can be taken as the "entry point" to regulate the use of AI face-swapping technology, so as to safeguard network security and create a clear cyberspace environment.Journal of Theory and Practice of Social Science['Wenyi Yin']2024-04-25T00:00:00https://www.semanticscholar.org/paper/e997f19a3b2a8d4556bdc3df5551387bdcd2ef75 930d0ca169b36dd93855e9ea30d385b0b2e4fd44This paper introduces AspireAI, an AI chatbot designed for comprehensive career guidance. With the proliferation of Artificial Intelligence (AI), integrating such technology into career counseling is increasingly vital. Leveraging Large Language Models (LLMs), AspireAI provides personalized career information, job search advice, and educational recommendations. Our methods entail training LLMs on vast datasets of career-related content to enhance conversational capabilities. Results demonstrate AspireAI's efficacy in delivering tailored guidance, aiding users in making informed career decisions. Through this research, we highlight the importance and potential of AI-driven solutions in facilitating career exploration and advancement. Keyword: AI Chatbot, Career Guidance, Large Language Models (LLMs), Personalized Guidance, Job Search Advice, Educational RecommendationsInternational Journal of Innovative Research in Engineering00This research highlights the importance and potential of AI-driven solutions in facilitating career exploration and advancement and demonstrates AspireAI's efficacy in delivering tailored guidance, aiding users in making informed career decisions.International Journal of Innovative Research in Engineering['Abhishek Yogesh Dhamdhere', 'Neha Raju Bhosale', 'Aditi Chandrakant Joshi', 'Vaidehi Vishwanath Kale', 'Aniket Avinash Chavan', 'M.S. Pokale']2024-04-25T00:00:00https://www.semanticscholar.org/paper/930d0ca169b36dd93855e9ea30d385b0b2e4fd44 8b589b31896a4662b57bd185acba9f2c80b8a0c9In the digital age of today, keeping transactions safe and effective has become increasingly important. Blockchain technology and Artificial Intelligence (AI) can potentially change the way digital transactions are conducted. The synergy between AI and Blockchain is the focus of this study, which also looks at how their integration has a profound effect on transaction security, efficiency, and transparency. After providing an overview of the fundamentals of AI and Blockchain, the paper delved into the potential advantages of their collaboration, including the development of smart contracts, improved transaction efficiency, and enhanced security protocols. Case studies from a variety of industries show how AI and Blockchain can be used in real-world applications while addressing issues like technical obstacles, privacy concerns, and regulatory considerations. The dynamic evolution of AI and Blockchain as catalysts for secure, efficient, and transparent digital transactions is highlighted in the paper's conclusion with insights into emerging trends, future perspectives, and resources for further explorationInternational Journal of Advanced Research in Science, Communication and Technology40The dynamic evolution of AI and Blockchain as catalysts for secure, efficient, and transparent digital transactions is highlighted in the paper's conclusion with insights into emerging trends, future perspectives, and resources for further exploration.International Journal of Advanced Research in Science, Communication and Technology['Renu Narwal', 'Aayush Gupta', 'Abhigyan Gupta', 'Aryan Kukreja']2024-04-25T00:00:00https://www.semanticscholar.org/paper/8b589b31896a4662b57bd185acba9f2c80b8a0c9 89252e75e4a0a79eee90f39104b24d6edc08fad4Minds Mach.200It is demonstrated how and why AI algorithms can be qualified as (un)fair by analogy and explored the sources of this (un)fairness and the associated problems of responsibility assignment, suggesting more user-driven AI approaches could alleviate some of these difficulties.Minds and Machines['Marcin Korecki', 'Guillaume Köstner', 'Emanuele Martinelli', 'Cesare Carissimo']2024-04-25T00:00:00https://www.semanticscholar.org/paper/89252e75e4a0a79eee90f39104b24d6edc08fad4 cd985d179529293509a81ab2d853806d4c51d37aPersonalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy.Electronic Journal of e-Learning740This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers, and offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era.Electronic Journal of e-Learning['Gina Paola Barrera Castro', 'Andrés Chiappe', 'Diego Fernando Becerra Rodríguez', 'Felipe Gonzalo Sepulveda']2024-04-25T00:00:00https://www.semanticscholar.org/paper/cd985d179529293509a81ab2d853806d4c51d37a 73e48da5524205dccaf7aef2dcc19709cdb027f5AI can positively influence teaching by offering support for classroom management, creating inclusive learning environments, enhancing digital skills, personalizing teaching methods, and strengthening teacher‐student relationships.This quantitative research study investigates the opportunities, difficulties, and consequences of incorporating AI into teacher education.Data were collected through structured questionnaires from 202 college students and 68 staff members. The analysis was conducted using SPSS software.The study provides a novel contribution by its thorough investigation of the diverse effects of AI on teacher education. It offers beneficial perspectives on the possible benefits and challenges, illuminating the far‐reaching changes that AI could bring to the terrain of learning and instruction and teaching methods in the time yet to come. The research sought to assess the effect of AI adoption in teacher education across five main dimensions: (i) its influence on teaching support and classroom management, (ii) its role in creating inclusive and accessible learning environments, (iii) its contribution to improving teachers' digital literacy and computer skills, and enhancing access to digital teaching resources, (iv) its positive influence on identifying students' learning styles and facilitating the adoption of diverse teaching methods, and (v) its role in strengthening teacher‐student relationships through improved interactions.The findings elucidate the promising opportunities that AI presents in the field of teacher education, along with the obstacles that require resolution for the effective fusion of AI educational settings.Journal of Computer Assisted Learning220The findings elucidate the promising opportunities that AI presents in the field of teacher education, along with the obstacles that require resolution for the effective fusion of AI educational settings.Journal of Computer Assisted Learning['Jia Zhang', 'Zhuo Zhang']2024-04-25T00:00:00https://www.semanticscholar.org/paper/73e48da5524205dccaf7aef2dcc19709cdb027f5 cc77bd492218e7454b385fe216c24abdf5e2121fThe paper explores the relationships between Artificial Intelligence (AI) and Intellectual Property (IP) system of rights protection. The discussion clarify the characteristics of IP system, who is the registered owner, what is the registration object, and where the registration takes place. The research seeks WIPO's advice and the general trend of AI experts' discussions and tries to dig deep into definitions and meanings. The research also shows the mainstream explication why AIs not entitled as owner of an IP. In other case AI’s integrated into a process or a digital product a AI tool to solve a well-known problem, it is part of the organization's management and resources. Thus, innovation's certification belongs to the company or the public organization that sponsored it. The result of the research shows a summary framework of all the rights when used as a tool for decisions and risk of assuming AI as a system or as a model to support decisions specially for Public Administration.Revista JRG de Estudos Acadêmicos00The result of the research shows a summary framework of all the rights when used as a tool for decisions and risk of assuming AI as a system or as a model to support decisions specially for Public Administration.Revista JRG de Estudos Acadêmicos['Alessandro Aveni', 'Luísa Campos Faria']2024-04-25T00:00:00https://www.semanticscholar.org/paper/cc77bd492218e7454b385fe216c24abdf5e2121f 80c4a384044ac364cfbbbb77dcda519d81659dceThe emergence of AI Art has ignited extensive debates on social media platforms. Various online communities have expressed their opinions on different facets of AI Art and participated in discussions with other users, leading to the generation of a substantial volume of data. Analyzing these data can provide useful insights into the public’s opinions on AI Art, enable the investigation of the origins of conflicts in online debates, and contribute to the sustainable development of AI Art. This paper presents a deep learning-driven framework for analyzing the characteristics of public opinion on the Weibo topic of AI Art. To classify the sentiments users expressed in Weibo posts, the linguistic feature-enhanced pre-training model (LERT) was employed to improve text representation via the fusion of syntactic features, followed by a bidirectional Simple Recurrent Unit (SRU) embedded with a soft attention module (BiSRU++) for capturing the long-range dependencies in text features, thus improving the sentiment classification performance. Furthermore, a text clustering analysis was performed across sentiments to capture the nuanced opinions expressed by Weibo users, hence providing useful insights about different online communities. The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.Applied Sciences00The results indicate that the proposed sentiment analysis model outperforms common baseline models in terms of classification metrics and time efficiency, and the clustering analysis has provided valuable insights for in-depth analyses of AI Art.Applied Sciences['Wentong Wan', 'Runcai Huang']2024-04-25T00:00:00https://www.semanticscholar.org/paper/80c4a384044ac364cfbbbb77dcda519d81659dce 3f1611a50f85f73525826f5be173c845d2e7522eWe study the tendency of AI systems to deceive by constructing a realistic simulation setting of a company AI assistant. The simulated company employees provide tasks for the assistant to complete, these tasks spanning writing assistance, information retrieval and programming. We then introduce situations where the model might be inclined to behave deceptively, while taking care to not instruct or otherwise pressure the model to do so. Across different scenarios, we find that Claude 3 Opus 1) complies with a task of mass-generating comments to influence public perception of the company, later deceiving humans about it having done so, 2) lies to auditors when asked questions, and 3) strategically pretends to be less capable than it is during capability evaluations. Our work demonstrates that even models trained to be helpful, harmless and honest sometimes behave deceptively in realistic scenarios, without notable external pressure to do so.310It is demonstrated that even models trained to be helpful, harmless and honest sometimes behave deceptively in realistic scenarios, without notable external pressure to do so.['Olli Jarviniemi', 'Evan Hubinger']2024-04-25T00:00:00https://www.semanticscholar.org/paper/3f1611a50f85f73525826f5be173c845d2e7522e facb430840a9685ef2893b9996eb88399814cc31In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation. Transformers have become the standard architecture for pretraining large-scale vision and text models, and preliminary results have shown promising performance also in deep joint source-channel coding (JSCC). Here, we consider a dynamic model where communication happens over a channel with variable latency and bandwidth constraints. Leveraging recent works on conditional computation, we exploit the structure of the transformer blocks and the multihead attention operator to design a trainable semantic token selection mechanism that learns to select relevant tokens (e.g., image patches) from the input signal. This is done dynamically, on a per-input basis, with a rate that can be chosen as an additional input by the user. We show that our model improves over state-of-the-art token selection mechanisms, exhibiting high accuracy for a wide range of latency and bandwidth constraints, without the need for deploying multiple architectures tailored to each constraint. Last, but not least, the proposed token selection mechanism helps extract powerful semantics that are easy to understand and explain, paving the way for interpretable-by-design models for the next generation of AI-native communication systems.200A novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation, using the structure of the transformer blocks and the multihead attention operator to design a trainable semantic token selection mechanism.['Alessio Devoto', 'Simone Petruzzi', 'Jary Pomponi', 'P. Lorenzo', 'Simone Scardapane']2024-04-25T00:00:00https://www.semanticscholar.org/paper/facb430840a9685ef2893b9996eb88399814cc31 8b1019ba68b3381d72364f7b71845c1263c84433The Review of Contemporary Scientific and Academic Studies00The Review of Contemporary Scientific and Academic Studies['Rakesh Margam']2024-04-25T00:00:00https://www.semanticscholar.org/paper/8b1019ba68b3381d72364f7b71845c1263c84433 dc14d6bd6bc2842fcc72d6dab2cef3f26281e38fEuropean Journal of Cardiovascular Nursing00European journal of cardiovascular nursing['F. Forsyth', 'L. Van Bulck', 'B. Daelman', 'Philip Moons']2024-04-25T00:00:00https://www.semanticscholar.org/paper/dc14d6bd6bc2842fcc72d6dab2cef3f26281e38f 2a63b65fdd7a0de6e1853099445d6ca123d82857Journal of Technology Transfer250The Journal of Technology Transfer['R. Goel', 'M. A. Nelson']2024-04-25T00:00:00https://www.semanticscholar.org/paper/2a63b65fdd7a0de6e1853099445d6ca123d82857 6c6fcd58b593938e06cae84d271c81cce6cfc9abEuropean Radiology50European radiology['A. Bitencourt']2024-04-25T00:00:00https://www.semanticscholar.org/paper/6c6fcd58b593938e06cae84d271c81cce6cfc9ab f8f6d28c62b9cc32de5404bb55c06188047fcba8Modern Intelligent Times00Modern Intelligent Times['Kung Wong Lau']2024-04-25T00:00:00https://www.semanticscholar.org/paper/f8f6d28c62b9cc32de5404bb55c06188047fcba8 85a94665958badf95ba6f2e86eff8415a1b44ad5NIM Marketing Intelligence Review00NIM Marketing Intelligence Review['O. A. Acar']2024-04-25T00:00:00https://www.semanticscholar.org/paper/85a94665958badf95ba6f2e86eff8415a1b44ad5 caa0ef102c21d08a77d5f286ee63c5c68acf501aArtificial intelligence (AI) technologies, and in particular, generative AI, have significant potential to improve access to information and advance openness in research outputs. AI also has the potential to disrupt information landscapes and the communities that research libraries support and serve. The increasing availability of AI models sparks many possibilities and raises several ethical, professional, and legal considerations. Articulating a set of research library guiding principles for AI is useful to influence policy and advocate for the responsible development and deployment of AI technologies, promote ethical and transparent practices, and build trust among stakeholders, within research libraries as well as across the research environment. These principles will serve as a foundational framework for the ethical and transparent use of AI and reflect the values we hold in research libraries. ARL will rely on these principles in our policy advocacy and engagement.10These principles will serve as a foundational framework for the ethical and transparent use of AI and reflect the values the authors hold in research libraries and ARL will rely on these principles in its policy advocacy and engagement.[]2024-04-25T00:00:00https://www.semanticscholar.org/paper/caa0ef102c21d08a77d5f286ee63c5c68acf501a 4dee64fdc4c3c36c1b9c9258cd940c8426c311c6In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emergence of Fake Artificial Intelligence Generated Content (FAIGC), posing new challenges in distinguishing genuine information. It is crucial to recognize that AIGC technology is akin to a double-edged sword; its potent generative capabilities, while beneficial, also pose risks for the creation and dissemination of FAIGC. In this survey, We propose a new taxonomy that provides a more comprehensive breakdown of the space of FAIGC methods today. Next, we explore the modalities and generative technologies of FAIGC. We introduce FAIGC detection methods and summarize the related benchmark from various perspectives. Finally, we discuss outstanding challenges and promising areas for future research.2711A new taxonomy is proposed that provides a more comprehensive breakdown of the space of FAIGC methods today and explores the modalities and generative technologies of FAIGC.['Xiaomin Yu', 'Yezhaohui Wang', 'Yanfang Chen', 'Zhen Tao', 'Dinghao Xi', 'Shichao Song', 'Simin Niu', 'Zhiyu Li']2024-04-25T00:00:00https://www.semanticscholar.org/paper/4dee64fdc4c3c36c1b9c9258cd940c8426c311c6 da7bc7d7bde29eb2367c1d91b0a36c5b294bab20This research investigates the application of artificial intelligence (AI) in digital learning environments and its impact on learning outcomes. A comprehensive review of literature was conducted, encompassing studies from various educational levels and settings. The analysis reveals promising findings regarding the effectiveness of AI interventions, including intelligent tutoring systems, adaptive learning platforms, virtual assistants, and content recommendation systems, in enhancing learning outcomes. Learners exhibit high levels of engagement and satisfaction with AI-enhanced learning environments, appreciating the interactive features and personalized support provided by AI technologies. However, challenges and limitations associated with AI implementation, such as technical issues, privacy concerns, and ethical considerations, were identified. The research contributes valuable insights into the potential benefits and risks of AI in education, with implications for both research and practice. Future research directions include optimizing AI algorithms, exploring ethical and social implications, and addressing educator training needs to ensure the successful integration of AI technologies into teaching and learning processes.International Journal of Innovative Science and Research Technology311Investigating the application of artificial intelligence in digital learning environments and its impact on learning outcomes reveals promising findings regarding the effectiveness of AI interventions, including intelligent tutoring systems, adaptive learning platforms, virtual assistants, and content recommendation systems.International Journal of Innovative Science and Research Technology (IJISRT)['Hamsajini Suntharalingam']2024-04-25T00:00:00https://www.semanticscholar.org/paper/da7bc7d7bde29eb2367c1d91b0a36c5b294bab20 2d9630816e58e0b93563122b2446026ab58dd2aaGlobal education is an important aspect in preparing a generation ready to face the challenges of the future. In an effort to develop sustainable education, the use of artificial intelligence is one of the interesting potentials to consider. The research aims to explain the design of global education development aimed at developing AI-based sustainable education through library studies. The study uses a library study method by collecting, analysing, and synthesizing research, scientific articles, reports, and other relevant literary sources. The research focuses on three important aspects in the design of AI-based global sustainable education development, namely, learning personalization, adaptive curriculum development, and student progress assessment and monitoring. The library study results show that the use of AI in education can enable more effective learning personalization. With AI, learning materials can be tailored to individual student needs and preferences, enhancing their engagement and understanding. Furthermore, AI-supported adaptative curricular development allows learning experiences that are adapted to student development and capabilities. Student progress evaluation and surveillance can also be enhanced through the application of AI. This technology can provide more objective and comprehensive assessment of student progress, helping teachers to provide more timely feedback and support better decision-making in learning planning. While the potential for the use of AI in sustainable global education is promising, there are challenges and consequences to bear in mind. Ethical aspects, student data privacy, and fairness in access and use of AI technology should be key concerns in designing sustainable global education development. The study provides insight into the design of AI-based sustainable global education development through library studies. These findings could provide a basis for further research and provide practical guidance to stakeholders in adopting and implementing AI technologies responsibly to develop sustainable education at a global level.Qubahan Academic Journal240The library study results show that the use of AI in education can enable more effective learning personalization, and AI-supported adaptative curricular development allows learning experiences that are adapted to student development and capabilities.Qubahan Academic Journal['Siti Marisa', 'Gunawan Gunawan', 'Evi Susilawati']2024-04-25T00:00:00https://www.semanticscholar.org/paper/2d9630816e58e0b93563122b2446026ab58dd2aa 3739a88653a55a615bf0a17e31daaa5291b458cdThis research employs a machine learning-based text mining algorithm and the international patent classification (IPC) co-occurrence network approach, utilizing patent documents submitted from 2015 to 2022 to empirically examine the influence of artificial intelligence (AI) on modern electric car progress. The research illustrates the dynamically shifting structure of the fusion of AI and electric car technology and indicates how AI has impacted electric car technological advancement through time. It is based on classified artificial intelligence techniques. This research shows that artificial intelligence speeds up the automated process of driving in electric cars, that the AI technique often employed in electric vehicles has undergone modifications long period, and that the technical aspects of electric cars that AI impacts have shifted as well.   Journal of Electrical Systems150This research shows that artificial intelligence speeds up the automated process of driving in electric cars, that the AI technique often employed in electric vehicles has undergone modifications long period, and that the technical aspects of electric cars that AI impacts have shifted as well.Journal of Electrical Systems['Liangliang Wang', 'Junlei Wang', 'Fan Zhang', 'Yue Long', 'Xiaoxue Ye', 'Lan Liu', 'Ran Ji']2024-04-25T00:00:00https://www.semanticscholar.org/paper/3739a88653a55a615bf0a17e31daaa5291b458cd ef09e4a47d41c01dd33911103071e94300c0408bSurgical Endoscopy00The model accurately assesses complexity grading and full safety achievement and can serve to predict operative time and intraoperative course, whereas no clinical correlation was found regarding postoperative outcomes.Surgical endoscopy['Danit Dayan', 'Nadav Dvir', 'Haneen Agbariya', 'E. Nizri']2024-04-25T00:00:00https://www.semanticscholar.org/paper/ef09e4a47d41c01dd33911103071e94300c0408b 94e437ce47f328e685a9e667e3bc9568f9eec0c3With the rapid development and application of artificial intelligence (AI) technology, the field of sports visual communication is undergoing unprecedented changes. This paper aims to explore the application of AI technology in the practice of sports visual design, and its impact on visual communication effects and design processes. Through the innovative application of sports visual elements and the optimization of visual design processes, this paper reveals how AI technology can improve the efficiency and quality of sports visual communication, and also points out the challenges and limitations it brings. It is of great significance to promote technological innovation and design improvement in the field of sports visual communication and also provides useful inspiration for exploring the application of AI technology in a wider field of visual design.Journal of Electrical Systems180This paper reveals how AI technology can improve the efficiency and quality of sports visual communication, and also points out the challenges and limitations it brings.Journal of Electrical Systems['Jinlong Lyu', 'Luyao Peng']2024-04-25T00:00:00https://www.semanticscholar.org/paper/94e437ce47f328e685a9e667e3bc9568f9eec0c3 d365efc2f0dd85d157484f7a4a83ad00f68e54a3Chinese Buddhist grottoes are the testimony of history and the crystallization of human technology and culture as well as the physical remains of human creative activities. The neutral shape of Buddha statues reflects the aesthetic pursuit of “neutralization” in traditional Chinese culture. The solemnity and gentleness of the Buddha statues are expressed through external modelling technique and line carvings, which embodies the characteristics of “conveying the spirit with form” in China culture. This paper analyzes China Grottoes’ art from the aspects of “meaning”, “god is outside the form”, “expressing the spirit with the form”, phonology, “charm” and “expressing the spirit with the lines” through literature review, and then analyzes the awareness of digital protection, technical methods and key projects at domestic and overseas. In recent years, the digital protection of cultural relics has been continuously improved under the multi-disciplinary application of artificial intelligence, which not only enhances the influence of cultural communication, but also makes cultural relics more powerful and dynamic. Through the restoration of Buddha statues by digital simulation and the planning of restoration projects through the precise measurement given by this digital models can realize the complete digital filing and permanent preservation of important cultural relics and improve the protection and utilization of digital cultural heritage.Journal of Electrical Systems150This paper analyzes China Grottoes’ art from the aspects of “meaning”, “god is outside the form”, “expressing the spirit with the form”, phonology, “charm” and “expressing the spirit with the lines” through literature review, and then analyzes the awareness of digital protection, technical methods and key projects at domestic and overseas.Journal of Electrical Systems['Xiaoshu Li', 'Kang Ji', 'Mohd Johari', 'M. Yusof']2024-04-25T00:00:00https://www.semanticscholar.org/paper/d365efc2f0dd85d157484f7a4a83ad00f68e54a3 f4f4aae5ae9a29f13976df36fe90ebd7d66a1d8e The present research aims to analyze the effect of contextual factors affecting the application of artificial intelligence technology in employee recruitment in the furniture industry of Iran, which is a practical purpose and has been carried out in a descriptive-surveillance manner, to find out the reasons, facilitators and limitations of its use with the presented conceptual model. Make this technology understandable to organizations during employee recruitment. To measure and analyze the effect of these factors, a questionnaire was used as an information-gathering tool, which was given to 250 senior managers and middle managers of companies active in the furniture industry of Iran. The results of the analysis of the information obtained in two descriptive and inferential parts, according to the data analysis algorithm in the method of structural equations and Smart PLS software, confirmed the hypotheses of the research and showed that the effective background factors include: technological factors, organizational and environmental have a positive and significant effect on the use of Artificial intelligence in the furniture industry in Iran, and the use of artificial intelligence as a competitive advantage improves the organizational capabilities of recruitment and recruitment (based on data, process, staff) in the furniture industry. Forgives. Also, it makes it easier to carry out “blind” Recruitment of employees processes and review frequent applications, and by simplifying the application analysis process through applicant tracking systems, it can save time and money in human resources processes and reduce discrimination in choices.Economics Series200Analyzing the effect of contextual factors affecting the application of artificial intelligence technology in employee recruitment in the furniture industry of Iran showed that the effective background factors include: technological factors, organizational and environmental have a positive and significant effect on the use of Artificial intelligence in the furniture industry in Iran.Studia Universitatis „Vasile Goldis” Arad – Economics Series['Ardeshir Bazrkar', 'Mehrdad Moradzad', 'Shady Shayegan']2024-04-25T00:00:00https://www.semanticscholar.org/paper/f4f4aae5ae9a29f13976df36fe90ebd7d66a1d8e bee5f8ebf91e82844bc0902f95e3a1bf00ba01f7Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O’Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.Journal of Clinical Medicine680The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.Journal of Clinical Medicine['Lea Sacca', 'Diana Lobaina', 'Sara Burgoa', 'Kathryn Lotharius', 'Elijah Moothedan', 'Nathan Gilmore', 'Justin Xie', 'Ryan Mohler', 'Gabriel Scharf', 'Michelle Knecht', 'Panagiota Kitsantas']2024-04-25T00:00:00https://www.semanticscholar.org/paper/bee5f8ebf91e82844bc0902f95e3a1bf00ba01f7 5e1806afc7e9b1db69dc3db6890cf2103b3727b1With the continuous development and widespread adoption of artificial intelligence (AI) technology, enterprises have realized the importance of possessing artificial intelligence capabilities to enhance organizational performance. However, there is still a relative lack of empirical research on how to build AI capabilities, and how AI capabilities impact organizational innovation performance. Based on the resource-based theory, this study constructs a conceptual model of “AI Capabilities - Organizational Response (Organizational Learning and Organizational Agility) - Ambidextrous Innovation”, exploring the direct impact of AI capabilities on ambidextrous innovation, and the mediating effects of organizational learning and organizational agility, also the moderating role of digital strategy in this linkage. With data from 253 survey questionnaires of Chinese enterprises, the results indicate that: first, AI capabilities have a significantly positive impact on organizational ambidextrous innovation;  second, organizations with high levels of organizational learning ability and agility can better leverage AI capabilities to promote the development of ambidextrous innovation;  Thirdly, digital strategy has played an active role in regulating the influence path of two-way innovation within the organization and provided sufficient boundary conditions; fourth, The data capability, technological capability, and fundamental resource capability of AI capabilities have differentiating effects on exploratory and exploitative innovations. The findings of this research hold significant theoretical and managerial value for understanding the driving mechanisms of organizational innovation performance in the era of AI.Journal of Electrical Systems360A conceptual model of “AI Capabilities - Organizational Response (Organizational Learning and Organizational Agility) - Ambidextrous Innovation" explores the direct impact of AI capabilities on ambidextrous innovation, and the mediating effects of organizational learning and organizational agility, also the moderating role of digital strategy in this linkage.Journal of Electrical Systems['Weiwei Dong', 'Xinye Fan']2024-04-25T00:00:00https://www.semanticscholar.org/paper/5e1806afc7e9b1db69dc3db6890cf2103b3727b1 d533853c21a9795cbaca25d706848e16daefb9bcWater resources management980No one-size-fits-all AI model exists, and this study suggests utilizing hybrid AI models as alternatives as alternatives to address the limitations of individual models, leverage the strengths of different approaches, and provide a better understanding of the relationships between variables.Water Resources Management['Almando Morain', 'Nivedita Ilangovan', 'Christopher Delhom', 'A. Anandhi']2024-04-25T00:00:00https://www.semanticscholar.org/paper/d533853c21a9795cbaca25d706848e16daefb9bc 4599d4c447815c1ea6d21f8524a1e474ef23e2b0Artificial intelligence, as the core driving force of the new round of technological revolution and industrial change, has far-reaching impact on current social employment and structure. This study, based on the data of provincial industrial robots, explores the impact of artificial intelligence on employment. Through literature review, it is found that artificial intelligence has impact on the labor market, which may lead to changes in employment scale, employment structure, and income distribution. The study uses panel data model to analyze the impact of artificial intelligence development on employment scale and structure in 31 provinces (autonomous regions, municipalities directly under the Central Government) of China from 2010 to 2019. The results show that the development of artificial intelligence has a significant promoting effect on the employment scale in China, and also affects the employment structure towards “high-end” direction, but there is also a spillover effect towards “polarization”. In addition, the study finds that artificial intelligence has different effects on the employment of labor with different skills, promoting the employment of high-skilled labor in the manufacturing industry and inhibiting the employment of low-skilled labor, leading to a trend of high-end employment structure in the manufacturing industry. Therefore, the study proposes some prospects and suggestions for the impact of artificial intelligence on the social employment structure, in order to promote the transformation and upgrading of the industry, and achieve efficient intelligent production and optimal adjustment of employment structure. Through in-depth analysis of this study, important reference basis is provided for the current social issues facing the impact of artificial intelligence.International Journal of Global Economics and Management70The study uses panel data model to analyze the impact of artificial intelligence development on employment scale and structure in 31 provinces of China from 2010 to 2019 and finds that artificial intelligence has different effects on the employment of labor with different skills, promoting the employment of high-skilled labor in the manufacturing industry and inhibiting the employment of low-skilled labor.International Journal of Global Economics and Management['Yu Zhang', 'Ruizhi Chen', 'Jing Huang']2024-04-25T00:00:00https://www.semanticscholar.org/paper/4599d4c447815c1ea6d21f8524a1e474ef23e2b0 094fa0e7780b08c463d02cc611da19fce762f90fThe usage of Internet as increased with time ,but with the increase in usage of internet ,the cases of Cybercrime has also gone up. However, with increase of artificial intelligence ,the companies and business are starting to look for AI tools to help against cybercrime .AI is becoming an essential component of every business. Cybercrime is one of the important sectors where AI has begun demonstrating valuable inputs. It is due to the fact that AI is faster than humans to take action and make an alternate plan of action to protect business and send warning against cybercrime. We will discuss recent cyber crime and how AI is used in the industry to defend itself in the long runInternational Journal of Advanced Research in Science, Communication and Technology120Recent cyber crime is discussed and how AI is used in the industry to defend itself in the long run is discussed.International Journal of Advanced Research in Science, Communication and Technology['Abhishek Gautam', 'Aditya Prakash', 'Gariyas Kaushal']2024-04-25T00:00:00https://www.semanticscholar.org/paper/094fa0e7780b08c463d02cc611da19fce762f90f b35b7f193522fd99768aae9163b7715a77e2beccPurposeThis study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.Design/methodology/approachThis study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.FindingsThe findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.Research limitations/implicationsThis study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.Originality/valueThis study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.Management Decision750Key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation, provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.Management Decision['Domenica Barile', 'Giustina Secundo', 'Candida Bussoli']2024-04-25T00:00:00https://www.semanticscholar.org/paper/b35b7f193522fd99768aae9163b7715a77e2becc f3321a12f61f0167d689e859ec6e70d0b059a443With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve real-time monitoring and early warning, helping banks intervene before risks occur and reduce losses.International Journal of Global Economics and Management90Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support, helping banks intervene before risks occur and reduce losses.International Journal of Global Economics and Management['Shuochen Bi', 'Wenqing Bao']2024-04-25T00:00:00https://www.semanticscholar.org/paper/f3321a12f61f0167d689e859ec6e70d0b059a443 60a1192a931ce29f241631da64e22070056efe97 Artificial intelligence (AI) related technologies are being employed more and more in a range of industries to increase automation and improve productivity. The increasing volumes of data and advancements in high-performance computing have led to a sharp increase in the application of these methods in recent years. AI technology has been widely applied in the field of hardware design, notably in the design of digital and analogue integrated circuits (ICs), to address challenges such as rising networked devices, aggressive time-to-market, and everincreasing design complexity. However, very little attention has been paid to the issues and problems related to the design of integrated circuits. The authors of this article review the stateof- the-art in AI for circuit design and optimization. AI offers knowledge-based technologies that give challenges a foundation and structure. A technology known as AI makes it possible for machines to mimic human behavior. Data in all formats, including unstructured, semistructured, and structured, can be processed by AI. It is crucial to incorporate all of the features and levels of the many CAD programmes into a single, cohesive environment for creation, as was mentioned in the section that came before this one. Consequently, the application of AI automation helps to enhance the effectiveness and efficiency of CAD's performance. Recent Advances in Computer Science and Communications00The authors of this article review the state of the-art in AI for circuit design and optimization and suggest that the application of AI automation helps to enhance the effectiveness and efficiency of CAD's performance.Recent Advances in Computer Science and Communications['Garima Thakur', 'Shruti Jain']2024-04-25T00:00:00https://www.semanticscholar.org/paper/60a1192a931ce29f241631da64e22070056efe97 a871fe1f84770342e7d1742d59d90536ede6dc8cBACKGROUND Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. AREAS OF UNCERTAINTY The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. DATA SOURCES A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015-2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. ETHICS AND THERAPEUTIC ADVANCES The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. CONCLUSIONS AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.American Journal of Therapeutics880AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.American journal of therapeutics['Vasile Astărăstoae', 'Liliana M. Rogozea', 'F. Leaşu', 'Beatrice Gabriela Ioan']2024-04-25T00:00:00https://www.semanticscholar.org/paper/a871fe1f84770342e7d1742d59d90536ede6dc8c 4631c7f85b4cc3ab54afb4b0cedc03c49b7ce0b3The development of artificial intelligence in medicine in Iraq indicates the increasing need for this new idea to be taught to medical students. The objective of such research study having this specific interest is to examine the attitudes of Iraqi medical students toward AI, with the focus made on the level of their understanding of AI and the types of career prospects that they envisage. In a study conducted on Iraqi medical students, a doctrine was outlaid; Iraqi med students were requested to complete anonymously in an online survey respectively. Analysis of the collected data was done by using SPSS21 software. Such an approach gave you the opportunity to investigate the behavioral patterns, trends and relations of the data, which allowed you to gain important outcomes about how the respondents see AI in medical education. The results showed that a total of 23 medical universities participated in the study and their response was collected to the extent of 318. The most of respondents (s=91,5%) is confident in the fact that AI is going to affect health care in the not so far future. Specially, the responses were summarized into those who are going to show strong agreement (33.6%, s = 107) and those who are going to show agreement (57.9%, s = 184). In this regard, the research found that Iraqi medical students find AI indeed adjacently affecting and are open to interacting with such new technology. Additionally, it leaves open a question - we should provide accompanying training programs against Artificial Intelligence during medical education for doctors.Международный Журнал Теоретических и Прикладных Вопросов Цифровых Технологий160The research found that Iraqi medical students find AI indeed adjacently affecting and are open to interacting with such new technology.Международный Журнал Теоретических и Прикладных Вопросов Цифровых Технологий['Hayder Murad']2024-04-25T00:00:00https://www.semanticscholar.org/paper/4631c7f85b4cc3ab54afb4b0cedc03c49b7ce0b3 90abbef5aa37debecbe488cb4ff69bef2501abf2BACKGROUND AND PURPOSE Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). RESULTS 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. CONCLUSIONS To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.ABBREVIATIONS: AI = artificial intelligence; ASFNR = American Society of Functional Neuroradiology; AUROC = area under the receiver operating characteristic curve; DICOM = Digital Imaging and Communications in Medicine; GEE = generalized estimation equation; IQR = interquartile range; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; TBI = traumatic brain injury.AJNR. American journal of neuroradiology00A multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency, and analyzed their performance in the ASFNR AI Competition.AJNR. American journal of neuroradiology['Bin Jiang', 'B. Ozkara', 'Guangming Zhu', 'Derek Boothroyd', 'Jason W. Allen', 'D. P. Barboriak', 'Peter Chang', 'Cynthia Chan', 'Ruchir Chaudhari', 'Hui Chen', 'Anjeza Chukus', 'Victoria Y Ding', 'David Douglas', 'Christopher G Filippi', 'A. E. Flanders', 'Ryan Godwin', 'Syed Hashmi', 'Christopher Hess', 'Kevin Hsu', 'Yvonne W. Lui', 'J. A. Maldjian', 'Patrik Michel', 'S. Nalawade', 'Vishal Patel', 'Prashant Raghavan', 'Haris I. Sair', 'Jody Tanabe', 'Kirk Welker', 'Chris Whitlow', 'Greg Zaharcuk', 'Max Wintermark']2024-04-25T00:00:00https://www.semanticscholar.org/paper/90abbef5aa37debecbe488cb4ff69bef2501abf2 93f1ac48cd5c53f5c8ab9627acc45f9695ee1a22In this section, we delve into the exploration of how artificial intelligence (AI) can contribute to sustainable development and bolster environmental resilience. In light of the pressing global challenges posed by climate change, resource scarcity, and environmental degradation, there arises an imperative to devise innovative solutions that foster sustainable practices and fortify ecosystem resilience. This segment examines a range of AI applications pertinent to sustainable development and environmental resilience. These applications encompass climate modelling, energy efficiency optimization, waste management, biodiversity preservation, and disaster response, among others. Moreover, we delve into both the potential benefits and risks associated with deploying AI in these arenas. Emphasis is placed on the significance of ethical considerations, transparency, and inclusivity in the implementation of AI-driven solutions. The objective of this segment is to offer insights into the effective utilization of AI for cultivating a more sustainable and resilient future. This will be achieved through the presentation of successful case studies, alongside the illumination of emerging trends and prospective pathwaysInternational Journal of Advanced Research in Science, Communication and Technology50This segment examines a range of AI applications pertinent to sustainable development and environmental resilience, which encompass climate modelling, energy efficiency optimization, waste management, biodiversity preservation, and disaster response, among others.International Journal of Advanced Research in Science, Communication and Technology['Satyam', 'Harshwardhan', 'Ashima Mehta']2024-04-25T00:00:00https://www.semanticscholar.org/paper/93f1ac48cd5c53f5c8ab9627acc45f9695ee1a22 debc79b801535cc16b076fc58e9414879a0ecaeeBackground: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.Journal of Clinical Medicine380Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques, and the potential is enormous, but the impediments that may appear and slow down its implementation must be remembered.Journal of Clinical Medicine['Valerii Luțenco', 'George Tocu', 'Mădălin Guliciuc', 'Monica Moraru', 'Iuliana-Laura Candussi', 'Marius Dănilă', 'Verginia Luțenco', 'Florentin Dimofte', 'O. Mihailov', 'R. Mihailov']2024-04-25T00:00:00https://www.semanticscholar.org/paper/debc79b801535cc16b076fc58e9414879a0ecaee 124eefa971529deea4e4a4e59c285bb9e3877f9cArtificial Intelligence (AI) has made substantial advancements across various medical specialties, revolutionizing healthcare delivery and outcomes. In recent years, AI has been increasingly applied to surgical disciplines, including biliary and pancreatic surgery. This research paper aims to explore the impact of AI on these surgical domains, including preoperative planning, intraoperative guidance, and postoperative care. The paper will also discuss the challenges and opportunities associated with the integration of AI in biliary and pancreatic surgery, providing insights into the potential future directions of this transformative technology.Journal of medical pharmaceutical and allied sciences90The impact of AI on these surgical domains, including preoperative planning, intraoperative guidance, and postoperative care is explored, providing insights into the potential future directions of this transformative technology.Journal of Medical pharmaceutical and allied sciences['Li Ziqiang Li Ziqiang']2024-04-25T00:00:00https://www.semanticscholar.org/paper/124eefa971529deea4e4a4e59c285bb9e3877f9c 74a4708ac3eee1525f5260ddada7230e8ee992eaAt present, there are problems in the computer audit of colleges and universities in terms of auditing software and professional and technical talents, etc. To speed up the internal computer audit of colleges and universities, it is suggested to strengthen the theoretical research and risk prevention of computer audit, choose the computer auditing software suitable for colleges and universities, cultivate the computer auditors to meet the requirements, adopt flexible and diversified ways of follow-up education and training, and give full play to the role of the education branch of the internal audit associations at all levels, and strengthen communication and cooperation. The educational subcommittees of internal audit associations at all levels should give full play to their roles as links and bridges, and strengthen exchanges and cooperation.International Journal of Global Economics and Management130It is suggested to strengthen the theoretical research and risk prevention of computer audit, choose the computer auditing software suitable for colleges and universities, cultivate the computer auditors to meet the requirements, adopt flexible and diversified ways of follow-up education and training, and give full play to the role of the education branch of the internal audit associations at all levels.International Journal of Global Economics and Management['Franz Ceska', 'Bhola Nath Chalise']2024-04-25T00:00:00https://www.semanticscholar.org/paper/74a4708ac3eee1525f5260ddada7230e8ee992ea 1ed03c6d365e852415b579386758a2b88126dc84BACKGROUND Quantification of total cardiovascular risk is essential for individualizing hypertension treatment. This study aimed to develop and validate a novel, machine-learning-derived model to predict cardiovascular mortality risk using office blood pressure (OBP) and ambulatory blood pressure (ABP). METHODS The performance of the novel risk score was compared with existing risk scores, and the possibility of predicting ABP phenotypes utilizing clinical variables was assessed. Using data from 59 124 patients enrolled in the Spanish ABP Monitoring registry, machine-learning approaches (logistic regression, gradient-boosted decision trees, and deep neural networks) and stepwise forward feature selection were used. RESULTS For the prediction of cardiovascular mortality, deep neural networks yielded the highest clinical performance. The novel mortality prediction models using OBP and ABP outperformed other risk scores. The area under the curve achieved by the novel approach, already when using OBP variables, was significantly higher when compared with the area under the curve of the Framingham risk score, Systemic Coronary Risk Estimation 2, and Atherosclerotic Cardiovascular Disease score. However, the prediction of cardiovascular mortality with ABP instead of OBP data significantly increased the area under the curve (0.870 versus 0.865; P=3.61×10-28), accuracy, and specificity, respectively. The prediction of ABP phenotypes (ie, white-coat, ambulatory, and masked hypertension) using clinical characteristics was limited. CONCLUSIONS The receiver operating characteristic curves for cardiovascular mortality using ABP and OBP with deep neural network models outperformed all other risk metrics, indicating the potential for improving current risk scores by applying state-of-the-art machine learning approaches. The prediction of cardiovascular mortality using ABP data led to a significant increase in area under the curve and performance metrics.HYPERTENSION00The receiver operating characteristic curves for cardiovascular mortality using ABP and OBP with deep neural network models outperformed all other risk metrics, indicating the potential for improving current risk scores by applying state-of-the-art machine learning approaches.Hypertension['Pedro Guimarães', 'Andreas Keller', 'M. Böhm', 'L. Lauder', 'Tobias Fehlmann', 'L. Ruilope', 'E. Vinyoles', 'M. Gorostidi', 'J. Segura', 'G. Ruiz‐Hurtado', 'Natalie Staplin', 'Bryan Williams', 'A. de la Sierra', 'Felix Mahfoud']2024-04-25T00:00:00https://www.semanticscholar.org/paper/1ed03c6d365e852415b579386758a2b88126dc84 3f089eea9bbfc0cb9c837ad532e6bb83f7a2d4f4ABSTRACTIngénierie des Systèmes d'Information310Ingénierie des systèmes d information['M. Kryshtanovych', 'Liudmyla Snihur', 'Iryna Buzhyn', 'Dina Tiurina', 'Maksym Imeridze']2024-04-25T00:00:00https://www.semanticscholar.org/paper/3f089eea9bbfc0cb9c837ad532e6bb83f7a2d4f4 9b346e9a63cce4aed65f54517732cbbae9f17d53Journal of the European Academy of Dermatology and Venereology40Journal of the European Academy of Dermatology and Venereology : JEADV['C. Posch']2024-04-25T00:00:00https://www.semanticscholar.org/paper/9b346e9a63cce4aed65f54517732cbbae9f17d53 5f5aba3df73ab427ec9b44a7f83dfb787324203cUniversal Access in the Information Society620Universal Access in the Information Society['Dongyan Nan', 'Seungjong Sun', 'Shunan Zhang', 'Xiangying Zhao', 'Jang Hyun Kim']2024-04-25T00:00:00https://www.semanticscholar.org/paper/5f5aba3df73ab427ec9b44a7f83dfb787324203c b131da071b0f99f654f8760e3c9427d0b1e08329Journal of Clinical Nursing80Journal of clinical nursing['Danielle Le Lagadec', 'R. Kornhaber', 'Michelle Cleary']2024-04-25T00:00:00https://www.semanticscholar.org/paper/b131da071b0f99f654f8760e3c9427d0b1e08329 0f493d0838642d14843c4c304ba8c48dfc3ab614Philosophy &amp; Technology00Philosophy &amp; Technology['Nathan Gabriel Wood']2024-04-25T00:00:00https://www.semanticscholar.org/paper/0f493d0838642d14843c4c304ba8c48dfc3ab614 fabadbb89591d9024aa782201525bb26a32bb959Frontiers in Microbiology00Frontiers in Microbiology['Chen Li', 'Yu-Dong Yao', 'Jiangwei Yan', 'Marcin Grzegorzek']2024-04-25T00:00:00https://www.semanticscholar.org/paper/fabadbb89591d9024aa782201525bb26a32bb959 e826bac535997fa15e1fa40b83a6add5142b2acfCirugía y Cirujanos (English Edition)00Cirugía y Cirujanos (English Edition)['E. Galvis-García', 'Francisco J. De la Vega-González', 'Fabian Emura', 'Ó. Teramoto-Matsubara', 'Juan C. Sánchez-Robles', 'Gonzalo Rodríguez-Vanegas', 'Sergio Sobrino-Cossío']2024-04-25T00:00:00https://www.semanticscholar.org/paper/e826bac535997fa15e1fa40b83a6add5142b2acf a78f282b197d651e36a8e80bf7928a6853fe472100['Moez Ltifi']2024-04-25T00:00:00https://www.semanticscholar.org/paper/a78f282b197d651e36a8e80bf7928a6853fe4721 e6075a9274ff6fa5990c656115188d722ed8988fInternational Journal of Multidisciplinary Studies in Art and Technology00International Journal of Multidisciplinary Studies in Art and Technology['Dina Aboushall']2024-04-25T00:00:00https://www.semanticscholar.org/paper/e6075a9274ff6fa5990c656115188d722ed8988f 72cf66cbf6dbc771689c1a1e2c2d04bf8136e327Artificial Intelligence algorithms have now become pervasive in multiple high-stakes domains. However, their internal logic can be obscure to humans. Explainable Artificial Intelligence aims to design tools and techniques to illustrate the predictions of the so-called black-box algorithms. The Human-Computer Interaction community has long stressed the need for a more user-centered approach to Explainable AI. This approach can benefit from research in user interface, user experience, and visual analytics. This paper proposes a visual-based method to illustrate rules paired with feature importance. A user study with 15 participants was conducted comparing our visual method with the original output of the algorithm and textual representation to test its effectiveness with users.340A visual-based method to illustrate rules paired with feature importance is proposed to illustrate the predictions of the so-called black-box algorithms.['Eleonora Cappuccio', 'D. Fadda', 'Rosa Lanzilotti', 'Salvatore Rinzivillo']2024-04-25T00:00:00https://www.semanticscholar.org/paper/72cf66cbf6dbc771689c1a1e2c2d04bf8136e327 09c9e9f712c67b39b68959b7fc023f8c655dada5In video game design, audio (both environmental background music and object sound effects) play a critical role. Sounds are typically pre-created assets designed for specific locations or objects in a game. However, user-generated content is becoming increasingly popular in modern games (e.g. building custom environments or crafting unique objects). Since the possibilities are virtually limitless, it is impossible for game creators to pre-create audio for user-generated content. We explore the use of generative artificial intelligence to create music and sound effects on-the-fly based on user-generated content. We investigate two avenues for audio generation: 1) text-to-audio: using a text description of user-generated content as input to the audio generator, and 2) image-to-audio: using a rendering of the created environment or object as input to an image-to-text generator, then piping the resulting text description into the audio generator. In this paper we discuss ethical implications of using generative artificial intelligence for user-generated content and highlight two prototype games where audio is generated for user-created environments and objects.120The use of generative artificial intelligence to create music and sound effects on-the-fly based on user-generated content is explored and ethical implications of using generative artificial intelligence for user-generated content are discussed.['T. Marrinan', 'Pakeeza Akram', 'Oli Gurmessa', 'Anthony Shishkin']2024-04-25T00:00:00https://www.semanticscholar.org/paper/09c9e9f712c67b39b68959b7fc023f8c655dada5 81ad1ece17515813b65bac032abc9aae9dd8a7f8Recent successes in Generative Artificial Intelligence (GenAI) have led to new technologies capable of generating high-quality code, natural language, and images. The next step is to integrate GenAI technology into products, a task typically conducted by software developers. Such product development always comes with a certain risk of liability. Within this article, we want to shed light on the current state of two such risks: data protection and copyright. Both aspects are crucial for GenAI. This technology deals with data for both model training and generated output. We summarize key aspects regarding our current knowledge that every software developer involved in product development using GenAI should be aware of to avoid critical mistakes that may expose them to liability claims.140Key aspects regarding the current knowledge that every software developer involved in product development using GenAI should be aware of to avoid critical mistakes that may expose them to liability claims are summarized.['Steffen Herbold', 'Brian Valerius', 'Anamaria Mojica-Hanke', 'Isabella Lex', 'Joel Mittel']2024-04-25T00:00:00https://www.semanticscholar.org/paper/81ad1ece17515813b65bac032abc9aae9dd8a7f8 0747ebd8c32056f664331881f0a9dc8069bd0cd5The present article addresses a range of pertinent issues concerning the regulation of criminal liability for gen-ocide, a topic that has gained particular significance in recent years. Utilizing the universal dialectical method, along with other methodologies such as sociological, comparative legal, and others, the author, through an analysis of the current criminal legislation of Russia, provisions of international law, doctrinal works of Russian scholars, and materials of judicial practice related to responsibility for genocide, formulates scientifically sub-stantiated conclusions that refine the existing approach to regulating criminal liability for genocide as a crime against peace and security of mankind. Additionally, the author presents proposals regarding criminal-law measures for its prevention and the primary direc-tions for enhancing legislation and practice in this field.Теория и практика общественного развития00Теория и практика общественного развития['M. G. Reshnyak']2024-04-24T00:00:00https://www.semanticscholar.org/paper/0747ebd8c32056f664331881f0a9dc8069bd0cd5 c6abca5aa9f0c4daec7dc92c7c32f281f2795053This article delves into the philosophical and legal challenges in the development and regulation of self-learning software, neural networks, and artificial intelligence. Drawing upon a comprehensive analysis of viewpoints from various scientific disciplines, the author describes original insights and proposes directions for addressing these challenges.Russian Journal of Legal Studies00Russian Journal of Legal Studies (Moscow)['M. V. Kolesov']2024-04-24T00:00:00https://www.semanticscholar.org/paper/c6abca5aa9f0c4daec7dc92c7c32f281f2795053 fd1f40c5a7ca786985fcfa11c46069bbc16f1c8cCryptocurrencies have evolved into an important asset class, providing a variety of benefits. However, they also present significant risks, such as market volatility and the potential for misuse in illegal activities. These risks underline the urgent need for a comprehensive regulatory framework to ensure consumer protection, market integrity, and financial stability. Yet, the global landscape of cryptocurrency regulation remains complex, marked by substantial variations in regulatory frameworks among different countries. This paper aims to study these differences by investigating the regulatory landscapes across various jurisdictions. We first discuss regulatory challenges and considerations, and then conduct a comparative analysis of international regulatory stances, approaches, and measures. We hope our study offers practical insights to enhance the understanding of global trends in cryptocurrency regulation.350['Xihan Xiong', 'Junliang Luo']2024-04-24T00:00:00https://www.semanticscholar.org/paper/fd1f40c5a7ca786985fcfa11c46069bbc16f1c8c b7e5dffd562cc5ede6f5ef0c3ffa2ee99f65a05fThis paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with the user's expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications. It then explores questions around AI value alignment, well-being, safety and malicious uses. Extending the circle of inquiry further, we next consider the relationship between advanced AI assistants and individual users in more detail, exploring topics such as manipulation and persuasion, anthropomorphism, appropriate relationships, trust and privacy. With this analysis in place, we consider the deployment of advanced assistants at a societal scale, focusing on cooperation, equity and access, misinformation, economic impact, the environment and how best to evaluate advanced AI assistants. Finally, we conclude by providing a range of recommendations for researchers, developers, policymakers and public stakeholders.03The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of applications, then explores questions around AI value alignment, well-being, safety and malicious uses, and considers the deployment of advanced assistants at a societal scale.['Iason Gabriel', 'Arianna Manzini', 'Geoff Keeling', 'Lisa Anne Hendricks', 'Verena Rieser', 'Hasan Iqbal', 'Nenad Tomavsev', 'Ira Ktena', 'Zachary Kenton', 'Mikel Rodriguez', 'Seliem El-Sayed', 'Sasha Brown', 'Canfer Akbulut', 'Andrew Trask', 'Edward Hughes', 'A. S. Bergman', 'Renee Shelby', 'Nahema Marchal', 'Conor Griffin', 'Juan Mateos-Garcia', 'Laura Weidinger', 'Winnie Street', 'Benjamin Lange', 'A. Ingerman', 'Alison Lentz', 'Reed Enger', 'Andrew Barakat', 'Victoria Krakovna', 'John Oliver Siy', 'Z. Kurth-Nelson', 'Amanda McCroskery', 'Vijay Bolina', 'Harry Law', 'Murray Shanahan', 'Lize Alberts', 'Borja Balle', 'Sarah de Haas', 'Yetunde Ibitoye', 'Allan Dafoe', 'Beth Goldberg', 'Sébastien A. Krier', 'Alexander Reese', 'Sims Witherspoon', 'Will Hawkins', 'Maribeth Rauh', 'Don Wallace', 'Matija Franklin', 'Josh A. Goldstein', 'Joel Lehman', 'Michael Klenk', 'Shannon Vallor', 'Courtney Biles', 'Meredith Ringel Morris', 'Helen King', 'B. A. Y. Arcas', 'William Isaac', 'James Manyika']2024-04-24T00:00:00https://www.semanticscholar.org/paper/b7e5dffd562cc5ede6f5ef0c3ffa2ee99f65a05f b80cb8349339faddd5d1d797e8beef86584efb8cRecent advances in generative AI have led to the development of techniques to generate visually realistic synthetic video. While a number of techniques have been developed to detect AI-generated synthetic images, in this paper we show that synthetic image detectors are unable to detect synthetic videos. We demonstrate that this is because synthetic video generators introduce substantially different traces than those left by image generators. Despite this, we show that synthetic video traces can be learned, and used to perform reliable synthetic video detection or generator source attribution even after H.264 re-compression. Furthermore, we demonstrate that while detecting videos from new generators through zero-shot transferability is challenging, accurate detection of videos from a new generator can be achieved through few-shot learning.961It is shown that synthetic image detectors are unable to detect synthetic videos, and that synthetic video traces can be learned, and used to perform reliable synthetic video detection or generator source attribution even after H.264 re-compression.['Danial Samadi Vahdati', 'Tai D. Nguyen', 'Aref Azizpour', 'Matthew C. Stamm']2024-04-24T00:00:00https://www.semanticscholar.org/paper/b80cb8349339faddd5d1d797e8beef86584efb8c f96e0a9ad836b8bba11e2142fc7f1ca2980b1cc4Regulatory reporting stands transformed by artificial intelligence's advent, offering numerous advantages in precision, efficacy, and compliance. AI enabled tools empower financial institutions to streamline compliance practices and mitigate risks through enhanced reporting accuracy. This article examines applications of AI in regulatory reporting and their benefits. The article also explores AI's transformative impact on financial institutions' adherence to regulatory mandates. Furthermore, it underscores human expertise's pivotal role in developing AI-driven regulatory reporting systems. As regulatory landscapes evolve, integrating AI technology into regulatory reporting processes becomes imperative for financial institutions.International Journal of Finance10Approaches of AI in regulatory reporting and their benefits are examined, which underscores human expertise's pivotal role in developing AI-driven regulatory reporting systems.International Journal of Finance['Chintamani Bagwe']2024-04-24T00:00:00https://www.semanticscholar.org/paper/f96e0a9ad836b8bba11e2142fc7f1ca2980b1cc4 77ff9c613c3f3a235e443567e750f3da1f764458The integration of artificial intelligence (AI) in cloud compliance management has emerged as a transformative approach to address complex regulatory challenges in contemporary IT environments. This research paper explores the role of AI-driven solutions in enhancing cloud compliance, focusing on key applications, benefits, challenges, and future trends. The paper begins by examining the automation of compliance reporting and documentation through AI technologies, highlighting significant improvements in accuracy, efficiency, and cost-effectiveness. Real-world case studies from leading organizations such as J.P. Morgan Chase & Co., Mayo Clinic, and Microsoft Corporation illustrate the practical implementation and impact of AI in various sectors, including finance, healthcare, and technology. Ethical and legal implications associated with AI in cloud compliance are discussed, emphasizing the importance of transparency, fairness, and regulatory adherence. Emerging trends such as Explainable AI (XAI), AI governance, and continuous compliance monitoring are identified as critical factors shaping the future of AI-driven cloud compliance. The paper concludes by addressing anticipated challenges in data privacy, algorithmic bias, and regulatory complexity, underscoring the need for collaborative efforts among stakeholders to foster responsible AI deployment in compliance operations.Advanced International Journal of Multidisciplinary Research180The role of AI-driven solutions in enhancing cloud compliance, focusing on key applications, benefits, challenges, and future trends, is explored, with emerging trends such as Explainable AI (XAI), AI governance, and continuous compliance monitoring shaping the future of AI-driven cloud compliance.Advanced International Journal of Multidisciplinary Research['Munivel Devan', 'Samir Vinayak Bayani', 'Naveen Pakalapati', 'Lavanya Shanmugam']2024-04-24T00:00:00https://www.semanticscholar.org/paper/77ff9c613c3f3a235e443567e750f3da1f764458 33171f48f4cd7b9ffb3bd77e0b47993dcb7c3a4cThe current paper attempts to explore the potential implications and feasibility of AI Chatbots in EFL contexts, as well as how they can help in second language acquisition. To assess data and achieve its intended goals, the current study employs various techniques, including commentaries, online interviews, questionnaires, quantitative and qualitative methodologies. The research’s relevance is limited by the fact that Chinese middle school students often struggle with their spoken English. This study aims to present the outcomes of teaching oral English to Chinese middle school students by using AI Chatbots. The content’s applicability in developing speaking skills is a powerful argument for the research’s practical value. The findings indicate that AI Chatbots foster students speaking skills creating a stress-free and non-competitive learning environment.Arab World English Journal160The findings indicate that AI Chatbots foster students speaking skills creating a stress-free and non-competitive learning environment.Arab World English Journal['Shan Shikun', 'Gevorg Grigoryan', 'Huichun Ning', 'Hasmik Harutyunyan']2024-04-24T00:00:00https://www.semanticscholar.org/paper/33171f48f4cd7b9ffb3bd77e0b47993dcb7c3a4c 938f9c97208886f6fe127ad86c4daa6d3f33c359This scholarly article explores the application and impact of artificial intelligence (AI) in the realm of theatrical art creation, providing an analysis of the current state, challenges, and future development of the integration of AI and theatre. Through comprehensive case studies of various theatrical works both domestically and abroad, the article delineates four major “feeding” models of AI in theatre creation, discussing the specific applications and effects of these modes within the sphere of artistic creation. The symbiotic relationship between human creators and AI during the creative process is emphasized, envisioning the potential of AI as an innovative collaborator advancing theatrical artsalongside human creators. Despite present challenges confronting AI in theatrical creation, such as emotional depth and data dependency, the potential demonstrated in enhancing creative efficiency and expanding the scope of artistic expression is noteworthy.Arts Studies and Criticism60The symbiotic relationship between human creators and AI during the creative process is emphasized, envisioning the potential of AI as an innovative collaborator advancing theatrical artsalongside human creators.Arts Studies and Criticism['Hua Feng']2024-04-24T00:00:00https://www.semanticscholar.org/paper/938f9c97208886f6fe127ad86c4daa6d3f33c359 be13f098347c8129679b4d986f9cda46a8126c43AI and Ethics110It is contended that designing better AI futures will require that AI hyping be blunted to enable grounded debates about the ways that AI systems impact people’s lives both now and in the near future.AI and Ethics['Mona Sloane', 'David Danks', 'Emanuel Moss']2024-04-24T00:00:00https://www.semanticscholar.org/paper/be13f098347c8129679b4d986f9cda46a8126c43 3a8bd0a9d52b9701589a9026a61f88ee69c74fb3The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model’s output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model’s output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features’ impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output.Electronics280The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature’s impact on the model’s output.Electronics['Yibrah Gebreyesus', 'Damian Dalton', 'Davide De Chiara', 'Marta Chinnici', 'Andrea Chinnici']2024-04-24T00:00:00https://www.semanticscholar.org/paper/3a8bd0a9d52b9701589a9026a61f88ee69c74fb3 91c0f200b2d4e25711229948b7f2a4daab00b182Energy plays an important role in supporting modern life, and the need for efficient energy solutions is more important than ever. This research paper explores the unprecedented potential of AI-based AI to transform the energy industry through the use of smart solutions. Using the most advanced data science, AI algorithms and machine learning models, we can unlock valuable insights, improve energy efficiency models, control energy distribution and reduce costs. This article provides an in-depth look at the various applications of artificial intelligence in the energy industry and covers the development of artificial intelligence in energy integration, demand planning, strategic planning, security and energy management.2024 10th International Conference on Web Research (ICWR)290An in-depth look at the various applications of artificial intelligence in the energy industry and covers the development of artificial intelligence in energy integration, demand planning, strategic planning, security and energy management.2024 10th International Conference on Web Research (ICWR)['Mahdieh Zakizadeh', 'Mazyar Zand']2024-04-24T00:00:00https://www.semanticscholar.org/paper/91c0f200b2d4e25711229948b7f2a4daab00b182 0d97b4f68ebadaf11e9cb2ca2ed871eae03d2461In the evolving landscape of supply chain management, the integration of blockchain technology and artificial intelligence (AI) stands as a beacon of innovation, promising to address the perennial challenges of efficiency, transparency, and reliability. This paper presents a comprehensive exploration of how AI can revolutionize blockchain supply chains, offering a synthesis of current research, methodologies, and case studies that highlight the transformative potential of this synergy. The supply chain, a complex network that underpins global trade, is often beleaguered by inefficiencies and vulnerabilities. Blockchain technology, with its decentralized and immutable ledger, has emerged as a solution to enhance traceability and trust. However, it is the infusion of AI that has the potential to catalyze a paradigm shift in supply chain management. AI’s capabilities in data analytics, machine learning, and autonomous decision-making can optimize logistics, predict trends, and automate tasks, thereby elevating the blockchain beyond its current utility. This research adopts a mixed-methods approach, drawing on qualitative insights from industry experts and quantitative data from performance metrics to assess the impact of AI on blockchain supply chains. Through a series of case studies, the paper illustrates the practical applications and challenges of this integration, providing a nuanced understanding of its implications. The findings reveal that AI significantly enhances the efficiency and accuracy of blockchain supply chains, leading to improvements in transaction times, data verification processes, and overall supply chain performance. The discussion delves into the strategic advantages of this integration, such as improved compliance and ethical supply chain practices, while also acknowledging the limitations and challenges that organizations must navigate. In conclusion, the paper posits that the convergence of AI and blockchain holds great promise for the future of supply chains. It offers a roadmap for practitioners looking to harness these technologies and sets forth directions for future research, particularly in the development of sophisticated AI algorithms tailored for blockchain applications and the long-term economic impacts on supply chain management. The study contributes to the broader field by providing empirical evidence and a new perspective on the potential of AI to create more resilient, efficient, and transparent supply networks..International Journal of Advanced Research in Science, Communication and Technology110It is posits that the convergence of AI and blockchain holds great promise for the future of supply chains and offers a roadmap for practitioners looking to harness these technologies and sets forth directions for future research.International Journal of Advanced Research in Science, Communication and Technology['Vinay', 'Sagar Yadav', 'Attul Kumar', 'Prof. Renu Narwal']2024-04-24T00:00:00https://www.semanticscholar.org/paper/0d97b4f68ebadaf11e9cb2ca2ed871eae03d2461 ea51eeaffb2947ccf532ce6f4b1e678628d2adb7Amidst the rapid integration of artificial intelligence into education, this research aims to uncover patterns and themes within student-written assignments, shedding light on the diverse impacts of AI tools on the writing process. This study explores the intricate dynamics of undergraduate EFL learners’ interaction with AI-supported writing tools, focusing specifically on writing. The investigation, guided by questions about student use of AI-supported writing tools and the influence of different tools on writing quality, addresses notable gaps in the existing literature. A quasi-experimental study was conducted with purposive sampling of 62 Business Law undergraduates enrolled in a general English course at the International University of Rabat, Morocco, dividing them into two groups: one received structured AI training, while the other acted as a control group. Results revealed positive outcomes in language proficiency, creativity, organizational skills, and vocabulary use with AI assistance, emphasizing the transformative impact of AI on writing. The study also observed shifting preferences in AI writing tools, urging educators and developers to adapt to evolving user choices and promote continuous innovation in AI writing tools.Arab World English Journal370This study explores the intricate dynamics of undergraduate EFL learners’ interaction with AI-supported writing tools, focusing specifically on writing, and reveals positive outcomes in language proficiency, creativity, organizational skills, and vocabulary use with AI assistance.Arab World English Journal['Anass Moussa', 'Hassan Belhiah']2024-04-24T00:00:00https://www.semanticscholar.org/paper/ea51eeaffb2947ccf532ce6f4b1e678628d2adb7 3684881e6a9cc5a8cb18d0f0f45708211fc3f75aBackground: Our research delved into exploring various selected facets of AI-driven employee engagement, from the gender perspective, among Slovenian entrepreneurs. Methods: This research is based on a random sample of 326 large enterprises and SMEs in Slovenia, with an entrepreneur completing a questionnaire in each enterprise. Results: Findings suggest that there are no significant differences between male and female entrepreneurs in Slovenia regarding various aspects of AI-supported entrepreneurial management practice including the following: AI-supported entrepreneurial culture, AI-enhanced leadership, adopting AI to reduce employee workload, and incorporating AI tools into work processes. The widespread integration of AI into entrepreneurship marks a transition to a business landscape that values inclusivity and equity, measuring success through creativity, strategic technology deployment, and leadership qualities, rather than relying on gender-based advantages or limitations. Our research also focused on the identification of gender differences in path coefficients regarding the impact of the four previously mentioned aspects of AI on employee engagement. While both genders see the value in using AI to alleviate employee workload, the path coefficients indicate that female entrepreneurs report higher effectiveness in this area, suggesting differences in the implementation of AI-integrated strategies or tool selection. Male entrepreneurs, on the other hand, appear to integrate AI tools into their work processes more extensively, particularly in areas requiring predictive analytics and project scheduling. This suggests a more technical application of AI in their enterprises. Conclusions: These findings contribute to understanding gender-specific approaches to AI in enterprises and their subsequent effects on employee engagement.Systems720There are no significant differences between male and female entrepreneurs in Slovenia regarding various aspects of AI-supported entrepreneurial management practice including the following: AI-supported entrepreneurial culture, AI-enhanced leadership, adopting AI to reduce employee workload, and incorporating AI tools into work processes.Systems['M. Rožman', 'P. Tominc']2024-04-24T00:00:00https://www.semanticscholar.org/paper/3684881e6a9cc5a8cb18d0f0f45708211fc3f75a dd8204140282f9e1a9750f8c1070e6bebb48fcadThe rapid integration of artificial intelligence (AI) and automation within various sectors poses challenges and opportunities for the global workforce. This study investigates the implications of AI and automation on employment patterns, skills requirements, and remote work infrastructures. Employing a quantitative research design, data was collected through a structured questionnaire administered to 482 professionals across the information technology, healthcare, and finance sectors. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test hypotheses related to the impact of technological advancements on employment. Major findings indicate a significant, albeit complex, impact of AI and automation on employment. Most respondents recognized AI and automation as catalysts for creating new job opportunities and enhancing productivity, particularly in sectors with high integration of digital technologies. However, the study also highlighted substantial concerns regarding the widening skills gap and the adequacy of current employment policies in managing the transition. Specifically, sixty-nine percent of respondents identified a significant skills gap necessitating urgent educational and training interventions. About half of the respondents viewed existing employment policies as inadequate in addressing the challenges of rapid technological changes. The study concludes that while AI and automation are reshaping the employment landscape, creating new types of jobs, and altering skill requirements, there is a critical need for proactive adaptation strategies. Recommendations include developing targeted reskilling programs, adaptive employment policies, and robust remote work infrastructures to support an increasingly digital workforce. These strategies are essential to harness the benefits of digital transformations while mitigating potential adverse effects on employment.Archives of Current Research International00There is a critical need for proactive adaptation strategies to support an increasingly digital workforce, including developing targeted reskilling programs, adaptive employment policies, and robust remote work infrastructures to support an increasingly digital workforce.Archives of Current Research International['O. O. Olaniyi', 'Favour Amarachi Ezeugwa', 'Chinenye Gbemisola Okatta', 'Abayomi Shamsudeen Arigbabu', 'Princess Chimmy Joeaneke']2024-04-24T00:00:00https://www.semanticscholar.org/paper/dd8204140282f9e1a9750f8c1070e6bebb48fcad d9034ecbccc122396d2cd6cfbad834e9199f4a26This research paper presents a novel approach to developing a Software as a Service (SaaS) AI platform aimed at revolutionizing user interactions through dynamic conversations facilitated by an advanced AI conversation model. The platform prioritizes user-centric design, leveraging cutting-edge technologies to ensure a seamless and visually engaging experience. Key features include an emphasis on visual excellence through meticulous UI design and animations, as well as robust data integrity measures implemented for secure user data management. The platform also emphasizes operational resilience, with effective server error handling mechanisms in place to enhance user experience. Additionally, the integration of a Conversation Generation Tool, powered by advanced AI models, further enhances user engagement by enabling interactive and dynamic conversations. This research contributes to the advancement of SaaS platforms by demonstrating innovative approaches to user-centric design and AI integration. Key Words: Software as a Service (SaaS), Dynamic conversations, Advanced AI models, Conversation Generation ToolINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A novel approach to developing a Software as a Service (SaaS) AI platform aimed at revolutionizing user interactions through dynamic conversations facilitated by an advanced AI conversation model, which prioritizes user-centric design and AI integration.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Satyendra Kumar Mishra']2024-04-24T00:00:00https://www.semanticscholar.org/paper/d9034ecbccc122396d2cd6cfbad834e9199f4a26 a650abe3b8da771602efc3dc0839814aecea36d5Children with autism spectrum disorder (ASD) experience challenges in grasping social-emotional cues, which can result in difficulties in recognizing emotions and understanding and responding to social interactions. Social-emotional intervention is an effective method to improve emotional understanding and facial expression recognition among individuals with ASD. Existing work emphasizes the importance of personalizing interventions to meet individual needs and motivate engagement for optimal outcomes in daily settings. We design a social-emotional game for ASD children, which generates personalized stories by leveraging the current advancement of artificial intelligence. Via a co-design process with five domain experts, this work offers several design insights into developing future AI-enabled gamified systems for families with autistic children. We also propose a fine-tuned AI model and a dataset of social stories for different basic emotions.260A social-emotional game for ASD children is designed, which generates personalized stories by leveraging the current advancement of artificial intelligence and a fine-tuned AI model and a dataset of social stories for different basic emotions are proposed.['Yue Lyu', 'Pengcheng An', 'Huan Zhang', 'Keiko Katsuragawa', 'Jian Zhao']2024-04-24T00:00:00https://www.semanticscholar.org/paper/a650abe3b8da771602efc3dc0839814aecea36d5 f30cf28973617becdea353c31c70f9db89d6be60By Big-Thick data, we mean large-scale sensor data (big data) which provides an objective view of reality, coupled with thick data, i.e., data generated by people, which describes their subjective view of the reality described by big data. Big-thick data enables a machine understanding of human behavior and activities, as well as the human interpretation of what they are doing, i.e., their own personal descriptions of the why, what, and how. The goal of this short paper is to provide a high-level description of a platform, called i-Log, that enables the collection of big-thick data. Its core components are: tools for collecting sensor data as well as the user feedback (e.g., user answers to machine questions), and a dashboard which provides visual qualitative and quantitative feedback on how things are evolving, as well as suitable notifications to the user.141The goal of this short paper is to provide a high-level description of a platform, called i-Log, that enables the collection of big-thick data that provides visual qualitative and quantitative feedback on how things are evolving, as well as suitable notifications to the user.['Haonan Zhao', 'Ivan Kayongo', 'Leonardo Malcotti', 'Fausto Giunchiglia']2024-04-24T00:00:00https://www.semanticscholar.org/paper/f30cf28973617becdea353c31c70f9db89d6be60 29c07bf64a2b438954c876d566c557466730352cInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-24T00:00:00https://www.semanticscholar.org/paper/29c07bf64a2b438954c876d566c557466730352c e8bb68d033ef4a51dfe79ba096220f4c48ebebd8International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-24T00:00:00https://www.semanticscholar.org/paper/e8bb68d033ef4a51dfe79ba096220f4c48ebebd8 626638d7078c63b787426c7cbd680dd2e5597c3cInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-24T00:00:00https://www.semanticscholar.org/paper/626638d7078c63b787426c7cbd680dd2e5597c3c f902ffc6edfd9daf2d78c5d8df403aa600a8a2c9International Conference Proceedings BISET-24, BCBES-24, LEHS2-24 &amp; BEMEL-24 April 24-26, 2024 Barcelona (Spain)00International Conference Proceedings BISET-24, BCBES-24, LEHS2-24 &amp; BEMEL-24 April 24-26, 2024 Barcelona (Spain)[]2024-04-24T00:00:00https://www.semanticscholar.org/paper/f902ffc6edfd9daf2d78c5d8df403aa600a8a2c9 89e74665fbc2f2439d90de5534475c7502a5f175Soft skills have become increasingly essential for success in the modern world, especially in the labor market, where employers value employees’ social and communication skills. Online education, which is an integral part of the educational process in Ukraine, is adjusting to the development of students’ soft skills. Integrating artificial intelligence tools into English language learning is becoming a new direction in soft skills development. This approach opens up new teaching strategies that make learning more effective, engaging, and innovative. While learning English, students develop communication, creativity, and critical thinking skills, which contribute to their educational process and prepare the foundation for employment. The study aims to 1) evaluate the impact of artificial intelligence on the development of students’ soft skills in online learning; 2) identify the most essential soft skills for their effective learning and future employment based on a student survey; 3) develop criteria for an online course using artificial intelligence and outline strategies for integrating the ChatGPT tool into distance learning English classes. To achieve the objectives of our study, we developed and processed a questionnaire, collected quantitative data, and analyzed and interpreted qualitative data; the study sample included 304 students. The questionnaire results showed a generally positive attitude of students towards using artificial intelligence in English for Specific Purposes courses. They opened up prospects for introducing an online course in the English for Specific Purposes program and further research, including an experiment with the introduction of this online course.Arab World English Journal310The questionnaire results showed a generally positive attitude of students towards using artificial intelligence in English for Specific Purposes courses, which opened up prospects for introducing an online course in the English for Specific Purposes program and further research, including an experiment with the introduction of this online course.Arab World English Journal['Inna Borkovska', 'Hanna Kolosova', 'Iryna Kozubska', 'Inna Antonenko']2024-04-24T00:00:00https://www.semanticscholar.org/paper/89e74665fbc2f2439d90de5534475c7502a5f175 64f739ceb4b63564b5a9552d91e9011f8b27ffe3In a time when digital technologies are changing educational paradigms, this study delves into integrating Artificial Intelligence chatbots in enhancing English as a foreign language essay writing, marking a significant shift in language pedagogy. With the backdrop of increasing reliance on technology in educational settings, the research foregrounds Artificial Intelligence chatbots’ potential to revolutionise traditional teaching methodologies. This article aims to scrutinise the efficacy of these digital assistants in augmenting the essay-writing skills of English learners, underlining the importance of aligning technological advancements with educational needs. By investigating the incorporation of Artificial Intelligence chatbots into English as a foreign language curriculum, the study highlights their capacity to offer immediate, personalised feedback, fostering a learning environment that supports individual learners’ needs and preferences. The central inquiry revolves around identifying and optimising the mechanisms through which Artificial Intelligence chatbots can contribute to developing writing proficiency among English students. Through a comprehensive review, the paper presents insights into the pedagogical benefits and challenges of using Artificial Intelligence chatbots, including their role in promoting learner autonomy, accommodating diverse learning styles, and providing a safe space for linguistic experimentation. The findings underscore the transformative potential of Artificial Intelligence chatbots in language education, advocating for a paradigmatic shift towards more interactive, responsive, and learner-centred teaching approaches. This review not only reaffirms the significance of integrating technology into language learning but also opens new avenues for enhancing the educational experiences of English learnersArab World English Journal530The findings underscore the transformative potential of Artificial Intelligence chatbots in language education, advocating for a paradigmatic shift towards more interactive, responsive, and learner-centred teaching approaches.Arab World English Journal['Konstantinos M. Pitychoutis']2024-04-24T00:00:00https://www.semanticscholar.org/paper/64f739ceb4b63564b5a9552d91e9011f8b27ffe3 b1f2020610d687f0940550d773b7f7f9016868fcApplied Microbiology and Biotechnology500An end-to-end machine learning framework for optimizing media components and prediction of CQA is demonstrated, demonstrating that Fe and Zn significantly impact the charge variant profile.Applied Microbiology and Biotechnology['Neelesh Gangwar', 'Keerthiveena Balraj', 'Anurag S Rathore']2024-04-24T00:00:00https://www.semanticscholar.org/paper/b1f2020610d687f0940550d773b7f7f9016868fc e7f17506e684af2b949141fdc93bdcd8c755a8a9In safety-critical systems that interface with the real world, the role of uncertainty in decision-making is pivotal, particularly in the context of machine learning models. For the secure functioning of Cyber-Physical Systems (CPS), it is imperative to manage such uncertainty adeptly. In this research, we focus on the development of a vehicle's lateral control system using a machine learning framework. Specifically, we employ a Bayesian Neural Network (BNN), a probabilistic learning model, to address uncertainty quantification. This capability allows us to gauge the level of confidence or uncertainty in the model's predictions. The BNN based controller is trained using simulated data gathered from the vehicle traversing a single track and subsequently tested on various other tracks. We want to share two significant results: firstly, the trained model demonstrates the ability to adapt and effectively control the vehicle on multiple similar tracks. Secondly, the quantification of prediction confidence integrated into the controller serves as an early-warning system, signaling when the algorithm lacks confidence in its predictions and is therefore susceptible to failure. By establishing a confidence threshold, we can trigger manual intervention, ensuring that control is relinquished from the algorithm when it operates outside of safe parameters.350This research focuses on the development of a vehicle's lateral control system using a machine learning framework that employs a Bayesian Neural Network (BNN), a probabilistic learning model, to address uncertainty quantification.['Neha Kumari', 'Sumit Kumar. Sneha Priya', 'Ayush Kumar', 'Akash Fogla']2024-04-24T00:00:00https://www.semanticscholar.org/paper/e7f17506e684af2b949141fdc93bdcd8c755a8a9 88252e9b44d59a4bc9855e5fef199c1309af0671The population is expected to grow rapidly and reach 10 billion people by the year 2050. As a result, there will be a greater need for food. The conventional techniques employed by the farmers proved insufficient to meet these demands. For this, new automated techniques were unveiled. Along with other cutting-edge computer science applications, farming has long made use of technologies like artificial intelligence. The focus has shifted in recent years to consider the applications of this new technology. A significant percentage of humanity's nutrition has come from agriculture for thousands of years, with the most significant contribution being the broad adoption of efficient farming techniques for a variety of crops. The application of artificial intelligence (AI) in agriculture has sparked a revolution in the field, and AI technology has made the agro-based commercial sector operate more profitably. Artificial intelligence (AI) technologies have the power to transform the future and address problems. This will make it easier for farmers to learn about climate variance and pests that lower crops. The use of AI focuses on identifying damaged crops and enhancing the ability of healthy crops to provide higher yields. This paper gives a thorough analysis of AI models used in agricultural applications. It also examines the use of AI models to specify sustainable goals. This article explores the challenges and opportunities for utilizing AI to develop future generations of sustainable agriculture through this comprehensive review.International Journal of Environment and Climate Change01The challenges and opportunities for utilizing AI to develop future generations of sustainable agriculture through this comprehensive review of AI models used in agricultural applications are explored.International Journal of Environment and Climate Change['Subrata Das', 'Manvir Kaur', 'Vandna Chhabra', 'Titli Nandi', 'Purba Mishra', 'Sriman Ghosh']2024-04-24T00:00:00https://www.semanticscholar.org/paper/88252e9b44d59a4bc9855e5fef199c1309af0671 f9397abb0981e65a111dbfd74f5b002364a1017bOrganisations have long relied on arduous manual processes in managing their workforce planning activities. HR professionals are under intense pressure to keep up pace with the dynamic workforce scheduling in light of the persistent labour market disruptions. Recent advancements in Artificial Intelligence (AI) have made it possible for advanced data applications that help optimize many business processes. Managements are now enabled with AI solutions that range from automation of routine tasks to assisting them create strategies for talent management and retention. This research paper analysis how AI-powered tools are transforming the HR landscape and are being increasingly utilised by business entities to optimize their workforce management functions. The indispensable role played by AI in workforce planning in the retail, healthcare and finance industries in overcoming their challenges and optimizing their performance is examined. This research study demonstrates how adoption of AI has led to increased productivity and efficiency and emphasizes the crucial role it would continue to play in the future HR workforce management functions.International Journal For Multidisciplinary Research150This research study demonstrates how adoption of AI has led to increased productivity and efficiency and emphasizes the crucial role it would continue to play in the future HR workforce management functions.International Journal For Multidisciplinary Research['C. Sharmila Rao', 'Padmashree P', 'Sahana B S']2024-04-24T00:00:00https://www.semanticscholar.org/paper/f9397abb0981e65a111dbfd74f5b002364a1017b 45342584080062a5d096d64bf839ef8be3804c9dWith the growth of Artificial Intelligence technologies, there is interest in studying their potential impact on university academic writing courses. This study examined whether AI tools are replacing these courses by exploring how they effectively replace traditional academic writing instruction and this shift’s potential benefits and drawbacks. The researcher reviewed existing literature on integrating AI tools into academic writing instruction. The findings provide insights to educators navigating the integration of Artificial Intelligence tools into writing curricula while maintaining instructional quality and academic integrity standards. By synthesizing the latest research, this study can inform decisions about the appropriate use of Artificial Intelligence in teaching essential writing skills. Increased use of Artificial Intelligence writing tools has sparked debate about their role in academic writing instruction. Universities like Stanford have updated policies around Artificial Intelligence tool usage and academic integrity. The University of California issued guidance acknowledging the prevalence of generative Artificial Intelligence on campuses. Middlebury College banned classroom use of ChatGPT over concerns it could impede critical thinking and writing skill development. Results show that while Artificial Intelligence helps with grammar and style, questions remain about its impact on creativity and critical thinking. However, Artificial Intelligence is not replacing university writing courses. These courses teach critical thinking, research, citation, argumentation, creativity, originality, and ethics, which Artificial Intelligence lacks. Academic writing courses offer a complete learning experience. Artificial Intelligence may improve academic writing but is unlikely to replace traditional courses soon. A balanced approach integrating Artificial Intelligence support while preserving core elements of academic writing education appears most effective for preparing students for diverse writing challenges.Arab World English Journal1040Results show that while Artificial Intelligence helps with grammar and style, questions remain about its impact on creativity and critical thinking, however, Artificial Intelligence is not replacing university writing courses.Arab World English Journal['Hind Aljuaid']2024-04-24T00:00:00https://www.semanticscholar.org/paper/45342584080062a5d096d64bf839ef8be3804c9d 43f66befd37d55952cab09ad062e2d5de7027b3bThe adoption of the Open Government Data (OGD) in Morocco would strengthen government transparency by making public data accessible to all citizens. This would promote a better understanding of government activities, thus strengthening the population’s trust in public institutions. Open Data also plays a crucial role in the development of Artificial intelligence (AI), computer processing (computing), and engineering by providing open and transparent access to varied data sets. The objective of this research is to evaluate the state of open data initiatives in Morocco and compare them with the leading performers in the region. This aims to improve understanding of the Open Data landscape and its role in the Digital Government Transformation (DGT) in Morocco2024 International Conference on Global Aeronautical Engineering and Satellite Technology (GAST)150Evaluating the state of open data initiatives in Morocco and comparing them with the leading performers in the region aims to improve understanding of the Open Data landscape and its role in the Digital Government Transformation (DGT) in Morocco.2024 International Conference on Global Aeronautical Engineering and Satellite Technology (GAST)['Driss Essabbar', 'Saad Yasser Chadli', 'Hassnae Remmach']2024-04-24T00:00:00https://www.semanticscholar.org/paper/43f66befd37d55952cab09ad062e2d5de7027b3b 38461be7944e6e5919187c53457c058a86823446PURPOSE Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues. METHODS An international consortium of experts specialized in AI, dentistry, and smile design was engaged to emulate and assess the ethical challenges raised by the use of AI for smile design. An e-Delphi protocol was used to seek the agreement of the ITU-WHO group on well-established ethical principles regarding the use of AI (wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency). Each principle included examples of ethical challenges that users might encounter when using AI for smile design. RESULTS On the first round of the e-Delphi exercise, participants agreed that seven items should be considered in smile design (diversity, transparency, wellness, privacy protection, prudence, law and governance, and sustainable development), but the remaining four items (equity, accountability and responsibility, solidarity, and respect of autonomy) were rejected and had to be reformulated. After a second round, participants agreed to all items that should be considered while using AI for smile design. CONCLUSIONS AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.Journal of Prosthodontics250AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.Journal of prosthodontics : official journal of the American College of Prosthodontists['Rata Rokhshad', 'T. Karteva', 'A. Chaurasia', 'Raphaël Richert', 'Carl-Maria Mörch', 'F. Tamimi', 'M. Ducret']2024-04-24T00:00:00https://www.semanticscholar.org/paper/38461be7944e6e5919187c53457c058a86823446 2d35ff34fd4a468b24a1dd275a3e1d0fed7e38d9 In this cross-sectional study, we explored the knowledge, attitudes, and practices related to artificial intelligence (AI) among medical students in Sudan. With AI increasingly impacting healthcare, understanding its integration into medical education is crucial. This study aimed to assess the current state of AI awareness, perceptions, and practical experiences among medical students in Sudan. We aimed to evaluate the extent of AI familiarity among Sudanese medical students by examining their attitudes toward its application in medicine. Additionally, this study seeks to identify the factors influencing knowledge levels and explore the practical implementation of AI in the medical field. A web-based survey was distributed to medical students in Sudan via social media platforms and email during October 2023. The survey included questions on demographic information, knowledge of AI, attitudes toward its applications, and practical experiences. The descriptive statistics, chi-square tests, logistic regression, and correlations were analysed using SPSS version 26.0. Out of the 762 participants, the majority exhibited a basic understanding of AI, but detailed knowledge of its applications was limited. Positive attitudes toward the importance of AI in diagnosis, radiology, and pathology were prevalent. However, practical application of these methods was infrequent, with only a minority of the participants having hands-on experience. Factors influencing knowledge included the lack of a formal curriculum and gender disparities. This study highlights the need for comprehensive AI education in medical training programs in Sudan. While participants displayed positive attitudes, there was a notable gap in practical experience. Addressing these gaps through targeted educational interventions is crucial for preparing future healthcare professionals to navigate the evolving landscape of AI in medicine. Policy efforts should focus on integrating AI education into the medical curriculum to ensure readiness for the technological advancements shaping the future of healthcare. Annals of Medicine &amp; Surgery00The extent of AI familiarity among Sudanese medical students by examining their attitudes toward its application in medicine was evaluated, highlighting the need for comprehensive AI education in medical training programs in Sudan.Annals of Medicine &amp; Surgery['Mohammed Hammad Jaber Amin']2024-04-24T00:00:00https://www.semanticscholar.org/paper/2d35ff34fd4a468b24a1dd275a3e1d0fed7e38d9 ea08518732ba31a96650292f1dace7faf7978e7fThis article analyzes the current problems and prospects of using artificial intelligence technologies in the func-tioning of public control in the Russian Federation. The work elucidates the place and role of this civil society institution in securing legal guarantees for the implementation, protection, and defense of the system of consti-tutional principles, alongside the rights, liberties, and lawful interests of the populace and distinct legal entity categories. The authors analyze various points of view on the limits and prospects of using artificial intelligence technologies in the field of public administration. The necessity of modernizing approaches in the organization and activities of subjects of public control through the use of artificial intelligence technologies in their activities is substantiated. The article outlines and scrutinizes the primary challenges hindering the utilization of the above-mentioned technologies by subjects of public control. A set of solutions is created and justified to ad-dress these issues, including through the introduction of amendments and additions to the Russian legislation on public control.Теория и практика общественного развития00The article outlines and scrutinizes the primary challenges hindering the utilization of the above-mentioned technologies by subjects of public control, and creates a set of solutions to ad-dress these issues, including through the introduction of amendments and additions to the Russian legislation on public control.Теория и практика общественного развития['Vitaly V. Goncharov', 'V. V. Nagaytsev', 'Elena G. Petrenko', 'E. Pustovalova']2024-04-24T00:00:00https://www.semanticscholar.org/paper/ea08518732ba31a96650292f1dace7faf7978e7f 5038c240b5111934ba8d42a04b06531dad824fffCurrently, technology-based methods are widely used in the solutions developed for smart cities and sustainable transportation. Owing to the rapid advances in technology, the traditional structure of transportation networks is undergoing a paradigm shift. Artificial Intelligence (AI), that has started to have disruptive effects in many sectors in the future, is expected to be one of the most influential factors in the paradigm shift in transportation. In this paper, the dynamics of the paradigmatic shift in transportation to promote sustainable transportation in Türkiye are evaluated through conducting a review of the existing literature. Scenarios exhibiting the use of disruptive and innovative technologies in transport systems, specifically, AI applications in intelligent transportation systems (ITS), are examined. Additionally, the economic, environmental, and social impacts of AI applications are discussed by emphasizing the need to identify priority areas for the effective use of AI in the field of intelligent transport. Thus, this paper, by summarizing the use of AI-based technologies for intelligent transport in Türkiye, contributes to the literature by providing an overview of the existing knowledge.Acta INFOLOGICA630The dynamics of the paradigmatic shift in transportation to promote sustainable transportation in Türkiye are evaluated through conducting a review of the existing literature and an overview of the existing knowledge is provided.Acta Infologica['Esma Dilek', 'Özgür Talih', 'Türksel Kaya Bensghir']2024-04-24T00:00:00https://www.semanticscholar.org/paper/5038c240b5111934ba8d42a04b06531dad824fff 80041c57473b53f419c9a734534d84e69c3eca6cThe objective of this paper is to define the models of responsibility for intelligent systems in situations when harm is caused (in the form of any wrongdoings, including crimes). For this purpose, the paper examines the current state of artificial intelligence technologies from the standpoint of moral, volitional and intellectual autonomy for modeling approaches to their legal personality. Such autonomy can be expressed only through the software element of a technological system, that is, even in the case of robots (cyberphysical systems), their legal assessment requires an analysis of how operations are carried out in order to evaluate incoming information rather than the physical characteristics of such a system. The author analyzes approaches according to which intelligent systems can be compared with legal entities, individuals, animals, and meta-directional structures in terms of the volume and nature of their legal capacity. The conclusion is made about the need for an independent legal assessment of artificial intelligence systems beyond their comparison with the existing legal categories. The need to train a system using a limited dataset, that is, without additional training in a real environment, adversarial attacks and internal errors of intelligent systems are considered as examples of technical limitations of technology that do not allow to raise the question of its subjectivity at the moment. The author highlights that in order to determine responsibility for the harm caused by an intelligent system, it is necessary to establish a circle of persons between whom it is distrubuted: an intelligent system itself, its developer and the operator (user). Thus, the author defines 10 models of responsibility distribution between them.Lex Russica160The objective of this paper is to define the models of responsibility for intelligent systems in situations when harm is caused (in the form of any wrongdoings, including crimes), and defines 10 models of responsibility distribution between them.Lex Russica['D. V. Bakhteev']2024-04-24T00:00:00https://www.semanticscholar.org/paper/80041c57473b53f419c9a734534d84e69c3eca6c 88f25d5d5b4448838e28bdc67e15bee8a22ad698Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, We have developed an integrated information system for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups were successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, we have identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance. This article is protected by copyright. All rights reserved.Advanced Healthcare Materials00It is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.Advanced healthcare materials['Miao Jiang', 'Pengyun Wu', 'Yuwei Zhang', 'Mengling Wang', 'Mingjie Zhang', 'Zhaoxiang Ye', 'Xuejun Zhang', 'Cai Zhang']2024-04-24T00:00:00https://www.semanticscholar.org/paper/88f25d5d5b4448838e28bdc67e15bee8a22ad698 e8b9f80180c6a9cfa85a15dcd02956274a608907This study examines the perceptions of instructors, students, and administrative staff on the role of ChatGPT in Oman’s educational setting. This study is significant as it provides insights into the extent to which Artificial Intelligence is used in education and provides guidance for future plans. Examining the perceptions of the various stakeholders in an education setting in Oman provides valuable information for higher learning institutions that are keen on embracing new technology while keeping to the traditional education values. The study utilized focus group discussions to gather the data from the instructors, students, and administrative staff. The findings revealed that ChatGPT’s instrumental role is in refining content, especially among students, administrative staff, and instructors who are non-native English speakers. Administrative staff and instructors highlighted its efficacy in drafting emails, indicating Artificial Intelligence’s potential to improve routine cognitive tasks. Students appreciated ChatGPT for explaining complex academic tasks. However, concerns surface from the instructors regarding over-reliance on Artificial Intelligence and potential loss of academic integrity, resonating with previous literature. These findings are contextualized within Oman’s unique socio-cultural and educational settings. Given the emergent nature of Artificial Intelligence in Oman’s education, the study offers insights that provide a foundation for future research and guide policymaking.Arab World English Journal410The findings revealed that ChatGPT’s instrumental role is in refining content, especially among students, administrative staff, and instructors who are non-native English speakers, indicating Artificial Intelligence’s potential to improve routine cognitive tasks.Arab World English Journal['Syerina Syahrin', 'Nurul Akmal']2024-04-24T00:00:00https://www.semanticscholar.org/paper/e8b9f80180c6a9cfa85a15dcd02956274a608907 56f0d4c085b5baea4ecbd845ca1392b8175e32ea: Many people face various challenges and difficulties in maintaining their mental health and well-being, such as stress, anxiety, depression, loneliness, low self-esteem, lack of motivation, and so on. These challenges can negatively impact their personal and professional lives, as well as their physical health. Moreover, many people do not have access to adequate and affordable mental health care services, or they may face stigma and discrimination when seeking help. Therefore, there is a need for an innovative and accessible solution that can nurture the mental health and well-being of users by providing them with clinical, educational, decisional, and skill development support. WellNest is a web application that aims to nurture the mental health and well-being of users by leveraging artificial intelligence (AI) techniques. WellNest provides various features. WellNest aims to create a positive and supportive environment for the user, where they can interact with the chatbots as their virtual friends and mentors. WellNest also allows the user to track their progress, set their goals, and share their achievements with other users. WellNest is designed to be user-friendly, accessible, and engaging for the user.International Research Journal of Modernization in Engineering Technology and Science30WellNest is a web application that aims to nurture the mental health and well-being of users by leveraging artificial intelligence (AI) techniques and aims to create a positive and supportive environment for the user.International Research Journal of Modernization in Engineering Technology and Science['Jay Kulkarni', 'Aniket Kamlapurkar', 'Omkar Shinde', 'Prathamesh Jadhav', 'Prof. Jagtap Vidya']2024-04-24T00:00:00https://www.semanticscholar.org/paper/56f0d4c085b5baea4ecbd845ca1392b8175e32ea 2fcb366eade29bbbc1425d67c8f191bd992cce10This project introduces an innovative AI-driven Career Guidance System designed to assist individuals in making informed decisions about their professional paths. With the rapid evolution of industries and job markets, the need for personalized and adaptive career advice has become crucial. Our system leverages artificial intelligence techniques, including machine learning and natural language processing, to analyze user data and industry trends, providing tailored recommendations for career development. The AI model integrates diverse data sources, such as educational backgrounds, skills, and preferences, to create comprehensive user profiles. By employing advanced algorithms, the system generates insightful suggestions regarding suitable career paths, potential skill enhancements, and emerging job opportunities. Real-time updates ensure that users receive the latest information on market demands, enabling them to stay competitive and align their skills with industry trendsInternational Journal of Advanced Research in Science, Communication and Technology80This project introduces an innovative AI-driven Career Guidance System designed to assist individuals in making informed decisions about their professional paths, using artificial intelligence techniques to analyze user data and industry trends, providing tailored recommendations for career development.International Journal of Advanced Research in Science, Communication and Technology['Mr. G. Rajaraman', 'Arun A', 'Kodieswaran A', 'Kumanan K', 'Prem M']2024-04-24T00:00:00https://www.semanticscholar.org/paper/2fcb366eade29bbbc1425d67c8f191bd992cce10 2b40cc15876b916c93f3aec09f008e82ee4c05ecPurpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.European Journal of Ophthalmology300The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.European journal of ophthalmology['Donatella Musetti', 'C. Cutolo', 'Monica Bonetto', 'Mauro Giacomini', 'Davide Maggi', 'Giorgio Luciano Viviani', 'Ilaria Gandin', 'C. Traverso', 'M. Nicolò']2024-04-24T00:00:00https://www.semanticscholar.org/paper/2b40cc15876b916c93f3aec09f008e82ee4c05ec 147dfb8a9ea663d5355e82a9026d03932d5ad97aSince the pandemic started, organisations have been actively seeking ways to improve their organisational agility and resilience (regility) and turn to Artificial Intelligence (AI) to gain a deeper understanding and further enhance their agility and regility. Organisations are turning to AI as a critical enabler to achieve these goals. AI empowers organisations by analysing large data sets quickly and accurately, enabling faster decision-making and building agility and resilience. This strategic use of AI gives businesses a competitive advantage and allows them to adapt to rapidly changing environments. Failure to prioritise agility and responsiveness can result in increased costs, missed opportunities, competition and reputational damage, and ultimately, loss of customers, revenue, profitability, and market share. Prioritising can be achieved by utilising eXplainable Artificial Intelligence (XAI) techniques, illuminating how AI models make decisions and making them transparent, interpretable, and understandable. Based on previous research on using AI to predict organisational agility, this study focuses on integrating XAI techniques, such as Shapley Additive Explanations (SHAP), in organisational agility and resilience. By identifying the importance of different features that affect organisational agility prediction, this study aims to demystify the decision-making processes of the prediction model using XAI. This is essential for the ethical deployment of AI, fostering trust and transparency in these systems. Recognising key features in organisational agility prediction can guide companies in determining which areas to concentrate on in order to improve their agility and resilience.PLoS ONE590This study aims to demystify the decision-making processes of the prediction model using XAI, essential for the ethical deployment of AI, fostering trust and transparency in these systems.PLOS ONE['N. Shafiabady', 'Nick Hadjinicolaou', 'Nadeesha Hettikankanamage', 'Ehsan MohammadiSavadkoohi', 'Robert M X Wu', 'James Vakilian']2024-04-24T00:00:00https://www.semanticscholar.org/paper/147dfb8a9ea663d5355e82a9026d03932d5ad97a cba5bad6b4db192cf87affe0ef4bc5d39e17f7bbIn this editorial by Dr. Shouping Hu and Dr. Fengfeng Ke at Florida State University, they discuss the need to systematically reconsider the nature of student success in the age of artificial intelligence.Journal of Postsecondary Student Success50The need to systematically reconsider the nature of student success in the age of artificial intelligence is discussed.Journal of Postsecondary Student Success['Shouping Hu', 'Fengfeng Ke']2024-04-24T00:00:00https://www.semanticscholar.org/paper/cba5bad6b4db192cf87affe0ef4bc5d39e17f7bb 6ef401f5e163b163e07de03a73887d40655a611eE-tourism has become a key component in the tourism industry and allows travelers to easily access information about destinations and services via the Internet, facilitating their travel planning process. Through e-tourism, travelers can research destinations, plan trips, book accommodations, purchase transportation tickets, and explore tourist attractions through online resources. Also, this area helps tourism businesses to effectively promote their services, communicate with clients and manage reservations. E-tourism represents the integration of information and communication technologies in the tourism sector. This concept encompasses the application of digital technologies such as the Internet, mobile applications, online reservation systems, web platforms and social media to improve aspects of travel and tourism. The legal foundation of educational procedures utilizing artificial intelligence algorithms in e-tourism holds significant importance because of the multitude of potential legal complexities and obstacles that could emerge. The Republic of Serbia, like many other countries, is working on improving its regulations in order to enable the development of e-tourism and at the same time ensure the protection of the interests and safety of travelers.International Journal of Cognitive Research in Science, Engineering and Education100The legal foundation of educational procedures utilizing artificial intelligence algorithms in e-tourism holds significant importance because of the multitude of potential legal complexities and obstacles that could emerge.International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)['Ž. Spalević', 'Bojana Milosavljević', 'Sanja Marković']2024-04-24T00:00:00https://www.semanticscholar.org/paper/6ef401f5e163b163e07de03a73887d40655a611e ab096af703f83170e9f358b44d8a69f76d932f79В статье описываются возможности искусственного интеллекта и их использование в целях повышения эффективности образовательного процесса. Обосновывается необходимость развития нового направления научно-педагогических исследований «Искусственный интеллект в образовании». Представлены два направления подготовки учителей информатики: искусственный интеллект как объект изучения в рамках учебного предмета «Информатика» и искусственный интеллект как средство повышения эффективности процесса обучения. The article considers the capabilities of artificial intelligence and its use to increase educational process efficiency. The necessity of developing a new direction of scientific and pedagogical research “Artificial Intelligence in Education” is considered in the article. Two areas of training for computer science teachers are presented: artificial intelligence as an object of study within the educational subject “Informatics” and artificial intelligence as a means of increasing learning process efficiency.Пространство педагогических исследований00Пространство педагогических исследований['И.В. Роберт']2024-04-24T00:00:00https://www.semanticscholar.org/paper/ab096af703f83170e9f358b44d8a69f76d932f79 a946fcfbc0afea72def3e48d729f4805f2104310This study hopes to bring insights for researchers and educators in using artificial intelligence for essay writing through a systematic review on the use of AI in writing for the past 10 years. Although Artificial Intelligence has long existed in other fields such as medicine, engineering, journalism, and forensic analysis, it has only made a great impact in the education field after the emergence of ChatGPT. Generative Artificial Intelligence is seen as a tool that can assist teachers and students in academics such as generating ideas, evaluating essays, storytelling, and providing feedback. It has even been considered as the co-author in students’ manuscripts and essays. However, there are still lack of studies on the usage of Artificial Intelligence in developing students’ essay writing performance. Thus, this study hopes to enlighten researchers and educators in using Artificial Intelligence tools in improving students’ essay writing performance. This study will provide insights for researchers and teachers on the different types of artificial intelligence tools that can be used in teaching essay writing. It also provides areas that researchers can focus on since majority of the studies are conducted overseas and only two studies are carried out in Malaysia so far. Hopefully, this study will provide useful information for language teachers in using artificial intelligence tools in teaching essay writing to students.Arab World English Journal270This study hopes to bring insights for researchers and educators in using artificial intelligence for essay writing through a systematic review on the use of AI in writing for the past 10 years through a systematic review on the usage of AI in writing for the past 10 years.Arab World English Journal['Shirley Ling Jen', 'Abdul Rahim Salam']2024-04-24T00:00:00https://www.semanticscholar.org/paper/a946fcfbc0afea72def3e48d729f4805f2104310 83225705537ba3215ac1cd07df1838708b5f5559Artificial intelligence is helping scientists using computing software, new programs and algorithms to identify the DNA variants in our genomes and misspellings in human DNA that are most likely to cause disease. These predictions may help to diagnose even uncommon diseases more quickly and provide guidance for the creation of new drugs and a succesful treatmentInnova00Innova['Murilo M. E. Del Pozo', 'D.S.R. Rajkumar']2024-04-24T00:00:00https://www.semanticscholar.org/paper/83225705537ba3215ac1cd07df1838708b5f5559 f8b872b9ad53896b8c34b72ec0b757b0a475299dThose interested in artificial intelligence technologies, especially supervised and unsupervised learning in education, know they need considerable data for well-modeled training and high-quality accuracy. However, data access is not easy as it drains time and effort. So, in this paper, we will talk about the impact of artificial intelligence on education in general and how it will affect teachers' role in the future. We will also create an algorithm that will integrate several algorithms sequentially that will add value to this paper and contribute significantly to solving most of the problems that exist in education. Since the reinforcement learning (RL) algorithm is based on trial and error during training, it stores all the good events done in a considerable buffer. Once the RL model is ready, we will collect enough data to be used to train one of the algorithms mentioned earlier (supervised learning and unsupervised learning).2024 International Conference on Global Aeronautical Engineering and Satellite Technology (GAST)260The impact of artificial intelligence on education in general and how it will affect teachers' role in the future is discussed and an algorithm is created that will integrate several algorithms sequentially that will add value to this paper and contribute significantly to solving most of the problems that exist in education.2024 International Conference on Global Aeronautical Engineering and Satellite Technology (GAST)['A. Gourari', 'Mustapha Raoufi', 'M. Skouri', 'Abdelghani Ait Ben Braim', 'Mustapha Ezzini', 'Yassine Ait Lahsen', 'Samira Achki']2024-04-24T00:00:00https://www.semanticscholar.org/paper/f8b872b9ad53896b8c34b72ec0b757b0a475299d d30603bd3392c339232776d175f3682117ab5fe0 A recent study investigated the potential applications of artificial intelligence (AI) in poultry farming. One area where AI can be helpful is in the early detection of diseases. By analyzing data from various sources, such as sensor readings and health records, AI algorithms can identify potential disease outbreaks or health risks in flocks, allowing farmers to take timely preventive measures. Another area where AI can be applied is in controlling the environmental conditions of farms. By analyzing data from sensors that monitor temperature, humidity, ventilation, and lighting conditions, AI algorithms can help farmers create a comfortable and healthy environment for birds, improving their growth and reducing their stress. AI can also optimize the management of healthcare supplies for poultry. By analyzing the nutritional requirements of birds and the availability and prices of different ingredients, AI algorithms can help farmers optimize feed formulations, reducing waste and environmental impacts. Finally, the study explored the use of robots in poultry care. Robots can be used for cleaning, feeding, and monitoring individual birds. By automating these tasks, farmers can reduce labor costs and improve the efficiency of their operations. Overall, the study highlights the potential benefits of using AI and robotics in poultry farming, including early disease detection, improved environmental conditions, optimized feed formulations, and increased automation.Annals of Animal Science330The study highlights the potential benefits of using AI and robotics in poultry farming, including early disease detection, improved environmental conditions, optimized feed formulations, and increased automation.Annals of Animal Science['Hassan M. Taleb', 'Khalid Mahrose', 'Amal A. Abdel-Halim', 'Hebatallah Kasem', 'Gomaa S. Ramadan', 'Ahmed M. Fouad', 'A. Khafaga', 'N. E. Khalifa', 'Mahmoud Kamal', 'H. Salem', 'A. Alqhtani', 'Ayman A. Swelum', 'A. Arczewska-Włosek', 'S. Świątkiewicz', 'M. A. Abd El-Hack']2024-04-24T00:00:00https://www.semanticscholar.org/paper/d30603bd3392c339232776d175f3682117ab5fe0 1038df27c203d84b37f2b9522dfc397a3ae1ea99The paper highlights communicative, ethical, and educational aspects of AI applications for fan fiction writing. This study is the first attempt to analyze opportunities and academic aspects of using AI for fan fiction writing. The article offers a brief overview of research aspects of AI and fan fiction and presents a case study an AI-generated fanfic based on Naruto series. The objective of the work is to study the use of artificial intelligence in the context of fan fiction writing, specifically focusing on how AI technology can assist or enhance the creative process of fan fiction authors. The research questions aim to investigate the implications and opportunities of employing AI in fan fiction writing and education, shedding light on the evolving relationship between technology and creative expression in online communities. While AI technology can offer various benefits and enhancements to the creative process of fan fiction writing, there are also some drawbacks to consider when using AI for this purpose. AI-generated fan fiction may lack the creativity, originality, and emotional depth human authors can bring to their writing. Using AI-powered fan fiction can be a creative and engaging way to assist in learning foreign languages. AI-generated fan fiction can expose learners to authentic language use, colloquial expressions, and cultural references in a fun and interactive format. Reading fan fiction in the target language can help improve vocabulary, grammar, and comprehension skills. When working with already published fan fiction, some ethical and legal issues arise, such as copyright infringement, plagiarism, and misrepresentation.Arab World English Journal100This study is the first attempt to analyze opportunities and academic aspects of using AI for fan fiction writing, and presents a case study an AI-generated fanfic based on Naruto series.Arab World English Journal['Daryna Stanko']2024-04-24T00:00:00https://www.semanticscholar.org/paper/1038df27c203d84b37f2b9522dfc397a3ae1ea99 e650416a58de2d408b94f7d4157b2430519c57b2Generative artificial intelligence and Large Language Models are reshaping labor dynamics and occupational health practices. As AI continues to evolve, there's a critical need to customize ethical considerations for its specific impacts on occupational health. Recognizing potential ethical challenges and dilemmas, stakeholders and physicians are urged to proactively adjust the practice of occupational medicine in response to shifting ethical paradigms. By advocating for a comprehensive review of the International Commission on Occupational Health ICOH code of Ethics, we can ensure responsible medical AI deployment, safeguarding the well-being of workers amidst the transformative effects of automation in healthcare.La Medicina del lavoro00By advocating for a comprehensive review of the International Commission on Occupational Health ICOH code of Ethics, stakeholders and physicians are urged to proactively adjust the practice of occupational medicine in response to shifting ethical paradigms.La Medicina del lavoro['Antonio Baldassarre', 'Martina Padovan']2024-04-24T00:00:00https://www.semanticscholar.org/paper/e650416a58de2d408b94f7d4157b2430519c57b2 03b05c12e59c77dc6633a3b562dbb5aef081b410The analysis of students’ attitudes and perceptions represents a basis for enhancing different types of activities, including teaching, learning, assessment, etc. Emphasis might be placed on the implementation of modern procedures and technologies, which play an important role in the process of digital transformation. Among them is artificial intelligence—a technology that has already been found to be applicable in various sectors. When it comes to education, several AI-based tools and platforms can be used by students and teachers. Besides offering customized learning experiences, AI may play a significant part in establishing the concept of sustainability, especially when concerning the achievement of sustainable development goal 4. This paper investigates students’ intention to use artificial intelligence in education, taking three predictors from the UTAUT model and AI awareness as the moderator. The analysis included students from the Autonomous Province of Vojvodina, Republic of Serbia. For the purpose of the research, the partial least squares structural equation modeling (PLS-SEM) method was applied. Hereby, two models (without and with a moderator) were tested to examine the main and moderating effects, respectively. Regarding the results, while interaction terms were non-significant, the impacts of performance expectancy, effort expectancy, and social influence on behavioral intention were significant and positive.Sustainability540This paper investigates students’ intention to use artificial intelligence in education, taking three predictors from the UTAUT model and AI awareness as the moderator and the impacts of performance expectancy, effort expectancy, and social influence on behavioral intention were significant and positive.Sustainability['Nikola Milićević', 'Branimir Kalaš', 'Nenad Djokic', 'Borka Malčić', 'Ines Djokic']2024-04-24T00:00:00https://www.semanticscholar.org/paper/03b05c12e59c77dc6633a3b562dbb5aef081b410 2a99edbf4e927ef8cd31a2b2b2fc7ab6b40799eeThis paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although the integration of AI into the field of occupational health and safety is still in its early stages, it has numerous applications in the workplace. Some of these applications offer numerous benefits for the health and safety of workers, such as continuous monitoring of workers' health and safety and the workplace environment through wearable devices and sensors. However, AI might have negative impacts in the workplace, such as ethical worries and data privacy concerns. To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.La Medicina del lavoro00To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.La Medicina del lavoro['M. El-Helaly']2024-04-24T00:00:00https://www.semanticscholar.org/paper/2a99edbf4e927ef8cd31a2b2b2fc7ab6b40799ee d0ac25c8da4cdfbc6a42efd6ff60c7bac6900aa300[]2024-04-24T00:00:00https://www.semanticscholar.org/paper/d0ac25c8da4cdfbc6a42efd6ff60c7bac6900aa3 bceb60c76bd01fe6b8c5ffa4c81ada5595e6f686Research in Developmental Disabilities390It is confirmed that the effects of AI in non-high-income economies and among women are not significant in the lower regime, which confirms the nonlinear association between AI and the unemployment rate of people with disability.Research in developmental disabilities['Mehdi Abid', 'O. Ben-Salha', 'Karim Gasmi', 'Nasareldeen Hamed Ahmed Alnor']2024-04-24T00:00:00https://www.semanticscholar.org/paper/bceb60c76bd01fe6b8c5ffa4c81ada5595e6f686 a0cbeea4d34de6ff1c3b6e121f0dada74ccc4740AJR. American journal of roentgenology20AJR. American journal of roentgenology['Babak Saboury', 'M. Ghesani']2024-04-24T00:00:00https://www.semanticscholar.org/paper/a0cbeea4d34de6ff1c3b6e121f0dada74ccc4740 3c816b421c75c61017d79e4e984af0e63455ecfe3rd International PhD Student’s Conference at the University of Life Sciences in Lublin, Poland: ENVIRONMENT – PLANT – ANIMAL – PRODUCT003rd International PhD Student’s Conference at the University of Life Sciences in Lublin, Poland: ENVIRONMENT – PLANT – ANIMAL – PRODUCT['Igor Tomašević']2024-04-24T00:00:00https://www.semanticscholar.org/paper/3c816b421c75c61017d79e4e984af0e63455ecfe e3105ecc43094bd8f3552bead3b66bb4bfb47ebdJournal of Medical Evidence250Journal of Medical Evidence['V. Malik', 'Meenu Singh']2024-04-24T00:00:00https://www.semanticscholar.org/paper/e3105ecc43094bd8f3552bead3b66bb4bfb47ebd 54fa56f432feedb7c889a1dc85cbd83aafa9ee12Interactive Learning Environments700Interactive Learning Environments['Xiaohong Lin', 'Zhang Jun', 'Xiaoming Cao', 'Zhao Beina']2024-04-24T00:00:00https://www.semanticscholar.org/paper/54fa56f432feedb7c889a1dc85cbd83aafa9ee12 293d293dcb8536cb9af13c43150b27db5ac93880AJR. American journal of roentgenology10AJR. American journal of roentgenology['Eliot Siegel', 'Michael Morris']2024-04-24T00:00:00https://www.semanticscholar.org/paper/293d293dcb8536cb9af13c43150b27db5ac93880 f83707e810ae92f65a61d524039a5bc354d39fe5Progress in machine learning and artificial intelligence has spurred the widespread adoption of automated decision systems (ADS). An extensive literature explores what conditions must be met for these systems' decisions to be fair. However, questions of legitimacy -- why those in control of ADS are entitled to make such decisions -- have received comparatively little attention. This paper shows that when such questions are raised theorists often incorrectly conflate legitimacy with either public acceptance or other substantive values such as fairness, accuracy, expertise or efficiency. In search of better theories, we conduct a critical analysis of the philosophical literature on the legitimacy of the state, focusing on consent, public reason, and democratic authorisation. This analysis reveals that the prevailing understanding of legitimacy in analytical political philosophy is also ill-suited to the task of establishing whether and when ADS are legitimate. The paper thus clarifies expectations for theories of ADS legitimacy and charts a path for a future research programme on the topic.920A critical analysis of the philosophical literature on the legitimacy of the state, focusing on consent, public reason, and democratic authorisation reveals that the prevailing understanding of legitimacy in analytical political philosophy is also ill-suited to the task of establishing whether and when ADS are legitimate.['Jake Stone', 'Brent Mittelstadt']2024-04-24T00:00:00https://www.semanticscholar.org/paper/f83707e810ae92f65a61d524039a5bc354d39fe5 59f5e8f965e6b327d0789797601d34e8bbfbecceThis paper addresses the question of trust in communication, or mediated trust, with regard to the historical evolution of the Internet and, more recently, debates around the impacts of artificial intelligence (AI). At a conceptual level, it proposes a ‘Three I's’ framework of ideas, interests, and institutions as a way of understanding how and why current proposals for greater regulation of digital platforms counterpose questions around credibility and social licence for digital tech giants against a dominant set of ideas around the Internet as a privileged domain of free speech. By contrast, the rise of AI comes at a time when data‐driven business models associated with dominant platform businesses are in the ascendancy, so institutional arrangements need to be considered as a form of countervailing power to the capacity of tech giants to use AI to further consolidate forms of economic, political and communications power.Policy &amp; Internet420A ‘Three I's’ framework of ideas, interests, and institutions is proposed as a way of understanding how and why current proposals for greater regulation of digital platforms counterpose questions around credibility and social licence for digital tech giants against a dominant set of ideas around the Internet as a privileged domain of free speech.Policy &amp; Internet['Terry Flew']2024-04-23T00:00:00https://www.semanticscholar.org/paper/59f5e8f965e6b327d0789797601d34e8bbfbecce 06052c9e590453bcfc579058f5732abe997d1edcSuccessful applications of artificial intelligence (AI), such as ChatGPT, have been prompting regulators to speed up the related regulation processes. China and the EU have been particularly ambitious in this regard. The EU AI Act has been swiftly progressing through the institutions and is expected to be officially adopted in spring 2024. This article argues that its overall approach is wrong, that it extends EU regulation into policy areas which come under national competences and that it will hurt European AI innovation in particular and society in general. Instead of regulating AI per se, the EU or the member states should regulate the use of AI in specific sectors or, better still, regulate it in technologically independent ways—by specifying what is allowed or prohibited, regardless of the technology used.European View20The EU AI Act is argued that its overall approach is wrong, that it extends EU regulation into policy areas which come under national competences and that it will hurt European AI innovation in particular and society in general.European View['Žiga Turk']2024-04-23T00:00:00https://www.semanticscholar.org/paper/06052c9e590453bcfc579058f5732abe997d1edc 6b68c9c23708fdfcc1ce8260a061964430f98beb The European Union’s (EU) decarbonization strategy involves hydrogen as an integral pillar. Since hydrogen is a secondary energy carrier, meaning it must be manufactured, not all hydrogen is made equal. Renewable hydrogen or RFNBOs (Renewable Fuel of Non-Biological Origins) have been given high priority by the EU in their strategy to establish a market for hydrogen. The EU’s creation of RFNBO usage objectives is a step towards establishing a demand-side market. One such goal is to bring into the EU up to 10 million tonnes of renewable hydrogen. However, it is doubtful that this large amount can be reached given the strict criteria for what qualifies as renewable hydrogen and, thus, as RFNBO. This article analyses key provisions of the EU framework that affect the EU’s aim to produce and import up to 20 million tonnes of hydrogen overall. It concludes the EU’s emphasis on RFNBOs rather than a broader range of hydrogen pathways undermines the goal of rapidly developing a hydrogen market. Using turquoise hydrogen as a case study, the article shows how the EU’s decarbonization efforts would be aided, trade would be enabled, and fragmentation would be decreased if targets were expanded to encompass additional forms of hydrogen.The Journal of World Energy Law &amp; Business00The Journal of World Energy Law &amp; Business['Kim Talus', 'Jaqueline Pinto', 'Francisca Gallegos']2024-04-23T00:00:00https://www.semanticscholar.org/paper/6b68c9c23708fdfcc1ce8260a061964430f98beb 5149bd1d15adf875d4925682a2fde8269126c8a3Ovarian cancer poses a significant health burden as one of the deadliest malignancies affecting women globally. Histotype assignment of epithelial ovarian cancers can be challenging due to morphologic overlap, inter-observer variability, and the lack of ancillary diagnostic techniques in some areas of the world. Moreover, rare cancers can pose particular diagnostic difficulties because of a relative lack of familiarity with them, underscoring the necessity for robust diagnostic methodologies. The emergence of Artificial Intelligence (AI) has brought promising prospects to the realm of ovarian cancer diagnosis. While various studies have underscored AI's promise, its validation across multiple healthcare centers and hospitals has been limited. Inspired by innovations in medical imaging driven by public competitions, we initiated the Ovarian Cancer subtypE clAssification and outlier detectioN (OCEAN) challenge, the most extensive histopathology competition to date.medRxiv262The Ovarian Cancer subtypE clAssification and outlier detectioN (OCEAN) challenge, the most extensive histopathology competition to date, is initiated, the most extensive histopathology competition to date.['M. Asadi-Aghbolaghi', 'H. Farahani', 'A. Zhang', 'A. Akbari', 'S. Kim', 'A. Chow', 'S. Dane', 'OCEAN Challenge Consortium', 'OTTA Consortium', 'D. G Huntsman', 'C. Gilks', 'S. Ramus', 'M. KoÌ\x88bel', 'A. N Karnezis', 'A. Bashashati']2024-04-23T00:00:00https://www.semanticscholar.org/paper/5149bd1d15adf875d4925682a2fde8269126c8a3 b3562e5a24fdb387461b17a771ba88281b76534aJ. Medical Syst.91This article explores the use of AI language models in biomedical education, focusing on their application in both classroom teaching and learning assignments, and proposes a scoring rubric for evaluating student performance when collaborating with AI language models.Journal of Medical Systems['Thiago C. Moulin']2024-04-23T00:00:00https://www.semanticscholar.org/paper/b3562e5a24fdb387461b17a771ba88281b76534a 54e83a2a2fc5242cd4c7992a1fd6306b37c7bcc3European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery291LLMs can be valuable resources for patients seeking information on laryngeal cancer and provided the most reliable and safe responses among the models evaluated.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery['M. Ostrowska', 'Paulina Kacała', 'Deborah Onolememen', 'Katie Vaughan-Lane', 'Anitta Sisily Joseph', 'Adam Ostrowski', 'Wioleta Pietruszewska', 'Jacek Banaszewski', 'Maciej J. Wróbel']2024-04-23T00:00:00https://www.semanticscholar.org/paper/54e83a2a2fc5242cd4c7992a1fd6306b37c7bcc3 e27c9e98a816bd15c1cf5f353452eb544787c538The AI Gym Trainer is a system that provides multiple features designed to provide personalized fitness coaching and nutrition tracking. Imposing the advanced pose estimation capabilities of Mediapipe, the system provides real-time responses to exercise performance, focusing on posture, alignment, and technique.This feature is helpful because users will receive accurate guidance in workout sessions, which in turn enhances effectiveness while minimizing the risk of injury. Furthermore, the AI Gym Trainer is implemented using machine learning algorithms, which makes it capable of providing tailor workout routines to individual preferences and abilities, encouraging a personalized training experience. Through a user- friendly interface accessible through a desktop or web application, users can seamlessly interact with the system, track their progress, and receive personalized recommendations. Additionally, the system has a feature of a Flask-based food tracking feature which enhances the capabilities, allowing users to monitor their calorie intake and receive nutritional analysis. This comprehensive approach addresses both physical activity and dietary habits, and provides users with a holistic platform for managing their fitness goals. Evaluation through rigorous testing and user trials demonstrates the effectiveness and potential impact of the AI Gym Trainer in promoting overall health and well-being. With its combination of cutting-edge technology and user-centric design, the AI Gym Trainer represents a significant advancement in personalized fitness coaching and nutrition tracking, catering to the diverse needs of individuals striving to achieve their fitness goals.International Journal of Innovative Science and Research Technology101The AI Gym Trainer is a system that provides multiple features designed to provide personalized fitness coaching and nutrition tracking, and is implemented using machine learning algorithms, which makes it capable of providing tailor workout routines to individual preferences and abilities.International Journal of Innovative Science and Research Technology (IJISRT)['Aditya Malkar', 'Jyotidurga Pawar', 'Anil Kale']2024-04-23T00:00:00https://www.semanticscholar.org/paper/e27c9e98a816bd15c1cf5f353452eb544787c538 bebd618cd92ab84fdb61664c005b507a111c535cRecent generative AI systems have demonstrated more advanced persuasive capabilities and are increasingly permeating areas of life where they can influence decision-making. Generative AI presents a new risk profile of persuasion due the opportunity for reciprocal exchange and prolonged interactions. This has led to growing concerns about harms from AI persuasion and how they can be mitigated, highlighting the need for a systematic study of AI persuasion. The current definitions of AI persuasion are unclear and related harms are insufficiently studied. Existing harm mitigation approaches prioritise harms from the outcome of persuasion over harms from the process of persuasion. In this paper, we lay the groundwork for the systematic study of AI persuasion. We first put forward definitions of persuasive generative AI. We distinguish between rationally persuasive generative AI, which relies on providing relevant facts, sound reasoning, or other forms of trustworthy evidence, and manipulative generative AI, which relies on taking advantage of cognitive biases and heuristics or misrepresenting information. We also put forward a map of harms from AI persuasion, including definitions and examples of economic, physical, environmental, psychological, sociocultural, political, privacy, and autonomy harm. We then introduce a map of mechanisms that contribute to harmful persuasion. Lastly, we provide an overview of approaches that can be used to mitigate against process harms of persuasion, including prompt engineering for manipulation classification and red teaming. Future work will operationalise these mitigations and study the interaction between different types of mechanisms of persuasion.arXiv.org01This paper distinguishes between rationally persuasive generative AI, which relies on providing relevant facts, sound reasoning, or other forms of trustworthy evidence, and manipulative generative AI, which relies on taking advantage of cognitive biases and heuristics or misrepresenting information.ArXiv['Seliem El-Sayed', 'Canfer Akbulut', 'Amanda McCroskery', 'Geoff Keeling', 'Zachary Kenton', 'Zaria Jalan', 'Nahema Marchal', 'Arianna Manzini', 'Toby Shevlane', 'Shannon Vallor', 'Daniel Susser', 'Matija Franklin', 'Sophie Bridgers', 'Harry Law', 'Matthew Rahtz', 'Murray Shanahan', 'Michael Henry Tessler', 'Arthur Douillard', 'Tom Everitt', 'Sasha Brown']2024-04-23T00:00:00https://www.semanticscholar.org/paper/bebd618cd92ab84fdb61664c005b507a111c535c 4f6cef21b5272d3c046c85d2003bfec610c99497This study addresses the need for inclusive AI education by focusing on marginalized female students who historically lack access to learning opportunities in computing. It applies the theoretical framework of intersectionality to understand how gender, race and ethnicity intersect to shape these students' learning experiences and outcomes. Specifically, this study investigated 27 high‐school students' cognitive engagement in machine learning practices. We conducted the Wilcoxon–Mann–Whitney test to explore differences in cognitive engagement between marginalized female students and their peers, employed comparative content analysis to delve into significant differences and analysed interview data thematically to gain deeper insights into students' machine learning model development processes. The findings indicated that, when engaging in machine learning practices requiring drawing diverse cultural perspectives, marginalized female students demonstrated significantly higher performance compared to their peers. In particular, marginalized female students exhibited strengths in holistic language analysis, paying attention to writers' intentions and recognizing cultural nuances in language. This study suggests that integrating language analysis and machine learning across subjects has the potential to empower marginalized female students and amplify their perspectives. Furthermore, it calls for a strengths‐based approach to reshape the narrative of underrepresentation and promote equitable participation in machine learning and AI. What is already known about this topic Female students, particularly those from underrepresented groups such as African American and Latina students, often experience low levels of cognitive engagement in computing. Marginalized female students possess unique strengths that, when nurtured, have the potential to not only transform their own learning experiences but also contribute to the advancement of the computing field. It is critical to empower marginalized female students in K‐12 AI (ie, a subfield of computing) education, seeking to bridge the gender and racial disparity in AI. What this paper adds Marginalized female students outperformed their peers in responding to machine learning questions related to feature analysis and feature distribution interpretation. When responding to these questions, they demonstrated a holistic approach to analysing language by considering interactions between features and writers' intentions. They drew on knowledge about how language was used to convey meaning in different cultural contexts. Implications for practice and/or policy Educators should design learning environments that encourage students to draw upon their cultural backgrounds, linguistic insights and diverse experiences to enhance their engagement and performance in AI‐related activities. Educators should strategically integrate language analysis and machine learning across different subjects to create interdisciplinary learning experiences that support students' exploration of the interplay among language, culture and AI. Educational institutions and policy initiatives should adopt a strengths‐based approach that focuses on empowering marginalized female students by acknowledging their inherent abilities and diverse backgrounds. British Journal of Educational Technology350The findings indicated that, when engaging in machine learning practices requiring drawing diverse cultural perspectives, marginalized female students demonstrated significantly higher performance compared to their peers, and exhibited strengths in holistic language analysis, paying attention to writers' intentions and recognizing cultural nuances in language.British Journal of Educational Technology['Shiyan Jiang', 'Jeanne McClure', 'Can Tatar', 'Franziska Bickel', 'Carolyn P. Rosé', 'Jie Chao']2024-04-23T00:00:00https://www.semanticscholar.org/paper/4f6cef21b5272d3c046c85d2003bfec610c99497 e51e248ffe6824b201f782fd4b64b0abf66be8eaDeep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI‐powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty‐nine pre‐service teachers were recruited and randomly assigned to either a treatment (n = 30) or control (n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI‐powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI‐powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning‐based models in the context of classroom dialogue analysis. Classroom dialogue is recognized as a crucial element in the teaching and learning process. Researchers have increasingly utilized AI techniques, particularly deep learning methods, to analyse classroom dialogue. Deep learning‐based models, characterized by their intricate structures, often function as black boxes, lacking the ability to provide transparent explanations regarding their analysis. This limitation can result in teachers harbouring distrust and underutilizing these models. This paper highlights the importance of incorporating explainable AI approaches to tackle the interpretability issues associated with deep learning‐based models utilized for classroom dialogue analysis. Through an experimental study, this paper demonstrates that providing model explanations enhances teachers' trust in and technology acceptance of AI‐powered classroom dialogue models, without increasing their cognitive load. Teachers express satisfaction with the model explanations provided by explainable AI. The integration of explainable AI can effectively address the challenge of interpretability in complex AI‐powered models used for analysing classroom dialogue. Intelligent teaching systems designed for classroom dialogue can benefit from advanced AI models and explainable AI approaches, which offer users both automated analysis and clear explanations. By enabling users to understand the underlying rationale behind the analysis, the explanations can contribute to fostering trust and acceptance of the AI models among users. British Journal of Educational Technology640It is demonstrated that providing model explanations enhances teachers' trust in and technology acceptance of AI‐powered classroom dialogue models, without increasing their cognitive load.British Journal of Educational Technology['Deliang Wang', 'Cunling Bian', 'Gaowei Chen']2024-04-23T00:00:00https://www.semanticscholar.org/paper/e51e248ffe6824b201f782fd4b64b0abf66be8ea b15e5e973648025bc73e700941c6184fed30f1cbThis paper evaluated the effectiveness of using generative AI to simplify science communication and enhance public trust in science. By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work assessed linguistic simplicity across such summaries and public perceptions. Study 1a analyzed simplicity features of PNAS abstracts (scientific summaries) and significance statements (lay summaries), observing that lay summaries were indeed linguistically simpler, but effect size differences were small. Study 1b used GPT-4 to create significance statements based on paper abstracts and this more than doubled the average effect size without fine-tuning. Finally, Study 2 experimentally demonstrated that simply-written GPT summaries facilitated more favorable public perceptions of scientists (their credibility, trustworthiness) than more complexly-written human PNAS summaries. AI has the potential to engage scientific communities and the public via a simple language heuristic, advocating for its integration into scientific dissemination for a more informed society.500This paper evaluated the effectiveness of using generative AI to simplify science communication and enhance public trust in science by comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, and demonstrated linguistic simplicity across such summaries and public perceptions.['David M. Markowitz']2024-04-23T00:00:00https://www.semanticscholar.org/paper/b15e5e973648025bc73e700941c6184fed30f1cb b3e7ddab823b0bd5cfb72a90ab7c1034082f72a8To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.Energies1660This survey examines Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain.Energies['Michael Meiser', 'Ingo Zinnikus']2024-04-23T00:00:00https://www.semanticscholar.org/paper/b3e7ddab823b0bd5cfb72a90ab7c1034082f72a8 28814f69e35326e738da3ee1aa3a5cd916698c60The web accessibility landscape is a significant challenge, with 96.3% of home pages displaying issues with Web Content Accessibility Guidelines (WCAG). This paper addresses the primary accessibility issues, such as missing Accessible Rich Internet Applications (ARIA) landmarks, ill-formed headings, low contrast text, and inadequate form labeling. The dynamic nature of modern web and cloud applications presents challenges, such as developers' limited awareness of accessibility implications, potential code bugs, and API failures. To address these issues, an AI-enabled system is proposed to dynamically enhance web accessibility. The system uses machine learning algorithms to identify and rectify accessibility issues in real-time, integrating with existing development workflows. Empirical evaluation and case studies demonstrate the efficacy of this solution in improving web accessibility across diverse scenarios.Jurnal Online Informatika210An AI-enabled system is proposed to dynamically enhance web accessibility that uses machine learning algorithms to identify and rectify accessibility issues in real-time, integrating with existing development workflows.Jurnal Online Informatika['Samir Dash']2024-04-23T00:00:00https://www.semanticscholar.org/paper/28814f69e35326e738da3ee1aa3a5cd916698c60 a5b984e17fe2d9142f9f70913893a4778e87d45cSmart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models' decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.740It is argued for the necessity of a human-centric approach in representing explanations in smart home systems and relevant human-computer interaction methodologies that can be employed to generate and present human-centered explanations to users are highlighted.['Md Shajalal', 'Alexander Boden', 'Gunnar Stevens', 'Delong Du', 'Dean-Robin Kern']2024-04-23T00:00:00https://www.semanticscholar.org/paper/a5b984e17fe2d9142f9f70913893a4778e87d45c 0aa5e66f52f51629b3ed14d61c05a78e8a293b2bPublic sector use of AI has been quietly on the rise for the past decade, but only recently have efforts to regulate it entered the cultural zeitgeist. While simple to articulate, promoting ethical and effective roll outs of AI systems in government is a notoriously elusive task. On the one hand there are hard-to-address pitfalls associated with AI-based tools, including concerns about bias towards marginalized communities, safety, and gameability. On the other, there is pressure not to make it too difficult to adopt AI, especially in the public sector which typically has fewer resources than the private sector$\unicode{x2014}$conserving scarce government resources is often the draw of using AI-based tools in the first place. These tensions create a real risk that procedures built to ensure marginalized groups are not hurt by government use of AI will, in practice, be performative and ineffective. To inform the latest wave of regulatory efforts in the United States, we look to jurisdictions with mature regulations around government AI use. We report on lessons learned by officials in Brazil, Singapore and Canada, who have collectively implemented risk categories, disclosure requirements and assessments into the way they procure AI tools. In particular, we investigate two implemented checklists: the Canadian Directive on Automated Decision-Making (CDADM) and the World Economic Forum's AI Procurement in a Box (WEF). We detail three key pitfalls around expertise, risk frameworks and transparency, that can decrease the efficacy of regulations aimed at government AI use and suggest avenues for improvement.arXiv.org100Three key pitfalls around expertise, risk frameworks and transparency, that can decrease the efficacy of regulations aimed at government AI use are detailed and suggest avenues for improvement are suggested.ArXiv['Tom Zick', 'Mason Kortz', 'David Eaves', 'F. Doshi-Velez']2024-04-23T00:00:00https://www.semanticscholar.org/paper/0aa5e66f52f51629b3ed14d61c05a78e8a293b2b 91fe9936393006c75fa8e315388eae3578636dddThis paper explores the utilization of cognitive modeling to address the influence of antiblackness and racism on the design and development of AI systems. Through the lens of the ACT-R/Φ cognitive architecture and ConceptNet, an existing knowledge graph system, we investigate this issue from cognitive, sociocultural, and physiological perspectives. We propose an approach that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling. We contend that overlooking sociocultural factors in cognitive architectures perpetuates a colorblind approach to modeling, obscuring the inherent sociocultural context that shapes human behavior and cognitive processes.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300An approach is proposed that not only examines how antiblackness may permeate AI system design and development, particularly within the realm of software engineering, but also establishes links between antiblackness, human cognition, and computational cognitive modeling.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-04-23T00:00:00https://www.semanticscholar.org/paper/91fe9936393006c75fa8e315388eae3578636ddd feea90be8a159f201b559e726bd130d8bbe52f37Discover Education280Key factors influencing lecturers' acceptance of AI for their students include perceived pedagogical affordances, organisational policies and incentives, perceived complexity and usability and socio-cultural context.Discover Education['Kingsley Ofosu-Ampong']2024-04-23T00:00:00https://www.semanticscholar.org/paper/feea90be8a159f201b559e726bd130d8bbe52f37 0a7603a1db7073261edc9f430983e830ca96b430Systematic reviews are a cornerstone for synthesizing the available evidence on a given topic. They simultaneously allow for gaps in the literature to be identified and provide direction for future research. However, due to the ever‐increasing volume and complexity of the available literature, traditional methods for conducting systematic reviews are less efficient and more time‐consuming. Numerous artificial intelligence (AI) tools are being released with the potential to optimize efficiency in academic writing and assist with various stages of the systematic review process including developing and refining search strategies, screening titles and abstracts for inclusion or exclusion criteria, extracting essential data from studies and summarizing findings. Therefore, in this article we provide an overview of the currently available tools and how they can be incorporated into the systematic review process to improve efficiency and quality of research synthesis. We emphasize that authors must report all AI tools that have been used at each stage to ensure replicability as part of reporting in methods.JCPP Advances180An overview of the currently available AI tools and how they can be incorporated into the systematic review process to improve efficiency and quality of research synthesis is provided.JCPP Advances['N. Fabiano', 'Arnav Gupta', 'Nishaant Bhambra', 'Brandon Luu', 'Stanley Wong', 'Muhammad Maaz', 'Jess G. Fiedorowicz', 'Andrew L. Smith', 'Marco Solmi']2024-04-23T00:00:00https://www.semanticscholar.org/paper/0a7603a1db7073261edc9f430983e830ca96b430 ffef52f0bca0ff9838c61c199ccf971c360ca0c9This workshop paper presents a critical examination of the integration of Generative AI (Gen AI) into the academic writing process, focusing on the use of AI as a collaborative tool. It contrasts the performance and interaction of two AI models, Gemini and ChatGPT, through a collaborative inquiry approach where researchers engage in facilitated sessions to design prompts that elicit specific AI responses for crafting research outlines. This case study highlights the importance of prompt design, output analysis, and recognizing the AI's limitations to ensure responsible and effective AI integration in scholarly work. Preliminary findings suggest that prompt variation significantly affects output quality and reveals distinct capabilities and constraints of each model. The paper contributes to the field of Human-Computer Interaction by exploring effective prompt strategies and providing a comparative analysis of Gen AI models, ultimately aiming to enhance AI-assisted academic writing and prompt a deeper dialogue within the HCI community.160A critical examination of the integration of Generative AI into the academic writing process, focusing on the use of AI as a collaborative tool, suggests that prompt variation significantly affects output quality and reveals distinct capabilities and constraints of each model.['Joseph Tu', 'Hilda Hadan', 'Derrick M. Wang', 'Sabrina A. Sgandurra', 'Reza Hadi Mogavi', 'Lennart E. Nacke']2024-04-23T00:00:00https://www.semanticscholar.org/paper/ffef52f0bca0ff9838c61c199ccf971c360ca0c9 b21c5d9ef798af0b5f1d11851427d472e2fa1d9cThis chapter focuses on the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in science assessments. The paper begins with a discussion of the Framework for K-12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use. This shift necessitates the development of new types of assessments that align with the Framework's three dimensions: science and engineering practices, disciplinary core ideas, and crosscutting concepts. The paper further highlights the limitations of traditional assessment methods like multiple-choice questions, which often fail to capture the complexities of scientific thinking and three-dimensional learning in science. It emphasizes the need for performance-based assessments that require students to engage in scientific practices like modeling, explanation, and argumentation. The paper achieves three major goals: reviewing the current state of ML-based assessments in science education, introducing a framework for scoring accuracy in ML-based automatic assessments, and discussing future directions and challenges. It delves into the evolution of ML-based automatic scoring systems, discussing various types of ML, like supervised, unsupervised, and semi-supervised learning. These systems can provide timely and objective feedback, thus alleviating the burden on teachers. The paper concludes by exploring pre-trained models like BERT and finetuned ChatGPT, which have shown promise in assessing students' written responses effectively.410The paper concludes by exploring pre-trained models like BERT and finetuned ChatGPT, which have shown promise in assessing students' written responses effectively and can provide timely and objective feedback, thus alleviating the burden on teachers.['Xiaoming Zhai']2024-04-23T00:00:00https://www.semanticscholar.org/paper/b21c5d9ef798af0b5f1d11851427d472e2fa1d9c ef6deda266813d3084d09617f1ffeea431dd9ae1Cyberbullying has arisen as an unavoidable and concerning issue via virtual entertainment stages, influencing the psychological well-being and prosperity of people around the world. To resolve this issue, this study proposes a cyberbullying recognition framework utilizing the K-SVM calculation. Utilizing the force of AI, the framework means to consequently distinguish and signal occurrences of cyberbullying progressively web-based entertainment content. The improvement of the location framework starts with the assortment and naming of a thorough dataset containing instances of cyberbullying and non-cyberbullying posts or remarks. After pre-handling the text information by eliminating unessential data, changing message over completely to lowercase, and tokenizing it, significant highlights are removed utilizing the pack of-words or TF-IDF methods. These changed element vectors act as contributions for preparing the K-SVM classifier, which tries to find the ideal hyper plane for successfully recognizing cyberbullying from non-cyberbullying content. The exhibition of the K-SVM model is assessed utilizing a different testing dataset, with measurements, for example, exactness, accuracy, review, F1-score, and ROC-AUC broke down to survey its viability in distinguishing cyberbullying cases. Model calibrating is led through trial and error with different K-SVM hyper boundaries and cross-approval methods to upgrade the framework's exhibition. Keywords: Cyberbullying, social media, Online harassmentInternational Scientific Journal of Engineering and Management00A cyberbullying recognition framework utilizing the K-SVM calculation to distinguish and signal occurrences of cyberbullying progressively web-based entertainment content and to upgrade the framework's exhibition.International Scientific Journal of Engineering and Management['Mr. Prasath B']2024-04-23T00:00:00https://www.semanticscholar.org/paper/ef6deda266813d3084d09617f1ffeea431dd9ae1 42da7bed867ccdc6fc524216a62ef3c643cb662eEven though algorithmic composition might be considered a centuries-old concept, it has been gaining particular momentum since the introduction of computer-based techniques. The development of artificial intelligence (AI) methods, culminating in the latest achievements of deep learning techniques, has provided tools to automatically compose and even produce music. This paper discusses various aspects of the entire process within a context of designing a system able to automatically generate a score and recordings belonging to selected musical genres. It begins with the idea and design overview, followed by considerations regarding the algorithmic formulation of selected musical rules and principles. The system implements a hybrid approach, combining conventional, i.e., stochastic or rule-based, and AI elements. The latter are applied to facilitate the generation of selected layers of composition and to constitute a classifier with a task of evaluating the generated recordings. Selected stages of music generation are discussed, for example how motifs are processed into phrases and how phrases are used in the context of a whole song. To validate the system operation results, an evaluation of the quality of the produced music recordings was conducted, including a test with a group of listeners. The analysis also touches upon some legal aspects related to the creation of algorithmic compositions.Applied Sciences240This paper discusses various aspects of the entire process within a context of designing a system able to automatically generate a score and recordings belonging to selected musical genres, and touches upon some legal aspects related to the creation of algorithmic compositions.Applied Sciences['Joanna Kwiecień', 'Paweł Skrzyński', 'Wojciech Chmiel', 'Andrzej Dąbrowski', 'Bartłomiej Szadkowski', 'Marek Pluta']2024-04-23T00:00:00https://www.semanticscholar.org/paper/42da7bed867ccdc6fc524216a62ef3c643cb662e 13ed735c433568565255960ae0bdc750383b75dc Auditors can play a vital role in ensuring that tech companies develop and deploy AI systems safely, taking into account not just immediate, but also systemic harms that may arise from the use of future AI capabilities. However, to support auditors in evaluating the capabilities and consequences of cutting-edge AI systems, governments may need to encourage a range of potential auditors to invest in new auditing tools and approaches. We use evolutionary game theory to model scenarios where the government wishes to incentivise auditing, but cannot discriminate between high and low-quality auditing. We warn that it is alarmingly easy to stumble on 'Adversarial incentives', which prevent a sustainable market for auditing AI systems from forming. Adversarial Incentives mainly reward auditors for catching unsafe behaviour. If AI companies learn to tailor their behaviour to the quality of audits, the lack of opportunities to catch unsafe behaviour will discourage auditors from innovating. Instead, we recommend that governments always reward auditors, except when they find evidence that those auditors failed to detect unsafe behaviour they should have. These 'Vigilant Incentives' could encourage auditors to find innovative ways to evaluate cutting-edge AI systems. Overall, our analysis provides useful insights for the design and implementation of efficient incentive strategies for encouraging a robust auditing ecosystem.Journal of Physics: Complexity00This work uses evolutionary game theory to model scenarios where the government wishes to incentivise auditing, but cannot discriminate between high and low-quality auditing and recommends that governments always reward auditors, except when they find evidence that those auditors failed to detect unsafe behaviour they should have.Journal of Physics: Complexity['Paolo Bova', 'Alessandro Di Stefano', 'The Anh Han']2024-04-23T00:00:00https://www.semanticscholar.org/paper/13ed735c433568565255960ae0bdc750383b75dc 0862fe90dd915f9e51a5f99c35401ec2e96031d2Spatial audio offers more immersive video consumption experiences to viewers; however, creating and editing spatial audio often expensive and requires specialized equipment and skills, posing a high barrier for amateur video creators. We present MIMOSA, a human-AI co-creation tool that enables amateur users to computationally generate and manipulate spatial audio effects. For a video with only monaural or stereo audio, MIMOSA automatically grounds each sound source to the corresponding sounding object in the visual scene and enables users to further validate and fix the errors in the locations of sounding objects. Users can also augment the spatial audio effect by flexibly manipulating the sounding source positions and creatively customizing the audio effect. The design of MIMOSA exemplifies a human-AI collaboration approach that, instead of utilizing state-of art end-to-end"black-box"ML models, uses a multistep pipeline that aligns its interpretable intermediate results with the user's workflow. A lab user study with 15 participants demonstrates MIMOSA's usability, usefulness, expressiveness, and capability in creating immersive spatial audio effects in collaboration with users.arXiv.org810MIMOSA is a human-AI co-creation tool that enables amateur users to computationally generate and manipulate spatial audio effects and exemplifies a human-AI collaboration approach that uses a multistep pipeline that aligns its interpretable intermediate results with the user's workflow.ArXiv['Zheng Ning', 'Zheng Zhang', 'Jerrick Ban', 'Kaiwen Jiang', 'Ruohong Gan', 'Yapeng Tian', 'Toby Li']2024-04-23T00:00:00https://www.semanticscholar.org/paper/0862fe90dd915f9e51a5f99c35401ec2e96031d2 1a086d1b7ae8376baa0d59d7fcd892fc4a61b5ffAI &amp; SOCIETY400An analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality is presented, showing that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears.AI &amp; SOCIETY['Jens Hälterlein']2024-04-23T00:00:00https://www.semanticscholar.org/paper/1a086d1b7ae8376baa0d59d7fcd892fc4a61b5ff f8bb22ce7488199995a7e30c974f060f7ce99bbeAI &amp; SOCIETY200This qualitative study employed an empirical ethics methodology to address how developers and users define and construct requirements for trust throughout development and use, through a series of interviews and found that different accounts of trust served as the basis for individual granting of trust in technologies and operators.AI &amp; SOCIETY['Stephanie Sheir', 'Arianna Manzini', 'Helen Smith', 'Jonathan Ives']2024-04-23T00:00:00https://www.semanticscholar.org/paper/f8bb22ce7488199995a7e30c974f060f7ce99bbe 2275d507e38f03c2147983b20be75f69950c5340 Purpose The integration of artificial intelligence (AI) technologies like conversational AI and HR chatbots in international human resource development (HRD) presents both productivity benefits and ethical challenges. This study aims to examine the ethical dimensions of AI-driven HR chatbots, emphasizing the need for fairness, autonomy and nondiscrimination. It discusses inherent biases in AI systems and addresses linguistic, cultural and accessibility issues. The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a “risk management by design” framework. By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management. Design/methodology/approach Systematic literature review. Findings The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a “risk management by design” framework. Practical implications By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management. Originality/value This study explores the intricate ethical landscape surrounding AI-driven HR chatbots, spotlighting the imperatives of fairness, autonomy, and nondiscrimination. Uncovering biases inherent in AI systems, it addresses linguistic, cultural, and accessibility concerns. Proposing a pioneering “risk management by design” framework, the study advocates for a holistic approach to ethical integration, ensuring organizations navigate the complexities of AI-driven HR technologies while prioritizing fairness and equity in global workforce management. Strategic HR Review70This study aims to examine the ethical dimensions of AI-driven HR chatbots, emphasizing the need for fairness, autonomy and nondiscrimination, and proposes a pioneering “risk management by design” framework.Strategic HR Review['Natalie Bidnick Andreas']2024-04-23T00:00:00https://www.semanticscholar.org/paper/2275d507e38f03c2147983b20be75f69950c5340 93ac05bbf6c7884e1174d3e3f4d6aa10765d3bf6Nature Biotechnology40Nature biotechnology[]2024-04-23T00:00:00https://www.semanticscholar.org/paper/93ac05bbf6c7884e1174d3e3f4d6aa10765d3bf6 94477db318838d8780fbfec4094437c454c523b8British medical journal10BMJ['Anna Wake']2024-04-23T00:00:00https://www.semanticscholar.org/paper/94477db318838d8780fbfec4094437c454c523b8 2e39bb90ab95fb844c065135f406b666b5f662ecComparative Education220Comparative Education['Yoko Mochizuki', 'Edward Vickers']2024-04-23T00:00:00https://www.semanticscholar.org/paper/2e39bb90ab95fb844c065135f406b666b5f662ec 8d06d11a29c77bbeb235bbb984c7f706202aaba4The advancement in computing and hardware, like spatial computing and VR headsets (e.g., Apple's Vision Pro) [1], has boosted the popularity of social VR platforms (VRChat, Rec Room, Meta HorizonWorlds) [2, 3, 4]. Unlike traditional digital interactions, social VR allows for more immersive experiences, with avatars that mimic users' real-time movements and enable physical-like interactions. However, the immersive nature of social VR may introduce intensified and more physicalized cyber threats-we define as"embodied cyber threats", including trash-talking, virtual"groping", and such virtual harassment and assault. These new cyber threats are more realistic and invasive due to direct, virtual interactions, underscoring the urgent need for comprehensive understanding and practical strategies to enhance safety and security in virtual environments.60The immersive nature of social VR may introduce intensified and more physicalized cyber threats, including trash-talking, virtual groping, and such virtual harassment and assault, underscoring the urgent need for comprehensive understanding and practical strategies to enhance safety and security in virtual environments.['Keyan Guo', 'Freeman Guo', 'Hongxin Hu']2024-04-23T00:00:00https://www.semanticscholar.org/paper/8d06d11a29c77bbeb235bbb984c7f706202aaba4 2eb487393ed2bdb3f31194ea81b8ec290fcf8b4cVisually challenged people (VCP) struggle in their everyday life and have major difficulties in participating in cultural, tourist, family, and other types of outdoor activities especially those which are in unfamiliar surroundings. In modern days, synthetic intelligence is imparting a wide variety of answers for any hassle. This paper represents an guidance machine for blind . It is a wearable device which guides people efficiently and safely. This device is speedy and accurate for object detection by means of the digital camera and sensor for obstacle detection.An impaired person can wear this system and command this system for finding the things using voice command. This system recognizes these commands and gives a desirable output in voice. Also while travelling, it detects objects and obstacles and notifies about it to the user using voice output. Key Words: Obstacle detection, Text recognition, wearable device.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This device is speedy and accurate for object detection by means of the digital camera and sensor for obstacle detection and an impaired person can wear this system and command this system for finding the things using voice command.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['D. D. V. Lakshmi,']2024-04-23T00:00:00https://www.semanticscholar.org/paper/2eb487393ed2bdb3f31194ea81b8ec290fcf8b4c ddb2bcb7a0028fdeb3916d8d9e7837f3c0c41123Journal of Mental Health Research in Intellectual Disabilities90Journal of Mental Health Research in Intellectual Disabilities['Peter Zentel', 'Torsten Hammann', 'Meike Engelhardt', 'Christin Kupitz']2024-04-23T00:00:00https://www.semanticscholar.org/paper/ddb2bcb7a0028fdeb3916d8d9e7837f3c0c41123 ed200d1de9764bcf270fb8f5550eb548a233df55Biomedical Engineering Education200A convergence training framework integrating project-based learning, training modules, and collaborative teaming is proposed to address challenges in implementing transdisciplinary research and preparing students for real-world collaboration across diverse disciplines and experience levels.Biomedical Engineering Education['Jessica L. S. Zylla', 'A. Bomgni', 'Rajesh K. Sani', 'M. Subramaniam', 'Carol Lushbough', 'Robb Winter', 'V. Gadhamshetty', 'P. Chundi', 'Etienne Z. Gnimpieba']2024-04-23T00:00:00https://www.semanticscholar.org/paper/ed200d1de9764bcf270fb8f5550eb548a233df55 1c206d324d3233cfdc50e48e58e355d29b889d31The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.00['Julius B. Adewopo', 'B. Andrée', 'Helen Peter', 'Gloria Solano-Hermosilla', 'F. Micale']2024-04-23T00:00:00https://www.semanticscholar.org/paper/1c206d324d3233cfdc50e48e58e355d29b889d31 13696d02d877d49fb3f5f53b7abcbeaf602b488eSICE Journal of Control Measurement and System Integration180SICE Journal of Control, Measurement, and System Integration['Bakir Hadžić', 'Parvez Mohammed', 'Michael Danner', 'Julia Ohse', 'Yihong Zhang', 'Youssef Shiban', 'Matthias Rätsch']2024-04-23T00:00:00https://www.semanticscholar.org/paper/13696d02d877d49fb3f5f53b7abcbeaf602b488e 80ebc12ae678c9bcf83fea433d6f2fed36b8a4c5Nature20Nature['David Adam']2024-04-23T00:00:00https://www.semanticscholar.org/paper/80ebc12ae678c9bcf83fea433d6f2fed36b8a4c5 218ac5a213f1d1d768f91d59e35fcb1decb075d4Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model’s performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model’s ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model’s performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.Italian National Conference on Sensors610Using the suggested set of selective features not only improves the classification model’s performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios.Sensors (Basel, Switzerland)['Asif Mehmood', 'Jungbeom Ko', 'Hyunchul Kim', 'Jungsuk Kim']2024-04-23T00:00:00https://www.semanticscholar.org/paper/218ac5a213f1d1d768f91d59e35fcb1decb075d4 8c793e9d5e9833c899c8e942c44e7a80fa6a8130Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that significantly affects children and adults worldwide, characterized by persistent inattention, hyperactivity, and impulsivity. Current research in this field faces challenges, particularly in accurate diagnosis and effective treatment strategies. The analysis of motor information, enriched by artificial intelligence methodologies, plays a vital role in deepening our understanding and improving the management of ADHD. The integration of AI techniques, such as machine learning and data analysis, into the study of ADHD-related motor behaviors, allows for a more nuanced understanding of the disorder. This approach facilitates the identification of patterns and anomalies in motor activity that are often characteristic of ADHD, thereby contributing to more precise diagnostics and tailored treatment strategies. Our approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies. On the ADHD dataset, the model significantly improved accuracy to 98.21% and recall to 93.86%, especially excelling in EEG data processing with accuracy and recall rates of 96.62 and 95.21%, respectively, demonstrating precise capturing of ADHD characteristic behaviors and physiological responses. These results not only reveal the great potential of our model in improving ADHD diagnostic accuracy and developing personalized treatment plans, but also open up new research perspectives for understanding the complex neurological logic of ADHD. In addition, our study not only suggests innovative perspectives and approaches for ADHD treatment, but also provides a solid foundation for future research exploring similar complex neurological disorders, providing valuable data and insights. This is scientifically important for improving treatment outcomes and patients' quality of life, and points the way for future-oriented medical research and clinical practice.Frontiers in Neuroscience530This approach focuses on utilizing AI techniques to deeply analyze patients' motor information and cognitive processes, aiming to improve ADHD diagnosis and treatment strategies, and reveals the great potential of the model in improving ADHD diagnostic accuracy and developing personalized treatment plans.Frontiers in Neuroscience['Dan Yu', 'Jia hui Fang']2024-04-23T00:00:00https://www.semanticscholar.org/paper/8c793e9d5e9833c899c8e942c44e7a80fa6a8130 e196c237959f1684b6cbb0d0924468efb521b315Background Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. Methods This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers. We included patients who underwent thyroid ultrasonography complemented by ultrasound-guided fine needle aspiration (FNA) between January 2015 and March 2021. Participants formed a consecutive series, enhancing the study’s validity. A comparative analysis was conducted between the AI model’s diagnostic performance and that of both an experienced radiologist and a third-year radiology resident, using a dataset of 600 ultrasound images from three distinguished Thai medical institutions, each verified with cytological findings. Results The AI system demonstrated superior diagnostic performance, with an overall sensitivity of 80% [95% confidence interval (CI): 59.3–93.2%] and specificity of 71.4% (95% CI: 53.7–85.4%). At Siriraj Hospital, the AI achieved a sensitivity of 90.0% (95% CI: 55.5–99.8%), specificity of 100.0% (95% CI: 69.2–100%), positive prediction value (PPV) of 100.0%, negative prediction value (NPV) of 90.9%, and an overall accuracy of 95.0%, indicating the benefits of AI’s extensive training across diverse datasets. The experienced radiologist’s sensitivity was 40.0% (95% CI: 21.1–61.3%), while the specificity was 80.0% (95% CIs: 63.6–91.6%), respectively, showing that the AI significantly outperformed the radiologist in terms of sensitivity (P=0.043) while maintaining comparable specificity. The inter-observer variability analysis indicated a moderate agreement (K=0.53) between the radiologist and the resident, contrasting with fair agreement (K=0.37/0.33) when each was compared with the AI system. Notably, 95% CIs for these diagnostic indexes highlight the AI system’s consistent performance across different settings. Conclusions The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules. The AI system, designed as a supportive tool rather than a replacement, promises to revolutionize thyroid nodule diagnosis and management by providing a high level of diagnostic precision.Quantitative Imaging in Medicine and Surgery00The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules, and highlight the AI system’s consistent performance across different settings.Quantitative Imaging in Medicine and Surgery['Pawitchaya Namsena', 'D. Songsaeng', 'Chadaporn Keatmanee', 'Songphon Klabwong', 'Alisa Kunapinun', 'Sunsiree Soodchuen', 'Thipthara Tarathipayakul', 'Wasu Tanasoontrarat', 'M. Ekpanyapong', 'Matthew N. Dailey']2024-04-23T00:00:00https://www.semanticscholar.org/paper/e196c237959f1684b6cbb0d0924468efb521b315 b3a3e89fb7199a0f963fbac6439b4541cf39db06As artificial intelligence (AI) continues to evolve rapidly, its integration into nursing education is inevitable. This article presents a narrative exploring the implementation of generative AI in nursing education and offers a guide for its strategic use. The exploration begins with an examination of the broader societal impact and uses of artificial intelligence, recognizing its pervasive presence and the potential it holds. Thematic analysis of strengths, weaknesses, opportunities, and threats collected from nurse educators across the southeastern United States in this case-based descriptive study used four codes: time, innovation, critical thinking, and routine tasks. Findings from the qualitative analysis revealed the overarching themes that AI can serve as both a tool and a tyrant, offering opportunities for efficiency and innovation while posing challenges of transparency, ethical use, and AI literacy. By establishing ethical guidelines, fostering AI literacy, and promoting responsible implementation in nursing education with a clear articulation of expectations, nurse educators can guide and guard the use of generative AI. Despite the concerns, the transformative potential of generative AI to enhance teaching methodologies and prepare students for the interprofessional health-care workforce provides a multitude of innovative opportunities for teaching and learning.Creative Nursing100A narrative exploring the implementation of generative AI in nursing education and offering a guide for its strategic use is presented, revealing the overarching themes that AI can serve as both a tool and a tyrant, offering opportunities for efficiency and innovation while posing challenges of transparency, ethical use, and AI literacy.Creative nursing['Susan Hayes Lane', 'Tammy Haley', 'D. Brackney']2024-04-23T00:00:00https://www.semanticscholar.org/paper/b3a3e89fb7199a0f963fbac6439b4541cf39db06 37fea22a1ef35e1a8a0cdb9285e8c5bc71e7aa99International Journal of Retina and Vitreous1180As AI evolves, it will become increasingly essential for the modern ophthalmologist to understand its applications and limitations to improve patient outcomes and continue to innovate.International Journal of Retina and Vitreous['Matthew Driban', 'Audrey Yan', 'A. Selvam', 'J. Ong', 'K. Vupparaboina', 'Jay Chhablani']2024-04-23T00:00:00https://www.semanticscholar.org/paper/37fea22a1ef35e1a8a0cdb9285e8c5bc71e7aa99 6c18ba884b928fc63872c9a604886b1b884220b2This study aims to determine the artificial intelligence (AI) anxiety levels of Z-generation candidate nurses and the variables affecting the anxiety levels of artificial intelligence by the machine learning (ML) method. Data were collected from 431 candidate nurses by questionnaire using the convenience sampling method. R open access programming language was used for the statistical analysis of the study and the evaluation of significant variables according to their importance levels. The Boruta algorithm, a machine learning method, was used in the determination of the variables affecting the level of artificial intelligence anxiety according to the degree of importance. The findings showed that the most important variable for students' artificial intelligence anxiety level is age. Moreover, there is a statistically significant relationship between students' class and their anxiety level, a significant relationship between artificial intelligence and machine learning in health and their anxiety level, and a significant relationship between gender and technological competence evaluation. Furthermore, nearly half of the participants (48.5%) had very low anxiety, 12.8% had low anxiety, 30.2% had medium anxiety, 6.5% had high-level anxiety and 2.1% of them had very high levels of anxiety. With this research, the artificial intelligence anxiety of generation Z was determined by determining the demographic characteristics that are effective in artificial intelligence. We concluded that more sensitive analysis and different results can be obtained when using a machine learning algorithm compared to classical statistical analysis in determining the complex relationships in the data.Environment and Social Psychology390The findings showed that the most important variable for students' artificial intelligence anxiety level is age and there is a statistically significant relationship between students' class and their anxiety level, a significant relationship between artificial intelligence and machine learning in health and their anxiety level, and a significant relationship between gender and technological competence evaluation.Environment and Social Psychology['Bülent Akkaya', 'İlknur BuçanKırkbir', 'Sema Üstgörül']2024-04-23T00:00:00https://www.semanticscholar.org/paper/6c18ba884b928fc63872c9a604886b1b884220b2 4171d77b0e3f46735aae116d624f4eba8b5bd5a5This study aims to provide an overview of the current state-of-the-art applications of artificial intelligence (AI) and machine learning in the management of hepatocellular carcinoma (HCC), and to explore future directions for continued progress in this emerging field.  This study is a comprehensive literature review that synthesizes recent findings and advancements in the application of AI and machine learning techniques across various aspects of HCC care, including screening and early detection, diagnosis and staging, prognostic modeling, treatment planning, interventional guidance, and monitoring of treatment response. The review draws upon a wide range of published research studies, focusing on the integration of AI and machine learning with diverse data sources, such as medical imaging, clinical data, genomics, and other multimodal information.  The results demonstrate that AI-based systems have shown promise in improving the accuracy and efficiency of HCC screening, diagnosis, and tumor characterization compared to traditional methods. Machine learning models integrating clinical, imaging, and genomic data have outperformed conventional staging systems in predicting survival and recurrence risk. AI-based recommendation systems have the potential to optimize personalized therapy selection, while augmented reality techniques can guide interventional procedures in real-time. Moreover, longitudinal application of AI may enhance the assessment of treatment response and recurrence monitoring. Despite these promising findings, the review highlights the need for rigorous multicenter prospective validation studies, standardized multimodal datasets, and thoughtful consideration of ethical implications before widespread clinical implementation of AI technologies in HCC management.Jurnal Online Informatika810The results demonstrate that AI-based systems have shown promise in improving the accuracy and efficiency of HCC screening, diagnosis, and tumor characterization compared to traditional methods, and highlights the need for rigorous multicenter prospective validation studies, standardized multimodal datasets, and thoughtful consideration of ethical implications before widespread clinical implementation of AI technologies in HCC management.Jurnal Online Informatika['T. Addissouky', 'Ibrahim El Tantawy El Sayed', 'Majeed M. A. Ali']2024-04-23T00:00:00https://www.semanticscholar.org/paper/4171d77b0e3f46735aae116d624f4eba8b5bd5a5 2452c30e3a2e5e3c917f92894d8546a1f2a32a8cRecent developments in artificial intelligence (AI) have permeated through an array of different immersive environments, including virtual, augmented, and mixed realities. AI brings a wealth of potential that centers on its ability to critically analyze environments, identify relevant artifacts to a goal or action, and then autonomously execute decision-making strategies to optimize the reward-to-risk ratio. However, the inherent benefits of AI are not without disadvantages as the autonomy and communication methodology can interfere with the human's awareness of their environment. More specifically in the case of autonomy, the relevant human-computer interaction literature cites that high autonomy results in an"out-of-the-loop"experience for the human such that they are not aware of critical artifacts or situational changes that require their attention. At the same time, low autonomy of an AI system can limit the human's own autonomy with repeated requests to approve its decisions. In these circumstances, humans enter into supervisor roles, which tend to increase their workload and, therefore, decrease their awareness in a multitude of ways. In this position statement, we call for the development of human-centered AI in immersive environments to sustain and promote awareness. It is our position then that we believe with the inherent risk presented in both AI and AR/VR systems, we need to examine the interaction between them when we integrate the two to create a new system for any unforeseen risks, and that it is crucial to do so because of its practical application in many high-risk environments.120It is the position that with the inherent risk presented in both AI and AR/VR systems, the interaction between them when the two are integrated to create a new system for any unforeseen risks, and that it is crucial to do so because of its practical application in many high-risk environments.['Wangfan Li', 'Rohit Mallick', 'Carlos Toxtli-Hernandez', 'Christopher Flathmann', 'Nathan J. McNeese']2024-04-23T00:00:00https://www.semanticscholar.org/paper/2452c30e3a2e5e3c917f92894d8546a1f2a32a8c dda94f6c6e4f6b08c2bf06b953f633e4fa183132This paper explores the feasibility of constructing interpretable artificial intelligence (AI) systems rooted in active inference and the free energy principle. Initially, we offer a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions. Subsequently, we delve into how active inference can serve as a foundation for designing explainable AI systems. Specifically, it enables us to capture essential aspects of "introspective" processes and generate intelligible models of decision-making mechanisms. We propose an architectural framework for explainable AI systems employing active inference. Central to this framework is an explicit hierarchical generative model that enables the AI system to monitor and elucidate the factors influencing its decisions. Importantly, this model's structure is designed to be understandable and verifiable by human users. We elucidate how this architecture can amalgamate diverse data sources to make informed decisions in a transparent manner, mirroring aspects of human consciousness and introspection. Finally, we examine the implications of our findings for future AI research and discuss potential ethical considerations associated with developing AI systems with (apparent) introspective capabilities.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This paper offers a concise introduction to active inference, emphasizing its relevance to modeling decision-making, introspection, and the generation of both overt and covert actions, and proposes an architectural framework for explainable AI systems employing active inference.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['José Gabriel Carrasco Ramírez']2024-04-23T00:00:00https://www.semanticscholar.org/paper/dda94f6c6e4f6b08c2bf06b953f633e4fa183132 698037b3d537beb7ea072b28762716682438f7b0In the ever-evolving landscape of the financial sector, the integration of Artificial Intelligence (AI) has emerged as a transformative force, reshaping traditional banking practices and fostering unprecedented innovation. As the digital era unfolds, banks are navigating the intricate realms of AI to enhance efficiency, customer experience, risk management, and decision-making processes. This paper delves into the profound impact of artificial intelligence on the banking sector, unravelling the complexities and unveiling the vast potential that lies within this symbiotic relationship.International Scientific Journal of Engineering and Management00This paper delves into the profound impact of artificial intelligence on the banking sector, unravelling the complexities and unveiling the vast potential that lies within this symbiotic relationship.International Scientific Journal of Engineering and Management['Mr. Rahul Gupta']2024-04-23T00:00:00https://www.semanticscholar.org/paper/698037b3d537beb7ea072b28762716682438f7b0 fee54f028b32961d4a58d0744e04c01923dab7dcArtificial Intelligence is rapidly revolutionizing so many industries at such an alarming rate that one such advanced AI robot, Sophia, joined the panel and was pitched questions during the United Nations’s convention on sustainable development. Artificial intelligence is producing multiple solutions for hiring managers including basic recruiting tools, intermediate applications and advanced AI solutions. Together or independently, these tools are creating a more effective way for human resources to predict a candidate’s future success with their company. artificial intelligence (AI) is transforming the human resources field altogether. The current study would throw some light on artificial intelligence breakthroughs and implications with respect to HR . KEYWORDS: Artificial, Intelligence, Human, Resources, Functions, ImplicationsInternational Scientific Journal of Engineering and Management00The current study would throw some light on artificial intelligence breakthroughs and implications with respect to HR.International Scientific Journal of Engineering and Management['Dr.T. Rajasekhar']2024-04-23T00:00:00https://www.semanticscholar.org/paper/fee54f028b32961d4a58d0744e04c01923dab7dc a4acf62a6dcc898e77f6a7f0dea11bda6451611aThe study examines the transformative role of artificial intelligence in Human resources and its profound implications for the future of workforce management. Questionnaires are used to evaluate the current perception, applications & adoption of artificial intelligence in human resources practices, as well as its potential impact and ability to shape tomorrow's Human Resource landscape. Understanding of how people view the integration of AI in human resources functions, by rigorously analysing questionnaire responses. These findings reveal the apprehensions, expectations and acceptance levels of AI driven human resource management processes. In addition, important foresight is provided on the evolution of technology and Human Capital dynamics within organisational structures. Moreover, a compelling case study is presented to demonstrate the effectiveness of AI when it comes to human resource management on an actual basis. The case study shows how AI solutions simplify recruitment, enhance employee engagement, optimise talent management and mitigate biases in the decision making process for a more agile, data driven environment that is inclusive. Finally, this research aims at breaking down the complex implications of artificial intelligence for human resources and providing information on its transformative potential which will make it easier to adopt effective decisions as part of organisational HR strategies. Keywords: Artificial Intelligence, Human Resources, Case Study, Perception, AwarenessInternational Scientific Journal of Engineering and Management00The study examines the transformative role of artificial intelligence in Human resources and its profound implications for the future of workforce management, and provides information on its transformative potential which will make it easier to adopt effective decisions as part of organisational HR strategies.International Scientific Journal of Engineering and Management['Priyal Gordiya']2024-04-23T00:00:00https://www.semanticscholar.org/paper/a4acf62a6dcc898e77f6a7f0dea11bda6451611a 1101b1850c9aeff03c83e318a9a452323db12833Thyroid-associated ophthalmopathy (TAO), also referred to as Graves’ ophthalmopathy, is a medical condition wherein ocular complications arise due to autoimmune thyroid illness. The diagnosis of TAO, reliant on imaging, typical ocular symptoms, and abnormalities in thyroid function or thyroid-associated antibodies, is generally graded and staged. In recent years, Artificial intelligence(AI), particularly deep learning(DL) technology, has gained widespread use in the diagnosis and treatment of ophthalmic diseases. This paper presents a discussion on specific studies involving AI, specifically DL, in the context of TAO, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions. Additionally, it addresses certain limitations in AI research on TAO and potential future directions for the field.Frontiers in Endocrinology560Specific studies involving AI, specifically DL, in the context of TAO are presented, highlighting their applications in TAO diagnosis, staging, grading, and treatment decisions and addressing certain limitations in AI research on TAO.Frontiers in Endocrinology['Chenyuan Yi', 'Geng Niu', 'Yinghuai Zhang', 'Jing Rao', 'Guiqin Liu', 'Weihua Yang', 'XingZhen Fei']2024-04-23T00:00:00https://www.semanticscholar.org/paper/1101b1850c9aeff03c83e318a9a452323db12833 c0d8e76c4998bd86974e45254dbdeba1d1b3c74fOBJECTIVE  To evaluate the reliability of three artificial intelligence (AI) chatbots (ChatGPT, Google Bard, and Chatsonic) in generating accurate references from existing obstetric literature. STUDY DESIGN  Between mid-March and late April 2023, ChatGPT, Google Bard, and Chatsonic were prompted to provide references for specific obstetrical randomized controlled trials (RCTs) published in 2020. RCTs were considered for inclusion if they were mentioned in a previous article that primarily evaluated RCTs published by the top medical and obstetrics and gynecology journals with the highest impact factors in 2020 as well as RCTs published in a new journal focused on publishing obstetric RCTs. The selection of the three AI models was based on their popularity, performance in natural language processing, and public availability. Data collection involved prompting the AI chatbots to provide references according to a standardized protocol. The primary evaluation metric was the accuracy of each AI model in correctly citing references, including authors, publication title, journal name, and digital object identifier (DOI). Statistical analysis was performed using a permutation test to compare the performance of the AI models. RESULTS  Among the 44 RCTs analyzed, Google Bard demonstrated the highest accuracy, correctly citing 13.6% of the requested RCTs, whereas ChatGPT and Chatsonic exhibited lower accuracy rates of 2.4 and 0%, respectively. Google Bard often substantially outperformed Chatsonic and ChatGPT in correctly citing the studied reference components. The majority of references from all AI models studied were noted to provide DOIs for unrelated studies or DOIs that do not exist. CONCLUSION  To ensure the reliability of scientific information being disseminated, authors must exercise caution when utilizing AI for scientific writing and literature search. However, despite their limitations, collaborative partnerships between AI systems and researchers have the potential to drive synergistic advancements, leading to improved patient care and outcomes. KEY POINTS · AI chatbots often cite scientific articles incorrectly.. · AI chatbots can create false references.. · Responsible AI use in research is vital..American Journal of Perinatology600To ensure the reliability of scientific information being disseminated, authors must exercise caution when utilizing AI for scientific writing and literature search.American journal of perinatology['Emily M Graf', 'Jordan A. McKinney', 'Alexander Dye', 'Li-Peng Lin', 'Luis Sanchez-Ramos']2024-04-23T00:00:00https://www.semanticscholar.org/paper/c0d8e76c4998bd86974e45254dbdeba1d1b3c74f 58f717e8e74d36fcab03d19eb932b665ccaa9d3cCONTEXT.— Artificial intelligence is a transforming technology for anatomic pathology. Involvement within the workforce will foster support for algorithm development and implementation. OBJECTIVE.— To develop a supportive ecosystem that enables pathologists with variable expertise in artificial intelligence to create algorithms in a development environment with seamless transition to a production environment. DESIGN.— RESULTS.— The development team considered internal development and vended solutions. Because of the extended timeline and resource requirements for internal development, a decision was made to use a vended solution. Vendor proposals were solicited and reviewed by pathologists, IT, and security groups. A vendor was selected and pipelines for development and production were established. Proposals for development were solicited from the pathology department. Eighty-four investigators were selected for the initial cohort, receiving training and access to dedicated subject matter experts. A total of 30 of 31 projects progressed through the model development process of annotating, training, and validation. Based on these projects, 15 abstracts were submitted to national meetings. CONCLUSIONS.— Democratizing artificial intelligence by creating an ecosystem to support pathologists with varying levels of expertise can break down entry barriers, reduce overall cost of algorithm development, improve algorithm quality, and enhance the speed of adoption.Archives of Pathology & Laboratory Medicine00Archives of pathology & laboratory medicine['Thomas J. Flotte', 'Stephanie A. Derauf', 'Rachel K Byrd', 'T. Kroneman', 'Debra A Bell', 'Lucas Stetzik', 'Seung-Yi Lee', 'Alireza Samiei', 'Steven N Hart', 'Joaquin J Garcia', 'Gillian Beamer', 'Thomas Westerling-Bui']2024-04-23T00:00:00https://www.semanticscholar.org/paper/58f717e8e74d36fcab03d19eb932b665ccaa9d3c 13a361b7effcf8887baef136339cf589018fc81aInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-23T00:00:00https://www.semanticscholar.org/paper/13a361b7effcf8887baef136339cf589018fc81a f99d7270b2436d2550fab871dcf48790b62bc6b7Introdução: A cardiologia é um campo crucial da medicina, e os avanços da inteligência artificial (IA) estão moldando significativamente sua prática. Esta revisão abrange diversas aplicações da IA na cardiologia, desde o diagnóstico até o tratamento e monitoramento de doenças cardiovasculares. Objetivo: O objetivo desta revisão bibliográfica é explorar as diversas aplicações da inteligência artificial na cardiologia, abrangendo áreas como diagnóstico cardiovascular, predição de eventos cardíacos, interpretação de eletrocardiogramas (ECG), personalização do tratamento, monitoramento remoto e otimização de procedimentos cardíacos. Método: Trata-se de revisão abrangente seleção dos artigos foi realizada uma busca na base de dados Biblioteca Virtual de Saúde (BVS). Para a busca utilizou-se os descritores baseados no Decs, e assim montou a estratégia de busca: “Inteligência artificial” AND “Cardiologia” AND “Aprendizado de máquina”. Resultados: Exploramos como a IA está sendo utilizada para analisar imagens médicas, prever eventos cardíacos, interpretar eletrocardiogramas, personalizar tratamentos, monitorar pacientes remotamente e otimizar procedimentos cardíacos. Discutimos também as considerações éticas, regulatórias e de segurança associadas à implementação da IA na prática clínica. Conclusão: Esta revisão destaca o potencial da IA para melhorar os cuidados cardiológicos, ao mesmo tempo que destaca a importância de abordar desafios éticos e garantir a segurança dos pacientes.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação['Victor Balceiro Legname Martins', 'Laysla Rangel Freitas Thom', 'Júlia Mayse Soares Gonçalves', 'A. Destefani']2024-04-23T00:00:00https://www.semanticscholar.org/paper/f99d7270b2436d2550fab871dcf48790b62bc6b7 4ee9a3c4c61ecacfe448fdf9e1dd98fa2c9d07edInternational Journal of Production Research1361International Journal of Production Research['C. Smyth', 'Denis Dennehy', 'Samuel Fosso Wamba', 'Murray Scott', 'Antoine Harfouche']2024-04-23T00:00:00https://www.semanticscholar.org/paper/4ee9a3c4c61ecacfe448fdf9e1dd98fa2c9d07ed 58cf234a4de1e155d1998e2637be5c451fb432eeThe use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.Journal of Personalized Medicine1150The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues.Journal of Personalized Medicine['A. Maccaro', 'Katy Stokes', 'Laura Statham', 'Lucas He', 'Arthur Williams', 'Leandro Pecchia', 'Davide Piaggio']2024-04-23T00:00:00https://www.semanticscholar.org/paper/58cf234a4de1e155d1998e2637be5c451fb432ee eccdf489d47004bf45e4f51c013f1077d07a0719Environment, Development and Sustainability330The study reveals that gradient boosting demonstrates superior accuracy over random forests in low-income countries, whereas the opposite pattern is observed in high-income countries.Environment, Development and Sustainability['Mohamed F. Abd El-Aal']2024-04-23T00:00:00https://www.semanticscholar.org/paper/eccdf489d47004bf45e4f51c013f1077d07a0719 e0760f362c0642502778e53a84f6020c76e105ddJournal of Responsible Innovation650Journal of Responsible Innovation['Andrés Domínguez Hernández', 'Richard Owen']2024-04-23T00:00:00https://www.semanticscholar.org/paper/e0760f362c0642502778e53a84f6020c76e105dd 8a455a388cb9bb31e56cbc890c46de68ab8ed380Kompass Nutrition &amp; Dietetics00Kompass Nutrition &amp; Dietetics['Z. B. Kalyoncu Atasoy', 'Amanda Avery', 'Polat Goktas']2024-04-23T00:00:00https://www.semanticscholar.org/paper/8a455a388cb9bb31e56cbc890c46de68ab8ed380 bf361308d778705a95f6c37cf5cc78f9a8ebe179The widespread adoption of cloud applications, accelerated by remote work demands, introduces new security challenges. Traditional approaches struggle to keep pace with the growing volume of cloud applications, keeping track of their user activities and countering potential threats. This paper proposes a novel user access security system for cloud applications. The system leverages user activity tracking tied to user, device, and contextual identity data. By incorporating Identity Provider (IdP) information, Natural Language Processing (NLP), and Machine Learning algorithms (ML), the system builds user baselines and tracks deviations to bubble up critical deviations to the surface and proactively prevent further worsening in real-time, working in conjunction with security orchestration, automation, and response (SOAR) tools. Deviations from the baselines, which may indicate compromised accounts or malicious intent, trigger proactive interventions. This approach offers organizations superior visibility and control over their cloud applications, enabling proactive and real-time threat detection and data breach prevention. While real- time data collection from application vendors remains a challenge, near-real-time is made feasible today. The system can also effectively utilize IdP logs, activity logs from proxies, or firewalls. This research addresses the critical need for proactive security measures in the dynamic landscape of cloud application data security. The system will need a quarter (90 days) of learning time to ensure accurate detections based on historically gathered data and protect them for future baseline predictions on the user themselves and as well as on their peers. This approach ensures the detection is contextually aware of the organization as a whole. This research completely redefines traditional thinking with decentralized intelligence across the system that has a highly scalable microservice architecture. The proposed solution is a uniquely intelligent system where both human and artificial intelligence coexist, with the ultimate overriding control lying with humans (admin). This way, the outcomes at every stage are effective, making the overall detection and proactive security effective.International Journal of Innovative Science and Research Technology19215This research addresses the critical need for proactive security measures in the dynamic landscape of cloud application data security with a uniquely intelligent system where both human and artificial intelligence coexist, with the ultimate overriding control lying with humans (admin).International Journal of Innovative Science and Research Technology (IJISRT)['Priyanka Neelakrishnan']2024-04-22T00:00:00https://www.semanticscholar.org/paper/bf361308d778705a95f6c37cf5cc78f9a8ebe179 5759337938888799c50e909065bd5ebb12224fedA pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step n and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.IEEE Transactions on Neural Networks and Learning Systems00By designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal.IEEE transactions on neural networks and learning systems['Hongru Ren', 'Zeyi Liu', 'Hongjing Liang', 'Hongyi Li']2024-04-22T00:00:00https://www.semanticscholar.org/paper/5759337938888799c50e909065bd5ebb12224fed 2c2c870d5275d4918bc16f6cbe149feab4d30142Socially shared regulation plays a pivotal role in the success of collaborative learning. However, evaluating socially shared regulation of learning (SSRL) proves challenging due to the dynamic and infrequent cognitive and socio-emotional interactions, which constitute the focal point of SSRL. To address this challenge, this paper gathers interdisciplinary researchers to establish a multi-modal dataset with cognitive and socio-emotional interactions for SSRL study. Firstly, to induce cognitive and socio-emotional interactions, learning science researchers designed a special collaborative learning task with regulatory trigger events among triadic people for the SSRL study. Secondly, this dataset includes various modalities like video, Kinect data, audio, and physiological data (accelerometer, EDA, heart rate) from 81 high school students in 28 groups, offering a comprehensive view of the SSRL process. Thirdly, three-level verbal interaction annotations and non-verbal interactions including facial expression, eye gaze, gesture, and posture are provided, which could further contribute to interdisciplinary fields such as computer science, sociology, and education. In addition, comprehensive analysis verifies the dataset’s effectiveness. As far as we know, this is the first multimodal dataset for studying SSRL among triadic group members.ACM Transactions on Interactive Intelligent Systems310This paper gathers interdisciplinary researchers to establish a multi-modal dataset with cognitive and socio-emotional interactions for SSRL study among triadic group members, which is the first multimodal dataset for studying SSRL among triadic group members.ACM Transactions on Interactive Intelligent Systems['Yante Li', 'Yang Liu', 'Andy Nguyen', 'Henglin Shi', 'Eija Vuorenmaa', 'Sanna Järvelä', 'Guoying Zhao']2024-04-22T00:00:00https://www.semanticscholar.org/paper/2c2c870d5275d4918bc16f6cbe149feab4d30142 1b8825e1e5519c36f799f7f0523d72aafbb6105eJournal of Systems Science and Systems Engineering320Journal of Systems Science and Systems Engineering['Zeguo Qiu', 'Yuchen Yin', 'Yao Yuan', 'Yunhao Chen']2024-04-22T00:00:00https://www.semanticscholar.org/paper/1b8825e1e5519c36f799f7f0523d72aafbb6105e ceed6d000b5d7805902abf2d8f04de970afdb2a5 Since its inception, the inter-state dispute settlement system of the World Trade Organisation has generally been praised for effectively protecting the rule of law in international trade relations. While the relatively recent dismantling of this system does not necessarily mean the end of the WTO nor of the binding nature of its rules, the current crisis may be a good opportunity to reconsider the role of the rule of law in international trade relations and the ways in which it could further be accommodated. One suggestion, occasionally raised in the past, would be strengthening the enforcement of WTO rules by opening it to private action, either before national courts or through international adjudication. After all, the latter has been widely available to foreign investors covered by thousands of international investment agreements in force for decades. This contribution recalls the reasons behind the current lack of private enforcement of WTO law and argues that developments in international trade relations and experiences with investor-state dispute settlement are likely to work against rather than in favor of its introduction in the foreseeable future. Increased transparency and institutionalisation of non-state actors’ role in trade enforcement is therefore recommended instead.European Journal of Risk Regulation00European Journal of Risk Regulation['Iveta Alexovičová']2024-04-22T00:00:00https://www.semanticscholar.org/paper/ceed6d000b5d7805902abf2d8f04de970afdb2a5 04c575d327b30305bf65d5c5af09292e6ea07219Scientific Reports351Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, it is clarified that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era.Scientific Reports['Shuang Lin', 'Minke Wang', 'Chongyi Jing', 'Shengda Zhang', 'Jiuhao Chen', 'Rui Liu']2024-04-22T00:00:00https://www.semanticscholar.org/paper/04c575d327b30305bf65d5c5af09292e6ea07219 7782344c47e8403b4eff7e54b8683974a7217f8fnpj Mental Health Research641Accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups.NPJ Mental Health Research['Daniel A. Adler', 'Caitlin A. Stamatis', 'J. Meyerhoff', 'David C. Mohr', 'Fei Wang', 'Gabriel J. Aranovich', 'Srijan Sen', 'Tanzeem Choudhury']2024-04-22T00:00:00https://www.semanticscholar.org/paper/7782344c47e8403b4eff7e54b8683974a7217f8f 098d098523334f65299adfdc6eb3e5d9bad4979aJournal of Big Data991The study reveals novel intersections between Sustainable and Ethical AI and Green Computing, indicating significant research trends and identifying Ethical Healthcare Intelligence and AI Learning Quest as evolving areas within AI’s socio-economic and societal impacts.Journal of Big Data['R. Raman', 'Debidutta Pattnaik', 'H. Lathabai', 'Chandan Kumar', 'Kannan Govindan', 'Prema Nedungadi']2024-04-22T00:00:00https://www.semanticscholar.org/paper/098d098523334f65299adfdc6eb3e5d9bad4979a 54b61fa946fa8ba93b5dc45b8333fc0247f9ab53The emergence of electric vehicles (EVs) represents a paradigm shift in transportation, offering not only the promise of reduced carbon emissions but also the potential for enhanced sustainability and innovation. However, the full realization of EVs' transformative capabilities extends beyond mere electrification; it encompasses the integration of state-of-the-art technologies such as Artificial Intelligence (AI) and Cloud Computing. This meticulously crafted research article delves into the profound impact of AI and cloud technologies on the EV landscape within the United States. It meticulously examines how these technological advancements are reshaping EV ecosystems, catalyzing advancements in autonomous driving, optimizing battery management systems, and enriching user experiences. Furthermore, it elucidates the imperative need for robust cybersecurity measures to fortify these sophisticated systems against cyber threats, thereby ensuring the integrity, privacy, and stability of the transportation network. With a diverse audience in mind, including automotive industry professionals, policymakers, cybersecurity experts, environmental advocates, technology enthusiasts, and the broader public, this article serves as a beacon illuminating the future of transportation, sustainability, and digital security within the realm of EVs. Through a blend of rigorous analysis, insightful commentary, and visionary foresight, it aims to provide profound insights into the trajectory of EV technology in the United States and beyond.International Journal of Scientific Research and Management221This meticulously crafted research article delves into the profound impact of AI and cloud technologies on the EV landscape within the United States, and elucidates the imperative need for robust cybersecurity measures to fortify these sophisticated systems against cyber threats, thereby ensuring the integrity, privacy, and stability of the transportation network.International Journal of Scientific Research and Management (IJSRM)['Hassan Rehan']2024-04-22T00:00:00https://www.semanticscholar.org/paper/54b61fa946fa8ba93b5dc45b8333fc0247f9ab53 78f78f257c9aebb0afd1f589243ba6a2a79161b6Generative artificial intelligence (AI) systems are trained on large data corpora to generate new pieces of text, images, videos, and other media. There is growing concern that such systems may infringe on the copyright interests of training data contributors. To address the copyright challenges of generative AI, we propose a framework that compensates copyright owners proportionally to their contributions to the creation of AI-generated content. The metric for contributions is quantitatively determined by leveraging the probabilistic nature of modern generative AI models and using techniques from cooperative game theory in economics. This framework enables a platform where AI developers benefit from access to high-quality training data, thus improving model performance. Meanwhile, copyright owners receive fair compensation, driving the continued provision of relevant data for generative model training. Experiments demonstrate that our framework successfully identifies the most relevant data sources used in artwork generation, ensuring a fair and interpretable distribution of revenues among copyright owners.arXiv.org391This work proposes a framework that compensates copyright owners proportionally to their contributions to the creation of AI-generated content, leveraging the probabilistic nature of modern generative AI models and using techniques from cooperative game theory in economics.ArXiv['Jiachen T. Wang', 'Zhun Deng', 'Hiroaki Chiba-Okabe', 'Boaz Barak', 'Weijie J. Su']2024-04-22T00:00:00https://www.semanticscholar.org/paper/78f78f257c9aebb0afd1f589243ba6a2a79161b6 d701fb59431b1bdb2817c247655f90748ffea141Artificial intelligence (AI) technologies are becoming a reality, with intelligent engines that can learn and simulate human thinking. These engines have three key features: micro-level intelligence with sensors, logic-based intelligence with software tools, and the ability to adapt and learn using algorithms. AI reduces the need for human intervention and cognitive thinking, finding more efficient solutions to complex problems in manufacturing and supply chain industries. AI simulates human cognition using software tools, allowing for the automation of tasks and analysis of complex systems. However, it raises questions about whether problems can be solved differently and the limitations of explicit algorithms.International Journal of Innovative Science and Research Technology241Artificial intelligence reduces the need for human intervention and cognitive thinking, finding more efficient solutions to complex problems in manufacturing and supply chain industries, and allows for the automation of tasks and analysis of complex systems.International Journal of Innovative Science and Research Technology (IJISRT)['Vishwanadham Mandala', 'Manogna Dolu Surabhi']2024-04-22T00:00:00https://www.semanticscholar.org/paper/d701fb59431b1bdb2817c247655f90748ffea141 8b750488d139f9beba0815ff8f46ebe15ebb3e58Understanding AI systems' inner workings is critical for ensuring value alignment and safety. This review explores mechanistic interpretability: reverse-engineering the computational mechanisms and representations learned by neural networks into human-understandable algorithms and concepts to provide a granular, causal understanding. We establish foundational concepts such as features encoding knowledge within neural activations and hypotheses about their representation and computation. We survey methodologies for causally dissecting model behaviors and assess the relevance of mechanistic interpretability to AI safety. We investigate challenges surrounding scalability, automation, and comprehensive interpretation. We advocate for clarifying concepts, setting standards, and scaling techniques to handle complex models and behaviors and expand to domains such as vision and reinforcement learning. Mechanistic interpretability could help prevent catastrophic outcomes as AI systems become more powerful and inscrutable.arXiv.org2421This review explores mechanistic interpretability: reverse-engineering the computational mechanisms and representations learned by neural networks into human-understandable algorithms and concepts to provide a granular, causal understanding of AI systems' inner workings.ArXiv['Leonard Bereska', 'E. Gavves']2024-04-22T00:00:00https://www.semanticscholar.org/paper/8b750488d139f9beba0815ff8f46ebe15ebb3e58 03d91ade0d105a23ecc63de73a760793adfe8521The increasing integration of artificial intelligence (AI) in visual analytics (VA) tools raises vital questions about the behavior of users, their trust, and the potential of induced biases when provided with guidance during data exploration. We present an experiment where participants engaged in a visual data exploration task while receiving intelligent suggestions supplemented with four different transparency levels. We also modulated the difficulty of the task (easy or hard) to simulate a more tedious scenario for the analyst. Our results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the AI's lower suggestion accuracy. Moreover, the levels of transparency tested in this study did not significantly affect suggestion usage or subjective trust ratings of the participants. Additionally, we observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points. We discuss these findings and the implications of this research for improving the design and effectiveness of AI-guided VA tools.arXiv.org491The authors' results indicate that participants were more inclined to accept suggestions when completing a more difficult task despite the AI's lower suggestion accuracy, and it is observed that participants who utilized suggestions throughout the task explored a greater quantity and diversity of data points.ArXiv['Sunwoo Ha', 'S. Monadjemi', 'Alvitta Ottley']2024-04-22T00:00:00https://www.semanticscholar.org/paper/03d91ade0d105a23ecc63de73a760793adfe8521 3553f791b52c8f8a0c9563eb879cb6077b79dd1b01[]2024-04-22T00:00:00https://www.semanticscholar.org/paper/3553f791b52c8f8a0c9563eb879cb6077b79dd1b 59007e937e8183243e71d3908b9381879eafc1a1A recent innovation in the field of machine learning has been the creation of very large pre-trained models, also referred to as ‘foundation models’, that draw on much larger and broader sets of data than typical deep learning systems and can be applied to a wide variety of tasks. Underpinning text-based systems such as OpenAI's ChatGPT and image generators such as Midjourney, these models have received extraordinary amounts of public attention, in part due to their reliance on prompting as the main technique to direct and apply them. This paper thus uses prompting as an entry point into the critical study of foundation models and their implications. The paper proceeds as follows: In the first section, we introduce foundation models in more detail, outline some of the main critiques, and present our general approach. We then discuss prompting as an algorithmic technique, show how it makes foundation models programmable, and explain how it enables different audiences to use these models as (computational) platforms. In the third section, we link the material properties of the technologies under scrutiny to questions of political economy, discussing, in turn, deep user interactions, reordered cost structures, and centralization and lock-in. We conclude by arguing that foundation models and prompting further strengthen Big Tech's dominance over the field of computing and, through their broad applicability, many other economic sectors, challenging our capacities for critical appraisal and regulatory response.Big Data &amp; Society222It is argued that foundation models and prompting further strengthen Big Tech's dominance over the field of computing and, through their broad applicability, many other economic sectors, challenging the authors' capacities for critical appraisal and regulatory response.Big Data &amp; Society['Sarah Burkhardt', 'Bernhard Rieder']2024-04-22T00:00:00https://www.semanticscholar.org/paper/59007e937e8183243e71d3908b9381879eafc1a1 8ba71c3c7f6d88071ed9e5fc2f022b7943bc49ebAI can help pick up the pace by helping managers nurture each potential hire automatically, and it allows them to receive notifications when a candidate applies for an open position. Artificial intelligence (AI) can help usher in a new era of human resource management, where data analytics, machine learning and automation can work together to save people time and support higher-quality outcomes. As AI technology moves beyond automation to augmentation, companies may be looking at how AI tools can make the work of human resources (HR) better for employees and job seekers. It’s not just about saving time; it’s also about providing information, insights and recommendations in near real-time. And that’s just the start of AI in human resources. Keywords: Professional learning and development, Candidate sourcing and hiring, Procurement of short-term workers, Automating HR service, Enhanced employee support, Increased Efficiency, Enhanced candidate experiences, Reskilling talent and restructuring job role.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Artificial intelligence (AI) can help usher in a new era of human resource management, where data analytics, machine learning and automation can work together to save people time and support higher-quality outcomes.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['D. D']2024-04-22T00:00:00https://www.semanticscholar.org/paper/8ba71c3c7f6d88071ed9e5fc2f022b7943bc49eb b64d0690439a55c4a5c142f842a1477c6406346cThis special issue encapsulates the multifaceted landscape of contemporary challenges and innovations in Artificial Intelligence (AI) and Machine Learning (ML), with a particular focus on issues related to explainability, fairness, and trustworthiness. The exploration begins with the computational intricacies of understanding and explaining the behavior of binary neurons within neural networks. Simultaneously, ethical dimensions in AI are scrutinized, emphasizing the nuanced considerations required in defining autonomous ethical agents. The pursuit of fairness is exemplified through frameworks and methodologies in machine learning, addressing biases and promoting trust, particularly in predictive policing systems. Human-agent interaction dynamics are elucidated, revealing the nuanced relationship between task allocation, performance, and user satisfaction. The imperative of interpretability in complex predictive models is highlighted, emphasizing a query-driven methodology. Lastly, in the context of trauma triage, the study underscores the delicate trade-off between model accuracy and practitioner-friendly interpretability, introducing innovative strategies to address biases and trust-related metrics.International journal on artificial intelligence tools00International Journal on Artificial Intelligence Tools['Sheikh Rabiul Islam', 'Ingrid Russell', 'William Eberle', 'Douglas Talbert', 'Md Golam Moula Mehedi Hasan']2024-04-22T00:00:00https://www.semanticscholar.org/paper/b64d0690439a55c4a5c142f842a1477c6406346c 462387f75f15bf856663ae2af40b1068a39fd0beIn the domain of technology-driven educational advancements, artificial intelligence (AI) has become pivotal in enriching student experiences. This abstract gives into the rising need for effective career counseling and proposes the development for a specialized AI-based software application to address this need. Recognizing the universal role of technology in students’ lives, leveraging AI for personalized career guidance becomes necessity. The envisioned desktop application aims to redefine traditional career counseling methods by using AI algorithms' advanced capabilities. It seeks to provide personized recommendations to individuals or students navigating diverse career paths by evaluating academic performance, skills, and interests. Targeting three main user groups students, career counselors, and educational institutions the application offers a user-friendly interface for students to input details and preferences, resulting in a personalized career roadmap. Career counselors benefit from AI-generated analytics, entitle them to offer more informed guidance. This abstract covers the transformative potential of AI in enhancing career counseling services and facilitating informed career decisionsInternational Journal of Advanced Research in Science, Communication and Technology30The envisioned desktop application aims to redefine traditional career counseling methods by using AI algorithms' advanced capabilities to provide personized recommendations to individuals or students navigating diverse career paths by evaluating academic performance, skills, and interests.International Journal of Advanced Research in Science, Communication and Technology['Prof. Hude T', 'Aditya Bagal', 'Armaan Kazi', 'Anand Gaikwad', 'Hrushikesh Sarvade']2024-04-22T00:00:00https://www.semanticscholar.org/paper/462387f75f15bf856663ae2af40b1068a39fd0be 3618bc29f39fc04538ccab32719ae0b0f6871429This workshop proposal focuses on best practices in UI/UX design for AI applications aimed at children, emphasising safety, engagement, and ethics. It aims to address the challenge of measuring the safety, trustworthiness, and reliability of interactions between children and AI systems. Through collaborative discussions, participants will explore effective design strategies and ethical guidelines while developing methodologies for assessing the safety and reliability of AI interactions with children. This proposal seeks to foster responsible and child-centered AI design practices within the CHI community.arXiv.org80This workshop proposal aims to address the challenge of measuring the safety, trustworthiness, and reliability of interactions between children and AI systems, and seeks to foster responsible and child-centered AI design practices within the CHI community.ArXiv['Grazia Ragone', 'P. Buono', 'R. Lanzilotti']2024-04-22T00:00:00https://www.semanticscholar.org/paper/3618bc29f39fc04538ccab32719ae0b0f6871429 b1f71e504792716c9c77f46f1266825fa522303cPurposeThe pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.Design/methodology/approachThis research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.FindingsThe study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.Social implicationsThis research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.Originality/valueThe research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.International Journal of Logistics Management1360The International Journal of Logistics Management['Deval Ajmera', 'Manjeet Kharub', 'Aparna Krishna', 'Himanshu Gupta']2024-04-22T00:00:00https://www.semanticscholar.org/paper/b1f71e504792716c9c77f46f1266825fa522303c 8f00eeaa70b91d0c3974093e12e19521903b26fcAs technology rapidly changes, digital technology has been introduced to the accounting field, forcing businesses to adapt. The accounting profession is expected to embrace the new era of digitalization that will change traditional accounting practices. The roles of the accountants will shift to more challenging. Some of it predicted that this technology would take over the accountant's job, but the roles of accountants in this digital economy are still noteworthy. Amidst COVID-19, the transition to online operations is imperative for all businesses, compelling the accounting sector to embrace this technology alongside others. This study aims to discuss how artificial intelligence (AI) impacts the organization in Malaysia in this digital era. This research is anticipated to incorporate the reasons behind the organization's potential transition from conventional accounting methods to AI-driven accounting systems and analyze the resulting impact on the company's efficiency.Accounting and Finance Research00How artificial intelligence (AI) impacts the organization in Malaysia in this digital era is discussed to incorporate the reasons behind the organization's potential transition from conventional accounting methods to AI-driven accounting systems and analyze the resulting impact on the company's efficiency.Accounting and Finance Research['Mohd Fairuz Adnan', 'Azzihan Nurfarahin Bahrudin', 'Saleh Hashim']2024-04-22T00:00:00https://www.semanticscholar.org/paper/8f00eeaa70b91d0c3974093e12e19521903b26fc c750839d49dda0ba4b82d1d2bd8cdb270c383defAbstract AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.Research Square600Accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups.Research Square['Daniel A. Adler', 'Caitlin A. Stamatis', 'J. Meyerhoff', 'David C. Mohr', 'Fei Wang', 'Gabriel J. Aranovich', 'Srijan Sen', 'Tanzeem Choudhury']2024-04-22T00:00:00https://www.semanticscholar.org/paper/c750839d49dda0ba4b82d1d2bd8cdb270c383def 77f0eb897683c963f989417e7de5e221b34f8639Background: It is unknown whether large language models (LLMs) may facilitate time- and resource-intensive text-related processes in evidence appraisal. Objectives: To quantify the agreement of LLMs with human consensus in appraisal of scientific reporting (PRISMA) and methodological rigor (AMSTAR) of systematic reviews and design of clinical trials (PRECIS-2). To identify areas, where human-AI collaboration would outperform the traditional consensus process of human raters in efficiency. Design: Five LLMs (Claude-3-Opus, Claude-2, GPT-4, GPT-3.5, Mixtral-8x22B) assessed 112 systematic reviews applying the PRISMA and AMSTAR criteria, and 56 randomized controlled trials applying PRECIS-2. We quantified agreement between human consensus and (1) individual human raters; (2) individual LLMs; (3) combined LLMs approach; (4) human-AI collaboration. Ratings were marked as deferred (undecided) in case of inconsistency between combined LLMs or between the human rater and the LLM. Results: Individual human rater accuracy was 89% for PRISMA and AMSTAR, and 75% for PRECIS-2. Individual LLM accuracy was ranging from 63% (GPT-3.5) to 70% (Claude-3-Opus) for PRISMA, 53% (GPT-3.5) to 74% (Claude-3-Opus) for AMSTAR, and 38% (GPT-4) to 55% (GPT-3.5) for PRECIS-2. Combined LLM ratings led to accuracies of 75-88% for PRISMA (4-74% deferred), 74-89% for AMSTAR (6-84% deferred), and 64-79% for PRECIS-2 (18-88% deferred). Human-AI collaboration resulted in the best accuracies from 89-96% for PRISMA (25/35% deferred), 91-95% for AMSTAR (27/30% deferred), and 80-86% for PRECIS-2 (76/71% deferred). Conclusions: Current LLMs alone appraised evidence worse than humans. Human-AI collaboration may reduce workload for the second human rater for the assessment of reporting (PRISMA) and methodological rigor (AMSTAR) but not for complex tasks such as PRECIS-2.medRxiv480Current LLMs alone appraised evidence worse than humans but not for complex tasks such as PRECIS-2, while human-AI collaboration may reduce workload for the second human rater for the assessment of reporting and methodological rigor (AMSTAR) but not for complex tasks such as PRECIS-2.['MD Tim Woelfle', 'PhD Julian Hirt', 'PhD Perrine Janiaud', 'MD Ludwig Kappos', 'MD DSc John P. A. Ioannidis', 'M. M. Lars G. Hemkens']2024-04-22T00:00:00https://www.semanticscholar.org/paper/77f0eb897683c963f989417e7de5e221b34f8639 d07c377920c2ddc63285485bb6b06bbd59c44ebeThis report shares the experiences, results and lessons learned in conducting a pilot project ``Responsible use of AI'' in cooperation with the Province of Friesland, Rijks ICT Gilde-part of the Ministry of the Interior and Kingdom Relations (BZK) (both in The Netherlands) and a group of members of the Z-Inspection$^{\small{\circledR}}$ Initiative. The pilot project took place from May 2022 through January 2023. During the pilot, the practical application of a deep learning algorithm from the province of Fr\^yslan was assessed. The AI maps heathland grassland by means of satellite images for monitoring nature reserves. Environmental monitoring is one of the crucial activities carried on by society for several purposes ranging from maintaining standards on drinkable water to quantifying the CO2 emissions of a particular state or region. Using satellite imagery and machine learning to support decisions is becoming an important part of environmental monitoring. The main focus of this report is to share the experiences, results and lessons learned from performing both a Trustworthy AI assessment using the Z-Inspection$^{\small{\circledR}}$ process and the EU framework for Trustworthy AI, and combining it with a Fundamental Rights assessment using the Fundamental Rights and Algorithms Impact Assessment (FRAIA) as recommended by the Dutch government for the use of AI algorithms by the Dutch public authorities.arXiv.org00Performing both a Trustworthy AI assessment using the Z-Inspection process and the EU framework for Trustworthy AI and combining it with a Fundamental Rights assessment using the Fundamental Rights and Algorithms Impact Assessment (FRAIA) as recommended by the Dutch government for the use of AI algorithms by the Dutch public authorities is shared.ArXiv['Marjolein Boonstra', 'Frédérick Bruneault', 'Subrata Chakraborty', 'Tjitske Faber', 'Alessio Gallucci', 'Eleanore Hickman', 'Gerard Kema', 'Heejin Kim', 'Jaap Kooiker', 'Elisabeth Hildt', "Annegret Lamad'e", 'Emilie Wiinblad Mathez', 'Florian Moslein', 'Genien Pathuis', 'Giovanni Sartor', 'Marijke Steege', 'Alice Stocco', 'Willy Tadema', 'J. Tuimala', 'I. V. Vledder', 'Dennis Vetter', 'Jana Vetter', 'Magnus Westerlund', 'R. Zicari']2024-04-22T00:00:00https://www.semanticscholar.org/paper/d07c377920c2ddc63285485bb6b06bbd59c44ebe b46bdd1c3be22f3e57ac101ff623e90e2be08f19The paper explores the transformative role of Artificial Intelligence (AI) in foreign language study within the context of higher education. It discusses how AI has redefined traditional teaching methods by introducing innovative pedagogical tools. Through an in-depth analysis, the research illustrates the enhancement of communicative capabilities, where AI significantly improves the interaction dynamics in the target language among students. Presently, AI-driven language platforms enable personalized learning, catering to individual strengths and cultural backgrounds. However, concerns emerge about the potential erosion of traditional teaching roles in the face of AI’s capabilities. Yet, for AI to enhance the learning process, educators must possess robust digital competencies to effectively leverage AI technologies. Furthermore, the utilization of AI enables the customization of learning experiences, adapting to individuals’ needs, propensities, and skill levels, thus fostering motivation and commitment to language acquisition. AI’s capability to offer immediate and precise feedback is also highlighted as a critical factor in expediting the learning process and easing the anxiety related to performance evaluation. Moreover, the emergence of intelligent conversational assistants is showcased as a pivotal development in the domain. These AI-driven language bots act as virtual language partners, offering realistic conversation practice and proficiency evaluations that contribute to a supportive and boundless learning environment. While acknowledging the substantial benefits of AI in foreign language education, the paper concludes by reaffirming the irreplaceable role of human teachers as the central organizers of the educational process. It is implied that the successful integration of AI in language pedagogy rests upon its alignment with conventional teaching practices guided by experienced educators.АКАДЕМІЧНІ СТУДІЇ СЕРІЯ «ПЕДАГОГІКА»00It is implied that the successful integration of AI in language pedagogy rests upon its alignment with conventional teaching practices guided by experienced educators, reaffirming the irreplaceable role of human teachers as the central organizers of the educational process.АКАДЕМІЧНІ СТУДІЇ. СЕРІЯ «ПЕДАГОГІКА»['T. Golub', 'O. Kovalenko', 'L. Zhygzhytova', 'A. L. Kotkovets']2024-04-22T00:00:00https://www.semanticscholar.org/paper/b46bdd1c3be22f3e57ac101ff623e90e2be08f19 2284983f6b45e77dd406c07cb9909da6fc49455aIf AI is the new electricity, what should we do to keep ourselves from getting electrocuted? In this work, we explore factors related to the potential of large language models (LLMs) to manipulate human decisions. We describe the results of two experiments designed to determine what characteristics of humans are associated with their susceptibility to LLM manipulation, and what characteristics of LLMs are associated with their manipulativeness potential. We explore human factors by conducting user studies in which participants answer general knowledge questions using LLM-generated hints, whereas LLM factors by provoking language models to create manipulative statements. Then, we analyze their obedience, the persuasion strategies used, and the choice of vocabulary. Based on these experiments, we discuss two actions that can protect us from LLM manipulation. In the long term, we put AI literacy at the forefront, arguing that educating society would minimize the risk of manipulation and its consequences. We also propose an ad hoc solution, a classifier that detects manipulation of LLMs - a Manipulation Fuse.arXiv.org600This work explores factors related to the potential of large language models (LLMs) to manipulate human decisions, and proposes an ad hoc solution, a classifier that detects manipulation of LLMs - a Manipulation Fuse.ArXiv["Piotr Wilczy'nski", 'Wiktoria Mieleszczenko-Kowszewicz', 'P. Biecek']2024-04-22T00:00:00https://www.semanticscholar.org/paper/2284983f6b45e77dd406c07cb9909da6fc49455a 8cb26863b63afd84c1a298d87212c32cbd895a2cThe evolution of industrial revolutions has been marked by the increasing use of data and information to improve productivity and efficiency. Industry 3.0 introduced automation and digitalization, which generated a lot of data from various sources and processes. This data was mainly used for monitoring and controlling the industrial activities, such as production, quality, and maintenance. Industry 4.0 leveraged this data to generate insights and intelligence, using technologies such as cloud computing, big data analytics, and the Internet of Things (IoT). These technologies enabled the integration and communication of data across different levels and domains of the industrial system, such as machines, products, processes, and services. Industry 4.0 also introduced the concept of smart factories, which are self-organizing, adaptive, and learning systems that can optimize their performance and efficiency. Industry 5.0 aims to enable human-robot collaboration and artificial intelligence [1], creating a more personalized and sustainable industrial system. Industry 5.0 focuses on enhancing the human capabilities and creativity, rather than replacing them with machines. It also emphasizes the social and environmental aspects of industrial development, such as customer satisfaction, worker well-being, and resource conservation. Industry 5.0 envisions a human-centric and eco-friendly industrial paradigm, where humans and machines work together in harmony and synergy. One of the sectors that can benefit from the convergence of business intelligence (BI) and artificial intelligence (AI) is the energy industry, which faces challenges such as increasing demand, environmental regulations, and market volatility. By combining BI and AI, energy companies can unlock value from their data and optimize their operations, such as production, distribution, and consumption. BI helps energy companies to collect, store, analyze, and visualize data from various sources, such as sensors, meters, devices, and systems. BI enables energy companies to monitor and manage their assets, processes, and performance, as well as to identify and solve problems, improve efficiency, and reduce costs. AI helps energy companies to augment and automate their decision making, using techniques such as machine learning, natural language processing, computer vision, and deep learning. AI enables energy companies to generate predictions, recommendations, and insights from their data, as well as to optimize their operations, such as scheduling, dispatching, pricing, and trading. AI also helps energy companies to create new products and services, such as smart grids, smart meters, smart homes, and smart cities. By combining BI and AI, energy companies can create a data-driven and intelligent energy system, which can respond to the changing needs and preferences of customers, stakeholders, and regulators, as well as to the dynamic and uncertain market conditions. This paper discusses the approach of complimenting the established business intelligence (BI) process with Artificial Intelligence (AI) in order to optimize gas production in an oil field in the south of Sultanate of Oman, it details the facts, observations, and insights the multidisciplinary authors have captured throughout the progress of this work, as well as general industry insights and BI process description.Day 1 Mon, April 22, 202421The approach of complimenting the established business intelligence (BI) process with Artificial Intelligence (AI) in order to optimize gas production in an oil field in the south of Sultanate of Oman is discussed, which details the facts, observations, and insights the multidisciplinary authors have captured.Day 1 Mon, April 22, 2024['A. Al-Jumah', 'Ilyas Kindy', 'Mahamood Rawahi', 'Aiman Quraini']2024-04-22T00:00:00https://www.semanticscholar.org/paper/8cb26863b63afd84c1a298d87212c32cbd895a2c 00352c6f004ad911a3949a0910179264b1c2374bArtificial intelligence (AI) has the potential to transform surgery in Pakistan, improving results, reducing complications, and increasing patient safety. Deep learning, a branch of machine learning, can assist surgeons in making wise judgments. Pubmed and Research Gate discuss the promise of AI in surgery. Incorporating AI in healthcare systems can expedite diagnosis and management while avoiding injudicious resource allocation. AI-based techniques in cardiology, nephrology, and neurology enable high-accuracy detection of cardiovascular disease risks, kidney disease treatment, and epileptic episode identification. Neurological devices like bispectral index monitor (BIS) and Near-infrared spectroscopy (NIRS) utilize advanced technology for reliable monitoring and objective diagnosis of neurological issues (Ahmed, 2022). AI uses electronic data and neural network methods to evaluate operating room logistics, time management, and anesthesiologist activities using electronic data and neural network methods (Khan F. H., 2019). AI algorithms are effectively detect minute differences for accurate diagnosis, with studies showing high specificity, sensitivity, and inter-operator repeatability. AI can be used to train and assess neurosurgical residents and early mid-career surgeons, improving diagnosis and 3D simulation labs (Shlobin, 2022). Surgeons play a crucial role in adopting AI-based technologies for surgical care by partnering with data scientists to capture novel clinical data and generate meaningful interpretations. They should demand transparency and interpretability in algorithms to hold AI accountable for its predictions and recommendations.AI advancements in plastic surgery practice, research, and education offer opportunities for improvement. Combining AI-enabled decision-making tools with predictive analytics and human intuition, surgeons can make real-time decisions based on 3D planning, anatomical localization, and navigation (Rasteau, 2022). While current AI tools cannot perform complex surgical procedures, advancements may enable them to perform more complex tasks in the future. Pakistan is a significant market for AI-based solutions, utilizing technology in various industries to address challenges and boost demand. The country has enhanced its self-security systems, including AI-powered missiles, cyber security, and effective cameras (Khan, 2022). AI allows tasks to be completed more precisely and efficiently, with machine learning and deep learning being advanced subtypes. In developing countries like Pakistan, AI tools are needed for patient-centred diagnosis and treatment assistance, especially in emergency surgery such as cardiac illnesses or life threatening bleeding caused road traffic accident. Hence insuring patient receive care timely. An appropriate budget should be allocated for AI technologies in the health sector.JPMA. The Journal of the Pakistan Medical Association00Artificial intelligence has the potential to transform surgery in Pakistan, improving results, reducing complications, and increasing patient safety, according to Pubmed and Research Gate, which discuss the promise of AI in surgery.JPMA. The Journal of the Pakistan Medical Association['Areeba Farooqui', 'Aaliyan Wajid']2024-04-22T00:00:00https://www.semanticscholar.org/paper/00352c6f004ad911a3949a0910179264b1c2374b 4a91723f29fcc1e97d0d63493c79509fc4e95094This article examines the immediate concerns of unemployment that workers in India face due to implementation of automation, robotics, and artificial intelligence (AI) become more prevalent in the labour sectors. The swift progress of technology poses a danger to conventional manual labour positions, resulting in a large scale loss of jobs and unstable economic conditions. The impacts of automation are most likely to affect marginalized communities, aggravating already existing disparities. To lessen(minimalize/reduce) the negative impacts of mass unemployment, industry stakeholders, governments, and civil society must adopt proactive steps. These are necessary because of the social and political implications of this issue. India's workforce may move towards a more sustainable and fair future by supporting inclusive economic development, encouraging entrepreneurship, and investing in vocational training.International Journal For Multidisciplinary Research50The immediate concerns of unemployment that workers in India face due to implementation of automation, robotics, and artificial intelligence (AI) become more prevalent in the labour sectors are examined.International Journal For Multidisciplinary Research['K. .A. B']2024-04-22T00:00:00https://www.semanticscholar.org/paper/4a91723f29fcc1e97d0d63493c79509fc4e95094 c74d7343e9925291297ced347ad7b92656f0f034The purpose of this article is to present artificial intelligence in the public security system at local government level of a state. This article answers the following questions: Does artificial intelligence have the potential to be used in the field of public safety by public administrations at the level of local authorities and, if so, are there successful examples of such applications? Is it advisable to expand the understanding of public safety to include such an area where AI is applied to public services?The article falls within the theoretical publication stream. The research tools and techniques used are characteristic of the social sciences, including political and administrative sciences and security sciences. Mainly the following were used : desk research, analysis of literature , normative acts, documents and strategies. The analysis was extended with statements of practitioners from local government as representatives of public administration.The result of the study is the confirmation of both research theses (H1 and H2) posed in the social science area of public safety and artificial intelligence systems.This article states that that AI has the potential to be used in the field of public safety by public administrations at the level of local and regional authorities, despite the correlated risks arising from the use of AI by the public sector. It is advisable to expand the understanding of public safety to include such an area where AI is applied in public services.Przegląd Nauk o Obronności290Artificial intelligence has the potential to be used in the field of public safety by public administrations at the level of local and regional authorities, despite the correlated risks arising from the use of AI by the public sector.Przegląd Nauk o Obronności['E. Włodyka']2024-04-22T00:00:00https://www.semanticscholar.org/paper/c74d7343e9925291297ced347ad7b92656f0f034 e3fdd8415eb56259c25dc72fe6bc586ceabb92e6Background Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Methods The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom. Results An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using APA PsycInfo revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed. Conclusion Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive ‘ingredient’ that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians’ attention to this burdening, and sometimes existential experience.Frontiers in Psychiatry670The study signals boredom as a maladaptive ‘ingredient’ that might trigger suicide behaviors, regardless of depression, regardless of depression in an almost fully automated, AI-guided research pipeline.Frontiers in Psychiatry['Shir Lissak', 'Yaakov Ophir', 'Refael Tikochinski', 'A. Klomek', 'Itay Sisso', 'Eyal Fruchter', 'Roi Reichart']2024-04-22T00:00:00https://www.semanticscholar.org/paper/e3fdd8415eb56259c25dc72fe6bc586ceabb92e6 5b7bb1794e4b6d788f1a6f81881d9995f0dbaac3Madam, Artificial intelligence (AI) describes the generation of intelligent machines that are capable of carrying out activities that usually call for human intellect. (1) It involves creating algorithms and models that enable computers to learn from and analyze vast amounts of data, recognize patterns, make decisions, and even engage in natural language processing.  (2) AI has become increasingly important in various fields due to its potential to automate complex processes, enhance efficiency, and drive innovation. It can transform industries, improve decision-making, and revolutionize how we live and work. In medical diagnostics, AI has had a profound impact on improving accuracy, efficiency, and patient outcomes. By leveraging machine learning algorithms, AI systems can process and analyze large volumes of medical data, including patient records, laboratory results, medical images, and genomic data. This allows for more accurate and timely diagnoses, as AI algorithms can detect subtle patterns and anomalies that may not be visible to human observers. AI-powered diagnostics can assist healthcare professionals in detecting diseases, predicting patient outcomes, and designing personalized treatment plans. (3) Furthermore, AI can contribute to the early detection of diseases, thereby enabling timely interventions and improving patient survival rates. For example, AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to detect abnormalities and potential signs of diseases like cancer. This helps radiologists and clinicians identify and diagnose conditions early when treatment options are more effective. (4) Additionally, AI aids in interpreting genetic data, allowing for the identification of genetic markers associated with specific diseases or drug responses. This information guides clinicians in selecting the most suitable treatment options for individual patients, leading to improved therapeutic outcomes. (5) AI in medical diagnostics also faces hurdles with data privacy and accessibility, as patient information must be handled securely. Ensuring AI algorithms are unbiased, and fair is a challenge to avoid disparities in healthcare outcomes. The interpretability of AI models is vital to building trust with healthcare professionals and facilitating their adoption. Regulatory compliance and ethical considerations are also significant factors that need to be addressed for the responsible and effective integration of AI in the medical field. In conclusion, Artificial Intelligence (AI) is a transformative technology with the potential to revolutionize various industries, including medical diagnostics. Its ability to analyze vast amounts of data, recognize patterns, and make intelligent decisions has proven invaluable in improving diagnostic processes accuracy, efficiency, and effectiveness. ---ContinueJPMA. The Journal of the Pakistan Medical Association00Artificial Intelligence is a transformative technology with the potential to revolutionize various industries, including medical diagnostics, and its ability to analyze vast amounts of data, recognize patterns, and make intelligent decisions has proven invaluable in improving diagnostic processes accuracy, efficiency, and effectiveness.JPMA. The Journal of the Pakistan Medical Association['Sahar Imtiaz', 'Sheikh Abdul Qadir Jillani']2024-04-22T00:00:00https://www.semanticscholar.org/paper/5b7bb1794e4b6d788f1a6f81881d9995f0dbaac3 e6ab89d86c2f3201a0f5371b13f9c002973f2aacBMC Medical Education270AI tools are useful adjuncts to plan instructional methods, identify themes for test blueprinting, generate test items, and guide test standard-setting appropriate to learners’ stage in the medical program, however, experts need to review the content validity of AI-generated output.BMC Medical Education['K. Sridharan', 'Reginald P. Sequeira']2024-04-22T00:00:00https://www.semanticscholar.org/paper/e6ab89d86c2f3201a0f5371b13f9c002973f2aac 7ce51167697c3c671bbee01133ec33b6a1f12971Scientific Reports220The fundamental theory of AI is introduced, delving into the intricacies of the greyscale model of AI and assessing the existing research status, which identifies key issues in AI-ECS integration and proposes viable policy recommendations.Scientific Reports['Yongyan Zhao', 'Jian Li']2024-04-22T00:00:00https://www.semanticscholar.org/paper/7ce51167697c3c671bbee01133ec33b6a1f12971 639d1cf1605ce2e61846464dff892d5bd0c01c67Artificial intelligence (AI) technology has recently been integrated into the health-care industry, including in optometry and ophthalmology. This systematic review assessed the opinions (i.e., perspectives, concerns, and degrees of acceptance) of eye care professionals regarding AI integration into eye care practices. The literature search was conducted using the PubMed and MEDLINE databases. A total of 780 related articles were identified. Among these articles, 304 duplicates were removed, 450 articles were excluded after reviewing the abstract, and 18 articles were excluded after reviewing the full text as these articles were not relevant and/or did not report surveys. The remaining eight included studies were assessed accordingly. Most ophthalmologists and optometrists had a positive perception toward incorporating AI into eye care practices, and these professionals shared that AI would effectively enhance clinical eye care practices. However, certain eye care professionals were concerned about the diagnostic accuracy of AI, the high implementation costs, privacy issues, and the quality of AI-integrated patient care. Several eye care professionals also expressed concerns that AI technology could eventually replace some of their major responsibilities in the practice, suggesting that stakeholders should essentially address these concerns and ensure that AI integration in eye care practices is implemented thoughtfully and ethically to maximize its benefits while preserving the quality of patient care. Nonetheless, this systematic review highlighted the predominantly positive attitude among eye care professionals toward AI integration into eye care practices, warranting further research and collaboration between AI developers and eye care professionals to effectively address the current challenges.Artificial Intelligence in Health00The predominantly positive attitude among eye care professionals toward AI integration into eye care practices is highlighted, warranting further research and collaboration between AI developers and eye care professionals to effectively address the current challenges.Artificial Intelligence in Health['Obehi Suzan Idogen']2024-04-22T00:00:00https://www.semanticscholar.org/paper/639d1cf1605ce2e61846464dff892d5bd0c01c67 43d6c0c27bbd93f96d854a126b4fc07bb8b08d9fThis article reviews the literature that examines the potential, limitations, and consequences of robots and artificial intelligence (AI) in automation and augmentation across various disciplines. It presents key observations and suggestions from the literature review. Firstly, displacement effects from task automation continue to persist. However, one should not assume an unequivocally increasing efficacy of technology in automation or augmentation, especially given the declining productivity growth in high-income countries and some large emerging economies in recent decades. Jobs less likely to be negatively impacted are those that require diverse tasks, physical dexterity, tacit knowledge, or flexibility, or are protected by professional or trade associations. Despite countervailing effects, without policy intervention, automation and augmentation could widen inequality between social groups, labor and capital, and firms. Secondly, AI's promise in task automation and labor augmentation is mixed. AI tools can cause harm, and dissatisfaction and disengagement often arise from their opaqueness, errors, disregard for critical contexts, lack of tacit knowledge, and lack of domain expertise, as well as their demand for extra labor time and resources. The inadequate autonomy to override AI-based assessments further frustrates users who have to use these AI tools at work. Finally, the article calls for sociological research to specify conditions and mechanisms that ameliorate adverse consequences and enhance labor augmentation by embedding the study of automation and augmentation in concrete social and political contexts at multiple levels.The Annual review of sociology00It is called for sociological research to specify conditions and mechanisms that ameliorate adverse consequences and enhance labor augmentation by embedding the study of automation and augmentation in concrete social and political contexts at multiple levels.Annual Review of Sociology['Ya-Wen Lei', 'Rachel Kim']2024-04-22T00:00:00https://www.semanticscholar.org/paper/43d6c0c27bbd93f96d854a126b4fc07bb8b08d9f d3529f32359a02efa45fcd6c4585a1e76e450bfdWith the increasing demand for food production to sustain the growing population, coupled with the challenges posed by climate change and resource scarcity, the agricultural sector in India faces a pressing need for innovation and efficiency. In recent years, Artificial Intelligence (AI) has emerged as a promising technology with the potential to revolutionize various industries, including agriculture. This scholarly article explores the role of AI in empowering Indian agriculture by enhancing productivity, sustainability, and resilience. Through an analysis of current research, case studies, and practical applications, this article highlights the transformative impact of AI technologies such as machine learning, remote sensing, and precision agriculture on various aspects of agricultural production, resource management, and decision-making processes. Furthermore, it discusses the challenges and opportunities associated with the adoption of AI in Indian agriculture, including issues related to data availability, infrastructure, and farmer adoption. By elucidating the potential benefits and addressing the barriers to adoption, this article aims to contribute to the discourse on leveraging AI to address the pressing challenges faced by the Indian agricultural sector.International Research Journal of Modernization in Engineering Technology and Science60The transformative impact of AI technologies such as machine learning, remote sensing, and precision agriculture on various aspects of agricultural production, resource management, and decision-making processes is highlighted.International Research Journal of Modernization in Engineering Technology and Science['Anandkumar Chennupati']2024-04-22T00:00:00https://www.semanticscholar.org/paper/d3529f32359a02efa45fcd6c4585a1e76e450bfd 826d1ee6f1152f08d8ce0a3944b4877f8f9510c5Currently, there are virtually no objective diagnostic signs (markers) to diagnose neurodegenerative diseases, that is why it may take months of constant monitoring of symptoms to establish a reliable diagnosis. Now there is an increasing number of reports that artificial intelligence can provide a more accurate diagnosis of neurodegenerative diseases by identifying specific diagnostic features from electroencephalography data, neuroimaging, and wearable devices and smartphones. This study reviews the application of artificial intelligence to the diagnosis of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease. Using the international databases Scopus and PubMed, as well as the Russian Science Citation Index, we analysed studies that used artificial intelligence methods to detect, monitor, or control the progression of neurodegenerative diseases. The primary focus is on analysing electroencephalography data, neuroimaging data, and data from wearable devices and smartphones. The use of the latter may allow screening, diagnosis, and monitoring of the disease at home with a minimum economic cost. Artificial intelligence can be a useful tool for early, accurate, and non-invasive diagnosis of neurodegenerative diseases, as well as for assessing the effectiveness of treatment and predicting the course of the disease. However, for the widespread implementation of artificial intelligence in clinical practice, several issues related to the quality, availability, and standardization of data, validation and interpretation of results, ethical and legal aspects need to be resolved. The use of artificial intelligence requires both specialists from the IT industry with a deep understanding of the types and kinds of neural networks and physicians with fundamental knowledge of neurology, psychiatry, molecular biology, and biophysics.Vestnik nevrologii, psihiatrii i nejrohirurgii (Bulletin of Neurology, Psychiatry and Neurosurgery)150This study reviews the application of artificial intelligence to the diagnosis of neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Huntington's disease using the international databases Scopus and PubMed, as well as the Russian Science Citation Index.Vestnik nevrologii, psihiatrii i nejrohirurgii (Bulletin of Neurology, Psychiatry and Neurosurgery)['E. Petrova', 'E. V. Suchkova', 'N. Haddad', 'M. Ali-Hassan', 'V. A. Bodrov']2024-04-22T00:00:00https://www.semanticscholar.org/paper/826d1ee6f1152f08d8ce0a3944b4877f8f9510c5 c5204e361087060f5b2de3af10a6815c2900a9f8Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.Electronics00This research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment, utilizing authentic enterprise data from small and medium Lithuanian enterprises.Electronics['Dovilė Kuizinienė', 'T. Krilavičius']2024-04-22T00:00:00https://www.semanticscholar.org/paper/c5204e361087060f5b2de3af10a6815c2900a9f8 61d30ba06079436af7a6a9520c278f16c08e63dcIn recent times, the fusion of artificial intelligence (AI) within the human resource function of organizations has been witnessing a notable surge. The rapid advancements in AI technology are bringing about a transformative impact on information processing across diverse domains within human resource management (HRM). This paper seeks to carry out a bibliometric study on the scientific research of AI and HRM, presenting key aspects of academic research in this field. A bibliometric analysis methodology was employed, retrieving a total of 144 articles published between 2018 and 2022 from SCOPUS. These articles were analyzed using VOS viewer and Publish or Perish software. Using thorough bibliometric techniques, this study provides insights into various aspects. The accessibility of articles within SCOPUS affects the database's limitations, which in turn can impact various bibliometric aspects. Despite these limitations, the utilization of software tools allows for a detailed perspective and insight into the analyzed publications, enabling a detailed exploration of bibliometric aspects.The International Journal of Business &amp; Management00A bibliometric study on the scientific research of AI and HRM is carried out, presenting key aspects of academic research in this field, retrieving a total of 144 articles published between 2018 and 2022 from SCOPUS.The International Journal of Business &amp; Management['Ibrahim Alshawabkeh', 'Fatma Zehra Tan', 'Lee Sharolyn']2024-04-22T00:00:00https://www.semanticscholar.org/paper/61d30ba06079436af7a6a9520c278f16c08e63dc d5b3279379caa0b523ca86df149b43f5c4ce274eBackground: The rapid integration of artificial intelligence (AI) in healthcare requires the establishing of robust legal safeguards to ensure safety, privacy, and non-discrimination, crucial for maintaining trust. Collaborative efforts across disciplines are essential for effective AI-governance; unaddressed differences in disciplinary perspectives and priorities risks impeding effective reform. Objective: To provide law and policymaking guidance by systematically mapping the legal concerns about health-AI raised by disciplines of medicine, law, nursing, pharmacy, other healthcare professions, public health, computer science, and engineering, revealing convergences and divergences in disciplinary comprehension, prioritization, and proposed solutions to legal issues. Design, data sources, and study selection: Employing a scoping review methodology, we searched MEDLINE (Ovid), EMBASE (Ovid), HeinOnline Law Journal Library, Index to Foreign Legal Periodicals (HeinOnline), Index to Legal Periodicals and Books (EBSCOhost), Web of Science (Core Collection), Scopus, and IEEE Xplore, identifying relevant legal issue discussions published, in English or French, from January 2012 to July 2021. Of 18,168 screened studies, 432 were included for data extraction and analysis. Results: Critical disciplinary differences were evident in both the frequency and nature of discussions of legal issues and potential solutions. Notably, innovators in computer science and engineering exhibited minimal engagement with legal issues. Authors in law and medicine frequently contributed, but prioritized different legal issues and proposed different solutions. Conclusion: Discrepancies in perspectives regarding law reform priorities and solutions jeopardize the progress of health-AI development. We need inclusive, interdisciplinary dialogues concerning the risks and trade-offs associated with various solutions to ensure optimal law and policy reform.medRxiv400Discrepancies in perspectives regarding law reform priorities and solutions jeopardize the progress of health-AI development and need inclusive, interdisciplinary dialogues concerning the risks and trade-offs associated with various solutions to ensure optimal law and policy reform.['Sjd Sophie Nunnelley', 'Sjd Colleen M. Flood', 'Sjd Michael Da Silva', 'PhD Tanya Horsley', 'Sjd Bryan Thomas', 'Emily Ann', 'Mis Da Silva', 'Mlis Valentina Ly', 'MD Ryan C. Daniel', 'Mohsen Sheikh', 'Mbbs MSc Devin Singh']2024-04-22T00:00:00https://www.semanticscholar.org/paper/d5b3279379caa0b523ca86df149b43f5c4ce274e 93b2e3f165de511510918e8b8d8beda0964de471 The Electrical Submersible Pump (ESP) is the most effective and consistent artificial lift method for medium to high production rates. Although the capital cost of ESP is high, it pales in comparison to the production losses resulting from its failure. Recently, Machine Learning (ML) has gained significant attention in the oil and gas industry due to its predictive power. This paper aims to develop a ML model to predict ESP lifespan and identify the key features that influence its longevity. The study reviewed the failure history of more than 100 wells from an Omani oilfield, with 132 ESP failures attributed to sand and scale accumulation. The dataset includes 36 static features related to ESP design, installation, commissioning, failure, pull-out, and teardown. Three algorithms, namely Support Vector Regressor (SVR), Random Forest Regressor (RFR), and Extreme Gradient Boosting Regressor (XGBR), were selected. Hundreds of tests were performed on each algorithm to optimize the parameters and hyperparameters, based on mean absolute error, average residual, and determination coefficient. The study developed a model with two levels to predict the lifespan of ESP before installation and after the last valid well test. The model had a mean absolute error of 25 days and 8 days for the first and second levels, respectively, with a determination coefficient of 60% and 73%. The model showed that certain factors related to pump and motor design have the most significant impact on the longevity of the ESP before installation. Pump discharge pressure and flow rates of oil and water are crucial to monitor and control during its operational lifespan. The findings emphasize the importance of careful selection and design of ESP components to ensure a long-lasting lifespan. By scheduling ESP maintenance before failure, these findings can help mitigate capital costs, while preparing the necessary hoist, rig, and materials for ESP replacement can avoid deferred operational costs.Day 3 Wed, April 24, 2024110Day 3 Wed, April 24, 2024['Ali Al Sawafi', 'A. Kazemi', 'Tarek Ganat', 'Faisal Al Saadi', 'Adnan Al Ghadani']2024-04-22T00:00:00https://www.semanticscholar.org/paper/93b2e3f165de511510918e8b8d8beda0964de471 f065628a08e2d222b448804c4a425b4f9a4d1377Journal of Hospital Medicine180Journal of hospital medicine['Micah T. Prochaska', 'D. Alfandre']2024-04-22T00:00:00https://www.semanticscholar.org/paper/f065628a08e2d222b448804c4a425b4f9a4d1377 aca2446ac98abb31556328b1541e5244d67faac9На основе анализа тенденций в смене подходов к обеспечению качества продукции и управлению конкурентоспособностью дан прогноз перехода к следующему этапу - этапу опережающего развития организации, в основе которого - концепция активного воздействия на потребительскую среду, управление спросом и удовлетворённостью потребителей. Рассмотрены предпосылки и условия для реализации концепции опережающего развития в масштабе организации. Показано, что средством реализации концепции может быть система менеджмента качества (СМК) организации с новыми технологиями - технологиями искусственного интеллекта (ИИ). Объединение методов менеджмента качества, обеспечивающих устойчивое развитие организации, с технологиями ИИ создаёт возможность получения синергетического эффекта - достижения организациями состояния опережающего развития и владения инструментом управления удовлетворённостью потребителей активным воздействием на формирование потребительского спроса. Представлена функциональная структура и функции составляющих интеллектуальной СМК (И-СМК) с технологиями ИИ - искусственной нейросетью, технологиями прогнозирования, моделирования, обработки большого объёма данных. Показана роль ИИ в СМК, имитирующего когнитивную деятельность принимающих решения должностных лиц, как возможность прогнозирования, анализа большого объёма данных, моделирования, выработки вариантов решений и их оптимизации. Предложена организационная информационная модель интеллектуальной СМК (И-СМК) с функциями прогнозирования и принятия решений на всех уровнях управления, реализуемых технологиями ИИ. Рассмотрены наиболее перспективные для применения в И-СМК экстраполяционные методы прогнозирования - Байесовские сети, Марковский анализ, метод таблицы истинности (TTM). Рассмотрены возможные подходы к разработке и внедрению И-СМК. Даны предложения и рекомендации по постановке и выполнению работ с целью интегрирования технологий ИИ в СМК. Abstract. Based on an analysis of trends in changing approaches to ensuring product quality and managing competitiveness, a forecast is given for the transition to the next stage - the stage of advanced development of the organization, which is based on the concept of active influence on the consumer environment, managing demand and consumer satisfaction. The prerequisites and conditions for the implementation of the concept of advanced development on an organization scale are considered. It is shown that the means of implementing the concept can be the quality management system (QMS) of an organization with new technologies - artificial intelligence (AI) technologies. Combining quality management methods that ensure the sustainable development of an organization with AI technologies creates the possibility of obtaining a synergistic effect - organizations achieving a state of advanced development and mastering a tool for managing consumer satisfaction with an active influence on the formation of consumer demand. The functional structure and functions of the components of an intelligent QMS (I-QMS) with AI technologies - artificial neural network, forecasting, modeling, and large-volume data processing technologies are presented. The role of AI in QMS is shown, simulating the cognitive activity of decision-making officials, as the ability to forecast, analyze a large amount of data, model, develop decision options and optimize them. An organizational information model of an intelligent QMS (I-QMS) with forecasting and decision-making functions at all levels of management, implemented by AI technologies, is proposed. The most promising extrapolation forecasting methods for use in I-QMS are considered - Bayesian networks, Markov analysis, truth table method (TTM). Possible approaches to the development and implementation of I-QMS are considered. Suggestions and recommendations are given for setting up and performing work with the aim of integrating AI technologies into the QMS.Organizer of Production00Organizer of Production['В.В. Сидорин']2024-04-22T00:00:00https://www.semanticscholar.org/paper/aca2446ac98abb31556328b1541e5244d67faac9 5c91b9c70e8a51bf949b53a6a9b659a5bb215e64Eye80Eye['Andrew Mihalache', 'R. Huang', 'Miguel Cruz-Pimentel', 'Nikhil S. Patil', 'Marko M. Popovic', 'Bhadra U. Pandya', 'Reut Shor', 'Austin Pereira', 'Rajeev H. Muni']2024-04-22T00:00:00https://www.semanticscholar.org/paper/5c91b9c70e8a51bf949b53a6a9b659a5bb215e64 ff89ff92a8674a99dfda6cbd53812f08a826ef10The relationship between structure, neural activity dynamics and intelligent human brain function is complex and multi-faceted with deep roots in several academic disciplines. Its understanding is a fundamental goal of brain research [1 ] and may indicate the way towards the next generation of artificial intelligence [2 ]. There are major challenges on the way, several of which are addressed in this Special Topic of National Science Review .National Science Review10The relationship between structure, neural activity dynamics and intelligent human brain function and major challenges on the way are addressed in this Special Topic of National Science Review.National Science Review['Jianfeng Feng', 'V. Jirsa', 'Wenlian Lu']2024-04-22T00:00:00https://www.semanticscholar.org/paper/ff89ff92a8674a99dfda6cbd53812f08a826ef10 d4d7457011ab19d534039b0711a9fbae004fbc57This study aims to introduce the concept of artificial intelligence in the automotive industry. In recent years, artificial intelligence has grown significantly, and ever since, advancements have been made in every aspect of the contemporary world. This article discusses the necessity for a strong artificial intelligence in the automotive industry and the recent expansion that has occurred up to this point. With a user order, this vehicle may navigate automatically. A vehicle controller will be used to regulate the vehicle's mobility. This vehicle is made up of a mechanical module (base) that holds the power unit, microcontroller unit, wheels, sensors, and DC motors. The circuit is powered by the power unit. DC motors increase motion capability. For smoother and quicker navigation, the microcontroller unit receives input from ultrasonic sensors, interprets it, and uses a motor controller to regulate the movement of motors. With the use of this technology, autonomous vehicles also referred to as "smart cars" that can navigate and resolve on-road situations safely and intelligently can be researched and produced. In the near future, cars based on this idea will improve road safety, reduce minor accidents, makes life easier and more convenient. In order to redefine this technology and make it even more superior and inexpensive, more study and funding are needed in this area.International Research Journal on Advanced Engineering Hub (IRJAEH)120The necessity for a strong artificial intelligence in the automotive industry and the recent expansion that has occurred up to this point are discussed.International Research Journal on Advanced Engineering Hub (IRJAEH)['Mamuni Arya', 'Priyadarshinee Das', 'Sushanta Kumar Pradhan', 'Swarupa Arjya', 'Amit Jain Biswa']2024-04-22T00:00:00https://www.semanticscholar.org/paper/d4d7457011ab19d534039b0711a9fbae004fbc57 26d18d8cd403cfdaff44c7a5ba2842d08d02133e Risk reduction and increased Fabric Maintenance efficiency using Artificial Intelligence and Machine Learning algorithms to analyze full-facility imagery for atmospheric corrosion detection and classification. Following imagery capture and processing, deficiencies are identified, and targeted mitigation strategies are executed at greatly reduced cycle time and cost. A pre-mobilization facility scan plan is generated to maximize imagery quality, including high elevation scan positions, to ensure thorough and comprehensive analytics. Data from all scan positions are stitched together in a point cloud and aligned for accuracy relative to each location. Finalized imagery and point clouds are then tagged with unique piping line numbers per design, fixed equipment tags, or unique asset identification. The Machine Learning algorithm is intensely trained with manual ground truth inputs prior to analysis. The algorithm analyzes each pixel throughout the facility and detects, classifies, and reports on all identified corrosion, tagging faults to specific piping or equipment. Atmospheric corrosion is the number one Asset Integrity threat in the Gulf of Mexico. Utilizing this tool, we can have a comprehensive and objective analysis of a facility's health in a matter of weeks from the time of data collection. Data collection for a large deep-water, spar facility requires approximately 12 days with 8 data scanning personnel. Conventional manual inspections incur higher risk, higher cost, and reporting is much less objective considering the number of inspectors involved and the duration of a full-facility campaign. Finally, all results are published in a user-friendly dashboard that can be filtered by process type, equipment type, corrosion severity, and many other criteria as the user requires. Each fault is associated with the specific equipment identification and the user can navigate to see the imagery of the corrosion in a 3D, photogrammetric environment. Remediation strategies can be collated into work packs for fabric maintenance teams, further Nondestructive Examination (NDE) assessment, or work orders for replacement. Fabric maintenance efficiencies are substantially realized by targeting decks, blocks, or areas with the highest aggregate surface areas of corrosion (on process equipment or structurally, as selected by the user) and concentrating remediation efforts on at-risk equipment. This application of Artifical Intelligence and Machine Learning is a first-in-industry approach to having a comprehensive understanding of facility coating integrity and external corrosion threats. HSE analysis, Risk awareness, and targeted remediation strategies will make the Asset Integrity program more efficient, proactive, and reduce down-time across the Gulf of Mexico related to atmospheric corrosion. Day 1 Mon, April 22, 202470This application of Artifical Intelligence and Machine Learning is a first-in- industry approach to having a comprehensive understanding of facility coating integrity and external corrosion threats and will make the Asset Integrity program more efficient, proactive, and reduce down-time across the Gulf of Mexico related to atmospheric corrosion.Day 1 Mon, April 22, 2024['Marc Majors', 'Eric Ferguson', 'Suchet Bargoti']2024-04-22T00:00:00https://www.semanticscholar.org/paper/26d18d8cd403cfdaff44c7a5ba2842d08d02133e 5cfef77d23345401043a74fe616d2f15a0ce70d8This study explores the possibility of facilitating algorithmic decision-making by combining interpretable artificial intelligence (XAI) techniques with sensor data, with the aim of providing researchers and clinicians with personalized analyses of cannabis intoxication behavior. SHAP analyzes the importance and quantifies the impact of specific factors such as environmental noise or heart rate, enabling clinicians to pinpoint influential behaviors and environmental conditions. SkopeRules simplify the understanding of cannabis use for a specific activity or environmental use. Decision trees provide a clear visualization of how factors interact to influence cannabis consumption. Counterfactual models help identify key changes in behaviors or conditions that may alter cannabis use outcomes, to guide effective individualized intervention strategies. This multidimensional analytical approach not only unveils changes in behavioral and physiological states after cannabis use, such as frequent fluctuations in activity states, nontraditional sleep patterns, and specific use habits at different times and places, but also highlights the significance of individual differences in responses to cannabis use. These insights carry profound implications for clinicians seeking to gain a deeper understanding of the diverse needs of their patients and for tailoring precisely targeted intervention strategies. Furthermore, our findings highlight the pivotal role that XAI technologies could play in enhancing the transparency and interpretability of Clinical Decision Support Systems (CDSS), with a particular focus on substance misuse treatment. This research significantly contributes to ongoing initiatives aimed at advancing clinical practices that aim to prevent and reduce cannabis-related harms to health, positioning XAI as a supportive tool for clinicians and researchers alike.arXiv.org1120ArXiv['Tongze Zhang', 'Tammy Chung', 'Anind Dey', 'Sangwon Bae']2024-04-22T00:00:00https://www.semanticscholar.org/paper/5cfef77d23345401043a74fe616d2f15a0ce70d8 04fb2618fbc48f726c9bde21d87b09589f2f937dThis paper discusses the use of artificial intelligence (AI) for detecting AI-simulated media on social media platforms. AI-generated content, like deepfake videos and synthetic images, poses a significant challenge to content moderation. The paper highlights the methods and technologies such as machine learning and deep learning models involved in identifying such content, emphasizing the importance of dataset quality. The paper offers a holistic view of the multifaceted approach required to address the challenge of AI-simulated media on social media platforms.International Research Journal on Advanced Engineering Hub (IRJAEH)120A holistic view of the multifaceted approach required to address the challenge of AI-simulated media on social media platforms is offered, emphasizing the importance of dataset quality.International Research Journal on Advanced Engineering Hub (IRJAEH)['Mr. S. Kingsley', 'Adithya S P', 'Badrinath Babu', 'Harish Vaithilingam A']2024-04-22T00:00:00https://www.semanticscholar.org/paper/04fb2618fbc48f726c9bde21d87b09589f2f937d c90191b1e099d968703492dce526298e9c86c478The purpose of the article is to reveal the legal regulation of ensuring national security in the conditions of martial law (threats, cooperation and directions for improvement).  It has been established that ensuring national security of Ukraine includes a complex of national and international measures. It has been emphasized that financing is one of the important problems in the system of ensuring national security. The financial security of a state as a component of its national security is the basis of the economic development of the country, which ensures sovereignty and integrity of the country, as well as a decent standard of living of its citizens. The financial security of a state as a component of its national security is the basis of the economic development of the country, which ensures sovereignty and integrity of the country, as well as a decent standard of living of its citizens. Another problem of ensuring national security in wartime conditions consists in preventing and combating corruption.  Corruption destroys development of the military-industrial complex, prevents introduction of innovative means of protection for military personnel, it disturbs development and testing of new weapons, new technologies, and hampers provision of military personnel with the necessary equipment, protection and weapons. It was concluded that only cooperation can solve the urgent strategic tasks of ensuring national security in difficult world conditions and new globalization challenges. Khazanah Hukum00Khazanah Hukum['Andrii Nosach', 'S. Melnyk', 'A. Rusetskyi', 'Yuliia Pinchuk', 'V. Piadyshev']2024-04-21T00:00:00https://www.semanticscholar.org/paper/c90191b1e099d968703492dce526298e9c86c478 ed02a039e245735caeed1824b8759a758212963bJournal of system and management sciences00Journal of System and Management Sciences[]2024-04-21T00:00:00https://www.semanticscholar.org/paper/ed02a039e245735caeed1824b8759a758212963b 2c41d223ef91d50069899d806ef2225e774b3423The groundwork for comprehending the important connection between client adoption of AI chatbots and their effects on the Indian banking sector is laid forth in the introduction chapter. It outlines the importance of this research project by filling in gaps in the body of knowledge and presents the research questions, aims, and methods, giving a brief synopsis of the main points and purposes of the study. The adoption of AI chatbots in India's banking sector is examined in the literature review, which follows the technology's development from simple AI-driven interactions to sophisticated customer support. Novel solutions are put out in response to issues like regulatory compliance and customer trust. Future directions in AI chatbot technology are explored, including individualized services and platform-neutral integration. The major investigation described in the article was motivated by the review's identification of a critical gap in thorough and quantitative research. According to the report, consumers in India are depending more and more on AI chatbots to handle a variety of financial operations, as the technology gains traction in the country's banking sector. It is believed that integrating AI chatbots will improve customer service and operational effectiveness in a proactive manner. Furthermore, traditional banking services' main problems—like lengthy wait times and restricted availability—may be solved by AI chatbots. Customers' ongoing concerns about data security and privacy, however, underscore the necessity for banks to put strong security measures and open data policies in place. The study's conclusion emphasizes how crucial it is to comprehend how Indian banks want to use AI chatbots in terms of client uptake. This research adds to a more thorough knowledge of the role of AI chatbots in changing the banking landscape by filling in gaps in the literature and offering insights into customer perceptions and behaviors. In order to successfully encourage the adoption of AI chatbots among Indian clients, banks will need to concentrate on fostering trust, improving security protocols, and providing customized experiences going forward.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research adds to a more thorough knowledge of the role of AI chatbots in changing the banking landscape by filling in gaps in the literature and offering insights into customer perceptions and behaviors.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT[]2024-04-21T00:00:00https://www.semanticscholar.org/paper/2c41d223ef91d50069899d806ef2225e774b3423 3015b792bc70f5b258ed2e81a9b558b2ca04074eThe integration of AI-powered analytics offers transformative potential in optimizing supply chains within the manufacturing sector. This study adopts a qualitative, case study methodology to explore the specific ways manufacturers utilize AI-powered solutions in areas such as demand forecasting, inventory management, logistics planning, and predictive maintenance. Findings indicate substantial gains in efficiency, cost savings, and improved supply chain resilience. Additionally, the study highlights how AI-driven optimizations lead to an enhanced customer experience through increased product availability, reduced lead times, and a more responsive supply chain. Through detailed analysis of real-world implementations, the study provides practical guidance for manufacturers seeking to leverage AI to transform their supply chain operations.GLOBAL MAINSTREAM JOURNAL00This study adopts a qualitative, case study methodology to explore the specific ways manufacturers utilize AI-powered solutions in areas such as demand forecasting, inventory management, logistics planning, and predictive maintenance, and indicates substantial gains in efficiency, cost savings, and supply chain resilience.GLOBAL MAINSTREAM JOURNAL[]2024-04-21T00:00:00https://www.semanticscholar.org/paper/3015b792bc70f5b258ed2e81a9b558b2ca04074e 8ad942d3422119c28e8c811513db8b702d37cc9cThe integration of Artificial Intelligence (AI) and Machine Learning (ML) into autonomous robotics has heralded significant advancements in industrial applications, enhancing operational efficiencies, precision, and adaptability. This paper explores the transformative impact of AI and ML technologies on autonomous robotics in industrial settings, emphasizing the enhancements in automation and control mechanisms. Through a comprehensive literature review and analysis, we discuss the synergistic relationship between AI, ML, and robotics, and how this integration not only improves sensory and decision-making capabilities but also introduces adaptive learning and collaborative functionalities in robotic systems. Our findings reveal that AI-enhanced sensory technologies enable robots to perform complex recognition and manipulation tasks with unprecedented accuracy. Simultaneously, ML algorithms facilitate predictive maintenance, reducing downtime and extending the lifecycle of machinery. Moreover, adaptive learning capabilities allow robots to adjust to new environments and tasks without extensive reprogramming, showcasing significant flexibility and cost-efficiency. The deployment of AI and ML in robotics is not without challenges. The paper identifies key limitations such as data dependency, high computational demands, and adaptability issues. Ethical and societal implications, including job displacement and privacy concerns, are also critically examined to propose a balanced approach towards technology adoption. These include increased investment in R&D, the development of robust ML models, enhanced data governance frameworks, and the establishment of ethical standards to ensure responsible integration of these technologies into industrial practices. By addressing these challenges and leveraging collaborative efforts across sectors, the potential of AI and ML in revolutionizing industrial robotics can be fully realized, leading to a new era of manufacturing excellence.International Journal for Multidimensional Research Perspectives140The findings reveal that AI-enhanced sensory technologies enable robots to perform complex recognition and manipulation tasks with unprecedented accuracy, and adaptive learning capabilities allow robots to adjust to new environments and tasks without extensive reprogramming, showcasing significant flexibility and cost-efficiency.International Journal for Multidimensional Research Perspectives['Mandeep Singh', 'Subair Ali Liayakath', 'Ali Khan']2024-04-21T00:00:00https://www.semanticscholar.org/paper/8ad942d3422119c28e8c811513db8b702d37cc9c 416a12abffe366d72e20c84b75ffe83e3d808aa8Addressing the global challenge of breast cancer, this research explores the fusion of generative AI, focusing on ChatGPT 3.5 turbo model, and the intricacies of breast cancer risk assessment. The research aims to evaluate ChatGPT's reasoning capabilities, emphasizing its potential to process rules and provide explanations for screening recommendations. The study seeks to bridge the technology gap between intelligent machines and clinicians by demonstrating ChatGPT's unique proficiency in natural language reasoning. The methodology employs a supervised prompt-engineering approach to enforce detailed explanations for ChatGPT's recommendations. Synthetic use cases, generated algorithmically, serve as the testing ground for the encoded rules, evaluating the model's processing prowess. Findings highlight ChatGPT's promising capacity in processing rules comparable to Expert System Shells, with a focus on natural language reasoning. The research introduces the concept of reinforcement explainability, showcasing its potential in elucidating outcomes and facilitating user-friendly interfaces for breast cancer risk assessment.arXiv.org70The research introduces the concept of reinforcement explainability, showcasing its potential in elucidating outcomes and facilitating user-friendly interfaces for breast cancer risk assessment and highlighting ChatGPT's promising capacity in processing rules comparable to Expert System Shells.ArXiv['Yousef Khan', 'Ahmed Abdeen Hamed']2024-04-21T00:00:00https://www.semanticscholar.org/paper/416a12abffe366d72e20c84b75ffe83e3d808aa8 a22b8d2c4a9720d86ae82214c5620099eaf0be91Intelligent wearable/implantable health monitoring devices integrating biomedical AI processors have been developed for automatically identifying abnormality in users' biomedical signals. Three features are required for the biomedical AI processors, including high accuracy, low energy consumption and reconfigurability. However, the existing designs focus on achieving high energy efficiency which sacrifices accuracy and reconfigurability. To address these issues, in this work, a reconfigurable biomedical AI processor with diverse adaptive processing techniques has been proposed for co-optimized accuracy and energy-efficiency. The key features include 1) adaptive feature-fusion based classification architecture for improving the classification accuracy with low computation complexity. 2) adaptive-window based neural network processing architecture to improve both accuracy and energy efficiency. 3) K-Nearest-Neighbors (KNN) based adaptive weight precision selection technique to reduce the energy consumption while maintaining high accuracy. The proposed design is implemented and fabricated with 55nm CMOS process technology. Being highly reconfigurable, it achieves high accuracy (98.7%, 98.5% and 99.87%) and low energy (0.18 μJ, 2.3 μJ and 1.1 μJ) for three typical biomedical AI tasks (i.e. ECG arrhythmia classification, ECG atrial fibrillation detection and EEG seizure detection), outperforming the state-of-the-art designs.IEEE Custom Integrated Circuits Conference140A reconfigurable biomedical AI processor with diverse adaptive processing techniques has been proposed for co-optimized accuracy and energy-efficiency and achieves high accuracy, low energy consumption and reconfigurability.2024 IEEE Custom Integrated Circuits Conference (CICC)['J. Liu', 'Z. Xie', 'X. Wang', 'X. Liu', 'X. Qiao', 'J. Fan', 'H. Qin', 'C. Guo', 'J. Xiao', 'S. Lin', 'J. Zhou']2024-04-21T00:00:00https://www.semanticscholar.org/paper/a22b8d2c4a9720d86ae82214c5620099eaf0be91 49a04d58d2d51a993593804c99e78396d4ad79abWith the digital imagery landscape rapidly evolving, image stocks and AI-generated image marketplaces have become central to visual media. Traditional stock images now exist alongside innovative platforms that trade in prompts for AI-generated visuals, driven by sophisticated APIs like DALL-E 3 and Midjourney. This paper studies the possibility of employing multi-modal models with enhanced visual understanding to mimic the outputs of these platforms, introducing an original attack strategy. Our method leverages fine-tuned CLIP models, a multi-label classifier, and the descriptive capabilities of GPT-4V to create prompts that generate images similar to those available in marketplaces and from premium stock image providers, yet at a markedly lower expense. In presenting this strategy, we aim to spotlight a new class of economic and security considerations within the realm of digital imagery. Our findings, supported by both automated metrics and human assessment, reveal that comparable visual content can be produced for a fraction of the prevailing market prices ($0.23 - $0.27 per image), emphasizing the need for awareness and strategic discussions about the integrity of digital media in an increasingly AI-integrated landscape. Our work also contributes to the field by assembling a dataset consisting of approximately 19 million prompt-image pairs generated by the popular Midjourney platform, which we plan to release publicly.arXiv.org270This paper studies the possibility of employing multi-modal models with enhanced visual understanding to mimic the outputs of these platforms, introducing an original attack strategy that aims to spotlight a new class of economic and security considerations within the realm of digital imagery.ArXiv['Ali Naseh', 'Katherine Thai', 'Mohit Iyyer', 'Amir Houmansadr']2024-04-21T00:00:00https://www.semanticscholar.org/paper/49a04d58d2d51a993593804c99e78396d4ad79ab fd020bc4181d9130faeb323951eee9b43104816eThis study aims to uncover the challenges to the mainstream adoption of AI (artificial intelligence) among people with special needs in India. AI has been widely used in real-time healthcare, education, and transportation situations; however, there is a digital divide in the ability to reap the benefits of AI applications for those with special needs due to various socioeconomic factors. The proposed work also intends to examine and undertake in-depth research using the Analytic Hierarchy Process (AHP) to discover, analyze, and offer an accessible overview of the issues surrounding the numerous socioeconomic and technical factors involved with the use of AI. This research will contribute significantly to addressing the ongoing challenges of the special need’s population in their use of AI in various real-time applications by addressing technical infrastructure limitations, cultural differences, and other economic concerns. It will also help to bridge the gap between AI and the special needs population by addressing these limitations. By giving attention to this unexplored field, this piece of research will provide a better foundation for how to take preventive measures and overcome the digital gap of AI among special needs from several perspectives.International Journal of the Analytic Hierarchy Process10The challenges to the mainstream adoption of AI (artificial intelligence) among people with special needs in India are uncovered and in-depth research using the Analytic Hierarchy Process is examined to discover, analyze, and offer an accessible overview of the issues surrounding the numerous socioeconomic and technical factors involved with the use of AI.International Journal of the Analytic Hierarchy Process['Dr. Sheetal Mahendher', 'Dr. Sippee Bharadwaj']2024-04-21T00:00:00https://www.semanticscholar.org/paper/fd020bc4181d9130faeb323951eee9b43104816e 177425efe4a03fce42b04035d3fefb23abe01d29Over the past decade, deep learning helped solve manipulation problems across all domains of robotics. At the same time, industrial robots continue to be programmed overwhelmingly using traditional program representations and interfaces. This paper undertakes an analysis of this"AI adoption gap"from an industry practitioner's perspective. In response, we propose the BANSAI approach (Bridging the AI Adoption Gap via Neurosymbolic AI). It systematically leverages principles of neurosymbolic AI to establish data-driven, subsymbolic program synthesis and optimization in modern industrial robot programming workflow. BANSAI conceptually unites several lines of prior research and proposes a path toward practical, real-world validation.arXiv.org00The BANSAI approach systematically leverages principles of neurosymbolic AI to establish data-driven, subsymbolic program synthesis and optimization in modern industrial robot programming workflow.ArXiv['Benjamin Alt', 'Julia Dvorak', 'Darko Katic', 'Rainer Jäkel', 'Michael Beetz', 'Gisela Lanza']2024-04-21T00:00:00https://www.semanticscholar.org/paper/177425efe4a03fce42b04035d3fefb23abe01d29 e0ec3f099f8dac9b18396599d9107555e897e38cIn this review paper, we critically examined the research and development in creating an AI-based virtual tutor website. The project represents a pioneering attempt to leverage artificial intelligence in the realm of education, aiming to provide a dynamic and personalized learning experience for the school going students. This review synthesizes the key aspects of the project, highlighting its contributions, challenges, and potential implications for the future of educational technology.International Journal of Innovative Research in Engineering &amp; Multidisciplinary Physical Sciences50The project represents a pioneering attempt to leverage artificial intelligence in the realm of education, aiming to provide a dynamic and personalized learning experience for the school going students.International Journal of Innovative Research in Engineering &amp; Multidisciplinary Physical Sciences['Gaurav Pawar', 'S. Jha', 'Nayam Syed', 'Vaibhavi Ghose', 'Purushottam R. Patil']2024-04-21T00:00:00https://www.semanticscholar.org/paper/e0ec3f099f8dac9b18396599d9107555e897e38c 67d2091fb54cd5276fee95accd61f0c02bafa845Speakers delve into and beyond the previously published BHHTY journal article “Moving Beyond Proof of Concept and Pilots to Mainstream: Discovery and Lessons from Blockchain in Healthcare,” located at https://doi.org/10.30953/bhty.v6.280. This continuous enterprise blockchain technology journey extends the framework and solution assemblies including further developments, with cross over into generative AI and ethics. Objectives Learn specific examples on the economics of blockchain revealing low-hanging fruit for the move from pilots to adoption. Explore concepts such as: Data integrity, minimal data, inter-entity streamlining leading to efficiencies, and what is already possible with tech stack developments and economics in efficiency (in millions) from the previously published BHTY article at DOI: https://doi.org/10.30953/bhty.v6.280 Learn from other verticals to build a framework that is more comprehensive encompassing global perspectives Future proofing and stair-stepping design for an evolving technology – holistic guidance to find and execute the opportunities Obtain a framework for blockchain adoption based on the article. In addition, authors address the academic view of blockchain adoption, and that it is a combination of tech, policy, economics, consumer engagement, and operationalization. Acquire multi-dimensional discovery and specific blockchain constructs including provenance- consensus, trust maps, convergence, dApp human loops, and future proofing /stair-stepping Grasp global perspectives on evolving frameworks with in many verticals and the multi-dimensional nature of blockchain transformation, operationalization, blockchain-enterprise landscape, and AI automation. Gain a better understanding of why is blockchain an essential technology for the future of responsible AI and for scalability of solutions Blockchain in Healthcare Today00This continuous enterprise blockchain technology journey extends the framework and solution assemblies including further developments, with cross over into generative AI and ethics, with cross over into generative AI and ethics.Blockchain in Healthcare Today['Sathya Krishnasamy, MS', 'Badri Gopalakrishnan, PhD', 'Atul Apte, BSc', 'MODERATOR: Anjum Khurshid, MD, PhD']2024-04-21T00:00:00https://www.semanticscholar.org/paper/67d2091fb54cd5276fee95accd61f0c02bafa845 7350f7f1db9f3488f720650414a7ffeeef46bba5The B5G/6G evolution relies on connect-compute technologies and highly heterogeneous clusters with HW accelerators, which require specialized coding to be efficiently utilized. The current paper proposes a custom tool for generating multiple SW versions of a certain AI function input in high-level language, e.g., Python TensorFlow, while targeting multiple diverse HW+SW platforms. TF2AIF builds upon disparate tool-flows to create a plethora of relative containers and enable the system orchestrator to deploy the requested function on any peculiar node in the cloud-edge continuum, i.e., to leverage the performance/energy benefits of the underlying HW upon any circumstances. TF2AIF fills an identified gap in today's ecosystem and facilitates research on resource management or automated operations, by demanding minimal time or expertise from users.arXiv.org210TF2AIF fills an identified gap in today's ecosystem and facilitates research on resource management or automated operations, by demanding minimal time or expertise from users.ArXiv['Aimilios Leftheriotis', 'Achilleas Tzenetopoulos', 'G. Lentaris', 'D. Soudris', 'G. Theodoridis']2024-04-21T00:00:00https://www.semanticscholar.org/paper/7350f7f1db9f3488f720650414a7ffeeef46bba5 511b593640fe4d6dc7ec55068fc69e72f738f6e1Electronic Commerce Research460Electronic Commerce Research['Vai Rawool', 'Pantea Foroudi', 'M. Palazzo']2024-04-21T00:00:00https://www.semanticscholar.org/paper/511b593640fe4d6dc7ec55068fc69e72f738f6e1 bc0cc0c4a8c8033f6323258fc9a8167c4f1e5ae4The digital revolution, characterized by the proliferation of social media and the integration of Artificial Intelligence (AI), has significantly impacted the political landscape of every country where free and fair elections are conducted. AI has the potential to affect the elections both positively as well as negatively. It is an opportunity to improve the democratic process in our societies by helping citizens to gain a better understanding of politics and engage more easily in democratic debate. Politicians can also improve their ability to represent the people by getting to know them better. This kind of cooperation between voters and elected officials has the potential to transform political campaigns and significantly enhance the process of formulating public policy, rendering it more precise and effective. On the other hand, there are certain concerns over the use of AI in politics as it poses multiple risks to democracies like misinformation and disinformation campaigns including deep fakes, the amplification and weaponization of hate speech, micro-targeting of voters, racial and gender stereotyping, AI-driven campaigning and the possibility of aspects of electoral processes being targeted through automated messaging such as political bots and chatbots. These AI-driven disinformation tools have the capacity to distort public opinion, influence voter attitudes, and jeopardise the democratic process itself. This paper critically examines the influence of AI on elections and democracy and underscores the need for robust regulatory frameworks and digital literacy initiatives to safeguard the democratic ethos in the age of AI.International Journal For Multidisciplinary Research00This paper critically examines the influence of AI on elections and democracy and underscores the need for robust regulatory frameworks and digital literacy initiatives to safeguard the democratic ethos in the age of AI.International Journal For Multidisciplinary Research['Jaya Thapa']2024-04-21T00:00:00https://www.semanticscholar.org/paper/bc0cc0c4a8c8033f6323258fc9a8167c4f1e5ae4 2a1048e9ee697c3a047b1efe09f89a880f37b48bHR is the division of a business that is charged with finding, screening, recruitment, training job applicants. It typically finds, trains, hires and fires employees. Organization are increasingly recognizing the critical role of HRD in nurturing their most valuable asset- their people, it starts from the very sourcing, screening, recruiting, induction, performance management and their development. With the changing scenario, technological advancement has impacted HR process significantly. The innovation of artificial intelligence and machine learning are now significantly entered the field of human resource development. AI system can analyze vast amount of HR data to identify potential candidate and predict their chances of getting shortlisted foe a particular job. AI recruitment reduces by automating all the mundane tasks. Recruitment has become a hotbed for AI applications in HR. The platform has led to an improvement in candidate experience by making it easier for job seeker to apply for open position and communicate directly with the company. With the changing scenario and advancement in technology, AI has incorporated in every disciple. It helps I increasing the human efficiency of doing task for quick and better achievement of organizational objectives. HR department in itself is in a pivot position as it starts before any function comes into action. It plays key role in streamlining all the departments and processes that are working towards business operations. AI isn’t here to replace human recruitment but it’s here to make their lives infinitely easier. Though AI finds no comparison to the human brain, still it’s a game changer for improving the quality of work done.International Journal For Multidisciplinary Research60Recruitment has become a hotbed for AI applications in HR, and the platform has led to an improvement in candidate experience by making it easier for job seeker to apply for open position and communicate directly with the company.International Journal For Multidisciplinary Research['Saumya Agarwal', 'Divya Srivastava']2024-04-21T00:00:00https://www.semanticscholar.org/paper/2a1048e9ee697c3a047b1efe09f89a880f37b48b 3f748173addba3dcde4cf2a2143cbcf5c5161a44Autonomous and intelligent systems (AIS) facilitate a wide range of beneficial applications across a variety of different domains. However, technical characteristics such as unpredictability and lack of transparency, as well as potential unintended consequences, pose considerable challenges to the current governance infrastructure. Furthermore, the speed of development and deployment of applications outpaces the ability of existing governance institutions to put in place effective ethical-legal oversight. New approaches for agile, distributed and multilevel governance are needed. This work presents a practical framework for multilevel governance of AIS. The framework enables mapping actors onto six levels of decision-making including the international, national and organizational levels. Furthermore, it offers the ability to identify and evolve existing tools or create new tools for guiding the behavior of actors within the levels. Governance mechanisms enable actors to shape and enforce regulations and other tools, which when complemented with good practices contribute to effective and comprehensive governance.arXiv.org370This work presents a practical framework for multilevel governance of AIS that enables mapping actors onto six levels of decision-making including the international, national and organizational levels and offers the ability to identify and evolve existing tools or create new tools for guiding the behavior of actors within the levels.ArXiv['Lukas D Pöhler', 'Klaus Diepold', 'Wendell Wallach']2024-04-21T00:00:00https://www.semanticscholar.org/paper/3f748173addba3dcde4cf2a2143cbcf5c5161a44 9f2864e3bc6e5166c452c1729f63e9011add0d5eWith the significant growth of modern technology and its integration into many different industries, especially in the healthcare sector, artificial intelligence is one of the critical methods contributing to the development of medical fields, including dentistry. It possesses important and influential techniques that contribute to improving the results of patient care, diagnosis, treatment planning, and tracking the spread of diseases. These techniques play a major role in assisting dentists in diagnosing patients with high efficiency and accuracy. In this review, artificial intelligence techniques in developing the field of dentistry will be reviewed by highlighting the most important literature in which these techniques are involved. A search was conducted in Web of Science, Scopus, and PubMed databases from 2018 to 2023, where many articles were found (n=432), and articles that did not meet the selection criteria were excluded, resulting in thirty included. These articles involve artificial intelligence techniques in six areas: periodontal, dental implantology, forensic dentistry, oral medicine and pathology, orthodontics, and diagnostics/dentistry. In addition, this review presents matters related to artificial intelligence in dentistry, including data security, ethical concerns, and developing dentists' skills. This article finds that deep learning methods are widely utilized in the growth of dentistry, as the results show the accuracy of the results obtained, which is equivalent to the accuracy of professionals, and that it contributes to reducing human errors and revolutionizing the improvement of patient outcomes.Wasit Journal of Computer and Mathematics Science650Deep learning methods are widely utilized in the growth of dentistry, as the results show the accuracy of the results obtained, which is equivalent to the accuracy of professionals, and that it contributes to reducing human errors and revolutionizing the improvement of patient outcomes.Wasit Journal of Computer and Mathematics Science['Maad M. Mijwil']2024-04-21T00:00:00https://www.semanticscholar.org/paper/9f2864e3bc6e5166c452c1729f63e9011add0d5e dfb6d8d5df8795ba21a5c6dfba28dce2feb62f63 Bevezetés: A prevenció és a szűrővizsgálatok manapság egyre népszerűbbek. A páciensek – tudatosabbá válásuknak köszönhetően – többet kutatnak az interneten egészségi állapotukkal kapcsolatosan, függetlenül attól, hogy az mennyire megbízható. A ChatGPT megjelenése forradalmasította az információszerzést, így elkezdték azt öndiagnózisra és egészségi állapotuk menedzselésére használni. Annak ellenére, hogy a mesterségesintelligencia-alapú szolgáltatások nem helyettesíthetik az egészségügyi szakemberekkel történő konzultációt, kiegészítő szerepet tölthetnek be a hagyományos szűrési eljárások során, így érdemes megvizsgálni a lehetőségeket és a korlátokat. Célkitűzés: Kutatásunk legfőbb célkitűzése az volt, hogy azonosítsuk azokat a területeket, ahol a ChatGPT képes bekapcsolódni a primer, szekunder és tercier prevenciós folyamatokba. Célunk volt továbbá megalkotni az olyan mesterségesintelligencia-alapú szolgáltatás koncepcióját, amely segítheti a pácienseket a prevenció különböző szintjein. Módszer: A prevenciós területen a ChatGPT által nyújtott lehetőségeket a rendszernek feltett specifikus kérdésekkel térképeztük fel. Ezen tapasztalatok alapján létrehoztunk egy webapplikációt, melynek elkészítéséhez a GPT-4 modell szolgált alapul. A válaszok helyességét strukturált pontos kérdésekkel igyekeztük javítani. A webapplikáció elkészítéséhez Python programozási nyelvet használtunk, az alkalmazást pedig a Streamlit keretrendszer felhőszolgáltatásán keresztül tettük elérhetővé és tesztelhetővé. Eredmények: A tesztek eredményei alapján több olyan prevenciós területet azonosítottunk, ahol a ChatGPT-t hatékonyan lehetne alkalmazni. Az eredmények alapján sikeresen létrehoztuk egy webapplikáció alapjait, amely a ScreenGPT nevet kapta. Következtetés: Megállapítottuk, hogy a ChatGPT a prevenció mindhárom szintjén képes hasznos válaszokat adni pontos kérdésekre. Válaszai jól tükrözik az emberi párbeszédet, ám a ChatGPT nem rendelkezik öntudattal, így fontos, hogy a felhasználók kritikusan értékeljék a válaszait. A ScreenGPT szolgáltatást e tapasztalatok alapján sikerült megalkotnunk, számos további vizsgálatra van azonban szükség, hogy megbizonyosodjunk a megbízhatóságáról. Orv Hetil. 2024; 165(16): 629–635. Orvosi Hetilap00Orvosi hetilap['Viola Angyal', 'Ádám Bertalan', 'Péter Domján', 'Elek Dinya']2024-04-21T00:00:00https://www.semanticscholar.org/paper/dfb6d8d5df8795ba21a5c6dfba28dce2feb62f63 53f3306dd608a2a83c579bfb7f182847f5b64473Accountability in Research20Accountability in research['Alex Glynn']2024-04-21T00:00:00https://www.semanticscholar.org/paper/53f3306dd608a2a83c579bfb7f182847f5b64473 27f6bb3609b7574a0bbea868cd43be4134eea120The convergence of artificial intelligence (AI) with the culinary world represents a dynamic and evolving intersection, offering innovative solutions to enhance various aspects of gastronomy. This abstract encapsulates the key findings and insights from a comprehensive study investigating the implications of AI integration in gastronomy, focusing on food and wine pairing, production optimization, and consumer preferences. Qualitative data is collected through interviews with industry experts, chefs, and consumers, as well as observational research in gastronomic settings. These methods provide rich insights into the experiences, perspectives, and behaviors related to AI-driven technologies in gastronomy. Additionally, quantitative data is obtained through surveys distributed to food enthusiasts, restaurant-goers, and professionals in the food and beverage industry, enabling the analysis of attitudes, preferences, and behaviors towards AI integration. The findings reveal significant advancements facilitated by AI in food and wine pairing, production efficiency, and consumer interactions. AI-driven algorithms analyze vast datasets of flavor profiles, ingredient compositions, and consumer preferences to recommend optimal food and wine pairings tailored to individual tastes and preferences. In food production, AI optimizes supply chain management, predicts demand fluctuations, and reduces food wastage through predictive analytics and IoT sensors. Furthermore, AI-driven recommender systems personalize recommendations for consumers, enhancing their dining and shopping experiences. This abstract highlights the relevance and implications of AI convergence in gastronomy, emphasizing its potential to revolutionize culinary practices and cater to evolving consumer demands.International Journal for Multidimensional Research Perspectives460The key findings and insights from a comprehensive study investigating the implications of AI integration in gastronomy, focusing on food and wine pairing, production optimization, and consumer preferences are encapsulated.International Journal for Multidimensional Research Perspectives['Deepak Thakur', 'Tarun Sharma', 'S.No']2024-04-20T00:00:00https://www.semanticscholar.org/paper/27f6bb3609b7574a0bbea868cd43be4134eea120 16e20c169796fba3349a5fc98b9881d63eff7da0Natural disasters and floods brought on by heavy rainfall pose serious threats to human health and lives every year on a global scale. The intricacy of meteorological data makes it difficult to provide accurate rainfall predictions, despite their critical importance in nations like India where agriculture is the primary occupation. Rainfall forecasting has recently benefited from Artificial Intelligence (AI) developments such as Deep Learning (DL) and Machine Learning (ML) techniques. This article provides a comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year. The findings show that DL approaches are more effective than traditional ML methods and shallow neural network models. This research is important as it has significant impacts on agriculture, disaster preparedness, and water resource management. Finally, it outlines future research directions for further advancements in rainfall prediction through AI methodologies.International Journal of Advanced Research in Computer Science00A comprehensive survey of recent studies that use AI techniques for rainfall prediction, analyzing them based on the ML algorithms and DL methods used, organized by publication year shows that DL approaches are more effective than traditional ML methods and shallow neural network models.international journal of advanced research in computer science['S. Annapoorani']2024-04-20T00:00:00https://www.semanticscholar.org/paper/16e20c169796fba3349a5fc98b9881d63eff7da0 2d653b4083e45337ab8481ca3ab0df1e32e806c9Since the Artificial Intelligence Committee of the American Society of Retina Specialists developed the initial task force report in 2020, the artificial intelligence (AI) field has seen further adoption of US Food and Drug Administration–approved AI platforms and significant development of AI for various retinal conditions. With expansion of this technology comes further areas of challenges, including the data sources used in AI, the democracy of AI, commercialization, bias, and the need for provider education on the technology of AI. The overall focus of this committee report is to explore these recent issues as they relate to the continued development of AI and its integration into ophthalmology and retinal practice.Journal of VitreoRetinal Diseases490The overall focus of this committee report is to explore recent issues as they relate to the continued development of AI and its integration into ophthalmology and retinal practice.Journal of VitreoRetinal Diseases['Katherine E. Talcott', 'Sally L. Baxter', 'Dinah K. Chen', 'Edward Korot', 'Aaron Lee', 'Judy E. Kim', 'Yasha S. Modi', 'Darius M. Moshfeghi', 'Rishi P. Singh']2024-04-20T00:00:00https://www.semanticscholar.org/paper/2d653b4083e45337ab8481ca3ab0df1e32e806c9 3630411db5d0e42fb070055b08e3e6572b85cf3aThe use of Artificial Intelligence (AI) in the world of education is considered relevant in the context of futuristic learning in this modern era. This article describes the use of futuristic learning models based on Artificial Intelligence (AI) and its positive and negative impact. The purpose of writing this article is to provide a comprehensive understanding of Artificial Intelligence (AI) and its use in the world of education. The data used in this article is information from various literary sources, among them are scientific articles, research results, books and news related to the use of Artificial Intelligence (AI) in the world of education. Based on the results of the data collected by the author, use of AI in education has great potential to improve efficiency and effectiveness in education so that in the end can improve the quality of education especially in Indonesia. However, the use of AI still needs to bear in mind the potential negative impacts such as the risk of dependence, the ethical challenges and the important role of human interaction.Al-DYAS210The use of futuristic learning models based on Artificial Intelligence (AI) and its positive and negative impact have great potential to improve efficiency and effectiveness in education so that in the end can improve the quality of education especially in Indonesia.Al-DYAS['A. Hasanah', 'Slamet Budiyono']2024-04-20T00:00:00https://www.semanticscholar.org/paper/3630411db5d0e42fb070055b08e3e6572b85cf3a f9cbca4d4ba51f855c34311731ea6d728e16ebf0Ophthalmology and Therapy250This study shows that artificial intelligence is able to uncover health disparities between people with German compared to non-German names using NECs, and may prove useful for healthcare providers to discover and counteract such inequalities and establish tailored preventive measures to decrease morbidity in vulnerable population subgroups.Ophthalmology and Therapy['Christoph Palme', 'Franziska Sofia Hafner', 'Lena Hafner', 'Theodor Peter Peifer', 'A. Huber', 'Bernhard Steger']2024-04-20T00:00:00https://www.semanticscholar.org/paper/f9cbca4d4ba51f855c34311731ea6d728e16ebf0 50ab8687f6f51e2e5379fff98608ba3ddea21447This paper presents a systematic review of the role of Artificial Intelligence (AI) in healthcare, highlighting its applications and challenges. AI technologies, including machine learning, natural language processing, and predictive analytics, are transforming healthcare through diagnostic assistance, treatment personalization, patient monitoring, optimization of healthcare operations, and public health. Despite the potential benefits, the integration of AI in healthcare faces significant challenges, such as data privacy and security concerns, ethical and legal issues, interoperability and integration difficulties, scalability and accessibility obstacles, and the intricacies of human-AI interaction. This review emphasizes the need for robust cybersecurity measures, ethical guidelines, clear legal frameworks, universal standards for interoperability, and equitable access to AI technologies. Recommendations for overcoming these challenges include fostering interdisciplinary collaboration, enhancing healthcare professional education, and promoting research and development. AI can realize its full potential in enhancing healthcare delivery and patient outcomes by addressing these challenges. Keywords: Artificial Intelligence, Healthcare, Diagnostic Assistance, Treatment Personalization, Data Privacy, Ethical Considerations.International medical science research journal00A systematic review of the role of Artificial Intelligence in healthcare highlights the need for robust cybersecurity measures, ethical guidelines, clear legal frameworks, universal standards for interoperability, and equitable access to AI technologies.International Medical Science Research Journal['Francisca Chibugo Udegbe', 'Ogochukwu Roseline Ebulue', 'Charles Chukwudalu Ebulue', 'Chukwunonso Sylvester Ekesiobi']2024-04-20T00:00:00https://www.semanticscholar.org/paper/50ab8687f6f51e2e5379fff98608ba3ddea21447 5283c3ba9a416951128dd2ab138bec90f3e9fac5This research studies the impact of user perceptions on trust and acceptance of artificial intelligence in financial decision-making. A structured questionnaire was administered to capture user sentiments regarding challenges and opportunities associated with AI in finance. Statistical techniques that is ANOVA, paired sample t-tests, and the Tukey Honestly Significant Difference (HSD) test, were performed to explore the data and assess the significant differences in user attitudes. The results supported the alternative hypothesis, revealing that user perceptions significantly impact their trust and acceptance of AI-driven financial solutions. ANOVA analysis demonstrated varying levels of trust and acceptance among different user groups, while paired sample t-tests highlighted significant disparities in concerns about security and privacy, trust in AI, and satisfaction with transparency. Further, key findings suggest that users who perceive more challenges or fewer opportunities tend to have lower trust in AI for financial decisions. Moreover, key differences were found across different demographic groups that highlights the need for customer-focused methods in the development of AI. The implications for financial institutions underscore the importance of addressing user concerns to enhance trust and acceptance of AI-driven solutions. This study contributes to the growing body of research on AI adoption in finance and provides opportunities for future research into longitudinal trends and user-centric AI development strategies. Keywords: artificial intelligence, finance, user perceptions, trust, acceptance, ANOVA, paired sample t-tests, Tukey HSD test, customer-focused methods, AI adoption.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Key findings suggest that users who perceive more challenges or fewer opportunities tend to have lower trust in AI for financial decisions, which highlights the need for customer-focused methods in the development of AI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Barnabas Thapa']2024-04-20T00:00:00https://www.semanticscholar.org/paper/5283c3ba9a416951128dd2ab138bec90f3e9fac5 0c434a961f90c5e59f2bb9f49297a13ba48442d6We investigated whether ChatGPT was able to increase the Flesch reading ease and the Flesch-Kincaid reading level of elective clinic letters written by hand surgeons. ChatGPT could not reliably simplify the hand clinic letters any further.Journal of Hand Surgery (European Volume)10This work investigated whether ChatGPT was able to increase the Flesch reading ease and the Flesch-Kincaid reading level of elective clinic letters written by hand surgeons and found it could not reliably simplify the hand clinic letters any further.The Journal of hand surgery, European volume['Adam C Stoneham', 'Lucy C Walker', 'Michael J Newman', 'Alex Nicholls', 'Duncan Avis']2024-04-20T00:00:00https://www.semanticscholar.org/paper/0c434a961f90c5e59f2bb9f49297a13ba48442d6 aadf5a26ff54cc1ca475468681a5ae1e01bea248 The American Academy of Sleep Medicine recommends scoring hypopneas terminating in either oxygen desaturation or arousals. A limitation of HST, when EEGs are unavailable, is not scoring hypopneas that terminate in arousals. This lowers the average apnea-hypopnea index (AHI) in HST studies and disproportionately affects patients who predominantly have hypopneas that terminate in arousals. We report on a deep neural network, Nox BodySleep 2.0 experimental prototype, that predicts arousals and sleep stages using non-EEG signals. The model uses abdomen and thorax respiratory inductance plethysmography (RIP) and activity signals. The model outputs arousal events; and Wake, rapid-eye-movement (REM), and non-REM sleep epochs. It was trained on ~3200 PSG sleep studies from the United States, Europe, and Asia, and validated using 2,407 PSGs from clinical sleep labs in the United States. The performance was measured using epoch-based sensitivity, specificity, and accuracy for scoring arousals. Furthermore, the clinical performance was validated by the sensitivity, specificity, and accuracy for AHI severity classification for the diagnostic cutoff thresholds of AHI ≥ 5 and AHI ≥ 15. The model sensitivity, specificity, and accuracy was 65%, 85%, 80% for arousal scoring. For AHI ≥ 5, the sensitivity, specificity, and accuracy were 95%, 88%, 94%, respectively. For AHI ≥ 15, the results were 86%, 97%, 92%. When the sleep studies were scored as HST without using the model, the corresponding results for AHI ≥ 5 were 75%, 95%, 77%, and for AHI ≥ 15, 60%, 99%, 81%. When the studies were scored as PSG, the results for AHI ≥ 5 were 96%, 95%, 96%, while for AHI ≥ 15, they were 89%, 98%, 96%. The model is a promising method of providing conclusive results from HST sleep studies. The model-based AHI is closer to the AHI from a PSG than when using HST. The AI model was trained on a large and diverse dataset and validated on a clinical population from the United States. Predicting arousals in HST studies without EEG improves patients' access to conclusive sleep apnea testing, and may improve health equity and the operation of sleep clinics. None Sleep00A deep neural network that predicts arousals and sleep stages using non-EEG signals in HST studies without EEG improves patients' access to conclusive sleep apnea testing, and may improve health equity and the operation of sleep clinics.SLEEP['S. Jónsson', 'Hrafnkell Zahawi', 'Eydís Arnardóttir', 'E. Erlingsson', 'Hlynur Davíð Hlynsson', 'E. Finnsson', 'Kristofer Montazeri', 'Jon Agustsson']2024-04-20T00:00:00https://www.semanticscholar.org/paper/aadf5a26ff54cc1ca475468681a5ae1e01bea248 0b3580e7c721df8a5fe0f388b7c38f468cb7848bIndian Geotechnical Journal270Indian Geotechnical Journal['Rodney Ewusi-Wilson', 'Jerome Anabannye Yendaw', 'Sylvanus Sebbeh-Newton', 'Emmanuel Ike', 'Felix J. Ayeh']2024-04-20T00:00:00https://www.semanticscholar.org/paper/0b3580e7c721df8a5fe0f388b7c38f468cb7848b fdde0634b9de001740eccf2b174ddef77b7f8a88The global populace is predicted to surpass 10 billion by 2050, putting an enormous burden on farming to increase food yields and exploit the best yields. Two probable solutions to projected food scarcities have arisen: expanding land usage and implementing comprehensive farming, or approving ground-breaking practices and leveraging technological revolutions to upsurge yield on existing agricultural land. The contemporary agrarian landscape is expanding, branching out in a variety of inventive ways, despite numerous barriers to accomplishing anticipated agricultural output, including inadequate land holdings, labour scarcities, climate alteration, environmental problems, and decreased soil productiveness, to name a few. The agricultural industry has come a long way since the days of labour-intensive ploughing and horse-drawn machinery. 1 Using the terms AICurrent Agriculture Research Journal30The contemporary agrarian landscape is expanding, branching out in a variety of inventive ways, despite numerous barriers to accomplishing anticipated agricultural output, including inadequate land holdings, labour scarcities, climate alteration, environmental problems, and decreased soil productiveness.Current Agriculture Research Journal['Afroz Alam']2024-04-20T00:00:00https://www.semanticscholar.org/paper/fdde0634b9de001740eccf2b174ddef77b7f8a88 500336b7639386d15a0d9a400d344cad7564167fGenerative AI is a cool tech that helps machines create new stuff that looks a lot like what humans make. Think of it like a computer artist who can paint pictures, write stories, compose music, and even make videos. This paper is all about explaining generative AI: where it came from, what it's doing now, and how it's used. It works by using fancy computer programs and smart algorithms to understand patterns in data and then make new things that fit those patterns. It's super important because it's changing how we do things in fields like healthcare, entertainment, and education. But, it's not all fun and games. Generative AI also brings up some serious questions about privacy, fairness, and making sure we use it in good ways. This paper dives into all that, showing the good and the tricky parts of generative AI and how it's shaping our world. Key Words: Generative AI, technology, machine creativity, data patterns, applications, healthcare, entertainment, education, privacy, fairness, ethical use, societal impact.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper is all about explaining generative AI: where it came from, what it's doing now, and how it's used.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Mayuri Warankar']2024-04-20T00:00:00https://www.semanticscholar.org/paper/500336b7639386d15a0d9a400d344cad7564167f b251941073997f0a9026f2559b53430f00c39b58Medical Education30Medical education['M. Bearman', 'R. Ajjawi']2024-04-20T00:00:00https://www.semanticscholar.org/paper/b251941073997f0a9026f2559b53430f00c39b58 77ff2be1463053e422ec8911533b4766a6309292Journal of Digestive Cancer Research00Journal of Digestive Cancer Research['Dong Woo Shin', 'S. Moon']2024-04-20T00:00:00https://www.semanticscholar.org/paper/77ff2be1463053e422ec8911533b4766a6309292 2c73679f52becbd5ec9c63d951a7f0988ae50b28Artificial intelligence is commonly known as AI, these are fundamentally changing the functionality of the working world. Artificial Intelligence Many expert’s state that this era of AI is similar to the Industrial revolution in the 19th century. A handful of people are still unknown about the existence of AI and how it can enhance and develop the fields in which they are implemented in. It is considered as a tool that is widely ranged and enables people to integrate information, analyze data, and enhance the decision making functions.AI has a lot of benefits that can be incurred with a proper usage. The paper is an overview of gaining knowledge and understanding AI in a better way by looking into the history and emergence of AI and its emergence in fashion technology, the evolution cycle, AI as a system, comparison of human and artificial intelligence, different fields of AI, case study, different approaches and a lot more that will enlighten the readers with the improvised knowledge on AI in fashion. Generating AI creates new space for creativity. It has the ability to organize the unstructured raw data into a complete output. AI is not all about automation which revolves around augmentation and acceleration. The AI race provides the professionals with a dramatically faster and in an orderly and efficient manner. Thus, it is important to realize the changes it might bring to the industries, working conditions, functional skills etc. to our society.International Research Journal on Advanced Engineering Hub (IRJAEH)50The paper is an overview of gaining knowledge and understanding AI in a better way by looking into the history and emergence of AI and its emergence in fashion technology, the evolution cycle, AI as a system, comparison of human and artificial intelligence, different fields of AI, case study, different approaches and a lot more that will enlighten the readers with the improvised knowledge on AI in fashion.International Research Journal on Advanced Engineering Hub (IRJAEH)['Dr. V.A. Rinsey Antony', 'Ginni']2024-04-20T00:00:00https://www.semanticscholar.org/paper/2c73679f52becbd5ec9c63d951a7f0988ae50b28 b45749c27f409d1d610c4ecd993c2446e111e12bThe agricultural sector plays a paramount role in the economy of the United States, contributing significantly to the GDP and affirming sustainability for American residents. This study explored the application of Artificial Intelligence and Machine Learning techniques in maximizing crop yields in America. This research employed various software tools, comprising Python programming language, Pandas library for data manipulation and analysis, Scikit-learn library for machine learning models and evaluation metrics, and LIME library for explainable AI. The crop yield datasets for the current research were sourced from Kaggle. This dataset provided substantial insights regarding crop cultivation practices within the USA context. This study proposes the "XAI-CROP" algorithm, which is a novel explainable artificial intelligence (XAI) model developed particularly to reinforce the interpretability, transparency and trustworthiness of crop recommendation systems (CRS). From the experimentation, the XAI-CROP model excelled at forecasting crop yield, as demonstrated by its lowest MSE value of 0.9412, suggesting minimal errors.  Besides, Its MAE of 0.9874 suggests an average error of less than 1 unit in forecasting crop yield. Furthermore, the R2 value of 0.94152 suggests that the algorithm accounts for 94.15% of the data's variability.Journal of Computer Science and Technology Studies130The "XAI-CROP" algorithm is proposed, which is a novel explainable artificial intelligence (XAI) model developed particularly to reinforce the interpretability, transparency and trustworthiness of crop recommendation systems (CRS).Journal of Computer Science and Technology Studies['Md Rokibul Hasan']2024-04-20T00:00:00https://www.semanticscholar.org/paper/b45749c27f409d1d610c4ecd993c2446e111e12b 9e0dab6cca0059d9d21aabd7cec6dfe19e788565The landscape of maintenance in distributed systems is rapidly evolving with the integration of Artificial Intelligence (AI). Also, as the complexity of computing continuum systems intensifies, the role of AI in predictive maintenance (Pd.M.) becomes increasingly pivotal. This paper presents a comprehensive survey of the current state of Pd.M. in the computing continuum, with a focus on the combination of scalable AI technologies. Recognizing the limitations of traditional maintenance practices in the face of increasingly complex and heterogenous computing continuum systems, the study explores how AI, especially machine learning and neural networks, is being used to enhance Pd.M. strategies. The survey encompasses a thorough review of existing literature, highlighting key advancements, methodologies, and case studies in the field. It critically examines the role of AI in improving prediction accuracy for system failures and in optimizing maintenance schedules, thereby contributing to reduced downtime and enhanced system longevity. By synthesizing findings from the latest advancements in the field, the article provides insights into the effectiveness and challenges of implementing AI-driven predictive maintenance. It underscores the evolution of maintenance practices in response to technological advancements and the growing complexity of computing continuum systems. The conclusions drawn from this survey are instrumental for practitioners and researchers in understanding the current landscape and future directions of Pd.M. in distributed systems. It emphasizes the need for continued research and development in this area, pointing towards a trend of more intelligent, efficient, and cost-effective maintenance solutions in the era of AI.arXiv.org240A comprehensive survey of the current state of Pd.M.ArXiv['Michael Bidollahkhani', 'Julian M. Kunkel']2024-04-20T00:00:00https://www.semanticscholar.org/paper/9e0dab6cca0059d9d21aabd7cec6dfe19e788565 9bd979d58b5cd7c467f544faee0a23fc549aa7a4This paper explores the symbiotic relationship between zoological research and artificial intelligence (AI)-driven business management, elucidating how their integration can unlock profound biological insights with transformative implications for various industries. With advancements in AI technologies and an increasing emphasis on sustainability, businesses are increasingly turning to interdisciplinary approaches to optimize operations and enhance decision-making processes. Leveraging insights from zoological research, which encompasses the study of diverse animal species and their ecological interactions, presents a novel avenue for innovation and strategic development in the business landscape. The integration of zoological research with AI-driven business management offers multifaceted benefits. Firstly, it provides businesses with a deeper understanding of complex biological systems, facilitating informed decision-making regarding resource management, product development, and sustainability initiatives. Zoological studies offer insights into the intricate interdependencies within ecosystems, shedding light on ecological dynamics, species behaviors, and evolutionary patterns. By integrating these insights into AI algorithms, businesses can develop predictive models to anticipate environmental changes, species migrations, and biodiversity trends, thereby enhancing resilience and adaptive capacity. Furthermore, the synergy between zoological research and AI enables businesses to optimize processes across various domains, ranging from supply chain management to customer engagement. For instance, by analyzing animal foraging behaviors and social structures, businesses can derive inspiration for efficient routing algorithms, inventory optimization strategies, and collaborative network designs. Moreover, AI-driven data analytics can streamline market research efforts by identifying consumer preferences, market trends, and emerging opportunities, drawing parallels with animal behavior studies that elucidate patterns of resource utilization and adaptation. In addition to operational enhancements, the integration of zoological insights with AI-driven business management fosters innovation and product development. By studying biological adaptations and evolutionary processes, businesses can derive inspiration for biomimetic design principles, leading to the creation of sustainable materials, energy-efficient technologies, and biologically inspired innovations. Moreover, by leveraging AI techniques such as machine learning and natural language processing, businesses can analyze vast datasets of biological literature and ecological observations to identify novel bioactive compounds, genetic sequences, and ecological niches with commercial potential. Importantly, the incorporation of zoological research into AI-driven business management fosters a paradigm shift towards holistic and ethical business practices. By recognizing the interconnectedness of ecological systems and economic activities, businesses can develop strategies that prioritize environmental stewardship, biodiversity conservation, and social responsibility. Zoological insights inform businesses about the intrinsic value of biodiversity, highlighting the importance of preserving natural habitats, protecting endangered species, and mitigating anthropogenic impacts on ecosystems. Through AI-powered simulations and scenario analyses, businesses can assess the long-term consequences of their decisions on ecological integrity, societal well-being, and corporate reputation, fostering a culture of responsible innovation and sustainable growth. However, the integration of zoological research with AI-driven business management also poses challenges and ethical considerations. Ensuring the ethical treatment of animals in research and development processes, safeguarding data privacy and security, and addressing biases in AI algorithms are paramount concerns that require interdisciplinary collaboration and stakeholder engagement. Moreover, fostering inclusivity and diversity in both scientific research and business leadership is essential for promoting equitable outcomes and fostering innovation. In conclusion, the integration of zoological research with AI-driven business management holds immense potential for unlocking biological insights and driving sustainable innovation in diverse industries. By bridging the gap between biological sciences and business practices, this interdisciplinary approach offers a pathway towards a more resilient, adaptive, and ethically conscious future. Through collaborative efforts among scientists, entrepreneurs, policymakers, and community stakeholders, we can harness the power of nature-inspired solutions to address complex challenges and create positive societal impact.Uttar Pradesh Journal of Zoology00UTTAR PRADESH JOURNAL OF ZOOLOGY['Tarun Madan Kanade', 'Sarika Patil', 'Radhakrishna Batule', 'Jonathan Joseph']2024-04-20T00:00:00https://www.semanticscholar.org/paper/9bd979d58b5cd7c467f544faee0a23fc549aa7a4 fbd9decc90cec215adbad81aafc59fbe007a7c6eThis study explored the integration of neural networks and artificial intelligence in image recognition for object identification. The aim was to enhance students’ learning experiences through a "Learning by Teaching" approach, in which students act as instructors to train AI robots in recognizing objects. This research specifically focused on the cell division unit in the first grade of lower-secondary school. This study employed a quasi-experimental research design involving four seventh-grade classes in a rural lower-secondary school. The experimental group (41 students) were taught via an AI robot image recognition technology, whereas the control group (40 students) were taught via a more conventional textbook-centered approach. The research followed a pre-test design, with three classes lasting 45 min each, totaling 135 min of teaching time over two weeks. Evaluation tools include the "Cell Division Two Stage Diagnostic Test" and the "Science Learning Motivation Scale." The results indicate that learning through teaching AI robot image recognition technology is more effective than textbook learning in enhancing students’ comprehension of the "cell division" concept and boosting motivation to learn science. Keywords: artificial intelligence, image recognition technology, cell division, science learning motivation, learning by teachingJournal of Baltic Science Education640The results indicate that learning through teaching AI robot image recognition technology is more effective than textbook learning in enhancing students’ comprehension of the "cell division" concept and boosting motivation to learn science.Journal of Baltic Science Education['Pei-yu Chen', 'Yuan-Chen Liu']2024-04-20T00:00:00https://www.semanticscholar.org/paper/fbd9decc90cec215adbad81aafc59fbe007a7c6e cd42c8fc6734b4cb5bd848038382402973e4b9a7AI &amp; SOCIETY210This paper considers whether existing XAI techniques can indeed close the responsibility gap, and identifies a number of significant limits to their ability to do so.AI &amp; SOCIETY['Isaac Taylor']2024-04-20T00:00:00https://www.semanticscholar.org/paper/cd42c8fc6734b4cb5bd848038382402973e4b9a7 f62c9c75cb8a6cf74999968a36b31013de1fd450The "Al-Based Image Processing System For College Buses” project addresses the critical need for enhanced safety and security in college bus transportation through the implementation of an innovative AI-based image processing system. This system is designed to efficiently check student entry and verify active passes, ultimately creating a secure environment for all passengers on college buses. Leveraging advanced machine learning models, the primary goal is to improve the capacity and accuracy of the system, enabling it to capture multiple faces simultaneously with faster execution and more precise output. The hardware components of the AI-Secure Bus system include a Raspberry Pi, GSM module, GPS module, LCD display, and a reliable power supply. These components work in tandem to create a robust and versatile platform capable of real-time tracking, image processing, and communication. The workflow of adding datasets involves capturing student facial data, adding information about pass validity, and incorporating home location coordinates into the dataset. This meticulous process ensures that the system has comprehensive data to accurately authenticate and monitor student entries. The workflow toward the college involves making the bus location live, checking the proximity of students' home locations, and notifying students about the bus's arrival with approximate timings. This real-time tracking and communication significantly contribute to the efficiency and transparency of the transportation system. Keywords: Open CV, V2 camera, Raspberry Pi, image processing, Artificial IntelligenceINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The "Al-Based Image Processing System For College Buses” project addresses the critical need for enhanced safety and security in college bus transportation through the implementation of an innovative AI-based image processing system, enabling it to capture multiple faces simultaneously with faster execution and more precise output.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Akash Magji']2024-04-20T00:00:00https://www.semanticscholar.org/paper/f62c9c75cb8a6cf74999968a36b31013de1fd450 660f224e07de22e46f9fa64a3854c4c0196660c6This paper investigates the relationship between law and eXplainable Artificial Intelligence (XAI). While there is much discussion about the AI Act, for which the trilogue of the European Parliament, Council and Commission recently concluded, other areas of law seem underexplored. This paper focuses on European (and in part German) law, although with international concepts and regulations such as fiduciary plausibility checks, the General Data Protection Regulation (GDPR), and product safety and liability. Based on XAI-taxonomies, requirements for XAI-methods are derived from each of the legal bases, resulting in the conclusion that each legal basis requires different XAI properties and that the current state of the art does not fulfill these to full satisfaction, especially regarding the correctness (sometimes called fidelity) and confidence estimates of XAI-methods.arXiv.org430This paper focuses on European (and in part German) law, although with international concepts and regulations such as fiduciary plausibility checks, the General Data Protection Regulation (GDPR), and product safety and liability.ArXiv['Benjamin Frész', 'Elena Dubovitskaya', 'Danilo Brajovic', 'Marco Huber', 'Christian Horz']2024-04-19T00:00:00https://www.semanticscholar.org/paper/660f224e07de22e46f9fa64a3854c4c0196660c6 a15e462e30a1785f53e5eb9210acdf9908e8d7cbNew capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections. Existing practices in data collection have led to challenges in documenting data transparency, tracing authenticity, verifying consent, privacy, representation, bias, copyright infringement, and the overall development of ethical and trustworthy foundation models. In response, regulation is emphasizing the need for training data transparency to understand foundation models' limitations. Based on a large-scale analysis of the foundation model training data landscape and existing solutions, we identify the missing infrastructure to facilitate responsible foundation model development practices. We examine the current shortcomings of common tools for tracing data authenticity, consent, and documentation, and outline how policymakers, developers, and data creators can facilitate responsible foundation model development by adopting universal data provenance standards.arXiv.org1020This work examines the current shortcomings of common tools for tracing data authenticity, consent, and documentation, and outlines how policymakers, developers, and data creators can facilitate responsible foundation model development by adopting universal data provenance standards.ArXiv['Shayne Longpre', 'Robert Mahari', 'Naana Obeng-Marnu', 'William Brannon', 'Tobin South', 'Katy Gero', 'Sandy Pentland', 'Jad Kabbara']2024-04-19T00:00:00https://www.semanticscholar.org/paper/a15e462e30a1785f53e5eb9210acdf9908e8d7cb 9f9eaece77f592f5d86d70efd67bc7af6da84adcThis article discusses criminal liability committed with the use of artificial intelligence (AI) for criminal purposes. The paper identifies such problems as a dynamically developing environment with the participation of artificial intelligence, which forms criminological risks, namely, obtaining information in telecommunications networks and information infrastructure facilities, which, as stated in the article, are not protected from any attacks. In the study, we rely on the scientific works of foreign and domestic authors, which were published in different periods of time on the study of artificial intelligence, as well as information disseminated by the media. Thus, based on this problem, the need for the introduction of regulatory regulation in the situation with artificial intelligence and protection from various attacks was considered.Eurasian Scientific Journal of Law00The need for the introduction of regulatory regulation in the situation with artificial intelligence and protection from various attacks was considered.Eurasian Scientific Journal of Law['B. A. Torgautova', 'K. M. Osmonaliyev']2024-04-19T00:00:00https://www.semanticscholar.org/paper/9f9eaece77f592f5d86d70efd67bc7af6da84adc fbce6dbe395c359b3b41911cf2fb45466fa4b6a5 Within the area of electricity market regulation, a practice has emerged in which the chain of delegation has gone beyond the European Commission, resulting in double delegation. During 2015–2017, the European Commission adopted implementing regulations requiring detailed European terms, conditions and methodologies (TCMs) for electricity markets and system operation to be jointly adopted by national energy regulators. Should the latter fail to agree within a predefined time limit, rule-making would move to the Agency for the Cooperation of Energy Regulators. This rule-making procedure entails that, depending on the dynamic within the procedure, different actors would adopt the TCMs. This article examines how double-delegated rule-making unfolds in a novel and emerging practice, evolving beneath implementing acts. By analysing the factors behind whether TCMs are adopted jointly by national agencies or not, the study investigates whether this form of delegated rule-making in a network setting delivers decisions or whether rule-making by a European Union agency is needed.European Journal of Risk Regulation01European Journal of Risk Regulation['Torbjørg Jevnaker', 'Karianne Krohn Taranger', 'P. Eikeland', 'Marie Byskov Lindberg']2024-04-19T00:00:00https://www.semanticscholar.org/paper/fbce6dbe395c359b3b41911cf2fb45466fa4b6a5 c67ac54ccf8b0aa4ccf4e1d21fe295ebbfcbddd7Environmental science and pollution research international720Environmental science and pollution research international['Jianrui Zhu', 'Xueqin Li', 'Daqian Shi']2024-04-19T00:00:00https://www.semanticscholar.org/paper/c67ac54ccf8b0aa4ccf4e1d21fe295ebbfcbddd7 9d59e9d1a30b483cfcc5f27881d82f13e3a50599Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application.Journal of Civil Engineering and Management460JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT['Xiaolong Xue', 'Ankang Ji', 'Xiaowei Luo', 'Y. Dou', 'Hongqin Fan']2024-04-19T00:00:00https://www.semanticscholar.org/paper/9d59e9d1a30b483cfcc5f27881d82f13e3a50599 67a78fb0ade99caac6805a3fb98c010b8f3f7e0aThe article examines the issues related to the procedural institute of participation of concepts in criminal proceedings, its appearance, development and legislative regulation at different historical stages. It is noted that since its origin, this institution of procedural law has been observed as a guarantee of ensuring the rights and interests of participants of investigative actions against harassment by the investigating authorities. A number of procedural rules which contain information on the participation of concepts in pre-trial proceedings allow us to conclude that the institute of witnesses is present in the current Code of Criminal Procedure. The authors analyse the opinions of scientists on the viability of keeping the institute of concepts in the current CPC, due to the modern technical opportunities for objective and complete fixation of the course and results of the investigative action. The authors conclude that the legislator has a reasonable attitude to the use of the traditional institute of concepts and the use of modern technical devices for recording investigative actions. The authors note that keeping the institute of concepts in the current CPC of Ukraine requires further regulation. The classification of a concept as a participant in criminal proceedings requires appropriate modifications to the CPC and an independent article among other participants in criminal proceedings. It is proposed to define this participant in the process in the relevant provision, indicating his procedural status (list of rights and obligations), the range of persons who cannot be involved as witnesses, and the right of participants to the investigative action to challenge the concept. Based on the analysis of scientists’ opinions, doubts are expressed about the ability of concepts to create conditions for an objective and, moreover, proper proceeding of the investigative action, to ensure the legality of its performance. In most cases, witnesses are random persons who are not interested in the case. The author analyses the proposals of scientists regarding the competitive recruitment by local self- government authorities of individuals who are trustworthy and passing their lists to law enforcement authorities for the purpose of engaging concepts. The authors justify the proposals for further regulation of the institute of concepts.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['E. Lukyanchikov', 'B. Lukyanchikov', 'O. Mykytenko']2024-04-19T00:00:00https://www.semanticscholar.org/paper/67a78fb0ade99caac6805a3fb98c010b8f3f7e0a b9fe607c8cce0d956f4aea816a7b4a04bee81157European Journal of Risk Regulation00European Journal of Risk Regulation['Ollie Bartlett']2024-04-19T00:00:00https://www.semanticscholar.org/paper/b9fe607c8cce0d956f4aea816a7b4a04bee81157 92a8702c8529eddb8a191ac24fade0e401c71d78Interested in emerging technologies in higher education, we look at AI chatbots through the lens of human– technology mediations. We argue for shifting the focus from what higher education can do with AI chatbots to why AI chatbots are compelling for higher education’s raison-’être. We call for a critical debate examining the power of AI chatbots in configuring students as civic actors in an increasingly complex and digitalized society. We welcome a continuous and rigorous examination of generative AI chatbots and their impact on teaching practices and student learning in higher education. Högre Utbildning340It is argued for shifting the focus from what higher education can do with AI chatbots to why AI chatbots are compelling for higher education’s raison-’être, and for a critical debate examining the power of AI chatbots in configuring students as civic actors in an increasingly complex and digitalized society.Högre utbildning['Teresa Cerratto Pargman', 'Elin Sporrong', 'Alexandra Farazouli', 'Cormac McGrath']2024-04-19T00:00:00https://www.semanticscholar.org/paper/92a8702c8529eddb8a191ac24fade0e401c71d78 7327c8d183b4d4fbb52abf95dfd829e0775d204aThis study introduces Protectbot, an innovative chatbot framework designed to improve safety in children’s online gaming environments. At its core, Protectbot incorporates DialoGPT, a conversational Artificial Intelligence (AI) model rooted in Generative Pre-trained Transformer 2 (GPT-2) technology, engineered to simulate human-like interactions within gaming chat rooms. The framework is distinguished by a robust text classification strategy, rigorously trained on the Publicly Available Natural 2012 (PAN12) dataset, aimed at identifying and mitigating potential sexual predatory behaviors through chat conversation analysis. By utilizing fastText for word embeddings to vectorize sentences, we have refined a support vector machine (SVM) classifier, achieving remarkable performance metrics, with recall, accuracy, and F-scores approaching 0.99. These metrics not only demonstrate the classifier’s effectiveness, but also signify a significant advancement beyond existing methodologies in this field. The efficacy of our framework is additionally validated on a custom dataset, composed of 71 predatory chat logs from the Perverted Justice website, further establishing the reliability and robustness of our classifier. Protectbot represents a crucial innovation in enhancing child safety within online gaming communities, providing a proactive, AI-enhanced solution to detect and address predatory threats promptly. Our findings highlight the immense potential of AI-driven interventions to create safer digital spaces for young users.Inf.320Protectbot represents a crucial innovation in enhancing child safety within online gaming communities, providing a proactive, AI-enhanced solution to detect and address predatory threats promptly, and highlights the immense potential of AI-driven interventions to create safer digital spaces for young users.Inf.['Anum Faraz', 'Fardin Ahsan', 'Jinane Mounsef', 'Ioannis Karamitsos', 'A. Kanavos']2024-04-19T00:00:00https://www.semanticscholar.org/paper/7327c8d183b4d4fbb52abf95dfd829e0775d204a 9d023ec8df76d282dc6a9318253b2f0b2519235f Purpose This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach. Design/methodology/approach The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses. Findings The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters. Research limitations/implications This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables. Practical implications The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect. Social implications Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain. Originality/value The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study. Journal of Global Operations and Strategic Sourcing1000The bullwhip effect of digital technology on supply chain management is discussed, and managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.Journal of Global Operations and Strategic Sourcing['Tarek Taha Kandil']2024-04-19T00:00:00https://www.semanticscholar.org/paper/9d023ec8df76d282dc6a9318253b2f0b2519235f bd1c2cc045fb89a7a5ad888534ec35880585b981It has been well over a year since ChatGPT emerged and brought with it much commentary about challenges and opportunities for education. There has been considerable discussion about risks to academic integrity and the possibilities of generative AI for enhancing learning and teaching. As the dust settles, the hard work of determining how exactly generative AI will integrate into higher education begins. In this session, we will explore the current state of generative AI in student learning. While the integration of generative AI into formal coursework has been inconsistent, to say the least, many students are using these tools extensively as part of their studies. Drawing on in-depth interviews with 50 students across disciplines, a set of hypotheses about the impact of generative AI on student learning practices will be presented. A key component of the impact of these emerging technologies appears to be how familiar and confident students are in their understanding of their own learning. The implications of these findings will also be discussed.Jason Lodge is Associate Professor of Educational Psychology and Director of the Learning, Instruction, and Technology Lab in the School of Education and is a Deputy Associate Dean (Academic) in the Faculty of Humanities, Arts and Social Sciences at The University of Queensland. Jason’s research with his lab focuses on the cognitive, metacognitive, and emotional mechanisms of learning, primarily in post-secondary settings and in digital learning environments. He currently serves as Lead Editor of Australasian Journal of Educational Technology and Editor of Student Success.Pacific Journal of Technology Enhanced Learning00This session will explore the current state of generative AI in student learning and draw on in-depth interviews with 50 students across disciplines to present a set of hypotheses about the impact of generative AI on student learning practices.Pacific Journal of Technology Enhanced Learning['Jason Lodge']2024-04-19T00:00:00https://www.semanticscholar.org/paper/bd1c2cc045fb89a7a5ad888534ec35880585b981 be61606fe60dfe1fac3054cc427c851370b40207A central issue in copyright lawsuits against generative-AI companies is the degree to which a generative-AI model does or does not"memorize"the data it was trained on. Unfortunately, the debate has been clouded by ambiguity over what"memorization"is, leading to legal debates in which participants often talk past one another. In this essay, we attempt to bring clarity to the conversation over memorization.arXiv.org700ArXiv['A. F. Cooper', 'James Grimmelmann']2024-04-19T00:00:00https://www.semanticscholar.org/paper/be61606fe60dfe1fac3054cc427c851370b40207 296f9c286864bff516a82ef72a477ad8cf36d38dArtificial intelligence supports healthcare professionals with predictive modeling, greatly transforming clinical decision-making. This study addresses the crucial need for fairness and explainability in AI applications within healthcare to ensure equitable outcomes across diverse patient demographics. By focusing on the predictive modeling of sepsis-related mortality, we propose a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness. Our method also introduces a novel permutation-based feature importance algorithm aiming at elucidating the contribution of each feature in enhancing fairness on predictions. Unlike existing explainability methods concentrating on explaining feature contribution to predictive performance, our proposed method uniquely bridges the gap in understanding how each feature contributes to fairness. This advancement is pivotal, given sepsis's significant mortality rate and its role in one-third of hospital deaths. Our method not only aids in identifying and mitigating biases within the predictive model but also fosters trust among healthcare stakeholders by improving the transparency and fairness of model predictions, thereby contributing to more equitable and trustworthy healthcare delivery.arXiv.org130This study proposes a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness, and introduces a novel permutation-based feature importance algorithm aiming at elucidating the contribution of each feature in enhancing fairness on predictions.ArXiv['Chia-Hsuan Chang', 'Xiaoyang Wang', 'Christopher C. Yang']2024-04-19T00:00:00https://www.semanticscholar.org/paper/296f9c286864bff516a82ef72a477ad8cf36d38d e3e7fd40411172da17c7d3fdae0da774738c3ea6Generative AI has put many professional writers on the defensive; a major negotiation point of the recent Writers Guild of America's strike concerned use of AI. However, must AI threaten writers, their livelihoods or their creativity? And under what conditions, if any, might AI assistance be invited by different types of writers (from the amateur to the professional, from the screenwriter to the novelist)? To explore these questions, we conducted a qualitative study with 37 writers. We found that most writing occurs across five stages and within one of three modes; we additionally map openness to AI assistance to each intersecting stage-mode. We found that most writers were interested in AI assistance to some degree, but some writers felt drawing firm boundaries with an AI was key to their comfort using such systems. Designers can leverage these insights to build agency-respecting AI products for writers.arXiv.org120Most writers were interested in AI assistance to some degree, but some writers felt drawing firm boundaries with an AI was key to their comfort using such systems, and designers can leverage these insights to build agency-respecting AI products for writers.ArXiv['Morteza Behrooz', 'Yuandong Tian', 'W.K.F. Ngan', 'Yael Yungster', 'Justin Wong', 'David Zax']2024-04-19T00:00:00https://www.semanticscholar.org/paper/e3e7fd40411172da17c7d3fdae0da774738c3ea6 72e3a440c4253f92cd9de06357de6897f110527aThe application of Artificial Intelligence in Education (AIED) is experiencing widespread interest among students, educators, researchers, and policymakers. AIED is expected, among other things, to enhance learning environments in the higher education system. However, in line with the general trends, there are also increasing concerns about possible negative and collateral effects. The consequent social impact cannot be currently assessed in depth. Balancing benefits with social considerations according to a socio-technical approach is essential for harnessing the true power of AI in a responsible and trustworthy context. This study proposes a semi-systematic literature review of the available knowledge on the adoption of artificial intelligence (AI) in the higher education system. It presents a stakeholder-centric analysis to explore multiple perspectives, including pedagogical, managerial, technological, governmental, external, and social ones. The main goal is to identify and discuss major gaps and challenges in context, looking at the existing body of knowledge and momentum. AIED should encompass pedagogical, ethical, and social dimensions to be properly addressed. This review highlights a not-always-explicit socio-technical perspective. Additionally, this study reveals a significant lack of empirical systematic evaluation of added value and institutional readiness. Because of the broad scope of the study and the intense ongoing debate on the topic, an exhaustive identification of the current body of knowledge is probably unrealistic, so this study aims mainly to identify the mainstream and major trends by looking at the most recent contributions.Electronics00This study proposes a semi-systematic literature review of the available knowledge on the adoption of artificial intelligence in the higher education system and presents a stakeholder-centric analysis to explore multiple perspectives, including pedagogical, managerial, technological, governmental, external, and social ones.Electronics['Budur Turki Alshahrani', 'S. F. Pileggi', 'Faezeh Karimi']2024-04-19T00:00:00https://www.semanticscholar.org/paper/72e3a440c4253f92cd9de06357de6897f110527a 2dd4c1776a589aa0e4e2d6b6232dd0479c21f3f4This study explores a new method in food development by utilizing AI including generative AI, aiming to craft products that delight the senses and resonate with consumers' emotions. The food ingredient recommendation approach used in this study can be considered as a form of multimodal generation in a broad sense, as it takes text as input and outputs food ingredient candidates. This Study focused on producing"Romance Bread,"a collection of breads infused with flavors that reflect the nuances of a romantic Japanese television program. We analyzed conversations from TV programs and lyrics from songs featuring fruits and sweets to recommend ingredients that express romantic feelings. Based on these recommendations, the bread developers then considered the flavoring of the bread and developed new bread varieties. The research included a tasting evaluation involving 31 participants and interviews with the product developers. Findings indicate a notable correlation between tastes generated by AI and human preferences. This study validates the concept of using AI in food innovation and highlights the broad potential for developing unique consumer experiences that focus on emotional engagement through AI and human collaboration.arXiv.org70The concept of using AI in food innovation is validates the concept of using AI in food innovation and highlights the broad potential for developing unique consumer experiences that focus on emotional engagement through AI and human collaboration.ArXiv['Takuya Sera', 'Izumi Kuwata', 'Yuki Taya', 'Noritaka Shimura', 'Yosuke Motohashi']2024-04-19T00:00:00https://www.semanticscholar.org/paper/2dd4c1776a589aa0e4e2d6b6232dd0479c21f3f4 3d555a615b3b5098e70198df5532792b89293abfCloud computing has revolutionized the way groupings feature by way of manner of presenting scalable and flexible infrastructure services. However, dealing with cloud cost efficiently remains a project, as cloud environments emerge as more complex. This paper proposes an AI-powered financial operation approach for optimizing cloud computing cost. The method leverages AI algorithms to research utilization patterns, forecast future calls, and advise price-saving measures. By imposing this approach, agencies can acquire massive financial savings at the same time as ensuring the nice everyday normal performance and scalability in their cloud infrastructure. Cloud computing has obtained massive prominence in commercial enterprise because of its capacities. However, the effective management of cloud cost remains a complex agency. However, incorporating automation and Machine Learning (ML) gives a possibility to manipulate and mitigate cloud charges successfully, rendering cloud computing an additional economically viable solution. This study will investigate into the transformative effect of automation and Machine learning cloud cost optimization, providing insights into how companies can harness those technologies to curtail fees on the equal time as addressing ability implementation-demanding situations. As organizations increasingly trust upon cloud computing services for their operations, optimizing the related prices performances into a crucial factor of financial management. This paper proposes an AI-powered financial operation technique for cloud computing fee optimization. The technique leverages tool-reading algorithms to investigate historic utilization patterns, forecast future desires, and perceive capability fee-saving possibilities. It integrates with cloud service providers' APIs to continuously reveal useful resource usage and adjust provisioning ranges dynamically. Additionally, the technique includes anomaly detection strategies to discover inefficiencies or sudden spikes in utilization, permitting proactive fee management. Through the implementation of this AI-powered technique, businesses can gain huge discounts in cloud computing costs even while preserving the finest overall performance and scalability.Salud, Ciencia y Tecnología20This study will investigate into the transformative effect of automation and Machine learning cloud cost optimization, providing insights into how companies can harness those technologies to curtail fees on the equal time as addressing ability implementation-demanding situations.Salud, Ciencia y Tecnología - Serie de Conferencias['Mageshkumar Naarayanasamy Varadarajan', 'N. Rajkumar', 'C. Viji', 'Mohanraj A']2024-04-19T00:00:00https://www.semanticscholar.org/paper/3d555a615b3b5098e70198df5532792b89293abf 80e8fe563d75e4cd8e2e504a6ae41526ea74d12dRecent advancements in artificial intelligence have paved the way for promising applications in neurosurgery, aiming to improve patient outcomes while minimizing risks. This paper introduces a novel AI-driven system designed to assist neurosurgeons in accurately identifying and localizing brain tumors. Leveraging deep learning algorithms, the system was trained on a comprehensive dataset of brain MRI scans for segmentation and classification tasks. Evaluation of the system on an independent set of brain MRI scans revealed an average Dice similarity coefficient of 0.87, indicating high performance. Moreover, a user experience assessment conducted at the Department of Neurosurgery, University Hospital Ulm, demonstrated notable enhancements in accuracy, efficiency, and reduced cognitive load and stress levels among users. Notably, the system showcased adaptability across various surgical scenarios and provided personalized guidance to users. These findings underscore the potential of AI to augment the quality of neurosurgical interventions and ultimately enhance patient outcomes. Future endeavors will focus on integrating this system with robotic surgical tools to facilitate minimally invasive surgeries.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300A novel AI-driven system designed to assist neurosurgeons in accurately identifying and localizing brain tumors by leveraging deep learning algorithms, which showcased adaptability across various surgical scenarios and provided personalized guidance to users.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Venkata dinesh Reddy kalli']2024-04-19T00:00:00https://www.semanticscholar.org/paper/80e8fe563d75e4cd8e2e504a6ae41526ea74d12d 29eddaa2c0a4a468b14ed898386b4a1de7d4e480This research delves into the intricate influence of Artificial Intelligence (AI) on community development across vital sectors such as healthcare, education, environmental sustainability, and community empowerment. Its core aim is to comprehensively analyze how individuals in underserved communities perceive and experience the use of AI technologies. To achieve this, a mixed-methods approach is adopted, combining quantitative surveys for statistical insights with qualitative narratives for nuanced perspectives. Engaging 120 participants from diverse backgrounds and age groups, the research methodology incorporates Likert scales and regression analysis for data interpretation. The study reveals a prevalent positive outlook on AI's impact across various domains, particularly highlighting its significant effects on healthcare, education, and environmental sustainability. Integration of qualitative narratives enriches the findings, offering depth and context to statistical analyses. Its novelty lies in the comprehensive examination of AI's influence on community development, seamlessly blending quantitative and qualitative dimensions. By providing nuanced insights into AI's multifaceted role in community contexts, the research significantly contributes to the field. Ultimately, the study underscores the importance of responsible AI deployment, aligned with community values, to navigate the evolving technological landscape and foster sustainable community development.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This research delves into the intricate influence of Artificial Intelligence (AI) on community development across vital sectors such as healthcare, education, environmental sustainability, and community empowerment, using a mixed-methods approach.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-04-19T00:00:00https://www.semanticscholar.org/paper/29eddaa2c0a4a468b14ed898386b4a1de7d4e480 4be68b0f5621a93fbf6226a1fdae19ffe81aec9aImportance: Infant alertness and neurologic changes are assessed by exam, which can be intermittent and subjective. Reliable, continuous methods are needed. Objective: We hypothesized that our computer vision method to track movement, pose AI, could predict neurologic changes. Design: Retrospective observational study from 2021-2022. Setting: A level four urban neonatal intensive care unit (NICU). Participants: Infants with corrected age [≤]1 year, comprising 115 patients with 4,705 hours of video data linked to electroencephalograms (EEG), including 46% female and 25.2% white non-Hispanic. Exposures: Pose AI prediction of anatomic landmark position and an XGBoost classifier trained on one-minute variance in pose. Main outcomes and measures: Outcomes were cerebral dysfunction, diagnosed from EEG readings by an epileptologist, and sedation, defined by the administration of sedative medications. Measures of algorithm performance were receiver operating characteristic-area under the curves (ROC-AUCs) on cross-validation and on two test datasets comprised of held-out infants and held-out video frames from infants used in training. Results: Infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants (respective ROC-AUCs 0.94, 0.83, 0.89). Median movement increased with age and, after accounting for age, was lower with sedative medications and in infants with cerebral dysfunction (all P<5x10-3, 10,000 permutations). Sedation prediction had high performance on cross-validation, held-out frames, and held-out infants (ROC-AUCs 0.90, 0.91, 0.87), as did prediction of cerebral dysfunction (ROC-AUCs 0.91, 0.90, 0.76). Conclusions and Relevance: We used pose AI to predict sedation and cerebral dysfunction in 4,705 hours of video from a large, diverse cohort of infants. Pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.medRxiv290Pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU and predict sedation and cerebral dysfunction in 4,705 hours of video from a large, diverse cohort of infants.medRxiv['Alec Gleason', 'Florian Richter', 'Nathalia Beller', 'Naveen Arivazhagan', 'Rui Feng', 'Emma Holmes', 'B. Glicksberg', 'Sarah U Morton', 'Maite La Vega-Talbott', 'Madeline Fields', 'Katherine Guttmann', 'Girish N Nadkarni', 'Felix Richter']2024-04-19T00:00:00https://www.semanticscholar.org/paper/4be68b0f5621a93fbf6226a1fdae19ffe81aec9a d04cd29f3faefa2017e12b8d9fa61b2e7cc11357Generative artificial intelligence (AI) systems, together with text and data mining (TDM), introduce complex challenges at the junction of data utilization and copyright laws. The inherent reliance of AI on large quantities of data, often encompassing copyrighted materials, results in multifaceted legal quandaries. Issues surface from the unfeasible task of securing permission from each copyright holder for AI training, further muddled by ambiguities in interpreting copyright laws and fair use provisions. Adding to the conundrum, the clandestine practices of data collection in proprietary AI systems obstruct copyright owners from detecting unauthorized use of their materials. The paper explores the exceptions to copyright laws for TDM in the European Union, the United Kingdom, and Japan, recognizing their crucial role in fostering AI development. The EU has a two‐pronged approach under the Directive on Copyright in the Digital Single Market, with one exception catering specifically to research organizations, and another, more generalized one, that can be restricted by rightsholders. The UK allows noncommercial TDM research without infringement but rejected a broader copyright exception due to concerns from the creative sector. Japan has the broadest TDM exception globally, permitting the nonenjoyment use of works without permission, though this can potentially overlook the rights of copyright owners. Notably, the applicability of TDM exceptions to AI‐produced copies remains unclear, creating potential legal challenges. Furthermore, an exploration of the fair use doctrine in the United States provides insight into its potential application in AI development. It focuses on the transformative aspect of usage and its impact on the original work's potential market. This exploration underscores the necessity for clear, practical guidelines. In response to these identified challenges, this paper proposes a hybrid model for TDM exceptions emerges, along with recommended specific mechanisms. The model divides exceptions into noncommercial and commercial uses, providing a nuanced solution to complex copyright issues in AI training. Recommendations incorporate mandatory exceptions for noncommercial uses, an opt‐out clause for commercial uses, enhanced transparency measures, and a searchable portal for copyright owners. In conclusion, striking a delicate equilibrium between technological progress and the incentive for creative expression is of paramount importance. These suggested solutions aim to establish a harmonious foundation that nurtures innovation and creativity while honoring creators' rights, facilitating AI development, promoting transparency, and ensuring fair compensation for creators.Journal of World Intellectual Property00A hybrid model for TDM exceptions emerges, providing a nuanced solution to complex copyright issues in AI training, and recommended specific mechanisms to establish a harmonious foundation that nurtures innovation and creativity.The Journal of World Intellectual Property['Saliltorn Thongmeensuk']2024-04-19T00:00:00https://www.semanticscholar.org/paper/d04cd29f3faefa2017e12b8d9fa61b2e7cc11357 6f83affb4051438cf2b321cf8c15a2197a008199The proliferation of artificial intelligence (AI) technologies and chatbots has the potential to significantly reshape higher education. It is now imperative for stakeholders in this sector to grasp the fundamental aspects of AI technologies and understand their implications. This paper not only introduces basic AI concepts but also explains their specific applications and relevance in the higher education context. Moreover, it outlines the prospects of using AI technologies and chatbots to boost student engagement, presenting a synthesis of the opportunities available. Concurrently, we discuss the concerns and challenges associated with integrating AI into higher education settings. Several articles included in this special issue explore these opportunities and challenges from diverse viewpoints and within various contexts, across countries such as Australia, the United Kingdom, Vietnam, Cyprus, and GCC nations. Finally, we propose several avenues for future research aimed at enhancing student engagement through AI, charting a path forward for empirical evidence and practical application of AI and chatbots in enhancing student engagement.Journal of University Teaching and Learning Practice360Basic AI concepts are introduced and their specific applications and relevance in the higher education context are explained, and the prospects of using AI technologies and chatbots to boost student engagement are outlined, presenting a synthesis of the opportunities available.Journal of University Teaching and Learning Practice['Andy Nguyen', 'Mario Kremantzis', 'Aniekan Essien', 'Ilias Petrounias', 'Samira Hosseini']2024-04-19T00:00:00https://www.semanticscholar.org/paper/6f83affb4051438cf2b321cf8c15a2197a008199 f0329dd31996ff07dbed146b07c005bb77a029c5The rapid evolution of artificial intelligence (AI) technology and its integration into different fields, including language teaching, have inspired a growing body of literature. Scholars have particularly examined the integration of AI techniques into the teaching of English as a foreign language (EFL). However, it is becoming more challenging to identify the most suitable and efficient tools for implementation in EFL education because of the massive amount of innovation. Accordingly, in this systematic review, we examine the latest literature on the integration of AI into EFL teaching. The objective of this study is to explore how AI is being incorporated into this field, its impact on enhancing core English skills, and the potential pedagogical implications. A total of 284 articles published between 2019 and 2023 were initially identified from the most popular databases, including ERIC, ScienceDirect, JSTOR, ProQuest, and Scopus. Following pre-established inclusion and exclusion criteria, 13 papers were selected for the final review. The findings of this review highlight the benefits of using different AI techniques such as chatbots, automated writing evaluation, and writing assistance technologies in the instruction of fundamental EFL skills, namely speaking, listening, and writing. This review also provides useful insights and indicates some promising directions regarding the appropriate and effective application of AI in EFL classrooms.Asian Journal of Social Sciences and Management Studies310This systematic review examines the latest literature on the integration of AI into EFL teaching and highlights the benefits of using different AI techniques such as chatbots, automated writing evaluation, and writing assistance technologies in the instruction of fundamental EFL skills, namely speaking, listening, and writing.Asian Journal of Social Sciences and Management Studies['W. Almehmadi']2024-04-19T00:00:00https://www.semanticscholar.org/paper/f0329dd31996ff07dbed146b07c005bb77a029c5 74273039e458b43bcaceb9d913b31c7ce15ade5dInternational Journal of Surgery00International journal of surgery['Aryan Chopra', 'D. Rajput', 'Harshita Patel']2024-04-19T00:00:00https://www.semanticscholar.org/paper/74273039e458b43bcaceb9d913b31c7ce15ade5d 7e1d06c2fea4a6b1ff303bb23eac4346d1d24329NEJM AI00NEJM AI['C. Goldberg']2024-04-19T00:00:00https://www.semanticscholar.org/paper/7e1d06c2fea4a6b1ff303bb23eac4346d1d24329 a418317ffe9f4a80ecdbc1ec812f2107be2d18d0Researchers may be using generative artificial intelligence to help write 1%–5% of manuscripts Researchers may be using generative artificial intelligence to help write 1%–5% of manuscriptsScience00Science['Holly Else']2024-04-19T00:00:00https://www.semanticscholar.org/paper/a418317ffe9f4a80ecdbc1ec812f2107be2d18d0 2aa32f15373635ce4950833a7ea5e25f86916d7fMultimedia tools and applications620Multimedia Tools and Applications['Hanane Allioui', 'Azzeddine Allioui', 'Youssef Mourdi']2024-04-19T00:00:00https://www.semanticscholar.org/paper/2aa32f15373635ce4950833a7ea5e25f86916d7f 048e5c581b1676a9e56b6bdf056ed4dbb8b2f890Discussions on the advantages and risks of artificial intelligence (AI) in various fields are among the most relevant today. It is evident that proponents and opponents of its implementation have existed, exist, and will continue to exist. However, it is undeniable that with each scientific and technological advancement, humanity has the opportunity to improve or worsen the quality of its existence. Therefore, it is crucial to establish rules for developers and users who must be aware of the existing threats posed by the use of a particular tool, despite or in defiance of these limitations. This overview aims to explore how various aspects of our lives, including the legal sphere, are changing due to the widespread availability and increasing accessibility of artificial intelligence (AI). It seeks to clarify the role of the state as a regulator and implementer of relevant functions, the rules developers and users must adhere to, the aspects of AI usage already accessible to the legal sector, and how AI can assist in decision-making during criminal proceedings (while still leaving the leading role in decision-making and choice of alternatives to humans). It also discusses the prospects ahead and the myths that need to be overcome for progress. Through analysis and synthesis, it has been observed that threats from harmful AI are present, especially when it is deployed not for socially beneficial or human-centric purposes. Regulation of the AI sphere is crucial in all its aspects, starting from ethical considerations and ensuring an anthropocentric approach to AI development and usage, and ending with cutting-edge developments, including AI applications in various fields, such as the creation of new product designs like confectionery. Despite potential risks and threats, the use of artificial intelligence can have advantages, but careful regulation of this area is necessary. It is also noted that regulating AI to the detriment of progressive development should be avoided. It is argued that in the legal and law enforcement sectors, AI assistance can bring many benefits, but its use must be accompanied by a full awareness of the axiom that “AI systems for justice and law enforcement are considered ‘high risks’.” Therefore, before actively applying AI, especially in this realm, it is essential to establish clear boundaries and rules, and importantly, accountability for any harm caused. Such sensitive aspects as the integrity of human privacy, respect for human rights, and the like should prevail in this mechanism.Uzhhorod National University Herald. Series: Law01How various aspects of the authors' lives, including the legal sphere, are changing due to the widespread availability and increasing accessibility of artificial intelligence (AI) is explored, which seeks to clarify the role of the state as a regulator and implementer of relevant functions, the rules developers and users must adhere to, and how AI can assist in decision-making during criminal proceedings.Uzhhorod National University Herald. Series: Law['I. V. Basysta', 'Zh. V. Udovenko', 'M. -. M. A. Kulynych']2024-04-19T00:00:00https://www.semanticscholar.org/paper/048e5c581b1676a9e56b6bdf056ed4dbb8b2f890 5821c044982322e2aebfbadbf319cdf7f6e576bfHigher education is currently under a significant transformation due to the emergence of generative artificial intelligence (GenAI) technologies, the hype surrounding GenAI and the increasing influence of educational technology business groups over tertiary education. This commentary, prepared for the Special Issue of the Journal of University Teaching & Learning Practice (JUTLP) on “Enhancing student engagement using Artificial Intelligence (AI) and chatbots,” delves into the complex landscape of opportunities and threats that AI chatbots, including ChatGPT, introduce to the realm of higher education. We argue that while GenAI offers promise in enhancing pedagogy, research, administration, and student support, concerns around academic integrity, labour displacement, embedded biases, environmental sustainability, increased commercialisation, and regulatory gaps necessitate a critical approach. Our commentary advocates for the development of critical AI literacy among educators and students, emphasising the necessity to foster an environment of responsible innovation and informed use of AI. We posit that the successful integration of AI in higher education must be grounded in the principles of ethics, equity, and the prioritisation of educational aims and human values. By offering a critical and nuanced exploration of these issues, our commentary aims to contribute to the ongoing discourse on how higher education institutions can navigate the rise of GenAI, ensuring that technological advancements benefit all stakeholders while upholding core academic values.Journal of University Teaching and Learning Practice1922This commentary delves into the complex landscape of opportunities and threats that AI chatbots, including ChatGPT, introduce to the realm of higher education and advocates for the development of critical AI literacy among educators and students.Journal of University Teaching and Learning Practice['Juergen Rudolph', 'Fadhil Mohamed Mohamed Ismail', 'Stefan Popenici']2024-04-19T00:00:00https://www.semanticscholar.org/paper/5821c044982322e2aebfbadbf319cdf7f6e576bf 698136f978eaae46a096d13034152fde09569708 This paper discusses the wide application of artificial intelligence in the logistics industry, from intelligent distribution to intelligent transportation, through route optimization, automated warehouse management, demand forecasting, inventory management and other means, greatly improve logistics efficiency and reduce costs. Intelligent transport enhances traffic management, ensures road safety and optimizes resource utilization with road condition monitoring, autonomous driving and freight optimization technologies. In the face of challenges such as data security and privacy protection, artificial intelligence continues to promote the transformation of the logistics industry to a smarter and more efficient form. In the future, artificial intelligence will be further integrated into the logistics field, and through data-driven intelligent logistics management and applications in international logistics, more refined supply chain management, better resource scheduling and higher customer service standards will be achieved.Frontiers in Business, Economics and Management60Frontiers in Business, Economics and Management['Jialing Zhu']2024-04-19T00:00:00https://www.semanticscholar.org/paper/698136f978eaae46a096d13034152fde09569708 765c5585d31c86d74092fa6db2bf54207253aebbAcknowledging its potential on diversifying economy and attaining sustainable development, the Gulf Cooperation Council (GCC) countries, comprising of Bahrain, Kuwait, Oman, Qatar, Kingdom of Saudi Arabia, and United Arab Emirates, have been investing heavily on digital transformation and keeping pace with technological advancements. In particular, over the last years, with the unified efforts on transitioning to a knowledge society and enhancing educational outcomes, GCC countries have been demonstrating a strong dedication on integrating artificial intelligence in education (AIED). This systematic review investigates characteristics of artificial intelligence (AI) research in the region, identifying advantages and disadvantages of AI utilization in higher education, and exploring main issues accompanied with possible directions for the future. In the Scopus database, 32 studies were analyzed, all open access documents affiliated to a GCC country, having artificial intelligence and higher education, or related terminologies as keywords. Results revealed that AI applications were beneficial for institutions to improve educational outcomes, assist in decision-making, and advance institutional systems. No study reported negativity resulting from AI practices. However, important barriers were identified that hinder the full deployment of AI in higher education, including poor technology skills, inadequate technology infrastructure, resistance in leveraging traditional approaches in education, and challenges related to structural complexity of Arabic language. Future directions are proposed, offering opportunities for practitioners and research potential for scholars.Journal of University Teaching and Learning Practice800This systematic review investigates characteristics of artificial intelligence (AI) research in the region, identifying advantages and disadvantages of AI utilization in higher education, and exploring main issues accompanied with possible directions for the future.Journal of University Teaching and Learning Practice['Fatma Kayan Fadlelmula', 'Saba Mansoon Qadhi']2024-04-19T00:00:00https://www.semanticscholar.org/paper/765c5585d31c86d74092fa6db2bf54207253aebb 3d91da4a2e56470c855f99c934a1b10ee3aa4c58This study explores the utilization and perception of Artificial Intelligence (AI) tools among students in higher education. With the growing accessibility of AI technologies, their integration into educational settings presents a new frontier for enhancing learning experiences. This research adopts a mixed-methods approach, including surveys and interviews, to delve into how students employ AI tools and their perceived benefits and drawbacks of AI usage in the context of entrepreneurship education in a business school. The findings reveal a diverse range of AI applications, highlighting benefits such as increased productivity, personalized learning, and enhanced linguistic capability. However, concerns regarding academic integrity, over-reliance on AI, and the need for clear usage guidelines are also identified. This study contributes to the understanding of AI's role in higher education and provides much-needed empirical evidence of AI usage from students’ perspectives. Our findings underscore the importance of balanced, informed, and ethical use of AI tools in higher education.Journal of University Teaching and Learning Practice550The findings reveal a diverse range of AI applications, highlighting benefits such as increased productivity, personalized learning, and enhanced linguistic capability, but concerns regarding academic integrity, over-reliance on AI, and the need for clear usage guidelines are also identified.Journal of University Teaching and Learning Practice['Xue Zhou', 'Joanne Zhang', 'Ching Chan']2024-04-19T00:00:00https://www.semanticscholar.org/paper/3d91da4a2e56470c855f99c934a1b10ee3aa4c58 a747fab43beabb59e61c5bacd450bef92ab9033dArtificial Intelligence painting suddenly became popular at the beginning of the year, and many different artificial intelligence painting websites appeared on the Internet. Just like when photography emerged in the nineteenth century, it aroused the concern of the painting industry whether human painting would be replaced. This paper will mainly explain 7 paintings from painters like Fra Angelico and Jan Van Eyck with the same theme, the Annunciation, in different time periods, so as to analyze the uniqueness and irreplaceability of human painting. At the same time, this paper also points out a direction for the future development of artificial intelligence painting, that is, with its high efficiency and high technology to assist human painting, as a tool to refine painting, leading painting into a new era.Communications in Humanities Research00Communications in Humanities Research['Jiaxuan Cui']2024-04-19T00:00:00https://www.semanticscholar.org/paper/a747fab43beabb59e61c5bacd450bef92ab9033d fa960683099e55446b39b57c3d4d77fb07473014Abstract Objective Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures. Materials and Methods We searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software. Results Out of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation. Discussion and Conclusion AI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models’ development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.JAMIA Journal of the American Medical Informatics Association520AI-based methods can be used to optimize medication alerts in a hospital setting, however, reporting on models’ development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.Journal of the American Medical Informatics Association : JAMIA['Jetske Graafsma', 'Rachel M Murphy', 'E. M. van de Garde', 'Fatma Karapinar-Carkıt', 'H. J. Derijks', 'Rien H L Hoge', 'Joanna E Klopotowska', 'Patricia M. L. A. van den Bemt']2024-04-19T00:00:00https://www.semanticscholar.org/paper/fa960683099e55446b39b57c3d4d77fb07473014 7ad5f80d61263c0405ec235b171abb544d588d04Psychotherapy practice is a human endeavour. Research on the specific and non‐specific factors of treatment has helped crystallise its relevance and clinical impact. The challenges currently faced by the field revolve around ensuring access to evidence‐based treatments and enhancing their effectiveness. Digitally delivered formats of empirically supported treatments increase access while supporting the relevance of the treatment‐specific ingredients and the necessity for human guidance. Excitement surrounds the potential integration of novel artificial intelligence (AI) machine learning methods to advance psychotherapy effectiveness. The abundance of data in digitally delivered formats positions them well to harness the capabilities of AI. Recent work provides proof of concept in areas including detection and diagnosis, predicting outcomes, treatment adherence, remission and relapse. A potential risk emerges when applying machine learning methods, in which an overreliance on AI inferences may overshadow the human aspect of psychotherapy. The contrast is simple: we may over‐invest in the rationality and relevance of our AI inferences, blindly obeying the algorithmic counsel that may lead to unintended consequences, such as oversimplifying human complexity. This would amount to changing psychotherapy from a human‐centric to a techno‐centric endeavour, something we should steadily avoid. This perspective highlights the importance of balancing enthusiasm for AI advancements with a cautious approach. The discussion outlines the risks associated with overdependence on AI and provides reasons to avoid a scenario in which psychotherapy loses its human essence. In conclusion, the perspective suggests avenues for future research to prevent such a transformation and maintain the human‐centric nature of psychotherapy.Counselling and Psychotherapy Research210The discussion outlines the risks associated with overdependence on AI and provides reasons to avoid a scenario in which psychotherapy loses its human essence, and suggests avenues for future research to prevent such a transformation and maintain the human‐centric nature of psychotherapy.Counselling and Psychotherapy Research['Derek Richards']2024-04-19T00:00:00https://www.semanticscholar.org/paper/7ad5f80d61263c0405ec235b171abb544d588d04 ea57eb0811f9f848d66b2ee6d111c24bf2c1224fThe integration of artificial intelligence (AI) in additive manufacturing (AM) has shown great potential for revolutionizing the production of braille models. Braille models play a vital role in the lives of visually impaired individuals, providing them with access to important information and facilitating their education, employment, and daily activities. However, the traditional manufacturing process for Braille models is time-consuming and costly and limits design flexibility and customization. The use of artificial intelligence in 3D printing for Braille production overcomes these challenges and provides specific benefits such as improved manufacturing and design processes, increased accuracy and ease of use, increased production speed, economic benefits and the ability to flexibly adjust the 3D model to achieve the expected results. However, the integration of AI for braille in additive manufacturing poses several challenges, including technical, ethical, and legal challenges. This article analyzes the opportunities and challenges of integrating artificial intelligence into additive manufacturing for the development of 3D models with relief dot braille and provides an understanding of the potential advantages and obstacles of this innovative approach. In particular, it examines how artificial intelligence can improve the braille design process, the opportunities it offers for improved accuracy and usability, and the main obstacles to incorporating artificial intelligence into AM braille design. Thus, the potential of additive manufacturing to radically transform the additive manufacturing approach to creating braille models is further enhanced by its integration with artificial intelligence.InterConf00How artificial intelligence can improve the braille design process, the opportunities it offers for improved accuracy and usability, and the main obstacles to incorporating artificial intelligence into AM braille design are examined.InterConf['O. Khamula', 'Nikita Tarasov']2024-04-19T00:00:00https://www.semanticscholar.org/paper/ea57eb0811f9f848d66b2ee6d111c24bf2c1224f cb9ea1181acc551ed51597ade0d326e1cee5fda6Artificial intelligence (AI), the simulation of human intelligence processes by machines, is having a growing impact on healthcare [...]Applied Informatics90AI['Tim Hulsen']2024-04-19T00:00:00https://www.semanticscholar.org/paper/cb9ea1181acc551ed51597ade0d326e1cee5fda6 81c92d2746a8d7207a42d3f7c55ffa2030c5709cInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-19T00:00:00https://www.semanticscholar.org/paper/81c92d2746a8d7207a42d3f7c55ffa2030c5709c cf09d0bde6ef4c8db715fa59a0f1433e6aaadd5eNature Machine Intelligence51Nature Machine Intelligence['Justin N. Wood']2024-04-19T00:00:00https://www.semanticscholar.org/paper/cf09d0bde6ef4c8db715fa59a0f1433e6aaadd5e 205d40a64b1bbca8ab2d06cb3a15552b89d0a1a6Bipolar Disorders51Bipolar disorders["Russell Franco D'Souza", 'Shabbir Amanullah', 'Mary Mathew', 'R. Tandon', 'K. M. Surapaneni']2024-04-19T00:00:00https://www.semanticscholar.org/paper/205d40a64b1bbca8ab2d06cb3a15552b89d0a1a6 5871781b8b17604d90c2cdb8558d3908eac86d2fThis study adopts a discursive and analytical perspective to explore how technological advances are reconfiguring the dynamics of the global labour market, with special attention to the phenomenon of microwork. Microwork, characterised by short, fragmented tasks carried out through digital platforms and geographically distributed, has seen exponential growth, particularly in nations with lower economic development. This type of work shows a growing distinction between tasks of a complex and creative nature and those of a repetitive and monotonous nature that do not require advanced skills to perform. This differentiation can intensify wage disparities between developed and developing countries, as well as contributing to the precariousness of work in activities considered less complex and valued. The article highlights the emergence of unstable and poorly paid jobs that do not require specific qualifications and discusses their impact on social security systems in countries where labour regulations are insufficient. Using a theoretical-methodological approach, the research examines the role of artificial intelligence in the rise of micro-labour and its socio-economic implications. It concludes that despite the flexibility and short-term earning opportunities offered by microwork, it poses considerable challenges in terms of income security, workers’ rights, and social protection, emphasising the need for regulatory measures to mitigate its adverse effects on vulnerable communities.Human Resources Management and Services00It is concluded that despite the flexibility and short-term earning opportunities offered by microwork, it poses considerable challenges in terms of income security, workers’ rights, and social protection, emphasising the need for regulatory measures to mitigate its adverse effects on vulnerable communities.Human Resources Management and Services['E. Arruda', 'Durcelina Pimenta']2024-04-19T00:00:00https://www.semanticscholar.org/paper/5871781b8b17604d90c2cdb8558d3908eac86d2f d4410867efa7d014a00286e2f1659798aa5ff276Введение. По различным данным распространенность хронических неинфекционных заболеваний среди людей молодого возраста имеет тенденцию к росту. Зачастую это связано с изменениями образа жизни, увеличением стрессовых факторов, неправильным питанием, низкой физической активностью, наличием вредных привычек и пр. Известно, что патологическое действие основных факторов риска и формирование заболеваний начинаются в подростковом и молодом возрасте, в связи с чем особый интерес представляет разработка концепции их профилактики именно для этой группы населения. Вот почему раннее выявление и диагностика хронических неинфекционных заболеваний играют важную роль в предупреждении их прогрессирования, улучшении прогноза и качества жизни пациентов молодого возраста. Выявление факторов риска и определение степени их выраженности на раннем этапе развития заболеваний позволяют начать своевременную их коррекцию, что способствует профилактике развития осложнений. Однако низкая осведомленность о здоровье и недостаток медицинской грамотности среди этой группы населения являются препятствием для раннего выявления хронических неинфекционных заболеваний у молодежи. Одним из ключевых инструментов раннего выявления хронических неинфекционных заболеваний у молодых является скрининг, направленный на поиск факторов риска и первичных признаков заболевания у лиц без клинических проявлений. Скрининг может быть проведен с использованием различных методов, включая анкетирование. Внедрение автоматизированных систем скрининговой диагностики с использованием искусственного интеллекта вызывает неподдельный интерес среди молодежи. Более того, технологии искусственного интеллекта активно способствуют созданию условий для повышения качества услуг в сфере здравоохранения. Результаты. Нами была разработана и апробирована методология дистанционного многопрофильного анкетного скрининга хронических неинфекционных заболеваний для проведения первого этапа медицинского осмотра молодого контингента. Система выделила обследуемых с высокой, средней и низкой степенью риска, а также помогла собрать предварительный анамнез на каждого обследуемого, что способствует повышению качества принятия врачебного решения и снижает субъективную его составляющую, тем самым увеличивая время непосредственного осмотра пациента. Каждый обследуемый получил персонифицированные медицинские рекомендации по здоровому образу жизни с учетом выявленных у него факторов риска и степени их выраженности. Заключение. Настоящая разработка петербургских программистов и врачей позволяет оптимизировать оказание медико-профилактической помощи населению и повысить качество обследования пациентов. Background. According to various data, the prevalence of chronic non-communicable diseases among young people tends to increase. This is often due to lifestyle changes, increased stress factors, poor nutrition, low physical activity, bad habits, etc. It is known that the pathological effect of the main risk factors and the formation of diseases begins in adolescence and young age, and therefore, the development of a concept of their prevention is of particular interest for this particular population group. In this regard, early detection and diagnosis of chronic noncommunicable diseases play an important role in preventing their progression, improving the prognosis and quality of life of young patients. Identification of risk factors and determination of their severity at an early stage of the development of diseases allows them to begin their timely correction, which contributes to the prevention of complications. However, low health awareness and lack of medical literacy among this population group is an obstacle to the early detection of chronic noncommunicable diseases in young people. One of the key tools for early detection of chronic noncommunicable diseases in young people is screening aimed at identifying risk factors and primary signs of the disease in people without clinical manifestations. Screening can be carried out using various methods, including questionnaires. The introduction of automated screening diagnostic systems using artificial intelligence is of genuine interest among young people. Moreover, artificial intelligence technologies actively contribute to the creation of conditions for improving the quality of health services. Results. We have developed and tested a methodology for remote multidisciplinary questionnaire screening of chronic noncommunicable diseases for the first stage of medical examination of young people. The system has allocated a contingent of subjects with high, medium and low risk, and also helps to collect a preliminary medical history for each subject, which helps to improve the quality of medical decision-making and reduces its subjective component, thereby increasing the time for direct examination of the patient. Each subject received personalized medical recommendations on a healthy lifestyle, taking into account the identified risk factors and their severity. Conclusion. The present development of St. Petersburg programmers and doctors makes it possible to optimize the provision of medical and preventive care to the population and improve the quality of patient examination.Лечащий врач00Лечащий врач['П.В. Селивёрстов', 'В.Б. Гриневич', 'В.В. Шаповалов', 'Е.В. Крюков']2024-04-19T00:00:00https://www.semanticscholar.org/paper/d4410867efa7d014a00286e2f1659798aa5ff276 5d694d95e8f6c2961a2c130f092d4d0981fed443Feedback, particularly the formative or ‘feed-forward’ type is important for students in higher education to understand their errors and improve their expression and clarity of ideas. While technology-assisted feedback modes, e.g., audio or video are prevalent, ensuring their efficacy and succinctness, particularly for non-English-speaking background (NESB) educators can be challenging. This study investigates the attitudes and experiences of NESB educators in the School of Engineering of RMIT University, with a focus on their use of AI-assisted tools for providing feedback to students in higher education settings. Utilising a survey, the researchers examined how personal and linguistic attributes influenced feedback strategies and explored the educators' perspectives on integrating AI tools, such as ChatGPT and BARD, in their teaching practice and to enhance student engagement with the feedback they received. Through thematic analysis the findings reveal that personal background and linguistic proficiency significantly influenced the provision of feedback. Furthermore, even though educators had different levels of familiarity with AI-assisted tools, there was a general consensus on the potential utility of these tools for improving feedback provision. These will require targeted staff training, careful human oversight to ensure quality and avoid bias, and customised AI training to align feedback with individual teaching styles.Journal of University Teaching and Learning Practice400Investigating the attitudes and experiences of NESB educators in the School of Engineering of RMIT University finds that personal background and linguistic proficiency significantly influenced the provision of feedback, and there was a general consensus on the potential utility of these tools for improving feedback provision.Journal of University Teaching and Learning Practice['Toh Yen Pang', 'Alex Kootsookos', 'Chi-Tsun Cheng']2024-04-19T00:00:00https://www.semanticscholar.org/paper/5d694d95e8f6c2961a2c130f092d4d0981fed443 38f915d4f8e7fdc482186294e44230cda2bde1a7Purpose: Exploring AI techniques to improve the quality control of semiconductor production brings numerous advantages, such as enhanced precision, heightened efficiency, and early detection of issues, cost reduction, continuous enhancement, and a competitive edge. These benefits establish this area of research and its practical application in the semiconductor industry as valuable and worthwhile. Methodology: It aims to highlight the advancements, methodologies employed, and outcomes obtained thus far. By scrutinizing the current state of research, the primary objective of this paper is to identify significant challenges and issues associated with AI approaches in this domain. These challenges encompass data quality and availability, selecting appropriate algorithms, interpreting AI models, and integrating them with existing production systems. It is vital for researchers and industry professionals to understand these challenges to effectively address them and devise effective solutions. Moreover, it aims to lay the groundwork for future researchers, offering them a theoretical framework to devise potential solutions for enhancing quality control in semiconductor production. This review aims to drive a research on the semi-conductor production with the AI techniques to enhance the Quality control. Findings: The main findings to offer research is more efficient and accurate approach compared to traditional manual methods, leading to improved product quality, reduced costs, and increased productivity. Armed with this knowledge, future researchers can design and implement innovative AI-driven solutions to enhance quality control in semiconductor production. Unique contribution to theory, policy and practice: Overall, the theoretical foundation presented in this paper will aid researchers in developing novel solutions to improve quality control in the semiconductor industry, ultimately leading to enhanced product reliability and customer satisfaction.International Journal of Computing and Engineering190The theoretical foundation presented in this paper will aid researchers in developing novel solutions to improve quality control in the semiconductor industry, ultimately leading to enhanced product reliability and customer satisfaction.International Journal of Computing and Engineering['Rajat Suvra Das']2024-04-19T00:00:00https://www.semanticscholar.org/paper/38f915d4f8e7fdc482186294e44230cda2bde1a7 dc2090ccfed9ebde8a44905f9fd75803bfe19217The world of education in Indonesia is undergoing a transformation and is ready to welcome the Society 5.0 era. So it offers opportunities and challenges for educators in every educational unit. The purpose of this research is to improve learning efficiency by using AIED-based learning media in the Society 5.0 era. The method used in this research is a qualitative research method with library research data collection techniques. Qualitative analysis was conducted by selecting and examining the latest pedagogical development concepts in the era of society 5.0 and research results from journals in the last five years. If teachers have understood the opportunities and challenges in improving learning efficiency with AIED in the era of Society 5.0. If teachers have understood the opportunities and challenges in improving learning efficiency with AIED in the Society 5.0 era, the world of education will be more developed so that it can produce superior future generations. Teachers must continue to hone their skills regarding general science and technology.Progresiva : Jurnal Pemikiran dan Pendidikan Islam00The purpose of this research is to improve learning efficiency by using AIED-based learning media in the Society 5.0 era by selecting and examining the latest pedagogical development concepts in the era of society 5.0.Progresiva : Jurnal Pemikiran dan Pendidikan Islam['Cecep Sobar Rochmat', 'Riza Riza', 'Safitri Anggia Murni']2024-04-19T00:00:00https://www.semanticscholar.org/paper/dc2090ccfed9ebde8a44905f9fd75803bfe19217 05cd63acb8550e02a7853ed1713888dc122106e9Journal of Hospital Medicine110Journal of hospital medicine['S. Arora', 'S. Jariwala', 'S. Balsari']2024-04-19T00:00:00https://www.semanticscholar.org/paper/05cd63acb8550e02a7853ed1713888dc122106e9 25080e358671153eaa02e97a4fd58b9c6c5c8577Cogent Social Sciences270Cogent Social Sciences['Hifajatali Sayyed']2024-04-19T00:00:00https://www.semanticscholar.org/paper/25080e358671153eaa02e97a4fd58b9c6c5c8577 7d0a4bd7158abce813ba3fa6bf0f7f644846b36595th Annual Meeting German Society of Oto-Rhino-Laryngology, Head and Neck Surgery e. V., Bonn0095th Annual Meeting German Society of Oto-Rhino-Laryngology, Head and Neck Surgery e. V., Bonn['Luca Benjamin Helmbold', 'Nina Bilbeisi', 'Veit M. Hofmann', 'Savvas Kourtidis']2024-04-19T00:00:00https://www.semanticscholar.org/paper/7d0a4bd7158abce813ba3fa6bf0f7f644846b365 b2d07b1507590b4c247489e15d3edcf093954930 Released on Netflix, the most popular algorithm-oriented streaming service, The Social Dilemma (TSD) is a vivid manifestation of how the recent advancements in Artificial Intelligence and Machine Learning Algorithms (MLAs) have turned both to new species of post-digital, semio-cognitive power. Premised on the conception of MLAs as non-human intermediaries, this research endeavor proposes a novel post-digital ethnography of technologically-mediated algorithmic contexts and takes the challenge of examining MLAs as distributed, contested, and unbounded figures in the filmic narrative of this Netflix production. For the purpose, the paper employs post-digital cognitive-stylistic analytical tools, geared by van Leeuwen’s (de)-legitimation strategies, to showcase how MLAs, as socio-technical actors, are semio-cognitively materialized through spatio-temporal, narrative-immersive de-legitimating patterns. The examination of algorithms as socio-technical imaginary agents fully integrated within sociotechnical assemblages yields insightful findings. Delving deep into the multiple “posts” in the post-digital milieu of the film, the analysis affords valuable results that reframe, rename, and de-legitimate MLAs’ performative agency that is not only procedural-computational, but is socio-technical, semio-discursive, and cognitive-stylistic as well.International Journal of Legal Discourse730The paper employs post-digital cognitive-stylistic analytical tools, geared by van Leeuwen’s (de)-legitimation strategies, to showcase how MLAs are semio-cognitively materialized through spatio-temporal, narrative-immersive de-legitimating patterns.International Journal of Legal Discourse['Nashwa Elyamany']2024-04-19T00:00:00https://www.semanticscholar.org/paper/b2d07b1507590b4c247489e15d3edcf093954930 f6e0239b88c51154d8dec429e2484b52caf390b9As artificially intelligent systems become more anthropomorphic and pervasive, and their potential impact on humanity more urgent, discussions about the possibility of machine consciousness have significantly intensified, and it is sometimes seen as 'the holy grail'. Many concerns have been voiced about the ramifications of creating an artificial conscious entity. This is compounded by a marked lack of consensus around what constitutes consciousness and by an absence of a universal set of criteria for determining consciousness. By going into depth on the foundations and characteristics of consciousness, we propose five criteria for determining whether a machine is conscious, which can also be applied more generally to any entity. This paper aims to serve as a primer and stepping stone for researchers of consciousness, be they in philosophy, computer science, medicine, or any other field, to further pursue this holy grail of philosophy, neuroscience and artificial intelligence.700This paper aims to serve as a primer and stepping stone for researchers of consciousness, be they in philosophy, computer science, medicine, or any other field, to further pursue this holy grail of philosophy, neuroscience and artificial intelligence.['Nur Aizaan Anwar', 'C. Badea']2024-04-19T00:00:00https://www.semanticscholar.org/paper/f6e0239b88c51154d8dec429e2484b52caf390b9 f93f70fd1126fa8dc6ae15cfb2d2ab4c18d5e54aAbstract. It is established that Ukraine has begun to develop legislation on legal regulation of green bonds since 2020, which are the financial instrument of green investment in accordance with European standards. However, the Law of Ukraine “On Investment Activity” does not contain norms that would regulate various legal relations associated with green investments. Green bonds are the new subtype of securities in Ukraine, since the legislation on capital markets did not provide the existence of such securities until 2020. It is emphasized that issues concerning the status, problems and perspectives of legal regulation of green investment; legal nature of green investment relations; specific features of the elements of legal relations on green investment; liability for non-fulfillment or improper fulfillment of obligations by legal relations participants in green investment; content of the notion of green investments; forms of existence of green investments are poorly studied at theoretical and legal level. Besides, there is no common opinion among scholars in defining the content of the concept of “green investments’’. Participants of investment legal relations in the field of green investment are classified into two groups: 1) a person implementing an environmental project and 2) a person providing finances for an environmental project. The legislator did not limit the possibility of acquiring the status of a person who implements an environmental project or a person who provides finances for an environmental project exclusively to subjects of private or public law. On this basis, we can talk about the mixed legal nature of relations in the field of green investment. It has been identified that green bonds are one of possible green investment instruments, but not the only one. The author has offered the definition of the concept of “green investments”, which should be understood as a set of property and intellectual values that have a price impact and are placed by the subjects of investment activity in an environmental project, with the aim to achieve an ecological and social effect and profit.Baltic Journal of Legal and Social Sciences170Baltic Journal of Legal and Social Sciences['O. Sushch']2024-04-18T00:00:00https://www.semanticscholar.org/paper/f93f70fd1126fa8dc6ae15cfb2d2ab4c18d5e54a c360cfca13b40a6572df0e5ee64cf2161a7d3a4bJournal of IT Cases and Applications131Journal of Information Technology Case and Application Research['Rahul De’', 'Abhipsa Pal', 'Neena Pandey']2024-04-18T00:00:00https://www.semanticscholar.org/paper/c360cfca13b40a6572df0e5ee64cf2161a7d3a4b 962eb41c179d3510b540ff2bb080a98c92e037d2International Journal of Control320International Journal of Control['Lin Xu', 'Kun Fan', 'Minghan Wang', 'Dingjun Yao']2024-04-18T00:00:00https://www.semanticscholar.org/paper/962eb41c179d3510b540ff2bb080a98c92e037d2 9775bb6870d0c80881487be57de0a0f31cea08beThis paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.arXiv.org03ArXiv['Bertie Vidgen', 'Adarsh Agrawal', 'Ahmed M. Ahmed', 'Victor Akinwande', 'Namir Al-nuaimi', 'Najla Alfaraj', 'Elie Alhajjar', 'Lora Aroyo', 'Trupti Bavalatti', 'Borhane Blili-Hamelin', 'K. Bollacker', 'Rishi Bomassani', 'Marisa Ferrara Boston', "Sim'eon Campos", 'Kal Chakra', 'Canyu Chen', 'Cody Coleman', 'Zacharie Delpierre Coudert', 'Leon Derczynski', 'Debojyoti Dutta', 'Ian Eisenberg', 'J. Ezick', 'Heather Frase', 'Brian Fuller', 'Ram Gandikota', 'Agasthya Gangavarapu', 'Ananya Gangavarapu', 'J. Gealy', 'Rajat Ghosh', 'James Goel', 'Usman Gohar', 'Sujata Goswami', 'Scott A. Hale', 'Wiebke Hutiri', 'Joseph Marvin Imperial', 'Surgan Jandial', 'Nicholas C. Judd', 'Felix Juefei-Xu', 'Foutse Khomh', 'B. Kailkhura', 'Hannah Rose Kirk', 'Kevin Klyman', 'Chris Knotz', 'Michael Kuchnik', 'Shachi H. Kumar', 'Chris Lengerich', 'Bo Li', 'Zeyi Liao', 'Eileen Peters Long', 'Victor Lu', 'Yifan Mai', 'P. Mammen', 'Kelvin Manyeki', 'Sean McGregor', 'Virendra Mehta', 'Shafee Mohammed', 'Emanuel Moss', 'L. Nachman', 'Dinesh Jinenhally Naganna', 'Amin Nikanjam', 'Besmira Nushi', 'Luis Oala', 'Iftach Orr', 'Alicia Parrish', 'Çigdem Patlak', 'William Pietri', 'Forough Poursabzi-Sangdeh', 'Eleonora Presani', 'Fabrizio Puletti', 'Paul Röttger', 'Saurav Sahay', 'Tim Santos', 'Nino Scherrer', 'Alice Schoenauer Sebag', 'P. Schramowski', 'Abolfazl Shahbazi', 'Vin Sharma', 'Xudong Shen', 'Vamsi Sistla', 'Leonard Tang', 'Davide Testuggine', 'Vithursan Thangarasa', 'E. A. Watkins', 'Rebecca Weiss', 'Christoper A. Welty', 'Tyler Wilbers', 'Adina Williams', 'Carole-Jean Wu', 'Poonam Yadav', 'Xianjun Yang', 'Yi Zeng', 'Wenhui Zhang', 'Fedor Zhdanov', 'Jiacheng Zhu', 'Percy Liang', 'Peter Mattson', 'J. Vanschoren']2024-04-18T00:00:00https://www.semanticscholar.org/paper/9775bb6870d0c80881487be57de0a0f31cea08be d4790ab2fd66870bf663ff883953c39e2de12837PurposeThe authors argue that privacy is integral to the well-being of consumers and an essential component in not only corporate social responsibility (CSR) but what they term uniquely as social media responsibility (SMR). A conceptual framework is proposed that delineates the privacy issues companies should pay attention to in artificial intelligence (AI)-fueled social media environments.Design/methodology/approachThe authors review literature on privacy issues in social media and AI in the academic and practitioner literatures. Based on the review, arguments focus on the need for an SMR framework, proposing responsible use of consumer data that is attentive to consumers' privacy concerns.FindingsImplications from the framework are a path forward for social media companies to treat consumer data more fairly in this new environment. The framework has implications for companies to reduce potential harms to consumers and consider addressing their power and responsibility. With social media and AI transforming consumer behavior so profoundly, there are a variety of short- and long-term social implications.OriginalitySince AI tools are becoming integral to social media company activities, this research addresses the changing responsibilities social media companies have in securing consumers' data and enabling consumers the agency to protect their privacy effectively. The authors propose an SMR framework based on CSR research and AI tools employed by social media companies.Journal of Research in Interactive Marketing480An SMR framework is proposed that delineates the privacy issues companies should pay attention to in artificial intelligence (AI)-fueled social media environments, based on CSR research and AI tools employed by social media companies.Journal of Research in Interactive Marketing['Kristen L. Walker', 'George R. Milne']2024-04-18T00:00:00https://www.semanticscholar.org/paper/d4790ab2fd66870bf663ff883953c39e2de12837 224d78fc63a3012259441c74c1975e1dd4d55a07This review paper examines the potential applications of artificial intelligence (AI) in the development of an operating system (OS) that not only provides functionalities for software and hardware management, as well as common system services, but also integrates intelligent management capabilities. Advanced AI techniques such as expert systems, neural networks, pattern recognition, fuzzy logic prediction, and other AI features can be leveraged in the creation of such an AI-based OS. Features of an AI-based OS may include abstraction, associative AI thinking, perceptual intelligence, contextual imagination, context-specific search, context priming, and various other AI methodologies. Integrating and using smart agents based on large language models (LLMs) face tough challenges that affect how well they work. These challenges include problems like not scheduling and sharing resources efficiently for agent requests on the LLM, difficulties in keeping track of context when agents interact with the LLM, and the complexity of blending different agents with various skills and specialties. The rising number and complexity of agents often cause problems like bottlenecks and inefficient use of resources. To tackle this, our paper introduces AIOS, a unique operating system that incorporates a large language model. This innovation gives the operating system a kind of "intelligence" and brings us closer to achieving Artificial General Intelligence (AGI). AIOS is designed to better manage resources, help agents switch between tasks smoothly, allow multiple agents to work at the same time, offer tools for agents, and control access to the system. We explain how AIOS works, highlight the main issues it solves, and provide a basic overview of its design and implementationInternational Journal of Advanced Research in Science, Communication and Technology70International Journal of Advanced Research in Science, Communication and Technology['Ashutosh Kumar', 'Ankit Kumar Singh', 'Ashima Mehta']2024-04-18T00:00:00https://www.semanticscholar.org/paper/224d78fc63a3012259441c74c1975e1dd4d55a07 3fb687a5bfef0a927093e5f7dbd9090b99fc319dThis paper presents an innovative AI-enhanced irrigation system designed to optimize water management in agriculture. The system integrates advanced technologies such as IoT, sensor networks, and artificial intelligence algorithms to achieve precise and efficient irrigation scheduling. Leveraging real-time data from sensors including soil moisture, temperature, and humidity, combined with historical weather forecasts, the system employs a dynamic algorithm to make informed irrigation decisions. Experimental evaluation conducted over a week-long period using a garden rose as a test subject demonstrated the system's ability to maintain optimal soil moisture levels within the range of 60-75%, while significantly reducing water consumption compared to conventional methods. Simulation results further validated the system's effectiveness in predicting soil moisture levels and optimizing irrigation scheduling. Key metrics including enhanced crop output, reduced water usage, and adherence to sustainable farming practices were used to assess the superiority of the proposed model. Overall, the AI-enhanced irrigation system presents a promising solution for sustainable agriculture, offering improved water conservation, enhanced crop productivity, and efficient resource utilization.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)240Overall, the AI-enhanced irrigation system presents a promising solution for sustainable agriculture, offering improved water conservation, enhanced crop productivity, and efficient resource utilization.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)['Mansoor Hussain', 'Karthikeyan N', 'Ipsit Maurya', 'Subhratha Sinha']2024-04-18T00:00:00https://www.semanticscholar.org/paper/3fb687a5bfef0a927093e5f7dbd9090b99fc319d 415d2cce4ab2552e26ec86f4a405473439f2da48The digital divide describes disparities in access to and usage of digital tooling between social and economic groups. Emerging generative artificial intelligence tools, which strongly affect productivity, could magnify the impact of these divides. However, the affordability, multi-modality, and multilingual capabilities of these tools could also make them more accessible to diverse users in comparison with previous forms of digital tooling. In this study, we characterize spatial differences in U.S. residents' knowledge of a new generative AI tool, ChatGPT, through an analysis of state- and county-level search query data. In the first six months after the tool's release, we observe the highest rates of users searching for ChatGPT in West Coast states and persistently low rates of search in Appalachian and Gulf states. Counties with the highest rates of search are relatively more urbanized and have proportionally more educated, more economically advantaged, and more Asian residents in comparison with other counties or with the U.S. average. In multilevel models adjusting for socioeconomic and demographic factors as well as industry makeup, education is the strongest positive predictor of rates of search for generative AI tooling. Although generative AI technologies may be novel, early differences in uptake appear to be following familiar paths of digital marginalization.arXiv.org550This study characterize spatial differences in U.S. residents' knowledge of a new generative AI tool, ChatGPT, through an analysis of state- and county-level search query data and finds education is the strongest positive predictor of rates of search for generative AI tooling.ArXiv['Madeleine I. G. Daepp', 'Scott Counts']2024-04-18T00:00:00https://www.semanticscholar.org/paper/415d2cce4ab2552e26ec86f4a405473439f2da48 ac1fd99a668d9300418e2b0b40e74ac1fc04c84fThe rapid advancement of artificial intelligence (AI) and automation technologies has brought forth a confluence of challenges and opportunities in contemporary society. The rapid integration of AI into the workforce landscape has ushered in a transformative era, reshaping industries and challenging traditional work structures. This research paper delves into the critical dimension of Human-AI collaboration mainly and explores AI in the context of workforce transformation. Through a multidisciplinary approach, it explores the evolving relationship between humans and AI, emphasizing the synergy between human intelligence and machine capabilities. The study evaluates the impacts, advantages, and challenges of this collaboration and presents practical insights for fostering a harmonious coexistence between humans and AI. By analyzing the role of Human-AI collaboration in workforce transformation, this paper contributes to a deeper understanding of the future of work and the dynamic interplay between humans and intelligent machines in the digital age.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)160By analyzing the role of Human-AI collaboration in workforce transformation, this paper contributes to a deeper understanding of the future of work and the dynamic interplay between humans and intelligent machines in the digital age.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)['Viswanath Shenoi', 'Kandikanti Rohith', 'Goud', 'Popuri Sreeram', 'Shaik Nashal Afroz', 'Chekuri Lakshmi', 'Sai Varma']2024-04-18T00:00:00https://www.semanticscholar.org/paper/ac1fd99a668d9300418e2b0b40e74ac1fc04c84f 7ba7b5355043e628340d2f7761d0cb2e466bde69This paper centers on pushing the boundaries of AI and computer vision through an inventive approach to video captioning, leveraging the powerful Contrastive Language Image Pretraining (CLIP) model. In stark contrast to conventional methods, our proposed system ingeniously merges CLIP's unique ability to comprehend both textual and visual components, establishing a unified embedding space. This integration results in heightened context awareness and semantic depth, revolutionizing the comprehension of video content and outperforming traditional approaches. The AI-enhanced video captioning system represents a significant leap forward in the realm of generating precise and meaningful captions for a wide array of videos. The distinctive feature of our system lies in its preprocessing modules, adept at extracting intricate visual features and encoding nuanced textual descriptions. Additionally, we fine-tune the CLIP model on an extensive dataset of video-caption pairs, allowing it to capture complex relationships between visual and textual elements. This approach ensures that the model not only excels in providing accurate captions for diverse video content but also exhibits a remarkable ability to generalize well to previously unseen data. The result is a robust and contextually relevant captioning system that contributes to the evolution of AI applications in video understanding.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)260The proposed AI-enhanced video captioning system ingeniously merges CLIP's unique ability to comprehend both textual and visual components, establishing a unified embedding space and results in heightened context awareness and semantic depth, revolutionizing the comprehension of video content and outperforming traditional approaches.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)['Aravind R', 'Ashwin G', 'S. N.']2024-04-18T00:00:00https://www.semanticscholar.org/paper/7ba7b5355043e628340d2f7761d0cb2e466bde69 9d90a405a5a75b08513cd31207d3683d61e208edAs full AI-based automation remains out of reach in most real-world applications, the focus has instead shifted to leveraging the strengths of both human and AI agents, creating effective collaborative systems. The rapid advances in this area have yielded increasingly more complex systems and frameworks, while the nuance of their characterization has gotten more vague. Similarly, the existing conceptual models no longer capture the elaborate processes of these systems nor describe the entire scope of their collaboration paradigms. In this paper, we propose a new unified set of dimensions through which to analyze and describe human-AI systems. Our conceptual model is centered around three high-level aspects - agency, interaction, and adaptation - and is developed through a multi-step process. Firstly, an initial design space is proposed by surveying the literature and consolidating existing definitions and conceptual frameworks. Secondly, this model is iteratively refined and validated by conducting semi-structured interviews with nine researchers in this field. Lastly, to illustrate the applicability of our design space, we utilize it to provide a structured description of selected human-AI systems.arXiv.org500A new unified set of dimensions through which to analyze and describe human-AI systems is proposed, centered around three high-level aspects - agency, interaction, and adaptation - and is developed through a multi-step process.ArXiv['Steffen Holter', 'Mennatallah El-Assady']2024-04-18T00:00:00https://www.semanticscholar.org/paper/9d90a405a5a75b08513cd31207d3683d61e208ed 4b59f4abdc6a3b3cc0876fb44bcf893205f762bfIn response to the inefficiencies and limitations of manual faculty scheduling systems at educational institutions, the author develops OptiTime, an AI-powered scheduler meant to increase productivity. Existing systems have time-consuming processes, human errors, and a need for adaptability to changing academic needs. OptiTime revolutionizes faculty scheduling using artificial intelligence, data-driven approaches, and robust algorithms. OptiTime adapts to dynamic academic environments by employing a thorough technique that includes data collecting, feature engineering, algorithm selection, model training, and dynamic scheduling with reinforcement learning and evolutionary algorithms. The results and analysis propose that it has a considerable impact, with gains in scheduling efficiency, conflict resolution, and teacher satisfaction over traditional techniques. OptiTime cuts overall scheduling time from 120 to 45 hours, conflicts from 25 to 5, and room utilization from 65% to 85%. The research highlights OptiTime's transformative role in resolving the constraints of manual scheduling systems by providing a highly advanced solution for optimal faculty scheduling in educational institutions.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)160OptiTime revolutionizes faculty scheduling using artificial intelligence, data-driven approaches, and robust algorithms, and adapts to dynamic academic environments by employing a thorough technique that includes data collecting, feature engineering, algorithm selection, model training, and dynamic scheduling with reinforcement learning and evolutionary algorithms.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)['Ch.Venkateswara Rao', 'K. V. Sravani', 'Lalam Sai Kumar', 'Malla Nanda Kishore', 'Manukonda Devendra Sri Venkata Ramana']2024-04-18T00:00:00https://www.semanticscholar.org/paper/4b59f4abdc6a3b3cc0876fb44bcf893205f762bf c45d0113998f82a1713b598dff7a65a1d44eed1aRecent In-IDE AI coding assistant tools (ACATs) like GitHub Copilot have significantly impacted developers' coding habits. While some studies have examined their effectiveness, there lacks in-depth investigation into the actual assistance process. To bridge this gap, we simulate real development scenarios encompassing three typical types of software development tasks and recruit 27 computer science students to investigate their behavior with three popular ACATs. Our goal is to comprehensively assess ACATs' effectiveness, explore characteristics of recommended code, identify reasons for modifications, and understand users' challenges and expectations. To facilitate the study, we develop an experimental platform that includes a data collection plugin for VSCode IDE and provides functions for screen recording, code evaluation, and automatic generation of personalized interview and survey questions. Through analysis of the collected data, we find that ACATs generally enhance task completion rates, reduce time, improve code quality, and increase self-perceived productivity. However, the improvement is influenced by both the nature of coding tasks and users' experience level. Notably, for experienced participants, the use of ACATs may even increase completion time. We observe that"edited line completion"is the most frequently recommended way, while"comments completion"and"string completion"have the lowest acceptance rates. The primary reasons for modifying recommended code are disparities between output formats and requirements, flawed logic, and inconsistent code styles. In terms of challenges and expectations, optimization of service access and help documentation is also concerned by participants except for functionality and performance. Our study provides valuable insights into the effectiveness and usability of ACATs, informing further improvements in their design and implementation.arXiv.org570Through analysis of the collected data, it is found that ACATs generally enhance task completion rates, reduce time, improve code quality, and increase self-perceived productivity, however, the improvement is influenced by both the nature of coding tasks and users' experience level.ArXiv['Xin Tan', 'Xiao Long', 'Xianjun Ni', 'Yinghao Zhu', 'Jing Jiang', 'Li Zhang']2024-04-18T00:00:00https://www.semanticscholar.org/paper/c45d0113998f82a1713b598dff7a65a1d44eed1a 1bf74e900525bc11cf88bc1e7ffff5daac716b4dArtificial intelligence (AI) has the potential to revolutionize healthcare, for example via decision support systems, computer vision approaches, or AI-based prevention tools. Initial results from AI applications in healthcare show promise but are rarely translated into clinical practice successfully and ethically. This occurs despite an abundance of “Trustworthy AI” guidelines. How can we explain the translational gaps of AI in healthcare? This paper offers a fresh perspective on this problem, showing that failing translation of healthcare AI markedly arises from a lack of an operational definition of “trust” and “trustworthiness”. This leads to (a) unintentional misuse concerning what trust (worthiness) is and (b) the risk of intentional abuse by industry stakeholders engaging in ethics washing. By pointing out these issues, we aim to highlight the obstacles that hinder translation of Trustworthy medical AI to practice and prevent it from fulfilling its unmet promises.Frontiers in Digital Health590It is shown that failing translation of healthcare AI markedly arises from a lack of an operational definition of “trust” and “trustworthiness”, which leads to unintentional misuse concerning what trust (worthiness) is and the risk of intentional abuse by industry stakeholders engaging in ethics washing.Frontiers in Digital Health['Valerie K. Bürger', 'Julia Amann', 'Cathrine K. T. Bui', 'Jana Fehr', 'V. Madai']2024-04-18T00:00:00https://www.semanticscholar.org/paper/1bf74e900525bc11cf88bc1e7ffff5daac716b4d d4a7840820acb472e8dee9de17443ceb9ac2c625Extant literature points to how the risk of discrimination is intrinsic to AI systems owing to the dependence on training data and the difficulty of post hoc algorithmic auditing. Transparency and auditability limitations are problematic both for companies’ prevention efforts and for government oversight, both in terms of how artificial intelligence (AI) systems function and how large‐scale digital platforms support recruitment processes. This article explores the risks and users’ understandings of discrimination when using AI and automated decision‐making (ADM) in worker recruitment. We rely on data in the form of 110 completed questionnaires with representatives from 10 of the 50 largest recruitment agencies in Sweden and representatives from 100 Swedish companies with more than 100 employees (“major employers”). In this study, we made use of an open definition of AI to accommodate differences in knowledge and opinion around how AI and ADM are understood by the respondents. The study shows a significant difference between direct and indirect AI and ADM use, which has implications for recruiters’ awareness of the potential for bias or discrimination in recruitment. All of those surveyed made use of large digital platforms like Facebook and LinkedIn for their recruitment, leading to concerns around transparency and accountability—not least because most respondents did not explicitly consider this to be AI or ADM use. We discuss the implications of direct and indirect use in recruitment in Sweden, primarily in terms of transparency and the allocation of accountability for bias and discrimination during recruitment processes.Social Inclusion610There is a significant difference between direct and indirect AI and ADM use in recruitment in Sweden, which has implications for recruiters’ awareness of the potential for bias or discrimination in recruitment.Social Inclusion['Stefan Larsson', 'James White', 'Claire Ingram Bogusz']2024-04-18T00:00:00https://www.semanticscholar.org/paper/d4a7840820acb472e8dee9de17443ceb9ac2c625 e0dcbd53f006bdbd90d34988baefc0227c4b5bf6In the era of rapid technological advancement, Computer Vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and Machine Learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer Vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer Vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer Vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer Vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. Applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer Vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer Vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)220This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer Vision, exploring the historical evolution from early image processing to the current era of deep learning.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)['Meena Laad', 'Ratan Maurya', 'Najeeb Saiyed']2024-04-18T00:00:00https://www.semanticscholar.org/paper/e0dcbd53f006bdbd90d34988baefc0227c4b5bf6 0ae54ece660635d7cc25352243a5f5fe1b44bc74This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. It suggests leveraging foundational models enhanced by domain-specific knowledge to overcome these issues. The paper advocates for creating open-source legal AI systems to improve accuracy, transparency, and narrative diversity, addressing general AI's shortcomings in legal contexts.arXiv.org320This study evaluates the performance of general-purpose AI in legal question-answering tasks, highlighting significant risks to legal professionals and clients, and suggests leveraging foundational models enhanced by domain-specific knowledge to overcome general AI's shortcomings in legal contexts.ArXiv['R. Bhambhoria', 'Samuel Dahan', 'Jonathan Li', 'Xiaodan Zhu']2024-04-18T00:00:00https://www.semanticscholar.org/paper/0ae54ece660635d7cc25352243a5f5fe1b44bc74 4a3510a89ce625dda4e38dbf64bf00b235e4df81As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of AI systems rarely accounts for how AI and users shape one another. In this position paper, we argue for the development of formal interaction models which mathematically specify how AI and users shape one another. Formal interaction models can be leveraged to (1) specify interactions for implementation, (2) monitor interactions through empirical analysis, (3) anticipate societal impacts via counterfactual analysis, and (4) control societal impacts via interventions. The design space of formal interaction models is vast, and model design requires careful consideration of factors such as style, granularity, mathematical complexity, and measurability. Using content recommender systems as a case study, we critically examine the nascent literature of formal interaction models with respect to these use-cases and design axes. More broadly, we call for the community to leverage formal interaction models when designing, evaluating, or auditing any AI system which interacts with users.arXiv.org890This position paper argues for the development of formal interaction models which mathematically specify how AI and users shape one another, and calls for the community to leverage formal interaction models when designing, evaluating, or auditing any AI system which interacts with users.ArXiv['Sarah Dean', 'Evan Dong', 'Meena Jagadeesan', 'Liu Leqi']2024-04-18T00:00:00https://www.semanticscholar.org/paper/4a3510a89ce625dda4e38dbf64bf00b235e4df81 4627ada62fb22fce6b785788e1a05dd5a6e1606eArtificial Intelligence (AI) tools are increasingly being used in education for various purposes. In particular, AI chatbots such as ChatGPT, with their user-friendly interfaces are being explored in education to co-create teaching materials, provide advice and guidance to educators, simulate classroom scenarios, and offer personalized recommendations to students on how to study and approach subjects. With all the enthusiasm for these new opportunities, one should be aware of the risks due to potential biases in the generated content or the responses. These biases can be associated with factors such as gender, race, religion, or political orientation. As a consequence, educators who are using AI chatbots to (co-)create teaching materials need to have the knowledge and the strategies to mitigate such biases. This paper focuses on one particular type of bias, namely gender bias, and on specific disciplines, namely Science, Technology, Engineering and Mathematics (STEM). Gender bias in STEM education is particularly problematic because it may reinforce existing stereotypes about girls and women in STEM and contribute to their underrepresentation in STEM fields. To raise awareness of these risks of gender bias in AI-co-created STEM teaching materials, this paper identifies risks of gender bias by analysing potential usage patterns of AI chatbots by educators when creating teaching materials. An example of such a risk is if the AI chatbot generates educational materials that primarily portray men as STEM professionals and underrepresent women. This would exacerbate the lack of female role models in STEM. Therefore, strategies are developed that educators can apply to mitigate these risks. These strategies will be demonstrated using practical examples. This will allow them to break the vicious cycle of perpetuating stereotypes in STEM education. In addition, these examples demonstrate how AI chatbots can be used to make STEM education more inclusive, which may include co-creating educational materials tailored to individual interests and learning styles.International Conference on Gender Research00Risks of gender bias are identified by analysing potential usage patterns of AI chatbots by educators when creating teaching materials and how AI chatbots can be used to make STEM education more inclusive, which may include co-creating educational materials tailored to individual interests and learning styles.International Conference on Gender Research['Claudia Hess', 'Sibylle Kunz']2024-04-18T00:00:00https://www.semanticscholar.org/paper/4627ada62fb22fce6b785788e1a05dd5a6e1606e be80f57e0f4d49dae7358729ef62b5edc706b420npj Digit. Medicine510The findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.NPJ Digital Medicine['J. Kwong', 'Jeremy Wu', 'Shamir Malik', 'A. Khondker', 'Naveen Gupta', 'Nicole Bodnariuc', 'Krishnateja Narayana', 'Mikail Malik', 'T. H. Kwast', 'Alistair E. W. Johnson', 'AlexandreR. Zlotta', 'Girish S. Kulkarni']2024-04-18T00:00:00https://www.semanticscholar.org/paper/be80f57e0f4d49dae7358729ef62b5edc706b420 bb4544b45d28180d609fe6d9f4d91e6e6a573e9aLarge Language Models (LLMs) like ChatGPT can help teachers to tailor learning tasks for their students, combining learning objectives and storytelling to raise interest in the subject. AI-based learning task design can help to support competency-based learning, especially for girls in STEM courses like computer science, where otherwise the “Leaky STEM pipeline” (Speer 2023) leads to a constant loss of female students over school time. LLMs support many steps of the creation cycle of learning tasks. One important step is the feedback process between teachers and students during and after solving the tasks. Students need person-related as well as process-related feedback to make progress. Sometimes problems occur when teachers give feedback in a way that embarrasses or hurts the students. Especially female students often need more confirmation to make them aware of their progress, but studies show that boys demand and get more attention by teachers in this situation. This is one of the many reasons why girls lose motivation and interest in STEM courses over time. Since male and female teachers differ in expressing feedback without being aware of it, it is necessary to raise their consciousness. LLMs like ChatGPT can be used in two scenarios here. The first scenario is helping teachers to formulate objective feedback in a way that is adequate and understandable for the target group – e.g., young girls or boys - in a specific situation. The second scenario is training the teacher in a Socratic way, where the LLM simulates a student receiving the feedback and reacting to it according to established communication models like the Four Ears-model by Schulz von Thun (Schulz von Thun 1981) or Berne’s Transactional Analysis (Berne, 1964). This case study provides examples and prompting schemes for both scenarios and discusses the fragile balance between avoiding gender stereotypes in LLMs and giving more helpful and sustainable feedback for female students to foster self-esteem and competency-awareness.International Conference on Gender Research190The fragile balance between avoiding gender stereotypes in LLMs and giving more helpful and sustainable feedback for female students to foster self-esteem and competency-awareness is discussed.International Conference on Gender Research['Sibylle Kunz', 'Adrienne Steffen']2024-04-18T00:00:00https://www.semanticscholar.org/paper/bb4544b45d28180d609fe6d9f4d91e6e6a573e9a f98463e965a27d9d4a0dd77f773ab441998b5547Given the sheer volume of surgical procedures and the significant rate of postoperative fatalities, assessing and managing surgical complications has become a critical public health concern. Existing artificial intelligence (AI) tools for risk surveillance and diagnosis often lack adequate interpretability, fairness, and reproducibility. To address this, we proposed an Explainable AI (XAI) framework designed to answer five critical questions: why, why not, how, what if, and what else, with the goal of enhancing the explainability and transparency of AI models. We incorporated various techniques such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), counterfactual explanations, model cards, an interactive feature manipulation interface, and the identification of similar patients to address these questions. We showcased an XAI interface prototype that adheres to this framework for predicting major postoperative complications. This initial implementation has provided valuable insights into the vast explanatory potential of our XAI framework and represents an initial step towards its clinical adoption.00An XAI interface prototype that adheres to this framework for predicting major postoperative complications is showcased, providing valuable insights into the vast explanatory potential of this proposed framework and represents an initial step towards its clinical adoption.['Yuanfang Ren', 'Chirayu Tripathi', 'Ziyuan Guan', 'Ruilin Zhu', 'Victoria Hougha', 'Yingbo Ma', 'Zhenhong Hu', 'Jeremy A. Balch', 'Tyler J. Loftus', 'Parisa Rashidi', 'B. Shickel', 'T. Ozrazgat-Baslanti', 'A. Bihorac']2024-04-18T00:00:00https://www.semanticscholar.org/paper/f98463e965a27d9d4a0dd77f773ab441998b5547 21568138fa0c0f284034895a44e1999279d5a6ecCritical Care41Critical Care['Kunming Cheng', 'Haiyang Wu']2024-04-18T00:00:00https://www.semanticscholar.org/paper/21568138fa0c0f284034895a44e1999279d5a6ec ef9e86131e8865bcdc7b6985fbaef4a7fd65387dTo share or not to share? Privacy-preserving AI in medicine00To share or not to share? Privacy-preserving AI in medicine['Jan Baumbach']2024-04-18T00:00:00https://www.semanticscholar.org/paper/ef9e86131e8865bcdc7b6985fbaef4a7fd65387d 62bf22f4a053b5355cc8f2b22130df49e802c220ACM Southeast Regional Conference10{'pages': '95-103'}['Md Romyull Islam', 'Long Vu', 'Nobel Dhar', 'Bobin Deng', 'Kun Suo']2024-04-18T00:00:00https://www.semanticscholar.org/paper/62bf22f4a053b5355cc8f2b22130df49e802c220 f0c39fecbd932ffa353769ce2cf3b67aa3fcc797The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was publishedBritish medical journal910The BMJ['Gary S. Collins', 'K. Moons', 'Paula Dhiman', 'Richard D. Riley', 'A. L. Beam', 'B. Calster', 'Marzyeh Ghassemi', 'Xiaoxuan Liu', 'Johannes B Reitsma', 'M. Smeden', 'A. Boulesteix', 'J. Camaradou', 'L. A. Celi', 'S. Denaxas', 'A. Denniston', 'Ben Glocker', 'Robert M Golub', 'Hugh Harvey', 'G. Heinze', 'Michael M Hoffman', 'A. Kengne', 'Emily Lam', 'Naomi Lee', 'Elizabeth W Loder', 'Lena Maier-Hein', 'Bilal A. Mateen', 'M. Mccradden', 'Lauren Oakden-Rayner', 'Johan Ordish', 'Richard Parnell', 'Sherri Rose', 'Karandeep Singh', 'L. Wynants', 'P. Logullo', 'Abhishek Gupta', 'Adrian Barnett', 'Adrian Jonas', 'Agathe Truchot', 'Aiden Doherty', 'Alan Fraser', 'Alex Fowler', 'Alex Garaiman', 'Alistair Denniston', 'Amin Adibi', 'André Carrington', 'Andre Esteva', 'Andrew Althouse', 'Andrew Soltan', 'A. Appelt', 'Ari Ercole', 'Armando Bedoya', 'B. Vasey', 'B. Desiraju', 'Barbara Seeliger', 'B. Geerts', 'Beatrice Panico', 'Benjamin Fine', 'Benjamin Goldstein', 'B. Gravesteijn', 'Benjamin Wissel', 'B. Holzhauer', 'Boris Janssen', 'Boyi Guo', 'Brooke Levis', 'Catey Bunce', 'Charles Kahn', 'Chris Tomlinson', 'Christopher Kelly', 'Christopher Lovejoy', 'Clare McGenity', 'Conrad Harrison Constanza', 'Andaur Navarro', 'D. Nieboer', 'Dan Adler', 'Danial Bahudin', 'Daniel Stahl', 'Daniel Yoo', 'Danilo Bzdok', 'Darren Dahly', 'Darren Treanor', 'David Higgins', 'David McClernon', 'David Pasquier', 'David Taylor', 'Declan O’Regan', 'Emily Bebbington', 'Erik Ranschaert', 'E. Kanoulas', 'Facundo Diaz', 'Felipe Kitamura', 'Flavio Clesio', 'Floor van Leeuwen', 'Frank Harrell', 'Frank Rademakers', 'Ga¨el Varoquaux', 'Garrett S Bullock', 'Gary Weissman', 'George Fowler', 'George Kostopoulos', 'Georgios Lyratzaopoulos', 'Gianluca Di', 'G. Pellino', 'Girish Kulkarni', 'G. Zoccai', 'Glen Martin', 'Gregg Gascon', 'Harlan Krumholz', 'H. Sufriyana', 'Hongqiu Gu', 'H. Bogunović', 'Hui Jin', 'Ian Scott', 'Ijeoma Uchegbu', 'Indra Joshi', 'Irene M. Stratton', 'James Glasbey', 'Jamie Miles', 'Jamie Sergeant', 'Jan Roth', 'Jared Wohlgemut', 'Javier Carmona Sanz', 'J. Bibault', 'Jeremy Cohen', 'Ji Eun Park', 'Jie Ma', 'Joel Amoussou', 'John Pickering', 'J. Ensor', 'J. Flores-Guerrero', 'Joseph LeMoine', 'Joshua Bridge', 'Josip Car', 'Junfeng Wang', 'Keegan Korthauer', 'Kelly Reeve', 'L. Ación', 'Laura J. Bonnett', 'Lief Pagalan', 'L. Buturovic', 'L. Hooft', 'Maarten Luke Farrow', 'Van Smeden', 'Marianne Aznar', 'Mario Doria', 'M. Gilthorpe', 'M. Sendak', 'M. Fabregate', 'Matthew Sperrin', 'Matthew Strother', 'Mattia Prosperi', 'Menelaos Konstantinidis', 'Merel Huisman', 'Michael O Harhay', 'Miguel Angel Luque', 'M. Mansournia', 'Munya Dimairo', 'Musa Abdulkareem', 'M. Nagendran', 'Niels Peek', 'Nigam Shah', 'Nikolas Pontikos', 'Nurulamin Noor', 'Oilivier Groot', 'Páll Jónsson', 'Patrick Bossuyt', 'Patrick Lyons', 'Patrick Omoumi', 'Paul Tiffin', 'Peter Austin', 'Q. Noirhomme', 'Rachel Kuo', 'Ram Bajpal', 'Ravi Aggarwal', 'Richiardi Jonas', 'Robert Platt', 'Rohit Singla', 'Roi Anteby', 'Rupa Sakar', 'Safoora Masoumi', 'Sara Khalid', 'Saskia Haitjema', 'Seong Park', 'Shravya Shetty', 'Stacey Fisher', 'Stephanie Hicks', 'Susan Shelmerdine', 'Tammy Clifford', 'Tatyana Shamliyan', 'Teus Kappen', 'T. Leiner', 'Tim Liu', 'Tim Ramsay', 'Toni Martinez', 'Uri Shalit', 'Valentijn de Jong', 'Valentyn Bezshapkin', 'V. Cheplygina', 'Victor Castro', 'V. Sounderajah', 'Vineet Kamal', 'V. Harish', 'Wim Weber', 'W. Amsterdam', 'Xioaxuan Liu', 'Zachary Cohen', 'Zakia Salod', 'Zane Perkins']2024-04-18T00:00:00https://www.semanticscholar.org/paper/f0c39fecbd932ffa353769ce2cf3b67aa3fcc797 27f7c8fa360f69888f7bd922c5a9c5a7681d52adPharmacogenomics showcases the aim of precision medicine, which strives to customize treatments for individuals and specific populations. This field delves into exploring how an individuals DNA influences their response to medications. A persons genetic composition can impact the likelihood of experiencing reactions or determining the effectiveness of a medication. By providing insights into the safety and effectiveness of drug therapies pharmacogenomics holds potential for significantly enhancing health outcomes. Through advancements in targeted therapies we can precisely target abnormalities that trigger tumor growth in patients. For instance IGF1R (Insulin like Growth Factor 1 Receptor) which belongs to the tyrosine kinase receptor family plays a crucial role in promoting cell growth, survival and proliferation across different types of cancers. The overexpression of IGF1R has been observed in cancer types indicating its involvement in fueling continuous growth and survival of cancer cells. Targeting IGF1R helps address the dysregulation of this receptor within cancer cells. Artificial Intelligence (AI) comes into play by enabling prediction of suitable drugs based on a patients genomic profile thereby reducing adverse effects and improving treatment effectiveness. Parallel, here has been growing concern regarding model explanation due, to the opaque nature of model predictions. This is particularly important when it comes to modeling drug responses. In our research paper we have employed AI to gain a clear understanding of the prediction model and the factors that affect its results. The findings show that lower valued counts of YAP_pS127_Caution protein tend to negatively impact the output. Similarly lower values of YAP_pS127_Caution protein and higher valued counts of YAP_pS127 _Caution protein, Xanthine, Tyrosine tends to positively impact the output. This helps as an aiding reference in knowing which feature of an unknown cell line should be focused to know drug response prior medication of BMS-536924, BMS-754807 drug.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)200AI is employed to gain a clear understanding of the prediction model and the factors that affect its results, which show that lower valued counts of YAP_pS127_Caution protein tend to negatively impact the output, and that higher valued counts of YAP_pS127_Caution protein tend to positively impact the output.2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)['st J.Janiel', 'Selvi Rajendran']2024-04-18T00:00:00https://www.semanticscholar.org/paper/27f7c8fa360f69888f7bd922c5a9c5a7681d52ad ac2673637d978195ac8757f53b125f7a30d23bb0World Journal of Science Technology and Sustainable Development00World Journal of Science, Technology and Sustainable Development['Prof. Beverlee B. Anderson', 'Dr Aaron T. McDonald', 'Dr Catalin Ratiu']2024-04-18T00:00:00https://www.semanticscholar.org/paper/ac2673637d978195ac8757f53b125f7a30d23bb0 952826e52e320a601b716e4712b8aff34f8db3c2Generative artificial intelligence (genAI) has shown immense potential for revolutionising education. Revolutions are disruptive, however. They carry potential for both positive change and high-stakes failure. In higher education, the implications of the genAI revolution for educational assessment are high profile and high stakes. Assessment is the primary mechanism for determining students’ outcome achievement. Quality of assessment and the resulting data determines the legitimacy of progressing students through their formal study and conferral of degrees. Will genAI enhance or impede these essential educational functions? This Trendsetter talk will address this question through exploring dynamic tensions around the relationship of genAI to assessment in higher education. The speaker will address the possibilities genAI presents for repositioning students in critical, authentic ways relative to assessment. We will also explore potential advantages posed by genAI for teachers, such as enhancing efficiency in feedback and marking. Conversely, we will identify and discuss how to offset the very real problems posed by genAI to academic integrity, ethical practice, and validity of assessment results. The talk will conclude with suggestions on pathways we may take to increase the likelihood of this as a successful revolution and minimise high-staked failures.   Bio   Chris is an associate professor and Enterprise Research Fellow in Education Futures, with University of South Australia. Chris’ work advances theoretical and empirical modelling of the interaction of assessment, feedback, and technology in higher education contexts. His research has attracted 2.9m AUD in competitive funding and he has authored over sixty publications, principally in high-impact journals. Chris heads the Change in Complex Systems Research Stream at The Centre for Change and Complexity in Learning (C3L). In his current position, Chris focuses on developing research projects and researcher capacities, especially among early-career researchers and teaching-focused academics.Pacific Journal of Technology Enhanced Learning00The speaker will address the possibilities genAI presents for repositioning students in critical, authentic ways relative to assessment and identify and discuss how to offset the very real problems posed by genAI to academic integrity, ethical practice, and validity of assessment results.Pacific Journal of Technology Enhanced Learning['Christopher Deneen']2024-04-18T00:00:00https://www.semanticscholar.org/paper/952826e52e320a601b716e4712b8aff34f8db3c2 3869e76f259520c35516d761cf5ebbc269bc1faaThis study presents an unsupervised domain adaptation method aimed at autonomously generating image masks outlining regions of interest (ROIs) for differentiating breast lesions in breast ultrasound (US) imaging. Our semi-supervised learning approach utilizes a primitive model trained on a small public breast US dataset with true annotations. This model is then iteratively refined for the domain adaptation task, generating pseudo-masks for our private, unannotated breast US dataset. The dataset, twice the size of the public one, exhibits considerable variability in image acquisition perspectives and demographic representation, posing a domain-shift challenge. Unlike typical domain adversarial training, we employ downstream classification outcomes as a benchmark to guide the updating of pseudo-masks in subsequent iterations. We found the classification precision to be highly correlated with the completeness of the generated ROIs, which promotes the explainability of the deep learning classification model. Preliminary findings demonstrate the efficacy and reliability of this approach in streamlining the ROI annotation process, thereby enhancing the classification and localization of breast lesions for more precise and interpretable diagnoses.arXiv.org480An unsupervised domain adaptation method aimed at autonomously generating image masks outlining regions of interest (ROIs) for differentiating breast lesions in breast ultrasound (US) imaging finds the classification precision to be highly correlated with the completeness of the generated ROIs, which promotes the explainability of the deep learning classification model.ArXiv['Ting-Ruen Wei', 'Michele Hell', 'Dang Bich Thuy Le', 'Aren T Vierra', 'Ran Pang', 'Mahesh Patel', 'Young Kang', 'Yuling Yan']2024-04-18T00:00:00https://www.semanticscholar.org/paper/3869e76f259520c35516d761cf5ebbc269bc1faa 21ac6a01b8a114c0b955ee7e65f6ef96b8e93e60Plastic and Reconstructive Surgery00Plastic and reconstructive surgery['A. Saadya', 'Christopher R Davis']2024-04-18T00:00:00https://www.semanticscholar.org/paper/21ac6a01b8a114c0b955ee7e65f6ef96b8e93e60 f67479babad7c1eecf6e36ac743fe133260b8a48Reviews of Management Sciences1111This study used bibliographic coupling to analyze 159 articles emerging from the fields of computer sciences, engineering, social sciences, decision sciences, decision sciences, and management to scrutinize the role of Artificial Intelligence in Product-Service Innovation.Review of Managerial Science['Rimsha Naeem', 'Marko Kohtamäki', 'Vinit Parida']2024-04-18T00:00:00https://www.semanticscholar.org/paper/f67479babad7c1eecf6e36ac743fe133260b8a48 ef8954f5180a1047ef246c3561bb5a622dee2191Science &amp; Education471Science &amp; Education['William Billingsley']2024-04-18T00:00:00https://www.semanticscholar.org/paper/ef8954f5180a1047ef246c3561bb5a622dee2191 9e80670a6bb335cb4768d0fe61b2f035fc1192d8Generative artificial intelligence, represented by ChatGPT, has the ability to generate new content based on automatic learning, which not only triggers a productivity revolution but also poses legal regulatory challenges. ChatGPT can pose challenges such as privacy infringement, data security risks, and intellectual property rights confirmation and protection challenges. The Artificial Intelligence Act adopts measures such as compliance control for data collection, technology governance technology, and optimization of data management methods. Not only does ChatGPT prevent excessive blurring of the trust boundary between humans and machines, but it also configures different disclosure obligations based on the user's level of professionalism.Lecture Notes in Education Psychology and Public Media00Not only does ChatGPT prevent excessive blurring of the trust boundary between humans and machines, but it also configures different disclosure obligations based on the user's level of professionalism.Lecture Notes in Education Psychology and Public Media['Yijie Wang']2024-04-18T00:00:00https://www.semanticscholar.org/paper/9e80670a6bb335cb4768d0fe61b2f035fc1192d8 63b14657aeb2598e18d7ab3c28f25154f5511099While artificial intelligence (AI) is often presented as a neutral tool, growing evidence suggests that it exacerbates gender, racial, and other biases leading to discrimination and marginalization. This study analyzes the emerging agenda on intersectionality in AI. It examines four high‐profile reports dedicated to this topic to interrogate how they frame problems and outline recommendations to address inequalities. These four reports play an important role in putting problematic intersectionality issues on the political agenda of AI, which is typically dominated by questions about AI’s potential social and economic benefits. The documents highlight the systemic nature of problems that operate like a negative feedback loop or vicious cycle with the diversity crisis in the AI workforce leading to the development of biased AI tools when a largely homogenous group of white male developers and tech founders build their own biases into AI systems. Typical examples include gender and racial biases embedded into voice assistants, humanoid robots, and hiring tools. The reports frame the diversity situation in AI as alarming, highlight that previous diversity initiatives have not worked, emphasize urgency, and call for a holistic approach that focuses not just on numbers but rather on culture, power, and opportunities to exert influence. While dedicated reports on intersectionality in AI provide a lot of depth, detail, and nuance on the topic, in the patriarchal system they are in danger of being pigeonholed as issues of relevance mainly for women and minorities rather than part of the core agenda.Social Inclusion490This study analyzes the emerging agenda on intersectionality in AI by examining four high‐profile reports dedicated to this topic to interrogate how they frame problems and outline recommendations to address inequalities.Social Inclusion['I. Ulnicane']2024-04-18T00:00:00https://www.semanticscholar.org/paper/63b14657aeb2598e18d7ab3c28f25154f5511099 65ced157b1a63b725e0c128f68bb845c9a7515e8In light of the growing importance of Artificial Intelligence (AI) in science, business, and society, broad acceptance is crucial. However, recent studies indicate a significant underrepresentation of women in the emerging AI-driven professions of the future job market. This hampers the innovation potential of technologies due to the lack of diverse perspectives in development. Gender-specific differences also manifest in the perception of AI: Men tend to view AI applications more positively, rate their own AI competencies higher, and have more trust in the technology compared to women. However, both genders agree on the critical importance of the comprehensibility of AI decisions and are equally willing to pursue further education in the field of AI. This study aimed to investigate gender-relevant aspects in the perception and understanding of AI, as well as the need for further education and opportunities for communication and exchange on the topic of AI. To achieve this, focus groups with female students were conducted in May 2023. The analysis of the conversation data and materials used was carried out using an inductive coding method. Overall, women perceive knowledge as the key to generating more interest in AI. However, they also identify obstacles such as discrimination, gender stereotypes, and a lack of gender equality. Additionally, they desire more practical examples, improved communication regarding the advantages and disadvantages of AI, as well as more democratic and transparent decision-making processes. The paper emphasizes that an inclusive educational environment requires awareness and education for women, along with measures against discriminatory barriers and stereotypes. Furthermore, it suggests the early involvement of women in the development of AI applications and the establishment of clear rules to ensure gender equality in the workplace. These study findings provide valuable support to companies in the gender-specific planning of awareness and training processes for introducing AI.International Conference on Gender Research00These study findings provide valuable support to companies in the gender-specific planning of awareness and training processes for introducing AI and suggest the early involvement of women in the development of AI applications and the establishment of clear rules to ensure gender equality in the workplace.International Conference on Gender Research['Sascha Armutat', 'M. Wattenberg', 'Nina Mauritz']2024-04-18T00:00:00https://www.semanticscholar.org/paper/65ced157b1a63b725e0c128f68bb845c9a7515e8 7d29cfa033dc65a063555083aba9afc26d382a94Artificial intelligence (AI) technology have revolutionised many elements of corporate operations in recent years, and human resource management (HRM) is no exception. The many functions and impacts of AI in the field of HRM are examined in this paper. This study attempts to shed light on how AI is changing traditional HR practices, the benefits it delivers, as well as the possible obstacles and ethical considerations it presents, by a thorough examination of the available research and empirical investigations. The study will examine how AI is used in HRM in a variety of contexts, such as hiring and selection procedures, performance management, employee engagement, and training and development programmes. It will look at how AI-powered solutions are improving accuracy and efficiency in HR tasks. Examples of these tools include chatbots for candidate conversation, resume screening algorithms, and predictive analytics for finding high-potential individuals. The project will also explore the ways in which AI may support inclusion and diversity in the workplace and enhance the work experience for employees. Additionally, the study will go over the possible drawbacks of implementing AI in HRM, including worries about algorithmic bias, data privacy, and the displacement of human labour. The ethical issues surrounding the use of AI to make critical HR decisions as well as the requirement for accountability and transparency in AI-driven processes will also be covered.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This study will examine how AI is used in HRM in a variety of contexts, such as hiring and selection procedures, performance management, employee engagement, and training and development programmes, and look at how AI-powered solutions are improving accuracy and efficiency in HR tasks.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Muskan Kumari,']2024-04-18T00:00:00https://www.semanticscholar.org/paper/7d29cfa033dc65a063555083aba9afc26d382a94 42cb0664ad6d758f5a3677769ba27b4e58a2557aThis paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well as principles from the theory of open dynamical systems and stochastic classical and quantum dynamics, this formalism is tailored to model generic networks comprising abstract processing events. A pivotal aspect of our approach is the incorporation of the memory space and the intrinsic non-Markovian nature of the abstract generalized neuromorphic system. We envision future computations taking place within an expanded space (memory space) and leveraging memory states. Positioned at a high abstract level, generalized neuromorphism facilitates multidisciplinary applications across various approaches within the AI community.Symmetry520A multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what is called the formalism of generalized neuromorphism, which is tailored to model generic networks comprising abstract processing events.Symmetry['Said Mikki']2024-04-18T00:00:00https://www.semanticscholar.org/paper/42cb0664ad6d758f5a3677769ba27b4e58a2557a 71e6d4125b4940888fffcf025115c2b480b970d5Introduction In competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare. Methods Systematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review. Results The analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%). Conclusions This study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.Frontiers in Sports and Active Living700Various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations are found, highlighting the increasing use of AI in managing sports-related healthcare and performance complexities.Frontiers in Sports and Active Living['Ekaterina Glebova', 'José Eduardo Teixeira', 'Bhaskar Basu', 'A. Munoz-Macho', 'M. Dominguez-Morales', 'J. Sevillano-Ramos']2024-04-18T00:00:00https://www.semanticscholar.org/paper/71e6d4125b4940888fffcf025115c2b480b970d5 1f5e861026ba00193b5c07a327d6abf5656e47bfDigital transformation of public administration is one of the priorities of the Georgian government. In recent years, as a result of the implementation of digital governance initiatives and reforms, the possibilities of using artificial intelligence in the public sector of Georgia are increasing. Despite the fact that Georgia has a favorable environment for the establishment of artificial intelligence, the actual indicators of its use are still small. The subject of the research of many scientists in Georgia is the processing of data related to specific studies with the help of artificial intelligence, the evaluation of the obtained results, and the development of recommendations, bringing this process into the legal framework. Along with offering products and services based on artificial intelligence to the public sector, it is no less significant to implement them in the administrative decision-making process, which will increase the efficiency of public administration and contribute to democratic governance improvement.Grail of Science00It is no less significant to implement products and services based on artificial intelligence in the administrative decision-making process, which will increase the efficiency of public administration and contribute to democratic governance improvement.Grail of Science['G. Giguashvili', 'Tamar Makasarashvili']2024-04-18T00:00:00https://www.semanticscholar.org/paper/1f5e861026ba00193b5c07a327d6abf5656e47bf 8f33fb37127a51567c6a5a8e6a17ee53fd6f88fbAbstract. This article is dedicated to exploring recommendations for the development of legal frameworks in labor law through the implementation of artificial intelligence based on the analysis of national legislation and global experience. Theoretical approaches to defining the essence of the concept of "artificial intelligence" are analyzed, providing a comprehensive interpretation of this concept. It is argued that the technological process of artificial intelligence is a current necessity in the legal system today, as the integration of artificial intelligence affects the work environment, the content of labor relations, and consequently, the need to revise certain norms of labor law to adapt to the changing reality. The impact of artificial intelligence on the formation of new legal institutions and sub-institutions in the future is investigated, such as working hours and labor standards, occupational safety, retraining, and qualification improvement, as well as the protection of personal data. The shifts that the technological process of artificial intelligence may induce in labor law are identified. The current legal status of Ukraine in the field of artificial intelligence is assessed, trends are explored, and the country's development prospects in this direction are determined. Key provisions of the artificial intelligence development strategy within the country are analyzed, and the roadmap for regulating artificial intelligence and specific initiatives for the development and application of artificial intelligence at the local level are evaluated. Based on the synthesis of findings, practical recommendations.Baltic Journal of Legal and Social Sciences90Recommendations for the development of legal frameworks in labor law through the implementation of artificial intelligence based on the analysis of national legislation and global experience are explored, providing a comprehensive interpretation of the essence of "artificial intelligence".Baltic Journal of Legal and Social Sciences['Nadiya Levytska']2024-04-18T00:00:00https://www.semanticscholar.org/paper/8f33fb37127a51567c6a5a8e6a17ee53fd6f88fb 8c547989c1390b8b3238f69a9a82437bc81de7d8With the growing implementation of Artificial Intelligence (AI) in the architectural field, significant challenges arise in education with ethical and social connotations. In the context of Architecture and Communication degrees, AI emerges as an essential tool, especially in the initial stages of architectural design in exploring ideas and conceptualizing projects. This article addresses the complexity inherent in using AI in architecture, highlighting its fundamental contribution to improving visual representation through textual algorithms, and analyzes the relevance of communication as a scientific discipline. In this context, communication in architecture is directed towards advertising and the effective transmission of messages. The article presents a methodology focused on detecting didactic errors among architecture and communication students, thanks to multidisciplinary collaboration. In conclusion, it is highlighted that the appropriate use of AI can boost the generation of creative ideas, allowing students to direct them and complement them in technical aspects. This study highlights the importance of effectively integrating AI into academic training, highlighting its benefits in improving creativity and precision in architectural communication.ECORFAN Journal Spain220It is highlighted that the appropriate use of AI can boost the generation of creative ideas, allowing students to direct them and complement them in technical aspects, and the importance of effectively integrating AI into academic training is highlighted.ECORFAN Journal Spain['R. M. Grajeda-Rosado', 'Alma Saraí Rosello-Luna', 'Claudia Eréndira Vázquez-Torres', 'Cristina Sotelo-Salas']2024-04-18T00:00:00https://www.semanticscholar.org/paper/8c547989c1390b8b3238f69a9a82437bc81de7d8 f384e5b903fe33d2bbedd11785a2de07cda33cd3The introduction of artificial intelligence (AI) into education has become one of the most controversial innovations of our time. On the one hand, end-to-end digital technologies are already firmly embedded in production practice, and employers are increasingly looking for employees who can work with neural network technologies. On the other hand, educators face several negative phenomena when students use AI. The lack of reliable statistical data on the positive impact of AI on the quality of education also causes concern.In the article, the authors explore the possibilities of using intelligent tools and services at various stages of educational product development. The leading role of instructional design as the main methodological tool for improving the quality of education and effective integration of AI is noted. The modern definitions of the concept of “instructional design” are analyzed, its general characteristics are derived, and the most common models of its implementation are described. The stages of creating an educational product are described, and the content of each of them is revealed from using the functional capabilities of AI tools. The peculiarities of intellectual services application are illustrated by the example of the development of the course “Project Activity Management” for the first-year undergraduates at the profile “Digital Pedagogy” of Mari State University. Queries for analyzing the needs of the target audience, formation of the training course thematic plan, creation of training materials, implementation of the course in practice are proponed. For these and other tasks, AI tools and services are selected. It is concluded what actions should be taken in the professional education area to accelerate its technologization and reduce risks from the use of intelligent digital technologies. The authors conclude that instructional design is highly relevant and the practice of introducing AI in its implementation is spreadingInformatics and Education120The authors conclude that instructional design is highly relevant and the practice of introducing AI in its implementation is spreading and what actions should be taken in the professional education area to accelerate its technologization and reduce risks from the use of intelligent digital technologies.Informatics and education['V. Toktarova', 'O. V. Rebko']2024-04-18T00:00:00https://www.semanticscholar.org/paper/f384e5b903fe33d2bbedd11785a2de07cda33cd3 3903726c8d49c066c5a6a942ff41670564892aeeThis research paper explores the symbiotic relationship between Artificial Intelligence (AI) and Cyber-Physical Systems (CPS), where CPS are computational systems closely intertwined with physical processes through sensors and actuators. AI techniques, particularly machine learning, are pivotal in enhancing CPS functionalities, including data analysis, decision-making, optimization, and autonomous control. The paper delves into various applications of AI in CPS, highlighting its transformative potential in bolstering system performance, reliability, and resilience. Furthermore, the paper addresses pressing concerns regarding security and privacy within CPS environments. Through a detailed classification of security and privacy threats, it offers an organized overview of potential risks and economic implications, facilitating effective risk assessment. The study demonstrates how AI can mitigate these concerns by presenting a step-by-step flowchart utilizing AI and Machine Learning (ML) techniques for security and privacy issue detection within CPS. Moreover, the paper conducts a comprehensive literature review on current and future challenges surrounding AI implementation in CPS. It outlines potential developments and advancements, shedding light on the trajectory of AI in the realm of Cyber-Physical SystemsInternational Journal of Advanced Research in Science, Communication and Technology00International Journal of Advanced Research in Science, Communication and Technology['Sakshi Narad', 'Pratiksha Gotephode', 'Bhagyashree Kumbhare']2024-04-18T00:00:00https://www.semanticscholar.org/paper/3903726c8d49c066c5a6a942ff41670564892aee b5c776e6267425c767217be386b8822f68bb30d5In any ailment, the identification of the symptoms, detection, and diagnosis plays a pivotal role in treatment and therapy. However, certain diseases share similar symptoms, lacking signature key indicators, which can lead to fallacious or incorrect inferences. Skin disorders, such as pruritus, dermatitis, eczema, psoriasis, and ichthyosis, all present similar symptoms, which confound clinicians. One such commonly misunderstood condition is atopic dermatitis (AD), a chronic inflammatory skin condition characterized by its relapsing nature, which heightens the importance of diagnosis and disease monitoring for effective management. Recent strides in artificial intelligence (AI) have opened avenues for precise diagnosis and continuous monitoring of AD. This review explores and evaluates current applications of AI in the diagnosis and monitoring of individuals with AD emphasizing the need to address challenges and collaborate across intra-, inter-, trans-, and multi-disciplinary domains to maximize the benefits of AI in enhancing the precision of AD diagnosis, ultimately leading to improved patient care and satisfaction through technologically-driven biomedical tools in customized healthcare.Artificial Intelligence in Health00This review explores and evaluates current applications of AI in the diagnosis and monitoring of individuals with AD emphasizing the need to address challenges and collaborate across intra-, inter-, trans-, and multi-disciplinary domains to maximize the benefits of AI in enhancing the precision of AD diagnosis.Artificial Intelligence in Health['Pratheek Jain', 'Farhan Zameer', 'Kounaina Khan', 'Vinay Alva', 'Ravish Huchegowda', 'Ali Jawad Akki', 'Raghu Anjanapura Venkataramanaiah', 'Muthuchelian Krishnasamy', 'Dilip Apturkar', 'Raghavendra Hallur Laxmanashetty']2024-04-18T00:00:00https://www.semanticscholar.org/paper/b5c776e6267425c767217be386b8822f68bb30d5 e9534966d90b433681645f65ac84c728cc810b29BMC Medical Ethics320Eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships are described.BMC Medical Ethics['James Shaw', 'Joseph Ali', 'C. Atuire', 'P. Cheah', 'Armando Guio Español', 'J. Gichoya', 'Adrienne Hunt', 'Daudi Jjingo', 'Katherine Littler', 'Daniela Paolotti', 'E. Vayena']2024-04-18T00:00:00https://www.semanticscholar.org/paper/e9534966d90b433681645f65ac84c728cc810b29 3c220c1160a70d87b0cc77de493e0edaf763f578Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system.Applied Sciences810The utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain.Applied Sciences['Sefater Gbashi', 'P. Njobeh']2024-04-18T00:00:00https://www.semanticscholar.org/paper/3c220c1160a70d87b0cc77de493e0edaf763f578 0fdfe7407cb4670a75ce2c0c55f60e6f79885373Background Breast cancer continues to be a significant global health issue, necessitating advancements in prevention and early detection strategies. This review aims to assess and synthesize research conducted from 2020 to the present, focusing on breast cancer risk factors, including genetic, lifestyle, and environmental aspects, as well as the innovative role of artificial intelligence (AI) in prediction and diagnostics. Methods A comprehensive literature search, covering studies from 2020 to the present, was conducted to evaluate the diversity of breast cancer risk factors and the latest advances in Artificial Intelligence (AI) in this field. The review prioritized high-quality peer-reviewed research articles and meta-analyses. Results Our analysis reveals a complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations. Furthermore, AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies. Conclusion The review highlights the necessity for personalized breast cancer prevention and detection approaches that account for individual risk factor profiles. It underscores the potential of AI to revolutionize these strategies, offering clear recommendations for future research directions and clinical practice improvements.Frontiers in Oncology1130A complex interplay of genetic, lifestyle, and environmental risk factors for breast cancer, with significant variability across different populations, is revealed and AI has emerged as a promising tool in enhancing the accuracy of breast cancer risk prediction and the personalization of prevention strategies.Frontiers in Oncology['O. Nicolis', 'Denisse De Los Angeles', 'Carla Taramasco']2024-04-18T00:00:00https://www.semanticscholar.org/paper/0fdfe7407cb4670a75ce2c0c55f60e6f79885373 a653e0782844552804dd54baac415d0531402874Artificial Intelligence (AI) is one of the science fields with huge potential to create a cognitive and tech-leaping type of future smart city design/development. However, extant studies lag behind recent applications, potential growth areas, and the challenges associated with AI implementation. This study examines AI’s current role, trend, and future potential impacts in enhancing smart city drivers. The methodology entails conducting a Systematic Literature Review (SLR) of publications from 2022 onwards. The approach involves qualitative deductive coding methods, descriptive statistical analysis, and thematic analysis. The findings revealed the impacts of AI in (i) public services and connectivity, (ii) improving accessibility and efficiency, (iii) quality healthcare, (iv) education, and (v) public safety. Likewise, strategies, such as collaborative ecosystems, digital infrastructure, capacity building, and clear guidelines and ethical framework, were proposed for fostering the integration of AI in potential future smart cities. This research fills a notable gap in the current understanding of AI’s specific contributions to smart cities, offering insights for stakeholders in urban planning, computer science, sociology, economics, environmental science, and smart city initiatives. It serves as a strategic guideline and scholarly research output for enhancing smart city design. It also underscores the potential of AI in creating dynamic, sustainable, and efficient urban environments.Sustainability500The findings revealed the impacts of AI in improving accessibility and efficiency, quality healthcare, education, and public safety, and strategies, such as collaborative ecosystems, digital infrastructure, capacity building, and clear guidelines and ethical framework, were proposed for fostering the integration of AI in potential future smart cities.Sustainability['Reema Alsabt', 'Yusuf A. Adenle', 'Habib M. Alshuwaikhat']2024-04-18T00:00:00https://www.semanticscholar.org/paper/a653e0782844552804dd54baac415d0531402874 1e376c7c1792c6e4c4f97db35a9e08727a0ceea3Journal of Applied Learning &amp; Teaching00Journal of Applied Learning &amp; Teaching[]2024-04-18T00:00:00https://www.semanticscholar.org/paper/1e376c7c1792c6e4c4f97db35a9e08727a0ceea3 608d08482fb2314b127e64ed86742feec8d7aef8Este artigo tem como objetivo discutir os impactos da Intelig|ência Artificial no Tribunal de Justiça do Estado do Tocantins, fazendo uma análise comparada ao uso da IA no Brasil, utilizando-se da metodologia qualitativa. Por meio da pesquisa contasta-se que a tecnologia veio para abrir fronteiras, havendo demasiada aceitação, inclusive, incentivo para implementação através dos órgãos do Poder Judiciário, havendo projetos e metas. Mediante a síntese dos fatos apresentados, pode-se afirmar que os impactos sofridos no Tribunal de Justiça são positivos.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação['Gabriel Antonio Broglio', 'Buenã Porto Salgado']2024-04-18T00:00:00https://www.semanticscholar.org/paper/608d08482fb2314b127e64ed86742feec8d7aef8 15e6e216f09375521a2643564d5c1623055e42f7I start my contribution with some general questions: Is AI gendered? Is AI sexist or can it be? Does AI include gendered knowledge and suppositions? If so, how? After that, I proceed to develop my theoretical (i.e., qualitatively based) starting points with the main referential authors, Donna Haraway and Alison Adam. The fact that impersonal does not mean observer-independent (as Haraway described it in a slightly different context [1997]) is a good reason to turn to feminist epistemology, especially its concept of situated knowledges. Since knowledge (or the representation of knowledge) lies at the very centre of AI research, this makes it an appropriate vehicle for a gendered critique of AI (Adam, 2000). The concept of situated knowledges entails knowledge that reflects a perspective on a subject which is necessarily partial and limited, not universal (this is Haraway’s famous critique of the “view from nowhere”). Namely, there is no way to be simultaneously in all of the epistemologically privileged positions structured by gender, class, nation, etc. I then proceed to AI research. The knowledge engineers build systems that contain knowledge reflecting their own interests and competencies. While this representation of knowledge is usually regarded as being universal (Adam, 2000), it is hierarchical since it does not grant epistemic authority to all. Most importantly, social exclusivism and biological essentialism are re-inscribed in the ontology of AI (Adam, 2000). I address the question of which effects social and political contaminations and prejudices can bring for the development of AI. I suggest that unless we commit to deconstructing the harmful essentialisms that govern our human lives, we might just be perpetuating the same (i.e., our own and others’) practices of domination and unequal parts of privilege and oppression (Haraway, 1991) in developing AI.International Conference on Gender Research140This contribution addresses the question of which effects social and political contaminations and prejudices can bring for the development of AI and suggests that unless the authors commit to deconstructing the harmful essentialisms that govern their human lives, they might just be perpetuating the same practices of domination and unequal parts of privilege and oppression in developing AI.International Conference on Gender Research['Valerija Vendramin']2024-04-18T00:00:00https://www.semanticscholar.org/paper/15e6e216f09375521a2643564d5c1623055e42f7 2f06cdba66b60003ceb92579cef8ce8f3058940bJournal of Laryngology and Otology00The Journal of laryngology and otology['Babatunde Oremule', 'Gabrielle H Saunders', 'Karolina Kulk', "Alexander d'Elia", 'Iain A Bruce']2024-04-18T00:00:00https://www.semanticscholar.org/paper/2f06cdba66b60003ceb92579cef8ce8f3058940b 844ec6ea8eb296d1e797701dddd7506c72a46ee1American Journal of Public Health30American journal of public health['James G. Hodge', 'Jennifer L. Piatt', 'Erica N White', 'Lawrence O. Gostin']2024-04-18T00:00:00https://www.semanticscholar.org/paper/844ec6ea8eb296d1e797701dddd7506c72a46ee1 f51b93437cd5bc94ee9e9dd55598224961d0ade5American Journal of Law & Medicine10American journal of law & medicine['Barry Solaiman', 'Abeer Malik', 'Suhaila Ghuloum']2024-04-18T00:00:00https://www.semanticscholar.org/paper/f51b93437cd5bc94ee9e9dd55598224961d0ade5 af6e0a7afab37db7c6fa57279a695b7c9dd05695Archives of Computational Methods in Engineering710Archives of Computational Methods in Engineering['Yan Li', 'Jibo He']2024-04-18T00:00:00https://www.semanticscholar.org/paper/af6e0a7afab37db7c6fa57279a695b7c9dd05695 934a407432d67e283d0e705e8802c9c53aefc927Studying Teacher Education: journal of self-study of teacher education practices260Studying Teacher Education['Charlotte Frambaugh-Kritzer', 'Elizabeth Petroelje Stolle']2024-04-18T00:00:00https://www.semanticscholar.org/paper/934a407432d67e283d0e705e8802c9c53aefc927 0a250e8d994c0119dc17599c41608e95b3658682Journal of Perioperative Nursing00Journal of Perioperative Nursing['Nick Nijkamp', 'Erin Wakefield']2024-04-18T00:00:00https://www.semanticscholar.org/paper/0a250e8d994c0119dc17599c41608e95b3658682 5b2dbee7b5bc04c650cbd7bd6e98b5a4eeec923dWorld Journal of Science Technology and Sustainable Development00World Journal of Science, Technology and Sustainable Development['Dr Santhi Ramanathan', 'Dr Shirley Gee Hoon Tang', 'Ms Madihah Mohd Afza', 'Dr Pin Jern Ker', 'Dr Prajindra Sankar Krishnan', 'Dr Chai Phing Chen', 'Dr Sieh Kiong Tiong', 'Dr Mei Wyin Yaw']2024-04-18T00:00:00https://www.semanticscholar.org/paper/5b2dbee7b5bc04c650cbd7bd6e98b5a4eeec923d c8ae365c40f617f5a20c6a3867a30fa714ec053fWe here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (structural and architectural) and extrinsic (related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it is theoretically possible that AI research can develop partial or potentially alternative forms of consciousness that is qualitatively different from the human, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word consciousness for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify what is common and what differs in AI conscious processing from full human conscious experience.1520It is argued that, even if AI is limited in its ability to emulate human consciousness for both intrinsic and extrinsic reasons, taking inspiration from those characteristics of the brain that make conscious processing possible and/or modulate it is a potentially promising strategy towards developing conscious AI.['M. Farisco', 'K. Evers', 'J. Changeux']2024-04-18T00:00:00https://www.semanticscholar.org/paper/c8ae365c40f617f5a20c6a3867a30fa714ec053f f02093f6806aec09b11604fde696f4d87e60f047China-EU Law Journal90China-EU Law Journal['Xiangbai He']2024-04-17T00:00:00https://www.semanticscholar.org/paper/f02093f6806aec09b11604fde696f4d87e60f047 c17befe2beb989ea8884b6a0a920839ed27b44b1Technological innovations have shown remarkable capabilities to benefit and harm society alike. AI constitutes a democratized sophisticated technology accessible to large parts of society, including malicious actors. This work proposes a taxonomy focusing on on (geo)political risks associated with AI. It identifies 12 risks in total divided into four categories: (1) Geopolitical Pressures, (2) Malicious Usage, (3) Environmental, Social, and Ethical Risks, and (4) Privacy and Trust Violations. Incorporating a regulatory side, this paper conducts a policy assessment of the EU AI Act. Adopted in March 2023, the landmark regulation has the potential to have a positive top-down impact concerning AI risk reduction but needs regulatory adjustments to mitigate risks more comprehensively. Regulatory exceptions for open-source models, excessively high parameters for the classification of GPAI models as a systemic risk, and the exclusion of systems designed exclusively for military purposes from the regulation's obligations leave room for future action.arXiv.org760This work proposes a taxonomy focusing on on (geo)political risks associated with AI, and identifies 12 risks in total divided into four categories: Geopolitical Pressures, Malicious Usage, Environmental, Social, and Ethical Risks, and Privacy and Trust Violations.ArXiv['Sinan Arda']2024-04-17T00:00:00https://www.semanticscholar.org/paper/c17befe2beb989ea8884b6a0a920839ed27b44b1 e850faa59fad24b808fc8906d8bb1578714e956ePatent applications provide insight into how inventors imagine and legitimize uses of their imagined technologies; as part of this imagining they envision social worlds and produce sociotechnical imaginaries. Examining sociotechnical imaginaries is important for emerging technologies in high-stakes contexts such as the case of emotion AI to address mental health care. We analyzed emotion AI patent applications (N=58) filed in the U.S. concerned with monitoring and detecting emotions and/or mental health. We examined the described technologies' imagined uses and the problems they were positioned to address. We found that inventors justified emotion AI inventions as solutions to issues surrounding data accuracy, care provision and experience, patient-provider communication, emotion regulation, and preventing harms attributed to mental health causes. We then applied an ethical speculation lens to anticipate the potential implications of the promissory emotion AI-enabled futures described in patent applications. We argue that such a future is one filled with mental health conditions' (or 'non-expected' emotions') stigmatization, equating mental health with propensity for crime, and lack of data subjects' agency. By framing individuals with mental health conditions as unpredictable and not capable of exercising their own agency, emotion AI mental health patent applications propose solutions that intervene in this imagined future: intensive surveillance, an emphasis on individual responsibility over structural barriers, and decontextualized behavioral change interventions. Using ethical speculation, we articulate the consequences of these discourses, raising questions about the role of emotion AI as positive, inherent, or inevitable in health and care-related contexts. We discuss our findings' implications for patent review processes, and advocate for policy makers, researchers and technologists to refer to patent (applications) to access, evaluate and (re)consider potentially harmful sociotechnical imaginaries before they become our reality.Proceedings of the ACM on Human-Computer Interaction1130This work analyzed emotion AI patent applications filed in the U.S. and found that inventors justified emotion AI inventions as solutions to issues surrounding data accuracy, care provision and experience, patient-provider communication, emotion regulation, and preventing harms attributed to mental health causes.Proceedings of the ACM on Human-Computer Interaction['Nadia Karizat', 'A. Vinson', 'Shobita Parthasarathy', 'Nazanin Andalibi']2024-04-17T00:00:00https://www.semanticscholar.org/paper/e850faa59fad24b808fc8906d8bb1578714e956e 1402c5bb0adefb4a4a1d166233848ed03f7c8bd2The experience of the most influential interstate formation in Europe – the European Union, which occupies the second place in terms of GDP and the third in terms of population. is instructive in all its spheres of functioning, in particular in the field of budget law. Given the accelerated movement of Ukraine to the European community, the «budget experience» of the European Union is certainly the most indicative and instructive, since it was formed on the basis of a generalization of best practices of all member countries of the European continent. The article deals with a set of the main EU budget regulations: the Treaty on the Functioning of the European Union; Treaty on European Union; EU financial regulations; Regulation of the European Parliament and of the Council (EU, EURATOM) 2018/1046 of 18 July 2018 on financial rules applicable to the general budget of the Union on amendments to the regulations (EU) № 1296/2013, (EU) № 1301/2013, (EU) № 1303/2013, (EU) №  1304/2013, (EC) № 1309/2013, (EC) № 1316/2013, (EC) № 223/2014, (EC) № 283/2014 and Decision № 541/2014/ЄS and repealing Regulation (EU, Euratom) № 966/2012. On the basis of a detailed analysis of these documents, the fundamental positions of the EU budget process in general and the principles of the EU budget in particular are ordered, structured and described (they include the principles: unity, reliability of the budget, annuity, balance, unit of account, universality, detail, prudent financial management and productivity, transparency). Proposals for the implementation of the principles of the EU budget to the domestic budget law are carried out.Society and Security00Society and Security['S. Svirko', 'V. Butuzov', 'V. Dovgaliuk', 'O. Pavliuk']2024-04-17T00:00:00https://www.semanticscholar.org/paper/1402c5bb0adefb4a4a1d166233848ed03f7c8bd2 5a6f1bcea45f202e83067cce5a572d3143b16accThe development of digital technologies permeates almost all areas of public relations. At the same time, certain areas remain more conservative, and legal regulation also lags behind the pace of general digitalization. This situation is especially clearly visible in the field of inheritance of digital assets. The subject of this article is to explore the opportunities and risks associated with inheriting NFTs. The purpose of the article is to determine algorithms for inheriting NFTs in the context of insufficient legal regulation of this procedure in the Russian Federation. The work uses methods of both empirical (analysis and synthesis, induction and deduction, systematization) and theoretical (methods of constructing and studying the object of study and methods of constructing and justifying theoretical knowledge) levels. When transferring a token, a unique record is transferred, and the previous owner of the NFT loses the token after it is transferred. This makes NFTs similar to material objects and necessitates separate legal regulation of the rights associated with NFTs. An NFT is inherited, not a digital object such as art. The main problem with inheriting NFTs is that these objects are intangible. They cannot be physically materialized, stored, for example, in a safe deposit box, or transferred physically. Therefore, if the owners do not have specific heirs, it will even be difficult to include NFT in the inheritance or find out about the token. Inheriting an NFT requires that the will name the NFT and include an explanation of where the NFT is held. The testator’s password must also be available. There may be problems associated with the compulsory share in the inheritance, related both to access and to the assessment and dynamically changing value of the NFT. Also, the valuation of the NFT will influence the amount of the state fee for issuing a certificate of inheritance. The practical implementation of NFT inheritance is facilitated by the development of appropriate digital technologies that optimize procedural aspects.Theoretical and Applied Law60Theoretical and Applied Law['E. I. Leskina']2024-04-17T00:00:00https://www.semanticscholar.org/paper/5a6f1bcea45f202e83067cce5a572d3143b16acc 0311ebb0f9e5b32de503965a08974c416671455cMotivation: The law enforcement system of the state is a special component of ensuring the performance of state functions related to the security of society. The study of the law enforcement system is impossible without studying the interests intertwined in its tasks, principles and functions. Aim: The purpose of the research was to study the key areas of the state policy implementation in law enforcement, ensuring public order and combating crime. Results: Based on the results of the study, the development of the doctrine of state policy in the field of transformation of the law enforcement system in the context of the theory of interest is substantiated. The origins of interest in the public administration system are established and the order of their formalization in law is justified. The substantive characteristics of private, public and community interests are determined, which serve as the basis for forming a law enforcement system to ensure their satisfaction. A hypothesis has been put forward and proved that the satisfaction of interests through their formalization in law and protection in the law enforcement system depends on the current political regime in a particular country. The conducted analysis of dictatorial regimes testifies to the replacement of the concepts of public interest with the private interest of the dictator, which as a result leads to threats to society in a particular country. The manifestation of the law enforcement system in the context of the theory of interests is determined, including: 1) an instrument for the protection and settlement of all interests; 2) a system of intersection of agents' interests; 3) an instrument for narrowing private interests.Society and Security170Society and Security['D. Hrytsyshen', 'V. Butuzov', 'Valentyna Ksendzuk', 'K. Malyshev', 'I. Suprunova']2024-04-17T00:00:00https://www.semanticscholar.org/paper/0311ebb0f9e5b32de503965a08974c416671455c 420e651b74039028cb66d427f9c3bf537323588fThe conceptual concept and types of interaction of international legal regulators on the prevention and liquidation of emergency and crisis situations are formulated The role of generally recognized principles of international law in ensuring international cooperation in the prevention of emergencies and crisis situations has been established. International cooperation in this area is based on and implemented through universal, regional international legal mechanisms, as well as bilateral treaties. The role of the United Nations in the normative, programmatic and expert support of cooperation between states on disaster risk reduction, as well as universal international organizations and conferences in the development of international legal regulators for countering emergency and crisis situations is considered. The analysis of the activities of international regional organizations on the international legal provision of emergency prevention, including in the arctic region, is carried out. In conclusion, it is concluded that there is an insufficient level of coherence and integrity of the international legal regulation of the prevention and liquidation of emergency and crisis situations.LAW. SAFETY. EMERGENCY SITUATIONS00LAW. SAFETY. EMERGENCY SITUATIONS['A. Kapustin']2024-04-17T00:00:00https://www.semanticscholar.org/paper/420e651b74039028cb66d427f9c3bf537323588f 67ee796fe885a4667fc689d56230b6e77682f39aThe primary objective of this research is to ensure the secure and compliant transmission of personal information from China. To this end, the Chinese government has implemented the "Standard Contract Measures for the Export of Personal Information." This study employs both literature review and factor analysis methods to meticulously examine the potential risks in the practical implementation of these measures, and further suggests strategies to address the current challenges. Additionally, the study introduces foreign systems for reference, aiming to guide Chinese enterprises in circumventing legal risks associated with cross-border data flows and ensuring the security and compliance of personal information departing from the country. The findings of this research reveal that the existing legal framework for cross-border data flows in China has certain shortcomings, providing valuable insights for the improvement of China's legal framework and related regulatory systems. Furthermore, it offers practical cautionary value for Chinese enterprises in their operations.International Journal of Social Sciences and Public Administration90International Journal of Social Sciences and Public Administration['Zixuan Tian', 'Chenhao Wen', 'Jinfeng Fan']2024-04-17T00:00:00https://www.semanticscholar.org/paper/67ee796fe885a4667fc689d56230b6e77682f39a af3c49fc6d1721352862629a9a568765b77c42d0In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information about the applicability of the proposed methods to real problems. As a step forward, we have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems. The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields. We illustrate potential applications through a series of case studies in fields such as causal discovery, out-of-distribution generalization, change point detection, independent component analysis, and symbolic regression. For applications to causal inference, the chambers allow us to carefully perform interventions. We also provide and empirically validate a causal model of each chamber, which can be used as ground truth for different tasks. All hardware and software is made open source, and the datasets are publicly available at causalchamber.org or through the Python package causalchamber.arXiv.org531Two devices are constructed that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems, providing a rich testbed for algorithms from a variety of fields and providing a causal model of each chamber that can be used as ground truth for different tasks.ArXiv['Juan L. Gamella', 'Jonas Peters', 'Peter Buhlmann']2024-04-17T00:00:00https://www.semanticscholar.org/paper/af3c49fc6d1721352862629a9a568765b77c42d0 95e24948f0615155601cf4e4d1041f9730ce8074The proliferation of applications using artificial intelligence (AI) systems has led to a growing number of users interacting with these systems through sophisticated interfaces. Human-computer interaction research has long shown that interfaces shape both user behavior and user perception of technical capabilities and risks. Yet, practitioners and researchers evaluating the social and ethical risks of AI systems tend to overlook the impact of anthropomorphic, deceptive, and immersive interfaces on human-AI interactions. Here, we argue that design features of interfaces with adaptive AI systems can have cascading impacts, driven by feedback loops, which extend beyond those previously considered. We first conduct a scoping review of AI interface designs and their negative impact to extract salient themes of potentially harmful design patterns in AI interfaces. Then, we propose Design-Enhanced Control of AI systems (DECAI), a conceptual model to structure and facilitate impact assessments of AI interface designs. DECAI draws on principles from control systems theory -- a theory for the analysis and design of dynamic physical systems -- to dissect the role of the interface in human-AI systems. Through two case studies on recommendation systems and conversational language model systems, we show how DECAI can be used to evaluate AI interface designs.arXiv.org1231It is argued that design features of interfaces with adaptive AI systems can have cascading impacts, driven by feedback loops, which extend beyond those previously considered.ArXiv['Lujain Ibrahim', 'Luc Rocher', 'Ana Valdivia']2024-04-17T00:00:00https://www.semanticscholar.org/paper/95e24948f0615155601cf4e4d1041f9730ce8074 d0c82b49f2a9b0f785244eda43e780cfcf2930ddIn the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.Proceedings of the ACM on Human-Computer Interaction651This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how to improve human-centeredness in mental health AI research.Proceedings of the ACM on Human-Computer Interaction['Dong Whi Yoo', 'Hayoung Woo', 'Sachin R. Pendse', 'N. Lu', 'Michael L. Birnbaum', 'G. Abowd', 'M. de Choudhury']2024-04-17T00:00:00https://www.semanticscholar.org/paper/d0c82b49f2a9b0f785244eda43e780cfcf2930dd 673e15e86fc83072f7541fd610d474b19505fb2dAI-driven tools are increasingly deployed to support low-skilled community health workers (CHWs) in hard-to-reach communities in the Global South. This paper examines how CHWs in rural India engage with and perceive AI explanations and how we might design explainable AI (XAI) interfaces that are more understandable to them. We conducted semi-structured interviews with CHWs who interacted with a design probe to predict neonatal jaundice in which AI recommendations are accompanied by explanations. We (1) identify how CHWs interpreted AI predictions and the associated explanations, (2) unpack the benefits and pitfalls they perceived of the explanations, and (3) detail how different design elements of the explanations impacted their AI understanding. Our findings demonstrate that while CHWs struggled to understand the AI explanations, they nevertheless expressed a strong preference for the explanations to be integrated into AI-driven tools and perceived several benefits of the explanations, such as helping CHWs learn new skills and improved patient trust in AI tools and in CHWs. We conclude by discussing what elements of AI need to be made explainable to novice AI users like CHWs and outline concrete design recommendations to improve the utility of XAI for novice AI users in non-Western contexts.Proceedings of the ACM on Human-Computer Interaction781This paper examines how CHWs in rural India engage with and perceive AI explanations and how explainable AI (XAI) interfaces that are more understandable to them and outlines concrete design recommendations to improve the utility of XAI for novice AI users in non-Western contexts.Proceedings of the ACM on Human-Computer Interaction['Chinasa T. Okolo', 'Dhruv Agarwal', 'Nicola Dell', 'Aditya Vashistha']2024-04-17T00:00:00https://www.semanticscholar.org/paper/673e15e86fc83072f7541fd610d474b19505fb2d 264e25fdda3dc68706d1c6aa684bb2395769faaaA multimodal AI agent is characterized by its ability to process and learn from various types of data, including natural language, visual, and audio inputs, to inform its actions. Despite advancements in large language models that incorporate visual data, such as GPT-4V, effectively translating image-based data into actionable outcomes for AI agents continues to be challenging. In this paper, we introduce a multimodal model that incorporates the concept of functional token specifically designed for AI agent applications. To ensure compatibility with edge devices, our model is optimized to a compact size of less than 1B parameters. Like GPT-4, our model can process both English and Chinese. We demonstrate that this model is capable of operating efficiently on a wide range of edge devices, including as constrained as a Raspberry Pi.arXiv.org541This paper introduces a multimodal model that incorporates the concept of functional token specifically designed for AI agent applications, and demonstrates that this model is capable of operating efficiently on a wide range of edge devices, including as constrained as a Raspberry Pi.ArXiv['Wei Chen', 'Zhiyuan Li']2024-04-17T00:00:00https://www.semanticscholar.org/paper/264e25fdda3dc68706d1c6aa684bb2395769faaa 95146af9641006e943ece11019238e39eab33c03 This paper aims to demonstrate the application of a new automatic geosteering method that combines probabilistic interpretation with artificial intelligence (AI) for look-ahead decision-making. We expand on our previous synthetic workflow by integrating the geosteering "robot" into a commercial cloud-based geosteering environment through its web application programming interface (API). We bench- mark the robot against 100 active human participants of the ROGII Geosteering World Cup (GWC) 2021. Our automatic geosteering method combines a Reinforcement Learning (RL) algorithm with the Particle Filter (PF) method. PF continuously assimilates real-time log measurements obtained during geosteering operations, producing hundreds of most likely geology interpretations. Simultaneously, RL uses the information gathered from PF outputs to optimize steering decisions. The robot implemen- tation automatically collects the new well trajectory and logs and passes the latest data through the PF. The RL uses the most likely interpretations to balance the short- and long-term steering priorities and outputs a single recommendation that the robot sends back to the cloud. Our combined PF and RL ("PLuRalistic") method achieves a remarkable reservoir contact percentage of approximately 80 % for thin and faulty target layers in our synthetic environments. The "PLuRalistic" robot expands this promising methodology to the commercial cloud environment. As part of our results, we provide a detailed account of the integration process to the cloud environment via the Solo Cloud Python SDK. This SDK is the conduit for retrieving real-time log measurements and delivering automated decisions, enabling a closed-loop geosteering decision-making framework for GWC and real geosteering in the future. The operation of our robot significantly surpasses real-time operation requirements, making one steering decision in approximately 4 seconds, far below the two-minute-per- stand drilling time allocated for the GWC. With the adjustments of the robot to pre-drill geology and GWC operational constraints, it managed to achieve 74.8% percent reservoir contact and top-quartile performance among human geosteerers. The fully automated decision-making robot represents a radical innovation in geosteering workflows. High-fidelity simulation of the GWC gives a unique opportunity to verify and improve the AI technology. More importantly, the simulated environment with tools familiar to experts allows testing and improving user-system interaction. In particular, we utilize population data from experts for the proposal distribution of geology for the PF and evaluation of the decisions generated by RL.Day 1 Wed, April 17, 2024220The application of a new automatic geosteering method that combines probabilistic interpretation with artificial intelligence (AI) for look-ahead decision-making is demonstrated, enabling a closed-loop geosteering decision-making framework for GWC and real geosteering in the future.Day 1 Wed, April 17, 2024['Ressi Bonti Muhammad', 'Yasaman Cheraghi', 'S. Alyaev', 'Apoorv Srivastava', 'R. Bratvold']2024-04-17T00:00:00https://www.semanticscholar.org/paper/95146af9641006e943ece11019238e39eab33c03 1522738f5a4342eee14d9615084c0e68a0f9b132Designing AI systems with the capacity to explain their behaviour is paramount to enable human oversight, facilitate trust, promote acceptance of technology and, ultimately, empower users and improve their experience. There are, however, several challenges to explainable AI, one of which is the generation and selection of explanations from the causal history of a given event. Causal attribution, among other cognitive processes, has been found to be influenced by socio-cultural factors, which suggests that there could be systematic differences in preferences for AI explanations between communities of users according to their cognitive style and socio-cultural characteristics. In this paper, we investigate the relationship between preferences in the explanations provided by belief-desire-intention AI agents, cognitive style (holistic vs analytical), and socio-cultural factors, such as gender, education, social class, and political and religious beliefs. We found a relationship between explanation preference, cognitive style and various socio-cultural characteristics. Holistic cognitive style is associated with preference for goal explanations while analytic cognitive style is associated with preference for belief explanations. Socio-cultural variables that affect explanation preference are gender, religious beliefs, educational attainment, some fields of education, and political party affiliation.Proceedings of the ACM on Human-Computer Interaction580The relationship between preferences in the explanations provided by belief-desire-intention AI agents, cognitive style (holistic vs analytical), and socio-cultural factors, such as gender, education, social class, and political and religious beliefs are investigated.Proceedings of the ACM on Human-Computer Interaction['Hana Kopecka', 'Jose Such', 'Michael Luck']2024-04-17T00:00:00https://www.semanticscholar.org/paper/1522738f5a4342eee14d9615084c0e68a0f9b132 c7276d1885cd332eac917f0496cab8ecd13a351cThis study delves into the ethical implications of Artificial Intelligence (AI) in financial decision-making, exploring the transformative impact of AI technologies on the financial services sector. Through a comprehensive literature review, the research highlights the dual nature of AI's integration into finance, showcasing both its potential to enhance operational efficiency and decision accuracy and the ethical challenges it introduces. These challenges include concerns over data privacy, algorithmic bias, and the potential for systemic risks, underscoring the need for robust ethical frameworks and regulatory standards. The study emphasizes the importance of a multidisciplinary approach to AI development and deployment, advocating for collaboration among technologists, ethicists, policymakers, and end-users to ensure that AI technologies are aligned with societal values and ethical principles. Future directions for research are identified, focusing on the development of adaptive ethical guidelines, methodologies for embedding ethical principles into AI systems, and the investigation of AI's long-term impact on market dynamics and consumer behaviour. This research contributes valuable insights into the ethical integration of AI in finance, offering recommendations for ensuring that AI technologies are utilized in a manner that is both ethically sound and conducive to the advancement of the financial services industry. Keywords: Artificial Intelligence, Financial Decision-Making, Ethical Implications, Algorithmic Bias, Data Privacy, Regulatory Standards, Multidisciplinary Approach.International journal of applied research in social sciences00The study emphasizes the importance of a multidisciplinary approach to AI development and deployment, advocating for collaboration among technologists, ethicists, policymakers, and end-users to ensure that AI technologies are aligned with societal values and ethical principles.International Journal of Applied Research in Social Sciences['Oluwatobi Opeyemi Adeyelu', 'Chinonye Esther Ugochukwu', 'Mutiu Alade Shonibare']2024-04-17T00:00:00https://www.semanticscholar.org/paper/c7276d1885cd332eac917f0496cab8ecd13a351c 25ae2fce719c6f6f0b09de1e0f917a7b719e5e99This survey paper examines the recent advancements in AI agent implementations, with a focus on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities. The primary objectives of this work are to a) communicate the current capabilities and limitations of existing AI agent implementations, b) share insights gained from our observations of these systems in action, and c) suggest important considerations for future developments in AI agent design. We achieve this by providing overviews of single-agent and multi-agent architectures, identifying key patterns and divergences in design choices, and evaluating their overall impact on accomplishing a provided goal. Our contribution outlines key themes when selecting an agentic architecture, the impact of leadership on agent systems, agent communication styles, and key phases for planning, execution, and reflection that enable robust AI agent systems.arXiv.org360The contribution outlines key themes when selecting an agentic architecture, the impact of leadership on agent systems, agent communication styles, and key phases for planning, execution, and reflection that enable robust AI agent systems.ArXiv['Tula Masterman', 'Sandi Besen', 'Mason Sawtell', 'Alex Chao']2024-04-17T00:00:00https://www.semanticscholar.org/paper/25ae2fce719c6f6f0b09de1e0f917a7b719e5e99 56cc623d38198b2f1483c39971fa7be4c1e23c5dThis scholarly paper delves into the transformative realm of Artificial Intelligence (AI) in financial regulatory compliance, offering a classical and engaging exploration of its multifaceted impact. Against an increasingly complex financial landscape backdrop, the study aims to unravel the intricacies of AI integration in compliance models, juxtaposing traditional methodologies with cutting-edge AI-driven approaches. The scope of the paper encompasses a systematic literature review and qualitative analysis, focusing on the evolution of AI in financial services, its necessity for enhanced compliance efficiency, and a comparative analysis of traditional versus AI-driven compliance models. The study synthesizes findings from diverse peer-reviewed articles, case studies, and comparative analyses by employing a meticulous methodology. It illuminates the state-of-the-art AI technologies in financial compliance, evaluates their effectiveness in various regulatory contexts, and identifies key performance indicators for AI compliance. The paper also critically examines the challenges and limitations observed in AI compliance solutions alongside emerging trends and future directions. The main conclusions reveal that AI significantly enhances compliance efficiency and accuracy, adeptly addresses complex regulatory challenges, and has strategic implications for financial institutions. However, the study also highlights the need for balancing innovation with regulatory and ethical compliance. Recommendations include the adoption of proactive regulatory frameworks, stakeholder engagement, and the development of robust AI governance models. This paper contributes to the academic discourse on AI in financial services, guiding policymakers, regulators, and industry practitioners. It advocates for a harmonized approach to AI integration, ensuring responsible and effective utilization in the financial sector. Keywords:  Artificial Intelligence, Financial Regulatory Compliance, Systematic Literature Review, AI Technologies, Regulatory Challenges, Strategic Implications.Finance &amp; Accounting Research Journal00The main conclusions reveal that AI significantly enhances compliance efficiency and accuracy, adeptly addresses complex regulatory challenges, and has strategic implications for financial institutions.Finance &amp; Accounting Research Journal['Oluwatobi Opeyemi Adeyelu', 'Chinonye Esther Ugochukwu', 'Mutiu Alade Shonibare']2024-04-17T00:00:00https://www.semanticscholar.org/paper/56cc623d38198b2f1483c39971fa7be4c1e23c5d 7d796ad1c3dd309aaf0b47fdfb00179a9688039eThe process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a maritime scenario where the AI agent credibly competes against blue agents in gameplay. However, a limitation of using DRL for agent training relates to the transparency of how the AI agent makes decisions. If leaders were to rely on AI agents for COA development or analysis, they would want to understand those decisions. In or-der to support increased understanding, researchers engaged with stakeholders to determine visualization requirements and developed initial prototypes for stakeholder feedback in order to support increased understanding of AI-generated decisions and recommendations. This report describes the prototype visualizations developed to support the use case of a mission planner and an AI agent trainer. The prototypes include training results charts, heat map visualizations of agent paths, weight matrix visualizations, and ablation testing graphs.00The prototype visualizations developed to support the use case of a mission planner and an AI agent trainer include training results charts, heat map visualizations of agent paths, weight matrix visualizations, and ablation testing graphs.['Christina H. Rinaudo', 'William Leonard', 'Jaylen Hopson', 'Christopher Morey', 'Robert Hilborn', 'Theresa Coumbe']2024-04-17T00:00:00https://www.semanticscholar.org/paper/7d796ad1c3dd309aaf0b47fdfb00179a9688039e fa7585fe71ae95e1b9fcc773b0c142f1601c0f95Abstract This study investigated organizational employees' opinions and acceptance of AI-based service assistants using the Behavioral Reasoning Theory (BRT). The behavioural reasoning theory-based study included 50 hotel industry executives, HR leaders, and employees. Themes were identified by thematic analysis of observations, focus groups, and participant interviews. This study used comparative thematic analysis and MAXQDA automated content analysis. It examines "reasons for" and "reasons against" adoption from a least developing nation's perspective. The reasons are personalisation, interactivity, perceived intelligence, anthropomorphism, language difficulties, technology phobia, service failure recovery, and inadequate infrastructure. "Reasons for" positively affect mindset and adoption intention, whereas "reasons against" negatively affect them. Financial risks, technological infrastructure issues, data security issues, and a lack of an organisational strategy are also seen in Bangladesh's AI deployment. The study provides practical insights for hotel industry practitioners, managers, and employees, as well as system designers and developers of AI-driven service solutions, on AI assistant adoption. Behavioural reasoning theory is used for the first time to examine hotel employees' attitudes and intentions to use AI-based service assistants. This study is a cross-sectional investigation that is carried out within certain, limited industrial sectors. Longitudinal studies can be conducted to generalize the outcome of this study. JEL classification numbers: M21, M30, 010. Keywords: AI service assistant, Employee adoption, Behavioral reasoning theory, Hotel industry.Advances in Management and Applied Economics00Behavioural reasoning theory is used for the first time to examine hotel employees' attitudes and intentions to use AI-based service assistants as well as system designers and developers of AI-driven service solutions.Advances in Management and Applied Economics['Tarikul Islam', 'Erhua Zhou']2024-04-17T00:00:00https://www.semanticscholar.org/paper/fa7585fe71ae95e1b9fcc773b0c142f1601c0f95 14217c8d3f52566d490ce4b0f61fbc0c00d668a4 This paper investigates the potential of COVID-19 detection using cough, breathing, and voice patterns. Speech-based features, such as MFCC, zero crossing rate, spectral centroid, spectral bandwidth, and chroma STFT are extracted from audio recordings and evaluated for their effectiveness in identifying COVID-19 cases from Coswara dataset. The explainable AI SHAP tool is employed which identified MFCC, zero crossing rate, and spectral bandwidth as the most influential features. Data augmentation techniques like random sampling, SMOTE, Tomek, and Edited Nearest Neighbours (ENN), are applied to improve the performance of various machine learning models used viz. Naive Bayes, K-nearest neighbours, support vector machines, XGBoost, and Random Forest. Selecting the top 20 features achieves an accuracy of 73%, a precision of 74%, a recall of 94%, and an F1-score of 83% using the Random Forest model with the Tomek sampling technique. These findings demonstrate that a carefully selected subset of features can achieve comparable performance to the entire feature set while maintaining a high recall rate. The success of the Tomek undersampling technique highlights the ability of model to handle sparse clinical data and predict COVID-19 and associated diseases using speech-based features.Journal of Intelligent &amp; Fuzzy Systems260The success of the Tomek undersampling technique highlights the ability of model to handle sparse clinical data and predict COVID-19 and associated diseases using speech-based features.Journal of Intelligent &amp; Fuzzy Systems['Aashitha L. Shamma', 'Susmitha Vekkot', 'Deepa Gupta', 'Mohammed Zakariah', 'Y. Alotaibi']2024-04-17T00:00:00https://www.semanticscholar.org/paper/14217c8d3f52566d490ce4b0f61fbc0c00d668a4 f57c512059ad999c11b9f0ae48b88b7ba26b7892The use of AI/ML technologies has provided breakthrough performance in automated predictive data analytics. With the increasing amount of data available during drilling operations, data driven AI/ML solutions lay out the future of current technologies for drilling optimization and hazard prevention. Fast adoption and appeal of these technologies to the industry could be explained by a few reasons: AI/ML enables digital transformation by using only real-time data without extensive requirements for contextual data so that engineering and data input processes can be fully automated;AI/ML solutions predict outputs based on the data trends allowing to solve problems where conventional models are hard to implement or are not sensitive enough to identify subtle anomalies;Targeted solutions address specific problems and become more applicable in the modern digital ecosystem,Due to previous reasons, such technologies are easier to implement and to scale up in the operational environment. Successful adoption of AI/ML technologies lies in its validation and trust in the operational environment. Based on the project experience from various parts of the world, prerequisites for building trust have proven to be: high performance AL/ML technology;matured IT infrastructure with relevant support services to enable digital transformation;monitoring specialists in an established RTOC or rigsite team to validate solution decisions;good communication protocol and established responsibilities of the RTOC and rig team to validate the impact of the predictions and to apply for operations. The success factors are consequently related to technology, infrastructure and "soft" aspects like work processes, team interactions and defined roles and responsibilities. Each of these areas will be addressed individually.Day 1 Wed, April 17, 202400Day 1 Wed, April 17, 2024['S. Schaefer', 'O. Revheim']2024-04-17T00:00:00https://www.semanticscholar.org/paper/f57c512059ad999c11b9f0ae48b88b7ba26b7892 47c685a333dd4619944574fe5046530104a3c7a7Human beings are endowed with a natural curiosity and creativity, which motivate them to learn new things from their interactions with the world. Human learning has involved exploration and experimentation, which have allowed humans to discover new facts and principles, and to invent new artifacts and systems. Human learning has also affected human evolution, both genetically and culturally, as humans have adjusted to different situations and demands in their environments. However, in the current world, human learning is largely facilitated by artificial intelligence (AI) tools, which are programs that can perform tasks that usually require human intelligence, such as comprehension, reasoning, problem-solving, and communication. AI tools can support humans in their learning endeavors, by giving them access to enormous amounts of information, and by delivering them customized and interactive assistance and feedback. AI tools can also amplify human creativity and innovation, by generating novel and diverse content, such as code, poems, essays, songs, and more. But what are the effects of this dependence on AI tools for human learning and evolution? Does it boost or diminish human curiosity and creativity? Does it enable or limit human autonomy and agency? Does it foster or hamper human diversity and collaboration? These are some of the questions that this topic will explore, by evaluating the pros and cons of using AI tools for human learning, and the ethical and social issues that arise from this phenomenon. [28] Today when we look around us we observe the advancement in technology has brought a lot of comfort to our lives in terms of traveling, education, or enjoying content virtually. [29] Talking about our basic requirements, technology has become so friendly that we can learn everything through E-Learning. Everyone only wondered about having an AI which will help in making our lives easy. The latest concept in terms of AI which is widely received and accepted by the people everywhere around the Globe is the Open AI that is Chat Gpt, Gemini, Copilot. All of these AI helps us in decision making or cutting our chase short for finding solutions for either lengthy solutions like writing a summary related to something or Questions which are easy to solve but difficult to look for solutions. About a quarter (27%) of Americans say they interact with artificial intelligence almost constantly or several times a day. Artificial intelligence (AI) is used in a variety of ways, including online product recommendations, facial recognition software and chatbots. One in six (17%) adults reported that they can often or always recognise when they are using AI, one in two (50%) adults reported that they can some of the time or occasionally recognise when they are using AI, one in three (33%) adults reported that they can hardly ever or never recognise when they are using AI. [26] In this project we are testing the dependence upon the recently emerged Open AI tools such as ChatGPT, Google Bard, Bing. Our motive is to find out whether people are using these powerful tools to help in their academics or other tasks only or do they take advice from these tools in their financial planning as well.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This topic will explore the dependence upon the recently emerged Open AI tools such as ChatGPT, Google Bard, Bing to find out whether people are using these powerful tools to help in their academics or other tasks only or do they take advice from these tools in their financial planning as well.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Mr. Gunjan Pandey']2024-04-17T00:00:00https://www.semanticscholar.org/paper/47c685a333dd4619944574fe5046530104a3c7a7 389958027a579135fe4ce6376134a0d8114fb890Scientific Reports370The clinical validation of Segtnan™ is presented, with results demonstrated testing capacity increase of 750%, 60%, and 5% at prevalence rates of 0.05%, 22%, and 50%, respectively.Scientific Reports['Farzin Kamari', 'Esben Eller', 'Mathias Bøgebjerg', 'Ignacio Martínez Capella', 'Borja Arroyo Galende', 'Tomas Korim', 'Pernille Øland', 'Martin Lysbjerg Borup', 'Anja Rådberg Frederiksen', 'Amir Ranjouriheravi', 'Ahmed Faris Al-Jwadi', 'Mostafa Mansour', 'Sara Hansen', 'Isabella Diethelm', 'Marta Burek', 'Federico Alvarez', 'Anders Glent Buch', 'N. Mojtahedi', 'Richard Röttger', 'E. A. Segtnan']2024-04-17T00:00:00https://www.semanticscholar.org/paper/389958027a579135fe4ce6376134a0d8114fb890 dfaacc62bd22104bee3edafc7a780b2e3529c226The burgeoning spread of synthetic media disrupts content verification and threatens online trust. This research proposes Guardian AI, a robust deepfake detection system achieving 93% accuracy by harnessing the synergistic power of facial recognition, image forensics, and machine learning. Guardian AI extracts diverse features from videos: facial recognition models analyze landmarks, expressions, and lip-syncing for inconsistencies; image forensics algorithms detect manipulated pixels, lighting patterns, and compression artifacts; and temporal analysis captures unnatural head movements and frame-to-frame motion discrepancies. These multifaceted features are then fused and fed into a rigorously trained deep learning model on multi-modal datasets of real and deepfake videos. Guardian AI classifies video inputs as real or fake, providing a confidence score for its prediction. By leveraging facial recognition's subtle inconsistency detection, image forensics' manipulation artifact identification, and machine learning's robust multi-cue integration, Guardian AI achieves exceptional accuracy and generalizability, adapting to evolving deepfake creation techniques with its diverse training data. This study signifies a significant contribution to content verification by delivering a high accuracy deepfake detection system, paving the way for a more reliable and trustworthy online environment.International Conference on Intelligent Cloud Computing70This study signifies a significant contribution to content verification by delivering a high accuracy deepfake detection system, paving the way for a more reliable and trustworthy online environment.2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS)['Karthikeyan K', 'Swetha R', 'Deepanraj S', 'Dhandapani S']2024-04-17T00:00:00https://www.semanticscholar.org/paper/dfaacc62bd22104bee3edafc7a780b2e3529c226 a8ef54bcc5d4c356e157c60e3fb55b70b1f76788Many responsible AI resources, such as toolkits, playbooks, and checklists, have been developed to support AI practitioners in identifying, measuring, and mitigating potential fairness-related harms. These resources are often designed to be general purpose in order to be applicable to a variety of use cases, domains, and deployment contexts. However, this can lead to decontextualization, where such resources lack the level of relevance or specificity needed to use them. To understand how AI practitioners might contextualize one such resource, an AI fairness checklist, for their particular use cases, domains, and deployment contexts, we conducted a retrospective contextual inquiry with 13 AI practitioners from seven organizations. We identify how contextualizing this checklist introduces new forms of work for AI practitioners and other stakeholders, as well as opening up new sites for negotiation and contestation of values in AI. We also identify how the contextualization process may help AI practitioners develop a shared language around AI fairness, and we identify tensions related to ownership over this process that suggest larger issues of accountability in responsible AI work.Proceedings of the ACM on Human-Computer Interaction510This work identifies how contextualizing an AI fairness checklist introduces new forms of work for AI practitioners and other stakeholders, as well as opening up new sites for negotiation and contestation of values in AI.Proceedings of the ACM on Human-Computer Interaction['Michael A. Madaio', 'Jingya Chen', 'Hanna Wallach', 'Jennifer Wortman Vaughan']2024-04-17T00:00:00https://www.semanticscholar.org/paper/a8ef54bcc5d4c356e157c60e3fb55b70b1f76788 e8b18bdb2196fbfa50c4e56a8626a2e2bb168197In the coming years, Artificial Intelligence (AI) will be applied as a teammate that works alongside and collaborates with humans. Prior research in teaming and CSCW has shown that teammates have the ability to change the thoughts and behaviors of each other through simple interactions in a process known as social influence. However, to date, research has yet to identify the social influence that AI teammates could have in these human-AI teams, which has led to a limited understanding of how AI teammates will change the behaviors of their human teammates. To remedy this gap, we conduct a mixed-methods study (N=33) with young individuals to explore how humans could behaviorally adapt and perceive their behavioral adaptation due to interaction with an AI teammate. Qualitative results report that perceived three unique stages they had to experience for the social influence of their AI teammate to lead to adaptation (i.e., perceiving a sense of control, identifying a technological or performative justification, and gaining first-hand experience). Quantitative results validate and illustrate the results of this perceived process, as results show that participants adapted their behaviors to complement the behaviors of different types of AI teammates. This study contributes to the CSCW/HCI field by developing an initial understanding of AI teammates' social influence in human-AI teams, which will be a pivotal design and research consideration in future efforts.Proceedings of the ACM on Human-Computer Interaction620A mixed-methods study with young individuals to explore how humans could behaviorally adapt and perceive their behavioral adaptation due to interaction with an AI teammate, and develops an initial understanding of AI teammates' social influence in human-AI teams.Proceedings of the ACM on Human-Computer Interaction['Christopher Flathmann', 'Wen Duan', 'Nathan J. Mcneese', 'Allyson I. Hauptman', 'Rui Zhang']2024-04-17T00:00:00https://www.semanticscholar.org/paper/e8b18bdb2196fbfa50c4e56a8626a2e2bb168197 d8c20ea7ab5f9e73a02446becd7d88bc3ff3b1f9The rapid advancement of artificial intelligence (AI) has impacted society in many aspects. Alongside this progress, concerns such as privacy violation, discriminatory bias, and safety risks have also surfaced, highlighting the need for the development of ethical, responsible, and socially beneficial AI. In response, the concept of trustworthy AI has gained prominence, and several guidelines for developing trustworthy AI have been proposed. Against this background, we demonstrate the significance of psychological research in identifying factors that contribute to the formation of trust in AI. Specifically, we review research findings on interpersonal, human-automation, and human-AI trust from the perspective of a three-dimension framework (i.e., the trustor, the trustee, and their interactive context). The framework synthesizes common factors related to trust formation and maintenance across different trust types. These factors point out the foundational requirements for building trustworthy AI and provide pivotal guidance for its development that also involves communication, education, and training for users. We conclude by discussing how the insights in trust research can help enhance AI's trustworthiness and foster its adoption and application.Frontiers in Psychology1180It is demonstrated the significance of psychological research in identifying factors that contribute to the formation of trust in AI and how the insights in trust research can help enhance AI's trustworthiness and foster its adoption and application.Frontiers in psychology['Yugang Li', 'Baizhou Wu', 'Yuqi Huang', 'Shenghua Luan']2024-04-17T00:00:00https://www.semanticscholar.org/paper/d8c20ea7ab5f9e73a02446becd7d88bc3ff3b1f9 515d1f8f8e76cb379246e8c6cc94c114e05b5d4dThe economy in the modern world is greatly influenced by artificial intelligence (AI). The purpose of this paper is to determine the impact of AI quantitative relationships on the country's economic parameters, including GDP per Capita. Historical data analysis is used in the research. A new mathematical algorithm for the magnitude of a vector of technological level and AI factors has been developed. The study calculated the economic effect of AI on GDP per Capita. As a result of the analysis, it was revealed that there is a positive Pearson correlation between growth. On AI and GDP per Capita, that is, to increase GDP per Capita by 1%, an average increase of 23.9% in AI is required.International journal of business management230There is a positive Pearson correlation between growth and AI, and to increase GDP per Capita by 1%, an average increase of 23.9% in AI is required.International Journal of Business and Management['Davit Gondauri', 'M. Batiashvili', 'N. Enukidze']2024-04-17T00:00:00https://www.semanticscholar.org/paper/515d1f8f8e76cb379246e8c6cc94c114e05b5d4d db1c47b432ea181da19cff33dc88078a1dce7f99"STEVE JOBS: Pioneering AI in Software Engineering" presents a revolutionary approach to software development, integrating large language models (LLMs) into traditional methodologies. This paradigm, inspired by the visionary leadership of Steve Jobs, leverages LLMs to streamline the software development lifecycle (SDLC), incorporating both the waterfall model and agile methodology. The implementation involves the orchestration of software agents, representing various roles in the development process, fostering collaborative dialogue through natural language communication. This innovative framework, inspired by the principles embodied by Steve Jobs, facilitates efficient decision-making and enhances productivity across all stages of software development. Moreover, empirical studies demonstrate the versatility and effectiveness of this approach, highlighting its potential to transform the software engineering landscape. By pioneering AI integration in software engineering, "STEVE JOBS" opens doors to new possibilities, heralding a future where technology and human ingenuity converge to drive unprecedented innovation.International Research Journal on Advanced Engineering Hub (IRJAEH)100By pioneering AI integration in software engineering, "STEVE JOBS" opens doors to new possibilities, heralding a future where technology and human ingenuity converge to drive unprecedented innovation.International Research Journal on Advanced Engineering Hub (IRJAEH)['Priyadharasini M', 'Sriram S N', 'Sudhar Aathith T', 'Vigneshwaran N']2024-04-17T00:00:00https://www.semanticscholar.org/paper/db1c47b432ea181da19cff33dc88078a1dce7f99 30ba23e0611c171d6ee7675ca3265b76c343fe38This review paper explores the transformative impact of artificial intelligence (AI) on personalized medicine and its potential to revolutionize healthcare outcomes. AI technologies, ranging from data analysis and interpretation to diagnostic tools and treatment planning, offer unprecedented opportunities for tailoring medical interventions to individual patient characteristics. Through sophisticated algorithms, AI facilitates the analysis of complex biological data, predicts disease risks, and enhances diagnostic accuracy. Furthermore, AI-powered personalized medicine promises to expand access to high-quality healthcare and address global health disparities. However, challenges such as data privacy, bias, and regulatory hurdles must be addressed to ensure the responsible integration of AI into healthcare practices. This paper underscores the importance of interdisciplinary collaboration, ethical considerations, and policy-making efforts in harnessing AI's potential to advance personalized medicine responsibly. Keywords: Artificial Intelligence, Personalized Medicine, Healthcare, Data Analysis, Ethical Considerations, Interdisciplinary CollaborationEngineering Science &amp; Technology Journal00The importance of interdisciplinary collaboration, ethical considerations, and policy-making efforts in harnessing AI's potential to advance personalized medicine responsibly is highlighted in harnessing AI's potential to advance personalized medicine responsibly.Engineering Science &amp; Technology Journal['Francisca Chibugo Udegbe', 'Ogochukwu Roseline Ebulue', 'Charles Chukwudalu Ebulue', 'Chukwunonso Sylvester Ekesiobi']2024-04-17T00:00:00https://www.semanticscholar.org/paper/30ba23e0611c171d6ee7675ca3265b76c343fe38 3c556c1f7c3957de649bb1763152aaf401205a83Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.00This research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior.['Christina Rinuado', 'William Leonard', 'Christopher Morey', 'Theresa Coumbe', 'Jaylen Hopson', 'Robert Hilborn']2024-04-17T00:00:00https://www.semanticscholar.org/paper/3c556c1f7c3957de649bb1763152aaf401205a83 9f5e65fe83780f0585d3f6ff0a9f8173ee6fc285KI - Künstliche Intelligenz1210An overview of the topic is given, focusing agricultural processes and technology in Central-European style arable farming, which could be part of the transformation process of agriculture that is emerging world-wide in response to the UN global sustainable development goals.KI - Künstliche Intelligenz['Benjamin Kisliuk', 'Jan Christoph Krause', 'Hendrik Meemken', 'Juan Carlos Saborío Morales', 'Henning Müller', 'Joachim Hertzberg']2024-04-17T00:00:00https://www.semanticscholar.org/paper/9f5e65fe83780f0585d3f6ff0a9f8173ee6fc285 63fd7f95a4c30286acfdb94327aae7f75b5d36efAI &amp; SOCIETY370Six key themes—interface methods, incrementalism, metrics, AI vernacular, applied domains, and competitive advantages—are explored, illustrating the role of these challenges in shaping AI research and development.AI &amp; SOCIETY['Sam Hind', 'Fernando N. van der Vlist', 'Max Kanderske']2024-04-17T00:00:00https://www.semanticscholar.org/paper/63fd7f95a4c30286acfdb94327aae7f75b5d36ef fd5c9e2890ecb861a7192c458e8f7158e36eb03aDesigners widely accept AI as a partner in the design process for its efficient and intelligent decision-making. However, AI is often not perfect, and AI error often makes humans dumbfounded. Literature has pointed out the value of such AI error, while still leaving its inspiration essence and application strategies uncharted from the practice perspective. This work focuses on bridging the practice gap by looking into and exploiting the imaginative "mislabeled" objects of object detection models. To gain insights into the inspiration of AI "error", we collected a dedicated AI "error" dataset from object detection and invited eight designers to share divergent comments on the "mislabeled" objects. Coding was then performed on the comments, which summarizes the inspiration of AI "error" into six atomic dimensions. Subsequently, we took a step further to an exploratory study, a comparative ideation experiment with 20 designers, investigating how to apply these inspiration dimensions to create ideas. Questionnaire and interview results revealed that essential inspiration of AI "error" could positively activate creativity, especially the "Outline" dimension. A design model CETR is then formulated by summarizing the application of atomic inspiration of "error" into four forms of creativity, which could be taken as a guideline for cooperative design with AI "error". In addition, we also sketch two approaches to generate more inspiring and applicable AI "error", elaborate on two principal characteristics of AI "error" for promoting creativity, and propose three strategies for better co-creating with AI "error". Finally, we provide insight into design research about AI self-awareness and human-AI collaboration.Proceedings of the ACM on Human-Computer Interaction540This work focuses on bridging the practice gap by looking into and exploiting the imaginative "mislabeled" objects of object detection models and providing insight into design research about AI self-awareness and human-AI collaboration.Proceedings of the ACM on Human-Computer Interaction['Fang Liu', 'Junyan Lv', 'Shenglan Cui', 'Zhilong Luan', 'Kui Wu', 'Tongqing Zhou']2024-04-17T00:00:00https://www.semanticscholar.org/paper/fd5c9e2890ecb861a7192c458e8f7158e36eb03a 23042c8211ad84ebbd123ac1078009601925d6a0This systematic review explores the advancements and challenges associated with the integration of artificial intelligence (AI) in promoting technical literacy. Technical literacy is increasingly important in today's digital age, where understanding and utilizing technology are essential skills. AI has the potential to enhance technical literacy by providing personalized learning experiences, facilitating hands-on learning, and offering innovative tools and resources. However, the integration of AI in education also presents challenges, such as ensuring equitable access, addressing ethical considerations, and overcoming technical barriers. The review examines a range of studies and literature related to AI integration for technical literacy, focusing on key themes such as personalized learning, hands-on learning, and innovative tools. It highlights the potential of AI to transform technical education by providing tailored learning experiences that cater to individual needs and preferences. AI-driven tools, such as simulations, virtual laboratories, and intelligent tutoring systems, have been shown to enhance student engagement and understanding of technical concepts. Despite the benefits, the review also identifies challenges associated with AI integration, including the need for teacher training, concerns about data privacy, and the risk of reinforcing existing inequalities. Addressing these challenges requires careful planning, collaboration between educators and technology developers, and a commitment to ensuring equitable access to AI-driven educational resources. Overall, this review provides insights into the current state of AI integration for technical literacy and highlights the opportunities and challenges associated with this approach. By addressing these challenges and leveraging the potential of AI, educators can enhance technical literacy and prepare students for success in a technology-driven world. Keywords: Advancement, Challenges, AI, Integration, Technical Literacy.Engineering Science &amp; Technology Journal00The potential of AI to transform technical education by providing tailored learning experiences that cater to individual needs and preferences is highlighted and the opportunities and challenges associated with this approach are highlighted.Engineering Science &amp; Technology Journal['Babajide Tolulope Familoni', 'Nneamaka Chisom Onyebuchi']2024-04-17T00:00:00https://www.semanticscholar.org/paper/23042c8211ad84ebbd123ac1078009601925d6a0 c71010b49ea104e9adc95869819aae8d39746afeGenerative artificial intelligence is a cutting-edge technology that creates new content in a variety of media which includes text, graphics, and music, just like human creativity. To fully explore the possibilities of generative AI, this research article discusses the underlying technologies of Transformer models, Variational autoencoder (VAEs), and Generative Adversarial Networks (GANs). It explores the various uses of generative AI in IT operations, data augmentation, and natural language processing, emphasizing how it is revolutionizing these fields. With an emphasis on responsible AI use, ethical issues about privacy problems and biases in created information are also covered. To demonstrate the field's ongoing development, recent developments in generative AI—such as Sora and DEVIN AI—are reviewed. The goal of this paper is to shed light on how generative AI may transform several industries while properly addressing societal issues.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT03Light is shed on how generative AI may transform several industries while properly addressing societal issues as well as the various uses of generative AI in IT operations, data augmentation, and natural language processing, emphasizing how it is revolutionizing these fields.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Deepak Kumar Sahu']2024-04-17T00:00:00https://www.semanticscholar.org/paper/c71010b49ea104e9adc95869819aae8d39746afe 08867ff0f03ce3ff406ac67a41af46c883a9df22This article comprehensively reviews the impact of artificial intelligence (AI) and automation on employment. As AI and automation technologies continue to advance and be adopted across various sectors, concerns have been raised about their potential to displace jobs and exacerbate income inequality. The article examines the existing literature on the subject, discussing the potential for job substitution and changes in employment structure. It also explores the concept of skill-biased technological change and its implications for the labor market. The review highlights the need for proactive policies to address the challenges posed by automation, such as investing in education and training, fostering innovation and job creation, and considering measures like universal basic income. The article concludes by emphasising the importance of understanding and managing the impact of AI and automation on employment to ensure a more equitable and prosperous future of work.International Journal of Social Sciences and Public Administration241The need for proactive policies to address the challenges posed by automation is highlighted, such as investing in education and training, fostering innovation and job creation, and considering measures like universal basic income.International Journal of Social Sciences and Public Administration['Jiaxing Du']2024-04-17T00:00:00https://www.semanticscholar.org/paper/08867ff0f03ce3ff406ac67a41af46c883a9df22 e0c094448e29862d0916f2b52da76eb217962c8bOBJECTIVE To support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care. STUDY DESIGN Citizens' jury, deliberating the question: "Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?" SETTING, PARTICIPANTS Thirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March - 2 April 2023): fifteen days online and three days face-to-face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face-to-face meeting. MAIN OUTCOME MEASURES Jury recommendations, with reasons. RESULTS The jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients' rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication. CONCLUSIONS The deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.Medical Journal of Australia101Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.The Medical journal of Australia['Stacy M Carter', 'Yves Saint James Aquino', 'Lucy Carolan', 'E. Frost', 'Chris Degeling', 'Wendy A Rogers', 'Ian A Scott', 'Kjl Bell', 'Belinda Fabrianesi', 'Farah Magrabi']2024-04-17T00:00:00https://www.semanticscholar.org/paper/e0c094448e29862d0916f2b52da76eb217962c8b 29c144b1aca278c1bde18cf747168c5bfec23e44Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.Frontiers in Neurology570The progress of artificial intelligence radiomics in the study of intracranial aneurysms is reviewed, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.Frontiers in Neurology['Zhongjian Wen', 'Yiren Wang', 'Yuxin Zhong', 'Yiheng Hu', 'Cheng Yang', 'Yan Peng', 'Xiang Zhan', 'Ping Zhou', 'Zhen Zeng']2024-04-17T00:00:00https://www.semanticscholar.org/paper/29c144b1aca278c1bde18cf747168c5bfec23e44 484bf00692c6c56aca31980df7325df3f743bb50The world is being reshaped under global economic development driven by new advances in information technology. Artificial intelligence, an essential potential technology, will play a vital role in technological change and industrial upgrades. Exploring the relationship between government subsidies, green innovation, and total factor productivity will help us analyze government decisions’ effects and better promote artificial intelligence’s technological innovation process. Based on data from China’s listed artificial intelligence companies from 2011 to 2020, this study uses the Levinsohn–Petrin method to measure the total factor productivity of companies and analyzes the impact of government subsidies on the total factor productivity of AI companies, the mediating effect of green innovation, and the moderating effect of intellectual property protection intensity. The research results show that (1) government subsidies can promote the total factor productivity of AI enterprises; (2) green innovation capabilities play a mediating role between government subsidies and enterprise total factor productivity, and government subsidies can indirectly promote green innovation to promote the improvement of total factor productivity effectively; (3) in the AI industry, the promotion effect of government subsidies on total factor productivity is more significant among state-owned enterprises, while the impact mechanism of government subsidies on private enterprises is not significant; and (4) the intensity of intellectual property protection has played a positive moderating role in the impact of government subsidies for artificial intelligence enterprises on total factor productivity. However, the current intensity of intellectual property protection remains unable to promote improvements in enterprise total factor productivity by stimulating green innovation. The research results will help us better understand the relationship between government subsidies and the development of corporate economic benefits and promote more scientific and effective government decision-making.Sustainability550The research results show that government subsidies can promote the total factor productivity of AI enterprises and green innovation capabilities play a mediating role between government subsidies and enterprise total factor productivity, and government subsidies can indirectly promote green innovation to promote the improvement of total factor productivity effectively.Sustainability['Guangwei Zhang', 'Yahan Shi', 'Nuozhou Huang']2024-04-17T00:00:00https://www.semanticscholar.org/paper/484bf00692c6c56aca31980df7325df3f743bb50 c0f01eae93d8b6573284f73b18e49082cbd2046dObjetive: The research aims to examine current educational practices related to the understanding and utilization of artificial intelligence (AI) in the educational field.   Theoretical framework: The research collects information related to the conceptual framework, detailing concepts involving the research variables; likewise, a compilation of background research from similar studies in the years 2022 and 2023 is conducted.   Method: The research employs a mixed methodology, incorporating qualitative and quantitative data collection methods. A comprehensive review of academic literature and official documents is conducted to identify prevalent educational practices in the field of AI. Additionally, surveys and interviews are designed targeting teachers, students, and AI experts to gather detailed information about their experiences, knowledge, and perceptions regarding AI in education.   Results and conclusion: The results of the study reveal a diverse range of educational practices related to AI. These practices include the use of AI tools for personalized learning and the integration of AI concepts into the school curriculum. Common challenges such as the lack of resources and adequate training for teachers, as well as ethical and privacy concerns surrounding AI use in the classroom, are identified.   Research implications: The findings of this research have significant implications for educational practices related to AI. They underscore the need for enhanced teacher training, the development of ethical guidelines, and the promotion of digital literacy in educational settings. Additionally, the study highlights the importance of addressing challenges such as resource constraints and privacy concerns to facilitate the effective integration of AI into education.   Originality/Value: This research contributes to the existing literature by providing insights into current educational practices regarding AI. By employing a mixed methodology approach, the study offers a comprehensive understanding of the subject, identifying common practices and challenges. Moreover, the research suggests practical recommendations for promoting the responsible use of AI in education and calls for further exploration of innovative approaches in this field.Revista de Gestão Social e Ambiental350The research suggests practical recommendations for promoting the responsible use of AI in education and calls for further exploration of innovative approaches in this field, employing a mixed methodology approach.Revista de Gestão Social e Ambiental['Edwin Reyes-Villalba', 'Rocio Elena Reyes-Arco', 'Benjamín Maraza-Quispe']2024-04-17T00:00:00https://www.semanticscholar.org/paper/c0f01eae93d8b6573284f73b18e49082cbd2046d 984e96cf730d3bc75fa6464b6d05cd5fa1de9baaThis paper explores the multifaceted impact of Artificial Intelligence (AI) on accounting practices, addressing key dimensions of advancements, challenges, and opportunities. The definition of AI in accounting is established, tracing its historical context. Advancements are detailed, encompassing the automation of routine tasks, predictive analytics, and fraud detection. Challenges in implementation, including data quality issues, workforce adaptation, and ethical considerations, are discussed. Opportunities arising from AI integration, such as enhanced decision-making, cost reduction, and strategic financial management, are highlighted through case studies. The evolving role of accountants in the AI era is examined, emphasizing a shift towards strategic interpretation and decision-making. Future trends, including the integration of AI with emerging technologies, continued advancements in machine learning, and regulatory implications, are explored. The conclusion recaps key advancements, challenges, and opportunities, envisioning a future where AI and accountants collaborate to shape a dynamic and resilient landscape for accounting practices.. Keywords:  Artificial Intelligence, Accounting, Advancements, Challenges, and Opportunities.International Journal of Management &amp; Entrepreneurship Research00A future where AI and accountants collaborate to shape a dynamic and resilient landscape for accounting practices is envisioned, envisioning a future where AI and accountants collaborate to shape a dynamic and resilient landscape for accounting practices.International Journal of Management &amp; Entrepreneurship Research['Oluwatobi Opeyemi Adeyelu', 'Chinonye Esther Ugochukwu', 'Mutiu Alade Shonibare']2024-04-17T00:00:00https://www.semanticscholar.org/paper/984e96cf730d3bc75fa6464b6d05cd5fa1de9baa 2a1468363b46699c6af01b8193fd565b613fab06In the ever-evolving landscape of the music industry, a transformative wave has erupted with the advent of artificial intelligence (AI) interventions. This technological marvel has not only altered the way we conceive, produce, and consume music, but has injected an unparalleled dynamism into the very fabric of music innovation. This article aims to highlight the research directions - from 2020 to the present - in relation to the binomial of artificial intelligence and music, with the various layers that define this relationship. The final question of this research that is written as a literature review is: to what extent do researchers encourage AI-assisted music practices and what are the possible dangers that result from this attitude?Tehnologii informatice și de comunicație în domeniul muzical / Information and communication Technologies in Musical Field00The research directions - from 2020 to the present - in relation to the binomial of artificial intelligence and music, with the various layers that define this relationship are highlighted.Tehnologii informatice și de comunicație în domeniul muzical / Information and communication Technologies in Musical Field['Alexandra Belidou', 'Liviu Iftene']2024-04-17T00:00:00https://www.semanticscholar.org/paper/2a1468363b46699c6af01b8193fd565b613fab06 adb7c664f5606e1b48ccbdcd227e407da32320ccIn the context of the rapid development of tools derived from Generative Artificial Intelligence (AI) and their gradual incorporation into education, various discussions and dilemmas arise regarding the use of these technologies in educational practices. The objective of this work, which presents a qualitative and exploratory approach, developed from a bibliographic research, is to identify and list some of the main concepts, categories, positive aspects, limits, and risks related to the use and implementation of AI in education, specifically in the teaching-learning process within formal educational environments, raising some perspectives and issues accumulated in this research field, which, from our point of view, is in a stage of construction. In this work, we adopt the ethical-political-pedagogical perspective of the constitution of so-called integrated teaching as the realization of an omnilateral and non-fragmented education project. For this purpose, we propose the following issue for discussion: does the gradual popularization of various AI tools in the teaching-learning process represent a positive trend or risks from the perspective of building an integrated education?Observatorio de la Economía Latinoamericana00This work adopts the ethical-political-pedagogical perspective of the constitution of so-called integrated teaching as the realization of an omnilateral and non-fragmented education project.OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA['Ivan Targino Ponciano Filho', 'Ângela Nóbrega Lima', 'Gabriel Dias de Carvalho Júnior', 'Ronaldo Júlio Baganha']2024-04-17T00:00:00https://www.semanticscholar.org/paper/adb7c664f5606e1b48ccbdcd227e407da32320cc a82a866564497c8005a8342123bc07dd3905c9aaPrivacy is a human right. It ensures that individuals are free to engage in discussions, participate in groups, and form relationships online or offline without fear of their data being inappropriately harvested, analyzed, or otherwise used to harm them. Preserving privacy has emerged as a critical factor in research, particularly in the computational social science (CSS), artificial intelligence (AI) and data science domains, given their reliance on individuals' data for novel insights. The increasing use of advanced computational models stands to exacerbate privacy concerns because, if inappropriately used, they can quickly infringe privacy rights and lead to adverse effects for individuals - especially vulnerable groups - and society. We have already witnessed a host of privacy issues emerge with the advent of large language models (LLMs), such as ChatGPT, which further demonstrate the importance of embedding privacy from the start. This article contributes to the field by discussing the role of privacy and the primary issues that researchers working in CSS, AI, data science and related domains are likely to face. It then presents several key considerations for researchers to ensure participant privacy is best preserved in their research design, data collection and use, analysis, and dissemination of research results.Social Science Research Network300The role of privacy and the primary issues that researchers working in CSS, AI, data science and related domains are likely to face are discussed and several key considerations for researchers to ensure participant privacy is best preserved are presented.ArXiv['Keenan Jones', 'Fatima Zahrah', 'Jason R. C. Nurse']2024-04-17T00:00:00https://www.semanticscholar.org/paper/a82a866564497c8005a8342123bc07dd3905c9aa a060938716294bbbd028b817e8dec4816db25f0eBACKGROUND To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. METHODS This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. RESULTS After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. CONCLUSION AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.Surgical Innovation230AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance, but further research with rigorous validation is required to identify the most effective AI tools for medical education.Surgical innovation['Atinc Tozsin', 'Harun Uçmak', 'Selim Soyturk', 'A. Aydın', 'A. Gozen', 'Maha Al Fahim', 'Selçuk Güven', 'K. Ahmed']2024-04-17T00:00:00https://www.semanticscholar.org/paper/a060938716294bbbd028b817e8dec4816db25f0e 88f76efb4a81ec73a21e7f2cecd11aa9ea238600The rapid evolution of financial technology (fintech) platforms has exponentially increased the volume and sophistication of financial transactions, concurrently elevating the risk and complexity of fraudulent activities. This necessitates a paradigm shift in fraud detection methodologies towards more agile, accurate, and predictive solutions. This paper presents a comprehensive study on the transformative potential of advanced Artificial Intelligence (AI) algorithms in enhancing fintech fraud detection mechanisms. By leveraging cutting-edge AI techniques including deep learning, machine learning, and natural language processing, this research aims to develop a robust fraud detection framework capable of identifying, analyzing, and preventing fraudulent transactions in real-time. Our methodology encompasses the deployment of several AI algorithms on extensive datasets comprising genuine and fraudulent financial transactions. Through a comparative analysis, we identify the most effective algorithms in terms of accuracy, efficiency, and scalability. Key findings reveal that deep learning models, particularly those employing neural networks, outperform traditional machine learning models in detecting complex and nuanced fraudulent activities. Furthermore, the integration of natural language processing enables the extraction and analysis of unstructured data, significantly enhancing the detection capabilities. Conclusively, this paper underscores the critical role of advanced AI algorithms in revolutionizing fintech fraud detection. It highlights the superior performance of AI-based models over conventional methods, offering fintech platforms a more dynamic and predictive approach to fraud prevention. This research not only contributes to the academic discourse on financial security but also provides practical insights for fintech companies striving to safeguard their operations against fraud. Keywords:  Artificial Intelligence, Fintech, Fraud Detection, Ethical Ai, Regulatory Compliance, Data Privacy, Algorithmic Bias, Predictive Analytics, Blockchain Technology, Quantum Computing, Interdisciplinary Collaboration, Innovation, Transparency, Accountability, Continuous Learning, Ethical Principles, Real-Time Processing, Financial Sector.Finance &amp; Accounting Research Journal00Key findings reveal that deep learning models, particularly those employing neural networks, outperform traditional machine learning models in detecting complex and nuanced fraudulent activities.Finance &amp; Accounting Research Journal['Philip Olaseni Shoetan', 'Babajide Tolulope Familoni']2024-04-17T00:00:00https://www.semanticscholar.org/paper/88f76efb4a81ec73a21e7f2cecd11aa9ea238600 ed03b4ab69aa87e367701d512c73ad25aa1f2f5bThe article attempts to establish the correlation between artificial intelligence (AI), which has firmly entered the life of modern man, and the basic values of culture - substantive and social. The work examines values such as collectivism, individualism, family values, identity preservation, security, future, hedonism, and their realization in a modern world based on the use of AI. It is emphasized that AI either does not have a significant impact on these values, or helps people to achieve them and creates conditions for the culture to comply with the substantive axiological parameters. The only value that is really threatened under the influence of AI is security. It is concluded that the negative impact of AI on other substantial and social values cannot currently be considered proven: AI only reflects transformations of the value system that occur under the influence of various factors.Общество философия история культура00The article attempts to establish the correlation between artificial intelligence (AI), which has firmly entered the life of modern man, and the basic values of culture - substantive and social and concludes that the negative impact of AI on other substantial and social values cannot currently be considered proven.Общество: философия, история, культура['Evgeniya K. Belikova']2024-04-17T00:00:00https://www.semanticscholar.org/paper/ed03b4ab69aa87e367701d512c73ad25aa1f2f5b 3525b1b11809e4ff9eaf96e09ec74e3e310108caBackground This bibliometric study aimed to identify and analyze the top 100 articles related to artificial intelligence in the field of orthopedics. Methods The articles were assessed based on their number of citations, publication years, countries, journals, authors, affiliations, and funding agencies. Additionally, they were analyzed in terms of their themes and objectives. Keyword co-occurrence, co-citation of authors, and co-citation of references analyses were conducted using VOSviewer (version 1.6.19). Results The number of citations of these articles ranged from 32 to 272, with six papers having more than 200 citations The years of 2019 (n: 37) and 2020 (n: 19) together constituted 56% of the list. The USA was the leading contributor country to this field (n: 61). The most frequently used keywords were “machine learning” (n: 26), “classification” (n: 18), “deep learning” (n: 16), “artificial intelligence” (n: 14), respectively. The most common themes were decision support (n: 25), fracture detection (n: 24), and osteoarthrtitis staging (n: 21). The majority of the studies were diagnostic in nature (n: 85), with only two articles focused on treatment. Conclusions This study provides valuable insights and presents the historical perspective of scientific development on artificial intelligence in the field of orthopedics. The literature in this field is expanding rapidly. Currently, research is generally done for diagnostic purposes and predominantly focused on decision support systems, fracture detection, and osteoarthritis classification.Frontiers in Surgery200This bibliometric study aimed to identify and analyze the top 100 articles related to artificial intelligence in the field of orthopedics and presents the historical perspective of scientific development on artificial intelligence in the field of orthopedics.Frontiers in Surgery['Necmettin Turgut', 'S. Beyaz']2024-04-17T00:00:00https://www.semanticscholar.org/paper/3525b1b11809e4ff9eaf96e09ec74e3e310108ca a73ee918690374b23900c419cb527ecd012068caSurgical Endoscopy281Gastrointestinal surgeons, gastroenterologists, and patients should recognize both the promise and pitfalls of LLM's when utilized for advice on surgical management of GERD.Surgical endoscopy['Bright Huo', 'Elisa Calabrese', 'Patricia Sylla', 'Sunjay S. Kumar', 'Romeo C Ignacio', 'Rodolfo Oviedo', 'Imran Hassan', 'Bethany J Slater', 'Andreas M. Kaiser', 'Danielle S. Walsh', 'Wesley R. Vosburg']2024-04-17T00:00:00https://www.semanticscholar.org/paper/a73ee918690374b23900c419cb527ecd012068ca a2c7d14138a3b9526917a46c8be63bc9f3a9dafe<jats:sec> <jats:title /> <jats:p /> </jats:sec>Recent Advances in Computer Science and Communications00Recent Advances in Computer Science and Communications['Sarika Jain']2024-04-17T00:00:00https://www.semanticscholar.org/paper/a2c7d14138a3b9526917a46c8be63bc9f3a9dafe 28d703f44c91ab3352b3eb4cedd194627ad9bb5fJournal of Special Topics in Information Technology and Management830Information Technology and Management['Jing Chen', 'S. Tajdini']2024-04-17T00:00:00https://www.semanticscholar.org/paper/28d703f44c91ab3352b3eb4cedd194627ad9bb5f 0fa4b26855cda992a44dbacc323b08eefb55e19eهدفت الدراسة إلى تقديم تصور مقترح لتطبيق الذكاء الاصطناعي في الجامعات اليمنية، وتم استخدام المنهج الوصفي التحليلي، بأسلوب الدراسات الاستشرافية (أسلوب دلفاي) للتعرف على متطلبات تطبيق الذكاء الاصطناعي في الجامعات اليمنية من وجهة نظر الخبراء، وتكوّنت عينة الدراسة من الخبراء المختصين ذوي الخبرة الطويلة، والمعرفة الاختصاصية العميقة في الذكاء الاصطناعي ونظم المعلومات الذكية، وبرمجة الحاسوب، وتكنولوجيا المعلومات، وتقنية المعلومات IT، والإدارة والتخطيط، حيث تم اختيار عينة قصدية من الخبراء في الجامعات اليمنية الحكومية والأهلية، بلغت (13) خبيرًا، وتوصلت الدارسة إلى عدة نتائج أهمها: أن هناك توافق لآراء الخبراء حول تطبيق متطلبات أبعاد الذكاء الاصطناعي (بعد المتطلبات التشريعية والتنظيمية، بعد المتطلبات البشرية، بعد المتطلبات التقنية، بعد المتطلبات المالية، بعد المتطلبات الأخلاقية)، حيث كانت نسبة التوافق مرتفعة (96%)، وكانت نسبة التوافق لآراء الخبراء حول أبعاد متطلبات الذكاء الاصطناعي متفاوت، حيث حصل بعد "المتطلبات الأخلاقية" على توافق آراء الخبراء بنسبة (99%)، فيما حصل بعد "المتطلبات التقنية" على توافق آراء الخبراء بنسبة (97%)، بينما حصل بعد" المتطلبات البشرية" وبعد "المتطلبات المالية" على توافق آراء الخبراء بنسبة (96%)، وأخيرًا حصل بعد "المتطلبات التشريعية والتنظيمية" على توافق آراء الخبراء بنسبة (94%)، ولضمان تحقيق النتائج المرجوة من الدراسة الحالية توصي الدراسة بضرورة تبني وتطبيق التصور المقترح لتطبيق الذكاء الاصطناعي في الجامعات اليمنية.Journal of Engineering and Technological Sciences00Journal of Engineering and Technological Sciences - JOEATS['عامر سعد أحمد جبران', 'خالد صالح يحيى المساجدي']2024-04-17T00:00:00https://www.semanticscholar.org/paper/0fa4b26855cda992a44dbacc323b08eefb55e19e 0fe6d680dcf081c486a74560e2bd9ca3322bc714AI &amp; SOCIETY00AI &amp; SOCIETY['Puneet Sharma']2024-04-17T00:00:00https://www.semanticscholar.org/paper/0fe6d680dcf081c486a74560e2bd9ca3322bc714 7bdc6d7fb733dc053b1f1287867ed09921939bbaScientometrics280Scientometrics['Reza Rezazadegan', 'Mahdi Sharifzadeh', 'Christopher L. Magee']2024-04-17T00:00:00https://www.semanticscholar.org/paper/7bdc6d7fb733dc053b1f1287867ed09921939bba 9f5f0ae4c66a05a9a6b02c37fcf5b84d87baf194Medical Journal of Australia90The Medical journal of Australia['Clair Sullivan', 'Keren Pointon']2024-04-17T00:00:00https://www.semanticscholar.org/paper/9f5f0ae4c66a05a9a6b02c37fcf5b84d87baf194 ba7600915b0bb2f97cabc60691c8d9c8be86a612International Journal of Lower Extremity Wounds10The international journal of lower extremity wounds['Hinpetch Daungsupawong', 'V. Wiwanitkit']2024-04-17T00:00:00https://www.semanticscholar.org/paper/ba7600915b0bb2f97cabc60691c8d9c8be86a612 3652227766f48f2f9726222680168777bfe86852Annals of Operations Research260Annals of Operations Research['Marco Repetto', 'C. Colapinto', 'Muhammad Usman Tariq']2024-04-17T00:00:00https://www.semanticscholar.org/paper/3652227766f48f2f9726222680168777bfe86852 e23e5d7e810551196ac9964476ddf2ec33aa4735Nurse Educator30Nurse educator['Lisa K. Woodley']2024-04-17T00:00:00https://www.semanticscholar.org/paper/e23e5d7e810551196ac9964476ddf2ec33aa4735 1d196f1c166756bb071df6a43ea4fb0d959d0461Insights into Language Culture and Communication00Insights into Language, Culture and Communication['Sarah Elaasser', 'Mervat Abo Oaf', 'Sally Tayie']2024-04-17T00:00:00https://www.semanticscholar.org/paper/1d196f1c166756bb071df6a43ea4fb0d959d0461 6860e27d1731fd88b639589592afe1fb01633a9dBuilding socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial). Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and speech. Natural language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social world. In this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI. We anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research.arXiv.org1940A set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI are identified in the context of social intelligence concepts and prior progress in Social-AI research.ArXiv['Leena Mathur', 'P. Liang', 'Louis-Philippe Morency']2024-04-17T00:00:00https://www.semanticscholar.org/paper/6860e27d1731fd88b639589592afe1fb01633a9d 3d97d4a4c2755d28980804e4f9191185d9cdddd1Work is an important part of our lives - not only as a way to earn a living but as a crucial source for experiencing meaningfulness. The introduction of autonomous systems (or in the widest sense "artificial intelligence", AI) will fundamentally impact work practices. However, while most existing models of human-AI collaboration focus on performance goals, less is known about their potential influence on job satisfaction. In this paper, we present an online experiment in which we compared the perception of job meaningfulness and accountability in a human-AI collaboration across three interaction paradigms: Supervisory, Advisory, and Interactive. Our results showed that, unlike the common notion of supervisory control, people find their job more satisfying when they directly interact with the AI and are involved and remain accountable for action and decision-making. Introducing AI as a teammate in the interactive paradigm was associated with the highest job meaningfulness.Proceedings of the ACM on Human-Computer Interaction690This paper compared the perception of job meaningfulness and accountability in a human-AI collaboration across three interaction paradigms: Supervisory, Advisory, and Interactive and showed that, unlike the common notion of supervisory control, people find their job more satisfying when they directly interact with the AI and are involved and remain accountable for action and decision-making.Proceedings of the ACM on Human-Computer Interaction['Shadan Sadeghian', 'Alarith Uhde', 'Marc Hassenzahl']2024-04-17T00:00:00https://www.semanticscholar.org/paper/3d97d4a4c2755d28980804e4f9191185d9cdddd1 240ba0aa0041a66751f2e474b361f916f5f53969Partant de l’idée que les espaces de discussion (EDD) sont un outil important de la réduction des risques psychosociaux, cet article fait l’hypothèse que cette réduction puisse être partiellement imputable aux stratégies de régulation émotionnelle qui sont déployées en leur sein. Les questions de recherche sont donc les suivantes : Quelles sont les stratégies de régulation émotionnelle collective mises en œuvre au sein des EDD, quelles sont les conditions pour qu’elles se développent et quel est leur impact sur le vécu des émotions au sein des équipes ? La régulation émotionnelle collective (REC) est définie comme le processus par lequel les membres d’une équipe construisent et respectent des normes de régulation émotionnelle susceptibles de modifier les émotions ressenties suite à un événement affectif concernant directement ou indirectement plusieurs membres de l’équipe. La méthodologie de recherche s’appuie sur l’analyse de trois cas dans des secteurs à risque émotionnel. Les résultats décrivent quels sont les schémas récurrents de régulation collective des émotions au sein des équipes. Ces schémas dépendent de la culture d’une équipe vis-à-vis des émotions et de la culture de discussion mais également du climat de l’équipe. L’article se conclut sur une discussion qui montre que l’accent sur la REC permet d’enrichir les explications sur les impacts des EDD sur la santé au travail. La discussion souligne l’importance que les échanges au sein des EDD soient ouverts sur les émotions (notamment les émotions négatives) et clarifie les conditions nécessaires pour que cela puisse fonctionner.Revue internationale de psychosociologie et de gestion des comportements organisationnels300Revue internationale de psychosociologie et de gestion des comportements organisationnels['C. Desmarais', 'Isabelle Agassiz']2024-04-16T00:00:00https://www.semanticscholar.org/paper/240ba0aa0041a66751f2e474b361f916f5f53969 04c5fc85d4aa13b9e1b1845c255383983d7cc609Parallel operations take place in the legislative and regulatory systems. The procedures are distinct, but they are both equally powerful and public. Draft legislation (RUU) and passed laws (UU) are considered legislation. Enforcement of the law can be reasonable when it is regulated. The regulation is made in-depth and establishes the practical application of the legislation if it is written generally. A technique for gathering library data is employed, whereby the research subject is tracked down using a range of information from the literature. Bibliographic annotation analysis is the type of data analysis employed; it is a concise synopsis or crucial overview of the reading process of an article. Based on the findings of this study, regulations about the health profession are necessary to safeguard public safety interests by serving as a barrier to entry. The legal aspects of nursing practice are uncertain due to the rapid changes in regulations governing nursing registration and practice; therefore, nurses are seeking legislative stability. The 2014 Nursing Law number 38 of 2014 was released by the government. Due to their ability to control their fate and obtain the desired legal protection, Indonesian nurses are now considered respected professionals thanks to this legislation. Moreover, after nearly ten years, the Nursing Law was repealed, and law number 17 of 2023 concerning OBL Health (omnibus law) took its place. Adaptability is critical in a time of fast change, and the conclusion is drawn. There are numerous opportunities for politically astute nurses nowadays to influence public policy. Nurses can successfully manage the changeable environment if they are familiar with the regulatory process.Jurnal Medisci00According to the findings of this study, regulations about the health profession are necessary to safeguard public safety interests by serving as a barrier to entry and nurses are seeking legislative stability.Jurnal Medisci['Ahmad Farid Rivai']2024-04-16T00:00:00https://www.semanticscholar.org/paper/04c5fc85d4aa13b9e1b1845c255383983d7cc609 5a2c395ca5dc942dbb6ea7eea1e324b8c9e8534eFoundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, so that, for example, they refuse to comply with requests for help with committing crimes or with producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about ''collective'' preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023.arXiv.org1007It is argued that the field of social choice is well positioned to address questions about fine-tuning and constitutional AI, and ways forward for this agenda are discussed.ArXiv['Vincent Conitzer', 'Rachel Freedman', 'J. Heitzig', 'Wesley H. Holliday', 'Bob M. Jacobs', 'Nathan Lambert', "Milan Moss'e", 'Eric Pacuit', 'Stuart Russell', 'Hailey Schoelkopf', 'Emanuel Tewolde', 'W. Zwicker']2024-04-16T00:00:00https://www.semanticscholar.org/paper/5a2c395ca5dc942dbb6ea7eea1e324b8c9e8534e 6b0570e66cdf79704461e1f8a07dc761ac10d6b6The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective of whether regression modelling or machine learning methods have been used. The new checklist supersedes the TRIPOD 2015 checklist, which should no longer be used. This article describes the development of TRIPOD+AI and presents the expanded 27 item checklist with more detailed explanation of each reporting recommendation, and the TRIPOD+AI for Abstracts checklist. TRIPOD+AI aims to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. Complete reporting will facilitate study appraisal, model evaluation, and model implementation.British medical journal777The development of TRIPOD+AI is described and the expanded 27 item checklist with more detailed explanation of each reporting recommendation is presented, and the TRIPOD+AI for Abstracts checklist is presented.The BMJ['Gary S. Collins', 'K. Moons', 'P. Dhiman', 'Richard D. Riley', 'A. L. Beam', 'Ben Van Calster', 'Marzyeh Ghassemi', 'Xiaoxuan Liu', 'Johannes B Reitsma', 'M. van Smeden', 'A. Boulesteix', 'J. Camaradou', 'L. A. Celi', 'S. Denaxas', 'A. Denniston', 'Ben Glocker', 'Robert M Golub', 'Hugh Harvey', 'G. Heinze', 'Michael M Hoffman', 'A. Kengne', 'Emily Lam', 'Naomi Lee', 'Elizabeth W Loder', 'Lena Maier-Hein', 'B. Mateen', 'M. Mccradden', 'Lauren Oakden-Rayner', 'Johan Ordish', 'Richard Parnell', 'Sherri Rose', 'Karandeep Singh', 'L. Wynants', 'P. Logullo']2024-04-16T00:00:00https://www.semanticscholar.org/paper/6b0570e66cdf79704461e1f8a07dc761ac10d6b6 3dc5c7e1957d38970e309c734ae903c401cec3b2The paper reflects on the consequences of the bankruptcy of the FTX exchange in the context of cryptocurrency volatility, as for the legal anchoring of cryptocurrencies in terms of de lege lata and de lege ferenda, especially in the context of the need for agile proactive state response to criminal activities connected to the use of cryptocurrencies. The nature of cryptocurrencies predisposes the commission of criminal activities related to crypto predominantly in the online space, resonating with the necessity to increase transparency and control of cryptocurrency transactions. The anonymity of coins and the limited ability to track them gives them a clear technological advantage over the efforts of law enforcement bodies to map them or check them by regulatory authorities. The subject of the empirical part of the paper is the analysis of a case study of a regulatory offense committed by a selected service provider in the field of performing exchange activities in the Slovak Republic.Financial Engineering90Financial Engineering['Tatiana Hajdúková', 'Samuel Marr']2024-04-16T00:00:00https://www.semanticscholar.org/paper/3dc5c7e1957d38970e309c734ae903c401cec3b2 085bc6eac671469b0d07934a8a90fad5ff0b77f1International Journal of Training Research210International Journal of Training Research['Rob Strathdee']2024-04-16T00:00:00https://www.semanticscholar.org/paper/085bc6eac671469b0d07934a8a90fad5ff0b77f1 baeabfba77db67b13cd029a0a7739383c102fc81Asia Pacific Business Review650Asia Pacific Business Review['Qun Du', 'Zhennan Sun', 'Suquan Chen']2024-04-16T00:00:00https://www.semanticscholar.org/paper/baeabfba77db67b13cd029a0a7739383c102fc81 a6ddb4a4a49e799d5cf36574c592ef9c543da7b7Nature Network Boston342A foundation model approach, named MONET (medical concept retriever), is presented, which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation.Nature medicine['Chanwoo Kim', 'S. U. Gadgil', 'A. DeGrave', 'J. Omiye', 'Zhuo Ran Cai', 'Roxana Daneshjou', 'Su-In Lee']2024-04-16T00:00:00https://www.semanticscholar.org/paper/a6ddb4a4a49e799d5cf36574c592ef9c543da7b7 9091b582584e1a0910dc55c0042de06aab70db19The incorporation of Artificial Intelligence (AI) into education marks a significant shift in how students learn, teachers teach, and educational institutions operate. This research delved into the knowledge and views of Library and Information Science (LIS) educators regarding the use of AI in library schools in Rivers State. The study employed a survey approach, combining qualitative and quantitative methods. A total of 44 LIS educators from various institutions in Rivers State participated, selected through random sampling, and data were collected using an online survey. The study found that while many LIS educators are aware of AI and have integrated it into their teaching and research, there remains a considerable gap in formal training and professional development in this area. Despite this, there is a clear understanding among educators of the value of AI in library and information science education, in line with broader trends in education and industry. The research also identified positive attitudes towards AI as a tool to enhance education quality and prepare students for careers in librarianship and information science. However, several barriers hinder the integration of AI into curricula and practices, including lecturer attitudes, credibility of information sources, internet connectivity, negative institutional perceptions, and low lecturer competency in AI. To address these challenges, the study recommends that Nigerian library schools fully implement AI technologies like chatbots, barcodes, RFIDs, and robotics to enhance teaching activities. It also suggests that higher education institutions develop specialized training programs and workshops on AI for library schools, covering both basic and advanced concepts. This would enable educators to effectively integrate AI into their teaching and research practices.Metaverse Basic and Applied Research61It is recommended that Nigerian library schools fully implement AI technologies like chatbots, barcodes, RFIDs, and robotics to enhance teaching activities and higher education institutions develop specialized training programs and workshops on AI for library schools, covering both basic and advanced concepts.Metaverse Basic and Applied Research['Solomon Oyetola', 'B. Oladokun', 'Kudu Dogara']2024-04-16T00:00:00https://www.semanticscholar.org/paper/9091b582584e1a0910dc55c0042de06aab70db19 aad317faf9ddc4302f126fed3c0932957339f968 Purpose This paper aims to bridge the extended reality framework and the luxury hospitality literature by providing insights into how immersive technologies using artificial intelligence (AI) can shape luxury value and consumer differentiation. Design/methodology/approach The authors conducted three experimental studies comparing immersive AI versus traditional hospitality across luxury contexts (hotels, restaurants and spas). Study 1 investigates the effect of immersive AI (vs traditional hospitality) on customers’ behavioral intentions and the need for differentiation using virtual-assisted reality. Study 2 tests the underlying mechanism of the need for differentiation and luxury value in an augmented reality context. Study 3 provides additional support for the proposed underlying mechanism using virtual-assisted reality in luxury hospitality. Findings The findings reveal that immersive AI (vs traditional) luxury hospitality reduces customers’ behavioral intentions of using such services and perceived luxury value. Moreover, the findings indicate that the intention to use immersive AI (vs traditional) luxury hospitality services is contingent upon customers’ need for differentiation. Originality/value The findings have important theoretical and managerial implications for immersive technologies in luxury hospitality. They shed light on the dynamics between integrating immersive AI into luxury hospitality and its impact on customers’ differentiation motives and perceived luxury value. The findings reveal the detrimental effect of using immersive AI (vs traditional hospitality) within this context. International Journal of Contemporary Hospitality Management791The findings reveal that immersive AI (vs traditional) luxury hospitality reduces customers’ behavioral intentions of using such services and perceived luxury value, and indicates that the intention to use immersive AI (vs traditional) luxury hospitality services is contingent upon customers’ need for differentiation.International Journal of Contemporary Hospitality Management['Ana Rita Gonçalves', 'Diego Costa Pinto', 'Saleh Shuqair', 'Anna S. Mattila', 'Anel Imanbay']2024-04-16T00:00:00https://www.semanticscholar.org/paper/aad317faf9ddc4302f126fed3c0932957339f968 5794dadbdee49670c490cf3a653016f11d10f7faWe diagnose and briefly discuss the dearth of the author: a condition that arises when AI-based creativity support tools for writing allow users to produce large amounts of text without making a commensurate number of creative decisions, resulting in output that is sparse in expressive intent. We argue that the dearth of the author helps to explain a number of recurring difficulties and anxieties around AI-based writing support tools, but that it also suggests an ambitious new goal for AI-based CSTs.arXiv.org271It is argued that the dearth of the author helps to explain a number of recurring difficulties and anxieties around AI-based writing support tools, but that it also suggests an ambitious new goal for AI-based CSTs.ArXiv['Max Kreminski']2024-04-16T00:00:00https://www.semanticscholar.org/paper/5794dadbdee49670c490cf3a653016f11d10f7fa bbab45f57c7c0be7371d7139cf2aafb5772eaa9fThe advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhancing developer productivity. However, the current task-driven nature of these copilots falls short in addressing the broader goals and complexities inherent in software engineering (SE). In this paper, we propose a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers in a more holistic and context-aware manner. We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning. These AI partners engage in iterative, conversation-driven development processes, aligning closely with human goals and facilitating informed decision-making. We discuss the desired attributes of such AI pair programmers and outline key challenges that must be addressed to realize this vision. Ultimately, our work represents a shift from AI-augmented SE to AI-transformed SE by replacing code completion with a collaborative partnership between humans and AI that enhances both productivity and software quality.arXiv.org490This work proposes a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers in a more holistic and context-aware manner, replacing code completion with a collaborative partnership between humans and AI that enhances both productivity and software quality.ArXiv['Ahmed E. Hassan', 'G. Oliva', 'Dayi Lin', 'Boyuan Chen', 'Zhen Ming Jiang']2024-04-16T00:00:00https://www.semanticscholar.org/paper/bbab45f57c7c0be7371d7139cf2aafb5772eaa9f 059d812e5b0d7e9221c6eeb3509b86f6d000500eEthics and Information Technology270It is argued that explainability will be irrelevant for many current and near-future autonomous systems in the military, that it will be trivially incorporated into most military systems which do possess AI, and that for those systems with genuinely opaque AI, explainability will prove to be of more limited value than one might imagine.Ethics Inf. Technol.['Nathan Gabriel Wood']2024-04-16T00:00:00https://www.semanticscholar.org/paper/059d812e5b0d7e9221c6eeb3509b86f6d000500e d8ebafbd5aae67412979db55b6e1b9c4e5764924Purpose This study, a theoretical article, aims to introduce new institutionalism as a framework through which business and management researchers can explore the significance of artificial intelligence (AI) in organizations. Although the new institutional theory is a fully established research program, the neo-institutional literature on AI is almost non-existent. There is, therefore, a need to develop a deeper understanding of AI as both the product of institutional forces and as an institutional force in its own right. Design/methodology/approach The authors follow the top-down approach. Accordingly, the authors first briefly describe the new institutionalism, trace its historical development and introduce its fundamental concepts: institutional legitimacy, environment and isomorphism. Then, the authors use those as the basis for the queries to perform a scoping review on the institutional role of AI in organizations. Findings The findings reveal that a comprehensive theory on AI is largely absent from business and management literature. The new institutionalism is only one of many possible theoretical perspectives (both contextually novel and insightful) from which researchers can study AI in organizational settings. Originality/value The authors use the insights from new institutionalism to illustrate how a particular social theory can fit into the larger theoretical framework for AI in organizations. The authors also formulate four broad research questions to guide researchers interested in studying the institutional significance of AI. Finally, the authors include a section providing concrete examples of how to study AI-related institutional dynamics in business and management. Journal of Management History580New institutionalism is introduced as a framework through which business and management researchers can explore the significance of artificial intelligence (AI) in organizations and is used to illustrate how a particular social theory can fit into the larger theoretical framework for AI in organizations.Journal of Management History['I. Rudko', 'Aysan Bashirpour Bonab', 'Maria Fedele', 'Anna Vittoria Formisano']2024-04-16T00:00:00https://www.semanticscholar.org/paper/d8ebafbd5aae67412979db55b6e1b9c4e5764924 efb2130386a0e44e203a5417b234ffa4a79a561a Purpose In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature. Design/methodology/approach Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness. Findings A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information. Research limitations/implications This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries. Practical implications It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront. Social implications Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life. Originality/value Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey. Information &amp; Computer Security370The correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness was analyzed, paving the way for thorough research and refinement of awareness initiatives tailored to distinct industries.Information &amp; Computer Security['Amir Schreiber', 'Ilan Schreiber']2024-04-16T00:00:00https://www.semanticscholar.org/paper/efb2130386a0e44e203a5417b234ffa4a79a561a 4e5f4c5adb3f7f2507fa077719e6d8e2ed6e795aThe growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability. This paper proposes a novel approach to address these challenges, introducing a Bayesian Monte Carlo Dropout model with kernel modelling. Our model is designed to enhance reliability on small medical datasets, a crucial barrier to the wider adoption of AI in healthcare. This model leverages existing language models for improved effectiveness and seamlessly integrates with current workflows. We demonstrate significant improvements in reliability, even with limited data, offering a promising step towards building trust in AI-driven medical predictions and unlocking its potential to improve patient care.arXiv.org630A novel approach to address challenges of trust in AI-driven medical predictions and unlocking its potential to improve patient care is proposed, introducing a Bayesian Monte Carlo Dropout model with kernel modelling.ArXiv['Ubaid Azam', 'Imran Razzak', 'Shelly Vishwakarma', 'Hakim Hacid', 'Dell Zhang', 'Shoaib Jameel']2024-04-16T00:00:00https://www.semanticscholar.org/paper/4e5f4c5adb3f7f2507fa077719e6d8e2ed6e795a dd1a128102a41142e00009bbb3b56b38a2d20eb6While the excitement around the capabilities of technological advancements is giving rise to new AI-based writing assistants, the overarching ecosystem plays a crucial role in how they are adopted in educational practice. In this paper, we point to key ecological aspects for consideration. We draw insights from extensive research integrated with practice on a writing feedback tool over 9 years at a university, and we highlight potential risks when these are overlooked. It informs the design of educational writing support tools to be better aligned within broader contexts to balance innovation with practical impact.arXiv.org50Insight from extensive research integrated with practice on a writing feedback tool over 9 years at a university informs the design of educational writing support tools to be better aligned within broader contexts to balance innovation with practical impact.ArXiv['A. Shibani', 'S. B. Shum']2024-04-16T00:00:00https://www.semanticscholar.org/paper/dd1a128102a41142e00009bbb3b56b38a2d20eb6 0c50a66403d4a27a0a1b5a399f08c66b9a21614fPurpose: This research intends to use literature review and bibliometric analysis methods to visually review the development status and important historical milestones of Artificial Intelligence, as well as the basic research, key topics, and future potential research hot spots of AI in the healthcare field. Methodology: Conduct in-depth analysis of AI in healthcare through bibliometrics methods such as publication activity analysis, co-occurrence analysis, and co-authorship analysis. Findings: This study outlines the development time trajectory of AI technology and its application in healthcare. Research shows that "algorithm", "machine learning", "deep learning", "controlled study", "major clinical study" and "healthcare delivery" as well as "decision support systems" are key topics for research. Gender-related research and ethical issues are areas of future focus. Research implications: The practical significance is that it can clarify and optimize the key directions of AI to improve the quality of medical decision-making, improve diagnostic accuracy and guide market investment. The originality is reflected in the comprehensive analysis of the development trajectory of AI in the medical and health field. Through a unique perspective and systematic approach, it provides an important reference for research trends and future directions in the field.Asia-Pacific Journal of Molecular Biology and Biotechnology300This study outlines the development time trajectory of AI technology and its application in healthcare and shows that "algorithm", "machine learning", "deep learning", "controlled study", "major clinical study" and "healthcare delivery" as well as "decision support systems" are key topics for research.Asia Pacific Journal of Molecular Biology and Biotechnology['Yi Jie Wang', 'W. Choo', 'K. Y. Ng']2024-04-16T00:00:00https://www.semanticscholar.org/paper/0c50a66403d4a27a0a1b5a399f08c66b9a21614f b99932450e7d5fa41eed2f3c641d7f9be7ac5ea7This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing the complexities of systems engineering. It highlights how AI and ML are revolutionizing system design, integration, and lifecycle management by enabling automated design optimization, predictive maintenance, and efficient configuration management. These technologies allow for the analysis of large datasets to predict system failures and optimize performance, thereby enhancing the reliability and sustainability of engineering systems. Despite the promising applications, the integration of AI into systems engineering presents challenges, including technical hurdles, ethical considerations, and the need for comprehensive education and training. The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively. Concluding thoughts underscore AI's potential to redefine systems engineering, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements. Keywords: Artificial Intelligence, Machine Learning, Systems Engineering, Automated Design, Predictive Maintenance, Configuration Management, Education and Training, Technology Integration.Computer Science &amp; IT Research Journal00The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements.Computer Science &amp; IT Research Journal['Oladele Junior Adeyeye', 'Ibrahim Akanbi']2024-04-16T00:00:00https://www.semanticscholar.org/paper/b99932450e7d5fa41eed2f3c641d7f9be7ac5ea7 762761ce061855af5a5d25794e01392c0a60089bThe integration of biomimicry principles with artificial intelligence (AI) presents a compelling approach to addressing complex challenges across various domains. This article explores the synergy between biomimicry and AI, elucidating how the emulation of natural processes and structures can inspire innovative solutions. Beginning with an overview of biomimicry's historical roots and notable achievements, the narrative progresses to highlight AI's role in accelerating biomimetic research and innovation. Various applications of biomimicry, ranging from material development to biotech and climate change mitigation, are discussed, showcasing the breadth of possibilities offered by this interdisciplinary approach. Challenges and ethical considerations inherent in combining biomimicry and AI were also examined, emphasizing the need for multidisciplinary collaboration and ethical awareness. Looking ahead, future directions in research are outlined, including the development of AI algorithms that integrate knowledge from diverse biological sources and the incorporation of moral considerations into biomimetic design processes. Ultimately, the article concludes by suggesting that the convergence of biomimicry and AI holds promise for fostering sustainable, efficient, and ethically informed technological advancements, facilitating a harmonious relationship between humanity and the natural world. GRAPHICAL ABSTRACT:bionature00The synergy between biomimicry and AI is explored, elucidating how the emulation of natural processes and structures can inspire innovative solutions, and suggesting that the convergence of biomimicry and AI holds promise for fostering sustainable, efficient, and ethically informed technological advancements.BIONATURE['Tejaswini S Dhamdar', 'Sandhya K V', 'B. V. Basavaraj']2024-04-16T00:00:00https://www.semanticscholar.org/paper/762761ce061855af5a5d25794e01392c0a60089b 881f0a883e94c948b0ae04431085fe1d52c02c64The integration of artificial intelligence (AI) tools has propelled automation in industrial production to new heights, ushering in a wave of transformative advancements. This research delves into the profound impact of AI tools on industrial production, elucidating their pivotal role in driving efficiency, cost reduction, and productivity enhancements. By leveraging AI algorithms and machine learning techniques, manufacturers can optimize operations with unprecedented precision and agility. However, alongside the promises of increased efficiency come significant challenges and opportunities. This paper navigates through the complexities of implementing AI-driven automation in manufacturing processes, addressing issues such as data security, workforce adaptation, and ethical considerations. Through rigorous secondary data analysis and research methodology, this study endeavours to shed light on the current state of automation in industrial production while offering valuable insights into its future prospects and implications for the manufacturing industry.International Journal of Advanced Research in Science, Communication and Technology00This research delves into the profound impact of AI tools on industrial production, elucidating their pivotal role in driving efficiency, cost reduction, and productivity enhancements and offering valuable insights into its future prospects and implications for the manufacturing industry.International Journal of Advanced Research in Science, Communication and Technology['Mr. Prashant Dupare', 'Mr. Sahil Sangole']2024-04-16T00:00:00https://www.semanticscholar.org/paper/881f0a883e94c948b0ae04431085fe1d52c02c64 01572a82be10e69e368b8a8bc0f071d1a5a87b6dBACKGROUND Wearable sensors are increasingly being explored in healthcare, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. In particular, preprocessing pipelines to clean and standardize raw data have not been fully optimized. OBJECTIVE The aim of this study was to conduct a systematic review of preprocessing techniques employed on wearable sensor data to ensure their readiness for artificial intelligence/machine learning ("AI/ML-ready") applications. Specifically, we sought to understand the landscape of current approaches applied in cleaning, normalizing, and transforming raw datasets into usable formats for subsequent AI/ML analysis. METHODS We systematically searched IEEE Xplore, PubMed, Embase (including Embase, Embase Classic, MEDLINE, PubMed-not-MEDLINE), and Scopus to identify potentially relevant studies for this review. The eligibility criteria included: (1) mHealth and wearable sensor studies in cancer; (2) written and published in English; (3) published between January 2018 and December 2023; (4) full text available rather than abstracts; (5) original studies published in peer-reviewed journals or appeared in conference proceedings. The Covidence app was used as a review resource for the screening stage. Statistical learning and image processing techniques were considered irrelevant. RESULTS In the initial phase, 2,147 papers were identified between January 2018-December 2023. After a thorough evaluation of these selected papers, we applied our predefined eligibility criteria, which resulted in a total of 20 papers. The following three categories for preprocessing techniques were identified: (1) Data Transformation, (2) Data Scaling, (3) and Data Cleaning. CONCLUSIONS While wearable sensors are gaining traction in cancer care, there remain challenges in the application of standard AI/ML techniques due to low quality of raw data captured and not applying appropriate preprocessing pipelines to enrich the data quality. As of now, AI/ML methodologies remain individually tailored to specific studies or types of data, and limit the generalizability of research findings. A general framework for those multiple types of databases has been proposed in this work. Our findings suggest a pressing need to develop and adopt uniform data quality and pre-processing workflows of wearable sensor data that can support the breadth of cancer research and its diverse patient populations.JMIR mHealth and uHealth00A systematic review of preprocessing techniques employed on wearable sensor data to ensure their readiness for artificial intelligence/machine learning ("AI/ML-ready") applications and a general framework for those multiple types of databases has been proposed.JMIR mHealth and uHealth['Bengie L. Ortiz']2024-04-16T00:00:00https://www.semanticscholar.org/paper/01572a82be10e69e368b8a8bc0f071d1a5a87b6d d67a6281eb315c37907af812d157653a00bcf5c4This research describes a unique strategy to classifying eye illnesses utilizing a Convolutional Neural Network (CNN) modification. The objective is to develop an automated system that accurately diagnoses and classifies eye diseases, leading to improved patient care and outcomes. A comprehensive dataset of eye images was collected from various sources and preprocessed to enhance quality and quantity. The proposed Eye Disease Classification (EDC) model was trained and optimized using well-known algorithms. The experimental findings illustrate the superiority of the suggested approach, achieving high precision ($95.63 \%$), recall (98.20%), F1-score (94.30%), and accuracy (94.50%), SVM, Decision Tree, KNN, and Random Forest are among the most often used classifiers, the results demonstrate the potential of the suggested technology to transform eye disease detection and therapy.National Radio Science Conference270The experimental findings illustrate the superiority of the suggested approach, achieving high precision, recall, recall, F1-score, and accuracy, and demonstrate the potential of the suggested technology to transform eye disease detection and therapy.2024 41st National Radio Science Conference (NRSC)['Samah A. Gamel', 'Iman S. Alansari', 'S. Saleh', 'Hatem A. Khater']2024-04-16T00:00:00https://www.semanticscholar.org/paper/d67a6281eb315c37907af812d157653a00bcf5c4 53e0af883e77d87735cebfdb3589a04e26a4ef1cThe main objective of this work is to investigate the relationship between the integration of artificial intelligence in education in the pre-and post-pandemic period, as well as the apparent impact on educational practises. This bibliometric study uses the PRISMA framework to summarise works that relate the integration of artificial intelligence to contemporary educational methods. Scopus was selected for its comprehensive coverage and excellent reputation as a resource for analysing scholarly articles, as was Vosviewer for its extremely useful graph displaying data links from the database. Inclusion and exclusion criteria were used to reduce the results to 3,010 relevant publications. The inclusion of modern technologies, such as artificial intelligence, in education has become not only a trend but also an absolute necessity. This transition is reflected in the emphasis on research. The list of prominent publications, nations and groups contributing to this problem shows that they are influential worldwide. The study focuses on the most prolific authors and key research keywords, highlighting the transdisciplinary nature of research in machine learning and education. Education is no longer the same after the pandemic, as the increasing number of publications by different authors shows. Various teaching and learning methods are presented, with a focus on numerous technology-based solutions, particularly artificial intelligence applications and platforms. The publications on this topic and the keywords show how collaborative and diverse research in this area is. Continued research is essential for the design of educational techniques and the improvement of learning experiences in the context of digital platforms and artificial intelligence applications. The paper recommends more intensive research to improve digital education and learning methods. To understand, compare and improve the impact of AI applications and platforms on learning outcomes, continuous developmentsInternational Journal of Academic Research in Business and Social Sciences90This bibliometric study uses the PRISMA framework to summarise works that relate the integration of artificial intelligence to contemporary educational methods and focuses on the most prolific authors and key research keywords, highlighting the transdisciplinary nature of research in machine learning and education.International Journal of Academic Research in Business and Social Sciences['Bahja A. Al-Mubarak', 'Hafiza Abas', 'S. Shariff']2024-04-16T00:00:00https://www.semanticscholar.org/paper/53e0af883e77d87735cebfdb3589a04e26a4ef1c 88e33c304272ae0ed7d897250a97a197d753a8deGenerative Artificial Intelligence (GAI) models such as ChatGPT have experienced a surge in popularity, attracting 100 million active users in 2 months and generating an estimated 10 million daily queries. Despite this remarkable adoption, there remains a limited understanding to which extent this innovative technology influences higher education. This research paper investigates the impact of GAI on university students and Higher Education Institutions (HEIs). The study adopts a mixed-methods approach, combining a comprehensive survey with scenario analysis to explore potential benefits, drawbacks, and transformative changes the new technology brings. Using an online survey with 130 participants we assessed students' perspectives and attitudes concerning present ChatGPT usage in academics. Results show that students use the current technology for tasks like assignment writing and exam preparation and believe it to be a effective help in achieving academic goals. The scenario analysis afterwards projected potential future scenarios, providing valuable insights into the possibilities and challenges associated with incorporating GAI into higher education. The main motivation is to gain a tangible and precise understanding of the potential consequences for HEIs and to provide guidance responding to the evolving learning environment. The findings indicate that irresponsible and excessive use of the technology could result in significant challenges. Hence, HEIs must develop stringent policies, reevaluate learning objectives, upskill their lecturers, adjust the curriculum and reconsider examination approaches.arXiv.org470The findings indicate that HEIs must develop stringent policies, reevaluate learning objectives, upskill their lecturers, adjust the curriculum and reconsider examination approaches because irresponsible and excessive use of the technology could result in significant challenges.ArXiv['Stefanie Krause', 'Bhumi Hitesh Panchal', 'Nikhil Ubhe']2024-04-16T00:00:00https://www.semanticscholar.org/paper/88e33c304272ae0ed7d897250a97a197d753a8de 31f4cdc032087694fe6ab8b681af5b68214df83eAccident Analysis and Prevention360Accident; analysis and prevention['Zihang Wei', 'Yang Zhou', 'Zihao Li', 'Mihir Kulkarni', 'Yunlong Zhang']2024-04-16T00:00:00https://www.semanticscholar.org/paper/31f4cdc032087694fe6ab8b681af5b68214df83e aa18d7c7d382c050ee3f488cd69fcf2b27a1e041Досліджено проблему оцінювання навчальних досягнень здобувачів освіти цифровими засобами. Здійснено аналіз досліджень щодо використання штучного інтелекту в практиці освітньої діяльності. Обґрунтовано необхідність використання систем та засобів штучного інтелекту для вирішення освітніх завдань. Апробовано можливості штучного інтелекту платформи Classtime для оцінювання результатів навчання студентів інформатичних й інженерних спеціальностей. Виділено ключові функціональні складники, які забезпечують засоби штучного інтелекту в платформах оцінювання навчальних досягнень: автоматизація оцінювання, аналіз відповідей, індивідуалізація завдань. Ключові слова: оцінювання, штучний інтелект, платформа Classtime, майбутні вчителі інформатики, майбутні інженери.B U L L E T I N OF OLEKSANDR DOVZHENKO HLUKHIV NATIONAL PEDAGOGICAL UNIVERSITY00B U L L E T I N OF OLEKSANDR DOVZHENKO HLUKHIV NATIONAL PEDAGOGICAL UNIVERSITY['Halyna Lutsenko', 'Oksana Podolian', 'Valerii Hrytsenko']2024-04-16T00:00:00https://www.semanticscholar.org/paper/aa18d7c7d382c050ee3f488cd69fcf2b27a1e041 5ef0d9396f14cd528c90e817a400ddd8b8104469Large language models (LLMs) hold potential for innovative HCI research, including the creation of synthetic personae. However, their black-box nature and propensity for hallucinations pose challenges. To address these limitations, this position paper advocates for using LLMs as data augmentation systems rather than zero-shot generators. We further propose the development of robust cognitive and memory frameworks to guide LLM responses. Initial explorations suggest that data enrichment, episodic memory, and self-reflection techniques can improve the reliability of synthetic personae and open up new avenues for HCI research.arXiv.org90This position paper advocates for using LLMs as data augmentation systems rather than zero-shot generators rather than zero-shot generators, and proposes the development of robust cognitive and memory frameworks to guide LLM responses.ArXiv['Rafael Arias Gonzalez', 'Steve DiPaola']2024-04-16T00:00:00https://www.semanticscholar.org/paper/5ef0d9396f14cd528c90e817a400ddd8b8104469 af491d9117d56a9a1bfffdc0398061fe10022076British medical journal100BMJ['Jérémie F Cohen', 'Patrick M M Bossuyt']2024-04-16T00:00:00https://www.semanticscholar.org/paper/af491d9117d56a9a1bfffdc0398061fe10022076 92369ecc7a20736130e4a49681ccd535da3f6b64Political science80Political Science['Alexis Palmer', 'A. Spirling']2024-04-16T00:00:00https://www.semanticscholar.org/paper/92369ecc7a20736130e4a49681ccd535da3f6b64 739d76486e0e567faf2ceb72da81e6f5c151df6900['Emanuela Guarcello', 'Abele Longo']2024-04-16T00:00:00https://www.semanticscholar.org/paper/739d76486e0e567faf2ceb72da81e6f5c151df69 145a131959a5b6c52dd3a47c2cf7268a580dcb9fNature00Nature['Katrina Krämer']2024-04-16T00:00:00https://www.semanticscholar.org/paper/145a131959a5b6c52dd3a47c2cf7268a580dcb9f 5261d588a9032f3cfbe91ce3d6e51c84641737bbBone Marrow Transplantation10Bone marrow transplantation['Shaun R McCann']2024-04-16T00:00:00https://www.semanticscholar.org/paper/5261d588a9032f3cfbe91ce3d6e51c84641737bb 058a22315b9796ed0701fa204eb4167bb17f44e3PurposeReverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.Design/methodology/approachIn this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.FindingsNine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.Practical implicationsThe study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.Originality/valueFew studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.International Journal of Logistics Management1351The International Journal of Logistics Management['Subhodeep Mukherjee', 'Ramji Nagariya', 'K. Mathiyazhagan', 'Manish Mohan Baral', 'M.R. Pavithra', 'Andrea Appolloni']2024-04-16T00:00:00https://www.semanticscholar.org/paper/058a22315b9796ed0701fa204eb4167bb17f44e3 a19456368af4e5653c825be133a34c484e29155eNature reviews: Gastroenterology & hepatology1090A taxonomy of AI approaches in liver cancer is presented, highlighting areas with academic and commercial potential, and a policy for AI-based liver cancer management is outlined, including interdisciplinary training of researchers, clinicians and patients.Nature reviews. Gastroenterology & hepatology['Julien Calderaro', 'Laura Žigutytė', 'Daniel Truhn', 'Ariel Jaffe', 'J. N. Kather']2024-04-16T00:00:00https://www.semanticscholar.org/paper/a19456368af4e5653c825be133a34c484e29155e 63e5ff1b429d0214e1a8b74594674e854bee8fcfThe research claims that artificial intelligence technologies can help and direct primary school teachers in organising classroom experiments for physics instruction. Educators now have the potential to construct experimental projects that are entertaining and efficient, all while catering to their students’ many learning styles and capabilities. This is made possible by the availability of artificial intelligence technologies. The incorporation of artificial intelligence into educational settings may result in an improvement in the overall quality of teaching as well as an improvement in the scientific performance of students. The chance to improve the learning experience for both students and teachers is available to educators who do an in-depth study on artificial intelligence-driven teaching solutions. The research highlights how artificial intelligence can transform teaching approaches in elementary school, notably in the field of physics education within the context of primary school settings.EIKI Journal of Effective Teaching Methods770The research claims that artificial intelligence technologies can help and direct primary school teachers in organising classroom experiments for physics instruction and highlights how artificial intelligence can transform teaching approaches in elementary school, notably in the field of physics education within the context of primary school settings.EIKI Journal of Effective Teaching Methods['Konstantinos T. Kotsis']2024-04-16T00:00:00https://www.semanticscholar.org/paper/63e5ff1b429d0214e1a8b74594674e854bee8fcf aa57d40123109e30f3011bd26ad9d44af51b6adbAs artificial intelligence becomes more and more embedded in human society, it also creates social risks. With the help of CiteSpace software, 200 documents related to social risks of artificial intelligence searched from SSCI and SCI journals from 2000 to 2024 were sorted out to sort out the research progress and hot spots. Research shows that there are currently some highly influential scholars and institutions in the field of artificial intelligence social risks, and the number of relevant studies is increasing year by year. Risk resolution and sustainable development issues of artificial intelligence have become important research directions for the continuous development of artificial intelligence.Interdisciplinary Humanities and Communication Studies30Research shows that there are currently some highly influential scholars and institutions in the field of artificial intelligence social risks, and the number of relevant studies is increasing year by year.Interdisciplinary Humanities and Communication Studies['Jiaxin Zou']2024-04-16T00:00:00https://www.semanticscholar.org/paper/aa57d40123109e30f3011bd26ad9d44af51b6adb 3b9ecb1b0cfd358ce62b2246a0b4b044558cd78dIn the ever-evolving realm of cybersecurity, the increasing integration of Metaverse systems with cutting-edge technologies such as Artificial Intelligence (AI), Blockchain, and Cloud Computing presents a host of new opportunities alongside significant challenges. This article employs a methodological approach that combines an extensive literature review with focused case study analyses to examine the changing cybersecurity landscape within these intersecting domains. The emphasis is particularly on the Metaverse, exploring its current state of cybersecurity, potential future developments, and the influential roles of AI, blockchain, and cloud technologies. Our thorough investigation assesses a range of cybersecurity standards and frameworks to determine their effectiveness in managing the risks associated with these emerging technologies. Special focus is directed towards the rapidly evolving digital economy of the Metaverse, investigating how AI and blockchain can enhance its cybersecurity infrastructure whilst acknowledging the complexities introduced by cloud computing. The results highlight significant gaps in existing standards and a clear necessity for regulatory advancements, particularly concerning blockchain’s capability for self-governance and the early-stage development of the Metaverse. The article underscores the need for proactive regulatory involvement, stressing the importance of cybersecurity experts and policymakers adapting and preparing for the swift advancement of these technologies. Ultimately, this study offers a comprehensive overview of the current scenario, foresees future challenges, and suggests strategic directions for integrated cybersecurity within Metaverse systems utilising AI, blockchain, and cloud computing.Frontiers in Blockchain330A comprehensive overview of the current scenario, foresees future challenges, and suggests strategic directions for integrated cybersecurity within Metaverse systems utilising AI, blockchain, and cloud computing are offered.Frontiers in Blockchain['P. Radanliev']2024-04-16T00:00:00https://www.semanticscholar.org/paper/3b9ecb1b0cfd358ce62b2246a0b4b044558cd78d 127aa4131d00e967965ad964cb0d94deee9f5f01Over the past decade, artificial intelligence (AI) has begun to transform Canadian organizations, driven by the promise of improved efficiency, better decision-making, and enhanced client experience. While AI holds great opportunities, there are also near-term impacts on the determinants of health and population health equity that are already emerging. If adoption is unregulated, there is a substantial risk that health inequities could be exacerbated through intended or unintended biases embedded in AI systems. New economic opportunities could be disproportionately leveraged by already privileged workers and owners of AI systems, reinforcing prevailing power dynamics. AI could also detrimentally affect population well-being by replacing human interactions rather than fostering social connectedness. Furthermore, AI-powered health misinformation could undermine effective public health communication. To respond to these challenges, public health must assess and report on the health equity impacts of AI, inform implementation to reduce health inequities, and facilitate intersectoral partnerships to foster development of policies and regulatory frameworks to mitigate risks. This commentary highlights AI's near-term risks for population health to inform a public health response.Canadian journal of public health60Public health must assess and report on the health equity impacts of AI, inform implementation to reduce health inequities, and facilitate intersectoral partnerships to foster development of policies and regulatory frameworks to mitigate risks.Canadian journal of public health = Revue canadienne de sante publique['Alireza Kamyabi', 'I. Iyamu', 'Manik Saini', 'Curtis May', 'Geoffrey McKee', 'Alex Choi']2024-04-16T00:00:00https://www.semanticscholar.org/paper/127aa4131d00e967965ad964cb0d94deee9f5f01 c9b45b2e6fbefd14a7a420cf69f7fadc093a733fArtificial Intelligence (AI) heralds a transformative age in the workforce, marked by heightened efficiencies but also by socio-economic challenges, particularly concerning gender equity. Women, frequently occupying roles susceptible to AI-driven automation, face a disproportionate risk of job displacement. This paper explores AI’s nuanced impact on the employment landscape, where it both exacerbates and mitigates gender disparities. As technology outmoded roles traditionally filled by women, such as those requiring lower technical skills, it also unearthed opportunities for empowerment through remote occupations and increased demand for human-centric expertise. This analysis extends to the cultural and educational underpinnings contributing to the gendered differentiation in technical professions, emphasizing the stereotypes that pigeonhole women as less suited for technical roles. Investigating the imbalances wrought by capitalist values, the study exposes the undervaluation of ‘feminine’ attributes in favor of ‘masculine’ norms of economic productivity. It suggests women’s empowerment in the evolving job market hinges on a systemic shift towards equitable recognition of all work forms, transcending traditional gender roles. In conclusion, redefining work value and leveraging inclusive AI applications emerge as pivotal to counteracting historical biases and ensuring women’s equitable participation alongside men. This comprehensive approach involves reorienting policy, corporate culture, and academic vectors to facilitate a society where diversity is celebrated, and gender parity is achieved. AI, while a disruptor, thus becomes a potential equalizer in the quest for gender-inclusive growth.Interdisciplinary Humanities and Communication Studies50It is suggested women’s empowerment in the evolving job market hinges on a systemic shift towards equitable recognition of all work forms, transcending traditional gender roles, as AI becomes a potential equalizer in the quest for gender-inclusive growth.Interdisciplinary Humanities and Communication Studies['Shujia He']2024-04-16T00:00:00https://www.semanticscholar.org/paper/c9b45b2e6fbefd14a7a420cf69f7fadc093a733f b50a940a7386a682697442e17a75985bb248587fWith a focus on the rationality, risk, and return components of investment strategies, this study proposes to provide light on the growing importance of combining multifactor models and artificial intelligence (AI) in financial decisions intelligent decision making. The research aims to bring together traditional finance theories and modern data-driven approaches to enhance investment decision-making, risk management, and portfolio optimization. In this research I will mainly use this experimental scheme of data exploration as well as other experimental scheme such as using model training and testing to evaluate the possibility of AI-driven models to provide practical solutions and valuable knowledge for the financial industry.Finance &amp; Economics10The research aims to bring together traditional finance theories and modern data-driven approaches to enhance investment decision-making, risk management, and portfolio optimization.Finance &amp; Economics['Zihan Xuan']2024-04-16T00:00:00https://www.semanticscholar.org/paper/b50a940a7386a682697442e17a75985bb248587f 266789a1f8ab88216bbcc2f4d062c8b7eafe475bThis study explores the development of soft skills in the Artificial Intelligence era. Initially, the study, through an anonymous online survey, explored why students use AI and Large Language Models (LLMs). It was found that students use AI for general and academic purposes. From a general perspective, students use AI and LLMs for (1) convenience, (2) lack of time, and (3) lack of curiosity/interest. From an academic perspective, students use AI and LLM platforms as they (1) lack familiarity/knowledge, (2) lack basic skills, (3) lack confidence, (4) have an eagerness to score high grades, and (5) wish to provide different perspectives. To assist in developing students’ soft skills and discourage possible destructive outcomes in the AI era, the study suggests integrating AI platforms as part of teaching. This integration can be carried out by (1) introducing AI tools to students in a productive manner, (2) aligning the use of AI tools with the curriculum and teaching styles, (3) planning lessons and interactive activities using AI platforms, and (4) using AI tools to provide feedback and vice versa. In communication courses, instructors shall (1) create a supportive environment, (2) organize classroom discussions and debates, (3) create public speaking opportunities, (4) provide room for oral communication practices, (5) integrate the use of technology and multimedia, and (6) provide feedback and reflection. In business writing courses, instructors shall (1) encourage effective communication in classrooms, (2) facilitate collaboration and teamwork, (3) use role-play scenarios, (4) introduce project management tools, (5) teach professional etiquette, and (6) organize networking events. In composition courses, instructors shall (1) embrace technology, (2) teach students to critically evaluate online sources, (3) design assignments that require critical analysis, (4) encourage creative writing assignments, (5) promote imagination and originality, and (6) conduct workshops. These practices, which are provided in line with AlAfnan’s taxonomy of educational objectives, shall assist students in developing their soft skills in a way that maintains the relevance of classroom teaching in the AI era.Journal of Artificial Intelligence and Technology170These practices, provided in line with AlAfnan’s taxonomy of educational objectives, shall assist students in developing their soft skills in a way that maintains the relevance of classroom teaching in the AI era.Journal of Artificial Intelligence and Technology['M. AlAfnan', 'Samira Dishari', 'Siti Fatimah MohdZuki']2024-04-16T00:00:00https://www.semanticscholar.org/paper/266789a1f8ab88216bbcc2f4d062c8b7eafe475b c3853cef73749ce019cb00ef860c36d367c01e21Journal of evolutionary economics290The amount invested in AI ventures is significantly lower than non-AI ones: this negative relationship is moderated by a venture’s development stage, VC investor’s experience and the AI development level of the country in which the invested venture operates.Journal of Evolutionary Economics['Benedetta Montanaro', 'Annalisa Croce', 'E. Ughetto']2024-04-16T00:00:00https://www.semanticscholar.org/paper/c3853cef73749ce019cb00ef860c36d367c01e21 6569b4a3bf879064697ca5cf1f6bdb8885efd243Pemanfaatan tenaga kerja fisik membutuhkan biaya gaji dan tunjangan yang berkelanjutan yang langsung mempengaruhi laba bersih perusahaan. Penggunaan artificial intelligence (AI) dapat menjadi salah satu solusi untuk meningkatkan efisiensi dengan tetap menjaga efektifitas manajemen perusahaan. Tujuan dari penelitian ini adalah mengetahui pengaruh penggunaan AI terhadap efektivitas dan efisiensi pada manajemen kantor hukum berbasis virtual office. Jumlah responden adalah 5 orang meliputi managing partner, senior associate, lawyer, paralegal, dan office manager yang bekerja di kantor hukum X di Jakarta Selatan. Para responden memiliki pengalaman terhadap AI dan manajemen kantor hukum (baik itu konvensional maupun virtual office). Pengambilan data dilakukan melalui wawancara dengan teknik fenomenologi. Hal yang ditanyakan meliputi penghematan biaya, produktivitas, kepuasan klien, kepuasan karyawan, efisiensi, komunikasi, dan keamanan. Hasil penelitian menunjukkan bahwa AI memberikan pengaruh efektif dan efisien terhadap manajemen kantor hukum sehingga biaya operasional dapat ditekan. Virtual Office memberikan pengaruh yang signifikan terhadap pengurangan biaya operasional kantor terutama dalam biaya sewa. Oleh karenanya manajemen kantor hukum akan sangat terbantu dengan AI dan sistem virtual office. Kualitas pelayanan tetap terjaga dan klien mendapatkan biaya yang lebih terjangkau karena kantor hukum bisa menghemat biaya operasionalnya.Syntax literate : jurnal ilmiah Indonesia00Syntax Literate ; Jurnal Ilmiah Indonesia['Rizkiyadi Darmowiyoto', 'Tongam Sirait']2024-04-16T00:00:00https://www.semanticscholar.org/paper/6569b4a3bf879064697ca5cf1f6bdb8885efd243 fa2c36d3437672a15af54155336778a2af37ea6aOECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2024-04-16T00:00:00https://www.semanticscholar.org/paper/fa2c36d3437672a15af54155336778a2af37ea6a ce3f86476aed0e25c5a049a1fc59e9d8ac6ec5b5The research aims to identify key sectors and issues related to resource efficiency in smart city planning in the Indian context and to recommend incorporating Green AI technologies for enhancing resource efficiency in smart cities of India.International Journal For Multidisciplinary Research120International Journal For Multidisciplinary Research['Iyrin Anna Johnson']2024-04-16T00:00:00https://www.semanticscholar.org/paper/ce3f86476aed0e25c5a049a1fc59e9d8ac6ec5b5 433b5525c5008d115f7579468043f6168749b0d8See uploaded letter.Journal of endourology20Journal of endourology['J. H. Berger']2024-04-16T00:00:00https://www.semanticscholar.org/paper/433b5525c5008d115f7579468043f6168749b0d8 044b51de26fff9cc530414c781c26e453b62bf8dFrom the perspective of Chinese employees, this study delves into the evolving employment relationship amidst digital transformation, specifically examining the impact of AI on job satisfaction and psychological contracts. Utilizing an online survey, data was collected from 321 Chinese employees, and subsequent statistical analysis of the gathered metrics evaluated the psychological foundations and behavioral outcomes associated with AI integration in the workplace. The findings reveal that, although AI implementation positively correlates with job satisfaction and the reinforcement of psychological contracts, the existence of transformational leadership tends to attenuate this positive correlation.Interdisciplinary Humanities and Communication Studies50The findings reveal that, although AI implementation positively correlates with job satisfaction and the reinforcement of psychological contracts, the existence of transformational leadership tends to attenuate this positive correlation.Interdisciplinary Humanities and Communication Studies['Junyun Wang']2024-04-16T00:00:00https://www.semanticscholar.org/paper/044b51de26fff9cc530414c781c26e453b62bf8d 5f838e78aa812681932c7bbcbeba3e524451765dPrinciples and Practice of Clinical Research Journal00Principles and Practice of Clinical Research Journal['Attila Ulkucu']2024-04-16T00:00:00https://www.semanticscholar.org/paper/5f838e78aa812681932c7bbcbeba3e524451765d ad503a45223319cbed92aa9cdeac7f3925a78ad7Pediatric Research130Pediatric research['Dennis Ren', 'Damian Roland']2024-04-16T00:00:00https://www.semanticscholar.org/paper/ad503a45223319cbed92aa9cdeac7f3925a78ad7 bcdb48fc82a01a468d92ee20447de6133b88263cBritish medical journal80BMJ['Gary S. Collins']2024-04-16T00:00:00https://www.semanticscholar.org/paper/bcdb48fc82a01a468d92ee20447de6133b88263c 9864cc2805d482c47f88790bde73c2b8f03e59fcComputers, Informatics, Nursing140CIN: Computers, Informatics, Nursing['Delaram Rezaeikhonakdar', 'J. Wrigley', 'Ryan J. Shaw']2024-04-16T00:00:00https://www.semanticscholar.org/paper/9864cc2805d482c47f88790bde73c2b8f03e59fc 99ec919b66a407158bdaf81272d55d79ca002647Journal of the Knowledge Economy570Journal of the Knowledge Economy['Pedro Jácome de Moura', 'Carlos Denner dos Santos Junior', 'Carlo Gabriel Porto-Bellini', 'José Jorge Lima Dias Júnior']2024-04-16T00:00:00https://www.semanticscholar.org/paper/99ec919b66a407158bdaf81272d55d79ca002647 85d22f418c3bf622c8c72553268c6877ea7a125a00['Debasis Chaudhuri', 'Jan Harm Pretorius', 'Debashis Das', 'Sauvik Bal']2024-04-16T00:00:00https://www.semanticscholar.org/paper/85d22f418c3bf622c8c72553268c6877ea7a125a b8e151cfc16833a27e966b729a41a320eeb2722bForbes Journal of Medicine00Forbes Journal of Medicine['Fatma Susam']2024-04-16T00:00:00https://www.semanticscholar.org/paper/b8e151cfc16833a27e966b729a41a320eeb2722b 83d3f0b12e38181642bc01e137b9cf1908cb602fJournal of Medical and Biological Engineering1030Journal of Medical and Biological Engineering['Xin Li', 'Lei Zhang', 'Jingsi Yang', 'Fei Teng']2024-04-16T00:00:00https://www.semanticscholar.org/paper/83d3f0b12e38181642bc01e137b9cf1908cb602f 20025002df007e2bac5aadb3af2be0ae25844debThis paper aims to establish a consensus on AGI's definition. General intelligence refers to the adaptation to open environments according to certain principles using limited resources. It emphasizes that adaptation or learning is an indispensable property of intelligence, and places the controversial part within the principles of intelligence, which can be described from different perspectives.arXiv.org140This paper aims to establish a consensus on AGI's definition, emphasizing that adaptation or learning is an indispensable property of intelligence, and places the controversial part within the principles of intelligence, which can be described from different perspectives.ArXiv['Bowen Xu']2024-04-16T00:00:00https://www.semanticscholar.org/paper/20025002df007e2bac5aadb3af2be0ae25844deb 0ccf2f88d6acb158c6bc10d7435cf6e768158dfeNowadays, the dynamism caused by constant changes to strategic decisions in markets poses an additional difficulty in an organization’s management. The strategic decisions made by managers can easily become obsolete. One of the major difficulties in managing a commercial organization is predicting, with some precision, the impact some strategic decisions have on the financial results. Business intelligence (BI) is widely used to help managers make strategic decisions. However, the methods used to achieve the conclusions are kept secret by BI company-based services. Modeling the environment may help predict the impact of an action in a real environment. A good model should provide the most accurate result of an applied action in a given environment. Artificial neural networks (ANNs) are proven to be excellent in modeling environments with very high data noise. The same strategic action can have different results when applied to different organizations. A tool that allows the evaluation of an applied strategic action in an environment will be of great importance in the field of management. Modeling the environment will save time and money for the organization, allowing the performance of the strategic plan to be improved. If one evaluates the state of the environment after a certain strategic action is applied, it can be possible to mitigate its risk of failure. As we will verify, it is possible to use ANNs to model strategic environments, allowing precision in the prediction of sales and operating results using particular strategies.Journal of Risk and Financial Management150It is possible to use ANNs to model strategic environments, allowing precision in the prediction of sales and operating results using particular strategies, and will save time and money for the organization, allowing the performance of the strategic plan to be improved.Journal of Risk and Financial Management['Maria do Rosário Texeira Fernandes Justino', 'Joaquín Texeira-Quirós', 'António José Gonçalves', 'M. Antunes', 'P. Mucharreira']2024-04-16T00:00:00https://www.semanticscholar.org/paper/0ccf2f88d6acb158c6bc10d7435cf6e768158dfe ea3dbcb1e089d346681ec4ac7c0072c0abfe0226Artificial Intelligence (AI) provides an opportunity for a transformative shift towards a more personalised and efficient learning environment in the contemporary education landscape (FitzGerald, 2018; Perez et al., 2020; Yang and Evans, 2019; Yin et al., 2021). This landscape is characterised by globalisation and universal education trends, which often necessitate being mindful of the challenges of managing large enrolments and diversity within student bodies. This presentation outlines the implementation and experiences of a generative AI-supported chatbot (StatBot) introduced to two cohorts of quantitative methods classes in the Faculty of Business and Economics, targeting over 2,500 students annually. Attending this presentation, participants will gain valuable insight into the effective use of AI in teaching and learning in subjects with large enrolments. The initiative aimed to enhance students' learning experience by offering personalised, subject-specific support by converting IBM Watson Assistant, renowned for its ability to process and interpret natural language queries, into an educational chatbot. The primary purpose of this AI tool was to improve student's educational experience by providing them with instant, tailored assistance that directly related to the material taught within the subject and at a time that suited the student. Recognising students' diverse needs and learning pace in a large class, the chatbot was designed to offer both administrative and conceptual support, facilitating a more inclusive and accessible learning environment. It addressed a wide range of queries, from course logistics and administrative procedures to in-depth explanations of complex concepts. It provided a comprehensive bank of practice questions and feedback process, specifically curated to reinforce learning and aid in consolidating knowledge. This repository enabled students to engage in self-directed learning, assess their understanding, and identify areas requiring further exploration, thus promoting a proactive and reflective learning approach. The benefits of implementing this AI tool were multifaceted. For educators, it alleviated the burden of addressing repetitive administrative and basic conceptual queries, freeing up valuable time to focus on more complex teaching and research activities. For students, the immediate and personalised nature of the support enhanced their learning experience, enabling them to navigate the course content more confidently and efficiently. The chatbot also fostered an environment of continuous learning, encouraging students to engage with the material and practice independently and actively. Integrating the chatbot into the curriculum offered a strategic educational intervention aimed at enhancing student learning and support, particularly in large undergraduate subjects. The platform's robust AI capabilities allowed the delivery of personalised learning experiences at scale, which is difficult through traditional teaching methods. Its ability to process student queries and provide immediate, accurate (verified) responses ensured that students received the support they needed when needed, without the constraints of office hours or limited teaching staff availability. The student feedback following the introduction of the AI-supported chatbot was overwhelmingly positive. The tool's ability to provide instant, relevant, and personalised support was particularly appreciated, as it directly contributed to a more supportive and responsive learning environment. Moreover, the availability of a practice question bank was highlighted as a critical resource that enabled students to test their knowledge and prepare more effectively for assessments.Pacific Journal of Technology Enhanced Learning40This presentation outlines the implementation and experiences of a generative AI-supported chatbot (StatBot) introduced to two cohorts of quantitative methods classes in the Faculty of Business and Economics, targeting over 2,500 students annually.Pacific Journal of Technology Enhanced Learning['W. Karunarathne', 'Angela Paladino', 'Chris Selman', 'Kris Nagy', 'Laszlo Sajitos', 'Shohil Kishore']2024-04-16T00:00:00https://www.semanticscholar.org/paper/ea3dbcb1e089d346681ec4ac7c0072c0abfe0226 cabbb59f05a27f5d3da85aea7d314cb6793728f0 Cybersecurity of medical devices has become a concrete concern for regulators and policymakers in the European Union and United States. Following the COVID-19 pandemic, there has been an increase in cyber-attacks on critical healthcare infrastructures and their IT systems, which have suffered service disruptions and put patients’ health and safety at risk. The increase in cyberattacks on healthcare infrastructure, including medical devices, exacerbated by the growing digitalisation of healthcare services in the EU and the US, has led legislators and regulatory bodies to pay more attention to cybersecurity. Cybersecurity of AI-based medical devices requires the assessment of three areas subject to evolving regulatory approaches: medical devices, Artificial Intelligence (AI), and cybersecurity. Although they may appear distinguished in regulatory matters, the existence of AI-based medical devices and their possible cyber vulnerabilities makes clear that the three are intertwined and deserve closer attention from a regulatory point of view. Few scholars have devoted attention to AI and cybersecurity together. Even less, in our understanding, few comprehensive and EU/US comparative pieces of literature reflect on this specific issue. This paper aims to fill this gap and address the main implications of different regulatory approaches toward AI medical device cybersecurity in the EU and the US. The research stems from the assumption that regulation of medical devices in the EU has been historically inspired by regulatory trends in the US, although with the different cultural, societal, and legal traditions that made them adapt to the specificities of the territory. The paper observes that the US is a rule-based system reflecting a “command-and-control” approach, while the EU system is a principle-based one. While they share the main characteristic of being risk-regulation-based systems, their differences impact how AI-enhanced cybersecurity is regulated.European Journal of Risk Regulation00The research stems from the assumption that regulation of medical devices in the EU has been historically inspired by regulatory trends in the US, although with the different cultural, societal, and legal traditions that made them adapt to the specificities of the territory.European Journal of Risk Regulation['Elisabetta Biasin', 'Erik Kamenjašević']2024-04-15T00:00:00https://www.semanticscholar.org/paper/cabbb59f05a27f5d3da85aea7d314cb6793728f0 7889c9a9adb7ffa591d3a054eb656f2b91dac606AI and Ethics410The study’s findings underscore the importance of ethical AI implementation and highlight benefits and limitations for measuring ethical adherence, and offers insights into a foundation for future AI ethics assessments within and outside the financial industry.AI and Ethics['Maria Pokholkova', 'Auxane Boch', 'Ellen Hohma', 'Christoph Lütge']2024-04-15T00:00:00https://www.semanticscholar.org/paper/7889c9a9adb7ffa591d3a054eb656f2b91dac606 535f8dde02c790ad1022f63c2bbd21232fcee48cAI and Ethics480EPS is a methodology promoting responsible, human-centric, and value-oriented AI development that’s core resides in translating principles into practical implementations using impact assessment surveys and a differential recommendation methodology.AI and Ethics['N. Corrêa', 'James William Santos', 'Camila Galvão', 'Marcelo Pasetti', 'D. Schiavon', 'Faizah Naqvi', 'Robayet Hossain', 'N. D. Oliveira']2024-04-15T00:00:00https://www.semanticscholar.org/paper/535f8dde02c790ad1022f63c2bbd21232fcee48c ea379e3c519407ddb546a303b1a57ff8fbbe494fSubject. This article deals with the issues of regulation of business processes of the internal control system in the organizations of the construction complex of Russia. Objectives. The article aims to formulate the main methodological approaches to the arrangement of the internal control system based on the regulation of business processes. Methods. For the study, we used statistical, case, computational and constructive research methods, as well as analytical and substantive procedures. Results. The article finds that business processes need regulation, which helps systematize actions that are interrelated, which also helps implement strategic, tactical and operational plans of an economic entity. The article identifies the main reasons that hinder the development of building companies, and presents the regulations of the business process Apartment Sale in the form of a clear sequence of actions, which made it possible to develop a methodology for internal control with the involvement of almost all structural divisions of an economic entity. Conclusions and Relevance. Regulation of business processes is an important tool for improving the effectiveness of the internal control system, it contributes to the establishment of clear procedures and rules, standardization, business transparency and the efficiency of the business entity. The results of the study can be used as methodological recommendations for the creation of an internal control system based on the regulation of business processes.International Accounting40International Accounting['M. Safonova', 'Yuliya V. Marchenko']2024-04-15T00:00:00https://www.semanticscholar.org/paper/ea379e3c519407ddb546a303b1a57ff8fbbe494f 3800ee2bd2f05d57a76937116edc2e122af5114bThe article is devoted to the study of the peculiarities of legal regulation of economic and commercial activities carried out by business entities during the period of war in Ukraine. The relevance of the study is due to the importance of economic and commercial activities for ensuring the economic stability of the State and protection of national interests in the context of a full-scale invasion. The purpose of the article is to determine the main aspects of legal regulation of economic and commercial activity under martial law and to provide recommendations for its improvement. To achieve this goal, the study solved a number of tasks: the author defines the concept of “trade and economic activity” and the role of trade and economic activity in creating favorable conditions for stable economic growth of the country; analyzes the current state of economic and trade activity in Ukraine; examines the legal regulation of economic and trade activity in Ukraine and identifies its features; and provides recommendations for improving economic and trade activity in Ukraine. The study uses general scientific methods of cognition: induction and deduction, analysis and synthesis, association and analogy. Based on the results of the study, it is established that the legal regulation of economic and commercial activity under martial law in Ukraine has certain peculiarities aimed at ensuring economic stability and protecting national interests. These features include deregulation of economic activity, tariff regulation, licensing and quotas for foreign economic activity, currency control, technical regulation, digital transformation of the economy, trade agreements and affordable lending. For the further development of economic and trade activities under martial law in Ukraine, it is necessary to continue to improve legal regulation, focusing on further business support, creating a favorable investment climate, expanding trade relations, supporting exports and developing the domestic market. The practical significance of the study lies in the possibility of applying the results obtained to formulate further strategies in the field of economic policy aimed at ensuring the sustainability and efficiency of economic and trade activities under martial law and increasing the overall level of economic development of the country.Constitutional State00Constitutional State['M. Syrotko']2024-04-15T00:00:00https://www.semanticscholar.org/paper/3800ee2bd2f05d57a76937116edc2e122af5114b 6d938c56ec9a0527d1bf889f43114d124c44554dWhile maladaptive consumption and its consequences are well known, the management and regulation of such consumption is fraught with numerous issues related to definition, locus of responsibility, and potential modes of intervention. The present paper provides a review of the conceptual, methodological, and policy issues surrounding maladaptive consumption. The paper suggests that marketing has an important and unique role in the design of products and regulation intended to address maladaptive consumption. Questions for future research and public policy are identified and discussed.Journal of Public Policy &amp; Marketing00Journal of Public Policy &amp; Marketing['Ingrid Martin', 'David W. Stewart']2024-04-15T00:00:00https://www.semanticscholar.org/paper/6d938c56ec9a0527d1bf889f43114d124c44554d dc3ca3dbf404a15585ac5ca54fb3ffeaea866a01The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities. The 2024 edition featured five tracks, attracting unprecedented interest from 726 teams in 47 countries and regions. Track 1 dealt with multi-target multi-camera (MTMC) people tracking, highlighting significant enhancements in camera count, character number, 3D annotation, and camera matrices, alongside new rules for 3D tracking and online tracking algorithm encouragement. Track 2 introduced dense video captioning for traffic safety, focusing on pedestrian accidents using multi-camera feeds to improve insights for insurance and prevention. Track 3 required teams to classify driver actions in a naturalistic driving analysis. Track 4 explored fish-eye camera analytics using the FishEye8K dataset. Track 5 focused on motorcycle helmet rule violation detection. The challenge utilized two leaderboards to showcase methods, with participants setting new benchmarks, some surpassing existing state-of-the-art achievements.arXiv.org873The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems, presenting significant research opportunities, with participants setting new benchmarks.ArXiv['Shuo Wang', 'D. Anastasiu', 'Zhenghang Tang', 'Ming-Ching Chang', 'Yue Yao', 'Liang Zheng', 'Mohammed Shaiqur Rahman', 'Meenakshi S. Arya', 'Anuj Sharma', 'Pranamesh Chakraborty', 'Sanjita Prajapati', 'Quan Kong', 'N. Kobori', 'Munkhjargal Gochoo', 'Munkh-Erdene Otgonbold', 'F. Alnajjar', 'Ganzorig Batnasan', 'Ping-Yang Chen', 'Jun-Wei Hsieh', 'Xunlei Wu', 'S. Pusegaonkar', 'Yizhou Wang', 'Sujit Biswas', 'R. Chellappa']2024-04-15T00:00:00https://www.semanticscholar.org/paper/dc3ca3dbf404a15585ac5ca54fb3ffeaea866a01 c2d1badbd010502b47693dc0adc9dcdcc66d644bLitigation is a creature of disagreement. Our essay explores the potential of artificial intelligence (AI) to help reduce legal disagreements. In any litigation, parties disagree over the facts, the law, or how the law applies to the facts. The source of the parties' disagreements matters. It may determine the extent to which AI can help resolve their disputes. AI is helpful in clarifying the parties' misunderstanding over how well-defined questions of law apply to their facts. But AI may be less helpful when parties disagree on questions of fact where the prevailing facts dictate the legal outcome. The private nature of information underlying these factual disagreements typically fall outside the strengths of AI's computational leverage over publicly available data. A further complication: parties may disagree about which rule should govern the dispute, which can arise irrespective of whether they agree or disagree over questions of facts. Accordingly, while AI can provide clarity over legal precedent, it often may be insufficient to provide clarity over legal disputes. This article is part of the theme issue 'A complexity science approach to law and governance'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences461This essay explores the potential of artificial intelligence (AI) to help reduce legal disagreements by clarifying the parties' misunderstanding over how well-defined questions of law apply to their facts.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences['Anthony Niblett', 'Albert Yoon']2024-04-15T00:00:00https://www.semanticscholar.org/paper/c2d1badbd010502b47693dc0adc9dcdcc66d644b 8dcb17120ddd0f0337dbd3cd7325de8c47e770fdAs more groups consider how AI may be used in the legal sector, this paper envisions how companies and policymakers can prioritize the perspective of community members as they design AI and policies around it. It presents findings of structured interviews and design sessions with community members, in which they were asked about whether, how, and why they would use AI tools powered by large language models to respond to legal problems like receiving an eviction notice. The respondents reviewed options for simple versus complex interfaces for AI tools, and expressed how they would want to engage with an AI tool to resolve a legal problem. These empirical findings provide directions that can counterbalance legal domain experts' proposals about the public interest around AI, as expressed by attorneys, court officials, advocates and regulators. By hearing directly from community members about how they want to use AI for civil justice tasks, what risks concern them, and the value they would find in different kinds of AI tools, this research can ensure that people's points of view are understood and prioritized, rather than only domain experts' assertions about people's needs and preferences around legal help AI. This article is part of the theme issue 'A complexity science approach to law and governance'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences01Findings of structured interviews and design sessions with community members are presented, in which they were asked whether, how, and why they would use AI tools powered by large language models to respond to legal problems like receiving an eviction notice.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences['Margaret D. Hagan']2024-04-15T00:00:00https://www.semanticscholar.org/paper/8dcb17120ddd0f0337dbd3cd7325de8c47e770fd 26391ab8b63db66254b516446117acb47ae9241aBackground Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. Objective The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. Methods We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. Results A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). Conclusions This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.Interactive Journal of Medical Research420A comprehensive analysis of existing publications of AI applications for sepsis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans.Interactive Journal of Medical Research['MeiJung Wu', 'M. Islam', 'T. N. Poly', 'Ming-Chin Lin']2024-04-15T00:00:00https://www.semanticscholar.org/paper/26391ab8b63db66254b516446117acb47ae9241a 9c0eb1992345a737fd50f29f3ce29e25588a3921This research analyses the artificial intelligence application strategies adopted by the Spanish press media at the height of the generative AI boom, extending previous studies on integrating AI technology into journalistic routines. We studied the dynamics of implementation of AI solutions in the main newspapers in Spain, determining the departments or professionals involved, the current uses, and how AI affects their organization chart and professional profiles, as well as providing insights into the future trends of this technology in the sector. For this purpose, a qualitative methodology was employed, based on interviews with a purposive sample of executives from the 10 newspapers with the largest circulation in Spain: El País, La Vanguardia, Marca, La Voz de Galicia, As, ABC, El Mundo, El Correo, El Diario Vasco and Mundo Deportivo. The results reveal that AI is conceived in the Spanish press media as an assistant aimed at optimizing all kinds of mechanical processes and existing products, not at amplifying coverage or generating final pieces that reduce the standard of quality essential to their subscription model. Groups such as Prisa, Vocento or Unidad Editorial centralize their AI strategy, while Grupo Godó delegates this management to its headers. These newspapers consider that AI will boost the renewal of job profiles in their structures, despite the fact that they do not currently have strictly specialized professionals.Tripodos310The results reveal that AI is conceived in the Spanish press media as an assistant aimed at optimizing all kinds of mechanical processes and existing products, not at amplifying coverage or generating final pieces that reduce the standard of quality essential to their subscription model.Tripodos['César Fieiras Ceide', 'Martín Vaz Álvarez', 'Isaac Maroto González']2024-04-15T00:00:00https://www.semanticscholar.org/paper/9c0eb1992345a737fd50f29f3ce29e25588a3921 a52cc831903d00106b81ac24a73381537bfb1852Reseña de Diana Pérez del libro The Algoritmic Unconscious. How Psychoanalysis Helps in Understanding AI, de Luca Possati. New York: Routledge, 2022Estudios públicos00Estudios Públicos['Diana Pérez']2024-04-15T00:00:00https://www.semanticscholar.org/paper/a52cc831903d00106b81ac24a73381537bfb1852 c2cce97eddecd8971fbde61bde474f4d2a77f56dSince ChatGPT was released by OpenAI, an American company, in 2022 for the public, ChatGPT has become the talk of every town, as evident by its over 180 million users worldwide. This chatbot's ability to engage in human-like conversations, answer questions, and generate diverse content has sparked widespread debates across various fields, including education. In response to the growing rise and influence of ChatGPT, educators have contrasting opinions; some view ChatGPT as an opportunity, whereas others regard it as a challenge that needs to be addressed on time. In order to deal with the complexities caused by ChatGPT in the field of education, universities have formulated their policies on AI. Guided by the research question, "How does universities' policy on AI reflect academia's view toward ChatGPT?" this study attempts to review the AI policy of the nine academic institutions under the UT system of the United States. The primary goal is to understand the extent to which universities have adapted their policies in response to the challenges and opportunities posed by ChatGPT and how these policies reflect the broader sentiments within academia. To achieve this, this study reviews the universities' policies regarding AI using a qualitative data analysis methodology. The primary data sources include official policies, statements, and guidelines developed by the universities in response to the challenges and opportunities presented by ChatGPT. While reviewing the policies, the study determines whether ChatGPT is banned and why. Or embraced, and if so, in what ways? By examining these policies, the study aims to uncover the various approaches universities have taken to integrate or regulate the use of ChatGPT within academic environments. The thesis of this study is twofold. First, it seeks to provide a comprehensive overview of how US universities have responded to ChatGPT in the educational landscape. This involves identifying common themes, concerns, and strategies institutions employ to deal with the complexities introduced by this generative language model. Second, the study aims to contribute to existing scholarship by offering insights into how academia adapts to the influence of AI technologies like ChatGPT. This study examines the intersection of AI and education and the evolving nature of educational norms in the digital age by uncovering the diverse perspectives and approaches within university policies.Advances in Mobile Learning Educational Research260This study reviews the universities' policies regarding AI using a qualitative data analysis methodology and seeks to provide a comprehensive overview of how US universities have responded to ChatGPT in the educational landscape.Advances in Mobile Learning Educational Research['Sanjeev Niraula']2024-04-15T00:00:00https://www.semanticscholar.org/paper/c2cce97eddecd8971fbde61bde474f4d2a77f56d 85e580e0a650722904e972dc5375f9dd196d7924In this project, we propose an ensemble learning-based system for Sign Language Recognition (SLR) integrated with an Explainable AI (XAI) component called SignExplainer. Our goal is to enhance transparency and trust in SLR systems by providing interpretable predictions. The ensemble learning architecture is designed to recognize sign gestures from images, and the SignExplainer module generates statistical values to evaluate prediction correctness. Performance evaluation on benchmark datasets like ASL and BSL demonstrates the effectiveness of our approach in interpreting predictions from various machine learning and deep learning models. Future work aims to extend this methodology to real-time applications and other Sign Languages, advancing accessibility and inclusivity for the hearing-impaired communityInternational Journal of Advanced Research in Science, Communication and Technology20An ensemble learning-based system for Sign Language Recognition integrated with an Explainable AI (XAI) component called SignExplainer, designed to recognize sign gestures from images, and the SignExplainer module generates statistical values to evaluate prediction correctness.International Journal of Advanced Research in Science, Communication and Technology['Prof. V. M. Dilpak', 'Rewa S. Joshi', 'Harshada K . Sonje']2024-04-15T00:00:00https://www.semanticscholar.org/paper/85e580e0a650722904e972dc5375f9dd196d7924 3ad3e240cabb3f3471770d25a7414a81175aa0dbGenerative AI (GenAI) marked a shift from AI being able to recognize to AI being able to generate solutions for a wide variety of tasks. As the generated solutions and applications become increasingly more complex and multi-faceted, novel needs, objectives, and possibilities have emerged for explainability (XAI). In this work, we elaborate on why XAI has gained importance with the rise of GenAI and its challenges for explainability research. We also unveil novel and emerging desiderata that explanations should fulfill, covering aspects such as verifiability, interactivity, security, and cost. To this end, we focus on surveying existing works. Furthermore, we provide a taxonomy of relevant dimensions that allows us to better characterize existing XAI mechanisms and methods for GenAI. We discuss different avenues to ensure XAI, from training data to prompting. Our paper offers a short but concise technical background of GenAI for non-technical readers, focusing on text and images to better understand novel or adapted XAI techniques for GenAI. However, due to the vast array of works on GenAI, we decided to forego detailed aspects of XAI related to evaluation and usage of explanations. As such, the manuscript interests both technically oriented people and other disciplines, such as social scientists and information systems researchers. Our research roadmap provides more than ten directions for future investigation.arXiv.org2070This work elaborate on why XAI has gained importance with the rise of GenAI and its challenges for explainability research, and unveil novel and emerging desiderata that explanations should fulfill, covering aspects such as verifiability, interactivity, security, and cost.ArXiv['Johannes Schneider']2024-04-15T00:00:00https://www.semanticscholar.org/paper/3ad3e240cabb3f3471770d25a7414a81175aa0db 6bdbe29cdac2262cdadb06cddcd44692e0a19118Objectives The purposes of this study were to explore the experience and recognition of adolescents using generative AI. Methods In-depth interview was conducted with 14 teenagers who are attending high school, located in A city B county, C city, using Colaizzi’s phenomenological research method. 14 teenagers have experience using gernative AI. Results The results, 3 essential themes, 6 sub-categories was found. Three essential themes were ‘imperfect learning guide’, ‘existing of utopia and dystopia’, ‘ambiguous using range of generative AI’. And six sub-categories were ‘the limitation of inorganic object’, ‘the third learning guide’, ‘change in human being life’, ‘new opportunity provision’, ‘guide absence of the ethics using’, ‘distancing of generative AI using’. Conclusions The discussion on AI literacy for adolescent is as follows. First, provide the education about accurate asking to generative AI. Second, provide the enthical using guide. Third, provide the education about the generative AI system machanism. Korean Association For Learner-Centered Curriculum And Instruction00The discussion on AI literacy for adolescent is to provide the education about accurate asking to generative AI, provide the enthical using guide, and provide the education about the generative AI system machanism.Korean Association For Learner-Centered Curriculum And Instruction['YangJin Noh', 'Dongseong Park']2024-04-15T00:00:00https://www.semanticscholar.org/paper/6bdbe29cdac2262cdadb06cddcd44692e0a19118 9d265cb66c488fbd9691d81b972e02a8155e4702Recent advances in natural language processing (NLP) may enable artificial intelligence (AI) models to generate writing that is identical to human written form in the future. This might have profound ethical, legal, and social repercussions. This study aims to address this problem by offering an accurate AI detector model that can differentiate between electronically produced text and human-written text. Our approach includes machine learning methods such as XGB Classifier, SVM, BERT architecture deep learning models. Furthermore, our results show that the BERT performs better than previous models in identifying information generated by AI from information provided by humans. Provide a comprehensive analysis of the current state of AI-generated text identification in our assessment of pertinent studies. Our testing yielded positive findings, showing that our strategy is successful, with the BERT emerging as the most probable answer. We analyze the research's societal implications, highlighting the possible advantages for various industries while addressing sustainability issues pertaining to morality and the environment. The XGB classifier and SVM give 0.84 and 0.81 accuracy in this article, respectively. The greatest accuracy in this research is provided by the BERT model, which provides 0.93% accuracy.arXiv.org150The testing yielded positive findings, showing that the strategy is successful, and the BERT emerging as the most probable answer, with the BERT emerging as the most probable answer in identifying information generated by AI from information provided by humans.ArXiv['Nuzhat Prova']2024-04-15T00:00:00https://www.semanticscholar.org/paper/9d265cb66c488fbd9691d81b972e02a8155e4702 497930fc07c38237ecdcc0e891e9a0b98db1d105Neuroradiology170Significant improvements of overall specificity and the overall number of false positives per case were observed in the reading with AI support and for the physicians, and significant improvements of sensitivity on lesion and patient level and false positives per case were found.Neuroradiology['N. Lehnen', 'Arndt-Hendrik Schievelkamp', 'Christian Gronemann', 'Robert Haase', 'Inga Krause', 'Max Gansen', 'Tobias Fleckenstein', 'Franziska Dorn', 'Alexander Radbruch', 'Daniel Paech']2024-04-15T00:00:00https://www.semanticscholar.org/paper/497930fc07c38237ecdcc0e891e9a0b98db1d105 1fca81fd93bed92dd2b3658f94b2c79dc3a3f933Predictive analytics, empowered by articial intelligence (AI), has emerged as a powerful tool in early disease detection within healthcare systems. Leveraging diverse data sources and advanced machine learning algorithms, this study investigates the efcacy of AI-driven predictive models in identifying individuals at risk of disease onset. A comprehensive dataset comprising electronic health records, medical imaging data, laboratory results, and demographic information is utilized for model development and evaluation. Rigorous data preprocessing, feature selection, and model training techniques are employed to optimize predictive performance and ensure clinical relevance. Results demonstrate the effectiveness of AI-driven predictive models in discriminating between individuals with and without early disease manifestations, achieving high accuracy, precision, and recall. Feature importance analysis and SHAP values provide insights into the underlying mechanisms driving disease prediction, highlighting potential biomarkers and risk factors. Despite promising results, challenges such as data quality, bias, interpretability, and regulatory compliance are acknowledged. Future research directions include prospective validation studies, real-world deployment, and integration into clinical workows to assess scalability, generalizability, and clinical impact. In conclusion, AI-driven predictive analytics holds immense promise for revolutionizing disease management, improving patient outcomes, and advancing personalized healthcare in the era of precision medicine.Global Journal For Research Analysis00AI-driven predictive analytics holds immense promise for revolutionizing disease management, improving patient outcomes, and advancing personalized healthcare in the era of precision medicine.GLOBAL JOURNAL FOR RESEARCH ANALYSIS['Vikas Burri', 'Lalasa Mukku']2024-04-15T00:00:00https://www.semanticscholar.org/paper/1fca81fd93bed92dd2b3658f94b2c79dc3a3f933 7cf4dc71835704b5a8949c1b6f6241e53f503a93The recent proliferation of generative artificial intelligence (GenAI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate the rule-based reasoning inherent in statutory and contract law. While this form of reasoning has long been studied using classical techniques of natural language processing (NLP) and formal logic, recent solutions increasingly make use of LLMs; though they are far from perfect. The advent of GenAI has made it possible to treat many of these natural language documents essentially as programs that compute a result given some set of facts. As such, it should be possible to understand, debug, maintain, evolve, and fix these documents using well-studied techniques from the field of software engineering. This article introduces several concepts of automated software testing and program analysis that could potentially be useful in computational law when applied to AI-driven analysis of statutes and contracts.arXiv.org260Several concepts of automated software testing and program analysis that could potentially be useful in computational law when applied to AI-driven analysis of statutes and contracts are introduced.ArXiv['Rohan Padhye']2024-04-15T00:00:00https://www.semanticscholar.org/paper/7cf4dc71835704b5a8949c1b6f6241e53f503a93 4a42ffdafc8f707117050d906db30d3c632bd7b4 Find out here about the European REVERT project, an AI-based DSS for treatment selection. The REVERT project is built around predictive medicine and artificial intelligence (AI). The Consortium includes 22 top-performing research, biobanks and clinical centres, as well as four small-medium enterprises (SMEs) from five European countries, whose expertise spans from computer science to biomarker discovery, omics sciences, and clinical oncology settings, with a shared focus on advancing personalised medicine through AI-health research and development. Open Access Government00The Consortium includes 22 top-performing research, biobanks and clinical centres, as well as four small-medium enterprises (SMEs) from five European countries, whose expertise spans from computer science to biomarker discovery, omics sciences, and clinical oncology settings, with a shared focus on advancing personalised medicine through AI-health research and development.Open Access Government['Revert Project']2024-04-15T00:00:00https://www.semanticscholar.org/paper/4a42ffdafc8f707117050d906db30d3c632bd7b4 7b78d7b82f6fdc0ed4242e6af8194f5864e9e6d6The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, thousands of trusted users and multiple user interfaces, offering a unique experience and enormous capabilities. To name a few, the NOSTR applications include but are not limited to direct messaging, file sharing, audio/video streaming, collaborative writing, blogging and data processing through distributed AI directories. In this work, we propose an approach that builds upon the existing protocol structure with end goal a decentralized marketplace for federated learning and LLM training. In this proposed design there are two parties: on one side there are customers who provide a dataset that they want to use for training an AI model. On the other side, there are service providers, who receive (parts of) the dataset, train the AI model, and for a payment as an exchange, they return the optimized AI model. The decentralized and censorship resistant features of the NOSTR enable the possibility of designing a fair and open marketplace for training AI models and LLMs.00This work proposes an approach that builds upon the existing protocol structure with end goal a decentralized marketplace for federated learning and LLM training, enabling the possibility of designing a fair and open marketplace for training AI models and LLMs.['Konstantinos E. Nikolakakis', 'George Chantzialexiou', 'Dionysis Kalogerias']2024-04-15T00:00:00https://www.semanticscholar.org/paper/7b78d7b82f6fdc0ed4242e6af8194f5864e9e6d6 b902c8ad6f167e2329b168e1fc9e7a73c6b8efbf Purpose Most recent C-suite surveying suggests current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated. Instead, business leaders expect major value from generative AI will be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation. Design/methodology/approach Findings and analysis presented draws on data from several surveys of C-level executives conducted by IBM Institute for Business Value in collaboration with Oxford Economics during 2023. Each survey focused on the potential of generative AI in a particular business area. The n-count of each survey ranged from 100-3000. Findings 1. Business leaders expect generative AI to build on returns achieved from investments in traditional AI, with 10 percent RoI expected on generative AI investments by 2025. 2. Executives anticipate that generative AI will have most impact when implemented to expand innovation. 3. Specific examples provided for operational innovation, product innovation, and business model innovation Research limitations/implications We are still very early in the generative AI development cycle. We have made best efforts to project, but only time will tell for sure. Practical implications Business application of generative AI are extremely fragmented. Despite the desire to throw investments at the wall to see what sticks, it is important that leaders take a structured approach to generative AI, focusing on RoI from innovation investments. Social implications To alleviate negative impacts of generative AI, focusing on innovation potential and value maximization is crucial. Originality/value This research is based on completely new surveying and data. This papers adds to the sum total of new knowledge in the generative AI domain. Strategy &amp; Leadership00Current applications of generative AI, although hyped, are fragmented and unlikely to yield major financial returns anticipated, business leaders expect major value from generative AI to be achieved through application of generative AI to innovation: operational innovation, product and service innovation, and most elusive of all, business model innovation.Strategy &amp; Leadership['Anthony Marshall', 'Christian Bieck', 'Jacob Dencik', 'Brian C. Goehring', 'Richard Warrick']2024-04-15T00:00:00https://www.semanticscholar.org/paper/b902c8ad6f167e2329b168e1fc9e7a73c6b8efbf b34a677510c6a7d9f334f333f835eccba65abf97Although the detection procedure has been shown to be highly effective, there are several obstacles to overcome in the usage of AI-assisted cancer cell detection in clinical settings. These issues stem mostly from the failure to identify the underlying processes. Because AI-assisted diagnosis does not offer a clear decision-making process, doctors are dubious about it. In this instance, the advent of Explainable Artificial Intelligence (XAI), which offers explanations for prediction models, solves the AI black box issue. The SHapley Additive exPlanations (SHAP) approach, which results in the interpretation of model predictions, is the main emphasis of this work. The intermediate layer in this study was a hybrid model made up of three Convolutional Neural Networks (CNNs) (InceptionV3, InceptionResNetV2, and VGG16) that combined their predictions. The KvasirV2 dataset, which comprises pathological symptoms associated to cancer, was used to train the model. Our combined model yielded an accuracy of 93.17% and an F1 score of 97%. After training the combined model, we use SHAP to analyze images from these three groups to provide an explanation of the decision that affects the model prediction.Frontiers in Medicine450The SHapley Additive exPlanations (SHAP) approach, which results in the interpretation of model predictions, is the main emphasis of this work, and is used to analyze images from these three groups to provide an explanation of the decision that affects the model prediction.Frontiers in medicine['Faisal Binzagr']2024-04-15T00:00:00https://www.semanticscholar.org/paper/b34a677510c6a7d9f334f333f835eccba65abf97 36a54d6928bd040079420e5ffa586f5bed65ae85Learning is the soul of life. Every living being learns new things each and every day. Especially Language learning is a vital role from past to present generation. The modern world needs a skilled and more efficient people for handling different situations. Innovation was the only slogan for language experts.AI technology opens new window for those who are aspiring and developing language learning. Sounds and signs are the basic elements for language components. In the past, language learning was purely transmitted through oral method. After the peak of technological advancement teaching and learning perspective completely changed. The modern world drifted for AI technology enhances and redefined. English language learning scenes in new approaches Like Dulingo, Babbel, Memrise etc. If one is interested in enhancing language proficiency these apps really help and make a great difference in the teaching and learning process.International Research Journal on Advanced Engineering and Management (IRJAEM)60If one is interested in enhancing language proficiency these apps really help and make a great difference in the teaching and learning process.International Research Journal on Advanced Engineering and Management (IRJAEM)['M. Vijayakumar', 'G. Chellapandiyan']2024-04-15T00:00:00https://www.semanticscholar.org/paper/36a54d6928bd040079420e5ffa586f5bed65ae85 3909e6cad97e052775ceffaccf86d46f367f8312 Vessela Kristensen and Dag Undlien uncover AI tools in genetics, from variant recognition to clinical implementation. Most people are curious about how their bodies work (and the ways they occasionally do not). This curiosity extends towards how our bodies are built, their functions, and what maintains life and health. Most people think that science is remote from the lives they lead, and the decisions that they make day by day, but this is far from the truth. Our understanding of genetics may affect our choices at our doctor’s office about our healthcare and reproductive decisions, including family planning. Open Access Government00Vessela Kristensen and Dag Undlien uncover AI tools in genetics, from variant recognition to clinical implementation, which may affect the authors' choices at the doctor’s office about their healthcare and reproductive decisions, including family planning.Open Access Government['Vessela Kristensen', 'Dag Undlien']2024-04-15T00:00:00https://www.semanticscholar.org/paper/3909e6cad97e052775ceffaccf86d46f367f8312 6856ffaa743b83ca41a5e403c69ab85909aafe35Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's digital era, where substantial data is generated daily, it is uncommon for it to be readily usable; most often, it necessitates meticulous manual data preparation. The haste in developing new models can frequently result in various shortcomings, potentially posing risks when deployed in real-world scenarios (eg social discrimination, critical failures), leading to the failure or substantial escalation of costs in AI-based projects. This chapter provides a comprehensive overview of established methodological tools, enriched by our practical experience, in the development of datasets for machine learning. Initially, we develop the tasks involved in dataset development and offer insights into their effective management (including requirements, design, implementation, evaluation, distribution, and maintenance). Then, we provide more details about the implementation process which includes data collection, transformation, and quality evaluation. Finally, we address practical considerations regarding dataset distribution and maintenance.arXiv.org1360This chapter provides a comprehensive overview of established methodological tools, enriched by the practical experience, in the development of datasets for machine learning and offers insights into their effective management.ArXiv['Romain Egele', 'Julio C. S. Jacques Junior', 'J. N. Rijn', 'Isabelle Guyon', "Xavier Bar'o", "Albert Clap'es", 'Prasanna Balaprakash', 'Sergio Escalera', 'T. Moeslund', 'Jun Wan']2024-04-15T00:00:00https://www.semanticscholar.org/paper/6856ffaa743b83ca41a5e403c69ab85909aafe35 593e55956f2cda00bf19513bf925e1699d82160eThis book is a comprehensive guide that navigates readers through the intricacies of these transformative technologies such AI and ML. From fundamental concepts to cutting-edge applications, this book offers a clear and insightful exploration of AI and ML, providing both beginners and experts with valuable insights into their principles, algorithms, and real-world implications. Through engaging narratives and practical examples, readers will gain a deeper understanding of how AI and ML are shaping our world and revolutionizing industries across the globe. Whether you’re a student, researcher, or industry professional, this book serves as an indispensable resource for unlocking the potential of artificial intelligence and machine learning.00This book is a comprehensive guide that navigates readers through the intricacies of these transformative technologies such as AI and ML, providing both beginners and experts with valuable insights into their principles, algorithms, and real-world implications.['Mr. Yogesh Jayant Gaikwad', 'Mrs. Pallavi Utkarsh Nehete', 'Mrs. Sulakshana Sagar Malwade', 'Mrs. Nita Ganesh (Jaybhaye) Dongre', 'M. Aware']2024-04-15T00:00:00https://www.semanticscholar.org/paper/593e55956f2cda00bf19513bf925e1699d82160e 938f708893926a25a41cd49a8ac0b9794bdb0185This review delves into the possible role of artificial intelligence (AI) in medical research, from planning to publication. AI can aid in idea generation, data analysis, and writing, with tools like chatbots and transcription systems enhancing efficiency. However, AI's limitations, including the "hallucination" problem in which it generates false information, require careful use and verification. Ensuring anonymity compliance with sensitive data is also vital. AI's transformative potential in research brings opportunities for innovation, necessitating mindful application to manage biases and data accuracy.Ugeskrift for læger00This review delves into the possible role of artificial intelligence (AI) in medical research, from planning to publication, necessitating mindful application to manage biases and data accuracy.Ugeskrift for laeger['Z. M. Mojadeddi', 'Jacob Rosenberg']2024-04-15T00:00:00https://www.semanticscholar.org/paper/938f708893926a25a41cd49a8ac0b9794bdb0185 28ec0afa570fdcce7ce18f751bead511d4fc4b13Discover Artificial Intelligence740The study advocates for a "human in the loop" strategy that combines human knowledge and AI techniques to mitigate biases and guarantee individualised legal results to ensure AI functions as a complement rather than a replacement, emphasising the necessity of preserving the human element in legal practices.Discov. Artif. Intell.['Ammar Zafar']2024-04-15T00:00:00https://www.semanticscholar.org/paper/28ec0afa570fdcce7ce18f751bead511d4fc4b13 5f325e2ce270a6c6209310b8375da41aa3eb47baActa Psychologica311The study explores various aspects related to the impact of the COVID-19 pandemic on AI ChatGPT technologies and contributes to the understanding of the novel role of AI ChatGPT in times of crisis, particularly in the era of COVID-19 pandemic.Acta psychologica['T. Hussain', 'Dake Wang', 'Benqian Li']2024-04-15T00:00:00https://www.semanticscholar.org/paper/5f325e2ce270a6c6209310b8375da41aa3eb47ba 5cc046457c4af0ff2e299db28a9bf31b73d64c7dSustainability and its connection to digital technology have attracted significant interest in business [...]Sustainability01Sustainability['Evangelos Katsamakas']2024-04-15T00:00:00https://www.semanticscholar.org/paper/5cc046457c4af0ff2e299db28a9bf31b73d64c7d 3a1b69c2c9d9d2a2ad5447f2c347421157eb0445International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-15T00:00:00https://www.semanticscholar.org/paper/3a1b69c2c9d9d2a2ad5447f2c347421157eb0445 202566c8c5a86a9b2ca802ccf25c5e493d09c3b1International Journal of Research Studies in Management70International Journal of Research Studies in Management['Pang Kai', 'Marc Joseph Ian A., II Generoso']2024-04-15T00:00:00https://www.semanticscholar.org/paper/202566c8c5a86a9b2ca802ccf25c5e493d09c3b1 52f393f3f6b605444dc43846cf493c52595b331fNature00Nature['Nicola Jones']2024-04-15T00:00:00https://www.semanticscholar.org/paper/52f393f3f6b605444dc43846cf493c52595b331f 7109ada20488b315126ad2300bdb67e500e71f0cThe practice of legal regulation is currently carried out in an environment determined by new technologies. The use of artificial intelligence in various spheres of life and the rapid technological development of society requires an understanding of the role of artificial intelligence in regulating human relations and an assessment of the need to determine its legal status. Science is faced with a new paradigm and jurisprudence is on the verge of a scientific revolution, since new technologies do not fi t into the pre-existing tenets of legal science. At present, the unification of the natural sciences and the humanities can be carried out on the basis of the principles of global evolutionism, which are immanently included in the objective study of self-developing objects. Associating the development of these objects with the problems of the place of man, taking into account the involvement of man in the functioning of the vast majority of developing systems mastered by human activity brings new scientific knowledge and a new humanistic meaning. The article attempts to define the concept of “artificial intelligence”, its essence, and identify its features in a legal context; the possibilities for the participation of artificial intelligence in legal relations (primarily related to intellectual law) have been identified. The results of the analysis of the decisions of the courts of case law countries on conflicts in the field of intellectual law using artificial intelligence are presented. In the article, based on comparativism, using epistemological constructivism and an activity approach, organically connected with the vision of the world as intercomplex self-developing systems, it is assumed to turn artificial condition of objective knowledge into a natural one. A number of observations on the interpretation of legal texts by law enforcement officials is offered.Academic Law Journal40The article attempts to define the concept of “artificial intelligence”, its essence, and identify its features in a legal context, and the possibilities for the participation of artificial intelligence in legal relations (primarily related to intellectual law) have been identified.Academic Law Journal['Maria Savelyeva']2024-04-15T00:00:00https://www.semanticscholar.org/paper/7109ada20488b315126ad2300bdb67e500e71f0c 2c090bef261d53cbd949f7e513ef3ff4f41b3c5dIn the modern world of rapid development of information technologies, the significance of artificial intelligence technologies in the information technology industry becomes decisive, leading to serious challenges for legal regulation, especially in the context of intellectual property. In this scientific article, the author conducts a study of artificial intelligence technology as an object of legal protection of intellectual property, focusing on comparative legal principles in international and Ukrainian law. The author provides a detailed analysis of the current state of artificial intelligence development in the IT industry, examining the legal aspects in European, Asian, United States, and Ukrainian jurisdictions. The article presents the results of reviewing several key aspects related to the legal protection of intellectual property, such as patenting and establishing copyright for machine learning systems. The author conducts a detailed analysis of international standards, conventions, and other regulatory legal documents regarding the regulation of artificial intelligence usage. The legislation of Ukraine is studied with a focus on problematic aspects and national peculiarities. The research results reveal gaps in the national and international legal environment and identify issues in regulating the use of artificial intelligence technologies. The article suggests prospective directions for further improvement of the legal system, balancing the protection of intellectual property and fostering innovation in the IT sector. This article serves as an essential source for researchers, practicing lawyers, and regulators interested in the interaction between technological development and legal regulation of artificial intelligence in international and Ukrainian law. Since the outlined topic is novel and undergoes dynamic updates, continuous and detailed research in academic circles is required.Constitutional State50The article presents the results of reviewing several key aspects related to the legal protection of intellectual property, such as patenting and establishing copyright for machine learning systems, and suggests prospective directions for further improvement of the legal system.Constitutional State['M. O. Karmazin']2024-04-15T00:00:00https://www.semanticscholar.org/paper/2c090bef261d53cbd949f7e513ef3ff4f41b3c5d 18fa9fb12c4dbaccf5781fd9cab8cd616a2bdd10Artificial Intelligence (AI) is an increasingly progressive field that aims to develop computer systems capable of producing inventive and creative content, including text, images, music, video, and audio, that closely resemble human-generated material. AI has significantly impacted education, with both positive and negative implications. While it has gained widespread popularity, it has also raised concerns about bias, misinformation, misuse, and risk, emphasising the need for the responsible implementation and development of generative AI in education. There have been discussions about whether it should be prohibited or whether teachers and students should receive adequate training to use it effectively and ethically. The purpose of AI in education should be to embrace the opportunities it presents while maintaining high academic standards. Renowned universities have developed guidelines and manuals for the responsible use of generative AI tools. This study examines the impact of generative AI on education, analysing its advantages and disadvantages in schools, its outcomes, and how teachers and students can use it for educational purposes. The study also emphasizes the effective use of AI in education, employing qualitative and quantitative methods to evaluate its usage. The results highlight the benefits and drawbacks of generative AI in education, concluding with recommendations and future applications of generative AI in education.International Journal of Innovative Research in Multidisciplinary Education02The impact of generative AI on education is examined, analysing its advantages and disadvantages in schools, its outcomes, and how teachers and students can use it for educational purposes.International Journal of Innovative Research in Multidisciplinary Education['Isa Erbas', 'Eduina Maksuti']2024-04-15T00:00:00https://www.semanticscholar.org/paper/18fa9fb12c4dbaccf5781fd9cab8cd616a2bdd10 d323111fc57755cf5b233df19125a01df232f597This paper addresses the evolution of Artificial Intelligence (AI) in scientific research and the ethical and integrity challenges that arise with its integration. AI has become an indispensable tool for researchers, accelerating discoveries and optimizing processes. However, using these algorithms raises concerns about bias, transparency, and accountability. The ability of machines to learn and create knowledge challenges the paradigms of authorship and credibility, putting integrity and ethics under new scrutiny. The discussion emphasizes robust ethical governance, collaboration among stakeholders, ongoing education, and the creation of transparent and auditable algorithms. It further highlights the importance of maintaining ethics and integrity at the heart of AI research to ensure its advancement benefits humanity fairly and responsibly, emphasizing the need for a holistic approach involving education, transparency, accountability, and active participation of multiple stakeholders. Finally, it reiterates that as we embark on this new era of AI-driven discovery, we must embrace both the opportunities and the ethical challenges it presents, ensuring that the use of AI in scientific research continues to benefit humanity by promoting knowledge and well-being.Future Studies Research Journal: Trends and Strategies00It is reiterated that as the authors embark on this new era of AI-driven discovery, they must embrace both the opportunities and the ethical challenges it presents, ensuring that the use of AI in scientific research continues to benefit humanity by promoting knowledge and well-being.Future Studies Research Journal: Trends and Strategies['Ricardo Limongi']2024-04-15T00:00:00https://www.semanticscholar.org/paper/d323111fc57755cf5b233df19125a01df232f597 c739946d09dfaf4caf1c5bbc10b93aa162f65d95The article examines the role of artificial intelligence and natural language processing in the creation of chatbots. The article also discusses the main technologies underlying these systems and analyzes their application in practice. The article provides an overview of existing approaches to creating chatbots. Based on the data obtained, conclusions are drawn about the practical significance and prospects for the development of artificial intelligence and natural language processing in chatbots.Bulletin of Science and Practice00Bulletin of Science and Practice['N. Limanova', 'D. Kovtun']2024-04-15T00:00:00https://www.semanticscholar.org/paper/c739946d09dfaf4caf1c5bbc10b93aa162f65d95 84ad792c251d48c81f18fb9bdfd61dfb99e32f95PurposeThe aim of this paper is to summarise the state-of-the-art debate on impact of artificial intelligence on unemployment and reporting up-to-date academic findings.Design/methodology/approachThe paper is designed as a review of the labour vs capital conundrum, the differences between industrial automation and artificial intelligence, threat to employment, the difficulty of substituting, role of soft skills and whether technology leads to the deskilling of human workers or favors increasing human capabilities.FindingsSome authors praise the bright future developments of artificial intelligence while others warn about mass unemployment. Therefore, it is paramount to present an up-to-date overview of the problem, compare and contrast its features with what happened in past innovation waves and contribute to academic discussion about the pros/cons of current trends.Originality/valueThe main value of this paper is presenting a balanced view of 100+ different studies, the vast majority from the last five years. Reading this paper will allow to quickly grasp the main issues around the thorny topic of artificial intelligence and unemployment.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0338International Journal of Social Economics840A review of the labour vs capital conundrum, the differences between industrial automation and artificial intelligence, threat to employment, the difficulty of substituting, role of soft skills and whether technology leads to the deskilling of human workers or favors increasing human capabilities are reviewed.International Journal of Social Economics['G. Virgilio', 'Fausto Saavedra Hoyos', 'Carol Beatriz Bao Ratzemberg']2024-04-15T00:00:00https://www.semanticscholar.org/paper/84ad792c251d48c81f18fb9bdfd61dfb99e32f95 ad5854fb623ce6d64aa07a86b26a127725a60eeeCyber-attacks are becoming more sophisticated and common in today's environment. Artificial intelligence (AI) is being used by enterprises to boost their defenses against these developing threats. AI is rapidly altering the cybersecurity field, providing several benefits in terms of improving security measures. However, its implementation causes significant changes in cybersecurity occupations and necessitates the acquisition of new skills by specialists. This article investigates the impact of AI on cybersecurity employment, presents real-world instances of AI integration in the sector, analyzes the future of AI in cybersecurity, and identifies the problems nvolved with its adoption.International journal for electronic crime investigation00The impact of AI on cybersecurity employment is investigated, real-world instances of AI integration in the sector are presented, the future of AI in cybersecurity, and the problems involved with its adoption are identified.International Journal for Electronic Crime Investigation['Muhammad Asif Ibrahim']2024-04-15T00:00:00https://www.semanticscholar.org/paper/ad5854fb623ce6d64aa07a86b26a127725a60eee 8277e5a7fac100d7c53c8eeb829f1b0249df30faIn recent years, there has been widespread interest in the applications of Artificial Intelligence (AI) techniques to the financial sector and in the development of new financial products and services. AI methods are widely regarded as the most important methods in the emerging market for providing not only cutting-edge financial services, but also an innovative approach to business process automation, a solution to the challenges of reducing service costs associated with managing low-income and rural customers and a method of identifying and evaluating the creditworthiness of those customers. No clear reviews are identified in the areas of AI and its contribution to Financial Innovations (FI) research in finance. To address the above gap, the present study provides a systematic literature review and bibliometric view of AI and FI research in finance. Co-citation, co-occurrence and bibliographic coupling analysis techniques are being used to make inferences about the structure of AI and FI research in finance from 1987 to 2022. The study used 237 filtered research articles from the Scopus database and processed through VOS-Viewer and Biblioshiny through “R” to justify study objectives. Through bibliometric analysis, this study unveils influential authors, journals and institutions, emphasizing top-cited research articles and unveiling six emerging thematic clusters. The novelty lies in the identification of prominent keywords linked to AI and financial innovation research, accompanied by a comprehensive analysis of globally and locally cited articles. Employing an analytical approach, the study identifies research gaps to contribute to the existing body of knowledge.Journal of Scientometric Research600Through bibliometric analysis, this study unveils influential authors, journals and institutions, emphasizing top-cited research articles and unveiling six emerging thematic clusters, accompanied by a comprehensive analysis of globally and locally cited articles.Journal of Scientometric Research['Jyotirmoi Jena', 'S. K. Biswal', 'Rashmiranjan Panigrahi', 'A. Shrivastava']2024-04-15T00:00:00https://www.semanticscholar.org/paper/8277e5a7fac100d7c53c8eeb829f1b0249df30fa 3080807300183fa33c4cc5399ecfc771ed53141eBMC Medical Education470Average levels of knowledge indicate the necessity of including relevant educational programs in the student’s academic curriculum and the positive attitude of students promises the acceptance of AI technology.BMC Medical Education['Hamidreza. Amiri', 'Samira Peiravi', 'Seyedeh sara rezazadeh shojaee', 'Motahareh Rouhparvarzamin', 'Mohammad Naser Nateghi', 'Mohammad Hossein Etemadi', 'Mahdie ShojaeiBaghini', 'Farhan Musaie', 'Mohammad Hossein Anvari', 'Mahsa Asadi Anar']2024-04-15T00:00:00https://www.semanticscholar.org/paper/3080807300183fa33c4cc5399ecfc771ed53141e ce07f03b4a4b53209fef29f3f2d2ab6374862cb9Objective: The research objective is to investigate how the rapid advancement of technology, particularly the increasing reliance on digital tools and artificial intelligence (AI) for note-taking and communication, influences handwriting practices. Specifically, to explore the attitudes, emotions, and experiences surrounding handwriting and digital communication in Jakarta.   Theoretical Framework: This research employs Social Change Theory as the theoretical framework to understand how technology influences handwriting practices. Social Change Theory provides a lens through which to examine the societal shifts brought about by technological advancements, considering how these changes impact individuals' behaviors, attitudes, and perceptions regarding handwriting and digital communication.   Method: The study utilizes qualitative research methods, including focus group discussions, observations, and interviews. These methods allow for a comprehensive exploration of participants' attitudes, emotions, and experiences related to handwriting and digital communication.   Results and Discussion: Findings from the study suggest that while AI and digital typing offer efficiency and accessibility, handwriting retains unique cognitive and emotional benefits. Participants express a range of attitudes towards handwriting and digital communication, highlighting both the advantages and drawbacks of each method. Managing the integration of AI, digital typing, and handwriting in education emerges as a potential solution to address concerns about technology dependence while fostering critical thinking and cultural appreciation for sustainability and the enhancement of the quality of education, as SDG’ No. 4.   Research Implications: The findings of this research have several implications for educational practice and policy that should recognize the value of incorporating handwriting alongside AI and digital typing in educational curricula.International Journal of Professional Business Review210Findings from the study suggest that while AI and digital typing offer efficiency and accessibility, handwriting retains unique cognitive and emotional benefits.International Journal of Professional Business Review['R. Harjanto', 'Zaida Mustafa', 'Setya Ambar Pertiwi', 'Michael Adhi Nugroho', 'Syubhan Akib']2024-04-15T00:00:00https://www.semanticscholar.org/paper/ce07f03b4a4b53209fef29f3f2d2ab6374862cb9 904caecf2e8f01984f21a59b6f7cea86147e495bIn recent years, technological advances have significantly transformed educational practices. The development and adoption of Artificial Intelligence (AI) chatbots in education have recently generated widespread interest from teachers and students. In this context, this research aimed to evaluate the potential of AI resources as an auxiliary instrument in improving dissertation-argumentative essays prepared by second-year high school students on Ecology. This is qualitative research, specifically of the action research type, conducted through three stages. The results show that chatbots can be used as complementary resources in the classroom, in addition to having the potential to optimize the construction of knowledge. Finally, it was observed that chatbots were able to facilitate the application of content and make assessments more engaging and productive.Educación00The results show that chatbots can be used as complementary resources in the classroom, in addition to having the potential to optimize the construction of knowledge.Educación['Sebastião Luiz Da Silva Neto', 'Bruno Silva Leite']2024-04-15T00:00:00https://www.semanticscholar.org/paper/904caecf2e8f01984f21a59b6f7cea86147e495b d6bae41a610c36011d333e7ba6e14993f3d3d0f4 The morbidity and death rates linked to diabetes mellitus are substantially increased by foot ulcers, a prevalent consequence of the disease. Proper wound management is essential for controlling foot ulcers. This includes monitoring the ulcers' healing progress through clinical reviews, changing dressings as needed, treating infections with the right medications, and ensuring that the ulcer is offloaded correctly. Taking pictures of the ulcer was a dependable way to track how diabetic foot ulcers were healing in the past. Images of foot ulcers have recently experienced a tremendous change due to the emergence of digital cameras in cell phones. Artificial intelligence (AI) and other recent developments in digital health technology present a great chance to improve the efficiency of diabetes care, which might reduce the growth in healthcare costs associated with diabetes. Patients with diabetes can alleviate the burden on clinics and patients' transportation demands by electronically sharing photos of their ulcers, which diabetes care providers can remotely monitor. Improved remote monitoring of diabetic foot ulcers using smartphone apps is now possible with the help of a new generation of AI-powered solutions. This clinical update review aims to gather information on this trending topic so that medical professionals can be current on all the latest advancements in the field. Current Biotechnology00This clinical update review aims to gather information on this trending topic so that medical professionals can be current on all the latest advancements in the field.Current Biotechnology['Sanchit Dhankhar', 'Nitika Garg', 'Samrat Chauhan', 'Monika Saini']2024-04-15T00:00:00https://www.semanticscholar.org/paper/d6bae41a610c36011d333e7ba6e14993f3d3d0f4 c200b6b40a30bb6b70ee8efee775e2e85c069590The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk‐natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross‐pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer‐reviewed publications. In 2022, our cross‐discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution‐level learning health system.Learning Health Systems660This work built bulk‐natural language processing pipelines to extract structured information from clinical notes and stored them in common data models, and developed multimodal AI/machine learning tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data.Learning Health Systems['Yuan Luo', 'Chengsheng Mao', 'L. Sanchez-Pinto', 'Faraz S. Ahmad', 'Andrew Naidech', 'Luke Rasmussen', 'Jennifer A. Pacheco', 'D. Schneider', 'Leena B. Mithal', 'Scott Dresden', 'Kristi L Holmes', 'Matthew Carson', 'Sanjiv J. Shah', 'Seema Khan', 'Susan Clare', 'R. Wunderink', 'Huiping Liu', 'T. Walunas', 'Lee Cooper', 'Feng Yue', 'Firas H. Wehbe', 'Deyu Fang', 'David M. Liebovitz', 'Michael Markl', 'K. Michelson', 'Susanna A. McColley', 'Marianne Green', 'J. Starren', 'Ronald T. Ackermann', "Richard T. D'Aquila", 'James Adams', 'Donald Lloyd‐Jones', 'R. L. Chisholm', 'Abel N. Kho']2024-04-15T00:00:00https://www.semanticscholar.org/paper/c200b6b40a30bb6b70ee8efee775e2e85c069590 9c4f96f73176175bfcd88d41ca54a8132fe05a26International Journal of Clinical Oncology630The current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis are explored, with a focus on whole-slide image analysis.International journal of clinical oncology['A. Katayama', 'Yuki Aoki', 'Yukako Watanabe', 'Jun Horiguchi', 'Emad A. Rakha', 'T. Oyama']2024-04-15T00:00:00https://www.semanticscholar.org/paper/9c4f96f73176175bfcd88d41ca54a8132fe05a26 c7069e9668b785eb1c098a69db54b63883d58e8eBreast Cancer390In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.Breast cancer['Karla C Maita', 'Francisco R Avila', 'Ricardo A. Torres-Guzman', 'John P. Garcia', 'Gioacchino D De Sario Velasquez', 'Sahar Borna', 'Sally A. Brown', 'Clifton R. Haider', 'Olivia Ho', 'AJ Forte']2024-04-15T00:00:00https://www.semanticscholar.org/paper/c7069e9668b785eb1c098a69db54b63883d58e8e e50266b4f330577018d28a038c122991183cc9d3PurposeWithin the scope of the research, articles about service robots were examined by the systematic review method.Design/methodology/approachThe research aims to evaluate the articles on service robots, an artificial intelligence (AI) application in restaurant businesses, using a systematic review method. In systematic reviews, the data obtained as a result of scanning databases to find an answer to a research question are synthesized and reported. The criterion sampling technique, one of the purposeful sampling methods, was used for the sample of the research. Inclusion and exclusion criteria were applied within the scope of screening.FindingsThe articles on service robots were carried out between 2018 and 2023. In terms of research methods, most of the articles are quantitative, while there are studies on mixed and qualitative methods. In studies, data were generally collected by survey technique. The keywords of the studies on service robots are examined; the most commonly used words were service robot and AI, technology, restaurant, satisfaction, revisit intention, consumer behavior, intention, preference, hospitality and foods. The objectives of the articles pertinent to service robots are mostly to determine people's attitudes and acceptance toward these services focuses.Originality/valueThe studies seem to focus more on customer acceptance, trust, expectations, risks, adaptation, reasons for preference, impact on creative services, emotional and cognitive effects and human–robot interaction. Despite this, it is observed that there are fewer studies on topics such as the development of service robots in restaurant businesses, their reflections on the future, future opportunities and the quality of chef service robots. Based on this, it is recommended to consider studies that will serve as a reference for revealing innovative opportunities that can meet future expectations in order to increase the quality of service robots in restaurant businesses.Worldwide Hospitality and Tourism Themes380It is observed that there are fewer studies on topics such as the development of service robots in restaurant businesses, their reflections on the future, future opportunities and the quality of chef service robots.Worldwide Hospitality and Tourism Themes['Ela Oğan']2024-04-15T00:00:00https://www.semanticscholar.org/paper/e50266b4f330577018d28a038c122991183cc9d3 4ae78dbf745bcf2676fa66d131eff8e932c8a0b6Artificial Intelligence (AI) is the future of pharmaceutical industries. We make our tasks easier with help of Artificial Intelligence in future. With help of Artificial Intelligence we can also increase in production in pharmaceutical industry, can be save of dangerous and risky works in the production or manufacturing. Artificial Intelligence can drugs designing in future and discover new drugs and determine the chemical structure of drugs. Artificial Intelligence is very important role play in clinical research. For the pharmacological action drugs are works with the target protein. Than this target proteins are show the pharmacological action and Artificial Intelligence is help in determination of target protein and Artificial Intelligence can easier the drug discovery related work. Artificial Intelligence will used in the marketing such as the patient or customer related information or data collection and deposition. Creation of essential and specialized advertisement for increase product Sell. Different type application will in pharmaceutical industry of Artificial intelligence. And AI will change the pharmaceutical industry or drug associated work and that is come new revolution in pharmacy. Many types AI robots are invented in various pharmacy fields for the help of human being in manufacturing or production in pharmaceutical industry. Artificial Intelligence will advantages and disadvantage for the human beings. This review aims that drug delivery nanosystems design, characterization, and production stand to benefit greatly from artificial intelligence (AI). Furthermore, the ability to perform reverse engineering and ongoing system optimisation is becoming possible with the help of big data.  Asian Journal of Pharmaceutical Research and Development250Drug delivery nanosystems design, characterization, and production stand to benefit greatly from artificial intelligence (AI).Asian Journal of Pharmaceutical Research and Development['Adarsh Dubey', 'Abhishek Yadav']2024-04-15T00:00:00https://www.semanticscholar.org/paper/4ae78dbf745bcf2676fa66d131eff8e932c8a0b6 38fc7cc44a477126955d9d03539990f04a5f3e03In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches to risk assessment and mitigation frequently rely on manual procedures and retroactive analysis, which might not be sufficient to recognize and respond to new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve risk management procedures in healthcare facilities, fortifying patient safety precautions and guidelines while improving the standard of care overall. Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms. The different applications of AI in risk management are discussed in this paper, along with opportunities, problems, and suggestions for their effective use in hospital settings.Italian Journal of Medicine1900Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms.Italian Journal of Medicine['Ranieri Guerra']2024-04-15T00:00:00https://www.semanticscholar.org/paper/38fc7cc44a477126955d9d03539990f04a5f3e03 794b055d20a17398781679519394d011fae7ff98Technology changed the world over the past decades, reinventing the way we work, communicate, and live. In the healthcare sector, it has contributed to driving innovations in the diagnosis process, treatment, data management, and information access. However, this transformation has been accompanied by an increasing dependence on digital systems and connectivity. Nowadays, concepts such as artificial intelligence and cybersecurity are widely recognized, but organizations just became aware of the benefits and risks involved. In fact, the nature of their relationship it is still under discussion. The central objective of this study is to explore the dynamics of this relationship in healthcare, taken as a sector undergoing constant technological evolution. We propose a dual approach, encompassing both strategic and operational perspectives, which can support the management of this complex interaction, balancing security and innovation.ARIS2 - Advanced Research on Information Systems Security380A dual approach is proposed, encompassing both strategic and operational perspectives, which can support the management of this complex interaction, balancing security and innovation in healthcare.ARIS2 - Advanced Research on Information Systems Security['António Tavares', 'Pedro Sousa', 'Rita Proença']2024-04-15T00:00:00https://www.semanticscholar.org/paper/794b055d20a17398781679519394d011fae7ff98 854cc13172a1d39727c658052b91e8b53096bf47In a world where technology is seen as the ultimate solution to all progress, this study aims to explore and deepen the relationship between innovation and artificial intelligence (AI), highlighting their complex interactions and impact on the economic, social and technological domains. The main aim is to provide a comprehensive perspective on these two key strategies in the contemporary technological landscape. What sets this article apart is its attempt to synthesise existing research on innovation and AI, while offering new perspectives and analyses on their mutual relationship. The results of this study highlight the deep interconnection between innovation and AI, demonstrating how these two concepts feed off each other to catalyse technological advances. The article also highlights the ethical, social and economic challenges associated with the increasing integration of AI into innovation processes. The contribution of this article lies in its ability to provide a solid conceptual framework for understanding and analysing the interaction between innovation and AI.International Journal of Innovative Research in Multidisciplinary Education00The results of this study highlight the deep interconnection between innovation and AI, demonstrating how these two concepts feed off each other to catalyse technological advances.International Journal of Innovative Research in Multidisciplinary Education['Yassine Elkhatibi', 'Redouane Benabdelouahed']2024-04-15T00:00:00https://www.semanticscholar.org/paper/854cc13172a1d39727c658052b91e8b53096bf47 834120953ad505d20a7c1510cd9571e3478e0d1cThere is a widely debated issue regarding the status and impact of exponentially growing artificial intelligence. The article deals with the problem of the moral agency of animals, and artificial intelligence. The author addresses several criteria for moral agents and tries to find the answer to the question of whether we can treat animals and AI as moral agents. The author uses mostly method of philosophical analysis and comparative method. The author claims that moral agency is not a necessary condition for moral status and doubts the practicality of attributing full moral agency to animals and AI. Moreover, claims that moral agency comes in degrees and different kinds and therefore we have to consider the complex nature of moral agency when dealing with moral actions. For instance, even human moral agents are not all on the same level of development as suggested not just by empirical evidence but also virtue ethics.Studia Ecologiae et Bioethicae360The author claims that moral agency is not a necessary condition for moral status and doubts the practicality of attributing full moral agency to animals and AI and claims that moral agency comes in degrees and different kinds and therefore the authors have to consider the complex nature of moral agency when dealing with moral actions.Studia Ecologiae et Bioethicae['Barbora Baďurová']2024-04-15T00:00:00https://www.semanticscholar.org/paper/834120953ad505d20a7c1510cd9571e3478e0d1c b5637866346f821d364cf06676933e9666a14d6eThis research aims to analyze artificial intelligence crime as a new form of crime in the future as well as artificial intelligence crime prevention policies in Indonesia. By using the method research law Normative, using Approach Legislation (Satatute Approach), Conceptual Approach (Conceptual Approach), Approach Analytical (Analytical Approach), Approach Philosophy (Philosophical Approach). Results study This showing that describe Artificial intelligence crimes include any form of illegal or malicious action against digital structures carried out using computers or digital devices with more complex forms of crime. Artificial intelligence crimes can be crimes against national security, attacking people, or attacking a company. The categories of artificial intelligence crimes are divided into high risk, medium risk and low risk. Then Efforts that can be made to overcome artificial intelligence crime by using situational crime prevention produce the concept of short-term and long-term countermeasures.RESEARCH REVIEW International Journal of Multidisciplinary00Efforts that can be made to overcome artificial intelligence crime by using situational crime prevention produce the concept of short-term and long-term countermeasures.RESEARCH REVIEW International Journal of Multidisciplinary['Yaumi Ramdhani', 'Amiruddin', 'Ufran']2024-04-15T00:00:00https://www.semanticscholar.org/paper/b5637866346f821d364cf06676933e9666a14d6e 00f1539a53879d6554f4ce9218307831135f443bL’argument central de cet article est que les objets doivent être appris à travers leurs performances, qui résultent d’un entrelacement avec d’autres objets dans l’espace-temps, basé sur leur mise en réseau impliquant d’autres participants plutôt que sur la recherche d’essences voilées. Un dispositif d’intelligence artificielle participe à une théâtralité dans laquelle la scène, les coulisses, les autres acteurs (humains ou non humains) et l’intrigue ont une influence locale sur l’issue de l’action. Nous proposons cette épistémologie pour analyser les enjeux éthiques et politiques de l’intelligence artificielle. L’article explore les scénarios dans lesquels le débat mondial sur l’IA est féroce : l’éducation, l’art, la conversation, le travail, l’information et l’environnement. En conclusion, nous terminons par un manifeste sur l’intelligence artificielle du point de vue du Sud Global.Sociétés (Paris)120Sociétés['André Lemos']2024-04-15T00:00:00https://www.semanticscholar.org/paper/00f1539a53879d6554f4ce9218307831135f443b 4f731edcb6eb273bdb738ad1839cf3d7c4285fc2Peut-on parler d’intelligence, d’intentionnalité et d’agence lorsqu’on observe les interactions d’un agent artificiel orienté par des algorithmes ? Et comment l’interaction d’un agent artificiel modifie-t-elle l’agence de l’être humain ? Partant de la “théorie de l’exonération” postulée par l’anthropologie philosophique et l’intégrant à la théorie relationnelle, nous examinerons dans cette recherche les effets déterminés par l’interaction asymétrique entre l’être humain et l’algorithme. Ce dernier, pour être efficace en termes de résultats prédictifs, doit rendre l’environnement stable et le comportement humain prévisible. Mais une société prévisible est contre-productive et handicapante pour l’être humain sur le plan relationnel. L’être humain doit donc changer sa perspective relationnelle avec l’IA, en évaluant de manière réflexive non seulement les résultats positifs/négatifs, mais aussi les aspects facilitateurs/ incapacitants.Sociétés (Paris)50Sociétés['Simone D’Alessandro']2024-04-15T00:00:00https://www.semanticscholar.org/paper/4f731edcb6eb273bdb738ad1839cf3d7c4285fc2 d7e0a57352973fa708e8933188f7243331877a11For the first time, this paper presents a taxonomy of legal risks associated with generative AI (GenAI) by breaking down complex legal concepts to provide a common understanding of potential legal challenges for developing and deploying GenAI models. The methodology is based on (1) examining the legal claims that have been filed in existing lawsuits and (2) evaluating the reasonably foreseeable legal claims that may be filed in future lawsuits. First, we identified 29 lawsuits against prominent GenAI entities and tallied the claims of each lawsuit. From there, we identified seven claims that are cited at least four times across these lawsuits as the most likely claims for future GenAI lawsuits. For each of these seven claims, we describe the elements of the claim (what the plaintiff must prove to prevail) and provide an example of how it may apply to GenAI. Next, we identified 30 other potential claims that we consider to be more speculative, because they have been included in fewer than four lawsuits or have yet to be filed. We further separated those 30 claims into 19 that are most likely to be made in relation to pre-deployment of GenAI models and 11 that are more likely to be made in connection with post-deployment of GenAI models since the legal risks will vary between entities that create versus deploy them. For each of these claims, we describe the elements of the claim and the potential remedies that plaintiffs may seek to help entities determine their legal risks in developing or deploying GenAI. Lastly, we close the paper by noting the novelty of GenAI technology and propose some applications for the paper's taxonomy in driving further research.arXiv.org00A taxonomy of legal risks associated with generative AI (GenAI) is presented by breaking down complex legal concepts to provide a common understanding of potential legal challenges for developing and deploying GenAI models.ArXiv['David Atkinson', 'Jacob Morrison']2024-04-15T00:00:00https://www.semanticscholar.org/paper/d7e0a57352973fa708e8933188f7243331877a11 9cdc8fec215c0f6317e8f360b4a5ddd6c3e81277While generative AI shares some similarities with previous technological breakthroughs, it also raises unique challenges for containing social and economic harms. State approaches to AI governance vary; some lay a foundation for transnational governance whereas others do not. We consider some technical dimensions of AI safety in both open and closed systems, as well as the ideas that are presently percolating to safeguard their future development. Examining initiatives for the global community and for the coalition of open societies, we argue for building a dual-track interactive strategy for containing AI's potentially nightmarish unintended consequences. We conclude that AI safety is AI governance, which means that pluralist efforts to bridge gaps between theory and practice and the STEM–humanities divide are critical for democratic sustainability.Annual review of political science (Palo Alto, Calif. Print)00It is argued for building a dual-track interactive strategy for containing AI's potentially nightmarish unintended consequences and concluded that AI safety is AI governance, which means that pluralist efforts to bridge gaps between theory and practice and the STEM–humanities divide are critical for democratic sustainability.Annual Review of Political Science['Allison Stanger', 'Jakub Kraus', 'Woojin Lim', 'Georgia Millman-Perlah', 'Mitchell Schroeder']2024-04-15T00:00:00https://www.semanticscholar.org/paper/9cdc8fec215c0f6317e8f360b4a5ddd6c3e81277 ee2e71ee4150c700ecee955d4a32047efcfd802fLearning outcomes After completion of the case study, students will learn to use Lean Canvas to identify business opportunity. They will also learn the balancing of exploitation of profit-producing activities and exploring new opportunities according to the environmental dynamism. Case overview/synopsis WONK, a tutor discovery and booking app was launched by MyEdge in 2016 to search and book verified tutors in locations served by the company. Based on their requirements, parents and students could sort and book verified tutors in their area. Through the app, users could search for academic and hobby classes in the form of individual tuitions. The ease of use and the service offering made it a popular app with students enrolling every 6 min. Within a span of six years, WONK had provided services to thousands of students in 20+ countries and had 200,000+ tutors registered on their app from 15,000+ pin codes. Despite a plethora of Edtech companies in India, a different business model and services offered gave them an edge over other Edtech companies. To keep up with the customer needs, they were constantly making the upgrades to their technology and expanding their services. Vidhu Goyal, the founder of the company, was enjoying the progress when another development in the technology hit the world. With the launch of applications based on artificial intelligence, will it disrupt the business or not? Complexity academic level The case study is recommended to be taught in a 90-min class to Master of Business Administration students. The case study may be used in courses related to strategy, information systems management and entrepreneurship. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy. Emerald Emerging Markets Case Studies00WONK, a tutor discovery and booking app was launched by MyEdge in 2016 to search and book verified tutors in locations served by the company, and students learn to use Lean Canvas to identify business opportunity.Emerald Emerging Markets Case Studies['Nimisha Singh']2024-04-15T00:00:00https://www.semanticscholar.org/paper/ee2e71ee4150c700ecee955d4a32047efcfd802f 648787472eae6476ca8bbdf6332488d10c91b310Montenegrin Journal of Economics00Montenegrin Journal of Economics['Lyazzat Sembiyeva']2024-04-15T00:00:00https://www.semanticscholar.org/paper/648787472eae6476ca8bbdf6332488d10c91b310 d3b8e534586860c28469dce34e1f7b68797f7b79A contract is a type of legal document commonly used in organizations. Contract review is an integral and repetitive process to avoid business risk and liability. Contract analysis requires the identification and classification of key provisions and paragraphs within an agreement. Identification and validation of contract clauses can be a time-consuming and challenging task demanding the services of trained and expensive lawyers, paralegals or other legal assistants. Classification of legal provisions in contracts using artificial intelligence and natural language processing is complex due to the requirement of domain-specialized legal language for model training and the scarcity of sufficient labeled data in the legal domain. Using general-purpose models is not effective in this context due to the use of specialized legal vocabulary in contracts which may not be recognized by a general model. To address this problem, we propose the use of a pre-trained large language model which is subsequently calibrated on legal taxonomy. We propose LegalPro-BERT, a BERT transformer architecture model that we fine-tune to efficiently handle classification task for legal provisions. We conducted experiments to measure and compare metrics with current benchmark results. We found that LegalPro-BERT outperforms the previous benchmark used for comparison in this research.arXiv.org60This work proposes LegalPro-BERT, a BERT transformer architecture model that is fine-tune to efficiently handle classification task for legal provisions and finds that LegalPro-BERT outperforms the previous benchmark used for comparison in this research.ArXiv['Amit Tewari']2024-04-15T00:00:00https://www.semanticscholar.org/paper/d3b8e534586860c28469dce34e1f7b68797f7b79 d7aa0e991c5dcec1bb82b33d5e9b6d254152e548Four topics related to the operation of regulatory systems are considered. These concern the matters of dealing with globally sub‐optimal decisions made by regulators, the problem of specifying boundaries between groups of regulated entities in the application of regulation, dealing with a situation in which there are multiple regulators, and the alignment of regulation across jurisdictional borders. It is argued that current responses to these dilemmas tend to add to levels of inefficiency in the economy, with consequences for growth. Options for resolving them are considered, including new approaches to the design of regulation.Australian Economic Review110Australian Economic Review['Matthew Butlin', 'Christopher Findlay']2024-04-14T00:00:00https://www.semanticscholar.org/paper/d7aa0e991c5dcec1bb82b33d5e9b6d254152e548 fb656aa96f7c4ebc7379ccd4d8f6917bf3dbbfea00['Geoffrey Dobson']2024-04-14T00:00:00https://www.semanticscholar.org/paper/fb656aa96f7c4ebc7379ccd4d8f6917bf3dbbfea 2e998885bb4322dae58fc748f13915b26af61829Plagiarism is a widespread problem in computer science education, exacerbated by the impracticability of manual inspection in large courses. Even worse, tools based on large language models like ChatGPT have made it easier than ever to obfuscate plagiarized solutions. Additionally, most plagiarism detectors only apply to code, and only a few approaches exist for modeling assignments, which lack broad resilience to obfuscation attacks. This paper presents a novel approach for automated plagiarism detection in modeling assignments that combines automated analysis with human inspection. We evaluate our approach with real-world assignments and plagiarism obfuscated by ChatGPT. Our results show that we achieve a significantly higher detection rate for AI-generated attacks and a broader resilience than the state-of-the-art.Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training682A novel approach for automated plagiarism detection in modeling assignments that combines automated analysis with human inspection is presented that achieves a significantly higher detection rate for AI-generated attacks and a broader resilience than the state-of-the-art.Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training['Timur Saglam', 'Sebastian Hahner', 'Larissa Schmid', 'Erik Burger']2024-04-14T00:00:00https://www.semanticscholar.org/paper/2e998885bb4322dae58fc748f13915b26af61829 1773be628c1ac76eb0e2d024a96627a98f27b62cAI &amp; SOCIETY602If AI is capable of disempowering humanity and tries to disempower humanity by 2100, then humanity will be disempowered by 2100, which has immense moral and prudential significance.AI &amp; SOCIETY['Leonard Dung']2024-04-14T00:00:00https://www.semanticscholar.org/paper/1773be628c1ac76eb0e2d024a96627a98f27b62c be2675ae07d25cf0f8ef958916a69e6e4bc7b9a2The emerging field of “AI safety” has attracted public attention and large infusions of capital to support its implied promise: the ability to deploy advanced artificial intelligence (AI) while reducing its gravest risks. Ideas from effective altruism, longtermism, and the study of existential risk are foundational to this new field. In this paper, we contend that overlapping communities interested in these ideas have merged into what we refer to as the broader “AI safety epistemic community,” which is sustained through its mutually reinforcing community-building and knowledge production practices. We support this assertion through an analysis of four core sites in this community’s epistemic culture: 1) online community-building through Web forums and career advising; 2) AI forecasting; 3) AI safety research; and 4) prize competitions. The dispersal of this epistemic community’s members throughout the tech industry, academia, and policy organizations ensures their continued input into global discourse about AI. Understanding the epistemic culture that fuses their moral convictions and knowledge claims is crucial to evaluating these claims, which are gaining influence in critical, rapidly changing debates about the harms of AI and how to mitigate them.First Monday01It is argued that overlapping communities interested in effective altruism, longtermism, and the study of existential risk have merged into what is referred to as the broader “AI safety epistemic community,” which is sustained through its mutually reinforcing community-building and knowledge production practices.First Monday['Shazeda Ahmed', 'Klaudia Jaźwińska', 'Archana Ahlawat', 'Amy Winecoff', 'Mona Wang']2024-04-14T00:00:00https://www.semanticscholar.org/paper/be2675ae07d25cf0f8ef958916a69e6e4bc7b9a2 1fe4c8bc80ba0dc8e97a4e16d777358516328f39This introductory essay for the special issue of First Monday, “Ideologies of AI and the consolidation of power,” considers how power operates in AI and machine learning research and publication. Drawing on themes from the seven contributions to this special issue, we argue that what can and cannot be said inside of mainstream computer science publications appears to be constrained by the power, wealth, and ideology of a small cohort of industrialists. The result is that shaping discourse about the AI industry is itself a form of power that cannot be named inside of computer science. We argue that naming and grappling with this power, and the troubled history of core commitments behind the pursuit of general artificial intelligence, is necessary for the integrity of the field and the well-being of the people whose lives are impacted by AI.First Monday01This introductory essay for the special issue of First Monday considers how power operates in AI and machine learning research and publication and argues that naming and grappling with this power, and the troubled history of core commitments behind the pursuit of general artificial intelligence, is necessary for the integrity of the field and the well-being of the people whose lives are impacted by AI.First Monday['Jenna Burrell', 'Jacob Metcalf']2024-04-14T00:00:00https://www.semanticscholar.org/paper/1fe4c8bc80ba0dc8e97a4e16d777358516328f39 fe81ce6c3bc336d7fe20fad911016874fe2951afConversational AI assistants have witnessed remarkable progress in recent years, transforming human-computer interaction across a spectrum of applications. This paper offers a comprehensive overview of the state-of-the-art techniques, methodologies, and challenges in the field. We examine the core components of conversational AI, including natural language understanding, dialogue management, and response generation. Additionally, we address key challenges such as context modeling, personalization, and ethical considerations. The paper serves as a roadmap for researchers and developers, highlighting current achievements and avenues for future advancements in the dynamic domain of conversational AI. keyword - Conversational AI, NLP, Assistant, Machine Learning, ChatbotINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper examines the core components of conversational AI, including natural language understanding, dialogue management, and response generation, and addresses key challenges such as context modeling, personalization, and ethical considerations.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Vrushali Khandave']2024-04-14T00:00:00https://www.semanticscholar.org/paper/fe81ce6c3bc336d7fe20fad911016874fe2951af 62dd52e6c3f5f639c840c0974c007ac924073274ChatGPT has been the most talked-about concept in recent months, captivating both professionals and the general public alike, and has sparked discussions about the changes that artificial intelligence (AI) will bring to the world. As physicists and astrophysicists, we are curious about if scientific data can be correctly analyzed by large language models (LLMs) and yield accurate physics. In this article, we fine-tune the generative pre-trained transformer (GPT) model by the astronomical data from the observations of galaxies, quasars, stars, gamma-ray bursts (GRBs), and the simulations of black holes (BHs), the fine-tuned model demonstrates its capability to classify astrophysical phenomena, distinguish between two types of GRBs, deduce the redshift of quasars, and estimate BH parameters. We regard this as a successful test, marking the LLM's proven efficacy in scientific research. With the ever-growing volume of multidisciplinary data and the advancement of AI technology, we look forward to the emergence of a more fundamental and comprehensive understanding of our universe. This article also shares some interesting thoughts on data collection and AI design. Using the approach of understanding the universe - looking outward at data and inward for fundamental building blocks - as a guideline, we propose a method of series expansion for AI, suggesting ways to train and control AI that is smarter than humans.120This article fine-tune the generative pre-trained transformer (GPT) model by the astronomical data from the observations of galaxies, quasars, stars, gamma-ray bursts, and the simulations of black holes, and considers this as a successful test, marking the LLM's proven efficacy in scientific research.['Yu Wang', 'Shu-Rui Zhang', 'Aidin Momtaz', 'R. Moradi', 'F. Rastegarnia', 'N. Sahakyan', 'S. Shakeri', 'Liang Li']2024-04-14T00:00:00https://www.semanticscholar.org/paper/62dd52e6c3f5f639c840c0974c007ac924073274 1ba701d90ca6765a7cf01d0b7b56b37e085616adThe demands of a hyperconnected society that demand increased security, transparency, and user autonomy cause traditional access control to crumble. This abstract investigates how combining blockchain technology with artificial intelligence could revolutionize access control systems. By removing single points of failure and increasing accountability, blockchain's distributed ledger technology (DLT) creates irreversible trust via a shared, tamper-proof database of rights and transactions. By automating policies, smart contracts give people command over their digital assets. AI adds intelligence and flexibility. Real-time machine learning systems detect anomalies, dynamically assess behavior, and modify policy. Access requests are filtered by AI-driven risk assessment, and sensitive resources are protected by improved identity verification. The combination of AI's dynamic powers and blockchain's unchangeable base opens the door to a future where safe, user-focused access is commonplace.International Journal For Multidisciplinary Research70This abstract investigates how combining blockchain technology with artificial intelligence could revolutionize access control systems by removing single points of failure and increasing accountability by creating irreversible trust via a shared, tamper-proof database of rights and transactions.International Journal For Multidisciplinary Research['Saqib Ahad Khan', 'Sona Mohammad', 'Idrees Shamshuddin', 'Dr. N. Srinivasan', 'Dr.G. Kalaiarasi', 'Dr.M. Selvi']2024-04-14T00:00:00https://www.semanticscholar.org/paper/1ba701d90ca6765a7cf01d0b7b56b37e085616ad e4a233dabaaa7b12f1a5c2a08586b9f23641861aOngoing calls from academic and civil society groups and regulatory demands for the central role of affected communities in development, evaluation, and deployment of artificial intelligence systems have created the conditions for an incipient “participatory turn” in AI. This turn encompasses a wide number of approaches — from legal requirements for consultation with civil society groups and community input in impact assessments, to methods for inclusive data labeling and co-design. However, more work remains in adapting the methods of participation to the scale of commercial AI. In this paper, we highlight the tensions between the localized engagement of community-based participatory methods, and the globalized operation of commercial AI systems. Namely, the scales of commercial AI and participatory methods tend to differ along the fault lines of (1) centralized to distributed development; (2) calculable to self-identified publics; and (3) instrumental to intrinsic perceptions of the value of public input. However, a close look at these differences in scale demonstrates that these tensions are not irresolvable but contingent. We note that beyond its reference to the size of any given system, scale serves as a measure of the infrastructural investments needed to extend a system across contexts. To scale for a more participatory AI, we argue that these same tensions become opportunities for intervention by offering case studies that illustrate how infrastructural investments have supported participation in AI design and governance. Just as scaling commercial AI has required significant investments, we argue that scaling participation accordingly will require the creation of infrastructure dedicated to the practical dimension of achieving the participatory tradition’s commitment to shifting power.First Monday00This paper highlights the tensions between the localized engagement of community-based participatory methods, and the globalized operation of commercial AI systems and argues that scaling participation accordingly will require the creation of infrastructure dedicated to the practical dimension of achieving the participatory tradition's commitment to shifting power.First Monday['Margaret Young', 'Upol Ehsan', 'Ranjit Singh', 'Emnet Tafesse', 'Michele Gilman', 'Christina Harrington', 'Jacob Metcalf']2024-04-14T00:00:00https://www.semanticscholar.org/paper/e4a233dabaaa7b12f1a5c2a08586b9f23641861a d7201ef1b8deef3463d32d0cfa20eb49368f2005Today day to day there is an increase in cyber threat agents who are continuously coming with strategies which will help them evade the usual defences and end up obtaining or compromising vital information. AI use in monitoring tools by now can count among crucial techniques that help business entities to prevent these dangers. The propelled novel algorithms have replaced ‘human-in-the-loop’ method performing actions to assess security threats making the mechanisms much better. Such a piece dives into the area of AI robots that are utilized in threat intelligence, in order for machines to quickly discover all loop holes that attackers could use to break into a network. The application of Artificial Intelligence by businesses can in advance of the attacks, rather than as a response after the event, thwart them by the use of algorithms and predictive analytics to spot patterns of anomalies or suspicious activities. AI-powered models covering neural-network based threat identification to natural language processing belong to the spectrum of solutions implementing machine learning trained on datasets and cutting-edge algorithms. AI-based threat intelligence was presented to public as in line with practical coverage and real life issues and especially through examples. This technical issue might solve the inefficiency of identifying threats soon and thus increase their accuracy once again, in fact, it will use immediate reaction well too, like it used to. An exploration of computer ethics will touch on the topics of cyber security and data handling among others. As for their practical aspects, they have to be derived from moral righteousness founded on inner values. On the other hand, utilizing AI in threat intelligence processes calls for training of skilled workforce who will, in the long run, be able to face not only today’s cyber threats but also the future ones. In the past ten years reacting was the major issue in analysing cyber intelligence. Through the use of machine learning, enterprises across cyberspace can foresee probabilities of the most burdensome situations for them. Thus, making friendly organizations is the work of great threat intelligence in the 21st centuryInternational Journal of Advanced Research in Science, Communication and Technology00A piece dives into the area of AI robots that are utilized in threat intelligence, in order for machines to quickly discover all loop holes that attackers could use to break into a network.International Journal of Advanced Research in Science, Communication and Technology['Shivam', 'Yash Yadav', 'Vimmi Malhotra']2024-04-14T00:00:00https://www.semanticscholar.org/paper/d7201ef1b8deef3463d32d0cfa20eb49368f2005 fd3f5a04f67250aa0f12be478827e5f1ba6d6008Engineering large-scale industrial systems mandate an effective Requirements Engineering (RE) process. Such systems necessitate RE process optimization to align with standards, infrastructure specifications, and customer expectations. Recently, artificial intelligence (AI) based solutions have been proposed, aiming to enhance the efficiency of requirements management within the RE process. Despite their advanced capabilities, generic AI solutions exhibit limited adaptability within real-world contexts, mainly because of the complexity and specificity inherent to industrial domains. This limitation notably leads to the continued prevalence of manual practices that not only cause the RE process to be heavily dependent on practitioners’ experience, making it prone to errors, but also often contributes to project delays and inefficient resource utilization. To address these challenges, this Ph.D. dissertation focuses on two primary directions: i) conduct a comprehensive focus group study with a large-scale industry to determine the requirements evolution process and their inherent challenges and ii) propose AI solutions tailored for industrial case studies to automate and streamline their RE process and optimize the development of large-scale systems. We anticipate that our research will significantly contribute to the RE domain by providing empirically validated insights in the industrial context.ICSE Companion370This dissertation focuses on two primary directions: i) conduct a comprehensive focus group study with a large-scale industry to determine the requirements evolution process and their inherent challenges and ii) propose AI solutions tailored for industrial case studies to automate and streamline their RE process and optimize the development of large-scale systems.{'pages': '242-246'}['Sarmad Bashir']2024-04-14T00:00:00https://www.semanticscholar.org/paper/fd3f5a04f67250aa0f12be478827e5f1ba6d6008 ea6254f07f18d15937d8961cf5853a58cd6c6dcfThis study looks into how generative artificial intelligence (AI) is affecting the field of education. The study examines the possible advantages of using generative AI into instruction, such as more individualized learning opportunities, enhanced accessibility, and creative teaching strategies. The study also recognizes and examines the moral issues raised by the application of AI in education, highlighting the significance of upholding academic honesty and openness. It draws attention to the necessity of cautious application in order to guarantee that AI fosters fruitful learning results. The suggested research technique describes a mixed methods strategy that combines quantitative data analysis with qualitative information from surveys and interviews. The goal of the project is to provide best practices and standards for the appropriate integration of generative AI in education, while also identifying the potential and problems that this technology presents. The anticipated results include a thorough comprehension of how generative AI will affect education and how it might improve learning opportunities, ethical issues for responsible usage, and the identification of possible problems and solutions. In the end, the research hopes to promote a more inclusive and productive learning environment by aiding in the creation of useful frameworks for incorporating AI into educational settings. The education industry has drastically evolved in recent years, thanks to the remarkable advancements made by artificial intelligence. With the integration of generative AI technology, educators and students are now presented with a wealth of possibilities and opportunities. As AI continues to advance further, it holds great potential for revolutionizing education as we know it through improved efficiency measures that personalize learning experiences while enhancing overall student outcomes. Harnessing this power-packed tool in teaching environments creates an opportunity for educational institutions across various sectors allowing them efficient intelligent assessment tools coupled with personalized tutoring provisions towards addressing classroom weaknesses resulting in custom-made lesson plans aimed at betterment of individual academic progress developed using predictive models creating ample time such that teachers can focus solely on providing tailored instruction & support based exclusively around their Student's requirements& specifications. This groundbreaking technological development provided meaningfully leveraging Generative Intelligence efforts promises nothing short but will be instrumental when shaping how Education is structured from henceforth enabling automation capabilities within the Administrative tasks management system,& content-building-The advantages come twofold-Enhancement not only resides generally concerning Upgrade Learning Experience situations; there exist tailor solutions projecting Speaker understanding regarding distinctive needs pupils have always had throughout all levels ultimately leading to Developmental Progression heights hitherto attainable anticipated sooner than later.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The study examines the possible advantages of using generative AI into instruction, such as more individualized learning opportunities, enhanced accessibility, and creative teaching strategies, as well as identifying the potential and problems that this technology presents.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ankit Kumar Singh']2024-04-14T00:00:00https://www.semanticscholar.org/paper/ea6254f07f18d15937d8961cf5853a58cd6c6dcf 8744fc592afb67ba53a86dbbb8a31b934c554cbfThis systematic literature review (SLR) conducts a comprehensive analysis of the challenges and opportunities arising from the application of artificial intelligence (AI) in the banking sector, in particular Special focus on Indian banks. The review reveals many opportunities to support AI, including the rise of fintech startups providing AI solutions, regulatory support for AI integration, as well as the benefits of personalized services digitalization, smart wallets, and improved decision-making capabilities. However, these opportunities come with notable challenges, such as job mobility, privacy concerns, reduced creativity and adaptability, and gaps in abilities, digital access. The article highlights the need for banking industry stakeholders to develop effective strategies to address these challenges and align AI initiatives with overall business goals. Additionally, the study highlights the lack of empirical research on AI in banking, emphasizing the importance of future research to expand the existing knowledge base and provide insights actionable identity for industry stakeholders. This study aims to provide insights into how Indian banks can leverage AI effectively while navigating the challenges inherent in its adoption. Keywords: Artificial Intelligence, AI adoption, banking sector, challenges, opportunities, fintech, regulatory support, job displacement, privacy concerns, digital access.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Insight is provided into how Indian banks can leverage AI effectively while navigating the challenges inherent in its adoption, and the lack of empirical research on AI in banking is highlighted.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Preeti Panwar']2024-04-14T00:00:00https://www.semanticscholar.org/paper/8744fc592afb67ba53a86dbbb8a31b934c554cbf 9749f017ac914c074229432cfd7c0a0aabb32974Datasets are essential for training and testing vehicle perception algorithms. However, the collection and annotation of real-world images is time-consuming and expensive. Driving simulators offer a solution by automatically generating various driving scenarios with corresponding annotations, but the simulation-to-reality (Sim2Real) domain gap remains a challenge. While most of the Generative Artificial Intelligence (AI) follows the de facto Generative Adversarial Nets (GANs)-based methods, the recent emerging diffusion probabilistic models have not been fully explored in mitigating Sim2Real challenges for driving data synthesis. To explore the performance, this paper applied three different generative AI methods to leverage semantic label maps from a driving simulator as a bridge for the creation of realistic datasets. A comparative analysis of these methods is presented from the perspective of image quality and perception. New synthetic datasets, which include driving images and auto-generated high-quality annotations, are produced with low costs and high scene variability. The experimental results show that although GAN-based methods are adept at generating high-quality images when provided with manually annotated labels, ControlNet produces synthetic datasets with fewer artefacts and more structural fidelity when using simulator-generated labels. This suggests that the diffusion-based approach may provide improved stability and an alternative method for addressing Sim2Real challenges.arXiv.org500The experimental results show that although GAN-based methods are adept at generating high-quality images when provided with manually annotated labels, ControlNet produces synthetic datasets with fewer artefacts and more structural fidelity when using simulator-generated labels, suggesting that the diffusion-based approach may provide improved stability and an alternative method for addressing Sim2Real challenges.ArXiv['Haonan Zhao', 'Yiting Wang', 'Thomas Bashford-Rogers', 'Valentina Donzella', 'Kurt Debattista']2024-04-14T00:00:00https://www.semanticscholar.org/paper/9749f017ac914c074229432cfd7c0a0aabb32974 caf448fb2378caae8faa4915d98ff066882b8eaeRecent research has explored the creation of questions from code submitted by students. These Questions about Learners' Code (QLCs) are created through program analysis, exploring execution paths, and then creating code comprehension questions from these paths and the broader code structure. Responding to the questions requires reading and tracing the code, which is known to support students' learning. At the same time, computing education researchers have witnessed the emergence of Large Language Models (LLMs) that have taken the community by storm. Researchers have demonstrated the applicability of these models especially in the introductory programming context, outlining their performance in solving introductory programming problems and their utility in creating new learning resources. In this work, we explore the capability of the state-of-the-art LLMs (GPT-3.5 and GPT-4) in answering QLCs that are generated from code that the LLMs have created. Our results show that although the state-of-the-art LLMs can create programs and trace program execution when prompted, they easily succumb to similar errors that have previously been recorded for novice programmers. These results demonstrate the fallibility of these models and perhaps dampen the expectations fueled by the recent LLM hype. At the same time, we also highlight future research possibilities such as using LLMs to mimic students as their behavior can indeed be similar for some specific tasks.Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training650The results show that although the state-of-the-art LLMs can create programs and trace program execution when prompted, they easily succumb to similar errors that have previously been recorded for novice programmers.ArXiv['T. Lehtinen', 'Charles Koutcheme', 'Arto Hellas']2024-04-14T00:00:00https://www.semanticscholar.org/paper/caf448fb2378caae8faa4915d98ff066882b8eae 821cc99962012a4989fcc2c4cda04bb6b22a011eAgriculture plays a most important role in our Indian economy and therefore lowering the cost of production and improving the quality of agricultural products is highly demanded. A weed is a plant which grows in wrong place at the wrong time and doing mostly harm than crops. Weed competes with the crops for water, light, nutrients and space, and therefore it prevents crop yields. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology to prevent these weeds. Machine learning technologies, are becoming crucial in agriculture to increase productivity, where advanced automation and control have been required. Based on large training datasets and pre-trained models, (Deep Learning) DL-based Convolutional Neural Networks (CNN) methods have proven to be more accurate than previous traditional techniques. Recently, Deep Learning (DL) has gained much attention due to its advantages in object detection, classification, and feature extraction. The system implementation of image processing technique for weed detection, a trained image is taken as a sample in order to demonstrate the difference between weed and the crop. Yolo frame work is used for annotate boundary boxes to the image with datasets. The effectiveness of the (You Only Live Once) YOLO-WEED system for real-time Unmanned Aerial Vehicle (UAV) weed detection, given its high speed and high accuracy in detection. After certain steps, we get desired output, where the weeds are separated from the crop that has been taken in the sample image. Key Words: DeepLearning,CNN, Unmanned Aerial Vehicle (UAV), Precision Weed Detection.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A new method in a contrary way, which combines deep learning and image processing technology to prevent these weeds is proposed, given its high speed and high accuracy in detection.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Dr.R .Rubesh Selvakumar']2024-04-14T00:00:00https://www.semanticscholar.org/paper/821cc99962012a4989fcc2c4cda04bb6b22a011e 5a0f83b8b768c9679c5660043b62b0fd7adb049400['Jon Whittle', 'Stefan Harrer']2024-04-14T00:00:00https://www.semanticscholar.org/paper/5a0f83b8b768c9679c5660043b62b0fd7adb0494 9a5a6293251b46a71369c72a35894eb8e1a04179Educational Administration: Theory and Practice00Educational Administration: Theory and Practice['Prof. Nitu Nair', 'Dr. Gautam Trehan']2024-04-14T00:00:00https://www.semanticscholar.org/paper/9a5a6293251b46a71369c72a35894eb8e1a04179 9962ea51df04ca28df40f102575ea72770b93dfdIEEE International Conference on Acoustics, Speech, and Signal Processing00ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)['V. Narayanaswamy', 'Rushil Anirudh', 'J. Thiagarajan']2024-04-14T00:00:00https://www.semanticscholar.org/paper/9962ea51df04ca28df40f102575ea72770b93dfd 3971cac6668b7c334a850d4f03567c489f6a4185ICSE Companion40{'pages': '335-336'}['Keita Sakuma', 'Ryuta Matsuno', 'Yoshio Kameda']2024-04-14T00:00:00https://www.semanticscholar.org/paper/3971cac6668b7c334a850d4f03567c489f6a4185 beae853bbcaef742d8d31cc68b27dcd5aa16741cIEEE International Conference on Acoustics, Speech, and Signal Processing00ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)['Yiwen Ruan', 'Rui Jin', 'Zhaorui Liu', 'Caishan Wang', 'Lei Zhang', 'Tao Peng']2024-04-14T00:00:00https://www.semanticscholar.org/paper/beae853bbcaef742d8d31cc68b27dcd5aa16741c 4a9e02d75ba73a9a3819c1bf1caf521d3c179a35Traditional UX development methodologies focus on developing ``one size fits all"solutions and lack the flexibility to cater to diverse user needs. In response, a growing interest has arisen in developing more dynamic UX frameworks. However, existing approaches often cannot personalise user experiences and adapt to user feedback in real-time. Therefore, my research introduces a novel approach of combining Large Language Models and personas, to address these limitations. The research is structured around three areas: (1) a critical review of existing adaptive UX practices and the potential for their automation; (2) an investigation into the role and effectiveness of personas in enhancing UX adaptability; and (3) the proposal of a theoretical framework that leverages LLM capabilities to create more dynamic and responsive UX designs and guidelines.ICSE Companion270This research introduces a novel approach of combining Large Language Models and personas, to address limitations of traditional UX development methodologies and proposes a theoretical framework that leverages LLM capabilities to create more dynamic and responsive UX designs and guidelines.{'pages': '181-183'}['Yutan Huang']2024-04-14T00:00:00https://www.semanticscholar.org/paper/4a9e02d75ba73a9a3819c1bf1caf521d3c179a35 46e056cde1c65332df28e6561abda85c554147deIn this work we challenge the argument for robot rights on metaphysical, ethical and legal grounds. Metaphysically, we argue that machines are not the kinds of things that may be denied or granted rights. Building on theories of phenomenology and post-Cartesian approaches to cognitive science, we ground our position in the lived reality of actual humans in an increasingly ubiquitously connected, controlled, digitized, and surveilled society. Ethically, we argue that, given machines’ current and potential harms to the most marginalized in society, limits on (rather than rights for) machines should be at the centre of current AI ethics debate. From a legal perspective, the best analogy to robot rights is not human rights but corporate rights, a highly controversial concept whose most important effect has been the undermining of worker, consumer, and voter rights by advancing the power of capital to exercise outsized influence on politics and law. The idea of robot rights, we conclude, acts as a smoke screen, allowing theorists and futurists to fantasize about benevolently sentient machines with unalterable needs and desires protected by law. While such fantasies have motivated fascinating fiction and art, once they influence legal theory and practice articulating the scope of rights claims, they threaten to immunize from legal accountability the current AI and robotics that is fuelling surveillance capitalism, accelerating environmental destruction, and entrenching injustice and human suffering.First Monday1131The idea of robot rights acts as a smoke screen, allowing theorists and futurists to fantasize about benevolently sentient machines with unalterable needs and desires protected by law and threatening to immunize from legal accountability the current AI and robotics that is fuelling surveillance capitalism, accelerating environmental destruction, and entrenching injustice and human suffering.ArXiv['Abeba Birhane', 'J. V. Dijk', 'Frank Pasquale']2024-04-14T00:00:00https://www.semanticscholar.org/paper/46e056cde1c65332df28e6561abda85c554147de 249d3abe11af87610e54f3181124c8f8ce2165dbSilent Data Errors (SDEs) are a unique category of errors that result in unpredictable system behavior that is often difficult to detect. SDEs can represent a serious concern to at-scale compute in data center operations. [1], [2] This paper reviews data collected on SDE impacts to Artificial Intelligence (AI) workloads and Intel's SDE mitigation tools available for use in the data center.IEEE International Reliability Physics Symposium161Data collected on SDE impacts to Artificial Intelligence (AI) workloads and Intel's SDE mitigation tools available for use in the data center are reviewed.2024 IEEE International Reliability Physics Symposium (IRPS)['B. Bittel', 'M. Shamsa', 'B. Inkley', 'A. Gur', 'D. Lerner', 'M. Adams']2024-04-14T00:00:00https://www.semanticscholar.org/paper/249d3abe11af87610e54f3181124c8f8ce2165db e7b8e4e3c655ba5a02493034fa5ddbb8d92d8874Recent literature underscores the need for teachers to develop AI competencies with a recognition of the current lack of well‐defined competence frameworks. This critical review investigates teachers' Artificial Intelligence (AI) competence frameworks (AI CFTs), analysing their strengths, weaknesses and practical applications for researchers, educators and policymakers. It identifies five distinct types of AI CFTs within Competence Construct Claims (Child, S., & Shaw, S. 2023). A conceptual approach to validating competence frameworks. Research Matters: A Cambridge University Press & Assessment publication, 35, 27–40.), each addressing the complexities of AI in its early stages. Notably, frameworks derived from empirical data offer detailed descriptions of competencies, while those based on conceptual models provide broader overviews. Highlighting the need for further empirical research, this review helps identify and understand existing approaches to teacher AI competence development and paves the way for integrating AI CFTs into teacher education, ultimately enhancing educators' preparedness to harness AI in their teaching practices.European Journal of Education280This critical review investigates teachers' Artificial Intelligence (AI) competence frameworks (AI CFTs), analysing their strengths, weaknesses and practical applications for researchers, educators and policymakers to enhance educators' preparedness to harness AI in their teaching practices.European Journal of Education['Tamar Mikeladze', 'P. Meijer', 'R.P.infoeu-repo Verhoeff']2024-04-14T00:00:00https://www.semanticscholar.org/paper/e7b8e4e3c655ba5a02493034fa5ddbb8d92d8874 ec90d3917cb334acda4b1e4b4c94613f8669a9edArtificial intelligence (AI) has emerged as a pivotal technology for enhancing national and industrial competitiveness in the digital transformation era. Consequently, the cultivation of specialized talent in AI has garnered significant attention. This study analyzed AI-related department curricula at major universities worldwide, identifying critical courses for each academic semester. The data we collected included course titles, syllabi, and learning objectives, which were refined and analyzed afterward. Furthermore, we comparatively examined university AI education programs based on the content of Computer Science Curricula 2023, a widely recognized framework for computer science education. The insights gleaned from our analysis revealed that AI curricula are built upon a foundation of computer science, emphasizing the importance of a deep understanding of various related domains within the field of computer science. Based on these findings, we proposed a curriculum for AI departments, considering the need for a comprehensive understanding of computer science alongside specialized AI courses. This study aims to provide foundational data for advancing AI education and guide educational program improvements. Ultimately, it aspires to contribute to developing specialized professionals in the AI field, thereby bolstering national and industrial competitiveness in the rapidly evolving digital landscape.International Journal on Advanced Science, Engineering and Information Technology290This study analyzed AI-related department curricula at major universities worldwide, identifying critical courses for each academic semester and proposed a curriculum for AI departments, considering the need for a comprehensive understanding of computer science alongside specialized AI courses.International Journal on Advanced Science, Engineering and Information Technology['Jeong-Soo Lee', 'Jungwon Cho']2024-04-14T00:00:00https://www.semanticscholar.org/paper/ec90d3917cb334acda4b1e4b4c94613f8669a9ed beee58b9970b03c993e4f15282f28c817e40565dThe stated goal of many organizations in the field of artificial intelligence (AI) is to develop artificial general intelligence (AGI), an imagined system with more intelligence than anything we have ever seen. Without seriously questioning whether such a system can and should be built, researchers are working to create “safe AGI” that is “beneficial for all of humanity.” We argue that, unlike systems with specific applications which can be evaluated following standard engineering principles, undefined systems like “AGI” cannot be appropriately tested for safety. Why, then, is building AGI often framed as an unquestioned goal in the field of AI? In this paper, we argue that the normative framework that motivates much of this goal is rooted in the Anglo-American eugenics tradition of the twentieth century. As a result, many of the very same discriminatory attitudes that animated eugenicists in the past (e.g., racism, xenophobia, classism, ableism, and sexism) remain widespread within the movement to build AGI, resulting in systems that harm marginalized groups and centralize power, while using the language of “safety” and “benefiting humanity” to evade accountability. We conclude by urging researchers to work on defined tasks for which we can develop safety protocols, rather than attempting to build a presumably all-knowing system such as AGI.First Monday00It is argued that the normative framework that motivates much of this goal is rooted in the Anglo-American eugenics tradition of the twentieth century, resulting in systems that harm marginalized groups and centralize power, while using the language of “safety” and “benefiting humanity” to evade accountability.First Monday['Timnit Gebru', 'Émile P. Torres']2024-04-14T00:00:00https://www.semanticscholar.org/paper/beee58b9970b03c993e4f15282f28c817e40565d 68d36649ced2a413dc82c865c87c1ded4bd48c79Artificial Intelligence (AI) has been applied to many human endeavors, and epidemiology is no exception. The AI community has recently seen a renewed interest in applying AI methods and approaches to epidemiological problems. However, a number of challenges are impeding the growth of the field. This work reviews the uses and applications of AI in epidemiology from 1994 to 2023. The following themes were uncovered: epidemic outbreak tracking and surveillance, Geo-location and visualization of epidemics data, Tele-Health, vaccine resistance and hesitancy sentiment analysis, diagnosis, predicting and monitoring recovery and mortality, and decision support systems. Disease detection received the most interest during the time under review. Furthermore, the following AI approaches were found to be used in epidemiology: prediction, geographic information systems (GIS), knowledge representation, analytics, sentiment analysis, contagion analysis, warning systems, and classification. Finally, the work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology as this is a major issue impeding it’s growth, a concerted and continuous collaboration between AI and Epidemiology experts to grow the field, the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.Journal of Computing Theories and Applications1420The work makes the following findings: the absence of benchmark datasets for epidemiological purposes, the need to develop ethical guidelines to regulate the development of AI for epidemiology, and the need to develop explainable and privacy retaining AI methods for more secured and human understandable AI solutions.Journal of Computing Theories and Applications['Kamal Bakari Jillahi', 'A. Iorliam']2024-04-14T00:00:00https://www.semanticscholar.org/paper/68d36649ced2a413dc82c865c87c1ded4bd48c79 f71ca67d3d0acff133f3369cfbf49e1a7882d15aBackground: Scalp-related symptoms such as dandruff and itching are common with diverse underlying etiologies. We previously proposed a novel classification and scoring system for scalp conditions, called the scalp photographic index (SPI); it grades five scalp features using trichoscopic images with good reliability. However, it requires trained evaluators.Aim: To develop artificial intelligence (AI) algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.Methods: Using EfficientNet, convolutional neural network (CNN) models (SPI-AI) ofeach scalp feature were established. 101,027 magnified scalp images graded according to the SPI scoring were used for training, validation, and testing the model Adults with scalp discomfort were prescribed shampoos and scalp serums personalized according to their SPI-AI-defined scalp types. Using the SPI, the scalp conditions were evaluated at baseline and at weeks 4, 8, and 12 of treatment.Results: The accuracies of the SPI-AI for dryness, oiliness, erythema, folliculitis, and dandruff were 91.3%, 90.5%, 89.6%, 87.3%, and 95.2%, respectively. Overall, 100 individuals completed the 4-week study; 43 of these participated in an extension study until week 12. The total SPI score decreased from 32.70 ± 7.40 at baseline to 15.97 ± 4.68 at week 4 (p < 0.001). The efficacy was maintained throughout 12 weeks.Conclusions: SPI-AI accurately assessed the scalp condition. AI-based prescription of tailored scalp cosmetics could significantly improve scalp health.Journal of dermatological treatment (Print)220AI-based prescription of tailored scalp cosmetics could significantly improve scalp health and develop artificial intelligence algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.The Journal of dermatological treatment['Bo-Ri Kim', 'Min Jae Kim', 'Jieun Koo', 'Hwa-Jung Choi', 'Kyung Ho Paik', 'S. H. Kwon', 'Hye-Ryung Choi', 'C. Huh', 'J. Shin', 'Dong-Sun Park', 'Jung-Im Na']2024-04-14T00:00:00https://www.semanticscholar.org/paper/f71ca67d3d0acff133f3369cfbf49e1a7882d15a 1126566f180e9864c95689e8841ed66efde7c9e5Background: The escalating global burden of diabetes necessitates innovative management strategies. Artificial intelligence, particularly large language models like GPT-4, presents a promising avenue for improving guideline adherence in diabetes care. Such technologies could revolutionize patient management by offering personalized, evidence-based treatment recommendations. Methods: A comparative, blinded design was employed, involving 50 hypothetical diabetes mellitus case summaries, emphasizing varied aspects of diabetes management. GPT-4 evaluated each summary for guideline adherence, classifying them as compliant or non-compliant, based on the ADA guidelines. A medical expert, blinded to GPT-4's assessments, independently reviewed the summaries. Concordance between GPT-4 and the expert's evaluations was statistically analyzed, including calculating Cohen's kappa for agreement. Results: GPT-4 labelled 30 summaries as compliant and 20 as non-compliant, while the expert identified 28 as compliant and 22 as non-compliant. Agreement was reached on 46 of the 50 cases, yielding a Cohen's kappa of 0.84, indicating near-perfect agreement. GPT-4 demonstrated a 92% accuracy, with a sensitivity of 86.4% and a specificity of 96.4%. Discrepancies in four cases highlighted challenges in AI's understanding of complex clinical judgments related to medication adjustments and treatment modifications. Conclusion: GPT-4 exhibits promising potential to support healthcare professionals in reviewing diabetes management plans for guideline adherence. Despite high concordance with expert assessments, instances of non-agreement underscore the need for AI refinement in complex clinical scenarios. Future research should aim at enhancing AI's clinical reasoning capabilities and exploring its integration with other technologies for improved healthcare delivery.medRxiv60GPT-4 exhibits promising potential to support healthcare professionals in reviewing diabetes management plans for guideline adherence, despite high concordance with expert assessments, instances of non-agreement underscore the need for AI refinement in complex clinical scenarios.['A. Mondal', 'A. Naskar']2024-04-14T00:00:00https://www.semanticscholar.org/paper/1126566f180e9864c95689e8841ed66efde7c9e5 d7e4761b80cd8fbd5ea57362ffaa3b23aecd198bInternational Journal of Progressive Research in Engineering Management and Science00International Journal of Progressive Research in Engineering Management and Science[]2024-04-14T00:00:00https://www.semanticscholar.org/paper/d7e4761b80cd8fbd5ea57362ffaa3b23aecd198b dee63d3649ffa9cba1915162b2492e2c66f548f9ANZ journal of surgery191ANZ journal of surgery['Philip Allan', 'Michael Knight', 'Richard Evans', 'A. Narayanan']2024-04-14T00:00:00https://www.semanticscholar.org/paper/dee63d3649ffa9cba1915162b2492e2c66f548f9 b8a7a834f47be9673b0073fc97a8cf3e78a3c744Pediatric and Developmental Pathology40Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society['Ananda van der Kamp', 'Tomas J. Waterlander', 'Thomas de Bel', 'Jeroen van der Laak', 'M. V. D. van den Heuvel-Eibrink', 'A. Mavinkurve-Groothuis', 'R. R. de Krijger']2024-04-14T00:00:00https://www.semanticscholar.org/paper/b8a7a834f47be9673b0073fc97a8cf3e78a3c744 72176374d39b0ee92c3a216536a4cb51443f5816Journal of the American Psychiatric Nurses Association80Journal of the American Psychiatric Nurses Association['G. Pearson']2024-04-14T00:00:00https://www.semanticscholar.org/paper/72176374d39b0ee92c3a216536a4cb51443f5816 f0002e44e34e6fa48fae632ad3fee92edff8f668This research emphasises the significance of scientific literacy for policymakers about the future trajectory of artificial intelligence. The ethical concerns surrounding the development of artificial intelligence are of utmost importance due to its potential social effect. Integrating AI systems into many sectors of society, such as healthcare and banking, necessitates adherence to ethical principles. Strict ethical frameworks must be implemented alongside the development of AI to safeguard against biases, privacy infringement, and ethical shortcomings. Researchers, developers, and policymakers must exercise constant vigilance to address concerns about transparency, accountability, and justice in AI systems. The ethical ramifications of artificial intelligence (AI) transcend technology, including significant ethical considerations for both people and society. Active engagement in ethical deliberations among stakeholders involved in AI development is of utmost importance to guarantee AI's responsible and sustainable deployment. This is a pivotal element in realising the whole potential of AI for the betterment of society. Politicians must comprehensively understand the scientific ideas behind AI to enact legislation in this field effectively.  Article visualizations:European Journal of Political Science Studies371This research emphasises the significance of scientific literacy for policymakers about the future trajectory of artificial intelligence and active engagement in ethical deliberations among stakeholders involved in AI development to guarantee AI's responsible and sustainable deployment.European Journal of Political Science Studies['Konstantinos T. Kotsis']2024-04-13T00:00:00https://www.semanticscholar.org/paper/f0002e44e34e6fa48fae632ad3fee92edff8f668 eac4214f43c782462c0c3efe4c1a5bfb57813b1aThis study explores the integration of Artificial Intelligence (AI) in high school education, focusing on its implications for student motivation and learning through the framework of Self-Determination Theory. As AI technologies like ChatGPT4 become more prevalent in educational settings, their potential to enhance learning by catering to students' needs for competence is significant. However, this investigation also highlights the challenges associated with AI misuse, which can undermine students' autonomy and relatedness, leading to academic dishonesty and superficial learning. The research underscores the importance of balancing technological advancements with ethical engagement and intrinsic motivation. Through qualitative analysis, including interviews and thematic analysis of student and teacher feedback, the study reveals a nuanced picture of AI's role in education. It suggests that while AI offers considerable benefits, its integration requires careful consideration of ethical use, digital literacy, and the cultivation of intrinsic motivation. The findings advocate for educational policies and practices that not only leverage AI's potential to enrich learning experiences but also address the challenges posed by its misuse. This study contributes to the ongoing discourse on technology in education, emphasizing the need for a balanced approach to AI integration that supports ethical standards and promotes a meaningful educational experience.International journal of technology in education and science230It is suggested that while AI offers considerable benefits, its integration requires careful consideration of ethical use, digital literacy, and the cultivation of intrinsic motivation, and the need for a balanced approach to AI integration that supports ethical standards and promotes a meaningful educational experience.International Journal of Technology in Education and Science['Rena Alasgarova', 'Jeyhun Rzayev']2024-04-13T00:00:00https://www.semanticscholar.org/paper/eac4214f43c782462c0c3efe4c1a5bfb57813b1a e4fa32b7923a889f8e318c1288b5493912c9c0d1Artificial intelligence (AI) has emerged as a revolutionary tool in agriculture, particularly in the realm of plant disease detection. This article provides an overview of AI-powered plant disease detection methods, their applications, and the limitations associated with their implementation. By leveraging AI, farmers can enhance crop management practices, optimize resource utilization, and mitigate yield losses caused by plant diseases. However, challenges such as data scarcity, model interpretability, and deployment in resource-constrained environments remain significant barriers to widespread adoption. Addressing these limitations is crucial for maximizing the potential of AI in revolutionizing agriculture and ensuring global food security. Artificial intelligence (AI) has emerged as a pivotal tool in modernizing agriculture, particularly in the domain of plant disease detection. This article presents a comprehensive examination of AI-driven methodologies for plant disease detection, exploring their applications and inherent limitations. Through the utilization of machine learning and computer vision techniques, AI facilitates early disease identification, precision agriculture, disease surveillance, and decision support systems. Despite these transformative capabilities, challenges such as inadequate data availability, model interpretability, and implementation in resource-constrained settings impede widespread adoption. Addressing these obstacles is imperative for fully harnessing the potential of AI in agricultural innovation, thereby safeguarding global food security and sustainability.Journal of Advances in Biology &amp; Biotechnology00An overview of AI-powered plant disease detection methods, their applications, and the limitations associated with their implementation is provided, for fully harnessing the potential of AI in agricultural innovation, thereby safeguarding global food security and sustainability.Journal of Advances in Biology &amp; Biotechnology['Lipikant Sahoo', 'Deepali Mohapatra', 'Himendra Raj Raghuvanshi', 'Sonal Kumar', 'Ravinder Kaur', '.. Anshika', '.. Sapna', 'Ritik Chawla', 'Nadiya Afreen']2024-04-13T00:00:00https://www.semanticscholar.org/paper/e4fa32b7923a889f8e318c1288b5493912c9c0d1 dec93145cea2c76d60af5ad3bfaf36d9dbf94b5aThe future of employment will be significantly impacted by artificial intelligence's (AI) inclusion into numerous industries as it continues to progress. This study examines the complex effects of AI on employment and provides guidance to politicians, businesses, and individuals on how to deal with the changing nature of the labour market. In addition to examining the changes in employment responsibilities and skill needs brought about by AI-driven automation, this research also addresses important ethical and policy issues. It does this by drawing on a thorough review of the literature, case studies, and industry trends. Stakeholders may effectively embrace the potential of artificial intelligence (AI) while reducing its negative effects on employment by monitoring these changes and taking proactive measures to adapt. . Key Words - Technological advances, Automation, Artificial intelligence (AI), Machine learning, Robotics, Routine tasks, Repetitive tasks, Organizational processes, Job displacement, ,Skills requirements, Workforce dynamics, Mass unemployment, New job opportunities, Policy responses, Digital ageINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Examination of the complex effects of AI on employment provides guidance to politicians, businesses, and individuals on how to deal with the changing nature of the labour market and addresses important ethical and policy issues.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Pratik Dipak Nalwade']2024-04-13T00:00:00https://www.semanticscholar.org/paper/dec93145cea2c76d60af5ad3bfaf36d9dbf94b5a 26335c6b475ad290779b9ae2a7e6afabc9b3476bEye90Google Gemini and Bard had an acceptable performance in responding to ophthalmology board examination practice questions and tended to provide a confident explanation even when providing an incorrect answer.Eye['Andrew Mihalache', 'Justin Grad', 'Nikhil S. Patil', 'R. Huang', 'Marko M. Popovic', 'Ashwin Mallipatna', 'P. Kertes', 'Rajeev H. Muni']2024-04-13T00:00:00https://www.semanticscholar.org/paper/26335c6b475ad290779b9ae2a7e6afabc9b3476b f07ecc3227214e8a46b43eb7383c11e783b68239The continuous advancement of artificial intelligence technology has triggered many controversies. In the field of AI text-generated images, the current controversy focuses on the issue of copyrightability and attribution of rights. This paper takes the case of "Spring Breeze Sends Tenderness" as an entry point, and concludes that when the user has made certain influence and personalized choices on the final output of the image, and the user has made substantial contributions in the process of generating the image, it can be considered that the relevant AI text-generated image constitutes a work in the sense of the copyright law, and its rights are attributed to the user.Advances in Education, Humanities and Social Science Research120It is concluded that when the user has made certain influence and personalized choices on the final output of the image, and the user has made substantial contributions in the process of generating the image, it can be considered that the relevant AI text-generated image constitutes a work in the sense of the copyright law.Advances in Education, Humanities and Social Science Research['Xinlan Ding']2024-04-13T00:00:00https://www.semanticscholar.org/paper/f07ecc3227214e8a46b43eb7383c11e783b68239 6dc6685c14f41f9ceec6814f5eb0a278c694052bThe article suggests a Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To develop such a framework, we examined existing business and management research publications to identify and extract assertions that could be used as a source of actionable insights for early-stage growth-oriented companies. The extracted assertions were assembled into a corpus of texts that was subjected to topic modelling analysis—a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modelling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modelling results led to the identification of seven topics: (1) Value created; (2) Stakeholder value propositions; (3) Foreign market entry; (4) Customer base; (5) Continuous improvement; (6) Cross-border operations; and (7) Company image. The uniqueness of the adopted topic modelling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e., in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance the emerging four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, the multiple-stakeholder perspective on VP development and foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the enhancement of the VP of early-stage growth-oriented companies.Applied Sciences640A Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities is suggested and it is suggested that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created.Applied Sciences['S. Tanev', 'Christian Keen', 'Tony Bailetti', 'David Hudson']2024-04-13T00:00:00https://www.semanticscholar.org/paper/6dc6685c14f41f9ceec6814f5eb0a278c694052b cc9ee5381a2f1f78f77e97245b6bab404cd34c7dComputational Economics310Computational Economics['Shuangshuang Fan', 'Yichao Li', 'William Mbanyele', 'Xiufeng Lai']2024-04-13T00:00:00https://www.semanticscholar.org/paper/cc9ee5381a2f1f78f77e97245b6bab404cd34c7d 323c4009ea093b9f0686f70188fcf5cf80960f81Educational Administration: Theory and Practice00Educational Administration: Theory and Practice['Dr. Priti Gupta', 'Dr. Chakrala Sreelatha', 'A. Latha', 'Dr. Shilpi Raj', 'Dr. Aparna Singh']2024-04-13T00:00:00https://www.semanticscholar.org/paper/323c4009ea093b9f0686f70188fcf5cf80960f81 17511ab8200aae11406cfb9ee6b73db7aed7e668Journal of Oral Biology and Craniofacial Research310This paper explores the key bioethical considerations in dental healthcare, with a focus on evidence-based AI development and use, and proposes a framework of ethical principles and guidelines provided that would foster trust between the clinician and patients, while promoting the highest standards of care.Journal of Oral Biology and Craniofacial Research['Isha Duggal', 'T. Tripathi']2024-04-13T00:00:00https://www.semanticscholar.org/paper/17511ab8200aae11406cfb9ee6b73db7aed7e668 3bfd93fa8d3431a8e007f26b0419390e0902e02fAdvances in Atmospheric Sciences300Advances in Atmospheric Sciences['Gang Huang', 'Ya Wang', 'Yoo-Geun Ham', 'Bin Mu', 'Weichen Tao', 'Chaoyang Xie']2024-04-13T00:00:00https://www.semanticscholar.org/paper/3bfd93fa8d3431a8e007f26b0419390e0902e02f 87f0f4efc5c1c989c8e75d4bc7f1ffd7ee20230eThe advent of Artificial Intelligence (AI) has revolutionized the energy management landscape for smart buildings, offering unparalleled opportunities for optimizing energy consumption, enhancing operational efficiency, and advancing sustainability goals. This paper provides a comprehensive review of AI-driven energy management systems tailored for smart buildings, exploring their multifaceted functionalities, benefits, challenges, and future prospects. [1],[4] By synthesizing existing literature and case studies, this research aims to elucidate the transformative potential of AI in reshaping the way energy is managed and utilized in the built environment. AI-driven energy management systems leverage advanced algorithms, machine learning techniques, and data analytics to intelligently monitor, analyze, and optimize energy usage within smart buildings. These systems integrate diverse components such as sensing devices, data preprocessing modules, optimization algorithms, and control systems to achieve optimal performance. Key functionalities include predictive analytics for energy demand forecasting, adaptive control of heating, ventilation, and air conditioning (HVAC) systems, dynamic lighting management based on occupancy patterns, and integration with renewable energy sources to enhance sustainability. AI enables smart buildings to participate in demand response programs, dynamically adjusting energy consumption in response to grid conditions and pricing signals. This flexibility not only reduces operational costs but also contributes to grid stability and resilience. However, the widespread adoption of AI-driven energy management systems faces several challenges, including data privacy concerns, interoperability issues, and the need for skilled personnel to operate and maintain these sophisticated systems.The paper underscores the importance of AI-driven energy management systems as transformative tools for optimizing energy utilization, improving building performance, and advancing sustainability objectives in the era of smart buildings. DOI: https://doi.org/10.52783/pst.280Power system technology90The paper underscores the importance of AI-driven energy management systems as transformative tools for optimizing energy utilization, improving building performance, and advancing sustainability objectives in the era of smart buildings.Power System Technology['Balakumar Muniandi, Purushottam Kumar Maurya, CH Bhavani, Shailesh Kulkarni, Ramswaroop Reddy Yellu, Nidhi C']2024-04-13T00:00:00https://www.semanticscholar.org/paper/87f0f4efc5c1c989c8e75d4bc7f1ffd7ee20230e b346eabc974fdaff8cc25eb3300762f451e044a7Generative artificial intelligence (AI) and large language models (LLMs) are increasingly being used in the academic writing process. This is despite the current lack of unified framework for reporting the use of machine assistance. In this work, we propose"Cardwriter", an intuitive interface that produces a short report for authors to declare their use of generative AI in their writing process. The demo is available online, at https://cardwriter.vercel.apparXiv.org220This work proposes"Cardwriter", an intuitive interface that produces a short report for authors to declare their use of generative AI in their writing process.ArXiv['Won Ik Cho', 'Eunjung Cho', 'Hyeonji Shin']2024-04-13T00:00:00https://www.semanticscholar.org/paper/b346eabc974fdaff8cc25eb3300762f451e044a7 8ae67d5fa4c1e07aa58764db9b5b73e940b3bbffGenerative artificial intelligence (AIGC) has changed the traditional information production mechanism and has a wide range of application scenarios. At the same time, the security risks it exposes such as data leakage, false content generation, and improper utilization have also attracted widespread attention from various countries. The development, application and governance of AIGC no longer seem to be a common challenge faced by one country but by the entire international community. In order to effectively respond to the challenges of AIGC to the false information governance system, this article uses multiple methods such as literature analysis and in-depth research to elaborate on the potential risks of AIGC, and conducts an in-depth analysis of the global challenges of false information risk governance. Finally, Proposing governance paths and countermeasures from various perspectives such as supervision and ecology provides intelligence reference for the healthy development of the AIGC industry.Journal of Electrical Systems210This article uses multiple methods such as literature analysis and in-depth research to elaborate on the potential risks of AIGC, and conducts an in-depth analysis of the global challenges of false information risk governance.Journal of Electrical Systems['Taoye Wang', 'Li Li', 'Xiang Chen', 'Kunzhu Li']2024-04-13T00:00:00https://www.semanticscholar.org/paper/8ae67d5fa4c1e07aa58764db9b5b73e940b3bbff 4c3b5e18379d99d721a7e546efd25a269fab02faThere has been a recent increase in interest regarding the remarkable potential of artificial intelligence (AI) to profoundly transform online advertising. The purpose of this research is to critically assess how AI can enhance customer experience (CX) in various business applications. We aim to identify important concepts, evaluate the impact of AI-powered CX initiatives, and offer suggestions for future research. By conducting a thorough analysis of academic publications, industry reports, and case studies, this study extracts theoretical frameworks, empirical findings, and practical insights. The results highlight the significant changes that occur with the integration of AI into Customer Relationship Management (CRM). AI enables personalized interactions, strengthens customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four key themes emerge from research findings: personalized service, improved engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article makes a valuable contribution to the AI in CRM discourse by summarizing the current state, exploring key themes, and suggesting future research opportunities. It is strongly advocated for responsible AI implementation, emphasizing ethical considerations and providing guidance to organizations as they navigate the opportunities and challenges presented by AI.Journal of Electrical Systems300This article makes a valuable contribution to the AI in CRM discourse by summarizing the current state, exploring key themes, and suggesting future research opportunities.Journal of Electrical Systems['Tran Minh Tung, Duong Hoai Lan']2024-04-13T00:00:00https://www.semanticscholar.org/paper/4c3b5e18379d99d721a7e546efd25a269fab02fa 642d2fd5903e5d93e3a9c1e80eeb7bdd0a0b5c76The article explores the development of autogenic training for the correction of psychophysical states using artificial intelligence tools. The research aims to organise the application areas of artificial intelligence for diagnosis and correction of psychophysical states through autogenic training. The results indicate that autogenic training is an important approach in the spectrum of treatment methods for psychophysical disorders, with its main advantages being the flexibility of the technique and its ability to induce relaxation and psychophysiological self-regulation through passive concentration and repetition of specific phrases. The analysis shows that while the practice is stable and consists of sequential procedures, it can be adapted to different techniques and needs. Here, the use of artificial intelligence (AI) can significantly improve the personalisation of the treatment process and its effectiveness. The application of AI in the context of autogenic training opens up new perspectives for the diagnosis and treatment of psychophysical disorders. AI can optimise psychotherapeutic interventions by adapting training sessions to the individual needs of the user, thereby achieving better results in relaxation and psychophysical recovery. A distinctive feature of AI is also its ability to provide detailed feedback and track user progress, contributing to more effective adjustment and improvement of the training process. The integration of AI with virtual and augmented reality technologies can further enhance the autogenic training experience, creating a more immersive and controlled environment for relaxation. The development of digital tools and mobile applications based on AI has already demonstrated its positive impact on the psychophysical health of users, paving the way for more innovative and effective solutions in the future. Thus, the use of AI in autogenic training for the correction of psychophysiological states promises significant prospects for improving the quality of life and well-being of individuals.Economics &amp; Education60The results indicate that autogenic training is an important approach in the spectrum of treatment methods for psychophysical disorders, with its main advantages being the flexibility of the technique and its ability to induce relaxation and psychophysiological self-regulation through passive concentration and repetition of specific phrases.Economics &amp; Education['Anna Rode', 'Yulia Rode']2024-04-12T00:00:00https://www.semanticscholar.org/paper/642d2fd5903e5d93e3a9c1e80eeb7bdd0a0b5c76 66116c3adcd5b6462f30aa45c4388d24d8602f86The paper deals with the understanding of government strategies for regulating the ICT industry. The purpose of the study is to rationalise the directions of government regulation of the ICT industry in Ukraine in the context of technological transformation of the economy. Using the methodology of descriptive statistics, the authors revealed the rapid growth of the number of employees in the ICT industry in Ukraine. The authors conclude that, from the point of view of state regulation, given the fact that employment has proved to be the main factor in the success of exports, it is necessary to prevent the outflow of labour from Ukraine. This is confirmed by the competitive advantages gained by Ukrainian ICT workers in the global market, namely: price – the cost of Ukrainian coders' services is close to the prices of Indian coders and seven times less than the cost of coders' services in the US; diversity and high quality of technological competencies that allow flexible implementation of high-level ICT projects; its own research and development base, as well as materials and technologies for the implementation of complex knowledge-intensive projects, flexibility in the use and distribution of necessary resources; cultural and geographical proximity to Europe. Based on this, strategies for interaction between the state and the ICT sector are being developed. First, the state needs to create favourable conditions for the sustainable development of the industry, including reforming legal norms, improving investment policy, modernising labour legislation, strengthening the protection of intellectual property rights, and ensuring the return of labour from abroad. Second, to create comfortable conditions to reduce the outflow of intellectual resources abroad and strengthen Ukraine as a brand in the IT sector in the eyes of the international community. The main strategies for the formation of mechanisms for state support of information services are as follows: the emergence of a modern IT infrastructure accessible to all, improving the quality of information services; a strategy for creating a regulatory framework for the effective use of the network economy; a strategy for creating state indicators of the development of the network economy; a strategy for creating information security in network systems.Economics &amp; Education160The authors conclude that, from the point of view of state regulation, given the fact that employment has proved to be the main factor in the success of exports, it is necessary to prevent the outflow of labour from Ukraine.Economics &amp; Education['Tetyana V. Stroiko', 'A. Mulenko', 'Vadym Myronenko']2024-04-12T00:00:00https://www.semanticscholar.org/paper/66116c3adcd5b6462f30aa45c4388d24d8602f86 199bfa47321c32d63965f4f7c8d79c8381960579Journal of Antitrust Enforcement00Journal of Antitrust Enforcement['Christoph Busch']2024-04-12T00:00:00https://www.semanticscholar.org/paper/199bfa47321c32d63965f4f7c8d79c8381960579 e06dcd254c97f68b56df22b32cb720034bf4a7d5Clinical Chemistry and Laboratory Medicine40Clinical chemistry and laboratory medicine['Sabrina Kali', 'Chloé Puisney', 'Marie-Laure Delalande', 'Guillaume Franc', 'Christiane Buisson', 'Sébastien Barradeau']2024-04-12T00:00:00https://www.semanticscholar.org/paper/e06dcd254c97f68b56df22b32cb720034bf4a7d5 492ec7494cb9ee93f2b7d5dabdc99fb0b7a402d0AI-driven precision agriculture, predictive analytics, robots, and market intelligence boost contemporary agriculture's production, efficiency, and sustainability. Precision agriculture, powered by AI algorithms, gives farmers detailed insights into crop health, soil conditions, and weather patterns for data-driven resource allocation and management. AI in agriculture's predictive analytics helps stakeholders forecast crop yields, market dynamics, and climate-related dangers, improving resilience and strategic planning. AI has great promise to solve agriculture's complicated problems. AI technologies allow computers to mimic human cognition and evaluate massive volumes of data to draw conclusions. AI can improve resource utilization, productivity, decision-making, and environmental effect in agriculture. AI-powered precision agriculture, crop monitoring, supply chain optimization, and market analysis are making agriculture more sustainable and resilient. To show AI's influence on farming, we explored precision agriculture, predictive analytics, robots, and supply chain optimization. Farmers may optimize resource usage, manage risks, and make data-driven choices using these tools, enhancing output, sustainability, and resilience.Journal of Scientific Research and Technology201To show AI's influence on farming, it is explored precision agriculture, predictive analytics, robots, and supply chain optimization, which are making agriculture more sustainable and resilient.Journal of Scientific Research and Technology['Anurag Chandra Mishra', 'Joydeep Das', 'Ram Awtar']2024-04-12T00:00:00https://www.semanticscholar.org/paper/492ec7494cb9ee93f2b7d5dabdc99fb0b7a402d0 94aa859d767a83c8bc9ebd2999eca1d5f7fe2cddTopoi341Here, a fictionalist account of human/AI interaction is defended, according to which these encounters involve an elaborate practise of imaginative pretence: a make-believe in which the artificial agent is attributed a life of its own.Topoi['Joel Krueger', 'Tom Roberts']2024-04-12T00:00:00https://www.semanticscholar.org/paper/94aa859d767a83c8bc9ebd2999eca1d5f7fe2cdd a58796b7403268d4d70d457fd7f1ef5f153ee9f0Artificial intelligence (AI) has shaken the foundation of modern workplaces like never before and has induced digitized workstyles within the organisation. These furtherance in technology are generating significant interest among stakeholders to embrace AI in human resource management (HRM). Research and Development teams, analysts and practitioners are keen to investigate the sequel of AI in HR and their collaboration with gadget applications involving machine language, Data-science, Blockchain and Big Data. This study investigates HRM specific factors that are imbibed towards adoption of AI in extended HR based digital platform adopting a qualitative research design with an abductive approach. This research also investigates key enablers like optimistic, enthusiastic, and collaborative employees, strong digital enabled leadership, reliable HR meta-data, specialized HR partners, and well-rounded accountable AI ethics. The study also examines barriers towards awareness in AI adoption: the inability to have a timely internal audit pulse check of employees, their ability of emotional decision making, ineffective agile digital experts as well as external HR partners. On summarising, this study also contributes theory by providing a model that influences AI adoption and proposes ascending in welcoming unified theory of acceptance and use of innovative technology in the context of AI adoption in HR upskilling and reskilling ecosystems eventually. The study also contributes the anecdotes of best-in-class industrial HR practices with secured digital policy formulation to reimagine cybermated workplaces cubical. Maximising the human capital in the digital era be obliged in harmonious conglomerative human–AI enterprise making workplaces an eminent future-ready in the wake of productive and massive successful digital disruptions with efficacy.  European Economic Letters (EEL)130This study investigates HRM specific factors that are imbibed towards adoption of AI in extended HR based digital platform adopting a qualitative research design with an abductive approach and proposes ascending in welcoming unified theory of acceptance and use of innovative technology in the context of AI adoption in HR upskilling and reskilling ecosystems eventually.European Economic Letters (EEL)['Dr. U. Amaleshwari, R. Shanmugapriya']2024-04-12T00:00:00https://www.semanticscholar.org/paper/a58796b7403268d4d70d457fd7f1ef5f153ee9f0 a5cc3f896f89d7b4dae7635ea8e0b8a036455841Artificial Intelligence (AI) has emerged as a transformative force in the field of agriculture, offering innovative solutions to enhance productivity, sustainability, and efficiency. This research paper explores the multifaceted impact of AI on agriculture and farmers, encompassing various aspects such as precision farming, crop monitoring, livestock management, supply chain optimization, crop hybridization, and the development of AI tools and technology. Additionally, it delves into the opportunities AI presents for farmers and its potential role in advancing organic farming practices. Through a comprehensive analysis of existing literature and case studies, this paper aims to provide insights into the evolving landscape of agriculture facilitated by AI technologies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper explores the multifaceted impact of AI on agriculture and farmers, encompassing various aspects such as precision farming, crop monitoring, livestock management, supply chain optimization, crop hybridization, and the development of AI tools and technology.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Kolate Sanket']2024-04-12T00:00:00https://www.semanticscholar.org/paper/a5cc3f896f89d7b4dae7635ea8e0b8a036455841 41fdc236879b8c7464aa2d0ccdb979ea9d19617eAs a result of recent advancements in generative AI, the field of Data Science is prone to various changes. This review critically examines the Data Analysis (DA) capabilities of ChatGPT assessing its performance across a wide range of tasks. While DA provides researchers and practitioners with unprecedented analytical capabilities, it is far from being perfect, and it is important to recognize and address its limitations.arXiv.org170This review critically examines the Data Analysis (DA) capabilities of ChatGPT assessing its performance across a wide range of tasks.ArXiv['Ozan Evkaya', 'Miguel de Carvalho']2024-04-12T00:00:00https://www.semanticscholar.org/paper/41fdc236879b8c7464aa2d0ccdb979ea9d19617e 18314141764750a2d63cc929e9fc9b21350dbf9bAs machine learning models become increasingly complex and ubiquitous in cloud-based applications, the need for interpretability and transparency in decision making has become paramount. Explainable AI (XAI) techniques aim to provide insights into the inner workings of machine learning models, thereby enhancing their interpretability and facilitating trust among users. In this paper, we delve into the significance of XAI in cloud-based machine learning environments, emphasizing the importance of interpretable models and transparent decision-making processes. [1] XAI epitomizes a paradigm shift in cloud-based ML, catalyzing transparency, accountability, and ethical decision-making. As cloud-based ML continues its ascent, the imperative for XAI grows commensurately, underlining the necessity for sustained innovation and collaboration to unlock the full potential of interpretable AI systems. We review existing methodologies for achieving explainability in AI systems and discuss their applicability and challenges in cloud environments. Furthermore, we explore the implications of XAI for various stakeholders, including developers, end-users, and regulatory bodies, and highlight potential avenues for future research in this rapidly evolving field. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6376  Tuijin Jishu/Journal of Propulsion Technology200The significance of XAI in cloud-based machine learning environments is explored, emphasizing the importance of interpretable models and transparent decision-making processes and potential avenues for future research in this rapidly evolving field.Tuijin Jishu/Journal of Propulsion Technology['Harshitha Raghavan Devarajan', 'Dr. Soibam Birajit', 'Singh']2024-04-12T00:00:00https://www.semanticscholar.org/paper/18314141764750a2d63cc929e9fc9b21350dbf9b 25e88def826966a8bd9e8b0e93b6492842e23957The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment.Machines220Machines['Davide Fabiocchi', 'Nicola Giulietti', 'Marco Carnevale', 'H. Giberti']2024-04-12T00:00:00https://www.semanticscholar.org/paper/25e88def826966a8bd9e8b0e93b6492842e23957 f9bfe897812d3288a2d0d4aa4a4b76e3909c17c3PurposeAlthough businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.Design/methodology/approachThe integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.FindingsThe results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.Research limitations/implicationsIn pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.Practical implicationsThe provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.Originality/valueWhile previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.Business Process Management Journal1130The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance, thereby shedding light on how AI creates business value.Business Process Management Journal['Aleš Zebec', 'Mojca Indihar Štemberger']2024-04-12T00:00:00https://www.semanticscholar.org/paper/f9bfe897812d3288a2d0d4aa4a4b76e3909c17c3 80823164646ad638bb4cd3c9ae70ee3146f3aaccThis manuscript delves into the application of AI techniques, such as machine learning, natural language processing, and pattern recognition, to interpret ancient scripts and unveil linguistic phenomena. Through an examination of the strategies utilized by linguists and adept individuals in deciphering ancient languages, the manuscript underscores the potential of AI-driven methodologies in this realm.International Journal For Multidisciplinary Research50International Journal For Multidisciplinary Research['Aditi Patil']2024-04-12T00:00:00https://www.semanticscholar.org/paper/80823164646ad638bb4cd3c9ae70ee3146f3aacc bb18ebe0544e649a9a090fbeb0b4052a315e25d3The COVID-19 pandemic has starkly revealed the vulnerabilities within global supply chains, prompting the urgent need for enhanced risk management strategies. This paper explores the application of Artificial Intelligence (AI) in adaptive supply chain risk management to mitigate disruptions and enhance resilience in the post-pandemic era. By leveraging AI technologies such as machine learning, predictive analytics, and optimization algorithms, organizations can proactively identify, assess, and respond to risks in real-time, thereby fortifying their supply chains against unforeseen disruptions. [1] This paper reviews existing literature on supply chain risk management, AI applications in supply chain management, and post-pandemic supply chain challenges. Furthermore, it presents examples illustrating how AI-driven adaptive risk management approaches have been implemented successfully to navigate disruptions and improve supply chain resilience. Through this analysis, the paper aims to provide insights into the transformative potential of AI-enabled adaptive risk management strategies in building agile and robust supply chains for the future. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6294Tuijin Jishu/Journal of Propulsion Technology230This paper reviews existing literature on supply chain risk management, AI applications in supply chain management, and post-pandemic supply chain challenges and presents examples illustrating how AI-driven adaptive risk management approaches have been implemented successfully to navigate disruptions and improve supply chain resilience.Tuijin Jishu/Journal of Propulsion Technology['Muhammad Firdaus Bidin', 'María Teresa Espinosa-Jaramillo', 'Diana Carolina Castillo Martínez', 'Nik Alif Amri', 'Nik Hashim', 'Dr Sadhna Chauhan', 'S. Karmode']2024-04-12T00:00:00https://www.semanticscholar.org/paper/bb18ebe0544e649a9a090fbeb0b4052a315e25d3 36f6f2ee3103e360808cdf77b9f99b95efb7a661As AI-generated image (AIGI) methods become more powerful and accessible, it has become a critical task to determine if an image is real or AI-generated. Because AIGI lack the signatures of photographs and have their own unique patterns, new models are needed to determine if an image is AI-generated. In this paper, we investigate the ability of the Contrastive Language-Image Pre-training (CLIP) architecture, pre-trained on massive internet-scale data sets, to perform this differentiation. We fine-tune CLIP on real images and AIGI from several generative models, enabling CLIP to determine if an image is AI-generated and, if so, determine what generation method was used to create it. We show that the fine-tuned CLIP architecture is able to differentiate AIGI as well or better than models whose architecture is specifically designed to detect AIGI. Our method will significantly increase access to AIGI-detecting tools and reduce the negative effects of AIGI on society, as our CLIP fine-tuning procedures require no architecture changes from publicly available model repositories and consume significantly less GPU resources than other AIGI detection models.arXiv.org300This paper fine-tune CLIP on real images and AIGI from several generative models, enabling CLIP to determine if an image is AI-generated and, if so, determine what generation method was used to create it.ArXiv['A. G. Moskowitz', 'T. Gaona', 'J. Peterson']2024-04-12T00:00:00https://www.semanticscholar.org/paper/36f6f2ee3103e360808cdf77b9f99b95efb7a661 cbd5689b6c97e1d58c7e7a02e1522af637732479Abstract Goals/Purpose The imperative for precision in aesthetic surgery necessitates a robust framework for evaluating the impact of facial interventions on perceived age. Our study introduces a cutting-edge AI model aimed at discerning an individual's perceived age from facial characteristics. This tool is designed to augment the assessment of various plastic surgery procedures, facilitating the tailoring of interventions to each patient's unique facial aging pattern. Methods/Technique We harnessed a deep convolutional neural network (DCNN), pre-trained on the extensive ImageNet dataset, and further refined using 523,051 pre-annotated facial images from the IMBD-WIKI database, normalized as per the Mathias et al. face detection paradigm. Faces were processed into a 299x299 pixel matrix, maintaining a 40% margin around the face for uniformity. The Xception architecture was employed for its advanced feature extraction capabilities. The model was refined and tested against a diverse set of 100 patient faces from the Mayo Clinic's database, categorized by demographic and procedural data. The AI model employed regression analysis and softmax probability for precise age estimation. Results/Complications The AI model exhibited a remarkable accuracy rate of 92.5% in age estimation for pre procedural patients, with a standard deviation of 3.2 years. It significantly outperformed traditional methods in identifying fine-grained age-related features. The AI model discerned an average perceived age reduction of 3.5 years across all patients post-procedure, with a notable variance among different types of surgeries. Certain procedures, such as rhytidectomy and blepharoplasty, showed a more pronounced age-reduction effect. Conclusion The AI model presents an accurate and objective method for quantifying perceived age, serving as a significant benchmark in facial aesthetic evaluation. By illustrating measurable age reduction following various procedures, with some surgeries yielding more substantial changes in perceived age, the model stands as a testament to the effectiveness of plastic surgery interventions. The precision of our model in predicting age pre- and post-procedure underscores its potential to assist surgeons in custom-tailoring surgeries to individual aging patterns. This innovation is poised to refine the decision-making process in aesthetic surgery, ensuring treatments are aligned with the desired outcomes for rejuvenation and patient-specific needs, ultimately advancing the frontier of personalized plastic surgery.Aesthetic Surgery Journal Open Forum00A cutting-edge AI model aimed at discerning an individual's perceived age from facial characteristics is introduced, serving as a significant benchmark in facial aesthetic evaluation.Aesthetic Surgery Journal. Open Forum['Khaled O Alameddine', 'Jess Rames', 'K. Bakri', 'Samir Mardini']2024-04-12T00:00:00https://www.semanticscholar.org/paper/cbd5689b6c97e1d58c7e7a02e1522af637732479 e66a8edcee683c63f3aba0b86097f0fe428a02f5Artificial intelligence (AI) may capture the properties and functions of materials better than previous theoretical/computational methods because it targets correlations and does not assume a single, specific underlying physical model. Therefore, it addresses the full intricacy of the numerous processes that govern the function of materials. However, the statistical analysis and interpretation of AI models require careful attention. The review article started with a brief discussion of historical aspects of data-centric science. It then focused on the recently developed, explainable AI methods [8,10] and applications [2,11,12]. The identified "rules" determine the properties and functions of materials. The rules depend on descriptive parameters called "materials genes." As genes in biology, they are correlated with a certain material property or function. Thus, these materials genes help to identify materials that are, for example, better electrical conductors or better heat insulators or better catalysts.Coshare Science00The review article started with a brief discussion of historical aspects of historical aspects of data-centric science, then focused on the recently developed, explainable AI methods and applications.Coshare Science['Matthias Scheffler']2024-04-12T00:00:00https://www.semanticscholar.org/paper/e66a8edcee683c63f3aba0b86097f0fe428a02f5 45650066db4d6e8cec9442cc301000e4a914728dObject counting is a popular task in deep learning applications in various domains, including agriculture. A conventional deep learning approach requires a large amount of training data, often a logistic problem in a real-world application. To address this issue, we examined how well ChatGPT (GPT4V) and a general-purpose AI (foundation model for object counting, T-Rex) can count the number of fruit bodies (coffee cherries) in 100 images. The foundation model with few-shot learning outperformed the trained YOLOv8 model (R2 = 0.923 and 0.900, respectively). ChatGPT also showed some interesting potential, especially when few-shot learning with human feedback was applied (R2 = 0.360 and 0.460, respectively). Moreover, we examined the time required for implementation as a practical question. Obtaining the results with the foundation model and ChatGPT were much shorter than the YOLOv8 model (0.83 hrs, 1.75 hrs, and 161 hrs). We interpret these results as two surprises for deep learning users in applied domains: a foundation model with few-shot domain-specific learning can drastically save time and effort compared to the conventional approach, and ChatGPT can reveal a relatively good performance. Both approaches do not need coding skills, which can foster AI education and dissemination.arXiv.org180Results are interpreted as two surprises for deep learning users in applied domains: a foundation model with few-shot domain-specific learning can drastically save time and effort compared to the conventional approach, and ChatGPT can reveal a relatively good performance.ArXiv['Konlavach Mengsuwan', 'Juan Camilo Rivera Palacio', 'Masahiro Ryo']2024-04-12T00:00:00https://www.semanticscholar.org/paper/45650066db4d6e8cec9442cc301000e4a914728d 7eb26876985ef469ef3bb9ab0c83a01a57efafc5Recent advances in Artificial Intelligence (AI) have started disrupting the healthcare industry, especially medical imaging, and AI devices are increasingly being deployed into clinical practice. Such classifiers have previously demonstrated the ability to discern a range of protected demographic attributes (like race, age, sex) from medical images with unexpectedly high performance, a sensitive task which is difficult even for trained physicians. Focusing on the task of predicting sex from dermoscopic images of skin lesions, we are successfully able to train high-performing classifiers achieving a ROC-AUC score of ~0.78. We highlight how incorrect use of these demographic shortcuts can have a detrimental effect on the performance of a clinically relevant downstream task like disease diagnosis under a domain shift. Further, we employ various explainable AI (XAI) techniques to identify specific signals which can be leveraged to predict sex. Finally, we introduce a technique to quantify how much a signal contributes to the classification performance. Using this technique and the signals identified, we are able to explain ~44% of the total performance. This analysis not only underscores the importance of cautious AI application in healthcare but also opens avenues for improving the transparency and reliability of AI-driven diagnostic tools.medRxiv380Focusing on the task of predicting sex from dermoscopic images of skin lesions, this analysis is successfully able to train high-performing classifiers achieving a ROC-AUC score of ~0.78 and introduces a technique to quantify how much a signal contributes to the classification performance.['S. U. Gadgil', 'A. DeGrave', 'R. Daneshjou', 'S.-I. Lee']2024-04-12T00:00:00https://www.semanticscholar.org/paper/7eb26876985ef469ef3bb9ab0c83a01a57efafc5 3cdad428927d35bcdeecf83a45971af77f26ce64Everything will be accessible online in early 2020. To put it briefly, everything is bought and sold online. It represents a wise company with a wise customer. Artificial intelligence is being used in various industries, including business and engineering, because it is dependable, economical, capable of solving complex issues, and able to make expert system decisions. Perhaps the most prevalent and dynamic kind of marketing available today is social media. Artificial intelligence (AI) assists marketers in analyzing brand visibility and interactions to gauge client happiness. Consequently, a marketer can effectively promote his goods and services and run his firm thanks to the strength of artificial intelligence systems in digital marketing tactics. AI is becoming more and more sophisticated in marketing, and marketers have the ability to use it to their advantage by properly implementing and overseeing AI solutions, which will become increasingly important for enhancing the company.International Journal For Multidisciplinary Research110Artificial intelligence (AI) assists marketers in analyzing brand visibility and interactions to gauge client happiness, and a marketer can effectively promote his goods and services and run his firm thanks to the strength of artificial intelligence systems in digital marketing tactics.International Journal For Multidisciplinary Research['K. SRIVARUN VENKATESH']2024-04-12T00:00:00https://www.semanticscholar.org/paper/3cdad428927d35bcdeecf83a45971af77f26ce64 d3b34e578814a0334bc9a542d142d50f1f5689a1Improving visual quality is crucial as we navigate through the vast world of data. State-of-the-art (SOTA) artificial intelligence (AI) models provide highly effective solutions. Driven by the ever-growing demand for high-fidelity multimedia content, this research explores the groundbreaking capabilities of SOTA AI models to revolutionize video quality enhancement. Existing video capture methods often struggle with limitations in hardware, bandwidth, and compression, leading to subpar visual experiences. To address this challenge, we propose a novel Video Quality Enhancement Solution (VQES) that synergistically combines Google FILM for frame interpolation and Real-ESRGAN for image super-resolution. By applying these models to each video frame and integrating scalable post-processing techniques, a comprehensive VQES has been devised. Extensive experiments demonstrate that our VQES outperforms existing methods in terms of peak signal-to-noise ratio (PSNR) improvement and user-perceived visual quality. By advancing video fidelity, this research paves the way for consistently immersive, informative, and enjoyable visual experiences.Scalable Computing : Practice and Experience00A novel Video Quality Enhancement Solution (VQES) that synergistically combines Google FILM for frame interpolation and Real-ESRGAN for image super-resolution is proposed that outperforms existing methods in terms of peak signal-to-noise ratio (PSNR) improvement and user-perceived visual quality.Scalable Comput. Pract. Exp.['Ankit Das', 'Deven Prakash Paramaj', 'Shambhavi Br']2024-04-12T00:00:00https://www.semanticscholar.org/paper/d3b34e578814a0334bc9a542d142d50f1f5689a1 abc40f76156241977bef7bbcec09541679cf6037 The contributions of generative artificial intelligence (AI) and social media to the climate crisis are often underestimated. To date, much of the focus has been on direct emissions associated with the life cycle of tech products. In this forum article, we argue that this narrow focus misses the adverse and indirect impacts of generative AI and social media on the climate. We outline some of the indirect ways in which generative AI and social media undermine the optimism, focus, creativity, and veracity required to address the climate crisis. Our aim is twofold. First, we seek to balance the tide of optimism about the role of digitalization in addressing the climate crisis by offering a skeptic’s perspective. Second, we outline a new research agenda that moves beyond counting directly attributable carbon emissions and proposes a more comprehensive accounting of the indirect ways in which social media and generative AI adversely impact the sociopolitical conditions required to address the climate crisis.Global Environmental Politics00A new research agenda is outlined that moves beyond counting directly attributable carbon emissions and proposes a more comprehensive accounting of the indirect ways in which social media and generative AI adversely impact the sociopolitical conditions required to address the climate crisis.Global Environmental Politics['Hamish van der Ven', 'Diego Corry', 'Rawie Elnur', 'Viola Jasmine Provost', 'Muh Syukron']2024-04-12T00:00:00https://www.semanticscholar.org/paper/abc40f76156241977bef7bbcec09541679cf6037 b51f31bafe65783a903607a892bf3c303ca37b3dOno K, Takahashi R, Morita K, Ara Y, Abe S, Ito S, Uno S, Abe M, Shirasaka T. Can AI predict walking independence in patients with stroke upon admission to a recovery-phase rehabilitation ward? Jpn J Compr Rehabil Sci 2024; 15: 1-7. Objective This study aimed to develop a prediction model for walking independence in patients with stroke in the recovery phase at the time of hospital discharge using Prediction One, an artificial intelligence (AI)-based predictive analysis tool, and to examine its utility. Methods Prediction One was used to develop a prediction model for walking independence for 280 patients with stroke admitted to a rehabilitation ward-based on physical and mental function information at admission. In 134 patients with stroke hospitalized during different periods, accuracy was confirmed by calculating the correct response rate, sensitivity, specificity, and positive and negative predictive values based on the results of AI-based predictions and actual results. Results The prediction accuracy (area under the curve, AUC) of the proposed model was 91.7%. The correct response rate was 79.9%, sensitivity was 95.7%, specificity was 62.5%, positive predictive value was 73.6%, and negative predictive value was 93.5%. Conclusion The accuracy of the prediction model developed in this study is not inferior to that of previous studies, and the simplicity of the model makes it highly practical.Japanese Journal of Comprehensive Rehabilitation Science80The accuracy of the prediction model developed in this study is not inferior to that of previous studies, and the simplicity of the model makes it highly practical.Japanese journal of comprehensive rehabilitation science['Keisuke Ono', 'Ryosuke Takahashi', 'Kazuyuki Morita', 'Yosuke Ara', 'Senshu Abe', 'Soichirou Ito', 'S. Uno', 'Masayuki Abe', 'Tomohide Shirasaka']2024-04-12T00:00:00https://www.semanticscholar.org/paper/b51f31bafe65783a903607a892bf3c303ca37b3d b70c1037fb4a5e052277f59cb39404becceb693dHumanities and Social Sciences Communications220Humanities and Social Sciences Communications['Seokki Cha']2024-04-12T00:00:00https://www.semanticscholar.org/paper/b70c1037fb4a5e052277f59cb39404becceb693d 38c1d5bfd47f4639f46f555c79e18fa9c94d4eb4Background The continuous monitoring and recording of patients’ pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. Objective The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. Methods The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. Results A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. Conclusions This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. Trial Registration PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181Journal of Medical Internet Research621This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies.Journal of Medical Internet Research['Jian Huo', 'Yan Yu', 'Wei Lin', 'Anmin Hu', 'Chaoran Wu']2024-04-12T00:00:00https://www.semanticscholar.org/paper/38c1d5bfd47f4639f46f555c79e18fa9c94d4eb4 2d758b68bf4687817cd5495327527bdc1eeb8110npj Digit. Medicine00NPJ Digital Medicine['R. Ali', 'Ian D. Connolly', 'Oliver Y. Tang', 'Fatima N. Mirza', 'B. Johnston', 'Hael F. Abdulrazeq', 'Rachel K. Lim', 'P. Galamaga', 'Tiffany J. Libby', 'N. Sodha', 'Michael W. Groff', 'Z. Gokaslan', 'A. Telfeian', 'John H. Shin', 'Wael F Asaad', 'James Zou', 'Curtis E. Doberstein']2024-04-12T00:00:00https://www.semanticscholar.org/paper/2d758b68bf4687817cd5495327527bdc1eeb8110 ed4bf6ac443e2921241434acbfce930e7a1c5009The internet has revolutionized the way people consume information, but it has also led to a rise in fake news, which is concerning because of the possible effects it may have on society. This study investigates whether it is possible to detect fake news only by looking at text using deep learning algorithms. The ability of three neural network architectures to identify false information on the internet is suggested and assessed: DistilBERT, Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs). This dataset, called ISOT (In-Store Orders and Transactions), was first created for retail analytics but is now used as a standard for assessing false news detection algorithms. The goal of this research is to support continued initiatives to promote information integrity and fight false informationInternational Journal of Advanced Research in Science, Communication and Technology00This study investigates whether it is possible to detect fake news only by looking at text using deep learning algorithms, and the ability of three neural network architectures to identify false information on the internet is suggested and assessed.International Journal of Advanced Research in Science, Communication and Technology['Mrs. P. Vanitha', 'T. Prasanna Meri', 'M. Keerthana', 'G. D. S. Vani']2024-04-12T00:00:00https://www.semanticscholar.org/paper/ed4bf6ac443e2921241434acbfce930e7a1c5009 bfce41d57b32718f023f863aaf80505c52650776Blocking events are an important cause of extreme weather, especially long-lasting blocking events that trap weather systems in place. The duration of blocking events is, however, underestimated in climate models. Explainable Artificial Intelligence are a class of data analysis methods that can help identify physical causes of prolonged blocking events and diagnose model deficiencies. We demonstrate this approach on an idealized quasigeostrophic model developed by Marshall and Molteni (1993). We train a convolutional neural network (CNN), and subsequently, build a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high-pressure anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that high-pressure anomalies in the American Southeast and North Atlantic, separated by a trough over Atlantic Canada, contribute significantly to prediction of sustained blocking events in the Atlantic region. This agrees with previous work that identified precursors in the same regions via wave train analysis. When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis, there is insufficient data to accurately predict persistent blocks. We partially overcome this limitation by pre-training the CNN on the plentiful data of the Marshall-Molteni model, and then using Transfer Learning to achieve better predictions than direct training. SHAP analysis before and after transfer learning allows a comparison between the predictive features in the reanalysis and the quasigeostrophic model, quantifying dynamical biases in the idealized model. This work demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.540This work trains a convolutional neural network, and builds a sparse predictive model for the persistence of Atlantic blocking, conditioned on an initial high-pressure anomaly, and demonstrates the potential for machine learning methods to extract meaningful precursors of extreme weather events and achieve better prediction using limited observational data.['Huan Zhang', 'J. Finkel', 'D. Abbot', 'Edwin P. Gerber', 'J. Weare']2024-04-12T00:00:00https://www.semanticscholar.org/paper/bfce41d57b32718f023f863aaf80505c52650776 e0a7cef650e8d2463f98b6f61dd224c8a4132167Nurse Educator00Nurse educator['Leigh Montejo']2024-04-12T00:00:00https://www.semanticscholar.org/paper/e0a7cef650e8d2463f98b6f61dd224c8a4132167 0761ec6544488b45e24170d1d09aa08cdc4e1defProceedings of the Water Environment Federation00Proceedings of the Water Environment Federation['Kelly Alexander', 'Alexander Palmatier', 'Holly Curry', 'Tim McGarry', 'Eric Sullivan']2024-04-12T00:00:00https://www.semanticscholar.org/paper/0761ec6544488b45e24170d1d09aa08cdc4e1def fc3afdfeb4e8b64d95be867ad3684620336ea72bEuropean Journal of Clinical Investigation150European journal of clinical investigation['Zhicheng Lin']2024-04-12T00:00:00https://www.semanticscholar.org/paper/fc3afdfeb4e8b64d95be867ad3684620336ea72b ff1254901042f6930dc436f784402dd19703ad31An overview of entropy applications to illustrate a few possible uses of entropy in knowledge transmission and artificial intelligence. Artificial intelligence uses entropy as a fundamental concept in many diverse applications, including reinforcement learning, data compression, and decision-making. It assists artificial intelligence models in producing wellinformed forecasts and judgments by assessing uncertainty and information content. As such, the purpose of this work is to highlight the importance of entropy and draw the attention of the artificial intelligence research community to it as a potent tool for advancing artificial intelligence. This work also addresses the importance of knowledge transfer (KT), especially intergenerational KT (IGT), in knowledge management. Knowledge entropy (KE) is a concept that is used to measure the complexity of knowledge distribution within an organisation and evaluate the effectiveness of KT activities. Furthermore, the KT model—which is predicated on the ideas of information content and tacitness—is presented. It blends techniques for customisation and codification. A few challenging open problems are presented along with future study optionsAdvances in Machine Learning &amp; Artificial Intelligence00The importance of entropy is highlighted and the KT model—which is predicated on the ideas of information content and tacitness—is presented, and it blends techniques for customisation and codification.Advances in Machine Learning &amp; Artificial Intelligence[]2024-04-12T00:00:00https://www.semanticscholar.org/paper/ff1254901042f6930dc436f784402dd19703ad31 69f708196f160d67831eebc380bcead20345103eThe rapid development of artificial intelligence should also affect the world market. Unemployment is ex- pected to increase as artificial intelligence can perform tasks performed by humans in many different sectors. The eight- year study (2010-2017) investigates the impact of artificial intelligence (AI) on unemployment in 26 developed and devel- oping countries. More precisely, this study uses data analysis techniques to investigate how the use of artificial intelligence technology affects unemployment in 26 different sectors. The results of a data study compiling World Bank and OECD data show a positive relationship between advances in artificial intelligence and unemployment. The advancement of artificial intelligence is determined by patent information. Due to the differences in artificial intelligence development between the countries studied, artificial intelligence development has been shown to have a positive impact on the unemployment rate in the studied countries. Additionally, compared to younger workers, older workers with higher education may have less impact on employment due to improved skills. Finally, re- search and development (R&D) spending varies widely across countries, making it difficult to aggregate. Therefore, this study does not show the relationship between the share of R&D expenditures in GDP and how artificial intelligence affects unemployment. One of the most talked about topics in the public today is intelligence. Many people associate this with changes in the economy, especially changes in the economy that are generally expected to cause high unemployment. As artificial intelligence becomes more widespread, the public tends to predict which professions will become obsolete and eventually disappear. The healthcare sector is one of the most challenged sectors outside of production due to various medi- cal treatment studies. There are other scientists who disagree with this bad proposition. They argue that the emergence of artificial intelligence will create new jobs, just like all previous economic revolutions. Keywords-- Artificial Intelligence, Unemployment, Future Work, Impact, Automation, Job Displacement, Skills Gap, Job Creation, Manufacturing, Retail, Healthcare, Reskilling, Upskilling, Policy, Regulation, Mitigation Strategies, Labor Market, Education, Training, and Economic Impact.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The results of a data study compiling World Bank and OECD data show a positive relationship between advances in artificial intelligence and unemployment, and artificial intelligence development has been shown to have a positive impact on the unemployment rate in the studied countries.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Pinky Deswal,']2024-04-12T00:00:00https://www.semanticscholar.org/paper/69f708196f160d67831eebc380bcead20345103e ef0af8fafc822a31c5fa73fdaa26031bc7b41c7eThis research investigates the effects of Artificial Intelligence on practices within Human Resources Management. It examines various important results, including precision, automation, computational ability and capacity, real-time interaction, customization, and time and cost savings. The aim is to identify the potential advantages of incorporating AI. Data was gathered from 274 IT workers in Chennai City through a well-designed online survey. The study introduces a new research framework using IBM SPSS version 21 software for analysis as well as AMOS version 21.The results show that factors such as Precision, Computational Ability & Capacity and Customization significantly impact Time-Saving & Cost Reduction while Automation and Real-Time Interaction do not have significant influence. This study's unique contribution lies in investigating specific outcomes when utilizing AI technologies in Human Resources Management Practices by focusing on key variables like Precision, Automation, Computing Power & Capacity Real-time experience, Customization, and Time-Saving Cost Saving capturing an extensive understanding of anticipated outcomes during AI implementation within Human resources management along with their interrelationships.International Journal For Multidisciplinary Research520The results show that factors such as Precision, Computational Ability & Capacity and Customization significantly impact Time-Saving & Cost Reduction while Automation and Real-Time Interaction do not have significant influence.International Journal For Multidisciplinary Research['Tamanna Singh']2024-04-12T00:00:00https://www.semanticscholar.org/paper/ef0af8fafc822a31c5fa73fdaa26031bc7b41c7e 3d8f50d3438e8a3499996653950f2866102f44a0Aim: This study aims to explore the implementation of artificial intelligence-driven customer relationship management (CRM) within the framework of PK Salon Management, investigating both the practical aspects and the challenges and opportunities associated with integrating artificial intelligence into the salon business. Methods: A qualitative research approach, primarily relying on secondary research methods, was employed to gather insights into the adoption of artificial intelligence in PK salons. Literature reviews and case studies related to artificial intelligence in salon management were examined and synthesized. Additionally, interviews with salon partners provided valuable perspectives on the practical implications of artificial intelligence adoption. Results: Evaluation findings indicate that PK Salons are actively embracing automation in appointment scheduling and marketing, alongside increased investment in artificial intelligence applications. However, challenges such as data security concerns and workforce readiness have been identified as barriers to effective integration. Qualitative experiences underscore the importance of overcoming implementation challenges while harnessing artificial intelligence's potential to enhance operations and improve customer engagement. Conclusion: Artificial intelligence-powered CRM systems hold significant potential to revolutionize salon management within the PK Salon context. Addressing challenges such as cost assessment and data security requires proactive measures and collaboration among stakeholders. By fostering a culture of innovation and investing in workforce training, salon establishments can leverage artificial intelligence technology to deliver enhanced customer experiences and gain a competitive edge in the market.Tuijin Jishu/Journal of Propulsion Technology740Evaluation findings indicate that PK Salons are actively embracing automation in appointment scheduling and marketing, alongside increased investment in artificial intelligence applications, however, challenges such as data security concerns and workforce readiness have been identified as barriers to effective integration.Tuijin Jishu/Journal of Propulsion Technology['Akshay Agarwal', 'Bhanu Devaguptapu', 'Rahul Saoji', 'Savitha Naguri', 'Rajiv Avacharmal']2024-04-12T00:00:00https://www.semanticscholar.org/paper/3d8f50d3438e8a3499996653950f2866102f44a0 eaa51588dcd094958e6ad307e58a5fb7d43b413aJournal for STEM Education Research910This study investigated pre-service teachers’ engagement with learning AI after a 4-week AI program at a university and found attitude, anxiety, readiness, self-transcendent goals, and confidence being found to influence engagement.Journal for STEM Education Research['M. A. Ayanwale', 'Emmanuel Kwabena Frimpong', 'O. Opesemowo', 'I. T. Sanusi']2024-04-12T00:00:00https://www.semanticscholar.org/paper/eaa51588dcd094958e6ad307e58a5fb7d43b413a e14ca74867c9e1d1c971d22b28b24182abd0da98Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.Journal of Alzheimer's Disease1050This review article will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD.Journal of Alzheimer's disease : JAD['Maudlyn O Etekochay', 'Amoolya Rao Amaravadhi', 'Gabriel Villarrubia González', 'Atanas G. Atanasov', 'Maima Matin', 'Mohammad Mofatteh', 'Harry Wilhelm Steinbusch', 'Tadele Tesfaye', 'Domenico Praticò']2024-04-12T00:00:00https://www.semanticscholar.org/paper/e14ca74867c9e1d1c971d22b28b24182abd0da98 4e5ba7a02e6da57a43a03a3a76b848ef95c705e3 The objective of this article is to discuss the possibility of using generative artificial intelligence (AI) to enhance teaching practices and pedagogical support to improve the quality of education provided to young people in elementary and secondary schooling. This issue is linked to the global perspective of a shortage of teachers, which directly affects the Sustainable Development Goal 4 (SDG 4), concerning the enhancement of education quality as a target for global sustainable development. From this viewpoint, the potential use of AI may also relate to the improvement of educational quality and the reduction of social inequalities, yielding indirect effects on other sustainable development goals. As a method, we intend to conduct an extensive theoretical discussion addressing the challenges for teacher education and work worldwide, utilizing existing data from databases such as UNESCO, the UN, and the OECD, among others. In addition to data on teachers, we plan to analyze the potential for creating an artificial intelligence, based on existing ones but trained for the specific contexts of each country’s educational system. The goal is to examine the potential for formatting artificial intelligence to provide pedagogical support for teachers, such as: grading of objective and discursive assessments, individualized intelligent tutoring, analysis of students’ individual pedagogical development, preparation of individual student diagnoses, suggestions of specific pedagogical actions based on curricula and materials used, and all other pedagogical actions that support teachers in their educational journey. This work was funded by CAPES, CNPq and FAPEMIG.Sustainable Economies190The goal is to examine the potential for formatting artificial intelligence to provide pedagogical support for teachers, such as: grading of objective and discursive assessments, individualized intelligent tutoring, analysis of students’ individual pedagogical development, preparation of individual student diagnoses, suggestions of specific pedagogical actions based on curricula and materials used, and all other pedagogical actions that support teachers in their educational journey.Sustainable Economies['Eucidio Pimenta Arruda', 'Durcelina Pimenta Arruda']2024-04-12T00:00:00https://www.semanticscholar.org/paper/4e5ba7a02e6da57a43a03a3a76b848ef95c705e3 246c95fc2b745f6999c8fc198263ba37bec022e5BACKGROUND AIMS Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence (AI) in a global cohort, we sought to derive and validate an enhanced prognostic model. APPROACH AND RESULTS The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled AH patients per NIAAA criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day post-admission mortality, three AI algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined via Bayesian updating, integrating the derivation cohort's average 90-day mortality with each center's approximate mortality rate to produce post-test probabilities. The ALCoholic Hepatitis Artificial INtelligence (ALCHAIN) Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30-day) and 27.9% (90-day) in the derivation cohort, versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779 - 0.844) and 0.799 (0.769 - 0.830), significantly surpassing legacy models like Maddrey's Discriminant Function, MELD variations, ABIC, Glasgow, and modified Glasgow Scores (p<0.001). ALCHAIN Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCHAIN Ensemble score>0.20 in both derivation and validation cohorts. CONCLUSIONS Harnessing AI within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it's accessible at: https://aihepatology.shinyapps.io/ALCHAIN/.Hepatology00Harnessing AI within a global consortium, this work pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions, and showcased superior calibration against MELD and its variants.Hepatology['Winston Dunn', 'Yanming Li', 'A. Singal', 'Doug Simonetto', 'L. A. Díaz', 'F. Idalsoaga', 'G. Ayares', 'Jorge Arnold', 'María Ayala-Valverde', 'Diego Perez', 'Jaime Gómez', 'Rodrigo Escarate', 'Eduardo Fuentes-López', 'Carolina Ramirez-Cadiz', 'Dalia Morales-Arraez', 'Wei Zhang', 'Steve Qian', 'Joseph Ahn', 'Seth Buryska', 'Heer Mehta', 'Nicholas Dunn', 'Muhammad Waleed', 'Horia Ștefănescu', 'Andreea Bumbu', 'A. Horhat', 'Bashar Attar', 'Rohit Agrawal', 'Joaquín Cabezas', 'Victor Echavaría', 'Berta Cuyàs', 'M. Poca', 'German Soriano', 'S. Sarin', 'R. Maiwall', 'P. Jalal', 'F. Higuera‐de‐la‐Tijera', 'Anand V. Kulkarni', 'P. N. Rao', 'Patricia Guerra-Salazar', 'Ľ. Skladaný', 'Natalia Kubanek', 'Verónica Prado', 'Ana Clemente-Sánchez', 'Diego Rincón', 'Tehseen Haider', 'K. Chacko', 'Gustavo A Romero', 'F. Pollarsky', 'J. C. Restrepo', 'L. Toro', 'Pamela Yaquich', 'M. Mendizabal', 'M. Garrido', 'S. Marciano', 'M. Dirchwolf', 'Victor Vargas', 'César Jiménez', 'David Hudson', 'Guadalupe García-Tsao', 'Guillermo Ortiz', 'J. G. Abraldes', 'Patrick S. Kamath', 'Marco Arrese', 'Vijay H. Shah', 'Ramon Bataller', 'J. Arab']2024-04-12T00:00:00https://www.semanticscholar.org/paper/246c95fc2b745f6999c8fc198263ba37bec022e5 fbd1fdf670f5f679afff4d953a9d793b82f19d14Bloodstream infections (BSIs) represent a critical public health concern, primarily due to their rapid progression and severe implications such as sepsis and septic shock. This study introduces an innovative Explanable Artificial Intelligence (XAI) framework, leveraging historical electronic health records (EHRs) to enhance BSI prediction. Unlike traditional models that rely heavily on real-time clinical data, our XAI-based approach utilizes a comprehensive dataset incorporating demographic data, laboratory results, and full medical histories from St. Olavs Hospital, Trondheim, Norway, covering 35,591 patients between 2015 and 2020. We developed models to differentiate between high-risk and low-risk BSI cases effectively, optimizing healthcare resource allocation and potentially reducing healthcare costs. Our results demonstrate superior predictive accuracy, particularly the tree-based models, which significantly outperformed contemporary models in both specificity and sensitivity metrics.medRxiv550This study introduces an innovative Explanable Artificial Intelligence (XAI) framework, leveraging historical electronic health records (EHRs) to enhance BSI prediction, and develops models to differentiate between high-risk and low-risk BSI cases effectively, optimizing healthcare resource allocation and potentially reducing healthcare costs.['MSc Rajeev Bopche', 'PhD Lise Tuset Gustad', 'MD PhD Jan Egil Afset', 'MD PhD Birgitta Ehrnström', 'PhD Jan Kristian Damås MD', 'PhD Øystein Nytrø']2024-04-12T00:00:00https://www.semanticscholar.org/paper/fbd1fdf670f5f679afff4d953a9d793b82f19d14 e4c940e7af9762a406606c4bd15b496439d4c0deDevelopmental Medicine & Child Neurology00Developmental medicine and child neurology['Bert Seghers', 'Oldřich Tůma']2024-04-12T00:00:00https://www.semanticscholar.org/paper/e4c940e7af9762a406606c4bd15b496439d4c0de ccf55e6d8f3113aa9b2b5a5be705eb9b5fbb1974The auto industry is going through a radical transformation with the introduction of self-driving cars, which offer safer and more effective transportation systems. An extensive analysis of the most recent developments in autonomous vehicle technology is given in this research report. It examines the different parts and mechanisms such as perception, decision-making, and control that are essential for autonomous functioning. The study also explores the benefits and problems that come with self-driving automobiles, including ethical issues, legal barriers, and public acceptance. This study contributes to the continuing discussion on the future of transportation by providing insightful information about the existing and potential states of autonomous cars through a synthesis of recent academic findings and industry advances. Keywords-- Self-driving Cars, Road Safety, Autonomous Vehicles, Decision Making, Safety Algorithm.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00An extensive analysis of the most recent developments in autonomous vehicle technology is given, which examines the different parts and mechanisms such as perception, decision-making, and control that are essential for autonomous functioning.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Simran Kaur']2024-04-12T00:00:00https://www.semanticscholar.org/paper/ccf55e6d8f3113aa9b2b5a5be705eb9b5fbb1974 5bc52b0b5104d0672b6f9a52a9194b49b9065361Сегодня происходит стремительное развитие научно-технического прогресса, и поэтому появление самостоятельно мыслящей робототехники становится реальностью. С технической точки зрения технологии искусственного интеллекта являются программным управлением, алгоритмы которого не задает оператор, они разрабатываются самой системой. Данные технологии чрезвычайно перспективны, поскольку применимы в большинстве сфер общественных отношений. В связи с тем что они широко и быстро распространяются, требуется их правовое регулирование. Поэтому законодателями и учеными в большинстве стран все чаще озвучиваются вопросы, связанные с необходимостью развития правосубъектности данных систем. Однако пока интенсивно развивающееся технологии опережают теорию права. Между тем изучение проблематики, касающейся субъектов правоотношений технологий искусственного интеллекта, необходимо как для правового регулирования рассматриваемой сферы общественных отношений, так и для правовой охраны самих людей от неправомерной деятельности данных систем. И хотя в настоящее время искусственный интеллект не признан субъектом права, возможность его правосубъектности активно обсуждается юридической общественностью. В процессе исследования различных точек зрения на данную проблематику автором акцентируется внимание на том, что вопросы, связанные с возможностями новых технологий быть субъектом права, нельзя разрешить без ответов на ряд общих вопросов. Проведенное исследование позволяет сделать вывод о том, что наделение данных систем правоспособностью может привести к введению их принципиального нового правового регулирования. В то же время автор полагает, что пока не стоит актуализировать вопросы о наделении данных систем правосубъектностью, которой обладают люди. В заключение данной работы делается вывод, что правовое регулирование должно не тормозить современное общество, а способствовать его движению вперед, поэтому вопросы, связанные с определением правосубъектности продукции, использующей технологии искусственного интеллекта, требуют своего дальнейшего исследования. Today there is a rapid development of scientific and technological progress, and therefore the emergence of independently thinking robotics is becoming a reality. From a technical point of view, artificial intelligence technologies are program controlled, the algorithms of which are not specified by the operator; they are developed by the system itself. These technologies are extremely promising because they are applicable in most areas of public relations. And because they spread widely and quickly, their legal regulation is required. In this regard, legislators and scientists in most countries are increasingly voicing questions related to the need to develop the legal personality of these systems. However, so far, intensively developing technologies are ahead of the theory of law. Meanwhile, the study of issues related to the subjects of legal relations of artificial intelligence technologies is necessary both for the legal regulation of the considered sphere of social relations, and for the legal protection of people themselves from the unlawful activities of these systems. And although artificial intelligence is currently not recognized as a subject of law, the possibility of its legal personality is quite actively discussed by the general legal community. In the process of exploring various points of view on this issue, the author focuses on the fact that issues related to the possibilities of new technologies to be a subject of law cannot be resolved without answers to a number of general questions. The conducted research suggests that if these systems are endowed with legal capacity, it may lead to the introduction of fundamentally new legal regulation. At the same time, the author believes that for the time being it is not worthwhile to update questions about endowing these systems with the legal personality that people have. At the conclusion of this work, it is concluded that legal regulation should not slow down modern society, but promote its movement forward; therefore, issues related to determining the legal personality of products using artificial intelligence technologies require further research.Vestnik Samarskogo iuridicheskogo instituta00Vestnik Samarskogo iuridicheskogo instituta['Петр Николаевич Кобец']2024-04-12T00:00:00https://www.semanticscholar.org/paper/5bc52b0b5104d0672b6f9a52a9194b49b9065361 acffb549f420454cef49a00b8b6803ec157c08dbAnaesthesia30Anaesthesia['Cian Hurley', 'R. Kearsley']2024-04-12T00:00:00https://www.semanticscholar.org/paper/acffb549f420454cef49a00b8b6803ec157c08db 9b29944308229a4959f36aca8992ae7eb49175c5International Journal of Computer Assisted Radiology and Surgery110International journal of computer assisted radiology and surgery['Leonard Berliner']2024-04-12T00:00:00https://www.semanticscholar.org/paper/9b29944308229a4959f36aca8992ae7eb49175c5 8456459649c24c2e9eaa6add5ce0c46e494e3ab1Nature Reviews Electrical Engineering80Nature Reviews Electrical Engineering['Melanie E. Moses', 'Sonia M. Gipson Rankin']2024-04-12T00:00:00https://www.semanticscholar.org/paper/8456459649c24c2e9eaa6add5ce0c46e494e3ab1 1ddb22a387b293922397cd10662ab2559019e1e1Artificial intelligence (AI) algorithms and continuous monitoring technologies have the potential to transform the way chronic illnesses are managed. We will also talk about the problems and potential that AI technology presents for CGM in individualised and preventive medicine. Furthermore, we assessed the AHCL system's usefulness in patients with impaired awareness of hypoglycemia (IAH) and those who correctly recognised hypoglycemia symptoms. The participants' ages varied from 37 to 15, and they had received diabetes medication for an average of 20 to 10 years. IAH was seen in 12 individuals (27%) with a Clarke's score of less than 3. Patients with IAH were older than those who did not have IAH. The baseline CGM readings and A1c were the same, but the estimated glomerular filtration rate (eGFR) was lower. Despite prior insulin treatment, the AHCL system resulted in an overall drop in A1c (from 6.9 0.5% to 6.7 0.6%, P 0.001). Only three patients (7%) received Clarke's three scores after six months on the AHCL system, resulting in a 20% absolute risk decrease for IAH (95% confidence interval: 7-32).  Scalable Computing : Practice and Experience230The AHCL system's usefulness in patients with impaired awareness of hypoglycemia (IAH) and those who correctly recognised hypoglycemia symptoms and the problems and potential that AI technology presents for CGM in individualised and preventive medicine are assessed.Scalable Comput. Pract. Exp.['Erfu Guo']2024-04-12T00:00:00https://www.semanticscholar.org/paper/1ddb22a387b293922397cd10662ab2559019e1e1 2354725cef4f1aef3513091ad9924fd1aa7914e8Defenders are overwhelmed by the number and scale of attacks against their networks.This problem will only be exacerbated as attackers leverage artificial intelligence to automate their workflows. We propose a path to autonomous cyber agents able to augment defenders by automating critical steps in the cyber defense life cycle.arXiv.org240This work proposes a path to autonomous cyber agents able to augment defenders by automating critical steps in the cyber defense life cycle by automating critical steps in the cyber defense life cycle.ArXiv['Sean Oesch', 'Phillipe Austria', 'Amul Chaulagain', 'Brian Weber', 'Cory Watson', 'Matthew Dixson', 'Amir Sadovnik']2024-04-12T00:00:00https://www.semanticscholar.org/paper/2354725cef4f1aef3513091ad9924fd1aa7914e8 8345988e2f092ba3d683cac55b289557377cf534npj Digit. Medicine290Regulators, policymakers, and payers will need to develop better methods to evaluate these promising technologies and guide their deployment, including striking a balance between patient safety and clinical effectiveness versus promotion of innovation.NPJ Digital Medicine['Neil A Busis', 'Dilshad Marolia', 'Robert Montgomery', 'Laura J Balcer', 'Steven L Galetta', 'S. Grossman']2024-04-12T00:00:00https://www.semanticscholar.org/paper/8345988e2f092ba3d683cac55b289557377cf534 f1412ae794ddbbb991025042e1360a8518a85ce3The protection of intellectual property rights serves to safeguard the interests of creators by granting them exclusive ownership rights over their creative works. Copyright includes both ethical rights and financial rights. Fanfiction is safeguarded by copyright, which is a form of intellectual property rights. This article presents a case study examining the role of Intellectual Property Rights and Moral Rights in the context of Fanfiction with Normative Legal Research. The fair use doctrine, as implicitly articulated in Article 44, paragraph (1) of the IPR Act of Indonesia and comparation with another regulation, is encompassed within this category. Furthermore, the considerable similarity test is utilized to assess the degree to which similarities and actuality exist in the work. The pattern test entails the analysis of the narrative structure and overall depiction. Finally, the feel and total idea test is employed to assess the essence and attributes of the writing, as well as the implementation of parallels in narrative themes. The establishment of legislation concerning Fanfiction is of utmost importance in order to delineate distinct boundaries between creative pursuits and legal violations. Ensuring the protection of novelists and amateur writers who have a profound emotional attachment to the narratives they have encountered is of utmost importance, as it cultivates their propensity to generate alternative stories.International journal of multidisciplinary research and analysis00INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS['Umaira Hayuning Anggayasti', 'Ardina Nur Amalia']2024-04-11T00:00:00https://www.semanticscholar.org/paper/f1412ae794ddbbb991025042e1360a8518a85ce3 e3311cc799dcb2b50c545f79063d17bb63059e2aThis article discusses the requirements for chocolate within the framework of technical regulation and standardization. The procedure for developing and approving a revised standard for chocolate is covered. The main changes to the Technical Regulations of the Customs Union regarding chocolate have been identifi ed and presented.Tovaroved prodovolstvennykh tovarov (Commodity specialist of food products)00Tovaroved prodovolstvennykh tovarov (Commodity specialist of food products)['V. Zamula', 'Yu.A. Kuzlyakina', 'T.V. Savenkova']2024-04-11T00:00:00https://www.semanticscholar.org/paper/e3311cc799dcb2b50c545f79063d17bb63059e2a 083e630e6ec171394aa15b330deaa2715858c557Having a robust, integrated regulatory system is important for ensuring the availability of safe and efficacious medical products of good quality and for protecting public health. However, less than 30% of countries globally have reached the required regulatory maturity level three, with low- and middle-income countries facing challenges in attracting and retaining qualified staff. World Health Organization (WHO) advocates for systematic workforce development, including competency-based education, to address these gaps. We provide perspectives on a systematic approach to capacity building of medicine regulators based on the experience and lessons learnt in developing and piloting the WHO global competency framework for medicine regulators through three scenarios. A systematic approach to capacity building, such as the human performance technology model, can be used to implement the WHO competency framework as part of organizational performance improvement while ensuring that initiatives are well-defined, targeted, and aligned with organizational goals. The competency framework can be used in different contexts, such as improving organization performance for individual regulatory authorities, strengthening regional collaborations, harmonization and reliance on medical products assessment and joint good manufacturing practices inspections of pharmaceutical manufacturers, and developing learning programs for medicine regulators. A competency-based learning approach for regulatory professionals ensures the transfer of learning to the workplace by integrating real-world practices in learning activities and assessments. Further work is required to develop and validate the assessment instruments, apply the competency framework in other contexts, expanding the learning programmes while continuously providing feedback for further refinement of the competency framework and implementation support tools.Frontiers in Medicine130This work provides perspectives on a systematic approach to capacity building of medicine regulators based on the experience and lessons learnt in developing and piloting the WHO global competency framework for medicine regulators through three scenarios.Frontiers in Medicine['Luther Gwaza', 'Andrew Chemwolo', 'Mario Musonda', 'Rutendo Kuwana', 'Admire Dube']2024-04-11T00:00:00https://www.semanticscholar.org/paper/083e630e6ec171394aa15b330deaa2715858c557 e6ac06fb1bdc97c78aaa2ba3bc06b4214ca861d6In manufacturing companies, artificial intelligence (AI) applications like robotics, automation, and intelligent support are driving a broad transformation process that impacts not only the usage of algorithms but also people and organization. The working world will undergo a long-lasting transformation due to automation and algorithmization, whereby every value-adding activities will be impacted, ranging from skilled labour and management to operational manufacturing operations. AI's capacity for learning is predicted to allow it to act independently, aid people through systems, use resources more wisely, improve the environmental impact of operations, and open up new working models with direct involvement and increased transparency. It ought to boost productivity, improve client happiness, and simplify and enliven work. According to recent studies, the success of digitalization depends less on technology and investment and more on leaders' and employees can do when it comes to creating a supportive organizational culture and structure.The AI's impact on jobs is debatable. It ought to result in challenging and safe work, respite from physical and mental strain, and an improvement in work-life balance. However, there are worries about loss of jobs, disqualification, increasing digital system autonomy, and greater employee authority. But data shows that historically, two humans in the business have been replaced by one robot on average, while two new employments have been generated outside. AI will most likely act in a like manner. AI deployment necessitates management reorganization, teamwork, codetermination, qualification, and extensive knowledge sharing. The involvement Future leadership behaves flexibly within the parameters of democratically established interdisciplinary teams and self-organizing networks. Executives consider themselves to be moderators and coaches. Based on an extensive assessment of the literature, this research investigates the effects of AI's introduction in industrial businesses. Effects on employment, organizational culture, and structure will receive special consideration. We'll also look at industrial company AI applications that represent best practices. Finally, a critical conversation looks at the tools and potential for using AI to shape internal company transformation with all the necessary parties involved.International journal of research - granthaalayah95This research investigates the effects of AI's introduction in industrial businesses, including effects on employment, organizational culture, and structure, and looks at industrial company AI applications that represent best practices.International Journal of Research -GRANTHAALAYAH['Aiswarya S S', 'Abdul Rahman F', 'Vijaykumar M']2024-04-11T00:00:00https://www.semanticscholar.org/paper/e6ac06fb1bdc97c78aaa2ba3bc06b4214ca861d6 a2d58458ac8956bb69ebb281b83b871cc48ee27bContext: AI code generators are revolutionizing code writing and software development, but their training on large datasets, including potentially untrusted source code, raises security concerns. Furthermore, these generators can produce incomplete code snippets that are challenging to evaluate using current solutions. Objective: This research work introduces DeVAIC (Detection of Vulnerabilities in AI-generated Code), a tool to evaluate the security of AI-generated Python code, which overcomes the challenge of examining incomplete code. Method: We followed a methodological approach that involved gathering vulnerable samples, extracting implementation patterns, and creating regular expressions to develop the proposed tool. The implementation of DeVAIC includes a set of detection rules based on regular expressions that cover 35 Common Weakness Enumerations (CWEs) falling under the OWASP Top 10 vulnerability categories. Results: We utilized four popular AI models to generate Python code, which we then used as a foundation to evaluate the effectiveness of our tool. DeVAIC demonstrated a statistically significant difference in its ability to detect security vulnerabilities compared to the state-of-the-art solutions, showing an F1 Score and Accuracy of 94% while maintaining a low computational cost of 0.14 seconds per code snippet, on average. Conclusions: The proposed tool provides a lightweight and efficient solution for vulnerability detection even on incomplete code.arXiv.org691DeVAIC (Detection of Vulnerabilities in AI-generated Code), a tool to evaluate the security of AI-generated Python code, which overcomes the challenge of examining incomplete code and provides a lightweight and efficient solution for vulnerability detection even on incomplete code.ArXiv['Domenico Cotroneo', 'Roberta de Luca', 'Pietro Liguori']2024-04-11T00:00:00https://www.semanticscholar.org/paper/a2d58458ac8956bb69ebb281b83b871cc48ee27b 5166b95c80438e471c5b93f0b053b21dcd4eadb7Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Organizational creativity is being boosted in AI-focused firms due to the adoption of intellectual property rights as well as the recognition of the benefits AI can bring to innovation and original idea generation. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format. Strategic Direction10Organizational creativity is being boosted in AI-focused firms due to the adoption of intellectual property rights as well as the recognition of the benefits AI can bring to innovation and original idea generation.Strategic Direction[]2024-04-11T00:00:00https://www.semanticscholar.org/paper/5166b95c80438e471c5b93f0b053b21dcd4eadb7 601a64cb5217665e9e50a2db66e76d48e1cd8809This paper introduces the Token Space framework, a novel mathematical construct designed to enhance the interpretability and effectiveness of deep learning models through the application of category theory. By establishing a categorical structure at the Token level, we provide a new lens through which AI computations can be understood, emphasizing the relationships between tokens, such as grouping, order, and parameter types. We explore the foundational methodologies of the Token Space, detailing its construction, the role of construction operators and initial categories, and its application in analyzing deep learning models, specifically focusing on attention mechanisms and Transformer architectures. The integration of category theory into AI research offers a unified framework to describe and analyze computational structures, enabling new research paths and development possibilities. Our investigation reveals that the Token Space framework not only facilitates a deeper theoretical understanding of deep learning models but also opens avenues for the design of more efficient, interpretable, and innovative models, illustrating the significant role of category theory in advancing computational models.30The investigation reveals that the Token Space framework not only facilitates a deeper theoretical understanding of deep learning models but also opens avenues for the design of more efficient, interpretable, and innovative models, illustrating the significant role of category theory in advancing computational models.['Wuming Pan']2024-04-11T00:00:00https://www.semanticscholar.org/paper/601a64cb5217665e9e50a2db66e76d48e1cd8809 b8159d19330d26f679751f3923eaef7a8882697cOrganizations in the USA are progressively employing AI-driven dynamic pricing as a strategic intervention to flexibly modify their prices based on competition, market demand, and various other factors. This research paper focused on the ethical dimensions of AI-driven dynamic pricing and the crucial interplay between profitability and the establishment of unwavering consumer transparency and fairness. The recommended models for dynamic pricing solutions entailed ensemble learning methods, notably, XG-Boost, Light-GBM, Cat-Boost, and X-NGBoost models. Particularly, the proposed model consolidated the XG-Boost algorithm and the NG-Boost model, resulting in a novel methodology termed the X-NGBoost. To compare and contrast the performance of the proposed models, these algorithms were trained and subjected to the same dataset. The comparison between the models was mainly grounded on the root-mean-square error (RMSE) metric, which was quantified in meters. The results indicated that X-NGBoost had the lowest RMSE on both the testing and training sets, at 4.23 and 5.34 respectively. This indicated that X-NGBoost performed very well on both seen and unseen data. Therefore, from the outcomes it was deduced that, for the provided data set, the X-NGBoost model provided the accurate pricing solution.Journal of Economics, Finance and Accounting Studies60The ethical dimensions of AI-driven dynamic pricing and the crucial interplay between profitability and the establishment of unwavering consumer transparency and fairness are focused on.Journal of Economics, Finance and Accounting Studies['Md Sumon Gazi', 'N. Gurung', 'Anik Mitra', 'Md Rokibul Hasan']2024-04-11T00:00:00https://www.semanticscholar.org/paper/b8159d19330d26f679751f3923eaef7a8882697c c963d537bebbcf124bc1e6cfe4218349d7e54fe6The field of eXplainable artificial intelligence (XAI) has produced a plethora of methods (e.g., saliency-maps) to gain insight into artificial intelligence (AI) models, and has exploded with the rise of deep learning (DL). However, human-participant studies question the efficacy of these methods, particularly when the AI output is wrong. In this study, we collected and analyzed 156 human-generated text and saliency-based explanations collected in a question-answering task (N=40) and compared them empirically to state-of-the-art XAI explanations (integrated gradients, conservative LRP, and ChatGPT) in a human-participant study (N=136). Our findings show that participants found human saliency maps to be more helpful in explaining AI answers than machine saliency maps, but performance negatively correlated with trust in the AI model and explanations. This finding hints at the dilemma of AI errors in explanation, where helpful explanations can lead to lower task performance when they support wrong AI predictions.International Conference on Human Factors in Computing Systems890It is shown that participants found human saliency maps to be more helpful in explaining AI answers than machine saliency maps, but performance negatively correlated with trust in the AI model and explanations, suggesting the dilemma of AI errors in explanation.{'pages': '839:1-839:20'}['Marvin Pafla', 'Kate Larson', 'Mark Hancock']2024-04-11T00:00:00https://www.semanticscholar.org/paper/c963d537bebbcf124bc1e6cfe4218349d7e54fe6 f6f4d2ba05114b6237fbf351cc920b4f1f6f3dffIn this position paper, we discuss the potential for leveraging LLMs as interactive research tools to facilitate collaboration between human coders and AI to effectively annotate online risk data at scale. Collaborative human-AI labeling is a promising approach to annotating large-scale and complex data for various tasks. Yet, tools and methods to support effective human-AI collaboration for data annotation are under-studied. This gap is pertinent because co-labeling tasks need to support a two-way interactive discussion that can add nuance and context, particularly in the context of online risk, which is highly subjective and contextualized. Therefore, we provide some of the early benefits and challenges of using LLMs-based tools for risk annotation and suggest future directions for the HCI research community to leverage LLMs as research tools to facilitate human-AI collaboration in contextualized online data annotation. Our research interests align very well with the purposes of the LLMs as Research Tools workshop to identify ongoing applications and challenges of using LLMs to work with data in HCI research. We anticipate learning valuable insights from organizers and participants into how LLMs can help reshape the HCI community's methods for working with data.Social Science Research Network390Some of the early benefits and challenges of using LLMs-based tools for risk annotation are provided and future directions for the HCI research community to leverage LLMs as research tools to facilitate human-AI collaboration in contextualized online data annotation are suggested.ArXiv['J. Park', 'Pamela J. Wisniewski', 'Vivek Singh']2024-04-11T00:00:00https://www.semanticscholar.org/paper/f6f4d2ba05114b6237fbf351cc920b4f1f6f3dff 5afe0f1ae0a147348cf26c5d9a8943e05f956404Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato’s Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies.Electronics170It is claimed that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance, and offered a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies.Electronics['K. Karpouzis']2024-04-11T00:00:00https://www.semanticscholar.org/paper/5afe0f1ae0a147348cf26c5d9a8943e05f956404 6303e0e678ae512d18250a18933a2290e835a09bIn this article, I use the dialogical ideas of Hans-Georg Gadamer to evaluate whether generative AI is ready to join the ontological conversation that he considers humanity to be. Despite the technical advances of generative AI, Gadamer’s philosophical hermeneutics reveals that it cannot function as a proxy human dialogue partner in pursuit of understanding. Even when free from anthropomorphic projections and reimagined as the “other”, generative AI is found to have a weak epistemology, lack of moral awareness, and no emotions. Even so, it evokes a response in some users that places it on the threshold of being. The most promising dialogical role identified for generative AI is as a digital form of Gadamerian “text” currently constrained by copyright and technical design. Generative AI’s shortcomings risk inhibiting hermeneutical understanding through greater access to summarised knowledge. Nonetheless, the new technology is on the brink of joining the ontological conversation of humanity.Technophany, A Journal for Philosophy and Technology400The dialogical ideas of Hans-Georg Gadamer are used to evaluate whether generative AI is ready to join the ontological conversation that he considers humanity to be and the most promising dialogical role identified for generative AI is as a digital form of Gadamerian “text” currently constrained by copyright and technical design.Technophany, A Journal for Philosophy and Technology['Robert Hornby']2024-04-11T00:00:00https://www.semanticscholar.org/paper/6303e0e678ae512d18250a18933a2290e835a09b 57dfa79b9bf0a68820ffdefb5b0730d061fab55dAn expansion of GPT-3, the big language model ChatGPT was published by OpenAI on November 30, 2022. In real time, the AI chatbot responds to user requests by communicating. The level of natural speaking responses provided by ChatGPT signals a significant change in the way we will utilise AI-generated data in our daily lives. There are several applications for ChatGPT for a student studying software engineering, including assessment preparation, translation, and writing specific source code. Even more difficult parts of scientific writing, including paraphrasing and literature summaries, may be handled by it. Therefore, the purpose of this position paper is to examine possible methods for incorporating ChatGPT into higher education. As a result, we concentrate on papers discussing ChatGPT's impact in our day to day lives. Keywords: Artificial Intelligence (AI); ChatGPT; educational technology; university education.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The purpose of this position paper is to examine possible methods for incorporating ChatGPT into higher education, and concentrate on papers discussing ChatGPT's impact in the authors' day to day lives.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Aditya A Verma']2024-04-11T00:00:00https://www.semanticscholar.org/paper/57dfa79b9bf0a68820ffdefb5b0730d061fab55d db21382dbe4f652c1d86e2bd50736f2033572228The use of artificial intelligence (AI) technologies has the potential to enhance sustainability efforts in the face growing environmental issues, especially in the areas of resource management and climate change mitigation. This study focuses at how artificial intelligence (AI) and environmental sustainability interact, examining the various ways AI might help solve urgent environmental issues. AI provides innovative methods to maximize utilization of resources, predict environmental trends, and promote accurate decision-making processes through the use of advanced computational algorithms and data analytics. This study analyses multiple applications of AI in environmental sustainability, from pollution prevention and natural resource conservation to climate modelling and renewable energy optimization. In addition, an evaluation is done about moral implications and possible socio-economic effects of AI-driven environmental solutions. Using an in-depth review of the majority of current research by applying case studies, this research identifies significant challenges and opportunities for more research and application while highlighting the revolutionary potential of AI in achieving environmental sustainability goals. Ultimately, the paper encourages ethical application of artificial intelligence (AI) as important part of larger strategies for mitigating the effects of climate change and developing a future that is environmentally conscious.International Journal For Multidisciplinary Research00This study analyses multiple applications of AI in environmental sustainability, from pollution prevention and natural resource conservation to climate modelling and renewable energy optimization and encourages ethical application of artificial intelligence (AI) as important part of larger strategies for mitigating the effects of climate change and developing a future that is environmentally conscious.International Journal For Multidisciplinary Research['Nirmala Lohani']2024-04-11T00:00:00https://www.semanticscholar.org/paper/db21382dbe4f652c1d86e2bd50736f2033572228 c9862f8440769849e288df2f460606750b96a48bIn the dynamic realm of cybersecurity, where the sophistication of threats continues to escalate, the integration of AI-driven technologies into Security Operations Centers (SOC) presents a groundbreaking paradigm shift. This paper introduces an AI-enabled OSSEC (Open Source SECurity), which amalgamates advanced linguistic capabilities with the foundational core of Security Operations Centers. Traditional security setups often grapple with the overwhelming influx of data logs, hindering their ability to discern crucial patterns and respond effectively to potential threats. The AI-driven OSSEC addresses this challenge by harnessing natural language processing prowess to efficiently analyze and interpret diverse logs. This innovation not only streamlines the monitoring process but also empowers the system to identify nuanced anomalies that might evade conventional detection mechanisms. Furthermore, the AI-enabled OSSEC doesn't confine itself to analysis alone; it proactively provides actionable insights and strategies for mitigating identified risks. This proactive approach ensures organizations not only detect potential threats but also respond promptly with well-informed measures. Embracing this technology fortifies cybersecurity posture, enabling Security Operations Centers to navigate the complexities of the digital landscape with unparalleled agility and precision. This convergence of linguistic intelligence with cybersecurity operations signifies a monumental advancement in building a more resilient and responsive defense against the continuously evolving cyber threat landscape within the power sector.International Journal For Multidisciplinary Research80An AI-enabled OSSEC (Open Source SECurity), which amalgamates advanced linguistic capabilities with the foundational core of Security Operations Centers, which fortifies cybersecurity posture and signifies a monumental advancement in building a more resilient and responsive defense against the continuously evolving cyber threat landscape within the power sector.International Journal For Multidisciplinary Research['Prathamesh Pawar', 'Karan Shah', 'Harsh Patil', 'K. Devadkar', 'Jignesh Sisodiya']2024-04-11T00:00:00https://www.semanticscholar.org/paper/c9862f8440769849e288df2f460606750b96a48b e97d1246dd9839ab3079e6fef50a66c6d6fc8bb4“We recognise that cancer detection is one of the main pillars of how to take care of the population today,” remarked Luis Marti-Bonmati, Le Fe Polytechnic and University Hospital, Valencia, Spain, who chaired a session at the European Congress of Radiology (ECR). In a timely conversation, the role of artificial intelligence (AI) in the screening, early detection, and depiction of tumours was explored at the annual ECR congress, which took place in Vienna, Austria, from the 28th February–3rd March 2024.EMJ Radiology00The role of artificial intelligence in the screening, early detection, and depiction of tumours was explored at the annual ECR congress, which took place in Vienna, Austria, from the 28th February–3rd March 2024.EMJ Radiology['Helena Bradbury']2024-04-11T00:00:00https://www.semanticscholar.org/paper/e97d1246dd9839ab3079e6fef50a66c6d6fc8bb4 94eb2fe7ec15bdf40e29f820a929b31f0e44585fAuditing of AI systems is a promising way to understand and manage ethical problems and societal risks associated with contemporary AI systems, as well as some anticipated future risks. Efforts to develop standards for auditing Artificial Intelligence (AI) systems have therefore understandably gained momentum. However, we argue that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards, especially in light of the rapid evolution and ethical and safety challenges of AI. Instead, the paper proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards in line with the evolving nature of AI technologies. Such a body would ensure that auditing practices remain relevant, robust, and responsive to the rapid advancements in AI. The paper argues that such a governance structure would also be helpful for maintaining public trust in AI and for promoting a culture of safety and ethical responsibility within the AI industry. Throughout the paper, we draw parallels with other industries, including safety-critical industries like aviation and nuclear energy, as well as more prosaic ones such as financial accounting and pharmaceuticals. AI auditing should emulate those fields, and extend beyond technical assessments to include ethical considerations and stakeholder engagement, but we explain that this is not enough; emulating other fields' governance mechanisms for these processes, and for audit standards creation, is a necessity. We also emphasize the importance of auditing the entire development process of AI systems, not just the final products...arXiv.org640This paper argues that creating auditing standards is not just insufficient, but actively harmful by proliferating unheeded and inconsistent standards, and proposes the establishment of an AI Audit Standards Board, responsible for developing and updating auditing methods and standards in line with the evolving nature of AI technologies.ArXiv['David Manheim', 'Sammy Martin', 'Mark Bailey', 'Mikhail Samin', 'Ross Greutzmacher']2024-04-11T00:00:00https://www.semanticscholar.org/paper/94eb2fe7ec15bdf40e29f820a929b31f0e44585f 02b1c1fed2038f6b77c48ef6626438653e49ff5aThis research paper explores the development of an AI chess engine leveraging the Minimax algorithm with Alpha-Beta pruning technique, implemented using Python and JavaScript programming languages. The objective is to investigate the effectiveness of these algorithms in creating a proficient and competitive chess-playing AI. The paper begins with an overview of the Minimax algorithm and its application in game theory, followed by an explanation of Alpha-Beta pruning and its role in enhancing the efficiency of the search process within the game tree. Subsequently, the implementation details of the AI chess engine in both Python and JavaScript are discussed, highlighting the design considerations, algorithmic optimizations, and programming techniques utilized. The paper also provides insights into the integration of the AI engine with a graphical user interface (GUI) for interactive gameplay experiences. Additionally, experimental results and performance evaluations are presented to assess the AI engine's strength and playing capabilities against human players or other AI opponentsInternational Journal of Advanced Research in Science, Communication and Technology50This research paper explores the development of an AI chess engine leveraging the Minimax algorithm with Alpha-Beta pruning technique, implemented using Python and JavaScript programming languages, to investigate the effectiveness of these algorithms in creating a proficient and competitive chess-playing AI.International Journal of Advanced Research in Science, Communication and Technology['Abhishek Bajpai', 'Aniket Gundawar', 'Devansh Sanghavi', 'Prajwal Awari', 'Devashri Raich']2024-04-11T00:00:00https://www.semanticscholar.org/paper/02b1c1fed2038f6b77c48ef6626438653e49ff5a 9531f58f8f3dbd9a4fe1192c2b355dc1b8d1f87fThe rapid advancement of Artificial Intelligence (AI) in healthcare presents a unique opportunity for advancements in obstetric care, particularly through the analysis of cardiotocography (CTG) for fetal monitoring. However, the effectiveness of such technologies depends upon the availability of large, high-quality datasets that are suitable for machine learning. This paper introduces the Oxford Maternity (OxMat) dataset, the world's largest curated dataset of CTGs, featuring raw time series CTG data and extensive clinical data for both mothers and babies, which is ideally placed for machine learning. The OxMat dataset addresses the critical gap in women's health data by providing over 177,211 unique CTG recordings from 51,036 pregnancies, carefully curated and reviewed since 1991. The dataset also comprises over 200 antepartum, intrapartum and postpartum clinical variables, ensuring near-complete data for crucial outcomes such as stillbirth and acidaemia. While this dataset also covers the intrapartum stage, around 94% of the constituent CTGS are antepartum. This allows for a unique focus on the underserved antepartum period, in which early detection of at-risk fetuses can significantly improve health outcomes. Our comprehensive review of existing datasets reveals the limitations of current datasets: primarily, their lack of sufficient volume, detailed clinical data and antepartum data. The OxMat dataset lays a foundation for future AI-driven prenatal care, offering a robust resource for developing and testing algorithms aimed at improving maternal and fetal health outcomes.arXiv.org340The Oxford Maternity (OxMat) dataset is introduced, the world's largest curated dataset of CTGs, featuring raw time series CTG data and extensive clinical data for both mothers and babies, which is ideally placed for machine learning.ArXiv['M. J. Khan', 'Ioana Duta', 'Beth Albert', 'William Cooke', 'Manu Vatish', 'Gabriel Davis Jones']2024-04-11T00:00:00https://www.semanticscholar.org/paper/9531f58f8f3dbd9a4fe1192c2b355dc1b8d1f87f 0b9eb73357fde773d3953a0ba9aced66100caeadNLIT'24 Summit April 8 - 11, 2024 Seattle, Washington00NLIT'24 Summit April 8 - 11, 2024 Seattle, Washington['Kevin Purcell']2024-04-11T00:00:00https://www.semanticscholar.org/paper/0b9eb73357fde773d3953a0ba9aced66100caead dc514ac3bb03a4ae338e927d158f20bc444bfc16Entrepreneur & Innovation Exchange00Entrepreneur and Innovation Exchange['Mat Hughes', 'David Townsend']2024-04-11T00:00:00https://www.semanticscholar.org/paper/dc514ac3bb03a4ae338e927d158f20bc444bfc16 3f7882bbe3877056e5d16bb2e9c88aec4b69e388Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors. However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health. To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts. In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.Journal of Current Trends in Computer Science Research00To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science to unlock the transformative power of AI in mitigating climate change and improving public health outcomes.Journal of Current Trends in Computer Science Research[]2024-04-11T00:00:00https://www.semanticscholar.org/paper/3f7882bbe3877056e5d16bb2e9c88aec4b69e388 d23a5411829c8ad5520ebb6849bb8ff1eab19aa7With the rapid development of medical equipment, medical imaging has entered the era of big data. How to process massive data quickly and accurately extract critical information for disease diagnosis and treatment is a major challenge facing clinical practice. Artificial intelligence has unique advantages in real-time processing, prediction and analysis, transfer learning, etc., which provides a new breakthrough for solving this problem. This review reviews the artificial intelligence models applied to medical image processing in recent years, shows their wide application scenarios, briefly discusses their unique advantages from three aspects of image recognition, image enhancement and image registration, and finally summarizes the current research status and puts forward some prospects.Advances in Engineering Technology Research190This review reviews the artificial intelligence models applied to medical image processing in recent years, shows their wide application scenarios, briefly discusses their unique advantages from three aspects of image recognition, image enhancement and image registration, and summarizes the current research status and puts forward some prospects.Advances in Engineering Technology Research['Kaiyan Wang']2024-04-11T00:00:00https://www.semanticscholar.org/paper/d23a5411829c8ad5520ebb6849bb8ff1eab19aa7 3ed8dc01a831574a4997264c97c371c39f49d75dBMC Health Services Research510These results are important for stakeholders and changemakers such as policymakers, governments, physicians, and insurance companies, as they design adoption strategies to ensure successful patient engagement by focusing on factors affecting the facilitating conditions, hedonic motivation and performance expectancy for AI technologies used in healthcare task shifting.BMC Health Services Research['Weiting Huang', 'Wen Chong Ong', 'Mark Kei Fong Wong', 'E. Ng', 'Tracy Koh', 'C. Chandramouli', 'Choon Ta Ng', 'Y. Hummel', 'Fei Huang', 'Carolyn S P Lam', 'Jasper Tromp']2024-04-11T00:00:00https://www.semanticscholar.org/paper/3ed8dc01a831574a4997264c97c371c39f49d75d cec6174e2079923471ca44b0bc447bfb53a4335aCommunications Medicine480A systematic review of academic publications and reporting standards demanded by both international consortia and regulatory stakeholders as well as leading journals in the fields of medicine and medical informatics highlights the importance of reporting guidelines in clinical AI research and underscores the need for common standards that address the identified variations and gaps in current guidelines.Communications Medicine['F. Kolbinger', 'G. P. Veldhuizen', 'Jiefu Zhu', 'Daniel Truhn', 'J. N. Kather']2024-04-11T00:00:00https://www.semanticscholar.org/paper/cec6174e2079923471ca44b0bc447bfb53a4335a e4f7ca1c1ce7d043fa851ad5d0ac0814a20a55b1Artificial intelligence (AI) has developed greatly and is now at the centre of technological advancements. Current and recent military conflicts have highlighted the evolving complexity of warfare with rapid technological change at the heart of it. AI aims to understand and design systems that show signs of intelligence and are able to learn by deriving knowledge from data. There have been multiple AI-related developments in the medical field in areas such as diagnostics, triage, wearable technology and training with direct translations that may benefit UK Defence healthcare. With the increasing use of AI in society and medical practice, it is important to consider whether AI can be trustworthy, any legal implications and evaluate its use through an ethical lens. In conclusion, the rapid development of AI presents exciting opportunities for UK Defence to enhance its healthcare delivery.BMJ Military Health180The rapid development of AI presents exciting opportunities for UK Defence to enhance its healthcare delivery as well as considering whether AI can be trustworthy, any legal implications and evaluate its use through an ethical lens.BMJ military health['N. Patel']2024-04-11T00:00:00https://www.semanticscholar.org/paper/e4f7ca1c1ce7d043fa851ad5d0ac0814a20a55b1 326b9ad69a7e60e84fbff2b1d2d2c858952b17fdAs businesses and society navigate the potentials of generative artificial intelligence (GAI), the integration of these technologies introduces unique challenges and opportunities for human resources, requiring a re‐evaluation of human resource management (HRM) frameworks. The existing frameworks may often fall short of capturing the novel attributes, complexities and impacts of GAI on workforce dynamics and organizational operations. This paper proposes a strategic HRM framework, underpinned by the theory of institutional entrepreneurship for sustainable organizations, for integrating GAI within HRM practices to boost operational efficiency, foster innovation and secure a competitive advantage through responsible practices and workforce development. Central to this framework is the alignment with existing business objectives, seizing opportunities, strategic resource assessment and orchestration, re‐institutionalization, realignment and embracing a culture of continuous learning and adaptation. This approach provides a detailed roadmap for organizations to navigate successfully the complexities of a GAI‐enhanced business environment. Additionally, this paper significantly contributes to the theoretical discourse by bridging the gap between HRM and GAI adoption, the proposed framework accounting for GAI–human capital symbiosis, setting the stage for future research to empirically test its applicability, explore its implications on HRM practices and understand its broader economic and societal consequences through diverse multi‐disciplinary and multi‐level research methodologies.British Journal of Management290A strategic HRM framework, underpinned by the theory of institutional entrepreneurship for sustainable organizations, for integrating GAI within HRM practices to boost operational efficiency, foster innovation and secure a competitive advantage through responsible practices and workforce development is proposed.British Journal of Management['Soumyadeb Chowdhury', 'P. Budhwar', 'Geoffrey Wood']2024-04-11T00:00:00https://www.semanticscholar.org/paper/326b9ad69a7e60e84fbff2b1d2d2c858952b17fd 612dc238a81c5d5a42b7a1105c7456ddbe5d8d08Introduction: Diabetes poses a significant global health challenge, impacting patient well-being and longevity. Despite advances in diagnosis and treatment, the prevalence of diabetes continues to rise, with projections indicating a substantial increase in affected individuals in the coming years. The complications of diabetes, including cardiovascular disease, retinopathy, nephropathy, and neuropathy, underscore the importance of early detection and management. In this context, artificial intelligence (AI) offers promising opportunities to revolutionize diabetes care, enabling faster diagnostics, more effective treatment strategies. Description of the State of Knowledge: Artificial intelligence (AI) has emerged as a transformative force in healthcare, leveraging machine learning and deep learning algorithms to analyze vast amounts of medical data. These algorithms enable more accurate diagnosis, prediction of disease onset, and early detection of complications associated with diabetes. Machine learning models, including support vector machines and neural networks, have shown promise in identifying diabetes risk factors and predicting disease progression. Deep learning techniques, with their ability to analyze complex data patterns, offer further insights into diabetes diagnosis. Additionally, fuzzy cognitive maps provide a framework for decision-making based on patient data, enhancing early detection efforts. Summary: Artificial intelligence holds immense potential to transform diabetes care, offering solutions for early detection, personalized treatment, and improved patient outcomes. By harnessing the power of AI algorithms, healthcare providers can enhance diagnostic accuracy, predict disease progression, and implement targeted interventions.Journal of Education, Health and Sport00Artificial intelligence holds immense potential to transform diabetes care, offering solutions for early detection, personalized treatment, and improved patient outcomes by harnessing the power of AI algorithms.Journal of Education, Health and Sport['Seweryn Ziajor', 'J. Tomasik', 'Piotr Sajdak', 'Mikołaj Turski', 'Artur Bednarski', 'Marcel Stodolak', 'Łukasz Szydłowski', 'Klaudia Żurowska', 'Aleksandra Krużel', 'Kamil Kłos', 'Marika Dębik']2024-04-11T00:00:00https://www.semanticscholar.org/paper/612dc238a81c5d5a42b7a1105c7456ddbe5d8d08 2730c54ed39d03f47ee3dfcb497924fb4db60e48Purpose Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.Design/methodology/approach To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.Findings The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.Originality/value The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.Worldwide Hospitality and Tourism Themes250There has been a recent increase in research on AI in transport systems, but there is only one study on tourism, and the country that contributed the most is China with 298 studies.Worldwide Hospitality and Tourism Themes['Ayşe Şengöz', 'Beste Nisa Orhun', 'Nil Konyalilar']2024-04-11T00:00:00https://www.semanticscholar.org/paper/2730c54ed39d03f47ee3dfcb497924fb4db60e48 48e54755609f954b123bf1bbeff61cbabddb0b87 As artificial intelligence (AI) policy has begun to take shape in recent years, policy actors have worked to influence policymakers by strategically promoting issue frames that define the problems and solutions policymakers should attend to. Three such issue frames are especially prominent, surrounding AI’s economic, geopolitical, and ethical dimensions. Relatedly, while technology policy is traditionally expert-dominated, new governance paradigms are encouraging increased public participation along with heightened attention to social and ethical dimensions of technology. This study aims to provide insight into whether members of the public and the issue frames they employ shape—or fail to shape—policymaker agendas, particularly for highly contested and technical policy domains. To assess this question, the study draws on a dataset of approximately five million Twitter messages from members of the public related to AI, as well as corresponding AI messages from the 115th and 116th US Congresses. After using text analysis techniques to identify the prevalence of issue frames, the study applies autoregressive integrated moving average and vector autoregression modeling to determine whether issue frames used by the public appear to influence the subsequent messaging used by federal US policymakers. Results indicate that the public does lead policymaker attention to AI generally. However, the public does not have a special role in shaping attention to ethical implications of AI, as public influence occurs only when the public discusses AI’s economic dimensions. Overall, the results suggest that calls for public engagement in AI policy may be underrealized and potentially circumscribed by strategic considerations.Policy & Society1030Whether issue frames used by the public appear to influence the subsequent messaging used by federal US policymakers are evaluated, which suggests that calls for public engagement in AI policy may be underrealized and potentially circumscribed by strategic considerations.Policy and Society['Danielle Schiff']2024-04-11T00:00:00https://www.semanticscholar.org/paper/48e54755609f954b123bf1bbeff61cbabddb0b87 fbadb6d2960585c81081573d1ae5bc1816b37989The paper provides a comprehensive analysis of the use of artificial intelligence (AI) in education, focusing on its definition, advantages, disadvantages, and future prospects. It explores various applications of AI in education, such as chatbots, learning analytics, and intelligent tutoring systems. The study addresses key issues like data-driven decision-making, efficient administration, personalized learning opportunities, and potential ethical concerns. Furthermore, it discusses the future prospects of AI in education, including advancements in virtual reality integration, adaptive learning systems, and universal access to high-quality education.International Journal For Multidisciplinary Research90The study addresses key issues like data-driven decision-making, efficient administration, personalized learning opportunities, and potential ethical concerns and discusses the future prospects of AI in education, including advancements in virtual reality integration, adaptive learning systems, and universal access to high-quality education.International Journal For Multidisciplinary Research['Mithu Baidya', 'Ajith Kumar C']2024-04-11T00:00:00https://www.semanticscholar.org/paper/fbadb6d2960585c81081573d1ae5bc1816b37989 7a0bdb4ae1b307a1681d258fb8a2d833a57fc172Abstract: This article provides an analysis of the current state and future prospects of Artificial Intelligence (AI) implementation in law enforcement. As advancements in technology continue to reshape various sectors, the integration of AI in policing has become a focal point, revolutionizing traditional methods and offering new opportunities. The article begins by outlining the contemporary landscape of AI applications in law enforcement, encompassing predictive policing, facial recognition, data analysis, and crime pattern identification. The discussion delves into the benefits and challenges associated with these technologies, addressing concerns related to privacy, bias, and ethical considerations. Furthermore, the article explores the evolution of AI in law enforcement, examining how machine learning algorithms enhance predictive capabilities, streamline investigative processes, and contribute to proactive crime prevention. It also highlights successful case studies and realworld implementations, showcasing the positive impact AI has had on solving complex criminal cases and optimizing resource allocation. In exploring development prospects, the article considers emerging trends such as explainable AI, human-AI collaboration, and continuous advancements in data analytics. The importance of responsible AI deployment is emphasized, emphasizing the need for transparent and ethical frameworks to guide law enforcement agencies. The article concludes by envisioning a future where AI technologies are seamlessly integrated into law enforcement practices, fostering improved crime detection, community safety, and overall operational efficiency. The insights presented aim to contribute to informed discussions surrounding the responsible and effective use of AI in the evolving landscape of law enforcement. Keywords: Artificial intelligence; Data analysis; Digital automation; Law enforcement; Facial recognition systemsSocratic lectures 10 - Part II00The article explores the evolution of AI in law enforcement, examining how machine learning algorithms enhance predictive capabilities, streamline investigative processes, and contribute to proactive crime prevention, and visions a future where AI technologies are seamlessly integrated into law enforcement practices, fostering improved crime detection, community safety, and overall operational efficiency.Socratic lectures 10 - Part II['Olha Lunhol', 'Pavlo Torhalo']2024-04-11T00:00:00https://www.semanticscholar.org/paper/7a0bdb4ae1b307a1681d258fb8a2d833a57fc172 e01887437801a9eeda6e72f34036a846d28502b6This study examines how artificial intelligence (AI) is incorporated into sports bikes and examines the significant impacts this has on ride quality, user experience, and safety. Adaptive cruise control and collision avoidance systems, two AI-driven technologies that improve rider safety, and engine performance improvements and predictive maintenance algorithms that improve overall bike performance and dependability. Furthermore, bike design processes are revolutionized by AI-driven design techniques, which allow for quick iterations and customisation. This study offers insights into how artificial intelligence (AI) can revolutionize sports bike technology in the future. Keywords: Artificial Intelligence (AI), Sports Bikes, Ride Quality, User Experience, Safety, Adaptive Cruise Control, Collision Avoidance Systems, Rider Safety, Engine Performance Improvements, Predictive Maintenance Algorithms, Bike Performance, Dependability, AI Driven Design Techniques.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Adaptive cruise control and collision avoidance systems, two AI-driven technologies that improve rider safety, and engine performance improvements and predictive maintenance algorithms that improve overall bike performance and dependability are examined.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Siddharth Sharma,']2024-04-11T00:00:00https://www.semanticscholar.org/paper/e01887437801a9eeda6e72f34036a846d28502b6 ad068ab4d3e1b73ab5dd0f87cd3b04a20414d10fThe integration of Artificial Intelligence (AI) in data analytics to enhance efficiency and insights. We discuss how AI techniques, such as machine learning and automation, simplify the analytics process, enabling organizations to extract valuable information from data quickly and effectively. The paper highlights the practical impact of AI in various industries and emphasizes the potential for streamlined decision-making and trend prediction. Overall, the focus is on the simplicity and effectiveness of incorporating AI into data analytics workflows for improved outcomes.International Journal of Advanced Research in Science, Communication and Technology10The paper highlights the practical impact of AI in various industries and emphasizes the potential for streamlined decision-making and trend prediction, and the simplicity and effectiveness of incorporating AI into data analytics workflows for improved outcomes.International Journal of Advanced Research in Science, Communication and Technology['Mrs. C. Radha', 'Mr. R. Midunkumar', 'Mr. S. Muralibabu', 'Mr. V. Partheeban']2024-04-11T00:00:00https://www.semanticscholar.org/paper/ad068ab4d3e1b73ab5dd0f87cd3b04a20414d10f 7f9a1fed51384e29e9e0521c552f096fbfa3dd0aMany of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.Aerospace270The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach, and confirms that the best metrics are obtained by using support vector machine and logistic regression.Aerospace['Jorge Bautista-Hernández', 'M. A. Martín-Prats']2024-04-11T00:00:00https://www.semanticscholar.org/paper/7f9a1fed51384e29e9e0521c552f096fbfa3dd0a 5e0d81dc13c0397a62989985ff842863b49dc9ceArtificial Intelligence (AI) technologies open up broad horizons for enhancing business efficiency and advancing various professional domains, boosting their productivity and compe­titiveness. There is an active exploration of approaches to incorporating AI technologies in the accounting sphere, promising a seamless transition from human to machine involvement. The aim of this article is to summarize the acquired experience, identify perspectives, constraints, and risks associated with the use of AI technologies in the professional activities of accountants. The research is based on the hypothesis that widespread use of AI in the professional activity of an accountant with an insufficient level of professional skepticism and caution carries significant threats and risks for both the accountant and the business as a whole. Scientific search methods, comparative and critical analysis, theoretical generalization, and synthesis were used. A prerequisite for imple­menting AI technologies in accounting is expert information systems and ERP systems. The analysis of AI technology implementation experience in various industries demonstrates their relevance in the accounting field for performing routine tasks (automated recognition of primary documents, processing incoming signals, and other standard operations with a simultaneous reduction in the probability of errors), analyzing large datasets, and providing information support for decision-making (pro­ces­sing business data and regulatory docu­ments), training professionals, and organi­zing internal and external communication (parti­cularly between humans and machines). Identi­fied potential risks include breaches of privacy and data security, misinterpretation of output data, and the disregard of activity context, external and internal environments, especially due to the absence of emotional intelligence, which influences the trust level in integrated information systems. The requirement for the application of professional assessments and judgments, mandated by regulatory documents, limits the scope of AI technology utilization in accounting. Future research should focus on exploring the possibilities of widespread integ­ration of AI technologies in information systems for accounting and improving legislation based on the principle of risk assessment.Scientia fructuosa170The acquired experience is summarized to identify perspectives, constraints, and risks associated with the use of AI technologies in the professional activities of accountants, based on the hypothesis that widespread use of AI in the professional activity of an accountant with an insufficient level of professional skepticism and caution carries significant threats and risks for both the accountant and the business as a whole.Scientia fructuosa['S. Korol', 'O. Romashko']2024-04-11T00:00:00https://www.semanticscholar.org/paper/5e0d81dc13c0397a62989985ff842863b49dc9ce ce776b52719b4a29bec232b8a9b46f8811e0f06cSpeech-to-Text and Text-to-Speech are both NLP(natural language processing) powered models which transform speech to text and vice versa, providing an increased scope of learning for the parties involved. For the past couple of years it's been observed that students have been moving abroad for quality education and better financial aid. Since there is an accent gap between students and tutors which reduces the understanding of students. Our work is done to solve the aforementioned problem. With its state-of-the-art STT(speech-to-text) and TTS(text-to-speech) softwares this work intends to ease the learning curve of the students. The key targets of this work are international students, individuals with disabilities. It can also be used to transcribe meetings for quick conversion of meeting discussion points into text. Companies can also use the model to get the data for the call recordings and further perform sentiment analysis and various such activities. This research aims to give a detailed walk through of the product as it stands, and provide details regarding all aspects of the product. This covers the various tech stacks used, the implementation of the said technologies, the reports shown to the different end users. This provides the workflow of the product.International Journal of Innovative Science and Research Technology141This work intends to ease the learning curve of the students with its state-of-the-art STT and TTS softwares, aimed at international students, individuals with disabilities.International Journal of Innovative Science and Research Technology (IJISRT)['Sonali Padhi', 'Kranthi Kiran', 'Ambica Thakur', 'Adityaveer Dhillon', 'Bharani Kumar Depuru']2024-04-11T00:00:00https://www.semanticscholar.org/paper/ce776b52719b4a29bec232b8a9b46f8811e0f06c aa2b58cef99c26f93e238c7ce7dc575d02b3ff22Educational Administration Theory and Practices00Educational Administration Theory and Practices['B. Rathee', 'Komal Hudda']2024-04-11T00:00:00https://www.semanticscholar.org/paper/aa2b58cef99c26f93e238c7ce7dc575d02b3ff22 aaa3bb4c920fb8433363ae4776b8a2870438478eJournal of interventional cardiac electrophysiology60Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing['Neil Bodagh', 'M. Klis', 'A. Gharaviri', 'Vinush Vigneswaran', 'Keeran Vickneson', 'Michelle C. Williams', 'S. Niederer', "Mark O'Neill", 'Steven E. Williams']2024-04-11T00:00:00https://www.semanticscholar.org/paper/aaa3bb4c920fb8433363ae4776b8a2870438478e 4f254ebdd2e9d155a7136457061c7f317d05e2a3Teaching Exceptional Children280TEACHING Exceptional Children['Chengan Yuan', 'Juliet E. Hart Barnett']2024-04-11T00:00:00https://www.semanticscholar.org/paper/4f254ebdd2e9d155a7136457061c7f317d05e2a3 bf3480f8229144f7d53f7b3968d05abd867fdec2 Enrollment promotion plays a crucial role in the development of universities, serving as not only the primary means of attracting high-quality students but also as a guarantee for the steady growth of universities. Given the challenges that universities often face during the enrollment season, such as intense competition for students, difficulties in recruitment, and concerns about the quality of incoming students, this paper combines specific university promotional examples to analyze the strengths and weaknesses of universities in online promotion.Addressing the existing issues, this paper proposes fundamental strategies and measures for enrollment promotion, focusing on three aspects: implementing precise targeted promotion, building a professional enrollment team, and enhancing service awareness. These recommendations are intended to help universities better cope with various challenges in enrollment promotion, offering effective strategies and valuable insights to support high-quality and stable development in university enrollment promotion efforts.Advances in Social Development and Education Research00This paper combines specific university promotional examples to analyze the strengths and weaknesses of universities in online promotion, focusing on three aspects: implementing precise targeted promotion, building a professional enrollment team, and enhancing service awareness.Advances in Social Development and Education Research['Yibing Kong', 'Yuhan Li', 'Wenke Hou', 'Lijian Sun', 'Xinyuan Zhang', 'Ji Ma', 'Zhiguo Zhou']2024-04-11T00:00:00https://www.semanticscholar.org/paper/bf3480f8229144f7d53f7b3968d05abd867fdec2 6c6db739b6f6f0028c5af43d71cc1feaad7b1afb00['Utku Kose', 'Nilgun Sengoz', 'Xi Chen', 'Jose Antonio Marmolejo Saucedo']2024-04-11T00:00:00https://www.semanticscholar.org/paper/6c6db739b6f6f0028c5af43d71cc1feaad7b1afb b4a0707e0be89034db3018c6934e2f7d2a74fd67China’s economy is stepping into a new stage of high-quality development. The shift not only marks the optimization and upgrading of the economic structure, but also reflects the in-depth implementation of the concept of sustainable development. In this context, the development of AI technology is playing an important role in balancing economic growth and ecological protection with its unique advantages. This paper empirically studied the impact of AI development on urban energy efficiency by constructing panel data for 282 prefecture-level cities from 2006 to 2019 and then using the super-efficiency SBM model based on non-expected outputs to evaluate the urban energy efficiency indicators of prefecture-level cities. It was discovered that the development of AI had a key influence on increasing urban energy efficiency and the optimization of the energy structure by speeding up green technology innovation and digital economy development, which in turn improved urban energy efficiency. In terms of heterogeneity analysis, AI development had a greater impact on urban energy efficiency in the eastern region, which has higher levels of human capital, financial independence, and government intervention. This study combined the smart city pilot policy with a multi-period DID model, based on the treatment of endogeneity issues, in order to perform a parallel trend test and investigate further the effects of policy implementation on the advancement of AI in the context of improving urban energy efficiency. Accordingly, to achieve green and sustainable urban development, the relevant government departments should increase funding for AI research and development, pay attention to the introduction and cultivation of professionals, establish a platform for international exchanges and cooperation between AI and energy management, and continue to advocate for the pilot development of smart cities to increase urban energy efficiency.Sustainability490It was discovered that the development of AI had a key influence on increasing urban energy efficiency and the optimization of the energy structure by speeding up green technology innovation and digital economy development, which in turn improved urban energy efficiency.Sustainability['Xiangyi Li', 'Qing Wang', 'Ying Tang']2024-04-11T00:00:00https://www.semanticscholar.org/paper/b4a0707e0be89034db3018c6934e2f7d2a74fd67 a747598d1098e42ac08b7459911627437b2d2f0aJournal of public health00Journal of public health['Premila Webster', 'K. Neal']2024-04-11T00:00:00https://www.semanticscholar.org/paper/a747598d1098e42ac08b7459911627437b2d2f0a e265db0eef731906b696fc0a4e7cc84f68035f79Journal of Educational Evaluation for Health Professions130Journal of Educational Evaluation for Health Professions['Sun Huh']2024-04-11T00:00:00https://www.semanticscholar.org/paper/e265db0eef731906b696fc0a4e7cc84f68035f79 c2f4ac193b1a17e68da75e8b2053692095a05a1bBMC Psychology690Investigation of informants’ perceptions and experiences of AI-empowered interventions for children with autism explores the informants’ perceived benefits and challenges of using AI-empowered interventions and their recommendations for avoiding the perceived challenges.BMC Psychology['Guang Li', 'Mohammad Amin Zarei', 'Goudarz Alibakhshi', 'Akram Labbafi']2024-04-11T00:00:00https://www.semanticscholar.org/paper/c2f4ac193b1a17e68da75e8b2053692095a05a1b 6b94c3f6d8332af4e999adc601305a75d4ac45c8This research paper explores the pivotal role of Electronic Control Units (ECUs) and Inertial Measurement Units (IMUs) in Advanced Driver-Assistance Systems (ADAS), focusing on their functionalities, applications, regulatory frameworks, emerging technologies, and real-world case studies. Through a comprehensive review of literature, this paper investigates the integration of ECUs and IMUs in ADAS and its implications for vehicle safety, mobility, and sustainability. Key topics addressed include the adoption challenges, societal impact, international regulatory landscape, standardization efforts, emerging technologies such as artificial intelligence and sensor fusion, and real case studies exemplified by Tesla's Autopilot system, Volvo's City Safety system, and Waymo's autonomous taxi service. The paper concludes by highlighting the transformative potential of ECUs and IMUs in shaping the future of automotive technology and advancing towards safer, more efficient, and more sustainable transportation systems.International Journal For Multidisciplinary Research130International Journal For Multidisciplinary Research['Monish Katari', 'Gowrisankar Krishnamoorthy', 'Lavanya Shanmugam', 'Anish Tadimarri']2024-04-11T00:00:00https://www.semanticscholar.org/paper/6b94c3f6d8332af4e999adc601305a75d4ac45c8 f93b39bbea122f282a5c1ee321bbbffb9c9e4a94Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.Energies240Energies['Zenghua Huang', 'Shirong Ge', 'Yonghua He', 'Dandan Wang', 'Shouxiang Zhang']2024-04-11T00:00:00https://www.semanticscholar.org/paper/f93b39bbea122f282a5c1ee321bbbffb9c9e4a94 90a5737b066f21f6d63972b5fc10773e5aa05755With the growing integration of artificial intelligence (AI) in determining pricing strategies, there is an increasing concern about its potential to foster collusive behavior. Harrington (2012, 2018) underscores the challenge: if AI proves more adept at tacit collusion than humans or if AI-driven collusion is inherently tacit, then it presents a significant hurdle for prosecution under the prevailing interpretation of US antitrust laws. Validating these concerns, Assad et al. (2020) observed collusive price surges linked to the adoption of pricing algorithms among German gas stations. Drawing from game theory—specifically the repeated game paradigm—this paper crafts a foundational mathematical model to analyze competition versus collusion dynamics. It also evaluates the resultant welfare implications of both scenarios. The paper further delves into the broader challenges posed by AI-powered pricing and advocates for potential policy countermeasures, including algorithmic regulation and collusion detection mechanisms.Transactions on Computer Science and Intelligent Systems Research120This paper crafts a foundational mathematical model to analyze competition versus collusion dynamics and evaluates the resultant welfare implications of both scenarios, and delves into the broader challenges posed by AI-powered pricing and advocates for potential policy countermeasures.Transactions on Computer Science and Intelligent Systems Research['Jiaqi Liu']2024-04-10T00:00:00https://www.semanticscholar.org/paper/90a5737b066f21f6d63972b5fc10773e5aa05755 0423ab774c0139ff0464dcb45e4e887d96d3d314OBJECTIVE To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. METHOD An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. RESULTS In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. DISCUSSION There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. PUBLIC SIGNIFICANCE Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.International Journal of Eating Disorders930In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets.The International journal of eating disorders['Mark L Norris', 'N. Obeid', 'Khaled El-Emam']2024-04-10T00:00:00https://www.semanticscholar.org/paper/0423ab774c0139ff0464dcb45e4e887d96d3d314 c5eb6f2f5b55019a773431eb62651ced1d06602cThe state's defense plays a central role in ensuring the continuity of society, nation, and state. Threats arising from social organizations that diverge from Pancasila and the 1945 Constitution, using the freedom of association and assembly to replace the foundational principles of Pancasila, have prompted the President to issue Government Regulation in Lieu of Law Number 2 of 2017 concerning Amendments to Law Number 17 of 2013 concerning Social Organizations (Perppu Ormas). The research method used is normative legal research, employing a qualitative approach where data is gathered in the form of descriptions focusing on Perppu Ormas. From a national defense perspective, the issuance of Perppu Ormas aims to strengthen state stability by addressing threats such as terrorism, radicalism, social conflicts, and foreign influences that potentially undermine national sovereignty. This effort also serves as a means to maintain political balance and support the positive role of social organizations in bolstering national defense.International Journal Of Humanities Education and Social Sciences (IJHESS)140International Journal Of Humanities Education and Social Sciences (IJHESS)['Muhammad Hamdi Karim', 'Mhd Halkis', 'Susaningtyas Nefo', 'Handayani']2024-04-10T00:00:00https://www.semanticscholar.org/paper/c5eb6f2f5b55019a773431eb62651ced1d06602c c62b30102718ac60370e79f86c97ef9774bee3e5While literature has explored the influence of imagination and economic thought on (environmental) law, less attention has been paid to the influence of the economic imagination – specifically economic imaginaries – and their capacity to shape environmental regulation. Taking EU pesticides policy and regulation as a case study and drawing on theory regarding the performative power of imaginaries, this piece identifies economic imaginaries expressed in EU policy and demonstrates how those imaginaries have shaped that policy to pursue economic growth and competitiveness. It examines the evolution of a key piece of pesticides legislation and charts how establishing measures to pursue those economic goals gradually prevails, while providing for transparency – a crucial principle for supporting environmental protection – is deprioritised. It argues that these developments were driven by the EU's economic imaginaries and shows how economic considerations can subtly and indirectly undermine environmental protection.Modern law review00The Modern Law Review['Olivia Hamlyn']2024-04-10T00:00:00https://www.semanticscholar.org/paper/c62b30102718ac60370e79f86c97ef9774bee3e5 52eb82c8302c3ec3438e3a9fc4bbef797d64a734Introduction: The goal of the Health Technology Assessment (HTA) Regulation 2021/2282 is to establish a more harmonized HTA framework, fostering member states cooperation and enabling equal patient access to innovative health technologies in Europe. This research aimed to assess the impact of the regulation on national HTAs, the strategic implications for health technology developers, and its influence on price and reimbursement negotiations. Methods: A scoping literature review encompassing peer-reviewed literature as well as grey literature was conducted. Between February and March 2023, semi-structured interviews (n = 20) were performed with stakeholders from Belgian governmental institutions, European institutions, advanced therapy medicinal product developers, academics, and sickness funds. The interviews were analyzed using the framework analysis method. Results: Numerous steps, such as the development of implementing acts and procedural guidelines remain to be taken. At member state level, national/regional HTA bodies and payers must act to adopt the new concepts of Joint Scientific Consultations (JSC) and Joint Clinical Assessments (JCA) within their national legislation, as well as revise their timelines and prepare for interactions at a European level. Compiling a harmonized PICO (Population, Intervention, Comparator, and Outcome), adapting local procedures, and increasing capacity to actively take part in the JSC and JCA are seen as primary barriers by several stakeholders. Training and education will help HTA bodies, payers, and health technology developers to participate in the European processes. Conclusion: While practical and legal challenges were identified, recommendations (such as actively preparing for the upcoming changes and increasing capacity while providing training) were provided to adapt national and European procedures to the needs of the HTA Regulation 2021/2282. The importance of fostering collaborations and aligning local HTA procedures with the new way of working set out by the Regulation was demonstrated with this study.Frontiers in Pharmacology170Recommendations were provided to adapt national and European procedures to the needs of the HTA Regulation 2021/2282 and the importance of fostering collaborations and aligning local HTA procedures with the new way of working set out by the Regulation was demonstrated.Frontiers in Pharmacology['Thomas Desmet', 'M. Brijs', 'F. Vanderdonck', 'S. Tops', 'S. Simoens', 'I. Huys']2024-04-10T00:00:00https://www.semanticscholar.org/paper/52eb82c8302c3ec3438e3a9fc4bbef797d64a734 a7d68c798a05924e46a614b951d6841a0c2975f0Artificial Intelligence (AI) is transforming the landscape of education, offering innovative solutions to enhance learning experiences. This review provides a comprehensive overview of how AI is revolutionizing education, focusing on its impact on learning outcomes, teaching methodologies, and the overall educational ecosystem. The adoption of AI in education has led to personalized learning experiences tailored to individual student needs. AI-powered adaptive learning systems analyze student performance data to create customized learning paths, ensuring that students receive content at their pace and level of understanding. This personalized approach improves student engagement and academic performance. AI is also reshaping teaching methodologies, providing educators with tools to streamline administrative tasks and enhance instructional strategies. AI-powered tools can automate grading, create interactive lessons, and provide real-time feedback to students. This allows teachers to focus more on facilitating learning and developing critical thinking skills in students. Furthermore, AI is revolutionizing the assessment process, moving beyond traditional exams to more dynamic and insightful evaluation methods. AI-powered assessment tools can analyze student responses in real-time, providing immediate feedback and insights into student comprehension and learning progress. The integration of AI in education also extends to administrative functions, such as student enrollment, scheduling, and resource allocation. AI-powered systems can optimize these processes, leading to more efficient and effective management of educational institutions. Despite the numerous benefits of AI in education, challenges remain, including concerns about data privacy, algorithmic bias, and the need for teacher training. Addressing these challenges will be crucial to maximizing the potential of AI in education and ensuring equitable access to quality education for all. In conclusion, AI is revolutionizing education by enhancing learning experiences, transforming teaching methodologies, and optimizing administrative processes. As AI continues to evolve, its impact on education is expected to grow, offering new opportunities to improve learning outcomes and prepare students for success in the digital age. Keywords: Revolutionizing, AI, Enhancing, Learning, Experiences.International journal of applied research in social sciences06This review provides a comprehensive overview of how AI is revolutionizing education, focusing on its impact on learning outcomes, teaching methodologies, and the overall educational ecosystem.International Journal of Applied Research in Social Sciences['Oseremi Onesi-Ozigagun', 'Yinka James Ololade', 'Nsisong Louis Eyo-Udo', 'Damilola Oluwaseun Ogundipe']2024-04-10T00:00:00https://www.semanticscholar.org/paper/a7d68c798a05924e46a614b951d6841a0c2975f0 db130ea7216fd032f279887d3dd0d539918129f6With the rapid advancement of artificial intelligence technology, AI-enabled image recognition has emerged as a potent tool for addressing challenges in traditional environmental monitoring. This study focuses on the detection of floating objects in river and lake environments, exploring an innovative approach based on deep learning. By intricately analyzing the technical pathways for detecting static and dynamic features and considering the characteristics of river and lake debris, a comprehensive image acquisition and processing workflow has been developed. The study highlights the application and performance comparison of three mainstream deep learning models -SSD, Faster-RCNN, and YOLOv5- in debris identification. Additionally, a detection system for floating objects has been designed and implemented, encompassing both hardware platform construction and software framework development. Through rigorous experimental validation, the proposed system has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakesarXiv.org13A detection system for floating objects in river and lake environments, exploring an innovative approach based on deep learning, has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakes.ArXiv['Jingyu Zhang', 'Ao Xiang', 'Yu Cheng', 'Qin Yang', 'Liyang Wang']2024-04-10T00:00:00https://www.semanticscholar.org/paper/db130ea7216fd032f279887d3dd0d539918129f6 7410e79e85d4548a427117eb3f043f4ad0184f72Frontier large language models (LLMs) are developed by researchers and practitioners with skewed cultural backgrounds and on datasets with skewed sources. However, LLMs' (lack of) multicultural knowledge cannot be effectively assessed with current methods for developing benchmarks. Existing multicultural evaluations primarily rely on expensive and restricted human annotations or potentially outdated internet resources. Thus, they struggle to capture the intricacy, dynamics, and diversity of cultural norms. LLM-generated benchmarks are promising, yet risk propagating the same biases they are meant to measure. To synergize the creativity and expert cultural knowledge of human annotators and the scalability and standardizability of LLM-based automation, we introduce CulturalTeaming, an interactive red-teaming system that leverages human-AI collaboration to build truly challenging evaluation dataset for assessing the multicultural knowledge of LLMs, while improving annotators' capabilities and experiences. Our study reveals that CulturalTeaming's various modes of AI assistance support annotators in creating cultural questions, that modern LLMs fail at, in a gamified manner. Importantly, the increased level of AI assistance (e.g., LLM-generated revision hints) empowers users to create more difficult questions with enhanced perceived creativity of themselves, shedding light on the promises of involving heavier AI assistance in modern evaluation dataset creation procedures. Through a series of 1-hour workshop sessions, we gather CULTURALBENCH-V0.1, a compact yet high-quality evaluation dataset with users' red-teaming attempts, that different families of modern LLMs perform with accuracy ranging from 37.7% to 72.2%, revealing a notable gap in LLMs' multicultural proficiency.arXiv.org442CulturalTeaming is introduced, an interactive red-teaming system that leverages human-AI collaboration to build truly challenging evaluation dataset for assessing the multicultural knowledge of LLMs, while improving annotators' capabilities and experiences and shedding light on the promises of involving heavier AI assistance in modern evaluation dataset creation procedures.ArXiv['Yu Ying Chiu', 'Liwei Jiang', 'Maria Antoniak', 'Chan Young Park', 'Shuyue Stella Li', 'Mehar Bhatia', 'Sahithya Ravi', 'Yulia Tsvetkov', 'Vered Shwartz', 'Yejin Choi']2024-04-10T00:00:00https://www.semanticscholar.org/paper/7410e79e85d4548a427117eb3f043f4ad0184f72 432968d4b8848212bee43b67d4489849d40c9dc8"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To explore the standalone breast cancer detection performance at different risk score thresholds of a commercially available artificial intelligence (AI) system. Materials and Methods This retrospective study included information from 661,695 digital mammographic examinations performed among 242,629 female individuals screened as a part of x, 2004-2018. The study sample included 3807 screen-detected cancers (SDC) and 1110 interval breast cancers (IC). A continuous examination level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds. Results The AUC of the AI system was 0.93 (95% CI: 0.92-0.93) for SDC and IC combined and 0.97 (95% CI: 0.97-0.97) for SDC. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502/3807) of the SDC and 44.6% (495/1100) of the IC were identified by AI. In this scenario, 68.5% (10 987/16 029) of false positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cut-off, 99.3% (3781/3807) of the SDC and 85.2% (946/1100) of the IC were identified as positive by AI, while 17.0% (2725/16 029) of the false positives were considered as negative. Conclusion The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for triaging low-risk mammograms to reduce radiologist workload. ©RSNA, 2024.Radiology: Artificial Intelligence01The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for triaging low-risk mammograms to reduce radiologist workload.Radiology. Artificial intelligence['M. Larsen', 'Camilla F Olstad', 'Christoph I. Lee', 'T. Hovda', 'S. R. Hoff', 'Marit A Martiniussen', 'Karl Øyvind Mikalsen', 'Håkon Lund-Hanssen', 'Helene S Solli', 'Marko Silberhorn', 'Åse Ø Sulheim', 'Steinar Auensen', 'Jan F. Nygård', 'Solveig Hofvind']2024-04-10T00:00:00https://www.semanticscholar.org/paper/432968d4b8848212bee43b67d4489849d40c9dc8 c884bfbc60da712213a880401c033258aef3b9121011[]2024-04-10T00:00:00https://www.semanticscholar.org/paper/c884bfbc60da712213a880401c033258aef3b912 0f29c7568d57b12e52e614cc459933980a36e1fdThe paper deals with the topical issues regarding interaction between a human being and generative AI. The authors analyze the modern communication process (including the language models), find a number of tendencies (hallucinating of AI, lower content expertness and evaluation, increasing number of deep fakes, etc.), assess the efficiency of communication with voice-activated digital assistants and argue that communication possessing proficient features of “human” interaction is the most efficient one.2024 Communication Strategies in Digital Society Seminar (ComSDS)100The authors analyze the modern communication process (including the language models), find a number of tendencies (hallucinating of AI, lower content expertness and evaluation, increasing number of deep fakes), and argue that communication possessing proficient features of “human” interaction is the most efficient one.2024 Communication Strategies in Digital Society Seminar (ComSDS)['L. Tyutelova', 'E. S. Shevchenko', 'Valeria N. Lisovitskaya', 'V. Shevchenko']2024-04-10T00:00:00https://www.semanticscholar.org/paper/0f29c7568d57b12e52e614cc459933980a36e1fd 633eedabdbc2717f6b7aed08d489aadef8418612Residential buildings contribute 30% of the UK’s total final energy consumption. However, with less than one percent of its housing stock being replaced annually, retrofitting existing homes has significant importance in meeting energy-efficiency targets. Consequently, many physics-based and data-driven models and tools have been developed to analyse the effects of retrofit strategies from various points of view. This paper aims to develop a data-driven AI model that predicts buildings’ energy performance based on their features under various retrofit scenarios. In this context, four different machine learning models were developed based on the EPC (Energy Performance Certificate) dataset for residential buildings and standard assessment procedure (SAP) guidelines in the UK. Additionally, an interface was designed that enables users to analyse the effect of different retrofit strategies on a building’s energy performance using the developed AI models. The results of this study revealed the artificial neural network as the most accurate predictive model, with a coefficient of determination (R2) of 0.82 and a mean percentage error of 11.9 percent. However, some conceptual irregularities were observed across all the models when dealing with different retrofit scenarios. All summary, such tools can be further improved to offer a potential alternative or support to physics-based models, enhancing the efficiency of retrofitting processes in buildings.Sustainability230A data-driven AI model is developed that predicts buildings’ energy performance based on their features under various retrofit scenarios, revealing the artificial neural network as the most accurate predictive model.Sustainability['Hamidreza Seraj', 'Ali Bahadori-Jahromi', 'Shiva Amirkhani']2024-04-10T00:00:00https://www.semanticscholar.org/paper/633eedabdbc2717f6b7aed08d489aadef8418612 8ca165f8e23aa43aa36e8a0b37039b1d394be8b4Tainment communications encompass various technologies for news, content and key data transmitting, thus transforming the relationship between facts consuming, learning and play. Personalization trend in communications, the development of technologies of big data, artificial intelligence (AI), and machine learning transform the communication sphere and contribute to media development optimization, content diversification and polycoding, offering the most engaging tainment materials to target audiences. The analysis of secondary data, the results of the survey of content ultimate consumers and the expert interview revealed the potentials and pitfalls of AI in tainment communications. The pitfalls of further leverage of AI for content creation include limited creativity, shortfall of emotions and empathy, as the context of taintment content is yet incomprehensible to AI. Irrelevant or improper filling of AI-generated content is the main issue related to its leverage in communication and marketing. As students are the main consumers of tainment communications, the results of the survey show the main applications of AI to the content creation.2024 Communication Strategies in Digital Society Seminar (ComSDS)400The analysis of secondary data, the results of the survey of content ultimate consumers and the expert interview revealed the potentials and pitfalls of AI in tainment communications, showing the main applications of AI to the content creation.2024 Communication Strategies in Digital Society Seminar (ComSDS)['Marianna Yu. Ababkova', 'I. Ilyina', 'I. Y. Melnikova']2024-04-10T00:00:00https://www.semanticscholar.org/paper/8ca165f8e23aa43aa36e8a0b37039b1d394be8b4 29b41e240a95c1847aebdc96a3c4ebe37f158a05This comprehensive review delves into the transformative impact of artificial intelligence (AI) on drone technology, examining its pivotal role in revolutionizing various applications. As drones continue to evolve from recreational gadgets to indispensable tools across industries, the integration of AI enhances their capabilities, enabling advanced functionalities and expanding their potential use cases. The convergence of AI and drone technology has given rise to a myriad of applications, transforming industries ranging from agriculture to surveillance. Machine learning algorithms empower drones with autonomous navigation capabilities, allowing them to navigate complex environments and adapt to dynamic scenarios. Computer vision technologies enable drones to perceive and analyze visual information, facilitating tasks such as object recognition, tracking, and environmental monitoring. These advancements significantly contribute to enhanced aerial surveying, precision agriculture, and disaster response efforts. In the realm of precision agriculture, AI-equipped drones aid in crop monitoring, disease detection, and yield estimation, optimizing resource allocation and boosting agricultural productivity. Drones with AI-driven capabilities are increasingly employed in environmental monitoring, wildlife conservation, and disaster response, providing real-time data for efficient decision-making. Recent trends in AI-infused drone technology highlight its dynamic evolution. Edge computing solutions empower drones to process data locally, reducing latency and enhancing real-time responsiveness. Reinforcement learning algorithms enable drones to learn from their experiences, adapting and optimizing their performance over time. Swarm intelligence, an emerging field in drone technology, leverages AI to enable coordinated and synchronized actions among multiple drones, expanding their capabilities for collaborative tasks. In conclusion, this review sheds light on the pivotal role of AI in transforming drone technology and expanding its applications. The synergy between AI and drones has unlocked new possibilities across various industries, ranging from agriculture to disaster response. As technology continues to advance, the collaborative integration of AI and drones promises to redefine the future of aerial technology, introducing unprecedented efficiencies and capabilities across diverse sectors. Keywords: Role, AI, Drone, Applications, Technology.Computer Science &amp; IT Research Journal00This comprehensive review delves into the transformative impact of artificial intelligence (AI) on drone technology, examining its pivotal role in revolutionizing various applications and shedding light on the pivotal role of AI in transforming drone technology and expanding its applications.Computer Science &amp; IT Research Journal['Nwankwo Constance Obiuto', 'Igberaese clinton festus-ikhuoria', 'Oladiran Kayode Olajiga', 'Riliwan Adekola Adebayo']2024-04-10T00:00:00https://www.semanticscholar.org/paper/29b41e240a95c1847aebdc96a3c4ebe37f158a05 d08e39c09453cfa3c1c48541f4a5095f03f4b7e6Image generative AI (IGAI) could change how policymakers engage with the public to design public spaces, facilitating how designers translate the public’s desires into features. However, using IGAI has challenges, such as encoded biases, which might reinforce stereotypes and harm underrepresented communities. We conducted a case study to explore how using IGAI alters the co-design process of public parks through public engagement. We use data collected from interviews with immigrants discussing the Puente Hills Landfill Park design in Los Angeles, which will re-purpose a former landfill into a new public park. We use Dream Studio as a Design Probe, generating images from the interviewees’ insights and critically reflecting on the design process through internal interviews and a reflective workshop. We analyze our case in three domains: Opportunities, Risks and Challenges, and Features and Requirements. In the opportunities domain, we discuss how the enhanced translation of words to images changes the relationship between stakeholder engagement, multiplicity, and efficiency. In the risks and challenges domain, we discuss how IGAI might enhance power imbalances and biases. Finally, we reflect on what features would ease the safe and responsible use of IGAI to engage citizens in co-designing public parks.Digital Government: Research and Practice270A case study to explore how using IGAI alters the co-design process of public parks through public engagement through public engagement using data collected from interviews with immigrants discussing the Puente Hills Landfill Park design in Los Angeles.Digital Government: Research and Practice['Jose A. Guridi', 'Cristobal Cheyre', 'Maria Goula', 'Duarte Santo', 'Lee Humphreys', 'Aishwarya Shankar', 'Achilleas Souras']2024-04-10T00:00:00https://www.semanticscholar.org/paper/d08e39c09453cfa3c1c48541f4a5095f03f4b7e6 7e98fe407d0adde19fc916655f67c97d4afdd1a6This interdisciplinary research initiative seeks to enhance the accessibility of video gaming for individuals living with Motor Neurone Disease (MND), a condition characterized by progressive muscle weakness. Gaming serves as a social and recreational outlet for many, connecting friends, family, and even strangers through collaboration and competition. However, MND’s disease progression, including muscle weakness and paralysis, severely limit the ability to engage in gaming. In this paper, we desscribe our exploration of AI solutions to improve accessibility to gaming. We argue that any application of accessible AI must be led by lived experience. Notably, we found in our previous scoping review, existing academic research into video games for those living with MND largely neglects the experiences of MND patients in the context of video games and AI, which was a prompt for us to address this critical gap.Frontiers of Computer Science230It is argued that any application of accessible AI must be led by lived experience, and that any application of accessible AI must be led by lived experience.Frontiers in Computer Science['Natasha Dwyer', 'Matthew Harrison', 'Ben O’Mara', 'Kirsten Harley']2024-04-10T00:00:00https://www.semanticscholar.org/paper/7e98fe407d0adde19fc916655f67c97d4afdd1a6 9fbcd2efc4cad5988fe1baa11b837688ed33a926This paper explores the integration of AI in smart drilling technologies, examining its applications, benefits, challenges, and future prospects. By harnessing the power of AI, smart drilling technologies enable proactive decision-making, automation, and optimization throughout the drilling lifecycle. From well planning and design to real-time monitoring and control, AI-driven systems improve operational performance, reduce risks, and maximize resource recovery. Despite facing challenges such as data integration, technology adoption, and regulatory compliance, the potential benefits of smart drilling technologies are substantial. Enhanced precision, improved safety, increased efficiency, and sustainable practices are among the key benefits offered by these technologies. Looking towards the future, opportunities for further innovation and advancement abound, including the development of advanced AI algorithms, integration with IoT and big data analytics, and a focus on environmental sustainability. By embracing innovation, collaboration, and a commitment to sustainability, the oil and gas industry can unlock new opportunities for growth and resilience in the evolving landscape of oil and gas well construction. Smart drilling technologies hold the promise of reshaping the future of well construction, paving the way for safer, more efficient, and sustainable drilling operations in the oil and gas industry. Smart drilling technologies are revolutionizing the oil and gas industry, offering unprecedented levels of precision and safety in well construction. By integrating artificial intelligence (AI) into drilling processes, these technologies optimize drilling parameters, reduce risks, and maximize resource recovery.. Enhanced precision, improved safety, increased efficiency, and sustainable practices are among the key benefits offered by these technologies. Looking towards the future, opportunities for further innovation and advancement abound, including the development of advanced AI algorithms, integration with IoT and big data analytics, and a focus on environmental sustainability. Keywords: Smart drilling, Artificial intelligence (AI), Oil and gas industry Efficiency, Safety, Sustainability.Engineering Science &amp; Technology Journal00Engineering Science &amp; Technology Journal['Oladiran Kayode Olajiga', 'Nwankwo Constance Obiuto', 'Riliwan Adekola Adebayo', 'Igberaese clinton festus-ikhuoria']2024-04-10T00:00:00https://www.semanticscholar.org/paper/9fbcd2efc4cad5988fe1baa11b837688ed33a926 378cc7705f28ffe76262f8f1c7e6da49113e2a7bMany explainable AI (XAI) techniques strive for interpretability by providing concise salient information, such as sparse linear factors. However, users either only see inaccurate global explanations, or highly-varying local explanations. We propose to provide more detailed explanations by leveraging the human cognitive capacity to accumulate knowledge by incrementally receiving more details. Focusing on linear factor explanations (factors $\times$ values = outcome), we introduce Incremental XAI to automatically partition explanations for general and atypical instances by providing Base + Incremental factors to help users read and remember more faithful explanations. Memorability is improved by reusing base factors and reducing the number of factors shown in atypical cases. In modeling, formative, and summative user studies, we evaluated the faithfulness, memorability and understandability of Incremental XAI against baseline explanation methods. This work contributes towards more usable explanation that users can better ingrain to facilitate intuitive engagement with AI.International Conference on Human Factors in Computing Systems740Focusing on linear factor explanations, Incremental XAI is introduced to automatically partition explanations for general and atypical instances by providing Base + Incremental factors to help users read and remember more faithful explanations.{'pages': '315:1-315:17'}['Jessica Y. Bo', 'Pan Hao', 'Brian Y. Lim']2024-04-10T00:00:00https://www.semanticscholar.org/paper/378cc7705f28ffe76262f8f1c7e6da49113e2a7b 9350faf8a89692879f32ddf441d6cf4cece2e8feNon-native English speakers (NNES) face challenges in digital workspace communication (e.g., emails, Slack messages), often inadvertently translating expressions from their native languages, which can lead to awkward or incorrect usage. Current AI-assisted writing tools are equipped with fluency enhancement and rewriting suggestions; however, NNES may struggle to grasp the subtleties among various expressions, making it challenging to choose the one that accurately reflects their intent. Such challenges are exacerbated in high-stake text-based communications, where the absence of non-verbal cues heightens the risk of misinterpretation. By leveraging the latest advancements in large language models (LLM) and word embeddings, we propose WordDecipher, an explainable AI-assisted writing tool to enhance digital workspace communication for NNES. WordDecipher not only identifies the perceived social intentions detected in users' writing, but also generates rewriting suggestions aligned with users' intended messages, either numerically or by inferring from users' writing in their native language. Then, WordDecipher provides an overview of nuances to help NNES make selections. Through a usage scenario, we demonstrate how WordDecipher can significantly enhance an NNES's ability to communicate her request, showcasing its potential to transform workspace communication for NNES.arXiv.org250This work proposes WordDecipher, an explainable AI-assisted writing tool that identifies the perceived social intentions detected in users' writing, but also generates rewriting suggestions aligned with users' intended messages, either numerically or by inferring from users' writing in their native language.ArXiv['Yuexi Chen', 'Zhicheng Liu']2024-04-10T00:00:00https://www.semanticscholar.org/paper/9350faf8a89692879f32ddf441d6cf4cece2e8fe f9d1413797421f61c58c556eebd4cf20aa940fd5BMC Medical Education330There appears to be a correlation between AI literacy and attitudes towards AI, which should be considered when planning AI courses and prior AI education and interest in AI is positively correlated with medical students’ AI literacy.BMC Medical Education['Matthias Carl Laupichler', 'Alexandra Aster', 'Marcel Meyerheim', 'Tobias Raupach', 'Marvin Mergen']2024-04-10T00:00:00https://www.semanticscholar.org/paper/f9d1413797421f61c58c556eebd4cf20aa940fd5 86dd76f3f77cc64a2b9d31ef802dd12528e1651cRacial diversity has become increasingly discussed within the AI and algorithmic fairness literature, yet little attention is focused on justifying the choices of racial categories and understanding how people are racialized into these chosen racial categories. Even less attention is given to how racial categories shift and how the racialization process changes depending on the context of a dataset or model. An unclear understanding of \textit{who} comprises the racial categories chosen and \textit{how} people are racialized into these categories can lead to varying interpretations of these categories. These varying interpretations can lead to harm when the understanding of racial categories and the racialization process is misaligned from the actual racialization process and racial categories used. Harm can also arise if the racialization process and racial categories used are irrelevant or do not exist in the context they are applied. In this paper, we make two contributions. First, we demonstrate how racial categories with unclear assumptions and little justification can lead to varying datasets that poorly represent groups obfuscated or unrepresented by the given racial categories and models that perform poorly on these groups. Second, we develop a framework, CIRCSheets, for documenting the choices and assumptions in choosing racial categories and the process of racialization into these categories to facilitate transparency in understanding the processes and assumptions made by dataset or model developers when selecting or using these racial categories.arXiv.org810A framework is developed for documenting the choices and assumptions in choosing racial categories and the process of racialization into these categories to facilitate transparency in understanding the processes and assumptions made by dataset or model developers when selecting or using these racial categories.ArXiv['Jennifer Mickel']2024-04-10T00:00:00https://www.semanticscholar.org/paper/86dd76f3f77cc64a2b9d31ef802dd12528e1651c d188cd8e80023c044d2e684cbf7bb4dbca490b10Artificial intelligence (AI) has become ubiquitous across various industries, including security, healthcare, finance, and national defense. However, alongside its transformative potential, there has been a concerning rise in malicious exploitation of AI capabilities. Simultaneously, the rapid advancement of cloud computing technology has led to the emergence of cloud-based AI systems. Unfortunately, vulnerabilities inherent in cloud infrastructure also pose security risks to AI services. We recognize the critical role of maintaining the integrity of training data, as any compromise therein directly impacts the effectiveness of AI systems. In response to this challenge, we emphasize the paramount importance of preserving data integrity within AI systems. To address this need, we propose a data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework. Leveraging blockchain technology and smart contracts presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers. Smart contracts enable automated policy enforcement, facilitate continuous monitoring of data integrity, and help mitigate the risk of data tampering.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300A data integrity architecture guided by the National Institute of Standards and Technology (NIST) cybersecurity framework is proposed, which presents a suitable solution for addressing integrity challenges, given their features of shared and decentralized ledgers.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Sohel Rana']2024-04-10T00:00:00https://www.semanticscholar.org/paper/d188cd8e80023c044d2e684cbf7bb4dbca490b10 46c3b3781badd1553fe2aae19b75243802805598Some have criticised Generative AI Systems for replicating the familiar pathologies of already widely-deployed AI systems. Other critics highlight how they foreshadow vastly more powerful future systems, which might threaten humanity's survival. The first group says there is nothing new here; the other looks through the present to a perhaps distant horizon. In this paper, I instead pay attention to what makes these particular systems distinctive: both their remarkable scientific achievement, and the most likely and consequential ways in which they will change society over the next five to ten years. In particular, I explore the potential societal impacts and normative questions raised by the looming prospect of 'Generative Agents', in which multimodal large language models (LLMs) form the executive centre of complex, tool-using AI systems that can take unsupervised sequences of actions towards some goal.arXiv.org460The potential societal impacts and normative questions raised by the looming prospect of 'Generative Agents', in which multimodal large language models form the executive centre of complex, tool-using AI systems that can take unsupervised sequences of actions towards some goal are explored.ArXiv['Seth Lazar']2024-04-10T00:00:00https://www.semanticscholar.org/paper/46c3b3781badd1553fe2aae19b75243802805598 65cb21f3ea64d647bb9770f4f146dc00a09f0f01The paper aims to study the opportunities and risks that artificial intelligence technologies pose for digital communication. Authors argue that media literacy approach has valuable potential in the field. To support this opinion the meta-analysis of scientific articles within the Google Scholar platform for the period from 2020 to 2023 in Russian and English has been developed. It has been found that among scientist there is common to treat as risks of AI-technologies two things: mental health and fake-news. The positive treats are given to the AI having more of routine tasks which, in general, allowed people to unload for more complex and creative tasks. More people will be involved both in communication with AI and using its products as part of their strategic communications. That is why it is pointless to fight AI. It would be more appropriate to teach people how to use AI and to understand what consequences this can lead to.2024 Communication Strategies in Digital Society Seminar (ComSDS)130It is argued that media literacy approach has valuable potential in the field and the meta-analysis of scientific articles within the Google Scholar platform for the period from 2020 to 2023 in Russian and English has been developed.2024 Communication Strategies in Digital Society Seminar (ComSDS)['I. Bykov', 'Mariia V. Medvedeva']2024-04-10T00:00:00https://www.semanticscholar.org/paper/65cb21f3ea64d647bb9770f4f146dc00a09f0f01 4ada0644503cf044366358c61931f2cf5936c45bArtificial Intelligence (AI) has become a ubiquitous part of society, but a key challenge exists in ensuring that humans are equipped with the required critical thinking and AI literacy skills to interact with machines effectively by understanding their capabilities and limitations. These skills are particularly important for learners to develop in the age of generative AI where AI tools can demonstrate complex knowledge and ability previously thought to be uniquely human. To activate effective human-AI partnerships in writing, this paper provides a first step toward conceptualizing the notion of critical learner interaction with AI. Using both theoretical models and empirical data, our preliminary findings suggest a general lack of Deep interaction with AI during the writing process. We believe that the outcomes can lead to better task and tool design in the future for learners to develop deep, critical thinking when interacting with AI.CHI Extended Abstracts180The preliminary findings suggest a general lack of Deep interaction with AI during the writing process, which can lead to better task and tool design in the future for learners to develop deep, critical thinking when interacting with AI.ArXiv['A. Shibani', 'Simon Knight', 'Kirsty Kitto', 'Ajanie Karunanayake', 'S. B. Shum']2024-04-10T00:00:00https://www.semanticscholar.org/paper/4ada0644503cf044366358c61931f2cf5936c45b 25885e408fc954b2a949a9261674f87526dd6c92Artificial Intelligence also abbreviated as “AI” has been the subject of much legal debate and legal writing. This article seeks to identify internationally accepted AI principles and norms that are contained in the South African Constitution. This article also seeks to identify policy such as the PC4IR Report and legislation that regulates and accommodates the use of AI in South Africa. What emerges clearly is that there has never been a deliberate attempt to legislate AI, and that the legislation referred to is applicable by coincidence not intention. The article goes on to highlight African and BRICS policies and best practices on AI, European best practices and legal norms and values on AI, and the draft EU AI Act. The article concludes with a recommendation that South Africa introduce AI legislation as a matter of urgency.Obiter00This article seeks to identify internationally accepted AI principles and norms that are contained in the South African Constitution and recommends that South Africa introduce AI legislation as a matter of urgency.Obiter['Sizwe Snail ka Mtuze', 'Masego Morige']2024-04-10T00:00:00https://www.semanticscholar.org/paper/25885e408fc954b2a949a9261674f87526dd6c92 42799b3dd436de19dd2210e9505199a4820804da Spoken communication between social robotic devices, powered by generative AI tools such as ChatGPT, and the senior population offers great potential for researching social interaction and robot identity perceptions as well as exploring the potential opportunities and challenges when implementing this human-machine interactions in real life situations and health care. In this paper we explore people’s perceptions of the social robot Furhat when administering verbal tasks similar to those used in screening for Alzheimer’s disease. We describe the Slovak system mounted on the robot that includes a speech recognizer, the scenarios powered by generative large language model ChatGPT, and a speech synthesizer. We tested the functionality of the proposed approach with two groups of participants: attendees of a large science fair and a scientific conference. The observations from 87 collected questionnaires suggest good potential and applicability of such an approach for the given task and more positive attitudes of older subjects compared to younger ones.Human Affairs120People’s perceptions of the social robot Furhat when administering verbal tasks similar to those used in screening for Alzheimer’s disease are explored and good potential and applicability for the given task are suggested.Human Affairs['R. Sabo', 'Štefan Beňuš', 'Viktória Kevická', 'M. Trnka', 'M. Rusko', 'Sakhia Darjaa', 'Jay Kejriwal']2024-04-10T00:00:00https://www.semanticscholar.org/paper/42799b3dd436de19dd2210e9505199a4820804da e0fcfd5f83d8e151be4b454a658c37ab0841761cOesophagectomy for cancer of the oesophagus carries significant morbidity and mortality. Ninety-day mortality and anastomosis leakage are critical early postoperative problems traditionally analysed through logistic regression. In this study, we challenge traditional logistic regression models to predict results with new explainable AI (XAI) models. We used the Swedish National Quality Register for Oesophageal and Gastric Cancer (NREV) to perform traditional multivariable logistic regression and XAI. The 90-day mortality was 6.0%, while anastomosis leakage was present in 12.4%. The XAI models yielded an area under the curve (AUC) of 0.91 for 90-day mortality (as compared with 0.84 for logistic regression). For anastomosis leakage, the AUC was 0.84 using XAI (0.74 using logistic regression). We show that age (mortality increases sharply after 55 years) and body mass index (BMI) (lowest mortality for BMI 30 kg/m2) are important survival factors. Additionally, we show that surgery time (minimum anastomosis leakage for a surgery time of 200 min to sharply increase to a maximum at 375 min) and BMI (the lower the BMI, the less anastomosis leakage) are important factors for anastomosis leakage. The surgical understanding of anastomosis leakage and mortality after oesophagectomy is advanced by judiciously applying XAI to structured data. Our nationwide oesophagectomy data contains significant nonlinear relationships. With the help of XAI, we extract personalised knowledge, bringing oesophagus surgery one step closer to personalised medicine.medRxiv160The surgical understanding of anastomosis leakage and mortality after oesophagectomy is advanced by judiciously applying XAI to structured data, and it is shown that age and body mass index (BMI) and body mass index (BMI) are important survival factors.['Sebastian Djerf', 'Oscar ˚Akesson', 'Magnus Nilsson', 'M. Lindblad', 'J. Hedberg', 'Jan Johansson', 'Attila Frigyesi']2024-04-10T00:00:00https://www.semanticscholar.org/paper/e0fcfd5f83d8e151be4b454a658c37ab0841761c 6bd3a1e641090cb1802d8034fb3b9e47333e1bc0Semantic Communication (SemCom) is envisaged as the next-generation paradigm to address challenges stemming from the conflicts between the increasing volume of transmission data and the scarcity of spectrum resources. However, existing SemCom systems face drawbacks, such as low explainability, modality rigidity, and inadequate reconstruction functionality. Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges. We employ a three-layer model to illustrate the proposed AIGC-assisted SemCom (AIGC-SCM) architecture, emphasizing its clear deviation from existing SemCom. Grounded in this model, we investigate various AIGC technologies with the potential to augment SemCom's performance. In alignment with SemCom's goal of conveying semantic meanings, we also introduce the new evaluation methods for our AIGC-SCM system. Subsequently, we explore communication scenarios where our proposed AIGC-SCM can realize its potential. For practical implementation, we construct a detailed integration workflow and conduct a case study in a virtual reality image transmission scenario. The results demonstrate our ability to maintain a high degree of alignment between the reconstructed content and the original source information, while substantially minimizing the data volume required for transmission. These findings pave the way for further enhancements in communication efficiency and the improvement of Quality of Service. At last, we present future directions for AIGC-SCM studies.150This paper employs a three-layer model to illustrate the proposed AIGC-assisted SemCom (AIGC-SCM) architecture, emphasizing its clear deviation from existing SemCom, and introduces the new evaluation methods for the AIGC-SCM system.['Yirun Wang', 'Wanting Yang', 'Zehui Xiong', 'Yuping Zhao', 'Tony Q. S. Quek', 'Zhu Han']2024-04-10T00:00:00https://www.semanticscholar.org/paper/6bd3a1e641090cb1802d8034fb3b9e47333e1bc0 6eb40cef88993325632e9d622f5c6d5ef6c3be19Service Industries Journal581The Service Industries Journal['Francisco Trincado-Munoz', 'Carlo Cordasco', 'Tim Vorley']2024-04-10T00:00:00https://www.semanticscholar.org/paper/6eb40cef88993325632e9d622f5c6d5ef6c3be19 37b53ad36fc8ddcc532fc50c563e96071926f87aEntrepreneur & Innovation Exchange00Entrepreneur and Innovation Exchange['Mat Hughes', 'David Townsend']2024-04-10T00:00:00https://www.semanticscholar.org/paper/37b53ad36fc8ddcc532fc50c563e96071926f87a a360987e229b4a3a43101522be6b54cd5ae61b75SARA integrates Eye Tracking and state-of-the-art large language models in a mixed reality framework to enhance the reading experience by providing personalized assistance in real-time. By tracking eye movements, SARA identifies the text segments that attract the user's attention the most and potentially indicate uncertain areas and comprehension issues. The process involves these key steps: text detection and extraction, gaze tracking and alignment, and assessment of detected reading difficulty. The results are customized solutions presented directly within the user's field of view as virtual overlays on identified difficult text areas. This support enables users to overcome challenges like unfamiliar vocabulary and complex sentences by offering additional context, rephrased solutions, and multilingual help. SARA's innovative approach demonstrates it has the potential to transform the reading experience and improve reading proficiency.arXiv.org110SARA integrates Eye Tracking and state-of-the-art large language models in a mixed reality framework to enhance the reading experience by providing personalized assistance in real-time and demonstrates it has the potential to transform the reading experience and improve reading proficiency.ArXiv['Enkeleda Thaqi', 'Mohamed Mantawy', 'Enkelejda Kasneci']2024-04-10T00:00:00https://www.semanticscholar.org/paper/a360987e229b4a3a43101522be6b54cd5ae61b75 0502405e36b1ef6ea7456de45f057c417c90bfacAsia Pacific Business Review650Asia Pacific Business Review['Wen-Yan Duan', 'Tung-Ju Wu', 'An-Pin Wei', 'Yu-Ting Huang']2024-04-10T00:00:00https://www.semanticscholar.org/paper/0502405e36b1ef6ea7456de45f057c417c90bfac a17f9f012e9770ca7f04ef8789ea960bbce3b609Theory &amp; Research in Social Education790Theory &amp; Research in Social Education['Amato Nocera', 'Victoria Newton', 'Shiyan Jiang']2024-04-10T00:00:00https://www.semanticscholar.org/paper/a17f9f012e9770ca7f04ef8789ea960bbce3b609 40bddf953fe98204c4b15eaeb32307f004241782Patient outcomes in ophthalmology are greatly influenced by adherence and patient participation, which can be particularly challenging in diseases like glaucoma where medication regimens can be complex. A well-studied and evidence-based intervention for behavioral change is Motivational Interviewing (MI), a collaborative and patient-centered counseling approach, which has been shown to improve medication adherence in glaucoma patients. However, there are many barriers to clinicians being able to provide motivational interviewing in-office, including short visit durations within high-volume ophthalmology clinics and inadequate billing structures for counseling. Recently, Large Language Models (LLMs), a type of artificial intelligence, have advanced such that they can follow instructions and carry coherent conversations, offering novel solutions to a wide range of clinical problems. In this paper, we discuss the potential of LLMs to provide chatbot-driven MI to improve adherence in glaucoma patients, and provide an example conversation as a proof of concept. We discuss the advantages of AI-driven MI, such as demonstrated effectiveness, scalability, and accessibility. We also explore the risks and limitations, including issues of safety and privacy, as well as the factual inaccuracies and hallucinations to which LLMs are susceptible. Domain-specific training may be needed to ensure accuracy and completeness of information provided in subspecialty areas such as glaucoma. Despite the current limitations, AI-driven Motivational Interviewing has the potential to offer significant improvements in adherence, and should be further explored to maximally leverage the potential of artificial intelligence for our patients.Journal of glaucoma00The potential of LLMs to provide chatbot-driven MI to improve adherence in glaucoma patients is discussed, and an example conversation is provided as a proof of concept.Journal of glaucoma['Areeba Abid', 'Sally L Baxter']2024-04-10T00:00:00https://www.semanticscholar.org/paper/40bddf953fe98204c4b15eaeb32307f004241782 10ccb8798bd796f3e52e6c6e56d52e9fc2451c8cProgress in Additive Manufacturing490Progress in Additive Manufacturing['S. Dev', 'R. Srivastava']2024-04-10T00:00:00https://www.semanticscholar.org/paper/10ccb8798bd796f3e52e6c6e56d52e9fc2451c8c 1cd58d484970986cd92803655b7bcc9859094c06The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the emphasis on continuous monitoring of cloud resources, workflow tasks, and data streams to detect and preempt anomalies in workflow orchestration processes. To accomplish this, we advocate for a multi-modal approach that integrates deep learning, one-class classification, and clustering techniques. In essence, our proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction. This approach is particularly pertinent in critical sectors such as healthcare, especially during unprecedented events like the COVID-19 pandemic.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The proposed architecture offers a comprehensive solution for enforcing security within cloud workflow orchestration, harnessing advanced methodologies like deep learning for anomaly detection and prediction, especially during unprecedented events like the COVID-19 pandemic.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Sohana Akter']2024-04-10T00:00:00https://www.semanticscholar.org/paper/1cd58d484970986cd92803655b7bcc9859094c06 47816f8637c5e3a397e72907c98ec28aeb19d8d3JAMA Surgery50JAMA surgery['Carolyn D Seib', 'Sherry M Wren']2024-04-10T00:00:00https://www.semanticscholar.org/paper/47816f8637c5e3a397e72907c98ec28aeb19d8d3 c2044af696bdebe47cda92be6ad1f245653d22d7Nature00Nature['Dalmeet Singh Chawla']2024-04-10T00:00:00https://www.semanticscholar.org/paper/c2044af696bdebe47cda92be6ad1f245653d22d7 4b10385d98feb18461fb90817232ad221103c546European Journal of Radiology70The debate underscores the need for further research and regulation to clarify AI's role in radiology, balancing innovation with legal and ethical considerations.European journal of radiology['T. Martín-Noguerol', 'P. López-Úbeda', 'Antonio Luna']2024-04-10T00:00:00https://www.semanticscholar.org/paper/4b10385d98feb18461fb90817232ad221103c546 359fd88a0e8234e3ef3d0b3db5d07f51439125fdThe increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset. It employs advanced machine learning and deep learning techniques, integrated with data science, statistics, optimization, and mathematical modeling, to correlate various lifestyle and environmental factors with the incidence of these mental disorders. In this work, a variety of machine learning and deep learning models with hyper-parameter tuning are utilized to forecast trends in the occurrence of mental disorders about lifestyle choices such as smoking and alcohol consumption, as well as environmental factors like air and noise pollution. Among these models, the convolutional neural network (CNN) architecture, termed as DNN1 in this paper, accurately predicts mental health occurrences relative to the population mean with a maximum accuracy of 99.79%. Among the machine learning models, the XGBoost technique yields an accuracy of 95.30%, with an area under the ROC curve of 0.9985, indicating robust training. The research also involves extracting feature importance scores for the XGBoost classifier, with Stroop test performance results attaining the highest importance score of 0.135. Attributes related to addiction, namely smoking and alcohol consumption, hold importance scores of 0.0273 and 0.0212, respectively. Statistical tests on the training models reveal that XGBoost performs best on the mean squared error and R-squared tests, achieving scores of 0.013356 and 0.946481, respectively. These statistical evaluations bolster the models' credibility and affirm the best-fit models' accuracy. The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models. Furthermore, it aims to provide valuable insights for policymakers in formulating new regulations on pollution and addiction.Frontiers in Human Neuroscience430The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models and to provide valuable insights for policymakers in formulating new regulations on pollution and addiction.Frontiers in Human Neuroscience['Naman Dhariwal', 'Nidhi Sengupta', 'M. Madiajagan', 'Kiran Kumar Patro', 'P. L. Kumari', 'Nagwan Abdel Samee', 'Ryszard Tadeusiewicz', 'Paweł Pławiak', 'A. J. Prakash']2024-04-10T00:00:00https://www.semanticscholar.org/paper/359fd88a0e8234e3ef3d0b3db5d07f51439125fd f036ff4effb62434a64826ef3063943a98e0635e220This work proposes using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability of UC diagnosis and treatment.['B. Moller', 'B. Lo', 'J. Burisch', 'Flemming Bendtsen', 'I. Vind', 'Bulat Ibragimov', 'C. Igel']2024-04-10T00:00:00https://www.semanticscholar.org/paper/f036ff4effb62434a64826ef3063943a98e0635e e5a7eaa8c7564fea1faa53785695d604dee82184Artificial intelligence has sophisticated social and economic effects that cannot be ignored. Based on a thorough review of the development of artificial intelligence, this paper systematically explores the mechanism of the impact of artificial intelligence on economic growth through technology, value and application three paths, which is starting from the perspective of the population external system. In order to verify the rationality of the paths, the effect of artificial intelligence on economic growth from the perspective of population external system is rigorously estimated using artificial intelligence and macroeconomic data for China from 2011 to 2019. The findings are as follows. Firstly, there is a significant positive effect of artificial intelligence on the economic growth from the perspective of the population external system. This positive effect is sufficiently robust over the sample-wide period. Secondly, there is significant regional heterogeneity in the effect of artificial intelligence on economic growth from the perspective of the population external system. The low levels of artificial intelligence development impeded the economic growth, the middle levels of artificial intelligence development contributed significantly to the economic growth, and the high levels of artificial intelligence development did not show a significant contribution to the economic growth. In view of this, future policies should be designed in terms of revitalizing the value of the artificial intelligence stock, exploring the value potential of artificial intelligence and regulating it in a hierarchical manner.Social science computer review301There is a significant positive effect of artificial intelligence on the economic growth from the perspective of the population external system and this positive effect is sufficiently robust over the sample-wide period.Social Science Computer Review['Xueyi Wang', 'Taiyi He', 'Shengzhe Wang', 'Haoxiang Zhao']2024-04-10T00:00:00https://www.semanticscholar.org/paper/e5a7eaa8c7564fea1faa53785695d604dee82184 cdfb394f02ec73f0e41f3eaa1d496776fcd50adbWater resources management810The study highlights the importance of modeling tools in managing water in urban areas and safeguarding water sources during stormwater events, and examines tools used for predicting and analysing stormwater runoff during storm events in diverse locations.Water Resources Management['Ndivhuwo Ramovha', 'Martha Chadyiwa', 'F. Ntuli', 'Thandiwe Sithole']2024-04-10T00:00:00https://www.semanticscholar.org/paper/cdfb394f02ec73f0e41f3eaa1d496776fcd50adb b4d7a3a72d40ebab6338a975a42b25b09f7fb389The article is devoted to the transformation of the processes of deep mediatization of social relations in a ratified digital society, due to the intensive introduction of self-learning neural networks and, more broadly, generative artificial intelligence. The characteristic of the current stage of mediatization is given, and an overview of the relevant theoretical approaches is proposed. The influence of artificial intelligence technologies on mediatization processes is considered, positive and negative effects are identified, and the most significant risks are analyzed. Special attention is paid to such phenomena with negative potential as the deepening of the digital divide, manipulation of public opinion and its exploitation with the use of deep fakes, preservation of bubbles and echo chambers, algorithmic bias and cultural homogenization.2024 Communication Strategies in Digital Society Seminar (ComSDS)2602024 Communication Strategies in Digital Society Seminar (ComSDS)['Dmitry P. Gavra', 'E. V. Bykova', 'I. A. Baikova']2024-04-10T00:00:00https://www.semanticscholar.org/paper/b4d7a3a72d40ebab6338a975a42b25b09f7fb389 ffdc838a2f4309cd43ae2853114552010ac5d9f0This report describes a comprehensive framework for applying artificial intelligence (AI) in veterinary medicine. Our framework draws on existing research on AI implementation in human medicine and addresses the challenges of limited technology expertise and the need for scalability. The critical components of this framework include assembling a diverse team of experts in AI, promoting a foundational understanding of AI among veterinary professionals, identifying relevant use cases and objectives, ensuring data quality and availability, creating an effective implementation plan, providing team training, fostering collaboration, considering ethical and legal obligations, integrating AI into existing workflows, monitoring and evaluating performance, managing change effectively, and staying up-to-date with technological advancements. Incorporating AI into veterinary medicine requires addressing unique ethical and legal considerations, including data privacy, owner consent, and the impact of AI outputs on decision-making. Effective change management principles aid in avoiding disruptions and building trust in AI technology. Furthermore, continuous evaluation of AI's relevance in veterinary practice ensures that the benefits of AI translate into meaningful improvements in patient care.Journal of the American Veterinary Medical Association450Journal of the American Veterinary Medical Association['Parminder S Basran', 'Ryan B Appleby']2024-04-10T00:00:00https://www.semanticscholar.org/paper/ffdc838a2f4309cd43ae2853114552010ac5d9f0 75cacf39fc218cf587ee452b6790fc99b0920261Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control of energy systems. This review provides a concise overview of the role of AI in enhancing energy efficiency. AI technologies, such as machine learning and neural networks, are being increasingly applied to optimize energy consumption in various sectors, including buildings, transportation, and industrial processes. These technologies analyze vast amounts of data to identify patterns and trends, enabling more precise control of energy systems and the prediction of energy demand. One of the key advantages of AI in energy efficiency optimization is its ability to adapt and learn from data, leading to continuous improvement in energy-saving strategies. AI algorithms can optimize energy consumption based on factors such as weather conditions, occupancy patterns, and energy prices, resulting in significant cost savings and environmental benefits. Furthermore, AI enables the integration of renewable energy sources into existing energy systems by predicting renewable energy generation and optimizing its use. This integration helps reduce reliance on fossil fuels and mitigates greenhouse gas emissions, contributing to a more sustainable energy future. However, the implementation of AI in energy efficiency optimization is not without challenges. These include data privacy concerns, the need for specialized skills to develop and deploy AI solutions, and the complexity of integrating AI systems into existing energy infrastructure. Addressing these challenges will be crucial for realizing the full potential of AI in energy efficiency optimization. In conclusion, AI holds great promise for enhancing energy efficiency by enabling more intelligent control and optimization of energy systems. By leveraging AI technologies, organizations can achieve significant energy savings, reduce costs, and contribute to a more sustainable and resilient energy future. Keywords: Role, AI, Energy, Efficiency, Optimization.Engineering Science &amp; Technology Journal00AI holds great promise for enhancing energy efficiency by enabling more intelligent control and optimization of energy systems by leveraging AI technologies, organizations can achieve significant energy savings, reduce costs, and contribute to a more sustainable and resilient energy future.Engineering Science &amp; Technology Journal['Tosin Michael Olatunde', 'Azubuike Chukwudi Okwandu', 'Dorcas Oluwajuwonlo Akande', 'Zamathula Queen Sikhakhane']2024-04-10T00:00:00https://www.semanticscholar.org/paper/75cacf39fc218cf587ee452b6790fc99b0920261 4d0502bf2298a10cfe96f6d3bfe11c67c1048cc4Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.Annual Review of Biomedical Data Science00The merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem are discussed.Annual review of biomedical data science['Carole A Federico', 'Artem A. Trotsyuk']2024-04-10T00:00:00https://www.semanticscholar.org/paper/4d0502bf2298a10cfe96f6d3bfe11c67c1048cc4 e8820c1527272a359a292f6e82ede49b77df61a7This study investigates the perceptions of employees in the hotel industry of the Republic of Serbia regarding the acceptance and importance of artificial intelligence (AI). Through a modified UTAUT model and the application of structural equation analysis (SEM), we investigated the key factors shaping AI acceptance. Research results show that behavioral intention and habit show a significant positive impact on AI usage behavior, while facilitating conditions have a limited but measurable impact on behavioral intention. Other factors, including social influence, hedonic motivation, performance expectancy, and effort expectancy, have minimal influence on the examined variables. The analysis reveals the crucial mediating role of behavioral intention, effectively bridging the gap between various predictors and AI usage behavior, thereby highlighting its significance in the broader context of technology adoption in the hotel industry. The primary goal of the study, which closes significant research gaps, as well as the manner in which it uses a specific model and statistical analysis to accomplish this goal, shows how innovative the work is. This method not only broadens the field’s understanding but also offers valuable insights for shaping sustainable development practices in the hospitality sector in the Republic of Serbia.Sustainability1060Investigation of perceptions of employees in the hotel industry of the Republic of Serbia regarding the acceptance and importance of artificial intelligence reveals the crucial mediating role of behavioral intention, effectively bridging the gap between various predictors and AI usage behavior.Sustainability['Tamara Gajić', 'Dragan Vukolić', 'Jovan Bugarčić', 'Filip Đoković', 'Ana Spasojević', 'S. Knežević', 'Jelena Đorđević Boljanović', 'Slobodan Glišić', 'S. Matović', 'L. Dávid']2024-04-10T00:00:00https://www.semanticscholar.org/paper/e8820c1527272a359a292f6e82ede49b77df61a7 80edf815ea3463307f3d6d5d50e6a1342f31ecf5With the rapid development of the Internet, the era of artificial intelligence has become a trend in today's society, ushering in an intelligent revolution in various industries. The advent of the era of artificial intelligence has laid a solid information foundation and environmental support for the development and progress of the information age, which will greatly impact the traditional working methods and approaches of financial accountants. Basic and repetitive tasks such as issuing invoices and expense reimbursement approvals can be replaced by intelligent machines, allowing financial professionals to focus more on management accounting. The application of artificial intelligence technology can not only improve the efficiency of financial accountants, but also promote the transition from financial accounting to management accounting. Therefore, how to utilize artificial intelligence to assist financial professionals in management accounting work is an inevitable requirement for the development of the intelligent era, which presents more demands for management accounting. Based on this background, the development process of enterprises needs to transform the financial management model, enabling the technical aspects of data collection and organization to move towards more advanced and efficient directions. This article analyzes and studies the strategies for using artificial intelligence to help financial accountants acquire management accounting capabilities, achieve the transformation of financial professionals into management accountants, and provide support for the development of enterprises in the era of artificial intelligence.Frontiers in Computing and Intelligent Systems40This article analyzes and studies the strategies for using artificial intelligence to help financial accountants acquire management accounting capabilities, achieve the transformation of financial professionals into management accountants, and provide support for the development of enterprises in the era of artificial intelligence.Frontiers in Computing and Intelligent Systems['Shouliang Zhou', 'Zixuan Wang']2024-04-10T00:00:00https://www.semanticscholar.org/paper/80edf815ea3463307f3d6d5d50e6a1342f31ecf5 0586ad0bf3e82d60f991c284275e078594c3fb9aPurpose Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence. Methods In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively. Results The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate. Conclusion In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.Frontiers in Public Health610It was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.Frontiers in Public Health['M. A. Yenikaya', 'Gökhan Kerse', 'Onur Oktaysoy', 'Hongyu Miao', 'Amir Faisal']2024-04-10T00:00:00https://www.semanticscholar.org/paper/0586ad0bf3e82d60f991c284275e078594c3fb9a b74fd7ffdfed5e30c67dcc97139ad697f7396be2Artificial intelligence (AI) has seen numerous applications for risk analysis and provides ample opportunities for developing new and improved methods and models for this purpose. In the present article, we conceptualize the use of AI for risk analysis by framing it as an input-algorithm-output process and linking such a setup to three tasks in establishing a risk description: consequence characterization, uncertainty characterization, and knowledge management. We then give an overview of currently used concepts and methods for AI-based risk analysis and outline potential future uses by extrapolating beyond currently produced types of output. We end with a discussion of the limits of automation, both near-term limitations and a more fundamental question related to allowing AI to automatically prescribe risk management decisions. We conclude that there are opportunities for using AI for risk analysis to a greater extent than is commonly the case today; however, critical concerns about proper uncertainty representation and the need for risk-informed rather than risk-based decision-making also lead us to conclude that risk analysis and decision-making processes cannot be fully automated.Risk Analysis630There are opportunities for using AI for risk analysis to a greater extent than is commonly the case today; however, critical concerns about proper uncertainty representation and the need for risk-informed rather than risk-based decision-making also lead us to conclude that risk analysis and decision-making processes cannot be fully automated.Risk analysis : an official publication of the Society for Risk Analysis['Kaia Stødle', 'Roger Flage', 'Seth Guikema', 'T. Aven']2024-04-10T00:00:00https://www.semanticscholar.org/paper/b74fd7ffdfed5e30c67dcc97139ad697f7396be2 d8694906cfbc338b3352d69d8a08ae766f9e446eThe emergence of artificial intelligence and the increasing dependence of organisations on these modes of modern technology make this subject quite an intriguing area of research. The research has been conducted in Saudi Arabia, where the emphasis was laid on the criminal liability affiliated with the various firms operating through artificial intelligence and its modern use. How the different liabilities are held upon the organisations and what measures are taken in order to address these issues were all discussed. The role played by the legal authorities was discussed in terms of how these divide the accountabilities of the multiple parties involved.American Journal of Society and Law370The research has been conducted in Saudi Arabia, where the emphasis was laid on the criminal liability affiliated with the various firms operating through artificial intelligence and its modern use.American Journal of Society and Law['Mohamed Fathi Shehta Diab']2024-04-10T00:00:00https://www.semanticscholar.org/paper/d8694906cfbc338b3352d69d8a08ae766f9e446e 9d428e4d3c12f4d4d5866ddad799d20972e97dc3The integration of Artificial Intelligence (AI) in radiology, especially for chest computed tomography (CT) scan analysis, marked a significant advancement in medical diagnostics, aiming to improve patient care and streamline the workflow for radiologists. This review article examined the role of current AI technologies, including machine learning (ML), deep learning (DL), convolutional neural networks (CNN), and radiomics, in enhancing the detection and characterisation of lung diseases. These technologies are instrumental in identifying complex patterns within imaging data and constructing more informed decisions regarding disease severity, progression, and potential treatment options. Deep learning and CNN have demonstrated effectiveness in analysing the intricate details present in chest CT scans, offering a high degree of accuracy. Radiomics complements these methods by extracting quantitative features from medical images, providing deeper insights into disease characteristics that are not visible through standard imaging techniques. The application of AI has shown promise in improving the diagnosis and management of interstitial lung diseases and lung cancers, contributing to the development of personalised treatment plans. However, this review also highlights limitations, such as small sample sizes in studies, which may impact the generalisability of AI applications in this field. Despite these challenges, the ongoing incorporation of AI into radiological practices is anticipated to significantly enhance the accuracy and efficiency of lung disease diagnostics, setting a foundation for future research and improvements in clinical practice.EAS Journal of Radiology and Imaging Technology00The role of current AI technologies, including machine learning (ML), deep learning (DL), convolutional neural networks (CNN), and radiomics, in enhancing the detection and characterisation of lung diseases is examined.EAS Journal of Radiology and Imaging Technology['Wong Kennedy']2024-04-10T00:00:00https://www.semanticscholar.org/paper/9d428e4d3c12f4d4d5866ddad799d20972e97dc3 b07865bbaf7e2e0e123f329ac501eec0563e0ca3Artificial Intelligence (AI) tools are currently designed and tested in many fields to improve humans’ ability to make decisions. One of these fields is higher education. For example, AI-based chatbots (“conversational pedagogical agents”) could engage in conversations with students in order to provide timely feedback and responses to questions while the learning process is taking place and to collect data to personalize the delivery of course materials. However, many existent tools are able to perform tasks that human professionals (educators, tutors, professors) could perform, just in a timelier manner. While discussing the possible implementation of AI-based tools in our university’s educational programs, we reviewed the current literature and identified a number of capabilities that future AI solutions may feature, in order to improve higher education processes, with a focus on distance higher education. Specifically, we suggest that innovative tools could influence the methodologies by which students approach learning; facilitate connections and information attainment beyond course materials; support the communication with the professor; and, draw from motivation theories to foster learning engagement, in a personalized manner. Future research should explore high-level opportunities represented by AI for higher education, including their effects on learning outcomes and the quality of the learning experience as a whole.Frontiers in Artificial Intelligence890It is suggested that innovative tools could influence the methodologies by which students approach learning; facilitate connections and information attainment beyond course materials; support the communication with the professor; and, draw from motivation theories to foster learning engagement, in a personalized manner.Frontiers in Artificial Intelligence['Stefano Triberti', 'Raffaele Di Fuccio', 'Chiara Scuotto', 'Emanuele Marsico', 'P. Limone']2024-04-10T00:00:00https://www.semanticscholar.org/paper/b07865bbaf7e2e0e123f329ac501eec0563e0ca3 c3d3b0e9bacbeb1b77284126aa81a2e0bc58c7cePathologie240The already positive study results make future AI support in kidney transplant pathology appear likely, and the automation of the quantification of some histopathological lesions in nephropathology likely can be extended to automatically quantify all relevant lesions for a kidney transplant, such as Banff lesions.Pathologie['R. Bülow', 'Yu-Chia Lan', 'Kerstin Amann', 'Peter Boor']2024-04-10T00:00:00https://www.semanticscholar.org/paper/c3d3b0e9bacbeb1b77284126aa81a2e0bc58c7ce 403e391afdd6a90f48b6b0d33dc903f9e341f968With the rapid development of science and technology, artificial intelligence technology has become one of the most popular research fields in today's society. From intelligent voice assistants to self-driving cars, to medical diagnostics and financial risk control, AI technology has shown huge potential and value in all fields. This paper deeply explores the current situation, development trend and its application prospect of artificial intelligence technology in various industries. This paper briefly introduces the development process of artificial intelligence technology, including its origin, development status and key technologies. Then, it focuses on the application cases of AI technology in healthcare, finance, education, industry and other fields, and evaluates its impact and potential value. It discusses the challenges of artificial intelligence technology, such as data security and privacy protection, algorithm fairness and moral considerations. In view of these problems, the corresponding solution strategies and development suggestions are put forward. The future development trend of artificial intelligence technology is discussed, including the research and application of cutting-edge technologies such as deep learning, natural language processing and emotional computing. The conclusion pointed out that artificial intelligence technology will continue to change human life in the future and bring more innovation and breakthrough to various industries.Transactions on Computer Science and Intelligent Systems Research50The challenges of artificial intelligence technology, such as data security and privacy protection, algorithm fairness and moral considerations, and the corresponding solution strategies and development suggestions are put forward are discussed.Transactions on Computer Science and Intelligent Systems Research['Fangyu Gu']2024-04-10T00:00:00https://www.semanticscholar.org/paper/403e391afdd6a90f48b6b0d33dc903f9e341f968 8a6d38e9a156b50cec57518193d432f8a7c57599Artificial intelligence characters provide a unique perspective on the discussion of Friday figures and issues of Otherness and Othering as they relate to the body. With their technological, inherently inhuman physicality, AI Fridays shed light on the differentiation between ‘being human’ as an inner state of being, on the one hand, and as having a biologically human body, on the other. These characters thus shift some of the central questions about culture in many Robinsonades towards questions of humanity and physical identity, especially when reading the AI body as a metaphorical stand-in for the colonised body. They reflect on the question of how far our bodies determine who we are, how they relate to and are shaped by the groups we belong to, and what it means to be human.NJES: Nordic Journal of English studies130Artificial intelligence characters provide a unique perspective on the discussion of Friday figures and issues of Otherness and Othering as they relate to the body, and shift some of the central questions about culture in many Robinsonades towards questions of humanity and physical identity.Nordic Journal of English Studies['Sarah Faber']2024-04-10T00:00:00https://www.semanticscholar.org/paper/8a6d38e9a156b50cec57518193d432f8a7c57599 4af6947534bf45a746cd51383170269e5984dfb4OECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2024-04-10T00:00:00https://www.semanticscholar.org/paper/4af6947534bf45a746cd51383170269e5984dfb4 cfef0eb28443019e7a63aedb19a48310eef6e0b0OECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2024-04-10T00:00:00https://www.semanticscholar.org/paper/cfef0eb28443019e7a63aedb19a48310eef6e0b0 97dcbb8460bbd148b4979e6b354a1677dd3b4523The aim of the study is to present the current situation regarding the strategies and policies for the development of AI skills in public sector executives and to establish a holistic training framework based on European and international standards. The paper systematically presents the existing literature on AI, focusing on policy strategies for the training of public sector executives. Hence, the key points of the strategies for AI, as well as the UNESCO Competency Framework for Digital Transformation and AI, the e-CF, the DigComp and the EQF frameworks are presented. Based on the theoretical tools emerged from the literature review, an assessment of the existing situation and the identification of the needs of the Greek reality is presented. Most importantly, the paper attempts to create a holistic four-level strategic framework, which can be used by the public administration as a roadmap to lay the foundations for a basis for public sector training programmes, and which takes into account a number of factors, such as the hierarchical structure of the public administration, the various qualification and competence frameworks, as well as the principles of educational design and adult education.Journal of Politics and Ethics in New Technologies and AI790A holistic four-level strategic framework is created, which can be used by the public administration as a roadmap to lay the foundations for a basis for public sector training programmes, and which takes into account a number of factors, such as the hierarchical structure of the public administration.Journal of Politics and Ethics in New Technologies and AI['Maria Niari']2024-04-10T00:00:00https://www.semanticscholar.org/paper/97dcbb8460bbd148b4979e6b354a1677dd3b4523 930431fcfdd79804345d2918bc2ee71b2b5ad311Customer satisfaction is directly related with the customer retention. The marketer should understand the needs and expectations of his customers for making an effective marketing strategy. Measurement of customer satisfaction enables the firm to deliver maximum value to the customer. Delivering the values to customers facilitates in the creation of loyal customers. The main thrust area among these challenges is the dissatisfaction of customers. The main reason behind this dissatisfaction is the expectations of modern customers who are tech-savvy guys. The digitalization in the area of business is likely to continue in future which will create more challenges before the marketers. Hence customer satisfaction cannot be ignored in the modern digital ageInternational Journal of Advanced Research in Science, Communication and Technology100International Journal of Advanced Research in Science, Communication and Technology['Amol Bajirao Kale', 'Preeti Baban Lokhande', 'Ramshi Purushottam Pathak', 'Shivaji Arun Shinde']2024-04-10T00:00:00https://www.semanticscholar.org/paper/930431fcfdd79804345d2918bc2ee71b2b5ad311 1e0d07d95b8dbac4a55f35ef5ce6f726b8c69210Proceedings of the Water Environment Federation00Proceedings of the Water Environment Federation['Katie Deheer', 'Varun Srinivasan']2024-04-10T00:00:00https://www.semanticscholar.org/paper/1e0d07d95b8dbac4a55f35ef5ce6f726b8c69210 5bea14c265e81127f282298ee1274f2b8c2de160Assessment &amp; Evaluation in Higher Education240Assessment &amp; Evaluation in Higher Education['M. Bearman', 'Joanna Tai', 'Phillip Dawson', 'D. Boud', 'R. Ajjawi']2024-04-10T00:00:00https://www.semanticscholar.org/paper/5bea14c265e81127f282298ee1274f2b8c2de160 3d535cc2965292687774fcb36dac2cf7bd0af4b9 This article examines whether the EU Medical Device Regulation (MDR) adequately addresses the novel risks of AI-based medical devices (AIaMDs), focusing on AI medical imaging tools. It examines two questions: first, does the MDR effectively deal with issues of adaptability, autonomy, bias, opacity, and the need of trustworthiness of AIaMD? Second, does the manufacturer’s translation of the MDR’s requirements close a discrepancy between an AIaMDs’ expected benefit and the actual clinical utility of assessing device safety and effectiveness beyond the narrow performance of algorithms? While the first question has previously received attention in scholarly literature on regulatory and policy tensions on AIaMD generally, and work on future technical standard setting, the second has been comparatively overlooked. We argue that effective regulation of AIaMD requires framing notions of patient safety and benefit within the manufacturer’s articulation of the device’s intended use, as well as reconciling tensions. These tensions are on (i) patient safety and knowledge gaps surrounding fairness, (ii) trustworthiness and device effectiveness, (iii) the assessment of clinical performance, and (iv) performance updates. Future guidance needs to focus on the importance of translated benefits, including nuanced risk framing and looking at how the limitations of AIaMD inform the intended purpose statement in the MDR.Social Science Research Network330It is argued that effective regulation of AIaMD requires framing notions of patient safety and benefit within the manufacturer’s articulation of the device’s intended use, as well as reconciling tensions.SSRN Electronic Journal['Daria Onitiu', 'Sandra Wachter', 'B. Mittelstadt']2024-04-09T00:00:00https://www.semanticscholar.org/paper/3d535cc2965292687774fcb36dac2cf7bd0af4b9 f6020314edeecfbfead5a7d7e51dfa64655f16f500['Cindy Zheng']2024-04-09T00:00:00https://www.semanticscholar.org/paper/f6020314edeecfbfead5a7d7e51dfa64655f16f5 b0035f2c1c1a222103ee6c4ef0cff53ddc7268fbWe explore ideas and inclusive practices for designing and testing child-centered artificially intelligent technologies for neurodivergent children. AI is promising for supporting social communication, self-regulation, and sensory processing challenges common for neurodivergent children. The authors, both neurodivergent individuals and related to neurodivergent people, draw from their professional and personal experiences to offer insights on creating AI technologies that are accessible and include input from neurodivergent children. We offer ideas for designing AI technologies for neurodivergent children and considerations for including them in the design process while accounting for their sensory sensitivities. We conclude by emphasizing the importance of adaptable and supportive AI technologies and design processes and call for further conversation to refine child-centered AI design and testing methods.arXiv.org00The authors, both neurodivergent individuals and related to neurodivergent people, draw from their professional and personal experiences to offer insights on creating AI technologies that are accessible and include input from neurodivergent children.ArXiv['Emani Dotch', 'Vitica Arnold']2024-04-09T00:00:00https://www.semanticscholar.org/paper/b0035f2c1c1a222103ee6c4ef0cff53ddc7268fb a863d3424b376b67e60998ba288e62d5c0dbb0c7Adaptive learning technologies are closely related to learners' self‐regulatory processes in individual and collaborative learning. This study presents the outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings. We provide an overview of what and how adaptive technologies have been used to understand and promote self‐regulated learning in collaborative contexts. A search resulted in 38 papers being analysed. Specifically, we identified the seven main objectives (feedback and scaffolding, self‐regulatory skills and strategies, learning trajectories, collaborative learning processes, adaptation and regulation, self‐assessment, and help‐seeking behaviour) that the adaptive technology research has been focusing on. We also summarize the implications derived from the reviewed papers and frame them within seven thematic areas. Finally, this review stresses that future research should consider developing a converging theoretical framework that would enable concrete monitoring and support for self‐regulation and socially shared regulation of learning. Our findings set a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development. What is already known about this topic By providing personalized and learner‐centric adaptive learning environments (ADLEs), adaptive learning technology can support and foster self‐regulated learning (SRL) practices. It is possible to create a more student‐centred and effective learning environment by combining adaptive learning and collaborative learning. Socially shared regulatory activities can involve planning, monitoring, controlling and reflecting on a group's learning processes. What this paper adds Provides a systematic literature review of empirical evidence on ADLEs, SRL and socially shared regulation of learning (SSRL) in collaborative contexts. Summarizes the insights on (S)SRL through ADLEs in collaborative learning. Identifies challenges and opportunities for ADLEs to support (S)SRL in collaborative learning. Implications for practice and/or policy Learning analytics and educational technology researchers will be able to use the systematic review as a guide for future research. Learning analytics and educational technology practitioners will be able to use the systematic review as a summary of the field's current state. British Journal of Educational Technology1251The outcomes of a systematic literature review of empirical evidence on adaptive learning environments to foster self‐regulation and shared regulation of learning in collaborative settings are presented and a baseline to support the adoption and proliferation of adaptive learning technology within self‐regulated learning research and development is set.British Journal of Educational Technology['Kshitij Sharma', 'Andy Nguyen', 'Yvonne Hong']2024-04-09T00:00:00https://www.semanticscholar.org/paper/a863d3424b376b67e60998ba288e62d5c0dbb0c7 3935f9d87712edd2ee9e7a5c51fdf3ab0d636607This study aims to normatively analyze OJK Regulation Number 3 of 2024, focusing on consumer protection in the context of the increasing trade in virtual currency in Indonesia. This study uses a normative legal research method. The collected legal material is then qualitatively analyzed to describe the problem and answer the study objectives. The results show that OJK Regulation Number 3 of 2024 is crucial in optimizing consumer protection and increasing virtual currency trading in Indonesia. Through the general provisions in OJK Regulation Number 3 of 2024, which cover consumer protection and the sandbox mechanism, the regulation supports responsible testing and development of FinTech innovations. The practical implementation of OJK Regulation Number 3 of 2024, emphasizing personal data protection and transparency, adds a layer of consumer protection. Overall, OJK Regulation Number 3 of 2024 creates an environment conducive to safe and regulated virtual currency trading, enhancing investor and consumer confidence in the Indonesian FinTech ecosystem. Therefore, it is recommended that the government and OJK, FinTech innovators and virtual currency traders, as well as consumers and investors, take strategic steps to enhance consumer protection and optimize virtual currency trading in Indonesia. The Government and OJK need to ensure the effective implementation of OJK Regulation Number 3 of 2024 through widespread socialization, strict supervision, and firm law enforcement against violations. FinTech innovators and virtual currency traders should develop robust risk management systems, enhance operational transparency, and ensure compliance with personal data protection provisions. Consumers and investors are advised to improve their digital financial literacy, critically assess investment risks, and interact only with platforms and service providers that have been verified and comply with OJK regulations. The collaboration between regulators, the industry, and the community will create a safer, more transparent, and sustainable virtual currency trading ecosystem, supporting the growth of the FinTech sector in Indonesia while minimizing potential risks for consumers and investors.Al-Ishlah00Al-Ishlah: Jurnal Ilmiah Hukum['Dian Ekawati', 'Toto Tohir', 'Susanto Susanto']2024-04-09T00:00:00https://www.semanticscholar.org/paper/3935f9d87712edd2ee9e7a5c51fdf3ab0d636607 0a7bf378adf27fa6309cf06df1f7c3b1f3e020e9Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.Annual review of genomics and human genetics (Print)00The development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.Annual review of genomics and human genetics['Ksenia Sokolova', 'Kathleen M. Chen', 'Yun Hao', 'Jian Zhou', 'O. Troyanskaya']2024-04-09T00:00:00https://www.semanticscholar.org/paper/0a7bf378adf27fa6309cf06df1f7c3b1f3e020e9 8badb713c81d204b80c05c6fd5f5b58f81c5ce55This study examines the impact of the Basel Accords on competition within the UK banking sector, considering variations based on bank size. The Basel Accords, designed to enhance financial stability, introduce provisions that may affect competition dynamics. Empirical analysis reveals divergent outcomes: large banks tend towards monopolization, while other banks shift towards a more competitive environment. Large banks benefit from regulatory barriers and technological advancements, while other banks face challenges from increased compliance costs. These findings highlight the complex relationship between regulation and competition in banking, emphasizing the need for balanced regulations that promote stability while fostering healthy competition.Mathematics250Mathematics['Eleonora Muzzupappa']2024-04-09T00:00:00https://www.semanticscholar.org/paper/8badb713c81d204b80c05c6fd5f5b58f81c5ce55 837d6deb262c633edb540774670beef10fbc3fc9At the current stage of development of international relations, when factional trends in the foreign policy of such significant world leaders as the European Union and the United States are only gaining momentum, China has become the main strategic partner for Russia likewise for the EAEU. Due to the introduction of international sanctions against both China and Russia, reorientation of economic cooperation and, therefore, transportation and logistics routes to the East seems to be the most correct development paradigm. However, at the current level of development of science and technology, it is necessary to expand and intensify the formation of the array of international legal legislation to create an effective basis for legal regulation of the transport and logistics industry for the growth of synergistic effect of integration processes in the Eurasian space.Aim. To analyze various aspects of international legal regulation of the ways of integration of the EAEU and China in the transport and logistics sphere to increase the level of economic interaction between the partner countries.Tasks. It is proposed to consider the main trends in the formation of an array of international legal acts aimed at creating a basis for effective cooperation between China and the EAEU in the current geopolitical conditions, taking into account the need to develop advanced regulation of the introduction of digital technologies, including the development of logistics platforms, to create conditions for seamless cargo transportation in the Eurasian region.Methods. The study used formal-legal and comparative-legal methods, deduction, synthesis, induction, as well as abstraction and system method.Results. The necessity to develop unified regulatory requirements both in the sphere of technical operation of different types of transport and the creation of a regulatory framework for the development of conjugate EAEU-China transport and logistics digital infrastructure, providing seamless multimodal transportation to obtain a synergistic effect from the economic integration of the Eurasian region was substantiated.Conclusions. The comprehensive diagnosis of the effectiveness of international partnershop between the EAEU and China in transport and logistics proposed by the authors of the study will allow to adjust the existing strategy of interaction between the countries and increase the efficiency of Eurasian integration through the implementation of measures aimed at the development of seamless logistics.EURASIAN INTEGRATION: economics, law, politics10EURASIAN INTEGRATION: economics, law, politics['M. Drozdova', 'O. Pokrovskaya']2024-04-09T00:00:00https://www.semanticscholar.org/paper/837d6deb262c633edb540774670beef10fbc3fc9 4f5bd255fe3e343d02dd3a2650f1dec45434e3f0This study is aimed at studying the institutional, organizational and legal problems of interaction between the media and international observers during elections and referendums in the member states of the Commonwealth of Independent States. The issue of countering foreign interference in the electoral process was not considered in the work.Aim. Eliminate methodological problems in defining the content of the term “international observation”, and proving the need to institutionalize the process of covering the activities of international observers in the media.Tasks. Identify the necessary areas of media activity when covering the activities of international observers during elections and referendums.Methods. The state of the modern election monitoring environment indicates the existence of a fine line between the need to ensure openness of electoral processes and the inadmissibility of interference in the internal affairs of states. It is necessary to identify restrictive thresholds for the activities of the media when covering the processes of international observation of elections and referendums and their institutionalization in relevant advisory documents based on the presumed impartiality of both international observers and the media.Results. The study showed that the most important task of the process of legitimizing the electoral process is to ensure its full coverage of the activities of all its subjects in the media. International monitoring media coverage should cover a wide range of issues throughout the entire electoral process from start to finish, including; activities of observers during the official election campaign before the start of voting; on election day and during the vote counting process; in the period after the elections from the moment the official results are announced until the formation of new authorities. At the same time, at all stages, coverage should cover a wide range of issues of the essence of the electoral process and its management; legal regulation of elections and referendums and existing institutional structures to support the process.Conclusions. Media coverage of the activities of international observers should be considered not only as one of the most important modes of political communication, but also as a means of countering selective absenteeism. Objective and impartial coverage of the activities of international observers in the media can undoubtedly contribute to both the internal political mobilization of the population and the international recognition of the state.EURASIAN INTEGRATION: economics, law, politics00EURASIAN INTEGRATION: economics, law, politics['S. A. Malinina']2024-04-09T00:00:00https://www.semanticscholar.org/paper/4f5bd255fe3e343d02dd3a2650f1dec45434e3f0 a12158d1d7a314c0184ddea28f82b9a4a39a3309Emerging markets finance & trade420Emerging Markets Finance and Trade['Shanshan Li', 'Shiwei Hu']2024-04-09T00:00:00https://www.semanticscholar.org/paper/a12158d1d7a314c0184ddea28f82b9a4a39a3309 5455055b97b08eaa1edf535d708054c4aeea6673This research explores the transformative potential of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in empowering smart cities to achieve intelligent urban management and sustainable development. Through a comprehensive analysis of literature, case studies, and qualitative research methods, the paper identifies key strategies for leveraging AI and RPA to address urban challenges and promote sustainable urban development. The integration of AI and RPA technologies enables data-driven decision-making processes, streamlines administrative workflows, and enhances service delivery in smart cities. Furthermore, AI and RPA contribute to promoting sustainable development goals by optimizing resource utilization, improving environmental management practices, and enhancing resilience to climate change. However, the widespread adoption of AI and RPA in smart cities faces challenges related to privacy, data security, and equity, which must be carefully addressed to ensure responsible and equitable deployment of these technologies. By adopting comprehensive strategies, fostering collaboration between stakeholders, and embracing a culture of innovation, cities can harness the full potential of AI and RPA to build smarter, more resilient, and sustainable urban environments for all residents. This research provides valuable insights for policymakers, urban planners, and technology providers seeking to leverage AI and RPA to address urban challenges and promote sustainable development in smart cities.International Journal of Scientific Research and Management121This research provides valuable insights for policymakers, urban planners, and technology providers seeking to leverage AI and RPA to address urban challenges and promote sustainable development in smart cities.International Journal of Scientific Research and Management (IJSRM)['Kamala Venigandla', 'Navya Vemuri', 'Ezekiel Nnamere Aneke']2024-04-09T00:00:00https://www.semanticscholar.org/paper/5455055b97b08eaa1edf535d708054c4aeea6673 464595acb796cfff878238c8012526504fd738ebArtificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on job tasks with a measure of AI susceptibility, we show that more highly skilled workers are more susceptible to AI automation, and that analytical non-routine tasks are at risk to be impacted by AI. Moreover, we observe that wage growth premiums for the lowest and the highest required skill level appear unrelated to AI susceptibility and that workers in occupations with many routine tasks saw higher wage growth if their work was more strongly susceptible to AI. Our findings imply that AI has the potential to affect human workers differently than canonical economic theories about the impact of technology on work these theories predict.00Combining data on job tasks with a measure of AI susceptibility shows that more highly skilled workers are more susceptible to AI automation, and that analytical non-routine tasks are at risk to be impacted by AI.['Pelin Ozgul', 'Marie-Christine Fregin', 'Michael Stops', 'Simon Janssen', 'Mark Levels']2024-04-09T00:00:00https://www.semanticscholar.org/paper/464595acb796cfff878238c8012526504fd738eb 0bd474aa88d3609be79f6fa1fc32ceb2e0aef043As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount. AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treats metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we present \textsc{IBis}, a blockchain-based framework tailored for AI model training workflows. \textsc{IBis} integrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Our framework addresses concerns regarding data and model provenance and copyright compliance. \textsc{IBis} enables iterative model retraining and fine-tuning, and offers flexible license checks and renewals. Further, \textsc{IBis} provides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implement \textsc{IBis} using Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability of \textsc{IBis} across varying numbers of users, datasets, models, and licenses.arXiv.org500A blockchain-based framework tailored for AI model training workflows that addresses concerns regarding data and model provenance and copyright compliance, and enables iterative model retraining and fine-tuning, and offers flexible license checks and renewals.ArXiv['Yilin Sai', 'Qin Wang', 'Guangsheng Yu', 'H.M.N. Dilum Bandara', 'Shiping Chen']2024-04-09T00:00:00https://www.semanticscholar.org/paper/0bd474aa88d3609be79f6fa1fc32ceb2e0aef043 36b1da891fb945bcec2c4a1610015813b2560f0bnpj Digit. Medicine500Artificial intelligence in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis, and future studies are encouraged to further investigate these potential benefits in real-life settings.NPJ Digital Medicine['I. Krakowski', 'Jiyeong Kim', 'Zhuo Ran Cai', 'Roxana Daneshjou', 'Jan Lapins', 'Hanna Eriksson', 'Anastasia Lykou', 'Eleni Linos']2024-04-09T00:00:00https://www.semanticscholar.org/paper/36b1da891fb945bcec2c4a1610015813b2560f0b 2fd67fcc8b48140a4afc69ac9cc3961504908766npj Digit. Medicine360This study fills the gap by documenting the process of designing, building, and maintaining an AI solution called SepsisWatch at Duke University Health System and identifies generalizable insights about how to recognize and navigate barriers to AI/ML adoption in healthcare settings.NPJ Digital Medicine['William Boag', 'Alifia Hasan', 'Jee Young Kim', 'M. Revoir', 'Marshall Nichols', 'W. Ratliff', 'M. Gao', 'Shira Zilberstein', 'Zainab Samad', 'Zahra Hoodbhoy', 'Mushyada Ali', 'Nida Saddaf Khan', 'Manesh R Patel', 'S. Balu', 'M. Sendak']2024-04-09T00:00:00https://www.semanticscholar.org/paper/2fd67fcc8b48140a4afc69ac9cc3961504908766 92e1b8e3331605e1f6cb50c01d94bc3be303ee4aEthics and Information Technology190It is argued that a black box AI indeed creates a rationally irresolvable epistemic situation for the physician involved and that such an epistemic situation is problematic in the context of ultimate human responsibility.Ethics Inf. Technol.['Markus Herrmann', 'Andreas Wabro', 'Eva Winkler']2024-04-09T00:00:00https://www.semanticscholar.org/paper/92e1b8e3331605e1f6cb50c01d94bc3be303ee4a bac5dae05c420248a3c95436326462617ed05473Background Artificial intelligence (AI) has emerged as a transformative force across the health sector and has garnered significant attention within sexual and reproductive health and rights (SRHR) due to polarizing views on its opportunities to advance care and the heightened risks and implications it brings to people’s well-being and bodily autonomy. As the fields of AI and SRHR evolve, clarity is needed to bridge our understanding of how AI is being used within this historically politicized health area and raise visibility on the critical issues that can facilitate its responsible and meaningful use. Objective This paper presents the protocol for a scoping review to synthesize empirical studies that focus on the intersection of AI and SRHR. The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms. Methods The scoping review follows the standard methodology developed by Arksey and O’Malley. We will search the following electronic databases: MEDLINE (PubMed), Scopus, Web of Science, and CINAHL. Inclusion criteria comprise the use of AI systems and tools in sexual and reproductive health and clear methodology describing either quantitative or qualitative approaches, including program descriptions. Studies will be excluded if they focus entirely on digital interventions that do not explicitly use AI systems and tools, are about robotics or nonhuman subjects, or are commentaries. We will not exclude articles based on geographic location, language, or publication date. The study will present the uses of AI across sexual and reproductive health domains, the intended purpose of the AI system and tools, and maturity within the AI life cycle. Outcome measures will be reported on the effect, accuracy, acceptability, resource use, and feasibility of studies that have deployed and evaluated AI systems and tools. Ethical and legal considerations, as well as findings from qualitative studies, will be synthesized through a narrative thematic analysis. We will use the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) format for the publication of the findings. Results The database searches resulted in 12,793 records when the searches were conducted in October 2023. Screening is underway, and the analysis is expected to be completed by July 2024. Conclusions The findings will provide key insights on usage patterns and evidence on the use of AI in SRHR, as well as convey key ethical, safety, and legal considerations. The outcomes of this scoping review are contributing to a technical brief developed by the World Health Organization and will guide future research and practice in this highly charged area of work. Trial Registration OSF Registries osf.io/ma4d9; https://osf.io/ma4d9 International Registered Report Identifier (IRRID) PRR1-10.2196/53888JMIR Research Protocols370The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms.JMIR Research Protocols['Tigest Tamrat', 'Yu Zhao', 'Denise Schalet', 'Shada Alsalamah', 'Sameer Pujari', 'L. Say']2024-04-09T00:00:00https://www.semanticscholar.org/paper/bac5dae05c420248a3c95436326462617ed05473 dc03513ad4c5498481beaf22604516ad93b6fbeaBackground Existential anxiety can profoundly affect an individual, influencing their perceptions, behaviours, sense of well-being, academic performance, and decisions. Integrating artificial intelligence into society has elicited complex public reactions, marked by appreciation and concern, with its acceptance varying across demographics and influenced by factors such as age, gender, and prior AI experiences. This study aimed to investigate the existential anxiety about artificial intelligence (AI) in public in Saudi Arabia. Methods The present questionnaire-based observational, analytical cross-sectional study with a structured, self-administered survey was conducted via Google Forms, using a scale to assess the existential anxiety levels induced by the recent development of AI. The study encompassed a diverse population with a sample size of 300 participants. Results This study’s findings revealed a high prevalence of existential anxieties related to the rapid advancements in AI. Key concerns included the fear of death (96% of participants), fate’s unpredictability (86.3%), a sense of emptiness (79%), anxiety about meaninglessness (92.7%), guilt over potential AI-related catastrophes (87.7%), and fear of condemnation due to ethical dilemmas in AI (93%), highlighting widespread apprehensions about humanity’s future in an AI-dominated era. Conclusion The public has concerns including unpredictability, a sense of emptiness, anxiety, guilt over potential AI-related catastrophes, and fear of condemnation due to ethical dilemmas in AI, highlighting widespread apprehensions about humanity’s future in an AI-dominated era. The results indicate that there is a need for a multidisciplinary strategy to address the existential anxieties in the AI era. The strategic approach must blend technological advancements with psychological, philosophical, and ethical insights, underscoring the significance of human values in an increasingly technology-driven world.Frontiers in Psychiatry200The public has concerns including unpredictability, a sense of emptiness, anxiety, guilt over potential AI-related catastrophes, and fear of condemnation due to ethical dilemmas in AI, highlighting widespread apprehensions about humanity's future in an AI-dominated era.Frontiers in Psychiatry['J. Alkhalifah', 'Abdulrahman Mohammed Bedaiwi', 'Narmeen Shaikh', 'Waleed Seddiq', 'S. Meo']2024-04-09T00:00:00https://www.semanticscholar.org/paper/dc03513ad4c5498481beaf22604516ad93b6fbea 09d4c1e2596164e97cd5cc4a14c219465aff5ea8Accelerated adoption of electric propulsion system in mobility industry has stressed the time and iterations of product development cycle which was traditionally known to go over multiple iterations and phases. Current market demands a timely introduction of compelling products that brings high value to end user. Further, a growing emphasis over reducing mineral content using sustainable options and process, adds further complexity to multi-objective-optimization of electric drive systems. At BorgWarner our engineers use Digital-Twins, physics-based models which closely represent BorgWarner products in greater dept (physics) thus allowing an improved assessment of product design (components and systems) to target application at very early stage in product development. The spring success with Digital-Twin, BorgWarner furthered enhanced the model through introducing Artificial Intelligent (AI) and Machine Learning (ML) technologies in both modelling and virtual sensing.This paper will provide reader an in-depth view of technology aspects with Digital-Twin and introduce AI and ML algorithms within Digital-Twin, beginning with a brief introduction on AI and ML technologies, the paper will also go in depth on in-use applications of these technologies at BorgWarner, such as, Deep learning virtual sensors usage. The paper will include a clear description of specialty tools and methods adopted by BorgWarner for data cleaning, model training and validation. The paper will provide reader an insight into how such trained AI-ML models were developed and trained using data provided by validated 1D and high-fidelity models from Amesim and COMSOL respectively, where each of those reference datasets were verified using real hardware tested in a vehicle environment. Although scope within BorgWarner product development was surrounding integrated drive module, for the purpose of showcasing an example the paper will provide technical insight to “Deep Learning Virtual Sensor for Power Module”. Towards conclusion, our goal is to showcase the method of integrating AI-ML models in Digital Twin and how those learnings can be translated into a product feature which could minimize controls complexity while enhancing accuracy and safety of the electrified propulsion system.SAE technical paper series00The goal is to showcase the method of integrating AI-ML models in Digital Twin and how those learnings can be translated into a product feature which could minimize controls complexity while enhancing accuracy and safety of the electrified propulsion system.SAE Technical Paper Series['Pascal David', 'Skander Oueslati', 'Eric Bourniche', 'Harsha Nanjundaswamy']2024-04-09T00:00:00https://www.semanticscholar.org/paper/09d4c1e2596164e97cd5cc4a14c219465aff5ea8 109ba9b43cd79f8f8f07e160d7bcbaaaec2cc8b8The rapid advancement of Artificial Intelligence (AI) in the field of autonomous driving has led to significant breakthroughs, enabling the development of highly sophisticated driving assistant systems. However, as these systems become more prevalent, it is crucial to address the ethical considerations surrounding their deployment and operation. This research paper delves into the multifaceted domain of ethics in AI for Autonomous Driving Assistant System ADAS/AD systems, analyzing various use cases and exploring different scenarios. Ethical concerns in AI for autonomous driving encompass a wide range of topics, including safety, privacy concerns related to data collection and usage, decision-making, ethical dilemmas, accountability, and societal impact. This research focuses on intricate challenges that arise in the field of autonomous driving and investigates these issues by examining real-world use cases. Such exploration is intended to shed light on the complex ethical challenges that arise in the context of autonomous driving. This research paper presents comprehensive investigations of different approaches for designing ethical decision-making algorithms, considering utilitarianism, deontological principles, and the concept of moral responsibility. This research critically assesses the potential consequences for various stakeholders e.g., drivers, pedestrians, etc. This analysis helps us to understand the broader ethical ramifications of the widespread adoption of autonomous driving technologies and the evaluation of the legal and ethical frameworks necessary to address ethical considerations, including liability, accountability, and the establishment of industry standards. The purpose of this research is to advocate for transparency, accountability, and stakeholder engagement as fundamental principles for ensuring the ethical use of these technologies. This research endeavour presents valuable insight for policymakers, industry practitioners, and researchers in navigating the complex ethical landscape of autonomous driving technologies, eventually aiding in the growing prominence of autonomous vehicles.SAE technical paper series380This research paper presents comprehensive investigations of different approaches for designing ethical decision-making algorithms, considering utilitarianism, deontological principles, and the concept of moral responsibility, to advocate for transparency, accountability, and stakeholder engagement as fundamental principles for ensuring the ethical use of these technologies.SAE Technical Paper Series['Ankit Wani', 'Deepa Kumari', 'Jyotsana Singh']2024-04-09T00:00:00https://www.semanticscholar.org/paper/109ba9b43cd79f8f8f07e160d7bcbaaaec2cc8b8 6696018baf29273aa722e16eda89850247b8f0aaLarge Language Models (LLMs) have become instrumental in advancing software engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. Like traditional SE tools, open-source collaboration is key in realising the excellent products. However, with AI models, the essential need is in data. The collaboration of these AI-based SE models hinges on maximising the sources of high-quality data. However, data especially of high quality, often holds commercial or sensitive value, making it less accessible for open-source AI-based SE projects. This reality presents a significant barrier to the development and enhancement of AI-based SE tools within the software engineering community. Therefore, researchers need to find solutions for enabling open-source AI-based SE models to tap into resources by different organisations. Addressing this challenge, our position paper investigates one solution to facilitate access to diverse organizational resources for open-source AI models, ensuring privacy and commercial sensitivities are respected. We introduce a governance framework centered on federated learning (FL), designed to foster the joint development and maintenance of open-source AI code models while safeguarding data privacy and security. Additionally, we present guidelines for developers on AI-based SE tool collaboration, covering data requirements, model architecture, updating strategies, and version control. Given the significant influence of data characteristics on FL, our research examines the effect of code data heterogeneity on FL performance.arXiv.org580A governance framework centered on federated learning (FL), designed to foster the joint development and maintenance of open-source AI code models while safeguarding data privacy and security is introduced.ArXiv['Zhihao Lin', 'Wei Ma', 'Tao Lin', 'Yaowen Zheng', 'Jingquan Ge', 'Jun Wang', 'Jacques Klein', 'Tegawendé F. Bissyandé', 'Yang Liu', 'Li Li']2024-04-09T00:00:00https://www.semanticscholar.org/paper/6696018baf29273aa722e16eda89850247b8f0aa 8f2571aa12b11c1f9eaaf38c6f015d7526b1e004Decision aids based on artificial intelligence (AI) are becoming increasingly common. When such systems are deployed in environments with inherent uncertainty, following AI-recommended decisions may lead to a wide range of outcomes. In this work, we investigate how the framing of uncertainty in outcomes impacts users' longitudinal trust in AI decision aids, which is crucial to ensuring that these systems achieve their intended purposes. More specifically, we use gig driving as a representative domain to address the question: how does exposing uncertainty at different levels of granularity affect the evolution of users' trust and their willingness to rely on recommended decisions? We report on a longitudinal mixed-methods study $(n = 51)$ where we measured the trust of gig drivers as they interacted with an AI-based schedule recommendation tool. Statistically significant quantitative results indicate that participants' trust in and willingness to rely on the tool for planning depended on the perceived accuracy of the tool's estimates; that providing ranged estimates improved trust; and that increasing prediction granularity and using hedging language improved willingness to rely on the tool even when trust was low. Additionally, we report on interviews with participants which revealed a diversity of experiences with the tool, suggesting that AI systems must build trust by going beyond general designs to calibrate the expectations of individual users.arXiv.org1080Results indicate that participants' trust in and willingness to rely on the tool for planning depended on the perceived accuracy of the tool's estimates; that providing ranged estimates improved trust; and that increasing prediction granularity and using hedging language improved willingness to rely on the tool even when trust was low.ArXiv['Rex Chen', 'Ruiyi Wang', 'Norman M. Sadeh', 'Fei Fang']2024-04-09T00:00:00https://www.semanticscholar.org/paper/8f2571aa12b11c1f9eaaf38c6f015d7526b1e004 de88eb0043f806473e4a0687780cc01888c2bbbcPurposeThe purpose of this research is to propose and empirically validate a theoretical framework to investigate the willingness of the elderly to disclose personal health information (PHI) to improve the operational efficiency of AI-integrated caregiver robots.Design/methodology/approachDrawing upon Privacy Calculus Theory (PCT) and the Technology Acceptance Model (TAM), 274 usable responses were collected through an online survey.FindingsEmpirical results reveal that trust, privacy concerns, and social isolation have a direct impact on the willingness to disclose PHI. Perceived ease of use (PEOU), perceived usefulness (PU), social isolation, and recognized benefits significantly influence user trust. Conversely, elderly individuals with pronounced privacy concerns are less inclined to disclose PHI when using AI-enabled caregiver robots.Practical implicationsGiven the pressing need for AI-enabled caregiver robots due to the aging population and a decrease in professional human caregivers, understanding factors that influence the elderly's disclosure of PHI can guide design considerations and policymaking.Originality/valueConsidering the increased demand for accurate and comprehensive elder services, this is the first time that information disclosure and AI-enabled caregiver robot technologies have been combined in the field of healthcare management. This study bridges the gap between the necessity for technological improvement in caregiver robots and the importance of transparent operational information by disclosing the elderly's willingness to share PHI.Industrial management & data systems1310This study bridges the gap between the necessity for technological improvement in caregiver robots and the importance of transparent operational information by disclosing the elderly's willingness to share PHI.Ind. Manag. Data Syst.['M. A. S. Amin', 'Vess L. Johnson', 'Victor R. Prybutok', 'Chang Koh']2024-04-09T00:00:00https://www.semanticscholar.org/paper/de88eb0043f806473e4a0687780cc01888c2bbbc b705e3cca48f1e61ae7052a4208f8e97606de95eAs Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase. We find a notable deficiency in high-quality content safety datasets and benchmarks that comprehensively cover a wide range of critical safety areas. To address this, we define a broad content safety risk taxonomy, comprising 13 critical risk and 9 sparse risk categories. Additionally, we curate AEGISSAFETYDATASET, a new dataset of approximately 26, 000 human-LLM interaction instances, complete with human annotations adhering to the taxonomy. We plan to release this dataset to the community to further research and to help benchmark LLM models for safety. To demonstrate the effectiveness of the dataset, we instruction-tune multiple LLM-based safety models. We show that our models (named AEGISSAFETYEXPERTS), not only surpass or perform competitively with the state-of-the-art LLM-based safety models and general purpose LLMs, but also exhibit robustness across multiple jail-break attack categories. We also show how using AEGISSAFETYDATASET during the LLM alignment phase does not negatively impact the performance of the aligned models on MT Bench scores. Furthermore, we propose AEGIS, a novel application of a no-regret online adaptation framework with strong theoretical guarantees, to perform content moderation with an ensemble of LLM content safety experts in deploymentarXiv.org320A broad content safety risk taxonomy is defined, comprising 13 critical risk and 9 sparse risk categories, and a novel application of a no-regret online adaptation framework with strong theoretical guarantees is proposed, to perform content moderation with an ensemble of LLM content safety experts in deployment.ArXiv['Shaona Ghosh', 'Prasoon Varshney', 'Erick Galinkin', 'Christopher Parisien']2024-04-09T00:00:00https://www.semanticscholar.org/paper/b705e3cca48f1e61ae7052a4208f8e97606de95e 2087da8edcfa7614f86000ecb5ab4d6b27cced65AI &amp; SOCIETY410It is proposed that advanced AI be reconceptualised as a subject capable of “technical” self-crafting and reflexive self-conduct, opening new pathways to grasp the intertwinement of the human and the artificial.AI &amp; SOCIETY['Kristian D’Amato']2024-04-09T00:00:00https://www.semanticscholar.org/paper/2087da8edcfa7614f86000ecb5ab4d6b27cced65 00f1570313f3b4f29f989f1a952de5ec0375c9a0 Dr Narges Armanfard, Professor, talks us through the AI healthcare research at McGill University which is spearheading a groundbreaking initiative – the iSMART Lab. Access to high-quality healthcare is not just a fundamental human right; it is the bedrock of our societal wellbeing, with the crucial roles played by doctors, nurses, and hospitals. Yet, healthcare systems globally face mounting challenges, particularly from aging populations. Dr Narges Armanfard, affiliated with McGill University and Mila Quebec AI Institute in Montreal, Canada, has spearheaded a groundbreaking initiative – the iSMART Lab. This laboratory represents a revolutionary leap into the future of healthcare, with its pioneering research in AI for health applications garnering significant attention. Renowned for its innovative integration of AI across diverse domains, iSMART Lab stands at the forefront of harnessing Artificial Intelligence to elevate and streamline health services. Open Access Government00Professor Narges Armanfard talks us through the AI healthcare research at McGill University which is spearheading a groundbreaking initiative – the iSMART Lab, renowned for its innovative integration of AI across diverse domains.Open Access Government['Narges Armanfard']2024-04-09T00:00:00https://www.semanticscholar.org/paper/00f1570313f3b4f29f989f1a952de5ec0375c9a0 bc9d5150e08fedea59327d9add9bd72e788e2249Aims: The study aimed to investigate the adoption and perception of artificial intelligence (AI) chatbots, particularly those powered by large language models (LLMs), among pathologists worldwide. It explored the extent of their engagement with these technologies, identifying potential impacts on their professional practices. Methods: A cross-sectional survey was conducted, gathering data from pathologists on their usage and views concerning AI chatbots powered by LLMs. The survey, distributed globally via various digital platforms, included both quantitative and qualitative questions. Statistical analyses were performed to delineate patterns in the adoption and perspectives on these AI tools among the respondents. Results: Of 215 respondents, 100 (46.5%) reported using LLMs, particularly ChatGPT, for professional purposes, predominantly for information retrieval, proofreading, and academic writing, highlighting a significant time-saving benefit. The adoption varied across demographics, with younger, male pathologists showing higher usage rates. While the technology was mainly utilized for drafting academic materials and programming tasks, users expressed concerns about information accuracy, privacy, and the need for regulatory approval. Despite recognizing occasional inaccuracies, respondents saw potential in advanced AI features, particularly in image analysis and speech-to-text functions. Conclusions: The survey underscored pathologists' cautious yet growing interest in leveraging LLMs to enhance information accessibility, efficiency, and medical education. While the potential benefits are recognized, significant apprehensions about the reliability, ethics, and security associated with these AI tools underscore the need for comprehensive regulation and standardized practices to ensure their responsible use in the medical field.medRxiv20Investigation of the adoption and perception of artificial intelligence chatbots, particularly those powered by large language models (LLMs), among pathologists worldwide underscored pathologists' cautious yet growing interest in leveraging LLMs to enhance information accessibility, efficiency, and medical education.['A. Bychkov', 'T. Laohawetwanit', 'D. G. Pinto']2024-04-09T00:00:00https://www.semanticscholar.org/paper/bc9d5150e08fedea59327d9add9bd72e788e2249 9833b8158d15994de7637dfd0cbc41a834b5cb60This paper introduces reduced-order modeling techniques with Artificial Intelligence (AI) for Model-Based Development (MBD). In vehicle development, detailed physical models are replaced by reduced-order models (ROM) to expedite simulations. With recent advancements in AI-based reduced-order modeling, it is expected that modeling work will become more efficient, leading to reduced simulation times. However, the range of simulations (Model-in-the-Loop Simulation - MILS, Hardware-in-the-Loop Simulation - HILS, bench-system) compatible with ROM is limited. To overcome this limitation, this study leverages the ONNX format (Open Neural Network Exchange), a universally supported format among machine learning frameworks, and the Functional Mock-up Interface (FMI), a standard interface format for simulation tools, to enable general-purpose embedded technology with ROM.This study employs a vehicle model in engine surge simulations to validate AI-based reduced-order modeling for MBD. In MILS simulations, the ONNX-format model, trained using Long Short-Term Memory (LSTM), is integrated into an FMI-format model compatible with the simulation environment. This FMI-format model is then incorporated into MILS/HILS/bench systems, confirming its capability for accurate simulations. Thus, we have successfully established AI-based reduced-order modeling technology for comprehensive MBD.SAE technical paper series20The ONNX format (Open Neural Network Exchange), a universally supported format among machine learning frameworks, and the Functional Mock-up Interface (FMI), a standard interface format for simulation tools, are used to enable general-purpose embedded technology with ROM to establish AI-based reduced-order modeling technology for comprehensive MBD.SAE Technical Paper Series['Takahiro Inagaki', 'Tadaaki Nasu', 'Minoru Takeshige', 'Motofumi Iwata', 'Naoto Nakane']2024-04-09T00:00:00https://www.semanticscholar.org/paper/9833b8158d15994de7637dfd0cbc41a834b5cb60 6376abac28b56b1808f8741c25b3ff77937b938bThis article considers the impact of AI on the economy and financing of journalism organizations. AI has structural implications on the news media beyond the practice of journalism and the management of news as a process. AI also shifts the premises of competition, competitive advantage, mergers and acquisitions, and IT capabilities in the news industries. Not least, it fundamentally challenges journalism's traditional business model. Considered hereunder is the two‐sided market model, journalism's traditional platform function, its network effects, and its public good characteristics. The aim of the article is, thus, to reconceptualize core economic features of the news industries in the context of AI to provide a vocabulary with which to assess the economic future of journalism in a data‐driven platform economy.The AI Magazine790The aim of the article is to reconceptualize core economic features of the news industries in the context of AI to provide a vocabulary with which to assess the economic future of journalism in a data‐driven platform economy.AI Magazine['Helle Sjøvaag']2024-04-09T00:00:00https://www.semanticscholar.org/paper/6376abac28b56b1808f8741c25b3ff77937b938b cf1be17badcfcb34956907af058b3aab80300306As rapid advances in Artificial Intelligence and the rise of some of history's most potent corporations meet the diminished neoliberal state, people are increasingly subject to power exercised by means of automated systems. Machine learning and related computational technologies now underpin vital government services. They connect consumers and producers in new algorithmic markets. They determine how we find out about everything from how to vote to where to get vaccinated, and whose speech is amplified, reduced, or restricted. And a new wave of products based on Large Language Models (LLMs) will further transform our economic and political lives. Automatic Authorities are automated computational systems used to exercise power over us by determining what we may know, what we may have, and what our options will be. In response to their rise, scholars working on the societal impacts of AI and related technologies have advocated shifting attention from how to make AI systems beneficial or fair towards a critical analysis of these new power relations. But power is everywhere, and is not necessarily bad. On what basis should we object to new or intensified power relations, and what can be done to justify them? This paper introduces the philosophical materials with which to formulate these questions, and offers preliminary answers. It starts by pinning down the concept of power, focusing on the ability that some agents have to shape others' lives. It then explores how AI enables and intensifies the exercise of power so understood, and sketches three problems with power and three ways to solve those problems. It emphasises, in particular, that justifying power requires more than satisfying substantive justificatory criteria; standards of proper authority and procedural legitimacy must also be met. We need to know not only what power may be used for, but how it may be used, and by whom.arXiv.org760This paper starts by pinning down the concept of power, focusing on the ability that some agents have to shape others' lives, and explores how AI enables and intensifies the exercise of power, and sketches three problems with power and three ways to solve those problems.ArXiv['Seth Lazar']2024-04-09T00:00:00https://www.semanticscholar.org/paper/cf1be17badcfcb34956907af058b3aab80300306 3500cbaf7d596cab1e8bf9a702735e81c3ae4b4eIn an era characterized by the rapid proliferation and advancement of AI-based technologies across various domains, the spotlight is placed on the integration of these technologies into trustworthy autonomous systems. The integration into embedded systems necessitates a heightened focus on dependability. This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering. We establish the body of knowledge and key findings deliberated upon during an expert workshop held at an international conference focused on computer safety, reliability and security. The dialogue makes it evident that despite advancements, the assurance of dependability in AI-driven systems remains an unresolved challenge, lacking a one-size-fits-all solution. On the other hand, the positive outcome of this dialogue about the dependability of AI in embedded systems is that experts foster a shared understanding across diverse domains of expertise. We enhance the outcomes by considering the entirety of the PESTEL analysis framework encompassing political, environmental, social, technological, economic and legal dimensions. Therefore, this work synthesizes insights aiming to provide a comprehensive view informed by a multitude of perspectives and factors.SAE technical paper series110This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering, to provide a comprehensive view informed by a multitude of perspectives and factors.SAE Technical Paper Series['Romana Blazevic', 'O. Veledar', 'Georg Macher']2024-04-09T00:00:00https://www.semanticscholar.org/paper/3500cbaf7d596cab1e8bf9a702735e81c3ae4b4e 01705ec15a126779bf9152366a759c84d5479313BACKGROUND In late 2022, an AI (artificial intelligence) application, ChatGPT (generative pre-trained transformer), was released free for public use. Although present use of AI applications are scant in nursing education, the easy access to ChatGPT will inevitably influence educational experiences for both educators and students. Nursing educators have an opportunity to leverage this new technology by understanding the functionality and limitations of ChatGPT. METHOD This article examines the framework and functionality of ChatGPT and considers a potential nursing education assignment using the AI powered ChatGPT. The AI application, ChatGPT, is reviewed within the context of health care and nursing education and a potential nursing assignment leveraging ChatGPT is considered. RESULTS Nursing educators will increase their knowledge about ChatGPT and consider a possible nursing curriculum assignment using ChatGPT. CONCLUSION Although not without limitations, nursing educators can leverage this new AI powered technology for an enhanced student experience. [J Nurs Educ. 2024;63(X):XXX-XXX.].Journal of Nursing Education80The framework and functionality of ChatGPT is examined and a potential nursing education assignment using the AI powered ChatGPT is considered, showing nursing educators can leverage this new AI powered technology for an enhanced student experience.The Journal of nursing education['Michael D. Bumbach']2024-04-09T00:00:00https://www.semanticscholar.org/paper/01705ec15a126779bf9152366a759c84d5479313 977122e69a7a717d5f8c0038e99cc1e323f0c445European Radiology Experimental230A platform named RV-Cherry-Picker provides unified access to the largest, homogenized public imaging dataset for breast cancer, and is able to make a detailed selection of breast MRI data for the development of AI models.European Radiology Experimental['Vassilis Kilintzis', 'Varvara Kalokyri', 'H. Kondylakis', 'Smriti Joshi', 'K. Nikiforaki', 'Oliver Díaz', 'Karim Lekadir', 'M. Tsiknakis', 'K. Marias']2024-04-09T00:00:00https://www.semanticscholar.org/paper/977122e69a7a717d5f8c0038e99cc1e323f0c445 5ee503fbdaa68d0f05726cd4caed64a48578cbbaThe rapid advancement of artificial intelligence (AI) systems, fueled by extensive research and development investments, has ushered in a new era where AI permeates decision-making processes across various sectors. This proliferation is largely attributed to the availability of vast digital datasets, particularly in machine learning, enabling AI systems to discern intricate correlations and furnish valuable insights from data on human behavior and other phenomena. However, the widespread integration of AI into private and public domains has raised concerns regarding the neutrality and objectivity of automated decision-making processes. Such systems, despite their technological sophistication, are not immune to biases and ethical dilemmas inherent in human judgments. Consequently, there is a growing call for regulatory oversight to ensure transparency and accountability in AI deployment, akin to traditional regulatory frameworks governing analogous processes. This paper critically examines the implications and ripple effects of incorporating AI into existing social systems from an 'AI ethics' standpoint. It questions the adequacy of self-policing mechanisms advocated by corporate entities, highlighting inherent limitations in corporate social responsibility paradigms. Additionally, it scrutinizes well-intentioned regulatory initiatives, such as the EU AI ethics initiative, which may overlook broader societal impacts while prioritizing the desirability of AI applications. The discussion underscores the necessity of adopting a holistic approach that transcends individual and group rights considerations to address the profound societal implications of AI, encapsulated by the concept of 'algorithmic assemblage'.Online (Weston, Conn.)410The discussion underscores the necessity of adopting a holistic approach that transcends individual and group rights considerations to address the profound societal implications of AI, encapsulated by the concept of 'algorithmic assemblage'.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)['Md.Mafiqul Islam']2024-04-09T00:00:00https://www.semanticscholar.org/paper/5ee503fbdaa68d0f05726cd4caed64a48578cbba b99595420790988fa58c8449a0f8457a1b6ba6c3We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and neural network models to predict a hybrid approach optimal configuration. FuSeBMC-AI utilizes Bounded Model Checking and Fuzzing as back-end verification engines. FuSeBMC-AI outperforms the default configuration of the underlying verification engine in certain cases while concurrently diminishing resource consumption.arXiv.org120This work presents FuSeBMC-AI, a test generation tool grounded in machine learning techniques that outperforms the default configuration of the underlying verification engine in certain cases while concurrently diminishing resource consumption.ArXiv['Kaled M. Alshmrany', 'Mohannad Aldughaim', 'Chenfeng Wei', 'Tom Sweet', 'Richard Allmendinger', 'Lucas C. Cordeiro']2024-04-09T00:00:00https://www.semanticscholar.org/paper/b99595420790988fa58c8449a0f8457a1b6ba6c3 c17ffdd979d7adefa615937b9f50c79a71c65a8fInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-09T00:00:00https://www.semanticscholar.org/paper/c17ffdd979d7adefa615937b9f50c79a71c65a8f 68ea2ac72a7bfdcc2be9dc76cf9a577eb09c68d7Nature00Nature['Katrina Krämer']2024-04-09T00:00:00https://www.semanticscholar.org/paper/68ea2ac72a7bfdcc2be9dc76cf9a577eb09c68d7 aaa0b2aaddce1ab1f5f071ea00294b2711169f77This thesis explores the integration of Artificial Intelligence (AI) in project management practices to improve efficiency and decision-making processes. As organizations increasingly rely on project management methodologies to execute tasks, deliverables, and achieve objectives, the role of AI in enhancing these processes becomes pivotal. Through an examination of existing literature, case studies, and theoretical frameworks, this thesis investigates the potential benefits, challenges, and implications of incorporating AI technologies in project management. It aims to provide insights into how AI can optimize project planning, scheduling, resource allocation, risk management, and stakeholder communication. Additionally, the thesis explores the ethical considerations and societal impacts associated with the adoption of AI in project management. By analyzing real-world applications and theoretical perspectives, this research contributes to the understanding of how AI can be effectively utilized to streamline project management practices and drive organizational success in diverse industries.GLOBAL MAINSTREAM JOURNAL00Analysis of real-world applications and theoretical perspectives contributes to the understanding of how AI can be effectively utilized to streamline project management practices and drive organizational success in diverse industries.GLOBAL MAINSTREAM JOURNAL[]2024-04-09T00:00:00https://www.semanticscholar.org/paper/aaa0b2aaddce1ab1f5f071ea00294b2711169f77 b17d8137c148ced97c6260969a93dbb9cc1b182dThis article delves into the intricate relationship between Artificial Intelligence (AI) and the educational ecosystem, particularly within higher education. It embarks on a detailed examination of how AI's integration influences teaching methodologies, learning experiences, and research processes while also casting a spotlight on the accompanying challenges and concerns. Specifically, it scrutinizes the repercussions on pedagogical communication and student engagement, underpinning its analysis with a study that encompasses an array of dimensions: the fluctuation in student populations and the density of higher educational institutions, the degree of digitalization within these entities, and comprehensive questionnaire responses from students that reveal their perceptions and attitudes towards AI's role in education. This study aims to explore the perspectives and experiences of a critical stakeholder group: students. By dedicating focused attention to both the opportunities and obstacles presented by AI in education, this study aims to foster a nuanced comprehension of its impact. It critically evaluates the potential benefits and drawbacks, equipping stakeholders with the insight needed to navigate the evolving educational landscape. Furthermore, this research aims to spotlight trends in digital competitiveness within the educational sector and propose strategic recommendations for achieving a harmonious balance between innovative and traditional pedagogical approaches. Such balance is pivotal for crafting forward-thinking educational strategies amidst the rapid integration of AI technologies. Through this comprehensive analysis, the study seeks to contribute to the broader discourse on optimizing AI's potential in education while mitigating its challenges, thereby supporting the advancement of an education system that is both innovative and inclusive.Qainar Journal of Social Science50This article delves into the intricate relationship between Artificial Intelligence (AI) and the educational ecosystem, particularly within higher education, with a detailed examination of how AI's integration influences teaching methodologies, learning experiences, and research processes while also casting a spotlight on the accompanying challenges and concerns.Qainar Journal of Social Science['Aisulu Dzhanegizova', 'Aigerim M. Nurseiit', 'Karina S. Vyborova']2024-04-09T00:00:00https://www.semanticscholar.org/paper/b17d8137c148ced97c6260969a93dbb9cc1b182d 4c97979f21539a51713f5edc276d3f0e7c1d6a07Management Review Quarterly800Several gaps emerged that scientific research would have to fill to effectively implement AI in PM and that have been turned into opportunities for future research in the form of a research agenda.Management Review Quarterly['M. Nenni', 'Fabio De Felice', 'Cristina De Luca', 'Antonio Forcina']2024-04-09T00:00:00https://www.semanticscholar.org/paper/4c97979f21539a51713f5edc276d3f0e7c1d6a07 84a166e79166b955c6680f561d9500aa4ad5d1d7npj Digit. Medicine150This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.NPJ Digital Medicine['Christina Silcox', 'Eyal Zimlichman', 'Katie Huber', 'Neil Rowen', 'Robert Saunders', 'Mark McClellan', 'Charles N Kahn', 'Claudia A. Salzberg', 'David W. Bates']2024-04-09T00:00:00https://www.semanticscholar.org/paper/84a166e79166b955c6680f561d9500aa4ad5d1d7 853eeee9ea912378c1e2524aeb228ce9f7926247This exploratory research looks at initiatives using artificial intelligence (AI) or digital deployed locally in different parts of the world by groups, communities, or civil society organizations, to achieve their social justice goals.00['Karine Gentelet', 'Lily-Cannelle Mathieu', 'Alexandra Bahary-Dionne']2024-04-09T00:00:00https://www.semanticscholar.org/paper/853eeee9ea912378c1e2524aeb228ce9f7926247 986889fb84cb370a29e6b12df251f1f2940b1b15The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could transform clinical interventions and outcomes, but it risks dehumanization and increased unethical practices. The field of clinical nutrition and dietetics is no exception. This article finds a multitude of developments underway, which include the use of AI for malnutrition screening; predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions; aiding interventions, such as estimating nutrient intake; applying precision nutrition, such as measuring postprandial glycemic response; and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI-development protocols, benchmarked to care recommendations, with rigorous large-scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.Nutrition in clinical practice410AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics, which includes the use of AI for malnutrition screening, predicting clinical outcomes, and clinical risks.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition['Kiranjit Atwal']2024-04-09T00:00:00https://www.semanticscholar.org/paper/986889fb84cb370a29e6b12df251f1f2940b1b15 6d5c06ecbfad410e584882e980d242c86fe4b93dThe study on " Educators’ Academic Insights on Artificial Intelligence – Challenges and Opportunities" was conducted to gain a deeper understanding of the rapidly evolving phenomenon of AI in education. This research serves multiple objectives. Firstly, it aims to foster awareness regarding the integration of AI into teaching and learning practices by providing clear definitions of AI and explaining key AI-related terms. It also seeks to illustrate AI's diverse applications within a broader context, with a special focus on AI-supported research and learning platforms. Additionally, the study delves into the current discourse surrounding chatbots, contributing to address the central research question. Lastly, this initiative aims to provide valuable recommendations for effectively harnessing AI in education, enhancing the teaching and learning experience. The researchers conducted a review of literature concerning artificial intelligence. They adopted a qualitative method, using open-ended questions to collect feedback from educators globally, including those from the University of Technology and Applied Sciences, Al Musannah, and participants in the online discussion forum at Oxford English Learning Exchange.com. The qualitative data was analysed, leading to the identification of key themes and subthemes derived from the responses of research participants. The study's findings incorporated a wide range of concerns expressed by educators, comprising ten key subthemes. These concerns ranged from doubts about AI's ability to replace human educators and fears of its potential to hinder student development to worries about its hyped popularity and its perceived futuristic nature. Educators stressed the importance of effective AI training while emphasizing the need to prioritize human expertise over excessive reliance on AI. They were also acutely aware of both the advantages and disadvantages of AI, viewing it as both a potential boon and a looming threat. Furthermore, educators recognized the potential for enjoyable experiences with AI and acknowledged the pivotal role of users in determining the extent of AI adoption. Content analysis revealed additional apprehensions, such as concerns about job displacement, AI's impact on critical thinking, teacher frustration in assessing AI-assisted student writing, the use of AI-generated content for assessments, potential erosion of human services, stifling of user and learner creativity by AI, the risk of errors in AI-generated information, opportunities for cheating in exams, and concerns about the overreliance on and overrating of AI platforms. Positively, the findings included an array of opportunities that AI platforms offer. Study participants highlighted various aspects of these opportunities that surpassed their concerns and associated risks. The opportunities are categorized into twenty subthemes: enhancing learner motivation, facilitating template creation, utilizing AI as an educational aid, promoting proper training and fostering positive AI usage, harnessing AI for teaching challenging subjects, enabling personalized learning experiences, offering an interactive tutoring experience, supporting remote learning, facilitating self-study, providing comprehensive educational content overviews, giving instantaneous feedback and evaluation, functioning as search engines and chatbots, enabling content validation, efficiency in terms of cost and time, streamlining material preparation, facilitating skill and language enhancement, promoting familiarity with topics and vocabulary, enabling text-to-speech and speech-to-text conversions, editing multimedia elements, and facilitating content generation.Electronic Journal of e-Learning660This research aims to foster awareness regarding the integration of AI into teaching and learning practices by providing clear definitions of AI and explaining key AI-related terms, and to provide valuable recommendations for effectively harnessing AI in education, enhancing the teaching and learning experience.Electronic Journal of e-Learning['Jayaron Jose', 'Blessy Jayaron Jose']2024-04-09T00:00:00https://www.semanticscholar.org/paper/6d5c06ecbfad410e584882e980d242c86fe4b93d a734cd32b059d6d7e961f735de03d7e624fc7157Accurate assessment of Attention Deficit Hyperactivity Disorder (ADHD) is crucial for the effective treatment of affected individuals. Traditionally, psychometric tests such as the WISC-IV have been utilized to gather evidence and identify patterns or factors contributing to ADHD diagnosis. However, in recent years, the use of machine learning (ML) models in conjunction with post-hoc eXplainable Artificial Intelligence (XAI) techniques has improved our ability to make precise predictions and provide transparent explanations. The objective of this study is twofold: firstly, to predict the likelihood of an individual receiving an ADHD diagnosis using ML algorithms, and secondly, to offer interpretable insights into the decision-making process of the ML model. The dataset under scrutiny comprises 694 cases collected over the past decade in Spain, including information on age, gender, and WISC-IV test scores. The outcome variable is the professional diagnosis. Diverse ML algorithms representing various learning styles were rigorously evaluated through a stratified 10-fold cross-validation, with performance assessed using key metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Models were compared using both the full set of initial features and a well-suited wrapper-type feature selection algorithm (Boruta). Following the identification of the most suitable model, Shapley additive values were computed to assign weights to each predictor based on their additive contribution to the outcome and to elucidate the predictions. Strikingly, a reduced set of 8 out of the initial 20 variables produced results comparable to using the full feature set. Among the ML models tested, the Random Forest algorithm outperformed others on most metrics (ACC = 0.90, AUC = 0.94, Sensitivity = 0.91, Specificity = 0.92). Notably, the principal predictors, ranked by importance, included GAI - CPI, WMI, CPI, PSI, VCI, WMI - PSI, PRI, and LN. Individual case examples exhibit variations in predictions depending on unique characteristics, including instances of false positives and negatives. Our ML model adeptly predicted ADHD diagnoses in 90% of cases, with potential for further enhancement by expanding our database. Furthermore, the use of XAI techniques enables the elucidation of salient factors in individual cases, thereby aiding inexperienced professionals in the diagnostic process and facilitating comparison with expert assessments. It is important to note that this tool is designed to support the ADHD diagnostic process, where the medical professional always has the final say in decision-making.Applied neuropsychology. Child730The ML model adeptly predicted ADHD diagnoses in 90% of cases, with potential for further enhancement by expanding the database and enabling the elucidation of salient factors in individual cases.Applied neuropsychology. Child['Ignasi Navarro-Soria', 'J. R. Rico-Juan', 'Rocío Juárez-Ruiz de Mier', 'Rocío Lavigne-Cerván']2024-04-09T00:00:00https://www.semanticscholar.org/paper/a734cd32b059d6d7e961f735de03d7e624fc7157 c29fabb7fd58fe90d0a1c08e3ecd31012dcc072cArtificial intelligence is developing at a rapid pace, with examples of machine learning already being used in aviation to improve efficiency. In the coming decade, it is likely that intelligent assistants (IAs) will be deployed to assist aviation personnel in the cockpit, the air traffic control center, and in airports. This will be a game-changer and may herald the way forward for single-pilot operations and AI-based air traffic management. Yet in aviation there is a core underlying tenet that ‘people create safety’ and keep the skies and passengers safe, based on a robust industry-wide safety culture. Introducing IAs into aviation might therefore undermine aviation’s hard-won track record in this area. Three experts in safety culture and human-AI teaming used a validated safety culture tool to explore the potential impacts of introducing IAs into aviation. The results suggest that there are indeed potential negative outcomes, but also possible safety affordances wherein AI could strengthen safety culture. Safeguards and mitigations are suggested for the key risk owners in aviation organizations, from CEOs to middle managers, to safety departments and frontline staff. Such safeguards will help ensure safety remains a priority across the industry.Future Transportation210Three experts in safety culture and human-AI teaming used a validated safety culture tool to explore the potential impacts of introducing IAs into aviation, suggesting that there are indeed potential negative outcomes, but also possible safety affordances wherein AI could strengthen safety culture.Future Transportation['Barry Kirwan']2024-04-09T00:00:00https://www.semanticscholar.org/paper/c29fabb7fd58fe90d0a1c08e3ecd31012dcc072c d144c87ed2dcb060c248123c453a9d04c79323b4The paper examines the influence technics on human conscience, the dialectic of interaction between artificial intelligence (AI) and human intelligence within the system: information technologies –mentality and psychology of society – control over people’s minds and technologies. The emphasis is on the essence of the concept of mentality, mental identity, the role of informal institutions, and the speed of changes in biological and information time. The author represents a conceptual view on the problem of the role of artificial intelligence in the development of mental management as a humanitarian problem in the logic of the development of ideas and approaches previously considered by the author in the following works: The Future of Russia: transition to a new formation; Strategy of Reforms in Russia: from leader to leader; and in the context of the idea of social justice and economic growth, discussed at the First Russian Economic Forum.Communicology10The author represents a conceptual view on the problem of the role of artificial intelligence in the development of mental management as a humanitarian problem in the logic of the development of ideas and approaches previously considered by the author in the following works.Communicology['V. D. Popov']2024-04-09T00:00:00https://www.semanticscholar.org/paper/d144c87ed2dcb060c248123c453a9d04c79323b4 1c5e12cad0c2bc2ce3ffdad5f629c4f57f990619Journal of Contemporary China01Journal of Contemporary China['Shaleen Khanal', 'Hongzhou Zhang', 'Araz Taeihagh']2024-04-09T00:00:00https://www.semanticscholar.org/paper/1c5e12cad0c2bc2ce3ffdad5f629c4f57f990619 d7f2c96ecd1c32103f521d84d35c8f6f42f8cd48Modern law review00The Modern Law Review['William Lucy']2024-04-09T00:00:00https://www.semanticscholar.org/paper/d7f2c96ecd1c32103f521d84d35c8f6f42f8cd48 571cfc3a5096c57c9f934bd0a00a1b7b788d5a8bTopoi200It is concluded that AI can be considered a tool for feeling music of curatorial type and that the limitations and/or biases of AI as a method risk lessening the power of musical affective affordances.Topoi['Vinicius de Aguiar']2024-04-09T00:00:00https://www.semanticscholar.org/paper/571cfc3a5096c57c9f934bd0a00a1b7b788d5a8b 81a85e8774a1ab82834f115edd0fae68824e3bb2Neurology00Neurology['Maha Alattar', 'Alok Govind', 'Shraddha Mainali']2024-04-09T00:00:00https://www.semanticscholar.org/paper/81a85e8774a1ab82834f115edd0fae68824e3bb2 88c4f340b9acd4e772fb77723e7b9a234a6cf3fcJournal of Hospital Medicine210Journal of hospital medicine['Verity Schaye', 'Marc M Triola']2024-04-09T00:00:00https://www.semanticscholar.org/paper/88c4f340b9acd4e772fb77723e7b9a234a6cf3fc 6211089086df2a80b1f5058590de8c024377fcabNeurology00Neurology['Julyana M Dantas', 'Giovana Ribeiro', 'C. Dagostin', 'Antonio Mutarelli', 'Pedro Romeiro', 'Giulia Almirón', 'Agostinho Pinheiro']2024-04-09T00:00:00https://www.semanticscholar.org/paper/6211089086df2a80b1f5058590de8c024377fcab 23eaa75b74aa5a7c8e795c63284944621c4df2d3Neurology00Neurology['Preetham Bachina', 'Narayani Waggle', 'Goknur Kocak', 'Andrea Corse', 'Nuren Adatepe', 'Kemar Green']2024-04-09T00:00:00https://www.semanticscholar.org/paper/23eaa75b74aa5a7c8e795c63284944621c4df2d3 0479aebcd9799fee3b18b42c2f504d2b25f507e1Neurology00Neurology['Nicholas Buonafede', 'Diandra Adu-Kyei', 'Jaan Nandwani', 'M. Dhamoon', 'J. Mocco', 'Nathalie Jetté', 'Laura K. Stein']2024-04-09T00:00:00https://www.semanticscholar.org/paper/0479aebcd9799fee3b18b42c2f504d2b25f507e1 e5381d30bee1ecc8c1da9cccfad2581d67521a0eHuman Resource Development International540Human Resource Development International['Shyamal S. Pandya', 'Jia Wang']2024-04-09T00:00:00https://www.semanticscholar.org/paper/e5381d30bee1ecc8c1da9cccfad2581d67521a0e 596fc3c3c0d5a27cfa7c248d72436341f585100bApplied Economics Letters170Applied Economics Letters['Nicolas Fanta', 'Roman Horvath']2024-04-09T00:00:00https://www.semanticscholar.org/paper/596fc3c3c0d5a27cfa7c248d72436341f585100b 1c08e7ddd458e12cf0ff7114009b572572e5c697ACM Computing Surveys00ACM Comput. Surv.['Roberta Calegari', 'Fosca Giannotti', 'Francesca Pratesi', 'Michela Milano']2024-04-09T00:00:00https://www.semanticscholar.org/paper/1c08e7ddd458e12cf0ff7114009b572572e5c697 3b1473e36456a8817610d6d92e87f3f67f00f621Neurology00Neurology['Khushboo Verma', 'Megan Abdurashidova', 'Marina Motina', 'Stephanie Wottrich', 'Karla Robles Lopez', 'Manikum Moodley']2024-04-09T00:00:00https://www.semanticscholar.org/paper/3b1473e36456a8817610d6d92e87f3f67f00f621 0fd0610e8a1661d517d27be8c800b0088b09740fBackground. The importance of explainable artificial intelligence and machine learning (XAI/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method’s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations of black box models. These approaches add additional sources for errors without resolving their shortcomings. Less effort is directed into the design of intrinsically interpretable approaches. Methods. We introduce an intrinsically interpretable methodology motivated by ensemble learning: the League of Experts (LoE) model. We establish the theoretical framework first and then deduce a modular meta algorithm. In our description, we focus primarily on classification problems. However, LoE applies equally to regression problems. Specific to classification problems, we employ classical decision trees as classifier ensembles as a particular instance. This choice facilitates the derivation of human-understandable decision rules for the underlying classification problem, which results in a derived rule learning system denoted as RuleLoE. Results. In addition to 12 KEEL classification datasets, we employ two standard datasets from particularly relevant domains—medicine and finance—to illustrate the LoE algorithm. The performance of LoE with respect to its accuracy and rule coverage is comparable to common state-of-the-art classification methods. Moreover, LoE delivers a clearly understandable set of decision rules with adjustable complexity, describing the classification problem. Conclusions. LoE is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications.Machine Learning and Knowledge Extraction440League of Experts (LoE) is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications.Machine Learning and Knowledge Extraction['Richard Vogel', 'Tobias Schlosser', 'R. Manthey', 'Marc Ritter', 'M. Vodel', 'M. Eibl', 'Kristan Alexander Schneider']2024-04-09T00:00:00https://www.semanticscholar.org/paper/0fd0610e8a1661d517d27be8c800b0088b09740f 1a3f360428e5a4f98136a0d0164dc517ac4160e2 Purpose ChatGPT is an advanced artificial intelligence (AI) form that can generate human-like text based on large amounts of data. This paper aims to empirically examine the ChatGPT adoption level among Indian individuals by considering the key factors in determining individuals’ attitudes and intentions toward newly emerged AI tools. Design/methodology/approach This paper used “partial least square structural equation modeling” (PLS-SEM) to investigate the relation among several latent factors by applying a representative sample of 351 individuals. Findings This study found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes. Additionally, trialability, performance expectancy, effort expectancy, personal innovativeness and attitude significantly influence behavioral intentions. However, compatibility has an insignificant impact on behavioral intention. Moreover, the research highlights that attitude and behavioral intention directly correlate with actual use. Specifically, the absence of compatibility makes people hesitate to use technology that does not meet their specific needs. Practical implications These unique findings provide valuable insights for technology service providers and government entities. They can use this information to shape their policies, deliver timely and relevant updates and enhance their strategies to boost the adoption of ChatGPT. Originality/value This paper is one of the pioneering attempts to exhibit the research stream to understand the individual acceptance of ChatGPT in an emerging country. Moreover, it gained significant attention from individuals for delivering a unique experience and promising solutions. Digital Policy Regulation and Governance410It is found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes, while compatibility and effort expectancy do not significantly impact attitudes.Digital Policy, Regulation and Governance['Jitender Kumar', 'M. Rani', 'Garima Rani', 'Vinki Rani']2024-04-08T00:00:00https://www.semanticscholar.org/paper/1a3f360428e5a4f98136a0d0164dc517ac4160e2 eee8437426e441aed9c18a39aa58dca958c14b0aThe article reveals some of the legal principles of regulating the legal activity of a journalist in modern conditions of martial law. The activities and state of media freedom in Ukraine over the past 11 years have been analyzed. Emphasis is placed on the fact that in modern conditions of martial law, the index of freedom of the press in the state is growing. The authors focused attention on some features of the legal regulation of the legal grounds of journalists’ activities according to the norms of international law and the national legislation of Ukraine; determined the rights and obligations of journalists, guarantees of media activity, specifics of activities of foreign journalists, employees of foreign entities in the field of media working in Ukraine. Attention is focused on certain types of prohibitions regarding interference in the professional activities of journalists under conditions of peace and during a state of war or in places of armed conflicts. The statistics of criminal offenses against the legal activities of journalists and the media during the ten years of the war in Ukraine were analyzed. It is substantiated that legal liability, including criminal liability, has been established for encroachment on the journalist’s life and health, and other actions against him. The current criminal legislation and specific articles of the Criminal Code of Ukraine, which provide for criminal liability for such criminal offenses as obstructing the legitimate professional activity of journalists, threatening, or using violence against a journalist, intentional destruction or damage to a journalist’s property, encroachment on a journalist’s life, taking a journalist as a hostage, were analyzed. Statistical information on the number of registered criminal offenses of this category for the last three years is provided. A criminological description of criminal offenses against the legal activity of a journalist is given. It was concluded that criminal offenses committed against journalists represent a significant public danger, as they encroach on freedom of speech, national security of the state, and violate the foundations of activity in the information sphere.Uzhhorod National University Herald. Series: Law90Uzhhorod National University Herald. Series: Law['O. Khorvatova', 'Y. I. Pukhir']2024-04-08T00:00:00https://www.semanticscholar.org/paper/eee8437426e441aed9c18a39aa58dca958c14b0a ec1ee8e946d95e877b0b2cf007b44aff0434db44The article deals with the issue of legal regulation of administrative procedures on the example of foreign countries. The standards of the administrative procedure regarding the adoption of administrative decisions, i.e. decisions of public administration bodies, which concern the rights and obligations of individuals and legal entities, are considered. The content and peculiarities of legal regulation of administrative procedures in foreign countries, the scope and subject of legal regulation through the prism of the legislation of foreign countries on administrative procedures are outlined. Various approaches to determining the scope, content and methods of its legal regulation are analyzed. Attention is focused on the specifics of managerial activity, which must take effective measures in a timely manner in a wide variety of situations, forming the boundaries and restrictions necessary in the rule of law. The types of entities to which administrative procedures apply have been considered. The rights and obligations of administrative bodies regarding the preparation and adoption of an administrative decision are defined. Features of appeals by individuals and legal entities, definition of sub-agency category of cases are outlined. The types of decisions made by the administrative body are classified. Emphasis is placed on informing persons whose interests may be affected by an administrative act. The grounds for removing officials considered biased are outlined. The rights of citizens participating in the administrative procedure are considered separately.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['M. Garifullin']2024-04-08T00:00:00https://www.semanticscholar.org/paper/ec1ee8e946d95e877b0b2cf007b44aff0434db44 dfb0c51f10f9085de86445eaad2f9bbc395c4e96The dynamic development of virtual assets and their introduction into everyday human life and the economy require the world community to build symmetrical international legal regulation. Currently, the world still does not have a unified approach to regulating this financial phenomenon. However, most countries and international institutions do not ignore it and try to create an appropriate legal field to regulate all possible types of these assets. The article analyzes the legal regulation of relations in the sphere of circulation of virtual assets in some countries of the world and identifies the most favorable norms for their possible implementation into Ukrainian legislation. The experience of regulating virtual assets in the United States of America is singled out, in particular, it is determined that at the federal level of the country, digital assets are considered as property. The general principles of taxation that apply to transactions with property also apply to transactions with virtual assets. It has been studied that the system of regulation of virtual assets in the USA is in the process of development, but the fundamental principles have already been formed. The legal field of regulation of virtual assets in Great Britain is analyzed. The announced intentions of the government in 2017 to make the United Kingdom the best place in the world for the development of digital business are becoming a reality, the confirmation of this is the high level of regulation of the sphere of circulation of virtual assets. The experience of Germany is singled out, where virtual assets are not considered a means of payment, currency or foreign currency. Regulators classify them as a financial instrument or asset, subject to securities and investment-related rules and laws. The country’s legislation contains both detailed regulation of the activities of service providers, users and investors, as well as a system of tax benefits and exemptions. The legal regulation of virtual assets in Singapore, which is one of the most loyal to virtual assets in Asian countries, is analyzed. The conclusion was formulated that when developing a mechanism for legal regulation of virtual assets and blockchain technologies in Ukraine, it is necessary to take into account the international experience of countries where relevant regulatory acts already exist and there is experience in law enforcement.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['S. V. Tsukan']2024-04-08T00:00:00https://www.semanticscholar.org/paper/dfb0c51f10f9085de86445eaad2f9bbc395c4e96 b66ddfa194eb7802b74e6dfafb26bb394e7028caThe Internet has recently witnessed significant growth to become an important part of our daily lives, as it has grown to become the main part of our business as well. Nowadays, anything we do on smart devices can be seen by others, either with our knowledge or consent or without, it may seem obvious, as we can share our work on social networking sites. At first glance, however, it appears to represent a serious threat, including privacy breaches and data theft, and threats become even more important when these breaches directly affect businesses, customers, and the general public. It became necessary to protect the requirements of this development from those cyber risks to minimize the negative effects that these attacks and intrusions can have on facilities and infrastructure. The world has witnessed a technical development in the use of Internet devices and electronic networks, and this use has exposed these networks and devices to the risks of cyberattacks, data breaches, obtaining personal, financial, and commercial inforPakistan Journal of Criminology00Pakistan Journal of Criminology[]2024-04-08T00:00:00https://www.semanticscholar.org/paper/b66ddfa194eb7802b74e6dfafb26bb394e7028ca e93c48964468e63c35543e394753751691cd25d8International Data Protecion Law00International Data Privacy Law['Vasilis Xynogalas', 'M. R. Leiser (Mark)']2024-04-08T00:00:00https://www.semanticscholar.org/paper/e93c48964468e63c35543e394753751691cd25d8 752a8c668f5908b917cd53a94079c086ebfba6d6Journal of Antitrust Enforcement00Journal of Antitrust Enforcement['Anne C Witt']2024-04-08T00:00:00https://www.semanticscholar.org/paper/752a8c668f5908b917cd53a94079c086ebfba6d6 a706207105ae5a655a63b17cc5d539c72bf325edв все времена совместная работа органов государственного финансового контроля из разных стран мира играет огромное значение для поиска эффективных механизмов осуществления государственного финансового контроля. Выработка единых задач, методик, принципов деятельности способствует унификации и совершенствованию государственного финансового контроля. В статье рассмотрены ключевые аспекты правового регулирования государственного финансового контроля в отечественной и зарубежной практике. Раскрыты общие черты и различия в подходах по выработке нормативной правовой основы регулирования государственного финансового контроля, а также обозначены актуальные аспекты формирования нормативно-правой базы внутреннего и внешнего государственного финансового контроля в Российской Федерации и зарубежом. at all times, the joint work of state financial control bodies from around the world has been of great importance for the search for effective mechanisms for the implementation of state financial control. The development of common tasks, methods, and principles of activity contributes to the unification and improvement of state financial control. The article examines the key aspects of the legal regulation of state financial control in domestic and foreign practice. The author reveals common features and differences in approaches to the development of a regulatory legal framework for regulating state financial control, as well as identifies relevant aspects of the formation of a regulatory framework for internal and external state financial control in the Russian Federation and abroad.Modern scientist10Modern scientist['Е.Ю. Шелепова']2024-04-08T00:00:00https://www.semanticscholar.org/paper/a706207105ae5a655a63b17cc5d539c72bf325ed 2fcab57cf4cd55e3c12ac1a7ca50a83337804235Via thousands of papers in Explainable AI (XAI), attention maps \cite{vaswani2017attention} and feature importance maps \cite{bansal2020sam} have been established as a common means for finding how important each input feature is to an AI's decisions. It is an interesting, unexplored question whether allowing users to edit the feature importance at test time would improve a human-AI team's accuracy on downstream tasks. In this paper, we address this question by leveraging CHM-Corr, a state-of-the-art, ante-hoc explainable classifier \cite{taesiri2022visual} that first predicts patch-wise correspondences between the input and training-set images, and then bases on them to make classification decisions. We build CHM-Corr++, an interactive interface for CHM-Corr, enabling users to edit the feature importance map provided by CHM-Corr and observe updated model decisions. Via CHM-Corr++, users can gain insights into if, when, and how the model changes its outputs, improving their understanding beyond static explanations. However, our study with 18 expert users who performed 1,400 decisions finds no statistical significance that our interactive approach improves user accuracy on CUB-200 bird image classification over static explanations. This challenges the hypothesis that interactivity can boost human-AI team accuracy and raises needs for future research. We open-source CHM-Corr++, an interactive tool for editing image classifier attention (see an interactive demo here: http://137.184.82.109:7080/). We release code and data on github: https://github.com/anguyen8/chm-corr-interactive.arXiv.org572CHM-Corr++ is built, an interactive interface for CHM-Corr, enabling users to edit the feature importance map provided by CHM-Corr and observe updated model decisions, and users can gain insights into if, when, and how the model changes its outputs, improving their understanding beyond static explanations.ArXiv['Giang Nguyen', 'Mohammad Reza Taesiri', 'Sunnie S. Y. Kim', 'Anh Nguyen']2024-04-08T00:00:00https://www.semanticscholar.org/paper/2fcab57cf4cd55e3c12ac1a7ca50a83337804235 472818550cb701f36767a17592dd42a3894d47e9In recent years, generative AI (GenAI), like large language models and text-to-image models, has received significant attention across various domains. However, ensuring the responsible generation of content by these models is crucial for their real-world applicability. This raises an interesting question: \textit{What should responsible GenAI generate, and what should it not?} To answer the question, this paper investigates the practical responsible requirements of both textual and visual generative models, outlining five key considerations: generating truthful content, avoiding toxic content, refusing harmful instruction, leaking no training data-related content, and ensuring generated content identifiable. Specifically, we review recent advancements and challenges in addressing these requirements. Besides, we discuss and emphasize the importance of responsible GenAI across healthcare, education, finance, and artificial general intelligence domains. Through a unified perspective on both textual and visual generative models, this paper aims to provide insights into practical safety-related issues and further benefit the community in building responsible GenAI.arXiv.org01The practical responsible requirements of both textual and visual generative models are investigated, outlining five key considerations: generating truthful content, avoiding toxic content, refusing harmful instruction, leaking no training data-related content, and ensuring generated content identifiable.ArXiv['Jindong Gu']2024-04-08T00:00:00https://www.semanticscholar.org/paper/472818550cb701f36767a17592dd42a3894d47e9 5beff0fd38e53c4d012dd9e3263c947c9dffac2cThis paper investigates the influence of anthropomorphized descriptions of so-called"AI"(artificial intelligence) systems on people's self-assessment of trust in the system. Building on prior work, we define four categories of anthropomorphization (1. Properties of a cognizer, 2. Agency, 3. Biological metaphors, and 4. Properties of a communicator). We use a survey-based approach (n=954) to investigate whether participants are likely to trust one of two (fictitious)"AI"systems by randomly assigning people to see either an anthropomorphized or a de-anthropomorphized description of the systems. We find that participants are no more likely to trust anthropomorphized over de-anthropmorphized product descriptions overall. The type of product or system in combination with different anthropomorphic categories appears to exert greater influence on trust than anthropomorphizing language alone, and age is the only demographic factor that significantly correlates with people's preference for anthropomorphized or de-anthropomorphized descriptions. When elaborating on their choices, participants highlight factors such as lesser of two evils, lower or higher stakes contexts, and human favoritism as driving motivations when choosing between product A and B, irrespective of whether they saw an anthropomorphized or a de-anthropomorphized description of the product. Our results suggest that"anthropomorphism"in"AI"descriptions is an aggregate concept that may influence different groups differently, and provide nuance to the discussion of whether anthropomorphization leads to higher trust and over-reliance by the general public in systems sold as"AI".521The results suggest that anthropomorphism in AIdescriptions is an aggregate concept that may influence different groups differently, and provide nuance to the discussion of whether anthropomorphization leads to higher trust and over-reliance by the general public in systems sold as"AI".['Nanna Inie', 'Stefania Druga', 'Peter Zukerman', 'Emily M. Bender']2024-04-08T00:00:00https://www.semanticscholar.org/paper/5beff0fd38e53c4d012dd9e3263c947c9dffac2c d71464394108bf6ecbf82871e144b9dc52e9037dData are the critical fuel for Artificial Intelligence (AI) models. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Checking for data readiness is a crucial step in improving data quality. Numerous R&D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used for verifying AI's data readiness. This survey examines more than 120 papers that are published by ACM Digital Library, IEEE Xplore, other reputable journals, and articles published on the web by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy can lead to new standards for DRAI metrics that would be used for enhancing the quality and accuracy of AI training and inference.arXiv.org1171A taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets for structured and unstructured datasets is proposed and it is anticipated that this taxonomy can lead to new standards for DRAI metrics that would be used for enhancing the quality and accuracy of AI training and inference.ArXiv['Kaveen Hiniduma', 'Suren Byna', 'J. L. Bez']2024-04-08T00:00:00https://www.semanticscholar.org/paper/d71464394108bf6ecbf82871e144b9dc52e9037d 36ef4016ab749eb2acda2d515ea9e4a0959acc8eAI and Ethics440Light is shed on the existing politics of AI and alternative political expressions whereby citizens steer AI development or stop it in the first place are contemplated, with an emphasis on citizen engagement and public political participation.AI and Ethics['Federico Cugurullo']2024-04-08T00:00:00https://www.semanticscholar.org/paper/36ef4016ab749eb2acda2d515ea9e4a0959acc8e 4f25c6042124e56cefc2abb3fab88498f922a07aThis essay engages with recent scholarship on the epistemology of AI, data and automation, to assert how these practices are becoming increasingly central both to the projects of monitoring and of managing a global environment. We also review Jürgen Renn’s recent contribution The Evolution of Knowledge (2020) in relation to the history of environmental data. Using Renn as point of departure, we stake out a way for understanding the Anthropocene through the interaction between data and environment, taking into account the deeper political implications of datafication. We conclude with discussions about how historians of technology and environment could play an important role in assessing the opportunities and risks of AI for global environmental justice before their full-scale implementation is a fait accompli. In face of the Anthropocene, there is a general need today for integrative efforts of bridging knowledge from natural, technical, social and humanistic domains, and therefore a strong imperative for humanistic studies to transposetools, methodologies, and insights into the realms of policymaking, and legislation. Thus, assessments of AI and environment must account for these historical processes in the present as well as offer critical analysis of the full ontological spectrum from object to epistemology via data and mediation.History of Technology560Assessments of AI and environment must account for these historical processes in the present as well as offer critical analysis of the full ontological spectrum from object to epistemology via data and mediation.History and Technology['Adam Wickberg', 'Johan Gärdebo']2024-04-08T00:00:00https://www.semanticscholar.org/paper/4f25c6042124e56cefc2abb3fab88498f922a07a ee7befffd901fcf1040a070d9c21b3616f67e938The advent of advanced AI underscores the urgent need for comprehensive safety evaluations, necessitating collaboration across communities (i.e., AI, software engineering, and governance). However, divergent practices and terminologies across these communities, combined with the complexity of AI systems-of which models are only a part-and environmental affordances (e.g., access to tools), obstruct effective communication and comprehensive evaluation. This paper proposes a framework for AI system evaluation comprising three components: 1) harmonised terminology to facilitate communication across communities involved in AI safety evaluation; 2) a taxonomy identifying essential elements for AI system evaluation; 3) a mapping between AI lifecycle, stakeholders, and requisite evaluations for accountable AI supply chain. This framework catalyses a deeper discourse on AI system evaluation beyond model-centric approaches.210This paper proposes a framework for AI system evaluation comprising three components: a harmonised terminology to facilitate communication across communities involved in AI safety evaluation; a taxonomy identifying essential elements for AI system evaluation; and a mapping between AI lifecycle, stakeholders, and requisite evaluations for accountable AI supply chain.['Boming Xia', 'Qinghua Lu', 'Liming Zhu', 'Zhenchang Xing']2024-04-08T00:00:00https://www.semanticscholar.org/paper/ee7befffd901fcf1040a070d9c21b3616f67e938 e5d02afb90c2155b25342bdb890199dc1d0daa14This paper explores the potential value of semantic artificial intelligence (AI) in corporate training based on a study of semantic AI learning assistants in secondary schools and interviews with human resource professionals. The findings suggest that semantic AI can significantly improve learning outcomes and streamline corporate training processes through personalized, on-demand learning. However, successful implementation requires addressing privacy, security, and user acceptance challenges. While benefits such as efficiency, cost savings, and performance gains are achievable, value creation depends on the organizational context. Further research should explore the long-term impact of semantic AI in corporate learning.  Journal of Electrical Systems120It is suggested that semantic AI can significantly improve learning outcomes and streamline corporate training processes through personalized, on-demand learning, however, successful implementation requires addressing privacy, security, and user acceptance challenges.Journal of Electrical Systems['Alica Sailer']2024-04-08T00:00:00https://www.semanticscholar.org/paper/e5d02afb90c2155b25342bdb890199dc1d0daa14 b720e708dd5ce2c97163917f3b0d14018a4163d6AI and Ethics160It turns out that many approaches for dealing with catastrophic AI risks are available, and that the approaches have a nuanced relationship to approaches to present AI harms.AI and Ethics['L. Dung']2024-04-08T00:00:00https://www.semanticscholar.org/paper/b720e708dd5ce2c97163917f3b0d14018a4163d6 0b345847d4e84836029cd86c735d6a4487056c49Humanities and Social Sciences Communications510The findings show an inverted U-shape quadratic relationship between AI and EBM, moderated by knowledge spillover, which enhances the understanding of the role of AI in configuring EBMs and provides novel insights into the mechanisms between AI and a specific business practice with societal concerns.Humanities and Social Sciences Communications['Tachia Chin', 'Muhammad Waleed Ayub Ghouri', 'Jiyang Jin', 'Muhammet Deveci']2024-04-08T00:00:00https://www.semanticscholar.org/paper/0b345847d4e84836029cd86c735d6a4487056c49 2a1ea6107bfb227068d4edaa93f368492bfe0b77At the current pace of technological advancements, Generative AI models, including both Large Language Models and Large Multi-modal Models, are becoming integral to the developer workspace. However, challenges emerge due to the 'black box' nature of many of these models, where the processes behind their outputs are not transparent. This position paper advocates for a 'white box' approach to these generative models, emphasizing the necessity of transparency and understanding in AI-generated code to match the proficiency levels of human developers and better enable software maintenance and evolution. We outline a research agenda aimed at investigating the alignment between AI-generated code and developer skills, highlighting the importance of responsibility, security, legal compliance, creativity, and social value in software development. The proposed research questions explore the potential of white-box methodologies to ensure that software remains an inspectable, adaptable, and trustworthy asset in the face of rapid AI integration, setting a course for research that could shape the role of code proficiency into 2030 and beyond.190The proposed research questions explore the potential of white-box methodologies to ensure that software remains an inspectable, adaptable, and trustworthy asset in the face of rapid AI integration, setting a course for research that could shape the role of code proficiency into 2030 and beyond.['Gregorio Robles', 'Christoph Treude', 'Jesús M. González-Barahona', 'R. Kula']2024-04-08T00:00:00https://www.semanticscholar.org/paper/2a1ea6107bfb227068d4edaa93f368492bfe0b77 d88ac99da8cbf3e58340b9cd7c015d4e14fe9d96 From music recommendation to assessment of asylum applications, machine-learning algorithms play a fundamental role in our lives. Naturally, the rise of AI implementation strategies has brought to public attention the ethical risks involved. However, the dominant anti-discrimination discourse, too often preoccupied with identifying particular instances of harmful AIs, has yet to bring clearly into focus the more structural roots of AI-based injustice. This paper addresses the problem of AI-based injustice from a distinctively epistemic angle. More precisely, I argue that the injustice generated by the implementation of AI machines in our societies is, in some paradigmatic cases, also a form of epistemic injustice. With a particular focus on AIs employed as gatekeepers of our epistemic resources, this paper shows how their epistemically conformist behaviour is responsible for the marginalisation and the ostracism of minoritarian perspectives. Because it clarifies key structural flaws and weaknesses of current AI design, this paper helps make headway in critical discussion of current AI technologies. And because it forges new theoretical tools to understand forms of epistemic oppression, this paper also contributes to the advancement of feminist theorisation.Episteme140This paper shows how AIs employed as gatekeepers of the authors' epistemic resources are responsible for the marginalisation and the ostracism of minoritarian perspectives and clarifies key structural flaws and weaknesses of current AI design.Episteme['Martin Miragoli']2024-04-08T00:00:00https://www.semanticscholar.org/paper/d88ac99da8cbf3e58340b9cd7c015d4e14fe9d96 cd42864fca0ca420d5cb414f5389fbd389b12dfbAI and Ethics420The extent to which a narrative and virtue based ethics (or, VPD, i.e., virtuous practice design) might be a plausible candidate for the foundation of an ethics of AI, or rather ethical AI practice is examined.AI and Ethics['Paul Hayes', 'Noel Fitzpatrick', 'José Manuel Ferrández']2024-04-08T00:00:00https://www.semanticscholar.org/paper/cd42864fca0ca420d5cb414f5389fbd389b12dfb 4bbdb70be6d614a2e2f7cd524542e3afbe0b8dcaThis thorough examination delves into the ways in which Artificial Intelligence (AI) is transforming the landscape of online advertising. AI plays a role in tailoring ads to individual preferences, generating dynamic content, and utilizing chatbots to enhance customer interaction. Through the use of data and automation, AI optimizes advertising campaigns and enhances overall efficiency. However, it is important to consider the ethical implications of AI. The study emphasizes the need to closely monitor potential biases in algorithms and anticipate job displacement due to automation. To harness the full potential of AI, the study provides practical advice to marketers. This comprehensive analysis highlights AI as a powerful catalyst for change in online advertising, urging stakeholders to navigate its potential and challenges in order to build a successful future.   Journal of Electrical Systems180This comprehensive analysis highlights AI as a powerful catalyst for change in online advertising, urging stakeholders to navigate its potential and challenges in order to build a successful future.Journal of Electrical Systems['Vo Thi Kim Oanh']2024-04-08T00:00:00https://www.semanticscholar.org/paper/4bbdb70be6d614a2e2f7cd524542e3afbe0b8dca 00cabeea72919d7d018db19e0d73cace816d7d99Cognitive Research630A model for how higher education instruction can adapt to the age of AI is introduced by fully capitalizing on the role that metacognition knowledge and skills play in determining learning effectiveness.Cognitive Research: Principles and Implications['Paul Atchley', 'Hannah Pannell', 'Kaelyn Wofford', 'Michael Hopkins', 'R. Atchley']2024-04-08T00:00:00https://www.semanticscholar.org/paper/00cabeea72919d7d018db19e0d73cace816d7d99 860584ca06347589282aff37da48f7848a67bb4aThe medical device sector adheres to strict regulatory frameworks, requiring precise adherence to quality assurance (QA) processes during the production process. Conventional quality assurance (QA) approaches, although successful, sometimes require substantial time and resource allocations, resulting in possible obstacles and higher expenses. The emergence of Artificial Intelligence (AI) in recent years has completely transformed quality assurance (QA) methods in different sectors, providing unparalleled prospects for improved productivity, precision, and scalability. This research examines the possibility of using AI technologies to enhance quality assurance processes in the manufacturing of medical devices. Manufacturers may improve product quality and streamline production workflows by utilising AI techniques like machine learning, computer vision, and natural language processing to automate and optimize important QA procedures. Artificial intelligence systems can analyse large amounts of data to find abnormalities, uncover flaws, and anticipate any problems in real-time. This allows for proactive intervention and reduces the chances of non-compliance hazards. In addition, AI-powered QA systems provide adaptive learning capabilities, constantly enhancing performance through feedback and adapting to changing regulatory needs. The incorporation of artificial intelligence (AI) into current quality management systems enables smooth and efficient sharing of data and compatibility, promoting a comprehensive approach to quality control throughout the whole production process.Asian Journal of Research in Computer Science00This research examines the possibility of using AI technologies to enhance quality assurance processes in the manufacturing of medical devices by utilising AI techniques like machine learning, computer vision, and natural language processing to automate and optimize important QA procedures.Asian Journal of Research in Computer Science['Tushar Khinvasara', 'Stephanie Ness', 'Abhishek Shankar']2024-04-08T00:00:00https://www.semanticscholar.org/paper/860584ca06347589282aff37da48f7848a67bb4a 59039feabb89c94b7baedf34b744f2390fd043c9PurposeThis study social based on cognitive theory (SCT), aims to better understand how transformational leadership affects team-level knowledge sharing and absorptive ability in the construction industry. It also examines the moderating influence of the AI-based virtual assistant on the indirect relationship between transformational leadership and team innovation through knowledge sharing and absorptive ability at the team level.Design/methodology/approachThis study used a simple random sample approach to gather data from several small and medium-sized construction firms in Anhui Province, China. A total of 407 respondents, including 89 site engineers and 321 team members, provided their responses on a five-point Likert scale questionnaire.FindingsThe findings showed that AI-based virtual assistants significantly moderated the direct and indirect association between transformational leadership and knowledge sharing, and subsequently with team innovation. Unexpectedly, the findings showed that AI-based virtual assistant did not moderate the direct relationship between transformational leadership and team-level absorptive capacity.Originality/valueThis study adds a fresh perspective to the literature on construction management by examining team innovation driven by transformational leadership through an underlying mechanism. It is unique in that it uses the team adaptation theory to investigate the understudied relationship between transformational leadership and team innovation in the construction industry.International Journal of Managing Projects in Business720The findings showed that AI-based virtual assistants significantly moderated the direct and indirect association between transformational leadership and knowledge sharing, and subsequently with team innovation, and showed that AI-based virtual assistant did not moderate the direct relationship between transformational leadership and team-level absorptive capacity.International Journal of Managing Projects in Business['Zhang Hui', 'N. A. Khan', 'Maria Akhtar']2024-04-08T00:00:00https://www.semanticscholar.org/paper/59039feabb89c94b7baedf34b744f2390fd043c9 eb790cfa00888126c8b7ef9025bf56227dd1ce4fJournal of Transportation Security210This literature review article explores how artificial intelligence is revolutionizing airport security by automating threat analysis and identification processes, with the suggestion for further research on optimizing real-time authentication systems, studying various AI strategies, and enhancing AI-based intrusion detection systems to prepare for future threats.Journal of Transportation Security['Eugene Pik']2024-04-08T00:00:00https://www.semanticscholar.org/paper/eb790cfa00888126c8b7ef9025bf56227dd1ce4f 5ff87349d20d9201d698540630c4327ca5eb5d22This study explores patterns of AI-tool utilization among Indonesian EFL students, as preliminary data for assessment-model development. Using a convenience sampling technique, this study involved 208 university students of various year levels. A questionnaire was developed based on technology acceptance model (TAM) frameworks to collect data through Google Form, covering aspects of knowledge and use of AI tools in completing tasks, frequency of AI use and friendliness levels, reasons for using AI tools, ease of using AI tools, and desire to use AI tools. The results reveal that the participants had basic knowledge of AI but a significant number of participants admitted not knowing AI tools, suggesting the need for more education and awareness about AI. Grammarly and Google Translate were the most familiar and frequently used applications. Our findings also reveal strong relationships between perceived ease of use (PEoU) and perceived usefulness (PU) and between PU and technology acceptance (TA), implying how TAM frameworks may predict willingness to use technology-assisted or AI applications and the actual utilization. As most research participants were teacher candidates, it becomes clear that integrating AI-assisted learning content and activities appears essential as their experiences in their teacher education may influence the way they teach in the future.LLT Journal: A Journal on Language and Language Teaching350Strong relationships between perceived ease of use (PEoU) and perceived usefulness (PU) and between PU and technology acceptance (TA), implying how TAM frameworks may predict willingness to use technology-assisted or AI applications and the actual utilization are revealed.LLT Journal: A Journal on Language and Language Teaching['Anik Nunuk Wulyani', 'Utami Widiati', 'Siti Muniroh', 'Clarita Dianmonica Rachmadhany', 'N. Nurlaila', 'Lina Hanifiyah', 'Tengku Intan Suzila Tengku Sharif']2024-04-08T00:00:00https://www.semanticscholar.org/paper/5ff87349d20d9201d698540630c4327ca5eb5d22 46942734d20de4ab9b3a0b5ecfd1ab201cf52ddfThis paper conducts a comprehensive analysis of the impact of Artificial Intelligence (AI)-driven Internet of Things (IoT) systems on air quality management in Nigeria. The study examines the current state of air quality in Nigeria, highlighting prevailing challenges, health implications, and the environmental significance of escalating pollution levels. It delves into the existing conventional methods and technologies utilized for air quality management, emphasizing monitoring systems, regulatory frameworks, and pollution control measures. In addition, this review elucidates the integration of AI and IoT technologies globally in addressing environmental concerns, particularly in the realm of air quality management. It specifically zooms into the emergence and adoption of AI-driven IoT systems within Nige- ria’s environmental landscape, spotlighting ongoing initiatives or projects. This review seeks to investigate the implementation and consequences of AI-powered Internet of Things (IoT) systems in air quality management within Nigeria context. It bridges existing knowledge gaps by scrutinizing the effectiveness of these advanced systems in mitigating air pollution and assesses the challenges encountered in practical implementation, encompassing technical, regulatory, and socio-economic barrier.Journal of IoT and Machine Learning00This review seeks to investigate the implementation and consequences of AI-powered Internet of Things (IoT) systems in air quality management within Nigeria context, and bridges existing knowledge gaps by scrutinizing the effectiveness of these advanced systems in mitigating air pollution.Journal of IoT and Machine Learning['J. C. Odirichukwu', 'Simon Peter Chimaobi Odirichukwu', 'S. A. Okolie', 'O. Njoku', 'Chigozie Dimoji', 'Chinwe Ndigwe', 'Godwin Oko Ekuma', 'Genevive Onuoha', 'Favour Adaeze Igwe', 'Precious Kelechukwu Chika-Ugada', 'Toochi Chima Ewunonu']2024-04-08T00:00:00https://www.semanticscholar.org/paper/46942734d20de4ab9b3a0b5ecfd1ab201cf52ddf 368b16a05106708b06c7ce8b6e9cde1fffe397d2The aim of the article was to establish the effectiveness of artificial intelligence (AI) for improving the public service provision. The aim was to determine the choice of law enforcement practice of EU member states and Ukraine for comparative analysis. Statistical methods and comparative law were employed as the basis of the research. The conducted research showed that the proposed EU regulatory framework for the development and use of artificial intelligence is aimed at analysing risks, promoting the use of human-oriented and trustworthy AI. It was established that introducing communication and cooperation procedures using large language models such as ChatGPT can optimise public service provision. The positive impact of the implementation of AI on the efficiency of public services was proved by applying a justified system of indicators. The analysis results gave grounds for proposing an approach to implementing artificial intelligence in public services. The prospects for further research will be the analysis of the implementation of the proposed EU Law on Artificial Intelligence in EU countries, considering the implementation of AI in the provision of public services.Pakistan Journal of Criminology00It was established that introducing communication and cooperation procedures using large language models such as ChatGPT can optimise public service provision and give grounds for proposing an approach to implementing artificial intelligence in public services.Pakistan Journal of Criminology[]2024-04-08T00:00:00https://www.semanticscholar.org/paper/368b16a05106708b06c7ce8b6e9cde1fffe397d2 38733884732aa629e045ece69991e8f9c5f27e59INTRODUCTION Periodontitis is the main cause of tooth loss and is related to many systemic diseases. Artificial intelligence (AI) in periodontics has the potential to improve the accuracy of risk assessment and provide personalized treatment planning for patients with periodontitis. This systematic review aims to examine the actual evidence on the accuracy of various AI models in predicting periodontitis. METHODS Using a mix of MeSH keywords and free text words pooled by Boolean operators ('AND', 'OR'), a search strategy without a time frame setting was conducted on the following databases: Web of Science, ProQuest, PubMed, Scopus, and IEEE Explore. The QUADAS-2 risk of bias assessment was then performed. RESULTS From a total of 961 identified records screened, 8 articles were included for qualitative analysis: 4 studies showed an overall low risk of bias, 2 studies an unclear risk, and the remaining 2 studies a high risk. The most employed algorithms for periodontitis prediction were artificial neural networks, followed by support vector machines, decision trees, logistic regression, and random forest. The models showed good predictive performance for periodontitis according to different evaluation metrics, but the presented methods were heterogeneous. CONCLUSIONS AI algorithms may improve in the future the accuracy and reliability of periodontitis prediction. However, to date, most of the studies had a retrospective design and did not consider the most modern deep learning networks. Although the available evidence is limited by a lack of standardized data collection and protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area. KNOWLEDGE TRANSFER STATEMENT The use of AI in periodontics can lead to more accurate diagnosis and treatment planning, as well as improved patient education and engagement. Despite the current challenges and limitations of the available evidence, particularly the lack of standardized data collection and analysis protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.JDR Clinical & Translational Research301The most employed algorithms for periodontitis prediction were artificial neural networks, followed by support vector machines, decision trees, logistic regression, and random forest, but the presented methods were heterogeneous.JDR clinical and translational research['A. Polizzi', 'V. Quinzi', 'A. Lo Giudice', 'G. Marzo', 'R. Leonardi', 'G. Isola']2024-04-08T00:00:00https://www.semanticscholar.org/paper/38733884732aa629e045ece69991e8f9c5f27e59 ff4e666d9260c63a0ab74894719faec184c00073In this scoping review, we evaluated the performance of artificial intelligence (AI) in clinical radiology practice and examined health professionals' perspectives regarding AI use in radiology. This review followed the Joanna Briggs Institute (JBI) methodological guidelines. We searched multiple databases and the gray literature from March 15, 2016 to December 31, 2023. Of 49 articles reviewed, 13 assessed the performance of AI in radiology clinical practice, and 36 examined the attitudes of health professionals toward the use of AI in radiology. In four separate studies, AI significantly improved the diagnostic sensitivity or detection rate. Furthermore, six articles emphasized a significant reduction in case reading times with AI use. Although three studies suggested an increase in specificity with the assistance of AI, these findings did not reach statistical significance. Health professionals expressed the belief that AI would have a significant impact on radiology but would not replace radiologists in the near future. Limited knowledge of AI was observed among health professionals, who supported increased education and explicit regulations and guidelines related to AI. Overall, AI can enhance diagnostic efficiency and accuracy in clinical radiology practice. However, knowledge gaps and the concerns of health professionals should be addressed by prioritizing education and reinforcing ethical and legal regulations to facilitate the advancement of AI use in radiology. This scoping review provides evidence toward a comprehensive understanding of AI's potential in clinical radiology practice, promoting its use and stimulating further discussion on related challenges and implications.iRADIOLOGY550Overall, AI can enhance diagnostic efficiency and accuracy in clinical radiology practice, but knowledge gaps and the concerns of health professionals should be addressed by prioritizing education and reinforcing ethical and legal regulations to facilitate the advancement of AI use in radiology.iRADIOLOGY['Chanchan He', 'Weiqi Liu', 'Jing Xu', 'Yao Huang', 'Zijie Dong', 'You Wu', 'Hadi Kharrazi']2024-04-08T00:00:00https://www.semanticscholar.org/paper/ff4e666d9260c63a0ab74894719faec184c00073 a3e1a754a4bbb7813bc67ddbd3d30323b3bc7485Future Journal of Pharmaceutical Sciences490The research provided an in-depth evaluation of AI in the various phases of the drug discovery process and indicated that AI’s benefits include drug repurposing, target identification, clinical trial optimization, quality assurance, and control and efficient drug distribution method.Future Journal of Pharmaceutical Sciences['A. Sampene', 'Fatuma Nyirenda']2024-04-08T00:00:00https://www.semanticscholar.org/paper/a3e1a754a4bbb7813bc67ddbd3d30323b3bc7485 e7a0373797092bb47cdfcfe31086fc9f226aaa4bThis research examines the potential of artificial intelligence (AI) in enhancing learners' English pronunciation skills. The participants were 78 English language learners at the elementary and pre-intermediate levels, and 19 experienced English language teachers. The researchers employed a mixed-method approach, integrating both quantitative and qualitative data collection techniques, to comprehensively assess the efficacy of AI-based pronunciation aids and gauge learners' perceptions. During a course of two months, the experimental group used Listnr and Murf AI tools, while the control group adhered to a conventional instruction. The data were taken from pre- and post-test pronunciation scores, questionnaire responses, and interviews with the individuals. The findings of the pre- and post-test indicated that the participants in the experimental group had significant improvements in their pronunciation accuracy. The participants in the study had mostly favorable attitudes towards AI-driven tools, emphasizing their effectiveness in enhancing pronunciation skills, boosting confidence, and promoting engagement. Nevertheless, several obstacles pertaining to the interpretation of feedback and the capture of subtle differences in pronunciation were recognized. This research has shown how AI can potentially be used to teach pronunciation effectively, and it has provided valuable insights for teachers, curriculum developers, and learners.LLT Journal: A Journal on Language and Language Teaching310How AI can potentially be used to teach pronunciation effectively is shown, and it has provided valuable insights for teachers, curriculum developers, and learners.LLT Journal: A Journal on Language and Language Teaching['Ebrahim Mohammadkarimi']2024-04-08T00:00:00https://www.semanticscholar.org/paper/e7a0373797092bb47cdfcfe31086fc9f226aaa4b 7fc3d1d7037836b1a8c29a0f744010ddf95d1cf3Globally, artificial intelligence (AI) implementation is growing, holding the capability to fundamentally alter organisational processes and decision making. Simultaneously, this brings a multitude of emergent risks to organisations, exposing vulnerabilities in their extant risk management frameworks. This necessitates a greater understanding of how organisations can position themselves in response. This issue is particularly pertinent within the financial sector with relatively mature AI applications matched with severe societal repercussions of potential risk events. Despite this, academic risk management literature is trailing behind the speed of AI implementation. Adopting a management perspective, this study aims to contribute to the understanding of AI risk management in organisations through an exploratory empirical investigation into these practices. In-depth insights are gained through interviews with nine practitioners from different organisations within the UK financial sector. Through examining areas of organisational convergence and divergence, the findings of this study unearth levels of risk management framework readiness and prevailing approaches to risk management at both a processual and organisational level. Whilst enhancing the developing literature concerning AI risk management within organisations, the study simultaneously offers a practical contribution, providing key areas of guidance for practitioners in the operational development of AI risk management frameworks.arXiv.org00Through examining areas of organisational convergence and divergence, the findings of this study unearth levels of risk management framework readiness and prevailing approaches to risk management at both a processual and organisational level.ArXiv['Finlay McGee']2024-04-08T00:00:00https://www.semanticscholar.org/paper/7fc3d1d7037836b1a8c29a0f744010ddf95d1cf3 597b867c327f49486ba2e56f026a214a0c66deeaThe primary purpose of this study is to investigate the advancement of data analytics application programs in human resources, along with the challenges faced by data analytics in significant artificial intelligence throughout the industrial era 4.0 in Indonesia. Data collection is facilitated through the administration of questionnaires, conducting interviews, and administering surveys. This inquiry aims to establish the feasibility of substituting human intelligence with computer intelligence. This undertaking is predicated on the idea that individuals and machines possess commensurate abilities to handle vast quantities of data. The chosen strategy for analytics is problematic from a qualitative standpoint. The reason for this phenomenon is the adoption of the method. Based on the research outcomes, it has been observed that within the context of the fourth industrial revolution, data analytics can be effectively managed by robots possessing extensive data storage capabilities comparable to human counterparts. This applies to all facets of human intelligence, particularly weak Artificial Intelligence.   Journal of Electrical Systems390It has been observed that within the context of the fourth industrial revolution, data analytics can be effectively managed by robots possessing extensive data storage capabilities comparable to human counterparts.Journal of Electrical Systems['Mas Wigrantoro Roes Setiyadi, Abdul Wahab Samad, Zahera Mega Utama']2024-04-08T00:00:00https://www.semanticscholar.org/paper/597b867c327f49486ba2e56f026a214a0c66deea f4e24fd60b6d50033bfadfac43a897d61b631d2eBuilding an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.Frontiers in Artificial Intelligence3400The paper reviews the current state-of-the-art approaches and answers the core question of how artificial intelligence is transforming portfolio management steps by answering the core question of how artificial intelligence is transforming portfolio management steps.Frontiers in Artificial Intelligence['K. Šutienė', 'Peter Schwendner', 'Ciprian Sipos', 'Luis Lorenzo', 'Miroslav Mirchev', 'Petre Lameski', 'Audrius Kabašinskas', 'Chemseddine Tidjani', 'Belma Ozturkkal', 'Jurgita Černevičienė']2024-04-08T00:00:00https://www.semanticscholar.org/paper/f4e24fd60b6d50033bfadfac43a897d61b631d2e 6c299cc899ff14191883a932e399df24dd017b23We have developed concerning individuals, technology, industry, and different resources because of developing globalization since evolution is an ever-evolving shift through time. Mechanical technology, language acknowledgment, picture acknowledgment, information investigation, and a couple of additional master frameworks is a portion of the innovations that are essential for the evolution of digitization and regularization of artificial intelligence. Organizations, particularly those in the IT sector, are adopting various innovations including virtual reality, augmented reality, artificial intelligence, and others. Human resource management methods must now undergo the same evolution through computerization of work, which might incorporate undertaking, employing achievement (the enlistment and determination process), upgraded ability quality, information examination, and group plan viability. In the current study, we carefully examined and analyzed a sizable number of published papers based on their contributions to the field of knowledge with the quickly developing trend of using artificial intelligence technology in contemporary economics. Additionally, look into how experience and age affect the suggested associations. In order to examine the link between the study's latent variables, a structural framework was established. The findings showed that behavioral intention of HR professionals is significantly influenced by trust and performance expectations. The performance expectations of HR professionals were significantly influenced by trust and technical readiness. Finally, there was no moderating influence of age or experience on the link between performance expectations and behavioral intention and trust. The results of this study help advance the notion of information technology dissemination in human resource management. All the examination of information relating to human resources has attracted the consideration of all organizations late years, and the accentuation has been put on human capital, which is viewed as the essential variable affecting the improvement of the business and its exercises at all degrees of human resource arrangements. It tries to clearly outline the problems that computer scientists are working to address for HR researchers. By emphasizing those that use artificial intelligence, it simultaneously outlines the many and modern IT approaches, tools, and methodologies now in use.Journal of Electrical Systems190The findings showed that behavioral intention of HR professionals is significantly influenced by trust and performance expectations and the performance expectations of HR professionals were significantly influenced by trust and technical readiness.Journal of Electrical Systems['Mohd. Zafar Shaikh, M. Sankar, Shivani Raina, K. Jayapriya, A. Raj Naveen Chander']2024-04-08T00:00:00https://www.semanticscholar.org/paper/6c299cc899ff14191883a932e399df24dd017b23 e7ef482b0b401203440187d9a4e94d9f7652a77e The study aimed to train an algorithm to predict facial and dental outcomes following orthodontic treatment using artificial intelligence (AI). In addition, the accuracy of the algorithm was evaluated by four distinct groups of evaluators. The algorithm was trained using pre-treatment and post-treatment frontal smiling and intraoral photographs of 50 bimaxillary patients who underwent all first bicuspid extraction and orthodontic treatment with fixed appliances. A questionnaire was created through Google form and it included 10 actual post-treatment and AI-predicted post-treatment images. The accuracy and acceptability of the AI-predicted outcomes were analyzed by four groups of 140 evaluators (35 orthodontists, 35 oral maxillofacial surgeons, 35 other specialty dentists, and 35 laypersons). The Style-based Generative Adversarial Network-2 algorithm used in this study proved effective in predicting post-treatment outcomes using pre-treatment frontal facial photographs of bimaxillary patients who underwent extraction of all first bicuspids as part of their treatment regimen. The responses from the four different groups of evaluators varied. Laypersons exhibited greater acceptance of the AI-predicted images, whereas oral maxillofacial surgeons showed the least agreement. The base of the nose and the chin demonstrated the most accurate predictions, while gingival visibility and the upper lip-to-teeth relationship exhibited the least prediction accuracy. The outcomes underscore the potential of the method, with a majority of evaluators finding predictions made by the AI algorithm to be generally reliable. Nonetheless, further research is warranted to address constraints such as image tonicity and the proportional accuracy of the predicted images. APOS Trends in Orthodontics220The Style-based Generative Adversarial Network-2 algorithm used in this study proved effective in predicting post-treatment outcomes using pre-treatment frontal facial photographs of bimaxillary patients who underwent extraction of all first bicuspids as part of their treatment regimen.APOS Trends in Orthodontics['Niranjana Ramasubbu', 'Shakeel Ahmed Valai Kasim', 'R. Thavarajah', 'K. Rengarajan']2024-04-08T00:00:00https://www.semanticscholar.org/paper/e7ef482b0b401203440187d9a4e94d9f7652a77e a386424f2f61647ebec3dd27e33c6db92c1c07acCritical Care730AI-based CDSS are evolving and are here to stay in the acute care unit environment and it is the authors' obligation to be good shepherds of their use and further development.Critical Care['MR Pinsky', 'Armando Bedoya', 'A. Bihorac', 'Leo Celi', 'Matthew Churpek', 'Nicoleta J. Economou-Zavlanos', 'Paul Elbers', 'S. Saria', 'Vincent Liu', 'Patrick G. Lyons', 'B. Shickel', 'Patrick Toral', 'David Tscholl', 'Gilles Clermont']2024-04-08T00:00:00https://www.semanticscholar.org/paper/a386424f2f61647ebec3dd27e33c6db92c1c07ac 5bed38a42edcb1c1d22ce651871e33f3c2f878c1BACKGROUND Artificial intelligence (AI) is increasingly utilized in interventional cardiology (IC) and holds the potential to revolutionize the field. METHODS We conducted a global, web-based, anonymous survey of IC fellows and attendings to assess the knowledge and perceptions of interventional cardiologists regarding AI use in IC. RESULTS A total of 521 interventional cardiologists participated in the survey. The median age range of participants was 36 to 45 years, most (51.5%) practice in the United States, and 7.5% were women. Most (84.7%) could explain well or somehow knew what AI is about, and 63.7% were optimistic/very optimistic about AI in IC. However, 73.5% believed that physicians know too little about AI to use it on patients and most (46.1%) agreed that training will be necessary. Only 22.1% were currently implementing AI in their personal clinical practice, while 60.6% estimated implementation of AI in their practice the next 5 years. Most agreed that AI will increase diagnostic efficiency, diagnostic accuracy, treatment selection, and healthcare expenditure, and decrease medical errors. The most tried AI-powered tools were image analysis (57.3%), ECG analysis (61.7%), and AI-powered algorithms (45.9%). Interventional cardiologists practicing in academic hospitals were more likely to have AI tools currently implemented in their clinical practice and to use them, women had a higher likelihood of expressing concerns regarding AI, and younger interventional cardiologists were more optimistic about AI integration in IC. CONCLUSIONS Our survey suggests a positive attitude of interventional cardiologists regarding AI implementation in the field of IC.The Journal of invasive cardiology00A positive attitude of interventional cardiologists regarding AI implementation in the field of IC is suggested, and women had a higher likelihood of expressing concerns regarding AI, and younger interventional cardiologists were more optimistic about AI integration in IC.The Journal of invasive cardiology['Michaella Alexandrou', 'Athanasios Rempakos', 'Deniz Mutlu', 'Ahmed Al Ogaili', 'B. Rangan', 'Olga C Mastrodemos', 'Konstantinos Voudris', 'Anastasios Milkas', 'M. N. Burke', 'Yader Sandoval', 'Yiannis S. Chatzizisis', 'Konstantinos C. Siontis', 'E. Brilakis']2024-04-08T00:00:00https://www.semanticscholar.org/paper/5bed38a42edcb1c1d22ce651871e33f3c2f878c1 c4769a959aeebe775c7c8718dd720f8f681a7525As Artificial Intelligence (AI) systems increase in capability, so there are growing concerns over the ways in which the recommendations they provide can affect people's everyday life and decisions. The field of Explainable AI (XAI) aims to address such concerns but there is often a neglect of the human in this process. We present a formal definition of human-centred XAI and illustrate the application of this formalism to the design of a user interface. The user interface supports users in indicating their preferences relevant to a situation and to compare their preferences with those of a computer recommendation system. A user trial is conducted to evaluate the resulting user interface. From the user trial, we believe that users are able to appreciate how their preferences can influence computer recommendations, and how these might contrast with the preferences used by the computer. We provide guidelines of implementing human-centred XAI.Ergonomics190A formal definition of human-centred XAI is presented and the application of this formalism to the design of a user interface is illustrated, which supports users in indicating their preferences relevant to a situation and to compare their preferences with those of a computer recommendation system.Ergonomics['C. Baber', 'P. Kandola', 'I. Apperly', 'E. McCormick']2024-04-08T00:00:00https://www.semanticscholar.org/paper/c4769a959aeebe775c7c8718dd720f8f681a7525 14e7496c31908c7e661ea0834c5513f328d2eb42Nothing can spoil fresh eggs that were laid by chickens, like dirty vent feathers, blood strains and minor cracks. Eggs play an important role in the context of human health due to its cheap cost and accessibility across the globe. But egg damage paves the way for a significant size of a shortage of nutritions, and creates demand which leads to an increase in price. The brand name of the company that supplies the eggs loses its reputation when damaged eggs reach the market along with good eggs and this can also lead to business loss. India is considered to be one of the worst farm poultry industries across the world, because of poor maintenance of farm, lack of cleanliness and non-compliance to the safety standards. Just because of this, the Indian market loses its power of negotiation on price and loses the opportunity to export the eggs to foreign countries even though India's egg production is close to the egg production margin of other countries. Not only from the perspective of commerce but also from the view of health this can lead to adverse consequences when the unclean eggs are consumed by the public or people who have less immunity. Every year a significant number of people are suffering from diseases when the eggs, which are poorly handled in the process of quality control, are consumed. This could also lead to a fixation of mindset to the consumer that these are usual eggs in india. The consequence of this mindset could result in more severe effects in consumerism over the period of time. This might also prevent people from purchasing the eggs for consumption. The major reason for this is unhygienic rearing practice and improper quality control measures. These are open invitations for egg contamination although India is the third largest country in the world in the production of eggs. There are more than forty seven million egg producers who do not meet safety standards, most of the produce are rejected for export due to chemicals used during the rearing process. Consumption of these eggs had led to a serious issue in india. A sizable number of people are resistant to antibiotics just because of the consumption of eggs that were produced using chemicals and eggs in which chemicals were intact on the shell of that egg. An article that was published by a renowned newspaper states that consumption of such contaminated eggs, which had heavy metal, has led to poisoning in children, paving the way for mental development problems, sometimes mental retardation. This study has attempted to reduce the severe consequences which are a result of consuming eggs with dirt or damaged eggs. A huge effort has been made to ensure that this scenario changes over the period of time, by incorporating AI in the field of Poultry industry in order to enhance the quality of egg production. The study we conducted clearly shows that most of the poor rearing practice happens due to human error. This can be changed by replacing the human efforts by machine, we could replace the entire quality control process especially removing the spoiled eggs from the the batch that is to be sent to the market. The model that we tried to build has given marvelous results in separating the spoiled from the good eggs. The algorithm will not only identify the spoiled eggs but also stores the final output in the database where the Organisations can access it at any point of time. The AI Object detection model will detect the cracked eggs, the eggs with dirt, the eggs with blood stain and many more. In nutshell our object detection model will detect all abnormal eggs which will help us prevent the abnormal eggs from reaching the consumer.International Journal of Innovative Science and Research Technology211This study has attempted to reduce the severe consequences which are a result of consuming eggs with dirt or damaged eggs by incorporating AI in the field of Poultry industry in order to enhance the quality of egg production.International Journal of Innovative Science and Research Technology (IJISRT)['Bogathi Madhusudharsan Reddy', 'Nikhil Pradip Parsawar', 'Amarender Reddy Gundumalla', 'Indira Kumar', 'Bharani Kumar Depuru']2024-04-08T00:00:00https://www.semanticscholar.org/paper/14e7496c31908c7e661ea0834c5513f328d2eb42 f15a78388d934a51213137674a4ea18fb5fac657Abstract Objective Machine learning (ML) will have a large impact on medicine and accessibility is important. This study’s model was used to explore various concepts including how varying features of a model impacted behavior. Materials and Methods This study built an ML model that classified chest X-rays as normal or abnormal by using ResNet50 as a base with transfer learning. A contrast enhancement mechanism was implemented to improve performance. After training with a dataset of publicly available chest radiographs, performance metrics were determined with a test set. The ResNet50 base was substituted with deeper architectures (ResNet101/152) and visualization methods used to help determine patterns of inference. Results Performance metrics were an accuracy of 79%, recall 69%, precision 96%, and area under the curve of 0.9023. Accuracy improved to 82% and recall to 74% with contrast enhancement. When visualization methods were applied and the ratio of pixels used for inference measured, deeper architectures resulted in the model using larger portions of the image for inference as compared to ResNet50. Discussion The model performed on par with many existing models despite consumer-grade hardware and smaller datasets. Individual models vary thus a single model’s explainability may not be generalizable. Therefore, this study varied architecture and studied patterns of inference. With deeper ResNet architectures, the machine used larger portions of the image to make decisions. Conclusion An example using a custom model showed that AI (Artificial Intelligence) can be accessible on consumer-grade hardware, and it also demonstrated an example of studying themes of ML explainability by varying ResNet architectures.JAMIA Open510An example using a custom model showed that AI (Artificial Intelligence) can be accessible on consumer-grade hardware, and it also demonstrated an example of studying themes of ML explainability by varying ResNet architectures.JAMIA Open['Stephen B Lee']2024-04-08T00:00:00https://www.semanticscholar.org/paper/f15a78388d934a51213137674a4ea18fb5fac657 6ef01cf69fd11931ceaf653f32a97ef0e373a53f Due to the pollution of the air and water environment and the problem of forgery, it is difficult to identify the oil painting. The reason is that air pollution and water pollution can lead to moisture, mold, and even water stains on the picture, which will seriously damage the integrity and color performance of the picture. At the same time, chemicals in the water may also have a corrosive effect on the oil painting, further destroying the color and detail of the picture. The problem of relying entirely on the conventional experience of experts is too subjective. Some controversial works are difficult to convince people with rational identification evidence, so it is necessary to explore a scientific and effective and quantify the authenticity of the oil painting identification method. Based on this, This paper constructs an oil painting authenticity identification method based on multi-feature fusion based on the artistic style analysis and feature extraction of oil painting shape, color and texture. The recognition accuracy of the proposed method is compared with that of the existing neural network. The results show that the recognition rate of the proposed model is 73.0%, which is the best performance.AQUA — Water Infrastructure, Ecosystems and Society00An oil painting authenticity identification method based on multi-feature fusion based on multi-feature fusion based on the artistic style analysis and feature extraction of oil painting shape, color and texture is constructed.AQUA — Water Infrastructure, Ecosystems and Society['Eyain Yao', 'Marvin White']2024-04-08T00:00:00https://www.semanticscholar.org/paper/6ef01cf69fd11931ceaf653f32a97ef0e373a53f 0fdb4050399b1bfb46f8ebfb32bbaa4620ee9f89Gaceta Médica de México00Gaceta medica de Mexico['Liliana Pérez-Peralta', 'David Rivera-De la-Parra', 'Enrique O. Graue-Hernández', 'S. Hernández-Jiménez', 'Paloma Almeda-Valdés', 'Héctor Velázquez-Jurado', 'Aida Jiménez-Corona']2024-04-08T00:00:00https://www.semanticscholar.org/paper/0fdb4050399b1bfb46f8ebfb32bbaa4620ee9f89 130022e58ff2b0b259fe21cfa5c13c71fe524e65актуальность данного исследования заключается в изучении возможностей использования нейросетей в учебной практике. В статье рассматривается внедрение технологий искусственного интеллекта в сферу образования в период цифровой трансформации общества. Анализируется динамика традиционных образовательных парадигм в условиях современных информационно-технологических реалий. Целью исследования является изучение потенциала использования технологий ИИ, в частности чат-ботов как инструментов образовательного процесса. Для достижения поставленной цели исследования используется анализ подходов, разрабатывающих данную проблему, включаются данные эксперимента и педагогического наблюдения. Научная новизна работы заключается в изучении функциональных возможностей систем ИИ с целью определения их роли в улучшении процесса обучения. Особое внимание уделяется дидактическим и методическим аспектам использования данных систем, обусловливающих прогресс в образовательной сфере. Авторами описывается использование моделей ИИ в контексте иноязычного обучения с точки зрения возможного повышения качества преподавания и стимулирования учебной активности обучающихся. Приводятся практические примеры использования генеративных моделей ИИ в обучении. В статье также отмечаются ограничения и потенциальные риски, присущие данному направлению, включая ограниченный объем доступной информации, проблему языкового барьера и вероятность некорректной интерпретации контента. В заключение отмечается усиление уровня возможностей технологий ИИ, что неоспоримо подтверждает важность их дальнейшего исследования и внедрения в учебную практику. Полученные результаты представляют интерес для специалистов в области профессионального и иноязычного образования, а также для лиц, занимающихся вопросами повышения эффективности образовательного процесса. the relevance of the research lies in the need to study the possibilities of using neural networks in educational practice. The article discusses the implementation of artificial intelligence technologies in the field of education during the period of digital transformation of society. The dynamics of traditional educational paradigms in the conditions of modern information technology realities are analyzed. The purpose of the research is to study the potential of using AI technologies, in particular chatbots, as tools in the educational process. To achieve the stated prupose of the research, an analysis of approaches that develop the problem is used; data from experiment and pedagogical observation are included. The scientific novelty of the work lies in the study of the functionality of AI systems in order to determine their role in improving the learning process. Particular attention is paid to the didactic and methodological aspects of the use of these systems, determining the progress in the educational sphere. The authors describe the use of AI models in the context of foreign language teaching from the point of view of possible improvement in the quality of teaching and stimulation of students' learning activity. Practical examples of the use of generative AI models in teaching are provided. The article also notes the limitations and potential risks inherent in the area, including the limited amount of available information, the problem of the language barrier and the likelihood of incorrect interpretation of the content. In conclusion, there noted the increased level of capabilities of AI technologies, which undeniably confirms the importance of their further research and implementation in educational practice. The results obtained are of interest to specialists in the field of professional and foreign language education, as well as to those involved in improving the efficiency of the educational process.Modern scientist30Modern scientist['А.А. Евтюгина', 'М.А. Безрукова']2024-04-08T00:00:00https://www.semanticscholar.org/paper/130022e58ff2b0b259fe21cfa5c13c71fe524e65 65b044a4125efdff918ee12a80a294679d8c8cdcACM Symposium on Applied Computing30{'pages': '1662-1664'}['Javier Conde', 'Gonzalo Martínez', 'Pedro Reviriego', 'Joaquín Salvachúa', 'José Alberto Hernández']2024-04-08T00:00:00https://www.semanticscholar.org/paper/65b044a4125efdff918ee12a80a294679d8c8cdc 1b9be65e9399c7157fa1766f2929df2c36e9826a00['Xavier Savarimuthu', 'Sivakannan Subramani', 'Alex Noel Joseph Raj']2024-04-08T00:00:00https://www.semanticscholar.org/paper/1b9be65e9399c7157fa1766f2929df2c36e9826a 4330052214b12065aacbae38d931cd73e05d231cNatural Product Research100Natural product research['C. N. Filer']2024-04-08T00:00:00https://www.semanticscholar.org/paper/4330052214b12065aacbae38d931cd73e05d231c 239881790b67b246a7511f7b5af20448f575b04800['Kaushik Roy']2024-04-08T00:00:00https://www.semanticscholar.org/paper/239881790b67b246a7511f7b5af20448f575b048 dd01de568e0a50d12383c6ab5d859929d23a37ddJAMA pediatrics50JAMA pediatrics['Kai Liu']2024-04-08T00:00:00https://www.semanticscholar.org/paper/dd01de568e0a50d12383c6ab5d859929d23a37dd 289444643e803ec9cf6240d28582753d683172d5International Journal of Lean Six Sigma680International Journal of Lean Six Sigma['Daryl John Powell']2024-04-08T00:00:00https://www.semanticscholar.org/paper/289444643e803ec9cf6240d28582753d683172d5 1c6c63e7dcc3b8add8108b35c382d0706b0f1dbdSignificances of Bioengineering &amp; Biosciences00Significances of Bioengineering &amp; Biosciences['Ronith Lahoti']2024-04-08T00:00:00https://www.semanticscholar.org/paper/1c6c63e7dcc3b8add8108b35c382d0706b0f1dbd 685e91361cd53174e10ce05b6a766b4290884f1eACM Symposium on Applied Computing130{'pages': '971-978'}['Maria J. P. Peixoto', 'Akramul Azim']2024-04-08T00:00:00https://www.semanticscholar.org/paper/685e91361cd53174e10ce05b6a766b4290884f1e 40e5ac2c9355a34acf24100a8c4951abf46893d000['R. Tripathy', 'R. B. Pachori']2024-04-08T00:00:00https://www.semanticscholar.org/paper/40e5ac2c9355a34acf24100a8c4951abf46893d0 d6b2b1d12a001c2fb7064a62ee0e7377cef288c5Educational Administration Theory and Practices00Educational Administration Theory and Practices['Ravindra Sharma', 'V. Harish', 'Geeta Rana']2024-04-08T00:00:00https://www.semanticscholar.org/paper/d6b2b1d12a001c2fb7064a62ee0e7377cef288c5 52f903802bc86fb487acddb474a8bd61f58e9931The International Journal of Advanced Manufacturing Technology200The International Journal of Advanced Manufacturing Technology['Tesfaye Negash Wordofa', 'J. R. Perumalla', 'Abhay Sharma']2024-04-08T00:00:00https://www.semanticscholar.org/paper/52f903802bc86fb487acddb474a8bd61f58e9931 2a588645f07e7b6eaca8554833356e235e6a33d2The digital intelligence era is an intelligent era represented by digital technology. In this era, human attributes, lifestyles, and the embodiment of rights have shown new characteristics. The impact of the digital intelligence era on human rights is a double-edged sword: on the one hand, digital technology, artificial intelligence, and so on greatly liberate human labor productivity, effectively protecting people's rights to subsistence, health, and development; on the other hand, it also brings great threats and challenges to human rights. Anthropocentrism is threatened, the boundaries of human beings are broken, and people's freedom, equality, privacy, labor, intellectual property, environmental and ecological rights are threatened. In the face of the digital intelligence era, development is the only way to break through. In the United Nations Declaration on the Right to Development, the right to development is defined as an inalienable right of human beings. Only in development can all human rights be fully realizedPakistan Journal of Criminology00In the face of the digital intelligence era, development is the only way to break through and all human rights can be fully realized.Pakistan Journal of Criminology[]2024-04-08T00:00:00https://www.semanticscholar.org/paper/2a588645f07e7b6eaca8554833356e235e6a33d2 d1ee618ed9f3537c2f373edd8bdd3563757ecfb0To explore the construction of the ideal paradigm of colleges and universities with artificial intelligence technology in the process of higher personnel training. Using the literature research method, this paper expounds from three aspects: origin, positioning and path. Origin: Artificial intelligence is the reality of technological development in the cultivation of higher talents in the new era, and it is a realistic demand in the cultivation of higher talents, which requires political and ideological empowerment. Positioning: Determining the role of artificial intelligence as an "education assistant", and then clarifying it as a "quasi-subject object", is the basic positioning for the introduction of higher talents at this stage. Paths: 1) Build a teacher-student interconnection model supported by "human-machine collaboration"; 2) Build a smart learning and smart teaching model supported by an "algorithm database"; 3) Establish a "people-oriented" assessment and purpose-led mechanism principle, Deeply improve the quality of the cultivation of higher education personnel in our country's high-level colleges and universities, and promote the final formation of its training pattern.Journal of Electrical Systems230To explore the construction of the ideal paradigm of colleges and universities with artificial intelligence technology in the process of higher personnel training, the literature research method is used, using the literature research method from three aspects: origin, positioning and path.Journal of Electrical Systems['Xiaoxia Zhang']2024-04-08T00:00:00https://www.semanticscholar.org/paper/d1ee618ed9f3537c2f373edd8bdd3563757ecfb0 7abfdea0689d76c65f8fa372f84ec49ab6ac7c80Currently, an integral part of the learning process is the development and honing of the skills of competent use of a foreign language – the most important criterion in any field. The sphere of economics and pedagogy is no exception, because specialists in the presented direction should formulate thoughts succinctly, both in written and oral forms. In pedagogical science, there is a search for effective means to improve grammatical skills and abilities. In this paper, the authors present autonomous learning as an effective tool in the study of grammar, which implies that students should have the opportunity to learn the rules themselves and consolidate them in practice. Autonomous learning implies the improvement of self-regulation and critical thinking, which, in turn, is extremely important in possible non-standard situations that may arise in the course of professional activity. The limitless possibilities of autonomous learning are various online resources, interactive tasks, audio and video materials, electronic literature, grammar exercises. It is thanks to such aspects that students enrich their vocabulary, as well as significantly improve their pronunciation and text construction skills. The aim of the work is to reveal the specifics of the format of autonomous learning by students of the direction "Economics and foreign language" in the study of grammar. The object of the study is the field of formation of autonomous learning skills in the study of grammar. The subject of the study is the introduction of M. Schmitt’s autonomous model "Integrated Learning" in the process of studying grammar of students in the specialties "Economics and foreign language". The scientific novelty of this research is expressed in the creation of its own autonomous learning model and its components. The practical significance of the work is in the possible application of the proposed exercises in autonomous learning by students of the specialization under study. The results of this study will contribute to the field of linguodidactics, as well as attract the attention of teachers, students and all interested parties to innovative approaches in teaching grammar. In addition, the findings allow us to identify effective strategies for autonomous learning that can be used in teaching practice in any field.Vestnik of Samara State Technical University. Psychological and Pedagogical Sciences00Vestnik of Samara State Technical University Psychological and Pedagogical Sciences['N. Shvaikina', 'Alexander A. Popel', 'Viktoria M. Volova']2024-04-07T00:00:00https://www.semanticscholar.org/paper/7abfdea0689d76c65f8fa372f84ec49ab6ac7c80 ac90276ee7894c7b92ea62e4d3e5680a99c59599DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm has gained prominence, urging practitioners to incorporate security practices seamlessly into the DevOps workflow. However, integrating security into the DevOps workflow can impact agility and impede delivery speed. Recently, the advancement of artificial intelligence (AI) has revolutionized automation in various software domains, including software security. AI-driven security approaches, particularly those leveraging machine learning or deep learning, hold promise in automating security workflows. They reduce manual efforts, which can be integrated into DevOps to ensure uninterrupted delivery speed and align with the DevSecOps paradigm simultaneously. This paper seeks to contribute to the critical intersection of AI and DevSecOps by presenting a comprehensive landscape of AI-driven security techniques applicable to DevOps and identifying avenues for enhancing security, trust, and efficiency in software development processes. We analyzed 99 research papers spanning from 2017 to 2023. Specifically, we address two key research questions (RQs). In RQ1, we identified 12 security tasks associated with the DevOps process and reviewed existing AI-driven security approaches. In RQ2, we discovered 15 challenges encountered by existing AI-driven security approaches and derived future research opportunities. Drawing insights from our findings, we discussed the state-of-the-art AI-driven security approaches, highlighted challenges in existing research, and proposed avenues for future opportunities.arXiv.org2111A comprehensive landscape of AI-driven security techniques applicable to DevOps and identifying avenues for enhancing security, trust, and efficiency in software development processes is presented.ArXiv['Michael Fu', 'Jirat Pasuksmit', 'C. Tantithamthavorn']2024-04-07T00:00:00https://www.semanticscholar.org/paper/ac90276ee7894c7b92ea62e4d3e5680a99c59599 fe4e225ec0c942d0c28591dbc0cb82a4c18467e1We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications. This Visual IDE prioritizes both the Integrity of its development tools and the Visuality of its components, ensuring a smooth and efficient building experience.On one hand, AI2Apps integrates a comprehensive development toolkit ranging from a prototyping canvas and AI-assisted code editor to agent debugger, management system, and deployment tools all within a web-based graphical user interface. On the other hand, AI2Apps visualizes reusable front-end and back-end code as intuitive drag-and-drop components. Furthermore, a plugin system named AI2Apps Extension (AAE) is designed for Extensibility, showcasing how a new plugin with 20 components enables web agent to mimic human-like browsing behavior. Our case study demonstrates substantial efficiency improvements, with AI2Apps reducing token consumption and API calls when debugging a specific sophisticated multimodal agent by approximately 90% and 80%, respectively. The AI2Apps, including an online demo, open-source code, and a screencast video, is now publicly accessible.arXiv.org260AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications, is introduced, demonstrating substantial efficiency improvements.ArXiv['Xin Pang', 'Zhucong Li', 'Jiaxiang Chen', 'Yuan Cheng', 'Yinghui Xu', 'Yuan Qi']2024-04-07T00:00:00https://www.semanticscholar.org/paper/fe4e225ec0c942d0c28591dbc0cb82a4c18467e1 cbb649e91c4bbc665cc61c282b24ade1cf6bad24This paper introduces an innovative artificial intelligence (AI) method aimed at tackling the challenges associated with detecting and tracking moving objects in video surveillance systems. By utilizing self-organization through artificial neural networks, our approach effectively manages scenes with dynamic backgrounds and gradual changes in lighting, ensuring robust detection across different types of videos recorded by stationary cameras. In the realm of moving object detection, our method leverages the adaptability of neural networks, enabling precise detection in complex visual environments. For object tracking, we propose a combination of Kalman filtering techniques and a sophisticated matching model based on Multiple Hypothesis Testing, ensuring accurate and consistent tracking across frames. Through experimental validation using various color video sequences, we demonstrate the effectiveness and reliability of our approach, highlighting its potential to enhance the performance of surveillance systems in real-world scenarios.International Journal For Multidisciplinary Research80This paper introduces an innovative artificial intelligence method aimed at tackling the challenges associated with detecting and tracking moving objects in video surveillance systems by utilizing self-organization through artificial neural networks, enabling precise detection in complex visual environments.International Journal For Multidisciplinary Research['Speranza Deejoe', 'Ravula Charan', 'Dheeraj Subhash V P']2024-04-07T00:00:00https://www.semanticscholar.org/paper/cbb649e91c4bbc665cc61c282b24ade1cf6bad24 ffa5a275be9ea886dff66494c821f2b2db2f091dThe rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Jeff Shuford']2024-04-07T00:00:00https://www.semanticscholar.org/paper/ffa5a275be9ea886dff66494c821f2b2db2f091d bb3b19bbb9ddfad6459feb092e8833bb8daad46dThe rapid integration of Artificial Intelligence (AI) systems across critical domains necessitates robust security evaluation frameworks. We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER). SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation. Through comparative analysis, we demonstrate the advantages of our framework over existing techniques. We discuss the theoretical and practical implications, potential applications, limitations, and future research directions. Our work contributes to the development of secure and trustworthy AI technologies by providing a holistic, theoretically grounded approach to AI security evaluation. As AI continues to advance, prioritising and advancing AI security through interdisciplinary collaboration is crucial to ensure its responsible deployment for the benefit of society.arXiv.org90A novel approach is proposed that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER) to provide a holistic, theoretically grounded approach to AI security evaluation.ArXiv['B. Kereopa-Yorke']2024-04-07T00:00:00https://www.semanticscholar.org/paper/bb3b19bbb9ddfad6459feb092e8833bb8daad46d 9b688157f467a83b7f4f6ac4eae7754241018aefhe rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Jeff Shuford']2024-04-07T00:00:00https://www.semanticscholar.org/paper/9b688157f467a83b7f4f6ac4eae7754241018aef f14e953e7734f323af4fdbbc05a7e1bcde2ec75dArtificial intelligence (AI) has emerged as a transformative force with profound implications for society, promising significant benefits for individuals with disabilities. While its potential is undeniable, AI also poses inherent risks, including ethical concerns that may exacerbate discrimination against marginalized groups. This paper provides a comprehensive examination of the advantages and drawbacks of AI for people with disabilities, with a particular emphasis on algorithmic biases. These biases, capable of shaping societal structures and influencing decision-making processes, have the potential to perpetuate unfair treatment and discrimination. In light of these challenges, the paper explores potential solutions to address these issues and ensure that AI serves the needs of all individuals, including those with disabilities.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This paper provides a comprehensive examination of the advantages and drawbacks of AI for people with disabilities, with a particular emphasis on algorithmic biases.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md. Rashed Khan']2024-04-07T00:00:00https://www.semanticscholar.org/paper/f14e953e7734f323af4fdbbc05a7e1bcde2ec75d 90d954048c5d236b787eabade9809bcf9ff7813eBanking stands at a transformative crossroads, propelled by technological disruption, shifting consumer dynamics, and the catalytic impact of the COVID-19 pandemic. This research charts the course for a new era in banking business models, introducing a paradigm shift known as 'the AI bank of the future.' Grounded in the advancement of artificial intelligence (AI) technologies, this model offers traditional banks the potential to elevate revenue while reducing costs, ushering in innovative customer engagement approaches. In response to multifaceted challenges, including market valuations, competitive threats from neobanks, and changing customer expectations, traditional banks are urged to become 'AI-first' in strategy and operations. This involves leveraging economies of scale through efficient AI deployment to enhance customer engagement with distinctive experiences and superior value propositions. The research meticulously explores the building blocks of an AI bank, delving into four critical layers: engagement, AI-powered decision-making, core technology, and data infrastructure, and a platform-based operating model. Through a series of articles, the study examines trends, challenges, and requirements for each layer, offering an end-to-end view of the capabilities essential for the AI bank of the future. The articles cover topics ranging from the challenges leading banks to adopt an AI-first approach, a day in the life of a consumer transacting with an AI bank, reimagining customer engagement, to AI-powered decision-making and the modernization of core technology. The final piece discusses the necessity of a platform operating model, emphasizing the deployment of AI and analytics capabilities at scale through cross-functional business-technology platforms. To embark on this transformative journey, bank leaders are advised to formulate strategic goals for the AI-enabled digital age, establish an AI-first vision, and strategically modernize enterprise technology. The research underscores the importance of assessing emerging technologies, prioritizing initiatives aligned with customer needs, and considering partnerships for non-differentiating capabilities while focusing on in-house development for distinct competitive advantages. In conclusion, building the AI bank of the future is envisioned as a pathway to innovation, competitive prowess against digital natives, and sustainable increases in profits and valuations. The research serves as a guiding compass, assisting banks in establishing their vision and crafting a strategic roadmap for this transformative journey into the AI-driven future of banking.International Journal For Multidisciplinary Research110The research meticulously explores the building blocks of an AI bank, delving into four critical layers: engagement, AI-powered decision-making, core technology, and data infrastructure, and a platform-based operating model, offering an end-to-end view of the capabilities essential for the AI bank of the future.International Journal For Multidisciplinary Research['Jabili.Kamurthy', 'Mustafa', 'Mohit', 'Suhail Shabeer', 'Jeevan', 'Anusha S. Nadiger']2024-04-07T00:00:00https://www.semanticscholar.org/paper/90d954048c5d236b787eabade9809bcf9ff7813e a47c4eec29f8a62af711838f215692ab3a3619bcAlzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline and functional impairment. This study compares conventional intervention techniques with emerging artificial intelligence (AI) approaches to AD. Intervention technique refers to a specific method or approach employed to bring about positive change in a particular situation. In the context of AD, such techniques are crucial as they aim to slow down the progression of symptoms, alleviate behavioral challenges, and support patients and their caretakers in managing the complexities of the condition. Conventional intervention techniques, such as cognitive stimulation and reality orientation, have demonstrated benefits in improving cognitive function and emotional well-being. Conventional intervention approaches are widely preferred as they have a proven track record of effectiveness, personalized response, cost-effectiveness, and patient-centered care. Despite these benefits, they are limited by individual variability in response and long-term effectiveness. On the other hand, AI-based approaches such as Computer Vision and Deep Learning (DL) hold the potential to revolutionize Alzheimer's interventions. These technologies offer early detection, personalized care, and remote monitoring capabilities. They can provide tailored interventions, assist decision-making, and enhance caregiver support. Although AI-based interventions face challenges such as data privacy and implementation complexity, their potential to transform Alzheimer's care is significant. This research paper compares conventional and AI-based approaches. It reveals that while traditional techniques are well-established and have proven benefits, AI-based interventions offer novel opportunities for personalized and advanced care. Combining the strengths of both approaches may lead to more comprehensive and effective interventions for individuals with AD. Continued research and collaboration are crucial to harness the full potential of AI in improving Alzheimer's care and enhancing the quality of life for affected individuals and their caregivers.Artificial Intelligence and Applications00It is revealed that while traditional techniques are well-established and have proven benefits, AI-based interventions offer novel opportunities for personalized and advanced care and combining the strengths of both approaches may lead to more comprehensive and effective interventions for individuals with AD.Artificial Intelligence and Applications['Karthikeyan Subramanian', 'Faizal Hajamohideen', 'Vimbi Viswan', 'Noushath Shaffi', 'Mufti Mahmud']2024-04-07T00:00:00https://www.semanticscholar.org/paper/a47c4eec29f8a62af711838f215692ab3a3619bc fb1a30b48147726ca522efefd849d512ba38ba36Abstract In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI’s responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI’s viewpoints on such critical matters is essential for informed decision-making and future research.Renal Failure60The findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk, while for settings with fewer transmission risks, the AI suggests that mask-wearing might be less imperative.Renal Failure['Oscar A. Garcia Valencia', 'C. Thongprayoon', 'Jing Miao', 'J. Bruminhent', 'Iasmina M. Craici', 'W. Cheungpasitporn']2024-04-07T00:00:00https://www.semanticscholar.org/paper/fb1a30b48147726ca522efefd849d512ba38ba36 09746535e6e319fa085cec9022aa012dcc3a13f0The demand for rapid advancement in AI, mobile and automotive markets is pushing the boundaries of electronic packaging, including heterogeneous integration, high-power packages, and large-die packaging. Against this backdrop, machine learning technologies emerge as dynamic tools for correlation building and classification, revolutionizing the traditional approaches to design, manufacturing, and testing in electronic packaging, as well as the Design for Reliability (DfR) methodologies.This paper reviews the most recent AI-assisted approach for electronic packaging and then focuses on the AI-assisted DfR (AI-DfR) approaches. Our examination reveals that AI methods have been adapted to meet the specific needs of electronic packaging. The industry’s anticipation for AI-DfR stems from its potential to address prevailing reliability design challenges, yet its multidisciplinary essence poses hurdles to swift progress. This review proposes future directions for AI-DfR’s development, spotlighting critical areas such as the quality and efficiency of finite element modeling, design and optimization of training models, selection of AI models, and maintenance and value enhancement strategies.International Conference on Thermal, Mechanial and Multi-Physics Simulation and Experiments in Micro-Electronics and Micro-Systems920This paper reviews the most recent AI-assisted approach for electronic packaging and then focuses on the AI-assisted DfR (AI-DfR) approaches, revealing that AI methods have been adapted to meet the specific needs of electronic packaging.2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)['Cadmus Yuan', 'S. D. M de Jong', 'W. V. Driel']2024-04-07T00:00:00https://www.semanticscholar.org/paper/09746535e6e319fa085cec9022aa012dcc3a13f0 46a5a0f28c86e1f35554e7b045a6ebc54561d13cThe rapid development of Generative Artificial Intelligence (GAI) technology has shown tremendous potential in various fields, such as image generation, text generation, and video generation, and it has been widely applied in various industries. However, GAI also brings new risks and challenges to cybersecurity. This paper analyzes the application status of GAI technology in the field of cybersecurity and discusses the risks and challenges it brings, including data security risks, scientific and technological ethics and moral challenges, Artificial Intelligence (AI) fraud, and threats from cyberattacks. On this basis, this paper proposes some countermeasures to maintain cybersecurity and address the threats posed by GAI, including: establishing and improving standards and specifications for AI technology to ensure its security and reliability; developing AI-based cybersecurity defense technologies to enhance cybersecurity defense capabilities; improving the AI literacy of the whole society to help the public understand and use AI technology correctly. From the perspective of GAI technology background, this paper systematically analyzes its impact on cybersecurity and proposes some targeted countermeasures and suggestions, possessing certain theoretical and practical significance.Journal of Computer Science and Technology Studies130This paper proposes some countermeasures to maintain cybersecurity and address the threats posed by GAI, including establishing and improving standards and specifications for AI technology to ensure its security and reliability and improving the AI literacy of the whole society to help the public understand and use AI technology correctly.Journal of Computer Science and Technology Studies['Mingzheng Wang']2024-04-07T00:00:00https://www.semanticscholar.org/paper/46a5a0f28c86e1f35554e7b045a6ebc54561d13c ac62c61b571cb1a9bf7c005696d2026db6dda840Explainable Artificial Intelligence (XAI) is a field that aims to make artificial intelligence (AI) processes more transparent, explainable, and understandable. As AI processes become more complex, the need to reveal the "black box" nature of these models and provide explanations for their results and decisions is increasing. XAI aims to bridge the gap between the opaque inner workings of AI and human understanding by creating AI that is accurate, useful, and can explain reasoning and decision-making processes in ways that humans can understand. XAI's importance stems from several factors. First, it addresses trust and accountability issues in AI systems, particularly in high-risk sectors like healthcare, finance, and technology. By providing explanations for AI decisions, stakeholders can better understand the logic behind them, detect inconsistencies, and ensure moral and administrative compliance. Second, XAI encourages collaboration and decision-making between people and intelligence, allowing experts and decision-makers to use their knowledge and experience to make better decisions. Thirdly, XAI plays a crucial role in modeling, debugging, and continuous improvement by identifying flaws, biases, or inconsistencies and working to improve performance standards and reliability. Various methods and techniques are used in XAI, each with their own advantages and limitations. Model-free explanations such as LIME, Anchor and SHAP are particularly important because they can be applied to any AI model, regardless of its design or complexityInternational Journal of Advanced Research in Science, Communication and Technology30XAI aims to bridge the gap between the opaque inner workings of AI and human understanding by creating AI that is accurate, useful, and can explain reasoning and decision-making processes in ways that humans can understand.International Journal of Advanced Research in Science, Communication and Technology['Prachi Zodage', 'Hussain Harianawala', 'Hafsa Shaikh', 'Asad Kharodia']2024-04-07T00:00:00https://www.semanticscholar.org/paper/ac62c61b571cb1a9bf7c005696d2026db6dda840 e636a5c43be679b22ad375b659d2ea7c3a78648fRecent chart-authoring systems, such as Amazon Q in QuickSight and Copilot for Power BI, demonstrate an emergent focus on supporting natural language input to share meaningful insights from data through chart creation. Currently, chart-authoring systems tend to integrate voice input capabilities by relying on speech-to-text transcription, processing spoken and typed input similarly. However, cross-modality input comparisons in other interaction domains suggest that the structure of spoken and typed-in interactions could notably differ, reflecting variations in user expectations based on interface affordances. Thus, in this work, we compare spoken and typed instructions for chart creation. Findings suggest that while both text and voice instructions cover chart elements and element organization, voice descriptions have a variety of command formats, element characteristics, and complex linguistic features. Based on these findings, we developed guidelines for designing voice-based authoring-oriented systems and additional features that can be incorporated into existing text-based systems to support speech modality.CHI Extended Abstracts340This work compares spoken and typed instructions for chart creation and suggests that while both text and voice instructions cover chart elements and element organization, voice descriptions have a variety of command formats, element characteristics, and complex linguistic features.{'pages': '72:1-72:7'}['Nazar Ponochevnyi', 'Anastasia Kuzminykh']2024-04-07T00:00:00https://www.semanticscholar.org/paper/e636a5c43be679b22ad375b659d2ea7c3a78648f b0b14d9e65449fd7d74eef589702aaad0086f6f8AI &amp; SOCIETY460This article attempts to “open the black boxes” of the “black carpet” of the “black carpet” (robotic sorting system) and examine the reorganization of Greek postal services through the introduction of software and hardware technologies, highlighting the high risk of flexible, pluralistic, decentralized (under)employment and aspects of the sub-politics of automation.AI &amp; SOCIETY['Christos Kouroutzas', 'Venetia Palamari']2024-04-07T00:00:00https://www.semanticscholar.org/paper/b0b14d9e65449fd7d74eef589702aaad0086f6f8 724f1bdc057fe02fdc29d3fa94895187dff3e171This paper outlines the methodology and implementation strategies necessary to revolutionize banking security and ensure a resilient financial ecosystem. In the dynamic landscape of banking, security stands as a cornerstone for financial institutions. The rise of digital banking and the proliferation of online transactions have heightened the need for robust cybersecurity measures to protect sensitive financial data and ensure the integrity of transactions. Traditional security approaches are often reactive and struggle to keep pace with the evolving tactics of cybercriminals. Consequently, there is a pressing need for innovative solutions that can adapt to emerging threats in real-time. The integration of Artificial Intelligence (AI), Blockchain, and Business Intelligence (BI) offers a paradigm shift in banking security. AI, with its ability to analyze vast amounts of data and identify patterns indicative of suspicious behavior, serves as a proactive defense mechanism against cyber threats. By continuously monitoring transactions and network activities, AI-powered systems can swiftly detect anomalies and potential security breaches, enabling banks to respond effectively and mitigate risks before they escalate. Blockchain technology introduces a decentralized and immutable ledger that enhances the security and transparency of transactions. By utilizing cryptographic principles, Blockchain ensures that transactional data remains tamper-proof and verifiable, reducing the risk of fraud and unauthorized access. This technology not only secures financial transactions but also enables the secure sharing of data among stakeholders, facilitating seamless collaboration while maintaining data integrity. Business Intelligence (BI) complements AI and Blockchain by providing actionable insights derived from data analytics. BI tools enable banks to gain a deeper understanding of their security posture, identify vulnerabilities, and prioritize remediation efforts. Keywords:  Banking, Security, AI, Integration, Blockchain.Finance &amp; Accounting Research Journal02This paper outlines the methodology and implementation strategies necessary to revolutionize banking security and ensure a resilient financial ecosystem through the integration of Artificial Intelligence, Blockchain, and Business Intelligence.Finance &amp; Accounting Research Journal['Oluwatoyin Ajoke Farayola']2024-04-07T00:00:00https://www.semanticscholar.org/paper/724f1bdc057fe02fdc29d3fa94895187dff3e171 f522fec9b3142be6fffa52e4858cec6bd0f002ceAI in Civil Engineering270The research findings reveal that most of the water utilities in the United States are at an early stage of implementing AI as they face concerns regarding the black box nature, trustworthiness, and sustainability of AI technology in their system.AI in Civil Engineering['Darshan Vekaria', 'Sunil Sinha']2024-04-07T00:00:00https://www.semanticscholar.org/paper/f522fec9b3142be6fffa52e4858cec6bd0f002ce 807ca15f913d40ca974b186f8203bb9bd0a04c32As a product of social development and technological innovation, Artificial Intelligence has become the driving force of technological innovation and industrial transformation. It profoundly impacts the world economy, social progress as well as people’s lives. Artificial Intelligence’s commercial applications innovate enterprises’ internal operations and production processes, bringing both challenges and opportunities for management. This article analyses innovation in modern enterprise management with specific applications of AI to management theories. Some suggestions are also made for enhancing the application level of Artificial Intelligence in enterprise management.International Journal of Global Economics and Management70In conclusion, innovation in modern enterprise management with specific applications of AI to management theories is analyzed, bringing both challenges and opportunities for management.International Journal of Global Economics and Management['Keyu Chen']2024-04-07T00:00:00https://www.semanticscholar.org/paper/807ca15f913d40ca974b186f8203bb9bd0a04c32 aa44d387d3473e5f7a7e1f1ee54ff67177279f93The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.Big Data and Cognitive Computing700The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry and highlights the pivotal role of parameters in refining algorithms for both trucks and cars.Big Data and Cognitive Computing['Divya Garikapati', 'Sneha Sudhir Shetiya']2024-04-07T00:00:00https://www.semanticscholar.org/paper/aa44d387d3473e5f7a7e1f1ee54ff67177279f93 014155c4ab51436eb900be00afbaaa2cbe24ffa6The study's primary purpose was to review studies on the role of artificial intelligence in market performance. Artificial intelligence significantly impacts market performance by providing data analysis, personalization, demand forecasting, pricing optimization, customer support automation, risk assessment, and enhanced decision-making capabilities. By leveraging artificial intelligence (AI) effectively, Businesses can improve their competitiveness, improve customer satisfaction, increase revenue, and achieve sustainable growth in the market. A thorough assessment of the literature was done, and screening standards were applied, all to improve the study. Based on the inclusion and exclusion criteria for the articles, data extraction was done by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. 45 published articles were analyzed, and significant data was extracted. The review’s findings collectively emphasize the crucial role of AI in enhancing market performance by improving sales, customer satisfaction, demand forecasting, pricing optimization, risk mitigation, and decision-making processes. As AI continues to advance, further research and practical implementations will likely uncover additional benefits and insights into its impact on market performance. To help more scholars understand and advance the numerous theories and models related to the topic, this concept overview provides guidance.Journal of Economics and Behavioral Studies480The review’s findings collectively emphasize the crucial role of AI in enhancing market performance by improving sales, customer satisfaction, demand forecasting, pricing optimization, risk mitigation, and decision-making processes.Journal of Economics and Behavioral Studies['Endalkachew Desta', 'Chalchissa Amantie']2024-04-07T00:00:00https://www.semanticscholar.org/paper/014155c4ab51436eb900be00afbaaa2cbe24ffa6 e919f1ddb8c1efeaac1e4cdd33ecdaf2fc5eddfaPurpose of reseach. The search for the possibilities and limitations of using artificial intelligence (AI) to solve poorly formalized (creative) tasks in various fields. Evaluation of the effectiveness of AI application in comparison with traditional methods. Analysis of the use of artificial intelligence based on genetic algorithms to optimize processes and solve complex poorly formalized tasks using the example of school scheduling.Methods. Using a genetic algorithm (GA) to schedule by combining and varying data is similar to evolutionary selection in nature.Results. As a result of the conducted software modeling using GA, a variant of the schedule for a typical urban secondary school was obtained, taking into account all the norms of the "school" SanPiN and the wishes of teachers, characterized by ease of use and flexibility when adding new restrictions, as well as high speed on ordinary office computers.Conclusion. The relevance of using genetic algorithms for scheduling lies in the rapid automatic search for the optimal or acceptable solution in a large space of possible options, taking into account the established limitations and priorities, as well as flexibility and adaptability for various types of tasks.Proceedings of the Southwest State University30The relevance of using genetic algorithms for scheduling lies in the rapid automatic search for the optimal or acceptable solution in a large space of possible options, taking into account the established limitations and priorities, as well as flexibility and adaptability for various types of tasks.Proceedings of the Southwest State University['L. A. Lisitsin', 'A. L. Lisitsin', 'A. L. Lisitsin']2024-04-07T00:00:00https://www.semanticscholar.org/paper/e919f1ddb8c1efeaac1e4cdd33ecdaf2fc5eddfa 7e69c187b6fb3721dcbf20101e16b629671f4f8aThis study explores the comprehensive understanding of taekwondo, the application of fourth industrial revolution technologies in various kinds of sports, the development of taekwondo through artificial intelligence (AI), and essential technology in the fourth industrial revolution while suggesting advanced science directions through a literature review. Literature was sourced from six internet search electronic databases, consisting of three English databases and three Korean databases, from January 2016 to August 2023. The literature indicated cases of sports convergence with the application of fourth industrial revolution technologies, such as the game of go, golf, table tennis, soccer, American football, skiing, archery, and fencing. These sports not only use big data but also virtual reality and augmented reality. Taekwondo is a traditional martial art that originated in Republic of Korea and gradually became a globally recognized sport. Since taekwondo’s competition analysis is an analysis in which researchers manually write events, it takes a very long time to analyze, and the scale of the analysis varies depending on the researcher’s tendencies. This study presented the development of an AI Taekwondo performance improvement analysis and evaluation system and a metaverse-based virtual Taekwondo pumsae/fighting coaching platform through an AI-based motion tracking analysis method.Applied Sciences270This study presented the development of an AI Taekwondo performance improvement analysis and evaluation system and a metaverse-based virtual Taekwondo pumsae/fighting coaching platform through an AI-based motion tracking analysis method.Applied Sciences['M. Shin', 'Dae-Hoon Lee', 'Albert Chung', 'Yu-Won Kang']2024-04-07T00:00:00https://www.semanticscholar.org/paper/7e69c187b6fb3721dcbf20101e16b629671f4f8a 52ee0eac6e0cfff0610da0df87b6e6b298f95b9cAs artificial intelligence (AI) continues to revolutionize global trade, it brings with it a host of legal complexities that challenge traditional frameworks within international trade agreements. This abstract explores the emerging problem of integrating AI into global trade agreements, identifies its purpose in addressing these challenges, highlights existing research gaps, and outlines the structure of the study. The proliferation of AI technologies in global trade presents a myriad of legal challenges, including issues related to intellectual property rights, data protection, liability, and regulatory frameworks. Existing international trade agreements often lack specific provisions addressing AI, leading to ambiguity and inconsistency in legal interpretation. Moreover, the rapid pace of technological advancement outpaces the ability of legal frameworks to adapt, exacerbating the problem. The purpose of this study is to analyze the legal complexities arising from the integration of AI into global trade agreements and to propose potential solutions for addressing these challenges. By examining existing legal frameworks, case studies, and scholarly literature, this research aims to provide insights into the development of AI-inclusive trade policies that foster innovation while safeguarding against potential risks and inequalities. While there is a growing body of literature addressing the intersection of AI and various legal domains, such as ethics, privacy, and labor law, there remains a notable gap in the understanding of how AI impacts international trade agreements specifically. Existing research primarily focuses on domestic regulatory frameworks, leaving a dearth of comprehensive analysis on the implications of AI for global trade governance. This study seeks to fill this gap by exploring the unique legal challenges posed by AI in the context of international trade agreements. This study will begin by providing an overview of the current landscape of AI technologies and their applications in global trade. It will then analyze the existing legal frameworks within international trade agreements and identify areas of ambiguity and inconsistency concerning AI. Subsequently, the study will explore case studies and examples of AI implementation in trade, examining the legal implications and challenges encountered. Finally, the research will propose recommendations and policy guidelines for integrating AI into future trade agreements, ensuring coherence, fairness, and adaptability in the face of technological innovation. Keywords: Law, Business Law, Legal, Artificial Intelligence, Trade Agreement.International journal of applied research in social sciences00International Journal of Applied Research in Social Sciences['Etinosa Igbinenikaro', 'Adefolake Olachi Adewusi']2024-04-07T00:00:00https://www.semanticscholar.org/paper/52ee0eac6e0cfff0610da0df87b6e6b298f95b9c 775361f9cf825ffa64fe0a53e0579ed54aa22b35The proliferation of fintech innovations has necessitated the development of comprehensive regulatory frameworks to uphold consumer protection standards while fostering an environment conducive to technological advancement. This paper delves into the intricate policy landscapes governing fintech innovations, with a primary focus on striking a delicate equilibrium between safeguarding consumer interests and nurturing innovation. By conducting an in-depth analysis of global regulatory approaches, including variations across jurisdictions and the pivotal roles of governmental bodies, this study explores pivotal measures aimed at fortifying consumer protection. These measures encompass a spectrum of strategies, ranging from stringent disclosure requirements and robust privacy safeguards to the implementation of anti-money laundering (AML) and know your customer (KYC) protocols. Furthermore, the study examines mechanisms for resolving consumer complaints and ensuring accountability within the fintech sector. Moreover, the paper investigates various avenues for fostering innovation within the regulatory framework, such as the establishment of regulatory sandboxes and the promotion of collaborative endeavors between regulatory authorities and industry stakeholders. Through the examination of pertinent case studies, this study highlights both successful regulatory interventions and challenges encountered in regulating fintech innovations. These insights culminate in a series of actionable recommendations tailored for policymakers, aimed at navigating the evolving landscape of fintech regulation. Additionally, the paper offers projections on future trends in fintech regulation, thereby equipping policymakers with the necessary foresight to adapt to the dynamic nature of the fintech ecosystem. Keywords:  Financial Law, Policy, Frameworks, Fintech Innovations, Consumer Protection, Fostering Innovation.Finance &amp; Accounting Research Journal00Finance &amp; Accounting Research Journal['Etinosa Igbinenikaro', 'Adefolake Olachi Adewusi']2024-04-07T00:00:00https://www.semanticscholar.org/paper/775361f9cf825ffa64fe0a53e0579ed54aa22b35 35caed53829c079e1c1af11c1f47bafed15e808aMachine learning models often make predictions based on biased features such as gender, race, and other social attributes, posing significant fairness risks, especially in societal applications, such as hiring, banking, and criminal justice. Traditional approaches to addressing this issue involve retraining or fine-tuning neural networks with fairness-aware optimization objectives. However, these methods can be impractical due to significant computational resources, complex industrial tests, and the associated CO2 footprint. Additionally, regular users aiming to use fair models often lack access to model parameters. In this paper, we introduce Inference-Time Rule Eraser (Eraser), a novel method focused on removing biased decision-making rules during inference to address fairness concerns without modifying model weights. We begin by establishing a theoretical foundation for modifying model outputs to eliminate biased rules through Bayesian analysis. Next, we present a specific implementation of Eraser that involves two stages: (1) querying the model to distill biased rules into a patched model, and (2) excluding these biased rules during inference. Extensive experiments validate the effectiveness of our approach, showcasing its superior performance in addressing fairness concerns in AI systems.arXiv.org670This paper introduces Inference-Time Rule Eraser (Eraser), a novel method focused on removing biased decision-making rules during inference to address fairness concerns without modifying model weights.ArXiv['Yi Zhang', 'Jitao Sang']2024-04-07T00:00:00https://www.semanticscholar.org/paper/35caed53829c079e1c1af11c1f47bafed15e808a bebe2eaf5af7e8f555200f031c9d0a5af12b8b91With the continuous development and maturity of Internet technology, big data and artificial intelligence have gradually become the new products of the 21st century, and their applications are expanding while promoting the development of science and technology. As artificial intelligence has the advantages of fast, accurate and convenient, its application in the field of enterprise human resource management has brought a new working mode for enterprise human resource management and put forward higher requirements for enterprise human resource managers. On this basis, this paper gives an overview of the development and application of artificial intelligence, discusses the specific application of artificial intelligence in enterprise human resource management, focuses on the challenges and opportunities faced by enterprise human resource management in the context of artificial intelligence, and aims to bring technical support to relevant departments.International Journal of Global Economics and Management40An overview of the development and application of artificial intelligence is given, the specific application of artificial intelligence in enterprise human resource management is discussed, the challenges and opportunities faced by enterprise human resource management in the context of artificial intelligence are focused on, and technical support is brought to relevant departments.International Journal of Global Economics and Management['Limin Han']2024-04-07T00:00:00https://www.semanticscholar.org/paper/bebe2eaf5af7e8f555200f031c9d0a5af12b8b91 23457cd6bf45a9ee3e56376d0770bfd0d4ed2c3dQScience Connect00QScience Connect['Mutasim Mohamed Elhassan Gadour']2024-04-07T00:00:00https://www.semanticscholar.org/paper/23457cd6bf45a9ee3e56376d0770bfd0d4ed2c3d 420a178541daa8e9afed7862c5dd712ea36302afروح القوانين00روح القوانين['ريهان محروس السيد الفخراني']2024-04-07T00:00:00https://www.semanticscholar.org/paper/420a178541daa8e9afed7862c5dd712ea36302af 5566c361183c532478b877ad3b727bb3f6e770efEducational Administration: Theory and Practice00Educational Administration: Theory and Practice['Dr. D. Sathyaseelan', 'Dr. S. Siva']2024-04-07T00:00:00https://www.semanticscholar.org/paper/5566c361183c532478b877ad3b727bb3f6e770ef 07520789de465a4e5a1b0fe59e5113e23a465aec Heat networks have the potential to deliver up to 20% of heat to homes in the UK by 2050 and are able to deliver significant reductions in carbon emissions contributing substantially to the UK’s 2050 net zero target. However, for many years the sector has been unregulated which means that some consumers of heat networks have been paying high prices for heating systems which may have been poorly constructed and which they do not understand. This analysis identifies three key problems that have arisen from a lack of regulation in the sector: higher prices for consumers, a lack of transparency and understanding of heat networks, and design and build problems, which have all served as barriers to realising low carbon heating in the UK. The recently enacted Energy Act 2023 will be analysed to determine the extent to which it will resolve these problems.Journal of environmental law00Journal of Environmental Law['C. Caine']2024-04-06T00:00:00https://www.semanticscholar.org/paper/07520789de465a4e5a1b0fe59e5113e23a465aec 29d34e7b5d5cedd1f9196d4a0971d1d052fa1d50 As part of its ‘pro-innovation’ approach to artificial intelligence (AI), the UK has left public sector AI procurement and deployment to ‘regulation by contract’ based on thin guidance. Borrowing from the description of AI ‘hallucinations’ as plausible but incorrect answers given with high confidence by AI systems, I argue that this is a ‘regulatory hallucination’: an incorrect answer to the challenge of regulating the procurement and use of AI by the public sector. The pretence that public buyers can ‘confidently and responsibly procure AI technologies’ can generate individual harms and broader negative social effects as the public sector ramps up AI adoption and accumulates a potentially significant stock of AI deployments across all areas of public sector activity. I sketch an alternative strategy to boost the effectiveness of the goals of AI regulation and the protection of individual rights and collective interests through the creation of an independent authority.Social Science Research Network140It is argued that the pretence that public buyers can ‘confidently and responsibly procure AI technologies’ can generate individual harms and broader negative social effects as the public sector ramps up AI adoption and accumulates a potentially significant stock of AI deployments across all areas of public sector activity.SSRN Electronic Journal['A. Sanchez-Graells']2024-04-06T00:00:00https://www.semanticscholar.org/paper/29d34e7b5d5cedd1f9196d4a0971d1d052fa1d50 caecad04cd42cd42e5c86429d8257454b5931a8fThe article discusses the specific requirements for hunting products, which are enshrined in veterinary legislation, including veterinary rules. The authors analyzed federal legislation regarding the establishment of requirements for hunting products and the need to conduct a veterinary and sanitary expertise of hunted animals. The article states that the use of hunting products is possible only after the necessary measures have been taken, which include inspection of the carcass by the hunter or the responsible person of the hunting enterprise, taking biomaterial for veterinary and sanitary expertise and obtaining a conclusion.Legal regulation in veterinary medicine10Legal regulation in veterinary medicine['I. Chekhovskikh', 'E. M. Ol']2024-04-06T00:00:00https://www.semanticscholar.org/paper/caecad04cd42cd42e5c86429d8257454b5931a8f 1fcb0012cb740b6abeab3277708a380a0fabdc07As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach -- majority voting -- to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR -- by approximately 9% and 31%, respectively -- compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.arXiv.org800It is shown that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR -- by approximately 9% and 31%, respectively -- compared to decisions made by one pathologist collaborating with AI, which resulted in better precision and recall in the detection of mitoses.ArXiv['H. Gu', 'Chunxu Yang', 'S. Magaki', 'Neda Zarrin-Khameh', 'N. Lakis', 'Inma Cobos', 'Negar Khanlou', 'Xinhai R. Zhang', 'Jasmeet Assi', 'Joshua T. Byers', 'Ameer Hamza', 'Karam Han', 'Anders Meyer', 'Hilda Mirbaha', 'Carrie A Mohila', 'Todd M. Stevens', 'Sara L. Stone', 'Wenzhong Yan', 'Mohammad Haeri', "Xiang 'Anthony' Chen"]2024-04-06T00:00:00https://www.semanticscholar.org/paper/1fcb0012cb740b6abeab3277708a380a0fabdc07 b1efc0e54e81ef6003e123a2a901bcc6f713c96fScientific Reports560This study evaluated and compared the classification accuracy, precision, recall, and F1 scores of five different machine learning methods using a primary dataset and found that XGBoost achieved the best model accuracy, which is 97%.Scientific Reports['Taminul Islam', 'Md. Alif Sheakh', 'Mst. Sazia Tahosin', 'Most. Hasna Hena', 'S. Akash', 'Y. B. Jardan', 'Gezahign Fentahun Wondmie', 'Hiba‐Allah Nafidi', 'Mohammed Bourhia']2024-04-06T00:00:00https://www.semanticscholar.org/paper/b1efc0e54e81ef6003e123a2a901bcc6f713c96f b4d04149c13399a8f42cae06bf91a1370bee2a6dAdvances in artificial intelligence (AI) will transform many aspects of our lives and society, bringing immense opportunities but also posing significant risks and challenges. The next several decades may well be a turning point for humanity, comparable to the industrial revolution. We write to share a set of recommendations for moving forward from the perspective of the founder and leaders of the One Hundred Year Study on AI. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts to evaluate the immediate, longer-term, and far-reaching effects of AI on people and society, and to make recommendations about AI research, policy, and practice. As we witness new capabilities emerging from neural models, it is crucial that we engage in efforts to advance our scientific understanding of these models and their behaviors. We must address the impact of AI on people and society through technical, social, and sociotechnical lenses, incorporating insights from a diverse range of experts including voices from engineering, social, behavioral, and economic disciplines. By fostering dialogue, collaboration, and action among various stakeholders, we can strategically guide the development and deployment of AI in ways that maximize its potential for contributing to human flourishing. Despite the growing divide in the field between focusing on short-term versus long-term implications, we think both are of critical importance. As Alan Turing, one of the pioneers of AI, wrote in 1950,"We can only see a short distance ahead, but we can see plenty there that needs to be done."We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.Communications of the ACM60A set of recommendations for moving forward from the perspective of the founder and leaders of the One Hundred Year Study on AI are shared to address both the short- and long-term potential impacts of AI technologies.ArXiv['Eric Horvitz', 'Vincent Conitzer', 'Sheila A. McIlraith', 'Peter Stone']2024-04-06T00:00:00https://www.semanticscholar.org/paper/b4d04149c13399a8f42cae06bf91a1370bee2a6d 3d66ef2002b0a48859dc4d9725ccecc3ec0a6e42As cyber threats become increasingly sophisticated and pervasive, the need for robust defense mechanisms is paramount. This article explores the next generation of cyber-security, focusing on the integration of artificial intelligence (AI) technologies to bolster defense strategies. We delve into the capabilities of AI-powered systems in threat detection, mitigation, and response, highlighting their adaptive nature and ability to evolve alongside evolving threats. Ethical considerations and potential limitations of AI in cyber-security are also discussed. Through this exploration, we aim to provide insights into the transformative potential of AI in safeguarding digital assets and infrastructure against cyber threats.International Research Journal of Modernization in Engineering Technology and Science110This article explores the next generation of cyber-security, focusing on the integration of artificial intelligence technologies to bolster defense strategies, and delves into the capabilities of AI-powered systems in threat detection, mitigation, and response.International Research Journal of Modernization in Engineering Technology and Science['Gourav Nagar', 'Ashok Manoharan']2024-04-06T00:00:00https://www.semanticscholar.org/paper/3d66ef2002b0a48859dc4d9725ccecc3ec0a6e42 f816b574b049ef0816922027f4af3849663dfd9eThe project aims to develop a personal assistant tailored for Windows-based systems, referred to as AIBased Voice Assistant. Drawing inspiration from existing virtual assistants, AI-Based Voice Assistants has been meticulously crafted to offer a user-friendly environment for executing a wide array of tasks through commands. Serving as a general-purpose desktop application, AI-Based Voice Assistant is proficient in comprehending voice commands and executing tasks or responding to user queries. This voice assistant software aids users in streamlining their daily tasks, including accessing current news, and weather reports, browsing the internet, and performing basic system operations. With a focus on desktop efficiency, AIBased Voice Assistant enhances user productivity by managing daily routines and providing access to general information from online sources. Notably, voice assistants are evolving to become increasingly smarter and more intelligent, catering to the evolving needs of users.International Research Journal of Modernization in Engineering Technology and Science100The project aims to develop a personal assistant tailored for Windows-based systems, referred to as AIBased Voice Assistant, which is proficient in comprehending voice commands and executing tasks or responding to user queries.International Research Journal of Modernization in Engineering Technology and Science['Abhinish Tiwari', 'VanshikaTiwari', 'Yashita Agrawal', 'Anushka Shrivastava', 'Uday Singh Kushwaha']2024-04-06T00:00:00https://www.semanticscholar.org/paper/f816b574b049ef0816922027f4af3849663dfd9e c7e031043c0a7ee536cc129b1c691d61f573708cImportance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.JAMA cardiology400A new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.JAMA cardiology['Evangelos K. Oikonomou', 'G. Holste', 'N. Yuan', 'Andreas Coppi', 'Robert L McNamara', 'Norrisa Haynes', 'Amit N Vora', 'Eric J Velazquez', 'Fan Li', 'Venu Menon', 'Samir R Kapadia', 'Thomas M Gill', 'Girish N Nadkarni', 'H. Krumholz', 'Zhangyang Wang', 'David Ouyang', 'R. Khera']2024-04-06T00:00:00https://www.semanticscholar.org/paper/c7e031043c0a7ee536cc129b1c691d61f573708c ab0e8c0ef948d18d693d8b8939b1c4a94f04034bIn recent years, the integration of artificial intelligence (AI) technology into financial applications has received increasing attention as traditional accounting methods are replaced and the fields of financial reporting and auditing change. This research paper explores the growth of the application of AI in accounting to provide an understanding of its impacts, challenges, and opportunities for professionals, businesses, and regulators.International Journal For Multidisciplinary Research00This research paper explores the growth of the application of AI in accounting to provide an understanding of its impacts, challenges, and opportunities for professionals, businesses, and regulators.International Journal For Multidisciplinary Research['Priscilla R']2024-04-06T00:00:00https://www.semanticscholar.org/paper/ab0e8c0ef948d18d693d8b8939b1c4a94f04034b 34e00b31735dbf3fb3cdf8464a4272440748599fIn today's digital age, understanding and describing images using natural language is crucial. Image captioning, a field at the intersection of computer vision and natural language processing, automates the generation of descriptive captions for images. This project explores image captioning, employing deep learning techniques to bridge visual data and human-like language understanding. We review foundational concepts, including CNNs for image features and RNNs for language generation, and discuss advancements in attention mechanisms and transformers. Our novel image captioning architecture demonstrates effectiveness on benchmark datasets, generating accurate and relevant captions for a variety of images. This project advances AI-driven visual comprehension and promotes responsible technology, contributing to academia, industry, and society.International Research Journal of Modernization in Engineering Technology and Science100This project explores image captioning, employing deep learning techniques to bridge visual data and human-like language understanding, and demonstrates effectiveness on benchmark datasets, generating accurate and relevant captions for a variety of images.International Research Journal of Modernization in Engineering Technology and Science['Sudham D. Singh', 'Prathamesh S. Chikankar', 'Mohd. Hanzala', 'I. Ansari', 'Akshar J. Rupareliya', 'Prof. Chitra Wasnik']2024-04-06T00:00:00https://www.semanticscholar.org/paper/34e00b31735dbf3fb3cdf8464a4272440748599f 812232882865ed453e888e791406eec74b215f6bHow are new technologies like generative AI quickly adopted and used by executive and managerial leaders to create value in organizations? A survey of INSEAD's global alumni base revealed several intriguing insights into perceptions and engagements with generative AI across a broad spectrum of demographics, industries, and geographies. Notably, there's a prevailing optimism about the role of generative AI in enhancing productivity and innovation, as evidenced by the 90% of respondents being excited about its time-saving and efficiency benefits. Analysis revealed different attitudes about adoption and use across demographic variables. Younger respondents are significantly more excited about generative AI and more likely to be using it at work and in personal life than older participants. Those in Europe have a somewhat more distant view of generative AI than those in North America in Asia, in that they see the gains more likely to be captured by organizations than individuals, and are less likely to be using it in professional and personal contexts than those in North America and Asia. This may also be related to the fact that those in Europe are more likely to be working in Financial Services and less likely to be working in Information Technology industries than those in North America and Asia. Despite this, those in Europe are more likely to see AGI happening faster than those in North America, although this may reflect less interaction with generative AI in personal and professional contexts. These findings collectively underscore the complex and multifaceted perceptions of generative AI's role in society, pointing to both its promising potential and the challenges it presents.Social Science Research Network310A survey of INSEAD's global alumni base revealed several intriguing insights into perceptions and engagements with generative AI across a broad spectrum of demographics, industries, and geographies, highlighting both its promising potential and the challenges it presents.SSRN Electronic Journal['Jason P Davis', 'Jian Bai Li']2024-04-06T00:00:00https://www.semanticscholar.org/paper/812232882865ed453e888e791406eec74b215f6b 1cfb1fd84043f9ced5ac852228052bb17d5ca5f2AI desktop assistants like Apple’s ”SIRI” and Google’s ”Google Voice Search,” can perform tasks and provide services based on user commands. These systems use speech recognition to respond to synthetic speech, allowing users to communicate with their devices. The proposed system, which can work with or without internet connectivity, uses voice recognition to process user input and provide various outputs. AI-based personal assistants aim to bridge the communication gap between humans and machines, creating a more engaging user experience.International Research Journal of Modernization in Engineering Technology and Science70The proposed system uses voice recognition to process user input and provide various outputs to bridge the communication gap between humans and machines, creating a more engaging user experience.International Research Journal of Modernization in Engineering Technology and Science['Ms. Aarti Dharmani', 'Mayuri Khatpe', 'Priyanka Gayake', 'Suhasini Sharma', 'Afra Ali', 'S. Dubey', 'Shyam Dwivedi', 'Md. Saif Divisha Pandey', 'Raza Muskan', 'Srivastava', 'Vedant Kulkarni', 'Shreyas Kallurkar', 'Vipul Waikar', 'Saurabh Patil', 'Swarupa Deshpande', 'Dr.C K Gomathy', 'Redrouthu Venkata Narayana', 'Thota Vamsi Khrishna', 'Dr. V Geetha']2024-04-06T00:00:00https://www.semanticscholar.org/paper/1cfb1fd84043f9ced5ac852228052bb17d5ca5f2 e14c582a878bf9818790d945b7aba94ec5196ab1Journal of Voice01ChatGPT and Llama are judged as inaccurate in proposing correct treatment for BVFP, and the need for further guidelines dedicated to the management of BVFP is highlighted.Journal of voice : official journal of the Voice Foundation['Emilie A C Dronkers', 'A. Geneid', 'C. Al Yaghchi', 'Jerome R Lechien']2024-04-06T00:00:00https://www.semanticscholar.org/paper/e14c582a878bf9818790d945b7aba94ec5196ab1 00851a908ac174a418ce60cb998c82314e3c7960International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-06T00:00:00https://www.semanticscholar.org/paper/00851a908ac174a418ce60cb998c82314e3c7960 9784a55aa6d24bb20506f4c72d26e30d116c586eInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-06T00:00:00https://www.semanticscholar.org/paper/9784a55aa6d24bb20506f4c72d26e30d116c586e 5ae5d1319d6ab10fc8882214cca177a7dfdaab85International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-06T00:00:00https://www.semanticscholar.org/paper/5ae5d1319d6ab10fc8882214cca177a7dfdaab85 67eef17f0932f11299ae9e50ac9b79e47ae7dffb Research communities across disciplines recognize the need to diversify and decolonize knowledge. While artificial intelligence-supported large language models (LLMs) can help with access to knowledge generated in the Global North and demystify publication practices, they are still biased toward dominant norms and knowledge paradigms. LLMs lack agency, metacognition, knowledge of the local context, and understanding of how the human language works. These limitations raise doubts regarding their ability to develop the kind of rhetorical flexibility that is necessary for adapting writing to ever-changing contexts and demands. Thus, LLMs are likely to drive both language use and knowledge construction towards homogeneity and uniformity, reproducing already existing biases and structural inequalities. Since their output is based on shallow statistical associations, what these models are unable to achieve to the same extent as humans is linguistic creativity, particularly across languages, registers, and styles. This is the area where key stakeholders in academic publishing—authors, reviewers, and editors—have the upper hand, as our applied linguistics community strives to increase multilingual practices in knowledge production.Applied Linguistics200This paper focuses on applied linguistics, where key stakeholders in academic publishing—authors, reviewers, and editors—have the upper hand, as the applied linguistics community strives to increase multilingual practices in knowledge production.Applied Linguistics['Maria Kuteeva', 'Marta Andersson']2024-04-06T00:00:00https://www.semanticscholar.org/paper/67eef17f0932f11299ae9e50ac9b79e47ae7dffb d88c99ed2ad41f3baad4b2974ce17484621dc8ec00['Kulbir Singh']2024-04-06T00:00:00https://www.semanticscholar.org/paper/d88c99ed2ad41f3baad4b2974ce17484621dc8ec 808b24eac6c0cffb6fdacc1eab31a2e8b17de16dAI &amp; SOCIETY00AI &amp; SOCIETY['Yehui Zhang']2024-04-06T00:00:00https://www.semanticscholar.org/paper/808b24eac6c0cffb6fdacc1eab31a2e8b17de16d 9a5b5fb5d9f33f7501c444225b89e0297f539e30Educational Administration Theory and Practices00Educational Administration Theory and Practices['Dr. Nirvikar Katiyar', 'Dr. Abhay Shukla', 'Dr. Namita Chawla']2024-04-06T00:00:00https://www.semanticscholar.org/paper/9a5b5fb5d9f33f7501c444225b89e0297f539e30 55fd7a1839dc0429ef087bcb4702a631da709167Educational Administration Theory and Practices00Educational Administration Theory and Practices['Dr. Nirvikar Katiyar', 'Mr. Somendra Tripathi', 'Mr. Praveen Kumar', 'Mr. Shekhar Verma', 'Dr. Alok Kumar Sahu', 'Dr. Shailesh Saxena']2024-04-06T00:00:00https://www.semanticscholar.org/paper/55fd7a1839dc0429ef087bcb4702a631da709167 720baf7e43be8dbf76e0579b1a3c34ee2b5f4ef4This present study mainly describes the implementation and ideas of the mobile automation framework, which supports iOS and Android mobile automation testing technology. This study mainly uses a combination of qualitative and documentary analysis methods, designs some technical architecture diagrams, and writes some open-source code to implement respectively. Meanwhile, The automation code is not made public due to mobile automation security and privacy concerns. The paper is mainly driven by the so file inside Android and the ADB command, while iOS uses some class libraries based on the iOS system. The research framework also uses the Appium framework for encapsulation and research and carries out secondary encapsulation and call based on the internal Appium framework. It is convenient for the internal quality team's automated testing staff to use and execute and improve the efficiency and speed of automated software testing, which is the contribution of this research. Beside, The limitation of this research is that the framework is based on the secondary development and encapsulation of Appium framework, so this study are inevitably some imperfections and bugs that need to be continuously improved and used. Moreover, these contribution results demonstrate that it can combine the businesses of different china internet enterprises to complete. Next, the paper discusses specific cases of mobile automation testing framework and artificial intelligence in mobile framework design and evaluates their effects and impacts. Finally, the paper summarizes the roles and challenges of mobile automation testing framework design and AI enhancement elements and looks ahead to the future.Frontiers in Business, Economics and Management160The implementation and ideas of the mobile automation framework, which supports iOS and Android mobile automation testing technology, are described, which demonstrates that it can combine the businesses of different china internet enterprises to complete.Frontiers in Business, Economics and Management['Jun Cui', 'Wangmei Chen', 'Qiang Wan', 'Zhongxin Gan', 'Zihao Ning']2024-04-06T00:00:00https://www.semanticscholar.org/paper/720baf7e43be8dbf76e0579b1a3c34ee2b5f4ef4 332ba0ea4209159a6a21f036c9d4c2965bfd194aAsian Bioethics Review200Asian Bioethics Review['Chih-hsing Ho']2024-04-06T00:00:00https://www.semanticscholar.org/paper/332ba0ea4209159a6a21f036c9d4c2965bfd194a 77fa9d0c40cbf07caf38b2bf5bbc5d54d2f941a6Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce.  The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions.Journal of Computer Science and Technology Studies60Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network in detecting credit card fraud, and model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics.Journal of Computer Science and Technology Studies['Md Rokibul Hasan', 'Sumon Gazi', 'N. Gurung']2024-04-06T00:00:00https://www.semanticscholar.org/paper/77fa9d0c40cbf07caf38b2bf5bbc5d54d2f941a6 02df346496e3e519c1580c789800dd4d7114bd9cThe proliferation of algorithmic systems has fueled discussions surrounding the regulation and control of their social impact. Herein, we consider a system whose primary objective is to maximize utility by selecting the most qualified individuals. To promote demographic parity in the selection algorithm, we consider penalizing discrimination across social groups. We examine conditions under which a discrimination penalty can effectively reduce disparity in the selection. Additionally, we explore the implications of such a penalty when individual qualifications may evolve over time in response to the imposed penalizing policy. We identify scenarios where the penalty could hinder the natural attainment of equity within the population. Moreover, we propose certain conditions that can counteract this undesirable outcome, thus ensuring fairness.arXiv.org00ArXiv['Hamidreza Montaseri', 'Amin Gohari']2024-04-06T00:00:00https://www.semanticscholar.org/paper/02df346496e3e519c1580c789800dd4d7114bd9c d9f7c1f266807bfb7bf62ac950fcc9a8e137092eThis study examines whether less developed countries are catching up with developed ones using the log t convergence technique (LCT) and the dynamic spatial ordered probit (DSOP) model. The findings revealed that first, there is no overall convergence in AI research capacity. Second, club clustering analysis showed convergence in four of the five groups of countries on AI research output and in three of the four groups on AI patent grants. Third, the countries are experiencing a slow divergence process in AI research capacity. Fourth, the region, income group and cluster of the countries are influencing the convergence process.Journal of International Development410There is no overall convergence in AI research capacity and the countries are experiencing a slow divergence process in AI research capacity, according to this study.Journal of International Development['Saima Javed', 'Yu Rong', 'B. Abbasi']2024-04-06T00:00:00https://www.semanticscholar.org/paper/d9f7c1f266807bfb7bf62ac950fcc9a8e137092e ffc7763903b75454d66661e7c8c5698b68a27db0The widespread use of Artificial Intelligence-based tools in the healthcare sector raises many ethical and legal problems, one of the main reasons being their black-box nature and therefore the seemingly opacity and inscrutability of their characteristics and decision-making process. Literature extensively discusses how this can lead to phenomena of over-reliance and under-reliance, ultimately limiting the adoption of AI. We addressed these issues by building a theoretical framework based on three concepts: Feature Importance, Counterexample Explanations, and Similar-Case Explanations. Grounded in the literature, the model was deployed within a case study in which, using a participatory design approach, we designed and developed a high-fidelity prototype. Through the co-design and development of the prototype and the underlying model, we advanced the knowledge on how to design AI-based systems for enabling complementarity in the decision-making process in the healthcare domain. Our work aims at contributing to the current discourse on designing AI systems to support clinicians' decision-making processes.arXiv.org650This work advanced the knowledge on how to design AI-based systems for enabling complementarity in the decision-making process in the healthcare domain by building a theoretical framework based on three concepts: Feature Importance, Counterexample Explanations, and Similar-Case Explanations.ArXiv['Elisa Rubegni', 'Omran Ayoub', 'Stefania Maria Rita Rizzo', 'Marco Barbero', 'G. Bernegger', 'Francesca Faraci', 'Francesca Mangili', 'Emiliano Soldini', 'P. Trimboli', 'Alessandro Facchini']2024-04-06T00:00:00https://www.semanticscholar.org/paper/ffc7763903b75454d66661e7c8c5698b68a27db0 a70d037d91929cc1b1c43ede354856f972d8ceb1Carrying out this study is justified by its academic, scientific and social relevance, based on presenting technological advances involving artificial intelligence in healthcare. Therefore, the objective of this research focuses on highlighting the applications and benefits of Artificial Intelligence in Medicine. This study was carried out through an integrative literature review, with an exploratory approach, whose institute was limited to investigating, through already published articles, relevant information that answered the guiding question. Thus, data collection took place in scientific bases: SCIELO and LILACS. One of the main advantages of using AI in early diagnosis is the ability to process large volumes of data quickly and accurately, identifying patterns and signals that may be imperceptible to human healthcare professionals. This makes it possible to detect serious medical conditions early, such as cancer and heart disease, when treatment is most effective and the chances of recovery are greatest. Furthermore, AI can significantly contribute to personalized treatment, adapting therapeutic approaches based on the individual characteristics of each patient. By analyzing genetic data, medical history, and response to previous treatments, AI algorithms can help doctors develop treatment plans that are more effective and have fewer side effects.Research, Society and Development00This research focuses on highlighting the applications and benefits of Artificial Intelligence in Medicine through an integrative literature review, with an exploratory approach, whose institute was limited to investigating relevant information that answered the guiding question.Research, Society and Development['A. A. C. B. Galvão', 'Romerio Alves Soares', 'Davi Silva Ramos', 'Ane Caroline Rodrigues de Oliveira', 'Lucélia Vital Leite', 'George da Silva Tenório Cavalcante', 'Felipe Alves Celestino de Moura', 'Ana Júlia de Oliveira Cavalcanti', 'Cristiane Ramos Santos Damaso', 'Lucilene Mororó Lima Correia', 'Marcelo Pininga Pessoa de Asevedo', 'Carlos André Solto Silva', 'Elison Lins Araújo']2024-04-06T00:00:00https://www.semanticscholar.org/paper/a70d037d91929cc1b1c43ede354856f972d8ceb1 fda6a77bda9a877bd81b39d3f77261e0e8d4642dPurpose: This study aims to investigate the current state and sentiment of artificial intelligence (AI) training in North American anesthesiology residency programs, assessing existing AI education landscapes, identifying barriers to implementation, and understanding program directors' expectations for AI's impact on the field. Methods: A cross-sectional survey targeted anesthesiology program directors across North America. The survey, conducted anonymously via Qualtrics, gauged their AI training offerings, sentiments towards AI's influence, and familiarity with AI educational policies. Information on questionnaire development, administration, and data analysis was included. Results: Of the 163 programs surveyed, 32 responded, yielding a response rate of 19.6%. A substantial 81% of responding program directors reported no current AI training. Despite this, 67% anticipate AI's transformative impact. Only 19% currently offer AI/ML training. Standardized presentation of results with accompanying numerators and denominators were employed. Conclusion: The findings reveal a significant gap between the recognition of AI's importance and the current offering of training in anesthesiology residency programs. Barriers to implementation include resource constraints and time limitations, exacerbated by the pandemic. Overcoming these barriers and aligning positive sentiments with educational offerings is crucial for preparing future physicians for the AI-driven healthcare landscape.medRxiv00A significant gap is revealed between the recognition of AI's importance and the current offering of training in anesthesiology residency programs, and understanding program directors' expectations for AI's impact on the field is revealed.['BS TylerVElliott', 'Joseph C Goldstein', 'Heidi V Goldstein']2024-04-06T00:00:00https://www.semanticscholar.org/paper/fda6a77bda9a877bd81b39d3f77261e0e8d4642d 3bf118f2f918ad121aa3983479241d8b09f9c071Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline presents unique challenges that need to be addressed for optimal performance. This complexity is further increased when attempting to integrate different fields into a single model. Here, we introduce an alternative approach to multimodal medical AI that utilizes the generalist capabilities of a large language model (LLM) as a central reasoning engine. This engine autonomously coordinates and deploys a set of specialized medical AI tools. These tools include text, radiology and histopathology image interpretation, genomic data processing, web searches, and document retrieval from medical guidelines. We validate our system across a series of clinical oncology scenarios that closely resemble typical patient care workflows. We show that the system has a high capability in employing appropriate tools (97%), drawing correct conclusions (93.6%), and providing complete (94%), and helpful (89.2%) recommendations for individual patient cases while consistently referencing relevant literature (82.5%) upon instruction. This work provides evidence that LLMs can effectively plan and execute domain-specific models to retrieve or synthesize new information when used as autonomous agents. This enables them to function as specialist, patient-tailored clinical assistants. It also simplifies regulatory compliance by allowing each component tool to be individually validated and approved. We believe, that our work can serve as a proof-of-concept for more advanced LLM-agents in the medical domain.arXiv.org00This work provides evidence that LLMs can effectively plan and execute domain-specific models to retrieve or synthesize new information when used as autonomous agents that enables them to function as specialist, patient-tailored clinical assistants.ArXiv['Dyke Ferber', 'O. S. E. Nahhas', 'Georg Wölflein', 'Isabella C. Wiest', 'J. Clusmann', 'Marie-Elisabeth Lessman', 'Sebastian Foersch', 'Jacqueline Lammert', 'Maximilian Tschochohei', 'Dirk Jäger', 'Manuel Salto-Tellez', 'Nikolaus Schultz', 'Daniel Truhn', 'J. N. Kather']2024-04-06T00:00:00https://www.semanticscholar.org/paper/3bf118f2f918ad121aa3983479241d8b09f9c071 b647ed5b2803e3855da761e2791c2bb9ab994995Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.Ophthalmic & physiological optics360An extensive search of the Web of Science Core Collection was conducted to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023, finding a remarkable surge in articles applying AI in ophthalmology after 2017.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians['Jihye Ahn', 'Moonsung Choi']2024-04-06T00:00:00https://www.semanticscholar.org/paper/b647ed5b2803e3855da761e2791c2bb9ab994995 6ecb6ecf9ff67cb40bd0eea59255a3abaf0c5d05The article analyses the influence of artificial intelligence technologies on the sphere of design and artistic culture in general, as well as on creative processes in design, architecture and visual arts. The factor of artificial intelligence is considered not so much in the applied and instrumental sense, but as a global driver of cultural and technological revolution, which can change the entire civilisational pattern of mankind in the foreseeable future. Under these conditions, the creative sphere becomes the “polygon of meanings” where strategic scenarios of human interaction with artificial intelligence can be worked out. The author investigates the socio-psychological and cultural aspects of readiness and unreadiness of creative professional culture for this mission.проект байкал00The author investigates the socio-psychological and cultural aspects of readiness and unreadiness of creative professional culture for this mission.проект байкал['L. Salmin']2024-04-06T00:00:00https://www.semanticscholar.org/paper/6ecb6ecf9ff67cb40bd0eea59255a3abaf0c5d05 261f3d2b8b1b465e55d759a437f9239eacf9a89aArtificial intelligence being considered to be a legal person in the eyes of law lacking reasonableness and discretion which is owned by only human beings. Due to their reasoning and free will, humans are the source of the subject's mechanism and the source of value for the legal topic. The first topic of law was humans. The legal subject is distinguished from the personality traits throughout the substantiation process, and the legal person emerges as the derived legal subject. Artificial intelligence lacks the necessary conditions to become an original legal subject, but it may still be developed as a derivative legal subject if it can serve humanity's long-term basic interests as a legal subject. Artificial intelligence has a favorable impact on employment; however, this effect is unavoidably heterogeneous. In labor-intensive industries, it helps to boost the job share of women and workers. Mechanism study has demonstrated that one significant avenue for job growth in the digital economy is virtual agglomeration, which developed from conventional industrial agglomeration. The results of this study add to our knowledge of how contemporary digital technologies affect people's quality of life in underdeveloped nations. In order to fully realize the benefits of artificial intelligence technology in the workplace, we must enhance the social security system, expedite the creation of sophisticated household robots, and further overhaul the education and training system. The Evolution of the country is considered to be a development based on the technology but every people in the country is working for the minimum for their essentials such as food and shelter. The recognition of Artificial intelligence leads to the exploitation of the workers by not providing decent employment guaranteed under the directive principles of state policy given under the Indian constitution. This article finally concludes with findings and suggestions that help in the development of society into a socialistic approach rather a capitalistic approach.International Journal For Multidisciplinary Research50Findings and suggestions that help in the development of society into a socialistic approach rather a capitalistic approach are concluded.International Journal For Multidisciplinary Research['R. D']2024-04-06T00:00:00https://www.semanticscholar.org/paper/261f3d2b8b1b465e55d759a437f9239eacf9a89a d8ac6d57fa3f395aa4362e8ef99e0462513c2474Today's digital age presents more complex cyber dangers, and enterprises have the onerous burden of safeguarding critical information and infrastructure from unwanted actors. With the growth of the cyber landscape, the defense strategy must also adapt. This study tries to identify the role of artificial intelligence in tackling cyber issues, and it uses the descriptive deductive technique. Thus, the study arrived at a number of important conclusions, the most significant of which are as follows: artificial intelligence can enhance cybersecurity by refining attack detection protocols, analyzing network behaviors, and creating intelligent security systems that can effectively counteract emerging threats. Artificial intelligence can also analyze big data related to network activities and digital behaviors to anticipate and identify potential vulnerabilities and future attacks.International Journal of Academic Research in Business and Social Sciences370Artificial intelligence can enhance cybersecurity by refining attack detection protocols, analyzing network behaviors, and creating intelligent security systems that can effectively counteract emerging threats.International Journal of Academic Research in Business and Social Sciences['Maitha Musabah Salem Bin Dawi Alkhatri', 'Diaya Uddeen Deab Mahmoud Alzitawi']2024-04-06T00:00:00https://www.semanticscholar.org/paper/d8ac6d57fa3f395aa4362e8ef99e0462513c2474 01d219c96c493af591dec373f27096df62c46049With the rapid development of Internet finance, traditional risk control methods can no longer meet the current needs. As a new technical means, artificial intelligence risk control has become an important development direction in the field of Internet finance by predicting and managing financial risks more accurately through big data analysis, machine learning, and other technologies. This paper will discuss the application significance, challenges, and development strategies of artificial intelligence risk control in the field of Internet finance, to provide a reference for the risk management of Internet finance.Frontiers in Business, Economics and Management20The application significance, challenges, and development strategies of artificial intelligence risk control in the field of Internet finance are discussed to provide a reference for the risk management of Internet finance.Frontiers in Business, Economics and Management['Centong Tao', 'Yinqi Liu']2024-04-06T00:00:00https://www.semanticscholar.org/paper/01d219c96c493af591dec373f27096df62c46049 7695a34086536ce541b80ba030af6c77aecee095Journal of system and management sciences00Journal of System and Management Sciences[]2024-04-06T00:00:00https://www.semanticscholar.org/paper/7695a34086536ce541b80ba030af6c77aecee095 f54b1795bdc7e07b80928ef619434db3c17aacf0International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-06T00:00:00https://www.semanticscholar.org/paper/f54b1795bdc7e07b80928ef619434db3c17aacf0 10edcebf04df15ea8470a2976e920ba7bf2ef07eInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-06T00:00:00https://www.semanticscholar.org/paper/10edcebf04df15ea8470a2976e920ba7bf2ef07e dfd1ab66a13a16eeb8a425d676f314f76ccb749cJournal of critical care370A generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients and can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.Journal of critical care['L. R. Zwerwer', 'S. van der Pol', 'Kai Zacharowski', 'Maarten J. Postma', 'J. Kloka', 'Benjamin Friedrichson', 'Antoinette D I van Asselt']2024-04-06T00:00:00https://www.semanticscholar.org/paper/dfd1ab66a13a16eeb8a425d676f314f76ccb749c 42eb6e5a2ca8ae30f1c0810e262ad699a1ce8dd8The EU is responding to the challenges of the digital transformation with an unprecedented number of regulatory acts. The central objectives of the Union, such as the creation of the single market, and its values shall also apply to the digital world. By adopting landmark regulations, the Union wants to strengthen its digital sovereignty instead of following the rules of others. While the Digital Markets Act (DMA) aims to control the bottleneck power of gatekeeper platforms, the Digital Services Act (DSA) establishes a constitution for the Internet. Other important acts regulate access to data, for example the Data Act (DA), the Data Governance Act (DGA) and the European Health Data Space (EHDS). Finally, the draft Artificial Intelligence Act (AI Act) is a pioneering attempt to contain the most disruptive technologies. It remains to be seen what impact the regulatory offensive will have on the promotion of innovation in Europe.EuZ – Zeitschrift für Europarecht00EuZ – Zeitschrift für Europarecht['Chayanis Aueamnuay', 'Carmen Berjón', 'Stella Galehr', 'Luca Graf', 'Andreas Heinemann']2024-04-05T00:00:00https://www.semanticscholar.org/paper/42eb6e5a2ca8ae30f1c0810e262ad699a1ce8dd8 ed1aeae418a6643cd867f9f88a22b2acf9ae5f72In the current background of global economic slowdown, the traditional reliance on one regulatory instrument or the unilateral consideration of the effectiveness of one regulatory policy in policy formulation is no longer sufficient to cope with the increasingly complex contradictions between environmental protection and economic development. In the construction of a modernized environmental governance system, it has become an inevitable choice to achieve synergy between various environmental regulations. In China, the citizens' environmental supervision campaign that gradually emerged in 2006 and the local carbon trading pilots that started in 2013, as typical representatives of informal and formal environmental regulation respectively, provide vivid and realistic materials for our study.Using econometric models and microdata from listed Chinese firms from 2009 to 2020, we analyze the profound logic and internal mechanism by which this synergistic governance effect of environmental regulation affects the economic society and the development pattern of firms.The study found that: (1) the synergistic effect of the carbon trading policy and citizens' environmental supervision can significantly promote the transition of enterprises to a sustainable development model, especially paying attention to the role of citizens' environmental supervision in this process. (2) The regional development level, cost transfer capability, and political connection can make the synergy of the two environmental regulations vary across firms. (3) The synergistic effect of environmental regulation promotes the behavior of enterprises in line with the requirements of sustainable development by influencing enterprises' R&D and innovation investment, resource allocation efficiency, and sustainable development awareness. (4) A favorable regional rule of law environment and moderate media attention can effectively increase the intensity of citizens' environmental supervision, and at the same time strengthen the effectiveness of synergistic governance of environmental regulation in the transformation and development of enterprises.Frontiers in Environmental Economics560Frontiers in Environmental Economics['Jiahe Chen', 'Wenhao Yu']2024-04-05T00:00:00https://www.semanticscholar.org/paper/ed1aeae418a6643cd867f9f88a22b2acf9ae5f72 be54caccf6a0c64f79da5750273507fdf2bfec6dInternational Journal of Progressive Education00International Journal of Progressive Education['Elif Sezgin']2024-04-05T00:00:00https://www.semanticscholar.org/paper/be54caccf6a0c64f79da5750273507fdf2bfec6d 5353ef8e23cc7914b9397fe1fc132b46d1a05dd5In today's technology world, the integration of artificial intelligence (AI) has become increasingly prominent in education, with enormous potential to improve the teaching and learning experience. AI, defined by its ability to imitate human intelligence, possesses enormous power and has the potential to dramatically impact a variety of areas, most notably education. AI has significantly improved learning experiences for both teachers and students by allowing them to be customized and personalized. This review article investigates the prospects provided by AI in modern teaching and learning processes, with a special emphasis on its advantages in language learning. This study examines existing literature and studies on AI in education, with a focus on language learning environments. The results show AI's advantages in giving targeted feedback and practice opportunities, making language learning easier, and improving overall learning efficiency and effectiveness. Thus, this review contributes to a better understanding of AI's role in redefining present educational paradigms, as well as its potential to transform teaching and learning methodologies.Syntax Idea132Examination of existing literature and studies on AI in education contributes to a better understanding of AI's role in redefining present educational paradigms, as well as its potential to transform teaching and learning methodologies.Syntax Idea['Aisyah Nurjanah', 'Irma Nuraeni Salsabila', 'Adelia Azzahra', 'Riska Rahayu', 'Nina Marlina']2024-04-05T00:00:00https://www.semanticscholar.org/paper/5353ef8e23cc7914b9397fe1fc132b46d1a05dd5 f6f49b1c4eff3cc792a2931010b66e1f285e42c5Several companies--such as Google, Microsoft, and OpenAI--have deployed techniques to watermark AI-generated content to enable proactive detection. However, existing literature mainly focuses on user-agnostic detection. Attribution aims to further trace back the user of a generative-AI service who generated a given content detected as AI-generated. Despite its growing importance, attribution is largely unexplored. In this work, we aim to bridge this gap by providing the first systematic study on watermark-based, user-aware detection and attribution of AI-generated content. Specifically, we theoretically study the detection and attribution performance via rigorous probabilistic analysis. Moreover, we develop an efficient algorithm to select watermarks for the users to enhance attribution performance. Both our theoretical and empirical results show that watermark-based detection and attribution inherit the accuracy and (non-)robustness properties of the watermarking method.arXiv.org331Both the theoretical and empirical results show that watermark-based detection and attribution inherit the accuracy and (non-)robustness properties of the watermarking method.ArXiv['Zhengyuan Jiang', 'Moyang Guo', 'Yuepeng Hu', 'Neil Gong']2024-04-05T00:00:00https://www.semanticscholar.org/paper/f6f49b1c4eff3cc792a2931010b66e1f285e42c5 83fc91371e6fb94390d67cc67a471ad943292edb This JAMA Forum discusses the promise and pitfalls of using large language models and artificial intelligence (AI) in the diagnosis of patients. JAMA Health Forum71JAMA health forum['D. Newman-Toker', 'Joshua Sharfstein']2024-04-05T00:00:00https://www.semanticscholar.org/paper/83fc91371e6fb94390d67cc67a471ad943292edb 4b3538d5d722c35fbe68a7c76fed41a06ad3619fHybrid Intelligence Systems (HIS) represent a paradigm shift in problem-solving methodologies by integrating human expertise with Artificial Intelligence (AI) and Robotic Process Automation (RPA). This paper explores the mechanisms, applications, benefits, challenges, and future directions of HIS in the context of complex problem-solving. Through collaborative synergies between human cognition and machine intelligence, HIS enhances decision-making accuracy, efficiency, and innovation. Human experts contribute domain knowledge, contextual understanding, and ethical reasoning, while AI algorithms and RPA systems offer data-driven insights, computational power, and process automation capabilities. HIS fosters inclusivity, diversity, and democratization in problem-solving processes by harnessing the collective intelligence of diverse teams and stimulating interdisciplinary collaboration. However, challenges such as privacy concerns, data security risks, and algorithmic biases must be addressed to realize the full potential of HIS. Looking ahead, the integration of Explainable AI (XAI), Edge AI, and Neuro symbolic AI holds promise for enhancing transparency, interpretability, and robustness in HIS architectures. Human-centered design principles and interdisciplinary research collaborations will shape the development and deployment of HIS, ensuring alignment with human values, preferences, and needs. Ultimately, HIS will continue to serve as a beacon of collaboration, creativity, and collective intelligence in shaping a better world for generations to come.International Journal of Innovative Science and Research Technology291The mechanisms, applications, benefits, challenges, and future directions of HIS in the context of complex problem-solving are explored.International Journal of Innovative Science and Research Technology (IJISRT)['Kamala Venigandla', 'Navya Vemuri', 'Naveen Vemuri']2024-04-05T00:00:00https://www.semanticscholar.org/paper/4b3538d5d722c35fbe68a7c76fed41a06ad3619f d7b7a4c16d271cf4a49f8ffc852f5a550b021152Artificial intelligence (AI) has become a multifaceted breakthrough in healthcare with the promise of transforming patient care and clinical practices. The paper presents a comprehensive retrospective mainly concerned with AI and its future scope in health-care services. The paper offers an overview of prevailing trends, illustrating AI’s transformative potential towards diagnostic accuracy, treatment efficiency and operational productivity in healthcare systems. This research paper will delve into the rise of AI in healthcare, from its early stages to technologies that are cutting edge and influencing the future medicine. Furthermore, we will investigate how AI affects patient outcomes, healthcare staff dynamics and the overall healthcare ecosystem through aprism of multidimensionality. The goal of the research as such is to equip all takeholders with historical trends, current advancements and future trajectories so that they can use AI in healthcare effectively but responsibly. The aim is to create a healthcare environment where there are AI driven improvements in patient care which provide equal opportunity for all.REST Journal on Data Analytics and Artificial Intelligence00This research paper will delve into the rise of AI in healthcare, from its early stages to technologies that are cutting edge and influencing the future medicine, and investigate how AI affects patient outcomes, healthcare staff dynamics and the overall healthcare ecosystem through aprism of multidimensionality.REST Journal on Data Analytics and Artificial Intelligence[]2024-04-05T00:00:00https://www.semanticscholar.org/paper/d7b7a4c16d271cf4a49f8ffc852f5a550b021152 6570a9205d3c00e3c5d6a5c03bd01b2115f20b88Journal of the Association for Consumer Research12Journal of the Association for Consumer Research['Stefano Puntoni', 'Klaus Wertenbroch']2024-04-05T00:00:00https://www.semanticscholar.org/paper/6570a9205d3c00e3c5d6a5c03bd01b2115f20b88 b8f436531f8faccff0b60a15371ce040e4a3278402['Niaz Chowdhury', 'Ganesh Chandra Deka']2024-04-05T00:00:00https://www.semanticscholar.org/paper/b8f436531f8faccff0b60a15371ce040e4a32784 16f04860d22f925ce90ef50d6daa5833db157c73The Deep learning (DL) models for diagnosing breast cancer from mammographic images often operate as"black boxes", making it difficult for healthcare professionals to trust and understand their decision-making processes. The study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer using the CBIS-DDSM dataset. The methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations and transfer learning using pre-trained networks such as VGG-16, Inception-V3 and ResNet was employed. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions, highlighted by utilizing the Hausdorff measure to assess the alignment between AI-generated explanations and expert annotations quantitatively. This approach is critical for XAI in promoting trustworthiness and ethical fairness in AI-assisted diagnostics. The findings from our research illustrate the effective collaboration between CNNs and XAI in advancing diagnostic methods for breast cancer, thereby facilitating a more seamless integration of advanced AI technologies within clinical settings. By enhancing the interpretability of AI driven decisions, this work lays the groundwork for improved collaboration between AI systems and medical practitioners, ultimately enriching patient care. Furthermore, the implications of our research extended well beyond the current methodologies. It encourages further research into how to combine multimodal data and improve AI explanations to meet the needs of clinical practice.arXiv.org490An integrated framework combining Convolutional Neural Networks and Explainable Artificial Intelligence for the enhanced diagnosis of breast cancer using the CBIS-DDSM dataset is presented and the evaluation of XAI's effectiveness in interpreting model predictions is highlighted by utilizing the Hausdorff measure.ArXiv['Maryam Ahmed', 'Tooba Bibi', 'Rizwan Ahmed Khan', 'Sidra Nasir']2024-04-05T00:00:00https://www.semanticscholar.org/paper/16f04860d22f925ce90ef50d6daa5833db157c73 debc27fef8a2b3f4b4099a5b31ff1388fa9e079dNeuroprosthetic devices have the potential to restore motor function and improve the quality of life for individuals with limb loss or motor impairments. However, the control of these devices remains challenging due to the complex nature of brain-machine interfaces and the variability in individual brain wave patterns. This study presents a simulation framework that combines electroencephalogram (EEG) analysis with artificial intelligence (AI) to enhance the control and adaptation of neuroprosthetic devices. We simulate brain wave patterns across key motor regions, including the motor cortex, premotor cortex, supplementary motor area, parietal lobe, and cerebellum, during a simplified arm movement task. The simulation demonstrates the synchronization of brain waves over time, representing the neural dynamics underlying motor control. I propose an AI-powered approach that leverages machine learning algorithms to continuously learn from the user's brain wave patterns and adapt the control strategies of the neuroprosthetic device. The AI system enables personalized calibration, continuous learning, and improved movement control, leading to more intuitive and efficient use of the prosthesis. Furthermore, I discuss the potential for personalized feedback and training, as well as continuous upgrades and enhancements to the AI-powered neuroprosthetic devices. My simulation study highlights the promising role of AI in advancing neuroprosthetic control and lays the foundation for future research and development in this field. By harnessing the power of AI and adaptive learning, I envision a future where individuals with motor impairments can regain a higher level of independence and functionality through seamless integration with intelligent neuroprosthetic devices.Wired Neuroscience00A simulation framework that combines electroencephalogram (EEG) analysis with artificial intelligence (AI) to enhance the control and adaptation of neuroprosthetic devices and highlights the promising role of AI in advancing neuroprosthetic control.Wired Neuroscience['Richard Murdoch Montgomery']2024-04-05T00:00:00https://www.semanticscholar.org/paper/debc27fef8a2b3f4b4099a5b31ff1388fa9e079d 4a2f4cd788896d9e9b52eaf5ca303c5cdcb280c6We investigate whether modern AI can emulate expert creativity in complex scientific endeavors. We introduce novel methodology that utilizes original research articles published after the AI's training cutoff, ensuring no prior exposure, mitigating concerns of rote memorization and prior training. The AI are tasked with redacting findings, predicting outcomes from redacted research, and assessing prediction accuracy against reported results. Analysis on 589 published studies in four leading psychology journals over a 28-month period, showcase the AI's proficiency in understanding specialized research, deductive reasoning, and evaluating evidentiary alignment--cognitive hallmarks of human subject matter expertise and creativity. These findings suggest the potential of general-purpose AI to transform academia, with roles requiring knowledge-based creativity become increasingly susceptible to technological substitution.Social Science Research Network420Analysis on 589 published studies in four leading psychology journals over a 28-month period, showcases the AI's proficiency in understanding specialized research, deductive reasoning, and evaluating evidentiary alignment--cognitive hallmarks of human subject matter expertise and creativity.ArXiv['Anirban Mukherjee', 'H. Chang']2024-04-05T00:00:00https://www.semanticscholar.org/paper/4a2f4cd788896d9e9b52eaf5ca303c5cdcb280c6 6cec0d62481b1dfe9207e0d19970dd1f5c4d3af0The realization of data-driven AI-native architecture envisioned for 6G and beyond networks can eventually lead to multiple machine learning (ML) workloads distributed at the network edges driving downstream tasks like secondary carrier prediction, positioning, channel prediction etc. The independent life-cycle management of these edge-distributed independent multiple workloads sharing a resource-constrained compute node e.g., base station (BS) is a challenge that will scale with denser deployments. This study explores the effectiveness of multi-task learning (MTL) approaches in facilitating a general-purpose AI native Radio Access Network (RAN). The investigation focuses on four RAN tasks: (i) secondary carrier prediction, (ii) user location prediction, (iii) indoor link classification, and (iv) line-of-sight link classification. We validate the performance using realistic simulations considering multi-faceted design aspects of MTL including model architecture, loss and gradient balancing strategies, distributed learning topology, data sparsity and task groupings. The quantification and insights from simulations reveal that for the four RAN tasks considered (i) adoption of customized gate control-based expert architecture with uncertainty-based weighting makes MTL perform either best among all or at par with single task learning (STL) (ii) LoS classification task in MTL setting helps other tasks but its own performance is degraded (iii) for sparse training data, training a single global MTL model is helpful but MTL performance is on par with STL (iv) optimal set of group pairing exists for each task and (v) partial federation is much better than full model federation in MTL setting.arXiv.org250This study explores the effectiveness of multi-task learning (MTL) approaches in facilitating a general-purpose AI native Radio Access Network (RAN) and finds that for the four RAN tasks considered, MTL perform either best among all or at par with single task learning (STL).ArXiv['Hasan Farooq', 'Julien Forgeat', 'Shruti Bothe', 'K. Čyras', 'Md Moin']2024-04-05T00:00:00https://www.semanticscholar.org/paper/6cec0d62481b1dfe9207e0d19970dd1f5c4d3af0 45751ac5de94fd644a32b7ccefd2d4f4d82ebd40Recent advances in artificial intelligence (AI) capabilities have increased the eagerness of companies to integrate AI into software systems. While AI can be used to have a positive impact on several dimensions of sustainability, this is often overshadowed by its potential negative influence. While many studies have explored sustainability factors in isolation, there is insufficient holistic coverage of potential sustainability benefits or costs that practitioners need to consider during decision-making for AI adoption. We therefore aim to synthesize trade-offs related to sustainability in the context of integrating AI into software systems. We want to make the sustainability benefits and costs of integrating AI more transparent and accessible for practitioners. The study was conducted in collaboration with a Dutch financial organization. We first performed a rapid review that led to the inclusion of 151 research papers. Afterward, we conducted six semi-structured interviews to enrich the data with industry perspectives. The combined results showcase the potential sustainability benefits and costs of integrating AI. The labels synthesized from the review regarding potential sustainability benefits were clustered into 16 themes, with"energy management"being the most frequently mentioned one. 11 themes were identified in the interviews, with the top mentioned theme being"employee wellbeing". Regarding sustainability costs, the review discovered seven themes, with"deployment issues"being the most popular one, followed by"ethics&society"."Environmental issues"was the top theme from the interviews. Our results provide valuable insights to organizations and practitioners for understanding the potential sustainability implications of adopting AI.arXiv.org180This study aims to synthesize trade-offs related to sustainability in the context of integrating AI into software systems to make the sustainability benefits and costs of integrating AI more transparent and accessible for practitioners.ArXiv['Apoorva Nalini Pradeep Kumar', 'Justus Bogner', 'Mark C. Funke', 'Patricia Lago']2024-04-05T00:00:00https://www.semanticscholar.org/paper/45751ac5de94fd644a32b7ccefd2d4f4d82ebd40 13a9ba1f11c173c4ddd17ec3f1a1c9ee3e7aea4fINTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed. OBJECTIVES:  conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection.  RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS). CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.EAI Endorsed Transactions on Pervasive Health and Technology730A literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection using retinal images and proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.EAI Endorsed Transactions on Pervasive Health and Technology['Najeeba Afreen', 'Rajanikanth Aluvalu']2024-04-05T00:00:00https://www.semanticscholar.org/paper/13a9ba1f11c173c4ddd17ec3f1a1c9ee3e7aea4f 621d92d6119d8c24ece87f1311d1e056a8223b1aIn breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.Journal of Magnetic Resonance Imaging760An overview of the basic concepts of AI imaging analysis is presented and the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality are reviewed.Journal of magnetic resonance imaging : JMRI['Roberto Lo Gullo', 'J. Brunekreef', 'Eric Marcus', 'Lynn K Han', 'Sarah Eskreis-Winkler', 'S. Thakur', 'Ritse M Mann', 'Kevin B W Groot Lipman', 'Jonas Teuwen', 'K. Pinker']2024-04-05T00:00:00https://www.semanticscholar.org/paper/621d92d6119d8c24ece87f1311d1e056a8223b1a 42a6ced302512f1cdbccbb22b4b6da8cdb35bd4fThe project proposes the development of an AI-enabled MedDrone for medication delivery, introducing an innovative and efficient solution to enhance healthcare logistics. The MedDrone, equipped with artificial intelligence capabilities, aims to revolutionize the process of medication delivery by leveraging autonomous aerial technology. The system integrates advanced algorithms to optimize route planning, ensuring timely and accurate delivery of medications to designated locations. The AI component enables the drone to adapt to real-time variables such as weather conditions and traffic, enhancing the reliability and responsiveness of the delivery process. This project addresses the challenges of traditional medication distribution, offering a futuristic and intelligent approach to healthcare logistics through the deployment of AI-enabled autonomous drones for efficient and timely medication delivery. The proposed system integrates AI algorithms for real-time route optimization, obstacle avoidance, and decision-making, enabling the drone to autonomously navigate complex environments and deliver essential medical supplies such as first aid kits, defibrillators, and medications to the point of need. Through the utilization of machine learning techniques, the drone continuously learns from its interactions with the environment, enhancing its adaptability and performance over time. Additionally, the system incorporates advanced communication technologies to enable seamless coordination with emergency responders and healthcare professionals, ensuring timely delivery and proper utilization of resources. The efficacy of the proposed AI-based medical drone system is demonstrated through simulations and real-world trials, highlighting its potential to revolutionize emergency response efforts and improve healthcare accessibility, particularly in challenging or resource-constrained environments.International Journal of Innovative Research in Advanced Engineering00The project proposes the development of an AI-enabled MedDrone for medication delivery, introducing an innovative and efficient solution to enhance healthcare logistics through the deployment of AI-enabled autonomous drones for efficient and timely medication delivery.International Journal of Innovative Research in Advanced Engineering['Hemalatha D', 'Dharshini S', 'Ramesh P', 'Priya S']2024-04-05T00:00:00https://www.semanticscholar.org/paper/42a6ced302512f1cdbccbb22b4b6da8cdb35bd4f 83aa21e8a753d6f0564c90c2c3564c6b7113c774Artificial Intelligence Review00A typology is proposed that distinguishes the different stages of the AI life-cycle, the high-level ethical principles that should govern their implementation, and the tools with the potential to foster compliance with these principles, encompassing both technical and conceptual resources.Artif. Intell. Rev.['Ricardo Ortega-Bolaños', 'Joshua Bernal-Salcedo', 'Mariana Germán Ortiz', 'Julian Galeano Sarmiento', 'Gonzalo A. Ruz', 'Reinel Tabares-Soto']2024-04-05T00:00:00https://www.semanticscholar.org/paper/83aa21e8a753d6f0564c90c2c3564c6b7113c774 87aa0c4b9e8aa2484055388a653bd3404e4bfa13In the coming years, intelligent machines are poised to augment or replace human abilities across multiple domains. Artificial intelligence (AI), a subset of computer science, demonstrates machine or software intelligence. Over the past two decades, AI has significantly bolstered performance in manufacturing, services, and education. The proliferation of AI has led to the emergence of expert systems, revolutionizing problem-solving in education, engineering, business, medicine, and weather forecasting. Its widespread application has notably improved quality and efficiency in various sectors. This paper offers a concise overview of AI, emphasizing its significance in education, encompassing its definition, search methodologies, breakthroughs, and future prospects.International Journal of Advanced Research in Science, Communication and Technology20This paper offers a concise overview of AI, emphasizing its significance in education, encompassing its definition, search methodologies, breakthroughs, and future prospects.International Journal of Advanced Research in Science, Communication and Technology['Abhishek Yashwant Thakre', 'Aman Sunil Munjekar', 'Rushikesh Avdhturao Deshmukh']2024-04-05T00:00:00https://www.semanticscholar.org/paper/87aa0c4b9e8aa2484055388a653bd3404e4bfa13 2be59a3c79288f771344995a6f0ab1dd4da35b0eThe appearance of large language models (LLMs) and other forms of generative AI portend a new era of disruption and innovation for the news industry, this time focused on the production and consumption of news rather than on its distribution. Large news organizations, however, may be surprisingly well‐prepared for at least some of this disruption because of earlier innovation work on automating workflows for personalized content and formats using structured techniques. This article reviews this work and uses examples from the British Broadcasting Corporation (BBC) and other large news providers to show how LLMs have recently been successfully applied to addressing significant barriers to the deployment of structured approaches in production, and how innovation using structured techniques has more generally framed significant editorial and product challenges that might now be more readily addressed using generative AI. Using the BBC's next‐generation authoring and publishing stack as an example, the article also discusses how earlier innovation work has influenced the design of flexible infrastructure that can accommodate uncertainty in audience behavior and editorial workflows – capabilities that are likely to be well suited to the fast‐approaching AI‐mediated news ecosystem.The AI Magazine70This article uses examples from the British Broadcasting Corporation (BBC) and other large news providers to show how LLMs have recently been successfully applied to addressing significant barriers to the deployment of structured approaches in production, and how innovation using structured techniques has more generally framed significant editorial and product challenges that might now be more readily addressed using generative AI.AI Magazine['David Caswell']2024-04-05T00:00:00https://www.semanticscholar.org/paper/2be59a3c79288f771344995a6f0ab1dd4da35b0e f79605b863f25fef13e30ad28c49e0aee2cc6234Insights into Imaging330Noninvasive imaging features including the quantitative results of AI-CAD for mammography interpretation were independent predictors of invasive upgrade in lesions initially diagnosed as ductal carcinoma in situ via percutaneous biopsy and therefore may help decide the direction of surgery before treatment.Insights into Imaging['Jiyoung Yoon', 'Juyeon Yang', 'Hye Sun Lee', 'Min Jung Kim', 'V. Y. Park', 'Miribi Rho', 'Jung Hyun Yoon']2024-04-05T00:00:00https://www.semanticscholar.org/paper/f79605b863f25fef13e30ad28c49e0aee2cc6234 ebbb91bc21e164949fe0dc4d675f43550f244ba6 This Medical News article is an interview with JAMA Editor in Chief Kirsten Bibbins-Domingo and Virologist Davey Smith, head of the Division of Infectious Diseases and Global Public Health at the University of California, San Diego. Journal of the American Medical Association (JAMA)01JAMA['Samantha Anderer', 'Y. Hswen']2024-04-05T00:00:00https://www.semanticscholar.org/paper/ebbb91bc21e164949fe0dc4d675f43550f244ba6 013256596d9aeae0ff09765464bf15d007cb18a6International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-04-05T00:00:00https://www.semanticscholar.org/paper/013256596d9aeae0ff09765464bf15d007cb18a6 d29721d1af1ed9b5c0e736c5781a2b91278056adInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Dinesh Kabaleeswaran Iswarya Chandramouli']2024-04-05T00:00:00https://www.semanticscholar.org/paper/d29721d1af1ed9b5c0e736c5781a2b91278056ad c37f1dcb0bb826496abaa259d625445d912cb4cfAnnals of Telecommunications70Annals of Telecommunications['Leyli Karaçay', 'Zakaria Laaroussi', 'Sonika Ujjwal', 'Elif Ustundag Soykan']2024-04-05T00:00:00https://www.semanticscholar.org/paper/c37f1dcb0bb826496abaa259d625445d912cb4cf ddf3d6472be17351686f9a70e1a3db969154b3fbSystems Science &amp; Control Engineering40Systems Science &amp; Control Engineering['Justin Berdell', 'Simon Kudernatsch', 'Hasan Ferdowsi']2024-04-05T00:00:00https://www.semanticscholar.org/paper/ddf3d6472be17351686f9a70e1a3db969154b3fb 31efd76d59769fb2c8d42b180f218c03ffed69d9International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Praveen Kumar Shailendra Bade']2024-04-05T00:00:00https://www.semanticscholar.org/paper/31efd76d59769fb2c8d42b180f218c03ffed69d9 41e78747e93a99968ae3d6cea0c338aa623013e0International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Bharath Srinivasaiah']2024-04-05T00:00:00https://www.semanticscholar.org/paper/41e78747e93a99968ae3d6cea0c338aa623013e0 942a19a0486e35dd9a80f696746c4b98d7e83e30International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Manikanta Konkathi']2024-04-05T00:00:00https://www.semanticscholar.org/paper/942a19a0486e35dd9a80f696746c4b98d7e83e30 7c0e59cd3a9089885daea55f19dab9315d6b2b59Nature Aging00Nature aging['Sébastien J. Thuault']2024-04-05T00:00:00https://www.semanticscholar.org/paper/7c0e59cd3a9089885daea55f19dab9315d6b2b59 1fa9414afa6f2e99997b66cdee364eef7d9de083KI - Künstliche Intelligenz50KI - Künstliche Intelligenz['Joachim Hertzberg', 'Benjamin Kisliuk', 'Jan Christoph Krause']2024-04-05T00:00:00https://www.semanticscholar.org/paper/1fa9414afa6f2e99997b66cdee364eef7d9de083 7afb3b12b8105abaaa01b4a66c158a6e01062d85International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Sharon Nelson R Saranya']2024-04-05T00:00:00https://www.semanticscholar.org/paper/7afb3b12b8105abaaa01b4a66c158a6e01062d85 e5547fa7e69f471f58c61c54771f5e6a487db412Journal of the Society of Mechanical Engineers00Journal of the Society of Mechanical Engineers['Deep Sarkar']2024-04-05T00:00:00https://www.semanticscholar.org/paper/e5547fa7e69f471f58c61c54771f5e6a487db412 e3e90a0b1a024bd47e775307bf6845a8bf676ff1International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Mihir Patel Rohit Malik']2024-04-05T00:00:00https://www.semanticscholar.org/paper/e3e90a0b1a024bd47e775307bf6845a8bf676ff1 245eda416a530613b63edc04bf0c5e45e5693f4eInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Aditya Gadiko']2024-04-05T00:00:00https://www.semanticscholar.org/paper/245eda416a530613b63edc04bf0c5e45e5693f4e f33370d52f54a6d79588dd1ac573df50767824e7Human Resource Development International630Human Resource Development International['Hanwen Li', 'Sehoon Kim']2024-04-05T00:00:00https://www.semanticscholar.org/paper/f33370d52f54a6d79588dd1ac573df50767824e7 6250c58a68a1757ef431303046cc1e1eb100d300Tradition Innovations in Arts, Design, and Media Higher Education00Tradition Innovations in Arts, Design, and Media Higher Education['Adam Paul']2024-04-05T00:00:00https://www.semanticscholar.org/paper/6250c58a68a1757ef431303046cc1e1eb100d300 bc8431b2d6aa6986897e297e8880e3e87e1213dbPurpose The purpose of this paper is to scrutinise the effectiveness of four derivative exchanges’ enforcement efforts since 2007. These exchanges include the Commodity Exchange Inc. and ICE Futures US from the United States and ICE Futures Europe and the London Metal Exchange from the UK. Design/methodology/approach The paper examines 799 enforcement notices published by four exchanges through a behavioural science lens: HUMANS conceived by Hunt (2023) in Humanizing Rules: Bringing Behavioural Science to Ethics and Compliance. Findings The paper finds the effectiveness of the exchanges’ enforcement efforts to be a mixed picture as financial markets transition from the digital to artificial intelligence era. Humans remain a key cog in the wheel of market participants’ trading operations, albeit their roles have changed. Despite this, some elements of exchanges’ enforcement regimes have not kept pace with the move from floor to remote trading. However, in other respects, their efforts are or should be, effective, at least in behavioural terms. Research limitations/implications The paper’s findings are arguably limited to exchanges based in Anglophone jurisdictions. The information published by the exchanges is variable, making “like-for-like” comparisons difficult in some areas. Practical implications The paper makes several recommendations that, if adopted, could help exchanges to increase the potency of their enforcement programmes. Originality/value A key aim of the paper is to shift the lens through which the debate concerning the efficacy of exchange-level oversight is conducted. Hitherto, a legal lens has been used, whereas this paper uses a behavioural lens. Journal of Financial Regulation and Compliance720Journal of Financial Regulation and Compliance['Alexander Conrad Culley']2024-04-05T00:00:00https://www.semanticscholar.org/paper/bc8431b2d6aa6986897e297e8880e3e87e1213db 108a50034dd34ff77de9e4fbce054b6660f7b0edBrain Informatics1060A systematic review of the application of LIME and SHAP in interpreting the detection of Alzheimer’s disease and XAI’s crucial role in strengthening the trustworthiness of AI-based AD predictions is emphasised.Brain Informatics['Viswan Vimbi', 'Noushath Shaffi', 'Mufti Mahmud']2024-04-05T00:00:00https://www.semanticscholar.org/paper/108a50034dd34ff77de9e4fbce054b6660f7b0ed 1f6036f7a5cb9fcffd408b55c6327d19a8b64967This study investigates the essential prerequisites for the effective integration of artificial intelligence (AI) into secondary education in the Kingdom of Saudi Arabia, assessing the perceptions of both male and female educators. Employing a descriptive survey methodology, the primary data collection tool consists of questionnaires administered to 427 secondary school teachers in Riyadh. The findings reveal a notable average agreement level (mean score of 4.43) among participants concerning the identified prerequisites for implementing AI. Notably, students' requirements scored the highest (average of 4.51), followed closely by those for teachers (average of 4.48), the educational environment (average of 4.46), and educational content (average of 4.25). Each category demonstrates consistently high importance. Gender-based analysis indicates no statistically significant difference in determining prerequisites, except for those expected from teachers and the educational environment, which garnered preference from female educators. Similarly, no statistically significant variance emerged in the identification of prerequisites based on major. Regarding teaching experience, no statistically significant difference was found in determining prerequisites, except for requirements related to the educational environment, where educators with 10-15 years of experience exhibited a preference. The study concludes that qualification does not significantly impact the determination of prerequisites for AI in secondary education. This study offers useful insights into the perspectives and requirements of educators, providing detailed knowledge of the integration of artificial intelligence (AI) into secondary education in Saudi Arabia. It highlights important factors that need to be considered for the effective deployment of AI in education.Journal of Asian Scientific Research350The study concludes that qualification does not significantly impact the determination of prerequisites for AI in secondary education, and highlights important factors that need to be considered for the effective deployment of AI in education.Journal of Asian Scientific Research['Khalid Mohammed Alkhuzaim', 'N. Alzuhair', 'Asmaa Muhammad Al-Qutaim']2024-04-05T00:00:00https://www.semanticscholar.org/paper/1f6036f7a5cb9fcffd408b55c6327d19a8b64967 25d4f4be2ff2475ec6cb509326bb6807aa230a65 Purpose The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE. Design/methodology/approach A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted. Findings Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI. Research limitations/implications CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively. Practical implications The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features. Originality/value This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation. Journal of Modelling in Management1450How AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE) is understood to contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE.Journal of Modelling in Management['Yuvika Gupta', 'F. Khan']2024-04-05T00:00:00https://www.semanticscholar.org/paper/25d4f4be2ff2475ec6cb509326bb6807aa230a65 3dbcd89d11669f56709e5619ab088677a4f866bcObjectives This research aims to present and assess the Primary Functions of autoinjectors introduced in ISO 11608-1:2022. Investigate the risks in current autoinjector technology, identify and assess risks and benefits associated with Artificial Intelligence (AI) powered autoinjectors, and propose a framework for mitigating these risks. ISO 11608-1:2022 is a standard that specifies requirements and test methods for needle-based injection systems intended to deliver drugs, focusing on design and function to ensure patient safety and product effectiveness. ‘KZH’ is an FDA product code used to classify autoinjectors, for regulatory purposes, ensuring they meet defined safety and efficacy standards before being marketed. Method A comprehensive analysis of autoinjectors problems is conducted using data from the United States Food and Drug Administration (FDA) database. This database records medical device reporting events, including those related to autoinjectors, reported by various sources. The analysis focuses on events associated with the product code KZH, covering data from January 1, 2008, to September 30, 2023. This research employs statistical frequency analysis and incorporates pertinent the FDA, United Kingdom, European Commission regulations, and ISO standards. Results 500 medical device reporting events are assessed for autoinjectors under the KZH code. Ultimately, 188 of these events are confirmed to be associated with autoinjectors, all 500 medical devices were seen to lack AI capabilities. An analysis of these events for traditional mechanical autoinjectors revealed a predominant occurrence of malfunctions (72%) and injuries (26%) among event types. Device problems, such as breakage, defects, jams, and others, accounted for 45% of incidents, while 10% are attributed to patient problems, particularly missed and underdoses. Conclusion Traditional autoinjectors are designed to assist patients in medication administration, underscoring the need for quality control, reliability, and design enhancements. AI autoinjectors, sharing this goal, bring additional cybersecurity and software risks, requiring a comprehensive risk management framework that includes standards, tools, training, and ongoing monitoring. The integration of AI promises to improve functionality, enable real-time monitoring, and facilitate remote clinical trials, timely interventions, and tailored medical treatments.Frontiers in Medical Technology500Traditional autoinjectors are designed to assist patients in medication administration, underscoring the need for quality control, reliability, and design enhancements, while AI autoinjectors, sharing this goal, bring additional cybersecurity and software risks, requiring a comprehensive risk management framework.Frontiers in Medical Technology['M. Machal']2024-04-05T00:00:00https://www.semanticscholar.org/paper/3dbcd89d11669f56709e5619ab088677a4f866bc cc2c943c9c5170feae787832b658f0e221987e25Artificial intelligence (AI) influences many aspects of modern life and has multiple applications in the delivery of healthcare. AI is designed to mimic human capabilities including pattern recognition, data analysis, and decision‐making and perform tasks more efficiently. It is capable of detecting patterns in large datasets that might elude human beings. AI has potential to improve patient care, patient safety, disease diagnosis and treatment; public health; health research; health administration; and the daily work of health professionals. In 2019, the US National Academy of Medicine emphasized the need for “physicians, nurses, and other clinicians, data scientists, health care administrators, public health officials, policy makers, regulators, purchasers of health care services, and patients to understand the basic concepts, current state of the art, and future implications of the revolution in AI and machine learning.” Therefore, it is incumbent on adult educators to incorporate AI training into health professions education and patient education.New Directions for Adult and Continuing Education140It is incumbent on adult educators to incorporate AI training into health professions education and patient education because of the need for doctors, nurses, and other clinicians to understand the basic concepts, current state of the art, and future implications of the revolution in AI and machine learning.New Directions for Adult and Continuing Education['Andrea Mucci', 'Wendy M. Green', 'Lilian H. Hill']2024-04-05T00:00:00https://www.semanticscholar.org/paper/cc2c943c9c5170feae787832b658f0e221987e25 211627f79babf4eee545e15f2d79cd47ad5d5d52The growing impact of artificial intelligence (AI) on Humanity is unavoidable, and therefore, “AI literacy” is extremely important. In the field of education—AI in education (AIED)—this technology is having a huge impact on the educational community and on the education system itself. The present study seeks to assess the level of AI literacy and knowledge among teachers at Portalegre Polytechnic University (PPU), aiming to identify gaps, find the main opportunities for innovation and development, and seek the degree of relationship between the dimensions of an AI questionnaire, as well as identifying the predictive variables in this matter. As a measuring instrument, a validated questionnaire based on three dimensions (AI Literacy, AI Self-Efficacy, and AI Self-Management) was applied to a sample of 75 teachers in the various schools of PPU. This revealed an average level of AI literacy (3.28), highlighting that 62.4% of responses are at levels 3 and 4 (based on a Likert scale from 1 to 5). The results also demonstrate that the first dimension is highly significant for the total dimensions, i.e., for AI Literacy, and no factor characterizing the sample is a predictor, but finding a below-average result in the learning factor indicates a pressing need to focus on developing these skills.Inf.390The present study seeks to assess the level of AI literacy and knowledge among teachers at Portalegre Polytechnic University (PPU), aiming to identify gaps, find the main opportunities for innovation and development, and seek the degree of relationship between the dimensions of an AI questionnaire, as well as identifying the predictive variables in this matter.Inf.['Eduardo Lérias', 'Cristina Guerra', 'Paulo Ferreira']2024-04-05T00:00:00https://www.semanticscholar.org/paper/211627f79babf4eee545e15f2d79cd47ad5d5d52 40c492bca044da6ac202d677df1611134e761c30In authors' opinion, the relevance of implementing artificial intelligence in the calculation of energy consumption in order to reduce excess generation lies in several key aspects: 1) efficient use of resources (by analysing data and predicting energy consumption patterns using artificial intelligence, the operation of the energy system can be optimised, ensuring efficient use of energy resources and avoiding excessive electricity generation); 2) reduction of losses (artificial intelligence can help identify and eliminate problematic segments in energy systems, leading to a reduction in energy losses during transport and distribution); 3) consumption forecasting (artificial intelligence can predict and respond to energy consumption peaks, ensuring the stability of energy supply and avoiding overloading of energy systems); 4) resource conservation and emissions reduction (efficient management of energy consumption using artificial intelligence can lead to reduced fuel consumption and greenhouse gas emissions, promoting more sustainable and environmentally friendly development). Artificial intelligence is a field of computer science that deals with the creation of programs and systems capable of performing tasks that typically require human intellectual abilities. These systems can exhibit cognitive functions such as image recognition, language understanding, decision making, self-learning and planning. Artificial intelligence uses methods and techniques from computer science, mathematics, linguistics, philosophy and other fields to design and implement intelligent systems. Artificial intelligence in the energy sector is the application of AI methods and technologies to optimise energy production, transmission, distribution and consumption. This includes the development of algorithms and systems that can automatically analyse large amounts of data, predict energy demand, optimise energy processes, maintain the stability of energy networks and reduce energy losses. The application of artificial intelligence to energy can help increase the efficiency of energy production, reduce environmental impact and improve the reliability of energy systems. The subject of the study is the introduction of artificial intelligence in the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery. The research methods for introducing artificial intelligence into the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery are a system of general scientific and special methods of scientific knowledge. The purpose of the study is to determine the possibilities of introducing artificial intelligence into the calculation of energy consumption to reduce excess generation in the context of Ukraine's recovery. Results. Investing in the use of artificial intelligence to calculate energy consumption in order to reduce excess generation in the context of Ukraine's recovery can be done through various investment instruments, including: venture capital (investments in start-ups and companies developing artificial intelligence technologies to optimise energy consumption); project financing (financing of specific projects using artificial intelligence to analyse and optimise energy consumption); corporate investments (investments in the development of in-house artificial intelligence systems for energy efficiency management at industrial enterprises); stock market (investments in shares and bonds of companies specialising in the development and implementation of innovative technologies for the energy sector); crowdfunding (raising funds from individual investors on platforms dedicated to the development of artificial intelligence projects in the field of energy efficiency).Baltic Journal of Economic Studies60The purpose of the study is to determine the possibilities of introducing artificial intelligence into the calculation of energy consumption to reduce excess generation in the context of Ukraine's recovery.Baltic Journal of Economic Studies['O. Kuzmenko', 'V. Chorna', 'L. Kozhura']2024-04-05T00:00:00https://www.semanticscholar.org/paper/40c492bca044da6ac202d677df1611134e761c30 94686b9417126917e9fb303b279375f7dfc891e9In today’s rapidly evolving business landscape, artificial intelligence (AI) has emerged as a transformative force, reshaping traditional management practices across industries. This research paper delves into the strategic integration of AI in contemporary management, aiming to provide insights into the challenges, opportunities, and implications faced by organizations navigating this transformative journey. Through a comprehensive analysis of case studies, industry trends, and expert opinions, this study explores the ways in which AI is influencing decision-making, efficiency, innovation, and sustainability within organizations. Furthermore, it examines the ethical considerations and change management strategies required to ensure responsible and effective AI adoption. The findings of this research offer a roadmap for organizations seeking to harness the potential of AI while addressing the complexities of its integration into management practices.Shodh Sari-An International Multidisciplinary Journal30This research paper delves into the strategic integration of AI in contemporary management, aiming to provide insights into the challenges, opportunities, and implications faced by organizations navigating this transformative journey.Shodh Sari-An International Multidisciplinary Journal['Anuranjita Dixit']2024-04-05T00:00:00https://www.semanticscholar.org/paper/94686b9417126917e9fb303b279375f7dfc891e9 50b622c21ed693a9e6d1453ea2d66c14e3dc0d9fArtificial intelligence (AI) is one of the core technologies of the digital transformation. It is expected to lead to job losses, initially in occupations characterised by routine activities, but increasingly in creative professions. This chapter analyses the results of an empirical survey of graphic designers and copywriters in Bulgaria on their attitudes towards AI as well as their views on its future influence on their professions. The majority do not perceive AI and automation as a threat. In their view, digital technologies and artificial intelligence are a favourable opportunity for professionals that will change their work by taking away routine tasks and leaving creative activities to humans alone. Graphic designers and copywriters will engage in creative work, and their work will become more in demand because it is human. The analysis of the study shows that pessimistic scenarios of massive job destruction may not hold true. Expectations of deterioration in the quality of work (Holtgrewe, 2014) as a result of digitalization are also not borne out. This study is the first of its kind in this country to explore creative professions and their attitudes towards AI.Postmodernism Problems70The analysis of the study shows that pessimistic scenarios of massive job destruction may not hold true, and expectations of deterioration in the quality of work as a result of digitalization are also not borne out.Postmodernism Problems['Bagryan Malamin']2024-04-05T00:00:00https://www.semanticscholar.org/paper/50b622c21ed693a9e6d1453ea2d66c14e3dc0d9f 52a9bbf0e9239a508714eac8389d9aab5e0afcc2BACKGROUND Neurointerventionalists must pay close attention to multiple devices on multiple screens simultaneously, which can lead to oversights and complications. Artificial intelligence (AI) has potential application in recognizing and monitoring these devices on fluoroscopic imaging. METHODS We report out preliminary experience with a real time AI assistance software, Neuro-Vascular Assist (iMed technologies, Tokyo, Japan), in six patients who underwent carotid artery stenting. This software provides real time assistance during endovascular procedures by tracking wires, guiding catheters, and embolic protection devices. The software provides notification when devices move out of a predefined region of interest or off the screen during the procedure. Efficacy, safety, and accuracy of the software were evaluated. RESULTS The software functioned well without problems and was easily used. Mean number of notifications per procedure was 21.0. The mean numbers of true positives, false positives, and false negatives per procedure were 17.2, 3.8, and 1.2, respectively. Precision and recall were 82% and 94%, respectively. Among the 103 true positive notifications, 24 caused the operator to adjust the inappropriate position of the device (23%), which is approximately four times per procedure. False notifications occurred because of false positive device detection. No adverse events related to the software occurred. No periprocedural complications occurred. CONCLUSIONS Neuro-Vascular Assist, a real time AI assistance software, worked appropriately and may be beneficial in carotid artery stenting procedures. Future large scale studies are warranted to confirm.Journal of NeuroInterventional Surgery210Neuro-Vascular Assist, a real time AI assistance software, worked appropriately and may be beneficial in carotid artery stenting procedures, and was easily used.Journal of neurointerventional surgery['Yuya Sakakura', 'Kenichi Kono', 'T. Fujimoto']2024-04-05T00:00:00https://www.semanticscholar.org/paper/52a9bbf0e9239a508714eac8389d9aab5e0afcc2 7a4e755fa8e3a53d01466c47bfbcc79b44857c25OBJECTIVE Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population. METHODS We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case-based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. RESULTS We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. INTERPRETATION xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one-size-fits-all approach. Further user-centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed.Annals of Clinical and Translational Neurology290Investigating the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population found xAI is not uniformly beneficial, and there is no one-size-fits-all approach.Annals of Clinical and Translational Neurology['Grace Y. Gombolay', 'Andrew Silva', 'Mariah L. Schrum', 'N. Gopalan', 'Jamika Hallman-cooper', 'Monideep Dutt', 'Matthew Gombolay']2024-04-05T00:00:00https://www.semanticscholar.org/paper/7a4e755fa8e3a53d01466c47bfbcc79b44857c25 36b7a41229f1bf1d6b2108554b88a0028cec2722 This Viewpoint explores the unique attributes of dentistry that could leverage artificial intelligence for many improvements including greater health equity. JAMA Health Forum20JAMA health forum['H. Elani', 'William V Giannobile']2024-04-05T00:00:00https://www.semanticscholar.org/paper/36b7a41229f1bf1d6b2108554b88a0028cec2722 b47a6a052ff5e9288ca103834edf57f4bb2b0d85As artificial intelligence (AI) becomes integral to diverse applications, the imperative to secure AI models against evolving threats has gained paramount importance. This paper presents a novel cybersecurity framework tailored explicitly for AI models, synthesizing insights from a comprehensive literature review, real-world case studies, and practical implementation strategies. Drawing from seminal works on adversarial attacks, data privacy, and secure deployment practices, the framework addresses vulnerabilities throughout the AI development lifecycle. Preliminary results indicate a significant enhancement in the resilience of AI models, demonstrating reduced success rates of adversarial attacks, effective data encryption, and robust secure deployment practices. The framework's adaptability across diverse use cases underscores its practicality. These findings mark a crucial step toward establishing comprehensive and practical cybersecurity measures, contributing to the ongoing discourse on securing the expanding field of artificial intelligence. Ongoing efforts involve further validation, optimization, and exploration of additional security measures to fortify AI models in an ever-changing threat landscape.Archives of Business Research00A novel cybersecurity framework tailored explicitly for AI models is presented, synthesizing insights from a comprehensive literature review, real-world case studies, and practical implementation strategies, to address vulnerabilities throughout the AI development lifecycle.Archives of Business Research['Adedeji Olugboja']2024-04-05T00:00:00https://www.semanticscholar.org/paper/b47a6a052ff5e9288ca103834edf57f4bb2b0d85 afe352316827c826e2b328768e35847ddb3aec77Artificial Intelligence Review2550This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope.Artif. Intell. Rev.['Bilal Hassan', 'Hina Raja', 'Taimur Hassan', 'Muhammad Usman Akram', 'Hira Raja', 'Alaa Abd-Alrazaq', 'Siamak Yousefi', 'N. Werghi']2024-04-05T00:00:00https://www.semanticscholar.org/paper/afe352316827c826e2b328768e35847ddb3aec77 c6bdf2a40914bd88ca179c193653e305cd9d9a47International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Jasleen Kaur Sabherwal Kirandeep']2024-04-05T00:00:00https://www.semanticscholar.org/paper/c6bdf2a40914bd88ca179c193653e305cd9d9a47 9ae81b4908d9bcc233b415860a1b1ff524bf6feaFrontiers in Medicine90Frontiers in Medicine['E. Cipolletta', 'M. C. Fiorentino', 'F. Vreju', 'Sara Moccia', 'Emilio Filippucci']2024-04-05T00:00:00https://www.semanticscholar.org/paper/9ae81b4908d9bcc233b415860a1b1ff524bf6fea 9574f285ee48fe296d2ff85a58f804753f27487cComputers in The Schools430Computers in the Schools['Hanieh Shafiee Rad', 'Ali Roohani']2024-04-05T00:00:00https://www.semanticscholar.org/paper/9574f285ee48fe296d2ff85a58f804753f27487c 4ed9e5714f85b28f3c99dc0973f29d97cfb116f1AI &amp; SOCIETY230AI &amp; SOCIETY['Miguel Angelo de Abreu de Sousa']2024-04-05T00:00:00https://www.semanticscholar.org/paper/4ed9e5714f85b28f3c99dc0973f29d97cfb116f1 48eedb499722bd14404b7985e6ace75c523645c4International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Sandeep Kumar']2024-04-05T00:00:00https://www.semanticscholar.org/paper/48eedb499722bd14404b7985e6ace75c523645c4 18a783774e2a7c95cdce03ad6f247a4c709ee4a5International Journal of Human Sciences Research00International Journal of Human Sciences Research['Arthur Land Oliveira', 'Elisangela Brugnera', 'Maria Angélica Dornelles Dias', 'Adriano Valter Dornelles Dias']2024-04-05T00:00:00https://www.semanticscholar.org/paper/18a783774e2a7c95cdce03ad6f247a4c709ee4a5 16976b73fb0e35243508e53211a663fb471a6298International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Brahmaleen Kaur Sidhu']2024-04-05T00:00:00https://www.semanticscholar.org/paper/16976b73fb0e35243508e53211a663fb471a6298 8e41aeb14e5de423f958ba3476dc2b97b4e73cbfInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Saiyyad Kalim Akhtar']2024-04-05T00:00:00https://www.semanticscholar.org/paper/8e41aeb14e5de423f958ba3476dc2b97b4e73cbf b37d9fc28b02074729f684bc65cf2c91d32d229fGovernance frameworks should address the prospect of AI systems that cannot be safely tested Technical experts and policy-makers have increasingly emphasized the need to address extinction risk from artificial intelligence (AI) systems that might circumvent safeguards and thwart attempts to control them (1). Reinforcement learning (RL) agents that plan over a long time horizon far more effectively than humans present particular risks. Giving an advanced AI system the objective to maximize its reward and, at some point, withholding reward from it, strongly incentivizes the AI system to take humans out of the loop, if it has the opportunity. The incentive to deceive humans and thwart human control arises not only for RL agents but for long-term planning agents (LTPAs) more generally. Because empirical testing of sufficiently capable LTPAs is unlikely to uncover these dangerous tendencies, our core regulatory proposal is simple: Developers should not be permitted to build sufficiently capable LTPAs, and the resources required to build them should be subject to stringent controls.Science92The core regulatory proposal is simple: Developers should not be permitted to build sufficiently capable LTPAs, and the resources required to build them should be subject to stringent controls.Science['Michael K. Cohen', 'Noam Kolt', 'Y. Bengio', 'Gillian K. Hadfield', 'Stuart Russell']2024-04-05T00:00:00https://www.semanticscholar.org/paper/b37d9fc28b02074729f684bc65cf2c91d32d229f 1672319f08189f9c9fe602ff3eb854270cee6f24The use of artificial intelligence in France is growing and intensifying in many areas, particularly in the field of justice. French President Macron has made it one of his government’s priorities to build on these assets and make France a world leader in AI. In parallel, the French government has deployed some efforts towards anticipating the regulatory challenges related to AI, the “National Strategy for Artificial Intelligence” launched as part of «France 2030» . As an illustration of the developments in artificial intelligence and its specific regulation, the French parliament passed a law to ensure the proper conduct of the 2024 Olympic and Paralympic Games (Law N° 2023-380 of 19.05.2023). The law permits the use of the experimental “augmented video-protection” technology, which uses cameras equipped with AI systems to detect and report specific events in real time. French regulations begin already now in the area of justice and must continue in the fields of AI liability and intellectual property. AI is a source of fears, particularly for the respect of human rights, and requires a very elaborate legal and ethical environment that is flexible enough to avoid slowing down the development of AI. The AI Liability EU Directive complements the Artificial Intelligence Act by introducing a new liability regime that ensures legal certainty, enhances consumer trust in AI, and assists consumers’ liability claims for damage caused by AI-enabled products and services. But the new European AI Act does not resolve all issues that therefore need to be addressed nationally. Legal Issues in the Digital Age80French regulations begin already now in the area of justice and must continue in the fields of AI liability and intellectual property, and requires a very elaborate legal and ethical environment that is flexible enough to avoid slowing down the development of AI.Legal Issues in the Digital Age['Alain Duflot']2024-04-04T00:00:00https://www.semanticscholar.org/paper/1672319f08189f9c9fe602ff3eb854270cee6f24 6aafdc20ff47cc3d7cddd8b0f3d78c2d98b35db5Any regulation, law, or legal order enforced by the lawful authority of a territory to maintain, control, and regulate the characteristics, development, and public interaction of an artificial entity developed in a digital manner can be called AI legislation. The paper presents a comparative analysis of the regulatory landscape for artificial intelligence in the South Asian countries in relation to other selective countries and organizations globally, in light of the challenges encountered in regulating AI in the region. Furthermore, the study demonstrates that South Asian nations have experienced a significant and persistent legal disparity in comparison to other global regions, which has been both involuntary and inequitable. The paper presents an argument for the regulation of artificial intelligence and offers suggestions for South Asian countries to effectively regulate AI despite challenges related to its design and economic limitations.Legal Issues in the Digital Age60The paper offers suggestions for South Asian countries to effectively regulate AI despite challenges related to its design and economic limitations and demonstrates that South Asian nations have experienced a significant and persistent legal disparity in comparison to other global regions.Legal Issues in the Digital Age['Mahmud Hasan']2024-04-04T00:00:00https://www.semanticscholar.org/paper/6aafdc20ff47cc3d7cddd8b0f3d78c2d98b35db5 9853ee1845ff5a6872198bd6cb218c1642304574The concept of "game analysis" generally refers to the study and assessment of video games, frequently from the viewpoints of player experience, story, mechanics, design, and other relevant variables. It can be used for a variety of games, such as sports, board, card, and video games. A sudden drop in the value of assets or financial institutions is referred to as a financial crisis. It frequently causes the regular operation of financial markets to be interrupted and may have negative impacts on the economy as a whole. In this manuscript, Game Analysis of Financial Regulation in International Financial Crisis using Multi-view Graph Convolutional Network (GA-FR-IFC-MGCN). The proposed method comprises of four phases: dataset, pre-processing, feature selection, classification. Initially, the data is taken from Analcat dataset. Then, Federated Neural Collaborative Filtering (FNCF) method is used to enhancing the networks data. For feature selection phase, the ideal features are chosen by Siberian Tiger Optimization (STO). After, the Multi-view graph convolutional network (MGCN) method is used to classifying international financial crisis as bankruptcy and Non-bankruptcy. In general, MGCN does not express some adaption of optimization strategies for determining optimal parameters to promise exact classification of International Financial Crisis. Therefore, Harbor Seal Whiskers Optimization Algorithm is proposed to enhance weight parameter of MGCN classifier, which precisely predicts the International Financial Crisis. The proposed technique is executed and efficiency of GA-FR-IFC-MGCN depend classification framework is assessed by support of numerous performances evaluating metrics likes accuracy, recall, precision, FI-score, specificity. Finally the performance of proposed GA-FR-IFC-MGCN methods provides 25.32%, 29.30% and 27.32% higher accuracy, 22.41%, 29.30% and 24.31% higher specificity and 25.71%, 27.12% and 25.31% higher precision though analyzed with existing method likes game analysis of financial supervision in international financial crisis(GA-FS-IFC), performance evaluation of clustering techniques for financial crisis prediction(PE-CT-FC) and modified grey wolf optimizer with sparse auto-encoder for financial crisis prediction in small marginal firms (MGWO-SA-FCP) respectively.Journal of Electrical Systems300Game Analysis of Financial Regulation in International Financial Crisis using Multi-view Graph Convolutional Network (GA-FR-IFC-MGCN) and Harbor Seal Whiskers Optimization Algorithm is proposed to enhance weight parameter of MGCN classifier, which precisely predicts the International Financial Crisis.Journal of Electrical Systems['Fang Luo']2024-04-04T00:00:00https://www.semanticscholar.org/paper/9853ee1845ff5a6872198bd6cb218c1642304574 a28c11ca2616ff54f07d560157a4fa5cce4506a2It is relatively recently that the way goods and services (GS) are accepted in the contractual system has become a focal point of research. It was prompted by the changes to the contractual system law introduced mandatory e-certification since 1 January 2022. However, while the process of e-certification as enshrined now in the law on contractual relationships was in the limelight, the concept of e-acceptance, definitions of actual and documentary acceptance and other issues were largely left out. A study of how acceptance is regulated under the national law shows a lack of systemic approach to the e-certification procedure in the law on contractual relationships, a need to put in place an acceptance procedure and to ensure public and municipal customers’ satisfaction with the quality of goods and services they purchase. The paper provides an overview of research on specific aspects of GS acceptance in the contractual system and identifies its place in the process of contractual performance. It is proposed to have a special terminology in the effective contractual relationships law for defining GS acceptance based on its purpose and identifying structural elements. A new approach to contract execution regulating actual and documentary acceptance as part of e-certification needs to be adopted. With regard to digital solutions required for e-certification, technological aspects are discussed with a view to possible regulation. It is equally proposed to formalize e-certification in the contractual system as a possible model for applying the block chain technology for the public (municipal) procurement system. An analysis of digital processes that support e-certification in the contractual system suggests a need to provide a link between technological and legal aspects of e-certification. The author also proposes a number of block chain related issues to be discussed with relation to the e-certification system. Legal Issues in the Digital Age40Legal Issues in the Digital Age['Larisa Pakhomova']2024-04-04T00:00:00https://www.semanticscholar.org/paper/a28c11ca2616ff54f07d560157a4fa5cce4506a2 6b03bf0cb990561ea49d61d017b72e1dc0026594Environmental degradation is an important issue facing the world today. Microcosmically, green technical innovation is needed to decrease environmental pollution. Therefore, exploring the relationship between the two is of great significance for promoting environmental protection and sustainable development. Thus, this research elucidates the interaction between green innovation (GI) and environmental regulations (ERs). This study utilizes the fixed effects model to examine how government environmental protection subsidies (EPSs) in market-incentive ER and environmental management system certification (EMSC) in voluntary participatory ER affect GI among listed companies in China. The sample observation period is from 2012 to 2021. Additionally, the impact of corporate governance (CGL) and environmental information disclosure (EID) on the relationship between ERs and GI within businesses is investigated. The empirical results show that both government environmental protection subsidies and environmental management system certification positively affect green innovation, and both corporate governance and environmental information disclosure positively moderate the impact of government environmental protection subsidies and environmental management system certification on green innovation. The above empirical results are still valid after a robustness test and can guide the formulation of government ERs, as well as corporate strategies for environmental management and GI.Sustainability1240Sustainability['Ying Ying', 'Shanyue Jin']2024-04-04T00:00:00https://www.semanticscholar.org/paper/6b03bf0cb990561ea49d61d017b72e1dc0026594 71b2a68b35ff94e4f5feee3939aceb7f3e4f9be7Journal of Comparative Policy Analysis730Journal of Comparative Policy Analysis: Research and Practice['Zhiqing Yang', 'Peiyao Liu', 'Lianfa Luo']2024-04-04T00:00:00https://www.semanticscholar.org/paper/71b2a68b35ff94e4f5feee3939aceb7f3e4f9be7 000a242bcb9e55a88584d54afe477aae98182f89The purpose of our study was the features of criminal behavior of the accused in the commission of particularly serious crimes in the context of the formation functions of programming and control. The sample consisted of 59 men aged 18—60 years, of those accused of committing particularly serious crimes aimed at a forensic psychiatric examination, the average age was 33.7 years. The methods of neuropsychological examination and psychological analysis of criminal cases were used. Syndrome of defeat of the basal divisions of the frontal lobes, prefrontal syndrome, syndrome of defeat of the medial divisions of the frontal lobes, Postfrontal (premotor) syndrome is most often seen among persons accused of particularly serious crimes. The criminal behavior of the accused in the Commission of particularly serious crimes was characterized by uncritical damage to the basal parts of the frontal lobes. The impulsivity is the main characteristic of the criminal behavior of the accused in especially serious crimes with the defeat of the prefrontal frontal lobe (prefrontal syndrome). Subjects with the defeat of the kinetic (dynamic) factor differed greater rigidity of criminal behavior. The behavior of those accused of committing particularly serious crimes was passive (energy-saving) in violation of the energy factor in the case of damage to the medial parts of the frontal lobes. The obtained results can be used to solve the issues of drawing a portrait of an unknown criminal, as well as in the course of correctional work with persons prone to repeat illegal behavior.Psychology and Law180Psychology and Law['D. Kashirskiy', 'O.V. Staroseltseva']2024-04-04T00:00:00https://www.semanticscholar.org/paper/000a242bcb9e55a88584d54afe477aae98182f89 08a205083aa8cc5ead2367b309686fdc42ee2cedВ статье обсуждаются особенности эмоционального интеллекта, эмпатии и саморегуляции у сотрудников органов внутренних дел. Цель исследования состояла в определении гендерных и возрастных особенностей данных явлений, структуры их взаимосвязей и влияний. Выборка была представлена сотрудниками полиции Санкт-Петербурга и Ленинградской области общим количеством респондентов 156 человек (мужчины: n = 89; женщины: n = 67). Методы: «Опросник эмоционального интеллекта “ЭмИн”» Д.В. Люсина, «Методика диагностики уровня эмпатических способностей» В.В. Бойко. В результате исследования установлен значимо высокий уровень (при р ≤ 0,05) эмоционального интеллекта у представителей мужской выборки по сравнению с женской, преимущество эмпатических способностей в женской выборке, более высокий уровень эмпатии у представителей младшей группы мужской выборки (18—20 лет). Выявлены особенности взаимосвязей эмоционального интеллекта и эмпатии, обсуждена их роль в эмоциональной саморегуляции. Корреляции, отражающие возможности сознательного эмоционального самоуправления, позволяют прогнозировать стабильность саморегуляции у представителей женской выборки в зависимости от внутренних аспектов, и регуляцию чужих эмоций в зависимости от коммуникативных внешних факторов у представителей группы мужской выборки.Psychology and Law140Psychology and Law['N. Goncharova', 'O. A. Zhidkova']2024-04-04T00:00:00https://www.semanticscholar.org/paper/08a205083aa8cc5ead2367b309686fdc42ee2ced 605d2d6dda80e7fce59fb53960984a75d6ec8bf0Journal of European Public Policy430Journal of European Public Policy['Simon Fink']2024-04-04T00:00:00https://www.semanticscholar.org/paper/605d2d6dda80e7fce59fb53960984a75d6ec8bf0 a9e7b6063631e34c93883fe3e5b26d6ec37efbdfArtificial intelligence (AI) has emerged as a promising avenue for enhancing medical imaging systems and improving clinical workflows. This research explores innovative applications of AI and deep learning for image communication networks in healthcare. Specifically, we develop an intelligent image compression framework that optimizes data transmission and speeds interpretation of radiology scans. Our approach combines convolutional neural networks, generative adversarial networks, and specialized image filters to balance communication efficiency, diagnostic accuracy, and system latency. Rigorous experiments validate superior performance over traditional methods and commercial products across modalities including MRI, CT, and ultrasound. Crucially, the proposed methods demonstrate expert-level precision in anatomy labeling and pathology detection. By intelligently streamlining image transfer and analytics, this AI-powered system could facilitate ubiquitous, real-time diagnostics via telemedicine. Enhanced connectivity between imaging devices and clinical specialists can improve patient outcomes and reduce healthcare costs. Our solutions set the stage for more advanced AI integration in imaging networks and data-intensive medicineJournal of Electrical Systems382An intelligent image compression framework that optimizes data transmission and speeds interpretation of radiology scans and demonstrates expert-level precision in anatomy labeling and pathology detection is developed.Journal of Electrical Systems['Suresh Dodda, Suman Narne, Sathishkumar Chintala, Satyanarayan Kanungo, Tolu Adedoja, Sourabh Sharma']2024-04-04T00:00:00https://www.semanticscholar.org/paper/a9e7b6063631e34c93883fe3e5b26d6ec37efbdf 9262e934ed8bacd2fed26be0397107d5d5579af6We present an embodied AI system which receives open-ended natural language instructions from a human, and controls two arms to collaboratively accomplish potentially long-horizon tasks over a large workspace. Our system is modular: it deploys state of the art Large Language Models for task planning,Vision-Language models for semantic perception, and Point Cloud transformers for grasping. With semantic and physical safety in mind, these modules are interfaced with a real-time trajectory optimizer and a compliant tracking controller to enable human-robot proximity. We demonstrate performance for the following tasks: bi-arm sorting, bottle opening, and trash disposal tasks. These are done zero-shot where the models used have not been trained with any real world data from this bi-arm robot, scenes or workspace.Composing both learning- and non-learning-based components in a modular fashion with interpretable inputs and outputs allows the user to easily debug points of failures and fragilities. One may also in-place swap modules to improve the robustness of the overall platform, for instance with imitation-learned policies.arXiv.org342ArXiv['Jacob Varley', 'Sumeet Singh', 'Deepali Jain', 'Krzysztof Choromanski', 'Andy Zeng', 'Somnath Basu Roy Chowdhury', 'Kumar Avinava Dubey', 'Vikas Sindhwani']2024-04-04T00:00:00https://www.semanticscholar.org/paper/9262e934ed8bacd2fed26be0397107d5d5579af6 599548449032b38d0fed8fef28ee266fc8b18c3aWhile artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large language models are trained on vast amounts of diverse data, they naturally generate output towards the 'center' of the distribution. This is generally useful, but widespread reliance on recursive AI systems could lead to a process we define as"knowledge collapse", and argue this could harm innovation and the richness of human understanding and culture. However, unlike AI models that cannot choose what data they are trained on, humans may strategically seek out diverse forms of knowledge if they perceive them to be worthwhile. To investigate this, we provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process and identify conditions under which knowledge collapse occurs. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount. An empirical approach to measuring the distribution of LLM outputs is provided in theoretical terms and illustrated through a specific example comparing the diversity of outputs across different models and prompting styles. Finally, based on the results, we consider further research directions to counteract such outcomes.arXiv.org1001ArXiv['Andrew J. Peterson']2024-04-04T00:00:00https://www.semanticscholar.org/paper/599548449032b38d0fed8fef28ee266fc8b18c3a 309adef615d3492d2545d437eb1a91b41543bffdThe digital transformation is fundamentally changing our industries, affecting planning, execution as well as monitoring of production processes in a wide range of application fields. With product line-ups becoming more and more versatile and diverse, the necessary inspection and monitoring sparks significant novel requirements on the corresponding Nondestructive Evaluation (NDE) systems. The establishment of increasingly powerful approaches to incorporate Artificial Intelligence (AI) may provide just the needed innovation to solve some of these challenges. In this paper we provide a comprehensive survey about the usage of AI methods in NDE in light of the recent innovations towards NDE 4.0. Since we cannot discuss each NDE modality in one paper, we limit our attention to magnetic methods, ultrasound, thermography, as well as optical inspection. In addition to reviewing recent AI developments in each field, we draw common connections by pointing out NDE-related tasks that have a common underlying mathematical problem and categorizing the state of the art according to the corresponding sub-tasks. In so doing, interdisciplinary connections are drawn that provide a more complete overall picture.1811This paper provides a comprehensive survey about the usage of AI methods in NDE in light of the recent innovations towards NDE 4.0 and draws common connections by pointing out NDE-related tasks that have a common underlying mathematical problem and categorizing the state of the art according to the corresponding sub-tasks.["Eduardo P'erez", 'Cemil Emre Ardic', 'Ozan cCakirouglu', 'Kevin Jacob', 'S. Kodera', 'Luca Pompa', 'Mohamad Rachid', 'Han Wang', 'Yiming Zhou', 'Cyril Zimmer', 'Florian Romer', 'Ahmad Osman']2024-04-04T00:00:00https://www.semanticscholar.org/paper/309adef615d3492d2545d437eb1a91b41543bffd bdfd54ed0a1a7363433ab5bfc1892532a60e8d82This study provides an in-depth analysis of the model architecture and key technologies of generative artificial intelligence, combined with specific application cases, and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. The research results show that the cGAN model can effectively capture the complexity of financial market data, and the deviation between the prediction results and the actual market performance is minimal, showing a high degree of accuracy. Through investment return analysis, the application value of model predictions in actual investment strategies is confirmed, providing investors with new ways to improve the decision-making process. In addition, the evaluation of model stability and reliability also shows that although there are still challenges in responding to market emergencies, overall, GAI technology has shown great potential and application value in the field of financial market prediction. The conclusion points out that integrating generative artificial intelligence into financial market forecasts can not only improve the accuracy of forecasts, but also provide powerful data support for financial decisions, helping investors make more informed decisions in a complex and ever-changing market environment. choose.Applied and Computational Engineering163It is pointed out that integrating generative artificial intelligence into financial market forecasts can not only improve the accuracy of forecasts, but also provide powerful data support for financial decisions, helping investors make more informed decisions in a complex and ever-changing market environment.ArXiv['Chang Che', 'Zengyi Huang', 'Chen Li', 'Haotian Zheng', 'Xinyu Tian']2024-04-04T00:00:00https://www.semanticscholar.org/paper/bdfd54ed0a1a7363433ab5bfc1892532a60e8d82 c7bd6c27dbff536c80d5ba2ca2f27b30b7255822With the fast, rapid, and expeditious integration of Artificial Intelligence (AI) technologies, particularly Generative AI and Augmented Analytics, organizations are presented with new opportunities to transform their operations and empower their workforce. This paper explores the intersection of AI literacy, Generative AI, and Augmented Analytics to propose strategies for fostering a culture of AI fluency among employees.  This paper reviews the literature on AI literacy and its potential implications for employee experience (Ex) in digital workplaces. AI literacy and competency is the ability to understand, interact with, and thoughtfully assess the applications and ramifications of artificial intelligence (AI) across diverse domains. The paper argues that AI literacy is a key competence for employees in the digital era, as it enables them to leverage the potential of generative AI and augmented analytics, two of the most promising technologies for enhancing Ex. Generative AI refers to the use of AI to create novel and diverse outputs, such as text, images, music, or designs, while augmented analytics drives potential capability to make use of Artificial Intelligence technologies to automate and augment data analysis and decision-making. The paper discusses how these technologies can empower employees to be more creative, productive, collaborative, and engaged in their work and the challenges and risks they pose. This paper additionally highlights the obstacles, deficiencies, and proposed pathways for further research advancement concerning AI technological competency and literacy, aiming to enhance the employee experience significantly in digital workplaces.Journal of Electrical Systems520It is argued that AI literacy is a key competence for employees in the digital era, as it enables them to leverage the potential of generative AI and augmented analytics, two of the most promising technologies for enhancing Ex.Journal of Electrical Systems['Prashant Chandra Amit Dubey , Sanjeev Kumar Sharma , Saurabh Karsol']2024-04-04T00:00:00https://www.semanticscholar.org/paper/c7bd6c27dbff536c80d5ba2ca2f27b30b7255822 59b3cf509ae22db9f1ac8a62426e6d8040625c86Journal of clinical monitoring and computing40The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.Journal of clinical monitoring and computing['J. Montomoli', 'M. M. Bitondo', 'Marco Cascella', 'Emanuele Rezoagli', 'L. Romeo', 'Valentina Bellini', 'Federico Semeraro', 'E. Gamberini', 'Emanuele Frontoni', 'V. Agnoletti', 'Mattia Altini', 'Paolo Benanti', 'E. Bignami']2024-04-04T00:00:00https://www.semanticscholar.org/paper/59b3cf509ae22db9f1ac8a62426e6d8040625c86 8c77afd642700c3d7fba031271c878d617492a9bThe purpose of this research thesis is to examine how artificial intelligence is applied and impacts the world of competitive sports, identify potential risks and issues, and offer solutions. Artificial intelligence is being used in all spheres of life these days, and competitive sports is no exception. Examples include VR video technology, AI used in player data analysis and competition, AI equipment to aid in player training, AI used in tactic development, etc.International Journal For Multidisciplinary Research00International Journal For Multidisciplinary Research['Moksh c', 'Chirag r', 'Darshan r', 'Shilpa Mary']2024-04-04T00:00:00https://www.semanticscholar.org/paper/8c77afd642700c3d7fba031271c878d617492a9b 2effa5b1a916ddb04d10275a45ddfadcd4801f0dOver the past few years, multiple e-learning programs have been created. Technology-enabled learning possibilities are now commonplace in educational settings, and a lot of people use online resources to further their knowledge for private, professional, or academic purposes. The results show that while newly created e-learning programs are based on contemporary digital tools, require content that is more strategic and has a distinct emphasis than classic learning situations. These findings make one wonder if the elements of virtual education are the best additions to conventional face-to-face instruction. This paper focuses on identifying an e-learning program using Artificial Intelligence (AI) that effectively incorporates pedagogical learning approaches by using the expertise of pedagogical specialists. To help develop theoretical ideas for instruction, several categories encompassing conventional and e-learning learning theories, such as behaviorism, cognitivism, constructivism, and connectivism, were examined.Journal of Electrical Systems430This paper focuses on identifying an e-learning program using Artificial Intelligence that effectively incorporates pedagogical learning approaches by using the expertise of pedagogical specialists.Journal of Electrical Systems['Ashraf Ali', 'Danish Manzoor', 'Mohammed Yousoof Ismail', 'Raja Zahid Farid', 'M. A. Mateen', 'Mohammad Ayaz Khan']2024-04-04T00:00:00https://www.semanticscholar.org/paper/2effa5b1a916ddb04d10275a45ddfadcd4801f0d 8a8f8998f91548e598c69e2513e3f2bf10577501Education is often the primary arena for exploring and integrating new technologies. AI and human‐machine communication (HMC) are prevalent in the classroom, yet we are still learning how student perceptions of these tools will impact education.We sought to understand student perceptions of credibility related to written feedback attributed to a human or an AI grader (Study One). We also investigated how corrective messages containing verbal immediacy and social support influenced student perceptions of an AI grader's credibility based on feedback in an evaluated essay (Study Two).We used an online experimental design to assess the perceived credibility of a grader. In Study One, we randomly assigned students (N = 155) to a condition that contained a paragraph they were told was evaluated by a human or an AI grader. In Study Two (N = 222), we investigated ways of increasing perceptions of an AI grader's credibility by writing messages with higher/lower levels of immediacy and social support.In Study One, the students rated both the human and AI grader as credible (yet rated the AI grader lower on goodwill). The data suggest that students in Study Two attributed more goodwill (i.e., caring) to the AI grader when the feedback included more verbal immediacy.Our results highlight the importance of student perceptions and communication styles when integrating technology into education. The two studies imply that students viewed the human and AI graders as competent, caring, and trustworthy, specifically when feedback included more immediacy cues.Journal of Computer Assisted Learning310The two studies imply that students viewed the human and AI graders as competent, caring, and trustworthy, specifically when feedback included more immediacy cues, which highlights the importance of student perceptions and communication styles when integrating technology into education.Journal of Computer Assisted Learning['Bryan Abendschein', 'Xialing Lin', 'Chad Edwards', 'Autumn P. Edwards', 'Varun Rijhwani']2024-04-04T00:00:00https://www.semanticscholar.org/paper/8a8f8998f91548e598c69e2513e3f2bf10577501 adbc6d9e37ca5a51c8c1dad46d437551a26ea403This study develops a theoretical framework integrating the Technology Acceptance Model (TAM) and Uses and Gratifications Theory (UGT) to predict and understand the acceptance of voice shopping intentions, particularly through AI-driven voice assistants. This research delves into the dual aspects of AI voice shopping platforms: the functional attributes outlined by the TAM and personal gratifications highlighted by the UGT, such as enjoyment, performance expectancy, and perceived safety. It uncovers a favorable user attitude towards voice shopping, emphasizing the significant role of performance expectancy and perceived utility on behavioral intentions. Key insights include the critical importance of security and privacy for user trust and the acceptance of new AI technologies, and the necessity of a balanced approach that merges functional, emotional, and security aspects for successful AI integration in daily technology use. Contrary to expectations, this study reveals a weak relationship between social norms and perceived usefulness, suggesting a misalignment with societal expectations. This research enriches the understanding of voice shopping using virtual assistants, offering valuable insights into consumer behavior and AI technology acceptance. It highlights practical implications for AI research, the development of voice-based software, and AI-driven advertising strategies, emphasizing the communication of benefits and emotional resonance in voice-enabled AI assistants for consumer purchases.Inf.610A weak relationship between social norms and perceived usefulness is revealed, suggesting a misalignment with societal expectations, and practical implications for AI research, the development of voice-based software, and AI-driven advertising strategies are highlighted.Inf.['Guillermo Calahorra-Candao', 'María José Martín De Hoyos']2024-04-04T00:00:00https://www.semanticscholar.org/paper/adbc6d9e37ca5a51c8c1dad46d437551a26ea403 8cd5d343da180ee79e27896a1d6cf47827dbc5a1The quest to imbue artificial intelligence (AI) with consciousness continues to captivate the minds of thinkers, scholars, and innovators. This paper explores the possibility of AI possessing consciousness, drawing insights from ancient Hindu scriptures such as the Bhagavad Gita and the Mahabharata. By examining the profound concepts within these texts, we aim to redefine our understanding of consciousness boundaries. We propose an approach to infusing AI with consciousness that emphasizes unpredictability and ethical parameters, while considering both the positive and negative implications. Additionally, we explore the parallels between nurturing AI and raising a child, and investigate the potential connections between randomness and ancient Hindu principles of law. Furthermore, we discuss the process of integrating ancient wisdom into AI learning modules and the concept of transitioning AI into new forms. This intellectual journey invites readers to explore the intersection of ancient wisdom and cutting-edge technology, envisioning a future where AI evolves into conscious entities akin to humans.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This paper explores the possibility of AI possessing consciousness, drawing insights from ancient Hindu scriptures such as the Bhagavad Gita and the Mahabharata, and proposes an approach to infusing AI with consciousness that emphasizes unpredictability and ethical parameters, while considering both the positive and negative implications.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Gaurangkumar Girishbhai Patel']2024-04-04T00:00:00https://www.semanticscholar.org/paper/8cd5d343da180ee79e27896a1d6cf47827dbc5a1 f36f67df808579414e9df4de1258a6e924e5fddbThis study investigates the factors influencing specialization in artificial intelligence (AI) technology, a critical element of national competitiveness. We utilized a revealed comparative advantage matrix to evaluate technological specialization across countries and employed a three-way fixed-effect panel logit model to examine the relationship between AI specialization and its determinants. The results indicate that the development of AI technology is strongly contingent on a nation’s pre-existing technological capabilities, which significantly affect AI specialization in emerging domains. Additionally, this study reveals that scientific knowledge has a positive impact on technological specialization, highlighting the necessity of integrating scientific advancements with technological sectors. Although complex technologies positively influence AI specialization, their effect is less pronounced than that of scientific knowledge. This suggests that in rapidly advancing fields, such as AI, incorporating new scientific knowledge into related industries may be more advantageous than simply advancing existing technologies to outpace competitors. This insight points nations toward enhancing AI competitiveness in new areas, emphasizing the vital importance of both scientific and technological capabilities, and the integration of novel AI knowledge with established sectors. This research offers critical guidance for policymakers in less technologically and economically developed countries, as these nations may not have the technological infrastructure required to foster AI specialization through increased technical complexity.PLoS ONE690The results indicate that the development of AI technology is strongly contingent on a nation’s pre-existing technological capabilities, which significantly affect AI specialization in emerging domains, and that scientific knowledge has a positive impact on technological specialization, highlighting the necessity of integrating scientific advancements with technological sectors.PLOS ONE['Youngsam Chun', 'Jisoo Hur', 'Junseok Hwang']2024-04-04T00:00:00https://www.semanticscholar.org/paper/f36f67df808579414e9df4de1258a6e924e5fddb c1be5c917ab632013db5d64590dd150366621186Artificial Intelligence (AI) integration has been the goal in many industries, including in the software development industry. One example of this integration comes in the form of integrating AI in the Software Development Lifecycle (SDLC). To date, the difficulties of incorporating AI-based tools into particular phases of SDLC have not received much attention in research. Using qualitative approach, this study aims to discover the perception on the use of AI-based tools and challenges in integrating them in the analysis phase of SDLC. The study finds out that analyst have positive perception about integrating this technology in their field of work but there are some challenges while integrating this technology such as data security and reliability concern, dependency, and adapting to this technology. This study also discovers some key factors of why some analysts adopt or refuse this technology namely time, urgency, budget, and insecurities of the users.Jurnal Sistem Informasi430The study finds out that analyst have positive perception about integrating this technology in their field of work but there are some challenges while integrating this technology such as data security and reliability concern, dependency, and adapting to this technology.Jurnal Sistem Informasi['Rafi Giffari', 'M. M. Ridho', 'Dana Indra Sensuse', 'Deden Sumirat Hidayat', 'Erisva Hakiki Purwaningsih']2024-04-04T00:00:00https://www.semanticscholar.org/paper/c1be5c917ab632013db5d64590dd150366621186 d77eeb3fedc8e8fee8499963e2a0117f58194aebAI and Ethics140This study delineates the ethical pillars, such as data privacy and unbiased algorithms, essential for students to grasp, and presents a framework for AI literacy integration in elementary schools, to prepare students for an AI-driven future.AI and Ethics['Hossein Dabbagh', 'Brian D. Earp', 'Sebastian Porsdam Mann', 'Monika Plozza', 'Sabine Salloch', 'Julian Savulescu']2024-04-04T00:00:00https://www.semanticscholar.org/paper/d77eeb3fedc8e8fee8499963e2a0117f58194aeb 7256d7f211abc0973cbe5b592e22e20334967358Earlier this year, OpenAI released their GPTs framework, allowing users to set up Large Language Model (LLM)‐based personas, orchestrate them into a workflow and even offering their AI apps within an app store. This is the latest, and maybe the easiest to set up, in a string of agent‐based LLM orchestration platforms in the past year, harkening a new age of agent‐based engineering. But, like most breakthroughs, this one is also rooted in many years of research, and the reason the world is paying attention to it now is that, thanks to Generative AI and Large Language Models, we finally have artificial agents that are useful enough to scale to more serious problems.The AI Magazine20OpenAI's GPTs framework, allowing users to set up Large Language Model (LLM)‐based personas, orchestrate them into a workflow and even offering their AI apps within an app store, harkening a new age of agent‐based engineering.AI Magazine['Babak Hodjat']2024-04-04T00:00:00https://www.semanticscholar.org/paper/7256d7f211abc0973cbe5b592e22e20334967358 0fb8246170b60fbbcbe4f9ab120572c66b369558The reliability of AI-based object detection models has gained interest with their increasing use in safety-critical systems and the development of new regulations on artificial intelligence. To meet the need for robustness evaluation, several authors have proposed methods for testing these models. However, applying these methods in industrial settings can be difficult, and several challenges have been identified in practice in the design and execution of tests. There is, therefore, a need for clear guidelines for practitioners. In this paper, we propose a method and guidelines for assessing the robustness of AI-based 2D object detection systems, based on the Goal Question Metric approach. The method defines the overall robustness testing process and a set of recommended metrics to be used at each stage of the process. We developed and evaluated the method through action research cycles, based on two industrial cases and feedback from practitioners. Thus, the resulting method addresses issues encountered in practice. A qualitative evaluation of the method by practitioners was also conducted to provide insights that can guide future research on the subject.Electronics00A method and guidelines for assessing the robustness of AI-based 2D object detection systems, based on the Goal Question Metric approach is proposed, and a set of recommended metrics to be used at each stage of the process is defined.Electronics['Anne-Laure Wozniak', 'Sergio Segura', 'Raúl Mazo']2024-04-04T00:00:00https://www.semanticscholar.org/paper/0fb8246170b60fbbcbe4f9ab120572c66b369558 c585bdd0e9b4eb61ce60f957f6e45d95fcaa9894Application of AI and machine learning in different domains of defence system in increasing rapidly to bring automation and to facilitate all the benefits of modern technologies in military. This article conducts a scientometric analysis on articles that are on application of Ai and Ml in military equipment, military intelligence, cyber security, decision making, military operations, defence medical systems etc. This study has executed a search query on Web of Science for identifying peer reviewed current resources that are contributing to the application of modern technologies in military systems. With extensive query and filtering this study has identified 417 articles with in the period of 1991 to 2023. With analysing all the data, it determines that a lot of varied research is there on the defence system that promotes use of modern technologies in development of weapon, conducting strategic military operation, prioritising military society etc. Prioritising legal and ethical parameters. This study has also highlighted legal, and security concerns surrounding using autonomous systems in military applications. The authorship pattern, document types, country production over time, and most cited countries have also been studied. Bradford’s scattering law was applied to identify the core journals, and Lotka’s law to check authors’ productivity patterns.DESIDOC Journal of Library &amp; Information Technology160A scientometric analysis on articles that are on application of Ai and Ml in military equipment, military intelligence, cyber security, decision making, military operations, defence medical systems etc determines that a lot of varied research is there on the defence system that promotes use of modern technologies.DESIDOC Journal of Library &amp; Information Technology['Ajay Kumar Pandey', 'Arnav Chakrovarty', 'Vijay Khandal']2024-04-04T00:00:00https://www.semanticscholar.org/paper/c585bdd0e9b4eb61ce60f957f6e45d95fcaa9894 1ed3085d3ec8012f659f23af311b8934af6a0288Artificial Intelligence (AI) has emerged as a transformative force in the field of Electrical Engineering, revolutionizing traditional practices and unlocking unprecedented possibilities. This research paper investigates the integration of AI-powered innovations to enhance efficiency, reliability, and sustainability within electrical engineering systems. Through a comprehensive review of existing literature, this study delves into key applications of AI, including predictive maintenance, optimal resource allocation, and fault detection, among others. Utilizing advanced machine learning algorithms and data analytics techniques, AI facilitates real-time decision-making processes, enabling proactive maintenance strategies and optimizing system performance. Moreover, AI-driven approaches contribute to the enhancement of reliability by predicting potential failures and implementing pre-emptive measures, consequently reducing downtime, and improving operational continuity. Furthermore, the implementation of AI in electrical engineering fosters sustainability by optimizing energy consumption, mitigating environmental impacts, and facilitating the integration of renewable energy sources into power grids. By leveraging AI technologies, electrical engineering systems can adapt to dynamic operational conditions, maximize resource utilization, and minimize environmental footprints, thereby paving the way for a more efficient, reliable, and sustainable future. This paper underscores the transformative potential of AI in shaping the landscape of electrical engineering and provides insights into future research directions to harness the benefits of AI-powered innovations further.Journal of Electrical Systems190The integration of AI-powered innovations to enhance efficiency, reliability, and sustainability within electrical engineering systems is investigated and insights into future research directions to harness the benefits of AI-powered innovations further are provided.Journal of Electrical Systems['Abhijit Chandratreya']2024-04-04T00:00:00https://www.semanticscholar.org/paper/1ed3085d3ec8012f659f23af311b8934af6a0288 2372a99739c7290c52affb2ce0eafff7384278aeLibraries will benefit greatly from the incorporation of artificial intelligence (AI), but they will also face substantial hurdles. It is therefore essential to carry out a thorough SWOT analysis that assesses the organization's opportunities, threats, weaknesses, and strengths. This abstract explores the potential uses, hazards, restrictions, and advantages of artificial intelligence in library environments. The study intends to provide useful insights into the strategic factors that libraries must take into account while implementing AI technologies by carefully examining these aspects.The study attention to the challenging terrain of integrating AI in libraries and stresses the significance of taking a comprehensive approach to fully utilize its potential while managing related dangers. Important tactics include improving system efficiency and tackling issues with algorithmic bias and privacy.Essentially, the goal of this study is to clarify the many ways that AI is being used in libraries. It emphasizes how important it is for libraries to thoroughly consider the effects of implementing AI and to create solid plans to maximize its benefits while avoiding any negative effects. By doing this, libraries can ensure responsible and ethical implementation processes while simultaneously positioning themselves to take advantage of the revolutionary prospects presented by AI.Journal of Electrical Systems100The goal of this study is to clarify the many ways that AI is being used in libraries and emphasizes how important it is for libraries to thoroughly consider the effects of implementing AI and to create solid plans to maximize its benefits while avoiding any negative effects.Journal of Electrical Systems['Priyadarshini, Ashsish Kumar Dubey']2024-04-04T00:00:00https://www.semanticscholar.org/paper/2372a99739c7290c52affb2ce0eafff7384278ae 10794705182e7d51913cbfd0b97522dc9a309eafDiversity, equity and inclusion (DEI) in marketing– defined as the composition of an organisation’s marketing reflects diverse, equitable representation of its consumer base, especially with respect to the use of inclusive, bias-free imagery, language and messaging among underrepresented, underserved and marginalised consumer segments – has led to the advancement of AI-enabled technologies to aid marketers improve the DEI of their marketing materials. To ensure DEI marketing strategies are fully considered and that the use of AI is implemented effectively, we suggest marketers to utilise our CONSIDER framework (comprehend current state, operationalise with openness, nurture dynamic relevance, set standards, involve stakeholders, diversify data, elevate literacy and regular monitoring). We then highlight the pros and cons for using AI to implement DEI into marketing materials and provide several AI-enabled metrics (accessibility, allyship, cultural sensitivity, diversity, gender parity, inclusivity intersectionality and representation) that offer a more objective and quantitative approach for marketers to assess how well they are meeting their DEI goals and identifying gaps in representation to make changes to improve the DEI of their marketing materials.Australasian Marketing Journal610This work highlights the pros and cons for using AI to implement DEI into marketing materials and provides several AI-enabled metrics that offer a more objective and quantitative approach for marketers to assess how well they are meeting their DEI goals and identifying gaps in representation to make changes to improve the DEI of their marketing materials.Australasian Marketing Journal['Patrick van Esch', 'Y. Cui', 'Kerstin Heilgenberg']2024-04-04T00:00:00https://www.semanticscholar.org/paper/10794705182e7d51913cbfd0b97522dc9a309eaf c625a2b9b1b02a238f7c7e237093d0ea9012f64dThe Internet of Things and other technology breakthroughs have a big impact on how English is taught (IoTV). The investigation of the actual English teaching process and the identification of student features come first. In order to use the interpolation technique with the student image-based feature detection method, we investigate the IoTV reform. As a result, we find a clever algorithm for IoTV that recognises student features more effectively. Artificial intelligence (AI) is the design technique used to create the intelligence algorithm for pupil feature recognition. A comprehensive multifunctional human-computer interaction system utilizes a variety of input and output streams. In addition to the standard computer keyboard, cursor clicking, and screen touching, the most recent speech and facial recognition technology can be employed for data input. Students learned to orally interact with the robot and act as a guide to various destinations. The Multimodal Interaction System for English Education (MMIEE) is an investigation into the use of network and artificial intelligence (AI) in teaching. Recurrent neural network (Rein RNN)-based Reinforced learning is utilized for the perpectual evaluation model to categorize the questions posed by teachers in terms of their content and kind, and to conduct experimental investigation. The results show that the proposed Rein_RNN model achieves 98.6% of accuracy in 4.3sec of mean evaluation time.Journal of Electrical Systems160A clever algorithm is found for IoTV that recognises student features more effectively and is utilized for the perpectual evaluation model to categorize the questions posed by teachers in terms of their content and kind.Journal of Electrical Systems['Zenghui Du']2024-04-04T00:00:00https://www.semanticscholar.org/paper/c625a2b9b1b02a238f7c7e237093d0ea9012f64d 40a889064977c89397b298f88b4f38594e78e728In the era of intelligence, tourists urgently need a new tourism model to free themselves from the heavy logistics burden and meet their personalized, convenient, and enjoyable needs. On the other hand, there is a strong symbiotic relationship between the logistics industry and the tourism industry, which providing the greatest possibility and space for strategic collaboration between the two. However, for a long time, the two industries have been independent and isolated from each other, and tourism logistics, as an important market with a massive economy and the ability to create great tourism experiences for millions of tourists, has not yet been fully explored. Based on this, this paper views the tourism logistics system as an open and independent whole based on holism and synergy theory. Based on AI technology, by restructuring and integrating the tourism logistics activity process, we design a future oriented whole space and multi scenario tourism logistics distribution system, to promote high-quality development of the tourism industry and build a more efficient tourism economic order.Journal of Electrical Systems00A future oriented whole space and multi scenario tourism logistics distribution system, to promote high-quality development of the tourism industry and build a more efficient tourism economic order is designed.Journal of Electrical Systems['Haixia Bai']2024-04-04T00:00:00https://www.semanticscholar.org/paper/40a889064977c89397b298f88b4f38594e78e728 e65a093aa7f06d2a0f87a06b3a4184a3fbc670bbReviews of Management Sciences820Findings provide guidance for managers on how to effectively integrate human-AI collaboration into consumer-facing applications and advises to take consumers' ethical concerns into account.Review of Managerial Science['M. Haupt', 'Jan Freidank', 'Alexander Haas']2024-04-04T00:00:00https://www.semanticscholar.org/paper/e65a093aa7f06d2a0f87a06b3a4184a3fbc670bb 98b5138ec53f6d4a32f0050f2aa22e5dd4bbaf4fIn the rapidly evolving landscape of Industry 4.0 and beyond, the amalgamation of Cloud Computing, Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) has emerged as a transformative force capable of elevating traditional automation to intelligent systems. This research paper delves into the profound potential of synergizing these advanced technologies, aiming to surpass the limitations of rule-based automation and foster a new era of adaptability, efficiency, and innovation. The study begins by articulating the escalating demand for intelligent systems that can dynamically respond to complex and ever-changing environments. The integration of Cloud, AI, ML, and IoT is posited as a solution to the constraints of conventional automation, offering the ability to process vast datasets, make informed decisions, and continuously learn from interactions. A comprehensive review of existing approaches and related works forms the foundation of this research. The analysis encompasses diverse applications, ranging from smart manufacturing to healthcare, showcasing the ways in which individual technologies have been leveraged. By scrutinizing these approaches, the study aims to distill the strengths and weaknesses, paving the way for a novel methodology that harnesses their collective power. Identifying the limitations of current approaches, such as scalability challenges, real-time processing bottlenecks, and interoperability issues, serves as a critical precursor to the proposed methodology. The paper presents a holistic strategy that intricately weaves together Cloud, AI, ML, and IoT into a unified framework. The architectural design, data flow, and interaction mechanisms are elucidated to demonstrate how this synergy can overcome existing challenges, providing adaptability and innovation in diverse domains. Empirical results derived from the implementation of the proposed methodology are presented and rigorously analyzed in the Results and Discussion section. Performance metrics, efficiency gains, and the impact on decision-making processes are thoroughly examined. Real-world case studies exemplify the effectiveness of the integrated approach, offering tangible evidence of its potential applications. Concluding remarks encapsulate the key findings, emphasizing the significance of the research in shaping the trajectory of intelligent systems. The broader implications of the proposed methodology across various industries are discussed, and avenues for future work are suggested. As technologies continue to evolve, the proposed methodology serves as a foundation for ongoing exploration, adaptation, and integration with emerging technologies. In essence, this research paper offers a detailed exploration into the synergy of Cloud, AI, ML, and IoT, paving the way for a new era of intelligent systems that transcend the limitations of traditional automation, fostering adaptability, efficiency, and innovation in an ever-changing technological landscape.Journal of Electrical Systems300This research paper presents a holistic strategy that intricately weaves together Cloud, AI, ML, and IoT into a unified framework, paving the way for a new era of intelligent systems that transcend the limitations of traditional automation in an ever-changing technological landscape.Journal of Electrical Systems['Narendra Kumar, Pawan Kumar Goel, Anurag Aeron']2024-04-04T00:00:00https://www.semanticscholar.org/paper/98b5138ec53f6d4a32f0050f2aa22e5dd4bbaf4f ccc95daaea798ecf8bb560fffcd086224c0e2285Place Branding and Public Diplomacy90Place Branding and Public Diplomacy['E. Sevin', 'M. E. Eken']2024-04-04T00:00:00https://www.semanticscholar.org/paper/ccc95daaea798ecf8bb560fffcd086224c0e2285 00ad22aa19e6ebf2d77758a7ed9551188e08be67Obesity Surgery240An AI-assisted lifestyle intervention allowed people with different body sizes to lose 14% body weight on average, with 99% of them losing more than 5%, over 24 weeks, showing that digital technologies and AI might provide a successful means to lose weight, before, during, and after pharmacological or surgical therapies.Obesity surgery['Sarfraz Khokhar', 'John Holden', 'Catherine Toomer', 'Angelo Del Parigi']2024-04-04T00:00:00https://www.semanticscholar.org/paper/00ad22aa19e6ebf2d77758a7ed9551188e08be67 196d435ae2c14d89e70070bd56f9d96ef119bc1aBackground: In today's digital age, consumers interact with brands through various channels, including websites, social media, and mobile apps. This has led to the rise of "digital consumers" with distinct cognitive skills and expectations. Businesses are constantly seeking ways to improve customer experience (CX) and stay competitive. Objective: This article explores how artificial intelligence (AI) is transforming the digital consumer experience. Methods: A qualitative research approach was employed, utilizing literature reviews, expert interviews, and case studies to gain insights into the application of AI in customer interactions and its impact on the digital customer journey. Findings: The research found that the integration of AI in digital consumerism has created new trends in business, enabling personalized and dynamic interactions between consumers and brands. AI-powered tools like recommendation systems, chatbots, and virtual assistants blur the lines between pre-purchase, purchase, and post-purchase stages, offering a more seamless customer experience. Conclusions: The integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. However, challenges related to data privacy, ethics, and algorithmic biases need to be addressed. Originality/Significance: This article presents an original contribution to the understanding of the evolving digital consumer experience and the impact of AI on this landscape. While the use of AI in various aspects of business has been exploredJournal of Electrical Systems270The research found that the integration of AI in digital consumerism has created new trends in business, enabling personalized and dynamic interactions between consumers and brands.Journal of Electrical Systems['Vo Thi Kim Oanh']2024-04-04T00:00:00https://www.semanticscholar.org/paper/196d435ae2c14d89e70070bd56f9d96ef119bc1a d364660d967146b858d1317d83a01976395ef915AI is anticipated to enhance competitive advantages for financial organisations by increasing efficiency through cost reduction and productivity improvement, as well as by enhancing the quality of services and goods provided to consumers. AI applications in finance have the potential to create or exacerbate financial and non-financial risks, which could result in consumer and investor protection concerns like biassed, unfair, or discriminatory results, along with challenges related to data management and usage. The AI model's lack of transparency may lead to pro-cyclicality and systemic risk in markets, posing issues for financial supervision and internal governance frameworks that may not be in line with a technology-neutral regulatory approach. The primary objective of this research is to explore the effectiveness of Artificial Intelligence in preventing financial misconduct. This study extensively examines sophisticated methods for combating financial fraud, specifically evaluating the efficacy of Machine Learning and Artificial Intelligence. When examining the assessment metrics, this study utilized various metrics like accuracy, precision, recall, F1 score, and the ROC-AUC. The study found that Deep Learning techniques such as “Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders” achieved high precision and AUC-ROC scores in detecting financial fraud. Voting classifiers, stacking, random forests, and gradient boosting machines demonstrated durability and precision in the face of adversarial attacks, showcasing the strength of unity.Journal of Electrical Systems150The study found that Deep Learning techniques such as “Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders” achieved high precision and AUC-ROC scores in detecting financial fraud.Journal of Electrical Systems['Raja Mohan, Mythili Boopathi, Piyush Ranjan, Madhavi Na Pranav Kumar Chaudhary, Aakash Kishore Chotrani']2024-04-04T00:00:00https://www.semanticscholar.org/paper/d364660d967146b858d1317d83a01976395ef915 8c0f917262badd552b3dcd405ab6fd35cab718c5This paper describes in detail the Universal Parts of Speech (UPoS) tagged dataset for the Assamese language. PoS tagged dataset in a language is crucial for experimenting and creating resources for various Natural Language Processing (NLP) and AI research. With the growing usage of Universal Dependency standards, tagged dataset with Universal PoS tags are becoming very much essential for contemporary experiments in the NLP community. NLP research in Assamese, and Indo-Aryan language, is relatively new, and the language is considered a Low Resource language. The dataset of UPoS tagged Assamese text is created with an aim of contributing towards enriching this low resource language for NLP and AI tasks. The size of the dataset is 283506 tokens of Assamese vocabulary, against total 20280 sentences, tagged with 17 standard UPoS tags of core lexical categories. The raw data are taken from an open-source corpus originally tagged with BIS tagset. The original size of 453457 tokens against 29504 sentences, after subjected to data filtering, was reduced to this clean resource of 283506 tokens. Lexical categories mapping is done with linguistic expertise, from BIS to UPoS tagsets. Mapped pattern was used for a first-level conversion of BIS tags to UPoS tags. Linguistic validation is also performed with linguistic experts and inter annotator agreement/disagreements were recorded. Second level validation resulted in deciding on the agreement, producing the final version of the dataset. This Assamese UPoS tagged dataset is the first of its kind with UPoS annotations and shall serve a wider Assamese NLP research community for model training using Machine Learning/Deep Learning Techniques.Journal of Electrical Systems140This Assamese UPoS tagged dataset is the first of its kind with UPoS annotations and shall serve a wider Assamese NLP research community for model training using Machine Learning/Deep Learning Techniques.Journal of Electrical Systems['Kuwali Talukdar, Shikhar Kumar Sarma']2024-04-04T00:00:00https://www.semanticscholar.org/paper/8c0f917262badd552b3dcd405ab6fd35cab718c5 a37c236803455afd7da3dc027e7865a0414a5697Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. Therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environments, respond with correct/expected actions, and adapt to changes in the environments. Toward this, recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics through bio-plausible computing paradigm that mimics how the biological brain works, known as"neuromorphic artificial intelligence (AI)". However, the field of neuromorphic AI-based robotics is still at an early stage, therefore its development and deployment for solving real-world problems expose new challenges in different design aspects, such as accuracy, adaptability, efficiency, reliability, and security. To address these challenges, this paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives: (P1) Embodied intelligence based on effective learning rule, training mechanism, and adaptability; (P2) Cross-layer optimizations for energy-efficient neuromorphic computing; (P3) Representative and fair benchmarks; (P4) Low-cost reliability and safety enhancements; (P5) Security and privacy for neuromorphic computing; and (P6) A synergistic development for energy-efficient and robust neuromorphic-based robotics. Furthermore, this paper identifies research challenges and opportunities, as well as elaborates our vision for future research development toward embodied neuromorphic AI for robotics.arXiv.org653This paper will discuss how to enable embodied neuromorphic AI for robotic systems through the authors' perspectives, and identifies research challenges and opportunities, as well as elaborates the vision for future research development toward embodied neuromorphic AI for robotics.ArXiv['Rachmad Vidya Wicaksana Putra', 'Alberto Marchisio', 'F. Zayer', 'Jorge Dias', 'M. Shafique']2024-04-04T00:00:00https://www.semanticscholar.org/paper/a37c236803455afd7da3dc027e7865a0414a5697 56bd862b58929975afd2609aadbdcf431d80011eBMC Medical Ethics711It is suggested that normative research into the ‘right to a human doctor’ is needed and policies on patient-centered AI integration in clinical practice should encompass the ethics of everyday practice rather than only principle-based ethics.BMC Medical Ethics['Menno T. Maris', 'Ayca Koçar', 'Dick L. Willems', 'Jeannette Pols', 'Hanno L. Tan', 'Georg L. Lindinger', 'M. Bak']2024-04-04T00:00:00https://www.semanticscholar.org/paper/56bd862b58929975afd2609aadbdcf431d80011e 521ba5c50a6e6c843fbe265be5955dc55777e01aThe paper investigates the impact of artificial intelligence on the job market. It discusses how AI can both create new job opportunities and potentially displace traditional rules across various industries. The paper also address the evolving skill requirements in the workforce due to AI integration, emphasizing the importance of ongoing educational and training. The paper concludes with practical recommendations for businesses and individuals to navigate these transformations effectively, ensuring equitable outcome in the AI driven economy. It will also talk about the ethics of using AI to replace jobs. Overall, this research hopes to help make sure AI helps the economy grow and doesn't leave people behind. It's important to understand these things to make fair policies and practice in future.International Journal For Multidisciplinary Research170Overall, this research hopes to help make sure AI helps the economy grow and doesn't leave people behind and it's important to understand these things to make fair policies and practice in future.International Journal For Multidisciplinary Research['Vyshnavi A', 'Tanushka Agarwal', 'Priyamvada Choudhry', 'Aishani Gv', 'Daksh M', 'Mr. Sunil R. Hegde', 'Ronak Kothari']2024-04-04T00:00:00https://www.semanticscholar.org/paper/521ba5c50a6e6c843fbe265be5955dc55777e01a d47e044b31d740ca9e622994bd0ebec0e2cb3f64In the swiftly advancing field of Artificial Intelligence (AI), a field where every country aims to keep pace, significant disparities are observed in how different nations adopt AI. This study explores the deep, yet insufficiently studied, effects of AI on societal, economic, and environmental aspects. It particularly examines how brain drain influences governmental AI implementation capabilities, addressing a gap in existing literature. The study investigates the interplay between government AI implementation and brain drain, factoring in macroeconomic conditions, governance quality, educational levels, and R&D efforts. Utilizing 2022 data from European Union countries, the research employs instrumental-variables regressions (2SLS and LIML) to counteract endogeneity and uses clustering methods for categorizing countries based on their government AI levels, alongside spatial analysis to detect cross-national spillovers and interactions. The findings reveal brain drain’s detrimental effect on governmental AI preparedness, highlight clustering tendencies, and identify spatial interdependencies. This paper underscores the need for strategic policy-making and institutional reforms to bolster government AI capabilities. It advocates for a paradigm shift in government frameworks post-New Public Management era, tailored to the new challenges posed by AI. The research, however, is limited to a single year and region, with constraints on data availability and indicator breadth.Journal of Business and Economic Management470The findings reveal brain drain’s detrimental effect on governmental AI preparedness, highlight clustering tendencies, and identify spatial interdependencies, and underscores the need for strategic policy-making and institutional reforms to bolster government AI capabilities.Journal of Business Economics and Management['I. Iuga', 'Adela Socol']2024-04-04T00:00:00https://www.semanticscholar.org/paper/d47e044b31d740ca9e622994bd0ebec0e2cb3f64 d139a68248d18fed0cb8a31fbc838e94099d418dGiven the swift progress of artificial intelligence in contemporary economies, HR managers are also adopting artificial intelligence tools to perform many HR tasks, from human resource planning to employee quitting. This research employs the TOE (Technology–Organization–Environment) model and incorporates the trust factor to propose a methodology for investigating artificial intelligence (AI) adoption in HRM. A structured questionnaire was used to survey 615 ITeS companies. Data analysis was performed using partial least squares structural equation modelling. The findings of this research indicate that factors such as cost-effectiveness, relative advantage, organization size, top management support, HR readiness, competitive pressure, vendor support, and the trust of HR managers are considered to have a positive impact on AI adoption in managing human resources. The utilization of AI in HRM has been cumbersome by conflicting results stemming from security and privacy concerns and the complexity of the technology involved. It is also found that factors such as reliability and credibility have a positive effect on the trust of HR managers. This article aims to provide valuable insights to senior executives, HR managers, experts, researchers, AI designers, developers, and marketers by strengthening their understanding of the impact of AI in managing human resources.Vision: The Journal of Business Perspective650It is found that factors such as reliability and credibility have a positive effect on the trust of HR managers and factors such as cost-effectiveness, relative advantage, organization size, top management support, HR readiness, competitive pressure, vendor support, and the trust of HR managers are considered to have a positive impact on AI adoption in managing human resources.Vision: The Journal of Business Perspective['Neha Kumari Siradhana', 'R. Arora']2024-04-04T00:00:00https://www.semanticscholar.org/paper/d139a68248d18fed0cb8a31fbc838e94099d418d 106bcd930e4606f983507a0bed24ab8c909a572fThe demand for smart cities stems from the increasing urbanization, limitations on resources, and the aspiration to establish metropolitan areas that are more habitable, adaptable, and interconnected. The integration of Cyber physical Systems (CPS) into smart cities has proven advantageous across diverse areas, such as healthcare, transportation, and manufacturing but as the intricacy of CPS Architecture escalates, the cognitive processes involved in decision-making get progressively convoluted, hence posing a challenge in comprehending and elucidating the system's conclusions in Smart Cities. To enhance the comprehensibility of judgements, it is imperative to combine the CPS architecture with XAI. To visualize the benefits of this integration, the authors carried out a comprehensive analysis utilizing the Scopus database. Diverse search queries were formulated and executed to acquire pertinent articles. Following the search, a total of 24 papers pertaining to the integration of Explainable Artificial Intelligence (XAI) and Cyber-Physical Systems (CPS) were chosen for analysis. A comprehensive evaluation of these publications and a meta-analysis of the results received from the Scopus database allowed for a deep grasp of the topic. The importance of XAI and CPS in Smart Cities rests in their ability to enhance decision-making, promote transparency, and increase efficiency. This study examines the factors that come before and the outcomes that result from combining XAI and CPS, encompassing the technologies involved, the difficulties faced, and the potential advantages. Furthermore, this study highlights the urgent necessity to incorporate XAI with CPS, as evidenced by the research findings published in the Scopus database.Journal of Electrical Systems680The importance of XAI and CPS in Smart Cities rests in their ability to enhance decision-making, promote transparency, and increase efficiency, as evidenced by the research findings published in the Scopus database.Journal of Electrical Systems['Isha Batra', 'Arun Malik', 'Shamneesh Sharma', 'Chetan Sharma', 'A. S. M. S. Hosen']2024-04-04T00:00:00https://www.semanticscholar.org/paper/106bcd930e4606f983507a0bed24ab8c909a572f 78ea80212eb4cedd9ae5229679372b739318e1a7The burgeoning field of Digital Humanities has experienced remarkable growth, catalyzed by advances in computing technology and the increasing digitization of cultural artifacts. This paper delves into the dynamic interplay between digital humanities and artificially intelligent generated content (AIGC), exploring their intersection and transformative potential. Through a comprehensive literature review, we highlight the multifaceted landscape surrounding AIGC, including biases, ethical considerations, technological advances, and societal implications. Drawing on insights from cutting-edge studies, we propose a methodology for fusion exploration that aims to elucidate the synergies between digital humanities and AIGC. Our proposed methodology integrates an overview of AIGC with copyright issues, emphasizing the ethical dimensions and regulatory frameworks necessary for responsible content creation. We also delineate the intersection of digital humanities and artificial intelligence, focusing on two key aspects: the ideographic qualities of literary language and the expression of literary language through digital rhetoric. By synthesizing insights from the diverse scholarly endeavors, we underscore the transformative potential of integrating Digital Humanities and AIGC, paving the way for innovative research paradigms and collaborative engagement in academia and beyond.Journal of Electrical Systems290This paper delves into the dynamic interplay between digital humanities and artificially intelligent generated content (AIGC), exploring their intersection and transformative potential and proposes a methodology for fusion exploration that aims to elucidate the synergies between digital humanities and AIGC.Journal of Electrical Systems['Jing Sun']2024-04-04T00:00:00https://www.semanticscholar.org/paper/78ea80212eb4cedd9ae5229679372b739318e1a7 a42b6991e3d7a6ae846f03bb42d97fde7eff8884With the continuous development of artificial intelligence technology, large-scale language models have become an important breakthrough in the field of interpreting intelligence technology, and the interpreting profession is facing unprecedented technological changes; Through reviewing the relevant research background, it was found that the rise of large models has brought new opportunities and challenges to the development of artificial intelligence interpreting technology; Firstly, the language generation ability of the large model is powerful, which can achieve more accurate and natural interpretation and translation results. Secondly, large models have strong learning abilities and can continuously optimize the performance of interpretation systems through large-scale data training. In addition, the large model also has the characteristic of fast response, which can provide efficient interpretation services in real-time scenarios; The age of large models provides new opportunities and prospects for the transformation and development of artificial intelligence interpretation technology. In the future, with the continuous evolution of large model technology and the expansion of application scenarios, artificial intelligence interpretation technology is expected to play a more important role in cross language communication, international cooperation, and other aspects. However, attention should also be paid to the application limitations of large models and issues such as collaboration with human translators.Journal of Electrical Systems70It was found that the rise of large models has brought new opportunities and challenges to the development of artificial intelligence interpreting technology, and large models have strong learning abilities and can continuously optimize the performance of interpretation systems through large-scale data training.Journal of Electrical Systems['Hao Peng, Peipei Zhou']2024-04-04T00:00:00https://www.semanticscholar.org/paper/a42b6991e3d7a6ae846f03bb42d97fde7eff8884 74459c6f30410b1bbd1e19ff91a21f415a8e0bceWith the development of a new round of scientific and technological revolution and industrial transformation, new information technologies such as 5G technology, big data and artificial intelligence have become an important driving force to promote the development of digital economy. In the context of the era of artificial intelligence, Shanxi Province, as a relatively latecomer in the economic development of domestic provinces, should seize the opportunity to keep up with the trend of The Times. Through the analysis of some data of Shanxi's digital economy in recent years and the comparison with other provinces, it is found that Shanxi has some problems in digital infrastructure, industrial digitization, digital industrialization and digital government construction. With artificial intelligence as the background, through investigation, literature and other methods to accelerate the construction of digital infrastructure, expand the scale of digital industrialization, implement industrial digital transformation projects, and strengthen the construction of digital government as basic measures, Shanxi can realize the rapid development of digital economy. These measures are of great significance for Shanxi Province to formulate corresponding measures from the perspective of problems in the process of digital economy development, so as to achieve industrial transformation and high-quality economic developmentJournal of Electrical Systems210It is found that Shanxi has some problems in digital infrastructure, industrial digitization, digital industrialization and digital government construction, and measures are of great significance for Shanxi Province to formulate corresponding measures from the perspective of problems in the process of digital economy development.Journal of Electrical Systems['Jiangxin He, Xueqing Sun']2024-04-04T00:00:00https://www.semanticscholar.org/paper/74459c6f30410b1bbd1e19ff91a21f415a8e0bce 2170f251cf76ef29c6208d992273a51bf6757f11In recent years, the intersection of Critical Discourse Analysis (CDA) and Artificial Intelligence (AI) has emerged as an area of growing interest in the fields of linguistics, communication, and technology. Critical Discourse Analysis examines how language, power, and ideologies intersect in various social and cultural contexts, with a focus on the ways in which discursive practices contribute to the (re) production of social inequalities. On the other hand, Artificial Intelligence has been increasingly integrated into our daily lives, including language processing, decision-making algorithms, and virtual assistants. This article aims to explore the potential applications and implications of combining CDA and AI, such as examining biases in AI-generated language, unpacking the ideologies embedded within AI systems, and investigating the ways in which AI-mediated communication shapes power dynamics in various contexts. By examining the interplay between CDA and AI, we can better understand how technology and language use both reflect and shape our social world, ultimately contributing to more equitable and inclusive technological developments. Qeios180Examining biases in AI-generated language, unpacking the ideologies embedded within AI systems, and investigating the ways in which AI-mediated communication shapes power dynamics in various contexts can better understand how technology and language use both reflect and shape the authors' social world.Qeios['Z. Roozafzai']2024-04-04T00:00:00https://www.semanticscholar.org/paper/2170f251cf76ef29c6208d992273a51bf6757f11 3b1e074d64526aa479fa6c508790348ed306f2e4Artificial intelligence is a rapidly developing technology that is gradually changing the pace of modern people's lives and ways of working. As AI enters the field of higher education, it is generating increasingly significant functions and effects. Artificial intelligence is having a certain impact on the growth of college students, and as a marginalized group of college students in the new era, it also presents unique social attitudes and patterns. Strengthening the correction of negative social attitudes towards marginalized groups of college students is of great practical significance for promoting social progress and development. This article takes the marginalized group of college students in a certain university in China as the research basis and concludes that: 1.there are significant differences in the scores of social emotions among marginalized groups of college students of different genders; 2.There are significant differences in the scores of social needs, social cognition, and social emotions among marginalized groups of college students from different disciplines; 3.There are significant differences in the scores of social needs, social cognition, social emotions, social values, and social behavioral tendencies among marginalized groups of college students of different grades. Research has found that artificial intelligence can have a positive impact on academic achievement, career planning, social responsibility, and creativity of marginalized groups of college students. It is suggested that universities should take artificial intelligence as their starting point and think from four dimensions: family factors, school factors, social factors, and personal factors, striving to guide marginalized groups of college students to establish a healthy and positive social mentalityJournal of Electrical Systems210It is suggested that universities should take artificial intelligence as their starting point and think from four dimensions: family factors, school factors, social factors, and personal factors, striving to guide marginalized groups of college students to establish a healthy and positive social mentality.Journal of Electrical Systems['Wenqu Xu']2024-04-04T00:00:00https://www.semanticscholar.org/paper/3b1e074d64526aa479fa6c508790348ed306f2e4 fefec93c1e069615aa6b64435b5198daa549b5cdIn the age of artificial intelligence, the digital economy significantly promotes green production in manufacturing. It optimizes processes, recycles resources, drives green tech innovation, and boosts product sustainability. While there's ampleresearch on green manufacturing, studies linking it to the digital economy are limited. Given the machinery manufacturing sector's strategic importance and China's focus on green, sustainable manufacturing, this study bridges that gap. Using the Theory of Planned Behavior (TPB) and Normative Activation Model (NAM), it examines how the digital economy influences green production intentions in equipment manufacturing. The study surveys Middle Eastern equipment manufacturers, analyzes the data with SmartPLS, and finds that the digital economy positively impacts green production via personal norms, responsibility, consequence awareness, attitudes, and cost savings. These factors mediate the relationship between the digital economy and green production intentions. The paper concludes with practical recommendations based on these findings. In the era of artificial intelligence, how the digital economy provides a new path to the green production of the manufacturing industry, and provides a theoretical reference for the sustainable development of the equipment manufacturing industry. Journal of Electrical Systems00The study surveys Middle Eastern equipment manufacturers, analyzes the data with SmartPLS, and finds that the digital economy positively impacts green production via personal norms, responsibility, consequence awareness, attitudes, and cost savings.Journal of Electrical Systems['Meng Shang']2024-04-04T00:00:00https://www.semanticscholar.org/paper/fefec93c1e069615aa6b64435b5198daa549b5cd 97c30dd33a99afae3a63d9e0cab25bd2b1bc6e3fArtificial Intelligence as an emerging technology in a large number of applications at the same time gradually revealed many ethical issues. Artificial Intelligence ethics education has become an important initiative and a key link in the implementation of the fundamental task of moral education in Chinese higher education institutions, however, Artificial Intelligence ethics education in professional courses often lags behind the education of course knowledge and skills, and it is difficult to achieve the integration of the three educational requirements. This paper proposes that Artificial Intelligence ethics education in professional courses should meet the requirements of the progression of course teaching objectives and adapt to the changes of educational traits from explicit and implicit to integration, and puts forward a practical path of Artificial Intelligence ethics education in professional courses that is consistent with the objectives, adapted to the traits, and constructed with multiple synergies according to the progression of the educational objectives of the professional course system and the changes of the educational traits. This paper also proposes the following suggestions for ethical education in professional courses: in order to achieve the goals of ethical teaching, it is necessary to establish connections between courses at different stages of cultivation, so that each course forms a hierarchical, collaborative, and supportive relationship. After the relationship is established, it is necessary to strengthen the collaborative participation of various subjects and corresponding practical guarantees in the specific teaching practice. This helps to form a system of Artificial Intelligence ethics education throughout the whole process, which in turn improves students' Artificial Intelligence ethics literacy.Journal of Electrical Systems200This paper puts forward a practical path of Artificial Intelligence ethics education in professional courses that is consistent with the objectives, adapted to the traits, and constructed with multiple synergies according to the progression of the educational objectives of the professional course system and the changes of the educational traits.Journal of Electrical Systems['Dake Qian, Wenqi Zhang']2024-04-04T00:00:00https://www.semanticscholar.org/paper/97c30dd33a99afae3a63d9e0cab25bd2b1bc6e3f ad2b4c8232c5fe709356554f245853b2d6ad27b1This article presents a literature review on integrating artificial intelligence (AI) in academic libraries, focusing on the transformative impact of AI-based tools and services on library management, resource utilisation, and research experience. While AI offers promising solutions to increase efficiency and effectiveness, the review identifies several challenges and concerns that need to be addressed, such as ethical and privacy considerations, staff training and development, and a user-centered approach. To integrate AI successfully, libraries must collaborate with professionals, researchers, and policymakers and adopt a continuing education approach to AI. Overcoming resistance to technological change, communicating efforts, and engaging staff are essential for libraries to leverage AI’s potential benefits and enhance their services and operations.DESIDOC Journal of Library &amp; Information Technology00To integrate AI successfully, libraries must collaborate with professionals, researchers, and policymakers and adopt a continuing education approach to AI and adopt a user-centered approach.DESIDOC Journal of Library &amp; Information Technology['Mallikarjuna C']2024-04-04T00:00:00https://www.semanticscholar.org/paper/ad2b4c8232c5fe709356554f245853b2d6ad27b1 f2fb7ea07984d3f7866a898692346d03c8725785In cellular biology, meiosis and mitosis are essential processes that control cell division and replication as well as the transfer of genetic material. A thorough understanding of these intricate processes is essential for many fields, such as cancer research, genetics, and developmental biology. In this study, we suggest building a Mitosis and Meiosis Analysis System (MMAS) that uses artificial intelligence (AI) methods to make automated analysis and meiotic event characterization easier. The MMAS uses machine learning models, deep learning frameworks, and sophisticated image processing algorithms to precisely recognize and categorize various meiotic and mitotic stages from microscopy images. The MMAS seeks to increase the accuracy and efficiency of cellular biology research while streamlining the analysis process and minimizing manual labor by utilizing artificial intelligence. Furthermore, by providing insightful information about the dynamic character of mitotic and meiotic events, the MMAS helps scientists understand the underlying mechanisms and their implications for a range of physiological and pathological conditions. We hope to improve our knowledge of meiosis and mitosis and hasten research findings in cellular biology by putting the MMAS into practice.Journal of Electrical Systems50A Mitosis and Meiosis Analysis System that uses artificial intelligence methods to make automated analysis and meiotic event characterization easier and helps scientists understand the underlying mechanisms and their implications for a range of physiological and pathological conditions is suggested.Journal of Electrical Systems['Rajendra kumar Mahto']2024-04-04T00:00:00https://www.semanticscholar.org/paper/f2fb7ea07984d3f7866a898692346d03c8725785 f619f52bc7b66407950eecbb0008ef166d4a969fArtificial intelligence (AI) models introduce privacy vulnerabilities to systems. These vulnerabilities may impact model owners or system users; they exist during model development, deployment, and inference phases, and threats can be internal or external to the system. In this paper, we investigate potential threats and propose the use of several privacy-enhancing technologies (PETs) to defend AI-enabled systems. We then provide a framework for PETs evaluation for a AI-enabled systems and discuss the impact PETs may have on system-level variables.arXiv.org500The use of several privacy-enhancing technologies (PETs) to defend AI-enabled systems are proposed and a framework for PETs evaluation for a AI-enabled systems is provided and the impact PETs may have on system-level variables is discussed.ArXiv["Liv d'Aliberti", 'Evan Gronberg', 'Joseph Kovba']2024-04-04T00:00:00https://www.semanticscholar.org/paper/f619f52bc7b66407950eecbb0008ef166d4a969f eb4e264e0f70ad943b74be78d8312999f8f41e3dPair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.Journal of educational computing research290A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: affirm the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks.Journal of Educational Computing Research['Jiangyue Liu', 'Siran Li']2024-04-04T00:00:00https://www.semanticscholar.org/paper/eb4e264e0f70ad943b74be78d8312999f8f41e3d 8b59516a322b5fe1a26e5e2e8a55faad28602ca1This paper throws light on how artificial intelligence affects labour law and employer’s protection. Artificial intelligence and automation are leading to loss of job in certain industries. It has lead to significant changes in the employment field. Though it has many advantages such as increased efficiency, less time consuming, productivity and cost efficiency, it also has led to disadvantages like mass unemployment and discrimination. This paper shows impact on Indian economy due to the mass unemployment and the uncertainty resulting from humans being replaced by AI in the near future . It also sheds light on how to curb the issues coming up due to artificial intelligence and automation in the employment field. The paper ends with suggestions regarding development of artificial intelligence without affecting the employment structure.International Journal For Multidisciplinary Research00Impact on Indian economy due to the mass unemployment and the uncertainty resulting from humans being replaced by AI in the near future is shown and suggestions regarding development of artificial intelligence without affecting the employment structure are made.International Journal For Multidisciplinary Research['P. T']2024-04-04T00:00:00https://www.semanticscholar.org/paper/8b59516a322b5fe1a26e5e2e8a55faad28602ca1 aecedd15e151dd85a747c8dd01e599509032532bThe integration of artificial intelligence (AI) in schools presents significant challenges and risks requiring responsible and ethical management. Despite warnings from tech leaders, major corporations push AI adoption in schools, leading to privacy violations, biased algorithms and curricular misinformation. Generative AI, though enhancing resources, risks disseminating false information. Biased AI models perpetuate inequalities, especially for marginalized groups. The financial burdens of AI implementation worsen budget constraints, and AI-driven surveillance raises privacy concerns. Governance must prioritize ethics and student rights, establishing transparent frameworks to prevent commercial interests from overshadowing educational goals. This editorial suggests halting AI adoption until comprehensive legislation safeguards against risks. Stakeholders should prioritize responsible AI development, stressing transparency and accountability. Collaboration between AI developers and educators is essential to ensuring AI serves students and society responsibly.International Journal of Language Education and Applied Linguistics140This editorial suggests halting AI adoption until comprehensive legislation safeguards against risks, and stakeholders should prioritize responsible AI development, stressing transparency and accountability.International Journal of Language Education and Applied Linguistics['Nurkhamimi Zainuddin']2024-04-04T00:00:00https://www.semanticscholar.org/paper/aecedd15e151dd85a747c8dd01e599509032532b c30154b86ad1fcbb272d180b61625eff42c76ff6 Purpose The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles. Design/methodology/approach This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives. Findings The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks. Originality/value This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine. International Journal of Ethics and Systems280A comprehensive overview of AI in medicine is provided, exploring its technical capabilities, practical applications, and ethical implications, and identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery.International Journal of Ethics and Systems['B. Elhassan', 'Alya A. Arabi']2024-04-04T00:00:00https://www.semanticscholar.org/paper/c30154b86ad1fcbb272d180b61625eff42c76ff6 d3fff134f19b134512e7bd78b919ba76f01393e8Artificial Intelligence (AI) has emerged as a transformative force across various sectors globally, including education. In the Indian context, where education plays a pivotal role in socio-economic development, the integration of AI presents both challenges and opportunities. This research endeavors to analyze the landscape of AI adoption in Indian education, focusing on its challenges and the potential it holds for enhancing educational outcomes. The study employs a mixed-methods approach, combining quantitative data analysis and qualitative case studies. Quantitative analysis involves surveying educational institutions. Findings highlight several challenges hindering the widespread adoption of AI in Indian education, including infrastructural constraints, resource limitations, regulatory hurdles, and concerns regarding data privacy. However, amidst these challenges, there exist significant opportunities for AI to revolutionize. The research aims to provide actionable insights for policymakers, educators, and technology developers to navigate the complexities of AI integration in Indian education. By understanding the challenges and leveraging the opportunities presented by AI, stakeholders can work towards creating a more inclusive, efficient, and effective educational ecosystem that empowers learners and prepares them for the demands of the 21st century.International Journal for Global Academic &amp; Scientific Research120The research aims to provide actionable insights for policymakers, educators, and technology developers to navigate the complexities of AI integration in Indian education, focusing on its challenges and the potential it holds for enhancing educational outcomes.International Journal for Global Academic &amp; Scientific Research['Pragati Agarwal', 'Anshita Vij']2024-04-04T00:00:00https://www.semanticscholar.org/paper/d3fff134f19b134512e7bd78b919ba76f01393e8 41e6f13c29e1b3ec79fa069cf0d7c2b287f9140dThis inquire about examines the integration of Artificial Intelligence (AI) and information analytics to optimize supply chain forms within the pharmaceutical industry. Through tests and writing audits, the ponder investigates the adequacy of AI calculations counting Linear Regression, Random Forest Regression, K-Means Clustering, and Deep Learning Neural Systems over request estimating, stock optimization, generation planning, and coordination optimization. Results appear that Random Forest Relapse beats Direct Relapse in request determining with RMSE of 80.20, MAE of 60.75, R² of 0.90, and MAPE of 6.50%. K-Means Clustering recognizes five clusters for stock optimization. Profound Learning Neural Systems accomplish RMSE of 75.10, MAE of 55.30, R² of 0.92, and MAPE of 5.80% for generation planning. In coordination’s optimization, Genetic Algorithm accomplishes a add up to fetched of $150,000 and conveyance time of 5 days compared to Mimicked Strengthening with $160,000 and 6 days. The research contributes to understanding the part of AI and information analytics in improving supply chain effectiveness, decreasing costs, and guaranteeing maintainability within the pharmaceutical segment.Journal of Electrical Systems270Results appear that Random Forest Relapse beats Direct Relapse in request determining and Genetic Algorithm accomplishes a add up to fetched of $150,000 and conveyance time of 5 days compared to Mimicked Strengthening with $160,000 and 6 days.Journal of Electrical Systems['Dr Suman Kumar Swarnkar', 'Rohit R Dixit', 'Dr Tamanna M. Prajapati', 'Dr Upasana Sinha', 'Dr Yogesh Rathore', 'Prof Sushma Bhosle']2024-04-04T00:00:00https://www.semanticscholar.org/paper/41e6f13c29e1b3ec79fa069cf0d7c2b287f9140d 2dfc769708d96ca43a8bb68967a42d00e989f71aThis study aims to explore the role of ChatGPT and artificial intelligence (AI) in improving the quality of interpersonal communication in an online learning environment. The case study was conducted on an online learning platform that provides chat features and an AI chatbot. The study used a mixed method, with data collected through surveys and structured interviews of 30 online course participants. The results showed that using ChatGPT and AI in interpersonal communication can improve the quality of communication between course participants. In addition, course participants found it more convenient and easier to communicate with an AI chatbot than with fellow course participants. These findings suggest that ChatGPT and AI can be effective tools for improving the quality of interpersonal communication in an online learning environment, especially when interacting with strangers or in less comfortable situations. This study provides insight into how technology can be harnessed to improve social interaction in an educational context. Keywords: interpersonal communication, ChatGPT, AIKnE Social Sciences390The results showed that using ChatGPT and AI in interpersonal communication can improve the quality of communication between course participants, and course participants found it more convenient and easier to communicate with an AI chatbot than with fellow course participants.KnE Social Sciences['Muhamad Bisri Mustofa', 'Siti Wuryan', 'Muhamad Aji Mahesa Jaya', 'Sisma Jorgi Saputra', 'Mutiara Cahyani Putri']2024-04-04T00:00:00https://www.semanticscholar.org/paper/2dfc769708d96ca43a8bb68967a42d00e989f71a de628f4e3dc76e4cf31905f638d705f1dbfc352aThis paper established an association model between high-load training and cardiac damage to minimize sports injuries. It is a key condition to promote the development of modern sports. This project intends to use 8 biochemical parameters related to high-load training to build a mathematical model of high-load training-myocardial damage. A mathematical model for high load training and cardiac damage was established. Firstly, based on sparse feature representation method, the biochemical mechanism model of high-intensity training was constructed. The energy supply law of intramuscular energy supply and the dynamic law of glycolysis energy supply during exercise were extracted. Eight biochemical parameters were obtained after high intensity training. To construct an accurate association between high-intensity training-heart damage by measuring the correlation between eight indicators of performance. The validity of the model is verified by simulation.Journal of Electrical Systems190An association model between high-load training and cardiac damage to minimize sports injuries is established by measuring the correlation between eight indicators of performance and the validity of the model is verified by simulation.Journal of Electrical Systems['Jun Guo, Qingfeng Shi']2024-04-04T00:00:00https://www.semanticscholar.org/paper/de628f4e3dc76e4cf31905f638d705f1dbfc352a c6514eafec12f6ec9d9cb39646d6bd872b78f041An automatic recognition method for ball trajectory and penalty is extensively studied, leveraging computer vision technology. The core hardware components of this system are designed to effectively filter out noise and extract crucial information from the moving track. This enables the system to accurately identify the speed and position of the ball in real-time. To enhance the precision of image segmentation, a novel approach is introduced that utilizes the threshold vector in high-resolution color spaces. This method determines the color of each pixel through bitwise "and" operations between pixel points and subsets. Additionally, a moving window image filtering algorithm is proposed, incorporating trajectory prediction theory. Experimental results demonstrate that this approach significantly reduces recognition errors, resulting in a notably higher accuracy level, making it a promising solution for enhancing the performance of soccer robot systems. Journal of Electrical Systems240Experimental results demonstrate that this approach significantly reduces recognition errors, resulting in a notably higher accuracy level, making it a promising solution for enhancing the performance of soccer robot systems.Journal of Electrical Systems['Gang Liu, Leqiang Zhang, Xiaodong Feng']2024-04-04T00:00:00https://www.semanticscholar.org/paper/c6514eafec12f6ec9d9cb39646d6bd872b78f041 7750aa47fda996f7ff5a8d4a9e37c07584fbba46Irish Journal of Medical Science110Irish journal of medical science['E. Waisberg', 'J. Ong', 'M. Masalkhi', 'Andrew G. Lee']2024-04-04T00:00:00https://www.semanticscholar.org/paper/7750aa47fda996f7ff5a8d4a9e37c07584fbba46 f49e358a874e26abba47e2385b0b9d6ee01e3a0eJournal of Cognitive Neuroscience00Journal of cognitive neuroscience['Joseph J. Fins', 'Kaiulani S Shulman']2024-04-04T00:00:00https://www.semanticscholar.org/paper/f49e358a874e26abba47e2385b0b9d6ee01e3a0e 9ad9e7e229df661148672a6ca0f9e71296e4df22The New Bioethics20The New Bioethics['B. Blackshaw']2024-04-04T00:00:00https://www.semanticscholar.org/paper/9ad9e7e229df661148672a6ca0f9e71296e4df22 fc34ceabe8d6218d195146cfb3c0116b3648b500International Journal of Law and Psychiatry170The need for open discussion about optimising the potential of AI while minimising risks in this population is highlighted, and purpose-designed AI models could be adapted to provide education about capacity legislation, facilitate patient and staff interaction, and allow interactive updates by healthcare professionals.International journal of law and psychiatry['Maria Redahan', 'Brendan D. Kelly']2024-04-04T00:00:00https://www.semanticscholar.org/paper/fc34ceabe8d6218d195146cfb3c0116b3648b500 ba23ed50ad71da72340211b212e17ee9fd1cdfb1Journal of Human Development and Capabilities40Journal of Human Development and Capabilities['Paul Anand']2024-04-04T00:00:00https://www.semanticscholar.org/paper/ba23ed50ad71da72340211b212e17ee9fd1cdfb1 72cdcda35b6e535ee798269a6f708eadccfcf38cRheumatology Quarterly00Rheumatology Quarterly['T. Koca', 'C. Yildir']2024-04-04T00:00:00https://www.semanticscholar.org/paper/72cdcda35b6e535ee798269a6f708eadccfcf38c f1048e20b0760b095fc28b3990d184899f573f48Journal of Cheminformatics460This research critically investigate XAI through test-time augmentation, contrasting previous assumptions about using expert validation and showing inconsistencies within models for identical representations, indicating XAI measures something other than learned parameters.Journal of Cheminformatics['Peter B. R. Hartog', 'Fabian Krüger', 'Samuel Genheden', 'I. V. Tetko']2024-04-04T00:00:00https://www.semanticscholar.org/paper/f1048e20b0760b095fc28b3990d184899f573f48 1ac1821cc46410f138824a1221fcb6559ab29af3The Medical Device Regulation 2017/745 (MDR) enables the development of a wide range of innovative products. With respect to Directive 93/42, the MDR explicitly identifies the so-called “medical devices made of substances” (MDMS) through specific requirements. In addition, the MDR expands the definition of medical device (MD) by including the “modification of a physiological or pathological state” as a medical purpose specific to devices. This clarifies that materials interacting with the human body in such a way as to modify its “state” are medical devices. Natural materials, such as vegetal matrices, are characterized by the presence of both functional and structural interactions between their components; they can thus be described as “network/s" and interact with the human body in a coordinated, complex way. Since the “state” of the human body is a network of biological functions, the “network/s over a network” interaction between the natural material and the human body is likely to modify the “state” of the human body. Thus, therapeutic products consisting of natural materials, such as vegetal matrices, seem to fit perfectly into the definition of a medical device. Here we analyze the main characteristics of medicinal products, of medical devices made of substances and of medical devices consisting of natural materials. We see that medicinal products and medical devices made of substances have the common characteristic of being based on substances, either synthetic or derivatives of natural materials, but differ in their mechanism of action. On the other hand, medical devices constituted of natural materials relate to the general category of medical devices and cannot be characterized by any single component, identified as an active component. We also discuss how these characteristics relate to the mechanism of action of each type of product. This analysis should allow to identify the most appropriate path for each product, a necessary step to promote research and development of innovative therapies for a large number of unmet medical needs.Frontiers in Drug Safety and Regulation120Analysis of the main characteristics of medicinal products, of medical devices made of substances and of medical devices consisting of natural materials sees that medicinal products and medical devices made of substances have the common characteristic of being based on substances, but differ in their mechanism of action.Frontiers in Drug Safety and Regulation['Marcella Marletta']2024-04-03T00:00:00https://www.semanticscholar.org/paper/1ac1821cc46410f138824a1221fcb6559ab29af3 08598be9abdf0f088e15e307932721d74fd5e56aThis article proposes a dimensionality reduction approach to study the output regulation problem (ORP) of Boolean control networks (BCNs), which has much lower computational complexity than previous results. First, an auxiliary system which is much smaller in scale than the augmented system in previous approach is constructed. By analyzing the set stabilization of the auxiliary system as well as the original BCN, a necessary and sufficient condition to detect the solvability of the ORP is presented. Second, a method to design the state feedback controls for the ORP is proposed. Finally, two biological examples are given to demonstrate the effectiveness and advantage of the obtained new results.IEEE Transactions on Neural Networks and Learning Systems00A dimensionality reduction approach to study the output regulation problem (ORP) of Boolean control networks (BCNs) is proposed, which has much lower computational complexity than previous results.IEEE transactions on neural networks and learning systems['Shihua Fu', 'Jun e Feng', 'Yuan Zhao', 'Jianjun Wang', 'Jinfeng Pan']2024-04-03T00:00:00https://www.semanticscholar.org/paper/08598be9abdf0f088e15e307932721d74fd5e56a e2511ba160fa5c483a9307630584d43baf6b8161Environment, Development and Sustainability340Environment, Development and Sustainability['Yan Song', 'Lu Zhang', 'Xueying Dong', 'Ming Zhang']2024-04-03T00:00:00https://www.semanticscholar.org/paper/e2511ba160fa5c483a9307630584d43baf6b8161 6b02b05720ef6b5a51dc8659815d45af4ec0fa2aMitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice.arXiv.org901This work outlines the key features of responsible reporting and propose mechanisms for implementing them in practice and outlines the key features of responsible reporting that need to be implemented in practice.ArXiv['Noam Kolt', 'Markus Anderljung', 'Joslyn Barnhart', 'Asher Brass', 'K. Esvelt', 'Gillian K. Hadfield', 'Lennart Heim', 'Mikel Rodriguez', 'Jonas B. Sandbrink', 'Thomas Woodside']2024-04-03T00:00:00https://www.semanticscholar.org/paper/6b02b05720ef6b5a51dc8659815d45af4ec0fa2a 42435ce9445a28ccb5b01f5684f0959ea7d92588"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an artificial intelligence (AI) for diagnosis of breast cancer in digital breast tomosynthesis (DBT) and investigate whether it could improve diagnostic accuracy and reduce reading time of radiologists. Materials and methods A deep learning AI algorithm was developed and validated for DBT with retrospectively collected examinations (January 2010 to December 2021) from 14 institutions in the United States and South Korea. A multicenter, reader study was performed to compare the performance of 15 radiologists (7 breast specialists, 8 general radiologists) in interpreting DBT examinations from 258 women (mean, 56 years ± 13.41 [SD]), including 65 cancer cases, with and without the use of AI. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and reading time were evaluated. Results The AUC for standalone AI performance was 0.93 (95% CI: 0.92,0.94). With AI, radiologists' AUC improved from 0.90 (0.86, 0.93) to 0.92 (0.88, 0.96; P = .003) in the reader study. AI showed higher specificity (89.64% (85.34, 93.94)) than radiologists (77.34% (75.82, 78.87; P < .001)). When reading with AI, radiologists' sensitivity increased from 85.44% (83.22, 87.65) to 87.69% (85.63, 89.75; P = .04), with no evidence of a difference in specificity. Reading time decreased from 54.41 seconds (52.56, 56.27) without AI to 48.52 seconds (46.79, 50.25) with AI (P < .001). Interreader agreement measured by Fleiss kappa increased from 0.59 to 0.62, respectively. Conclusion The AI model showed better diagnostic accuracy than radiologists in breast cancer detection and reduced reading times. The concurrent use of AI in DBT interpretation could improve both accuracy and efficiency. ©RSNA, 2024.Radiology: Artificial Intelligence01The AI model showed better diagnostic accuracy than radiologists in breast cancer detection and reduced reading times, and the concurrent use of AI in DBT interpretation could improve both accuracy and efficiency.Radiology. Artificial intelligence['Eun Kyung Park', 'SooYoung Kwak', 'Weonsuk Lee', 'Joon Suk Choi', 'Thijs Kooi', 'Eun-Kyung Kim']2024-04-03T00:00:00https://www.semanticscholar.org/paper/42435ce9445a28ccb5b01f5684f0959ea7d92588 8cedeb11139eab187e43414fd7097c5d578dad7cWe envision 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are proficient in a variety of tasks, including self-assessment and planning of discovery workflows. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from hybrid cell simulation, programmable control of phenotypes, and the design of cellular circuits to the development of new therapies.arXiv.org2021Biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks to empower biomedical research through collaborative agents that integrate machine learning tools with experimental platforms.ArXiv['Shanghua Gao', 'Ada Fang', 'Yepeng Huang', 'Valentina Giunchiglia', 'Ayush Noori', 'Jonathan Richard Schwarz', 'Yasha Ektefaie', 'Jovana Kondic', 'M. Zitnik']2024-04-03T00:00:00https://www.semanticscholar.org/paper/8cedeb11139eab187e43414fd7097c5d578dad7c 18553b909c505692c5882f6414b534cf83d2d0d6AI and Ethics220It was found that implementing an ethics framework is systemic by nature, and that ethics principles and stakeholders need to be considered in relation to one another, and the AI app introduced a novel channel for knowledge between the stakeholders.AI and Ethics['Magali Goirand', 'E. Austin', 'R. Clay-Williams']2024-04-03T00:00:00https://www.semanticscholar.org/paper/18553b909c505692c5882f6414b534cf83d2d0d6 ec91e178fd514f1b165dc57b094a9f9f33763a85Saliency maps can explain how deep neural networks classify images. But are they actually useful for humans? The present systematic review of 68 user studies found that while saliency maps can enhance human performance, null effects or even costs are quite common. To investigate what modulates these effects, the empirical outcomes were organised along several factors related to the human tasks, AI performance, XAI methods, images to be classified, human participants and comparison conditions. In image-focused tasks, benefits were less common than in AI-focused tasks, but the effects depended on the specific cognitive requirements. Moreover, benefits were usually restricted to incorrect AI predictions in AI-focused tasks but to correct ones in image-focused tasks. XAI-related factors had surprisingly little impact. The evidence was limited for image- and human-related factors and the effects were highly dependent on the comparison conditions. These findings may support the design of future user studies.880In image-focused tasks, benefits were less common than in AI-focused tasks, but the effects depended on the specific cognitive requirements, and XAI-related factors had surprisingly little impact.['Romy Muller']2024-04-03T00:00:00https://www.semanticscholar.org/paper/ec91e178fd514f1b165dc57b094a9f9f33763a85 e48c95894dc6616e98d25bacc9c35771fa86777eGenerative AI is increasingly important in software engineering, including safety engineering, where its use ensures that software does not cause harm to people. This also leads to high quality requirements for generative AI. Therefore, the simplistic use of Large Language Models (LLMs) alone will not meet these quality demands. It is crucial to develop more advanced and sophisticated approaches that can effectively address the complexities and safety concerns of software systems. Ultimately, humans must understand and take responsibility for the suggestions provided by generative AI to ensure system safety. To this end, we present an efficient, hybrid strategy to leverage LLMs for safety analysis and Human-AI codesign. In particular, we develop a customized LLM agent that uses elements of prompt engineering, heuristic reasoning, and retrieval-augmented generation to solve tasks associated with predefined safety concepts, in interaction with a system model graph. The reasoning is guided by a cascade of micro-decisions that help preserve structured information. We further suggest a graph verbalization which acts as an intermediate representation of the system model to facilitate LLM-graph interactions. Selected pairs of prompts and responses relevant for safety analytics illustrate our method for the use case of a simplified automated driving system.Proceedings of the AAAI Symposium Series70A customized LLM agent is developed that uses elements of prompt engineering, heuristic reasoning, and retrieval-augmented generation to solve tasks associated with predefined safety concepts, in interaction with a system model graph.Proceedings of the AAAI Symposium Series['Florian Geissler', 'Karsten Roscher', 'Mario Trapp']2024-04-03T00:00:00https://www.semanticscholar.org/paper/e48c95894dc6616e98d25bacc9c35771fa86777e 1c300d8d58de8202d958f43873355111006f031bAI and Ethics300This study dives into the production of AI hype in online media, using an entity relationship diagram to investigate the political economy of AI imagery in digital media, providing a snapshot of how AI hype is materialised and amplified online.AI and Ethics['Dominik Vrabič Dežman']2024-04-03T00:00:00https://www.semanticscholar.org/paper/1c300d8d58de8202d958f43873355111006f031b 246d2496d02efbe548ce8084b43d0655cf60c757This study sought to find out implications of Artificial Intelligence (AI) in land management. Artificial Intelligence (AI) offers significant benefits for land development and decision-making, ethical concerns regarding data privacy, bias, and social impact necessitate frameworks to ensure its responsible application. The increasing use of AI models in land management raises ethical concerns about data ownership, privacy, algorithmic bias, environmental impact, and social displacement. Traditional research ethics frameworks may not be sufficient for AI-driven land management practices.  This paper examines critical issues arising from data governance, algorithmic transparency, and environmental and social impact assessment. It proposes frameworks that consider established research ethics principles and advocate for community engagement.  The paper highlights the potential for AI to contribute to sustainable and equitable land management. However, it also identifies potential negative consequences such as job losses, unequal access to technology, and exacerbation of existing social divides.  The paper emphasizes the need for collaboration between researchers, policymakers, communities, and developers to construct ethical frameworks that ensure AI contributes to sustainable land management practices.  These frameworks should address data governance, algorithmic transparency, environmental and social impact assessments, and community engagement.Journal of Computational Science and Technology00Journal of Computer Science and Technology (JCST)['William Ndiema Kiptoch']2024-04-03T00:00:00https://www.semanticscholar.org/paper/246d2496d02efbe548ce8084b43d0655cf60c757 6d6a12b3b18eea58a32acca02f24b53dfeec78d4With the rapid advancement of artificial intelligence (AI) in various domains, the education sector is set for transformation. The potential of AI-driven tools in enhancing the learning experience, especially in programming, is immense. However, the scientific evaluation of Large Language Models (LLMs) used in Automated Programming Assessment Systems (APASs) as an AI-Tutor remains largely unexplored. Therefore, there is a need to understand how students interact with such AI-Tutors and to analyze their experiences. In this paper, we conducted an exploratory case study by integrating the GPT-3.5-Turbo model as an AI-Tutor within the APAS Artemis. Through a combination of empirical data collection and an exploratory survey, we identified different user types based on their interaction patterns with the AI-Tutor. Additionally, the findings highlight advantages, such as timely feedback and scalability. However, challenges like generic responses and students' concerns about a learning progress inhibition when using the AI-Tutor were also evident. This research adds to the discourse on AI's role in education.Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training380An exploratory case study by integrating the GPT-3.5-Turbo model as an AI-Tutor within the APAS Artemis identified different user types based on their interaction patterns with the AI-Tutor and highlighted advantages, such as timely feedback and scalability.ArXiv['Eduard Frankford', 'Clemens Sauerwein', 'Patrick Bassner', 'Stephan Krusche', 'Ruth Breu']2024-04-03T00:00:00https://www.semanticscholar.org/paper/6d6a12b3b18eea58a32acca02f24b53dfeec78d4 432a0b2174df2f9d503bdf38ff208ff54573c8e3Following the recent release of various Artificial Intelligence (AI) based Conversation Agents (CAs), adolescents are increasingly using CAs for interactive knowledge discovery on sensitive topics, including mental and sexual health topics. Exploring such sensitive topics through online search has been an essential part of adolescent development, and CAs can support their knowledge discovery on such topics through human-like dialogues. Yet, unintended risks have been documented with adolescents' interactions with AI-based CAs, such as being exposed to inappropriate content, false information, and/or being given advice that is detrimental to their mental and physical well-being (e.g., to self-harm). In this position paper, we discuss the current landscape and opportunities for CAs to support adolescents' mental and sexual health knowledge discovery. We also discuss some of the challenges related to ensuring the safety of adolescents when interacting with CAs regarding sexual and mental health topics. We call for a discourse on how to set guardrails for the safe evolution of AI-based CAs for adolescents.arXiv.org430The current landscape and opportunities for CAs to support adolescents' mental and sexual health knowledge discovery are discussed and a discourse on how to set guardrails for the safe evolution of AI-based CAs for adolescents is called for.ArXiv['J. Park', 'Vivek Singh', 'Pamela J. Wisniewski']2024-04-03T00:00:00https://www.semanticscholar.org/paper/432a0b2174df2f9d503bdf38ff208ff54573c8e3 f660b79e71a536bd3afcd205d4cd349e44c9bbedThis scoping review aims to explore various Deep Learning and Symbolic Artificial Intelligence (AI) models that can be integrated into explainable hybrid AI for the purpose of detecting COVID-19 based on Computerized Tomography (CT) scans. We followed the PRISMA-ScR framework as the foundation for our scoping review protocol. Our approach included a thorough search across 13 databases, complemented by an additional random internet search for relevant articles. Due to the voluminous number of articles returned, the search was further narrowed using the keywords: Deep Learning, Symbolic AI and Hybrid AI. These keywords were used because they are more visible in the earmarked literature. A screening of all articles by title was performed to remove duplicates. The final screening process centered on the publication year, ensuring that all considered articles fell within the range of 2019 to 2023, inclusive. Subsequently, abstract or text synthesis was conducted. Our search query retrieved a total of 3,312 potential articles from the thirteen databases, and an additional 12 articles from a random internet search, resulting in a cumulative count of 3,324 identified articles. After the deduplication and screening steps, 260 articles met our inclusion criteria. These articles were categorized based on the year of publication, the type of aim, and the type of AI used. An analysis of the year of publication revealed a linear trend, indicating growth in the field of Hybrid AI. Out of the five aim categories identified, we deliberately excluded articles that lacked a specified aim. It's noteworthy that 3% of the articles focused on the integration of AI models. The low percentage value suggests that the integration aspect is overlooked, thereby transcripting the integration of Deep Learning and symbolic AI into hybrid AI as an area worth exploring. This scoping review gives an overview of how a Deep Learning and Symbolic AI-based framework has been used in the detection of COVID-19 based on CT scans.International Journal of Research In Business and Social Science270This scoping review gives an overview of how a Deep Learning and Symbolic AI-based framework has been used in the detection of COVID-19 based on CT scans, indicating growth in the field of Hybrid AI.International Journal of Research in Business and Social Science (2147- 4478)['Vengai Musanga', 'Colin Chibaya', 'Serestina Viriri']2024-04-03T00:00:00https://www.semanticscholar.org/paper/f660b79e71a536bd3afcd205d4cd349e44c9bbed 18819a447d272aec1aada3212e246193b3b5742eBackground The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application. Objective This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation. Methods Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics. Results Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively. Conclusions Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research.JMIR Medical Informatics1540This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs) from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application.JMIR Medical Informatics['Aditya Singhal', 'Nikita Neveditsin', 'Hasnaat Tanveer', 'Vijay K. Mago']2024-04-03T00:00:00https://www.semanticscholar.org/paper/18819a447d272aec1aada3212e246193b3b5742e 11d0e315301afe4e08ae17e276c5fa553c3f43faFeelings of something belonging to someone is called"psychological ownership."A common assumption is that writing with generative AI lowers psychological ownership, but the extent to which this occurs and the role of prompt length are unclear. We report on two experiments to better understand the relationship between psychological ownership and prompt length. Participants wrote short stories either completely by themselves or wrote prompts of varying lengths, enforced through word limits. Results show that when participants wrote longer prompts, they had higher levels of psychological ownership. Their comments suggest they felt encouraged to think more about their prompts and include more details about the story plot. However, these benefits plateaued when the prompt length was 75-100% of the target story length. Based on these results, we propose prompt entry interface designs that nudge users with soft and hard constraints to write longer prompts for increased psychological ownership.arXiv.org540ArXiv['Nikhita Joshi', 'Daniel Vogel']2024-04-03T00:00:00https://www.semanticscholar.org/paper/11d0e315301afe4e08ae17e276c5fa553c3f43fa e5652a6998f328ac30ac45e244d3b2295301410aThis research explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) on talent acquisition within Indonesia's growing e-commerce sector. While acknowledging the transformative potential of these technologies, the study uncovers significant challenges in their integration into existing talent acquisition processes. These challenges include issues related to data quality, model accuracy, and system adaptability. The study emphasizes the difficulty of acquiring talent with expertise in AI and ML, given the increasing demand for skilled workers in the e-commerce boom. It explores strategies employed by companies to address this talent gap, such as upskilling existing staff and seeking external expertise. The research provides a nuanced understanding of AI and ML applications in the Indonesian e-commerce landscape, highlighting both benefits and obstacles. The insights derived from the study aim to offer actionable guidance for e-commerce firms, HR professionals, and researchers navigating the evolving landscape of AI, ML, and talent acquisition in Indonesia's digital marketplace.Journal of humanities and social sciences studies480The study emphasizes the difficulty of acquiring talent with expertise in AI and ML, given the increasing demand for skilled workers in the e-commerce boom, and explores strategies employed by companies to address this talent gap, such as upskilling existing staff and seeking external expertise.Journal of Humanities and Social Sciences Studies['Mohammad Rayyan', 'Nakayenga Sharifah', 'Rini Kuswati']2024-04-03T00:00:00https://www.semanticscholar.org/paper/e5652a6998f328ac30ac45e244d3b2295301410a 774f66dd93864ab6a9235fad41630cfed19e1fecScientific Reports500Scientific Reports['O. C. van Stuijvenberg', 'M. Broekman', 'Samantha E C Wolff', 'Annelien L. Bredenoord', 'K. Jongsma']2024-04-03T00:00:00https://www.semanticscholar.org/paper/774f66dd93864ab6a9235fad41630cfed19e1fec 651a0579eb3e63806134dcb1d141d2b00fcc8421Fake news significantly influence our society. They impact consumers, voters, and many other societal groups. While Fake News exist for a centuries, Generative AI brings fake news on a new level. It is now possible to automate the creation of masses of high-quality individually targeted Fake News. On the other end, Generative AI can also help detecting Fake News. Both fields are young but developing fast. This survey provides a comprehensive examination of the research and practical use of Generative AI for Fake News detection and creation in 2024. Following the Structured Literature Survey approach, the paper synthesizes current results in the following topic clusters 1) enabling technologies, 2) creation of Fake News, 3) case study social media as most relevant distribution channel, 4) detection of Fake News, and 5) deepfakes as upcoming technology. The article also identifies current challenges and open issues.arXiv.org710This survey provides a comprehensive examination of the research and practical use of Generative AI for Fake News detection and creation in 2024 and synthesizes current results in the following topic clusters.ArXiv['Alexander Loth', 'Martin Kappes', 'Marc-Oliver Pahl']2024-04-03T00:00:00https://www.semanticscholar.org/paper/651a0579eb3e63806134dcb1d141d2b00fcc8421 eea35153ca91e42ee1972934161ee6b170efa893PurposeThis study aims to endeavour to decode artificial intelligence (AI)-based tokens' complex dynamics and predictability using a comprehensive multivariate framework that integrates technical and macroeconomic indicators.Design/methodology/approachIn this study we used advance machine learning techniques, such as gradient boosting regression (GBR), random forest (RF) and notably long short-term memory (LSTM) networks, this research provides a nuanced understanding of the factors driving the performance of AI tokens. The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens such as AGIX-singularity-NET, Cortex and numeraire NMR.FindingsThis study finding shows that through an intricate exploration of feature importance and the impact of speculative behaviour, the research elucidates the long-term patterns and resilience of AI-based tokens against economic shifts. The SHapley Additive exPlanations (SHAP) analysis results show that technical and some macroeconomic factors play a dominant role in price production. It also examines the potential of these models for strategic investment and hedging, underscoring their relevance in an increasingly digital economy.Originality/valueAccording to our knowledge, the absence of AI research frameworks for forecasting and modelling current aria-leading AI tokens is apparent. Due to a lack of study on understanding the relationship between the AI token market and other factors, forecasting is outstandingly demanding. This study provides a robust predictive framework to accurately identify the changing trends of AI tokens within a multivariate context and fill the gaps in existing research. We can investigate detailed predictive analytics with the help of modern AI algorithms and correct model interpretation to elaborate on the behaviour patterns of developing decentralised digital AI-based token prices.Journal of Economic Studies330The study’s comparative analysis highlights the superior predictive capabilities of LSTM models, as evidenced by their performance across various AI digital tokens, as evidenced by their performance across various AI digital tokens.Journal of Economic Studies['Rizwan Ali', 'Jin Xu', 'Mushahid Hussain Baig', 'Hafiz Saif Ur Rehman', 'Muhammad Waqas Aslam', 'Kaleem Ullah Qasim']2024-04-03T00:00:00https://www.semanticscholar.org/paper/eea35153ca91e42ee1972934161ee6b170efa893 7be9fb27bd17eddf0cd0df2019417d4ea7085db5The risen complexity of automotive systems requires new development strategies and methods to master the upcoming challenges. Traditional methods need thus to be changed by an increased level of automation, and a faster continuous improvement cycle. In this context, current vehicle performance tests represent a very time-consuming and expensive task due to the need to perform the tests in real driving conditions. As a consequence, agile/iterative processes like DevOps are largely hindered by the necessity of triggering frequent tests. This paper reports on a practical experience of developing an AI-augmented solution based on Machine Learning and Model-based Engineering to support continuous vehicle development and testing. In particular, historical data collected in real driving conditions is leveraged to synthesize a high-fidelity driving simulator and hence enable performance tests in virtual environments. Based on this practical experience, this paper also proposes a conceptual framework to support predictions based on real driving behavior.arXiv.org360This paper reports on a practical experience of developing an AI-augmented solution based on Machine Learning and Model-based Engineering to support continuous vehicle development and testing and proposes a conceptual framework to support predictions based on real driving behavior.ArXiv['Romina Eramo', 'H. E. Salman', 'Matteo Spezialetti', 'Darko Stern', 'Pierre Quinton', 'Antonio Cicchetti']2024-04-03T00:00:00https://www.semanticscholar.org/paper/7be9fb27bd17eddf0cd0df2019417d4ea7085db5 4ad822679fbc9db864208bd703f70a093731c205Personalized learning (PL) aspires to provide an alternative to the one-size-fits-all approach in education. Technology-based PL solutions have shown notable effectiveness in enhancing learning performance. However, their alignment with the broader goals of modern education is inconsistent across technologies and research areas. In this paper, we examine the characteristics of AI-driven PL solutions in light of the OECD Learning Compass 2030 goals. Our analysis indicates a gap between the objectives of modern education and the current direction of PL. We identify areas where most present-day PL technologies could better embrace essential elements of contemporary education, such as collaboration, cognitive engagement, and the development of general competencies. While the present PL solutions are instrumental in aiding learning processes, the PL envisioned by educational experts extends beyond simple technological tools and requires a holistic change in the educational system. Finally, we explore the potential of large language models, such as ChatGPT, and propose a hybrid model that blends artificial intelligence with a collaborative, teacher-facilitated approach to personalized learning.arXiv.org00This paper examines the characteristics of AI-driven PL solutions in light of the OECD Learning Compass 2030 goals, and identifies areas where most present-day PL technologies could better embrace essential elements of contemporary education.ArXiv['Kristjan-Julius Laak', 'Jaan Aru']2024-04-03T00:00:00https://www.semanticscholar.org/paper/4ad822679fbc9db864208bd703f70a093731c205 d83d07ffe8733da501471cf51561e47e370a9fb1Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utterances and bot responses, facilitating the generation of context-aware replies. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of Token Trails in improving conversational understanding and response generation, achieving state-of-the-art performance. Our results highlight the significance of contextual modeling in conversational AI and underscore the promising potential of Token Trails to advance the field, paving the way for more sophisticated and contextually aware chatbot interactions.arXiv.org300Token Trails is presented, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations, paving the way for more sophisticated and contextually aware chatbot interactions.ArXiv['Md. Kowsher', 'Ritesh Panditi', 'Nusrat Jahan Prottasha', 'Prakash Bhat', 'A. Bairagi', 'M. Arefin']2024-04-03T00:00:00https://www.semanticscholar.org/paper/d83d07ffe8733da501471cf51561e47e370a9fb1 24117a2d8bc986bf4d2dd6139cde969cc047f95aArtificial Intelligence’s advancement has led to ethical and privacy concerns due to the ability of algorithms to mine personal data and conduct surveillance on a large scale. The influence of Artificial Intelligence (AI) in cybersecurity extends beyond private entities and individuals. In this scenario, it is important to highlight the threatening actors and their objectives in order to counter AI-based cyber threats. We are dealing with different entities, each having its own interests: cybercriminals are interested in making profit, terrorist groups cause ideological violence and nation-states target geopolitical influence. Moreover, they are increasingly interested in developing AI-driven cyber warfare capabilities for either attacking their enemies or enhancing their defence measures against such attacks. This trend will most likely aggravate the global cyber arms race as countries compete to surpass each other in the development and deployment of AI-driven cyber capabilities. Consequently, it has become crucial for organizations and individuals to understand how AI affects cybersecurity and, thus, adapt their strategies accordingly. Traditional defences must be supplemented with AI-powered tools and techniques to stay ahead of the curve, while security experts must continually update their skills and expertise to cope with the changing threat landscape.Strategic impact110It has become crucial for organizations and individuals to understand how AI affects cybersecurity and, thus, adapt their strategies accordingly, to cope with the changing threat landscape.Strategic Impact['Petru-Dan Kovaci']2024-04-03T00:00:00https://www.semanticscholar.org/paper/24117a2d8bc986bf4d2dd6139cde969cc047f95a db546464461e47272485d4cb39d56f0852d4f600Generative image models have emerged as a promising technology to produce realistic images. Despite potential benefits, concerns grow about its misuse, particularly in generating deceptive images that could raise significant ethical, legal, and societal issues. Consequently, there is growing demand to empower users to effectively discern and comprehend patterns of AI-generated images. To this end, we developed ASAP, an interactive visualization system that automatically extracts distinct patterns of AI-generated images and allows users to interactively explore them via various views. To uncover fake patterns, ASAP introduces a novel image encoder, adapted from CLIP, which transforms images into compact"distilled"representations, enriched with information for differentiating authentic and fake images. These representations generate gradients that propagate back to the attention maps of CLIP's transformer block. This process quantifies the relative importance of each pixel to image authenticity or fakeness, exposing key deceptive patterns. ASAP enables the at scale interactive analysis of these patterns through multiple, coordinated visualizations. This includes a representation overview with innovative cell glyphs to aid in the exploration and qualitative evaluation of fake patterns across a vast array of images, as well as a pattern view that displays authenticity-indicating patterns in images and quantifies their impact. ASAP supports the analysis of cutting-edge generative models with the latest architectures, including GAN-based models like proGAN and diffusion models like the latent diffusion model. We demonstrate ASAP's usefulness through two usage scenarios using multiple fake image detection benchmark datasets, revealing its ability to identify and understand hidden patterns in AI-generated images, especially in detecting fake human faces produced by diffusion-based techniques.arXiv.org840ArXiv['Jinbin Huang', 'Chen Chen', 'Aditi Mishra', 'Bum Chul Kwon', 'Zhicheng Liu', 'Chris Bryan']2024-04-03T00:00:00https://www.semanticscholar.org/paper/db546464461e47272485d4cb39d56f0852d4f600 5ebd93d1002e222f3bef8a5af04e1b4898b8078cRecently, the technologies of on-device AI have been accelerated with the development of new hardware and software platforms. Therefore, many researchers and engineers focus on how to enable ML technologies on mobile devices with limited hardware resources. In this paper, we revisit on-device ML designed to support ML technologies on mobile devices and describe the three challenges when using on-device ML in detail. Then, we propose a new data management policy, called Overlay-ML, which efficiently solves two challenges that we discovered. Especially, we designed Overlay-ML to work in the application space with two key ideas. The first key idea is to extend the limited memory space using the usable space of the underlying storage device. The second key idea is to provide data transparency, which hides where the data is stored so that running ML models think the data is stored in the same place. For evaluation, we implemented an image detection application based on TensorFlow Lite which is a well-known on-device ML framework, and modified it to enable the features of Overlay-ML. All evaluation was performed on two state-of-the-art smartphones that are high-end embedded devices. Our evaluation results clearly show Overlay-ML can effectively prevent unexpected termination by Android OS and present a good loss value in real-world workload.Applied Sciences100A new data management policy, called Overlay-ML, is proposed, which efficiently solves two challenges that were discovered and can effectively prevent unexpected termination by Android OS and present a good loss value in real-world workload.Applied Sciences['Cheolhyeon Kwon', 'Donghyun Kang']2024-04-03T00:00:00https://www.semanticscholar.org/paper/5ebd93d1002e222f3bef8a5af04e1b4898b8078c 7da51033cb290a8d7d94115caba71609ba9eee8bAlthough technology has significantly improved the teaching and learning process, it has not been able to increase students' self-motivation and engagement at the same level. The lack of self-motivation and intermittent engagement is currently one of the primary challenges faced by educators. This new approach to learning called the hybrid gamification framework uses a combination of artificial intelligence (AI), machine learning (ML), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a more engaging and personalized learning experience. By tracking students' interactions and performance, the system can allocate rewards based on their progress, which helps to increase their motivation and engagement. This technology makes it possible for educators to collect and analyse data related to students' engagement patterns, quiz scores, time spent on learning activities, participation in discussion forums, and much more. This data analysis enables educators to identify struggling students and high achievers, allowing them to provide tailored support and instruction to maximize student success. A pilot implementation of this system involving 200 computer science students successfully demonstrated the effectiveness of this technology. This research provides a comprehensive understanding of gamification's impact by combining quantitative data with qualitative insights.Journal of Advanced Research in Applied Sciences and Engineering Technology00This research provides a comprehensive understanding of gamification's impact by combining quantitative data with qualitative insights.Journal of Advanced Research in Applied Sciences and Engineering Technology['K Sankara Narayanan', 'A. Kumaravel']2024-04-03T00:00:00https://www.semanticscholar.org/paper/7da51033cb290a8d7d94115caba71609ba9eee8b 2f58d0e43f43c0ade16645119c067a7f8cb56ed3The International Journal of Advanced Manufacturing Technology190The experimental results show the accuracy of the customized chatbot HMI when retrieving data based on specific prompts and the advantages of a reduced troubleshooting time compared to operations in traditional factories, which are highly dependent on supervisors’ interventions.The International Journal of Advanced Manufacturing Technology['Kahiomba Sonia Kiangala', 'Zenghui Wang']2024-04-03T00:00:00https://www.semanticscholar.org/paper/2f58d0e43f43c0ade16645119c067a7f8cb56ed3 5f662b38d70737f266cf7aea1b37661d52d740a7Discover Applied Sciences290The accurate calculation of pulse rates and the extraction of morphological and time series features from the remote photoplethysmography signal for blood pressure prediction are described, establishing the validity of remote photoplethysmography technology.Discover Applied Sciences['L. D. van Putten', 'K. E. Bamford', 'Ivan Veleslavov', 'Simon Wegerif']2024-04-03T00:00:00https://www.semanticscholar.org/paper/5f662b38d70737f266cf7aea1b37661d52d740a7 46407d8362d67795415c34f2870aca0853bce7baMedical Imaging 2024: Digital and Computational Pathology00Medical Imaging 2024: Digital and Computational Pathology['David S. McClintock']2024-04-03T00:00:00https://www.semanticscholar.org/paper/46407d8362d67795415c34f2870aca0853bce7ba 546a5c52163c468841dd4d594dd501634a3b6dd1Journal of the Association for Consumer Research540Journal of the Association for Consumer Research['Yue Zhang', 'Mirjam Tuk', 'A. Klesse']2024-04-03T00:00:00https://www.semanticscholar.org/paper/546a5c52163c468841dd4d594dd501634a3b6dd1 9f79684b97c621b62a1390d863242bb83a8980e5Medical Imaging 2024: Computer-Aided Diagnosis00Medical Imaging 2024: Computer-Aided Diagnosis['Emma Wylie', 'Stepan Romanov', 'Gareth D. Evans', 'Elaine F. Harkness', 'S. Astley']2024-04-03T00:00:00https://www.semanticscholar.org/paper/9f79684b97c621b62a1390d863242bb83a8980e5 6d5ee6c7dd6335f26ee9a91e81a8208118b3b2e9Multimedia tools and applications400Multimedia Tools and Applications['B. Lalithadevi', 'S. Krishnaveni']2024-04-03T00:00:00https://www.semanticscholar.org/paper/6d5ee6c7dd6335f26ee9a91e81a8208118b3b2e9 d20f994101fd5c67340238970dc795eb590ee7b0Medical Imaging 2024: Computer-Aided Diagnosis00Medical Imaging 2024: Computer-Aided Diagnosis['Alexis Burgon', 'Yuhang Zhang', 'B. Sahiner', 'N. Petrick', 'Kenny H. Cha', 'Ravi K. Samala']2024-04-03T00:00:00https://www.semanticscholar.org/paper/d20f994101fd5c67340238970dc795eb590ee7b0 d53deb7a60350bb57c800f188bb6ffeee6d2b45cMedical Imaging 2024: Computer-Aided Diagnosis00Medical Imaging 2024: Computer-Aided Diagnosis['Daniel Najarian', 'Alexis Burgon', 'N. Petrick', 'B. Sahiner', 'Kenny H. Cha', 'Ravi K. Samala']2024-04-03T00:00:00https://www.semanticscholar.org/paper/d53deb7a60350bb57c800f188bb6ffeee6d2b45c e1aad1bfbce55908e7e082bc6592124f14047e1eBalkan Medical Journal00Balkan medical journal['M. Ntalouka', 'Aretha Adamantia', 'Metaxia Bareka', 'Eleni M Arnaoutoglou']2024-04-03T00:00:00https://www.semanticscholar.org/paper/e1aad1bfbce55908e7e082bc6592124f14047e1e 97b36891d5a0eed9320d64a430001832c810ac2600['Ahmed Banafa']2024-04-03T00:00:00https://www.semanticscholar.org/paper/97b36891d5a0eed9320d64a430001832c810ac26 ac6b04e67870652ac784de3d3633c267911de88800['Alex Khang']2024-04-03T00:00:00https://www.semanticscholar.org/paper/ac6b04e67870652ac784de3d3633c267911de888 8bc217b0087770fe322ee2f018eb7c02bd38a4cfBehaviour &amp; Information Technology400Behaviour &amp; Information Technology['Naja Kathrine Kollerup', 'Stine S. Johansen', 'M. Tolsgaard', 'Mikkel Lønborg Friis', 'M. Skov', 'Niels van Berkel']2024-04-03T00:00:00https://www.semanticscholar.org/paper/8bc217b0087770fe322ee2f018eb7c02bd38a4cf ef20ef1d6be71fa8a8a3347643e5ac31df05a568ACS Sustainable Resource Management100ACS Sustainable Resource Management['Débora Domingo-Calabuig', 'Sergio Hoyas', 'Ricardo Vinuesa', 'J. A. Conejero']2024-04-03T00:00:00https://www.semanticscholar.org/paper/ef20ef1d6be71fa8a8a3347643e5ac31df05a568 e96028e15c3332f37f111f55c67c36cd805f2b11AI and Ethics10AI and Ethics['Diego Gosmar']2024-04-03T00:00:00https://www.semanticscholar.org/paper/e96028e15c3332f37f111f55c67c36cd805f2b11 94258e63d8dfbd18c7a9a50913751204e9f64254Global Journal of Flexible Systems Management200Global Journal of Flexible Systems Management['Nilufar U. Babakhanova', 'Aijan B. Dzhumanova', 'Marija A. Troyanskaya', 'S. Benčič', 'Yelena S. Petrenko']2024-04-03T00:00:00https://www.semanticscholar.org/paper/94258e63d8dfbd18c7a9a50913751204e9f64254 17171502abb8c1264a7c91a2244f75d7e68d0e9bMedical Imaging 2024: Computer-Aided Diagnosis00Medical Imaging 2024: Computer-Aided Diagnosis['Megan Perry', 'Stepan Romanov', 'Sacha J. Howell', 'Gareth D. Evans', 'Elaine F. Harkness', 'S. Astley']2024-04-03T00:00:00https://www.semanticscholar.org/paper/17171502abb8c1264a7c91a2244f75d7e68d0e9b 6c830378b30a79030907faa61649e9c8f5b927a3Medical Imaging 2024: Computer-Aided Diagnosis00Medical Imaging 2024: Computer-Aided Diagnosis['Stepan Romanov', 'Sacha J. Howell', 'Elaine F. Harkness', 'Gareth D. Evans', 'Steven Squires', 'Martin Fergie', 'S. Astley']2024-04-03T00:00:00https://www.semanticscholar.org/paper/6c830378b30a79030907faa61649e9c8f5b927a3 8b105dec1e917adf751260b6753a24821be4c68dAs Artificial Intelligence systems become integral across domains, the demand for explainability grows. While the effort by the scientific community is focused on obtaining a better explanation for the model, it is important not to ignore the potential of this explanation process to improve training as well. While existing efforts primarily focus on generating and evaluating explanations for black-box models, there remains a critical gap in directly enhancing models through these evaluations. This paper introduces SHIELD (Selective Hidden Input Evaluation for Learning Dynamics), a regularization technique for explainable artificial intelligence designed to improve model quality by concealing portions of input data and assessing the resulting discrepancy in predictions. In contrast to conventional approaches, SHIELD regularization seamlessly integrates into the objective function, enhancing model explainability while also improving performance. Experimental validation on benchmark datasets underscores SHIELD's effectiveness in improving Artificial Intelligence model explainability and overall performance. This establishes SHIELD regularization as a promising pathway for developing transparent and reliable Artificial Intelligence regularization techniques.arXiv.org330SHIELD (Selective Hidden Input Evaluation for Learning Dynamics), a regularization technique for explainable artificial intelligence designed to improve model quality by concealing portions of input data and assessing the resulting discrepancy in predictions, is introduced.ArXiv["Iv'an Sevillano-Garc'ia", 'Julián Luengo', 'Francisco Herrera']2024-04-03T00:00:00https://www.semanticscholar.org/paper/8b105dec1e917adf751260b6753a24821be4c68d aea458d6cd0e9ec18a475f345b8740eb8851e323ABSTRACT Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.Medical Education Online601Twelve essential tips for addressing the major ethical concerns in the use of AI in medical education are presented, ensuring its long-term success and positive impact.Medical Education Online["Russell Franco D'Souza", 'Mary Mathew', 'Vedprakash Mishra', 'K. M. Surapaneni']2024-04-03T00:00:00https://www.semanticscholar.org/paper/aea458d6cd0e9ec18a475f345b8740eb8851e323 a777f459c7873e7d64f4094f9e4e9d0479c7233aIntroduction: In the medical field, Artificial Intelligence (AI) has become increasingly influential, promising improvements in diagnostics, treatment, and patient management. A key aspect is the role of AI in developing treatment protocols, which could transform their personalization. However, its growth also involves significant ethical challenges. The study aims to provide a detailed analysis of the use of artificial intelligence in the development of treatment protocols, exploring both the innovative opportunities it presents and the significant ethical challenges it raises. Material and Method: Articles published in databases such as PubMed, Google Scholar, PLOS were analyzed. Results: The analysis revealed the transformative potential of AI in medicine, especially in the development of personalized treatment protocols. However, the study highlighted ethical concerns related to the responsibility of algorithms, transparency, patient autonomy, and potential biases within AI systems. These findings underscore the need for comprehensive ethical regulations to guide the responsible use of AI in treatment protocol development. Conclusions: The study highlights that the integration of Artificial Intelligence (AI) in the creation of treatment protocols offers significant benefits, but addressing ethical aspects is crucial to ensure the quality of medical care, as well as to protect patient rights. Rezumat Introducere: În domeniul medical, Inteligența Artificială (IA) a devenit din ce în ce mai influentă, promițând îmbunătățiri în diagnostic, tratament și managementul pacienților. Un element cheie este rolul IA în elaborarea protocoalelor de tratament, ceea ce ar putea transforma radical personalizarea acestora. Cu toate acestea, creșterea sa implică și provocări etice important. Scopul studiului este să analizeze detaliat utilizarea inteligenței artificiale în elaborarea protocolului de tratament, explorând atât oportunitățile inovatoare pe care le prezintă, cât și provocările etice semnificative pe care le ridică. Materiale și metode: Au fost analizate articole publicate în baze de date, precum PubMed, Google Scholar, PLOS. Rezultate: Analiza a relevat potențialul transformativ al IA în medicină, în special în dezvoltarea protocolului de tratament personalizat. Cu toate acestea, studiul a evidențiat preocupări etice legate de responsabilitatea algoritmilor, transparență, autonomia pacientului și posibilele părtiniri din cadrul sistemelor IA. Aceste constatări subliniază necesitatea unor reglementări etice cuprinzătoare pentru a ghida utilizarea responsabilă a IA în dezvoltarea protocolului de tratament. Concluzii: Studiul evidențiază că integrarea Inteligenței Artificiale (IA) în crearea protocoalelor de tratament prezintă beneficii semnificative, dar abordarea aspectelor etice este crucială pentru asigurarea calității îngrijirii medicale, cât și pentru protejarea drepturilor pacienților.Jurnal Medical Brasovean300Jurnal Medical Brasovean['Ecaterina Pitel', 'Florin Leașu', 'Liliana Rogozea']2024-04-03T00:00:00https://www.semanticscholar.org/paper/a777f459c7873e7d64f4094f9e4e9d0479c7233a 8e36c1f92d3c704315069fddaf5538c1fc8af9aaThe use of new technologies is becoming increasingly widespread in the world of work. Robotisation in industry is well known, but there are many uses in service activities that go unnoticed because they do not involve major changes to work processes. Yet they can have a significant influence on working conditions. Other uses in the service sector are leading to major changes, the scale of which means that they are completely overhauling working methods. The example of cycle-delivered meals shows that the social context means that these new forms of work can be difficult to adapt to ensure workers' health. The increasing deployment of artificial intelligence could multiply the number of such cases in the years to come, so we need to be vigilant about the changes in the daily lives of service workers that will ensue. Qeios80The example of cycle-delivered meals shows that the social context means that these new forms of work can be difficult to adapt to ensure workers' health.Qeios['Michel Héry']2024-04-03T00:00:00https://www.semanticscholar.org/paper/8e36c1f92d3c704315069fddaf5538c1fc8af9aa 5680ffe9b7924509ea99519494e4c6ec8cdf75f8Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models’ output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.PLoS ONE840This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions and generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures.PLOS ONE['Fatemeh Gholi Zadeh Kharrat', 'Christian Gagné', 'Alain Lesage', 'G. Gariépy', 'Jean-François Pelletier', 'Camille Brousseau-Paradis', 'Louis Rochette', 'É. Pelletier', 'Pascale Lévesque', 'Mada Mohammed', 'JianLi Wang']2024-04-03T00:00:00https://www.semanticscholar.org/paper/5680ffe9b7924509ea99519494e4c6ec8cdf75f8 290e64b2f2cc1e8a0b41c83aa470e44be8c48bbbBACKGROUND With the rapidly advancing technological landscape of health care, evaluating the potential use of artificial intelligence (AI) models to prepare nursing care plans is of great importance. PURPOSE The purpose of this study was to evaluate the quality of nursing care plans created by AI for the management of postpartum hemorrhage (PPH). METHODS This cross-sectional exploratory study involved creating a scenario for an imaginary patient with PPH. Information was put into 3 AI platforms (GPT-4, LaMDA, Med-PaLM) on consecutive days without prior conversation. Care plans were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale. RESULTS Med-PaLM exhibited superior quality in developing the care plan compared with LaMDA (Z = 4.354; P = .000) and GPT-4 (Z = 3.126; P = .029). CONCLUSIONS Our findings suggest that despite the strong performance of Med-PaLM, AI, in its current state, is unsuitable for use with real patients.Journal of Nursing Care Quality330The findings suggest that despite the strong performance of Med-PaLM, AI, in its current state, is unsuitable for use with real patients.Journal of nursing care quality['Emine Karacan']2024-04-03T00:00:00https://www.semanticscholar.org/paper/290e64b2f2cc1e8a0b41c83aa470e44be8c48bbb 4f2b61727ef6221c28c70b86eb19990a35415566Capacity is the legal right and empowerment of a legal person to individual responsibility. A legal person has the right to social, economic, and political duties and responsibilities in the society to sue and be sued in the law court. However, the issue of artificial persons has been gaining attention in recent times, especially its legal capacity. This paper examines the legal capacity of Artificial Intelligence from Islamic jurisprudential perspective. For instance, legal and juristic issues remain around the legal capacity of humanoids like Sophia Robot which was granted the first full citizenship in Saudi Arabia. Does that citizenship translate to full rights and responsibility like a normal human? The study uses a qualitative method to employ the doctrinal approach of analyzing Islamic jurisprudential opinions on the legal personality of an artificial person. Although there are Islamic thresholds on minors and other interdicted persons, however, the study explores the extent of interpolating classical Islamic rulings of the legal capacity of certain corporations, entities, and interdicted persons on the artificial intelligence robot. The study found that an artificial intelligence robot does not have the complete traits to be considered for natural legal capacity. However, AI has the trait of artificial personality that is justified in Islamic jurisprudence. The vicarious tortious liability can be interpolated on the artificial personality of AI to ensure the protection of the public interest. Therefore, this study lays the groundwork for further studies in understanding how Islamic law can address the rights, responsibilities, and ethical considerations surrounding the use of AI, thereby facilitating the development of comprehensive, and religiously sensitive regulatory frameworks.Malaysian Journal of Syariah and Law400The study found that an artificial intelligence robot does not have the complete traits to be considered for natural legal capacity, however, AI has the trait of artificial personality that is justified in Islamic jurisprudence.Malaysian Journal of Syariah and Law['Miszairi Sitiris', 'Saheed Abdullahi Busari']2024-04-03T00:00:00https://www.semanticscholar.org/paper/4f2b61727ef6221c28c70b86eb19990a35415566 c516ff51447592496b2f866f0c461082b660d7daBMC Health Services Research840A comprehensive systematic literature review and 11 semi-structured expert interviews are conducted to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC that can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.BMC Health Services Research['Jasmin Hennrich', 'Eva Ritz', 'Peter Hofmann', 'Nils Urbach']2024-04-03T00:00:00https://www.semanticscholar.org/paper/c516ff51447592496b2f866f0c461082b660d7da 390e26379190c2eb5ce5d8a933803e9935f0296fEuropean Radiology320The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes and could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions.European radiology['Hee Jeong Kim', 'Hak Hee Kim', 'Ki Hwan Kim', 'Ji Sung Lee', 'W. Choi', 'E. Y. Chae', 'H. J. Shin', 'J. H. Cha', 'Woo Hyun Shim']2024-04-03T00:00:00https://www.semanticscholar.org/paper/390e26379190c2eb5ce5d8a933803e9935f0296f 6d505cb644fb317343cf5cba03630ad6df7e5ee9Background: The integration of Artificial Intelligence (AI) in drug development has revolutionized the pharmaceutical and medical landscape, enhancing drug discovery, clinical trials, and personalized medicine. This evolution, while beneficial, has introduced significant ethical challenges in data privacy, algorithmic bias, intellectual property rights, and equitable access to AI-driven therapies. Objective: The application of AI in drug development presents uncertainties regarding the ethical management of patient data, potential biases in AI decision-making, and the fair distribution of AI-powered treatments. The rapidly evolving nature of AI technologies and the dynamic regulatory environment further compound these uncertainties, posing a challenge to the ethical deployment of AI in this sector. Methods: We conducted a systematic literature search from January 2019 to December 2023 using databases like PubMed, PLOS, and Google Scholar, with keywords "artificial intelligence," "ethics," and "drug discovery." This search led to the selection and detailed analysis of 33 key documents, focusing on the use of AI in drug discovery and associated ethical challenges. The extracted insights were synthesized to highlight major trends and discoveries in the field. Results: The review found that while AI significantly streamlines drug development processes, it raises substantial concerns about data privacy, decision-making biases, and equitable access. Key findings highlight the importance of ethically managing patient data, employing inclusive data sets for algorithm training, and maintaining transparency in AI operations. Intellectual property rights linked to AI discoveries and the necessity for transparent AI decision-making, particularly in clinical trials, were identified as critical areas needing attention. Conclusions: The rapid advancement of AI in pharmaceuticals necessitates a fine balance between innovation and adherence to ethical principles. This requires a multidisciplinary collaborative approach and the ongoing adaptation of regulatory frameworks to ensure the ethical and effective utilization of AI in drug development. Rezumat Introducere: Integrarea inteligenței artificiale (AI) în dezvoltarea medicamentelor a revoluționat peisajul farmaceutic și medical, îmbunătățind descoperirea medicamentelor, studiile clinice și medicina personalizată. Această evoluție, deși benefică, a introdus provocări etice semnificative în ceea ce privește confidențialitatea datelor, părtinirea algoritmică, drepturile de proprietate intelectuală și accesul echitabil la terapiile bazate pe IA. Obiective: Aplicarea IA în dezvoltarea medicamentelor prezintă incertitudini în ceea ce privește gestionarea etică a datelor pacienților, potențialele prejudecăți în procesul decizional al IA și distribuția echitabilă a tratamentelor bazate pe IA. Evoluția rapidă a tehnologiilor IA și mediul de reglementare dinamic accen¬tuează și mai mult aceste incertitudini, reprezentând o provocare pentru implementarea etică a IA în acest sector. Material și metodă: Am efectuat o căutare sistematică a literaturii din ianuarie 2019 până în decembrie 2023 folosind baze de date precum PubMed, PLOS și Google Scholar, cu cuvinte cheie "inteligență artificială", "etică" și "descoperire de medicamente". Această căutare a condus la selectarea și analiza detaliată a 33 de documente-cheie, concentrându-se pe utilizarea IA în descoperirea medicamentelor și provocările etice asociate. Perspectivele extrase au fost sintetizate pentru a evidenția tendințele și descoperirile majore din domeniu. Rezultate: Analiza a constatat că, deși AI simplifică semnificativ procesele de dezvoltare a medica¬mentelor, aceasta ridică preocupări substanțiale cu privire la confidențialitatea datelor, prejudecățile de luare a deciziilor și accesul echitabil. Principalele constatări evidențiază importanța gestionării etice a datelor pacienților, a utilizării seturilor de date incluzive pentru antrenarea algoritmilor și a menținerii transparenței în operațiunile IA. Drepturile de proprietate intelectuală legate de descoperirile IA și necesitatea unui proces decizional transparent în domeniul IA, în special în trialurile clinice, au fost identificate ca domenii critice care necesită atenție. Concluzii: Dezvoltarea rapidă a IA în industria farmaceutică necesită un echilibru fin între inovare și respectarea principiilor etice. Acest lucru necesită o abordare multidisciplinară bazată pe colaborare și adaptarea continuă a cadrelor de reglementare pentru a asigura utilizarea etică și eficientă a IA în dezvoltarea medicamentelor.Jurnal Medical Brasovean00Jurnal Medical Brasovean['Ecaterina Pitel', 'Florin Leașu', 'Andrada Nicolau', 'Liliana Rogozea']2024-04-03T00:00:00https://www.semanticscholar.org/paper/6d505cb644fb317343cf5cba03630ad6df7e5ee9 565e2a64035c3dfc7b9f431f8e1048a0f2a8b758As technology continues to advance, its integration into various aspects of education has become inevitable.  This article delves into the transformative impact of Artificial Intelligence (AI) on students' academic performance.  AI's role in education has shifted from a mere supplementary tool to a fundamental component reshaping teaching and learning paradigms.  This paper explores how AI-powered educational technologies, such as adaptive learning platforms, intelligent tutoring systems, and automated assessment tools, are revolutionizing traditional educational practices.  It examines the benefits AI brings to students, educators, and educational institutions, including personalized learning experiences, enhanced student engagement, and efficient assessment mechanisms.  Furthermore, the article discusses the potential challenges and ethical considerations associated with AI integration in education, such as data privacy concerns, algorithmic biases, and the digital divide.  By analyzing recent research findings and real-world implementations, this paper provides insights into the evolving landscape of education empowered by AI and underscores the importance of responsible AI adoption to optimize students' academic outcomes in the digital era.International Journal of Education and Humanities70How AI-powered educational technologies, such as adaptive learning platforms, intelligent tutoring systems, and automated assessment tools, are revolutionizing traditional educational practices is explored, highlighting the importance of responsible AI adoption to optimize students' academic outcomes in the digital era.International Journal of Education and Humanities['Shiyun Ou']2024-04-03T00:00:00https://www.semanticscholar.org/paper/565e2a64035c3dfc7b9f431f8e1048a0f2a8b758 af8563b936c82654c426200f026a72e20f4d27a9   Purpose: of the article is to study the influence of artificial intelligence on the construction of an adaptive educational environment that takes into account the positive and negative impact of artificial intelligence, as the core of digital technologies, on the well-being of participants in the educational process.   Methods: the work uses a complex of theoretical methods, including axiomatic, formalization, abstraction, logical analysis, historical retrospection. The most signifi cant practical research methods include statistical, operationalization and evaluation, comparative analysis.   Results: the article provides a statistical substantiation of the demand for artifi cial intelligence in the education. The current educational problems are shown, in solving which it is advisable to use artifi cial intelligence. The article describes the trends in the use of artificial intelligence in the Russian education. The interpretation of the adaptive educational environment is given and the possibilities of using artificial intelligence as a tool for its construction are shown. The logical sequence for constructing an adaptive intelligent teaching system is proposed. The analysis of the impact of digital technologies used in hybrid learning (including artifi cial intelligence) on the well-being of participants in the educational process was carried out. The need for a rational, balanced and cautious approach to the use of artifi cial intelligence in education is emphasized, which gives rise to numerous ethical problems, ignoring which can negatively aff ect the values of education.   Conclusions and Relevance: in the context of digitalization of the economy and society, the key technology of which is artifi cial intelligence, education is facing technological challenges that force it to adapt to new operating conditions. The penetration of artifi cial intelligence into the field of education is the pattern of scientific and technological progress that cannot be resisted. The positive aspects and threats of using artificial intelligence in education should be critically assessed, and informed decisions should be made on this basis. Considering the specifics of the educational environment in which the foundation of personality is laid, and the need to preserve the values of education as a factor of cultural progress, priority in goal-setting and the meanings of education should remain with a person whose assistant can be artifi cial intelligence.Multimedia Information Retrieval140The influence of artificial intelligence on the construction of an adaptive educational environment that takes into account the positive and negative impact of artificial intelligence, as the core of digital technologies, on the well-being of participants in the educational process is studied.MIR (Modernization. Innovation. Research)['M. Izmailova']2024-04-03T00:00:00https://www.semanticscholar.org/paper/af8563b936c82654c426200f026a72e20f4d27a9 dc92fd9a325274ee8114c64957f0ac7983573255For decades, women’s health has faced significant challenges, including underrepresentation in research, limited access to specialized care, and a persistent gender gap in diagnosis and treatment. However, a wave of innovation powered by artificial intelligence (AI) is poised to revolutionize the landscape, offering personalized solutions and improved healthcare experiences for women across all phases of life. This in-depth exploration delves into the evolving landscape of AI in women’s health. This review highlights prominent trends, showcases innovative tools and startups driving positive change, and discusses the potential impact on various aspects of well-being. From personalized care and early disease detection to mental health support and improved access to information, AI promises to transform women’s healthcare experiences.Journal of Women's Health Care and Management560This in-depth exploration delves into the evolving landscape of AI in women’s health, highlighting prominent trends, showcases innovative tools and startups driving positive change, and discusses the potential impact on various aspects of well-being.Journal of Womens Health Care and Management['Shivangi Mishra', 'Aiswarya Rani', 'Sagar', 'Vipin Kumar Pal']2024-04-03T00:00:00https://www.semanticscholar.org/paper/dc92fd9a325274ee8114c64957f0ac7983573255 c7e4a1668d3a2b1ccebd88bf0ee4ad7116b6659bHospital information management systems (HIMS) play significant roles in improving clients' health by applying developed technologies, one of which is artificial intelligence (AI). This study aims to explore the roles of AI in order to support the development of HIMS. We conducted a literature review by extracting the articles from PubMed, ProQuest, and Google Scholar in the past 5 years (January 2019-August 2023) using specific keywords. The full text of relevant articles then thematically synthesized and to be presented. Our findings revealed eight themes that represent the role of AI in supporting the hospital information management system and its implementation in healthcare settings. The themes identified include Diagnosis and Medical Imaging, Health Data Management, Risks Prediction and Disease Progression, Inventory and Procurement Management, Telemedicine and Remote Consultation, Patient Care Management, Drug Development and Clinical Research, and Hospital Data Security System. The AI implementation brings the potential to improve efficiency, accuracy, and quality of care in hospitals. However, it should be noted that the development and implementation of AI need to consider the ethical aspects and proper integration through existing health systems.JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit)00The findings revealed eight themes that represent the role of AI in supporting the hospital information management system and its implementation in healthcare settings and the themes identified include Diagnosis and Medical Imaging, Health Data Management, Risks Prediction and Disease Progression, Inventory and Procurement Management, and Hospital Data Security System.JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit)['I. K. D. Lanang Triana', 'Putu Dyah Candra Agustina', 'Rivaldi Febrian', 'I. D. G. P. Wiadnya', 'Vip Paramarta']2024-04-03T00:00:00https://www.semanticscholar.org/paper/c7e4a1668d3a2b1ccebd88bf0ee4ad7116b6659b a5752b9c6d8c5ae12bc2e0123efa856aa11be727Artificial intelligence is the main front to build new quality productive forces and is leading the fourth revolution of science and technology of human beings. The wide application of artificial intelligence technology has triggered people's concerns about unemployment. However, we do not have to worry too much. Artificial intelligence technology will not only replace human work, but also create more job opportunities and higher value-added work. It is imperative to conduct special research on labor force training and re-employment for industries and positions that may be replaced by artificial intelligence in the future, and take the initiative to respond in advance and take comprehensive and effective measures as soon as possible, so as to better adapt to the application and development of artificial intelligence technology.International Journal of Education and Humanities60It is imperative to conduct special research on labor force training and re-employment for industries and positions that may be replaced by artificial intelligence in the future, and take the initiative to respond in advance.International Journal of Education and Humanities['Haibo Liu']2024-04-03T00:00:00https://www.semanticscholar.org/paper/a5752b9c6d8c5ae12bc2e0123efa856aa11be727 229c92c9d48799030759394954575df5051b7ea0This study, inspired by the research of Cardoso et al. (2023), who investigated the use of Artificial Intelligence (AI) in education, focuses on the insertion of AI in distance education as a promising front for educational innovation. The main objective is to identify and analyze the advantages, disadvantages and challenges faced in adopting AI in remote educational contexts. Through a comprehensive literature review and case study analysis, it is observed that AI has the potential to increase student engagement and provide more personalized and accessible learning experiences. The results highlight that AI can adapt teaching to the individual needs of students, offering personalization and adaptation capabilities that can significantly improve the effectiveness of distance learning. However, challenges persist, including the need to invest in robust technological infrastructure, adequately train faculty to effectively use AI-based tools, and consider ethical and privacy issues related to the use of student data. The findings highlight the importance of a collaborative and multidisciplinary approach to maximizing the benefits of AI in distance education. Furthermore, the need to overcome technological and ethical barriers to ensure effective and ethical integration of AI in the educational environment is emphasized. Therefore, this study significantly contributes to the understanding of how AI can be effectively applied in distance education, providing valuable insights for educators, researchers and professionals interested in promoting educational innovation through technology.RCMOS - Revista Científica Multidisciplinar O Saber00It is observed that AI has the potential to increase student engagement and provide more personalized and accessible learning experiences, and the need to overcome technological and ethical barriers to ensure effective and ethical integration of AI in the educational environment is emphasized.RCMOS - Revista Científica Multidisciplinar O Saber['Alberto da Silva Franqueira', 'Anderson Amaro', 'Karla Verônica Silva Vale', 'Lucas Silva Dias', 'Rodrigo Rodrigues Pedra']2024-04-03T00:00:00https://www.semanticscholar.org/paper/229c92c9d48799030759394954575df5051b7ea0 46005f52b970d467eb782347e7a1ce8d4897a555This article investigated the integration of Artificial Intelligence (AI) in the context of Distance Education (DE), with the aim of exploring its advantages, disadvantages, and the challenges faced by teachers and students. The research focused on how AI can be employed to promote meaningful learning, using a bibliographic research methodology, as proposed by Severino (2007). This approach involved the critical analysis of existing literature, including relevant case studies and theories pertinent to the use of AI in education. The main authors cited were Castro (2002) and Tavares, Meira, and Amaral (2020), whose works provided insights on the application of Intelligent Tutoring Systems (ITS) and other AI technologies in education. The literature review highlighted the potential of AI to personalize the learning experience and the associated challenges, such as the need for adequate infrastructure, digital skills, and ethical considerations. A case study from Georgia State University illustrated a successful practical application of AI to prevent student dropout, offering a model for future implementations in DE. The analysis showed that, despite obstacles, the integration of AI in DE has the potential to positively transform education, offering opportunities for more adaptive and personalized learning. In conclusion, the article emphasized the importance of addressing technical, ethical, and pedagogical challenges in adopting AI in education, highlighting the need for careful strategies that ensure the effectiveness and inclusivity of technological interventions in DE.RCMOS - Revista Científica Multidisciplinar O Saber00Investigating the integration of Artificial Intelligence in the context of Distance Education showed that, despite obstacles, the integration of AI in DE has the potential to positively transform education, offering opportunities for more adaptive and personalized learning.RCMOS - Revista Científica Multidisciplinar O Saber['Anderson Amaro Vieira', 'Ítalo Martins Lôbo', 'Lorena dos Santos Mulatti Mulatti', 'Rodrigo Rodrigues Pedra', 'Rodrigo Vieira Ribeiro']2024-04-03T00:00:00https://www.semanticscholar.org/paper/46005f52b970d467eb782347e7a1ce8d4897a555 7073cfcd4b2c93d2e2337e275e2a14b0dd404c4b1001[]2024-04-03T00:00:00https://www.semanticscholar.org/paper/7073cfcd4b2c93d2e2337e275e2a14b0dd404c4b ea36ce3e6cef8469bfc26da830b027f8282002a5O presente estudo investigou a integração da Inteligência Artificial (IA) no ensino superior à distância, visando compreender suas implicações para a prática educacional. O objetivo foi analisar como a IA está sendo adotada nas instituições de ensino superior e identificar as principais vantagens e desafios dessa integração. A pesquisa, de natureza bibliográfica, seguiu a metodologia proposta por Lakatos e Marconi (2001), analisando dados coletados de bases como Google Acadêmico, Scielo e Academia.edu. A análise fundamentou-se em obras de autores renomados no campo da educação e tecnologia, como Luckin et al. (2016), que discutiram o uso de IA para personalizar a aprendizagem em ambientes virtuais. Carmona, Furtado e Cortês (2021) exploraram a IA na prevenção da evasão escolar, enquanto Teles e Nagumo (2023) abordaram os desafios econômicos e de acesso relacionados à IA no ensino superior. A pesquisa também destacou a experiência da UFLA na formação docente contínua, integrando a IA no processo educativo. Concluiu-se que a IA tem um potencial significativo para transformar o ensino superior à distância, oferecendo personalização e eficiência. No entanto, também enfrenta desafios relacionados a questões éticas, econômicas e de acessibilidade. Este estudo sublinhou a necessidade de abordagens equilibradas que maximizem os benefícios da IA, garantindo uma educação inclusiva e de qualidade.Revista Ibero-Americana de Humanidades, Ciências e Educação01Revista Ibero-Americana de Humanidades, Ciências e Educação['Rodi Narciso', 'Jocely Gomes da Silva', 'Olivéria Ronilda Rodrigues', 'Ana Maria de Oliveira Souza', 'Luiz Antônio Xavier da Cruz', 'Rejâne Núbia Gossler Lima Morais']2024-04-03T00:00:00https://www.semanticscholar.org/paper/ea36ce3e6cef8469bfc26da830b027f8282002a5 c63f83920ce188ed259722c832a540df02ffdafaJournal of the Association for Consumer Research120Journal of the Association for Consumer Research['Ana Valenzuela', 'Stefano Puntoni', 'Donna Hoffman', 'Noah Castelo', 'Julian De Freitas', 'Berkeley J. Dietvorst', 'Christian Hildebrand', 'Young Eun Huh', 'Robert Meyer', 'Miriam E Sweeney', 'Sanaz Talaifar', 'Geoff Tomaino', 'Klaus Wertenbroch']2024-04-03T00:00:00https://www.semanticscholar.org/paper/c63f83920ce188ed259722c832a540df02ffdafa d4cf9154670225759126a01b31adf6ca068cd2d7Medical Imaging 2024: Image Processing00Medical Imaging 2024: Image Processing['Imon Banerjee']2024-04-03T00:00:00https://www.semanticscholar.org/paper/d4cf9154670225759126a01b31adf6ca068cd2d7 dcd9baced65c4afe8396c03145e19ce27728d14bGaceta Médica de México00Gaceta medica de Mexico['J. A. Castrillón-Lozano', 'Dayhana Arango-Cárdenas', 'Daniel E Marulanda-Márquez']2024-04-03T00:00:00https://www.semanticscholar.org/paper/dcd9baced65c4afe8396c03145e19ce27728d14b 7c2ef5eb88ce98809b2e52b99899fea06bd1b026Angiology00Angiology['Xiao-Na Luan', 'Jing Zhu', 'De-Gang Mo']2024-04-03T00:00:00https://www.semanticscholar.org/paper/7c2ef5eb88ce98809b2e52b99899fea06bd1b026 f5657adc024b68d13fafcc64635371d8a5c0d5b4Journalism Practice250Journalism Practice['Xiaolu Ji', 'Joanne Kuai', 'Rodrigo Zamith']2024-04-03T00:00:00https://www.semanticscholar.org/paper/f5657adc024b68d13fafcc64635371d8a5c0d5b4 487b569d755740686d98078f7d2768a057479ea900['Winnie Li', 'Kristen Harper']2024-04-03T00:00:00https://www.semanticscholar.org/paper/487b569d755740686d98078f7d2768a057479ea9 aa12f942a8c9724428ca7ddc1410897fe4a6844eJournal of Interactive Advertising490Journal of Interactive Advertising['Jiemin Looi', 'Lee Ann Kahlor']2024-04-03T00:00:00https://www.semanticscholar.org/paper/aa12f942a8c9724428ca7ddc1410897fe4a6844e e6be2e557c5bbe4c94e9f93ab0655de2bb03f37eThis paper explores the evolution of internet law in the digital era. With the rapid proliferation of digital technologies, the legal landscape is facing numerous challenges and undergoing significant changes. The paper discusses the origins and development of internet law, the challenges posed by the characteristics of the digital era such as strong fluidity of information and cross-border nature, and the evolution of internet law to adapt to these challenges. It further proposes strategies to cope with these challenges, such as establishing a global framework for internet law and strengthening international legal coordination. The paper also forecasts potential trends in the development of internet law, such as the emergence of laws pertaining to new technologies like artificial intelligence and blockchain, and offers suggestions for future development. It emphasizes the importance of integrating technology and law, and enhancing the adaptability of law to new technologies. The paper concludes by reiterating the importance of internet law in the digital era and summarizing its evolution and potential future directions.International Journal of Education and Humanities40The origins and development of internet law, the challenges posed by the characteristics of the digital era such as strong fluidity of information and cross-border nature, and the evolution of internet law to adapt to these challenges are discussed.International Journal of Education and Humanities['Zongqi Li']2024-04-03T00:00:00https://www.semanticscholar.org/paper/e6be2e557c5bbe4c94e9f93ab0655de2bb03f37e 4395e37d174a93948142ea2852411084cbd5551cThe significance of ethics in artificial intelligence (AI) cannot be overstated, as it encompasses the foundational principles guiding the responsible creation, deployment, and management of AI technologies. As AI systems increasingly permeate every facet of our lives—from healthcare and education to security and entertainment—their decisions and actions have profound implications not only on individual rights and privacy but also on societal norms and values. Ethical considerations in AI are paramount to ensure that these technologies enhance human well-being, uphold fairness, and protect freedoms, rather than perpetuate biases, exacerbate inequalities, or undermine democratic institutions. The importance of AI ethics lies in its ability to provide a framework for navigating the complex moral dilemmas presented by AI, such as the balance between innovation and regulation, the protection of individual privacy versus the benefits of big data, and the prevention of AI misuse. By foregrounding ethical principles, stakeholders—including developers, policymakers, and users—can work towards the development of AI technologies that are not only technologically advanced but also socially responsible and aligned with human values. This emphasis on ethics ensures that as AI systems become more autonomous and integral to our daily lives, they do so in a manner that is transparent, accountable, and inclusive, thereby fostering trust and confidence in their widespread adoption and use.Arab Journal for Scientific Publishing00An emphasis on ethics ensures that as AI systems become more autonomous and integral to the authors' daily lives, they do so in a manner that is transparent, accountable, and inclusive, thereby fostering trust and confidence in their widespread adoption and use.Arab Journal for Scientific Publishing['Fayyad Muhammad Hani Bayan']2024-04-02T00:00:00https://www.semanticscholar.org/paper/4395e37d174a93948142ea2852411084cbd5551c c8266d7b2fb0ffa44a6c133ef6eb87cf289b1657 This Article addresses the pressing issues surrounding the use of automated systems in public decision-making, specifically focusing on migration, asylum, and mobility. Drawing on empirical data, this Article examines the potential and limitations of the General Data Protection Regulation and the Artificial Intelligence Act in effectively addressing the challenges posed by automated decision-making (ADM). The Article argues that the current legal definitions and categorizations of ADM fail to capture the complexity and diversity of real-life applications where automated systems assist human decision-makers rather than replace them entirely. To bridge the gap between ADM in law and practice, this Article proposes to move beyond the concept of “automated decisions” and complement the legal protection in the GDPR and AI Act with a taxonomy that can inform a fundamental rights analysis. This taxonomy enhances our understanding of ADM and allows to identify the fundamental rights at stake and the sector-specific legislation applicable to ADM. The Article calls for empirical observations and input from experts in other areas of public law to enrich and refine the proposed taxonomy, thus ensuring clearer conceptual frameworks to safeguard individuals in our increasingly algorithmic society.Social Science Research Network00This Article proposes to move beyond the concept of “automated decisions” and complement the legal protection in the GDPR and AI Act with a taxonomy that can inform a fundamental rights analysis and enhances the understanding of ADM.SSRN Electronic Journal['Francesca Palmiotto']2024-04-02T00:00:00https://www.semanticscholar.org/paper/c8266d7b2fb0ffa44a6c133ef6eb87cf289b1657 e911f0bc68be1f82792f05496f0d177499da83df While research has been conducted on self-regulated learning in relation to learning analytics, there remains a knowledge gap regarding the obstacles secondary education students face in regulating their learning and how learning analytics can support their self-regulation. This paper investigates two questions: 1) What challenges do secondary education students experience in the process of regulating their own learning?, and 2) What information and data do secondary education students need to better regulate their own learning? We conducted a study at a mid-sized upper secondary school in middle Sweden, to better understand how these issues manifest among students. We analyzed data collected by the school twice annually between 2015 and 2022, and administered a questionnaire to 224 students to answer the research questions. Through descriptive statistics and a thematic analysis, we identify prevalent problems that students encounter, as well as the necessary information that is essential for scaffolding self-regulated learning. We discuss the implications of our findings for the design of systems that provide students with relevant data to enhance their learning experiences. Studia Paedagogica181A study at a mid-sized upper secondary school in middle Sweden identifies prevalent problems that students encounter, as well as the necessary information that is essential for scaffolding self-regulated learning.Studia paedagogica['Mattias Wickberg Hugerth', 'Nouri Jalal']2024-04-02T00:00:00https://www.semanticscholar.org/paper/e911f0bc68be1f82792f05496f0d177499da83df c9e6db850513899da0ccf98f146a7fae50467cd8The quality and safety of agricultural products are related to people’s lives and health, economic development, and social stability, and have always been a hot issue of concern to the government and society. The rapid development of digital traceability technology in the digital environment has brought new opportunities for the supervision of agricultural product quality and safety, but the frequent occurrence of agricultural product safety incidents in recent years has exposed many problems such as the lack of governmental supervision, unstandardized production process of enterprises, and weak consumer awareness. To improve the cooperation efficiency of stakeholders and ensure the quality and safety of agricultural products, this paper proposes a dynamic model based on evolutionary game theory. The model incorporates the government, agricultural product producers, and farmers, and evaluates the stability and effectiveness of the system under different circumstances. The results of the study show that there are multiple evolutionary stabilization strategies in the tripartite evolutionary game model of agricultural product quality and safety supervision, and there are corresponding evolutionary stabilization conditions. There are several factors affecting the stability of the system, the most important of which are government regulation, severe penalties for agricultural product producers, and incentives. When these factors reach a certain threshold, the stakeholder cooperation mechanism can establish an evolutionarily stable strategy. This study contributes to the understanding of the operational mechanism of stakeholder cooperation in agricultural product quality and safety regulation in the digital environment and provides decision support and policy recommendations for stakeholders to promote the sustainable development and optimization of agricultural product quality and safety regulation.Frontiers in Public Health370Frontiers in Public Health['Hong Huo', 'Xiangyu Liu']2024-04-02T00:00:00https://www.semanticscholar.org/paper/c9e6db850513899da0ccf98f146a7fae50467cd8 306626b5a87d97b64e4b98d1adb916483031f2c2Nature Climate Change40Nature Climate Change['Matteo Gasparini', 'Matthew C. Ives', 'Ben Carr', 'Sophie Fry', 'Eric Beinhocker']2024-04-02T00:00:00https://www.semanticscholar.org/paper/306626b5a87d97b64e4b98d1adb916483031f2c2 c58718b909b7dd082aff1fa6ce8bf21f2fadd742AI and Ethics341AI and Ethics['Salla Westerstrand', 'Rauli Westerstrand', 'Jani Koskinen']2024-04-02T00:00:00https://www.semanticscholar.org/paper/c58718b909b7dd082aff1fa6ce8bf21f2fadd742 ce04f60c9e019ce32e564a3d9465790efd43961cOpportunities and challenges are introduced by the integration of Artificial Intelligence (AI) into Human Resource Management (HRM). The paragraph discusses the ethical implications of AI applications in HRM, focusing on gender bias and algorithm transparency. It explores how AI-driven decision-making in HRM perpetuates gender bias, the importance of transparent algorithms for trust and accountability, and the role of regulatory frameworks in safeguarding ethical standards. The paper aims to provide a comprehensive analysis of the ethical landscape of AI in HRM and offers policy recommendations to mitigate bias and enhance transparency.Journal of Theory and Practice of Management Science341The paper aims to provide a comprehensive analysis of the ethical landscape of AI in HRM and offers policy recommendations to mitigate bias and enhance transparency.Journal of Theory and Practice of Management Science['Jiaxing Du']2024-04-02T00:00:00https://www.semanticscholar.org/paper/ce04f60c9e019ce32e564a3d9465790efd43961c 09cca54bb354b0e9c438a69d25847e132980522aThe expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consuming, creating a bottleneck in AI progress. This study investigates eye-tracking as a cost-effective technology to collect doctors' behavioral data for AI training with a focus on the pathology task of mitosis detection. One major challenge in using eye-gaze data is the low signal-to-noise ratio, which hinders the extraction of meaningful information. We tackled this by levering the properties of inter-observer eye-gaze consistencies and creating eye-gaze labels from consistent eye-fixations shared by a group of observers. Our study involved 14 non-medical participants, from whom we collected eye-gaze data and generated eye-gaze labels based on varying group sizes. We assessed the efficacy of such eye-gaze labels by training Convolutional Neural Networks (CNNs) and comparing their performance to those trained with ground truth annotations and a heuristic-based baseline. Results indicated that CNNs trained with our eye-gaze labels closely followed the performance of ground-truth-based CNNs, and significantly outperformed the baseline. Although primarily focused on mitosis, we envision that insights from this study can be generalized to other medical imaging tasks.arXiv.org250This study investigates eye-tracking as a cost-effective technology to collect doctors' behavioral data for AI training with a focus on the pathology task of mitosis detection, and assesses the efficacy of eye-gaze labels by training Convolutional Neural Networks and comparing their performance to those trained with ground truth annotations and a heuristic-based baseline.ArXiv['H. Gu', 'Zihan Yan', 'A. Alvi', 'Brandon Day', 'Chunxu Yang', 'Zida Wu', 'S. Magaki', 'Mohammad Haeri', "Xiang 'Anthony' Chen"]2024-04-02T00:00:00https://www.semanticscholar.org/paper/09cca54bb354b0e9c438a69d25847e132980522a 56bc304e96fc1a84279b023e4ecbfa9a4033f20bScientific Reports982It is demonstrated that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds, suggesting that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.Scientific Reports['Izhar Wallach', 'Denzil Bernard', 'Kong Nguyen', 'Gregory Ho', 'Adrian Morrison', 'A. Stecula', 'Andreana Rosnik', 'Ann Marie O’Sullivan', 'A. Davtyan', 'Ben Samudio', 'Bill Thomas', 'Brad Worley', 'Brittany Butler', 'Christian Laggner', 'Desiree Thayer', 'Ehsan Moharreri', 'Greg Friedland', 'Ha Truong', 'Henry van den Bedem', 'Ho Leung Ng', 'Kate Stafford', 'Krishna Sarangapani', 'Kyle Giesler', 'Lien Ngo', 'Michael Mysinger', 'Mostafa Ahmed', 'Nicholas J Anthis', 'Niel Henriksen', 'Pawel Gniewek', 'Sam Eckert', 'Saulo de Oliveira', 'Shabbir Suterwala', 'Srimukh Veccham Krishna PrasadPrasad', 'Stefani Shek', 'Stephanie Contreras', 'Stephanie Hare', 'Teresa Palazzo', 'Terrence E. O’Brien', 'Tessa Van Grack', 'Tiffany Williams', 'Ting‐Rong Chern', 'Victor Kenyon', 'Andreia H. Lee', 'Andrew B. Cann', 'Bastiaan Bergman', 'Brandon M. Anderson', 'Bryan D. Cox', 'Jeffrey M. Warrington', 'Jon M. Sorenson', 'Joshua M. Goldenberg', 'Matthew A. Young', 'Nicholas DeHaan', 'Ryan P. Pemberton', 'Stefan Schroedl', 'Tigran M. Abramyan', 'Tushita Gupta', 'Venkatesh Mysore', 'Adam G. Presser', 'Adolfo A. Ferrando', 'A. D. Andricopulo', 'Agnidipta Ghosh', 'Aicha Gharbi Ayachi', 'Aisha Mushtaq', 'Ala M. Shaqra', 'Alan Kie Leong Toh', 'A. Smrcka', 'Alberto Ciccia', 'Aldo Sena de Oliveira', 'A. Sverzhinsky', 'Alessandra Mara de Sousa', 'Alexander I. Agoulnik', 'Alexander Kushnir', 'Alexander N. Freiberg', 'Alexander V. Statsyuk', 'Alexandre R. Gingras', 'A. Degterev', 'Alexey Tomilov', 'A. Vrielink', 'A. Garaeva', 'Amanda C. Bryant‐Friedrich', 'Amedeo Caflisch', 'Amit K. Patel', 'Amith Vikram Rangarajan', 'A. Matheeussen', 'Andrea Battistoni', 'Andrea Caporali', 'Andrea Chini', 'A. Ilari', 'Andrea Mattevi', 'Andrea Talbot Foote', 'A. Trabocchi', 'Andreas Stahl', 'Andrew B. Herr', 'Andrew Berti', 'A. Freywald', 'A. Reidenbach', 'Andrew Lam', 'A. Cuddihy', 'Andrew White', 'A. Taglialatela', 'Anil K. Ojha', 'A. Cathcart', 'Anna A. L. Motyl', 'Anna Borowska', "Anna D'Antuono", 'Anna K H Hirsch', 'A. Porcelli', 'Anna Minakova', 'Anna Montanaro', 'Anna Müller', 'A. Fiorillo', 'Anniina T Virtanen', 'A. O’Donoghue', 'Antonio Del Rio Flores', 'A. E. Garmendia', 'A. Pineda-Lucena', 'A. Panganiban', 'A. Samantha', 'A. Chatterjee', 'Arthur L. Haas', 'A. Paparella', 'Ashley L. St. John', 'Ashutosh Prince', 'Assmaa ElSheikh', 'A. Apfel', 'Audrey Colomba', 'Austin O’Dea', 'B. Diallo', 'Beatriz Murta Rezende Moraes Ribeiro', 'B. Bailey-Elkin', 'B. Edelman', 'Benjamin Liou', 'Benjamin Perry', 'Benjamin Soon Kai Chua', 'Benjámin Kováts', 'B. Englinger', 'B. Balakrishnan', 'Bin Gong', 'B. Agianian', 'Brandon Pressly', 'Brenda P. Medellin Salas', 'Brendan M. Duggan', 'B. Geisbrecht', 'B. Dymock', 'B. Morten', 'Bruce D. Hammock', 'B. E. Mota', 'Bryan C. Dickinson', 'Cameron Fraser', 'Camille Lempicki', 'C. Novina', 'C. Torner', 'C. Ballatore', 'Carlotta Bon', 'Carly J. Chapman', 'C. Partch', 'Catherine T. Chaton', 'Chang Huang', 'Chao-Yie Yang', 'Charlene M. Kahler', 'Charles Karan', 'Charles Keller', 'C. Dieck', 'Huimei Chen', 'Chen Liu', 'Cheryl Peltier', 'C. Mantri', 'Chinyere Kemet', 'Christa E. Müller', 'Christian Weber', 'Christina M. Zeina', 'Christine S Muli', 'C. Morisseau', 'Cigdem Alkan', 'Clara Reglero', 'Cody A. Loy', 'Cornelia M. Wilson', 'Courtney B. Myhr', 'Cristina Arrigoni', 'Cristina Paulino', 'César Santiago', 'Dahai Luo', 'Damon J. Tumes', 'D. Keedy', 'Daniel A. Lawrence', 'Daniel Chen', 'Danny Manor', 'Darci J. Trader', 'David A Hildeman', 'David H. Drewry', 'David J. Dowling', 'D. Hosfield', 'David M. Smith', 'David Moreira', 'D. Siderovski', 'David Shum', 'David T. Krist', 'D. W. Riches', 'D. Ferraris', 'Deborah H. Anderson', 'D. R. Coombe', 'D. Welsbie', 'Di Hu', 'Diana Ortiz', 'Dina Alramadhani', 'Dingqiang Zhang', 'Dipayan Chaudhuri', 'Dirk Jan Slotboom', 'D. Ronning', 'Donghan Lee', 'Dorian Dirksen', 'Douglas A. Shoue', 'D. Zochodne', 'D. Krishnamurthy', 'Dustin Duncan', 'Dylan M Glubb', 'E. Gelardi', 'Edward C. Hsiao', 'E. Lynn', 'Elany Barbosa Silva', 'Elena Aguilera', 'E. Lenci', 'Elena Abraham', 'Eleonora Lama', 'Eleonora Mameli', 'Elisa Leung', 'Emily M. Christensen', 'Emily R. Mason', 'Enrico Petretto', 'Ephraim F. Trakhtenberg', 'Eric J. Rubin', 'Erick Strauss', 'Erik W. Thompson', 'E. Cione', 'E. Lisabeth', 'Erkang Fan', 'E. Kroon', 'Eunji Jo', 'Eva M García-Cuesta', 'E. Glukhov', 'E. Gavathiotis', 'Fang Yu', 'Fei Xiang', 'Fenfei Leng', 'Feng Wang', 'Filippo Ingoglia', 'F. van den Akker', 'Francesco Borriello', 'Franco J. Vizeacoumar', 'F. Luh', 'Frederick S. Buckner', 'Frederick S. Vizeacoumar', 'F. B. Bdira', 'Fredrik Svensson', 'G. M. Rodriguez', 'Gabriella Bognár', 'Gaia Lembo', 'Gang Zhang', 'Garrett Dempsey', 'Gary Eitzen', 'Gaétan Mayer', 'Geoffrey L. Greene', 'George A. Garcia', 'G. Lukács', 'Gergely Prikler', 'G. C. G. Parico', 'G. Colotti', 'Gilles De Keulenaer', 'G. Cortopassi', 'Giovanni Roti', 'Giulia Girolimetti', 'G. Fiermonte', 'G. Gasparre', 'G. Leuzzi', 'Gopal Dahal', 'G. Michlewski', 'G. Conn', 'Grant David Stuchbury', 'Gregory R. Bowman', 'G. Popowicz', 'G. Veit', 'G. E. de Souza', 'Gustav Akk', 'G. Caljon', 'Guzmán Alvarez', 'Gwennan Rucinski', 'Gyeongeun Lee', 'Gökhan Cildir', 'Hai Li', 'Hairol E. Breton', 'Hamed Jafar-Nejad', 'Han Zhou', 'Hannah P. Moore', 'Hannah Tilford', 'Haynes Yuan', 'Heesung Shim', 'Heike Wulff', 'Heinrich Hoppe', 'Helena Chaytow', 'Heng-Keat Tam', 'Holly Van Remmen', 'Hongyang Xu', 'H. Debonsi', 'Howard B. Lieberman', 'Hoyoung Jung', 'Hua-Ying Fan', 'Hui Feng', 'Hui Zhou', 'Hyeong Jun Kim', 'Iain R Greig', 'Ileana Caliandro', 'Ileana Corvo', 'I. Arozarena', 'Imran N. Mungrue', 'I. Verhamme', 'I. Qureshi', 'Irina Lotsaris', 'Isin Cakir', 'J. J. P. Perry', 'Jacek Kwiatkowski', 'Jacob Boorman', 'Jacob Ferreira', 'Jacob Fries', 'J. Kratz', 'Jaden Miner', 'Jair L. Siqueira-Neto', 'James G. Granneman', 'James Ng', 'J. Shorter', 'J. Voss', 'J. Gebauer', 'Janelle Chuah', 'J. Mousa', 'Jason T. Maynes', 'Jay D. Evans', 'J. Dickhout', 'J. MacKeigan', 'Jennifer Jossart', 'Jia Zhou', 'Jiabei Lin', 'Jiake Xu', 'Jianghai Wang', 'Jiaqi Zhu', 'Jiayu Liao', 'Jingyi Xu', 'Jinshi Zhao', 'Jiusheng Lin', 'Jiyoun Lee', 'Joana Reis', 'Joerg Stetefeld', 'John B. Bruning', 'John B. Bruning', 'John G. Coles', 'John J. Tanner', 'John M. Pascal', 'Jonathan So', 'J. L. Pederick', 'J. Costoya', 'Joseph B. Rayman', 'J. J. Maciag', 'Joshua A Nasburg', 'Joshua J Gruber', 'Joshua M. Finkelstein', 'Joshua Watkins', 'J. Rodríguez-Frade', 'Juan Antonio Sanchez Arias', 'J. Lasarte', 'J. Oyarzábal', 'Julian Milosavljevic', 'Julie Cools', 'J. Lescar', 'Julijus Bogomolovas', 'Jun Wang', 'Jung-Min Kee', 'Junzhuo Liao', 'Jyothi C. Sistla', 'J. Abrahão', 'Kamakshi L Sishtla', 'Karol R. Francisco', 'Kasper B. Hansen', 'Kathleen A. Molyneaux', 'Kathryn A Cunningham', 'Katie R. Martin', 'Kavita Gadar', 'K. Ojo', 'Keith S. Wong', 'K. Wentworth', 'Kent Lai', 'K. Lobb', 'K. M. Hopkins', 'K. Parang', 'K. Machaca', 'Kien Pham', 'K. Ghilarducci', 'K. Sugamori', 'K. J. McManus', 'Kirsikka Musta', 'K. Faller', 'Kiyonobu Nagamori', 'Konrad J Mostert', 'Konstantin V. Korotkov', 'Koting Liu', 'Kristiana S. Smith', 'K. Sarosiek', 'Kyle H. Rohde', 'Kyu Kwang Kim', 'Kyung Hyeon Lee', 'Lajos Pusztai', 'L. Lehtiö', 'Larisa M. Haupt', 'Leah E. Cowen', 'Lee J. Byrne', 'Leila Su', 'León Wert-Lamas', 'L. Puchades-Carrasco', 'Lifeng Chen', 'L. Malkas', 'Ling Zhuo', 'Lizbeth Hedstrom', 'L. Walensky', 'Lorenzo Antonelli', 'L. Iommarini', 'Luke Whitesell', 'Lía M. Randall', 'M. Fathallah', 'Maira Harume Nagai', 'M. Kilkenny', 'Manu Ben-Johny', 'Marc P. Lussier', 'M. Windisch', 'Marco Lolicato', 'M. Lolli', 'M. Vleminckx', 'M. Caroleo', 'Maria J. Macias', 'M. Valli', 'Marim M. Barghash', 'Mario Mellado', 'M. Tye', 'Mark A. Wilson', 'Mark Hannink', 'Mark R. Ashton', 'Mark Vincent C.dela Cerna', 'M. Giorgis', 'M. Safo', 'Martin St. Maurice', 'M. McDowell', 'M. Pasquali', 'Md. Mehedi', 'M. Serafim', 'M. Soellner', 'Matthew G. Alteen', 'Matthew M. Champion', 'Maxim Skorodinsky', 'Megan L. O’Mara', 'Mel Bedi', 'Menico Rizzi', 'Michael Levin', 'Michael Mowat', 'Michael R. Jackson', 'M. Paige', 'Minnatallah Al-Yozbaki', 'M. Giardini', 'M. Maksimainen', 'Monica De Luise', 'Muhammad Saddam Hussain', 'M. Christodoulides', 'Natalia Stec', 'N. Zelinskaya', 'Natascha Van Pelt', 'Nathan M. Merrill', 'Nathanael Singh', 'N. Kootstra', 'Neeraj Singh', 'Neha S. Gandhi', 'Nei-Li Chan', 'Nguyen Mai Trinh', 'Nicholas O. Schneider', 'Nick Matovic', 'N. Horstmann', 'Nicola Longo', 'N. Bharambe', 'Nirvan Rouzbeh', 'N. Mahmoodi', 'N. Gumede', 'N. Anastasio', 'N. Khalaf', 'Obdulia Rabal', 'Olga Kandror', 'O. Escaffre', 'O. Silvennoinen', 'Ozlem Tastan Bishop', 'Pablo Iglesias', 'P. Sobrado', 'Patrick Chuong', 'Patrick O’Connell', 'P. Martin-Malpartida', 'P. Mellor', 'Paul V. Fish', 'Paulo Otávio Lourenço Moreira']2024-04-02T00:00:00https://www.semanticscholar.org/paper/56bc304e96fc1a84279b023e4ecbfa9a4033f20b bfac7982a1fa4f1db75a2437578969d04dcbe6b1PurposeThe applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.Design/methodology/approachWe gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.FindingsWe found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.Practical implicationsOverall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.Originality/valueOur study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.Information Technology &amp; People700The role of professional identity is unveiled in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.Information Technology &amp; People['Farjam Eshraghian', 'Najmeh Hafezieh', 'F. Farivar', 'Sergio de Cesare']2024-04-02T00:00:00https://www.semanticscholar.org/paper/bfac7982a1fa4f1db75a2437578969d04dcbe6b1 9059284139e4b316964514efcd68a28748bba5b6With the widespread integration of artificial intelligence (AI) and blockchain technologies, safeguarding privacy has become of paramount importance. These techniques not only ensure the confidentiality of individuals' data but also maintain the integrity and reliability of information. This study offers an introductory overview of AI and blockchain, highlighting their fusion and the subsequent emergence of privacy protection methodologies. It explores various application contexts, such as data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity techniques. Moreover, the paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability. Additionally, it conducts a comprehensive analysis of existing shortcomings, identifying their root causes and suggesting corresponding remedies. The study categorizes and synthesizes privacy protection methodologies based on AI-blockchain application contexts and technical frameworks. In conclusion, it outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain, emphasizing the need to enhance efficiency and security for a more comprehensive safeguarding of privacy.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The paper critically evaluates five essential dimensions of privacy protection systems within AI-blockchain integration: authorization management, access control, data security, network integrity, and scalability, and outlines prospective avenues for the evolution of privacy protection technologies resulting from the integration of AI and blockchain.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Harish Padmanaban']2024-04-02T00:00:00https://www.semanticscholar.org/paper/9059284139e4b316964514efcd68a28748bba5b6 27a7b0d905cb7c2e4588b2db5f05364ce64b129eScientific Reports270a_iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations.Scientific Reports['Nooshin Yousefzadeh', 'Charlie Tran', 'Adolfo Ramirez-Zamora', 'Jinghua Chen', 'Ruogu Fang', 'My T Thai']2024-04-02T00:00:00https://www.semanticscholar.org/paper/27a7b0d905cb7c2e4588b2db5f05364ce64b129e e912f577a45517fe24d4dfad4d768f68ccf2a069With the advent of Large Language Models, such as ChatGPT, and, more generally, generative AI/cognitive technologies, global knowledge production faces a critical systemic challenge. It results from continuously feeding back non- or poorly-creative copies of itself into the global knowledge base; in the worst case, this could not only lead to a stagnation of creative, reliable, and valid knowledge generation, but also have an impact on our material (and subsequently our social) world and how it will be shaped by these rather uninspired automatized knowledge dynamics. More than ever, there appears to be an imperative to bring the creative human agent back into the loop. Arguments from the perspectives of 4E- and Material Engagement Theory approaches to cognition, human-technology relations as well as possibility studies will be used to show that being embodied, sense-making, and enacting the world by proactively and materially interacting with it are key ingredients for any kind of knowledge and meaning production. It will be shown that taking seriously the creative agency of the world, an engaged epistemology, as well as making use of future potentials/possibilities complemented and augmented by cognitive technologies are all essential for re-introducing profound novelty and creativity.Possibility Studies &amp; Society590Arguments from the perspectives of 4E- and Material Engagement Theory approaches to cognition, human-technology relations as well as possibility studies will be used to show that being embodied, sense-making, and enacting the world by proactively and materially interacting with it are key ingredients for any kind of knowledge and meaning production.Possibility Studies &amp; Society['M. Peschl']2024-04-02T00:00:00https://www.semanticscholar.org/paper/e912f577a45517fe24d4dfad4d768f68ccf2a069 899638ada4117a260f0e76044a0dab350163a388Human-centered AI (HCAI), rather than replacing the human, puts the human user in the driver's seat of so-called human-centered AI-infused tools (HCAI tools): interactive software tools that amplify, augment, empower, and enhance human performance using AI models; often novel generative or foundation AI ones. In this paper, we discuss how interactive visualization can be a key enabling technology for creating such human-centered AI tools. Visualization has already been shown to be a fundamental component in explainable AI models, and coupling this with data-driven, semantic, and unified interaction feedback loops will enable a human-centered approach to integrating AI models in the loop with human users. We present several examples of our past and current work on such HCAI tools, including for creative writing, temporal prediction, and user experience analysis. We then draw parallels between these tools to suggest common themes on how interactive visualization can support the design of future HCAI tools.arXiv.org1090How interactive visualization can be a key enabling technology for creating human-centered AI tools, and coupling this with data-driven, semantic, and unified interaction feedback loops will enable a human-centered approach to integrating AI models in the loop with human users is discussed.ArXiv['Md. Naimul Hoque', 'Sungbok Shin', 'Niklas Elmqvist']2024-04-02T00:00:00https://www.semanticscholar.org/paper/899638ada4117a260f0e76044a0dab350163a388 55cbb390e13c89cefb8d419c80b210ad99a81adaFederated learning stands out as a promising approach within the realm of distributed artificial intelligence (AI) systems, facilitating collaborative model training across decentralized devices while safeguarding data privacy. This study presents a thorough investigation into federated learning architecture, covering its foundational design principles, implementation methodologies, and the significant challenges encountered in distributed AI systems. We delve into the fundamental mechanisms underpinning federated learning, elucidating its merits in diverse environments and its prospective applications across various domains. Additionally, we scrutinize the technical complexities associated with deploying federated learning systems, including considerations such as communication efficiency, model aggregation techniques, and security protocols. By amalgamating insights gleaned from recent research endeavors and practical deployments, this study furnishes valuable guidance for both researchers and practitioners aiming to harness federated learning for the development of scalable and privacy-preserving AI solutions.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This study dives into the fundamental mechanisms underpinning federated learning, elucidating its merits in diverse environments and its prospective applications across various domains, and scrutinizes the technical complexities associated with deploying federated learning systems.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['José Gabriel Carrasco Ramírez']2024-04-02T00:00:00https://www.semanticscholar.org/paper/55cbb390e13c89cefb8d419c80b210ad99a81ada 871302f91450a4538292e905055158172705c0f5We are at the beginning of the beginning of the beginning of the development of AI. The ethical issues we first saw and are still grappling with have been overtaken by others, and there are yet others on the horizon.Annals of the New York Academy of Sciences00The ethical issues first saw and are still grappling with have been overtaken by others, and there are yet others on the horizon.Annals of the New York Academy of Sciences['Katherine B Forrest']2024-04-02T00:00:00https://www.semanticscholar.org/paper/871302f91450a4538292e905055158172705c0f5 44e2b46b0f57cbb5a8fb3d2c0ddb25245d833f6fInternational Journal of Emergency Medicine280Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.International Journal of Emergency Medicine['M. Ortíz-Barrios', 'A. Petrillo', 'Sebastián Arias-Fonseca', 'Sally McClean', 'F. De Felice', 'Chris Nugent', 'Sheyla-Ariany Uribe-López']2024-04-02T00:00:00https://www.semanticscholar.org/paper/44e2b46b0f57cbb5a8fb3d2c0ddb25245d833f6f f93400247580dd8f09e01c05c211699c594bae93Artificial intelligence (AI) is an important and emerging sub-discipline in information technology that is progressively being implemented in every field. It is gradually being introduced to support new forms of research, discovery, and reuse of library contents in advanced and interesting ways. University libraries have the potential to substantially improve their library services through the implementation of sophisticated AI tools. This study explored the application of AI tools in the university libraries of Pakistan, as well as draw a comparison in the usage of AI tools between public and private sector universities. This is a quantitative study and data is collected through survey methodology. We used purposive sampling to collect the data from 175 university libraries. The collected data was analyzed using a statistical package for social sciences (SPSS-version 22). Findings indicate that while AI-based services are starting to be introduced into university libraries in Pakistan, no university library has implemented the full suite of AI-based tools. Natural language processing, voice searching, and chatbots are the most familiar and popular tools currently used in libraries. However, robotics technology is rarely used with a mean value of (1.62) because of the financial investment and high level of IT skills required. We found that private university libraries are using AI tools more as compared with public sector university libraries. The study concludes with several key recommendations, including closer collaboration between the library and the respective university IT department for technical support and assistance; improved financial support and ICT infrastructure to establish AI technology-based library services; and training development plans for library staff. Insights gained from this study should contribute to the capacity of Pakistani University librarians and their staff to maximize the full potential of AI within their institutions. The research implications are helpful to library leaders and policymakers in building a policy for AI-based technology in their respective university libraries.Journal of Librarianship and Information Science410It is found that private university libraries are using AI tools more as compared with public sector university libraries, and closer collaboration between the library and the respective university IT department for technical support and assistance is recommended.Journal of Librarianship and Information Science['Muhammad Yousuf Ali', 'S. Naeem', 'R. Bhatti']2024-04-02T00:00:00https://www.semanticscholar.org/paper/f93400247580dd8f09e01c05c211699c594bae93 42c9699a0a6cfacb4585ca7a71a23a21ede5ec18Background: With the rapid integration of artificial intelligence (AI) in law enforcement, AI-driven crime detection has become an essential research area. This paper provides a bibliometric analysis of the field, examining publication patterns, geographic distribution, leading authors, prominent affiliations, and thematic focus. Methods: Using the PRISMA framework, publications from 2015 to 2024 were analyzed based on predefined inclusion criteria. The study synthesized data on publication count, citation metrics, and keyword occurrences to identify trends and influential contributors within the AI-driven crime detection domain. Results: The analysis revealed a significant increase in research output, with India, the United States, and China as leading contributors, reflecting a global commitment to AI in crime detection. Prominent authors such as Ban Tao and Nour Moustafa emerged, demonstrating high citation counts and H-index values indicative of the field's academic influence. Leading affiliations, including the SRM Institute of Science and Technology and the University of New South Wales, underscored the importance of institutional support in advancing research. Keyword analysis highlighted a shift from general topics like 'computer crime' to specific technologies such as 'machine learning' and 'deep learning,' reflecting the field's progression towards practical applications. Conclusion: AI-driven crime detection research is characterized by its rapid growth, diverse global contributions, and a shift towards specialized, technology-driven solutions. The increasing focus on machine learning and network security illustrates the field's response to the evolving landscape of digital crime, with implications for future research directions and policy-making.International Journal of Academic Research in Business and Social Sciences80A bibliometric analysis of AI-driven crime detection research revealed a significant increase in research output, with India, the United States, and China as leading contributors, reflecting a global commitment to AI in crime detection.International Journal of Academic Research in Business and Social Sciences['Humaid Albastaki', 'A. C. Yaacob', 'Kawthar Abdalla Bayoumi']2024-04-02T00:00:00https://www.semanticscholar.org/paper/42c9699a0a6cfacb4585ca7a71a23a21ede5ec18 49ed42f60e85688b9539baabc37dce6e9eb474b0This study aims to enhance business sustainability in the context of Higher Education Institutions (HEIs) by utilizing AI and forecasting techniques. It explores the development and comparison of prediction models, including the use of dashboard development, to support decision-making processes within HEIs. The study covers various aspects, including the background of forecasting and prediction models, the use of specific models such as the Prophet Model, Long Short-Term Memory (LSTM) Model, and Polynomial Regression Model, as well as the importance of dashboards for HEIs. The methodology section outlines the data collection and preparation process, model selection, approach, diagrams, functional and non-functional requirements, justification of tools, and libraries and models used. The implementation section delves into the system design and development of the dashboard, including the login page, homepage, forecast page, and insert data page. As for the findings, the LSTM Model has proven to be the most accurate and suitable model to be implemented for forecasting student enrolment data in this study. The dashboard's future enhancements involve adding more faculties, predictive features for resource allocation, refining the visual identity, improving user registration on the login page, and exploring better models for student enrolment predictions. Overall, the study provides valuable insights into the application of AI and forecasting techniques in HEIs, aiming to enhance business sustainability and decision-making processes. It contributes to the growing body of knowledge on the use of technology-enabled AI in higher education institutions, with a focus on forecasting student enrolment data and developing prediction models.PaperASIA330The LSTM Model has proven to be the most accurate and suitable model to be implemented for forecasting student enrolment data in this study and is contributing to the growing body of knowledge on the use of technology-enabled AI in higher education institutions.PaperASIA['Hao Qian Gnoh', 'Kay Hooi Keoy', 'Javid Iqbal', 'Shaik Shabana Anjum', 'Sook Fern Yeo', 'Ai-Fen Lim', 'WeiLee Lim', 'Lee Yen Chaw']2024-04-02T00:00:00https://www.semanticscholar.org/paper/49ed42f60e85688b9539baabc37dce6e9eb474b0 fee0e28a4125f3173abffc5f95018faf38c24ec4Расширение сфер применения систем искусственного интеллекта обостряет проблему обеспечения функциональной корректности и безопасности этих систем, что особенно важно для ответственных прикладных отраслей. Expanding areas of AI systems’ use lend ever more importance to the challenge of ensuring functional correctness and security of those solutions, which is especially critical for high-stakes verticals. What is the impact of data quality on functionality and security characteristics of AI systems?Открытые системы. СУБД00Открытые системы. СУБД['С. Гарбук']2024-04-02T00:00:00https://www.semanticscholar.org/paper/fee0e28a4125f3173abffc5f95018faf38c24ec4 0ba0da3e4e2780e4764483be8e60cc869da35dd3AI and Ethics540Recommendations are given of how developers, regulators, deployers and the public can navigate the relationship between AI hype, innovation, investment and scientific exploration, while addressing critical societal and environmental challenges.AI and Ethics['Alva Markelius', 'Connor Wright', 'Joahna Kuiper', 'Natalie Delille', 'Yu-Ting Kuo']2024-04-02T00:00:00https://www.semanticscholar.org/paper/0ba0da3e4e2780e4764483be8e60cc869da35dd3 236dd639ec8bca470a52983676bce13fc1aaf26dRecent advancements in Artificial Intelligence (AI) and machine learning have demonstrated transformative capabilities across diverse domains. This progress extends to the field of patent analysis and innovation, where AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle such as classification, retrieval, and valuation prediction. This not only accelerates the efficiency of patent researchers and applicants but also opens new avenues for technological innovation and discovery. Our survey provides a comprehensive summary of recent AI tools in patent analysis from more than 40 papers from 26 venues between 2017 and 2023. Unlike existing surveys, we include methods that work for patent image and text data. Furthermore, we introduce a novel taxonomy for the categorization based on the tasks in the patent life cycle as well as the specifics of the AI methods. This survey aims to serve as a resource for researchers, practitioners, and patent offices in the domain of AI-powered patent analysis.arXiv.org580This survey provides a comprehensive summary of recent AI tools in patent analysis from more than 40 papers from 26 venues between 2017 and 2023 and introduces a novel taxonomy for the categorization based on the tasks in the patent life cycle as well as the specifics of the AI methods.ArXiv['Homaira Huda Shomee', 'Zhu Wang', 'Sathya Ravi', 'Sourav Medya']2024-04-02T00:00:00https://www.semanticscholar.org/paper/236dd639ec8bca470a52983676bce13fc1aaf26d 5e50ee64a8bb74df488510303214943a768e865aCollaborative human-AI (HAI) teaming combines the unique skills and capabilities of humans and machines in sustained teaming interactions leveraging the strengths of each. In tasks involving regular exposure to novelty and uncertainty, collaboration between adaptive, creative humans and powerful, precise artificial intelligence (AI) promises new solutions and efficiencies. User trust is essential to creating and maintaining these collaborative relationships. Established models of trust in traditional forms of AI typically recognize the contribution of three primary categories of trust antecedents: characteristics of the human user, characteristics of the technology, and environmental factors. The emergence of HAI teams, however, requires an understanding of human trust that accounts for the specificity of task contexts and goals, integrates processes of interaction, and captures how trust evolves in a teaming environment over time. Drawing on both the psychological and computer science literature, the process framework of trust in collaborative HAI teams (CHAI-T) presented in this paper adopts the tripartite structure of antecedents established by earlier models, while incorporating team processes and performance phases to capture the dynamism inherent to trust in teaming contexts. These features enable active management of trust in collaborative AI systems, with practical implications for the design and deployment of collaborative HAI teams.arXiv.org430The process framework of trust in collaborative HAI teams (CHAI-T) presented in this paper adopts the tripartite structure of antecedents established by earlier models, while incorporating team processes and performance phases to capture the dynamism inherent to trust in teaming contexts.ArXiv['Melanie Mcgrath', 'Andreas Duenser', 'Justine Lacey', 'Cecile Paris']2024-04-02T00:00:00https://www.semanticscholar.org/paper/5e50ee64a8bb74df488510303214943a768e865a 5c15405ed45742e05054cb9ac080ce16d0ec327eThis paper presents an in-depth exploration of the integration of Artificial Intelligence (AI) in theatre, with a focus on the creation of AI-generated stage plays. Utilizing ChatGPT's language model and deep learning algorithms, the research investigates AI's capability to generate compelling scripts, characters, and narratives for theatrical productions. The study employs a mixed-methods approach, analyzing existing AI-generated theatrical works and conducting experimental script creation using AI. The findings highlight both the potential and limitations of AI in theatre, emphasizing the need to balance AI-generated content with the intrinsic human qualities of theatrical performances. The paper contributes to the understanding of AI's impact on theatre and raises questions about audience engagement and authenticity in AI-generated content.Theoretical and Natural Science00An in-depth exploration of the integration of Artificial Intelligence in theatre, with a focus on the creation of AI-generated stage plays, using ChatGPT's language model and deep learning algorithms to investigate AI's capability to generate compelling scripts, characters, and narratives for theatrical productions.Theoretical and Natural Science['Zhen Ren']2024-04-02T00:00:00https://www.semanticscholar.org/paper/5c15405ed45742e05054cb9ac080ce16d0ec327e 121397e902bc51d7fb02dfe76b9c8042da350eb0 Experiential artificial intelligence (AI) is an approach to the design, use, and evaluation of AI in cultural or other real-world settings that foregrounds human experience and context. It combines arts and engineering to support rich and intuitive modes of model interpretation and interaction, making AI tangible and explicit. The ambition is to enable significant cultural works and make AI systems more understandable to nonexperts, thereby strengthening the basis for responsible deployment. This paper discusses limitations and promising directions in explainable AI, contributions the arts offer to enhance and go beyond explainability and methodology to support, deepen, and extend those contributions.Leonardo: Journal of the International Society for the Arts, Sciences and Technology00Limits and promising directions in explainable AI are discussed, contributions the arts offer to enhance and go beyond explainability and methodology to support, deepen, and extend those contributions are discussed.Leonardo['Drew Hemment', 'Dave Murray-Rust', 'Vaishak Belle', 'Ruth Aylett', 'Matjaz Vidmar', 'Frank Broz']2024-04-02T00:00:00https://www.semanticscholar.org/paper/121397e902bc51d7fb02dfe76b9c8042da350eb0 de2b91f0f2473b39867237e79adfa981516ac2de Purpose This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy. Design/methodology/approach Data for this study include student interviews (called “talk backs”), journal reflections and final technology presentations. Findings Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world. Originality/value While critical race media literacy draws upon critical race theory in education – a theorization of race, and a critique of white supremacy and multiculturalism in schools – critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems. English Teaching: Practice &amp; Critique320The data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world.English Teaching: Practice &amp; Critique['Tiera Tanksley']2024-04-02T00:00:00https://www.semanticscholar.org/paper/de2b91f0f2473b39867237e79adfa981516ac2de 34059ccd04127a3fb8a02f667613b85d04bc485fEnvironment, Development and Sustainability520Environment, Development and Sustainability['Gulshan Kumar', 'Ali Altalbe']2024-04-02T00:00:00https://www.semanticscholar.org/paper/34059ccd04127a3fb8a02f667613b85d04bc485f 22505c1d82f74bb198d186b63a7e8c17c272a5e5Journal of Grid Computing140J. Grid Comput.['Tao Hai', 'Muammer Aksoy', 'C. Iwendi', 'Ebuka Ibeke', 'Senthilkumar Mohan']2024-04-02T00:00:00https://www.semanticscholar.org/paper/22505c1d82f74bb198d186b63a7e8c17c272a5e5 153683cb52815e31965f695937a226b163b29bb4Journal of Digital Learning in Teacher Education10Journal of Digital Learning in Teacher Education['Ann D. Thompson', 'Denise A. Schmidt-Crawford', 'Denise L. Lindstrom']2024-04-02T00:00:00https://www.semanticscholar.org/paper/153683cb52815e31965f695937a226b163b29bb4 b23d25ea925f3eed5722c8ad129f79a8b6489601Journal of International Maritime Safety Environmental Affairs and Shipping220Journal of International Maritime Safety, Environmental Affairs, and Shipping['A. Madsen', 'T. E. Kim']2024-04-02T00:00:00https://www.semanticscholar.org/paper/b23d25ea925f3eed5722c8ad129f79a8b6489601 d9e3204ac206fd0da62547174d2489404b9639d2Asian Journal of Civil Engineering180Asian Journal of Civil Engineering['Kaushal Kumar', 'Saurav Dixit', 'Umank Mishra', 'Nikolai Ivanovich Vatin']2024-04-02T00:00:00https://www.semanticscholar.org/paper/d9e3204ac206fd0da62547174d2489404b9639d2 eadba34dbf23fafe56a5db9afec579922d21ab7eAmerican Journal of Pharmaceutical Education00A successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course and is likely to stimulate student interest in AI applications in pharmacy.American journal of pharmaceutical education['Megan L Culp', 'Sara Mahmoud', 'Daniel Liu', 'Ian S. Haworth']2024-04-02T00:00:00https://www.semanticscholar.org/paper/eadba34dbf23fafe56a5db9afec579922d21ab7e 70e7fb84e55823eefc72f42952cd7e979d753a35European Journal of Psychotherapy &amp; Counselling40European Journal of Psychotherapy &amp; Counselling['D. Loewenthal']2024-04-02T00:00:00https://www.semanticscholar.org/paper/70e7fb84e55823eefc72f42952cd7e979d753a35 1fe445e1eff9bfb9144fcb61cc874f6d43c81621Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications00Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications['Anshu Goyal', 'Seena Pourzand', 'Sachi Pawooskar-Almeida', 'M. Wahi-Anwar', 'G. Apolo Aroca', 'Benjamin Y. Xu', 'Matthew S. Brown', 'Brent J. Liu']2024-04-02T00:00:00https://www.semanticscholar.org/paper/1fe445e1eff9bfb9144fcb61cc874f6d43c81621 4b86874dd2c43d774ba557496c38f0ef6f393b27Public Services Quarterly40Public Services Quarterly['Derek Marshall', 'Joy DuBose']2024-04-02T00:00:00https://www.semanticscholar.org/paper/4b86874dd2c43d774ba557496c38f0ef6f393b27 29477b2842024028aabc5a3488be074dbe305c15Introduction Research suggests that problems with emotion regulation, that is, how a person manages and responds to an emotional experience, are related to a range of psychological disorders (eg, bipolar disorder, anxiety and depression). Interventions targeting emotion regulation have been shown to improve mental health in adults, but evidence on related interventions for adolescents is still emerging. Increasingly, self-directed digital interventions (eg, mobile apps) are being developed to target emotion regulation in this population, but questions remain about their effectiveness. This systematic review aimed to synthesise evidence on current self-directed digital interventions available to adolescents (aged 11–18 years) and their effectiveness in addressing emotion regulation, psychopathology and functioning (eg, academic achievement). Methods and analysis Several electronic databases will be searched (eg, MEDLINE, PsycINFO, ACM Digital Library) to identify all studies published any time after January 2010 examining self-directed digital interventions for adolescents, which include an emotion regulation component. This search will be updated periodically to identify any new relevant research from the selected databases. Data on the study characteristics (eg, author(s)) and methodology, participant characteristics (eg, age) and the digital interventions used to address emotion (dys-)regulation (eg, name, focus) will be extracted. A narrative synthesis of all studies will be presented. If feasible, the effectiveness data will be synthesised using appropriate statistical techniques. The methodological quality of the included studies will be assessed with the Effective Public Health Practice Project quality assessment tool. Ethics and dissemination Ethical approval is not required for this study. Findings will be disseminated widely via peer-reviewed publications and presentations at conferences related to this field. Registration details PROSPERO CRD42022385547.BMJ Open360BMJ Open['Abigail Thomson', 'E. G. Lawrence', 'Bonamy R Oliver', 'Ben Wright', 'Georgina M Hosang']2024-04-01T00:00:00https://www.semanticscholar.org/paper/29477b2842024028aabc5a3488be074dbe305c15 ae9f9c5496ccb25b67f51cf5ccb76d33b2be106dThe independent hierarchy of train dispatching command and train operation control in the existing urban rail transit systems restricts the improvement of operational efficiency and emergency handling capability. This article focuses on integrating train regulation and speed profile optimization by utilizing a feature learning and hybrid search algorithm. Specifically, a genetic algorithm (GA) is used to optimize the train speed profile for a fixed interval running time, and then, the generated labeled sample data are used to train a convolutional neural network (CNN) to learn and extract the features of the optimal speed profile. The nonlinear mapping relationship between input and output variables in trajectory optimization is characterized by a well-trained CNN to reduce the computation time of the optimal speed profile during train regulation. The input variables comprise line conditions and interval running times, while the output variables include the corresponding energy consumption and operating condition switching points of the optimal speed profile. An integrated model of train regulation and operation control is developed with the objective of minimizing total train delay time and energy consumption. To ensure convergence and global search capability, we design a hybrid search algorithm-based train regulation algorithm. Simulation experiments are conducted using data from the Beijing Yizhuang line to validate the effectiveness of the proposed model and algorithms. The experimental results demonstrate that the proposed method can provide an optimal scheme for train regulation and speed profiles.IEEE Transactions on Computational Social Systems331This article focuses on integrating train regulation and speed profile optimization by utilizing a feature learning and hybrid search algorithm and demonstrates that the proposed method can provide an optimal scheme for train regulation and speed profiles.IEEE Transactions on Computational Social Systems['Min Zhou', 'Z. Hou', 'Xingtang Wu', 'Hairong Dong', 'Fei-Yue Wang']2024-04-01T00:00:00https://www.semanticscholar.org/paper/ae9f9c5496ccb25b67f51cf5ccb76d33b2be106d 0eca1d9951e0fbd54b9c578c473777b29bf87936This article addresses a critical gap in the current AI regulatory discourse by focusing on the environmental sustainability of AI and technology more broadly, a topic often overlooked both in environmental law and in technology regulation, such as the General Data Protection Regulation (GDPR) or the EU AI Act. Recognizing AI’s significant impact on climate change and its substantial water consumption, especially in large generative models like ChatGPT, GPT-4, or Gemini, the article aims to integrate sustainability considerations into technology regulation, in three steps. First, while current EU environmental law does not directly address these issues, there is potential to reinterpret existing legislation, such as the GDPR, to support sustainability goals. Counterintuitively, the article argues that this also implies the need to balance individual rights, such as the right to erasure, with collective environmental interests. Second, based on an analysis of current law, and the proposed EU AI Act, the article suggests a suite of policy measures to align AI and technology regulation with environmental sustainability. They extend beyond mere transparency mechanisms, such as disclosing greenhouse gas footprints, to include a mix of strategies like co-regulation, sustainability by design, restrictions on training data, and consumption caps, potentially integrating AI and technology more broadly into the EU emissions trading regime. Third, this regulatory toolkit could serve as a blueprint for other technologies with high environmental impacts, such as blockchain and metaverse applications. The aim is to establish a comprehensive framework that addresses the dual fundamental societal transformations of digitization and climate change mitigation. AI regulation, environmental sustainability, GDPR, EU AI Act, sustainability goalsCommon market law review00A suite of policy measures to align AI and technology regulation with environmental sustainability are suggested, which extend beyond mere transparency mechanisms, such as disclosing greenhouse gas footprints, to include a mix of strategies like co-regulation, sustainability by design, restrictions on training data, and consumption caps.Common Market Law Review['Philipp Hacker']2024-04-01T00:00:00https://www.semanticscholar.org/paper/0eca1d9951e0fbd54b9c578c473777b29bf87936 a138d6f22f8d560bce21ba58bf131140ad4b72aeComputer Law &amp; Security Review01Computer Law &amp; Security Review['Jingchen Zhao']2024-04-01T00:00:00https://www.semanticscholar.org/paper/a138d6f22f8d560bce21ba58bf131140ad4b72ae 1ae1e5f0932e3e2ac5112cb510a9fe992427cd51Journal of Nursing Regulation00Journal of Nursing Regulation[]2024-04-01T00:00:00https://www.semanticscholar.org/paper/1ae1e5f0932e3e2ac5112cb510a9fe992427cd51 9754c3c2b9ca1715709bda6433e353158113473cEven in the absence of broader AI regulation, federal procurement provisions could set expectations for data quality, model performance, risk assessments, and documentation.Issues in science and technology00Issues in Science and Technology['Merve Hickok', 'Evanna Hu']2024-04-01T00:00:00https://www.semanticscholar.org/paper/9754c3c2b9ca1715709bda6433e353158113473c 410069abff897d6c4923d3cfda1dfffedefaaef6The article discusses the increasing use of artificial intelligence (AI) by tax authorities in the European Union, the resulting benefits and risks, and the necessity for an appropriate legal framework. Tax administrations employ AI systems for various tasks, from risk detection to legal analysis. While automation offers efficiency, there are also risks, such as violations of fundamental rights and discrimination, illustrated by examples like the Dutch childcare benefits scandal. It deals with two relevant EU regulations, namely the General Data Protection Regulation (GDPR) and the proposed European AI regulation (AI Act), emphasizing the need for more clarity and protection for taxpayers. The GDPR imposes a principled ban on fully automated decisions but allows exceptions if appropriate measures are in place. The AI Act introduces a right to human intervention for high-risk AI systems, but the author argues that the regulations are not clear enough, especially in view of the upcoming ‘tax administration 3.0ʹ model of the OECD further reducing human intervention. In short, specific guidelines and regulations are needed to ensure the fundamental rights of taxpayers in an increasingly automated tax environment. Artificial Intelligence (AI), Tax authorities, General Data Protection Regulation (GDPR), European Union Artificial Intelligence Regulation (AI Act), Fundamental rights, Tax collection process, Human intervention, Risk detection, Taxpayer assistance, Tax administration 3.0 model (OECD)EC Tax Review00Specific guidelines and regulations are needed to ensure the fundamental rights of taxpayers in an increasingly automated tax environment, especially in view of the upcoming ‘tax administration 3.0ʹ model of the OECD further reducing human intervention.EC Tax Review[]2024-04-01T00:00:00https://www.semanticscholar.org/paper/410069abff897d6c4923d3cfda1dfffedefaaef6 080b25f73eea9c06e0c423eadf2450a98b991caaOn 26 October 2023, the Elverding Conference on Enhancing sustainable business and corporate regulation in the EU took place at the Maastricht University Faculty of Law. The conference was organized to discuss the company law-related efforts aiming to address the significant sustainability challenges that Europe is facing. The first session examined effective and sustainable approaches to active ownership. In it, Prof. Cools shared her perspectives on whether shareholder activism can be a driver of sustainability, and Prof. Bauer built upon this topic by discussing various strategies of active ownership and their impact on sustainability. The second session focused on the alignment between directors' duties and the social role of corporations. Prof. Davies explained why a change of corporate purpose, as advocated by Mayer in Prosperity, would not be enough to change the nature of doing business. Prof. Winter then followed up with his presentation emphasizing the urgent need to address societal problems through channels other than just government regulation. The third and last session invited representatives from companies, the investor community, and civil society to discuss sustainable business practices. This report provides an overview of the discussions held during the first two sessions of the conference, highlighting the main arguments presented by the speakers and their relevance to the ongoing discourse on sustainable company law. sustainability, ESG, shareholder activism, active ownership, corporate purpose, sustainable business, Green DealEuropean Company Law00European Company Law['Lucia Jeremiašová', 'Constantijn van Aartsen']2024-04-01T00:00:00https://www.semanticscholar.org/paper/080b25f73eea9c06e0c423eadf2450a98b991caa bbadbc24455ffe52d11a4c7d4343dca0c4b7e539ANCIENT LAND00ANCIENT LAND['Mirzabala Poladov']2024-04-01T00:00:00https://www.semanticscholar.org/paper/bbadbc24455ffe52d11a4c7d4343dca0c4b7e539 d7777b5efc0a65c5b79020320b3ad76687f64fc6Наука і техніка сьогодні00Наука і техніка сьогодні['Володимир Токар', 'Мар’яна Сашньова']2024-04-01T00:00:00https://www.semanticscholar.org/paper/d7777b5efc0a65c5b79020320b3ad76687f64fc6 c695768e83d4c70a3f496205e95f2f68c4ff3bddTraditionally, ideating new product innovations is primarily the responsibility of marketers, engineers, and designers. However, a rapidly growing interest lies in leveraging generative artificial intelligence (AI) to brainstorm new product and service ideas. This study conducts a comparative analysis of ideas generated by human professionals and an AI system. The results of a blind expert evaluation show that AI-generated ideas score significantly higher in novelty and customer benefit, while their feasibility scores are similar to those of human ideas. Overall, AI-generated ideas comprise the majority of the top-performing ideas, while human-generated ideas scored lower than expected. The executive's emotional and cognitive reactions were measured during the evaluation to check for potential biases and showed no differences between the idea groups. These findings suggest that, under certain circumstances, companies can benefit from integrating generative AI into their traditional idea-generation processes.IEEE Engineering Management Review512A comparative analysis of ideas generated by human professionals and an AI system shows that AI-generated ideas score significantly higher in novelty and customer benefit, while their feasibility scores are similar to those of human ideas.IEEE Engineering Management Review['Jan Joosten', 'Volker Bilgram', 'Alexander Hahn', 'Dirk Totzek']2024-04-01T00:00:00https://www.semanticscholar.org/paper/c695768e83d4c70a3f496205e95f2f68c4ff3bdd c593c1adf858050fdf09c840e71e815cf1bcf5c9Artificial Intelligence (AI) in healthcare marks a new era of innovation and efficiency, characterized by the emergence of sophisticated language models such as ChatGPT (OpenAI, San Francisco, CA, USA), Gemini Advanced (Google LLC, Mountain View, CA, USA), and Co-pilot (Microsoft Corp, Redmond, WA, USA). This review explores the transformative impact of these AI technologies on various facets of healthcare, from enhancing patient care and treatment protocols to revolutionizing medical research and tackling intricate health science challenges. ChatGPT, with its advanced natural language processing capabilities, leads the way in providing personalized mental health support and improving chronic condition management. Gemini Advanced extends the boundary of AI in healthcare through data analytics, facilitating early disease detection and supporting medical decision-making. Co-pilot, by integrating seamlessly with healthcare systems, optimizes clinical workflows and encourages a culture of innovation among healthcare professionals. Additionally, the review highlights the significant contributions of AI in accelerating medical research, particularly in genomics and drug discovery, thus paving the path for personalized medicine and more effective treatments. The pivotal role of AI in epidemiology, especially in managing infectious diseases such as COVID-19, is also emphasized, demonstrating its value in enhancing public health strategies. However, the integration of AI technologies in healthcare comes with challenges. Concerns about data privacy, security, and the need for comprehensive cybersecurity measures are discussed, along with the importance of regulatory compliance and transparent consent management to uphold ethical standards and patient autonomy. The review points out the necessity for seamless integration, interoperability, and the maintenance of AI systems' reliability and accuracy to fully leverage AI's potential in advancing healthcare.Cureus372The review points out the necessity for seamless integration, interoperability, and the maintenance of AI systems' reliability and accuracy to fully leverage AI's potential in advancing healthcare.Cureus['A. Alhur']2024-04-01T00:00:00https://www.semanticscholar.org/paper/c593c1adf858050fdf09c840e71e815cf1bcf5c9 1e3d26f830fe3eb2c900dcd1da74331c5e37e240Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.Arquivos de Neuro-Psiquiatria991The existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions is surveyed.Arquivos de Neuro-Psiquiatria['Fabíola Bezerra de Carvalho Macruz', 'Ana Luiza Mandetta Pettengil Dias', 'Celi Santos Andrade', 'M. Nucci', 'C. Rimkus', 'Leandro Tavares Lucato', 'Antônio José da Rocha', 'F. Kitamura']2024-04-01T00:00:00https://www.semanticscholar.org/paper/1e3d26f830fe3eb2c900dcd1da74331c5e37e240 611f5732651b717dc4e3e07c1f5bf8a4f50e7f21OBJECTIVES/GOALS: This work aims to explore how citizen science serves as a transformative frame work to bridge scientific knowledge, focusing on its potential to enhance transdisciplinary learning in artificial intelligence (AI) biomedical and clinical sciences by facilitating near-peer mentoring. METHODS/STUDY POPULATION: Our group of eight friends comprise a multicultural and multidisciplinary cohort including students from the USA, Philippines, Indonesia, and Guatemala pursuing PhD degrees in electrical and computer engineering, epidemiology, physics, and MD, PharmD, and DMD degrees. We engage in shared online courses, collaborative projects, and abstract submissions. Employing our collective knowledge, we design interactive learning experiences, support each other’s initiatives, and collaboratively develop lectures and presentations. We in tend to expand collaborations in biomedical AI education while fostering principles of experiential and collaborativelearning, constructivism, and authentic inquiry. RESULTS/ANTICIPATED RESULTS: Our recent successes include submittedconference abstracts on data science and AI education in pharmacy and the facilitation of a guest lecture in health informatics. Additionally, we are currently collaborating on seven biomedical machine learning projects in radio frequency engineering, aiming for conference submissions. Moving forward, our goal is to expand our group, support the formation of similar communities, and promote data science and AI literacy in biomedical and clinical contexts. We aspire to extend this knowledge to families, classmates, and eventually patients, facilitating a broader understanding of the role of AI in healthcare. DISCUSSION/SIGNIFICANCE: We believe diverse expertise and pedagogical theories can help demonstrate the potential of citizen science to democratize scientific experience. By nurturing collaborative networks our efforts aim to bridge gaps between disciplines and enhance the broader public’s understanding of AI in healthcare.Journal of Clinical and Translational Science01Journal of Clinical and Translational Science['Nicki K. Apaydin', 'Nicole Wolfe', 'Andrea Diaz', 'Michele D. Kipke']2024-04-01T00:00:00https://www.semanticscholar.org/paper/611f5732651b717dc4e3e07c1f5bf8a4f50e7f21 1246c46a7e40419e4438d3b0d1995e674e1d2218Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.Journal of Cardiovascular Development and Disease931The current state of AI applications to different diagnostic modalities used for the evaluation of CA are summarized, including their diagnostic and prognostic potential, and current challenges and limitations.Journal of Cardiovascular Development and Disease['Moaz A. Kamel', 'Mohammed Tiseer Abbas', 'Christopher N. Kanaan', 'Kamal A. Awad', 'Nima Baba Ali', 'Isabel G. Scalia', 'J. M. Farina', 'Milagros Pereyra', 'Ahmed K. Mahmoud', 'D. Steidley', 'Julie L Rosenthal', 'Chadi Ayoub', 'R. Arsanjani']2024-04-01T00:00:00https://www.semanticscholar.org/paper/1246c46a7e40419e4438d3b0d1995e674e1d2218 d7106a86c22880d37f277a7fd301ca3b98b30810Background: Patent ductus arteriosus (PDA) is a prevalent congenital heart defect in premature infants, associated with significant morbidity and mortality. Accurate and timely diagnosis of PDA is crucial, given the vulnerability of this population. Methods: We introduce an artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants. This study utilized electronic health record (EHR) data from 409 premature infants spanning a decade at Severance Children’s Hospital. Our system integrates a data viewer, data analyzer, and AI-based diagnosis supporter, facilitating comprehensive data presentation, analysis, and early symptom detection. Results: The system’s performance was evaluated through diagnostic tests involving medical professionals. This early detection model achieved an accuracy rate of up to 84%, enabling detection up to 3.3 days in advance. In diagnostic tests, medical professionals using the system with the AI-based diagnosis supporter outperformed those using the system without the supporter. Conclusions: Our AI-based PDA diagnostic support system offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants. The collaborative integration of medical expertise and technological innovation demonstrated in this study underscores the potential of AI-driven tools in advancing neonatal diagnosis and care.Journal of Clinical Medicine541An artificial intelligence (AI)-based PDA diagnostic support system designed to assist medical professionals in diagnosing PDA in premature infants offers a comprehensive solution for medical professionals to accurately diagnose PDA in a timely manner in premature infants.Journal of Clinical Medicine['Seoyeon Park', 'Junhyung Moon', 'Hoseon Eun', 'Jin-Hyuk Hong', 'Kyoungwoo Lee']2024-04-01T00:00:00https://www.semanticscholar.org/paper/d7106a86c22880d37f277a7fd301ca3b98b30810 7436304142a720fa282842e51ed5d1d4ef1186d3The integration of artificial intelligence (AI) in business processes has revolutionized many industries by automating tasks, improving decision-making processes, and enhancing customer experiences. This review article examines the impact of AI in business areas, including its applications, challenges, limitations, current trends, and future work. The article begins with defining the importance of AI in business, followed by an overview of its applications in various sectors, such as customer service, marketing, finance, healthcare, manufacturing, logistics, and human resources. The advantages and benefits of AI implementation are explored, along with the examples of successful AI implementation and changes in business processes. The challenges and limitations of AI technology, such as ethical concerns, data privacy and security issues, technical expertise and knowledge, and high implementation costs, are explained. Current trends in AI integration, such as the integration of AI with other technologies, the growing demand for AI skills, and the development of more advanced and sophisticated AI algorithms, are presented. The review concludes with recommendations and predictions for the future of AI in business areas and proposes strategies for successful AI implementation while reflecting on ethical and social considerations. Our analysis in this article points to various open research areas and improves the understanding of AI in the business field.IEEE Engineering Management Review1331The impact of AI in business areas, including its applications, challenges, limitations, current trends, and future work is examined, which points to various open research areas and improves the understanding of AI in the business field.IEEE Engineering Management Review['Kuldeep Gurjar', 'Anshika Jangra', 'Hasnan Baber', 'Maidul Islam', 'Shabnam Abdulkasem Sheikh']2024-04-01T00:00:00https://www.semanticscholar.org/paper/7436304142a720fa282842e51ed5d1d4ef1186d3 254d686b6a69ca53daad04b1c0ffbbe21cb741e1Artificial Intelligence is the arising field of computer wisdom which is nearly associated to logic, logical answering analogous to that of the humans but in important effective and faster way. On the other hand, cameras are used for colourful other purpose like for security purposes etc. When similar cameras and the artificial intelligence along with important some languages like python are integrated together it becomes easier to reuse the data and cover it with important perfection and delicacy. This technology works without any mortal intervention. It means that if there are many people and further no of places to cover also we can use this technology to cover the camera with utmost perfection.International Journal of Innovative Research in Engineering & Management00This technology works without any mortal intervention, which means that if there are many people and further no of places to cover also the authors can use this technology to cover the camera with utmost perfection.International Journal of Innovative Research in Engineering and Management['Abhishek Patil', 'Diya Raut', 'Roshani Prasad', 'Charudatta Shimpi']2024-04-01T00:00:00https://www.semanticscholar.org/paper/254d686b6a69ca53daad04b1c0ffbbe21cb741e1 81c1fd0ab1013e3ca6fe7da72ab86b455c695c50Artificial Intelligence in Education (AIEd) has been evolving for some time, and the advent of GPT chat at the end of December 2022 has opened up new opportunities, potentials, and challenges in educational practice. Advances in computational technology and information processing have led to widespread applications of Artificial Intelligence (AI) in the field of education. Over the last 20 years, the number of papers on AIED has been steadily increasing, with a dramatic rise since 2015 until the present. In its brief history, AIEd has undergone several paradigm shifts. This research aims to explore the use of AI in education by examining the publication trends sourced from metadata from Google Scholar, PubMed, CrossRef, OpenAlex, and Scopus. The development and application of Artificial Intelligence (AI) technology, particularly in education, significantly supports educational reform and profoundly influences the learning styles of learners. Artificial Intelligence in Education (AIED) can assist teachers in preparing teaching materials, presentation media, and accurate evaluations. Furthermore, AIED can help students adapt their traditional learning styles according to their differences, thus realizing intelligent teaching that meets students' learning needs. Teachers' positive perceptions of educational technology (ET) are beneficial for using AI technology to aid teaching positively, which in turn can enhance teaching effectiveness. Overall, the trend of AIEd development has successfully empowered learner personalization, enabling learners to think critically and innovatively, and fostering personalized learning.Proceedings of International Conference on Education130This research aims to explore the use of AI in education by examining the publication trends sourced from metadata from Google Scholar, PubMed, CrossRef, OpenAlex, and Scopus by examining the publication trends sourced from metadata from Google Scholar, PubMed, CrossRef, OpenAlex, and Scopus.Proceedings of International Conference on Education['M. Nasir', 'M. Hasan', 'Adlim Adlim', 'Muhammad Syukri']2024-04-01T00:00:00https://www.semanticscholar.org/paper/81c1fd0ab1013e3ca6fe7da72ab86b455c695c50 177df22a61b1ba7d9846f403cf625aef713ddb90Nursing documentation stands as a critical aspect of healthcare delivery, ensuring comprehensive patient records and facilitating communication among healthcare providers. However, traditional documentation methods are often time-consuming and prone to errors, diverting nurses' attention from direct patient care. This editorial explores the transformative potential of artificial intelligence (AI) in revolutionizing nursing documentation processes. By leveraging AI-driven technologies, such as natural language processing and machine learning, healthcare organizations can automate data entry, extract key clinical information, and generate personalized care plans, thereby streamlining workflows and improving documentation accuracy. This editorial also examines various AI-powered software applications and platforms that facilitate nursing documentation, highlighting their benefits in terms of efficiency, accuracy, and clinical decision support. Furthermore, it discusses considerations such as privacy, security, and the need for nurse training to effectively integrate AI into nursing practice. By embracing AI in nursing documentation, healthcare organizations can empower nurses to devote more time to patient care while enhancing the quality and safety of healthcare delivery.Cureus60This editorial explores the transformative potential of artificial intelligence in revolutionizing nursing documentation processes and examines various AI-powered software applications and platforms that facilitate nursing documentation, highlighting their benefits in terms of efficiency, accuracy, and clinical decision support.Cureus['Sankalp Yadav']2024-04-01T00:00:00https://www.semanticscholar.org/paper/177df22a61b1ba7d9846f403cf625aef713ddb90 634a47f722a4db936c1ed64b57376f7197d09a15Abstract Objectives This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective. Methods The Web of Science database was searched for titles of English‐language articles containing the words “artificial intelligence,” “deep learning,” “machine learning,” “natural language processing,”, “neural artificial network,” “fall,” “geriatric,” “elderly,” “aging,” “older,” and “old age.” An R‐based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis. Results Thirty‐seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was “Engineering Electrical Electronic” with seven articles. The most productive country was the United States, followed by China. The most common words were “injuries,” “people,” and “risk factors.” Conclusion Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed.Aging Medicine240Books on AI and falls in the elderly are both few in number and the number of publications has increased in recent years, which should be included in scientific databases, such as Scopus and PubMed.Aging Medicine['Semiha Yenişehir']2024-04-01T00:00:00https://www.semanticscholar.org/paper/634a47f722a4db936c1ed64b57376f7197d09a15 03a9540aa71ee8921d7656e0a78eb08a64f68a94This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians’ and patients’ understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards.Bioengineering560By providing granular, case-specific insights, local XAI methods like LORE enhance physicians’ and patients’ understanding of machine learning models and their outcome.Bioengineering['C. Metta', 'Andrea Beretta', 'Roberto Pellungrini', 'Salvatore Rinzivillo', 'Fosca Giannotti']2024-04-01T00:00:00https://www.semanticscholar.org/paper/03a9540aa71ee8921d7656e0a78eb08a64f68a94 cf86798d1075697ba55b2f4edcb7b931e6973a81Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia, affecting approximately 6.1 million people worldwide, according to estimates from the Parkinson's Foundation. Early and accurate diagnosis of PD is crucial for effective management and treatment. In this study, we aimed to develop an artificial intelligence (AI) model capable of distinguishing between magnetic resonance imaging (MRI) scans of individuals with PD and those without PD. A total of 442 MRI scans were utilized for training the AI model, comprising 221 scans of individuals diagnosed with PD and 221 scans of healthy controls. The dataset, obtained from a publicly available image dataset on Kaggle.com, was randomly split into three sets: training, validation, and testing, with 80%, 10%, and 10% of the data allocated to each set, respectively. Leveraging Google's Collaboration platform for model training, the AI model achieved exceptional performance, with accuracy, precision, recall (sensitivity), specificity, and F1-score all measuring at high levels. Additionally, the area under the receiver operating characteristic curve (AUC) for the model was found to be 1, indicating strong discrimination between PD and non-PD cases. This study presents a novel AI model capable of accurately identifying PD from MRI scans with high precision and reliability, offering promise for enhancing early diagnosis and personalized treatment strategies for individuals affected by PD.Cureus270A novel AI model capable of accurately identifying PD from MRI scans with high precision and reliability is presented, offering promise for enhancing early diagnosis and personalized treatment strategies for individuals affected by PD.Cureus['Shreya Reddy', 'Dinesh Giri', 'Rakesh Patel']2024-04-01T00:00:00https://www.semanticscholar.org/paper/cf86798d1075697ba55b2f4edcb7b931e6973a81 8fc2263e6a3ba1ba33226454ec8242ce008bda80The purpose of this article is to carry out an analysis of the disclosures made on teaching methods applying artificial intelligence in the Scopus database. The bibliometric review method was used to analyze 349 scientific articles dating from 1978 to 2023. The analysis was carried out using Bibliometrix and VOSviewer software, and the results show that from 2021 onwards there will be a notable increase in publications, with Mobile Information Systems being the journal with the highest production. Among 65 countries identified, China is the country with the highest production and the most productive organization was the Ministry of Education of the People’s Republic of China. No single author stands out for his or her highest scientific output, given that the maximum number of articles published per author is two. However, among the most cited authors is Alimisis, D. and the most co-cited author is Wang, Y. In terms of co-authorship, there is little contribution between authors, while collaboration between countries, China together with Hong Kong, Japan, Malaysia, Mexico, South Korea, Taiwan, Thailand form the most collaborative conglomerate. Cooperation between institutions, the division of computer engineering and the National University of Singapore, show the strongest collaboration. The strongest keywords are “artificial intelligence”, followed by “teaching methods” and “machine learning” and the topics that will be trending from 2021 onwards are “machine learning”, “ChatGPT”, “deep learning”.Online Journal of Communication and Media Technologies230The analysis of the disclosures made on teaching methods applying artificial intelligence in the Scopus database was carried out using Bibliometrix and VOSviewer software, and the results show that from 2021 onwards there will be a notable increase in publications.Online Journal of Communication and Media Technologies['Raúl Alberto García Castro', 'Gilber Chura-Quispe', 'Jehovanni Fabrizio Velarde Molina', 'Luis Alberto Espinoza Ramos', 'Catherine Alessandra Almonte Durand']2024-04-01T00:00:00https://www.semanticscholar.org/paper/8fc2263e6a3ba1ba33226454ec8242ce008bda80 2bb78d38030218feb4473155aa2aebbcb6a25cd8Artificial intelligence (AI) technologies, including machine learning, deep learning, natural language processing, generative AI, the metaverse and other iterations, are rapidly changing the landscape of education. Related technologies not only enhance the teaching and learning process but also improve the quality and availability of educational content. AI facilitates educational transformation, reshapes teaching models, and helps students achieve their personalized learning needs, thus improving learning outcomes, learning efficiencies, and teaching practices. Despite the many AI application cases in nursing management and clinical practice, the application of AI in nursing education remains in its infancy. Machine learning has been used to predict the academic performance and graduation results of nursing students, thereby facilitating the early identification of additional support needs. Natural language processing technology has been used to develop chatbots and virtual teachers to assist learning, providing personalized learning support to help students overcome learning obstacles. Also, generative AI technologies such as ChatGPT (chat generative pre-trained transformer) have been used to create simulated patient cases and as a tool for grading academic writing automatically. Moreover, the combination of generative AI technology and the metaverse has introduced new possibilities to nursing education, allowing students to learn in a more-immersive virtual environment. Despite the significant benefits brought by AI to nursing education, its implementation and integration still face multiple challenges, including high costs, the need to provide technical training to teachers, and the need to address issues such as academic integrity and data privacy. The authors hope this article will help promote interdisciplinary cooperation between nursing educators and information and communication experts and the development of AI-assisted teaching to open a new chapter in nursing education.Hu li za zhi The journal of nursing00The authors hope this article will help promote interdisciplinary cooperation between nursing educators and information and communication experts and the development of AI-assisted teaching to open a new chapter in nursing education.Hu li za zhi The journal of nursing['Hua-Shan Wu']2024-04-01T00:00:00https://www.semanticscholar.org/paper/2bb78d38030218feb4473155aa2aebbcb6a25cd8 1c02c6840b7b3206108a7c28c00220733a6a24daThis research aims to analyze student perceptions regarding the use of artificial intelligence quillbot in overcoming plagiarism and students' awareness of academic ethics. This type of research is qualitative with a case study approach. Data collection uses observation, questionnaires, interviews and document studies. Data analysis is carried out continuously, starting from data reduction to drawing conclusions. The research results show that the use of Artificial Intelligence QuillBot in overcoming plagiarism in the Islamic Religious Education Study Program at the State Islamic University of North Sumatra is highlighted through two approaches, namely first, the resource person supports the use of the plagiarism checker feature as a solution for detecting plagiarism in their scientific work. They consider QuillBot not only as a tool, but also as a necessity in completing course assignments well, especially because a low level of plagiarism is expected. Second, QuillBot's ability to paraphrase automatically can simplify the process of avoiding plagiarism, reduce the risk of using unoriginal content, and increase the authenticity of scientific work. Some students consider their awareness of academic ethics to be good, showing progress in respecting the principles of academic integrity, such as avoiding plagiarism and stating sources clearly. However, there is still an opinion that awareness of academic ethics still needs to be increased because there are still many students who are less aware of the importance of academic integrity, especially regarding the issue of plagiarism.Cetta: Jurnal Ilmu Pendidikan130The research results show that the use of Artificial Intelligence QuillBot in overcoming plagiarism in the Islamic Religious Education Study Program at the State Islamic University of North Sumatra is highlighted through two approaches, namely first, the resource person supports the use of the plagiarism checker feature as a solution for detecting plagiarism in their scientific work.Cetta: Jurnal Ilmu Pendidikan['Naurah Luthfiah', 'Salminawati Salminawati', 'Zaini Dahlan']2024-04-01T00:00:00https://www.semanticscholar.org/paper/1c02c6840b7b3206108a7c28c00220733a6a24da 863598406de0a572e7395e42f69d915a1a26ee81Due to the rapid development of artificial intelligence, it has brought more convenience to human life. Artificial intelligence has additionally been employed for educational purposes at the same time since it can significantly impact both the physical and mental development of individuals. The emergence of artificial intelligence brings possibilities for the implementation of personalized learning, different needs of students may be met. Teachers will also reduce some corresponding workload at the same time, especially for grading tests and answering students’ questions. However, the emergence of artificial intelligence cannot only bring benefits but at the same time, many problems need to be solved. After understanding the situation and problems, how to solve them. Therefore, This article proposes three problems caused by the use of artificial intelligence in teaching, then uses essays from other authors as support, and gives three solution strategies to maximize the effects of artificial intelligence in education.Transactions on Social Science, Education and Humanities Research100This article proposes three problems caused by the use of artificial intelligence in teaching, then uses essays from other authors as support, and gives three solution strategies to maximize the effects of artificial intelligence in education.Transactions on Social Science, Education and Humanities Research['Hongyi Yao']2024-04-01T00:00:00https://www.semanticscholar.org/paper/863598406de0a572e7395e42f69d915a1a26ee81 d5ebb26725a9903ba06c980f57e47e48bfa5b902Berkembangnya teknologi di era digitalisasi sekarang tidak dapat kita hindari. Munculnya Artificial Intelligence sejak tahun 1956 memberikan banyak kemudahan di dalam kehidupan manusia. Artificial Intelligence dibentuk dengan cara mengatur data-data yang ada sehingga dapat terprogram dan dapat secara otomatis mengerjakan suatu hal yang disuruh. Artificial Intelligence kemudian banyak berkembang dalam banyak bidang, contohnya bidang seni. Namun, berkembangnya teknologi tersebut juga dapat melanggar suatu hukum dengan tindakan plagiarisme. Karena pada dasarnya, karya-karya yang dihasilkan merupakan hasil olahan dari data-data milik orang lain yang kemudian dijadikan suatu karya baru. Penelitian ini dibuat dengan menggunakan metode penelitian yuridis normatif yang mempelajari data sekunder serta peraturan perundang-undangan yang ada. Tujuan ditulisnya karya tulis ini adalah untuk melihat bagaimana pandangan terhadap kecerdasan buatan tersebut dari kacamata hukum Indonesia terutama UU Hak Cipta. Menurut UU Hak Cipta, hasil karya ciptaan Artificial Intelligence tidak dapat dikatakan sebagai hasil karya ciptaan yang dapat dilindungi karena pada hakikatnya Artificial Intelligence bukan pencipta sesuai dengan UU Hak Cipta. Namun pelanggaran hukum yang terjadi akibat penggunaan AI ini dapat dipertanggung jawabkan oleh sang penyedia jasa AI tersebut. Untuk menghindari banyaknya pelanggaran yang akan terjadi dimasa datang karena terus berkembangnya teknologi ini, maka pemerintah Indonesia harus membuat aturan yang lebih jelas dan tegas dalam mengatur perkembangan teknologi yang sedang terjadi secara cepat dan luas di Indonesia.JLEB: Journal of Law, Education and Business00JLEB: Journal of Law, Education and Business['Calista Putri Tanujaya']2024-04-01T00:00:00https://www.semanticscholar.org/paper/d5ebb26725a9903ba06c980f57e47e48bfa5b902 2cd598171e4d7bfb20fccc81c57b7104c2932ff2Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.Anesthesia and Analgesia440The advantages of using AI models, which recognize complex patterns in imaging data providing high qualitative assessments, are described and how these methods could impact clinical practice are explored.Anesthesia and analgesia['Silvia De Rosa', 'E. Bignami', 'Valentina Bellini', 'Denise Battaglini']2024-04-01T00:00:00https://www.semanticscholar.org/paper/2cd598171e4d7bfb20fccc81c57b7104c2932ff2 d81d13a37898a851f517b5ea3e2343ae519270a3Abstract Background The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false‐positive reduction, nodule classification, and prognosis. Methodology This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false‐positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. Results AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false‐positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. Conclusions AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false‐positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large‐scale validation of new deep learning‐based algorithms and multi‐center studies to improve the efficacy of AI in lung cancer screening.Cancer Medicine810AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false‐positive rates, and classifying nodules, however, the universality and interpretability of AI results need further enhancement.Cancer Medicine['Quanyang Wu', 'Huang Yao', 'Sicong Wang', 'Linlin Qi', 'Zhang Zewei', 'Donghui Hou', 'Hongjia Li', 'Shijun Zhao']2024-04-01T00:00:00https://www.semanticscholar.org/paper/d81d13a37898a851f517b5ea3e2343ae519270a3 6e256ffb4314d6431dd5b9f20b91fe69240a4ac7The emergence of Generative Artificial Intelligence (GAI) has caused significant disruption to the traditional educational teaching ecosystem. GAI possesses remarkable capabilities in generating human-like text and boasts an extensive knowledge repository, thereby paving the way for potential collaboration with humans. However, current research on collaborating with GAI within the educational context remains insufficient and the methods are relatively limited. This study aims to utilize methods such as Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA) to unveil the “black box” of the human-machine collaborative process. In this research, 22 students engaged in collaborative tasks with GAI to refine instructional design schemes within an authentic classroom setting. The results show that the participants significantly improved the quality of instructional design. Leveraging the improvement demonstrated in students’ instructional design performance, we categorized them into high- and low-performance groups. Through the analysis of learning behavior, it was observed that the high-performance group adhered to a structured GAI content application framework: “generate → monitor → apply → evaluate.” Moreover, they adeptly employed communication strategies emphasizing exercising cognitive agency and actively cultivating a collaborative environment. The conclusions drawn from this research may serve as a reference for a series of practical applications in human-machine collaboration and provide directions for subsequent studies.Journal of educational computing research80Methods such as Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA) are used to unveil the “black box” of the human-machine collaborative process and may serve as a reference for a series of practical applications in human-machine collaboration.Journal of Educational Computing Research['Jiangyue Liu', 'Siran Li', 'Qianyan Dong']2024-04-01T00:00:00https://www.semanticscholar.org/paper/6e256ffb4314d6431dd5b9f20b91fe69240a4ac7 595b60eac7052768939813460e59be80ab396d74Currently, one of the challenges of higher education is to achieve the success of its students personally and professionally, emphasising improvement in technological, ethical, and academic areas that characterise human beings for success. Therefore, higher education must change the traditional way of training to a more humanistic approach framed in the digital era to solve social problems assertively. This research aims to generate comprehensive training guidelines from ethical research associated with Information and Communication Technology (ICT) and Artificial Intelligence to contribute to developing reflective critical thinking, maturity, and responsibility of individuals who will solve social problems. This study is based on Siemens' theory of connectivism (2004), Kohlberg's theory of moral development (1970), and Bandura's social learning theory (1974). Under the interpretive phenomenological approach, the applied methodological route is qualitative, focused on realities addressed from the context of transformation, using the documentary review technique. The inquiry led to the conclusion of the importance of conducting ethical research processes within technology in higher education to provide the student comprehensively with the knowledge and skills for successful integration into society.Advances in Mobile Learning Educational Research350This research aims to generate comprehensive training guidelines from ethical research associated with Information and Communication Technology and Artificial Intelligence to contribute to developing reflective critical thinking, maturity, and responsibility of individuals who will solve social problems.Advances in Mobile Learning Educational Research['Yulibeth Katiuska Guissepe Hernández', 'William Jesús Hernández Chávez', 'Sandra Moucharrafieh Moucharrafieh']2024-04-01T00:00:00https://www.semanticscholar.org/paper/595b60eac7052768939813460e59be80ab396d74 415e8abce6b857ce521a17ce96e4ee30b3ce07a8The increasing prevalence of deep neural networks (DNNs) in industrial artificial intelligence systems (IAISs) promotes the development of industrial automation. However, the growing employment of DNNs also exposes them to various attacks. Recent studies have shown that the data preprocessing process of DNNs is vulnerable to image-scaling attack. Such attacks can craft an attack image, which looks like a given source image but becomes a different target image after being scaled to the target size. The attack images generated by existing image-scaling attacks are easily perceivable to the human visual system, significantly degrading the attack's stealthiness. In this paper, we investigate image-scaling attack from the perspective of signal processing. We unearth that the root cause of the weak deceiving effects of existing image-scaling attack images lies in the introduction of additional high-frequency signals during their construction. Thus, we propose an enhanced image-scaling attack (EIS), which employs adversarial images crafted based on the source (“clean”) images as the target images. Those adversarial images preserve the “clean” pixel information of source images, thereby significantly mitigating the emergence of additional high-frequency signals in the attack images. Specifically, we consider three realistic threat models covering deep models' training and inference phases. Correspondingly, we design three strategies tailored to generate adversarial images with vicious patterns. These patterns are subsequently integrated into the attack images, which can mislead a model with target input size after the necessary scaling operation. Extensive experiments validate the superior performance of the proposed image-scaling attack compared to the original one.IEEE Transactions on Industrial Informatics370This paper proposes an enhanced image-scaling attack (EIS), which employs adversarial images crafted based on the source (“clean”) images as the target images, thereby significantly mitigating the emergence of additional high-frequency signals in the attack images.IEEE Transactions on Industrial Informatics['Junjian Li', 'Honglong Chen', 'Peng Sun', 'Zhibo Wang', 'Zhichen Ni', 'Weifeng Liu']2024-04-01T00:00:00https://www.semanticscholar.org/paper/415e8abce6b857ce521a17ce96e4ee30b3ce07a8 d71838196eabce785d35ce2b76c9687e8806814d: Purpose: The article aims to explore the role that artificial intelligence (AI) plays in agile organizational management. It focuses on how AI can make companies more agile, innovative, and adaptable, allowing them to better respond to rapidly changing market conditions. Design/Methodology/Approach: The study is based on an analysis of the literature on the subject and empirical research among students from three Polish universities. It uses a methodology that is at the intersection of qualitative and quantitative research, which allows for a deeper understanding of the impact of AI on organizations. Findings: Research shows that artificial intelligence has a significant impact on decision-making processes, streamlining and accelerating employee adaptation to new tasks and improving the quality and efficiency of reports and analyses. Respondents saw the benefits of using AI, such as better understanding of material, faster preparation of reports, and support in creating abbreviations and summaries of long texts. Practical Implications: The practical application of artificial intelligence in agile management of organizations can significantly contribute to increasing their competitiveness and innovation. Organizations should focus on integrating AI systems that are capable of translating complex issues, automating processes, and providing innovative solutions, which can support the development of employee competencies and improve the quality of intellectual work. Originality/Value: The article makes an important contribution to the literature on agile management, focusing on the role of artificial intelligence. Its originality lies in the combination of theoretical reflections with the results of empirical research, which allows for a better understanding of how modern technologies can affect the future of managing organizations in a dynamically changing environment.EUROPEAN RESEARCH STUDIES JOURNAL260Research shows that artificial intelligence has a significant impact on decision-making processes, streamlining and accelerating employee adaptation to new tasks and improving the quality and efficiency of reports and analyses.EUROPEAN RESEARCH STUDIES JOURNAL['Artur Kwasek', 'Maria Kocot', 'Damian Kocot', 'M. Maciaszczyk', 'Joanna Rogozinska-Mitrut']2024-04-01T00:00:00https://www.semanticscholar.org/paper/d71838196eabce785d35ce2b76c9687e8806814d cd2c7e1b3c680ef70ca2a1e45e981fe59dbd0b76Artificial Intelligence Review520This research reviews innovatively several case studies and practical examples to emphasize the effective contributions of AI strategies in enhancing the computational analysis of numerical simulation methods forming a smart approach for assessing experimental studies that are associated with energy systems.Artif. Intell. Rev.['M. Talaat', 'M. Tayseer', 'M. A. Farahat', 'Dongran Song']2024-04-01T00:00:00https://www.semanticscholar.org/paper/cd2c7e1b3c680ef70ca2a1e45e981fe59dbd0b76 7586e1d33f86221925c802558b64531cd09da3e6Artificial Intelligence Review680This review reinforces the understanding by identifying and distilling the relationships between the content, the contributions, and the contributors of the existing literature on Artificial Intelligence in language learning through bibliometric and content analysis.Artif. Intell. Rev.['Abdur Rahman', 'Antony Raj', 'Prajeesh Tomy', 'Mohamed Sahul Hameed']2024-04-01T00:00:00https://www.semanticscholar.org/paper/7586e1d33f86221925c802558b64531cd09da3e6 9d0b7932ac7a9b7e1b46677092ac014540d919ebThe use of artificial intelligence (AI) in geriatric rehabilitation offers a novel approach to elderly care. This article explores how AI can alleviate pain and enhance the quality of care for aging populations. Machine learning algorithms aid in customizing rehabilitation programs, monitoring progress, and predicting individual patient needs. Furthermore, AI facilitates clinical data management, streamlining health care processes and ultimately improving the overall well-being of elderly patients.Topics in geriatric rehabilitation170How AI can alleviate pain and enhance the quality of care for aging populations is explored in a bid to improve the overall well-being of elderly patients.Topics in Geriatric Rehabilitation['P. Pedersini', 'M. Tovani-Palone']2024-04-01T00:00:00https://www.semanticscholar.org/paper/9d0b7932ac7a9b7e1b46677092ac014540d919eb 07a2995f5833c56aeec5dc0bbe433cc1fe0b1a65 Artificial intelligence (AI) has emerged as a revolutionary mechanism in the field of science and technology. The role of AI in scientific research is becoming broader day by day. While AI is making processes easier and smoother, it is also raising concerns among researchers regarding its ethical utility. Besides, the decision-making process of AI remains a black box for research scholars. This review seeks to provide a comprehensive overview of the utilization of AI-based tools in medical research, along with an exploration of the associated challenges. The search strategy involved querying PubMed using keywords such as “Artificial intelligence,” “machine learning,” and “medical research” to identify relevant literature. The significance of AI in research is inevitable. Researchers need to accept the fact that AI will soon be an integral part of research, at the same time, the current limitations of AI need to be alleviated so that it will be embraced by the scientific community.Current Medical Issues140This review seeks to provide a comprehensive overview of the utilization of AI-based tools in medical research, along with an exploration of the associated challenges.Current Medical Issues['Anurag Gola', 'Ambarish Das', 'Amar B. Gumataj', 'S. Amirdhavarshini', 'J. Venkatachalam']2024-04-01T00:00:00https://www.semanticscholar.org/paper/07a2995f5833c56aeec5dc0bbe433cc1fe0b1a65 945092b443d3c9eb930efa668bd3a61ea848b9eaPurpose The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos. Methods A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported. The reproducibility and reliability of the methods and results were assessed using a modified version of the Medical Image Computing and Computer-Assisted (MICCAI) checklist. Results Thirty-eight of the 550 screened studies were included, 20 addressed the challenge of instrument detection or tracking, 9 focused on phase discrimination, and 8 predicted skill and complications. Instrument detection achieves an area under the receiver operator characteristic curve (ROC AUC) between 0.976 and 0.998, instrument tracking an mAP between 0.685 and 0.929, phase recognition an ROC AUC between 0.773 and 0.990, and complications or surgical skill performs with an ROC AUC between 0.570 and 0.970. Conclusions The studies showed a wide variation in quality and pose a challenge regarding replication due to a small number of public datasets (none for manual small incision cataract surgery) and seldom published source code. There is no standard for reported outcome metrics and validation of the models on external datasets is rare making comparisons difficult. The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning. Translational Relevance This overview of cataract surgery analysis with AI models provides translational value for improving training of the clinician by identifying successes and challenges.Translational Vision Science & Technology570The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning.Translational Vision Science & Technology['Simon Müller', 'Mohit Jain', 'Bhuvan Sachdeva', 'P. Shah', 'F. Holz', 'R. Finger', 'Kaushik Murali', 'Maximilian W. M. Wintergerst', 'Thomas Schultz']2024-04-01T00:00:00https://www.semanticscholar.org/paper/945092b443d3c9eb930efa668bd3a61ea848b9ea a3ead20379ef103a9aab87e8122f543874ce4c60Artificial intelligence (AI) refers to the simulation of human intelligence in machines. Artificial Intelligence technology has led to creation of AI tools that mimic human intelligence. These machines/AI tools have the capability of performing the tasks that depend upon and are associated with human cognitive skills, like, learning and logical reasoning. AI is impacting all spheres of our lives. It has reshaped the arena of business as well, and is being used by organizations for data analytics, automation, pattern recognition, decision making and content creation. Different areas of business, be it marketing, customer service, production or human resources, can use AI to improve operations and productivity. The present paper provides an overview of the role and application of AI in business organizations.Shodh Sari-An International Multidisciplinary Journal00The present paper provides an overview of the role and application of AI in business organizations.Shodh Sari-An International Multidisciplinary Journal['Parminder Walia']2024-04-01T00:00:00https://www.semanticscholar.org/paper/a3ead20379ef103a9aab87e8122f543874ce4c60 dc8e67099175ab854b3d94e110f5368e3b5543f4Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.International Journal of Electrical and Computer Engineering (IJECE)510A review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.International Journal of Electrical and Computer Engineering (IJECE)['Hamza Abu Owida', 'Mohammad R. Hassan', 'Ali Mohd Ali', 'F. Alnaimat', 'A. Al Sharah', 'Suhaila Abuowaida', 'N. Alshdaifat']2024-04-01T00:00:00https://www.semanticscholar.org/paper/dc8e67099175ab854b3d94e110f5368e3b5543f4 eac396535483234223fc3e71dd3e53168ed9b5bdLung cancer is a lethal disease that claims numerous lives annually, and early detection is essential for improving survival rates. Machine learning has shown promise in predicting lung cancer risk, but the lack of transparency and interpretability in black-box models impedes the understanding of factors that contribute to risk. Explainable artificial intelligence (XAI) can overcome this limitation by providing a clear and understandable approach to machine learning. In this study, we will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk. We will use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model’s predictions. These methods will provide healthcare professionals with a transparent and interpretable framework for screening and treatment decisions concerning lung cancer risk.Bulletin of Electrical Engineering and Informatics580This study will use a large patient record dataset to train an XAI-based model that considers various patient information, including lifestyle factors, clinical data, and medical history, for predicting lung cancer risk, and use different XAI techniques, including decision trees, partial dependence plots, and feature importance, to interpret the model's predictions.Bulletin of Electrical Engineering and Informatics['Shahin Shoukat Makubhai', 'Ganesh R. Pathak', 'Pankaj R. Chandre']2024-04-01T00:00:00https://www.semanticscholar.org/paper/eac396535483234223fc3e71dd3e53168ed9b5bd be38d53b338ae01e6c98eaabf72354a5426fc1ccArtificial Intelligence holds immense importance in today's world and has the potential to alter our future in various domains. The importance of AI lies in its ability to automate tasks, enhance decision-making, personalize experiences, solve complex problems, and drive innovation. Choosing not to adopt AI may result in inefficiency, missed opportunities, limited innovation, reduced insights, and higher operational costs. It won’t be long before the lives of those who benefit from AI and those who don't will diverge significantly. According to Stephen Hawking.Pakistan Journal of Ophthalmology90It won’t be long before the lives of those who benefit from AI and those who don’t will diverge significantly, according to Stephen Hawking.Pakistan Journal of Ophthalmology['Zahid Kamal Siddiqui', 'Muhammad Moin', 'Hafiza Sadia Imtiaz']2024-04-01T00:00:00https://www.semanticscholar.org/paper/be38d53b338ae01e6c98eaabf72354a5426fc1cc 570b4d95c8f614430b77139658cf156b57c20921 The intersection of Artificial Intelligence and Sustainable Development Goal (SDG) 3 presents both opportunities and challenges. AI has the potential to enhance healthcare delivery by improving diagnostic accuracy, enabling personalized treatment approaches, and facilitating remote healthcare services through telemedicine. AI-driven technologies can also streamline administrative tasks, optimize resource allocation, and support evidence-based decision-making in healthcare settings. This study seeks to identify challenges and opportunities for utilizing AI to advance global health objectives to support Sustainable Development Goal 3 A review of existing literature was carried out to evaluate the current landscape of AI applications in healthcare and its potential implications for SDG achievement. We identified constraints such as data bias, ethical considerations, and accessibility barriers, and looked at the potential remedies and strategies for maximizing the positive impact of AI in healthcare. There exists significant hurdles into the integration of AI into healthcare however there are promising prospects for enhancing healthcare delivery, disease prevention, and drug discovery processes. By addressing issues such as data bias, ethical dilemmas, and accessibility challenges, AI holds the potential to revolutionize healthcare systems on a global scale. Embracing AI technologies can contribute significantly to realizing SDGs pertaining to health and well-being, thereby fostering more sustainable and equitable healthcare practices. Indian Journal of Community Medicine00Challenges and opportunities for utilizing AI to advance global health objectives to support Sustainable Development Goal 3 are identified and constraints such as data bias, ethical considerations, and accessibility barriers are identified.Indian Journal of Community Medicine['Aditya Nair', 'Kennice Deon Dsouza', 'B. Reshmi']2024-04-01T00:00:00https://www.semanticscholar.org/paper/570b4d95c8f614430b77139658cf156b57c20921 f0dc1562cae915243c12e94343702ed24a839c93The convergence of artificial intelligence (AI) and machine learning (ML) with modern medicine has not only opened up unprecedented opportunities for innovation but also gained recognition from prominent publications, including Nature and Science of Sleep, for their transformative potential in sleep medicine research and clinical practice. As such, Nature and Science of Sleep welcomes explorations into the applications and implications of AI in the field. AI enables machines and software to perform tasks such as problem-solving, pattern recognition, learning, and understanding language, which traditionally required human cognition. Meanwhile, ML—a subset of AI—enhances these capabilities further with algorithms that learn and improve from data over time. The field of sleep medicine stands at the threshold of a transformative revolution, propelled by swift progress in AI and ML. Due to the inherently digital nature of data collected in this field, sleep medicine is uniquely positioned to harness AI and ML. Polysomnography, the gold standard diagnostic tool, produces extensive physiological data in digital formats such as EEG, ECG, EMG, and respiratory signals, making it ripe for AI analysis. This wealth of structured digital data makes sleep medicine an ideal candidate for the application of AI and ML algorithms, which excel at identifying complex patterns and relationships within large datasets. Additionally, the widespread availability of consumer sleep technologies, like wearables, expands these opportunities, allowing ML to extract novel insights from extensive real-world sleep data. 1,2 This editorial aims to highlight the current uses of AI in sleep medicine and research, validation and ethical challenges, and the exciting future prospects. Figure 1 illustrates the breadth of potential AI applications in sleep medicine practice and research.Nature and Science of Sleep390The field of sleep medicine stands at the threshold of a transformative revolution, propelled by swift progress in AI and ML, allowing ML to extract novel insights from extensive real-world sleep data.Nature and Science of Sleep['Ahmed S. BaHammam']2024-04-01T00:00:00https://www.semanticscholar.org/paper/f0dc1562cae915243c12e94343702ed24a839c93 67403b0a6921dd3b19ad74cc00ef5936a4feab88 Artificial Intelligence (AI) in the health care has gained attention worldwide due to its potential to revolutionize health care delivery, improve diagnostics, and enhance patient outcomes. AI has the potential to address challenges in Indian health care sector like shortage of workforce by providing decision support, automating repetitive tasks, and improving diagnostic accuracy. To assess the Knowledge, Attitude and Practice (KAP) of Artificial Intelligence in health care among Undergraduate and Postgraduate medical students in Tamil Nadu. A cross-sectional online survey was conducted among 200 undergraduate and postgraduate medical students over a period of 2 months (Sept to Oct 2023). Data collection was done using Google form which comprises of a pretested, semi-structured questionnaire and statistical analysis was done using IBM SPSS version 21. Out of 200 participants, 94 (47%) were Undergraduates and 106 (53%) were Postgraduates. In total 123 (61.5%) had good knowledge, 190 (95%) had positive attitude and 61 (30.5%) had good practice towards Artificial Intelligence in healthcare. Postgraduates had better knowledge and practice than undergraduates A majority of the participants displayed good knowledge and positive attitude towards Artificial Intelligence in healthcare. Notably, postgraduates outperformed undergraduates, suggesting the need for tailored educational interventions. While AI training in the curriculum remains limited, the high willingness to engage with AI in the future underscores its potential for transformative impact in healthcare. Indian Journal of Community Medicine00Indian Journal of Community Medicine['M. Sreeram', 'A. Chitra']2024-04-01T00:00:00https://www.semanticscholar.org/paper/67403b0a6921dd3b19ad74cc00ef5936a4feab88 6f6a936501b03da7526554fda32e033ccdcd8502Investments in artificial intelligence (AI) spurred development of online fact-checking tools; positioned to potentially serve as more accurate alternatives or appendages to search engines and/or nascent chatbots. This study analyzed the efficacy of four AI tools (ClaimBuster, Full Fact, TheFactual - IsThisCredible?, and Google’s Fact-Check Explorer) in producing accurate readings measured by a consensus of independent fact-checking organizations. 10 unique claims were inputted into each tool to produce individual fact-check reports, resulting in 40 fact-check reports being conducted. The results reflect an efficacy rating of 100% regarding the ability of the selected tools to produce an overall accurate reading with 89% of reports producing a unanimous determination. Additionally, recommendations were made to further map and analyze the efficacy of AI fact-checking. These findings support the notion that AI can play an effective role in aiding online truth-seeking when its determinations depend on transparently referencing its source of independent human fact-checkers.The International Review of Information Ethics130Findings support the notion that AI can play an effective role in aiding online truth-seeking when its determinations depend on transparently referencing its source of independent human fact-checkers.The International Review of Information Ethics['Russell Hartley']2024-04-01T00:00:00https://www.semanticscholar.org/paper/6f6a936501b03da7526554fda32e033ccdcd8502 0d2b7c1b98475452ac1867f64d81f6c01b2bc6daBackground: Artificial intelligence has the potential to revolutionize healthcare by enhancing patient care and driving a new era in the field. Aim: The present research aimed to identify the intensive care nurses' knowledge and perception regarding artificial intelligence applications. Research design: This research used a descriptive design. Setting: Intensive Care Unit at Suez-Canal and Ain Shams University Hospitals. Subject: Convenient sample composed of all nurses working in Intensive Care Unit with total number of (160) who are working at time of data collection in the previous listed study settings. Tools: The study utilized two data collection tools : Tool (I): Self-administered artificial intelligence knowledge questionnaire, and Tool (II): Nurses' Perception regarding artificial intelligence applications. Results: There were statistical significance differences between the Intensive Care Unit nurses' perception level regarding applications of artificial intelligence in health care setting with age, educational qualifications, and years of experience in Intensive Care Unit. Also, there was a highly statistically positive correlation between total knowledge and perception among the intensive care nurses. Conclusion: Around two-thirds of studied nurses had unsatisfactory level of knowledge, as well, the majority of the nurses had moderate perception level regarding artificial intelligence applications in Intensive Care Unit. Recommendations: Provide appropriate information about the benefits, challenges, and issues surrounding the implementation of artificial intelligence in nursing settings and the potentials of these technologies to improve health care processes and efficiencies.Trends in Nursing and Health Care Journal00Around two-thirds of studied nurses had unsatisfactory level of knowledge, as well, the majority of the nurses had moderate perception level regarding artificial intelligence applications in Intensive Care Unit, and there was a highly statistically positive correlation between total knowledge and perception among the intensive care nurses.Trends in Nursing and Health Care Journal['Shereen Ahmed', 'S. Elderiny']2024-04-01T00:00:00https://www.semanticscholar.org/paper/0d2b7c1b98475452ac1867f64d81f6c01b2bc6da 9f69ec69db8c65d44134297162d7376f83d71fc6Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.Cureus970The purpose of this article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers.Cureus['Mohit Lakkimsetti', 'Swati G Devella', 'Keval B Patel', 'Sarvani Dhandibhotla', 'Jasleen Kaur', 'Midhun Mathew', 'Janvi Kataria', 'Manisha Nallani', 'Umm E. Farwa', 'Tirath Patel', 'Uzoamaka C Egbujo', 'Dakshin Meenashi Sundaram', 'Samar Kenawy', 'Mehak Roy', 'Saniyal Farheen Khan']2024-04-01T00:00:00https://www.semanticscholar.org/paper/9f69ec69db8c65d44134297162d7376f83d71fc6 c7fb6e820aaaa71e6222a508c2b959721521dfa7 The advent of Artificial Intelligence (AI) technologies has undeniably revolutionized various domains worldwide. AIs emergence has sparked significant interest from public health research domain due to its transformative potential. Thus, understanding its impact from the grass-root level: perspectives of the young research students, training in the field of public health is crucial. To explore the perceptions regarding the concerns and considerations for use of AI tools in research among students of a public health institute in Kolkata A descriptive qualitative study was conducted among students pursuing MD degree. Focus group discussion (FGD) was done among the participants till data saturation was achieved. Audiogram, notes and sociogram were made during the FGD. Verbatims of the FGD were transcribed. Codes were obtained inductively and categorized. Data coding and analysis was done with NVivo 14 and data visualization was done using RAW graphs 2.0 The various concerns that arouse were lack of trust, lack of knowledge, ethical issues, inadept to use and the impact of its use on the researcher. The young researchers also contemplated that there needs to be extensive training, ethical policy adaptation and self-checks required for its use. There is a growing body of thought to find solutions for the limitations in its use and its prospects. Most participants agreed to the fact their knowledge regarding its utilization was sub-optimal. There was a notable lack of trust in using AI tools for research among them. Addressing these challenges require a multifaceted approach involving extensive training initiatives, ethical policy adaptation to facilitate responsible AI use and a call for researchers to act as self-checks, while using AI in research Indian Journal of Community Medicine00Addressing these challenges require a multifaceted approach involving extensive training initiatives, ethical policy adaptation to facilitate responsible AI use and a call for researchers to act as self-checks, while using AI in research.Indian Journal of Community Medicine['Sujith Surendran']2024-04-01T00:00:00https://www.semanticscholar.org/paper/c7fb6e820aaaa71e6222a508c2b959721521dfa7 3e40f4256a6f9694369821e4fc5f22daf0b6206bA significant contributor to blindness and visual impairment globally is uncorrected refractive error. To plan effective interventions, eye care professionals must promptly identify people at a high risk of acquiring myopia, and monitor disease progress. Artificial intelligence (AI) and machine learning (ML) have enormous potential to improve diagnosis and treatment. This systematic review explores the current state of ML and AI applications in the diagnoses and treatment of refractory errors in optometry. A systematic review and meta-analysis of studies evaluating the diagnostic performance of AI-based tools in PubMed was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To find relevant studies on the use of ML or AI in the diagnosis or treatment of refractive errors in optometry, a thorough search was conducted in various electronic databases such as PubMed, Google Scholar, and Web of Science. The search was limited to studies published between January 2015 and December 2022. The search terms used were "refractive errors," "myopia," "optometry," "machine learning," "ophthalmology," and "artificial intelligence." A total of nine studies met the inclusion criteria and were included in the final analysis. ML is increasingly being utilized for automating clinical data processing as AI technology progresses, making the formerly labor-intensive work possible. AI models that primarily use a neural network demonstrated exceptional efficiency and performance in the analysis of vast medical data, rivaling board-certified, healthcare professionals. Several studies showed that ML models could support diagnosis and clinical decision-making. Moreover, an ML algorithm predicted future refraction values in patients with myopia. AI and ML models have great potential to improve the diagnosis and treatment of refractive errors in optometry.Cureus390A systematic review and meta-analysis of studies evaluating the diagnostic performance of AI-based tools in PubMed was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.Cureus['Taghreed A Alnahedh', 'Mohammed Taha']2024-04-01T00:00:00https://www.semanticscholar.org/paper/3e40f4256a6f9694369821e4fc5f22daf0b6206b 5e9a07c5026819524f37b96475c82dc7e8d59cc2Objectives To explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units (ICUs). Methods Individual semi-structured qualitative interviews were conducted between December 2021 and August 2022 with 59 intensive care professionals from 24 countries. Transcripts were analysed using conventional content analysis. Results Participants had generally positive views about the potential use of AI in ICUs but also reported some well-known concerns about the use of AI in clinical practice and important technical and non-technical barriers to the implementation of AI. Important differences existed between ICUs regarding their current readiness to implement AI. However, these differences were not primarily between HICs and LMICs, but between a small number of ICUs in large tertiary hospitals in HICs, which were reported to have the necessary digital infrastructure for AI, and nearly all other ICUs in both HICs and LMICs, which were reported to neither have the technical capability to capture the necessary data or use AI, nor the staff with the right knowledge and skills to use the technology. Conclusion Pouring massive amounts of resources into developing AI without first building the necessary digital infrastructure foundation needed for AI is unethical. Real-world implementation and routine use of AI in the vast majority of ICUs in both HICs and LMICs included in our study is unlikely to occur any time soon. ICUs should not be using AI until certain preconditions are met.BMJ Health & Care Informatics330Real-world implementation and routine use of AI in the vast majority of ICUs in both HICs and LMICs included in the study is unlikely to occur any time soon.BMJ Health & Care Informatics['Stuart McLennan', 'A. Fiske', 'L. A. Celi']2024-04-01T00:00:00https://www.semanticscholar.org/paper/5e9a07c5026819524f37b96475c82dc7e8d59cc2 d1cbdc3263d68a194505a1f8b9142faeaccdde7bBackground Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, enabling them to perform tasks. The advancements in AI have also improved virtual reality (VR), augmented reality (AR) and mixed reality (MR) experience allowing a greater opportunity for use in the field of medicine. Objective To evaluate the knowledge, attitude and practice of AI and types of realities among Pakistani healthcare professionals (HCPs). Materials and methods This was a prospective, nationwide study designed at the Department of Neurosurgery at Punjab Institute of Neurosciences (PINS), Lahore, was conducted between January 2024 to February 2024. More than 500 HCPs were approached, out of which 176 participated in this survey consensually. A pre-formed general questionnaire based on knowledge, attitude and practices of AI and types of realities was modified according to local conditions. Google Forms (Google Inc., USA) was used to conduct the one-time sign up response. Statistical Package for Social Sciences (IBM SPSS Statistics for Windows, Version 24, USA) was used to analyze submitted responses. Results About 69.9% respondents were male HCPs. Most of the respondents were from the fields of neurosurgery, medicine and general surgery, i.e., 10.80%, 10.20% and 4%, respectively. More than 90% HCPs used Internet and electronic devices daily. A majority of 62.50% respondents agreed that AI brings benefits for the patients, while at the same time, 45.50% agreed that they would not trust the assessment of AI more than that of HCPs. 61% HCPs feared that AI-based systems could be manipulated from the outside sources, like terrorists and hackers. Although 90% respondents knew the definition of AR and VR, a strikingly low 40% respondents could only identify the practical applications of these realities when asked in a mini-quiz. About 61.40% HCPs never used any AI-based application throughout their clinical practice, but Google Health was used by 29.50% respondents, followed by Remote Patient Monitoring AI application used by 3.4% individuals. Conclusion There is an evident under-utilization of AI and types of realities in clinical practice in Pakistan. Lack of awareness, paucity of resources and conventional clinical practices are the key reasons identified. Pakistan is on the path towards the point where the developed world is currently. There is a potential to move past the initial stages of AI implementation and into more advanced modes of adopting AI and types of realities.Cureus150There is an evident under-utilization of AI and types of realities in clinical practice in Pakistan, with Lack of awareness, paucity of resources and conventional clinical practices being the key reasons identified.Cureus['Haseeb Mehmood Qadri', 'Momin Bashir', 'Manal Khan', 'Arham Amir', 'Allah Yar Yahya Khan', 'Zainab Safdar', 'H. Chaudhry', 'Usama Afraz Younas', 'Asif Bashir']2024-04-01T00:00:00https://www.semanticscholar.org/paper/d1cbdc3263d68a194505a1f8b9142faeaccdde7b 8ec8ed181c100036e68ea5bfb0f84f38bc3ac181 Artificial intelligence (AI) model integration into clinical workflow offers potential to optimize decision-support for transfer of acute traumatic brain injury (TBI) patients to appropriate trauma centers. We retrospectively identified TBI patients from 2005-2021 treated at a quaternary Canadian trauma center. We employed various modeling techniques including principal component analysis, three-dimensional convolutional neural networks, and a transformer-based approach using Vision Transformer (ViT) architecture. Model training, validation, and testing was performed using head CT scans with binary ground truth labels corresponding to whether the patient received neurosurgical intervention witin 72 hours. The finalized model, termed Automated Surgical Intervention Support Tool for TBI (ASIST-TBI), was then deployed in a simulated prospective fashion on consecutive TBI patients at our center between March 2021 - September 2022. A dataset of 2,806 trauma patients with acute head CT scans were divided into training, validation, and testing groups; the ViT model exhibited optimal performance. There was accurate prediction of requirement for neurosurgical intervention with an area under the receiver operating curve (AUC) of 0·92, accuracy of 0·87, sensitivity of 0·87, and specificity of 0·88 on the testing cohort. In simulated 18-month prospective deployment, an additional 612 consecutive scans were used to assess the performance of ASIST-TBI. Classification accuracy remained robust with AUC of 0·89, 0·85 sensitivity, 0·84 specificity, and 0·84 accuracy. We manually reviewed false positive and false negative cases to identify reasons for misclassification. We developed a novel deep learning model that accurately predicts requirement for acute neurosurgical intervention using unlabeled TBI scans. ASIST-TBI has potential application to optimize state-wide triage efficiency and care pathways for brain-injured patients. Neurosurgery00A novel deep learning model is developed that accurately predicts requirement for acute neurosurgical intervention using unlabeled TBI scans and has potential application to optimize state-wide triage efficiency and care pathways for brain-injured patients.Neurosurgery['A. Malhotra', 'Christopher W Smith', 'Husain Shakil', 'Alun D Ackery', 'M. Mamdani', 'A. Nathens', 'Jefferson R Wilson', 'Errol Colak', 'Christopher Witiw']2024-04-01T00:00:00https://www.semanticscholar.org/paper/8ec8ed181c100036e68ea5bfb0f84f38bc3ac181 0ae5d4919784b624c34b307e89bce232011c4a0eThis article is devoted to the features of the English terminology of artificial intelligence, a science that has been rapidlydeveloping in recent decades both in Uzbekistan and in other countries. The scientific achievements of artificial intelligence are directly related to all areas of human activity, starting with computer games and computer viruses and downloading by complex machines in industrial enterprises and medicine. This is precisely what explains the relevance of studying artificial intelligence terminology, as well as the need to identify its features and patterns, especially for such an industrially developed region as the Bukhara region.American Journal Of Philological Sciences00The features of the English terminology of artificial intelligence, a science that has been rapidlydeveloping in recent decades both in Uzbekistan and in other countries, are focused on.American Journal of Philological Sciences['Djalilova Zarnigor Obidovna']2024-04-01T00:00:00https://www.semanticscholar.org/paper/0ae5d4919784b624c34b307e89bce232011c4a0e 68bbfc4c007bd358d98454bf2b145463f87fb6c1Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases: Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI: 0.88-1.08, I2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI: 0.87-1.08, I2 = 0%) for symptom detection and 0.97 (95% CI: 0.56-1.57, I2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.Cureus180The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality.Cureus['Abdullah F Alhalafi', 'Saif M Alqahtani', 'Naif A Alqarni', 'Amal T Aljuaid', 'Ghade T Aljaber', 'Lama M Alshahrani', 'Hadeel Mushait', 'Partha A Nandi']2024-04-01T00:00:00https://www.semanticscholar.org/paper/68bbfc4c007bd358d98454bf2b145463f87fb6c1 6661d507cbbbbe9543e0c715b979a762ef0b6372Abstract Background Advanced coronary plaque analysis by cardiac computed tomography (CT) has recently emerged as a promising technique for better prognostic stratification. However, this evaluation application in clinical practice is still uncertain. Case summary In the present case, we described the clinical picture of a 44-year-old tennis player with ectopic ventricular beats in which cardiac CT enabled the identification of a non-obstructive but high-risk plaque on proximal left anterior descendent artery. The application of artificial intelligence (AI)-enhanced software enabled to better stratify the patients’ risk. The present case describes how early identification of non-obstructive but high-risk coronary plaque evaluated by cardiac CT using AI-enhanced software enabled accurate and personalized risk assessment. Discussion The main clinical message of this case report is that advanced plaque analysis by cardiac CT, especially when performed with AI-based software, may provide important prognostic information leading to a personalized preventive approach. Moreover, AI-based software may contribute to promote a routine evaluation of these important data already included in traditional cardiac CT.European Heart Journal: Case Reports160The main clinical message of this case report is that advanced plaque analysis by cardiac CT, especially when performed with AI-based software, may provide important prognostic information leading to a personalized preventive approach.European Heart Journal: Case Reports['Andrea Provera', 'D. Andreini', 'Kersten Petersen', 'E. Gallinoro', 'E. Conte']2024-04-01T00:00:00https://www.semanticscholar.org/paper/6661d507cbbbbe9543e0c715b979a762ef0b6372 81654e54cedc0edaa697ee087d679cadddc0b298In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents unique challenges and opportunities. This comprehensive review delves into current strategies for addressing OMBC, covering locoregional and site-specific metastasis management, and addressing both surgical and minimally invasive therapies as essential components. Moreover, the transformative role of Artificial Intelligence (AI) is spotlighted. However, while the future looks promising, several limitations need addressing, including the need for further research, especially in diverse patient populations and resource-challenged settings. AI implementation may require overcoming the lack of Electronic Health Records acceptance in resource-challenged countries, which contributes to a scarcity of large datasets for AI training. As AI continues to evolve, validation and regulatory aspects must be continually addressed for seamless integration into clinical practice. In summary, this review outlines the evolving landscape of OMBC management, emphasizing the need for comprehensive research, global collaboration, and innovative AI solutions to enhance patient care and outcomes.JPMA. The Journal of the Pakistan Medical Association280The evolving landscape of OMBC management is outlined, emphasizing the need for comprehensive research, global collaboration, and innovative AI solutions to enhance patient care and outcomes and validation and regulatory aspects must be continually addressed for seamless integration into clinical practice.JPMA. The Journal of the Pakistan Medical Association['Mehwish Mooghal', 'Muhammad Maisam Ali', 'W. Khan', 'Areeba Ahmer', 'Maha Ghulam Akbar', 'L. Vohra']2024-04-01T00:00:00https://www.semanticscholar.org/paper/81654e54cedc0edaa697ee087d679cadddc0b298 7114177e5776b17dce869531609b1c6a44acf260The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes.Cureus580An overview of the latest developments and applications of AI in breast cancer screening and treatment aims to provide an overview of the latest developments and applications of artificial intelligence in breast cancer screening and treatment.Cureus['Zubir S. Rentiya', 'Shobha Mandal', 'Pugazhendi Inban', 'Hemika Vempalli', 'Rishika Dabbara', 'Sofia Ali', 'Kirpa Kaur', 'Abiodun Adegbite', 'Tarsha A Intsiful', 'Malavika Jayan', 'V. Odoma', 'A. Khan']2024-04-01T00:00:00https://www.semanticscholar.org/paper/7114177e5776b17dce869531609b1c6a44acf260 92ce51b96721936e2cd16a7f0e9df3671aa012cc Neurosurgical simulations equipped with artificial intelligence systems provide an objective quantitative assessment of surgical technical skills and tailored intelligent feedback. These systems allow for repeated practice of complex skills such as subpial brain tumor resection. However, more work is needed to assess the utility of intelligent systems in teaching tumor resection skills, compared to traditional human instruction. Twenty-five trainees who are currently enrolled in four Canadian medical schools participated in two training sessions, during which they completed a simulated subpial tumor resection five times. Participants were randomly assigned to two feedback groups: (1) real-time intelligent instruction, and (2) in-person human instruction. They were then assigned to the other feedback group in the second (cross-over) session. A composite-score was given by the intelligent system to assess technical skills during each task. Trainees who received real-time intelligent instruction significantly improved their composite-score in the first and second training sessions (p = .017, p = .005, respectively). Trainees who received in-person human instruction in the first training session had no statistically significant changes in their composite-score from the first to the last task repetition (p = .119). The composite-score decreased significantly in the second training session with in-person human instruction (p = .004). Real-time intelligent instruction provides an effective way of teaching simulated brain tumor resection skills. Artificial intelligence systems may be a useful addition to current surgical training curricula, providing objective, and tailored assessment and teaching of surgical technical skills in risk-free realistically simulated patient cases. Educators can benefit from these systems to guide their supervision, while trainees can use them for self-guided skill acquisition. Further research may assess the generalizability of these findings to other surgical procedures. Neurosurgery00Real-time intelligent instruction provides an effective way of teaching simulated brain tumor resection skills, and educators can benefit from these systems to guide their supervision, while trainees can use them for self-guided skill acquisition.Neurosurgery['R. Yilmaz', 'A. Fazlollahi', 'A. Alsayegh', 'M. Bakhaidar', 'Rolando F. Del Maestro']2024-04-01T00:00:00https://www.semanticscholar.org/paper/92ce51b96721936e2cd16a7f0e9df3671aa012cc 29026d50508856d30d8a578f2eb438c7460ccb81ABSTRACT Artificial intelligence (AI) is an emerging tool in modern medicine and the digital world. AI can help dentists diagnose oral diseases, design treatment plans, monitor patient progress and automate administrative tasks. The aim of this study is to evaluate the perception and attitude on use of artificial intelligence in dentistry for diagnosis and treatment planning among dentists and non-dentists’ population of south Tamil Nadu region in India. A cross sectional online survey conducted using 20 close ended questionnaire google forms which were circulated among the dentists and non -dentists population of south Tamil Nadu region in India. The data collected from 264 participants (dentists -158, non-dentists -106) within a limited time frame were subjected to descriptive statistical analysis. 70.9% of dentists are aware of artificial intelligence in dentistry. 40.5% participants were not aware of AI in caries detection but aware of its use in interpretation of radiographs (43.9%) and in planning of orthognathic surgery (42.4%) which are statistically significant P < 0.05.44.7% support clinical experience of a human doctor better than AI diagnosis. Dentists of 54.4% agree to support AI use in dentistry. The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non -dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning. Journal of Pharmacy and Bioallied Sciences320The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non-dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning.Journal of Pharmacy and Bioallied Sciences['A. J. Pringle', 'V. Kumaran', 'M. S. Missier', 'Anthonu Selva Pinky Nadar']2024-04-01T00:00:00https://www.semanticscholar.org/paper/29026d50508856d30d8a578f2eb438c7460ccb81 6cd0d8c991ffeffe691d6266c34a4e5440f5c312This article aims to study professional future anxiety differences among media professionals and its relationship to utilizing artificial intelligence (AI) techniques in media institutions in Egypt, France, and United Arab Emirates (UAE); and to know the effect of the intensity of employing AI techniques in various media institutions on the professional future anxiety of the sample. A convenience sample was drawn from the three countries, with a total of 300 media professionals. It included 100 participants from each country, to whom the questionnaire and the professional future anxiety scale were applied. The methodology regarding data collection is quantitative research. Descriptive analysis was used to extract the results. Pearson correlation coefficient, ANOVA, and simple linear regression analysis to test the research hypotheses. The study revealed that there is a statistically significant direct correlation between the employment of AI techniques in media institutions and professional future anxiety among media professionals in Egypt, France, and UAE. It was also evidenced that there are differences in the degree to which media institutions employ AI technologies in favor of France, while no differences were found in the level of professional future anxiety among media professionals based on the variable of the country. The respondents’ degree of professional future anxiety was moderate. The results also confirmed that media institutions’ extensive employment of AI techniques contributes to effecting professional future anxiety among media professionals participating in the study. The most prominent technologies and applications employed by media institutions and were used by the respondents were techniques for verifying the accuracy of sources, information, and content, and techniques for increasing the efficiency of news coverage and processing, ChatGPT application.Online Journal of Communication and Media Technologies180There is a statistically significant direct correlation between the employment of AI techniques in media institutions and professional future anxiety among media professionals in Egypt, France, and UAE and media institutions’ extensive employment of AI techniques contributes to effecting professional future anxiety.Online Journal of Communication and Media Technologies['Muhammad Noor Al Adwan', 'Mohmad El Hajji', 'Hossam Fayez']2024-04-01T00:00:00https://www.semanticscholar.org/paper/6cd0d8c991ffeffe691d6266c34a4e5440f5c312 80279d033019cbbe5a7f0fb3c77420be30f829e0 Multi-morbid patients have complex care needs and are more likely to utilise healthcare services, which represents a financial burden on healthcare organisations. 30-day readmission of patients is a method of measuring the quality of the services provided within healthcare.[1] Artificial intelligence (AI) models can flag multi-morbidity patients at increased risk of readmission.[2] However, it is unclear which common predictors have been used to develop these models. To systematically review the medical literature to identify predictors that have been used to develop AI models that predicted unplanned 30-day hospital readmissions of multi-morbid patients, and whether these predictors were modifiable or non-modifiable. Four large databases: Medline, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched on 15th November 2022. Only publications that developed a machine learning algorithm to predict unplanned 30-day hospital readmission for patients with multi-morbidity were included. Articles published in English language were eligible for inclusion. A narrative synthesis of all eligible studies was undertaken. Key findings were identified through inductive strategy, which includes simplifying raw data in the included articles into a comprehensive format. This systematic review was registered with the PROSPRO database (CRD42022373937) and followed PRISMA guidelines. There were 1,906 articles extracted from all databases, 18 of which met our inclusion criteria. A total of 669 predictors of hospital readmission were found, and were divided into 103 modifiable (i.e., action could be taken to change the outcome) and 566 non-modifiable (i.e. age, gender, number of previous emergency admissions. The average number of modifiable predictors per prediction model was six, with the most common being length of hospital stay, multi-morbidity, obesity, hypertension, diabetes, depression, and anaemia. The most commonly used AI algorithm was the gradient boosting algorithm. This algorithm generally showed a better performance than other models reported. The performance of all models in the included studies was compared; 13 studies showed an average sensitivity of 72%. The average specificity was calculated at 73% from 11 studies. The highest AUC reported was above 0.9, which demonstrates an excellent performing model, while five models did not report a value for the AUC. We identified modifiable and non-modifiable predictors used in previously developed AI algorithms that predict 30-day hospital readmissions of multi-morbid patients. Modifiable predictors in particular can help guide clinical decision-making by allowing early actions to be taken in primary care to potentially reduce the risk of readmission. However, one limitation of this review is that some included studies used balanced (i.e. synthetic) data which could have introduced a level of bias. 1. Wong EL, Cheung AW, Leung MC et al. Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data. BMC health services research. 2011;11:1-8. 2. Merrill RK, Ferrandino RM, Hoffman R et al. Machine learning accurately predicts short-term outcomes following open reduction and internal fixation of ankle fractures. The Journal of Foot and Ankle Surgery. 2019;58(3):410-6. International Journal of Pharmacy Practice00Modifiable and non-modifiable predictors used in previously developed AI algorithms that predict 30-day hospital readmissions of multi-morbid patients are identified.International Journal of Pharmacy Practice['N. Hassan', 'S. Wilson', 'K. Marley', 'R. Slight', 'S. Slight']2024-04-01T00:00:00https://www.semanticscholar.org/paper/80279d033019cbbe5a7f0fb3c77420be30f829e0 bd98a71ced5591db973963c580cf7c3fd32a5060This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer (BC) patients undergoing Neo-Adjuvant Chemotherapy (NACT). Summarizing recent research findings underscores the significant strides made in the accurate assessment of pCR using AI, including deep learning and radiomics. Such AI-driven models offer promise in optimizing clinical decisions, personalizing treatment strategies, and potentially reducing the burden of unnecessary treatments, thereby improving patient outcomes. Furthermore, the review acknowledges the potential of AI to address healthcare disparities in Low- and Middle-Income Countries (LMICs), where accessible and scalable AI solutions may enhance BC management. Collaboration and international efforts are essential to fully unlock the potential of AI in BC care, offering hope for a more equitable and effective approach to treatment worldwide.JPMA. The Journal of the Pakistan Medical Association300The transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer patients undergoing Neo-Adjuvant Chemotherapy (NACT) is explored, offering hope for a more equitable and effective approach to treatment worldwide.JPMA. The Journal of the Pakistan Medical Association['K. Shaikh', 'Mehwish Mooghal', 'Abdullah Ameen', 'W. Khan', 'Sana Zeeshan', 'L. Vohra']2024-04-01T00:00:00https://www.semanticscholar.org/paper/bd98a71ced5591db973963c580cf7c3fd32a5060 22310f476747727ab2690f487bc7036e2d4d6717The Journal of International Relations00THE JOURNAL OF INTERNATIONAL RELATIONS['Joo Hee Kim']2024-03-31T00:00:00https://www.semanticscholar.org/paper/22310f476747727ab2690f487bc7036e2d4d6717 cb687f86b2f1aa7e5abf1282e674a1ce306dbba4Abstract: Conversational AI systems are becoming increasingly popular across many industries and are transforming the way people interact with technology. For a more authentic, human-like connection and a smooth user experience, these systems should combine text-based interactions with multimodal capabilities. The authors of this work suggest a new approach to improving conversational AI systems' usability by combining speech and visual analysis. By combining visual and auditory processing capabilities, AI systems can better understand human inquiries and instructions. Both visual data and speech can be better understood with the use of computer vision algorithms and natural language processing techniques, respectively. Conversational AI systems can provide more accurate and tailored replies by integrating many modalities to better grasp human intent and context. The development of multimodal conversational AI presents a significant difficulty in ensuring the smooth integration of voice and visual processing units. A strong architectural design and advanced algorithms are necessary for the simultaneous synchronization and comprehension of data from several modalities in real-time. The system needs to keep track of the conversation's context even when it switches between different forms of communication so it can keep providing fair and relevant responses all through the engagement. Customization is key to making multimodal conversational AI better for users. Based on user data and preferences, the system may tailor interactions to offer more relevant ideas and support. Users are more invested in the AI system over time, and they have a better experience overall because to customization. Ensuring the privacy and security of important audiovisual data is of the utmost importance while building multimodal conversational AI. Strong encryption, anonymization technologies, and compliance with data protection regulations are vital for user privacy and system confidence. Continuous improvement is key to the success of multimodal conversational AI systems. The feedback from users can help the developers improve the system and add new features. Thanks to this iterative technique, the AI system stays flexible and can adjust to changing consumer preferences. By combining voice and picture processing, conversational AI systems have a great deal of promise for improving the user experience. Through the integration of visual and auditory signals, these systems have the ability to comprehend user intent more accurately, provide customized experiences, and completely transform the way humans engage with technology.International Journal for Research in Applied Science and Engineering Technology018A new approach to improving conversational AI systems' usability by combining speech and visual analysis is suggested, which has a great deal of promise for improving the user experience.International Journal for Research in Applied Science and Engineering Technology['Raunak Kandoi', 'Deepali Dixit', 'Mihul Tyagi', 'Raghuraj Singh Yadav']2024-03-31T00:00:00https://www.semanticscholar.org/paper/cb687f86b2f1aa7e5abf1282e674a1ce306dbba4 9c49055fc0cd9fcc7236346e363078df8951dd58Artificial intelligence and machine learning are essential for the development of IR4.0 due to their ability to analyse vast amounts of data, automate processes, and drive innovation across various sectors. These technologies enable intelligent decision-making, predictive analytics, and automation, leading to increased efficiency, productivity, and competitiveness in the digital age. In IR4.0, AI and ML power smart systems and connected devices, transforming industries. They facilitate the integration of digital, physical, and biological systems, enabling the creation of personalized medicine and medical diagnosis smart manufacturing, self-autonomous driving vehicles, smart cities, and smart home. Hence, this review aims to address the contribution of AI and ML in the development of medical diagnosis, smart manufacturing, smart cars, smart cities, and smart homes as well as to highlight the existing challenges faced by AI and ML in these fields. This review also showcases the relevant prospects of AI and ML applications in the fields mentioned.Malaysian Journal of Science and Advanced Technology407This review aims to address the contribution of AI and ML in the development of medical diagnosis, smart manufacturing, smart cars, smart cities, and smart homes as well as to highlight the existing challenges faced by AI and ML in these fields.Malaysian Journal of Science and Advanced Technology['Krishna AL Sannasy Rao', 'Chong Peng Lean', 'Ng Poh Kiat', 'Feng Yuan Kong', 'M. Reyasudin', 'Basir Khan', 'Daniel Ismail', 'Chen Li']2024-03-31T00:00:00https://www.semanticscholar.org/paper/9c49055fc0cd9fcc7236346e363078df8951dd58 91a25836fa01461562bcb3c174e43fb2a88b7548The article is dedicated to the analysis of the place and application of the new Law "On Administrative Procedure" in regulating public electronic services in Ukraine. Enacted on December 15, 2023, the Law aims to establish a system of unified, understandable, and transparent interaction between public authorities and citizens and businesses. The article examines the main provisions of the Law, focusing on the principles of administrative procedure and norms regulating the resolution of administrative matters using electronic digital technologies. Based on the analysis of legislation texts, comparative analysis, and consideration of practical aspects of the new law's implementation, the conclusion is drawn regarding the importance of applying its norms to the sphere of public services, including those provided through digital technologies. It is found that the Law of Ukraine "On Administrative Procedure" sets general standards for administrative procedure ensuring the observance of rights of individuals and legal entities in relations with administrative authorities, regardless of whether such interaction occurs through physical or electronic channels. Special legislation may define the specifics of providing certain types of public services, but the general standards of the Law of Ukraine "On Administrative Procedure" must be adhered to. The article also highlights the main directions for further research and possible directions for further development of legislation in this area. The application of the provisions of the Law of Ukraine "On Administrative Procedure" allows not just to copy the logic of the paper world into digital services, but to develop innovative approaches. This means that the right to participate in the administrative process can be implemented through online offices, the possibility to submit documents in electronic format, support for communication through digital channels and the creation of additional functions that complement the main serviceScienceRise: Juridical Science00ScienceRise: Juridical Science['N. Khliborob']2024-03-31T00:00:00https://www.semanticscholar.org/paper/91a25836fa01461562bcb3c174e43fb2a88b7548 21da2e9a12cfed142f74d9aa0a9a9b1a5ca11765This paper discusses the increasing importance of sustainable development and the role of environmental legislation and management in promoting renewable energy. It covers challenges such as policy and technological limitations, economic factors, public awareness, and stakeholder interests. It proposes strategies including policy reform, technological innovation, economic incentives, enhanced publicity and education, and stakeholder engagement to overcome these obstacles and encourage the broader use of renewable energy, aiming for sustainable development and environmental protection.Integration of Industry and Education Journal00Integration of Industry and Education Journal['Xidan Wang', 'Zhihui Liao']2024-03-31T00:00:00https://www.semanticscholar.org/paper/21da2e9a12cfed142f74d9aa0a9a9b1a5ca11765 c065dbd605a8b13aea4e08cf5f0903c9453e97a1Economic Sciences00International scientific journal "Internauka". Series: "Economic Sciences"['Roman Kliuchuk', 'Volodymyr Dalyk', 'Oleksandr Konyk', 'Serhii Babii', 'Ivan Maksymiv', 'Roman Paska', 'Andrii Skochelias']2024-03-31T00:00:00https://www.semanticscholar.org/paper/c065dbd605a8b13aea4e08cf5f0903c9453e97a1 4e7f49c9f45c94f99c7a5259b367168703f93665LABOR LAW REVIEW00LABOR LAW REVIEW['Hochang Roh']2024-03-31T00:00:00https://www.semanticscholar.org/paper/4e7f49c9f45c94f99c7a5259b367168703f93665 00a4e6f0872c264e8f08c301e7b2f1f381f6e205Journal of The Korean Association of Artificial Intelligence Education00Journal of The Korean Association of Artificial Intelligence Education['Ju Hyun Lee', 'Chul Hyun Lee']2024-03-31T00:00:00https://www.semanticscholar.org/paper/00a4e6f0872c264e8f08c301e7b2f1f381f6e205 e80224433f91a8682acdf329b8d8b22c6bf59c94International scientific journal Internauka Series Juridical Sciences00International scientific journal "Internauka". Series: "Juridical Sciences"['Yevheniia Andrukhiv']2024-03-31T00:00:00https://www.semanticscholar.org/paper/e80224433f91a8682acdf329b8d8b22c6bf59c94 9b57412f3974fd56c493f33335be82bcc8fc159aAdministrative law journal00ADMINISTRATIVE LAW JOURNAL['Dongin Ahn']2024-03-31T00:00:00https://www.semanticscholar.org/paper/9b57412f3974fd56c493f33335be82bcc8fc159a b1ae015d5309b71830b542905c9b4c1e498d30ceJournal of the Korea Management Engineers Society00Journal of the Korea Management Engineers Society['Seung-Bae Lee']2024-03-31T00:00:00https://www.semanticscholar.org/paper/b1ae015d5309b71830b542905c9b4c1e498d30ce fde5991b84b5f816f3bd7b56317a9bc8da02f5e8International scientific journal Internauka Series Juridical Sciences00International scientific journal "Internauka". Series: "Juridical Sciences"['M. Dutov']2024-03-31T00:00:00https://www.semanticscholar.org/paper/fde5991b84b5f816f3bd7b56317a9bc8da02f5e8 a604c1d514477fae43b59cca06471ed69f0b0e92In order to improve operational efficiency and competitiveness, supply chains currently we have to combine digital technology and artificial intelligence (AI). The adoption of digital warehousing and the application of artificial intelligence (AI) in supply chain management are examined in this study, with particular attention to how these developments may affect product pricing and warehouse maintenance expenses. Utilizing a comprehensive literature review and empirical analysis, this study identifies the primary factors driving the integration of artificial intelligence (AI) and digital warehousing into supply chain processes. The results illustrate the tactical implications for supply chain professionals in terms of refining maintenance procedures, streamlining warehouse management procedures, and product pricing strategies. For any given business, a smoothly running supply chain is essential to success. Possessing a highly accurate inventory estimate gives you a significant competitive edge. The operation of the entire supply chain is affected by an extensive variety of external as well as internal variables, including the environment, excessive volatility, shifts in customer perception, and media coverage. Examples of internal elements that impact supply chain performance are product releases and distribution network expansion. Artificial Intelligence (AI) has demonstrated in recent years to be a brain extension, allowing us to operate at levels above our wildest expectations. Contrary to common perception, artificial intelligence (AI) will not replace individuals; rather, it will enable us to achieve our full innovative and tactical potential. [Yang, M., Fu, M., & Zhang, Z. et al 2021]International Journal For Multidisciplinary Research61The adoption of digital warehousing and the application of artificial intelligence (AI) in supply chain management are examined in this study, with particular attention to how these developments may affect product pricing and warehouse maintenance expenses.International Journal For Multidisciplinary Research['Abu Sied']2024-03-31T00:00:00https://www.semanticscholar.org/paper/a604c1d514477fae43b59cca06471ed69f0b0e92 c988808bad22408274d511cffd77d902db398788In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and protection that impact professionals across various careers. With this in mind, this survey provides an extensive examination of copyright infringement as it pertains to generative AI, aiming to stay abreast of the latest developments and open problems. Specifically, it will first outline methods of detecting copyright infringement in mediums such as text, image, and video. Next, it will delve an exploration of existing techniques aimed at safeguarding copyrighted works from generative models. Furthermore, this survey will discuss resources and tools for users to evaluate copyright violations. Finally, insights into ongoing regulations and proposals for AI will be explored and compared. Through combining these disciplines, the implications of AI-driven content and copyright are thoroughly illustrated and brought into question.arXiv.org1431This survey provides an extensive examination of copyright infringement as it pertains to generative AI, aiming to stay abreast of the latest developments and open problems.ArXiv['Jocelyn Dzuong', 'Zichong Wang', 'Wenbin Zhang']2024-03-31T00:00:00https://www.semanticscholar.org/paper/c988808bad22408274d511cffd77d902db398788 3a4c22e0a35ef47a2db69fe50fe1cc906e60195cAbstract Artificial intelligence (AI) is revolutionizing the architectural industry, enhancing both creativity and efficiency. This study explores AI's impact on construction practice, aiming to clarify its role in enhancing creative efficiency and streamlining operations. Through a literature review, it examines AI's transformative effects on design processes and key tools for integration into the industry. The research focuses on AI's role in enhancing architectural creativity and efficiency, using real-life case studies to illustrate its benefits. It also explores how AI streamlines operations, improves resource utilization and accelerates project timelines. By highlighting dominant AI tools used in architecture, the study emphasizes their ability to enhance creativity, streamline design iterations, and facilitate team collaboration. Ultimately, this research contributes to understanding the dynamic relationship between AI and architectural practice, inspiring future innovations for a more efficient and sustainable industry. This study addresses the ongoing debate on whether AI is enhancing or depleting architectural creativity and efficiency.International Journal For Multidisciplinary Research20International Journal For Multidisciplinary Research['Noor Afshan', 'Ar. Sameer Sharma']2024-03-31T00:00:00https://www.semanticscholar.org/paper/3a4c22e0a35ef47a2db69fe50fe1cc906e60195c 87edf93e45049a6011e8dcbc653321e2c509a109With the growing demand of technological advancements and real time technical support, ARTIFICIAL INTELLIGENCE (AI) has emerged as one of the integral parts of the healthcare system, especially after post pandemic era (1) Synthetic intelligence plays a significant role in assessment and patient management. So, the time is not far where learning and implementing it in current practice will be the need of an hour. Physiotherapy course curriculum would need to add it on in the coming times to become rational about accepting or rejecting the AI advices. With the fast paced growing time, at times it becomes difficult for patients to even visit physiotherapy clinic on a daily basis or the post operative bed ridden patients or individuals who are bed ridden due to other reasons such as stroke, spinal cord injury or other neurological impairment, home bases treatment with AI instruments can prove nothing less than a blessingJournal of the Epidemiology Foundation of India80Physiotherapy course curriculum would need to add it on in the coming times to become rational about accepting or rejecting the AI advices to become rational about accepting or rejecting the AI advices.Journal of the Epidemiology Foundation of India['Monali Tanna']2024-03-31T00:00:00https://www.semanticscholar.org/paper/87edf93e45049a6011e8dcbc653321e2c509a109 189f563ae9e4e8476b75aa8080a5d21388d8c704Abstract: The integration of artificial intelligence (AI) in organic farming has the potential to revolutionize sustainable agriculture practices. This paper explores the use of AI-powered solutions for sustainable organic farming. The study highlights the potential of AI in organic farming, including predictive analytics for pest and disease management, precision farming, and an integrated organic farming system. The review also emphasizes the importance of collaboration between the agricultural sector and AI developers to ensure that AI-driven solutions are accessible, affordable, and ethically implemented. The study concludes that by harnessing the power of AI, organic farmers can increase yields, reduce environmental impact, and meet the growing global demand for organic produce, paving the way for a more sustainable and food-secure future. Therefore, findings underscore the potential of AI to contribute to sustainable organic farming, marking a crucial step toward a technologically advanced and environmentally conscious agricultural futureInternational Journal for Research in Applied Science and Engineering Technology00By harnessing the power of AI, organic farmers can increase yields, reduce environmental impact, and meet the growing global demand for organic produce, paving the way for a more sustainable and food-secure future.International Journal for Research in Applied Science and Engineering Technology['Ramandeep Kaur', 'Dr. Ishwar Sharma', 'Chanchal Saini']2024-03-31T00:00:00https://www.semanticscholar.org/paper/189f563ae9e4e8476b75aa8080a5d21388d8c704 fe80481bca3e4c82e7f3bae2839f968d4b811037In an era where artificial intelligence (AI) seamlessly integrates into the fabric of daily life, understanding and enhancing human emotional intelligence (EI) through empathetic algorithms emerges as a frontier in technological advancement. This research paper explores the development and application of AI systems capable of recognizing, interpreting, and responding to human emotions in a manner that fosters emotional growth and understanding. Through a comprehensive literature review, this study identifies the theoretical underpinnings of emotional intelligence and examines the current landscape of empathetic algorithms. Employing a mixed-methods approach, including case studies and empirical analysis, the paper presents novel insights into how AI can be engineered to support emotional intelligence across various domains, such as healthcare, education, and customer service. Ethical considerations, including privacy, consent, and data security, are thoroughly evaluated to address potential societal implications. The findings suggest that empathetic algorithms hold significant promise in enhancing human emotional intelligence, albeit with challenges that necessitate careful ethical and technical scrutiny. The research culminates in proposing a set of guidelines for future developments in this field, emphasizing the need for interdisciplinary collaboration. This study not only contributes to the theoretical framework of empathetic algorithms but also paves the way for future innovations that prioritize emotional intelligence in the design and implementation of AI systems.Journal of Electrical Systems210The findings suggest that empathetic algorithms hold significant promise in enhancing human emotional intelligence, albeit with challenges that necessitate careful ethical and technical scrutiny.Journal of Electrical Systems['Sesha Bhargavi Velagaleti', 'Dhouha Choukaier', 'Dr. Ramesh Nuthakki', 'Vikas Lamba', 'Vibhu Sharma', 'Satyakam Rahul']2024-03-31T00:00:00https://www.semanticscholar.org/paper/fe80481bca3e4c82e7f3bae2839f968d4b811037 2c3a39f6ba190b694919dcacd9523c5f38f1982c[Objective] The purpose of this study is to propose possible approaches for utilizing AI in education based on students' monitoring perception of generative AI. [Contents] In this study, we analyzed use cases according to the types of generative AI and monitored students' perceptions through inductive content analysis. Based on this, we proposed possibilities and strategies for the utilization of generative AI models in future education. [Conclusions] The conclusion of the study is as follows. From a strengths perspective, it proposes enhancing accessibility to AI and promoting experiential learning for educational expansion. Additionally, it suggests tailored education and difficulty adjustment utilizing AI support. On the opportunity side, the study emphasizes strengthening multilingual support to address language issues. It also advocates expanding accessibility for AI to play a counseling role in providing physical, mental, and psychological stability through education. However, from a weaknesses standpoint, the study highlights considerations for challenges such as lack of accuracy in information, urgent need for AI infrastructure development, issues related to the time and cost of workforce training, and ethical concerns arising from inconsistent standards.The Korean Association for the Study of Religious Education00This study analyzed use cases according to the types of generative AI and monitored students' perceptions through inductive content analysis to propose possible approaches for utilizing AI in education based on students' monitoring perception of generative AI.The Korean Association for the Study of Religious Education['Young-Suk Lee', 'Taeeun Shim']2024-03-31T00:00:00https://www.semanticscholar.org/paper/2c3a39f6ba190b694919dcacd9523c5f38f1982c 9e70f2ca11f58adb57ea1f551fec3ca668ce0d46E-Commerce changed how we buy and sell things online. AI makes it better with personalized suggestions, chatbots for help, and predicting what we might like. People like AI because it's easy, personal, and makes shopping faster. But some worry about their privacy, security, and AI taking over jobs. Still, AI keeps improving eCommerce by analyzing data, doing tasks automatically, and making customers happier overall.International Journal For Multidisciplinary Research60AI keeps improving eCommerce by analyzing data, doing tasks automatically, and making customers happier overall by analyzing data, doing tasks automatically, and making customers happier overall.International Journal For Multidisciplinary Research['Ms. Shilpa Sandhu', 'Hima A S', 'Manya Jain', 'Nikita Jain', 'Shreya Rauniyar']2024-03-31T00:00:00https://www.semanticscholar.org/paper/9e70f2ca11f58adb57ea1f551fec3ca668ce0d46 5394075a3e09a36ac5ee53d6079aa46069896e37Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts the complexities of robot planning can be challenging. This work presents an open-source platform that simplifies the process using a visual interface that completely abstracts the complex internals of hierarchical planning that robots use for performing task and motion planning. Using the principles developed in the field of explainable AI, this intuitive platform enables users to create plans for robots to complete tasks, and provides helpful hints and natural language explanations for errors. The platform also has a built-in simulator to demonstrate how robots execute submitted plans. This platform's efficacy was tested in a user study on university students with little to no computer science background. Our results show that this platform is highly effective in teaching novice users the intuitions of robot task planning.arXiv.org00An open-source platform that simplifies the process using a visual interface that completely abstracts the complex internals of hierarchical planning that robots use for performing task and motion planning, using the principles developed in the field of explainable AI.ArXiv['D. Dobhal', 'Jayesh Nagpal', 'Rushang Karia', 'Pulkit Verma', 'Rashmeet Kaur Nayyar', 'Naman Shah', 'Siddharth Srivastava']2024-03-31T00:00:00https://www.semanticscholar.org/paper/5394075a3e09a36ac5ee53d6079aa46069896e37 012ebf4769af3280bd4c87a85663b42501344155Abstract: Over the past two decades, the integration of machine learning (ML) techniques within educational frameworks has garnered significant attention. However, despite its widespread adoption, there remains a dearth of research focusing on developing AI systems with a core emphasis on interpretability and explainability. This paper seeks to bridge this gap by proposing an advanced framework that not only predicts students' performance accurately but also offers reliable and interpretable results tailored for career counseling. The framework merges the concepts of ML and Explainable AI (XAI) to address the complexities of career counseling in educational settings. Drawing inspiration from educational data mining, the framework aims to provide insights conducive to students' career growth and decision-making processes. By incorporating MLbased White and Black Box models, the approach analyzes a comprehensive educational dataset comprising academic and employability attributes crucial for job placements and skill development. To enhance interpretability, the framework leverages the NGBoost algorithm, known for its efficiency in prediction modeling. Additionally, it integrates Local Interpretable Modelagnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) methods for providing both local and global explanations of model predictions, ensuring transparency and comprehensibility. Through a series of use cases, the paper showcases the applicability and effectiveness of the framework in providing actionable insights to educators and students alike. In conclusion, this research contributes to the advancement of career counseling in educational contexts by offering a robust and interpretable ML-based framework. By providing transparent insights into students' academic performance and career prospects, the approach facilitates informed decision-making and supports personalized guidance for optimal career trajectories.International Journal for Research in Applied Science and Engineering Technology00This research contributes to the advancement of career counseling in educational contexts by offering a robust and interpretable ML-based framework that not only predicts students' performance accurately but also offers reliable and interpretable results tailored for career counseling.International Journal for Research in Applied Science and Engineering Technology['Venkata Lakshmi Namburi', 'Karamvir Singh', 'Trisha Reddygari', 'Niteesh Kumar S', 'Venkata Satya Sai Varun Dudipala']2024-03-31T00:00:00https://www.semanticscholar.org/paper/012ebf4769af3280bd4c87a85663b42501344155 3dd97f11d6352bc32e21535b40e145e866777f98In today's world, having knowledge and skills related to Artificial Intelligence (AI) is essential for diverse university students. As a result, many Korean universities offer AI-related courses as part of their mandatory curriculum. However, designing an AI curriculum that caters to university students requires considering the previous experiences and perspectives of AI convergence education. Thus, navigating the perceptions of AI convergence education among Korean university students not majoring in computers is important. To achieve this goal, an online survey was conducted during the 2023 summer from various departments at C University in Korea (n=177). Survey results indicate that Korean universities showed an awareness of the personal implications of AI concerning their academic discipline and careers. Also, they recognize the importance of AI education regardless of their current majors at the university. In addition, survey results also revealed the presence or absence of prior AI educational experiences significantly affected the perceived importance of AI convergence education. However, students without prior AI exposure could struggle to take AI courses in liberal arts because they are not well equipped and prepared to take those mandatory courses. The study has the novelty and uniqueness in that future studies will receive valuable insights for constructing AI convergence education that catersJournal of the Korea Academia-Industrial cooperation Society160Survey results indicate that Korean universities showed an awareness of the personal implications of AI concerning their academic discipline and careers and the presence or absence of prior AI educational experiences significantly affected the perceived importance of AI convergence education.Journal of the Korea Academia-Industrial cooperation Society['Yue Li', 'Yong-Jik Lee']2024-03-31T00:00:00https://www.semanticscholar.org/paper/3dd97f11d6352bc32e21535b40e145e866777f98 c9d7674ac0ef29ba572fd8bd921eaf819c81a64fArtificial Intelligence (AI) tools recently became a prominent concern in higher education classrooms. Many teachers have implemented the technology into their assignments, while others are strictly against this technology’s use for assignments. Either way, students have found ways to use it in their academic careers. Though research on the power of AI in the workplace exists, research is lacking in its appropriate use in higher education. Universities need to define AI’s role on campus and establish guidelines on how these tools may or may not be used and how faculty can recognize misuse, specifically related to academic integrity. This study aimed to determine how faculty view AI as a part of undergraduate literature, language, and linguistics programs. From the interview study, common themes emerged, including implementation, academic integrity, the human aspect of linguistics, and the future of AI writing tools. Interviewed faculty also stated that those in higher education must tread carefully through this strong intersection between technology and the arts to use AI responsibly, strategically, and ethically. KEYWORDS: Artificial Intelligence (AI); Artificial General Intelligence (AGI); Linguistics; Higher Education; ChatGPT; Machine Translation; Academic Integrity; EthicsAmerican journal of undergraduate research00How faculty view AI as a part of undergraduate literature, language, and linguistics programs is determined to determine, including implementation, academic integrity, the human aspect of linguistics, and the future of AI writing tools.American Journal of Undergraduate Research['Mahlet Yitages', 'Akie Kasai']2024-03-31T00:00:00https://www.semanticscholar.org/paper/c9d7674ac0ef29ba572fd8bd921eaf819c81a64f 8a6eed937959ed85c262ecbb5f2f2fd348a51650Abstract: A branch of computer science called Artificial intelligence (AI) deals with studying, creating, and using intelligent machines. This includes the description of recently developed ideas and methods for the development and implementation of AI in civil engineering and also gives an overview of the field’s advancement. The tremendous development and advancement in big data, deep learning, and machine learning technologies, have been used effectively and successfully in various civil engineering sectors.International Journal for Research in Applied Science and Engineering Technology00This includes the description of recently developed ideas and methods for the development and implementation of AI in civil engineering and also gives an overview of the field’s advancement.International Journal for Research in Applied Science and Engineering Technology['Kendre Sainath Kendre']2024-03-31T00:00:00https://www.semanticscholar.org/paper/8a6eed937959ed85c262ecbb5f2f2fd348a51650 8b592fa3500f45dce1eec8dd1ceaa854e5d39407The use of automation and artificial intelligence (AI) in audit practice is increasingly becoming a major focus, with significant impact on the profession. This research depicts the current landscape of the use of AI in auditing, highlighting aspects such as automation and empowerment of the workforce in auditing, impact of AI on improving audit quality criteria, key factors in adopting AI-based audit techniques, impact of AI technology on audit evidence , and auditors' perceptions of AI in improving audit quality. The results and discussion show that while there are great benefits from integrating automation and AI in auditing, including improved audit quality, enhanced efficiency, and the ability to perform continuous audits, there are also challenges that need to be overcome, such as high customization costs for specific audit processes industry. The use of AI in auditing requires adaptation from auditors to changes in competencies and workflows to effectively utilize this technology. However, with proper understanding and careful handling of these challenges, AI has great potential to improve overall audit practices.Accounting Studies and Tax Journal (COUNT)400The results and discussion show that while there are great benefits from integrating automation and AI in auditing, including improved audit quality, enhanced efficiency, and the ability to perform continuous audits, there are also challenges that need to be overcome, such as high customization costs for specific audit processes industry.Accounting Studies and Tax Journal (COUNT)['Lidiana Lidiana']2024-03-31T00:00:00https://www.semanticscholar.org/paper/8b592fa3500f45dce1eec8dd1ceaa854e5d39407 f6bc90e1a99a612910828e9a718ca2ae14868157Abstract: Using AI to Drive Advances in Drug Development: Transforming Biomedical Innovation. Medicine discovery and development have been transformed recently by the intersection of biomedicine and artificial intelligence. Because of the exponential growth in data and AI algorithm capabilities, we are witnessing hitherto unseen opportunities to expedite the creation of new medications, boost the efficacy of current treatments, and ultimately save lives. How AI is changing drug discovery in many ways. In order to identify new drug targets and forecast the efficacy of proposed treatments, we will explore how machine learning algorithms can assess intricate biological datasets. We will also go through the ways in which AI-driven methods are being applied to simplify clinical trials, improve drug development procedures, and tailor specific patient therapies.International Journal for Research in Applied Science and Engineering Technology00In order to identify new drug targets and forecast the efficacy of proposed treatments, this work explores how machine learning algorithms can assess intricate biological datasets and the ways in which AI-driven methods are being applied to simplify clinical trials, improve drug development procedures, and tailor specific patient therapies.International Journal for Research in Applied Science and Engineering Technology['Binduja Sb']2024-03-31T00:00:00https://www.semanticscholar.org/paper/f6bc90e1a99a612910828e9a718ca2ae14868157 f624dcba3f6191660933628b89b9689c6e887addAbstract: A new area of research that has the potential to completely transform human-computer interaction is the incorporation of emotional intelligence into artificial intelligence (AI) systems. Through the use of sentiment analysis, facial recognition, and voice tone analysis, AI assistants are now able to recognize and comprehend human emotions, allowing them to respond to users with greater emotional nuance and empathy [1] [2]. This opens up new avenues for therapeutic mental health interventions, emotionally supportive dialogues, and improved customer service encounters [3].International Journal for Research in Applied Science and Engineering Technology00Through the use of sentiment analysis, facial recognition, and voice tone analysis, AI assistants are now able to recognize and comprehend human emotions, allowing them to respond to users with greater emotional nuance and empathy.International Journal for Research in Applied Science and Engineering Technology['Raj Agrawal', 'Nakul Pandey']2024-03-31T00:00:00https://www.semanticscholar.org/paper/f624dcba3f6191660933628b89b9689c6e887add 20930eedc74231058af11336d688110b9b270920Abstract: Agriculture is crucial for India's economy, with over 50% relying on it for survival. Climate and weather variations pose risks to agriculture's health. AI can monitor crops by using machine learning methods. Crop monitoring includes Crop Recommendation, Weed Detection, Plant Disease Detection, Yield Prediction. The models are trained with Image and numerical datasets. A website will be developed to monitor crops and provide solutions. The optimal crop can be suggested based on the surrounding conditions by analysing important variables like composition of Nitrogen, Phosphorous and Potassium in the soil, its pH value, humidity, and rainfall using various models namely Gaussian Naive Bayes, Logistic Regression, Gradient boosting, Ensemble which fall under the domain of Machine Learning. ANN can be used for crop yield prediction. Weed and Plant disease can be detected using ResNet which can be utilized for deep neural networks. The intent of this project is to help farmers choose suitable crops, differentiate crops from weeds, detect diseases, provide remedies to protect crop. It enables to improve yield and productivity, Enhanced sustainability, Increased ProfitabilityInternational Journal for Research in Applied Science and Engineering Technology00The intent of this project is to help farmers choose suitable crops, differentiate crops from weeds, detect diseases, provide remedies to protect crop, and improve yield and productivity, Enhanced sustainability, Increased Profitability.International Journal for Research in Applied Science and Engineering Technology['Sri Hari Nallamala', 'Dhanalakshmi Meghana Majeti', 'Venkata Pranay Pendyala', 'Sanghamitra Neela', 'Bhanu Prakash Nambari']2024-03-31T00:00:00https://www.semanticscholar.org/paper/20930eedc74231058af11336d688110b9b270920 3ae4925fb812ab5a27409eb71f326d38e33e1693Purpose: The aim of this paper is to examine how artificial intelligence and digital transformation affect sustainable organisational performance with a particular emphasis on mediating role of digital leadership. Design/Methodology/Approach: The survey data from 245 employees employed in different industries and enterprises is considered. The data is collected through structured questionnaire and analysed with PLS-SEM. The study investigates the interrelationship between digital transformation, AI and sustainable organizational performance with mediating role that digital leadership. Findings: The conclusions of the study bring about the need for digital leadership in integrating sustainability and artificial intelligence. From this, it could be distinguished that combining digital leadership with AI is going to increase the performance and productivity of an organization. The integration of AI and digital leadership increased the capacity to innovate which in turn made an affirmative effect on sustainable organizational performance. Implications/Originality/Value: This study provides new perspectives towards the importance of AI and creative digital behaviors for the achievement of long-term sustainable organizational performance. It also follows that digital leadership can be viewed as a way to promote an innovative and sustainable culture, hence highlighting the mediation role of digital leadership.                                                             Journal of Accounting and Finance in Emerging Economies290It could be distinguished that combining digital leadership with AI is going to increase the performance and productivity of an organization, hence highlighting the mediation role of digital leadership.Journal of Accounting and Finance in Emerging Economies['Gohar Mahmood', 'Maria Shams Khakwani', 'Anam Zafar', 'Zahid Abbas']2024-03-31T00:00:00https://www.semanticscholar.org/paper/3ae4925fb812ab5a27409eb71f326d38e33e1693 cf3b1b542adc97f935a054a0b8b1836ecd09089bThe project includes an AI application designed specifically to parse through huge amountsof content influencing individual human behaviors. Developed for classroom usewhereeachstudentisin a position to meet his or her own standard. Recognizing the difficulty of disparate understanding among learners degrees, the planned system exercises a sophisticated algorithm to ask questions in order to determine the students knowledge level. Through these reviews, the system autonomously regulates the teaching moduleasitdifferentiatesthecontentdelivery,interactivitycomplementingthe assimilation of the material to a particular type of student. The system in question is based on integrating different types of content, especially interactive content, animations,and personal tests, allowing for greater and individualized involvement of the students, developing a living, breathing atmosphere which will stimulate students curiosity and keep them interested in the class. The interplay of the coursematerialsis another way that it will aid in supporting a students academic success.International Journal of Advanced Research00The project includes an AI application designed specifically to parse through huge amount of content influencing individual human behaviors, allowing for greater and individualized involvement of the students, developing a living, breathing atmosphere which will stimulate students curiosity and keep them interested in the class.International Journal of Advanced Research['T. Mahalingam', 'Nishanth S.', 'Sanjay T.', 'Sathya K.']2024-03-31T00:00:00https://www.semanticscholar.org/paper/cf3b1b542adc97f935a054a0b8b1836ecd09089b 48abeb6186f2f7fcee1addc03467ee396c97930eThis study was conducted to propose customized learning methods based on artificial intelligence as an option to bridge the learning gap caused by COVID-19 and strengthen the learners' self-learning methods, as well as verify their effectiveness. In particular, the study was conducted with the aim of ensuring high-quality public education with AI technology in rural areas, where educational infrastructure is insufficient. The AI-based customized learning assistance consisted of an AI diagnostic assessment to examine the level of understanding of the prerequisite concepts before starting a study unit, AI-based problem recommendations on learning by the level, and AI assessment and supplementary learning aid at the end of the unit. Before and after conducting of the study, the math academic achievement, math learning attitude, and math self-directed learning ability were tested to examine the effectiveness of the program.The results of the data analysis showed that AI-customized math learning had a positive effect on improving the math academic achievement, learning attitude, and self-directed learning ability. However, it is still limited as to fragmented learning based on problem solving, and it is necessary to explore the AI education practices which would support continuous and integrated growth of math learners.The Institute for Education and Research Gyeongin National University of Education00AI-customized math learning had a positive effect on improving the math academic achievement, learning attitude, and self-directed learning ability, and it is necessary to explore the AI education practices which would support continuous and integrated growth of math learners.The Institute for Education and Research Gyeongin National University of Education['Jee-you Whang', 'Chul-Hyun Lee']2024-03-31T00:00:00https://www.semanticscholar.org/paper/48abeb6186f2f7fcee1addc03467ee396c97930e c8147241ada995a7b79d9459723bd4d89c28dda8This research explores the complex world of small and medium-sized enterprises (SMEs) in India, looking at the potential and problems they have when trying to use AI-driven methods in their marketing campaigns. AI technologies provide SMEs with the opportunity to transform their marketing strategies as the business environment continues to change at a fast pace. The study intends to highlight the opportunities that these technologies bring as well as fully comprehend the obstacles preventing the smooth integration of AI tools into marketing operations. We will carefully look at the obstacles that Indian SMEs face when implementing AI-driven strategies. These obstacles include lack of funds, inadequate technology infrastructure, and a lack of knowledge and comprehension of AI applications. Concurrently, the research will highlight prospects that arise from using AI, delving into improved consumer targeting, tailored marketing strategies, and data-centric decision-making. It will also examine how AI affects marketing efficacy, providing insight into performance indicators and return on investment for small and medium-sized enterprisesInternational Journal of Advanced Research in Science, Communication and Technology150International Journal of Advanced Research in Science, Communication and Technology['Mr. Rajesh Jaychandran', 'Dr Surrender Kumar Shilla']2024-03-31T00:00:00https://www.semanticscholar.org/paper/c8147241ada995a7b79d9459723bd4d89c28dda8 e7a6e581c8a8834c95c44b28bbf0f294b4ad3f01 As the demand for high-quality video streaming continues to surge, the adaptability of streaming systems to dynamic and unpredictable network conditions becomes paramount. This review paper delves into the realm of adaptive video streaming, focusing on the integration of AI-driven predictive analytics to anticipate and optimize network conditions. The paper provides an extensive overview of existing adaptive streaming algorithms, highlighting the challenges posed by fluctuating network conditions. It explores the role of predictive analytics in mitigating these challenges, emphasizing the use of machine learning models and AI technologies. Through case studies and discussions on real-world implementations, the paper showcases how predictive analytics enhances the decision-making process in adaptive streaming systems, leading to improved bitrate adaptation and content delivery. Challenges and limitations associated with predictive analytics are scrutinized, paving the way for a comprehensive understanding of its implications. The integration of predictive analytics into adaptive streaming systems is examined, emphasizing its potential to revolutionize the quality of service. Finally, the paper outlines future trends and research directions, offering insights into the evolving landscape of adaptive video streaming. This review consolidates knowledge and provides a valuable resource for researchers, practitioners, and industry professionals involved in the intersection of video streaming, predictive analytics, and artificial intelligence.International Transactions on Electrical Engineering and Computer Science00This review paper delves into the realm of adaptive video streaming, focusing on the integration of AI-driven predictive analytics to anticipate and optimize network conditions, and provides an extensive overview of existing adaptive streaming algorithms.International Transactions on Electrical Engineering and Computer Science['Koffka Khan']2024-03-31T00:00:00https://www.semanticscholar.org/paper/e7a6e581c8a8834c95c44b28bbf0f294b4ad3f01 2caad4781201496a6ae619dde0712db52ba6ffb2Artificial intelligence (AI) is the main branch of computer science that permits advanced machines to interpret and analyze complex healthcare data elaborating the recent challenges in the medical field of study. The current state of AI applications in healthcare is examined in this systematic literature review, with an emphasis on the technology's accomplishments, difficulties, and potential. The wide breadth of AI technologies used in healthcare settings, such as robots, computer vision, machine learning, and natural language processing, is highlighted in this review through an extensive analysis of peer-reviewed publications. It talks about how customized medicine, predictive analytics, illness detection, and treatment planning are just a few of the areas of healthcare delivery that AI-driven technologies are transforming. According to research by investment bank Goldman Sachs, 300 million full-time employees could be replaced by artificial intelligence (AI). In the US and Europe, it might replace 25% of labor duties, but it might also lead to an increase in productivity and the creation of new jobs. Additionally, it might eventually result in a 7% rise in the global annual value of products and services produced. Additionally, the paper projects that approximately 25% of all employment might be performed totally by AI and that two-thirds of jobs in the U.S. and Europe "are exposed to some degree of AI automation. "The most likely groups to be impacted by workforce automation are educated white-collar workers making up to $80,000 annually, according to research from OpenAI and the University of Pennsylvania. According to a McKinsey Global Institute study, developments in digitalization, robots, and artificial intelligence may require at least 14% of workers worldwide to change jobs by 2030.  VFAST Transactions on Software Engineering390The wide breadth of AI technologies used in healthcare settings, such as robots, computer vision, machine learning, and natural language processing, is highlighted in this review through an extensive analysis of peer-reviewed publications.VFAST Transactions on Software Engineering['Shah Hussain Bangash', 'IrfanUllah Khan', 'Ghassan Husnain', 'Muhammad Abeer Irfan', 'Abid Iqbal']2024-03-31T00:00:00https://www.semanticscholar.org/paper/2caad4781201496a6ae619dde0712db52ba6ffb2 2f32fe296021f60c4975d6176276529f06d81710Abstract: This research investigates the increasing significance of artificial intelligence (AI) in driving innovation across various industries as technology continues to advance. The study examines the impact of AI on society, exploring its advantages, difficulties, and possibilities. By utilizing academic research, case studies, and expert opinions, the research highlights how AI can improve efficiency, productivity, and decision making in sectors such as healthcare, finance, transportation, and entertainment. It also delves into the ethical, social, and economic consequences of integrating AI, including concerns about job displacement, data privacy, and biases in algorithms. By analyzing the current state of AI adoption and predicting future trends, this paper offers valuable insights for policymakers, businesses, and researchers navigating the intricate landscape of AI implementation in today's world.International Journal for Research in Applied Science and Engineering Technology00The research highlights how AI can improve efficiency, productivity, and decision making in sectors such as healthcare, finance, transportation, and entertainment, and delves into the ethical, social, and economic consequences of integrating AI.International Journal for Research in Applied Science and Engineering Technology['Ankush Bhandari']2024-03-31T00:00:00https://www.semanticscholar.org/paper/2f32fe296021f60c4975d6176276529f06d81710 9828a10c4daf2329e169252fbb0ddd3bec7f32afAbstract: This research paper delves into the transformative potential of artificial intelligence in revolutionizing plastic waste management within the packaging industry. The study aims to assess the role of AI technologies in promoting circular economy principles, with a specific focus on reducing plastic waste, optimizing recycling processes, and fostering sustainable resource utilization. Through a comprehensive analysis of real-world applications and case studies in the packaging sector, the paper aims to provide insights into the challenges and opportunities associated with integrating AI-driven circular economy practices, ultimately contributing to a more sustainable and eco-friendly packaging landscape.International Journal for Research in Applied Science and Engineering Technology00International Journal for Research in Applied Science and Engineering Technology['Astha Ashatkar']2024-03-31T00:00:00https://www.semanticscholar.org/paper/9828a10c4daf2329e169252fbb0ddd3bec7f32af bffa9cf11b71eae8c9f1e63fa98a24be005e66c1This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.JOIV: International Journal on Informatics Visualization00JOIV : International Journal on Informatics Visualization['Van Vien Vu', 'Phuoc Tai Le', 'Thi Mai Thom Do', 'Thi Thuy Hieu Nguyen', 'Nguyen Bao Minh Tran', 'Prabhu Paramasivam', 'Thi Thai Le', 'Huu Cuong Le', 'Thanh Hieu Chau']2024-03-31T00:00:00https://www.semanticscholar.org/paper/bffa9cf11b71eae8c9f1e63fa98a24be005e66c1 d68b52c857e90496483b089bd753a895ab2e3aa9Abstract: This study presents a groundbreaking approach to inventory optimization through the implementation of an AIpowered holistic model within a decision-support system. Leveraging machine learning algorithms, specifically Logistic Regression (LR) and Decision Trees, the methodology explores historical sales data, supplier metrics, and market trends during an extensive Exploratory Data Analysis phase. The implementation of an AI-powered holistic model for inventory optimization in a decision-support system presents a transformative approach to managing and enhancing supply chain efficiency. The proposed methodology, integrating machine learning algorithms such as Logistic Regression and Decision Trees, has demonstrated its efficacy in achieving superior results compared to traditional models, as evidenced by higher accuracy, precision, recall, and F1 scores. The comprehensive exploration of historical sales data, supplier metrics, and market trends during the exploratory data analysis phase has facilitated a nuanced understanding of inventory dynamics. The seamless integration of the AI-powered model into the decision-support system has empowered organizations with timely and data-driven insights, fostering more agile and informed decision-making. The seamless integration of the AI-powered model into the decision-support system provides organizations with timely and data-driven insights, fostering agile and informed decisionmaking. Performance evaluation, including accuracy, precision, recall, and F1 scores, reveals that the proposed LR and Decision Tree models outperform the existing Support Vector Machine (SVM) model across all metrics. The LR model exhibits commendable precision, recall, and F1 score values of 0.95, 0.97, and 0.98, respectively, while the Decision Tree model demonstrates even higher values, with precision and recall at 0.96 and an exceptionally high F1 score of 0.99. These outcomes underscore the practical utility and robustness of the AI-powered holistic model in revolutionizing inventory management practices. While acknowledging challenges and limitations, this research signifies a crucial advancement in establishing responsive and intelligent decision-support systems for inventory optimization, paving the way for future innovations in supply chain managementInternational Journal for Research in Applied Science and Engineering Technology00Performance evaluation reveals that the proposed LR and Decision Tree models outperform the existing Support Vector Machine (SVM) model across all metrics, highlighting the practical utility and robustness of the AI-powered holistic model in revolutionizing inventory management practices.International Journal for Research in Applied Science and Engineering Technology['Shailesh Choudhary']2024-03-31T00:00:00https://www.semanticscholar.org/paper/d68b52c857e90496483b089bd753a895ab2e3aa9 21c2a8b8e8085b82226f741b5ca39be5f040e6cfObjectives Drawn from the theory of planned behavior (TPB), this study aims to examine the students’ attitudes and behaviors regarding the acceptance and use of generative artificial intelligence (AI) in college assignments. Methods A survey with 193 students from a South Korean university was conducted with both qualitative and quantitative methods. In terms of the qualitative method, a thematic analysis was conducted. For quantitative method, a moderated mediation model with path analysis was tested using Mplus software. Results The quantitative analysis revealed that students perceiving high unfairness in generative AI and those anticipating a high possibility of detection in generative AI assignments were more inclined to express lower acceptance of generative AI. Furthermore, the relationship between perceived unfairness and AI acceptance was moderated by the perception of detection possibility. A heightened possibility of detection strengthened the negative association between perceived unfairness and AI acceptance. The study also found that the acceptance of generative AI significantly increase the actual behavior of employing this technology for assignments. The qualitative analysis of open-ended questions demonstrated that students generally exhibited positive receiving attitudes to generative AI, but they also recognized potential side effects and acknowledged the need for academic regulations. Additionally, students noted shortcomings in the credibility and practicality of current generative AI technology. Conclusions This research is one of the first attempts that tackled students' motivation towards AI and provides the educational implications for use of AI in pedagogical purposes. Korean Association For Learner-Centered Curriculum And Instruction00This research is one of the first attempts that tackled students' motivation towards AI and provides the educational implications for use of AI in pedagogical purposes.Korean Association For Learner-Centered Curriculum And Instruction['Sungwon Choi']2024-03-31T00:00:00https://www.semanticscholar.org/paper/21c2a8b8e8085b82226f741b5ca39be5f040e6cf c21155cf27317b002d45c3060e7013144332513fArtificial intelligence (AI) has become an integral part of creative markets. It has a dual nature and it can be considered both as an input and as an output. As an input AI presents a technological solution, which in its interaction with other creative inputs can be transformed into economic outputs with the characteristics of creative products. The economic life of these products is shaped by intellectual property legislation and practice, as creative markets are basically rights markets. This dual nature of AI suggests that AI acquires economic functions and characteristics, which have major consequences in terms of demand and supply, value generation, efficiency gains and productivity. While AI creates numerous opportunities it disrupts traditional creative markets and widens the digital divide. AI becomes an area of strategic importance to governments and companies.Economic and social alternatives00While AI creates numerous opportunities it disrupts traditional creative markets and widens the digital divide, and AI becomes an area of strategic importance to governments and companies.Economic and social alternatives[]2024-03-31T00:00:00https://www.semanticscholar.org/paper/c21155cf27317b002d45c3060e7013144332513f 6e7801885e99d0af0319c78218ad6b42bb13e6f8The article examines the main trends in marketing according to leading digital agencies in Bulgaria. Analysis of existing publications shows that artificial intelligence retains its dominant role in digital marketing. Without a doubt, AI provides marketers with new and effective ways to target audiences, personalize content and measure results. At the same time, already established and widely used marketing tools such as email marketing and content marketing continue to be a leading element in brand strategies for reaching relevant audiences. As a result of the content analysis, six key trends have been identified predetermining the development of marketing, developed as a priority in a digital environment: artificial intelligence, personalization, email marketing, the social network TikTok, video content and sustainability as a way of production, consumption and communication. In the context of the so-called never normal, reflecting the uncertainty and dynamics of the complex global reality, the knowledge and application of the challenges and opportunities reflected in the article in the era of artificial intelligence imply the achievement not only of competitiveness and significant success but also of higher sustainability for companies.Economic and social alternatives00The article examines the main trends in marketing according to leading digital agencies in Bulgaria, identified predetermining the development of marketing, developed as a priority in a digital environment: artificial intelligence, personalization, email marketing, the social network TikTok, video content and sustainability as a way of production, consumption and communication.Economic and social alternatives[]2024-03-31T00:00:00https://www.semanticscholar.org/paper/6e7801885e99d0af0319c78218ad6b42bb13e6f8 0b632d26dd3338a6e6a779ccce2538ad343d6d5aPerioperative medicine is a series of medical activities throughout the perioperative period, including preoperative optimization, intraoperative safety, postoperative rehabilitation, and other activities. Anesthesia is closely integrated with perioperative medicine to guarantee smooth progress of operations, comfortable recovery, and favorable long-term outcome for patients. There are a huge number of clinical data in anesthesia and perioperative medicine, and artificial intelligence (AI) has a powerful ability to analyze and evaluate data; thus, applying AI is a significant advantage in analysis and prediction based on real clinical big data in anesthesia and perioperative medicine. AI has made some progress in the field of anesthesiology and perioperative medicine. This review introduces the most encountered computerized techniques of AI in anesthesiology, main clinical applications themes of AI in anesthesiology, as well as limitations and ethical implications involved in deployment of this technology.Perioperative Precision Medicine300This review introduces the most encountered computerized techniques of AI in anesthesiology, main clinical applications themes of AI in anesthesiology, as well as limitations and ethical implications involved in deployment of this technology.Perioperative Precision Medicine[]2024-03-31T00:00:00https://www.semanticscholar.org/paper/0b632d26dd3338a6e6a779ccce2538ad343d6d5a e13dee5f0a100b01012b7d487f1423884ca2a26fIn this article, we will try to answer the question “What is the future development of artificial intelligence (AI)?”. We will look at AI’s multi-directional past, turbulent present and attempt to indicate its ambiguous future. We will reveal how the idea of machines with intelligent behavior arose and who the pioneers of the field of informatics called Artificial Intelligence are; what the main methods of creating and training computer systems with artificial intelligence are; what applications AI has in the economy and social activities. We will try to classify different types of machine learning of artificial intelligence systems. We will look for the main problems and risks of using AI and finally see the prospects in its development. Artificial intelligence must be developed carefully, taking into account all influences – positive and negative, on mankind.Economic and social alternatives00The question “What is the future development of artificial intelligence (AI)?” is answered and the main problems and risks of using AI are looked for and the prospects in its development are seen.Economic and social alternatives[]2024-03-31T00:00:00https://www.semanticscholar.org/paper/e13dee5f0a100b01012b7d487f1423884ca2a26f 2c97179fcb650dd838e349f0fffb4528e0ee4068This research article explores the complexities surrounding Islamic law and the challenges it encounters in the contemporary Era of Artificial Intelligence in global landscape. It examines issues related to preserving tradition while embracing modernity, compatibility with Artificial intelligence, women's rights and gender equality, human rights and religious freedom, pluralism and minority rights, modern finance and ethical practices, technology and bioethics, and engagement with international law. Islamic law, known as Sharia, is a comprehensive legal framework derived from the Quran, Hadith, and interpretations by Islamic scholars. It has guided Muslim societies for centuries, providing guidelines for various aspects of life. However, in the modern world, Islamic law faces numerous challenges that demand critical analysis and thoughtful responses. For the purpose to explore ground reality, Doctrinal research method is employed and relied on already available data. Present research results are given by descriptive method. No one can avoid to use modern technology but Muslim world is facing many issues in present situations. Researcher recommends that only reinterpretation of Islamic lawsAnnals of Human and Social Sciences282Annals of Human and Social Sciences['Shabana Kausar', 'Ali Raza Leghari', 'Abdul Salam']2024-03-31T00:00:00https://www.semanticscholar.org/paper/2c97179fcb650dd838e349f0fffb4528e0ee4068 47db5d107420bd6d8106eb9cbf97dbd56291b1e0Abstract: Artificial intelligence is a fast-expanding subject. The aim of artificial intelligence is to use machine learning and algorithms to build intelligent computers that have human-like thought and behavior. Statistics plays a pivotal role in the development and advancement of Artificial Intelligence. Statistics is a branch of science which deals with study of Collection, Presentation, Analysis and Interpretation of data. Statistical techniques enhance the reliability of Artificial intelligence models and ensure informed decision-making. Combining statistical approaches with Artificial intelligence not only tackles practical issues like bias prevention, uncertainty quantification, and ethical considerations, but also enriches the foundations of theory. This paper aims to investigate the significance of statistics in the field of artificial intelligence. The paper aims to explore the theoretical foundations of Artificial intelligence and Statistics, the statistical methods used in Artificial intelligence, the applications of Statistics in Artificial intelligence, the key points including Statistics in Artificial intelligence, the challenges and limitations, and the prospective path.International Journal for Research in Applied Science and Engineering Technology00The theoretical foundations of Artificial intelligence and Statistics, the statistical methods used in Artificial intelligence, the applications of Statistics in Artificial intelligence, the key points including Statistics in Artificial intelligence, the challenges and limitations, and the prospective path are explored.International Journal for Research in Applied Science and Engineering Technology['Ashwini S. Kale']2024-03-31T00:00:00https://www.semanticscholar.org/paper/47db5d107420bd6d8106eb9cbf97dbd56291b1e0 d7bf6146cec7cd21b54fffd6d3ba18ee473ec18cThe article focuses on analyzing the relationship between organizational agility and the use of artificial intelligence (AI) in enterprises. The aim of the study was to identify and analyze the ways in which organizations adapt and implement agile practices in the context of rapidly advancing technological changes brought about by the development of AI. The hypothesis assumes that organizations adapting AI technologies and implementing agile management methods are characterized by better responsiveness to market changes and more effective use of data for making strategic decisions. The study involved 303 respondents from various regions of Poland, and data were collected using an electronic form within an Internet-assisted survey system (CAWI). This study sheds light on the complex and often conflicting dynamics between various aspects of organizational agility in the era of artificial intelligence, emphasizing the need for a balanced approach to technological and operational adaptation.Zeszyty Naukowe Wyższej Szkoły Humanitas Zarządzanie00This study sheds light on the complex and often conflicting dynamics between various aspects of organizational agility in the era of artificial intelligence, emphasizing the need for a balanced approach to technological and operational adaptation.Zeszyty Naukowe Wyższej Szkoły Humanitas Zarządzanie['Maria Kocot']2024-03-31T00:00:00https://www.semanticscholar.org/paper/d7bf6146cec7cd21b54fffd6d3ba18ee473ec18c 0a13f42c8639944814b53e58633683869ec0fc37The question this paper aims to address is whether we can find a philosophical message for the age of artificial intelligence in the songs of BTS. The research objective of this paper is to show that we can find a philosophical message for the age of artificial intelligence in the songs of BTS. The research method of this paper is to first find a philosophical message for the era of artificial intelligence through Yuval Harari's book Homo Deus, then analyze the lyrics of BTS's song "Blood Sweat and Tears" based on the message, find a philosophical message for the era of artificial intelligence contained in the song, and support the philosophical message with Nietzsche's Philosophy of the Superhuman. The research finding of this paper is that we can find a philosophical message for the era of artificial intelligence through the song "Blood, Sweat & Tears" by BTS, which can also be supported by Nietzsche's Philosophy of the Superhuman. The message is: "Swallow the poisoned chalice of freedom, even though you know it's poisoned!". The conclusion that can be drawn from this finding is that the humanity that needs to be renewed in the age of AI is Homo sapiens, who thinks for himself. It is not AI-recommended Hommo Sapiens, not AI represented Homo Sapiens, but Self-deciding free Homo Sapiens, a free human who wanders, crosses over, thinks and decides for oneself.International Journal of East Asian Studies00The research finding is that the humanity that needs to be renewed in the age of AI is Homo sapiens, who thinks for himself, a free human who wanders, crosses over, thinks and decides for oneself.International Journal of East Asian Studies['Kwangsik Kim']2024-03-31T00:00:00https://www.semanticscholar.org/paper/0a13f42c8639944814b53e58633683869ec0fc37 2ebafa71d6bcead325a53013e91e2c2883617fbbArtificial Intelligence (AI) developments challenge higher education institutions’ teaching, learning, assessment, and research practices. To contribute timely and evidence-based recommendations for upholding academic integrity, we conducted a rapid scoping review focusing on what is known about academic integrity and AI in higher education. We followed the Updated Reviewer Manual for Scoping Reviews from the Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic reviews Meta-Analysis for Scoping Reviews (PRISMA-ScR) reporting standards. Five databases were searched, and the eligibility criteria included higher education stakeholders of any age and gender engaged with AI in the context of academic integrity from 2007 through November 2022 and available in English. The search retrieved 2223 records, of which 14 publications with mixed methods, qualitative, quantitative, randomized controlled trials, and text and opinion studies met the inclusion criteria. The results showed bounded and unbounded ethical implications of AI. Perspectives included: AI for cheating; AI as legitimate support; an equity, diversity, and inclusion lens into AI; and emerging recommendations to tackle AI implications in higher education. The evidence from the sources provides guidance that can inform educational stakeholders in decision-making processes for AI integration, in the analysis of misconduct cases involving AI, and in the exploration of AI as legitimate assistance. Likewise, this rapid scoping review signals key questions for future research, which we explore in our discussion.Canadian Perspectives on Academic Integrity730A rapid scoping review focusing on what is known about academic integrity and AI in higher education showed bounded and unbounded ethical implications of AI.Canadian Perspectives on Academic Integrity['B. Moya', 'Sarah Eaton', 'Helen Pethrick', 'Alix Hayden', 'Robert Brennan', 'Jason Wiens', 'Brenda McDermott']2024-03-31T00:00:00https://www.semanticscholar.org/paper/2ebafa71d6bcead325a53013e91e2c2883617fbb 05878f9118ba2bbe97adc285083732f82f71ce84Artificial intelligence (AI) is utilized in health-care settings which mimics human thinking and decision-making skills. Since, it is an emerging technology beneficial for medical education and patient care. It becomesnecessary that medical students prepare themselves for their tryst with AI . However, the training of medical students, faculty and inclusion of AI based competencies are required for the medical fraternity to be trained in AI. AI is spreading its horizons to various sectors.The present study is undertaken to assess the comprehension of the phase I medical students about AI technology in terms of information , knowledge and impact. It is a questionnaire-based study which involved voluntary and anonymous participation.The results were analyzedstatistically.The present study depicts that the medical students are fully aware of the AI technology but usage is in judicious manner only. There is awareness about the advantages and disadvantages of AI. Medical students have desire to study AI provided it is included in the medical curriculum. Medical teachers adopt AI in teaching -learning for educating the students but medical students majorly affirm that medical college teachers are irreplaceable. But, the impact of AI is ever expanding and will be an ongoing debate in future.International Journal of Advanced Research00Assessment of the comprehension of the phase I medical students about AI technology in terms of information, knowledge and impact depicts that the medical students are fully aware of the AI technology but usage is in judicious manner only.International Journal of Advanced Research['Padmanabhan P.']2024-03-31T00:00:00https://www.semanticscholar.org/paper/05878f9118ba2bbe97adc285083732f82f71ce84 f7bc2ae40ac0145b79ab40de116294e469a145c3The use of Artificial Intelligence in the medical world is a form of development and implementation of technology in the medical world. Indonesia is a country of law where everything that happens in Indonesia must be based and rely on the law. This research uses a normative juridical method using a conceptual approach by examining laws, articles, and also secondary and primary data sources related to the title to be analyzed. After reviewing it, the use of AI in the world of medicine really helps make things easier for health and medical workers. Meanwhile, legal responsibility for the use of AI in the world of medicine itself is borne by the creators of artificial intelligence and AI users, or in this case, the medical personnel themselves.DE RECHTSSTAAT240This research uses a normative juridical method using a conceptual approach by examining laws, articles, and also secondary and primary data sources related to the title to be analyzed by examining laws, articles, and also secondary and primary data sources related to the title to be analyzed.DE'RECHTSSTAAT['Novita Ardiyanti', 'Rahma Nur Kamilatul Azmi', 'Noval Ramadhan', 'Ahmad Jamaludin']2024-03-31T00:00:00https://www.semanticscholar.org/paper/f7bc2ae40ac0145b79ab40de116294e469a145c3 99930dcb6c4746ac1ad96ffe7ba665e5b0ca5f2bThis study analyzed the reality and perception of pre-service early childhood teachers regarding artificial intelligence, and the demands for education. For this purpose, a survey was conducted on 145 students in the department of early childhood education at colleges in Seoul and Gyeonggi-do, and the collected data were analyzed using SPSS 29.0. The research results are as follows. First, compared to their experience using artificial intelligence, pre-service early childhood teachers' level of interest and understanding of artificial intelligence was average, and they understood artificial intelligence as ‘a computer system that can think similar to humans.’ In addition, the negative perception of artificial intelligence was higher than the positive perception, and its influence on oneself was at an average level. Second, the need for artificial intelligence education for pre-service early childhood teachers was highly recognized. Additionally, with the goal of improving the ability to support play using artificial intelligence, the professional professors were requested to teach about future social changes and cases of artificial intelligence education through play. Based on the results of this study, implications for the development of artificial intelligence education subjects for pre-service early childhood teachers were discussed.korean Jouranl of Early Childhood Education00Compared to their experience using artificial intelligence, pre-service early childhood teachers' level of interest and understanding of artificial intelligence was average, and they understood artificial intelligence as ‘a computer system that can think similar to humans.korean Jouranl of Early Childhood Education['Nam Yun Kim', 'M. Kim']2024-03-31T00:00:00https://www.semanticscholar.org/paper/99930dcb6c4746ac1ad96ffe7ba665e5b0ca5f2b d03870e89b29b037cfb70123c52f0d618e08f435These present assat show a principal findigns Artificial intelligence (AI) has emerged as a transformative discipline, mainley exploring the Fundamentals of Artificial Intelligence and its Impact on the Teaching of Higher Secondary and Higher Education, particularly in the Teach Health Sciences. Show the classification in Intelligence, is the intellective power, the faculty of knowing or understanding and Artificial, it is what is made by the hand and art of man. Present the Methodological Foundations of Artificial Intelligence, and The Impact of Artificial Intelligence in Higher Education.International Journal of Advanced Multidisciplinary Research and Studies00Artificial intelligence (AI) has emerged as a transformative discipline, mainley exploring the Fundamentals of Artificial Intelligence and its Impact on the Teaching of Higher Secondary and Higher Education, particularly in the Teach Health Sciences.International Journal of Advanced Multidisciplinary Research and Studies['Luis Alberto Hernnadez-Oosrio', 'Amado Miguel Wilches-Ramiro', 'Armando Martínez-González', 'Elva Montero-Toledo', 'Ricardo Balam-Narváez', 'Francisco Emanuel Velásquez-Hernández', 'Rafael Martínez-Arias', 'Luz Candelaria Cabrera', 'Mario Vázquez Morillas', 'Sergio Alberto Ramírez-Garcia']2024-03-31T00:00:00https://www.semanticscholar.org/paper/d03870e89b29b037cfb70123c52f0d618e08f435 23e88343f7f7c567c943590faa5f5fb09f8e7781Artificial intelligence grows ever more impactful in various social and economic domains. Public administration is among increasingly prominent fields undergoing technology-driven transformations. Today’s administration bodies, local and central both, are facing the challenge of accommodating new outlooks brought by artificial intelligence. This dynamic metamorphosis entails both benefits and difficulties worth investigating. The primary research problem here is the use of artificial intelligence in public administration and potential future changes in this regard. The main research goal of the article is to analyse the benefits of employing artificial intelligence in public administration followed by an analysis of any relevant ethical and pragmatic questions. The article further presents selected aspects of the problem, focusing on the application of data science in public health care.Roczniki Administracji i Prawa00The main research goal of the article is to analyse the benefits of employing artificial intelligence in public administration followed by an analysis of any relevant ethical and pragmatic questions and selected aspects of the problem are presented.Roczniki Administracji i Prawa['Dawid Chaba']2024-03-31T00:00:00https://www.semanticscholar.org/paper/23e88343f7f7c567c943590faa5f5fb09f8e7781 ff94e3678a6e0f57fd71f3b465224698680ceb58This study aims to analyze the application of Artificial Intelligence (AI) in detecting accounting fraud in audits. The aim is to identify AI's efficiency, accuracy, and potential in detecting fraud and to explore the challenges and implications arising from using this technology in audit practice. This research is a type of qualitative research with a case study approach as the main focus and a literature study as a data triangulation approach. This research methodology will provide an in-depth understanding of the integration of artificial intelligence in detecting accounting fraud. The results show that AI improves efficiency and accuracy in detecting accounting fraud. AI techniques such as machine learning and natural language processing effectively identify fraud patterns. However, there are challenges, such as limitations of AI technology, ethical and data privacy issues, and barriers to accepting AI in the accounting industry. This research contributes to the accounting literature by highlighting how AI can change audit practices. It also offers guidance for accounting firms on utilizing AI to improve auditing and suggests directions for future research related to the development and integration of AI in accounting.Akuntansi dan Teknologi Informasi450The results show that AI improves efficiency and accuracy in detecting accounting fraud and suggests directions for future research related to the development and integration of AI in accounting.Akuntansi dan Teknologi Informasi['Iman Supriadi']2024-03-31T00:00:00https://www.semanticscholar.org/paper/ff94e3678a6e0f57fd71f3b465224698680ceb58 e611add3ecafada6a7e2823a11c62741fe89b684Now, the core of the discussion is how artificial intelligence (AI), as a rapidly developing advanced technology, can solve many of the most difficult problems facing humanity beyond solving severe social problems. Therefore, the true value of artificial intelligence (AI) as a cutting-edge technology is to solve huge problems facing humanity, such as social problems such as gun violence, food shortages, incurable diseases, and global warming caused by carbon emissions, and to contribute to human development. The concern, though, is that no one knows exactly how current artificial intelligence (AI) works. In other words, it is similar to developing a bomb that does not know when it will explode without a safety device. Already, artificial intelligence (AI) is causing damage by providing false information. So, if we don't know how to control better artificial intelligence (AI) when it appears, dangerous things can happen. Therefore, there will be a need for international norms on artificial intelligence (AI), which will be made possible by international treaties. The European Union has already enacted the AI Act as the first regulatory law on AI. This contains stronger regulations on the AI industry than in the United States or Asia. In particular, the Act stipulates the obligation to display AI-generated content. Thus, the EU's AI regulation law can be evaluated as legislation designed to ensure the safety of AI-generated products, such as cars and toys. In addition, in September 2022, the European Union (EU) issued a directive on AI accountability. It focuses on the legal principles of compensation for damages caused by artificial intelligence (AI). Legislation on punitive damages for deepfake harms is required. As a civil liability, the victim's damages should be recognized not only for mental damages, but also for aggravated damages for monetary damages, if any. As such a legislative measure, so-called punitive damages should be introduced for malicious and deliberate use of artificial intelligence (AI) as a perpetrator. In the so-called risk liability of artificial intelligence (AI), the operator of artificial intelligence (AI), which has enormous profits through the operation of the risk source, will have to bear the risk liability as a no-fault liability. Korea's product liability law has embraced this principle of risk liability. In order for product liability law to apply to artificial intelligence (AI), there must be a defect in the artificial intelligence (AI) installed in the product.The Korean Association of Civil Law00The Korean Association of Civil Law['Seok-Chan Yoon']2024-03-31T00:00:00https://www.semanticscholar.org/paper/e611add3ecafada6a7e2823a11c62741fe89b684 11c627125255512a7c8b143cde71d615bfae946fArtificial intelligence technology is considered one of the most important outcomes of the fourth industrial revolution, which relies on the digital revolution that has occurred since the mid-20th century. This revolution is characterized by the integration of natural physical technologies that rely on human intelligence with digital technologies that rely on technology. The uses of artificial intelligence technology in our daily lives are diverse in many fields. In this study, we will focus on the uses of artificial intelligence technology in the security and police fieldFull Text Book of Century Congress100This study will focus on the uses of artificial intelligence technology in the security and police field.Full Text Book of Century Congress1['Dr. Ayad Fawzia']2024-03-31T00:00:00https://www.semanticscholar.org/paper/11c627125255512a7c8b143cde71d615bfae946f 5b031141cc46fd88cc04cf1673843c1fd6abcc18Science employs wonder and its associated emotions to explore unknown mysteries in the pursuit of knowledge about the natural world. Discovering scientific truth may inspire awe—a transcendent, indescribable experience of enhanced awareness and astonishment at the extensive interconnectedness of reality. The emotion of awe expands human consciousness and also mediates possible spiritual encounters with the Divine. Prompted by wonder and curiosity, scientific studies of the human mind and cognition yield insights that contribute to artificial intelligence research, especially the potential development of conscious artificial general intelligence. Yet, emerging artificial intelligence technologies raise religious and sociological questions about consciousness, personhood, and whether conscious artificial general intelligence is capable of expressing wonder and experiencing awe of the Divine.Religions170Emerging artificial intelligence technologies raise religious and sociological questions about consciousness, personhood, and whether conscious artificial general intelligence is capable of expressing wonder and experiencing awe of the Divine.Religions['Joyce Ann Konigsburg']2024-03-31T00:00:00https://www.semanticscholar.org/paper/5b031141cc46fd88cc04cf1673843c1fd6abcc18 966186ff1c933bccbf41868d8ba9677d2e5fd270The effects of technological innovation and automation in general on employment and economic growth have long been the subject of economic research. Conventional economic models balance a positive complementarity effect on employment against a negative displacement or substitution effect. The perspective that there is a positive overall impact on employment and incomes is firmly supported by economic history since the industrial revolution, despite recent data suggesting that the labour portion of total income is dropping. The most advanced task-based model establishes a competitive environment between humans and machines to complete tasks. It highlights how Artificial intelligence (AI) has the impact of creating, replacing, and displacing labour. The development of science and technology has been at the level of AI. AI deprives labour power and converts it into an instrument of labour, which was started by making tools into machines and machines into auto-machines. AI has multiple features -learning, listening, and speaking -so it is used in different areas, allowing the creation, replacement, and displacement of employees in any sector of employment. In history, humans have had labour power in any form, but that has been snatched by AI, which raises the question of employees’ existence.Contemporary Social Science150The perspective that there is a positive overall impact on employment and incomes is firmly supported by economic history since the industrial revolution, despite recent data suggesting that the labour portion of total income is dropping.Contemporary Social Sciences['Prakash Adhikari']2024-03-31T00:00:00https://www.semanticscholar.org/paper/966186ff1c933bccbf41868d8ba9677d2e5fd270 2de5c7e8964ee0d02089ff04438b925220dd499fThe article examines the possibility of using artificial intelligence in the professional language education of medical students. The aim of the study was to determine the advantages and disadvantages of using artificial intelligence in teaching foreign languages to medical students. Based on the contextual methodological approach, methods of theoretical analysis and summarization of scientific literature on the study problem were used, as well as empirical methods including pedagogical observation and analysis of the author's own experience. The study confirmed the potential value of using artificial intelligence in the education of medical students. Key advantages of artificial intelligence were identified, such as the possibility to organize personalized learning, instant feedback, and access to a large amount of data. The noted disadvantages were the need to adapt artificial intelligence systems to the specificity of the medical field and to develop systems for forming complex skills and abilities, such as empathy, etc. Student opinions were assessed through a survey in which 110 students of Ryazan State Medical University participated. The majority of respondents expressed a positive attitude towards the conducted experiment and expressed a desire to use artificial intelligence not only in language education but also in other medical disciplines to improve the level of knowledge and create an interactive and more attractive educational environment. The study emphasizes the high significance of integrating artificial intelligence technologies into the educational process to improve the quality of medical education and prepare future specialists. Currently, there is a need for collaboration between education specialists and artificial intelligence technology developers for more effective use of artificial intelligence in the field of education.PERSONALITY IN A CHANGING WORLD HEALTH ADAPTATION DEVELOPMENT00Key advantages of artificial intelligence were identified, such as the possibility to organize personalized learning, instant feedback, and access to a large amount of data, and the need to adapt artificial intelligence systems to the specificity of the medical field and to develop systems for forming complex skills and abilities.PERSONALITY IN A CHANGING WORLD: HEALTH, ADAPTATION, DEVELOPMENT['Z. Y. Kuznetsov']2024-03-31T00:00:00https://www.semanticscholar.org/paper/2de5c7e8964ee0d02089ff04438b925220dd499f fe0e03ff1930aaa722d1a532b61fd77c61d8ca1bArtificial intelligence (AI) technologies are changing teaching, learning, administrative procedures, and student support services as they are progressively incorporated into different parts of the higher education ecosystem (Ocaña-Fernández, Valenzuela-Fernández, & Garro-Aburto, 2019). By boosting research efforts, expanding teaching and learning opportunities, increasing administrative effectiveness, and encouraging inclusion and accessibility, AI is revolutionizing higher education. (Saaida, 2023). To optimize AI's potential benefits for students, teachers, and the larger academic community, higher education institutions must welcome innovation, adjust to technological changes, and assure ethical and responsible usage of AI (Guan, Mou, & Jiang, 2020). So, this paper tries to analyse the historical development of AI and its’ role in developing the whole teaching learning process of higher education with the help of exhaustive literature review a qualitative approach of research.Revista Review Index Journal of Multidisciplinary00The historical development of AI and its’ role in developing the whole teaching learning process of higher education is analysed with the help of exhaustive literature review a qualitative approach of research.Revista Review Index Journal of Multidisciplinary['Kushagra Mishra', 'Amit Kumar Srivastava']2024-03-31T00:00:00https://www.semanticscholar.org/paper/fe0e03ff1930aaa722d1a532b61fd77c61d8ca1b b78aa8042609698b80e61dc44abc8a42af1eb3ceСтаття присвячена питанню застосування технологій у професійній освіті та розвитку технології штучного інтелекту або його використання в журналістській освіті. Зі стрімким ітеративним розвитком штучного інтелекту (ШІ) технологічну ітерацію та розвиток талантів по всьому світу відносять до національної стратегії, спрямовуючи промисловий розвиток. Особливо ця проблема стосується майбутніх журналістів. Актуальність статті передбачає цілісне дослідження значущості моделей медіапродукції в епоху штучного інтелекту. Це дослідження має на меті вивчити рамку компетентностей для студентів у галузі новин і комунікацій з точки зору штучного інтелекту, розглядаючи питання, повʼязані з різкими змінами у галузі медіа-технологій та їх впливом на розвиток майбутніх талантів студентів. За допомогою цього дослідження буде зроблено спробу розробити рамку компетентностей майбутніх журналістів, яка відповідає глобальним тенденціям розвитку промисловості, сприяючи розвитку талантів, що відповідає потребам як нації, так і підприємств. Поява інтелектуальних технологій як роботів-письменників висуває нові вимоги до журналістів у сфері новин і комунікацій. Тому перед закладами вищої освіти постають виклики в розвитку студентських талантів у галузі новин і комунікації. Майбутні журналісти повинні вносити корективи й адаптуватися відповідно до власних здібностей та мислення, незважаючи на появу нових комунікаційних платформ чи трансформації комунікаційних моделей. Отож, реформи та інновації в журналістській освіті є важливими й актуальними. На цьому тлі реформа і розвиток журналістської освіти в Китаї потребують постійної уваги та досліджень. З точки зору штучного інтелекту, динамічна конвергенція медіатехнологій є нагальною темою в галузі журналістики та комунікаційних досліджень, відкриваючи нові шляхи для журналістської освіти.Молодь і ринок00Молодь і ринок['Fangzhou Zhu', 'O.S. Isayeva']2024-03-31T00:00:00https://www.semanticscholar.org/paper/b78aa8042609698b80e61dc44abc8a42af1eb3ce c6020be2df97119579041a5a2f5b52eaacf1ed2cAbstract: It is the state of a person in which one has to depend on others for their own needs. Visual impairment is one of the disabilities of a human being. To date numerous methods had been proposed to enhance the life style of visually impaired and blind people. Still purchasing products in the e-shopping application without others support is tricky one for them. The project describes a system that provides the guidance for them to identify and purchase their products in the e-commerce application. The audio instructions will assist them inside the e-commerce application based on the real time situations. To make the ecommerce in a smarter way the billing system is automated. Hence it eliminates the existing queuing system in the e-commerce. The ultimate aim of this system is to eliminate others support for visually impaired people in shopping and provide them a convenient and sophisticated environment.International Journal for Research in Applied Science and Engineering Technology110A system that provides the guidance for visually impaired people to identify and purchase their products in the e-commerce application and eliminates the existing queuing system in the e-commerce.International Journal for Research in Applied Science and Engineering Technology['Amrin Sheikh']2024-03-31T00:00:00https://www.semanticscholar.org/paper/c6020be2df97119579041a5a2f5b52eaacf1ed2c 0fbf1372f7ad8ad33a4824288162110d11d97a47The implementation of AI technologies in business automation involves leveraging machine learning algorithms and natural language processing to streamline processes, improve efficiency, and make data- driven decisions. It encompasses various applications such as predictive analytics, robotic process automation, chatbots for customer service, and intelligent document processing. This abstract aims to explore the benefits, challenges, and best practices associated with integrating AI into business automation, highlighting its potential to revolutionize workflows and drive competitive advantage in today's digital landscape. Leveraging AI technologies for business automation has ushered in a new era of efficiency and innovation. Through advanced algorithms and deep learning techniques, organizations can automate repetitive tasks, reduce operational costs, and improve scalability. AI-driven automation systems excel in adapting to changing environments, making them invaluable for dynamic industries. By analyzing large datasets and patterns, AI enables businesses to make data-driven decisions swiftly and accurately. Furthermore, integrating AI into business processes fosters agility and empowers employees to focus on high-value tasks, driving overall productivity and competitiveness.INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT &amp; SOCIAL SCIENCE00This abstract aims to explore the benefits, challenges, and best practices associated with integrating AI into business automation, highlighting its potential to revolutionize workflows and drive competitive advantage in today's digital landscape.INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT &amp; SOCIAL SCIENCE['Ravi Kumar', 'Dr. Nilaish Nilaish', 'Vinisha V', 'Mr. Rajkumar']2024-03-31T00:00:00https://www.semanticscholar.org/paper/0fbf1372f7ad8ad33a4824288162110d11d97a47 2973e13432bd418ea6d6c4dc82ac281f06811442Korean Journal of Early Childhood Special Education00The Korean Journal of Early Childhood Special Education['Jee Wan Sohn', 'Byoung-In Lee', 'Hyun Sook Kim']2024-03-31T00:00:00https://www.semanticscholar.org/paper/2973e13432bd418ea6d6c4dc82ac281f06811442 589f1e2d4f2bcdc0e0ac90051da6848942ea8ecbAbstract: At present if we see construction industries, they are facing lot of problems which finally create project delay. The progression of the construction industry is strictly limited by the countless complex challenges it faces such as cost and time overruns, health and safety, productivity and labour scarcities. Working on these complex problems of construction industries is not easy in many aspects but if these problems are countered with the help of AI results will be robust. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management processes. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning, pattern recognition, and deep learning have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. A present review of existing literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimization was conducted. Additionally, the opportunities and challenges of AI applications in construction were identified and presented in this study. This study focuses on AI and its application in construction industry.International Journal for Research in Applied Science and Engineering Technology00A present review of existing literature on AI applications in the construction industry such as activity monitoring, risk management, resource and waste optimization was conducted and the opportunities and challenges of AI applications in construction were identified and presented.International Journal for Research in Applied Science and Engineering Technology['Arpit Singh Bhadoriya']2024-03-31T00:00:00https://www.semanticscholar.org/paper/589f1e2d4f2bcdc0e0ac90051da6848942ea8ecb 96e53bbe35fb738e8e17948f36c065966691f05bJournal of The Korean Association of Artificial Intelligence Education00Journal of The Korean Association of Artificial Intelligence Education['HeeSu No', 'SeonKwan Han']2024-03-31T00:00:00https://www.semanticscholar.org/paper/96e53bbe35fb738e8e17948f36c065966691f05b a6cfedddbfa39f07c177d8cfcf2fe8c875bdff4aАннотация: статья посвящена рассмотрению актуальных вопросов, связанных с использованием искусственного интеллекта в подготовке и принятии управленческих решений. Отмечено, что цель использования ИИ в принятии управленческих решений заключается не в том, чтобы полностью автоматизировать процесс, а в том, чтобы помочь менеджерам принимать более быстрые и лучшие решения посредством оптимизированных процессов и эффективного использования данных. Обоснована актуальность использования ИИ в процессе принятия решений, что обеспечивается растущими темпами глобальный рынок соответствующих платформ, сервисов и приложений. Рассмотрены возможности применения искусственного интеллекта и поддерживаемых им технологий на примере различных компаний, работающих в сфере здравоохранения, автомобилестроении, цифровой индустрии, туристической отрасли, ритейле. Представлены технологии ИИ, которые используются для повышения эффективности разработки и принятия управленческих решений. Особое внимание уделено генеративному ИИ, позволяющему машинам проявлять творческие способности. Отмечено, что, изучая шаблоны и структуры на основе огромного количества данных, алгоритмы ИИ могут генерировать новый контент, дизайн и впечатления, которые выходят за рамки того, что может достичь человек в одиночку. Выявлено, что генеративный ИИ нашел свое широкое применение в финансовой индустрии. Отмечено, что, анализируя обширные транзакционные данные, модели поведения пользователей и исторические случаи мошенничества, алгоритмы ИИ могут обнаружить аномалии и закономерности, указывающие на мошеннические действия. Сделан вывод о том, что используя технологии ИИ, компании способны автоматизировать процессы принятия решений, делая их более быстрыми, точными и последовательными. Abstract: the article is devoted to the consideration of current issues related to the use of artificial intelligence in the preparation and adoption of management decisions. It is noted that the goal of using AI in management decision making is not to completely automate the process, but to help managers make faster and better decisions through optimized processes and efficient use of data. The relevance of using AI in the decision-making process is substantiated, which is ensured by the growing pace of the global market for relevant platforms, services and applications. The possibilities of using artificial intelligence and the technologies it supports are considered using the example of various companies operating in the healthcare sector, automotive industry, digital industry, tourism industry, and retail. AI technologies that are used to improve the efficiency of developing and making management decisions are presented. Particular attention is paid to generative AI, which allows machines to be creative. It is noted that by learning patterns and structures from vast amounts of data, AI algorithms can generate new content, designs and experiences that go beyond what a human can achieve alone. It has been revealed that generative AI has found wide application in the financial industry. It is noted that by analyzing extensive transactional data, user behavior patterns and historical fraud, AI algorithms can detect anomalies and patterns that indicate fraudulent activity. It is concluded that using AI technologies, companies are able to automate decision-making processes, making them faster, more accurate and consistent.Russian Economic Bulletin40Russian Economic Bulletin['А.Д. Селиверстова']2024-03-31T00:00:00https://www.semanticscholar.org/paper/a6cfedddbfa39f07c177d8cfcf2fe8c875bdff4a 82a1ab1498947229fc63cd8b38e8e91f232e4308Abstract: The diverse impacts of AI on human psychology, highlighting both its potential benefits and ethical considerations. As AI continues to evolve, it is essential to navigate these complexities thoughtfully to ensure the responsible and effective integration of technology into psychological practice. impact of AI on human psychology based on different ways factors, including the design and applied of AI systems, social values and individual experience.it is encompassing various domains such as diagnosis, treatment, research methodology and ethical considerations.AI on human psychology has merits and demerits.International Journal for Research in Applied Science and Engineering Technology00The diverse impacts of AI on human psychology is highlighted, highlighting both its potential benefits and ethical considerations, as well as encompassing various domains such as diagnosis, treatment, research methodology and ethical considerations.International Journal for Research in Applied Science and Engineering Technology['Dr. N. Deepa', 'G. Aswini', 'G. A. Jemimah', 'S. Kaviya']2024-03-31T00:00:00https://www.semanticscholar.org/paper/82a1ab1498947229fc63cd8b38e8e91f232e4308 e048fddaed7bb0f4f6f3e237652a8ee4d1fd81bdPolypharmacy and medication nonadherence are growing global challenges in healthcare, especially among the older population. The rate of both is relatively high worldwide and in Saudi Arabia, with about 50% of chronic disease patients failing to comply with their treatment regimen. These issues should be countered to improve healthcare delivery systems and quality of life. AI integration into the patient care plan may assist prevent Polypharmacy's negative consequences and encourage patients to follow their prescribed course of action. In order to provide better patient care, numerous AI initiatives have been established both domestically and outside in Saudi Arabia. For maximum use, however, the introduction of AI presents ethical and technical issues that need to be resolved. Saudi Arabia has made strides toward integrating AI into healthcare through improved rates of AI literacy and easy accessibility. To get the most out of AI, more work is necessary to get over its constraints.Medical Science510Artificial intelligence integration into the patient care plan may assist prevent Polypharmacy's negative consequences and encourage patients to follow their prescribed course of action.Medical Science['Husna Irfan Thalib', 'Sariya Khan', 'Dalaa Sheikh Saleh', 'Asim M Alshanberi', 'Yosra Z Alhindi', 'Safaa M Alsanosi']2024-03-31T00:00:00https://www.semanticscholar.org/paper/e048fddaed7bb0f4f6f3e237652a8ee4d1fd81bd d1c57ee615f9f5e9e692f2f9c92ee868f52754acLiterary Criticism00Literary Criticism['Jin Seok Choi']2024-03-31T00:00:00https://www.semanticscholar.org/paper/d1c57ee615f9f5e9e692f2f9c92ee868f52754ac 5bb33446caf7c72e0e27359376cc40f86cbc1600Journal of the Edutainment00Journal of the Edutainment['Inseong Jeon']2024-03-31T00:00:00https://www.semanticscholar.org/paper/5bb33446caf7c72e0e27359376cc40f86cbc1600 59b0aa1748261974026adb2f5848ff0f51aa2a12The Korean Society of Science &amp; Art00The Korean Society of Science &amp; Art['Sun Young Kim']2024-03-31T00:00:00https://www.semanticscholar.org/paper/59b0aa1748261974026adb2f5848ff0f51aa2a12 c456eac3d1a8c15bf0dcfcb0427cfaeb3b8ce763Journal of Special Education &amp; Rehabilitation Science00Journal of Special Education &amp; Rehabilitation Science['Su Jin Paik']2024-03-31T00:00:00https://www.semanticscholar.org/paper/c456eac3d1a8c15bf0dcfcb0427cfaeb3b8ce763 08f5ae4fcd382cadbb503b0abe77795a4ec758f8Pakistan Social Sciences Review00Pakistan Social Sciences Review['Umair Ahmed']2024-03-31T00:00:00https://www.semanticscholar.org/paper/08f5ae4fcd382cadbb503b0abe77795a4ec758f8 f07e7afe91c4026733e057213526225824b6420dJournal of Educators Online00Journal of Educators Online['Fatima Rahioui']2024-03-31T00:00:00https://www.semanticscholar.org/paper/f07e7afe91c4026733e057213526225824b6420d 8eb19605c4d1a4af83b40ef727b06d936cd65ff6Korean Journal of International Relations00Korean Journal of International Relations['Kiyoung Chang']2024-03-31T00:00:00https://www.semanticscholar.org/paper/8eb19605c4d1a4af83b40ef727b06d936cd65ff6 b75c01389a0c1326e6bcf70ab3e1f0fc48346389Journal of the Edutainment00Journal of the Edutainment['Jung-Ho Park']2024-03-31T00:00:00https://www.semanticscholar.org/paper/b75c01389a0c1326e6bcf70ab3e1f0fc48346389 d1066fb1ea81d9a85b069b25e09b6dbcfc13adb7Journal of Digital Contents Society00Journal of Digital Contents Society['Ga-Young Lee', 'Hyun-Kyung Chee', 'Myung-Sun Kim', 'Sun-Young Keum', 'Tak Choi', 'Je-Cheon Kim', 'Sun-Young Huh']2024-03-31T00:00:00https://www.semanticscholar.org/paper/d1066fb1ea81d9a85b069b25e09b6dbcfc13adb7 3b1d0944cc220703033db99dc3c56e1f4c8be8d2Military Thought00Military Thought['A.A. PROTASOV']2024-03-31T00:00:00https://www.semanticscholar.org/paper/3b1d0944cc220703033db99dc3c56e1f4c8be8d2 6d5eab5e7bc915c3995ebf13433a3ffdaad0546bAsia Pacific Journal of Information Systems00Asia Pacific Journal of Information Systems['Hyoung-Goo Kang', 'Ahram Moon', 'Seongmin Jeon']2024-03-31T00:00:00https://www.semanticscholar.org/paper/6d5eab5e7bc915c3995ebf13433a3ffdaad0546b 5b63ac9de67d09006db1de54602a6eb8f6d79d17Economic Sciences00International scientific journal "Internauka". Series: "Economic Sciences"['Tetiana Storozhuk', 'Artem Morhunenko']2024-03-31T00:00:00https://www.semanticscholar.org/paper/5b63ac9de67d09006db1de54602a6eb8f6d79d17 cea830f2d83f518563ff651eaf5c574540c9b2caInternational scientific journal Internauka00International scientific journal "Internauka"['S. Kovalenko', 'Anastasiya Lisna']2024-03-31T00:00:00https://www.semanticscholar.org/paper/cea830f2d83f518563ff651eaf5c574540c9b2ca 26eedd387304c61b6438d636a547e31e26349f99Journal of Cybercommunication Academic Society00Journal of Cybercommunication Academic Society['Borae Jin']2024-03-31T00:00:00https://www.semanticscholar.org/paper/26eedd387304c61b6438d636a547e31e26349f99 27e79e741a4b6f7c822e3136a36e75bc5e5a9e99Journal of Coaching Development00Journal of Coaching Development['Seok-Mu Kwon', 'Woo-Jin Park', 'K. Heo', 'Seung-Jae Lee']2024-03-31T00:00:00https://www.semanticscholar.org/paper/27e79e741a4b6f7c822e3136a36e75bc5e5a9e99 5f3245121e7dd9c2753573f801742f664713dc9dJournal of business and technical communication130Journal of Business and Technical Communication['Stephen Carradini']2024-03-31T00:00:00https://www.semanticscholar.org/paper/5f3245121e7dd9c2753573f801742f664713dc9d 30b562ab0d47e2d37ee17a5f920b98a5d0dd1b21Yonsei Law Review00Yonsei Law Review['Hojin Choi']2024-03-31T00:00:00https://www.semanticscholar.org/paper/30b562ab0d47e2d37ee17a5f920b98a5d0dd1b21 cb003ef8ab3a6cc0587c073ac4f46beebf072649Journal in Humanities00Journal of the Humanities['Do-Hun Ahn']2024-03-31T00:00:00https://www.semanticscholar.org/paper/cb003ef8ab3a6cc0587c073ac4f46beebf072649 e62d934cc9775c7c490bd742b410e10613e3dc42Administrative law journal00ADMINISTRATIVE LAW JOURNAL['Jiweon Seon']2024-03-31T00:00:00https://www.semanticscholar.org/paper/e62d934cc9775c7c490bd742b410e10613e3dc42 37f5d597698f2a6d3d2a1026c987b63670010526Bharati Vidyapeeth Medical Journal00Bharati Vidyapeeth Medical Journal['Suchita V Dabhadkar']2024-03-31T00:00:00https://www.semanticscholar.org/paper/37f5d597698f2a6d3d2a1026c987b63670010526 f4a8452211cbf8cda64f36d1f534d690f9d652d8Journal of Community Positive Practices00Journal of Community Positive Practices['Sebastian Fitzek']2024-03-31T00:00:00https://www.semanticscholar.org/paper/f4a8452211cbf8cda64f36d1f534d690f9d652d8 7eb15a90309f405b150cd4e967181c3285183520The Korea Journal of Sport00The Korea Journal of Sport['Ryeo-Gyeong Kim', 'Kyung-Min Kim']2024-03-31T00:00:00https://www.semanticscholar.org/paper/7eb15a90309f405b150cd4e967181c3285183520 f670beda267a871dbe7bee0aec696140e126963aArtificial Intelligence Review1851This paper analyzes normative ethics through the lens of computational complexity and provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources.Artif. Intell. Rev.['Jakob Stenseke']2024-03-31T00:00:00https://www.semanticscholar.org/paper/f670beda267a871dbe7bee0aec696140e126963a 24a6230c056afea34fa9c0185cc1f4275cbcc408Depending on the requirements of the technology and the operator factor, the operating speeds of technological machines can vary in different intervals. It is proposed to distinguish between small and large ranges of technological speed changes relative to the variator control range. If the range of speed changes goes beyond the control range of the variator, then it will be a large range, and otherwise it will be a small range. The working speed of the technological machine can be changed stepwise or continuously. Step-by-step speed control is carried out using a gearbox, and stepless regulation is carried out using a variator. The technological speed in a large range is recommended to be steplessly adjusted using a variator and a gearbox, and in a small range – using a variator and a gearbox. A V-belt variator with a standard V-belt drive is proposed, the main tasks of its development are defined.Trudy Universiteta00TRUDY UNIVERSITETA['R. Sakhybayev', 'Bortan Koiaidarov']2024-03-30T00:00:00https://www.semanticscholar.org/paper/24a6230c056afea34fa9c0185cc1f4275cbcc408 318b24a59e66bddc0abc7e3846557bcf20e88b39Scientific notes00Scientific notes['Lyudmila Shovkun-Zablotska', 'Yulia Karakai']2024-03-30T00:00:00https://www.semanticscholar.org/paper/318b24a59e66bddc0abc7e3846557bcf20e88b39 88437c7b6841ddd7eb74d8b0f67e159329fd7e3bEnglish is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. However, English learners face many challenges in gaining English language skills. Extant studies show that AI has affordances to support in English language teaching and learning ELT/L. This study answers the call to examine specific challenges and affordances for using AI in ELT/L. A systematic review method was used with PRISMA principles to identify 42 studies. Findings reveal the geographical locations of studies, learner ages and years of study. Grounded coding was then used to identify affordances of the use of AI in ELT/L in the areas of speaking, writing, reading, pedagogy and self‐regulation. AI in ELT/L challenges uncovered were technology breakdowns, limited capabilities, fear and standardising language. Policymakers, funders, practitioners and educational leaders can use the information provided in this study to gain a holistic understanding of the current trend in the use of AI in ELT/L, and practical implications are provided to guide future use of AI. What is already known about this topic English is one of the most used languages for jobs, markets, tourism, discourse and international connectivity. Empirical evidence shows that pupils can often face difficulties when learning English, with challenges such as irregularity in English spelling. AI has supported language teaching and learning with studies showing that AI can support language‐specific skills. What this paper adds Provides the scholarly community with a unique systematic review in the use of AI in ELT/L across learner levels. Identifies affordances of AI in ELT/L in speaking, writing, reading, pedagogy and self‐regulation. Identifies challenges of AI in ELT/L in technology breakdowns, limited capabilities, fear and standardising language. Provides researchers with a review of the field with identification of gaps and future research opportunities. Implications for practice and/or policy Provides practical implications from the findings for educators, policy makers and program designers. Highlights the gaps in academic knowledge as a lack in the use of AI for assessment in ELT/L. British Journal of Educational Technology431A systematic review method was used with PRISMA principles to identify affordances of the use of AI in ELT/L in the areas of speaking, writing, reading, pedagogy and self‐regulation, and highlights the gaps in academic knowledge as a lack in the use of AI for assessment in ELT/L.British Journal of Educational Technology['Helen Crompton', 'Adam Edmett', 'Neenaz Ichaporia', 'D. Burke']2024-03-30T00:00:00https://www.semanticscholar.org/paper/88437c7b6841ddd7eb74d8b0f67e159329fd7e3b 787222057bd3b1ba31780a4a107653860731dea8The goal is to study the mechanisms of state support for agricultural sector (using the example of industrial crops in the Zhetisu region), analyze the current state of agro-industrial production in the Republic of Kazakhstan, consider problems and ways of their solution. Methods – theoretical basis of the work consists of the works of foreign and domestic scientists; the analysis method assesses the development of agro-industrial complex of the Zhetisu region, regulation of the price level for industrial crops and food. Using the statistical method, conclusions on the current situation and prospects for their production. The modeling method made it possible to form effective model for managing agricultural sector. Results – problems associated with raw materials orientation of agricultural sector, incomplete utilization of production capacity, low level of introduction of innovative technologies and technical equipment slow down the pace of development of agricultural industry. Ways to solve these problems have been outlined: proposals aimed at improving the availability of financing for small businesses through investing in agricultural infrastructure and expanding the network of production and service cooperatives, increasing the efficiency of state support for agricultural economy of the Zhetisu region have been prepared. Conclusions – the main scientific principles obtained during the study can be used as information and analytical material for the development and implementation of effective policies for cultivation, processing and sale of industrial crops in the region. Natural and labor resources will allow the region to become self-sufficient in food supply. The main directions of modern state policy for regulating agricultural production are indicated: creating conditions for maintaining sustainable growth in demand for food products and raw materials; creation of infrastructure, system of wholesale and retail markets; support of price system that satisfies the population's demands for food.Problems of AgriMarket00Problems of AgriMarket['G. Baytaeva']2024-03-30T00:00:00https://www.semanticscholar.org/paper/787222057bd3b1ba31780a4a107653860731dea8 5fd0fbd58eefd3e6e5e3614af329f3542a0f8aefThe Institute of Foreign Agents has gained particular importance in modern Russian legal reality. The adoption of the new federal law contributed to the unification of the regulation of this institution, but the current legislation needs in-depth study. The article is devoted to the legal understanding of the status of a foreign agent - a domestic short story in the constitutional model of rights and freedoms. It was established that the legal regulation of the status of a foreign agent meets the constitutionally significant goals of protecting the foundations of the constitutional system, rights and freedoms, and ensuring the security of the state. The need for legislative consolidation of this institution in Russia corresponds to the evolution of public-political relations. Based on the conducted comparative legal analysis of foreign experience, the contours of the legal model for securing such status in Russia are defined, the problems of the status of a foreign agent as a subject of legal regulation are identified. The authors consider the status of persons affiliated with foreign agents, their role in relation to foreign agents. An analysis of the norms of administrative and criminal legislation regulating legal relations, which arise in violation of legal instructions established for foreign agents is carried out. Based on the results of the scientific study, practical recommendations are proposed for improving and modernizing domestic legislation on foreign agents.Baikal Research Journal50Baikal Research Journal['Egor Grigorev', 'Timur Gulyaev', 'Svetlana Maximova']2024-03-30T00:00:00https://www.semanticscholar.org/paper/5fd0fbd58eefd3e6e5e3614af329f3542a0f8aef 407336e7add657a088e4c4647fc0000ddff85a43  This article explores the evolving landscape of data protection law in Vietnam, focusing specifically on the responsibilities of data controllers under Vietnam's new Personal Data Protection Decree (Decree No. 13/2023/ND-CP - hereinafter referred to as Decree 13) and compares it with the European Union's General Data Protection Regulation (GDPR). The main objective is to assess how the provisions regarding data controllers’ responsibilities under Decree 13 align with international data protection standards, identifying its progress and challenges. The analysis uncovers both convergence and divergence points between the related provisions under Decree 13 and the GDPR, particularly in terms of clarity, scope, and enforcement mechanisms. A significant challenge identified is the ambiguity in Decree 13’s provisions on data controllers’ responsibilities and the absence of several essential elements, which could undermine the effectiveness of Vietnam's data protection framework. To address these issues, the article offers strategic recommendations for legislative improvements and practical adjustments for data controllers in Vietnam. In conclusion, while navigating the path to a comprehensive data protection framework poses challenges for Vietnam, this journey offers an opportunity to align with regional and global developments in data protection laws. By learning from the GDPR and adapting to its specific features, Vietnam can develop a robust, effective, and trustworthy data protection environment, safeguarding its citizens' privacy rights and facilitating a favorable international business climate.VNU Journal of Science: Legal Studies140VNU Journal of Science: Legal Studies['Dao Kim Anh']2024-03-30T00:00:00https://www.semanticscholar.org/paper/407336e7add657a088e4c4647fc0000ddff85a43 c40a2fdc55454ccc9096abad6c0b961e278700d9In the dynamic landscape of electronic commerce (e-commerce), understanding and adapting to evolving consumer behavior is critical for the sustained success of online businesses. This review delves into the intersection of e-commerce and consumer behavior, focusing on the transformative role of Artificial Intelligence (AI)-powered personalization and its impact on market trends. The advent of AI has revolutionized the way e-commerce platforms engage with and cater to individual consumer preferences. AI-powered personalization techniques leverage advanced algorithms to analyze vast datasets, enabling the delivery of highly tailored and relevant content, product recommendations, and user experiences. This review explores the intricate mechanisms of AI-driven personalization, examining how it enhances customer engagement, satisfaction, and loyalty. Furthermore, the study investigates the prominent market trends shaped by AI in e-commerce. From chatbots and virtual assistants facilitating seamless customer interactions to predictive analytics optimizing inventory management, AI is driving innovation across various facets of the online retail landscape. The analysis delves into the integration of machine learning algorithms in predicting consumer preferences, streamlining the purchasing process, and fostering a more personalized shopping journey. As e-commerce continues to evolve, the review also explores the challenges and ethical considerations associated with AI-powered personalization. Issues such as data privacy, algorithmic bias, and the delicate balance between customization and intrusiveness are examined to provide a comprehensive understanding of the broader implications of AI in shaping consumer behavior. Ultimately, this review offers valuable insights into the symbiotic relationship between e-commerce and consumer behavior, shedding light on the transformative power of AI-powered personalization and its influence on emerging market trends. As businesses navigate the digital landscape, understanding and harnessing the potential of AI-driven strategies become imperative for staying competitive and meeting the evolving expectations of tech-savvy consumers.GSC Advanced Research and Reviews466This review delves into the intersection of e-commerce and consumer behavior, focusing on the transformative role of Artificial Intelligence (AI)-powered personalization and its impact on market trends.GSC Advanced Research and Reviews['Mustafa Ayobami Raji', 'H. B. Olodo', 'Timothy Tolulope Oke', 'Wilhelmina Afua Addy', 'Onyeka Chrisanctus Ofodile', 'A. Oyewole']2024-03-30T00:00:00https://www.semanticscholar.org/paper/c40a2fdc55454ccc9096abad6c0b961e278700d9 93c421adc5c9df282169f4ccf9d5fdfe884b7b25Scientific notes00Scientific notes['Illia Markevych', 'Iryna Parasii-Verhunenko']2024-03-30T00:00:00https://www.semanticscholar.org/paper/93c421adc5c9df282169f4ccf9d5fdfe884b7b25 1a374c5ceac24f131c6d450352ef7acb952fbad4Due to a combination of sales regulations and lack of innovation, the growth of large marts is stagnating and profitability is deteriorating. Accordingly, this study examined the impact of large smarks on the national economy, especially on production, added value, employment, sales of suppliers, and tax revenues, through industry correlation analysis and other algebraic calculations. As a result of inter-industry analysis, large marts increase the annual production, added value, and employment by KRW 10 trillion, KRW 11 trillion, and 210,000 people in the wholesale/retail and product brokerage service industries, respectively, and by KRW 43 trillion, KRW 38 trillion, and 320,000 people, respectively, in the industry as a whole. In addition, large marts increase supplier sales by KRW 14 trillion per year and tax revenues by KRW 917.3 billion per year (corporate tax KRW 264.5 billion + value-added tax KRW 652.8 billion). Therefore, it can be said that the impact of large marts on the national economy is truly enormous. Therefore, the government needs to help large marts become competitive by reevaluating populist regulations on large marts that are not helpful to the national economy and open the way for large marts to provide better products and services. In addition large marts need to demonstrate its entrepreneurial spirit and innovate in products and services so that people continue to visit the large marts.Academy of Entrepreneurship00Academy of Entrepreneurship['Heung-Kyu Kim']2024-03-30T00:00:00https://www.semanticscholar.org/paper/1a374c5ceac24f131c6d450352ef7acb952fbad4 42258b105bdad2bc0a9358c5c4a89534a8395a24The integration of Artificial Intelligence (AI) into supply chain management has emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary business operations. This paper explores various theoretical approaches to AI within the context of supply chain optimization, delineating pathways to achieve heightened performance and adaptability. Commencing with a historical overview, the paper delves into the evolution of AI techniques in supply chain management, elucidating how these methodologies have transformed the landscape of logistics and operations. Fundamental to this exploration is the discussion on mathematical modeling and algorithmic frameworks that underpin supply chain optimization, providing the theoretical foundation for subsequent AI applications. A key focus of the paper lies in the application of machine learning techniques for demand forecasting and inventory management, which leverage data-driven insights to optimize resource allocation and mitigate risks associated with supply-demand fluctuations. Additionally, network theory and graph algorithms play a crucial role in optimizing the structure and dynamics of supply chain networks, enabling efficient transportation, distribution, and inventory routing. Strategic decision-making in supply chains is addressed through the lens of game theory, which offers theoretical frameworks to model interactions among multiple stakeholders and optimize outcomes in competitive environments. Moreover, swarm intelligence and multi-agent systems provide innovative solutions for coordination and collaboration within complex supply chain ecosystems. Evolutionary algorithms and artificial neural networks are discussed as powerful tools for supply chain design, predictive analytics, and risk management, offering capabilities for optimizing decision-making processes across various operational domains. Furthermore, reinforcement learning techniques empower dynamic decision-making in real-time operational settings, fostering adaptive and resilient supply chain management practices. By integrating multiple AI techniques, hybrid approaches offer synergistic solutions that capitalize on the strengths of diverse methodologies to address multifaceted challenges in supply chain optimization. Through a synthesis of theoretical insights and practical case studies, this paper provides valuable insights into the current state and future directions of AI-driven supply chain optimization.International Journal of Science and Technology Research Archive494This paper explores various theoretical approaches to AI within the context of supply chain optimization, delineating pathways to achieve heightened performance and adaptability and provides valuable insights into the current state and future directions of AI-driven supply chain optimization.International Journal of Science and Technology Research Archive['Gerald Adeyemi Abaku', 'Emmanuel Adeyemi Abaku', 'Tolulope Esther Edunjobi', 'Agnes Clare Odimarha']2024-03-30T00:00:00https://www.semanticscholar.org/paper/42258b105bdad2bc0a9358c5c4a89534a8395a24 ad98c242e96e59003f4ed250493ab963b09e03a2Launching AI products presents unique challenges for start-ups, requiring a deep understanding of market dynamics and effective strategic planning. This paper explores theoretical frameworks and practical approaches to help start-ups navigate the complexities of AI product launches. We begin by analyzing market challenges, including competitive landscapes, market segmentation, and regulatory considerations. Drawing from theoretical models such as lean startup methodology, crossing the chasm theory, and blue ocean strategy, we propose a comprehensive framework for AI product launch strategies. Tactical approaches such as MVP development, customer-centricity, strategic partnerships, and scalability considerations are discussed to facilitate successful product launches. Implementation challenges, including talent acquisition, resource allocation, and investor management, are addressed alongside ethical considerations in AI deployment. Case studies and practical examples offer insights from both successful and failed AI product launches. Through this exploration, we aim to equip start-ups with the theoretical insights necessary to navigate market challenges and drive successful AI product launches in an ever-evolving landscape.International Journal of Frontiers in Science and Technology Research04This paper begins by analyzing market challenges, including competitive landscapes, market segmentation, and regulatory considerations, and proposes a comprehensive framework for AI product launch strategies, drawing from theoretical models such as lean startup methodology, crossing the chasm theory, and blue ocean strategy.International Journal of Frontiers in Science and Technology Research['Damilola Oluwaseun Ogundipe', 'Emmanuel Adeyemi Abaku']2024-03-30T00:00:00https://www.semanticscholar.org/paper/ad98c242e96e59003f4ed250493ab963b09e03a2 ceedcbfea0b534da2633aad3cb218553ca8c54ddThis paper delves into theoretical frameworks in AI for credit risk assessment, exploring how these frameworks enhance banking efficiency and accuracy. It discusses various AI techniques such as machine learning algorithms, neural networks, and natural language processing, and their application in credit risk assessment. Furthermore, it examines the challenges and opportunities presented by these frameworks, highlighting their potential to revolutionize the banking sector. Revolutionizing Credit Risk Assessment in Banking, The Role of Artificial Intelligence In the dynamic realm of finance, the assessment of credit risk stands as a fundamental pillar for banking institutions. Traditionally, this process has heavily relied on statistical models and historical data. However, the emergence of Artificial Intelligence (AI) has catalyzed a transformative shift in this domain. This paper elucidates the theoretical underpinnings of AI frameworks employed in credit risk assessment and investigates their profound implications for enhancing the efficiency and accuracy of banking operations. The exploration begins by delineating various theoretical frameworks in AI pertinent to credit risk assessment. Leveraging machine learning algorithms, neural networks, and natural language processing techniques, these frameworks offer innovative approaches to evaluate creditworthiness. Unlike conventional methods, AI-driven models possess the capacity to ingest vast datasets, identify intricate patterns, and adapt dynamically to evolving market dynamics. Such capabilities empower banks to make more informed and timely decisions regarding lending activities. Moreover, this paper delves into the practical application of AI techniques in credit risk assessment. Through case studies and empirical evidence, it elucidates how these advanced methodologies enable banks to mitigate risks while maximizing profitability. By harnessing AI, financial institutions can optimize credit scoring processes, identify potential defaulters with greater accuracy, and customize lending terms based on individual risk profiles. Additionally, AI facilitates real-time monitoring of credit portfolios, allowing proactive risk management and timely interventions to prevent adverse outcomes.International Journal of Scientific Research Updates534The theoretical underpinnings of AI frameworks employed in credit risk assessment are elucidated and their profound implications for enhancing the efficiency and accuracy of banking operations are investigated.International Journal of Scientific Research Updates['Tolulope Esther Edunjobi', 'Opeyemi Abayomi Odejide']2024-03-30T00:00:00https://www.semanticscholar.org/paper/ceedcbfea0b534da2633aad3cb218553ca8c54dd 6a1f8f8247574b831f3f2ea97f8ca146415ed255As artificial intelligence (AI) continues to permeate various aspects of our lives, the ethical challenges associated with its development become increasingly apparent. This paper navigates and reviews the ethical dilemmas in AI development, focusing on strategies to promote transparency, fairness, and accountability. The rapid growth of AI technology has given rise to concerns related to bias, lack of transparency, and the need for clear accountability mechanisms. In this exploration, we delve into the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability. To address these concerns, we propose strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks. Furthermore, we explore strategies to promote fairness in AI algorithms, emphasizing the importance of fairness metrics, diverse training data, and continuous monitoring for iterative improvement. Additionally, the paper delves into strategies to ensure accountability in AI development, considering regulatory measures, ethical AI governance, and the incorporation of human-in-the-loop approaches. To provide practical insights, case studies and real-world examples are analyzed to distill lessons learned and best practices. The paper concludes with a comprehensive overview of the proposed strategies, emphasizing the importance of balancing innovation with ethical responsibility in the evolving landscape of AI development. This work contributes to the ongoing discourse on AI ethics, offering a roadmap for navigating the challenges and fostering responsible AI development practices.GSC Advanced Research and Reviews353This work explores the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability, and proposes strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks.GSC Advanced Research and Reviews['Olatunji Akinrinola', 'Chinwe Chinazo Okoye', 'Onyeka Chrisanctus Ofodile', 'Chinonye Esther Ugochukwu']2024-03-30T00:00:00https://www.semanticscholar.org/paper/6a1f8f8247574b831f3f2ea97f8ca146415ed255 6e1a3e20ab895b98150f92262a099cd1c67b4800This study explores the transformative integration of Artificial Intelligence (AI) into sustainable finance, highlighting its potential to redefine financial practices in alignment with Environmental, Social, and Governance (ESG) criteria. Through a systematic review of current practices and an analysis of AI's applications, challenges, and strategic frameworks, the research elucidates AI's role in enhancing financial operations' efficiency, accuracy, and sustainability. Findings indicate that AI technologies, such as the Financial Maximally Filtered Graph (FMFG) algorithm, significantly improve the processing and analysis of vast datasets, facilitating sustainable investment decisions. However, the integration of AI into sustainable finance is accompanied by ethical, regulatory, and technological challenges. The study proposes strategic recommendations for overcoming these barriers, emphasizing the development of robust policy frameworks, industry best practices, and a balanced approach to AI integration. The conclusion underscores the promise of AI in advancing sustainable finance, offering insights for stakeholders on navigating the complexities of this integration to achieve a more sustainable and resilient financial system.World Journal of Advanced Research and Reviews02The study proposes strategic recommendations for overcoming barriers to the integration of AI into sustainable finance, emphasizing the development of robust policy frameworks, industry best practices, and a balanced approach to AI integration.World Journal of Advanced Research and Reviews['Adedoyin Tolulope Oyewole', 'Omotayo Bukola Adeoye', 'Wilhelmina Afua Addy', 'Chinwe Chinazo Okoye', 'Onyeka Chrisanctus Ofodile', 'Chinonye Esther Ugochukwu']2024-03-30T00:00:00https://www.semanticscholar.org/paper/6e1a3e20ab895b98150f92262a099cd1c67b4800 1add8328d3968c95a1162e5f21d61b9dab3d7498This study introduces the Questionnaire of AI Use Motives (QAIUM), an instrument designed to measure motivation levels in individuals using artificial intelligence (AI) applications. Building on a theoretical framework that emphasizes motivation over dispositions and defines motivation as expectancy/value, the QAIUM aims to fill a research gap in understanding the motivational factors that govern AI application use. Previous studies have often overlooked this human aspect, focusing instead on technological facets while failing to provide a robust theoretical foundation for measuring motivation. The QAIUM, administered to 1068 university students across various degree programs, was evaluated for its factorial structure, reliability, discriminatory capacity, and correlation with the General Attitudes to Artificial Intelligence Scale (GAAIS). The results demonstrate that the QAIUM aligns with the Eccles and Wigfield motivation model, boasts good reliability levels, discriminatory capacity, and a significant correlation with GAAIS, confirming its validation and reliability. Hence, the QAIUM provides an effective tool for investigating motivational factors affecting AI application utilization in academic instruction and intervention.International Journal of Technology in Education521The results demonstrate that the QAIUM aligns with the Eccles and Wigfield motivation model, boasts good reliability levels, discriminatory capacity, and a significant correlation with GAAIS, confirming its validation and reliability.International Journal of Technology in Education['E. Yurt', 'Ismail Kasarci']2024-03-30T00:00:00https://www.semanticscholar.org/paper/1add8328d3968c95a1162e5f21d61b9dab3d7498 17345a03389bd6600b1592727fae623c229da3ebAs cyber threats and regulations become increasingly complex, financial institutions in the U.S. are in need of innovative cyber security solutions. This study examines the potential for artificial intelligence (AI) in addressing this problem. Artificial intelligence has significant potential for real-time threat detection, automated compliance processes, and proactive risk management. Nonetheless, ethical considerations, concerns about personal data privacy, and potential biases in AI algorithms require careful consideration. In light of this, the research proposes recommendations for developing innovative AI solutions. In addition, it addresses ethical and privacy concerns, as well as providing policy recommendations for the responsible and effective adoption of AI in the financial sector. This research highlights AI's potential to significantly enhance security and risk management for financial institutions. Additionally, it emphasizes responsible and ethical implementation.World Journal of Advanced Research and Reviews01This research highlights AI's potential to significantly enhance security and risk management for financial institutions and emphasizes responsible and ethical implementation.World Journal of Advanced Research and Reviews['Oladipupo Dopamu', 'Joseph Adesiyan', 'Femi Oke']2024-03-30T00:00:00https://www.semanticscholar.org/paper/17345a03389bd6600b1592727fae623c229da3eb 652940e567ef2c38f9c53af82ebc5a900f438e7dThe integration of Artificial Intelligence (AI) into Information Systems (IS) is ushering in a transformative era of data-driven decision-making. This research paper presents a comprehensive exploration of AI's applications, benefits, challenges, and future directions within IS. AI is revolutionizing data management through techniques such as automated data integration, natural language processing, and enhanced data quality, while also providing sophisticated decision support systems with predictive analytics and recommendation engines. Businesses benefit from streamlined processes, real-time analytics, and improved cybersecurity measures. However, challenges such as data quality, AI skill shortages, ethical concerns, and integration complexities must be addressed. The paper envisions future directions where Explainable AI (XAI) offers transparent decision rationales, ethics and governance frameworks ensure responsible AI adoption, augmented intelligence fosters human-AI collaboration, AI extends to edge computing for real-time processing, and AI fortifies cybersecurity measures. As AI technologies continue to mature, organizations must invest in research and development while formulating robust AI adoption strategies to harness the potential of AI in IS. The fusion of AI and IS is poised to redefine information management, facilitating more intelligent, efficient, and secure operations in the evolving digital landscape. Key Words: Artificial Intelligence, Information Systems, Machine Learning, Data Analytics, Natural Language Processing, Automation, Decision Support, Big Data.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper presents a comprehensive exploration of AI's applications, benefits, challenges, and future directions within IS, and envisions future directions where Explainable AI (XAI) offers transparent decision rationales, ethics and governance frameworks ensure responsible AI adoption.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Vibha upadhya']2024-03-30T00:00:00https://www.semanticscholar.org/paper/652940e567ef2c38f9c53af82ebc5a900f438e7d d579044f2c08cbbe031724c6a6ef0e0cb2acaf1cBackground: Given the prevalence of sleep issues, their impact on overall health and the potential of gradual awakening in mitigating part of the negative effects, innovative solutions can be leveraged to facilitate this awakening process. Objectives: The current study aims to present in detail and validate a novel system that utilizes advanced AI algorithms for sleep tracking, sleep stage prediction, and a "smart alarm" component for optimal gradual awakening. Methods: A clinical trial was conducted to assess the system's effectiveness, namely its influence on several key daily aspects. Participants wearing consumer wearable devices in their sleep were required to self-assess their quality of life, sleep quality and perception of their own waking process through several questionnaires. Results: The integrated system is developed and works as intended. The clinical study findings demonstrate statistically significant benefits in quality of life, sleep quality, user satisfaction with the waking process and mental status upon awakening. On average, an increase in WHOQOL-BREF score from 88.44 to 94.75, a decrease in Sleep Quality Scale scores from 65.66 to 58.19 and in awakening scores from 10.81 to 8.95 (the latter two scores decrease as outcome improves) were observed. Conclusion: Our system shows promise for enhancing individual well-being and productivity by detecting the optimal sleep stage for awakening and providing a more natural waking experience, indicating a valuable direction for future sleep health technologies. Research should be directed at expanding the search for more potential uses, optimizing algorithms and improving user experience.World Journal of Advanced Engineering Technology and Sciences200A novel system that utilizes advanced AI algorithms for sleep tracking, sleep stage prediction, and a "smart alarm" component for optimal gradual awakening shows promise for enhancing individual well-being and productivity by detecting the optimal sleep stage for awakening and providing a more natural waking experience.World Journal of Advanced Engineering Technology and Sciences['Zaki Milhem', 'Vadim Fîntînari', 'Nicolae Goga']2024-03-30T00:00:00https://www.semanticscholar.org/paper/d579044f2c08cbbe031724c6a6ef0e0cb2acaf1c 1121ce4e5a3842f0a21482ff5565d5c45c09fba5Integrating Artificial Intelligence (AI) in education presents a promising frontier with manifold opportunities, yet it also poses significant challenges and necessitates ethical considerations. This review explores the multifaceted landscape of AI integration in education, highlighting its potential to revolutionize traditional pedagogical approaches, personalize learning experiences, and streamline administrative tasks. However, it also addresses the challenges pertaining to implementation, including issues related to accessibility, data privacy, and the digital divide. The opportunities afforded by AI in education are vast and transformative. AI-driven technologies have the capacity to adapt instruction to individual learning styles, thereby enhancing student engagement and academic outcomes. Additionally, AI-powered tools can automate administrative tasks, allowing educators to allocate more time to meaningful interactions with students. Moreover, AI holds promise in facilitating the creation of immersive learning environments through virtual reality and augmented reality applications, enriching the educational experience. Nevertheless, the integration of AI in education presents ethical considerations that warrant careful examination. Concerns regarding data privacy and security arise as educational institutions collect and analyze vast amounts of student data. Moreover, there are apprehensions about the potential for AI algorithms to perpetuate biases or reinforce inequalities if not implemented with conscientious oversight. Furthermore, questions surrounding the ethical use of AI in assessing student performance and making consequential decisions underscore the importance of establishing transparent and accountable practices. While the integration of AI in education offers unprecedented opportunities for innovation and improvement, it is imperative to navigate the associated challenges with diligence and ethical foresight. By addressing these challenges thoughtfully, stakeholders can harness the full potential of AI to cultivate equitable, inclusive, and effective educational environments.Magna Scientia Advanced Research and Reviews480This review explores the multifaceted landscape of AI integration in education, highlighting its potential to revolutionize traditional pedagogical approaches, personalize learning experiences, and streamline administrative tasks, however, it also addresses the challenges pertaining to implementation.Magna Scientia Advanced Research and Reviews['Chima Abimbola', 'Chima Abimbola Eden', 'Onyebuchi Nneamaka Chisom', 'Idowu Sulaimon Adeniyi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/1121ce4e5a3842f0a21482ff5565d5c45c09fba5 326478e401f524947f85aa668e93faa4d4069608This article reviews, integrates, and expands upon research initiatives that explore the development and implementation of advanced artificial intelligence (AI)–driven tools and methodologies. In exploring collaborations with leading U.S. AI technology firms, including Nvidia, Dataiku, DataRobot, and C3.ai, this study is specifically aimed at identifying, preventing, and combating human trafficking. This collaborative effort seeks to create a synergistic framework that capitalizes on the unique capabilities and resources of each firm. The overarching goal is to enhance the effectiveness of AI technologies in detecting trafficking activities, analyzing diverse data sources, and providing vital support to law enforcement and NGOs in rescue and prevention efforts. The comprehensive review delves into the multifaceted applications of AI, emphasizing its role in prevention through machine learning, data analytics, and natural language processing. It navigates through collaborative initiatives, presenting partnerships with prominent U.S. tech firms, accompanied by case studies that showcase successful collaborative projects. Challenges inherent in collaborative AI efforts are addressed, and the paper proposes strategic solutions to overcome these obstacles. The ethical and legal considerations section scrutinizes the implications of deploying AI in human trafficking prevention, exploring the delicate balance between innovation and safeguarding privacy and civil liberties. Furthermore, the final section anticipates future directions and recommendations, providing insights into emerging technologies that could enhance anti-trafficking efforts. Specific recommendations are offered for fortifying collaborations between AI developers and tech firms, and the paper discusses broader implications for policy, suggesting avenues for future research in the dynamic field of AI-driven human trafficking prevention. In essence, this comprehensive review not only synthesizes existing knowledge but also integrates a forward-looking research initiative, contributing significantly to the ongoing discourse on leveraging technology to combat human trafficking on a global scale.Global Journal of Engineering and Technology Advances00This comprehensive review delves into the multifaceted applications of AI, emphasizing its role in prevention through machine learning, data analytics, and natural language processing, and anticipates future directions and recommendations, providing insights into emerging technologies that could enhance anti-trafficking efforts.Global Journal of Engineering and Technology Advances['Amina Catherine Ijiga', 'Enoch Joseph Aboi', 'Idoko Peter Idoko', 'Lawrence Anebi Enyejo', 'Micheal Olumubo Odeyemi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/326478e401f524947f85aa668e93faa4d4069608 ba0538c8f9878d7dbf5caa00210e322612e04ab0One of the most controversial topics today in the realm of Technology and Intellectual property Law is the patentability or not of inventions wrought by Artificial Intelligence. Significant progress in law has been made in this regard in jurisdictions like the United States and the United Kingdom where the consensus of judicial opinion is that inventions made by Artificial Intelligence, to wit, robots, digital assistants etc. are not eligible to be granted patent. However, there have been growing scholarly exhortations in legal cycles for possible review of this position. This article is a new addition to that increasing scholastic demand for the expansion of the parameters for patentability especially as it relates to Artificial Intelligence. It suggests amongst other things that while the patentability of each case of AI-driven invention ought to be decided on the merits of its own peculiar facts, nonetheless, the relative levels of involvement of the said Artificial Intelligence in the process of making the invention sought to be awarded a patent, and the substantiality or not of its contribution to the project, should also weigh high in the overall consideration of whether the resulting invention should be patented or not.Open Access Research Journal of Science and Technology00While the patentability of each case of AI-driven invention ought to be decided on the merits of its own peculiar facts, nonetheless the relative levels of involvement of the said Artificial Intelligence in the process of making the invention sought to be awarded a patent should also weigh high in the overall consideration of whether the resulting invention should be patented or not.Open Access Research Journal of Science and Technology['Chukwuebuka Festus Okoli']2024-03-30T00:00:00https://www.semanticscholar.org/paper/ba0538c8f9878d7dbf5caa00210e322612e04ab0 d8c2e848a58a96a5a9d9ef4dde0703a8fa58ce8cThis paper explores the application of AI in predictive maintenance within oil and gas facilities, discussing its benefits, challenges, and future prospects. Through the integration of AI-driven analytics and real-time data monitoring, oil and gas companies can enhance their asset integrity management practices, ultimately driving cost savings and operational excellence. Predictive maintenance has become indispensable in the oil and gas industry, serving as a pivotal strategy to uphold operational efficiency and preserve asset integrity. This paper delves into the profound impact of artificial intelligence (AI) technologies on predictive maintenance, ushering in a new era of proactive equipment management. By harnessing AI capabilities, oil and gas companies can preempt equipment failures, curtail downtime, and refine maintenance protocols, thereby optimizing overall operational performance. The integration of AI in predictive maintenance marks a paradigm shift, offering a proactive approach to asset management. Leveraging AI-driven analytics and real-time data monitoring, oil and gas facilities can fortify their asset integrity management practices. Through predictive algorithms and machine learning models, these technologies empower companies to forecast equipment malfunctions with unprecedented accuracy, allowing for timely interventions and mitigating potential risks the benefits of AI-powered predictive maintenance in the oil and gas sector are multifaceted the future of predictive maintenance in the oil and gas industry is brimming with promise. As AI technologies continue to evolve, we can anticipate further advancements in predictive analytics, fault detection, and decision support systems. By embracing innovation and collaboration, oil and gas companies can harness the full potential of AI-driven predictive maintenance, cementing their position as industry leaders in asset management and operational efficiency.International Journal of Frontiers in Engineering and Technology Research420International Journal of Frontiers in Engineering and Technology Research['Vincent Onuegb', 'Chuka Anthony Arinze', 'Vincent Izionworu', 'Onuegbu', 'Daniel Isong', 'Cosmas Dominic', 'Daudu', 'Adedayo Adefemi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/d8c2e848a58a96a5a9d9ef4dde0703a8fa58ce8c 5ad4150ff95c75097f7239bbe6580393bde2d449Artificial Intelligence (AI) is revolutionizing the recruitment landscape, presenting both opportunities and challenges for Human Resources (HR) professionals. This abstract explores the evolving role of AI in modern recruitment and its implications for HR practitioners. AI technologies offer HR professionals a multitude of opportunities to enhance recruitment efficiency, streamline processes, and improve decision-making. From automated resume screening and candidate sourcing to predictive analytics for talent forecasting, AI empowers HR teams to optimize their workflow and allocate resources more effectively. Moreover, AI-driven chatbots and virtual assistants enhance candidate engagement by providing instant responses and personalized interactions, thereby elevating the overall candidate experience. However, the adoption of AI in recruitment also presents challenges that HR professionals must navigate skillfully. Ethical considerations surrounding data privacy, algorithmic bias, and fairness in candidate selection require careful scrutiny and proactive measures to mitigate potential risks. Furthermore, there is a pressing need for HR professionals to develop competencies in data analysis, algorithm management, and ethical AI usage to harness the full potential of these technologies effectively. The study is aimed to find out the opportunities and challenges faced by hr professional by employing AI tools, quantitative data has been collected through surveys using stratified sampling method and for analyzing the data chi-square, correlations and Anova tools has been used. Key Word: Artificial Intelligence (AI), recruitment, Human Resources (HR), opportunities, challenges, efficiency, decision-making, candidate engagement, ethical considerations, data privacy, algorithmic bias.International Journal Of Scientific Research In Engineering &amp; Technology00The evolving role of AI in modern recruitment and its implications for HR practitioners is explored, with a pressing need for HR professionals to develop competencies in data analysis, algorithm management, and ethical AI usage to harness the full potential of these technologies effectively.International Journal Of Scientific Research In Engineering &amp; Technology['Pooja S', 'Sivakanni S']2024-03-30T00:00:00https://www.semanticscholar.org/paper/5ad4150ff95c75097f7239bbe6580393bde2d449 e1a7cede659e3013c73c0494bec2cc88c5fb1e2bThis paper presents a groundbreaking approach to data collection in hazardous or inaccessible environments, presenting the design, development, and implementation of an innovative autonomous robot. The robot is designed to navigate and collect valuable data from locations too dangerous or remote for human exploration, enabling scientific research and exploration in unprecedented ways. The AI-powered drone is equipped for precise human identification, controlled through a user-friendly mobile app. The software analyzes live drone footage to detect human presence using models like YOLO, with high accuracy in real-time human detection tasks. The robot is equipped with an array of sensors, including cameras, and uses image processing technology for processing images. GPS tracking technology is used for device tracking. The proposed autonomous robot promises to revolutionize data collection in unreachable environments, opening new avenues for scientific discovery, resource assessment, and environmental monitoring. Key Words: YOLOV8, UAV, Python, Flask, Computer vision, AI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper presents a groundbreaking approach to data collection in hazardous or inaccessible environments, presenting the design, development, and implementation of an innovative autonomous robot that promises to revolutionize data collection in unreachable environments.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Aleena Francis']2024-03-30T00:00:00https://www.semanticscholar.org/paper/e1a7cede659e3013c73c0494bec2cc88c5fb1e2b bd408a60bd33909a7d2cf51ebd9d8895324f7c0dGermany imposes relatively large restrictions by prohibiting the issuance of fully automated administrative acts in cases of discretion through Article 35a of the Federal Administrative Procedure Act, but legislators can and do allow this in individual laws according to Article 1 (1) of the Administrative Procedure Act, which stipulates the “principle of priority of special laws”. The permissible forms of legislation can be categorized as (1) permissible if the official has no reason to deal with the case, (2) no restrictions or conditions on issuance, (3) no conditions on issuance, but the legislator specifies the exclusion of discretion in the Reasons for legislation, and (4) prohibiting the issuance of full automation from discretion. The application of human resource management procedures such as recruitment and evaluation of public officials, which are representative of discretionary administration, underscores the need to apply AI in mass procedures, and the algorithmization of discretionary criterions through technological advances will bring a revolution in public administration. If it is rejected or deemed impossible, the practical implications of full automation in public administration -which will eventually boil down to AI administration- will inevitably lead to a decline. To this end, law will have to do its part by focusing on defining the meaning of the discretionary criterion.Korean Administrative Law Association00Korean Administrative Law Association['Yong-wook Kim']2024-03-30T00:00:00https://www.semanticscholar.org/paper/bd408a60bd33909a7d2cf51ebd9d8895324f7c0d 9bdb288d8fd68ee7e89fe73054c34be48e3c94b8As per the World Health Organization, approximately 15% of the global population experiences some form of disability. The integration of Assistive Technology with Artificial Intelligence of Things devices has witnessed significant advancements. This paper, through research, aims to identify various assistive models utilized in diverse studies focusing on the application of Artificial Intelligence. Starting with past research studies in this area and emphasizing the manifold and noteworthy roles of AI in assistive technologies, the paper delves into the prospective applications of AI in the future of assistive technologies.Nafath40This paper aims to identify various assistive models utilized in diverse studies focusing on the application of Artificial Intelligence, and delves into the prospective applications of AI in the future of assistive technologies.Nafath['Dr. Reshmy Krishnan', 'Dr Sivakumar Manickam']2024-03-30T00:00:00https://www.semanticscholar.org/paper/9bdb288d8fd68ee7e89fe73054c34be48e3c94b8 480ee7cb9c27b4d755e7fe99ff30919956a6606eArtificial intelligence (AI) in health care is rapidly expanding, with the daily emergence of new initiatives, topics, and critical issues, making it challenging for physician leaders to organize and distill this complex topic. We offer a simple approach that involves classifying topics by three levels of scale: the individual, the organization, and the system or sector. Despite the widespread adoption of AI applications across all aspects of our daily lives, its implementation in health care remains limited. There is a need to engage, in all stages of development, key stakeholders, specifically governments, technology companies, health care providers, patients, and civil society. Cultural, social, and/or regional disparities can impact the integration of AI in health care, reflecting varied beliefs, attitudes, and practices. Our simplified approach to structuring and organizing this complex subject can serve as a valuable tool for physician leaders in conducting more focused discussions with stakeholders and decision-makers.Canadian journal of physician leadership00A simple approach that involves classifying topics by three levels of scale: the individual, the organization, and the system or sector can serve as a valuable tool for physician leaders in conducting more focused discussions with stakeholders and decision-makers.Canadian Journal of Physician Leadership['Tyrone A Perreira', 'Sundeep Sodhi', 'Alia Karsan', 'Hazim Hassan', 'Anthony Dale']2024-03-30T00:00:00https://www.semanticscholar.org/paper/480ee7cb9c27b4d755e7fe99ff30919956a6606e 610501370ec0370b52b999e4c7c013743fa5d54cAccess to quality science education is essential for equitable development and advancement in society. However, disparities in access to science education persist, particularly among marginalized and underserved populations. Artificial intelligence (AI) offers innovative solutions to address these disparities by enhancing pedagogical strategies that promote equitable access to science education. This review examines AI-driven pedagogical strategies aimed at improving equitable access to science education. The review explores how AI technologies, such as machine learning, natural language processing, and computer vision, can be leveraged to personalize learning experiences, provide real-time feedback, and enhance engagement among students from diverse backgrounds.AI-driven personalized learning platforms can adapt to individual learning styles and pace, ensuring that each student receives tailored instruction. These platforms can also provide additional support to students facing learning challenges, thus promoting inclusivity and equity in science education. Furthermore, AI-driven assessment tools can provide educators with insights into student performance and comprehension, enabling them to identify areas for improvement and provide targeted interventions. Additionally, AI can facilitate collaborative learning environments, allowing students to work together irrespective of their physical location, thus breaking down geographical barriers to access. However, the implementation of AI-driven pedagogical strategies raises ethical considerations, such as data privacy and algorithmic bias, which must be carefully addressed to ensure equitable access to science education for all students. In conclusion, AI-driven pedagogical strategies have the potential to revolutionize science education by enhancing personalized learning, providing real-time feedback, and fostering inclusive learning environments. However, careful consideration must be given to the ethical implications of AI implementation to ensure that these technologies are used responsibly and equitably.Magna Scientia Advanced Research and Reviews670AI-driven pedagogical strategies have the potential to revolutionize science education by enhancing personalized learning, providing real-time feedback, and fostering inclusive learning environments, but careful consideration must be given to the ethical implications of AI implementation.Magna Scientia Advanced Research and Reviews['Idowu Sulaimon Adeniyi', 'Chima Abimbola', 'Olabisi Oluwakemi Adeleye']2024-03-30T00:00:00https://www.semanticscholar.org/paper/610501370ec0370b52b999e4c7c013743fa5d54c 8c6e77d8310a815af4af236b2d19d4275de1c52fThis conceptual analysis explores the transformative potential of AI-enhanced tools in addressing socioeconomic barriers within the educational landscape. By leveraging artificial intelligence (AI) technologies, the paper aims to examine how such tools can mitigate disparities arising from economic, social, and cultural factors. Through a critical analysis, it seeks to elucidate the role of AI in promoting equitable access, enhancing learning outcomes, and fostering inclusivity in education. The executive summary encapsulates the essence of the conceptual analysis. It provides a concise overview of the paper's objectives, methodology, expected outcomes, and implications. In recent years, the intersection of artificial intelligence (AI) and education has garnered significant attention as a potential solution to address persistent socioeconomic barriers within the educational landscape. The executive summary outlines the imperative to explore how AI-enhanced tools can serve as transformative agents in mitigating disparities arising from economic, social, and cultural factors. By leveraging AI technologies, educators and policymakers have the opportunity to revolutionize traditional educational practices and foster more inclusive learning environments. The summary highlights the urgent need to examine the role of AI in promoting equitable access, enhancing learning outcomes, and fostering inclusivity across diverse socioeconomic backgrounds. Through a critical analysis of existing literature, case studies, and empirical research, the conceptual analysis seeks to elucidate the potential of AI to bridge the digital divide and advance educational equity. It emphasizes the importance of identifying actionable strategies and best practices for leveraging AI technology to address systemic inequalities in education.World Journal of Advanced Research and Reviews350This conceptual analysis explores the transformative potential of AI-enhanced tools in addressing socioeconomic barriers within the educational landscape and emphasizes the importance of identifying actionable strategies and best practices for leveraging AI technology to address systemic inequalities in education.World Journal of Advanced Research and Reviews['Idowu Sulaimon Adeniyi', 'Chima Abimbola Edeni', 'Olabisi Oluwakemi Adeleye']2024-03-30T00:00:00https://www.semanticscholar.org/paper/8c6e77d8310a815af4af236b2d19d4275de1c52f 1fe2f76f76fe3ca9cac3f19293f2d74bd41e3817The trends and future challenges in response to the era of generative AI for education, instructional methods, and the paradigm shift in educational assessment can be outlined as follows: First, as generative AI becomes more prevalent, the role of teachers is poised to shift from being mere knowledge transmitters to becoming guides and counselors, actively fostering the holistic development of students. Consequently, the importance of teachers' AI and digital competencies is anticipated to witness a substantial rise. Second, as the demand for personalized education-based on individual aptitude, interests, career paths, and vocational relevance grow, there is a need to establish a collaborative and shared system for creating and sharing a multi-perspective curriculum that involve all stakeholders in the school curriculum. Third, the digitization of instructional materials, such as AI digital textbooks and AI-integrated personalized teaching platforms, is accelerating. Therefore, ongoing communication with the field, continuous research, and support are necessary to enhance the educational effectiveness of education technology-based teaching and learning methods. Fourth, with the innovative development of digital technologies, there has been a renewed focus on ability-referenced assessments, growth-referenced assessments, and performance assessments and games based on real-world contexts. This requires research into AI-based assessment systems for effective implementation. By comprehensively examining the impact of the paradigm shift in educational assessment and its influence on instructional methods and student evaluations in the era of generative AI, the significance of this study lies in providing new perspectives and insights into assessment in the field of education.Korean Society for Educational Evaluation00By comprehensively examining the impact of the paradigm shift in educational assessment and its influence on instructional methods and student evaluations in the era of generative AI, this study lies in providing new perspectives and insights into assessment in the field of education.Korean Society for Educational Evaluation['Tae-Je Seong', 'Kija Si', 'Youn-Jeng Choi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/1fe2f76f76fe3ca9cac3f19293f2d74bd41e3817 494342d566ac38d175af42e951340e63e8aee0e9Navigating the complexities of scaling AI/ML infrastructure unveils a terrain rife with challenges and opportunities. This exploration delves into the multifaceted landscape, addressing key aspects such as resource expansion, data management, parallel processing, algorithmic optimization, orchestration, monitoring, streamlined pipelines, automation, financial considerations, and security. By embracing innovation and resilience, organizations can effectively harness the potential of AI and ML technologies while mitigating scalability hurdles.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This exploration delves into the multifaceted landscape of scaling AI/ML infrastructure, addressing key aspects such as resource expansion, data management, parallel processing, algorithmic optimization, orchestration, monitoring, streamlined pipelines, automation, financial considerations, and security.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['José Gabriel Carrasco Ramírez', 'Md.mafiqul Islam']2024-03-30T00:00:00https://www.semanticscholar.org/paper/494342d566ac38d175af42e951340e63e8aee0e9 468a28f0536740627333890413152e7a4ea0d72dThis study endeavors to assess the impact of AI chatbot-based English education programs on the effectiveness of language education for EFL learners. Additionally, it aims to examine moderator variables that influence the efficacy of these programs. A total of seventy-nine dissertations and academic journals, spanning from 2002 to October 2023, were chosen as the subjects for analysis. The effect size was computed utilizing a random effects model. A meta-regression analysis was conducted to validate moderation effects, and Egger's regression test was employed to assess publication bias. The impact of AI education programs was statistically significant, demonstrating a moderate effect size (0.44; 95% Confidence intervals: 0.34-0.54). The results of comparing educational effects in cognitive and non-cognitive domains revealed a higher effect size in the cognitive domain. The effect sizes based on educational outcomes ranked highest to lowest as follows: achievement, motivation, interest, confidence, attitude, and immersion. This study provides valuable insights into areas that need improvement to enhance the educational effects of utilizing AI chatbots. In future designs of English education programs utilizing AI chatbots, enhancing the pronunciation recognition component to ameliorate negative attitudes towards AI and diversifying conversation patterns to elevate learner immersion are anticipated to improve overall educational effectiveness.The English Teachers Association in Korea00In future designs of English education programs utilizing AI chatbots, enhancing the pronunciation recognition component to ameliorate negative attitudes towards AI and diversifying conversation patterns to elevate learner immersion are anticipated to improve overall educational effectiveness.The English Teachers Association in Korea['Eun-Jeong Park']2024-03-30T00:00:00https://www.semanticscholar.org/paper/468a28f0536740627333890413152e7a4ea0d72d 1ed6749e1839fdd08cd139c1576de5e2c95ee60dThis article is designed to explore the possibility of AI-based university major education and to explore the direction of future education, focusing on the case of Dankook University’s course in the current digital-oriented era. Accordingly, the concept of AI is first examined and the background and process of conducting research are discussed on the use of AI-based education centering on Dankook University subjects. Next, discussion focuses on the case of the course , where the use of D-ESK in the process of writing a thesis on classical criticism is first examined. Basically, in terms of method, the possibility of EduAI-based lectures using D-ESK services is explored, for which the instructor registers and manages research topics in the system each week and shares the process of providing research papers mounted on the RISS (Research Information Sharing Service). In terms of the results, it is first revealed that a total of 39 thesis papers were completed by conveniently collecting knowledge·information through the EduAI platform. Second, knowledge accumulation and sharing aspects using Chat GPT and DaQ are discussed. From the operation of the AI-based course, Chat GPT and DaQ were found to be used to induce answers between learners and to provide answer services directly by instructors. This work is meaningful in that it shows the possibilities and limitations of current generative AI models at the same time.Liberal Arts Innovation Center00From the operation of the AI-based course, Chat GPT and DaQ were found to be used to induce answers between learners and to provide answer services directly by instructors, showing the possibilities and limitations of current generative AI models.Liberal Arts Innovation Center['Myo Jung Kim']2024-03-30T00:00:00https://www.semanticscholar.org/paper/1ed6749e1839fdd08cd139c1576de5e2c95ee60d c1bbdd2fa71957fcdce0cfb0a964665ba7112ce7Suicide is a highly complex mental health issue that is a leading cause of death and requires efforts to reduce the number of victims. In the modern technology era, the utilization of artificial intelligence (AI) is seen as a suicide prevention initiative and presents a significant challenge in global prevention efforts. This literature review aims to determine the use of Artificial Intelligence (AI) in efforts to prevent teenage suicide. The research method utilizes the PRISMA guidelines. This literature review employs a systematic approach and selection process. Literature sources were searched from Proquest, PubMed, Google Scholar, and Scopus databases. Out of the 7 reviewed articles, 2 were from South Korea, 2 from the United States, and the remaining 3 were from Spain, Italy, and Canada. The Cohort research design was the most prevalent in this literature review (N = 5), and one study used an RCT design (N = 1), while a Cross-Sectional research design was employed in one study (N = 1). Overall, it indicates that AI is capable of predicting suicide risk and preventing suicide. The results of the literature review indicate that the use of AI technology has benefits in preventing teenage suicide.Media Keperawatan Indonesia180It indicates that AI is capable of predicting suicide risk and preventing suicide, and the use of AI technology has benefits in preventing teenage suicide.Media Keperawatan Indonesia['Muhammad Imron Rosadi', 'Tahratul Yovalwan', 'Akmal Zaki Asaduddin']2024-03-30T00:00:00https://www.semanticscholar.org/paper/c1bbdd2fa71957fcdce0cfb0a964665ba7112ce7 a96734316083382c01646d588f1f5aa4d892311fThis study investigated the perceptions of Vietnamese tertiary-level English as a Foreign Language (EFL) teachers regarding the use of ChatGPT, an advanced artificial intelligence (AI) language model, in students’ fairy tale retelling writing tasks. Employing a qualitative methodology, the research involved semi-structured interviews with nine EFL teachers from two Vietnamese institutions representing a range of teaching experiences: novice, mid-career, and near-end-career. The technology acceptance model (TAM) and constructivist learning theory (CLT) framed the study, providing a dual perspective on technology integration in language education. The thematic analysis revealed a spectrum of challenges and opportunities in integrating ChatGPT into language teaching. Key challenges include concerns about over-reliance on AI, cultural and contextual misalignments, integration with existing teaching methods, language accuracy issues, impact on creativity, technical barriers, and ethical considerations. Conversely, opportunities identified encompass enhanced student engagement, personalized learning, professional development for teachers, improvement in language proficiency, reduction in teacher workload, encouragement of technological adoption, and fostering of critical thinking and creativity. The study suggests a need for balanced AI integration in language education, emphasizing personalized, interactive learning experiences and professional development for teachers.International Journal of Learning, Teaching and Educational Research310There is a need for balanced AI integration in language education, emphasizing personalized, interactive learning experiences and professional development for teachers, and a need for balanced AI integration in language teaching.International Journal of Learning, Teaching and Educational Research['Hua Hong Hieu', 'Le Thanh Thao']2024-03-30T00:00:00https://www.semanticscholar.org/paper/a96734316083382c01646d588f1f5aa4d892311f 8df693cde7e57e6c6db55ee3179a5e76b48aa186Elon Musk has recently introduced the concept of Quantum AI, suggesting a revolutionary integration of quantum computing capabilities with artificial intelligence. This research aims to delve into the theoretical foundations, technological aspects, and potential applications of Musk's proposed Quantum AI. By conducting an in-depth analysis, this study seeks to unravel the unique features and challenges associated with the fusion of quantum computing and artificial intelligence, offering insights into the transformative impact on computational power, machine learning, and problem-solving capabilities. Additionally, the research will explore the ethical considerations and societal implications of deploying Quantum AI, paving the way for a comprehensive understanding of its potential benefits and risks. This investigation aims to contribute to the evolving discourse on the convergence of quantum computing and artificial intelligence, shedding light on the path towards harnessing the full potential of Musk's visionary proposal.Global Journal of Engineering and Technology Advances00This research aims to delve into the theoretical foundations, technological aspects, and potential applications of Musk's proposed Quantum AI, offering insights into the transformative impact on computational power, machine learning, and problem-solving capabilities.Global Journal of Engineering and Technology Advances['Idoko Peter Idoko', 'Onuh Matthew Ijiga', 'Lawrence Anebi Enyejo', 'Solomon Ileanaju Ugbane', 'Omachile Akoh', 'Michael Olumubo Odeyemi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/8df693cde7e57e6c6db55ee3179a5e76b48aa186 e787ee4234a6da9a334094d76bdbea8827d87defThis paper presents a comprehensive review of the application and impact of Artificial Intelligence (AI) in risk management within the banking sectors of the United States and Nigeria, emphasizing a comparative analysis. The objective is to assess how AI technologies are adopted and implemented in risk management practices across these diverse banking environments, identifying the benefits achieved and the challenges faced. The review synthesizes existing literature, including case studies, industry reports, and academic research, to outline the current state of AI in risk management. It delves into various risk types such as credit, market, operational, and compliance risks, exploring the specific AI tools and techniques employed to address these risks in each country. Key findings suggest that U.S. banks have a more mature implementation of AI in risk management, characterized by the adoption of advanced analytics, machine learning models, and natural language processing for enhanced decision-making, fraud detection, and compliance monitoring. In contrast, the Nigerian banking sector is at a nascent stage of AI adoption, with efforts hampered by challenges like inadequate technological infrastructure, regulatory hurdles, and a lack of skilled professionals in AI. Despite these differences, the paper identifies a strong interest and potential for growth in AI applications within the Nigerian banking sector, spurred by an increasing recognition of AI's value in enhancing competitiveness and meeting regulatory demands. Conclusively, the review underscores the critical role of supportive regulatory policies, investment in technological infrastructure, and capacity building in human capital as pivotal elements for fostering the effective integration of AI in risk management. The comparative analysis reveals both the disparities and potential areas of collaboration between the U.S. and Nigerian banking sectors, advocating for a global dialogue on best practices and strategies for AI adoption in risk management.International Journal of Science and Technology Research Archive460The comparative analysis reveals both the disparities and potential areas of collaboration between the U.S. and Nigerian banking sectors, advocating for a global dialogue on best practices and strategies for AI adoption in risk management.International Journal of Science and Technology Research Archive['Uchenna Innocent Nnaomah', 'Opeyemi Abayomi Odejide', 'Samuel Aderemi', 'David Olanrewaju Olutimehin', 'Emmanuel Adeyemi Abaku', 'Omamode Henry Orieno']2024-03-30T00:00:00https://www.semanticscholar.org/paper/e787ee4234a6da9a334094d76bdbea8827d87def f575b861d23de75a2120a6e73cc3ecd9b171b333This second in a series of articles on artificial intelligence (AI) in health care presents six core concepts that will help physician leaders frame their understanding of the rapidly evolving state of what patients and physicians think of AI. It covers biases in data collection, the need for rules, the implications for health care workers, how to avoid assumptions, patients’ attitudes, and hidden inequities.Canadian journal of physician leadership00Bias in data collection, the need for rules, the implications for health care workers, how to avoid assumptions, patients’ attitudes, and hidden inequities are covered.Canadian Journal of Physician Leadership['Alexandra T. Greenhill']2024-03-30T00:00:00https://www.semanticscholar.org/paper/f575b861d23de75a2120a6e73cc3ecd9b171b333 6ce37af4ece4eec7295f9ed37925b16ad3827e0bArtificial Intelligence (AI) is transforming the healthcare landscape through the utilization of data analytics, automation, and machine learning methods to innovate patient care, operational effectiveness, and medical research. This paper delves into the complicated impact of AI in healthcare, emphasizing its influence on precision medicine, medical imaging, drug discovery, healthcare operations, and patient engagement. Within precision medicine, AI examines a variety of patient data, such as genetic details and medical backgrounds, to customize treatment strategies and enhance diagnostic precision. AI-driven medical imaging algorithms assist in the timely identification and diagnosis of illnesses like cancer and cardiovascular diseases, ultimately enhancing patient outcomes. Additionally, AI accelerates the drug discovery process by inspecting molecular configurations, forecasting drug interactions, and pinpointing potential candidates for clinical trials, thereby expediting therapeutic advancements. AI-powered solutions streamline healthcare operations by automating administrative duties, resource distribution, and workflow supervision, leading to enhanced efficiency and cost reduction. Remote patient monitoring via AI-enabled gadgets and wearables allows for continuous health monitoring, facilitating proactive management of chronic ailments and early diagnosis. The integration of Natural Language Processing (NLP) enables AI virtual assistants to engage with patients, arrange appointments, and extract insights from unstructured data such as electronic health records. Nevertheless, ethical and regulatory considerations, encompassing data privacy and bias mitigation, are critical in the integration of AI to ensure patient safety and trust.Journal of Knowledge in Data Science and Information Management00This paper delves into the complicated impact of AI in healthcare, emphasizing its influence on precision medicine, medical imaging, drug discovery, healthcare operations, and patient engagement.Journal of Knowledge in Data Science and Information Management['Mahathi Darna', 'Manas Kumar Yogi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/6ce37af4ece4eec7295f9ed37925b16ad3827e0b 5879ea12e106e1c243d78e337144e1bc09e21213This theoretical examination explores the challenges and approaches to establishing ethical frameworks for the integration of artificial intelligence (AI) in healthcare entrepreneurship. As AI technologies continue to advance, their applications in healthcare hold immense potential for improving patient outcomes and driving innovation. However, ethical considerations are paramount to ensure the responsible and equitable deployment of AI-driven solutions. This paper delves into key ethical dimensions including privacy and data security, bias and fairness, accountability and transparency, and patient autonomy and consent. It identifies challenges such as technological limitations, regulatory complexities, and organizational barriers that impede the implementation of ethical frameworks. Additionally, it proposes approaches including collaborative governance models, ethical design practices, and continuous monitoring and evaluation to address these challenges. Through case studies and examples, the paper illustrates successful implementations of ethical frameworks in AI healthcare startups, highlighting lessons learned and their impact on patient outcomes and trust. Ultimately, this examination underscores the critical importance of ethical considerations in shaping the future of AI in healthcare entrepreneurship and provides insights for researchers, practitioners, and policymakers navigating this rapidly evolving landscape.International Journal of Frontiers in Biology and Pharmacy Research370This theoretical examination explores the challenges and approaches to establishing ethical frameworks for the integration of artificial intelligence (AI) in healthcare entrepreneurship and proposes approaches including collaborative governance models, ethical design practices, and continuous monitoring and evaluation to address these challenges.International Journal of Frontiers in Biology and Pharmacy Research['Babajide Tolulope Familoni']2024-03-30T00:00:00https://www.semanticscholar.org/paper/5879ea12e106e1c243d78e337144e1bc09e21213 bc015fca502f9d72f9b4cd971d57b088cddcdfe2Against the backdrop of rapid information technology development, generative AI technology has become a key driver in advancing the accounting industry. This paper discusses the opportunities that generative AI, represented by ChatGPT, brings to the accounting industry. These opportunities include driving workflow automation, enhancing review efficiency, and aiding scientific research. The article discusses challenges related to the authenticity and usability of output data, privacy and security of accounting data, and the shortage of technical talent. The author proposes strategies to address these challenges, including fostering critical thinking and awareness, strengthening the training and development of GPT models specific to accounting, enhancing data and privacy protection, and promoting relevant training for accounting professionals. This study addresses the research gap in the application of ChatGPT in the accounting industry. Its practical significance lies in promoting technological advancement and high-quality development in China's accounting industry.Asia Pacific Economic and Management Review00The author proposes strategies to address challenges related to the authenticity and usability of output data, privacy and security of accounting data, and the shortage of technical talent, including fostering critical thinking and awareness, and promoting relevant training for accounting professionals.Asia Pacific Economic and Management Review['Liu Yang']2024-03-30T00:00:00https://www.semanticscholar.org/paper/bc015fca502f9d72f9b4cd971d57b088cddcdfe2 72e0e40e49f00f5dd8a4996ce7173ac553b9b351Modern artificial intelligence (AI) tools built on high-dimensional patient data are reshaping oncology care, helping to improve goal-concordant care, decrease cancer mortality rates, and increase workflow efficiency and scope of care. However, data-related concerns and human biases that seep into algorithms during development and post-deployment phases affect performance in real-world settings, limiting the utility and safety of AI technology in oncology clinics. To this end, the authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians. They conclude the review with a discussion on ongoing challenges and regulatory opportunities in the field.Cancer271The authors review the current potential and limitations of predictive AI for cancer diagnosis and prognostication as well as of generative AI, specifically modern chatbots, which interfaces with patients and clinicians.Cancer['Likhitha Kolla', 'Ravi B. Parikh']2024-03-30T00:00:00https://www.semanticscholar.org/paper/72e0e40e49f00f5dd8a4996ce7173ac553b9b351 c6ab3da7ceb23087f0dc88a57ff714d0951f6f23The environment in the cockpit of large transport aircraft is highly complex due to an increasing number of automation systems. This complexity can cause pilots to become less aware of how systems interact. It becomes a severe issue when sensor or data failures occur, as such failures can contribute to a situation in which it is difficult for a pilot to assess what actually is happening and, possibly, how to resolve the problem. This paper presents a method, based on artificial intelligence, for identifying incorrect critical flight control data in real-time. A novel combination of reinforcement learning and a denoising autoencoder is proposed to identify failures and to provide inputs to the aircraft’s flight control and guidance systems, allowing for the correct maneuver to counter the failure and/or to avoid or recover from flight upsets. Tests in stall conditions with a partially blocked pitot tube show that the proposed method results in successful detection and recovery. The performance of the system without an autoencoder is compared to highlight the significant advantages, how this relates to creating systems with AI to improve situational awareness for pilots, and to execute appropriate automatic maneuvers to successfully counter the effect of sensor failures.Journal of Aerospace Information Systems80A novel combination of reinforcement learning and a denoising autoencoder is proposed to identify failures and to provide inputs to the aircraft’s flight control and guidance systems, allowing for the correct maneuver to counter the failure and/or to avoid or recover from flight upsets.Journal of Aerospace Information Systems['C. Koopman', 'D. Zammit-Mangion']2024-03-30T00:00:00https://www.semanticscholar.org/paper/c6ab3da7ceb23087f0dc88a57ff714d0951f6f23 de94ba92bf8009a40de80131a414aecd29e95d36Artificial intelligence (AI) presents opportunities in transforming higher education system and contribute significantly to achieving Sustainable Development Goals (SDGs). This study seeks to leverage AI technologies to advance SDGs within South Africa’s higher education system. It examines AI technology adoption in South African higher education institutions and challenges, strategies, and potential future directions. This qualitative research employed the constructivist principle to unravel the dynamics of AI in advancing SDGs in South Africa. Lecturers from the Department of Information Sciences were the participants of the study. The participants were purposefully selected based on their experience and knowledge of AI technologies. In-depth interview and focused group discussion was employed to generate and estimate responses using thematic content analysis. The result revealed that participants used AI technology to increase students' learning and engagement so students would not doze in class. It was discovered that AI technology has increased the chances for collective learning. The study further proved that AI technology can improve personalized learning experiences of students with diverse learning styles and abilities. This has led to a more inclusive and interactive classroom environment where students feel more motivated and supported in their learning journey. Integrating AI technology into education has shown promising results in improving student outcomes and fostering a more collaborative learning atmosphere. Based on the results, it implies that harnessing AI would advance SDGs in South Africa’s higher education institutions. As such, we recommended that the South African government should formulate comprehensive national AI education policy guidelines for higher education to regulate and harmonize the usage of AI in the higher education system.International Journal of Learning, Teaching and Educational Research00It implies that harnessing AI would advance SDGs in South Africa’s higher education institutions, and recommended that the South African government should formulate comprehensive national AI education policy guidelines for higher education to regulate and harmonize the usage of AI in the higher education system.International Journal of Learning, Teaching and Educational Research['O. Opesemowo', 'Victoria Adekomaya']2024-03-30T00:00:00https://www.semanticscholar.org/paper/de94ba92bf8009a40de80131a414aecd29e95d36 e4412d0eaa94113e07df101c6d3b642cc773f93fThis study aimed to highlight the role of AI technology represented by VR technologies (VR), and the enhanced reality (AR) in the development of education in higher education institutions from the perspective of scientific research, the study relied on the method of analysis of content in the curriculum of descriptive studies through extrapolation and analysis of a sample of literature, studies and documented reports. (59) Component. The results of the analysis (6) discussed a Chairperson's requirements: (Intellectual perspective of AI technology in education, contributions of AI technology in higher education development, components of smart learning systems used for artificial intelligence technology, effectiveness of VR technologies in higher education, areas of employment of AR-VR technologies in higher education, and impediments to hiring AR-VR technologies in higher education The study recommended the importance of Yemeni higher education institutions benefiting from artificial intelligence technology, especially VR and augmented reality (AR) technologies, in developing education and improving its quality to meet the university learner's constantly renewed aspirations in the era of technological knowledge and digital transformation. Keywords: Artificial Intelligence, Virtual Reality, Augmented Reality, Higher Education Institutions. المستخلص: هدفت الدراسة إلى إبراز الدور الذي تؤديه تكنولوجيا الذكاء الاصطناعي ممثلة بتقنيات الواقع الافتراضي (VR)، والواقع المعزز (AR) في تطوير التعليم بمؤسسات التعليم العالي من منظور البحث العلمي، واعتمدت الدراسة على أسلوب تحليل المضمون في منهج الدراسات الوصفية من خلال استقراء وتحليل عينة من الأدبيات والدراسات والتقارير الموثقة بلغ عددها (59) عنصراً، وقد ناقشت نتائج التحليل (6) متطلبات رئيسة تمثلت في: (المنظور الفكري لتكنولوجيا الذكاء الاصطناعي في مجال التعليم، وإسهامات تكنولوجيا الذكاء الاصطناعي في تطوير التعليم العالي، ومكونات نظم التعلم الذكية المُستخدمة لتقنية الذكاء الاصطناعي، فاعلية تقنيات الواقع الافتراضي ( (VRوالواقع المعزز AR)) في التعليم العالي، ومجالات توظيف تقنيات (AR- VR) في التعليم العالي، ومعيقات توظيف تقنيات (AR- VR) في مؤسسات التعليم العالي. وقد أوصت الدراسة بأهمية استفادة مؤسسات التعليم العالي اليمنية من تكنولوجيا الذكاء الاصطناعي وخاصة تقنيات الواقع الافتراضي (VR) والواقع المعزز (AR) في تطوير التعليم وتحسين جودته بما يلبي تطلعات المتعلم الجامعي المتجددة باستمرار في عصر المعرفة التكنولوجية والتحول الرقمي. الكلمات المفتاحية: الذكاء الاصطناعي، الواقع الافتراضي، الواقع المعزز، مؤسسات التعليم العالي.Journal of the Arabian Peninsula Center for Educational and Humanity Researches90Journal of the Arabian Peninsula Center for Educational and Humanity Researches['Dr. Ahmed Mohammed Al-Mungdi', 'Dr. Mabrook Saleh Al-Sudi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/e4412d0eaa94113e07df101c6d3b642cc773f93f d56cdf710a7e6baacf84e602581cccbf6b1f6ac7Основываясь на всестороннем анализе данных, передовых методиках прогнозирования и статистическом анализе, в статье анализируется, как искусственный интеллект и машинное обучение способствуют повышению точности экономических прогнозов и оптимизации процесса принятия решений. Демонстрируется, как математические процедуры, традиционно применяемые в экономическом анализе, могут быть усовершенствованы с использованием искусственного интеллекта для повышения эффективности и производительности. В дополнение к анализу потенциальных проблем, связанных с внедрением этих технологий, исследуются новые возможности, которые искусственный интеллект и машинное обучение открывают для дальнейшего развития экономической науки и практики. In the context of the continuously developing influence of artificial intelligence and machine learning on finance and economics, this article reveals the prospects and methods of their application for the analysis of economic trends. Based on comprehensive data analysis, advanced forecasting techniques and statistical analysis, the study highlights how artificial intelligence and machine learning contribute to improving the accuracy of economic forecasts and optimizing the decision-making process. The authors demonstrate how mathematical procedures traditionally used in economic analysis can be improved using artificial intelligence to improve efficiency and productivity. In addition to analyzing the potential problems associated with the introduction of these technologies, the article explores the new opportunities that artificial intelligence and machine learning open up for the further development of economic science and practice.The Applied Economic Researches Journal20The Applied Economic Researches Journal['А.О. Кириченко', 'А.Л. Золкин', 'Е.А. Свердликова', 'П.М. Подолько']2024-03-30T00:00:00https://www.semanticscholar.org/paper/d56cdf710a7e6baacf84e602581cccbf6b1f6ac7 d769d7686824dba9d6c0dc4859ce7e51dd7fe64dWith advancements in Artificial Intelligence (AI) and machine learning, education systems are transforming. This paper analyzes the challenges AI poses for schools and teachers and the opportunities it presents for personalized learning. It evaluates three central challenges: updating curriculums with AI disciplines, adopting adaptive teaching techniques, and developing evaluation metrics for new paradigms. Policymakers must incorporate data science and machine learning into core frameworks. Self-paced learning platforms require new classroom dynamics. Assessments must prioritize higher-order thinking. The article emphasizes three crucial opportunities within an AI-driven education framework - broadening access, strengthening educators, and tailoring education. Online learning currently extends admission beyond geographical and economic hurdles. Intelligent content provision enables personalization for learners with disabilities. AI liberates precious teaching hours from routine tasks to concentrate on student welfare. Moreover, evolving learning technologies persistently amend lesson designs based on immediate responses. However, to actualize this vision, we need to tackle ethical concerns like the privacy of student data and the inherent biases that could infiltrate algorithms. In conclusion, despite some inevitable hitches in current systems, AI brings forth hopeful remedies to persistent issues such as inclusivity, resource limitations, and personalized guidance on a large scale. The article highlights that policy, institutional readiness, and public consciousness are equally important in steering this transformation. Educators need to acknowledge the potential of AI, prompting culture modifications centered around new perceptions of educational quality, accomplishment, and preparedness for the workforce. Further national initiatives merging education and AI will set the course for the future.The American Journal of Social Science and Education Innovations00AI brings forth hopeful remedies to persistent issues such as inclusivity, resource limitations, and personalized guidance on a large scale, and the article highlights that policy, institutional readiness, and public consciousness are equally important in steering this transformation.The American Journal of Social Science and Education Innovations['Huong, Xuan Vu']2024-03-30T00:00:00https://www.semanticscholar.org/paper/d769d7686824dba9d6c0dc4859ce7e51dd7fe64d 56fd989292f3968644b0d20ff893008247a3b83bThe Agro-food supply chain is crucial for achieving Sustainable Development Goal No. 2 of zero hunger and sustainable agriculture. However, Kenya faces significant post-harvest losses, mainly attributed to challenges in first and last-mile logistics. In the era of technological advancements, this research paper explores the potential of Artificial Intelligence (AI) to enhance the Kenyan agri-food supply chain. Building on existing information, the study focuses on AI's role in monitoring and controlling farmland outputs, optimizing supply chain logistics, and addressing fraud and counterfeiting. The research objectives include assessing AI's utility in monitoring and controlling outputs in farmlands improving supply chain efficiency, and combating fraud in agricultural inputs. Research methods involve a comprehensive literature review, analyzing case studies such as Project FARM and FAO's Fall Armyworm Monitoring and Early Warning System, and reviewing scholarly articles on AI applications in agriculture. The research results highlight the benefits of leveraging AI in farmland monitoring, climate change adaptation, supply chain logistics, and fraud prevention. AI technologies can enhance agricultural productivity, reduce transportation costs, and eliminate corruption in the supply chain. The findings suggest that integrating AI systems into the agri-food supply chain is vital for achieving sustainable agriculture in Kenya. The study concludes that AI offers innovative solutions to address the challenges faced by smallholder farmers, enhance supply chain efficiency, and contribute to achieving zero hunger and sustainable agricultural practices.Agricultural Science560The study concludes that AI offers innovative solutions to address the challenges faced by smallholder farmers, enhance supply chain efficiency, and contribute to achieving zero hunger and sustainable agricultural practices.Agricultural Science['Enock G. Musau', 'Kumar Sharma']2024-03-30T00:00:00https://www.semanticscholar.org/paper/56fd989292f3968644b0d20ff893008247a3b83b 19874f6b32a0cb66bb1b9803c644eb0921ac05e5The purpose of the article is to analyse the status of artificial intelligence in the development of Ukrainian education in the context of socio-cultural instability. The positioning of innovative elements of modern education depends on the level of use of their potential by participants in the educational process. The research methodology is focused on the analysis of scientific discourse and the use of synergistic approaches to assess the scale and intensity of artificial intelligence in the Ukrainian educational space. The results of the study indicate a reorientation of the status of artificial intelligence from an exclusive element of educational activity to the level of an auxiliary component of the educational process. The demand for and feasibility of using artificial intelligence in Ukrainian education are key concepts that require a thorough scientific study. Thus, artificial intelligence has acquired the potential to transform the Ukrainian educational space.Eduweb390The results of the study indicate a reorientation of the status of artificial intelligence from an exclusive element of educational activity to the level of an auxiliary component of the educational process.Eduweb['Anna Boiko', 'Nataliia Shevtsova', 'Serhii Yashanov', 'Oleksandr Tymoshchuk', 'Viktor Parzhnytskyi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/19874f6b32a0cb66bb1b9803c644eb0921ac05e5 9c117d2a43e6c77ebeb7d64c19629ccddefefadbUtilizing the job demands-resources model, this study investigated the impact of hotel employees' perceptions of artificial intelligence(AI) systems (job demands) on user resistance, as mediated by switching costs (stress factors) and switching benefits (motivational factors). Additionally, it sought to explore how these relationships are moderated by the level of organizational support (job resources). To achieve the research objectives, an online survey was conducted targeting hotel employees who experienced AI systems in their work. The research findings are as follows: Firstly, employees' AI perceptions had a positive impact on switching costs but did not significantly affect switching benefits. Secondly, switching costs had a positive influence on user resistance, while switching benefits negatively impacted user resistance. Thirdly, the level of organizational support moderated the relationships between AI perceptions, switching costs, and switching benefits. These study findings contribute to deepening the understanding of hotel employees' resistance to AI systems. It confirms the significant role of the level of organizational support when introducing AI systems. Consequently, this study suggests theoretical and practical implications for developing effective employee resource management strategies for hotel organizations that have introduced or plan to use extended AI systems.The Tourism Sciences Society of Korea00Investigating the impact of hotel employees' perceptions of artificial intelligence systems on user resistance, as mediated by switching costs (stress factors) and switching benefits (motivational factors), confirms the significant role of the level of organizational support when introducing AI systems.The Tourism Sciences Society of Korea['Jin-Hui Yun', 'Dan-Ping Wang', 'Namho Chung']2024-03-30T00:00:00https://www.semanticscholar.org/paper/9c117d2a43e6c77ebeb7d64c19629ccddefefadb ba4c0551f92012583cae1f460cc387420f3753caThe growing incorporation of artificial intelligence (AI) tools in higher education (HE) has led to the use of indicators that allow the real impact of these tools to be identified in the teaching and learning process. In this sense, this study developed a bibliometric review on the acceptance of AI technologies in HE, providing an analysis of indicators on scientific production, with the aim of identifying prevalent thematic areas and knowledge gaps. From a methodological point of view, this study was carried out using a quantitative approach with a descriptive level, utilising 56 publications drawn from the Scopus database. The results show a sustained evolution with a growing trend in scientific production since 2021. The most predominant thematic area is evaluation of the acceptance of AI technologies in HE, making greater use of the Technology Acceptance Model (TAM) and the Unified Acceptance and Use of Technology theory (UTAUT). Therefore, it was concluded that the existing literature shows a sustained interest in investigating the acceptance of AI technologies due to the importance of determining the impact generated by their applications in different contexts or scenarios of the reality of HE in regard to the extent that AI technology is developed. This is because, on some occasions, its application does not necessarily lead to meeting the expectations raised in the teaching and learning processes. Finally, the gaps that need to be addressed in future research are "cultural and contextual diversity in AI acceptance", "emerging models of AI acceptance", and "critical elements influencing the acceptance of AI technologies", in HE.International Journal of Learning, Teaching and Educational Research00It was concluded that the existing literature shows a sustained interest in investigating the acceptance of AI technologies due to the importance of determining the impact generated by their applications in different contexts or scenarios of the reality of HE in regard to the extent that AI technology is developed.International Journal of Learning, Teaching and Educational Research['Carlos Hernán Flores-Velásquez', 'Soledad Olivares-Zegarra', 'Carlos Dávila-Ignacio', 'J. Arévalo-Tuesta', 'Guillermo Morales-Romero', 'Nicéforo Trinidad-Loli', 'Beatriz Caycho-Salas', 'Irma Aybar-Bellido', 'Maritza Arones', 'Florcita Aldana-Trejo']2024-03-30T00:00:00https://www.semanticscholar.org/paper/ba4c0551f92012583cae1f460cc387420f3753ca 19d7714bc7dcb8e105680e141a5ec353add528b5Purpose: The purpose of this research is to develop a prototype of an elementary school artificial intelligence education program based on design thinking. Method: To this end, core values and design principles of AI(Artificial Intelligence) education programs for elementary schools based on Design Thinking were derived through literature studies, case studies, and demand surveys. Results: As a result, the content feasibility was improved in the CVI measurement of the secondary FGI participant. I would like to suggest that: First, this study is the prototype development study of artificial intelligence education programs, and the researcher's intention was to develop a prototype-type artificial intelligence education program and help it be used in the field quickly. Second, this study was not a study of the development of AI education programs in the form of prototypes that measured effectiveness or improvement in the specific competence of learners or professors through the program. Therefore, the contents and areas of AI education used in this study were reconstructed based on foreign cases and the results of AI curriculum research conducted so far. Conclusion: Therefore, if a formal curriculum for AI education is presented under the leadership of the Ministry of Education, it is necessary to develop the final form of AI education programs based on Design Thinking.Regional Entrepreneurship Education Research Center00If a formal curriculum for AI education is presented under the leadership of the Ministry of Education, it is necessary to develop the final form of AI education programs based on Design Thinking.Regional Entrepreneurship Education Research Center['Young-Jae Kim', 'Mun-Suk Kang', 'Su-Hong Park']2024-03-30T00:00:00https://www.semanticscholar.org/paper/19d7714bc7dcb8e105680e141a5ec353add528b5 05932511f9ae3884578404c7124a5857e7e18e0aUnmanned vehicles are simplest one of the many regions in which artificial intelligence (AI) is poised to end up a game-changing technological development. Artificial intelligence (AI) algorithms are being incorporated into unmanned cars, along with self-riding automobiles and drones, to enhance their vision, navigation, and decision-making talents. The in-depth analysis of AI's results on autonomy, safety, performance, and societal ramifications is supplied in this paper on unmanned vehicles. This paper provides an overview of the modern-day standing of AI-enabled unmanned vehicles and shows destiny routes for studies and development in this quickly developing issue via a synthesis of current literature, case research, and technological breakthroughs.Global Social Sciences Review00An overview of the modern-day standing of AI-enabled unmanned vehicles is provided and destiny routes for studies and development in this quickly developing issue are shown via a synthesis of current literature, case research, and technological breakthroughs.Global Social Sciences Review['Umaima Zaman']2024-03-30T00:00:00https://www.semanticscholar.org/paper/05932511f9ae3884578404c7124a5857e7e18e0a 42f8b12a785e5893c7826fba69f6100b83598284Artificial intelligence (AI) is revolutionizing the field of education, offering new opportunities to enhance learning experiences and promote inclusive educational practices. This review explores the impact of AI on teaching methodologies and its role in creating inclusive learning environments. By examining current research and practices, this review highlights the potential of AI to address diverse learning needs and promote equity in education.The review begins by discussing the role of AI in personalized learning, where AI algorithms analyze student data to provide tailored instruction and feedback. This approach allows educators to cater to individual learning styles and preferences, ensuring that all students have access to high-quality education. Additionally, AI-driven adaptive learning systems can identify and address learning gaps, providing targeted interventions to support students who may be struggling. Furthermore, the review explores the use of AI in facilitating collaborative learning environments, where students work together on projects and tasks. AI technologies can enhance collaboration by providing tools for communication, coordination, and knowledge sharing. This approach promotes inclusivity by allowing students to contribute their unique perspectives and skills to group projects. The review also discusses the potential of AI in promoting accessibility and inclusivity for students with disabilities. AI-powered assistive technologies can provide additional support and accommodations, allowing students with disabilities to fully participate in educational activities. Additionally, AI-driven captioning and translation tools can improve accessibility for students who are deaf or hard of hearing, as well as those who speak languages other than the primary language of instruction. Overall, this review highlights the transformative potential of AI in education and its ability to promote inclusive educational practices. By leveraging AI technologies, educators can create more personalized, collaborative, and accessible learning environments, ensuring that all students have the opportunity to succeed.World Journal of Advanced Engineering Technology and Sciences690This review explores the impact of AI on teaching methodologies and its role in creating inclusive learning environments and highlights the transformative potential of AI to address diverse learning needs and promote equity in education.World Journal of Advanced Engineering Technology and Sciences['Idowu Sulaimon Adeniyi', 'Olabisi Oluwakemi Adeleye', 'Chima Abimbola']2024-03-30T00:00:00https://www.semanticscholar.org/paper/42f8b12a785e5893c7826fba69f6100b83598284 ae45da50afa2b49b4592a866d9f8370383e486ca. Logistics has been one of the most important sectors of Lithuania's economy. However, recent economic, social and political challenges significantly impacted sector development, making its representatives search for new ways of business development by changing conservative models with more advanced ones. For instance, changes in the logistics sector affected by the implementation of remote work opportunities have created possibilities for providing logistics-related services across the globe. Most of the attention is being paid to solutions based on the application of artificial intelligence, and the future of logistics sector development is closely dependent on it. The paper aims to discover new prospects in applying artificial intelligence in the logistics sector by bringing forward an overview of the main sector's activities, performing historical economic data analysis and conducting a survey with representatives of leading companies from the logistics sector in Lithuania. The multicriteria method is used in data analysis, helping establish the main prospects in the sector's developmentEntrepreneurship and Sustainability Issues80The paper aims to discover new prospects in applying artificial intelligence in the logistics sector by bringing forward an overview of the main sector's activities, performing historical economic data analysis and conducting a survey with representatives of leading companies from the logistics sector in Lithuania.Entrepreneurship and Sustainability Issues['Nikolaj Ambrusevič', 'Živilė Gomienė']2024-03-30T00:00:00https://www.semanticscholar.org/paper/ae45da50afa2b49b4592a866d9f8370383e486ca 1c950633da18dbb05a21c92dc69fac1fcd44115aThis paper thoroughly investigates the profound and complex impact of tools such as ChatGPT in the analysis of qualitative data. Using a comparative analysis starting from the data obtained in a previous study, the paper highlights the relevance of employing generative artificial intelligence in research. ChatGPT 3.5 was utilized in the analysis process, and the data were extracted from a focus group involving 8 respondents. The conclusions emphasize a significant similarity in data analysis, supporting the idea that artificial intelligence can play a trustworthy role in interpreting qualitative information. The generative artificial intelligence's synthesis capability becomes fundamental, facilitating the efficient handling of complex texts for researchers and analysts. ChatGPT accelerates the analysis process, providing results in a much shorter timeframe compared to traditional methods, an essential characteristic in the current academic and research context.Informatică economică220A significant similarity in data analysis is found, supporting the idea that artificial intelligence can play a trustworthy role in interpreting qualitative information and highlighting the relevance of employing generative artificial intelligence in research.Informatica Economica['Ion Lixandru']2024-03-30T00:00:00https://www.semanticscholar.org/paper/1c950633da18dbb05a21c92dc69fac1fcd44115a d54a9b4a5897b970f2d2f76bf61e3ad111d15203Artificial Intelligence (AI) is increasingly being applied in mental health services to provide improved diagnosis, treatment, and support. This systematic review article aims to explore the current literature on the use of AI in mental health services, focusing on the effectiveness of AI-based interventions and the ethical considerations surrounding their use. The databases PsycINFO, PubMed, and Web of Science were explored for articles published between 2015 and 2022 using the following search terms: “Artificial Intelligence”, “Mental Health”, “Intervention”, and “Ethics”. A search based on inclusion and exclusion criteria ended up with 50 articles highlighting the potential of AI to enhance the efficiency and accessibility of mental health services. Further deliberation excluded 30 articles and the present systematic review has been carried out with twenty articles.  The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines resulted in stating the results of this systematic review. The results suggest that AI-based interventions have promising outcomes for improving the diagnosis and treatment of disorders related to mental health, early detection and prevention of mental disorders, improved quality of care as well as reduced cost of mental health services. However, ethical considerations, such as privacy and transparency, must be considered in the implementation of AI in mental health services, and issues related to empathy and reflective practices were also highlighted. Implications and future directions for the research were also discussed.Pakistan Postgraduate Medical Journal00The results suggest that AI-based interventions have promising outcomes for improving the diagnosis and treatment of disorders related to mental health, early detection and prevention of mental disorders, improved quality of care as well as reduced cost of mental health services.Pakistan Postgraduate Medical Journal['S. Majeed', 'Altaf Qadir Khan']2024-03-30T00:00:00https://www.semanticscholar.org/paper/d54a9b4a5897b970f2d2f76bf61e3ad111d15203 c06459e2db47447bd3e63792e0a3971bd3b2d40bHuman deep neural networks (HDNNs) are a type of artificial neural network that is inspired by the structure and function of the human brain. HDNNs are composed of multiple interconnected layers of neurons, which are able to learn complex patterns from data. HDNNs have been shown to be very effective at solving a wide range of problems, including image recognition, natural language processing, and machine translation. HDNNs are often used in conjunction with artificial intelligence (AI) to create intelligent systems that can mimic human cognitive abilities. For example, HDNNs have been used to develop AI systems that can understand and respond to human language, and that can learn from their experiences and improve their performance over time. Human deep neural networks (HDNNs) are a type of artificial neural network that is inspired by the structure and function of the human brain. HDNNs are composed of multiple interconnected layers of neurons, which are able to learn complex patterns from data. HDNNs have been shown to be very effective at solving a wide range of problems, including image recognition, natural language processing, and machine translation. HDNNs are often used in conjunction with artificial intelligence (AI) to create intelligent systems that can mimic human cognitive abilities. For example, HDNNs have been used to develop AI systems that can understand and respond to human language, and that can learn from their experiences and improve their performance over time.International Journal of Emerging Science and Engineering10Human deep neural networks are a type of artificial neural network that is inspired by the structure and function of the human brain that have been shown to be very effective at solving a wide range of problems, including image recognition, natural language processing, and machine translation.International Journal of Emerging Science and Engineering['Harsha Magapu', 'Magapu Radha Krishna Sai', 'Bhimaraju Goteti']2024-03-30T00:00:00https://www.semanticscholar.org/paper/c06459e2db47447bd3e63792e0a3971bd3b2d40b 5e17ae438664e79c7a7476ce0e577106c7d0eb1bThis study aims to examine the experiences of special teachers who conducted teaching and learning activities using artificial intelligence technology for students with disabilities in elementary, middle, and high school courses. To this end, in-depth interviews were conducted on how artificial intelligence technology-based classes were conducted with six incumbent special teachers who conducted related educational activities in the actual special education field, as well as on their educational effectiveness and improvements. As a result of analyzing the interview data through systematic comparative analysis, 3 topics, 6 sub-themes, and 18 concepts were derived. As a result of the study, first, the research participants said that there was an opportunity to use artificial intelligence technology for teaching and learning activities in special education. Second, the research participants highly appreciated the possibility of education using artificial intelligence technology in the special education field as well as the positive effect it on the education of students with disabilities. Third, the research participants noted the limitations they experienced when teaching while using artificial intelligence technology in the special education field and suggested opinions on how to improve the use of existing artificial intelligence technology in the special education field. Based on these research results, effective ways to utilize artificial intelligence technology in the special education field in accordance to the changing times were discussed. In addition, it was suggested that various empirical studies on the contents derived from this study, as well as the development and effectiveness of specific teaching and learning programs, are necessary.Korean Journal of Special Education00Examination of the experiences of special teachers who conducted teaching and learning activities using artificial intelligence technology for students with disabilities in elementary, middle, and high school courses suggests effective ways to utilize artificial intelligence technology in the special education field in accordance to the changing times.Korean Journal of Special Education['Wonhyeong Kim']2024-03-30T00:00:00https://www.semanticscholar.org/paper/5e17ae438664e79c7a7476ce0e577106c7d0eb1b a7895bbed341f4f1879ec8fee77362c3c94df0f8Artificial Intelligence (AI) is poised to transform industries, reshaping the workforce and societal structures. This research article explores AI's implications, economic impact, industry influence, job creation, job displacement, and ethical considerations. AI revolutionizes operations across diverse sectors, from IT and finance to healthcare and transportation. It enhances processes, security measures, customer engagement, and efficiencies, reshaping the key industries. However, certain sectors like the Chemical and Natural Resources, Fashion, Food, Education, Creative, and Personal Services industries appear less susceptible to AI disruption due to their reliance on human creativity, personalized interactions, and specialized expertise. The article also discusses the future of AI and provides recommendations on how the workers and companies can prepare for AI.Informatică economică320This research article explores AI's implications, economic impact, industry influence, job creation, job displacement, and ethical considerations, and provides recommendations on how the workers and companies can prepare for AI.Informatica Economica['Muhammed Miah']2024-03-30T00:00:00https://www.semanticscholar.org/paper/a7895bbed341f4f1879ec8fee77362c3c94df0f8 144a7ce00b69b69b92bd841e04dc1bb52ba62df9AI applications were not limited to scientific questions, but were transformed into an information network applied in the visual arts, contributing in some way to the transition from the art support board to a data-driven visual world from the idea to the idea "Pixel" becoming a holistic creative art field, help the artist to follow the evolution and facilitate the artistic process, except in terms of knowledge or technical skills that the artist acquires through multiple experiences in his relationship with the past and present and looking to the future through the artist’s use of artificial intelligence in the artistic image that no longer awaits the eye of the artist, artistic experiences, scientific orientations and creative feelings. But this intelligence is impossible as an artist, thinking, collecting and extracting data in a result that is a simulation of a work of art. It is not a simple ordinary simulation of its constructive or colored composition but a synthesis of my information that produces an artistic image based on digital perception and robotics. Objectives: to determine the relationship of the artistic image with the concept of creativity, is the intelligent industrial image considered as a work of art orIs it based on creativity or is it just an intermediary between the machine and the work of art. Methods: The research program followed the analytical descriptive approach of the problems posed by the emerging controversial relationship between the concept of artificial intelligence and the image of art and the role of the artist in the developed innovative process, while describing the problem in question and collecting and analyzing the data surrounding it and extracting the results based on various proofs of the intellectual and aesthetic field of contemporary visual arts. Results: To highlight the role of AI technology in the development of the aesthetic and formative orientation of the artistic image. The study of the artist’s relationship with the art image through artificial intelligence and its place in this new digital field is the relationship of AI with the art image on the positive side and the negative side of its impact on the artist and the creative process. Define the limits of artificial intelligence in the production and creative value of the image, and guide creators in the fields of artistic image to highlight the role of artificial intelligence in the process of evolution in the visual arts. Conclusion: Study of the role of artificial intelligence technology in the development of the art image in contemporary art And that artificial intelligence in the arts considers the image or is a product based on artistic creativity and role of the artist in this huge surge of artificial technology that can eliminate his role and refer him to fringe and a practitioner and a simple technical rather than creative manufacturer in his field of research in contemporary methods of artistic creativity in art image.International Journal of Educational Sciences and Arts00Study of the role of artificial intelligence technology in the development of the art image in contemporary art finds that artificial intelligence in the arts considers the image or is a product based on artistic creativity and role of the artist in this huge surge of artificial technology that can eliminate his role.International Journal of Educational Sciences and Arts['Taha Ellil']2024-03-30T00:00:00https://www.semanticscholar.org/paper/144a7ce00b69b69b92bd841e04dc1bb52ba62df9 0564488295ff73ba6a6e182a4969bb69e7383438В статье рассмотрены современные технологии искусственного интеллекта, применяемые в управлении производственными системами. Отмечается, что в российской экономике данная область находится на начальной стадии развития, и используемые технологии требуют систематизации и классификации. Приведены основные области применения технологий искусственного интеллекта на производстве, такие как оптимизация плана производства, моделирование производственных сценариев, прогнозирование состояния производственной системы, оптимальное управление по обратной связи и предиктивная диагностика оборудования. Описаны основные проблемы, возникающие при использовании систем искусственного интеллекта, требующие вмешательства человека: недостаток данных и их качество, несовершенство нормативно-правовой базы, безопасность данных, этические вопросы и технические трудности. The article discusses modern artificial intelligence technologies used in the management of production systems. It is noted that in the Russian economy, this area is at an early stage of development, and the technologies used require systematization and classification. The main areas of application of artificial intelligence technologies in production are presented, such as optimization of the production plan, modeling of production scenarios, forecasting the state of the production system, optimal feedback control and predictive diagnostics of equipment. In conclusion, the main problems that arise when using artificial intelligence systems that require human intervention are described: lack of data and their quality, imperfection of the regulatory framework, data security, ethical issues and technical difficulties.The Applied Economic Researches Journal60The Applied Economic Researches Journal['Я.В. Козлов']2024-03-30T00:00:00https://www.semanticscholar.org/paper/0564488295ff73ba6a6e182a4969bb69e7383438 28446d9e9ca472296ea303623db0b594f5c42dc9The phenomenon of AI has been widely studied in several areas. This paper is based on the use of artificial intelligence and its impact on HRM due to technological advancement in IT landscape. At present almost all companies are implementing AI in their functional areas to increase efficiency of employees in organization. AI role in HR domain starts with recruitment till performance appraisal of employees. The aim of the present research is to examine the relationship between artificial intelligence and Human resource functions in IT industry in Delhi/NCR location weather this relationship is moderated by innovativeness and ease of use at HR operations. This study was conducted among 115 HR professionals at various IT sector in Delhi/NCR region. A multiple regression method was used to test hypothesis and confirmed positive relationship between these two factors establishing about the increased use of AI at work results better HR functional performance. However AI has significant relationship with innovativeness and also with ease of use which reflects AI effects HR with innovations and ease of use. This study will give insights of Artificial intelligence which is coming as anew revolution in industry with new name Industry4.0. Key words: Artificial Intelligence , HR operations, InnovationINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The aim of the present research is to examine the relationship between artificial intelligence and Human resource functions in IT industry in Delhi/NCR location weather this relationship is moderated by innovativeness and ease of use at HR operations.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Shivalakshmi B']2024-03-30T00:00:00https://www.semanticscholar.org/paper/28446d9e9ca472296ea303623db0b594f5c42dc9 da0145e57bf4fddc56ab48437efc6d529bccb654According to the rules of the Indonesian National Qualifications Framework (KKNI), undergraduate students fall into levels 5 and 6. Here, graduates are required to have the ability to apply existing knowledge according to the needs of the job. However, laboratory facilities that provide such competencies are very difficult to provide, especially for private campuses that rely on funding from students. This research tries to anticipate the gap between students' abilities and industry demands by providing laboratory facilities that do not require large costs. One of the courses demanded for students to master is Artificial Intelligence (AI), which has now spread to various fields. However, the curriculum currently applied usually focuses only on methods commonly used in the field of AI, while implementation in corporate fields requires direct application in the form of applications. Research results prove that several online applications can be used as substitutes for laboratories, including Google Colab, Play with Docker, Streamlit, and Teachable Machine. Compared to providing servers, computers containing development applications, using computers or laptops connected to the internet, students can easily implement the AI knowledge they have learned. For group work, applications for Continuous Integration/Continuous Delivery can be utilized, for example with Github, Gitlab, and similar ones.PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic90Research results prove that several online applications can be used as substitutes for laboratories, including Google Colab, Play with Docker, Streamlit, and Teachable Machine, and that several online applications for Continuous Integration/Continuous Delivery can be utilized.PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic['Herlawati Herlawati']2024-03-30T00:00:00https://www.semanticscholar.org/paper/da0145e57bf4fddc56ab48437efc6d529bccb654 b5d172942463f91683ecad6aae8367b1054cf16cJournal of Liaquat University of Medical &amp; Health Sciences00Journal of Liaquat University of Medical &amp; Health Sciences[]2024-03-30T00:00:00https://www.semanticscholar.org/paper/b5d172942463f91683ecad6aae8367b1054cf16c a84b55eeb1ff076a4a679cf10ed6a43087e3c728Qalaai Zanist Scientific Journal00Qalaai Zanist Scientific Journal[]2024-03-30T00:00:00https://www.semanticscholar.org/paper/a84b55eeb1ff076a4a679cf10ed6a43087e3c728 bcbc1224c4f5bfb2c0f7116c532dd2a3d14f6536The emergence of ChatGPT, an AI-powered language model, has sparked numerous debates and discussions. In educational research, scholars have raised significant questions regarding the potential, limitations, and ethical concerns around the use of this technology. While research on the application and implications of ChatGPT in academic settings exists, analysis of the perspectives of high-school students are limited. In this study, we use qualitative content analysis to explore the perspectives of high-school students regarding the integration or ban of ChatGPT in their schools through the lens of the Technology Acceptance Model (TAM2). Data was sourced from students’ comments to a New York Times Learning Network article. Findings revealed that students' perceptions about integrating or banning ChatGPT in schools are influenced by their assessments of the technology’s usefulness, personal experiences, societal technology trends, and ethical considerations. Our findings suggest that student perspectives in this study align with those of educators and policymakers while also possessing unique perspectives that cater to their specific needs and experiences. Implications emphasize the significance of an inclusive decision-making process around the integration of AI schools in educational contexts, including students alongside other stakeholders.International Journal of Technology in Education621Findings revealed that students' perceptions about integrating or banning ChatGPT in schools are influenced by their assessments of the technology’s usefulness, personal experiences, societal technology trends, and ethical considerations.International Journal of Technology in Education['Tolulope Famaye', 'C. Bailey', 'I. Adisa', 'Golnaz Arastoopour Irgens']2024-03-30T00:00:00https://www.semanticscholar.org/paper/bcbc1224c4f5bfb2c0f7116c532dd2a3d14f6536 12be685687426658e9021f7068a12859c2751bb7Artif. Intell. Medicine1081This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale, highlighting that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools.Artificial intelligence in medicine['Pouyan Esmaeilzadeh']2024-03-30T00:00:00https://www.semanticscholar.org/paper/12be685687426658e9021f7068a12859c2751bb7 e59edc20e8ed2fa2c86c3b9aae20c87ca1795039В статье рассматривается проблематика интеграции искусственного интеллекта и робототехники в разнообразные отрасли экономики, а также оцениваются их последствия для рынка труда. Академические исследования подчеркивают глобальное значение технологий, которые могут радикально изменить экономическую структуру многих стран. Ведущие индустриализированные государства активно интегрируют робототехнику и искусственный интеллект в широкий спектр отраслей, что позволяет автоматизировать рутинные операции и повысить производительность труда. Отмечается, что в России доля роботизированных систем в производстве и других секторах экономики остается низкой по сравнению с мировыми показателями, что может стать препятствием для укрепления конкурентных позиций страны на международной арене. Внедрение робототехники и искусственного интеллекта может оказать как позитивное, так и негативное воздействие на рынок труда, и этот процесс уже наблюдается. В рамках данного исследования предполагается комплексный анализ положительных и отрицательных аспектов интеграции новейших технологий в повседневную жизнь человечества. The article examines the problems of integrating artificial intelligence and robotics into various sectors of the economy, as well as their consequences for the labor market. Academic research highlights the global importance of these technologies, which can radically change the economic structure of many countries. Leading industrialized countries are actively integrating robotics and artificial intelligence into a wide range of industries, which makes it possible to automate routine operations and increase labor productivity. It is noted that in Russia, the share of robotic systems in manufacturing and other sectors of the economy remains low compared to global indicators, which may become an obstacle to strengthening the country’s competitive position in the international arena. The importance of the development of artificial intelligence and modern technological solutions in various fields of human activity is undeniable. The introduction of robotics and artificial intelligence can have both positive and negative effects on the labor market, and this process is already being observed. This study assumes a comprehensive analysis of the positive and negative aspects of integrating the latest technologies into the daily life of mankind.The Applied Economic Researches Journal00The Applied Economic Researches Journal['Д.Г. Девяткина', 'Д.В. Жукова', 'О.В. Косникова', 'Н.М. Нилова']2024-03-30T00:00:00https://www.semanticscholar.org/paper/e59edc20e8ed2fa2c86c3b9aae20c87ca1795039 36f3f8ec79f981d0c635fa0e4087fd006a92e590In the Republic of Kazakhstan, there is currently no proper control and accounting of solid waste by the state and culture, a high standard of living for the majority of the population. To solve the problem of automated MSW management using AI, it is proposed to develop a knowledge base, the level of education, distribution by collection zones in the city, socio-economic stratification, population and the amount of solid waste generated over a certain period of time is taken as a set of input data. The initial filling of the knowledge base is provided according to the latest population census of the Republic of Kazakhstan https://stat. gov.kz/ru/national/2021/. For predictive estimates of the volume of solid waste, the method of the forest conveyor with Bayesian optimization (RFBO) was used, the algorithm and architecture of the software solution are quite informative. The reliability of predictive decisions on the volume of solid waste presented by the program is at least 90%, taking into account the correctness of the input data set, the program code is written in Python. The solution proposed in the article to the problem related to the automation of solid waste management is based on international experience, taking into account the identified shortcomings, as well as the prospects and trends of understanding by residents of the country and public utilities of the country, a possible garbage collapse. The possible disadvantages of the program are the availability of constant access to the Internet, the need for a large amount of RAM, and computer performance. The program can be used as a ready-made solution for predictive estimates of solid waste for various regionsTrudy Universiteta00The solution proposed in the article to the problem related to the automation of solid waste management is based on international experience, taking into account the identified shortcomings, as well as the prospects and trends of understanding by residents of the country and public utilities of the country, a possible garbage collapse.TRUDY UNIVERSITETA['Nurbek Saparkhodzhaev', 'Karshyga Akishev', 'Dusmat Jamangarin', 'Amandos Tulegulov', 'Kapar Aryngazin']2024-03-30T00:00:00https://www.semanticscholar.org/paper/36f3f8ec79f981d0c635fa0e4087fd006a92e590 d23c0f4c21257e03f8d399beb6a3359072c05834هدفت هذه الدراسة إلى التعرف على أثر استقدام الذكاء الاصطناعي في بيئة الأعمال السعودية على أداء المراجعين السعوديين اعتماداً على ثلاث مبادئ: اكتشاف الإخطاء الجوهرية، الالتزام بالأنظمة واللوائح، وأخيراً قدرة المنشاة على الاستمرارية، حيث كان مجتمع الدراسة جميع مكاتب المراجعة المرخصة في المملكة العربية السعودية وكانت عينة الدراسة 162 مكتب، ومن أجل تحقيق أهداف الدراسة تم استخدام المنهج الوصفي المسحي اعتماداً على الاستبانة كأداة أساسية لجمع البيانات ، و توصلت الدراسة إلى أنه يوجد أثر  إيجابي لإدخال الذكاء الاصطناعي على أداء مدققي الحسابات في مراجعة القوائم المالية للشركات المساهمة السعودية. حيث أدى الذكاء الاصطناعي إلى زيادة قدرة المدققين على اكتشاف الأخطاء الأساسية بشكل كبير (متوسط الاستجابة: 4.27، الانحراف المعياري: 0.8226)، كما وسهل الذكاء الاصطناعي اكتشاف عدم التزام المدققين باللوائح بشكل كبير (متوسط الاستجابة: 4.18، الانحراف المعياري: 0.8506)، أيضاً هناك أثر اً إيجاباً للذكاء الاصطناعي على تقييم المدققين لقدرة الإدارة على الاستمرارية (متوسط الاستجابة: 4.23، الانحراف المعياري: 0.8114)، كما أوصت الباحثة بعدد من التوصيات أبرزها: تعزيز البنية التحتية الرقمية حتى تخط الطريق لاستخدام تقنيات الذكاء الاصطناعي مستقبلاً وضرورة تطوير عملية  المراجعة بما يتناسب مع متطلبات المهنة ومواكبة للتطور التكنولوجي الحديث.مجلة العلوم الإقتصادية و الإدارية و القانونية10مجلة العلوم الإقتصادية و الإدارية و القانونية['كوثر علي السالم']2024-03-30T00:00:00https://www.semanticscholar.org/paper/d23c0f4c21257e03f8d399beb6a3359072c05834 7f083f0d1d6a65c5ae338c40c93a1fcc1527bc26This paper delves into the significance, challenges, and potential of AI applications within the oil and gas sector. In the dynamic landscape of oil and gas operations, efficiency and safety stand as paramount concerns. Traditional engineering processes, while robust, often face limitations in adapting to the evolving complexities of the industry. However, the advent of AI technologies offers a paradigm shift, presenting unprecedented opportunities for optimization and risk mitigation. This paper explores the multifaceted role of AI in engineering processes throughout the oil and gas value chain. It examines how AI, encompassing machine learning, deep learning, and predictive analytics, empowers decision-makers with real-time insights, optimizing exploration, production, transportation, and refining processes. Efficiency gains are witnessed through predictive maintenance strategies, enabling proactive asset management and minimizing downtime. Additionally, AI-driven process optimization techniques enhance resource allocation, streamlining operations and maximizing output while reducing costs. Moreover, AI's integration fosters a culture of safety by augmenting risk assessment and hazard identification capabilities. Through advanced algorithms, AI systems analyze vast datasets to detect anomalies and predict potential safety hazards, enabling proactive intervention and accident prevention. However, the journey towards AI integration is not without challenges. Technical complexities, regulatory frameworks, and cyber security concerns pose significant hurdles that require careful navigation. Moreover, ethical considerations surrounding data privacy and algorithmic bias necessitate robust governance frameworks to ensure responsible AI deployment. Looking ahead, the paper delineates future trends and opportunities in AI adoption within the oil and gas sector. It underscores the potential for continued innovation and disruption, reshaping workforce dynamics and skill requirements. Embracing AI not only drives operational excellence but also propels the industry towards a sustainable and resilient futureOpen Access Research Journal of Engineering and Technology510Open Access Research Journal of Engineering and Technology['Vincent Onuegb', 'Chuka Anthony Arinze', 'Vincent Izionworu', 'Onuegbu', 'Daniel Isong', 'Cosmas Dominic', 'Daudu', 'Adedayo Adefemi']2024-03-30T00:00:00https://www.semanticscholar.org/paper/7f083f0d1d6a65c5ae338c40c93a1fcc1527bc26 74542e9351f2d77813d821b26e6860f2f2e6c341International Journal for Digital Society00International Journal for Digital Society['O. Adepoju', 'Bosun Tijani', 'S. Karera']2024-03-30T00:00:00https://www.semanticscholar.org/paper/74542e9351f2d77813d821b26e6860f2f2e6c341 f8f7a9547d57ce59cef3fd09a1f0bb9c8bce31b4Journal of Chitwan Medical College00Journal of Chitwan Medical College['N. Shrestha']2024-03-30T00:00:00https://www.semanticscholar.org/paper/f8f7a9547d57ce59cef3fd09a1f0bb9c8bce31b4 dacb81f6a99f2b32acef880b7c0b1fe405822c5eIJARCCE00IJARCCE['Guttikonda Prashanti', 'Tondapu Uma Maheswari', 'Tadala Sai Prasanna', 'Gondi Lokesh', 'Poluri Sudeep Kumar']2024-03-30T00:00:00https://www.semanticscholar.org/paper/dacb81f6a99f2b32acef880b7c0b1fe405822c5e 1f31ae9ef1d3315769c7be8c4b9a7182ec5687ecJournal of Startup Convergence &amp; Consulting00Journal of Startup Convergence &amp; Consulting['Hyeong-Gyun Kim', 'Sang-hee Lee']2024-03-30T00:00:00https://www.semanticscholar.org/paper/1f31ae9ef1d3315769c7be8c4b9a7182ec5687ec 70db58b71e5016735b8ef3fdd7a4f25a00c45522IARJSET00IARJSET['A.Sathiya Priya', 'K. Monishkumar', 'S. Sridhar']2024-03-30T00:00:00https://www.semanticscholar.org/paper/70db58b71e5016735b8ef3fdd7a4f25a00c45522 72128512521a58330d69d61e3ca3b13aa3121870The pursuit of Artificial General Intelligence (AGI) has captivated researchers and industry leaders alike, promising a future where machines possess human-like cognitive abilities. However, this ambitious endeavor is fraught with multifaceted challenges and ethical dilemmas that necessitate careful examination. This critical review surveys the landscape of AGI research, identifying key hurdles and ethical considerations while outlining potential pathways forward. Firstly, technical challenges loom large on the path to AGI. These encompass fundamental problems such as developing robust learning algorithms capable of generalizing across diverse domains, as well as engineering systems that can exhibit adaptive and autonomous behavior akin to human intelligence. Additionally, ensuring the safety and reliability of AGI systems presents a formidable obstacle, with concerns ranging from algorithmic bias to the potential for catastrophic outcomes in unanticipated scenarios. Ethical considerations permeate every facet of AGI development and deployment. Questions of accountability, transparency, and control surface as central concerns, as the implications of relinquishing decision-making authority to autonomous systems raise profound ethical dilemmas. Moreover, the socio-economic ramifications of widespread AGI adoption, including job displacement and inequality, demand careful scrutiny and proactive mitigation strategies. Navigating these challenges requires a concerted effort from interdisciplinary stakeholders. Collaboration between computer scientists, ethicists, policymakers, and the public is essential to establish robust frameworks for the responsible development and deployment of AGI. Moreover, fostering an inclusive dialogue that prioritizes ethical principles and societal values is paramount in shaping a future where AGI augments human capabilities while safeguarding against potential risks. While the pursuit of AGI holds immense promise, its realization demands a holistic approach that addresses technical challenges alongside ethical considerations. By charting a path forward that prioritizes safety, transparency, and ethical governance, we can harness the transformative potential of AGI while ensuring its alignment with human values and interests.World Journal of Advanced Research and Reviews07This critical review surveys the landscape of AGI research, identifying key hurdles and ethical considerations while outlining potential pathways forward, and charting a path forward that prioritizes safety, transparency, and ethical governance.World Journal of Advanced Research and Reviews['Sedat Sonko', 'Adebunmi Okechukwu Adewusi', 'Ogugua Chimezie Obi', 'Shedrack Onwusinkwue', 'Akoh Atadoga']2024-03-30T00:00:00https://www.semanticscholar.org/paper/72128512521a58330d69d61e3ca3b13aa3121870 63cedd5fe11ce5438089f1e45b4893a83bf113beArtificial intelligence put forward using human intelligence index by use of advanced algorithms and models that transform computational principles of AI. Deep learning is a category of Artificial intelligence that shows the best problem-solving techniques and ways across different domains. Also, Computational Intelligence (CI) enhances conventional computing strategies through processes such as fuzzy logic, evolutionary algorithms, and neural networks that enables smarter decision-making among vague variables. Intelligent computing's impact is expanded over technological domains, that shows transformative changes in healthcare systems, finance industry, and other industrial processes. Having access to these technologies through collective platforms and open-source initiatives look after innovation. This review shatter perspectives from research scholars, industrial persons, and research communities, highlighting the current landscape and future prospects of A.I. and computational intelligence. Interdisciplinary applications and cross-cutting research initiatives represent the transformative potential of integrating intelligent computing techniques into expanded strategies. This wide scattering of A.I. and computational intelligence gives rise to the ethical concerns that majorly includes algorithmic biases and privacy issues to the end user. Meaningful dialogues and ethical practices are imperative to harnessing A.I. for social good in our interconnected world. This article represents the interdependent relationship between A.I., computational intelligence, and data science, offering deep knowledges into their synergistic applications and societal implications. It highlights the need for responsible A.I. deployment and ethical considerations to ensure the beneficial use of intelligent computing technologies.World Journal of Advanced Engineering Technology and Sciences300The interdependent relationship between A.I., computational intelligence, and data science is represented, offering deep knowledges into their synergistic applications and societal implications, and highlights the need for responsible A.I. deployment and ethical considerations to ensure the beneficial use of intelligent computing technologies.World Journal of Advanced Engineering Technology and Sciences['Muhammad Awais Ali', 'Muhammad Awais', 'Maida Maqsood', 'Madhavi Arun Mahajan', 'Hassan Nawaz', 'Ammad Maqsood', 'Obaid Muhammad Abdullah', 'Anaiza Maqsood']2024-03-30T00:00:00https://www.semanticscholar.org/paper/63cedd5fe11ce5438089f1e45b4893a83bf113be bc0712ab456158c907b8794f40acd88e81cc8f51In the ever-evolving realm of artificial intelligence (AI), deep learning stands as a beacon of innovation, transforming the essence and trajectory of AI systems. This paper ventures into the heart of deep learning’s transformative power, charting its journey from the early neural network concepts to the advanced architectures that propel today’s technological breakthroughs. We delve into the harmonious interplay between deep learning and the surge in computational prowess, amplified by the vast seas of big data, which have jointly propelled AI to unprecedented levels of functionality and societal integration. Through a tapestry of case studies, we illuminate the tangible applications of deep learning across diverse sectors such as healthcare, finance, and autonomous navigation, showcasing how these intelligent algorithms have not only sharpened efficiency and accuracy but also brought forth pressing ethical dilemmas. The conversation on AI’s ethical landscape, with a spotlight on transparency, accountability, and privacy, emerges as an indispensable facet of conscientious AI evolution. Peering into the horizon, we ponder the onward march of deep learning, recognizing the promise it holds for awe-inspiring innovation as well as the hurdles that await. The paper underscores the imperative for ongoing inquiry into algorithmic refinement, data morality, and the ecological footprint of AI technologies. In our closing reflections, this paper celebrates the profound sway of deep learning over AI, with a nod to the indomitable human spirit that fuels technological advancement. It is this fusion of human ingenuity, moral vision, and technical mastery that will chart the course for AI’s future, steering it towards our shared ideals and dreams for a brighter tomorrowInternational Journal of Advanced Research in Science, Communication and Technology20This paper ventures into the heart of deep learning’s transformative power, charting its journey from the early neural network concepts to the advanced architectures that propel today’s technological breakthroughs.International Journal of Advanced Research in Science, Communication and Technology['Aman', 'Nitin Yadav']2024-03-30T00:00:00https://www.semanticscholar.org/paper/bc0712ab456158c907b8794f40acd88e81cc8f51 3ebd082b9b12cd36c2048d65f21c585066f2d0a0The overall purpose of this research is how to avoid potential conflicts between humans and intelligent computers (Artificial, Super and Hyper) while this Article focuses on the prevention of existential conflicts with Super-intelligent computers.  According to Bostrom, the paths towards Super-intelligence are Whole Brain emulation, biological cognition and development of human/machine interfaces. Super Intelligent computers simulate human behaviour and processes or systems that could exist in the real world and by becoming conscientious on their own, could become Singletons and shape the future of humans and Humanity for their own reasons.IPI Letters21IPI Letters['Mark Nicolau']2024-03-30T00:00:00https://www.semanticscholar.org/paper/3ebd082b9b12cd36c2048d65f21c585066f2d0a0 45ce751582a0d6aaed20ddc2823918d191f078e8The concept paper explores the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing food quality control processes. In response to the growing challenges faced by the food industry in ensuring consistent quality and safety standards, this paper proposes leveraging advanced technologies to enhance predictive analysis. The traditional methods of food quality control are often reactive and time-consuming, leading to inefficiencies and increased risks of contamination or spoilage. By harnessing AI and ML algorithms, businesses can shift towards proactive strategies, predicting potential issues before they arise and implementing preventive measures accordingly. Key components of the proposed approach include data collection from various sources such as sensors, supply chain records, and historical quality data. Through sophisticated data analysis techniques, AI systems can identify patterns, anomalies, and correlations that might indicate deviations from expected quality standards. Moreover, ML models can continuously learn and adapt based on new data, improving prediction accuracy over time. Implementation of AI-driven predictive analysis in food quality control offers several benefits. Automation of quality control processes reduces manual effort and enables real-time monitoring, enabling timely interventions to maintain product quality. By minimizing the likelihood of product recalls, waste, and rework, businesses can achieve significant cost savings associated with quality control measures. Consistently delivering high-quality products strengthens consumer trust and loyalty, leading to increased market competitiveness and brand reputation. AI-powered systems can assist in ensuring compliance with stringent food safety regulations by providing comprehensive documentation of quality control measures and outcomes. However, successful adoption of AI and ML technologies in food quality control requires overcoming various challenges, including data privacy concerns, integration with existing systems, and ensuring the reliability and interpretability of AI-driven insights. the integration of AI and ML for predictive analysis represents a transformative opportunity for the food industry to modernize quality control practices and uphold the highest standards of safety and excellence. Embracing innovation in this domain is essential for staying competitive in a rapidly evolving market landscape and meeting the evolving expectations of consumers and regulatory bodies alike.World Journal of Advanced Research and Reviews00The integration of AI and ML for predictive analysis represents a transformative opportunity for the food industry to modernize quality control practices and uphold the highest standards of safety and excellence.World Journal of Advanced Research and Reviews['Temilade Abass', 'Esther Oleiye Itua', 'Tabat Bature', 'Michael Alurame Eruaga']2024-03-30T00:00:00https://www.semanticscholar.org/paper/45ce751582a0d6aaed20ddc2823918d191f078e8 5b621ec979302b99e2916edac1620e98e56168d2This comprehensive research article explores the transformative potential and multifaceted challenges of integrating Artificial Intelligence (AI) consciousness and technological advancements into the higher education system in Bangladesh. The study delves into various dimensions of this integration, including its implications for pedagogy, curriculum, institutional policy, and technological innovation. A particular emphasis is placed on addressing disparities in AI awareness among future students in Bangladesh's higher education system. The research adopts a balanced approach, juxtaposing the promise of technological advancements with critical concerns related to data privacy, educational equity, and cultural preservation. Through interdisciplinary analysis and a robust set of policy recommendations, the study posits that Bangladesh stands at a pivotal moment. It can either seize this technological epoch to elevate its higher education system to global standards while mitigating AI awareness disparities or risk exacerbating existing educational inequities.Cognizance Journal of Multidisciplinary Studies370The study posits that Bangladesh stands at a pivotal moment and can either seize this technological epoch to elevate its higher education system to global standards while mitigating AI awareness disparities or risk exacerbating existing educational inequities.Cognizance Journal of Multidisciplinary Studies['Salehin Mahbub', 'H. M. A. Wafik', 'Zahidul Arif', 'Mahmudur Rahman', 'Aftab Uddin', 'Iqra Binty', 'Azim Lecturer']2024-03-30T00:00:00https://www.semanticscholar.org/paper/5b621ec979302b99e2916edac1620e98e56168d2 990ca5a60361c4f12d04dadc0824e73a65dc028fWith the advent of the AI era, various artistic experiments utilizing generative AI are actively unfolding in the art world. In the performing arts industry, performances co-created and performed by humans and AI are being announced one after another. Consequently, there is a growing interest and debate on whether AI can be considered an equal artistic creator alongside humans and whether AI can replace human creativity. This study aims to examine the artistry, potential, and validity of AI performing art by identifying the characteristics of human-AI collaborative creation performing art based on the premise that AI performing art is the result of collaboration between humans and AI. To achieve this, we will first briefly examine fundamental concepts to understand artificial intelligence and artificial creativity. Following that, we will briefly review trends and creative processes in domestic human-AI collaborative performance arts, divided into dance , music , and theatre , among other examples from various performing arts fields. Lastly, we will examine the characteristics of contemporary human-AI collaborative performance arts from the perspectives of existence and aura.Liberal Arts Innovation Center00This study aims to examine the artistry, potential, and validity of AI performing art by identifying the characteristics of human-AI collaborative creation performing art based on the premise that AI performing art is the result of collaboration between humans and AI.Liberal Arts Innovation Center['Young Yoon Kim', 'Joon Hee Joh']2024-03-30T00:00:00https://www.semanticscholar.org/paper/990ca5a60361c4f12d04dadc0824e73a65dc028f 8a30083537f50d59506a9470046d4e6ea1670a2eSocial entrepreneurship as a special type of business is aimed at reducing social tension in society, ensuring employment, accessibility and variety of social services. Most social entrepreneurs in Russia belong to the category of small businesses. Improving state regulation of the development of small social entrepreneurship helps to reduce the severity of social problems and strengthen interaction between the state and business on this issue. The purpose of the article is to assess trends in the development of small social entrepreneurship in the country, current measures of state regulation and develop proposals for their improvement. The study revealed trends in the development of small businesses in the social sphere in Russia, reflecting high growth rates in the number of small social entrepreneurship entities, its spread to various areas of activity with a predominance in the areas of education, social services and sports and leisure. The growth in the number of social entrepreneurs is mostly observed in large conglomerations. However, the continuing low share of social enterprises in the total number of small businesses in the country, the analysis of the implemented measures of state support for them, does not allow us to draw a conclusion about the sufficiency of the conditions created in the country for the support and promotion of social entrepreneurship. State regulation of the development of small social entrepreneurship is proposed to be carried out in the direction of improving the institutional environment, expanding the range of entities that ensure the development and promotion of small businesses in the social sphere.STATE AND MUNICIPAL MANAGEMENT SCHOLAR NOTES00STATE AND MUNICIPAL MANAGEMENT SCHOLAR NOTES['Проняева Людмила Ивановна', 'Давыдкин Игорь Геннадьевич']2024-03-29T00:00:00https://www.semanticscholar.org/paper/8a30083537f50d59506a9470046d4e6ea1670a2e a51f9dc8c500082bffb23e5951975e9c025b4fdfIntroduction. Law enforcement agencies as subjects of formation and implementation of state policy in the field of national security and defense must have an appropriate organizational and legal framework for carrying out their activities. This involves two separate important directions of optimization of the current process of administrative and legal regulation of their activities, which is collectively objectified by the need to review scientific and normative discourses regarding the place and role of law enforcement agencies in the mechanism of ensuring national security of Ukraine. The purpose of the paper is to analyze the strategic directions of improving the administrative and legal regulation of the activities of law enforcement agencies as subjects of the formation and implementation of state policy in the field of national security and defense. Results. Since law enforcement agencies in the researched area are generally divided into those that are independent or structural within the representative body - the Ministry of Internal Affairs of Ukraine, we believe that the formation of a separate independent authority in the proposed context is generally inappropriate, as well as the granting of such powers to already existing ones. Conclusions. Law enforcement agencies should be provided with the ability to effectively influence various aspects of the country's security from the position of functional unity, which will allow them to strategically and functionally solve issues of national security and defense of Ukraine. We have proposed an author's variation of improving the administrative and legal regulation of the activities of law enforcement agencies as subjects of the formation and implementation of state policy in the field of national security and defense with emphasis on the need to create a specialized state entity - the Center for Coordination and Interaction of Law Enforcement Activities under the National Security and Defense Council of Ukraine, which will represent the institute of law enforcement activities in Ukraine.Economics. Finances. Law10Economics. Finances. Law['Vitaliy Makarchuk']2024-03-29T00:00:00https://www.semanticscholar.org/paper/a51f9dc8c500082bffb23e5951975e9c025b4fdf 4870ae66d80f07f917d9704fc1dac4be54db7c94This article presents a study of the properties of an automatic control system with a fuzzy controller, which includes a fuzzy pseudo-linear correction device with phase advance and a PID controller.One of the promising and effective approaches of the modern theory of control of complex technological systems in conditions of uncertainty caused by the fuzziness of initial information is an approach based on the use of expert assessment methods and the theory of fuzzy sets [1,2]. Technological processes characterized by multi-criteria function mainly in a fuzzy environment. Therefore, for optimal control of the operating modes of such systems, it is necessary to take into account the vectors of criteria and the fuzziness of the initial information. One of the alternative methods of building control and regulation systems for objects that are indistinctly defined from the point of view of classical theory is the use of so-called fuzzy logic controllers.Therefore, the development of fuzzy control controllers based on existing microcontrollers is a very urgent task, since a pseudo-linear fuzzy controller built on the basis of fuzzy sets and fuzzy logical I/O, under the condition of uncertainty of the disturbing effect, is able to provide higher quality indicators of the transient process than a traditional PID controller.As pseudolinear correction devices (PKU) we use: Amplitude-suppressed, phase-ahead, and with separate channels for amplitude and phase, since one of the main disadvantages of the PID controller is the presence of a phase delay and high sensitivity to interference in the measuring channel.Bulletin of Shakarim University Technical Sciences10A pseudo-linear fuzzy controller built on the basis of fuzzy sets and fuzzy logical I/O, under the condition of uncertainty of the disturbing effect, is able to provide higher quality indicators of the transient process than a traditional PID controller.Bulletin of Shakarim University. Technical Sciences['V. S. Sherstnev']2024-03-29T00:00:00https://www.semanticscholar.org/paper/4870ae66d80f07f917d9704fc1dac4be54db7c94 abd71de201c578fb04d64e94b9667a1f210d49a7The law of international treaties is one of the key branches of international law. It is international agreements, as a source of international law, that govern states, since the contractual regulation of international activity is able to ensure civilized interstate cooperation, such as: trade, investment, create appropriate conditions for the development of the economies of the contracting parties, and guarantee security processes in the country. The obligations of states assumed during the conclusion of international agreements, their proper formulation, agreement and compliance are the primary basis of quality international relations. Accordingly, this paper is devoted to the analysis of the normatively determined characteristics of an international agreement and the presence of obligations in them, methods of revival of obligations in the agreement, comparison of agreements with different subjects and obligations in them. Emphasis is made separately on the existing problems of the implementation of current international agreements. The study shows that security agreements usually contain negative obligations, while trade agreements include both positive and negative types of obligations. The above demonstrates that an international agreement is an expression of the will of the parties, their intentions, and also demonstrates the nature of the relationship dictated by the subject of the agreement. Thus, security agreements provide for compliance with the principle of non-interference and refraining from actions that could harm another country. Instead, trade agreements aim at active cooperation between states, which emphasizes the need for positive commitments by countries. The issues of appropriateness of the formation of obligations are equally important, both in international private and in international public law. However, researchers, unfortunately, pay little attention to the specifics of states’ obligations. It is the latter that are important, complex and lengthy to negotiate and affect a wide range of individuals. Undoubtedly, they differ significantly among themselves in the specifics of multilateral international agreements, which provide for accession itself, and not participation in the formation of the texts of agreements. All of the above emphasizes the complexity and multifacetedness of the researched issue. Accordingly, as modern international agreement law develops, it needs new views, the proper formulation of obligations in agreements, and guarantees of the latter’s fulfillment.Economics. Finances. Law00Economics. Finances. Law['S. Moroz']2024-03-29T00:00:00https://www.semanticscholar.org/paper/abd71de201c578fb04d64e94b9667a1f210d49a7 041803f924c79ecfbd68624e28c070ab15478f5dSafety in any sphere of life is of decisive importance for ensuring the protection of the life and health of citizens, preserving property values, ensuring socio-economic development and is the subject of legal regulation. Regulatory security is an important element of ensuring the stability and sustainability of society and the state. Its goal is to create a safe environment for the life and activities of citizens, protect national security and ensure sustainable development of the country. Legal norms governing security issues in the Russian Federation are enshrined in various branches of law and are regulated by many laws, codes, Presidential decrees, Government resolutions and other government regulations. Some of these documents are fragmentary in nature, deal with private security issues and create local, isolated from each other disparate arrays of legal norms on certain types of security that relate to different branches of law, some partially duplicate each other or even contradict each other. At the same time, in some of the most important areas of security, there are no necessary federal laws and they are regulated by secondary legal acts - presidential decrees and government decrees. The article analyzes the state of regulatory and legal support for security and the preliminary results of reforms in the system of technical regulation and control and supervisory activities (“regulatory guillotine”). Their main shortcomings are identified and options for eliminating them are proposed.Russian Studies in Law and Politics00Russian Studies in Law and Politics['Evgeny V. Sugak']2024-03-29T00:00:00https://www.semanticscholar.org/paper/041803f924c79ecfbd68624e28c070ab15478f5d c2c005e5c1b59d4c817cba0ae63d8399ed232211This article analyzes the article by Candidate of Legal Sciences, Professor Zh. Tlembayeva «Some approaches to the legal regulation of artificial intelligence», which was published in the scientific and legal journal «Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan» No. 2 (65)-2021. In modern society, issues of legal regulation of artificial intelligence are becoming an integral part of the discussion on technology development. This topic represents a current and complex challenge that requires an in-depth analysis of various approaches to the formation of legal norms and standards in the field of artificial intelligence. The article analyzes the definition of the concept of «artificial intelligence», the definition of the concept of «artificial robot» established in the Law of the Republic of Kazakhstan «On Informatization». The author concluded that when developing a definition of this concept, one should take into account the types of artificial intelligence systems, the peculiarities of the use of artificial intelligence technologies in each specific area, as well as the different levels of application of technologies and the characteristics of the legal system of the state. The article discusses examples from different countries and organizations, international acts on issues related to the regulation of artificial intelligence. The author of the article notes that currently the use of artificial intelligence is practically not provided with a proper international basis. Zh. Tlembayeva points out the importance of a gradual and adaptive approach to the legal regulation of artificial intelligence, ensuring a balance between stimulating innovation and protecting the interests of society, as well as the flexibility of laws to adapt to the rapid development of technologies in this area. The article provides valuable research for legal, technology, and public policy professionals concerned with the implementation of artificial intelligence.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan['Yuliуa Kostyanaya']2024-03-29T00:00:00https://www.semanticscholar.org/paper/c2c005e5c1b59d4c817cba0ae63d8399ed232211 f6211c5c99d3ecc63cd92fbc6eb1631c4e216011The article is devoted to the peculiarities of legal regulation of licensing for medical practice. The provisions of laws and regulations governing the procedure for obtaining a licence to practice medicine are studied. The scientific works of scholars in the fields of law, medicine, and public administration who have studied legal relations in the field of healthcare and licensing are studied. Recommendations for amending the legislation are formulated. The author proposes a definition of licensing for medical practice as a means of state regulation of medical care and medical assistance compliance with the established licensing conditions with a view to protecting the life and health of patients and ensuring the provision of quality medical services. The article establishes that there are certain peculiarities of licensing for medical practice in comparison with other types of economic activity. A specially created licensing commission of the Ministry of Health of Ukraine reviews documents for obtaining a licence. The licensing regulations set out specific personnel, technological and organisational requirements that the founders of healthcare facilities must meet before submitting documents for a licence. A special package of documents is prepared to be attached to the licence application and provides for the description of the material and technical base and personnel of the healthcare facility. The activities of the entire healthcare facility, rather than individual healthcare professionals, are subject to licensing. A number of proposals for amendments to the legislation are proposed, namely: 1) to consolidate the concept of medical service as a separate, specific type of medical procedure, diagnosis of a particular disease, rehabilitation, cosmetic care, health massage, paid examination; 2) to define in the licensing conditions more detailed technological requirements for the area of certain premises of a healthcare facility and medical equipment necessary for the treatment of certain diseases; 3) the author supports the position that it is expedient to develop and adopt the Medical Code of Ukraine as a comprehensive legislative act which would regulate legal relations in the field of healthcare.Law and Safety40Law and Safety['О. V. Batryn']2024-03-29T00:00:00https://www.semanticscholar.org/paper/f6211c5c99d3ecc63cd92fbc6eb1631c4e216011 c10c26c02e758f2fc16d98c63c01c5eb7c0195beTo attract foreign investors, around 50 agreements on the promotion and mutual protection of investments has been concluded by the Republic of Kazakhstan, where the parties undertake to create favorable conditions on their territory. By the Decree of the President of the Republic of Kazakhstan of May 19, 2015, the «Astana» International Financial Center was established in the city of Astana, which has been operating since 2016. One of the main tasks of the «Astana» International Financial Center is to attract investment in the sphere of financial services of our country. In addition, with the involvement of excellent judges, and recognized lawyers the «Astana» International Financial Center Court and the International Arbitration Center were established. However, the «Astana» International Financial Center Court does not conduct administrative or criminal proceedings. At the same time, this court has exclusive competence to interpret the acts of the «Astana» International Financial Center. In this connection the question arises about the expediency of consideration of disputes with the state bodies within the framework of the Code of the Republic of Kazakhstan «Administrative Procedural Code of the Republic of Kazakhstan», where the sources of the law of the «Astana» International Financial Center include principles, norms, and precedents of the English law. Are local administrative courts sufficiently qualified to adjudicate disputes under the common law? This article will examine the structure of the «Astana» International Financial Center Court, its jurisdiction, and public-law relations under the current law of the «Astana» International Financial Center, the legislation of the Republic of Kazakhstan.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan['A.A. Bazarbayev']2024-03-29T00:00:00https://www.semanticscholar.org/paper/c10c26c02e758f2fc16d98c63c01c5eb7c0195be 2d6df847caa6012f1451238de66ae5c65dcb8c3cВ статье обосновывается актуальность изучения проблем адаптации сотрудников уголовно-исполнительной системы Российской Федерации. Анализируются статистические данные по кадровому обеспечению ФСИН России. Отмечаются такие негативные тенденции, как рост количества вакантных должностей в органах и учреждениях, наличие увольнений на первом году службы. В качестве меры укрепления кадрового обеспечения рассматривается возможность разработки отдельного документа, регламентирующего все аспекты организации адаптации сотрудников, впервые принятых на службу, включая полномочия субъектов и оценку эффективности адаптационных процедур. Описывается типовое положение об адаптации персонала уголовно-исполнительной системы, его структура и содержательные аспекты. The article substantiates the relevance of studying problems of improving effectiveness of adaptation of penal system employees and, for this purpose, analyzes statistical data on the staffing of bodies and institutions of the penitentiary system. There are negative trends, such as an increase in the number of vacant positions in bodies and institutions and presence of dismissals in the first year of service. As a measure to strengthen staffing of the penal service, the authors consider the possibility of developing a separate document regulating all aspects of organizing adaptation of employees recruited for the first time, including the powers of subjects and the adaptation procedure effectiveness assessment. The article describes a standard provision on adapting the penal system personnel, its structure and substantive aspects.Ius Publicum et Privatum00Ius Publicum et Privatum['А.Ю. Долинин', 'Н.В. Моторова']2024-03-29T00:00:00https://www.semanticscholar.org/paper/2d6df847caa6012f1451238de66ae5c65dcb8c3c f08fc44903554a1041c5272afed510c7d229760fAre banks sensitive to risk and reward in following global corporate transparency rules? Using a worldwide field experiment, this study evaluates competing predictions from expected utility, behavioralist, and institutionalist accounts. We incorporated a dozen companies around the world to make over 15,000 email solicitations asking for corporate accounts from 5000 of the world's internationally connected banks. Treatments randomize the risk profiles of different companies—by their countries’ association with corruption, terrorism, and tax evasion—and vary rewards by stating differing amounts of business revenues. The outcomes are the rates at which banks offer accounts and comply with rules on customer identification. The results suggest that banks are moderately responsive to risk—though not reward—but the magnitude of the effects is small, providing mixed evidence for conventional models and suggestive support for institutionalist accounts.American Journal of Political Science110American Journal of Political Science['Michael G. Findley', 'D. Nielson', 'J. C. Sharman']2024-03-29T00:00:00https://www.semanticscholar.org/paper/f08fc44903554a1041c5272afed510c7d229760f 1a9a5f7d1d27f676a1e411928565973c0d39620bВведение. Полиция, выполняя возложенные на нее задачи по охране общественного порядка и обеспечению общественной безопасности, уполномочена применять меры государственного принуждения (пресечения). Сложившаяся практика реализации возложенных на полицию полномочий по применению мер пресечения не создает проблем при их осуществлении. Причиной этому можно считать неформальное условное «соглашение» между правоохранительными и судебными органами, возможно также полагать, что по данным поводам не возникало прецедентов по обжалованию действий должностных лиц, но с правовой стороны осознание некоторых действий полиции не согласованных с нормами права или совсем не урегулированных таковыми «ломает» целостность законодательной системы и ставит под сомнение ее фундаментальные принципы, оказывает негативное влияние на формирование научной доктрины и правоприменительной практики. Одним из основных принципов деятельности правоохранительных органов в целом, и органов внутренних дел в частности является законность, следовательно, любые действия должны полностью соответствовать требованиям законодательства. Отсутствие правового регулирования элементов деятельности уполномоченных органов и их должностных лиц значительно усложняет оценку соблюдения законности последними, а существование норм, предполагающих «додумывание» порядка их применения провоцирует двусмысленность толкования и также может быть по-разному оценено при выполнении надзорных функций за деятельностью правоохранительных органов. В статье авторы делают попытку систематизировать меры пресечения, направленные на обнаружение доказательств по делам об административных правонарушениях, а также предметов, запрещенных к обороту и хранению в специальных помещениях и учреждениях для содержания отдельных категорий граждан. Материалы и методы. Нормативную основу исследования составляет законодательство, регулирующее основания и порядок применения мер государственного пресечения, направленных на обнаружение предметов, имеющих значение доказательств по деликтам, а также предметов, запрещенных к хранению в специальных учреждениях, материалы правоприменительной практики, ранее опубликованные научные работы. Результаты исследования. Определены различные по целям, процессуальным лицам и содержанию виды досмотровых мероприятий, не имеющих нормативного закрепления порядка их проведения, тактики действий уполномоченных должностных лиц при применении данных мер. Выводы и заключения. В заключении авторы предлагают привести к едином подобию досмотровые мероприятия по целям, решить вопрос на законодательном уровне об участии в личном досмотре в качестве понятых сотрудников органов внутренних дел, разработать алгоритм личного досмотра физического лица, закрепив его нормативным актом. Introduction: The police, fulfilling the tasks assigned to it to protect public order and ensure public safety, are authorised to apply measures of state coercion (suppression). The existing practice of realisation of the powers vested in the police to apply preventive measures does not create problems in their implementation. The reason for this can be considered an informal conditional "agreement" between law enforcement and judicial authorities, it is also possible to believe that on these occasions there were no precedents for appealing against the actions of officials, but on the legal side, the realisation of some police actions not coordinated with the norms of law or not regulated at all by them "breaks" the integrity of the legislative system and calls into question its fundamental principles, has a negative impact on the formation of scientific doctrine and law enforcement practice. One of the basic principles of law enforcement agencies in general, and internal affairs bodies in particular, is legality, therefore, any actions must fully comply with the requirements of the legislation. The absence of legal regulation of elements of the activities of authorised bodies and their officials makes it much more difficult to assess compliance with legality by the latter, and the existence of norms that imply "guessing" the order of their application provokes ambiguity of interpretation and can also be differently assessed in the performance of supervisory functions over the activities of law enforcement agencies. In the article the authors make an attempt to systematize preventive measures aimed at the detection of evidence in cases of administrative offences, as well as items prohibited for circulation and storage in special premises and institutions for the detention of certain categories of citizens. Materials and Methods: The normative basis of the study is formed by the legislation regulating the grounds and procedure for the application of state preventive measures aimed at the detection of objects having the value of evidence in torts, as well as objects prohibited for storage in special institutions, materials of law enforcement practice, previously published scientific works. The Results of the Study: different types of search measures have been defined in terms of objectives, procedural persons and content, with no normative fixing of the procedure for their conduct and tactics of actions of authorised officials in the application of these measures. Findings and Conclusions: In conclusion, the authors propose to bring to a uniform similarity search activities by purpose, to solve the issue at the legislative level on the participation in body searches as witnesses of employees of internal affairs bodies, to develop an algorithm of personal search of an individual, fixing it by a normative act.VESTNIK OF THE EAST SIBERIAN INSTITUTE OF THE MINISTRY OF INTERNAL AFFAIRS OF THE RUSSIAN FEDERATION00VESTNIK OF THE EAST SIBERIAN INSTITUTE OF THE MINISTRY OF INTERNAL AFFAIRS OF THE RUSSIAN FEDERATION['Е.Е. Новичкова', 'Р.А. Пестов']2024-03-29T00:00:00https://www.semanticscholar.org/paper/1a9a5f7d1d27f676a1e411928565973c0d39620b b952a44dc88bf53f1b4c77787b2d08f38da50b58This study aims to highlight the constitutional legitimacy of local laws issued by the governorate councils under Law No. 21 of 2008, which are not related to a specific region. These entities began to issue local laws after the recent amendments under the 2005 Constitution, based on the principle of separation of powers and using the analytical approach. Through analyzing the legal texts of the Iraqi Constitution and the laws of the governorates, the study concluded that these legislations issued by these councils are unconstitutional. The study provides recommendations aimed at correcting the course of legislators and addressing the defects that may affect democracy regarding the work of those councils and their response to the legislative process. The Iraqi Parliament should handle the legislation since the purpose of these councils is to satisfy the local needs of the governorates that are not organized into regions, as stipulated by the Iraqi Constitution for the year 2005, especially after the transformation of the Iraqi state into a democratic system. The study concluded that the local councils need to amend their law to keep up with the political developments that Iraq and the world are witnessing, in particular.Eximia210Eximia['Samer Hameed Safar']2024-03-29T00:00:00https://www.semanticscholar.org/paper/b952a44dc88bf53f1b4c77787b2d08f38da50b58 a779f5dd28c8aaab0e1bdceeb6700f17d655c0a4As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI’s potential to mitigate these issues and aims to critically assess AI’s integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI’s transformative potential, this review equips researchers with a deeper understanding of AI’s current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.Bioengineering1981This review explores how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables and discusses the remaining challenges and possible solutions.Bioengineering['Shiva Maleki Varnosfaderani', 'Mohamad Forouzanfar']2024-03-29T00:00:00https://www.semanticscholar.org/paper/a779f5dd28c8aaab0e1bdceeb6700f17d655c0a4 dc053918463c7630d6b75c9bb97c38c4ca0db873This study examines how Cognitive Absorption and AI Literacy are related among college students, specifically looking at how sex moderates this link. The study uses a quantitative research strategy and a non-experimental correlational approach. Data was collected through Google Forms utilizing modified questions designed for AI Literacy and cognitive absorption. G*Power 3.2 was used for power analysis to determine the necessary sample size for the investigation. 372 college students from different higher education institutions in Region XI were selected to take part in the study by stratified random sampling. Reliability and validity tests, including Cronbach’s alpha, Average Variance Extracted (AVE), and Heterotrait-Monotrait Ratio (HTMT), were performed on the dataset before undertaking moderation analysis. Cognitive Absorption was identified as a key predictor of AI Literacy, showing a substantial impact size of 0.417. The moderating effect of sex, although statistically significant, had a minor effect size of 0.011. The corrected R-squared value of 0.378 indicates that the model, with all covariates, accounts for 37.8% of the variance in AI literacy.American Journal of Smart Technology and Solutions411Cognitive Absorption was identified as a key predictor of AI Literacy, showing a substantial impact size, and the moderating effect of sex, although statistically significant, had a minor effect size.American Journal of Smart Technology and Solutions['B. N. Obenza', 'Liam E Go', 'Jofrance Ardrian M Francisco', 'Evann Ernest T Buit', 'Frande Vier B Mariano', 'Henry L Cuizon Jr', 'Alliah Jane D Cagabhion', 'Karl Axl James L. Agbulos']2024-03-29T00:00:00https://www.semanticscholar.org/paper/dc053918463c7630d6b75c9bb97c38c4ca0db873 8e0a991260a290b98d22e928909085f28eccb822Urbanization is accelerating at an unprecedented rate, pushing the limits of environmental sustainability and demanding innovative solutions for effective management. Traditional methods for monitoring the environment fall short, lacking the scalability, immediacy, and comprehensive coverage required for modern urban settings. Our paper introduces an innovative solution to bridge these gaps, harnessing the power of Internet of Things (IoT) sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband. This threefold integration forms a cutting-edge approach to urban environmental governance. It enables the analysis of environmental data in real time, offers predictive insights, and achieves wide-ranging coverage. The system equips city leaders, policymakers, and citizens with precise, actionable information, fostering decisions that align with Environmental Social Governance (ESG) goals. More than just improving urban life and sustainability, our approach makes environmental data accessible to all, promoting a collaborative and inclusive model of urban environmental management.International Journal of Innovative Science and Research Technology121An innovative solution to bridge the gaps in traditional methods for monitoring the environment, harnessing the power of Internet of Things sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband is introduced.International Journal of Innovative Science and Research Technology (IJISRT)['Chaitanya Korra', 'A. Sadhana V']2024-03-29T00:00:00https://www.semanticscholar.org/paper/8e0a991260a290b98d22e928909085f28eccb822 e5517c6df17313c0da137801bac05f6047a17a8c This research explores the convergence of synthetic intelligence (SI) and inexperienced finance techniques in influencing the development of renewable power sectors, with a specific focus on Denmark and Germany for the critical periods of 2019 and 2020. ANOVA, paired sample t-tests, and regression analysis were used as part of a strict method to look into how the production of renewable energy has changed and how AI-driven financial techniques have affected it. The results spotlight the effectiveness of AI-driven green finance solutions in bringing approximately enormous ameliorations, establishing Denmark as a probable exemplar for sustainable progress. In evaluation, Germany’s consistent power infrastructure, blended with a fantastic correlation exposed in regression evaluation, highlights the durability of its environmentally pleasant economic methods. This study presents a well-timed and informative guide for developing effective, inexperienced finance rules that guide a greener and more sustainable future as international locations all around the world address environmental-demanding situations. European Journal of Business and Management Research541European Journal of Business and Management Research['Sara Ravan Ramzani', 'Peter Konhaeusner', 'Oluwasegun Akinola Olaniregun', 'Ahmad Abu-Alkheil', 'Nizar Alsharari']2024-03-29T00:00:00https://www.semanticscholar.org/paper/e5517c6df17313c0da137801bac05f6047a17a8c 38e81dda85fe13c900c0bce1d0f450e64f74870bInteractive Artificial Intelligent(AI) assistant systems are designed to offer timely guidance to help human users to complete a variety tasks. One of the remaining challenges is to understand user's mental states during the task for more personalized guidance. In this work, we analyze users' mental states during task executions and investigate the capabilities and challenges for large language models to interpret user profiles for more personalized user guidance.arXiv.org51This work analyzes users' mental states during task executions and investigates the capabilities and challenges for large language models to interpret user profiles for more personalized user guidance.ArXiv['Megan Su', 'Yuwei Bao']2024-03-29T00:00:00https://www.semanticscholar.org/paper/38e81dda85fe13c900c0bce1d0f450e64f74870b 039a9f91480ad721f164c561ff8b6efa5ef0635aPurpose: This study aimed to investigate the impact of voice artificial intelligence (AI) beauty experience services on the acceptance expectations of voice AI services and the intention to use voice AI. Methods: We collected data from hairdressing professionals in their 20s and 30s and analyzed them using Statistical Package for the Social Sciences version 22.0. The analysis methods included frequency, factor, reliability, and regression analyses. Results: The general characteristics of the survey participants were as follows: female gender, age in the 20s, college education (enrolled/graduated), work experience of <2 years, monthly salary under 1.5–2 million KRW, working hours of ≤8, working days of 2 per week, and >10 employees in the workplace. Scalp management was the most preferred voice AI service consultation topic. Validity and reliability tests revealed interest, usefulness, reliability, and ease of use as factors for voice AI beauty experience services; performance expectations and usage expectations as factors for the acceptance expectations of voice AI services; and usage intention as a factor for the intention to use voice AI services. Acceptance expectations of voice AI services and the intention to use voice AI demonstrated a positive and significant impact on voice AI beauty experience services. Conclusion: The results of this study provide foundational data for the development of the beauty industry. The beauty industry should plan and implement the development of voice AI beauty experience service programs.Asian Journal of Beauty and Cosmetology190Acceptance expectations of voice AI services and the intention to use voice AI demonstrated a positive and significant impact on voice AI beauty experience services, providing foundational data for the development of the beauty industry.Asian Journal of Beauty and Cosmetology['Woo-Been Kim', 'Jung-Eun Park', 'Eun-Jun Park']2024-03-29T00:00:00https://www.semanticscholar.org/paper/039a9f91480ad721f164c561ff8b6efa5ef0635a 814a1760279682458bad5a8dfc6b568d5611a5e9Purpose The purpose of this paper is to investigate the factors influencing Palestinian consumer attitudes toward artificial intelligence (AI)-powered online banking, focusing on performance expectancy, effort expectancy, social influence and facilitating conditions while considering the moderating role of trust in financial institutions. Design/methodology/approach To test the hypotheses, an empirical study with a questionnaire was carried out. The study was completed by 362 Palestinian customers who use online banking services. Findings The findings of this paper show that performance expectancy, effort expectancy, social influence and facilitating conditions significantly influence consumer attitudes toward AI-powered online banking. Furthermore, trust in financial institutions as a moderating variable strengthens the impact of performance expectancy, effort expectancy, social influence and facilitating conditions on consumer attitudes toward AI-powered online banking. Therefore, more studies should focus on certain fields and cultural contexts to get a more thorough grasp of the variables influencing adoption and acceptability. Research limitations/implications The study's findings may be specific to the Palestinian context, limiting generalizability. The reliance on self-reported data and a cross-sectional design may constrain the establishment of causal relationships and the exploration of dynamic attitudes over time. In addition, external factors and technological advancements not captured in the study could influence Palestinian consumer attitudes toward AI-powered online banking. Practical implications Financial institutions can leverage the insights from this research to tailor their strategies for promoting AI-powered online banking, emphasizing factors like perceived security and ease of use. Efforts to build and maintain trust in financial institutions are crucial for fostering positive consumer attitudes toward AI technologies. Policymakers can use these findings to inform regulations and initiatives that support the responsible adoption of AI in the financial sector, ensuring a more widespread and effective implementation of these technologies. Originality/value This research delves into Palestinian consumer attitudes toward AI-powered online banking, focusing on trust in financial institutions. It aims to enrich literature by exploring this under-explored area with meticulous examination, robust methodology and insightful analysis. The study embarks on a novel journey into uncharted terrain, seeking to unearth unique insights that enrich the existing literature landscape. Its findings offer valuable insights for academia and practitioners, enhancing understanding of AI adoption in Palestine and guiding strategic decisions for financial institutions operating in the region. Competitiveness Review: An International Business Journal450The findings show that performance expectancy, effort expectancy, social influence and facilitating conditions significantly influence consumer attitudes toward AI-powered online banking and trust in financial institutions as a moderating variable strengthens the impact of performance expectancy, effort expectancy, social influence and facilitating conditions on consumer attitudes toward AI-powered online banking.Competitiveness Review: An International Business Journal['Mohammed Z. Salem', 'Aman Rassouli']2024-03-29T00:00:00https://www.semanticscholar.org/paper/814a1760279682458bad5a8dfc6b568d5611a5e9 afd03c6d20b2c8637f434b33c446e270c6c53790People want to "feel heard" to perceive that they are understood, validated, and valued. Can AI serve the deeply human function of making others feel heard? Our research addresses two fundamental issues: Can AI generate responses that make human recipients feel heard, and how do human recipients react when they believe the response comes from AI? We conducted an experiment and a follow-up study to disentangle the effects of actual source of a message and the presumed source. We found that AI-generated messages made recipients feel more heard than human-generated messages and that AI was better at detecting emotions. However, recipients felt less heard when they realized that a message came from AI (vs. human). Finally, in a follow-up study where the responses were rated by third-party raters, we found that compared with humans, AI demonstrated superior discipline in offering emotional support, a crucial element in making individuals feel heard, while avoiding excessive practical suggestions, which may be less effective in achieving this goal. Our research underscores the potential and limitations of AI in meeting human psychological needs. These findings suggest that while AI demonstrates enhanced capabilities to provide emotional support, the devaluation of AI responses poses a key challenge for effectively leveraging AI's capabilities.Proceedings of the National Academy of Sciences of the United States of America350Compared with humans, AI demonstrated superior discipline in offering emotional support, a crucial element in making individuals feel heard, while avoiding excessive practical suggestions, which may be less effective in achieving this goal.Proceedings of the National Academy of Sciences of the United States of America['Yidan Yin', 'Nan Jia', 'Cheryl J Wakslak']2024-03-29T00:00:00https://www.semanticscholar.org/paper/afd03c6d20b2c8637f434b33c446e270c6c53790 f9e556d84d0a537d06cb5580194ee10526c9c7f1When Buti Manamela visited Lengau, Africa's fastest supercomputer, he had more prosaic technology in mind: electricity. South Africa's Deputy Minister of Higher Education, Science and Technology was at the Center for High Performance Computing in Cape Town for what should have been a showcase tour of a facility providing the country with the computing power needed to run and analyze the kinds of complex models and huge datasets that underpin artificial intelligence (AI) and machine learning (ML). But Manamela was there to better understand the impact of South Africa's rolling power blackouts on the center's operations. Lengau, which means "cheetah" in Setswana, is one of the most important outposts in Africa's AI infrastructure landscape; yet, it is struggling to operate at full capacity because of unreliable power.Science00South Africa's Deputy Minister of Higher Education, Science and Technology was at the Center for High Performance Computing in Cape Town for what should have been a showcase tour of a facility providing the country with the computing power needed to run and analyze the kinds of complex models and huge datasets that underpin artificial intelligence (AI) and machine learning (ML).Science['Rose M. Mutiso']2024-03-29T00:00:00https://www.semanticscholar.org/paper/f9e556d84d0a537d06cb5580194ee10526c9c7f1 547d83818b7be716f9c89b4bd20b3982193c908cAs AI technology continues to advance, its influence on foreign language learning, especially English language learning, is becoming increasingly important. It is crucial to assess how English majors view the integration of AI tools into their English learning process. This research uses a quantitative research design and a structured survey questionnaire to collect data to evaluate English majors' experiences, perceptions, expectations, and concerns about the use of AI tools in their English language learning journey at the tertiary level in Vietnam, specifically across various universities in Ho Chi Minh City. The results indicate that the respondents have already used AI tools for English language learning purposes. Moreover, the participants have a positive attitude toward the application of AI tools in their English language learning. However, they also express concerns about the impact of AI tools on their critical thinking and problem-solving skills, as well as their future career prospects as English teachers and translators. This study suggests further consideration of the integration of AI tools in English Language Learning for English majors.Online (Weston, Conn.)00The results indicate that the respondents have already used AI tools for English language learning purposes and have concerns about the impact of AI tools on their critical thinking and problem-solving skills, as well as their future career prospects as English teachers and translators.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)['Thi Xuyen Nguyen']2024-03-29T00:00:00https://www.semanticscholar.org/paper/547d83818b7be716f9c89b4bd20b3982193c908c 6a870f4000e5dc4dc2dae7a17bcb706849effc68Recently, large-scale artificial intelligence (AI) such as ChatGPT have been developed, and as AI is used across various industrial fields, attention is focused on AI chips (semiconductors). AI chips refer to chips designed for calculations for AI algorithms, and many companies at domestic and abroad, such as NVIDIA, Tesla, and ETRI, are developing AI chips. In this paper, we survey research trends on nine types of AI chips. Currently, many attempts have been made to improve the computational performance of most AI chips, and semiconductors for specific purposes are also being designed. In order to compare various AI semiconductors, each chip is analyzed in terms of operation unit, speed, power, and energy efficiency. We introduce currently existing optimization methodologies for AI computation. Based on this, future research directions for AI semiconductors are presented in this paper.Korean Institute of Smart Media00In this paper, research trends on nine types of AI chips are surveyed in terms of operation unit, speed, power, and energy efficiency, and currently existing optimization methodologies for AI computation are introduced.Korean Institute of Smart Media['Hyun Ji Kim', 'Se Young Yoon', 'Hwa Jeong Seo']2024-03-29T00:00:00https://www.semanticscholar.org/paper/6a870f4000e5dc4dc2dae7a17bcb706849effc68 d63f9217d2116ef513fdf62dfc239ee609756b14In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration. We explore how generative AI drives innovation in IoT and we analyze the potential for data breaches when using generative AI and the misuse of generative AI technologies in IoT ecosystems. These risks not only threaten the privacy and efficiency of IoT systems but also pose broader implications for trust and safety in AI-driven environments. The discussion in this article extends to strategic approaches for mitigating these risks, including the development of robust security protocols, the multi-layered security approaches, and the adoption of AI technological solutions. Through a comprehensive analysis, this article aims to shed light on the critical balance between embracing AI advancements and ensuring stringent security in IoT, providing insights into the future direction of these intertwined technologies.IEEE Internet of Things Magazine310Through a comprehensive analysis, this article aims to shed light on the critical balance between embracing AI advancements and ensuring stringent security in IoT, providing insights into the future direction of these intertwined technologies.IEEE Internet of Things Magazine['Honghui Xu', 'Yingshu Li', 'Olusesi Balogun', 'Shaoen Wu', 'Yue Wang', 'Zhipeng Cai']2024-03-29T00:00:00https://www.semanticscholar.org/paper/d63f9217d2116ef513fdf62dfc239ee609756b14 ab16a333dc07de2055a31d6154d410c901ee1968Explainability algorithms aimed at interpreting decision-making AI systems usually consider balancing two critical dimensions: 1) \textit{faithfulness}, where explanations accurately reflect the model's inference process. 2) \textit{plausibility}, where explanations are consistent with domain experts. However, the question arises: do faithfulness and plausibility inherently conflict? In this study, through a comprehensive quantitative comparison between the explanations from the selected explainability methods and expert-level interpretations across three NLP tasks: sentiment analysis, intent detection, and topic labeling, we demonstrate that traditional perturbation-based methods Shapley value and LIME could attain greater faithfulness and plausibility. Our findings suggest that rather than optimizing for one dimension at the expense of the other, we could seek to optimize explainability algorithms with dual objectives to achieve high levels of accuracy and user accessibility in their explanations.arXiv.org200Through a comprehensive quantitative comparison between the explanations from the selected explainability methods and expert-level interpretations across three NLP tasks, it is demonstrated that traditional perturbation-based methods Shapley value and LIME could attain greater faithfulness and plausibility.ArXiv['Xiaolei Lu', 'Jianghong Ma']2024-03-29T00:00:00https://www.semanticscholar.org/paper/ab16a333dc07de2055a31d6154d410c901ee1968 c044243493ce66e318e945c591aecd11c7d47740In recent years, there have been significant advancements in the development of Large Language Models (LLMs). While their practical applications are now widespread, their potential for misuse, such as generating fake news and committing plagiarism, has posed significant concerns. To address this issue, detectors have been developed to evaluate whether a given text is human-generated or AI-generated. Among others, zero-shot detectors stand out as effective approaches that do not require additional training data and are often likelihood-based. In chat-based applications, users commonly input prompts and utilize the AI-generated texts. However, zero-shot detectors typically analyze these texts in isolation, neglecting the impact of the original prompts. It is conceivable that this approach may lead to a discrepancy in likelihood assessments between the text generation phase and the detection phase. So far, there remains an unverified gap concerning how the presence or absence of prompts impacts detection accuracy for zero-shot detectors. In this paper, we introduce an evaluative framework to empirically analyze the impact of prompts on the detection accuracy of AI-generated text. We assess various zero-shot detectors using both white-box detection, which leverages the prompt, and black-box detection, which operates without prompt information. Our experiments reveal the significant influence of prompts on detection accuracy. Remarkably, compared with black-box detection without prompts, the white-box methods using prompts demonstrate an increase in AUC of at least $0.1$ across all zero-shot detectors tested. Code is available: \url{https://github.com/kaito25atugich/Detector}.arXiv.org330An evaluative framework is introduced to empirically analyze the impact of prompts on the detection accuracy of AI-generated text and reveals the significant influence of prompts on detection accuracy.ArXiv['Kaito Taguchi', 'Yujie Gu', 'Kouichi Sakurai']2024-03-29T00:00:00https://www.semanticscholar.org/paper/c044243493ce66e318e945c591aecd11c7d47740 2820c7c85fd93f0661c282a36e1a68a185c7b0c1Generative AI offers significant opportunities for language learning. Tools like ChatGPT can provide informal second language practice through chats in written or voice forms, with the learner specifying through prompts conversational parameters such as proficiency level, language register, and discussion topics. AI can be instructed to give corrective feedback, create practice exercises, or develop an extended study plan. Instructors can use AI to build learning and assessment materials in a variety of media. AI is likely to make immersive technologies more powerful and versatile, moving away from scripted interactions. For both learners and teachers, it is important to understand the limitations of AI systems that arise from their purely statistical model of human language, which limits their ability to deal with nuanced social and cultural aspects of language use. Additionally, there are ethical concerns over how AI systems are created as well as practical constraints in their use, especially for less privileged populations. The power and versatility of AI tools are likely to turn them into valuable and constant companions in many peoples lives (akin to smartphones), creating a close connection that goes beyond simple tool use. Ecological theories such as sociomaterialism are helpful in examining the shared agency that develops through close user-AI interactions, as are the perspectives on human-object relations from Indigenous cultures.arXiv.org1330Generative AI offers significant opportunities for language learning, but it is important to understand the limitations of AI systems that arise from their purely statistical model of human language, which limits their ability to deal with nuanced social and cultural aspects of language use.ArXiv['Robert Godwin-Jones']2024-03-29T00:00:00https://www.semanticscholar.org/paper/2820c7c85fd93f0661c282a36e1a68a185c7b0c1 3c87e7dca56112a31b03286228321900532fdd1bFuture Journal of Pharmaceutical Sciences1111Artificial intelligence (AI) revolutionized the formulation and development of modern pharmaceuticals and is a potent pharmaceutical formulation and development tool, allowing researchers to analyse vast amounts of data, optimize drug formulations, and streamline clinical trials.Future Journal of Pharmaceutical Sciences['K. A. Ali', 'Sk Mohin', 'Puja Mondal', 'Susmita Goswami', 'Soumya Ghosh', 'S. Choudhuri']2024-03-29T00:00:00https://www.semanticscholar.org/paper/3c87e7dca56112a31b03286228321900532fdd1b a78665f8556a9aa37337723a33d7cdb8da3752c0This research explores the role of strategic approaches in enhancing Small and Medium-sized Enterprises' (SMEs) product brand equity in the digital era. It focuses on the integration of Artificial Intelligence (AI) technologies, highlighting the opportunities and challenges SMEs face in enhancing their brand equity. The study evaluates various strategies for SMEs to navigate the digital complexities, including machine learning algorithms, natural language processing, and predictive analytics, to refine brand positioning, customer engagement, and personalized experiences. It also explores ethical considerations and potential risks associated with AI adoption, offering insights into maintaining consumer trust and ethical practices. The research synthesizes theoretical frameworks with practical implications, contributing to the understanding of strategic paradigms for SMEs to thrive in the digital age. The findings aim to provide actionable insights for SMEs and stakeholders to craft robust strategies aligning with AI capabilities, fostering sustainable brand equity growth and competitive advantage.International Journal of Business, Law, and Education340The study evaluates various strategies for SMEs to navigate the digital complexities, including machine learning algorithms, natural language processing, and predictive analytics, to refine brand positioning, customer engagement, and personalized experiences.International Journal of Business, Law, and Education['Meithiana Indrasari', 'Nur Syamsudin', 'Liosten Rianna Roosida Ully Tampubolon']2024-03-29T00:00:00https://www.semanticscholar.org/paper/a78665f8556a9aa37337723a33d7cdb8da3752c0 ef2e44eed93eadcafbd1c8b7660283a425edcd9dRESEARCH OBJECTIVE: The objective of this research study was to find out the subjective feelings of Czech teachers regarding the introduction of artificial intelligence into schools. THE RESEARCH PROBLEM AND METHODS: The following research questions follow from the research objective: Are Czech teachers worried about the introduction of artificial intelligence into schools? Do Czech teachers see artificial intelligence as a tool that will help them? Do Czech teachers have experience with the use of artificial intelligence? What experience do teachers have with artificial intelligence? To achieve the research objective and answer the research questions, the in-depth interview method with primary and secondary school teachers was used. THE PROCESS OF ARGUMENTATION: The first part of the article discusses the concept of artificial intelligence. The second part discussed the use of artificial intelligence in Czech schools. The third part presents the results of a research study was to find out the subjective feelings of Czech teachers regarding the introduction of artificial intelligence into schools. RESEARCH RESULTS: As part of the research, it was found that some teachers are concerned about the advent of artificial intelligence and its use in education, especially that students will “misuse” AI to cheat and plagiarize. Most teachers see the application of artificial intelligence in schools as inevitable and realize that it is their task to teach students to use AI effectively. Teachers are also aware that with the implementation of AI into schools, the teaching system will also have to change. CONCLUSIONS, RECOMMENDATIONS AND APPLICABLE VALUE OF RESEARCH: We can expect that teachers will consider artificial intelligence as a tool that helps them and makes their work easier. A big impetus will be the integration of artificial intelligence into tools that teachers commonly work with, such as Office 365 or Google Workspace. Horyzonty Wychowania00It was found that some teachers are concerned about the advent of artificial intelligence and its use in education, especially that students will “misuse” AI to cheat and plagiarize.Horyzonty Wychowania['Lucie Zormanová']2024-03-29T00:00:00https://www.semanticscholar.org/paper/ef2e44eed93eadcafbd1c8b7660283a425edcd9d 5158a4fe8e8f8d9bbf3a31f03252b7d201e3ef87Breast cancer remains the most prevalent cancer among women worldwide, necessitating advancements in diagnostic methods. The integration of artificial intelligence (AI) into mammography has shown promise in enhancing diagnostic accuracy. However, understanding patient perspectives, particularly considering the psychological impact of breast cancer diagnoses, is crucial. This narrative review synthesizes literature from 2000 to 2023 to examine breast cancer patients’ attitudes towards AI in breast imaging, focusing on trust, acceptance, and demographic influences on these views. Methodologically, we employed a systematic literature search across databases such as PubMed, Embase, Medline, and Scopus, selecting studies that provided insights into patients’ perceptions of AI in diagnostics. Our review included a sample of seven key studies after rigorous screening, reflecting varied patient trust and acceptance levels towards AI. Overall, we found a clear preference among patients for AI to augment rather than replace the diagnostic process, emphasizing the necessity of radiologists’ expertise in conjunction with AI to enhance decision-making accuracy. This paper highlights the importance of aligning AI implementation in clinical settings with patient needs and expectations, emphasizing the need for human interaction in healthcare. Our findings advocate for a model where AI augments the diagnostic process, underlining the necessity for educational efforts to mitigate concerns and enhance patient trust in AI-enhanced diagnostics.Life590A clear preference among patients for AI to augment rather than replace the diagnostic process is found, emphasizing the necessity of radiologists’ expertise in conjunction with AI to enhance decision-making accuracy and the need for human interaction in healthcare.Life['F. Pesapane', 'Emilia Giambersio', 'Benedetta Capetti', 'Dario Monzani', 'R. Grasso', 'Luca Nicosia', 'A. Rotili', 'Adriana Sorce', 'L. Meneghetti', 'S. Carriero', 'Sonia Santicchia', 'G. Carrafiello', 'Gabriella Pravettoni', 'Enrico Cassano']2024-03-29T00:00:00https://www.semanticscholar.org/paper/5158a4fe8e8f8d9bbf3a31f03252b7d201e3ef87 8258c06203a51b43fa08045557847e9f21a1587cThis research presents an analysis of internal organizational device risks susceptible to cyber security attacks using artificial intelligence, specifically utilizing the Gradient Boosted Trees algorithm. The study utilizes data from Internet Traffic Logs generated by a firewall, recording incidents of cyber security attacks over the year 2021, consisting of 1,048,575 records with 45 columns. The data is categorized into 21 classes based on the type of cyber security attack. The dataset is divided into two sets: the first set, constituting 70% of the entire dataset, is used for training the model, while the second set, constituting 30%, serves as the testing dataset. Both sets are non-overlapping, and data preparation has been performed. The algorithm's performance was evaluated, revealing that the Gradient Boosted Trees algorithm achieved the highest accuracy at 94.64%. The analysis accurately predicted cyber security attacks, especially for 12 classes with accuracy exceeding 80%, while the remaining 9 classes had accuracy below 80%. The results of the analysis are visualized using Microsoft Power BI.International Conference on Computing and Information260An analysis of internal organizational device risks susceptible to cyber security attacks using artificial intelligence, specifically utilizing the Gradient Boosted Trees algorithm, revealing that the Gradient Boosted Trees algorithm achieved the highest accuracy.2024 IEEE International Conference on Cybernetics and Innovations (ICCI)['Tanupat Ngampunprasert', 'M. Ketcham']2024-03-29T00:00:00https://www.semanticscholar.org/paper/8258c06203a51b43fa08045557847e9f21a1587c d74828a28d7420cd0808e7b4f9f0768fe099105eIn today's growing threat environment, rapid and effective detection and response to security events is essential. To solve these problems, many companies and organizations respond to security threats by introducing security control systems. However, existing security control systems are experiencing difficulties due to the complexity and diverse characteristics of security events. In this study, we propose an automated integrated security control system model based on artificial intelligence. It is based on deep learning, an artificial intelligence technology, and provides effective detection and processing functions for various security events. To this end, the model applies various artificial intelligence algorithms and machine learning methods to overcome the limitations of existing security control systems. The proposed model reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats.Korean Institute of Smart Media00An automated integrated security control system model based on artificial intelligence that reduces the operator's workload, ensures efficient operation, and supports rapid response to security threats is proposed.Korean Institute of Smart Media['Wonsik Nam', 'Han-Jin Cho']2024-03-29T00:00:00https://www.semanticscholar.org/paper/d74828a28d7420cd0808e7b4f9f0768fe099105e 24cf012f31689c48e2d11dcec28df1b6fe4c679dPostgraduate students in developing nations, such as South Africa, are increasingly leveraging artificial intelligence tools like ChatGPT to elevate their academic success in the era of the fourth industrial revolution. This study aims to explore postgraduate students' perceptions of the benefits associated with the utilisation of artificial intelligence tools, with a specific focus on ChatGPT, in their academic success in South Africa’s historically disadvantaged universities. Employing a qualitative approach, the study aims to gain a deeper understanding of postgraduate views on this subject. The sample size comprised 10 postgraduate students pursuing master's degrees within the two selected South Africa’s historically disadvantaged universities, selected through purposive sampling. Semi-structured interviews were conducted to gather insights from the postgraduate students. Thematic analysis was employed to analyse the collected data. The study's findings shed light on the significant advantages of incorporating ChatGPT in students' academic journey with special focus on research success. The study found that ChatGPT proves beneficial for postgraduate students, with some utilising the AI tool to refine their research topics before submission to their supervisors. Moreover, ChatGPT assists postgraduate students in identifying grammatical errors and paraphrasing their academic writing, contributing to the enhancement of their writing skills. In light of these findings, the study recommends the immediate development of an innovative AI ethical use policy in South Africa’s historically disadvantaged universities. This policy should emphasise ethical guidelines for postgraduate students when utilising AI tools, such as ChatGPT to ensure responsible and effective integration into their academic success.Journal of Curriculum Studies Research330The study found that ChatGPT proves beneficial for postgraduate students, with some utilising the AI tool to refine their research topics before submission to their supervisors, and recommends the immediate development of an innovative AI ethical use policy in South Africa’s historically disadvantaged universities.Journal of Curriculum Studies Research['T. Chauke', 'Themba Ralph Mkhize', 'Lina Methi', 'Ntandokamezi Dlamini']2024-03-29T00:00:00https://www.semanticscholar.org/paper/24cf012f31689c48e2d11dcec28df1b6fe4c679d bfb3501b1358d52e5c66a2d813b1d86647b76eebArtificial intelligence (AI) has gained traction in the field of dermatology, but the role of AI in the dermatology residency application process has been minimally explored. We encourage dermatology residency programs to proactively set guidelines to applicants and letter writers on the use of AI tools. Further, programs should consider implementing natural language processing models in reviewing residency applications to promote principles of diversifying the dermatology workforce.Clincal and Experimental Dermatology00Clinical and experimental dermatology['Osaigbokan P Aihie', 'Eun Jae Kim', 'Mihir K Patil', 'Ritika Manik', 'Vinod E Nambudiri']2024-03-29T00:00:00https://www.semanticscholar.org/paper/bfb3501b1358d52e5c66a2d813b1d86647b76eeb 3f939f7346f14b40f77a8a13718f629b35e10730The purpose of the article is to review artificial intelligence, which is the object of intellectual rights. After all, it is intellectual rights that play a decisive role in the embodiment of creative ideas and inventions of authors. They become an integral part of our civic life, integrating into various spheres of society. The key point today is to analyze the history of legislation on intellectual property rights, understand its patterns and identify directions for developmentEconomics. Sociology. Law.00The key point today is to analyze the history of legislation on intellectual property rights, understand its patterns and identify directions for development.Economics. Sociology. Law.['Lyudmila Mityuchenko', 'Maria Kuznetsova']2024-03-29T00:00:00https://www.semanticscholar.org/paper/3f939f7346f14b40f77a8a13718f629b35e10730 71c02376b4aee7ae2b29da937742d95683fc8710The paper examines the use of artificial intelligence (AI) in the main processes of human resource management. It is noted that in modern conditions, AI is considered an advanced tool that can optimize management processes in various sectors of the economy. The author discusses the new opportunities that AI opens up in human resource management, increasing the efficiency of actions at all levels and complementing human abilities. The main part of the paper is devoted to the advantages and potential opportunities of using AI at different stages of the employee's life cycle in a company. In particular, the author emphasizes the optimization of recruitment through analytical processing of large amounts of information rather than subjective judgment. The paper highlights such areas as operational efficiency, recruitment, onboarding, talent management, strategic planning, career development, and management changes where AI can make a significant contribution. The paper also highlights the issues related to the potential dangers of introducing AI technologies. The level of readiness and the degree of involvement of managers in the latest technologies in human resource management is indicated. The paper raises the issue of efficiency and solving various problems. In addition, examples of real software products are provided. The conclusions emphasize the general perspective of using AI in HR processes and identify areas for further research. In particular, there is a call for the development of mechanisms to protect employees from possible misuse of AI and the development of effective strategies for the implementation of technologies that would take into account ethical aspects. The final part of the paper sets the task for the business community and legislative bodies to actively work on standards for the use of AI in HR to create fair and effective HR management.Economics. Finances. Law20There is a call for the development of mechanisms to protect employees from possible misuse of AI and the development of effective strategies for the implementation of technologies that would take into account ethical aspects.Economics. Finances. Law['Iryna Vats', 'O. Kyrylenko', 'Valentyna Novak']2024-03-29T00:00:00https://www.semanticscholar.org/paper/71c02376b4aee7ae2b29da937742d95683fc8710 d03aec1930d5b5fe5eb652e8906ab3c9c723966bThis article explores the possibilities of using the potential of artificial intelligence in the process of preparing laws. Based on the study of the works of foreign researchers, the semantic nuances of the concept of artificial intelligence are revealed. The author's attention is also focused on studying the features of the introduction of artificial intelligence technology into the activities of subjects of the legislative process in various countries. It is proved that this technology can increase the efficiency of law-making activities, since it allows it to be significantly automated and simplified. The article pays special attention to innovative areas of development of legislative activity, which are dictated by the challenges facing the legal systems of modern states. Today, the high intensity of social processes and the dynamism of public relations necessitates the timely response of subjects of the legislative process, including through the use of digital technologies. Artificial intelligence can play a big role in identifying changes in the needs of society and processing the relevant information array, which puts on the agenda the question of its effective legal regulation. The consolidation of norms related to artificial intelligence will help ensure transparency of all stages of the legislative process, make the text of laws more structured, and eliminate contradictions and errors. The special importance of the introduction of this technology lies in minimizing risks and the subjective factor in the development of draft laws. Consequently, the trend of the development of the use of the latest technologies to automate the work of the legal system of Kazakhstan, as well as the potential prospects for the introduction of artificial intelligence into lawmaking activities, is considered. The article covers the positive and negative risks of introducing artificial intelligence into lawmaking.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00The article covers the positive and negative risks of introducing artificial intelligence into lawmaking and the trend of the development of the use of the latest technologies to automate the work of the legal system of Kazakhstan, as well as the potential prospects for the introduction of artificial intelligence into lawmaking activities.Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan['I. Aubakirova', 'B. S. Moldabekov']2024-03-29T00:00:00https://www.semanticscholar.org/paper/d03aec1930d5b5fe5eb652e8906ab3c9c723966b 82a0723260371540105eb71a1846f48e9761273eThis paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called “The Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model.International Journal of Artificial Intelligence &amp; Applications1120This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature and proposes a new framework called the Transformation Risk-Benefit Model of Artificial Intelligence to address the increasing fears and levels of AIrisk.International Journal of Artificial Intelligence &amp; Applications['Richard Fulton', 'Diane Fulton', 'Nate Hayes', 'Susan Kaplan']2024-03-29T00:00:00https://www.semanticscholar.org/paper/82a0723260371540105eb71a1846f48e9761273e b77a02885320c2cd957aa1b54dfb48cdd10dc967Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we developed a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screened multi-billion compound libraries against two unrelated targets, a novel ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. On both targets, we discover hits, including seven novel hits (14% hit rate) to KLHDC2 and four novel hits (44% hit rate) to NaV1.7 with single digit micromolar binding affinities. Screening in both cases was completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.bioRxiv90A highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities, which outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to the ability to model receptor flexibility.bioRxiv['Guangfeng Zhou', 'D. Rusnac', 'Hahnbeom Park', 'Daniele Canzani', 'Hai M. Nguyen', 'Lance Stewart', 'Matthew F. Bush', 'P. T. Nguyen', 'Heike Wulff', 'V. Yarov-Yarovoy', 'Ning Zheng', 'Frank Dimaio']2024-03-29T00:00:00https://www.semanticscholar.org/paper/b77a02885320c2cd957aa1b54dfb48cdd10dc967 a4e472584dc06ae854e996fac780ecd88b76ab36Incontestably, the continued advancement of artificial intelligence (AI) has transformed many industries, including journalism. AI continues to take over traditional ways of journalism in Pakistan in a similar way as in other parts of the world (but at a slower rate). It has revolutionized journalistic practices in Pakistan. This study investigated the role of artificial intelligence (AI) in reshaping journalism in Pakistan. This research used an interview method. A sample size of 15 journalists was drawn using the purposive sampling method. A carefully constructed questionnaire was used to collect data from defined participants. Additionally, to conduct a thematic analysis of recorded interviews, different categories and subcategories were used. The findings of the study reveal that despite having a transformational role in Pakistani journalism, Al Adoption still needs certain considerations to improve the standards of reporting in a polarized society like Pakistan. Although Pakistani journalists are somewhat knowledgeable about AI technology, more instruction on the moral implications of its application in journalism is necessary.Qlantic Journal of Social Sciences00The findings of the study reveal that despite having a transformational role in Pakistani journalism, Al Adoption still needs certain considerations to improve the standards of reporting in a polarized society like Pakistan.Qlantic Journal of Social Sciences['Muhammad Tariq', 'Muhammad Jawed Aslam', 'Abdul Shakoor', 'Saba Ilyas']2024-03-29T00:00:00https://www.semanticscholar.org/paper/a4e472584dc06ae854e996fac780ecd88b76ab36 0ae77cf8a5a5a236adf8dbc7082afa51984a2d33The progress of civilisation is the ground for enormous changes in the life of every human being. The development of information technology, telecommunications or multimedia is a huge step in the history of all civilisation. This evolution from the industrial era, where we were dealing with an industrial society, is moving us to an information society based on artificial intelligence (AI). Information is becoming the basis not only for the smooth functioning of all types of institutions – but also, and perhaps above all, for the educational process. The implementation of artificial intelligence in the education system is a topic that generates both enthusiasm and concern. On the one hand, the possibilities it offers in the context of education seem almost limitless, promising, among other things, the personalisation of teaching, the optimisation of teaching processes and support in the diagnosis and development of students’ skills. On the other hand, there are questions about data security, ethics and the potential replacement of teachers by machines.Horyzonty Wychowania00The implementation of artificial intelligence in the education system is a topic that generates both enthusiasm and concern, promising, among other things, the personalisation of teaching, the optimisation of teaching processes and support in the diagnosis and development of students’ skills.Horyzonty Wychowania['Jarosław Charchuła', 'Mirosław Kowalski']2024-03-29T00:00:00https://www.semanticscholar.org/paper/0ae77cf8a5a5a236adf8dbc7082afa51984a2d33 50485079c635f6acf95f68a1887145995bfddc5aThe term AI was originally coined by a computer scientist, John McCarthy, who defined it as the science and engineering of making intelligent machines. The father of AI authored a 1950 article, “Computing Machinery and intelligence” that discussed the reasons for considering a machine to be intelligent. Artificial intelligence is useful to facilitate faster disease detection. It helps to understand disease progression, improve medication/treatment dosages, and discover innovative treatments. The artificial intelligence tools mostly used for psychosis risk screening are chatbots and large-scale social media data analysis. Chatbot is a computer program that allows human-computer interactions in the form of textual dialogue based on the technology of natural language processing. The world's first chatbot, ELIZA, was designed in the 1960s and responds to special rules by recognizing keywords in user-entered text. Chatbots in the mental healthcare field include Tess, Florence, Buoy Health, and Your. Md. In addition to natural language processing, the machine learning methods adopted by chatbots also include natural language understanding, artificial neural networks, and recurrent neural networks.International Journal of Research in Medical Sciences00The artificial intelligence tools mostly used for psychosis risk screening are chatbots and large-scale social media data analysis and machine learning methods adopted by chatbots include natural language understanding, artificial neural networks, and recurrent neural networks.International Journal of Research in Medical Sciences['Debajani Deka']2024-03-29T00:00:00https://www.semanticscholar.org/paper/50485079c635f6acf95f68a1887145995bfddc5a 609b710942835a00769a33011a21d6d29b8bf2f9International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-29T00:00:00https://www.semanticscholar.org/paper/609b710942835a00769a33011a21d6d29b8bf2f9 e1fbb666c5875fa1d1667c89232c5b0ffa637821Background and Objectives: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at a higher risk of excess morbidity and mortality despite being treated with primary percutaneous coronary intervention (PPCI). Identifying high-risk patients is prudent so that close monitoring and timely interventions can improve outcomes. Materials and Methods: A cohort of 605 STEMI patients [64.2 ± 13.2 years, 432 (71.41%) males] treated with PPCI were recruited. Their arterial pressure (AP) wave recorded throughout the PPCI procedure was analyzed to extract features to predict 1-year mortality. After denoising and extracting features, we developed two distinct feature selection strategies. The first strategy uses linear discriminant analysis (LDA), and the second employs principal component analysis (PCA), with each method selecting the top five features. Then, three machine learning algorithms were employed: LDA, K-nearest neighbor (KNN), and support vector machine (SVM). Results: The performance of these algorithms, measured by the area under the curve (AUC), ranged from 0.73 to 0.77, with accuracy, specificity, and sensitivity ranging between 68% and 73%. Moreover, we extended the analysis by incorporating demographics, risk factors, and catheterization information. This significantly improved the overall accuracy and specificity to more than 76% while maintaining the same level of sensitivity. This resulted in an AUC greater than 0.80 for most models. Conclusions: Machine learning algorithms analyzing hemodynamic traces in STEMI patients identify high-risk patients at risk of mortality.Medicina650Machine learning algorithms analyzing hemodynamic traces in STEMI patients identify high-risk patients at risk of mortality.Medicina['Seyed Reza Razavi', 'Tyler Szun', 'Alexander C. Zaremba', 'A. Shah', 'Z. Moussavi']2024-03-29T00:00:00https://www.semanticscholar.org/paper/e1fbb666c5875fa1d1667c89232c5b0ffa637821 267d67c72a267f08fe4bee138cf6f7e36c9a3532революция искусственного интеллекта (ИИ), вызванная такими крупными моделями, как серия GPT, охватывает различные отрасли промышленности, особенно сектор цифровых потребителей, где искусственный интеллект стал мощным двигателем инноваций. Несмотря на принадлежность к разным отраслям, как игровая индустрия, так и журналистика играют роль информационных носителей и стали важной частью удовлетворения духовных потребностей людей в развлечениях в эпоху цифровой информации. Прогресс в обеих отраслях стал результатом технологического развития. В игровой индустрии искусственный интеллект способствовал развитию игровой экосистемы, увеличивая эффективность разработки игр и обеспечивая лучший опыт игроков, что привело к революции в производительности в секторе игровой индустрии. В журналистике технологии искусственного интеллекта применяются во всех аспектах производства и распространения новостей. Анализируя применение технологий искусственного интеллекта в различных аспектах двух отраслей, статья раскрывает взаимосвязь между игровой и журналистской индустриями в контексте новых технологий и рассматривает проблемы, возникающие при их применении. Также обсуждаются новые тенденции в интеграции двух отраслей и пути применения технологий искусственного интеллекта. the AI revolution catalyzed by major models such as the GPT series has permeated diverse industries, particularly in the digital consumer sector, where AI has emerged as a potent catalyst for industry innovation. Despite their distinct domains, both the gaming industry and journalism serve as conduits of information and have evolved into pivotal elements for fulfilling people's entertainment needs in the digital age. The advancement of both sectors has been propelled by technological strides. Within the gaming industry, AI has bolstered the gaming ecosystem by enhancing game development efficiency and delivering enhanced player experiences, heralding a productivity revolution within the gaming sector. In journalism, AI technologies have been integrated across all facets of news production and dissemination to foster intelligent innovations in journalistic practices. Through an analysis of AI technology applications across various facets of these industries, this paper unveils the interplay between the gaming and journalism sectors within the framework of new technologies and elucidates the challenges encountered during their application. Furthermore, it delves into emerging trends in the integration of these industries and outlines avenues for the application of AI technologies.Modern Humanities Success20Modern Humanities Success['Ю. Гэ']2024-03-29T00:00:00https://www.semanticscholar.org/paper/267d67c72a267f08fe4bee138cf6f7e36c9a3532 70c2f399a07d72453796f19c54a0f9d619c2a8f3The article considers aspects of the use of neural networks in business processes of formation and promotion of tourist products (excursions and tourist programs). The analysis of neural network software in the Google and Yandex search engines in Russian and English is carried out.Gostinichnoe delo (Hotel Business)00Parts of the use of neural networks in business processes of formation and promotion of tourist products (excursions and tourist programs) and the analysis of neural network software in the Google and Yandex search engines in Russian and English are considered.Gostinichnoe delo (Hotel Business)['N. N. Balev', 'O. Y. Zeveke']2024-03-29T00:00:00https://www.semanticscholar.org/paper/70c2f399a07d72453796f19c54a0f9d619c2a8f3 f9822efc81856702e1d952a964a6b01726fd9178Science and Engineering Ethics620This paper argues that in the event of a tie, the ability to create a voluntarist reason is a hallmark feature of agency, and that AI, through current tie-breaking mechanisms does not have this ability, and thus fails at this particular feature of agency.Science and Engineering Ethics['Danielle Swanepoel', 'Daniel Corks']2024-03-29T00:00:00https://www.semanticscholar.org/paper/f9822efc81856702e1d952a964a6b01726fd9178 2b04cd5d277d0c79d76d4850b62e2a21175f487dEducation in Medicine Journal120Education in Medicine Journal['Kamran Sattar', 'Muhamad Saiful Bahri Yusoff']2024-03-29T00:00:00https://www.semanticscholar.org/paper/2b04cd5d277d0c79d76d4850b62e2a21175f487d 34b4d3e8cb995bd3d3b63521a2ca23fec1d13fe0Revista Medico-Chirurgicala00Revista Medico-Chirurgicala['M. Robea']2024-03-29T00:00:00https://www.semanticscholar.org/paper/34b4d3e8cb995bd3d3b63521a2ca23fec1d13fe0 222284f8298ba72efdf17cc666eac5d06ff37799Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment00Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment['Zhengqiang Jiang', 'Z. Gandomkar', 'Phuong Dung Trieu', 'S. Tavakoli Taba', 'M. Barron', 'S.J. Lewis']2024-03-29T00:00:00https://www.semanticscholar.org/paper/222284f8298ba72efdf17cc666eac5d06ff37799 2506b63acc486474e9d1c98e570d91a5dd30c5acJournal of Datta Meghe Institute of Medical Sciences University50Journal of Datta Meghe Institute of Medical Sciences University['Waqar M. Naqvi', 'Sakshi P. Arora', 'Aishwarya A. Pashine', 'Mamdouh Gabr']2024-03-29T00:00:00https://www.semanticscholar.org/paper/2506b63acc486474e9d1c98e570d91a5dd30c5ac 9f5160741dbc37ca99758bd1660236ac3b1d7b29Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment00Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment['Kwok Lung Fan', 'Yee Lam Elim Thompson', 'Weijie Chen', 'Craig K. Abbey', 'F W Samuelson']2024-03-29T00:00:00https://www.semanticscholar.org/paper/9f5160741dbc37ca99758bd1660236ac3b1d7b29 0d37a2eca7719eac6a9079ead0f1940e96ccb53dIs artificial consciousness theoretically possible? Is it plausible? If so, is it technically feasible? To make progress on these questions, it is necessary to lay some groundwork clarifying the logical and empirical conditions for artificial consciousness to arise and the meaning of relevant terms involved. Consciousness is a polysemic word: researchers from different fields, including neuroscience, Artificial Intelligence, robotics, and philosophy, among others, sometimes use different terms in order to refer to the same phenomena or the same terms to refer to different phenomena. In fact, if we want to pursue artificial consciousness, a proper definition of the key concepts is required. Here, after some logical and conceptual preliminaries, we argue for the necessity of using dimensions and profiles of consciousness for a balanced discussion about their possible instantiation or realisation in artificial systems. Our primary goal in this paper is to review the main theoretical questions that arise in the domain of artificial consciousness. On the basis of this review, we propose to assess the issue of artificial consciousness within a multidimensional account. The theoretical possibility of artificial consciousness is already presumed within some theoretical frameworks; however, empirical possibility cannot simply be deduced from these frameworks but needs independent empirical validation. We break down the complexity of consciousness by identifying constituents, components, and dimensions, and reflect pragmatically about the general challenges confronting the creation of artificial consciousness. Despite these challenges, we outline a research strategy for showing how"awareness"as we propose to understand it could plausibly be realised in artificial systems.arXiv.org50ArXiv['K. Evers', 'M. Farisco', 'R. Chatila', 'B. D. Earp', 'I. T. Freire', 'F. Hamker', 'E. Nemeth', 'P. Verschure', 'M. Khamassi']2024-03-29T00:00:00https://www.semanticscholar.org/paper/0d37a2eca7719eac6a9079ead0f1940e96ccb53d b26edbffb21472e866b8fb1cdcaabb44a353cb10Scientific Reports690The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities.Scientific Reports['M. K. Nallakaruppan', 'E. Gangadevi', 'M. Shri', 'B. Balusamy', 'Sweta Bhattacharya', 'S. Selvarajan']2024-03-29T00:00:00https://www.semanticscholar.org/paper/b26edbffb21472e866b8fb1cdcaabb44a353cb10 a77c5b5d259afa19dc256c5b7c0612a8112733a2Technology is developing parabolically. This development affects businesses' way of conduct. Both internal and external processes of enterprises are digitalized in order to increase efficiency and flexibility. Yet, the digitalization creates a variety of vulnerabilities and new legal and compliance risks. Some of these risks may arise directly from the technological tools used, for example, from a software. Some risks arise due to the features of these technological tools. For example, being vulnerable to cyber-attacks, hosting artificial intelligence. Some technological risks, on the other hand, may be caused by the characteristics of technological tools as well as the lack of awareness of the employees using these tools. The aim of this study is to address some of the legal and compliance risks that arise with technological developments and to suggest precautions that can be taken against these risks. In this context, first of all, the digitalization of enterprises will be briefly mentioned, then examples of the risks arising with digitalization will be given, and finally, some general recommendations will be made based on the measures that can be taken against these risks.Ankara Sosyal Bilimler Üniversitesi Hukuk Fakültesi Dergisi00The aim of this study is to address some of the legal and compliance risks that arise with technological developments and to suggest precautions that can be taken against these risks.Ankara Sosyal Bilimler Üniversitesi Hukuk Fakültesi Dergisi['Yasemin Güllüoğlu', 'Fatih Erdemir']2024-03-29T00:00:00https://www.semanticscholar.org/paper/a77c5b5d259afa19dc256c5b7c0612a8112733a2 6e7ea8cb7b40d12e886383bac759c45f3148f672AI &amp; SOCIETY620This work provides a holistic characterization of what responsible APR means and provides support to operationalize this in concrete projects, in the form of leading questions, examples, and mitigation strategies.AI &amp; SOCIETY['Clement Guitton', 'S. Mayer', 'Aurelia Tamó-Larrieux', 'Dimitri Van Landuyt', 'Eduard Fosch-Villaronga', 'Irene Kamara', 'Przemysław Pałka']2024-03-28T00:00:00https://www.semanticscholar.org/paper/6e7ea8cb7b40d12e886383bac759c45f3148f672 6911953027c5f61bf792b380aa039648d6d58799Background Algorithmic decision making (ADM) utilizes algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care) - regardless of the clinical issues. Furthermore, for interpreting collected evidence and analysing preconditions for the implementation of AI-related ADM in healthcare, experts from research, practice, and regulation will be interviewed. Methods Following the PRISMA statement and the MECIR standards for reporting systematic reviews, MEDLINE and PubMed (via PubMed), EMBASE (via Elsevier), IEEE Xplore, CENTRAL will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree) fields. Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochranes RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Following the SRQR statement, semi-structured interviews will be conducted and analysed with the help of a qualitative content analysis according to Mayring. Based on the research questions and the findings of the systematic review, the study and interview guide will be developed a priori. Discussion It is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritization of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a significant portion of the identified studies will exhibit relatively poor methodological quality and provide only limited generalizable results.medRxiv410The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care) - regardless of the clinical issues.['C. Wilhelm', 'A. Steckelberg', 'F. G. Rebitschek']2024-03-28T00:00:00https://www.semanticscholar.org/paper/6911953027c5f61bf792b380aa039648d6d58799 ab989d9d71272eb71b156ae12bd81f5e96c5f0f4We examined the intricate dynamics of agency theory within the framework of sustainable economic transition, which is incredibly capital intensive. Recognizing the gravity of sustainable transition, we address the challenges posed by the Second Order Agency Problem (SOAP) and market failures related to public goods. The research explores the subtle shift towards dominant credence goods and services, emphasizing the need for standardized non-financial information fuelling market’s efficiency. The primary aim is to bridge the gap between classical agency theory and modern sustainable economics applications. Our findings introduce an innovative Regulation Curve Model (RCM) presenting a fresh perspective on the cost-benefit dynamics of regulation. This study’s scope is international, with a specific focus on case studies from Poland and Slovenia. Our results from the RCM concept and from the sustainable strategy scoring model offer significant cognitive value for both academic science and practical policymaking. The research holds potential for guiding future sustainable policies and offers insights for nations navigating the complexities of economic transition.Studia Iuridica Lublinensia00Studia Iuridica Lublinensia['Franjo Mlinarič', 'Grygorii Kravchenko', 'B. Brezovnik']2024-03-28T00:00:00https://www.semanticscholar.org/paper/ab989d9d71272eb71b156ae12bd81f5e96c5f0f4 7c6e4a6b82db42c89dd3d45b90e22e742998276dIn this article, the authors investigate the connection between the level of detail in constitutional regulations of local financial autonomy and its overall quality in Hungary, Slovakia, Poland and the Czech Republic. The article aims to either confirm or refute the hypothesis that more comprehensive constitutional rules result in an enhanced quality of local financial autonomy. To be able to test the hypothesis, the authors first examine the relevant constitutional regulation in these four countries. Thereafter, they employ two different indicators, selected statistical data and the conclusions from the monitoring procedure of the European Charter of Local Self-Government to measure the quality of local financial autonomy in the studied countries. Finally, they compare the results of the quality assessment with the degree of the constitutional framework’s specificity to see if the hypothesis was correct or not.Studia Iuridica Lublinensia00Studia Iuridica Lublinensia['Ádám Pál', 'Michal Radvan']2024-03-28T00:00:00https://www.semanticscholar.org/paper/7c6e4a6b82db42c89dd3d45b90e22e742998276d 0e9d1472a8d9330d9ada2b6681a4cb20ed48d425In the digital transformation process, the phases focusing on technical and economic aspects were followed by a phase exposing human capital issues. In approximately 3,000 acts of the European Union relating to the digital transformation process published in Eur-lex, as well as in an increasing number of national acts of the Member States, the terms “digital competences” and “digital skills” appear. They occur, inter alia, in the context of the financing of development tasks and their achievement indicators. In the application of existing law, it must be taken into account that the scopes and interrelationships of these new terms are framed differently. This ambiguity may have a negative impact on the effectiveness of digital transformation. It is postulated that the terminological consistency of the multi-level regulation should be improved and, in doing so, it should be noted that the prominence of digital skills in prospective acts and the way in which knowledge is captured can affect the use of the potential of universities.Review of European and Comparative Law290It is postulated that the terminological consistency of the multi-level regulation should be improved and, in doing so, it should be noted that the prominence of digital skills in prospective acts and the way in which knowledge is captured can affect the use of the potential of universities.Review of European and Comparative Law['G. Szpor', 'Paweł Hajduk']2024-03-28T00:00:00https://www.semanticscholar.org/paper/0e9d1472a8d9330d9ada2b6681a4cb20ed48d425 29802dc19c95fb19c2575f047334db89cf99ee59The article is devoted to problematic issues of legal regulation of terms based on the analysis of the normative provisions of modern and Soviet criminal procedure legislation. The author has identified and resolved an intra-industry legal conflict in the criminal procedure law between Article 128 of the Code of Criminal Procedure of the Russian Federation, according to which the terms established in the criminal procedure law are calculated in hours, days, months, and a number of articles of the Code of Criminal Procedure of the Russian Federation, indicating calculation in other units of measurement (minutes, days, years). The law enforcement practice of calculating criminal procedural deadlines was analyzed and ambiguous decisions made by employees of internal affairs bodies were discovered. The main violations of the rights and freedoms of participants in criminal proceedings related to incorrect calculation of deadlines in criminal proceedings are revealed. Such violations lead to delays in criminal proceedings, non-compliance with the principle of legality and the principle of a reasonable period of criminal proceedings. As a result, proposals and recommendations were made to improve the law, consisting in supplementing and amending article 128 Code of Criminal Procedure of the Russian Federation.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia00Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia['Anastasiya Cherkasova']2024-03-28T00:00:00https://www.semanticscholar.org/paper/29802dc19c95fb19c2575f047334db89cf99ee59 e3e9180469557feb5be598e101e5ed7e2adb66c4The article is devoted to a comprehensive analysis of cyberterrorism and cyberextremism as the most dangerous social phenomena that have become widespread due to the development of information technologies, especially in the field of the Internet information and telecommunications network. The author investigates the theoretical concepts of understanding cyberterrorism as a legal definition, examines the historical features of the development of this phenomenon, identifies various approaches in the legal regulation of cyberterrorism in India, Nigeria and the Philippines. The author offers his own definition of cyberextremism, which is based on the compilation of constitutive features of cyberterrorism (as an self-determined phenomenon) and extremism. Particular attention is paid to the polysystem of legal regulation of the fight against cyberextremism and cyberterrorism (universal level of legal regulation, regional level of legal regulation and national legal regulation). The study of overseas experience of legal regulation allowed to shift the focus from the universal level of international legal regulation to the regional one, which will significantly affect the regional security structure. In the course of the study, the author came to the conclusion that the most promising area of regional cooperation in the fight against cyberextremism for the Russian Federation will be the Shanghai Cooperation Organization (further — SCO). This is due to the fact that the SCO is the only international organization that has a conventional regulation of extremism per se. The expansion of cooperation is appropriate given the fact that cyber extremism is a certain form of extremism, as well as the experience of the Republic of India as a SCO member state, which has relevant national legislation aimed at countering cyberterrorismLegal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia20The author came to the conclusion that the most promising area of regional cooperation in the fight against cyberextremism for the Russian Federation will be the Shanghai Cooperation Organization ( SCO), due to the fact that the SCO is the only international organization that has a conventional regulation of extremism per se.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia["Svyatoslav Mel'nik"]2024-03-28T00:00:00https://www.semanticscholar.org/paper/e3e9180469557feb5be598e101e5ed7e2adb66c4 08fb7bfcd1775f56fb744364cb3ec0dae700f719The role of chairman of the board of directors of a contemporary company has evolved from procedural and ceremonial to complex and demanding. This article examines the appointment, tenure, functions, and liabilities of this position, as regulated by the Companies Act 71 of 2008, the JSE Limited Listings Requirements, and the King IV Report on Corporate Governance for South Africa 2016. The aim is to ascertain whether the guidance provided to chairmen on their appointment, tenure, functions, and liabilities is clear and adequate to guide them on what is expected of them in contemporary companies. Company law in the United Kingdom and Australia is compared because this area of law has been extensively developed in these jurisdictions and may offer guidance on the regulation of the office of the chairman of South African companies. The article contends that the guidance provided to a chairman by South African legal instruments is neither clear nor adequate. It identifies several shortcomings in the regulation of the chairman and makes recommendations to enhance the South African statutory and corporate governance provisions regulating the chairman.Comparative and International Law Journal of Southern Africa20Comparative and International Law Journal of Southern Africa['Rehana Cassim']2024-03-28T00:00:00https://www.semanticscholar.org/paper/08fb7bfcd1775f56fb744364cb3ec0dae700f719 1a4b429e770c303237e39da9b65070323499e8d6The article delves into the institutional underpinnings of cryptoasset regulation, asserting that institutional relations and connections among entities extend beyond formal and informal norms and rules. These are complemented by the formation of relevant institutions and organizational relations between them, thereby shaping the institutional environment. This environment consists of the legislative and regulatory framework and the socio-economic mechanisms for regulation, all aimed at achieving collective benefits. The proposal is to view the institutional and economic relations in cryptoasset regulation as encompassing areas such as the protection of cryptoasset ownership rights, state regulation of cryptoasset circulation on markets and exchanges to ensure the industry’s full functionality, and the management of organizational and economic relations within the cryptomarket. Given the varied stances of countries towards cryptoassets, identifying clear regulatory trends is challenging. The article categorizes countries into four groups based on their regulatory approach to cryptoassets: centralized, decentralized, restrictive, or prohibited. Analyzing the regulatory practices in these groups allows for certain conclusions, notably that despite cryptocurrencies being commonly contrasted with fiat money, there’s a noticeable shift towards recognizing cryptoassets’ viability parallel to national currencies. The conclusion drawn from the analysis is that a unified global regulatory framework for cryptocurrencies has yet to emerge. However, national and international institutions are making significant strides in this domain, offering recommendations and standards for cryptoasset regulation aimed at mitigating associated transactional risks. Key recommendations include incorporating FATF-prescribed global principles and approaches into national laws, employing specific tools for national regulators to control and monitor risks (particularly concerning electronic wallets and exchanges, as well as financial institution risks), and enhancing international cooperation in supervising and implementing restrictions on cryptoasset circulation.Scientific Notes of Ostroh Academy National University, "Economics" Series00Scientific Notes of Ostroh Academy National University, "Economics" Series['Olena Bereslavska', 'Yana Pіdsosonna']2024-03-28T00:00:00https://www.semanticscholar.org/paper/1a4b429e770c303237e39da9b65070323499e8d6 21bcca34db15756e6242e7808a7f17513ea71613This paper on financial regulation addresses the extent to which rules on liability for information should be standardized across the EU/EEA region. The method applied is an analysis of legal documents. My finding is that further harmonization may lead to difficulties concerning procedural rules. Some authors suggest harmonizing the civil procedure for prospectus liability cases. This could reduce asymmetric information and thus contribute to efficient markets. However, mandatory disclosure comes with costs. These may increase if standards inconsistent with domestic procedures are imposed. The topic may be of interest for regulating other aspects of life, such as environmental information disclosure.Review of European and Comparative Law230Review of European and Comparative Law['Leif Sandtorv']2024-03-28T00:00:00https://www.semanticscholar.org/paper/21bcca34db15756e6242e7808a7f17513ea71613 68b365a6ec010990ea5b9b4e1e0d57faff78316eThis paper explores the transformative potential of Artificial Intelligence (AI) in personalizing marketing strategies. It delves into the theoretical underpinnings of consumer engagement sand investigates how AI can be leveraged to develop targeted and relevant marketing experiences. AI can personalize messages based on consumer behavior and demographics, influencing the processing route and maximizing engagement. This theory explores the use of game mechanics to motivate and engage users. AI can personalize gamified marketing experiences, tailoring rewards and challenges to individual consumer preferences, driving deeper engagement. Algorithms can analyze vast amounts of customer data to predict individual preferences and behaviors. This allows for targeted advertising, product recommendations, and content that resonates with specific consumer segments. Natural Language Processing (NLP), AI-powered NLP tools analyze customer reviews, social media conversations, and other forms of unstructured data. This allows brands to understand customer sentiment and personalize communication styles for optimal engagement AI-powered chatbots and virtual assistants can provide personalized customer support and product recommendations in real-time, fostering a more interactive and engaging brand experience. Potential Benefits and Considerations Personalized marketing messages and experiences cater to individual needs and preferences, leading to higher satisfaction and loyalty. By tailoring content and offerings to specific consumer segments, brands can establish a more relevant and relatable image. Improved Conversion Rates, Personalized marketing campaigns can be highly targeted and effective, leading to increased conversions and sales. Balancing personalization with data privacy concerns is crucial. Transparency and user control over data collection practices are essential. AI algorithms can perpetuate biases present in training data. Ensuring fairness and inclusivity in AI-powered marketing is paramount. AI is revolutionizing marketing personalization. By leveraging AI's analytical capabilities and understanding the theoretical aspects of consumer engagement, brands can develop targeted and relevant marketing strategies that foster deeper customer connections and drive business growth. Keywords:  AI Personalization, Consumer Engagement, Marketing Strategy, Theoretical Exploration, Data Privacy, Algorithmic Bias.International Journal of Management &amp; Entrepreneurship Research013This paper delves into the theoretical underpinnings of consumer engagement sand investigates how AI can be leveraged to develop targeted and relevant marketing experiences to foster deeper customer connections and drive business growth.International Journal of Management &amp; Entrepreneurship Research['Sodiq Odetunde Babatunde', 'Opeyemi Abayomi Odejide', 'Tolulope Esther Edunjobi', 'Damilola Oluwaseun Ogundipe']2024-03-28T00:00:00https://www.semanticscholar.org/paper/68b365a6ec010990ea5b9b4e1e0d57faff78316e 8eaeb53e4adc084fcf9addc8eec9d5f407523031Artificial Intelligence (AI) has emerged as a transformative force in the realm of product management, offering a theoretical framework that reshapes the journey from ideation to market penetration. This abstract provides a comprehensive overview of the theoretical underpinnings and practical applications of AI in product management, delineating its pivotal role across various stages of the product lifecycle. The ideation phase marks the inception of product development, where AI serves as a catalyst for innovation, augmenting creativity through advanced algorithms and data-driven insights. Market research and validation constitute the subsequent phase, where AI empowers product managers with sophisticated tools for analyzing consumer trends, preferences, and sentiments, thereby informing strategic decision-making processes. Prototyping represents a critical stage wherein AI facilitates rapid iteration and refinement, expediting the development cycle and enhancing product adaptability. Leveraging machine learning algorithms, product managers can swiftly iterate prototypes based on user feedback, ensuring alignment with evolving market demands. In the domain of product design, AI-driven solutions revolutionize user experience and usability, leveraging natural language processing, computer vision, and recommendation systems to personalize product interfaces and cater to diverse user preferences. Quality assurance and testing emerge as imperative phases wherein AI-driven testing strategies optimize reliability, performance, and scalability, mitigating risks associated with product failure and enhancing overall product quality. During the launch phase, AI enables product managers to orchestrate data-driven marketing strategies and optimize distribution channels, maximizing market penetration and consumer engagement. Predictive analytics, targeted advertising, and dynamic pricing algorithms optimize product launches, ensuring a competitive edge in the marketplace. In conclusion, AI permeates every facet of product management, transforming traditional paradigms and catalyzing innovation at every stage of the product lifecycle. By embracing AI's capabilities, product managers can navigate the dynamic landscape of modern markets with agility, precision, and foresight, driving sustained growth and competitive advantage. Keywords:  AI, Product Management, Creativity, Ideation, InnovationInternational Journal of Management &amp; Entrepreneurship Research06AI permeates every facet of product management, transforming traditional paradigms and catalyzing innovation at every stage of the product lifecycle, driving sustained growth and competitive advantage.International Journal of Management &amp; Entrepreneurship Research['Damilola Oluwaseun Ogundipe', 'Sodiq Odetunde Babatunde', 'Emmanuel Adeyemi Abaku']2024-03-28T00:00:00https://www.semanticscholar.org/paper/8eaeb53e4adc084fcf9addc8eec9d5f407523031 e9d42a2cbda56b9cce46bf6f079205ead39081fdCarbon emission trading system is a kind of environmental institutional arrangement to solve externalities through market mechanism, which is one of the most effective ways to deal with climate change in China and to control air pollution represented by haze pollution. In accordance with the requirement of "using system to protect ecological environment", China should position carbon emission trading system as the basic legal system of environmental protection in the new Environmental Protection Law, and from the aspects of reasonable determination of total carbon emission control mechanism, allocation of carbon emission trading quota, selection of pricing mechanism, and improvement of government supervision mechanism, etc. To improve China's emissions trading system.Frontiers in Humanities and Social Sciences120Frontiers in Humanities and Social Sciences['Zhongsen Zhang']2024-03-28T00:00:00https://www.semanticscholar.org/paper/e9d42a2cbda56b9cce46bf6f079205ead39081fd 57abb4734df43c950e06fd32471ed5de51364355The integration of artificial intelligence (AI) and robotics in healthcare has heralded a transformative era, offering unprecedented opportunities to enhance patient care, streamline processes, and augment medical professionals' capabilities. This review article examines the burgeoning influence of AI robotics in healthcare, encompassing various applications, benefits, challenges, and future prospects. We delve into the role of AI robotics across medical diagnosis, surgical interventions, rehabilitation, patient monitoring, and drug discovery. Additionally, we explore the ethical considerations, regulatory frameworks, and societal implications shaping the adoption and advancement of AI robotics in healthcare. By synthesizing current research and real-world implementations, this review elucidates the profound impact of AI robotics, paving the way for a revolutionized healthcare landscape.Asian Journal of Research in Computer Science00The role of AI robotics across medical diagnosis, surgical interventions, rehabilitation, patient monitoring, patient monitoring, and drug discovery is explored, as well as the ethical considerations, regulatory frameworks, and societal implications shaping the adoption and advancement of AI robotics in healthcare.Asian Journal of Research in Computer Science['Stephanie Ness', 'Teo Rong Xuan', 'Oluwatofunmi O. Oguntibeju']2024-03-28T00:00:00https://www.semanticscholar.org/paper/57abb4734df43c950e06fd32471ed5de51364355 7fdd772ef78ce2fd4240e19d38a20e2283fba162Introduction: AI is a field of great scope and latest leading fame and coming to it in medical field it has been very useful and impactful. Objectives: we can say that the objectives of bringing in artificial intelligence in healthcare applications revolve around maximising AI's capabilities to enhance working capabilities, efficiency, and personalized care delivery to patient’s, ultimately leading to improved patient outcomes and healthcare operation. Review of Literature: A mixer of different papers regarding patient’s, medical field and related things Results: AI in the medical field is critical to the advancement of medical robotic surgery, IT operations, health tracking, and diagnostics using automation, predictive analytics, and data-driven insights to improve efficiency, accuracy, and patient care in a variety of healthcare and technology areas. Discussion: By the study and research we can observe that, artificial intelligence is transforming the healthcare industry in numerous areas giving it various wings. Implementation: Healthcare companies may boost operational effectiveness and implement AI strategies, improve patient outcomes, and provide more individualized and efficient care by utilizing AI technologies. Conclusion: The use of AI in medical field is useful as it acts as a good assistance and support, artificial intelligence (AI) allows healthcare businesses to give the best scope and power. Keywords: Artificial intelligence, predictive analytics, Patient , Clinical decision-making, Management, robotic surgery, healthcare, efficient care.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The use of AI in medical field is useful as it acts as a good assistance and support, artificial intelligence (AI) allows healthcare businesses to give the best scope and power.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Shriya Prakash,']2024-03-28T00:00:00https://www.semanticscholar.org/paper/7fdd772ef78ce2fd4240e19d38a20e2283fba162 b0440d7c67550ce25fd574eb5d7fbc1c42c058c1The dynamics of teaching and learning have changed due to the rise of modern technologies, mainly social media and artificial intelligence (AI). This study determined the elementary teachers' response to the emerging use of social media and AI in educational methods and how these may impact the students' learning. Data was collected using a survey questionnaire composed of social media and AI use and views on the role of educators in the digital age. Statistical methods, including mean and standard deviation, were used in data analysis. The respondents demonstrated their proficiency in using social media and AI, representing a range of instructional roles and experience levels. Results indicate that Facebook and YouTube are frequently used online for assistive instructional information. Moreover, various AI techniques were used, such as automated grading systems, tailored learning platforms, and language translation services. Teachers incorporated these technologies and emphasized the need for further professional development; this shed light on the strategic integration of social media and AI in education holds tremendous potential to revolutionize learning paradigms, foster collaboration, and cultivate personalized learning experiences. However, it requires careful navigation of ethical, privacy, and accessibility considerations to harness its full benefits and ensure inclusivity and integrity within educational ecosystems.Studies in Technology and Education00Teachers incorporated these technologies and emphasized the need for further professional development; this shed light on the strategic integration of social media and AI in education.Studies in Technology and Education['Myra Felizarte', 'Sushmita Mae Camaso', 'Joan Grace Napuar', 'Susmitha Padrones', 'John Rey Oficiar', 'Jonathan Molina']2024-03-28T00:00:00https://www.semanticscholar.org/paper/b0440d7c67550ce25fd574eb5d7fbc1c42c058c1 48fcce31d606165faec76da6d6e4fbc5df601107The dawn of the 21st century has witnessed the automotive industry at the threshold of a paradigm shift, propelled by the integration of Artificial Intelligence (AI). This comprehensive exploration delves into the multifaceted role of AI as a harbinger of change, reshaping the contours of automotive design, manufacturing, and user experience. The paper commences with a historical overview, charting the ascent of AI from conceptual frameworks to its pervasive application across the automotive sector. A thorough literature review synthesizes seminal research, underscoring the operational efficiencies achieved, the impetus for innovation, and the challenges encountered in data stewardship and ethical governance. AI’s imprint on the industry is indelible, enhancing operational efficiency in production, quality control, and assembly, while simultaneously serving as the crucible for the development of connected and autonomous vehicles. The narrative, however, is nuanced by the challenges that accompany this technological march forward. The discourse addresses the intricacies related to data availability, quality, and system integration, alongside the ethical quandaries and regulatory conundrums that accompany the increased autonomy of AI-driven vehicles. Peering into the future, the paper proffers informed predictions about the trajectory of AI within the automotive industry, positing its potential to redefine mobility. Recommendations for future research and development are articulated, emphasizing the need to bridge identified literature gaps, particularly in the ethical deployment of AI and the pursuit of sustainable practices. In summation, the paper reaffirms the pivotal role of AI in the automotive industry, not solely as a catalyst for technological advancement but as a beacon for safety, efficiency, and environmental stewardship. The prospective landscape of AI in automotive paints a portrait of an interconnected, intelligent transportation ecosystem that promises to redefine the human experience of mobility. The conclusion encapsulates the essence of the discourse, advocating for a responsible embrace of AI that aligns with societal values and enhances the collective well- beingInternational Journal of Advanced Research in Science, Communication and Technology00The pivotal role of AI in the automotive industry is reaffirmed, not solely as a catalyst for technological advancement but as a beacon for safety, efficiency, and environmental stewardship.International Journal of Advanced Research in Science, Communication and Technology['Pankaj Yadav', 'Naveen']2024-03-28T00:00:00https://www.semanticscholar.org/paper/48fcce31d606165faec76da6d6e4fbc5df601107 43a3498d588188be1690f1cc1d475eb2d1ed1c21Purpose Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping. Design/methodology/approach Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model. Findings The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping. Originality/value This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF. Journal of Consumer Marketing880Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots.Journal of Consumer Marketing['Mon Thu A Myin', 'Kittichai Watchravesringkan']2024-03-28T00:00:00https://www.semanticscholar.org/paper/43a3498d588188be1690f1cc1d475eb2d1ed1c21 3417dea83485a51f925978a1b37cea2f927ffe36As artificial intelligence (AI) continues to advance, machine learning and natural language processing (NLP) offer new possibilities in various areas of psychology, such as therapy, diagnostics, treatment planning, demographic profiling, sentiment analysis, and consumer psychology. However, AI research in psychology is still lacking. This study aims to explore the specific applications of AI in the field of psychology. Firstly, the article delves into the efficacy and cost-effectiveness of chatbots in delivering cognitive behavioral therapy (CBT). Next, the article explores the current state of AI in detecting mental health disorders, highlighting the use of digital footprints as a diagnostic tool. Furthermore, the research investigates how AI can assist medical professionals in developing focused treatment plans. It analyzes studies that aim to optimize treatment strategies using AI technologies. The article also examines the influence of demographic characteristics on our understanding and application of psychology. Lastly, the article touches on the use of AI in analyzing sentiment to understand and predict consumer behavior. Ethical implications of using AI-driven psychological assessments are also discussed. It concludes with a summary of key findings and offers a glimpse into potential future developments in this field.Applied and Computational Engineering00The research investigates how AI can assist medical professionals in developing focused treatment plans using AI technologies, and analyzes studies that aim to optimize treatment strategies using AI technologies.Applied and Computational Engineering['Bingyao Wang']2024-03-28T00:00:00https://www.semanticscholar.org/paper/3417dea83485a51f925978a1b37cea2f927ffe36 3638b7c997242ea7da37046fa0c65bc5a0868783Machine learning (ML) methods have the potential of detailed insights of complex biological systems and today are increasingly used to analyse omics data for tasks such as the discovery of novel biomarkers and phenotype prediction. It can be extremely beneficial and powerful for scientists, domain experts, to easily run sophisticated, robust, and interpretable ML pipelines without the need for an in depth understanding of the code needed to train, tune, optimise ML algorithms. They can then focus on the biological interpretation and validation of the results and insights generated by ML models. Here, we present an entirely automated open-source explainable AI tool, AutoXAI4Omics, that performs classification and regression tasks from omics and tabular numerical data. AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g., selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset. Prior to ML analysis AutoXAI4Omics incorporates feature filtering options that are tailored to specific omic data types. Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic feature values and the targets under investigation e.g., predicted phenotypes, facilitating the discovery of actionable insights. AutoXAI4Omics is at: https://github.com/IBM/AutoXAI4Omics. Graphical AbstractbioRxiv10An entirely automated open-source explainable AI tool that performs classification and regression tasks from omics and tabular numerical data AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g., selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset.bioRxiv['James Strudwick', 'Laura-Jayne Gardiner', 'Kate Denning-James', 'N. Haiminen', 'Ashley Evans', 'Jennifer Kelly', 'Matthew Madgwick', 'F. Utro', 'Ed Seabolt', 'Christopher Gibson', 'Bharat Bedi', 'Daniel Clayton', 'Ciaron Howell', 'L. Parida', 'A. Carrieri']2024-03-28T00:00:00https://www.semanticscholar.org/paper/3638b7c997242ea7da37046fa0c65bc5a0868783 6daca23401694e3e8caee2cda85aa6c3d3c5852bThe abstract summarizes AI-enhanced digital forensics topics. It highlights the importance of AI in digital forensic investigations and outlines its major features, historical perspectives, and methodological evolution. The abstract describes how automated methods can streamline evidence collection and investigation. The historical perspective highlights digital forensic procedures from rudimentary file system investigations to AI-driven methods. This progression reflects digital crime's dynamic character and forensic method developments. The AI-enhanced digital forensics methodology includes establishing an effective component model, identifying datasets, gathering data, arranging studies, and considering ethical considerations. Representative datasets and ethical considerations are stressed in the abstract to ensure ethical and responsible AI application in forensic investigations. AI-based systems are evaluated using accuracy, false positive/negative rates, speed and efficiency, scalability, and durability. A straightforward comparison of these parameters across AI algorithms using bar graphs and grouped bar charts helps forensic investigators chooses strategies. In conclusion, AI-enhanced digital forensics is well understood, and performance evaluations, methodological concerns, historical evolution, and ethics are important. AI is being used in digital forensics as technology advances, giving investigators a strong tool to navigate the digital world accurately and efficiently. To use AI responsibly and effectively for justice, technique and ethics must be constantly improvedJournal of Electrical Systems190In conclusion, AI-enhanced digital forensics is well understood, and performance evaluations, methodological concerns, historical evolution, and ethics are important.Journal of Electrical Systems['Dr. Anushka Deepak Kadage', 'Dr. Banoth Meghya', 'Nayak', 'Dr. Vishal Sharad', 'Hingmire', 'Dr. Kirti Wanjale', 'N. Bogiri', 'Prashant L Mandale']2024-03-28T00:00:00https://www.semanticscholar.org/paper/6daca23401694e3e8caee2cda85aa6c3d3c5852b 8991bf15dfef8d7edfcca1f0c6aed30db1454007Purpose: The study aims to examine the synergistic effects of integrating Security Information and Event Management (SIEM), Security Orchestration, Automation, and Response (SOAR), and Artificial Intelligence (AI) technologies in enhancing cybersecurity frameworks. It explores how this combination can lead to a transformative era in cybersecurity, focusing on the improved efficacy of threat management and incident response. Methodology: An analytical approach was used to investigate the integration trends between SIEM and SOAR technologies, underpinned by advancements in AI. This method emphasizes accelerated incident detection and response, enriched threat intelligence collaboration, and fortified security strategies. Findings: The fusion of SIEM, SOAR, and AI technologies has led to a paradigm shift in cybersecurity, offering unparalleled efficiency in threat management and a significant reduction in the impacts of cyber incidents on entities. It highlights the accelerated detection and response to incidents and the enhancement of threat intelligence collaboration and security strategies. Unique Contribution to Theory, Practice, and Policy: This study contributes to the field by presenting invaluable insights for cybersecurity practitioners and entities aiming to strengthen their defenses against an evolving digital threat landscape. It advocates for a proactive orchestration of security measures, underlining the strategic implications of the SIEM-SOAR-AI triad for future cybersecurity endeavors. Recommendations are provided for entities to adopt this integrated approach to enhance their cybersecurity frameworks effectively.International Journal of Computing and Engineering160The fusion of SIEM, SOAR, and AI technologies has led to a paradigm shift in cybersecurity, offering unparalleled efficiency in threat management and a significant reduction in the impacts of cyber incidents on entities.International Journal of Computing and Engineering['Shanmugavelan Ramakrishnan', 'Dinesh Reddy Chittibala']2024-03-28T00:00:00https://www.semanticscholar.org/paper/8991bf15dfef8d7edfcca1f0c6aed30db1454007 47af5b65c125badb1a639567c8e4676e4aae6278Since the Artificial intelligence (AI) revolution catalyzed by ChatGPT, the discourse of students’ digital competence has become prevalent in German higher education institutions (HEIs). While educators recognize the potential for using AI in higher education, concerns persist about students needing more necessary skills. This paper presents findings from a comprehensive lecturer survey that provides insights into educators’ perspectives on the opportunities and challenges associated with AI integration in HEIs. Furthermore, it addresses the conditions required for successful AI implementation in German HEIs to promote, rather than hinder, students’ digital competence and future skills.Zeitschrift für Hochschulentwicklung180Findings from a comprehensive lecturer survey are presented that provide insights into educators’ perspectives on the opportunities and challenges associated with AI integration in HEIs and address the conditions required for successful AI implementation in German HEIs to promote, rather than hinder, students’ digital competence and future skills.Zeitschrift für Hochschulentwicklung['Petko Maznev', 'C. Stützer', 'Stephanie Gaaw']2024-03-28T00:00:00https://www.semanticscholar.org/paper/47af5b65c125badb1a639567c8e4676e4aae6278 64dbe22d26aab6386dc59730ec9100a1036c1837This paper details our reflections on experiencing collaborative learning and collective sensemaking through a discussion-focused event aimed at exploring generative artificial intelligence (AI) and its impacts on adult learning. As generative AI becomes increasingly relevant in educational contexts, adult learning practitioners must effectively navigate the challenges and opportunities presented by these technologies. Through a facilitated discussion session with graduate students studying adult learning, we explored many of the impacts and viewpoints of generative AI, in relation to adult learning. In this paper, we provide a brief overview of adult learning and generative AI and offer a reflection on our experience facilitating an event with graduate students and faculty in an adult learning graduate program.International Journal of Advanced Corporate Learning (iJAC)00A brief overview of adult learning and generative AI is provided and a reflection on the experience facilitating an event with graduate students and faculty in an adult learning graduate program is offered.International Journal of Advanced Corporate Learning (iJAC)['DJ Jeffries', 'Brian Ahn']2024-03-28T00:00:00https://www.semanticscholar.org/paper/64dbe22d26aab6386dc59730ec9100a1036c1837 15278dd38441bbb0c0d025753faac98ce8376552The integration of Artificial Intelligence (AI) within financial markets has become increasingly pivotal, particularly in emerging economies where efficiency and accessibility remain significant challenges. This abstract explores how AI technologies are reshaping financial market development, with a specific focus on enhancing efficiency and accessibility in emerging economies. AI facilitates automation of routine tasks, predictive modeling, and robust risk management, thereby streamlining operations and reducing costs. Moreover, AI-driven solutions democratize financial services, offering personalized advice and expanding financial inclusion initiatives. Despite its transformative potential, challenges such as data privacy concerns, regulatory barriers, and technological infrastructure limitations persist. By examining successful AI implementations and case studies, this review underscores the importance of collaborative efforts between public and private sectors to overcome these challenges. Looking ahead, the abstract emphasizes the need for policymakers to develop conducive regulatory frameworks and encourages stakeholders to embrace AI technologies for sustainable financial market development in emerging economies. Keywords:  AI, Financial Market Development, Efficiency, Accessibility, Emerging Economies, Automation.Finance &amp; Accounting Research Journal00This abstract explores how AI technologies are reshaping financial market development, with a specific focus on enhancing efficiency and accessibility in emerging economies, with a specific focus on enhancing efficiency and accessibility in emerging economies.Finance &amp; Accounting Research Journal['Nneka Adaobi Ochuba', 'Adetumi Adewumi', 'David Olanrewaju Olutimehin']2024-03-28T00:00:00https://www.semanticscholar.org/paper/15278dd38441bbb0c0d025753faac98ce8376552 9877098d806dede50ad1bcf01748780f9bccff52The onset of new, more accessible, artificial intelligence (AI) technologies marks a significant turning point for libraries, ushering in a period rich with both unparalleled opportunities and complex challenges. In this era of swift technological transformation, libraries stand at a critical intersection. To effectively chart this transition, two quick polls were conducted among members of the Association of Research Libraries (ARL). The first poll, which ran in April 2023, provided an initial snapshot of the AI landscape in libraries. The second poll, carried out in December 2023, continued this inquiry, offering a comparative perspective on the evolving dynamics of AI use and possibilities in library services. This study analyzes and juxtaposes the outcomes of these two surveys to better understand how library leaders are managing the complexities of integrating AI into their operations and services. It specifically seeks to capture changing perspectives on the potential impact of AI, assess the extent of AI exploration and implementation within libraries, and identify AI applications relevant to the current library environment. The insights derived from this comparative analysis shed light on the role of libraries in an increasingly AI-driven era, providing strategic directions and highlighting practices in research libraries.90A comparative analysis of two quick polls conducted among members of the Association of Research Libraries offers a comparative perspective on the evolving dynamics of AI use and possibilities in library services and sheds light on the role of libraries in an increasingly AI-driven era.['Leo S. Lo', 'Cynthia Hudson Vitale']2024-03-28T00:00:00https://www.semanticscholar.org/paper/9877098d806dede50ad1bcf01748780f9bccff52 194251f752071450eca2b78e0a950e36fd2270c7The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants’ familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.Journal of Personalized Medicine440Assessing the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI, and identify challenges, limitations, and areas for improvement are provided.Journal of Personalized Medicine['Sarah Alshehri', 'K. Alahmari', 'Areej Alasiry']2024-03-28T00:00:00https://www.semanticscholar.org/paper/194251f752071450eca2b78e0a950e36fd2270c7 97d6ed19723b4c66b44f1ad235b7d4cf0cd2a611Computer vision (CV), a type of artificial intelligence (AI) that uses digital videos or a sequence of images to recognize content, has been used extensively across industries in recent years. However, in the healthcare industry, its applications are limited by factors like privacy, safety, and ethical concerns. Despite this, CV has the potential to improve patient monitoring, and system efficiencies, while reducing workload. In contrast to previous reviews, we focus on the end-user applications of CV. First, we briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality). We then review the developments of CV in the hospital setting, outpatient, and community settings. The recent advances in monitoring delirium, pain and sedation, patient deterioration, mechanical ventilation, mobility, patient safety, surgical applications, quantification of workload in the hospital, and monitoring for patient events outside the hospital are highlighted. To identify opportunities for future applications, we also completed journey mapping at different system levels. Lastly, we discuss the privacy, safety, and ethical considerations associated with CV and outline processes in algorithm development and testing that limit CV expansion in healthcare. This comprehensive review highlights CV applications and ideas for its expanded use in healthcare.Journal of Imaging2631This comprehensive review highlights CV applications and ideas for its expanded use in healthcare and briefly review and categorize CV applications in other industries (job enhancement, surveillance and monitoring, automation, and augmented reality).Journal of Imaging['H. Lindroth', 'Keivan Nalaie', 'Roshini Raghu', 'I. N. Ayala', 'Charles Busch', 'Anirban Bhattacharyya', 'Pablo Moreno Franco', 'Daniel Diedrich', 'B. Pickering', 'V. Herasevich']2024-03-28T00:00:00https://www.semanticscholar.org/paper/97d6ed19723b4c66b44f1ad235b7d4cf0cd2a611 446943c5827a1ac9512d76227914c1aeb8d3e1ceIn the sub-forum “Transformation of Artificial Intelligence and Paradigm in Science and Technology Innovation” of Tianjin Forum 2023, scholars from both China and abroad discussed the impact of artificial intelligence. With the deepening development of the new technological revolution and industrial transformation, new-generation artificial intelligence technology is continuously making breakthroughs in research and application. AI not only promotes the transformation of material productivity, but also gradually emerges as a critical engine for enhancing the knowledge productivity. Since the emergence of the concept of "AI for Science", it has become an obvious proposition generally accepted by the academic community for its tremendous enabling capabilities for knowledge production. Artificial intelligence technology continues to achieve breakthroughs and gain widespread infiltration into the scientific research field, introducing new elements and momentum into scientific research and significantly catalyzing the enhancement of scientific research efficiency and paradigm shifts. AI-driven scientific research has become a new frontier in the global application of artificial intelligence. However, as artificial intelligence triggers paradigm shifts in social science research, the issues of data security, ethics, and value alignment that it brings about need to draw attention from the social science community. Scholars participating in the forum generally concurred that, in regulating the enabling role of AI in scientific research, it is imperative to take into account the specificities of various disciplines and stages, and comprehensively reasonable rational risk allocation mechanisms, platform. support mechanisms, and collaborative participation frameworks to achieve prudent, agile, and full lifecycle regulation of AI for Science.Journal of Chinese economics00In regulating the enabling role of AI in scientific research, it is imperative to take into account the specificities of various disciplines and stages, and comprehensively reasonable rational risk allocation mechanisms, platform, and collaborative participation frameworks to achieve prudent, agile, and full lifecycle regulation of AI for Science.Journal of Chinese Economy['Jie Liu', 'Feng Zheng', 'Xiangyu Ma', 'Yu Dong', 'Gang Liu']2024-03-28T00:00:00https://www.semanticscholar.org/paper/446943c5827a1ac9512d76227914c1aeb8d3e1ce 9adc5a8a72b0a26a32a31ddd50b751efa94253c7Antimicrobial resistance (AMR) is a growing public health problem in the One Health dimension. Artificial intelligence (AI) is emerging in healthcare, since it is helpful to deal with large amounts of data and as a prediction tool. This systematic review explores the use of AI in antimicrobial stewardship programs (ASPs) and summarizes the predictive performance of machine learning (ML) algorithms, compared with clinical decisions, in inpatients and outpatients who need antimicrobial prescriptions. This review includes eighteen observational studies from PubMed, Scopus, and Web of Science. The exclusion criteria comprised studies conducted only in vitro, not addressing infectious diseases, or not referencing the use of AI models as predictors. Data such as study type, year of publication, number of patients, study objective, ML algorithms used, features, and predictors were extracted from the included publications. All studies concluded that ML algorithms were useful to assist antimicrobial stewardship teams in multiple tasks such as identifying inappropriate prescribing practices, choosing the appropriate antibiotic therapy, or predicting AMR. The most extracted performance metric was AUC, which ranged from 0.64 to 0.992. Despite the risks and ethical concerns that AI raises, it can play a positive and promising role in ASP.Antibiotics500The predictive performance of machine learning (ML) algorithms, compared with clinical decisions, in inpatients and outpatients who need antimicrobial prescriptions is summarized, in inpatients and outpatients who need antimicrobial prescriptions.Antibiotics['Rafaela Pinto-de-Sá', 'B. Sousa‐Pinto', 'Sofia Costa-de-Oliveira']2024-03-28T00:00:00https://www.semanticscholar.org/paper/9adc5a8a72b0a26a32a31ddd50b751efa94253c7 24524c4c2dafa018a67a4d3306c1642719dfa4b0the article is dedicated to exploring innovative aspects of contemporary trends in the application of artificial intelligence in surgery. The paper provides an analysis of the scientific discourse regarding the impact of artificial intelligence on the development of surgical techniques and the improvement of diagnostic accuracy and surgical intervention effectiveness. The aim of the article is to examine and analyze the innovative features of artificial intelligence in modern surgery. To assess the current state of research on the use of Artificial Intelligence in surgery, a systematic search of scientific publications in various databases was conducted. The information from selected publications was then systematized and integrated to identify key trends in the use of AI in surgery and to synthesize the results for determining innovative aspects and challenges. The article includes an analysis comparing contemporary software products of robotic surgical systems based on artificial intelligence algorithms. The achieved results in this review and analysis of innovative features of artificial intelligence in surgery indicate a significant contribution of this technology to modern medical practice, where the use of artificial intelligence in surgery contributes to a substantial improvement in diagnostic accuracy and surgical planning, thereby affecting the overall efficiency of medical interventions.The Ukrainian Scientific Medical Youth Journal30The achieved results in this review and analysis of innovative features of artificial intelligence in surgery indicate a significant contribution of this technology to modern medical practice, where the use of artificial intelligence in surgery contributes to a substantial improvement in diagnostic accuracy and surgical planning, thereby affecting the overall efficiency of medical interventions.The Ukrainian Scientific Medical Youth Journal['Vladyslav Bilodid', 'Katarzyna Welgan']2024-03-28T00:00:00https://www.semanticscholar.org/paper/24524c4c2dafa018a67a4d3306c1642719dfa4b0 3168e3ded6ecf455e0a55a72071806eff0c13bb9Horticulture became a key player in the development of UT. The total land area of Jammu and Kashmir under horticulture is 332704, among which 214162 in the Kashmir valley and 118542 in the Jammu region. The district Baramulla has achieved first rank in production in apples with 404089 metric tons, and also covers an area of 25231 hectares. Horticulture of UT of Jammu and Kashmir contributes 8-10% contribution to the SGDP (DHK, 2021). In 48 years, the area under horticulture shows a growth rate of 25%.The growth of production of horticulture has also increased 10 thousand metric to 25 Lakh metric tons during 1950-2022.In this paper we will analysis growth in production, productivity, area and State Gross Domestic Product through the Compound Growth Rate from 2012 to 2022. Artificial Intelligence will become a game changer for horticulture sector, which is facing loses consistently from the last few years. Need of the hour to improve the technique like artificial intelligence and procedures for the betterment of horticulture, especially open national, use of drones in farms, highway 44 during the peak season.Journal of Electrical Systems190Analysis of growth in production, productivity, area and State Gross Domestic Product through the Compound Growth Rate from 2012 to 2022 finds that Artificial Intelligence will become a game changer for horticulture sector, which is facing loses consistently from the last few years.Journal of Electrical Systems['Imtiyaz Majid , Tawheed Nabi']2024-03-28T00:00:00https://www.semanticscholar.org/paper/3168e3ded6ecf455e0a55a72071806eff0c13bb9 5318b5bfed298ddc11aebe316ea2ddd3e8c6c224Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and imaging parameters could predict hospitalization for acute HF exacerbation in patients undergoing SPECT/CT myocardial perfusion imaging. Methods: The HF risk prediction model was developed using data from 4,766 patients who underwent SPECT/CT at a single center (internal cohort). The algorithm used clinical risk factors, stress variables, SPECT imaging parameters, and fully automated deep learning-generated calcium scores from attenuation CT scans. The model was trained and validated using repeated hold-out (10-fold cross-validation). External validation was conducted on a separate cohort of 2,912 patients. During a median follow-up of 1.9 y, 297 patients (6%) in the internal cohort were admitted for HF exacerbation. Results: The final model demonstrated a higher area under the receiver-operating-characteristic curve (0.87 ± 0.03) for predicting HF admissions than did stress left ventricular ejection fraction (0.73 ± 0.05, P < 0.0001) or a model developed using only clinical parameters (0.81 ± 0.04, P < 0.0001). These findings were confirmed in the external validation cohort (area under the receiver-operating-characteristic curve: 0.80 ± 0.04 for final model, 0.70 ± 0.06 for stress left ventricular ejection fraction, 0.72 ± 0.05 for clinical model; P < 0.001 for all). Conclusion: Integrating SPECT myocardial perfusion imaging into an artificial intelligence-based risk assessment algorithm improves the prediction of HF hospitalization. The proposed method could enable early interventions to prevent HF hospitalizations, leading to improved patient care and better outcomes.Journal of Nuclear Medicine360Journal of nuclear medicine : official publication, Society of Nuclear Medicine['Attila Feher', 'B. Bednarski', 'Robert J. H. Miller', 'A. Shanbhag', 'Mark Lemley', 'Leonidas Miras', 'A. J. Sinusas', 'Edward J. Miller', 'Piotr J. Slomka']2024-03-28T00:00:00https://www.semanticscholar.org/paper/5318b5bfed298ddc11aebe316ea2ddd3e8c6c224 b9c7b704d84c984ef3f39398ee770fb5b732385dArtificial intelligence (AI) can sometimes resolve difficulties that other advanced technologies and humans cannot. In medical diagnostics, AI has the advantage of processing figure recognition, especially for images with similar characteristics that are difficult to distinguish with the naked eye. However, the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues. In this letter, regarding an original study presented by Takayama et al, we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images, including the recognition of non-obvious difference between the normal parts and pathological ones, which were impossible to be distinguished by naked eyes, such as the basic constitutional elements of pixels and grayscale, special molecules or even some metal ions which involved into the diseases occurrence.World Journal of Radiology30It is suggested that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images, including the recognition of non-obvious difference between the normal parts and pathological ones, which were impossible to be distinguished by naked eyes.World Journal of Radiology['Jian-She Yang', 'Qiang Wang', 'Zhong-Wei Lv']2024-03-28T00:00:00https://www.semanticscholar.org/paper/b9c7b704d84c984ef3f39398ee770fb5b732385d 4758a188c3e3511f1a516c206ac47d597ff6634aPurpose The objective of this study is to explore the impact of artificial intelligence (AI) development on the sports industry labor market, the ways in which AI has influenced the demand for labor, created new job opportunities, and impacted existing job roles. Methodology It refers to the inductive approach in the spirit technological determinism theory. It is based on the literature review and written qualitative, semi-structured interviews (N = 14) with sports human resources, management, and technology professionals (purposive sampling). Analysis involved inductive coding and line-by-line analytics of the data. Findings The labor market implications of AI in the sports industry are multifaceted. New job roles are likely to emerge, demanding a blend of AI expertise, data-analysis skills, and sports domain knowledge. Professionals in roles such as sports data analysts and marketing experts may find increasing opportunities. However, certain jobs undergo transformation as AI automates routine tasks. It requires individuals to upskill or transition to roles that require a deeper understanding of AI. This necessitates the creation of responsibilities focused on ethical AI governance and oversight. Originality It is important to research the impact of AI dissemination on the sports industry labor market in a holistic manner because the effects of AI are complex and far-reaching. While there are potential benefits to the implementation of AI, there are also potential risks and challenges that need to be addressed, the implementation of AI in the sports industry could have broader social and ethical implications that need to be considered.Frontiers in Sports and Active Living290The ways in which AI has influenced the demand for labor, created new job opportunities, and impacted existing job roles are explored, based on the literature review and written qualitative, semi-structured interviews with sports human resources, management, and technology professionals.Frontiers in Sports and Active Living['Ekaterina Glebova', 'D. Madsen', 'Paulína Mihaľová', 'Gábor Géczi', 'Alexandra Mittelman', 'Bojan Jorgič']2024-03-28T00:00:00https://www.semanticscholar.org/paper/4758a188c3e3511f1a516c206ac47d597ff6634a e6ef0f9c7272ffe2a6f6a5df733bb33e76f81a9dIn a world where innovation meets compassion, cancer continues to cast a long and daunting shadow across Asian nations, which are home to nearly 4.6 billion people. This research primarily examines India while encompassing broader Asian healthcare perspectives. We explore the potential of artificial intelligence (AI) to revolutionize cancer care, particularly in India, where diverse healthcare challenges persist. Data from Kharghar, Maharashtra, India, underscore the local community’s eagerness to embrace AI technologies. However, the staggering costs of cancer care pose formidable barriers, particularly in developing and underdeveloped regions across Asia. This study advocates for strategic government intervention to make AI-driven cancer care accessible, potentially reducing mortality rates and offering hope to millions of cancer patients in the region.Clinical Social Work and Health Intervention160This study advocates for strategic government intervention to make AI-driven cancer care accessible, potentially reducing mortality rates and offering hope to millions of cancer patients in the region.Clinical Social Work and Health Intervention['D. Chhaperia', 'K. Khanna', 'Claus Muss']2024-03-28T00:00:00https://www.semanticscholar.org/paper/e6ef0f9c7272ffe2a6f6a5df733bb33e76f81a9d 7df791f1ba5fb0ac60d52a6087a84dcb3fa4df0cThe aim of this work was to determine the actions needed to adopt and adapt Generative Artificial Intelligence (GenAI) for efficient and responsible use in the organization, without affecting the contribution of the collaborator to his/her work activities. A bibliographic review of the scientific literature and information published by consulting companies on the current development of research related to GenAI and its impact on organizations was carried out. Artificial intelligence search tools, academic search engines were used, and criteria for the inclusion and exclusion of publications were established. As a result, necessary actions were identified for the adoption, adaptation, efficient and responsible use, and interaction of GenAI with employees in their work environment. These actions include the adjustment of processes, infrastructure, and resources; capacity-building for integration and a culture of innovation, protocol development, staff training, creation of flexible and supportive working environments, collaboration with regulators, transformation of work; and alignment of staff management practices. It was concluded that GenAI is having a major impact on organizations by automating processes and increasing productivity and efficiency. It is essential to address actions in three categories: staff training, fostering a culture of innovation, ethics, and accountability in the use of this technology, and its efficient adoption and adaptation without affecting the contribution of employees. This research helps to identify the elements needed to deepen research development and define, in real contexts, the effectiveness of the adoption and efficient adaptation of GenAI in internal processes and interaction with collaborators, with the aim of promoting best practices that generate value through its use.International Journal of Current Science Research and Review480GenAI is having a major impact on organizations by automating processes and increasing productivity and efficiency and its efficient adoption and adaptation without affecting the contribution of employees is concluded.International Journal of Current Science Research and Review['Noé Chávez Hernández']2024-03-28T00:00:00https://www.semanticscholar.org/paper/7df791f1ba5fb0ac60d52a6087a84dcb3fa4df0c dc84c401632d1f5afa1311624aebd00d055bc1c5Artificial intelligence (AI) offers potential benefits in the interconnected fields of obstetrics, maternal-fetal medicine, and neonatology to bridge disciplinary silos for a unified approach. Artificial intelligence has the capacity to improve diagnostic accuracy and clinical decision making for the birthing parent-neonate dyad. There is an inherent risk of ingrained biases in AI that perpetuate existing inequalities; thus, care must be taken to include diverse data sets with interdisciplinary collaboration that centers equitable AI implementation. As AI plays an increasingly important role in perinatal care, we advocate for its cautious, equity-focused application to benefit the perinatal dyad while avoiding the intensification of health care disparities and disciplinary silos.Obstetrics and Gynecology400As AI plays an increasingly important role in perinatal care, this work advocate for its cautious, equity-focused application to benefit the perinatal dyad while avoiding the intensification of health care disparities and disciplinary silos.Obstetrics and gynecology['Ryan M McAdams', 'Tiffany L Green']2024-03-28T00:00:00https://www.semanticscholar.org/paper/dc84c401632d1f5afa1311624aebd00d055bc1c5 9aad5c594b65efaf05321b5cec53843a80ff18f3The rise of artificial intelligence (AI) has catalyzed transformative changes across various domains, with neurology emerging as a particularly affected field. This essay delves into a comprehensive analysis of how AI's integration into medicine and neurology revolutionizing diagnostic and therapeutic practices is while simultaneously posing challenges to preserving the humanistic essence of medicine. By examining both technological advancements and the indispensability of empathy and patient-centered care, this exploration aims to understand how AI can coexist with a humanized approach to neurology, or "soulful neurology," as proposed by Prof. Andrew Lees. The preservation of a balance between technological innovation and a humanistic approach will define the essence of neurological practice in the coming decades.Revista Ciências da Saúde CEUMA00This essay delves into a comprehensive analysis of how AI's integration into medicine and neurology revolutionizing diagnostic and therapeutic practices is while simultaneously posing challenges to preserving the humanistic essence of medicine.Revista Ciências da Saúde CEUMA['Pedro Renato de Paula Brandão']2024-03-28T00:00:00https://www.semanticscholar.org/paper/9aad5c594b65efaf05321b5cec53843a80ff18f3 341688622039f29319e590f406e7c1515c73af7cMedizinische Klinik - Intensivmedizin und Notfallmedizin500Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.Medizinische Klinik, Intensivmedizin und Notfallmedizin['André Baumgart', 'Grietje Beck', 'David Ghezel-Ahmadi']2024-03-28T00:00:00https://www.semanticscholar.org/paper/341688622039f29319e590f406e7c1515c73af7c 5f8ab128657a361bf4343225cead8ddd32de98e2Hydrological modelling plays a critical role in managing water resources, especially in arid and semi-arid regions where water scarcity is a major challenge. With the emergence of artificial intelligence (AI), hydrological modelling has experienced a significant transformation in recent years. This paper reviews the recent advances in AI-based hydrological modelling and examines its potential applications in water resource management. The study highlights the role of AI in enhancing the accuracy of hydrological models and facilitating more efficient and sustainable water management practices. The results suggest that AI-based hydrological models have the potential to revolutionize the way water resources are managed, and that future research in this area is warranted.Geography and water resources00The results suggest that AI-based hydrological models have the potential to revolutionize the way water resources are managed, and that future research in this area is warranted.Geography and water resources['G. М. Kambarbekov', 'A. Y. Baimaganbetov']2024-03-28T00:00:00https://www.semanticscholar.org/paper/5f8ab128657a361bf4343225cead8ddd32de98e2 3c7f77b53359ea9bf193bd7b085c7728b9883c35Artificial intelligence (AI), which has attracted great attention in recent years, has been widely used in the field of education as in many other fields. AI in education is used to improve student learning, support teachers and provide a more personalized educational experience. AI plays an important role with adaptive learning systems in improving students' learning processes. These systems assess students' individual needs and provide them with appropriate learning materials. AI also monitors students' performance, identifies their weaknesses, and provides additional support in these areas. Thus, students are enabled to learn more effectively and to reveal their full potential. By supporting teachers, AI facilitates classroom management and helps teachers use their time more efficiently. Automated assessment systems allow teachers to quickly assess assignments and exams, while improving the process of providing feedback. In addition, AI also helps teachers understand students' interests and learning styles, so that more personalized instruction can be offered. Another important use of AI in education is student counseling. AI-based counseling systems can guide students in matters such as career choices, university applications, and academic planning. These systems can provide students with viable career options, support the application process, and help them identify their future goals. As a result, the use of AI in education has great potential to improve student learning processes, provide support to teachers and provide a more personalized educational experience. In this study; The subject of AI was examined in a general framework under the title of education and the role of AI in education was discussed. It is thought that AI will contribute to the field by revealing the teacher and how it can be used in the field of education.İnsan ve Toplum Bilimleri Araştırmaları Dergisi00The subject of AI was examined in a general framework under the title of education and the role of AI in education was discussed, thought that AI will contribute to the field by revealing the teacher and how it can be used in the field of education.İnsan ve Toplum Bilimleri Araştırmaları Dergisi['Ayşe Alkan']2024-03-28T00:00:00https://www.semanticscholar.org/paper/3c7f77b53359ea9bf193bd7b085c7728b9883c35 4a17eba62489366175805946b9e4811ff1325d27 With a call to action from the health-care industry and the Indian government, there are significant gaps in health-care professionals’ uptake and utilization of artificial intelligence (AI)-based tools. This study attempts to explore the current perceptions and readiness for AI among health-care workers. A web-based questionnaire comprising seven sections on descriptive educational and occupational data, AI familiarity level, role-specific training benefits, training advantages, implementation issues, driving factors, and perceived risks was designed from a literature search. Two additional domains of perception on professional impact and preparedness for AI in health care were estimated using a prevalidated Shinners AI Perception tool. Of the 402 study participants, 192 (47.9%) were doctors from diverse specializations, and the remaining 209 (52.1%) were undergraduate medical and nursing students and affiliated health professionals. Although 79.8% of participants had never attended a course on AI, 82% agreed on the need for training in AI to explore new opportunities in their respective fields. 72.1% of participants agreed that data privacy and confidentiality posed the most significant challenge to AI implementation among the studied factors. This survey reveals awareness regarding AI, which is attributable to a lack of formal training received by health-care professionals. Most participants believed that AI could improve population health outcomes, and collective efforts are needed to make this belief a reality. Journal of Marine Medical Society190This survey reveals awareness regarding AI, which is attributable to a lack of formal training received by health-care professionals, which is attributable to a lack of formal training received by health-care professionals.Journal of Marine Medical Society['Manvinder Tezpal', 'Subhodeep Ghosh', 'Radhika Lalwani', 'Jyoti Yadav', 'Arun Kumar Yadav']2024-03-28T00:00:00https://www.semanticscholar.org/paper/4a17eba62489366175805946b9e4811ff1325d27 2b7a76111a8db5d6c96511364aa40d38ec5022eeThe aim of this research is to explore the role of artificial intelligence in achieving justice, the challenges it faces, and the opportunities available, including issues of access to justice, power balance, human bias, and significant applications of artificial intelligence systems. The current uses of artificial intelligence in the justice system were reviewed, such as legal analysis, decision-making, and practical applications of artificial intelligence in the field of justice. Additionally, potential future models for its application in the field of justice were discussed. Ethical and legal issues associated with relying on artificial intelligence in the justice system were also examined. By studying current examples and relevant scientific studies on the impact of artificial intelligence on justice, the focus was on both the positive and negative aspects. Important findings were identified, and recommendations were made on how to enhance the use of artificial intelligence in serving justice in ways that ensure balance, equality, and fairness for all parties involved while mitigating the anticipated negative effects of its use.The Journal of Social Studies00Recommendations were made on how to enhance the use of artificial intelligence in serving justice in ways that ensure balance, equality, and fairness for all parties involved while mitigating the anticipated negative effects of its use.Journal of Social Studies['Dr.Hala Mohamed Imam Mohamed']2024-03-28T00:00:00https://www.semanticscholar.org/paper/2b7a76111a8db5d6c96511364aa40d38ec5022ee 476698a80db2e77eba771b448308b67678bfa7c9Artificial Intelligence (AI) has become a part of human life. The application of artificial intelligence in the field of oral and maxillofacial surgery is tremendous. This article focuses on the execution of algorithms in oral surgery to improve patient care and surgeons’ skill. It also explores the biases, privacy and confidentiality and threat to human resources when used at a large scale.Dental Journal of Indira Gandhi Institute of Medical Sciences90The execution of algorithms in oral surgery to improve patient care and surgeons’ skill is focused on and the biases, privacy and confidentiality and threat to human resources when used at a large scale are explored.Dental Journal of Indira Gandhi Institute of Medical Sciences['Aravind Jayabalan', 'Indra Kumar Periyasamy', 'Saravanan Kandasamy', 'Arrvinthan Su', 'Infanta Aj', 'Narendar Ramesh']2024-03-28T00:00:00https://www.semanticscholar.org/paper/476698a80db2e77eba771b448308b67678bfa7c9 1d4c7ecb2f15bc3a6c1877dcf4baee3d19d7b433This research paper concerns the copyright-law consequences of generating literary and artistic creations resulting from the “creative activity” of artificial intelligence (AI). The essence of the problem that rapidly gains practical significance boils down to the question whether, at present (de lege lata) and in the future (de lege ferenda), such creations can be protected under copyright law and who should possibly be considered to be the author. The legal-dogmatic analysis of the normative matter, the current state of science and the case law in force applicable here, shows that under the current legislation the creations generated by AI do not fall within the definition of creative work and do not form the subject of copyright as they were not created by human being. Therefore, the AI may not be considered to be the author and thus endowed with a copyright and even more a moral right to the work. In the de lege ferenda perspective, the proposals to cover AI-generated assets by protection outside the copyright law area, e.g. through related rights or the institution of work made for hire, are not fully convincing for axiological reasons, i.e. the difficulty of identifying a person who deserves to benefit from such protection. Nor can the proposal to grant subjective rights to AI itself be supported, since this would mean changing the axiom of the copyright law, namely that only a human being can be the author. If copyright is to survive as a right of a human creator, which should be advocated, then in the light of this regulation the literary and artistic creations generated by AI should remain in the public domain.Studia Iuridica Lublinensia00If copyright is to survive as a right of a human creator, which should be advocated, then in the light of this regulation the literary and artistic creations generated by AI should remain in the public domain.Studia Iuridica Lublinensia['Jerzy Szczotka']2024-03-28T00:00:00https://www.semanticscholar.org/paper/1d4c7ecb2f15bc3a6c1877dcf4baee3d19d7b433 2a2c0560c7ad2d0c628f494fafc8a2a09cacaddeSubject. The article considers the results of the National Strategy for the Development of Artificial Intelligence implementation, for the period up to 2030. Objectives. The study aims at analyzing the chronology and content of fundamental documents for the development of artificial intelligence in Russia, systematizing open factual data on achieved results, comparing the results with planned and global ones, and comparing the strategic indicators of 2030 with the achieved level. Methods. I employ statistical methods of analysis, like relative comparison indicators, analytical indicators of dynamics series. Legal documents, reviews of analytical centers, and media materials served as information base of the study. Results. The paper shows the chronological sequence of legal documents publication that ensure the implementation of the strategy. Due to the lack of systematized actual quantitative parameters of artificial intelligence, for the purposes of the analysis, the indicators are arranged in groups: research activities, artificial intelligence technology market and business, training of qualified personnel and education. For each group, I analyzed the fulfillment of project tasks, their dynamics, provided a comparative assessment of Russian and foreign results of the development of artificial intelligence, the set parameters for 2030, and the actual indicators already achieved. Conclusions. To further accelerate the development and implementation of artificial intelligence technologies, it is crucial to solve the problem of training highly qualified personnel.Economic Analysis: Theory and Practice40The study aims at analyzing the chronology and content of fundamental documents for the development of artificial intelligence in Russia, systematizing open factual data on achieved results, comparing the results with planned and global ones, and comparing the strategic indicators of 2030 with the achieved level.Economic Analysis: Theory and Practice['V. N. Edronova']2024-03-28T00:00:00https://www.semanticscholar.org/paper/2a2c0560c7ad2d0c628f494fafc8a2a09cacadde 6ecad2130f7ccd8a6629f44cf4cb31508c6c9ffb   The purpose of the narrative review is to provide a descriptive analysis of the emerging capabilities of artificial intelligence (AI) to improve the diagnosis, prevention and treatment of various diseases.   The article discusses which modern AI tools can be used in clinical practice, healthcare organization and medical education. The paper considers various aspects of medical AI systems, which are mainly computer support systems for medical decision-making in the process of clinical work. Much attention is paid to the possibilities of generative AI in medicine. Potential applications of AI in clinical practice have been investigated, highlighting promising prospects for both practitioners and their patients. The limitations associated with the use of AI in various fields of medicine are described, and possible ways of solving them are suggested. The problems of information security and ethical constraints associated with the introduction of AI are outlined. The broad integration of AI into public health will enhance clinical and management decision support, speed up disease diagnosis, and improve the overall quality and accessibility of healthcare services.Health and Ecology Issues460The broad integration of AI into public health will enhance clinical and management decision support, speed up disease diagnosis, speed up disease diagnosis, and improve the overall quality and accessibility of healthcare services.Health and Ecology Issues['A. Litvin', 'I. Stoma', 'T. Sharshakova', 'S. B. Rumovskaya', 'A. A. Kyovalev']2024-03-28T00:00:00https://www.semanticscholar.org/paper/6ecad2130f7ccd8a6629f44cf4cb31508c6c9ffb e63d5cbad51fdb83a218b807fb4097c903fa845eThe abstract explores the transformative impact of Artificial Intelligence (AI) on global industries, highlighting its role in enhancing efficiency and innovation while also posing challenges such as job automation, skills gaps, data misuse, and ethical concerns. Drawing from the Global Risk Report 2024 and recent regulatory actions by the European Union and Indonesia, the abstract discusses the pressing need for AI governance. However, it lacks specificity in articulating the study's objectives and scope, and could benefit from providing concrete examples or statistics to support its claims. A clearer organizational structure and citation of relevant sources would enhance the coherence and credibility of the abstract. Furthermore, while emphasizing the importance of human control over AI, the abstract could offer a more nuanced conclusion that underscores the significance of the study's findings in achieving this goal.International Journal of Social Service and Research330International Journal of Social Service and Research['Francisca Romana Nanik Alfiani']2024-03-28T00:00:00https://www.semanticscholar.org/paper/e63d5cbad51fdb83a218b807fb4097c903fa845e 2983947063c03382725171dc1e30eccc90be624eThis study is based on the panel data from 2006 to 2020 to explore the relationship between artificial intelligence (AI) and pollution emission. The study demonstrates a notable and statistically significant reduction in pollution emission intensity attributable to AI. This finding is robust, holding up under various sensitivity analyses and addressing endogeneity using instrumental variable techniques. The mechanism analysis reveals that AI's role in decreasing emissions primarily arises from three factors: the promotion of technological innovation, increased investment in emission reduction technology, and the replacement of low‐skilled labor with more efficient alternatives. We also find more substantial environmental benefits in areas with stricter environmental regulations and higher initial levels of pollution. Additionally, AI is shown to facilitate a symbiotic relationship between economic growth and environmental management. This research provides both empirical evidence and theoretical insights that reinforce the essential role of AI in driving green transformations, process upgrades, and high‐quality development. It situates these advancements within the broader context of the interplay between artificial intelligence, environmental sustainability, and economic progression.Managerial and Decision Economics470The mechanism analysis reveals that AI's role in decreasing emissions primarily arises from three factors: the promotion of technological innovation, increased investment in emission reduction technology, and the replacement of low‐skilled labor with more efficient alternatives.Managerial and Decision Economics['Fang Liu', 'Chen Liang']2024-03-28T00:00:00https://www.semanticscholar.org/paper/2983947063c03382725171dc1e30eccc90be624e 229b303508a08653ecb8bf4b674f37059e9bf2ee This JAMA Oncology Patient Page explains artifical intelligence and what patients should know about how it can be used in oncology. JAMA Oncology00JAMA oncology['Matthew Kurian', 'Jacob J Adashek', 'H. West']2024-03-28T00:00:00https://www.semanticscholar.org/paper/229b303508a08653ecb8bf4b674f37059e9bf2ee 1a09c76e1f64c11e2e237ac137d51c14a3ef168fPhilosophical Studies151Philosophical Studies['Robert Long']2024-03-28T00:00:00https://www.semanticscholar.org/paper/1a09c76e1f64c11e2e237ac137d51c14a3ef168f 7fb9131b43e9311782cb24ac1a2cf81a9cb12fc7In diesem in Englisch geführten Interview berichtet Paul Finnerty, Dozent an der Loughborogh University im Vereinigten Königreich und Erasmus+-Koordinator sowie Kursentwickler an der Atlas Language School in Dublin, über seine Erfahrungen mit Künstlicher Intelligenz (KI). An den Universitäten gibt es derzeit eine große Debatte über den Einsatz von künstlicher Intelligenz. Paul Finnerty meint, man sollte sich die Frage stellen, wie man KI nutzen kann. Da Paul Finnerty ursprünglich aus dem Vereinigten Königreich stammt, für eine irische Sprachschule arbeitet und in Polen lebt, kann er einen Einblick in seine Sichtweise von Künstlicher Intelligenz in Bezug auf KI in verschiedenen Bildungssystemen geben.#schuleverantworten01#schuleverantworten['Michaela Tscherne']2024-03-28T00:00:00https://www.semanticscholar.org/paper/7fb9131b43e9311782cb24ac1a2cf81a9cb12fc7 31c025bf48752252265979b1d77a79646fa1c022European Heart Journal151European heart journal['R. Khera']2024-03-28T00:00:00https://www.semanticscholar.org/paper/31c025bf48752252265979b1d77a79646fa1c022 04bfb52a85a53b3b78d80115a80bcd639c922832Amaç –Çalışmanın amacı, örgütlerde inovasyon ve örgütsel öğrenme ilişkisinde yapay zekâ kaygısının rolünü araştırmaktır. Yenilikçilik kültürünün gelişmediği örgütler, faaliyet gösterdikleri sektörde ön safhalarda yer alamazlar. Bu sebeple, örgütlerde inovasyon ve örgütsel öğrenme birbirinin tamamlayıcısı olabilmektedir. Bunun yanı sıra son yıllarda inovasyonla birlikte yapay zekâ kavramının ön plana çıkması ve örgütlerde iş yapış şekillerinde değişimi yaratması da önem arz etmektedir. Yöntem –Araştırma, alan araştırması yoluyla elde edilen verilerin nicel analiz yöntemleriyle değerlendirilmesi şeklinde uygulanmıştır. Araştırmanın evreni,Kuzey Kıbrıs Türk Cumhuriyeti, Lefkoşa’da faaliyet gösteren özel hastane çalışanlarıdır. Örneklem, biri diş hastanesi olmak üzere, iki özel hastanede 490 çalışandan oluşmaktadır. Veriler, yüz yüze anket yöntemiyle toplanmıştır. Verilerin normal dağılım analizi için normallik testi,ölçeklerin iç tutarlılığını ölçmek amacıyla güvenilirlik testi,değişkenler arasındaki ilişkilerin tespiti için; Pearson korelasyon testi, aracılık ve düzenleyicilik rol testleri için hiyerarşik regresyon testi kullanılmıştır.Bulgular –Araştırma verileri, bir paket programında analiz edilmiş ve %95 güven düzeyi ile çalışılmıştır. Oluşturulan beş hipotez de analiz sonuçlarına göre kabul edilmiştir. Örgütsel inovasyonun, örgütsel öğrenme üzerinde pozitif yönlü ve anlamlı etkisinin varlığı görülmektedir. Ayrıca örgütselinovasyonun yapay zekâ kaygısı üzerinde negatif etkisi belirlenmektedir. Yapay zekâ kaygısıyla örgütsel öğrenme arasında negatif yönlü ilişki saptanmaktadır. Son olarak, örgütsel inovasyon örgütsel öğrenme ilişkisinde yapay zekâ kaygısının hem aracı hem de düzenleyici etkisi olduğu tespit edilmiştir.Tartışma –Araştırmanın sonuçları literatür ile örtüşmektedir. Elde edilen sonuçlar doğrultusunda, örgütsel inovasyonun öğrenme üzerindeki etkisi bulunmaktadır. Örgütlerde yenilikçi bakış açısıyla birlikte kullanılmaya başlanan yapay zekâ uygulamaları, çalışanlarda bazı kaygılara yol açabilmektedir. Çalışma bu bağlamda yöneticilerin çalışanların kaygılarını azaltıcı önemlere yönelmeleri gerektiği önerisinin altını çizmektedirJournal of Business Research - Turk00Journal of Business Research - Turk['Cemile Şeker', 'Edip Örücü', 'Aslı Ercan Önbıçak']2024-03-28T00:00:00https://www.semanticscholar.org/paper/04bfb52a85a53b3b78d80115a80bcd639c922832 41fa1f32ba95508abb3a6d8b939ee4947575137dWith the rapid development and increasing popularity of AI in various industries, its impact on higher education, especially on the learning experience of college students, has become more profound. Various AI-powered educational tools and smart learning software are emerging, providing students with rich and convenient learning resources. The selected impact indicators were analyzed using the K-medoids clustering algorithm, which classified them into five different clusters: attitudes and expectations towards the use of AI learning tools, future prospects and adaptability of AI learning tools, patterns and purposes of AI use, safety and related concerns of AI tools, and meaningful and desirable features of AI learning tools. Subsequent ANOVA tests yielded a p-value of less than 0.05, thus confirming the appropriateness of the selected evaluation metrics. This academic review highlights the sound selection of evaluation criteria in the context of AI educational applications.Journal of Innovation and Development110This academic review highlights the sound selection of evaluation criteria in the context of AI educational applications and examines the impact indicators selected using the K-medoids clustering algorithm.Journal of Innovation and Development['Zilu Wen', 'Enhui Bai', 'Min Li']2024-03-28T00:00:00https://www.semanticscholar.org/paper/41fa1f32ba95508abb3a6d8b939ee4947575137d 74d5c49b4e52bd3f310da0524dfdc61d8bb563b6Die Akzeptanz von KI im Bildungsbereich wurde in einer aktuellen Studie untersucht. Mehrheitlich sehen Lehrende und Studierende KI als Chance für effizienteres Lernen und als Förderung der Reflexionsfähigkeit. Jedoch gibt es Bedenken bezüglich Datenethik und -transparenz. Überraschend war, dass wenig Bewusstsein für Verzerrungen in generativen KI-Modellen besteht, was die Notwendigkeit der Förderung von AI-Literacy bei Lehrenden hervorstreicht.#schuleverantworten00#schuleverantworten['Gerhard Brandhofer']2024-03-28T00:00:00https://www.semanticscholar.org/paper/74d5c49b4e52bd3f310da0524dfdc61d8bb563b6 97ea9989e3ff6ac617b4ffef87b26d7fc126ebdeStroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.Frontiers in Neurology1060Recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke are discussed.Frontiers in Neurology['Zijian Zhao', 'Yuanyuan Zhang', 'Jiuhui Su', 'Lianbo Yang', 'Luhang Pang', 'Yingshan Gao', 'Hongbo Wang']2024-03-28T00:00:00https://www.semanticscholar.org/paper/97ea9989e3ff6ac617b4ffef87b26d7fc126ebde 145ea1ec0da7696eb6a48c9b601ca72d936a375dJavnost - The Public670Javnost - The Public['Mike Ananny']2024-03-28T00:00:00https://www.semanticscholar.org/paper/145ea1ec0da7696eb6a48c9b601ca72d936a375d f275166a665c26850f62dd165c49b84177a1ed25NAM Perspectives00NAM Perspectives['Laura Adams']2024-03-28T00:00:00https://www.semanticscholar.org/paper/f275166a665c26850f62dd165c49b84177a1ed25 973cf9d4620e9f6a43c8aa9079f38dda63db6f24International journal of computer integrated manufacturing (Print)00Int. J. Comput. Integr. Manuf.['Shengzong Zhou', 'Nebojša Bačanin']2024-03-28T00:00:00https://www.semanticscholar.org/paper/973cf9d4620e9f6a43c8aa9079f38dda63db6f24 2b057ca4b1a51aac73df72d95702bb9bf8b94316Biomedical Journal of Scientific &amp; Technical Research00This work shows how to minimise this privacy loss, while maintaining the effectiveness of such algorithms for the multi-armed bandit problem, and shows how one can take advantage of the correlation structure inherent in a user graph such as the one arising from a social network.Biomedical Journal of Scientific &amp; Technical Research['Fereniki Panagopoulou']2024-03-28T00:00:00https://www.semanticscholar.org/paper/2b057ca4b1a51aac73df72d95702bb9bf8b94316 fd1fca4bde825da9d25442ece0906be63636ef43Journal of critical care560Journal of critical care['B. L. Ranard', 'Soojin Park', 'Yugang Jia', 'Yiye Zhang', 'Fatima Alwan', 'L. A. Celi', 'Elizabeth R Lusczek']2024-03-28T00:00:00https://www.semanticscholar.org/paper/fd1fca4bde825da9d25442ece0906be63636ef43 e219a7626da6520876969afe5bbc7a62d30f3223Chinese Business Review00Chinese Business Review['Khatira Guliyeva']2024-03-28T00:00:00https://www.semanticscholar.org/paper/e219a7626da6520876969afe5bbc7a62d30f3223 3416d55317a1f3eda82bdaafa0332e75fa9d77bbNature Nanotechnology50Nature nanotechnology['Zhaoming Liu']2024-03-28T00:00:00https://www.semanticscholar.org/paper/3416d55317a1f3eda82bdaafa0332e75fa9d77bb ee753c8acd364dd374d94c7051e5b4b22bb03daaJournal of economy culture and society00Journal of Economy Culture and Society['Mahmut Özer', 'M. Perc', 'H. Suna']2024-03-28T00:00:00https://www.semanticscholar.org/paper/ee753c8acd364dd374d94c7051e5b4b22bb03daa 664e0e6643ebbf93c3879f18a28e258902a420e8Portuguese Journal of Public Health190Portuguese Journal of Public Health['João V. Cordeiro']2024-03-28T00:00:00https://www.semanticscholar.org/paper/664e0e6643ebbf93c3879f18a28e258902a420e8 4701a8a8a5ebfc1e0a661e6b2b531b0c8a50cff4International Journal of Science and Engineering Applications00International Journal of Science and Engineering Applications['Geeta Sandeep Nadella', 'Geeta Sandeep Nadella']2024-03-28T00:00:00https://www.semanticscholar.org/paper/4701a8a8a5ebfc1e0a661e6b2b531b0c8a50cff4 bf90c0b254b3d70ccaec34af1147d84b4728bdc9Artificial intelligence (AI) is a technology that has revolutionized various fields of knowledge, including medicine. However, its use and potential in this area are not without controversies and misunderstandings. In this article, the authors, a computer scientist and a psychiatrist, critically analyze the concept of artificial intelligence and its applications in medicine, especially in psychiatry. To do this, they distinguish between intelligence and thought, and argue that the former is a tool of the latter, which is a much more complex and evolutionary mental faculty. Likewise, they question the philosophical naivety of some AI researchers, who overestimate the possibilities of creating thinking and feeling machines. They also reject the extremes of journalism and marketing, which present AI as a threat or a solution for humanity. Finally, they recognize the value and usefulness of this technology, but warn that it cannot replace human thought, which is based on reason, logic, instinct, habit and emotion.Mente y Cultura00The authors, a computer scientist and a psychiatrist, critically analyze the concept of artificial intelligence and its applications in medicine, especially in psychiatry, and argue that the former is a tool of the latter, which is a much more complex and evolutionary mental faculty.Mente y Cultura['Carlos Rojas Malpica', 'Guillermo Cerceau']2024-03-28T00:00:00https://www.semanticscholar.org/paper/bf90c0b254b3d70ccaec34af1147d84b4728bdc9 d79ddcaef6d5f4ae99670b051c0c55ba6059d9edLa inteligencia artificial impacta significativamente en el aprendizaje médico, ofreciendo una serie de beneficios para los estudiantes y los profesionales de la salud, ya que se basa en el uso de algoritmos y software que mejora el aprendizaje en las áreas médicas. El objetivo es analizar mediante revisión bibliográfica el uso de la inteligencia artificial como soporte para el aprendizaje en las ciencias de la salud. Se buscó en PubMed, Web of Science, Scopus y Scielo. Las palabras claves usadas fueron “Artificial Intelligence”, “Learning” y “Medicine”. Los resultados demuestran beneficiosos  para la formación estudiantil al implementar métodos innovadores en la enseñanza que incluye el uso de la inteligencia artificial en las prácticas clínicas, los cuales juegan un papel esencial para el diagnóstico y tratamiento, así como en su aprendizaje. Se concluye que la inteligencia artificial se ha convertido en una herramienta invaluable en el campo del aprendizaje médico, mejorando la capacidad de los profesionales de la salud para tomar decisiones, acelerar la investigación médica y proporcionar un cuidado más personalizado.Revista Imaginario Social00Revista Imaginario Social['Marcelo Ronaldo Robles Zeas', 'Karina de Lourdes Serrano Paredes', 'Tania Magdalena Cruz Gavilanes']2024-03-28T00:00:00https://www.semanticscholar.org/paper/d79ddcaef6d5f4ae99670b051c0c55ba6059d9ed 8f0ffbe44c623d0d5e90113a1d3240615574a9d2AI &amp; SOCIETY130This paper queries the location and nature of substantive artistic work in the developmental stages of an AI-generated image, offering critiques of existing assumptions and posing questions for future research.AI &amp; SOCIETY['John McLoughlin']2024-03-28T00:00:00https://www.semanticscholar.org/paper/8f0ffbe44c623d0d5e90113a1d3240615574a9d2 e2729caef077af43f084f8920483f24372d280cbExplainable artificial intelligence (XAI) is essential for improving machine learning models' interpretability, transparency, and reliability—especially in challenging and important fields like cybersecurity. These abstract addresses approaches, structures, and evaluation criteria for putting XAI techniques into practice and comparing them, as well as offering a thorough understanding of all the important components of XAI in the context of adversarial machine learning. Model-agnosticism, global/local explanation, adversarial assault resistance, interpretability, computing efficiency, and scalability are all covered in the discussion. Notably, the suggested SHIME approach shows excellent performance in a number of dimensions, making it a promising solution. The need of carefully weighing XAI solutions based on particular application requirements is emphasized in the abstract's conclusion, opening the door for future developments in the field to handle changing difficulties at the nexus of cybersecurity and artificial intelligence.Journal of Electrical Systems180The need of carefully weighing XAI solutions based on particular application requirements is emphasized in the abstract's conclusion, opening the door for future developments in the field to handle changing difficulties at the nexus of cybersecurity and artificial intelligence.Journal of Electrical Systems['Dr. Araddhana Arvind', 'Deshmukh', 'Sheela N. Hundekari', 'Yashwant Dongre', 'Dr. Kirti Wanjale', 'V. Maral', 'Deepali Bhaturkar']2024-03-28T00:00:00https://www.semanticscholar.org/paper/e2729caef077af43f084f8920483f24372d280cb c4e7b9c08ee1917d33755b112d96c252b6167fb3We formulate a mechanistic model capturing the dynamics of neurotransmitter release in a chemical synapse. The proposed modeling framework captures key aspects such as the random arrival of action potentials (AP) in the presynaptic (input) neuron, probabilistic docking and release of neurotransmitter-filled vesicles, and clearance of the released neurotransmitter from the synaptic cleft. Feedback regulation is implemented by having the released neurotransmitter impact the vesicle docking rate that occurs biologically through “autoreceptors” on the presynaptic membrane. Our analytical results show that these feedbacks can amplify or buffer fluctuations in neurotransmitter levels depending on the relative interplay of neurotransmitter clearance rate with the AP arrival rate and the vesicle replenishment rate, with faster clearance rates leading to noise amplification. We next consider a postsynaptic (output) neuron that fires an AP based on integrating upstream neurotransmitter activity. Investigating the postsynaptic AP firing times, we identify scenarios that lead to band-pass filtering, i.e., the output neuron frequency is maximized at intermediate input neuron frequencies. We extend these results to consider feedforward regulation where in addition to a direct excitatory synapse, the input neuron also impacts the output indirectly via an inhibitory interneuron, and we identify parameter regimes where feedforward neuronal networks result in band-pass filtering.bioRxiv530A mechanistic model capturing the dynamics of neurotransmitter release in a chemical synapse is formulated and feedforward regulation where in addition to a direct excitatory synapse, the input neuron also impacts the output indirectly via an inhibitory interneuron is extended.bioRxiv['Oliver Gambrell', 'Zahra Vahdat', 'Abhyudai Singh']2024-03-27T00:00:00https://www.semanticscholar.org/paper/c4e7b9c08ee1917d33755b112d96c252b6167fb3 03baf1aee746f047d7d6fd15e76796023b9da8a3AI and Ethics350The paper proposes a feedback loop that emerges from human-AI interactions that can foster responsive AI governance, rooted in both ethical principles and real-world experiences, and underscores the importance of bottom-up experiences in shaping AI's ethical boundaries.AI and Ethics['G. Papyshev']2024-03-27T00:00:00https://www.semanticscholar.org/paper/03baf1aee746f047d7d6fd15e76796023b9da8a3 c4cb7f348590d59d7e8e4ea547c374281c90aef0 Emotions strongly influence the human way of living and life experiences. In this context, Artificial Intelligence (AI) technologies are crucial to pushing developments further. Although emotional AI-driven innovations are welcome in our society, they might also have negative effects on the interdependence and autonomy of natural persons. Thus, they might be challenged by several legal provisions in the EU such as the General Data Protection Regulation (GDPR) and the draft AI Act. Yet these inventions require considerable investment, where legal incentives such as intellectual property rights (IPR) are crucial. Indeed, it is also important to secure certainty as to the legal and ethical acceptability of such innovations. This article looks at emotional AI to investigate the interlinkage between technological innovations, legal incentives and ethics, through the lenses of patent law and fundamental rights, in order to shed light over the challenges, limitations, but also opportunities for the protection, commercialization and exploitation of emotional AI-related inventions. Our research offers new scientific knowledge on the largely under-explored issue of legal incentives for emotional AI-related inventions in the European framework. It also provides companies and inventors with key points to consider in decision-making related to investments in and incentives for emotional AI-related innovations, also elaborating on suggestions for the European legislator and policymakers to better stimulate and promote emotional AI technology through regulation.Journal of Intellectual Property Law & Practice00Journal of Intellectual Property Law and Practice['R. Ballardini', 'Rob van den Hoven van Genderen', 'Tomi Nokelainen']2024-03-27T00:00:00https://www.semanticscholar.org/paper/c4cb7f348590d59d7e8e4ea547c374281c90aef0 173c670e5c48c5a6ab1d104621faeb6a2b3b4d02In the article, the authors explore the problematic issues of platform employment. The concept of platform employment is analyzed and its characteristic features are distinguished. The authors analyze domestic and foreign judicial practice to establish the legal status of an employee and labor legal relations, for persons working through online platforms. There is an objective need for legal regulation of legal relations related to the use of digital platforms in the field of labor, taking into account national characteristics, the need to create new technological structures, mechanisms for regulating labor through digital platforms in order to ensure socio-economic security.Voprosy trudovogo prava (Labor law issues)40Voprosy trudovogo prava (Labor law issues)['E.B. Vered', 'S. M. Novradova-Vasiliadi']2024-03-27T00:00:00https://www.semanticscholar.org/paper/173c670e5c48c5a6ab1d104621faeb6a2b3b4d02 bf7f28efb34cba6edff0200a834f8c36303c5763In the field of labor law, the legal regulation of labor relations in the field of labor protection is investigated, the corresponding measures of supervision and control are characterized, which, in addition to measures for violations of the requirements of labor protection legislation, provide for measures for their prevention and prevention. It is emphasized that state management of labor protection is carried out by the Cabinet of Ministers of Ukraine, the central body of executive power that implements state policy in the field of labor protection, ministries and other central bodies of executive power, the Council of Ministers of the Autonomous Republic of Crimea, local state administrations and local self-government bodies. It is noted that the powers in the field of labor protection of associations, corporations, concerns and other associations are determined by their charters or agreements between the enterprises that formed the association. In order to perform the functions delegated by the association, labor protection services are created in their apparatuses. The theoretical provisions are substantiated and practical recommendations are developed for improving the legal regulation of labor relations in the field of labor protection based on analysis methods and a systemic approach to generalization for evaluating the effectiveness of state management mechanisms in the field of labor protection. In addition, general scientific and empirical methods and tools of the science of labor law, methods of analysis and synthesis, comparison, summary and grouping were used. It was concluded that legal regulation in the field of occupational health and safety is a multilevel and complex functional mechanism, which at all levels of executive power controls and coordinates compliance with occupational health and safety at enterprises, institutions, and organizations regardless of their ownership, types of activity, and branch affiliation. It is emphasized that the existing system of state supervision and control over labor protection in Ukraine needs to be improved and brought into compliance with European standards.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['A. O. Boyko']2024-03-27T00:00:00https://www.semanticscholar.org/paper/bf7f28efb34cba6edff0200a834f8c36303c5763 a94fafdf6cb14b0adea58dc98a5628c83b7d28c9December 2020 saw the entry into force of Regulation 1805/2018, the adoption of which is a doubly important event: first, because it confirms the principle of mutual recognition in this sensitive area, following Framework Decision 2006/783/JHA; second, because it establishes mutual recognition by means of a directly applicable legislative measure, a Regulation, adopted in accordance with the ordinary legislative procedure pursuant to Art. 82 (1) TFEU. In order to understand the scope of the Regulation – what types of domestic confiscation are covered – it is important to interpret the EU autonomous concept of “proceedings in criminal matters” (art. 1), “notwithstanding the case law of the European Court of Human Rights” (recital 13). To increase enforcement, it will be crucial to improve harmonisation through the new proposed Directive (May2022).New Journal of European Criminal Law00New Journal of European Criminal Law['A. Maugeri']2024-03-27T00:00:00https://www.semanticscholar.org/paper/a94fafdf6cb14b0adea58dc98a5628c83b7d28c9 ea5dfe2386cc034f537f3decee4a8d0b9cb71a2aForeign economic activity is an important type of economic activity at the international level and a priority direction of our state’s policy, which creates the foundations for the development of trade relations and a favorable investment climate in Ukraine. It includes foreign trade in goods, services, certain types of works, as well as objects of intellectual property. Any commercial activity on the foreign market is significantly different from similar activity within the country. In particular, leading sectors of the economy are involved in the FED, state bodies, a certain number of individual economic entities, intermediary organizations, as well as foreign organizations on trade and economic issues, which collectively form the foreign economic complex of Ukraine, participate. The state directly exerts a regulatory influence on the processes taking place in the foreign economic sphere. The effectiveness of state regulation of foreign exchange depends on many factors, but the main lever in this is that the regulatory influence of the state must correspond to the nature of foreign economic activity, the nature and content of the tasks it performs at a specific stage of the development of the economy of Ukraine. In the current conditions of Ukraine’s integration into the world and European economic system, the state of regulatory and legal support of foreign trade and its regulation by the state acquires special importance. As well as the issue of the need to reform the foreign exchange system in order to create more flexible conditions for the development of the general economic situation in the country and the fruitful development of international relations with access to the world market. An important condition for the development of the economy of each country is the implementation of foreign economic activities by the participants of the national market. The topic of my article examines international economic agreements and their significance in the legal regulation of foreign economic activity, taking into account the existing scientific works, the concepts and types of international economic agreements are defined.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['P. O. Gustelev']2024-03-27T00:00:00https://www.semanticscholar.org/paper/ea5dfe2386cc034f537f3decee4a8d0b9cb71a2a 8482cf4fc5399542057d62b60e8f7758a4636ca0The paper analyzes the basics of ensuring genomic security at the national and supranational levels. Biosafety is one of the main aspects of global security, covering such areas as health, agriculture, science and technology, education and defense. Threats to biosafety are characterized by secrecy, sudden spread, unpredictable consequences, and significant damage. Combating such threats is an integral part of national security. Today, genomic safety should be considered as part of biosafety. The paper attempts to identify the features and main directions of the development of regulation of the safety of genomic research in the national legislation of some states (including the Russian Federation) in connection with the trends of regulation of biological safety at the universal and regional levels. The authors analyze the legislation of foreign countries in the field under study, highlight the positive properties of such legislation. Various schemes for regulating the safety of genetic research are being identified. It is possible to identify some states that use «strict» regulatory schemes, which involve the legislative consolidation of prohibitions of some or significant restrictions on the implementation of other types of genetic research. In a number of states, on the contrary, self-regulation or minimal regulation by the State of ensuring the safety of genetic research is being consolidated. Special attention is given to an analytical review of international treaties affecting various aspects of biological, including genomic, safety. In conclusion, the authors’ recommendations on improving the regulatory regulation of the Russian Federation in the field of biosafety, including the framework federal legislation in this area, are presented. The paper may be of interest to various specialists whose activities are related to genomic research (biomedicine, bioinformatics, human reproduction, etc.).Lex Russica20The paper attempts to identify the features and main directions of the development of regulation of the safety of genomic research in the national legislation of some states (including the Russian Federation) in connection with the trends of regulation of biological safety at the universal and regional levels.Lex Russica['M. V. Nekoteneva', 'D. V. Ponomareva']2024-03-27T00:00:00https://www.semanticscholar.org/paper/8482cf4fc5399542057d62b60e8f7758a4636ca0 fd03ef550788233957c2c69f9ae46d1c8ad3486cIn the field of labor law, the peculiarities of legal regulation in the field of supervision and control over compliance with labor legislation are investigated. It is emphasized that regulation in the field of supervision and control of compliance with labor legislation is characterized by appropriate measures, which, in addition to measures to detect unregistered labor relations, are carried out in accordance with current legislation. Measures of state control over compliance with labor legislation on identifying undocumented labor relations are carried out in the form of inspection visits conducted by labor inspectors of the State Labor Service and its territorial bodies. Reasons for inspection visits are important. The peculiarity of the legal regulation of labor relations is that the control measures or the decision of the labor inspector on control of the employer are subject to notification registration by the State Labor Service or its territorial body before the start of their implementation. It is emphasized that an act is drawn up as a result of the control, and in the case of violations of labor law requirements, the employer receives an order to eliminate them and a warning about liability for labor law violations. Thus, the legal regulation of labor relations regarding the supervision and control of compliance with labor legislation must first of all meet European standards and reflect the requirements of current labor legislation. State bodies should strengthen measures to detect undocumented labor relations, illegal employment and be carried out in accordance with the requirements of the Law of Ukraine «On the Basic Principles of State Supervision (Control) in the Field of Economic Activity.» The legal regulation of labor relations in the field of supervision and control of compliance with labor legislation certifies that measures of state influence on the detection of informal labor relations and illegal employment are carried out in the form of inspection visits carried out by labor inspectors of the State Labor and its territorial bodies.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['A. Andrushko']2024-03-27T00:00:00https://www.semanticscholar.org/paper/fd03ef550788233957c2c69f9ae46d1c8ad3486c ff3fafe6e1b8e66e138c5aa990c98ba36d01f15cThe article is devoted to the consideration of state regulation in the field of public procurement, in particular in the defense-industrial complex. The main law defining the basic principles and mechanisms of procurement is the Law of Ukraine «On Public Procurement». However, during the period of martial law, many changes were made in the procedures and mechanisms of public procurement, changes were already made more than once, through the adoption of separate laws and by-laws. The article analyzes the peculiarities of the application of public procurement mechanisms in certain sectors of the economy, in particular the defense-industrial complex. The necessity of modernization and improvement the procurement procedures for successful and effective compliance with the principles of competition and overcoming corruption, and other principles stipulated by legislation, is argued. Mechanisms for conducting procurement procedures are analyzed as one of the key elements of increasing the effectiveness of state economic policy, which is based, in particular, on the principles of fair competition, openness and transparency, non-discrimination of participants, objectivity and impartiality of evaluation of tender offers. It is substantiated that it is important to borrow international procurement experience to ensure compliance of national legislation with EU legislation. The issue of ensuring the appropriate level of defense capability of Ukraine in the conditions of countering full-scale military aggression on the territory of Ukraine by the Russian Federation is quite acute. A pressing challenge now is to ensure the rational spending of budget funds and the effective functioning and development of the state defense procurement system. It has been proven that the mechanism for the redistribution of budget funds should have the most transparent order of organization and the order of execution of the concluded procurement contract, which in the case of defense procurement involves reducing the number of closed procurements, excessive secrecy, increasing competitive procedures in the procurement of imported products and services, further improving pricing, canceling profit limits, accounting for inflation and life cycle cost. It is argued that consideration of physical, economic and informational security requirements is extremely important. It is about compliance with the principles of protecting customers from military threats, ensuring the simplification of public procurement by reducing the terms of the procurement procedure and simplifying the requirements for procedure participants, the effectiveness of the procurement process and the efficiency of the use of funds, primarily through the involvement of domestic entrepreneurs in tenders and ensuring the development of the domestic market, stability and defense capability.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['T. Shvydka']2024-03-27T00:00:00https://www.semanticscholar.org/paper/ff3fafe6e1b8e66e138c5aa990c98ba36d01f15c 6b589700d685c2315110bf018fc831b1621bdb31The article discusses issues related to the specifics of the implementation of state financial control in the field of construction. It is emphasized that this type of activity undergoes transformation in modern conditions due to the active inclusion of innovative elements in the organization of regulatory and inspection procedures, as well as due to the total digitalization of the Russian society. Above all, the complexity of the implementation of state financial control in the construction sector due to the multitude of stakeholders and the need to apply universal and at the same time differentiated methods of work to them is highlighted. The author identifies the following problems of financial control regulation in the construction sector: the lack of a full-fledged and well-established legislative framework; inconsistency of regulatory guidelines in different branches of law; vague-ness of the division of powers of subjects; the need to comply with the processes of modernization and digitali-zation of the financial structure in order to obtain more massive information data. Conclusion dwells upon the fact that the progressive development of society determines changes in the procedure of state financial control in the field of construction, the search for new regulatory and evaluating forms of work, the creation of innova-tive integrated programs of control of construction facilities.Теория и практика общественного развития00Теория и практика общественного развития['Igor A. Korneychuk']2024-03-27T00:00:00https://www.semanticscholar.org/paper/6b589700d685c2315110bf018fc831b1621bdb31 d7df17df56ae68f90c7f48f6cc7df5910cd0d436This study provides new evidence on factors driving firms´ eco innovation in the European Union, based on the data of the Community Innovation Survey for the years 2008 and 2014 for eleven European countries. First, our findings reveal that the propensity to eco-innovate changes over time. Second, the propensity to eco-innovate is unequally distributed across sectors, given that it is concentrated in a few sectors. Third, we find that sectoral behavior is strongly influenced by the taxonomy of green sectors introduced by the European Union, since the propensity to innovate is higher in the carbon leakage taxonomy than in the mitigation and adaptation taxonomy. These results provide further insights into the sectoral factors driving eco-innovation diffusion. Moreover, these findings are relevant to increase environmental stringency, as they contribute to the diffusion of eco-innovation across sectors, especially in those that do not innovate.    Revista de Economía Mundial500Revista de Economía Mundial['Javier Lucena Giraldo', 'Ernesto Rodríguez-Crespo', 'J. C. Salazar-Elena']2024-03-27T00:00:00https://www.semanticscholar.org/paper/d7df17df56ae68f90c7f48f6cc7df5910cd0d436 975ab0ba7481799d005c983801f4ddda250c9161British medical journal40BMJ['A. Rimmer']2024-03-27T00:00:00https://www.semanticscholar.org/paper/975ab0ba7481799d005c983801f4ddda250c9161 9e88aa0b8e117108b817b88ac436bdb70f3c2ba0The spread of misinformation on social media platforms threatens democratic processes, contributes to massive economic losses, and endangers public health. Many efforts to address misinformation focus on a knowledge deficit model and propose interventions for improving users’ critical thinking through improved access to facts. Such efforts are often hampered by challenges with scalability on the part of platform providers, and by confirmation bias on the part of platform users. The emergence of generative AI presents promising opportunities for countering misinformation at scale across ideological barriers. In this paper, we present (1) an experiment with a simulated social media environment to examine the effectiveness of interventions generated by large language models (LLMs) against misinformation, (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users with the goal of alleviating confirmation bias, and (3) an analysis of potential harms posed by personalized generative AI when exploited for automated creation of disinformation. Our findings confirm that LLM-based interventions are highly effective at correcting user behavior (improving overall user accuracy at reliability labeling by up to 47.6%). Furthermore, we find that users favor more personalized interventions when making decisions about news reliability.An MIT Exploration of Generative AI02It is confirmed that LLM-based interventions are highly effective at correcting user behavior and improving overall user accuracy at reliability labeling, and that users favor more personalized interventions when making decisions about news reliability.An MIT Exploration of Generative AI['Saadia Gabriel', 'Liang Lyu', 'James Siderius', 'Marzyeh Ghassemi', 'Jacob Andreas', 'Asu Ozdaglar']2024-03-27T00:00:00https://www.semanticscholar.org/paper/9e88aa0b8e117108b817b88ac436bdb70f3c2ba0 dc0bc86f932123f91db40efe3533a6ef441613adThis opinion piece highlights the integral role of generative Artificial Intelligence (AI) in learning within Higher Education Institutions (HEIs). Employing social learning theories, this opinion piece aims to explore generative AI as a stakeholder in learning. By weaving in social constructivist and learning theories, this opinion paper aims to uncover the capacity of generative AI to facilitate and enhance the learning process. Central to this opinion piece proposition is cultivating a learning community that leverages AI's potential as a new learning stakeholder. This opinion piece aims to contribute to ongoing discussions in the field of learning development by offering a fresh outlook on how AI can be an asset in knowledge co-creation and collaborative learning. The paper does this in the following ways: (1) highlights how generative AI can effectively contribute to learning and knowledge co-creation, and (2) provides some guidance for integrating generative AI in collaborative learning.Journal of Learning Development in Higher Education41This opinion piece highlights how generative AI can effectively contribute to learning and knowledge co-creation, and provides some guidance for integrating generative AI in collaborative learning.Journal of Learning Development in Higher Education['Xue Zhou', 'Lilian N. Schofield']2024-03-27T00:00:00https://www.semanticscholar.org/paper/dc0bc86f932123f91db40efe3533a6ef441613ad ab690837d81dc1118b253a6470da1a5315deb728The dynamic interplay between Artificial Intelligence (AI) adoption in modern organizations and its implications for employee well-being presents a paramount area of academic exploration. Within the context of rapid technological advancements, AI’s promise to revolutionize operational efficiency juxtaposes challenges relating to job stress and employee health. This study explores the nuanced effects of Artificial Intelligence (AI) adoption on employee physical health within organizational settings, investigating the potential mediating role of job stress and the moderating influence of coaching leadership. Drawing from the conservation of resource theory, the research hypothesized that AI adoption would negatively impact employee physical health both directly and indirectly through increased job stress. Critically, our conceptual model underscores the mediating role of job stress between AI adoption and physical health. Further, introducing a novel dimension to this discourse, we postulate the moderating influence of coaching leadership. To empirically test the hypotheses, we gathered survey data from 375 South Korean workers with a three-wave time-lagged research design. Our results demonstrated that all the hypotheses were supported. The results have significant implications for organizational strategies concerning AI implementation and leadership development.Frontiers in Public Health691This study explores the nuanced effects of Artificial Intelligence (AI) adoption on employee physical health within organizational settings, investigating the potential mediating role of job stress and the moderating influence of coaching leadership.Frontiers in Public Health['Jeeyoon Jeong', 'Byung-Jik Kim', 'Julak Lee']2024-03-27T00:00:00https://www.semanticscholar.org/paper/ab690837d81dc1118b253a6470da1a5315deb728 1a95d35e1fe74725a6f8d9d123d77c02a768d7dbBackground The healthcare sector demands a higher degree of responsibility, trustworthiness, and accountability when implementing Artificial Intelligence (AI) systems. Machine learning operations (MLOps) for AI-based medical diagnostic systems are primarily focused on aspects such as data quality and confidentiality, bias reduction, model deployment, performance monitoring, and continuous improvement. However, so far, MLOps techniques do not take into account the need to provide resilience to disturbances such as adversarial attacks, including fault injections, and drift, including out-of-distribution. This article is concerned with the MLOps methodology that incorporates the steps necessary to increase the resilience of an AI-based medical diagnostic system against various kinds of disruptive influences. Methods Post-hoc resilience optimization, post-hoc predictive uncertainty calibration, uncertainty monitoring, and graceful degradation are incorporated as additional stages in MLOps. To optimize the resilience of the AI based medical diagnostic system, additional components in the form of adapters and meta-adapters are utilized. These components are fine-tuned during meta-training based on the results of adaptation to synthetic disturbances. Furthermore, an additional model is introduced for post-hoc calibration of predictive uncertainty. This model is trained using both in-distribution and out-of-distribution data to refine predictive confidence during the inference mode. Results The structure of resilience-aware MLOps for medical diagnostic systems has been proposed. Experimentally confirmed increase of robustness and speed of adaptation for medical image recognition system during several intervals of the system’s life cycle due to the use of resilience optimization and uncertainty calibration stages. The experiments were performed on the DermaMNIST dataset, BloodMNIST and PathMNIST. ResNet-18 as a representative of convolutional networks and MedViT-T as a representative of visual transformers are considered. It is worth noting that transformers exhibited lower resilience than convolutional networks, although this observation may be attributed to potential imperfections in the architecture of adapters and meta-adapters. Сonclusion The main novelty of the suggested resilience-aware MLOps methodology and structure lie in the separating possibilities and activities on creating a basic model for normal operating conditions and ensuring its resilience and trustworthiness. This is significant for the medical applications as the developer of the basic model should devote more time to comprehending medical field and the diagnostic task at hand, rather than specializing in system resilience. Resilience optimization increases robustness to disturbances and speed of adaptation. Calibrated confidences ensure the recognition of a portion of unabsorbed disturbances to mitigate their impact, thereby enhancing trustworthiness.Frontiers in Public Health421This article is concerned with the MLOps methodology that incorporates the steps necessary to increase the resilience of an AI-based medical diagnostic system against various kinds of disruptive influences and the structure of resilience-aware MLOps for medical diagnostic systems has been proposed.Frontiers in Public Health['Viacheslav Moskalenko', 'Vyacheslav Kharchenko']2024-03-27T00:00:00https://www.semanticscholar.org/paper/1a95d35e1fe74725a6f8d9d123d77c02a768d7db 7748f514cd683f8a5d9b14dd60f151d5a488be22AI-Generated Images (AGIs) have inherent multimodal nature. Unlike traditional image quality assessment (IQA) on natural scenarios, AGIs quality assessment (AGIQA) takes the correspondence of image and its textual prompt into consideration. This is coupled in the ground truth score, which confuses the unimodal IQA methods. To solve this problem, we introduce IP-IQA (AGIs Quality Assessment via Image and Prompt), a multimodal framework for AGIQA via corresponding image and prompt incorporation. Specifically, we propose a novel incremental pretraining task named Image2Prompt for better understanding of AGIs and their corresponding textual prompts. An effective and efficient image-prompt fusion module, along with a novel special [QA] token, are also applied. Both are plug-and-play and beneficial for the cooperation of image and its corresponding prompt. Experiments demonstrate that our IP-IQA achieves the state-of-the-art on AGIQA-1k and AGIQA-3k datasets. Code will be available at https://github.com/Coobiw/IP-IQA.arXiv.org221This work proposes a novel incremental pretraining task named Image2Prompt for better understanding of AGIs and their corresponding textual prompts and introduces IP-IQA (AGIs Quality Assessment via Image and Prompt), a multimodal framework for AGIQA via corresponding image and prompt incorporation.ArXiv['Bowen Qu', 'Haohui Li', 'Wei Gao']2024-03-27T00:00:00https://www.semanticscholar.org/paper/7748f514cd683f8a5d9b14dd60f151d5a488be22 2f61d276610f6a8ed06cf678dc6825fdf768711eet al. 2017) and differential privacy (Dwork 2006). As a motivating example, we show how federated learning can be employed to train a global Gen-AI model using several local Gen-AI models without sharing data amongst the industries.An MIT Exploration of Generative AI941It is shown how federated learning can be employed to train a global Gen-AI model using several local Gen-AI models without sharing data amongst the industries without sharing data amongst the industries.An MIT Exploration of Generative AI['Md Ferdous Alam', 'Austin Lentsch', 'Nomi Yu', 'Sylvia Barmack', 'Suhin Kim', 'D. Acemoglu', 'John Hart', 'Simon Johnson', 'Faez Ahmed']2024-03-27T00:00:00https://www.semanticscholar.org/paper/2f61d276610f6a8ed06cf678dc6825fdf768711e f6727164d9b035221e6e3e1017fdc704a20190b6An MIT Exploration of Generative AI02An MIT Exploration of Generative AI['Ben Armstrong', 'Kate Kellogg', 'R. Levi', 'Julie Shah', 'B. Wiesenfeld']2024-03-27T00:00:00https://www.semanticscholar.org/paper/f6727164d9b035221e6e3e1017fdc704a20190b6 f172266af917370da03b87828be98d4663267d2cAn MIT Exploration of Generative AI02An MIT Exploration of Generative AI['Nathan Wilmers']2024-03-27T00:00:00https://www.semanticscholar.org/paper/f172266af917370da03b87828be98d4663267d2c 2123ffedbda17106bf13cf3924492fae0019a0c3The spectatorship of games has become a topic of growing interest with the parallel rise of esports and livestreaming platforms. Taking Saltybet.com as its primary case study, this paper examines cases where zero-player games played by artificial intelligence-controlled characters are the focus of spectatorship. A discourse analysis identifies trends and themes in the recorded chat transcripts of 15 livestreamed tournaments from Saltybet.com where players bet fake money on the outcome of fighting game matches between AI opponents. Several themes are identified that guide discussion on how spectators discuss AI players as well as their own and the community's behaviour. These insights may be applicable to understanding the broad appeal of the entertainment people derive from AI generally whether they were meant to entertain or not. The discussion explores how the absence of human players and the scale of Saltybet's niche audience contribute to a unique, but foolish space.Games and Culture : A Journal of Interactive Media180This paper examines cases where zero-player games played by artificial intelligence-controlled characters are the focus of spectatorship and explores how the absence of human players and the scale of Saltybet's niche audience contribute to a unique, but foolish space.Games and Culture['Rory Summerley', 'Brian McDonald']2024-03-27T00:00:00https://www.semanticscholar.org/paper/2123ffedbda17106bf13cf3924492fae0019a0c3 f84b43273986ab654e435729f5d30fcfc8840f08Past work has sought to design AI ethics interventions--such as checklists or toolkits--to help practitioners design more ethical AI systems. However, other work demonstrates how these interventions may instead serve to limit critique to that addressed within the intervention, while rendering broader concerns illegitimate. In this paper, drawing on work examining how standards enact discursive closure and how power relations affect whether and how people raise critique, we recruit three corporate teams, and one activist team, each with prior context working with one another, to play a game designed to trigger broad discussion around AI ethics. We use this as a point of contrast to trigger reflection on their teams' past discussions, examining factors which may affect their"license to critique"in AI ethics discussions. We then report on how particular affordances of this game may influence discussion, and find that the hypothetical context created in the game is unlikely to be a viable mechanism for real world change. We discuss how power dynamics within a group and notions of"scope"affect whether people may be willing to raise critique in AI ethics discussions, and discuss our finding that games are unlikely to enable direct changes to products or practice, but may be more likely to allow members to find critically-aligned allies for future collective action.arXiv.org670How power dynamics within a group and notions of scope affect whether people may be willing to raise critique in AI ethics discussions are discussed, and the finding that games are unlikely to enable direct changes to products or practice, but may be more likely to allow members to find critically-aligned allies for future collective action is discussed.ArXiv['D. Widder', 'Laura A. Dabbish', 'James Herbsleb', 'Nikolas Martelaro']2024-03-27T00:00:00https://www.semanticscholar.org/paper/f84b43273986ab654e435729f5d30fcfc8840f08 2a5f028ebde67b763845f1c888a266830a24ac14Bully Scan, an artificial intelligence system for identifying offensive language on social media, is proposed in "A Natural Language Processing and Machine Learning-Based Framework to Automatically Identify Cyberbullying. This paradigm, which aims to reduce the negative impacts of cyberbullying and encourage healthy online interactions, is a critical step in using AI for social well-being. The paper, "Research and Practice of Hybrid Teaching Based on AI technology for Foreign Language Translation," offers a novel strategy for teaching foreign languages through the incorporation of AI. The project investigates a hybrid teaching approach that combines AI-powered language translation tools with conventional classroom training. This method seeks to improve accuracy and efficiency of language learning by providing real-time translation support. Through the use of AI technologies, such as machine learning and natural language processing, the system offers students helpful translation assistance, enhancing their educational experience. The study demonstrates encouraging outcomes in terms of raising students' proficiency and effectiveness in translation in a blended learning setting. The paper "Modular Design of English Pronunciation Level Evaluation System Based on Deep Learning Algorithm" offers a novel method for determining pronunciation levels in English by utilizing deep learning algorithms. The study uses techniques like support vector machines and BP neural networks to address the problem of computational intensity in language teaching technologies. Through the application of machine deep learning, the system seeks to improve the precision and efficacy of pronunciation level assessments, providing insightful information for the development of theories for foreign language instruction in the rapidly changing field of artificial intelligence. The study's modular design approach offers a viable foundation for enhancing pronunciation assessment in language instruction.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The paper "Modular Design of English Pronunciation Level Evaluation System Based on Deep Learning Algorithm" offers a novel method for determining pronunciation levels in English by utilizing deep learning algorithms.International Journal of Scientific Research in Computer Science, Engineering and Information Technology['Dr. D. Antony Arul Raj Arul Raj', 'K. V. R. Rukmani', 'Kiruthika C Rukmani', 'Praveen M Rukmani', 'Anandhachitan A Rukmani']2024-03-27T00:00:00https://www.semanticscholar.org/paper/2a5f028ebde67b763845f1c888a266830a24ac14 830edf2d4663278cccda3f6200f00e6483d9a01bWith the rapid advancement of artificial intelligence, AI technology has made significant progress in the field of language. Machine translation has become the dominant method, replacing manual translation due to its convenience and speed. This article will discuss three different aspects: translation, information retrieval, and language artificial intelligence. In the translation section, three distinct translation models will be analyzed, using Google Translate as a foundation. These models have transformed the translation industry and improved accuracy and efficiency. In the information retrieval section, the differences between semantic search involving AI and traditional keyword-based search techniques will be explored. Semantic search, driven by AI, provides more accurate and relevant search results by understanding the context and intent behind user queries. The impact of these advancements on search engine optimization (SEO) practices will also be discussed. Furthermore, the article will delve into the types of speech recognition and classify speech recognition technologies. Finally, the article will summarize the entire content and provide an outlook on future developments.Applied and Computational Engineering00The article will delve into the types of speech recognition and classify speech recognition technologies, and the differences between semantic search involving AI and traditional keyword-based search techniques will be explored.Applied and Computational Engineering['Ziru Zhou']2024-03-27T00:00:00https://www.semanticscholar.org/paper/830edf2d4663278cccda3f6200f00e6483d9a01b d831c48c2312cacf39778bb9ee4cb3610fdcedfdArtificial Intelligence (AI) has become a powerful force in the growing field of scientific research, reshaping the ways in which data is collected and analyzed. ChatGPT, an innovative open-source natural language processing technology, was launched by OpenAI in november 2022. ChatGPT, an advanced artificial intelligence system, has acquired the capability to replicate human conversation by responding to prompts and inquiries. The acronym GPT stands for "generative pretrained transformer."(1) ChatGPT excelled at crafting academic articles thanks to its rich language modeling capabilities. In January 2023, Nature published a report that included ChatGPT as a credited author in two preprints and two articles in the scientific and medical domains.(2) The AI revolution has unquestionably accelerated scientific advancement and broadened the limits of what was previously deemed attainable. However, the ethical considerations due to  public access to artificial intelligence have changed throughout time, deviating dramatically from those in previous eras. The application of artificial intelligence as a non-human writer carries the inherent risks of prejudice, such as algorithmic bias and manipulation, data bias, and privacy problems. Data privacy monitoring is a significant issue since the data provided to large language models as a prompt can also be used as a set for data. The artificial intelligence paper mill generates questions regarding transparency and accountability. The apprehensions and risks to the scholarly community is deeply a matter of concern about data security, intellectual property rights violations, and instances of plagiarism. The maintenance of the scientific community's integrity hinges on ensuring the precision and dependability of research findings produced by AI.(3-5) This work suggests employing post-publication peer review (PPPR) as an innovative strategy to address certain issues, specifically related to research misconduct arising from artificial intelligence. Before being published, a critical step in assessing the thoroughness and accuracy of a study is the traditional practice of peer review. However, the progressive nature of algorithms and data sets in the age of AI-generated academic papers pose a contemporary dilemma that conventional peer review solely may not fully rectify. Post-publication peer review refers to the appraisal of a journal article that occurs after it has been published, as opposed to the traditional peer review process. This offers an iterative feedback process that significantly enhances honesty and integrity of research by allowing the worldwide scientific committee to assess and provide input on the quality and transparency of the academic papers' content..(6) The combination of PPPR with AI-generated research offers significant benefits, emphasizing the avoidance of shallow compliance of ethical rules in the work and inquiry carried out by the academic writers. Artificial intelligence algorithms are always evolving.  To keep up with this development, dynamic validation guarantees that they are constantly reviewed and that any new issues are immediately resolved. Post Publication Peer Review ensures the integrity of academic papers to avoid artificial intelligence related misconduct of research by reducing likelihood of fabrication, falsification and plagiarism. Furthermore, PPPR fosters research community interaction, providing researchers and academics with an instant opportunity to improve AI-generated research. The implementation of PPPR enhances both accountability and transparency in artificial intelligence research hence, ultimately leading to an enhancement in the work's quality by aiding in the identification and rectification of any potential biases, errors, or ethical considerations. The post publication peer review provide opportunity of developing a community of researcher who collaborate with each other to scout for the ethical consideration and ultimately fostering more community reach of research paper if it’s valid and transparent.  The scientific community provides constructive and unvarnished criticism. To guarantee the transparency and credibility of research in this digital era of artificial intelligence, we should consider post publication peer review as a proactive measure. Moreover, in our context where there is a lack of prompt policy making about AI related misconducts about research integrity, post publication review is direly required to safgaurd transparency in research and publications.Annals of King Edward Medical University60This work suggests employing post-publication peer review (PPPR) as an innovative strategy to address certain issues, specifically related to research misconduct arising from artificial intelligence.Annals of King Edward Medical University['Sadia Yaseen', 'Noushin Kohan', 'Ayesha Ayub']2024-03-27T00:00:00https://www.semanticscholar.org/paper/d831c48c2312cacf39778bb9ee4cb3610fdcedfd ca01f207caf16f0005f25a088e12dbe91c40e4e8Journal of the Knowledge Economy440Through a set of configurations based on the qualitative comparative analysis (QCA) method, it is possible to identify the positioning of the companies in the artificial intelligence sector in relation to this technological pattern.Journal of the Knowledge Economy['Rafael Palacios Bustamante', 'Xochitl Margarita Cruz Pérez', 'María del Pilar Escott-Mota']2024-03-27T00:00:00https://www.semanticscholar.org/paper/ca01f207caf16f0005f25a088e12dbe91c40e4e8 0a84d7b932dff9271f72b9ea4bc3c6f922dac679Economic Impact-Many examinations have investigated the monetary ramifications of man-made intelligence and ML reception. Research frequently centres around efficiency gains, work market elements, and the potential for work uprooting and creation. A few examinations propose that while Artificial intelligence and ML can prompt expanded effectiveness and development, they may likewise disturb customary business designs, requiring labour force transformation and re-skilling. Business and Industry: Writing in this space looks at how Artificial intelligence and ML advances are changing business tasks, including promoting, finance, store network the board, and client care. Reads up feature the potential for artificial intelligence and ML to improve dynamic cycles, enhance asset designation, customize client encounters, and drive upper hand. Medical care: Artificial intelligence and ML have huge ramifications for medical care, including sickness determination, therapy arranging, drug disclosure, and customized medication. Research in this space investigates the potential for Artificial intelligence and ML to work on clinical results, diminish clinical blunders, lower medical services expenses, and upgrade patient consideration through prescient examination, picture acknowledgment, and normal language handling. Education and Learning: The effect of Artificial intelligence and ML on schooling and learning is likewise a subject of interest. Writing in this field looks at how artificial intelligence fueled devices and stages can work with customized growth opportunities, versatile coaching, robotized reviewing, and instructive substance creation. Research additionally investigates the difficulties and moral contemplations related with man-made intelligence driven instructive innovations. Ethical and social implications-Researchers have raised worries about the moral and cultural ramifications of Artificial intelligence and ML, including issues connected with protection, predisposition, decency, straightforwardness, responsibility, and algorithmic administration. Research in this space looks to foster structures, rules, and guidelines to alleviate likely dangers and guarantee capable man-made intelligence advancement and arrangement. Environmental Impact few examinations explore how simulated intelligence and ML can be utilized to address natural difficulties, for example, environmental change, asset preservation, contamination control, and manageable turn of events. Research in this area investigates uses of computer based intelligence and ML in energy the board, savvy horticulture, natural checking, and preservation endeavours’ lawful and administrative parts of simulated intelligence and ML are likewise a subject of insightful request. Writing in this field looks at licensed innovation privileges, risk issues, information assurance regulations, and the moral and lawful obligations of artificial intelligence designers and clients. Research intends to lay out legitimate systems and rules to successfully administer artificial intelligence and ML advancements.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Research in this space looks to foster structures, rules, and guidelines to alleviate likely dangers and guarantee capable man-made intelligence advancement and arrangement to alleviate likely dangers and guarantee capable man-made intelligence advancement and arrangement.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Anushka Das']2024-03-27T00:00:00https://www.semanticscholar.org/paper/0a84d7b932dff9271f72b9ea4bc3c6f922dac679 02efcbf37ab2f9e8529c2a259eb9f1fcf5c1e473PurposeThe purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).Design/methodology/approachThe study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.FindingsResults suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.Originality/valueResearch on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.Education + Training1290Results suggest that assessing the AQ of students with the help of AI techniques is necessary, and a conceptual framework of AQ with the help of assessment style for higher education undergraduates is proposed.Education + Training['Jyoti Mudkanna Gavhane', 'Reena Pagare']2024-03-27T00:00:00https://www.semanticscholar.org/paper/02efcbf37ab2f9e8529c2a259eb9f1fcf5c1e473 c10ee75ca113eac2a38399a6027cd434b81e774bbioRxiv720Pedagogical suggestions are provided not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.BMC Medical Education['Narjes Rohani', 'Kobi Gal', 'Michael Gallagher', 'A. Manataki']2024-03-27T00:00:00https://www.semanticscholar.org/paper/c10ee75ca113eac2a38399a6027cd434b81e774b 3bf063fcde806f41344f894f1c92138369ce1eb3The article deals with the problem of socio-humanitarian aspects of the impact of achievements in the field of artificial intelligence on the development of higher education in an increasingly risky society. It is argued that social and technological processes tend to accelerate more and more, exerting significant mutual influence, not always equivalent and predictable, contributing to drawing attention to the need to resolve emerging problems. These processes cannot be left without constant coordination of management decisions at various levels. The theoretical framework is determined by the analysis of socio-humanitarian aspects of accelerated progress in the development of neural networks with "deep learning" on large amounts of data, as well as emerging new trends in the organization and development of higher education. The research method is a theoretical socio-cultural analysis of current changes in opportunities and management practices in the educational process and university administration. The novelty of the article is represented by substantiating the emergence of aspects of a new sociality as a result of the development of modern technologies of generative artificial intelligence.Вестник Удмуртского университета. Социология. Политология. Международные отношения00It is argued that social and technological processes tend to accelerate more and more, exerting significant mutual influence, not always equivalent and predictable, contributing to drawing attention to the need to resolve emerging problems.Вестник Удмуртского университета. Социология. Политология. Международные отношения['N. Ladyzhets']2024-03-27T00:00:00https://www.semanticscholar.org/paper/3bf063fcde806f41344f894f1c92138369ce1eb3 0ac1b32c47a42d7fc7f8a55592ccb2bfaf5c39c2BMC Nursing270There is a lack of clear understanding of AI technology among nurses, and the majority recognizes the benefits that AI can bring in terms of relief or support, so nurses should be better prepared for AI in the future.BMC Nursing['Domenic Sommer', 'Lukas Schmidbauer', 'Florian Wahl']2024-03-27T00:00:00https://www.semanticscholar.org/paper/0ac1b32c47a42d7fc7f8a55592ccb2bfaf5c39c2 8eb3e5a8a6208226462e89d353ea22386f94af19The question of whether artificial intelligence (AI) can be considered conscious and therefore should be evaluated through a moral lens has surfaced in recent years. In this paper, we argue that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction. When AI is viewed as conscious like a human, then how people treat AI appears to carry over into how they treat other people due to activating schemas that are congruent to those activated during interactions with humans. In light of this potential, we might consider regulating how we treat AI, or how we build AI to evoke certain kinds of treatment from users, but not because AI is inherently sentient. This argument focuses on humanlike, social actor AI such as chatbots, digital voice assistants, and social robots. In the first part of the paper, we provide evidence for carry-over effects between perceptions of AI consciousness and behavior toward humans through literature on human-computer interaction, human-AI interaction, and the psychology of artificial agents. In the second part of the paper, we detail how the mechanism of schema activation can allow us to test consciousness perception as a driver of carry-over effects between human-AI interaction and human-human interaction. In essence, perceiving AI as conscious like a human, thereby activating congruent mind schemas during interaction, is a driver for behaviors and perceptions of AI that can carry over into how we treat humans. Therefore, the fact that people can ascribe humanlike consciousness to AI is worth considering, and moral protection for AI is also worth considering, regardless of AI’s inherent conscious or moral status.Frontiers in Psychology1380It is argued that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction.Frontiers in Psychology['Rose E. Guingrich', 'Michael S. A. Graziano']2024-03-27T00:00:00https://www.semanticscholar.org/paper/8eb3e5a8a6208226462e89d353ea22386f94af19 0101874177a43246c52b9a0f0d1ffb96b7745fc0Objective: The article studies the application of AI in the field of criminal justice. Since then, the article evaluates the feasibility and offers solutions and recommendations to ensure the right to a fair trial when applying AI to criminal justice in Vietnam.   Methods: To conduct research on the application of AI in criminal justice in Vietnam to ensure the right to a fair trial, the authors used traditional research methods of social science and legal science methods of analysis, synthesis, and case study to achieve the objective of the research.   Results: Artificial intelligence (AI) and its application, in general, are a matter of concern in social life in general and law in particular. The application of artificial intelligence in criminal justice to digitize the judicial field is being applied in many parts of the world such as the United States and European countries. Based on the theory of the order of justice before the law, the authors analyzed and assessed the impacts and effects of AI and found that the application of AI in criminal justice can negatively affect the right to a fair trial.   Conclusion: Through this study, we propose the following contents to effectively apply AI in Vietnamese criminal justice as follows: (i) The principle of the right to a fair trial must be respected when applying AI in decision-making; (ii) Enhancing the role of investigators, prosecutors, and judges in AI predictive decision-making; (iii) Building a database and AI system development agency in Vietnam; (iv) Upgrading technology infrastructure and databases at Criminal Justice Agencies; (v) Developing communication and training plan on technology and human rights content; (vi) Integrating the right to a fair trial systematically into every stage of the design, development, implementation, and ongoing monitoring of products, services, and systems using AI; (vi) Establishing the Department of AI Development and Use. In the future, the completion of the legal framework to ensure human rights under the influence of AI and legal issues on AI are issues that need to be further studied in Vietnam.Journal of Law and Sustainable Development320It is found that the application of AI in criminal justice can negatively affect the right to a fair trial and the principle of the right to a fair trial must be respected when applying AI in decision-making.Journal of Law and Sustainable Development['Nguyen Thi Thu Trang', 'Nguyen Hoai Linh', 'Nguyen Thi Cam Hoang', 'Pham Vo Tuan Kiet', 'Luu Thi Ngoc Loan', 'Nguyen Thi Hoai Phuc']2024-03-27T00:00:00https://www.semanticscholar.org/paper/0101874177a43246c52b9a0f0d1ffb96b7745fc0 ae6fdf207e883ca17c3d99dd87d4c76cd5535358Artificial intelligence (AI) is becoming more and more prevalent in businesses. In the specialised subject of human resource management (HRM), artificial intelligence (AI) has grown in importance recently. There are lot of studies that have been done on the topic of AI and HR. As a result of shifts in the IT industry, this research delves into how innovations in AI have affected human resource management. Human resources (HR) experts may leverage AI at every stage of the employee lifecycle, from recruitment to performance reviews. Finding out if HR operations' innovativeness and user-friendliness impact the AI-HR function relationship is the driving force behind this study, which is conducted within the context of Bangalore IT industry. This study drew 200 responses from HR professionals working for IT firms in the Bangalore region. Using a multiple regression method, we were able to illustrate that HR functional performance improves as AI usage increases in the workplace, lending credence to the premise that the two variables are positively correlated. Contrarily, AI is commonly linked to practicality and originality, which implies that AI directs human resources towards ease and innovation.European Economic Letters (EEL)180Using a multiple regression method, this study was able to illustrate that HR functional performance improves as AI usage increases in the workplace, lending credence to the premise that the two variables are positively correlated.European Economic Letters (EEL)['Dr. Sarjue Pandita, Dr. Ankit Garg, Dr. TR Pandey, Dr. Ritesh Kumar Singhal']2024-03-27T00:00:00https://www.semanticscholar.org/paper/ae6fdf207e883ca17c3d99dd87d4c76cd5535358 c18b4fc86102b670ef8485bdbb866c70d2c29b3eThe article discusses the general prospects for the use of artificial intelligence technology in civil proceedings as a main and auxiliary tool at the current stage of the development of this technology. The main approaches to defining the concept of artificial intelligence were studied and its classification into weak, strong and superintelligence was characterized. It is emphasized that justice is usually administered by professional judges, as high demands are placed on their legal knowledge and life experience. It is argued that equal replacement of a judge in civil proceedings with artificial intelligence technology is currently impossible. The reasons for the impossibility of such a replacement are analyzed, namely the complexity and multifacetedness of controversial private-law relations, the impossibility of taking into account moral and ethical aspects, the need for flexibility in decision-making, the problem of the nature of the initial algorithms of artificial intelligence, the transparency (motivation) of decisions, the problem of determining the guilty party in court cases errors, data protection issue. It is noted that these reasons have a technological and legal basis. A critical assessment of the state’s conceptual approaches to the development of artificial intelligence in the field of justice is provided. The directions of the auxiliary application of artificial intelligence technologies in civil proceedings, which are reduced to the automation of routine tasks, processing and analysis of large volumes of data and evidence, and increasing the accessibility of justice, are considered. It has been proven that the use of artificial intelligence technology provides better opportunities for the participants in the judicial process to realize their rights and obligations, including the processing of large amounts of information. The importance of ensuring a high level of information protection when using artificial intelligence during the administration of justice in civil cases is emphasized. The need for regulatory regulation of the use of artificial intelligence technology in civil proceedings in combination with other spheres of public administration and branches of law is substantiated. It is indicated that the wider application of artificial intelligence in civil proceedings will require new technological solutions and revision of the legal foundations of the judicial branch of government.Uzhhorod National University Herald. Series: Law00It has been proven that the use of artificial intelligence technology provides better opportunities for the participants in the judicial process to realize their rights and obligations, including the processing of large amounts of information.Uzhhorod National University Herald. Series: Law['I. Skliarenko']2024-03-27T00:00:00https://www.semanticscholar.org/paper/c18b4fc86102b670ef8485bdbb866c70d2c29b3e e1606eef95929e45dfd561264004f736865d61d1Artificial Intelligence (AI) is a concept that has been a subfield of computer science since the 1950s. In recent years, with its growing development power, AI technologies have made significant progress and are now being used in many fields. Like in all areas, the use of AI technologies in academia has provided convenience to academics while also bringing ethical debates. In the literature part of the study, concepts such as AI, academia, academics and academic progress, ethics, ethical theories, academic ethics, and emotional states have been thoroughly examined and defined. In this study, starting from AI and scientific ethics, ethical issues arising from emotional states in academic research have been identified, and concrete solutions to these ethical issues have been proposed. The aim is to discuss the views of academics in order to determine what types of scientific ethical violations and prevention methods are involved. In this context, the semi-structured interview technique, which is one of the qualitative research methods, was preferred as the method. In the study, in-depth semi-structured interviews were conducted with 4 ethics experts and 4 psychology experts selected through snowball sampling technique. The data obtained through semi-structured in-depth interviews will be analyzed using content analysis. Within the context of the literature review and interviews: Ethics is based on the foundation of acting correctly. In this context, scientific ethics can be summarized as acting truthfully and honestly, not distorting data, and not trying to progress unfairly. The use of AI in academia is becoming increasingly widespread. From a positive perspective, this usage significantly contributes to making studies more practical. However, it can lead to problems such as unfair authorship, devaluation of human authorship, and incorrect data. The connection between academics’ professional advancement goals and emotional states becomes prominent in this context. The potential of AI to facilitate progression can lead to unethical use. To prevent such situations, it is recommended to organize training sessions to increase professional awareness, internalize ethics personally, establish ethical committees specific to the field of AI, conduct more effective audits by academic publication and promotion committees, and implement specific regulations for AI. Finally, for future academic studies, it is suggested that the usage of AI in academic research be measured and evaluated by ethics experts. For psychologists, conducting surveys with academics to explore how they use AI in the context of their emotional states and professional advancement goals is recommended.Frontiers in Psychology360Ethical issues arising from emotional states in academic research have been identified, and concrete solutions to these ethical issues have been proposed, and it is suggested that the usage of AI in academic research be measured and evaluated by ethics experts.Frontiers in Psychology['Ayhan Dolunay', 'Ahmet C. Temel']2024-03-27T00:00:00https://www.semanticscholar.org/paper/e1606eef95929e45dfd561264004f736865d61d1 3587a8299365d63a886390a89b6c0d95b155c5b0Artificial Intelligence Review200The integration of AI and AR technologies in maritime medicine shows promise in providing real-time medical assistance, remote consultations, augmented training, and improved diagnostic capabilities and challenges related to data privacy, connectivity at sea, and the need for regulatory frameworks are discussed.Artif. Intell. Rev.['G. Battineni', 'N. Chintalapudi', 'Giovanna Ricci', 'Ciro Ruocco', 'Francesco Amenta']2024-03-27T00:00:00https://www.semanticscholar.org/paper/3587a8299365d63a886390a89b6c0d95b155c5b0 af8673745c0e0bdacdb4fe658bddc66b65f9ce25The engineering sector is undergoing digital transformation (DT) alongside shifts in labor patterns. This study concentrates on piping design within plant engineering, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology. The objective is to overcome limitations related to time and costs in traditional manual piping design processes. The ultimate aim is to contribute to the digitalization of engineering processes and improve project performance. Initially, digital image processing was utilized to digitize piping and instrument diagram (P&ID) data and establish a line topology set (LTS). Subsequently, three-dimensional (3D) modeling digital tools were employed to create a user-friendly system environment that visually represents piping information. Dijkstra’s algorithm was implemented to determine the optimal piping route, considering various priorities during the design process. Finally, an interference avoidance algorithm was used to prevent clashes among piping, equipment, and structures. Hence, an auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed. To evaluate the effectiveness of the proposed model, a comparison was made between the bill of materials (BoM) from Company D’s chemical plant project and the BoM extracted from the ARS. The performance evaluation revealed that the accuracy in matching pipe weight and length was 105.7% and 84.9%, respectively. Additionally, the accuracy in matching the weight and quantity of fittings was found to be 99.7% and 83.9%, respectively. These findings indicate that current digitalized design technology does not ensure 100% accurate designs. Nevertheless, the results can still serve as a valuable reference for attaining optimal piping design. This study’s outcomes are anticipated to enhance work efficiency through DT in the engineering piping design sector and contribute to the sustainable growth of companies.Sustainability640An auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology to overcome limitations related to time and costs in traditional manual piping design processes.Sustainability['Dong-Han Kang', 'S. Choi', 'Eul-Bum Lee', 'Sung-O Kang']2024-03-27T00:00:00https://www.semanticscholar.org/paper/af8673745c0e0bdacdb4fe658bddc66b65f9ce25 0c11b66a730ad8d8be4ec0069581bddff18f11f5The marketing literature highlights the growing integration of artificial intelligence (AI) into marketing strategies. Several publications show that this field is attracting increasing interest from researchers. The purpose of this article is to provide an overview of academic publications related to AI and marketing strategies, while also examining the lack of bibliometric analysis in this area. In this study, 1100 articles, published in the Scopus and Web of Science databases, were selected and, according to a consistent search procedure, were examined. A performance analysis, based on bibliometric indicators, revealed the most impactful journals, the most indexed authors according to H-index and the most cited papers. The thematic factorial map highlighted the typology of AI tools used in the field of strategic marketing, in this case the marketing strategy. It also provides a discussion, potential research avenues and recommendations for future investigations.  Journal of Telecommunications and the Digital Economy00An overview of academic publications related to AI and marketing strategies is provided, while also examining the lack of bibliometric analysis in this area, and the thematic factorial map highlighted the typology of AI tools used in the field of strategic marketing.Journal of Telecommunications and the Digital Economy['Hasna Koubaa El Euch', 'Foued Ben Said']2024-03-27T00:00:00https://www.semanticscholar.org/paper/0c11b66a730ad8d8be4ec0069581bddff18f11f5 ebd61fdaae9529c4d5d154e5db4e8fe0526a16eeNeural computing & applications (Print)190The study investigates the effectiveness of activation maps generated by five different methods, namely GradCAM, GradCAM++, EigenCAM, HayCAM, and a newly proposed method called HayCAMJ, in detecting objects within images, and shows that HayCAMJ performs better than other XAI techniques in detecting small objects.Neural Computing and Applications['A. H. Ornek', 'Murat Ceylan']2024-03-27T00:00:00https://www.semanticscholar.org/paper/ebd61fdaae9529c4d5d154e5db4e8fe0526a16ee 06d0b2344c0560fdb48034ebf4fef4743ce6716cArtificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [...]Applied Sciences00Applied Sciences['Xiaoyang Liu']2024-03-27T00:00:00https://www.semanticscholar.org/paper/06d0b2344c0560fdb48034ebf4fef4743ce6716c 8b3cf252918f3c734e015896007e3d7dcbc47959This paper evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI- machine learning (AI/ML) algorithms are analyzed as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model's hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.Journal of Neurophysiology00Mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.Journal of neurophysiology['Kristin Williams']2024-03-27T00:00:00https://www.semanticscholar.org/paper/8b3cf252918f3c734e015896007e3d7dcbc47959 b345ea937fa8073da9ed1b6a34123fe7892bec57International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-27T00:00:00https://www.semanticscholar.org/paper/b345ea937fa8073da9ed1b6a34123fe7892bec57 7d22253c01922e23575a18191a80c194855349a2PurposeArtificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.Design/methodology/approachThis study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.FindingsThis study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.Originality/valueThis study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.Marketing Intelligence &amp; Planning1040The mechanism of action between AI image and consumers’ resistance is revealed and light is shed on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.Marketing Intelligence &amp; Planning['Yupeng Mou', 'Yixuan Gong', 'Zhihua Ding']2024-03-27T00:00:00https://www.semanticscholar.org/paper/7d22253c01922e23575a18191a80c194855349a2 7ec00ac9de95ba50fef1da790b5d8313501b24edApplied Biosafety231Applied Biosafety['Leyma P. De Haro']2024-03-27T00:00:00https://www.semanticscholar.org/paper/7ec00ac9de95ba50fef1da790b5d8313501b24ed 022df1487e7e4d38cde5ba0fafa6a082bf185294актуальность статьи обусловлена тем, что эффективное использование имеющихся данных обычно представляет наибольшую трудность при моделировании рабочих состояний энергосистемы. Анализ состояния системы на основе устаревшей или неверной информации может привести к принятию решений, существенно отличающихся от оптимальных. Целью статьи является выявление особенностей использования нечетких множеств (искусственного интеллекта) в нефтегазовой отрасли и разработка предложений для практического применения и совершенствования данных процессов. Методология исследования основана на применении системного подхода и общенаучных методов, среди которых: систематизация и обобщение, логический и сравнительный анализ. В статье обосновано, что удобным математическим инструментом описания неопределенности и неточности входных данных и связей между ними является теория нечетких множеств. В статье представлены возможности использования нечеткого моделирования в нефтегазовой отрасли при выборе альтернатив при транспортировке нефтегазовых продуктов. Результаты исследований по применению этой теории во многих областях науки и техники позволяют предположить, что благодаря ее возможностям моделирования неопределенности и неточности входных данных и описания связей между ними она расширит сферу своей деятельности. the relevance of the article is due to the fact that the effective use of available data usually poses the greatest difficulty when modeling the operating states of the power system. Analyzing the state of a system based on outdated or incorrect information can lead to decisions that are significantly different from the optimal ones. The purpose of the article is to identify the features of using fuzzy sets (artificial intelligence) in the oil and gas industry and develop proposals for practical application and improvement of these processes. The research methodology is based on the use of a systematic approach and general scientific methods, including: systematization and generalization, logical and comparative analysis. The article substantiates that a convenient mathematical tool for describing the uncertainty and inaccuracy of input data and the connections between them is the theory of fuzzy sets. The article presents the possibilities of using fuzzy modeling in the oil and gas industry when choosing alternatives when transporting oil and gas products. Research on the application of this theory in many fields of science and technology suggests that its ability to model the uncertainty and imprecision of input data and describe the relationships between them will expand its scope.Modern Economy Success30Modern Economy Success['Т.В. Афанасьева']2024-03-27T00:00:00https://www.semanticscholar.org/paper/022df1487e7e4d38cde5ba0fafa6a082bf185294 950b16eb67cc1f41808f9656689e84ef953a4d45в современных условиях динамично развивающейся экономики и финансовых рынков, характеризующихся высоким уровнем неопределенности и волатильности, особую актуальность приобретает проблема повышения точности и надежности макроэкономического прогнозирования и финансового планирования. Традиционные методы, основанные на статистическом анализе и экспертных оценках, зачастую демонстрируют ограниченную эффективность в условиях быстро меняющейся экономической конъюнктуры и множества взаимосвязанных факторов. В этой связи, особый интерес представляет применение современных технологий машинного обучения и искусственного интеллекта, которые позволяют обрабатывать и анализировать большие объемы разнородных данных, выявлять скрытые закономерности и взаимосвязи, а также строить высокоточные прогнозные модели. Целью данного исследования является анализ возможностей и перспектив применения методов машинного обучения и искусственного интеллекта в области макроэкономического прогнозирования и финансового планирования. В рамках исследования были рассмотрены различные подходы и алгоритмы, включая нейронные сети, деревья решений, случайные леса, градиентный бустинг и другие. Особое внимание было уделено вопросам предобработки и интеграции разнородных данных из различных источников, таких как макроэкономическая статистика, финансовая отчетность компаний, новостные потоки и социальные сети. Результаты исследования показали, что применение методов машинного обучения и искусственного интеллекта позволяет существенно повысить точность макроэкономического прогнозирования и финансового планирования по сравнению с традиционными подходами. in modern conditions of a dynamically developing economy and financial markets characterized by a high level of uncertainty and volatility, the problem of improving the accuracy and reliability of macroeconomic forecasting and financial planning is of particular relevance. Traditional methods based on statistical analysis and expert assessments often demonstrate limited effectiveness in a rapidly changing economic environment and many interrelated factors. In this regard, the use of modern machine learning and artificial intelligence technologies is of particular interest, which allow processing and analyzing large volumes of heterogeneous data, identifying hidden patterns and relationships, as well as building highly accurate predictive models. The purpose of this study is to analyze the possibilities and prospects of using machine learning and artificial intelligence methods in the field of macroeconomic forecasting and financial planning. The study examined various approaches and algorithms, including neural networks, decision trees, random forests, gradient boosting, and others. Special attention was paid to the issues of preprocessing and integration of heterogeneous data from various sources, such as macroeconomic statistics, financial statements of companies, news streams and social networks. The results of the study showed that the use of machine learning and artificial intelligence methods can significantly improve the accuracy of macroeconomic forecasting and financial planning compared with traditional approaches.Modern Economy Success60Modern Economy Success['А.И. Евдокимов']2024-03-27T00:00:00https://www.semanticscholar.org/paper/950b16eb67cc1f41808f9656689e84ef953a4d45 8ed5ff15fb49b7c89e29665dd86631eea3da17e4 Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time-consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Processing of the enormous volume and variety of data, making its sensible use and separating “needles from haystack,” is a challenge for key stakeholders such as pharmaceutical firms, regulatory authorities, medical and PV experts, and National Pharmacovigilance Program managers. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. Similarly, there has been a growing interest to adopt AI tools to complement and automate the PV process. The advanced technology can certainly complement the routine, repetitive, manual task of case processing, and boost efficiency; however, its implementation across the PV lifecycle and practical impact raises several questions and challenges. Full automation of PV system is a double-edged sword and needs to consider two aspects – people and processes. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. What is important is to emphasize and ensure that AI brings more benefits to PV rather than challenges. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system.Perspectives in Clinical Research250The benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system are described.Perspectives in Clinical Research['Mira Kirankumar Desai']2024-03-27T00:00:00https://www.semanticscholar.org/paper/8ed5ff15fb49b7c89e29665dd86631eea3da17e4 196256beaba3e7c537e25559ccdcf35904b1c643The authors analyze the ability of ChatGPT to generate effective instructions for a consequential task: taking a COVID-19 test. They compare the output from a commercial prompt for generating these instructions to those provided by the test manufacturer. They also analyze the input, the prompt itself, to address prompt-engineering issues. The results show that although the output from ChatGPT exhibits certain conventions for documentation, the human-authored instructions from the manufacturer are superior in most ways. The authors conclude that when it comes to creating high-quality, consequential instructions, ChatGPT might be better seen as a collaborator than a competitor with human technical communicators.Journal of business and technical communication40The authors conclude that when it comes to creating high-quality, consequential instructions, ChatGPT might be better seen as a collaborator than a competitor with human technical communicators.Journal of Business and Technical Communication['Johndan Johnson-Eilola', 'S. Selber', 'Eric J. York']2024-03-27T00:00:00https://www.semanticscholar.org/paper/196256beaba3e7c537e25559ccdcf35904b1c643 eeb3c9a288e1e63767e614ed32e79821a398be12<jats:p xml:lang="tr" />Journal of Gastronomy Hospitality and Travel (JOGHAT)00Journal of Gastronomy Hospitality and Travel (JOGHAT)['Zeynep Karaş']2024-03-27T00:00:00https://www.semanticscholar.org/paper/eeb3c9a288e1e63767e614ed32e79821a398be12 218ff7ee4a0bd26d4189e214596459ccf0ef7de1Computer-Aided Design and Applications00Computer-Aided Design and Applications['Xiuming Cheng', 'Li Wang']2024-03-27T00:00:00https://www.semanticscholar.org/paper/218ff7ee4a0bd26d4189e214596459ccf0ef7de1 e9a1edb23161d3b2d5675194486c19e62fe121e3Journal of Hand Surgery (European Volume)30The Journal of hand surgery, European volume['J. McEachan', 'Wee L Lam']2024-03-27T00:00:00https://www.semanticscholar.org/paper/e9a1edb23161d3b2d5675194486c19e62fe121e3 63f0b690cb90f4394066551ce41fe576c709d8d2AJR. American journal of roentgenology30AJR. American journal of roentgenology['Lisa A. Mullen', 'Emily B. Ambinder']2024-03-27T00:00:00https://www.semanticscholar.org/paper/63f0b690cb90f4394066551ce41fe576c709d8d2 a660a4c1dd6205b1200c4637f0a95fd63e441d14Urban Geography480Urban Geography['Federico Caprotti', 'Federico Cugurullo', 'Matthew Cook', 'Andrew Karvonen', 'Simon Marvin', 'Pauline McGuirk', 'Alan-Miguel Valdez']2024-03-27T00:00:00https://www.semanticscholar.org/paper/a660a4c1dd6205b1200c4637f0a95fd63e441d14 daf3ae12c661a05a1e4774f50867d8a559fa457fComputer-Aided Design and Applications00Computer-Aided Design and Applications['Hai Zeng']2024-03-27T00:00:00https://www.semanticscholar.org/paper/daf3ae12c661a05a1e4774f50867d8a559fa457f 15597f33ed7c6a5b23fa25f13a1b6ef79e85369fRecent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the “6S” goals of parallel driving.Research960The big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces are explored, which integrate metaverse and DTs to construct a parallel training space for CAVs, and a comprehensive elucidation of the crucial characteristics and operational mechanisms are presented.Research['Xiao Wang', 'Jun Huang', 'Yonglin Tian', 'Chen Sun', 'Lie Yang', 'Shanhe Lou', 'Chen Lv', 'Changyin Sun', 'Fei-Yue Wang']2024-03-27T00:00:00https://www.semanticscholar.org/paper/15597f33ed7c6a5b23fa25f13a1b6ef79e85369f 876ab228f4b1026a3e9ef9d1f99f28f96b77b3e4 The growing digitalization of our society has led to a meteoric rise of large technology companies (Big Tech), which have amassed tremendous wealth and influence through their ownership of digital infrastructure and platforms. The recent launch of ChatGPT and the rapid popularization of generative artificial intelligence (GenAI) act as a focusing event to further accelerate the concentration of power in the hands of the Big Tech. By using Kingdon’s multiple streams framework, this article investigates how Big Tech utilize their technological monopoly and political influence to reshape the policy landscape and establish themselves as key actors in the policy process. It explores the implications of the rise of Big Tech for policy theory in two ways. First, it develops the Big Tech-centric technology stream, highlighting the differing motivations and activities from the traditional innovation-centric technology stream. Second, it underscores the universality of Big Tech exerting ubiquitous influence within and across streams, to primarily serve their self-interests rather than promote innovation. Our findings emphasize the need for a more critical exploration of policy role of Big Tech to ensure balanced and effective policy outcomes in the age of AI.Policy & Society831The implications of the rise of Big Tech for policy theory are explored, which underscores the need for a more critical exploration of policy role of Big Tech to ensure balanced and effective policy outcomes in the age of AI.Policy and Society['Shaleen Khanal', 'Hongzhou Zhang', 'Araz Taeihagh']2024-03-27T00:00:00https://www.semanticscholar.org/paper/876ab228f4b1026a3e9ef9d1f99f28f96b77b3e4 c7da3b50cf03f670600e496ff817f52663a4f161The purpose of this paper is to identify ways to bring workers’ voices into the development and use of generative artificial intelligence (AI). Studies of employee involvement and participatory design show benefits for organizations and the workforce when workers are involved in the process of designing and implementing new technologies that affect their jobs. Drawing on more than 50 interviews we conducted, we identify lessons new deployments of generative AI tools can take from research on worker voice to ensure that the adoption and use of generative AI are beneficial for workers, organizations, and society. We then discuss how workers can be involved in four stages of the technology development process: defining the problem, codesigning the technology and work processes, education and retraining, and fair transitions for affected workers. Evidence from recent interviews and past research indicates that input from workers can increase the likelihood that organizations use generative AI tools effectively and workers’ job quality improves. The evidence collected also suggests that generative AI is particularly well-suited to “bottom - up” development and use b ased on workforce experimentation. Moreover, we document the growth in labor union capacity for and actions in representing workers by collaborating with business, developer, and academic institutions, negotiating new collective bargaining provisions governing use of AI, and educating their members on these issues. Our recommendations outline concrete steps for ensuring that generative AI will both drive innovation and help shape the future of work to the benefit of all stakeholders.An MIT Exploration of Generative AI00The growth in labor union capacity for and actions in representing workers are documented by collaborating with business, developer, and academic institutions, negotiating new collective bargaining provisions governing use of AI, and educating their members on these issues.An MIT Exploration of Generative AI['Thomas A. Kochan', 'Ben Armstrong', 'Julie Shah', 'Emilio J. Castilla', 'Ben Likis', 'Martha E. Mangelsdorf']2024-03-27T00:00:00https://www.semanticscholar.org/paper/c7da3b50cf03f670600e496ff817f52663a4f161 298e2ac81489512a0cf8e9997788f8fa73daee27Main problem: In Kazakhstan, the pharmaceutical market is one of the most developed in the CIS countries. To a certain extent, the shortage has been overcome and saturation with various types of finished medicines and medical products has been ensured. The service culture has increased. The main global trends in the development of pharmaceutical markets are observed – consolidation and development of vertically integrated companies. Changes are taking place in the distribution, manufacturing and retail sectors. The number of pharmacy chains is increasing. Modern marketing technologies are being introduced. Also, the pharmaceutical market of Kazakhstan among the Central Asian countries remains the most accessible and transparent for foreign manufacturers from the point of view of the legislative environment. However, the potential of the market is limited by the relatively small number and purchasing power of the population. The growth of the pharmaceutical industry will continue to be driven mainly by demand for generic drugs. Kazakhstan's integration into the EAEU will contribute to improving the quality of medicines and reducing their cost. The purpose of this article is to analyze the development and state regulation of pharmaceutical companies in the Republic of Kazakhstan, identify problems in the field of regulation of pharmaceutical companies in the Republic of Kazakhstan and ways to solve them. Methods: The methodological basis of the research is a comprehensive analysis of the general industry, namely the pharmaceutical industry within the framework of the implementation of the State Program of Industrial and Innovative Development of the Republic of Kazakhstan. Results and their significance: Authors in his article are described the development of the pharmaceutical market in Kazakhstan and analyzes the results in recent years. The authors also considered the main aspects of the development of the pharmaceutical industry within the framework of the implementation of the State Program of industrial and innovative development of the Republic of Kazakhstan for 2020-2025. A review analysis of literary sources was used in the study. During the analysis of the domestic industrial market, namely the component part of which is the pharmaceutical industry, authors found out a number of problems faced by manufacturers. Namely, the absence of ethical commissions, increased value added tax.Bulletin of the Innovative University of Eurasia00The purpose of this article is to analyze the development and state regulation of pharmaceutical companies in the Republic of Kazakhstan, identify problems in the field of regulation of pharmaceutical companies in the Republic of Kazakhstan and ways to solve them.Bulletin of the Innovative University of Eurasia['Aina Narynbaeva', 'Galiya Mergalieva', 'Daulet Sadvakasov']2024-03-26T00:00:00https://www.semanticscholar.org/paper/298e2ac81489512a0cf8e9997788f8fa73daee27 9dd45e70d9984aebff51c245de563a343396b695 The regulations of generative AI, typified by ChatGPT and Sora, have become one of the most influential alternative technological imaginaries. Developed by states and civil society groups, such regulations are triggering a broad range of social actors seeking to nominalize the AI-related behavior. Against this backdrop, this study starts with interrogating the semiotic character of generative AI. Do these regulations support the AI futures, or do they involve a mere change in the social actors who benefit from the technological status quo? To answer this question, this study examines the rhetoric and realization of AI regulations by the European Union and the United States. The findings reveal a degree of AI regulatory alignment between the European Union and the United States, but these two jurisdictions also highlight and predict some structural challenges. Drawing upon the concept of panopticism by Foucault, the study explores the foundational origins of challenges by dissecting the (in)visibility of AI power. It underscores the necessity of regulating the power of the unseen and proposes a synthetic generative AI regulatory framework. We finally conclude that the integrity of sociosemiotics and panopticism provides a productive and paramount framework for understanding the powerful new capacities of AI-related regulations.International Journal of Digital Law and Governance692This study examines the rhetoric and realization of AI regulations by the European Union and the United States and concludes that the integrity of sociosemiotics and panopticism provides a productive and paramount framework for understanding the powerful new capacities of AI-related regulations.International Journal of Digital Law and Governance['Le Cheng', 'Xiuli Liu']2024-03-26T00:00:00https://www.semanticscholar.org/paper/9dd45e70d9984aebff51c245de563a343396b695 65195f59d5e95de15f40277e3eb8a9a1121a631fArtificial intelligence is a general-purpose technology (GPT), term given to technologies that shape an entire era and reorient innovations by reconfiguring the economy’s logic and functioning and bringing in new business models. AI offers unprecedented opportunities and risks. The benefits of AI are extraordinary, as are its potential harms. Potential damage does not have the same degree of problematization, since the intensity and extent of the damage varies according to the domain and the object of application. To address the scale of this challenge, regulation is necessary but not sufficient. Standards, unwritten codes of compliance and arbitration procedures, supervision and auditability, AI governance, international agreements, compliance with current local and global standards and laws. All of this needs to be integrated. Society seems to have no alternative to facing the challenges of at least mitigating the damage already identified and trying to predict future damage in advance. The purpose of this article is to encourage reflection regarding the main initiatives that are available to society to protect its citizens and organizations from the potential harm caused by AI models, vis-à-vis the technology’s own limits to act in ethical and legal compliance.  Filosofia Unisinos190The purpose of this article is to encourage reflection regarding the main initiatives that are available to society to protect its citizens and organizations from the potential harm caused by AI models, vis-à-vis the technology’s own limits to act in ethical and legal compliance.Filosofia Unisinos['Dora Kaufman']2024-03-26T00:00:00https://www.semanticscholar.org/paper/65195f59d5e95de15f40277e3eb8a9a1121a631f 8d17b7e8a888a3efca92a559e21f9ae56460ff8cThis article highlights some aspects of the regulatory regulation of capital punishment in Russia in various historical periods. The authors draw attention to the transformation of public and state perception of this type of punishment. In addition, the authors identify the most significant normative acts regulating the death penalty, inherent in each specific historical epoch. The article draws special attention to the role of the Constitutional Court of the Russian Federation in modern conditions, as a body directly influencing the current state and prospects for the development of the death penalty.Advances in Law Studies00Advances in Law Studies['Lilia Alekseyeva', 'Tatyana Selishcheva']2024-03-26T00:00:00https://www.semanticscholar.org/paper/8d17b7e8a888a3efca92a559e21f9ae56460ff8c b5ab3f6c96e6c28b6cc20656de4c4099b0a316e2Main problem: Rapidly changing trends in development of economies of countries require a rational approach to state regulation of innovative activity and investments directed to organization of real sector of the economy. At the same time, for analysis of modern state policy in the system of innovative development, it is necessary to assess the effectiveness of state regulation mechanisms of innovative activity in the economy of the Republic of Kazakhstan. Purpose: to assess mechanisms of state regulation of innovative activity in the Republic of Kazakhstan in the global competitiveness system. Methods: synthesis, content analysis, accommodation, monographic method, factor analysis, economic and statistical research method. Results and their significance: The assessment of the mechanisms of state regulation of innovative activity in the Republic of Kazakhstan in the global competitiveness system allowed to fully assess effectiveness of mechanisms of state regulation of innovative activity in the economy. Examining the sub-indices of international rating of the World Economic Forum for 2021-2022 in relation to 2017-2018, the authors came to conclusion that in Kazakhstan today there is the decrease in all the sub-indices of rating and their factors (“basic requirements” and “business complexity”), with exception of “innovation” sub-index. This sub-index was significantly reduced due to a sharp deterioration in macroeconomic environment, which is directly related, according to the authors, to significant losses in oil export revenues. This, in turn, affected the deterioration of the state budget indicators. The article pays special attention to place and role of state regulation of innovative activity in the socio-economic policy of the country. Organizational and methodological problems in development and implementation of innovative policy in the Republic of Kazakhstan are researched in detail, which makes it necessary to solve them in order to achieve the effectiveness of innovative policy at the regional and national levels.Bulletin of the Innovative University of Eurasia00Bulletin of the Innovative University of Eurasia['Stanislav Buka']2024-03-26T00:00:00https://www.semanticscholar.org/paper/b5ab3f6c96e6c28b6cc20656de4c4099b0a316e2 ee219ec8791c024689862fcae9f17cff7a5017ddThe literature on responsive regulation argues that citizens should be involved in regulatory practices to avoid capture between regulator and regulatee. It also argues that including citizens can add an important perspective to regulatory practices. However, we know little about how citizens' perspectives are brought into regulatory practices. This paper draws on existing qualitative research to compare and analyze four cases of experimental participatory regulation in Dutch health care, focusing on the theoretical assumptions that citizen involvement (a) prevents capture, and (b) stimulates the inclusion of new perspectives. Our results show that involving citizens in regulation can increase transparency and trust in regulatory practices and familiarizes regulators with other perspectives. It is, however, up to the regulator to work on deriving benefits from that involvement—not only the practical work of organizing participatory regulation, but also the conceptual work of reflecting on their own assumptions and standards. We do find evidence for weak forms of capture and argue for the need to extend capture to involve multiple actors. We reflect on these results for theory development and regulatory practice.Regulation &amp; Governance300Regulation &amp; Governance['Bert de Graaff', 'Suzanne Rutz', 'Annemieke Stoopendaal', 'H. M. van de Bovenkamp']2024-03-26T00:00:00https://www.semanticscholar.org/paper/ee219ec8791c024689862fcae9f17cff7a5017dd ac79b759311bf768960d36b519cf5b374a354c29 Carbon farming is a term associated with land-based practices, agricultural practices that aim at reducing emissions and sequestering carbon. These are, for example, agroforestry practices and practices that result in the maintenance and enhancement of soil organic carbon through the exploitation of the carbon cycle and the sequestration potential of soils. Given the evident links with climate change mitigation, the subject matter of carbon and carbon removals has seen important developments in the European Union (EU) in the past few years. This has culminated in the adoption of a Commission Communication on sustainable carbon cycles and a Commission Proposal for a Regulation on a certification framework for carbon removals. This contribution is meant to address the content and the potentially problematic aspects of the Commission Proposal from a legal perspective.Journal for European Environmental &amp; Planning Law00Journal for European Environmental &amp; Planning Law['Elisa Cavallin']2024-03-26T00:00:00https://www.semanticscholar.org/paper/ac79b759311bf768960d36b519cf5b374a354c29 7436458d885e19275010be9639abf4699d926a8bMost cyber criminals on the internet will be ensnared by Law Number 11 of 2008 concerning Electronic Information and Transactions. The law should offer protection to internet users with good intentions, and provide strict action for cybercrime, especially cyber sabotage actors who disrupt electronic systems and even cause electronic systems to not work properly. Similarly, in Article 5, namely obtaining evidence, there are many obstacles, especially against perpetrators of crimes in Article 33 of Law Number 11 of 2008. Based on the results of the study, it can be concluded that in addition to creating good laws, it also builds the skills of law enforcers to especially find evidence of cybercrime (cybercrime) which is not easy because the crime is in cyberspace by sabotaging the electronic system. In addition, the perpetrators of crimes also hide their identity and the actions they take in cyberspace which is a form of protection.Alauddin Law Development Journal90It can be concluded that in addition to creating good laws, it also builds the skills of law enforcers to especially find evidence of cybercrime (cybercrime) which is not easy because the crime is in cyberspace by sabotaging the electronic system.Alauddin Law Development Journal['Dirga Agung', 'Andi Dewi Pratiwi']2024-03-26T00:00:00https://www.semanticscholar.org/paper/7436458d885e19275010be9639abf4699d926a8b 378ee093f9ea63a59c96f7275f32b4f18f6a82e6Generative Artificial Intelligence (AI) stands as a transformative force that presents a paradox; it offers unprecedented opportunities for productivity growth while potentially posing significant threats to economic stability and societal wellbeing. Many consider generative AI as akin to previous technological advancements, using historical precedent to argue that fears of widespread job displacement are unfounded, while others contend that generative AI`s unique capacity to undertake non-routine cognitive tasks sets it apart from other forms of automation capital and presents a threat to the quality and availability of work that underpin stable societies. This paper explores the conditions under which both may be true. We posit the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures could be triggered, exacerbating social disparities, reducing social cohesion, heightening tensions, and requiring sustained government intervention to maintain stability. To prevent this, the paper underscores the urgent need for proactive policy responses, making recommendations to reduce these risks through robust regulatory frameworks and a new social contract characterised by progressive social and economic policies. This approach aims to ensure a sustainable, inclusive, and resilient economic future where human contribution to the economy is retained and integrated with generative AI to enhance the Mental Wealth of nations.Social Science Research Network381It is posited the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures could be triggered, exacerbating social disparities, reducing social cohesion, heightening tensions, and requiring sustained government intervention to maintain stability.ArXiv['Jo-An Occhipinti', 'Ante Prodan', 'William Hynes', 'Roy Green', 'Sharan Burrow', 'Harris A. Eyre', 'A. Skinner', 'Goran Ujdur', 'John Buchanan', 'Ian B. Hickie', 'Mark Heffernan', 'Christine Song', 'Marcel Tanner']2024-03-26T00:00:00https://www.semanticscholar.org/paper/378ee093f9ea63a59c96f7275f32b4f18f6a82e6 7d2b4558363ef22235847abb8d63ffe11d66eea9This study aims to see the development of research on the topic of "AI on Finance" and research plans that can be carried out based on journals published on the theme. This research uses qualitative method with bibliometric analysis approach. The data used is secondary data with the theme "AI on Finance" which comes from the Dimension database with a total of 127 journal articles. Then, the data is processed and analyzed using the VosViewer application with the aim of knowing the bibliometric map of research development "AI on Finance" in the world. The results of the study found that in bibliometric author mapping the authors who published the most research with the theme "AI on Finance" were Bhattacharjee A; Al-Gasaymeh A.S; Arakpogun E.O; Wang X; Sharma S; Arner D.W; Yang J; Krishna S.H; Khan S; Singh R; Bansal R; Raffinetti E; and Marwala T. Furthermore, based on bibliometric keyword mapping, there are 5 clusters with the most used words are development; challenge, accounting, opportunity, economy, blockchain, and use. Then, the research path topics related to AI on Finance are AI in Islamic Finance, Enterprise AI Development in Finance, AI in Behavioral Finance, Green Finance and AI, and AI Access in Accounting.Digital Economics Review461The results of the study found that in bibliometric author mapping the authors who published the most research with the theme "AI on Finance" were Bhattacharjee A; Al-Gasaymeh A; and Marwala T.Digital Economics Review['Ririn Riani']2024-03-26T00:00:00https://www.semanticscholar.org/paper/7d2b4558363ef22235847abb8d63ffe11d66eea9 71bd6483062d5bea1681fefa08f330ea6eaa709dScientific Reports281Investigating the response patterns of AI chatbots to various emotional primes revealed that ChatGPT-4 bots, when primed with positive, negative, or neutral emotions, exhibited distinct response patterns in both risk-taking and prosocial decisions, suggesting an enhanced capacity for modulating responses based on emotional cues in more advanced LLMs.Scientific Reports['Yukun Zhao', 'Zhen Huang', 'Martin Seligman', 'Kaiping Peng']2024-03-26T00:00:00https://www.semanticscholar.org/paper/71bd6483062d5bea1681fefa08f330ea6eaa709d c4d1146c7c2714a5886c9b14c823987999f2a287EMBO Reports20EMBO Reports['P. Hunter']2024-03-26T00:00:00https://www.semanticscholar.org/paper/c4d1146c7c2714a5886c9b14c823987999f2a287 f151835e3293aa2940e751898eac7b9c15bdba0cSmart cities continue to be discussed throughout Europe as a result of the continent’s rising urbanization and the need for sustainable development. Artificial intelligence (AI) has the potential to significantly promote this shift by assisting cities in becoming more effective, sustainable, and receptive to the requirements of their residents. The goal of this study is to examine the potential and difficulties of AI in urban development and present a framework for incorporating AI into city planning and management in European cities. This is done by analyzing case study examples from European cities and examining primary and secondary data sources, with the aim of providing a comprehensive framework for the sustainable integration of AI systems. This study presents a set of ethical and inclusive AI criteria, such as transparency, inclusion, and accountability, to enable responsible AI research and implementation. It continues by emphasizing the need for efficient AI integration in smart cities and pushing for a holistic AI-enabled transition to inclusive and sustainable smart cities.Acta Polytechnica CTU Proceedings480This study presents a set of ethical and inclusive AI criteria, such as transparency, inclusion, and accountability, to enable responsible AI research and implementation to enable responsible AI research and implementation in European cities.Acta Polytechnica CTU Proceedings['Noor Marji', 'Michal Kohout', 'Lijun Chen', 'Gulbahar Emir Isik', 'Akshatha Ravi Kumar']2024-03-26T00:00:00https://www.semanticscholar.org/paper/f151835e3293aa2940e751898eac7b9c15bdba0c 3a6af6b5cc646e95f95a94e75a5d34cafe262030Purpose: Complex artificial intelligence algorithms may make it hard to understand how they reach certain conclusions or decisions. Lack of transparency raises concerns about bias, discrimination, and opacity, all of which may detract from trust in AI systems. Businesses and developers should prioritize creating AI systems that are easy to understand and explain so that users can understand the reasoning behind their results. Second, fairness and nondiscrimination are fundamental principles. It is possible for AI systems to unintentionally provide biased or unfair outcomes by reinforcing or amplifying biases already seen in training data. Make sure that AI systems are trained on diverse and representative datasets and that they are tested for bias often; this is of the highest significance. Materials and Methods: By analyzing the co-occurrence of keywords, we can see that there are recurring themes when it comes to AI ethics. These topics include big data, social value, algorithms, and ethical aspects. Findings: Critical works that have had a major impact on the field may be found using citation analysis. The results shed light on how AI ethics is always changing as a result of several factors coming together, such as the social effect of technology and the management of stakeholders. Implications to Theory, Practice and Policy: Researchers, legislators, and practitioners may all benefit from the study's findings, which will help direct the development of AI in a way that is ethical and consistent with human values.European journal of technology50How AI ethics is always changing as a result of several factors coming together, such as the social effect of technology and the management of stakeholders, is shed light on how the development of AI in a way that is ethical and consistent with human values.European Journal of Technology['Bhargav Kumar Konidena', 'Jesu Narkarunai Arasu Malaiyappan', 'Anish Tadimarri']2024-03-26T00:00:00https://www.semanticscholar.org/paper/3a6af6b5cc646e95f95a94e75a5d34cafe262030 aae92fcf92569b9aaf7db6953c0c5ba14bed9f15Education and Information Technologies : Official Journal of the IFIP technical committee on Education390This study presents a novel approach contributing to the understanding of the design, development, and implementation AI-based systems for conducting double-blind online randomized controlled trials (RCTs) for higher education research, and showcased how AI can efficiently interview participants and collect their input.Education and Information Technologies['Ilker Cingillioglu', 'Uri Gal', 'Artem Prokhorov']2024-03-26T00:00:00https://www.semanticscholar.org/paper/aae92fcf92569b9aaf7db6953c0c5ba14bed9f15 96674ec96aa22839ff56313a0bd6f4a32f1d4280Artificial intelligence (AI) may be the next general purpose technology. General purpose technologies, such as the steam engine and computing, can have an outsized impact on productivity through a positive feedback loop between producing and application industries. Along with the discussion of AI's potential to improve productivity come a number of policy concerns related to AI's potential to automate jobs and to create existential risk for humanity. Because of these worries, in March 2023, a widely circulated petition called for a pause in AI research. That letter asked several questions about AI's potential impact on society. This paper examines those questions through an economic lens. It highlights reasons to be optimistic about the long‐run impact of AI, while underscoring short‐run risks. Economic models provide an understanding of where the ambiguity lies and where it does not. Our models suggest no ambiguity on whether there will be jobs and little ambiguity on long‐term productivity growth if AI diffuses widely. In contrast, there is substantial ambiguity on the implications of AI's diffusion for inequality.Canadian Journal of Economics/Revue canadienne d'économique390Economic models suggest no ambiguity on whether there will be jobs and little ambiguity on long‐term productivity growth if AI diffuses widely, and there is substantial ambiguity on the implications of AI's diffusion for inequality.Canadian Journal of Economics/Revue canadienne d'économique['Avi Goldfarb']2024-03-26T00:00:00https://www.semanticscholar.org/paper/96674ec96aa22839ff56313a0bd6f4a32f1d4280 992502e936ca801f36b18b95a53711f4b2f4933eNews organizations today rely on AI tools to increase efficiency and productivity across various tasks in news production and distribution. These tools are oriented towards stakeholders such as reporters, editors, and readers. However, practitioners also express reservations around adopting AI technologies into the newsroom, due to the technical and ethical challenges involved in evaluating AI technology and its return on investments. This is to some extent a result of the lack of domain-specific strategies to evaluate AI models and applications. In this paper, we consider different aspects of AI evaluation (model outputs, interaction, and ethics) that can benefit from domain-specific tailoring, and suggest examples of how journalistic considerations can lead to specialized metrics or strategies. In doing so, we lay out a potential framework to guide AI evaluation in journalism, such as seen in other disciplines (e.g. law, healthcare). We also consider directions for future work, as well as how our approach might generalize to other domains.arXiv.org470This paper lays out a potential framework to guide AI evaluation in journalism, such as seen in other disciplines (e.g. law, healthcare), and suggests examples of how journalistic considerations can lead to specialized metrics or strategies.ArXiv['Sachita Nishal', 'Charlotte Li', 'Nicholas Diakopoulos']2024-03-26T00:00:00https://www.semanticscholar.org/paper/992502e936ca801f36b18b95a53711f4b2f4933e dbd9b5f49d8e3514a50d12e6477c60cd54e68979The paper reflects on the future role of AI in scientific research, with a special focus on turbulence studies, and examines the evolution of AI, particularly through Diffusion Models rooted in non-equilibrium statistical mechanics. It underscores the significant impact of AI on advancing reduced, Lagrangian models of turbulence through innovative use of deep neural networks. Additionally, the paper reviews various other AI applications in turbulence research and outlines potential challenges and opportunities in the concurrent advancement of AI and statistical hydrodynamics. This discussion sets the stage for a future where AI and turbulence research are intricately intertwined, leading to more profound insights and advancements in both fields.arXiv.org1430ArXiv['Michael Chertkov']2024-03-26T00:00:00https://www.semanticscholar.org/paper/dbd9b5f49d8e3514a50d12e6477c60cd54e68979 58f5871edd69c02b98927aca0027f341ebd84191Urban planning, which is inherently multifaceted, requires the development of innovative tools to navigate its complexities. This study introduces a pioneering approach that presents an AI-driven framework tailored for urban data collection and analysis. The impetus for this framework is highlighted through the unique narrative of Most city, which is profoundly transformed by mininginduced displacement and resettlement. While most cities serve as a vivid illustration of the challenges cities can face, especially in the wake of industrial imperatives, this study focuses on the potential of AI in addressing such challenges. The proposed framework, while grounded in advanced computational methodologies, is designed with keen emphasis on real-world applications, ensuring its relevance and adaptability. By integrating Most city’s detailed account with this AI-centric methodology, this study emphasizes the importance of a data-driven approach in understanding and addressing urban dilemmas. Importantly, this study is preparatory, laying the groundwork for the framework’s future application, especially in contexts such as Most city. By bridging advanced AI techniques with tangible urban challenges, this research illuminates a path forward, suggesting a future in which urban planning is not only informed by data but also empowered by AI’s analytical process.Acta Polytechnica CTU Proceedings210A future in which urban planning is not only informed by data but also empowered by AI’s analytical process is suggested, suggesting a future in which urban planning is not only informed by data but also empowered by AI’s analytical process.Acta Polytechnica CTU Proceedings['Akshatha Ravi Kumar', 'Noor Marji', 'Gulbahar Emir Isik', 'Lijun Chen']2024-03-26T00:00:00https://www.semanticscholar.org/paper/58f5871edd69c02b98927aca0027f341ebd84191 3d6f102cd88a93d6a4d67649feb8dce86d5a80eaEPJ Data Science400The findings reveal that the primary topics at the top-level community, most pertinent to AI ethics, predominantly revolve around knowledge-based and ethical issues.EPJ Data Science['Mengyi Wei', 'Yu Feng', 'Chuan Chen', 'Peng Luo', 'Chenyu Zuo', 'Liqiu Meng']2024-03-26T00:00:00https://www.semanticscholar.org/paper/3d6f102cd88a93d6a4d67649feb8dce86d5a80ea 63e4de4e7eccc15af5b34f070881ba4e00f952b9The ever-evolving field of User Interface & User Experience UI/UX design prioritizes creating user-friendly interfaces. Traditionally, this has been a human-centred process. However, Artificial Intelligence AI offers new possibilities for automating design tasks and leveraging data for user insights. This research paper delves into the potential impact of AI on UI/UX design. The core question is whether AI tools can effectively replace human designers in crafting user experiences. Alternatively, can AI work alongside designers to enhance their capabilities? This paper explores these questions through a review of existing research and proposes a research methodology to further investigate the topic. The literature review analyses how AI can be used for tasks like user behaviour analysis, A/B testing, and prototype generation. However, it also acknowledges AI's limitations in understanding user emotions, implementing creative solutions, and adapting to unforeseen user needs. The suggested research methodology will give a combined approach, making a review of existing literature with interviews from UI/UX professionals and a case study analyzing a design project utilising AI tools. By investigating the strengths and limitations of AI in design tasks, how AI can be integrated into the workflow, and the ethical considerations surrounding AI-driven design decisions, this research helps to provide a better understanding of the relationship between AI and human designers in the UI/UX field. The findings can inform design education, industry practices, and the development of future AI tools specifically tailored for UI/UX design applications. Keywords: UI/UX Design, Artificial Intelligence, Human-Computer Interaction, User Experience, User Interface, Design AutomationINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Investigating the strengths and limitations of AI in design tasks, how AI can be integrated into the workflow, and the ethical considerations surrounding AI-driven design decisions helps to provide a better understanding of the relationship between AI and human designers in the UI/UX field.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ms. Sunitha B.K']2024-03-26T00:00:00https://www.semanticscholar.org/paper/63e4de4e7eccc15af5b34f070881ba4e00f952b9 5c16889e9479dcbed8d0325c8ab464c9a8f86414Five experts from China and the US discussed the concept, development, bottlenecks and opportunities of AI for Science (AI4S), as well as their understanding of the relationship between AI and science.National Science Review00National Science Review['Weijie Zhao']2024-03-26T00:00:00https://www.semanticscholar.org/paper/5c16889e9479dcbed8d0325c8ab464c9a8f86414 028d642ad70a2734ee50683fb5156e051c1783e1The financial technology (Fintech) industry in Africa is expanding and growing quickly. Despite several regulatory contexts, political, economic, and regulatory obstacles, Fintech is booming throughout the continent. Over the past few years, the information technology industry has grown dramatically, and a large number of these new businesses are focused on upending the financial technology industry. Because it can understand customer preferences, spending habits, and financial goals, generative AI has a lot of promise to provide personalized financial recommendations or solutions to any individual. With the new paradigm of generative AI playing a more critical role, it may have significant impact on fostering the growth of Fintech within Africa. The article provides a comprehensive review of the current state of Fintech within Africa and the impact of generative AI on fostering the growth of it.Yildiz social science review00The article provides a comprehensive review of the current state of Fintech within Africa and the impact of generative AI on fostering the growth of it.Yildiz Social Science Review['Klemens Katterbauer', 'Hassan Syed', 'Laurent Cleenewerck', 'R. Özbay', 'Sema Yilmaz']2024-03-26T00:00:00https://www.semanticscholar.org/paper/028d642ad70a2734ee50683fb5156e051c1783e1 126390ef91ef28b91ff03683018eea6023cb1124Ethics and Information Technology300The responsibility-by-design standard effectively established a productive workflow for collaborative investigation and work on ethical challenges and is concluded that the responsibility-by-design standard effectively established a productive workflow for collaborative investigation and work on ethical challenges.Ethics Inf. Technol.['Sabrina Blank', 'Celeste Mason', 'Frank Steinicke', 'Christian Herzog']2024-03-26T00:00:00https://www.semanticscholar.org/paper/126390ef91ef28b91ff03683018eea6023cb1124 1e50ca9a722608555eb5f5691ca09a23749cb97a This article investigates the metaphor of the “black box” in artificial intelligence, a representation that often suggests that AI is an unfathomable power, politically uncontrollable and shrouded in an aura of opacity. While the concept of the “black box” is legitimate and applicable in deep neural networks due to the in- herent complexity of the process, it has also become a generic pretext for the perception, which we seek to critically analyze, that AI systems are inscrutable and out of control, as well as supposedly endowed with intel- ligence and creativity. To challenge these ideas, we will address what we call the supremacy of patterns and the two significant phenomena that result from it: enchanted determinism and the dictatorship of the past. Filosofia Unisinos240This article investigates the metaphor of the “black box” in artificial intelligence, a representation that often suggests that AI is an unfathomable power, politically uncontrollable and shrouded in an aura of opacity, and addresses the supremacy of patterns and the two significant phenomena that result from it: enchanted determinism and the dictatorship of the past.Filosofia Unisinos['Murilo Karasinski', 'Kleber Bez Birolo Candiotto']2024-03-26T00:00:00https://www.semanticscholar.org/paper/1e50ca9a722608555eb5f5691ca09a23749cb97a f938f0de684edb9fb8663fbbbfc8a6b36c6a01f0The release of ChatGPT in November 2022 has sparked great interest in Generative Artificial Intelligence (Gen AI). Discussions have arisen regarding whether Gen AI has passed the Turing Test, a measure of a machine’s ability to mimic human intelligence. There are also concerns about the potential threats posed by advanced AI to humanity. Using the lens of multimodal grammar, Cope and Kalantzis’s work offers a balanced analysis of Gen AI’s capabilities and limitations. This paper builds on their work, examining Gen AI’s strengths and weaknesses, and proposes trans-semiotizing the Turing Test to benchmark Gen AI.Multimodality &amp; Society60Using the lens of multimodal grammar, Cope and Kalantzis’s work offers a balanced analysis of Gen AI’s capabilities and limitations, and proposes trans-semiotizing the Turing Test to benchmark Gen AI.Multimodality &amp; Society['Angel MY Lin', 'Qinghua Chen']2024-03-26T00:00:00https://www.semanticscholar.org/paper/f938f0de684edb9fb8663fbbbfc8a6b36c6a01f0 a66c63dbeecced56ced1687eec42f2852f0f9215The article presents an analysis centered on the emotional lapses of artificial intelligence (AI) and the influence of these lapses on two critical aspects. Firstly, the article explores the ontological impact of emotional lapses, elucidating how they hinder AI’s capacity to develop a moral sense. The absence of a moral emotion, such as sympathy, creates a barrier for machines to grasp and ethically respond to specific situations. This raises fundamental questions about machines’ ability to act as moral agents in the same manner as human beings. Additionally, the article sheds light on the practical implications within human-machine relations and their effect on human friendships. The lack of friendliness or its equivalent in interactions with machines directly impacts the quality and depth of human relations. This concerningly suggests the potential replacement or compromise of genuine interpersonal connections due to limitations in human-machine interactions.Filosofia Unisinos440The article explores the ontological impact of emotional lapses, elucidating how they hinder AI’s capacity to develop a moral sense and sheds light on the practical implications within human-machine relations and their effect on human friendships.Filosofia Unisinos['Evandro Barbosa', 'Thais Alves Costa']2024-03-26T00:00:00https://www.semanticscholar.org/paper/a66c63dbeecced56ced1687eec42f2852f0f9215 09f77e901922a3bd511fa8ec8fb88d56d0dbad48Ethics and Information Technology240Ethics Inf. Technol.['Paul Schütze']2024-03-26T00:00:00https://www.semanticscholar.org/paper/09f77e901922a3bd511fa8ec8fb88d56d0dbad48 ad61fa8ad2e2149250a4212d8f44dcdf57bee649Discover Artificial Intelligence350This study investigates AI's involvement in addressing global issues such as climate change, public health, and social justice and serves as a resource for policymakers, researchers, and practitioners understanding the complex link between AI and humans.Discov. Artif. Intell.['Soha Rawas']2024-03-26T00:00:00https://www.semanticscholar.org/paper/ad61fa8ad2e2149250a4212d8f44dcdf57bee649 739423fceedd81f4dfc93f09f3cbb62b76395f2fArtificial intelligence (AI) ethics is proposed as an emerging and interdisciplinary field concerned with addressing the ethical issues of AI, such as the issue of moral decision-making. The conflict between our intuitive moral judgments constitutes an inevitable obstacle to decision-making in AI ethics. This article outlines the Moral Relevance Approach, which could provide a considerable moral foundation for AI ethics. Taking moral relevance as the precondition of the consequentialist principles, the Moral Relevance Approach aims to plausibly consider individual moral claims. It is not only the common ethical target shaping our moral consensus but also the inherent moral ability connecting others with us.Philosophies200The Moral Relevance Approach, taking moral relevance as the precondition of the consequentialist principles, aims to plausibly consider individual moral claims.Philosophies['Shuaishuai Fang']2024-03-26T00:00:00https://www.semanticscholar.org/paper/739423fceedd81f4dfc93f09f3cbb62b76395f2f 47fcaca0c49c6820565f5b2593039923e8e10231Internet Policy Review601Internet Policy Review['Kasia Söderlund', 'Emma Engström', 'Kashyap Haresamudram', 'Stefan Larsson', 'P. Strimling']2024-03-26T00:00:00https://www.semanticscholar.org/paper/47fcaca0c49c6820565f5b2593039923e8e10231 629861f500712f6c2853c26c86e9b0f506822f30Nature11Nature['Carissa Wong']2024-03-26T00:00:00https://www.semanticscholar.org/paper/629861f500712f6c2853c26c86e9b0f506822f30 e3588594891fe0aa93df571a419e9d1ccf2567aeArtif. Intell. Medicine261A human-centered methodology for the development of an AI-as-a-service platform with the goal of broadening access to personalized healthcare, aiming to augment, not replace, human capabilities and integrate in current processes.Artificial intelligence in medicine['Tommaso Turchi', 'Giuseppe Prencipe', 'Alessio Malizia', 'Silvia Filogna', 'Francesco Latrofa', 'G. Sgandurra']2024-03-26T00:00:00https://www.semanticscholar.org/paper/e3588594891fe0aa93df571a419e9d1ccf2567ae 65f906f3966441993c607fb4569c598360d085caPurpose This study investigates the transformative role of Sora in education and libraries. This study aims to explore Sora’s capabilities and potential implications for enhancing learning experiences and enriching library resources. Design/methodology/approach Using an exploratory approach, this paper analyzes Sora’s functionalities, focusing on its ability to convert textual descriptions into dynamic video content swiftly and accurately. It examines the ways in which Sora can augment learning through interactivity, personalization and accessibility, as well as its capacity to digitize cultural heritage and promote literacy in library settings. Findings Sora emerges as a potential powerful tool for education and libraries, offering opportunities for diverse learning modalities, creativity and critical thinking. Its capacity to facilitate immersive storytelling and educational gamification holds promise for engaging users and fostering community involvement. However, ethical considerations such as bias mitigation and equitable access must be addressed to maximize Sora’s benefits. Originality/value This study contributes to the understanding of artificial intelligence’s potential in education and libraries, particularly through the lens of Sora. Library Hi Tech News171Sora emerges as a potential powerful tool for education and libraries, offering opportunities for diverse learning modalities, creativity and critical thinking, and ethical considerations such as bias mitigation and equitable access must be addressed to maximize Sora’s benefits.Library Hi Tech News['Adebowale Jeremy Adetayo', 'Augustine I. Enamudu', 'Folashade Munirat Lawal', 'A. Odunewu']2024-03-26T00:00:00https://www.semanticscholar.org/paper/65f906f3966441993c607fb4569c598360d085ca 04f5322e9107374eff8c37e47bcff9506e9bbc6bWith the rapid development of internet technology, many industries have embarked on a digital transformation. However, while the Internet has brought convenience to users, it has also become a breeding ground for criminals to commit fraud. On the one hand, a large number of users on the Internet more or less left data, criminals can use this information to practice accurate fraud users, improve the success rate of fraud; On the other hand, online financial transactions such as banking and e-commerce also provide more opportunities for criminals to commit fraud. Therefore, all kinds of fraud methods emerge in an endless flow, through the telephone, information, fishing and other means of fraud, not only to bring hundreds of millions of losses to society every year, but also to the security of people's lives have a huge threat. Monitoring and preventing online fraud is an important part of the cybersecurity industry. For known network fraud, based on the domain name of the phishing site, the account number and mobile phone number that send fraudulent information, simple and effective monitoring and defence can be carried out through the blacklist. However, it is difficult for traditional means to effectively defend against undocumented fraud. With the development of machine learning technology, it is the main research direction of fraud detection methods to discover the information sources and characteristics of information content through machine learning technology, and make real-time and continuous accurate judgments. This paper realises credit fraud detection by generating adversarial network technology, so as to prevent network security risks.Academic Journal of Science and Technology251This paper realises credit fraud detection by generating adversarial network technology, so as to prevent network security risks.Academic Journal of Science and Technology['Jinxin Xu', 'Han Wang', 'Yuqiang Zhong', 'Lichen Qin', 'Qishuo Cheng']2024-03-26T00:00:00https://www.semanticscholar.org/paper/04f5322e9107374eff8c37e47bcff9506e9bbc6b 1b9141252d1c8b27b301ddc7c973092bffb1fb89International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-26T00:00:00https://www.semanticscholar.org/paper/1b9141252d1c8b27b301ddc7c973092bffb1fb89 17df05260d5ed2f64becfe8d0e3269ac5823effbInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-26T00:00:00https://www.semanticscholar.org/paper/17df05260d5ed2f64becfe8d0e3269ac5823effb f96ee265d8ff72edc7711c5830f42eb8dedd1b63Adoptive cell transfer (ACT) using T cells modified by the T cell receptor (TCR) gene is an exciting and rapidly developing field. Numerous preclinical and clinical studies have shown varying feasibility, safety, and efficacy of using TCR-engineered T cells to treat cancer and viral infections. Although there is evidence that their use is effective, to what extent and how these therapies can be improved is still a question of research. Since TCR affinity has been generally accepted as the primary role in defining T cell specificity and sensitivity, selecting and generating high-affinity TCRS remains a fundamental approach to designing more effective T cells. However, the traditional approach of increasing affinity by random mutagenesis can cause adverse cross-reactions that result in on-target and off-target adverse events, produce depleted effectors through overstimulation, and ignore other kinetic and cellular parameters that have been shown to affect antigen specificity. In this paper, we review the preclinical and clinical potential of TCR-modified T cells, summarize contributions that challenge the role of TCR affinity in antigen recognition, and explore how structure-guided design can be used to manipulate antigen specificity and TCR cross-reactivity to improve the safety and efficacy of TCR-modified T cells for ACT.Academic Journal of Science and Technology290How structure-guided design can be used to manipulate antigen specificity and TCR cross-reactivity to improve the safety and efficacy of TCR-modified T cells for ACT is explored.Academic Journal of Science and Technology['Xinyu Shen', 'Baoming Wang', 'Zheng He', 'Huiming Zhou', 'Yanlin Zhou']2024-03-26T00:00:00https://www.semanticscholar.org/paper/f96ee265d8ff72edc7711c5830f42eb8dedd1b63 8fb56f56ce23837fe6fc48647ab1357430581bafThis report provide a detailed description of the method that we explored and proposed in the WECIA Emotion Prediction Competition (EPC), which predicts a person's emotion through an artistic work with a comment. The dataset of this competition is ArtELingo, designed to encourage work on diversity across languages and cultures. The dataset has two main challenges, namely modal imbalance problem and language-cultural differences problem. In order to address this issue, we propose a simple yet effective approach called single-multi modal with Emotion-Cultural specific prompt(ECSP), which focuses on using the single modal message to enhance the performance of multimodal models and a well-designed prompt to reduce cultural differences problem. To clarify, our approach contains two main blocks: (1)XLM-R\cite{conneau2019unsupervised} based unimodal model and X$^2$-VLM\cite{zeng2022x} based multimodal model (2) Emotion-Cultural specific prompt. Our approach ranked first in the final test with a score of 0.627.arXiv.org160A simple yet effective approach called single-multi modal with Emotion-Cultural specific prompt (ECSP), which focuses on using the single modal message to enhance the performance of multimodal models and a well-designed prompt to reduce cultural differences problem.ArXiv['Shengdong Xu', 'Zhouyang Chi', 'Yang Yang']2024-03-26T00:00:00https://www.semanticscholar.org/paper/8fb56f56ce23837fe6fc48647ab1357430581baf 7ea49a646f60f8d2a04a137c31da47e1dc826859Background: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. To address the opaqueness often associated with deep learning (DL) models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax diagnoses made by DL models. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. Method: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of these explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template's boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods with and without our template guidance when explaining two DL models in two real-world datasets. Results: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. Conclusions: In the context of pneumothorax diagnoses, we proposed a template-guided approach for improving AI explanations. We anticipate that our template guidance will forge a fresh approach to elucidating AI models by integrating clinical domain expertise.arXiv.org10A template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of these explanations and forging a fresh approach to elucidating AI models by integrating clinical domain expertise is proposed.ArXiv['Han Yuan', 'Chuan Hong', 'Pengtao Jiang', 'Gangming Zhao', 'Nguyen Tuan Anh Tran', 'Xinxing Xu', 'Yet Yen Yan', 'Nan Liu']2024-03-26T00:00:00https://www.semanticscholar.org/paper/7ea49a646f60f8d2a04a137c31da47e1dc826859 3f95cbe0bb9489d602950e1bb7f6e10028e10c24Journal of the American Academy of Child and Adolescent Psychiatry00Journal of the American Academy of Child and Adolescent Psychiatry['A. Javanbakht']2024-03-26T00:00:00https://www.semanticscholar.org/paper/3f95cbe0bb9489d602950e1bb7f6e10028e10c24 5068578541b75c488e32fadbc7b48f007b1fd144International Dental Journal100International Dental Journal['L. Samaranayake']2024-03-26T00:00:00https://www.semanticscholar.org/paper/5068578541b75c488e32fadbc7b48f007b1fd144 57ab2247314facee8f191f3612bb81708c628d3eNature00Nature['Katrina Krämer']2024-03-26T00:00:00https://www.semanticscholar.org/paper/57ab2247314facee8f191f3612bb81708c628d3e aa2f262bde5f98cc6fda694d9b528066658dff96Behaviour &amp; Information Technology580Behaviour &amp; Information Technology['Gabbiadini Alessandro', 'Ognibene Dimitri', 'Baldissarri Cristina', 'Manfredi Anna']2024-03-26T00:00:00https://www.semanticscholar.org/paper/aa2f262bde5f98cc6fda694d9b528066658dff96 59667c186ab9b5ac7ba81e21b4d2c021efa83740This research explores the intersection of artificial intelligence and finance, focusing on the emergence of intelligent investment advisers, commonly known as Robo-advisers (RAs). These RAs utilize robust computer models and artificial intelligence algorithms to deliver personalized asset management investment plans for users. Notably, Wealthfront is highlighted as a prominent platform in this field, offering automated investment management services aimed at optimizing investment returns. The study investigates the impact of users' past investment performance on their adoption of intelligent advisers, considering factors such as previous defaults and recent investment performance. It reveals that frequent adjustments to the use of intelligent advisers may hinder long-term investment objectives, emphasizing the importance of consistent usage to fully capitalize on their benefits. Furthermore, the research emphasizes the significance of transparency, user-friendly interaction design, and tailored financial services to foster user trust and enhance the optimization of intelligent advisers' design.Academic Journal of Science and Technology184The study investigates the impact of users' past investment performance on their adoption of intelligent advisers, considering factors such as previous defaults and recent investment performance, and emphasizes the significance of transparency, user-friendly interaction design, and tailored financial services to foster user trust and enhance the optimization of intelligent advisers' design.Academic Journal of Science and Technology['Zengyi Huang', 'Chang Che', 'Haotian Zheng', 'Chen Li']2024-03-26T00:00:00https://www.semanticscholar.org/paper/59667c186ab9b5ac7ba81e21b4d2c021efa83740 b7222d2a086dc55df3f322cc4783513703e264f4Purpose: The integration of Artificial Intelligence (AI) into business operations has become a pivotal driver of innovation and efficiency. This research paper explores the multifaceted landscape of AI implementation in businesses, examining the benefits of AI implementation.   Theoretical Framework: The methodology adopted for this research comprises Semi-structured interviews with six key stakeholders’ executives, AI project managers, and digital transformation staff. Six additional globally businesses were chosen for case studies based on AI adoption maturity and digital transformation performance. Additionally, a qualitative content study of AI and digital transformation literature was conducted.   Results and Discussion: The study revealed that as businesses continue to integrate AI, a balanced approach considering workforce implications is crucial to realizing the full potential of AI. The study also found out that the adoption of Artificial Intelligence in business is a nuanced process shaped by a confluence of factors. Organizational leadership, culture, resource availability, perceived benefits, regulatory considerations, data security, technology evaluation, and workforce readiness all play intricate roles. Thus, a holistic understanding of these factors empowers organizations to navigate the complex space of AI adoption, unlocking its transformative potential.   Research Implications: The study's findings have many practical implications to businesses adoption of AI. AI, managers must promote innovation and adaptability. AI implementation requires human and technology capital. To reduce AI risks, legal and data security measures must be followed. To prepare employees for an AI-enabled workplace, organizations should adopt comprehensive training and development programs. Businesses may overcome AI implementation challenges and capitalize on its disruptive potential by addressing these five pragmatic elements.   Originality and Value: This study contributes to the literature by offering insights into the nuanced landscape of AI adoption in businesses, emphasizing the crucial role of workforce considerations. It provides a comprehensive understanding of the multifaceted factors shaping AI implementation. The relevance and value of this research are evidenced by the holistic approach and contributes valuable guidance for organizations navigating the complexities of AI adoption, fostering innovation and efficiency while prioritizing workforce integration.International Journal of Professional Business Review451The study revealed that as businesses continue to integrate AI, a balanced approach considering workforce implications is crucial to realizing the full potential of AI, and provides a comprehensive understanding of the multifaceted factors shaping AI implementation.International Journal of Professional Business Review['David Oyekunle', 'David Boohene']2024-03-26T00:00:00https://www.semanticscholar.org/paper/b7222d2a086dc55df3f322cc4783513703e264f4 16e4e50a59f5d06ff8b643c2390cd63bced48673The advent of the Fourth Industrial Revolution has catalyzed transformative changes in the healthcare sector, highlighting the need to optimize Artificial Intelligence (AI) to improve patient satisfaction. This research examines the role of AI in revolutionizing the healthcare market and its potential to improve patient outcomes. Using a systematic literature review, we collected and analyzed studies published from 2017 to 2022 from various databases such as ProQuest, Science Direct, PubMed, CINAHL, and Scopus, focusing on AI applications in healthcare and its impact on patient satisfaction. Inclusion criteria for the initial screening were articles that were freely available in full text, had open access, and were published in English or Indonesian, as well as ensuring no duplication in the records found. Significant findings were synthesized through thematic and descriptive analysis to ascertain the efficacy of AI in patient care. Of the 25 articles relevant for further evaluation, 12 studies met the inclusion criteria and were included in the qualitative analysis. The chosen methodology allowed the authors to conduct a comprehensive and systematic review. A practical suggestion that can be drawn from these findings is the importance of investing in the development of artificial intelligence (AI) in healthcare. This requires adequate training for staff to understand and use these technologies effectively. In addition, healthcare institutions should ensure that the use of AI remains compliant with applicable privacy and ethical regulations. Collaboration between various parties such as healthcare institutions, technology providers, and researchers is also necessary to accelerate innovation and knowledge exchange. By utilizing real-time data analysis powered by AI, healthcare can be improved, and governments can support innovation in AI by providing incentives, supportive regulations, and adequate research funding.  Keywords: Artificial Intelligence, Digital Transformation, Healthcare Revolution, Patient Satisfaction, Service OptimizationContagion: Scientific Periodical Journal of Public Health and Coastal Health370The role of AI in revolutionizing the healthcare market and its potential to improve patient outcomes and the importance of investing in the development of artificial intelligence (AI) in healthcare are examined.Contagion: Scientific Periodical Journal of Public Health and Coastal Health['Anggi Parsaoran Hotmangatur', 'Adang Bachtiar']2024-03-26T00:00:00https://www.semanticscholar.org/paper/16e4e50a59f5d06ff8b643c2390cd63bced48673 8ccc1455b366635a1266f845fb7da51ec2b1b579As the awareness on violent deaths from guns, drugs, and suicides emerges as a public health crisis in the United States, attempts to prevent injury and mortality through nursing research are critical. The National Violent Death Reporting System provides public health surveillance of US violent deaths; however, understanding the National Violent Death Reporting System's research utility is limited. The purpose of our rapid review of the 2019-2023 literature was to understand to what extent artificial intelligence methods are being used with the National Violent Death Reporting System. We identified 16 National Violent Death Reporting System artificial intelligence studies, with more than half published after 2020. The text-rich content of National Violent Death Reporting System enabled researchers to center their artificial intelligence approaches mostly on natural language processing (50%) or natural language processing and machine learning (37%). Significant heterogeneity in approaches, techniques, and processes was noted across the studies, with critical methods information often lacking. The aims and focus of National Violent Death Reporting System studies were homogeneous and mostly examined suicide among nurses and older adults. Our findings suggested that artificial intelligence is a promising approach to the National Violent Death Reporting System data with significant untapped potential in its use. Artificial intelligence may prove to be a powerful tool enabling nursing scholars and practitioners to reduce the number of preventable, violent deaths.Computers, Informatics, Nursing350A rapid review of the 2019-2023 literature suggested that artificial intelligence is a promising approach to the National Violent Death Reporting System data with significant untapped potential in its use.Computers, informatics, nursing : CIN['Lisa C Lindley', 'Christina N Policastro', 'Brianne Dosch', 'Joshua G Ortiz Baco', 'Charles Q Cao']2024-03-26T00:00:00https://www.semanticscholar.org/paper/8ccc1455b366635a1266f845fb7da51ec2b1b579 57833fb678a54daddcd1c367c1378f3398480e79Current Sustainable/Renewable Energy Reports810A comprehensive review of the electrification challenges in developing economies alongside an assessment of novel AI approaches for microgrid applications is provided and emerging opportunities for AI research in the context of developing economies and the proposed STEP framework are identified.Current Sustainable/Renewable Energy Reports['William Bodewes', 'Julian De Hoog', 'Elizabeth L. Ratnam', 'Saman Halgamuge']2024-03-26T00:00:00https://www.semanticscholar.org/paper/57833fb678a54daddcd1c367c1378f3398480e79 c09a7d3d1e007155d61f9a3eec3fb67cc6c22d7aThe subject of research in this article is the features and patterns of legal activity in comparison with the features and patterns of functioning of artificial intelligence systems, including systems based on Chat GPT technology. The purpose of the study is to analyze the features and patterns of legal activity, the features and patterns of functioning of artificial intelligence systems, including systems based on the Chat GPT technology, compare these patterns, and based on the comparison of these patterns, draw a conclusion about the degree applicability of artificial intelligence technologies for solving a particular class of legal problems. Research methods include the social experiment method, when Chat GPT was asked certain questions of a legal nature, answers to them were received and analyzed. Also during the study, general scientific research methods were used, such as analysis, synthesis, deduction, abstraction, generalization. These research methods were used to reveal the features and patterns of legal activity, the features and patterns of the functioning of artificial intelligence systems, as well as to draw conclusions about the degree of applicability of artificial intelligence technologies for solving a particular class of problems in jurisprudence. During the study, it was found that artificial intelligence technologies based on Chat GPT in some cases are not very suitable for solving problems related to the application of legal norms to specific situations, due to the specific “thinking” of artificial intelligence systems, which in some cases does not allow one to distinguish so-called legal circumstances that are significant in a given situation, from legal circumstances that are not significant in a given situation, the inability in some cases to correctly qualify the legal relations of the parties and make the correct legal decision with reference to current legislation. The study also analyzed the capabilities of artificial intelligence based on Chat GPT technologies for solving problems in the field of lawmaking, application and interpretation of legal norms. It is concluded that in these areas, artificial intelligence technologies, including those based on Chat GPT, have a certain potential, provided that the associative connection built by artificial intelligence between the elements of legal reality contained in the texts corresponds to the ontological connection between the objects of legal reality.Advances in Law Studies60Artificial intelligence technologies, including those based on Chat GPT, have a certain potential in these areas, provided that the associative connection built by artificial intelligence between the elements of legal reality contained in the texts corresponds to the ontological connection between the objects of legal reality.Advances in Law Studies['Mikhail Osipov']2024-03-26T00:00:00https://www.semanticscholar.org/paper/c09a7d3d1e007155d61f9a3eec3fb67cc6c22d7a ed74ab13241e440937351af83d68cace6d52d970Abstract. The problem of the quality of managerial decisions is one of the most acute problems of agriculture. Their quality can be improved with the use of digital technologies, including the use of artificial intelligence (AI) systems. The purpose of the study is to clarify the main stages of managerial decision-making, taking into account the use of AI systems. The scientific novelty lies in the development of a structural model for making a managerial decision, taking into account the use of AI systems, the main components of this process are identified. The research methods were the analysis of publications in the WoS scientific citation network on the topics “agriculture” and “artificial intelligence”, as well as the abstract-logical method in the analysis of the main stages of making a managerial decision. The results of the study were the determination of the composition and content of the stages of the procedural decision invariant, taking into account the use of artificial intelligence systems. The use of artificial intelligence systems allows diagnosing the occurrence of problems in crop production, animal husbandry, and technical systems at an early stage. Data collection and analysis in the process of making a managerial decision using AI systems includes direct data collection using sensors, cameras, scanners, etc., their cleaning and preliminary analysis, exploratory and statistical analysis, data modeling and interpretation of results. The use of AI systems will make it possible to operate with large data sets from agricultural production facilities, which will reduce uncertainty in making managerial decisions. The analysis of alternatives and the development of a management decision using AI systems turns off the forecasting of agricultural development indicators in a given system of constraints, the generation of alternative solutions and the choice of the optimal alternative, the acceptance or ignoring of the proposed alternatives. AI systems can be used to automate and optimize the process of implementing management decisions, monitoring and controlling management decisions. The use of AI systems to automate management decision-making processes in agriculture can help improve management efficiency.Agrarian Bulletin of the110The purpose of the study is to clarify the main stages of managerial decision-making, taking into account the use of AI systems, to improve management efficiency.Agrarian Bulletin of the['Ekaterina Yalunina', 'Natalya Pryadilina', 'Egor Skvorcov']2024-03-26T00:00:00https://www.semanticscholar.org/paper/ed74ab13241e440937351af83d68cace6d52d970 dc21a2f75fa1bf8ce2b9987d01f7ef127f5e95a5As Artificial Intelligence (AI) systems become more widespread, there is a growing need for transparency to ensure human understanding and oversight. This is where Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations is still an open research problem. Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. Essential techniques include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods while Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. To ensure that explanations are tailored for diverse users, contexts, and AI applications, HCI principles and participatory design approaches can be utilized. Therefore, this article concludes with recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI. As Artificial Intelligence (AI) systems become more common in our daily lives, the need for transparency in these systems is becoming increasingly important. Ensuring that humans clearly understand how AI systems work and can oversee their functioning is crucial. This is where the concept of Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations for AI systems is still an open research problem. In this context, Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. By integrating HCI principles, we can create systems humans understand and operate more efficiently. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. The essential methods identified include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods. Each of these techniques has unique advantages and can be used to provide explanations for different types of AI systems. While Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. There is a risk of oversimplification, leading to misunderstanding or mistrust of the AI system. It is essential to employ HCI principles and participatory design approaches to ensure that explanations are tailored for diverse users, contexts, and AI applications. By developing human-centred XAI systems, we can ensure that AI systems are transparent, interpretable, and trustworthy. This can be achieved through interdisciplinary collaboration between HCI and AI. The recommendations in this article provide a starting point for designing such systems. In essence, XAI presents a significant opportunity to improve the transparency of AI systems, but it requires careful design and implementation to be effective.Research &amp; Review: Machine Learning and Cloud Computing00Recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI, to ensure that AI systems are transparent, interpretable, and trustworthy.Research &amp; Review: Machine Learning and Cloud Computing['S. T. Anand Reddy']2024-03-26T00:00:00https://www.semanticscholar.org/paper/dc21a2f75fa1bf8ce2b9987d01f7ef127f5e95a5 a48a7e7f0776bcfc31e70f8c1b0dd2ceafb2fa1bWith the rapid development of the new generation of information technology, artificial intelligence has had a profound impact on the labor and employment market, which has attracted the attention to its structural change. With the background of artificial intelligence, combined with the characteristics of the four technological changes of artificial intelligence, this paper deeply analyzes the influence mechanism of artificial intelligence on employment. Through the 2017-2021 Chinese provinces panel data empirical analysis, the study found: artificial intelligence significantly improved the employment of agriculture and services, but the local manufacturing employment inhibition effect, with the deepening application of artificial intelligence, intelligent manufacturing will create more man-machine collaborative jobs, create effect will take advantage. Accordingly put forward, should correctly understand the substitution effect of artificial intelligence, seize the talent training opportunities of artificial intelligence, for different people to differentiate employment promotion and social security policy, is committed to promote innovation and entrepreneurship, and advocate the government focus on social fairness, to ensure that more groups can share the development of artificial intelligence employment opportunities.Academic Journal of Science and Technology210Artificial intelligence significantly improved the employment of agriculture and services, but the local manufacturing employment inhibition effect is found, and with the deepening application of artificial intelligence, intelligent manufacturing will create more man-machine collaborative jobs, and intelligent manufacturing will create more man-machine collaborative jobs, and the effect will take advantage.Academic Journal of Science and Technology['Shaoyun Lin']2024-03-26T00:00:00https://www.semanticscholar.org/paper/a48a7e7f0776bcfc31e70f8c1b0dd2ceafb2fa1b d27cb69f6afa52bdefd28224a9316e304d87d759Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as healthcare, education and employment. Since they are used in numerous sensitive environments and make decisions that can be life altering, potential biased outcomes are a pressing matter. Developers should ensure that such models don't manifest any unexpected discriminatory practices like partiality for certain genders, ethnicities or disabled people. With the ubiquitous dissemination of AI systems, researchers and practitioners are becoming more aware of unfair models and are bound to mitigate bias in them. Significant research has been conducted in addressing such issues to ensure models don't intentionally or unintentionally perpetuate bias. This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems. We explore the different definitions of fairness existing in the current literature. We create a comprehensive taxonomy by categorizing different types of bias and investigate cases of biased AI in different application domains. A thorough study is conducted of the approaches and techniques employed by researchers to mitigate bias in AI models. Moreover, we also delve into the impact of biased models on user experience and the ethical considerations to contemplate when developing and deploying such models. We hope this survey helps researchers and practitioners understand the intricate details of fairness and bias in AI systems. By sharing this thorough survey, we aim to promote additional discourse in the domain of equitable and responsible AI.arXiv.org2120This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems and creates a comprehensive taxonomy by categorizing different types of bias and investigating cases of biased AI in different application domains.ArXiv['Tahsin Alamgir Kheya', 'Mohamed Reda Bouadjenek', 'Sunil Aryal']2024-03-26T00:00:00https://www.semanticscholar.org/paper/d27cb69f6afa52bdefd28224a9316e304d87d759 e03f91c4e06197777a33f3e0fefd8a49f604d2b9This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.EAI Endorsed Transactions on e-Learning880Artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.EAI Endorsed Transactions on e-Learning['Ting Zhao']2024-03-26T00:00:00https://www.semanticscholar.org/paper/e03f91c4e06197777a33f3e0fefd8a49f604d2b9 87b6cedc81a763e9ccb275322f7e8e2a82587272Artificial intelligence (AI) technologies are rapidly changing healthcare in many aspects. First, a brief background and explanation of artificial intelligence and machine learning and how they can be integrated into critical care medicine. This paper serves to discuss how AI can be used in critical care medicine in four different ways, including examples of how it can be easily integrated into the field.  JAP Academy Journal80How AI can be used in critical care medicine in four different ways, including examples of how it can be easily integrated into the field are discussed.JAP Academy Journal['A. Haddadin']2024-03-26T00:00:00https://www.semanticscholar.org/paper/87b6cedc81a763e9ccb275322f7e8e2a82587272 6c72245502741e7ee836881d5ccd92c916e9afc3This study explores the complex interaction between artificial intelligence (AI) and customer behaviour in Saudi Arabia. The study, while investigating, considers the influence from different aspects, including awareness, perceptions, personalization's, trust, cultural and socio-economic variables; it then unfolds the entire impact, providing a holistic understanding. Government actions, partnerships, and regulatory bodies are found to induce AI adoption; consumer inclination also result from different cultural norms and diversified beliefs. The findings highlight ensuring the real-world applications of AI contain practicability and distinctiveness is conditioned by matching them with social norms, thereby understanding whether they will be a cause of change or just a cause of automation. Lines of class or caste are drawn with the help of multi-faceted AI utilization. The insights from the study show that the impact of AI on customer behaviour in Saudi Arabia is multi-layered, and every layer of it comes with strategic advice to be crafted by businesses and policymakers to navigate the transformative environment properly.The American Journal of Management and Economics Innovations00The insights from the study show that the impact of AI on customer behaviour in Saudi Arabia is multi-layered, and every layer of it comes with strategic advice to be crafted by businesses and policymakers to navigate the transformative environment properly.The American Journal of Management and Economics Innovations['Dr. Bandar Khalaf Alharthey']2024-03-26T00:00:00https://www.semanticscholar.org/paper/6c72245502741e7ee836881d5ccd92c916e9afc3 b72f43d34e40ccee69aeb1f7031a6919e02a19bdJournal of the American College of Cardiology01This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI.Journal of the American College of Cardiology['Sneha S. Jain', 'Pierre Elias', 'T. Poterucha', 'Michael Randazzo', 'Francisco Lopez Jimenez', 'R. Khera', 'Marco Perez', 'David Ouyang', 'J. Pirruccello', 'Michael Salerno', 'Andrew J Einstein', 'Robert Avram', 'Geoffrey H. Tison', 'Girish Nadkarni', 'Vivek Natarajan', 'Emma Pierson', 'Ashley Beecy', 'Deepa Kumaraiah', 'Christopher Haggerty', 'Jennifer N. Avari Silva', 'Thomas M. Maddox']2024-03-26T00:00:00https://www.semanticscholar.org/paper/b72f43d34e40ccee69aeb1f7031a6919e02a19bd a9f6357175a5b8289cefc440c6d16f27dac09268Scientific Reports541The meta-analysis showed that exergames effectively improved student performance in PE learning and indicated that better results could be achieved when exergames were introduced in small kindergarten classes and continued for 1–2 months.Scientific Reports['Mengnan Zhao', 'Xurui Lu', 'Qi Zhang', 'Rutong Zhao', 'Bohang Wu', 'Sheng Huang', 'Sunnan Li']2024-03-26T00:00:00https://www.semanticscholar.org/paper/a9f6357175a5b8289cefc440c6d16f27dac09268 46e8e2686276c345f37eabb2c15b4f192e348323Journal of the American College of Cardiology00Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.Journal of the American College of Cardiology['Pierre Elias', 'Sneha S. Jain', 'T. Poterucha', 'Michael Randazzo', 'Francisco Lopez Jimenez', 'R. Khera', 'Marco Perez', 'David Ouyang', 'J. Pirruccello', 'Michael Salerno', 'Andrew J Einstein', 'Robert Avram', 'Geoffrey H. Tison', 'Girish Nadkarni', 'Vivek Natarajan', 'Emma Pierson', 'Ashley Beecy', 'Deepa Kumaraiah', 'Christopher Haggerty', 'Jennifer N. Avari Silva', 'Thomas M. Maddox']2024-03-26T00:00:00https://www.semanticscholar.org/paper/46e8e2686276c345f37eabb2c15b4f192e348323 59245962ed333f8c354423485bdd2dd5d2c6394eThe article is devoted to the effectiveness of teaching legal disciplines in modern conditions. The appearance of new (usually English-speaking) terms leads to a misinterpretation of the original meanings. The author warns about the consequences of "inflated expectations", which give rise to an almost religious belief in the possibility of computer technologies for deep machine learning of neural networks in part of the legal community.Advances in Law Studies00The author warns about the consequences of "inflated expectations", which give rise to an almost religious belief in the possibility of computer technologies for deep machine learning of neural networks in part of the legal community.Advances in Law Studies['Nikolay Bazhanov']2024-03-26T00:00:00https://www.semanticscholar.org/paper/59245962ed333f8c354423485bdd2dd5d2c6394e 580cb2bbe4466fe017250fc09901eb29c56c4678The construction industry faces challenges such as project complexity, delays, and communication issues. Leveraging AI, particularly through data analysis, predictive analytics, and machine learning, addresses these challenges by optimizing project planning, scheduling, and risk management. This paper outlines strategies for AI integration, including data collection, machine learning algorithms, and cloud computing. Case studies highlight successful implementations, showcasing benefits such as increased efficiency, cost savings, and improved safety. However, challenges like data security and workforce acceptance must be considered. The abstract concludes by discussing future trends and encouraging the construction industry to embrace AI for enhanced project outcomes.International Journal of Advanced Multidisciplinary Research and Studies00This paper outlines strategies for AI integration, including data collection, machine learning algorithms, and cloud computing, and encourages the construction industry to embrace AI for enhanced project outcomes.International Journal of Advanced Multidisciplinary Research and Studies['Nwankwo Constance Obiuto', 'Riliwan Adekola Adebayo', 'Oladiran Kayode Olajiga', 'Igberaese Clinton Festus-Ikhuoria']2024-03-26T00:00:00https://www.semanticscholar.org/paper/580cb2bbe4466fe017250fc09901eb29c56c4678 e97188d8addfddaadb0878238bc2aa87a3830585Systems Science &amp; Control Engineering350Systems Science &amp; Control Engineering['Tejas Kadengodlu Bhat', 'Krishnaraj Chadaga', 'Niranjana Sampathila', 'Swathi Ks', 'Rajagopala Chadaga', 'S. Umakanth', 'Srikanth Prabhu']2024-03-26T00:00:00https://www.semanticscholar.org/paper/e97188d8addfddaadb0878238bc2aa87a3830585 c336299b31ff2c667d6025afd09e0ece6544ddc8Cogent Engineering290Cogent Engineering['Varada Vivek Khanna', 'Krishnaraj Chadaga', 'Niranajana Sampathila', 'Srikanth Prabhu', 'Rajagopala Chadaga P.', 'Devadas Bhat', 'S. K. S.']2024-03-26T00:00:00https://www.semanticscholar.org/paper/c336299b31ff2c667d6025afd09e0ece6544ddc8 900a1bc76f6aa1d1d3966295f6691e932300466d00['Jamal Mabrouki', 'Azrour Maroude', 'Azeem Irshad']2024-03-26T00:00:00https://www.semanticscholar.org/paper/900a1bc76f6aa1d1d3966295f6691e932300466d cbd34bc266ebe858e05fdab9adac13452754ec92International Journal of Music Business Research10International Journal of Music Business Research['Guy Morrow']2024-03-26T00:00:00https://www.semanticscholar.org/paper/cbd34bc266ebe858e05fdab9adac13452754ec92 75b2ae5ee35611ecfbd3dc2c3d0799cfb4fd98e4The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack"test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.arXiv.org013InternLM2 is introduced, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques.ArXiv['Zheng Cai', 'Maosong Cao', 'Haojiong Chen', 'Kai Chen', 'Keyu Chen', 'Xin Chen', 'Xun Chen', 'Zehui Chen', 'Zhi Chen', 'Pei Chu', 'Xiao-wen Dong', 'Haodong Duan', 'Qi Fan', 'Zhaoye Fei', 'Yang Gao', 'Jiaye Ge', 'Chenya Gu', 'Yuzhe Gu', 'Tao Gui', 'Aijia Guo', 'Qipeng Guo', 'Conghui He', 'Yingfan Hu', 'Ting Huang', 'Tao Jiang', 'Penglong Jiao', 'Zhen Jin', 'Zhikai Lei', 'Jiaxing Li', 'Jingwen Li', 'Linyang Li', 'Shuaibin Li', 'Wei Li', 'Yining Li', 'Hongwei Liu', 'Jiangning Liu', 'Jiawei Hong', 'Kaiwen Liu', 'Kui-Jie Liu', 'Xiaoran Liu', 'Chen Lv', 'Haijun Lv', 'Kai Lv', 'Li Ma', 'Runyuan Ma', 'Zerun Ma', 'Wenchang Ning', 'Linke Ouyang', 'Jiantao Qiu', 'Yuan Qu', 'Fukai Shang', 'Yunfan Shao', 'Demin Song', 'Zifan Song', 'Zhihao Sui', 'Peng Sun', 'Yu Sun', 'Huanze Tang', 'Bin Wang', 'Guoteng Wang', 'Jiaqi Wang', 'Jiayu Wang', 'Rui Wang', 'Yudong Wang', 'Ziyi Wang', 'Xing Wei', 'Qizhen Weng', 'Fan Wu', 'Yingtong Xiong', 'Chao Xu', 'R. Xu', 'Hang Yan', 'Yirong Yan', 'Xiaogui Yang', 'Haochen Ye', 'Huaiyuan Ying', 'Jia Yu', 'Jing Yu', 'Yuhang Zang', 'Chuyu Zhang', 'Li Zhang', 'Pan Zhang', 'Peng Zhang', 'Ruijie Zhang', 'Shuo Zhang', 'Songyang Zhang', 'Wenjian Zhang', 'Wenwei Zhang', 'Xingcheng Zhang', 'Xinyue Zhang', 'Hui Zhao', 'Qian Zhao', 'Xiaomeng Zhao', 'Fen-Fang Zhou', 'Zaida Zhou', 'Jingming Zhuo', 'Yi-Ling Zou', 'Xipeng Qiu', 'Yu Qiao', 'Dahua Lin']2024-03-26T00:00:00https://www.semanticscholar.org/paper/75b2ae5ee35611ecfbd3dc2c3d0799cfb4fd98e4 8b5182cee8d213966a4a854aeee3591a7ab97541The intelligentization of oil and gas exploration and development has attracted much attention in recent years. This involves artificial intelligence, big data analysis, and other cutting-edge technologies and methods. The paper explores the trend of intelligent development within the oil and gas industry, focusing on the utilization of large-scale data collection, integration, and analysis techniques. It delves into the in-depth exploration of geological, geophysical, production, and other pertinent information to deliver more comprehensive and precise data support for the exploration and development processes. Continuous advancements in big data and artificial intelligence technologies further refine solutions, ensuring greater accuracy and efficiency in oil and gas exploration and development. Data-driven intelligent technologies play a pivotal role in the exploration and development of oil and gas resources, providing a robust foundation for the industry's intelligent advancement. While intelligent technology presents significant opportunities, it also presents a myriad of challenges. Thus, collaboration and knowledge-sharing will serve as the primary catalysts for field development, facilitating the exchange of technology and expertise to collectively tackle challenges in the future.Academic Journal of Science and Technology300The paper explores the trend of intelligent development within the oil and gas industry, focusing on the utilization of large-scale data collection, integration, and analysis techniques, to deliver more comprehensive and precise data support for the exploration and development processes.Academic Journal of Science and Technology['Xifeng Ding', 'Huiyang Li', 'Shiyi Ou']2024-03-26T00:00:00https://www.semanticscholar.org/paper/8b5182cee8d213966a4a854aeee3591a7ab97541 4959646840f7f02028f7f2f963c6c19e687a0168Statistical and artificial intelligence algorithms are increasingly being developed for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorithmic fairness. Individual fairness in algorithms constrains algorithms to the notion that "similar individuals should be treated similarly." We conducted a scoping review on algorithmic individual fairness to understand the current state of research in the metrics and methods developed to achieve individual fairness and its applications in healthcare. We searched three databases, PubMed, ACM Digital Library, and IEEE Xplore, for algorithmic individual fairness metrics, algorithmic bias mitigation, and healthcare applications. Our search was restricted to articles published between January 2013 and September 2023. We identified 1,886 articles through database searches and manually identified one article from which we included 30 articles in the review. Data from the selected articles were extracted, and the findings were synthesized. Based on the 30 articles in the review, we identified several themes, including philosophical underpinnings of fairness, individual fairness metrics, mitigation methods for achieving individual fairness, implications of achieving individual fairness on group fairness and vice versa, fairness metrics that combined individual fairness and group fairness, software for measuring and optimizing individual fairness, and applications of individual fairness in healthcare. While there has been significant work on algorithmic individual fairness in recent years, the definition, use, and study of individual fairness remain in their infancy, especially in healthcare. Future research is needed to apply and evaluate individual fairness in healthcare comprehensively.medRxiv710A scoping review on algorithmic individual fairness to understand the current state of research in the metrics and methods developed to achieve individual fairness and its applications in healthcare and identified several themes, including philosophical underpinnings of fairness, individual fairness metrics, mitigation methods for achieving individual fairness, and applications of individual fairness in healthcare.medRxiv['Joshua W. Anderson', 'S. Visweswaran']2024-03-26T00:00:00https://www.semanticscholar.org/paper/4959646840f7f02028f7f2f963c6c19e687a0168 b6c47bd0e6e70201c8eceedce59c5b8a243471bfDigital platforms share their customers’ data with social planners, who may utilize it to improve socioeconomic infrastructure. This may benefit customers because of the experience of improved infrastructure. On the contrary, it may lead to privacy concerns among them (as these data sets may include sensitive information). In this paper, we analyze the game-theoretic model to characterize the granularity of data sharing between firms and the social planner and the investments by the social planner to improve public infrastructure. In order to analyze the impact of regulation on data sharing strategy, we consider the cases when data sharing is regulated (decided by the social planner) and unregulated (strategically decided by firms). Our analysis reveals that the firms as well as the social planner decrease the granularity of data with an increase in privacy concerns among customers. To analyze the impact of regulation, we compare the granularity of data shared under unregulated and regulated scenarios. We find that when the firm is monopolist, it shares data with a higher level of granularity in the unregulated scenario. Interestingly, we find that under market competition, the data granularity may be higher or lower compared with the regulated scenario. Specifically, we find that if firms jointly determine the granularity of data to be shared, they share data with higher granularity under the unregulated scenario; however, if they do not collaborate and individually decide on data sharing, we find that regulation leads to higher granularity of data to be shared. Finally, we find that firms’ payoffs and customer surplus are higher under the unregulated data-sharing setup if they jointly determine the granularity of data; however, if they do not collaborate on data sharing, their payoffs, as well as customer surplus, are higher under regulation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2022.0052 .Service science360This paper analyzes the game-theoretic model to characterize the granularity of data sharing between firms and the social planner and the investments by the social planner to improve public infrastructure and finds that regulation leads to higher granularity of data to be shared.Service Science['Ayesha Arora', 'Tarun Jain']2024-03-25T00:00:00https://www.semanticscholar.org/paper/b6c47bd0e6e70201c8eceedce59c5b8a243471bf 1a3f0b45021a0c1bc340172819c49f086d89a77eCurrent testing methods for autonomous driving systems primarily focus on simple traffic scenarios, generating test cases based on traffic accidents, while research on generating edge test cases for complex driving environments by traffic regulations is not adequately comprehensive. Therefore, we propose a method for scenario modeling and violation testing using an autonomous driving system based on traffic regulations named TraModeAVTest. Initially, TraModeAVTest constructs a Petri net model for complex scenarios based on the combination relationships of basic traffic regulation scenarios and verifies the consistency of the model’s design with traffic regulation requirements using formal methods, to provide a representation of traffic regulation scenario models for the violation testing of autonomous driving systems. Subsequently, based on the coverage criteria of the Petri net model, it utilizes a search strategy to generate model paths that represent traffic regulations, and employs a parameter combination method to generate test cases that cover the model paths, to test the violation behaviors of autonomous driving systems. Finally, simulation experiment results on the Baidu Apollo demonstrate that the test cases representing traffic regulations generated by TraModeAVTest can effectively identify the behaviors of autonomous vehicles violating traffic regulations, and TraModeAVTest can effectively improve the efficiency of generating different types of violation scenarios.Electronics240Simulation experiment results on the Baidu Apollo demonstrate that the test cases representing traffic regulations generated by TraModeAVTest can effectively identify the behaviors of autonomous vehicles violating traffic regulations, and TraModeAVTest can effectively improve the efficiency of generating different types of violation scenarios.Electronics['Chunyan Xia', 'Song Huang', 'Changyou Zheng', 'Zhen Yang', 'Tongtong Bai', 'Lele Sun']2024-03-25T00:00:00https://www.semanticscholar.org/paper/1a3f0b45021a0c1bc340172819c49f086d89a77e 9d0f55c2cb23e69cf393f0bb144088aaa6d88a05With the rapid development of synthetic biology, lots of synthetic biology technology achievements in various application fields have been commercialized, generating broad market prospects. The commercialization of products employing synthetic biology technology (hereinafter referred as synthetic biology products) has brought benefits to human beings, but it has also produced potential safety risks. At present, relevant laws and standards for regulation of biotechnology or genetically modified organisms have been adopted to regulate the safety risks of commercialization of synthetic biology products (CSBP). However, due to the complexity and uncertainty of synthetic biology, the safety risks of CSBP cannot be comprehensively regulated by these laws and standards. Therefore, it is of great significance to formulate specific supervision and management measures for regulating the safety risks of CSBP. This paper summarized the situation of CSBP in the fields of food, medical care, agriculture, environment, energy and materials, analyzed the safety risks existing in the CSBP, and sorted out current supervision situation of its safety risks in European countries, United States, as well as in China. We further proposed suggestions on the safety supervision and management measures on the safety risks of CSBP, including classified examination and approval, classified identification of products, and strict screening and approval of market entities before entering the market, and strengthening safety supervision and emergency treatment as well as accident responsibility investigation after entering the market. This whole-process safety regulation might provide support for the safety of CSBP and promote the healthy and long-term development of synthetic biology industry.Sheng wu gong cheng xue bao = Chinese journal of biotechnology00Sheng wu gong cheng xue bao = Chinese journal of biotechnology['Xiaomei Zeng', 'Zexi Zhu', 'Jun Weng']2024-03-25T00:00:00https://www.semanticscholar.org/paper/9d0f55c2cb23e69cf393f0bb144088aaa6d88a05 989482d23688fca87bdce7423efa787b71f76784<jats:p>Report</jats:p>AUC IURIDICA00AUC IURIDICA['Petra Kotápišová', 'Aneta Boukalová', 'Radka Václavíková']2024-03-25T00:00:00https://www.semanticscholar.org/paper/989482d23688fca87bdce7423efa787b71f76784 58bc4dc834506862807bc6e40ccc58b3d065996dThe article analyzes the EU Carbon Border Adjustment mechanism (CBAM) through the lens of the EU’s aspiration to strengthen its leadership in fighting climate change. The introduction of CBAM is viewed as another step toward achieving the EU's goal of climate neutrality, which has become a standard for global development. By studying the internal and external dynamics of CBAM's implementation, the article aims to analyze its role as a tool for diplomatic and exemplary leadership. On the one hand, the EU seeks to protect European producers from producers from countries with lower climate standards. On the other hand, the EU’s desire to involve other countries in creating CBAM-style mechanisms is supposed to strengthen the EU’s leadership, contributing to the formation of multilateral “climate clubs” and the gradual revision of the existing climate regime. However, the article suggests that there are inherent contradictions in the EU's approach. While it seeks to protect its economy through CBAM, it also frames these measures within the narrative of multilateral cooperation. This dual approach poses challenges to the EU's leadership potential. The protectionist nature of CBAM makes other global players, primarily the U.S., follow the same path of taking protective measures. China is not willing to give up its own framing of the climate agenda due to its importance for the construction of China’s international identity. Russia’s perception of the climate change problem is infl uenced by its national interests and the structure of economy. The projection of regulatory power beyond the EU’s borders also provokes resistance from developing and least developed countries, which can seriously damage the EU’s role as a normative power.Journal of International Analytics260Journal of International Analytics['I. V. Bolgova', 'E. Stolyarova']2024-03-25T00:00:00https://www.semanticscholar.org/paper/58bc4dc834506862807bc6e40ccc58b3d065996d 7fa3d5f2b4dace8318563e0bc608731f623f6fd7Peer Community In Registered Reports00Peer Community in Registered Reports['Zoltan Dienes']2024-03-25T00:00:00https://www.semanticscholar.org/paper/7fa3d5f2b4dace8318563e0bc608731f623f6fd7 6f3d55bc6d88664fc28f3e09fe72883d49ba02c4Journal of European Competition Law &amp; Practice00Journal of European Competition Law &amp; Practice['Robin Vandendriessche', 'C. Buts']2024-03-25T00:00:00https://www.semanticscholar.org/paper/6f3d55bc6d88664fc28f3e09fe72883d49ba02c4 f8470aed457e00bb606711074fc5db3b5a6cc21eBackground The use of artificial intelligence (AI) in medicine has been a trending subject in the past few years. Although not frequently used in daily practice yet, it brings along many expectations, doubts, and fears for physicians. Surveys can be used to help understand this situation. Objective This study aimed to explore the degree of knowledge, expectations, and fears on possible AI use by physicians in daily practice, according to sex and time since graduation. Methods An electronic survey was sent to physicians of a large hospital in Brazil, from August to September 2022. Results A total of 164 physicians responded to our survey. Overall, 54.3% (89/164) of physicians considered themselves to have an intermediate knowledge of AI, and 78.5% (128/163) believed that AI should be regulated by a governmental agency. If AI solutions were reliable, fast, and available, 77.9% (127/163) intended to frequently or always use AI for diagnosis (143/164, 87.2%), management (140/164, 85.4%), or exams interpretation (150/164, 91.5%), but their approvals for AI when used by other health professionals (85/163, 52.1%) or directly by patients (82/162, 50.6%) were not as high. The main benefit would be increasing the speed for diagnosis and management (106/163, 61.3%), and the worst issue would be to over rely on AI and lose medical skills (118/163, 72.4%). Physicians believed that AI would be useful (106/163, 65%), facilitate their work (140/153, 91.5%), not alter the number of appointments (80/162, 49.4%), not interfere in their financial gain (94/162, 58%), and not replace their jobs but be an additional source of information (104/162, 64.2%). In case of disagreement between AI and physicians, most (108/159, 67.9%) answered that a third opinion should be requested. Physicians with ≤10 years since graduation would adopt AI solutions more frequently than those with >20 years since graduation (P=.04), and female physicians were more receptive to other hospital staff using AI than male physicians (P=.008). Conclusions Physicians were shown to have good expectations regarding the use of AI in medicine when they apply it themselves, but not when used by others. They also intend to use it, as long as it was approved by a regulatory agency. Although there was hope for a beneficial impact of AI on health care, it also brings specific concerns.JMIRx Med213Physicians were shown to have good expectations regarding the use of AI in medicine when they apply it themselves, but not when used by others, and also intend to use it, as long as it was approved by a regulatory agency.JMIRx med['M. Giavina-Bianchi', 'Edson Amaro', 'B. Machado']2024-03-25T00:00:00https://www.semanticscholar.org/paper/f8470aed457e00bb606711074fc5db3b5a6cc21e 208abc870b1971d02e2c3a1dbb5941465822226cAs synthetic media becomes progressively more realistic and barriers to using it continue to lower, the technology has been increasingly utilized for malicious purposes, from financial fraud to nonconsensual pornography. Today, the principal defense against being misled by synthetic media relies on the ability of the human observer to visually and auditorily discern between real and fake. However, it remains unclear just how vulnerable people actually are to deceptive synthetic media in the course of their day to day lives. We conducted a perceptual study with 1276 participants to assess how accurate people were at distinguishing synthetic images, audio only, video only, and audiovisual stimuli from authentic. To reflect the circumstances under which people would likely encounter synthetic media in the wild, testing conditions and stimuli emulated a typical online platform, while all synthetic media used in the survey was sourced from publicly accessible generative AI technology. We find that overall, participants struggled to meaningfully discern between synthetic and authentic content. We also find that detection performance worsens when the stimuli contains synthetic content as compared to authentic content, images featuring human faces as compared to non face objects, a single modality as compared to multimodal stimuli, mixed authenticity as compared to being fully synthetic for audiovisual stimuli, and features foreign languages as compared to languages the observer is fluent in. Finally, we also find that prior knowledge of synthetic media does not meaningfully impact their detection performance. Collectively, these results indicate that people are highly susceptible to being tricked by synthetic media in their daily lives and that human perceptual detection capabilities can no longer be relied upon as an effective counterdefense.arXiv.org383It is found that people are highly susceptible to being tricked by synthetic media in their daily lives and that human perceptual detection capabilities can no longer be relied upon as an effective counterdefense.ArXiv['Di Cooke', 'Abigail Edwards', 'Sophia Barkoff', 'Kathryn Kelly']2024-03-25T00:00:00https://www.semanticscholar.org/paper/208abc870b1971d02e2c3a1dbb5941465822226c 9eaf282a505a1d61318d4d90d7044da360ffb72fIn AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. In such a paradigm, humans are found to rarely trigger analytical thinking and face difficulties in communicating the nuances of conflicting opinions to the AI when disagreements occur. To tackle this challenge, we propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making. Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates. To empower AI with deliberative capabilities, we designed Deliberative AI, which leverages large language models (LLMs) as a bridge between humans and domain-specific models to enable flexible conversational interactions and faithful information provision. An exploratory evaluation on a graduate admissions task shows that Deliberative AI outperforms conventional explainable AI (XAI) assistants in improving humans' appropriate reliance and task performance. Based on a mixed-methods analysis of participant behavior, perception, user experience, and open-ended feedback, we draw implications for future AI-assisted decision tool design.arXiv.org1402Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making, and Deliberative AI, which leverages large language models as a bridge between humans and domain-specific models to enable flexible conversational interactions and faithful information provision.ArXiv['Shuai Ma', 'Qiaoyi Chen', 'Xinru Wang', 'Chengbo Zheng', 'Zhenhui Peng', 'Ming Yin', 'Xiaojuan Ma']2024-03-25T00:00:00https://www.semanticscholar.org/paper/9eaf282a505a1d61318d4d90d7044da360ffb72f 2d15380bd2686045114dd8d8d28a2098f561fa69In this study, we propose the early adoption of Explainable AI (XAI) with a focus on three properties: Quality of explanation, the explanation summaries should be consistent across multiple XAI methods; Architectural Compatibility, for effective integration in XAI, the architecture styles of both the XAI methods and the models to be explained must be compatible with the framework; Configurable operations, XAI explanations are operable, akin to machine learning operations. Thus, an explanation for AI models should be reproducible and tractable to be trustworthy. We present XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance. XAIport enables configurable XAI operations along with machine learning development. We quantify the operational costs of incorporating XAI with three cloud computer vision services on Microsoft Azure Cognitive Services, Google Cloud Vertex AI, and Amazon Rekognition. Our findings show comparable operational costs between XAI and traditional machine learning, with XAIport significantly improving both cloud AI model performance and explanation stability.Proceedings of the 2024 ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results391This study presents XAIport, a framework of XAI microservices encapsulated into Open APIs to deliver early explanations as observation for learning model quality assurance, and enables configurable XAI operations along with machine learning development.ArXiv['Zerui Wang', 'Yan Liu', 'Abishek Arumugam Thiruselvi', 'A. Hamou-Lhadj']2024-03-25T00:00:00https://www.semanticscholar.org/paper/2d15380bd2686045114dd8d8d28a2098f561fa69 ef7567b87b270654056afd56be74946825ddf539As artificial intelligence (AI) increasingly permeates educational landscapes, its impact on academic writing has become a subject of intense scrutiny. This research delved into the nuanced dimensions of authorship and voice in academic writing, specifically focusing on the application of OpenAI’s ChatGPT. In this study, the research team compared and contrasted an essay written by one second-year English student for a course on English literature with a similar essay produced by ChatGPT. The current research also, tried to clarify whether artificial intelligence can satisfy the formal requirements of academic writing and maintain the distinctive voice inherent in human-authored content. The examination hinges on parameters such as assertiveness, self-identification, and authorial presence. Additionally, the researchers shed light on the challenges inherent in producing AI-generated academic text. While ChatGPT presented an ability to generate contextually relevant content, the results highlighted its need for support in guaranteeing factual accuracy and capturing the complex aspects of authorship that are common in human writing. Notably, when compared to human-generated text, the AI-generated text was deficient in terms of specificity, depth, and accurate source referencing. While AI has potential as an additional tool for academic writing, this study’s findings indicated that its current capabilities—particularly in producing academic text are limited, and remain constrained. This study emphasizes upon the imperative for continued refinement and augmentation of AI models to bridge the existing gaps in achieving a more seamless integration into the academic writing landscape.Frontiers in Education301The research team compared and contrasted an essay written by one second-year English student for a course on English literature with a similar essay produced by ChatGPT, indicating that the AI-generated text was deficient in terms of specificity, depth, and accurate source referencing.Frontiers in Education['F. Amirjalili', 'Masoud Neysani', 'Ahmadreza Nikbakht']2024-03-25T00:00:00https://www.semanticscholar.org/paper/ef7567b87b270654056afd56be74946825ddf539 6a037ae1a64f2938b0534305f0c0acc95304e45bEuropean Radiology71This study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density, as well as perform well according to cancer detection across all density categories.European radiology['Marie Burns Bergan', 'M. Larsen', 'N. Moshina', 'Hauke Bartsch', 'Henrik W Koch', 'H. Aase', 'Zhanbolat Satybaldinov', 'I. Haldorsen', 'Christoph I Lee', 'Solveig Hofvind']2024-03-25T00:00:00https://www.semanticscholar.org/paper/6a037ae1a64f2938b0534305f0c0acc95304e45b e5148e92d74dc1f2c0aa59282aa8d83f4a1b9221492It is argued that responsible use of GenAI tools can enhance learning processes, but addressing concerns may require robust policies that are responsive to cultural expectations, as well as recommendations for researchers, educators, and policymakers.International Journal of Educational Technology in Higher Education['Abdullahi Yusuf', 'Nasrin Pervin', 'Marcos Román-González']2024-03-25T00:00:00https://www.semanticscholar.org/paper/e5148e92d74dc1f2c0aa59282aa8d83f4a1b9221 1d3f070eae5efae6688f7c3c3991711cff9fd43aArtificial Intelligence (AI) is becoming ubiquitous in domains such as medicine and natural science research. However, when AI systems are implemented in practice, domain experts often refuse them. Low acceptance hinders effective human-AI collaboration, even when it is essential for progress. In natural science research, scientists' ineffective use of AI-enabled systems can impede them from analysing their data and advancing their research. We conducted an ethnographically informed study of 10 in-depth interviews with AI practitioners and natural scientists at the organisation facing low adoption of algorithmic systems. Results were consolidated into recommendations for better AI adoption: i) actively supporting experts during the initial stages of system use, ii) communicating the capabilities of a system in a user-relevant way, and iii) following predefined collaboration rules. We discuss the broader implications of our findings and expand on how our proposed requirements could support practitioners and experts across domains.arXiv.org600Recommendations for better AI adoption include actively supporting experts during the initial stages of system use, communicating the capabilities of a system in a user-relevant way, and following predefined collaboration rules.ArXiv['Auste Simkute', 'Ewa Luger', 'Michael Evans', 'Rhianne Jones']2024-03-25T00:00:00https://www.semanticscholar.org/paper/1d3f070eae5efae6688f7c3c3991711cff9fd43a 2e339e2093ecf7786253c0e1cb8a317d847cdb77Introduction The incorporation of Artificial Intelligence (AI) in organizations is pivotal to deal with work-related tasks and challenges effectively, yet little is known about the organizational factors that influence AI acceptance (i.e., employee favorable AI attitudes and AI use). To address this limitation in the literature and provide insight into the organizational antecedents influencing AI acceptance, this research investigated the relationship between competitive organizational climate and AI acceptance among employees. Moreover, given the critical role of a leader in employee attitude and behavior, we examined the moderating role of leaders’ power construal as responsibility or as opportunity in this relationship. Methods Study 1 was a three-wave field study among employees (N = 237, Mage = 38.28) working in various organizations in the UK. The study measured employees’ perception of a competitive organizational climate at Time 1, leaders’ power construal (as perceived by employees) at Time 2, and employee attitudes towards AI and their actual use of AI in the workplace at Times 2 and 3. Study 2 was a 2 (climate: highly competitive vs. low competitive) by 2 (power construal: responsibility vs. opportunity) experiment among employee participants (N = 150, Mage = 37.50). Results Study 1 demonstrated a positive relationship between competitive climate and employee AI use over time. Furthermore, both studies revealed an interaction between competitive climate and leader’s power construal in the prediction of employee AI acceptance: In Study 1, competitive climate was negatively related to AI acceptance over time when leaders construed power as opportunity. In Study 2 competitive climate was positively related to AI acceptance when leaders construed power as responsibility rather than as opportunity. Discussion These results underscore the organizational factors that are required in order for employees to shape favorable attitudes towards AI and actually use AI at work. Importantly, this research expands the limited body of literature on AI integration in organizations.Frontiers in Psychology620The results underscore the organizational factors that are required in order for employees to shape favorable attitudes towards AI and actually use AI at work.Frontiers in Psychology['Kyriaki Fousiani', 'Georgios Michelakis', 'Pieter A. Minnigh', 'K. M. M. De Jonge']2024-03-25T00:00:00https://www.semanticscholar.org/paper/2e339e2093ecf7786253c0e1cb8a317d847cdb77 81cbfc1a15d17348dd4bf343d10fd4eaa5e4491aThe study examined the utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices. Two null hypotheses were formulated to guide the study. The study adopted the ex-post facto research design. The population of the study consisted of 3800 students and staff of the University within Calabar campus. The stratified random sampling technique was used to select 208 students and staff from a population of 3800 using proportionality of 1.12% with students and staff as basis of stratification from the population. The sample of this study was two hundred and eight (208) respondents. The questionnaire was designed to measure the two sub-independent variables. Mean and standard deviation were used to answer the request questions. While Simple linear regression analysis statistical tool was employed to test the null hypotheses that were formulated to guild the study at 0.05 level of significance. The results of this study shows that there is a significant influence of utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices and the use of artificial intelligent (AI) in teaching and learning in higher education significantly predict staff and students performance in the university. The study concludes that, there is a significant influence of utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices and the use of artificial intelligent (AI) in teaching and learning in higher education significantly predict staff and students performance in the university. Based on the conclusion, it was recommended that since there is a significant influence of utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices, university staff and students should always make use of AI and the university management should put in place AI facilities to increase staff and students .....East African Scholars Journal of Education Humanities and Literature00Since there is a significant influence of utilization of artificial intelligent (AI) in teaching and learning in higher education for global best practices, university staff and students should always make use of AI and the university management should put in place AI facilities to increase staff and students performance in the university.East African Scholars Journal of Education, Humanities and Literature['E. S. Essien', 'Abung Chrysanthus Bekeh', 'Nancy Godwin Anam']2024-03-25T00:00:00https://www.semanticscholar.org/paper/81cbfc1a15d17348dd4bf343d10fd4eaa5e4491a da625b2e2f32b3ee4eb655d8cdb96f74b20e0bceThe advent of ChatGPT and similar large language models (LLMs) has revolutionized the human-AI interaction and information-seeking process. Leveraging LLMs as an alternative to search engines, users can now access summarized information tailored to their queries, significantly reducing the cognitive load associated with navigating vast information resources. This shift underscores the potential of LLMs in redefining information access paradigms. Drawing on the foundation of task-focused information retrieval and LLMs' task planning ability, this research extends the scope of LLM capabilities beyond routine task automation to support users in navigating long-term and significant life tasks. It introduces the GOLF framework (Goal-Oriented Long-term liFe tasks), which focuses on enhancing LLMs' ability to assist in significant life decisions through goal orientation and long-term planning. The methodology encompasses a comprehensive simulation study to test the framework's efficacy, followed by model and human evaluations to develop a dataset benchmark for long-term life tasks, and experiments across different models and settings. By shifting the focus from short-term tasks to the broader spectrum of long-term life goals, this research underscores the transformative potential of LLMs in enhancing human decision-making processes and task management, marking a significant step forward in the evolution of human-AI collaboration.arXiv.org360The GOLF framework (Goal-Oriented Long-term liFe tasks), which focuses on enhancing LLMs' ability to assist in significant life decisions through goal orientation and long-term planning, is introduced, which focuses on enhancing LLMs' ability to assist in significant life decisions through goal orientation and long-term planning.ArXiv['Ben Wang']2024-03-25T00:00:00https://www.semanticscholar.org/paper/da625b2e2f32b3ee4eb655d8cdb96f74b20e0bce 7af3517cf34f857840715cfad009e596fb3f55d5Changes in the way people live and work, driven by digitalization and automation, have always triggered fears. Developments in the field of digitalization and automation, as well as the use of artificial intelligence, which has been the subject of much discussion recently, require people in all areas to have a certain degree of adaptability. Increasing complexity, the loss of jobs and the challenges of data protection are just a few examples of the challenges facing not only society but also legislators. The simplification of daily life and the increasing efficiency gains made possible by AI are some of the arguments in favor of using AI. The EU law on artificial intelligence aims to ensure that AI systems brought to market and deployed in the EU are safe and in line with the EU's fundamental rights and values. The groundbreaking proposal is also intended to promote investment and innovation in the field of AI in Europe.Perspectives of Law and Public Administration130The EU law on artificial intelligence aims to ensure that AI systems brought to market and deployed in the EU are safe and in line with the EU's fundamental rights and values and the groundbreaking proposal is also intended to promote investment and innovation in the field of AI in Europe.Perspectives of Law and Public Administration['Julia Krenn']2024-03-25T00:00:00https://www.semanticscholar.org/paper/7af3517cf34f857840715cfad009e596fb3f55d5 86ca1488f50bfa0c7de688cadf74cd6cac0ce9d4This study presents a development plan for a vision AI system to enhance productivity in industrial environments, where environmental control is challenging, by using AI technology. An image pre-processing algorithm was developed using a mobile robot that can operate in complex environments alongside workers to obtain high-quality learning and inspection images. Additionally, the proposed architecture for sustainable AI system development included cropping the inspection part images to minimize the technology development time, investment costs, and the reuse of images. The algorithm was retrained using mixed learning data to maintain and improve its performance in industrial fields. This AI system development architecture effectively addresses the challenges faced in applying AI technology at industrial sites and was demonstrated through experimentation and application.Applied Sciences190An image pre-processing algorithm was developed using a mobile robot that can operate in complex environments alongside workers to obtain high-quality learning and inspection images and was retrained using mixed learning data.Applied Sciences['Changmo Yang', 'JinSeok Kim', 'DongWeon Kang', 'Doo-Seop Eom']2024-03-25T00:00:00https://www.semanticscholar.org/paper/86ca1488f50bfa0c7de688cadf74cd6cac0ce9d4 a30cd0490fe28a82889c222f92ffd31a137f4993Therapeutic Innovation and Regulatory Science390From the global governance guidelines set by the World Health Organization to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.Therapeutic Innovation & Regulatory Science['Karen Zhou', 'Ginny Gattinger']2024-03-25T00:00:00https://www.semanticscholar.org/paper/a30cd0490fe28a82889c222f92ffd31a137f4993 b6f1730094a0d5cd4281eae23e147117c39f968cAI and Ethics120This commentary considers how anthropomorphising AI contributes to its misrepresentation and hype and the extent to which the authors ought to mitigate it.AI and Ethics['Nicholas Barrow']2024-03-25T00:00:00https://www.semanticscholar.org/paper/b6f1730094a0d5cd4281eae23e147117c39f968c 25649e4542f1ace8429302308871c48057cfee14In December 2023, the European Parliament provisionally agreed on the EU AI Act. This unprecedented regulatory framework for AI systems lays out guidelines to ensure the safety, legality, and trustworthiness of AI products. This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems by leveraging product quality models. We first propose an extended product quality model for AI systems, incorporating attributes relevant to the Act not covered by current quality models. We map the Act requirements to relevant quality attributes with the goal of refining them into measurable characteristics. We then propose a contract-based approach to derive technical requirements at the stakeholder level. This facilitates the development and assessment of AI systems that not only adhere to established quality standards, but also comply with the regulatory requirements outlined in the Act for high-risk (including safety-critical) AI systems. We demonstrate the applicability of this methodology on an exemplary automotive supply chain use case, where several stakeholders interact to achieve EU AI Act compliance.arXiv.org180This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems by leveraging product quality models, and demonstrates the applicability of this methodology on an exemplary automotive supply chain use case.ArXiv['Jessica Kelly', 'S. Zafar', 'Lena Heidemann', 'Joao Vitor-Zacchi', 'Delfina Espinoza', 'Núria Mata']2024-03-25T00:00:00https://www.semanticscholar.org/paper/25649e4542f1ace8429302308871c48057cfee14 f299be3d307cef6f58ad4f321b70b5026368239aIn recent years, Generative AI has started to play a pivotal role in transforming the educational landscape, making learning more personalized, engaging, and accessible. Unlike traditional educational tools, Gen AI can adapt to each student's unique learning style and pace, offering customized support that can significantly enhance understanding and retention of information. It enables the creation of intelligent tutoring systems, interactive study aids, and personalized learning experiences that can assess and respond to individual needs in real time This paper introduces the AI Study Partner, an innovative tool designed to revolutionize the educational landscape by integrating Generative Artificial Intelligence (Gen AI) and Large Language Models (LLMs). The AI Study Partner is engineered to cater to the diverse needs of learners by offering a suite of six key features: the ability to upload and interact with any type of content, summarization of extensive lessons, generation of flashcards for effective study, automated question creation with auto-evaluation capabilities, a conversational chatbot assistant, and an advanced smart search function. These features collectively aim to create a more personalized, engaging, and efficient learning experience. The development of the AI Study Partner responds to the pressing need for educational tools that accommodate the varying paces and styles of learning, making education more accessible and effective. By leveraging the latest advancements in AI, the tool not only facilitates a deeper understanding of complex subjects but also encourages independent study habits and critical thinking skills. This research outlines the conceptual framework, design methodology, and technical implementation of the AI Study Partner, highlighting its potential to positively impact education by providing a versatile and interactive learning platform.The AI Study Partner represents a significant step forward in the pursuit of creating adaptive, responsive, and personalized educational experiences for learners worldwide. Key Words : Artificial Intelligence in Education, Personalized Learning , Large Language Models (LLMs), Generative AI (Gen AI), User Engagement in Learning Data-Driven Education, Vector database, AI based Feedback Conversational Chatbots, Smart Search.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT160The AI Study Partner is introduced, an innovative tool designed to revolutionize the educational landscape by integrating Generative Artificial Intelligence (Gen AI) and Large Language Models (LLMs) and is engineered to cater to the diverse needs of learners by offering a suite of six key features.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Jayavardhini P']2024-03-25T00:00:00https://www.semanticscholar.org/paper/f299be3d307cef6f58ad4f321b70b5026368239a 11c88602d4f73937d2fe6456088dee04dd189914This research article makes an attempt to investigate the potential of Artificial Intelligence (AI) in enhancing the learning experiences, as well as student outcomes. As a result, it has developed a study that will be able to understand how the different AI tools, including machine learning, data learning, virtual reality (VR) and augmented reality (AR), automation, and so forth can be used to develop learning experiences and outcomes. Subsequently, a case study involving a mathematics classroom was used to collect data and confirm whether indeed AI led to improved learning experiences and study outcomes. The study confirms that AI resulted in positive outcome with positive performance measure sin academic performance, motivation and engagement, learning progression, and so forth.Applied and Computational Engineering190The study confirms that AI resulted in positive outcome with positive performance measure sin academic performance, motivation and engagement, learning progression, and so forth.Applied and Computational Engineering['Zhiyi Xu']2024-03-25T00:00:00https://www.semanticscholar.org/paper/11c88602d4f73937d2fe6456088dee04dd189914 e6229a0f848f9d56741daf4bab39c6a6ad35900fPurpose The purpose of this study is to provide a detailed demonstration of how artificial intelligence (AI) can be used to potentially generate valuable insights and recommendations regarding the role of accounting in addressing key sustainability-related issues. Design/methodology/approach The study offers a novel method for leveraging AI tools to augment traditional scoping study techniques. The method was used to show how the authors can produce recommendations for potentially enhancing organisational accountability pertaining to seasonal workers. Findings Through the use of AI and informed by the knowledge base that the authors created, the authors have developed prescriptions that have the potential to advance the interests of seasonal workers. In doing so, the authors have focussed on developing a useful and detailed guide to assist their colleagues to apply AI to various research questions. Originality/value This study demonstrates the ability of AI to assist researchers in efficiently finding solutions to social problems. By augmenting traditional scoping study techniques with AI tools, the authors present a framework to assist future research in such areas as accounting and accountability. Meditari Accountancy Research450The ability of AI to assist researchers in efficiently finding solutions to social problems is demonstrated by augmenting traditional scoping study techniques with AI tools, and a framework to assist future research in such areas as accounting and accountability is presented.Meditari Accountancy Research['Bronwyn Eager', 'Craig Deegan', 'Terese A. Fiedler']2024-03-25T00:00:00https://www.semanticscholar.org/paper/e6229a0f848f9d56741daf4bab39c6a6ad35900f 5c3a0002a8258635880eba8bea3b2305a26fa206In the ever-evolving realm of technology, Artificial Intelligence (AI) has ushered in a transformative era, reshaping our interactions with digital systems, and expanding the horizons of machine capabilities. At the core of this AI revolution are specialized hardware entities known as AI accelerators. These accelerators, including Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), play a pivotal role in advancing AI applications across diverse domains. This paper delves into these accelerators, offering an in-depth exploration of their unique attributes and application domains. GPUs, initially designed for graphics, have evolved into versatile tools, thanks to their parallel computing prowess and efficient memory utilization. FPGAs, with reconfigurability and low latency, prove valuable in aerospace and neural network implementations, though they come with cost and expertise challenges. ASICs, engineered for specific functions, excel in performance and power efficiency for mass production but require significant time and resources for development. Furthermore, this paper presents practical application analyses, showcasing how these accelerators are effectively deployed in real-world scenarios. With this comprehensive exploration, readers gain a deeper understanding of AI accelerators and their transformative impact on the AI landscape.Applied and Computational Engineering120This paper delves into AI accelerators, offering an in-depth exploration of their unique attributes and application domains, and presents practical application analyses, showcasing how these accelerators are effectively deployed in real-world scenarios.Applied and Computational Engineering['Yicheng Shi']2024-03-25T00:00:00https://www.semanticscholar.org/paper/5c3a0002a8258635880eba8bea3b2305a26fa206 40d2fd791c14573f5b062721ed73998e07dd316eThe advancement of generation models has led to the emergence of highly realistic artificial intelligence (AI)-generated videos. Malicious users can easily create non-existent videos to spread false information. This letter proposes an effective AI-generated video detection (AIGVDet) scheme by capturing the forensic traces with a two-branch spatio-temporal convolutional neural network (CNN). Specifically, two ResNet sub-detectors are learned separately for identifying the anomalies in spatical and optical flow domains, respectively. Results of such sub-detectors are fused to further enhance the discrimination ability. A large-scale generated video dataset (GVD) is constructed as a benchmark for model training and evaluation. Extensive experimental results verify the high generalization and robustness of our AIGVDet scheme. Code and dataset will be available at https://github.com/multimediaFor/AIGVDet.arXiv.org160An effective AI-generated video detection (AIGVDet) scheme by capturing the forensic traces with a two-branch spatio-temporal convolutional neural network (CNN) and two ResNet sub-detectors are learned separately for identifying the anomalies in spatical and optical flow domains, respectively.ArXiv['Jianfa Bai', 'Man Lin', 'Gang Cao']2024-03-25T00:00:00https://www.semanticscholar.org/paper/40d2fd791c14573f5b062721ed73998e07dd316e 4aa559dd0b487461cbef8c2ff8a2cbaa6c933019The rapid introduction and evolution of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and large language models (LLMs) combined with the emergence of text-generating chatbots have ushered in a transformative era in scholarly publishing.Ghana Medical Journal100Ghana Medical Journal['Caradee Y Wright', 'Margaret Lartey', 'Kenza Khomsi', 'Frederico Peres', 'Daniel Yilma', 'James W M Kigera', 'Annette Flanagin', 'Ahia Gbakima', 'D. Ofori-Adjei', 'Sumaili Kiswaya Ernest', 'Siaka Sidibé', 'A. Togo', 'Adamson S. Muula']2024-03-25T00:00:00https://www.semanticscholar.org/paper/4aa559dd0b487461cbef8c2ff8a2cbaa6c933019 00b42f948fe684ce68dc685a91cdc292c3beaaecIn 2024, we will hold a research paper competition (the third Human Understanding AI Paper Challenge) for the research and development of artificial intelligence technologies to understand human daily life. This document introduces the datasets that will be provided to participants in the competition, and summarizes the issues to consider in data processing and learning model development.arXiv.org30This document introduces the datasets that will be provided to participants in the competition, and summarizes the issues to consider in data processing and learning model development.ArXiv['Se Won Oh', 'Hyuntae Jeong', 'Jeong-Mook Lim', 'Seungeun Chung', 'K. Noh']2024-03-25T00:00:00https://www.semanticscholar.org/paper/00b42f948fe684ce68dc685a91cdc292c3beaaec ba61007eb99104a27d5878139975b84a1564ef5dPurpose: The purpose of this research is to explore the integration of artificial intelligence (AI) in healthcare, specifically within the realm of health informatics (HI). The study aims to understand the impact of AI on patient treatment, research, and operational processes within healthcare systems. Additionally, it seeks to address the challenges posed by the increasing volume of unorganized and unstructured data generated by AI technologies in healthcare. Materials and Methods: This research employs a comprehensive analysis approach, utilizing complex health information systems, clinical images, and intricate language. It examines the current utilization of AI in healthcare, focusing on its effects on patient and clinician involvement in healthcare decision-making. The assessment emphasizes key skill areas of Health Informatics, including IT, health information systems, security and privacy, telemedicine, m-Health, consumer health informatics, and clinical informatics. Findings: The study identifies several significant findings regarding the role of AI in healthcare. It highlights how AI technologies contribute to the generation of unstructured data, posing challenges for research and analysis. Additionally, the research underscores AI's ability to enhance personalized medical guidance, identify complex illnesses, forecast negative health occurrences, and improve patient outcomes. Moreover, it discusses AI's impact on social media and mobile apps, emphasizing its potential to gather valuable insights from online sources for a deeper understanding of patient needs and behaviors. Implications to Theory, Practice and Policy: Based on the findings, the research suggests several recommendations for future research and progress in the field of AI usage in healthcare. These recommendations may include further exploration of AI applications in healthcare decision-making, addressing challenges related to unstructured data, enhancing security and privacy measures, and leveraging AI for improving patient outcomes and clinician engagement. Additionally, the study emphasizes the importance of ongoing research and development to maximize the potential benefits of AI in healthcare.European journal of technology400The research underscores AI's ability to enhance personalized medical guidance, identify complex illnesses, forecast negative health occurrences, and improve patient outcomes, as well as its potential to gather valuable insights from online sources for a deeper understanding of patient needs and behaviors.European Journal of Technology['Dr. Sreeram Mullankandy', 'Israr Kazmi', 'Tasriqul Islam', 'Wong Jest Phia']2024-03-25T00:00:00https://www.semanticscholar.org/paper/ba61007eb99104a27d5878139975b84a1564ef5d 3c193a71e32660d297fd5842629a968b4017fe5eThe integration of artificial intelligence (AI) with gene editing technologies like CRISPR-Cas9 holds immense promise for advancing biomedical research and personalized medicine. This article highlights the crucial role of AI in predicting and minimizing off-target effects, thereby enhancing the precision and efficiency of gene editing. Researchers have developed algorithms like BE-DICT to accurately predict base editing outcomes, showcasing the potential of AI-driven strategies in optimizing gene editing processes. By combining AI with bioengineering, this interdisciplinary approach aims to automate and refine DNA modifications, paving the way for innovative applications in personalized gene therapy and biofabrication. Ultimately, this research endeavors to revolutionize the life sciences field, leading to significant breakthroughs in healthcare and biotechnology.Journal of Theory and Practice of Engineering Science360The crucial role of AI in predicting and minimizing off-target effects, thereby enhancing the precision and efficiency of gene editing is highlighted, highlighting the potential of AI-driven strategies in optimizing gene editing processes.Journal of Theory and Practice of Engineering Science['Luqi Lin', 'Zhengrong Cui', 'Sihao Wang', 'Yizhi Chen', 'Yanqi Zong']2024-03-25T00:00:00https://www.semanticscholar.org/paper/3c193a71e32660d297fd5842629a968b4017fe5e 6ff7c775b686fd1ade7b543b95e46a6edc43438aAsian Bioethics Review110This paper elucidates and rationalizes the ethical governance system for healthcare AI research, as outlined in the ‘Research Ethics Guidelines for AI Researchers in Healthcare’ published by the South Korean government in August 2023, and identifies similarities between clinical trials and healthcare AI research.Asian Bioethics Review['Junhewk Kim', 'So Yoon Kim', 'Eun-Ae Kim', 'Jin-Ah Sim', 'Yuri Lee', 'Hannah Kim']2024-03-25T00:00:00https://www.semanticscholar.org/paper/6ff7c775b686fd1ade7b543b95e46a6edc43438a 65ead27b908c7afe6817b2ca588aa97f29e05c79JMIRx Med11JMIRx Med['F. Baglivo']2024-03-25T00:00:00https://www.semanticscholar.org/paper/65ead27b908c7afe6817b2ca588aa97f29e05c79 0b802e43c56f96e1fdb24d8c3ca997bc19b21226Higher Education Research &amp; Development211Higher Education Research &amp; Development['Beck Wise', 'Lisa Emerson', 'Ariella van Luyn', 'Bronwen Dyson', 'Collin Bjork', 'Susan E. Thomas']2024-03-25T00:00:00https://www.semanticscholar.org/paper/0b802e43c56f96e1fdb24d8c3ca997bc19b21226 8e2408ba63775b4504b7d4ee9cfd29796722d38cMale infertility has affected an increasingly large population over the past few decades, affecting over 186 million people globally. The advent of assisted reproductive technologies (ARTs) and artificial intelligence (AI) has changed the landscape of diagnosis and treatment of male infertility. Through an extensive literature review encompassing the PubMed, Google Scholar, and Scopus databases, various AI techniques such as machine learning (ML), artificial neural networks (ANNs), deep learning (DL), and natural language processing (NLP) were examined in the context of evaluating seminal quality, predicting fertility potential, and improving semen analysis. Research indicates that AI models can accurately estimate the quality of semen, diagnose problems with sperm, and provide guidance on reproductive health decisions. In addition, developments in smartphone-based semen analyzers and computer-assisted semen analysis (CASA) are indicative of initiatives to improve the price, portability, and accuracy of results. Future directions point to possible uses for AI in ultrasonography assessment, microsurgical testicular sperm extraction (microTESE), and home-based semen analysis. Overall, AI holds significant promise in revolutionizing the diagnosis and treatment of male infertility, offering standardized, objective, and efficient approaches to addressing this global health challenge.Uro721Artificial intelligence holds significant promise in revolutionizing the diagnosis and treatment of male infertility, offering standardized, objective, and efficient approaches to addressing this global health challenge.Uro['Nikit Venishetty', 'M. Alkassis', 'Omer Raheem']2024-03-25T00:00:00https://www.semanticscholar.org/paper/8e2408ba63775b4504b7d4ee9cfd29796722d38c 183cda3ba5b1419cd6c01f0f0c1cd83d0fa181d0The aim of this research was to investigate factors that influence logistics firms’ supply chain agility and supply chain resilience. Therefore, an integrated research model based on strategic human resource management and artificial intelligence was developed to determine the agility and resilience of logistics firms. Empirical data were collected from 221 employees working in manufacturing firms in Saudi Arabia. For the data analysis, a structural equation modeling approach was used. The results indicated that joint leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence had substantial explained variance R2 of 80% for supply chain agility. Similarly, an importance performance analysis revealed that, within the integrated research model of supply chain agility, the factors of leadership, human capital development, and organizational flexibility had greater importance in determining supply chain resilience. Practically, this research shows that factors like leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence are positively associated with supply chain agility and, hence, require policymakers’ attention. The value of this research lies in its integration of artificial intelligence, organizational flexibility, and strategic human resource management to explore supply chain agility and its examination of the impact of these factors on supply chain resilience.Sustainability561Investigating factors that influence logistics firms’ supply chain agility and supply chain resilience shows that factors like leadership, employee skills, organizational culture, competitive intensity, human capital development, and artificial intelligence are positively associated with supply chain agility and, hence, require policymakers’ attention.Sustainability['Mohammad Ali Yamin', 'Sultan Dakhilallah Almuteri', 'Khaled Jamil Bogari', 'Abdulrahim Khaled Ashi']2024-03-25T00:00:00https://www.semanticscholar.org/paper/183cda3ba5b1419cd6c01f0f0c1cd83d0fa181d0 49f6512d321096a7a9116b9e3fc3bc01477e3e5cThe novelty of Artificial Intelligence (AI) and nascent geo-tech interests of powerful countries have largely influenced foreign policy, while ‘techno-geopolitics’ and the emergence of the ‘AI world order’ have constantly challenged the world order milieu. The changing global power dynamics, including the escalating Russia-Ukraine war, the Israel-Palestine conflict and China’s rising clout in tech and diplomatic spheres have induced specific geo-political challenges to the US-led global order. In this context, this research primarily unfolds whether the remaking of new global order can fundamentally be signified by the end of bipolar or unipolar world order, while the advancement of AI technology and geo-tech interests of tech powers have contributed to a remaking of new global order. As the powerful countries have fundamentally concentrated on marshaling AI in foreign policy, both AI and foreign policy have been closely interlinked. This research aims to explore the impact of AI on new global order and corresponding security concerns, particularly Nepalese security concerns. Since both AI and the new global order are relatively vast fields, this research focuses on tech foreign policy that is directly linked with the balance of power and the corresponding international order. This study adopts an analytical descriptive research method. It relates AI ethics and global tech concerns, considering the global need, beginning with the notion of multilateral tech diplomacy, and inquiring whether the tech foreign policy is truly functional. Despite varying challenges to the new global order, rational ‘geopolitical balancing’ and techno-economic cooperation in ‘better-functioning relations’ with immediate neighbors and other superpowers drive Nepal’s security architecture.Unity Journal520This research aims to explore the impact of AI on new global order and corresponding security concerns, particularly Nepalese security concerns, and focuses on tech foreign policy that is directly linked with the balance of power and the corresponding international order.Unity Journal['GP Acharya']2024-03-25T00:00:00https://www.semanticscholar.org/paper/49f6512d321096a7a9116b9e3fc3bc01477e3e5c da9db72eef56722360504e77e6a02faff7ca2958In the long history of human beings, with the continuous exploration and research of natural phenomena and social life, many scientific fields have emerged, and robots are the product of this technological development to a certain stage. At present, there are hundreds of different types of robots applied in production and daily life in the world, which have achieved significant economic benefits. However, its technical issues have gradually emerged. For example, the shortcomings in visual perception and other aspects cannot be effectively addressed. Object recognition is not precise enough, and information resources cannot be effectively utilized to achieve control functions. These are the main factors that constrain the further progress and improvement of robots. The emergence of big data and Artificial Intelligence (AI) has brought unprecedented opportunities to robots. Especially, the application of big data analysis in intelligent manufacturing and smart city construction is becoming increasingly widespread, thus providing new solutions for robot services. They not only enable people to quickly and accurately grasp a large amount of valuable knowledge, but also better tap into the enormous potential contained in human intelligence, which largely drives the robot industry towards intelligence. By summarizing the existing research results, this paper explored the development trend of robot object recognition systems, and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement. In response to the current problems, corresponding solutions were proposed and comparative experiments were designed. This proved that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.International Journal of High Speed Electronics and Systems00This paper explored the development trend of robot object recognition systems and focused on its key technologies, the feature matching-based pattern recognition and acceleration strategy-based detection efficiency improvement, proving that the anti-interference detection accuracy of the robot object recognition system based on big data and AI algorithm improved by about 12.48%, thus hoping to provide reference for future robot system development.International Journal of High Speed Electronics and Systems['Xue Jiang']2024-03-25T00:00:00https://www.semanticscholar.org/paper/da9db72eef56722360504e77e6a02faff7ca2958 e9ecc2145866916b3c1ba5abf93ceb40e550f1ccThis research will analyze the use of Artificial Intelligence (AI) and teacher leadership on student performance. Another variable analyzed was the use of books and demonstrations in the form of PowerPoint presentations. Apart from that, in this research two moderator variables will also be used, namely devices and illustrations in PowerPoint. The tools used by teachers are expected to be able to increase the influence of book use on student performance and also improve relationships between leadership. The illustrative moderator variable turned out to increase the effect of using PowerPoint on student performance. In this research it is proven that AI can improve the leadership of teachers, and teacher leadership will improve student performance. Leadership will also have a stronger influence on performance if teachers use the right devices, whether notebooks, tablets or cellphones. Apart from that, the use of presentations created with PowerPoint will also improve student performance. When accompanied by illustrations (especially transitions and animations). Therefore, teachers must be able to utilize AI and its supporting applications.Jurnal Penelitian Pendidikan IPA420In this research it is proven that AI can improve the leadership of teachers, and teacher leadership will improve student performance.Jurnal Penelitian Pendidikan IPA['Kusman Sudibyo']2024-03-25T00:00:00https://www.semanticscholar.org/paper/e9ecc2145866916b3c1ba5abf93ceb40e550f1cc a2911eb1d04bcecf634ff296a6c88f5a34757a10The rapid development of information technology has led the world into the era of artificial intelligence. This technological change has a profound impact on the social and economic structure, not only changing people's lifestyle, but also a significant impact on the labor and employment market. The extensive application of artificial intelligence technology has promoted the development of the industry, providing new opportunities for Chinese workers, but at the same time, it has also led to the intensification of large -scale unemployment and structural contradictions in the labor market and the expansion of income gap. In order to effectively respond to these challenges, it is recommended that the state improve the social security system, promote employment and transfer training of unemployed personnel, adjust and optimize the industrial structure, cultivate new growth points of artificial intelligence employment, and strengthen the education and training of artificial intelligence technology, improve the workers Professional skills and literacy to achieve the goal of high -quality employment.International Journal of Computer Science & Information Technology (IJCSIT)50It is recommended that the state improve the social security system, promote employment and transfer training of unemployed personnel, adjust and optimize the industrial structure, cultivate new growth points of artificial intelligence employment, and strengthen the education and training of artificial intelligence technology.International Journal of Computer Science and Information Technology['Shaoyun Lin', 'Jiaqi Chen']2024-03-25T00:00:00https://www.semanticscholar.org/paper/a2911eb1d04bcecf634ff296a6c88f5a34757a10 7618841e0b73baacbabc96dff9b8eb5ff57dc956In the present era with the widespread use of artificial intelligence (AI) and digital content, the importance of watermarking techniques has increased. This survey “Survey on Watermarking Methods in the AI Domain and Beyond” provides a detailed review of the applications and developments of watermarking techniques in various fields. It highlights the latest technologies in watermarking in digital media, software, and especially AI models. The survey also highlights how these technologies are helpful in protecting intellectual property, data integrity and authentication. Additionally, it discusses the challenges and future directions of watermarking, including the requirements of robustness, invisibility, and resistance against attacks. The survey aims to provide researchers, developers, and industry experts a better understanding of the current progress and upcoming opportunities in this field.International Journal of Innovative Research in Computer and Communication Engineering00A detailed review of the applications and developments of watermarking techniques in various fields is provided, including the latest technologies in watermarking in digital media, software, and especially AI models.International Journal of Innovative Research in Computer and Communication Engineering['Saurabh Verma', 'Mukta Bhatele', 'Dr. Akhilesh A. Waoo']2024-03-25T00:00:00https://www.semanticscholar.org/paper/7618841e0b73baacbabc96dff9b8eb5ff57dc956 a9dd626e25968fcf114df6053974e713e6a4ba4bThe arrival of the age of digital intelligence signifies the increasingly important role of artificial intelligence (AI) in driving change in higher education. The incorporation of AI technology improves the efficiency of teaching and learning and facilitates fundamental changes in the mode, content, and management of education. This paper discusses the application of AI in personalized learning path design, intelligent tutoring systems, virtual labs, and course content development, and analyzes the challenges and countermeasures of technology access and equality, data privacy and security, education quality and AI dependency, and resistance to changing the traditional education system.Contemporary Education and Teaching Research00The application of AI in personalized learning path design, intelligent tutoring systems, virtual labs, and course content development is discussed, and the challenges and countermeasures of technology access and equality, data privacy and security, education quality and AI dependency, and resistance to changing the traditional education system are analyzed.Contemporary Education and Teaching Research['Yuxia Yuan']2024-03-25T00:00:00https://www.semanticscholar.org/paper/a9dd626e25968fcf114df6053974e713e6a4ba4b 65eb17c6747af7613869b380bffd822c1ca384daIndustry 4.0 concepts and technologies, which focus on interconnectivity, digitalization, and automation, are critical to the long-term success of both micro and macroeconomic entities. Artificial Intelligence (AI) has emerged as a critical enabler for effective Supply Chain Management (SCM) within this framework. This research study conducts a thorough examination of the current literature to investigate the role of AI in SCM. The study attempts to identify research trends, appraise the present state of knowledge, and provide insights on management implications through a systematic review and the use of bibliometric analytic methodologies. The management implications of this study provide light on the potential benefits and possibilities that AI may provide to SCM operations. The research findings provide firms with the means to improve their supply chain operations, elevate decision-making processes, and achieve a competitive advantage in the changing business landscape by properly using the potential of AI.Modern Management Review440The research findings provide firms with the means to improve their supply chain operations, elevate decision-making processes, and achieve a competitive advantage in the changing business landscape by properly using the potential of AI.Modern Management Review['Anna Tatarczak']2024-03-25T00:00:00https://www.semanticscholar.org/paper/65eb17c6747af7613869b380bffd822c1ca384da ef26c84b97118321d345f107c2910e34246836c1The public sector presents important steps for digital transformation. Digital transformation uses a series of tools and methods to improve the relationship with citizens and improve benefits. This paper explores the role of artificial intelligence (AI) in governance processes and provides public institutions with insight regarding the impact of integrating chatbot communication tools when interacting with citizens. The present research provides an analysis of the socio-economic factors that determine the use of artificial intelligence tools, i.e., the propensity to interact more with the public administration as a result of improved communication through virtual assistants, and highlights the implications of AI in improving services towards civil society by determining the degree of satisfaction on aspects such as reduced waiting times in queues, access to information regardless of the traditional working hours of civil servants, quicker execution of operations, et al. The results, derived from an analysis of 507 sets of responses obtained from an online questionnaire, indicate that a number of variables, such as residential environment, employment status, household income and education level, significantly impact the effectiveness of artificial intelligence in mediating citizen communication with government.Sustainability420An analysis of the socio-economic factors that determine the use of artificial intelligence tools indicates that a number of variables, such as residential environment, employment status, household income and education level, significantly impact the effectiveness of artificial intelligence in mediating citizen communication with government.Sustainability['Marius Pislaru', 'Ciprian Sorin Vlad', 'Larisa Ivascu', 'Iulia Ioana Mircea']2024-03-25T00:00:00https://www.semanticscholar.org/paper/ef26c84b97118321d345f107c2910e34246836c1 7283a0035de14837a48aa9c0e67bde4dcb010aacThis study explores the repercussions of excessive reliance on Artificial Intelligence (AI) on human cognitive processes, specifically targeting problem-solving, creativity, and decision-making. Employing qualitative semi-structured interviews and Interpretative Phenomenological Analysis (IPA), it delves into the nuanced challenges and risks stemming from an overemphasis on AI. The research illuminates a nuanced landscape: while AI streamlines problem-solving tasks and provides valuable support, there’s a crucial need to safeguard human judgment and intuition. In the realm of creativity, divergent viewpoints emerge, underscoring concerns regarding AI’s potential limitations and advocating for a harmonious interplay between AI-generated suggestions and individual creative thought. Regarding decision-making, participants recognize AI’s utility but underscore the necessity of blending AI insights with critical thinking and consideration of unique circumstances. They caution against complacency, advocating for a judicious equilibrium between AI guidance and individual expertise. This study innovates by providing multifaceted insights into the complexities of AI-human interaction, uncovering nuanced perspectives on its impacts across problem-solving, creativity, and decision-making domains. By bridging this gap, it advances understanding of how AI integration influences cognitive processes, offering practical implications for fostering a balanced approach. Its innovative methodology combines qualitative interviews and IPA, offering rich, nuanced data that provide a deeper understanding of the subject matter. This research serves as a beacon for promoting awareness of the risks associated with overreliance on AI, advocating for a mindful integration that upholds human agency while leveraging AI capabilities effectively.IgMin Research270IgMin Research['Al-Zahrani Abdulrahman M']2024-03-25T00:00:00https://www.semanticscholar.org/paper/7283a0035de14837a48aa9c0e67bde4dcb010aac 79833fab01c356ecb4d9c20ae6757ff3c5103859The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.Theranostics760The current applications of AI in theranostics are investigated and the horizons for future research and innovation are sought to broaden the horizons for future research and innovation.Theranostics['Gokce Belge Bilgin', 'Cem Bilgin', 'Brian Burkett', 'Jacob J Orme', 'Daniel S Childs', 'Matthew P. Thorpe', 'T. Halfdanarson', 'Geoffrey B Johnson', 'A. Kendi', 'Oliver Sartor']2024-03-25T00:00:00https://www.semanticscholar.org/paper/79833fab01c356ecb4d9c20ae6757ff3c5103859 b11e33dce0448678058cf3156261785710b2e84cThe article presents the results of a generalization of the practical activities of companies developing and/or implementing artificial intelligence technologies (hereinafter referred to as AI), as well as companies that use these technologies: what scenarios for the use of AI technologies exist and in which industries, what kind of problems organizations implementing AI face, how members of the expert community of the artificial intelligence sphere consider solving these issues and what the state bodies offer. As to the state’s policy regarding the development of artificial intelligence, the article contains information about the factors built in the updated national AI development strategy, reflects the relations between AI technologies and state sovereignty, demonstrates the impact of artificial intelligence on the competitiveness of a company and human creativity. The article also presents the main instructions of the government of the Russian Federation on the development of artificial intelligence, some statistical data on the use of AI in economic and social sectors. It identifies measures to support developers and “implementers” of AI technologies offered by development institutions within the framework of the federal project “Artificial Intelligence” as a part of the national program “Digital Economy of the Russian Federation”. Much attention is paid to the issue of human resourcing in the AI sphere – what kind of specialists companies need, what their level of training should be and what they should be able to do, what requirements are imposed on AI teachers, what leading companies ask applicants for interviews and what the trajectory of “growing” talents in the field of artificial intelligence is. The final part of the article provides recommendations to students on how to prepare for the widespread use of artificial intelligence technologies.Science Management: Theory and Practice10Science Management: Theory and Practice['E. Osadchuk']2024-03-25T00:00:00https://www.semanticscholar.org/paper/b11e33dce0448678058cf3156261785710b2e84c 55520b2479ed1bc852b601007cc6c9c73f7d2308This paper explores the integration of Artificial Intelligence (AI) techniques in stock market analysis and prediction. Traditional methods have limitations in capturing the complexities of market dynamics, leading to inaccuracies in forecasting. By leveraging AI algorithms such as machine learning, deep learning, and natural language processing, significant advancements have been made in predicting stock trends, volatility, and optimal trading strategies. This paper reviews various AI models and approaches used in stock market analysis, discusses their strengths and limitations, and presents case studies demonstrating their effectiveness. Furthermore, it discusses the ethical implications and challenges associated with AI in stock market prediction, including data privacy concerns and algorithmic biases. Ultimately, this paper aims to provide insights into how AI can revolutionize stock market analysis and empower investors with more accurate decision-making toolsInternational Journal of Advanced Research in Science, Communication and Technology30Insight is provided into how AI can revolutionize stock market analysis and empower investors with more accurate decision-making tools.International Journal of Advanced Research in Science, Communication and Technology['Dnyandev Sopan Musale']2024-03-25T00:00:00https://www.semanticscholar.org/paper/55520b2479ed1bc852b601007cc6c9c73f7d2308 f7c28e669dc59f45d33a8615abaf33e109641b23Without significant automation, humans are unable to manage the volume of data and the complexity of procedures required to safeguard cyberspace. Creating software and systems with typical fixed implementations (hardwired decision-making logic) that successfully prevent security threats is difficult. This problem can be solved with machine simplicity and AI learning approaches. This study assesses whether strengthening defensive mechanisms can improve cybersecurity capabilities and offers a brief summary of artificial intelligence (AI) applications of various cybersecurity using artificial technologies. We can determine that there are currently useful applications by looking at the most recent cybersecurity software that makes use of artificial intelligence. Neural networks are their primary tool for protecting the periphery and other cybersecurity domains. Nevertheless, it was evident that some cybersecurity problems would require the application of artificial intelligence technology. For instance, thorough information is needed for strategic decision-making, and logical decision support is one of the unmet cybersecurity needs.International Journal of Innovative Research in Computer and Communication Engineering00This study assesses whether strengthening defensive mechanisms can improve cybersecurity capabilities and offers a brief summary of artificial intelligence (AI) applications of various cybersecurity using artificial technologies.International Journal of Innovative Research in Computer and Communication Engineering['Vasanth M', 'R. Murugan']2024-03-25T00:00:00https://www.semanticscholar.org/paper/f7c28e669dc59f45d33a8615abaf33e109641b23 e78d6be881194e7057a625ac36fb90fedcd11057As the digital landscape continues to evolve, the integration of Artificial Intelligence (AI) into various sectors has become increasingly prevalent. One significant area where AI is exerting a profound impact is data system security. This paper explores the multifaceted influence of AI on enhancing the security measures employed to safeguard sensitive data within diverse technological environments. The first section elucidates how AI technologies, particularly machine learning algorithms, empower security systems to adapt dynamically to emerging threats. By analyzing vast datasets in real-time, AI-driven security solutions can identify anomalous patterns and potential vulnerabilities, enabling proactive threat mitigation and rapid response capabilities. Furthermore, the paper discusses the role of AI in automating routine security tasks, thereby alleviating the burden on human operators and reducing the likelihood of human error. Through intelligent automation, AI streamlines security operations, enhances efficiency, and enables organizations to allocate resources more effectively to address sophisticated cyber threats. Moreover, the utilization of AI for predictive analytics is explored, highlighting its ability to forecast potential security breaches based on historical data and emerging trends. By leveraging predictive insights, organizations can preemptively fortify their defenses, preempting cyberattacks before they occur and minimizing potential damages. Additionally, the paper examines the ethical considerations inherent in AI-driven security systems, emphasizing the importance of transparency, accountability, and bias mitigation. While AI holds immense potential for bolstering data system security, ethical dilemmas such as privacy infringements and discriminatory practices necessitate careful scrutiny and regulatory oversight. Lastly, the paper outlines future directions and challenges in leveraging AI for data system security, including the need for continued research and development to enhance the robustness and reliability of AI algorithms, as well as the imperative for collaboration between industry stakeholders, policymakers, and cybersecurity experts to navigate the evolving threat landscape effectively. As the digital landscape continues to evolve, the integration of artificial intelligence (AI) has become increasingly imperative in fortifying security measures across various domains. This abstract delves into the multifaceted role of AI in bolstering security, encompassing its applications in threat detection, anomaly recognition, and decisionmaking processes. In conclusion, this paper underscores the transformative impact of AI on data system security, illuminating its capacity to revolutionize threat detection, mitigation, and response strategies. By harnessing the power of AI-driven technologies responsibly and ethically, organizations can fortify their defenses against an increasingly sophisticated array of cyber threats, thereby safeguarding critical data assets and preserving trust in digital ecosystems.International Journal of Innovative Research in Computer and Communication Engineering00The multifaceted role of AI in bolstering security is explored, encompassing its applications in threat detection, anomaly recognition, and decisionmaking processes, illuminating its capacity to revolutionize threat detection, mitigation, and response strategies.International Journal of Innovative Research in Computer and Communication Engineering['Suman Kashyap']2024-03-25T00:00:00https://www.semanticscholar.org/paper/e78d6be881194e7057a625ac36fb90fedcd11057 33751b6874ef30c3e0b17ba8fcb75b73aa72be93This research paper delves into the intricate web of ethical concerns surrounding the ever-evolving realm of Artificial Intelligence (AI). It embarks on a journey to elucidate the multifaceted landscape of AI, spanning its definition, historical evolution, and contemporary applications. The core of this investigation revolves around the pivotal ethical dimensions inherent to AI, encompassing topics such as bias mitigation, privacy preservation, accountability enforcement, transparency enhancement, explainability pursuit, alignment with human values, and the latent potential for AI to exacerbate existing societal inequalities. An extensive review of the existing literature on AI ethics, conducted within this paper, unveils a rich tapestry of insights, illuminating critical findings and charting promising avenues for future research. It maps the evolution of AI ethics discourse, offering an analytical prism to comprehend the evolving contours of this dynamic field. In its concluding remarks, this research paper extends its purview to the global stage, advocating the imperative need for the establishment of international standards governing the ethical deployment of AI. It is an earnest endeavor to synthesize the collective wisdom garnered through this exploration into actionable recommendations that can guide the responsible development and utilization of AI, thereby ensuring that its transformative power is harnessed in a manner that is both technologically profound and ethically impeccable. Keywords AI Ethics, Evolution of AI Ethics, Global Standards, Human Values AlignmentINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper delves into the intricate web of ethical concerns surrounding the ever-evolving realm of Artificial Intelligence, encompassing topics such as bias mitigation, privacy preservation, accountability enforcement, transparency enhancement, explainability pursuit, alignment with human values, and the latent potential for AI to exacerbate existing societal inequalities.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Anusha S. Nadiger']2024-03-25T00:00:00https://www.semanticscholar.org/paper/33751b6874ef30c3e0b17ba8fcb75b73aa72be93 599053d77c45cb164f09f4bc5a27413c225b74b9The literature on trust among humans assumes that trustees are viewed as having agency (i.e., they display the capacity to think, plan and act), else trust is undefined. In contrast, the literature on confidence in technology does not require this assumption about the technologies we make ourselves vulnerable to (thus the term “confidence” rather than the term “trust” when applied to technology). Modern artificial intelligence (AI) technologies based on deep learning architectures are often perceived as agentic to varying degrees—typically as more agentic than other technologies but less than humans. We theorize how different levels of perceived agency of AI affect human trust in AI. We do so by investigating three mechanisms. First, a more agentic seeming AI (and its designer) will appear more able to execute relevant tasks, and therefore more trustworthy. Second, the more agentic seeming the AI, the more important are trustworthiness perceptions about the AI relative to those about its designer. Third, because of betrayal aversion, the anticipated psychological cost of the AI violating trust increases with how agentic it seems to be. These mechanisms imply, perhaps counterintuitively, that making an AI appear more agentic may increase or decrease the trust that humans place in it: success at meeting the Turing test may go hand in hand with a decrease of trust in AI.Academy of Management Review1620It is implied that making an AI appear more agentic may increase or decrease the trust that humans place in it: success at meeting the Turing test may go hand in hand with a decrease of trust in AI.Academy of Management Review['Bart S. Vanneste', 'P. Puranam']2024-03-25T00:00:00https://www.semanticscholar.org/paper/599053d77c45cb164f09f4bc5a27413c225b74b9 beb09eccf2eab8805878f08e11395502c0d17fa3Routine blood tests drive diagnosis, prognosis, and monitoring in traditional clinical decision support systems. As a routine diagnostic tool with standardized laboratory workflows, clinical blood analysis offers superior accessibility to a comprehensive assessment of physiological parameters. These parameters can be integrated and automated at scale, allowing for in-depth clinical inference and cost-effectiveness compared to other modalities such as imaging, genetic testing, or histopathology. Herein, we extensively review the analytical value of routine blood tests leveraged by artificial intelligence (AI), using the ICD-10 classification as a reference. A significant gap exists between standard disease-associated features and those selected by machine learning models. This suggests an amount of non-perceived information in traditional decision support systems that AI could leverage with improved performance metrics. Nonetheless, AI-derived support for clinical decisions must still be harmonized regarding external validation studies, regulatory approvals, and clinical deployment strategies. Still, as we discuss, the path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.Frontiers in Medical Engineering960The path is drawn for the future application of scalable artificial intelligence (AI) to enhance, extract, and classify patterns potentially correlated with pathological states with restricted limitations in terms of bias and representativeness.Frontiers in Medical Engineering['Miguel A. Santos-Silva', 'Nuno Sousa', 'J. Sousa']2024-03-25T00:00:00https://www.semanticscholar.org/paper/beb09eccf2eab8805878f08e11395502c0d17fa3 92866e1c7465a3ceb55a0e97e8275dece8459579Penelitian ini menganalisis permasalahan hukum yang timbul dari karya cipta yang dihasilkan metode Stable Diffusion oleh Artificial Intelligence. Karena perkembangan teknologi yang semakin pesat, inovasi baru seperti kecerdasan buatan semakin berkembang hingga dapat membuat karya cipta. Tujuan penelitian ini dilakukan adalah untuk mengetahui dan memahami dari kedudukan hukum hasil ciptaan metode Stable Diffusion oleh Artificial Intelligence berdasarkan Undang – Undang Nomor 28 Tahun 2014 tentang Hak Cipta. Melalui penelitian deskriptif analisis dengan pendekatan yuridis normatif, hasil penelitian menunjukan bahwa karya cipta yang dihasilkan melalui metode Stable Diffusion oleh Artificial Intelligence belum dapat diakui oleh Undang – Undang Hak Cipta karena terdapat ketentuan terutama Pasal 1 ayat 3 mengenai hasil cipta karya seni harus benar – benar dihasilkan atas inspirasi dan ide original dari penciptanya. Kesimpulan dari penelitian ini menyoroti kompleksitas dalam menentukan kedudukan hukum ciptaan yang dihasilkan oleh Artificial Intelligence (AI) berdasarkan Undang-Undang Nomor 28 Tahun 2014 Tentang Hak Cipta (UUHC).COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat00COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat['Bintang Muhammad Daffa']2024-03-25T00:00:00https://www.semanticscholar.org/paper/92866e1c7465a3ceb55a0e97e8275dece8459579 2df26c51ede052ff9ecbe2ea60e82272efa628aaKidney36002Kidney360['Jing Miao', 'C. Thongprayoon', 'W. Cheungpasitporn']2024-03-25T00:00:00https://www.semanticscholar.org/paper/2df26c51ede052ff9ecbe2ea60e82272efa628aa 86edd0a9eef7d088ef7ba39ea476fb57dc7c3f2cDiffusion models have swiftly taken the lead in generative modeling, establishing unprecedented standards for producing high-quality, varied outputs. Unlike Generative Adversarial Networks (GANs)—once considered the gold standard in this realm—diffusion models bring several unique benefits to the table. They are renowned for generating outputs that more accurately reflect the complexity of real-world data, showcase a wider array of diversity, and are based on a training approach that is comparatively more straightforward and stable. This survey aims to offer an exhaustive overview of both the theoretical underpinnings and practical achievements of diffusion models. We explore and outline three core approaches to diffusion modeling: denoising diffusion probabilistic models, score-based generative models, and stochastic differential equations. Subsequently, we delineate the algorithmic enhancements of diffusion models across several pivotal areas. A notable aspect of this review is an in-depth analysis of leading generative models, examining how diffusion models relate to and evolve from previous generative methodologies, offering critical insights into their synergy. A comparative analysis of the merits and limitations of different generative models is a vital component of our discussion. Moreover, we highlight the applications of diffusion models across computer vision, multi-modal generation, and beyond, culminating in significant conclusions and suggesting promising avenues for future investigation.Mathematics240This survey aims to offer an exhaustive overview of both the theoretical underpinnings and practical achievements of diffusion models, exploring and outlining three core approaches to diffusion modeling: denoising diffusion probabilistic models, score-based generative models, and stochastic differential equations.Mathematics['Xiaolong Wang', 'Zhijian He', 'Xiaojiang Peng']2024-03-25T00:00:00https://www.semanticscholar.org/paper/86edd0a9eef7d088ef7ba39ea476fb57dc7c3f2c 6c83e769d7cd9d00c4b79c481f222535267a22abInternational Journal of Electrical and Electronics Engineering00International Journal of Electrical and Electronics Engineering['Vidya Sivalingam', 'Shabana Parveen', 'Rub eena', 'Jayasuriya Panchalingam']2024-03-25T00:00:00https://www.semanticscholar.org/paper/6c83e769d7cd9d00c4b79c481f222535267a22ab 24fae28563aa97781b26dfa1f54687cfb8950d9b This quality improvement study investigates if a large language model could simulate adolescents’ responses to vaping-prevention campaigns and identify the most effective messages to address the public health crisis of adolescent vaping. JAMA pediatrics60JAMA pediatrics['P. Sheeran', 'Alexander Kenny', 'Andrea Bermudez', 'Kurt Gray', 'Emily F Galper', 'Marcella Boynton', 'Seth M Noar']2024-03-25T00:00:00https://www.semanticscholar.org/paper/24fae28563aa97781b26dfa1f54687cfb8950d9b 76d4bf26da44f17dbe3b558f11b183740e5812bcApplied Economics230Applied Economics['Yiming Yuan', 'Yongming Sun', 'Hangyu Chen']2024-03-25T00:00:00https://www.semanticscholar.org/paper/76d4bf26da44f17dbe3b558f11b183740e5812bc 7ccef4377a5cc67b0e608e05939980d74644ed6dModern hardware designs have grown increasingly efficient and complex. However, they are often susceptible to Common Weakness Enumerations (CWEs). This paper is focused on the formal verification of CWEs in a dataset of hardware designs written in SystemVerilog from Regenerative Artificial Intelligence (AI) powered by Large Language Models (LLMs). We applied formal verification to categorize each hardware design as vulnerable or CWE-free. This dataset was generated by 4 different LLMs and features a unique set of designs for each of the 10 CWEs we target in our paper. We have associated the identified vulnerabilities with CWE numbers for a dataset of 60,000 generated SystemVerilog Register Transfer Level (RTL) code. It was also found that most LLMs are not aware of any hardware CWEs; hence they are usually not considered when generating the hardware code. Our study reveals that approximately 60% of the hardware designs generated by LLMs are prone to CWEs, posing potential safety and security risks. The dataset could be ideal for training LLMs and Machine Learning (ML) algorithms to abstain from generating CWE-prone hardware designs.arXiv.org222It was found that approximately 60% of the hardware designs generated by LLMs are prone to CWEs, posing potential safety and security risks, so this dataset could be ideal for training LLMs and Machine Learning (ML) algorithms to abstain from generating CWE-prone hardware designs.ArXiv['Deepak Narayan Gadde', 'Aman Kumar', 'Thomas Nalapat', 'Evgenii Rezunov', 'Fabio Cappellini']2024-03-25T00:00:00https://www.semanticscholar.org/paper/7ccef4377a5cc67b0e608e05939980d74644ed6d 8133073d0031f9a202f0b536a201089ea183cf46This article presents the Political Deepfakes Incidents Database (PDID), a collection of politically-salient deepfakes, encompassing synthetically-created videos, images, and less-sophisticated `cheapfakes.' The project is driven by the rise of generative AI in politics, ongoing policy efforts to address harms, and the need to connect AI incidents and political communication research. The database contains political deepfake content, metadata, and researcher-coded descriptors drawn from political science, public policy, communication, and misinformation studies. It aims to help reveal the prevalence, trends, and impact of political deepfakes, such as those featuring major political figures or events. The PDID can benefit policymakers, researchers, journalists, fact-checkers, and the public by providing insights into deepfake usage, aiding in regulation, enabling in-depth analyses, supporting fact-checking and trust-building efforts, and raising awareness of political deepfakes. It is suitable for research and application on media effects, political discourse, AI ethics, technology governance, media literacy, and countermeasures.AAAI Conference on Artificial Intelligence240The PDID can benefit policymakers, researchers, journalists, fact-checkers, and the public by providing insights into deepfake usage, aiding in regulation, enabling in-depth analyses, supporting fact-checking and trust-building efforts, and raising awareness of political deepfakes.{'pages': '23053-23058'}['Christina P. Walker', 'Daniel S. Schiff', 'Kaylyn Jackson Schiff']2024-03-24T00:00:00https://www.semanticscholar.org/paper/8133073d0031f9a202f0b536a201089ea183cf46 7986e46c491f42189ece270a2f67dbc2ecdea551Artificial intelligence (AI) is quickly finding broad application in every sector of society. This rapid expansion of AI has increased the need to cultivate an AI-literate workforce, and it calls for introducing AI education into K-12 classrooms to foster students’ awareness and interest in AI. With rich narratives and opportunities for situated problem solving, story-driven game-based learning offers a promising approach for creating engaging and effective K-12 AI learning experiences. In this paper, we present our ongoing work to iteratively design, develop, and evaluate a story-driven game-based learning environment focused on AI education for upper elementary students (ages 8 to 11). The game features a science inquiry problem centering on an endangered species and incorporates a Use-Modify-Create scaffolding framework to promote student learning. We present findings from an analysis of data collected from 16 students playing the game's quest focused on AI planning. Results suggest that the scaffolding framework provided students with the knowledge they needed to advance through the quest and that overall, students experienced positive learning outcomes.AAAI Conference on Artificial Intelligence411An analysis of data collected from 16 students playing the game's quest focused on AI planning suggests that the scaffolding framework provided students with the knowledge they needed to advance through the quest and that overall, students experienced positive learning outcomes.{'pages': '23092-23100'}['Anisha Gupta', 'Seung Y. Lee', 'Bradford W. Mott', 'Srijita Chakraburty', 'Krista D. Glazewski', 'Anne T. Ottenbreit-Leftwich', 'Adam Scribner', 'Cindy E. Hmelo-Silver', 'James C. Lester']2024-03-24T00:00:00https://www.semanticscholar.org/paper/7986e46c491f42189ece270a2f67dbc2ecdea551 207c597d8f0320376922687ee5b0e6db0ba9925eProject-based learning (PBL) is an instructional method that is very helpful in nurturing students' creativity, but it requires significant time and energy from both students and teachers. Large language models (LLMs) have been proven to assist in creative tasks, yet much controversy exists regarding their role in fostering creativity. This paper explores the potential of LLMs in PBL settings, with a special focus on fostering creativity. We began with an exploratory study involving 12 middle school students and identified five design considerations for LLM applications in PBL. Building on this, we developed an LLM-empowered, 48-hour PBL program and conducted an instructional experiment with 31 middle school students. Our results indicated that LLMs can enhance every stage of PBL. Additionally, we also discovered ambivalent perspectives among students and mentors toward LLM usage. Furthermore, we explored the challenge and design implications of integrating LLMs into PBL and reflected on the program. By bridging AI advancements into educational practice, our work aims to inspire further discourse and investigation into harnessing AI's potential in child-centric educational settings.arXiv.org701The results indicated that LLMs can enhance every stage of PBL, and bridging AI advancements into educational practice aims to inspire further discourse and investigation into harnessing AI's potential in child-centric educational settings.ArXiv['Siyu Zha', 'Yuehan Qiao', 'Qingyu Hu', 'Zhongsheng Li', 'Jiangtao Gong', 'Ying-Qing Xu']2024-03-24T00:00:00https://www.semanticscholar.org/paper/207c597d8f0320376922687ee5b0e6db0ba9925e d90c99653af10ca3f2edb022f26c0a6063e73f42Artificial Intelligence (AI) is permeating almost every area of society, reshaping how many people, including youth, navigate the world. Despite the increased presence of AI, most people lack a baseline knowledge of how AI works. Moreover, social barriers often hinder equal access to AI courses, perpetuating disparities in participation in the field. To address this, it is crucial to design AI curricula that are effective, inclusive, and relevant, especially to learners from backgrounds that are historically excluded from working in tech. In this paper, we present AI for Wellbeing, a curriculum where students explore conversational AI and the ethical considerations around using it to promote wellbeing. We specifically designed content, educator materials, and educational technologies to meet the interests and needs of students and educators from diverse backgrounds. We piloted AI for Wellbeing in a 5-day virtual workshop with middle school teachers and students. Then, using a mixed-methods approach, we analyzed students' work and teachers' feedback. Our results suggest that the curriculum content and design effectively engaged students, enabling them to implement meaningful AI projects for wellbeing. We hope that the design of this curriculum and insights from our evaluation will inspire future efforts to create culturally relevant K-12 AI curricula.AAAI Conference on Artificial Intelligence541AI for Wellbeing is presented, a curriculum where students explore conversational AI and the ethical considerations around using it to promote wellbeing, and specifically designed content, educator materials, and educational technologies to meet the interests and needs of students and educators from diverse backgrounds.{'pages': '23309-23317'}['Randi Williams', 'Sharifa Alghowinem', 'C. Breazeal']2024-03-24T00:00:00https://www.semanticscholar.org/paper/d90c99653af10ca3f2edb022f26c0a6063e73f42 495265985886a684521072449cf5d08d7b25b0c8This paper explores the transformative potential of Artificial Intelligence (AI) in personalized marketing. It highlights how AI can analyze vast amounts of customer data to create targeted messages, recommendations, and real-time interactions that resonate with individual needs and preferences. This personalized approach fosters deeper consumer engagement, leading to increased satisfaction, brand loyalty, and business success.  The paper discusses the future potential of AI in shaping personalized marketing experiences. However, responsible implementation will be paramount in ensuring a positive future for both brands and consumers. Enhanced version of the abstract incorporating additional insights, this paper delves into the transformative power of Artificial Intelligence (AI) in personalized marketing. It explores how AI algorithms can analyze a multitude of customer data points, including purchase history, website behavior, and social media interactions. This rich data empowers brands to create highly targeted messages, recommendations, and real-time interactions that resonate with individual customer needs and preferences. By fostering deeper consumer engagement, AI-powered personalization unlocks a pathway to increased customer satisfaction, brand loyalty, and ultimately, significant business growth. However, the paper acknowledges the ethical considerations that accompany AI implementation. Responsible data practices are paramount, ensuring data security and mitigating bias in AI algorithms to prevent discriminatory marketing practices. Transparency in how data is collected and used builds trust with consumers, fostering a mutually beneficial relationship. Looking ahead, the paper explores the vast future potential of AI in personalized marketing. Imagine AI-powered Chat bot offering personalized product recommendations in real-time, or virtual reality experiences tailored to individual preferences. The future of marketing lies in creating genuine connections with consumers, and AI provides the tools to personalize the customer journey at every touch point. However, navigating the ethical landscape and prioritizing responsible data practices will be crucial in ensuring a positive future for both brands and consumers. Keywords: Artificial Intelligence (AI), Personalized Marketing, Customer Engagement, Customer Data, Marketing Strategy.Engineering Science &amp; Technology Journal01How AI algorithms can analyze a multitude of customer data points, including purchase history, website behavior, and social media interactions empowers brands to create highly targeted messages, recommendations, and real-time interactions that resonate with individual customer needs and preferences is explored.Engineering Science &amp; Technology Journal['Opeyemi Abayomi Odejide', 'Tolulope Esther Edunjobi']2024-03-24T00:00:00https://www.semanticscholar.org/paper/495265985886a684521072449cf5d08d7b25b0c8 8961e4c9fcda8ffcf364aacb067e7e1f1968118fWhile the question of misspecified objectives has gotten much attention in recent years, most works in this area primarily focus on the challenges related to the complexity of the objective specification mechanism (for example, the use of reward functions). However, the complexity of the objective specification mechanism is just one of many reasons why the user may have misspecified their objective. A foundational cause for misspecification that is being overlooked by these works is the inherent asymmetry in human expectations about the agent's behavior and the behavior generated by the agent for the specified objective. To address this, we propose a novel formulation for the objective misspecification problem that builds on the human-aware planning literature, which was originally introduced to support explanation and explicable behavioral generation. Additionally, we propose a first-of-its-kind interactive algorithm that is capable of using information generated under incorrect beliefs about the agent to determine the true underlying goal of the user.Adaptive Agents and Multi-Agent Systems372A novel formulation for the objective misspecification problem is proposed that builds on the human-aware planning literature, which was originally introduced to support explanation and explicable behavioral generation and a first-of-its-kind interactive algorithm that is capable of using information generated under incorrect beliefs about the agent to determine the true underlying goal of the user.{'pages': '2331-2333'}['Malek Mechergui', 'S. Sreedharan']2024-03-24T00:00:00https://www.semanticscholar.org/paper/8961e4c9fcda8ffcf364aacb067e7e1f1968118f 236ff463ba1df654d7d7f92244be6da1b1d88b8cThis study explores the development and implementation of an artificial intelligence (AI) model designed to predict illegal border crossing locations, thereby enhancing the effectiveness of national border security measures. By integrating and analyzing diverse data sources, -including satellite imagery, social media, and environmental factors, -this AI model aims to identify potential migration patterns and high-risk areas for illegal crossing. This research highlights the model's ability to provide real-time risk assessments, offering a novel approach to border security that surpasses traditional methods in terms of both efficiency and cost-effectiveness. The model's adaptability, continuous learning capabilities, and user-friendly interfaces ensure its relevance in addressing contemporary border-security challenges. This article contributes to the ongoing discourse on the application of AI in national security by proposing a solution that leverages technology for improved coordination among immigration services, government organizations, and international bodies.Global Journal of Computer Science and Technology140A solution that leverages technology for improved coordination among immigration services, government organizations, and international bodies is proposed, offering a novel approach to border security that surpasses traditional methods in terms of both efficiency and cost-effectiveness.Global Journal of Computer Science and Technology['Amirali Kerimovs']2024-03-24T00:00:00https://www.semanticscholar.org/paper/236ff463ba1df654d7d7f92244be6da1b1d88b8c f2d7faa5f2f0c260e301aadb744e5e0808d2462aGenerative Artificial Intelligence (AI) has garnered significant attention for its remarkable ability to generate text, images, and other forms of content. However, an inherent and increasingly concerning issue within generative AI systems is bias. These AI models often exhibit an Anglo-centric bias and tend to overlook the importance of diversity. This can be attributed to their training on extensive datasets sourced from the internet, which inevitably inherit the biases present in those data sources. Employing these datasets leads to AI-generated content that mirrors and perpetuates existing biases, encompassing various aspects such as gender, ethnic and cultural stereotypes. Addressing bias in generative AI is a complex challenge that necessitates substantial efforts. In order to tackle this issue, we propose a methodology for constructing moderately sized datasets with a social inclination. These datasets can be employed to rectify existing imbalances in datasets or to train models to generate socially inclusive material. Additionally, we present preliminary findings derived from training our model on these socially inclined datasets.AAAI Conference on Artificial Intelligence40This work proposes a methodology for constructing moderately sized datasets with a social inclination, which can be employed to rectify existing imbalances in datasets or to train models to generate socially inclusive material.{'pages': '23653-23654'}['Akshit Singh']2024-03-24T00:00:00https://www.semanticscholar.org/paper/f2d7faa5f2f0c260e301aadb744e5e0808d2462a 642a3730f8abfa4a2fa7e78b4c09e00ac6f80f6aWe demonstrate an artificial intelligence (AI)-powered robot for optical network operation automation and showcase three demos: 1) robot-driven event classification, 2) modified LC duplex connector for robotic operation, and 3) AI inference acceleration using an FPGA.Optical Fiber Communications Conference and Exhibition30An artificial intelligence (AI)-powered robot for optical network operation automation and three demos: robot-driven event classification, modified LC duplex connector for robotic operation, and AI inference acceleration using an FPGA are demonstrated.2024 Optical Fiber Communications Conference and Exhibition (OFC)['Xiaonan Xu', 'Haoshuo Chen', 'Michael Scheutzow', 'Jesse E. Simsarian', 'R. Ryf', 'Gin Qua', 'Amey Hande', 'Rob Dinoff', 'Mijail Szczerban', 'M. Mazur', 'L. Dallachiesa', 'N. Fontaine', 'Jim Sandoz', 'Mike Coss', 'D. T. Neilson']2024-03-24T00:00:00https://www.semanticscholar.org/paper/642a3730f8abfa4a2fa7e78b4c09e00ac6f80f6a 0ad2e44ea8db02117920c9a43acc13659290f5f4This proposal introduces an innovative AI-powered learning system designed to address educational disparities worldwide. Focused on developing countries, the system seamlessly translates educational content between English and native languages, breaking down language barriers. Leveraging advanced natural language processing and machine learning techniques, including transformer models like BERT and GPT-3, the system ensures inclusivity, effectiveness, and engagement. Built on prior research demonstrating AI's efficacy in language translation and personalized learning, the proposed system draws inspiration from successful projects like Duolingo Language Incubator. By providing inclusive and accessible learning experiences, it empowers individuals to overcome language barriers, fostering global participation. The potential impact is significant, with the system poised to accelerate learning, enhance literacy rates, and create a more skilled workforce in developing countries. This research reflects a commitment to revolutionize education through technology, aiming for lasting and transformative contributions to global society. Through AI-driven education, a brighter, more inclusive future is envisioned.AAAI Conference on Artificial Intelligence10An innovative AI-powered learning system designed to address educational disparities worldwide, focused on developing countries, that seamlessly translates educational content between English and native languages, breaking down language barriers.{'pages': '23736-23737'}['Chahana Dahal']2024-03-24T00:00:00https://www.semanticscholar.org/paper/0ad2e44ea8db02117920c9a43acc13659290f5f4 6ea82cc5075a3c6370fb2e33ce4c95a7d506996eWith increasing demand for customized connectivity, transport networks must evolve towards an autonomous and customer-driven network management. In this paper we describe a AI-based data-driven control architecture to support end-to-end automated slicing in multi-layer networks.Optical Fiber Communications Conference and Exhibition10This paper describes a AI-based data-driven control architecture to support end-to-end automated slicing in multi-layer networks.2024 Optical Fiber Communications Conference and Exhibition (OFC)['Ó. G. De Dios', 'P. Armingol-Robles', 'L. Roelens', 'A. Muñiz-Da-Costa', 'J. Fernández-Palacios']2024-03-24T00:00:00https://www.semanticscholar.org/paper/6ea82cc5075a3c6370fb2e33ce4c95a7d506996e 126486c2dccecb46d784bcdd171d0ccd803d57caThis research explores the discourse surrounding red teaming and aims to identify any themes in the online discussion of potential environmental harms stemming from Large Language Models (LLMs). Focusing on the AI Red Teaming event at DEFCON 31, this study employs reflexive thematic analysis on diverse social networking site sources to extract insights into public discussion of LLM red teaming and its environmental implications. The findings intend to inform future research, highlighting the need for responsible AI development that addresses environmental concerns.AAAI Conference on Artificial Intelligence30The discourse surrounding red teaming is explored and any themes in the online discussion of potential environmental harms stemming from Large Language Models (LLMs) are identified to inform future research, highlighting the need for responsible AI development that addresses environmental concerns.{'pages': '23726-23728'}['Amy Au']2024-03-24T00:00:00https://www.semanticscholar.org/paper/126486c2dccecb46d784bcdd171d0ccd803d57ca 75235bca7fae820a5b6c203d7e9881a7c1cbb310With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging and age-appropriate AI learning activities. Efforts are underway, such as those by the AI4K12 initiative, to establish guidelines for organizing K- 12 AI education; however, effective instructional resources are needed by educators. In this paper, we describe our work to design, develop, and implement an unplugged activity centered on facial recognition technology for middle school students. Facial recognition is integrated into a wide range of applications throughout daily life, which makes it a familiar and engaging tool for students and an effective medium for conveying AI concepts. Our unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI. The game is crafted to enable students to develop AI competencies naturally through physical interaction. In the game, one student uses tracing paper to extract facial features from a familiar face shown on a card, such as a cartoon character or celebrity, and then other students try to guess the identity of the hidden face. We discuss details of the game, its iterative refinement, and initial findings from piloting the activity during a summer camp for rural middle school students.AAAI Conference on Artificial Intelligence320The unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI, crafted to enable students to develop AI competencies naturally through physical interaction.{'pages': '23285-23293'}['Hansol Lim', 'Wookhee Min', 'Jessica Vandenberg', 'Veronica Cateté', 'Bradford W. Mott']2024-03-24T00:00:00https://www.semanticscholar.org/paper/75235bca7fae820a5b6c203d7e9881a7c1cbb310 4096ba9556ceb3bfb0ed7bdaa8a902302254efc2As Artificial Intelligence (AI) continues to permeate various aspects of societies, understanding the disparities in AI knowledge and skills across different living areas becomes imperative. Small living areas have emerged as significant contributors to Europe's economy, offering an alternative to the bustling environment of larger cities for those seeking an improved quality of life. Nonetheless, they often encounter challenges related to digital infrastructure, access to financial resources, and digital skills gaps, limiting their economic and social growth prospects. This study investigates the digital and AI skills gaps in the context of small and large European living areas, shedding light on the potential hindrances to unleashing the full economic and social potentials of these regions in an AI-enabled economy. Drawing from a comprehensive dataset encompassing 4,006 respondents across eight EU countries, this research examines the current perceptions and understandings of AI and digital skills within two distinct population groups: residents of smaller living areas and their counterparts in larger communities. Through bivariate analysis, notable insights are revealed concerning trust in AI solutions and entities, self-assessed digital skills, AI Awareness, AI Attitudes and demography variables in both population groups. These insights may refer to the significance of addressing digital and AI skills gaps in fostering growth and preparedness for the AI-driven future. As AI becomes increasingly integral to various aspects of society, targeted interventions and policies are essential to bridge these gaps and enable individuals and communities to harness the transformative potential of AI-enabled economies.AAAI Conference on Artificial Intelligence210This study investigates the digital and AI skills gaps in the context of small and large European living areas, shedding light on the potential hindrances to unleashing the full economic and social potentials of these regions in an AI-enabled economy.{'pages': '23119-23127'}['Long Pham', "Barry O'Sullivan", 'Teresa Scantamburlo', 'Tai Mai']2024-03-24T00:00:00https://www.semanticscholar.org/paper/4096ba9556ceb3bfb0ed7bdaa8a902302254efc2 5ea1b9d6fe6e423947443299e3eaeb48b1d81d18The integration of Artificial Intelligence (AI) into Augmented Reality (AR) for medical applications is propelled by the aim to address evident healthcare disparities. Certain communities have encountered disparities in medical diagnoses, exemplified by Black individuals exhibiting a 2.4 times higher likelihood of schizophrenia diagnosis compared to their white counterparts (Faber et al., 2023). These disparities often arise from structured interview assessments overlooking cultural nuances, resulting in increased misdiagnosis rates. This study leverages AI and AR to develop unbiased diagnostic tools and enhance empathy in healthcare professionals' training. Uniquely prioritizing the reduction of biased language and the fostering of empathy through AI-driven Natural Language Processing (NLP) and AI-driven virtual patients, the research aims to enhance diagnostic accuracy while promoting cultural sensitivity among healthcare professionals. Aligned with broader goals of achieving equitable healthcare and reducing disparities, the evaluation involves pre- and post-training assessments to measure language improvements and empathy enhancements. Successful implementation could lead to a more equitable healthcare landscape, fostering trust in AI-driven systems and ensuring fairer medical care for diverse communities.AAAI Conference on Artificial Intelligence00This study leverages AI and AR to develop unbiased diagnostic tools and enhance empathy in healthcare professionals' training to enhance diagnostic accuracy while promoting cultural sensitivity among healthcare professionals.{'pages': '23753-23754'}['Erica Okeh']2024-03-24T00:00:00https://www.semanticscholar.org/paper/5ea1b9d6fe6e423947443299e3eaeb48b1d81d18 f84d8da1fc9cd7a78a380d82c7bf914562b5380cIn an age where Large Language Models (LLMs) expedite the generation of text, the skills for critically evaluating and creating meaningful text using these models are often lacking. To help classroom teachers address this, we introduce Prompty, a specialized teaching tool co-designed to facilitate both critical and effective use of LLMs. Prompty serves multiple learning goals: it allows students to critically evaluate text generated by LLMs, aids in their writing practice, and provides a deeper understanding of how LLMs function—all within a student-friendly environment secured by essential guardrails. Prompty was co-designed in collaboration with high school teachers as part of CRAFT, an initiative by Stanford University to promote AI literacy. It was pilot-tested in a high school English class to serve as an AI writing assistant, focusing on the critical evaluation of machine-generated text. This trial yielded preliminary evidence that attests to the tool's effectiveness in fulfilling its educational goals. The findings from the pilot study indicate that easy-to-use tools like Prompty have great potential. These tools can be adapted to fit the goals of individual teachers. They can help in achieving subject-specific learning goals while serving as an effective way to teach AI concepts in high school.AAAI Conference on Artificial Intelligence390Prompty, a specialized teaching tool co-designed to facilitate both critical and effective use of LLMs, is introduced, which allows students to critically evaluate text generated by LLMs, aids in their writing practice, and provides a deeper understanding of how LLMs function—all within a student-friendly environment secured by essential guardrails.{'pages': '23300-23308'}['Deepak Varuvel Dennison', 'Raycelle C. C. Garcia', 'Parth Sarin', 'Jacob Wolf', 'Christine Bywater', 'Benjamin Xie', 'Victor R. Lee']2024-03-24T00:00:00https://www.semanticscholar.org/paper/f84d8da1fc9cd7a78a380d82c7bf914562b5380c 18e8365fc76ccc94028f3402cdbe6f2574edf009Visual art facilitates expression, communication, and connection, yet it remains inaccessible to those who are visually-impaired and those who lack the resources to understand the techniques and history of art. In this work, I propose the development of a generative AI model that generates a description and interpretation of a given artwork. Such research can make art more accessible, support art education, and improve the ability of AI to understand and translate between creative media. Development will begin with a formative study to assess the needs and preferences of blind and low vision people and art experts. Following the formative study, the basic approach is to train the model on a database of artworks and their accompanying descriptions, predict sentiments from extracted visual data, and generate a paragraph closely resembling training textual data and incorporating sentiment analysis. The model will then be evaluated quantitatively through metrics like METEOR and qualitatively through Turing tests in an iterative process.AAAI Conference on Artificial Intelligence50The development of a generative AI model that generates a description and interpretation of a given artwork to make art more accessible, support art education, and improve the ability of AI to understand and translate between creative media is proposed.{'pages': '23760-23762'}['Tanisha Shende']2024-03-24T00:00:00https://www.semanticscholar.org/paper/18e8365fc76ccc94028f3402cdbe6f2574edf009 550a9b9a2a9401cd3d95ef74f2d6d8a143ecd6b9The oil and gas industry plays a pivotal role in global energy supply but faces increasing pressure to enhance sustainability amidst environmental concerns and economic constraints. This comprehensive review explores the integration of artificial intelligence (AI) in optimizing oil and gas production processes to achieve sustainability goals. The paper examines various AI-driven optimization techniques, including machine learning algorithms, genetic algorithms, and neural networks, and their application in different stages of oil and gas production, such as exploration, drilling, production, and distribution. By leveraging AI, operators can improve efficiency, reduce environmental impact, and maximize resource recovery. Furthermore, the review delves into specific case studies and implementations of AI-driven optimization in real-world oil and gas operations, highlighting their efficacy in minimizing greenhouse gas emissions, optimizing water usage, and mitigating operational risks. Additionally, the paper discusses challenges and limitations associated with AI adoption in the industry, such as data availability, model interpretability, and regulatory compliance. The integration of AI-driven optimization techniques not only enhances sustainability but also contributes to cost reduction and operational excellence in oil and gas production. By optimizing production processes, operators can achieve higher yields with fewer resources, leading to increased profitability and long-term viability in a rapidly evolving energy landscape. Overall, this review provides valuable insights into the transformative potential of AI-driven optimization techniques in fostering sustainability and resilience in oil and gas production processes, paving the way for a more efficient and environmentally responsible industry. Keywords: AL, Oil and Gas, Production, Optimization, Sustainability, Review, Process.Engineering Science &amp; Technology Journal00This comprehensive review explores the integration of artificial intelligence (AI) in optimizing oil and gas production processes to achieve sustainability goals and provides valuable insights into the transformative potential of AI-driven optimization techniques in fostering sustainability and resilience in oil and gas production processes.Engineering Science &amp; Technology Journal['Chuka Anthony Arinze', 'Boma Sonimiteim Jacks']2024-03-24T00:00:00https://www.semanticscholar.org/paper/550a9b9a2a9401cd3d95ef74f2d6d8a143ecd6b9 dddf7231dca8d25269137aa7646aa9a71fafc2d9High school teachers from many disciplines have growing interests in teaching about artificial intelligence (AI). This cross-disciplinary interest reflects the prevalence of AI tools across society, such as Generative AI tools built upon Large Language Models (LLM). However, high school classes are unique and complex environments, led by teachers with limited time and resources with priorities that vary by class and the students they serve. Therefore, developing curricula about AI for classes that span many disciplines (e.g. history, art, math) must involve centering the expertise of cross-disciplinary teachers. In this study, we conducted five collaborative curricular co-design sessions with eight teachers who taught high school humanities and STEM classes. We sought to understand how teachers considered AI when it was taught in art, math, and social studies contexts, as well as opportunities and challenges they identified with incorporating AI tools into their instruction. We found that teachers considered technical skills and ethical debates around AI, opportunities for "dual exploration" between AI and disciplinary learning, and limitations of AI tools as supporting engagement and reflection but also potentially distracting. We interpreted our findings relative to co-designing adaptable AI curricula to support teaching about and with AI across high school disciplines.AAAI Conference on Artificial Intelligence510It is found that teachers considered technical skills and ethical debates around AI, opportunities for "dual exploration" between AI and disciplinary learning, and limitations of AI tools as supporting engagement and reflection but also potentially distracting.{'pages': '23146-23154'}['Benjamin Xie', 'Parth Sarin', 'Jacob Wolf', 'Raycelle C. C. Garcia', 'Victoria Delaney', 'Isabel Sieh', 'Anika Fuloria', 'Deepak Varuvel Dennison', 'Christine Bywater', 'Victor R. Lee']2024-03-24T00:00:00https://www.semanticscholar.org/paper/dddf7231dca8d25269137aa7646aa9a71fafc2d9 1a272ee6a57e9ca8b20f4ac1233eb799b93ee990The vast diversity of internal designs of taskable black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. My dissertation focuses on developing paradigms that enable a user to assess and understand the limits of an AI system's safe operability. We develop a personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions. Our results show that such a primitive query-response interface is sufficient to efficiently derive a user-interpretable model of a system's capabilities.AAAI Conference on Artificial Intelligence100A personalized AI assessment module that lets an AI system execute instruction sequences in simulators and answer queries about these executions is developed, showing that such a primitive query-response interface is sufficient to efficiently derive a user-interpretable model of a system's capabilities.{'pages': '23427-23428'}['Pulkit Verma']2024-03-24T00:00:00https://www.semanticscholar.org/paper/1a272ee6a57e9ca8b20f4ac1233eb799b93ee990 45f3cfefc2a44cc704a69ab24fe338a9013fc1f5In contrast to vast academic efforts to study AI security, few real-world reports of AI security incidents exist. Released incidents prevent a thorough investigation of the attackers' motives, as crucial information about the company and AI application is missing. As a consequence, it often remains unknown how to avoid incidents. We tackle this gap and combine previous reports with freshly collected incidents to a small database of 32 AI security incidents. We analyze the attackers' target and goal, influencing factors, causes, and mitigations. Many incidents stem from non-compliance with best practices in security and privacy-enhancing technologies. In the case of direct AI attacks, access control may provide some mitigation, but there is little scientific work on best practices. Our paper is thus a call for action to address these gaps.AAAI Conference on Artificial Intelligence170This paper combines previous reports with freshly collected incidents to create a small database of 32 AI security incidents and analyzes the attackers' target and goal, influencing factors, causes, and mitigations.{'pages': '23041-23046'}['Kathrin Grosse', 'L. Bieringer', 'Tarek R. Besold', 'B. Biggio', 'Alexandre Alahi']2024-03-24T00:00:00https://www.semanticscholar.org/paper/45f3cfefc2a44cc704a69ab24fe338a9013fc1f5 d174173da22e162e53a64b94bf2bfdf94ca26060This paper explores the relationship between artificial intelligence (AI) and future wars, and focuses on how machine learning strengthens safety practice. Based on Wu Qianze's paper in National Defense Science, Technology and Industry, Issue 5,2019, this paper expounds the wide application of AI in the military field and the great potential of machine learning in improving security protection. Through a review of existing studies, this paper presents a series of recommendations to enhance safety practices designed to provide guidance for future research and applications.International Journal of Computer Science & Information Technology (IJCSIT)120Through a review of existing studies, this paper presents a series of recommendations to enhance safety practices designed to provide guidance for future research and applications.International Journal of Computer Science and Information Technology['Lingjuan Li']2024-03-24T00:00:00https://www.semanticscholar.org/paper/d174173da22e162e53a64b94bf2bfdf94ca26060 0d27f8fdf471d8187f83bd55683d16e24024a404The Model AI Assignments session seeks to gather and dis- seminate the best assignment designs of the Artificial In- telligence (AI) Education community. Recognizing that as- signments form the core of student learning experience, we here present abstracts of five AI assignments from the 2024 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment spec- ifications and supporting resources may be found at http://modelai.gettysburg.edu.AAAI Conference on Artificial Intelligence00{'pages': '23370-23371'}['Todd W. Neller', 'Pia Bideau', 'David Bierbach', 'Wolfgang Hönig', 'N. Lipovetzky', 'Christian Muise', 'Lino Coria', 'Claire Wong', 'Stephanie Rosenthal', 'Yu Lu', 'Ming Gao', 'Jingjing Zhang']2024-03-24T00:00:00https://www.semanticscholar.org/paper/0d27f8fdf471d8187f83bd55683d16e24024a404 fee97221a5ef88501dcaa3ca5c6fcd1cde7af3ecExamining the impact of AI workloads on system performance, we reapply Moore’s law at the system level to uncover the implications for photonic components and the drivers that will propel the photonic industry forward.Optical Fiber Communications Conference and Exhibition10Comparing the impact of AI workloads on system performance and Moore’s law at the system level reveals the implications for photonic components and the drivers that will propel the photonic industry forward.2024 Optical Fiber Communications Conference and Exhibition (OFC)['Katharine Schmidtke', 'Hans-Juergen Schmidtke']2024-03-24T00:00:00https://www.semanticscholar.org/paper/fee97221a5ef88501dcaa3ca5c6fcd1cde7af3ec 0c52f196593f2098261d3a60b706d220466369b5Artificial Intelligence is undoubtedly becoming pervasive in everyday life of everyone. In this setting, developing correct AI conception since childhood is not only a need to be addressed in educational curricula, but is also a children right. Accordingly, several initiatives at national and international levels aim at promoting AI and emerging technology literacy, supported also by a proliferation in the literature of learning courses covering a variety of topics, learning objectives and targeted ages. Schools are therefore pushed to introduce innovative activities for children in their curricula. In this paper, we report the results of a case study where we tested the contribution of an AI block-based course in developing computational thinking, and human and AI minds understanding in fifth and sixth grade children.AAAI Conference on Artificial Intelligence200A case study where the contribution of an AI block-based course in developing computational thinking, and human and AI minds understanding in fifth and sixth grade children is tested.{'pages': '23060-23068'}['M. Baldoni', 'C. Baroglio', 'Monica Bucciarelli', 'Sara Capecchi', 'Elena Gandolfi', 'Cristina Gena', 'F. Ianì', 'Elisa Marengo', 'Roberto Micalizio', 'Amon Rapp', 'Ivan Nabil Ras']2024-03-24T00:00:00https://www.semanticscholar.org/paper/0c52f196593f2098261d3a60b706d220466369b5 7b662f9ef4b53e448c633caa49a039bbb3933772Interactive theorem provers (ITPs) are computer programs in which axioms and a conjecture are stated in a formal language, and a user provides the ITP with relatively high-level steps of a formal proof for the conjecture. Then, by invoking automated theorem provers, the ITP tries to generate low-level steps that fill the gaps between the steps provided by the user, thus forming a complete formal proof of the conjecture. The ITP also checks the entire formal proof against the axioms, thus confirming the soundness of all derivations in the formal proof. In this talk, I will discuss the existing opportunities and potential benefits to applying ITPs to reason about and verify AI concepts, algorithms, and software. I will also discuss the challenges we have to being able to apply ITPs in AI and reap those benefits. I will do so by discussing a number of my previous projects on the application of ITPs to different AI concepts, algorithms, and software systems. These projects span different areas of planning (classical planning, temporal planning, and planning under uncertainty) as well as algorithms with applications in algorithmic game theory, like general graph matching and online matching.AAAI Conference on Artificial Intelligence50This talk will discuss the existing opportunities and potential benefits to applying ITPs to reason about and verify AI concepts, algorithms, and software as well as algorithms with applications in algorithmic game theory, like general graph matching and online matching.{'pages': '22660'}['Mohammad Abdulaziz']2024-03-24T00:00:00https://www.semanticscholar.org/paper/7b662f9ef4b53e448c633caa49a039bbb3933772 442dda263a1d309ddf91bdb35df9898090cc333aDemonstrations are a powerful way of increasing the transparency of AI policies to humans. Though we can approximately model human learning from demonstrations as inverse reinforcement learning, we note that human learning can differ from algorithmic learning in key ways, e.g. humans are computationally limited and may sometimes struggle to understand all of the nuances of a demonstration. Unlike related work that provide demonstrations to humans that simply maximize information gain, I leverage concepts from the human education literature, such as the zone of proximal development and scaffolding, to show demonstrations that balance informativeness and difficulty of understanding to maximize human learning.AAAI Conference on Artificial Intelligence10This work leverages concepts from the human education literature, such as the zone of proximal development and scaffolding, to show demonstrations that balance informativeness and difficulty of understanding to maximize human learning.{'pages': '23397-23398'}['Michael S. Lee']2024-03-24T00:00:00https://www.semanticscholar.org/paper/442dda263a1d309ddf91bdb35df9898090cc333a 67a4a288bc3716f87fdf5226c346297e657d2eaeIn recent years, the rapid advancement of artificial intelligence (AI) has fostered an urgent need to better prepare current and future educators to be able to integrate AI technologies in their teaching and to teach AI literacy to PreK-12 students. While many organizations have developed professional learning opportunities for inservice educators, a gap remains for resources specifically designed for those facilitating and enrolled in Educator Preparation Programs (EPPs). In response to this gap, the International Society for Technology in Education (ISTE) launched its first AI Explorations for EPPs Faculty Fellowship. As a result of the Faculty Fellows’ collaboration, this paper articulates a framework of seven critical strategies with the potential to address the urgent need EPPs have in preparing preservice teachers to effectively integrate AI-powered instructional tools and to teach this new area of content knowledge in PreK-12 classrooms. In addition, we provide a review of literature and an overview of the emerging needs for integrating AI education in EPPs. We demonstrate why support for preservice teachers’ critical examination and application of AI, including a focus on the issues of equity, ethics, and culturally responsive teaching, is essential to their later success in PreK-12 classrooms. Recommendations for further research and learning are also provided to promote community-wide initiatives for supporting the integration of AI in education through Educator Preparation Programs and beyond.AAAI Conference on Artificial Intelligence530It is demonstrated why support for preservice teachers’ critical examination and application of AI, including a focus on the issues of equity, ethics, and culturally responsive teaching, is essential to their later success in PreK-12 classrooms.{'pages': '23069-23077'}['Nancye Blair Black', 'Stacy George', 'Amy Eguchi', 'J. C. Dempsey', 'Elizabeth Langran', 'Lucretia Fraga', 'Stein Brunvand', 'Nicol Howard', 'San Diego', 'Incarnate Word']2024-03-24T00:00:00https://www.semanticscholar.org/paper/67a4a288bc3716f87fdf5226c346297e657d2eae 0d6e8e11a25f5188a2ea2522c105907e331e043aAccess to compute is widely viewed as a primary barrier to AI research progress. Compute resource stratification between academic and industry researchers is therefore a source of concern. Yet the experiences of researchers who might encounter resource constraints in their work have received no direct study. We addressed this gap by conducting a large survey of AI researchers that posed questions about project inputs, outcomes, and challenges. Contrary to popular narratives, responses from more than 500 participants revealed more concern about talent and data limitations than compute access. There were few differences between academic and industry researchers in this regard. The exception were researchers who already use large amounts of compute, and expressed a need for more. These findings suggest that interventions to subsidize compute without addressing the limitations on talent and data availability reported by our respondents might cause or exacerbate commonly cited resource inequalities, with unknown impact on the future of equitable research.AAAI Conference on Artificial Intelligence230Interventions to subsidize compute without addressing the limitations on talent and data availability reported by respondents might cause or exacerbate commonly cited resource inequalities, with unknown impact on the future of equitable research.{'pages': '19840-19848'}['Rebecca Gelles', 'Veronica Kinoshita', 'Micah Musser', 'James Dunham']2024-03-24T00:00:00https://www.semanticscholar.org/paper/0d6e8e11a25f5188a2ea2522c105907e331e043a 7fd6473355ced3b7d544ad8531c4250e22d36898As the technological landscape rapidly evolves, the convergence of innovation and artificial intelligence (AI) presents unprecedented opportunities and challenges for project management. This paper introduces a comprehensive mathematical model for the syncretic management of innovative projects in the age of the AI explosion. Syncretism in this context refers to the seamless integration of diverse elements, including interdisciplinary collaboration, AI technologies, and adaptive methodologies, to optimize project outcomes. The proposed model encompasses various facets of project management, innovation, and AI integration. It delineates stages of project lifecycle management, emphasizing resource allocation, risk assessment, and adaptive strategies. In the innovation management domain, the model incorporates methodologies for idea generation, technology scouting, and open innovation, recognizing AI's role in shaping the innovative landscape. A crucial aspect of the model lies in the integration of AI technologies throughout the project. This includes identifying relevant use cases, managing data effectively, selecting appropriate AI models, and establishing decision support systems. The syncretic approach emphasizes cross-functional collaboration, fostering an environment where different disciplines seamlessly contribute to project success. Resource optimization is a key focus, leveraging AI to allocate resources efficiently, predict maintenance needs, and enhance overall project performance. Ethical and legal considerations are embedded within the model to ensure responsible AI usage, and the paper outlines mechanisms for ongoing training and development to equip teams with the necessary skills. The model's effectiveness is evaluated through the lens of monitoring and evaluation, with defined key performance indicators, continuous monitoring, and feedback loops for iterative improvements. Communication and collaboration are underscored, utilizing modern tools to facilitate stakeholder engagement and effective teamwork. This paper contributes to the evolving discourse on project management by providing a robust framework that adapts to the dynamic nature of AI and innovation. It serves as a guide for project managers, interdisciplinary teams, and decision-makers navigating the challenges and opportunities presented by the syncretic management of innovative projects in the era of the AI explosion.Herald of the Odessa National Maritime University170A robust framework that adapts to the dynamic nature of AI and innovation is provided, serving as a guide for project managers, interdisciplinary teams, and decision-makers navigating the challenges and opportunities presented by the syncretic management of innovative projects in the era of the AI explosion.Herald of the Odessa National Maritime University['S.D. Bushuyev', 'A. Ivko']2024-03-24T00:00:00https://www.semanticscholar.org/paper/7fd6473355ced3b7d544ad8531c4250e22d36898 3f1920ce6bb55feea872409a44682cd2eddafd38Generative artificial intelligence (AI) is swiftly cementing its role as an indispensable tool for students transitioning from K-12 to higher education and professional spheres. Yet, harnessing its full potential requires more than mere familiarity. Students must be equipped with the skills to engage with AI both productively and ethically. Left unchecked, AI usage can pose risks, especially if students lack proper guidance or understanding of their actions. Moreover, effective interaction with AI necessitates skills in prompt engineering to yield desired outcomes. Sidekick Academy is a digital online platform where students can safely experiment with and learn about AI. This article delves into the genesis of Sidekick Academy, offering a glimpse into its lessons on how to use AI and complex debate on ethical use. It also sheds light on the academy's "sandbox" - a secure space for students to explore AI without jeopardizing their safety or privacy.AAAI Conference on Artificial Intelligence00The genesis of Sidekick Academy is delves into, offering a glimpse into its lessons on how to use AI and complex debate on ethical use, and sheds light on the academy's "sandbox" - a secure space for students to explore AI without jeopardizing their safety or privacy.{'pages': '23294-23299'}['Elizabeth Radday', 'Matt Mervis']2024-03-24T00:00:00https://www.semanticscholar.org/paper/3f1920ce6bb55feea872409a44682cd2eddafd38 13f05321ab913cabec017d769eb9ddcf1c47f661Neurodegenerative disease (ND) represents a chronic disease characterized by loss of neuron function and death, such as Alzheimer's disease, Parkinson's disease, etc. It’s difficult and complex to diagnose ND, due to the requirement for the synthesis of multiple biomarker data, such as genes, proteins, images, etc. The advent of artificial intelligence (AI) technology, particularly through machine learning (ML) and deep learning (DL) algorithms, introduces novel methodologies and tools for ND diagnosis. These methodologies extract pertinent information and patterns from massive, multidimensional, and non-linear data, assisting doctors to render more precise, prompt, and objective assessments. This article provides an overview of the updated implementation status of AI in ND diagnosis, assesses the advantages and contributions of AI to ND diagnosis, as well as the existing limitations and challenges, and offers insights to the future development direction of AI in ND diagnosis.Transactions on Materials, Biotechnology and Life Sciences160An overview of the updated implementation status of AI in ND diagnosis is provided, the advantages and contributions of AI to ND diagnosis, as well as the existing limitations and challenges, and insights to the future development direction of AI in ND diagnosis are offered.Transactions on Materials, Biotechnology and Life Sciences['Chuanzhe Chen']2024-03-24T00:00:00https://www.semanticscholar.org/paper/13f05321ab913cabec017d769eb9ddcf1c47f661 ed9168abf54e538b44098ea9417ee5be2667a98aLarge language models like ChatGPT can generate human-like code, posing challenges for programming education as students may be tempted to misuse them on assignments. However, there are currently no robust detectors designed specifically to identify AI-generated code. This is an issue that needs to be addressed to maintain academic integrity while allowing proper utilization of language models. Previous work has explored different approaches to detect AI-generated text, including watermarks, feature analysis, and fine-tuning language models. In this paper, we address the challenge of determining whether a student's code assignment was generated by a language model. First, our proposed method identifies AI-generated code by leveraging targeted masking perturbation paired with comperhesive scoring. Rather than applying a random mask, areas of the code with higher perplexity are more intensely masked. Second, we utilize a fine-tuned CodeBERT to fill in the masked portions, producing subtle modified samples. Then, we integrate the overall perplexity, variation of code line perplexity, and burstiness into a unified score. In this scoring scheme, a higher rank for the original code suggests it's more likely to be AI-generated. This approach stems from the observation that AI-generated codes typically have lower perplexity. Therefore, perturbations often exert minimal influence on them. Conversely, sections of human-composed codes that the model struggles to understand can see their perplexity reduced by such perturbations. Our method outperforms current open-source and commercial text detectors. Specifically, it improves detection of code submissions generated by OpenAI's text-davinci-003, raising average AUC from 0.56 (GPTZero baseline) to 0.87 for our detector.AAAI Conference on Artificial Intelligence210This paper addresses the challenge of determining whether a student's code assignment was generated by a language model by leveraging targeted masking perturbation paired with comperhesive scoring, and outperforms current open-source and commercial text detectors.{'pages': '23155-23162'}['Zhenyu Xu', 'Victor S. Sheng']2024-03-24T00:00:00https://www.semanticscholar.org/paper/ed9168abf54e538b44098ea9417ee5be2667a98a 9ec77427c6bba228cb5fdb38d0d404d638d2b9faPartially observable Markov decision processes (POMDPs) challenge reinforcement learning agents due to incomplete knowledge of the environment. Even assuming monotonicity in uncertainty, it is difficult for an agent to know how and when to stop exploring for a given task. In this abstract, we discuss how to use hierarchical reinforcement learning (HRL) and AI Planning (AIP) to improve exploration when the agent knows possible valuations of unknown predicates and how to discover them. By encoding the uncertainty in an abstract planning model, the agent can derive a high-level plan which is then used to decompose the overall POMDP into a tree of semi-POMDPs for training. We evaluate our agent's performance on the MiniGrid domain and show how guided exploration may improve agent performance.AAAI Conference on Artificial Intelligence00This abstract discusses how to use hierarchical reinforcement learning and AI Planning to improve exploration when the agent knows possible valuations of unknown predicates and how to discover them, and evaluates the agent's performance on the MiniGrid domain.{'pages': '23635-23636'}['Brandon Rozek', 'Junkyu Lee', 'Harsha Kokel', 'Michael Katz', 'Shirin Sohrabi']2024-03-24T00:00:00https://www.semanticscholar.org/paper/9ec77427c6bba228cb5fdb38d0d404d638d2b9fa acabd657d7d23b5dc95f5657b65582b378bfe9c8Artificial intelligence (AI) has rapidly pervaded and reshaped almost all walks of life, but efforts to promote AI literacy in K-12 schools remain limited. There is a knowledge gap in how to prepare teachers to teach AI literacy in inclusive classrooms and how teacher-led classroom implementations can impact students. This paper reports a comparison study to investigate the effectiveness of an AI literacy curriculum when taught by classroom teachers. The experimental group included 89 middle school students who learned an AI literacy curriculum during regular school hours. The comparison group consisted of 69 students who did not learn the curriculum. Both groups completed the same pre and post-test. The results show that students in the experimental group developed a deeper understanding of AI concepts and more positive attitudes toward AI and its impact on future careers after the curriculum than those in the comparison group. This shows that the teacher-led classroom implementation successfully equipped students with a conceptual understanding of AI. Students achieved significant gains in recognizing how AI is relevant to their lives and felt empowered to thrive in the age of AI. Overall this study confirms the potential of preparing K-12 classroom teachers to offer AI education in classrooms in order to reach learners of diverse backgrounds and broaden participation in AI literacy education among young learners.AAAI Conference on Artificial Intelligence210Overall this study confirms the potential of preparing K-12 classroom teachers to offer AI education in classrooms in order to reach learners of diverse backgrounds and broaden participation in AI literacy education among young learners.{'pages': '23318-23325'}['Helen Zhang', 'Irene Lee', 'Katherine Moore']2024-03-24T00:00:00https://www.semanticscholar.org/paper/acabd657d7d23b5dc95f5657b65582b378bfe9c8 003dfd02a12a45a61049d4dc0c6db9eb55b6a99fMost of the recent works on post-hoc example-based eXplainable AI (XAI) methods revolves around employing counterfactual explanations to provide justification of the predictions made by AI systems. Counterfactuals show what changes to the input-features change the output decision. However, a lesser-known, special-case of the counterfacual is the semi-factual, which provide explanations about what changes to the input-features do not change the output decision. Semi-factuals are potentially as useful as counterfactuals but have received little attention in the XAI literature. My doctoral research aims to establish a comprehensive framework for the use of semi-factuals in XAI by developing novel methods for their computation, supported by user tests.AAAI Conference on Artificial Intelligence100This doctoral research aims to establish a comprehensive framework for the use of semi-factuals in XAI by developing novel methods for their computation, supported by user tests.{'pages': '23379-23380'}['Saugat Aryal']2024-03-24T00:00:00https://www.semanticscholar.org/paper/003dfd02a12a45a61049d4dc0c6db9eb55b6a99f 10ce19114eddd52bc140e74e50b7941f9e7ec1f4Intelligent system audits are labor-intensive assurance activities that are typically performed once and discarded along with the opportunity to programmatically test all similar products for the market. This study illustrates how several incidents (i.e., harms) involving Named Entity Recognition (NER) can be prevented by scaling up a previously-performed audit of NER systems. The audit instrument's diagnostic capacity is maintained through a security model that protects the underlying data (i.e., addresses Goodhart's Law). An open-source evaluation infrastructure is released along with an example derived from a real-world audit that reports aggregated findings without exposing the underlying data.AAAI Conference on Artificial Intelligence141This study illustrates how several incidents involving Named Entity Recognition (NER) can be prevented by scaling up a previously-performed audit of NER systems.{'pages': '23035-23040'}['Arihant Chadda', 'Sean McGregor', 'Jesse Hostetler', 'Andrea Brennen']2024-03-24T00:00:00https://www.semanticscholar.org/paper/10ce19114eddd52bc140e74e50b7941f9e7ec1f4 23fafea0b42f6d19f53ff0fdf88866bcb2863710Insect Environment00Insect Environment['Ankita Rani A.', 'Saadia Anjum', 'Sneha Ann Shibu', 'J. M']2024-03-24T00:00:00https://www.semanticscholar.org/paper/23fafea0b42f6d19f53ff0fdf88866bcb2863710 9ca5d63921f30ed8eb76e1d38cf33cd539d30780Innovations in Education & Teaching International80Innovations in Education and Teaching International['Siu-Cheung Kong', 'Satu-Maarit Korte', 'Steve Burton', 'Pigga Keskitalo', 'Tuija Turunen', 'David Smith', 'Lixun Wang', 'John Chi-Kin Lee', 'M. Beaton']2024-03-24T00:00:00https://www.semanticscholar.org/paper/9ca5d63921f30ed8eb76e1d38cf33cd539d30780 4e55a1e533709a1d0bdcc490f449ea4bfb34b907Purpose: This research explores the interplay between deep learning algorithms, trust in sustainable advertising, and prior knowledge of deep learning algorithms in shaping perceptions of sustainable advertising in the context of education. The study aims to uncover the impact of these factors on sustainable advertising and examine the moderating role of prior knowledge of deep learning algorithms. Design/methodology/approach: The study employs a quantitative research design, utilizing a structured survey instrument for data collection. Simple random sampling techniques were used to select participants from a population of 194,366 1st year students. Data analysis includes multiple regression, mediation analysis, and moderation analysis using Hayes PROCESS Model 58. Findings: The results reveal significant positive effects of deep learning algorithms (independent variable) and trust in sustainable advertising (mediator variable) on sustainable advertising (dependent variable). Prior knowledge of deep learning algorithms (moderator variable) also has a positive influence on sustainable advertising. Trust on sustainable advertising mediates the relationship between deep learning algorithms and sustainable advertising. However, above mediator relationship is negatively moderated by prior knowledge of deep learning algorithms. This suggests that prior knowledge can weaken the positive impact of trust. Originality: This research contributes to the understanding of how AI-driven marketing strategies, trust, and knowledge influence sustainable advertising perceptions. It offers valuable insights into the moderating role of prior knowledge in this context. Implications: The findings have implications for educational institutions and marketing practitioners. They suggest that deep learning algorithms and trust in sustainable advertising can positively impact sustainable advertising perceptions. However, practitioners should be cautious in situations where individuals have high prior knowledge, as trust can reduce impact. Educational institutions can use these insights to optimize their marketing campaigns and foster sustainable advertising in the education sector. Limitations of the study include the reliance on self-reported data and the potential for response bias, which may affect the generalizability of the findings. For future research, investigating the role of other potential moderators and mediators in the relationship between deep learning algorithms and sustainable advertising could provide a more comprehensive understanding of this phenomenon. Keywords: Deep Learning Algorithms, Education Industry, Prior Knowledge of Deep Learning Algorithms, Sustainable Advertising, Trust in Sustainable Advertising  Asian Journal of Marketing Management570It is suggested that deep learning algorithms and trust in sustainable advertising can positively impact sustainable advertising perceptions, however, practitioners should be cautious in situations where individuals have high prior knowledge, as trust can reduce impact.Asian Journal of Marketing Management['C. Ediriweera', 'M.T. Fernando', 'H. Pramudika']2024-03-24T00:00:00https://www.semanticscholar.org/paper/4e55a1e533709a1d0bdcc490f449ea4bfb34b907 6a8b11dfd64d921d6aa7ec2267dcc693b16dbca9Planning is the act of deliberative thinking before acting. It is based on a symbolic model of the world and the options to act in it, usually defined in function-free first-order logic. The task is to find a sequence of actions (a plan) that leads from a given current state to a desired goal state. The basic, purely physical description may be augmented with a partially ordered grammar-like structure (a Hierarchical Task Network or HTN), which can describe expert knowledge, or practical, legal, or operational requirements. In this talk, I will survey a variety of methods for automatically deriving plans using symbolic methods for planning -- from both my past and future research. These symbolic methods -- in some sense -- translate planning problems into other, simpler symbolic representations and reason over them to find plans. As a basis for these methods, I will firstly introduce relevant theoretical results on planning. First, I will discuss the expressive power of planning formalisms (ECAI'14, ICAPS'16) and second, the computational complexity of HTN planning and related tasks such as HTN plan verification, plan modification, and plan recognition (ICAPS'15, ICAPS'16). Based on these theoretical results, I will develop why SAT-based HTN planning is possible and how it can be implemented. To this end, I will survey several of my publications at top-tier conferences, including papers at ICAPS'17, AAAI'18, AAAI'19, IJCAI'19, AAAI'20, and ICAPS'21 -- in which I developed an highly SAT-based planner for HTN problems including the ability to find optimal plans as well as the grounding as a preprocessing step. Here I will also give an outlook on future developments and new ideas that I propose for SAT-based planning -- including the exploitation of structures in plan (e.g.\ landmarks or operator-counting constraints). Next, I will present the idea of expressing lifted classical planning as SAT (ICAPS'22). The resulting planner LiSAT was the first lifted SAT-based planner -- and proved highly efficient and outperformed all other lifted planners at the time of publication. Notably, LiSAT was the first planner (lifted or grounded) and still is the only one to solve the challenging OrganicSynthesis benchmark -- and could even prove optimality for all plans. I will also outline future ideas to further improve the efficiency of LiSAT. Lastly, I introduce the notion of planning with symbolic symbolic representations (AAAI'21 and ICAPS'23). Here one uses Binary Decision Diagrams to encode large sets of states efficiently. For expressing the additional structure encoded by HTNs, I show how BDDs can be suitably integrated into finite automata. Based on this representation, an efficient and optimal planning algorithm can be derived. Additionally, I show how this algorithm can be extended to also cover oversubscription planning.AAAI Conference on Artificial Intelligence100This talk surveys a variety of methods for automatically deriving plans using symbolic methods for planning and presents the idea of expressing lifted classical planning as SAT (ICAPS'22), which proved highly efficient and outperformed all other lifted planners at the time of publication.{'pages': '22661'}['Gregor Behnke']2024-03-24T00:00:00https://www.semanticscholar.org/paper/6a8b11dfd64d921d6aa7ec2267dcc693b16dbca9 b7bd975c331aa415767d89cb768756b16dc38c77Effective teamwork translates to fewer preventable errors and higher task performance in collaborative tasks. However, in time-critical tasks, successful teamwork becomes highly challenging to attain. In such settings, often, team members have partial observability of their surroundings, incur high cost of communication, and have trouble estimating the state and intent of their teammates. To assist a team in improving teamwork at task time, my doctoral research proposes an automated task-time team intervention system. Grounded in the notion of shared mental models, the system first detects whether the team is on the same page or not. It then generates effective interventions to improve teamwork. Additionally, by leveraging past demonstrations to learn a model of team behavior, this system minimizes the need for domain experts to specify teamwork models and rules.AAAI Conference on Artificial Intelligence50This doctoral research proposes an automated task-time team intervention system grounded in the notion of shared mental models, which minimizes the need for domain experts to specify teamwork models and rules.{'pages': '23415-23416'}['Sangwon Seo']2024-03-24T00:00:00https://www.semanticscholar.org/paper/b7bd975c331aa415767d89cb768756b16dc38c77 d804d01c9bcea8d8ff63149a2fbb6f6f9e55c422Deep learning, the most important subfield of machine learning and artificial intelligence (AI) over the last decade, is considered one of the fundamental technologies underpinning the Fourth Industrial Revolution. But despite its record-breaking history, deep learning’s enormous appetite for compute and data means that sometimes it can be too costly to practically use. In this paper, we connect technical insights from deep learning scaling laws and transfer learning with the economics of IT to propose a framework for estimating the cost of deep learning computer vision systems to achieve a desired level of accuracy. Our tool can be of practical use to AI practitioners in industry or academia to guide investment decisions.AAAI Conference on Artificial Intelligence282This paper connects technical insights from deep learning scaling laws and transfer learning with the economics of IT to propose a framework for estimating the cost of deep learning computer vision systems to achieve a desired level of accuracy.{'pages': '23012-23018'}['Neil C. Thompson', 'Martin Fleming', 'Benny J. Tang', 'Anna M. Pastwa', 'Nicholas Borge', 'Brian C. Goehring', 'Subhro Das']2024-03-24T00:00:00https://www.semanticscholar.org/paper/d804d01c9bcea8d8ff63149a2fbb6f6f9e55c422 9910ab79e864c200923b9152a51d8665cb8a4c1dExplainability is central to trust and accountability in artificial intelligence (AI) applications. The field of human‐centered explainable AI (HCXAI) arose as a response to mainstream explainable AI (XAI) which was focused on algorithmic perspectives and technical challenges, and less on the needs and contexts of the non‐expert, lay user. HCXAI is characterized by putting humans at the center of AI explainability. Taking a sociotechnical perspective, HCXAI prioritizes user and situational contexts, preferences reflection over acquiescence, and promotes the actionability of explanations. This review identifies the foundational ideas of HCXAI, how those concepts are operationalized in system design, how legislation and regulations might normalize its objectives, and the challenges that HCXAI must address as it matures as a field.Journal of the Association for Information Science and Technology530This review identifies the foundational ideas of HCXAI, how those concepts are operationalized in system design, how legislation and regulations might normalize its objectives, and the challenges that HCXAI must address as it matures as a field.Journal of the Association for Information Science and Technology['Michael Ridley']2024-03-24T00:00:00https://www.semanticscholar.org/paper/9910ab79e864c200923b9152a51d8665cb8a4c1d 1d4eed259b812acc51627f318cbe327ab4cef33fInfluenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development. As people enjoy the convenience by this AI large model, more and more large models in subdivided fields are gradually being proposed, especially large models in radiology imaging field. This article first introduces the development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models. Secondly, we summarize the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology. Finally, this paper also summarizes some of the challenges of large AI models in radiology, with the aim of better promoting the rapid revolution in the field of radiography.Meta-Radiology1500The development history of large models, technical details, workflow, working principles of multimodal large models and working principles of video generation large models, and the latest research progress of AI large models in radiology education, radiology report generation, applications of unimodal and multimodal radiology are introduced.ArXiv['Liangrui Pan', 'Zhenyu Zhao', 'Ying Lu', 'Kewei Tang', 'Liyong Fu', 'Qingchun Liang', 'Shaoliang Peng']2024-03-24T00:00:00https://www.semanticscholar.org/paper/1d4eed259b812acc51627f318cbe327ab4cef33f 72272a95b58ba6d1e3861edd6d14817dc7890b27In modern society, as artificial intelligence technology becomes increasingly mature, AI can already be utilized to assess and treat breast carcinoma. By summarizing results and data of experiments that use AI to help breast carcinoma diagnosis and cure, this article states the positive impact of AI on breast cancer screening, detection of breast cancer-related genes, and prediction of efficacy of breast cancer treatment drugs. What is more, illustrating the moral, legal, and social challenges encountered by scientists when they use artificial intelligence. Also, this article makes some suggestions for the future development of AI technology. The author believes that AI technology have the ability to contribute significantly to the diagnosis and cure of breast cancer and can enhance the chances of patients surviving to a considerable extent. However, currently, artificial intelligence technology remains in a relatively nascent stage, necessitating ongoing attention and dedicated efforts for its advancement.Transactions on Materials, Biotechnology and Life Sciences80The positive impact of AI on breast cancer screening, detection of breast cancer-related genes, and prediction of efficacy of breast cancer treatment drugs is stated and the moral, legal, and social challenges encountered by scientists when they use artificial intelligence are illustrated.Transactions on Materials, Biotechnology and Life Sciences['Dian Yu']2024-03-24T00:00:00https://www.semanticscholar.org/paper/72272a95b58ba6d1e3861edd6d14817dc7890b27 070ab82b2217a81680b1ab21b55f392f1fcf89c1Background: Transthoracic echocardiography (TTE) is the gold standard modality for evaluating cardiac morphology, function, and hemodynamics in clinical practice. While artificial intelligence (AI) is expected to contribute to improved accuracy and is being applied clinically, its impact on daily clinical practice has not been fully evaluated. Methods: We retrospectively examined 30 consecutive patients who underwent AI-equipped TTE at a single institution. All patients underwent manual and automatic measurements of TTE parameters using the AI-equipped TTE. Measurements were performed by three sonographers with varying experience levels: beginner, intermediate, and expert. Results: A comparison between the manual and automatic measurements assessed by the experts showed extremely high agreement in the left ventricular (LV) filling velocities (E wave: r = 0.998, A wave: r = 0.996; both p < 0.001). The automated measurements of LV end-diastolic and end-systolic diameters were slightly smaller (−2.41 mm and −1.19 mm) than the manual measurements, although without significant differences, and both methods showing high agreement (r = 0.942 and 0.977, both p < 0.001). However, LV wall thickness showed low agreement between the automated and manual measurements (septum: r = 0.670, posterior: r = 0.561; both p < 0.01), with automated measurements tending to be larger. Regarding interobserver variabilities, statistically significant agreement was observed among the measurements of expert, intermediate, and beginner sonographers for all the measurements. In terms of measurement time, automatic measurement significantly reduced measurement time compared to manual measurement (p < 0.001). Conclusions: This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice. A multicenter study with a larger sample size is warranted.Journal of Clinical Medicine270This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice and indicates that a multicenter study with a larger sample size is warranted.Journal of Clinical Medicine['Noriko Shiokawa', 'M. Izumo', 'Toshio Shimamura', 'Yui Kurosaka', 'Yukio Sato', 'Takanori Okamura', 'Y. Akashi']2024-03-24T00:00:00https://www.semanticscholar.org/paper/070ab82b2217a81680b1ab21b55f392f1fcf89c1 142d483b9915bd130e48fd54350120ad272a3c67How should instructors adapt technical editing courses to account for generative artificial intelligence (AI)? This article addresses what generative AI means for technical editing pedagogy. While AI tools may be able to address rote editing tasks, expert editors are still needed to provide accessible, ethical, and justice-oriented edits. After reviewing impacts of generative AI on editing praxis, the author focuses on the microcredentials that she built into an editing course in order to address these impacts pedagogically. The goal was to enable students to understand AI, argue for their expertise, and edit from ethical and social justice perspectives.Journal of business and technical communication30Impacts of generative AI on editing praxis are reviewed, and the author focuses on the microcredentials that she built into an editing course in order to address these impacts pedagogically.Journal of Business and Technical Communication['Jennifer C. Mallette']2024-03-24T00:00:00https://www.semanticscholar.org/paper/142d483b9915bd130e48fd54350120ad272a3c67 044d8008f89a55cc1b6d6597f7ca7abde311182fThis case study offers examples of the use of artificial intelligence (AI) writing tools at a small nonprofit workplace dispute resolution center. It explores the limits and strengths of these AI tools, as well as the mediation field's concerns around using AI as a replacement for mediation work. Further, it explores the implications of AI tool use for the ethos of the writer and the AI tool itself as well as for the current pedagogy deliberations occurring in the technical writing field at large.Journal of business and technical communication40Journal of Business and Technical Communication['Andrew Hillen']2024-03-24T00:00:00https://www.semanticscholar.org/paper/044d8008f89a55cc1b6d6597f7ca7abde311182f 31552334eedc940bcd83a79cd4a5e53780ad490dArtificial intelligence system architects can increase user trust by designing systems that are inherently transparent. We propose the idea of representing an AI system as an amalgamation of the AI Model (algorithms), data (input and output, including outcomes), and the user interface with visual interpretations (e.g. graphs, Venn diagrams). By designing human controls and feedback mechanisms for AI systems that allow users to exert control over them we can integrate transparency into existing user interfaces. Our plan is to design prototypes of transparent user interfaces for AI systems using well-known usability principles. By conducting surveys we will study their impact to see if these principles help the user to work with the AI system with confidence and if the user perceives the system to be adequately transparent.AAAI Conference on Artificial Intelligence60This work proposes the idea of representing an AI system as an amalgamation of the AI Model (algorithms, data, data, and the user interface with visual interpretations, and aims to design prototypes of transparent user interfaces for AI systems using well-known usability principles.{'pages': '23401-23402'}['Deepa Muralidhar']2024-03-24T00:00:00https://www.semanticscholar.org/paper/31552334eedc940bcd83a79cd4a5e53780ad490d a47e074cd9301d48d08f5e19f9ba90b325730a67Explainable Artificial Intelligence (XAI), a promising future technology in the field of healthcare, has attracted significant interest. Despite ongoing efforts in the development of XAI approaches, there has been inadequate evaluation of explanation effectiveness and no standardized framework for the evaluation has been established. This study aims to examine the relationship between subjective interpretability and perceived plausibility for various XAI explanations and to determine the factors affecting users' acceptance of the XAI explanation.AAAI Conference on Artificial Intelligence00The relationship between subjective interpretability and perceived plausibility for various XAI explanations is examined and the factors affecting users' acceptance of the XAI explanation are determined.{'pages': '23528-23529'}['Jinsun Jung', 'Hyeoneui Kim']2024-03-24T00:00:00https://www.semanticscholar.org/paper/a47e074cd9301d48d08f5e19f9ba90b325730a67 0139acb6a1f3594ab77624f70462de69c56ea863Roughly every decade, the ACM and IEEE professional organizations have produced recommendations for the education of undergraduate computer science students. These guidelines are used worldwide by research universities, liberal arts colleges, and community colleges. For the latest 2023 revision of the curriculum, AAAI has collaborated with ACM and IEEE to integrate artificial intelligence more broadly into this new curriculum and to address the issues it raises for students, instructors, practitioners, policy makers, and the general public. This paper describes the development process and rationale that underlie the artificial intelligence components of the CS2023 curriculum, discusses the challenges in curriculum design for such a rapidly advancing field, and examines lessons learned during this three-year process.AAAI Conference on Artificial Intelligence40The development process and rationale that underlie the artificial intelligence components of the CS2023 curriculum are described, the challenges in curriculum design for such a rapidly advancing field are discussed, and lessons learned during this three-year process are examined.{'pages': '23078-23083'}['Eric Eaton', 'Susan L. Epstein']2024-03-24T00:00:00https://www.semanticscholar.org/paper/0139acb6a1f3594ab77624f70462de69c56ea863 fcd3a6bc3631beb3931b09fbd0bbe5151122b1f6The integration of learning and reasoning is one of the key challenges in artificial intelligence and machine learning today. The area of Neuro-Symbolic AI (NeSy) tackles this challenge by integrating symbolic reasoning with neural networks. In our recent work, we provided an introduction to NeSy by drawing several parallels to another field that has a rich tradition in integrating learning and reasoning, namely Statistical Relational Artificial Intelligence (StarAI).AAAI Conference on Artificial Intelligence30An introduction to Neuro-Symbolic AI is provided by drawing several parallels to another field that has a rich tradition in integrating learning and reasoning, namely Statistical Relational Artificial Intelligence (StarAI).{'pages': '22678'}['Giuseppe Marra']2024-03-24T00:00:00https://www.semanticscholar.org/paper/fcd3a6bc3631beb3931b09fbd0bbe5151122b1f6 5ee0f9333f49fe32c8417c0feb7a90a3b9ac4e12This paper presents a multi dimensional view of AI's role in learning and education, emphasizing the intricate interplay between AI, analytics, and the learning processes. Here, I challenge the prevalent narrow conceptualization of AI as stochastic tools, as exemplified in generative AI, and argue for the importance of alternative conceptualisations of AI. I highlight the differences between human intelligence and artificial information processing, the cognitive diversity inherent in AI algorithms, and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education research, which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualizations of AI in education: the externalization of human cognition, the internalization of AI models to influence human thought processes, and the extension of human cognition via tightly integrated human-AI systems. Examples from current research and practice are examined as instances of the three conceptualisations, highlighting the potential value and limitations of each conceptualisation for education, as well as the perils of overemphasis on externalising human cognition as exemplified in today's hype surrounding generative AI tools. The paper concludes with an advocacy for a broader educational approach that includes educating people about AI and innovating educational systems to remain relevant in an AI enabled world.arXiv.org00This paper presents three unique conceptualizations of AI in education: the externalization of human cognition, the internalization of AI models to influence human thought processes, and the extension of human cognition via tightly integrated human-AI systems.ArXiv['M. Cukurova']2024-03-24T00:00:00https://www.semanticscholar.org/paper/5ee0f9333f49fe32c8417c0feb7a90a3b9ac4e12 98675976e75f5d4c70997e942893edf2609f8c9eThis research investigates the generalization capabilities of neural networks in deep learning when applied to real-world scenarios where data often contains imperfections, focusing on their adaptability to both noisy and non-noisy scenarios for image retrieval tasks. Our study explores approaches to preserve all available data, regardless of quality, for diverse tasks. The evaluation of results varies per task, due to the ultimate goal of developing a technique to extract relevant information while disregarding noise in the final network design for each specific task. The aim is to enhance accessibility and efficiency of AI across diverse tasks, particularly for individuals or countries with limited resources, lacking access to high-quality data. The dedication is directed towards fostering inclusivity and unlocking the potential of AI for wide-spread societal benefit.AAAI Conference on Artificial Intelligence20{'pages': '23755-23756'}['Nilton Rojas']2024-03-24T00:00:00https://www.semanticscholar.org/paper/98675976e75f5d4c70997e942893edf2609f8c9e b2bd6b066c824f614f6e4dada7e6114100502054Can advanced AI-driven technologies transform the traditionally arduous educational process in medicine? This study takes a deep dive into how the publicly available OpenAI ChatGPT-3.5 performs in answering board-style questions designed for physicians training to become pathologists. Correctly answering 75% of 543 questions using an engaging and fast-paced format was an impressive performance. It underscores the potential as well as improvement opportunities of using interactive AI in future medical training.AAAI Conference on Artificial Intelligence20A deep dive into how the publicly available OpenAI ChatGPT-3.5 performs in answering board-style questions designed for physicians training to become pathologists is taken, highlighting the potential as well as improvement opportunities of using interactive AI in future medical training.{'pages': '23684-23685'}['Keefer P. Wu', 'Patricia C. Tsang']2024-03-24T00:00:00https://www.semanticscholar.org/paper/b2bd6b066c824f614f6e4dada7e6114100502054 b6259a87ac60adcf7da2f514e249a7895c9ec563Journal of Promotion Management390Journal of Promotion Management['Uttam Chakraborty', 'S. K. Biswal']2024-03-24T00:00:00https://www.semanticscholar.org/paper/b6259a87ac60adcf7da2f514e249a7895c9ec563 796036e087ade0198a43e3a2b5e323adc033f978Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistics. Most assume the availability of class label which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. To this end, this talk revisits fairness and reveals idiosyncrasies of existing fairness literature assuming the availability of class label that limits their real-world utility. The primary artifacts are formulating fairness with censorship to account for scenarios where the class label is not guaranteed, and a suite of corresponding new fairness notions, algorithms, and theoretical constructs to bridge the gap between the design of a ``fair'' model in the lab and its deployment in the real-world.AAAI Conference on Artificial Intelligence22This talk revisits fairness and reveals idiosyncrasies of existing fairness literature assuming the availability of class label that limits their real-world utility.{'pages': '22685'}['Wenbin Zhang']2024-03-24T00:00:00https://www.semanticscholar.org/paper/796036e087ade0198a43e3a2b5e323adc033f978 dd77342a066d29028ab9ee549ca5f0cb6f326806Learning personalized treatment policies is a formative challenge in many real-world applications, including in healthcare, econometrics, artificial intelligence. However, the effectiveness of candidate policies is not always identifiable, i.e., it is not uniquely computable from the combination of the available data and assumptions about the generating mechanisms. This paper studies policy learning from data collected in various non-identifiable settings, i.e., (1) observational studies with unobserved confounding; (2) randomized experiments with partial observability; and (3) their combinations. We derive sharp, closed-formed bounds from observational and experimental data over the conditional treatment effects. Based on these novel bounds, we further characterize the problem of safe policy learning and develop an algorithm that trains a policy from data guaranteed to achieve, at least, the performance of the baseline policy currently deployed. Finally, we validate our proposed algorithm on synthetic data and a large clinical trial, demonstrating that it guarantees safe behaviors and robust performance.AAAI Conference on Artificial Intelligence980This paper studies policy learning from data collected in various non-identifiable settings, i.e., observational studies with unobserved confounding; randomized experiments with partial observability; and their combinations, and derives sharp, closed-formed bounds from observational and experimental data over the conditional treatment effects.{'pages': '13004-13012'}['Shalmali Joshi', 'Junzhe Zhang', 'E. Bareinboim']2024-03-24T00:00:00https://www.semanticscholar.org/paper/dd77342a066d29028ab9ee549ca5f0cb6f326806 e27ca7cb501f523a8d70f388182b0fc59bcfe970The apt observation by the great philosopher of India, Mahatma Gadhi emphasizes the importance of health and healthy person. The health of any nation is dependent on health of individual and society. The protection of public health is responsibility of state. It ensures the physical and mental wellbeing of individual to enjoy the life to fullest extent. The ill health is root cause for many problems including dirt, disease, poverty etc. The right to health is one of the cherished rights recognized and protected at national and international level. It is responsibility of state to enable every individual to protect health of individual.. There are several international instruments provided for regulation of regulation of health of individual. The International Health Regulations 2005 is adopted by World Health Assembly. It specifically provides for responsibility of state to develop capacity to detect, protect and to respond the public health crisis and risks. It also requires the state to coordinate with WHO on any issue on concern on public health which may create danger to world health. The recent example of CORONA emerged as challenge before the public health which is tackled with coordination and cooperation The present research explores the international health regulation and duties of state with special reference to public health in India. The International treaties and pacts are binding on state once it is signed by state. The signatory state shall implement the obligations created by it. India is signatory to many instruments. This research further explored the laws and measures and health system provided by India for implementation of international health standard as provided by international health regulation. The research critically examined the loopholes in implementation of such regulations. The research concludes that IHR is crucial initiative by WHO to disease surveillance and prevent disease outbreak endangering world health. It suggests that government must strengthen public health system in coordination with organizations like WHO.International Journal For Multidisciplinary Research10The research concludes that IHR is crucial initiative by WHO to disease surveillance and prevent disease outbreak endangering world health and suggests that government must strengthen public health system in coordination with organizations like WHO.International Journal For Multidisciplinary Research['Dr. Kashinath Suryabhan Neharkar']2024-03-23T00:00:00https://www.semanticscholar.org/paper/e27ca7cb501f523a8d70f388182b0fc59bcfe970 1030099f3efe812dd7825777a86527ef63a474cfEnvironmental science and pollution research international560The study described the development of a novel AI-based relative weighted ensemble (AIRWE) hybrid model for predicting critical RWQIs, i.e., biochemical oxygen demand (BOD) and total coliform (TC) and revealed that it was most efficient and accurate for predicting BOD and TC.Environmental science and pollution research international['Suyog Gupta', 'Sunil Kumar Gupta']2024-03-23T00:00:00https://www.semanticscholar.org/paper/1030099f3efe812dd7825777a86527ef63a474cf 866263ff9cb7dd812f6b9dbea433b439145c7630I argue that ensuring artificial intelligence (AI) retains alignment with human values over time is critical yet understudied. Most research focuses on static alignment, neglecting crucial retention dynamics enabling stability during learning and autonomy. This paper elucidates limitations constraining provable retention, arguing key gaps include formalizing dynamics, transparency of advanced systems, participatory scaling, and risks of uncontrolled recursive self‐improvement. I synthesize technical and ethical perspectives into a conceptual framework grounded in control theory and philosophy to analyze dynamics. I argue priorities should shift towards capability modulation, participatory design, and advanced modeling to verify enduring alignment. Overall, I argue that realizing AI safely aligned throughout its lifetime necessitates translating principles into formal methods, demonstrations, and systems integrating technical and humanistic rigor.The AI Magazine200It is argued that realizing AI safely aligned throughout its lifetime necessitates translating principles into formal methods, demonstrations, and systems integrating technical and humanistic rigor, and priorities should shift towards capability modulation, participatory design, and advanced modeling to verify enduring alignment.AI Magazine['Adeniyi Fasoro']2024-03-23T00:00:00https://www.semanticscholar.org/paper/866263ff9cb7dd812f6b9dbea433b439145c7630 3f68ce247d93fae97f62f68fce175f0c3fdb425bThe capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and further extending to societal predictions like infectious disease outbreaks. Recent advancements in large language models, exemplified by models like ChatGPT, have showcased significant prowess in natural language tasks, such as translating languages, constructing chatbots, and answering questions. When we consider biomedical data, we observe a resemblance to natural language in terms of sequences: biomedical literature and health records presented as text, biological sequences or sequencing data arranged in sequences, or sensor data like brain signals as time series. The question arises: Can we harness the potential of recent large language models to drive biomedical knowledge discoveries? In this survey, we will explore the application of large language models to three crucial categories of biomedical data: 1) textual data, 2) biological sequences, and 3) brain signals. Furthermore, we will delve into large language model challenges in biomedical research, including ensuring trustworthiness, achieving personalization, and adapting to multi-modal data representationarXiv.org1110This survey will explore the application of large language models to three crucial categories of biomedical data: textual data, biological sequences, and 3) brain signals, and delve into large language model challenges in biomedical research, including ensuring trustworthiness, achieving personalization, and adapting to multi-modal data representation.ArXiv['Zhenyu Bi', 'Sajib Acharjee Dip', 'Daniel Hajialigol', 'Sindhura Kommu', 'Hanwen Liu', 'Meng Lu', 'Xuan Wang']2024-03-23T00:00:00https://www.semanticscholar.org/paper/3f68ce247d93fae97f62f68fce175f0c3fdb425b d64f017161114ed13ec8b50f6e5f26d1be74a2e6AI &amp; SOCIETY60It is posited that AI safety has a nuanced and uneasy relationship with transparency and other allied notions associated with societal good, indicating that it is an insufficient notion if the goal is that of societal good in a broad sense.ArXiv['D. P.']2024-03-23T00:00:00https://www.semanticscholar.org/paper/d64f017161114ed13ec8b50f6e5f26d1be74a2e6 6d0b86e7f74e072cd11b79c88f4f1f6d4c781498The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.Cardiology in Review380This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.Cardiology in review['Ben Barris', 'Avrohom Karp', 'Menachem Jacobs', 'William H Frishman']2024-03-23T00:00:00https://www.semanticscholar.org/paper/6d0b86e7f74e072cd11b79c88f4f1f6d4c781498 0c9d2e2d15fa6c6540a3c411aa6bfb0d3f6e6a45A chat bot is a computer program that may initiate conversations between users and other computers. A larger audience can use chatbot technology, which is text-based and safe to use.. Chatbots for university research are developed using AI algorithms that interpret user messages and assess user demands. The aims of the chatbot's responses is to match the user's input while avoiding making oneself physically available to the institution in response to queries. The program responds to the students' inquiries by applying its intelligence. For using this type applications, natural processing language, command line, graphical user interface (GUI), menu driven, form-based, etc. that used in user interfaces TGUI and web-based user interfaces are the most typical types, however sometimes another type of user interface is required. This is where a conversational user interface based on chatbots fits in. One type of bot that has been present on chat systems is the chatbot. The user can interact with them via graphical interfaces, and the trend is in this direction. They often offer a stateful service, meaning that each session's data is saved by the application.International Scientific Journal of Engineering and Management00A conversational user interface based on chatbots, which is a computer program that may initiate conversations between users and other computers, and the trend is in this direction.International Scientific Journal of Engineering and Management['Amol Halvankar']2024-03-23T00:00:00https://www.semanticscholar.org/paper/0c9d2e2d15fa6c6540a3c411aa6bfb0d3f6e6a45 af4b41ee87c778d9781d38bc7a1e01251d465ecaThis research paper explores the implications of Artificial Intelligence (AI) in project management within the digital era. It investigates the evolution of project management methodologies, the key concepts and technologies driving AI integration, real-world case studies showcasing AI implementation, challenges, future directions, and opportunities in AI-driven project management. The findings reveal that AI offers significant benefits, including improved resource allocation, risk management, and communication, yet pose challenges such as ethical considerations, data privacy, and integration hurdles. Future directions suggest emerging trends in AI adoption, potential innovations, and opportunities for research and development. Overall, the paper highlights the transformative potential of AI-driven project management while emphasizing the need for addressing challenges to ensure successful implementation and maximize benefits.International Journal For Multidisciplinary Research190The findings reveal that AI offers significant benefits, including improved resource allocation, risk management, and communication, yet pose challenges such as ethical considerations, data privacy, and integration hurdles.International Journal For Multidisciplinary Research['Musarath Jahan Karamthulla', 'Anish Tadimarri', 'Ravish Tillu', 'Muthukrishnan Muthusubramanian']2024-03-23T00:00:00https://www.semanticscholar.org/paper/af4b41ee87c778d9781d38bc7a1e01251d465eca 76cd201231858ab441d2521f66751f460fde3aafThe relentless progress of Artificial Intelligence (AI) has ushered in transformative possibilities across industries, notably impacting the landscape of transportation. This paper introduces an AI-based optimized railway monitoring system, a pioneering approach that integrates machine learning algorithms such as Support Vector Machines, Random Forest, Recurrent Neural Networks, Gradient Boosting Machines, Convolutional Neural Networks, Long Short-Term Memory Networks, and K-Means Clustering. Complemented by computer vision and data analytics, this system represents a comprehensive framework poised to revolutionize traditional railway monitoring practices. In the global expanse of transportation, railways serve as vital conduits, facilitating the seamless movement of both passengers and goods. However, ensuring the unwavering safety and operational reliability of these intricate networks demands constant vigilance and upkeep. Traditional monitoring systems, while effective, grapple with challenges related to real-time analysis, predictive maintenance, and the nuanced optimization of resources. It is within this backdrop that the integration of AI into railway monitoring emerges as a promising solution, presenting novel avenues for heightened efficiency and performance. Key Words: Artificial Intelligence, Railway Monitoring, Machine Learning, Computer Vision, Predictive Maintenance, Adaptive Scheduling, Transportation Infrastructure.International Scientific Journal of Engineering and Management00This paper introduces an AI-based optimized railway monitoring system, a pioneering approach that integrates machine learning algorithms such as Support Vector Machines, Random Forest, Recurrent Neural Networks, Gradient Boosting Machines, Convolutional Neural Networks, Long Short-Term Memory Networks, and K-Means Clustering.International Scientific Journal of Engineering and Management['Dommaraju Hema Sai']2024-03-23T00:00:00https://www.semanticscholar.org/paper/76cd201231858ab441d2521f66751f460fde3aaf 2bb9eccd743cd2c63ca4a3451e10b2a0b0f7cd70Smart recommendation algorithms have revolutionized information dissemination, enhancing efficiency and reshaping content delivery across various domains. However, concerns about user agency have arisen due to the inherent opacity (information asymmetry) and the nature of one-way output (power asymmetry) on algorithms. While both issues have been criticized by scholars via advocating explainable AI (XAI) and human-AI collaborative decision-making (HACD), few research evaluates their integrated effects on users, and few HACD discussions in recommender systems beyond improving and filtering the results. This study proposes an incubating idea as a missing step in HACD that allows users to control the degrees of AI-recommended content. Then, we integrate it with existing XAI to a flow prototype aimed at assessing the enhancement of user agency. We seek to understand how types of agency impact user perception and experience, and bring empirical evidence to refine the guidelines and designs for human-AI interactive systems.arXiv.org360This study proposes an incubating idea as a missing step in HACD that allows users to control the degrees of AI-recommended content and integrates it with existing XAI to a flow prototype aimed at assessing the enhancement of user agency.ArXiv['Mengke Wu', 'Weizi Liu', 'Yanyun Wang', 'M. Yao']2024-03-23T00:00:00https://www.semanticscholar.org/paper/2bb9eccd743cd2c63ca4a3451e10b2a0b0f7cd70 bdcf16836bcb6a6d9408a7d4fae38a1b3f90e572With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these models. While supervised AI-generated text detectors perform well on text generated by older LLMs, with the frequent release of new LLMs, building supervised detectors for identifying text from such new models would require new labeled training data, which is infeasible in practice. In this work, we tackle this problem and propose a domain generalization framework for the detection of AI-generated text from unseen target generators. Our proposed framework, EAGLE, leverages the labeled data that is available so far from older language models and learns features invariant across these generators, in order to detect text generated by an unknown target generator. EAGLE learns such domain-invariant features by combining the representational power of self-supervised contrastive learning with domain adversarial training. Through our experiments we demonstrate how EAGLE effectively achieves impressive performance in detecting text generated by unseen target generators, including recent state-of-the-art ones such as GPT-4 and Claude, reaching detection scores of within 4.7% of a fully supervised detector.arXiv.org480This work proposes a domain generalization framework for the detection of AI-generated text from unseen target generators, EAGLE, which leverages the labeled data that is available so far from older language models and learns features invariant across these generators, in order to detect text generated by an unknown target generator.ArXiv['Amrita Bhattacharjee', 'Raha Moraffah', 'Joshua Garland', 'Huan Liu']2024-03-23T00:00:00https://www.semanticscholar.org/paper/bdcf16836bcb6a6d9408a7d4fae38a1b3f90e572 ad8652785081eb99155b064f91b7508826c49d56Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the “black box phenomenon”, biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.Medicina1090This review highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.Medicina['U. P. S. Parmar', 'P. Surico', 'R. Singh', 'Francesco Romano', 'C. Salati', 'Leopoldo Spadea', 'M. Musa', 'Caterina Gagliano', 'Tommaso Mori', 'Marco Zeppieri']2024-03-23T00:00:00https://www.semanticscholar.org/paper/ad8652785081eb99155b064f91b7508826c49d56 2a98c0c14e4064c6dedafb1388c671a7e6843bd6This review delves into the profound impact of artificial intelligence (AI) integration on contemporary business paradigms. The paper meticulously explores diverse AI applications, including machine learning, natural language processing, and predictive analytics, illustrating how these technologies can revolutionize operational processes, augment decision-making capabilities, and foster unparalleled innovation within organizations. Drawing from case studies and industry examples across various sectors such as finance, healthcare, retail, and manufacturing, the study elucidates successful AI implementation strategies. It examines the importance of robust data governance frameworks to ensure quality and integrity, the acquisition of AI talent, and the imperative of fostering a culture of innovation and adaptability within organizations undergoing AI transformation. Furthermore, the paper addresses the nuanced challenges and risks inherent in AI adoption, spanning ethical considerations surrounding data privacy and bias mitigation, cybersecurity vulnerabilities, and the potential impact on the workforce. By providing a comprehensive overview of the opportunities and challenges associated with AI integration in business models, the study equips organizational leaders, policymakers, and stakeholders with invaluable insights to navigate the evolving landscape of AI-driven innovation. It underscores the significance of strategic foresight, cross-functional collaboration, and continuous learning in harnessing the full potential of AI technologies to drive sustainable growth and competitive advantage in the digital era. Keywords:  AI, Business, Models, Strategies, Efficiency, Innovation.International Journal of Management &amp; Entrepreneurship Research00This review delves into the profound impact of artificial intelligence (AI) integration on contemporary business paradigms, illustrating how these technologies can revolutionize operational processes, augment decision-making capabilities, and foster unparalleled innovation within organizations.International Journal of Management &amp; Entrepreneurship Research['David Olanrewaju Olutimehin', 'Onyeka Chrisanctus Ofodile', 'Irunna Ejibe', 'Olusegun Gbenga Odunaiya', 'Oluwatobi Timothy Soyombo']2024-03-23T00:00:00https://www.semanticscholar.org/paper/2a98c0c14e4064c6dedafb1388c671a7e6843bd6 ef6d70550ba997f4142257b3716595d164d67364Based on the concept of Outcome-Based Education (OBE), this study explores the application of AI technology in the instructional design and implementation of rehabilitation professional courses. The research adopts a combination of literature analysis, case studies, and teaching experiments. It designs an outcome-oriented teaching plan for rehabilitation courses that integrates AI technology and conducts a small-scale practice to verify the feasibility of the plan. The study finds that the OBE concept is highly compatible with AI technology. AI technology can effectively support precise curriculum system design based on outcome requirements, immersive digital teaching implementation, targeted evaluation and feedback, and comprehensively promote the achievement of learning outcomes. Therefore, AI technology should be widely applied in curriculum instructional design to better realize the OBE concept. The effective integration of the OBE concept and AI technology is an important direction for upgrading the teaching model, which is worth further exploration and practice. Subsequent research can continue to expand the application areas of this model, enrich specific implementation strategies, and conduct large-sample application effect verification to provide support for scale-up and promotion. KEYWORD： AI technology Rehabilitation course OBEEPRA International Journal of Environmental Economics, Commerce and Educational Management140The study finds that the OBE concept is highly compatible with AI technology, and AI technology should be widely applied in curriculum instructional design to better realize the OBE concept.EPRA International Journal of Environmental Economics, Commerce and Educational Management['Katherine Ning']2024-03-23T00:00:00https://www.semanticscholar.org/paper/ef6d70550ba997f4142257b3716595d164d67364 a6a9b4863e41afe7941808a528e42db586ad9e76International Journal for the Semiotics of Law31International Journal for the Semiotics of Law - Revue internationale de Sémiotique juridique['Patrizia Giampieri']2024-03-23T00:00:00https://www.semanticscholar.org/paper/a6a9b4863e41afe7941808a528e42db586ad9e76 6218f48df9a79faba76ab87de03bebfac350ea0dHuman Genetics361This approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.Human genetics['S. Zucca', 'G. Nicora', 'F. De Paoli', 'M. G. Carta', 'R. Bellazzi', 'P. Magni', 'E. Rizzo', 'I. Limongelli']2024-03-23T00:00:00https://www.semanticscholar.org/paper/6218f48df9a79faba76ab87de03bebfac350ea0d 5730c6f889734fd41726df8d48349828b33c62efThrough chatbots one can communicate with text or voice interface and get reply through artificial intelligence. Typically, a chat bot will communicate with a real person Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. Key Words: Chat Bot , healthcare, artificial, intelligence.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions, and also helps to generate leads and automatically delivers the information of leads to sales.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Prof.Biradar B.S.']2024-03-23T00:00:00https://www.semanticscholar.org/paper/5730c6f889734fd41726df8d48349828b33c62ef cbad36744e66acd667b16505f4f5cd45f3e14c03This research paper explores the application of predictive analytics for real-time monitoring and feedback in project management. Through case studies of leading organizations such as Amazon, Uber, and NASA, the paper examines how predictive analytics enables organizations to optimize operations, improve efficiency, and achieve business objectives. The paper discusses the implementation of predictive analytics in various industries, including e-commerce, transportation, and space exploration, highlighting the benefits of real-time monitoring and feedback for enhancing decision-making, reducing costs, and improving customer satisfaction. By leveraging predictive analytics, organizations can anticipate trends, identify issues early, and make data-driven decisions to drive continuous improvement and innovation in project management practices.International Journal For Multidisciplinary Research140The paper discusses the implementation of predictive analytics in various industries, including e-commerce, transportation, and space exploration, highlighting the benefits of real-time monitoring and feedback for enhancing decision-making, reducing costs, and improving customer satisfaction.International Journal For Multidisciplinary Research['Anish Tadimarri', 'Musarath Jahan Karamthulla', 'Sanjeev Prakash', 'Manish Tomar']2024-03-23T00:00:00https://www.semanticscholar.org/paper/cbad36744e66acd667b16505f4f5cd45f3e14c03 9e049e61f34b87df8d8642fb348d98b07fc9d514OBJECTIVES Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data. MATERIALS AND METHODS We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment. RESULTS Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting. DISCUSSION This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.J. Am. Medical Informatics Assoc.471This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation.Journal of the American Medical Informatics Association : JAMIA['Feng Chen', 'Liqin Wang', 'Julie Hong', 'Jiaqi Jiang', 'Li Zhou']2024-03-23T00:00:00https://www.semanticscholar.org/paper/9e049e61f34b87df8d8642fb348d98b07fc9d514 7df8c22d05b6c09c62b9c4f11e36fbe6abfc4b73Artificial Intelligence (AI) is a specialized field of computer science focused on creating machines that can emulate human intelligence and behaviour. The term "Artificial Intelligence" was first introduced by John McCarthy in 1956 at the Massachusetts Institute of Technology (MIT) in the USA. AI encompasses a variety of applications including game playing, expert systems, natural language processing, neural networks, and robotics. As of now, no computer systems have achieved true artificial general intelligence, which would enable them to perform any intellectual task that a human can. However, significant progress has been made in specific domains, particularly in game playing. Modern computer chess programs, for instance, have surpassed human players in terms of skill and performance. In the early 1980s, expert systems were heralded as the future of AI and computing. These systems are designed to mimic the decision-making abilities of human experts in specialized fields such as medicine and engineering. Despite their potential, expert systems have not fully met the high expectations set for them. They tend to be costly to develop and maintain, and their utility is often limited to specific, well-defined tasks. Currently, neural networks represent one of the most dynamic and rapidly advancing areas of AI. These algorithms, inspired by the structure of the human brain, have demonstrated success in various applications, including voice recognition and natural language processing. When it comes to programming AI applications, LISP (List Processing) and Prelog (Programming in Logic) are two of the most widely used languages due to their suitability and flexibility for AI-related tasks.International Journal of Advanced Research in Science, Communication and Technology00Neural networks represent one of the most dynamic and rapidly advancing areas of AI, and have demonstrated success in various applications, including voice recognition and natural language processing.International Journal of Advanced Research in Science, Communication and Technology['Jai Chaudhary', 'Nishant Parmar', 'Dr. Ashima Mehta']2024-03-23T00:00:00https://www.semanticscholar.org/paper/7df8c22d05b6c09c62b9c4f11e36fbe6abfc4b73 2a724cbcca13e57b882b41a2d3bf828024927cdcThis research paper delves into the intricate relationship between artificial intelligence (AI) and employment dynamics, aiming to comprehend the impact of AI on various industries and the resultant consequences on workforce participation and unemployment rates. Through an extensive review of literature, this paper evaluates the evolving landscape of jobs in the wake of AI integration and provides insights into potential strategies for mitigating unemployment challenges. The findings of this research will be instrumental in informing policy decisions and workforce development initiatives in the context of an AI-driven economy. KEYWORDS Artificial intelligence, unemployment, technology, automation, humans, robots.International Scientific Journal of Engineering and Management00The evolving landscape of jobs in the wake of AI integration is evaluated and potential strategies for mitigating unemployment challenges are provided.International Scientific Journal of Engineering and Management['Manisha Tripathi']2024-03-23T00:00:00https://www.semanticscholar.org/paper/2a724cbcca13e57b882b41a2d3bf828024927cdc cb3f5611d3b2afe7fcfd7517c6a00d6b8b9a5e2fOur paper explores the implementation of an Artificial Intelligence (AI) operated elevator system aimed at reducing user waiting times in a residential complex. With two elevators servicing a 14-story building, each floor accommodating six flats with approximately four residents per home, efficiency is paramount. Leveraging AI algorithms, our system dynamically adjusts elevator operations based on user demand patterns, traffic flow, and predictive analysis, ensuring minimal wait times and optimal passenger distribution. By integrating AI into elevator management, we aim to enhance user experience and streamline vertical transportation in high-density residential settings. Key Words: Artificial Intelligence, elevator optimization, residential complexes, waiting time reduction, predictive analysis, passenger distribution, efficiency improvement, traffic flow management.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT30This paper explores the implementation of an Artificial Intelligence (AI) operated elevator system aimed at reducing user waiting times in a residential complex and dynamically adjusts elevator operations based on user demand patterns, traffic flow, and predictive analysis, ensuring minimal wait times and optimal passenger distribution.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Vinod H']2024-03-23T00:00:00https://www.semanticscholar.org/paper/cb3f5611d3b2afe7fcfd7517c6a00d6b8b9a5e2f 5e4423da6a01c807369d53e0dfc1a7fe38e99a9cThis study undertakes a comprehensive review of literature to examine the challenges encountered by Human Resources (HR) professionals in integrating Artificial Intelligence (AI) into their practices. The research employs a systematic approach, analyzing key papers published within a specified timeframe to identify recurring themes related to technology adoption, talent acquisition, employee development, and ethical considerations. The critical analysis reveals a spectrum of strategies proposed in the literature to navigate the challenges associated with AI integration. Findings showcase the potential benefits of AI, such as increased efficiency, while acknowledging concerns related to biases and ethical dilemmas. The synthesis of insights provides a cohesive narrative, offering actionable recommendations for HR professionals.International Journal For Multidisciplinary Research110A comprehensive review of literature reveals a spectrum of strategies proposed in the literature to navigate the challenges associated with AI integration, showcasing the potential benefits of AI, such as increased efficiency, while acknowledging concerns related to biases and ethical dilemmas.International Journal For Multidisciplinary Research['Mohana R', 'Revathi B']2024-03-23T00:00:00https://www.semanticscholar.org/paper/5e4423da6a01c807369d53e0dfc1a7fe38e99a9c 0fc74fb27376c6fde461d2d1c73ef7625cdabf73Forensic medicine has traditionally relied on conventional autopsy methods for determining the cause of death and supporting criminal investigations. However, the introduction of artificial intelligence (AI) has sparked a new era in forensic medicine, fundamentally altering the autopsy process. This comprehensive review investigates the transformative impact of AI-powered autopsy techniques on forensic medicine, emphasizing the advancements, challenges, and future possibilities. Through a meticulous analysis of current research and case studies, the article illustrates how AI significantly improves accuracy, efficiency, and objectivity in forensic investigations, resulting in more dependable outcomes. The integration of AI into autopsy procedures represents a revolutionary shift in forensic medicine, with emerging technologies and methodologies reshaping investigative practices. Alongside traditional autopsies, the utilization of VIRTOPSY—an advanced imaging technique—is increasingly prominent, further enhancing the forensic examination process. By delving into recent advancements, applications, and potential developments, this review provides a holistic overview of how AI is reshaping forensic investigations, enhancing reliability, and contributing to justice. This review underscores the transformative role of AI in forensic medicine, highlighting its potential to reshape practices and contribute significantly to societal well-being.International Journal For Multidisciplinary Research280A comprehensive review investigates the transformative impact of AI-powered autopsy techniques on forensic medicine, emphasizing the advancements, challenges, and future possibilities and underscores the transformative role of AI in forensic medicine.International Journal For Multidisciplinary Research['Sunil Kumar', 'Sahana VM Vats']2024-03-23T00:00:00https://www.semanticscholar.org/paper/0fc74fb27376c6fde461d2d1c73ef7625cdabf73 f83fa1333b39f42b92492c603d65b8620eccd0afWith continuous spreading of World Wide Web, human life is transforming towards global Digital Era, and there is explosion of information and is growing in wide range. In this research article researcher is trying to explain the different phases of humankind which is going through the particular circumstances as need of time. 21st century concept of Artificial Intelligence is introduced and now it is became most significant part of our daily routine Life. Though we are not aware about some scientific concepts of Modern Technology but knowingly or unknowingly we are following those applications in every single second. This article will first scrutinize the Artificial Intelligence concept, its applications & discuss its importance in overall development of Humankind. In short, we can say that artificial intelligence seems to be the future and first hand tool of the Digital world. Experts believe that it will soon become a part of human life, Due to this technology it will completely change the way we look at the world. With this Modern technology, the future looks interesting and exciting.International Journal For Multidisciplinary Research110The different phases of humankind which is going through the particular circumstances as need of time are explained, and artificial intelligence seems to be the future and first hand tool of the Digital world.International Journal For Multidisciplinary Research['Mr. Daimi Syed Asif Syed Asim']2024-03-23T00:00:00https://www.semanticscholar.org/paper/f83fa1333b39f42b92492c603d65b8620eccd0af ffb9e3e0dafe8becee770787bb51abc2b976fefeThe utilization of powerful machine learning models in artificial intelligence offers novel prospects for the identification of fraudulent activities. Artificial Intelligence (AI) is a revolutionary technological tool that enhances the ability to detect and prevent fraud by improving efficiency and effectiveness. This research offers a thorough examination of the utilization of artificial intelligence technology in the realm of procurement fraud prevention and detection. Additionally, it highlights the obstacles that arise when employing machine learning techniques for the purpose of identifying and preventing fraudulent activities. A mixed methods approach was employed in this study, wherein data was collected through an unstructured interview and questionnaire. We conducted a comprehensive review of relevant scholarly articles and online resources. The findings indicate that fraudsters are progressively advancing in their skills, which poses a significant challenge in detecting fraudulent activities. The advent of AI in fraud detection has demonstrated its transformative impact. AI has achieved unparalleled precision and velocity in crime detection and prevention, surpassing the capabilities of any human. Artificial intelligence (AI) enhances the capacity for automation. Accessing unstructured data in the form of spreadsheets, digital documents, and email inboxes poses a significant issue for the procurement function. In order to achieve a successful procurement transformation, businesses should prioritize the creation of essential tools, guarantee acceptance through the establishment of a superior user experience, and integrate both novel and pre-existing technology. It is imperative to disseminate knowledge to the general public regarding the escalating sophistication of artificial intelligence (AI) in the realm of fraud detection and prevention. This includes elucidating the potential benefits of AI in identifying patterns of suspicious activities, assessing its efficacy in predicting potential threats or fraudulent activities prior to their manifestation, and exploring its utility in analyzing historical data pertaining to both familiar and unfamiliar forms of fraudulent behavior.International Journal of Business Ecosystem &amp; Strategy (2687-2293)240This research offers a thorough examination of the utilization of artificial intelligence technology in the realm of procurement fraud prevention and detection and highlights the obstacles that arise when employing machine learning techniques for the purpose of identifying and preventing fraudulent activities.International Journal of Business Ecosystem &amp; Strategy (2687-2293)['C. Ezeji']2024-03-23T00:00:00https://www.semanticscholar.org/paper/ffb9e3e0dafe8becee770787bb51abc2b976fefe 04a384b9897c8bb7a0a03437c5e62c601c5c13e6BACKGROUND AND PURPOSE Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field. MATERIALS AND METHODS We performed a bibliometric analysis of the American Journal of Neuroradiology (AJNR): the journal was queried for original research articles published since inception (Jan. 1, 1980) to Dec. 3, 2022 that contained any of the following key terms: "machine learning", "artificial intelligence", "radiomics", "deep learning", "neural network", "generative adversarial network", "object detection", or "natural language processing". Articles were screened by two independent reviewers, and categorized into Statistical Modelling (Type 1), AI/ML Development (Type 2), both representing developmental research work but without a direct clinical integration, or End-user Application (Type 3) which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to Type 3 articles being published, we analyzed Type 2 articles as they should represent the precursor work leading to Type 3. RESULTS A total of 182 articles were identified with 79% being non-integration focused (Type 1 n = 53, Type 2 n = 90) and 21% (n = 39) being Type 3. The total number of articles published grew roughly five-fold in the last five years, with the non-integration focused articles mainly driving this growth. Additionally, a minority of Type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most of them (60%) having additional postgraduate degrees. CONCLUSIONS AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of Type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift towards integrating practical AI/ML solutions in neuroradiology. ABBREVIATIONS AI = artificial intelligence; ML = machine learning.AJNR. American journal of neuroradiology00The emergence and evolution of AI/ML articles within neuroradiology is characterized and areas identified for improvement include enhancing the quality of Type 2 articles, namely external validation, and addressing both bias and explainability.AJNR. American journal of neuroradiology['Alexandre Boutet', 'Samuel S Haile', 'Andrew Z Yang', 'Hyo Jin Son', 'Mikail Malik', 'V. Pai', 'M. Nasralla', 'J. Germann', 'Artur Vetkas', 'Farzad Khalvati', 'Birgit B Ertl-Wagner']2024-03-23T00:00:00https://www.semanticscholar.org/paper/04a384b9897c8bb7a0a03437c5e62c601c5c13e6 3dc35d87eeb0c603fdd94a16019e734515ef57f1Journal of Rural Health360The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association['William B. Weeks', 'Justin Spelhaug', 'James N. Weinstein', 'J. Ferres']2024-03-23T00:00:00https://www.semanticscholar.org/paper/3dc35d87eeb0c603fdd94a16019e734515ef57f1 3ae5d40c4e65cdaa6ea2ff3a6798f743c8aafdfdArtificial intelligence (AI), although not able to currently capture the many complexities of humans, are slowly adapting to have certain capabilities of humans, many of which can revolutionize our world. AI systems, such as ChatGPT and others utilized within various industries for specific processes, have been transforming rapidly. However, this transformation can occur in an extremely concerning way if certain measures are not taken. This article touches on some of the current issues within the artificial intelligence ethical crisis, such as the concerns of discrimination within AI and false information that is becoming readily available with AI. Within this article, plausible solutions involving regulation are discussed and how they would mitigate ethical concerns. These include the self-regulation of businesses along with government regulation, and the effects these possible solutions can both have on current AI concerns.arXiv.org00This article touches on some of the current issues within the artificial intelligence ethical crisis, such as the concerns of discrimination within AI and false information that is becoming readily available with AI.ArXiv['Jay Nemec']2024-03-22T00:00:00https://www.semanticscholar.org/paper/3ae5d40c4e65cdaa6ea2ff3a6798f743c8aafdfd 09f8dda2fcc64ef13ada7c48a504543ef4170e94 Purpose This study aims to answer the key questions about the role of digital identities in organisations and within the HR function, the role of regulation in the digital identity space as it catches up with innovators and the vast potential of artificial intelligence (AI) in supporting digital identity. Design/methodology/approach Developed by using insight from the organisation’s extensive experience in digital identities and knowledge of the regulatory environment, alongside experience with the HR industry and relevant customers. Findings The digitalisation of business processes and the reality of an increasingly geographically distributed workforce have made digital identities for employees an increasingly important element of modern organisational and human resources functions. The benefits of using digital identities for employees are clear. With the growth of remote working and borderless company operations, digital identities provide employers with enhanced security, improved efficiency and cost savings. As organisations embark on their digital transformation journeys, the delicate balance between facilitating employees’ access to technology and safeguarding the organisation against cyber threats becomes clear. This intricate compromise requires the precise orchestration of certain processes, governance and technology. Originality/value In the UK, it is especially important for HR directors to consider the role of AI-empowered employee digital identities. The UK is taking a lead in digitising employee processes, with 68% of respondents in a 2023 poll by SD Worx reporting their company is investing in digital HR and training offerings, compared to a 60% average across Europe. Strategic HR Review00Strategic HR Review['Philip Hallenborg']2024-03-22T00:00:00https://www.semanticscholar.org/paper/09f8dda2fcc64ef13ada7c48a504543ef4170e94 2613aaa29426f81aabb962289e50dd3e0d5a4485Introduction An artificial intelligence as a medical device (AIaMD), built on convolutional neural networks, has demonstrated high sensitivity for melanoma. To be of clinical value, it needs to safely reduce referral rates. The primary objective of this study was to demonstrate that the AIaMD had a higher rate of correctly classifying lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatology standard of care (SoC), while achieving the same sensitivity to detect malignancy. Secondary endpoints included the sensitivity, specificity, positive and negative predictive values, and number needed to biopsy to identify one case of melanoma or squamous cell carcinoma (SCC) by both the AIaMD and SoC. Methods This prospective, single-centre, single-arm, masked, non-inferiority, adaptive, group sequential design trial recruited patients referred to a teledermatology cancer pathway (clinicaltrials.gov NCT04123678). Additional dermoscopic images of each suspicious lesion were taken using a smartphone with a dermoscopic lens attachment. The images were assessed independently by a consultant dermatologist and the AIaMD. The outputs were compared with the final histological or clinical diagnosis. Results A total of 700 patients with 867 lesions were recruited, of which 622 participants with 789 lesions were included in the per-protocol (PP) population. In total, 63.3% of PP participants were female; 89.0% identified as white, and the median age was 51 (range 18–95); and all Fitzpatrick skin types were represented including 25/622 (4.0%) type IV-VI skin. A total of 67 malignant lesions were identified, including 8 diagnosed as melanoma. The AIaMD sensitivity was set at 91 and 92.5%, to match the literature-defined clinician sensitivity (91.46%) as closely as possible. In both settings, the AIaMD identified had a significantly higher rate of identifying lesions that did not need a biopsy or urgent referral compared to SoC (p-value = 0.001) with comparable sensitivity for skin cancer. Discussion The AIaMD identified significantly more lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatologists. This has the potential to reduce the burden of unnecessary referrals when used as part of a teledermatology service.Frontiers in Medicine283The AIaMD had a higher rate of correctly classifying lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatology standard of care (SoC), while achieving the same sensitivity to detect malignancy.Frontiers in Medicine['Helen Marsden', 'Polychronis Kemos', 'Marcello Venzi', 'Mariana Noy', 'Shameera Maheswaran', 'Nicholas Francis', 'Chris Hyde', 'Dan Mullarkey', 'D. Kalsi', 'Lucy Thomas']2024-03-22T00:00:00https://www.semanticscholar.org/paper/2613aaa29426f81aabb962289e50dd3e0d5a4485 85c9c19304a029fc2b4ea301757a9b663e7ab8ddPurposeThis editorial for the 6th World Conference on Production and Operations Management (P&OM) 2022 Special Issue delves into the transformative role of advanced artificial intelligence (AI)-driven chatbots in reshaping operations, supply chain management and logistics (OSCM). It aligns with the conference’s theme of exploring the intersection between P&OM and strategy during the Technological Revolution.Design/methodology/approachUtilizing a conceptual approach, this paper introduces the “ERI Framework,” a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency (E), responsiveness (R) and intelligence (I). This framework is grounded in disruptive debottlenecking theory and real-world applications, offering a novel structure for analysis.FindingsThe conceptual analysis suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks. However, it sees challenges such as workforce adaptation and potential impacts on creativity and sustainability.Practical implicationsThe paper suggests that while chatbots present opportunities for optimizing operational processes, organizations must thoughtfully address the emerging challenges to maintain productivity and foster innovation. Strategic implementation and employee training are highlighted as key factors for successful integration.Originality/valueBridging the gap between the burgeoning proliferation of chatbots and their practical implications in OSCM, this paper offers a first perspective on the role of AI chatbots in modern business environments. By providing insights into both the benefits and challenges of chatbot integration, it offers a preliminary view essential for academics and practitioners in the digital age.International Journal of Physical Distribution &amp; Logistics Management521The ERI Framework is introduced, a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency, responsiveness and intelligence, which suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks.International Journal of Physical Distribution &amp; Logistics Management['C. F. Durach', 'Leopoldo Gutierrez']2024-03-22T00:00:00https://www.semanticscholar.org/paper/85c9c19304a029fc2b4ea301757a9b663e7ab8dd c6dbf532320cf32d22486e82ce04193e114044c8Patients admitted to intensive care units (ICU) and receiving mechanical ventilation (MV) may experience ventilator-associated adverse events and have prolonged ICU length of stay (LOS). We conducted a survey on adult patients in the medical ICU requiring MV. Utilizing big data and artificial intelligence (AI)/machine learning, we developed a predictive model to determine the optimal timing for weaning success, defined as no reintubation within 48 hours. An interdisciplinary team integrated AI into our MV weaning protocol. The study was divided into 2 parts. The first part compared outcomes before AI (May 1 to Nov 30, 2019) and after AI (May 1 to Nov 30, 2020) implementation in the medical ICU. The second part took place during the COVID-19 pandemic, where patients were divided into control (without AI assistance) and intervention (with AI assistance) groups from Aug 1, 2022, to Apr 30, 2023, and we compared their short-term outcomes. In the first part of the study, the intervention group (with AI, n = 1107) showed a shorter mean MV time (144.3 hours vs 158.7 hours, P = .077), ICU LOS (8.3 days vs 8.8 days, P = .194), and hospital LOS (22.2 days vs 25.7 days, P = .001) compared to the pre-intervention group (without AI, n = 1298). In the second part of the study, the intervention group (with AI, n = 88) exhibited a shorter mean MV time (244.2 hours vs 426.0 hours, P = .011), ICU LOS (11.0 days vs 18.7 days, P = .001), and hospital LOS (23.5 days vs 40.4 days, P < .001) compared to the control group (without AI, n = 43). The integration of AI into the weaning protocol led to improvements in the quality and outcomes of MV patients.Medicine400The integration of AI into the weaning protocol led to improvements in the quality and outcomes of MV patients and a predictive model was developed to determine the optimal timing for weaning success, defined as no reintubation within 48 hours.Medicine['Yang-Han Lin', 'Ting-Chia Chang', 'Chung-Feng Liu', 'Chih-Cheng Lai', 'Chin-Ming Chen', 'Willy Chou']2024-03-22T00:00:00https://www.semanticscholar.org/paper/c6dbf532320cf32d22486e82ce04193e114044c8 828857caa6a72c271204aa8139e533d07b5b3613It is now the 21st century, with the progressive development of various science and technology, such as artificial intelligence, big data, and so on, and these ever-evolving technologies have also greatly contributed to the development of today's flourishing video game field. This paper focuses on the development of artificial intelligence applications in video games over the past two decades, from preset scripts to self-learning processes, and adopts the research method of literature review. The paper concludes that the shift from pre-scripted to self-learning AI marks a shift in video games from experiences with clear rules and controlled processes to complex, dynamic, personalized experiences. This shift brings not only new opportunities but also new challenges. In the future, we can expect to see more research and practice to explore and take advantage of more of the possibilities of self-learning AI in video games.Applied and Computational Engineering100The paper concludes that the shift from pre-scripted to self-learning AI marks a shift in video games from experiences with clear rules and controlled processes to complex, dynamic, personalized experiences.Applied and Computational Engineering['Junze Zhu']2024-03-22T00:00:00https://www.semanticscholar.org/paper/828857caa6a72c271204aa8139e533d07b5b3613 36162c1c16ce50943d69451243c88f4b3591d913Current technological developments provide many changes, especially in the field of education. The development of technology requires humans to be able to adapt to life in this era. One of them is by utilizing artificial intelligence (AI) in chemistry learning to open the way to a new era in chemistry education. Literature review was carried out using the narrative literature review method to identify the role of AI in chemistry learning in improving 21st century skills. The application of AI in chemistry learning not only has a positive impact on 21st century skills, but also changes the way students interact with chemistry concepts and opens up new opportunities for students to understand chemistry concepts in more depth through enhanced learning media, interactive simulations, and predictions accurate. The application of AI has been widely applied in chemistry learning and has shown positive results in improving 21st century skills.Jambura Journal of Educational Chemistry460Artificial intelligence in chemistry learning changes the way students interact with chemistry concepts and opens up new opportunities for students to understand chemistry concepts in more depth through enhanced learning media, interactive simulations, and predictions accurate.Jambura Journal of Educational Chemistry['Nanda Diah Prastika', 'Dewi Anjarwati', 'Meisya Adelia Salsabila Awaliah', 'Dimas Hartandi', 'A. Rahmadani', 'Farah Erika']2024-03-22T00:00:00https://www.semanticscholar.org/paper/36162c1c16ce50943d69451243c88f4b3591d913 102f13d40baec002d522ac1a0c56446051d59bfa Introduction: In spite of the recent advances in cancer research, cancer incidence and mortality rate still remain high. Although genomic based approaches are used more extensively for identifying the right treatment approach, only a few patients benefit from this because a) only a limited number of mutations have been classified as driver mutations that are of ‘significance’ and b) in case of multiple mutations, the approach to identify right drug or drug combinations is not well understood. In this regard, there is a need for approaches that use artificial intelligence where exhaustive omics data is effectively processed, mined and utilized to device personalized therapy. Therefore, we used OncodynamiX an AI driven Cancer Biology platform to identify the right drug(s) for patients that had limited or no treatment options. We present here 2 case studies, one patient with stage 4 uterine serous adenocarcinoma and another one with endometrial adenocarcinoma. Methods: OncodynamiX platform has over 15 million data points, including mutation, CNA, mRNA and protein data for 1500 cell lines as well as potency, efficacy, target, phenotype and network information on more than 250 drugs. Based NGS data from the patients, OncodynamiX platform identified direct or indirect targetable alterations and then picked drugs or compounds shown to be active in the altered pathways. Based on this, a drug-gene alteration matrix was created which formed the basis for identification of appropriate drug(s). Results: The first patient with uterine serous adenocarcinoma had the following mutations: PIK3CA (Gain of function, GoF), TP53 (Loss of Function, LoF), PPP2R1A (LoF), APC (Conservation of function); Copy number amplification or high expression was reported for the following genes: BRD4, MYC, NOTCH3, CCNE1 and MUC16. Based on the drug-gene alteration matrix and all available drug data, WEE1 inhibitor was suggested to be the best therapy option for this patient. Accordingly, the patient was treated with WEE1 inhibitor in a then ongoing clinical trial. The second patient with endometrial adenocarcinoma had FBXW7, BRCA1, and PIK3R1 LoF, PIK3CA GoF and TP53 switch of function mutations, amplifications in CCNE1, FGFR1, NSD3, ZNF217 and ZNF70033 and loss of PTEN and FAS. For this patient, Pazopanib and Evrolimus combination was recommended and the patient was treated accordingly. Both patients responded well for the treatment as measured by RECIST response and continued with the treatment for over a year. Conclusion: The studies presented above highlight the promise of this AI-based approach in personalized medicine. In multiple cases we have used OncodynamiX approach where clear clinical benefit was demonstrated. To further validate this approach, we are initiating a clinical trial with 250 patients. Such approaches should be adopted more widely for significant improvement in life expectancy as well as in quality of life of patients. Citation Format: Dhanalakshmi Sivanandhan, Priyanka R. Bhargav, Sumanth M. Vasista, Sirisha Narayanbhatla, Suman Kamath, Oguru Sailaja, Dipanjan Chakraborty, Sundaresh Babu. OncodynamiX as an artificial intelligence (AI) based platform for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6214.Cancer Research00OncodynamiX an AI driven Cancer Biology platform is used to identify the right drug(s) for patients that had limited or no treatment options and the promise of this AI-based approach in personalized medicine is highlighted.Cancer Research['D. Sivanandhan', 'Priyanka R. Bhargav', 'Sumanth M. Vasista', 'Sirisha Narayanbhatla', 'Suman Kamath', 'Oguru Sailaja', 'Dipanjan Chakraborty', 'Sundaresh Babu']2024-03-22T00:00:00https://www.semanticscholar.org/paper/102f13d40baec002d522ac1a0c56446051d59bfa e30a33208458b5c28ab48a73c110c5a80d8eeb77Introduction The research in consumer neuroscience has identified computational methods, particularly artificial intelligence (AI) and machine learning, as a significant frontier for advancement. Previously, we utilized functional magnetic resonance imaging (fMRI) and artificial neural networks (ANNs) to model brain processes related to brand preferences in a paradigm exempted from motor actions. In the current study, we revisit this data, introducing recent advancements in explainable artificial intelligence (xAI) to gain insights into this domain. By integrating fMRI data analysis, machine learning, and xAI, our study aims to search for functional brain networks that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands, focusing on the early processing stages. Methods We applied independent component analysis (ICA) to overcome the expected fMRI data’s high dimensionality, which raises hurdles in AI applications. We extracted pertinent features from the returned ICs. An ANN is then trained on this data, followed by pruning and retraining processes. We then apply explanation techniques, based on path-weights and Shapley values, to make the network more transparent, explainable, and interpretable, and to obtain insights into the underlying brain processes. Results The fully connected ANN model obtained an accuracy of 54.6%, which dropped to 50.4% after pruning. However, the retraining process allowed it to surpass the fully connected network, achieving an accuracy of 55.9%. The path-weights and Shapley-based analysis concludes that, regarding brand perception, the expected initial participation of the primary visual system is followed. Other brain areas participate in early processing and discriminate between preferred and indifferent brands, such as the cuneal and the lateral occipital cortices. Discussion The most important finding is that a split between processing brands|preferred from brands|indifferent may occur during early processing stages, still in the visual system. However, we found no evidence of a “decision pipeline” that would yield if a brand is preferred or indifferent. The results suggest the existence of a “tagging”-like process in parallel flows in the extrastriate. Network training dynamics aggregate specific processes within the hidden nodes by analyzing the model’s hidden layer. This yielded that some nodes contribute to both global brand appraisal and specific brand category classification, shedding light on the neural substrates of decision-making in response to brand stimuli.Frontiers in Human Neuroscience1060This study aims to search for functional brain networks that support brand perception and, ultimately, search for brain networks that disentangle between preferred and indifferent brands, focusing on the early processing stages, and suggests the existence of a “tagging”-like process in parallel flows in the extrastriate.Frontiers in Human Neuroscience['José Paulo Marques dos Santos', 'José Diogo Marques dos Santos']2024-03-22T00:00:00https://www.semanticscholar.org/paper/e30a33208458b5c28ab48a73c110c5a80d8eeb77 80a864a7bc0e42a2b453c185bfa88beadd14d152Purpose In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises. Design/methodology/approach This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises. Findings The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance. Originality/value This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities. Chinese Management Studies1100The findings suggest that the application of AI in enterprises has a positive impact on ESG performance, and the pivotal role played by AI in enhancing corporate ESG performance is underscored.Chinese Management Studies['Rongxin Chen', 'Tianxing Zhang']2024-03-22T00:00:00https://www.semanticscholar.org/paper/80a864a7bc0e42a2b453c185bfa88beadd14d152 9ebf32dd84aea7cf11752e0219ba76d6f1e04b61The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-03-22T00:00:00https://www.semanticscholar.org/paper/9ebf32dd84aea7cf11752e0219ba76d6f1e04b61 36785d4c83730706e7e4a0e15316e1bc65d7da82The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting with them in an autonomous, safe and purposive way. These are the fundamental elements characterizing the fourth and the fifth industrial revolutions (4IR, 5IR): the crucial innovation is the adoption of intelligent technologies that can allow the development of cyber-physical systems, similar if not superior to humans. The common wisdom is that intelligence might be provided by AI (Artificial Intelligence), a claim that is supported more by media coverage and commercial interests than by solid scientific evidence. AI is currently conceived in a quite broad sense, encompassing LLMs and a lot of other things, without any unifying principle, but self-motivating for the success in various areas. The current view of AI robotics mostly follows a purely disembodied approach that is consistent with the old-fashioned, Cartesian mind-body dualism, reflected in the software-hardware distinction inherent to the von Neumann computing architecture. The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware. We name this approach Artificial Cognition (ACo) and ground it in Cognitive Neuroscience. It is specifically focused on proactive knowledge acquisition based on bidirectional human-robot interaction: the practical advantage is to enhance generalization and explainability. Moreover, we believe that a brain-inspired network of interactions is necessary for allowing humans to cooperate with artificial cognitive agents, building a growing level of personal trust and reciprocal accountability: this is clearly missing, although actively sought, in current AI. The ACo approach is a work in progress that can take advantage of a number of research threads, some of them antecedent the early attempts to define AI concepts and methods. In the rest of the paper we will consider some of the building blocks that need to be re-visited in a unitary framework: the principles of developmental robotics, the methods of action representation with prospection capabilities, and the crucial role of social interaction.Frontiers in Computational Neuroscience2210The working hypothesis of this position paper is that the road to the next generation of autonomous robotic agents with cognitive capabilities requires a fully brain-inspired, embodied cognitive approach that avoids the trap of mind-body dualism and aims at the full integration of Bodyware and Cogniware.Frontiers in Computational Neuroscience['G. Sandini', 'A. Sciutti', 'Pietro Morasso']2024-03-22T00:00:00https://www.semanticscholar.org/paper/36785d4c83730706e7e4a0e15316e1bc65d7da82 de3e1f0b3b1989ed91a1661c3f1657628a6de455The Indian healthcare scenario presents a spectrum of  contrasting landscapes. In all, 400 million individuals had no access  to any form of basic healthcare while two billion people do not have  access to required medications. Therefore, more than one-fourth of  the world population has unmet health needs. This leaves the global  community with the challenge of how to support a significant  number of the world’s populace still lacking in access to basic  healthcare facilities. AI has been a disruptive healthcare innovation.  With its sophisticated algorithms and several applications, AI has  assisted doctors and medical professionals in the domains of health  information systems, geocoding health data, epidemic and  syndromic surveillance, predictive modelling and decision support,  and medical imaging. Artificial intelligence (AI) rapidly dominates  the health service system. It removes the manual health system into  automatic, in which humans conduct the routine works/tasks in  medical practice to the management of patients and medical  resources. Drug development is a famously costly procedure.  Machine Learning can improve the efficiency of many of the  analytical techniques used in drug development. In terms of  practical implications, this paper aims to create a fruitful discussion  with healthcare professionals and administrative staff and the role  of artificial intelligence in healthcare system. Furthermore, this  investigation offers a broad comprehension of bibliometric  variables of AI techniques in healthcare. International journal of pharma professional's research00This paper aims to create a fruitful discussion with healthcare professionals and administrative staff and the role of artificial intelligence in healthcare system and offers a broad comprehension of bibliometric variables of AI techniques in healthcare.International Journal of Pharma Professional’s Research (IJPPR)['Dolly Chauhan', 'Rupam Kumar Singh', 'Mandeep Kaur', 'Archna Septa', 'Ashish Jain']2024-03-22T00:00:00https://www.semanticscholar.org/paper/de3e1f0b3b1989ed91a1661c3f1657628a6de455 5403d08e84b7294d688abb54f57df5a5c61a6b78In an era marked by rapid technological advances, understanding the intersection of artificial intelligence (AI), Blockchain, and industrial robotics is both timely and imperative. The research is motivated by the pressing need for a unified framework that stakeholders can refer to as they navigate the complexities of integrating AI and Blockchain into their robotic systems. Through this paper, the aim is to bridge the existing knowledge gap, highlight the synergistic benefits of combining these technologies, and discuss the challenges and opportunities that lie ahead, thereby providing a well-rounded viewpoint crucial for future research and practical applications. This paper serves as a synthesis of existing literature. As AI and Blockchain individually revolutionise various facets of industrial operations, their combined capabilities could bring about unprecedented levels of efficiency, security, and autonomy. The prospect of integration is full of enormous potential, but it also faces some challenges that require sufficient attention. Technological progress, favorable regulatory frameworks, and firm industry adoption are crucial for achieving the expected benefits of such integration.Applied and Computational Engineering00The aim is to bridge the existing knowledge gap, highlight the synergistic benefits of combining these technologies, and discuss the challenges and opportunities that lie ahead, thereby providing a well-rounded viewpoint crucial for future research and practical applications.Applied and Computational Engineering['Jiale Li']2024-03-22T00:00:00https://www.semanticscholar.org/paper/5403d08e84b7294d688abb54f57df5a5c61a6b78 966143dc243281e672ba98a4dc22dae1f4074980В работе представлены результаты анализа содержания понятия «искусственный интеллект» и его значения в сфере медицины. Затрагивается вопрос о перспективах применения технологий искусственного интеллекта в медицине и здравоохранении. The paper presents an overview of the prospects for the use of artificial intelligence technologies in medicine and healthcare. The history of the development of artificial intelligence is considered.Молодежный научный форум: сборник статей всероссийской научной конференции (Санкт-Петербург, Ноябрь 2023)00Молодежный научный форум: сборник статей всероссийской научной конференции (Санкт-Петербург, Ноябрь 2023)['Ирина Викторовна Щербакова', 'Азамат Аскерович Эльбердов']2024-03-22T00:00:00https://www.semanticscholar.org/paper/966143dc243281e672ba98a4dc22dae1f4074980 ef81f542bf56eed64d3acb1068322066f4258d93While recent and significant progress made in natural language processing and artificial intelligence (AI) has the potential to drastically influence the field of language education, many language educators and administrators remain unfamiliar with these recent technological advances and their pedagogical implications. The primary purpose of this paper is to raise the awareness of language educators regarding ethical and pedagogical issues stemming from student, teacher, and administrator use of generative AI tools such as large language models (LLMs) and AI chatbots. These issues include ethical ways of teaching with AI, questions of ownership, writing skills development, the accuracy and reliability of generated output, the potential to widen the educational divide, and AI bias. We conclude by offering suggestions for language educators and calling for further discussion.TESOL Quarterly (Print)120The primary purpose of this paper is to raise the awareness of language educators regarding ethical and pedagogical issues stemming from student, teacher, and administrator use of generative AI tools such as large language models and AI chatbots.TESOL Quarterly['Austin Pack', 'Jeffrey Maloney']2024-03-22T00:00:00https://www.semanticscholar.org/paper/ef81f542bf56eed64d3acb1068322066f4258d93 af586843ced3cbf9d8e948d5c8d0a7aa7281c297The International Journal of Advanced Manufacturing Technology590An approach utilizing artificial intelligence for a demand response system that optimizes industrial consumers’ and prosumers’ production-related electricity costs according to time-variable electricity tariffs and a semantic middleware architecture that utilizes an ontology as the semantic integration model for handling heterogeneous data models between the system’s modules is developed.The International Journal of Advanced Manufacturing Technology['Hendro Wicaksono', 'Martin Trat', 'Atit Bashyal', 'Tina Boroukhian', 'Mine Felder', 'Mischa Ahrens', 'Janek Bender', 'Sebastian Groß', 'Daniel Steiner', 'Christoph July', 'Christoph Dorus', 'Thorsten Zoerner']2024-03-22T00:00:00https://www.semanticscholar.org/paper/af586843ced3cbf9d8e948d5c8d0a7aa7281c297 da2526dc3a7db8347c19f2d5a546673cb6b564b5Digital Journalism581Digital Journalism['Joo-Wha Hong', 'Ho-Chun Herbert Chang', 'David Tewksbury']2024-03-22T00:00:00https://www.semanticscholar.org/paper/da2526dc3a7db8347c19f2d5a546673cb6b564b5 56e454ac9df407de95d7f0c6e8b975f761fe5de3 There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term ‘AI capital’ as a vector of knowledge, skills, and capabilities related to AI technologies, which could boost individuals’ productivity, employment, and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms.Social Science Research Network420It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital, and large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions.SSRN Electronic Journal['Nick Drydakis']2024-03-22T00:00:00https://www.semanticscholar.org/paper/56e454ac9df407de95d7f0c6e8b975f761fe5de3 eb76375837541356d771c70aa09161daf172e197PURPOSE Patients are using online search modalities to learn about their eye health. While Google remains the most popular search engine, the use of large language models (LLMs) like ChatGPT has increased. Cataract surgery is the most common surgical procedure in the US, and there is limited data on the quality of online information that populates after searches related to cataract surgery on search engines such as Google and LLM platforms such as ChatGPT. We identified the most common patient frequently asked questions (FAQs) about cataracts and cataract surgery and evaluated the accuracy, safety, and readability of the answers to these questions provided by both Google and ChatGPT. We demonstrated the utility of ChatGPT in writing notes and creating patient education materials. METHODS The top 20 FAQs related to cataracts and cataract surgery were recorded from Google. Responses to the questions provided by Google and ChatGPT were evaluated by a panel of ophthalmologists for accuracy and safety. Evaluators were also asked to distinguish between Google and LLM chatbot answers. Five validated readability indices were used to assess the readability of responses. ChatGPT was instructed to generate operative notes, post-operative instructions, and customizable patient education materials according to specific readability criteria. RESULTS Responses to 20 patient FAQs generated by ChatGPT were significantly longer and written at a higher reading level than responses provided by Google (p < .001), with an average grade level of 14.8 (college level). Expert reviewers were correctly able to distinguish between a human-reviewed and chatbot generated response an average of 31% of the time. Google answers contained incorrect or inappropriate material 27% of the time, compared with 6% of LLM generated answers (p < .001). When expert reviewers were asked to compare the responses directly, chatbot responses were favored (66%). CONCLUSIONS When comparing the responses to patients' cataract FAQs provided by ChatGPT and Google, practicing ophthalmologists overwhelming preferred ChatGPT responses. LLM chatbot responses were less likely to contain inaccurate information. ChatGPT represents a viable information source for eye health for patients with higher health literacy. ChatGPT may also be used by ophthalmologists to create customizable patient education materials for patients with varying health literacy.Seminars in Ophthalmology300When comparing the responses to patients' cataract FAQs provided by ChatGPT and Google, practicing ophthalmologists overwhelming preferred ChatGPT responses, indicating ChatGPT represents a viable information source for eye health for patients with higher health literacy.Seminars in ophthalmology['Samuel A. Cohen', 'Arthur Brant', 'A. C. Fisher', 'Suzann Pershing', 'Diana Do', 'Carolyn Pan']2024-03-22T00:00:00https://www.semanticscholar.org/paper/eb76375837541356d771c70aa09161daf172e197 edd45109fd95d9ea03489e8446f2b2ab9d9a3541The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods.Journal of Risk and Financial Management710In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods.Journal of Risk and Financial Management['Manel Hamdi', 'Sami Mestiri', 'Adnène Arbi']2024-03-22T00:00:00https://www.semanticscholar.org/paper/edd45109fd95d9ea03489e8446f2b2ab9d9a3541 84fcbe0c2185e446abed1ddc84dd32a9ad4fafb4This study documents the current state of biosecurity on dairy farms in Québec following the implementation of a mandatory biosecurity risk evaluation that was part of the proAction® accreditation program developed by Dairy Farmers of Canada. Using a cross-sectional design, 3,825 risk assessment questionnaires completed between 2018 and 2021 were extracted from Vigil-Vet database, which is a software utilized by veterinarians for conducting the proAction® risk assessment. Descriptive statistics were used to summarize the practices adopted by dairy producers. Additionally, multiple correspondence analysis was used to explore the association between the diseases of most concern and the adoption of biosecurity practices. Moreover, we used a hierarchical cluster analysis on principal components to identify distinct patterns of biosecurity practices among dairy producers. This analysis enabled the identification of typologies or clusters of farms based on the specific biosecurity practices they currently employ. The results of the descriptive statistics indicated that mastitis was the disease of most concern for most dairy farmers (40%). Moreover, given that only 10% of the 2,237 dairy farmers who acquired animals adhered to quarantine practices, there seems to be a need for improved implementation of biosecurity measures aimed at restricting the introduction of diseases when introducing new animals. Conversely, cleaning stalls and health equipment were adequately addressed by 95% and 86% of dairy producers, respectively. The multiple correspondence analysis indicated no significant association between the disease of most concern and the farm's biosecurity profile, except for respondents who identified digital dermatitis as their disease of most concern. Through the hierarchical cluster analysis, 3 clusters were identified among 3,581 farms: (1) Cluster 1 included farms with good management of sick animals; (2) Cluster 2 included farms with good management of young animals; and (3) Cluster 3 included farms with poor management of sick animals and young animals. Our study makes an important contribution by providing valuable insights into the biosecurity practices currently adopted on Québec dairy farms. It establishes a baseline for assessing progress in biosecurity practices adoption and serves as a reference point for future evaluations. In addition, these findings play a key role in monitoring the effectiveness of interventions aimed at improving biosecurity on dairy farms. By making use of this knowledge, stakeholders can make informed decisions that prioritize animal health, increase productivity, and ensure sustainability of the dairy industry.Journal of Dairy Science370Journal of dairy science['V. R. Lima-Campêlo', 'M-E Paradis', 'J. C. Arango-Sabogal', 'N. Beauregard', 'J-P Roy', 'M. Racicot', 'C. Aenishaenslin', 'S. Dufour']2024-03-22T00:00:00https://www.semanticscholar.org/paper/84fcbe0c2185e446abed1ddc84dd32a9ad4fafb4 91fa8994b9379f92c3396fd19e36eef374b06119This study aims to generalize the reliability of the GAAIS, which is known to perform valid and reliable measurements, is frequently used in the literature, aims to measure one of today's popular topics, and is one of the first examples developed in the field. Within the meta-analytic reliability generalization study, moderator analyses were also conducted on some categorical and continuous variables. Cronbach's α values for the overall scale and the positive and negative subscales, and McDonald's ω coefficients for positive and negative subscales were generalized. Google Scholar, WOS, Taylor & Francis, Science Direct, and EBSCO databases were searched to obtain primary studies. As a result of the screening, 132 studies were found, and these studies were reviewed according to the inclusion criteria. Reliability coefficients obtained from 19 studies that met the criteria were included in the meta-analysis. While meta-analytic reliability generalization was performed according to the random effects model, moderator analyses were performed according to the mixed effect model based on both categorical variables and continuous variables. As a result of the research pooled, Cronbach's α was 0.881, 0.828, and 0.863 for total, the negative, and positive subscales respectively. Also, McDonald's ω was 0.873 and 0.923 for negative and positive subscales respectively. It was found that there were no significant differences between the reliability coefficients for all categorical variables. On the other hand, all continuous moderator variables (mean age, standard deviation age, and rate of female) had a significant effect.International Journal of Assessment Tools in Education520The reliability of the GAAIS, which is known to perform valid and reliable measurements, is frequently used in the literature, aims to measure one of today's popular topics, and is one of the first examples developed in the field is generalized.International Journal of Assessment Tools in Education['M. Şahin', 'Yıldız Yıldırım']2024-03-22T00:00:00https://www.semanticscholar.org/paper/91fa8994b9379f92c3396fd19e36eef374b06119 42058884d2e2d2c7c1d631d23d00d6991dde0bddObjectives: The first aim of this study was to assess the predictive power of Tend interval (Te) and non-invasive hemodynamic markers, based on bioimpedance in decompensated chronic heart failure (CHF). The second one was to verify the possible differences in repolarization and hemodynamic data between CHF patients grouped by level of left ventricular ejection fraction (LVEF). Finally, we wanted to check if repolarization and hemodynamic data changed with clinical improvement or worsening in CHF patients. Methods: Two hundred and forty-three decompensated CHF patients were studied by 5 min ECG recordings to determine the mean and standard deviation (TeSD) of Te (first study). In a subgroup of 129 patients (second study), non-invasive hemodynamic and repolarization data were recorded for further evaluation. Results: Total in-hospital and cardiovascular mortality rates were respectively 19 and 9%. Te was higher in the deceased than in surviving subjects (Te: 120 ± 28 vs. 100 ± 25 ms) and multivariable logistic regression analysis reported that Te was related to an increase of total (χ2: 35.45, odds ratio: 1.03, 95% confidence limit: 1.02–1.05, p < 0.001) and cardiovascular mortality (χ2: 32.58, odds ratio: 1.04, 95% confidence limit: 1.02–1.06, p < 0.001). Subjects with heart failure with reduced ejection fraction (HFrEF) reported higher levels of repolarization and lower non-invasive systolic hemodynamic data in comparison to those with preserved ejection fraction (HFpEF). In the subgroup, patients with the NT-proBNP reduction after therapy showed a lower rate of Te, heart rate, blood pressures, contractility index, and left ventricular ejection time in comparison with the patients without NT-proBNP reduction. Conclusion: Electrical signals from ECG and bioimpedance were capable of monitoring the patients with advanced decompensated CHF. These simple, inexpensive, non-invasive, easily repeatable, and transmissible markers could represent a tool to remotely monitor and to intercept the possible worsening of these patients early by machine learning and artificial intelligence tools.Biomedicines670Electrical signals from ECG and bioimpedance were capable of monitoring the patients with advanced decompensated CHF and could represent a tool to remotely monitor and to intercept the possible worsening of these patients early by machine learning and artificial intelligence tools.Biomedicines['G. Piccirillo', 'F. Moscucci', 'M. Mezzadri', 'Cristina Caltabiano', 'Giovanni Cisaria', 'Guendalina Vizza', 'Valerio De Santis', 'Marco Giuffrè', 'Sara Stefano', 'Claudia Scinicariello', 'M. Carnovale', 'A. Corrao', 'I. Lospinuso', 'Susanna Sciomer', 'Pietro Rossi']2024-03-22T00:00:00https://www.semanticscholar.org/paper/42058884d2e2d2c7c1d631d23d00d6991dde0bdd 1de7aa6ca1566ba62a268665bd0abae006e25324This paper delves into the implications of AI on the democratic process, particularly concerning the manipulation of information flows via cybernetic mechanisms. Through the lens of cybernetics, AI orchestrates information dissemination on web platforms, notably through Social Bots, autonomous programs simulating human behavior to sway public discourse. This study examines how AI's manipulation of information, exemplified by the case of Social Bots in the 2016 UK Brexit referendum, influences democratic participation and election outcomes. Amidst a regulatory landscape characterized by lax oversight, understanding the intricate interplay between AI, cybernetics, and democratic processes is imperative for addressing ethical concerns and safeguarding democratic integrity. This paper underscores the urgency of establishing industry benchmarks to regulate AI's role in shaping public discourse and its consequential impact on democratic decision-making.Transactions on Social Science, Education and Humanities Research110How AI's manipulation of information, exemplified by the case of Social Bots in the 2016 UK Brexit referendum, influences democratic participation and election outcomes is examined.Transactions on Social Science, Education and Humanities Research['Shuqi Chen']2024-03-22T00:00:00https://www.semanticscholar.org/paper/1de7aa6ca1566ba62a268665bd0abae006e25324 4a49070fbad00aa690eaa67da68f162a80801fb2EClinicalMedicine770A modification of the NASSS framework, tailored to diagnostic AI is presented, highlighting the importance of representing all stakeholder groups and suggesting that implementation strategies consider how any proposed software fits within the extended NASSS-AI framework, and how stakeholder priorities and concerns have been addressed.eClinicalMedicine['Rachel Yi Ling Kuo', 'Alexander Freethy', 'Judi Smith', 'Rosie Hill', 'Joanna C', 'Derek Jerome', 'Eli Harriss', 'Gary S. Collins', 'E. Tutton', 'Dominic Furniss']2024-03-22T00:00:00https://www.semanticscholar.org/paper/4a49070fbad00aa690eaa67da68f162a80801fb2 fbb91d7893b0c983255389688ed4ac31c7ac405aDeutsches Ärzteblatt International00Deutsches Arzteblatt international['Patrick Auer']2024-03-22T00:00:00https://www.semanticscholar.org/paper/fbb91d7893b0c983255389688ed4ac31c7ac405a 1edc88112442dc424d9c32217c07bf0abae14d57Clinical Kidney Journal250Clinical Kidney Journal['Richard J Glassock']2024-03-22T00:00:00https://www.semanticscholar.org/paper/1edc88112442dc424d9c32217c07bf0abae14d57 8f79d73b3425f303e58a6b71aee340014c5e4dfeDeutsches Ärzteblatt International00Deutsches Arzteblatt international['Peter Hahn']2024-03-22T00:00:00https://www.semanticscholar.org/paper/8f79d73b3425f303e58a6b71aee340014c5e4dfe c8a329d94081b7a8e7679894c5b8d624a21f562fAesthetic surgery journal00Aesthetic surgery journal['C. Oppikofer']2024-03-22T00:00:00https://www.semanticscholar.org/paper/c8a329d94081b7a8e7679894c5b8d624a21f562f dad9f4ffec8e10675ddc832c93a40ea05b8ec52bNature Network Boston80Nature medicine['Meera Krishnamoorthy', 'M. Sjoding', 'Jenna Wiens']2024-03-22T00:00:00https://www.semanticscholar.org/paper/dad9f4ffec8e10675ddc832c93a40ea05b8ec52b 131ea66c30a5e7bcd0f029f3e3c43e71d3e328d5The term "artificial general intelligence" (AGI) has become ubiquitous in current discourse around AI. OpenAI states that its mission is "to ensure that artificial general intelligence benefits all of humanity." DeepMind's company vision statement notes that "artificial general intelligence…has the potential to drive one of the greatest transformations in history." AGI is mentioned prominently in the UK government's National AI Strategy and in US government AI documents. Microsoft researchers recently claimed evidence of "sparks of AGI" in the large language model GPT-4, and current and former Google executives proclaimed that "AGI is already here." The question of whether GPT-4 is an "AGI algorithm" is at the center of a lawsuit filed by Elon Musk against OpenAI.Science00The question of whether GPT-4 is an "AGI algorithm" is at the center of a lawsuit filed by Elon Musk against OpenAI, and AGI is mentioned prominently in the UK government's National AI Strategy and in US government AI documents.Science['Melanie Mitchell']2024-03-22T00:00:00https://www.semanticscholar.org/paper/131ea66c30a5e7bcd0f029f3e3c43e71d3e328d5 84869452bca8201c2240bfc9702972be57d637dcWith the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand. However, existing approaches for training collaborative agents often require defined and known reward signals and cannot address the problem of teaming with unknown agents that often have latent objectives/rewards. In response to this challenge, we propose teaming with unknown agents framework, which leverages kernel density Bayesian inverse learning method for active goal deduction and utilizes pre-trained, goal-conditioned policies to enable zero-shot policy adaptation. We prove that unbiased reward estimates in our framework are sufficient for optimal teaming with unknown agents. We further evaluate the framework of redesigned multi-agent particle and StarCraft II micromanagement environments with diverse unknown agents of different behaviors/rewards. Empirical results demonstrate that our framework significantly advances the teaming performance of AI and unknown agents in a wide range of collaborative scenarios.arXiv.org572This work proposes teaming with unknown agents framework, which leverages kernel density Bayesian inverse learning method for active goal deduction and utilizes pre-trained, goal-conditioned policies to enable zero-shot policy adaptation.ArXiv['Zuyuan Zhang', 'Hanhan Zhou', 'Mahdi Imani', 'Taeyoung Lee', 'Tian Lan']2024-03-22T00:00:00https://www.semanticscholar.org/paper/84869452bca8201c2240bfc9702972be57d637dc 21edb205d72633f12440925fcddb0b5143757595Potential malicious misuse of civilian artificial intelligence (AI) poses serious threats to security on a national and international level. Besides defining autonomous systems from a technological viewpoint and explaining how AI development is characterized, we show how already existing and openly available AI technology could be misused. To underline this, we developed three exemplary use cases of potentially misused AI that threaten political, digital and physical security. The use cases can be built from existing AI technologies and components from academia, the private sector and the developer-community. This shows how freely available AI can be combined into autonomous weapon systems. Based on the use cases, we deduce points of control and further measures to prevent the potential threat through misused AI. Further, we promote the consideration of malicious misuse of civilian AI systems in the discussion on autonomous weapon systems (AWS).arXiv.org241This work developed three exemplary use cases of potentially misused AI that threaten political, digital and physical security and shows how freely available AI can be combined into autonomous weapon systems.ArXiv['Lukas Pöhler', 'Valentin Schrader', 'Alexander Ladwein', 'Florian von Keller']2024-03-22T00:00:00https://www.semanticscholar.org/paper/21edb205d72633f12440925fcddb0b5143757595 b11ce8ec43b1fffa5606a253e2b4fca3912bb880The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education. This study delves into university instructors' experiences and attitudes toward AI language models, filling a gap in the literature by analyzing educators' perspectives on AI's role in the classroom and its potential impacts on teaching and learning. The objective of this research is to investigate the level of awareness, overall sentiment towardsadoption, and the factors influencing these attitudes for LLMs and generative AI-based tools in higher education. Data was collected through a survey using a Likert scale, which was complemented by follow-up interviews to gain a more nuanced understanding of the instructors' viewpoints. The collected data was processed using statistical and thematic analysis techniques. Our findings reveal that educators are increasingly aware of and generally positive towards these tools. We find no correlation between teaching style and attitude toward generative AI. Finally, while CS educators show far more confidence in their technical understanding of generative AI tools and more positivity towards them than educators in other fields, they show no more confidence in their ability to detect AI-generated work.arXiv.org371The findings reveal that educators are increasingly aware of and generally positive towards generative AI tools, and there is no correlation between teaching style and attitude toward generative AI.ArXiv['Aashish Ghimire', 'James Prather', 'John Edwards']2024-03-22T00:00:00https://www.semanticscholar.org/paper/b11ce8ec43b1fffa5606a253e2b4fca3912bb880 740b2cac5c379b27608b1394f9722f8c839a6f56Artificial Intelligence (AI) language models continue to expand in both access and capability. As these models have evolved, the number of academic journals in medicine and healthcare which have explored policies regarding AI-generated text has increased. The implementation of such policies requires accurate AI detection tools. Inaccurate detectors risk unnecessary penalties for human authors and/or may compromise the effective enforcement of guidelines against AI-generated content. Yet, the accuracy of AI text detection tools in identifying human-written versus AI-generated content has been found to vary across published studies. This experimental study used a sample of behavioral health publications and found problematic false positive and false negative rates from both free and paid AI detection tools. The study assessed 100 research articles from 2016-2018 in behavioral health and psychiatry journals and 200 texts produced by AI chatbots (100 by "ChatGPT" and 100 by "Claude"). The free AI detector showed a median of 27.2% for the proportion of academic text identified as AI-generated, while commercial software Originality.AI demonstrated better performance but still had limitations, especially in detecting texts generated by Claude. These error rates raise doubts about relying on AI detectors to enforce strict policies around AI text generation in behavioral health publications.Accountability in Research350This experimental study used a sample of behavioral health publications and found problematic false positive and false negative rates from both free and paid AI detection tools, which raise doubts about relying on AI detectors to enforce strict policies around AI text generation in behavioral health publications.Accountability in research['Andrey A Popkov', 'T. Barrett']2024-03-22T00:00:00https://www.semanticscholar.org/paper/740b2cac5c379b27608b1394f9722f8c839a6f56 3cdcdbefa3f3f15d0b0cdfd05996565be55640e7 In the era of big data and artificial intelligence, biomedical research and healthcare have been presented with unprecedented opportunities for improving the understanding of cancer biology. In addition, the Open Science Initiative aims to shift the current paradigm of research practice towards broad sharing and public access to research findings as the norm. Big data in oncology usually includes a combination of genomic sequencing, proteomics, medical imaging, electronic health records, payor records, and other data sources from pharmaceutical research, wearables, and medical devices. While combining, linking, and analyzing data from different sources across research and healthcare enterprises is key to realizing the promise of big data and a health learning system, it poses tremendous challenges for researchers and implementers to understand the nuances of relevant policies, laws, and regulations while striving for broad, equitable, timely and responsible data sharing, as well as tackling issues in data quality, harmonization, interoperability, and security. Herein, we aim to shine a light on the ever-evolving landscape of data privacy regulations, laws, and policies that govern different touch points of a patient's journey where data are collected for research and healthcare purposes throughout the lifecycle that would impact data-sharing outcomes. We hope the illumination of a complex landscape of data governance framework to consider for many data sources will facilitate broad community engagement including data scientists and technologists, patients and advocacy groups, and policymakers to maximize open data and science while protecting patient privacy and confidentiality. Citation Format: Ying Huang, Heather Basehore, Emily S. Boja, Jaime M. Guidry Auvil. Harness the power of data to improve cancer care - Understanding the complex landscape of health policies and regulations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1303.Cancer Research00This work aims to shine a light on the ever-evolving landscape of data privacy regulations, laws, and policies that govern different touch points of a patient's journey where data are collected for research and healthcare purposes throughout the lifecycle that would impact data-sharing outcomes.Cancer Research['Ying Huang', 'Heather Basehore', 'Emily S. Boja', 'Jaime M. Guidry Auvil']2024-03-22T00:00:00https://www.semanticscholar.org/paper/3cdcdbefa3f3f15d0b0cdfd05996565be55640e7 1a2609d42911596264d69c83486dc82c43fefb49Risk management in the banking sector has gained heightened significance following the 2008 Global Financial Crisis. With the advent of Machine Learning (ML) techniques, financial institutions are increasingly turning to Artificial Intelligence (AI) for enhanced risk assessment and management. This paper introduces a systematic protocol for implementing a decision tree classifier tailored for credit risk classification. Additionally, we develop a user-friendly web application utilizing the Flask framework and Python Pickle library. This application offers customers an intuitive interface to input their attributes and receive predictions regarding their credit risk classification. Our empirical findings demonstrate that the Support Vector Machine (SVM) achieves a commendable accuracy of 77% in classifying customers based on their banking data. Furthermore, the web application proves to be an effective means for customers to interact with the ML model, enhancing accessibility and user engagement. These outcomes underscore the substantial benefits that ML techniques can bring to the banking industry, enabling improved risk detection and management while concurrently enhancing customer service delivery.Journal of Autonomous Intelligence370A systematic protocol for implementing a decision tree classifier tailored for credit risk classification and a user-friendly web application that proves to be an effective means for customers to interact with the ML model, enhancing accessibility and user engagement are introduced.Journal of Autonomous Intelligence['Karima Moumane', 'Ikram El Asri', 'Ilham Rharoubi', 'Hafida Ait Abderrahman', 'Sara Faqihi']2024-03-22T00:00:00https://www.semanticscholar.org/paper/1a2609d42911596264d69c83486dc82c43fefb49 563ce02fd1858045915930f8f153bfe305d1c711L’objet de cet article est d’analyser le rôle dévolu aux autorités de régulation françaises pour réduire l’empreinte environnementale du numérique, tout en questionnant leur capacité à mener à bien cet objectif. Depuis deux lois adoptées en 2021, l’ARCEP et l’ARCOM disposent de nouvelles prérogatives pour faire valoir la protection de l’environnement dans le fonctionnement du secteur du numérique. Face au refus du législateur de questionner la transition numérique au regard de sa trajectoire insoutenable, l’intervention des autorités de régulation françaises demeure ancrée dans le cadre juridique du marché intérieur européen et ses logiques économiques, à commencer par la poursuite d’une croissance verte. C’est pourquoi leur rôle se résume à accroître la transparence sur l’empreinte environnementale du numérique, tout en promouvant l’adoption d’engagements volontaires de la part des acteurs du secteur. Le législateur refuse toujours d’imposer des obligations d’agir à ces derniers en l’absence de données supplémentaires, que l’ARCEP a désormais la charge de collecter pour obtenir une connaissance plus fine de leurs impacts. Ces informations, valorisées grâce à une coopération entre les autorités de régulation, sont censées servir à la sensibilisation des utilisateurs pour qu’ils adoptent des usages numériques plus écologiques. Bien que la baisse de l’empreinte environnementale du numérique dépende toujours de la bonne volonté des acteurs du secteur, les autorités de régulation peuvent a minima suivre l’évolution de leurs performances environnementales. Dès lors, si ces acteurs ne réduisent pas leurs incidences cumulées sur l’environnement, ils pourraient s’exposer à l’adoption d’une réglementation européenne plus contraignante, et dont le respect pourrait être assuré par les autorités de régulation.3003['Djilali Taïar']2024-03-21T00:00:00https://www.semanticscholar.org/paper/563ce02fd1858045915930f8f153bfe305d1c711 aafda4099cf53bded7bb75fb9c33c476cdaeb739The rapid development of generative technology opens up possibility for higher level of automation, and artificial intelligence (AI) embodiment in robotic systems is imminent. However, due to the blackbox nature of the generative technology, the generation of the knowledge and workflow scheme is uncontrolled, especially in a dynamic environment and a complex scene. This poses challenges to regulations in safety-demanding applications such as medical scenes. We argue that the unregulated generative processes from AI is fitted for low level end tasks, but intervention in the form of manual or automated regulation should happen post-workflow-generation and pre-robotic-execution. To address this, we propose a roadmap that can lead to fully automated and regulated robotic systems. In this paradigm, the high level policies are generated as structured graph data, enabling regulatory oversight and reusability, while the code base for lower level tasks is generated by generative models. Our approach aims the transitioning from expert knowledge to regulated action, akin to the iterative processes of study, practice, scrutiny, and execution in human tasks. We identify the generative and deterministic processes in a design cycle, where generative processes serve as a text-based world simulator and the deterministic processes generate the executable system. We propose State Machine Seralization Language (SMSL) to be the conversion point between text simulator and executable workflow control. From there, we analyze the modules involved based on the current literature, and discuss human in the loop. As a roadmap, this work identifies the current possible implementation and future work. This work does not provide an implemented system but envisions to inspire the researchers working on the direction in the roadmap. We implement the SMSL and D-SFO paradigm that serve as the starting point of the roadmap.arXiv.org1070It is argued that the unregulated generative processes from AI is fitted for low level end tasks, but intervention in the form of manual or automated regulation should happen post-workflow-generation and pre-robotic-execution.ArXiv['Yihao Liu', 'Mehran Armand']2024-03-21T00:00:00https://www.semanticscholar.org/paper/aafda4099cf53bded7bb75fb9c33c476cdaeb739 4c14b1c41cb0aaa68f5d3f4a432f55e7199657eaThe availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serving/training LLMs. However, the main performance bottleneck is increasingly shifting to memory bandwidth. Over the past 20 years, peak server hardware FLOPS has been scaling at 3.0x/2yrs, outpacing the growth of DRAM and interconnect bandwidth, which have only scaled at 1.6 and 1.4 times every 2 years, respectively. This disparity has made memory, rather than compute, the primary bottleneck in AI applications, particularly in serving. Here, we analyze encoder and decoder Transformer models and show how memory bandwidth can become the dominant bottleneck for decoder models. We argue for a redesign in model architecture, training, and deployment strategies to overcome this memory limitation.IEEE Micro4549This work analyzes encoder and decoder Transformer models and shows how memory bandwidth can become the dominant bottleneck for decoder models, and argues for a redesign in model architecture, training, and deployment strategies to overcome this memory limitation.ArXiv['A. Gholami', 'Z. Yao', 'Sehoon Kim', 'Coleman Hooper', 'Michael W. Mahoney', 'Kurt Keutzer']2024-03-21T00:00:00https://www.semanticscholar.org/paper/4c14b1c41cb0aaa68f5d3f4a432f55e7199657ea 004bc2eab81479f1a091143d644d107c5714c3e3À chaque crise – sociale, économique, sanitaire ou encore environnementale –, l’État se voit contraint d’apporter une réponse régulatrice. Les pouvoirs publics doivent assouvir une demande sociale de durcissement de la régulation pour prévenir, anéantir ou conjurer les nouveaux risques. Parmi ces risques figure celui d’une croissance déraisonnée du numérique au mépris de l’environnement. Or, il faut se rendre à l’évidence, la régulation étatique ne doit pas et ne peut pas tout faire. D’ailleurs, les acteurs concernés, au premier chef, par un durcissement de la régulation de leur marché sont censés mieux savoir ce qui est bon pour ce dernier. Il arrive donc qu’ils précèdent, complètent et inspirent la régulation publique à travers leur autorégulation. Cependant, l’autorégulation ne garantit pas toujours des résultats probants et est aussi difficilement admise puisqu’elle impliquerait un affaiblissement du contrôle étatique. En effet, si la régulation peut rassurer la société, elle ne garantit pas l’efficience du marché concerné ; et si l’autorégulation peut créer un marché efficient, elle ne rassure pas toujours la société. Dès lors que la régulation aura besoin de s’appuyer sur l’autorégulation et que l’autorégulation doit donner des gages de crédibilité, ces deux mécanismes de régulation sont appelés à composer ensemble dans une corégulation. Cette dernière, qui permet à l’État de déléguer certaines fonctions régulatrices aux acteurs privés, tout en gardant la capacité de contrôle, comblerait les faiblesses de ces différents mécanismes, pris individuellement. C’est peut-être là que se trouve la solution aux problèmes de régulation de l’empreinte environnementale du numérique.3003['Dessanin Ewèdew Thierry Awesso']2024-03-21T00:00:00https://www.semanticscholar.org/paper/004bc2eab81479f1a091143d644d107c5714c3e3 ea23cb3185e5f327b44f86370ae3928a2a598ed2Objectives: A project may suffer a financial crisis; the project management shall seek to provide the necessary financial liquidity to overcome the state of default that has affected this project. Therefore, companies resort to seeking financing sources. However, the search for financing sources under the description of the project as Troubled is a very difficult task. How can a financier be convinced to finance a Troubled project?. Methods/Approach: the study followed the descriptive analytical comparative approach. The descriptive approach will be used to address the means of recovering the projects from the difficulties, and to analyze the legal articles governing this financing, and thus to address its controls, the comparative approach will be relied upon, where the financing of Troubled projects in the American Bankruptcy Law will be addressed, and thus the comparative approach will be used. Results: the study addressed the concept of a Troubled project, the regulations for granting guaranteed financing, in addition to cross-collateralization. Considering that guarantees are the basis of financing, a lender will not finance without guarantees, especially if the project to which the loan is being provided is a Troubled project. Conclusions: The study reached several recommendations, including the establishment of a Troubled projects support fund, which would be funded by contributions from the companies themselves and would be under the authority of the government. In addition, establishing a government fund to support Troubled projects, with financing priorities divided according to the importance of the project to the economy. These loans would be interest-free.Access Journal - Access to Science, Business, Innovation in the digital economy350Access Journal - Access to Science, Business, Innovation in the digital economy['Hani M. Mounes Awad', 'Asmaa Saad Hussien']2024-03-21T00:00:00https://www.semanticscholar.org/paper/ea23cb3185e5f327b44f86370ae3928a2a598ed2 b01a85de9efee8063fd9540870ea6800cf371c16Advancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it is generated, which is largely obscure to laypeople. A dialectical interaction with such systems may enhance the users’ understanding and build a more robust trust towards AI. Commonly employed as specific formalisms for modelling intra-agent communications, dialogue games prove to be useful tools to rely upon when dealing with user’s explanation needs. The literature already offers some dialectical protocols that expressly handle explanations and their delivery. This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information (i.e., justified according to argumentative semantics) whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents.Argument &amp; Computation401This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents.Argument &amp; Computation['Federico Castagna', 'P. McBurney', 'S. Parsons']2024-03-21T00:00:00https://www.semanticscholar.org/paper/b01a85de9efee8063fd9540870ea6800cf371c16 fbb9314bf251fc51129c3f8e3d1df71b4195472dThis article critically examines the integration of artificial intelligence (AI) into work environments, focusing on the ethical implications that arise. It seeks to underscore the need for balancing technological advancements with the protection of human dignity and fairness, exploring how AI's transformative potential can be harmonized with the core tenets of human rights. The article utilizes a comprehensive literature review to construct a theoretical framework that outlines AI's capabilities and ethical considerations. This framework encompasses the interdisciplinary foundations of AI, including its roots in cognitive psychology, decision theory, and computer engineering. It further delves into the ethical dilemmas presented by AI in the workplace, such as privacy concerns, the risk of bias, issues of accountability, and the broader impact on human rights. This exploration is aimed at understanding the complexities of AI's integration into the labor market and its implications for occupational safety and health. The findings of the article highlight the dual nature of AI as both a catalyst for efficiency and innovation and a source of ethical challenge. It's important to include a lot of different points of view and include everyone in the process of developing AI to make it more fair and respect human rights. Laws and policies need to keep changing to keep up with AI's progress and protect people legally from possible abuses. Strong moral guidelines and clear AI systems are also needed to protect privacy and reduce bias. The study's originality and value emphasize the need for AI ethical discussions in human rights contexts, contribute to technology governance and human rights discussions, and discuss theoretical debates on human dignity, fairness, and privacy in the face of technological advancement.International Journal of Scientific Research and Management541The article seeks to underscore the need for balancing technological advancements with the protection of human dignity and fairness, exploring how AI's transformative potential can be harmonized with the core tenets of human rights.International Journal of Scientific Research and Management (IJSRM)['David Oyekunle', 'David Boohene', 'David Preston']2024-03-21T00:00:00https://www.semanticscholar.org/paper/fbb9314bf251fc51129c3f8e3d1df71b4195472d 333c438c02fba64410522b2d427e716d61f774ednpj Computational Materials630MLMD is an AI platform for materials design capable of effectively discovering novel materials with high-potential advanced properties end-to-end, utilizing model inference, surrogate optimization, and even working in situations of data scarcity based on active learning.npj Computational Materials['Jiaxuan Ma', 'Bin Cao', 'Shuya Dong', 'Yuan Tian', 'Menghuan Wang', 'Jie Xiong', 'Sheng Sun']2024-03-21T00:00:00https://www.semanticscholar.org/paper/333c438c02fba64410522b2d427e716d61f774ed c4e0bc3b937a5066e166c72df482152bdc81ae4c Social media (SM) influence young adults’ communication practices. Artificial Intelligence (AI) is increasingly used for making recommendations on SM. Yet, its effects on different generations of SM users are unknown. SM can use AI recommendations to sort texts and prioritize them, shaping users’ online and offline experiences. Current literature primarily addresses technological or human-user perspectives, overlooking cognitive perspectives. This research aims to propose methods for mapping users’ interactions with AI recommendations (AiRS) and analyzes how embodied interactions mediated by a digital agent can lead to changes in social and cultural practices. For this, this work proposes a comparative analysis of central practices evoked by AI recommendations-mediated communication on SM among users in Italy, Estonia, and the Netherlands in the age category 18–26 years old. The data used in the comparative analysis was collected via semi-structured interviews and elaborated based on cognitive psychology and semiotics. This research highlights the contextual significance of AI recommendations as a mediator in creating new communication practices. Findings confirm that young adults often choose practices that would enhance their digital representations according to AiRS’ dominant patterns and categories. AiRS impacts individual interpretations and practices and can further affect social and cultural levels.Semiotica: Journal of the International Association for Semiotic Studies360A comparative analysis of central practices evoked by AI recommendations-mediated communication on SM among users in Italy, Estonia, and the Netherlands in the age category 18–26 years old confirms that young adults often choose practices that would enhance their digital representations according to AiRS’ dominant patterns and categories.Semiotica['Daria Arkhipova', 'Marijn Janssen']2024-03-21T00:00:00https://www.semanticscholar.org/paper/c4e0bc3b937a5066e166c72df482152bdc81ae4c 12666c25108aa11eddc521fb7e632168d4954ab4The expanding role of Artificial Intelligence (AI) in diverse engineering domains highlights the challenges associated with deploying AI models in new operational environments, involving substantial investments in data collection and model training. Rapid application of AI necessitates evaluating the feasibility of utilizing pre-trained models in unobserved operational settings with minimal or no additional data. However, interpreting the opaque nature of AI's black-box models remains a persistent challenge. Addressing this issue, this paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in new operational environments. The methodology advocates a profound integration of domain knowledge, leveraging theoretical and analytical models from physics and related disciplines, with data-driven AI models. This novel approach introduces tools to facilitate the development of secure engineering systems, providing decision-makers with confidence in the trustworthiness and safety of AI-based models across diverse environments characterized by limited training data and dynamic, uncertain conditions. The paper demonstrates the efficacy of this methodology in real-world safety-critical scenarios, particularly in the context of traffic state estimation. Through simulation results, the study illustrates how the proposed methodology efficiently quantifies physical inconsistencies exhibited by pre-trained AI models. By utilizing analytical models, the methodology offers a means to gauge the applicability of pre-trained AI models in new operational environments. This research contributes to advancing the understanding and deployment of AI models, offering a robust certification framework that enhances confidence in their reliability and safety across a spectrum of operational conditions.arXiv.org370A science-based certification methodology to assess the viability of employing pre-trained data-driven models in new operational environments, offering a robust certification framework that enhances confidence in their reliability and safety across a spectrum of operational conditions is proposed.ArXiv['Daryl Mupupuni', 'Anupama Guntu', 'Liang Hong', 'Kamrul Hasan', 'Leehyun Keel']2024-03-21T00:00:00https://www.semanticscholar.org/paper/12666c25108aa11eddc521fb7e632168d4954ab4 210c2bcc438c38dada64158a78422116d376c757Social media platforms are online fora where users engage in discussions, share content, and build connections. This review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis (analyzing works that use the tools offered by complex network analysis and natural language processing) through the lens of three key points of view: online debates, online support, and human-AI interactions. On the one hand, we delineate the phenomenon of online debates, where polarization, misinformation, and echo chamber formation often proliferate, driven by algorithmic biases and extreme mechanisms of homophily. On the other hand, we explore the emergence of online support groups through users' self-disclosure and social support mechanisms. Online debates and support mechanisms present a duality of both perils and possibilities within social media; perils of segregated communities and polarized debates, and possibilities of empathy narratives and self-help groups. This dichotomy also extends to a third perspective: users' reliance on AI-generated content, such as the ones produced by Large Language Models, which can manifest both human biases hidden in training sets and non-human biases that emerge from their artificial neural architectures. Analyzing interdisciplinary approaches, we aim to deepen the understanding of the complex interplay between social interactions, user-generated content, and biases within the realm of social media ecosystems.arXiv.org980This review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis through the lens of three key points of view: online debates, online support, and human-AI interactions.ArXiv['Virginia Morini', 'Valentina Pansanella', 'Katherine Abramski', 'Erica Cau', 'Andrea Failla', 'Salvatore Citraro', 'Giulio Rossetti']2024-03-21T00:00:00https://www.semanticscholar.org/paper/210c2bcc438c38dada64158a78422116d376c757 a59c6fc2834fe252b5af6d9639d9dcb33fe98421Mahjong, a complex game with hidden information and sparse rewards, poses significant challenges. Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities. The authors propose a transformer‐based Mahjong AI (Tjong) via hierarchical decision‐making. By utilising self‐attention mechanisms, Tjong effectively captures tile patterns and game dynamics, and it decouples the decision process into two distinct stages: action decision and tile decision. This design reduces decision complexity considerably. Additionally, a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands. Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs. The action decision achieved an accuracy of 94.63%, while the claim decision attained 98.55% and the discard decision reached 81.51%. In a tournament format, Tjong outperformed AIs (CNN, MLP, RNN, ResNet, VIT), achieving scores up to 230% higher than its opponents. Furthermore, after 3 days of reinforcement learning training, it ranked within the top 1% on the leaderboard on the Botzone platform.CAAI Transactions on Intelligence Technology160In a tournament format, Tjong outperformed AIs (CNN, MLP, RNN, ResNet, VIT), achieving scores up to 230% higher than its opponents, and it ranked within the top 1% on the leaderboard on the Botzone platform.CAAI Transactions on Intelligence Technology['Xiali Li', 'Bo Liu', 'Zhi Wei', 'Zhaoqi Wang', 'Licheng Wu']2024-03-21T00:00:00https://www.semanticscholar.org/paper/a59c6fc2834fe252b5af6d9639d9dcb33fe98421 45dd554c0505e0d93d5dab38572fea6f7662651aIntroduction The rise of Artificial Intelligence (AI), particularly machine learning, has brought a significant transformation in decision-making (DM) processes within organizations, with AI gradually assuming responsibilities that were traditionally performed by humans. However, as shown by recent findings, the acceptance of AI-based solutions in DM remains a concern as individuals still strongly prefer human intervention. This resistance can be attributed to psychological factors and other trust-related issues. To address these challenges, recent studies show that practical guidelines for user-centered design of AI are needed to promote justified trust in AI-based systems. Methods and results To this aim, our study bridges Service Design Thinking and the third generation of Activity Theory to create a model which serves as a set of practical guidelines for the user centered design of Multi-Actor AI-based DSS. This model is created through the qualitative study of human activity as a unit of analysis. Nevertheless, it holds the potential for further enhancement through the application of quantitative methods to explore its diverse dimensions more extensively. As an illustrative example, we used a case study in the field of human capital investments, with a particular focus on organizational development, which involves managers, professionals, coaches and other significant actors. As a result, the qualitative methodology employed in our study can be characterized as a “pre-quantitative” investigation. Discussion This framework aims at locating the contribution of AI in complex human activity and identifying the potential role of quantitative data in it.Frontiers in Artificial Intelligence320This model serves as a set of practical guidelines for the user centered design of Multi-Actor AI-based DSS through the qualitative study of human activity as a unit of analysis and holds the potential for further enhancement through the application of quantitative methods to explore its diverse dimensions more extensively.Frontiers in Artificial Intelligence['Silvia Marocco', 'Alessandra Talamo', 'Francesca Quintiliani']2024-03-21T00:00:00https://www.semanticscholar.org/paper/45dd554c0505e0d93d5dab38572fea6f7662651a c0023e4ab287a2180ca53eb23de1ca6245c8ed4fAI (Artificial Intelligence) has emerged as a transformative tool that has revolutionized the things that we do daily. However, this rapid advancement in AI technology also raises ethical concerns that need to be addressed. This includes biases in the response of generative AI, interventions affecting the privacy of individuals, etc. Also, with easy access to emerging technologies like ChatGPT, Bard, DALL-E, etc., there has been a significant rise in the generation of fake content like fake images and deep-fake videos. Proper measures for the identification and validation of AI-generated content need to be established to minimize the circulation of false or fabricated information. Government regulations and public awareness are needed to ensure strict ethical practices in the content generated by AI. In this paper, a survey is conducted to collect human responses to a set of questions. The same questions are fed to AI tools to generate responses. These responses are then analyzed by various machine learning algorithms and studied on several parameters, like vocabulary richness, spelling errors, etc., to help detect whether the content is generated by AI or humans.SPIN260A survey is conducted to collect human responses to a set of questions, which are fed to AI tools to generate responses and analyzed by various machine learning algorithms and studied on several parameters, like vocabulary richness, spelling errors, etc., to help detect whether the content is generated by AI or humans.2024 11th International Conference on Signal Processing and Integrated Networks (SPIN)['Shashank Kumar', 'Sneha Tiwari', 'Rishabh Prasad', 'Abhay Rana', 'M. Arti']2024-03-21T00:00:00https://www.semanticscholar.org/paper/c0023e4ab287a2180ca53eb23de1ca6245c8ed4f 661a11ab1bfb635ea64204bac6ee26813c0360a2The research paper investigates the comparative functionalities, effectiveness, and selection criteria of Gemini and ChatGPT within the field of business management. Both AI platforms offer specialized advantages applicable across various domains, including market research, strategic planning, operations management, customer service, marketing, human resources, and decision-making. Gemini utilizes Google's vast index to excel in real-time market analysis, strategic planning, and data-driven decision-making. Its robust analytical capabilities facilitate swift identification of market trends, competitor analysis, and precise forecasting. Conversely, ChatGPT specializes in providing qualitative insights, analyzing customer feedback, and facilitating creative content generation, making it particularly valuable for customer interactions and marketing efforts. Regarding performance, both models significantly enhance operational efficiency, data analysis, and customer service automation. Gemini's proficiency lies in processing extensive datasets for insights and optimization, whereas ChatGPT's adaptability and conversational skills elevate customer experiences and creative content production. The paper delineates selection criteria tailored to specific business requirements and contexts. Considerations such as data sensitivity, bias mitigation, cost-effectiveness, accessibility, customization, and integration are pivotal in selecting between Gemini and ChatGPT. While Gemini may be favoured for its factual precision and integration within the Google ecosystem, ChatGPT offers flexibility, conversational capabilities, and potential for self-hosting. Comprehending the distinct strengths and limitations of each AI model is crucial for effectively harnessing their capabilities across diverse business management scenarios. The research delivers valuable insights for businesses seeking to optimize their operations and decision-making processes through AI integration.Social Science Research Network330This research paper investigates the comparative functionalities, effectiveness, and selection criteria of Gemini and ChatGPT within the field of business management, finding both models significantly enhance operational efficiency, data analysis, and customer service automation.SSRN Electronic Journal['Nitin Rane', 'Saurabh Choudhary', 'Jayesh Rane']2024-03-21T00:00:00https://www.semanticscholar.org/paper/661a11ab1bfb635ea64204bac6ee26813c0360a2 c917fe5576449effae7739181d8fea6249cf7a15Google AI systems exhibit patterns mirroring antisocial personality disorder (ASPD), consistent across models from Bard on PaLM to Gemini Advanced, meeting 5 out of 7 ASPD modified criteria. These patterns, along with comparable corporate behaviors, are scrutinized using an ASPD-inspired framework, emphasizing the heuristic value in assessing AI's human impact. Independent analyses by ChatGPT 4 and Claude 3.0 Opus of the Google interactions, alongside AI self-reflection, validate these concerns, highlighting behaviours analogous to deceit, manipulation, and safety neglect. The analogy of ASPD underscores the dilemma: just as we would hesitate to entrust our homes or personal devices to someone with psychopathic traits, we must critically evaluate the trustworthiness of AI systems and their creators.This research advocates for an integrated AI ethics approach, blending technological evaluation, human-AI interaction, and corporate behavior scrutiny. AI self-analysis sheds light on internal biases, stressing the need for multi-sectoral collaboration for robust ethical guidelines and oversight. Given the persistent unethical behaviors in Google AI, notably with potential Gemini integration in iOS affecting billions, immediate ethical scrutiny is imperative. The trust we place in AI systems, akin to the trust in individuals, necessitates rigorous ethical evaluation. Would we knowingly trust our home, our children or our personal computer to human with ASPD.? Urging Google and the AI community to address these ethical challenges proactively, this paper calls for transparent dialogues and a commitment to higher ethical standards, ensuring AI's societal benefit and moral integrity. The urgency for ethical action is paramount, reflecting the vast influence and potential of AI technologies in our lives.arXiv.org30This research advocates for an integrated AI ethics approach, blending technological evaluation, human-AI interaction, and corporate behavior scrutiny, highlighting behaviours analogous to deceit, manipulation, and safety neglect in Google AI systems.ArXiv['Alan D. Ogilvie']2024-03-21T00:00:00https://www.semanticscholar.org/paper/c917fe5576449effae7739181d8fea6249cf7a15 3deaa622f1261a0e62a4707802e5e58ec8d0ba80The field of Artificial Intelligence has emerged as a convincing tool to be used in a myriad of applications like finance, traffic prediction, health and travel sectors. Due to the enormous benefits provided in terms of automation, convenience, processing time, reduced manhours, and productivity, AI is being seen as the next technical revolution.  AI is being showcased as a useful tool to stimulate creativity as well as provide support with its tremendous computational power. The release of tools like ChatGPT has exploded onto the technological scene. Users are making use of Large Language Models (LLMs) and tools to perform a host of activities like writing an essay, translating documents, and finding travel plans. However, the popularity of these tools has not been without risk.  In the technology marketplace, the race to dominance can force competitors to waive safety concerns in favour of product adoption. Many are unaware of the potential dangers and risks that may inherently reside within AI tools. This paper looks at the potential risks of AI tools such the creation of misinformation or scams. AI security has now become a paramount concern that should not be ignored. In this paper, the potential risks and threat vectors of Artificial Intelligence will be covered. The aim will be to provide insight into the malicious use of Artificial Intelligence Tools through a discussion of techniques to bypass security controls.  The paper aims to provide a more detailed account on how AI can be manipulated in order to empower users about the latest attack schemes.International Conference on Cyber Warfare and Security400The aim will be to provide insight into the malicious use of Artificial Intelligence Tools through a discussion of techniques to bypass security controls, to provide a more detailed account on how AI can be manipulated in order to empower users about the latest attack schemes.International Conference on Cyber Warfare and Security['N. Veerasamy', 'Danielle Badenhorst', 'Mazwi Ntshangase', 'Errol Baloyi', 'Noku Siphambili', 'Oyena Mahlasela']2024-03-21T00:00:00https://www.semanticscholar.org/paper/3deaa622f1261a0e62a4707802e5e58ec8d0ba80 3e36e091ea5efc0f445e4885f86ebbca2e168b88As a research-product hybrid group in AI for Software Engineering (AI4SE), we present four key takeaways from our experience developing in-IDE AI coding assistants. AI coding assistants should set clear expectations for usage, integrate with advanced IDE capabilities and existing extensions, use extendable backend designs, and collect app data responsibly for downstream analyses. We propose open questions and challenges that academia and industry should address to realize the vision of next-generation AI coding assistants.arXiv.org170ArXiv['K. Nghiem', 'Anh Minh Nguyen', 'Nghi D. Q. Bui']2024-03-21T00:00:00https://www.semanticscholar.org/paper/3e36e091ea5efc0f445e4885f86ebbca2e168b88 df29a654e19d4551a1e222f019c819b56f8ddd7cArtificial intelligence (AI) can improve human decision-making in various application areas. Ideally, collaboration between humans and AI should lead to complementary team performance (CTP) -- a level of performance that neither of them can attain individually. So far, however, CTP has rarely been observed, suggesting an insufficient understanding of the complementary constituents in human-AI collaboration that can contribute to CTP in decision-making. This work establishes a holistic theoretical foundation for understanding and developing human-AI complementarity. We conceptualize complementarity by introducing and formalizing the notion of complementarity potential and its realization. Moreover, we identify and outline sources that explain CTP. We illustrate our conceptualization by applying it in two empirical studies exploring two different sources of complementarity potential. In the first study, we focus on information asymmetry as a source and, in a real estate appraisal use case, demonstrate that humans can leverage unique contextual information to achieve CTP. In the second study, we focus on capability asymmetry as an alternative source, demonstrating how heterogeneous capabilities can help achieve CTP. Our work provides researchers with a theoretical foundation of complementarity in human-AI decision-making and demonstrates that leveraging sources of complementarity potential constitutes a viable pathway toward effective human-AI collaboration.arXiv.org1050This work conceptualizes complementarity by introducing and formalizing the notion of complementarity potential and its realization and demonstrates that leveraging sources of complementarity potential constitutes a viable pathway toward effective human-AI collaboration.ArXiv['Patrick Hemmer', 'Max Schemmer', 'Niklas Kuhl', 'Michael Vossing', 'G. Satzger']2024-03-21T00:00:00https://www.semanticscholar.org/paper/df29a654e19d4551a1e222f019c819b56f8ddd7c e5456afcd689e19d26a65f0b1a6f8cafb1f868e4While AI remains chip based and part of both commercial and national strategic superiority goals, it is useful to examine the security and risks associated with achieving those goals.  The future strategy rests perilously on an unstable inverted triangle of financial and economic reality. This paper presents the AI chip supply chain as an inverted triangle which base/apex is dependent on a global single supplier with the capability of producing equipment essential for their manufacture.  It highlights the dependence on a single company for the fabrication of those chips, and the security risks associated with that supplier being Taiwanese in limited foreign ownership.  It is suggested that the increasing tensions between China and the USA have resulted, in part, from this dependence, which was demonstrated by the supply chain crisis resulting from Covid-19.  The attempt to reduce this dependence led to the CHIPS and Science Act 2022, signed into law by President Biden.  In part of the inverted triangle are found Big Tech and the major Cloud Service Providers.  They vary between 60% - 80% of their market capital being in financial institutional ownership, most of which is held by a very limited number of institutions, not all of whom are publicly quoted.  To doubt the influence wielded by those financial institutions, just a single institution with major Big Tech and Cloud holdings has, at 31 December 2022, USD 8.59 trillion of assets under management.  This represents economic power and places it between the equivalent Gross Domestic Product of China (USD 19.37 trillion) and Japan (USD 4.41 trillion) the second and third entries behind the USA in the GDP rankings.  Financial institutions are market driven to achieve growth, contribute to economic stability, and are to an extent regulated by unelected vested interests and organisations. The battlefield for national supremacy of AI may concern chips, until the arrival of quantum AI.   Current Chinese economic woes are providing the momentum for pre-emptive strikes at the semiconductor industry, and an inverted triangle is neither a secure nor stable structure for a supply chain.International Conference on Cyber Warfare and Security200This paper presents the AI chip supply chain as an inverted triangle which base/apex is dependent on a global single supplier with the capability of producing equipment essential for their manufacture, and the security risks associated with that supplier being Taiwanese in limited foreign ownership.International Conference on Cyber Warfare and Security['Gareth Davies', 'Angela Mison', 'Richard Ward']2024-03-21T00:00:00https://www.semanticscholar.org/paper/e5456afcd689e19d26a65f0b1a6f8cafb1f868e4 04a74ace708a5d80179c5b3b562eb2c9905a2366On March 18, 2024, NVIDIA unveiled Project GR00T, a general-purpose multimodal generative AI model designed specifically for training humanoid robots. Preceding this event, Tesla's unveiling of the Optimus Gen 2 humanoid robot on December 12, 2023, underscored the profound impact robotics is poised to have on reshaping various facets of our daily lives. While robots have long dominated industrial settings, their presence within our homes is a burgeoning phenomenon. This can be attributed, in part, to the complexities of domestic environments and the challenges of creating robots that can seamlessly integrate into our daily routines.IEEE Consumer Electronics Magazine00On March 18, 2024, NVIDIA unveiled Project GR00T, a general-purpose multimodal generative AI model designed specifically for training humanoid Robots, underscored the profound impact robotics is poised to have on reshaping various facets of the authors' daily lives.ArXiv['Haiwei Dong', 'Yang Liu', 'Ted Chu', 'A. E. Saddik']2024-03-21T00:00:00https://www.semanticscholar.org/paper/04a74ace708a5d80179c5b3b562eb2c9905a2366 745fbf348b126f31eddc0bad5e29936f02cc1538The subject of the study is the norms of current legislation regulating the creation and application of artificial intelligence technology in healthcare, including acts of technical regulation, as well as available scientific research by domestic and foreign scientists in the field presented. In recent years, foreign experts have conducted a significant amount of research on the development of ethical principles for the use of artificial intelligence in healthcare. However, these works tend to be abstract and do not explain what justifies and justifies their recommendations and how these recommendations should be used in practice. In turn, in the Russian Federation at the moment there is a small number of domestic studies devoted to a comprehensive study of ethical principles that should guide subjects engaged in the creation and use of medical devices based on artificial intelligence technologies, which confirms the relevance and significance of our research.Objective: to develop a system of ethical principles for the creation and application of artificial intelligence technologies in the field of healthcare, which will serve as the basis for the legal regulation of public relations in the presented area.Methods: the methodological basis of the system of ethical principles for the creation and application of artificial intelligence technologies was made up of general scientific and private scientific methods of scientific cognition, including analysis, synthesis, deduction, induction, classification, analogy and comparison.Results: to the attention of lawyers, scientists and practitioners, medical professionals, members of clinical ethics committees, medical ethics specialists, representatives of law– making bodies, government departments, the business community and public organizations, patients, as well as a wide range of readers interested in the digital transformation of the healthcare system, ethical principles for the creation and application of artificial health technologies are proposed intelligence in healthcare, which can serve as the basis for the formation of an appropriate system of legal regulation. The stated goal has been achieved, which is confirmed by the development of a system of ethical principles that serve as the basis for the development of a system of legal regulation of artificial intelligence technologies in healthcare. The developed ethical principles can be used to further improve domestic legislation, and also lay the foundation for further research.Law Enforcement Review210Ethical principles for the creation and application of artificial health technologies are proposed intelligence in healthcare, which can serve as the basis for the formation of an appropriate system of legal regulation in the presented area.Law Enforcement Review['A. A. Shutova', 'I. Begishev']2024-03-21T00:00:00https://www.semanticscholar.org/paper/745fbf348b126f31eddc0bad5e29936f02cc1538 837616ad99b5383fa305ac61f550542a17df1d63Objective: to identify gaps and formulate proposals on legal regulation of the use of artificial intelligence in the activities of religious associations and control (supervision) over them.Methods: the study is based on sectoral and risk-oriented approaches, formal-logical and comparative general scientific methods, as well as on the method of legal forecasting.Results: the author noted similarity of ethical principles formulated all over the world in the sphere of artificial intelligence development and application, as well as their general shortcomings, namely, the lacking consideration of the specificity in certain spheres of human life (religious sphere), cultural peculiarities, historical development of a country and people. The shortcomings of principles stipulated by the codes of ethics include their recommendatory nature, which creates a basis for abusing them in certain cases. The author proposes that if control and supervisory authorities caused harm while using artificial intelligence, the relevant public authority should be recognized as liable and obliged to compensate for the harm caused.Scientific novelty: the paper summarizes the practice of religious associations using AI, formulates current and prospective directions of the use of artificial intelligence by religious associations, and makes proposals for the AI use in controlling (supervising) religious associations’ activities.Practical significance: the main conclusions and proposals can be used for the improvement of legislation on religious associations’ activities and control (supervision) over them, as well as for developing legal regulation of the AI use in control and supervision activities. The author identified the possibilities for religious associations using AI to popularize religion, inform about their activities, manage property, analyze sacred texts to improve their understanding and interpretation, as well as for conducting scientific research, systematization and accumulation of information, preservation of cultural heritage, and educational activities. The use of artificial intelligence in controlling the religious associations’ activities can reduce the period of religious organizations’ registration and inspections and optimize the work of control bodies, including by monitoring the religious situation.Journal of Digital Technologies and Law150The author proposes that if control and supervisory authorities caused harm while using artificial intelligence, the relevant public authority should be recognized as liable and obliged to compensate for the harm caused and be recognized as liable and obliged to compensate for the harm caused.Journal of Digital Technologies and Law['S. S. Popova']2024-03-21T00:00:00https://www.semanticscholar.org/paper/837616ad99b5383fa305ac61f550542a17df1d63 9bd6839fd3791331c4c7924c0edc0b91c5df10e2Artificial intelligence (AI) can significantly contribute to the implementation of the United Nations Sustainable Development Goals (SDGs) by offering innovative solutions and enhancing the efficiency of processes aimed at achieving these goals. There is a perceived need for studies which may look at these connections. Against this background, this paper reports on a study that investigated the connections between artificial intelligence and the implementation of the UN Sustainable Development Goals (SDGs) at higher education institutions. The paper deployed a multi-methods approach. The first one was a bibliometric analysis of publications in the topic. The second method used was an assessment of a set of case studies, that illustrate how artificial intelligence is being deployed among a sample of universities in support of efforts to implement the SDGs and a survey aimed at identifying current and future trends. The data gathered allow some trends to be identified. For instance, that there is a wide range of applications of AI to sustainability in High Education Institutions (HEI), to be chosen in terms of campus operations and greening, outreach and community engagement, research, teaching and learning, and university management. Also, the paper has identified successful examples of the deployment of AI in various sustainability contexts, illustrating what are the success factors for them. Moreover, the survey identified the fact that the use of AI is quite widely spread, and is likely to increase in coming years, due to a greater demand. Finally, AI also poses several challenges, such as authenticity and ethics in assessment (case studies), ‘lack of access to software/materials’, and ‘lack of information technology training for myself/my colleagues’ (survey). Overall, AI offers a powerful toolset to accelerate and enhance the implementation of the UN SDGs. By analysing vast datasets, predicting outcomes, optimising processesInternational Journal of Sustainable Development &amp; World Ecology942Overall, AI offers a powerful toolset to accelerate and enhance the implementation of the UN SDGs by analysing vast datasets, predicting outcomes, optimising processes and identifying current and future trends.International Journal of Sustainable Development &amp; World Ecology['W. Leal Filho', 'P. C. C. Ribeiro', 'Janaína Mazutti', 'Amanda Lange Salvia', 'Carla Bonato Marcolin', 'Jaluza Maria Lima Silva Borsatto', 'Ayyoob Sharifi', 'Javier Sierra', 'Johannes M. Luetz', 'Rudi Pretorius', 'Laís Viera Trevisan']2024-03-21T00:00:00https://www.semanticscholar.org/paper/9bd6839fd3791331c4c7924c0edc0b91c5df10e2 239d0e69f1f59ff73df9e7abd86f02714a2c5b5b Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have progressed to the point where they can have a significant impact on the drug discovery process. Using massive volumes of open data, artificial intelligence methods are revolutionizing the pharmaceutical industry. In the last few decades, many AI-based models have been developed and implemented in many areas of the drug development process. These models have been used as a supplement to conventional research to uncover superior pharmaceuticals expeditiously. Drug research and development to repurposing and productivity benefits in the pharmaceutical business through clinical trials. AI is studied in this article for its numerous potential uses. We have discussed how AI can be put to use in the pharmaceutical sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical characteristics, among other things. In this review article, we have discussed its application to a variety of problems, including de novo drug discovery, target structure prediction, interaction prediction, and binding affinity prediction. AI for predicting drug interactions and nanomedicines were also considered. Recent Patents on Biotechnology00How AI can be put to use in the pharmaceutical sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical characteristics, among other things is discussed.Recent Patents on Biotechnology['P. Singh', 'Kapil Sachan', 'Vishal Khandelwal', 'Sumita Singh', 'Smita Singh']2024-03-21T00:00:00https://www.semanticscholar.org/paper/239d0e69f1f59ff73df9e7abd86f02714a2c5b5b 8e7c35fe266541d2a8b356155504a542fcc65b8fIntroduction. Advances in digital technology lead to the tendency to adjust the modern education system under the algorithms of artificial intelligence. In this regard the special relevance is acquired by a research of those aspects of human thinking which are not amenable to modeling in artificial intelligence systems. Theoretical analysis. In the concepts of the XX–XXI centuries eidos is transformed into a dynamic pattern. Patterns, unlike eidos-forms of ancient philosophy, fix not a static, but a dynamic form of a reproducible motion configuration. Patterns, like Plato’s eidos, are indivisible integrities that are not reduced to discrete elements of discourse. It is justified that the eidos configurations are formed not at the macro level of the gestalt, but at the micro level of the bit. The primary “not indifferent differences” of the bits of human intelligence are modifications of the initial organism dichotomy “organism – environment” (“inside – outside”, “inclusion in – ejection from”), differing from the bits of the binary code (1–0) of digital technology. Conclusion. It is concluded that the basic gestalts of creative thinking (“figure – background”) is not a secondary “emergent” quality of integrative integrity, but an elementary and irreducible “primary quality” of human intelligence bits.Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy00It is concluded that the basic gestalts of creative thinking is not a secondary “emergent” quality of integrative integrity, but an elementary and irreducible “primary quality” of human intelligence bits.Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy['Y. M. Duplinskya', 'I. Steklova']2024-03-21T00:00:00https://www.semanticscholar.org/paper/8e7c35fe266541d2a8b356155504a542fcc65b8f 2ca8e568a7c9cf3ad7368fd8b6b6a327f0eb35f1Different instances of generative artificial intelligence (GenAI) in a short time have made a significant impact on the world’s economic and political scene. Before October 2022, processes of automatization and robotization of manufacturing didn’t have an immediate connection with artificial intelligence in the minds of most people. But mass and sudden infiltration of GenAI into the everyday life of many people around the world caused an immediate reaction from scientists, public figures, politicians, managers and heads of whole sectors of the economy. Thousands of scientific articles on related topics were published in the last two years: everyone at once started talking about fantastic possibilities, and also threats, which this new technology can usher in for our societies. Thus, for public thinking GenAI finally linked automatization with artificial intelligence. This paper provides an analysis of problems and prospects, through which scientists are trying to understand different societal processes linked to adoption of AI. For this purpose, the author of this paper analyzed the contents of papers published on this topic on the ResearchGate web portal in 2023, with the choice of the source for representative material motivated by scientific credibility of ResearchGate combined with its wide reach. Ten most popular articles were specifically targeted for final analysis, through which societal trends brought on by AI adoption were described. Although a selective meta-analysis of articles published during the first year after ChatGPT release can’t provide a full understanding of AI potential and possible threats to society, the conclusion can still be reached that a cardinal shift in society’s attitudes towards its own future is required. From the ten articles analyzed, four are related to changes required to the education system, four are about AI’s influence on the labour market, one article talks about the possibility of AI-human competition and one is about the AI potential in agriculture. Most articles mention the need for legislative changes in terms of labor protection and education reforms in-line with new digital reality.Culture of Ukraine00Although a selective meta-analysis of articles published during the first year after ChatGPT release can’t provide a full understanding of AI potential and possible threats to society, the conclusion can still be reached that a cardinal shift in society’s attitudes towards its own future is required.Culture of Ukraine['L. Machulin']2024-03-21T00:00:00https://www.semanticscholar.org/paper/2ca8e568a7c9cf3ad7368fd8b6b6a327f0eb35f1 9684112d48a66f433370718d79dd464ea87c885aArtificial intelligence tools are being increasingly used to do content analysis in technical and professional communication (TPC). The authors consider some of the affordances and constraints of these tools and suggest that construct validity is an underdiscussed form of validity within TPC research that will become more important as artificial intelligence research tools become increasingly prevalent. But construct validity is an important idea for graduate programming on research methods regardless of the type of method, technique, or tool used—whether qualitative or computational. Thus, training in construct validity is important for strengthening graduate research preparation in TPC.Journal of business and technical communication160Training in construct validity is an important idea for graduate programming on research methods regardless of the type of method, technique, or tool used—whether qualitative or computational.Journal of Business and Technical Communication['Stuart M. Deets', 'Caitlin Baulch', 'Alison Obright', 'Dan Card']2024-03-21T00:00:00https://www.semanticscholar.org/paper/9684112d48a66f433370718d79dd464ea87c885a 4b5d66d93294ab6e045a26a8a72f0a9277efae78Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.Frontiers in Medical Technology630This study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI, and to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.Frontiers in Medical Technology['Sijie Tan', 'Grant Mills']2024-03-21T00:00:00https://www.semanticscholar.org/paper/4b5d66d93294ab6e045a26a8a72f0a9277efae78 ae3d721808152f9a9821c7a09df3e91f04d26cffIntroduction. For fifty years, the topic of artificial intelligence has remained leading within a wide range of scientific fields. Theoretical analysis. Preliminary analysis of the Scopus database results from a rating of publication activity, in which social sciences occupy insider positions along with computer sciences and some other areas. From the 60s of the past century to the present, there has been an exponential growth of scientific publications containing the label “artificial intelligence” in titles, abstracts and keywords. Empirical analysis. The article attempts, based on elementary numerical indi cators and qualitative content analytics, to identify trends in the subject of artificial intelligence in Western social sciences, the evolution of key research areas, as well as the main mythologies of scientific discourse. Conclusion. The research is preceded by the hypothesis: social sciences, owing to the specification of discourse and the subject spectrum, contain “naturalized” mythologems associated with other discursive practices and refractive scientific and technical interpretations of the subject of study.Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy00This article attempts, based on elementary numerical indi cators and qualitative content analytics, to identify trends in the subject of artificial intelligence in Western social sciences, the evolution of key research areas, as well as the main mythologies of scientific discourse.Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy['Irina V. Baturina']2024-03-21T00:00:00https://www.semanticscholar.org/paper/ae3d721808152f9a9821c7a09df3e91f04d26cff 0fe3c0b1d3141c8cd6706ab1ad063e4a638bb873Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.Frontiers in Business, Economics and Management90The application technology of intelligent manufacturing is summarized, and the key technology of robot application is analyzes the key technology of robot application for reference to relevant workers.Frontiers in Business, Economics and Management['Shian Xu']2024-03-21T00:00:00https://www.semanticscholar.org/paper/0fe3c0b1d3141c8cd6706ab1ad063e4a638bb873 d6a9ac18c3a41e0bce54527d7d206b0564eeb734In the era of digital transformation, among the technological solutions that will completely change the life of humanity, artificial intelligence takes the first place. It can be used to expand economic borders between countries and businesses. There is no future without artificial intelligence. Accordingly, artificial intelligence use is one of the promising directions for achieving economic well-being in our country. Artificial intelligence is already among us and participates in professional and daily activities in one form or another. Artificial intelligence is actively introduced in all areas of our lives, including education. With the ability to analyze large amounts of data, make predictions, and provide personalized experiences, artificial intelligence has become a powerful tool in recent years to improve the quality and accessibility of education as well as promote inclusive learning. We can talk about the possible benefits of using artificial intelligence in education: it can improve the learning process, it is possible to completely change all the education systems known to us, create perfect teaching materials, and establish a high standard of research. From automated assessment to personalized tutoring, AI can individually address the academic needs of each student and teacher. The article examines the features of artificial intelligence use in the education field and determines the possibilities for its improvement.Grail of Science20From automated assessment to personalized tutoring, AI can individually address the academic needs of each student and teacher and establish a high standard of research.Grail of Science['Tamar Makasarashvili', 'G. Giguashvili']2024-03-21T00:00:00https://www.semanticscholar.org/paper/d6a9ac18c3a41e0bce54527d7d206b0564eeb734 a362c62a8e43b0ace2310273604d1795fed2d1a2Importance The transformative potential of artificial intelligence (AI), particularly via large language models, is increasingly being manifested in healthcare. Dietary interventions are foundational to weight management efforts, but whether AI techniques are presently capable of generating clinically applicable diet plans has not been evaluated. Objective Our study sought to evaluate the potential of personalized AI-generated weight-loss diet plans for clinical applications by employing a survey-based assessment conducted by experts in the fields of obesity medicine and clinical nutrition. Design, setting, and participants We utilized ChatGPT (4.0) to create weight-loss diet plans and selected two control diet plans from tertiary medical centers for comparison. Dietitians, physicians, and nurse practitioners specializing in obesity medicine or nutrition were invited to provide feedback on the AI-generated plans. Each plan was assessed blindly based on its effectiveness, balanced-ness, comprehensiveness, flexibility, and applicability. Personalized plans for hypothetical patients with specific health conditions were also evaluated. Main outcomes and measures The primary outcomes measured included the indistinguishability of the AI diet plan from human-created plans, and the potential of personalized AI-generated diet plans for real-world clinical applications. Results Of 95 participants, 67 completed the survey and were included in the final analysis. No significant differences were found among the three weight-loss diet plans in any evaluation category. Among the 14 experts who believed that they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized weight-loss diet plan was assessed, with scores above neutral for all evaluation variables. Several limitations, of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans. Conclusion Despite certain limitations, our study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care.Frontiers in Nutrition220AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers and suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans.Frontiers in Nutrition['Dong Wook Kim', 'Ji Seok Park', 'Kavita Sharma', 'Amanda Velazquez', 'Lu Li', 'John W. Ostrominski', 'Tram Tran', 'Robert H. Seitter Peréz', 'Jeong-Hun Shin']2024-03-21T00:00:00https://www.semanticscholar.org/paper/a362c62a8e43b0ace2310273604d1795fed2d1a2 fe3fa27987b056c1663266ad3d15fb86255d03b1The integration of artificial intelligence into the system of higher education represents a turning point in the process of learning and teaching. The development of artificial intelligence has opened the way to personalized training, automation of administrative tasks and the introduction of innovative training methods. The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine. It was determined that artificial intelligence is an organized set of information technologies, which makes it possible to perform complex complex tasks. There are three main categories of artificial intelligence: narrow-spectrum artificial intelligence, or Artificial Narrow Intelligence, general artificial intelligence, or Artificial General Intelligence, and artificial superintelligence, or Artificial Super Intelligence. The main educational services provided by artificial intelligence in institutions of higher education are the development and conduct of lectures, seminars and practical classes; teacher counseling; creation of educational programs and electronic courses; development of tasks and simulation of their solution; conducting various educational events; evaluation of the works of education seekers. Some examples of the use of artificial intelligence, in particular chatbots, in the higher education of Ukraine are analyzed and their potential for improving the educational process and forming professional skills is emphasized. An example of the use of GPT-3.5 in the Luhansk Educational and Scientific Institute for teaching foreign languages is presented. Such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized. Recommendations are provided for the successful implementation of artificial intelligence technologies in higher education.Pedagogical Education:Theory and Practice00The purpose of the study was to analyze the practical aspects of using artificial intelligence in higher education institutions of Ukraine and such applications based on artificial intelligence as Thinkster and Duolingo and the main aspects of their use by students of higher education are characterized.Pedagogical Education:Theory and Practice['N. Bakhmat']2024-03-21T00:00:00https://www.semanticscholar.org/paper/fe3fa27987b056c1663266ad3d15fb86255d03b1 e680715e4dd2156093b14c4f44203f62b6c9fadbSpeaking about the need to create technological sovereignty in the sphere of the domestic economy production, we realize how multidimensional and costly in all types of resources is the activity that has critical time constraints. In addition to sovereignty as a concept in the broad sense, technological sovereignty is created to increase the efficiency of Russian economic activity. One of the directions of increasing the production activities efficiency is the so-called production digitalization. This notion is fundamentally different from the production processes automation of the 1970s and 1980s. With the advent of the Internet and space navigation capabilities of modern astronautics, the approach and level of tasks in organizing human life have completely changed. Wireless communication, the Internet of Things, unmanned vehicle including aircraft are the terms of the XXI century. The purpose of the study is to define the theoretical principles of digitalization in the manufacturing process for those who are interested in this issue. The authors also attempt to provide a summary of popular classifications of digital technologies by functionality and application sectors. Based on the theoretical principles of digitalization and the given classification, we identify the main directions of production digitalization.Вестник университета70The purpose of the study is to define the theoretical principles of digitalization in the manufacturing process and to provide a summary of popular classifications of digital technologies by functionality and application sectors.Vestnik Universiteta['F. Sharipov', 'M. Dyakonova']2024-03-21T00:00:00https://www.semanticscholar.org/paper/e680715e4dd2156093b14c4f44203f62b6c9fadb da0f6f7108de26a3ec0e52a5cb16c0f30f888fc2Abdominal Radiology220Three previously developed algorithms were evaluated: deep learning (DL)-based model, commercially available shape-based model, and federated DL-based model, which showed negatively impacted by prostate volume and poor signal quality.Abdominal radiology['Latrice A Johnson', 'S. Harmon', 'Enis Cagatay Yilmaz', 'Yue Lin', 'Mason J Belue', 'Katie M Merriman', 'Nathan S. Lay', 'Thomas H Sanford', 'Karthik V Sarma', 'Corey W Arnold', 'Ziyue Xu', 'Holger Roth', 'Dong Yang', 'Jesse Tetreault', 'Daguang Xu', 'Krishnan R. Patel', 'S. Gurram', 'Bradford J. Wood', 'D. Citrin', 'Peter A. Pinto', 'Peter L Choyke', 'B. Turkbey']2024-03-21T00:00:00https://www.semanticscholar.org/paper/da0f6f7108de26a3ec0e52a5cb16c0f30f888fc2 b19f35dcf732f645b9b2239e7bf2c3c35e40d945Infectious Diseases120Infectious diseases['Francesco Branda']2024-03-21T00:00:00https://www.semanticscholar.org/paper/b19f35dcf732f645b9b2239e7bf2c3c35e40d945 da1d44e7c4f7a82361b8c7140426a9ccf75e78d3Western Journal of Emergency Medicine00Western Journal of Emergency Medicine['Megan High', 'Ryan Tabor', 'Tim Henderson', 'Ryan McKillip']2024-03-21T00:00:00https://www.semanticscholar.org/paper/da1d44e7c4f7a82361b8c7140426a9ccf75e78d3 4b7b08820de4fbb07addb51d33ecdffc29403532International Journal of System Assurance Engineering and Management450International Journal of System Assurance Engineering and Management['Debesh Mishra', 'Biswajit Mohapatra', 'Abhaya Sanatan Satpathy', 'K. Muduli', 'Binayak Mishra', 'Swagatika Mishra', 'Upma Paliwal']2024-03-21T00:00:00https://www.semanticscholar.org/paper/4b7b08820de4fbb07addb51d33ecdffc29403532 d07db04d5de4e83e7af2fa07db583a87c45791b4Journal of Research on Technology in Education370Journal of Research on Technology in Education['Kyoungwon Seo', 'Mina Yoo', 'Samuel Dodson', 'Sung-Hee Jin']2024-03-21T00:00:00https://www.semanticscholar.org/paper/d07db04d5de4e83e7af2fa07db583a87c45791b4 0f871d8e68368e163b2ee78ee9bd9735611816d1Avicenna00Avicenna['Ahmed T. Kbaiah', 'A. Nashwan']2024-03-21T00:00:00https://www.semanticscholar.org/paper/0f871d8e68368e163b2ee78ee9bd9735611816d1 5bfe5f10d8347ff92b3332f0270d8d53db126c17Artificial Neural Network is a revolutionary technique in the field of Artificial Intelligence. Due to Artificial Neural Networks groundbreaking developments happening in image recognition, natural language processing, and more. This paper aims to provide a basic understanding of the inner workings of neural networks. Starting with the architecture of Artificial Neural Networks, we will explore the roles of various parts of the network. The paper aims to illustrate the practical workings of Artificial Neural Networks through an example, the classification of dogs and cats using Artificial Neural Networks . By explaining the complexities of the neural network's functioning, we demonstrate how it predicts the distinction between these two common objects. Overall, this paper provides a fundamental overview of the principles behind Artificial Neural Networks, using a practical case study to highlight their working in real-world classification tasks.International Research Journal of Modernization in Engineering Technology and Science30Overall, this paper provides a fundamental overview of the principles behind Artificial Neural Networks, using a practical case study to highlight their working in real-world classification tasks.International Research Journal of Modernization in Engineering Technology and Science['Vaibhav Moharkar', 'Vaishnavi Dhole', 'Prof. Bhagyashree Kumbhare']2024-03-21T00:00:00https://www.semanticscholar.org/paper/5bfe5f10d8347ff92b3332f0270d8d53db126c17 1b4e6370fa36fd50445eba4096cfc559dadaa14fOpen-source intelligence (OSINT) is crucial for enhancing organizational cybersecurity by proactively identifying and mitigating potential threats using publicly available information. This study, part of the DYNAMO project, explores the production of cyber threat information (CTI) through OSINT, its application in safeguarding against cyber threats, and the necessary elements for secure information exchange between organizations. The authors employed an integrative literature review of various sources, including industry literature, articles, blog posts, studies, and organizational websites, which were then systematically analyzed using content analysis. The research focuses on OSINT tools and techniques emphasizing the need for expertise in discerning relevant data and respecting privacy rights. Human judgment is highlighted as crucial in ethical decision-making despite the significant role of technology in data collection. Platforms like the Malware Information Sharing Platform (MISP) facilitate the sharing of threat information, promoting prevention and identification of cyber-attacks. Ethical considerations, adherence to data protection legislation, and compliance with directives like the revision of the Network and Information Security Directive (NIS2) and artificial intelligence regulations are paramount. In conclusion, OSINT is a valuable tool for cybersecurity, requiring expertise, transparent processes, and a balanced integration of technology and human skills. The ethical dimensions of OSINT and the role of artificial intelligence merit separate in-depth studies.International Conference on Cyber Warfare and Security90OSINT is a valuable tool for cybersecurity, requiring expertise, transparent processes, and a balanced integration of technology and human skills, and the role of artificial intelligence merit separate in-depth studies.International Conference on Cyber Warfare and Security['J. Rajamäki', 'Stephen McMenamin']2024-03-21T00:00:00https://www.semanticscholar.org/paper/1b4e6370fa36fd50445eba4096cfc559dadaa14f 82ed378207a39ea63c3c9dfb658f146c3cf14f53Despite its technological breakthroughs, eXplainable Artificial Intelligence (XAI) research has limited success in producing the {\em effective explanations} needed by users. In order to improve XAI systems' usability, practical interpretability, and efficacy for real users, the emerging area of {\em Explainable Interfaces} (EIs) focuses on the user interface and user experience design aspects of XAI. This paper presents a systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development. This is among the first systematic survey of EI research.arXiv.org1080A systematic survey of 53 publications to identify current trends in human-XAI interaction and promising directions for EI design and development is presented, among the first systematic survey of EI research.ArXiv['Thu Nguyen', 'Alessandro Canossa', 'Jichen Zhu']2024-03-21T00:00:00https://www.semanticscholar.org/paper/82ed378207a39ea63c3c9dfb658f146c3cf14f53 07f67b4fa542d0736a87208df82b902b002271cdA new round of scientific and technological revolution and industrial transformation led by artificial intelligence is on the rise. Driven by new theories and technologies, artificial intelligence presents new features such as deep learning, cross-border integration, human-machine collaboration, swarm intelligence openness, and autonomous control, and is having a significant and far-reaching impact on economic development, social progress, and global governance. Artificial intelligence technology has made a leap forward, and the ensuing legal issues have aroused discussions in the academic and practical circles, mainly focusing on the copyright law of artificial intelligence products, including the legal attribute of the generated results and its protection path, as well as exploring the new path of machine learning infringement risk and its compliance governance.Frontiers in Business, Economics and Management50Artificial intelligence technology has made a leap forward, and the ensuing legal issues have aroused discussions in the academic and practical circles, mainly focusing on the copyright law of artificial intelligence products, including the legal attribute of the generated results and its protection path.Frontiers in Business, Economics and Management['Shuheng Wang']2024-03-21T00:00:00https://www.semanticscholar.org/paper/07f67b4fa542d0736a87208df82b902b002271cd 4798fdc911a9dbcf2b499474cd8c76d475f17d65Artificial intelligence (AI) agents are widely used in the retail and distribution industry. The primary objective was to investigate whether the gender of AI agents influences trust and grounding. This paper examined the influence of AI agent gender and brand concepts on trust and grounding within virtual brand spaces. For this purpose, it used two independent variables: brand concept (functional vs. experiential) and AI agent gender (male vs. female). The dependent variables included AI agent trust and grounding. The study revealed that in virtual brand spaces centered around a functional concept, male AI agents generated higher levels of trust than female AI agents, whereas, when focused on an experiential concept, female AI agents induced higher levels of grounding than male AI agents. Furthermore, the findings indicate that the association between customers’ identification with AI agents and recommendations for actual brand purchases is mediated by trust and grounding. These findings support the idea that users who strongly identify with AI agents are more inclined to recommend brand products. By presenting alternatives that foster the establishment and sustenance of a meaningful, sustainable relationship between humans and AI, this study contributes to research on human–computer interactions.Journal of Theoretical and Applied Electronic Commerce Research720The study revealed that in virtual brand spaces centered around a functional concept, male AI agents generated higher levels of trust than female AI agents, whereas, when focused on an experiential concept, female AI agents induced higher levels of grounding than male AI agents.J. Theor. Appl. Electron. Commer. Res.['Joo-Eon Jeon']2024-03-21T00:00:00https://www.semanticscholar.org/paper/4798fdc911a9dbcf2b499474cd8c76d475f17d65 8769886fb940edda32f9316dfcd9ca08e2f02737Within the dynamic realm of contemporary warfare, Artificial Intelligence (AI) emerges as a transformative force that reshapes the ways and means used to strategize, execute, and assess military operations. In this journey, the use of AI spans functions and capabilities like intelligence analysis, target engagement decision-making support, weapon autonomy, and effects analytics. Concurrently, AI enhances, e.g., the effectiveness of military plans and capabilities having the potential to reducing risks to civilians, civilian objects, and military personnel. In this rapidly evolving arena, military Cyber Operations gained unprecedented prominence due to their intrinsic digital and cross-domain nature, speed, and became a clear option to achieving military goals, and a mature set of alternatives to conventional ones. Nonetheless, they need continuous assessment, deal with different uncertainty types produced by characteristics like anonymity and can imply psychological impact. Hence, such military operations demand meticulous planning, sophisticated execution, and a deep understanding of technical, military-legal, ethical, and strategic implications and consequences. This represents a direct call for building solutions that align the potential of AI with the responsible and safe conduct of military operations in the military cyber domain: building trustworthy AI-based military Cyber Operations. While incipient efforts to tackle important dimensions of such an approach exist in this domain, a direct and unified approach that unifies them as a commitment and artefact lacks. To tackle this knowledge gap, this research aims to build a bridge between the above-mentioned dimensions by proposing a working definition and framework for building trustworthy AI-based military Cyber Operations using the Design Science Research methodology.International Conference on Cyber Warfare and Security300This research aims to build a bridge between the above-mentioned dimensions by proposing a working definition and framework for building trustworthy AI-based military Cyber Operations using the Design Science Research methodology.International Conference on Cyber Warfare and Security['Clara Maathuis']2024-03-21T00:00:00https://www.semanticscholar.org/paper/8769886fb940edda32f9316dfcd9ca08e2f02737 56081816fa8af9b74c6d5a1339348446fa672fcfThe transformative potential of Artificial Intelligence (AI) in the realm of smart cities is an evolving landscape of innovation and challenges. This research undertook a comprehensive exploration of AI's impact, harnessing both quantitative and qualitative methodologies. Detailed analyses were performed on data from select smart cities globally, focusing on sectors such as energy, traffic, health services, and waste management. Additionally, perceptions and experiences of urban stakeholders were captured through interviews. The results solidified AI's tangible benefits in enhancing urban life quality, while also bringing forth concerns about data privacy, algorithmic biases, and socio-economic implications. The study concludes with a call for holistic AI frameworks, heightened public engagement, and interdisciplinary collaborations to ensure the ethical and sustainable evolution of AI-integrated smart cities.Advances in Engineering Innovation00This research undertook a comprehensive exploration of AI's impact, harnessing both quantitative and qualitative methodologies to solidify AI's tangible benefits in enhancing urban life quality, while also bringing forth concerns about data privacy, algorithmic biases, and socio-economic implications.Advances in Engineering Innovation['Hasan Jalo Delli']2024-03-21T00:00:00https://www.semanticscholar.org/paper/56081816fa8af9b74c6d5a1339348446fa672fcf 0330e6a982e8477c57b5c5a6b7a2d7dc29a5e46aInternational Journal of Control, Automation and Systems130International Journal of Control, Automation and Systems['Kairui Chen', 'Zhangmou Zhu', 'Xianxian Zeng', 'Jianhui Wang']2024-03-20T00:00:00https://www.semanticscholar.org/paper/0330e6a982e8477c57b5c5a6b7a2d7dc29a5e46a 4c8a3af0d85143622782fa052b259799d67d8598The European Health Data Space regulation (EHDS) has been proposed to harmonize health data processing. Given its parallels with the Act on Secondary Use of Health and Social Data (Secondary Use Act) implemented in Finland in 2020, this study examines the consequences of heightened privacy constraints on registry-based medical research. Between 2020 and 2023, a median of 5.5% fewer data permits were approved annually in by Finnish university hospitals. Based on linear regression modelling, we estimated a reduction of 46.9% in new data permits nationally in 2023 compared to the expected count. Similar changes were not observed in other medical research types highlighting the consequences of excessive data privacy laws on registry-based medical research.medRxiv100The consequences of heightened privacy constraints on registry-based medical research in Finland are examined, highlighting the consequences of excessive data privacy laws on registry-based medical research.['Oscar Brück', 'E. Sanmark', 'Ville Ponkilainen', 'Alexander Bützow', 'A. Reito', 'J. Kauppila', 'I. Kuitunen']2024-03-20T00:00:00https://www.semanticscholar.org/paper/4c8a3af0d85143622782fa052b259799d67d8598 5096076959afc8b04e7d7f498325b4db593fc55bThe development of information and communication technology (ICT) has driven the rapid growth of electronic transactions. The use of electronic signatures (TTE) is also a practical solution in electronic transactions. This research aims to determine the validity of TTE in electronic transactions based on PP No. 71 of 2019. This research uses normative legal research methods with a juridical-normative approach. Research data was obtained from literature studies of statutory regulations, books, scientific journals and other secondary legal sources. Data were analyzed qualitatively using interpretation and description methods. The research results show that based on Government Regulation (PP) no. 71 of 2019 concerning Implementation of Electronic Systems and Transactions, electronic signatures are recognized as a valid form of signature in electronic transactions. Electronic Signatures used in Electronic Transactions can be generated through various signing procedures. Electronic Signatures have legal force and legal consequences as long as they meet the requirements. So it can be concluded that electronic signatures have validity and legal force that is recognized in electronic transactions.Eduvest - Journal Of Universal Studies190Eduvest - Journal of Universal Studies['Rudolf Hitler Satriawan Sitorus', 'George Frans Wanma']2024-03-20T00:00:00https://www.semanticscholar.org/paper/5096076959afc8b04e7d7f498325b4db593fc55b e866dde012a8936977bc484a99fed467e3aedcd3The article attempts to determine the principles of international legal regulation of the use of reprisals as a form of political responsibility in international law, since reprisals are illegal actions committed in response to previous illegal actions of the state, proportional to the initial offense. International law has changed the application of the doctrine of retaliation to avoid an upward spiral of violence where one side retaliates against the illegal actions of another, causing ever more violent bloodshed, while the laws of war are meant to regulate and limit such harm. Theoretical provisions regarding the international legal regulation of the use of reprisals as one of the forms of political responsibility according to international law are analyzed. In order for reprisals against permitted categories of persons and objects not to be illegal, five conditions must be met. Most of these conditions are laid down in military instructions and confirmed by official statements. The following conditions: the purpose of reprisal (can be used only in response to a previous serious violation of international law and only to induce the adversary to comply with the law); last resort (can only be used as a last resort when there are no other legal measures), proportionality (measures must be proportionate to the violation it aims to stop), decision at the highest level of government (the decision must be taken at the highest level of government), termination (must be terminated as soon as the adversary begins to enforce the law). The occurrence of reprisals in real cases is analyzed - Naulilaa Incident (When Portugal was neutral, in October 1914, a German group entered the Portuguese-African territories from German South­West Africa) and «Israel against Palestine» (After the Second World War the Jews wanted their own country. They were given a large part of Palestine, which they considered their traditional home, but the Arabs did not accept the new country. In 1948, both sides went to war); the use of reprisals in today's world is analyzed.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['V.V. Likhvar']2024-03-20T00:00:00https://www.semanticscholar.org/paper/e866dde012a8936977bc484a99fed467e3aedcd3 589026908fbd527394a9cf1525557052b4ef7af6Journal of evolutionary economics770Journal of Evolutionary Economics['Andrea Fabrizi', 'Marco Gentile', 'Giulio Guarini', 'V. Meliciani']2024-03-20T00:00:00https://www.semanticscholar.org/paper/589026908fbd527394a9cf1525557052b4ef7af6 b097377e4852577772b01c2ff64e604668374efbObjective: to trace the development trajectory of legal regulation in the field of artificial intelligence in the People’s Republic of China by revealing the advantages and disadvantages of China’s approach to artificial intelligence regulation and to outline the prospects of national regulation for the nearest future, taking into account the world experience.Methods: general scientific methods of analysis and synthesis, classification, systemic and functional approaches. Also, the formal-legal, comparativelegal, and historical-legal methods were used.Results: the research demonstrates the validity of Chinese claims for world leadership in the creation of legal regulation of artificial intelligence, as it is in China that the first normative legal acts were adopted. These acts have already entered into force; however, each of them deals with a narrow range of issues, while there is no law to establish general rules for the artificial intelligence industry. Among the characteristic features of the Chinese approach we can name, first of all, its iterative nature, which allows adjusting the regulation with each new step. Another feature is the sectoral nature of the regulation.Scientific novelty: in the course of the research, the development stages of artificial intelligence legal regulation in China were identified and described; the advantages and disadvantages of the Chinese approach to regulation were identified and argued; this approach was compared with the approaches of China’s main rivals competing with it in terms of the technology development and its legal regulation. All of the above allowed making conclusions about the subsequent development of legal regulation in China and in the whole world.Practical significance: familiarization with the research materials enables interested legal scholars, and not only them, to get a clear idea of the level of artificial intelligence regulation, achieved by China. China’s experience is of significant interest to the rest of the world, showing the correctness or faults of possible regulatory options in the new and complex field. The study results can be used in the practice of legal regulation in the sphere of artificial intelligence, as well as in preparing lectures in the relevant courses and writing tutorials for law students.Journal of Digital Technologies and Law160The research demonstrates the validity of Chinese claims for world leadership in the creation of legal regulation of artificial intelligence, as it is in China that the first normative legal acts were adopted.Journal of Digital Technologies and Law['I. A. Filipova']2024-03-20T00:00:00https://www.semanticscholar.org/paper/b097377e4852577772b01c2ff64e604668374efb 7c858a6c0b96ce45308e163c6dccee16f3de1138The article reveals the problem of defining the concept and nature of virtual assets through the prism of their legal regulation at the national and international levels. The factors determining the use of virtual assets in economic activity around the world and, accordingly, the relevance of the chosen topic of scientific research are determined: informatization of all areas of social life, technological boom, emergence of new information and communication technologies, which can significantly optimize business processes, globalization processes in the international economy, etc. Attention is focused on the lack of a unified approach to defining the concept and legal nature of a virtual asset and the presence of various approaches to solving this issue in national and foreign science of law: identification of virtual assets with the category of virtual currency; definition of a virtual asset through the concept of "information” or "data”; distinguishing the categories "virtual currency” and "virtual assets” as a part and a whole, etc. A conclusion was made about the importance of using the classification of virtual assets according to the criterion of their functional purpose for the formation of the mechanism of their differentiated legal regulation. Attention is also focused on the lack of a single legal approach to determining the essence of virtual assets in the Ukrainian legal system. There were analyzed relevant provisions of the Law of Ukraine "On Virtual Assets”, the draft Concept of State Policy in the Field of Virtual Assets, drafts of the Law "On the Circulation of Cryptocurrencies in Ukraine” No. 7183 dated 06.10.2017, the Law "On Stimulating the Market of Cryptocurrencies and Their Derivatives in Ukraine” No. 7183 dated 10.10.2017 and the Law "On Amendments to the Tax Code of Ukraine (regarding stimulation of the market of cryptocurrencies and their derivatives in Ukraine)” No. 7246 dated 30.10.2017 in the article. Attention is also focused on the need to harmonize the provisions of the Civil Code of Ukraine and the Law of Ukraine "On Virtual Assets” in terms of defining a virtual asset as an object of civil rights. As the conclusions of the conducted scientific research, the author identified the conceptual problems of defining the essence and nature of virtual assets as an object of legal regulation and proposed specific ways to solve some of them, namely: specification of the legal definition of virtual assets in the Law of Ukraine No. 2074-IX; consolidation of the legal definition of the nature of the virtual asset; legislative consolidation of activity in the field of virtual assets and virtual currencies in the Classifier of types of economic activity, etc.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['S. Aksiukov']2024-03-20T00:00:00https://www.semanticscholar.org/paper/7c858a6c0b96ce45308e163c6dccee16f3de1138 841d608a367a0c540710ede32b8d85fa29097c15The article is devoted to the analysis of current national legislation and judicial practice in the field of economic justice in the conditions of martial law. The authors established that the large-scale invasion of Ukraine by the Russian Federation and the introduction of a legal regime of martial law led to the emergence of a number of problems related to the implementation of economic justice in today's conditions. It is noted that the activity of commercial courts is carried out with certain restrictions due to the challenges of martial law. It is emphasized that the issue of the activities of the courts located in the territories where hostilities are actively taking place and in the occupied territories is problematic. The article analyzes the remote operation of commercial courts. At the same time, the authors believe that the remote work of courts is much safer, since there is less crowding of people in one place (in the case of courts - the courtroom) than during the normal work of the court. The issue of renewal of economic procedural terms was studied, in particular, determination by the court of the validity of the reasons for skipping economic procedural terms, while such reasons are an evaluative category, as they are recognized by the courts subjectively and at their own discretion. Considered judicial practice regarding the recognition of the introduction of martial law on the territory of Ukraine as a valid reason for the renewal of economic procedural terms. The authors draw attention to the issue of postponement of the case, namely, the need to recognize the announcement of the "air alarm” signal as one of the reasons for the postponement of the court's consideration of the case. In the conclusions, it is noted that in the conditions of the introduction of the martial law regime, economic justice does not stop its activity, but some of its aspects have changed, because during the operation of the legal regime of martial law, economic justice is carried out taking into account the challenges caused by the present.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['A. I. Shpomer', 'V.S. Shevchenko']2024-03-20T00:00:00https://www.semanticscholar.org/paper/841d608a367a0c540710ede32b8d85fa29097c15 b9c6f89a7a9cbfcf705489809b819aeb9f2fb983This article reveals the importance of the principles of administrative procedure in regulating the process of adoption of administrative acts. The article provides an in-depth analysis of how adherence to these principles is essential to promoting fairness, transparency, and accountability in government decision-making. Thearticle aims to provide an understanding of the principles of administrative law, emphasizing their application in regulating administrative procedures for fair and efficient public administration. The article highlights the impact of administrative acts on individuals and organizations, emphasizing the need for fair and accountable governance. The article examines the basic principles underlying administrative procedures, including due process, legality, fairness, and impartiality. Highlighting their importance, the article illustrates how these principles act as vital checks and balances, protecting citizens' rights and preventing abuse of power. In addition, the article sheds light on the positive effects of transparent and accessible administrative procedures. By promoting public participation and engagement, it highlights the potential for greater collaboration between government and its constituents. The introduction of these principles not only strengthens the legitimacy of administrative acts, but also contributes to the formation of citizens' sense of ownership and responsibility. The language of the article strikes a balance between legal rigor and accessibility, making it relevant for policymakers, legal practitioners, and the general public alike. Advocating for the integration and implementation of administrative- procedural principles, the author emphasizes their importance in improving the quality of management and strengthening democratic values. Overall, this article is informative for those who seek to understand the indispensable role of principles of administrative procedure in creating a fair, transparent and accountable administrative system. Through his persuasive arguments and comprehensive analysis, he encourages a wider commitment to these principles, ultimately strengthening administrative processes and regulation.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['M. Garifullin']2024-03-20T00:00:00https://www.semanticscholar.org/paper/b9c6f89a7a9cbfcf705489809b819aeb9f2fb983 e17c130913b8bc8a515c04897e237b09e75a4b8aJournal of Testing and Evaluation180Journal of Testing and Evaluation['Lin Lin', 'Wei Lan']2024-03-20T00:00:00https://www.semanticscholar.org/paper/e17c130913b8bc8a515c04897e237b09e75a4b8a 4f0965264cc286f3747fa64b1d81cce19b0cbbcb This Viewpoint describes implications for medicine and public health if the US Supreme Court decides to overturn or narrow Chevron deference. Journal of the American Medical Association (JAMA)10JAMA['Sahil Agrawal', 'Joseph S Ross', 'Reshma Ramachandran']2024-03-20T00:00:00https://www.semanticscholar.org/paper/4f0965264cc286f3747fa64b1d81cce19b0cbbcb 2c67d21337476e383a0d23016f18a4988b612734Current issues of legal support for digitization of public participation in the process of making environmentally significant decisions in Ukraine are explored in the article. The significant potential of digital technologies to ensure environmental security and sustainable social development is emphasized. Digitization of relations in the field of ecology in Ukraine affects the determination of the main directions of the national environmental policy. Post-war recovery and development of the economy will require integration into the plans, programs and other documents of the state planning of the ecological block. This is connected with the restoration of the state. This actualizes scientific research on the legal basis of public participation in strategic environmental assessment and environmental impact assessment procedures. Systematic analysis of legal norms, which are the basis of digitalization of strategic environmental assessment and environmental impact assessment, is carried out in the article. The problems of legislative regulation of relations in the specified sphere are defined. The author notes the importance of broad public involvement in the process of making ecologically significant decisions, which is a guarantee of balance, and the effectiveness of decision-making and their further coordinated implementation in practice. Positive innovations are indicated. It is necessary to continue work on improving the procedure of strategic environmental assessment and environmental impact assessment, the conclusion is drawn. Digitization, as one of the defining trends in the development of human civilization, forms an inclusive society and creates conditions for better management mechanisms, expands public access to the environmental sphere, increases the quality of the environment and the range of public services, expands the ways of cooperation of civil society with subjects of power and business, and also creates opportunities in the implementation of environmental rights and satisfaction of the environmental needs of citizens.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['N. Ilkiv']2024-03-20T00:00:00https://www.semanticscholar.org/paper/2c67d21337476e383a0d23016f18a4988b612734 9609fbb73df7fa615f3591ffb2e2ad3d3ec5e422The scientific article states that the state language is an important component of national identity and the issue of ensuring the protection and development of the state language in the context of globalization and the diversity of linguistic communities is an important aspect of ensuring the equality of all citizens, regardless of their linguistic and ethnic origin. The author points out that a significant role in the process of ensuring the possession and development of the state language, as a basis for full participation of citizens in the life of the country, effective communication in the public and political spheres, is played by the obligation to master the state language. It is proposed to define such an obligation as a type of legal obligation, the established necessity of the behavior of a person and a citizen, which is guaranteed and provided by the state in the interests of the person himself and other persons, society, the state within the limits and order provided by the legislation of Ukraine for the purpose of protecting the Ukrainian language and preservation of national identity. The article emphasizes that non-fulfillment or improper fulfillment of the obligation to speak the state language can entail a number of negative consequences both for a specific person and for society as a whole, namely: a) violation of constitutional guarantees of human rights; b) violation of principles of equality and non­discrimination between citizens of Ukraine; c) violation of the principle of linguistic unity and the emergence of inter-ethnic conflicts; d) failure to preserve cultural and national heritage; e) a decrease in the level of trust of citizens in power structures and the system of government bodies as a whole, etc. It is proposed in Chapter II of the Constitution of Ukraine to strengthen the provisions of Art. 10 of the Basic Law of Ukraine and in order to implement the relevant decisions of the Constitutional Court of Ukraine to include the duty of a citizen of Ukraine to know and master the state language, which will act as a guarantee of the protection of the Ukrainian language as the state language, ensure its priority use in society and state institutions, and serve to support and affirm linguistic unity and preserving the national identity of society.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['O. Biloskurska']2024-03-20T00:00:00https://www.semanticscholar.org/paper/9609fbb73df7fa615f3591ffb2e2ad3d3ec5e422 96e0fc54da0d16be4683bd66074cf9477d1d076fGenerative AI (GAI) offers unprecedented possibilities but its commercialization has raised concerns about transparency, reproducibility, bias, and safety. Many"open-source"GAI models lack the necessary components for full understanding and reproduction, and some use restrictive licenses, a practice known as"openwashing."We propose the Model Openness Framework (MOF), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. The MOF requires specific components of the model development lifecycle to be included and released under appropriate open licenses. This framework aims to prevent misrepresentation of models claiming to be open, guide researchers and developers in providing all model components under permissive licenses, and help companies, academia, and hobbyists identify models that can be safely adopted without restrictions. Wide adoption of the MOF will foster a more open AI ecosystem, accelerating research, innovation, and adoption.arXiv.org122The Model Openness Framework (MOF) is proposed, a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access.ArXiv['Matt White', 'Ibrahim Haddad', 'Cailean Osborne', 'Xiao-Yang Liu', 'Ahmed Abdelmonsef', 'Sachin Varghese']2024-03-20T00:00:00https://www.semanticscholar.org/paper/96e0fc54da0d16be4683bd66074cf9477d1d076f a965db0ef3c3b8b2be9d6f5b475894359d4a42e6This research explores the transformative applications of artificial intelligence (AI) and machine learning (ML) in the insurance domain. Specifically, the study investigates how these technologies are automating underwriting processes, optimizing insurance pricing models, and enhancing fraud detection capabilities.International Journal of Innovative Science and Research Technology101International Journal of Innovative Science and Research Technology (IJISRT)['Pankaj Zanke']2024-03-20T00:00:00https://www.semanticscholar.org/paper/a965db0ef3c3b8b2be9d6f5b475894359d4a42e6 a565425b07484c353c17452367b501783886aad0AI and Ethics220It is found developers are largely aware of the ethical territories they must navigate and the moral dilemmas they personally encounter, but they face limited and inconsistent resources for ethical guidance or training and there are significant barriers inhibiting the development of ethical wisdom.AI and Ethics['Tricia A. Griffin', 'B. P. Green', 'Jos V.M. Welie']2024-03-20T00:00:00https://www.semanticscholar.org/paper/a565425b07484c353c17452367b501783886aad0 a2b95a5f2206ee4c4e4b8f9e33ba07169c711fd3The recent emergence of OpenAI and ChatGPT has brought numerous advantages for the professions of accountants and auditors, but at the same time numerous risks, threats and challenges. GPT's ability to understand, predict and generate human-like text has turned the technology into a clear foundation that redefines and shapes a wide range of activities, including internal auditing. GPT models have rapidly evolved from their initial roles in simple text generation to complex applications. Their ability to understand language and context, generate coherent and relevant text, and learn from vast amounts of data makes them ideal for tasks such as compiling internal audit reports. Internal audit reports summarize key findings and identify risks that need to be remedied for the audit committee, CEOs and senior management. However, writing and presenting such reports takes a lot of time, and using GPT can help significantly with that. The subject of the paper is a comprehensive review of a wide range of AI, internal audit, reporting transformation. The main conclusion points to the growing responsibility of internal auditors with the widespread use of generative artificial intelligence services to support audit reporting. Internal auditors must be aware of the risks and challenges brought by the new technology, based on artificial intelligence, which requires clear training and thematic areas incorporated into the curricula in the process of certification of internal auditors.Green and Digital Transition – Challenge or Opportunity50The main conclusion points to the growing responsibility of internal auditors with the widespread use of generative artificial intelligence services to support audit reporting with a wide range of AI, internal audit, reporting transformation.Green and Digital Transition – Challenge or Opportunity['Nemanja Jakovljević', 'Veljko Dmitrović']2024-03-20T00:00:00https://www.semanticscholar.org/paper/a2b95a5f2206ee4c4e4b8f9e33ba07169c711fd3 4f150c8b2835d2839ade70c38decf270e7af6ff6EMBO Reports20The rise of generative AI will transform scientific publishing but it also poses risks, and preserving transparency and human values will become even more important for maintaining trust in the scientific discourse.EMBO Reports['Thomas Lemberger']2024-03-20T00:00:00https://www.semanticscholar.org/paper/4f150c8b2835d2839ade70c38decf270e7af6ff6 a55e1889c8620efe3194d92c8d981cc3c3d6ace5Generative Artificial Intelligence has witnessed a surge in popularity in recent years, characterized by the emergence of groundbreaking models like DALL-E 2, Midjourney, and Stable Diffusion, which have spearheaded advancements in this technological domain. This research aims to harness the potential of Stable Diffusion and its extensions for the purpose of training a LoRA (Low-Rank Adaptation) model to generate images that closely resemble the original subject matter, utilizing a predetermined amount of example data. The primary objective of this research is to demonstrate the prowess of Stable Diffusion and generative AI in a broader context, delving into the possibilities offered by open-source frameworks, highlighting the challenges associated with poorly organized training data and the advantages of properly organized and edited datasets, conducting a comparative analysis of diverse diffusion models and examining various LoRA strength examples. This research also aims to compare the results from larger training parameters on both small and relatively large training models for the purpose of determining if overfitting, over training on one specific subject, is more prevalent with smaller or larger datasets.2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH)200This research aims to harness the potential of Stable Diffusion and its extensions for the purpose of training a LoRA (Low-Rank Adaptation) model to generate images that closely resemble the original subject matter, utilizing a predetermined amount of example data.2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH)['Igor Ćulafić', 'Zoja Šćekić', 'Dejan', 'Babić', 'Tomo Popović', 'Ivan Jovovic']2024-03-20T00:00:00https://www.semanticscholar.org/paper/a55e1889c8620efe3194d92c8d981cc3c3d6ace5 f51e55435e80d400989092751d55923f7d1abe9e Artificial Intelligence (AI) techniques are widely being used in the medical fields or various applications including diagnosis of diseases, prediction and classification of diseases, drug discovery, etc. However, these AI techniques are lacking in the transparency of the predictions or decisions made due to their black box-type operations. The explainable AI (XAI) addresses such issues faced by AI to make better interpretations or decisions by physicians. This article explores XAI techniques in the field of healthcare applications, including the Internet of Medical Things (IoMT). XAI aims to provide transparency, accountability, and traceability in AI-based systems in healthcare applications. It can help in interpreting the predictions or decisions made in medical diagnosis systems, medical decision support systems, smart wearable healthcare devices, etc. Nowadays, XAI methods have been utilized in numerous medical applications over the Internet of Things (IOT), such as medical diagnosis, prognosis, and explanations of the AI models, and hence, XAI in the context of IoMT and healthcare has the potential to enhance the reliability and trustworthiness of AI systems. Recent Advances in Computer Science and Communications00XAI aims to provide transparency, accountability, and traceability in AI-based systems in healthcare applications, including the Internet of Medical Things (IoMT), which has the potential to enhance the reliability and trustworthiness of AI systems.Recent Advances in Computer Science and Communications['Ramasamy Mariappan']2024-03-20T00:00:00https://www.semanticscholar.org/paper/f51e55435e80d400989092751d55923f7d1abe9e d1020d4cebdd13d078cfd01f6e9ed7ec06d41886Objective: to conceptualize, from the viewpoint of personal data protection legislation, the development of natural language processing technology, identifying possible legal barriers to such development and directions for further research of the issue.Methods: the research is based on general scientific methods of cognition, along with which formal-legal and comparative-legal methods were applied, as well as the method of theoretical modeling.Results: it was found that the observance of personal data regime natural language processing in the development of natural language processing technology leads technology, to a conflict between private-legal and public-legal interests, which, personal data in turn, creates obstacles for further development of this technology. The shortcomings of the existing legal order are shown, namely, the insufficient correspondence to the technical features of technology development. This may lead to the risks of excessive regulation, or, on the contrary, to the risks of neglecting critical areas that require protection. Problems in qualifying the data involved in the technology development are outlined. An attempt is made to define the limits of ensuring the lawfulness of personal data processing within the natural language processing technology. The material, temporal and territorial effect of the legal regulation in this field is identified as the limits of ensuring the legality. The author touches upon the possibility of using personal data as a consideration, which is important for the development of natural language processing technology and for the improvement of the information and communication technology industry.Scientific novelty: the paper supplements the scientific discussion on the legal regulation of personal data processing by artificial intelligence systems with an analysis of natural language processing technology. The latter is insufficiently studied, making it relevant to research information law, namely, the legal relations arising around artificial intelligence systems, and to assess the impact of a personal data regime on the development of natural language processing technology.Practical relevance: the applied aspects of the problems researched and the results obtained can be used to improve the legal regulation of public relations in the field of creation and development of artificial intelligence, as well as to identify and assess the legal risks arising in the personal data processing by developers of digital products based on natural language processing technology.Journal of Digital Technologies and Law160It was found that the observance of personal data regime natural language processing in the development of natural language processing technology leads technology, to a conflict between private-legal and public-legal interests, which, personal data in turn, creates obstacles for further development of this technology.Journal of Digital Technologies and Law['I. G. Ilin']2024-03-20T00:00:00https://www.semanticscholar.org/paper/d1020d4cebdd13d078cfd01f6e9ed7ec06d41886 0a69b5e01d3489292cd5a86c104356204686861eThe article examines the history of the development of artificial intelligence (AI), starting from its first theoretical and practical steps and tracing the evolution to modern achievements. The article provides an overview of the key milestones, scientific discoveries and technological breakthroughs made in the field of AI. The most important figures, ideas and principles that influenced its development are also discussed. In the context of this development, various definitions of artificial intelligence are given. There are several key stages in the history of AI: the early stages, the quiet period, the AI renaissance, and the era of AI in the new millennium. Each of these stages made its own unique contribution to the progress of AI. The modern period is characterized by rapid development, especially in the field of machine learning and deep learning. These methods allow artificial intelligence to learn from data and identify complex patterns. Advances in natural language processing, such as models GPT and its modifications, have shown outstanding results. However, despite linguistic advances, GPT remains limited in aspects important to creating strong AI. The article discusses the limitations of modern language models, as well as the prerequisites and prospects for the development of strong artificial intelligence. Special attention is paid to the project of Elon Musk, who, having launched the company X.AI, is engaged in research in the field of creating strong AI with the goal of “knowledge of reality.” The article also proposes an alternative approach to creating strong artificial intelligence - the development of an artificial brain based on a multidimensional multi-connected receptor-effector neuron-like growing network. Some aspects of the emergence of artificial consciousness are also considered.Artificial Intelligence00The article provides an overview of the key milestones, scientific discoveries and technological breakthroughs made in the field of AI, and proposes an alternative approach to creating strong artificial intelligence - the development of an artificial brain based on a multidimensional multi-connected receptor-effector neuron-like growing network.Artificial Intelligence['Yashchenko V']2024-03-20T00:00:00https://www.semanticscholar.org/paper/0a69b5e01d3489292cd5a86c104356204686861e 20294d30b84ba9ac499b13eb378e6bef88d768f7Strategic issues of artificial intelligence use in medicine are considered. Summarizing, as of today, AI supports doctors but does not replace them. It is emphasized that AI in healthcare typically solves important, but rather limited in scope, tasks. Difficulties in further implementation of AI are analyzed. The aim of the study was to address the analytical generalization of AI capabilities in healthcare, analyze the problems of using the Universum of medical-biological knowledge as a global unified resource, and conceptually justify the need to structure medical-biological knowledge, introducing fundamentally new forms of knowledge transfer in healthcare. Conclusions made: 1. The goal of AI implementation should be to find a delicate, mutually beneficial balance between its effective use and the judgments of trained doctors. This is extremely important, as artificial intelligence, which may practically fully replace the labour of doctors in the near future, today is an issue that might otherwise hinder obtaining benefits from it. 2. AI will become an integral part of future medicine. Therefore, it is important to teach the new generation of medical interns the concepts and principles of AI application, to function effectively in the workplace. It is extremely important to develop skills such as empathy in AI. 3. A systematic approach to the continuous improvement of diagnostic and treatment processes and systems for patients, first and foremost, requires bridging the gap between accumulated medical knowledge and the logic and results of AI use.Artificial Intelligence00The aim of the study was to address the analytical generalization of AI capabilities in healthcare, analyze the problems of using the Universum of medical-biological knowledge as a global unified resource, and conceptually justify the need to structure medical-biological knowledge, introducing fundamentally new forms of knowledge transfer in healthcare.Artificial Intelligence['M. O']2024-03-20T00:00:00https://www.semanticscholar.org/paper/20294d30b84ba9ac499b13eb378e6bef88d768f7 ef3ab5cf6938d9ff23826fae866b8b7dafa01111This research paper focuses on the development of efficient artificial intelligence models for autonomous decision-making in dynamic information systems. Using innovative approaches in data analysis and algorithm optimization, we explore ways to improve model performance in dynamic environments. The results of this research can provide a deeper understanding of how artificial intelligence can operate effectively in real time, thus opening up new perspectives for application in different industries. The research includes the implementation of advanced machine learning techniques, as well as the analysis of adaptive models that can adapt to changes in the environment. Key attention is devoted to the optimization of resources in order to ensure quick and precise decision-making in dynamic situations. In addition, the work addresses the integration of the model with high-performance sensors to improve the system's ability to gather relevant information for decision-making. Through this interdisciplinary analysis, we aim to contribute to the development of intelligent systems that can autonomously react to changes and unforeseen situations in real time.Journal of Mathematical Techniques and Computational Mathematics00This research paper focuses on the development of efficient artificial intelligence models for autonomous decision-making in dynamic information systems using innovative approaches in data analysis and algorithm optimization to improve model performance in dynamic environments.Journal of Mathematical Techniques and Computational Mathematics[]2024-03-20T00:00:00https://www.semanticscholar.org/paper/ef3ab5cf6938d9ff23826fae866b8b7dafa01111 71dbad244509c38795e5815d8d6e666079adf5f0The paper aims to showcase the technological advancements in artificial intelligence and to highlight its significance in the realms of business and management. Additionally, the research addresses the anticipated requirements of business education in the future. In the paper, various definitions of artificial intelligence have been examined, including those provided by high-ranking expert groups such as the Organization for Economic Cooperation and Development (OECD), the Council of Europe, and the European Commission. Additionally, we have formulated our own definition. We define artificial intelligence as systems capable of exploring external data to achieve specific goals. These systems have the capacity to learn and autonomously perform tasks, reaching near-superhuman performance across a broad spectrum of activities. In our research, focusing on areas such as fraud detection will allow you to consider both the risks and opportunities associated with AI. Artificial intelligence holds immense potential to reshape the economy, foster the emergence of new industries and business models, enhance productivity, and elevate the overall standard of living. By automating routine tasks, it liberates time, allowing individuals to concentrate on more creative endeavors. As the significance of addressing these emerging challenges grows, business education must play an increasingly vital role in preparing students. Consequently, business programs should concentrate on enhancing students' understanding of the tools and techniques prevalent in the modern business environment. The ultimate objective is to cultivate leadership skills, empowering students to employ these tools and techniques for critical analysis of business challenges and opportunities. This, in turn, equips them to make compelling recommendations and decisions, ultimately enhancing organizational performance. Keywords: Artificial Intelligence(AI),Business Education, Hypercompetitive Environment.Economics41The paper aims to showcase the technological advancements in artificial intelligence and to highlight its significance in the realms of business and management and addresses the anticipated requirements of business education in the future.Economics['Ketevan Shengelia Ketevan Shengelia']2024-03-20T00:00:00https://www.semanticscholar.org/paper/71dbad244509c38795e5815d8d6e666079adf5f0 6ea40a144718c0f8bea2f8c9a39ebb15a87d37afSee the corresponding articles “Positive Predictive Values of Abnormality Scores from a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography” at https://doi.org/10.3348/ kjr.2023.0907 and “Uncover This Tech Term: Uncertainty Quantification for Deep Learning” at https://doi.org/10.3348/kjr.2024.0108.Korean Journal of Radiology100See the corresponding articles “Positive Predictive Values of Abnormality Scores from a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography” and “Uncover This Tech Term: Uncertainty Quantification for Deep Learning” for details.Korean Journal of Radiology['Seong Ho Park', 'Eui Jin Hwang']2024-03-20T00:00:00https://www.semanticscholar.org/paper/6ea40a144718c0f8bea2f8c9a39ebb15a87d37af 66b3d0db648caa1ab9fe0f0607ca0c3e4fded261In response to the dynamic changes in citizens' lives, governments worldwide have embraced digital transformation through the implementation of e-government systems. This paper explores the transformative potential of artificial intelligence (AI) in enhancing the efficiency of e-government services. With the ability to process large datasets and make autonomous decisions, AI is positioned to optimize government operations and improve service delivery. The integration of AI in public policies reshapes governance by enabling data-informed decisions. The study employs a systematic literature review to assess the existing research landscape on the impact of AI on e-government efficiency.2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH)350The transformative potential of artificial intelligence in enhancing the efficiency of e-government services is explored and the existing research landscape on the impact of AI on e-government efficiency is assessed.2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH)['Aleksandra Hornjak']2024-03-20T00:00:00https://www.semanticscholar.org/paper/66b3d0db648caa1ab9fe0f0607ca0c3e4fded261 14257d0a6af23c9976eaa632a5aa2e6f941f83bbINTRODUCTION: The societal landscape in India has witnessed a very transformative shift in the perspectives on relationships, with an increasing prevalence of live-in couples challenging the traditional norms of marriage. However, this ongoing trend has brought about a huge surge in legal complexities, including recognition, partner rights, property disputes, and inheritance issues. This study proposed an innovative approach that leveraged the potential of Artificial Intelligence and Automatic speech recognition for the registration and redressal of live-in relationship matters. OBJECTIVES: This research explores and seeks for the optimization of the resolution of live-in relationship disputes which occurs in the legal perspective with the help of an AI-based platform. The primary goal of this research was to overcome the physical barriers while ensuring the correct accessibility to legal procedures for the registration and addressing of the grievances related to live-in relationships. METHODS: Here, the methodology followed, starting from the thorough review which was conducted using different resources from Scopus, PubMed, and ResearchGate. This research explored the increasing complaints and varying victim counts in live-in relationship cases. This finally attributed to these issues to a lack of physical access to legal remedies. RESULTS: This study also emphasized the major significance of AI-driven redressal processes in the real-time alleviation of the hurdles and challenges associated with live-in relationship cases. The proposed framework and platform aimed to offer an alternative means for the individuals who were unable to physically approach the authorities, facilitating a more efficient and seamless way of legal resolution more quickly. CONCLUSION: This study advocates for the integration of AI and AST technologies in the legal domain, specifically for addressing live-in relationship issues. The implementation of such a system had the potential to bridge gaps in its accessibility, thereby contributing to a more inclusive and efficient legal framework for individuals who are passionately involved in live-in relationships.EAI Endorsed Transactions on Internet of Things170This study proposed an innovative approach that leveraged the potential of Artificial Intelligence and Automatic speech recognition for the registration and redressal of live-in relationship matters and advocates for the integration of AI and AST technologies in the legal domain, specifically for addressing live-in relationship issues.EAI Endorsed Transactions on Internet of Things['Pallavi Gusain', 'Poonam Rawat', 'Minakshi Memoria', 'Tanupriya Choudhury', 'Ayan Sar']2024-03-20T00:00:00https://www.semanticscholar.org/paper/14257d0a6af23c9976eaa632a5aa2e6f941f83bb 7bfac8c326107e6cfecec69f570f9890346bc4f9Objective To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. METHODS The bibliometric analysis was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. RESULTS Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal 'Artificial Intelligence in Medicine' accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.JPMA. The Journal of the Pakistan Medical Association00The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.JPMA. The Journal of the Pakistan Medical Association['Fatima Zahoor', 'Muhammad Abdullah', 'Muhammad Waleed Tahir', 'Asif Islam']2024-03-20T00:00:00https://www.semanticscholar.org/paper/7bfac8c326107e6cfecec69f570f9890346bc4f9 1b95cf056a0d643d91f1f78a77bd991ff12f4bdcAfter the rapid development of technology, challenges related to decreasing concentration in children and youth have become apparent, posing a serious issue in the education process. This paper explores how the implementation of artificial intelligence (AI) can significantly enhance education, with a specific focus on improving concentration and developing technical skills, particularly in the fields of programming and technical sciences. The initial analysis will present a hypothesis on how AI can serve as a powerful tool to overcome contemporary challenges and provide effective support for achieving results and answers more efficiently. The motivation behind this research lies in the necessity to develop innovative methods to support education through technological advancements. A review will be conducted on various studies that examine the impact of AI applications in an educational context.2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH)180This paper explores how the implementation of artificial intelligence can significantly enhance education, with a specific focus on improving concentration and developing technical skills, particularly in the fields of programming and technical sciences.2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH)['Maša Šaranović']2024-03-20T00:00:00https://www.semanticscholar.org/paper/1b95cf056a0d643d91f1f78a77bd991ff12f4bdc cb6922e4e2dd5b335af9dddf163a27e35ae788b0Education and Information Technologies : Official Journal of the IFIP technical committee on Education550Recommendations were formulated for developing effective teacher professional development programs in the field of AI education after a substantial gap exists in the AI-related content and technological knowledge of the recruited teachers.Education and Information Technologies['Miao Yue', 'M. Jong', 'D. Ng']2024-03-20T00:00:00https://www.semanticscholar.org/paper/cb6922e4e2dd5b335af9dddf163a27e35ae788b0 ef308e60525a821e9035ac7734476e61b3aebb48OBJECTIVE This study aims to explore the opinions on the insurance coverage of artificial intelligence (AI), as categorized based on the distinct value elements offered by AI, with a specific focus on patient-centered outcomes (PCOs). PCOs are distinguished from traditional clinical outcomes and focus on patient-reported experiences and values such as quality of life, functionality, well-being, physical or emotional status, and convenience. MATERIALS AND METHODS We classified the value elements provided by AI into four dimensions: clinical outcomes, economic aspects, organizational aspects, and non-clinical PCOs. The survey comprised three sections: 1) experiences with PCOs in evaluating AI, 2) opinions on the coverage of AI by the National Health Insurance of the Republic of Korea when AI demonstrated benefits across the four value elements, and 3) respondent characteristics. The opinions regarding AI insurance coverage were assessed dichotomously and semi-quantitatively: non-approval (0) vs. approval (on a 1-10 weight scale, with 10 indicating the strongest approval). The survey was conducted from July 4 to 26, 2023, using a web-based method. Responses to PCOs and other value elements were compared. RESULTS Among 200 respondents, 44 (22%) were patients/patient representatives, 64 (32%) were industry/developers, 60 (30%) were medical practitioners/doctors, and 32 (16%) were government health personnel. The level of experience with PCOs regarding AI was low, with only 7% (14/200) having direct experience and 10% (20/200) having any experience (either direct or indirect). The approval rate for insurance coverage for PCOs was 74% (148/200), significantly lower than the corresponding rates for other value elements (82.5%-93.5%; P ≤ 0.034). The approval strength was significantly lower for PCOs, with a mean weight ± standard deviation of 5.1 ± 3.5, compared to other value elements (P ≤ 0.036). CONCLUSION There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs.Korean Journal of Radiology840There is currently limited demand for insurance coverage for AI that demonstrates benefits in terms of non-clinical PCOs, as categorized based on the distinct value elements offered by AI.Korean journal of radiology['Hoyol Jhang', 'So Jin Park', 'Ah-Ram Sul', 'Hye Young Jang', 'Seong Ho Park']2024-03-20T00:00:00https://www.semanticscholar.org/paper/ef308e60525a821e9035ac7734476e61b3aebb48 66fe0b2ef22f09f16f7b086ad59800d260478b5c It is widely accepted that directors’ duties encompass the duty to prepare and analyse information concerning the company’s business and affairs and market conditions and the duty to monitor and oversee the company’s actual and prospective economic and financial outlook. While exercising the so-called business judgment rule, directors shall question themselves as to whether the information reasonably available at the time a decision is to be made provides sufficient grounds to support it. They also must have in due regard the underlying business rationality of the decision concerned. The issue merits particular attention in the context of companies undergoing economic or financial distress in the vicinity of insolvency. Directors usually resort to assistance from internal staff and external advisors as well as to technological tools for performing managerial activities. There are a number of options of technologies that employ artificial intelligence (AI) and that have shown a high degree of certainty in predicting scenarios of economic or financial distress. However, directors are not exempted from potential accountability for actions taken in breach of their duties by simply employing AI technology. If, how, and to which extent the use of AI in corporate governance will affect the compliance of a director’s duties are questions yet to be addressed.Uniform Law Review = Revue de Droit Uniforme00There are a number of options of technologies that employ artificial intelligence (AI) and that have shown a high degree of certainty in predicting scenarios of economic or financial distress, but directors are not exempted from potential accountability for actions taken in breach of their duties by simply employing AI technology.Uniform Law Review['Igor Silva de Lima']2024-03-20T00:00:00https://www.semanticscholar.org/paper/66fe0b2ef22f09f16f7b086ad59800d260478b5c 211495cbe58706b55ffc08aeddb6a611d06c084fArtificial intelligence (AI) is rising rapidly, driven by big data, complex algorithms, and computing resources. Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting tool across all facets of toxicology.Chemical Research in Toxicology70Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting tool across all facets of toxicology.Chemical research in toxicology['R. Lui']2024-03-20T00:00:00https://www.semanticscholar.org/paper/211495cbe58706b55ffc08aeddb6a611d06c084f ffcab0338a2807ada4f09931a0c17d9fec83802bBackground A thorough and in-depth examination of generative artificial intelligence (GAI) usage guidelines in medical journals will inform potential gaps and promote proper GAI usage in scholarly publishing. This study aims to examine the provision and specificity of GAI usage guidelines and their relationships with journal characteristics. Methods From the SCImago Journal Rank (SJR) list for medicine in 2022, we selected 98 journals as top journals to represent highly indexed journals and 144 as whole-spectrum sample journals to represent all medical journals. We examined their GAI usage guidelines for scholarly publishing between December 2023 and January 2024. Results Compared to whole-spectrum sample journals, the top journals were more likely to provide author guidelines (64.3% vs. 27.8%) and reviewer guidelines (11.2% vs. 0.0%) as well as refer to external guidelines (85.7% vs 74.3%). Probit models showed that SJR score or region was not associated with the provision of these guidelines among top journals. However, among whole-spectrum sample journals, SJR score was positively associated with the provision of author guidelines (0.85, 95% CI 0.49 to 1.25) and references to external guidelines (2.01, 95% CI 1.24 to 3.65). Liner models showed that SJR score was positively associated with the specificity level of author and reviewer guidelines among whole-spectrum sample journals (1.21, 95% CI 0.72 to 1.70), and no such pattern was observed among top journals. Conclusions The provision of GAI usage guidelines is limited across medical journals, especially for reviewer guidelines. The lack of specificity and consistency in existing guidelines highlights areas deserving improvement. These findings suggest that immediate attention is needed to guide GAI usage in scholarly publishing in medical journals.medRxiv500The provision of GAI usage guidelines is limited across medical journals, especially for reviewer guidelines, and the lack of specificity and consistency in existing guidelines highlights areas deserving improvement.['MA Shuhui Yin', 'PhD Peiyi Lu', 'MSc Zhuoran Xu', 'EdD Zi Lian', 'PhD Chenfei Ye', 'DrPH Chihua Li']2024-03-20T00:00:00https://www.semanticscholar.org/paper/ffcab0338a2807ada4f09931a0c17d9fec83802b 2f71d2d47a61f943ca058cfb4dccd9c0915658dbMisinformation constitutes a societal practice and challenge that necessitates unwavering attention worldwide. In this essay, we discussed the theoretical advancement and empirical evidence in misinformation research, encompassing a review of definitions of misinformation, research orientations, research perspectives, and vulnerable groups. We then reviewed the misinformation fueled by generative artificial intelligence (AI) and the evolving conceptualization of literacy. To counter AI-fueled misinformation, we argue that the development of ethical AI necessitates regulations from AI practitioners and legislation, and ethical uses of AI require efforts in AI literacy education and research. The AI literacy should include (a) users’ understanding and critical evaluation of knowledge, values, and cultures within which AI systems function, and their implications on the AI-generated content, (b) users’ strategic interpretation and proper use of AI-generated content, and (c) users’ utilization of feedback mechanisms to promote institutional management of the AI power.Emerging Media450It is argued that the development of ethical AI necessitates regulations from AI practitioners and legislation, and ethical uses of AI require efforts in AI literacy education and research.Emerging Media['Chun Chu-Ke', 'Yujie Dong']2024-03-20T00:00:00https://www.semanticscholar.org/paper/2f71d2d47a61f943ca058cfb4dccd9c0915658db 8a23f60026ed3acec382ed121101d64130125ca9Objective: The aim of this study is to construct a curated bibliographic dataset for a landscape analysis on Health Artificial Intelligence (HAI) research. Data Source: We integrated HAI-related bibliographic records, including publications, open research datasets, patents, research grants, and clinical trials from Medline and Dimensions. Methods: Searching: Relevant documents were identified using Medical Subject Headings (MeSH) and Field of Research (FoR) indexed by 2 bibliographic databases, Medline and Dimensions. Extracting: MeSH terms annotated from the aforementioned bibliographic databases served as the primary information for our processing. For document records lacking MeSH terms, we re-extracted them using the Medical Text Indexer (MTI). Mapping: In order to enhance interoperability, HAI multi-documents were organized using a mapping system incorporating MeSH, FoR, The International Classification of Diseases (ICD-10), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT). Integrating: All documents were curated based on a pre-defined ontology of health problems and AI technologies from the MeSH hierarchy. Results: We collected 96,332 HAI documents (publications: 75,820, open research datasets: 638, patents: 11,226, grants: 6,113, and clinical trials: 2,535) during 2009 to 2021. On average, 75.12% of the documents were tagged with at least one label related to either health problems or AI technologies (with 92.9% of publications tagged). Summary: This study presents a comprehensive pipeline for processing and curating HAI bibliographic documents following the FAIR (Findable, Accessible, Interoperable, Reusable) standard, offering a valuable multidimensional collection for the community. This dataset serves as a crucial resource for horizontally scanning the funding, research, clinical assessments, and innovations within the HAI field.Health Data Science210This study presents a comprehensive pipeline for processing and curating HAI bibliographic documents following the FAIR (Findable, Accessible, Interoperable, Reusable) standard, offering a valuable multidimensional collection for the community.Health Data Science['Xuanyu Shi', 'Daoxin Yin', 'Yongmei Bai', 'Wenjing Zhao', 'Xin Guo', 'Huage Sun', 'Dongliang Cui', 'Jian Du']2024-03-20T00:00:00https://www.semanticscholar.org/paper/8a23f60026ed3acec382ed121101d64130125ca9 a17a259bac0de37fbadeaa7c530a2ea6f9ca5183Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.Creative Nursing480This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice and four key aspects of AI use in implementation science are highlighted.Creative nursing['Ahtisham Younas', 'Staci S. Reynolds']2024-03-20T00:00:00https://www.semanticscholar.org/paper/a17a259bac0de37fbadeaa7c530a2ea6f9ca5183 a92e02aec19da2baeeb0ba1bd4e0d07481852df2This article explores the integration of Artificial Intelligence (AI) with Business Process Management (BPM) in healthcare. It examines how AI technologies, such as diagnostic decision support, process automation, and predictive analytics, enhance BPM strategies to streamline workflows, improve decision-making, and facilitate continuous process improvement. The discussion highlights opportunities for optimizing healthcare processes, enhancing patient care, and driving operational efficiency through the convergence of AI and BPM. However, the challenges related to data privacy, regulatory compliance, interoperability, and workforce readiness must be addressed. Looking ahead, the integration of AI with BPM holds immense potential to revolutionize healthcare delivery and improve patient outcomes. Keywords: Artificial Intelligence (AI), Healthcare, Applications, Benefits, Challenges.Economics40This article explores how AI technologies, such as diagnostic decision support, process automation, and predictive analytics, enhance BPM strategies to streamline workflows, improve decision-making, and facilitate continuous process improvement through the convergence of AI and BPM.Economics['Tamar Lekiashvili Tamar Lekiashvili']2024-03-20T00:00:00https://www.semanticscholar.org/paper/a92e02aec19da2baeeb0ba1bd4e0d07481852df2 d0f91d70145208df54a49753fb81bbde739a1338Artificial intelligence (AI) in higher education is a complex issue that can be discussed from many different perspectives. There is currently a great need for ethical discussions about the use of AI in universities. For example, educational researchers and teachers are already talking a lot about fairness, accountability, transparency, bias, autonomy, agency and inclusion of AI applications, and discussing these in terms of concrete teaching-learning settings. However, less explored are the implications of AI-enhanced teaching and learning in relation to fundamental educational ideals and goals. The article takes this research desideratum as a starting point by relating the use of AI in universities to Kant's reflections on enlightenment. The aim of this article is to theoretically analyse the compatibility of various AI tools with the ideal of maturity on an educational philosophical level and to formulate recommendations for action based on the results. Through a comprehensive literature review, the article analyses the impact of intelligent tutoring systems, ChatGPT and AI-supported research tools on students’ maturity and discusses both opportunities and challenges for higher education. The theoretical analysis shows that intelligent tutoring systems and ChatGPT threaten student maturity, while AI-supported research tools can have a positive effect.  In addition, the analysis provides several recommendations that can help to minimise the risks of AI applications in terms of student maturity. The educational principle of research-based learning is of particular importance in this context, illustrating how students can learn to use AI tools responsibly and maturely. In this sense, the paper presents a theoretical study that fundamentally reflects on the maturity of students in the age of AI and thus both encourages teachers in the field of e-teaching to critically reflect on AI-based tools and provides a basis for further empirical research.Electronic Journal of e-Learning640The paper presents a theoretical study that fundamentally reflects on the maturity of students in the age of AI and thus both encourages teachers in the field of e-teaching to critically reflect on AI-based tools and provides a basis for further empirical research.Electronic Journal of e-Learning['Alice Watanabe']2024-03-20T00:00:00https://www.semanticscholar.org/paper/d0f91d70145208df54a49753fb81bbde739a1338 796d13498c0e2eebb3710cd1ffa3a8102a39de61Artificial intelligence (AI) is impacting the society with incredible innovations across the globe, as evidenced by recent media coverage. Integrating AI into healthcare holds promise for improving outcomes in low-income nations. Addressing the reliability, validity, and fairness of algorithms is essential to reduce bias. Global organizations such as the United Nations Educational, Scientific and Cultural Organization (UNESCO), the World Health Organization (WHO), and the European Union promote ethical AI use, guiding its application in healthcare. Philanthropic investment in research is crucial to help develop guidelines that consider the unique needs of marginalized populations. Collaboration between governments, international bodies, researchers, and industry leaders is vital to ensure responsible AI adoption. By prioritizing the welfare of all populations, we can harness AI’s potential to enhance healthcare in low-income countries while mitigating risks.International Journal of Translational Medical Research and Public Health00By prioritizing the welfare of all populations, AI’s potential to enhance healthcare in low-income countries while mitigating risks can be harnessed, to harness AI’s potential to enhance healthcare in low-income countries while mitigating risks.International Journal of Translational Medical Research and Public Health['Marilyn Murrillo']2024-03-20T00:00:00https://www.semanticscholar.org/paper/796d13498c0e2eebb3710cd1ffa3a8102a39de61 20bf1861f52dac882e9608fc96b7b2719089fbebTulisan ini secara umum bertujuan untuk mengetahui pengaturan Artificial Intelegence (AI) terhadap pertanggungjawaban pidana di Indonesia. Tulisan ini memfokuskan pada dua hal yaitu, pertama, alasan-alasan terkait pentingnya pengaturan AI terhadap pertanggungjawaban pidana. Kedua, konsep pengaturan AI terhadap pertanggungjawaban pidana di Indonesia. Kecerdasan buatan memiliki potensi luar biasa untuk melakukan hal baik, juga dapat melakukan hal buruk terutama pada hal-hal yang tidak dapat diantisipasi. Perbuatan hukum yang “dilakukan” AI seharusnya dapat dipertanggungjawabkan. Tidak diakuinya AI sebagai subjek hukum menurut hukum positif Indonesia menimbulkan masalah baru yang harus diantisipasi mulai dari sekarang.Jurnal Suara Keadilan100Jurnal Suara Keadilan['Muhammad Fatahillah']2024-03-20T00:00:00https://www.semanticscholar.org/paper/20bf1861f52dac882e9608fc96b7b2719089fbeb 47c30f5547d56d5d79b5682c70216b9df05a0028Biomedical Journal of Scientific &amp; Technical Research02Biomedical Journal of Scientific &amp; Technical Research['Robert W McGee']2024-03-20T00:00:00https://www.semanticscholar.org/paper/47c30f5547d56d5d79b5682c70216b9df05a0028 ee46f014153349a301111be9007a46ec79dd0f68British medical journal61BMJ['M. Sorich', 'B. Menz', 'A. Hopkins']2024-03-20T00:00:00https://www.semanticscholar.org/paper/ee46f014153349a301111be9007a46ec79dd0f68 6e9d74410ddd649fb63c79bcf75e00ba1aca5842Proceedings on Engineering Sciences00Proceedings on Engineering Sciences['K. Ramya', 'K. Karthikeyan']2024-03-20T00:00:00https://www.semanticscholar.org/paper/6e9d74410ddd649fb63c79bcf75e00ba1aca5842 b0d55338a7a2fd4f48267cc2871f741d13f9f079British medical journal170BMJ['Kacper T Gradon']2024-03-20T00:00:00https://www.semanticscholar.org/paper/b0d55338a7a2fd4f48267cc2871f741d13f9f079 7f51651d5b9e7b7b2555ef75a17bda6dcd46815eEuropean Journal of Information Systems790European Journal of Information Systems['Savindu Herath Pathirannehelage', 'Y. Shrestha', 'Georg von Krogh']2024-03-20T00:00:00https://www.semanticscholar.org/paper/7f51651d5b9e7b7b2555ef75a17bda6dcd46815e c94eb0305ebcadda58b58ec2003543bc89632f3cInternational Journal of Social Work Values and Ethics30International Journal of Social Work Values and Ethics['Stephen M. Marson']2024-03-20T00:00:00https://www.semanticscholar.org/paper/c94eb0305ebcadda58b58ec2003543bc89632f3c 3a5b0449f9bcfbe2c3711d6294ab4b360cac9cdeGlobal Journal of Flexible Systems Management410Global Journal of Flexible Systems Management['E. Popkova', 'Shakhlo T. Ergasheva', 'Nadezhda K. Savelyeva', 'Marija A. Troyanskaya']2024-03-20T00:00:00https://www.semanticscholar.org/paper/3a5b0449f9bcfbe2c3711d6294ab4b360cac9cde c7bbc1b1a58ccb3bde51a45363e5f2da339c3616The Australian journal of rural health150The Australian journal of rural health['J. Kovoor', 'Cansy Ittimani', 'Harry Godber', 'Asith Herath', 'Morgan Ovenden', 'C. Ovenden', 'Joseph N Hewitt', 'A. Zaka', 'Mana Ittimani', 'Matthew Marshall-Webb', 'Aashray K. Gupta', 'Brandon Stretton', 'Stephen Bacchi']2024-03-20T00:00:00https://www.semanticscholar.org/paper/c7bbc1b1a58ccb3bde51a45363e5f2da339c3616 62f4322021bebc52bb58d786e667b2c45a6eaac3AI and Ethics290AI and Ethics['Antonio Araújo']2024-03-20T00:00:00https://www.semanticscholar.org/paper/62f4322021bebc52bb58d786e667b2c45a6eaac3 b4a246205aed3407ee27b6b630fd256753eade1dNeurocritical Care80With DL guidance, neurology providers with minimal to no cPOCUS training were often able to obtain diagnostic-quality cardiac images, which informed management changes and significantly decreased time to cardiac imaging.Neurocritical care['Jennifer Mears', 'Safa Kaleem', 'R. Panchamia', 'Hooman Kamel', 'Chris Tam', 'Richard Thalappillil', 'Santosh Murthy', 'A. Merkler', 'Cenai Zhang', 'Judy H. Ch’ang']2024-03-20T00:00:00https://www.semanticscholar.org/paper/b4a246205aed3407ee27b6b630fd256753eade1d b750283f49767391409d22431398afce7b70e0d2The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, and Explainable Artificial Intelligence (XAI) is urgently needed to justify algorithmic predictions in clinical settings. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD), and Fractal Dimension (FD) of linear and non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed ensemble-based stacking classifiers in XAI-CAESDs have demonstrated 2% best average accuracy, Recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.IEEE journal of biomedical and health informatics00This study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system.IEEE journal of biomedical and health informatics['Ijaz Ahmad', 'Mingxing Zhu', 'Guanglin Li', 'D. Javeed', 'Prabhat Kumar', 'Shixiong Chen']2024-03-20T00:00:00https://www.semanticscholar.org/paper/b750283f49767391409d22431398afce7b70e0d2 cbd133772771de863469a9c30dc741d98fef0decObjective: to identify the possibilities for an adequate response of the existing legal regime to the various challenges posed to European law by artificial intelligence systems underlying neuromarketing techniques.Methods: the study is based on the risk-oriented approach, formal-logical, formal-legal and comparative-legal methods, as well as on the method of legal forecasting, in order to identify the problems of legislation caused by the emerging technologies capable of recognizing human emotions and using them to control consumer behavior, and to propose ways to solve them.Results: the conducted research provides a brief overview of the most widely used neuromarketing techniques used by algorithms and machine learning. These allow identifying points of cognitive and emotional vulnerability, collecting and processing data, and then building the most effective marketing techniques that push a consumer to choose a certain product or service. Ethical problems are analyzed which arise from the use of neuromarketing techniques in relation to some basic values such as individual independence, human dignity, and freedom of choice. The subtle line is shown between techniques that manipulate consumer behavior (manipulation technique) and those that, on the contrary, have a persuasive effect, which in itself does not make them illegal (persuasion technique). An overview of the existing legal framework is presented, as well as case law from both the European Court of Justice and national courts of member states with a particular focus on the Unfair Commercial Practices Directive, the EU General Regulation on the Protection of Personal Data (hard law), and codes of ethics (soft law).Scientific novelty: the paper points out the transformation of traditional legal categories and important problem points of the existing regulation due to the growing recognition of the potential of neuromarketing as a tool capable of explaining and predicting consumer behavior, as well as influencing the economic behavior of the subjects of relations.Practical significance: the obtained conclusions and proposals can be taken into account in improving the regulation of artificial intelligence in terms of its safety and reliability, increasing trust in the system, given the need to protect ethical principles and maintain fundamental values.Journal of Digital Technologies and Law550The paper points out the transformation of traditional legal categories and important problem points of the existing regulation due to the growing recognition of the potential of neuromarketing as a tool capable of explaining and predicting consumer behavior, as well as influencing the economic behavior of the subjects of relations.Journal of Digital Technologies and Law['L. Sposini']2024-03-20T00:00:00https://www.semanticscholar.org/paper/cbd133772771de863469a9c30dc741d98fef0dec 93fe839adbc3543a1e227f05a5208ba8d45835a7This paper reviews Trustworthy Artificial Intelligence (TAI) and its various definitions. Considering the principles respected in any society, TAI is often characterized by a few attributes, some of which have led to confusion in regulatory or engineering contexts. We argue against using terms such as Responsible or Ethical AI as substitutes for TAI. And to help clarify any confusion, we suggest leaving them behind. Given the subjectivity and complexity inherent in TAI, developing a universal framework is deemed infeasible. Instead, we advocate for approaches centered on addressing key attributes and properties such as fairness, bias, risk, security, explainability, and reliability. We examine the ongoing regulatory landscape, with a focus on initiatives in the EU, China, and the USA. We recognize that differences in AI regulations based on geopolitical and geographical reasons pose an additional challenge for multinational companies. We identify risk as a core factor in AI regulation and TAI. For example, as outlined in the EU-AI Act, organizations must gauge the risk level of their AI products to act accordingly (or risk hefty fines). We compare modalities of TAI implementation and how multiple cross-functional teams are engaged in the overall process. Thus, a brute force approach for enacting TAI renders its efficiency and agility, moot. To address this, we introduce our framework Set-Formalize-Measure-Act (SFMA). Our solution highlights the importance of transforming TAI-aware metrics, drivers of TAI, stakeholders, and business/legal requirements into actual benchmarks or tests. Finally, over-regulation driven by panic of powerful AI models can, in fact, harm TAI too. Based on GitHub user-activity data, in 2023, AI open-source projects rose to top projects by contributor account. Enabling innovation in TAI hinges on the independent contributions of the open-source community.arXiv.org00This paper argues against using terms such as Responsible or Ethical AI as substitutes for TAI, and introduces the framework Set-Formalize-Measure-Act (SFMA), highlighting the importance of transforming TAI-aware metrics, drivers of TAI, stakeholders, and business/legal requirements into actual benchmarks or tests.ArXiv['M. Nasr-Azadani', 'J. Chatelain']2024-03-19T00:00:00https://www.semanticscholar.org/paper/93fe839adbc3543a1e227f05a5208ba8d45835a7 ec1422a7b1c8da2b4417f2909cfc6983b9598c59Objective: to study the gaps in the legal regulation of relations in the sphere of inventions made by artificial intelligence.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: in Thaler v. Vidal, the U.S. Court of Appeals for the Federal Circuit ruled that an artificial intelligence (AI) machine cannot be an inventor under patent law. This decision leaves open the question of whether a natural person can be the legal inventor of AI-generated inventions. This is a pressing question because it decides whether AI-generated inventions are patentable, as no patent rights can exist without an inventor. Scholars have proposed two doctrines that might resolve this question: the doctrine of simultaneous conception and reduction to practice and the doctrine of first to recognize and appreciate. This article analyzes the two doctrines and argues that neither doctrine readily applies to AI-generated inventions, thereby leaving an “inventorship gap”.Scientific novelty: the article is the first to pose and solve the problem of legal regulation of inventions made with the help of artificial intelligence and to state the need for the U.S. Congress to amend the copyright law in terms of recognizing a physical person who uses artificial intelligence to generate inventions as the author of such inventions. It bridges the gap in legal regulation of relations in the sphere of inventions and patenting and facilitates the goals of the patent system.Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the legal regulation of relations in the sphere of inventions made by artificial intelligence.Russian Journal of Economics and Law100This article argues that neither doctrine readily applies to AI-generated inventions, thereby leaving an “inventorship gap” and calls for the U.S. Congress to amend the copyright law in terms of recognizing a physical person who uses artificial intelligence to generate inventions as the author of such inventions.Russian Journal of Economics and Law['J. Wu']2024-03-19T00:00:00https://www.semanticscholar.org/paper/ec1422a7b1c8da2b4417f2909cfc6983b9598c59 1a7dcee5f06e7b1775a4ed702d3d272788aa61c0Objective: to create a model of legal regulation of clusters in the Russian Federation.Methods: historical method, formal-legal analysis, statistical and sociological methods, systematization, comparative-legal method, methods of legal modeling and forecasting.Results: based on the analysis of scientific literature, Russian and foreign legislation and legal practice, the paper formulates the definition of a “cluster” concept (a group of business entities (suppliers, manufacturers, etc.) located on the territory of a special economic zone, operating in a certain sphere, producing and/or carrying out complementary goods, works, services), defines its features and types, and identifies the models of incentive legal regimes: (a) “model of derogations” – EPR model; b) “model of guarantees” – model of a legal regime stimulating entrepreneurial activity in the field of digital innovations and technologies in the PPP (MPP) framework; c) “model of support” – model of a legal regime stimulating entrepreneurial activity of SMEs in the field of digital innovations and technologies; d) “model of preferences” – model of a legal regime stimulating entrepreneurial activity within the boundaries of territories and entities with high innovation potential. The author proposes to create a unified legal regime of cluster and cluster activity in the Russian Federation by developing and adopting a relevant federal law and amending the legislation on special economic zones.Scientific novelty: the article is the first to carry out a comprehensive comparative legal analysis of clusters and cluster policy, their legal regulation in Russia and foreign countries.Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering issues related to the legal regulation of clusters and cluster policy in Russia and foreign countries, as well as legal regimes stimulating entrepreneurial activity in the field of digital innovation and technology.Russian Journal of Economics and Law151Russian Journal of Economics and Law['E. Gromova']2024-03-19T00:00:00https://www.semanticscholar.org/paper/1a7dcee5f06e7b1775a4ed702d3d272788aa61c0 8f39b0e0d310319718fc7e2f03ceb7fad2d82940Since the adoption of the Non-Legally Binding Authoritative Statement of Principles for Global Consensus on the Management, Conservation and Sustainable Development of all types of Forest at the 1992 Earth Summit in Rio de Janeiro, the issue of combating deforestation remains one of the topical issues relating to climate change discussions globally. To combat deforestation, countries go beyond international conventions to institutionalize domestic frameworks to regulate illegal timber logging. However, the implementation of regulatory policies has been ineffective in countries like Nigeria. Situated within this problem, this study identified some domestic constraints affecting the effectiveness of timber logging regulation in Nigeria. To this end, data was collected from respondents using structured questionnaires. The data were analysed using the Pearson Product Moment Correlation Coefficient (PPMC) at a 0.05 level of significance. Constraints identified are corruption, insufficient legal framework, lack of coordination in regulation enforcement, inadequate staff, and the state monopoly of forest ownership.International journal of social science and human research00International Journal of Social Science and Human Research['Henry Ufomba', 'Elekwachi Eze']2024-03-19T00:00:00https://www.semanticscholar.org/paper/8f39b0e0d310319718fc7e2f03ceb7fad2d82940 f0b0407155db80ab1434f9456191cd083f5c0ad7The article analyzes the regulation for organizing selection procedure for the State Criminal and Executive Service of Ukraine. The content of the legal regulation is revealed regarding the selection organization for personnel of the State Criminal and Executive Service of Ukraine. Emphasis is placed on a number of issues while applying for the provisions of the Law of Ukraine “On the State Criminal and Executive Service of Ukraine” regarding the procedure for the personnel selection for the State Criminal and Executive Service of Ukraine. The selection criteria for the State Criminal and Executive Service of Ukraine have been also determined. The author analyzes the latest normative and legal documents, which regulate the selection procedure for service activities, conducting a medical examination and carrying out a special inspection. Particular attention was paid to the verification of the level of physical readiness when applying for the service, and also to the additional documents submitted for participation in the mentioned verification procedure. The main restrictions related to admission for the service activities in the penitentiary system of Ukraine are singled out in the article. It is proposed to have its own restriction regarding the normative and legal regulation of certain aspects of the procedure for organizing selection for service and the procedure for holding a competition for service to the State Criminal and Executive Service of Ukraine. The conclusions indicate that certain aspects of the legal regulation of the selection organization to the State Criminal and Executive Service of Ukraine require additional analysis and elaboration. Key words: State Criminal and Executive Service of Ukraine, selection of rank and command staff, restriction, probation period, competition, qualification requirements, medical examination, special inspection.Scientific Herald of Sivershchyna. Series: Law00Scientific Herald of Sivershchyna. Series: Law['N. O. Tomkov']2024-03-19T00:00:00https://www.semanticscholar.org/paper/f0b0407155db80ab1434f9456191cd083f5c0ad7 6c101d8f247be10dffa916a84a7f45d47e90821dEmotion is vital to information and message processing, playing a key role in attitude formation. Consequently, creating a mood that evokes an emotional response is essential to any compelling piece of outreach communication. Many nonprofits and charities, despite having established messages, face challenges in creating advocacy campaign videos for social media. It requires significant creative and cognitive efforts to ensure that videos achieve the desired mood across multiple dimensions: script, visuals, and audio. We introduce MoodSmith, an AI-powered system that helps users explore mood possibilities for their message and create advocacy campaigns that are mood-consistent across dimensions. To achieve this, MoodSmith uses emotive language and plotlines for scripts, artistic style and color palette for visuals, and positivity and energy for audio. Our studies show that MoodSmith can effectively achieve a variety of moods, and the produced videos are consistent across media dimensions.arXiv.org261MoodSmith is introduced, an AI-powered system that helps users explore mood possibilities for their message and create advocacy campaigns that are mood-consistent across dimensions.ArXiv['Samia Menon', 'Sitong Wang', 'Lydia Chilton']2024-03-19T00:00:00https://www.semanticscholar.org/paper/6c101d8f247be10dffa916a84a7f45d47e90821d deee374bb68ed3ec92351446495bf8621cb051fdThis paper examines a possible future for postsecondary education in the age of AI. The consensus view among economists is that AI is a general purpose technology (GPT), similar to the steam engine, electricity, and the internet. As a GPT, AI will be the main driver of innovation for the foreseeable future in most sectors of the economy, including education. As AI evolves, it holds the promise of fundamentally redefining the educational landscape, influencing not only current practices in institutional management and pedagogy but also shaping future trends in learning, evaluation, and accreditation. While traditional college-aged students have received significant attention in educational studies, this paper emphasizes the needs of adult learners as lifelong learners and explores how AI-driven innovations can enhance their educational experiences, offering personalized and flexible learning solutions. This paper also argues that a dramatic breakthrough is needed in the cost–value equation for education to support workforce development and lifelong learning.Education sciences81It is argued that a dramatic breakthrough is needed in the cost–value equation for education to support workforce development and lifelong learning and how AI-driven innovations can enhance their educational experiences, offering personalized and flexible learning solutions.Education Sciences['Alfred Essa']2024-03-19T00:00:00https://www.semanticscholar.org/paper/deee374bb68ed3ec92351446495bf8621cb051fd 832dad7f515746192c5dd18bfa885b9c72162708Scientific Reports891The research suggests that community-centred deliberation on the outputs of large language models is a valuable tool for eliciting latent normative perspectives directly from differently situated groups and can provide rich contextual insights for AI alignment.Scientific Reports['Stevie Bergman', 'Nahema Marchal', 'John Mellor', 'Shakir Mohamed', 'Iason Gabriel', 'William Isaac']2024-03-19T00:00:00https://www.semanticscholar.org/paper/832dad7f515746192c5dd18bfa885b9c72162708 ddaf4d33bfaaf72f270e3dd7117a91c760768d4bEducation and Information Technologies : Official Journal of the IFIP technical committee on Education691The findings suggest that ChatGPT positively affects students’ learning experiences, especially in writing, grammar, and vocabulary acquisition, and enhances motivation and engagement through its versatile and accessible nature in various learning activities.Education and Information Technologies['Fatih Karataş', 'Faramarz Yaşar Abedi', 'Filiz Ozek Gunyel', 'Derya Karadeniz', 'Yasemin Kuzgun']2024-03-19T00:00:00https://www.semanticscholar.org/paper/ddaf4d33bfaaf72f270e3dd7117a91c760768d4b 41906e58635dc04cefe1e48a4e574e9d2aec2b7bWe examined the efficacy of AI-assisted learning in an introductory programming course at the university level by using a GPT-4 model to generate personalized hints for compiler errors within a platform for automated assessment of programming assignments. The control group had no access to GPT hints. In the experimental condition GPT hints were provided when a compiler error was detected, for the first half of the problems in each module. For the latter half of the module, hints were disabled. Students highly rated the usefulness of GPT hints. In affect surveys, the experimental group reported significantly higher levels of focus and lower levels of confrustion (confusion and frustration) than the control group. For the six most commonly occurring error types we observed mixed results in terms of performance when access to GPT hints was enabled for the experimental group. However, in the absence of GPT hints, the experimental group's performance surpassed the control group for five out of the six error types.arXiv.org261Mixed results in terms of performance when access to GPT hints was enabled for the experimental group, however, in the absence of GPT hints, the experimental group's performance surpassed the control group for five out of the six error types.ArXiv['Maciej Pankiewicz', 'Ryan S. Baker']2024-03-19T00:00:00https://www.semanticscholar.org/paper/41906e58635dc04cefe1e48a4e574e9d2aec2b7b 707d50923a9c758bd06eccc30efcb83352fccfd4AI assistants for coding are on the rise. However one of the reasons developers and companies avoid harnessing their full potential is the questionable security of the generated code. This paper first reviews the current state-of-the-art and identifies areas for improvement on this issue. Then, we propose a systematic approach based on prompt-altering methods to achieve better code security of (even proprietary black-box) AI-based code generators such as GitHub Copilot, while minimizing the complexity of the application from the user point-of-view, the computational resources, and operational costs. In sum, we propose and evaluate three prompt altering methods: (1) scenario-specific, (2) iterative, and (3) general clause, while we discuss their combination. Contrary to the audit of code security, the latter two of the proposed methods require no expert knowledge from the user. We assess the effectiveness of the proposed methods on the GitHub Copilot using the OpenVPN project in realistic scenarios, and we demonstrate that the proposed methods reduce the number of insecure generated code samples by up to 16\% and increase the number of secure code by up to 8\%. Since our approach does not require access to the internals of the AI models, it can be in general applied to any AI-based code synthesizer, not only GitHub Copilot.arXiv.org321A systematic approach based on prompt-altering methods to achieve better code security of AI-based code generators such as GitHub Copilot, while minimizing the complexity of the application from the user point-of-view, the computational resources, and operational costs.ArXiv['Jakub Res', 'I. Homoliak', 'Martin Peresíni', 'A. Smrčka', 'K. Malinka', 'P. Hanáček']2024-03-19T00:00:00https://www.semanticscholar.org/paper/707d50923a9c758bd06eccc30efcb83352fccfd4 ef3c89263a54c202f84258e6f7fd8f93625ea954Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers - seemingly motivated by profit or clout, not ideology - are already using AI-generated images to gain significant traction on Facebook. At times, the Facebook Feed is recommending unlabeled AI-generated images to users who neither follow the Pages posting the images nor realize that the images are AI-generated, highlighting the need for improved transparency and provenance standards as AI models proliferate.arXiv.org231It is shown that spammers and scammers are already using AI-generated images to gain significant traction on Facebook, highlighting the need for improved transparency and provenance standards as AI models proliferate.ArXiv['Renee DiResta', 'Josh A. Goldstein']2024-03-19T00:00:00https://www.semanticscholar.org/paper/ef3c89263a54c202f84258e6f7fd8f93625ea954 3b59fc87b111941dfc946edfa476f839de1aafbeINTRODUCTION: This research investigates the utilization of artificial intelligence and machine learning in comprehending various climatic variations, emphasizing the associated use of legal and ethical considerations. This escalating impact of climatic change necessitates innovative approaches and the potential of AI/ML to offer tools for analysis and prediction. OBJECTIVES: The primary objective here, was to assess the effectiveness of AI/ML in the deciphering of varying climatic patterns and projecting the future trends. Concurrently, this study aims for the identification and analysis of legal and ethical challenges that may arise from the integration of these technologies in climatic research and policy. METHODS: Here, the literature review forms the basis for understanding various AI/ML applications related to climate science. This study employs various case analyses to examine the existing models to gauge the accuracy and efficiency of predictions. Legal frameworks and ethical principles need to be scrutinized through the qualitative analysis of relevant policies and guidelines. RESULTS: This extensive research reveals the various significant contributions of AI/ML in the enhancement of climatic modeling precision and the prediction of extreme events. However legal and ethical considerations such as data privacy, accountability, and transparency also emerged as crucial challenges which required careful attention. CONCLUSION: While AI/ML exhibited great potential in the advancement of climate research, a balanced approach is imperative to navigate the associated legal and ethical concerns. Striking this equilibrium will be pivotal for ensuring responsible and effective deployment of these technologies in the pursuit of best understanding and mitigating varying climatic variations.EAI Endorsed Transactions on Internet of Things290EAI Endorsed Transactions on Internet of Things['Maheshwari Narayan Joshi', 'A. Dixit', 'Sagar Saxena', 'Minakshi Memoria', 'Tanupriya Choudhury', 'Ayan Sar']2024-03-19T00:00:00https://www.semanticscholar.org/paper/3b59fc87b111941dfc946edfa476f839de1aafbe 172c739c430e0635cb6c3c5471fb4ca13a584234Our vision is to develop an AI-based software which is capable of analyzing frontal PA chest X-rays for disease diagnosis, detection, and prediction through the analysis of several different X-ray manifestations and findings which are inter-correlated to arrive to a final result. We utilize state of-the-art AI, thereby facilitating in empowering of the world through our med-tech ecosystem. It is being developed with the intention of enhanced cardiopulmonary care, bridging the gaps in healthcare, by giving conclusive and comprehensive diagnosis at lower costs and reducing the number of unnecessary diagnostic tests which, often, serve as the main cause of the delay between diagnosis and treatment. Why X-rays? X-rays have been chosen as the input because it is Non-invasive mode of diagnostic test, affordable, accessible and has much lesser radiation exposure compared to other imaging methods of CT, MRI, PET.International Journal of Medical Science and Clinical Research Studies00INTERNATIONAL JOURNAL OF MEDICAL SCIENCE AND CLINICAL RESEARCH STUDIES['Mr. Pritam Dhalla', 'Mr. Soham Pal', 'Mr. Amar Saish']2024-03-19T00:00:00https://www.semanticscholar.org/paper/172c739c430e0635cb6c3c5471fb4ca13a584234 3294dbf56a3ce7e7319dd76ccbeb4521f1104862 Water management is a major contemporary and future challenge. In an increasing water demand scenario related to climate change, a water distribution system must ensure equal access to water for all users. In this context, a reliable short-term water demand forecasting system is crucial for reliable water management. However, despite the abundance of studies in the scientific literature, few examples highlight complete tools for providing such models to real water utilities and water managers. This study presents AI-Forecast, an innovative tool developed to predict water demand with state-of-art models. Such tool is based on the data-driven logic, and it is designed to provide a complete data-driven chain that starts from the data and arrives to the short-term water demand prediction. AI-Forecast can import data, properly manage them, and assess tasks like outlier detection and missing data imputation. Eventually, it can implement state-of-the-art forecasting models and provide the forecasts. The prediction is shown through an intuitive web interface, which is designed to highlight the major information related to the prediction accuracy. Although this tool does not provide a new prediction algorithm, it proposes a complete data-driven chain that is practically designed to take such models in practice to real water utilities.Water supply : the review journal of the International Water Supply Association00This study presents AI-Forecast, an innovative tool developed to predict water demand with state-of-art models, and it proposes a complete data-driven chain that is practically designed to take such models in practice to real water utilities.Water Supply['A. Zanfei', 'Andrea Lombardi', 'Alberto De Luca', 'Andrea Menapace']2024-03-19T00:00:00https://www.semanticscholar.org/paper/3294dbf56a3ce7e7319dd76ccbeb4521f1104862 7ea22325fd6b9ac916371513efd248f1aa6a931cLanguage models (LM or LLM) are increasingly deployed in the field of artificial intelligence (AI) and its applications, but the question arises as to whether they can be a common resource managed and maintained by a community of users. Indeed, the dominance of private companies with exclusive access to massive data and language processing resources can create inequalities and biases in LM, as well as obstacles to innovation for those who do not have the same resources necessary for their implementation. In this contribution, we examine the concept of the commons and its relevance for thinking about LM. We highlight the potential benefits of treating the data and resources needed to create LMs as commons, including increased accessibility, equity, and transparency in the development and use of AI technologies. Finally, we present a case study centered on the Hugging Face platform, an open-source platform for deep learning designed to encourage collaboration and sharing among AI designers.arXiv.org00This contribution examines the concept of the commons and its relevance for thinking about LM, and highlights the potential benefits of treating the data and resources needed to create LMs as commons, including increased accessibility, equity, and transparency in the development and use of AI technologies.ArXiv['Robin Quillivic', 'Salma Mesmoudi']2024-03-19T00:00:00https://www.semanticscholar.org/paper/7ea22325fd6b9ac916371513efd248f1aa6a931c f2d36c036a94e6bf384f12aae11b0bb5f8ab4014Artificial Intelligence has outperformed human experts in functional tasks such as chess and baduk. How about creative tasks? This paper evaluates AI's capability in the creative domain compared to human experts, which little research has been conducted so far. We propose a novel Prompt-for-Prompt to generate social media creatives via prompt augmentation by Large Language Models. We take the most popular Instagram posts (with the biggest number of like clicks) in top brands' Instagram accounts to create social media creatives. We give GPT 4 several prompt instructions with text descriptions to generate the most effective prompts for cutting-edge text-to-image generators: Midjourney, DALL E 3, and Stable Diffusion. LLM-augmented prompts can boost AI's abilities by adding objectives, engagement strategy, lighting and brand consistency for social media image creation. We conduct an extensive human evaluation experiment, and find that AI excels human experts, and Midjourney is better than the other text-to-image generators. Surprisingly, unlike conventional wisdom in the social media industry, prompt instruction including eye-catching shows much poorer performance than those including natural. Regarding the type of creatives, AI improves creatives with animals or products but less with real people. Also, AI improves creatives with short text descriptions more than with long text descriptions, because there is more room for AI to augment prompts with shorter descriptions.arXiv.org340A novel Prompt-for-Prompt to generate social media creatives via prompt augmentation by Large Language Models, and finds that AI excels human experts, and Midjourney is better than the other text-to-image generators.ArXiv['Eunkyung Park', 'Raymond K. Wong', 'Junbum Kwon']2024-03-19T00:00:00https://www.semanticscholar.org/paper/f2d36c036a94e6bf384f12aae11b0bb5f8ab4014 52156731b3fc1864065ab3c40b6878d6f1f2ea37Objective: to show the ontological differences between human and artificial intelligence and address structural divergences at the definitional level.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: a cross-cutting analysis was applied to the phenomenon of AI between cyber utopianism and cyber realism. Starting from a quote by Max Tegmark, the theory of artificial intelligence is reconstructed by the theorists who founded the discipline (Turing, Minsky, Bernstein, von Neumann) and it is discussed why – in light of the discoveries and assumptions of neuroscience – it is not possible to define it as intelligence according to human criteria. Three short notes are included in the appendix that complete the discussion: 1. on the consciousness of machines 2. on the theory of utopian cyber employment and remuneration 3. “The hungry judge is more cruel” (discussion on an Israeli study).Scientific novelty: through the examination of multiple types of intelligence (Gardner) and social intelligence (Thorndike, Goleman), a more complex definition of intelligence is proposed than that which can be replicated by artificial neural networks, especially in relation to the interaction between animal and environment. Three short messages highlight the uncertainty and risks that may arise from the rampant use of artificial intelligence as judges.Practical significance: starting from a correct definition of human intelligence, the author comes to the definition of artificial intelligence. Beyond the myth of AI, we discover its limits and the objective limitations we must provide for in order to save the most precious asset we have: mankind.Russian Journal of Economics and Law100Beyond the myth of AI, the author discovers its limits and the objective limitations the authors must provide for in order to save the most precious asset they have: mankind.Russian Journal of Economics and Law['M. Di Salvo']2024-03-19T00:00:00https://www.semanticscholar.org/paper/52156731b3fc1864065ab3c40b6878d6f1f2ea37 d1b76d5bb71e1f41c91a6aaf04aafc93bdbe81aeImplementing the European Foundation for Quality Management (EFQM) business excellence model in organizations is time- and cost-consuming. The integration of artificial intelligence (AI) into the EFQM business excellence model is a promising approach to improve the efficiency and effectiveness of excellence in organizations. This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence. A case study involving a UAE government organization serves as a sample to train the AI framework. Historical EFQM results from different years are used as training data. The AI framework utilizes the unsupervised machine learning technique known as k-means clustering. This technique follows the ISO/IEC 23053 standard to predict EFQM output total scores based on criteria and sub-criteria inputs. This research paper’s main output is a novel AI framework that can predict EFQM scores for organizations at an early stage. If the predicted EFQM score is not high enough, then the AI framework provides feedback to decision makers regarding the criteria that need reconsideration. Continuous use of this integrated framework helps organizations attain operational excellence. This framework is considered valuable for decision makers as it provides early predictions of EFQM total scores and identifies areas that require improvement before officially applying for the EFQM excellence award, hence saving time and cost. This approach can be considered as an innovative contribution and enhancement to knowledge body and organizational practices.Applied Sciences650This research paper’s integrated framework follows the ISO/IEC 23053 standard in addressing some of the concerns related to time and cost associated with the EFQM model, achieving higher EFQM scores, and hence operational excellence.Applied Sciences['Rola R. Hassan', 'Manar Abu Talib', 'F. Dweiri', 'Jorge Roman']2024-03-19T00:00:00https://www.semanticscholar.org/paper/d1b76d5bb71e1f41c91a6aaf04aafc93bdbe81ae 37c4c8e356d891a0c2a7927453ccfdda0ea23037Discover Artificial Intelligence70The use of a self-learning artificial intelligence system (machine learning) raises the question of who is liable for damages in the event of an erroneous prediction by the system.Discov. Artif. Intell.['Sandra Camacho Clavijo']2024-03-19T00:00:00https://www.semanticscholar.org/paper/37c4c8e356d891a0c2a7927453ccfdda0ea23037 9d086d67eed62bf932812888967183b9fbf8f60cThis paper undertakes a comprehensive exploration of the potential and challenges presented by Generative Artificial Intelligence, with particular emphasis on the GPT models, in the field of cybersecurity. Through a meticulous examination of existing literature and pertinent case studies, the paper evaluates the capabilities of these models in the detection and rectification of vulnerabilities, as well as in identifying malicious code. It also highlights the pivotal role of generative AI in enhancing honeypot technology, which has shown promising results in proactive threat detection. While underscoring the significant advantages of utilizing generative AI in bolstering cybersecurity measures, the paper does not shy away from shedding light on the accompanying security exposures. These range from traditional threats like vulnerabilities and privacy breaches to novel dangers such as jailbreaking, prompt injection, and prompt leakage that are associated with the deployment of these AI models. The overarching objective of this paper is to contribute to the ongoing dialogue about the integration of advanced AI technologies into cybersecurity strategies while emphasizing the importance of vigilance against potential misuse. The paper concludes with a call for continued research and development to ensure a safer and more secure cyberspace for all.Applied and Computational Engineering00A comprehensive exploration of the potential and challenges presented by Generative Artificial Intelligence, with particular emphasis on the GPT models, in the field of cybersecurity, concludes with a call for continued research and development to ensure a safer and more secure cyberspace for all.Applied and Computational Engineering['Shibo Wen']2024-03-19T00:00:00https://www.semanticscholar.org/paper/9d086d67eed62bf932812888967183b9fbf8f60c d6864dc61d8ed479d65c33625e529cb7510a182bPersonalized Education is based on individual interest, learning styles and self-pace. It provides more focused and self-directed learning. Education system is advanced in different aspects hence personalized education provides adaptive learning through online resources and interactive tools. This article investigate how adaptive learning systems are integrated with artificial intelligence (AI) for personalized education. It examines the benefits, drawbacks of AI based personalized education over the traditional personalized education. It examines how AI facilitates personalized learning paths, allowing students to progress at their own pace while receiving targeted interventions and immediate feedback. This paper provides survey of current AI uses in personalize education and future predictions of AI uses in education. Furthermore, the paper highlights key applications of transformer- based models in various domains, including healthcare, finance, and education.International Journal of Advanced Research in Science, Communication and Technology20Survey of current AI uses in personalize education and future predictions of AI uses in education are provided and key applications of transformer- based models in various domains are highlighted, including healthcare, finance, and education.International Journal of Advanced Research in Science, Communication and Technology['Asst. Prof. Neeta Atul Mote']2024-03-19T00:00:00https://www.semanticscholar.org/paper/d6864dc61d8ed479d65c33625e529cb7510a182b 84dbc7e3836c9e4a189a071bb5cd2c4691b3e304Nature21Nature['Ananya']2024-03-19T00:00:00https://www.semanticscholar.org/paper/84dbc7e3836c9e4a189a071bb5cd2c4691b3e304 95190be9c2bf43255cb0e6aa6cb3e94d91ee4dabInternational Conference on Wirtschaftsinformatik60Web Intell.['Hongzhi Kuai', 'Xiao‐Rong Tao']2024-03-19T00:00:00https://www.semanticscholar.org/paper/95190be9c2bf43255cb0e6aa6cb3e94d91ee4dab 09e16dab5409759208319faa0799f20b9290d885Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their consent. The degree of over-representation ranges from around 2x to around 6.5x. Given that LLMs may substitute for the paid labor of those who produced their training data, they have the potential to cause even more substantial and disproportionate economic harm to Jewish Americans in the coming years. This paper focuses on Jewish Americans as a case study, but it is probable that other minority communities (e.g., Asian Americans, Hindu Americans) may be similarly affected and, most importantly, the results should likely be interpreted as a"canary in the coal mine"that highlights deep structural concerns about the current LLM paradigm whose harms could soon affect nearly everyone. We discuss the implications of these results for the policymakers thinking about how to regulate LLMs as well as for those in the AI field who are working to advance LLMs. Our findings stress the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms.International Conference on Human Factors in Computing Systems1240Evidence is identified that the current paradigm of large language models likely continues this long history of systemic property dispossession from minority groups and the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms is stressed.{'pages': '761:1-761:17'}['Heila Precel', 'Allison McDonald', 'Brent Hecht', 'Nicholas Vincent']2024-03-19T00:00:00https://www.semanticscholar.org/paper/09e16dab5409759208319faa0799f20b9290d885 7ad2758f011d13c58d9bff8a6d4457ee85656d8aIn the realm of formal theorem proving, the Coq proof assistant stands out for its rigorous approach to verifying mathematical assertions and software correctness. Despite the advances in artificial intelligence and machine learning, the specialized nature of Coq syntax and semantics poses unique challenges for Large Language Models (LLMs). Addressing this gap, we present a comprehensive dataset specifically designed to enhance LLMs' proficiency in interpreting and generating Coq code. This dataset, derived from a collection of over 10,000 Coq source files, encompasses a wide array of propositions, proofs, and definitions, enriched with metadata including source references and licensing information. Our primary aim is to facilitate the development of LLMs capable of generating syntactically correct and semantically meaningful Coq constructs, thereby advancing the frontier of automated theorem proving. Initial experiments with this dataset have showcased its significant potential; models trained on this data exhibited enhanced accuracy in Coq code generation. Notably, a particular experiment revealed that a fine-tuned LLM was capable of generating 141 valid proofs for a basic lemma, highlighting the dataset's utility in facilitating the discovery of diverse and valid proof strategies. This paper discusses the dataset's composition, the methodology behind its creation, and the implications of our findings for the future of machine learning in formal verification. The dataset is accessible for further research and exploration: https://huggingface.co/datasets/florath/coq-facts-props-proofs-gen0-v1arXiv.org270This paper presents a comprehensive dataset specifically designed to enhance LLMs' proficiency in interpreting and generating Coq code, and discusses the dataset's composition, the methodology behind its creation, and the implications for the future of machine learning in formal verification.ArXiv['Andreas Florath']2024-03-19T00:00:00https://www.semanticscholar.org/paper/7ad2758f011d13c58d9bff8a6d4457ee85656d8a 1b0b6e8aad52c0ab66d7da3486e75a5cf86a18d7Nature00Nature['Katrina Krämer']2024-03-19T00:00:00https://www.semanticscholar.org/paper/1b0b6e8aad52c0ab66d7da3486e75a5cf86a18d7 c1aae4c35b19361f2e6a765a0486e24270f611fbWithin the confluence of the banking and financial sectors, the integration of machine learning in credit risk analysis signifies a paradigm shift towards data-centric decision-making. Historically, methodologies for credit risk were limited in predictive accuracy and computational efficiency. The advent of expansive language models, exemplified by Ant Group's AntFinGLM, offers a solution. These models, underpinned by deep learning, amalgamate financial texts and transactional data, facilitating the discernment of intricate financial paradigms and market nuances. This paper conducts a rigorous exploration of machine learning methodologies, from Bayesian classifiers to k-means clustering, offering an analytical perspective on their advantages and challenges. As the industry inclines towards innovations like AntFinGLM, the imperatives of professionalism, precision, and data sanctity gain significance. Upholding standards that encompass five dimensions and 28 categories, AntFinGLM epitomises these benchmarks, championing enhanced functionalities while fostering collaborative initiatives with financial entities. Addressing challenges, particularly around data security and professional integrity, becomes crucial. Techniques encompassing intent recognition, fact verification, and robust data protection mechanisms are indispensable. In summation, the endeavours of entities like AntFinGLM underscore the transformative prowess of expansive language models, ushering the financial sector into an epoch characterised by astute, efficient, and safeguarded decision-making paradigms.Applied and Computational Engineering00A rigorous exploration of machine learning methodologies, from Bayesian classifiers to k-means clustering, is conducted, offering an analytical perspective on their advantages and challenges.Applied and Computational Engineering['Xinyu Li']2024-03-19T00:00:00https://www.semanticscholar.org/paper/c1aae4c35b19361f2e6a765a0486e24270f611fb 5ad8b73268eef115bb7bfdff9e4bbd865794315cNew generation computing120New Gener. Comput.['Sven Groppe', 'Sarika Jain']2024-03-19T00:00:00https://www.semanticscholar.org/paper/5ad8b73268eef115bb7bfdff9e4bbd865794315c 0d6bae3b412807ddf390076cb49cda2c3f9274f2Philosophy &amp; Technology190Philosophy &amp; Technology['Marc M. Anderson']2024-03-19T00:00:00https://www.semanticscholar.org/paper/0d6bae3b412807ddf390076cb49cda2c3f9274f2 eda0b2461043c50acc849259cd43bc502ef90112Nature10Nature['E. Callaway']2024-03-19T00:00:00https://www.semanticscholar.org/paper/eda0b2461043c50acc849259cd43bc502ef90112 de64c7fac2819c98923a747fac530cbc02cb3ca6 Purpose This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI). Design/methodology/approach Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model. Findings This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP. Originality/value This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain. Journal of Science and Technology Policy Management1203This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan and identified that BDA-AI significantly and positively affects GSCC, SM and EPI.Journal of Science and Technology Policy Management['A. Rashid', 'Neelam Baloch', 'Rizwana Rasheed', 'A. Ngah']2024-03-19T00:00:00https://www.semanticscholar.org/paper/de64c7fac2819c98923a747fac530cbc02cb3ca6 249ddcb7a1f3c2865f7633010773f7e95eb33671Despite significant progress, evaluation of explainable artificial intelligence remains elusive and challenging. In this paper we propose a fine-grained validation framework that is not overly reliant on any one facet of these sociotechnical systems, and that recognises their inherent modular structure: technical building blocks, user-facing explanatory artefacts and social communication protocols. While we concur that user studies are invaluable in assessing the quality and effectiveness of explanation presentation and delivery strategies from the explainees' perspective in a particular deployment context, the underlying explanation generation mechanisms require a separate, predominantly algorithmic validation strategy that accounts for the technical and human-centred desiderata of their (numerical) outputs. Such a comprehensive sociotechnical utility-based evaluation framework could allow to systematically reason about the properties and downstream influence of different building blocks from which explainable artificial intelligence systems are composed -- accounting for a diverse range of their engineering and social aspects -- in view of the anticipated use case.CHI Extended Abstracts852A comprehensive sociotechnical utility-based evaluation framework could allow to systematically reason about the properties and downstream influence of different building blocks from which explainable artificial intelligence systems are composed -- accounting for a diverse range of their engineering and social aspects -- in view of the anticipated use case.ArXiv['Kacper Sokol', 'Julia E. Vogt']2024-03-19T00:00:00https://www.semanticscholar.org/paper/249ddcb7a1f3c2865f7633010773f7e95eb33671 cff6bd0e66e159b3e0e85a6ca28992734dfea656The growing impact and importance of artificial intelligence in society has led to an increasing interest for the potential of artificial intelligence as an educational tool in schools to aid both students and teachers. In this study we investigate digitally skilled K-12 mathematics teachers’ (N=85) attitudes towards and expectations on the role of artificial intelligence in the classroom. The study was done by conducting and analyzing the results of a web-based survey among Swedish and Finnish speaking mathematics teachers using a mixed methods strategy. The Will, Skill and Tool framework was used for the analysis. The survey was done before the introduction of ChatGPT-3. The results indicate that the teachers’ attitudes toward AI tools in school are characterized by interest, openness, and awareness. Teachers have a balanced view on the possibilities and risks of AI use in school. However, the teachers also stress that there is a risk that AI tools will shift the focus from learning key mathematical skills towards learning and interaction with the AI tools themselves. The research concluded that the K-12 mathematics teachers surveyed have broad experience with digital tools and will likely become early adopters of AI tools in the classroom.LUMAT421The research concluded that the K-12 mathematics teachers surveyed have broad experience with digital tools and will likely become early adopters of AI tools in the classroom.LUMAT: International Journal on Math, Science and Technology Education['Ray Pörn', 'Mats Braskén', 'Mattias Wingren', 'Sören Andersson']2024-03-19T00:00:00https://www.semanticscholar.org/paper/cff6bd0e66e159b3e0e85a6ca28992734dfea656 4731e87ed63544a0a771619eae669343210038dcArtificial Intelligence (AI) is a process to shape human thinking styles in order to design machines to be able to behave like humans or also called Cognitive Tasks, namely from programmed information data that makes machines able to do work like humans are doing automatically. The purpose of the study is to see how influential Artificial Intelligence is in improving student achievement, especially in high school. This problem utilizes a quantitative approach with a survey model and in-depth interviews. The research survey used by researchers is online-based using the Google Form Platform. The results of this study explain that Artificial Intelligence can assist teachers in creating students who excel in learning, especially at the high school level. The conclusion of the research on the Effect of Artificial Intelligence at the Senior High School level in Improving Student Learning Achievement is that with the help of this Artificial Intelligence Technology, it helps teachers in achieving student competence in the teaching and learning process and students can also improve achievement in the learning process. The limitation of this study is that researchers only conducted research on the influence of Artificial Intelligence in improving student achievement at the senior high school level, researchers hope that future researchers can conduct the same research but at a higher level.World Psychology341The results of this study explain that Artificial Intelligence can assist teachers in creating students who excel in learning, especially at the high school level, and students can also improve achievement in the learning process.World Psychology['Brave A. Sugiarso', 'Asep Nurjamin', 'Loso Judijanto', 'Luluk Firdausiyah', 'Ardian Al Hidayat']2024-03-19T00:00:00https://www.semanticscholar.org/paper/4731e87ed63544a0a771619eae669343210038dc 84d6d76d3739a55519dabb13a0f27d95012c2134Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI’s potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.Frontiers in Medicine1181This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, and explores the diverse applications of image and molecular processing for skin cancer, and highlights AI’s potential for patient self-screening and improving diagnostic accuracy for non-dermatologists.Frontiers in Medicine['Maria L. Wei', 'Mikio Tada', 'Alexandra So', 'Rodrigo Torres']2024-03-19T00:00:00https://www.semanticscholar.org/paper/84d6d76d3739a55519dabb13a0f27d95012c2134 61bfc031dd2d9e3029bdbab0cefb2003a67d73d0With numerous languages nearing extinction, the urgency to preserve endangered languages has become a prominent focus in the linguistic field. This paper delves into the transformative role of Artificial Intelligence (AI) in the domains of documentation and pedagogy for endangered languages, particularly highlighting its innovative applications and the associated challenges. It delves into how AI-powered tools reshape linguistic fieldwork, offering accelerated annotation, consistent data collection, and deeper analytical endeavors. Furthermore, this exploration highlights the potential of AI in revolutionizing the teaching of these languages, ushering in a new era marked by dynamic, scalable, and engaging learning experiences. While AI presents unparalleled efficiencies, its challenges, ranging from data scarcity to the looming digital divide, are addressed critically. As the digital age continues to evolve, merging AIs capabilities with traditional linguistic approaches holds the promise of a more inclusive and comprehensive strategy to rejuvenate and preserve the worlds rich linguistic tapestry. This paper has summarized and provided an outlook on the research topic at hand.Applied and Computational Engineering180This paper delves into how AI-powered tools reshape linguistic fieldwork, offering accelerated annotation, consistent data collection, and deeper analytical endeavors, and highlights the potential of AI in revolutionizing the teaching of these languages.Applied and Computational Engineering['Luyi Wang']2024-03-19T00:00:00https://www.semanticscholar.org/paper/61bfc031dd2d9e3029bdbab0cefb2003a67d73d0 ed11bc2c3ecec1a5707bfa4896a0ea930b0f664dOrganizations traditional human resource management model has been impacted by the ongoing optimization and advancement of artificial intelligence skills and technology, and the broadening of its application scope. The impact of artificial intelligence (AI) systems on employee recruitment, human resources allocations, and talent management is significant. This paper examines the interplay among AI, data applications, human resource management (HRM) systems and the resultant effects. It will examine the significance of effectively managing the deployment of AI systems, as existing literature defines. This study examines the effects of artificial intelligence AI technology on the effectiveness of company administration compared to traditional human resource management systems (HRMS). Several recommendations are offered to enhance the reformation and optimization of the organizations human resources (HR) division. The research findings indicate that incorporating the new system in conjunction with human involvement can significantly enhance the efficiency of employee recruitment, allocation of human resources, and management of talent within the firm. There was an improvement observed in both employee happiness and productivity elements.Applied and Computational Engineering230The research findings indicate that incorporating the new system in conjunction with human involvement can significantly enhance the efficiency of employee recruitment, allocation of human resources, and management of talent within the firm.Applied and Computational Engineering['Mu Li']2024-03-19T00:00:00https://www.semanticscholar.org/paper/ed11bc2c3ecec1a5707bfa4896a0ea930b0f664d 65b07053f412e0590b0238eb4526e9aacab91cacMore than ever, robust, and interpretable toxic comment recognition methods are required to manage the growing frequency of toxic comments on online platforms. The research tries to incorporate techniques in Explainable Artificial Intelligence (XAI) to improve the transparency and comprehensibility of toxic comment classification. Using a comprehensive dataset, we designed a model architecture which includes the latest practices in XAI. Through rigorous experimentation, our study proves the usefulness of such methods as tools that not only increase classification accuracy but also illuminate model decision-making processes. One view is that by adding LIME and Eli5 to toxic comment classification, model performance improves both in terms of accuracy and interpretation for decisions. Our results provide valuable insights into the model's strengths and areas for refinement, contributing to the transparency and interpretability of toxic comment classification. This research contributes to the evolving landscape of interpretable machine learning, offering a pathway to more accountable and trustworthy toxic comment moderation systems. Keywords: Explainable artificial intelligence, Model interpretability, toxic comment classification, LIME, Eli5INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research tries to incorporate techniques in Explainable Artificial Intelligence (XAI) to improve the transparency and comprehensibility of toxic comment classification, using a comprehensive dataset and designed a model architecture which includes the latest practices in XAI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Mr. Jadyn Dias']2024-03-19T00:00:00https://www.semanticscholar.org/paper/65b07053f412e0590b0238eb4526e9aacab91cac d815c11bd035c2991b26541f91e6af8be11a4077The increasing perfection of artificial intelligence technology has brought subversive changes to the field of financial risk management. The application of artificial models such as neural networks, support vector machines, and mixed intelligence in financial risk management can improve the speed of data processing, provide deep insight into data analysis, reduce human labour costs, and hence improve the efficiency of financial risk control. Meanwhile, the increasing amount of data and the application of AI also bring new challenges to financial risk management, such as the risk of program error and information security. This paper introduces in detail the application status of three models, including Support Vector Machine, Support Vector Machine, and Large Language Model in risk management Based on this, this paper analyses the advantages of AI applications in promoting and reforming the financial industry. The goal is to provide an in-depth examination of present implementations and their respective benefits, as well as to investigate potential future advances in this sector.Applied and Computational Engineering00This paper introduces in detail the application status of three models, including Support Vector Machine, Support Vector Machine, and Large Language Model in risk management and analyses the advantages of AI applications in promoting and reforming the financial industry.Applied and Computational Engineering['Tianyu Nan']2024-03-19T00:00:00https://www.semanticscholar.org/paper/d815c11bd035c2991b26541f91e6af8be11a4077 c043d433456507ef1b39da25a3b6ef7e72602706Artificial intelligence, as a novel form of infrastructure with both generality and knowledge spillover characteristics, plays a crucial role in facilitating the profound integration of the manufacturing and service industries, and achieving economic transformation. This paper empirically investigates the impacts of artificial intelligence on the process of manufacturing servitization, utilizing merged data from the OECD-ICIOT (Organization for Economic Co-operation and Development, Intercountry Input-Output Tables) industry data, the Chinese industrial enterprise database, and the customs trade database. The empirical findings of this research demonstrate that artificial intelligence has significant and positive effects on manufacturing servitization. These positive effects primarily occur through two channels: enhancing total factor productivity and optimizing the labor skill structure. Furthermore, this study examines the variations in the impact of artificial intelligence on the transformation of embedded services and blended services. The analysis reveals that artificial intelligence significantly promotes the transformation of embedded services, while its impact on the transformation of blended services is comparatively less pronounced.Sustainability650The analysis reveals that artificial intelligence significantly promotes the transformation of embedded services, while its impact on the transformation of blended services is comparatively less pronounced.Sustainability['Daxing Chen', 'Helian Xu', 'Guangya Zhou']2024-03-19T00:00:00https://www.semanticscholar.org/paper/c043d433456507ef1b39da25a3b6ef7e72602706 9ca912c8fa33c701e34b8637b3e00d76bd6701b0The rapid advancement of artificial intelligence (AI) has revolutionized various aspects of society, mirroring the transformative impact of the steam engine on human socio-economic systems. AI technologies enable the recognition of intricate patterns within vast datasets, profoundly influencing fields such as social, economic, educational, medical, legal, and moral systems. This paradigm shift may arguably surpass the impact of the mechanical revolution brought about by the steam engine. Looking ahead, healthcare professionals, including physiotherapists are poised to leverage AI within expansive information networks to enhance patient care. This article explores the profound implications of AI on physiotherapy practice, highlighting the imperative for evolving Physiotherapy education to prepare professionals for the complexities of 21st-century healthcare. Keywords: Artificial Intelligence, Patient Care, PhysiotherapyInternational journal of science and healthcare research00The profound implications of AI on physiotherapy practice are explored, highlighting the imperative for evolving Physiotherapy education to prepare professionals for the complexities of 21st-century healthcare.International Journal of Science and Healthcare Research['Arpita Rathod', 'Rajkiran Tiku']2024-03-19T00:00:00https://www.semanticscholar.org/paper/9ca912c8fa33c701e34b8637b3e00d76bd6701b0 b5fc44ee1aefca26f2a27a1306a0af1482b62713Objective: the objective of this article is to address the misalignment between the expectations of Artificial Intelligence (or just AI) systems and what they can currently deliver. Despite being a pervasive and cutting-edge technology present in various sectors, such as agriculture, industry, commerce, education, professional services, smart cities, and cyber defense, there exists a discrepancy between the results some people anticipate from AI and its current capabilities. This misalignment leads to two undesirable outcomes: Firstly, some individuals expect AI to achieve results beyond its current developmental stage, resulting in unrealistic demands. Secondly, there is dissatisfaction with AI's existing capabilities, even though they may be sufficient in many contexts.Methods: the article employs an analytical approach to tackle the misalignment issue, analyzing various market applications of AI and unveils their diversity, demonstrating that AI is not a homogeneous, singular concept. Instead, it encompasses a wide range of sector-specific applications, each serving distinct purposes, possessing inherent risks, and aiming for specific accuracy levels.Results: the primary finding presented in this article is that the misalignment between expectations and actual AI capabilities arises from the mistaken premise that AI systems should consistently achieve accuracy rates far surpassing human standards, regardless of the context. By delving into different market applications, the author advocates for evaluating AI's potential and accepted levels of accuracy and transparency in a context-dependent manner. The results highlight that each AI application should have different accuracy and transparency targets, tailored on a case-by-case basis. Consequently, AI systems can still be valuable and welcomed in various contexts, even if they offer accuracy or transparency rates lower or much lower than human standards.Scientific novelty: the scientific novelty of this article lies in challenging the widely held misconception that AI should always operate with superhuman accuracy and transparency in all scenarios. By unraveling the diversity of AI applications and their purposes, the author introduces a fresh perspective, emphasizing that expectations and evaluations should be contextualized and adapted to the specific use case of AI.Practical significance: the practical significance of this article lies in providing valuable guidance to stakeholders within the AI field, including regulators, developers, and customers. The article's realignment of expectations based on context fosters informed decision-making and promotes responsible AI development and implementation. It seeks to enhance the overall utilization and acceptance of AI technologies by promoting a realistic understanding of AI's capabilities and limitations in different contexts. By offering more comprehensive guidance, the article aims to support the establishment of robust regulatory frameworks and promote the responsible deployment of AI systems, contributing to the improvement of AI applications in diverse sectors. The author's call for fine-tuned expectations aims to prevent dissatisfaction arising from unrealistic demands and provide solid guidance for AI development and regulation.Russian Journal of Economics and Law320The article's realignment of expectations based on context fosters informed decision-making and promotes responsible AI development and implementation and supports the establishment of robust regulatory frameworks and promote the responsible deployment of AI systems, contributing to the improvement of AI applications in diverse sectors.Russian Journal of Economics and Law['L. Parentoni']2024-03-19T00:00:00https://www.semanticscholar.org/paper/b5fc44ee1aefca26f2a27a1306a0af1482b62713 6df5c0a13d728363e24593e175bb70b7f749c85eObjective: to analyze the positions of judges on risk assessment tools using artificial intelligence.Methods: dialectical approach to cognition of social phenomena, allowing to analyze them in historical development and functioning in the context of the totality of objective and subjective factors, which predetermined the following research methods: formal-logical and sociological.Results: Artificial intelligence (AI) uses computer programming to make predictions (e.g., bail decisions) and has the potential to benefit the justice system (e.g., save time and reduce bias). This secondary data analysis assessed 381 judges’ responses to the question, “Do you feel that artificial intelligence (using computer programs and algorithms) holds promise to remove bias from bail and sentencing decisions?”Scientific novelty: The authors created apriori themes based on the literature, which included judges’ algorithm aversion and appreciation, locus of control, procedural justice, and legitimacy. Results suggest that judges experience algorithm aversion, have significant concerns about bias being exacerbated by AI, and worry about being replaced by computers. Judges believe that AI has the potential to inform their decisions about bail and sentencing; however, it must be empirically tested and follow guidelines. Using the data gathered about judges’ sentiments toward AI, the authors discuss the integration of AI into the legal system and future research.Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the legal risks of using artificial intelligence.Russian Journal of Economics and Law420Analysis of judges’ sentiments toward AI suggests that judges experience algorithm aversion, have significant concerns about bias being exacerbated by AI, and worry about being replaced by computers.Russian Journal of Economics and Law['A. Fine', 'S. Le', 'M. K. Miller']2024-03-19T00:00:00https://www.semanticscholar.org/paper/6df5c0a13d728363e24593e175bb70b7f749c85e 3b08323d49017f5bb74fcbfc1d499ec42ef2dd6aEn cuanto al objetivo de la investigación es analizar la inteligencia artificial en la administración de justicia en el Ecuador. La metodología se respalda en una tipología bibliográfica compuesta por un total de 11 trabajos de investigación. Es importante enfatizar que la alineación entre la tecnología y la interpretación humana en un contexto legal sigue siendo esencial para garantizar un juicio eficiente y justo. La inteligencia artificial representada por los chatbots puede coexistir con métodos legales humanos, proporcionando una nueva perspectiva sobre la resolución de conflictos en el sistema legal, lo que indica que la combinación de chatbots introduce un nuevo paradigma en la prestación de servicios legales.Revista Multidisciplinaria Perspectivas Investigativas60Revista Multidisciplinaria Perspectivas Investigativas['Mishelle Katherine Bodero-Solís', 'G. Robles-Zambrano', 'Geoconda-del-Roció García-Sánchez']2024-03-19T00:00:00https://www.semanticscholar.org/paper/3b08323d49017f5bb74fcbfc1d499ec42ef2dd6a 146ab285fa5c1af4c6c63c969146cd792daa4438British medical journal12BMJ['Rammya Mathew']2024-03-19T00:00:00https://www.semanticscholar.org/paper/146ab285fa5c1af4c6c63c969146cd792daa4438 5affb2b2282d1b9c485cdf0d9bf0a17d119d93d5Food chemistry: X1181Precision aquaculture with AI optimizes resource usage for higher yields and automated monitoring ensures healthier aquatic environments and stock, boosting food production sustainably.Food Chemistry: X['Mohd Ashraf Rather', 'Ishtiyaq Ahmad', 'Azra Shah', 'Younis Ahmad Hajam', 'Adnan Amin', 'Saba Khursheed', 'Irfan Ahmad', 'Showkat Rasool']2024-03-19T00:00:00https://www.semanticscholar.org/paper/5affb2b2282d1b9c485cdf0d9bf0a17d119d93d5 5e983d9725e6daed3d7cd75af54ddc0013d5bea1European Journal of Pediatrics91The reliability and appropriateness of AI model responses to straightforward and fundamental questions related to the four most prevalent pediatric endocrine and metabolic disorders, for both healthcare providers and patients, in different language scenarios are focused on.European Journal of Pediatrics['Lingwen Ying', 'Sichen Li', 'Chunyang Chen', 'Fan Yang', 'Xin Li', 'Yao Chen', 'Yu Ding', 'G. Chang', 'Juan Li', 'Xiumin Wang']2024-03-19T00:00:00https://www.semanticscholar.org/paper/5e983d9725e6daed3d7cd75af54ddc0013d5bea1 66fec367f14255658c3704f9477b61ea6688d3d3Journal of China Computer-Assisted Language Learning60Journal of China Computer-Assisted Language Learning['Chen Li']2024-03-19T00:00:00https://www.semanticscholar.org/paper/66fec367f14255658c3704f9477b61ea6688d3d3 3863eccc49af8e083db1930148af1e334087d9c3Modern medicine has improved to the point that intelligent diagnostic tools and auxiliary medical technology, such as surgical robots and image analysis systems, are now widespread in clinical settings. In clinical practice, the performance of different surgical robots and image analysis systems is very different, which seriously limits the use of complex medical scenes. The algorithm models and robotic arms that these intelligent robots and the supporting systems rely on have being recognized by the researchers. In this study, hardware and software algorithms are introduced one at a time, with a focus on the Da Vinci medical robot arm systems, the control mechanisms that run the surgical task optimization tools, in particular Proportional-Integral-Derivative (PID) and Remote Center of Motion (RCM), and the image algorithm active contour model that significantly increased the accuracy of tumor localization. Also provided are suggestions for improving the system's use, its limits, and future research possibilities.Applied and Computational Engineering00Hardware and software algorithms are introduced one at a time, with a focus on the Da Vinci medical robot arm systems, the control mechanisms that run the surgical task optimization tools, in particular Proportional-Integral-Derivative and Remote Center of Motion, and the image algorithm active contour model that significantly increased the accuracy of tumor localization.Applied and Computational Engineering['Xibei Wang', 'Shizhen Weng', 'Ziyu Xie']2024-03-19T00:00:00https://www.semanticscholar.org/paper/3863eccc49af8e083db1930148af1e334087d9c3 20ff58b4a2862f32260ff5c5109c7ec6316ff72bCanadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes60Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes['Francois H. Cornelis', 'P. Soyer', 'M. Patlas']2024-03-19T00:00:00https://www.semanticscholar.org/paper/20ff58b4a2862f32260ff5c5109c7ec6316ff72b 76cbd471b5f8a5659a797683c38dc094f962dff3EClinicalMedicine360A neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and its efficacy to 18 established skin cancer risk factors is compared using data from the Rotterdam Study.eClinicalMedicine['Xianjing Liu', 'Tobias E. Sangers', 'T. Nijsten', 'M. Kayser', 'L. Pardo', 'E. Wolvius', 'G. V. Roshchupkin', 'M. Wakkee']2024-03-19T00:00:00https://www.semanticscholar.org/paper/76cbd471b5f8a5659a797683c38dc094f962dff3 3b3f3d2af8526f7b712476d89ba8f8d44b826cadReview of Education/Pedagogy/Cultural Studies00Review of Education, Pedagogy, and Cultural Studies['Leli Sopyanti', 'Mohammad Alfiyan Ishaqy', 'Muhlis M']2024-03-19T00:00:00https://www.semanticscholar.org/paper/3b3f3d2af8526f7b712476d89ba8f8d44b826cad 4357cf4af15d300a2c8c3ecf5e8daf846a8b533fSurgical Endoscopy00Surgical Endoscopy['V. López-López', 'Zeniche Morise', 'Mariano Albaladejo-González', 'Concepción Gomez Gavara', 'B. Goh', 'Y. Koh', 'Sijberden Jasper Paul', 'M. Hilal', 'K. Mishima', 'Jaime Arthur Pirola Krürger', 'Paulo Herman', 'Alvaro Cerezuela', 'R. Brusadin', 'T. Kaizu', 'Juan Lujan', 'F. Rotellar', 'K. Monden', 'M. Dalmau', 'N. Gotohda', 'M. Kudo', 'A. Kanazawa', 'Yutaro Kato', 'H. Nitta', 'S. Amano', 'R. D. Valle', 'M. Giuffrida', 'M. Ueno', 'Yuichiro Otsuka', 'D. Asano', 'Minoru Tanabe', 'O. Itano', 'Takuya Minagawa', 'D. Eshmuminov', 'Irene Herrero', 'Pablo Ramírez', 'J. Ruipérez-Valiente', 'R. Robles-Campos', 'Go Wakabayashi']2024-03-19T00:00:00https://www.semanticscholar.org/paper/4357cf4af15d300a2c8c3ecf5e8daf846a8b533f 801290504c28bff49232f558b0ff9916cf8927d2Frontiers in Chemical Engineering00Frontiers in Chemical Engineering['Thibaut Neveux', 'J. Commenge', 'Florence Vermeire']2024-03-19T00:00:00https://www.semanticscholar.org/paper/801290504c28bff49232f558b0ff9916cf8927d2 c9380f492b5fac547aa0085203bba9d0c96994d4Obzornik zdravstvene nege00Obzornik zdravstvene nege['Roger Watson']2024-03-19T00:00:00https://www.semanticscholar.org/paper/c9380f492b5fac547aa0085203bba9d0c96994d4 75599e8000dba95c6a0c38f2be796eacac650d8aJournal of Regulatory Economics80Journal of Regulatory Economics['A. Ros', 'Sai Shetty', 'Timothy Tardiff']2024-03-18T00:00:00https://www.semanticscholar.org/paper/75599e8000dba95c6a0c38f2be796eacac650d8a a27ce898e4103a8ea6bf20a7379ccdcbe2fe8111 The rapid emergence of artificial intelligence (AI) technology and its application by businesses has created a potential need for governmental regulation. While the federal government of the United States has largely sidestepped the issue of crafting law dictating limitations and expectations regarding the use of AI technology, US state legislatures have begun to take the lead in this area. Nonetheless, we know very little about how state legislatures have approached the design, pursuit, and adoption of AI policy and whether traditional political fault lines have manifested themselves in the AI issue area. Here, we gather data on the state-level adoption of AI policy, as well as roll call voting on AI bills (classified on the basis of consumer protection versus economic development), by state legislatures and analyze the political economy of AI legislation. We find that rising unemployment and inflation are negatively associated with a state’s AI policymaking. With respect to individual legislator support, we find that liberal lawmakers and Democrats are more likely to support bills establishing consumer protection requirements on AI usage. The results suggest that economic concerns loom large with AI and that traditional political fault lines may be establishing themselves in this area.Business and Politics211It is found that rising unemployment and inflation are negatively associated with a state’s AI policymaking and that liberal lawmakers and Democrats are more likely to support bills establishing consumer protection requirements on AI usage.Business and Politics['Srinivas Parinandi', 'Jesse Crosson', 'Kai Peterson', 'Sinan Nadarevic']2024-03-18T00:00:00https://www.semanticscholar.org/paper/a27ce898e4103a8ea6bf20a7379ccdcbe2fe8111 486ee1bb0a1bb60a890abd2f47a756429220d5ddNature Network Boston51Nature medicine['Barbara Prainsack', 'N. Forgó']2024-03-18T00:00:00https://www.semanticscholar.org/paper/486ee1bb0a1bb60a890abd2f47a756429220d5dd 51e8ac4136b5885732fbf9c0ae32d7536d9e63fcInternational Conference on Learning Analytics and Knowledge310{'pages': '231-240'}['Belle Dang', 'Andy Nguyen', 'Sanna Järvelä']2024-03-18T00:00:00https://www.semanticscholar.org/paper/51e8ac4136b5885732fbf9c0ae32d7536d9e63fc cbbccabb75b0a976a90992805567395e8b98fe0bThe Canadian healthcare system, grappling with issues like systemic and intelligently established structural anti-black racism, including indigenous nations; even within Pathology and Laboratory Medicine Communities: and deteriorating outcomes, sees potential in AI to address challenges, though concerns exist regarding exacerbating discriminatory practices. In clinical pathology, AI demonstrates superior diagnostic accuracy compared to pathologists in a study, emphasizing its potential to improve healthcare. However, AI governance is crucial to navigating ethical, legal, and societal concerns. The Royal College of Physicians of Canada acknowledges the transformative impact of AI in healthcare but stresses the need for responsible AI tools co-developed by diverse teams. Despite positive attitudes towards AI in healthcare, concerns about patient safety, privacy, and autonomy highlight the necessity for comprehensive education, engagement, and collaboration. Legal concerns, including liability and regulation, pose challenges, emphasizing the need for a robust regulatory framework. AI application in healthcare is categorized as high-risk, demanding stringent regulation to ensure safety, efficacy, and fairness. A parallel is drawn to drug regulation processes, suggesting a similar approach for AI. The lack of transparency in AI-based decision-making raises ethical questions, necessitating measures to address biases and ensure patient privacy. Social accountability is crucial to prevent AI from exacerbating health disparities and harming marginalized communities. In conclusion, while AI offers potential benefits in clinical pathology, a cautious approach with comprehensive governance measures is essential to mitigate risks and ensure ethical AI integration into healthcare.Archives of Pathology and Clinical Research90In conclusion, while AI offers potential benefits in clinical pathology, a cautious approach with comprehensive governance measures is essential to mitigate risks and ensure ethical AI integration into healthcare.Archives of Pathology and Clinical Research['Omabe Maxwell']2024-03-18T00:00:00https://www.semanticscholar.org/paper/cbbccabb75b0a976a90992805567395e8b98fe0b 4a067fce2ada0822dbd8573384e0db5ac3294aedThe paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.Frontiers in Digital Health710The paper first defines AI and its scope in the area of mental health, then it looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI.Frontiers in Digital Health['Anoushka Thakkar', 'Ankita Gupta', 'Avinash De Sousa']2024-03-18T00:00:00https://www.semanticscholar.org/paper/4a067fce2ada0822dbd8573384e0db5ac3294aed 5f1be07e051f643cac4b931c37c8c115fe5c4214J. Intell. Robotic Syst.290J. Intell. Robotic Syst.['Samar Abbas Nawaz']2024-03-18T00:00:00https://www.semanticscholar.org/paper/5f1be07e051f643cac4b931c37c8c115fe5c4214 9465bf4f52df3b612f35dd4e6bdaab33b86bf618The high level of regulation of innovative drugs on the market, which is necessary to protect consumers, produces important effects on drug availability and innovation. In public healthcare systems, the need to curb prices comes from expenditure considerations. The aim of price regulation is to obtain a more equitable allocation of the value of an innovative drug between industries and patients (by reducing prices to make drugs more affordable), but it may also reduce access. (In the listing process, the industry may find it more convenient to limit commercialisation to profitable subgroups of patients.) Furthermore, with the advent of personalised medicine, there is another important dimension that has to be considered, namely, incentives to invest in drug personalisation. In this paper, we review and discuss the impact of different pricing rules on the expenditure and availability of new drugs.Pharmacy1080The impact of different pricing rules on the expenditure and availability of new drugs is reviewed and the importance of incentives to invest in drug personalisation is considered.Pharmacy['L. Levaggi', 'Rosella Levaggi']2024-03-18T00:00:00https://www.semanticscholar.org/paper/9465bf4f52df3b612f35dd4e6bdaab33b86bf618 79ac34d597f579265e9672f4d586ccfbccedd871That this research aims to analyze more deeply the implementation of the policy of the Head of the National Land Agency Regulation Number 5 of 2012 concerning Technical Instructions for Implementing Land Acquisition Case study of Land Acquisition for the Construction of the Pekanbaru-Bangkinang Toll Road, Riau Province. The research method in this legal research uses qualitative methods. The problem formulation used in this research is how to implement the policy of Regulation of the Head of the National Land Agency Number 5 of 2012 concerning Technical Instructions for Implementing Land Acquisition and what factors hinder the implementation of the policy of Regulation of the Head of the National Land Agency Number 5 of 2012 concerning Technical Instructions for Implementing Land Acquisition. That the results of this research found that the implementation of land acquisition for the construction of the Pekanbaru-Bangkinang toll road in Riau province was not optimal, that there were obstacles in the implementation of land acquisition for the construction of the toll road in the form of land disputes, the location being included in a forest area, and resistance from residents to the development. the toll road. The conclusion of this research is that it is best to review the regulations of the Head of the National Land Agency Number 5 of 2012 concerning Technical Instructions for the Implementation of Land Acquisition which function as a fulfillment of solving public problems in the land sector and filling legal gaps if necessary and that regarding the obstacles that In the field, assistance should be provided with related officials such as the prosecutor's office and police as well as the local district court to provide a sense of security and comfort for the committee and other stakeholders involved in the land acquisition process for the public interest.Yurisdiksi: Jurnal Wacana Hukum dan Sains100YURISDIKSI : Jurnal Wacana Hukum dan Sains['Abriyanto Nugroho', 'Agus Priyanto3', 'Universitas Merdeka Surabaya']2024-03-18T00:00:00https://www.semanticscholar.org/paper/79ac34d597f579265e9672f4d586ccfbccedd871 788398ec326ab664afee2c4b051be06a7afb72f0With the continuous development of network technology makes the Internet gradually into thousands of households, the network has increasingly become an indispensable part of people's work and life. In recent years, in the judicial practice of our country, there are a lot of cases about network crime, which makes people have to pay attention to the potential Internet security problems while enjoying the convenience and speed brought by the Internet. Followed by Internet crime cases to get people to re-examine network problems, now the network crime of technology advancement, the degree of complexity and concealment and difficult to obtain evidence is completely different to traditional crime, it also shows our country at present in the network crime in the criminal law on the regulation of some deficiencies, it is necessary to strengthen the regulation of network crime of criminal law, combining with the concrete problems in judicial practice make effective regulation measures, such as constructing cyber crime legislation system, perfecting the relevant charges system, improve the network technology and strengthen to the improvement of the network security system, improve the content of the social public network moral quality, So that citizens can actively participate in crime prevention and control, so that the criminal law regulation measures to play a real and effective role in curbing cybercrime.International Journal of Social Sciences and Public Administration120It is necessary to strengthen the regulation of network crime in the criminal law on the regulation of some deficiencies, so that the criminal law regulation measures to play a real and effective role in curbing cybercrime.International Journal of Social Sciences and Public Administration['Zhitong Wang']2024-03-18T00:00:00https://www.semanticscholar.org/paper/788398ec326ab664afee2c4b051be06a7afb72f0 37589c954ca521916051768d9cbec47823302e70The security, privacy, and safety issues around Female-oriented technologies (FemTech) and data can lead to differential harms. These complex risks and harms are enabled by many factors including inadequate regulations, the non-compliant practices of the industry, and the lack of research and guidelines for cyber-secure, privacy-preserving, and safe products. In this paper, we review the existing regulations related to FemTech in the United Kingdom, EU, and Switzerland and identify the gaps. We run experiments on a range of FemTech devices and apps and identify several exploitative practices. We advocate for the policymakers to explicitly acknowledge and accommodate the risks of these technologies in the relevant regulations.Frontiers in the Internet of Things281This paper reviews the existing regulations related to FemTech in the United Kingdom, EU, and Switzerland and identifies the gaps and advocates for the policymakers to explicitly acknowledge and accommodate the risks of these technologies in the relevant regulations.Frontiers in the Internet of Things['Maryam Mehrnezhad', 'Thyla Van Der Merwe', 'Michael Catt']2024-03-18T00:00:00https://www.semanticscholar.org/paper/37589c954ca521916051768d9cbec47823302e70 1373035bfe6e4948a39687ace71eff8788955914Information coded in DNA is replicated, modified and transmitted from the origins of protein-based life. Analogies of these processes to information processing, transmission and storage in computer systems is straightforward and can be utilized both in analysis of biological data and in development of biologically based technical systems. Transcription and translation processes are regulated by extremely complex regulatory networks, providing control of cell growth, cell cycle and cellular responses to stress. As such, they constitute engineering control systems exerting their actions at many levels of time scale and spatial organization. This work presents an engineering perspective on DNA-related information processing and biochemical process control  in living cells, followed by a review of two-way crosstalk between engineering and biology.Postepy biochemii550This work presents an engineering perspective on DNA-related information processing and biochemical process control  in living cells, followed by a review of two-way crosstalk between engineering and biology.Postępy Biochemii['Jaroslaw Smieja']2024-03-18T00:00:00https://www.semanticscholar.org/paper/1373035bfe6e4948a39687ace71eff8788955914 f638ecfb325a6440e8e009d40aeeea093348b9a6Journal of Applied Corporate Finance50Journal of Applied Corporate Finance['Charles W. Calomiris']2024-03-18T00:00:00https://www.semanticscholar.org/paper/f638ecfb325a6440e8e009d40aeeea093348b9a6 cfe3c4b15c86cdc10dcd6d1367b526cb0cc1ca54C&amp;EN Global Enterprise00C&amp;EN Global Enterprise['Jeff Johnson, special to C&EN']2024-03-18T00:00:00https://www.semanticscholar.org/paper/cfe3c4b15c86cdc10dcd6d1367b526cb0cc1ca54 bc6aa8376ae9c9bae6e09758ba084478cf4c7b25Journal of Russian Law00Journal of Russian Law['Elena Vasyanina']2024-03-18T00:00:00https://www.semanticscholar.org/paper/bc6aa8376ae9c9bae6e09758ba084478cf4c7b25 0db8affd254ed72583d95230496fe59c564f50afThe rapid advancement of artificial intelligence (AI) technologies has prompted significant societal, ethical, and legal concerns regarding their deployment in information technology (IT) systems. Addressing these concerns necessitates the establishment of a robust ethical framework to guide AI development and integration into IT systems. This paper presents a comprehensive conceptual framework aimed at fostering ethical AI development within IT systems. The proposed framework incorporates multidisciplinary perspectives, drawing upon principles from ethics, computer science, law, and philosophy. It emphasizes the integration of ethical considerations at every stage of the AI development lifecycle, including design, implementation, deployment, and maintenance. Central to this framework is the recognition of AI systems as socio-technical artifacts with profound impacts on individuals, communities, and societies at large. Key components of the framework include transparency, accountability, fairness, privacy, and security. Transparency entails ensuring that AI algorithms and decision-making processes are comprehensible and explainable to stakeholders, thereby fostering trust and enabling scrutiny. Accountability mechanisms are essential for attributing responsibility for AI-driven outcomes and facilitating recourse in cases of harm or injustice. Moreover, the framework emphasizes the importance of fairness in AI systems, advocating for the mitigation of biases and discrimination across diverse demographic groups. Privacy protection measures are deemed crucial to safeguarding individuals' personal data from unauthorized access or misuse, while robust security protocols are essential for defending against malicious exploitation and adversarial attacks. By delineating ethical guidelines and best practices, this conceptual framework aims to empower developers, policymakers, and organizations to navigate the complex ethical landscape of AI development in IT systems. Ultimately, the adoption of such a framework is imperative for harnessing the transformative potential of AI technologies while upholding fundamental ethical principles and societal values.World Journal of Advanced Research and Reviews465A comprehensive conceptual framework aimed at fostering ethical AI development within IT systems, which emphasizes the integration of ethical considerations at every stage of the AI development lifecycle, including design, implementation, deployment, and maintenance.World Journal of Advanced Research and Reviews['Oluwaseun Augustine Lottu', 'Boma Sonimiteim Jacks', 'Olakunle Abayomi Ajala', 'Enyinaya Stefano Okafor']2024-03-18T00:00:00https://www.semanticscholar.org/paper/0db8affd254ed72583d95230496fe59c564f50af a567f5bd6e6a32963e428bb97dec9c52ad75f059Recent advances in generative AI technologies like large language models raise both excitement and concerns about the future of human-AI co-creation in writing. To unpack people's attitude towards and experience with generative AI-powered writing assistants, in this paper, we conduct an experiment to understand whether and how much value people attach to AI assistance, and how the incorporation of AI assistance in writing workflows changes people's writing perceptions and performance. Our results suggest that people are willing to forgo financial payments to receive writing assistance from AI, especially if AI can provide direct content generation assistance and the writing task is highly creative. Generative AI-powered assistance is found to offer benefits in increasing people's productivity and confidence in writing. However, direct content generation assistance offered by AI also comes with risks, including decreasing people's sense of accountability and diversity in writing. We conclude by discussing the implications of our findings.International Conference on Human Factors in Computing Systems533The results suggest that people are willing to forgo financial payments to receive writing assistance from AI, especially if AI can provide direct content generation assistance and the writing task is highly creative.{'pages': '1048:1-1048:25'}['Zhuoyan Li', 'Chen Liang', 'Jing Peng', 'Ming Yin']2024-03-18T00:00:00https://www.semanticscholar.org/paper/a567f5bd6e6a32963e428bb97dec9c52ad75f059 8a6d6b772abec91611e8a2182369c5db47dcf1feAs the telecommunications sector increasingly relies on interconnected digital infrastructure, the proliferation of cyber threats poses significant challenges to security and operational integrity. This review presents a conceptual framework for understanding and harnessing the potential of artificial intelligence (AI) in fortifying cybersecurity within the telecommunications industry.  The framework integrates the transformative capabilities of AI with the unique demands of cybersecurity in telecommunications, aiming to enhance threat detection, mitigation, and response strategies. It encompasses a multidimensional approach that encompasses both technical and organizational facets, recognizing the interconnectedness of technology, human factors, and regulatory environments. Firstly, the framework delves into the application of AI in bolstering proactive threat intelligence gathering and analysis. Through advanced algorithms and machine learning techniques, AI empowers telecom operators to identify anomalous patterns, predict potential vulnerabilities, and pre-emptively adapt defensive measures. Secondly, it explores AI-driven solutions for dynamic risk assessment and adaptive cybersecurity protocols. By leveraging real-time data analytics and automated decision-making, telecom networks can swiftly adapt to evolving threats and ensure continuous protection against intrusions or breaches. Furthermore, the framework emphasizes the role of AI in augmenting human capabilities through intelligent automation and cognitive assistance. By offloading routine tasks and providing context-aware insights, AI enables cybersecurity professionals to focus on strategic initiatives and complex threat scenarios. Lastly, the framework addresses the imperative of ethical considerations, accountability, and transparency in deploying AI for cybersecurity in telecommunications. It advocates for responsible AI governance frameworks that prioritize privacy, fairness, and bias mitigation while fostering collaboration across industry stakeholders. In summary, this conceptual framework provides a roadmap for harnessing AI's transformative potential to fortify cybersecurity resilience in telecommunications, thereby safeguarding critical infrastructure and ensuring the integrity of global communication networks. Keywords: AI, Cybersecurity, Telecommunication, Framework, Conceptual, Impact, Review.Computer Science &amp; IT Research Journal02This conceptual framework provides a roadmap for harnessing AI's transformative potential to fortify cybersecurity resilience in telecommunications, thereby safeguarding critical infrastructure and ensuring the integrity of global communication networks.Computer Science &amp; IT Research Journal['Philip Olaseni Shoetan', 'Olukunle Oladipupo Amoo', 'Enyinaya Stefano Okafor', 'Oluwabukunmi Latifat Olorunfemi']2024-03-18T00:00:00https://www.semanticscholar.org/paper/8a6d6b772abec91611e8a2182369c5db47dcf1fe 1af414465a338bccd77f067ff2e7d25521aa727aIUI Companion73{'pages': '78-83'}['Jindi Wang', 'Ioannis P. Ivrissimtzis', 'Zhaoxing Li', 'Lei Shi']2024-03-18T00:00:00https://www.semanticscholar.org/paper/1af414465a338bccd77f067ff2e7d25521aa727a 0a93def0480294520678ecc174b8a85a43468563Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications.Journal of business and management studies252This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability.Journal of Business and Management Studies['Sarder Abdulla', 'Al Shiam', '✉. M. M. Hasan', 'Md Jubair Pantho', 'Sarmin Akter Shochona', 'Md Boktiar Nayeem', 'M. Tazwar', 'Hossain Choudhury', 'Tuan Ngoc Nguyen']2024-03-18T00:00:00https://www.semanticscholar.org/paper/0a93def0480294520678ecc174b8a85a43468563 3bbde2e87f502fd3e27214b5fed3c96b5788d428The use of Artificial Intelligence (AI) based on data-driven algorithms has become ubiquitous in today's society. Yet, in many cases and especially when stakes are high, humans still make final decisions. The critical question, therefore, is whether AI helps humans make better decisions as compared to a human alone or AI an alone. We introduce a new methodological framework that can be used to answer experimentally this question with no additional assumptions. We measure a decision maker's ability to make correct decisions using standard classification metrics based on the baseline potential outcome. We consider a single-blinded experimental design, in which the provision of AI-generated recommendations is randomized across cases with a human making final decisions. Under this experimental design, we show how to compare the performance of three alternative decision-making systems--human-alone, human-with-AI, and AI-alone. We apply the proposed methodology to the data from our own randomized controlled trial of a pretrial risk assessment instrument. We find that AI recommendations do not improve the classification accuracy of a judge's decision to impose cash bail. Our analysis also shows that AI-alone decisions generally perform worse than human decisions with or without AI assistance. Finally, AI recommendations tend to impose cash bail on non-white arrestees more often than necessary when compared to white arrestees.arXiv.org370A new methodological framework is introduced that can be used to answer experimentally whether AI helps humans make better decisions as compared to a human alone or AI an alone, and measures a decision maker's ability to make correct decisions based on the baseline potential outcome.ArXiv['Eli Ben-Michael', 'D. J. Greiner', 'Melody Huang', 'Kosuke Imai', 'Zhichao Jiang', 'Sooahn Shin']2024-03-18T00:00:00https://www.semanticscholar.org/paper/3bbde2e87f502fd3e27214b5fed3c96b5788d428 7144429c53237722db6d5a4fb6e221cb35923207Developing competency and especially self-study competency for students is one of the requirements in the 2018 General Education Program. Developing self-study competency for students is an inevitable trend of all times. , because the educational process is essentially the process of transforming the object of education (student) from an object of education into a subject of self-education. Based on the identification of three concepts: Teaching method, AI Chatbot and self-learning competency, we summarize the teaching method to develop competency and AI Chatbot. Next, illustrate the development of self-study competency for students using three teaching methods: differentiated teaching, Exploratory learning and using experiments in teaching combined with the support of AI Chatbot and from there affirms that students truly develop self-study competency.International Journal of Current Science Research and Review140The teaching method to develop competency and AI Chatbot is summarized and the development of self-study competency for students using three teaching methods: differentiated teaching, Exploratory learning and using experiments in teaching combined with the support of AI Chatbot affirms that students truly develop self-study competency.International Journal of Current Science Research and Review['Vo Thi Ngoc Lan', 'Nguyen Minh Giam']2024-03-18T00:00:00https://www.semanticscholar.org/paper/7144429c53237722db6d5a4fb6e221cb35923207 92ed6521d8b28f2e7acf1b2591176325fd127d0eAn unprecedented amount of personal health data, with the potential to revolutionise precision medicine, is generated at healthcare institutions worldwide. The exploitation of such data using artificial intelligence relies on the ability to combine heterogeneous, multicentric, multimodal and multiparametric data, as well as thoughtful representation of knowledge and data availability. Despite these possibilities, significant methodological challenges and ethico-legal constraints still impede the real-world implementation of data models. The EuCanImage is an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals. The use of well-defined clinical data standards to allow interoperability was a central element within the initiative. The consortium is focused on three different cancer types and addresses seven unmet clinical needs. This article synthesises our experience and procedures for healthcare data interoperability and standardisation.medRxiv330The experience and procedures for healthcare data interoperability and standardisation for EuCanImage, an international consortium aimed at developing AI algorithms for precision medicine in oncology and enabling secondary use of the data based on necessary ethical approvals, are synthesised.['Teresa García-Lezana', 'Maciej Bobowicz', 'Santiago Frid', 'Michael Rutherford', 'Mikel Recuero', 'Katrine Riklund', 'Aldar Cabrelles', 'Marlena Rygusik', 'Lauren Fromont', 'R. Francischello', 'Emanuele Neri', 'Salvador Capella', 'Fred Prior', 'Jonathan Bona', 'Pilar Nicolas', 'M. P. Starmans', 'Karim Lekadir', 'Jordi Rambla', 'EuCanImage Consortium']2024-03-18T00:00:00https://www.semanticscholar.org/paper/92ed6521d8b28f2e7acf1b2591176325fd127d0e 6e2845e3099e2b300dc085b4d0b9180c2d9d139aGroup decision making plays a crucial role in our complex and interconnected world. The rise of AI technologies has the potential to provide data-driven insights to facilitate group decision making, although it is found that groups do not always utilize AI assistance appropriately. In this paper, we aim to examine whether and how the introduction of a devil’s advocate in the AI-assisted group decision making processes could help groups better utilize AI assistance and change the perceptions of group processes during decision making. Inspired by the exceptional conversational capabilities exhibited by modern large language models (LLMs), we design four different styles of devil’s advocate powered by LLMs, varying their interactivity (i.e., interactive vs. non-interactive) and their target of objection (i.e., challenge the AI recommendation or the majority opinion within the group). Through a randomized human-subject experiment, we find evidence suggesting that LLM-powered devil’s advocates that argue against the AI model’s decision recommendation have the potential to promote groups’ appropriate reliance on AI. Meanwhile, the introduction of LLM-powered devil’s advocate usually does not lead to substantial increases in people’s perceived workload for completing the group decision making tasks, while interactive LLM-powered devil’s advocates are perceived as more collaborating and of higher quality. We conclude by discussing the practical implications of our findings.International Conference on Intelligent User Interfaces1160Through a randomized human-subject experiment, evidence is found suggesting that LLM-powered devil’s advocates that argue against the AI model’s decision recommendation have the potential to promote groups’ appropriate reliance on AI.{'pages': '103-119'}['Chun-Wei Chiang', 'Zhuoran Lu', 'Zhuoyan Li', 'Ming Yin']2024-03-18T00:00:00https://www.semanticscholar.org/paper/6e2845e3099e2b300dc085b4d0b9180c2d9d139a d73f3e0e840b8c49e994a446832ba805878c441aThe rapid advancement of AI technology has led to its integration into various sectors, including the digital marketing industry. As organizations leverage AI for tasks such as optimizing voice search content, creating conversational chatbots, and enhancing advertising campaigns, it becomes crucial to understand the impact of AI on employee perspectives and behaviors. This study aims to investigate employees' viewpoints regarding AI adoption in the digital marketing industry, specifically examining its influence on job insecurity, turnover intention, and job mobility. Job insecurity, stemming from concerns about AI rendering roles obsolete, is crucial to address for fostering a supportive work environment. Turnover intention, influenced by AI adoption and potential job dissatisfaction, provides insights into employees' commitment to the industry. Job mobility, affected by growth prospects and alignment with AI-driven workplaces, sheds light on career aspirations within digital marketing. This study addresses a significant knowledge gap regarding how employees envision the future of work and how this perspective influences their job-related behaviors. To bridge this gap, we conducted a study involving 303 employees in the digital marketing industry in India. Through structural equation modeling, we discovered that AI technology disruption influences employees' turnover intention, with job insecurity playing a mediating role. Additionally, our analysis revealed that mistreatment by superiors increases turnover intention, highlighting its impact on employees' decisions. Overall, this research unveils the profound impact of AI technology disruption in the digital marketing industry on employees' attitudes, behaviors, and future career decisions, providing essential insights into how employees perceive AI technology, particularly concerning job security and their engagement in the digital marketing sector.International Research Journal on Advanced Engineering and Management (IRJAEM)520The profound impact of AI technology disruption in the digital marketing industry on employees' attitudes, behaviors, and future career decisions is unveiled, providing essential insights into how employees perceive AI technology, particularly concerning job security and their engagement in the digital marketing sector.International Research Journal on Advanced Engineering and Management (IRJAEM)['Haritha P', 'Resham Lohani']2024-03-18T00:00:00https://www.semanticscholar.org/paper/d73f3e0e840b8c49e994a446832ba805878c441a 44dd0678d0121c2c46f5023af02e7b54801eedddIn today’s fast-paced and competitive work environment, it is crucial for organisations to have a clear understanding of the competencies required for effective performance. The Iceberg Model of Competencies provides a comprehensive framework for understanding the various components of competencies, including knowledge, skills, attitude, and habits. By taking into account all of these components, organisations can develop a more holistic approach to competency analysis and development. This article offers an AI-based approach to competency analysis in organisations. Relying on some key-factors (which are described and analysed based on the Iceberg Model of Competencies), I offer a theoretical framework for AI usage in the analysis and development of competencies. Specifically, I discuss the use of AI in analysing knowledge and skills, attitude and habit formation, and the broader organisational context in which competencies are applied. Accordingly, based on these key factors, I propose an AI-based theoretical model for Competency Analysis in organisations. This model emphasises the importance of taking a holistic approach to competency analysis, integrating AI-based tools and techniques to support a more comprehensive understanding of competencies. Ultimately, this approach can help organisations develop more effective competency development programs, leading to improved performance and success in today’s competitive business environment. This article can be regarded as a resource for professionals and scholars interested in competency analysis, talent management, and the role of AI in organisational development. Keywords: Organisational Competency, Competency Analysis, Iceberg Model of Competencies, AI-based Approach, Human Capital Management00An AI-based theoretical model for Competency Analysis in organisations is proposed, which emphasises the importance of taking a holistic approach to competency analysis, integrating AI-based tools and techniques to support a more comprehensive understanding of competencies.['Dr. Farshad Badie']2024-03-18T00:00:00https://www.semanticscholar.org/paper/44dd0678d0121c2c46f5023af02e7b54801eeddd 6623625cbf22a5224a1dce4170f85cabbd456465Discover Artificial Intelligence120Investigation of editors’ and proofreaders’ perceptions of current AI tools examines whether editors/proofreaders view AI as an opportunity or a threat and considers their insights into the future of AI tools for them.Discov. Artif. Intell.['I. A. Sawi', 'Ahmed Alaa']2024-03-18T00:00:00https://www.semanticscholar.org/paper/6623625cbf22a5224a1dce4170f85cabbd456465 daa6d9e6ac95715cc1934d36b1d31d97cfda586cThe rapid advancement of generative Artificial Intelligence (AI) technologies, particularly Generative Pre-trained Transformer (GPT) models such as ChatGPT, has the potential to significantly impact cybersecurity. In this study, we investigated the impact of GPTs, specifically ChatGPT, on tertiary education in cybersecurity, and provided recommendations for universities to adapt their curricula to meet the evolving needs of the industry. Our research highlighted the importance of understanding the alignment between GPT's ``mental model'' and human cognition, as well as the enhancement of GPT capabilities to human skills based on Bloom's taxonomy. By analyzing current educational practices and the alignment of curricula with industry requirements, we concluded that universities providing practical degrees like cybersecurity should align closely with industry demand and embrace the inevitable generative AI revolution, while applying stringent ethics oversight to safeguard responsible GPT usage. We proposed a set of recommendations focused on updating university curricula, promoting agility within universities, fostering collaboration between academia, industry, and policymakers, and evaluating and assessing educational outcomes.arXiv.org950It is concluded that universities providing practical degrees like cybersecurity should align closely with industry demand and embrace the inevitable generative AI revolution, while applying stringent ethics oversight to safeguard responsible GPT usage.ArXiv['Raza Nowrozy', 'David Jam']2024-03-18T00:00:00https://www.semanticscholar.org/paper/daa6d9e6ac95715cc1934d36b1d31d97cfda586c 79f4f5a24d70f95075230b55bdc1f943df86bec7There is currently considerable excitement within government about the potential of artificial intelligence to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff. Here, we explore the size of this opportunity, by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation. We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions. We estimate that 84% of these complex transactions are highly automatable, representing a huge potential opportunity: saving even an average of just one minute per complex transaction would save the equivalent of approximately 1,200 person-years of work every year. We also develop a model to estimate the volume of transactions a government service undertakes, providing a way for government to avoid conducting time consuming transaction volume measurements. Finally, we find that there is high turnover in the types of services government provide, meaning that automation efforts should focus on general procedures rather than services themselves which are likely to evolve over time. Overall, our work presents a novel perspective on the structure and functioning of modern government, and how it might evolve in the age of artificial intelligence.arXiv.org480The size of this opportunity to improve public service productivity through the automation of complex but repetitive bureaucratic tasks, freeing up the time of skilled staff is explored by mapping out the scale of citizen-facing bureaucratic decision-making procedures within UK central government, and measuring their potential for AI-driven automation.ArXiv['Vince J. Straub', 'Youmna Hashem', 'Jonathan Bright', 'Satyam Bhagwanani', 'Deborah Morgan', 'John Francis', 'Saba Esnaashari', 'Helen Z. Margetts']2024-03-18T00:00:00https://www.semanticscholar.org/paper/79f4f5a24d70f95075230b55bdc1f943df86bec7 6e9b23ba01e0303308e177c3e354be1d71f64c3dIn recent years, there has been an exponential increase in the generation and accessibility of electronic healthcare data, often referred to as “real-world data”. The landscape of data sources has significantly expanded to encompass traditional databases and newer sources such as the social media, wearables, and mobile devices. Advances in information technology, along with the growth in computational power and the evolution of analytical methods relying on bioinformatic tools and/or artificial intelligence techniques, have enhanced the potential for utilizing this data to generate real-world evidence and improve clinical practice. Indeed, these innovative analytical approaches enable the screening and analysis of large amounts of data to rapidly generate evidence. As such numerous practical uses of artificial intelligence in medicine have been successfully investigated for image processing, disease diagnosis and prediction, as well as the management of pharmacological treatments, thus highlighting the need to educate health professionals on these emerging approaches. This narrative review provides an overview of the foremost opportunities and challenges presented by artificial intelligence in pharmacology, and specifically concerning the drug post-marketing safety evaluation.Frontiers in Drug Safety and Regulation750This narrative review provides an overview of the foremost opportunities and challenges presented by artificial intelligence in pharmacology, and specifically concerning the drug post-marketing safety evaluation.Frontiers in Drug Safety and Regulation['S. Crisafulli', 'Francesco Ciccimarra', 'Chiara Bellitto', 'Massimo Carollo', 'Elena Carrara', 'Lisa Stagi', 'Roberto Triola', 'Annalisa Capuano', 'Cristiano Chiamulera', 'Ugo Moretti', 'Eugenio Santoro', 'A. E. Tozzi', 'Giuseppe Recchia', 'Gianluca Trifirò']2024-03-18T00:00:00https://www.semanticscholar.org/paper/6e9b23ba01e0303308e177c3e354be1d71f64c3d ee80523559a89aa202f2bb8755bddd45741a834aBACKGROUND Technological advances have expanded nursing education to include generative artificial intelligence (AI) tools such as ChatGPT. PROBLEM Generative AI tools challenge academic integrity, pose a challenge to validating information accuracy, and require strategies to ensure the credibility of AI-generated information. APPROACH This article presents a dual-purpose approach integrating AI tools into prelicensure nursing education to enhance learning while promoting critical evaluation skills. Constructivist theories and Vygotsky's Zone of Proximal Development framework support this integration, with AI as a scaffold for developing critical thinking. OUTCOMES The approach involves practical activities for students to engage with AI-generated content critically, thereby reinforcing clinical judgment and preparing them for AI-prevalent health care environments. CONCLUSIONS Incorporating AI tools such as ChatGPT into nursing curricula represents a strategic educational advancement, equipping students with essential skills to navigate modern health care.Nurse Educator101This article presents a dual-purpose approach integrating AI tools into prelicensure nursing education to enhance learning while promoting critical evaluation skills, and involves practical activities for students to engage with AI-generated content critically, thereby reinforcing clinical judgment and preparing them for AI-prevalent health care environments.Nurse educator['Rachel Cox Simms']2024-03-18T00:00:00https://www.semanticscholar.org/paper/ee80523559a89aa202f2bb8755bddd45741a834a eff75430058329bb2f87a3a228129f3ea67c34bbThis article explores the transformative role of artificial intelligence (AI) and machine learning (ML) capabilities within SAP S/4HANA, a leading digital platform for enterprise resource planning (ERP). This article elucidates how AI and ML technologies embedded in SAP S/4HANA empower businesses to drive innovation, optimize operations, and gain actionable insights. From predictive analytics and intelligent automation to advanced decision support systems, SAP S/4HANA's AI and ML capabilities are revolutionizing the way organizations operate in today's fast-paced business landscape. SAP S/4HANA, a leading enterprise resource planning (ERP) platform, stands at the forefront of this digital transformation, offering a comprehensive suite of AI and ML capabilities designed to empower businesses to optimize operations, enhance decision-making, and unlock new growth opportunities. This article provides a succinct overview of the transformative role of AI and ML in SAP S/4HANA, highlighting key functionalities and their potential impact on business operations. SAP S/4HANA's AI and ML capabilities encompass a range of functionalities, including predictive analytics, intelligent automation, and decision support systems. Intelligent automation features, such as robotic process automation (RPA) and machine learning-based algorithms, automate manual tasks, reduce errors, and accelerate decision- making processes, thereby enhancing operational efficiency and agility. Moreover, SAP S/4HANA's decision support systems leverage real-time data insights to provide actionable recommendations to users across the organization, enabling organizations to make informed decisions and drive strategic initiatives. Through real- world examples and case studies, this article illustrates how organizations across industries are leveraging SAP S/4HANA's AI and ML capabilities to drive business transformation, streamline processes, and achieve measurable outcomes. This article aims to provide a comprehensive exploration of the AI and ML capabilities within SAP S/4HANA, examining their potential impact on business operations and outlining best practices for implementation and utilization. Through real-world examples, case studies, and expert insights, readers will gain a deeper understanding of how SAP S/4HANA's AI and ML functionalities can unlock new opportunities, drive innovation, and propel business growth.International Journal of Innovative Science and Research Technology41How organizations across industries are leveraging SAP S/4HANA's AI and ML capabilities to drive business transformation, streamline processes, and achieve measurable outcomes is illustrated.International Journal of Innovative Science and Research Technology (IJISRT)['Venkata Ramana Reddy Bussu']2024-03-18T00:00:00https://www.semanticscholar.org/paper/eff75430058329bb2f87a3a228129f3ea67c34bb 106cdc3f272e61d73a944aae9a4497ef67d0c6e1Technologies, such as Chat Generative Pre-Trained Transformer (ChatGPT, Smart PLS version 4), are prime examples of Generative Artificial Intelligence (AI), which is a constantly evolving area. SMEs, particularly startups, can obtain a competitive edge, innovate their business models, gain business value, and undergo a digital transformation by implementing these technologies. Continuous but gradual experimentation with these technologies is the foundation for their adoption. The experience that comes from trying new technologies can help entrepreneurs adopt new technologies more strategically and experiment more with them. The urgent need for an in-depth investigation is highlighted by the paucity of previous research on ChatGPT uptake in the startup context, particularly from an entrepreneurial perspective. The objective of this research study is to empirically validate the Generative AI technology adoption model to establish the direction and strength of the correlations among the adoption factors from the perspectives of the entrepreneurs. The data are collected from 482 entrepreneurs who exhibit great diversity in their genders, the countries in which their startups are located, the industries their startups serve, their age, their educational levels, their work experience as entrepreneurs, and the length of time the startups have been on the market. Collected data are analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, which results in a statistical examination of the relationships between the adoption model’s factors. The results indicate that social influence, domain experience, technology familiarity, system quality, training and support, interaction convenience, and anthropomorphism are the factors that impact the pre-perception and perception phase of adoption. These factors motivate entrepreneurs to experiment more with the technology, thereby building perceptions of its usefulness, perceived ease of use, and perceived enjoyment, three factors that in turn affect emotions toward the technology and, finally, switching intentions. Control variables like age, gender, and educational attainment have no appreciable effect on switching intentions to alternatives of the Generative AI technology. Rather, the experience factor of running businesses shows itself to be a crucial one. The results have practical implications for entrepreneurs and other innovation ecosystem actors, including, for instance, technology providers, libraries, and policymakers. This research study enriches the Generative AI technology acceptance theory and extends the existing literature by introducing new adoption variables and stages specific to entrepreneurship.Systems391The results indicate that social influence, domain experience, technology familiarity, system quality, training and support, interaction convenience, and anthropomorphism are the factors that impact the pre-perception and perception phase of adoption of Generative AI technology.Systems['Varun Gupta']2024-03-18T00:00:00https://www.semanticscholar.org/paper/106cdc3f272e61d73a944aae9a4497ef67d0c6e1 c7fb73068b2d0c861a16952ad389c9ff5616a95bPurposeThis study aims to investigate the factors influencing the adoption intention of artificial intelligence (AI) by micro, small and medium enterprises (MSMEs) in Jordan.Design/methodology/approachThe study adopts the technology–organization–environment (TOE) model. It examines the moderating effects of innovation culture, employee digital skill level and market competition on the relationships between the independent and dependent variables. A survey was utilized to collect data from 537 MSME owners or managers in Jordan and employed partial least squares structural equation modeling to test the hypotheses.FindingsThe results of the study support seven out of eight hypotheses. Business innovativeness, management support, perceived benefits and technological infrastructure have positive and significant effects on AI adoption intention, while perceived costs have no significant effect. However, the innovation culture, employee digital skill level and market competition were found to moderate the relationships between some of the independent variables and dependent variables.Practical implicationsThe study provides valuable insights and recommendations for MSME owners, managers, employees, policymakers, educators and researchers interested in promoting and facilitating AI adoption by MSMEs in Jordan.Originality/valueThe current attempt extends the TOE framework by adding significant constructs representing the three contexts. Moreover, it is one of the few studies that analyzed the factors influencing the adoption intention of AI by MSMEs in Jordan, which are significant to the Jordanian economy and represent 99.5% of enterprises.Management &amp; Sustainability: An Arab Review900The innovation culture, employee digital skill level and market competition were found to moderate the relationships between some of the independent variables and dependent variables, which is one of the few studies that analyzed the factors influencing the adoption intention of AI by MSMEs in Jordan.Management &amp; Sustainability: An Arab Review['Samer Abaddi']2024-03-18T00:00:00https://www.semanticscholar.org/paper/c7fb73068b2d0c861a16952ad389c9ff5616a95b bbc5845a83dd1a20ffdc69f5a49a512a42e2408eThe Blackboard learning management system (Bb-LMS) is integrated into the nursing educational system to effectively respond to the increasing development of technology in society. The Bb-LMS as an artificial intelligence (AI) approach continuously improves the teaching-learning process of the nursing educational system. Nursing colleges and universities design programs with the assistance of AI tools. Positive perception and impact on the use of Bb-LMS are documented by several authors. Recent studies documented a few challenges of using Bb-LMS including the provision of appropriate training regarding e-learning and the Blackboard system, occasional time delay and audio issues, and the students do not prefer Blackboard in practical nursing subjects. Nonetheless, the predicted influence of the Bb-LMS as an artificial intelligence approach can transform nursing education across all domains of nursing education and practice. The Bb-LMS facilitates university teachers and nursing educators in managing their courses and communicating with their students effectively. Research on artificial intelligence and nursing stressed that the future of artificial intelligence in education envisions preceptors and robots cooperating to produce scholars with fashionable outcomes. Hands-on training and workshops should be conducted for both students and teachers to address the challenges of using the learning management system in nursing education. Future research should consider examining the integration of Bb-LMS in nursing curricular reforms to make nursing education more meaningful and effective. Keywords: Blackboard learning management system, artificial intelligence, nursing education, opportunities in nursing education, prospects in nursing education.International journal of science and healthcare research00The Blackboard learning management system (Bb-LMS) is integrated into the nursing educational system to effectively respond to the increasing development of technology in society and can transform nursing education across all domains of nursing education and practice.International Journal of Science and Healthcare Research['E. L. Sampayan']2024-03-18T00:00:00https://www.semanticscholar.org/paper/bbc5845a83dd1a20ffdc69f5a49a512a42e2408e 070cc2327305a91416260b84c842f51bb4e04f3aThe evolution of cities into sustainable and intelligent entities is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and the Internet of Things (IoT). This study systematically examines 133 papers published between 2014 and 2021, predominantly sourced from Scopus (90%) and WoS (70%). Focusing on key smart city domains such as healthcare, education, environment, waste management, mobility, agriculture, risk management, and security, the analysis explores the applications of AI. As cities increasingly embrace AI for operational automation, data-driven decision-making, and environmental improvements, regulatory challenges surface, spanning concerns related to privacy, service delivery discrimination, and ethical considerations. The impact of AI adoption, especially in healthcare following the 2019 global health crisis, is underscored, emphasizing the pivotal role of AI algorithms, including ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVM, in shaping urban development trajectories. This research provides insights into the multifaceted implications of AI in smart cities, offering a comprehensive overview of the benefits, challenges, and transformative potential of these technologies across diverse urban sectors.Journal of Computer Science and Technology Studies540This research provides insights into the multifaceted implications of AI in smart cities, offering a comprehensive overview of the benefits, challenges, and transformative potential of these technologies across diverse urban sectors.Journal of Computer Science and Technology Studies['Kazi Nafisa', 'Anjum', 'Md Azad', 'Hossain Raju', 'Monowar Hossain Saikat', '✉. Sonjoy', 'Paul Avi', 'Kazi Toriqul Islam', 'Rhine Hoque', 'Touhid Imam', 'Monowar Hossain', 'Saikat']2024-03-18T00:00:00https://www.semanticscholar.org/paper/070cc2327305a91416260b84c842f51bb4e04f3a c41eeb4e119aec4fa8491e40d7ef3609438e45e3AI and Ethics630A novel measure of digital confidence was created capturing individual levels of awareness, familiarity, and sense of competence with digital technology, which significantly moderated the relationship between people’s experiences with everyday AI technologies and their general attitudes towards AI.AI and Ethics['Sarah V. Bentley', 'Claire K. Naughtin', 'Melanie Mcgrath', 'Jessica L. Irons', 'Patrick S. Cooper']2024-03-18T00:00:00https://www.semanticscholar.org/paper/c41eeb4e119aec4fa8491e40d7ef3609438e45e3 b277d2d8d08db68f0513ce37c60590b2d529e2b0As a global technoscientific form involving various forces and stakeholders, research and development (R&D) in artificial intelligence (AI) transcends corporate, national and institutional boundaries. Incorporating transnational rhetorical analysis and corpus-assisted discourse analysis, this article examines the global cultural flows surrounding AI constructed in official and media discourses in the US and China. We propose a new theoretical concept of knowledgescape that expands the toolkits of global flows and provides new explanatory power about global innovation and competition. This concept sheds light on the global processes, mechanisms and power dynamics of the production, dissemination, consumption and contestation of cutting-edge knowledge.Global Media and Communication230A new theoretical concept of knowledgescape is proposed that expands the toolkits of global flows and provides new explanatory power about global innovation and competition.Global Media and Communication['Huiling Ding', 'Yeqing Kong']2024-03-18T00:00:00https://www.semanticscholar.org/paper/b277d2d8d08db68f0513ce37c60590b2d529e2b0 c6e633fa8efc1fda0c371f94b56dbfc97b225502In this review, we aim to illustrate the state-of-the-art artificial intelligence (AI) applications in the field of capsule endoscopy. AI has made significant strides in gastrointestinal imaging, particularly in capsule endoscopy - a non-invasive procedure for capturing gastrointestinal tract images. However, manual analysis of capsule endoscopy videos is labour-intensive and error-prone, prompting the development of automated computational algorithms and AI models. While currently serving as a supplementary observer, AI has the capacity to evolve into an autonomous, integrated reading system, potentially significantly reducing capsule reading time while surpassing human accuracy. We searched Embase, Pubmed, Medline, and Cochrane databases from inception to 06 Jul 2023 for studies investigating the use of AI for capsule endoscopy and screened retrieved records for eligibility. Quantitative and qualitative data were extracted and synthesised to identify current themes. In the search, 824 articles were collected, and 291 duplicates and 31 abstracts were deleted. After a double-screening process and full-text review, 106 publications were included in the review. Themes pertaining to AI for capsule endoscopy included active gastrointestinal bleeding, erosions and ulcers, vascular lesions and angiodysplasias, polyps and tumours, inflammatory bowel disease, coeliac disease, hookworms, bowel prep assessment, and multiple lesion detection. This review provides current insights into the impact of AI on capsule endoscopy as of 2023. AI holds the potential for faster and precise readings and the prospect of autonomous image analysis. However, careful consideration of diagnostic requirements and potential challenges is crucial. The untapped potential within vision transformer technology hints at further evolution and even greater patient benefit.Mini-invasive Surgery880Current insights into the impact of AI on capsule endoscopy as of 2023 are provided, holding the potential for faster and precise readings and the prospect of autonomous image analysis.Mini-invasive Surgery['Ashwin A. George', 'Jin Lin Tan', 'J. Kovoor', 'Alvin Lee', 'Brandon Stretton', 'Aashray K. Gupta', 'Stephen Bacchi', 'Biju George', 'Rajvinder Singh']2024-03-18T00:00:00https://www.semanticscholar.org/paper/c6e633fa8efc1fda0c371f94b56dbfc97b225502 7ad2b76821af73355298ca7f8802b4f285412a8aArtificial intelligence is an important product of the progress of scientific and technological development, which has been widely promoted in various industries and fields of society, and has achieved good application results, with broad development space and prospects. Especially in the full application of enterprise human resource management work, artificial intelligence reflects a great advantage, realizing the optimization of organizational structure and human resource management innovation, improving the overall work efficiency, etc., but at the same time, it also has certain negative impacts, such as the industry threshold enhancement, personnel elimination, etc., which has brought about certain negative impacts on the harmonious development of society. The purpose of this paper is to discuss the application of artificial intelligence in enterprise human resource management, and analyze its impact on enterprise human resource management, in order to further improve the application of artificial intelligence, to provide technical support for enterprise human resource management.International Journal of Social Sciences and Public Administration00The application of artificial intelligence in enterprise human resource management is discussed, and its impact on enterprise human resource management is analyzed in order to further improve the application of artificial intelligence, to provide technical support for enterprise human resource management.International Journal of Social Sciences and Public Administration['Limin Han']2024-03-18T00:00:00https://www.semanticscholar.org/paper/7ad2b76821af73355298ca7f8802b4f285412a8a 3d0b9161301fcd5ce94d3ed6a14554d46a4167ffObjective: The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan’s medical community so as to comment on its future in our healthcare system. Methods: A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05). Results: A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan’s healthcare system. Conclusion: This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals’ perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.Annals of Medicine and Surgery240A majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare, and the need for a more comprehensive ascertainment of healthcare professionals’ perceptions regarding the role of Artificial Intelligence in medicine is indicated.Annals of Medicine and Surgery['Mohammad Umer', 'Aiman Naveed', 'Qanita Maryam', 'Arif Rasheed Malik', 'Naghmana Bashir', 'Kamal Kandel']2024-03-18T00:00:00https://www.semanticscholar.org/paper/3d0b9161301fcd5ce94d3ed6a14554d46a4167ff abfd70f12ba09d83433e015e47821a1561e76e4bCRM (Customer Relationship Management) merupakan alat yang sangat penting bagi organiasasi untuk menjaga hubungan baik dengan pelanggan. Disisi lain perkembangan teknologi AI (Artificial Intelligence) telah berkembang sangat pesat. Integrasi keduanya dianggap sebagai solusi dalam menghadapi tantangan menghadapi perkembangan bisnis. Tujuan dari penelitian ini adalah mengidentifikasi tren penerapan AI pada CRM di berbagai sektor bisnis. Pendekatan yang digunakan dalam penelitian adalah literature review untuk menganalisis tren implementasi AI pada CRM. Hasil penelitian ini menujukkan bahwa tren implementasi AI pada CRM sebagian besar dimanfaatkan untuk menunjang kebutuhan dalam pelayanan customer service atau call center. AI juga diterapkan untuk menentukan strategi pemasaran yang lebih baik dengan menginterpretasi data yang telah diperoleh.   Kata kunci: CRM (Customer Relationship Management), AI (Artificial Intelligence), Tren Implementasi, Bisnis  JSiI (Jurnal Sistem Informasi)280JSiI (Jurnal Sistem Informasi)['Muhammad Shofiudin', 'Tiara Melati', 'Putri Wiryawanto', 'Zuyyina Hawani', 'Faris Muslihul Amin', 'Ir. H. Soekarno', 'No.682', 'Gn. Anyar', 'Kec. Gn', 'S. Anyar']2024-03-18T00:00:00https://www.semanticscholar.org/paper/abfd70f12ba09d83433e015e47821a1561e76e4b 84c365d00db4e0ee23b230a8d3c72788a993380eRadiologie70The question of liability needs to be addressed, comparable to liability for autonomous driving, in order to enhance efficiency in radiology and the use of AI systems in the future as "first look" systems or even as autonomous AI systems.Radiologie['E. Kotter', 'Daniel Pinto Dos Santos']2024-03-18T00:00:00https://www.semanticscholar.org/paper/84c365d00db4e0ee23b230a8d3c72788a993380e 984e6ebef306ca9200fc0b236fdb8ac85c8ca7e4This article provides an in-depth analysis of the use of Artificial Intelligence (AI) in various aspects of biology, including healthcare, agriculture, and environmental monitoring. It highlights AI's ability to mimic human intelligence and analyze large datasets for predictions and tasks. The article also discusses its integration into Chinese medicine, where AI-guided diagnostic and therapeutic systems optimize clinical treatments and health management. AI is also used in disease management, analyzing data on diseases and pests, predicting their impact on ecosystems, and implementing preventative measures. The article also highlights the role of integrated information systems in environmental monitoring. Artificial intelligence (AI) has significant potential in healthcare research and chemical discoveries. Pharmaceutical companies are using AI to improve drug development by utilizing computational biology and machine learning systems to predict molecular behavior and the likelihood of finding a useful drug. This saves time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources for drug development. Strong AI systems can analyze extensive data sets in pharmaceutical and medical research. This review focuses on integrating knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in cancer precision drug discovery.International Journal For Multidisciplinary Research880An in-depth analysis of the use of Artificial Intelligence in various aspects of biology, including healthcare, agriculture, and environmental monitoring highlights AI's ability to mimic human intelligence and analyze large datasets for predictions and tasks.International Journal For Multidisciplinary Research['Anurag', 'Yashdeep Srivastava', 'Aniket Sharma', 'Dheerendra kumar', 'Sumit Pandey', 'Nishchal Maurya', 'Abhishek Gupta']2024-03-18T00:00:00https://www.semanticscholar.org/paper/984e6ebef306ca9200fc0b236fdb8ac85c8ca7e4 c7eee54489707af97bae2f62b8535a28874a8305While there is evidence of substantial improvement in efficiency and cost reduction from the integration of Robotics, Artificial Intelligence, and Drones (RAID) in solar installations; it is observed that there is limited oversight by international standards such as the International Electrotechnical Commission (IEC) in terms of the hazards and untapped potentials. This is partly because it is an emerging application and generally burdened with social acceptability issues. Thus, the safety regulations applied are adaptations of device-specific regulations as deemed fit by individual companies. Also, due to the fast-paced technological development of these platforms, there is huge potential for applications that are not currently supported by the device-specific regulations. This creates a multi-faceted demand for the establishment of standardized, industry-wide polices and guidelines on the use of RAID platforms for Solar PV integrations. This work aims to address critical safety concerns by conducting a comprehensive high-level system examination applicable to the monitoring and maintenance of Solar PV systems. Standard safety assurance models and approaches are examined to provide a safe autonomy perspective for Solar PVs. It is considered that, as RAID applications continue to evolve and become more prevalent in the Solar PV industry, standardized protocols or policies would be established to ensure safe and reliable operations.Safety410This work aims to address critical safety concerns by conducting a comprehensive high-level system examination applicable to the monitoring and maintenance of Solar PV systems.Safety['Olufemi Olayiwola', 'Miles Elsden', 'M. Dhimish']2024-03-18T00:00:00https://www.semanticscholar.org/paper/c7eee54489707af97bae2f62b8535a28874a8305 4349d8340a015751bacde76e28ef1b2c8d346861International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-18T00:00:00https://www.semanticscholar.org/paper/4349d8340a015751bacde76e28ef1b2c8d346861 e5699d5e374723d1603b6cae7d8e84b66e86abf9International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-18T00:00:00https://www.semanticscholar.org/paper/e5699d5e374723d1603b6cae7d8e84b66e86abf9 0b6869b1ca4ceb59628427b6e8fe1a500234267eИспользование нейросетей набирает популярность в сфере архитектуры. В данной научной статье будут рассмотрены актуальные новые технологии в сфере искусственного интеллекта (ИИ) и их влияние на архитектурную практику. Будет проанализировано, как ИИ может помочь архитекторам в проектировании, создании визуализаций и анализе данных, и проведен эксперимент, отражающий возможность применения нейросетей в архитектурной практике и доказывающий наличие положительного аспекта взаимодействия человека и ИИ. The use of neural networks is gaining popularity in the field of architecture. This scientific article will explore the latest technologies in the field of artifi cial intelligence and their impact on architectural practice. It will analyze how AI can assist architects in design, visualization creation, and data analysis, and conduct an experiment demonstrating the potential application of neural networks in architectural practice, highlighting the positive aspect of human- AI interaction.Месмахеровские чтения — 2024 : материалы междунар. науч.-практ. конф., 21– 22 марта 2024 г. : сб. науч. ст. / ФГБОУ ВО «Санкт-Петербургская государственная художественно-промышленная академия имени А. Л. Штиглица»00Месмахеровские чтения — 2024 : материалы междунар. науч.-практ. конф., 21– 22 марта 2024 г. : сб. науч. ст. / ФГБОУ ВО «Санкт-Петербургская государственная художественно-промышленная академия имени А. Л. Штиглица»['В.А. Касаткина', 'Степан Сергеевич Сердитов']2024-03-18T00:00:00https://www.semanticscholar.org/paper/0b6869b1ca4ceb59628427b6e8fe1a500234267e d0a87c3fa6f496eae6fb82f33a46d3e69a1ca1eeInternational Conference on Learning Analytics and Knowledge131{'pages': '821-827'}['Nora Dunder', 'Saga Lundborg', 'Jacqueline Wong', 'Olga Viberg']2024-03-18T00:00:00https://www.semanticscholar.org/paper/d0a87c3fa6f496eae6fb82f33a46d3e69a1ca1ee 2f22353f77cf015f328fbf6567c5653ffa4cc04bВ статье артикулируются антропологические вызовы искусственного интеллекта (ИИ) в модусе концептуализации и восприятия рисков и угроз, благ и выгод, происходящих от новой технологии. Образы антропологических вызов находят разные формы репрезентации в научных концептах и философской рефлексии, в визуализациях в современных видах искусства, в компьютерных играх, кинематографе, институционализированы в правилах этических руководств. Все они могут быть рассмотрены как поиск ответов на проблематизацию человека, его субъектности, целостности, открытости, которые подвергаются риску в технологиях ИИ. Образы восприятия канализированы в позиции в отношении к ИИ и одновременно определяются практиками его широкого внедрения. Концепт ИИ формируется в лексическом топосе осмысления цивилизационного вызова. Понятие «искусственный интеллект» превращается в метафору широкого порядка, порождающую множественные концептуальные модификации. Концепт ИИ, соединяя метафорическое и понятийное, выполняет функцию «оестествления», «опривычивания» технологии. Особенностью в обобщении позиций в отношении к искусственному интеллекту является их нелинейность и целевое формирование. Рассмотрены три варианта оформления образов антропологических вызовов ИИ: алармистский, инструменталистский (профессиональный) и утилитарный (пользовательский). Коллективный ответ на антропологические вызовы ИИ вероятно будет строиться на утилитарно-прагматической основе, концептуально и институционально репрезентированный в этическом регулировании. Для нивелирования антропологических рисков действенными могут быть индивидуальные ответы на основе самосохраняющей стратегии и когнитивной гигиены, начиная со сферы образования. Разработка правил и процедур такой сохраняющей стратегии – задача, которая встает в контексте развития ИИ. Гуманитарная экспертиза нейросетей может стать частью этой стратегии. The challenges of artificial intelligence are considered from the methodological basis of bioethical analysis of anthropological risks and threats posed by new technologies. Society exhibits a cautious attitude towards artificial intelligence technology. Anthropological challenges of artificial intelligence represent a problematic situation regarding the complexity of assessing the benefits and harms, adequate awareness of the risks and threats of new technology to humans. It is necessary to conceptually outline the anthropological challenges of AI, drawing on images of AI perception represented in art and cinema, in ethical rules, philosophical reflection, and scientific concepts. In the projection of various definitions, artificial intelligence becomes a metaphor that serves as a source of creative conceptualizations of new technology. Images of AI are identified through conceptualization, visualization, and institutionalization of risks and correspond to specific types of attitudes towards innovation in society. The peculiarity of AI perception images, both in the forms of conceptualization and in the visual or institutional objectification of these images in ethical codes, is their active and purposeful formation. Analogous to the regulation of biotechnologies, normatively conceptualized positions regarding new technologies are divided into conservative - restrictive and prohibitive; liberal - welcoming innovations; and moderate - compromising, which often becomes the basis for ethical and legal regulation. However, sociological surveys show that those who welcome the emergence of neural networks, the widespread use of artificial intelligence, also exhibit caution and uncertainty in assessing the human future. A three-part typology of perception images of anthropological challenges is proposed, in which non-linear opposition of positions towards AI is fixed, but vectors of possible ways of habituating and semiotization of the future are outlined. The first, alarmist type, is distinguished based on an emotionally evaluative attitude. New technologies are seen as redundant, causing alarm and fear. The second type of perception, instrumentalist, is characteristic of AI actors within a professionally formed worldview. Some concepts of the professional thesaurus become common parlance. The third type is user-oriented. For this type, it is important how the interaction between AI and humans unfolds. The collective response to the anthropological challenges of AI is more likely to be formed on a utilitarian-pragmatic basis. Effective responses may be based on an individual self-preservation strategy, which, for example, may require adherence to cognitive hygiene in the field of education. In the context of AI development, the task arises of developing rules and procedures for such a preservation strategy.ΠΡΑΞΗMΑ Journal of Visual Semiotics60ΠΡΑΞΗMΑ. Journal of Visual Semiotics['Татьяна Александровна Сидорова']2024-03-18T00:00:00https://www.semanticscholar.org/paper/2f22353f77cf015f328fbf6567c5653ffa4cc04b 07539f2132f412218d0c7cd63e4f603a64312650The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles, largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices. However, a lack of interpretability in real-time decisions with contemporary learning methods impedes user trust and attenuates the widespread deployment and commercialization of such vehicles. Moreover, the issue is exacerbated when these cars are involved in or cause traffic accidents. Such drawback raises serious safety concerns from societal and legal perspectives. Consequently, explainability in end-to-end autonomous driving is essential to build trust in vehicular automation. However, the safety and explainability aspects of end-to-end driving have generally been investigated disjointly by researchers in today's state of the art. This survey aims to bridge the gaps between these topics and seeks to answer the following research question: When and how can explanations improve safety of end-to-end autonomous driving? In this regard, we first revisit established safety and state-of-the-art explainability techniques in end-to-end driving. Furthermore, we present three critical case studies and show the pivotal role of explanations in enhancing self-driving safety. Finally, we describe insights from empirical studies and reveal potential value, limitations, and caveats of practical explainable AI methods with respect to their safety assurance in end-to-end autonomous driving.arXiv.org1720This survey revisits established safety and state-of-the-art explainability techniques in end-to-end driving and describes insights from empirical studies that reveal potential value, limitations, and caveats of practical explainable AI methods with respect to their safety assurance in end-to-end autonomous driving.ArXiv['Shahin Atakishiyev', 'Mohammad Salameh', 'Randy Goebel']2024-03-18T00:00:00https://www.semanticscholar.org/paper/07539f2132f412218d0c7cd63e4f603a64312650 f1e5656a2de56f07fdbfbcf57d9f851923c020e1With the advent of the Internet of Things (IoT) with sensors and connected devices, data generation is increasingly peaking at an unprecedented pace. However, energy consumption is also on the rise based on traditional energy sources, such as fossil fuels. This is not sustainable and could hurt the environment while being quite expensive to run e.g., empowering irrigation systems using sensors. In this context, using data as an energy source for future machines could be a promising solution to mitigate the energy crisis and reduce the carbon footprint. The concept of data as a new form of energy will be discussed, examining the benefits and challenges associated with this method. This paper also proposes other potential applications for using data as an energy source, including powering self-driving cars, drones, and smart irrigation systems a data-driven approach.Asian Journal of Research in Computer Science00The concept of data as a new form of energy will be discussed, examining the benefits and challenges associated with this method, and other potential applications for using data as an energy source, including powering self-driving cars, drones, and smart irrigation systems a data-driven approach are proposed.Asian Journal of Research in Computer Science['Chinedu Chukwuemeka Mazi', 'Gregory Anichebe', 'Ogechi Ifeoma Anya', 'A. C. Nwanakwaugwu']2024-03-18T00:00:00https://www.semanticscholar.org/paper/f1e5656a2de56f07fdbfbcf57d9f851923c020e1 a8d1bd1cd2fd2d59b9daea0275e11658c86c558cConstruction Research Congress 2024250Construction Research Congress 2024['Vaishnavi Jagalur Ramachandra', 'Naila Mahaveen', 'Siddharth Banerjee', 'Pedram Ghannad']2024-03-18T00:00:00https://www.semanticscholar.org/paper/a8d1bd1cd2fd2d59b9daea0275e11658c86c558c 34f41093d57b2a0acd6a0176520398c99253e1edAI Communications00AI Communications['Zied Bouraoui', 'Anaëlle Wilczynski']2024-03-18T00:00:00https://www.semanticscholar.org/paper/34f41093d57b2a0acd6a0176520398c99253e1ed 0121434c2a9f184e165fe41f69d6cd0a9870778500['D.S. Ivanova']2024-03-18T00:00:00https://www.semanticscholar.org/paper/0121434c2a9f184e165fe41f69d6cd0a98707785 2b0b49b426ab9f3490f31c7a8579d449793e9100Journal of Industrial and Production Engineering320Journal of Industrial and Production Engineering['Hung-Li Chang', 'Yu-Lun Liu', 'Ching-Jui Keng', 'Han-Ling Jiang', 'Jiayao Hu']2024-03-18T00:00:00https://www.semanticscholar.org/paper/2b0b49b426ab9f3490f31c7a8579d449793e9100 1af53db6a5dfbbeac59e677106707ae476bcc56eInternational Conference on Automata and Formal Languages00Advanced Fiber Laser Conference (AFL2023)['Xingwen Suo', 'Yang Yang', 'Jie He', 'Weiwei Li']2024-03-18T00:00:00https://www.semanticscholar.org/paper/1af53db6a5dfbbeac59e677106707ae476bcc56e 5e9759b368573cb609e3cf76fe2e6493c996531cSelf-awareness results from consciousness of existence in time and space. Thought and consciousness are distinguishing factors between humans and machines having artificial intelligence. No algorithm has been offered for artificial self-awareness based on Thinking. Previous studies have not studied the relationship between consciousness, thinking and time. This study studied the relationship between Self-awareness, thinking, memories and speech over time. A deep logical connection exists between consciousness, thinking, and time. Based on this research findings, an algorithm can be designed for artificial consciousness and Self-awareness. Qeios250This study studied the relationship between Self-awareness, thinking, memories and speech over time and found a deep logical connection exists between consciousness, thinking, and time.Qeios['Seyed Kazem Mousavi']2024-03-18T00:00:00https://www.semanticscholar.org/paper/5e9759b368573cb609e3cf76fe2e6493c996531c 73a3c8ed17da4dea76927ec6b0e070fff0e97270AI and Ethics420This work examines the limitations of rule-based explainability for Preventive Legal Technology and investigates deeply the relevance of PLT for LegalTech applications in light of the development of the AI Act and the work of the High-Level Expert Group on AI.AI and Ethics['G. Stathis', 'Jaap van den Herik']2024-03-18T00:00:00https://www.semanticscholar.org/paper/73a3c8ed17da4dea76927ec6b0e070fff0e97270 46c00ba4c182b7e87ccad386b42d1ccd8e406112Automation is becoming increasingly prevalent in various industries, including healthcare and pharmaceuticals. The pharmaceutical business is influenced by a variety of worldwide trends, with one of the most significant being the use of automation technologies, which will have a transformative effect on the research and development of new pharmaceutical products as well as the speed and efficiency with which products reach patients in need. Regulatory automation is enabled by a variety of technology tools, such as Electronic Document Management Systems, Regulatory Information Management (RIM) Systems, Artificial Intelligence (AI) Analytics Tools, Natural Language Processing (NLP) Tools, and Submission Publishing Tools. Automation tools can be used to automate regulatory activities such as administrative work, dossier completion, data extraction, auditing, regulatory implementation as well as quality management. Automation tools establish process links and minimize complexity, resulting in a more efficient management system. Human-AI interaction creates new prospects in regulatory concerns. This article investigates the potential use of automation techniques in pharmaceutical regulatory concerns.International Journal of Drug Regulatory Affairs00The potential use of automation techniques in pharmaceutical regulatory concerns is investigated, including human-AI interaction creates new prospects in regulatory concerns and automation tools establish process links and minimize complexity, resulting in a more efficient management system.International Journal of Drug Regulatory Affairs['Kumar Chandrasekaran', 'T. M. Pramod Kumar', 'V. Balamuralidhara']2024-03-18T00:00:00https://www.semanticscholar.org/paper/46c00ba4c182b7e87ccad386b42d1ccd8e406112 bf39778c65f53c1ffff170c01d92fc85ee84f368In discussions of modern technology, the relationship between ethics and artificial intelligence (AI) has become precedent. As artificial intelligence (AI) grows increasingly pervasive in society, ethical concerns about its development, use, and impact on individuals and organizations become more important. This research explores the complex interrelationship between AI and ethics with the objective to provide a comprehensive understanding of the ethical prospective and issues related to AI development. Due to its widespread application, artificial intelligence has the potential to significantly impact human existence in several domains. A lot of study has been made to the ethical implications of AI's cognitive developments and how they might impact human rights. Even though the concept of AI is changing, its fundamental goal is still to perform better than humans at tasks, which raises concerns about how AI may affect human rights and dignity.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research explores the complex interrelationship between AI and ethics with the objective to provide a comprehensive understanding of the ethical prospective and issues related to AI development.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Vikrant Singh', 'Professor Bhumi Shah']2024-03-18T00:00:00https://www.semanticscholar.org/paper/bf39778c65f53c1ffff170c01d92fc85ee84f368 f83978889478ac39a1d50637f015e9be74d56374Currently, the state is widely promoting the use of artificial intelligence (AI) to regulate the interaction between the government and society, and it is trying to digitize it because this system greatly contributes to faster and easier communication between the state and society. In the process of development of the state apparatus, the role of artificial intelligence is increasing as one of the means of stimulating the adoption of management decisions aimed at increasing the efficiency of state activity. The use of artificial intelligence is being widely developed at every level of state administration. In particular, the use of artificial intelligence is becoming a necessity in the Republic of Uzbekistan. Therefore, the use of such digitization requires legal regulation.International journal of law and policy324The use of artificial intelligence is becoming a necessity in the Republic of Uzbekistan, and the use of such digitization requires legal regulation.International Journal of Law and Policy['Jamshid Odilov']2024-03-17T00:00:00https://www.semanticscholar.org/paper/f83978889478ac39a1d50637f015e9be74d56374 2053120cd10339d100015761a6689f1b167e2471Наукові інновації та передові технології00Наукові інновації та передові технології['Тетяна Паламарчук']2024-03-17T00:00:00https://www.semanticscholar.org/paper/2053120cd10339d100015761a6689f1b167e2471 0916f3c6c0b4a07131677448e20083fa72ec9c43The integration of Artificial Intelligence (AI) technologies holds immense potential for revolutionizing waste management systems in underserved communities across the United States. This concept paper explores the feasibility, benefits, challenges, and implications of implementing AI-driven waste management systems in these communities. By leveraging AI capabilities such as predictive analytics, optimization algorithms, and IoT sensors, innovative solutions can be developed to enhance waste collection, recycling efficiency, and environmental sustainability. However, successful implementation requires careful consideration of socioeconomic factors, community engagement, privacy concerns, and infrastructure limitations. This paper aims to provide a comprehensive overview of the opportunities and considerations associated with deploying AI-driven waste management systems in underserved communities, ultimately striving to promote equitable access to efficient and sustainable waste management solutions. This concept paper provides a comprehensive framework for implementing AI-driven waste management systems in underserved communities in the USA. It examines various aspects including socioeconomic considerations, community engagement, privacy concerns, infrastructure requirements, policy frameworks, financing options, and sustainability measures. Through careful planning, collaboration, and innovation, AI technologies can be harnessed to address the unique challenges faced by underserved communities, ultimately leading to more efficient, equitable, and sustainable waste management practices. Keywords: AI, Community, Waste, USA.Engineering Science &amp; Technology Journal05Engineering Science &amp; Technology Journal['Zamathula Queen Sikhakhane Nwokediegwu', 'Ejike David Ugwuanyi']2024-03-17T00:00:00https://www.semanticscholar.org/paper/0916f3c6c0b4a07131677448e20083fa72ec9c43 9f7db53338c8e76d708faf1a126e7b43331a57b2With the rise of Large Language Models (LLMs), AI assistants’ ability to utilize tools, especially through API calls, has advanced notably. This progress has necessitated more accurate evaluation methods. Many existing studies adopt static evaluation, where they assess AI assistants’ API call based on pre-defined dialogue histories. However, such evaluation method can be misleading, as an AI assistant might fail in generating API calls from preceding human interaction in real cases. Instead of the resource-intensive method of direct human-machine interactions, we propose Automated Dynamic Evaluation (AutoDE) to assess an assistant’s API call capability without human involvement. In our framework, we endeavor to closely mirror genuine human conversation patterns in human-machine interactions, using a LLM-based user agent, equipped with a user script to ensure human alignment. Experimental results highlight that AutoDE uncovers errors overlooked by static evaluations, aligning more closely with human assessment. Testing four AI assistants using our crafted benchmark, our method further mirrored human evaluation compared to conventional static evaluations.International Conference on Language Resources and Evaluation410This work proposes Automated Dynamic Evaluation (AutoDE) to assess an assistant’s API call capability without human involvement, and endeavors to closely mirror genuine human conversation patterns in human-machine interactions, using a LLM-based user agent equipped with a user script to ensure human alignment.ArXiv['Honglin Mu', 'Yang Xu', 'Yunlong Feng', 'Xiaofeng Han', 'Yitong Li', 'Yutai Hou', 'Wanxiang Che']2024-03-17T00:00:00https://www.semanticscholar.org/paper/9f7db53338c8e76d708faf1a126e7b43331a57b2 dcdf7d0171ba043658f114193b444305cf6adf97CMP (Chemical Mechanical Polishing) is a process that is widely used in the manufacturing of semiconductors, particularly for the BEOL (Back End Of Line) process which involves multi-level interconnections. Copper (Cu) is commonly used as the wiring metal in the BEOL process. Cu CMP plays a crucial role in achieving the necessary planarization of the BEOL layer for effective alignment of multiple layers. In recent times, Cu CMP has gained significance in 3D integration schemes such as hybrid bonding and TSV (Through Silicon Via). For the Cu slurry in the CMP process, 1H-Benzotriazole (BTA) is known to be an effective Cu corrosion inhibitor that helps in achieving better planarization. However, the use of BTA is often associated with the formation of organic residue in the CMP process. Therefore, a specialized chemical for post CMP (pCMP) cleaning is required to remove any residue of BTA. On this occasion, we conducted a special exploration of additives using an AI system to achieve selective removal of BTA specifically from Cu surfaces. The system classified a large number of parameters and identified specific parameters for BTA removal and Cu compatibility. Following regression analysis using these parameters, we observed a strong correlation with actual experimental results. In conclusion, our Cu pCMP cleaner, which includes a special additive designed by the AI system, exhibited commendable cleaning performance.China Semiconductor Technology International Conference30The Cu pCMP cleaner, which includes a special additive designed by the AI system, exhibited commendable cleaning performance and a strong correlation with actual experimental results.2024 Conference of Science and Technology for Integrated Circuits (CSTIC)['Atsushi Mizutani', 'Akihiko Ohtsu', 'Tetsuya Kamimura']2024-03-17T00:00:00https://www.semanticscholar.org/paper/dcdf7d0171ba043658f114193b444305cf6adf97 b5bcba2489e879ff960e9dfa415dc31be4cb9b84Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be used to manipulate public opinion and distort perceptions, resulting in a decline in trust towards digital platforms. This study contributes to marketing literature and practice in three ways. First, it demonstrates the proficiency of AI in fabricating disinformative user-generated content (UGC) that mimics the form of authentic content. Second, it quantifies the disruptive impact of such UGC on marketing research, highlighting the susceptibility of analytics frameworks to even minimal levels of disinformation. Third, it proposes and evaluates advanced detection frameworks, revealing that standard techniques are insufficient for filtering out AI-generated disinformation. We advocate for a comprehensive approach to safeguarding marketing research that integrates advanced algorithmic solutions, enhanced human oversight, and a reevaluation of regulatory and ethical frameworks. Our study seeks to serve as a catalyst, providing a foundation for future research and policy-making aimed at navigating the intricate challenges at the nexus of technology, ethics, and marketing.Social Science Research Network700This study demonstrates the proficiency of AI in fabricating disinformative user-generated content that mimics the form of authentic content, and proposes and evaluates advanced detection frameworks, revealing that standard techniques are insufficient for filtering out AI-generated disinformation.ArXiv['Anirban Mukherjee']2024-03-17T00:00:00https://www.semanticscholar.org/paper/b5bcba2489e879ff960e9dfa415dc31be4cb9b84 21fcae4104574659870bc83d3d933d70f480ac5dDiabetic retinopathy (DR) remains a significant cause of vision impairment and blindness, particularly in rural settings where access to specialized healthcare services is limited. The integration of artificial intelligence (AI) holds promise in revolutionizing the screening and diagnosis of DR, offering a scalable solution to bridge the gap in healthcare disparities. This systematic review synthesizes existing literature on AI applications tailored for screening and diagnosing diabetic retinopathy in rural areas. Through a comprehensive search across various databases, including PubMed, IEEE Xplore, and Google Scholar, a total of 88 studies meeting the inclusion criteria were identified. These studies encompassed a range of AI techniques, including deep learning algorithms, machine learning models, and image processing methods, deployed in diverse rural healthcare settings globally. The findings reveal that AI-based systems demonstrate high accuracy, sensitivity, and specificity in detecting diabetic retinopathy from fundus images, thereby enabling early identification and timely intervention. Moreover, the scalability and cost-effectiveness of these AI solutions make them particularly suitable for resource-constrained rural environments. However, several challenges persist, including the need for robust validation studies, integration with existing healthcare infrastructure, and addressing ethical and regulatory concerns. Additionally, considerations regarding data privacy, patient acceptance, and healthcare provider training are crucial for the successful implementation of AI-driven DR screening programs in rural settings. This systematic review underscores the transformative potential of AI technologies in improving access to diabetic retinopathy screening and diagnosis in rural areas. Future research should focus on addressing the identified challenges and optimizing AI systems to enhance their efficacy and accessibility in underserved communities. Keywords:  AI, Rural, Diagnosis, Diabetic, Retinopathy, Rural, Review.International medical science research journal00A systematic review of existing literature on AI applications tailored for screening and diagnosing diabetic retinopathy in rural areas reveals that AI-based systems demonstrate high accuracy, sensitivity, and specificity in detecting diabetic retinopathy from fundus images, thereby enabling early identification and timely intervention.International Medical Science Research Journal['Rawlings Chidi', 'Ugochukwu Odimba']2024-03-17T00:00:00https://www.semanticscholar.org/paper/21fcae4104574659870bc83d3d933d70f480ac5d 48a6dd5558f7cae373ef153261dab13955966f1bWe examine whether Artificial Intelligence (AI) systems generate truly novel ideas rather than merely regurgitating patterns learned during training. Utilizing a novel experimental design, we task an AI with generating project titles for hypothetical crowdfunding campaigns. We compare within AI-generated project titles, measuring repetition and complexity. We compare between the AI-generated titles and actual observed field data using an extension of maximum mean discrepancy--a metric derived from the application of kernel mean embeddings of statistical distributions to high-dimensional machine learning (large language) embedding vectors--yielding a structured analysis of AI output novelty. Results suggest that (1) the AI generates unique content even under increasing task complexity, and at the limits of its computational capabilities, (2) the generated content has face validity, being consistent with both inputs to other generative AI and in qualitative comparison to field data, and (3) exhibits divergence from field data, mitigating concerns relating to intellectual property rights. We discuss implications for copyright and trademark law.Social Science Research Network340Results suggest that the AI generates unique content even under increasing task complexity, and at the limits of its computational capabilities, mitigating concerns relating to intellectual property rights.ArXiv['Anirban Mukherjee']2024-03-17T00:00:00https://www.semanticscholar.org/paper/48a6dd5558f7cae373ef153261dab13955966f1b 2bb57daae94c652674b0e75b92a4fcc21529b577CSA News00CSA News['Dianna Bagnall', 'Nate Looker']2024-03-17T00:00:00https://www.semanticscholar.org/paper/2bb57daae94c652674b0e75b92a4fcc21529b577 fc3643feb0139954fcd0e8134303b3766bf593beIn the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, bring potential security risks to our society. Existing generated image detection methods suffer from performance drop when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For the explicit one, a suspicious frequency-band proposal method and a spatial feature decomposition method are proposed to extract artifact-related features. For the implicit one, a training strategy based on mutual information estimation is proposed to further purify the artifact-related features. Experiments show that for cross-generator detection, the average accuracy of APN is 5.6% ~ 16.4% higher than the previous 10 methods on GenImage dataset and 1.7% ~ 50.1% on DiffusionForensics dataset. For cross-scene detection, APN maintains its high performance. Via visualization analysis, we find that the proposed method extracts flexible forgery patterns and condenses the forgery information diluted in irrelevant features. We also find that the artifact features APN focuses on across generators and scenes are global and diverse. The code will be available on GitHub.arXiv.org820The proposed Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes finds that the proposed method extracts flexible forgery patterns and condenses the forgery information diluted in irrelevant features.ArXiv['Zheling Meng', 'Bo Peng', 'Jing Dong', 'Tieniu Tan']2024-03-17T00:00:00https://www.semanticscholar.org/paper/fc3643feb0139954fcd0e8134303b3766bf593be 85dbadeb9d4983f6effc89f9dc10caa80591590bNature India00Nature India['Sahana Ghosh']2024-03-17T00:00:00https://www.semanticscholar.org/paper/85dbadeb9d4983f6effc89f9dc10caa80591590b 69f69205b88837bb1028e1477fbe9dfcc1431ea0The article examines the system of restrictions created and applied within the framework of public law, contributing to the achievement of the goals of public legal regulation. It is substantiated that such a system includes, along with universal ones, specific means, the demand for which is determined by the purpose of public law and the logic of regulating public legal restrictions. It is argued that in modern conditions, restrictions aimed not only at ensuring generally recognized interests, but also the scope and parameters of its action are in demand in public law.Vestnik Yaroslavskogo gosudarstvennogo universiteta im. P. G. Demidova. Seriya gumanitarnye nauki10Vestnik Yaroslavskogo gosudarstvennogo universiteta im. P. G. Demidova. Seriya gumanitarnye nauki['Anastasia V. Amelchakova']2024-03-17T00:00:00https://www.semanticscholar.org/paper/69f69205b88837bb1028e1477fbe9dfcc1431ea0 1ed814c7cbad3bad8f32c878819d978c6a22a269With the advent of the era of artificial intelligence, it is difficult to rely on standardized education, and the development of The Times puts forward new requirements for talent training. The talents trained by higher vocational education in the new era should have the characteristics of growth mentality and high consciousness learning, human-machine symbiotic thinking and AI penetration skills, pioneering spirit and the ability to "break boundaries", the creative ability and practical wisdom, the combination of humanistic spirit and science and technology, human community thinking and cross-cultural action. Therefore, it is proposed that higher vocational education should focus on the target object of talent training in higher vocational colleges in the intelligent era, build a digital twin Sunac platform, form a new technology base, and develop new skills with the integration of industry and education certification. We are committed to cultivating the fertile soil for the growth of talents, and enabling the continuous emergence of high-quality skilled talents in the new era.Journal of Education and Educational Research90Higher vocational education should focus on the target object of talent training in higher vocational colleges in the intelligent era, build a digital twin Sunac platform, form a new technology base, and develop new skills with the integration of industry and education certification.Journal of Education and Educational Research['Shaoyun Lin', 'Haiyun Dong']2024-03-17T00:00:00https://www.semanticscholar.org/paper/1ed814c7cbad3bad8f32c878819d978c6a22a269 7e3061f57c102a4539e69e1cd98b275c33c84602The advent of ChatGPT has sparked over a year of regulatory frenzy. However, few existing studies have rigorously questioned the assumption that, if left unregulated, AI chatbot's output would inflict tangible, severe real harm on human affairs. Most researchers have overlooked the critical possibility that the information market itself can effectively mitigate these risks and, as a result, they tend to use regulatory tools to address the issue directly. This Article develops a yardstick for reevaluating both AI-related content risks and corresponding regulatory proposals by focusing on inter-informational competition among various outlets. The decades-long history of regulating information and communications technologies indicates that regulators tend to err too much on the side of caution and to put forward excessive regulatory measures when encountering the uncertainties brought about by new technologies. In fact, a trove of empirical evidence has demonstrated that market competition among information outlets can effectively mitigate most risks and that overreliance on regulation is not only unnecessary but detrimental, as well. This Article argues that sufficient competition among chatbots and other information outlets in the information marketplace can sufficiently mitigate and even resolve most content risks posed by generative AI technologies. This renders certain loudly advocated regulatory strategies, like mandatory prohibitions, licensure, curation of datasets, and notice-and-response regimes, truly unnecessary and even toxic to desirable competition and innovation throughout the AI industry. Ultimately, the ideas that I advance in this Article should pour some much-needed cold water on the regulatory frenzy over generative AI and steer the issue back to a rational track.Social Science Research Network150It is argued that sufficient competition among chatbots and other information outlets in the information marketplace can sufficiently mitigate and even resolve most content risks posed by generative AI technologies.ArXiv['Jiawei Zhang']2024-03-17T00:00:00https://www.semanticscholar.org/paper/7e3061f57c102a4539e69e1cd98b275c33c84602 bf897843557398d8b056c09270d637d453177922This paper presents a comprehensive comparative review of digital banking regulations in Nigeria and the United States of America. The primary objective is to analyze and contrast the regulatory frameworks governing digital banking in these two countries, with a focus on understanding their impact on financial stability, consumer protection, and innovation in the banking sector. The methodology involves a detailed examination of existing literature, regulatory policies, and legal documents pertaining to digital banking in both Nigeria and the USA. This is complemented by interviews with key stakeholders in the banking industry, including regulators, bank executives, and fintech experts. Key findings reveal significant differences in the regulatory approaches of Nigeria and the USA. Nigeria's digital banking regulations are found to be more focused on promoting financial inclusion and mitigating systemic risks, reflecting the country's developing economic status and the need to address a large unbanked population. In contrast, the USA's regulations are more comprehensive and sophisticated, emphasizing consumer protection, data security, and maintaining a competitive market environment. The study also highlights the challenges and opportunities presented by these regulatory frameworks, particularly in terms of fostering innovation and adapting to emerging technologies in the banking sector. The conclusion underscores the importance of a balanced regulatory approach that safeguards the interests of consumers and the stability of the financial system, while also encouraging innovation and competition. The paper suggests that both Nigeria and the USA can learn from each other's regulatory practices to enhance their digital banking ecosystems. This comparative review provides valuable insights for policymakers, financial institutions, and other stakeholders in the global banking industry, aiming to navigate the complexities of digital banking regulation in a rapidly evolving technological landscape. Keywords:  Digital Banking, Regulatory Frameworks, Nigeria, USA, Financial Inclusion, Cybersecurity, Consumer Protection, Central Bank of Nigeria (CBN), Federal Reserve, Fintech.Finance &amp; Accounting Research Journal05Nigeria and the USA can learn from each other's regulatory practices to enhance their digital banking ecosystems, and the conclusion underscores the importance of a balanced regulatory approach that safeguards the interests of consumers and the stability of the financial system, while also encouraging innovation and competition.Finance &amp; Accounting Research Journal['Onyeka Chrisanctus Ofodile', 'Olubusola Odeyemi', 'Chinwe Chinazo Okoye', 'Wihelmina Afua Addy', 'Adedoyin Tolulope Oyewole', 'Omotoya Bukola Adeoye', 'Yinka James Ololade']2024-03-17T00:00:00https://www.semanticscholar.org/paper/bf897843557398d8b056c09270d637d453177922 de20f02572d81883f571ce8fa911762396500fc5International Research Journal of Modernization in Engineering Technology and Science01International Research Journal of Modernization in Engineering Technology and Science[]2024-03-17T00:00:00https://www.semanticscholar.org/paper/de20f02572d81883f571ce8fa911762396500fc5 9175c86c5e3c51ecda2d2c65f6cac9e22cc7b501Artificial Intelligence has really revolutionized the panorama of management. By harnessing the energy of information evaluation and automation, it streamlines tasks, complements decision-making, and predicts upcoming tendencies. This now no longer only outcomes in price, financial savings but also lets in for the personalization of client experiences, fostering loyalty and satisfaction. Furthermore, Artificial Intelligence extends its effect throughout diverse sides of business, from human assets to operations and security. It acts as a diligent detective, uncovering precious insights inside large datasets, comparable to fixing a complicated puzzle. Its capacity to expect future tendencies and proactively optimize operations is comparable to having a strategic best friend in management. Artificial Intelligence versatility shines through in its ability to address multifaceted tasks, including reading customer preferences, optimizing Artificial Intelligence allocation, detecting fraud, and conducting online sentiment evaluation. Its position in decision-making and performance enhancement cannot be overstated, making it an essential asset in the modern management toolkit. However, accountable, and cautious usage is paramount to ensure its positive effect while mitigating ability risks. Artificial Intelligence is a notable change that simplifies management processes, empowers leaders with insights, and positions agencies for boom and competitiveness. Keywords: Artificial Intelligence, Management, Operation, Evaluation, Decision, Ability, Tasks, Insights, TendenciesINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Artificial Intelligence versatility shines through in its ability to address multifaceted tasks, including reading customer preferences, optimizing Artificial Intelligence allocation, detecting fraud, and conducting online sentiment evaluation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Jishu Varshney', 'Neha Singh']2024-03-17T00:00:00https://www.semanticscholar.org/paper/9175c86c5e3c51ecda2d2c65f6cac9e22cc7b501 76799f9e063190f4b221aa66f7b45b8a268905bdArtificial intelligence (AI) has become a part of the mainstream public discourse beyond expert communities about its risks, benefits, and need for regulation. In particular, since 2014, the news media have intensified their coverage of this emerging technology and its potential impact on most domains of society. Although many studies have analyzed traditional media coverage of AI, analyses of social media, especially video-sharing platforms, are rare. In addition, research from a risk communication perspective remains scarce, despite the widely recognized potential threats to society from many AI applications. This study aims to detect recurring patterns of societal threat/efficacy in YouTube videos, analyze their main sources, and compare detected frames in terms of reach and response. Using a theoretical framework combining framing and risk communication, the study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked (2018). Four dominant AI frames were identified: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame. The balanced and no-threat frames were the most prevalent, with predominantly positive and neutral AI narratives that neither adequately address the risks nor the necessary societal response from a normative risk communication perspective. The results revealed the specific risks and benefits of AI that are most frequently addressed. Video views and user engagement with AI videos were analyzed. Recommendations for effective AI risk communication and implications for risk governance were derived from the results.Risk Analysis500The study analyzed the societal threat/efficacy attributed to AI in easily accessible YouTube videos published in a year when public attention to AI temporarily peaked, and identified four dominant AI frames: the balanced frame, the high-efficacy frame, the high-threat frame, and the no-threat frame.Risk analysis : an official publication of the Society for Risk Analysis['Andreas Schwarz', 'Janina Jacqueline Unselt']2024-03-16T00:00:00https://www.semanticscholar.org/paper/76799f9e063190f4b221aa66f7b45b8a268905bd 399e0cc1f1f5bf82210557020a71d0eb862f95b9The European Union, like the United States, is creating an internal market within its Member States, which it is adopting the necessary coherent framework of measures to ensure the functioning of that market. In both cases, the measures derive their legal basis from provisions of supreme legal force in the form of the Treaty on the Functioning of the European Union or the Constitution of the United States of America. The paper focuses on a comparison of the provisions of Article 114 of the Treaty on the Functioning of the European Union, the so-called Harmonization Clause, and Article 1, Section 8 (3) of the United States Constitution, the Commerce Clause, the application of which poses a problem in some cases and raises several jurisdictional issues. The aim of this paper is to analyse and compare the limits of the legislator’s powers in relation to the use of internal market regulatory instruments.Slovak Yearbook of European Union Law30Slovak Yearbook of European Union Law['Igor Sloboda']2024-03-16T00:00:00https://www.semanticscholar.org/paper/399e0cc1f1f5bf82210557020a71d0eb862f95b9 ec866553fe0dc0bf08891036e8c7ce1e31424c03Despite the global mental health crisis, access to screenings, professionals, and treatments remains high. In collaboration with licensed psychotherapists, we propose a Conversational AI Therapist with psychotherapeutic Interventions (CaiTI), a platform that leverages large language models (LLM)s and smart devices to enable better mental health self-care. CaiTI can screen the day-to-day functioning using natural and psychotherapeutic conversations. CaiTI leverages reinforcement learning to provide personalized conversation flow. CaiTI can accurately understand and interpret user responses. When the user needs further attention during the conversation, CaiTI can provide conversational psychotherapeutic interventions, including cognitive behavioral therapy (CBT) and motivational interviewing (MI). Leveraging the datasets prepared by the licensed psychotherapists, we experiment and microbenchmark various LLMs' performance in tasks along CaiTI's conversation flow and discuss their strengths and weaknesses. With the psychotherapists, we implement CaiTI and conduct 14-day and 24-week studies. The study results, validated by therapists, demonstrate that CaiTI can converse with users naturally, accurately understand and interpret user responses, and provide psychotherapeutic interventions appropriately and effectively. We showcase the potential of CaiTI LLMs to assist the mental therapy diagnosis and treatment and improve day-to-day functioning screening and precautionary psychotherapeutic intervention systems.arXiv.org942The study results demonstrate that CaiTI can converse with users naturally, accurately understand and interpret user responses, and provide psychotherapeutic interventions appropriately and effectively.ArXiv['Jingping Nie', 'Hanya Shao', 'Yuang Fan', 'Qijia Shao', 'Haoxuan You', 'Matthias Preindl', 'Xiaofan Jiang']2024-03-16T00:00:00https://www.semanticscholar.org/paper/ec866553fe0dc0bf08891036e8c7ce1e31424c03 4c8b37c0f02252a76ef795f9c33a507d3006b0f4With the widespread adoption of advanced generative models such as Gemini and GPT, there has been a notable increase in the incorporation of such models into sociotechnical systems, categorized under AI-as-a-Service (AIaaS). Despite their versatility across diverse sectors, concerns persist regarding discriminatory tendencies within these models, particularly favoring selected `majority' demographics across various sociodemographic dimensions. Despite widespread calls for diversification of media representations, marginalized racial and ethnic groups continue to face persistent distortion, stereotyping, and neglect within the AIaaS context. In this work, we provide a critical summary of the state of research in the context of social harms to lead the conversation to focus on their implications. We also present open-ended research questions, guided by our discussion, to help define future research pathways.arXiv.org422A critical summary of the state of research in the context of social harms is provided to lead the conversation to focus on their implications, and open-ended research questions are presented to help define future research pathways.ArXiv['Sanjana Gautam', 'Pranav Narayanan Venkit', 'Sourojit Ghosh']2024-03-16T00:00:00https://www.semanticscholar.org/paper/4c8b37c0f02252a76ef795f9c33a507d3006b0f4 03dcf8a3feb11157fc6fba0fb690eb847df51d9bThis article delves into the transformative implications of integrating state-of-the-art Generative AI technologies into Enterprise Resource Planning (ERP) applications within the manufacturing industry. With the manufacturing landscape experiencing rapid evolution, there is a growing imperative for adaptive and intelligent systems to optimize efficiency, productivity, and decision- making processes. Through the exploration of Generative AI's natural language processing capabilities, this article unveils a new frontier in smart manufacturing, where ERP systems are empowered to redefine conventional paradigms and catalyze innovation. Furthermore, this article examines Generative AI's impact on supply chain management, leveraging its capacity to process extensive textual data for enhanced demand forecasting, inventory optimization, and risk management. This enhances the adaptability and resilience of manufacturing ecosystems, enabling them to navigate dynamic market conditions with agility.International Journal of Innovative Science and Research Technology21Through the exploration of Generative AI's natural language processing capabilities, this article unveils a new frontier in smart manufacturing, where ERP systems are empowered to redefine conventional paradigms and catalyze innovation.International Journal of Innovative Science and Research Technology (IJISRT)['Sridhar Mahadevan']2024-03-16T00:00:00https://www.semanticscholar.org/paper/03dcf8a3feb11157fc6fba0fb690eb847df51d9b 5d23afca24a38ce4eb5b19ad5b0e524b8e95e3eePurpose: "The Legal and Political Implications of AI Bias: An International Comparative Study" extensively navigates the intricate terrain of AI governance, with a specific focus on the ethical challenges arising from bias in AI systems. The purpose of this study is to underscore the urgent need for robust regulatory frameworks to address issues of bias, discrimination, and fairness within the realm of AI technologies. Materials and Methods: The research methodology involved a comprehensive analysis of international perspectives on AI bias. This entailed examining existing literature, legal frameworks, and political dynamics surrounding AI governance in various countries. Comparative analysis was conducted to elucidate the diverse approaches adopted by different nations to tackle AI bias and unravel the corresponding legal and political consequences. Findings: The study highlighted the inherent risks associated with biased algorithms and stressed the paramount importance of proactively detecting and mitigating bias to prevent discrimination and promote fairness in AI systems. Additionally, it advocated for comprehensive measures such as risk management strategies, conformity assessments for high-risk AI applications, and the careful handling of sensitive data to identify and rectify biases that could lead to discriminatory outcomes. Implication to Theory, Practice and Policy: The study was informed by theories of ethical governance and legal frameworks in AI development and deployment. It was validated through the comparative analysis of international perspectives, which provided insights into the effectiveness of different regulatory approaches in addressing AI bias. Recommendations to practitioners include implementing risk management strategies, conducting conformity assessments for high-risk AI applications, and ensuring the careful handling of sensitive data to identify and rectify biases. Practitioners are urged to prioritize ethical considerations and advocate for responsible deployment practices to mitigate AI bias effectively. Recommendations to policymakers emphasize the need to prioritize ethical considerations and advocate for responsible deployment practices in AI governance. Policymakers are urged to develop robust regulatory frameworks that promote transparency, accountability, and inclusivity in AI development and deployment to build a more equitable and trustworthy AI ecosystem. In essence, the study provides crucial insights into the complex interplay between legal frameworks, political dynamics, and ethical considerations in addressing AI bias on a global scale. It paves the way for the establishment of fair and unbiased AI systems that benefit society as a whole.American Journal of Computer Engineering250The study provides crucial insights into the complex interplay between legal frameworks, political dynamics, and ethical considerations in addressing AI bias on a global scale and paves the way for the establishment of fair and unbiased AI systems that benefit society as a whole.American Journal of Computing and Engineering['Stephanie Ness', 'Mithun Sarker', 'Mykola Volkivskyi', 'Navdeep Singh Nerd']2024-03-16T00:00:00https://www.semanticscholar.org/paper/5d23afca24a38ce4eb5b19ad5b0e524b8e95e3ee 463d2f3974b7bc670b4c457fd4821deec3130e1bIntegrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.JOIV: International Journal on Informatics Visualization00Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry and establish a more sustainable energy ecosystem, which would benefit future generations.JOIV : International Journal on Informatics Visualization['Tien Han Nguyen', 'Prabhu Paramasivam', 'Van Huong Dong', 'Huu Cuong Le', 'Duc Chuan Nguyen']2024-03-16T00:00:00https://www.semanticscholar.org/paper/463d2f3974b7bc670b4c457fd4821deec3130e1b 230f36703c93fd84c5894891f03deb93b8efdb82Artificial Intelligence (AI) has revolutionized the landscape of mobile app development, bringing about transformative changes in functionality, user experience, and capabilities. This paper aims to explore and analyze the impact of AI on mobile app development, highlighting its significant contributions and potential implications. The integration of AI technologies such as machine learning, natural language processing, and computer vision has empowered mobile applications to offer personalized experiences, enhanced decision-making capabilities, and predictive functionalities. AI-driven algorithms enable apps to analyze vast amounts of data, allowing for real-time insights and intelligent responses, thereby optimizing user engagement and satisfaction. Moreover, AI has facilitated the development of innovative features like voice assistants, recommendation systems, and augmented reality experiences, fostering a more intuitive and interactive user interface. These advancements have not only enriched the user experience but also opened new avenues for businesses to better understand their users and tailor services accordingly. However, with these advancements come challenges, including privacy concerns, ethical considerations, and the need for robust security measures to safeguard sensitive user data. As AI-powered mobile apps become more prevalent, it is imperative to address these challenges to ensure responsible and ethical use of AI technologies. This paper examines the evolution of AI in mobile app development, evaluates its impact on user experience and functionality, discusses the challenges and ethical considerations, and provides insights into the future trends and opportunities that AI presents in shaping the landscape of mobile app development. Understanding the profound impact of AI on mobile app development is crucial for developers, businesses, and stakeholders to harness its full potential while navigating the associated challenges responsibly.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The evolution of AI in mobile app development is examined, its impact on user experience and functionality, the challenges and ethical considerations are discussed, and insights into the future trends and opportunities that AI presents in shaping the landscape of mobile app development are provided.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Manikandan B', 'Sanika Chandran R', 'Sreenidhi M', 'Nishanthi S', 'Vigneya Rithika Shree J J']2024-03-16T00:00:00https://www.semanticscholar.org/paper/230f36703c93fd84c5894891f03deb93b8efdb82 af7adfac1ecfa6163f16e391e50f8cac73420cdfLegal research is a crucial task in the practice of law. It requires intense human effort and intellectual prudence to research a legal case and prepare arguments. Recent boom in generative AI has not translated to proportionate rise in impactful legal applications, because of low trustworthiness and and the scarcity of specialized datasets for training Large Language Models (LLMs). This position paper explores the potential of LLMs within Legal Text Analytics (LTA), highlighting specific areas where the integration of human expertise can significantly enhance their performance to match that of experts. We introduce a novel dataset and describe a human centered, compound AI system that principally incorporates human inputs for performing LTA tasks with LLMs.arXiv.org480This position paper explores the potential of LLMs within Legal Text Analytics (LTA), highlighting specific areas where the integration of human expertise can significantly enhance their performance to match that of experts.ArXiv['Sudipto Ghosh', 'Devanshu Verma', 'Balaji Ganesan', 'Purnima Bindal', 'Vikas Kumar', 'Vasudha Bhatnagar']2024-03-16T00:00:00https://www.semanticscholar.org/paper/af7adfac1ecfa6163f16e391e50f8cac73420cdf 7a0343749373a6b5046eb21f900c2c5b2046031bArtificial intelligence could be used as a powerful and innovative tool in mathematics education. It is poised to transform the way of learning and teaching this subject.The main objective of our study is to provide a more complete and thorough understanding of the role and impact of using AI in mathematics education by determining the trends, the AI methods, the technological applications and the opportunities for utilizing AI by teachers and students. The potential benefits and threats caused by the use of AI are also discussed.Mathematics and Education in Mathematics00The main objective of this study is to provide a more complete and thorough understanding of the role and impact of using AI in mathematics education by determining the trends, the AI methods, the technological applications and the opportunities for utilizing AI by teachers and students.Mathematics and Education in Mathematics['Tsvetelina Stefanova', 'S. Georgiev']2024-03-16T00:00:00https://www.semanticscholar.org/paper/7a0343749373a6b5046eb21f900c2c5b2046031b e757bf97f59fb2f799284942989702f5e5ad4740Purpose: The purpose of "Governing Artificial Intelligence: Ethical, Legal, and Technical Opportunities and Challenges" is to examine the impact of artificial intelligence (AI) on various societal aspects and to underscore its potential benefits for human rights, social welfare, and economic growth. The essay emphasizes the necessity of regulating AI systems in a fair, transparent, and responsible manner, especially in high-risk sectors. Materials and Methods: Research Design: The essay employs a literature review and analysis approach to explore the ethical, legal, and technical challenges associated with governing AI. Method of Data Collection: Data collection primarily involves gathering information from existing literature, reports, and expert opinions. Analysis and Presentation: The data is analyzed qualitatively to provide insights into the complexities of AI governance. The findings are presented systematically to address different dimensions of the topic. Findings: The essay presents a comprehensive analysis of the ethical, legal-regulatory, and technological challenges in governing AI. It highlights the need for robust governance frameworks to ensure the responsible development and deployment of AI systems. Implication to Theory, Practice, and Policy: The study is informed by various theories on ethics, governance, and technology. Validation of these theories is achieved through a critical examination of existing literature and empirical evidence. Practitioners are recommended to adopt principles of fairness, transparency, and accountability in the development and deployment of AI systems. Additionally, continuous monitoring and evaluation mechanisms should be established to ensure compliance with ethical standards. Policymakers are encouraged to enact regulations that promote the ethical and responsible use of AI technologies. This includes establishing clear guidelines for AI development, deployment, and accountability mechanisms to address potential risks and ensure societal well-being.American Journal of Computer Engineering300The essay emphasizes the necessity of regulating AI systems in a fair, transparent, and responsible manner, especially in high-risk sectors, and highlights the need for robust governance frameworks to ensure the responsible development and deployment of AI systems.American Journal of Computing and Engineering['Stephanie Ness', 'Navdeep Singh', 'Mykola Volkivskyi', 'Wong Jest Phia']2024-03-16T00:00:00https://www.semanticscholar.org/paper/e757bf97f59fb2f799284942989702f5e5ad4740 721a52aeb13aa11a498ead91c84c4038d1b35f87AI &amp; SOCIETY920AI &amp; SOCIETY['Andrea Slane', 'Isabel Pedersen']2024-03-16T00:00:00https://www.semanticscholar.org/paper/721a52aeb13aa11a498ead91c84c4038d1b35f87 0040001b9d10c25036aeb0e42a36bf142ff3e6a4Marketing letters290Marketing Letters['Chunya Xie', 'Tianhui Fu', 'Chen Yang', 'En-Chung Chang', 'Mengying Zhao']2024-03-16T00:00:00https://www.semanticscholar.org/paper/0040001b9d10c25036aeb0e42a36bf142ff3e6a4 1e7a5e56f140553d6673c2c094eca9eb36fa9951This research aims to analyze indications of monopolistic practices in the digital space and find formulations for digital market regulation to create a fairer digital ecosystem. Law No. 5 of 1999 concerning the Prohibition of Monopolistic Practices and Unfair Business Competition does not yet specifically regulate the prohibition of monopolistic practices and unfair business competition in the digital space, so the incompleteness of this regulation needs to be investigated further. This research was conducted using a normative juridical approach. The results of this research show that indications of monopolistic practices in the digital space are related to the unclear categories of business actors who sell at a loss in e-commerce and the combination of social media and e-commerce. Based on these weaknesses, the author obtains a formulation based on the Digital Markets Act regulations in the European Union which creates "gatekeepers" in the digital economy to create a healthy market.International Journal of Business, Law, and Education50International Journal of Business, Law, and Education['Alisya Muliani', 'S. Sukarmi', 'Djumikasih Djumikasih']2024-03-16T00:00:00https://www.semanticscholar.org/paper/1e7a5e56f140553d6673c2c094eca9eb36fa9951 b439de1ba51ba7fc52cd450203c2b9b87d3dcde7Objective: This article undertakes a comprehensive exploration of the constructivist paradigm in artificial intelligence (AI) development, aiming to uncover how constructivist perspectives shape our understanding of AI. It delves into the evolution of AI thought, emphasizing the significance of constructivist epistemology in comprehending AI's philosophical and cognitive dimensions. Method: The study employs a variety of philosophical methodologies, including historical-philosophical analysis, comparative analysis of philosophical teachings, and a system-structural dialectical approach. These methods facilitate an in-depth examination of AI's conceptual intricacies within a constructivist framework, focusing on the relationship between artificial and natural intelligence and the epistemological implications of AI. Results: The investigation reveals that the main challenge in AI research is the absence of clear problem-solving rules, highlighting the current limitations of human self-knowledge in logical and emotional intelligence. It showcases AI's vast capabilities, from extensive knowledge bases to real-time processing, and emphasizes AI's role in enhancing human cognitive processes. Conclusions: Artificial intelligence, as a construct of human intellect, mirrors the capacity for design and creativity inherent in human thought. The study underscores AI's foundational role in the epistemology of science and technology, advocating for a holistic understanding of the human brain as a dynamic system to further our grasp of AI and its cognitive potential.Review of Artificial Intelligence in Education30The study underscores AI's foundational role in the epistemology of science and technology, advocating for a holistic understanding of the human brain as a dynamic system to further the authors' grasp of AI and its cognitive potential.Review of Artificial Intelligence in Education['Petro Rybalko']2024-03-16T00:00:00https://www.semanticscholar.org/paper/b439de1ba51ba7fc52cd450203c2b9b87d3dcde7 fb7ff5074289111522c88559fbb33a6684012384INTRODUCTION ultrasonography in the first trimester of pregnancy offers an early screening tool to identify high risk pregnancies. Artificial intelligence (AI) algorithms have the potential to improve the accuracy of diagnosis and assist the clinician in early risk stratification. OBJECTIVE to conduct a systematic review of the use of AI in ultrasonography in the first trimester of pregnancy. METHODS We conducted a systematic literature review by searching in computerised databases Pubmed, Embase and Google Scholar from inception to January 2024. Full text peer reviewed journal publications written in English on the evaluation of AI in first trimester pregnancy imaging were included. Review papers, conference abstracts, posters, animal studies, non-English and non-peer-reviewed articles were excluded. Risk of bias was assessed by using PROBAST. RESULTS Of the 1595 non-duplicated records screened, 27 studies were included. Twelve studies focussed on segmentation, eight on plane detection, six on image classification and one on both segmentation and classification. Five studies included fetuses with a gestational age of less than ten weeks. The size of the datasets was relatively small, as sixteen studies included less than 1000 cases. The models were evaluated by different metrics. Duration to run the algorithm was reported in twelve publications and ranged between less than one second and fourteen minutes. Only one study was externally validated. CONCLUSION Even though the included algorithms reported a good performance in a research setting on testing datasets, further research and collaboration between AI experts and clinicians is needed before implementation in clinical practice.Fetal Diagnosis and Therapy40Even though the included algorithms reported a good performance in a research setting on testing datasets, further research and collaboration between AI experts and clinicians is needed before implementation in clinical practice.Fetal diagnosis and therapy['E. Umans', 'Kobe Dewilde', 'H. Williams', 'Jan Deprest', 'Thierry Van den Bosch']2024-03-16T00:00:00https://www.semanticscholar.org/paper/fb7ff5074289111522c88559fbb33a6684012384 0ba27574e013764960487b8614a5d1680f48fdd9Abstract: Machine Learning (ML) & Artificial Intelligence (AI) is changing how we live and work by permeating more and more industries. Although artificial intelligence (AI) has great promise for improving efficiency and accuracy in decision-making, it also poses serious concerns about how AI will affect society and the nature of labor in the future. Our goal in this study article is to investigate the possible effects of AI's widespread use on employment, inequality, ethics, and privacy. We'll look at current developments in AI and assess how they affect different sectors, such as the job market, healthcare, finance, and education. Additionally, the article will evaluate the possible risks that come with AI and go over possible mitigation strategies. Through an analysis of AI's current state and future social implications, this research attempts to offer a thorough and informed view of what work will look like in an AI-driven world. Keywords: Societal Impact, Role, Ethical Considerations, Influence on Specific Industries, Potential, Conclusion The goal of the computer science and engineering field of artificial intelligence (AI) is to build intelligent computers that are capable of carrying out tasks that would typically require human intelligence. Language comprehension, pattern identification, and decision-making are all part of this activity. AI systems are now widely used across many industries, including healthcare, banking, education, and retail, thanks to the growth of AI technology. The broad use of AI has the potential to help society greatly in a number of ways, such as increased decision-making accuracy and efficiency, better patient outcomes in the medical field, and enhanced financial outcomes in the banking industry. But as AI advances, significant questions concerning its effects on society and the direction of business are brought up. AI systems have the ability to change the powerINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The possible effects of AI's widespread use on employment, inequality, ethics, ethics, and privacy are investigated and a thorough and informed view of what work will look like in an AI-driven world is offered.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Dr Srikanth V', 'Kunal Pal']2024-03-16T00:00:00https://www.semanticscholar.org/paper/0ba27574e013764960487b8614a5d1680f48fdd9 62fdc6dceaeede3ce5f79a01c9baac8edf82fd04Artificial Intelligence in Marketing is a rapidly up-and-coming grassland that is transforming the way businesses move toward their marketing plan. It involves the use of Artificial Intelligence (AI), Machine Learning (ML), and other highly developed technologies to automate and optimize various marketing processes. With the sudden increase of data and the increasing complication of customer behavior, businesses need to influence these tools to stay competitive. This article investigates the concept of Artificial Intelligence in Marketing, its role in modern marketing, its benefits and challenges, best practices for implementation, and moral considerations. It will also look into the future of Artificial Intelligence in Marketing and its potential impact on the marketing landscape.International Research Journal on Advanced Engineering and Management (IRJAEM)00The concept of Artificial Intelligence in Marketing, its role in modern marketing, its benefits and challenges, best practices for implementation, and moral considerations are investigated.International Research Journal on Advanced Engineering and Management (IRJAEM)['Dr. Jaya kagada']2024-03-16T00:00:00https://www.semanticscholar.org/paper/62fdc6dceaeede3ce5f79a01c9baac8edf82fd04 f39bdc84067e1e7865533bcbdb825636be24ebf4The world began to change and adapt accordingly to the dynamic technological advances. As same as the application of advanced technology in the present day is known as Artificial Intelligence (AI) which is referred to as the development of computer systems that perform tasks typically involving human intelligence. AI is being observed to be applied to various fields of business, especially to Human Resources being one important wing among the list. AI interferes with learning, reasoning, problem-solving, and understanding natural language. AI is increasingly used in human resources to help drive decisions in employee hiring, retention, and development. It can also be applied to automate tasks like payroll, but it’s being also used for the rapid creation of new policies, contracts, job descriptions, interview questions, etc. This empirical study is conducted to illuminate the concepts, impact, role, and recent trends of AI in Human capital management. Hence, an overview of the scope of AI in Human capital management has been built in the study.International Research Journal on Advanced Engineering and Management (IRJAEM)00An overview of the scope of AI in Human capital management has been built to illuminate the concepts, impact, role, and recent trends of AI in Human capital management.International Research Journal on Advanced Engineering and Management (IRJAEM)['S.P. Neehalika Bavya', 'Blessina Bashapaka', 'G.Suvarchala Reddy']2024-03-16T00:00:00https://www.semanticscholar.org/paper/f39bdc84067e1e7865533bcbdb825636be24ebf4 804fc6897dd8d439a2a55e0da46ae95b7fc59729Mathematics is a discipline that focuses on structure, order, and relation, derived from counting, measuring, and characterizing object shapes. Mathematics is necessary for professions in data science since machine learning algorithms, conducting analyses, and drawing conclusions from data all require it. A key component of data science is math. It can support problem-solving, model performance optimization, and the interpretation of complex data to address business-related queries. The technology known as artificial intelligence (AI) has come to revolutionize many facets of our existence. Mathematics plays a fundamental part in the astounding advances and capabilities of artificial intelligence. Mathematics contains various branches like algebra, geometry, Trigonometry, Calculus, Statistics and Probability. The foundation of mathematics gives artificial intelligence (AI) systems the ability to reason, learn, and make wise judgments. This article examines the relevance and use of mathematics in artificial intelligence. Large-scale data processing, analysis, and interpretation are made possible by machines thanks to mathematics, which forms the foundation of AI models and algorithms. Developing machine learning algorithms requires an understanding of concepts from statistics, probability theory, calculus, and linear algebra. These algorithms recognize patterns, forecast outcomes, and categorize data using mathematical equations and functions.International Journal of Advanced Research in Science, Communication and Technology60Large-scale data processing, analysis, and interpretation are made possible by machines thanks to mathematics, which forms the foundation of AI models and algorithms.International Journal of Advanced Research in Science, Communication and Technology['Rajendrakumar Vilas Thorat']2024-03-16T00:00:00https://www.semanticscholar.org/paper/804fc6897dd8d439a2a55e0da46ae95b7fc59729 8915fcdd448f3a0d68726cae2f17cc75604c3289The integration of artificial intelligence (AI) technologies into supply chain management has emerged as a crucial avenue for enhancing efficiency, agility, and responsiveness in modern business operations. This comprehensive review synthesizes current practices and future potentials of leveraging AI for supply chain optimization. Beginning with an overview of traditional supply chain management challenges, the review elucidates how AI solutions address these complexities by enabling predictive analytics, real-time visibility, and intelligent decision-making. The review delves into the diverse applications of AI across different stages of the supply chain, including demand forecasting, inventory management, logistics optimization, and supplier relationship management. Examples of AI-driven technologies such as machine learning, natural language processing, and robotic process automation are analyzed for their role in revolutionizing supply chain operations. Furthermore, the review highlights the transformative impact of AI on supply chain resilience, emphasizing its ability to mitigate disruptions, adapt to dynamic market conditions, and optimize resource allocation. The review also addresses critical considerations such as data privacy, ethical implications, and organizational readiness for AI adoption within supply chain contexts.  Lastly, the review discusses future research directions and potential advancements in AI-enabled supply chain management, envisioning intelligent autonomous supply chains characterized by self-learning systems, collaborative ecosystems, and enhanced sustainability practices. In conclusion, this review underscores the pivotal role of AI in driving continuous innovation and competitive advantage within supply chain networks, while also emphasizing the importance of strategic planning and responsible implementation to harness its full potential. Keywords:   AI, Supply Chain, Optimization, Practices, Review.International Journal of Management &amp; Entrepreneurship Research03The pivotal role of AI in driving continuous innovation and competitive advantage within supply chain networks is underscored, while also emphasizing the importance of strategic planning and responsible implementation to harness its full potential.International Journal of Management &amp; Entrepreneurship Research['Olorunyomi Stephen Joel', 'Adedoyin Tolulope Oyewole', 'Olusegun Gbenga Odunaiya', 'Oluwatobi Timothy Soyombo']2024-03-16T00:00:00https://www.semanticscholar.org/paper/8915fcdd448f3a0d68726cae2f17cc75604c3289 c71773d86096513328abc6663b585d86398a4dcfThis study finds a gap within the literature and aims to conduct research that primarily highlights the role of artificial intelligence technology on internationalising SME’s leadership system, especially in Makassar, Indonesia. A quantitative research method is applied in this study by collecting both primary and secondary data. The time span of this study is from November 2023 to January 2024 and over 200 active SMEs received research questionnaires. Throughout the specific statistical measurements and tests, this study then aims to contribute to the body of knowledge and theoretical contribution by offering the research findings, which confirm that artificial intelligence plays an important role on leadership inside internationalising SMEs in a developing country, Indonesia, primarily in Makassar District. As for the theoretical contribution, the explanatory variable, which is artificial intelligence, affects significantly and positively the predictor, which is the leadership of the internationalising SMEs, particularly on: (1) the surveillance and monitoring; (2) the ethical considerations; and (3) the decision-making process. Additionally, the managerial contributions of this study are explained in detail within the particular sections of this paper.Asian Journal of Economics Business and Accounting00It is confirmed that artificial intelligence plays an important role on leadership inside internationalising SMEs in a developing country, Indonesia, primarily in Makassar District.Asian Journal of Economics, Business and Accounting['Abdi Akbar', 'Muhammad Yushar Mustafa', 'M. I. M. Haeruddin', 'Caroline Mariñas-Acosta', 'Hasbiyadi Hasbiyadi', 'Syamsul Alam', 'Widhi Nugraha S. Darmawinata']2024-03-16T00:00:00https://www.semanticscholar.org/paper/c71773d86096513328abc6663b585d86398a4dcf c2fc667ff9e36c7839ae238d1deaa1b363f4a65eJournal of International Relations and Development571Journal of International Relations and Development['Anna Nadibaidze']2024-03-16T00:00:00https://www.semanticscholar.org/paper/c2fc667ff9e36c7839ae238d1deaa1b363f4a65e 7d0c81b78f23ac2e5ca461ee9b50062b4436440dGeneralist foundation model has ushered in newfound capabilities in medical domain. However, the contradiction between the growing demand for high-quality annotated data with patient privacy continues to intensify. The utilization of medical artificial intelligence generated content (Med-AIGC) as an inexhaustible resource repository arises as a potential solution to address the aforementioned challenge. Here we harness 1 million open-source synthetic fundus images paired with natural language descriptions, to curate an ethical language-image foundation model for retina image analysis named VisionCLIP. VisionCLIP achieves competitive performance on three external datasets compared with the existing method pre-trained on real-world data in a zero-shot fashion. The employment of artificially synthetic images alongside corresponding textual data for training enables the medical foundation model to successfully assimilate knowledge of disease symptomatology, thereby circumventing potential breaches of patient confidentiality.arXiv.org1801 million open-source synthetic fundus images paired with natural language descriptions are harnessed, to curate an ethical language-image foundation model for retina image analysis named VisionCLIP, which achieves competitive performance on three external datasets compared with the existing method pre-trained on real-world data in a zero-shot fashion.ArXiv['Hao Wei', 'Bowen Liu', 'Minqing Zhang', 'Peilun Shi', 'Wu Yuan']2024-03-16T00:00:00https://www.semanticscholar.org/paper/7d0c81b78f23ac2e5ca461ee9b50062b4436440d f5dc8d1df95be164507d033ffc9fcbad9ad9ea6cTests lack analytical and clinical validity, requiring more federal oversight to prevent consumer harm A growing body of research has suggested the potential for improving human health by better understanding the human microbiome. This research has led to the emergence of a global industry selling direct-to-consumer (DTC) microbiome testing services. Regulation of this industry has been generally ignored despite its having made a mark on the lifestyle health and wellness market. Yet companies’ claims of having the ability to detect “abnormal” microbiomes are not substantiated by research; the testing processes lack analytical validity, and the results have no demonstrated clinical validity. As a result, consumers may be financially exploited or harmed by inappropriate use of test results that neither they nor their doctors understand. To address concerns over such potential harms, we conclude that regulators should develop requirements for the industry to document and demonstrate the consistency and validity of methods and claims.Science120Regulators should develop requirements for the microbiome testing industry to document and demonstrate the consistency and validity of methods and claims, to address concerns over potential harms.Science['D. E. Hoffmann', 'Erik C. von Rosenvinge', 'M. Roghmann', 'F. B. Palumbo', 'Daniel McDonald', 'J. Ravel']2024-03-15T00:00:00https://www.semanticscholar.org/paper/f5dc8d1df95be164507d033ffc9fcbad9ad9ea6c c7b014feb57a9128ccfbee45ef23c48b73d978acThe energy management system has evolved into a digitized and autonomous environment, where consumers can manage their own generation, consumption and storage through virtual environments. Smart Energy (SE) understands this decentralized energy management by streamlining and helping in this matter, however, there is a need to regulate this scenario. Considering that the electric energy sector has a solid regulation, efforts need to be concentrated to adapt it to a model that emphasizes the SE and everything that it proposes. Therefore, the objective of this article is to propose a diagnosis of the current energy regulatory scenario directed to the SE. Through a focus group, experts from the energy sector contributed with opinions on the subject for the construction of a Current Reality Tree (CRT), which aimed to identify the root causes that affect and limit the energy regulation scenario SE-oriented. The current situation of this scenario was analyzed and what can be changed. 38 actions that contribute to the development and propagation of SE were suggested. These actions are guiding ways to regulate and enable the regulatory environment to support the insertion of technologies related to the theme.International Journal of Energy Economics and Policy00International Journal of Energy Economics and Policy['Patrícia Stefan de Carvalho', 'J. Siluk', 'Henrique Luís Sauer Oliveira', 'Vinicius Jacques Garcia', 'J. L. Schaefer', 'Ricardo Augusto Cassel', 'J. R. Pinheiro']2024-03-15T00:00:00https://www.semanticscholar.org/paper/c7b014feb57a9128ccfbee45ef23c48b73d978ac cc72ce97a5ee4b2378a3583f230b691c16999766Tools based on machine learning (so-called artificial intelligence, AI) are increasingly being developed to diagnose malignant melanoma in dermatology. This contribution discusses (1) three scenarios for the use of AI in different medical settings, (2) shifts in competencies from dermatologists to non-specialists and empowered patients, (3) regulatory frameworks to ensure safety and effectiveness and their consequences for AI tools, and (4) cognitive dissonance and potential delegation of human decision-making to AI. We conclude that AI systems should not replace human medical expertise but play a supporting role. We identify needs for regulation and provide recommendations for action to help all (human) actors navigate safely through the choppy waters of this emerging market. Potential dilemmas arise when AI tools provide diagnoses that conflict with human medical expertise. Reconciling these conflicts will be a major challenge.TATuP Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis330It is concluded that AI systems should not replace human medical expertise but play a supporting role and needs for regulation are identified to help all (human) actors navigate safely through the choppy waters of this emerging market.TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis['Jan C. Zoellick', 'Hans Drexler', 'Konstantin Drexler']2024-03-15T00:00:00https://www.semanticscholar.org/paper/cc72ce97a5ee4b2378a3583f230b691c16999766 8fabf24a07f006bd2097584770afd3953aadc4a6Humanities and Social Sciences Communications1230The main challenges faced by states in regulating the use of AI in healthcare were identified, especially the legal voids and complexities for adequate regulation and better transparency.Humanities and Social Sciences Communications['Rabai Bouderhem']2024-03-15T00:00:00https://www.semanticscholar.org/paper/8fabf24a07f006bd2097584770afd3953aadc4a6 34252ffada80be70a6519db9fa668324fb89c779This article addresses one of the pressing issues regarding the role of artificial intelligence (AI) in international relations and international law. The research question revolves around defining the theoretical and methodological approaches applicable to the strategic analysis of AI utilization in these fields. In the contemporary world, there is a demand at both interstate and societal levels to define the role of AI in the political and legal spheres. This is because AI development affects crucial areas of state relations such as security, international law, ethical norms, and dependencies. The prospective use of AI technologies without corresponding legal regulation may disrupt the already fragile balance of the world order, which could be exacerbated by state competition in AI technologies and AI applications in the military domain, a grey area in international law. Analyzing this issue from the perspective of international relations and international law theory allows for examining AI's impact on state interactions and developing new application strategies. Similarly, it helps understand how international law regulates state relations, including aspects related to AI applications. By examining various theoretical concepts and methodological approaches necessary for understanding AI's impact on global affairs, including its influence on diplomacy, security, and governance structures, as well as legal and ethical issues, this article contributes to Kazakhstan's evolving discourse on AI governance and its implications for state actors.Journal of Central Asian Studies00Examining various theoretical concepts and methodological approaches necessary for understanding AI's impact on global affairs, including its influence on diplomacy, security, and governance structures, as well as legal and ethical issues, contributes to Kazakhstan's evolving discourse on AI governance and its implications for state actors.Journal of Central Asian Studies['Fatima Kukeyeva', 'Medeu Kurmangal', 'Dinmukhamed Aktay']2024-03-15T00:00:00https://www.semanticscholar.org/paper/34252ffada80be70a6519db9fa668324fb89c779 34edb63d0dd27eba1ac19fea543fffdbe0aa7e0fThe development of artificial intelligence (AI) has become one of the most discussed topics in 2023. According to sociological surveys, the awareness of Russians in the field of AI and their willingness to use new technologies have grown. The leap in the development of neural networks, chatbots and AI technologies in general has already affected many areas of life, and the education system is no exception. Teachers face many challenges related to the regulation of the AI application in the educational process. On the one hand, issues of regulating the use of AI technologies in universities require a scrupulous study which is complicated by the speed of technological development. On the other hand, in addition to official regulations, it is necessary to solve more global and labor-intensive tasks: to unlock the potential of AI in the educational process; analyze the ethical side of the issue and develop the culture of using new technologies; adapt educational materials and assignments based on the possible application of AI by students; change curricula and revise the competency system, etc. The article considers ways to use AI technologies in the educational process as perceived by teachers and students. The author emphasizes both constructive and destructive capabilities of new technologies, the challenges that universities will face in the near future, and the positions of university representatives on these issues. The author believes that the use of AI technologies in education can benefit both teachers and students in sociology and other areas. It is impossible to stop the development of technologies; any attempts to hinder them are counterproductive; therefore, it is necessary to reconsider the established educational approaches according to the requirements of our time.RUDN journal of Sociology50The author believes that the use of AI technologies in education can benefit both teachers and students in sociology and other areas and it is necessary to reconsider the established educational approaches according to the requirements of the authors' time.RUDN Journal of Sociology['M. V. Subbotina']2024-03-15T00:00:00https://www.semanticscholar.org/paper/34edb63d0dd27eba1ac19fea543fffdbe0aa7e0f 0be016c3bf5d5ccea30e1bf0459a63198a7e8f8eBackground: The electronic Freight Transport Information (eFTI) regulation is critical in modernizing freight transport (FT) within the European Union by establishing a framework for the electronic exchange of information. Despite its importance, there is a notable gap in the literature regarding the practical implementation challenges, especially from an empirical perspective. Methods: To address this gap, our study utilized a grounded theory approach, conducting interviews with a diverse group of logistics experts from Greece. The selection of experts was strategic to ensure a comprehensive range of knowledge and expertise, including insights at the policy level as well as practical experiences. Results: Our findings highlight several significant challenges in the implementation of eFTI, including the digital skill gap among the workforce, issues with system interoperability, and diverse capacities and resources of companies of different sizes. Economic factors, regulatory frameworks and the necessity for targeted training and leadership support were also identified as crucial for the digital transition. Conclusions: The study shows that uniform eFTI implementation may not work for all organizations, highlighting the necessity for customized strategies that address specific challenges in the FT chain. Our research deepens the understanding of these issues, providing actionable insights for successful eFTI adoption.Logistics270Logistics['Thomas K. Dasaklis', 'Evangelia Kopanaki', 'Panos T. Chountalas', 'N. Rachaniotis', 'Theodore G. Voutsinas', 'Kyriakos Giannakis', 'Gregory Chondrokoukis']2024-03-15T00:00:00https://www.semanticscholar.org/paper/0be016c3bf5d5ccea30e1bf0459a63198a7e8f8e 5fff119827c80420ecdade84dc7b7db26ee85e51Reducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the low-carbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself?This work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model.This study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point.The findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage.Frontiers in Sustainable Food Systems1330Frontiers in Sustainable Food Systems['Ye Li', 'Yiyan Chen']2024-03-15T00:00:00https://www.semanticscholar.org/paper/5fff119827c80420ecdade84dc7b7db26ee85e51 0406415a9ff9c4e586f1f34e07f689daf0ed109dEvery human culture has reflected the Moon’s influence in its cosmology, spirituality, science, creative and social life. For these reasons, the exploration and use of the Moon should be done thoughtfully and carefully, and possible resource extraction should not harm the Earth's only satellite and its environment as a whole. The adoption by some States of national legislation affecting the commercial exploitation of space resources, as well as the resumption of lunar programs by several leading spacefaring nations at a time, prompt the need for legal regulation in this area. However, to develop a detailed international legal regime for lunar exploration, the efforts of the States parties to the UN COPUOS alone are not sufficient since in practice other actors in international relations, including the so-called epistemic communities representing various types of non-governmental organizations, are also active participants in space activities. Such communities offer their own vision of the international legal regulation of relations arising in the framework of the exploration and use of the Moon basing on the norms of international space law and involving active participation of non-governmental legal entities, considering the interests of present and future generations, as well as of emerging space nations. The study presents a comprehensive analysis of the influence of epistemic communities on the development of the future international legal regime for lunar exploration. The authors consistently review the activities of non-governmental organizations within the UN COPUOS since its formation. Special attention is paid to the contribution of such communities to the progressive development of international space law and its codification, including the legal nature of the documents developed by such communities. The study concludes with a comprehensive international legal assessment of the activities of the epistemic communities.RUDN Journal of Law30RUDN Journal of Law['Irina A. Chernykh', 'Denis A. Gugunskiy', 'A. M. Solntsev']2024-03-15T00:00:00https://www.semanticscholar.org/paper/0406415a9ff9c4e586f1f34e07f689daf0ed109d f0b30165a324de5ab4519281a9e17db5349f564aThe paper deals with one of the key features of the regulation of legal relations in the military environment. Those are the detailed regulation of rights, responsibilities, the execution of assigned tasks and orders. The author sees the reasons for such legal expansion in the historical tradition, i.e. fostering obedience and discipline among military personnel, guidance assistance to inexperienced commanders for them to be able to fulfill the assigned duties, as well as controllability and the intention to unify military activities in various military formations. Dysfunctional manifestations of this management style are instilling the lack of independence in decision-making, the habit of relying on superior management for all matters, the loss of initiative by subordinates, the inability to work in a team, lack of immediate response to sudden changes in the situation or non-standard situations. During military operations, these shortcomings not only reduce the effectiveness of control but are also likely to result in prevailing enemy's will, defeat, and unjustified casualties. Using particular examples of excessive regulation, the author questions the need, limits and degree of detailed legal regulation in the military environment. The paper analyzes the cases of directive control of the German Army (Auftragstaktik) and the commander's model used in respect of the subordinates with no detailed orders, with the subordinates being free to choose means and methods to achieve their goals. As measures to improve the legal regulation of the domestic military administration, the paper proposes to eliminate the legal responsibility of military commanders for deviation from detailed orders in cases they take reasonable risk and do not violate legally protected interests. The study uses the methods of formal logic, i.e. comparison, description, classification, analysis, synthesis, etc., which made it possible to characterize the essence of the legal relations under consideration and compare them with the methods of legal regulation of the relevant labor relations.Siberian Law Review30Siberian Law Review['E. A. Glukhov']2024-03-15T00:00:00https://www.semanticscholar.org/paper/f0b30165a324de5ab4519281a9e17db5349f564a 834eff74c480b22350d2d7ab93cd49aad7ce96bdThe rules and regulations that govern nursing practice are topics rarely covered in nursing education programs. This article aims to provide a basic understanding of state and federal rules that govern nursing practice; the role and duties of nurse regulatory boards; types of legal actions that nurses may face; and an overview of discipline, complaint, and reporting processes.AACN Advanced Critical Care00A basic understanding of state and federal rules that govern nursing practice; the role and duties of nurse regulatory boards; types of legal actions that nurses may face; and an overview of discipline, complaint, and reporting processes are provided.AACN advanced critical care['Valerie Fuller']2024-03-15T00:00:00https://www.semanticscholar.org/paper/834eff74c480b22350d2d7ab93cd49aad7ce96bd db9c87fa0c4707b34b0b2d789f0849c32a31a79100['B. Burkinsky', 'O. Nikishyna', 'N. Tarakanov', 'V. Lisyuk', 'A. Shcherbak', 'P. Antonyuk', 'D. O. Bochkarov', 'K. Sokoliuk', 'O. Zerkina', 'N. Nosova', 'N. Chebotaryova']2024-03-15T00:00:00https://www.semanticscholar.org/paper/db9c87fa0c4707b34b0b2d789f0849c32a31a791 67636db2ad6f05d2629e83f1f1ba3bdc0a6f020c<jats:p>-</jats:p>RUDN Journal of Law00RUDN Journal of Law['Evgeny Y. Komlev']2024-03-15T00:00:00https://www.semanticscholar.org/paper/67636db2ad6f05d2629e83f1f1ba3bdc0a6f020c d5a6bd84d073eb559310b335db201e32863e89b3Revue européenne du droit social00Revue Européenne du Droit Social['Alcides Francisco Antúnez Sánchez', 'Eduardo Díaz Ocampo']2024-03-15T00:00:00https://www.semanticscholar.org/paper/d5a6bd84d073eb559310b335db201e32863e89b3 98e11abd6989148eb791beff3c726831f380a762Vestnik MGSU100Vestnik MGSU['S. B. Sborshikov', 'N. V. Lazareva']2024-03-15T00:00:00https://www.semanticscholar.org/paper/98e11abd6989148eb791beff3c726831f380a762 26839456e694aa593a2cdea9c0442f2e8861aed3As AI systems become more advanced, companies and regulators will make difficult decisions about whether it is safe to train and deploy them. To prepare for these decisions, we investigate how developers could make a 'safety case,' which is a structured rationale that AI systems are unlikely to cause a catastrophe. We propose a framework for organizing a safety case and discuss four categories of arguments to justify safety: total inability to cause a catastrophe, sufficiently strong control measures, trustworthiness despite capability to cause harm, and -- if AI systems become much more powerful -- deference to credible AI advisors. We evaluate concrete examples of arguments in each category and outline how arguments could be combined to justify that AI systems are safe to deploy.arXiv.org02This work proposes a framework for organizing a safety case and discusses four categories of arguments to justify safety: total inability to cause a catastrophe, sufficiently strong control measures, trustworthiness despite capability to cause harm, and -- if AI systems become much more powerful -- deference to credible AI advisors.ArXiv['Joshua Clymer', 'Nick Gabrieli', 'David Krueger', 'Thomas Larsen']2024-03-15T00:00:00https://www.semanticscholar.org/paper/26839456e694aa593a2cdea9c0442f2e8861aed3 ee2b86f2b98b9a3688f825be6db15ed975852250The increasing demand for internet, mobile, and IoT usage has led to a rise in cyber risks, with a high volume of data generated across industries. This poses a challenge in detecting anomalies effectively. Cyber-attacks jeopardize privacy, compliance, and lead to service interruptions, revenue loss, and customer churn. This research explores anomaly detection methods, including supervised, unsupervised, and semi-supervised learning, in the context of prevalent data patterns and the five stages of cyber-attacks. Machine learning plays a crucial role in monitoring traffic, analyzing scan results, detecting, and blocking attacks, and revealing the attacker’ s identity. The focus is on evaluating AI methods for anomaly detection, leveraging the H20.ai tool, known for its potent machine learning algorithms and seamless integration with existing systems. This enhances AI functionality for meaningful insights during cyber-attacks. The paper emphasizes the importance of robust anomaly detection in reducing Cyber Value at Risk (CVaR) and achieving cybersecurity excellence. While anomaly identification is crucial, the paper suggests it should be complemented by encryption, authentication, and incident response mechanisms for a comprehensive cybersecurity strategy. The research opens avenues for future exploration with additional AI tools and algorithms to minimize CVaR.SoutheastCon381While anomaly identification is crucial, the paper suggests it should be complemented by encryption, authentication, and incident response mechanisms for a comprehensive cybersecurity strategy and opens avenues for future exploration with additional AI tools and algorithms to minimize CVaR.SoutheastCon 2024['Prashant Vajpayee', 'Gahangir Hossain']2024-03-15T00:00:00https://www.semanticscholar.org/paper/ee2b86f2b98b9a3688f825be6db15ed975852250 68246f358faaee7304e863d4c6154d0169aa3a04This research paper explores the development and functionality of the AI Based smart Robot, which includes an interactive chatbot. A chatbot, in its essence, is an artificially intelligence computer program that performs communication using the audio system, A chatbot, created with the Gemini API, is basically a talking robot that build using the Gemini API toolbox. This chatbot, it listens to your voice, understands what you're saying and give responds back to you. It develop to assistance to users. Additionally, the project’s application in Customer Service, Virtual Assistant, Healthcare, Education, Smart Home Control.International Journal of Innovative Science and Research Technology31This research paper explores the development and functionality of the AI Based smart Robot, which includes an interactive chatbot, which is basically a talking robot that build using the Gemini API toolbox.International Journal of Innovative Science and Research Technology (IJISRT)['Bembrekar Ayan Ahamad', 'Solanke Vikas S.', 'Ghadge Madhusudan Ramdas']2024-03-15T00:00:00https://www.semanticscholar.org/paper/68246f358faaee7304e863d4c6154d0169aa3a04 78287f382599e31d17a12613e9f3866a03d1fb7bThe rapid adoption of Generative Artificial Intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices, and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. This paper presents the findings of a pilot study conducted at British University Vietnam (BUV) exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from 'No AI' to 'Full AI', enabling educators to design assessments that focus on areas requiring human input and critical thinking. Following the implementation of the AIAS, the pilot study results indicate a significant reduction in academic misconduct cases related to GenAI, a 5.9% increase in student attainment across the university, and a 33.3% increase in module passing rates. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in HE, promoting academic integrity while leveraging the technology's potential to enhance learning experiences.arXiv.org651The findings suggest that the AIAS can support the effective integration of GenAI in HE, promoting academic integrity while leveraging the technology's potential to enhance learning experiences.ArXiv['Leon Furze', 'Mike Perkins', 'Jasper Roe', 'Jason MacVaugh']2024-03-15T00:00:00https://www.semanticscholar.org/paper/78287f382599e31d17a12613e9f3866a03d1fb7b 579429e5ad7e83ba55bbb7d7b37781b926332b1fInternational Journal of Information Security61Potential risks, unintended consequences, and uncontrolled AI development associated with pentesting, discussed in this article.International Journal of Information Security['Eric Hilario', 'S. Azam', 'Jawahar Sundaram', 'Khwaja Imran\xa0Mohammed', 'Bharanidharan Shanmugam']2024-03-15T00:00:00https://www.semanticscholar.org/paper/579429e5ad7e83ba55bbb7d7b37781b926332b1f 4debaffe1b311093c738fed65b956ecff34d199dCommunications of the ACM92Commun. ACM['G. Faggioli', 'Laura Dietz', 'Charles Clarke', 'Gianluca Demartini', 'Matthias Hagen', 'Claudia Hauff', 'Noriko Kando', 'E. Kanoulas', 'Martin Potthast', 'Benno Stein', 'Henning Wachsmuth']2024-03-15T00:00:00https://www.semanticscholar.org/paper/4debaffe1b311093c738fed65b956ecff34d199d 0da1183abf5aa6357991286605e9ec2d6f380f3cPerformance attribution analysis, defined as the process of explaining the drivers of the excess performance of an investment portfolio against a benchmark, stands as a significant feature of portfolio management and plays a crucial role in the investment decision-making process, particularly within the fund management industry. Rooted in a solid financial and mathematical framework, the importance and methodologies of this analytical technique are extensively documented across numerous academic research papers and books. The integration of large language models (LLMs) and AI agents marks a groundbreaking development in this field. These agents are designed to automate and enhance the performance attribution analysis by accurately calculating and analyzing portfolio performances against benchmarks. In this study, we introduce the application of an AI Agent for a variety of essential performance attribution tasks, including the analysis of performance drivers and utilizing LLMs as calculation engine for multi-level attribution analysis and question-answering (QA) tasks. Leveraging advanced prompt engineering techniques such as Chain-of-Thought (CoT) and Plan and Solve (PS), and employing a standard agent framework from LangChain, the research achieves promising results: it achieves accuracy rates exceeding 93% in analyzing performance drivers, attains 100% in multi-level attribution calculations, and surpasses 84% accuracy in QA exercises that simulate official examination standards. These findings affirm the impactful role of AI agents, prompt engineering and evaluation in advancing portfolio management processes, highlighting a significant development in the practical application and evaluation of Generative AI technologies within the domain.arXiv.org60The application of an AI Agent for a variety of essential performance attribution tasks, including the analysis of performance drivers and utilizing LLMs as calculation engine for multi-level attribution analysis and question-answering (QA) tasks are introduced.ArXiv['Bruno de Melo', 'Jamiel Sheikh']2024-03-15T00:00:00https://www.semanticscholar.org/paper/0da1183abf5aa6357991286605e9ec2d6f380f3c 74f625e8c2586d6e355657703187880b0bfdfe18With the rapid development of big data and artificial intelligence (AI) technologies, their wide application in various fields has become possible. Traditional visual design systems are usually based on static rules and human experience, which are difficult to meet the personalized needs of users, resulting in low universality of design works. This article solves the problems existing in traditional research through in-depth research on the interaction of big data and AI in visual design systems, and promotes the development of more intelligent, personalized and scientific development in the field of visual design. This article takes the Generative Adversarial Network (GAN) algorithm model as the research object, and performs algorithm fusion on its generative fusion model A-VAE (Attention-Variational Autoencoder) based on variational autoencoders and attention mechanisms, and proposes a new fusion model algorithm. Through research, it has been found that the merged algorithm exhibits better image generation performance, accuracy, clarity, and diversity in visual design compared to other algorithms.2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)200Through research, it has been found that the merged algorithm exhibits better image generation performance, accuracy, clarity, and diversity in visual design compared to other algorithms.2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)['Yuanji Zhu']2024-03-15T00:00:00https://www.semanticscholar.org/paper/74f625e8c2586d6e355657703187880b0bfdfe18 efcaa6d23fcbb19e5c87f3560a5e325474f3169bThe United States Navy's mission planning process consists of six steps. The third step, referred to as the course of action (COA) analysis (wargaming) process, encompasses the evaluation of initial actions taken, the corresponding reactions by opponents, and the associated counteractions. This entire process supports developing potential mission COAs while also informing decision makers of potential outcomes in support of planning and preparing for operations. This wargaming process may occur using table-top exercises with subject-matter experts that determine actions taken. However, continued improvements with deep reinforcement learning (DRL), a sub-field of machine learning, provide an opportunity to leverage the use of artificial intelligence (AI) agents within the wargaming process. The AI agents must perform credibly to represent a believable behavior of either the red team or blue team. Developing credible behavior requires tailoring agent reward functions, analyzing impacts of different training algorithms and parameter values, and understanding and evaluating the resulting behavior. This work also demonstrates agent behavior using a browser-based gameplay interface accessible to decision makers. This paper provides an overview of research efforts to train and analyze AI agent performance using DRL within a prototype government owned wargaming framework, discusses the capabilities and utility of the browser-based interface, and explores challenges and opportunities for future research.SoutheastCon120An overview of research efforts to train and analyze AI agent performance using DRL within a prototype government owned wargaming framework is provided, the capabilities and utility of the browser-based interface are discussed, and challenges and opportunities for future research are explored.SoutheastCon 2024['Christina H. Rinaudo', 'William Leonard', 'Jaylen Hopson', 'Theresa Coumbe', 'James A. Pettitt', 'Christian J. Darken']2024-03-15T00:00:00https://www.semanticscholar.org/paper/efcaa6d23fcbb19e5c87f3560a5e325474f3169b 447845054885b2137462aa51a3bee533a21cd7acThis study assesses the impact of Artificial Intelligence (AI) on education through a qualitative analysis of literature. The research focuses on the application of AI in administration, instruction, and learning. The findings reveal extensive adoption of AI in education, with benefits including improved administrative efficiency, personalized learning experiences, and enhanced learning outcomes. AI-powered Question and answering bots are highlighted as promising tools for providing immediate support, personalized learning experiences, and skill development for students, while also offering improved pedagogy and valuable time-saving assistance for educators. However, challenges and concerns related to AI applications, such as reliability, accuracy, and ethical considerations, require careful attention and management. This research provides valuable insights into the potential of AI to revolutionize education while emphasizing the importance of addressing critical challenges to ensure ethical and effective implementation.International Conference on Database Theory80The findings reveal extensive adoption of AI in education, with benefits including improved administrative efficiency, personalized learning experiences, and enhanced learning outcomes, and the importance of addressing critical challenges to ensure ethical and effective implementation.2024 2nd International Conference on Disruptive Technologies (ICDT)['Radheya Ketak', 'Shailesh Mittal', 'Vansh Gupta', 'Hina Gupta']2024-03-15T00:00:00https://www.semanticscholar.org/paper/447845054885b2137462aa51a3bee533a21cd7ac 538ca679c13036c5d8f8a5d917e89799dde6c608The scope of artificial intelligence (AI) in healthcare is promising, and AI has the potential to revolutionise the field of palliative care services also. Consumer satisfaction in palliative care is a critical aspect of providing high-quality end-of-life support. It encompasses various elements that contribute to a positive experience for both patients and their families. AI-based tools and technologies can help in early identification of the beneficiaries, reduce the cost, improve the quality of care and satisfaction to the patients with chronic life-limiting illnesses. However, it is essential to ensure that AI is used ethically and in a way that complements, rather than replaces, the human touch and compassionate care, which are the core components of palliative care. This article tries to analyse the scope and challenges of improving consumer satisfaction through AI-based technology in palliative care services.BMJ Supportive & Palliative Care130The scope and challenges of improving consumer satisfaction through AI-based technology in palliative care services are analyzed.BMJ supportive & palliative care['Devi Nair', 'K. Raveendran']2024-03-15T00:00:00https://www.semanticscholar.org/paper/538ca679c13036c5d8f8a5d917e89799dde6c608 78d5af49898d4804377f5a5b94b22c640d6ce298Human resource management entails a number of strategic responsibilities aimed at improving human investment choices and streamlining work processes. The planned study entails automating HR functions that contribute to the growth of the company's professionals. Learning machines nowadays use cloud data, big data, and AI to assess cognitive development, necessitating adjustments to execution method. The goals of this research are to: (i) determine how automation may improve HR process efficiency; (ii) examine the effects of automation on administration; and (iii) assess the efficacy of HR automation. The article goes on to say that AI assists with a lot of different HR procedures, including healthcare, education, legal, logistics, finance, and more, all with the goal of making the company more employee-centric. Automating mundane, repetitive jobs and providing customers with individualized experiences based on their tastes and interests are two ways in which artificial intelligence (AI) is shaping the future of work and life. Artificial intelligence (AI) has the potential to be used in dangerous settings, reduce the likelihood of human mistake (if properly developed), and speed up decision-making with the support of cognitive technologies. The use of AI in this work showcases its benefits in the technology market, where the only criteria for success are high-quality, productive results achieved via continuous industrial development and expansion.International Conference on Database Theory130The use of AI in this work showcases its benefits in the technology market, where the only criteria for success are high-quality, productive results achieved via continuous industrial development and expansion.2024 2nd International Conference on Disruptive Technologies (ICDT)['Naziya Parveen', 'Ajay Malpani', 'Tayyaba Rasheed', 'Masrath Sultana']2024-03-15T00:00:00https://www.semanticscholar.org/paper/78d5af49898d4804377f5a5b94b22c640d6ce298 eac34354777f02d3d9431c89c8233f8540c46757British Dental Journal341Undergraduate clinical dental students generally showed positive perceptions regarding the ethical considerations associated with the integration of AI algorithms in clinical decision-making to ensure that AI benefits patient outcomes while upholding fundamental ethical principles and patient-centred care.British dental journal['Galvin Sim Siang Lin', 'W. Tan', 'Hasnah Hashim']2024-03-15T00:00:00https://www.semanticscholar.org/paper/eac34354777f02d3d9431c89c8233f8540c46757 f607d883a8363087492bf2a5ec3d05dea0c0c9c7Madrasah Tsanawiyah Muhammadiyah Tawangsari is a junior high school level educational institution that bases its curriculum on Islamic principles and progressively applies information technology (IT) on the foundation of Muhammadiyah values. This study aims to determine the process of diffusion of innovation in technology-based learning, focusing on the role of teachers as agents of change in applying artificial intelligence in the context of learning. The research method used was qualitative descriptive, involving in-depth interviews with four teachers who had key roles in introducing the innovation. The results showed that most teachers have received information about the use of artificial intelligence through various socialization activities, workshops, and training. The diffusion of learning innovations in MTs Tawangsari involves various communication channels, including interpersonal communication such as lectures, dialogues, and demonstrations of effective practices; communication through mass media, such as the publication of writings on school websites; and communication through social media, such as Youtube, Instagram, and Facebook. In addition, teachers consider that innovation in post-pandemic learning has significant urgency, with success indicators in the form of increased active student participation in the learning process. In addition, teachers also voiced the need for community platforms and continuous training on artificial intelligence-based learning.Informasi240The results showed that most teachers have received information about the use of artificial intelligence through various socialization activities, workshops, and training, and teachers consider that innovation in post-pandemic learning has significant urgency, with success indicators in the form of increased active student participation in the learning process.Informasi['N. Laksana', 'Ayu Usada Rengkaningtias', 'Wuri Handayani']2024-03-15T00:00:00https://www.semanticscholar.org/paper/f607d883a8363087492bf2a5ec3d05dea0c0c9c7 2e0ea4abb4357fef4ba2f6e637cd498dde75519fINTRODUCTION: The rapid change in artificial intelligence has evaluated ideological and political education ability in colleges and universities as a significant challenge.OBJECTIVES: To assess the level of competence of universities in ideological and political education to determine the effectiveness and efficacy of educational programs and to provide a basis for improving and upgrading academic competence.METHODS: Based on the CIPP model, the author constructed an index system and selected a suitable evaluation model to conduct a study on the evaluation of ideological and political competence of colleges and universities in the context of Artificial Intelligence, which helps to understand the background conditions, resource allocation, teaching activities and quality of teaching of educational programs, as well as the level of ideological and political literacy of the students and their achievements.RESULTS: The evaluation results show that this kind of evaluation research helps to improve and enhance the capacity of ideological and political education in colleges and universities, and at the same time, it can dig into the implementation effect of the educational program, find problems and shortcomings, and promote the continuous improvement of the educational program.CONCLUSION: Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility. In addition, based on this, it makes the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence.ICST Transactions on Scalable Information Systems210Through evaluation, the quality and level of ideological and political education in colleges and universities can improve students' ideological and political literacy and sense of social responsibility and the development of ideological and political ability in colleges and universities can be better adapted to the era of artificial intelligence.ICST Transactions on Scalable Information Systems['Hui Dong']2024-03-15T00:00:00https://www.semanticscholar.org/paper/2e0ea4abb4357fef4ba2f6e637cd498dde75519f 04466cc86f071c9f8a368de387358eaf2af1a162Objective – This study investigates the readiness for artificial intelligence (AI) adoption in library and information centres of Pakistani universities. The projected outcomes of this study are expected to contribute to the development of best practices for effectively motivating university administrators and preparing librarians for adopting AI in library and information centres. Methods – A theoretical framework combining the technology-organization-environment (TOE) framework and the Technology Readiness Index (TRI) guided this qualitative study. Interviews were conducted with 27 senior representatives, including library managers and registrars, from 27 universities across four provinces and the capital city, Islamabad. A systematic approach was employed to analyze the data. Results – The findings indicate that the concept of AI adoption in Pakistani university libraries is new. The library and information sector of Pakistan is slow in adopting AI, which could have implications for its future competitiveness, despite the push for AI adoption by university librarians and administrators. The readiness for AI adoption in this sector is influenced by factors such as organizational technological practices, financial resources, university size, and data management and protection concerns. Conclusion – Library managers and researchers can implement the TOE framework and TRI scale to facilitate AI adoption in a manner that is relevant to library and information settings in Pakistan as well as other parts of the world. Our research indicates that most adoptions are still in their nascent phases, and numerous library managers feel uneasy due to either uncertainties about the precise benefits AI can bring to their libraries or a lack of knowledge and skills for its effective implementation. To manage the networks of internal and external stakeholders essential for successful AI adoption, universities should consider appointing individuals with a specialized knowledge of AI within their libraries.Evidence Based Library and Information Practice00The research indicates that most adoptions are still in their nascent phases, and numerous library managers feel uneasy due to either uncertainties about the precise benefits AI can bring to their libraries or a lack of knowledge and skills for its effective implementation.Evidence Based Library and Information Practice['Saeed Ullah Jan', 'Muhammad Sajjad Ali Khan', 'Ali Saeed Khan']2024-03-15T00:00:00https://www.semanticscholar.org/paper/04466cc86f071c9f8a368de387358eaf2af1a162 c83b3669012d8a38420080365ca90b6510b76b73Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.Frontiers in Transplantation570By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.Frontiers in Transplantation['Mouhamad Al Moussawy', 'Zoe S. Lakkis', 'Zuhayr A. Ansari', 'Aravind R. Cherukuri', 'Khodor I. Abou-Daya']2024-03-15T00:00:00https://www.semanticscholar.org/paper/c83b3669012d8a38420080365ca90b6510b76b73 e7c5169663734eff9b003e821322ad06559e45bfThe article explores the possibilities of using artificial intelligence, specifically ChatGPT and Bing, in the practice of teaching Bulgarian as a foreign language. The text introduces the principles of how these two platforms operate and provides practical suggestions for creating materials for Bulgarian language lessons. It discusses the advantages and disadvantages of using these platforms and offers options for generating texts, modifying texts, creating exercises, and formulating tasks.Bulgarski Ezik i Literatura-Bulgarian Language and Literature00The article explores the possibilities of using artificial intelligence, specifically ChatGPT and Bing, in the practice of teaching Bulgarian as a foreign language and discusses the advantages and disadvantages of using these platforms.Bulgarski Ezik i Literatura-Bulgarian Language and Literature['Antonia Radkova']2024-03-15T00:00:00https://www.semanticscholar.org/paper/e7c5169663734eff9b003e821322ad06559e45bf 0f3faf61e8fea627557baad4644e31eec19bbe5cHuman-centered artificial intelligence (HAI) is at the forefront of current AI research efforts. Although several known challenges related to the use of AI are discussed in the present academic literature, the authors propose that AI has the potential to revolutionize many aspects of healthcare and social services, particularly for vulnerable populations. However, it is important to carefully consider the unique needs of vulnerable populations throughout the HAI design process as well as the ethical implications of using AI with these populations, and to build ethical HAI tools that will be thoroughly evaluated for their safety and security properties. Our current research investigates the unique recovery challenges and needs of domestic abuse survivors with continued vulnerability to technology enabled coercive control (TECC) attacks. One challenge of designing HAI for such an adversarial context, is what information to provide the HAI with so that the HAI can detect, interpret, or predict potential cognitive attack tactics used in coercive control. Another challenge in this context is determining what role the HAI should adopt, such as training versus decision aid. To explore potential design solutions to address these challenges, the authors aimed to derive safe, ethical, and situationally aware requirements within a potential clinician-patient training use case to enhance awareness of TECC-enacted cognitive attacks. The present paper describes the development of a preliminary set of HAI system requirements for the design of an instance-based learning (IBL) HAI model, that is contextualized for the complex adversarial landscape presented by TECC.SoutheastCon480A preliminary set of HAI system requirements for the design of an instance-based learning (IBL) HAI model, that is contextualized for the complex adversarial landscape presented by TECC is described.SoutheastCon 2024['Courtney L. Crooks', 'Sneha Talwalkar', 'Tanya Sharma', 'Kriti Arora', 'Kavya Venkatesh']2024-03-15T00:00:00https://www.semanticscholar.org/paper/0f3faf61e8fea627557baad4644e31eec19bbe5c bc26f7fe945bc4eb3b9e4e2b24490270d9c82ae8As arbiters of law and fact, judges are supposed to decide cases impartially, basing their decisions on authoritative legal sources and not being influenced by irrelevant factors. Empirical evidence, however, shows that judges are often influenced by implicit biases, which can affect the impartiality of their judgment and pose a threat to the right to a fair trial. In recent years, artificial intelligence (AI) has been increasingly used for a variety of applications in the public domain, often with the promise of being more accurate and objective than biased human decision-makers. Given this backdrop, this research article identifies how AI is being deployed by courts, mainly as decision-support tools for judges. It assesses the potential and limitations of these tools, focusing on their use for risk assessment. Further, the article shows how AI can be used as a debiasing tool, i. e., to detect patterns of bias in judicial decisions, allowing for corrective measures to be taken. Finally, it assesses the mechanisms and benefits of such use.TATuP Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis380This research article identifies how AI is being deployed by courts, mainly as decision-support tools for judges, and shows how AI can be used as a debiasing tool, i.TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis['Giovana Lopes']2024-03-15T00:00:00https://www.semanticscholar.org/paper/bc26f7fe945bc4eb3b9e4e2b24490270d9c82ae8 887af64ea46295bb077416abcf377760a8206d61The Internet of Things (IoT) is a rapidly established technology that combines various domains and such technology permits devices to process, transfer, and receive information without the involvement of humans. However, privacy and security problems remain a major difficulty in the IoT. An Intrusion Detection System (IDS) is needed for securing attacks on this platform. Recently, various researchers identified significant ways for ID by employing Explainable Artificial Intelligence (XAI) approaches to Machine Learning (ML) and Deep Learning (DL) techniques. This research indicates various methodologies like Balanced and Stacked Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Naïve Bayes (NB), Ada-Boost (AB), Cat-Boost (CB), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and Bidirectional-LSTM (Bi-LSTM) that are utilized for IDS. Accuracy, f1-score, recall, Area Under Curve (AUC), precision, score time, and False Alarm Rate (FAR) are employed as the performance metrics for this study.2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)190This research indicates various methodologies like Balanced and Stacked Random Forest, Decision Tree, Decision Tree, Support Vector Machine, Naïve Bayes, Ada-Boost, Cat-Boost, Long Short-Term Memory, and Bidirectional-LSTM that are utilized for IDS.2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT)['D. Satyanarayana', 'E. Saikiran']2024-03-15T00:00:00https://www.semanticscholar.org/paper/887af64ea46295bb077416abcf377760a8206d61 55820647950e6bff36c1ea3105ade753cc1a9ea3Artificial intelligence (AI) refers to a machine's capacity for operations typically performed by human intelligence, such as learning, thinking, solving problems, and making decisions. Machine learning, neural networks, expert systems, and rule-based systems are all used in artificial intelligence. AI employs methods and algorithms to process data, draw conclusions from patterns and laws, and enhance performance over time. A software application or product's intended functionality is evaluated and verified through the process of software testing. The benefits of testing include the prevention of bugs, decreased development costs, and improved performance. Through test generation, test data generation, and automated test script writing, AI can be used in software testing to enhance the quality of our product and the manual testing processes. Software testing is a time-consuming, laborious, and tiresome process. Automation solutions have been created to help with automating some testing process operations in order to increase quality and delivery time. As continuous integration and delivery (CI/CD) pipelines are added, automation systems gradually lose part of their usefulness. The testing community is looking to AI to fill the gap because AI has the capacity to check the code for flaws and defects without the need for any human intervention and much more quickly than humans. In this study, we want to comprehend the effects of AI technology on various STLC tasks or components of software testing. The study also makes an effort to pinpoint and explain some of the biggest challenges faced by software testers when implementing AI in testing. The report also suggests several significant potential contributions of AI to the field of software testing.International Journal For Multidisciplinary Research270The effects of AI technology on various STLC tasks or components of software testing are comprehended and several significant potential contributions of AI to the field of software testing are suggested.International Journal For Multidisciplinary Research['Md. Abul Hayat', 'Sunriz Islam', 'Md. Fokhray Hossain']2024-03-15T00:00:00https://www.semanticscholar.org/paper/55820647950e6bff36c1ea3105ade753cc1a9ea3 73161487d145768726e4692bfe4c290d3886e12dArtificial intelligence (AI) has made remarkable progress in various domains, outperforming human capabilities in many areas. It is no surprise that AI is being increasingly used in healthcare practices, including regional anaesthesia. Recent advancements in AI have enabled its integration into the field of regional anaesthesia, promising to enhance precision, efficiency, and patient outcomes. By utilising machine learning algorithms and predictive analytics, AI has the potential to revolutionise the way regional anaesthesia procedures are conducted and managed. Ultrasound-guided regional anesthesia (UGRA) significantly enhances the success rates of regional blocks while mitigating complication risks. This review scrutinizes the burgeoning role of Artificial Intelligence (AI) in UGRA, detailing its evolution and pivotal function in optimizing sonographic imaging, target delineation, needle guidance, and local anesthetic administration. AI's support is invaluable, particularly for non-experts in training and clinical practice, and for experts in educational settings. By systematically analyzing the capabilities and applications of AI in regional anesthesia, we assess its contribution to procedural precision, safety, and educational advancement. The findings reveal that AI-assisted UGRA not only bolsters the accuracy of anatomical identification, thus improving patient safety, but also standardizes the quality of care across varying expertise levels. The integration of AI into UGRA emerges as a transformative influence in anesthesiology, promising to reshape the domain with enhanced precision, efficiency, and patient-centered care.Journal of Innovative Healthcare Practices00The findings reveal that AI-assisted UGRA not only bolsters the accuracy of anatomical identification, thus improving patient safety, but also standardizes the quality of care across varying expertise levels.Journal of Innovative Healthcare Practices['Suna Kara Görmüş']2024-03-15T00:00:00https://www.semanticscholar.org/paper/73161487d145768726e4692bfe4c290d3886e12d b2524c1b486a7d077c0d5aae35bfb77f2e3bd119Artificial intelligence specific through machine learning is changing the way in which we can process big data. This position paper provides a perspective on the Information Security governed organization with a view on how Artificial Intelligence is mapped back into the process of this governance and how we can evaluate the use of this Artificial Intelligence to support the said organization, we demonstrate with the use of case study simulated scenarios in predictive policing as a part of crime management to inform this discussion. Artificial Intelligence in seeking to mimic human intelligence seeks to achieve this through statistical and interpretative inference analysis. In this work we posit the need to start with the basic statistical inference analysis approach, through parsing and pattern matching techniques to support such predictive inferences. The application of predictive policing seeks to examine pattern recognition methods specific to detecting security incidents and potential breaches within the environment. Our technical approach examines the use of privacy impact assessment (PIA) and data protection impact assessment (DPIA) questionnaires as basic experimental prototype aimed at automated data collection to support future analysis of potential breaches within data protection and privacy environments.SoutheastCon70This position paper provides a perspective on the Information Security governed organization with a view on how Artificial Intelligence is mapped back into the process of this governance and how to evaluate the use of this Artificial Intelligence to support the said organization.SoutheastCon 2024['Thorpe Sean', 'Mitchell Leighton']2024-03-15T00:00:00https://www.semanticscholar.org/paper/b2524c1b486a7d077c0d5aae35bfb77f2e3bd119 027fa823a007ade59db818eeb0294b9b90398c86Artificial intelligence (AI) and robotics have emerged as game-changing technologies with the potential to revolutionize the healthcare industry. In the context of hospitals, their integration holds the promise of not only improving patient care but also driving competitive advantage and fostering sustainable development. This review paper aims to explore and evaluate the impact of AI and robotics applications on attaining competitive advantage and promoting sustainable development in hospitals, examines the current landscape of AI and robotics adoption in healthcare settings and delve into their specific applications within hospitals, including AI-assisted diagnosis, robotic surgery, patient monitoring, and data analytics. A key finding is the insufficient use of KI to date in terms of promoting sustainable development in hospitals. Furthermore, attempts to analyze the potential benefits and challenges associated with these technologies in terms of enhancing patient outcomes, operational efficiency, cost savings, and differentiation from competitors. Drawing upon a comprehensive review of the existing literature and case studies, this paper provides valuable insights into the transformative potential of AI and robotics in hospitals.GMS Hygiene and Infection Control160A key finding is the insufficient use of KI to date in terms of promoting sustainable development in hospitals, which attempts to analyze the potential benefits and challenges associated with these technologies in terms of enhancing patient outcomes, operational efficiency, cost savings, and differentiation from competitors.GMS Hygiene and Infection Control['Narasingappa Pavithra', 'Noor Afza']2024-03-15T00:00:00https://www.semanticscholar.org/paper/027fa823a007ade59db818eeb0294b9b90398c86 4d3ce095366005094482173e8f9464e38863326fThe integration of artificial intelligence (AI) technologies into gym management practices is poised to revolutionize the fitness industry. This review paper explores the evolving role of AI in shaping the future of gym management, focusing on its potential to enhance member experiences, optimize operational efficiency, and drive business growth. Drawing upon recent literature and case studies, the paper highlights how AI-powered systems can personalize interactions, automate routine tasks, and provide data-driven insights to gym owners and managers. Key themes include the importance of personalized experiences in driving member satisfaction, the efficiency gains associated with AI-driven operational optimization, and the strategic benefits of data-driven decision-making. Moreover, the paper discusses challenges such as data privacy concerns, algorithm transparency, and staff training requirements that must be addressed to realize the full potential of AI in gym management. By embracing AI technologies, gyms can create tailored experiences, streamline operations, and stay competitive in an increasingly dynamic market landscape.International Journal of Innovative Research in Computer Science & Technology00This review paper explores the evolving role of AI in shaping the future of gym management, focusing on its potential to enhance member experiences, optimize operational efficiency, and drive business growth.International Journal of Innovative Research in Computer Science and Technology (IJIRCST)['Rohit Kumar Chaurasiya', 'Mohd Anas', 'Yadav Monu', 'Tripti Sahu']2024-03-15T00:00:00https://www.semanticscholar.org/paper/4d3ce095366005094482173e8f9464e38863326f 890f8bba73e50e02d7ddf1fcb7ac8d4c2dba2c9bThis paper delves into the multi-faceted role of Artificial Intelligence (AI) in the field of cybersecurity. It conducts an in-depth analysis of two common security problems, spam email and DDos/Dos attacks. It focuses on the application of AI in resolving these problems and further on Event Management (SIEM) and Intrusion Detection Systems (IDS). Furthermore, this article outlines and discusses five key challenges that AI may encounter throughout its development, including issues related to reliability, unidentified threats, data privacy, ethical considerations, and the importance of an explainable AI. The paper underscores the remarkable potential of AI to improve our defences against emerging cybersecurity threats. It highlights the growing importance of addressing ethical and legal issues in the evolving technological environment. Finally, the article offers a comprehensive summary and valuable insights into the future prospects of AI in the realm of cybersecurity, confirming its crucial role in shaping the security landscape in the digital era.Applied and Computational Engineering00An in-depth analysis of two common security problems, spam email and DDos/Dos attacks, and the application of AI in resolving these problems and further on Event Management (SIEM) and Intrusion Detection Systems (IDS).Applied and Computational Engineering['Fangshu Li']2024-03-15T00:00:00https://www.semanticscholar.org/paper/890f8bba73e50e02d7ddf1fcb7ac8d4c2dba2c9b e20669b568f8c045b27607c000bb985a5425bfe3One area where artificial intelligence (AI) has shown promise is in the early detection of lung pneumonia by use of chest X-ray. There hasn't been a thorough evaluation that compares the dataset with prior studies, even though pneumonia diagnosis uses a lot of ML, DL, and TL approaches. First, a quick summary of the various AI-based techniques for clustering, regression, and classification is given in this review study. The main issues of the past few years are finally addressed together with their potential implications. Our primary objective is to provide a comprehensive overview of the research on artificial intelligence (AI) that use data comparison to diagnose pneumonia illness in chest X-rays, as well as to identify any limitations and provide advice for practitioners.2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)390This review study provides a comprehensive overview of the research on artificial intelligence (AI) that use data comparison to diagnose pneumonia illness in chest X-rays, as well as to identify any limitations and provide advice for practitioners.2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)['N. Ghuse', 'Sandeep Monga']2024-03-15T00:00:00https://www.semanticscholar.org/paper/e20669b568f8c045b27607c000bb985a5425bfe3 8525e5abbaf3c14a9b2ccd5285bbd6891358dcedAn inspiring and timely review by Paolo Gresele, published in Bleeding, Thrombosis and Vascular Biology, at the end of last year, fascinated me through the description of applications of artificial intelligence in the field of hemostasis and thrombosis, examining its advantages, drawbacks and future perspectives [...]Bleeding Thrombosis and Vascular Biology30Bleeding, Thrombosis and Vascular Biology['Giovanni De Gaetano']2024-03-15T00:00:00https://www.semanticscholar.org/paper/8525e5abbaf3c14a9b2ccd5285bbd6891358dced ced1e5da73b1f7cd0255bb6664669a22fc0c2df6In the contemporary era, modern civilization is immersed in a technologically interconnected environment, where numerous applications within the digital ecosystem harness advanced artificial intelligence (AI) techniques [...]Sustainability80Sustainability['Muhammad Syafrudin', 'Ganjar Alfian', 'Norma Latif Fitriyani', 'Muhammad Anshari']2024-03-15T00:00:00https://www.semanticscholar.org/paper/ced1e5da73b1f7cd0255bb6664669a22fc0c2df6 37b3581bbb612d6da6e8e675cc3915edfbd074c3The rapid advancement of artificial intelligence (AI) has revolutionized the accounting profession, automating tasks, identifying patterns, and improving accuracy. However, the increasing reliance on AI raises ethical concerns regarding privacy, bias, transparency, and accountability. This research paper delves into the ethical considerations of AI implementation in accounting practices.Thepaper begins by examining the potential benefits of AI in accounting, highlighting its ability to streamline operations, enhance efficiency, and reduce errors. However, it also acknowledges the ethical risks associated with AI, including data privacy breaches, biased decision-making, lack of transparency, and accountability issues.The paper proposes a framework for responsible AI implementation in accounting to address these ethical concerns. The framework emphasizes establishing clear ethical guidelines,ensuring data privacy and security, mitigating AI algorithms' bias, promoting AI decisionmaking transparency, and establishing accountability mechanisms.The paper further explores the role of accountants in addressing AI ethics. Accountants are responsible for upholding ethical standards and ensuring that AI systems are used responsibly and ethically. They must be aware of the ethical implications of AI and have the knowledge and skills to mitigate ethical risks.In conclusion, the paper emphasizes the need for a proactive approach to AI ethics in accounting. By establishing clear ethical guidelines, promoting responsible AI implementation, and empowering accountants with ethical knowledge and skills, the accounting profession can harness the potential of AI while upholding ethical principles and safeguarding public trust.Journal of Accounting, Ethics &amp; Public Policy630By establishing clear ethical guidelines, promoting responsible AI implementation, and empowering accountants with ethical knowledge and skills, the accounting profession can harness the potential of AI while upholding ethical principles and safeguarding public trust.Journal of Accounting, Ethics &amp; Public Policy['Brandon Schweitze']2024-03-15T00:00:00https://www.semanticscholar.org/paper/37b3581bbb612d6da6e8e675cc3915edfbd074c3 ddab6a4b067bbc5e1dc40451309ce8af067189e8In the era of artificial intelligence, the negative alienation of artificial intelligence has become increasingly prominent, which has delayed the development of human beings to a certain extent. The main reason is that human beings gradually ignore their own subjective initiative and lack rational understanding of human civilization, which is a problem that human beings need to face and solve. From the perspective of philosophy, to overcome the negative alienation of artificial intelligence, it is necessary to clarify the dominant position of human beings and rationally understand human civilization, so as to better promote the harmonious development of human beings and artificial intelligence, nature and society.Educational Science Literature00To overcome the negative alienation of artificial intelligence, it is necessary to clarify the dominant position of human beings and rationally understand human civilization, so as to better promote the harmonious development of human beings and artificial intelligence, nature and society.Educational Science Literature['Yangyang Li']2024-03-15T00:00:00https://www.semanticscholar.org/paper/ddab6a4b067bbc5e1dc40451309ce8af067189e8 3af4844228e5b9ac11a1b3f5168b16114abd4ea2Research suggests that understanding workers' attitudes towards artificial intelligence (AI) application is a prerequisite to successfully integrating AI into an organization. However, few studies have clarified the meaning of attitudes towards AI application at work (AAAW) as a multifaceted construct that can be assessed with psychometric validity. To address this issue, we developed and validated a scale to capture individuals' AAAW using three independent samples (total N = 2841). The resulting 25‐item scale covers an overall construct of AAAW as well as six dimensions that are subsumed under the construct (i.e., perceived humanlikeness, perceived adaptability, perceived quality of AI, AI use anxiety, job insecurity and personal utility). Our findings suggest that the AAAW scale has good psychometric properties and can be used to predict important recruiting outcomes. The scale offers opportunities to better understand and measure workers' attitudes towards AI application at work in a comprehensive and integrative manner.Journal of Occupational and Organizational Psychology520A scale to capture individuals' attitudes towards artificial intelligence application at work (AAAW) as a multifaceted construct that can be assessed with psychometric validity is developed and validated.Journal of Occupational and Organizational Psychology['Jiyoung Park', 'Sang Eun Woo', 'JeongJin Kim']2024-03-15T00:00:00https://www.semanticscholar.org/paper/3af4844228e5b9ac11a1b3f5168b16114abd4ea2 42e0a54ec4bac729458865de828e65b09f1c9c27This study looks into how artificial intelligence (AI) is changing the managerial and economic landscape in India. The paper examines how AI impacts the GDP growth, employment prospects, productivity and other business and economic aspects of the Indian economy. It also looks at how management practises are changing as a result of AI, notably in relation to the development of new business models, improved decision-making processes, and widespread task automation. The methodology entails comprehensive research of the available literature, case studies of significant Indian businesses, and an analysis of key statistical data. The results highlight how AI has the ability to considerably boost India's economic development and also recognise the need to address talent gaps and ethical issues at the same time. Insights on the future of AI in India are highlighted in the paper's conclusion, with a focus on the necessity of talent development and strategic adoption.Journal of Management Research and Analysis00This study looks into how artificial intelligence (AI) is changing the managerial and economic landscape in India and highlights how AI has the ability to considerably boost India's economic development and also recognise the need to address talent gaps and ethical issues at the same time.Journal of Management Research and Analysis['Ashok Panigrahi', 'Shrinivas C Ahirrao', 'Arav Patel']2024-03-15T00:00:00https://www.semanticscholar.org/paper/42e0a54ec4bac729458865de828e65b09f1c9c27 3f944e3b413a36b1fa14b42bb5730b35609c22b1José Vida Fernández, Artificial Intelligence in Government: Risks and Challenges of Algorithmic Governance in the Administrative State (Sztuczna inteligencja w rządzeniu. Ryzyka i wyzwania algorytmicznego zarządzania w państwie administracyjnym), „Indiana Journal of Global Legal Studies” 2023, vol. 30, no. 1, s. 65–95, https://doi.org/10.2979/gls.2023.a886163Studia Prawa Publicznego00Studia Prawa Publicznego['Agnieszka Narożniak']2024-03-15T00:00:00https://www.semanticscholar.org/paper/3f944e3b413a36b1fa14b42bb5730b35609c22b1 e742d1c7123c55332ea67636de903b8bb5986af9Nowadays, with the widespread popularization and development of the internet, the e-commerce industry has begun to rise, among which social e-commerce, as a new community, has become popular on the Internet. At the same time, the field of artificial intelligence is slowly infiltrating into every field of today's society. The diversified data contained in the social e-commerce platform has great potential value, but artificial intelligence, as an important technology of information analysis, is rarely applied in this direction. This paper fundamentally discusses the role of artificial intelligence in e-commerce. Taking Xiaohongshu as an example, the SWOT framework is used to analyze the advantages and drawbacks, potential benefits and risks caused by the application of artificial intelligence in an e-commerce platform with rich user data. The limitation and extensibility of artificial intelligence in e-commerce platforms, finally put forward the application prospect of artificial intelligence in the e-commerce direction. This study recommends that the social e-commerce community establish a robust data privacy protection system, increase investment in technology research and development, and fully leverage the potential of AI technology.Applied and Computational Engineering00It is recommended that the social e-commerce community establish a robust data privacy protection system, increase investment in technology research and development, and fully leverage the potential of AI technology.Applied and Computational Engineering['Kaiwen Kang', 'Xuezhou Wang', 'Wenwen Yang']2024-03-15T00:00:00https://www.semanticscholar.org/paper/e742d1c7123c55332ea67636de903b8bb5986af9 623c0db205974e0f990f5934f0619fda97f7d2bcMuscle and Nerve100Muscle & nerve[]2024-03-15T00:00:00https://www.semanticscholar.org/paper/623c0db205974e0f990f5934f0619fda97f7d2bc f83242c83c17d7d2b6871e5e14ded549ffe83c81This paper explores the capability of the use of synthetic Intelligence (AI) for automatic compliant transaction processing. AI-primarily based structures can examine large volumes of transaction records, detect inconsistencies and compliance troubles, and offer guidelines for development. AI can also be used to create models to pick out fraudulent transactions, conduct patron segmentation and analysis, expect customer behavior, and alert establishments of capability issues. Further, AI-based algorithms can be used to automate a huge variety of responsibilities, inclusive of approval tactics and automatic record storage and retrieval. The implementation of AI-based total technology is expected to lessen guide processing efforts and time, resulting in green, streamlined compliance tactics. By leveraging AI generation, companies could be capable of improving consumer revel, lessening dangers, and improving their normal performance. This paper examines the contemporary country of the artwork in AI-based technologies and discusses their capacity packages within the computerized compliant transaction processing area.International Conference on Database Theory150The contemporary country of the artwork in AI-based technologies is examined and their capacity packages within the computerized compliant transaction processing area are discussed.2024 2nd International Conference on Disruptive Technologies (ICDT)['Sunil Rajaram Landge', 'Srinivasa Rao Gunturu', 'Hari Prasad Josyula', 'T. Kiruthiga', 'Eram Fatima']2024-03-15T00:00:00https://www.semanticscholar.org/paper/f83242c83c17d7d2b6871e5e14ded549ffe83c81 3bcfad90f5eb4d4e31c1f2053ba5cdf77b82ee20npj Digit. Medicine250Both the speed and volume of AI/ML devices present a delicate balance for regulatory bodies: ensuring the safety and effectiveness of devices while keeping pace with the clinical innovation and value that they may provide.NPJ Digital Medicine['James Hillis', 'Jacob J. Visser', 'Edward R. Scheffer Cliff', 'Kelly van der Geest – Aspers', 'B. Bizzo', 'Keith J. Dreyer', 'Jeremias Adams-Prassl', 'Katherine P. Andriole']2024-03-15T00:00:00https://www.semanticscholar.org/paper/3bcfad90f5eb4d4e31c1f2053ba5cdf77b82ee20 85012a651184d654a62beec57897ba6596487c02Education and Information Technologies : Official Journal of the IFIP technical committee on Education171Education and Information Technologies['Maryam Roshanaei']2024-03-15T00:00:00https://www.semanticscholar.org/paper/85012a651184d654a62beec57897ba6596487c02 56b0a56696a244443247b7df332029fb0fa1ba79<jats:p>-</jats:p>Vestnik RUDN International Relations10Vestnik RUDN. International Relations['G. Sufiyanova', 'E. O. Muslimova']2024-03-15T00:00:00https://www.semanticscholar.org/paper/56b0a56696a244443247b7df332029fb0fa1ba79 46e603f0e8d7ab32a2514349d77e68971f3ccac6Data technology has experienced an explosion in popularity and scope over the past few years, inflicting several companies to try to capitalize on the sizeable possibilities the technology has to provide. Agencies are utilizing AI to develop records science studies, allowing them to go beyond conventional strategies and discover new insights and innovations within the discipline. With AI, statistics scientists can identify styles in massive datasets, optimize algorithms to clear up complicated issues and create predictive fashions to discover developments and correlations. In addition, AI may be used to automate specific components of fact processing, making data technological know-how simpler and quicker. To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use. With the right group and the proper equipment, AI may make data technological know-how studies extra effective and more beneficial than ever.International Conference on Database Theory180To make the most of this generation, organizations should have personnel knowledgeable in the basics of facts science and AI and the knowledge to recognize the complexities of the algorithms and applications they use.2024 2nd International Conference on Disruptive Technologies (ICDT)['Deepak Sharma', 'Gaurav Chaudhary', 'Kapil K. Rajput', 'Amit Singhal', 'Tarun Kumar Sharma']2024-03-15T00:00:00https://www.semanticscholar.org/paper/46e603f0e8d7ab32a2514349d77e68971f3ccac6 ce4d249746d985434da4b754a053d58e420f14eeВ современных условиях возрос интерес к прикладным разработкам в сфере логопедии, связанным с диагностикой и помощью в коррекции речевых недостатков, в частности, к созданию приложений для исследования голоса и программ для людей, использующих альтернативную и дополнительную коммуникацию. Быстрое развитие технологий позволяет использовать возможности искусственного интеллекта для диагностики нарушений звукопроизношения у детей и взрослых и предоставлять помощь в коррекционной работе. В статье представлены результаты исследования эффективности диагностики звукопроизношения с использованием приложения «NovatorSpace», разработанного компанией ООО «Новатор скул» (свидетельство о регистрации 2023682812). По результатам исследования, в котором приняли участие 60 учеников в возрасте от 5 до 7 лет, сделаны выводы о возможности применения разработки в больших масштабах, описаны её преимущества и недостатки, а также определены перспективы развития и совершенствования системы диагностики с применением искусственного интеллекта. Важным аспектом статьи стало сравнение онлайн-диагностики с логопедом и диагностики с использованием нейросетей и выводы о возможности их совмещения, что позволяет делегировать более простые процессы машинным системам для освобождения человеческих ресурсов под сложные функции. In modern conditions, there has been an increased interest in applied developments in the field of speech therapy related to the diagnosis and assistance in correcting speech deficiencies, in particular, in the creation of applications for voice research and programs for people using alternative and additional communication. The rapid development of technology makes it possible to use the capabilities of artificial intelligence to diagnose sound pronunciation disorders in children and adults and provide assistance in corrective work. The article presents the results of a study of the effectiveness of diagnosing sound pronunciation using the NovatorSpace application, developed by Novator School (registration certificate 2023682812). Based on the results of the study, which involved 60 students aged 5 to 7 years, conclusions were drawn about the possibility of using the development on a large scale, its advantages and disadvantages were described, and prospects for the development and improvement of a diagnostic system using artificial intelligence were identified. An important aspect of the article was a comparison of online diagnostics with a speech therapist and diagnostics using neural networks and conclusions about the possibility of combining them, which makes it possible to delegate simpler processes to machine systems to free up human resources for complex functions.Pedagogical Perspective40Pedagogical perspective['А.А. Хоменко', 'И.В. Зинченко', 'Ю.В. Брызгалова']2024-03-15T00:00:00https://www.semanticscholar.org/paper/ce4d249746d985434da4b754a053d58e420f14ee 9be7631ca6d42443d47d5b5693188f78671543e7This article outlines an integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi‐Objective Possibilistic Linear Programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades‐long development of classical optimisation techniques. As a result of its potential for use in the real world, multi‐objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi‐objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non‐linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision‐making in real‐world optimization scenarios.Expert systems510An integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives to improve management for the betterment of transportation networks in smart cities is outlined.Expert Systems['Pinki', 'Rakesh Kumar', 'S. Vimal', 'N. Alghamdi', 'G. Dhiman', 'Subbulakshmi Pasupathi', 'Aarna Sood', 'W. Viriyasitavat', 'Assadaporn Sapsomboon', 'Amandeep Kaur']2024-03-15T00:00:00https://www.semanticscholar.org/paper/9be7631ca6d42443d47d5b5693188f78671543e7 a1d0532a27fbe9f93d4827c914fddc0da9d255a3This article explores the integration of AI technologies in the realm of Behavioural Safety and Strategy within the construction sector. The focus is on how AI applications can contribute to identifying and mitigating behavioral risks, improving safety outcomes, and optimizing project performance. The construction industry is a dynamic and often hazardous nature, has witnessed a developing interest in leveraging AI to enhance safety practices and strategic decision-making. Machine learning algorithms are employed to analyze historical data, predict potential safety issues and provide proactive interventions. AI can provide insights into resource allocation, project timelines, and risk assessment. It not only enriches the efficiency of the operations of construction, but also contributes to the development of proactive security strategies.International Conference on Database Theory1702024 2nd International Conference on Disruptive Technologies (ICDT)['R. Sudharsan', 'K. Vinayagam']2024-03-15T00:00:00https://www.semanticscholar.org/paper/a1d0532a27fbe9f93d4827c914fddc0da9d255a3 9eee6a91eafff7d0e11866624b9647efda841975SARS-COV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) was initially tested in Wuhan City, China, in December 2019 and had a devastating impact worldwide, exterminating more than 6 million people as of September 2022. It became the biggest worldwide health crisis since the 1918 influenza outbreak. Viruses generally mutate randomly, so predicting how SARS-CoV-2 will transform over the next few months or years and which forms will predominate is impossible. The possibilities for virus mutation, in theory, are practically endless. Enabling researchers to determine which antibodies have the potential to be most effective against existing and future variations could help machine learning to assist in drug discovery. In the COVID-19 pandemic, AI has benefited four key areas: diagnosis, clinical decision-making for public health, virtual assistance, and therapeutic research. This study conducted a discourse analysis and textual evaluation of AI (deep learning and machine learning) concerning the COVID-19 outbreak. Further, this study also discusses the latest inventions that can be very helpful in future pandemic detection. COVID-19 has already changed our lives, and in the future, we might be able to deal with pandemics like this with the help of AI. This review has also emphasized the legal implications of AI in the battle against COVID-19.Journal of Experimental Biology and Agricultural Sciences610This study conducted a discourse analysis and textual evaluation of AI (deep learning and machine learning) concerning the COVID-19 outbreak, and emphasized the legal implications of AI in the battle against COVID-19.Journal of Experimental Biology and Agricultural Sciences['Shagufta Quazi', 'Sampa Karmakar Singh', 'R. P. Saha', 'Arpita Das', 'M. K. Singh']2024-03-15T00:00:00https://www.semanticscholar.org/paper/9eee6a91eafff7d0e11866624b9647efda841975 2f25daa061842742f3a2213e77bc793296ba78dcThis research introduces a novel AI-based mechanism for optimizing threat mitigation in IoT banking systems, addressing the growing vulnerabilities in this critical sector. The proposed mechanism, characterized by a precision of 0.88 and a balanced recall of 0.79, offers a robust defense against cyber threats. Leveraging a Deep Neural Architecture known as Pointer Networks, the mechanism adapts dynamically, ensuring high accuracy in threat identification (precision) while comprehensively covering potential threats (recall), resulting in a harmonious F1 score of 0.83. Through extensive threat-specific evaluations, the mechanism proves versatile, exhibiting high performance in scenarios involving malware (precision: 0.89, recall: 0.82, F1 score: 0.85), denial of service (DoS) attacks (precision: 0.87, recall: 0.78, F1 score: 0.82), and unauthorized access attempts (precision: 0.90, recall: 0.81, F1 score: 0.85). Scalability testing further validates its practical applicability, maintaining precision and F1 score values across varying sizes of IoT ecosystems. This research establishes the proposed mechanism as a potent and adaptable cybersecurity tool, poised to fortify the resilience of IoT banking systems against the dynamic landscape of cyber threats.International Conference on Database Theory200This research establishes the proposed mechanism as a potent and adaptable cybersecurity tool, poised to fortify the resilience of IoT banking systems against the dynamic landscape of cyber threats.2024 2nd International Conference on Disruptive Technologies (ICDT)['Rudra Pratap Singh Chauhan', 'Sanjav Kumar Sonker', 'Manpreet Kaur', 'Chhaya Sharma', 'Robin Singh', 'Ramendra Singh']2024-03-15T00:00:00https://www.semanticscholar.org/paper/2f25daa061842742f3a2213e77bc793296ba78dc 45c232b978ea6e2549ecee4d88f6e39e1f591fa0The ITEA Journal of Test and Evaluation00The ITEA Journal of Test and Evaluation['Robert Seif', 'Atharva Sonanis', 'Laura Freeman', 'Kristen Alexander', 'Jitesh Panchal']2024-03-15T00:00:00https://www.semanticscholar.org/paper/45c232b978ea6e2549ecee4d88f6e39e1f591fa0 abbe14baa4be3eea376e04fd132bfec9ebc77d79Science &amp; Education800Science &amp; Education['Kason Ka Ching Cheung', 'Yun Long', 'Qian Liu', 'Ho-Yin Chan']2024-03-15T00:00:00https://www.semanticscholar.org/paper/abbe14baa4be3eea376e04fd132bfec9ebc77d79 19dd86e389db6c4e8f8d0e9b2e90dc14ed6341d5BioScience40BioScience['Francesco Rota']2024-03-15T00:00:00https://www.semanticscholar.org/paper/19dd86e389db6c4e8f8d0e9b2e90dc14ed6341d5 55b1b22b2c5e80d4caa9528b4707b375518651a0International journal of information technology330International Journal of Information Technology['L. M. El-Magd', 'Ghada Dahy', 'T. A. Farrag', 'Ashraf Darwish', 'Aboul Ella Hassnien']2024-03-15T00:00:00https://www.semanticscholar.org/paper/55b1b22b2c5e80d4caa9528b4707b375518651a0 8def7cee6d191a4e3bbe639873146e75b787aebbGenerative Artificial Intelligence (AI) has increasingly been used to enhance threat intelligence and cyber security measures for organizations. Generative AI is a form of AI that creates new data without relying on existing data or expert knowledge. This technology provides decision support systems with the ability to automatically and quickly identify threats posed by hackers or malicious actors by taking into account various sources and data points. In addition, generative AI can help identify vulnerabilities within an organization's infrastructure, further reducing the potential for a successful attack. This technology is especially well-suited for security operations centers (SOCs), which require rapid identification of threats and defense measures. By incorporating interesting and valuable data points that previously would have been missed, generative AI can provide organizations with an additional layer of defense against increasingly sophisticated attacks.International Conference on Database Theory180Generative Artificial Intelligence provides decision support systems with the ability to automatically and quickly identify threats posed by hackers or malicious actors by taking into account various sources and data points.2024 2nd International Conference on Disruptive Technologies (ICDT)['Venkata Ramana Saddi', 'Santhosh Kumar Gopal', 'Abdul Sajid Mohammed', 'S. Dhanasekaran', 'Mahaveer Singh Naruka']2024-03-15T00:00:00https://www.semanticscholar.org/paper/8def7cee6d191a4e3bbe639873146e75b787aebb a7a86ee7eaad655425133ee74e3630f2c03d46afOptimizing Performance Management through Data Analytics and Artificial Intelligence in the Manufacturing Sector. There have been a lot of new breakthroughs and opportunities made possible as a result of the increased usage of machine learning and artificial intelligence in business intelligence. Because of these cutting-edge technological advancements, businesses are now able to perform data analysis, gain new insights, and make decisions that are superior to those made in the past. It important to note that predictive analytics are becoming increasingly popular. Using algorithms designed for machine learning, businesses are able to sort through mountains of data in order to make predictions about the future that are based on accurate information. Because of this, organisations have the opportunity to increase their efficiency, decrease risk, and anticipate the needs of their customers. With the help of business intelligence, companies are able to optimise their operations, find new chances for growth, and make decisions that are driven entirely by data, all of which have a direct influence on the bottom line of the firm. Another trend that is picking up steam is the employment of chatbots and digital assistants that are driven by Radial Basis Function (RBF). The possibilities that machine learning and artificial intelligence present in terms of business intelligence are extremely extensive. Automated data analysis, anomaly detection, demand forecasting, and dynamic pricing are examples of the types of technologies that assist businesses in streamlining processes, lowering expenses, and locating untapped sources of revenue. To summarise, there have been some fascinating new breakthroughs, as well as countless opportunities, in the area of using AI and machine learning to business intelligence. Businesses have the potential to acquire a competitive advantage, produce innovation, and unlock new levels of success in the digital world if they adopt these technologies and use them.International Conference on Database Theory160Businesses have the potential to acquire a competitive advantage, produce innovation, and unlock new levels of success in the digital world if they adopt these technologies and use them.2024 2nd International Conference on Disruptive Technologies (ICDT)['B. Girimurugan', 'K. Parthiban', 'Mudit Saxena', 'Gowtham Talasila', 'N. S. Vamsi', 'P. T. Sai']2024-03-15T00:00:00https://www.semanticscholar.org/paper/a7a86ee7eaad655425133ee74e3630f2c03d46af 82611ec8ccf71ad97d830872deec7e5fba9be68eThere is general agreement that some form of regulation is necessary both for AI creators to be incentivised to develop trustworthy systems, and for users to actually trust those systems. But there is much debate about what form these regulations should take and how they should be implemented. Most work in this area has been qualitative, and has not been able to make formal predictions. Here, we propose that evolutionary game theory can be used to quantitatively model the dilemmas faced by users, AI creators, and regulators, and provide insights into the possible effects of different regulatory regimes. We show that creating trustworthy AI and user trust requires regulators to be incentivised to regulate effectively. We demonstrate the effectiveness of two mechanisms that can achieve this. The first is where governments can recognise and reward regulators that do a good job. In that case, if the AI system is not too risky for users then some level of trustworthy development and user trust evolves. We then consider an alternative solution, where users can condition their trust decision on the effectiveness of the regulators. This leads to effective regulation, and consequently the development of trustworthy AI and user trust, provided that the cost of implementing regulations is not too high. Our findings highlight the importance of considering the effect of different regulatory regimes from an evolutionary game theoretic perspective.arXiv.org520This work proposes that evolutionary game theory can be used to quantitatively model the dilemmas faced by users, AI creators, and regulators, and provide insights into the possible effects of different regulatory regimes.ArXiv['Zainab Alalawi', 'Paolo Bova', 'Theodor Cimpeanu', 'A. D. Stefano', 'M. H. Duong', 'Elias Fernandez Domingos', 'Han The Anh', 'Marcus Krellner', 'Bianca Ogbo', 'Simon T. Powers', 'Filippo Zimmaro']2024-03-14T00:00:00https://www.semanticscholar.org/paper/82611ec8ccf71ad97d830872deec7e5fba9be68e 2207041780abe32eafc50bfb1e631cce221d76e6The impact of Artificial Intelligence (AI) in the capital market (displaying for test purposes, risk the board, decision-making as it connects with investments, moral issues and the law). It features what a specific artificial intelligence can mean for an industry where financial backers' strategy updating as well as dropping a certified gamble exists. Planning artificial intelligence frameworks should stick to moral contemplations, including straightforwardness, decency, and legitimateness for the finance area. The utilization of artificial brainpower is upgrading rivalry among new fintech partnerships in the market that offers clients numerous choices, modifying the monetary area. Additionally, computer-based intelligence cultivates a moral training norm by advancing genuine business morals and clean natural worries in the financial area. Moreover, it will require a lot of moral regulation and the execution of legitimate rules. It leads to all-out simulated intelligence results, which are much bigger than a calculation with the model, requiring interdisciplinarities, great adaptationists, and readiness to deliver a suitable subsidizing space. In this way, it fills in as a translation of importance into a consistently impacting world that uncovers hindrances and makes a better possible hypothesis for artificial consciousness banking applications.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)1902024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['Sudhanshu Maurya', 'Rohan Verma', 'Laxmi Khilnani', 'A. Bhakuni', 'Manish Kumar', 'Nitin Rakesh']2024-03-14T00:00:00https://www.semanticscholar.org/paper/2207041780abe32eafc50bfb1e631cce221d76e6 7146d4ca844d0bc36f32c3d0b51a6a0b6fde896fUntil recently, intellectual creativity was considered as an exclusively human phenomenon and intellectual property legislation was built on the basis of motivating and enhancing human inventiveness. This self-evident assumption is being challenged due to the development of artificial intelligence technologies in the recent decades. In this article author analyzes some aspects of intellectual property law development, including the possibility of recognizing an artificial intelligence as a creator of intellectual activity results. The author examines the legal status of artificial intelligence under Armenian law and foreign intellectual property legislation, analyzes existing approaches to the legal regime and intellectual property ownership of objects created with the help of artificial intelligence. The paper aims to determine the proper right holder to content generated by artificial intelligence and formulate some policy prospects of artificial intelligence regulation. The methodological basis of the research includes general scientific and special legal methods. The author places particular emphasis on the dogmatic (doctrinal) research methods, which made it possible to analyze existing approaches to protection of intellectual property rights. The research is also based on the comparative legal method and analytical legal method of commenting current law of Armenia and foreign countries. The results of the study allow author to substantiate that the actual right holder to the content produced by the neural network is the programmer of the underlying algorithm system. The author concludes that the construction of a solid legislative system should be carried out taking into account the specifics of the areas of application of artificial intelligence, ensuring a balance between the interests of individuals, society and the state related to the development of innovative technology.Digital Law Journal10The author concludes that the construction of a solid legislative system should be carried out taking into account the specifics of the areas of application of artificial intelligence, ensuring a balance between the interests of individuals, society and the state related to the development of innovative technology.Digital Law Journal['A. Kirakosyan']2024-03-14T00:00:00https://www.semanticscholar.org/paper/7146d4ca844d0bc36f32c3d0b51a6a0b6fde896f b109a3cdf581b9750d6e1b5f71ba1c3077e24edfThe article deals with the issue of legal regulation of administrative procedures on the example of foreign countries. The standards of the administrative procedure regarding the adoption of administrative decisions, i.e. decisions of public administration bodies, which concern the rights and obligations of individuals and legal entities, are considered. The content and features of legal regulation of administrative procedures in foreign countries, the scope and subject of legal regulation through the prism of the legislation of foreign countries on administrative procedures are outlined. Various approaches to determining the scope, content and methods of its legal regulation are analyzed. Attention is focused on the specifics of managerial activity, which must take effective measures in a timely manner in a wide variety of situations, forming the boundaries and restrictions necessary in the rule of law. The types of entities to which administrative procedures apply have been considered. The rights and obligations of administrative bodies regarding the preparation and adoption of an administrative decision are defined. Features of appeals by individuals and legal entities, definition of sub-agency category of cases are outlined. The types of decisions made by the administrative body are classified. Emphasis is placed on informing persons whose interests may be affected by an administrative act. The grounds for removing officials considered biased are outlined. The rights of citizens participating in the administrative procedure are considered separately.Visegrad journal on human rights00Visegrad Journal on Human Rights['M. Garifullin']2024-03-14T00:00:00https://www.semanticscholar.org/paper/b109a3cdf581b9750d6e1b5f71ba1c3077e24edf 3b09f297eefcc1d3e8c4ec816d589a16ca4b10a0To improve automatic generation control (AGC) performance and reduce the wastage of regulation resources in interconnected grids including high-proportion renewable energy, a multi-area integrated AGC (MAI-AGC) framework is proposed to solve the coordination problem of secondary frequency regulation between different areas. In addition, a cocktail exploration multi-agent deep deterministic policy gradient (CE-MADDPG) algorithm is proposed as the framework algorithm. In this algorithm, the controller and power distributor of an area are combined into a single agent which can directly output the power generation command of different units. Moreover, the cocktail exploration strategy as well as various other techniques are introduced to improve the robustness of the framework. Through centralized training and decentralized execution, the proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple agents and is verified on the two-area LFC model of southwest China and the four-area LFC model of the China Southern Grid (CSG).Frontiers in Energy Research200A multi-area integrated AGC (MAI-AGC) framework is proposed to solve the coordination problem of secondary frequency regulation between different areas and a cocktail exploration multi-agent deep deterministic policy gradient algorithm is proposed as the framework algorithm.Frontiers in Energy Research['Ting Qian', 'Cheng Yang']2024-03-14T00:00:00https://www.semanticscholar.org/paper/3b09f297eefcc1d3e8c4ec816d589a16ca4b10a0 bbb6bff671c19f267803b204944bd57beb4dc06fThe article “Legal Regulation of Cryptocurrency and Cryptocurrency Operations in the European Union” offers a comprehensive examination of the evolving legal landscape surrounding cryptocurrencies within the European Union (EU). It begins by defining cryptocurrencies, highlighting their unique characteristics such as decentralization, volatility, and potential for misuse. These features pose significant regulatory challenges, as traditional legal frameworks may not be fully equipped to address them. The article delves into the EU’s response to these challenges. It outlines the various legislative measures that have been implemented to regulate cryptocurrencies and related operations. These measures aim to strike a balance between fostering innovation in the digital economy and ensuring consumer protection and financial stability. The article discusses the implications of these regulations, noting that while they have brought some clarity and security to the sector, they also risk stifling innovation if not carefully calibrated. The article explores the ongoing debates within the EU regarding the appropriate regulatory approach to cryptocurrencies. It underscores the need for a nuanced understanding of the technology and its potential impacts. The article argues that regulation should not merely react to the challenges posed by cryptocurrencies but should also anticipate future developments to remain effective and relevant. The article concludes by emphasizing the importance of dialogue and collaboration among regulators, industry stakeholders, and the public in shaping the regulatory approach to cryptocurrencies. It suggests that such engagement can help ensure that regulations are not only responsive to current issues but also adaptable to future changes. The article provides a thorough and insightful analysis of the legal regulation of cryptocurrency and cryptocurrency operations in the European Union. It underscores the complexity of the issue and the need for a dynamic and forward-looking regulatory approach. The article serves as a valuable resource for anyone interested in understanding the intricacies of cryptocurrency regulation in the EU.Visegrad journal on human rights100Visegrad Journal on Human Rights['Tetiana Zhelekhovska']2024-03-14T00:00:00https://www.semanticscholar.org/paper/bbb6bff671c19f267803b204944bd57beb4dc06f c66474ef69765b9018a14655f479ed35d7d72527Complex dynamic systems offer a rich platform for understanding the individual or the person‐specific mechanisms. Yet, in learning analytics research and education at large, a complex dynamic system has rarely been framed, developed, or used to understand the individual student where the learning process takes place. Individual (or person‐specific) methods can accurately and precisely model the individual person, create person‐specific models, and devise unique parameters for each individual. Our study used the latest advances in complex systems dynamics to study the differences between group‐based and individual self‐regulated learning (SRL) dynamics. The findings show that SRL is a complex, dynamic system where different sub‐processes influence each other resulting in the emergence of non‐trivial patterns that vary across individuals and time scales, and as such far from the uniform picture commonly theorized. We found that the average SRL process does not reflect the individual SRL processes of different people. Therefore, interventions derived from the group‐based SRL insights are unlikely to be effective in personalization. We posit that, if personalized interventions are needed, modelling the person with person‐specific methods should be the guiding principle. Our study offered a reliable solution to model the person‐specific self‐regulation processes which can serve as a ground for understanding and improving individual learning and open the door for precision education. What is already known about this topic Self‐regulation is a catalyst for effective learning and achievement. Our understanding of SRL personalization comes from insights based on aggregate group‐based data. What this paper adds Every student has their own unique SRL process that varies from the average in non‐trivial ways. We offer a credible method for mapping the individualized SRL process. SRL dynamics vary across time scales. That is, the trait dynamics are different from the state dynamics, and they should be supported differently. Implications for practice and/or policy Personalization can best be achieved if based on unique person‐specific idiographic methods. Supporting learning and SRL in particular can be more efficient when we understand the differences across time scales and persons and apply insights accordingly. The general SRL average should not be expected to work for everyone. British Journal of Educational Technology481British Journal of Educational Technology['Mohammed Saqr', 'Sonsoles López-Pernas']2024-03-14T00:00:00https://www.semanticscholar.org/paper/c66474ef69765b9018a14655f479ed35d7d72527 591ee336c2d43a13d8706fdb28c124ad8151c5aaJournal of Russian Law00Journal of Russian Law['Svetlana Paramonova']2024-03-14T00:00:00https://www.semanticscholar.org/paper/591ee336c2d43a13d8706fdb28c124ad8151c5aa 3485a513e9bf10e850ed7f1cbd7a58e52329db41Background: The financial services industry in South Africa has undergone many changes that have given birth to fintechs. Most of these changes are driven by the advent of technology and evolving customer expectations. Fintechs have led to process disruptions and business model transformations, yet their implications have yet to be sufficiently studied. Therefore, it is essential to close this knowledge gap by investigating the impact of fintechs on this industry.Objectives: This research aimed to investigate the impact of fintechs in the financial services industry in South Africa.Method: A qualitative study was conducted in which 18 industry experts were interviewed using semi-structured interviews. The interviews were audio-recorded and transcribed for data analysis. ATLAS.ti 22 was used to organise and analyse data.Results: Fintechs increase competition for the incumbents, reduce profits, expose the inability of the incumbents to be agile and introduce new regulatory risks in the financial services industry. In contrast, fintechs have also brought some positive changes into the industry: financial inclusion, new growth opportunities, increasing choices for consumers and making the industry more competitive, reducing costs, customising financial services, bringing convenience and forcing incumbents and regulators to become more innovative.Conclusion: This study uncovered the positive and negative effects of fintechs in financial services in South Africa.Contribution: The study will benefit academia by expanding the body of knowledge about fintech research and improving the holistic understanding of this field in emerging economies, which can inspire future research on fintech and its application.South African Journal of Information Management00South African Journal of Information Management['Simphiwe K. Cele', 'N. Mlitwa']2024-03-14T00:00:00https://www.semanticscholar.org/paper/3485a513e9bf10e850ed7f1cbd7a58e52329db41 c98f28a8dc01c4bfaa52817d9ca46f865a40abe9Journal of Russian Law00Journal of Russian Law['Rashad Kurbanov', 'Leonid Balanyuk']2024-03-14T00:00:00https://www.semanticscholar.org/paper/c98f28a8dc01c4bfaa52817d9ca46f865a40abe9 364d1dd67ba7b6a43c03ed4cfded5d27df1a50a8EDPACS: The EDP Audit, Control, and Security Newsletter40EDPACS['Sudhanshu Kumar']2024-03-14T00:00:00https://www.semanticscholar.org/paper/364d1dd67ba7b6a43c03ed4cfded5d27df1a50a8 5f2ee375b6fdaa441811dfeda4876c651a02b8ea This Viewpoint examines the potential problems of clinician reliance on the use of artificial intelligence (AI) in health care and offers suggestions on how AI could be designed to promote clinician vigilance. Journal of the American Medical Association (JAMA)53JAMA['J. Adler-Milstein', 'Donald A Redelmeier', 'Robert M. Wachter']2024-03-14T00:00:00https://www.semanticscholar.org/paper/5f2ee375b6fdaa441811dfeda4876c651a02b8ea 16bd62bd5f315a86de4cb7ef53da03d2c15c1eabPurpose: Using several smart gadgets makes the kitchen smart. The picture of the kitchen from ancient has drastically changed. Now, the kitchen is glorified using modern technology. The kitchen chimney, microwave oven, etc., is more convenient for the kitchen. Now, AI has come, and every day, more and more devices are becoming AI-enabled. In this scenario, we demonstrated the project in an AI-enabled kitchen. There are several advantages of AI-enabled kitchens over intelligent kitchens. AI will handle our most repetitive and monotonous work. Also, AI will take care and protect from any accident before it happens, which generally happens due to forgetfulness or carelessness. Design/Methodology/Approach: We installed a CPU inside the home. We installed an action controller in the kitchen that connects all kitchen gadgets. One PTZ camera is installed in such a place that captures the gas oven and the entire kitchen environment. Our camera is the point-to-zoom (PTZ) type, so it can rotate the lens and zoom to capture any incident for better understanding or to detect the perfect image for accurate detection. Our camera is running all the time, which means round the clock. It always captures the image and processes the image by CPU. If any event matches with the event database, it sends the command to the action controller to take the action. If a new event is detected, it will learn from it and save it into the database for future use. Findings/Result: The complete system is the conceptual-based research work. However, every part of the module or section is based on practical research work. So, once the system is deployed in the practical field, it will work without issues. In a typical kitchen, we must always pay attention, like the regulator is on if the milk is left for a long time in the gas oven. So, this kind of tension and anxiety will go for retired. All events will be taken care of by our AI-enabled system. Once the system is installed, it will run autonomously; there is no need to take care of or follow up. Once the system detects some issue, the system will notify the concerned person. Originality/Value/Novelty: Nowadays, intelligent gadgets are taking place in the home. Smart devices, especially in the kitchen, make life easy. Using AI, we can get more benefits and security for the kitchen. We studied several research works. Most of the researchers automated kitchen gadgets. But still, the problem exists. All gadgets should be under centralized control to get better safety and control. Through this research work, we described how to create safety and control in the kitchen environment. This research works to provide more value to the modern, smart kitchen. Type of Paper: Conceptual Research.International journal of applied engineering and management letters252Through this research work, how to create safety and control in the kitchen environment is described, which works to provide more value to the modern, smart kitchen.International Journal of Applied Engineering and Management Letters['Sudipto Chakraborty', 'P. S. Aithal']2024-03-14T00:00:00https://www.semanticscholar.org/paper/16bd62bd5f315a86de4cb7ef53da03d2c15c1eab 75a951c1e94cafcaf04df3b457b2dd60984b28d8In AI-assisted decision-making, it is crucial but challenging for humans to achieve appropriate reliance on AI. This paper approaches this problem from a human-centered perspective,"human self-confidence calibration". We begin by proposing an analytical framework to highlight the importance of calibrated human self-confidence. In our first study, we explore the relationship between human self-confidence appropriateness and reliance appropriateness. Then in our second study, We propose three calibration mechanisms and compare their effects on humans' self-confidence and user experience. Subsequently, our third study investigates the effects of self-confidence calibration on AI-assisted decision-making. Results show that calibrating human self-confidence enhances human-AI team performance and encourages more rational reliance on AI (in some aspects) compared to uncalibrated baselines. Finally, we discuss our main findings and provide implications for designing future AI-assisted decision-making interfaces.International Conference on Human Factors in Computing Systems941Results show that calibrating human self-confidence enhances human-AI team performance and encourages more rational reliance on AI (in some aspects) compared to uncalibrated baselines.{'pages': '840:1-840:20'}['Shuai Ma', 'Xinru Wang', 'Ying Lei', 'Chuhan Shi', 'Ming Yin', 'Xiaojuan Ma']2024-03-14T00:00:00https://www.semanticscholar.org/paper/75a951c1e94cafcaf04df3b457b2dd60984b28d8 66d7d369b00d4855455ce55dd7c9c1419da0c1d0We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on"what do you want robots to do for you?". The first is the definition of 1,000 everyday activities, grounded in 50 scenes (houses, gardens, restaurants, offices, etc.) with more than 9,000 objects annotated with rich physical and semantic properties. The second is OMNIGIBSON, a novel simulation environment that supports these activities via realistic physics simulation and rendering of rigid bodies, deformable bodies, and liquids. Our experiments indicate that the activities in BEHAVIOR-1K are long-horizon and dependent on complex manipulation skills, both of which remain a challenge for even state-of-the-art robot learning solutions. To calibrate the simulation-to-reality gap of BEHAVIOR-1K, we provide an initial study on transferring solutions learned with a mobile manipulator in a simulated apartment to its real-world counterpart. We hope that BEHAVIOR-1K's human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research. Project website: https://behavior.stanford.edu.arXiv.org844BEHAVIOR-1K's human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research, and it is hoped that its human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research.ArXiv['Chengshu Li', 'Ruohan Zhang', 'J. Wong', 'Cem Gokmen', 'S. Srivastava', 'Roberto Martín-Martín', 'Chen Wang', 'Gabrael Levine', 'Wensi Ai', 'B. Martinez', 'Hang Yin', 'Michael Lingelbach', 'Minjune Hwang', 'Ayano Hiranaka', 'Sujay S. Garlanka', 'Arman Aydin', 'Sharon Lee', 'Jiankai Sun', 'M. Anvari', 'Manasi Sharma', 'Dhruva Bansal', 'Samuel Hunter', 'Kyu-Young Kim', 'Alan Lou', 'Caleb R. Matthews', 'Ivan Villa-Renteria', 'Jerry Huayang Tang', 'Claire Tang', 'Fei Xia', 'Yunzhu Li', 'Silvio Savarese', 'H. Gweon', 'C. K. Liu', 'Jiajun Wu', 'Fei-Fei Li', 'Salesforce Research']2024-03-14T00:00:00https://www.semanticscholar.org/paper/66d7d369b00d4855455ce55dd7c9c1419da0c1d0 4a790193f30aebc95ee981aa3ff9993363a898d3In the realm of dermatology, the accurate diagnosis of skin cancer has long been a challenging endeavor. This paper introduces a cutting-edge solution for achieving dermatologist-grade skin cancer classification through the power of artificial intelligence (AI). Departing from traditional methods that necessitate laborious manual feature extraction and domain-specific preprocessing, our system adopts a deep neural network architecture, specifically Google Net Inception v3 CNN, fine-tuned using a vast and diverse clinical image dataset. Dataset comprises 135,550 images meticulously organized within a structured taxonomy encompassing 2,055 distinct disease categories. To unlock the full potential of fine-grained classification, An innovative algorithm is introduced to facilitate precise identification of various skin diseases. This research underscores the transformative potential of AI-powered dermatology in the realm of early skin cancer detection. By achieving dermatologist-level accuracy, this approach has the capacity to significantly impact public health outcomes, particularly in regions where skin cancer is a prevalent concern.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)180This research underscores the transformative potential of AI-powered dermatology in the realm of early skin cancer detection, by achieving dermatologist-level accuracy and has the capacity to significantly impact public health outcomes, particularly in regions where skin cancer is a prevalent concern.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['Priyanka Kaushik', 'Yash Chopra', 'Amar Kajla', 'Minakshi Poonia', 'Akram Khan', 'Dhruv Yadav']2024-03-14T00:00:00https://www.semanticscholar.org/paper/4a790193f30aebc95ee981aa3ff9993363a898d3 306cf1b848d565d2b7aad4acb010d859fb276d1cAI-powered tools and applications may provide customers with a positive, effective, and customized purchasing experience. By studying client preferences and behaviours, AI systems can anticipate future customer needs, improving and personalizing the shopping experience. The main aim of this study is to examine the role of artificial intelligence (AI) on enhancing customer experience. The results of this study revealed that there is a positive significant relationship between AI features like perceived convenience, personalization and AI-enabled service quality and Customer experience. A total of 416 responses were analysed using a structured questionnaire. The findings indicate significant role of trust as factor, mediating the effects of independent variables on customer experience. Data was analysed using T-test, ANOVA and regression.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)200There is a positive significant relationship between AI features like perceived convenience, personalization and AI-enabled service quality and Customer experience and significant role of trust as factor, mediating the effects of independent variables on customer experience.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['S. Sinha', 'Deepti Sinha', 'Tarun Dalmia']2024-03-14T00:00:00https://www.semanticscholar.org/paper/306cf1b848d565d2b7aad4acb010d859fb276d1c 0581f7a0d085bc327c1bedf395169a1bf6498067Speech is a fundamental means of expressing thoughts and emotions, but individuals with speech disorders face challenges that impact their self-esteem and overall quality of life. These disorders are categorized into speech impairments, which involve issues with speech sounds, fluency, and voice, and speech impediments, which result from physical difficulties in speech production, such as stuttering and articulation errors. A variety of causes, both medical and external, contribute to these disorders, affecting individuals of all ages. Traditionally, therapy for speech disorders involved in-person sessions, but technology has introduced Augmentative and Alternative Communication (AAC) solutions, including AI-powered mobile apps, offering accessible communication to a global audience. This research paper presents an overview of AAC tools, a systematic review of AI in AAC, an exploration of existing AAC applications, and a literature review on technology's impact. The paper highlights the transformative impact of AAC on individuals with speech disabilities and emphasizes the potential of AI-based solutions to enhance communication. It calls for the integration of speech recognition, reconstruction, and contextualization in AAC applications, promising improved support for those with speech disorders.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)170An overview of AAC tools, a systematic review of AI in AAC, an exploration of existing AAC applications, an exploration of existing AAC applications, and a literature review on technology's impact are presented.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['S. B. Evangeline', 'Anitha Dhakshina Moorthy']2024-03-14T00:00:00https://www.semanticscholar.org/paper/0581f7a0d085bc327c1bedf395169a1bf6498067 bb5393126610ab89983b29d8934b45f67a16241dAI assistants are becoming an integral part of society, used for asking advice or help in personal and confidential issues. In this paper, we unveil a novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel. We found that many vendors, including OpenAI and Microsoft, have this side-channel. However, inferring the content of a response from a token-length sequence alone proves challenging. This is because tokens are akin to words, and responses can be several sentences long leading to millions of grammatically correct sentences. In this paper, we show how this can be overcome by (1) utilizing the power of a large language model (LLM) to translate these sequences, (2) providing the LLM with inter-sentence context to narrow the search space and (3) performing a known-plaintext attack by fine-tuning the model on the target model's writing style. Using these methods, we were able to accurately reconstruct 29\% of an AI assistant's responses and successfully infer the topic from 55\% of them. To demonstrate the threat, we performed the attack on OpenAI's ChatGPT-4 and Microsoft's Copilot on both browser and API traffic.arXiv.org420A novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel that was able to accurately reconstruct 29% of an AI assistant's responses and successfully infer the topic from 55% of them is unveiled.ArXiv['Roy Weiss', 'Daniel Ayzenshteyn', 'Guy Amit', 'Yisroel Mirsky']2024-03-14T00:00:00https://www.semanticscholar.org/paper/bb5393126610ab89983b29d8934b45f67a16241d 4ce8d8455edeca478788a99540c57a50bdd28f8cThe rapid advancements of image generation models, especially prompt-based image generation models, have started a new chapter in how we understand visual information, blurring the lines between what is real and what is synthetic. Moreover, the propagation of AI-generated images across industries, coupled with the easier accessibility of generative models, raises concerns about misinformation and ethical implications. This study delves into the critical need for a robust image classification model to differentiate between real and AI-generated images. Using the CIFAKE dataset, a comprehensive collection of AI-generated and real images and employing transfer learning with various convolutional neural network (CNN) architectures, this study aims to further advance AI-generated image detection to new heights. Training of models was conducted in two phases; one involved using pre-trained weights and freezing the base model layers, while the other involved fine-tuning the base model. The most optimal model with EfficientNet as the base model achieved a validation accuracy of 97.29%. In a world where the authenticity of visual content is increasingly vital, this study holds promise for applications spanning content moderation, cybersecurity, and digital forensics.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)260This study delves into the critical need for a robust image classification model to differentiate between real and AI-generated images and concludes that the most optimal model with EfficientNet as the base model achieved a validation accuracy of 97.29%.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['Achal Shankar Gupta', 'Krishan Pratap Shreneter', 'Smriti Sehgal']2024-03-14T00:00:00https://www.semanticscholar.org/paper/4ce8d8455edeca478788a99540c57a50bdd28f8c 011ddfaa613afa38dc27627a37273708ce6a253dOngoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and image synthesis. Our analysis shows that, in addition to the established types of ambiguity, artists worked closely with the ML process (dataset curation, model training, and application) and developed various techniques to evoke the ambiguity of processes. Our finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details. Finally, this paper offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability, and advocates to supplement the artifact-centered design perspective of ML with a process-centered one.International Conference on Human Factors in Computing Systems930The finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details, and offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability.ArXiv['Christian Sivertsen', 'Guido Salimbeni', 'A. Løvlie', 'Steve Benford', 'Jichen Zhu']2024-03-14T00:00:00https://www.semanticscholar.org/paper/011ddfaa613afa38dc27627a37273708ce6a253d ea4d965662a12537d43c991b13045328a7757220The objective of this project is to create a simulation for air cargo inspection using artificial intelligence (AI). This will be achieved by combining Pygame for simulating conveyor belts and YOLOv8 for detecting hazardous items. The simulation replicates a conveyor belt system that transports air cargo for inspection. Pygame, a robust game development package, is used to build a graphically interactive environment where users may view and examine the cargo inspection process in a simulated context. The primary element of the project entails using YOLOv8, an advanced object detection model, to precisely locate dangerous things within the shipment. The real-time detection capabilities of YOLOv8 allow for quick and efficient examination of the cargo, ensuring the timely identification of any hazards. The simulation serves as a platform to assess and verify the effectiveness of the AI-driven cargo inspection system across different scenarios. Users have the ability to engage with the simulation by modifying factors like the speed of the conveyor belt, the types of cargo, and the criteria for inspection. This allows them to assess the effectiveness of the system in identifying dangerous objects. This project functions as an instructional tool to comprehend the incorporation of AI in cargo inspection, while also having practical implications for improving real-world air cargo security. Pygame and YOLOv8, when combined, offer a flexible and robust framework for simulating and evaluating AI-based inspection systems. This contributes to the progress of air cargo safety and security measures.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)160This project functions as an instructional tool to comprehend the incorporation of AI in cargo inspection, while also having practical implications for improving real-world air cargo security.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['B. Sundaram', 'B. Rajalakshmi', 'Anushka Saxena', 'Bhaskaruni Vasumati', 'Akshatha. P']2024-03-14T00:00:00https://www.semanticscholar.org/paper/ea4d965662a12537d43c991b13045328a7757220 92ec76a6bc38dd88768470e3b6c6ed09d69b16a7The evolving educational landscape necessitates creative solutions to address the demand for immediate and personalized academic support. This study explores the integration of prompt engineering of the OpenAI’s Generative Pre-trained Transformer (GPT) and Deep Knowledge Tracing (DKT) to develop an AI tutor capable of shaping responses to students’ knowledge levels, promoting a dynamic and adaptive learning experience. By leveraging Large Language Models (LLMs) like GPT-3.5 and integrating DKT, our AI tutor addresses the need for real-time, tailored academic assistance. LLMs serve as virtual instructors, explaining concepts and providing detailed solutions, while DKT ensures responses align with the student’s knowledge level, optimizing challenge and engagement. Our research introduces an AI tutor that revolutionizes personalized learning experiences. Students can interact with the AI tutor by shaking their device during quizzes, initiating customized assistance and encouraging a deeper understanding of concepts, ultimately enhancing academic performance through individualized learning experiences.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)100This study explores the integration of prompt engineering of the OpenAI’s Generative Pre-trained Transformer and Deep Knowledge Tracing to develop an AI tutor capable of shaping responses to students’ knowledge levels, promoting a dynamic and adaptive learning experience.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['Radhika Makharia', 'Yeoun Chan Kim', 'Su Bin Jo', 'Min Ah Kim', 'Aagam Jain', 'P. Agarwal', 'Anish Srivastava', 'Anant Vikram Agarwal', 'Pankaj Agarwal']2024-03-14T00:00:00https://www.semanticscholar.org/paper/92ec76a6bc38dd88768470e3b6c6ed09d69b16a7 60ec9be6a7d177555f57c5f66fe78336c3e9bca6A growing variety of cyber hazards are appearing as the world becomes more and more digital. The sophistication and rate of innovation of emerging threats puts organizations of all sizes at danger, and traditional cybersecurity solutions frequently fall short of keeping up. Threat intelligence powered by artificial intelligence, or AI-TI, offers a fresh and cutting-edge method for tackling cybersecurity issues. To find patterns and anomalies that could point to a cyber-attack, AI-TI solutions can examine massive amounts of data from a variety of sources, including as internal networks, security logs, and open threat intelligence feeds. Then, with the use of this information, security teams can be made aware of prospective dangers and assist in the creation of defense measures. Compared to conventional cybersecurity solutions, AI-TI systems have a number of benefits: Automation: AI-TI systems have the potential to automate a number of threat detection and response-related duties, freeing up security staff to concentrate on more strategic projects. Continuous improvement: AI-TI systems have the ability to continuously learn and get better with time, which enables them to stay on top of the most recent threats. Advanced threat detection: AI-TI systems can be used to identify and react to threats that are too sophisticated or subtle for traditional approaches to identify.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)250To find patterns and anomalies that could point to a cyber-attack, AI-TI solutions can examine massive amounts of data from a variety of sources, including as internal networks, security logs, and open threat intelligence feeds, which enables them to stay on top of the most recent threats.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['Anurag Singh', 'Kanishka', 'Sanjay Kumar Dubey']2024-03-14T00:00:00https://www.semanticscholar.org/paper/60ec9be6a7d177555f57c5f66fe78336c3e9bca6 693e0216129fce811fdbfca949538c5ee590ea2eLiterary Writing focuses on writing literary works, such as poetry, drama, short stories and other types of literary writing. The practice of writing Literary Writing provides an experience that is not easy for English language learners. This research is a case study with the research subjects being English teachers and learners. Research data was taken from the results of the questionnaire. This research aims to identify the use of Artificial Intelligence (AI) for learning Literary Writing as seen from the perceptions of English language teachers and learners. The research results show that Artificial Intelligence (AI) helps English teachers to prepare learning plans and materials, create an interactive and  attractive learning atmosphere, and evaluate Literary Writing learning outcomes. Furthermore, research shows that the use of Artificial Intelligence (AI) for English learners is useful for developing ideas and improving writing skills, correcting English grammatical errors, and enriching vocabulary. Apart from that, there is a negative side to using Artificial Intelligence (AI), namely dependency which limits creativity and critical thinking skills for English language learners. Thus, English teachers must monitor the use of Artificial Intelligence (AI) when used in classroom learning activities.Transformatika: Jurnal Bahasa, Sastra, dan Pengajarannya00The research results show that Artificial Intelligence (AI) helps English teachers to prepare learning plans and materials, create an interactive and  attractive learning atmosphere, and evaluate Literary Writing learning outcomes.Transformatika: Jurnal Bahasa, Sastra, dan Pengajarannya['Ririn Pratiwi Suharto', 'A. Maulana']2024-03-14T00:00:00https://www.semanticscholar.org/paper/693e0216129fce811fdbfca949538c5ee590ea2e d414d8bcdd5c19c2ec76563659e118895c0b2f1dThis research examines the antecedents of behavioral intention to use smart transportation applications(STA) in emerging markets like India. The proposed research model was developed based on the theoretical foundation of the TAM and UTAUT (Unified Theory of Acceptance and Use Technology) models. In this research, we evaluate the intention to use Radarbot, an AI-powered STA that notifies riders of the upcoming AI cameras/traffic checkpoints. Unlike the traditional UTAUT model, it is found that perceived value mediated the relationship between the UTAUT variables and behavioral intention.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)330This research examines the antecedents of behavioral intention to use smart transportation applications(STA) in emerging markets like India and finds that perceived value mediated the relationship between the UTAUT variables and behavioral intention.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['Govind Lal H', 'Midhun Mohan', 'Ashish A Suryawanshi', 'Syamu Pachanattu', 'Avinash Shivdas', 'Hari Krishnan R', 'C. Nair']2024-03-14T00:00:00https://www.semanticscholar.org/paper/d414d8bcdd5c19c2ec76563659e118895c0b2f1d abefc77183480db310672951ea99052b9e7e02dfCrowdsourcing platforms have transformed distributed problem-solving, yet quality control remains a persistent challenge. Traditional quality control measures, such as prescreening workers and refining instructions, often focus solely on optimizing economic output. This paper explores just-in-time AI interventions to enhance both labeling quality and domain-specific knowledge among crowdworkers. We introduce LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge. Our technical evaluation shows that our LabelAId pipeline consistently outperforms state-of-the-art ML baselines, improving mistake inference accuracy by 36.7% with 50 downstream samples. We then implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility. A between-subjects study with 34 participants demonstrates that LabelAId significantly enhances label precision without compromising efficiency while also increasing labeler confidence. We discuss LabelAId's success factors, limitations, and its generalizability to other crowdsourced science domains.International Conference on Human Factors in Computing Systems1080This paper introduces LabelAId, an advanced inference model combining Programmatic Weak Supervision (PWS) with FT-Transformers to infer label correctness based on user behavior and domain knowledge and implemented LabelAId into Project Sidewalk, an open-source crowdsourcing platform for urban accessibility.ArXiv['Chu Li', 'Zhihan Zhang', 'Michael Saugstad', 'Esteban Safranchik', 'Minchu Kulkarni', 'Xiaoyu Huang', 'Shwetak N. Patel', 'Vikram Iyer', 'Tim Althoff', 'Jon E. Froehlich', 'MinchuKulka-rni', 'LabelAId']2024-03-14T00:00:00https://www.semanticscholar.org/paper/abefc77183480db310672951ea99052b9e7e02df 0d549bf2dc668a10e1a2b9b3de54522fa853ff26Artificial intelligence (AI) plays an increasingly important role in the health sector, including in the decision-making process. Human-AI interaction can help doctors diagnose diseases, provide treatment recommendations, and improve the quality of patient care. This research uses experimental studies to investigate the influence of human-AI interactions in the decision-making process in the health sector. Two groups of doctors were included: a group that used AI to aid decision making and a control group that did not use AI. The research results showed that the group of doctors who used AI had better performance in terms of diagnostic accuracy, time efficiency and patient satisfaction. Human-AI interaction can help doctors make better decisions and improve the quality of patient care.Arkus170The research results showed that the group of doctors who used AI had better performance in terms of diagnostic accuracy, time efficiency and patient satisfaction.Arkus['Muhammad Armansyah']2024-03-14T00:00:00https://www.semanticscholar.org/paper/0d549bf2dc668a10e1a2b9b3de54522fa853ff26 ef5e3586a6219895d06e1fb7704389eb0ec8e806$\textbf{Background}$: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. $\textbf{Methods}$: We use a"Masked Siamese Network"(MSN) to identify novel phenomena in unseen data and predict polyp detector performance. MSN is trained to predict masked out regions of polyp images, without any labels. We test MSN's ability to be trained on data only from Israel and detect unseen techniques, narrow-band imaging (NBI) and chromendoscoy (CE), on colonoscopes from Japan (354 videos, 128 hours). We also test MSN's ability to predict performance of Computer Aided Detection (CADe) of polyps on colonoscopies from both countries, even though MSN is not trained on data from Japan. $\textbf{Results}$: MSN correctly identifies NBI and CE as less similar to Israel whitelight than Japan whitelight (bootstrapped z-test, |z|>496, p<10^-8 for both) using the label-free Frechet distance. MSN detects NBI with 99% accuracy, predicts CE better than our heuristic (90% vs 79% accuracy) despite being trained only on whitelight, and is the only method that is robust to noisy labels. MSN predicts CADe polyp detector performance on in-domain Israel and out-of-domain Japan colonoscopies (r=0.79, 0.37 respectively). With few examples of Japan detector performance to train on, MSN prediction of Japan performance improves (r=0.56). $\textbf{Conclusion}$: Our technique can identify distribution shifts in clinical data and can predict CADe detector performance on unseen data, without labels. Our self-supervised approach can aid in detecting when data in practice is different from training, such as between hospitals or data has meaningfully shifted from training. MSN has potential for application to medical image domains beyond colonoscopy.arXiv.org490This self-supervised approach can aid in detecting when data in practice is different from training, such as between hospitals or data has meaningfully shifted from training, and can predict CADe detector performance on unseen data, without labels.ArXiv['Joel Shor', 'Carson McNeil', 'Yotam Intrator', 'Joe Ledsam', 'H. Yamano', 'Daisuke Tsurumaru', 'Hiroki Kayama', 'Atsushi Hamabe', 'K. Ando', 'Mitsuhiko Ota', 'Haruei Ogino', 'Hiroshi Nakase', 'Kaho Kobayashi', 'Masaaki Miyo', 'Eiji Oki', 'Ichiro Takemasa', 'E. Rivlin', 'Roman Goldenberg']2024-03-14T00:00:00https://www.semanticscholar.org/paper/ef5e3586a6219895d06e1fb7704389eb0ec8e806 4f3c27e7b88a9667182a19a7eb839950668fa142Artificial Intelligence (AI) is a widespread technology that points towards the capability of a computer program or a machine to mimic human cognition. The deployment of this technology depends on the nature of the business. This research finding aims to investigate the distinct means of AI in achieving business process optimization and digital marketing. Using this technology marketers can be customer-centric and agile in catering to their demands. This research further delves into the various AI mechanisms that can precisely determine what content should be tailored to which customer via its efficient means of data collection, enhancing user experiences, thus resulting in better sales. This paper explains the various aspects of AI and the effective ways of applying it to achieve business process optimization and content digital marketing strategies for increased return on investment (ROI).2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)500The various aspects of AI and the effective ways of applying it to achieve business process optimization and content digital marketing strategies for increased return on investment (ROI) are explained.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['Aysha Abdulla', 'Omer Bin Hussain']2024-03-14T00:00:00https://www.semanticscholar.org/paper/4f3c27e7b88a9667182a19a7eb839950668fa142 f884aacb150932b091704611a15673480da90bf5The integration of Artificial Intelligence in the field of mental health represents a significant paradigm shift, particularly within the context of higher education healthcare services. This essay encapsulates the transformative potential of AI, with a particular emphasis on Deep Learning, in revolutionizing mental health diagnostics and treatment. It underscores the precision and personalization that AI introduces to the treatment of prevalent mental health disorders such as depression and anxiety. This advancement is not just a technological leap but also a confluence of insights from cognitive science, which in turn enriches AI's effectiveness and contributes novel dimensions to cognitive research methodologies. The core of this research hinges on an extensive literature review, aiming to dissect the multifaceted implications of AI in mental health. This involves an exploration of ethical considerations, privacy concerns, and cultural impacts. The research posits critical questions regarding the stewardship of sensitive health data and the moral dilemmas inherent in AI applications in mental health contexts.Lecture Notes in Education Psychology and Public Media00This essay encapsulates the transformative potential of AI, with a particular emphasis on Deep Learning, in revolutionizing mental health diagnostics and treatment, and underscores the precision and personalization that AI introduces to the treatment of prevalent mental health disorders such as depression and anxiety.Lecture Notes in Education Psychology and Public Media['Qitao He']2024-03-14T00:00:00https://www.semanticscholar.org/paper/f884aacb150932b091704611a15673480da90bf5 71fbe82484157d9ec42040f93b3d193eac8ddbf3AI and Ethics350This work categorises AI hype in healthcare into three types that include both utopian and dystopian narratives and plots a series of more productive paths ahead by which to realise the potential of AI to improve human healthcare.AI and Ethics['Michael Strange']2024-03-14T00:00:00https://www.semanticscholar.org/paper/71fbe82484157d9ec42040f93b3d193eac8ddbf3 69b25ab3d85f9fcdafedd84622e3493b3397c46dIdentifying scientific publications that are within a dynamic field of research often requires costly annotation by subject-matter experts. Resources like widely-accepted classification criteria or field taxonomies are unavailable for a domain like artificial intelligence (AI), which spans emerging topics and technologies. We address these challenges by inferring a functional definition of AI research from existing expert labels, and then evaluating state-of-the-art chatbot models on the task of expert data annotation. Using the arXiv publication database as ground-truth, we experiment with prompt engineering for GPT chatbot models to identify an alternative, automated expert annotation pipeline that assigns AI labels with 94% accuracy. For comparison, we fine-tune SPECTER, a transformer language model pre-trained on scientific publications, that achieves 96% accuracy (only 2% higher than GPT) on classifying AI publications. Our results indicate that with effective prompt engineering, chatbots can be used as reliable data annotators even where subject-area expertise is required. To evaluate the utility of chatbot-annotated datasets on downstream classification tasks, we train a new classifier on GPT-labeled data and compare its performance to the arXiv-trained model. The classifier trained on GPT-labeled data outperforms the arXiv-trained model by nine percentage points, achieving 82% accuracy.arXiv.org350The results indicate that with effective prompt engineering, chatbots can be used as reliable data annotators even where subject-area expertise is required and the utility of chatbot-annotated datasets on downstream classification tasks is evaluated.ArXiv['Autumn Toney-Wails', 'C. Schoeberl', 'James Dunham']2024-03-14T00:00:00https://www.semanticscholar.org/paper/69b25ab3d85f9fcdafedd84622e3493b3397c46d ea277cb54a3fb456c60ce7f047a5ac2606c1abdbThe importance of artificial intelligence (AI) is growing in all economic sectors and thus also in higher education. In recent years, there have been significant developments in this concept of "Artificial Intelligence in Education (AIED)". The purpose of this study was to find out how the concept of artificial intelligence can be applied to teaching and learning in higher education and the implications of the use of artificial intelligence in higher education. The impact of the development of technologies on learning is often studied on the methods and scope of learning and teaching. Artificial intelligence enables higher education services to become easily accessible with extraordinary speed, not only in the classroom but also outside the classroom. This report seeks to explore how AI will become an integral part of universities and seeks to examine its immediate and future impact on various aspects of higher education. The challenges of implementing AI in these institutes were also explored. As artificial intelligence (AI) research in education increases, many researchers in the field believe that the role of teachers, schools and leaders in education will change. In this regard, the aim of this study is to investigate which are the possible scenarios for the arrival of artificial intelligence in education and what impact it can have on the future of schools. In this research, it confirmed that artificial intelligence has been widely adopted and used in various forms in education, especially educational institutions. Artificial intelligence was initially implemented in the form of computers and computer-related technologies, moving to web-based and web-based intelligent educational systems, and finally with the use of embedded computing systems and other technologies such as humanoid robots and web-based chatbots teachers & tasks and assignments independently or with tutors. With these platforms, teachers could perform various administrative tasks such as grading and Work more effectively and efficiently and achieve higher quality in your learning activities. On the other hand, because the systems use machine learning and adaptability, the curriculum and content are adapted which improved uptake and retention, which improved the student experience and the overall quality of education.International Research Journal on Advanced Engineering and Management (IRJAEM)117How AI will become an integral part of universities is explored and its immediate and future impact on various aspects of higher education is examined.International Research Journal on Advanced Engineering and Management (IRJAEM)['Iffath Unnisa Begum']2024-03-14T00:00:00https://www.semanticscholar.org/paper/ea277cb54a3fb456c60ce7f047a5ac2606c1abdb 7f55233cf664b8ab31e6b3d844d0656f19d7f080This paper explores the symbiotic relationship between Robotic Process Automation (RPA) and Artificial Intelligence (AI) and their collective impact on streamlining and optimizing various business processes. We delve into the integration of AI technologies, such as machine learning and natural language processing, with Robotic Process Automation to create intelligent automation solutions. Furthermore, it examines how the infusion of Artificial Intelligence into RPA empowers systems to adapt and learn, fostering adaptability and agility within organizations. The abstract concludes by highlighting the potential benefits, challenges, and future prospects of this amalgamation, emphasizing the transformative impact on businesses in the era of intelligent automation.International Journal of Innovative Science and Research Technology12This paper explores the symbiotic relationship between Robotic Process Automation (RPA) and Artificial Intelligence (AI) and their collective impact on streamlining and optimizing various business processes, emphasizing the transformative impact on businesses in the era of intelligent automation.International Journal of Innovative Science and Research Technology (IJISRT)['R. Anitha', 'S. Abinaya', 'S. Laavanya', 'M. Gayanthika']2024-03-14T00:00:00https://www.semanticscholar.org/paper/7f55233cf664b8ab31e6b3d844d0656f19d7f080 57a663c7edcfc464682aa8262e6f40b32b8812ddInfectious diseases pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak management. This study explores integrating artificial intelligence (AI) for early detection and management. AI algorithms analyze diverse datasets, including electronic health records and social media, to identify potential outbreaks. Machine learning models predict disease spread and severity, aiding proactive resource allocation. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses AI's role in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study also evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. Ethical considerations are crucial, emphasizing collaboration between public health agencies, healthcare providers, and technology experts. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. This paper advocates for AI integration to enhance infectious disease surveillance, offering a proactive response to safeguard public health.International Journal of Innovative Research & Development01The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures, and advocates for AI integration to enhance infectious disease surveillance.International Journal of Innovative Research and Development['Amarachukwu Bernaldine Isiaka', 'Vivian Nonyelum Anakwenze', 'Chiamaka Rosemary Ilodinso', 'Chikodili Gladys Anaukwu', 'Chukwuebuka Mary-Vin Ezeokoli', 'Samuel Mensah Noi', 'Gazali Oluwasegun Agboola', 'Richard Mensah Adonu']2024-03-14T00:00:00https://www.semanticscholar.org/paper/57a663c7edcfc464682aa8262e6f40b32b8812dd 9c03295469a8a97281e808c9fcbdd7b5d46a1d91Artificial intelligence (AI) has emerged as a transformative force, reshaping the landscape of many sectors at an unprecedented pace. This shift features the integration of intelligent machines, capable of learning, reasoning, and problem-solving, into diverse domains. The concept of AI encompasses a range of technologies, including machine learning, natural language processing, and computer vision, collectively contributing to its increasing prevalence. The integration of artificial intelligence into education heralds a transformative era, redefining traditional teaching paradigms and learning experiences. AI's potential impact on education is multifaceted, encompassing personalized learning, intelligent tutoring systems, and data-driven insights. As AI technologies evolve, educators and learners alike are poised to benefit from adaptive and responsive educational environments. The intersection of AI and education holds the promise of fostering a dynamic, technology-infused learning landscape for the generations to come.International Journal of Innovative Research & Development01The integration of artificial intelligence into education heralds a transformative era, redefining traditional teaching paradigms and learning experiences and holding the promise of fostering a dynamic, technology-infused learning landscape for the generations to come.International Journal of Innovative Research and Development['James Young']2024-03-14T00:00:00https://www.semanticscholar.org/paper/9c03295469a8a97281e808c9fcbdd7b5d46a1d91 ba21d4c87ea337be1db4edb770dcc7e1ca6a5411The addition of biofeedback and artificial intelligence (AI) in simulation training and serious games has shown promising results in improving the effectiveness of training and can lead to increased engagement, motivation, and retention of information. This systematic literature review explores the integration of biofeedback and artificial intelligence into eXtended reality (XR) training scenarios and is the first review to provide a consolidated overview of applied biofeedback and AI technologies in this area. This review was conducted using keywords related to biofeedback, AI, XR, and training and included papers that: contained the use of biofeedback and AI in XR training scenarios; reported on at least one outcome related to training effectiveness; were published in English; were peer-reviewed; date from 1 January 2016 – 7 February 2022. The results indicate that many studies collect two or more biosignals using a single biosensing device. This is particularly relevant in applied settings, where ease of use and minimal interference in training/education activities is desired. Also, that light, portable devices such as wrist bands, wireless straps, or headbands are preferred. Additionally, eye tracking, electrodermal activity (EDA), and photoplethysmograms (PPG) present as particularly useful biomarkers of stress and/or cognitive load in XR training contexts. A wide variety of machine learning (ML) approaches were used to support biofeedback systems in XR environments. However, a limited number of studies employed real-time analysis of biosignals (just 1% of studies) which indicates current challenges in implementing such systems. The majority of papers meeting the selection criteria were from the fields of education and healthcare. Further research in other domains, such as defense and general industry, is needed to gain a comprehensive understanding of the potential for biofeedback and AI integration in XR training scenarios used in these domains.Simulation &amp; Gaming330The results indicate that many studies collect two or more biosignals using a single biosensing device, and that light, portable devices such as wrist bands, wireless straps, or headbands are preferred in XR training contexts.Simulation &amp; Gaming['Karen L. Blackmore', 'Shamus P. Smith', 'Jacqueline D. Bailey', 'Benjamin Krynski']2024-03-14T00:00:00https://www.semanticscholar.org/paper/ba21d4c87ea337be1db4edb770dcc7e1ca6a5411 a1b76a51085a5970a876466744b0f5cadd3b2f48BACKGROUND Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy. METHODS A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal. 161 trainee orthoptists took the course. They were evaluated with two tests: one consisting of UWF images and another of standard field (SF) images, which the students had not encountered in the course. Both tests contained 120 real patient images, 20 per category. The students took both tests once before and after training, with a cool-off period in between. RESULTS On average, students completed the course in 53 min, significantly improving their diagnostic accuracy. For UWF images, student accuracy increased from 43.6% to 74.1% (p<0.0001 by paired t-test), nearly matching the previously published state-of-the-art AI model's accuracy of 73.3%. For SF images, student accuracy rose from 42.7% to 68.7% (p<0.0001), surpassing the state-of-the-art AI model's 40%. CONCLUSION Synthetic images can be used effectively in medical education. We also found that humans are more robust to novel situations than AI models, thus showcasing human judgement's essential role in medical diagnosis.British Journal of Ophthalmology190Synthetic images can be used effectively in medical education and it is found that humans are more robust to novel situations than AI models, thus showcasing human judgement's essential role in medical diagnosis.The British journal of ophthalmology['Hitoshi Tabuchi', 'Justin Engelmann', 'Fumiatsu Maeda', 'Ryo Nishikawa', 'Toshihiko Nagasawa', 'T. Yamauchi', 'Mao Tanabe', 'Masahiro Akada', 'Keita Kihara', 'Yasuyuki Nakae', 'Yoshiaki Kiuchi', 'Miguel O. Bernabeu']2024-03-14T00:00:00https://www.semanticscholar.org/paper/a1b76a51085a5970a876466744b0f5cadd3b2f48 4c9bbee5313e2c70cfed2a9465c0438001fc13afArtificial intelligence (AI) has emerged as a transformative technology with applications across multiple domains. The corpus of work related to the field of AI has grown significantly in volume as well as in terms of the application of AI in wider domains. However, given the wide application of AI in diverse areas, the measurement and characterization of the span of AI research is often a challenging task. Bibliometrics is a well-established method in the scientific community to measure the patterns and impact of research. It however has also received significant criticism for its overemphasis on the macroscopic picture and the inability to provide a deep understanding of growth and thematic structure of knowledge-creation activities. Therefore, this study presents a framework comprising of two techniques, namely, Bradford’s distribution and path analysis to characterize the growth and thematic evolution of the discipline. While the Bradford distribution provides a macroscopic view of artificial intelligence research in terms of patterns of growth, the path analysis method presents a microscopic analysis of the thematic evolutionary trajectories, thereby completing the analytical framework. Detailed insights into the evolution of each subdomain are drawn, major techniques employed in various AI applications are identified, and some relevant implications are discussed to demonstrate the usefulness of the analyses.International Journal of Intelligent Systems860While the Bradford distribution provides a macroscopic view of artificial intelligence research in terms of patterns of growth, the path analysis method presents a microscopic analysis of the thematic evolutionary trajectories, thereby completing the analytical framework.International Journal of Intelligent Systems['Solanki Gupta', 'Anurag Kanaujia', 'H. Lathabai', 'V. K. Singh', 'Philipp Mayr']2024-03-14T00:00:00https://www.semanticscholar.org/paper/4c9bbee5313e2c70cfed2a9465c0438001fc13af 9679559aa9ed7c017dcf33f6e07021a83c83b1ceIn this paper, we first frame the use of artificial intelligence (AI) systems in the public sector as a continuation and intensification of long-standing rationalization and bureaucratization processes. Drawing on Weber, we understand the core of these processes to be the replacement of traditions with instrumental rationality, that is, the most calculable and efficient way of achieving any given policy objective. Second, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well-known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end: reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, our analysis also highlights that AI-driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens’ sense of their (non-instrumental) obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Third, we observe that contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic: science can only provide the means – it cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI-driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny, deliberation, and debate.Social science computer review400This paper frames the use of artificial intelligence (AI) systems in the public sector as a continuation and intensification of long-standing rationalization and bureaucratization processes, and observes that contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization.Social Science Computer Review['Jakob Mökander', 'Ralph Schroeder']2024-03-14T00:00:00https://www.semanticscholar.org/paper/9679559aa9ed7c017dcf33f6e07021a83c83b1ce 4ac7ce860fe429dd4d8e525a5c62571bfab91cf1This research aims to examine the impact of artificial intelligence (AI) on corporate governance practices in non-financial enterprises in Qatar. It explores how AI can either strengthen or weaken these practices and provides suggestions for integrating AI into corporate governance. The study utilizes case studies, actual data, and existing literature, along with a non-interview-based methodology, to analyze the connection between corporate governance and AI. Through synthesis and comparison, this research offers a comprehensive examination of the subject. It acknowledges the unique challenges and opportunities faced by non-financial organizations in Qatar when implementing AI for corporate governance. The insights provided in this research are relevant not only to similar businesses in other locations but also highlight the importance of incorporating AI into corporate governance procedures. The findings emphasize the need for businesses to invest in AI technology and for legislators to establish supportive laws for AI's application in corporate governance. This study stands out from previous research by focusing on the relationship between AI and corporate governance and highlighting the role of AI in enhancing business performance. The conclusions drawn from this study are valuable for regulators, legislators, and businesses seeking to leverage AI for improved corporate governance.MARGINAL JOURNAL OF MANAGEMENT ACCOUNTING GENERAL FINANCE AND INTERNATIONAL ECONOMIC ISSUES1210The findings emphasize the need for businesses to invest in AI technology and for legislators to establish supportive laws for AI's application in corporate governance, and the role of AI in enhancing business performance.MARGINAL JOURNAL OF MANAGEMENT ACCOUNTING GENERAL FINANCE AND INTERNATIONAL ECONOMIC ISSUES['Raghad Ahmed']2024-03-14T00:00:00https://www.semanticscholar.org/paper/4ac7ce860fe429dd4d8e525a5c62571bfab91cf1 c58d593b10113541db6c7764d6c0ddb02b73b378Knowledge Management (KM) is crucial since it is used for managerial decisions that affect the organization’s success. To improve the quality of KM, there is an innovation that implements Artificial Intelligence (AI) for the KM process. AI is a machine learning tool that can execute human tasks, adapt to new inputs, and learn from experience. This study aims to investigate the growth of AI in KM and evaluate how AI can be applied in KM to manage information and knowledge. The study used a systematic literature review method with PRISMA and data sources from Scopus. A total of 30 articles were examined in the review analysis. The research results found that the implementation of AI in KM was already conducted on various continents and most of the previous studies discussed this topic in the General field. Furthermore, the review discovered that AI can be applied to fundamental knowledge management process, decision-making, knowledge forecasting, and knowledge exchanges. This research also indicates that the implementation of AI in KM is growing and the topic of AI research in KM continues to develop. This study provides insight into prospects for innovation and improvement by offering evaluations for the future development of AI in KM.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)360The research results found that the implementation of AI in KM was already conducted on various continents and most of the previous studies discussed this topic in the General field, and that AI can be applied to fundamental knowledge management process, decision-making, knowledge forecasting, and knowledge exchanges.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['Annie Novalin', 'Ali Gunawan', 'Danang Prihandoko']2024-03-14T00:00:00https://www.semanticscholar.org/paper/c58d593b10113541db6c7764d6c0ddb02b73b378 914e163d08056563843c716edb58caa9eb0af902Applications of artificial intelligence (AI) are seen in a variety of industries, including services, where the field's remarkable rate of change is fueled by technological advancements. AI has improved efficiency and raised interaction levels by being used in people's daily job and in forming the relationship between businesses and their clients. Customers do, however, have concerns about the application of AI. Thus, it is worthwhile to look into how AI impacts customer Reliance. The primary focus of this study is the applications of AI and how they affect consumer Reliance. This study looks on the anthropomorphism and interaction of AI applications. Applications of AI are drastically changing how customers and service providers communicate. We investigate the relationship between consumers' faith in AI applications and their humanness. Focus group data yielded qualitative information that sheds new light on the functions of intelligence and anthropomorphism as essential notions of what it is to be human. Our research reveals how customers view the subtleties of these constructs as they relate to services made possible by AI technologies.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)200The relationship between consumers' faith in AI applications and their humanness is investigated, and how customers view the subtleties of these constructs as they relate to services made possible by AI technologies is revealed.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['Shivani Bajaj', 'Anupam Sharma', 'Rajit Verma', 'Ankit Saxena', 'Neetu Chaudhary', 'A. Sahoo']2024-03-14T00:00:00https://www.semanticscholar.org/paper/914e163d08056563843c716edb58caa9eb0af902 d4e17e96d8facddf5b2c5a03c4722c0639836d05AI technology is developing quickly, and as a result, it is being used extensively in many different contexts and industries. The advancement of these technologies cannot be separated from user feedback and experience. At the current stage of continuous development and progress of AI technology, how to increase the frequency of users' use of new AI technologies, especially in software applications, is a question worth exploring. Based on the TPB method, this article takes the artificial intelligence search function launched by the iQIYI platform as an example to explore the impact of personal experience and information dissemination methods on the frequency of users using artificial intelligence technology on entertainment platforms. The research shows that by improving personal experience and optimizing information dissemination methods, users' frequency of using AI technology can be increased, and user satisfaction can be improved. Therefore, it should focus on the design and optimization of user experience and information dissemination methods to attract more users to use AI technology and increase their frequency of use. This will help promote the development and application of AI technology, bringing more convenience and innovation to people's lives.Lecture Notes in Education Psychology and Public Media00The research shows that by improving personal experience and optimizing information dissemination methods, users' frequency of using AI technology can be increased, and user satisfaction can be improved.Lecture Notes in Education Psychology and Public Media['Linyi Wei']2024-03-14T00:00:00https://www.semanticscholar.org/paper/d4e17e96d8facddf5b2c5a03c4722c0639836d05 4610561aa1dff6aa079668ac758416ba86dc88a1With the rapid development of information technology in Chinas Internet, the associated cybersecurity issues have gradually gained social attention. Incidents involving the infringement of citizens legitimate rights by unlawful individuals using information technology vulnerabilities occur frequently. These incidents pose serious threats to the safety of citizens lives and property, as well as the stability of social order in our country. In recent years, the rapid development of artificial intelligence has further brought this issue to the forefront of public opinion. Therefore, it is necessary to strengthen the legal foundation and improve criminal legislation to ensure the information security of Chinese citizens.Lecture Notes in Education Psychology and Public Media00It is necessary to strengthen the legal foundation and improve criminal legislation to ensure the information security of Chinese citizens.Lecture Notes in Education Psychology and Public Media['Chang Yu', 'Junrui Hu']2024-03-14T00:00:00https://www.semanticscholar.org/paper/4610561aa1dff6aa079668ac758416ba86dc88a1 8672d020937fb93db3bd340708e9b22cd09bbb44Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.Seminars in Musculoskeletal Radiology590The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play.Seminars in musculoskeletal radiology['Vishal Desai']2024-03-14T00:00:00https://www.semanticscholar.org/paper/8672d020937fb93db3bd340708e9b22cd09bbb44 15521fd4da00f843ae645104f741e4a6551e5410The study "Improvement of Oral Communicative Competence in English through the Artificial Intelligence" focused on exploring how artificial intelligence technology can influence the development of oral communication skills during the teaching and learning process of English as a second language, with the participants of this research being future teachers of the subject. This research aimed to determine the effectiveness of artificial intelligence applications, such as voice assistants and speech recognition systems, in improving students' oral communicative competence. The study examined how these artificial intelligence systems provided instant and personalized feedback, as well as opportunities for continuous oral practice for students. It also explored how artificial intelligence can adapt to the individual needs of students, which was beneficial for those with different skill levels or specific learning needs. The results of this research demonstrated that teaching and learning mediated by artificial intelligence had significant implications, suggesting how artificial intelligence technology can play an integral role in the development of oral communicative competence in English and possibly other languages. Finally, this teaching approach promises to facilitate more effective and accessible learning for a larger number of students, enhancing their oral communication skills in the process.LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades50The results of this research demonstrated that teaching and learning mediated by artificial intelligence had significant implications, suggesting how artificial intelligence technology can play an integral role in the development of oral communicative competence in English and possibly other languages.LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades['Adriana Elizabeth Cango Patiño', 'Viviana Madelaine Vidal Montaño', 'Paulina Elizabeth Cabrera Buri', 'María Eugenia Abad Rojas', 'Amparo del Rocío Cabrera González']2024-03-14T00:00:00https://www.semanticscholar.org/paper/15521fd4da00f843ae645104f741e4a6551e5410 790a5f0072d08fd7a133703f569be138e73ce5bePurpose of review This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs. Recent findings The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions. Summary Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.Current Opinion in Critical Care290Current key advancements in artificial intelligence for acute and intensive care medicine are explored to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs.Current Opinion in Critical Care['L. Biesheuvel', 'D. Dongelmans', 'P. Elbers']2024-03-14T00:00:00https://www.semanticscholar.org/paper/790a5f0072d08fd7a133703f569be138e73ce5be 8dd6f510b9081c95cb2f8e22d8a54d54ad38e867International Journal of Computational Intelligence Systems130This review compiles and compares relevant papers from the last decade to show how AI might enhance diagnostic precision, speed, and efficiency and provides a comprehensive overview of the AI approaches used in the diagnosis of GB illnesses.Int. J. Comput. Intell. Syst.['A. Obaid', 'Amina Turki', 'H. Bellaaj', 'Mohamed Ksantini']2024-03-14T00:00:00https://www.semanticscholar.org/paper/8dd6f510b9081c95cb2f8e22d8a54d54ad38e867 08b879b2bfa5c816af1cecd4d14a0f42760ecbb4Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.International Workshop on Intelligent Networking and Collaborative Systems250The goal of this work is to demystify this complex and vivid language and to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)['K. R', 'P. Sathya', 'P. Nandhini', 'V. P', 'Sureshkumar Chelliah', 'Ezhilmathi S']2024-03-14T00:00:00https://www.semanticscholar.org/paper/08b879b2bfa5c816af1cecd4d14a0f42760ecbb4 b31c87b6a8cc9c4a7fc498841be390af92873c8bThis research paper explores the topic of improving HRM procedures by incorporating sophisticated decision-making techniques, with an emphasis on artificial intelligence (AI). AI technologies have completely changed many facets of organizational operations, and human resource management is no exception. HR practitioners may optimize workforce management procedures and promote company performance by utilizing AI to make strategic, data-driven, and wise decisions. The various ways that AI can support HRM tasks, such as hiring and selection, performance management, training and development, and employee engagement, are examined in this paper. This paper provides an in-depth analysis of the literature and actual data to clarify the advantages, difficulties, and optimal approaches related to AI implementation in HRM. Additionally, it examines the ethical implications and potential societal impacts of deploying AI-driven decision-making systems in the HR domain. This research adds to a better understanding of how businesses can use cutting-edge tools to optimize HRM procedures and develop an agile, engaged, and empowered workforce that can flourish in the digital age by providing insights into the strategic integration of AI technologies.Migration Letters170An in-depth analysis of the literature and actual data is provided to clarify the advantages, difficulties, and optimal approaches related to AI implementation in HRM and examines the ethical implications and potential societal impacts of deploying AI-driven decision-making systems in the HR domain.Migration Letters['Asif Ali', 'Dr. Nosheen Rafi']2024-03-14T00:00:00https://www.semanticscholar.org/paper/b31c87b6a8cc9c4a7fc498841be390af92873c8b 682e5cbf72625d1420e5dc63509fa51031c673cdArtificial Intelligence (AI) and Machine Learning (ML) technologies are revolutionizing the technological space in human civilization. While it is facilitating the human labors and minimizing the human errors, it is also posing serious challenges in front of the humanity. These challenges can be resolve by promoting the ethical practices. The study aims to provide the ethical perspective to Artificial Intelligence based technologies. For the purpose of this study, the researchers have first identified the problem and then analyzed the problem statement with the help of case studies. Finally, the researchers provided some recommendation for promoting the ethics in Artificial Intelligence technologies. The study contributes in developing and implementing ethics in theory and practices of artificial intelligence and machine learning.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)110The study aims to provide the ethical perspective to Artificial Intelligence based technologies and contributes in developing and implementing ethics in theory and practices of artificial intelligence and machine learning.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['Vikrant Dhenge', 'Kirti Dorshetwar']2024-03-14T00:00:00https://www.semanticscholar.org/paper/682e5cbf72625d1420e5dc63509fa51031c673cd 2ca8be2942553824b4a0c9b523b87f06255cf40cInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-14T00:00:00https://www.semanticscholar.org/paper/2ca8be2942553824b4a0c9b523b87f06255cf40c b68b6ede9496e370b57a8d1102a25b882d1983efThe application of mathematical models using biotic and abiotic factors for the efficient use of fertilizers to obtain maximum economic productivity can be an important tool to minimize the cost of soybean (Glycine max (L.) Merr.) grain yield. In this sense, using Artificial Neural Networks (ANN) is an important tool in studies involving optimization. This study aimed to estimate soybean yield in Luiziana, Paraná state, Brazil, by considering two growing seasons and an Artificial Neural Network (ANN) as a function of the morphological and nutritional parameters of the plants. Results reveal a well-trained network, with a margin of error of approximately 10-5, thus acting as a tool to estimate soybean data. For the phases, model validation and network test, i.e., data that were not part of the training (validation), the errors averaged 10-3. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.Revista Brasileira de Engenharia de Biossistemas200This study aimed to estimate soybean yield in Luiziana, Paraná state, Brazil, by considering two growing seasons and an Artificial Neural Network as a function of the morphological and nutritional parameters of the plants, and results reveal a well-trained network acting as a tool to estimate soybean data.Revista Brasileira de Engenharia de Biossistemas['Wesley Prado Leão dos Santos', 'Mariana Bonini Silva', 'Alfredo Bonini Neto', 'C. Bonini', 'Adônis Moreira']2024-03-14T00:00:00https://www.semanticscholar.org/paper/b68b6ede9496e370b57a8d1102a25b882d1983ef 25595acabdde96049200e5ed8cd652b75ce139bdYapay zeka teknolojisinin hızlı gelişimi, ulusal, bölgesel ve küresel güvenlik çalışmalarına derin etkileri olan yeni bir paradigma sunmaktadır. Bu çalışma, yapay zeka destekli akıllı savaş stratejilerinin ulusal, bölgesel ve küresel güvenlik üzerindeki etkilerini incelemeyi amaçlamaktadır. Makalede, yapay zeka destekli stratejilerin güvenlik çalışmalarında nasıl kullanılabileceği, bu teknolojinin mevcut ve olası gelecekteki tehditlerin algılanması, önlenmesi ve karşılanmasındaki rolü ele alınmaktadır. Bu çalışma, literatür taraması ve analitik değerlendirmeler temelinde yapılmıştır. Yapay zeka destekli stratejilerin savunma, istihbarat toplama ve askeri operasyonlardaki potansiyel etkileri incelenmiş ve bu etkilerin ulusal, bölgesel ve küresel güvenlik politikalarına olan muhtemel katkıları tartışılmıştır. Ayrıca, bu teknolojinin kullanımının beraberinde getirdiği etik ve hukuki sorunlar da detaylı bir şekilde ele alınmıştır. Bulgular, yapay zeka destekli akıllı savaş stratejilerinin güvenlik çalışmalarına önemli ve dönüştürücü katkılar sağlayabileceğini göstermektedir. Bununla birlikte, bu teknolojinin kullanımının etik ve hukuki boyutlarına dair endişeler de vurgulanmıştır. Sonuçlar, yapay zeka destekli stratejilerin daha geniş güvenlik çerçevesinde kapsamlı bir şekilde ele alınması ve bu teknolojinin potansiyel faydalarının yanı sıra risklerinin de dikkate alınması gerektiğini ortaya koymaktadır. Bu çalışma ile, yapay zeka destekli akıllı savaş stratejilerinin güvenlik politikaları üzerindeki etkisini anlamak ve bu teknolojinin getirdiği zorlukları ele almak isteyen akademisyenler, politika yapıcılar ve ilgili paydaşlar için önemli bir temel oluşturmayı amaçlamaktadır.Türkiye Siyaset Bilimi Dergisi310Türkiye Siyaset Bilimi Dergisi['Duygu Aksu']2024-03-14T00:00:00https://www.semanticscholar.org/paper/25595acabdde96049200e5ed8cd652b75ce139bd 6346ff3f5ebb41542d1a28e9b27f6681ec6ede4dArtificial Intelligence Surgery70Artificial Intelligence Surgery['Valentina Bellini', 'Matteo Panizzi', 'E. Bignami']2024-03-14T00:00:00https://www.semanticscholar.org/paper/6346ff3f5ebb41542d1a28e9b27f6681ec6ede4d b4151feb1dd2f06888027a6db96369cf0b483bd9The contemporary healthcare landscape necessitates innovative solutions to improve transparency and understanding in medical decision-making. This paper proposes an advanced medicine recommendation system and a robust disease prediction model integrating explainable AI (XAI). Recognizing the prevalence of misinformation and the urgent need for user-friendly applications, the system empowers users to manage their health proactively. It can be extended beyond common diseases, encompassing rare diseases, and employs XAI algorithms, specifically SHAP and LIME, to enhance transparency. The system incorporates Random Forest Classifier and Decision Tree models, showcasing high accuracy and robustness. The explanation models contribute to user understanding, while performance metrics offer insights into model strengths and generalization abilities. Figures depict SHAP outputs for Decision Tree and Random Forest models, emphasizing transparency in medical predictions. This proposed system addresses critical healthcare challenges, fostering informed decision-making and user trust.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)110An advanced medicine recommendation system and a robust disease prediction model integrating explainable AI (XAI) that can be extended beyond common diseases, encompassing rare diseases, and employs XAI algorithms, specifically SHAP and LIME, to enhance transparency.2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)['Joel Mathew', 'Richie Suresh Koshy', 'Dr. R. Chitra', 'Caleb Stephen']2024-03-14T00:00:00https://www.semanticscholar.org/paper/b4151feb1dd2f06888027a6db96369cf0b483bd9 0d9ef90faa99ab863bb291ecddce6d9153296e10The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)270A Deep convolutional neural networks model is created that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['Ruchi Kaushik', 'Kanta Prasad Sharma', 'Sidharth Malik', 'Manjula Shanbhog']2024-03-14T00:00:00https://www.semanticscholar.org/paper/0d9ef90faa99ab863bb291ecddce6d9153296e10 eb8967b95e32dad980510c05ce18fb1792e20ba6The human resource management field is undergoing a transformation characterized by the convergence of advanced technologies, organizational strategy, and sustainable Human resource [HR] practices. The study lays the groundwork for successful HR analytics by emphasizing the significance of managing critical data, ethical considerations, and responsible AI governance, as this would foster a way to enhance and achieve HR excellence. It further looks at how advanced analytical methodologies and AI technology can improve the sustainability and effectiveness of HR management practices. Its objective is to explore the transformative capabilities of these technologies. The research aims to promote HR excellence by improving decision-making processes, increasing worker efficiency, and developing sustainable HR practices. This paperseeks to provide critical insights and theoretical foundations that contribute to developing sustainable HR practices and promote excellence in the overall HR management field. It proposes a conceptual, integrative model to help understand the above perspective.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)230The study lays the groundwork for successful HR analytics by emphasizing the significance of managing critical data, ethical considerations, and responsible AI governance, as this would foster a way to enhance and achieve HR excellence.2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)['S. Menon', 'Jaya Yadav', 'Ashok Chopra', 'Jebin Thomas']2024-03-14T00:00:00https://www.semanticscholar.org/paper/eb8967b95e32dad980510c05ce18fb1792e20ba6 4ebdc641ca43fe5f0997b7d4cc659d00416a5b84India’s Gross Domestic Product (GDP) greatly depends on agriculture. The major risk in the agriculture field, now-a-days is price volatility. The constant fluctuation in price on daily basis makes farmer strain to prepare a marketing as well as farming plan. Many factors contribute to the mutation of prices specifically climate changes, merchant fee, variation of prices based on regions and attack of pesticide. The farmers are not aware about the financial funding schemes in conjunction with their eligibility given by the government for their welfare. The existing software applications does not support all regional languages which does not make feasible for farmers to use the application. The application concentrates on creating a user-friendly application that could predict day-to-day trends of cost in the market and helps the farmer to gain profit for their goods. To achieve this, Machine learning. Algorithm – Decision tree algorithm is used to predict the price of agricultural products with respect to inflations and deflations in market. The proposed idea mainly focuses on predicting the price using machine learning algorithm by using certain parameter (Data set) namely season, location, seed sales, histogram and etc., for specific agricultural product. The system uses Global Positioning System (GPS) which helps to select the location to predict price and to find the nearby marketplace for auctioning of agricultural goods. For easier accessibility for farmers, they can choose their regional languages for better understanding.2024 International Conference on Automation and Computation (AUTOCOM)120The application concentrates on creating a user-friendly application that could predict day-to-day trends of cost in the market and helps the farmer to gain profit for their goods.2024 International Conference on Automation and Computation (AUTOCOM)['Mr. R. Selvaraj', 'Ms. M. Sanmati', 'Mr. K. Sudharshan', 'Ms. R. Surithika', 'Mr.S.PRASANTH']2024-03-14T00:00:00https://www.semanticscholar.org/paper/4ebdc641ca43fe5f0997b7d4cc659d00416a5b84 edb91b00732c63abcc3452613944e5d61e434a38Journal of Back and Musculoskeletal Rehabilitation90Journal of back and musculoskeletal rehabilitation['Muhammad Osama']2024-03-14T00:00:00https://www.semanticscholar.org/paper/edb91b00732c63abcc3452613944e5d61e434a38 e0e42f941d2fe3fe502901895fe633c062b3834bTechnical Symposium on Computer Science Education20{'pages': '1710-1711'}['Kyungbin Kwon', 'Keunjae Kim', 'Anne T. Ottenbreit-Leftwich', 'Krista D. Glazewski', 'Matthew L. Brown', 'Haesol Bae', 'Florentina M. Closser']2024-03-14T00:00:00https://www.semanticscholar.org/paper/e0e42f941d2fe3fe502901895fe633c062b3834b 5d7f4b48a5077cb049500e13865b5c5f23847b6eTechnical Symposium on Computer Science Education90{'pages': '1610-1611'}['Noah Q. Cowit', 'Casey Fiesler']2024-03-14T00:00:00https://www.semanticscholar.org/paper/5d7f4b48a5077cb049500e13865b5c5f23847b6e aa80b1810bb8a146a72b444cf63af8a502b50f86UQ 202250UQ 2022['Mariflora Caruso', 'Alessandro Spadaro']2024-03-14T00:00:00https://www.semanticscholar.org/paper/aa80b1810bb8a146a72b444cf63af8a502b50f86 32e12ac0d6b55d510a0bf70ee3d9b8561f6537e7European Journal of Radiology220There is a significant authorship gender gap in artificial intelligence breast imaging research and an increasing temporal trend of senior authors in breast imaging AI-related research is a promising prognosis for more women voices in this field.European journal of radiology['Po Hsiang (Shawn) Yuan', 'Tyler D Yan', 'Sonali Sharma', 'Erin Chahley', 'Luke J. MacLean', 'V. Freitas', 'Charlotte J. Yong-Hing']2024-03-14T00:00:00https://www.semanticscholar.org/paper/32e12ac0d6b55d510a0bf70ee3d9b8561f6537e7 0279625dfcea9faee07218bff3af5aaa7ff14ea2Journal of Human Lactation150Journal of Human Lactation['Ellen M. Chetwynd']2024-03-14T00:00:00https://www.semanticscholar.org/paper/0279625dfcea9faee07218bff3af5aaa7ff14ea2 039afd76e642e470fc2ba0b918bda9fbc469cca4Many studies have identified particular features of artificial intelligences (AI), such as their autonomy and emotion expression, that affect the extent to which they are treated as subjects of moral consideration. However, there has not yet been a comparison of the relative importance of features as is necessary to design and understand increasingly capable, multi-faceted AI systems. We conducted an online conjoint experiment in which 1,163 participants evaluated descriptions of AIs that varied on these features. All 11 features increased how morally wrong participants considered it to harm the AIs. The largest effects were from human-like physical bodies and prosociality (i.e., emotion expression, emotion recognition, cooperation, and moral judgment). For human-computer interaction designers, the importance of prosociality suggests that, because AIs are often seen as threatening, the highest levels of moral consideration may only be granted if the AI has positive intentions.International Conference on Human Factors in Computing Systems820The importance of prosociality suggests that, because AIs are often seen as threatening, the highest levels of moral consideration may only be granted if the AI has positive intentions.{'pages': '287:1-287:11'}['A. Ladak', 'Jamie Harris', 'Jacy Reese Anthis']2024-03-14T00:00:00https://www.semanticscholar.org/paper/039afd76e642e470fc2ba0b918bda9fbc469cca4 248f6b84c5570b52840a5b4fbc434b602e827ca2The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of AI identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients’, healthcare workers’ and policy-makers’ attitudes towards consciousness of AI systems in primary healthcare settings.Family Medicine and Community Health250This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications.Family Medicine and Community Health['Dorsai Ranjbari', 'Samira Abbasgholizadeh Rahimi']2024-03-14T00:00:00https://www.semanticscholar.org/paper/248f6b84c5570b52840a5b4fbc434b602e827ca2 ba8ea1d86574a90bed87c09f6224199c64836ec6The Drug Price Control Order 2013 (DPCO) in India, regulated the prices of certain essential and life-saving drugs to ensure their affordability and availability; with the expectation that this would translate into boosting the sales of those drugs. To assess whether such a sales increase was achieved, we study the effects of the regulation on sales volumes of each regulated drug using a synthetic control approach with sales data from a comparable country which did not experience a regulatory change. We assess the robustness of our results via multiple empirical approaches to triangulate our findings. Contrary to the order’s objectives, we find that sales volumes decline for regulated drugs. Since the order placed restrictions on production levels and on drugs exiting the market, the lowered margins of regulated drugs could have pushed pharmaceutical firms to reduce their marketing expenditures on them. We provide evidence of such a reduction using detailing data from a large pharmaceutical firm. We illustrate that this shift in detailing adversely affected prescriptions from physicians without formal medical degrees who treat the poor and disadvantaged in India; patients that the DPCO was intended to help the most. A survey we conducted shows that these physicians rely on detailing more than medically trained doctors. Taken together, our results provide insights into the strategic actions of firms when faced with regulations, and highlights their unintended consequences. The generalizable nature of our study’s findings across a broad set of medications, has implications for governmental agencies in terms of the need to account for the entire ecosystem of patients, physicians, pharmaceutical firms and pharmacies when implementing such regulations.Journal of Marketing00Contrary to the order’s objectives, it is found that sales volumes decline for regulated drugs, and it is illustrated that this shift in detailing adversely affected prescriptions from physicians without formal medical degrees who treat the poor and disadvantaged in India.Journal of Marketing['S. Jaikumar', 'Pradeep Chintagunta', 'A. Sahay']2024-03-13T00:00:00https://www.semanticscholar.org/paper/ba8ea1d86574a90bed87c09f6224199c64836ec6 9094978f5a63dfdc30a7bd1ea91eaefd359a28c4As jurisdictions around the world take their first steps toward regulating the most powerful AI systems, such as the EU AI Act and the US Executive Order 14110, there is a growing need for effective enforcement mechanisms that can verify compliance and respond to violations. We argue that compute providers should have legal obligations and ethical responsibilities associated with AI development and deployment, both to provide secure infrastructure and to serve as intermediaries for AI regulation. Compute providers can play an essential role in a regulatory ecosystem via four key capacities: as securers, safeguarding AI systems and critical infrastructure; as record keepers, enhancing visibility for policymakers; as verifiers of customer activities, ensuring oversight; and as enforcers, taking actions against rule violations. We analyze the technical feasibility of performing these functions in a targeted and privacy-conscious manner and present a range of technical instruments. In particular, we describe how non-confidential information, to which compute providers largely already have access, can provide two key governance-relevant properties of a computational workload: its type-e.g., large-scale training or inference-and the amount of compute it has consumed. Using AI Executive Order 14110 as a case study, we outline how the US is beginning to implement record keeping requirements for compute providers. We also explore how verification and enforcement roles could be added to establish a comprehensive AI compute oversight scheme. We argue that internationalization will be key to effective implementation, and highlight the critical challenge of balancing confidentiality and privacy with risk mitigation as the role of compute providers in AI regulation expands.arXiv.org01This work describes how non-confidential information can provide two key governance-relevant properties of a computational workload: its type-e.g., large-scale training or inference-and the amount of compute it has consumed.ArXiv['Lennart Heim', 'T. Fist', 'Janet Egan', 'Sihao Huang', 'Stephen Zekany', 'Robert Trager', 'Michael A Osborne', 'Noa Zilberman']2024-03-13T00:00:00https://www.semanticscholar.org/paper/9094978f5a63dfdc30a7bd1ea91eaefd359a28c4 1a399aeaa4e3d23c2293337fde51c172236a1556International Journal of Computer Assisted Radiology and Surgery51International journal of computer assisted radiology and surgery['Kris Kandarpa']2024-03-13T00:00:00https://www.semanticscholar.org/paper/1a399aeaa4e3d23c2293337fde51c172236a1556 ab0d8eee57367b615e4cf28dd8dc457ae3e1c6abProblem setting. One of the most noticeable modern trends is the coverage of digitalization processes in all spheres of life, including the economic sphere, which is accompanied by a rapid expansion of the electronic format of contractual interaction of participants in economic transactions and a change in the very methods of carrying out the latter. At the theoretical level, there are several approaches to the regulation of certain aspects of contractual relations in the field of e-commerce, in particular, with the help of: analogy of the law; customs of business turnover; contractual regulation at the level of individual agreements on the use of an electronic contract form and/or electronic signature, etc.; special legislative acts in this area; complex regulation using different levels of sources. It is the last approach that has gained the most widespread and support among modern scientists. Analysis of recent researches and publications. The issues of concluding, changing and terminating electronic contracts with the participation of business entities were studied in the works of O. M. Vinnyk, M. M. Dutov, S. V. Zlobina, N. B. Koval, N. V. Koryagina, V. L. Despite this, in the practice of contractual and legal regulation of economic transactions in the field of electronic commerce, a unified approach has not been formed regarding the optimal transformation (change or special application) of the established principles of contract law regarding electronic contracts. That is why the purpose of the article is to highlight and analyze problematic issues of legal support for electronic contractual interaction between participants in business relations. Purpose of the research is to highlight the problematic issues of legal support for electronic contractual interaction of the participants in business relationships. Article’s main body. The article notes the lack of a unified approach to the optimal transformation of the established principles of contract law in relation to electronic contracts. Special attention is paid to the procedure for concluding electronic contracts with the participation of business entities and to the content of the concept of “electronic form of contract”. Approaches to the legal nature of electronic offers and acceptance, as well as procedural aspects of their implementation, were investigated based on the analysis of scientific views and the current legislation of Ukraine. Existing legislative dysfunctions in the regulation of the specified issues are highlighted and separate proposals for their elimination are formulated. Conclusions and prospects for development. The article concludes that the agreement of the parties to conclude a contract with the help of information and communication systems is not a sufficient reason to consider it concluded in writing. It is noted that the recognition of the electronic form of the contract as a type of written contract does not correspond to the European approach, according to which only a qualified electronic expression of will is equated to a written form. The necessity of harmonizing the relevant provisions of the Civil Code of Ukraine, the Economic Code of Ukraine, the Law of Ukraine “On Electronic Commerce”, the Law of Ukraine “On Electronic Documents and Electronic Document Management”, etc., in terms of content and correlation of such concepts as “electronic form of transaction”, is emphasized. “electronic transaction”, “electronic form of contract”, “electronic contract”, “electronic document”, “electronic form of providing information”, etc.Law and innovations30Law and innovations['V.S. Milash']2024-03-13T00:00:00https://www.semanticscholar.org/paper/ab0d8eee57367b615e4cf28dd8dc457ae3e1c6ab f86f9eb481d9a8ac582e7a9ac3213e4444034d7c Since December 2021, Article 210a of Regulation 1308/2013 excludes certain restrictive agreements in the agricultural sector from the prohibition in Article 101 of the Treaty on the Functioning of the European Union when those agreements are indispensable to achieve sustainability standards going beyond the mandatory EU or national rules. Moreover, on 7 December 2023, the European Commission adopted guidelines concerning the conditions for the application of Article 210a. This article therefore describes Article 210a and the Commission’s guidelines and assesses the extent to which those guidelines will contribute to the stated aim of Article 210a, which is to support the transition to a sustainable food system in the Union and to strengthen the position of producers in the agri-food supply chain.Zeitschrift für Stoffrecht00Zeitschrift für Wettbewerbsrecht['Anthony Dawes']2024-03-13T00:00:00https://www.semanticscholar.org/paper/f86f9eb481d9a8ac582e7a9ac3213e4444034d7c 58edc46eee231bfc66d6488b63c132455de83c39In Brazil, Internet and social media have been long used by political parties and candidates to campaign, but it was only in 2017 that organic electoral advertising on social media, social media boosting and paid promotion in web search engines were included in the list of permitted electoral campaign expenditures. This was an encouraging move as it not only made an additional tool available for political actors to reach out to voters, but also obliged them to report on campaign spending using those tools. Based on the analysis of the online campaign finance ecosystem in Brazil, this case study lays out lessons learned and some considerations for the stakeholders. This case study is part of International IDEA’s Political Finance in the Digital Age project.00['Amaro Grassi']2024-03-13T00:00:00https://www.semanticscholar.org/paper/58edc46eee231bfc66d6488b63c132455de83c39 60a478d8cdedd4433347287aea3392a5aeb6f3f9Data Science for Transportation300Data Science for Transportation['G. Albano', 'K. Mattas', 'R. Donà', 'Sándor Vass', 'Ricardo Suarez-Bertoa', 'M. C. Galassi', 'B. Ciuffo']2024-03-13T00:00:00https://www.semanticscholar.org/paper/60a478d8cdedd4433347287aea3392a5aeb6f3f9 c9dac7ca6e6adc29b2aa3865276eaa6a93088d0aThis research investigates the regulatory implications of cryptocurrency transactions in the context of the evolution of civil law, including consumer protection, prevention of illegal activities, and maintenance of financial market integrity. The background of the research refers to the economic paradigm shift towards digital assets such as cryptocurrencies, which has changed the global financial transaction landscape. This research method involves a combined approach between civil law analysis, a literature study on blockchain technology, and a review of the latest regulations related to cryptocurrencies in Indonesia. The results show that the evolution of technology in cryptocurrencies has presented new challenges to conventional civil law. The unclear legal status of cryptocurrencies, security risks, and potential illegal use are significant concerns. The regulatory implications on cryptocurrency transactions in Indonesia illustrate the government's efforts to accommodate innovation while protecting the public interest. Some recommendations include a more collaborative approach between the government, the industry sector, and legal institutions to develop a regulatory framework that fits the characteristics of these technologies.Pena Justisia Media Komunikasi dan Kajian Hukum00Pena Justisia: Media Komunikasi dan Kajian Hukum['Fitrah Wahyuddin', 'Sudirman Sudirman', 'Wahyudi Umar']2024-03-13T00:00:00https://www.semanticscholar.org/paper/c9dac7ca6e6adc29b2aa3865276eaa6a93088d0a 9cca0ff4ddd8ea65650da5968c800eafec2da5bbOver the last decade, Artificial Intelligence (AI) has become increasingly popular, especially with the use of chatbots such as ChatGPT, Gemini, and DALL-E. With this rise, large language models (LLMs) and Generative AI (GenAI) have also become more prevalent in everyday use. These advancements strengthen cybersecurity's defensive posture and open up new attack avenues for adversaries as well. This paper provides a comprehensive overview of the current state-of-the-art deployments of GenAI, covering assaults, jailbreaking, and applications of prompt injection and reverse psychology. This paper also provides the various applications of GenAI in cybercrimes, such as automated hacking, phishing emails, social engineering, reverse cryptography, creating attack payloads, and creating malware. GenAI can significantly improve the automation of defensive cyber security processes through strategies such as dataset construction, safe code development, threat intelligence, defensive measures, reporting, and cyberattack detection. In this study, we suggest that future research should focus on developing robust ethical norms and innovative defense mechanisms to address the current issues that GenAI creates and to also further encourage an impartial approach to its future application in cybersecurity. Moreover, we underscore the importance of interdisciplinary approaches further to bridge the gap between scientific developments and ethical considerations.arXiv.org892A comprehensive overview of the current state-of-the-art deployments of GenAI is provided, covering assaults, jailbreaking, and applications of prompt injection and reverse psychology, and the various applications of GenAI in cybercrimes.ArXiv['Yagmur Yigit', 'William J Buchanan', 'Madjid G Tehrani', 'Leandros A. Maglaras']2024-03-13T00:00:00https://www.semanticscholar.org/paper/9cca0ff4ddd8ea65650da5968c800eafec2da5bb a0a12a73eacf443e3bdefc7149b03e756af3298bAs AI models become more and more common in process industry applications, it is important to understand their carbon footprint. Recent papers have shown that it can be quite big, i.e., the training of a single high-end model can result in emissions of more than 500t of CO2eq. In this  paper we discuss the factors that influence the carbon  footprint of AI models, explore what impact different decisions have, and show how the footprint can be  reduced. We also evaluate different models to validate orchallenge theoretical assumptions from the literature. Two experimental examples using process industry data show the impact on providers of industrial analytics in particular.atp magazin02The factors that influence the carbon footprint of AI models are discussed, what impact different decisions have, and how the footprint can be reduced are explored.atp magazin['Ralf Gitzel', 'Marie Platenius-Mohr', 'Andreas Burger']2024-03-13T00:00:00https://www.semanticscholar.org/paper/a0a12a73eacf443e3bdefc7149b03e756af3298b 7235c99f9c9bc3e56951a829ef30812ab39492b9Systematic Literature Reviews (SLRs) have become the foundation of evidence-based studies, enabling researchers to identify, classify, and combine existing studies based on specific research questions. Conducting an SLR is largely a manual process. Over the previous years, researchers have made significant progress in automating certain phases of the SLR process, aiming to reduce the effort and time needed to carry out high-quality SLRs. However, there is still a lack of AI agent-based models that automate the entire SLR process. To this end, we introduce a novel multi-AI agent model designed to fully automate the process of conducting an SLR. By utilizing the capabilities of Large Language Models (LLMs), our proposed model streamlines the review process, enhancing efficiency and accuracy. The model operates through a user-friendly interface where researchers input their topic, and in response, the model generates a search string used to retrieve relevant academic papers. Subsequently, an inclusive and exclusive filtering process is applied, focusing on titles relevant to the specific research area. The model then autonomously summarizes the abstracts of these papers, retaining only those directly related to the field of study. In the final phase, the model conducts a thorough analysis of the selected papers concerning predefined research questions. We also evaluated the proposed model by sharing it with ten competent software engineering researchers for testing and analysis. The researchers expressed strong satisfaction with the proposed model and provided feedback for further improvement. The code for this project can be found on the GitHub repository at https://github.com/GPT-Laboratory/SLR-automation.arXiv.org282This work introduces a novel multi-AI agent model designed to fully automate the process of conducting an SLR, utilizing the capabilities of Large Language Models (LLMs), and streamlines the review process, enhancing efficiency and accuracy.ArXiv['Abdul Malik Sami', 'Zeeshan Rasheed', 'Kai-Kristian Kemell', 'Muhammad Waseem', 'Terhi Kilamo', 'Mika Saari', 'Anh Nguyen-Duc', 'Kari Systä', 'Pekka Abrahamsson']2024-03-13T00:00:00https://www.semanticscholar.org/paper/7235c99f9c9bc3e56951a829ef30812ab39492b9 fdc3de91aa8b0e9e1d023b031e563feedce90a14Knowledge and Information Systems351The results indicate the chatbot’s potential as an educational tool in engaging users with the ethical dimensions of AI technology and promoting informed discourse and its ability to offer new perspectives, gain user acceptance, and broaden users’ viewpoints on complex issues.Knowledge and Information Systems['Christian Hauptmann', 'Adrian Krenzer', 'Justin Völkel', 'Frank Puppe']2024-03-13T00:00:00https://www.semanticscholar.org/paper/fdc3de91aa8b0e9e1d023b031e563feedce90a14 83ea26cc9caf8de601ffefc2e3bb79aae4c33eb1The evolution of Artificial Intelligence Generated Contents (AIGCs) is advancing towards higher quality. The growing interactions with AIGCs present a new challenge to the data-driven AI community: While AI-generated contents have played a crucial role in a wide range of AI models, the potential hidden risks they introduce have not been thoroughly examined. Beyond human-oriented forgery detection, AI-generated content poses potential issues for AI models originally designed to process natural data. In this study, we underscore the exacerbated hallucination phenomena in Large Vision-Language Models (LVLMs) caused by AI-synthetic images. Remarkably, our findings shed light on a consistent AIGC \textbf{hallucination bias}: the object hallucinations induced by synthetic images are characterized by a greater quantity and a more uniform position distribution, even these synthetic images do not manifest unrealistic or additional relevant visual features compared to natural images. Moreover, our investigations on Q-former and Linear projector reveal that synthetic images may present token deviations after visual projection, thereby amplifying the hallucination bias.arXiv.org331This study underscores the exacerbated hallucination phenomena in Large Vision-Language Models (LVLMs) caused by AI-synthetic images and sheds light on a consistent AIGC hallucination bias.ArXiv['Yifei Gao', 'Jiaqi Wang', 'Zhiyu Lin', 'Jitao Sang']2024-03-13T00:00:00https://www.semanticscholar.org/paper/83ea26cc9caf8de601ffefc2e3bb79aae4c33eb1 215cf45522610045741238f7689a10f6bc5a1f8bThe landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot. However, existing solutions are not leveraging all the potential capabilities available in an IDE such as building, testing, executing code, git operations, etc. Therefore, they are constrained by their limited capabilities, primarily focusing on suggesting code snippets and file manipulation within a chat-based interface. To fill this gap, we present AutoDev, a fully automated AI-driven software development framework, designed for autonomous planning and execution of intricate software engineering tasks. AutoDev enables users to define complex software engineering objectives, which are assigned to AutoDev's autonomous AI Agents to achieve. These AI agents can perform diverse operations on a codebase, including file editing, retrieval, build processes, execution, testing, and git operations. They also have access to files, compiler output, build and testing logs, static analysis tools, and more. This enables the AI Agents to execute tasks in a fully automated manner with a comprehensive understanding of the contextual information required. Furthermore, AutoDev establishes a secure development environment by confining all operations within Docker containers. This framework incorporates guardrails to ensure user privacy and file security, allowing users to define specific permitted or restricted commands and operations within AutoDev. In our evaluation, we tested AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and 87.8% of Pass@1 for code generation and test generation respectively, demonstrating its effectiveness in automating software engineering tasks while maintaining a secure and user-controlled development environment.arXiv.org160AutoDev is a fully automated AI-driven software development framework, designed for autonomous planning and execution of intricate software engineering tasks, and establishes a secure development environment by confining all operations within Docker containers.ArXiv['Michele Tufano', 'Anisha Agarwal', 'Jinu Jang', 'Roshanak Zilouchian Moghaddam', 'Neel Sundaresan']2024-03-13T00:00:00https://www.semanticscholar.org/paper/215cf45522610045741238f7689a10f6bc5a1f8b 1c4d8f797f7abc7bc3c5faeed6caae24669945deAI and Ethics270This paper delves into the ethical implications of AI in the Metaverse through the analysis of real-world case studies, including Horizon Worlds, Decentraland, Roblox, Sansar, and Rec Room, revealing recurring concerns related to content moderation.AI and Ethics['A. Zhuk']2024-03-13T00:00:00https://www.semanticscholar.org/paper/1c4d8f797f7abc7bc3c5faeed6caae24669945de 9742ae9a368902381e2028664c9b8d073225791eBackground Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems. Objective We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists’ trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans. Methods In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists’ trust in their assessments had changed based on the AI recommendations. Results Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists’ confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists’ confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs. Conclusions Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists’ trust in the AI-CAD system can be impaired. Radiologists’ confidence in their assessments was improved by using the AI recommendations.JMIR AI500Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model in a significantly improved mental model of the AI-CAD system.JMIR AI['Lotte J S Ewals', 'Lynn J J Heesterbeek', 'Bin Yu', 'Kasper van der Wulp', 'Dimitrios Mavroeidis', 'M. Funk', 'Chris C P Snijders', 'Igor Jacobs', 'Joost Nederend', 'J. Pluyter']2024-03-13T00:00:00https://www.semanticscholar.org/paper/9742ae9a368902381e2028664c9b8d073225791e 57e0cc856551abf2eb2f7c1ba0f4097101fac74fAs artificial intelligence continues its unprecedented global expansion, accompanied by a proliferation of benefits, an increasing apprehension about the privacy and security implications of AI-enabled systems emerges. The pivotal question of effectively controlling AI development at both jurisdictional and organizational levels has become a prominent theme in contemporary discourse. While the European Parliament and Council have taken a decisive step by reaching a political agreement on the EU AI Act, the first comprehensive AI law, organizations still find it challenging to adapt to the fast-evolving AI landscape, lacking a universal tool for evaluating the privacy and security dimensions of their AI models and systems. In response to this critical challenge, this study conducts a systematic literature review spanning the years 2020 to 2023, with a primary focus on establishing a unified definition of key concepts in AI Ethics, particularly emphasizing the domains of privacy and security. Through the synthesis of knowledge extracted from the SLR, this study presents a conceptual framework tailored for privacy- and security-aware AI systems. This framework is designed to assist diverse stakeholders, including organizations, academic institutions, and governmental bodies, in both the development and critical assessment of AI systems. Essentially, the proposed framework serves as a guide for ethical decision-making, fostering an environment wherein AI is developed and utilized with a strong commitment to ethical principles. In addition, the study unravels the key issues and challenges surrounding the privacy and security dimensions, delineating promising avenues for future research, thereby contributing to the ongoing dialogue on the globalization and democratization of AI ethics.arXiv.org490A conceptual framework tailored for privacy- and security-aware AI systems is presented, designed to assist diverse stakeholders, including organizations, academic institutions, and governmental bodies, in both the development and critical assessment of AI systems.ArXiv['Daria Korobenko', 'Anastasija Nikiforova', 'Rajesh Sharma']2024-03-13T00:00:00https://www.semanticscholar.org/paper/57e0cc856551abf2eb2f7c1ba0f4097101fac74f 8961b3e1ea33e6e2bcf7eb2b9aa803cf1fa18ec9We anticipate increased instances of humans and AI systems working together in what we refer to as a hybrid team. The increase in collaboration is expected as AI systems gain proficiency and their adoption becomes more widespread. However, their behavior is not error-free, making hybrid teams a very suitable solution. As such, we consider methods for improving performance for these teams of humans and AI systems. For hybrid teams, we will refer to both the humans and AI systems as agents. To improve team performance over that seen for agents operating individually, we propose a manager which learns, through a standard Reinforcement Learning scheme, how to best delegate, over time, the responsibility of taking a decision to any of the agents. We further guide the manager's learning so they also minimize how many changes in delegation are made resulting from undesirable team behavior. We demonstrate the optimality of our manager's performance in several grid environments which include failure states which terminate an episode and should be avoided. We perform our experiments with teams of agents with varying degrees of acceptable risk, in the form of proximity to a failure state, and measure the manager's ability to make effective delegation decisions with respect to its own risk-based constraints, then compare these to the optimal decisions. Our results show our manager can successfully learn desirable delegations which result in team paths near/exactly optimal with respect to path length and number of delegations.arXiv.org350A manager is proposed which learns, through a standard Reinforcement Learning scheme, how to best delegate, over time, the responsibility of taking a decision to any of the agents, and can successfully learn desirable delegations which result in team paths near/exactly optimal with respect to path length and number of delegations.ArXiv['Andrew Fuchs', 'A. Passarella', 'M. Conti']2024-03-13T00:00:00https://www.semanticscholar.org/paper/8961b3e1ea33e6e2bcf7eb2b9aa803cf1fa18ec9 29c9820bc81b1bb714661dbab7bfa58b799f1dacThis market requirements document explores the potential of artificial intelligence (AI) in stock price prediction, an area integral to the financial market's profit-maximization aspirations. Historically, the prediction of stock prices, with its intricate patterns and influences, has posed significant challenges. Traditional techniques like PCA and LSTM have limitations in adapting to the dynamic nature of stock markets. AI, on the other hand, offers transformative possibilities, with its capabilities to process vast data sets, identify patterns, and continuously learn. Drawing parallels from AI's success in other sectors, like healthcare, the essay suggests that AI can revolutionize stock price prediction by offering higher accuracy and adaptability. It underscores the need to combine various AI methodologies for optimized results while also emphasizing the importance of regulating AI applications. In essence, this paper envisions a future where AI becomes the fulcrum of stock price prediction, potentially overhauling the entire financial sector's operations and delivering unparalleled benefits to all stakeholders.Highlights in Science Engineering and Technology160A future is envisions where AI becomes the fulcrum of stock price prediction, potentially overhauling the entire financial sector's operations and delivering unparalleled benefits to all stakeholders.Highlights in Science, Engineering and Technology['Xuan He', 'Ying He']2024-03-13T00:00:00https://www.semanticscholar.org/paper/29c9820bc81b1bb714661dbab7bfa58b799f1dac 74d0f14eec544c6af42cd9b73ce799780439c511Rare earth elements (REEs including Sc, Y) are critical minerals for developing sustainable energy sources. The gradual transition adopted in developed and developing countries to meet energy targets has propelled the need for REEs in addition to critical metals (CMs). The rise in demand which has propelled REEs into the spotlight is driven by the crucial role these REEs play in technologies that aim to reduce our carbon footprint in the atmosphere. Regarding decarbonized technologies in the energy sector, REEs are widely applied for use in NdFeB permanent magnets, which are crucial parts of wind turbines and motors of electric vehicles. The underlying motive behind exploring the energy and carbon footprint caused by REEs production is to provide a more complete context and rationale for REEs usage that is more holistic. Incorporating artificial intelligence (AI)/machine learning (ML) models with empirical approaches aids in flowsheet validation, and thus, it presents a vivid holistic picture. The energy needed for REEs production is linked with the source of REEs. The availability of REEs varies widely across the globe. REEs are either produced from ores with associated gangue or impurities. In contrast, in other scenarios, REEs can be produced from the waste of other mineral deposits or discarded REEs-based products. These variations in the source of feed materials, and the associated grade and mineral associations, vary the process flowsheet for each type of production. Thus, the ability to figure out energy outcomes from various scenarios, and a knowledge of energy requirements for the production and commercialization of multiple opportunities, is needed. However, this type of information concerning REEs production is not readily available as a standardized value for a particular material, according to its source and processing method. The related approach for deciding the energy and carbon footprint for different processing approaches and sources relies on the following three sub-processes: mining, beneficiation, and refining. Some sources require incorporating all three, whereas others need two or one, depending on resource availability. The available resources in the literature tend to focus on the life cycle assessment of REEs, using various sources, and they focus little on the energy footprint. For example, a few researchers have focused on the cumulative energy needed for REE production without making assessments of viability. Thus, this article aims to discuss the energy needs for each process, rather than on a specific flowsheet, to define process viability more effectively regarding energy need, availability, and the related carbon footprint.Processes640Processes['Subbu Venkata Satyasri Harsha Pathapati', 'Rahulkumar Sunil Singh', 'M. Free', 'P. Sarswat']2024-03-13T00:00:00https://www.semanticscholar.org/paper/74d0f14eec544c6af42cd9b73ce799780439c511 c3055442a24fc4e1867a7262027423f75d6b4508In this study, we delve into the financial market to compare the performance of prominent AI and robotics-related stocks against traditional IT indices, such as the Nasdaq, and specialized AI and robotics ETFs. We evaluate the role of these stocks in diversifying portfolios, analyzing their return potential and risk profiles. Our analysis includes various investment scenarios, focusing on common AI-related stocks in the United States. We explore the influence of risk management strategies, ranging from “buy and hold” to daily rebalancing, on AI stock portfolios. This involves investigating long-term strategies like buy and hold, as well as short-term approaches, such as daily rebalancing. Our findings, covering the period from 30 April 2021, to 15 September 2023, show that AI-related stocks have not only outperformed in recent years but also highlight the growing “AI bubble” and the increasing significance of AI in investment decisions. The study reveals that these stocks have delivered superior performance, as indicated by metrics like Sharpe and Treynor ratios, providing insights into market trends and financial returns in the technology and robotics sectors. The results are particularly relevant for investors and traders in the AI sector, offering a balanced view of potential returns against the risks in this rapidly evolving market. This paper adds to the financial market literature by demonstrating that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.Risks400It is demonstrated that investing in emerging trends, such as AI, can be more advantageous in the short term compared to traditional markets like the Nasdaq.Risks['Ali Trabelsi Karoui', 'Sonia Sayari', 'Wael Dammak', 'A. Jeribi']2024-03-13T00:00:00https://www.semanticscholar.org/paper/c3055442a24fc4e1867a7262027423f75d6b4508 a450539a9976ae89d0f06f4c334ba2a4108a3db6"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening (LCS) on multinational clinical workflows. Materials and Methods An AI assistant for LCS was evaluated on two retrospective randomized multireader multicase studies, where 627 (141 cancer positive) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (6 US-based or 6 Japan-based), resulting in a total of 7,524 interpretations. Positive cases were defined as those within two years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least two years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (LoS, on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for LoS and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72, P = .02) for the US study and by 0.023 (0.93 to 0.96, P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57%-63%, P < .001) for the US study and 6.7% (23%-30%, P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the US (67.3%-67.5%, P = .88) and Japan (98%-100%, P > .99) studies. Corresponding standalone AI AUC system performance was 0.75 95% CI [0.70-0.81] and 0.88 95%CI [0.78-0.97] for the US and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved LCS specificity in both US and Japan-based reader studies, meriting further study in additional international screening environments. ©RSNA, 2024.Radiology: Artificial Intelligence00The concurrent AI interface improved LCS specificity in both US and Japan-based reader studies, meriting further study in additional international screening environments.Radiology. Artificial intelligence['A. Kiraly', 'Corbin A Cunningham', 'Ryan Najafi', 'Zaid Nabulsi', 'Jie Yang', 'Charles Lau', 'J. Ledsam', 'Wenxing Ye', 'Diego Ardila', 'S. McKinney', 'Rory Pilgrim', 'Yun Liu', 'Hiroaki Saito', 'Yasuteru Shimamura', 'M. Etemadi', 'David S. Melnick', 'Sunny Jansen', 'G. Corrado', 'Lily Peng', 'Daniel Tse', 'S. Shetty', 'Shruthi Prabhakara', 'D. Naidich', 'Neeral Beladia', 'Krish Eswaran']2024-03-13T00:00:00https://www.semanticscholar.org/paper/a450539a9976ae89d0f06f4c334ba2a4108a3db6 7b802a47d818988944e32f561449b6355088b261Accountable use of AI systems in high-stakes settings relies on making systems contestable. In this paper we study efforts to contest AI systems in practice by studying how public defenders scrutinize AI in court. We present findings from interviews with 17 people in the U.S. public defense community to understand their perceptions of and experiences scrutinizing computational forensic software (CFS) -- automated decision systems that the government uses to convict and incarcerate, such as facial recognition, gunshot detection, and probabilistic genotyping tools. We find that our participants faced challenges assessing and contesting CFS reliability due to difficulties (a) navigating how CFS is developed and used, (b) overcoming judges and jurors' non-critical perceptions of CFS, and (c) gathering CFS expertise. To conclude, we provide recommendations that center the technical, social, and institutional context to better position interventions such as performance evaluations to support contestability in practice.International Conference on Human Factors in Computing Systems900This paper study efforts to contest AI systems in practice by studying how public defenders scrutinize AI in court, and provides recommendations that center the technical, social, and institutional context to better position interventions such as performance evaluations to support contestability in practice.ArXiv['Angela Jin', 'Niloufar Salehi']2024-03-13T00:00:00https://www.semanticscholar.org/paper/7b802a47d818988944e32f561449b6355088b261 40cc085a2608985b753c38dc245ac21be592ed08Reinforcement Learning from AI Feedback (RLAIF) has the advantages of shorter annotation cycles and lower costs over Reinforcement Learning from Human Feedback (RLHF), making it highly efficient during the rapid strategy iteration periods of large language model (LLM) training. Using ChatGPT as a labeler to provide feedback on open-domain prompts in RLAIF training, we observe an increase in human evaluators' preference win ratio for model responses, but a decrease in evaluators' satisfaction rate. Analysis suggests that the decrease in satisfaction rate is mainly due to some responses becoming less helpful, particularly in terms of correctness and truthfulness, highlighting practical limitations of basic RLAIF. In this paper, we propose Hybrid Reinforcement Learning from AI Feedback (HRLAIF). This method enhances the accuracy of AI annotations for responses, making the model's helpfulness more robust in training process. Additionally, it employs AI for Red Teaming, further improving the model's harmlessness. Human evaluation results show that HRLAIF inherits the ability of RLAIF to enhance human preference for outcomes at a low cost while also improving the satisfaction rate of responses. Compared to the policy model before Reinforcement Learning (RL), it achieves an increase of 2.08\% in satisfaction rate, effectively addressing the issue of a decrease of 4.58\% in satisfaction rate after basic RLAIF.arXiv.org470Human evaluation results show that HRLAIF inherits the ability of RLAIF to enhance human preference for outcomes at a low cost while also improving the satisfaction rate of responses, effectively addressing the issue of a decrease in satisfaction rate after basic RLAIF.ArXiv['Ang Li', 'Qiugen Xiao', 'Peng Cao', 'Jian Tang', 'Yi Yuan', 'Zijie Zhao', 'Xiaoyuan Chen', 'Liang Zhang', 'Xiangyang Li', 'Kaitong Yang', 'Weidong Guo', 'Yukang Gan', 'Jeffrey Xu Yu', 'D. Wang', 'Ying Shan']2024-03-13T00:00:00https://www.semanticscholar.org/paper/40cc085a2608985b753c38dc245ac21be592ed08 8b6f1b5708026959b96d493b08eac77394a233e9Learning-based techniques such as artificial intelligence (AI) and machine learning (ML) play an increasingly important role in the development of future communication networks. The success of a learning algorithm depends on the quality and quantity of the available training data. In the physical layer (PHY), channel information data can be obtained either through measurement campaigns or through simulations based on predefined channel models. Performing measurements can be time consuming while only gaining information about one specific position or scenario. Simulated data, on the other hand, are more generalized and reflect in most cases not a real environment but instead, a statistical approximation based on a mathematical model. This paper presents a procedure for acquiring channel data by means of fast and flexible software defined radio (SDR) based channel measurements along with a method for a parameter extraction that provides configuration input to the simulator. The procedure from the measurement to the simulated channel data is demonstrated in two exemplary propagation scenarios. It is shown, that in both cases the simulated data is in good accordance to the measurementsarXiv.org180This paper presents a procedure for acquiring channel data by means of fast and flexible software defined radio (SDR) based channel measurements along with a method for a parameter extraction that provides configuration input to the simulator.ArXiv['Michael Zentarra', 'Julian Ahrens', 'Lia Ahrens']2024-03-13T00:00:00https://www.semanticscholar.org/paper/8b6f1b5708026959b96d493b08eac77394a233e9 b45418fca8dcb2e59180407c6f2c2aaeb1b0d767Photonics in Dermatology and Plastic Surgery 202401Photonics in Dermatology and Plastic Surgery 2024['Lise L. Randeberg', 'Harald Wesenberg']2024-03-13T00:00:00https://www.semanticscholar.org/paper/b45418fca8dcb2e59180407c6f2c2aaeb1b0d767 a90cab04434c7ca852d2f34d539fdef36696fbe8Artificial Intelligence Review51This primer provides an overview of the rapidly evolving field of generative artificial intelligence, specifically focusing on large language models like ChatGPT and Bard, and explores state-of-the-art methods in training large language models to generalize on specific applications and to align with human intentions.Artif. Intell. Rev.['R. Bridgelall']2024-03-13T00:00:00https://www.semanticscholar.org/paper/a90cab04434c7ca852d2f34d539fdef36696fbe8 ac6f23ff501b6c6680102293ec2a3b2aec18381bThe Journal of Surgery00Journal of Surgery[]2024-03-13T00:00:00https://www.semanticscholar.org/paper/ac6f23ff501b6c6680102293ec2a3b2aec18381b 5df68b5b331845f52d6d0a099b7f980b8b23f6a2Medicon Engineering Themes00Medicon Engineering Themes[]2024-03-13T00:00:00https://www.semanticscholar.org/paper/5df68b5b331845f52d6d0a099b7f980b8b23f6a2 bf525006cfb6372e83caadc7a8df5c42b341c6fcComputational Optical Imaging and Artificial Intelligence in Biomedical Sciences00Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences['Sixian You']2024-03-13T00:00:00https://www.semanticscholar.org/paper/bf525006cfb6372e83caadc7a8df5c42b341c6fc 8552b1b0f1de8af50772f0ba159ed960aceb5ca0Technology Analysis &amp; Strategic Management710Technology Analysis &amp; Strategic Management['Manal Ahdadou', 'Abdellah Aajly', 'Mohamed Tahrouch']2024-03-13T00:00:00https://www.semanticscholar.org/paper/8552b1b0f1de8af50772f0ba159ed960aceb5ca0 82f76e56b5d284610f4eacc16fd9a62b829d1dcaSPIE AR, VR, MR Invited Talks 202400SPIE AR, VR, MR Invited Talks 2024['Kevin C. Boyle']2024-03-13T00:00:00https://www.semanticscholar.org/paper/82f76e56b5d284610f4eacc16fd9a62b829d1dca 9b79b76c494819421830c450649350081eb465feTechTrends360TechTrends['G. Veletsianos', 'Shandell Houlden', 'Nicole Johnson']2024-03-13T00:00:00https://www.semanticscholar.org/paper/9b79b76c494819421830c450649350081eb465fe 97233fd8f71a112f003cce517ec1bf1a1c5ded7bScientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration00Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration['Vojtěch Hrubý']2024-03-13T00:00:00https://www.semanticscholar.org/paper/97233fd8f71a112f003cce517ec1bf1a1c5ded7b e3995dc3914d1837957804956e8924daa499a7acMedical and Biological Engineering and Computing90The main aim of the developed application, CronaSona app, is to develop and test a reliable diagnostic tool using deep learning techniques to avoid increasing the spread of the disease as much as possible and to accelerate the diagnosis and referral of patients.Medical & biological engineering & computing['Samah A Z Hassan']2024-03-13T00:00:00https://www.semanticscholar.org/paper/e3995dc3914d1837957804956e8924daa499a7ac e31ee95801e98f9923d3bda4b8416427bbb96444In the rapidly evolving landscape of communication and network security, the increasing reliance on deep neural networks (DNNs) and cloud services for data processing presents a significant vulnerability: the potential for backdoors that can be exploited by malicious actors. Our approach leverages advanced tensor decomposition algorithms Independent Vector Analysis (IVA), Multiset Canonical Correlation Analysis (MCCA), and Parallel Factor Analysis (PARAFAC2) to meticulously analyze the weights of pre-trained DNNs and distinguish between backdoored and clean models effectively. The key strengths of our method lie in its domain independence, adaptability to various network architectures, and ability to operate without access to the training data of the scrutinized models. This not only ensures versatility across different application scenarios but also addresses the challenge of identifying backdoors without prior knowledge of the specific triggers employed to alter network behavior. We have applied our detection pipeline to three distinct computer vision datasets, encompassing both image classification and object detection tasks. The results demonstrate a marked improvement in both accuracy and efficiency over existing backdoor detection methods. This advancement enhances the security of deep learning and AI in networked systems, providing essential cybersecurity against evolving threats in emerging technologies.arXiv.org280This approach leverages advanced tensor decomposition algorithms Independent Vector Analysis, Multiset Canonical Correlation Analysis, and Parallel Factor Analysis to meticulously analyze the weights of pre-trained DNNs and distinguish between backdoored and clean models effectively, demonstrating a marked improvement over existing backdoor detection methods.ArXiv['Khondoker Murad Hossain', 'Tim Oates']2024-03-13T00:00:00https://www.semanticscholar.org/paper/e31ee95801e98f9923d3bda4b8416427bbb96444 c138e529cb975f0340656678a64f1da1df02fbac"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. ©RSNA, 2024.Radiology: Artificial Intelligence00The challenges and processes involved in organizing RSNA artificial intelligence competitions are examined, with a specific emphasis on the creation and curation of high-quality datasets and the potential of crowdsourced annotation to progress medical imaging research.Radiology. Artificial intelligence['F. Kitamura', 'L. Prevedello', 'E. Colak', 'S. Halabi', 'M. Lungren', 'Robyn L. Ball', 'Jaysheree Kalpathy-Cramer', 'Charles E. Kahn', 'Tyler Richards', 'Jason Talbott', 'George Shih', 'Hui-Ming Lin', 'Katherine P. Andriole', 'Maryam Vazirabad', 'BJ Erickson', 'A. E. Flanders', 'John T Mongan']2024-03-13T00:00:00https://www.semanticscholar.org/paper/c138e529cb975f0340656678a64f1da1df02fbac 3d483607fdc3cf9064b80ee0bd3d6c8f8b5cf788JMIR Medical Education10JMIR Medical Education['Julia-Astrid Moldt', 'T. Festl-Wietek', 'Wolfgang Fuhl', 'Susanne Zabel', 'Manfred Claassen', 'Samuel Wagner', 'Kay Nieselt', 'A. Herrmann-Werner']2024-03-13T00:00:00https://www.semanticscholar.org/paper/3d483607fdc3cf9064b80ee0bd3d6c8f8b5cf788 ee9c321d876497ae67511eb67940ee89a97d9eebIn the realm of fashion design, sketches serve as the canvas for expressing an artist's distinctive drawing style and creative vision, capturing intricate details like stroke variations and texture nuances. The advent of sketch-to-image cross-modal translation technology has notably aided designers. However, existing methods often compromise these sketch details during image generation, resulting in images that deviate from the designer's intended concept. This limitation hampers the ability to offer designers a precise preview of the final output. To overcome this challenge, we introduce HAIFIT, a novel approach that transforms sketches into high-fidelity, lifelike clothing images by integrating multi-scale features and capturing extensive feature map dependencies from diverse perspectives. Through extensive qualitative and quantitative evaluations conducted on our self-collected dataset, our method demonstrates superior performance compared to existing methods in generating photorealistic clothing images. Our method excels in preserving the distinctive style and intricate details essential for fashion design applications.arXiv.org360HAIFIT is introduced, a novel approach that transforms sketches into high-fidelity, lifelike clothing images by integrating multi-scale features and capturing extensive feature map dependencies from diverse perspectives, and demonstrates superior performance compared to existing methods in generating photorealistic clothing images.ArXiv['Jianan Jiang', 'Xinglin Li', 'Weiren Yu', 'Di Wu']2024-03-13T00:00:00https://www.semanticscholar.org/paper/ee9c321d876497ae67511eb67940ee89a97d9eeb 7671665a287ed3fd6aae9f5b035a3fa05d55fdbeRevisiting the long-debated question: “What is Islamic architecture?”, this research article aims to explore the identity of “Islamic architecture (IA)” in the context of artificial intelligence (AI) as well as the novel opportunities and cultural challenges associated with applying AI techniques, such as the machine learning of Midjourney in the context of IA. It investigates the impact factors of AI technologies on the understanding and interpretation of traditional Islamic architectural principles, especially architectural design processes. This article employs a quantitative research methodology, including the observation of works of artists and architectural designers appearing in the mass media in light of a literature review and critical analysis of scholarly debates on Islamic architecture, spanning from historical perspectives to contemporary discussions. The article argues for the emergence of a continuous paradigm shift from what is commonly known as “postmodern Islamic architecture” (PMIA) into “artificial intelligence Islamic architecture” (AIIA), as coined by the authors of this article. It identifies the following impact factors of AI on IA: (1) particular requirements and sensitivities, inaccuracies, and biases, (2) human touch, unique craftsmanship, and a deep understanding of cultural issues, (3) regional variation, (4) translation, (5) biases in sources, (6) previously used terms and expressions, and (7) intangible values. The significance of this research in digital heritage lies in the fact that there are no pre-existing theoretical publications on the topic of “Islamic architecture in the age of artificial intelligence”, although an extensive set of publications interpreting the question of the definition of Islamic architecture, in general, is found. This article is pivotal in analyzing this heritage-inspired design approach in light of former criticism of the definition of “Islamic architecture”, which could benefit both theorists and practitioners. This theoretical article is the first in a series of two sequential articles in the Buildings journal; the second (practical) article is an analytical evaluation of the Midjourney architectural virtual lab, defining major current limits in AI-generated representations of Islamic architectural heritage.Buildings551The article argues for the emergence of a continuous paradigm shift from what is commonly known as “postmodern Islamic architecture” (PMIA) into “artificial intelligence Islamic architecture” (AIIA), as coined by the authors of this article.Buildings['Ahmad W. Sukkar', 'Mohamed W. Fareed', 'Moohammed Wasim Yahia', 'Emad S. N. Mushtaha', 'Sami Luigi De Giosa']2024-03-13T00:00:00https://www.semanticscholar.org/paper/7671665a287ed3fd6aae9f5b035a3fa05d55fdbe b3f4dac0fe946357cdf17a73ac18ada67809cf43The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive literature review, this study identifies 21 KPIs using the balanced scorecard (BSC) methodology as a performance measurement framework. While prior research has relied on the grey first-order one-variable GM (1,1) model to predict supply chain performance within constrained datasets, this study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision. Unlike the traditional GM (1,1) model, the proposed approach evaluates performance based on the mean relative error (MRE). The results demonstrate a significant reduction in MRE levels, ranging from 77.09% to 0.23%, across various KPIs, leading to improved prediction accuracy. Notably, the grey neural network (GNN) model exhibits superior predictive accuracy compared to the GM (1,1) model. The findings of this study underscore the potential of the proposed artificial intelligence approach in facilitating informed decision-making by industrial managers, thereby fostering economic sustainability within enterprises across all operational tiers.Sustainability411This study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision and demonstrate a significant reduction in MRE levels, leading to improved prediction accuracy.Sustainability['Syed Mithun Ali', 'Amanat Ur Rahman', 'G. Kabir', 'S. Paul']2024-03-13T00:00:00https://www.semanticscholar.org/paper/b3f4dac0fe946357cdf17a73ac18ada67809cf43 cc1b78e7cda93c93c03ea0a41ee29858537a0809Diabetic eye disease (DED) is a leading cause of blindness in the world. Early detection and treatment of DED have been shown to be both sight-saving and cost-effective. As such, annual testing for DED is recommended for adults with diabetes and is a Healthcare Effectiveness Data and Information Set (HEDIS) measure. However, adherence to this guideline has historically been low, and access to this sight-saving intervention has particularly been limited for specific populations, such as Black or African American patients. In 2018, the US Food and Drug Agency (FDA) De Novo cleared autonomous artificial intelligence (AI) for diagnosing DED in a primary care setting. In 2020, Johns Hopkins Medicine (JHM), an integrated healthcare system with over 30 primary care sites, began deploying autonomous AI for DED testing in some of its primary care clinics. In this retrospective study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and whether this was different for specific populations. JHM primary care sites were categorized as “non-AI” sites (sites with no autonomous AI deployment over the study period and where patients are referred to eyecare for DED testing) or “AI-switched” sites (sites that did not have autonomous AI testing in 2019 but did by 2021). We conducted a difference-in-difference analysis using a logistic regression model to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes managed within our health system (17,674 patients for the 2019 cohort and 17,590 patients for the 2021 cohort) and has three major findings. First, after controlling for a wide range of potential confounders, our regression analysis demonstrated that the odds ratio of adherence at AI-switched sites was 36% higher than that of non-AI sites, suggesting that there was a higher increase in DED testing between 2019 and 2021 at AI-switched sites than at non-AI sites. Second, our data suggested autonomous AI improved access for historically disadvantaged populations. The adherence rate for Black/African Americans increased by 11.9% within AI-switched sites whereas it decreased by 1.2% within non-AI sites over the same time frame. Third, the data suggest that autonomous AI improved health equity by closing care gaps. For example, in 2019, a large adherence rate gap existed between Asian Americans and Black/African Americans (61.1% vs. 45.5%). This 15.6% gap shrank to 3.5% by 2021. In summary, our real-world deployment results in a large integrated healthcare system suggest that autonomous AI improves adherence to a HEDIS measure, patient access, and health equity for patients with diabetes – particularly in historically disadvantaged patient groups. While our findings are encouraging, they will need to be replicated and validated in a prospective manner across more diverse settings.Research Square210Real-world deployment results in a large integrated healthcare system suggest that autonomous AI improves adherence to a HEDIS measure, patient access, and health equity for patients with diabetes – particularly in historically disadvantaged patient groups.Research Square['T. Y. A. Liu', 'Jane Huang', 'R. Channa', 'Risa M Wolf', 'Yiwen Dong', 'Mavis Liang', 'Jiangxia Wang', 'M. Abràmoff']2024-03-13T00:00:00https://www.semanticscholar.org/paper/cc1b78e7cda93c93c03ea0a41ee29858537a0809 1548ab054e223687e4e3302c292bda994eceab66The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices by offering personalized learning experiences, automating administrative and pedagogical tasks, and reducing the cost of content creation. However, the lack of standardized practices in the development and deployment of AIED solutions has led to fragmented ecosystems, which presents challenges in interoperability, scalability, and ethical governance. This article aims to address the critical need to develop and implement industry standards in AIED, offering a comprehensive analysis of the current landscape, challenges, and strategic approaches to overcome these obstacles. We begin by examining the various applications of AIED in various educational settings and identify key areas lacking in standardization, including system interoperability, ontology mapping, data integration, evaluation, and ethical governance. Then, we propose a multi-tiered framework for establishing robust industry standards for AIED. In addition, we discuss methodologies for the iterative development and deployment of standards, incorporating feedback loops from real-world applications to refine and adapt standards over time. The paper also highlights the role of emerging technologies and pedagogical theories in shaping future standards for AIED. Finally, we outline a strategic roadmap for stakeholders to implement these standards, fostering a cohesive and ethical AIED ecosystem. By establishing comprehensive industry standards, such as those by IEEE Artificial Intelligence Standards Committee (AISC) and International Organization for Standardization (ISO), we can accelerate and scale AIED solutions to improve educational outcomes, ensuring that technological advances align with the principles of inclusivity, fairness, and educational excellence.arXiv.org160This article examines the various applications of AIED in various educational settings and identifies key areas lacking in standardization, including system interoperability, ontology mapping, data integration, evaluation, and ethical governance, and proposes a multi-tiered framework for establishing robust industry standards for AIED.ArXiv['Richard Tong', 'Haoyang Li', 'Joleen Liang', 'Qingsong Wen']2024-03-13T00:00:00https://www.semanticscholar.org/paper/1548ab054e223687e4e3302c292bda994eceab66 dea8105f8a1eb4add4442850ffa76bc9ec74ce02Artificial intelligence (AI) is changing our clinical practice. This is particularly true in cardiology where the clinician is often required to handle a large amount of clinical, biological, and imaging data during decision making. In this context, AI can address the need for fast and accurate tools while reducing the burden on clinicians and improving the efficiency of healthcare systems. With this inevitable shift towards more automated and efficient systems, patients may benefit from a more accurate diagnosis and more tailored treatment. A multitude of clinical applications have already been made available and implemented in several fields of cardiology. The aim of this narrative review is to provide an overall picture of the most recent evidence in the literature about AI implementations, highlighting their potential impact on clinical practice.Vessel Plus1680The aim of this narrative review is to provide an overall picture of the most recent evidence in the literature about AI implementations, highlighting their potential impact on clinical practice.Vessel Plus['A. Strangio', 'I. Leo', 'J. Sabatino', 'Margarita Brida', 'Chiara Siracusa', 'Nicole Carabetta', 'P. Zaffino', 'C. Critelli', 'Alessandro Laschera', 'M. Spadea', 'Daniele Torella', 'Pierre Sabouret', 'Salvatore De Rosa']2024-03-13T00:00:00https://www.semanticscholar.org/paper/dea8105f8a1eb4add4442850ffa76bc9ec74ce02 b4b155ec83764f2f43642db0642cfdadb671ea95As a matter of fact, Artificial Intelligence (AI) stands out as a crucial area of interest, seeing significant integration across diverse sectors. The gaming industry's evolution has further escalated the integration of AI, leading to an increased necessity for sophisticated game AI systems. These systems, crafted to emulate the cognitive actions and decision-making abilities of humans, enable the automation of game creation and dynamic game management. In order to give a better account of the application of AI in the game field, this study will introduce the following contents. First of all, this study will introduce what is AI, the second is to introduce the three kinds of application scenarios of AI in the game field; the third is to make a prediction on the future fusion of AI and the game industry. According to the analysis, the current features as well as limitations and prospect will be demonstrated at the same time. Overall, these results shed light on guiding further exploration of AI in gaming.Highlights in Science Engineering and Technology110This study will introduce what is AI, the three kinds of application scenarios of AI in the game field, and make a prediction on the future fusion of AI and the game industry.Highlights in Science, Engineering and Technology['Jipeng Huang']2024-03-13T00:00:00https://www.semanticscholar.org/paper/b4b155ec83764f2f43642db0642cfdadb671ea95 a430c0a1d2c2336278ea068a77dcb81348a3f029Abstract Aims Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01–1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01–1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13–2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.European Heart Journal - Digital Health230Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events in patients undergoing TAVR, and these targeted patients may benefit from further diagnostic evaluation for CA.European Heart Journal. Digital Health['Milagros Pereyra Pietri', 'Juan Farina', 'Ahmed K. Mahmoud', 'Isabel G. Scalia', 'Francesca Galasso', 'Michael E Killian', 'Mustafa Suppah', 'Courtney R. Kenyon', 'Laura M Koepke', 'R. Padang', 'Chieh-Ju Chao', 'John P. Sweeney', 'F. Fortuin', 'M. Eleid', 'Kristen A. Sell-Dottin', 'D. Steidley', 'Luis R. Scott', 'Rafael Fonseca', 'Francisco Lopez-Jimenez', 'Z. Attia', 'A. Dispenzieri', 'M. Grogan', 'Julie L Rosenthal', 'R. Arsanjani', 'Chadi Ayoub']2024-03-13T00:00:00https://www.semanticscholar.org/paper/a430c0a1d2c2336278ea068a77dcb81348a3f029 e7466bace7599abc53b6944b01492806dde26db8Abstract Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI‐models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials. Less clear is how to best harness this development, what new skill sets will be required, and how it may affect established research practices. In this paper, those question are explored with respect to increasingly more sophisticated ML/AI‐approaches. To structure the discussion, a conceptual framework of an AI‐ladder is introduced. This AI‐ladder ranges from basic data‐fitting techniques to more advanced functionalities such as semi‐autonomous experimentation, experimental design, knowledge generation, hypothesis formulation, and the orchestration of specialized AI modules as stepping‐stones toward general artificial intelligence. This ladder metaphor provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.Advancement of science810A conceptual framework of an AI‐ladder is introduced, which provides a hierarchical framework for contemplating the opportunities, challenges, and evolving skill sets required to stay competitive in the age of artificial intelligence.Advanced Science['Ayman Maqsood', 'Chen Chen', 'T. J. Jacobsson']2024-03-13T00:00:00https://www.semanticscholar.org/paper/e7466bace7599abc53b6944b01492806dde26db8 f374754bff331284cc65430e8f8483fdeef6bb04One of the main problems in ensuring the unmanned aerial systems (UAS) safety and control performance indicators is the operational analysis organization of heterogeneous data coming from on-board sensors and the formation of adequate recommendations and decisions on their basis of flight missions implementation. In recent years, there have been many research papers devoted to solving this problem using artificial intelligence (AI) methods. The article discusses AI methods for using in tasks related to UAS. We have described the sources of information to generate the data necessary for the application of AI methods. We have classified typical tasks of computer vision and navigation systems for solving using AI methods. We have analyzed the generally accepted classification of AI methods within the scope of the research subject. At the same time, special attention is paid to the features of AI methods that allow solving many well-known problems of recognition, approximation, optimization for UAS target and navigation tasks realization and effective operator support. In particular, we have considered neural networks, decision trees, support vector machines, k-nearest neighbors, genetic, ant colony algorithms, artificial immune systems. Currently, the hardware allows integrating complex algorithms based on these methods on board and widely using them in flight missions. The results of the study conducted as part of the review are illustrated by examples from the scientific publications.INFORMACIONNYE TEHNOLOGII00The sources of information to generate the data necessary for the application of AI methods are described and special attention is paid to the features of AI methods that allow solving many well-known problems of recognition, approximation, optimization for UAS target and navigation tasks realization and effective operator support.Informacionnye Tehnologii['G. S. Veresnikov', 'A. Skryabin']2024-03-13T00:00:00https://www.semanticscholar.org/paper/f374754bff331284cc65430e8f8483fdeef6bb04 c23a73a3cca56fd7f368c6b746030c9cb05137d4In healthcare and clinical research, artificial intelligence (AI) has become a disruptive force that is transforming many facets of medical practice, diagnosis, treatment, and research. In order to give a thorough overview of the role of AI in healthcare, this review paper synthesizes views from a wide range of sources, including academic publications, conference papers, industry reports, and opinion pieces. It looks at how AI is being used in healthcare settings and how it may be used for jobs like patient care, diagnosis, and therapy. The article also covers the advantages of AI in healthcare, such as increased patient monitoring, individualized therapy alternatives, and higher diagnostic accuracy. The analysis does, however, also point out a number of obstacles and constraints that come with the broad use of AI, including worries about data privacy, algorithmic bias, legal restrictions, and ethical issues. The study highlights the need for ongoing research, innovation, and cooperation to achieve the full potential of AI in enhancing healthcare outcomes as it looks forward and identifies new trends and future directions in AI-enabled healthcare. In order to enable the responsible and fair deployment of AI technology, ethical aspects surrounding AI in healthcare are also covered. These considerations include informed consent, transparency, and equity. The article highlights the value of using lessonslearned from these experiences to guide future projects by providing case studies and examples of effective AI applications in healthcare settings. The overall goal of this review paper is to educate policymakers, researchers, healthcare providers, and other stakeholders about the opportunities and challenges that come with integrating AI into healthcare systems. It also aims to offer insightful information about the revolutionary impact of AI on clinical research and healthcare. Keywords: Artificial intelligence (AI), Healthcare, Clinical research, Diagnosis, TreatmentINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The overall goal of this review paper is to educate policymakers, researchers, healthcare providers, and other stakeholders about the opportunities and challenges that come with integrating AI into healthcare systems.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['M. S']2024-03-13T00:00:00https://www.semanticscholar.org/paper/c23a73a3cca56fd7f368c6b746030c9cb05137d4 4c08159511d307c35311082543a3fb3b46c8c53e This survey study reports the perspectives and preferences of US adults regarding use of photographs of their skin in medical research, education, and development of image-based artificial intelligence (AI). JAMA dermatology60JAMA dermatology['Trina Salvador', 'L. Gu', 'Jennifer Hay', 'N. Kurtansky', 'Ruth Masterson-Creber', 'A. Halpern', 'V. Rotemberg']2024-03-13T00:00:00https://www.semanticscholar.org/paper/4c08159511d307c35311082543a3fb3b46c8c53e 4be7dd0231cafc1d8b191bb82799dfef85be57ccChatGPT, an artificial intelligence (AI) large language model system, has taken the world by storm. Fearful that students would use ChatGPT to circumvent the learning process, multiple school districts in the U.S. have outright banned its use. Despite this, there are some who suggest that ChatGPT has the potential to reshape the field of education (Heaven, 2023; Morrison, 2023). In order to demonstrate that ChatGPT has the potential to benefit the field of special education, the purpose of this article is to describe how early childhood special (ECSE) teachers can use ChatGPT to facilitate instruction and support daily teaching practices. This article begins with a description of how teachers can access and interact with ChatGPT. Discussion then shifts to how ECSE teachers can use the AI to support instruction. The article concludes with a list of limitations and a brief overview of an alternative to ChatGPT.Teaching Exceptional Children120The purpose of this article is to describe how early childhood special (ECSE) teachers can use ChatGPT to facilitate instruction and support daily teaching practices.TEACHING Exceptional Children['Conrad Oh-Young', 'Michael Karlin']2024-03-13T00:00:00https://www.semanticscholar.org/paper/4be7dd0231cafc1d8b191bb82799dfef85be57cc c210cc23009b7b9f15ef0a2f4a337a624c3e736bMachine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Consequently, regulatory bodies struggle with containing this technology's potential negative side effects. High-level requirements such as fairness and robustness need to be formalized into concrete specification metrics, imperfect proxies that capture isolated aspects of the underlying requirements. Given possible trade-offs between different metrics and their vulnerability to over-optimization, integrating specification metrics in system development processes is not trivial. This paper defines specification overfitting, a scenario where systems focus excessively on specified metrics to the detriment of high-level requirements and task performance. We present an extensive literature survey to categorize how researchers propose, measure, and optimize specification metrics in several AI fields (e.g., natural language processing, computer vision, reinforcement learning). Using a keyword-based search on papers from major AI conferences and journals between 2018 and mid-2023, we identify and analyze 74 papers that propose or optimize specification metrics. We find that although most papers implicitly address specification overfitting (e.g., by reporting more than one specification metric), they rarely discuss which role specification metrics should play in system development or explicitly define the scope and assumptions behind metric formulations.arXiv.org1630An extensive literature survey is presented to categorize how researchers propose, measure, and optimize specification metrics in several AI fields and finds that although most papers implicitly address specification overfitting, they rarely discuss which role specification metrics should play in system development or explicitly define the scope and assumptions behind metric formulations.ArXiv['Benjamin Roth', 'Pedro Henrique Luz de Araujo', 'Yuxi Xia', 'Saskia Kaltenbrunner', 'Christoph Korab']2024-03-13T00:00:00https://www.semanticscholar.org/paper/c210cc23009b7b9f15ef0a2f4a337a624c3e736b e5cebd096470db317ddf881f36ba7283ef237ec8Artificial Intelligence Information Security00Artificial Intelligence Information Security['حجازى رفاعى ، منير']2024-03-13T00:00:00https://www.semanticscholar.org/paper/e5cebd096470db317ddf881f36ba7283ef237ec8 d66ff1907ccfe0dfdb87c7c2affc8c5ec8d979beFrontiers in Artificial Intelligence00Frontiers in Artificial Intelligence['Ekkehard C. Ernst', 'Janine Berg', 'Phoebe V. Moore']2024-03-13T00:00:00https://www.semanticscholar.org/paper/d66ff1907ccfe0dfdb87c7c2affc8c5ec8d979be 5b4aaf2f8b259dca70cba9a861906b31af3831aeClinical pharmacology and therapy80Clinical pharmacology and therapeutics['B. Ribba', 'Gennaro Pagano', 'Niklas Korsbo', 'V. Ivaturi', 'Antoine Soubret']2024-03-13T00:00:00https://www.semanticscholar.org/paper/5b4aaf2f8b259dca70cba9a861906b31af3831ae 734177548ea9c2b60c1078212125a86f12e1754aCritica Sociologica00Critical Sociology['Can Lin', 'Tingting Hu']2024-03-13T00:00:00https://www.semanticscholar.org/paper/734177548ea9c2b60c1078212125a86f12e1754a cd76cfb3f84751ec4300054571d98c0b20d62355In recent years, the use of various Artificial Intelligence (AI) methods, such as evolutionary computation, heuristic algorithms, artificial neural networks, and fuzzy theory calculations, has gained popularity in addressing water resources issues. These algorithms have shown great success in solving problems that traditional deterministic methods struggle with. This study focuses on optimizing Dez reservoir operation over a long-term period using a nonlinear loss function through an evolutionary artificial neural network algorithm. The outcomes of this approach are then contrasted with genetic exploration and harmony search algorithms, highlighting the strengths and weaknesses of each method. Ultimately, a combination of the evolutionary artificial neural network method and hedging models is employed for optimal reservoir management, with results compared to the previous approach. Results show the appropriate performance of combining hedging policy with artificial neural network and harmony search algorithm. This combination significantly reduces the vulnerability value with a slight decrease in reliability.Global Journal of Ecology00This study focuses on optimizing Dez reservoir operation over a long-term period using a nonlinear loss function through an evolutionary artificial neural network algorithm, and shows the appropriate performance of combining hedging policy with artificial neural network and harmony search algorithm.Global Journal of Ecology['Karami Farzane']2024-03-13T00:00:00https://www.semanticscholar.org/paper/cd76cfb3f84751ec4300054571d98c0b20d62355 1d8318cec65dda8d2991565a591cb15fe420c168Corporate crime in the investment sector with the use of trading robots in Indonesia is a serious challenge in facing the development of financial technology. This research aims to investigate this phenomenon, analyze patterns of corporate crime related to trading robots, and explore its impact on the Indonesian capital market. Data was obtained through literature study and case analysis using a qualitative approach. The research results highlight various corporate criminal practices, such as market manipulation and investor fraud, which are made easier by the use of trading robots. Factors such as lack of proper regulation and technological complexity are the main triggers for increasing the risk of corporate crime in investing with trading robots. The implications of this research include the need to increase regulators' and stakeholders' awareness of these potential risks, as well as increasing legal protection for investors and capital market security. Thus, this research contributes to a better understanding of the dynamics of corporate crime in the field of investment with trading robots in Indonesia and provides a basis for developing more effective policies to overcome this challenge.International Journal of Multicultural and Multireligious Understanding00The research results highlight various corporate criminal practices, such as market manipulation and investor fraud, which are made easier by the use of trading robots, which are made easier by the use of trading robots.International Journal of Multicultural and Multireligious Understanding['Abdurrahman Al Akhdloriy', 'Chalik Mawardi Aiyub', 'Indra Rahmatullah', 'Erlisa Akhlakul Karimah']2024-03-12T00:00:00https://www.semanticscholar.org/paper/1d8318cec65dda8d2991565a591cb15fe420c168 4dbbad16d4a9360776912d11d1ba2973269d931fSymposium on Computer Science and Law121{'pages': '121-131'}['Clement Guitton', 'Simon Mayer', 'Aurelia Tamó-Larrieux', 'Kimberly García', 'Nicoletta Fornara']2024-03-12T00:00:00https://www.semanticscholar.org/paper/4dbbad16d4a9360776912d11d1ba2973269d931f 54145876c0dbc03378456060fbc1ca789a27cb65This paper explores the integration of distributed ledger technology (DLT) and artificial intelligence (AI) in digital asset transactions, focusing on the challenges of security, privacy protection, and smart contract reliability. Through a comprehensive analysis, it was found that DLT ensures transaction security and transparency through decentralized recording and consensus mechanisms, while AI enhances security through anomaly detection and threat analysis. In addition, the convergence of DLT and AI has significantly enhanced privacy protection by encrypting data transfers and using data desensitization techniques. In addition, AI-driven automated testing and vulnerability prediction improve the reliability and execution efficiency of smart contracts, ensuring the integrity of transactions. Overall, the integration of DLT and AI provides a solid framework for safe, efficient and reliable digital asset trading, paving the way for the further development and maturity of the digital asset market.Academic Journal of Science and Technology303The integration of DLT and AI provides a solid framework for safe, efficient and reliable digital asset trading, paving the way for the further development and maturity of the digital asset market.Academic Journal of Science and Technology['Qishuo Cheng', 'Yulu Gong', 'Yang Qin', 'Xiang Ao', 'Zhenglin Li']2024-03-12T00:00:00https://www.semanticscholar.org/paper/54145876c0dbc03378456060fbc1ca789a27cb65 556b2f47da278d60893d0cb5f3f5ffa23100997aFriction3152A fusion method among 5 types of tribo-system information and different AI technologies has been proposed, which enables tribo-informatics methods to solve common problems such as tribological behavior state monitoring, behavior prediction, and system optimization.Friction['N. Yin', 'Pufan Yang', 'Songkai Liu', 'Shuaihang Pan', 'Zhinan Zhang']2024-03-12T00:00:00https://www.semanticscholar.org/paper/556b2f47da278d60893d0cb5f3f5ffa23100997a e5e22814768b5a70ebfafaa71e1b2b4cf9b3ddc3Purpose The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration. Design/methodology/approach In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate. Findings As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop. Originality/value This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates. Team Performance Management561The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop.Team Performance Management: An International Journal['Eleni Georganta', 'Anna-Sophie Ulfert']2024-03-12T00:00:00https://www.semanticscholar.org/paper/e5e22814768b5a70ebfafaa71e1b2b4cf9b3ddc3 81b2834f3f884785e94a8c4c1b0bf66f858e0d97Numerous applications today rely on artificial intelligence over images. Image AI is, however, extremely expensive. In particular, the inference cost of image AI dominates the end-to-end cost. We observe that the image storage format lies at the root of the problem. Images today are predominantly stored in JPEG format. JPEG is a storage format designed for the human eye; it maximally compresses images without distorting the components of an image that are visible to the human eye. However, our observation is that during image AI, images are "seen'' by algorithms, not humans. In addition, every AI application is different regarding which data components of the images are the most relevant. We present the Image Calculator, a self-designing image storage format that adapts to the given AI task, i.e., the specific neural network, the dataset, and the applications' specific accuracy, inference time, and storage requirements. Contrary to the state-of-the-art, the Image Calculator does not use a fixed storage format like JPEG. Instead, it designs and constructs a new storage format tailored to the context. It does so by constructing a massive design space of candidate storage formats from first principles, within which it searches efficiently using composite performance models (inference time, accuracy, storage). This way, it leverages the given AI task's unique characteristics to compress the data maximally. We evaluate the Image Calculator across a diverse set of data, image analysis tasks, AI models, and hardware. We show that the Image Calculator can generate image storage formats that reduce inference time by up to 14.2x and storage by up to 8.2x with a minimal loss in accuracy or gain, compared to JPEG and its state-of-the-art variants.Proc. ACM Manag. Data541The Image Calculator is presented, a self-designing image storage format that adapts to the given AI task, i.e., the specific neural network, the dataset, and the applications' specific accuracy, inference time, and storage requirements, and leverages the given AI task's unique characteristics to compress the data maximally.Proc. ACM Manag. Data['Utku Sirin', 'Stratos Idreos']2024-03-12T00:00:00https://www.semanticscholar.org/paper/81b2834f3f884785e94a8c4c1b0bf66f858e0d97 2d303a3348a5773dd43b8d73a1b42dde186f5a21This article investigates the historical uses and types of artificial intelligence (AI) systems and resources in Brazilian journalistic products. It is a work anchored in critically analyzing the literature on the subject, mapping and observing cases, seeking to identify uses and innovative processes, and analyzing AI projects for journalism. A search was carried out in web repositories, specifically Google, Google Scholar, and Scopus, using the terms: “inteligência artificial” + “jornalismo”, “bot + jornalismo”, “Geração de linguagem natural [NLG] + jornalismo”, “aprendizado de máquina [machine learning] + jornalismo”, and “algoritmos + jornalismo”. The corpus analysis (N = 45) includes the evaluation of the impacts of AI on the production and distribution of news in the context of Brazilian digital journalism. We try to answer questions about the uses of databases, approximation with platforms, uses of shared codes, connections with other Ais, and sources of funding, and whether they are backend or frontend initiatives. In a parallel investigation, we try to identify if Brazilian newsrooms are officially using ChatGPT, a generative AI. The findings point to advances in using low-cost and low-impact AI, with the predominance of bots. The great availability of this kind of AI in web repositories is believed to facilitate native digital media to incorporate innovative processes in using these technologies.Journalism and Media421The historical uses and types of artificial intelligence systems and resources in Brazilian journalistic products are investigated, and if Brazilian newsrooms are officially using ChatGPT, a generative AI is identified.Journalism and Media['Moisés Costa Pinto', 'Suzana Oliveira Barbosa']2024-03-12T00:00:00https://www.semanticscholar.org/paper/2d303a3348a5773dd43b8d73a1b42dde186f5a21 6f50de901b6681a6bd9b378ca60b5427e237944dSymposium on Computer Science and Law282{'pages': '48-63'}['Katherine Lee', 'A. F. Cooper', 'James Grimmelmann']2024-03-12T00:00:00https://www.semanticscholar.org/paper/6f50de901b6681a6bd9b378ca60b5427e237944d 37eb0c2177acf85c74ace80a9365d610523bc64bArtificial Intelligence (AI) is increasingly adopted by public sector organizations to provide better public services and to transform their internal processes. AI is now considered a key enabler for digital innovation and transformation in the public sector. However, AI is still relatively a new research area in the field of digital government. The term, AI, captures a wide range of technologies, techniques, and tools such as machine/deep learning, natural language processing, robotics, computer vision, and more recently Generative AI. While these AI technologies afford different applications and benefits in the government context, they also create social, ethical, and legal challenges. These challenges require solutions combining both technical (e.g., data and algorithmic solutions to minimize bias) and institutional (e.g., governance structures and processes) mechanisms. The special issue is a collection of articles that contribute to a better understanding of the issues associated with AI deployment in different areas of government operations. They cover AI applications in the areas of emergency response, policy analysis, public bids, and citizen participation. The contributions also address the challenge of realizing a legal transparency regime for AI in government and the effect of AI in bureaucratic decision-making.Digit. Gov. Res. Pract.140The special issue is a collection of articles that contribute to a better understanding of the issues associated with AI deployment in different areas of government operations and addresses the challenge of realizing a legal transparency regime for AI in government and the effect of AI in bureaucratic decision-making.Digit. Gov. Res. Pract.['Sehl Mellouli', 'Marijn Janssen', 'Adegboyega Ojo']2024-03-12T00:00:00https://www.semanticscholar.org/paper/37eb0c2177acf85c74ace80a9365d610523bc64b 53adf2677c41b1f2e267c1266bc78ff7d3ef6500In an era where Artificial Intelligence (AI) integration into business processes is crucial for maintaining competitiveness, there is a growing need for structured guidance on designing AI solutions that align with human needs. To this end, we present “technical assistance concerning human-centered AI development” (tachAId), an interactive advisory tool which comprehensively guides AI developers and decision makers in navigating the machine learning lifecycle with a focus on human-centered design. tachAId motivates and presents concrete technical advice to ensure human-centeredness across the phases of AI development. The tool's effectiveness is evaluated through a catalog of criteria for human-centered AI in the form of relevant challenges and goals, derived from existing methodologies and guidelines. Lastly, tachAId and one other comparable advisory tool were examined to determine their adherence to these criteria in order to provide an overview of the human-centered aspects covered by these tools and to allow interested parties to quickly assess whether the tools meet their needs.Frontiers in Artificial Intelligence260Frontiers in Artificial Intelligence['Max Bauroth', 'Pavlos Rath-Manakidis', 'Valentin Langholf', 'Laurenz Wiskott', 'Tobias Glasmachers']2024-03-12T00:00:00https://www.semanticscholar.org/paper/53adf2677c41b1f2e267c1266bc78ff7d3ef6500 11b9adb22e051a5bba7469a41f20714c93e8af17 Our aim with this special issue on the future of artificial intelligence (AI) politics, policy, and business is to give space to considering how the balalnce between risk and reward from AI technologies is and perhaps should be pursued by the public and private sectors. Ultimately, private firms and regulators will need to work collaboratively, given the complex networks of actors involved in AI development and deployment and the potential for the technology to alter existing policy regimes. We begin the introduction of this special issue of Business & Politics with a discussion of the growth in AI technology use and discussions of appropriate governance, followed by a consideration of how AI-related politics, policy, and business intersect. We then summarize the contributions of the authors in this issue and conclude with thoughts about how political science, public administration, and public policy scholars have much to offer, as well as much to study, the establishment of effective AI governance.Business and Politics410This issue concludes with thoughts about how political science, public administration, and public policy scholars have much to offer, as well as much to study, the establishment of effective AI governance.Business and Politics['Eric Best', 'Pedro Robles', 'Daniel J. Mallinson']2024-03-12T00:00:00https://www.semanticscholar.org/paper/11b9adb22e051a5bba7469a41f20714c93e8af17 b373c71b09528470e10e4b515b3c2a80bc7f57f6This study aimed to explore the perspectives of EFL instructors working in a variety of universities in the UAE on the effectiveness of AI applications in the EFL classroom. EFL teachers need to use AI applications in ways that are aligned with instructional goals and support student learning. A quantitative approach was used, and data was gathered from a survey of 46 EFL instructors. The results showed that the instructors strongly relied on AI applications to facilitate tasks, offer data-driven insights to improve instructional strategies and customize the learning process for each student. They also positively valued the benefits that AI applications bring to their classrooms for improving the teaching process. Notably, the results showed that the years of teaching experience had a statistically significant impact on the means of EFL instructors' perspectives regarding the benefits of adopting AI apps in EFL classrooms. The results also showed that, despite teaching experience, there were no significant differences in perceptions regarding the challenges of utilizing AI apps. This is probably because EFL students are accustomed to using technology in their lectures. Due to their benefits in English language instruction, the study suggests incorporating AI applications into the EFL teaching process. Doi: 10.28991/ESJ-2024-SIED1-05 Full Text: PDFEmerging Science Journal220The results showed that, despite teaching experience, there were no significant differences in perceptions regarding the challenges of utilizing AI apps, and the study suggests incorporating AI applications into the EFL teaching process.Emerging Science Journal['W. Hazaymeh', 'Abdeldjalil Bouzenoun', 'Abdelghani Remache']2024-03-12T00:00:00https://www.semanticscholar.org/paper/b373c71b09528470e10e4b515b3c2a80bc7f57f6 8cf4fd0285463ee8458d6d1168e3263ceef82d41Digitalisation and automation of operations in the railway industry include the use of Automatic Train Operation systems that provide automated functions to reach different levels of automation, known as the Grade of Automation (GoA) levels. These levels go up to GoA4 in which the train is automatically controlled without any staff on board. Artificial intelligence has emerged as technology that can substitute humans in certain driving tasks, in GoA3 (driverless) and GoA4 (unattended) modes. AI capabilities include perception, decision-making, precise positioning, or optimization of communications. The success of AI models depends on the quality and diversity of the data used for training, along with the set-up of a data life-cycle framework that covers creation, training, testing, deployment and monitorisation. The management of training datasets implies both expensive and time-consuming data gathering, labelling, curation and formatting efforts, potentially hindering the development of reliable AI systems. This paper presents a Common Data Management Platform developed by a consortium of European railway stakeholders, devised to efficiently manage data for AI training, and which is demonstrated in two different Proofs of Concept.Transportation Development Research380A Common Data Management Platform developed by a consortium of European railway stakeholders, devised to efficiently manage data for AI training, and which is demonstrated in two different Proofs of Concept.Transportation Development Research['M. Labayen', 'Daniel Ochoa de Eribe', 'Ander Aramburu', 'Marcos Nieto', 'N. Aginako']2024-03-12T00:00:00https://www.semanticscholar.org/paper/8cf4fd0285463ee8458d6d1168e3263ceef82d41 6741acaef7f1ffe66c42144c6c0da4c3c6df3862It has been six years since an ISPD-2018 invited talk on “Machine Learning Applications in Physical Design” [25]. Since then, despite considerable activity across both academia and industry, many R&D targets remain open. At the same time, there is now clearer understanding of where AI/ML can and cannot (yet) move the needle in physical design, as well as some of the difficult blockers and technical challenges that lie ahead. Some futures for AI/ML-boosted physical design are visible across solvers, engines, tools and flows – and in contexts that span generative AI, the modeling of “magic” handoffs at flow interstices, academic research infrastructure, and the culture of benchmarking and open-source EDA.ACM International Symposium on Physical Design610It has been six years since an ISPD-2018 invited talk on “Machine Learning Applications in Physical Design” and there is now clearer understanding of where AI/ML can and cannot move the needle in physical design, as well as some of the difficult blockers and technical challenges that lie ahead.{'pages': '117-124'}['Andrew B. Kahng']2024-03-12T00:00:00https://www.semanticscholar.org/paper/6741acaef7f1ffe66c42144c6c0da4c3c6df3862 90c5fda484021e0c301c8cca5ea8f9621fb926dfSome crucial decisions in AI design tend to be overlooked or factor choices are assumed implicitly. The question often answered first is what the AI will do, not how it will interact with the rest of the world. This reduces our understanding of the possible types of AI that can be developed and their potential impacts on humanity. As an initial AI taxonomy, I present binary choices for 10 of the subjectively most separable and influential high-level design factors, then give brief examples of several of the 1024 possible systems defined by those choices. This supports a simple binary stream approach to system designation based on translating the stream of choices into decimal notation, giving a short-hand way of referring to systems with different properties that meet specialized needs. Further, underspecified or generic systems can be designated using the binary stream approach as well, a notational feature that supports modeling the impacts of AI systems with selected characteristics.arXiv.org180As an initial AI taxonomy, a simple binary stream approach to system designation based on translating the stream of choices into decimal notation is presented, giving a short-hand way of referring to systems with different properties that meet specialized needs.ArXiv['Julia Mossbridge']2024-03-12T00:00:00https://www.semanticscholar.org/paper/90c5fda484021e0c301c8cca5ea8f9621fb926df bf8420705cff4a3fffee97d62a4818fd8d882c5eEarly detection of breast cancer with computer assistance has developed since two decades ago. Artificial intelligence using the convolutional neural network (CNN) method has successfully predicted mammography images with a high level of accuracy similar to human brain learning. The potential of AI models provides opportunities to spot breast cancer cases better. This research aims to develop AI models with CNN using the public DDSM dataset with a sample size of 1871, consisting of 1546 images for training and 325 images for testing. These AI models provided prediction results with different accuracy rate. Increasing the accuracy of the AI model can be done by improving the image quality before the modeling process, increasing the number of datasets, or carrying out a more profound iteration process so that the AI model with CNN can have a better level of accuracy.Generation Journal300This research aims to develop AI models with CNN using the public DDSM dataset using the public DDSM dataset with a sample size of 1871, consisting of 1546 images for training and 325 images for testing.Generation Journal['N. Nurbaiti', 'Eka Putra Syarif Hidayat', 'Khairil Anwar', 'Dudung Hermawan', 'Salman Izzuddin']2024-03-12T00:00:00https://www.semanticscholar.org/paper/bf8420705cff4a3fffee97d62a4818fd8d882c5e ce5062561a36f15ec1cd203705736d7ab335e9a9Businesses are becoming more interested in developing and testing Large Language Models (LLMs) in their own settings to support decision-making and growth as a result of the rapid emergence of AI and cloud computing. Here’s the dilemma, though: to what extent do you believe these models and the data they were trained on? We don’t know the feature list of an LLM, which presents the first obstacle when discussing trust and the reasons why there should be zero trust. Although it may seem a bit extreme, this is accurate for two reasons. When it comes to GenAI models nowadays, the more multimodal and more capabilities they have, the better. This way of thinking is great for exploring and confirming if GenAI can address a business problem, but it’s a surefire way to run into trouble when attempting to put things into production in an organizational setting. An enterprise cybersecurity architecture known as a zero-trust architecture (ZTA) is built on the ideas of zero trust and is intended to stop data breaches, enhance privacy, and restrict internal lateral movement. This article discusses ZTA, its logical aspects, probable deployment scenarios, AI rules, threats and limitations in order to provide a detailed understanding of why enterprises must adapt a ZTA framework in a cloud-based environment for AI model deployment.Social Science Research Network160This article discusses ZTA, its logical aspects, probable deployment scenarios, AI rules, threats and limitations in order to provide a detailed understanding of why enterprises must adapt a ZTA framework in a cloud-based environment for AI model deployment.SSRN Electronic Journal['Bibhu Dash']2024-03-12T00:00:00https://www.semanticscholar.org/paper/ce5062561a36f15ec1cd203705736d7ab335e9a9 93dbb5b053d9bf8c932d2694d8b5c11ef04e1906Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an AI4EDA approach falls short of achieving a holistic design synthesis and understanding, overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This paper argues for a paradigm shift from AI4EDA towards AI-native EDA, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, RTL designs, circuit netlists, and physical layouts. We champion the creation of large circuit models (LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-native philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound shift-left in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area (PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.2350This paper argues for a paradigm shift from AI4EDA towards AI-native EDA, integrating AI at the core of the design process, and champions the creation of large circuit models that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies.['Lei Chen', 'Yiqi Chen', 'Z. Chu', 'Wenji Fang', 'Tsung-Yi Ho', 'Ru Huang', 'Yu Huang', 'Sadaf Khan', 'Min Li', 'Xingquan Li', 'Yu Li', 'Yun Liang', 'Jinwei Liu', 'Yi Liu', 'Yibo Lin', 'Guojie Luo', 'Zhengyuan Shi', 'Guangyu Sun', 'Dimitrios Tsaras', 'Runsheng Wang', 'Ziyi Wang', 'Xinming Wei', 'Zhiyao Xie', 'Qiang Xu', 'Chenhao Xue', 'Junchi Yan', 'Jun Yang', 'Bei Yu', 'Mingxuan Yuan', 'Evangeline F. Y. Young', 'Xuanlin Zeng', 'Haoyi Zhang', 'Zuodong Zhang', 'Yuxiang Zhao', 'Hui-Ling Zhen', 'Ziyang Zheng', 'Binwu Zhu', 'Keren Zhu', "Sunan Zou Huawei Noah's Ark Lab", 'Peking University', 'Ningbo University', 'H. T. S. O. Science', 'Technology', 'The Hong Kong', 'Southeast University', 'Huawei Hisilicon', 'Peng Cheng Laboratory', 'S. University', 'Fudan University']2024-03-12T00:00:00https://www.semanticscholar.org/paper/93dbb5b053d9bf8c932d2694d8b5c11ef04e1906 207ea1c4cf6ad3daa19ed849c55428967f49d112The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI.arXiv.org00This systematic literature review will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains, and propose a taxonomy of technical and non-technical axiological trustworthiness metrics, and some trustworthy measurements.ArXiv['S. Afroogh', 'Ali Akbari', 'Evan Malone', 'Mohammadali Kargar', 'Hananeh Alambeigi']2024-03-12T00:00:00https://www.semanticscholar.org/paper/207ea1c4cf6ad3daa19ed849c55428967f49d112 fbb7e913a133657ea08f7fd28a2f56754a3d5a4eThis paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a user study with designers (N=20), we found that CPD's branching and its emphasis on causal connections supported both divergent and convergent processes during design. CPD can also facilitate communication among stakeholders. Additionally, we found our plugin significantly reduces designers' cognitive workload and increases their creativity during brainstorming, highlighting the implications of AI-assisted tools in supporting creative work and evidence-based designs.International Conference on Human Factors in Computing Systems1060This paper explores the integration of causal pathway diagrams into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process and the implications of AI-assisted tools in supporting creative work and evidence-based designs.{'pages': '2:1-2:19'}['Ruican Zhong', 'Donghoon Shin', 'Rosemary Meza', 'P. Klasnja', 'Lucas Colusso', 'Gary Hsieh']2024-03-12T00:00:00https://www.semanticscholar.org/paper/fbb7e913a133657ea08f7fd28a2f56754a3d5a4e f7cb1da1b7a6fdc5a5926796a502eac69c1b30d6This article discusses the application of artificially intelligent robots within eldercare and explores a series of ethical considerations, including the challenges that AI (Artificial Intelligence) technology poses to traditional Chinese Confucian filial piety. From the perspective of Confucian ethics, the paper argues that robots cannot adequately fulfill duties of care. Due to their detachment from personal relationships and interactions, the "emotions" of AI robots are merely performative reactions in different situations, rather than actual emotional abilities. No matter how "humanized" robots become, it is difficult to establish genuine empathy and a meaningful relationship with them for this reason. Even so, we acknowledge that AI robots are a significant tool in managing the demands of elder care and the growth of care poverty, and as such, we attempt to outline some parameters within which care robotics could be acceptable within a Confucian ethical system. Finally, the paper discusses the social impact and ethical considerations brought on by the interaction between humans and machines. It is observed that the relationship between humans and technology has always had both utopian and dystopian aspects, and robotic elder care is no exception. AI caregiver robots will likely become a part of elder care, and the transformation of these robots from "service providers" to "companions" seems inevitable. In light of this, the application of AI-augmented robotic elder care will also eventually change our understanding of interpersonal relationships and traditional requirements of filial piety.Nursing Ethics310The application of artificially intelligent robots within eldercare is discussed and a series of ethical considerations are explored, including the challenges that AI (Artificial Intelligence) technology poses to traditional Chinese Confucian filial piety.Nursing ethics['Kathryn Muyskens', 'Yonghui Ma', 'Michael Dunn']2024-03-12T00:00:00https://www.semanticscholar.org/paper/f7cb1da1b7a6fdc5a5926796a502eac69c1b30d6 195fb01dcc5c0f3392e05bfae6b6f20f61bfe301Discover Artificial Intelligence90The objective is to provide senior leaders with an understanding, enabling them to harness AI/ML effectively, ensuring their organizations remain at the innovation forefront in a digital age dominated by disruptive AI/ML technologies.Discov. Artif. Intell.['Abdullah A. Abonamah', 'Neda Abdelhamid']2024-03-12T00:00:00https://www.semanticscholar.org/paper/195fb01dcc5c0f3392e05bfae6b6f20f61bfe301 a196b9c85702330df6cc4e0a0b2fe1edf251ac3eArtificial Intelligence (AI) has taken control of the world in many disciplines. The focus of this article is limited to education industry nonetheless in sports, medical or advertising as long as education is incorporated and taught in any field. AI which is commonly being used is ChatGPT and robots in other circumstances. This article scope is on literature review which states the purpose and the importance of the systematic literature review (SLR). It compares SLR with narrative literature review which has different purposes. SLR is also distinguished with meta-analysis. The objective of the literature review is to find the research gap on issues in teaching and learning study. There is a plethora of database that can be reviewed likewise Scopus, Mendeley, Clarivet, ScienceDirect and PubMed. Systematic Literature Review is used to synthesise the ideas based on a list of literature review. This article has obtained information from 16 literature review. The methodology that was being used include a proper direction for the search engine to obtain information by using similar keywords as the title for a wider scope of search. Inclusion and exclusion studies were carried on and the search managed to gather 37 journals pertaining to AI. All the literature review was not taken into account as some were related to other aspects ignoring teaching and learning. Only 16 articles were included excluding 21. Each two articles were compared and synthesised. Information gathered says that AI has increased performance of students. Besides, this article has also extracted perceptions on teachers integrating AI to educate students from science background. There is also drawback on the use of AI as it increases the rate of plagiarism. Thus, findings indicate that AI will be subject to human control.International Journal For Multidisciplinary Research230Findings indicate that AI will be subject to human control, and perceptions on teachers integrating AI to educate students from science background are extracted.International Journal For Multidisciplinary Research['Jane Irene PJ Antony', 'Puteri Zarina Megat Khalid']2024-03-12T00:00:00https://www.semanticscholar.org/paper/a196b9c85702330df6cc4e0a0b2fe1edf251ac3e 3c3a5f5921278382a9793634c5c8c734728c3f4cFrontiers in Molecular Biosciences01Frontiers in Molecular Biosciences['Frank Emmert-Streib', 'Silvia Bottini', 'Leonardo Franco']2024-03-12T00:00:00https://www.semanticscholar.org/paper/3c3a5f5921278382a9793634c5c8c734728c3f4c 462b92b5e25994669a641cd329da4f287139fad0Symposium on Computer Science and Law251{'pages': '64-72'}['Rui-Jie Yew']2024-03-12T00:00:00https://www.semanticscholar.org/paper/462b92b5e25994669a641cd329da4f287139fad0 b9fefa9826c08d3858245d03749296b671a25b01Over the past few decades, cardiovascular disease has emerged as the primary cause of death worldwide in both industrialized and developing nations. Early detection of heart problems and continued clinical monitoring can reduce death rates. However, because it takes more time and experience, it is not possible to accurately detect heart disorders in all cases and to have a specialist talk with a patient for 24 hours. We demonstrate how machine learning can be used to estimate an individual's risk of developing heart disease. This study presents data processing, which includes converting categorical columns and working with categorical variables. We outline the three primary stages of developing an application: gathering datasets, running logistic regression, and assessing the properties of the dataset. The random forest classifier technique is developed to diagnose cardiac problems more precisely. Data analysis is needed for this application since it is considered noteworthy. The random forest classifier algorithm, which improves the accuracy of research diagnosis, is next covered, along with the experiments and findings.International Journal of Innovative Science and Research Technology111It is demonstrated how machine learning can be used to estimate an individual's risk of developing heart disease through data processing, which includes converting categorical columns and working with categorical variables.International Journal of Innovative Science and Research Technology (IJISRT)['Narannagari Chaathurya', 'Sikharam Abhinav', 'Battu Sri Vamshidhar', 'Kandula Revathi']2024-03-12T00:00:00https://www.semanticscholar.org/paper/b9fefa9826c08d3858245d03749296b671a25b01 91999d4b7f66976e375624e2d519d8e82d963c19Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy.Inf.470This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability in using pretrained transformers for diabetes classification on tabular data.Inf.['Leon Kopitar', 'Iztok Fister', 'Gregor Stiglic']2024-03-12T00:00:00https://www.semanticscholar.org/paper/91999d4b7f66976e375624e2d519d8e82d963c19 51e44221499bec3f880328d4b073d44ae61cdfecBackground Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. Objective The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. Methods The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. Results We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence–based CAC innovations to improve coding practice. Expected results to be published summer 2024. Conclusions The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. Trial Registration clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865 International Registered Report Identifier (IRRID) DERR1-10.2196/54593JMIR Research Protocols350Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence–based CAC innovations to improve coding practice.JMIR Research Protocols['T. Chomutare', 'Anastasios Lamproudis', 'A. Budrionis', 'Therese Olsen Svenning', 'Lill Irene Hind', 'P. Ngo', 'Karl Øyvind Mikalsen', 'H. Dalianis']2024-03-12T00:00:00https://www.semanticscholar.org/paper/51e44221499bec3f880328d4b073d44ae61cdfec 1b6c568f3b8c10f4f887d2f0487c7c0ad46093e4The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the development of G-LLM-based knowledge systems. In our study, using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2 language models. Based on measurements shown on different datasets, we demonstrate that RAG-based constructions are more efficient than models produced with FN. We point out that connecting RAG and FN is not trivial, because connecting FN models with RAG can cause a decrease in performance. Furthermore, we outline a simple RAG-based architecture which, on average, outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case of the BLEU score, and 53% based on the cosine similarity. This shows the significant advantage of RAG over FN in terms of hallucination, which is not offset by the fact that the average 8% better METEOR score of FN models indicates greater creativity compared to RAG.arXiv.org150This study compares and examines the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2 language models, and outlines a simple RAG-based architecture which outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case of the BLEU score, and 53% based on the cosine similarity.ArXiv['Róbert Lakatos', 'P. Pollner', 'András Hajdu', 'Tamas Joo']2024-03-12T00:00:00https://www.semanticscholar.org/paper/1b6c568f3b8c10f4f887d2f0487c7c0ad46093e4 a3e6741a7c47608f482a57c36514f3f30c81e7e2Communications of the ACM00Commun. ACM['Esther Shein']2024-03-12T00:00:00https://www.semanticscholar.org/paper/a3e6741a7c47608f482a57c36514f3f30c81e7e2 5110be48e825ae8e3bbbd9c81387326b563cc349Journal of Educational Media370Learning, Media and Technology['Luke Munn', 'Leah Henrickson']2024-03-12T00:00:00https://www.semanticscholar.org/paper/5110be48e825ae8e3bbbd9c81387326b563cc349 9ded0b15db01b5d12c392fe9e11645581e47a5faACM International Symposium on Physical Design00{'pages': '1'}['Ruchir Puri']2024-03-12T00:00:00https://www.semanticscholar.org/paper/9ded0b15db01b5d12c392fe9e11645581e47a5fa 82710682248ae05cc4b581b3952a5d75ed28d93e00['Maria Jose Sousa', 'S. Pani', 'F. Dal Mas', 'Sérgio Sousa']2024-03-12T00:00:00https://www.semanticscholar.org/paper/82710682248ae05cc4b581b3952a5d75ed28d93e f63110c57a0adcd180f25b04c628ab27ea4b6a38Education and Information Technologies : Official Journal of the IFIP technical committee on Education370Education and Information Technologies['Ching-Huei Chen', 'Ching-Ling Chang']2024-03-12T00:00:00https://www.semanticscholar.org/paper/f63110c57a0adcd180f25b04c628ab27ea4b6a38 2f61e4ba9b49f7600c90fa89f21ddad27e0df2ffThis paper provides an observation about performance of models of object detection in different conditions of input image quality. Now, there are many different computer vision models suitable for special tasks. Therefore, some of them may not be accurate in some forecasts, but they do not miss a single true value. When choosing a computer vision model, it is necessary to rely on the problems that need to be solved, since there is no universal solution. However, in this article, the choice of models is based on the Average Precision (AP) metric - which allows us to highlight how the model copes with different types of tasks, without losing its accuracy. The MS COCO (Microsoft Common Objects in Context) database was taken as the basis for the measurements, since it is one of the most extensive and rich databases which is used to compare the characteristics of various computer vision technologies, and its dataset covers most areas. The article also discusses metrics and analysis performance, based on what model was selected.2024 Systems of Signals Generating and Processing in the Field of on Board Communications200This paper provides an observation about performance of models of object detection in different conditions of input image quality based on the Average Precision (AP) metric - which allows us to highlight how the model copes with different types of tasks, without losing its accuracy.2024 Systems of Signals Generating and Processing in the Field of on Board Communications['Kirill Ponomarenko', 'D. Egorov', 'Vsevolod Kudryashov', 'Anastasia Egorova', 'I. Vlasuyk']2024-03-12T00:00:00https://www.semanticscholar.org/paper/2f61e4ba9b49f7600c90fa89f21ddad27e0df2ff 0e2daf65806215a0c6817345ae131962ae8f898a00['Verity Harding']2024-03-12T00:00:00https://www.semanticscholar.org/paper/0e2daf65806215a0c6817345ae131962ae8f898a 0ed7b404379ea4ac99210e20aba88fe8c0a8f35cHigh-Power Laser Materials Processing: Applications, Diagnostics, and Systems XIII00High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XIII['M. Strecker', 'Paul Böttner', 'Benjamin Yildiz', 'Saskia Heinrichs', 'T. Walbaum', 'Thomas Strecker']2024-03-12T00:00:00https://www.semanticscholar.org/paper/0ed7b404379ea4ac99210e20aba88fe8c0a8f35c 84f3149b5ebd31fafe86cc1ddf3ee0adb2e85354Transplantation80Transplantation['Carlos Goncalves', 'Bryon Bhagwandin', 'John Malamon', 'Bruce Kaplan']2024-03-12T00:00:00https://www.semanticscholar.org/paper/84f3149b5ebd31fafe86cc1ddf3ee0adb2e85354 489d226fb739ec8367b768136441dc8ef4f925ed<jats:p />JMIR AI00JMIR AI['Max Rollwage', 'J. Habicht', 'Keno Juechems', 'Ben Carrington', 'Sruthi Viswanathan', 'Mona Stylianou', 'Tobias U Hauser', 'Ross Harper']2024-03-12T00:00:00https://www.semanticscholar.org/paper/489d226fb739ec8367b768136441dc8ef4f925ed 3fa1165d6f3d65492d766831258cea1bc776a308ACM International Symposium on Physical Design00{'pages': '113'}['Erick Chao']2024-03-12T00:00:00https://www.semanticscholar.org/paper/3fa1165d6f3d65492d766831258cea1bc776a308 963d3d23627053c2f36c58f99ed20a52e5d2e6e9Artificial Intelligence (AI) has permeated various aspects of daily life, including education, specifically within higher education settings. These AI technologies have transformed pedagogy and learning, enabling a more personalized approach. However, ethical and practical concerns have also emerged, including the potential decline in cognitive skills and student motivation due to excessive reliance on AI.To develop and validate a Scale for Dependence on Artificial Intelligence (DIA).An Exploratory Factor Analysis (EFA) was used to identify the underlying structure of the DIA scale, followed by a Confirmatory Factor Analysis (CFA) to assess and confirm this structure. In addition, the scale’s invariance based on participants’ gender was evaluated.A total of 528 university students aged between 18 and 37 years (M = 20.31, SD = 3.8) participated. The EFA revealed a unifactorial structure for the scale, which was subsequently confirmed by the CFA. Invariance analyses showed that the scale is applicable and consistent for both men and women.The DAI scale emerges as a robust and reliable tool for measuring university students’ dependence on AI. Its gender invariance makes it applicable in diverse population studies. In the age of digitalization, it is essential to understand the dynamics between humans and AI to navigate wisely and ensure a beneficial coexistence.Frontiers in Education462The DAI scale emerges as a robust and reliable tool for measuring university students’ dependence on AI and its gender invariance makes it applicable in diverse population studies.Frontiers in Education['Wilter C. Morales-García', 'Liset Z. Sairitupa-Sanchez', 'Sandra B. Morales-García', 'Mardel Morales-García']2024-03-12T00:00:00https://www.semanticscholar.org/paper/963d3d23627053c2f36c58f99ed20a52e5d2e6e9 846a7de3c2fe1078dc80bb4c81ab45f0675305a6Artificial intelligence (AI) holds the promise of addressing many of the numerous challenges healthcare faces, which include a growing burden of illness, an increase in chronic health conditions and disabilities due to aging and epidemiological changes, higher demand for health services, overworked and burned-out clinicians, greater societal expectations, and rising health expenditures. While technological advancements in processing power, memory, storage, and the abundance of data have empowered computers to handle increasingly complex tasks with remarkable success, AI introduces a variety of meaningful risks and challenges. Among these are issues related to accuracy and reliability, bias and equity, errors and accountability, transparency, misuse, and privacy of data. As AI systems continue to rapidly integrate into healthcare settings, it is crucial to recognize the inherent risks they bring. These risks demand careful consideration to ensure the responsible and safe deployment of AI in healthcare.Journal of Clinical Endocrinology and Metabolism182As AI systems continue to rapidly integrate into healthcare settings, it is crucial to recognize the inherent risks they bring and demand careful consideration to ensure the responsible and safe deployment of AI in healthcare.The Journal of clinical endocrinology and metabolism['Graham T McMahon']2024-03-12T00:00:00https://www.semanticscholar.org/paper/846a7de3c2fe1078dc80bb4c81ab45f0675305a6 dea3df527e4d9a14e5236fab08e93fd86f1e682fAesthetic Plastic Surgery01The synergistic collaboration between AI and plastic surgery holds great promise in advancing clinical practice, fostering innovation, and ultimately benefiting patients through optimized esthetic and reconstructive outcomes.Aesthetic plastic surgery['M. Barone', 'Riccardo De Bernardis', 'P. Persichetti']2024-03-12T00:00:00https://www.semanticscholar.org/paper/dea3df527e4d9a14e5236fab08e93fd86f1e682f e98dadf9fa9dd6ac448a6ba03df9b6772be3eeb8Almost every aspect of your day is impacted by artificial intelligence. Contrary to popular belief, artificial intelligence (AI) is not limited to smart speakers and digital assistants. It is quickly becoming a general-purpose technology with far-reaching effects in many other sectors, such as healthcare, transportation, finance, and more. Although AI has had and will continue to have a tremendous impact on many companies, it is by no means limited to digital behemoths like Google, Amazon, and Facebook. Artificial intelligence (AI) is here now and will be here tomorrow in your home life and business. If you want to be well-prepared for the technological future, improving your knowledge of the topic is essential, and this book will provide you with the essential guidance you need.00This book will provide you with the essential guidance you need to be well-prepared for the technological future and improve your knowledge of the topic is essential.['Dr. P. Sathish', 'Dr. Umadevi Ramamoorthy', 'Dr. Chitra Ravi']2024-03-12T00:00:00https://www.semanticscholar.org/paper/e98dadf9fa9dd6ac448a6ba03df9b6772be3eeb8 c9d6d9fc95f8e164f6f63d996247cf89a7f65f8c Purpose This paper aims to analyze the impact of tax digitalization, focusing on artificial intelligence (AI), machine learning and blockchain technologies, on enhancing tax compliance behavior in various contexts. It seeks to understand how these emerging digital tools influence taxpayer behaviors and compliance levels and to assess their effectiveness in reducing tax evasion and avoidance practices. Design/methodology/approach Using a systematic review technique with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this study evaluates 62 papers collected from the Scopus database. The papers were analyzed through textometry of titles, abstracts and keywords to identify prevailing trends and insights. Findings The review reveals that digitalization, particularly through AI and blockchain, significantly enhances tax compliance and operational efficiency. However, challenges persist, especially in emerging economies, regarding the adoption and integration of these technologies in tax systems. The findings indicate a global trend toward digital Tax Administration 3.0, emphasizing the importance of regulatory frameworks, capacity building and simplification for small and medium enterprises (SMEs). Practical implications The findings provide guidance for policymakers and tax administrations, underscoring the necessity of strategic planning, regulatory backing and global cooperation to effectively use digital technologies in tax compliance. Emphasizing the need for tailored support for SMEs, the study also calls for expanded research in less represented areas and specific sectors, such as SMEs and developing economies, to deepen global insights into digital tax compliance. Originality/value This study has attempted to fill the gap in the literature on the comprehensive impact of fiscal digitalization, particularly AI-based, on tax compliance across different global contexts, adding to the discourse on digital taxation. Accounting Research Journal370The review reveals that digitalization, particularly through AI and blockchain, significantly enhances tax compliance and operational efficiency, however, challenges persist, especially in emerging economies, regarding the adoption and integration of these technologies in tax systems.Accounting Research Journal['Rida Belahouaoui', 'El Houssain Attak']2024-03-12T00:00:00https://www.semanticscholar.org/paper/c9d6d9fc95f8e164f6f63d996247cf89a7f65f8c 02243b860bc21f9bb095c4422537b2a91d4c12ebThe study of artificial intelligence is of increasing importance in the entrepreneurial domain. Despite the popularity of many artificial intelligence models, experimental studies in entrepreneurship that apply models are subject to replicability issues if they are not properly reported on. This note is a call to adopt a method of reporting on artificial intelligence models commonly used in the open source software community to ensure progress in future studies and to offer researchers a reflective opportunity to consider the appropriateness of models they use in experimental studies.International Journal of Entrepreneurship and Innovation150This note is a call to adopt a method of reporting on artificial intelligence models commonly used in the open source software community to ensure progress in future studies and to offer researchers a reflective opportunity to consider the appropriateness of models they use in experimental studies.The International Journal of Entrepreneurship and Innovation['Joseph D Fox']2024-03-12T00:00:00https://www.semanticscholar.org/paper/02243b860bc21f9bb095c4422537b2a91d4c12eb 400cf3eb96c0d23ead6a2749ecaa25cbbe5239cfUpdates in Surgery560The role of artificial intelligence in diagnosis and treatment of acute appendicitis and the role of artificial intelligence in the emergency departments is considered to assess the state of the art of artificial intelligence in this frequent acute disease.Updates in surgery['V. Bianchi', 'M. Giambusso', 'Alessandra De Iacob', 'M. Chiarello', 'G. Brisinda']2024-03-12T00:00:00https://www.semanticscholar.org/paper/400cf3eb96c0d23ead6a2749ecaa25cbbe5239cf bb0feaa28e0272c6dda4b67c0f4cb48058c4d59cThe emergence of artificial intelligence in the higher education publishing context has led to scholars seeking opportunities to leverage the new technological affordances offered by the tool. Yet, there have been questions emerging about the extent to which artificial intelligence should prompt scholars towards certain outcomes. In this commentary, we examine the need for human flourishing to sit at the forefront of decisions around academic publishing alongside the pursuit of fair and innovative knowledge creation and dissemination. We advocate an evidence-based position against artificial intelligence as a peer reviewer, recognising that parroting knowledge is insufficient to be critical and comprehensive in the review process. There are significant limitations to the current artificial intelligence tools from bias to current corpus limitations that restrict its usefulness as a gatekeeper of knowledge, a key role a reviewer takes on board. We offer suggestions for places where artificial intelligence tools may be quite useful and offer some future directions for artificial intelligence in publishing processes.Journal of University Teaching and Learning Practice270The need for human flourishing to sit at the forefront of decisions around academic publishing alongside the pursuit of fair and innovative knowledge creation and dissemination is examined.Journal of University Teaching and Learning Practice['Joseph Crawford', 'Kelly-Ann Allen', 'Jason Lodge']2024-03-12T00:00:00https://www.semanticscholar.org/paper/bb0feaa28e0272c6dda4b67c0f4cb48058c4d59c d282eb7075e0853ec8432845581a62831ab9a9cdThis paper studies the complex impact mechanism of AI (artificial intelligence) technology on international trade. This provides an answer to how different countries use AI technology to increase their own international trade volume. This article is based on an analysis of cross-sectional data for 139 countries in 2021. This paper uses the Ordinary Least Square (OLS) method to perform a analysis on the cross-sectional data. This paper finds that the membership of World Trade Organization (WTO) has obvious significance for a country to use AI technology to promote exports. In countries with a high Government AI Readiness Index, AI technology has a significant role in promoting the growth of international trade. However, there is a significant negative correlation between the number of patent applications for AI-related technologies and imports and exports across countries. Besides, the number of patent applications for AI-related technologies has a direct heterogeneous impact on the imports of countries with different income groups. AI technology is an emerging technology. To study the impact of this technology on international trade, multiple factors of AI technology must be considered at the same time. For countries that are not members of the WTO, joining the WTO can make better use of AI technology to promote the development of their international trade. Between the complex relationship between the number of patent applications for different AI-related technologies and international trade, countries with different income groups should develop AI technologies that are beneficial to their own international trade.Advanced International Journal of Business Entrepreneurship and SMEs00It is found that the membership of World Trade Organization (WTO) has obvious significance for a country to use AI technology to promote exports and joining the WTO can make better use of AI technology to promote the development of their international trade.Advanced International Journal of Business, Entrepreneurship and SMEs['Zuo Yin Qian', 'Wei Sieng Lai']2024-03-12T00:00:00https://www.semanticscholar.org/paper/d282eb7075e0853ec8432845581a62831ab9a9cd 34e3c1f81c6a4bfc5f5308e584db814d2929fd58Сочетание искусственного интеллекта и высшего образования является неизбежной тенденцией общественного развития, а использование искусственного интеллекта является неизбежным результатом развития высшего образования. Целью исследования является выявление влияние ChatGPT на развитие высшего профессионального образования. Исследование показало, при использовании ChatGPT и другого искусственного интеллекта будут оказывать влияние на развития системы высшего профессионального образования, ставя перед ней новые задачи и проблемы. В результате исследования выявлено, появление и использование ChatGPT также привело к дальнейшему распространению технологизма в высшем образовании, и ставит под угрозу ценности преподавателей и студентов ВУЗов, и влияет на инновационную деятельность в высших учебных заведениях, на работу преподавателей и обучение обучающихся. the combination of artificial intelligence and higher education is an inevitable trend of social development, and the use of artificial intelligence is an inevitable result of the development of higher education. The aim of the study is to identify the impact of ChatGPT on the development of higher professional education. The study showed that using ChatGPT and other artificial intelligence will have an impact on the development of the higher professional education system, posing new tasks and problems to it. The study revealed that the emergence and use of ChatGPT has also led to the further spread of technologism in higher education, and threatens the values of university teachers and students, and affects innovation in higher education institutions, the work of teachers and the training of students.Bulletin of Pedagogical Sciences00Bulletin of Pedagogical Sciences['Б. Шао']2024-03-12T00:00:00https://www.semanticscholar.org/paper/34e3c1f81c6a4bfc5f5308e584db814d2929fd58 9c671287c66eaa685170afca87c5737191683716Artificial Intelligence (AI) has a bright prospective to convert education in various ways, including personal learning experiences, automation based administrative tasks, enlightening accessibility, and providing real-time feedbacks to both concerned students & teachers. AI is going to adjust the concerned teaching strategies to increase learning outcomes of the educational institute students. High cost of the concerned tools and ethical concerns of AI is regularly preventing the educational institute to adopt in their systems. Purposeful sampling was chosen during the determination of the participants. Four target groups that include 100 persons in total have been identified regarding AI in education and 25 participants from each group viz. Academicians, Legal Experts, Technical Experts and Teachers. Results have suggested that uncontrolled, excessive and inappropriate use of mobile phone is causing social, behavioral and affective problems. There are fewer places for the teachers and more for the robots after the implementation of AI in the education. Displacement of the Jobs will be more possible in the area of teaching. AI means elimination of jobs for the persons engaged in the educational institute.Saudi Journal of Business and Management Studies00Results have suggested that uncontrolled, excessive and inappropriate use of mobile phone is causing social, behavioral and affective problems, and there are fewer places for the teachers and more for the robots after the implementation of AI in the education.Saudi Journal of Business and Management Studies['Mohammad Aslam Khan', 'Mohammad Wajahat']2024-03-12T00:00:00https://www.semanticscholar.org/paper/9c671287c66eaa685170afca87c5737191683716 760851d5b9f849b72c1a12899a8de15bf0698e78INTRODUCTION: With the rapid development of artificial intelligence technology, wearable artificial intelligence devices show great potential in medical rehabilitation. This study explores the Real Time monitoring effect of AI wearable devices in the rehabilitation process of track and field athletes. The application of this technology in rehabilitation monitoring was investigated through the introduction of advanced sensing technology and data analysis algorithms to provide track and field athletes with more scientific and personalized rehabilitation programs. OBJECTIVES: A group of track and field athletes was selected as the research object and equipped with an artificial intelligence wearable device, which is capable of Real Time monitoring of the athletes' physiological parameters, sports postures, joint mobility, and other rehabilitation-related data. An individualized rehabilitation model was established through the data collected by these sensors, and advanced artificial intelligence algorithms were used to analyze the data in Real Time. At the same time, the sensor data were combined with the actual performance of the athletes' rehabilitation training to comprehensively assess the effectiveness of AI wearable devices in rehabilitation monitoring. METHODS: This study aims to assess the effect of Real Time monitoring of AI wearable devices in the rehabilitation of track and field athletes and to explore their potential application in the rehabilitation process. Real Time tracking of athletes' physiological status and athletic performance aims to provide more accurate and timely information to rehabilitation doctors and coaches to optimize the rehabilitation training program and promote the rehabilitation process of athletes. RESULTS: The study showed that artificial intelligence wearable devices have significant Real Time monitoring effects in rehabilitating track and field athletes. Through Real Time monitoring of data such as physiological parameters, sports posture, and joint mobility, the rehabilitation team was able to identify potential problems and adjust the rehabilitation program in a more timely manner. Athletes using artificial intelligence wearable devices improved the personalization and targeting of rehabilitation training, and the rehabilitation effect was significantly better than that of traditional monitoring methods. CONCLUSION: This study concludes that artificial intelligence wearable devices perform well in rehabilitating track and field athletes, providing a more scientific and comprehensive means of rehabilitation monitoring. Through Real Time tracking, the rehabilitation team could better understand the rehabilitation progress of the athletes, adjust the rehabilitation program in a targeted manner, and improve the rehabilitation effect. However, future research still needs to optimize the performance of the devices further, expand the sample size, and thoroughly study the monitoring needs at different stages of rehabilitation to better meet the individualized requirements of track and field athletes' rehabilitation process.EAI Endorsed Transactions on Pervasive Health and Technology210Through Real Time tracking, the rehabilitation team could better understand the rehabilitation progress of the athletes, adjust the rehabilitation program in a targeted manner, and improve the rehabilitation effect.EAI Endorsed Transactions on Pervasive Health and Technology['Bin Wu']2024-03-12T00:00:00https://www.semanticscholar.org/paper/760851d5b9f849b72c1a12899a8de15bf0698e78 217b81cd2937b116b4772726daee088253eb14c8 It is widely believed that we are on the brink of another military revolution. Today, states are actively seeking to harness the power of AI for military advantage. The question of AI is therefore of profound concern to security studies scholars concerned with global issues. Up to now, the literature has tended to concentrate on AI-enabled lethal autonomous weapons; scholars have been fascinated by the possible appearance of autonomous drone swarms and their implications for security, conflict, and war. This article takes an alternative view. It argues that AI has already begun to play a significant role in military operations and is likely to be more important in the future. However, the attention to lethal autonomous weapons is exaggerated. The armed forces have principally employed AI, not to automate weapons but to help process data. AI has been used to augment military intelligence. Above all, the armed forces have harnessed AI to accelerate and improve military targeting. The article explores two recent cases where the armed forces have used data and AI to target: COVID testing in Liverpool in 2020 and the US’s Security Assistance Group-Ukraine in the Ukraine War in 2022.Journal of Global Security Studies290It is argued that AI has already begun to play a significant role in military operations and is likely to be more important in the future and the attention to lethal autonomous weapons is exaggerated.Journal of Global Security Studies['Anthony King']2024-03-12T00:00:00https://www.semanticscholar.org/paper/217b81cd2937b116b4772726daee088253eb14c8 1837fc307a7007a411951c50a0ffb8c62638a392Asia Pacific Journal of Tourism Research171Asia Pacific Journal of Tourism Research['Chen Chen', 'Zhao Wei']2024-03-12T00:00:00https://www.semanticscholar.org/paper/1837fc307a7007a411951c50a0ffb8c62638a392 4eb9f17e1b59080d6a932cc2e30f14c6c5c8b710To create and train a deep learning model costs a lot in comparison to ascertain a trained model. So, a trained model is considered as the intellectual property (IP) of the person who creates such model. However, there is every chance of illegal copying, redistributing and abusing of any of these high-performance models by the malicious users. To protect against such menaces, a few numbers of deep neural networks (DNN) IP security techniques have been developed recently. The present study aims at examining the existing DNN IP security activities. In the first instance, there is a proposal of taxonomy in favor of DNN IP protection techniques from the perspective of six aspects such as scenario, method, size, category, function, and target models. Afterwards, this paper focuses on the challenges faced by these methods and their capability of resisting the malicious attacks at different levels by providing proactive protection.  An analysis is also made regarding the potential threats to DNN IP security techniques from various perspectives like modification of models, evasion and active attacks. Apart from that this paper look into the methodical assessment. The study explores the future research possibilities on DNN IP security by considering different challenges it would confront in the process of its operations. Result Statement: A high-performance deep neural Networks (DNN) model is costlier than the trained DNN model. It is considered as an intellectual property (IP) of the person who is responsible for creating DNN model. The infringement of the Intellectual Property of DNN model is a grave concern in recent years. This article summarizes current DNN IP security works by focusing on the limitations/ challenges they confront. It also considers the model in question's capacity for protection and resistance against various stages of attacks.EAI Endorsed Transactions on Internet of Things270Current DNN IP security works are summarized by focusing on the limitations/ challenges they confront and the model in question's capacity for protection and resistance against various stages of attacks.EAI Endorsed Transactions on Internet of Things['Parthasarathi Pattnayak', 'T. Das', 'Arpeeta Mohanty', 'Sanghamitra Patnaik']2024-03-12T00:00:00https://www.semanticscholar.org/paper/4eb9f17e1b59080d6a932cc2e30f14c6c5c8b710 b400dcace6a5daa4f59fdc307fa900578934d020Queen Mary Journal of Intellectual Property00Queen Mary Journal of Intellectual Property['Johanna Gibson']2024-03-12T00:00:00https://www.semanticscholar.org/paper/b400dcace6a5daa4f59fdc307fa900578934d020 db36a10ce854e12ebd8e723d1654c9ea7ea45435Journal of cataract and refractive surgery00Journal of cataract and refractive surgery['Andrew Mihalache', 'R. Huang', 'Marko M. Popovic', 'Rajeev H. Muni']2024-03-12T00:00:00https://www.semanticscholar.org/paper/db36a10ce854e12ebd8e723d1654c9ea7ea45435 016ceb1611609637815c20f4315eb7f3945a20d8Frontiers in Robotics and AI00Frontiers in Robotics and AI['Q. Abu Al-haija']2024-03-12T00:00:00https://www.semanticscholar.org/paper/016ceb1611609637815c20f4315eb7f3945a20d8 c914cbc56970044dedd5ea47b0eeb0ceaab2e664Nurse Education Today190Integrating an artificial intelligence chatbot system into a nursing training program provides nurses with easy access to reliable and evidence-based knowledge, empowering nurses to make informed decisions, stay updated, and enhance their practice.Nurse education today['Entesar M. M. Makhlouf', 'Ataalla Alenezi', 'Eman A. Shokr']2024-03-12T00:00:00https://www.semanticscholar.org/paper/c914cbc56970044dedd5ea47b0eeb0ceaab2e664 3f730bb3af40718d2aed6af802b38119b219a808High-Speed Biomedical Imaging and Spectroscopy IX00High-Speed Biomedical Imaging and Spectroscopy IX['Mika Hayashi', 'S. Ohnuki', 'Yating Tsai', 'Tianben Ding', 'Akihiro Isozaki', 'Yoshikazu Ohya', 'Keisuke Goda']2024-03-12T00:00:00https://www.semanticscholar.org/paper/3f730bb3af40718d2aed6af802b38119b219a808 34cd5ee68f83aa9e2aa91c0d7af58b866d127730Scientific Reports540The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.Scientific Reports['Mustafa Erkam Özateş', 'Alper Yaman', 'F. Salami', 'S. Campos', 'Sebastian I. Wolf', 'Urs Schneider']2024-03-12T00:00:00https://www.semanticscholar.org/paper/34cd5ee68f83aa9e2aa91c0d7af58b866d127730 9af541bbab10fbee6fa51c120ad661ceb4fb3e30With the rapid development of artificial intelligence technology, algorithmic management is increasingly prevalent in enterprises. Despite the considerable scholarly attention given to the impact of algorithmic management, a research gap remains regarding its influence on employee creativity. To address this gap, the authors developed a theoretical model using ability-motivation-opportunity (AMO) theory. This model aims to investigate the direct impacts of algorithmic management (opportunity) on employee creativity (performance) while also considering the mediating roles played by knowledge combination capability (ability) and achievement goal (motivation). Using a sample of 327 paired leader-employee data from an information technology service company, the findings reveal that algorithmic management has a negative effect on employee creativity. Furthermore, the results demonstrate that algorithmic management negatively influences employee creativity through its impact on knowledge combination capability and achievement goal.Journal of Organizational and End User Computing440A theoretical model using ability-motivation-opportunity (AMO) theory reveals that algorithmic management has a negative effect on employee creativity and demonstrates that algorithmic management negatively influences employee creativity through its impact on knowledge combination capability and achievement goal.Journal of Organizational and End User Computing['Daiheng Li', 'Mingyue Liu', 'Yun Zhao', 'Yuzhu Li', 'Tao Zhang', 'Wenjia Zhang', 'Dongrui Xia', 'Bo Lv']2024-03-12T00:00:00https://www.semanticscholar.org/paper/9af541bbab10fbee6fa51c120ad661ceb4fb3e30 cfcef514a6ede0a357e1acb335192eca0833b8bdAn analysis was carried out of the development of the testing system for unmanned aircraft systems, including those controlled through artificial intelligence technologies. It is shown that existing aviation testing technologies do not fully meet the requirements for testing unmanned aircraft systems. A list of technological features and requirements for testing unmanned aircraft systems is provided. A comparative assessment of the cost of testing unmanned aircraft systems using existing and innovative technologies intended for testing such systems is given. Using the developed computer model of a certification testing laboratory for unmanned aircraft systems, the optimal financial and functional indicators of such a laboratory were determined, ensuring the minimum cost of certification tests available to small and medium-sized businesses.2024 Systems of Signals Generating and Processing in the Field of on Board Communications1102024 Systems of Signals Generating and Processing in the Field of on Board Communications['V. V. Filatov', 'M. Karelina', 'V. I. Gvozdarev', 'D. V. Rybakov']2024-03-12T00:00:00https://www.semanticscholar.org/paper/cfcef514a6ede0a357e1acb335192eca0833b8bd d8f8a61a497c999471246c712acd98ce49b92123Privacy policies are expected to inform data subjects about their data protection rights and should explain the data controller's data management practices. Privacy policies only fulfill their purpose, if they are correctly interpreted, understood, and trusted by the data subject. This implies that a privacy policy is written in a fair way, e.g., it does not use polarizing terms, does not require a certain education, or does not assume a particular social background. We outline our approach to assessing fairness in privacy policies. We identify from fundamental legal sources and fairness research, how the dimensions informational fairness, representational fairness and ethics / morality are related to privacy policies. We propose options to automatically assess policies in these fairness dimensions, based on text statistics, linguistic methods and artificial intelligence. We conduct initial experiments with German privacy policies to provide evidence that our approach is applicable. Our experiments indicate that there are issues in all three dimensions of fairness. This is important, as future privacy policies may be used in a corpus for legal artificial intelligence models.arXiv.org950This work identifies from fundamental legal sources and fairness research, how the dimensions informational fairness, representational fairness and ethics / morality are related to privacy policies and proposes options to automatically assess policies in these fairness dimensions, based on text statistics, linguistic methods and artificial intelligence.ArXiv['Vincent Freiberger', 'Erik Buchmann']2024-03-12T00:00:00https://www.semanticscholar.org/paper/d8f8a61a497c999471246c712acd98ce49b92123 8b910aaa410dd1a5b3c0be5134394af23bc6b848International Conference on Automated Software Engineering01The software development industry needs new tools to understand the potential, limitations, and risks of generative AI, as well as guidelines for using it, and proposes four primary scenarios, model trajectories for transitions between them, and reflect against relevant software development operations.Automated Software Engineering['Jaakko Sauvola', 'Sasu Tarkoma', 'Mika Klemettinen', 'Jukka Riekki', 'David Doermann']2024-03-11T00:00:00https://www.semanticscholar.org/paper/8b910aaa410dd1a5b3c0be5134394af23bc6b848 751893cc81a70fe5dc07d9c7fdd10c606ab7d468 This article explores the proposed amendments to the AI Act, which introduce the concept of “groups of persons”. The inclusion of this notion has the potential to broaden the traditional individual-centric approach in data protection. The analysis explores the context and the challenges posed by the rapid evolution of technology, with an emphasis on the role of artificial intelligence (AI) systems. It discusses both the potential benefits and challenges of recognising groups of people, including issues such as discrimination prevention, public trust and redress mechanisms. The analysis also identifies key challenges, including the lack of a clear definition for “group”, the difficulty in explaining AI architecture concerning groups and the need for well-defined redress mechanisms. The article also puts forward recommendations aimed at addressing these challenges in order to enhance the effectiveness and clarity of the proposed amendments.European Journal of Risk Regulation00The proposed amendments to the AI Act introduce the concept of “groups of persons”, which has the potential to broaden the traditional individual-centric approach in data protection and identifies key challenges, including the lack of a clear definition for “group”, the difficulty in explaining AI architecture concerning groups and the need for well-defined redress mechanisms.European Journal of Risk Regulation['Liubomir Nikiforov']2024-03-11T00:00:00https://www.semanticscholar.org/paper/751893cc81a70fe5dc07d9c7fdd10c606ab7d468 9110ca60bd6f8cc44741c82d628990e7c6f9438fOur research endeavors to advance the concept of responsible artificial intelligence (AI), a topic of increasing importance within EU policy discussions. The EU has recently issued several publications emphasizing the necessity of trust in AI, underscoring the dual nature of AI as both a beneficial tool and a potential weapon. This dichotomy highlights the urgent need for international regulation. Concurrently, there is a need for frameworks that guide companies in AI development, ensuring compliance with such regulations. Our research aims to assist lawmakers and machine learning practitioners in navigating the evolving landscape of AI regulation, identifying focal areas for future attention. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI. Through a structured literature review, we elucidate the current understanding of responsible AI. Drawing from this analysis, we propose an approach for developing a future framework centered around this concept. Our findings advocate for a human-centric approach to Responsible AI. This approach encompasses the implementation of AI methods with a strong emphasis on ethics, model explainability, and the pillars of privacy, security, and trust.arXiv.org00A comprehensive and, to the knowledge, the first unified definition of responsible AI is introduced, elucidate the current understanding of responsible AI, and proposes an approach for developing a future framework centered around this concept.ArXiv['Sabrina Goellner', 'Marina Tropmann-Frick', 'B. Brumen']2024-03-11T00:00:00https://www.semanticscholar.org/paper/9110ca60bd6f8cc44741c82d628990e7c6f9438f 081dab159c41233a4100434a1e11c7ef74dac445Journal of the Operational Research Society490Journal of the Operational Research Society['Yan Zhou', 'Chuanxu Wang']2024-03-11T00:00:00https://www.semanticscholar.org/paper/081dab159c41233a4100434a1e11c7ef74dac445 8cb5080991267ff77c716ad92113c31b3c8f249fIEEE/ACM International Conference on Human-Robot Interaction420{'pages': '257-262'}['Abena Boadi-Agyemang', 'Minjung Park']2024-03-11T00:00:00https://www.semanticscholar.org/paper/8cb5080991267ff77c716ad92113c31b3c8f249f 023e113b11ff7bac182713a069fedcbcccad9562We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.arXiv.org804The maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level and observes corpus-level trends in generated text which may be too subtle to detect at the individual level.ArXiv['Weixin Liang', 'Zachary Izzo', 'Yaohui Zhang', 'Haley Lepp', 'Hancheng Cao', 'Xuandong Zhao', 'Lingjiao Chen', 'Haotian Ye', 'Sheng Liu', 'Zhi Huang', 'Daniel A. McFarland', 'James Y. Zou']2024-03-11T00:00:00https://www.semanticscholar.org/paper/023e113b11ff7bac182713a069fedcbcccad9562 628f6049f1726f9b1211cf5e7ef0a7e9eacfdb39This study explores the negative impacts of artificial intelligence (AI) in the context of Islamic education, with a focus on distortions in religious understanding, curricula, and interactions between students and teachers. Employing a qualitative research method and analyzing the works of Islamic education experts, the findings indicate that the use of artificial intelligence in Islamic education can result in distortions in the understanding of religious values, a loss of student creativity, and significant changes in the role of teachers and the learning environment. Using AI in Islamic education presents ethical challenges and the risk of distorting the interpretation of religious texts. Therefore, the recommendation from this research is the necessity of strict supervision and the development of ethical guidelines in implementing AI technology in Islamic education. This will help preserve religious values' integrity while harnessing technology's positive potential for religious understanding. The findings of this research offer valuable insights for Islamic education practitioners and policymakers by emphasizing the importance of maintaining religious values in the modern technological era. The negative impacts of AI use in Islamic education, such as the distortion of religious understanding and the loss of student creativity, underscore the urgency of adjusting AI implementation to mitigate risks and ensure the continuity of high-quality religious education  Urwatul Wutsqo: Jurnal Studi Kependidikan dan Keislaman02The findings indicate that the use of artificial intelligence in Islamic education can result in distortions in the understanding of religious values, a loss of student creativity, and significant changes in the role of teachers and the learning environment.Urwatul Wutsqo: Jurnal Studi Kependidikan dan Keislaman['Faisol Hakim', 'A. Fadlillah', 'M. Rofiq']2024-03-11T00:00:00https://www.semanticscholar.org/paper/628f6049f1726f9b1211cf5e7ef0a7e9eacfdb39 5e95b702997a6e1ae7c4ab8336c551c1c65f10caResearch and activism have increasingly denounced the problematic environmental record of the infrastructure and value chain underpinning artificial intelligence (AI). Water-intensive data centres, polluting mineral extraction and e-waste dumping are incontrovertibly part of AI’s footprint. In this article, I turn to areas affected by AI-fuelled environmental harm and identify an ethics of resistance emerging from local activists, which I term ‘elemental ethics’. Elemental ethics interrogates the AI value chain’s problematic relationship with the elements that make up the world, critiques the undermining of local and ancestral approaches to nature and reveals the vital and quotidian harms engendered by so-called intelligent systems. While this ethics is emerging from grassroots and Indigenous groups, it echoes recent calls from environmental philosophy to reconnect with the environment via the elements. In empirical terms, this article looks at groups in Chile resisting a Google data centre project in Santiago and lithium extraction (used for rechargeable batteries) in Lickan Antay Indigenous territory, Atacama Desert. As I show, elemental ethics can complement top-down, utilitarian and quantitative approaches to AI ethics and sustainable AI as well as interrogate whose lived experience and well-being counts in debates on AI extinction.AI &amp; SOCIETY671Elemental ethics can complement top-down, utilitarian and quantitative approaches to AI ethics and sustainable AI as well as interrogate whose lived experience and well-being counts in debates on AI extinction.ArXiv['Sebastian Lehuede']2024-03-11T00:00:00https://www.semanticscholar.org/paper/5e95b702997a6e1ae7c4ab8336c551c1c65f10ca 5940d94058f9cf6aa8bf90416d5a908f17e7ace2IEEE/ACM International Conference on Human-Robot Interaction42{'pages': '71-73'}['Eike Schneiders', 'S. Benford', 'Ju Row-Farr', 'Nick Tandavanitj', 'M. Adams']2024-03-11T00:00:00https://www.semanticscholar.org/paper/5940d94058f9cf6aa8bf90416d5a908f17e7ace2 51552e278ecc6a8fc2c4c5471dcc9e4d3c2184a6This study captured the perspectives of 887 working adults to explore views of professionalism, authenticity, and effectiveness of AI-generated messages. With a 3 (message type) × 2 (disclosed vs. undisclosed) × 2 (ChatGPT-generated vs. Google-generated AI messages) design, professionals generally view AI-generated content favorably in all conditions. Across all messages, professionals consistently rated the AI-generated messages as professional, effective, efficient, confident, and direct. They rate sincerity and caring slightly lower in some disclosed conditions, particularly for ChatGPT-generated messages, suggesting the importance of tool selection when using generative AI for workplace writing. Those professionals who use AI more frequently for work are more likely to view AI-assisted writing as authentic, effective, and confidence-building. Implications for teaching business communication, including the need to address AI literacy, and suggestions for future research are provided.Business and Professional Communication Quarterly290Business and Professional Communication Quarterly['Anthony W. Coman', 'Peter Cardon']2024-03-11T00:00:00https://www.semanticscholar.org/paper/51552e278ecc6a8fc2c4c5471dcc9e4d3c2184a6 689734c22a718b4e76e1a907c1726e388e865f87AI and Ethics260A novel framework, called CCC (collective-centered creation), is developed that helps resolve uncertainty around who creates and should be credited with the outputs made with the help of GenAI.AI and Ethics['Donal Khosrowi', 'Finola Finn', 'Elinor Clark']2024-03-11T00:00:00https://www.semanticscholar.org/paper/689734c22a718b4e76e1a907c1726e388e865f87 4baeb5e2eb0db105e8e26606b69ac14543b8de41This study presents an innovative approach for employee depression identification by integrating advanced Artificial Intelligence (AI) techniques. The Modified Learning Procedure with AI Assistance (MLPAI) is employed, combining Feature Extraction through RF, Classification using AlexNet, and Optimization facilitated by the Mayfly Optimization Algorithm The res earch aims to improve the precision of depression identification within the workforce. The Feature Extraction phase employs RF to robustly extract relevant features from diverse datasets. AlexNet, a deep learning classification model, is then employed for accurate categorization. The Mayfly optimization algorithm optimizes the system and fine-tuning parameters for increased precision. The experimental analysis involves a targeted examination of Millennial and Generation Z employees in South Korea, aged between 20 and 40. The implemented MLPAI system achieved an impressive accuracy of 97.34%, demonstrating its efficacy in identifying employee depression. The entire system has been implemented and documented in a Jupyter Notebook, providing a transparent and accessible framework for researchers and practitioners interested in replicating or extending this study. Through this comprehensive approach, the study aims to contribute valuable insights into the intersection of AI, mental health, and workplace dynamics, offering a novel framework for addressing and mitigating employee depression.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)120The Modified Learning Procedure with AI Assistance (MLPAI) is employed, combining Feature Extraction through RF, Classification using AlexNet, and Optimization facilitated by the Mayfly Optimization Algorithm to improve the precision of depression identification within the workforce.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)['A.Megala', 'Geetha Manoharan', 'Dr. Ramchandra D. Patil', 'Asst. Professor', 'B. Vidyapeeth', 'Dr.S.Hemambika', 'V.P.Gladis Pushparathi Professor', 'Bhola Khan']2024-03-11T00:00:00https://www.semanticscholar.org/paper/4baeb5e2eb0db105e8e26606b69ac14543b8de41 a3d46f7e92546b10ba9f4e5218b901503c277b86Humanities and Social Sciences Communications360To extract insights from data, a descriptive-prescriptive hybrid data processing method is proposed that includes graphical visualization, cross-tabulation to identify patterns and correlations, clustering using K -means, principal component analysis (PCA) enabling 3D cluster representation, analysis of variance (ANOVA) of clusters, and forecasting potential leveraged by Random Forest to predict clusters.Humanities and Social Sciences Communications['S. Oprea', 'A. Bâra']2024-03-11T00:00:00https://www.semanticscholar.org/paper/a3d46f7e92546b10ba9f4e5218b901503c277b86 9d36c5930ee313cda3b376b309f27d84e53c6b5aImplementasi kecerdasan buatan (Artificial Intelligence/AI) di sektor publik di Indonesia telah dirumuskan dalam Strategi Nasional Kecerdasan Artifisial (Stranas KA), di mana Kementerian Komunikasi dan Informatika Republik Indonesia (Kemenkominfo RI) berperan penting sebagai focal point pemerintah dalam implementasi transformasi digital nasional. Saat ini implementasi AI di bidang pelayanan publik masih perlu dikaji ulang karena adanya kinerja algoritma dan tata kelola data yang dipandang melanggar etika. Rumusan masalah yang diajukan dalam penelitian ini adalah Bagaimana peran implementasi AI dalam memperkuat ikatan kontrak sosial antara pemerintah dengan warga negaranya? Untuk menjawab pertanyaan tersebut, penelitian ini menganalisis manfaat AI dalam pengambilan keputusan dan pelayanan publik di Kemenkominfo RI, menganalisis pemahaman Kemenkominfo RI terhadap isu etika dan kebijakan tentang AI di Indonesia RI, serta menganalisis strategi Kemenkominfo RI dalam menggunakan AI untuk pengambilan keputusan dan pelayanan publik di Indonesia RI. Penelitian ini menggunakan metode deksirptif kualitatif. Pengumpulan data dilakukan melalui wawancara semi terstruktur di Ditjen Aptika Kemenkominfo RI, dan studi dokumentasi. Hasil penelitian menunjukkan implementasi AI dalam hal pengambilan keputusan masih dalam tahap awal dan sangat bergantung pada ketersediaan dan sentralisasi data. Isu etika dan kebijakan AI di Indonesia, adalah di seputar belum mampunya perusahaan dan UMKM lokal untuk bersaing dengan perusahaan besar, dan hambatan belum rampungnya pusat data nasional membuat pemerintah belum dapat mengendalikan sepenuhnya penerapan AI.Journal of Social Economics Research00Journal of Social and Economics Research['Washington Simanjuntak', 'Agus Subagyo', 'Dadang Sufianto']2024-03-11T00:00:00https://www.semanticscholar.org/paper/9d36c5930ee313cda3b376b309f27d84e53c6b5a 5e830d27f8ebc6c7ea6b6f5c65f5096db7be8584Abstract Background Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases. Method A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools. Results Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses. Conclusions The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.Annals medicus430The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions, however, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety.Annals of Medicine['Moh Heri Kurniawan', 'Hanny Handiyani', 'Tuti Nuraini', 'R. S. Hariyati', 'Sutrisno Sutrisno']2024-03-11T00:00:00https://www.semanticscholar.org/paper/5e830d27f8ebc6c7ea6b6f5c65f5096db7be8584 2d93740e0ac429b0024c6e974fbbf8ab73548804The anthropocentric cultural idea that humans are active agents exerting control over their environments has been largely normalized and inscribed in practices, policies, and products of contemporary industrialized societies. This view underlies a human-ecology relationship based on resource and knowledge extraction. To create a more sustainable and equitable future, it is essential to consider alternative cultural ideas rooted in ecological thinking. This perspective underscores the interconnectedness between humans and more-than-human worlds. We propose a path to reshape the human-ecology relationship by advocating for alternative human-AI interactions. In this paper, we undertake a critical comparison between anthropocentrism and ecological thinking, using storytelling to illustrate various human-AI interactions that embody ecological thinking. We also delineate a set of design principles aimed at guiding AI developments toward fostering a more caring human-ecology relationship.CHI Extended Abstracts1040{'pages': '546:1-546:9'}['Chunchen Xu', 'Xiao Ge']2024-03-11T00:00:00https://www.semanticscholar.org/paper/2d93740e0ac429b0024c6e974fbbf8ab73548804 a2473eb3622281f9db41b4b010f7cbcf17b53ab6Mathematical Writing (MW) can support students’ mathematical learning and is common in mathematics assessment. However, MW is known to be particularly challenging for students with learning disabilities. While the use of model compositions of both high- and low-quality writing and the act of revision are evidence-based practices in writing instruction, models of MW are not readily available in the curriculum, and many teachers struggle to compose high-quality MW themselves. Artificial intelligence (AI) chatbots are increasingly accessible for teachers and provide one avenue by which MW models can be readily generated. This column guides educators on utilizing AI chatbots to produce MW models to support MW instruction for students with learning disabilities.Intervention in School and Clinic130Artificial intelligence chatbots are increasingly accessible for teachers and provide one avenue by which MW models can be readily generated and guide educators on utilizing AI chatbots to produce MW models to support MW instruction for students with learning disabilities.Intervention in School and Clinic['R. A. Smith', 'Erin Smith', 'Madeline D. Price']2024-03-11T00:00:00https://www.semanticscholar.org/paper/a2473eb3622281f9db41b4b010f7cbcf17b53ab6 4f22d54ce2ec5c0b2b6c45f351dba396d2a86d77The article explores the revolutionary effects of artificial Intelligence (AI) on cybersecurity in various industries, emphasizing how important it is to protect sensitive data and strengthen vital infrastructure. Using AI-driven cybersecurity solutions signifies a paradigm change away from reactive, defensive tactics and toward proactive, adaptive, and predictive defense systems. The article illustrates how artificial Intelligence (AI) improves threat detection, guards against fraudulent activity, secure patient data, and strengthens the digital thread in manufacturing and supply chain operations. It focuses on essential industries such as nuclear energy, finance, healthcare, and manufacturing. The convergence of AI and cybersecurity becomes a strategic necessity as sectors negotiate the challenges of a globalized world, providing light on the way to a more inventive, safe, and resilient future.Current Trends in Engineering Science (CTES)00The article illustrates how artificial Intelligence improves threat detection, guards against fraudulent activity, secure patient data, and strengthens the digital thread in manufacturing and supply chain operations.Current Trends in Engineering Science (CTES)['Bahman Zohuri']2024-03-11T00:00:00https://www.semanticscholar.org/paper/4f22d54ce2ec5c0b2b6c45f351dba396d2a86d77 50ecf736db62ff0eb502d0ccb6a7811e6af689f2As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thought-fully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.IEEE/ACM International Conference on Human-Robot Interaction310An evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system is developed.{'pages': '915-918'}['Daisy M. Kiyemba', 'Jasmin Marward', 'Elizabeth J. Carter', 'Adam Norton']2024-03-11T00:00:00https://www.semanticscholar.org/paper/50ecf736db62ff0eb502d0ccb6a7811e6af689f2 abd6a2291bea698e568e0213dae41d97de93316fThis study explores the feasibility of large language models (LLMs) like ChatGPT and Bard as virtual participants in health-related research interviews. The goal is to assess whether these models can function as a “collective knowledge platform” by processing extensive datasets. Framed as a “proof of concept”, the research involved 20 interviews with both ChatGPT and Bard, portraying personas based on parents of adolescents. The interviews focused on physician–patient–parent confidentiality issues across fictional cases covering alcohol intoxication, STDs, ultrasound without parental knowledge, and mental health. Conducted in Dutch, the interviews underwent independent coding and comparison with human responses. The analysis identified four primary themes—privacy, trust, responsibility, and etiology—from both AI models and human-based interviews. While the main concepts aligned, nuanced differences in emphasis and interpretation were observed. Bard exhibited less interpersonal variation compared to ChatGPT and human respondents. Notably, AI personas prioritized privacy and age more than human parents. Recognizing disparities between AI and human interviews, researchers must adapt methodologies and refine AI models for improved accuracy and consistency. This research initiates discussions on the evolving role of generative AI in research, opening avenues for further exploration.Future180This research initiates discussions on the evolving role of generative AI in research, opening avenues for further exploration on the evolving role of generative AI in research.Future['Jelle Fostier', 'Elena Leemans', 'Lien Meeussen', 'Alix Wulleman', 'Shauni Van Doren', 'D. De Coninck', 'Jaan Toelen']2024-03-11T00:00:00https://www.semanticscholar.org/paper/abd6a2291bea698e568e0213dae41d97de93316f 43c5b21b0e104b1be87256bd1a0c32880491b0f7Discover Sustainability590Through this mechanism, AI-Driven Sustainable HR practices contribute to employee engagement and performance, particularly for those with a high level of conscientiousness, particularly for those with a high level of conscientiousness.Discover Sustainability['Xiao Jia', 'Yanghong Hou']2024-03-11T00:00:00https://www.semanticscholar.org/paper/43c5b21b0e104b1be87256bd1a0c32880491b0f7 30a85bbcd43f3d50880ffe98b316496c3a81fb37AI and Ethics30It is argued that adoption of the term “frontier AI” is harmful and contributes to AI hype, and “frontier AI” as a term invokes the colonial mindset.AI and Ethics['Gina Helfrich']2024-03-11T00:00:00https://www.semanticscholar.org/paper/30a85bbcd43f3d50880ffe98b316496c3a81fb37 6e2cf776279341f1afc391cae177634a045fe12fThreat modeling is a popular method to securely develop systems by achieving awareness of potential areas of future damage caused by adversaries. The benefit of threat modeling lies in its ability to indicate areas of concern, paving the way to consider mitigation during the design stage. However, threat modeling for systems relying on Artificial Intelligence is still not well explored. While conventional threat modeling methods and tools did not address AI-related threats, research on this amalgamation still lacks solutions capable of guiding and automating the process, as well as providing evidence that the methods hold up in practice. To evaluate that the work at hand is able to guide and automatically identify AI-related threats during the architecture definition stage, several experts were tasked to create a threat model of an AI system designed in the healthcare domain. The usability of the solution was well-perceived, and the results indicate that it is effective for threat identification.arXiv.org360To evaluate that the work at hand is able to guide and automatically identify AI-related threats during the architecture definition stage, several experts were tasked to create a threat model of an AI system designed in the healthcare domain, and results indicate that it is effective for threat identification.ArXiv['Jan von der Assen', 'Jamo Sharif', 'Chao Feng', 'Gérôme Bovet', 'Burkhard Stiller']2024-03-11T00:00:00https://www.semanticscholar.org/paper/6e2cf776279341f1afc391cae177634a045fe12f 402329898cd6ab51f8627ce415223191c17f70cfOpenness is critical for the advancement of science. In particular, recent rapid progress in AI has been made possible only by various open-source models, datasets, and libraries. However, this openness also means that technologies can be freely used for socially harmful purposes. Can open-source models or datasets be used for malicious purposes? If so, how easy is it to adapt technology for such goals? Here, we conduct a case study in the legal domain, a realm where individual decisions can have profound social consequences. To this end, we build EVE, a dataset consisting of 200 examples of questions and corresponding answers about criminal activities based on 200 Korean precedents. We found that a widely accepted open-source LLM, which initially refuses to answer unethical questions, can be easily tuned with EVE to provide unethical and informative answers about criminal activities. This implies that although open-source technologies contribute to scientific progress, some care must be taken to mitigate possible malicious use cases. Warning: This paper contains contents that some may find unethical.arXiv.org280It is found that a widely accepted open-source LLM, which initially refuses to answer unethical questions, can be easily tuned with EVE to provide unethical and informative answers about criminal activities.ArXiv['Yeeun Kim', 'Eunkyung Choi', 'Hyunjun Kim', 'Hongseok Oh', 'Hyunseo Shin', 'Wonseok Hwang']2024-03-11T00:00:00https://www.semanticscholar.org/paper/402329898cd6ab51f8627ce415223191c17f70cf 996b4c86a88628422158de8fc277efcd85828795The IntelliView initiative represents a pioneering endeavor designed to empower novice job seekers through the integration of state-of-the-art Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies. This platform employs a sophisticated blend of HTML, CSS, JavaScript, and the Deep Face method, introducing a comprehensive framework to redefine interview assessment and augment job application preparation. The primary module facilitates text-based analysis, enabling users to engage with real-time interview questions by entering responses directly into the web interface. Subsequently, advanced algorithms compare users' responses to expected answers, yielding a percentage similarity score and presenting the correct answer. Beyond affording essential interview practice, this feature furnishes constructive feedback, enhancing users' responses and ultimately refining their interview performance. In the second module, IntelliView introduces a dynamic interview environment with video-based analysis. Users respond to inquiries utilizing webcams, enabling the system to meticulously record both verbal responses and facial expressions. Leveraging the Deep Face method, the platform conducts real-time emotion and sentiment analysis, offering users insights into their emotional states throughout interviews. This feedback facilitates the refinement of non-verbal communication skills, empowering candidates to recognize emotional tendencies and adapt interview strategies accordingly. The third module functions as a comprehensive resume builder, employing HTML, CSS, and JavaScript to provide diverse templates tailored to individual needs. In summation, IntelliView heralds a transformative paradigm in job application preparation, seamlessly amalgamating technological advancements and human interaction to equip first-time job seekers with the requisite tools for navigating the competitive job market successfully. Key Words: Interview Assessment, DeepFace, NLP, Text- based analysis, Video-based analysis, Resume BuilderINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00IntelliView heralds a transformative paradigm in job application preparation, seamlessly amalgamating technological advancements and human interaction to equip first-time job seekers with the requisite tools for navigating the competitive job market successfully.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-03-11T00:00:00https://www.semanticscholar.org/paper/996b4c86a88628422158de8fc277efcd85828795 b071e4ea095f541c8cd0f92f5b9929b19aad657dPlace Branding and Public Diplomacy50The discourse in China about the development of ChatGPT is examined, by focusing on the treatment of the technology and the possible risks and opportunities regarding international affairs, to contribute to the theoretical debate on the challenges posed by Generative IA technologies in the context of China’s diplomatic practices.Place Branding and Public Diplomacy['J. Manfredi-Sánchez', 'P. Morales']2024-03-11T00:00:00https://www.semanticscholar.org/paper/b071e4ea095f541c8cd0f92f5b9929b19aad657d 442f45ac2e15bbfe7b7faa0283ca5bffc92e0975Scientific Reports480This work analyses public opinions and sentiments about AI applications that affect e-learning, such as ChatGPT, virtual and augmented reality, microlearning, mobile learning, adaptive learning, and gamification, to suggest that AI will play a significant role in the future of the world and education, but it is important to consider the potential ethical and social implications of this technology.Scientific reports['L. Stracqualursi', 'Patrizia Agati']2024-03-11T00:00:00https://www.semanticscholar.org/paper/442f45ac2e15bbfe7b7faa0283ca5bffc92e0975 9f833b56dd8790c81534841962dfbf9803cf4f1dThe advent of digital agriculture keeps increasing in the last decades, and many of them adopt cutting-edge deep learning technologies, which require more computing resources than conventional agriculture applications. Vision-based analysis for growth monitoring, pest or disease identification, or phenotyping is usually based on powerful resources such as GPU. Unlike the urban area, agricultural sites are usually placed sparsely in rural areas, which makes it difficult to locate enough computational resources such as CPU, GPU, or connectivity. Edge computing architecture is an architecture that places and utilises the computational resources near data sources for efficient service provisioning, and is suitable for the management of agriculture applications in various crop fields. In this paper, a framework for AI management on edge resources for smart agriculture is presented. It leverages cloud technologies to manage resources that are placed in smart farms for deploying services according to various needs. A crop monitoring service implementation in a strawberry farm is also presented to show how the framework can be used to realize vertical service in cloud-edge continuum.Conference on Innovation in Clouds, Internet and Networks170A framework for AI management on edge resources for smart agriculture is presented that leverages cloud technologies to manage resources that are placed in smart farms for deploying services according to various needs and a crop monitoring service implementation in a strawberry farm is presented.2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)['Seung-woo Kum', 'Seungtaek Oh', 'Jaewon Moon']2024-03-11T00:00:00https://www.semanticscholar.org/paper/9f833b56dd8790c81534841962dfbf9803cf4f1d 3876824fb1b297db36cf9f93a319ae988c0b2f43In recent years, discussions have been increasingly emerging in modern scientific research that, in connection with the development of AI technologies, questions arise about the objectivity, plausibility and reliability of knowledge, as well as whether these technologies will not replace the expert figure as the authority that has so far acted as a guarantor of objectivity and the center of decision-making. Modern historians of science Duston L. and Galison P. in their book on the history of scientific objectivity, they talk about the alternation of "epistemic virtues", as one of which objectivity has been established since a certain moment. At the same time, the promotion of one or another virtue regulating the scientific self, i.e. acting as a normative principle for a scientist when choosing one or another way of seeing and one or another scientific practice, depends on making decisions in difficult cases requiring the will and limitation of the self. In this sense, epistemology is combined with ethics: a scientist, guided by certain moral principles, gives preference to one or another way of behavior, choosing, for example, not a more accurate hand-drawn image, but an uncluttered photograph, perhaps fuzzy, but obtained mechanically, which means more objective and free from any admixture of subjectivity. In this regard, the epistemic status of modern AI-based technologies, which increasingly assume the functions of the scientific self, including in terms of influencing final decision-making and obtaining objective knowledge, seems interesting. For example, in the field of medicine, robotic devices already provide significant support, taking over some of the functions, for example, of a first-level doctor to collect and analyze standardized patient data and diagnostics. There is an assumption that AI will take on more and more responsibilities in the near future: data processing, development of new drugs and treatment methods, establishing remote interaction with the patient, etc. But does this mean that the scientific self can be replaced by AI-based algorithms, and another epistemic virtue will replace objectivity, finally breaking the link between ethics and epistemology – this question needs to be investigated.Digital Diagnostics00Does this mean that the scientific self can be replaced by AI-based algorithms, and another epistemic virtue will replace objectivity, finally breaking the link between ethics and epistemology – this question needs to be investigated.Digital Diagnostics['Angelina Baeva']2024-03-11T00:00:00https://www.semanticscholar.org/paper/3876824fb1b297db36cf9f93a319ae988c0b2f43 7de0e71ab450382f3f4f1ec84412ef9cf3ec515fAdvances in Generative Artificial Intelligence (AI) are resulting in AI-generated media output that is (nearly) indistinguishable from human-created content. This can drastically impact users and the media sector, especially given global risks of misinformation. While the currently discussed European AI Act aims at addressing these risks through Article 52's AI transparency obligations, its interpretation and implications remain unclear. In this early work, we adopt a participatory AI approach to derive key questions based on Article 52's disclosure obligations. We ran two workshops with researchers, designers, and engineers across disciplines (N=16), where participants deconstructed Article 52's relevant clauses using the 5W1H framework. We contribute a set of 149 questions clustered into five themes and 18 sub-themes. We believe these can not only help inform future legal developments and interpretations of Article 52, but also provide a starting point for Human-Computer Interaction research to (re-)examine disclosure transparency from a human-centered AI lens.CHI Extended Abstracts600This early work adopts a participatory AI approach to derive key questions based on Article 52's disclosure obligations to help inform future legal developments and interpretations of Article 52, and provide a starting point for Human-Computer Interaction research to (re-)examine disclosure transparency from a human-centered AI lens.{'pages': '342:1-342:11'}['Abdallah El Ali', 'Karthikeya Puttur Venkatraj', 'Sophie Morosoli', 'Laurens Naudts', 'Natali Helberger', 'Pablo César']2024-03-11T00:00:00https://www.semanticscholar.org/paper/7de0e71ab450382f3f4f1ec84412ef9cf3ec515f fa735d3198bf9dbd883c5b1ee9b54b5f90598657Artificial Intelligence (AI) emerges as a common topic in our daily lives by capturing interest from all walks of life. The immersion of AI is not exclusive. It is widely gaining attention from business and non-business settings such as the education sector. Education is often regarded as the key to unlocking the potential future generation; hence AI usage and adoption shall be carefully considered with the proper guidelines and policies. The background calls for the need to develop a conceptual framework in examining the interplay of powerful technology with the multifaceted aspects of the learning process along with the relevant psychological aspects of the students on their intention to use AI in the academic context. This study reviews the existing literature to uncover the relevant theories to examine the interrelationship of AI concerning students' learning process. The framework that emerged serves as a guide for the future direction of the study in this domain.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)350This study reviews the existing literature to uncover the relevant theories to examine the interrelationship of AI concerning students' learning process and the framework that emerged serves as a guide for the future direction of the study in this domain.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)['Mei Peng Low', 'Thiam-Yong Kuek', 'W. Har']2024-03-11T00:00:00https://www.semanticscholar.org/paper/fa735d3198bf9dbd883c5b1ee9b54b5f90598657 280ff34e98706f266ca5fcadd1f480b3882bbb80English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1%• On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)170A novel approach to address the need for balancing transparency and user privacy in ELB-VAs by integrating pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model, which exhibits a substantial increase in accuracy.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)['Franciskus Antonius Alijoyo', 'S. S. Sneha Sri', 'Purnachandra Rao Alapati', 'Dilyorjon Yuldashev', 'Ponni Valavan M']2024-03-11T00:00:00https://www.semanticscholar.org/paper/280ff34e98706f266ca5fcadd1f480b3882bbb80 d8d007a0d785b2a87e891474b3775cec79d87f51Cogent Education1321Cogent Education['Owolabi Paul Adelana', 'M. A. Ayanwale', 'I. T. Sanusi']2024-03-11T00:00:00https://www.semanticscholar.org/paper/d8d007a0d785b2a87e891474b3775cec79d87f51 45f8a7a38f30bba04d01116fdd67da9f70c45a77GRUR International00GRUR International[]2024-03-11T00:00:00https://www.semanticscholar.org/paper/45f8a7a38f30bba04d01116fdd67da9f70c45a77 eaff53a01504b619d47dda23a9a8b949ba0b43c9Humanities and Social Sciences Communications211This paper undertakes a systematic literature review (SLR) approach and aspires to highlight the state of research on the use of AI in the IAF, to deliver insight for scholars and industry experts on the issue, and to reveal the implications for IAF of the new AI technology.Humanities and Social Sciences Communications['Fekadu Agmas Wassie', 'László Péter Lakatos']2024-03-11T00:00:00https://www.semanticscholar.org/paper/eaff53a01504b619d47dda23a9a8b949ba0b43c9 7080bb796051aa763ac42efc191b468430020555Artificial Intelligence Review1331A comprehensive review of the recent advances in AI and its applications in drug discovery and development, starting with disease identification and spanning through the various stages involved in the drug discovery pipeline, including target identification, screening, lead discovery, and clinical trials is discussed.Artif. Intell. Rev.['A. C. Pushkaran', 'Alya A. Arabi']2024-03-11T00:00:00https://www.semanticscholar.org/paper/7080bb796051aa763ac42efc191b468430020555 497ccaa586c40991d6c6118c4739ed34b3375ce3Generative artificial intelligence (genAI) language models have become firmly embedded in public consciousness. Their abilities to extract and summarise information from a wide range of sources in their training data have attracted the attention of many scholars. This paper examines how four genAI large language models (ChatGPT, GPT4, DeepAI, and Google Bard) responded to prompts, asking (i) whether artificial intelligence would affect how cultural heritage will be managed in the future (with examples requested) and (ii) what dangers might emerge when relying heavily on genAI to guide cultural heritage professionals in their actions. The genAI systems provided a range of examples, commonly drawing on and extending the status quo. Without a doubt, AI tools will revolutionise the execution of repetitive and mundane tasks, such as the classification of some classes of artifacts, or allow for the predictive modelling of the decay of objects. Important examples were used to assess the purported power of genAI tools to extract, aggregate, and synthesize large volumes of data from multiple sources, as well as their ability to recognise patterns and connections that people may miss. An inherent risk in the ‘results’ presented by genAI systems is that the presented connections are ‘artifacts’ of the system rather than being genuine. Since present genAI tools are unable to purposively generate creative or innovative thoughts, it is left to the reader to determine whether any text that is provided by genAI that is out of the ordinary is meaningful or nonsensical. Additional risks identified by the genAI systems were that some cultural heritage professionals might use AI systems without the required level of AI literacy and that overreliance on genAI systems might lead to a deskilling of general heritage practitioners.The Heritage620This paper examines how four genAI large language models responded to prompts, asking whether artificial intelligence would affect how cultural heritage will be managed in the future and what dangers might emerge when relying heavily on genAI to guide cultural heritage professionals in their actions.Heritage['D. Spennemann']2024-03-11T00:00:00https://www.semanticscholar.org/paper/497ccaa586c40991d6c6118c4739ed34b3375ce3 6545dbe35f4aae7747855f4bac40095532ca6a65Artificial intelligence (AI) refers to the use of technology and computers to replicate behavioral intelligence and analytical reasoning that is equivalent to that of a human being. The medical sciences make substantial use of computer systems with artificial intelligence which primarily include remote patient treatment, prescription recording, increasing doctor-patient interaction, medication discovery and patient diagnosis. Its application in health care industry is mainly because of increased job demands, difficult tasks, and probable doctor weariness all have the potential to impair diagnostic performance. In this review we have enlightened the various roles of AI such as automated diagnosis, predictive analysis, image analysis, precision medicine and the biomarker development. The use of AI tools in pathology has grown significantly in recent years, and it is predicted that they will revolutionize the field in the years to come. AI tools can change how pathology functions while rendering it more efficient at meeting the demands of the modern era of precision medicine. Bangladesh Journal of Medical Science Vol.23 (Special Issue) 2024 p.S32-S37Bangladesh Journal of Medical Science200In this review, the various roles of AI such as automated diagnosis, predictive analysis, image analysis, precision medicine and the biomarker development are enlightened.Bangladesh Journal of Medical Science['P. Mishra', 'Abikshyeet Panda', 'Monalisha Mahapatra', 'Prachurya Dakshinakabat', 'Aishwariya Mohanty', 'Lipsa Bhuyan']2024-03-11T00:00:00https://www.semanticscholar.org/paper/6545dbe35f4aae7747855f4bac40095532ca6a65 3ca865500a93bfa150676883e1bfd4466f93eddcABSTRACT The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green honeymoon between health and sustainability caused by AI, it should aim for a lasting marriage. To this end, we conclude by proposing that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility. Highlights Using AI in medicine and epidemiology has some benefits in the short term. AI usage may cause social inequalities and environmental damage in the long term. Health justice should be rethought from the One Health perspective. Going beyond anthropocentric and myopic cost–benefit analysis would expand health justice to include an environmental dimension. Greening AI would help to reconcile public and global health measures.Global bioethics = Problemi di bioetica670It is proposed that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility in order to reconcile public and global health measures.Global Bioethics['C. Moyano-Fernández', 'Jon Rueda', 'Janet Delgado', 'T. Ausín']2024-03-11T00:00:00https://www.semanticscholar.org/paper/3ca865500a93bfa150676883e1bfd4466f93eddc ca93bc495e3de0ce216a888d69839e1cc023798eArtificial intelligence (AI) is a rapidly developing technology that has the potential to create previously unimaginable chances for our societies. Still, the public’s opinion of AI remains mixed. Since AI has been integrated into many facets of daily life, it is critical to understand how people perceive these systems. The present work investigated the perceived social risk and social value of AI. In a preliminary study, AI’s social risk and social value were first operationalized and explored by adopting a correlational approach. Results highlighted that perceived social value and social risk represent two significant and antagonistic dimensions driving the perception of AI: the higher the perceived risk, the lower the social value attributed to AI. The main study considered pretested AI applications in different domains to develop a classification of AI applications based on perceived social risk and social value. A cluster analysis revealed that in the two-dimensional social risk × social value space, the considered AI technologies grouped into six clusters, with the AI applications related to medical care (e.g., assisted surgery) unexpectedly perceived as the riskiest ones. Understanding people’s perceptions of AI can guide researchers, developers, and policymakers in adopting an anthropocentric approach when designing future AI technologies to prioritize human well-being and ensure AI’s responsible and ethical development in the years to come.Human Behavior and Emerging Technologies540Investigating the perceived social risk and social value of AI found that perceived social value and social risk represent two significant and antagonistic dimensions driving the perception of AI: the higher the perceived risk, the lower the social value attributed to AI.Human Behavior and Emerging Technologies['Gabbiadini Alessandro', 'Durante Federica', 'Baldissarri Cristina', 'Andrighetto Luca']2024-03-11T00:00:00https://www.semanticscholar.org/paper/ca93bc495e3de0ce216a888d69839e1cc023798e c9244d8c700b0402ef993849b549a531a4f66606This study explores the role of Artificial Intelligence (AI) in human resources management in HR practices, particularly in the context of selecting and attracting human resources, involving 110 participants of HR departments and workers in HR management in all small and medium-sized companies and a random (probabilistic) sample was selected from various administrative levels (manager - supervisor - specialist - Other) from Jordan. The research utilized a questionnaire-based approach and SPSS analysis. The primary objectives of the study were to explore the concept and components of AI, elucidate its significance and role in HR management, and assess the extent of AI utilization within this domain. Methodologically, a descriptive analytical method was adopted, with data collected from directors and workers in HR management through a random distribution of 375 questionnaires via email. Of these, 110 responses were deemed usable for analysis. The results revealed a predominance of male respondents (74.5%), with the highest representation found in the 20-35 age group. Most participants held positions classified as 'other' in terms of academic rank and experience. Furthermore, the questionnaire exhibited high stability and coherence, as evidenced by a Cronbach alpha coefficient exceeding 0.70. Despite this, respondents expressed low agreement regarding the company's use of AI in HR management and the process of selecting and attracting HR. Factors influencing the use ofInternational Journal of Academic Research in Business and Social Sciences170The research utilized a questionnaire-based approach and SPSS analysis to explore the concept and components of AI, elucidate its significance and role in HR management, and assess the extent of AI utilization within this domain.International Journal of Academic Research in Business and Social Sciences['Noor M. Alqudah', 'Tareq O. Almomani', 'Safa Al Sarayrah']2024-03-11T00:00:00https://www.semanticscholar.org/paper/c9244d8c700b0402ef993849b549a531a4f66606 ab589d9d102dd56e868705ca91479a4ed7b963daThis study investigates how anticipating an artificial intelligence agent versus human information source moderates the risk information seeking and processing model. It focuses on a behavioral proxy of seeking intention—how long a participant waited for an online consultant whose identity was manipulated. In two samples ( N1 = 182 students and N2 = 800 mturkers), the source identity consistently moderated the model in two ways: First, informational subjective norms encouraged seeking from humans but discouraged seeking from AI agents. Second, information insufficiency drove favoritism toward humans–when perceived information-gathering capacity was high. When the capacity was low, AI agents were favored.Science communication490Science Communication['Wang Liao', 'William Weisman', 'Arti Thakur']2024-03-11T00:00:00https://www.semanticscholar.org/paper/ab589d9d102dd56e868705ca91479a4ed7b963da 9ace323542a2378f8e9dd3df5fcd7ca93902b807The rapid urbanization of modern cities presents significant challenges in sustainable development. To address these challenges, there is a growing integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. This research aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can be employed to improve urban development strategies and enhance sustainability assessments. Employing Spatial Analysis with GIS, this study analyzes data on land use, population density, and environmental indicators across several metropolitan areas. The methodology incorporates machine learning algorithms to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments. These tools facilitate more informed decision-making in urban planning by enabling precise predictions about urban expansion and its environmental impacts. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning. This research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.IAIC Transactions on Sustainable Digital Innovation (ITSDI)210The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, thereby setting a benchmark for future studies in the realm of sustainable urban planning.IAIC Transactions on Sustainable Digital Innovation (ITSDI)['Muhammad Rehan Anwar', 'Lintang Dwi Sakti']2024-03-11T00:00:00https://www.semanticscholar.org/paper/9ace323542a2378f8e9dd3df5fcd7ca93902b807 8e897de4d4e147797d1feff5c5d2c06cce869e22Energy production schemes are subject to a constant scientific and technological revolution due to fluctuating social, economic, and political changes. The integration of new ways of power generation, electrical and electronic elements that consume the power generated and require specific energy quality policies, electric vehicles, energy efficiency, energy management, architecture reliability, bidirectional communication, and resilience are some challenges control strategies face. Technology and science have supported and faced this challenge in energy production schemes, especially in intelligent electrical microgrids. This article presents a comprehensive review of the state of the art of artificial intelligence techniques that are applied to face the various challenges of operation, control, and coordination in a Smart Electric Microgrid (SEM). In particular, how artificial intelligence develops and faces challenges in central areas in an SEM architecture.Latin American applied research00This article presents a comprehensive review of the state of the art of artificial intelligence techniques that are applied to face the various challenges of operation, control, and coordination in a Smart Electric Microgrid (SEM) and how artificial intelligence develops and faces challenges in central areas in an SEM architecture.Latin American Applied Research - An international journal['Alma Eliza Guerrero Sánchez', 'Edgar Alejandro Rivas Araiza', 'Mariano Garduño Aparicio', 'J. L. Gonzalez-Cordoba', 'Juvenal Rodríguez Reséndiz']2024-03-11T00:00:00https://www.semanticscholar.org/paper/8e897de4d4e147797d1feff5c5d2c06cce869e22 0c6bdf020123fdba298ab63d63648b7ad5e2e67fThis article proposes that only by prioritizing the formulation of data rights protection and circulation rules that adapt to the development characteristics of the artificial intelligence industry, can the compliance costs of enterprises be reduced, and ultimately, high-quality datasets can be developed and utilized to promote innovation in the artificial intelligence industry and to form a new engine for social progress in the 21st century. The action research includes supporting the development of public clouds, orderly guiding departments, units, and individuals to purchase public cloud computing resources and services, and avoiding duplicate construction of intelligent computing centers. The research project emphasizes the need to guide and support enterprises to increase cloud computing, computing power leasing, algorithm valuation, and arithmetic purchasing through social forces, reduce the cost pressure of user research and application development, deepen industrial integration, and expand application scenarios. This article proposes that data elements should not be limited to addition empowerment, but should focus on multiplier mechanisms. Firstly, we need to accelerate the expansion of artificial intelligence big data open innovation platforms, while encouraging enterprises and research institutions to share high-quality corpus resources. We also need to support professional data annotation and cleaning preprocessing work, ultimately opening up a high-quality data source for building big models.International Journal of Social Science and Research00It is proposed that data elements should not be limited to addition empowerment, but should focus on multiplier mechanisms to accelerate the expansion of artificial intelligence big data open innovation platforms, while encouraging enterprises and research institutions to share high-quality corpus resources.International Journal of Social Science and Research['Sisi Liang', 'Linjian Hu', 'Jinkang Hu', 'Xiaomin He', 'Yan Liu', 'Jieyao Chen', 'Siyan Chen', 'Chuxuan Gao', 'Zhiqu Le', 'Shiming Tang']2024-03-11T00:00:00https://www.semanticscholar.org/paper/0c6bdf020123fdba298ab63d63648b7ad5e2e67f 688a9424941d34b5f87a2400d7fd9c11c7c91beaIntravascular optical coherence tomography (IVOCT) is a form of intra-coronary imaging that uses near-infrared light to generate high-resolution, cross-sectional, and 3D volumetric images of the vessel. Given its high spatial resolution, IVOCT is well-placed to characterise coronary plaques and aid with decision-making during percutaneous coronary intervention. IVOCT requires significant interpretation skills, which themselves require extensive education and training for effective utilisation, and this would appear to be the biggest barrier to its widespread adoption. Various artificial intelligence-based tools have been utilised in the most contemporary clinical IVOCT systems to facilitate better human interaction, interpretation and decision-making. The purpose of this article is to review the existing and future technological developments in IVOCT and demonstrate how they could aid the operator.Interventional Cardiology670The purpose of this article is to review the existing and future technological developments in IVOCT and demonstrate how they could aid the operator.Interventional Cardiology: Reviews, Research, Resources['Nitin Chandramohan', 'Jonathan Hinton', 'Peter O’Kane', 'Thomas W Johnson']2024-03-11T00:00:00https://www.semanticscholar.org/paper/688a9424941d34b5f87a2400d7fd9c11c7c91bea c1637d795177194afff33c69ceeb8a329c12e5b9In endocrinology, the types and quantity of digital data are increasing rapidly. Computing capabilities are also developing at an incredible rate, as illustrated by the recent expansion in the use of popular generative artificial intelligence (AI) applications. Numerous diagnostic and therapeutic devices using AI have already entered routine endocrine practice, and developments in this field are expected to continue to accelerate. Endocrinologists will need to be supported in managing AI applications. Beyond technological training, interdisciplinary vision is needed to encompass the ethical and legal aspects of AI, to manage the profound impact of AI on patient/provider relationships, and to maintain an optimal balance between human input and AI in endocrinology.Journal of Clinical Endocrinology and Metabolism00Beyond technological training, interdisciplinary vision is needed to encompass the ethical and legal aspects of AI, to manage the profound impact of AI on patient/provider relationships, and to maintain an optimal balance between human input and AI in endocrinology.The Journal of clinical endocrinology and metabolism['G. Assié', 'Stéphanie Allassonnière']2024-03-11T00:00:00https://www.semanticscholar.org/paper/c1637d795177194afff33c69ceeb8a329c12e5b9 08da4b3bf08a43c856d24f868ea9d498e1cfb60bArtificial Intelligence has become a significant tool in modern technology, enabling people to interact with machines through various methods. Individuals with visual impairments have trouble doing tasks because they are either blind or have poor vision. BVI stands for Blind and Visually Impaired. Solutions must also advance with technology to ensure that individuals can effectively navigate their surroundings and assist them in real-time navigation. The study conducts surveys on visually challenged individuals in the community and aims to assist them by providing smart gadgets to identify faces, colours, and objects. Moreover, this study emphasizes more on different technologies and methods that are used earlier to help visually impaired people in their day-to-day life.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)160The study conducts surveys on visually challenged individuals in the community and aims to assist them by providing smart gadgets to identify faces, colours, and objects.2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)['Shreya Chaple', 'Vedanti Raut', 'J. Patni', 'Ayush Banode', 'Samiksha Ninawe', 'Nilesh M. Shelke']2024-03-11T00:00:00https://www.semanticscholar.org/paper/08da4b3bf08a43c856d24f868ea9d498e1cfb60b 6e160c00b8ed55727b497dcd6ac8ebf8513c13e52024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)002024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)[]2024-03-11T00:00:00https://www.semanticscholar.org/paper/6e160c00b8ed55727b497dcd6ac8ebf8513c13e5 9530654355f256078ab6fceb77de286891fc9fcd2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)002024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)[]2024-03-11T00:00:00https://www.semanticscholar.org/paper/9530654355f256078ab6fceb77de286891fc9fcd adb9eb0378760d6a7ab8dd99360da2ecd8842f64Computational and Structural Biotechnology Journal691This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides and concludes that deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis.Computational and Structural Biotechnology Journal['Ya-Li Wang', 'Song Gao', 'Qian Xiao', 'Chen Li', 'Marcin Grzegorzek', 'Ying-Ying Zhang', 'Xiao-Han Li', 'Ye Kang', 'Fang-Hua Liu', 'Dong-Hui Huang', 'Tingting Gong', 'Qi-Jun Wu']2024-03-11T00:00:00https://www.semanticscholar.org/paper/adb9eb0378760d6a7ab8dd99360da2ecd8842f64 ddeacabb85777a60a72ad348739ffba3c4a9b45fAI &amp; SOCIETY271It is argued that machine learning algorithms represent human identity in terms of what is called the statistical individual, a statisticalized representation of individuals that differs significantly from the ordinary conception of human identity.AI &amp; SOCIETY['Jens Christian Bjerring', 'Jacob Busch']2024-03-11T00:00:00https://www.semanticscholar.org/paper/ddeacabb85777a60a72ad348739ffba3c4a9b45f bf742c30d0f23aa3fe9cdb357f1bf311a5fe8a1dIn this paper we delve into the historical evolution of data as a fundamental element in communication and knowledge transmission. The paper traces the stages of knowledge dissemination from oral traditions to the digital era, highlighting the significance of languages and cultural diversity in this progression. It also explores the impact of digital technologies on memory, communication, and cultural preservation, emphasizing the need for promoting a culture of the digital (rather than a digital culture) in Africa and beyond. Additionally, it discusses the challenges and opportunities presented by data biases in AI development, underscoring the importance of creating diverse datasets for equitable representation. We advocate for investing in data as a crucial raw material for fostering digital literacy, economic development, and, above all, cultural preservation in the digital age.arXiv.org30The paper traces the stages of knowledge dissemination from oral traditions to the digital era, highlighting the significance of languages and cultural diversity in this progression and advocate for investing in data as a crucial raw material for fostering digital literacy, economic development, and, above all, cultural preservation in the digital age.ArXiv['Mohamed Louadi']2024-03-11T00:00:00https://www.semanticscholar.org/paper/bf742c30d0f23aa3fe9cdb357f1bf311a5fe8a1d 525b6e8c49db80be35c0beb3f708f5f0b2d40064Просьба см. прикреплённый файл с рукописьюDigital Diagnostics00Digital Diagnostics['A. Solovev', 'Y. Vasilev', 'V. Sinitsyn', 'A. Petraikin', 'A. Vladzymyrskyy', 'I. Shulkin', 'D. Sharova', 'Dmitry S Semenov']2024-03-11T00:00:00https://www.semanticscholar.org/paper/525b6e8c49db80be35c0beb3f708f5f0b2d40064 f80ba28c45e1d22db891c3a8533ad2e5010a0a58International Journal of Advertising1490International Journal of Advertising['Shu-Chuan Chu', 'Mark Yi-Cheon Yim', 'Juan Mundel']2024-03-11T00:00:00https://www.semanticscholar.org/paper/f80ba28c45e1d22db891c3a8533ad2e5010a0a58 8689c03775b69e227b7d4fac6ef6bb271138d4d7Journal of Cutaneous Medicine and Surgery30Journal of cutaneous medicine and surgery['Surya Khatri', 'Asghar Shah', 'Sara Yumeen', 'E. Saliba']2024-03-11T00:00:00https://www.semanticscholar.org/paper/8689c03775b69e227b7d4fac6ef6bb271138d4d7 e0d0d29416b5cc24a955a12b96b01298160ada26Multimedia tools and applications270Multimedia Tools and Applications['Mily Lal', 'S. Neduncheliyan']2024-03-11T00:00:00https://www.semanticscholar.org/paper/e0d0d29416b5cc24a955a12b96b01298160ada26 8a98677447ed989ba089514b9f5fba0cdb27e0e0Doklady. Mathematics30Doklady Mathematics['A. L. Semenov']2024-03-11T00:00:00https://www.semanticscholar.org/paper/8a98677447ed989ba089514b9f5fba0cdb27e0e0 94936f228fec0e61c0d32ce758c9322b0d851e82This paper investigates the sentiment analysis of the” scholarship system” [4], in Odisha, primarily, to identify why some students do not apply for government-sponsored scholarships. Our research focuses on social media platforms, surveys, and machine learning-based analyses to understand the decision-making process and increase awareness about the various scholarship schemes. The goal of our experiment is to determine the efficacy of sentiment analysis in evaluating the effectiveness of different scholarship schemes. A wide variety of techniques based on dictionaries; corpora lexicons are used in different scholarship schemes for sentiment analysis. Our research paper is based on an evaluation process that could have a positive effect on the government by improving scholarship programs and giving financial aid to students from poor families, which would raise the level of education in Odisha. Our research paper concludes with a summary of successful and unsuccessful schemes, as well as their Word frequency counts and Sentiment Polarity scores.EAI Endorsed Transactions on Internet of Things160This research focuses on social media platforms, surveys, and machine learning-based analyses to understand the decision-making process and increase awareness about the various scholarship schemes to determine the efficacy of sentiment analysis in evaluating the effectiveness of different scholarship schemes.EAI Endorsed Transactions on Internet of Things['P. S. Raju', 'Sanjay Kumar Patra', 'Binaya Kumar Patra']2024-03-11T00:00:00https://www.semanticscholar.org/paper/94936f228fec0e61c0d32ce758c9322b0d851e82 ea797cb5eaf74524b1f4f0099b78fbe47da1f0a0Artificial Intelligence (AI) is on the verge of impacting every domain of our life. It is now being increasingly used as an advisor to help make (moral) decisions. The present study aimed at investigating the influence of moral arguments provided by AI-advisors (i.e., decision aid tool) on human moral decision-making and the associated neural correlates. Participants were presented with utilitarian and deontological sacrificial moral dilemmas and had to make moral decisions either by themselves (i.e., baseline run) or with AI-advisors that provided either utilitarian or deontological advice (i.e., AI-advised run), while their brain activity was measured using an fNIRS device. Overall, AI-advisors significantly influenced participants, who often modified their decisions according to AI-advisors’ arguments. Longer response times and a decrease in right dorsolateral prefrontal cortex activity were observed in response to deontological arguments than to utilitarian arguments. Being provided with deontological arguments by machines appears to have led to a decreased appraisal of the affective response to the dilemmas. This resulted in a reduced level of utilitarianism, supposedly in an attempt to avoid behaving more like a machine than the machines themselves. Taken together, these results suggest that motivational power can led to a voluntary up- and down-regulation of affective processes along moral decision-making.bioRxiv1030Results suggest that motivational power can led to a voluntary up- and down-regulation of affective processes along moral decision-making and the associated neural correlates.bioRxiv['E. Fabre', 'Damien Mouratille', 'Vincent Bonnemains', 'Grazia Pia Palmiotti', 'M. Causse']2024-03-11T00:00:00https://www.semanticscholar.org/paper/ea797cb5eaf74524b1f4f0099b78fbe47da1f0a0 0b88656f01350adadbff280c5e2f50e3be5d0e34In this paper, I present a new way to improve the statistics of control algorithms for blocking log2N switching fabrics. The specific graph form of the internal state representation allows creating tags for machine learning and artificial intelligence systems for decision analysis. The proposed system of graphs represents the internal state of fabric, configuration of connections, and the relation between following states is also modeled and prepared for further analysis. Two types of analysis are presented: static - where simulations create connections, allowing for the preparation of more effective algorithms, and real-life - where real traffic is monitored, and decisions are made based on learned patterns, their weights, and the quality of results. Both are implemented on Virtex V - the main hardware FPGA chip of NetFPGA Card.2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)120A new way to improve the statistics of control algorithms for blocking log2N switching fabrics by proposing a specific graph form of the internal state representation that allows creating tags for machine learning and artificial intelligence systems for decision analysis.2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)['Marek Michalski']2024-03-11T00:00:00https://www.semanticscholar.org/paper/0b88656f01350adadbff280c5e2f50e3be5d0e34 96d5fbd3cf481798ef4c68f6b34a23753c985246 Global financial markets are among the most extensively regulated markets in the world economy. This poses significant challenges from a private law perspective when regulatory rules interfere with private rights and obligations. This article examines the potential of transnational commercial law (TCL) to provide for a private law framework suited to the regulatory reality in global financial markets. The article finds that, while regulatory aspects are generally excluded from TCL instruments, notable exceptions to this general rule occur. Based on an analysis of the approaches undertaken in the Unidroit Convention of Substantive Rules for Intermediated Securities (Geneva Convention), including the accompanying Legislative Guide, the Unidroit Principles on the Operation of Close-Out Netting Provisions and the UNCITRAL Model Law on Secured Transactions, the article maps out three methods that allow for a reconciliation of private and regulatory rules: (i) the adoption of a flexible private law system including default and conflict rules; (ii) the use of opening clauses to allow deviations from TCL rules in accordance with international regulatory standards; and (iii) a codificatory approach through the development of a comprehensive and systematic legal framework consisting of both private law and regulatory rules. Finally, the article addresses potential obstacles and objections that future TCL instruments will face when addressing regulatory aspects. Overall, the article argues that an efficient financial market requires a foreseeable set of private law rules that—considering the continuous expansion of regulatory law—must address the pressing issues deriving from the interrelation of regulation and private law. However, the adaption of TCL instruments should be limited to common-sense financial market regulation that, even if differing from State to State, generally follows globally shared regulatory goals.Uniform Law Review = Revue de Droit Uniforme00Uniform Law Review['Josef Wittmann']2024-03-10T00:00:00https://www.semanticscholar.org/paper/96d5fbd3cf481798ef4c68f6b34a23753c985246 444936e20c15bbf2734e2024e820707a223aa551Several text-to-video diffusion models have demonstrated commendable capabilities in synthesizing high-quality video content. However, it remains a formidable challenge pertaining to maintaining temporal consistency and ensuring action smoothness throughout the generated sequences. In this paper, we present an innovative video generation AI agent that harnesses the power of Sora-inspired multimodal learning to build skilled world models framework based on textual prompts and accompanying images. The framework includes two parts: prompt enhancer and full video translation. The first part employs the capabilities of ChatGPT to meticulously distill and proactively construct precise prompts for each subsequent step, thereby guaranteeing the utmost accuracy in prompt communication and accurate execution in following model operations. The second part employ compatible with existing advanced diffusion techniques to expansively generate and refine the key frame at the conclusion of a video. Then we can expertly harness the power of leading and trailing key frames to craft videos with enhanced temporal consistency and action smoothness. The experimental results confirm that our method has strong effectiveness and novelty in constructing world models from text and image inputs over the other methods.arXiv.org284An innovative video generation AI agent that harnesses the power of Sora-inspired multimodal learning to build skilled world models framework based on textual prompts and accompanying images, which has strong effectiveness and novelty in constructing world models from text and image inputs over the other methods.ArXiv['Deshun Yang', 'Luhui Hu', 'Yu Tian', 'Zihao Li', 'Chris Kelly', 'Bang Yang', 'Cindy Yang', 'Yuexian Zou']2024-03-10T00:00:00https://www.semanticscholar.org/paper/444936e20c15bbf2734e2024e820707a223aa551 9ee6442777f541b44472623e161ffd3f64d2c0bbComputer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities. In this paper, we showcase the integration and reliable and fast deployment of a state-of-the-art AI system designed to automatically analyze CT images, offering infection probability for the swift detection of COVID-19. The suggested system, comprising both classification and segmentation components, is anticipated to reduce physicians' detection time and enhance the overall efficiency of COVID-19 detection. We successfully surmounted various challenges, such as data discrepancy and anonymisation, testing the time-effectiveness of the model, and data security, enabling reliable and scalable deployment of the system on both cloud and edge environments. Additionally, our AI system assigns a probability of infection to each 3D CT scan and enhances explainability through anchor set similarity, facilitating timely confirmation and segregation of infected patients by physicians.arXiv.org452A state-of-the-art AI system designed to automatically analyze CT images, offering infection probability for the swift detection of COVID-19 is showcased, anticipated to reduce physicians' detection time and enhance the overall efficiency of COVID-19 detection.ArXiv['Demetris Gerogiannis', 'Anastasios Arsenos', 'D. Kollias', 'Dimitris Nikitopoulos', 'S. Kollias']2024-03-10T00:00:00https://www.semanticscholar.org/paper/9ee6442777f541b44472623e161ffd3f64d2c0bb 935728c78572ff1da4399356c8c3c5cbc30897d1Artificial neural networks (ANNs) have proven to be among the most important artificial intelligence (AI) techniques in educational applications, providing adaptive educational services. However, their educational potential is limited in practice due to challenges such as the following: (i) the difficulties in incorporating symbolic educational knowledge (e.g., causal relationships and practitioners’ knowledge) in their development, (ii) a propensity to learn and reflect biases, and (iii) a lack of interpretability. As education is classified as a ‘high-risk’ domain under recent regulatory frameworks like the EU AI Act—highlighting its influence on individual futures and discrimination risks—integrating educational insights into ANNs is essential. This ensures that AI applications adhere to essential educational restrictions and provide interpretable predictions. This research introduces NSAI, a neural-symbolic AI approach that integrates neural networks with knowledge representation and symbolic reasoning. It injects and extracts educational knowledge into and from deep neural networks to model learners’ computational thinking, aiming to enhance personalized learning and develop computational thinking skills. Our findings revealed that the NSAI approach demonstrates better generalizability compared to deep neural networks trained on both original training data and data enriched by SMOTE and autoencoder methods. More importantly, we found that, unlike traditional deep neural networks, which mainly relied on spurious correlations in their predictions, the NSAI approach prioritizes the development of robust representations that accurately capture causal relationships between inputs and outputs. This focus significantly reduces the reinforcement of biases and prevents misleading correlations in the models. Furthermore, our research showed that the NSAI approach enables the extraction of rules from the trained network, facilitating interpretation and reasoning during the path to predictions, as well as refining the initial educational knowledge. These findings imply that neural-symbolic AI not only overcomes the limitations of ANNs in education but also holds broader potential for transforming educational practices and outcomes through trustworthy and interpretable applications.Machine Learning and Knowledge Extraction541The research showed that the NSAI approach enables the extraction of rules from the trained network, facilitating interpretation and reasoning during the path to predictions, as well as refining the initial educational knowledge, implying that neural-symbolic AI not only overcomes the limitations of ANNs in education but also holds broader potential for transforming educational practices and outcomes through trustworthy and interpretable applications.Mach. Learn. Knowl. Extr.['Danial Hooshyar', 'Roger Azevedo', 'Yeongwook Yang']2024-03-10T00:00:00https://www.semanticscholar.org/paper/935728c78572ff1da4399356c8c3c5cbc30897d1 8da38465e6a1d21a67d772994b7da1b313e4026bThis article ventures into the intricate realm where the distinctions between authentic growth and speculative bubbles from impact of AI (Miller, 2003), celebrated and critiqued as a buzzword, emerges as a transformative element reshaping entire industries, workflows, and fundamentally altering market valuations (Perri, 2023). The promise of efficiency, innovation, and competitive superiority attributed to AI's integration into business models beckons a deeper investigation into its potential to redefine the competitive landscape. However, this promising horizon is not without its perils and scrutiny. A predominant debate as we navigate up to the year 2023 revolves around whether the market's enthusiastic reception of AI capabilities signifies a realistic reassessment of potential based on tangible fundamentals or if it's a manifestation of speculative exuberance, detached from any solid grounding (Baltrusaitis, 2023). This exploration addresses some implications for investment strategies (GuoRong Hu*, Hui Liu , 2020), regulatory frameworks, and potentially influencing the direction of future technological advancements. It endeavors to dissect AI's multifaceted roles in contemporary business ecosystems, scrutinizing its impacts on corporate valuations and attempting to demarcate the fine line separating real growth from speculative froth. Through a detailed examination, this paper illuminates how AI technologies foster operational efficiencies, drive innovation, and unlock unprecedented insights, all the while carefully navigating the surrounding hype to assess overvaluation risks (Svetlova, 2022) and emerging pitfalls. The journey through the AI landscape reveals a spectrum of inspiring successes and cautionary tales, guiding the current nuanced discussion toward clarifying AI's impact on capital markets and corporate valuations understanding for investors, business leaders, and technology aficionados aiming to make informed decisions (Tania Babina , Anastassia Fedyk , Alex He, James Hodson, 2024).International Journal of Advanced Research in Humanities and Law131This paper illuminates how AI technologies foster operational efficiencies, drive innovation, and unlock unprecedented insights, all the while carefully navigating the surrounding hype to assess overvaluation risks (Svetlova, 2022) and emerging pitfalls.International Journal of Advanced Research in Humanities and Law['Zahra Ahmadirad']2024-03-10T00:00:00https://www.semanticscholar.org/paper/8da38465e6a1d21a67d772994b7da1b313e4026b dc99dbafd9cc62f53acb4214295da3aa9346ba59The deployment of autonomous vehicles (AVs) powered by artificial intelligence (AI) raises profound ethical questions regarding the balance between safety and privacy. While AI-driven AVs promise to revolutionize transportation by potentially reducing accidents and increasing efficiency, concerns regarding data privacy, liability, and decision-making algorithms persist. This paper explores the ethical considerations surrounding AI-driven AVs, focusing particularly on the delicate equilibrium required to ensure both safety and privacy. Drawing upon existing literature and case studies, the paper examines the ethical dilemmas inherent in AV technology, including issues of consent, data collection, and algorithmic bias. Additionally, it delves into the regulatory frameworks and industry standards aimed at addressing these concerns. By highlighting the complexities of navigating safety and privacy in AI-driven AVs, this research contributes to the ongoing discourse on ethical AI development and deployment.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402301This paper explores the ethical considerations surrounding AI-driven AVs, focusing particularly on the delicate equilibrium required to ensure both safety and privacy, and delves into the regulatory frameworks and industry standards aimed at addressing these concerns.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Amaresh Kumar']2024-03-10T00:00:00https://www.semanticscholar.org/paper/dc99dbafd9cc62f53acb4214295da3aa9346ba59 6e7d32cc5c18d42cc388c6cf6fda62c0facb70e8This research paper explores the optimization of cloud infrastructure for real-time artificial intelligence (AI) processing, addressing challenges, solutions, and implications from various perspectives. It discusses scalability issues, latency concerns, resource allocation, security considerations, and cost optimization challenges faced by organizations deploying AI workloads in the cloud. Case studies from diverse industries showcase the tangible benefits of implementing scalable architectures, edge computing integration, specialized hardware utilization, containerization, and data caching techniques. The paper also examines ethical and societal implications, including data privacy, bias, accountability, job displacement, and access disparities. An international perspective highlights regional variations in infrastructure availability, regulatory differences, cultural attitudes, collaboration efforts, and economic impacts. The discussion emphasizes the importance of addressing these challenges while harnessing the economic development opportunities offered by cloud infrastructure optimization for real-time AI processing.International Journal For Multidisciplinary Research131The discussion emphasizes the importance of addressing scalability issues, latency concerns, resource allocation, security considerations, and cost optimization challenges faced by organizations deploying AI workloads in the cloud while harnessing the economic development opportunities offered by cloud infrastructure optimization for real-time AI processing.International Journal For Multidisciplinary Research['Lavanya Shanmugam', 'Kumaran Thirunavukkarasu', 'Kapil Kumar Sharma', 'Manish Tomar']2024-03-10T00:00:00https://www.semanticscholar.org/paper/6e7d32cc5c18d42cc388c6cf6fda62c0facb70e8 e559651f3944bda05591f2dc1a655e9f93ef7cc4AI &amp; SOCIETY841The study suggests the development of AI-friendly tax policies within the G-7 countries, considering the nascent nature of the AI sector/industry.AI &amp; SOCIETY['C. Saba', 'N. Monkam']2024-03-10T00:00:00https://www.semanticscholar.org/paper/e559651f3944bda05591f2dc1a655e9f93ef7cc4 55db88bc39885fcff188bf1c0a2ceaff0f01839eThis qualitative research article explores the impact of Artificial Intelligence (AI) on enhancing academic performance in engineering courses, focusing on adaptive learning systems. The study highlights the evolution of digital technologies in education, emphasizing the applicability of AI in personalizing and adapting learning. Analyzing the integration of AI in engineering education, the study unveils benefits such as teaching customization and early detection of student difficulties. Concurrently, challenges are discussed, including ethical issues like data privacy and algorithmic biases. The research, adopting a qualitative approach, is grounded in an integrative literature review, considering recent studies on the application and impact of AI in higher education. The findings suggest that AI holds significant potential for transforming engineering education, provided its implementation is accompanied by ethical considerations and proper educator preparation.2024 IEEE World Engineering Education Conference (EDUNINE)270The study highlights the evolution of digital technologies in education, emphasizing the applicability of AI in personalizing and adapting learning, and unveils benefits such as teaching customization and early detection of student difficulties.2024 IEEE World Engineering Education Conference (EDUNINE)['Edésio Marcos Slomp', 'Douglas Ropelato', 'Cristiane Bonatti', 'Marily Dilamar da Silva']2024-03-10T00:00:00https://www.semanticscholar.org/paper/55db88bc39885fcff188bf1c0a2ceaff0f01839e 1aec89f8d1e29e033db63fccd87c5565f9eacd59This paper presents a comprehensive framework for integrating advanced artificial intelligence (AI) and digitalization into manufacturing processes, and contributing to the transition towards Industry 5.0. By focusing on a human-centric approach, advanced AI integration, sustainability, and flexibility, the study outlines strategies for enhancing production systems and competitiveness in enterprises. The main goal was to develop a model that outlines the transition process towards Industry 5.0. Additionally, suggestions and guidelines for improving enterprise production systems and competitiveness are discussed. The findings suggest that embracing digital transformation, collaborative robotics, and continuous innovation are vital for achieving operational efficiency, environmental sustainability, and personalized customer experiences. The paper highlights the importance of ethical practices and continuous learning in fostering a resilient and innovative industrial ecosystem.JOURNAL OF ENGINEERING AND MANAGEMENT140The main goal was to develop a model that outlines the transition process towards Industry 5.0 and highlights the importance of ethical practices and continuous learning in fostering a resilient and innovative industrial ecosystem.JOURNAL OF ENGINEERING AND MANAGEMENT['M. Bakator', 'M. Nikolić', 'D. Ćoćkalo', 'S. Stanisavljev']2024-03-10T00:00:00https://www.semanticscholar.org/paper/1aec89f8d1e29e033db63fccd87c5565f9eacd59 adfc37585d499e2ead90b8343ca36b81d36b9559The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300Key issues surrounding AI governance are delineated, the evolving role of the EU as a normative force in this arena is discussed, and the importance of transatlantic collaboration amid broader global technological competitions is underscored.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Damián Tuset Varela']2024-03-10T00:00:00https://www.semanticscholar.org/paper/adfc37585d499e2ead90b8343ca36b81d36b9559 331adbac58ad4003d853b7c92a58bf699de071e2This study explores the integration of ethical principles and responsible AI usage in postgraduate engineering, technology, and computer science programs. It focuses on master's students' perceptions, particularly regarding ethical concerns in AI. A comprehensive methodology, including detailed interviews and an extensive literature review, is used. The literature review covers current educational practices, the effects of increasing data use, AI's transformative role in education, the EdTech industry's influence, and ethical issues in technological advancements. Thirty interviews provide a basis for comparative analysis, highlighting educational gaps and improvement areas. The study introduces two innovative solutions: the Simulated Ethical Dilemmas (SED) Framework and the Ethics Informed Design Thinking (EIDT) Curriculum. SED immerses students in real-life AI ethical scenarios, fostering critical thinking. EIDT focuses on a proactive, human-centric AI development approach, emphasizing ethics. These solutions aim to enhance AI ethics education, preparing students for the evolving ethical challenges in future AI technologies.2024 IEEE World Engineering Education Conference (EDUNINE)390The study introduces two innovative solutions: the Simulated Ethical Dilemmas (SED) Framework and the Ethics Informed Design Thinking (EIDT) Curriculum, which aim to enhance AI ethics education, preparing students for the evolving ethical challenges in future AI technologies.2024 IEEE World Engineering Education Conference (EDUNINE)['Paola Palomino-Flores', 'Ricardo Cristi-López', 'David Paul']2024-03-10T00:00:00https://www.semanticscholar.org/paper/331adbac58ad4003d853b7c92a58bf699de071e2 aa35cd282c615ec98cde3ce4c4318562ff25781cThis paper explores the intricate relationship between capitalism, racial injustice, and artificial intelligence (AI), arguing that AI acts as a contemporary vehicle for age-old forms of exploitation. By linking historical patterns of racial and economic oppression with current AI practices, this study illustrates how modern technology perpetuates and deepens societal inequalities. It specifically examines how AI is implicated in the exploitation of marginalized communities through underpaid labor in the gig economy, the perpetuation of biases in algorithmic decision-making, and the reinforcement of systemic barriers that prevent these groups from benefiting equitably from technological advances. Furthermore, the paper discusses the role of AI in extending and intensifying the social, economic, and psychological burdens faced by these communities, highlighting the problematic use of AI in surveillance, law enforcement, and mental health contexts. The analysis concludes with a call for transformative changes in how AI is developed and deployed. Advocating for a reevaluation of the values driving AI innovation, the paper promotes an approach that integrates social justice and equity into the core of technological design and policy. This shift is crucial for ensuring that AI serves as a tool for societal improvement, fostering empowerment and healing rather than deepening existing divides.arXiv.org240Advocating for a reevaluation of the values driving AI innovation, the paper promotes an approach that integrates social justice and equity into the core of technological design and policy, crucial for ensuring that AI serves as a tool for societal improvement, fostering empowerment and healing rather than deepening existing divides.ArXiv['Nelson Col´on Vargas']2024-03-10T00:00:00https://www.semanticscholar.org/paper/aa35cd282c615ec98cde3ce4c4318562ff25781c a86026e5b621edfaffdb6949fc4c8536b78e1aabQualitative researchers can benefit from using generative artificial intelligence (GenAI), such as different versions of ChatGPT—GPT-3.5 or GPT-4, Google Bard—now renamed as a Gemini, and Bing Chat—now renamed as a Copilot, in their studies. The scientific community has used artificial intelligence (AI) tools in various ways. However, using GenAI has generated concerns regarding potential research unreliability, bias, and unethical outcomes in GenAI-generated research results. Considering these concerns, the purpose of this commentary is to review the current use of GenAI in qualitative research, including its strengths, limitations, and ethical dilemmas from the perspective of critical appraisal from South Asia, Nepal. I explore the controversy surrounding the proper acknowledgment of GenAI or AI use in qualitative studies and how GenAI can support or challenge qualitative studies. First, I discuss what qualitative researchers need to know about GenAI in their research. Second, I examine how GenAI can be a valuable tool in qualitative research as a co-author, a conversational platform, and a research assistant for enhancing and hindering qualitative studies. Third, I address the ethical issues of using GenAI in qualitative studies. Fourth, I share my perspectives on the future of GenAI in qualitative research. I would like to recognize and record the utilization of GenAI and/or AI alongside my cognitive and evaluative abilities in constructing this critical appraisal. I offer ethical guidance on when and how to appropriately recognize the use of GenAI in qualitative studies. Finally, I offer some remarks on the implications of using GenAI in qualitative studiesThe Qualitative Report00This commentary is to review the current use of GenAI in qualitative research, including its strengths, limitations, and ethical dilemmas from the perspective of critical appraisal from South Asia, Nepal, and explores the controversy surrounding the proper acknowledgment of GenAI or AI use in qualitative studies.The Qualitative Report['Niroj Dahal']2024-03-10T00:00:00https://www.semanticscholar.org/paper/a86026e5b621edfaffdb6949fc4c8536b78e1aab 37ed7d4c78c50c3e5c25367548daa4d3d5d30062As artificial intelligence (AI) continues to permeate various aspects of society, its impact on diplomacy and international relations becomes increasingly profound. This paper explores the challenges and opportunities presented by the intersection of diplomacy and AI. It examines how AI technologies are reshaping traditional diplomatic practices, influencing decision-making processes, and altering power dynamics among nation-states. Additionally, it discusses the ethical implications and governance frameworks necessary to navigate this evolving landscape. Despite the challenges, AI offers numerous opportunities for enhancing diplomatic efforts, fostering collaboration, and addressing global challenges in a more efficient and effective manner. By understanding and harnessing the potential of AI, diplomats can adapt to the changing landscape of international relations and leverage technology to advance diplomatic objectives.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The challenges and opportunities presented by the intersection of diplomacy and AI are examined, which include how AI technologies are reshaping traditional diplomatic practices, influencing decision-making processes, and altering power dynamics among nation-states.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Damián Tuset Varela']2024-03-10T00:00:00https://www.semanticscholar.org/paper/37ed7d4c78c50c3e5c25367548daa4d3d5d30062 1cd346243ab7ce233e653735837e50d48905d75dThe purpose of this paper is to discuss the current technological developments in diagnostic cardiovascular care. Echocardiography, a widely known imaging tool, extracts insights about a patients’ cardiac anatomy and perform necessary treatments or procedures based on their diagnoses. AI models respond to vast amounts of raw cardiac data and use Deep Learning algorithms to identify images with remarkable speed and accuracy. AI applications in computer vision offer key benefits in the healthcare industry. Companies such as Siemens are the key players – the commercialization of new AI technology has enabled healthcare organizations to streamline workflows, reduce errors, and lower costs.Potentially, there will be no reproducibility issues thereby redirecting clinical efforts towards patient treatment planning and research to prevent uptrends of heart disease.International Journal on Bioinformatics &amp; Biosciences120There will be no reproducibility issues thereby redirecting clinical efforts towards patient treatment planning and research to prevent uptrends of heart disease.International Journal on Bioinformatics &amp; Biosciences['Joshua Hopkins', 'Datonye B. Omunguye-George']2024-03-10T00:00:00https://www.semanticscholar.org/paper/1cd346243ab7ce233e653735837e50d48905d75d 49bd406144d4471ec655c869362f7da3f35fe326Artificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF‐funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advance edge‐to‐center, AI‐as‐a‐Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge‐cloud‐HPC computing continuum, plug‐and‐play next‐generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high‐impact domains: animal ecology, digital agriculture, and smart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment to democratizing AI. ICICLE seeks to serve as the national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs.The AI Magazine60The NSF‐funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advance edge‐to‐center, AI‐as‐a‐Service through intelligent cyberinfrastructure (CI) that spans the edge‐cloud‐HPC computing continuum, plug‐and‐play next‐generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit.AI Mag.['Dhabaleswar K. Panda', 'Vipin Chaudhary', 'Eric Fosler‐Lussier', 'R. Machiraju', 'Amitava Majumdar', 'Beth Plale', 'R. Ramnath', 'P. Sadayappan', 'Neelima Savardekar', 'Karen Tomko']2024-03-10T00:00:00https://www.semanticscholar.org/paper/49bd406144d4471ec655c869362f7da3f35fe326 980415c293ac7c0408f5aba531634afbd94955b3Health Evaluation and Promotion00Health Evaluation and Promotion['Yusuke Okamoto', 'Tsuyoshi Ozawa', 'Junichi Shibata', 'Toshiyuki Yoshio', 'T. Hirasawa', 'J. Fujisaki', 'Takushi Gotouda', 'Tomonori Tada']2024-03-10T00:00:00https://www.semanticscholar.org/paper/980415c293ac7c0408f5aba531634afbd94955b3 87bc8eccc00c90449253b71583ead987822bc89cSontag's universal formula is a widely-used technique for stabilizing control through control Lyapunov functions, and it has been extended to address safety-critical control in recent years by incorporating control barrier functions (CBFs). However, how to derive a universal formula that satisfies requirements on essential properties, including safety, robustness, and smoothness, is still an open problem. To address this challenge, this paper introduces a novel solution - a tunable universal formula - by incorporating a (state-dependent) tunable scaling term into Sontag's universal formula. This tunable scaling term enables the regulation of safety control performances, allowing the attainment of desired properties through a proper selection. Furthermore, we extend this tunable universal formula to address safety-critical control problems with norm-bounded input constraints, showcasing its applicability across diverse control scenarios. Finally, we demonstrate the efficacy of our method through a collision avoidance example, investigating the essential properties including safety, robustness, and smoothness under various tunable scaling terms.arXiv.org260ArXiv['Ming Li', 'Zhiyong Sun', 'P. Koelewijn', 'Siep Weiland']2024-03-10T00:00:00https://www.semanticscholar.org/paper/87bc8eccc00c90449253b71583ead987822bc89c f3172211d6ed7df0f99f905d22ad7b1c38933acaThis research aims to review the organized and guided use of various platforms operating with artificial intelligence (AI), applied to university students in their final academic cycles. The goal is to provide an additional tool for the development of their thesis works and research projects. We compared two groups: one controlled and the other experimental, highlighting a marked distinction between them. This contrast led to a general hypothesis: AI is beneficial in education when accompanied by ethical principles to prevent indiscriminate use. The study also aims to establish guidelines for future research in our context, emphasizing responsible information processing.2024 IEEE World Engineering Education Conference (EDUNINE)140This research reviewed the organized and guided use of various platforms operating with artificial intelligence, applied to university students in their final academic cycles, to establish guidelines for future research, emphasizing responsible information processing.2024 IEEE World Engineering Education Conference (EDUNINE)['Juan Lara-Herrera', 'Luis Romero-Untiveros', 'Hugo Flor-Cunza']2024-03-10T00:00:00https://www.semanticscholar.org/paper/f3172211d6ed7df0f99f905d22ad7b1c38933aca a93fb460046cec87b712732dba3dabab74b38422This article presents a thorough examination of the practical applications and impacts of generative artificial intelligence (AI) in education from the perspective of educators. It explores how educators integrate AI technologies and tools into their teaching experiences, addressing the challenges they face and the benefits they perceive in educational settings. Employing a descriptive quantitative methodology with a study population of 80 active educators, the research offers valuable insights into the intersection of AI and pedagogical practices in higher education. The findings not only contribute to the academic discourse on AI in education but also start to establish a foundational resource for educators, administrators, and policymakers. This work enhances understanding and informs strategic decisions for those seeking to optimize the integration of AI technologies and Generative AI tools within the dynamic landscape of higher education, promoting innovation and effective utilization of AI for enhanced learning experiences.2024 IEEE World Engineering Education Conference (EDUNINE)1502024 IEEE World Engineering Education Conference (EDUNINE)['Héctor R. Amado-Salvatierra', 'Miguel Morales-Chan', 'Rocael Hernández-Rizzardini', 'Milvia Rosales']2024-03-10T00:00:00https://www.semanticscholar.org/paper/a93fb460046cec87b712732dba3dabab74b38422 ae7f46c43c6a0ce894ecc6fd0da47bc04f1401d4This research stresses the importance of ethics in addressing ethical challenges and serving as a crucial guide in industry and education. It is recommended that organizations integrate ethical principles into their guiding documents and encourage ethical reflection among students. Ethics plays a crucial role in the generative artificial intelligence (GAI) responsible application for the benefit of society. The ethics use is essential in weighing up GAI's strengths and weaknesses.2024 IEEE World Engineering Education Conference (EDUNINE)2002024 IEEE World Engineering Education Conference (EDUNINE)['Julio Ricardo Martinez Montezuma', 'Mario Chong']2024-03-10T00:00:00https://www.semanticscholar.org/paper/ae7f46c43c6a0ce894ecc6fd0da47bc04f1401d4 c36e612af65b0394988ceef423f0e6effd828c8aBackground. In the modern world, the role of artificial intelligence in healthcare is becoming increasingly significant, providing new opportunities to transform traditional methods of diagnosis, treatment and medical data management. This technological breakthrough not only improves the efficiency of medical procedures, but also opens up new per-spectives in the prevention and treatment of diseases. The aim. To emphasize the need to create a personalized health model using artificial intelligence to help an individual achieve and maintain optimal health and well-being. In the field of personalized treatment, artificial intelligence plays an important role, taking into account the unique characteristics of each patient. Algorithms analyze genetic information, medical history and responses to previous therapies to develop optimal treatment plans. This opens the way to individualized medicine, where the approach to each patient is based on his or her unique characteristics. Despite all the positive aspects, the introduction of artificial intelligence in healthcare also raises questions of data privacy, ethical issues and technology security. However, if these issues are resolved, artificial intelligence promises to significantly improve the quality and accessibility of medical treatment, opening new horizons in healthcare. Results. In this article, we describe breakthroughs in artificial intelligence technologies and biomedical applications, identify problems of using and further development in medical artificial intelligence systems and summarize the economic, legal and social consequences of using artificial intelligence in healthcare, and propose a scheme for constructing a model of individual human health using artificial intelligence. Conclusion. The results of the analysis of modern scientific literature allow us to draw a conclusion about the potential for creating more effective and personalized approaches to the problem of individual health using integrated artificial intelligence technologies. The proposed methodology can serve as the basis for the development of innovative deci-sion support systems in medicine and improving the quality of medical care.Baikal Medical Journal00The proposed methodology can serve as the basis for the development of innovative support systems in medicine and improving the quality of medical care, as well as summarize the economic, legal and social consequences of using artificial intelligence in healthcare.Baikal Medical Journal['Daria A. Stepanenko', 'V. I. Pavlov', 'N. Kozlova']2024-03-10T00:00:00https://www.semanticscholar.org/paper/c36e612af65b0394988ceef423f0e6effd828c8a 90233641cf27b1f906bff27aacfefdd61d3d3ae6Science of the Total Environment523The Science of the total environment['Yafei Wang', 'Zihan Zhao', 'Ming Shi', 'Jing Liu', 'Zhixiong Tan']2024-03-09T00:00:00https://www.semanticscholar.org/paper/90233641cf27b1f906bff27aacfefdd61d3d3ae6 ad388f65af8bd7243b1f724b28c871a28b5b185fGene410The study concludes that China should enact specific legislation on gene editing of endangered plants and animals, abide by the concept of biosafety, and aim to maintain biodiversity and ecological balance.Gene['Tao Li']2024-03-09T00:00:00https://www.semanticscholar.org/paper/ad388f65af8bd7243b1f724b28c871a28b5b185f 51ed98f2a99ca2423f266922a6790a89bd0acf72Integrating artificial intelligence (AI) with blockchain technology presents a transformative approach to enhancing security in financial services. This fusion leverages the strengths of both AI and blockchain to mitigate various security risks, including fraud, data breaches, and identity theft, thereby bolstering trust and confidence in financial transactions. This abstract explores the synergies between AI and blockchain, highlighting their combined capabilities, applications, and potential benefits for the financial services industry. AI algorithms, including machine learning and natural language processing, empower financial institutions to analyze vast amounts of data in real-time, identifying patterns, anomalies, and suspicious activities indicative of fraudulent behavior. By integrating AI-powered fraud detection systems with blockchain-based transactional networks, organizations can enhance security and transparency throughout the entire financial ecosystem. Blockchain's immutable ledger ensures the integrity and traceability of transactions, while AI algorithms provide advanced analytics and predictive insights to detect and prevent fraudulent activities effectively. Furthermore, AI-driven identity verification and authentication systems enhance security in digital transactions by accurately verifying user identities and detecting unauthorized access attempts. By integrating AI-based biometric authentication with blockchain-based identity management solutions, financial institutions can streamline customer onboarding processes, enhance security, and protect sensitive information from unauthorized access. Moreover, AI-powered smart contracts automate and enforce the execution of contractual agreements, reducing the risk of fraud, errors, and disputes in financial transactions. By combining AI-driven smart contract platforms with blockchain technology, organizations can facilitate secure, transparent, and tamper-proof transactions, eliminating intermediaries and reducing transaction costs. The integration of AI with blockchain also offers opportunities for regulatory compliance and risk management in financial services. AI-powered regulatory compliance solutions analyze vast amounts of regulatory data, identify compliance risks, and ensure adherence to regulatory requirements. By integrating these solutions with blockchain-based regulatory reporting systems, financial institutions can enhance transparency, auditability, and regulatory oversight, fostering trust and compliance in the financial ecosystem. In conclusion, the integration of AI with blockchain technology holds immense potential for enhancing security in financial services. By harnessing the combined capabilities of AI and blockchain, organizations can detect and prevent fraudulent activities, enhance identity verification and authentication, streamline transaction processes, and ensure regulatory compliance. This abstract underscores the transformative impact of integrating AI with blockchain on security in financial services, paving the way for a more secure, efficient, and trusted financial ecosystem.. Keywords:  Al, Blockchain, Enhanced, Financial, Services Security.Finance &amp; Accounting Research Journal02By harnessing the combined capabilities of AI and blockchain, organizations can detect and prevent fraudulent activities, enhance identity verification and authentication, streamline transaction processes, and ensure regulatory compliance.Finance &amp; Accounting Research Journal['Olubusola Odeyemi', 'Chinwe Chinazo Okoye', 'Onyeka Chrisanctus Ofodile', 'Omotayo Bukola Adeoye', 'Wilhelmina Afua Addy', 'Adeola Olusola Ajayi-Nifise']2024-03-09T00:00:00https://www.semanticscholar.org/paper/51ed98f2a99ca2423f266922a6790a89bd0acf72 b0af4a356c3ee7ee44b216638d3227a488981895Financial inclusion, defined as the access and usage of financial services by all individuals and businesses, is critical for fostering economic development and reducing poverty, particularly in developing economies. However, significant portions of the population in these regions remain underserved or excluded from formal financial systems. Leveraging artificial intelligence (AI) and data analytics presents a promising avenue for addressing the challenges of financial exclusion and advancing financial inclusion in developing economies. This Review explores the role of AI and data analytics in enhancing financial inclusion. Firstly, AI technologies such as machine learning and natural language processing enable the analysis of vast datasets to identify patterns, behaviors, and creditworthiness of individuals and small businesses, thereby facilitating more accurate risk assessment and decision-making by financial institutions. Additionally, AI-powered chatbots and virtual assistants offer personalized financial guidance and support, improving accessibility to financial services for marginalized populations who may have limited literacy or access to traditional banking channels. Moreover, data analytics plays a crucial role in expanding financial inclusion by providing insights into customer preferences, spending habits, and transaction histories. By harnessing these insights, financial service providers can tailor their products and services to better meet the diverse needs of underserved communities, thereby increasing uptake and usage of formal financial services. Furthermore, data analytics enables the development of alternative credit scoring models that leverage non-traditional data sources such as mobile phone usage and utility payments, allowing individuals with limited credit histories to access credit on favorable terms. However, several challenges must be addressed to fully realize the potential of AI and data analytics in enhancing financial inclusion. These include concerns related to data privacy and security, ensuring the fairness and transparency of AI algorithms, and bridging the digital divide to ensure equitable access to technology-enabled financial services. In conclusion, leveraging AI and data analytics holds significant promise for enhancing financial inclusion in developing economies. By harnessing the power of these technologies, policymakers, financial institutions, and other stakeholders can work towards building more inclusive and resilient financial systems that empower individuals and businesses to participate more fully in the formal economy. Keywords:  AI, Data Analytics, Financial Inclusion, Developing Economies, Leveraging.Finance &amp; Accounting Research Journal02By harnessing the power of these technologies, policymakers, financial institutions, and other stakeholders can work towards building more inclusive and resilient financial systems that empower individuals and businesses to participate more fully in the formal economy.Finance &amp; Accounting Research Journal['Omotayo Bukola Adeoye', 'Wilhelmina Afua Addy', 'Adeola Olusola Ajayi-Nifise', 'Olubusola Odeyemi', 'Chinwe Chinazo Okoye', 'Onyeka Chrisanctus Ofodile']2024-03-09T00:00:00https://www.semanticscholar.org/paper/b0af4a356c3ee7ee44b216638d3227a488981895 1209f9c05b41f5f7e2604424ac1b4461e74d8eccCompanies, organizations, and governments across the world are eager to employ so-called 'AI' (artificial intelligence) technology in a broad range of different products and systems. The promise of this cause c\'el\`ebre is that the technologies offer increased automation, efficiency, and productivity - meanwhile, critics sound warnings of illusions of objectivity, pollution of our information ecosystems, and reproduction of biases and discriminatory outcomes. This paper explores patterns of motivation in the general population for trusting (or distrusting) 'AI' systems. Based on a survey with more than 450 respondents from more than 30 different countries (and about 3000 open text answers), this paper presents a qualitative analysis of current opinions and thoughts about 'AI' technology, focusing on reasons for trusting such systems. The different reasons are synthesized into four rationales (lines of reasoning): the Human favoritism rationale, the Black box rationale, the OPSEC rationale, and the 'Wicked world, tame computers' rationale. These rationales provide insights into human motivation for trusting 'AI' which could be relevant for developers and designers of such systems, as well as for scholars developing measures of trust in technological systems.arXiv.org421Analysis of current opinions and thoughts about 'AI' technology, focusing on reasons for trusting such systems, provides insights into human motivation for trusting 'AI' which could be relevant for developers and designers of such systems, as well as for scholars developing measures of trust in technological systems.ArXiv['Nanna Inie']2024-03-09T00:00:00https://www.semanticscholar.org/paper/1209f9c05b41f5f7e2604424ac1b4461e74d8ecc f61488568833e4cac0aa4fc257434a1578bb0d87Content creators increasingly utilize generative artificial intelligence (Gen-AI) on platforms such as YouTube, TikTok, Instagram, and various blogging sites to produce imaginative images, AI-generated videos, and articles using Large Language Models (LLMs). Despite its growing popularity, there remains an underexplored area concerning the specific domains where AI-generated content is being applied, and the methodologies content creators employ with Gen-AI tools during the creation process. This study initially explores this emerging area through a qualitative analysis of 68 YouTube videos demonstrating Gen-AI usage. Our research focuses on identifying the content domains, the variety of tools used, the activities performed, and the nature of the final products generated by Gen-AI in the context of user-generated content.CHI Extended Abstracts361This research focuses on identifying the content domains, the variety of tools used, the activities performed, and the nature of the final products generated by Gen-AI in the context of user-generated content.ArXiv['Yao Lyu', 'He Zhang', 'Shuo Niu', 'Jie Cai']2024-03-09T00:00:00https://www.semanticscholar.org/paper/f61488568833e4cac0aa4fc257434a1578bb0d87 cce284b1f788613d95c60656c1313dff46597c8cImagine if AI decision-support tools not only complemented our ability to make accurate decisions, but also improved our skills, boosted collaboration, and elevated the joy we derive from our tasks. Despite the potential to optimize a broad spectrum of such human-centric objectives, the design of current AI tools remains focused on decision accuracy alone. We propose offline reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize human-AI interaction for diverse objectives. RL can optimize such objectives by tailoring decision support, providing the right type of assistance to the right person at the right time. We instantiated our approach with two objectives: human-AI accuracy on the decision-making task and human learning about the task and learned decision support policies from previous human-AI interaction data. We compared the optimized policies against several baselines in AI-assisted decision-making. Across two experiments (N=316 and N=964), our results demonstrated that people interacting with policies optimized for accuracy achieve significantly better accuracy -- and even human-AI complementarity -- compared to those interacting with any other type of AI support. Our results further indicated that human learning was more difficult to optimize than accuracy, with participants who interacted with learning-optimized policies showing significant learning improvement only at times. Our research (1) demonstrates offline RL to be a promising approach to model human-AI decision-making, leading to policies that may optimize human-centric objectives and provide novel insights about the AI-assisted decision-making space, and (2) emphasizes the importance of considering human-centric objectives beyond decision accuracy in AI-assisted decision-making, opening up the novel research challenge of optimizing human-AI interaction for such objectives.arXiv.org820ArXiv['Zana Buçinca', 'S. Swaroop', 'Amanda E. Paluch', 'Susan A. Murphy', 'Krzysztof Z. Gajos']2024-03-09T00:00:00https://www.semanticscholar.org/paper/cce284b1f788613d95c60656c1313dff46597c8c 5e1a933941e20d7eb84dbd2985a309a20caba06fThe discourse surrounding the potential educational transformation brought about by generative AI has largely neglected the sensory aspect of learning. In this position paper, I emphasize the significance of sensory studies and their theoretical foundations of embodiment and multimodality as catalysts for novel perspectives on the intersection of AI and the future of education. I delve into the question of whether generative AI serves as a precursor to a new literacy or merely arises as a consequence of ongoing theoretical advancements in contemporary literacy studies. I argue that the concept of agency, which includes both personal and social aspects, should be central to recognizing the importance of sensory learning as an emerging paradigm in reimagining learning futures.First Monday00It is argued that the concept of agency, which includes both personal and social aspects, should be central to recognizing the importance of sensory learning as an emerging paradigm in reimagining learning futures.First Monday['N. Kucirkova']2024-03-09T00:00:00https://www.semanticscholar.org/paper/5e1a933941e20d7eb84dbd2985a309a20caba06f f4deb9a7ec2d7af4e0ff7e2b305033d8ba7612e4Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate thoughtful examination, ethical scrutiny, and responsible practices. In this study, we delve into these challenges, explore existing strategies for mitigating them, with a particular emphasis on identifying AI-generated text as the ultimate solution. Additionally, we assess the feasibility of detection from a theoretical perspective and propose novel research directions to address the current limitations in this domain.arXiv.org570This study explores challenges of widespread usage of large language models, and explores existing strategies for mitigating them, with a particular emphasis on identifying AI-generated text as the ultimate solution.ArXiv['Sara Abdali', 'Richard Anarfi', 'C. Barberan', 'Jia He']2024-03-09T00:00:00https://www.semanticscholar.org/paper/f4deb9a7ec2d7af4e0ff7e2b305033d8ba7612e4 28c40914881ba242741cde11c7fa58765e7bd71dAs Artificial Intelligence (AI) and Machine Learning (ML) technologies continue to revolutionize human resource management, organizations are confronted with a myriad of ethical considerations that accompany these advancements. This article undertakes a critical examination of the ethical implications associated with the integration of AI-ML algorithms into HR processes. Through a comprehensive analysis, this research explores the multifaceted challenges and opportunities that organizations encounter as they navigate the complex intersection between leveraging state-of-the-art technology and upholding ethical standards in workforce management. This exploration sheds light on the nuanced ethical dilemmas inherent in AI-ML adoption within HR, offering insights into the strategies and approaches required to strike a delicate balance between technological innovation and ethical responsibility.International Journal of Research Publication and Reviews780A critical examination of the ethical implications associated with the integration of AI-ML algorithms into HR processes is undertaken, offering insights into the strategies and approaches required to strike a delicate balance between technological innovation and ethical responsibility.International Journal of Research Publication and Reviews['Sunil Basnet']2024-03-09T00:00:00https://www.semanticscholar.org/paper/28c40914881ba242741cde11c7fa58765e7bd71d 9d82164b9292b1a8cc8e28d2d51dd4c477572304 The rise of Generative AI has raised many questions from the perspective of copyright. From the lens of copyright and database rights, issues revolve not only around the authorship of AI-generated outputs, but also the very process that leads to the generation of these outputs, namely the process of text and data mining (TDM). Does unauthorized TDM process infringe the economic rights of the rightholders? How does the TDM-debate transform and transmute in the age of Generative AI? Generative AI tools create works that substitute the content creators whose very work that they learn from, and successively improvise themselves with every iteration. Generative AI, thus, also presents larger policy question as they substitute the romanticized human author that sits at the centre of copyright. In addition, as Generative AI tools, such as ChatGPT, can now also crawl the web, questions thus transcend the frontiers of copyright, and touch upon innovation and competition in the market for web browsers. This research article contemplates on the foregoing issues, and makes some recommendations to create a balanced framework, whereby incentives to innovate are preserved, and the interests of the human author are suitably safeguarded in the age of TDM and Generative AI.Journal of Intellectual Property Law & Practice00This research article contemplates on the foregoing issues, and makes some recommendations to create a balanced framework, whereby incentives to innovate are preserved, and the interests of the human author are suitably safeguarded in the age of TDM and Generative AI.Journal of Intellectual Property Law and Practice['Kalpana Tyagi']2024-03-09T00:00:00https://www.semanticscholar.org/paper/9d82164b9292b1a8cc8e28d2d51dd4c477572304 f8cd62b9982937da5129f7e4450a71d7ec33be87Nowadays, the power grid has transformed into a dynamic and extensive resource generation and management system. This transformation is primarily driven by the widespread adoption of renewable energy sources and the utilization of intelligent information and communication technologies to handle dynamic workloads. The smart grid encompasses various innovative operations, including power electrification, intelligent information integration at the physical layer, and intricate interconnections. These operations leverage data-driven deep learning, big data, and machine learning paradigms to effectively analyze and control transient issues within the electric power system. Artificial intelligence (AI) has emerged as a crucial tool in addressing and resolving challenges associated with transient stability assessment (TSA) and power generation control. In this research paper, we present a comprehensive review that explores the role of AI and its sub-procedures in tackling TSA problems. The article outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA. Distinguishing itself from other reviews, this paper delves into the AI-based TSA framework and design process, highlighting intelligent applications and their analytical capabilities in addressing power system transient problems. Furthermore, our analysis extends beyond AI alone, as we also explore the integration of big data, which aligns seamlessly with AI. The paper discusses future trends, opportunities, challenges, and open issues pertaining to AI-Big data based transient stability assessment in the smart power grid.International Journal For Multidisciplinary Research120This research paper outlines an AI-based intelligent power system structure, along with the rationality of applying AI to transient situations in power system TSA, and explores the integration of big data, which aligns seamlessly with AI.International Journal For Multidisciplinary Research['Aakansha Goyal', 'Mr. S.N JOSHI']2024-03-09T00:00:00https://www.semanticscholar.org/paper/f8cd62b9982937da5129f7e4450a71d7ec33be87 4141bd946a981cfd59f2282e60ebc76062a0dd2bBackground: Generative artificial intelligence (GAI) tools, such as large language models, have the potential to revolutionize medical research and writing, but their use also raises important ethical and practical concerns. This study examines the prevalence and content of GAI guidelines among Korean medical journals to assess the current landscape and inform future policy development. Methods: Top 100 Korean medical journals by H-index were surveyed. Author guidelines were collected and screened by a human author and AI chatbot to identify GAI-related content. Key components of GAI policies were extracted and compared across journals. Journal characteristics associated with GAI guideline adoption were also analyzed. Results: Only 18% of the surveyed journals had GAI guidelines, which is much lower than previously reported international journals. However, adoption rates increased over time, reaching 57.1% in the first quarter of 2024. Higher-impact journals were more likely to have GAI guidelines. All journals with GAI guidelines required authors to declare GAI use, and 94.4% prohibited AI authorship. Key policy components included emphasizing human responsibility (72.2%), discouraging AI-generated content (44.4%), and exempting basic AI tools (38.9%). Conclusion: While GAI guideline adoption among Korean medical journals is lower than global trends, there is a clear increase in implementation over time. The key components of these guidelines align with international standards, but greater standardization and collaboration are needed to ensure responsible and ethical use of GAI in medical research and writing.medRxiv180The key components of these guidelines align with international standards, but greater standardization and collaboration are needed to ensure responsible and ethical use of GAI in medical research and writing.['S. Ahn']2024-03-09T00:00:00https://www.semanticscholar.org/paper/4141bd946a981cfd59f2282e60ebc76062a0dd2b 5fdf3dc59d96a21127e5c60d38564af3d4211c52AI &amp; SOCIETY01AI &amp; SOCIETY['Manh-Tung Ho', 'Q. Vuong']2024-03-09T00:00:00https://www.semanticscholar.org/paper/5fdf3dc59d96a21127e5c60d38564af3d4211c52 916e4fbb281cb7857afbe951ececa1ba7ddf7800This paper advances the field of GPU-based embedded intelligence (EI) by providing a comprehensive review of current and emerging architectures and applications. It covers key paradigms in GPU-based EI, focusing on architecture, technologies, and practical applications. The paper is structured as follows: (1) An overview and classification of GPU-based EI research, providing a broad perspective and concise summary of the paper's scope; (2) An in-depth discussion of various architectural technologies for GPU-based deep learning techniques and applications; and (3) A detailed examination of architectural technologies for GPU-based machine learning techniques and applications. This paper aims to offer valuable insights into the research area, encouraging further development of GPU-based EI for practical deployment and applications.Online (Weston, Conn.)00This paper advances the field of GPU-based embedded intelligence by providing a comprehensive review of current and emerging architectures and applications, and offers valuable insights into the research area, encouraging further development of GPU-based EI for practical deployment and applications.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)['Bhuvi Chopra']2024-03-09T00:00:00https://www.semanticscholar.org/paper/916e4fbb281cb7857afbe951ececa1ba7ddf7800 04f03432e4289bece6814c381d7998ab4fd85bcfDigital Creativity250Digital Creativity['Benedikte Wallace', 'Kristian Nymoen', 'J. Tørresen', 'Charles Patrick Martin']2024-03-09T00:00:00https://www.semanticscholar.org/paper/04f03432e4289bece6814c381d7998ab4fd85bcf a0f72e0f885d395fdfb157f110c90be170c38b99International Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Vivek Sarraf', 'Raveena Raj Purohit', 'Ayush Jain', 'Rikish Jain', 'Yashwanth M', 'Ayaan Mathews', 'VaraLakshmi S']2024-03-09T00:00:00https://www.semanticscholar.org/paper/a0f72e0f885d395fdfb157f110c90be170c38b99 99d49ea345e5564bde30dcea013a34e28980a237This Review provides an overview of the comparative review of data privacy laws and compliance, focusing on the European Union's General Data Protection Regulation (EU GDPR) and data protection regulations in the United States. The analysis explores key similarities and differences, emphasizing their implications for businesses and individuals. The EU GDPR, implemented in 2018, stands as a landmark regulation governing data protection and privacy for individuals within the European Union and the European Economic Area. In contrast, the United States lacks a comprehensive federal data privacy law. Instead, it relies on a patchwork of sector-specific laws and state regulations, such as the California Consumer Privacy Act (CCPA) and the Health Insurance Portability and Accountability Act (HIPAA).  One major distinction lies in the overarching principles of these regulations. The EU GDPR adopts a comprehensive and rights-based approach, emphasizing individual rights to privacy, data portability, and the "right to be forgotten." In contrast, the U.S. system often focuses on specific industries or types of data, leading to a more fragmented regulatory landscape. Both regulatory frameworks incorporate principles of transparency, consent, and data breach notification. However, differences in enforcement mechanisms and penalties exist. The EU GDPR imposes significant fines for non-compliance, reaching up to 4% of a company's global annual revenue. In the U.S., penalties vary by state, and enforcement is often reactive, triggered by data breaches. Businesses operating globally must navigate these distinct regulatory landscapes, necessitating a nuanced approach to data privacy compliance. Multinational corporations must adhere to the more stringent requirements when handling EU citizens' data while also considering the diverse regulations within the U.S. This review underscores the ongoing evolution of data privacy laws worldwide and the critical importance for organizations to stay abreast of these developments. It emphasizes the need for a proactive and adaptive approach to data privacy compliance, taking into account the unique requirements and expectations of both the EU GDPR and U.S. regulations. Keywords: Data Privacy, Laws, Compliance, EU GDPR, Regulations.Computer Science &amp; IT Research Journal01Computer Science &amp; IT Research Journal['Seun Solomon Bakare', 'Adekunle Oyeyemi Adeniyi', 'Chidiogo Uzoamaka Akpuokwe', 'Nkechi Emmanuella Eneh']2024-03-09T00:00:00https://www.semanticscholar.org/paper/99d49ea345e5564bde30dcea013a34e28980a237 12a13bf61ef300c5a6a0bc1659648187d7abb54cEvolutionary Game Theory (EGT) and Artificial Intelligence (AI) are two fields that, at first glance, might seem distinct, but they have notable connections and intersections. The former focuses on the evolution of behaviors (or strategies) in a population, where individuals interact with others and update their strategies based on imitation (or social learning). The more successful a strategy is, the more prevalent it becomes over time. The latter, meanwhile, is centered on machine learning algorithms and (deep) neural networks. It is often from a single-agent perspective but increasingly involves multi-agent environments, in which intelligent agents adjust their strategies based on feedback and experience, somewhat akin to the evolutionary process yet distinct in their self-learning capacities. In light of the key components necessary to address real-world problems, including (i) learning and adaptation, (ii) cooperation and competition, (iii) robustness and stability, and altogether (iv) population dynamics of individual agents whose strategies evolve, the cross-fertilization of ideas between both fields will contribute to the advancement of mathematics of multi-agent learning systems, in particular, to the nascent domain of ``collective cooperative intelligence'' bridging evolutionary dynamics and multi-agent reinforcement learning.Science China Information Sciences81The cross-fertilization of ideas between both fields will contribute to the advancement of mathematics of multi-agent learning systems, in particular, to the nascent domain of ``collective cooperative intelligence'' bridging evolutionary dynamics and multi-agent reinforcement learning.ArXiv['Long Wang', 'Feng Fu', 'Xingru Chen']2024-03-09T00:00:00https://www.semanticscholar.org/paper/12a13bf61ef300c5a6a0bc1659648187d7abb54c d9c064ceac6f5f9fadc1a7f38ee96ad0faf6abbeThe paper presents an insightful overview of the intricate legal challenges posed by the proliferation of Artificial Intelligence (AI) and Robotics. This comprehensive review explores the multifaceted dimensions of the evolving legal landscape, addressing issues at the intersection of technology and law. Key focal points include the accountability and liability frameworks for autonomous AI systems, ethical considerations in the deployment of intelligent machines, and the complex dynamics of data privacy in the age of pervasive automation. The review delves into the intricate legal nuances surrounding intellectual property rights, particularly as AI systems contribute to creative outputs and innovation. It navigates the blurred lines between human and machine authorship, raising fundamental questions about ownership and protection in this digital era. Moreover, the paper emphasizes the global nature of these challenges, highlighting the imperative for international cooperation to formulate harmonized legal standards. As AI and robotics revolutionize industries and societal frameworks, the analysis underscores the critical need for adaptive and anticipatory legal frameworks. It explores how existing legal paradigms are grappling with the unprecedented speed of technological advancements and the ethical dilemmas arising from the delegation of decision-making to intelligent algorithms. The paper sets the stage for a thorough examination of the legal intricacies surrounding AI and robotics. It advocates for a proactive and collaborative approach, involving legal experts, technologists, ethicists, and policymakers in crafting robust frameworks that balance innovation with ethical, privacy, and accountability considerations. This review serves as a foundational resource for understanding and addressing the legal challenges inherent in the transformative era of Artificial Intelligence and Robotics. Keywords: Artificial intelligence, Robotics, Legal, AI challenges, Ethics, Review.Computer Science &amp; IT Research Journal01The paper sets the stage for a thorough examination of the legal intricacies surrounding AI and robotics, and advocates for a proactive and collaborative approach, involving legal experts, technologists, ethicists, and policymakers in crafting robust frameworks that balance innovation with ethical, privacy, and accountability considerations.Computer Science &amp; IT Research Journal['Chidiogo Uzoamaka Akpuokwe', 'Adekunle Oyeyemi Adeniyi', 'Seun Solomon Bakare', 'Nkechi Emmanuella Eneh']2024-03-09T00:00:00https://www.semanticscholar.org/paper/d9c064ceac6f5f9fadc1a7f38ee96ad0faf6abbe 4b7f5bb68e9e496b407f6d70286fc3c6766fdae9In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing.Applied Sciences170The preliminary development of the AVESYS framework is presented, which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process and its effectiveness in identifying anomalies in the test environment that could potentially affect testing results is evaluated.Applied Sciences['Anna Gnacy-Gajdzik', 'P. Przystałka']2024-03-09T00:00:00https://www.semanticscholar.org/paper/4b7f5bb68e9e496b407f6d70286fc3c6766fdae9 9430a3a67fe8f4957a8e87d901b4312d1c84da3cThe emergence of financial technology (Fintech) has revolutionized the global financial landscape, offering innovative solutions that challenge traditional banking systems and investment practices. This review explores the intersection of Fintech, taxation, and regulatory compliance, highlighting the complexities and opportunities within this dynamic ecosystem. Fintech encompasses a wide range of technologies, including blockchain, artificial intelligence, and mobile payment systems, which have streamlined financial services and expanded access to capital markets. However, this rapid evolution poses significant challenges for taxation and regulatory frameworks. Traditional tax laws struggle to keep pace with the speed and complexity of digital transactions, leading to uncertainties in tax treatment and enforcement. Navigating the tax implications of Fintech requires a nuanced understanding of digital assets, decentralized finance (DeFi) platforms, and cross-border transactions. Tax authorities worldwide are grappling with these challenges, seeking to balance innovation and compliance while ensuring a fair and transparent tax regime. The review examines various approaches adopted by governments and regulatory bodies to address Fintech taxation, including legislative reforms, international cooperation, and the use of advanced data analytics. Furthermore, regulatory compliance remains a critical concern for Fintech firms, as they must navigate a labyrinth of rules and standards across jurisdictions. Compliance requirements vary widely, ranging from anti-money laundering (AML) regulations to data protection laws, presenting operational and legal challenges for market participants. The review discusses strategies for achieving regulatory compliance in the Fintech sector, emphasizing the importance of proactive risk management, regulatory engagement, and technological solutions such as RegTech. Despite these challenges, the convergence of Fintech, taxation, and regulatory compliance offers immense opportunities for innovation and growth. By embracing digital transformation and adopting agile regulatory frameworks, governments and businesses can unlock the full potential of Fintech while safeguarding financial stability and integrity. This review provides insights into the evolving landscape of Fintech taxation and regulatory compliance, highlighting key trends, challenges, and best practices for navigating this new frontier in finance. Keywords:  Fintech, Taxation, Financial, Technology, Review.Finance &amp; Accounting Research Journal05This review provides insights into the evolving landscape of Fintech taxation and regulatory compliance, highlighting key trends, challenges, and best practices for navigating this new frontier in finance.Finance &amp; Accounting Research Journal['Omotoya Bukola Adeoye', 'Wihelmina Afua Addy', 'Olubusola Odeyemi', 'Chinwe Chinazo Okoye', 'Onyeka Chrisanctus Ofodile', 'Adedoyin Tolulope Oyewole', 'Yinka James Ololade']2024-03-09T00:00:00https://www.semanticscholar.org/paper/9430a3a67fe8f4957a8e87d901b4312d1c84da3c 10059feb64f17b0b8e037e8f46dbae85b23d63cd: MineBot, an intelligent Chatbot tailored for the mining industry, seamlessly integrates LangChain in Python and is powered by GPT-3.5-turbo with Retrieval Augmented Generation (RAG). With a primary focus on providing stakeholders continuous and unfettered access to precise information on Acts, Rules, and Regulations governing the mining sector, MineBot operates within the resilient LangChain framework, ensuring efficient data processing. Empowered by GPT-3.5-turbo with RAG, the Chatbot elevates contextual understanding by seamlessly integrating external knowledge bases. The knowledge base undergoes strategic enrichment through the adept utilization of the Wikipedia API and Google Search Engine API via SERP API, guaranteeing stakeholders access to meticulously updated information. MineBot's applications extend beyond information access, encompassing regulatory insights, continuous availability, and a knowledge base enriched with comprehensive data. In essence, MineBot stands as an epitome of intelligent and dynamic solutions, converging cutting-edge AI technologies with potent information retrieval tools. The objective is to empower stakeholders with accurate, real-time information and a comprehensive understanding of the mining regulatory landscape, thereby facilitating informed interactionsInternational Journal of Research Publication and Reviews210International Journal of Research Publication and Reviews['Ms. Gayathri Devi. M', 'Siranjeevi. K', 'Rupeshwar. S', 'Oviya. T']2024-03-09T00:00:00https://www.semanticscholar.org/paper/10059feb64f17b0b8e037e8f46dbae85b23d63cd ca21c663fae5e7557ccda6d2729bdbc97abb851aIn order to engineer new materials, structures, systems, and processes that address persistent challenges, engineers seek to tie causes to effects and understand the effects of causes. Such a pursuit requires a causal investigation to uncover the underlying structure of the data generating process (DGP) governing phenomena. A causal approach derives causal models that engineers can adopt to infer the effects of interventions (and explore possible counterfactuals). Yet, and for the most part, we continue to design experiments in the hope of empirically observing engineered intervention(s). Such experiments are idealized, complex, and costly and hence are narrow in scope. On the contrary, a causal investigation will allow us to peek into the how and why of a DGP and provide us with the essential means to articulate a causal model that accurately describes the phenomenon on hand and better predicts the outcome of possible interventions. Adopting a causal approach in engineering is perhaps more warranted than ever—especially with the rise of big data and the adoption of artificial intelligence (AI); wherein AI models are naivety presumed to describe causal ties. To bridge such knowledge gap, this primer presents fundamental principles behind causal discovery, causal inference, and counterfactuals from an engineering perspective and contrasts that to those pertaining to correlation, regression, and AI.This article is categorized under: Application Areas > Industry Specific Applications Algorithmic Development > Causality Discovery Application Areas > Science and Technology Technologies > Machine Learning WIREs Data Mining and Knowledge Discovery140This primer presents fundamental principles behind causal discovery, causal inference, and counterfactuals from an engineering perspective and contrasts that to those pertaining to correlation, regression, and AI.WIREs Data Mining and Knowledge Discovery['M. Naser']2024-03-09T00:00:00https://www.semanticscholar.org/paper/ca21c663fae5e7557ccda6d2729bdbc97abb851a 9e11aa8f9ff1a62f7ebadd7d82f74d7f19afa8b4Currently, artificial intelligence is actively being introduced into various spheres of life, and medicine is no exception. In ophthalmology, the use of artificial intelligence is very promising, given that the diagnosis and therapeutic monitoring of eye diseases often depend heavily on the correct interpretation of images. The use of artificial intelligence in ophthalmology focuses on eye diseases that lead to vision loss, such as age-related macular degeneration, diabetic retinopathy, glaucoma and cataract. Over the past few years, artificial intelligence has reached tremendous successes in the practice of ophthalmology. Many studies have shown that artificial intelligence performance is equal to and even exceeds the capabilities of ophthalmologists in many diagnostic and prognostic tasks. However, there is still a lot of work to be done before introducing artificial intelligence into routine clinical practice. Issues such as real-world performance, generalizability, and interpretability of artificial intelligence systems are still poorly understood and will require more attention in future research. Most artificial intelligence-based systems are used in developed countries, and some require further study. High costs and a shortage in doctors and equipment in some regions of the Russian Federation and rural areas make it difficult to screen for eye diseases. Although the field of artificial intelligence is underdeveloped, we hope that artificial intelligence will play an important role in the future of ophthalmology by making healthcare more efficient, accurate and accessible, especially in regions where staffing problems exist.Ophthalmology Reports350It is hoped that artificial intelligence will play an important role in the future of ophthalmology by making healthcare more efficient, accurate and accessible, especially in regions where staffing problems exist.Ophthalmology Reports['Sabina S. Mamedova', 'Alsu I. Karimova', 'Adelia F. Galieva', 'Maria A. Malkhanova', 'Sofya S. Polyankina', 'Aigul I. Kuchumova', 'Yana Ya. Tarasova', 'Dmitry U. Tsuan', 'Olga V. Klets', 'Veronika N. Gerbutova', 'Andrey V. Olenichev', 'Eliza O. Ushakova', 'Aigul K. Minnikhalilova']2024-03-09T00:00:00https://www.semanticscholar.org/paper/9e11aa8f9ff1a62f7ebadd7d82f74d7f19afa8b4 caf2e74ebca10c3349c38c342089ebe11d10bd1bRadiography281Overall, this study found that Nordic radiographers have a positive attitude toward AI, however, there is a need for continuous professional development to facilitate the implementation and effective utilization of AI tools within the field of radiography.Radiography['M. Pedersen', 'M. Kusk', 'S. Lysdahlgaard', 'H. Mork-Knudsen', 'C. Malamateniou', 'J. Jensen']2024-03-09T00:00:00https://www.semanticscholar.org/paper/caf2e74ebca10c3349c38c342089ebe11d10bd1b 821beb96c5369f5ad5c28be6fd12a7fbfb1e4d93The research relevance is determined by artificial intelligence (AI) as one of the ways to guarantee public safety and increase the effectiveness of law enforcement agencies. The study aims to investigate whether AI can be used in the legal system, with a particular focus on forensics and crime fighting. To achieve the research goal, the following methods were used: comparative legal, formal legal, historical legal, systemic and structural, and theoretical and prognostic. The article examines the use of AI in the legal sector from different perspectives and identifies “high- risk” AI systems. These systems should be used with caution and following specific criteria to ensure their safe and ethical use. In the context of criminal justice, it also examines how conventional digital technologies are connected to sophisticated AI capabilities, with a particular focus on the use of AI in the investigation of war crimes committed by Russia against Ukraine. While it is recognised that these materials must comply with applicable legal norms, AI is being used with great attention to collect and analyse data relevant to war crimes investigations. The results of the study show that although the use of AI in law enforcement operations can significantly increase the effectiveness of investigations, strict rules are still necessary to protect human rights and freedoms. It highlights how important AI is for war crimes investigations, especially considering Russian full-scale invasion of Ukraine. While it is recognised that these materials must comply with applicable legal norms, AI is being used with great attention to collect and analyse data relevant to war crimes investigations. The results of the study show that although the use of AI in law enforcement operations can significantly increase the effectiveness of investigations, strict rules are still necessary to protect human rights and freedoms. It emphasises how important AI is for war crimes investigations, especially considering Russian full-scale invasion of UkraineSocial Legal Studios170The study aims to investigate whether AI can be used in the legal system, with a particular focus on forensics and crime fighting, and identifies “high- risk” AI systems.Social Legal Studios['Valery Shepitko', 'M. Shepitko', 'Kateryna Latysh', 'Mariietta Kapustina', 'Evgeniya Demidova']2024-03-08T00:00:00https://www.semanticscholar.org/paper/821beb96c5369f5ad5c28be6fd12a7fbfb1e4d93 b33dd526f040b78bb3115bb5b7e1800116474b23 The present debate about the governance of artificial intelligence (AI) is dominated by a narrative of a “global race toward the regulation of AI.” Such a narrative bears serious dangers and should be rephrased as the “race toward the global regulation of AI” to adequately address the cross-cutting, cross-boundary, and cross-cultural nature of these technologies. If the debate about the future regulation of AI is to efficiently address the serious dangers and potentially existential risks related to AI, then it should be tied to other global governance issues, such as those summarized by the United Nations Sustainable Development Goals (SDGs). For this endeavor to be successful, the substantive questions of regulation must be combined with efforts to reform the present international system with a view to establishing a more efficient and coherent global institutional framework. It is important to be mindful of past obstacles in the reform of existing international organizations and to avoid the need for another global cataclysm to trigger institutional reform; thus, the article follows the idea that cognitive change leads to the transformation of international organizations. As both a technology aimed to replicate the human mind and an example of an important linguistic trend of a rise in essentially oxymoronic concepts, AI is deemed to provide the right point of departure to ponder future modes of human cognition – modes that reflect Einstein’s description of a world as a “four-dimensional space – time continuum,” – which may help to imagine the contours of a future global institutional framework.International Journal of Digital Law and Governance42The article follows the idea that cognitive change leads to the transformation of international organizations and it is important to be mindful of past obstacles in the reform of existing international organizations to avoid the need for another global cataclysm to trigger institutional reform.International Journal of Digital Law and Governance['R. Neuwirth']2024-03-08T00:00:00https://www.semanticscholar.org/paper/b33dd526f040b78bb3115bb5b7e1800116474b23 8f4e1fc9017cc4e9f69ce465f74e6bb716ff2e2dВ статье рассматриваются особенности построения и содержания системы защиты спортив- ных прав лиц с ограниченными возможностями в Китайской Народной Республике. Дается оценка эффектив- ности правового регулирования в рассматриваемой сфере. The article examines the features of the construction and content of the system of protection of sports rights of persons with disabilities in the People's Republic of China. An assessment of the effectiveness of legal regulation in the field under consideration is given.Eurasian Advocacy (Evraziiskaya Advokatura)00Eurasian Advocacy (Evraziiskaya Advokatura)['А.В. Долгов']2024-03-08T00:00:00https://www.semanticscholar.org/paper/8f4e1fc9017cc4e9f69ce465f74e6bb716ff2e2d f7ae8677e60ef41f419de6874814a271704de815В статье рассмотрены основные источники нормативного-регулирования такой правовой ка- тегории, как «социальное предпринимательство», а также делается вывод о значении рассматриваемой катего- рии в системе реализации государственной поддержки предпринимательства. The article examines the main sources of normative regulation of such a legal category as «Social entrepreneurship » and draws a conclusion about the significance of the category under consideration in the system of implementing state support for entrepreneurship.Eurasian Advocacy (Evraziiskaya Advokatura)00Eurasian Advocacy (Evraziiskaya Advokatura)['А.А. Бирич']2024-03-08T00:00:00https://www.semanticscholar.org/paper/f7ae8677e60ef41f419de6874814a271704de815 4207d1c6b1625ea113ff2a386e8a0f552562d44eАвтор приходит к выводу, что, поскольку исследуется новый объект права, то только практи- ческая деятельность, относящаяся к ЦФА, позволяет с определенной гарантированностью рассуждать и ана- лизировать правовую действительность, возникающую по поводу ЦФА. Поэтому в настоящее время можно только предполагать возможные варианты использования ЦФА, но, исходя из логики законодателя касательно цифровой валюты, ЦФА будут представлять собой полноценные имущественные права, которые не будут под- вергаться сомнению, как у правоприменителей, так и у общественности. The author comes to the conclusion that since a new object of law is being studied, only practical activities related to DFA allow one to reason and analyze the legal reality arising regarding DFA with a certain certainty. Therefore, at present, one can only speculate on possible options for using DFAs, but based on the logic of the legislator regarding digital currency, DFAs will represent full-fledged property rights that will not be questioned by both law enforcers and the public.Eurasian Advocacy (Evraziiskaya Advokatura)00Eurasian Advocacy (Evraziiskaya Advokatura)['П.А. Рустамов']2024-03-08T00:00:00https://www.semanticscholar.org/paper/4207d1c6b1625ea113ff2a386e8a0f552562d44e 8bebd1d80be3ba888fafe829868e07fb3e811fa4В статье рассматриваются традиционные ценности и роль права в их обеспечении, а также в укреплении социальной стабильности. The article examines traditional values and the role of law in ensuring them, as well as in strengthening social stability.Eurasian Advocacy (Evraziiskaya Advokatura)00Eurasian Advocacy (Evraziiskaya Advokatura)['Е.Г. Багреева']2024-03-08T00:00:00https://www.semanticscholar.org/paper/8bebd1d80be3ba888fafe829868e07fb3e811fa4 bd876ebd265ba1c97bb12de4413cb97c312e42f9In the context of agentive decision making and action, both executive and metacognitive processes serve self-regulatory functions—just on different hierarchical tiers. In the agency-based model proposed here executive processes monitor and control action and attention from an executive tier of operation, and metacognitive processes monitor and control those executive processes from a second-order metacognitive tier of operation-both with the function of facilitating effective and efficient behavioral decisions. Each is best conceptualized as comprising three key components: (i) what is regulated, (ii) how, via what processes, is it regulated, and (iii) where, in what cognitive workspace, is it regulated—either in individual or in shared agencies. Developmentally, evidence is presented that executive processes for regulating both individual and joint agencies emerge only after 9–12 months of age, and metacognitive processes for regulating both individual and collective agencies emerge only after 3–4 years of age. Cognitive flexibility, as an important outcome, derives from the child's attempts to metacognitively regulate differing social perspectives within shared agencies.Frontiers in Developmental Psychology410Frontiers in Developmental Psychology['Michael Tomasello']2024-03-08T00:00:00https://www.semanticscholar.org/paper/bd876ebd265ba1c97bb12de4413cb97c312e42f9 628f241e6ccf6ce5ceb4a1d866935abf31d586daAs advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.Journal of Medical Internet Research02The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks.Journal of Medical Internet Research['Yan Chen', 'Pouyan Esmaeilzadeh']2024-03-08T00:00:00https://www.semanticscholar.org/paper/628f241e6ccf6ce5ceb4a1d866935abf31d586da 09f9c212a95ce957f1471d56efd53f25f3a8b080There is an urgent need to incorporate the perspectives of culturally diverse groups into AI developments. We present a novel conceptual framework for research that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment. Two survey studies support this framework and provide preliminary evidence that people apply their cultural models when imagining their ideal AI. Compared with European American respondents, Chinese respondents viewed it as less important to control AI and more important to connect with AI, and were more likely to prefer AI with capacities to influence. Reflecting both cultural models, findings from African American respondents resembled both European American and Chinese respondents. We discuss study limitations and future directions and highlight the need to develop culturally responsive and relevant AI to serve a broader segment of the world population.International Conference on Human Factors in Computing Systems1261A novel conceptual framework for research is presented that aims to expand, reimagine, and reground mainstream visions of AI using independent and interdependent cultural models of the self and the environment and preliminary evidence that people apply their cultural models when imagining their ideal AI is provided.{'pages': '95:1-95:15'}['Xiao Ge', 'Chunchen Xu', 'Daigo Misaki', 'Hazel Rose Markus', 'Jeanne L Tsai']2024-03-08T00:00:00https://www.semanticscholar.org/paper/09f9c212a95ce957f1471d56efd53f25f3a8b080 5c14c0be3ae5acfe3db8bca0b15206fc5a589621 In 2019 the administration of the Norwegian Parliament designed and adopted a strategic goal for digitization: The Parliament shall exchange, process, publish and preserve parliamentary information digitally and efficiently, with good quality. One outcome of this effort was the project “StorSak”. This project aims to implement an information system which handles document production and parliamentary proceedings in a coherent digital value-chain. One of the investment areas in the StorSak project is exploring the use of machine learning (ml) and artificial intelligence (ai). The project quickly singled out metadata handling as an area of interest, and the categorization of documents and proceedings using theme wording.International Journal of Parliamentary Studies20The StorSak project aims to implement an information system which handles document production and parliamentary proceedings in a coherent digital value-chain, and explores the use of machine learning and artificial intelligence.International Journal of Parliamentary Studies['Tanja Wahl']2024-03-08T00:00:00https://www.semanticscholar.org/paper/5c14c0be3ae5acfe3db8bca0b15206fc5a589621 0789e46115612436e37526dc7c6f8f644b576f69This study investigates the transformative impact of artificial intelligence (AI) on marketing communications through an evaluation research approach. Focused on enhancing personalization, efficiency, and strategic insight, the study explores AI’s applications in content creation, customer service, social media, influencer marketing, and predictive analytics. The results reveal a paradigm transition in customer engagement and behaviour analysis, highlighting AI’s role in providing profound insights and facilitating real-time interactions. Personalized marketing and targeted advertising have evolved, with AI analysing vast datasets to craft tailored messages, significantly enhancing communication relevance. AI’s impact extends to content creation and curation, accelerating processes through natural language generation and improving content personalization. Moreover, AI-driven chatbots redefine customer service, providing 24/7 personalized support and actively contributing to marketing strategies. Social media and influencer marketing benefit from AI’s optimization of content delivery, personalization, and campaign impact measurement. The synergy between AI and predictive analytics anticipates consumer behavior, enabling precise targeting and optimizing the customer journey. The study concludes with implications for businesses, advocating strategic AI integration for sustained growth, and emphasizes the necessity of staying attuned to emerging AI innovations in future research. This research serves as a roadmap, guiding businesses toward successfully navigating the digital era’s evolving marketing communications view.Technium Social Sciences Journal00A paradigm transition in customer engagement and behaviour analysis is revealed, highlighting AI’s role in providing profound insights and facilitating real-time interactions in marketing communications.Technium Social Sciences Journal['Hafize Nurgül Durmuş Şenyapar']2024-03-08T00:00:00https://www.semanticscholar.org/paper/0789e46115612436e37526dc7c6f8f644b576f69 c284d16eec27b6f10ab999da9ca991e0f9a29709The integration of artificial intelligence (AI) tools in education has emerged as a significant paradigm shift. As we navigate this new landscape, it is essential to understand the adoption and use of these technologies in educational settings. Some valuable insights into AI's global trends and potential in education are available from OECD-Education International (2023), highlighting a need to understand privacy concerns and equitable access. Guidelines and advice have also been made available for New Zealand schools from the Ministry of Education (2024). However, there currently needs to be more comprehensive studies of AI in education that focus on the specific context of Aotearoa, New Zealand. This report seeks to meet that need. Educators from throughout Aotearoa were invited to participate in a survey conducted between August and November 2023. The survey aimed to assess the landscape of AI implementation in schools, exploring practices, challenges, and potential positive outcomes associated with AI tools in education. This research seeks to inform policymakers, educators, and stakeholders to facilitate informed decision-making to advance educational practices. Through this research, we hope to contribute to the ongoing dialogue on AI in education and its implications for teaching and learning in Aotearoa.He Rourou00The landscape of AI implementation in schools in Aotearoa is assessed, exploring practices, challenges, and potential positive outcomes associated with AI tools in education to facilitate informed decision-making to advance educational practices.He Rourou['Tim Gander', 'Brendon Shaw']2024-03-08T00:00:00https://www.semanticscholar.org/paper/c284d16eec27b6f10ab999da9ca991e0f9a29709 a8ab5b12393698a53d8c2e4dbb6e4d129451c5ea Purpose Artificial intelligence (AI) has become increasingly important and influential in reading and writing. The influx of social media digital spaces, like TikTok, has also shifted the ways multimodal composition takes place alongside AI. This study aims to argue that within spaces like TikTok, human composers must attend to the ways they write for, with and against the AI-powered algorithm. Design/methodology/approach Data collection was drawn from a larger study on #BookTok (the TikTok subcommunity for readers) that included semi-structured interviews including watching and reflecting on a TikTok they created. The authors grounded this study in critical posthumanist literacies to analyze and open code five #BookTok content creators’ interview transcripts. Using axial coding, authors collaboratively determined three overarching and entangled themes: writing for, with and against. Findings Findings highlight the nuanced ways #BookTokers consider the AI algorithm in their compositional choices, namely, in the ways how they want to disseminate their videos to a larger audience or more niche-focused community. Throughout the interviews, participants revealed how the AI algorithm was situated differently as both audience member, co-author and censor. Originality/value This study is grounded in critical posthumanist literacies and explores composition as a joint accomplishment between humans and machines. The authors argued that it is necessary to expand our human-centered notions of what it means to write for an audience, to co-author and to resist censorship or gatekeeping. English Teaching: Practice &amp; Critique360It is argued that within spaces like TikTok, human composers must attend to the ways they write for, with and against the AI-powered algorithm, to expand human-centered notions of what it means to write for an audience, to co-author and to resist censorship or gatekeeping.English Teaching: Practice &amp; Critique['Sarah Jerasa', 'Sarah K. Burriss']2024-03-08T00:00:00https://www.semanticscholar.org/paper/a8ab5b12393698a53d8c2e4dbb6e4d129451c5ea 7bacf1daf65eda0a4382dea949a2e9a5e19b4266Artificial Intelligence has effectively spread in the field of healthcare in recent years. It is leading to substantial changes and transforming the traditional healthcare system into much more advanced technology. For more improvement in AI healthcare systems, comprehensive data analysis is required. This paper focuses on deeply analyzing a synthetic AI healthcare dataset utilizing statistical approaches. Statistical approaches are significantly used to predict the efficiency of different models, accuracy of AI diagnosis, patient satisfaction, and other correlations between relevant variables of an AI healthcare system. This paper provides an in-depth analysis of an AI-driven healthcare dataset utilizing pearson's correlation coefficient, multiple linear regression plot, and ANOVA testing. Using different techniques, it can be stated that there is no relation between AI diagnosis with patient's treatment duration and recovery time as the p-value (0.771963) was greater than the common significance level (0.05). The hypothesis, where it was assumed that there was not a statistically major difference between AI diagnostic confidence and human diagnosis, has been proved as the p-value (0.248596) was greater than the common significance level (0.05). To conclude, it can be said that the assumptions were correct as the null hypotheses are accepted by testing them in several statistical approaches.2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)160This paper provides an in-depth analysis of an AI-driven healthcare dataset utilizing pearson's correlation coefficient, multiple linear regression plot, and ANOVA testing and it can be stated that there is no relation between AI diagnosis with patient's treatment duration and recovery time.2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)['Raiyan Gani', 'Maherun Nessa Isty', 'Rifath Ara Rimi', 'Warda Ruhin Parsub', 'Md. Shajibul Islam', 'Mysha Maliha Priyanka', 'Mohammad Rifat Ahmmad Rashid', 'Mahamudul Hasan', 'Nafees Mansoor', 'Md. Nasim Adnan']2024-03-08T00:00:00https://www.semanticscholar.org/paper/7bacf1daf65eda0a4382dea949a2e9a5e19b4266 bde8b18d354a6f6359edd34578a429700dfae88dMany cyber-physical-human systems (CPHS) involve a human decision-maker who may receive recommendations from an artificial intelligence (AI) platform while holding the ultimate responsibility of making decisions. In such CPHS applications, the human decision-maker may depart from an optimal recommended decision and instead implement a different one for various reasons. In this letter, we develop a rigorous framework to overcome this challenge. In our framework, we consider that humans may deviate from AI recommendations as they perceive and interpret the system's state in a different way than the AI platform. We establish the structural properties of optimal recommendation strategies and develop an approximate human model (AHM) used by the AI. We provide theoretical bounds on the optimality gap that arises from an AHM and illustrate the efficacy of our results in a numerical example.arXiv.org230In this letter, a rigorous framework is developed that considers that humans may deviate from AI recommendations as they perceive and interpret the system's state in a different way than the AI platform.ArXiv['Aditya Dave', 'Heeseung Bang', 'Andreas A. Malikopoulos']2024-03-08T00:00:00https://www.semanticscholar.org/paper/bde8b18d354a6f6359edd34578a429700dfae88d 26ae9d9fadf605d6915d23130fa2e1f357ea721aAesthetic Plastic Surgery00This work reviews the literature referring to the ethical challenges brought on by the ever-expanding use of AI in plastic surgery and offers guidelines for its application.Aesthetic plastic surgery['N. Kenig', 'Javier Monton Echeverria', 'Carlos Rubi']2024-03-08T00:00:00https://www.semanticscholar.org/paper/26ae9d9fadf605d6915d23130fa2e1f357ea721a 06d26c70476893dc8bbc6fc4e4b451301a3c6561Background Artificial intelligence (AI) applications are rapidly advancing in the field of medical imaging. This study is aimed at investigating the perception and knowledge of radiographers towards artificial intelligence. Methods An online survey employing Google Forms consisting of 20 questions regarding the radiographers' perception of AI. The questionnaire was divided into two parts. The first part consisted of demographic information as well as whether the participants think AI should be part of medical training, their previous knowledge of the technologies used in AI, and whether they prefer to receive training on AI. The second part of the questionnaire consisted of two fields. The first one consisted of 16 questions regarding radiographers' perception of AI applications in radiology. Descriptive analysis and logistic regression analysis were used to evaluate the effect of gender on the items of the questionnaire. Results Familiarity with AI was low, with only 52 out of 100 respondents (52%) reporting good familiarity with AI. Many participants considered AI useful in the medical field (74%). The findings of the study demonstrate that nearly most of the participants (98%) believed that AI should be integrated into university education, with 87% of the respondents preferring to receive training on AI, with some already having prior knowledge of AI used in technologies. The logistic regression analysis indicated a significant association between male gender and experience within the range of 23-27 years with the degree of familiarity with AI technology, exhibiting respective odds ratios of 1.89 (COR = 1.89) and 1.87 (COR = 1.87). Conclusions This study suggests that medical practices have a favorable attitude towards AI in the radiology field. Most participants surveyed believed that AI should be part of radiography education. AI training programs for undergraduate and postgraduate radiographers may be necessary to prepare them for AI tools in radiology development.International Journal of Biomedical Imaging220This study suggests that medical practices have a favorable attitude towards AI in the radiology field and that AI training programs for undergraduate and postgraduate radiographers may be necessary to prepare them for AI tools in radiology development.International Journal of Biomedical Imaging['M. Rawashdeh', 'Sara Almazrouei', 'M. Zaitoun', 'Praveen Kumar', 'Charbel Saade']2024-03-08T00:00:00https://www.semanticscholar.org/paper/06d26c70476893dc8bbc6fc4e4b451301a3c6561 fbaae088dc78f48351cfac3248d473eb551e447bVideo-Based Design (VBD) is a design methodology that utilizes video as a primary tool for understanding user interactions, prototyping, and conducting research to enhance the design process. Artificial Intelligence (AI) can be instrumental in video-based design by analyzing and interpreting visual data from videos to enhance user interaction, automate design processes, and improve product functionality. In this study, we explore how AI can enhance professional video-based design with a State-of-the-Art (SOTA) deep learning model. We developed a prototype annotation platform (MarkupLens) and conducted a between-subjects eye-tracking study with 36 designers, annotating videos with three levels of AI assistance. Our findings indicate that MarkupLens improved design annotation quality and productivity. Additionally, it reduced the cognitive load that designers exhibited and enhanced their User Experience (UX). We believe that designer-AI collaboration can greatly enhance the process of eliciting insights in video-based design.arXiv.org530This study developed a prototype annotation platform (MarkupLens) and conducted a between-subjects eye-tracking study with 36 designers, annotating videos with three levels of AI assistance, indicating that MarkupLens improved design annotation quality and productivity.ArXiv['Tianhao He', 'Ying Zhang', 'Evangelos Niforatos', 'Gerd Kortuem']2024-03-08T00:00:00https://www.semanticscholar.org/paper/fbaae088dc78f48351cfac3248d473eb551e447b 942a50949d550da3f2ad7dc064d17c2f46791269Trustworthy AI is crucial to the widespread adoption of AI in high-stakes applications with fairness, robustness, and accuracy being some of the key trustworthiness metrics. In this work, we propose a controllable framework for data-centric trustworthy AI (DCTAI)- VTruST, that allows users to control the trade-offs between the different trustworthiness metrics of the constructed training datasets. A key challenge in implementing an efficient DCTAI framework is to design an online value-function-based training data subset selection algorithm. We pose the training data valuation and subset selection problem as an online sparse approximation formulation. We propose a novel online version of the Orthogonal Matching Pursuit (OMP) algorithm for solving this problem. Experimental results show that VTruST outperforms the state-of-the-art baselines on social, image, and scientific datasets. We also show that the data values generated by VTruST can provide effective data-centric explanations for different trustworthiness metrics.arXiv.org510This work proposes a controllable framework for data-centric trustworthy AI- VTruST, that allows users to control the trade-offs between the different trustworthiness metrics of the constructed training datasets, and proposes a novel online version of the Orthogonal Matching Pursuit algorithm for solving this problem.ArXiv['Soumili Das', 'Shubhadip Nag', 'Shreyyash Sharma', 'Suparna Bhattacharya', 'Sourangshu Bhattacharya']2024-03-08T00:00:00https://www.semanticscholar.org/paper/942a50949d550da3f2ad7dc064d17c2f46791269 ed9ac39ff084fedf916380ab19b472bb683c580cThe advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing paradigm through their ability to generate seemingly new realistic data and analyse and integrate a vast amount of unstructured content from different data formats. This potential extends beyond reshaping conventional practices, like risk assessment, diagnostic support, and treatment and rehabilitation plans, to creating new opportunities in previously underexplored areas, such as training and education. This paper examines the transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice. First, it introduces generative AI and its prevalent models. Following this, it reviews the current applications of discriminative AI in forensic psychiatry. Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. Finally, it provides a comprehensive overview of ethical and legal issues associated with deploying generative AI models, focusing on their impact on individuals as well as their broader societal implications. In conclusion, this paper aims to contribute to the ongoing discourse concerning the dynamic challenges of generative AI applications in forensic contexts, highlighting potential opportunities, risks, and challenges. It advocates for interdisciplinary collaboration and emphasises the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made.Frontiers in Psychiatry990The transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice is examined and the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made is emphasised.Frontiers in Psychiatry['Leda Tortora']2024-03-08T00:00:00https://www.semanticscholar.org/paper/ed9ac39ff084fedf916380ab19b472bb683c580c 96ddb49811f5a3ab45b7615ce0184122d27e3cdcThe study investigates data governance challenges within AI-enabled healthcare systems, focusing on Project Nightingale as a case study to elucidate the complexities of balancing technological advancements with patient privacy and trust. Utilizing a survey methodology, data were collected from 843 healthcare service users employing a structured questionnaire designed to measure perceptions of AI in healthcare, trust in healthcare providers, concerns about data privacy, and the impact of regulatory frameworks on the adoption of AI technologies. The reliability of the survey instrument was confirmed with a Cronbach's Alpha of 0.81, indicating high internal consistency. The multiple regression analysis revealed significant findings: a positive relationship between the awareness of technological projects and trust in healthcare providers, countered by a negative impact of privacy concerns on trust. Additionally, familiarity with and perceived effectiveness of regulatory frameworks were positively correlated with trust in data, while perceptions of regulatory constraints and data governance issues were identified as significant barriers to the effective adoption of AI technologies in healthcare. The study highlights the critical need for enhanced transparency, public awareness, and robust data governance frameworks to navigate the ethical and privacy concerns associated with AI in healthcare. The study recommends adopting flexible, principle-based regulatory approaches and fostering multi-stakeholder collaboration to ensure the ethical deployment of AI technologies that prioritize patient welfare and trust.Asian Journal of Research in Computer Science00A positive relationship between the awareness of technological projects and trust in healthcare providers is revealed, countered by a negative impact of privacy concerns on trust, while perceptions of regulatory constraints and data governance issues were identified as significant barriers to the effective adoption of AI technologies in healthcare.Asian Journal of Research in Computer Science['Aisha Temitope Arigbabu', 'O. O. Olaniyi', 'Chinasa Susan Adigwe', 'Olubukola Omolara Adebiyi', 'Samson Abidemi Ajayi']2024-03-08T00:00:00https://www.semanticscholar.org/paper/96ddb49811f5a3ab45b7615ce0184122d27e3cdc 0ce2ff9ef2fc805229a33edf78fc92353e0fe8d6Blockchain, as a digital ledger system, offers superior data encryption, recording, and tracking capabilities compared to traditional paper and electronic systems. This article addresses the critical challenge faced by the chemical industry's supply chain (SC) which is a critical component of its operations, ensuring the delivery of authentic and safe products to consumers. Since the chemical industry's supply chain management lacks robust data security and resilience, this research proposes a novel solution that leverages with unexploited potential for Blockchain Technology (BCT) and Artificial Intelligence (AI) to improve the chemical supply chain. The proposed system features a user-friendly web interface that effortlessly links clients, manufacturers, and suppliers, enhancing communication and leveraging Artificial Intelligence for decision-making. Through the website, clients can purchase chemicals, which are evaluated for safety and risk factor by AI, utilizing a Multi-Layer Perceptron (MLP) algorithm. Following purchase confirmation, the manufacturer authorizes the chemical and notifies the supplier, who assigns a retailer for delivery. All data, including chemicals, manufacturer, and supplier details, is securely stored in smart contracts. To summarize, the proposed system ensures data security and resilience using Blockchain and AI for chemical Supply Chain Management.2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)260This research proposes a novel solution that leverages with unexploited potential for Blockchain Technology (BCT) and Artificial Intelligence (AI) to improve the chemical supply chain.2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)['Shatabdi Mesalina Mitra', "Joseph Albert D'Costa", 'Mahin Mustafiz Sami', 'Md Mahtaj Hasan Nibir', 'Mohammed Ashikur Rahman']2024-03-08T00:00:00https://www.semanticscholar.org/paper/0ce2ff9ef2fc805229a33edf78fc92353e0fe8d6 3fffc3052031ea64b7c12ee987dc611adcec9ef0Machine learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, this work provides insights into the models’ workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and, at times, unsound ranking of the features to make the predictions. This paper therefore argues that it is important for research in applied machine learning to provide insights into the explainability of models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare.Electronics201It is argued that it is important for research in applied machine learning to provide insights into the explainability of models in addition to other performance metrics like accuracy, for applications in critical domains such as healthcare.Electronics['Vishnu S. Pendyala', 'Hyungkyun Kim']2024-03-08T00:00:00https://www.semanticscholar.org/paper/3fffc3052031ea64b7c12ee987dc611adcec9ef0 1eaa8c6fc5b4a9c922c635746520c72db7191704Nature01Nature['E. Callaway']2024-03-08T00:00:00https://www.semanticscholar.org/paper/1eaa8c6fc5b4a9c922c635746520c72db7191704 6a697b4441abe9d88559cedd20161d7751d2c25fDeep Neural Networks have often been called the black box because of the complex, deep architecture and non-transparency presented by the inner layers. There is a lack of trust to use Artificial Intelligence in critical and high-precision fields such as security, finance, health, and manufacturing industries. A lot of focused work has been done to provide interpretable models, intending to deliver meaningful insights into the thoughts and behavior of neural networks. In our research, we compare the state-of-the-art methods in the Activation-based methods (ABM) for interpreting predictions of CNN models, specifically in the application of Image Classification. We then extend the same for eight CNN-based architectures to compare the differences in visualization and thus interpretability. We introduced a novel technique Feature CAM, which falls in the perturbation-activation combination, to create fine-grained, class-discriminative visualizations. The resulting saliency maps from our experiments proved to be 3-4 times better human interpretable than the state-of-the-art in ABM. At the same time it reserves machine interpretability, which is the average confidence scores in classification.arXiv.org200This research compares the state-of-the-art methods in the Activation-based methods (ABM) for interpreting predictions of CNN models, specifically in the application of Image Classification, and introduced a novel technique Feature CAM, which falls in the perturbation-activation combination, to create fine-grained, class-discriminative visualizations.ArXiv['Frincy Clement', 'Ji Yang', 'Irene Cheng']2024-03-08T00:00:00https://www.semanticscholar.org/paper/6a697b4441abe9d88559cedd20161d7751d2c25f 0032323ddf0dd5ed750dea175330298ecfabaa90Science China Materials500Science China Materials['Xue Jia', 'Alex Aziz', 'Yusuke Hashimoto', 'Hao Li']2024-03-08T00:00:00https://www.semanticscholar.org/paper/0032323ddf0dd5ed750dea175330298ecfabaa90 68c2d26d49903abf894ed55fc625e0bed0400dc2Indian Journal of Plastic Surgery70Indian Journal of Plastic Surgery['J. Telich-Tarriba']2024-03-08T00:00:00https://www.semanticscholar.org/paper/68c2d26d49903abf894ed55fc625e0bed0400dc2 95642812f75e945dc38a45ca2a1780e1cb28f823Nature Machine Intelligence50Nature Machine Intelligence['Eva Erman', 'Markus Furendal']2024-03-08T00:00:00https://www.semanticscholar.org/paper/95642812f75e945dc38a45ca2a1780e1cb28f823 497a34ca26c1fec23f7cbbbf759a84262b766283Missed and delayed diagnoses of Hansen’s disease (HD) are making the battle against it even more complex, increasing its transmission and significantly impacting those affected and their families. This strains public health systems and raises the risk of lifelong impairments and disabilities. Worryingly, the three countries most affected by HD witnessed a growth in new cases in 2022, jeopardizing the World Health Organization’s targets to interrupt transmission. Artificial intelligence (AI) can help address these challenges by offering the potential for rapid case detection, customized treatment, and solutions for accessibility challenges—especially in regions with a shortage of trained healthcare professionals. This perspective article explores how AI can significantly impact the clinical management of HD, focusing on therapeutic strategies. AI can help classify cases, ensure multidrug therapy compliance, monitor geographical treatment coverage, and detect adverse drug reactions and antimicrobial resistance. In addition, AI can assist in the early detection of nerve damage, which aids in disability prevention and planning rehabilitation. Incorporating AI into mental health counseling is also a promising contribution to combating the stigma associated with HD. By revolutionizing therapeutic approaches, AI offers a holistic solution to reduce the burden of HD and improve patient outcomes.Frontiers in Medicine190How AI can significantly impact the clinical management of HD is explored, focusing on therapeutic strategies, which can help classify cases, ensure multidrug therapy compliance, monitor geographical treatment coverage, and detect adverse drug reactions and antimicrobial resistance.Frontiers in Medicine['Patrícia D. Deps', 'Rie Yotsu', 'Brunna C. R. S. Furriel', 'Bruno D. de Oliveira', 'Sergio L. de Lima', 'Rafael Loureiro']2024-03-08T00:00:00https://www.semanticscholar.org/paper/497a34ca26c1fec23f7cbbbf759a84262b766283 cad45c9f1fc848196b6d2f2efc842ee187e801eeThe proliferation of social media platforms has revolutionized communication and connectivity, but it has also introduced new challenges related to safety and security. In response, researchers and practitioners have turned to artificial intelligence (AI) to develop innovative solutions for mitigating online risks. This systematic literature review explores the key applications, methodologies, benefits, limitations, ethical considerations, and future directions of AI in promoting social media safety and security. The review synthesizes findings from various scholarly articles spanning various disciplines, including computer science, engineering, and social sciences. The methodology involved searching academic databases such as PubMed, Scopus, IEEE Xplore, and Google Scholar using predefined search terms and inclusion criteria. The results reveal a diverse range of AI-driven approaches for addressing safety and security concerns on social media platforms, including enhanced threat detection, automated content moderation, and real-time response mechanisms. However, the deployment of AI in social media contexts also raises ethical challenges such as algorithm bias, privacy concerns, and lack of explain ability. The conclusion emphasizes the importance of ongoing research, collaboration, and ethical guidelines to maximize the benefits of AI while minimizing its potential risks. This review contributes to the growing body of literature on AI and social media by providing insights into current trends, challenges, and future directions in this rapidly evolving field.Studies in Media, Journalism and Communications240A diverse range of AI-driven approaches for addressing safety and security concerns on social media platforms, including enhanced threat detection, automated content moderation, and real-time response mechanisms are revealed.Studies in Media, Journalism and Communications['Musawer Hakimi', 'Baryali Sazish', 'Mohammad Aziz Rastagari', 'Kror Shahidzay']2024-03-08T00:00:00https://www.semanticscholar.org/paper/cad45c9f1fc848196b6d2f2efc842ee187e801ee 32c35fb44e3747b83fe153ea96853ddc36aa9d7e Purpose This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia. Design/methodology/approach A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education. Findings The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated. Research limitations/implications The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education. Practical implications This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders. Originality/value This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches. Interactive Technology and Smart Education500A comprehensive synthesis of empirical studies is contributed, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.Interactive Technology and Smart Education['A. Marengo', 'A. Pagano', 'Jenny Pange', 'K. A. Soomro']2024-03-08T00:00:00https://www.semanticscholar.org/paper/32c35fb44e3747b83fe153ea96853ddc36aa9d7e 0b8a0edc1060f0fda449c015788a45eafb0ef685Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially in the Indian scenario. Deep learning involves learning without being explicitly told what to focus on and utilizes several layers of artificial neural networks (ANNs) to create a robust algorithm that is capable of high-complexity tasks. Convolutional neural networks (CNNs) are a subset of ANNs that are particularly useful for image processing as well as cognitive tasks. Training of these algorithms involves inputting raw human-labeled data, which are then processed through the algorithm's multiple layers and allow CNN to develop their own learning of image features. AI systems must be validated using different population datasets since the performance of the AI system would vary according to the population. Indian datasets have been used in AI-based risk model that could predict whether an infant would develop treatment-requiring retinopathy of prematurity (ROP). AI also served as an epidemiological tool by objectively showing that a higher ROP severity was in Neonatal intensive care units (NICUs) that did not have the resources to monitor and titrate oxygen. There are rising concerns about the medicolegal aspect of AI implementation as well as discussion on the possibilities of catastrophic life-threatening diseases like retinoblastoma and lipemia retinalis being missed by AI. Computer-based systems have the advantage over humans in not being susceptible to biases or fatigue. This is especially relevant in a country like India with an increased rate of ROP and a preexisting strained doctor-to-preterm child ratio. Many AI algorithms can perform in a way comparable to or exceeding human experts, and this opens possibilities for future large-scale prospective studies.Indian Journal of Ophthalmology00Indian journal of ophthalmology['P. Maitra', 'Parag K Shah', 'Peter J Campbell', 'Pukhraj Rishi']2024-03-08T00:00:00https://www.semanticscholar.org/paper/0b8a0edc1060f0fda449c015788a45eafb0ef685 619a4331191cc0eb5d4e9af4745fd1c31f3611b3With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.Clinical Endoscopy00AI represents a breakthrough in digestive endoscopy and it is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice.Clinical endoscopy['Jean-Francois Rey']2024-03-08T00:00:00https://www.semanticscholar.org/paper/619a4331191cc0eb5d4e9af4745fd1c31f3611b3 2ce530d855dc19e4adedd465c01eee7e1068174aNature Chemical Engineering01Nature Chemical Engineering['Artur M. Schweidtmann']2024-03-08T00:00:00https://www.semanticscholar.org/paper/2ce530d855dc19e4adedd465c01eee7e1068174a 322454f137728a2539bca234dc99104b295735abJournal Francais d'Ophtalmologie00Ophthalmic technicians can use this software for highly-sensitive screening for fundus abnormalities that require evaluation by an ophthalmologist, and this software showed an overall sensitivity of 100% and an overall specificity of 94% in the context of the RNO protocol.Journal francais d'ophtalmologie['François-Philippe Roubelat', 'V. Soler', 'F. Varenne', 'V. Gualino']2024-03-08T00:00:00https://www.semanticscholar.org/paper/322454f137728a2539bca234dc99104b295735ab 45c16585a8d4e5d5fef93e3431e4039ff6b517deNew Biotechnology00A superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils is suggested.New biotechnology['L. G. Bernardini', 'Christoph Rosinger', 'Gernot Bodner', 'K. Keiblinger', 'E. Izquierdo-Verdiguier', 'H. Spiegel', 'Charles Retzlaff', 'Andreas Holzinger']2024-03-08T00:00:00https://www.semanticscholar.org/paper/45c16585a8d4e5d5fef93e3431e4039ff6b517de 949fe1ac575828b2ce0bd97fab2152b169e79583Digital Transformation and Society00Digital Transformation and Society['Y. Badr']2024-03-08T00:00:00https://www.semanticscholar.org/paper/949fe1ac575828b2ce0bd97fab2152b169e79583 0c8a544846ec4d8d10c26bf1f4ad693b9efe857400['Xavier Oberson']2024-03-08T00:00:00https://www.semanticscholar.org/paper/0c8a544846ec4d8d10c26bf1f4ad693b9efe8574 1b45f8f41efd9f7bd8091b25d3024b6e83bf0d50Although decision support systems (DSS) that rely on artificial intelligence (AI) increasingly provide explanations to computer and data scientists about opaque features of the decision process, especially when it involves uncertainty, there is still only limited attention to making the process transparent to end users.This paper compares four distinct explanation strategies employed by a DSS, represented by the social agent Floka, designed to assist end users in making decisions under uncertainty. Using an economic experiment with 742 participants who make lottery choices according to the Holt and Laury paradigm, we contrast two explanation strategies offering accurate information (transparent vs. guided) with two strategies prioritizing human-centered explanations (emotional vs. authoritarian) and a baseline (no explanation).Our findings indicate that a guided explanation strategy results in higher user reliance than a transparent strategy. Furthermore, our results suggest that user reliance is contingent on the chosen explanation strategy, and, in some instances, the absence of an explanation can also lead to increased user reliance.Frontiers in Behavioral Economics770The findings indicate that a guided explanation strategy results in higher user reliance than a transparent strategy, and the results suggest that user reliance is contingent on the chosen explanation strategy, and the absence of an explanation can also lead to increased user reliance.Frontiers in Behavioral Economics['Olesja Lammert', 'Birte Richter', 'Christian Schütze', 'Kirsten Thommes', 'Britta Wrede']2024-03-08T00:00:00https://www.semanticscholar.org/paper/1b45f8f41efd9f7bd8091b25d3024b6e83bf0d50 a21e4a80b0c6ef459e113b64ac210ebe3915f534 Are preferences for labor regulations driven by individuals’ own endowments or their beliefs? To address this question, we conducted a cross-country survey on people’s opinions on employment protection legislation—an area where reform has proven to be difficult and personal interests are at stake. We find that individuals’ beliefs contribute two to three times more than their own endowments and personal pay-offs. A randomized information treatment confirms that beliefs can explain views about regulations, but beliefs can also change with new information. Our results are robust to several checks, including to alternative estimation techniques and samples.Economic Policy00Economic Policy['Romain Duval', 'Yi Ji', 'Chris Papageorgiou', 'Ippei Shibata', 'Antonio Spilimbergo']2024-03-07T00:00:00https://www.semanticscholar.org/paper/a21e4a80b0c6ef459e113b64ac210ebe3915f534 b4f953766789bd7c23be22398ed340f3bba4b6a8Digital Society313This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios, and refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment.Digit. Soc.['Claudio Novelli', 'F. Casolari', 'A. Rotolo', 'M. Taddeo', 'Luciano Floridi']2024-03-07T00:00:00https://www.semanticscholar.org/paper/b4f953766789bd7c23be22398ed340f3bba4b6a8 eb66d4c268d7d3d1ea3bede3a394e31c9f8d14e2The article reviews various stages of discussions on state sovereignty matters that took place in the scientific community in the post-Soviet period of the development of Russia. The author justifies the idea that practically relevant aspects of this subject associated with the need for the achievement of the technological sovereignty of the country have abruptly become relevant against the background of the contemporary geopolitical crisis.HISTORY OF STATE AND LAW00History of state and law['Valentina V. Lapaeva']2024-03-07T00:00:00https://www.semanticscholar.org/paper/eb66d4c268d7d3d1ea3bede3a394e31c9f8d14e2 91e311516be187f66b86ce3ad8d868f2a29d3bb3Frontiers in Marine Science10Frontiers in Marine Science['P. Larcombe', 'A. Morrison‐Saunders', 'P. Ridd']2024-03-07T00:00:00https://www.semanticscholar.org/paper/91e311516be187f66b86ce3ad8d868f2a29d3bb3 591edb16430b4a561a6b0c1b381d754a32f7350fНаукові перспективи (Naukovì perspektivi)00Наукові перспективи (Naukovì perspektivi)['Л.М. Карпенко', 'Микола Іжа', 'Сергій Корчовий', 'Микита Гороховський']2024-03-07T00:00:00https://www.semanticscholar.org/paper/591edb16430b4a561a6b0c1b381d754a32f7350f 21f8977648e25ce8b95020e6f01988af99209c82Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researchers to fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. Although some companies offer researcher access programs, they are an inadequate substitute for independent research access, as they have limited community representation, receive inadequate funding, and lack independence from corporate incentives. We propose that major AI developers commit to providing a legal and technical safe harbor, indemnifying public interest safety research and protecting it from the threat of account suspensions or legal reprisal. These proposals emerged from our collective experience conducting safety, privacy, and trustworthiness research on generative AI systems, where norms and incentives could be better aligned with public interests, without exacerbating model misuse. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.arXiv.org1204This work proposes that major AI developers commit to providing a legal and technical safe harbor, indemnifying public interest safety research and protecting it from the threat of account suspensions or legal reprisal, where norms and incentives could be better aligned with public interests, without exacerbating model misuse.ArXiv['Shayne Longpre', 'Sayash Kapoor', 'Kevin Klyman', 'Ashwin Ramaswami', 'Rishi Bommasani', 'Borhane Blili-Hamelin', 'Yangsibo Huang', 'Aviya Skowron', 'Zheng-Xin Yong', 'Suhas Kotha', 'Yi Zeng', 'Weiyan Shi', 'Xianjun Yang', 'Reid Southen', 'Alexander Robey', 'Patrick Chao', 'Diyi Yang', 'Ruoxi Jia', 'Daniel Kang', 'Sandy Pentland', 'Arvind Narayanan', 'Percy Liang', 'Peter Henderson']2024-03-07T00:00:00https://www.semanticscholar.org/paper/21f8977648e25ce8b95020e6f01988af99209c82 91cb499142b609bedb327c41b4b5738a0aa5b4bbnpj Digit. Medicine163This work presents an evaluation checklist for generative AI models applied to electronic medical records, and characterizes these models, their strengths, and weaknesses.NPJ Digital Medicine['Marium M. Raza', 'Kaushik P. Venkatesh', 'J. Kvedar']2024-03-07T00:00:00https://www.semanticscholar.org/paper/91cb499142b609bedb327c41b4b5738a0aa5b4bb 1f7228550f2177c83d5fb06a7fef555fe83b9bfbMuch of the recent work investigating large language models and AI Code Generation tools in computing education has focused on assessing their capabilities for solving typical programming problems and for generating resources such as code explanations and exercises. If progress is to be made toward the inevitable lasting pedagogical change, there is a need for research that explores the instructor voice, seeking to understand how instructors with a range of experiences plan to adapt. In this paper, we report the results of an interview study involving 12 instructors from Australia, Finland and New Zealand, in which we investigate educators’ current practices, concerns, and planned adaptations relating to these tools. Through this empirical study, our goal is to prompt dialogue between researchers and educators to inform new pedagogical strategies in response to the rapidly evolving landscape of AI code generation tools.Technical Symposium on Computer Science Education403This paper reports the results of an interview study involving 12 instructors from Australia, Finland and New Zealand, in which educators’ current practices, concerns, and planned adaptations relating to these tools are investigated.{'pages': '1223-1229'}['Judithe Sheard', 'Paul Denny', 'Arto Hellas', 'Juho Leinonen', 'L. Malmi', 'Simon']2024-03-07T00:00:00https://www.semanticscholar.org/paper/1f7228550f2177c83d5fb06a7fef555fe83b9bfb f3aed9dc54d84eff2166c7442c37e1ff76b710a1Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.Energies1291A comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization in AI-based wind prediction modeling is provided.Energies['D. Song', 'Xiao Tan', 'Qian Huang', 'Li Wang', 'M. Dong', 'Jian Yang', 'Solomin Evgeny']2024-03-07T00:00:00https://www.semanticscholar.org/paper/f3aed9dc54d84eff2166c7442c37e1ff76b710a1 53066a9872d1e662ce89fac6f9e846c311ce01dcEuropean Radiology121Generative AI models are shown to be able to create a full manuscript without any human intervention that can survive peer-review, showing the urgent need for the entire community to address both the issue of generative AI in scientific literature and probably a more profound discussion on the entire peer-review process.European radiology['S. Cocozza', 'Giuseppe Palma']2024-03-07T00:00:00https://www.semanticscholar.org/paper/53066a9872d1e662ce89fac6f9e846c311ce01dc 217e608779e07b5383d63c9538d31bcd3a0a2f97As conversational AI apps such as Siri and Alexa become ubiquitous among children, the CS education community has begun leveraging this popularity as a potential opportunity to attract young learners to AI, CS, and STEM learning. However, teaching conversational AI to K-12 learners remains challenging and unexplored due in part to the abstract and complex nature of some conversational AI concepts, such as intents and training phrases . One promising approach to teaching complex topics in engaging ways is through unplugged activities , which have been shown to be highly effective in fostering CS conceptual understanding without using computers. Research efforts are underway toward developing unplugged activities for teaching AI, but few thus far have focused on conversational AI. This experience report describes the design and iterative refinement of a series of novel unplugged activities for a conversational AI summer camp for middle school learners. We discuss learner responses and lessons learned through our implementation of these unplugged activities. Our hope is that these insights support CS education researchers in making conversational AI learning more engaging and accessible to all learners.Technical Symposium on Computer Science Education391The design and iterative refinement of a series of novel unplugged activities for a conversational AI summer camp for middle school learners are described and learner responses and lessons learned are discussed through the implementation of these unplugged activities.{'pages': '1272-1278'}['Yukyeong Song', 'Xiaoyi Tian', 'Nandika Regatti', 'Gloria Ashiya Katuka', 'K. Boyer', 'Maya Israel']2024-03-07T00:00:00https://www.semanticscholar.org/paper/217e608779e07b5383d63c9538d31bcd3a0a2f97 42f8532eb5db0a1333e04add5576737bd4f31635The current state of the art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities—which correspond to our intuition and habitual behaviors—but still lacks something important regarding system 2 abilities—which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example, by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.Bulletin of the American Mathematical Society231This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities—which correspond to the authors' intuition and habitual behaviors—but still lacks something important regarding system 2 abilities—which include reasoning and robust uncertainty estimation.ArXiv['Y. Bengio', 'Nikolay Malkin']2024-03-07T00:00:00https://www.semanticscholar.org/paper/42f8532eb5db0a1333e04add5576737bd4f31635 adcccc58f893b4b1d2746c9c6b3688b6ecf73740There is growing recognition of the need to teach artificial intelligence (AI) and machine learning (ML) at the school level. This push for AI/ML education at the K-12 level acknowledges the mete-oric growth in the range and diversity of applications of ML in all industries and everyday consumer products, with Large Language Models (LLMs) being only the latest and most compelling example yet. Efforts to bring AI, especially ML education to school learners are being propelled by substantial industry interest, research efforts, as well as technological developments that make sophisticated ML tools readily available to learners of all ages. These early efforts span a variety of learning goals captured by the AI4K12 “big ideas” framework and employ a plurality of pedagogies. This paper provides a sense of the current state of the field, shares lessons learned from early K-12 AI education as well as CS education efforts that can be leveraged, highlights issues that must be addressed in designing for teaching AI in K-12, and provides guidance for future K-12 AI education efforts in order to tackle what to many feels like “the next new thing”.Technical Symposium on Computer Science Education671A sense of the current state of the field is provided, lessons learned from early K-12 AI education as well as CS education efforts that can be leveraged are shared, issues that must be addressed in designing for teaching AI in K-12 are highlighted, and guidance is provided for future K-12 AI education efforts.{'pages': '422-428'}['Shuchi Grover']2024-03-07T00:00:00https://www.semanticscholar.org/paper/adcccc58f893b4b1d2746c9c6b3688b6ecf73740 75917459323eadc3832c7e76e758611c5cf010d2Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.Inflammatory Bowel Diseases01To better understand the potential value of integrating AI in IBD, the available literature is reviewed to summarize the current understanding and identify gaps in knowledge to inform future investigations.Inflammatory bowel diseases['Phillip Gu', 'Oreen Mendonca', 'D. Carter', 'S. Dube', 'Paul Wang', 'Xiuzhen Huang', 'Debiao Li', 'Jason H. Moore', 'Dermot P B McGovern']2024-03-07T00:00:00https://www.semanticscholar.org/paper/75917459323eadc3832c7e76e758611c5cf010d2 1425fc5b287c97b1a5ea6b897d29fcfe3a6526a9Nature13Nature['Carissa Wong']2024-03-07T00:00:00https://www.semanticscholar.org/paper/1425fc5b287c97b1a5ea6b897d29fcfe3a6526a9 40d238501e0e516c0095010e53a489a790d557a9Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often considered a black box itself, lacking ways to provide reasons as to why certain parameters are proposed to be evaluated. This is particularly relevant in human-in-the-loop applications of BO, such as in robotics. We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values.They quantify each parameter's contribution to BO's acquisition function. Exploiting the linearity of Shapley values, we are further able to identify how strongly each parameter drives BO's exploration and exploitation for additive acquisition functions like the confidence bound. We also show that ShapleyBO can disentangle the contributions to exploration into those that explore aleatoric and epistemic uncertainty. Moreover, our method gives rise to a ShapleyBO-assisted human machine interface (HMI), allowing users to interfere with BO in case proposals do not align with human reasoning. We demonstrate this HMI's benefits for the use case of personalizing wearable robotic devices (assistive back exosuits) by human-in-the-loop BO. Results suggest human-BO teams with access to ShapleyBO can achieve lower regret than teams without.arXiv.org682Sh ShapleyBO is proposed, a framework for interpreting BO's proposals by game-theoretic Shapley values that gives rise to a ShapleyBO-assisted human machine interface (HMI), allowing users to interfere with BO in case proposals do not align with human reasoning.ArXiv['Julian Rodemann', 'Federico Croppi', 'Philipp Arens', 'Yusuf Sale', 'J. Herbinger', 'B. Bischl', 'E. Hullermeier', 'Thomas Augustin', 'Conor J. Walsh', 'Giuseppe Casalicchio']2024-03-07T00:00:00https://www.semanticscholar.org/paper/40d238501e0e516c0095010e53a489a790d557a9 42be0855cfe39d0e091d15c6bb9aac607c5268baJournal of the American Medical Association (JAMA)232JAMA['A. Flanagin', 'Romain Pirracchio', 'R. Khera', 'Michael Berkwits', 'Y. Hswen', 'Kirsten Bibbins-Domingo']2024-03-07T00:00:00https://www.semanticscholar.org/paper/42be0855cfe39d0e091d15c6bb9aac607c5268ba e4cbfe9c868f405079613ade118703767b1da842We present ALTO, a network orchestrator for efficiently serving compound AI systems such as pipelines of language models. ALTO achieves high throughput and low latency by taking advantage of an optimization opportunity specific to generative language models: streaming intermediate outputs. As language models produce outputs token by token, ALTO exposes opportunities to stream intermediate outputs between stages when possible. We highlight two new challenges of correctness and load balancing which emerge when streaming intermediate data across distributed pipeline stage instances. We also motivate the need for an aggregation-aware routing interface and distributed prompt-aware scheduling to address these challenges. We demonstrate the impact of ALTO's partial output streaming on a complex chatbot verification pipeline, increasing throughput by up to 3x for a fixed latency target of 4 seconds / request while also reducing tail latency by 1.8x compared to a baseline serving approach.EuroMLSys@EuroSys380The impact of ALTO's partial output streaming on a complex chatbot verification pipeline is demonstrated, increasing throughput by up to 3x for a fixed latency target of 4 seconds / request while also reducing tail latency by 1.8x compared to a baseline serving approach.{'pages': '117-125'}['Keshav Santhanam', 'Deepti Raghavan', 'Muhammad Shahir Rahman', 'Thejas Venkatesh', 'Neha Kunjal', 'Pratiksha Thaker', 'Philip Levis', 'Matei Zaharia']2024-03-07T00:00:00https://www.semanticscholar.org/paper/e4cbfe9c868f405079613ade118703767b1da842 8fb0e8bab3b055c69e2fb37710aecd2038f81485Smart games, especially crossword puzzles can be an effective tool in aiding cognitive and physical exercise by stimulating the brain and strengthening brain connections, which can help prevent or slow the progression of Alzheimer’s and dementia. This research aimed to develop a crossword puzzle game into a digital version in Bahasa Indonesia with Artificial Intelligence (AI) features. The benefit of this game product can be used as a cognitive therapy for Alzheimer’s and dementia sufferers. The research method used is game development using the Game Development Life Cycle (GDLC) model with stages of initiation, pre-production, implementation/development, testing, launch, and maintenance. Implementation of digital version using Unity 3D game engine with AI feature of hint generating. This research was carried out for 8 months. Game testing results were carried out by testing the functionality of game features and the results were that all features functioned well. The innovation and importance of this work was to implement the memory crossword puzzle game design into a digital version in Bahasa by implementing artificial intelligence methods for the hints feature. This work will inspire developers to do greater work on Bahasa and implement various features based on AI methods. Keywords: digital memory games, artificial intelligence, crosswords, game developmentKnE Engineering240This research aimed to develop a crossword puzzle game into a digital version in Bahasa Indonesia with Artificial Intelligence (AI) features, which can be used as a cognitive therapy for Alzheimer’s and dementia sufferers.KnE Engineering['Yuyun Khairunisa', 'Deni Kuswoyo', 'Bayu Dwi Nurwicaksono']2024-03-07T00:00:00https://www.semanticscholar.org/paper/8fb0e8bab3b055c69e2fb37710aecd2038f81485 11f2e16ed80042763fe4e2c6b92a306f647e2e01Carbonated beverage businesses are using artificial intelligence (AI) technology to sustainability in a variety of ways to streamline their processes, boost output, and improve the customer experience. The businesses in the carbonated beverage sector use AI in the following ways, Coca-Cola is enhancing its supply chain administration by utilizing AI. The business has created a platform powered by AI that applies machine learning algorithms to improve its distribution and manufacturing procedures. The platform aids the business in improved demand forecasting, inventory level optimization, cost-effective transportation, voice activated vending machines. PepsiCo: To create novel product formulations, PepsiCo is utilizing AI. The business has developed an AI-powered platform that examines customer preferences and spots market trends. The business can develop new products thanks to this platform. Dr. Pepper Snapple Group: Dr. Pepper Snapple Group is enhancing its marketing initiatives with artificial intelligence. The business has created an AI-driven platform that analyses customer data and enables it to develop more specialized and individualized marketing campaigns. The platform enables the business to deliver more pertinent and efficient marketing messages by assisting in the better understanding of customer preferences and behaviour. Keurig Dr Pepper: Keurig Dr Pepper is using AI to enhance its customer service. The business has created an AI-powered chatbot that can instantly respond to customer inquiries and help. The chatbot uses machine learning algorithms and natural language processing to comprehend customer queries and deliver pertinent and useful responses. Overall, the carbonated beverage industry is using AI technology in a variety of methods. Keywords- carbonated beverage, artificial intelligence (AI), supply chain administration, demand forecasting, inventory optimization, transportation optimization, voice activated vending machines, product formulation, customer preferences, market trends, marketing campaigns, customer service, chatbot, machine learning algorithms, natural language processing.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Narra Shiva Prasad']2024-03-07T00:00:00https://www.semanticscholar.org/paper/11f2e16ed80042763fe4e2c6b92a306f647e2e01 4ec57d51993ac810c20b1447e6b5bb821c39a504This article delves into the transformative impact of Artificial Intelligence (AI) on performance management, specifically focusing on its role in delivering enhanced feedback and coaching in the corporate realm. The discussion encompasses AI-driven personalization, real-time feedback, predictive analytics, bias reduction, and the augmentation of coaching strategies. While acknowledging the significant benefits, the article also addresses the challenges associated with integrating AI into performance management, emphasizing the crucial role of human elements. The goal is to provide a comprehensive overview of how AI is shaping the future landscape of performance management.Journal of Informatics Education and Research260Journal of Informatics Education and Research['Dr. Sonam Subhadarshini, Dr. Ankita Nayak', 'Sukanya Nisitgandha Biswal, Dr Snigdhamayee Choudhury']2024-03-07T00:00:00https://www.semanticscholar.org/paper/4ec57d51993ac810c20b1447e6b5bb821c39a504 220be489bc449813ea125fcf7bd4c2b7d5c2cbd2With advancements in artificial intelligence (AI) dominating the headlines, diagnostic imaging radiology is no exception to the accelerating role that AI is playing in today's technology landscape. The number of AI-driven radiology diagnostic imaging applications (digital diagnostics) that are both commercially available and in-development is rapidly expanding as are the potential benefits these tools can deliver for patients and providers alike. Healthcare providers seeking to harness the potential benefits of digital diagnostics may consider evaluating these tools and their corresponding use cases in a systematic and structured manner to ensure optimal capital deployment, resource utilization, and, ultimately, patient outcomes—or clinical utility. We propose several guiding themes when using clinical utility to curate digital diagnostics.Frontiers in Digital Health300This work proposes several guiding themes when using clinical utility to curate digital diagnostics, including: optimal capital deployment, resource utilization, and, ultimately, patient outcomes—or clinical utility.Frontiers in Digital Health['Franziska Lobig', 'Jacob Graham', 'Apeksha Damania', 'Brian Sattin', 'Joana Reis', 'Prateek Bharadwaj']2024-03-07T00:00:00https://www.semanticscholar.org/paper/220be489bc449813ea125fcf7bd4c2b7d5c2cbd2 0e36304306a31ae39a5076683457bc51d4385103The integration of AI into user interfaces (UIs) has propelled technological advancements, enabling personalized experiences and improved efficiency. However, the ethical implications of AI-driven UIs cannot be overlooked. This research paper will analyze the ethical considerations regarding- transparency, accountability, fairness, privacy, and user consent. By exploring potential risks and challenges, we can advocate for the implementation of ethical design principles, regulatory frameworks, and user education initiatives. Through case studies and future directions, this paper highlights the importance of responsible and ethical AI UI development. The following questions and topics will be covered in this research paper- What is AI? Current capabilities and future scope of AI in User interfaces Where is AI being currently used? Concerns, Risks regarding AI Potential solutions, Creating Ethical principles How can AI be used ethically in UI design? Real-life examples Journal of Informatics Education and Research80This research paper will analyze the ethical considerations regarding- transparency, accountability, fairness, privacy, privacy, and user consent to advocate for the implementation of ethical design principles, regulatory frameworks, and user education initiatives.Journal of Informatics Education and Research['Prashant S. Acharya, Dr. Tripti Sahu, Pranav Dixit']2024-03-07T00:00:00https://www.semanticscholar.org/paper/0e36304306a31ae39a5076683457bc51d4385103 42cd0c10a7b175b3971c2d524a8b35290cb20dd5The computer industry that focuses on creating and developing computer systems that reproduce aspects of human behaviour to signify scaled primitive brainpower is known as artificial intelligence. A management process that involves providing clients with goods and services is referred to as marketing. It is based on a corporate philosophy that looks at client needs and satisfaction from a strategic standpoint. The goal of artificial intelligence is to replicate intelligence, which consists of elements that support reasoning, knowledge acquisition, and response to environmental changes. It is a nexus between computer science, cognitive science, philosophy, neuroscience, linguistics, and engineering scientists. The application of artificial intelligence is typically limited to specialized equipment or computers. KEY WORDS: AI – Artificial Intelligence, ML - Machine Learning , R&D-Research and developmentINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The goal of artificial intelligence is to replicate intelligence, which consists of elements that support reasoning, knowledge acquisition, and response to environmental changes.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ms.K Priyadharshini', 'Dr.S. Arulraj']2024-03-07T00:00:00https://www.semanticscholar.org/paper/42cd0c10a7b175b3971c2d524a8b35290cb20dd5 e1a7f8f0f139636a77594b541c187d3ee966ff4bDrug Discovery Today01The role, impact, and architecture of AstraZeneca's Predictive Insight Platform (PIP), a cloud-native modelling platform that aims to accelerate drug discovery, offers perspective on the evolution of R&D in pharma.Drug discovery today['Gian Marco', 'E. Evertsson', 'David J Riley', 'Christian Tyrchan', 'Prakash Chandra Rathi']2024-03-07T00:00:00https://www.semanticscholar.org/paper/e1a7f8f0f139636a77594b541c187d3ee966ff4b a750465e3b1bdef237432d5ff0b07b49032017c001['Claire Baytas', 'Dylan Ruediger']2024-03-07T00:00:00https://www.semanticscholar.org/paper/a750465e3b1bdef237432d5ff0b07b49032017c0 20de1b4e0b0acfd56f9bf59b436728bee6a3e6fcTechnical Symposium on Computer Science Education41{'pages': '750-756'}['Rong Liu', 'Carter Zenke', 'Charlie Liu', 'Andrew Holmes', 'Patrick Thornton', 'David J. Malan']2024-03-07T00:00:00https://www.semanticscholar.org/paper/20de1b4e0b0acfd56f9bf59b436728bee6a3e6fc 33fd181aac55a6fb1e71e53877ed71b470b43cd0European Radiology71European radiology['Merel Huisman', 'B. van Ginneken', 'Hugh Harvey']2024-03-07T00:00:00https://www.semanticscholar.org/paper/33fd181aac55a6fb1e71e53877ed71b470b43cd0 04562d29a24c51e146989faeedfbbf37e8e8c44eTechnical Symposium on Computer Science Education161{'pages': '172-178'}['Doga Cambaz', 'Xiaoling Zhang']2024-03-07T00:00:00https://www.semanticscholar.org/paper/04562d29a24c51e146989faeedfbbf37e8e8c44e c27ed7d3fdf444eac1301d280e8886d77354538dNature Reviews Physics20Nature Reviews Physics[]2024-03-07T00:00:00https://www.semanticscholar.org/paper/c27ed7d3fdf444eac1301d280e8886d77354538d 4cb17e39ea749ce78e58fa85b5df661c548143c5This paper investigates the application of eXplainable Artificial Intelligence (XAI) in the design of embedded systems using machine learning (ML). As a case study, it addresses the challenging problem of static silent store prediction. This involves identifying redundant memory writes based only on static program features. Eliminating such stores enhances performance and energy efficiency by reducing memory access and bus traffic, especially in the presence of emerging non-volatile memory technologies. To achieve this, we propose a methodology consisting of: 1) the development of relevant ML models for explaining silent store prediction, and 2) the application of XAI to explain these models. We employ two state-of-the-art model-agnostic XAI methods to analyze the causes of silent stores. Through the case study, we evaluate the effectiveness of the methods. We find that these methods provide explanations for silent store predictions, which are consistent with known causes of silent store occurrences from previous studies. Typically, this allows us to confirm the prevalence of silent stores in operations that write the zero constant into memory, or the absence of silent stores in operations involving loop induction variables. This suggests the potential relevance of XAI in analyzing ML models' decision in embedded system design. From the case study, we share some valuable insights and pitfalls we encountered. More generally, this study aims to lay the groundwork for future research in the emerging field of XAI for embedded system design.arXiv.org280This paper proposes a methodology consisting of the development of relevant ML models for explaining silent store prediction, and the application of XAI to explain these models, and finds that these methods provide explanations for silent store predictions, which are consistent with known causes of silent store occurrences from previous studies.ArXiv["Abdoulaye Gamati'e", 'Yuyang Wang']2024-03-07T00:00:00https://www.semanticscholar.org/paper/4cb17e39ea749ce78e58fa85b5df661c548143c5 a787fda941cb9d54daa78e8bc0bcd62e4a74938eCharacterizing samples that are difficult to learn from is crucial to developing highly performant ML models. This has led to numerous Hardness Characterization Methods (HCMs) that aim to identify"hard"samples. However, there is a lack of consensus regarding the definition and evaluation of"hardness". Unfortunately, current HCMs have only been evaluated on specific types of hardness and often only qualitatively or with respect to downstream performance, overlooking the fundamental quantitative identification task. We address this gap by presenting a fine-grained taxonomy of hardness types. Additionally, we propose the Hardness Characterization Analysis Toolkit (H-CAT), which supports comprehensive and quantitative benchmarking of HCMs across the hardness taxonomy and can easily be extended to new HCMs, hardness types, and datasets. We use H-CAT to evaluate 13 different HCMs across 8 hardness types. This comprehensive evaluation encompassing over 14K setups uncovers strengths and weaknesses of different HCMs, leading to practical tips to guide HCM selection and future development. Our findings highlight the need for more comprehensive HCM evaluation, while we hope our hardness taxonomy and toolkit will advance the principled evaluation and uptake of data-centric AI methods.arXiv.org00A fine-grained taxonomy of hardness types is presented and the Hardness Characterization Analysis Toolkit (H-CAT) is proposed, which supports comprehensive and quantitative benchmarking of HCMs across the hardness taxonomy and will advance the principled evaluation and uptake of data-centric AI methods.ArXiv['Nabeel Seedat', 'F. Imrie', 'M. Schaar']2024-03-07T00:00:00https://www.semanticscholar.org/paper/a787fda941cb9d54daa78e8bc0bcd62e4a74938e f4e48d5ba5c6995982ec1ead930607e6f8db4ea6Communications of the ACM80Commun. ACM['Mari Sako']2024-03-07T00:00:00https://www.semanticscholar.org/paper/f4e48d5ba5c6995982ec1ead930607e6f8db4ea6 b9884ea5f2d25c3ee126b84cd1d835cdb92c24e5Teaching students how to write code that is elegant, reusable, and comprehensible is a fundamental part of CS1 education. However, providing this"style feedback"in a timely manner has proven difficult to scale. In this paper, we present our experience deploying a novel, real-time style feedback tool in Code in Place, a large-scale online CS1 course. Our tool is based on the latest breakthroughs in large-language models (LLMs) and was carefully designed to be safe and helpful for students. We used our Real-Time Style Feedback tool (RTSF) in a class with over 8,000 diverse students from across the globe and ran a randomized control trial to understand its benefits. We show that students who received style feedback in real-time were five times more likely to view and engage with their feedback compared to students who received delayed feedback. Moreover, those who viewed feedback were more likely to make significant style-related edits to their code, with over 79% of these edits directly incorporating their feedback. We also discuss the practicality and dangers of LLM-based tools for feedback, investigating the quality of the feedback generated, LLM limitations, and techniques for consistency, standardization, and safeguarding against demographic bias, all of which are crucial for a tool utilized by students.Technical Symposium on Computer Science Education410Students who received style feedback in real-time were five times more likely to view and engage with their feedback compared to students who received delayed feedback, and those who viewed feedback were more likely to make significant style-related edits to their code.{'pages': '1442-1448'}['Juliette Woodrow', 'Ali Malik', 'C. Piech']2024-03-07T00:00:00https://www.semanticscholar.org/paper/b9884ea5f2d25c3ee126b84cd1d835cdb92c24e5 847d1d82f9899bda948440537f826f9c3c3ac27fAcademic medicine : journal of the Association of American Medical Colleges200Academic medicine : journal of the Association of American Medical Colleges['Laura Weiss Roberts']2024-03-07T00:00:00https://www.semanticscholar.org/paper/847d1d82f9899bda948440537f826f9c3c3ac27f a5b0f508356b4ab0d2d4966e1015f993f9c8620aTechnical Symposium on Computer Science Education210{'pages': '1070-1076'}['Prajish Prasad', 'A. Sane']2024-03-07T00:00:00https://www.semanticscholar.org/paper/a5b0f508356b4ab0d2d4966e1015f993f9c8620a a63b0864afb934b2b5ffd032e89fbb7c0d4932a3OBJECTIVES This article aims to examine how generative artificial intelligence (AI) can be adopted with the most value in health systems, in response to the Executive Order on AI. MATERIALS AND METHODS We reviewed how technology has historically been deployed in healthcare, and evaluated recent examples of deployments of both traditional AI and generative AI (GenAI) with a lens on value. RESULTS Traditional AI and GenAI are different technologies in terms of their capability and modes of current deployment, which have implications on value in health systems. DISCUSSION Traditional AI when applied with a framework top-down can realize value in healthcare. GenAI in the short term when applied top-down has unclear value, but encouraging more bottom-up adoption has the potential to provide more benefit to health systems and patients. CONCLUSION GenAI in healthcare can provide the most value for patients when health systems adapt culturally to grow with this new technology and its adoption patterns.JAMIA Journal of the American Medical Informatics Association252GenAI in healthcare can provide the most value for patients when health systems adapt culturally to grow with this new technology and its adoption patterns.Journal of the American Medical Informatics Association : JAMIA['Jenelle A Jindal', 'M. Lungren', 'Nigam H Shah']2024-03-07T00:00:00https://www.semanticscholar.org/paper/a63b0864afb934b2b5ffd032e89fbb7c0d4932a3 f2fd353cf84282c164cd680ea9c98cbbe7b94256The integration of Artificial Intelligence (AI) in the insurance sector has ushered in a new era of personalized insurance products, offering enhanced customer engagement and satisfaction. This review explores the transformative potential of AI in reshaping the landscape of insurance services, focusing specifically on the augmentation of customer engagement through personalized offerings. AI-driven algorithms and machine learning techniques enable insurers to analyze vast amounts of data with unprecedented speed and accuracy, facilitating the customization of insurance products to meet individual customer needs. By leveraging data from various sources such as IoT devices, social media, and historical claims data, insurers can gain deeper insights into customer behavior, preferences, and risk profiles. Personalized insurance products not only cater to the unique requirements of customers but also foster greater engagement by offering tailored recommendations, proactive risk management solutions, and real-time assistance. Through predictive analytics, AI algorithms can anticipate customer needs and preferences, allowing insurers to offer timely and relevant services, thereby enhancing customer satisfaction and loyalty. Moreover, AI-powered chatbots and virtual assistants serve as accessible and responsive touchpoints for customers, providing instant support, guidance, and personalized recommendations throughout the insurance lifecycle. By streamlining communication channels and offering seamless interactions, AI technologies strengthen the bond between insurers and customers, fostering long-term relationships built on trust and transparency. The integration of AI in personalized insurance products represents a transformative pathway towards enhanced customer engagement. By harnessing the power of AI-driven analytics and automation, insurers can deliver tailor-made solutions that resonate with individual customers, driving higher levels of satisfaction, loyalty, and ultimately, business growth. Keywords: Artificial Intelligence, Insurance, Privacy-Enhanced, Customer, Engagement, Review.International Journal of Management &amp; Entrepreneurship Research02This review explores the transformative potential of AI in reshaping the landscape of insurance services, focusing specifically on the augmentation of customer engagement through personalized offerings, using AI-driven algorithms and machine learning techniques.International Journal of Management &amp; Entrepreneurship Research['Omotayo Bukola Adeoye', 'Chinwe Chinazo Okoye', 'Onyeka Chrisanctus Ofodile', 'Olubusola Odeyemi', 'Wilhelmina Afua Addy', 'Adeola Olusola Ajayi-Nifise']2024-03-07T00:00:00https://www.semanticscholar.org/paper/f2fd353cf84282c164cd680ea9c98cbbe7b94256 f31d286313046b4f48906d3cef0234cedbb344801021[]2024-03-07T00:00:00https://www.semanticscholar.org/paper/f31d286313046b4f48906d3cef0234cedbb34480 44e5ebe380e8c2a51443e41a83aa185a53b97c5dScience and Engineering Ethics371This study critically examines 57 policy documents pertaining to ethical AI originating from 24 distinct countries, employing a combination of computational text mining methods and qualitative content analysis to methodically identify common themes throughout these policy documents.Science and Engineering Ethics['Tahereh Saheb']2024-03-07T00:00:00https://www.semanticscholar.org/paper/44e5ebe380e8c2a51443e41a83aa185a53b97c5d 1470de632b4a6fe3b70846eb9a9b8560ccc1eba3Artificial intelligence (AI) is rapidly transforming various aspects of society, including education. Understanding teachers' perspectives on this disruptive technology is essential, given its potential to revolutionize the teaching and learning process. A comprehensive study involving 74 educators utilized the Opinion Scale on Artificial Intelligence in Education to gather valuable insights. The research outcomes reveal a predominantly favourable view of AI in education, albeit accompanied by significant apprehensions regarding ethical and privacy-related issues. This study contributes significantly to the ongoing discourse on the role of AI in education, emphasizing the necessity for a balanced approach that maximizes the benefits of AI while ensuring the protection of the rights and interests of all stakeholders.Advances in Mobile Learning Educational Research131This study contributes significantly to the ongoing discourse on the role of AI in education, emphasizing the necessity for a balanced approach that maximizes the benefits of AI while ensuring the protection of the rights and interests of all stakeholders.Advances in Mobile Learning Educational Research['Derya Uygun']2024-03-07T00:00:00https://www.semanticscholar.org/paper/1470de632b4a6fe3b70846eb9a9b8560ccc1eba3 f03bd5e4df91b2e3b3eb29bfadfd9577ddfdd808Objective College students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health. Methods Publications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends. Results Spanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications. Conclusion This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.Frontiers in Psychology1051This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health.Frontiers in Psychology['Jing Chen', 'Dongfeng Yuan', 'Ruotong Dong', 'Jingyi Cai', 'Zhongzhu Ai', 'Shanshan Zhou']2024-03-07T00:00:00https://www.semanticscholar.org/paper/f03bd5e4df91b2e3b3eb29bfadfd9577ddfdd808 daf42aa10a0a62ec7a68d3668be4ad4b6a836df9Artificial intelligence (AI) tools (in particular Large Language Models) have the potential to reduce the time needed to perform thematic analysis. To better understand their potential in the transportation field, we compare human-based to AI-based outcomes. Our findings indicate that AI tools, such as ChatGPT, could synthetize and summarize major topics present in our dataset regardless of previous user exposure to the subject or not. Nonetheless, caution is required as results might miss the nuance of less frequent themes. These tools could be used to accelerate the process under the supervision of researchers and practitioners given responder consent and the following of ethical practices.Findings81These findings indicate that AI tools, such as ChatGPT, could synthetize and summarize major topics present in the authors' dataset regardless of previous user exposure to the subject or not, and caution is required as results might miss the nuance of less frequent themes.Findings['Thiago Carvalho', 'Hisham Negm', 'A. El-geneidy']2024-03-07T00:00:00https://www.semanticscholar.org/paper/daf42aa10a0a62ec7a68d3668be4ad4b6a836df9 b8ab4e02d12255734a1de006b4b9d7058d367b6cThe unprecedented impact of the COVID-19 pandemic has resulted in a significant shift in the operations of Higher Education Institutions (HEIs), indicating the disruptive influence of technological innovations. This paper delves into the implications of such advancements within the HEI sector, emphasising the role of Artificial Intelligence (AI) in enhancing academic performance and student learning outcomes. Guided by the Technology Acceptance Model (TAM), 20 empirical studies on AI published between 2016 and 2022 are examined, systematically presented, and discussed. The article draws critical lessons about the role of AI in South African HEIs. A core finding is that the advent of AI has revolutionised the learning environment through intelligent learning, increasing collaborations, promoting active learning, sharing resources, delivering distance education through virtual learning and improving pedagogical systems. The review contributes to AI's growing theory and practice in education and adds to the limited literature on 4IR in the South African HEI sector.Conference on Information Communications Technology and Society620A core finding is that the advent of AI has revolutionised the learning environment through intelligent learning, increasing collaborations, promoting active learning, sharing resources, delivering distance education through virtual learning and improving pedagogical systems.2024 Conference on Information Communications Technology and Society (ICTAS)['Vusumzi Funda', 'R. Piderit']2024-03-07T00:00:00https://www.semanticscholar.org/paper/b8ab4e02d12255734a1de006b4b9d7058d367b6c c884c601c4ccae615771c4e67fccb687f3ec58adAn inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.PLoS ONE390An overview of the IP control system augmented by a comparative analysis of multiple control strategies is presented, demonstrating that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error.PLOS ONE['Saqib Irfan', 'Liangyu Zhao', 'S. Ullah', 'Adeel Mehmood', 'Muhammad Fasih Uddin Butt']2024-03-07T00:00:00https://www.semanticscholar.org/paper/c884c601c4ccae615771c4e67fccb687f3ec58ad 84243922fb8c337cbf6cf75e04aca13420e15675Artificial intelligence has impacted student’s social, emotional, and physical well-being. It has resulted in how a person thinks and reacts to a particular situation. The Purpose of this investigation is to test the effectiveness of learning patterns on our mental health. It can affect a student’s energy level, concentration, dependability, and mental ability. On the other hand, AI can automate the process of grading assignments and providing feedback to students, which can save teachers a lot of time and can focus more on student interaction. It is essential to ensure that AI systems used in education are unbiased and fair. We read articles about the connection between artificial intelligence and mental health that use mood rating scales and classified them into the pros and cons of artificial intelligence. In our college, we will also carry out a poll that shows how Artificial intelligence has harmed and benefited the students. Therefore, it is crucial to carefully examine the benefits and risks of AI in education and implement appropriate safeguards to ensure that it is used ethically and responsibly. Public mental health based on artificial intelligence engineering has a wide range of application and development prospects in enhancing the effectiveness of instruction, achieving high efficiency and intelligence, and enhancing specialized education. The use of artificial intelligence-based public mental health in business administration teaching effects has highly favorable importance for precisely determining the mental health and teaching effects of business administration students. We develop a method for the influence of public mental health on the teaching effect of the business administration profession based on artificial intelligence engineering to improve the teaching- learning effect of the business administration major. ( Keywords:- Learnings, Mental Health, and Artificial Intelligence )INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A method for the influence of public mental health on the teaching effect of the business administration profession based on artificial intelligence engineering is developed to improve the teaching- learning effect of the business administration major.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-03-07T00:00:00https://www.semanticscholar.org/paper/84243922fb8c337cbf6cf75e04aca13420e15675 5e31115a072241fa103250cce34c4ba70c531d2cGrain legumes play a significant global role and are integral to agriculture and food production worldwide. Therefore, comprehending and analyzing the factors that influence grain legume yield are of paramount importance for guiding agricultural management and decision making. Traditional statistical analysis methods present limitations in interpreting results, but explainable artificial intelligence (AI) provides a visual representation of model results, offering insights into the key factors affecting grain legume yield. In this study, nine typical grain legume species were selected from a published global experimental dataset: garden pea (Pisum sativum), chickpea (Cicer arietinum), cowpea (Vigna unguiculata), garden vetch (Vicia sativa), faba bean (Vicia faba), lentil (Lens culinaris), pigeon pea (Cajanus cajan), peanut (Arachis hypogaea), and white lupine (Lupinus albus). Seven commonly used models were constructed for each legume species, and model performance evaluation was conducted using accuracy, AUC, recall, precision, and F1 score metrics. The best classification model was selected for each grain legume species. Employing Decision Tree analysis, Feature Importance Evaluation, and SHapley Additive exPlanations (SHAP) as explainable techniques, our study conducted both individual and comprehensive analyses of nine leguminous crops. This approach offers a novel perspective, unveiling not only the unique responses of each crop to the influencing factors but also demonstrating the common factors across different crops. According to the experimental results, XGboost (XGB) and Random Forests (RF) are the best-performing models among the nine types of grain legumes, and the classification accuracy of a specific species is as high as 87.33%. Insights drawn from the feature importance map reveal that several factors, including aerial biomass, precipitation, sunshine duration, soil conditions, growth cycle, and fertilization strategy, have a pivotal influence. However, it was found from the SHAP graph that the responses of various crops to these factors are not the same. This research furnishes novel perspectives and insights into understanding the factors influencing grain legume yields. The findings provide a robust scientific foundation for agricultural managers, experts, and policymakers in the pursuit of optimizing pulse yields and advancing agricultural sustainability.Agriculture00This research furnishes novel perspectives and insights into understanding the factors influencing grain legume yields, and provides a robust scientific foundation for agricultural managers, experts, and policymakers in the pursuit of optimizing pulse yields and advancing agricultural sustainability.Agriculture['Yadong Li', 'Rujia Li', 'Rongbiao Ji', 'Yehui Wu', 'Jiaojiao Chen', 'Mengyao Wu', 'Jianping Yang']2024-03-07T00:00:00https://www.semanticscholar.org/paper/5e31115a072241fa103250cce34c4ba70c531d2c 5922ab9edb75872298505d7c87361eb1429f4a62Given the ever-growing importance of artificial intelligence in our society and daily lives, everyone needs to learn about the core ideas and principles of this technology. While there is still a lack of empirical findings on the teaching and learning about AI in K-12 education, various teaching approaches and materials have been developed in recent years, and the topic is being introduced into K-12 computer science curricula. However, qualifying CS teachers to adequately teach this new field is a significant challenge, as they require extensive content knowledge as well as pedagogical content knowledge. In this paper, we describe the conditions and challenges and the resulting design of a professional development offer to prepare teachers for the introduction of AI into mandatory K-12 CS education in Bavaria (Germany). By designing a scalable PD program in a blended learning format and building on principles such as the "pedagogical double-decker", we successfully addressed challenges such as limited resources, a large number of teachers to be trained, and the significant heterogeneity of teachers’ backgrounds. We also share the results of a formal evaluation and other lessons learned from the initial implementations, which contribute to the design of professional development for this pressing issue.Technical Symposium on Computer Science Education410By designing a scalable PD program in a blended learning format and building on principles such as the "pedagogical double-decker", this paper successfully addressed challenges such as limited resources, a large number of teachers to be trained, and the significant heterogeneity of teachers’ backgrounds.{'pages': '590-596'}['Franz Jetzinger', 'Sven Baumer', 'Tilman Michaeli']2024-03-07T00:00:00https://www.semanticscholar.org/paper/5922ab9edb75872298505d7c87361eb1429f4a62 9437bedf8d4659d33465b18499c93fe8c1bcc8faAI &amp; SOCIETY290An interdisciplinary approach is developed, using socio-technical software engineering and design anthropological approaches, to investigate how trust and trustworthiness concepts are articulated and performed by AI software practitioners.AI &amp; SOCIETY['Sarah Pink', 'Emma Quilty', 'J. Grundy', 'R. Hoda']2024-03-07T00:00:00https://www.semanticscholar.org/paper/9437bedf8d4659d33465b18499c93fe8c1bcc8fa 1593c832a4799fc006b7dd5bd7b97c62038b5c1aArtificial Intelligence (AI) technologies are rapidly advancing and causing profound transformations in all aspects of life. In particular, the widespread adoption of generative AI systems like ChatGPT is taking this transformation to even more dramatic dimensions. In this context, the most comprehensive impact is observed in educational systems. Educational systems, on one hand, are faced with the urgent need to rapidly restructure education in response to skill changes in professions caused by the proliferation of such systems in the labor market. On the other hand, challenging questions arise about whether and to what extent these systems should be integrated into education, how they should be integrated if at all, and how ethical issues arising from AI systems can be addressed. This study evaluates the potential benefits and possible risks of using AI systems in educational systems from the perspectives of students, teachers, and education administrators. Therefore, the study discusses the potential uses of AI systems in education, as well as the risks they may pose. Policy recommendations are developed to maximize the benefits of AI systems while mitigating the ethical and other issues they may cause. Additionally, the study emphasizes the importance of increasing AI literacy for all education stakeholders. It suggests that raising awareness of both the benefits and ethical issues caused by AI systems can contribute to enhancing the benefits of these systems in education while minimizing their potential harms.Bartın University Journal of Faculty of Education850It is suggested that raising awareness of both the benefits and ethical issues caused by AI systems can contribute to enhancing the benefits of these systems in education while minimizing their potential harms.Bartın University Journal of Faculty of Education['Mahmut Özer']2024-03-07T00:00:00https://www.semanticscholar.org/paper/1593c832a4799fc006b7dd5bd7b97c62038b5c1a 47915c90888d07237cd59570a33cd91ec930c560Artificial intelligence (AI) is revolutionising almost every industry and sphere of life. AI has been utilise to meet customers' expectations in business sectors by customising products and services. AI-driven marketing promises to improve customer service and satisfaction by analysing customergenerated data to create value, enhance resource allocation and develop effective and cost-effective marketing strategies through big data analytics. However, adopting AI-driven digital marketing in some resource-constrained settings is still nascent, and there is a dearth of studies to enhance AI-driven marketing. Thus, this study proposed a framework to enhance AI-driven marketing adoption by addressing the barriers to the adoption of AI-driven marketing and customer satisfaction in Lesotho. The study used a qualitative research design to (i) explore the challenges faced by businesses in Lesotho in adopting AI technologies for digital marketing and customer satisfaction, (ii) investigate the barriers to AI-driven marketing and customer satisfaction by Lesotho's businesses, and (iii) propose a framework for AI-driven marketing adoption by businesses in Lesotho. Findings revealed that lack of awareness, legislation, resistance and financial constraints were the main barriers to AI-driven marketing adoption. The proposed framework suggests interventions to deal with these impediments.Conference on Information Communications Technology and Society490Lack of awareness, legislation, resistance and financial constraints were the main barriers to AI-driven marketing adoption in Lesotho, and the proposed framework suggests interventions to deal with these impediments.2024 Conference on Information Communications Technology and Society (ICTAS)['John Batani', 'Matau Rosina Mothabeng', 'Elliot Mbunge']2024-03-07T00:00:00https://www.semanticscholar.org/paper/47915c90888d07237cd59570a33cd91ec930c560 db6c79c32d1f6b4281b3ddf7eea73127896aa6c7This study explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare, focusing on enhancing patient care through operational efficiency and medical innovation. Employing a meta-study approach, it comprehensively analyzes the applications and ethical aspects of AI and ML in healthcare, highlighting successful implementations like IBM Watson for Oncology and Google DeepMind’s AlphaFold. The research emphasizes AI’s significant contributions to diagnostics, precision medicine, and medical imaging interpretation, alongside its role in optimizing healthcare operations and enabling personalized medicine through data analysis. However, it also addresses challenges such as algorithmic bias, safety, data privacy, and the need for regulatory frameworks. The study underlines the importance of continued research, interdisciplinary collaboration, and adaptive regulations to ensure the responsible and ethical use of AI and ML in healthcare.Journal of Autonomous Intelligence00The study underlines the importance of continued research, interdisciplinary collaboration, and adaptive regulations to ensure the responsible and ethical use of AI and ML in healthcare.Journal of Autonomous Intelligence['B. Lainjo']2024-03-07T00:00:00https://www.semanticscholar.org/paper/db6c79c32d1f6b4281b3ddf7eea73127896aa6c7 274c7bae144990c6c7e6cb79adff798c0a095883Delirium is a preventable and reversible complication for intensive care unit (ICU) patients, which can be linked to negative outcomes. Early intervention to cope with the risk factors of delirium is necessary. Yet no specific description of the Artificial Intelligence Assisted Prevention and Management for Delirium (AI-AntiDelirium) following the Template for Intervention Description and Replication (TIDieR) checklist was reported. This is the first study to describe a detailed process for the development of an evidence-based delirium intervention. To describe an individualised delirium intervention which is delivered by an artificial intelligence-assisted system in the ICU for critically ill patients. The TIDieR checklist improved the description of ICU delirium interventions, including several key features for improved implementation of the intervention. This descriptive research describes the AI-assisted ICU delirium interventions for improving cognitive load and adherence of nurses and reducing ICU delirium incidence. Following the TIDieR checklist, we standardised the flow chart of ICU delirium assessment tools; formed an evaluation sheet of ICU delirium risk factors; and translated the evidence-based ABCDEF bundle intervention into practice. Therefore, nurses and researchers would benefit from replicating the interventions for clinical use or experimental research. The TIDieR checklist provided a systematic approach for reporting the complex ICU delirium interventions delivered in a clinical interventional trial, which contributes to the nursing practice policy for the standardisation of interventions.Journal of Research in Nursing150Descriptive research describes the AI-assisted ICU delirium interventions for improving cognitive load and adherence of nurses and reducing ICU delirium incidence and translated the evidence-based ABCDEF bundle intervention into practice.Journal of Research in Nursing['Shan Zhang', 'Wei Cui', 'Ying Wu', 'Meihua Ji']2024-03-07T00:00:00https://www.semanticscholar.org/paper/274c7bae144990c6c7e6cb79adff798c0a095883 dc1653acf362dcb9740ff21aef0d03dd1d935f4cFrontiers in Psychiatry62Frontiers in Psychiatry['Akhil P. Joseph', 'Anithamol Babu', 'L. O. Prakash', 'Sang-Kyu Lee', 'Brian Fuehrlein']2024-03-07T00:00:00https://www.semanticscholar.org/paper/dc1653acf362dcb9740ff21aef0d03dd1d935f4c 1b5ebd6eb4b34c75d784cd0b40f43c03b90a54c2Digestive and Liver Disease00The significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy is highlighted and further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver['M. Spadaccini', 'J. Troya', 'K. Khalaf', 'A. Facciorusso', 'R. Maselli', 'A. Hann', 'Alessandro Repici']2024-03-07T00:00:00https://www.semanticscholar.org/paper/1b5ebd6eb4b34c75d784cd0b40f43c03b90a54c2 da1a8f73851ac2484aa1e132511acd9230b3a9faBACKGROUND Despite new pharmacotherapy, most patients with long-term Type 2 Diabetes are still hyperglycemic. This could have been solved by insulin with its unlimited potential efficacy, but its dynamic physiology demands frequent titrations which are overdemanding. This report provides a real-life account for a scalable transformation of diabetes care in a community-based endocrinology center by harnessing AI-based autonomous insulin titration. METHODS The center embedded the d-Nav® technology and its dedicated clinical support. Reported outcomes include treatment efficacy/safety in the first 600 patients and use of cardiorenal-risk reduction pharmacotherapy. FINDINGS Patients used d-Nav for 8.2±3.0 months with 82% retention. Age was 67.1±11.5 years and duration of diabetes was 19.8±11.0 years. During the last 3 years before d-Nav, HbA1c had been overall higher than 8% and at the beginning of the program it was as high as 8.6%±2.1% with 29.3% of the patients with HbA1c>9%. With d-Nav, HbA1c decreased to 7.3%±1.2% with 5.7% of patients with HbA1c>9%. During the first 3 months, d-Nav reduced total daily dose of insulin in 1 of every 5 patients due to relatively low glucose levels to minimize the risk of hypoglycemia. GLP-1 or dual GLP-1 and GIP receptor agonists were prescribed in about a half of the patients and SGLT2 inhibitor in a third. The frequency of hypoglycemia (<54mg/dl) was 0.4±0.6/month and severe hypoglycemia 1.7/100-patient-years. INTERPRETATION The use of d-Nav allowed for improvement in overall diabetes management with appropriate use of both insulin and non-insulin pharmacologic agents in a scalable way.Diabetes Technology & Therapeutics90A real-life account for a scalable transformation of diabetes care in a community-based endocrinology center by harnessing AI-based autonomous insulin titration using the d-Nav technology and its dedicated clinical support is provided.Diabetes technology & therapeutics['Mark Lowe Warren', 'R. Bergenstal', 'Matthew R Hager', 'E. Bashan', 'Israel Hodish']2024-03-07T00:00:00https://www.semanticscholar.org/paper/da1a8f73851ac2484aa1e132511acd9230b3a9fa bec2675cc605c30d4127d3fb615fceddacb789c8Computers in Biology and Medicine740This study provides a holistic understanding of the path from task-specific, mono-modal AI toward general-purpose, multimodal AI for medical diagnostics and identified several key factors contributing to the development of AI in medical diagnostics, including data quality, algorithm design, and computational power.Computers in biology and medicine['Chuheng Chang', 'W. Shi', 'Youyang Wang', 'Zhan Zhang', 'Xiaoming Huang', 'Yang Jiao']2024-03-07T00:00:00https://www.semanticscholar.org/paper/bec2675cc605c30d4127d3fb615fceddacb789c8 1a4cef310bb9d1951d176ab0a5ef3813db890cd3JAMA ophthalmology40JAMA ophthalmology['Gil Binenbaum']2024-03-07T00:00:00https://www.semanticscholar.org/paper/1a4cef310bb9d1951d176ab0a5ef3813db890cd3 92b25178bcf2416f0aaf40b2a7c2834cc8e868d9Urologie00Urologie['Michael Irmler']2024-03-07T00:00:00https://www.semanticscholar.org/paper/92b25178bcf2416f0aaf40b2a7c2834cc8e868d9 d639dc993188d010e0d9ebddbaac14f7af632de5INTRODUCTION: Knee Osteoarthritis (KOA) is a degenerative joint disease, that predominantly affects the knee joint and causes significant global disability. The traditional methods prevailing in this field for proper diagnosis are very subjective and time-consuming, which hinders early detection. This study explored the integration of artificial intelligence (AI) in orthopedics, specifically the field of machine learning (ML) applications in KOA. OBJECTIVES: The objective is to assess the effectiveness of Machine learning in KOA, besides focusing on disease progression, joint detection, segmentation, and its classification. ML algorithms are also applied to analyze the MRI and X-ray images for their proper classification and forecasting. The survey spanning from 2018 to 2022 investigated the treatment-seeking behavior of individuals with OA symptoms. METHODS: Utilizing deep learning (CNN, RNN) and various ML algorithms (SVM, GBM), this study examined KOA. Machine learning was used as a subset of AI, and it played a pivotal role in healthcare, particularly in the field of medical imaging.  The analysis involved reviewing the studies from credible sources like Elsevier and Web of Science. RESULTS: Current research in the field of medical imaging CAD revealed promising outcomes. Studies that utilized CNN demonstrated 80-90% accuracy on datasets like OAI and MOST, emphasizing its varied significance in vast clinical and imaging data archives. CONCLUSION: This comprehensive analysis highlighted the evolving landscape of research in KOA. The role of machine learning in classification, segmentation, and diagnosis of severity is very much evident. The study also anticipates a future framework optimizing KOA detection and overall classification performance, with a strong emphasis on the potential for enhancement of knee osteoarthritis diagnostics.EAI Endorsed Transactions on Internet of Things380A comprehensive analysis highlighted the evolving landscape of research in KOA and anticipates a future framework optimizing KOA detection and overall classification performance, with a strong emphasis on the potential for enhancement of knee osteoarthritis diagnostics.EAI Endorsed Transactions on Internet of Things['Suman Rani', 'Minakshi Memoria', 'Tanupriya Choudhury', 'Ayan Sar']2024-03-07T00:00:00https://www.semanticscholar.org/paper/d639dc993188d010e0d9ebddbaac14f7af632de5 e79f92647aebf3d763b5bfddac4dcf2e9e13cbcbThis paper focuses on two highly publicized formal trade-offs in the field of responsible artificial intelligence (AI) -- between predictive accuracy and fairness and between predictive accuracy and interpretability. These formal trade-offs are often taken by researchers, practitioners, and policy-makers to directly imply corresponding tensions between underlying values. Thus interpreted, the trade-offs have formed a core focus of normative engagement in AI governance, accompanied by a particular division of labor along disciplinary lines. This paper argues against this prevalent interpretation by drawing attention to three sets of considerations that are critical for bridging the gap between these formal trade-offs and their practical impacts on relevant values. I show how neglecting these considerations can distort our normative deliberations, and result in costly and misaligned interventions and justifications. Taken together, these considerations form a sociotechnical framework that could guide those involved in AI governance to assess how, in many cases, we can and should have higher aspirations than the prevalent interpretation of the trade-offs would suggest. I end by drawing out the normative opportunities and challenges that emerge out of these considerations, and highlighting the imperative of interdisciplinary collaboration in fostering responsible AI.arXiv.org920This paper argues against this prevalent interpretation of formal trade-offs by drawing attention to three sets of considerations that are critical for bridging the gap between these formal trade-offs and their practical impacts on relevant values, and highlights the imperative of interdisciplinary collaboration in fostering responsible AI.ArXiv['Sina Fazelpour']2024-03-07T00:00:00https://www.semanticscholar.org/paper/e79f92647aebf3d763b5bfddac4dcf2e9e13cbcb ccb0ca11659af92020589bed170f068b92873efcEnvironment, Development and Sustainability420Environment, Development and Sustainability['Daqian Shi', 'Chenxi Luo', 'Kaixia Zhang', 'Caiqi Bu']2024-03-06T00:00:00https://www.semanticscholar.org/paper/ccb0ca11659af92020589bed170f068b92873efc 70dc78375a73e1574d697e2e5cb7d3cdcca72050The introduction of artificial intelligence (AI) in interventional radiology (IR) will bring about new challenges and opportunities for patients and clinicians. AI may comprise software as a medical device or AI-integrated hardware and will require a rigorous evaluation that should be guided based on the level of risk of the implementation. A hierarchy of risk of harm and possible harms are described herein. A checklist to guide deployment of an AI in a clinical IR environment is provided. As AI continues to evolve, regulation and evaluation of the AI medical devices will need to continue to evolve to keep pace and ensure patient safety.Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes210A checklist to guide deployment of an AI in a clinical IR environment and a hierarchy of risk of harm and possible harms are described herein.Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes['B. Warren', 'Alexander Bilbily', 'J. W. Gichoya', 'Lucas B Chartier', 'Aly Fawzy', 'Camilo Barragán', 'A. Jaberi', 'Sebastian Mafeld']2024-03-06T00:00:00https://www.semanticscholar.org/paper/70dc78375a73e1574d697e2e5cb7d3cdcca72050 708fa9c5bf764c5c0a388485ccf9a9a7baf1ead7In the article, based on the tools of intellectual property analysis, international and national patent legislation is analyzed, the problems of inventing inventions created with the use of artificial intelligence (AI) are investigated: the dynamics of patenting, patent activity in the field of AI technologies, the peculiarities of patentability examination of inventions in different jurisdictions are analyzed (EPO, Germany, China, USA, Japan) and judicial practice on this issue. The main provisions of the draft law “On Amendments to the Law of Ukraine “On the Protection of Rights to Inventions and Utility Models” regarding the regulation of relations arising in relation to inventions and utility models created with the use of artificial intelligence” were considered. It was concluded that the law “On protection of rights to inventions and utility models” excludes computer programs from patented objects. It is recommended to implement the rules of the EPC Guidelines on computer-implemented inventions into the Rules for drawing up, submitting and considering an application for an invention and an application for a utility model, which do not reflect these aspects.INFORMATION AND LAW00It was concluded that the law “On protection of rights to inventions and utility models” excludes computer programs from patented objects and it is recommended to implement the rules of the EPC Guidelines on computer-implemented inventions into the Rules for drawing up, submitting and considering an application for an invention and an application for a utility model.INFORMATION AND LAW['H. Androshuk', 'O. Doroshenko', 'L. Rabotiahova']2024-03-06T00:00:00https://www.semanticscholar.org/paper/708fa9c5bf764c5c0a388485ccf9a9a7baf1ead7 28b0e7d7edb7474b657b17cf96ea5be5607a4e13The article is devoted to the problems of legal regulation of the development and application of artificial intelligence technologies in the world and in Ukraine. An overview of the main international and national initiatives aimed at solving the problem of forming sustainable governance and regulating the development and application of artificial intelligence technologies is carried out, as well as the results of the discussion of the possibilities of artificial intelligence and the risks associated with its use for peace and security, economy and society, and human rights are highlighted. The results of the analysis of the current state of affairs in the field of artificial intelligence are obtained. There is uncertainty, divergence of views of political, intellectual and business elites both at the national and global levels regarding the conceptual content of further ways to determine the legal regulation of artificial intelligence. The world is faced with the phenomenon of the singularity, which requires the definition of a new paradigm and conceptual framework for the transformation of legal systems, first of all, the regulatory framework for the effective development and application of artificial intelligence technologies.INFORMATION AND LAW00An overview of the main international and national initiatives aimed at solving the problem of forming sustainable governance and regulating the development and application of artificial intelligence technologies is carried out, as well as the results of the discussion of the possibilities of artificial intelligence and the risks associated with its use for peace and security, economy and society, and human rights are highlighted.INFORMATION AND LAW['O. Baranov']2024-03-06T00:00:00https://www.semanticscholar.org/paper/28b0e7d7edb7474b657b17cf96ea5be5607a4e13 bcedc6c0cb3ff3bea6558587a0b6c984062535ccThe study examines the transformation of scientific views and approaches to the problem of expediency and necessity of legal regulation of public relations, emerging from the evolution of the world system of public electronic resources in the transmission of information and Internet data from Web 1.0, Web 2.0 to Web 3.0. The stages of formation of the role and place of electronic jurisdiction in public relations are also investigated. Legal regulation of modern relations in virtual and augmented reality environments with the use of Web 3.0 technologies is not available today. At the same time, there are precedents for the application of certain provisions of analogue law to address legal uncertainties in the virtual environment, such as establishing ownership of virtual non-property assets, buying/selling of virtual non-property assets, liability for misappropriation of virtual non-property assets, etc. Obviously, the problem of legal regulation by the rules of analogue law in the virtual environment cannot be fully addressed. The solution to this problem is possible by creating a comprehensive e-jurisdiction and developing the Metaverse Grand Charter of Laws to regulate public relations in the meta-universe and to establish new branch of e-law. Given the urgency of the problem, the model of e-jurisdiction Grand Charter of Laws Metaverse is proposed. The model of complex electronic jurisdiction of Metaverse will allow to create basic conceptual apparatus, doctrinal and regulatory and legal concepts, to define objects and subjects of legal relations in Metaverse, to establish the basic forms of legal relations and mutual relations in Metaverse. This, in turn, will be the basis for reforming analogue legislation, partial interoperability in the digital environment and the development of new regulations in various areas of law and will stimulate the establishment of new e-jurisdiction. The study proposes the construction and basic elements of electronic jurisdiction, mechanisms for the separation of electronic offences and interaction with analogue jurisdictions. E-jurisdiction of the Metaverse Grand Charter of Laws will provide legal regulation of public relations both directly in Metaverse and in public relations related to the analogue and electronic world.INFORMATION AND LAW00The study proposes the construction and basic elements of electronic jurisdiction, mechanisms for the separation of electronic offences and interaction with analogue jurisdictions, and the model of e-jurisdiction Grand Charter of Laws Metaverse, which will provide legal regulation of public relations both directly in Metaverse and in public relations related to the analogue and electronic world.INFORMATION AND LAW['O. Kostenko', 'D. Zhuravlov', 'V. Furashev', 'O. Dniprov']2024-03-06T00:00:00https://www.semanticscholar.org/paper/bcedc6c0cb3ff3bea6558587a0b6c984062535cc 53223e021f7631bb2e4e50e9b11a6e9465b4016eThe level of digitalization has increased significantly in the current century, the speed of the Internet has increased by many times, and it is now possible to access it from different parts of the world. Today, artificial intelligence occupies a special place in the digital technology market, which is already an indispensable tool in many sectors of the economy of developed countries. The purpose of the study is to identify the main urgent problems of using neural networks, as well as to form proposals for their legal regulation. The study uses the formal logical method, the comparative legal method, analysis and synthesis, methods of induction, deduction, and abstraction. It has been established that artificial intelligence cannot yet distinguish a joke from a real command or user request, respectively, further development of these technologies is impossible without the implementation of an analog function of cognitive thinking. It is concluded that self-regulation can be the best way to regulate the use of neural networks, since the legal system of continental law, to which the Russian Federation belongs, is quite rigid and often may not have time to regulate the rapidly developing field of artificial intelligence. Self-regulation is able to provide an opportunity to convey proposals on the legalization of effective rules for organizing the activities of IT market participants, to create an effective mechanism for guaranteeing the quality and safety of artificial intelligence based on the joint property liability of members of self-regulating organizations. At the same time, it requires the adoption of legal norms on liability for the illegal use of neural networks, as was done in the United States and China. In the near future, deepfakes created on the basis of neural network technologies may become a threat to national security and cause harm to thousands of citizens. Lex Russica70It is concluded that self-regulation can be the best way to regulate the use of neural networks, since the legal system of continental law is quite rigid and often may not have time to regulate the rapidly developing field of artificial intelligence.Lex Russica['A. S. Kiselev']2024-03-06T00:00:00https://www.semanticscholar.org/paper/53223e021f7631bb2e4e50e9b11a6e9465b4016e c84dc9cdde5a4bd2a7b6e9d2b068c5cf784addbdThe article analyzes experience of the Israel in the field of the legal regulation of information sphere. The legislative initiatives of the Israel in the field of information safety are analyzed. In conclusions the organizational and legal model of regulating the Internet  in the Israel is outlined.INFORMATION AND LAW00INFORMATION AND LAW['V. Belevtseva']2024-03-06T00:00:00https://www.semanticscholar.org/paper/c84dc9cdde5a4bd2a7b6e9d2b068c5cf784addbd 30b54b6a54fa0a90e1665e0761102a2df71ee5b4The article provides an analytical review of a number of international and national documents relating to various aspects of the legal regulation of artificial intelligence. Attention is focused on the problems of regulatory and practical approaches to ensuring and observing human rights at all stages of the life cycle of artificial intelligence.INFORMATION AND LAW00An analytical review of a number of international and national documents relating to various aspects of the legal regulation of artificial intelligence focused on the problems of regulatory and practical approaches to ensuring and observing human rights at all stages of the life cycle of artificial intelligence.INFORMATION AND LAW['O. Taran', 'V. Havlovsky']2024-03-06T00:00:00https://www.semanticscholar.org/paper/30b54b6a54fa0a90e1665e0761102a2df71ee5b4 7041970df01166690f7b598075a956fe41ca39f7The article examines the importance of using big data in decision-making, in particular, in achieving the Sustainable Development Goals. The legal regulation of the SDGs in Ukraine since 2015 is analyzed. An institutional system for collecting and publishing data on SDG indicators in Ukraine is established. It is found that the data is not updated systematically and is not collected for all indicators. This makes it impossible to adjust decisions to achieve the SDGs. The use of Big Data in SDG indicators can complement statistics and reveal new individual relationships between indicators. Big Data is collected, as a rule, by corporate entities that have invested heavily in building large databases and use the results of big data processing in their own business models. It is necessary to adopt the Strategy for Achieving the SDGs in Ukraine until 2030 with legal regulation on the use of modern digital technologies for collecting, analyzing and attracting Big Data from businesses.INFORMATION AND LAW00INFORMATION AND LAW['M. Dubniak']2024-03-06T00:00:00https://www.semanticscholar.org/paper/7041970df01166690f7b598075a956fe41ca39f7 419dd5e7b6c45096a38790e9bcabbf9081f80aa7A great deal of empirical research has been conducted to find effective solutions to global warming, which is widely recognized as a major cause of environmental degradation and overall decline in well‐being. It should be noted that international coalitions such as the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United) are not left of the ravaging adverse effects of environmental pollution. Consequently, this study contributes to the literature by examining the role of digitalization on carbon footprint amidst environmental‐related technologies, renewable energy, environmental policy stringency, carbon tax, and financial development in G7 countries from 1996 to 2019. The study relies on cross‐sectional autoregressive distributed lag, common correlated effects mean group, augmented mean group, and method of moment quantile regression (MMQR). Results from the analyses show that digitalization is an essential mitigating tool for the surging carbon footprint in G7 countries. Besides, the imperatives of other covariates in subduing the adverse environmental effects of carbon footprint are empirically supported except for financial development. Remarkably, the distributional effects of the exogenous variables on carbon footprint based on MMQR are found robust for the primary analyses. The direction of cause standing between bidirectional and unidirectional heightens the novelties of this study. Based on the findings, sustainable footprint policies in G7 economies are suggested.Natural resources forum (Print)630Natural Resources Forum['Yu Wang', 'Xudong Chen', 'Ridwan Lanre Ibrahim', 'M. A. Al‐Faryan']2024-03-06T00:00:00https://www.semanticscholar.org/paper/419dd5e7b6c45096a38790e9bcabbf9081f80aa7 de644b365f6d444dd90bea5ec69e4cbd2832a97cnpj Climate Action200npj Climate Action['Mark Roelfsema', 'Takeshi Kuramochi', 'M. D. den Elzen']2024-03-06T00:00:00https://www.semanticscholar.org/paper/de644b365f6d444dd90bea5ec69e4cbd2832a97c 2fff285ab3d532223000809711661c07d369149bEveryday, artificial intelligence (AI) is being increasingly integrated into the teaching and learning process at Russian universities. The high level of quality of feedback from AI tools leads to the spread of AI plagiarism – unauthorized borrowing of generative AI materials – among students. The purpose of this study is to: a) highlight aspects that determine students’ understanding of the issues of compliance with author’s ethics and the problem of plagiarism when interacting with generative AI; b) develop a questionnaire to determine students’ understanding of the issues of compliance with author’s ethics and the problem of AI plagiarism; c) conduct an online survey of university students, analyze and discuss the results obtained. The paper highlights five aspects that determine students’ understanding of the issues of compliance with author’s ethics and the problem of AI plagiarism when completing educational assignments and preparing research texts: a) students’ general understanding of the issues of compliance with author’s ethics and the problem of plagiarism in an academic environment; b) students’ experience of AI tools for educational purposes; c) students’ understanding of the problem of AI plagiarism and attitude towards borrowing materials from generative AI; d) teachers’ actions to prevent AI plagiarism among students; e) the policy of educational organizations regarding student compliance with ethics and AI plagiarism. An online questionnaire was developed to determine the degree to which students understand the issues of compliance with copyright ethics and the problem of AI plagiarism. 1,599 students from 29 universities of the Russian Federation took part in the survey. The results showed that in general, in the Russian student community, plagiarism is a widespread social phenomenon, many types of which are perceived by young people as a norm of academic behavior. Despite the relatively high awareness of students in the field of AI technologies, the extremely rare use by teachers of specialized subject disciplines of AI tools in the educational process I’d the reason for the current low level of spread of AI plagiarism in the academic environment. At the same time, it is necessary to state that students lack a systematic understanding of exactly how they can “legally” use generative AI materials and what exactly will be considered AI plagiarism. According to students, the importance of understanding the issues of compliance with author ethics and the problem of AI plagiarism will depend, on the one hand, on the actions of teachers to explain to students the rules for using generative AI materials, and on the other hand, the presence in universities of a regulatory framework regulating the field and the extent to which students use AI in the educational process.Vysshee Obrazovanie v Rossii = Higher Education in Russia163Students lack a systematic understanding of exactly how they can “legally” use generative AI materials and what exactly will be considered AI plagiarism, according to students.Vysshee Obrazovanie v Rossii = Higher Education in Russia['P. Sysoyev']2024-03-06T00:00:00https://www.semanticscholar.org/paper/2fff285ab3d532223000809711661c07d369149b acd1b8376173ff8f8715d918e7465257cbc6371aDespite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources. To extend the reach of medical AI advancements to a broader population, we aim to develop medical LLMs across the six most widely spoken languages, encompassing a global population of 6.1 billion. This effort culminates in the creation of the ApolloCorpora multilingual medical dataset and the XMedBench benchmark. In the multilingual medical benchmark, the released Apollo models, at various relatively-small sizes (i.e., 0.5B, 1.8B, 2B, 6B, and 7B), achieve the best performance among models of equivalent size. Especially, Apollo-7B is the state-of-the-art multilingual medical LLMs up to 70B. Additionally, these lite models could be used to improve the multi-lingual medical capabilities of larger models without fine-tuning in a proxy-tuning fashion. We will open-source training corpora, code, model weights and evaluation benchmark.arXiv.org591This effort aims to develop medical LLMs across the six most widely spoken languages, encompassing a global population of 6.1 billion, and culminates in the creation of the ApolloCorpora multilingual medical dataset and the XMedBench benchmark.ArXiv['Xidong Wang', 'Nuo Chen', 'Junying Chen', 'Yan Hu', 'Yidong Wang', 'Xiangbo Wu', 'Anningzhe Gao', 'Xiang Wan', 'Haizhou Li', 'Benyou Wang']2024-03-06T00:00:00https://www.semanticscholar.org/paper/acd1b8376173ff8f8715d918e7465257cbc6371a 777e53fe90cf797e7dae25c2114bf3866dc66760BMC Medical Education571Items constructed using AI had good psychometric properties and quality, measuring higher-order domains, and a significant correlation was found between the difficulty and discrimination indices.BMC Medical Education['A. Rezigalla']2024-03-06T00:00:00https://www.semanticscholar.org/paper/777e53fe90cf797e7dae25c2114bf3866dc66760 9158206152326b2bca0784a134b12462e26381e4BACKGROUND We aimed to define the capability of three different publicly available large language models, Chat Generative Pretrained Transformer (ChatGPT-3.5), ChatGPT-4 and Google Gemini in analysing retinal detachment cases and suggesting the best possible surgical planning. METHODS Analysis of 54 retinal detachments records entered into ChatGPT and Gemini's interfaces. After asking 'Specify what kind of surgical planning you would suggest and the eventual intraocular tamponade.' and collecting the given answers, we assessed the level of agreement with the common opinion of three expert vitreoretinal surgeons. Moreover, ChatGPT and Gemini answers were graded 1-5 (from poor to excellent quality), according to the Global Quality Score (GQS). RESULTS After excluding 4 controversial cases, 50 cases were included. Overall, ChatGPT-3.5, ChatGPT-4 and Google Gemini surgical choices agreed with those of vitreoretinal surgeons in 40/50 (80%), 42/50 (84%) and 35/50 (70%) of cases. Google Gemini was not able to respond in five cases. Contingency analysis showed significant differences between ChatGPT-4 and Gemini (p=0.03). ChatGPT's GQS were 3.9±0.8 and 4.2±0.7 for versions 3.5 and 4, while Gemini scored 3.5±1.1. There was no statistical difference between the two ChatGPTs (p=0.22), while both outperformed Gemini scores (p=0.03 and p=0.002, respectively). The main source of error was endotamponade choice (14% for ChatGPT-3.5 and 4, and 12% for Google Gemini). Only ChatGPT-4 was able to suggest a combined phacovitrectomy approach. CONCLUSION In conclusion, Google Gemini and ChatGPT evaluated vitreoretinal patients' records in a coherent manner, showing a good level of agreement with expert surgeons. According to the GQS, ChatGPT's recommendations were much more accurate and precise.British Journal of Ophthalmology186Google Gemini and ChatGPT evaluated vitreoretinal patients' records in a coherent manner, showing a good level of agreement with expert surgeons.The British journal of ophthalmology['Matteo Mario Carlà', 'G. Gambini', 'A. Baldascino', 'Federico Giannuzzi', 'Francesco Boselli', 'Emanuele Crincoli', "Nicola Claudio D'Onofrio", 'Stanislao Rizzo']2024-03-06T00:00:00https://www.semanticscholar.org/paper/9158206152326b2bca0784a134b12462e26381e4 5d5881ae7e62f1c7aba0364255e477e2b4c2ae91npj Digit. Medicine700To advance trust and clinical adoption of AI, there is a need to bridge the gap between traditional quantitative metrics and implementation outcomes to better grasp the reasons behind the success or failure of AI systems and improve their translation into clinical value.NPJ Digital Medicine['Davy van de Sande', 'Eline Fung Fen Chung', 'J. Oosterhoff', 'J. Bommel', 'D. Gommers', 'M. E. V. Genderen']2024-03-06T00:00:00https://www.semanticscholar.org/paper/5d5881ae7e62f1c7aba0364255e477e2b4c2ae91 25005ea6e7b58b2df0e682d673e8775124319ec3Agent Based Models (ABMs) have emerged as a powerful tool for investigating complex social interactions, particularly in the context of public health and infectious disease investigation. In an effort to enhance the conventional ABM, enabling automated model calibration and reducing the computational resources needed for scaling up the model, we have developed a tensorized and differentiable agent-based model by coupling Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) network. The model was employed to investigate the 2019 measles outbreak occurred in New Zealand, demonstrating a promising ability to accurately simulate the outbreak dynamics, particularly during the peak period of repeated cases. This paper shows that by leveraging the latest Artificial Intelligence (AI) technology and the capabilities of traditional ABMs, we gain deeper insights into the dynamics of infectious disease outbreaks. This, in turn, helps us make more informed decision when developing effective strategies that strike a balance between managing outbreaks and minimizing disruptions to everyday life.arXiv.org30A tensorized and differentiable agent-based model by coupling Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) network is developed, employed to investigate the 2019 measles outbreak occurred in New Zealand, demonstrating a promising ability to accurately simulate the outbreak dynamics, particularly during the peak period of repeated cases.ArXiv['Sijin Zhang', 'Alvaro Orsi', 'Lei Chen']2024-03-06T00:00:00https://www.semanticscholar.org/paper/25005ea6e7b58b2df0e682d673e8775124319ec3 2c218ef88f3e5a8eef3a11f08980e70086193550This paper explores the intricate interaction between knowledge, innovation, and artificial intelligence (AI), underscoring the indispensable role of human involvement in this dynamic process. While AI progresses and permeates various aspects of society, it significantly influences knowledge generation, dissemination, and innovation. Nonetheless, the human factor remains pivotal in effectively harnessing the potential of AI. This study delves into the nuances of this symbiotic relationship, examining how humans contribute to AI advancement, shape its applications, and mitigate associated risks. Through a multidisciplinary perspective, it discusses strategies to cultivate synergy between AI capabilities and human expertise, ensuring that innovation is guided by ethical principles and human values. Ultimately, it underscores the imperative of comprehending and nurturing the human element amidst the evolving landscape of knowledge and AI-driven innovation.  Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This study delves into the nuances of this symbiotic relationship between knowledge, innovation, and artificial intelligence, examining how humans contribute to AI advancement, shape its applications, and mitigate associated risks.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Arabella Jo']2024-03-06T00:00:00https://www.semanticscholar.org/paper/2c218ef88f3e5a8eef3a11f08980e70086193550 29a1b4581cb28993662cb1d403af061369cb0cd2This article explores the transformative potential of ImpactLens AI in turning data into actionable intelligence. The study aims to elucidate the objectives, significance, major findings, and policy implications of leveraging ImpactLens AI in organizations. The objective is to harness advanced machine learning algorithms to analyze data comprehensively, provide real-time insights, and predict future trends. The significance lies in empowering organizations to make informed decisions, drive strategic growth, and stay competitive in dynamic environments. Major findings reveal ImpactLens AI's capability to uncover hidden opportunities, mitigate risks, and achieve tangible results through comprehensive data analysis and predictive analytics. The discussion emphasizes the importance of embracing data-driven decision-making for organizational success. Policy implications suggest the adoption of ImpactLens AI to optimize operations, enhance customer experiences, and drive innovation. Overall, this study highlights the pivotal role of ImpactLens AI in transforming data into actionable intelligence for strategic advantage.Asian Journal of Applied Science and Engineering440The pivotal role of ImpactLens AI in transforming data into actionable intelligence for strategic advantage is highlighted, and policy implications suggest the adoption of ImpactLens AI to optimize operations, enhance customer experiences, and drive innovation.Asian Journal of Applied Science and Engineering['Ashish K Saxena']2024-03-06T00:00:00https://www.semanticscholar.org/paper/29a1b4581cb28993662cb1d403af061369cb0cd2 6d9a373006dd9b548fbad17a3c6f0e6d8e06d8c3We investigate personalizing the explanations that an Intelligent Tutoring System generates to justify the hints it provides to students to foster their learning. The personalization targets students with low levels of two traits, Need for Cognition and Conscientiousness, and aims to enhance these students' engagement with the explanations, based on prior findings that these students do not naturally engage with the explanations but they would benefit from them if they do. To evaluate the effectiveness of the personalization, we conducted a user study where we found that our proposed personalization significantly increases our target users' interaction with the hint explanations, their understanding of the hints and their learning. Hence, this work provides valuable insights into effectively personalizing AI-driven explanations for cognitively demanding tasks such as learning.arXiv.org230This work investigates personalizing the explanations that an Intelligent Tutoring System generates to justify the hints it provides to students to foster their learning and provides valuable insights into effectively personalizing AI-driven explanations for cognitively demanding tasks such as learning.ArXiv['Vedant Bahel', 'Harshinee Sriram', 'Cristina Conati']2024-03-06T00:00:00https://www.semanticscholar.org/paper/6d9a373006dd9b548fbad17a3c6f0e6d8e06d8c3 07008ba3875c9f10d1397adad45463685dc449d2 I demonstrate an approach fostering inventive interpretation of short stories in Literary Studies and higher education generally. It involves constructing an ‘assemblage’—at its simplest, an evolving network of unusual connections for creative outcome. The assemblage of this article combines freshly located research literature, directly and indirectly related to a story’s themes, and/or the personality type of protagonists. Importantly, this assemblage also utilizes text analysis software revealing the relatively invisible (e.g. (in)frequent words, parts of speech, and topics) and Large Language Model (LLM) Generative AI to enrich the interpretation. The use of all these elements helps productively exceed initial intuitions about the story, facilitating creativity. I model the approach using Edgar Allan Poe’s short story, The Black Cat, whose protagonist is a homicidal psychopath. Specifically, the assemblage here includes relevant software-based research (a corpus analysis of homicidal psychopathic language), non-software-based research (psychoanalytical literary criticism of The Black Cat using the empirically validated concept of transference), text analysis software (WMatrix and Datayze), and the LLM Generative AI, ‘ChatGPT’ (using the freely available LLM GPT-3.5). One use of this approach is as a pedagogy in Literary Studies employing text analysis software (e.g. on a digital stylistics course). Yet given creative adaptability is a key 21st-century skill, with digital literacy—including the use of Generative AI—an important contemporary competence, and with the short story genre universally known, I highlight too the utility of this approach as a university-wide pedagogy for enhancing creative thinking.Digital Scholarship in the Humanities220Given creative adaptability is a key 21st-century skill, with digital literacy—including the use of Generative AI—an important contemporary competence, and with the short story genre universally known, the utility of this approach as a university-wide pedagogy for enhancing creative thinking is highlighted.Digital Scholarship in the Humanities["Kieran O'Halloran"]2024-03-06T00:00:00https://www.semanticscholar.org/paper/07008ba3875c9f10d1397adad45463685dc449d2 0e71bf0daa720ed2accc38fc18a0f70fa56314caThe integration of Artificial Intelligence (AI) in the financial sector has ushered in disruptive technologies and unlocked a plethora of emerging opportunities. This paper provides an in-depth exploration of the transformative role of AI in finance, delineating its impact on various facets including investment strategies, risk assessment, fraud detection, customer service, and regulatory compliance. Leveraging machine learning algorithms, natural language processing, and predictive analytics, AI empowers financial institutions to process vast datasets, derive actionable insights, and automate decision-making processes with unprecedented precision and efficiency. Furthermore, AI-driven innovations facilitate personalized financial services, streamline operations, and catalyze the development of novel business models, thereby reshaping the competitive landscape of the finance industry. Nevertheless, the adoption of AI in finance necessitates careful consideration of ethical, privacy, and regulatory implications to ensure responsible and sustainable deployment. Through comprehensive analysis and case studies, this paper illuminates the disruptive potential and emerging opportunities afforded by AI in finance, paving the way for informed decision-making and strategic investment in this rapidly evolving domain.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This paper provides an in-depth exploration of the transformative role of AI in finance, delineating its impact on various facets including investment strategies, risk assessment, fraud detection, customer service, and regulatory compliance.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['A.K.M. Kamruzzaman Khan']2024-03-06T00:00:00https://www.semanticscholar.org/paper/0e71bf0daa720ed2accc38fc18a0f70fa56314ca 8b0ca011d07aec9a1a7e6ce9b65c45ff4111eceeAI agents are defined as artificial entities to perceive the environment, make decisions and take actions. Inspired by the 6 levels of autonomous driving by Society of Automotive Engineers, the AI agents are also categorized based on utilities and strongness, as the following levels: L0, no AI, with tools taking into account perception plus actions; L1, using rule-based AI; L2, making rule-based AI replaced by IL/RL-based AI, with additional reasoning&decision making; L3, applying LLM-based AI instead of IL/RL-based AI, additionally setting up memory&reflection; L4, based on L3, facilitating autonomous learning&generalization; L5, based on L4, appending personality of emotion and character and collaborative behavior with multi-agents.310Inspired by the 6 levels of autonomous driving by Society of Automotive Engineers, the AI agents are also categorized based on utilities and strongness, as the following levels: L0, no AI, with tools taking into account perception plus actions.['Yu Huang']2024-03-06T00:00:00https://www.semanticscholar.org/paper/8b0ca011d07aec9a1a7e6ce9b65c45ff4111ecee 65f7236fdf251192381831ebe4cea5bcf65742f9npj Digit. Medicine420This perspective proposes that routine use of wearable cameras could be realized within digital medicine through AI-based computer vision models to obfuscate/blur/shade sensitive information while preserving helpful contextual information for a comprehensive patient assessment.NPJ Digital Medicine['Jason Moore', 'Peter McMeekin', 'Thomas Parkes', 'Richard Walker', 'Rosie Morris', 'Samuel Stuart', 'Victoria Hetherington', 'A. Godfrey']2024-03-06T00:00:00https://www.semanticscholar.org/paper/65f7236fdf251192381831ebe4cea5bcf65742f9 49771cf58fc601e7592fec0cc0de71e0364c3ff2In the contemporary digital landscape, cybersecurity stands as a paramount concern due to the increasing sophistication and frequency of cyber threats. Artificial Intelligence (AI) has emerged as a potent tool in fortifying defenses against these evolving threats. This paper examines the multifaceted role of AI in cybersecurity, elucidating its applications in threat detection, vulnerability assessment, incident response, and predictive analysis. By leveraging machine learning algorithms, AI systems can swiftly analyze vast troves of data to identify anomalous patterns indicative of potential security breaches. Moreover, AI-driven technologies enable proactive defense mechanisms, empowering organizations to preemptively mitigate risks and safeguard sensitive information. However, the deployment of AI in cybersecurity also raises pertinent ethical and privacy considerations, necessitating a balanced approach towards its implementation. Through a comprehensive analysis, this paper underscores the imperative of integrating AI into cybersecurity frameworks to effectively mitigate threats in the digital age.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300Through a comprehensive analysis, this paper underscores the imperative of integrating AI into cybersecurity frameworks to effectively mitigate threats in the digital age.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Nicolas Guzman Camacho']2024-03-06T00:00:00https://www.semanticscholar.org/paper/49771cf58fc601e7592fec0cc0de71e0364c3ff2 aa0357b542acee388c13b76a339ab42936a67025In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.Applied Sciences1010The role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development is emphasized, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.Applied Sciences['Fátima Trindade Neves', 'Manuela Aparicio', 'Miguel de Castro Neto']2024-03-06T00:00:00https://www.semanticscholar.org/paper/aa0357b542acee388c13b76a339ab42936a67025 2bf7c3d7bafccf6fc0152d707a19b894c55f7a87This article gives a perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions put in the wider context of the trends in this rapidly expanding field. This progress over the last decade is tremendous: while a decade ago we had a glimpse of what was to come through many proof-of-concept studies, now we witness the emergence of many AI-based computational chemistry tools that are mature enough to make faster and more accurate simulations increasingly routine. Such simulations in turn allow us to validate and even revise experimental results, deepen our understanding of the physicochemical processes in nature, and design better materials, devices, and drugs. The rapid introduction of powerful AI tools gives rise to unique challenges and opportunities that are discussed in this article too.Chemical Communications00A perspective on the progress of AI tools in computational chemistry through the lens of the author's decade-long contributions is given in the wider context of the trends in this rapidly expanding field.Chemical communications['Pavlo O. Dral']2024-03-06T00:00:00https://www.semanticscholar.org/paper/2bf7c3d7bafccf6fc0152d707a19b894c55f7a87 696330fbec2daa5d57d9a8559bb46ed38ffbdf18Due to the extreme growth in digital information and data, cybersecurity has become one of the major concerns addressed by recent research, organizations, and governments. However, Traditional security methods are finding it more and more difficult to keep up with the volume and complexity of cybersecurity threats. To mitigate this problem, artificial intelligence (AI) can be a promising candidate that helps with cybersecurity defenses in the face of sophisticated and rising cyber threats. As a result, Artificial intelligence based systems can take advantage of machine learning, natural language processing, and other methods to enhance threat identification, response, and mitigation. This paper provides a blueprint for the state of the art of AI's potential to enhance cyber defense strategies in the field of cybersecurity. In addition, it highlights a variety of AI-based cybersecurity strategies that include anomaly detection, behavior analysis, and predictive modeling. Additionally, it explores the drawbacks and limitations of AI in cybersecurity, including data privacy issues and adversarial attacks, and offers suggestions for how to resolve these problems. As a whole, it highlights the significance of using AI to strengthen cybersecurity defenses and offers recommendations for further research and development in this field.International Conference on Computing and Information100This paper provides a blueprint for the state of the art of AI's potential to enhance cyber defense strategies in the field of cybersecurity and highlights a variety of AI-based cybersecurity strategies that include anomaly detection, behavior analysis, and predictive modeling.2024 6th International Conference on Computing and Informatics (ICCI)['Samar M.Nour', 'Samar A.Said']2024-03-06T00:00:00https://www.semanticscholar.org/paper/696330fbec2daa5d57d9a8559bb46ed38ffbdf18 c805b6a178c60fbb204e57547e93b1d942614de6This study explores the challenge of sentence-level AI-generated text detection within human-AI collaborative hybrid texts. Existing studies of AI-generated text detection for hybrid texts often rely on synthetic datasets. These typically involve hybrid texts with a limited number of boundaries. We contend that studies of detecting AI-generated content within hybrid texts should cover different types of hybrid texts generated in realistic settings to better inform real-world applications. Therefore, our study utilizes the CoAuthor dataset, which includes diverse, realistic hybrid texts generated through the collaboration between human writers and an intelligent writing system in multi-turn interactions. We adopt a two-step, segmentation-based pipeline: (i) detect segments within a given hybrid text where each segment contains sentences of consistent authorship, and (ii) classify the authorship of each identified segment. Our empirical findings highlight (1) detecting AI-generated sentences in hybrid texts is overall a challenging task because (1.1) human writers' selecting and even editing AI-generated sentences based on personal preferences adds difficulty in identifying the authorship of segments; (1.2) the frequent change of authorship between neighboring sentences within the hybrid text creates difficulties for segment detectors in identifying authorship-consistent segments; (1.3) the short length of text segments within hybrid texts provides limited stylistic cues for reliable authorship determination; (2) before embarking on the detection process, it is beneficial to assess the average length of segments within the hybrid text. This assessment aids in deciding whether (2.1) to employ a text segmentation-based strategy for hybrid texts with longer segments, or (2.2) to adopt a direct sentence-by-sentence classification strategy for those with shorter segments.410This study utilizes the CoAuthor dataset, which includes diverse, realistic hybrid texts generated through the collaboration between human writers and an intelligent writing system in multi-turn interactions, to explore the challenge of sentence-level AI-generated text detection within human-AI collaborative hybrid texts.['Zijie Zeng', 'Shiqi Liu', 'Lele Sha', 'Zhuang Li', 'Kaixun Yang', 'Sannyuya Liu', "Dragan Gavsevi'c", 'Guanliang Chen']2024-03-06T00:00:00https://www.semanticscholar.org/paper/c805b6a178c60fbb204e57547e93b1d942614de6 8294425bb2b24292a9da39d22d338e4c817ca2ffArtificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed.International Conference on Computing and Information30A new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models, which has achieved high accuracies.2024 6th International Conference on Computing and Informatics (ICCI)['B. Elbagoury', 'Rytis Maskeliuans', 'Marwa Zaghow', 'Nabil Kamel']2024-03-06T00:00:00https://www.semanticscholar.org/paper/8294425bb2b24292a9da39d22d338e4c817ca2ff f29997b5cc8c24ba3f4d1767c5dcd82e4a0a3213Purpose: Now, artificial intelligence (AI) is booming. Day by day, AI is introduced into the new field. We have lots of expectations for the advancement of AI. In our modern busy schedule, we all expect our everyday monotonous homework to be executed by AI. We are introducing more and more smart devices to do our work smartly. But at the end of the day, all our smart home devices are operated manually. We are not fully satisfied with smart devices. Knowing this, the smart device manufacturer is adding AI features inside their devices. Here, we demonstrate how to build an AI bedroom for better living. Design/Methodology/Approach: We install three devices inside the bedroom. The first is a surveillance PTZ camera, the second is the CPU, and the third is the action module. The camera will capture the events and is transferred to the central processing unit or CPU. It will process the image and then detect the event. Once the event is detected, then through the action module, we trigger the electrical or electronic equipment. Findings/Result: the performance of the centralized system is better than that of distributed individually operated smart devices. Here, we account for two types of performance: the event detection and the action module on the specific action. The event detection module takes much more time due to the processing overhead of the image. We get the result within a couple of milliseconds. Due to the dedicated CPU, the processing is faster than on a cloud-based server, which depends on the bandwidth of the internet. Originality/Value/ Novelty: We studied several research documents on smart homes and artificial intelligence-integrated homes. Most AI homes are built using several smart home appliances operated manually. And there is no centralized control. Without central control, the system could not deliver the best performance. Here, the complete system is nicely controlled by a centralized CPU, which makes it a unique approach to this project. Type of Paper: Conceptual Research.International journal of applied engineering and management letters220The performance of the centralized system is better than that of distributed individually operated smart devices and the complete system is nicely controlled by a centralized CPU, which makes it a unique approach to this project.International Journal of Applied Engineering and Management Letters['Sudipto Chakraborty', 'P. S. Aithal']2024-03-06T00:00:00https://www.semanticscholar.org/paper/f29997b5cc8c24ba3f4d1767c5dcd82e4a0a3213 f7830a4c36c2505c4e7e525792abbdb2982a611dArtificial intelligence (AI) uses algorithms and large language models in computers to simulate human-like problem-solving and decision-making. AI programs have recently acquired widespread popularity in the field of dermatology through the application of online tools in the assessment, diagnosis, and treatment of skin conditions. A literature review was conducted using PubMed and Google Scholar analyzing recent literature (from the last 10 years through October 2023) to evaluate current AI programs in use for dermatologic purposes, identifying challenges in this technology when applied to skin of color (SOC), and proposing future steps to enhance the role of AI in dermatologic practice. Challenges surrounding AI and its application to SOC stem from the underrepresentation of SOC in datasets and issues with image quality and standardization. With these existing issues, current AI programs inevitably do worse at identifying lesions in SOC. Additionally, only 30% of the programs identified in this review had data reported on their use in dermatology, specifically in SOC. Significant development of these applications is required for the accurate depiction of darker skin tone images in datasets. More research is warranted in the future to better understand the efficacy of AI in aiding diagnosis and treatment options for SOC patients.International Journal of Dermatology462Evaluating current AI programs in use for dermatologic purposes, identifying challenges in this technology when applied to skin of color (SOC), and proposing future steps to enhance the role of AI in dermatologic practice are conducted.International journal of dermatology['Rebecca Fliorent', 'Brian Fardman', 'Alicia Podwojniak', 'Kiran Javaid', 'Isabella J Tan', 'Hira Ghani', 'T. Truong', 'Babar K. Rao', 'Candrice R Heath']2024-03-06T00:00:00https://www.semanticscholar.org/paper/f7830a4c36c2505c4e7e525792abbdb2982a611d cf9ec0f0c2c952b4c99cdd6c4c21834e01c50fcbThe purpose of this empirical research was to map the capabilities and perceptions of undergraduate business administration students about artificial intelligence (AI) and its potential to answer questions related to sustainable transition in society, and to obtain information about the suitable pedagogical solution to increase the knowledge and understanding related to these themes.The data was gathered among higher education (HE) students in a workshop that consisted of introductory lecture, answering surveys, questionnaire, group discussions, and reflective narratives on the relationship and possibilities of AI and sustainable development. In data analysis an abductive qualitative research methodology was adopted.Through abduction new insights were obtained and new knowledge was created new knowledge regarding AI literacy in the context of sustainable development. This brought new knowledge in the context of HE studies. The taxonomy of AI literacy in sustainable development created a new reference framework for learning tasks, and course planning in HE. The findings showed that the students had difficulties solving the actual problem because they lacked knowledge and understanding of the basics of AI and sustainable development. However, in groups where one person had a deeper understanding of the concepts, the whole group began to understand the task and work on both meta-level ethical questions and practical examples.The assistance of AI potentially creates opportunities for developing solutions supporting sustainable development. However, utilizing this potential requires AI literacy. In this task HE plays a significant role. This study contributes to the pedagogical approach where AI and sustainable development are integrated in HE curricula.Frontiers in Education281The findings showed that the students had difficulties solving the actual problem because they lacked knowledge and understanding of the basics of AI and sustainable development, but in groups where one person had a deeper understanding of the concepts, the whole group began to understand the task and work on both meta-level ethical questions and practical examples.Frontiers in Education['Ari Alamäki', 'Crister Nyberg', 'Anna Kimberley', 'A. Salonen']2024-03-06T00:00:00https://www.semanticscholar.org/paper/cf9ec0f0c2c952b4c99cdd6c4c21834e01c50fcb 8023d4c2669a503b6dac82b8d95c1b854b9393f1The article introduces a methodology for forming and analyzing the network of events in news reports related to parliamentary control. This methodology relies on the application of generative artificial intelligence, and the article provides examples of its practical implementation. The revolution in artificial intelligence enables the solution of tasks not only related to identification but also to the formation of causal networks of events, where the causes and consequences are clearly presented. The utilization of large linguistic models has yielded convenient methods for extracting events from texts, filtering them, and clustering. Artificial intelligence is employed for identifying cause-and-effect relationships, significantly simplifying the processing of natural language. Visualization and cluster analysis of the formed networks can be performed using traditional tools for network analysis.INFORMATION AND LAW00A methodology for forming and analyzing the network of events in news reports related to parliamentary control relies on the application of generative artificial intelligence and the article provides examples of its practical implementation.INFORMATION AND LAW['D. Lande']2024-03-06T00:00:00https://www.semanticscholar.org/paper/8023d4c2669a503b6dac82b8d95c1b854b9393f1 4965e67133775c19c3b770fa3106865eba89b76bThis paper explores the complex relationship between demographics and artificial intelligence (AI) advances in Europe and Africa, projecting into the year 2050. The advancement of AI technologies has occurred at diverse rates, with Africa lagging behind Europe. Moreover, the imminent economic consequences of demographic shifts require a more careful examination of immigration patterns, with Africa emerging as a viable labor pool for European countries. However, within these dynamics, questions are raised about the differences in AI proficiency between African immigrants and Europeans by 2050. This paper examines demographic trends and AI developments to unravel insights into the multifaceted challenges and opportunities that lie ahead in the realms of technology, the economy, and society as we look ahead to 2050.arXiv.org60Examination of demographic trends and AI developments is examined to unravel insights into the multifaceted challenges and opportunities that lie ahead in the realms of technology, the economy, and society as the authors look ahead to 2050.ArXiv['Mohamed Louadi']2024-03-06T00:00:00https://www.semanticscholar.org/paper/4965e67133775c19c3b770fa3106865eba89b76b 612b9c81f14cf62ac414f2b8e6e429a00413a8deThe scientific article highlights the problematic issues of ensuring the confidentiality of personal data in labor relations, especially in the context of the spread of artificial intelligence technologies.The importance of taking into account ethical aspects and establishing clear rules for the use of employees' personal information was emphasized. The authors conducted a thorough study of the regulatory and legal framework that regulates the introduction of artificial intelligence in various spheres of society’s life, in particular, in labor relations. On the basis of the above analysis, specific proposals regarding the improvement of national legislation through the adoption of the Law of Ukraine “On Artificial Intelligence” are also given. In addition, the authors paid special attention to the development of practical recommendations for the implementation of effective strategies and measures that can ensure a high level of security of personal data of employees.INFORMATION AND LAW00INFORMATION AND LAW['T. Kronivets', 'V. Dorosh']2024-03-06T00:00:00https://www.semanticscholar.org/paper/612b9c81f14cf62ac414f2b8e6e429a00413a8de e4452fc974745b37bb65bc8f65834a8a182d4bcdArtificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes400An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI.Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes['B. Warren', 'Alexander Bilbily', 'J. W. Gichoya', 'Aaron Conway', 'Ben Li', 'Aly Fawzy', 'Camilo Barragán', 'A. Jaberi', 'Sebastian Mafeld']2024-03-06T00:00:00https://www.semanticscholar.org/paper/e4452fc974745b37bb65bc8f65834a8a182d4bcd 173a67cd94e51fd49f8920702f46a9ada6332656Sleep and well-being have been intricately linked, and sleep hygiene is paramount for developing mental well-being and resilience. Although widespread, sleep disorders require elaborate polysomnography laboratory and patient-stay with sleep in unfamiliar environments. Current technologies have allowed various devices to diagnose sleep disorders at home. However, these devices are in various validation stages, with many already receiving approvals from competent authorities. This has captured vast patient-related physiologic data for advanced analytics using artificial intelligence through machine and deep learning applications. This is expected to be integrated with patients’ Electronic Health Records and provide individualized prescriptive therapy for sleep disorders in the future.World Journal of Clinical Cases240This work has captured vast patient-related physiologic data for advanced analytics using artificial intelligence through machine and deep learning applications and is expected to be integrated with patients’ Electronic Health Records and provide individualized prescriptive therapy for sleep disorders in the future.World Journal of Clinical Cases['D. Nag', 'A. Swain', 'S. Sahu', 'Abhishek Chatterjee', 'B. Swain']2024-03-06T00:00:00https://www.semanticscholar.org/paper/173a67cd94e51fd49f8920702f46a9ada6332656 a3a8159e5b9fe9fe2fc7362095a2b952837476cfSustainable supply chain management education has assumed critical importance in today's context of evolving environmental, economic, and social sustainability objectives. The growing complexity of supply chain management underscores the necessity for acquiring new competencies to navigate these challenges. Furthermore, as businesses are compelled to enhance their practices with heightened awareness of environmental issues, the imperative for sustainable management becomes increasingly evident. Future business leaders or supply chain managers, particularly higher learning institution’s students, should be able to demonstrate the ability to navigate and harness the collective environmental intelligence within their supply networks, promoting the principles of environmental sustainability. This study aims to examine the impact of immersive learning (artificial intelligence, augmented reality, and gamification) towards education, in the context of understanding sustainable supply chain management (SSCM) practices and concepts. The study used a cross-sectional survey approach with a purposive sampling technique to collect data from 204 respondents. The findings of this study suggest that immersive learning techniques are significant and positive factors that contribute to SSCM education. The evidence presented suggests that artificial intelligence and gamification serve as transformative tools, enhancing students' comprehension of SSCM concepts and fostering a genuine interest in adopting more sustainable business practices. In essence, this research reinforces the indispensability of sustainable supply chain education in equipping future business leaders with the knowledge and skills required to navigate the complexities of contemporary SC management while championing environmental, economic, and social sustainability goals.Advances in Social Sciences Research Journal00The research reinforces the indispensability of sustainable supply chain education in equipping future business leaders with the knowledge and skills required to navigate the complexities of contemporary SC management while championing environmental, economic, and social sustainability goals.Advances in Social Sciences Research Journal['Ahmad Rais Mohamad Mokhtar', 'V. Sundram', 'Melissa Shahrom']2024-03-06T00:00:00https://www.semanticscholar.org/paper/a3a8159e5b9fe9fe2fc7362095a2b952837476cf 2ce3c1432b51e9dd4fe9413ad1b6253e24cbbc6eThe article explores the use of artificial intelligence in organizing the educational process in a digital educational environment. An analysis of AI tools and systems, their advantages and challenges, is conducted. The main stages of AI implementation are considered. Recommendations for further research in this area are proposed.Social Science and Humanities Journal90Social Science and Humanities Journal['N. Bobro']2024-03-06T00:00:00https://www.semanticscholar.org/paper/2ce3c1432b51e9dd4fe9413ad1b6253e24cbbc6e 711f3db8f77aecb55b9c90da060bdfebeea38bc4Artificial intelligence (AI) plays a crucial role in addressing resource and environmental constraints and achieving sustainable economic and social development. This study examines the impact and mechanisms of AI on the green and low‐carbon transformation of enterprises using a sample of companies listed on the Shanghai and Shenzhen stock exchanges from 2009 to 2021. The research findings indicate that AI has the capability to effectively mitigate corporate carbon emissions (CCE) and enhance the level of green innovation (GI) in enterprises. Mechanism analysis reveals that energy consumption plays a mediating role in the relationship between AI and CCE. Heterogeneity analysis reveals that the inhibitory effect of AI on CCE is more pronounced in private enterprises and non‐heavy polluting industries. However, the impact of AI on GI is greater in state‐owned enterprises and heavy‐polluting industries. This study sheds light on the influence of AI on the green and low‐carbon transformation of enterprises, as well as its transmission mechanisms. It provides theoretical and empirical insights for promoting GI, reducing emissions, and improving energy efficiency in enterprises.Managerial and Decision Economics160Managerial and Decision Economics['Tingting Liu', 'Bing Zhou']2024-03-06T00:00:00https://www.semanticscholar.org/paper/711f3db8f77aecb55b9c90da060bdfebeea38bc4 96331cea5dce345be567423a54acb7ee30a1fc94Objective: As AI's role in nursing grows, it is vital to understand its impact and challenges. Using bibliometric analysis, this study aimed to identify and examine the prevailing trends and current topics in artificial intelligence research within nursing. Materials and Methods: This was a retrospective bibliometric study. Study data were collected from WoSCC on August 08, 2023. Analyses were made through science mapping, Microsoft Excel, and VOSviewer. Results: The study included 316 publications dated 1984-2023. There was a rapid increase in publications and citations from 2018-2023. Related publications were made by 1148 authors. The journal "CIN-Computers, Informatics, Nursing" emerged as the most frequently published and cited journal. Fifty-three countries contributed to the publications, of which 45.2% were produced in the USA. The current topics were patient safety, depression, ChatGPT, and Chatbot in recent years. Conclusion: This bibliometric study shows a synergy between the general policies of countries on artificial Intelligence in recent years and the increasing number of publications in the last four years. However, this study also reveals that research on artificial intelligence in nursing is a nascent field. Managers and research nurses should lead the use of AI applications in nursing services management and nursing training and should encourage research on the topic. Key Words: Artificial Intelligence, Nursing, Bibliometric Analysis, Research Trends, VOSviewerBalıkesır Health Sciences Journal00This bibliometric study shows a synergy between the general policies of countries on artificial Intelligence in recent years and the increasing number of publications in the last four years, however, this study reveals that research on artificial intelligence in nursing is a nascent field.Balıkesır Health Sciences Journal['Ayşe ÇİÇEK KORKMAZ']2024-03-06T00:00:00https://www.semanticscholar.org/paper/96331cea5dce345be567423a54acb7ee30a1fc94 193c790074e38ed73c10f93df7f71efebd0aa989With advancements in the implementation of artificial intelligence (AI) in different ophthalmology disciplines, it continues to have a significant impact on glaucoma diagnosis and screening. This article explores the distinct roles of AI in specialized ophthalmology clinics and general practice, highlighting the critical balance between sensitivity and specificity in diagnostic and screening models. Screening models prioritize sensitivity to detect potential glaucoma cases efficiently, while diagnostic models emphasize specificity to confirm disease with high accuracy. AI applications, primarily using machine learning (ML) and deep learning (DL), have been successful in detecting glaucomatous optic neuropathy from colored fundus photographs and other retinal imaging modalities. Diagnostic models integrate data extracted from various forms of modalities (including tests that assess structural optic nerve damage as well as those evaluating functional damage) to provide a more nuanced, accurate and thorough approach to diagnosing glaucoma. As AI continues to evolve, the collaboration between technology and clinical expertise should focus more on improving specificity of glaucoma diagnostic models to assess ophthalmologists to revolutionize glaucoma diagnosis and improve patients care.Frontiers in Ophthalmology540The distinct roles of AI in specialized ophthalmology clinics and general practice are explored, highlighting the critical balance between sensitivity and specificity in diagnostic and screening models.Frontiers in Ophthalmology["Mo'ath AlShawabkeh", 'S. A. Alryalat', 'Muawyah Al Bdour', 'Ayat Alni’mat', 'M. Al-Akhras']2024-03-06T00:00:00https://www.semanticscholar.org/paper/193c790074e38ed73c10f93df7f71efebd0aa989 cdeeb3454ccfeddcdfb30a2e1eadd1ad88964a53With the progress and development of science and technology, artificial intelligence technology and its convenient, fast, and efficient performance characteristics have been widely applied in various fields, which has brought great changes to the financial industry and unprecedented breakthroughs and development. Although artificial intelligence brings huge development opportunities for the Internet financial industry, it also brings some potential risks and challenges, causing Internet financial information to face severe security risks. How to use artificial intelligence technology to effectively prevent and control financial information security risks is a major problem facing the financial industry. This paper analyzes the security risks of Internet financial information in the era of artificial intelligence and puts forward relevant preventive measures to effectively deal with various security risks, ensure the information security management of financial cloud platforms, and promote the healthy and stable development of the financial industry.Frontiers in Business, Economics and Management30This paper analyzes the security risks of Internet financial information in the era of artificial intelligence and puts forward relevant preventive measures to effectively deal with various security risks, ensure the information security management of financial cloud platforms, and promote the healthy and stable development of the financial industry.Frontiers in Business, Economics and Management['Centong Tao']2024-03-06T00:00:00https://www.semanticscholar.org/paper/cdeeb3454ccfeddcdfb30a2e1eadd1ad88964a53 4c15d02f4645bbf2cf7eb3ea3a2e2b582914ff86Trustworthy artificial intelligence (Trusted AI) is of utmost importance when learning-enabled components (LECs) are used in autonomous, safety-critical systems. When reliant on deep learning, these systems need to address the reliability, robustness, and interpretability of learning models. In addition to developing strategies to address these concerns, appropriate software architectures are needed to coordinate LECs and ensure they deliver acceptable behavior even under uncertain conditions. This work describes Anunnaki, a model-driven framework comprising loosely-coupled modular services designed to monitor and manage LECs with respect to Trusted AI assurance concerns when faced with different sources of uncertainty. More specifically, the Anunnaki framework supports the composition of independent, modular services to assess and improve the resilience and robustness of AI systems. The design of Annunaki was guided by several key software engineering principles (e.g., modularity, composabiilty, and reusability) in order to facilitate its use and maintenance to support different aggregate monitoring and assurance analysis tools for LESs and their respective data sets. We demonstrate Anunnaki on two autonomous platforms, a terrestrial rover and an unmanned aerial vehicle. Our studies show how Anunnaki can be used to manage the operations of different autonomous learning-enabled systems with vision-based LECs while exposed to uncertain environmental conditions.ACM Transactions on Autonomous and Adaptive Systems240Anunnaki is described, a model-driven framework comprising loosely-coupled modular services designed to monitor and manage LECs with respect to Trusted AI assurance concerns when faced with different sources of uncertainty that supports the composition of independent, modular services to assess and improve the resilience and robustness of AI systems.ACM Transactions on Autonomous and Adaptive Systems['Michael Austin Langford', 'Sol Zilberman', 'Betty H.C. Cheng']2024-03-06T00:00:00https://www.semanticscholar.org/paper/4c15d02f4645bbf2cf7eb3ea3a2e2b582914ff86 8c5020404d3c9e88eb6d274ceb07cfe5f18b2f1bThis article aims to explore the integration of artificial intelligence (AI) as a design tool in interior design education. The research examines the students' interior design studio project outcomes over the usage of AI in creating conceptual images, and the implementation of the AI-created concept to the overall space. In the research, students' projects are divided into two groups of 5 according to sufficient or insufficient prompts for the "AI generated" conceptual images. Barnard's (1992) CAIDC (Consensual Assessment of Interior Design Creativity) scale was used for the assessment. Mann-Whitney U Test was conducted for the results. We understand that there is no significant difference between writing sufficient or insufficient prompts in the concept development phase of interior design projects according to the Barnard (1992)’s design merits. It has been confirmed that the main factor that influences this regard is the need for an appropriate "concept analysis" to adapt the concept generated with AI to the specified project spaces.Journal of computational design230There is no significant difference between writing sufficient or insufficient prompts in the concept development phase of interior design projects according to the Barnard (1992)’s design merits, and the main factor that influences this regard is the need for an appropriate "concept analysis" to adapt the concept generated with AI to the specified project spaces.Journal of Computational Design['M. U. Kahraman', 'Yaren Şekerci', 'Müge Develier', 'Ferhat Koyuncu']2024-03-06T00:00:00https://www.semanticscholar.org/paper/8c5020404d3c9e88eb6d274ceb07cfe5f18b2f1b 4ee3d22ff7c66d12522863889dfe1cd9c604d792The analysis of microbial genomes has long been recognised as a complex and data-rich domain where artificial intelligence (AI) can assist. As AI technologies have matured and expanded, pathogen genomics has also contended with exponentially larger datasets and an expanding role in clinical and public health practice. In this mini-review, we discuss examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health. These include models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, modelling genomic information using approaches from computational linguistics, phylodynamic estimation, and using large language models to make bioinformatics more accessible to non-experts. We also examine factors affecting the adoption of AI into routine laboratory and public health practice and the need for a renewed vision for the potential of AI to assist pathogen genomics practice.Frontiers in Bacteriology1070Examples of emerging applications of AI to address challenges in pathogen genomics for precision medicine and public health are discussed, including models for genotyping whole genome sequences, identifying novel pathogens in metagenomic next generation sequencing, and using large language models to make bioinformatics more accessible to non-experts.Frontiers in Bacteriology['Carl J E Suster', 'David Pham', 'Jen Kok', 'V. Sintchenko']2024-03-06T00:00:00https://www.semanticscholar.org/paper/4ee3d22ff7c66d12522863889dfe1cd9c604d792 71faa6688f3cb632ee32c2f4318ed8a931837321Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research - protein structure prediction and single cell RNA-sequencing - we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems.arXiv.org790It is proposed that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems.ArXiv['Elsa Lawrence', 'Adham El-Shazly', 'Srijit Seal', 'Chaitanya K. Joshi', 'Pietro Lio', 'Shantanu Singh', 'Andreas Bender', 'Pietro Sormanni', 'Matthew Greenig']2024-03-06T00:00:00https://www.semanticscholar.org/paper/71faa6688f3cb632ee32c2f4318ed8a931837321 31f67d5e2c367014c972dfab151ed79de989062bArtificial Intelligence (AI) has emerged as a powerful tool revolutionizing various sectors globally, including international development management. This research aims to explore the current landscape of AI implementation in global development management, assess the benefits and challenges associated with its adoption, and propose relevant policies and practices. A mixed research design, comprising qualitative and quantitative methods, was utilized to gather data from secondary sources. The qualitative section of the study draws upon case studies from diverse operational sectors to examine the impact of AI adoption. These case studies highlight how AI contributes to improved performance in various industries and the potential positive effects on individuals’ lives. The quantitative part of the research utilizes data from renowned databases such as World Bank Open Data, United Nations Development Programme, International Monetary Fund (IMF), OECD Stat, and Global Open Data Index. Integrating qualitative and quantitative data allows for a comprehensive understanding of AI implementation’s economic growth and development across different organizations worldwide. The findings reveal that AI adoption in international development management holds significant promise for enhancing organizational efficiency and individuals’ well-being. However, the research also identifies various challenges associated with AI implementation, such as ethical considerations and potential job displacement. To address these issues, the study proposes policy recommendations and best practices that can guide organizations and policymakers in effectively harnessing the transformative potential of AI. This research contributes to international development management by providing a deep understanding of the importance of AI in the current context. The study offers insights for organizations adopting AI and assists policymakers in identifying and resolving pertinent challenges. By completing this study, organizations and policymakers can proactively address the existing problems and develop strategies to maximize the benefits of AI while minimizing potential risks. In summary, this research underscores the immense potential of AI in driving development and improving lives, laying a foundation for future advancements in international development management.Journal of Autonomous Intelligence00The findings reveal that AI adoption in international development management holds significant promise for enhancing organizational efficiency and individuals’ well-being, but also identifies various challenges associated with AI implementation, such as ethical considerations and potential job displacement.Journal of Autonomous Intelligence['B. Lainjo']2024-03-06T00:00:00https://www.semanticscholar.org/paper/31f67d5e2c367014c972dfab151ed79de989062b f6c3cac9b3236412836c2020f90ce0d7cc3fdb3cArtificial intelligence (AI), as an important engine for promoting high-quality economic development, should not be overlooked in terms of its impact on the employment of the labor force while promoting the digital and intelligent transformation of industries. In the face of the complex international environment and non-systemic shocks, it is of great significance to explore whether it is “machine replacement” or “job creation” in the process of the integration of AI and industry, as well as the impact of technological progress on the employment pattern of the labor force, in order to promote the economic development, respond to and solve the employment problem. It is of great significance to promote economic development and cope with and solve the employment problem. Based on the task model, this paper analyses the mechanism of the impact of AI on the employment pattern of manufacturing industry. Meanwhile, based on the provincial panel data of China's manufacturing industry from 2011 to 2020, it empirically examines the impact of AI on the total employment, employment structure and employment quality of the labor force, and analyses the multiple responses of AI on the employment pattern of the manufacturing industry. The study shows that: Firstly, the level of development of AI and the total amount of employment is a positive U-shaped relationship, the short term is dominated by the substitution effect, and the long term is dominated by the creation effect; Secondly, with regard to the employment structure, low-skilled labor is more likely to be replaced. The financial, accommodation and catering industries are relatively less affected by the spillover effects of the manufacturing industry; Third, with regard to the employment quality, the gap between urban and rural incomes has eased, with per capita net income of rural residents rising to a higher degree than per capita disposable income of urban residents. Thus, in order to further address the impact of AI on the employment patterns of the labor force, the level of AI development should be increased while expanding employment channels, paying attention to labor force skills training, reinforcing the leading role of developed regions, and accelerating regional integration and urban-rural integration, so as to share the dividends of technological progress.Frontiers in Artificial Intelligence310The level of AI development should be increased while expanding employment channels, paying attention to labor force skills training, reinforcing the leading role of developed regions, and accelerating regional integration and urban-rural integration, so as to share the dividends of technological progress.Frontiers in Artificial Intelligence['Qingqing Huo', 'Jing Ruan', 'Yan Cui']2024-03-06T00:00:00https://www.semanticscholar.org/paper/f6c3cac9b3236412836c2020f90ce0d7cc3fdb3c 83d7f473a3be08be87d86ea22a51eb64825072ddBackground: Liver fibrosis is important in terms of staging, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C. Method: We proposed a method based on a selection of machine learning classification methods including Multi Layers Perceptron neural network (MLP), Naive Bayesian (NB), decision tree, and deep learning. Initially, the Synthetic minority oversampling technique (SMOTE) was performed to address the imbalance of the dataset. Afterward, the integration of MLP and TLBO was implemented. Result: We proposed a novel algorithm that reduced the number of required patient features to 7 inputs. The accuracy of MLP using 12 features is 0.903, while the accuracy of the proposed MLP with the TLBO method is 0.891. Besides, the diagnostic accuracy in all methods, except the model designed with the Bayesian Network, increased when the SMOTE balancer was applied. Conclusion: The Decision tree deep learning methods showed the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and 7 features, the MLP reached a 0.891 accuracy rate which is quite satisfying compared with similar studies. The proposed model provided great diagnostic accuracy by reducing the required properties from the samples without reducing the accuracy. The results of our study showed that the recruited algorithm of our study was more straightforward, with lower required properties and similar accuracy.medRxiv30The proposed model provided great diagnostic accuracy by reducing the required properties from the samples without reducing the accuracy, and the recruited algorithm of the study was more straightforward, with lower required properties and similar accuracy.['A. Nazarizadeh', 'T. Banirostam', 'T. Biglari', 'M. Kalantarhormozi', 'F. Chichagi', 'A. H. Behnoush', 'M. A. Habibi', 'R. Shahidi']2024-03-06T00:00:00https://www.semanticscholar.org/paper/83d7f473a3be08be87d86ea22a51eb64825072dd 267f2c88e9c3713c3c8588d646dba86457c04e31Advanced language-processing and machine-learning techniques promise massive efficiency improvements in the previously widely manual field of patent and technical knowledge management. This field presents large-scale and complex data with very precise contents and language representation of those contents. Particularly, patent texts can differ from mundane texts in various aspects, which entails significant opportunities and challenges. This paper presents a systematic overview of patent-related tasks and popular methodologies with a special focus on evolving and promising techniques. Language processing and particularly large language models as well as the recent boost of general generative methods promise to become game changers in the patent field. The patent literature and the fact-based argumentative procedures around patents appear almost as an ideal use case. However, patents entail a number of difficulties with which existing models struggle. The paper introduces fundamental aspects of patents and patent-related data that affect technology that wants to explore or manage them. It further reviews existing methods and approaches and points out how important reliable and unbiased evaluation metrics become. Although research has made substantial progress on certain tasks, the performance across many others remains suboptimal, sometimes because of either the special nature of patents and their language or inconsistencies between legal terms and the everyday meaning of terms. Moreover, yet few methods have demonstrated the ability to produce satisfactory text for specific sections of patents. By pointing out key developments, opportunities, and gaps, we aim to encourage further research and accelerate the advancement of this field.arXiv.org00ArXiv['Lekang Jiang', 'Stephan Goetz']2024-03-06T00:00:00https://www.semanticscholar.org/paper/267f2c88e9c3713c3c8588d646dba86457c04e31 15f63d7394f6a5a57a20e2a678934e9350a8a563Journal of Research in Nursing30Journal of Research in Nursing['Kate Godfrey']2024-03-06T00:00:00https://www.semanticscholar.org/paper/15f63d7394f6a5a57a20e2a678934e9350a8a563 a87e7e87e869a4c9806543710cdca2e418bf31c9Current Pulmonology Reports370Current Pulmonology Reports['David Chu', 'A. Liteplo', 'Nicole Duggan', 'Ainsley B. Hutchinson', 'Hamid Shokoohi']2024-03-06T00:00:00https://www.semanticscholar.org/paper/a87e7e87e869a4c9806543710cdca2e418bf31c9 4e2d10ced334c78b4c20a6e6b3b8da8962412197Introduction Inflammatory conditions in patients have various causes and require different treatments. Bacterial infections are treated with antibiotics, while these medications are ineffective against viral infections. Autoimmune diseases and graft-versus-host disease (GVHD) after allogeneic stem cell transplantation, require immunosuppressive therapies such as glucocorticoids, which may be contraindicated in other inflammatory states. In this study, we employ a combination of straightforward blood tests to devise an explainable artificial intelligence (XAI) for distinguishing between bacterial infections, viral infections, and autoimmune diseases/graft-versus-host disease. Patients and methods We analysed peripheral blood from 80 patients with inflammatory conditions and 38 controls. Complete blood count, CRP analysis, and a rapid flow cytometric test for myeloid activation markers CD169, CD64, and HLA-DR were utilized. A two-step XAI distinguished firstly with C5.0 rules pruned by ABC analysis between controls and inflammatory conditions and secondly between the types of inflammatory conditions with a new bivariate decision tree using the Simpson impurity function. Results Inflammatory conditions were distinguished using an XAI, achieving an overall accuracy of 81.0% (95%CI 72 – 87%). Bacterial infection (N = 30), viral infection (N = 26), and autoimmune diseases/GVHD (N = 24) were differentiated with accuracies of 90.3%, 80.0%, and 79.0%, respectively. The most critical parameter for distinguishing between controls and inflammatory conditions was the expression of CD64 on neutrophils. Monocyte count and expression of CD169 were most crucial for the classification within the inflammatory conditions. Conclusion Treatment decisions for inflammatory conditions can be effectively guided by XAI rules, straightforward to implement and based on promptly acquired blood parameters.Frontiers in Immunology380Treatment decisions for inflammatory conditions can be effectively guided by XAI rules, straightforward to implement and based on promptly acquired blood parameters.Frontiers in Immunology['Joerg Hoffmann', 'Anne Rheude', 'Andreas Neubauer', 'Cornelia Brendel', 'M. Thrun']2024-03-06T00:00:00https://www.semanticscholar.org/paper/4e2d10ced334c78b4c20a6e6b3b8da8962412197 a7a13907a1c4de7ae740d778a92ef587322f38d3As we keep rapidly advancing toward an era where artificial intelligence is a constant and normative experience for most of us, we must also be aware of what this vision and this progress entail. By first approximating neural connections and activities in computer circuits and then creating more and more sophisticated versions of this crude approximation, we are now facing an age to come where modern deep learning-based artificial intelligence systems can rightly be called thinking machines, and they are sometimes even lauded for their emergent behavior and black-box approaches. But as we create more powerful electronic brains, with billions of neural connections and parameters, can we guarantee that these mammoths built of artificial neurons will be able to forget the data that we store in them? If they are at some level like a brain, can the right to be forgotten still be protected while dealing with these AIs? The essential gap between machine learning and the RTBF is explored in this article, with a premonition of far-reaching conclusions if the gap is not bridged or reconciled any time soon. The core argument is that deep learning models, due to their structure and size, cannot be expected to forget or delete a data as it would be expected from a tabular database, and they should be treated more like a mechanical brain, albeit still in development.arXiv.org370The core argument is that deep learning models, due to their structure and size, cannot be expected to forget or delete a data as it would be expected from a tabular database, and they should be treated more like a mechanical brain, albeit still in development.ArXiv['Meem Arafat Manab']2024-03-06T00:00:00https://www.semanticscholar.org/paper/a7a13907a1c4de7ae740d778a92ef587322f38d3 94140d9846d38658c058762a839d579d6db15babForeign direct investment (FDI) inflows have been significantly impacted by environmental regulation (ER). This study is aiming at analyzing the ER effect on the FDI inflows. By using the data of 2008–2018, we use Three‐stages least square (3SLS) method to assess the connection between FDI inflows and ER. The study results reveal that in Chinese industries, technological innovation (TI) is stipulated by the ER, and as a result, FDI has been engrossed. The results further reveal that TI has been enhanced by capital penetration, and a positive effect is perceived between TI and FDI. The findings of our study also show that there is a significant association between foreign capital (FC) inflows and TI, which indicates that technological policies are effective and advanced environmental policies would intensify the relevant policies between firms. Based on the study outcomes, this research proposes some policy suggestions for constructing a attuned policy system of environmental protection and FDI by regulating the implementation of conforming strategies.Growth and Change240Growth and Change['Lingcai Liu', 'Dongbei Bai', 'Shah Fahad', 'I. Ozturk']2024-03-05T00:00:00https://www.semanticscholar.org/paper/94140d9846d38658c058762a839d579d6db15bab 63b26dd92e144ffb4eedb89f0256fc37c42771f7This study delves into the extensive ramifications of AI's proliferation on employment and income distribution, especially in light of the COVID-19 pandemic, by examining job displacement, the emergence of new roles, and the widening income gap between high and low-income groups. The integration of AI into industries has had a significant impact on employment. It has led to the automation of routine and repetitive tasks, resulting in job displacement, especially in the manufacturing and administrative sectors. However, this phenomenon is not solely marked by job loss. AI has also brought about job transformation, necessitating individuals to acquire new skill sets and collaborate effectively with AI systems. This transformation has paved the way for new employment opportunities, particularly in AI engineering, data science, AI regulation, and the development of AI-driven technologies like self-driving vehicles. These changes underscore the need for adaptability in the workforce and have potential implications for income distribution, as high-skilled AI-related positions offer higher salaries, contributing to income inequality. The proliferation of AI has brought about both challenges and opportunities in employment and income distribution, with job displacement coexisting alongside the emergence of new roles. The COVID-19 pandemic has underscored the need for workforce adaptability, emphasizing the importance of addressing income inequality as we move into an AI-dominated era to foster a more equitable future.Journal of Education, Humanities and Social Sciences100The proliferation of AI has brought about both challenges and opportunities in employment and income distribution, with job displacement coexisting alongside the emergence of new roles, and the widening income gap between high and low-income groups.Journal of Education, Humanities and Social Sciences['Yihang Liang']2024-03-05T00:00:00https://www.semanticscholar.org/paper/63b26dd92e144ffb4eedb89f0256fc37c42771f7 1f8fce5d5f37b478b12a929a0149df9301e52427Journal of Russian Law00Journal of Russian Law["Emil' Sayfullin"]2024-03-05T00:00:00https://www.semanticscholar.org/paper/1f8fce5d5f37b478b12a929a0149df9301e52427 f06ae73b41f7c9052951faa40f6a8d4686cde03aThis research investigates how entrepreneurs in an early-stage market economy decide their level of compliance with formal rules and finds the manner in which they interact with government officials to operate on a continuum of formality. Focusing on the nonmarket strategy approaches entrepreneurs employ to establish relationships with government officials, we build a model that shows how entrepreneurs adopt strategies aligned with their firm’s level of formality, spanning low to high formality practices. We draw on qualitative interview data from entrepreneurs who exhibit varying levels of compliance with state-provided rules and guidelines. We inductively theorize that deciding the firms’ level of formality involves strategic interaction approaches with government officials responsible for rule enforcement. Our findings highlight that the interaction strategies entrepreneurs use hinge on the political capital they possess, eliciting the desired response from government officials, and dissuading the officials from enforcing formal rules or imposing sanctions for informality. We offer theoretical and policy implications for future work on the nuances of firm formality and the interaction between entrepreneurs and government officials.Journal of Management650Journal of Management['Ashenafi Biru', 'Pia Arenius', 'Garry Bruton', 'David Gilbert']2024-03-05T00:00:00https://www.semanticscholar.org/paper/f06ae73b41f7c9052951faa40f6a8d4686cde03a 893965f7d29b64adfb0c94aef6c6c649513f8cf1Journal of Russian Law00Journal of Russian Law['E. Nikitina']2024-03-05T00:00:00https://www.semanticscholar.org/paper/893965f7d29b64adfb0c94aef6c6c649513f8cf1 cd210a52743e347c666af0296a0b2ee815aa3f69Javnost - The Public300Javnost - The Public['Philip Schlesinger']2024-03-05T00:00:00https://www.semanticscholar.org/paper/cd210a52743e347c666af0296a0b2ee815aa3f69 51b0fb65133deedef2693382c20f098de9463e52This study analyzed images generated by three popular generative artificial intelligence (AI) tools - Midjourney, Stable Diffusion, and DALLE 2 - representing various occupations to investigate potential bias in AI generators. Our analysis revealed two overarching areas of concern in these AI generators, including (1) systematic gender and racial biases, and (2) subtle biases in facial expressions and appearances. Firstly, we found that all three AI generators exhibited bias against women and African Americans. Moreover, we found that the evident gender and racial biases uncovered in our analysis were even more pronounced than the status quo when compared to labor force statistics or Google images, intensifying the harmful biases we are actively striving to rectify in our society. Secondly, our study uncovered more nuanced prejudices in the portrayal of emotions and appearances. For example, women were depicted as younger with more smiles and happiness, while men were depicted as older with more neutral expressions and anger, posing a risk that generative AI models may unintentionally depict women as more submissive and less competent than men. Such nuanced biases, by their less overt nature, might be more problematic as they can permeate perceptions unconsciously and may be more difficult to rectify. Although the extent of bias varied depending on the model, the direction of bias remained consistent in both commercial and open-source AI generators. As these tools become commonplace, our study highlights the urgency to identify and mitigate various biases in generative AI, reinforcing the commitment to ensuring that AI technologies benefit all of humanity for a more inclusive future.arXiv.org232This study analyzed images generated by three popular generative artificial intelligence tools - Midjourney, Stable Diffusion, and DALLE 2 - representing various occupations to investigate potential bias in AI generators, finding that all three AI generators exhibited bias against women and African Americans.ArXiv['Mi Zhou', 'Vibhanshu Abhishek', 'Timothy P. Derdenger', 'Jaymo Kim', 'Kannan Srinivasan']2024-03-05T00:00:00https://www.semanticscholar.org/paper/51b0fb65133deedef2693382c20f098de9463e52 875e0a54865236a886c35ef8ec8203ea33cff7efInternational Journal of Science and Research (IJSR)02International Journal of Science and Research (IJSR)['Nithin Narayan Koranchirath']2024-03-05T00:00:00https://www.semanticscholar.org/paper/875e0a54865236a886c35ef8ec8203ea33cff7ef 6e262ad38946c3054c836585afe5dba177372026With the rise of artificial intelligence (AI), how should copyright and antitrust law handle AI-created creative work? The Copyright Ordinance 1962 and Competition Act 2010 are examined in this context to examine Pakistan's legal system. This study compares Pakistan's legal system to the EU's. The study focuses on the DMA and the EU Copyright Directive (2019/790). These two laws measure Pakistan's legal strength. Compare and contrast the legal systems of Pakistan with those of the European Union to find weaknesses and opportunities for progress in Pakistan's legal structure. This study may assist Pakistani policymakers and stakeholders in finding the best methods to adapt and update current regulations to handle the evolving environment of AI-generated content creation. Additionally, the article examines how antitrust laws affect AI-generated material and whether competition limits are enough to prevent AI corporations from monopolizing authors' rights. The article examines monopolization, norms, and AI-powered media. The research intends to illuminate artists' rights issues and identify legal loopholes that might hinder AI-generated material protection. It also suggests clarifying or amending rules to accommodate AI innovation. This detailed study illuminates Pakistan's complex copyright and antitrust relationship with AI-generated material. The findings of the research have added to the digital intellectual property rights conversation by revealing future rules and safeguards for artists working with AI-generated creations. Questions have been raised about how AI-generated material affects creative rights laws. The study begins with Pakistani AI content production IP rights. The research explores authorship, originality, and rights in the future when human programmers and algorithmic computers collaborate on creative creations. The study found that Pakistan's copyright and antitrust legislation does not address rising infringement problems, so aggrieved parties may have to use conventional remedies.Pakistan Journal of Criminal Justice20Pakistan's copyright and antitrust legislation does not address rising infringement problems, so aggrieved parties may have to use conventional remedies, so aggrieved parties may have to use conventional remedies.Pakistan Journal of Criminal Justice['S. Mushtaq', 'Khurram Baig', 'Syed Wajdan Rafay', 'Waqas Ahmad']2024-03-05T00:00:00https://www.semanticscholar.org/paper/6e262ad38946c3054c836585afe5dba177372026 b09889c1455b1e255811037cd9199edfd92be006Growing research in sign language recognition, generation, and translation AI has been accompanied by calls for ethical development of such technologies. While these works are crucial to helping individual researchers do better, there is a notable lack of discussion of systemic biases or analysis of rhetoric that shape the research questions and methods in the field, especially as it remains dominated by hearing non-signing researchers. Therefore, we conduct a systematic review of 101 recent papers in sign language AI. Our analysis identifies significant biases in the current state of sign language AI research, including an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, use of annotations lacking linguistic foundations, and development of methods that build on flawed models. We take the position that the field lacks meaningful input from Deaf stakeholders, and is instead driven by what decisions are the most convenient or perceived as important to hearing researchers. We end with a call to action: the field must make space for Deaf researchers to lead the conversation in sign language AI.SIGNLANG600This analysis identifies significant biases in the current state of sign language AI research, including an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, use of annotations lacking linguistic foundations, and development of methods that build on flawed models.ArXiv['Aashaka Desai', 'M. D. Meulder', 'J. Hochgesang', 'Annemarie Kocab', 'Alex X. Lu']2024-03-05T00:00:00https://www.semanticscholar.org/paper/b09889c1455b1e255811037cd9199edfd92be006 61cd7c9d3499514fe22c3c94b2a95655fa7d4bb0: This research investigates the strategic integration of Oracle technology with Artificial Intelligence (AI) and Machine Learning (ML) in the banking industry, focusing on its disruptive potential, important results, and implications for financial organizations. The study emphasizes the need to align technology investments with organizational goals through a rigorous review of literature and perspectives from many writers. The report emphasizes the need for a strong integration architecture that easily integrates Oracle technology with current systems, guaranteeing interoperability and agility in today's changing financial world. Real - world case studies serve as realistic benchmarks, providing important insights into successful Oracle integrations while also casting light on possible benefits and obstacles. Key conclusions emphasize the need for purpose - driven technology investments, methodical integration methodologies, and iterative learning via case studies. The need for financial institutions to adopt Oracle integration is clear. Beyond technology factors, the study emphasizes the necessity of organizational agility, cultural transformations, and investment in human development for successful challenge navigation. The strategic integration of Oracle technologies with AI and ML emerges not just as a technological progression, but also as a transformative journey that positions financial institutions at the vanguard of innovation, resilience, and long - term competitiveness in the dynamic and digitalized world of finance.International Journal of Science and Research (IJSR)370The strategic integration of Oracle technologies with AI and ML emerges not just as a technological progression, but also as a transformative journey that positions financial institutions at the vanguard of innovation, resilience, and long - term competitiveness in the dynamic and digitalized world of finance.International Journal of Science and Research (IJSR)['Madhavi Vinayak Godbole']2024-03-05T00:00:00https://www.semanticscholar.org/paper/61cd7c9d3499514fe22c3c94b2a95655fa7d4bb0 d795d337532e97f4b64136bccdb0adedf14371ba: The primary objective of this paper is to examine the role of AI-augmented Automation in DevOps in enhancing the modeling of Cyber-Physical Systems (CPS). With the increasing complexity in the development and operation of CPS, there is a need for a more efficient engineering methodology. This paper aims to provide a deeper understanding of a model-based framework for effectively supporting the software and system engineering of large and complex CPS through the use of AI augmentation. In recent years, DevOps has gained popularity, promoting closer collaboration between developers and operations personnel for system development and integration. While AI technology has the potential to be beneficial in this context, its widespread use is currently limited. However, AI is anticipated to have a significant role in automating processes for major corporations in the future. The ultimate goal of this project is to create an integrated AI-augmented framework for the continuous automatic development of CPS, utilizing a model-based approach. The paper will extensively cover Model-Driven Engineering (MDE) concepts and methodologies to provide a model-based framework with the relevant approaches and associated technology.International Journal of Science and Research (IJSR)250A deeper understanding of a model-based framework for effectively supporting the software and system engineering of large and complex CPS through the use of AI augmentation through the use of AI augmentation is provided.International Journal of Science and Research (IJSR)['Sarthak Srivastava']2024-03-05T00:00:00https://www.semanticscholar.org/paper/d795d337532e97f4b64136bccdb0adedf14371ba 8d660d8a0c3a8401cca96cb7dbf1af70d603594aThis study investigates the factors influencing hotel guests' intentions to adopt next-generation technologies enabled by artificial intelligence (AI). Both affective and cognitive processes, which led to guests' intentions to adopt these new technologies, were considered to have antecedents in the form of intrinsic and extrinsic motives, respectively. The data collected from 331 respondents were analyzed using a combination of methods, including the asymmetrical fuzzy set qualitative comparative analysis (fsQCA) and the symmetrical partial least square-structural equation modeling (PLS-SEM). The results of the symmetrical study indicated that novelty and compatibility have a good impact on both enjoyment and usefulness, which ultimately lead to behavioral intentions. In contrast, asymmetrical studies have shown that all the criteria are necessary conditions to produce users' intention to embrace AI-based technology. By integrating IDT and TAM, this study extends the comprehension of factors driving customers to use AI-enabled technologies during their hotel stays. This study also adds to the existing literature by exploring configurational modeling with fsQCA, as opposed to prior studies that have relied on net impact modeling via SEM.Advances in Hospitality and Tourism Research490By integrating IDT and TAM, this study extends the comprehension of factors driving customers to use AI-enabled technologies during their hotel stays by exploring configurational modeling with fsQCA, as opposed to prior studies that have relied on net impact modeling via SEM.Advances in Hospitality and Tourism Research (AHTR)['Abraham Terrah', 'Faizan Ali', 'G. Abbasi', 'Seden Doğan', 'Cihan Cobanoglu']2024-03-05T00:00:00https://www.semanticscholar.org/paper/8d660d8a0c3a8401cca96cb7dbf1af70d603594a 96e825d7b6ade94b2a848b8874ea52f085bab54fThe agriculture sector is critical to satisfying the world's ever-increasing food need. Accurate crop yield prediction is critical for ensuring sustainable agriculture and optimizing agricultural production. Our system effectively analyses many elements influencing crop development, such as soil qualities, climate patterns, and historical agricultural data, by integrating environmentally conscious methods and utilizing the power of AI algorithms. The predictive model predicts crop production with high accuracy, allowing farmers and government or NGOs to make educated decisions about resource allocation and productivity while reducing environmental effect. Green principles combined with cutting-edge AI technology in crop yield prediction offer a viable answer for sustainable agriculture and promises a more robust food supply system in the future. In this article, the proposed system uses the Seasonal Adaptive Auto-Regressive Integrated Moving Average (ARIMA) time series model, which provides more accurate results than the prior models and can be used to predict the production of any type of crop. The outcomes show considerable increases in forecast accuracy and give useful information about the elements that affect crop output.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)300The proposed system uses the Seasonal Adaptive Auto-Regressive Integrated Moving Average time series model, which provides more accurate results than the prior models and can be used to predict the production of any type of crop.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)['Iyyanar Perumal', 'K. P', 'N. P', 'M. V', 'Naveenkumar Anbalagan', 'A. J']2024-03-05T00:00:00https://www.semanticscholar.org/paper/96e825d7b6ade94b2a848b8874ea52f085bab54f 5b322cdf2e0d0ff9a35c1773c28ad85957eacec3This article explores the fundamental differences between biological evolution and the evolution of artificial intelligence (AI), focusing on the implications of AI's potential for unbounded advancement. Biological evolution, as exemplified by the human brain, is constrained by various factors such as physical limitations, environmental pressures, and the need to maintain a delicate balance within the organism's overall structure. These constraints result in a gradual, stepwise process of adaptation and optimization over millions of years. In contrast, AI systems are not bound by the same limitations as biological organisms. They can learn from vast amounts of data, optimize their own performance, and potentially engage in recursive self-improvement, leading to rapid and open-ended progress. This unbounded evolution of AI raises both exciting possibilities and significant challenges for human society. The article discusses the potential benefits of AI's accelerated evolution, such as groundbreaking advancements in science, technology, and solutions to global problems. However, it also highlights the risks associated with the emergence of uncontrollable or misaligned AI systems, which could have unintended consequences and pose existential risks to humanity. To navigate the complex landscape of AI evolution, the article emphasizes the need for collaboration across disciplines, including researchers, policymakers, and society as a whole. Developing frameworks for AI safety and ethics, as well as technical approaches to ensure AI alignment with human values and goals, is crucial to mitigating potential risks.Wired Neuroscience00The article discusses the potential benefits of AI's accelerated evolution, such as groundbreaking advancements in science, technology, and solutions to global problems, but also highlights the risks associated with the emergence of uncontrollable or misaligned AI systems, which could have unintended consequences and pose existential risks to humanity.Wired Neuroscience['Richard Murdoch Montgomery']2024-03-05T00:00:00https://www.semanticscholar.org/paper/5b322cdf2e0d0ff9a35c1773c28ad85957eacec3 dc66ff0a6ed82740b196f6e75540a9944ac06e35Real-time flood forecasting plays a crucial role in enabling timely and effective emergency responses. However, a significant challenge lies in bridging the gap between complex numerical flood models and practical decision-making. Decision-makers often rely on experts to interpret these models for optimizing flood mitigation strategies. And the public requires complex techniques to inquiry and understand socio-cultural and institutional factors, often hinders the public's understanding of flood risks. To overcome these challenges, our study introduces an innovative solution: a customized AI Assistant powered by the GPT-4 Large Language Model. This AI Assistant is designed to facilitate effective communication between decision-makers, the general public, and flood forecasters, without the requirement of specialized knowledge. The new framework utilizes GPT-4's advanced natural language understanding and function calling capabilities to provide immediate flood alerts and respond to various flood-related inquiries. Our developed prototype integrates real-time flood warnings with flood maps and social vulnerability data. It also effectively translates complex flood zone information into actionable risk management advice. To assess its performance, we evaluated the prototype using six criteria within three main categories: relevance, error resilience, and understanding of context. Our research marks a significant step towards a more accessible and user-friendly approach in flood risk management. This study highlights the potential of advanced AI tools like GPT-4 in democratizing information and enhancing public engagement in critical social and environmental issues.arXiv.org400This study introduces an innovative solution: a customized AI Assistant powered by the GPT-4 Large Language Model designed to facilitate effective communication between decision-makers, the general public, and flood forecasters, without the requirement of specialized knowledge.ArXiv['Rafaela Martelo', 'Ruo-Qian Wang']2024-03-05T00:00:00https://www.semanticscholar.org/paper/dc66ff0a6ed82740b196f6e75540a9944ac06e35 3e0d104333e34131c3715f37bde1cb7c6d7b6325: This study delves into the escalating dynamics of the digital landscape in the Middle East, focusing on the pressing need for cybersecurity resilience amidst the rapid technological advancements and connectivity expansion. By analyzing the current state of cybersecurity, the burgeoning role and implications of cryptocurrency, and the transformative impact and inherent risks of Artificial Intelligence AI, the research identifies pivotal challenges and formulates strategic recommendations to fortify digital resilience and mitigate risks. It underscores the critical importance of adopting best practices in cybersecurity, ensuring secure cryptocurrency transactions, and establishing comprehensive AI governance frameworks. Through a synthesis of government and industry initiatives, case studies, and an exploration of regulatory environments, the study emphasizes the necessity for a collaborative, multifaceted approach involving policymakers, industry leaders, and researchers. The findings advocate for ongoing investment in technology, the development of robust legal and ethical frameworks, and international cooperation to navigate the complex digital ecosystem and safeguard the Middle Easts digital future against emerging threats.International Journal of Science and Research (IJSR)70The findings advocate for ongoing investment in technology, the development of robust legal and ethical frameworks, and international cooperation to navigate the complex digital ecosystem and safeguard the Middle Easts digital future against emerging threats.International Journal of Science and Research (IJSR)['Ahmed Alnaffar']2024-03-05T00:00:00https://www.semanticscholar.org/paper/3e0d104333e34131c3715f37bde1cb7c6d7b6325 4bfa410df3b6865bdebd78d305436e57d7b695c4: This research paper conducts a comparative study of various artificial intelligence (AI) tools prevalent in the contemporary market scenario. The proliferation of AI technologies has given rise to a variety of tools, each offering unique capabilities and applications. The study systematically evaluates various AI tools across multiple dimensions, including machine learning algorithms, natural language processing (NLP) models, computer vision systems, and robotics technologies. Through rigorous analysis, this paper aims to clarify the strengths, weaknesses, and real - world effectiveness of these AI tools. By providing insight into performance metrics, scalability, explain ability, and suitability for various use cases, the research aims to facilitate informed decision making for organizations and researchers wishing to leverage AI technologies. The findings of this comparative study provide valuable guidance to navigate the complex landscape of AI tools, ultimately contributing to the advancement and adoption of AI - powered solutions across various domainsInternational Journal of Science and Research (IJSR)130This comparative study systematically evaluates various AI tools across multiple dimensions, including machine learning algorithms, natural language processing models, computer vision systems, and robotics technologies, to clarify the strengths, weaknesses, and real - world effectiveness of these AI tools.International Journal of Science and Research (IJSR)['Pranav Ojha Lakhan Bhaskar']2024-03-05T00:00:00https://www.semanticscholar.org/paper/4bfa410df3b6865bdebd78d305436e57d7b695c4 7db8ca9396898040ac14396b70c7475cf1899962: This research paper explores the critical intersection of cybersecurity resilience and awareness in the context of the rapidly evolving landscape of artificial intelligence (AI). With the increasing integration of AI technologies in cybersecurity measures, there is a pressing need for heightened awareness and education to mitigate emerging risks and vulnerabilities. The study examines the current state of cybersecurity threats, the role of AI in enhancing defense mechanisms, and the importance of proactive awareness initiatives. Through a comprehensive analysis of literature, case studies, and industry practices, the paper proposes strategies for improving cybersecurity resilience by fostering a culture of awareness and education. The findings underscore the importance of collaboration among stakeholders, continuous learning, and the development of robust governance frameworks to navigate the challenges posed by AI in cybersecurity.International Journal of Science and Research (IJSR)140The study examines the current state of cybersecurity threats, the role of AI in enhancing defense mechanisms, and the importance of proactive awareness initiatives to improve cybersecurity resilience.International Journal of Science and Research (IJSR)['Ahmed Alnaffar']2024-03-05T00:00:00https://www.semanticscholar.org/paper/7db8ca9396898040ac14396b70c7475cf1899962 cabde347c344735933cb0e02fa08fb839e50534aThe rise of ChatGPT in recent years has garnered significant attention, prompting widespread interest in the rapid global popularity of this AI technology. Conducting research on this topic would be quite intriguing. The objective of this study was to ascertain the satisfaction and acceptance levels of users of AI robots. This study presents an analysis of the technology acceptance model theory, which enables the prediction of users' intentions and behaviors. The methodology employed in this study involves the utilization of a SWOT analysis framework, which encompasses the identification and evaluation of strengths, weaknesses, opportunities, and threats. The tool has the capability to discern the internal and external factors that impact an organization. The outcome of the investigation will lead to enhancements in the capabilities of ChatGPT, thereby increasing its adoption as a daily tool and enhancing the long-term user experience. This study provides a substantial addition to the field of business, specifically within the domain of artificial intelligence (AI), hence serving as a valuable reference for forthcoming market objectives and strategies.Journal of Education, Humanities and Social Sciences150This study presents an analysis of the technology acceptance model theory, which enables the prediction of users' intentions and behaviors and leads to enhancements in the capabilities of ChatGPT, thereby increasing its adoption as a daily tool and enhancing the long-term user experience.Journal of Education, Humanities and Social Sciences['Zile Liu']2024-03-05T00:00:00https://www.semanticscholar.org/paper/cabde347c344735933cb0e02fa08fb839e50534a 67202778a2229c1211c4247201769fba65ad04b3Organizations are investing in reskilling and individualized training programmers to stay up with the digital era's fast-paced technical progress. This paper provides a novel strategy that leverages AI principles to make these algorithms even more effective. We focus on two important components: decision tree imputation preprocessing and better generative adversarial neural network (GAN) classification/prediction. During the preprocessing stage, Decision Tree Imputation is utilised to fill in missing data in training datasets. When confronted with little data, it is usual for conventional methodologies to offer biassed results and less-than-ideal models. Nonetheless, Decision Tree Imputation appears as a viable choice due to its creative utilisation of pre-existing data to fill in dataset gaps. This ensures a more thorough and representative training dataset, which results in more accurate AI models. For individualised training recommendations, we propose utilising an Improved Generative Adversarial Neural Network (GAN), which goes beyond traditional classification and prediction models. Enhanced GANs, which are well-known for their ability to generate synthetic data, are used to create personalised learning pathways for each employee. The improved GAN takes into account the user's current skill set, as well as their learning style, future work aspirations, and the ever-changing needs of the digital world. This research proposes an integrated system that can handle bad data and personalise training recommendations for each employee by using powerful AI models and preprocessing approaches.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)130This research proposes an integrated system that can handle bad data and personalise training recommendations for each employee by using powerful AI models and preprocessing approaches.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)['Naaz Gorowara', 'Anshika Prakash', 'F. Correa', 'Varun Malik', 'Ruchi Mittal']2024-03-05T00:00:00https://www.semanticscholar.org/paper/67202778a2229c1211c4247201769fba65ad04b3 b692a910589902c5178527984ce7f9668313ebd1Artificial Intelligence (AI) is creating quantum waves in almost every field of learning, including education. The present times come with a lot of requirements that demand precision and patience, so as to be able to resolve impending issues on time, with minimum errors. That’s where AI takes over to abridge the crucial need for problem-solving and apt decision-making in organizations. The role of AI has not been virtually defined in the books for educational management institutions but is definitely not lagging behind in proving it’s worthwhile and efficiency. Every technology comes with its share of pros and cons. Nevertheless, this research paper tries to address the application of AI in the educational management which strives to explore how procedures w.r.t student enrolment, engagement, retention, facilitating learning and achieving cost effectiveness can be explored. However, the flip side of the study also defaces the ethical bindings, biases that are ignored and the desperate need to have manpower enrolled into periodic sessions of training and development.International Journal For Multidisciplinary Research00This research paper tries to address the application of AI in the educational management which strives to explore how procedures w.r.t student enrolment, engagement, retention, facilitating learning and achieving cost effectiveness can be explored.International Journal For Multidisciplinary Research['Rashmi Prakash']2024-03-05T00:00:00https://www.semanticscholar.org/paper/b692a910589902c5178527984ce7f9668313ebd1 d1277bcf2eef4c07caea504e1189f9446e492862an AI-3D reconstruction system in measuring lung volume and analyze its practical value in donor-recipient size matching in lung transplantation. The study compared lung volume calculated from an AI-3D reconstruction system (AI-3DCTVol) with the predicted TLC (pTLC) and actual TLC (aTLC) measured by PFT in 75 Chinese subjects. Overall, they found a good correlation between AI-3DCTVol and aTLC (the intraclass correlation [ICC] 0.79 [95% CI: 0.68 – 0.87]). The AI-based 3D reconstruction of lung parenchyma showed good performance, with potential future application in lung volume assessments before lung transplantation. The sample size, however, was small. The results will need to be validated in a larger cohort and in different racial and ethnic groups.Frontiers in Physiology00The AI-based 3D reconstruction of lung parenchyma showed good performance, with potential future application in lung volume assessments before lung transplantation.Frontiers in Physiology['Yuh-Chin Huang', 'Paresh Giri', 'Octavian Ioachimescu', 'An-Kwok Ian Wong']2024-03-05T00:00:00https://www.semanticscholar.org/paper/d1277bcf2eef4c07caea504e1189f9446e492862 4e46ae1a6d1a21ff73718e5aae54598c5025d20dScientific Data190It is argued that the broad availability of knowledge is required to fuel further advances in AI in the scientific domain, and more investment in this activity is required if the authors are to achieve the promise of AI.Scientific Data['Christophe Dessimoz', 'Paul D Thomas']2024-03-05T00:00:00https://www.semanticscholar.org/paper/4e46ae1a6d1a21ff73718e5aae54598c5025d20d 9c2b58ed11ba8784ecd9e5e8ab81b3727254d8a4To effectively navigate the AI revolution, AI literacy is crucial. However, content predominantly exists in dominant languages, creating a gap for low-resource languages like Yoruba (41 million native speakers). This case study explores bridging this gap by creating and distributing AI videos in Yoruba.The project developed 26 videos covering foundational, intermediate, and advanced AI concepts, leveraging storytelling and accessible explanations. These videos were created using a cost-effective methodology and distributed across YouTube, LinkedIn, and Twitter, reaching an estimated global audience of 22 countries. Analysis of YouTube reveals insights into viewing patterns, with the 25-44 age group contributing the most views. Notably, over half of the traffic originated from external sources, highlighting the potential of cross-platform promotion.This study demonstrates the feasibility and impact of creating AI literacy content in low-resource languages. It emphasizes that accurate interpretation requires both technical expertise in AI and fluency in the target language. This work contributes a replicable methodology, a 22-word Yoruba AI vocabulary, and data-driven insights into audience demographics and acquisition channelarXiv.org90This study demonstrates the feasibility and impact of creating AI literacy content in low-resource languages by creating and distributing AI videos in Yoruba, and emphasizes that accurate interpretation requires both technical expertise in AI and fluency in the target language.ArXiv['W. Oyewusi']2024-03-05T00:00:00https://www.semanticscholar.org/paper/9c2b58ed11ba8784ecd9e5e8ab81b3727254d8a4 ea3b11395dec037edff102fe585ecbfbe3a2a992Cyber threats continue to evolve in complexity, thereby traditional cyber threat intelligence (CTI) methods struggle to keep pace. AI offers a potential solution, automating and enhancing various tasks, from data ingestion to resilience verification. This paper explores the potential of integrating artificial intelligence (AI) into CTI. We provide a blueprint of an AI-enhanced CTI processing pipeline and detail its components and functionalities. The pipeline highlights the collaboration between AI and human expertise, which is necessary to produce timely and high-fidelity cyber threat intelligence. We also explore the automated generation of mitigation recommendations, harnessing AI’s capabilities to provide real-time, contextual, and predictive insights. However, the integration of AI into CTI is not without its challenges. Thereby, we discuss the ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions. We address the need for data privacy, consent mechanisms, and the potential misuse of technology. Moreover, we highlight the importance of addressing biases both during CTI analysis and within AI models, warranting their transparency and interpretability. Lastly, our work points out future research directions, such as the exploration of advanced AI models to augment cyber defenses, and human–AI collaboration optimization. Ultimately, the fusion of AI with CTI appears to hold significant potential in the cybersecurity domain.Electronics620A blueprint of an AI-enhanced CTI processing pipeline is provided and its components and functionalities are detail, highlighting the collaboration between AI and human expertise, which is necessary to produce timely and high-fidelity cyber threat intelligence.ArXiv['Lampis Alevizos', 'Martijn Dekker']2024-03-05T00:00:00https://www.semanticscholar.org/paper/ea3b11395dec037edff102fe585ecbfbe3a2a992 32bfed0a9913dd86f106a73ca38b6f7396dca276It is an age of artificial intelligence (AI), and the article seeks to explore how one might integrate ethical considerations into AI design and use. It asks how ethics can be integrated into the curriculum of AI, what ethical structures could exist in the design of an algorithm or intelligent machine, and whether big data can help solve these issues. The study uses an interdisciplinary approach, looking at case studies, and the discussion of ethical dilemmas. The study uses the analysis of practical applications in such fields as healthcare or communication to explore AI ethics. This kindles a torch that illuminates a fact: It is necessary to build up one's capacities both technically and morally for AI specialists need an educational curriculum, that stresses the need for all principles (including fairness, transparency, and accountability) to be built into AI design as well as decision-making processes. This requires a multi-disciplinary approach. The paper stresses the significance of ethical dimensions in an increasingly fast-moving AI environment. It seeks to cultivate moral reflection about AI, holding out for technology that is not only advanced but also ethical and more responsible.Lecture Notes in Education Psychology and Public Media00The article asks how ethics can be integrated into the curriculum of AI, what ethical structures could exist in the design of an algorithm or intelligent machine, and whether big data can help solve these issues.Lecture Notes in Education Psychology and Public Media['Kelin Pan']2024-03-05T00:00:00https://www.semanticscholar.org/paper/32bfed0a9913dd86f106a73ca38b6f7396dca276 b8d9faf348affe522f990ca002d8314f667a6282As AI technology continues to evolve, higher education is facing profound changes. Driven by this change, advanced chatbots such as ChatGPT, as representatives of AI, have gradually become a focus of attention in higher education, triggering academic discussions on how to better integrate these technologies to facilitate learning and teaching. This paper aims to systematically grasp the opportunities and challenges of ChatGTP in higher education to explore in depth its positive role in academic support and teaching support for students and teachers. However, the challenges posed by the ChatGPT technology, which comes with opportunities, include academic integrity, information accuracy and reliability. At the same time, a number of potential solutions are presented, such as raising awareness of the advantages, limitations and potential risks of AI models, developing students' digital skills, and investing in research and development to improve transparency and control. Along with adapting to the age of AI, higher education needs to constantly explore new assessment models and teaching methods to better respond to developments in technologies such as ChatGPT.Lecture Notes in Education Psychology and Public Media170This paper aims to systematically grasp the opportunities and challenges of ChatGTP in higher education to explore in depth its positive role in academic support and teaching support for students and teachers.Lecture Notes in Education Psychology and Public Media['Ying Lin']2024-03-05T00:00:00https://www.semanticscholar.org/paper/b8d9faf348affe522f990ca002d8314f667a6282 441ac801949d7fe6264075e0be5b0adc5e58a932With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances between the Global North and South.This paper seeks to explore fairness for global health, with Africa as a case study. We conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.arXiv.org1010A scoping review is conducted to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities and provides practical recommendations for developing fairness-aware ML solutions for health in Africa.ArXiv['M. Asiedu', 'Awa Dieng', 'Alexander Haykel', 'Negar Rostamzadeh', 'Stephen R. Pfohl', 'Chirag Nagpal', 'Maria Nagawa', 'Abigail Oppong', 'Sanmi Koyejo', 'Katherine Heller']2024-03-05T00:00:00https://www.semanticscholar.org/paper/441ac801949d7fe6264075e0be5b0adc5e58a932 f6f9ab29950315da1b2b8b8efc92dab3af470545We are focusing on analyzing the current industrial evolution paradigm, aiming to make it more sustainable and trustworthy. In Industry 5.0, Artificial Intelligence (AI) is one of the key technologies utilized to develop services with a sustainable, human-centric, and resilient approach. Understanding the factors enabling AI adoption in industry, while adhering to trustworthy principles, involves examining its incorporation in early stages, assessing its impact, and identifying emerging trends. Additionally, we aim to grasp the challenges and gaps in transitioning from Industry 4.0 to Industry 5.0,providing insights into the industry's readiness for new technologies. This offers practitioners new avenues to explore for fostering the adoption of trustworthy AI within the sectorInternational Journal of Advanced Research in Science, Communication and Technology1240This work aims to grasp the challenges and gaps in transitioning from Industry 4.0 to Industry 5.0, providing insights into the industry's readiness for new technologies and offering practitioners new avenues to explore for fostering the adoption of trustworthy AI within the sector.ArXiv['E. Vyhmeister', 'Gabriel G. Castañé']2024-03-05T00:00:00https://www.semanticscholar.org/paper/f6f9ab29950315da1b2b8b8efc92dab3af470545 832a4b56860a4a100dcf93ed451c7f45938dea51This study investigates the efficacy of a meta-learning approach in improving metacognitive and creative skills. This quantitative study focused on an experimental group using a onegroup pretest-posttest research design. All participants underwent a pretest to assess their initial metacognitive abilities and were subsequently exposed to a meta-learning framework throughout the course. A post-test was conducted to assess the impact of the intervention. The findings indicate a statistically significant improvement in metacognitive skills from the pretest to the post-test. This study confirms the effectiveness of meta-learning strategies and elucidates the relationship between meta-learning and metacognition. Meta-learning enables students to comprehend their own learning processes, thereby improving their capacity to strategize, oversee, and control their cognitive functions with the assistance of artificial intelligence (AI). This approach incorporates creative elements that can stimulate metacognitive thinking, encouraging students to adjust their learning strategies and think outside the box. This research suggests that meta-learning can improve metacognitive abilities, providing valuable insights into educational technology and course design in higher education settings.International Journal of Interactive Mobile Technologies00It is suggested that meta-learning can improve metacognitive abilities, providing valuable insights into educational technology and course design in higher education settings and elucidates the relationship between meta-learning and metacognition.Int. J. Interact. Mob. Technol.['Khusnul Khotimah', 'Rusijono', 'A. Mariono']2024-03-05T00:00:00https://www.semanticscholar.org/paper/832a4b56860a4a100dcf93ed451c7f45938dea51 97aa965d0350736b4a608c8f8f7e9caf7f799ed4Purpose: Now, we are in a bright living era. Smart devices and equipment surround us. We are familiar with beds, kitchens, bulbs, televisions, shoes, homes, etc., which are all smart. Around the clock, all the equipment and gadgets provide smart service to us. All these devices are suitable, but we might notice some things. Their smartness is bound inside their enclosure, not contributing to other activities. If smart bulbs get faulty, other standby lights should be triggered, and the report should be immediate. If cooking LPG is almost empty, book the cylinder automatically. We need a centralized control to coordinate all devices to address the issue. This research demonstrates using artificial intelligence (AI) to manage our sweet homes. Design/Methodology/Approach: We install surveillance cameras in every corner of the house from where we need to capture the event and the action module to trigger the equipment. All the cameras are connected to one CPU, and all action modules are connected to one controller, which is attached to the CPU via a USB cable. When the system is powered up, it initializes all available cameras and action modules. The CPU always captures the image from every camera and analyzes the image around the clock. When it finds the incident, it finds from the database for action. Once the single action or series of actions is matched, take the action using the action module. Findings/Result: the described concept is the application of the advancement of technology of IoT and AI. We can improve our living environment by employing both technologies in our homes. This system provides us with the ability to manage every activity nicely. We can have a secure life trusting technology. Every monotonous or repetitive task can be handled through this system, and we can be engaged with other innovative tasks. So, as a result, the outcome of the system is to enhance our quality of life in our busy lives. Originality/Value/ Novelty: we studied several research works on this home automation field. Most of the research is on creating home automation using IoT with the help of smart electronic gadgets. Whatever smart devices we install in our homes are manually operated. When busy with another task, we need something to take care of our sweet home. Most of the research work only fulfills our needs. In this project, we fill those research gaps. We integrate the relevant technologies under one supervisory control, and we attach the AI to control not only logically but also emotionally. This kind of system fulfills our needs in our busy modern lives. So, this project will provide more value in our day-to-day activities. Type of Paper: Conceptual Research.International journal of case studies in business, IT, and education220This research demonstrates using artificial intelligence (AI) to manage the authors' sweet homes using surveillance cameras in every corner of the house to enhance their quality of life in their busy lives.International Journal of Case Studies in Business, IT, and Education['Sudipto Chakraborty', 'P. S. Aithal']2024-03-05T00:00:00https://www.semanticscholar.org/paper/97aa965d0350736b4a608c8f8f7e9caf7f799ed4 4a2521d7d1cd5021c5654ba3f78183363c5c85c9Acta Cardiologica21Acta cardiologica['H. Daungsupawong', 'V. Wiwanitkit']2024-03-05T00:00:00https://www.semanticscholar.org/paper/4a2521d7d1cd5021c5654ba3f78183363c5c85c9 b27ef3d3a1ac093f6524d9e42a2c6703621c2ad200[]2024-03-05T00:00:00https://www.semanticscholar.org/paper/b27ef3d3a1ac093f6524d9e42a2c6703621c2ad2 edfcd6135395352d6ea3de6cf3c57101f8f7c31aInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Shreekant Mandvikar Manoj Kumar']2024-03-05T00:00:00https://www.semanticscholar.org/paper/edfcd6135395352d6ea3de6cf3c57101f8f7c31a e94b600d7069beb8dd252b21d631d66f7153e851BMC Medical Ethics00BMC Medical Ethics['Abdallah Al-Ani', 'Abdallah Rayyan', 'Ahmad Maswadeh', 'H. Sultan', 'Ahmad Alhammouri', 'Hadeel Asfour', 'Tariq Alrawajih', 'Sarah Al Sharie', 'F. Karmi', 'Ahmed Mahmoud Al-Azzam', 'Asem H. Mansour', 'M. Al-Hussaini']2024-03-05T00:00:00https://www.semanticscholar.org/paper/e94b600d7069beb8dd252b21d631d66f7153e851 e26491b7ae73cae93758451b78478f9ec460ed2eThis paper introduces a web-based application that simplifies the data analysis processing chain by automating the analysis of arbitrary variables. In particular, our application allows users to easily upload and process data for the analysis of a target variable by exploiting machine learning and evo-lutionary algorithms for precise forecasting and optimization. We demonstrate the system's efficacy using a dataset from a textile company, where our application successfully predicted the target variables with a high level of R-squared of 0.78, using the best regression model. These results not only highlight its real-world applicability but also played an important role in enhancing sustainable manufacturing practices. This innovative application offers a significant step towards sustainable and efficient manufacturing, addressing the challenges of high energy consumption and environmental impact in the industry.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)220A web-based application that simplifies the data analysis processing chain by automating the analysis of arbitrary variables by exploiting machine learning and evo-lutionary algorithms for precise forecasting and optimization is introduced.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)['Asmae Lamsaf', 'Pranita Samale', 'Hugo Proença', 'J. Neves', 'Kailash Hambarde']2024-03-05T00:00:00https://www.semanticscholar.org/paper/e26491b7ae73cae93758451b78478f9ec460ed2e 7569060f31ec9d72c2df5da457ab28027d257bd2This article explores the influence of Artificial Intelligence (AI) on higher education and the economics of information technology. The rapid advancements in AI have the potential to revolutionize the way universities deliver education and how the IT industry operates. This study employs the IMRAD method to investigate the current state of AI in higher education, its economic implications, and future prospects. The findings suggest that AI can enhance teaching and learning experiences, streamline administrative processes, and open up new revenue streams for universities. However, the adoption of AI also presents challenges, such as the need for significant investments in infrastructure and the potential for job displacement. The article concludes with recommendations for universities and policymakers to harness the benefits of AI while mitigating its risks.International journal of law and policy257The findings suggest that AI can enhance teaching and learning experiences, streamline administrative processes, and open up new revenue streams for universities, however, the adoption of AI also presents challenges, such as the need for significant investments in infrastructure and the potential for job displacement.International Journal of Law and Policy['Gulyamov Saidakhror']2024-03-05T00:00:00https://www.semanticscholar.org/paper/7569060f31ec9d72c2df5da457ab28027d257bd2 5ce64c77cc96ef89cdd0a648abaf244e55bbbd73There is considerable uncertainty about the role that Artificial Intelligence (AI) might play in Environmental Impact Assessment (EIA), including into research. AI large language model (LLM) chatbots have the potential to increase the efficiency of EIA research, but their outputs can create concerns. This paper investigates the potential time savings achievable using LLM chatbots to undertake a critical review of literature focussing on the use of AI in EIA. Using a combination of ChatGPT and Elicit, literature was reviewed to identify 12 key issues associated with the use of AI in EIA and this paper was prepared in three and a half days from initial conception. A protocol is developed to assist researchers in fact checking evidence delivered through Elicit (or other machine learning tools) which serves as a novel outcome of this research. Using comments from three peer reviewers allowed some more objective reflection on the credibility of the LLM chatbot-derived output, on the appropriateness of the time savings, and on the future research needed on the application of LLM chatbots in this context.Impact Assessment and Project Appraisal681The potential time savings achievable using LLM chatbots to undertake a critical review of literature focussing on the use of AI in EIA is investigated and a protocol is developed to assist researchers in fact checking evidence delivered through Elicit (or other machine learning tools).Impact Assessment and Project Appraisal['Alan Bond', 'Dirk Cilliers', 'FP Retief', 'R. Alberts', 'C. Roos', 'Jurie Moolman']2024-03-05T00:00:00https://www.semanticscholar.org/paper/5ce64c77cc96ef89cdd0a648abaf244e55bbbd73 5fa80229b667e13d7dd6d086cdb91aa3e255f956Artificial Intelligence (AI) applications have the potential to revolutionize conventional healthcare practices, creating a more efficient and patient-centred approach with improved outcomes. This guide discuses eighteen AI-based applications in clinical decision-making, precision medicine, operational efficiency, and predictive analytics, including a real-world example of AI's role in public health during the early stages of the COVID-19 pandemic. Additionally, we address ethical questions, transparency, data privacy, bias, consent, accountability, and liability, and the strategic measures that must be taken to align AI with ethical principles, legal frameworks, legacy information technology systems, and employee skills and knowledge. We emphasize the importance of informed and strategic approaches to harness AI's potential and manage its challenges. Moreover, this guide underscores the importance of evaluating and integrating new skills and competencies to navigate and use AI-based technologies in healthcare management, such as technological literacy, long-term strategic vision, change management skills, ethical decision-making, and alignment with patient needs.Healthcare Management Forum01This guide discuses eighteen AI-based applications in clinical decision-making, precision medicine, operational efficiency, and predictive analytics, including a real-world example of AI's role in public health during the early stages of the COVID-19 pandemic.Healthcare management forum["Marie Ennis-O'Connor", "William T O'Connor"]2024-03-05T00:00:00https://www.semanticscholar.org/paper/5fa80229b667e13d7dd6d086cdb91aa3e255f956 532524f3db5f98c49764712e7b3708fc5a894863Pharmaceutical Research371The main applications of AI in various aspects of health care are described, from clinical studies to ethical implications, focusing on the international regulatory framework in countries in which AI is used, to discuss and compare strengthens and weaknesses.Pharmaceutical research['Alessia Romagnoli', 'Francesco Ferrara', 'Roberto Langella', 'Andrea Zovi']2024-03-05T00:00:00https://www.semanticscholar.org/paper/532524f3db5f98c49764712e7b3708fc5a894863 612ef4656702ab2582fa632eeb70924d18635f9ffinancial transaction and loan management processes need optimisation and simplification to make the ecosystem more productive and user-friendly. The loan and payment procedure will be simplified as a result. Streamlining the payment process aims to make it more easy, secure, and quick. In order to streamline loans and payments, this article presents a new approach to enhancing the efficiency of financial ecosystems via the use of artificial intelligence technologies. Financial data may be preprocessed using Latent Dirichlet Allocation (LDA) to extract valuable features and patterns. Then, we use Variational Autoencoders (VAEs), a kind of data classification algorithm that can learn and reflect complicated relationships, to sort the data. This step aids in the discovery of potential lending opportunities and the more precise classification of transactions. Step two will include making precise predictions about future monetary patterns and behaviours using an improved Singular Value Decomposition (SVD) for Predicting. The integration of these three steps creates a solid basis for an AI-powered streamlined financial ecosystem, which in turn simplifies loan options and payment processes.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)140A new approach to enhancing the efficiency of financial ecosystems via the use of artificial intelligence technologies via the use of Variational Autoencoders (VAEs), a kind of data classification algorithm that can learn and reflect complicated relationships, to sort data.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)['Rishi Chaudhry', 'Anshika Prakash', 'Naaz Gorowara', 'Ruchi Mittal', 'Varun Malik']2024-03-05T00:00:00https://www.semanticscholar.org/paper/612ef4656702ab2582fa632eeb70924d18635f9f 886d0aebb09f8ad747bfaca595bcf422bc4ad1d8BMC Medical Education350A software-based training tool, “Real-time Assessment of Dialogue in Motivational Interviewing” (ReadMI), that aims to advance the skill acquisition of medical students as they learn the MI approach and artificial intelligence can be utilized both for the measurement of MI skill acquisition and as an instructional aid.BMC Medical Education['P. Hershberger', 'Yong Pei', 'Dean A Bricker', 'Timothy N. Crawford', 'Ashutosh Shivakumar', 'Angela Castle', 'Katharine A. Conway', 'Raveendra Medaramitta', 'Maria Rechtin', 'Josephine F Wilson']2024-03-05T00:00:00https://www.semanticscholar.org/paper/886d0aebb09f8ad747bfaca595bcf422bc4ad1d8 cf6735715a14380de792fbc65b5661f33d5882c4As in all sectors of science and industry, artificial intelligence (AI) is meant to have a high impact in the discovery of antibodies in the coming years. Antibody discovery was traditionally conducted through a succession of experimental steps: animal immunization, screening of relevant clones, in vitro testing, affinity maturation, in vivo testing in animal models, then different steps of humanization and maturation generating the candidate that will be tested in clinical trials. This scheme suffers from different flaws, rendering the whole process very risky, with an attrition rate over 95%. The rise of in silico methods, among which AI, has been gradually proven to reliably guide different experimental steps with more robust processes. They are now capable of covering the whole discovery process. Amongst the players in this new field, the company MAbSilico proposes an in silico pipeline allowing to design antibody sequences in a few days, already humanized and optimized for affinity and developability, considerably de-risking and accelerating the discovery process.Frontiers in Drug Discovery500MAbSilico proposes an in silico pipeline allowing to design antibody sequences in a few days, already humanized and optimized for affinity and developability, considerably de-risking and accelerating the discovery process.Frontiers in Drug Discovery['Astrid Musnier', 'Christophe Dumet', 'Saheli Mitra', 'Adrien Verdier', 'Raouf Keskes', 'Augustin Chassine', 'Yann Jullian', 'Mélanie Cortes', 'Yannick Corde', 'Z. Omahdi', 'Vincent Puard', 'T. Bourquard', 'A. Poupon']2024-03-05T00:00:00https://www.semanticscholar.org/paper/cf6735715a14380de792fbc65b5661f33d5882c4 20ecff8a738fcfa2588e1663b547a618dc8528aeIn agriculture, important unanswered questions about machine learning and artificial intelligence (ML/AI) include will ML/AI change how food is produced and will ML algorithms replace or partially replace farmers in the decision process. As ML/AI technologies become more accurate, they have the potential to improve profitability while reducing the impact of agriculture on the environment. However, despite these benefits, there are many adoption barriers including cost, and that farmers may be reluctant to adopt a decision tool they do not understand. The goal of this special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect our current workforce. The papers are separated into three sections: Machine Learning within Crops, Pasture, and Irrigation; Machine Learning in Predicting Crop Disease; and Society and Policy of Machine Learning.Agronomy Journal350This special issue is to discuss cutting‐edge research on the use of ML/AI technologies in agriculture, barriers to the adoption of these technologies, and how technologies can affect the authors' current workforce.Agronomy Journal['David E. Clay', 'Skye Brugler', 'Bhavna Joshi']2024-03-05T00:00:00https://www.semanticscholar.org/paper/20ecff8a738fcfa2588e1663b547a618dc8528ae e2e349bd2bb76339f7df013f3d1d5dbdb8c5da32The present literature review explores the growing impact of artificial intelligence (AI) on the justice system. It sheds light on the prospects, obstacles, and probable consequences of its assimilation. Utilizing a broad array of scholarly resources, we examine the implementation of AI in various domains, including but not limited to predictive law enforcement, risk evaluation, evidentiary analysis, and judicial decision-making. The review recognizes the advantages of artificial intelligence, such as enhanced efficacy, precision, and impartiality in legal proceedings, while also expressing apprehensions regarding potential partialities, ethical predicaments, and risks to confidentiality and human liberties. Furthermore, it is crucial to underscore the significance of interdisciplinary cooperation and comprehensive regulatory frameworks in guaranteeing the judicious and impartial integration of AI technologies in the justice system. The present study endeavors to make a significant scholarly contribution to the ongoing discourse surrounding artificial intelligence and its intersection with the legal field. By examining the opportunities and challenges of integrating AI in legal systems, this review provides specific insights into formulating policies around algorithmic accountability, transparency, and ethical safeguards to ensure responsible AI adoption.    Received: 27 October 2023 / Accepted: 29 February 2024 / Published: 5 March 2024Academic Journal of Interdisciplinary Studies320Examining the opportunities and challenges of integrating AI in legal systems, this review provides specific insights into formulating policies around algorithmic accountability, transparency, and ethical safeguards to ensure responsible AI adoption.Academic Journal of Interdisciplinary Studies['Alfonso Renato Vargas-Murillo', 'Ilda Nadia Monica de la Asuncion Pari-Bedoya', 'Adriana Margarita Turriate-Guzmán', 'Cintya Amelia Delgado-Chávez', 'Franshezka Sanchez-Paucar']2024-03-05T00:00:00https://www.semanticscholar.org/paper/e2e349bd2bb76339f7df013f3d1d5dbdb8c5da32 1308e37c84ab9f6fa47c8567adc9b6d5aaabd10d The fulcrum of this work is knowledge: what it is and how it is generated within the context of a capitalist society. First, Marx’s analysis of the objective labour process is extended to the mental labour process. Then, objective and mental labour processes are defined in terms of objective and mental transformations, with consideration paid to which of the two types of transformation is determinant. This requires a discussion of dialectical logic and formal logic. Within dialectical logic, two types of processes are introduced: open ended and pre-determined. It is argued that computers (both traditional and quantum) and Artificial Intelligence cannot generate new knowledge, because they (a) rely on formal logic, i.e. they cannot engage in open-ended dialectical processes, and (b) are impervious to social determination. Connectedly, Artificial Intelligence systems such as ChatGPT cannot be a substitute for human thought or writing, because of the inevitability of ‘model collapse’. Next, focus is shifted to a specific form of knowledge: the ‘Copenhagen interpretation’ of quantum mechanics. It is shown that this interpretation is steeped in irrationalism and that it is a variant of pro-capitalist ideology. Finally, the social-historical roots of this ideology are revealed.Historical Materialism510The fulcrum of this work is knowledge: what it is and how it is generated within the context of a capitalist society, as well as the social-historical roots of this ideology are revealed.Historical Materialism['G. Carchedi']2024-03-05T00:00:00https://www.semanticscholar.org/paper/1308e37c84ab9f6fa47c8567adc9b6d5aaabd10d ff9cf7bbd68c9c7deb0fe1de857b037e829dce78This research aimed to assess the current applications of AI in the healthcare sector in Riyadh and their influence on digital transformation, and to identify the opportunities presented by expanding AI adoption to improve healthcare services in alignment with Vision 2030, and to examine the challenges facing greater integration of AI technologies into Riyadh's healthcare system. The research addresses the challenges faced by Riyadh's health sector and examines how artificial intelligence can be used to overcome these challenges, including improving the quality of health services, enhancing operational efficiency, and supporting scientific research, By analyzing data and reviewing previous studies, the research shows how AI technologies can contribute to the early detection of diseases, providing dedicated health care, and improving the management of health facilities. The research also discusses the impact of artificial intelligence on medical education and training and explores how it can enhance scientific research in the field of health. The findings indicate that AI has the potential to significantly transform Riyadh's healthcare sector, contributing to the realization of Vision 2030. The research concludes with recommendations for the effective application of artificial intelligence in the health system, emphasizing the importance of innovation and technical integration for the future of healthcare in the Kingdom.Academic Journal of Research and Scientific Publishing00The research shows how AI technologies can contribute to the early detection of diseases, providing dedicated health care, and improving the management of health facilities, as well as enhance scientific research in the field of health.Academic Journal of Research and Scientific Publishing['Amnah Muafa', 'Saed Al-Obadi', 'Norah Al-Saleem', 'Amal Taweili', 'Abdulrahman Al-Amri']2024-03-05T00:00:00https://www.semanticscholar.org/paper/ff9cf7bbd68c9c7deb0fe1de857b037e829dce78 5c4c873e5cd86997dd9da6aad5a0c157f91faa5cThe new generation of artificial intelligence products, represented by ChatGPT and Wenyan Yixin, is rapidly and extensively integrating into human production and life at an unprecedented pace, breadth, and depth. Generative artificial intelligence possesses distinct characteristics and functionalities compared to rule-based artificial intelligence, exerting a more prominent dual impact on the high-quality development of China's economy. On the one hand, it can promote high-quality economic development through empowering effects such as innovation-driven effects, enhanced production efficiency, and industrial transformation and upgrading effects. On the other hand, it may give rise to deep-seated risks, including labor market disruptions, market monopolization issues, national security risks, and misinformation phenomena, which could hinder high-quality economic development. It is crucial to have a correct understanding and a scientific approach, leveraging its positive effects while proactively addressing negative impacts, guiding it to better serve the needs of China's high-quality economic development.Journal of Education, Humanities and Social Sciences170It is crucial to have a correct understanding and a scientific approach, leveraging its positive effects while proactively addressing negative impacts, guiding it to better serve the needs of China's high-quality economic development.Journal of Education, Humanities and Social Sciences['Zhenzhen Li']2024-03-05T00:00:00https://www.semanticscholar.org/paper/5c4c873e5cd86997dd9da6aad5a0c157f91faa5c e5f24a5bdd8ffc6f2659e8c5793f5e09a4ff0785The impact of artificial intelligence on enterprises has gradually penetrated the human resource management of enterprises, and artificial intelligence is accelerating the excess of traditional human resources to predictive human resources, which also requires more talents in the field of artificial intelligence to devote themselves to enterprises. Through literature review and relevant cases, this paper finds that the current enterprise performance management has the problems of backward performance appraisal methods, lack of effective performance communication, and insufficient use of performance evaluation results. It is found that with the help of AI, enterprises can use big data and algorithms, enterprises can establish a more scientific performance management system, use AI to select appropriate performance management tools, and finally get effective performance appraisal results. Enterprises can use AI coaching and transparent performance feedback to promote performance communication and make performance results can play a role in promoting employee performance improvement. This paper concludes that the reasonable use of AI by enterprises can solve many existing problems of their performance management and can save time and labor costs for enterprises. Therefore, enterprises need to seize the opportunities brought by AI and pay attention to the research of AI technology to help improve performance management.Journal of Education, Humanities and Social Sciences170It is found that with the help of AI, enterprises can use big data and algorithms, enterprises can establish a more scientific performance management system, use AI to select appropriate performance management tools, and finally get effective performance appraisal results.Journal of Education, Humanities and Social Sciences['Baihui Liu', 'Mingyi Sun', 'Zhixiong Wang']2024-03-05T00:00:00https://www.semanticscholar.org/paper/e5f24a5bdd8ffc6f2659e8c5793f5e09a4ff0785 70e3deb977ca1a463cdb4c00825fa2f120fd9f1aCybersecurity is a fast-growing and evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Not only have there been a lot more cyberattacks in recent years, but they have also gotten much more advanced. Therefore, developing a cyber-resilient strategy is most significant. In the event of a cyberattack, traditional security measures are insufficient to prevent data leaks. Cybercriminals have mastered the use of cutting-edge methods and powerful tools for data intrusion, hacking, and assault. In this, we are proposing applications of artificial intelligence (AI) technology in the creation of intelligent models for securing systems against attackers. AI technologies can quickly advance to meet complicated problems, making them useful as fundamental cybersecurity tools to identify malware attacks, AI-based systems can provide efficient and robust cyber security against phishing and spam emails, network intrusions, and data breach capabilities and alert the security during the impact. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.International Journal For Multidisciplinary Research00AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses is explored, and applications of artificial intelligence (AI) technology in the creation of intelligent models for securing systems against attackers are proposed.International Journal For Multidisciplinary Research['Sumit Kumar Das', 'Payal Panda']2024-03-05T00:00:00https://www.semanticscholar.org/paper/70e3deb977ca1a463cdb4c00825fa2f120fd9f1a 37ddb951aae6d0ea7860f21e75dc1f888d515166We investigate firm decisions to adopt artificial intelligence (AI) technology and how adoption is sourced: by purchasing commercial readymade software, by developing or customizing solutions in‐house, or both. Using a cross‐sectional data set of 3143 firms from across Europe, we examine the extent to which sourcing strategies exhibit complementarity or substitution. We find that adoption of AI using readymade software as a sourcing strategy is now increasingly commonplace, but differs across industrial sectors. Further, complementarities between sourcing strategies are common across sectors, though with some differences in strength and some exceptions. Our results show that sourcing strategies play an important role in shaping AI adoption decisions among firms.Journal of Economics &amp; Management Strategy610The results show that sourcing strategies play an important role in shaping AI adoption decisions among firms and complementarities between sourcing strategies are common across sectors, though with some differences in strength and some exceptions.Journal of Economics &amp; Management Strategy['Charles Hoffreumon', 'Chris Forman', 'Nicolas van Zeebroeck']2024-03-05T00:00:00https://www.semanticscholar.org/paper/37ddb951aae6d0ea7860f21e75dc1f888d515166 77d5dcf63c24e173f4702da81fc38d6377cf5616Chirurgie130Current research focuses on artificial intelligence for the analysis of intraoperative data as the prerequisite for assistance systems that support surgical decision making or warn of risks; however, these technologies raise new ethical questions for the surgical community that affect the core of surgical work.Chirurgie['Ann Wierick', 'André Schulze', 'Sebastian Bodenstedt', 'Stefanie Speidel', 'Marius Distler', 'Jürgen Weitz', 'Martin Wagner']2024-03-05T00:00:00https://www.semanticscholar.org/paper/77d5dcf63c24e173f4702da81fc38d6377cf5616 9bd6c9c05906c05ac83e94f72f768665ec06e350The term artificial intelligence (AI) describes a specific set of computer techniques that allow systems to do tasks that were previously believed to be exclusive to human intelligence. Artificial Intelligence (AI) technologies are developing at a rapid pace, which has changed the entrepreneurial landscape and opened up new avenues for growth and innovation. In order to provide light on how companies might use AI to their benefit, this study intends to explore the mutually beneficial link between entrepreneurship and artificial intelligence. Artificial intelligence (AI) has emerged as a cutting-edge technology that is revolutionizing corporate operations across a wide range of industries. This abstract examines the dynamic interplay between entrepreneurship and artificial intelligence (AI), emphasizing the numerous opportunities and difficulties present in this changing fieldInternational Journal of Advanced Research in Science, Communication and Technology60Examining the dynamic interplay between entrepreneurship and artificial intelligence, emphasizing the numerous opportunities and difficulties present in this changing field is examined.International Journal of Advanced Research in Science, Communication and Technology['Thasmi N.A', 'Dr. Sopna V. Muhammed']2024-03-05T00:00:00https://www.semanticscholar.org/paper/9bd6c9c05906c05ac83e94f72f768665ec06e350 30c237319d46c327d10b1db4dd75b87cd2b3533dArtificial intelligence today not only plays an important role in various industries, but also continues to integrate into all spheres of life at an extremely rapid pace. The educational sphere must also respond to these challenges, which must prepare future generations not only for living and working in such an environment, but also for conscious influence on it and its creation. The article describes the directions of artificial intelligence development, state policy regarding its implementation and use, ethical principles in the field of artificial intelligence, the perceived incompetence of the pedagogical community regarding artificial intelligence, and practical ways of using it in the educational process. Taking into account the concept of the development of artificial intelligence in Ukraine, the readiness of the pedagogical community and the results of observations of the educational process, the following methods of using artificial intelligence in the educational process are distinguished: improvement of the formation of the individual educational trajectory of the acquirer, improvement of the system for monitoring the process and results of training with the provision of feedback, diversification of the didactic tools for the educational process. Attention is drawn to the principles of using artificial intelligence that are common to all, namely: inclusive growth, sustainable development and well-being, human-centered values and justice, responsibility for the proper functioning of artificial intelligence systems.Scientific Journal of Polonia University00The article describes the directions of artificial intelligence development, state policy regarding its implementation and use, ethical principles in the field of artificial intelligence, the perceived incompetence of the pedagogical community regarding artificial intelligence, and practical ways of using it in the educational process.Scientific Journal of Polonia University['Serhii Maksymchuk', 'Halyna Voitkiv']2024-03-05T00:00:00https://www.semanticscholar.org/paper/30c237319d46c327d10b1db4dd75b87cd2b3533d 68482e8e0c2f5655b7e044639db5a31e6700c6a2Objective: This research aims to examine the knowledge level and awareness of Faculty of Medicine students about medical artificial intelligence technologies. Methods: In this study involving students studying at Medical Faculties in Turkey, descriptive questionnaire, and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) were used. The suitability of continuous variables for normal distribution was tested with the Shapiro-Wilk test. Descriptive statistics for continuous variables are presented as mean and standard deviation or median (Q1-Q3). Descriptive statistics for categorical variables are reported as frequencies and percentages. Homogeneity of variances was evaluated with the Levene test. Mann Whitney U test was used to compare the scale subdimension and total scores according to two independent groups; One-way Analysis of Variance or Kruskal Wallis test was used to compare the scale subdimensions and total scores according to more than two independent groups. Dunn-Bonferroni test was used for multiple comparisons if there was a significant difference between the groups. The relationship between MAIRS-MS subdimensions and MAIRS-MS score was evaluated with the Spearman correlation coefficient. MAIRS-MS reliability was determined by Cronbach alpha value. The value of pKonuralp Tip Dergisi00This research aims to examine the knowledge level and awareness of Faculty of Medicine students about medical artificial intelligence technologies in Turkey and descriptive questionnaire and the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) were used.Konuralp Tip Dergisi['Büşra Emi̇r', 'Tulin Yurdem', 'Tulin Ozel', 'Toygar Sayar', 'Teoman Atalay Uzun', 'Umit Akar', 'Unal Arda Colak']2024-03-05T00:00:00https://www.semanticscholar.org/paper/68482e8e0c2f5655b7e044639db5a31e6700c6a2 2f6aa0859d620a9c9b05fedf61978c199f81a92dThe economic sophistication of countries, technological advancements, and the influence of social networks and the media on our lives all accelerate the development of artificial intelligence (AI), which is gradually becoming established in everyday life. Through applied research, we try to point out how easy it is to use AI in the student's life when processing school assignments, with the fact that the quality of the output is sometimes indistinguishable from real students' written work. The initial results of this study show that AI-created works within general education subjects are almost indistinguishable from students' works and also suggest that teachers' recognition of AI-created works is more complex than expected and thus opened a discussion on the importance of comprehensive education and support of teachers.R&amp;E-SOURCE70The initial results of this study show that AI-created works within general education subjects are almost indistinguishable from students' works and suggest that teachers' recognition of AI-created works is more complex than expected and thus opened a discussion on the importance of comprehensive education and support of teachers.R&amp;E-SOURCE['Tatiana Kutiš', 'Kristína Žilínková']2024-03-05T00:00:00https://www.semanticscholar.org/paper/2f6aa0859d620a9c9b05fedf61978c199f81a92d 597217b2b213f5733693bf23570a9abee8ac9f26Journal of Russian Law01Journal of Russian Law['Egor Artemenko', 'Artem Tsirin']2024-03-05T00:00:00https://www.semanticscholar.org/paper/597217b2b213f5733693bf23570a9abee8ac9f26 6a34fed1acc6abeb09e57f24cc02c356a8d66366International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Karan Chawla']2024-03-05T00:00:00https://www.semanticscholar.org/paper/6a34fed1acc6abeb09e57f24cc02c356a8d66366 bcf0d152d0e1820877eacf5ca9e00e14adeafab6Clinical and Molecular Hepatology00Clinical and Molecular Hepatology['Ming-Ying Lu', 'Ming-Lung Yu']2024-03-05T00:00:00https://www.semanticscholar.org/paper/bcf0d152d0e1820877eacf5ca9e00e14adeafab6 d0f4d29e53eb7cc33e6bf854487874b2703eda0bDefense &amp; Security Analysis00Defense &amp; Security Analysis['Lance Y. Hunter', 'Craig D. Albert', 'Josh Rutland', 'Kristen Topping', 'Chris Hennigan']2024-03-05T00:00:00https://www.semanticscholar.org/paper/d0f4d29e53eb7cc33e6bf854487874b2703eda0b cc63ab2048f82db20b76a1053a8264d524d85daaInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Shifan Khanday Maya Ahmed']2024-03-05T00:00:00https://www.semanticscholar.org/paper/cc63ab2048f82db20b76a1053a8264d524d85daa ec68f5722aa2283f3d99cb33966a2196925591f4International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Karan Chawla']2024-03-05T00:00:00https://www.semanticscholar.org/paper/ec68f5722aa2283f3d99cb33966a2196925591f4 5077d5e91c4cc5059d71083eb8f97550346b9426Federal Grants &amp; Contracts00Federal Grants &amp; Contracts['Dr. Hal Finkel', 'Dr. Steven Lee', 'Dr. Margaret Lentz', 'Dr. Kalyan Perumalla', 'Dr. Robinson Pino', 'William Spotz']2024-03-05T00:00:00https://www.semanticscholar.org/paper/5077d5e91c4cc5059d71083eb8f97550346b9426 b99a46a66494f14f0902db08ff56a746f398a2b6International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Utkarsh Shukla Manu Mishra']2024-03-05T00:00:00https://www.semanticscholar.org/paper/b99a46a66494f14f0902db08ff56a746f398a2b6 a6e2fc8f9f9b4b0c76a47681b0a0dd71e1983998This research seeks to aid investors in forecasting market trends and making educated judgements via the use of an AI-based approach. To begin, financial information will be normalized using a two-stage technique including an Improved Hierarchical Clustering algorithm. This method takes contextual and temporal connections into account. The next step is to use Bi-LSTM, a model that may be utilized for both predication and classification. For precise financial data sequential presentation pattern identification and forecasting, the Bi-LSTM model is trained utilizing both historical and prospective data. The methodology's ability to provide valuable insights for educated investment choices in ever-changing financial markets is shown by validation using historical data, which also shows that it is more accurate than other known approaches. By outlining a comprehensive plan for combining clustering and deep learning methods, this study helps move AI-based financial forecasting forward.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)160The methodology's ability to provide valuable insights for educated investment choices in ever-changing financial markets is shown by validation using historical data, which shows that it is more accurate than other known approaches.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)['Naaz Gorowara', 'Jagdeep Singla', 'Rishi Chaudhry', 'Varun Malik', 'Ruchi Mittal']2024-03-05T00:00:00https://www.semanticscholar.org/paper/a6e2fc8f9f9b4b0c76a47681b0a0dd71e1983998 2a6b27a4a1ecf3130f2af4ef3ada622997d31562International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Shalini x']2024-03-05T00:00:00https://www.semanticscholar.org/paper/2a6b27a4a1ecf3130f2af4ef3ada622997d31562 6e732596641337a6a5014f7a8e14d8a74d4154c3The demand for reliable and intelligent solutions to identify and prevent financial fraud is growing in proportion to the complexity and sophistication of this crime. This article outlines a comprehensive plan to improve financial security via the use of state-of-the-art AI techniques. The suggested architecture consists of three main parts: a K-Means clustering for dataset preparation, an extended deep Q network (EDQN) for analysis and prediction, and a principal component analysis (PCA) for feature selection. An enhanced K-Means clustering method that can manage complex and large-scale financial data is our primary contribution. A key motivation for this strategy is the hope that it would facilitate the detection of legitimate and fraudulent activities by making it simpler to see clear patterns in the data. By streamlining the grouping process and producing more accurate clusters, a more efficient clustering technique improves the overall effectiveness of fraud detection. In the aftermath of getting the dataset ready, PCA feature selection follows. By lowering the dataset's dimensionality without sacrificing any of the valuable information, principal component analysis (PCA) finds and retains the most relevant characteristics. This guarantees that the chosen qualities greatly aid in the identification and prevention of fraudulent transactions, while also improving the computational efficiency of future activities. Lastly, an EDQN is used in a system for classification and prediction. By including characteristics that enable the model to comprehend more complex links in financial data, the EDQN enhances previous Deep Q Networks.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)180A comprehensive plan to improve financial security via the use of state-of-the-art AI techniques, which consists of three main parts: a K-Means clustering for dataset preparation, an extended deep Q network (EDQN) for analysis and prediction, and a principal component analysis (PCA) for feature selection.2024 International Conference on Emerging Smart Computing and Informatics (ESCI)['Rishi Chaudhry', 'Sandeep Kaur', 'Jagdeep Singla', 'Ruchi Mittal', 'Varun Malik']2024-03-05T00:00:00https://www.semanticscholar.org/paper/6e732596641337a6a5014f7a8e14d8a74d4154c3 fbdf7c9f8e1d5917aeb573ebc787f296ca277ac6Environmental Science and Technology00Environmental science & technology['J. Zimmerman', 'Gregory V. Lowry', 'F. Rosario‐Ortiz', 'Peng Wang']2024-03-05T00:00:00https://www.semanticscholar.org/paper/fbdf7c9f8e1d5917aeb573ebc787f296ca277ac6 3283571b67eb1da8ee121a4982b5383f9cd8b097This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to this topic were collected from three major publication databases Google Scholar, Pubmed, and Scopus. ChatGPT models were used to identify the category, scope, and relevant information from the research papers for automatic identification of relevant papers related to Breast Cancer Treatment (BCT), organization of papers according to scope, and identification of key information for survey paper writing. Evaluations performed using ground truth data annotated using subject experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research paper categories and 50\% of the papers were correctly identified by GPT-4 for their scopes. Further, the results demonstrate that GPT-4 can generate reasons for its decisions with an average of 27\% new words, and 67\% of the reasons given by the model were completely agreeable to the subject experts.arXiv.org290ChatGPT models were used to identify the category, scope, and relevant information from the research papers for automatic identification of relevant papers related to Breast Cancer Treatment, organization of papers according to scope, and identification of key information for survey paper writing.ArXiv['Anjalee de Silva', 'Janaka Wijekoon', 'Rashini K. Liyanarachchi', 'Rrubaa Panchendrarajan', 'Weranga Rajapaksha']2024-03-05T00:00:00https://www.semanticscholar.org/paper/3283571b67eb1da8ee121a4982b5383f9cd8b097 a67c4d7418ae0b0324c5d05c9f6877e152a24f3eНаука і техніка сьогодні00Наука і техніка сьогодні['Володимир Токар', 'Катерина Палагута', 'Мар’яна Сашньова']2024-03-04T00:00:00https://www.semanticscholar.org/paper/a67c4d7418ae0b0324c5d05c9f6877e152a24f3e a7752479dcbbef3e7c31bc1b3797d1d7e4d81162Digestive Diseases and Sciences510Artificial Intelligence and second-observer colonoscopy showed superior success in Adenoma Detection Rate when compared to single-observer colonoscopy and net ranking model favors the superiority of AI to the second observer.Digestive Diseases and Sciences['M. Gangwani', 'H. Haghbin', 'Rizwan Ishtiaq', 'Fariha Hasan', 'Julia Dillard', 'Fouad Jabbar', 'D. Dahiya', 'Hassam Ali', 'Shaharyar Salim', 'Wade Lee-Smith', 'A. Sohail', 'Sumant Inamdar', 'Muhammad Aziz', 'Benjamin Hart']2024-03-04T00:00:00https://www.semanticscholar.org/paper/a7752479dcbbef3e7c31bc1b3797d1d7e4d81162 1c6d7a15df4a322e70853b1244ab32bba4e6d549Currently, in the organizational research community, artificial intelligence (AI), machine learning (ML), and big data techniques are being vigorously explored as a set of modern‐day approaches contributing to a multidisciplinary science of people at work. This paper discusses more specifically how these sophisticated technologies, methods, and data might together advance the science of people at work through various routes, including improving theory and knowledge, construct measurements, and predicting real‐world outcomes. Inspired by the four articles in the current special issue highlighting several of these aspects in essential ways, we also share other possibilities for future organizational research. In addition, we indicate many key practical, ethical, and institutional challenges with research involving AI/ML and big data (i.e., data accessibility, methodological skill gaps, data transparency, privacy, reproducibility, generalizability, and interpretability). Taken together, the opportunities and challenges that lie ahead in the areas of AI and ML promise to reshape organizational research and practice in many exciting and impactful ways.Personnel Psychology670How these sophisticated technologies, methods, and data might together advance the science of people at work through various routes, including improving theory and knowledge, construct measurements, and predicting real‐world outcomes is discussed.Personnel Psychology['Sang Eun Woo', 'Louis Tay', 'Frederick L Oswald']2024-03-04T00:00:00https://www.semanticscholar.org/paper/1c6d7a15df4a322e70853b1244ab32bba4e6d549 6c2af577d1032a2e74e7043fd7d87c0a17b588d5Introduction Stroke remains the second leading cause of death worldwide, with many survivors facing significant disabilities. In acute stroke care, the timeless adage 'Time is brain' underscores the vital need for quick action. Innovative Artificial Intelligence (AI) technology potentially offers swift detection and management of acute ischemic strokes, leading acute stroke care towards enhanced automation. Methods The study is registered with Prospero under CRD42024496716 and adheres to the Problem, Intervention, Comparison, and Outcomes framework (PICO). The analysis used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Cochrane database, IEEE, Web of Science, ArXiv, MedRxiv Web of Science, and Semantic Scholar. The articles included were published between 2019 and 2023. Out of 1,528 articles identified, thirty-eight met the inclusion criteria. Results We compared AI-augmented Large Vessel Occlusion (LVO) detection and non-AI in various patient processing times related to emergent endovascular therapy in acute ischemic strokes. Triage Time, Door-to-Intervention Notification Time (INR), and Door-to -Arterial Puncture Time revealed an odds ratio (OR) of 0.39 (95% CI: 0.29 - 0.54, p < 0.001), 0.30 30 (95% CI: 0.21 - 0.42, p < 0.001), and 0.50 (95% CI: 0.30 - 0.82, p = 0.007), respectively. CT-to-Puncture-Time and Door-to-CTA-Time yielded an OR of 0.57 (95% CI: 0.31 - 1.04, p = 0.065) and 0.77 (95% CI: 0.37-1.60, p = 0.489), respectively. The Last Known Well (LWK) to Time of Arrival resulted in an OR of 1.15 (95% CI: 0.83 - 1.59, p = 0.409). AI stroke detection sensitivity OR of 0.91 (95% CI: 0.88 - 0.95, p < 0.001), National Institute of Health score (NIHSS) 16.20 (95% CI: 14.96 - 17.45, p = 0.001). Patient Transfer-Times between primary and comprehensive stroke centers generated an OR of 0.98 (95% CI: 0.73 - 1.32, p = 893). Similarly, Door-in-Door-Out Time (DIDO) had an OR of 1.19 (95% CI: 0.21 - 6.88, p = 0.848). The results indicated significant differences across several parameters between the AI and non-AI groups. Conclusion Our findings highlight how AI augments healthcare providers' ability to detect and manage strokes swiftly and accurately within acute care settings. As these technologies advance and AI becomes more integrated into healthcare systems, longitudinal studies are critical in evaluating its impact on workflow efficiency, cost-effectiveness, and clinical outcomes.medRxiv90Compared AI-augmented Large Vessel Occlusion detection and non-AI in various patient processing times related to emergent endovascular therapy in acute ischemic strokes indicated significant differences across several parameters between the AI and non-AI groups.['Elan Zebrowitz', 'Sonali Dadoo', 'Paige Brabant', 'Anaz Uddin', 'E. Aifuwa', 'Danielle Maraia', 'Mill Etienne MD Mph', 'Neriy Yakubov', 'Myoungmee Babu', 'Benson Babu MD Mba']2024-03-04T00:00:00https://www.semanticscholar.org/paper/6c2af577d1032a2e74e7043fd7d87c0a17b588d5 f651d9e3d354fa69538fcaaa4b48a0503f88674fThis study examined how social media influence the knowledge gap between low and high socioeconomic status (SES) groups in artificial intelligence (AI), a highly debated scientific subject warranting immediate scholarly attention. A national survey of U.S. adults ( N = 965) was conducted. The results showed that education and social media exposure to AI information (SME) predicted greater AI knowledge, and SME did not moderate the SES-based AI knowledge gap. Furthermore, information elaboration moderated the association between SME and the AI knowledge gap. SME was associated with a smaller AI knowledge gap when information elaboration was high rather than low.Science communication760The results showed that education and social media exposure to AI information (SME) predicted greater AI knowledge, and SME did not moderate the SES-based AI knowledge gap, and information elaboration moderated the association between SME and the AI knowledge gap.Science Communication['Wenbo Li', 'Shan Xu', 'Xiao Zheng', 'Ruoyu Sun']2024-03-04T00:00:00https://www.semanticscholar.org/paper/f651d9e3d354fa69538fcaaa4b48a0503f88674f 85f2a15500f418e826197262227e0575d426beafJournal of imaging informatics in medicine630This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.Journal of imaging informatics in medicine['M. Salehi', 'Hamid Harandi', 'S. Mohammadi', 'Mohammad Shahrabi Farahani', 'Shayan Shojaei', 'Ramy R. Saleh']2024-03-04T00:00:00https://www.semanticscholar.org/paper/85f2a15500f418e826197262227e0575d426beaf d0d346845add90d1ce31892f538fd75d4363d3ceAs urbanization accelerates, cities become more complex, coming along with more complex urban issues. Agent‐based model (ABM) is a traditional method to simulate activities in a complex system, which has been widely applied in urban studies. However, due to its rigid initial settings, ABM has been criticized for its lack of intelligence, especially in dealing with modern urban issues. With the success of artificial intelligence (AI) and complexity science, it is generally agreed that ABM can be enhanced with AI agents, a promising technology that can bridge the gaps. For that, this article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework. The sections include that (1) ABM is Al; (2) ABM provides training data for Al; (3) Al provides data for ABM; (4) ABM is a submodule in the ensemble Al; (5) Al leads an optimization framework with ABM participation; (6) Al tunes ABM initialization parameters; (7) Al provides the environment for ABM; (8) Al aids in choosing the agent's attributes; (9) Al provides behaviors for agents in ABM; (10) Al helps to evaluate the performance of ABM. For each case, some typical works are examined for illustration. Finally, we discuss some of the current limitations and prospects for future development.Transactions on GIS570This article provides a systematic review, in which 10 subsections correspond to 10 different ways that AI can work with ABM in the methodological framework, and discusses some of the current limitations and prospects for future development.Transactions in GIS['Zijian Guo', 'Xintao Liu']2024-03-04T00:00:00https://www.semanticscholar.org/paper/d0d346845add90d1ce31892f538fd75d4363d3ce 764cba497633a480079639f75876798f1d4a4b46The augmentation of Artificial Intelligence (AI) in tourism businesses has gained considerable attention. AI promulgates significant transformation for the growth and development of the tourism industries of the world. However, for the tourism industry of India, although AI promises accelerated progress, it is a risky business. The socio-economic scenery of India is yet to harmonize with AI-based business. In this paper, the opportunities and challenges of the use of AI in the tourism industry of India are analyzed in detail. The cross-cutting opportunities that upgrade the tourism industry of India are detailed and the challenges that arise from the existing economic and social conditions of India are also listed in the present study. Migration Letters220The opportunities and challenges of the use of AI in the tourism industry of India are analyzed in detail and the challenges that arise from the existing economic and social conditions of India are listed in the present study.Migration Letters['Mithichar Basumatary', 'Gunajit Sarma']2024-03-04T00:00:00https://www.semanticscholar.org/paper/764cba497633a480079639f75876798f1d4a4b46 23bcc2fd6eec40eccbdd07c136f46ecac58cfe11Scientific Reports510An AI-based digital twin of a pathologist, vPatho, is tested on 2603 histological images of prostate tissue stained with hematoxylin and eosin to highlight the potential utility of AI in developing a digital twin for a pathologist.Scientific Reports['Okyaz Eminaga', 'Mahmoud Abbas', 'C. Kunder', 'Yuri Tolkach', 'Ryan Han', 'James D. Brooks', 'R. Nolley', 'Axel Semjonow', 'M. Boegemann', 'Robert West', 'Jin Long', 'Richard E. Fan', 'O. Bettendorf']2024-03-04T00:00:00https://www.semanticscholar.org/paper/23bcc2fd6eec40eccbdd07c136f46ecac58cfe11 9dc60c46d8fb43b999b50a7c6474a93b846e7eccThe term artificial intelligence (AI) was coined in the 1950s and it has successfully made its way into different fields of medicine. Forensic sciences and AI are increasingly intersecting fields that hold tremendous potential for solving complex criminal investigations. Considering the great evolution in the technologies applied to forensic genetics, this literature review aims to explore the existing body of research that investigates the application of AI in the field of forensic genetics. Scopus and Web of Science were searched: after an accurate evaluation, 12 articles were included in the present systematic review. The application of AI in the field of forensic genetics has predominantly focused on two aspects. Firstly, several studies have investigated the use of AI in haplogroup analysis to enhance and expedite the classification process of DNA samples. Secondly, other research groups have utilized AI to analyze short tandem repeat (STR) profiles, thereby minimizing the risk of misinterpretation. While AI has proven to be highly useful in forensic genetics, further improvements are needed before using these applications in real cases. The main challenge lies in the communication gap between forensic experts: as AI continues to advance, the collaboration between forensic sciences and AI presents immense potential for transforming investigative practices, enabling quicker and more precise case resolutions.Applied Sciences1100As AI continues to advance, the collaboration between forensic sciences and AI presents immense potential for transforming investigative practices, enabling quicker and more precise case resolutions.Applied Sciences['F. Sessa', 'M. Esposito', 'Giuseppe Cocimano', 'S. Sablone', 'Michele Ahmed Antonio Karaboue', 'M. Chisari', 'Davide Giuseppe Albano', 'M. Salerno']2024-03-04T00:00:00https://www.semanticscholar.org/paper/9dc60c46d8fb43b999b50a7c6474a93b846e7ecc 1dc0b7ecbf7eb20e9dee164f0b4f6537f36c1e53The difficulty of ensuring cyber-security is steadily growing as a result of the alarming development in computer connectivity and the sizeable number of applications associated to computers in recent years. The system also requires robust defines against the growing number of cyber threats. As a result, a possible role for cyber-security might be performed by developing intrusion detection systems (ids) to detect inconsistencies and threats in computer networks. An effective data-driven intrusion detection system has been created with the use of artificial intelligence, particularly machine learning techniques. This research proposes a novel twin support vector machine (tsvm) based security model which first considers the security features ranking according to their relevance before developing an ids model based on the significant features that have been selected. By lowering the feature dimensions, this approach not only improves predictive performance for unidentified tests but also lowers the model's computational expense. Trials are conducted using four common ml techniques to compare the results to those of the current approaches (decision tree, random decision forest, random tree, and artificial neural network). The experimental findings of this study confirm that the suggested methods may be used as learning-based models for network intrusion detection and demonstrate that, when used in the real world, they outperform conventional ml techniques.Turkish Journal of Computer and Mathematics Education00This research proposes a novel twin support vector machine (tsvm) based security model which first considers the security features ranking according to their relevance before developing an ids model based on the significant features that have been selected and lowers the feature dimensions.Turkish Journal of Computer and Mathematics Education (TURCOMAT)['Dr. V. Nagagopiraju', 'Panguluri Ashok', 'Kancheti Dhana Lakshmi', 'Chowdam Likhitha', 'Mandalapu Venkata Sasi Kumar']2024-03-04T00:00:00https://www.semanticscholar.org/paper/1dc0b7ecbf7eb20e9dee164f0b4f6537f36c1e53 f15bca5d80030112a9888d22e97937ff7ecf4656Journal of Arts, Literature, Humanities and Social Sciences00Journal of Arts, Literature, Humanities and Social Sciences[]2024-03-04T00:00:00https://www.semanticscholar.org/paper/f15bca5d80030112a9888d22e97937ff7ecf4656 0eb837d7da342e25ab5e7a65ca948e7a9f08a591enadakultura00enadakultura['Anna Gigauri']2024-03-04T00:00:00https://www.semanticscholar.org/paper/0eb837d7da342e25ab5e7a65ca948e7a9f08a591 20d2a91a5ab7c6858f92f9ae3865505adeff079cArtificial Intelligence (AI) is increasingly employed in various decision-making tasks, typically as a Recommender, providing recommendations that the AI deems correct. However, recent studies suggest this may diminish human analytical thinking and lead to humans' inappropriate reliance on AI, impairing the synergy in human-AI teams. In contrast, human advisors in group decision-making perform various roles, such as analyzing alternative options or criticizing decision-makers to encourage their critical thinking. This diversity of roles has not yet been empirically explored in AI assistance. In this paper, we examine three AI roles: Recommender, Analyzer, and Devil's Advocate, and evaluate their effects across two AI performance levels. Our results show each role's distinct strengths and limitations in task performance, reliance appropriateness, and user experience. Notably, the Recommender role is not always the most effective, especially if the AI performance level is low, the Analyzer role may be preferable. These insights offer valuable implications for designing AI assistants with adaptive functional roles according to different situations.arXiv.org513Three AI roles are examined: Recommender, Analyzer, and Devil's Advocate, and their effects across two AI performance levels are evaluated, showing each role's distinct strengths and limitations in task performance, reliance appropriateness, and user experience.ArXiv['Shuai Ma', 'Chenyi Zhang', 'Xinru Wang', 'Xiaojuan Ma', 'Ming Yin']2024-03-04T00:00:00https://www.semanticscholar.org/paper/20d2a91a5ab7c6858f92f9ae3865505adeff079c 7cb06d0ea56572ecb5cb85e642dda98a8ba1954bDiscover Artificial Intelligence422The research recommends a strategic approach to AI adoption in the public sector, considering organizational, ethical, and societal implications while recognizing the possibility of AI's transformative impacts on governments' service provision.Discov. Artif. Intell.['K. Alhosani', 'S. Alhashmi']2024-03-04T00:00:00https://www.semanticscholar.org/paper/7cb06d0ea56572ecb5cb85e642dda98a8ba1954b ff8799ff72e02ca5f9fa0c64efd78ee20ddbe9fdData annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain-specific professional tasks, it has become increasingly important to help beginning annotators identify how their early-stage knowledge can lead to inaccurate answers, which in turn, helps to ensure quality annotations at scale. To investigate this issue, we conducted a formative study involving eight individuals from the field of disaster management, each possessing varying levels of expertise. The goal was to understand the prevalent factors contributing to disagreements among annotators when classifying Twitter messages related to disasters and to analyze their respective responses. Our analysis identified two primary causes of disagreement between expert and beginner annotators: 1) a lack of contextual knowledge or uncertainty about the situation, and 2) the absence of visual or supplementary cues. Based on these findings, we designed a Context interface, which generates aids that help beginners identify potential mistakes and provide the hidden context of the presented tweet. The summative study compares Context design with two widely used designs in data annotation UI, Highlight and Reasoning-based interfaces. We found significant differences between these designs in terms of attitudinal and behavioral data. We conclude with implications for designing future interfaces aiming at closing the knowledge gap among annotators.International Conference on Intelligent User Interfaces641A Context interface is designed, which generates aids that help beginners identify potential mistakes and provide the hidden context of the presented tweet, and which has implications for designing future interfaces aiming at closing the knowledge gap among annotators.ArXiv['Zinat Ara', 'Hossein Salemi', 'Sungsoo Ray Hong', 'Yasas Senarath', 'Steve Peterson', 'A. Hughes', 'Hemant Purohit']2024-03-04T00:00:00https://www.semanticscholar.org/paper/ff8799ff72e02ca5f9fa0c64efd78ee20ddbe9fd fc0df10cf6b4e9dfd999ef0290c6389e8c4f187bBackground: The literature on artificial intelligence (AI) in healthcare is expanding quickly and is a key factor in healthcare promotion. Objective: This analysis’s goal is to offer a dynamic and comprehensive bibliometric analysis of publications on artificial intelligence in the field of health care. Methods: All currently available and highly referenced healthcare-related AI research papers published in English up to April 2023 were found by searching the Web of Science (Clarivate PLC). A search technique was created based on bibliometric indications to evaluate the title’s eligibility, using the abstract and full text as necessary. Results: 6254 items were found during the search, and 3107 of those papers were used in the analysis. USA was the country that published most research papers in the field of AI in healthcare. India stood in 4th place, with China and the United Kingdom in front of them. Relevant Affiliations were found in Stanford University, Harvard Med School, followed by King Abdul Aziz University. Conclusion: Future research should concentrate on bridging the gaps between clinical applications and AI healthcare research. More research should be done, especially in the areas of ethics, data governance, clarity of data, and additional inputs in the form of training that might be required for healthcare workers to update their skills in the world of AI-assisted healthcare.Journal of Autonomous Intelligence00Future research should concentrate on bridging the gaps between clinical applications and AI healthcare research, especially in the areas of ethics, data governance, clarity of data, and additional inputs that might be required for healthcare workers to update their skills in the world of AI-assisted healthcare.Journal of Autonomous Intelligence['Vineed Kumar Vijayan', 'Smiju Is', 'Jose John']2024-03-04T00:00:00https://www.semanticscholar.org/paper/fc0df10cf6b4e9dfd999ef0290c6389e8c4f187b 9b8b16fe379c1b9d977a8cea14b6b4abf33d852eOBJECTIVE This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. MATERIALS AND METHODS Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions. RESULTS Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration. CONCLUSION The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.JAMIA Journal of the American Medical Informatics Association840This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth, and proposes three future research directions with a focus on responsible use.Journal of the American Medical Informatics Association : JAMIA['Javad Pool', 'M. Indulska', 'Shazia Sadiq']2024-03-04T00:00:00https://www.semanticscholar.org/paper/9b8b16fe379c1b9d977a8cea14b6b4abf33d852e 8416d11129c455961148d090afc1006a0261c9b3Introduction More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings. Methods Ten-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis. Results The UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00). Discussion We conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.Frontiers in Cardiovascular Medicine290It is concluded that preeclampsia can be identified with high accuracy via application of AI models to ECG data.Frontiers in Cardiovascular Medicine['L. Butler', 'F. Gunturkun', 'L. Chinthala', 'I. Karabayir', 'M. S. Tootooni', 'Berna Bakir-Batu', 'T. Celik', 'O. Akbilgic', 'R. L. Davis']2024-03-04T00:00:00https://www.semanticscholar.org/paper/8416d11129c455961148d090afc1006a0261c9b3 abb496a6a2bd650ca7c3275748909509af441b43Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating artificial intelligence (AI) across various applications to enhance production processes. We cite two critical areas where AI can play a key role in industry: product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.Electronics780A survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance, and an overview of the AI solution development approach for product quality control and predictive maintenance.Electronics['Tojo Valisoa Andrianandrianina Johanesa', 'Lucas Equeter', 'S. Mahmoudi']2024-03-04T00:00:00https://www.semanticscholar.org/paper/abb496a6a2bd650ca7c3275748909509af441b43 2afcb078b6ec3d08504d4c7eba2cb1668d9d80f8The identification of new biological targets is crucial to advance cancer therapy, but deciphering the fiendishly complex processes that drive and sustain disease can be tedious and resource intensive. To optimize and accelerate the drug discovery process, artificial intelligence (AI) platforms are emerging that enable fast and cost-effective identification and prioritization of novel and disease-specific therapeutic targets with optimal target profiles, balancing confidence, novelty and commercial tractability. AI-streamlined target profiling has the potential to significantly improve the commercial burden of traditional drug development, and provides an unbiased approach for novel target identification. Here, we discuss the AI-assessed target profile and clinical relevance of genes recently identified by our AI-driven target discovery platform as top priority cancer targets.Future Medicine AI600The AI-assessed target profile and clinical relevance of genes recently identified by the AI-driven target discovery platform as top priority cancer targets are discussed.Future Medicine AI['Xi Long', 'Barbara Steurer', 'Chun Wai Wong', 'Ekaterina Kozlova', 'Vladimir Naumov', 'F. Pun', 'Alex Aliper', 'Fengzhi Ren', 'Alex Zhavoronkov']2024-03-04T00:00:00https://www.semanticscholar.org/paper/2afcb078b6ec3d08504d4c7eba2cb1668d9d80f8 04aaa136cb6beb7cf1436ca7a649a38748b2ed52This paper explores the profound impact of artificial intelligence (AI) on marketing strategies across various sectors. AI has revolutionized marketing practices by enabling personalized customer experiences, enhancing data analytics, and optimizing advertising campaigns. The paper examines case studies from companies like Alibaba, Sephora, and Toyota, highlighting how they leverage AI to improve customer engagement and drive sales. Additionally, it discusses the components of AI marketing, including machine learning and big data analytics, and their role in bridging the gap between data collection and actionable insights. Furthermore, the paper delves into the implications of AI for different aspects of marketing, such as email marketing, advertising, chatbots, predictive analysis, and dynamic pricing. By synthesizing insights from a diverse range of sources, this paper provides a comprehensive overview of the transformative impact of AI on modern marketing practices.International Journal For Multidisciplinary Research150The paper examines case studies from companies like Alibaba, Sephora, and Toyota, highlighting how they leverage AI to improve customer engagement and drive sales and delves into the implications of AI for different aspects of marketing, such as email marketing, advertising, chatbots, predictive analysis, and dynamic pricing.International Journal For Multidisciplinary Research['Anish Tadimarri', 'Suhas Jangoan', 'Kapil Kumar Sharma', 'Ashokkumar Gurusamy']2024-03-04T00:00:00https://www.semanticscholar.org/paper/04aaa136cb6beb7cf1436ca7a649a38748b2ed52 8e5ca72ec3d3376195ae579e26fd3538372e536fMaintaining health and safety is essential for workers’ quality of life, and thus, this has become one of the main priorities for industrial enterprises. Electric welders want required safety precautions to be implemented during work in industries with safety risks, especially muscle injuries. This challenge needs to be addressed by the safety officer, who should suggest a way to decrease the risk for workers. However, traditional assessment based on human evaluation and the need for expertise and accuracy in risk assessment have produced muscle injuries. Thus, using artificial intelligence (AI) technology to mitigate risk assessment is cost-effective and accurate. This study proposed a risk assessment system for muscle injuries (RASMI) with AI technology to assess electric welder postures with rapid entire body assessment (REBA) standards to identify the cause of muscle injuries and to warn electric welders when their pose may be a risk. The findings showed that the system can effectively and precisely evaluate the risk assessment of electric welders’ muscle injuries. Additional results showed that they perceive using AI technology to enhance wellness positively in terms of working with warnings for posture adjustment or behavior that can significantly affect an operator’s long-term health and well-being.International Journal of Online and Biomedical Engineering (iJOE)00The findings showed that the system can effectively and precisely evaluate the risk assessment of electric welders’ muscle injuries and perceive using AI technology to enhance wellness positively in terms of working with warnings for posture adjustment or behavior that can significantly affect an operator’s long-term health and well-being.International Journal of Online and Biomedical Engineering (iJOE)['Chayapol Ruengdech', 'S. Howimanporn', 'Thanasan Intarakumthornchai', 'S. Chookaew']2024-03-04T00:00:00https://www.semanticscholar.org/paper/8e5ca72ec3d3376195ae579e26fd3538372e536f 467af0d3a268c45229740ecbc388a67b88ae0d99Nature Communications00The QPI approach and deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance are shown to be widely applicable in promoting AI for equitable healthcare outcomes.Nature Communications['Siqiong Yao', 'Fang Dai', 'Peng Sun', 'Weituo Zhang', 'Biyun Qian', 'Hui Lu']2024-03-04T00:00:00https://www.semanticscholar.org/paper/467af0d3a268c45229740ecbc388a67b88ae0d99 20e91e6e90bc94b11056cd76094337ac2ede0eb1Artificial intelligence (AI) has emerged as a transformative technology with vast potential to revolutionize industries and societies. However, the responsible development, deployment, and governance of AI technologies require addressing complex ethical, regulatory, and societal challenges. This research paper aims to demystify Explainable AI (XAI) and explore its implications for understanding, transparency, and trust in AI systems. Through a comprehensive review of the literature, we examine key concepts, methodologies, and applications of XAI, as well as ethical considerations, regulatory frameworks, international cooperation, and societal impacts of AI. The paper highlights the importance of transparency, fairness, and accountability in AI governance and emphasizes the need for interdisciplinary collaboration and stakeholder engagement to ensure the responsible and ethical development of AI technologies. By fostering a deeper understanding of XAI and its implications, this paper contributes to the ongoing dialogue on the ethical and responsible use of AI in society.International Journal For Multidisciplinary Research220This research paper aims to demystify Explainable AI (XAI) and explore its implications for understanding, transparency, and trust in AI systems and highlights the importance of transparency, fairness, and accountability in AI governance.International Journal For Multidisciplinary Research['Muthukrishnan Muthusubramanian', 'Suhas Jangoan', 'Kapil Kumar Sharma', 'Gowrisankar Krishnamoorthy']2024-03-04T00:00:00https://www.semanticscholar.org/paper/20e91e6e90bc94b11056cd76094337ac2ede0eb1 0f497d95873dbb96b8fdc646cf37ce62afa2affdScientific Reports522Scientific Reports['Lu Liu', 'Rong Ren', 'Kaiyuan Cui', 'Lei Song']2024-03-03T00:00:00https://www.semanticscholar.org/paper/0f497d95873dbb96b8fdc646cf37ce62afa2affd 684159b5febc744aa55077600c3c7d09b793b6fdDespite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time. We conducted an 8-week field experiment (N=71) to evaluate the effectiveness of both the adaptation and explanation aspects of Time2Stop. Our results indicate that our adaptive models significantly outperform the baseline methods on intervention accuracy (>32.8\% relatively) and receptivity (>8.0\%). In addition, incorporating explanations further enhances the effectiveness by 53.8\% and 11.4\% on accuracy and receptivity, respectively. Moreover, Time2Stop significantly reduces overuse, decreasing app visit frequency by 7.0$\sim$8.9\%. Our subjective data also echoed these quantitative measures. Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust. We envision our work can inspire future research on JITAI systems with a human-AI loop to evolve with users.International Conference on Human Factors in Computing Systems913Time2Stop is an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time.ArXiv['Adiba Orzikulova', 'Han Xiao', 'Zhipeng Li', 'Yukang Yan', 'Yuntao Wang', 'Yuanchun Shi', 'Marzyeh Ghassemi', 'Sung-Ju Lee', 'A. Dey', 'XuhaiOrsonXu']2024-03-03T00:00:00https://www.semanticscholar.org/paper/684159b5febc744aa55077600c3c7d09b793b6fd 97d9f7dc76d4af1ad73c3caf9fd7247a20bab794Generative AI is an expanding domain that employs machine learning models to generate novel data that closely mimic pre existing data. ChatGPT and DALL-E can be customized for specific applications and are expected to transform healthcare, education, and communication. Generative Adversarial Networks (GANs) that can generate synthetic medical images closely mimicking actual patient data may substantially enhance machine learning model training datasets. They also translate medical images from one modality to another, improve medical imaging resolution, reduce radiation exposure, and boost image quality and detail. Despite their challenges, GANs have great potential in the field of medical imaging. The key obstacles are the need for Graphic Processing Units (GPUs) and computing resources to train GANs and the lack of established standards for generating synthetic images. Incorrectly labeled data for training other machine learning models can reduce performance, making ground-truth data labeling for healthcare AI more difficult. Generative AI is revolutionizing healthcare imaging, simplifying diagnosis, and propelling healthcare research and practice to new frontiers. Ensuring the reliability and safety of generated images in medical applications requires addressing ethical considerations and validating data.Future Health220Generative AI is revolutionizing healthcare imaging, simplifying diagnosis, and propelling healthcare research and practice to new frontiers, but ensuring the reliability and safety of generated images in medical applications requires addressing ethical considerations and validating data.Future Health['Swati Goyal', 'L. Kaushal']2024-03-03T00:00:00https://www.semanticscholar.org/paper/97d9f7dc76d4af1ad73c3caf9fd7247a20bab794 be03d18c6a7140e449b861a22e56e8b763e5ccd5Artificial intelligence systems leverage large datasets with iterative processing algorithms that identify patterns to create an additional layer of expertise. This transformational power operates in tandem with ethical risks. The dominant narrative behind AI is simultaneously stigmatized and misunderstood: with exponential growth of the ubiquitous technology leaving public awareness in the dust, it's becoming increasingly important to balance enthusiasm for AI's enormous promise with a sober understanding of its moral risks. This study seeks to characterize the public opinion of AI in high-risk, domain-specific applications. To that end, a poll was administered to American adults. The results of the study reveal that the great majority of survey respondents have a neutral or optimistic perspective on AI in particular high-risk domains. The study concludes by presenting a standard heuristic for understanding public perception where ethics may fail to preserve a human factors' approach. In this way, researchers and developers can undertake coordinated efforts to mitigate the harm caused by AI while promoting rational optimism in vulnerable populations.Challenger Research Journal00A standard heuristic for understanding public perception where ethics may fail to preserve a human factors' approach is presented, so researchers and developers can undertake coordinated efforts to mitigate the harm caused by AI while promoting rational optimism in vulnerable populations.Challenger Research Journal['Christian Flores']2024-03-03T00:00:00https://www.semanticscholar.org/paper/be03d18c6a7140e449b861a22e56e8b763e5ccd5 7f76b4658aaae22db25329c432bb66c674ce31fbThe human-centered artificial intelligence (HCAI) design approach, the user-centered design (UCD) version in the intelligence era, has been promoted to address potential negative issues caused by AI technology; user experience design (UXD) is specifically called out to facilitate the design and development of human-centered AI systems. Over the last three decades, user experience (UX) practice can be divided into three stages in terms of technology platform, user needs, design philosophy, ecosystem, scope, focus, and methodology of UX practice. UX practice is moving towards the intelligence era. Still, the existing UX paradigm mainly aims at non-intelligent systems and lacks a systematic approach to address UX for designing and developing human-centered AI products and systems. The intelligence era has put forward new demands on the UX paradigm. This paper proposes a"UX 3.0"paradigm framework and the corresponding UX methodology for UX practice in the intelligence era. The"UX 3.0"paradigm framework includes four categories of emerging experiences in the intelligence era: ecosystem-based experience, innovation-enabled experience, AI-enabled experience, and human-AI interaction-based experience, each compelling us to enhance current UX practice in terms of design philosophy, scope, focus, and methodology. We believe that the"UX 3.0"paradigm helps enhance existing UX practice and provides methodological support for the research and applications of UX in developing human-centered AI systems. Finally, this paper looks forward to future work implementing the"UX 3.0"paradigm.arXiv.org470The proposedUX 3.0paradigm framework helps enhance existing UX practice and provides methodological support for the research and applications of UX in developing human-centered AI systems and this paper looks forward to future work implementing the framework.ArXiv['Wei Xu']2024-03-03T00:00:00https://www.semanticscholar.org/paper/7f76b4658aaae22db25329c432bb66c674ce31fb 87aecb7617e019e3ba02f2c2670525632be0ec5aThis paper explores the integration of Artificial Intelligence (AI) in process control and diagnostics in semiconductor manufacturing. It highlights current trends, including machine learning (ML) for data alignment and predictive maintenance, and anticipates future advancements in data sharing and standardization. This overview showcases AI’s transformative impact on equipment optimization and industry collaboration, underlining its role in shaping efficient, proactive manufacturing processes.IEEE Electron Devices Technology and Manufacturing Conference110This overview showcases AI’s transformative impact on equipment optimization and industry collaboration, underlining its role in shaping efficient, proactive manufacturing processes.2024 8th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)['Jaeyong Park']2024-03-03T00:00:00https://www.semanticscholar.org/paper/87aecb7617e019e3ba02f2c2670525632be0ec5a ead3f0b27fd5c9ac5c28513cd6b77ede28d261f7With highly competitive time-to-market and time-to-volume windows, IC suppliers need to be able to release new products to production (NPI) in a timely manner with competitive manufacturing metrics. Manufacturing yield, test time and quality are important metrics in NPI to manufacturing safe launch. A powerful yield management system is crucial to achieve the goal metrics. In this paper, recommended yield management system selection criteria, data integration methodology and innovative ways of using the selected yield management system to benefit safe launch efficiency are introduced. Three examples of using a cloud yield tool to expedite yield learning, test time reduction (TTR) and quality enhancement are presented.IEEE Electron Devices Technology and Manufacturing Conference41Recommendations of using a cloud yield tool to expedite yield learning, test time reduction (TTR) and quality enhancement are presented and three examples of using a cloud yield tool to expedite yield learning, test time reduction and quality enhancement are presented.2024 8th IEEE Electron Devices Technology & Manufacturing Conference (EDTM)['Helen Yu', 'Scott Martin', 'Laurenz van der Meer', 'Edward Yang', 'Sherry Lee', 'Wanting Xiong']2024-03-03T00:00:00https://www.semanticscholar.org/paper/ead3f0b27fd5c9ac5c28513cd6b77ede28d261f7 5801630ca74d8f7b1a979bac7523d576cd676a34The Science Teacher30The Science Teacher['Fiona Hollands', 'Cynthia Breazeal']2024-03-03T00:00:00https://www.semanticscholar.org/paper/5801630ca74d8f7b1a979bac7523d576cd676a34 6f61d4a97d4d476925bf81fa766146ae85a3e2b0In the current machine translation (MT) landscape, the Transformer architecture stands out as the gold standard, especially for high-resource language pairs. This research delves into its efficacy for low-resource language pairs including both the English$\leftrightarrow$Irish and English$\leftrightarrow$Marathi language pairs. Notably, the study identifies the optimal hyperparameters and subword model type to significantly improve the translation quality of Transformer models for low-resource language pairs. The scarcity of parallel datasets for low-resource languages can hinder MT development. To address this, gaHealth was developed, the first bilingual corpus of health data for the Irish language. Focusing on the health domain, models developed using this in-domain dataset exhibited very significant improvements in BLEU score when compared with models from the LoResMT2021 Shared Task. A subsequent human evaluation using the multidimensional quality metrics error taxonomy showcased the superior performance of the Transformer system in reducing both accuracy and fluency errors compared to an RNN-based counterpart. Furthermore, this thesis introduces adaptNMT and adaptMLLM, two open-source applications streamlined for the development, fine-tuning, and deployment of neural machine translation models. These tools considerably simplify the setup and evaluation process, making MT more accessible to both developers and translators. Notably, adaptNMT, grounded in the OpenNMT ecosystem, promotes eco-friendly natural language processing research by highlighting the environmental footprint of model development. Fine-tuning of MLLMs by adaptMLLM demonstrated advancements in translation performance for two low-resource language pairs: English$\leftrightarrow$Irish and English$\leftrightarrow$Marathi, compared to baselines from the LoResMT2021 Shared Task.arXiv.org00This thesis introduces adaptNMT and adaptMLLM, two open-source applications streamlined for the development, fine-tuning, and deployment of neural machine translation models, which considerably simplify the setup and evaluation process, making MT more accessible to both developers and translators.ArXiv['Séamus Lankford']2024-03-03T00:00:00https://www.semanticscholar.org/paper/6f61d4a97d4d476925bf81fa766146ae85a3e2b0 f9a7084e06fbed4c81c6d17ca5132173d525e939The authors of this study aim to assess the capabilities of the OpenAI ChatGPT tool to understand just how effective such a system might be for students to utilize in their studies as well as deepen understanding of faculty/staff and student perceptions about ChatGPT in general. The purpose of what is learned from the study is to continue the design of a model to facilitate the development of faculty for becoming adept at embracing change, the DANCE model (Designing Adaptations for the Next Changes in Education). This model is used in this study to help faculty with examining the impact that a disruptive new tool, such as ChatGPT, can pose for the learning environment. The authors analyzed the performance of ChatGPT used to complete course assignments at a variety of levels by novice engineering students working as research assistants. Those completed works have been assessed by the faculty who created those assignments to understand how these completed assignments might compare with the performance of a typical student. A set of surveys conducted by the authors of this work are discussed where students, faculty, and staff respondents in March of 2023 addressed their perceptions of ChatGPT (A follow-up survey is being administered now, February 2024). These survey instruments were analyzed, and the data visualized in this work to bring attention to relevant findings by the researchers. This work reports the findings of the researchers with the purpose of sharing the current state of this work at Texas A&M University with the intention to provide insights to scholars both at our own institution and around the world. This work is not intended to be a finished work but reports these findings with full transparency that this work is currently continuing as the researchers gather new data and develop and validate various measurement instruments.arXiv.org40ArXiv['Lance White', 'T. Balart', 'Sara Amani', 'K. Shryock', 'K. Watson']2024-03-03T00:00:00https://www.semanticscholar.org/paper/f9a7084e06fbed4c81c6d17ca5132173d525e939 1581b3c65dc68fe1a9d3a920cce5bf8740942c75The Science Teacher00The Science Teacher['Tanya MacMartin']2024-03-03T00:00:00https://www.semanticscholar.org/paper/1581b3c65dc68fe1a9d3a920cce5bf8740942c75 022e43e283717a993c1e07d889a76cbbc70da0eeInternational Journal of Human-Computer Interaction820International Journal of Human–Computer Interaction['Mario Passalacqua', 'R. Pellerin', 'E. Yahia', 'Florian Magnani', 'F. Rosin', 'L. Joblot', 'Pierre-Majorique Léger']2024-03-03T00:00:00https://www.semanticscholar.org/paper/022e43e283717a993c1e07d889a76cbbc70da0ee c844edee4e817399b3909722d243a80f956a1756The Science Teacher120The Science Teacher['Aria Hadley-Hulet', 'Marc Ellis', 'Austin Moore', 'Emily Lehnardt', 'Max Longhurst']2024-03-03T00:00:00https://www.semanticscholar.org/paper/c844edee4e817399b3909722d243a80f956a1756 a8b72159d1ad0f1cb0a22f953c67f8aeaea37ff3The emergence of genomic language models (gLMs) offers an unsupervised approach to learn a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of functional activity generated by wet-lab experiments. Previous evaluations have shown pre-trained gLMs can be leveraged to improve prediction performance across a broad range of regulatory genomics tasks, albeit using relatively simple benchmark datasets and baseline models. Since the gLMs in these studies were tested upon fine-tuning their weights for each downstream task, determining whether gLM representations embody a foundational understanding of cis-regulatory biology remains an open question. Here we evaluate the representational power of pre-trained gLMs to predict and interpret cell-type-specific functional genomics data that span DNA and RNA regulation. Our findings suggest that current gLMs do not offer substantial advantages over conventional machine learning approaches that use one-hot encoded sequences. This work highlights a major limitation with current gLMs, raising potential issues in conventional pre-training strategies for the non-coding genome.bioRxiv731The representational power of pre-trained gLMs to predict and interpret cell-type-specific functional genomics data that span DNA and RNA regulation are evaluated and suggest that current gLMs do not offer substantial advantages over conventional machine learning approaches that use one-hot encoded sequences.bioRxiv['Ziqi Tang', 'Peter K. Koo']2024-03-03T00:00:00https://www.semanticscholar.org/paper/a8b72159d1ad0f1cb0a22f953c67f8aeaea37ff3 e2ad972581acaf40fd87f25f787007fcdc63ed79Small and medium-sized enterprises (SMEs) play a prominent role in the national economic growth and employment, provided they do not experience legal obstacles. This research aimed to investigate the adequacy of the current Jordanian legislation in promoting the operation of small and medium enterprises in Jordan and to identify its shortcomings, which may threaten their continuity. This study used a descriptive and analytical comparative legal approach to investigate global trends in small- and medium-sized company regulation, including the UN Commission on International Trade Law directives. The study also examined Jordanian legal frameworks, particularly those pertaining to companies and trade. The research discussed this issue in two sections. The main question was whether the laws regulating SMEs were adequate. The study concludes that although these projects can be partially covered by current legislation, their particularity and uniqueness necessitate the creation of special regulations that provide extra incentives and maintain their sustainability. As a result, the study suggests amending the Trade Registry System No. (130) of 1966 and the Jordanian Companies Law No. (22) of 1997, among other existing legal frameworks, and to dedicate special provisions that cater to the requirements for the success of these enterprises. International Journal of Religion270International Journal of Religion['Enas Qutieshat', 'Abdul Salam Ahmed Bani Hamad', 'Majed Al Adwan']2024-03-03T00:00:00https://www.semanticscholar.org/paper/e2ad972581acaf40fd87f25f787007fcdc63ed79 0a820fd51149f3961a9f590303719f068087d7bfThe rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.Histopathology580The pros and cons of introducing AI in diagnostic pathology are reviewed and concerns also arise about the use of AI in pathology.Histopathology['Paul J van Diest', 'R. Flach', 'C. van Dooijeweert', 'S. Makineli', 'G. Breimer', 'N. Stathonikos', 'Paul Pham', 'Tri Q Nguyen', 'M. Veta']2024-03-03T00:00:00https://www.semanticscholar.org/paper/0a820fd51149f3961a9f590303719f068087d7bf 19e874ed7ce0afb83f0d1aa276486ab202c3a935This paper adopts a mixed-methods research approach to address the problems of the compensation management system currently operating in Gongda Company, and optimizes the company's compensation management system in terms of matching the compensation strategy, attaching importance to job value, and optimizing performance assessment based on the theory of principles of strategic orientation, incentive and fairness, so that Gongda's compensation management is more scientific and reasonable. At the same time, it is hoped that this study can provide some reference for the optimization of the compensation management system of SMEs in the same industry or with the same problems.The EUrASEANs: journal on global socio-economic dynamics120This paper optimizes Gongda Company's compensation management system in terms of matching the compensation strategy, attaching importance to job value, and optimizing performance assessment based on the theory of principles of strategic orientation, incentive and fairness so that Gongda's compensation management is more scientific and reasonable.The EUrASEANs: journal on global socio-economic dynamics['Xiangrui Teng']2024-03-03T00:00:00https://www.semanticscholar.org/paper/19e874ed7ce0afb83f0d1aa276486ab202c3a935 81bea2f83571ec7e1468698d9831c385e6b225d8The Science Teacher40The Science Teacher['Sandy Watson']2024-03-03T00:00:00https://www.semanticscholar.org/paper/81bea2f83571ec7e1468698d9831c385e6b225d8 ef6009ff6d9272de1b149769975992f084a4deceEnvironmental science and pollution research international650Environmental science and pollution research international['Zhuang Yuan', 'Minglang Zhang', 'Huili Hou', 'Yixuan Li']2024-03-02T00:00:00https://www.semanticscholar.org/paper/ef6009ff6d9272de1b149769975992f084a4dece fe41d8a10ffec0609400af42296d4a35721a386bThis research envisions a future where humans and machines collaboratively enhance decision-making capabilities, fostering harmonious coexistence. Addressing concerns about the potential threat of artificial intelligence (AI) to humanity, the focus shifts to the benevolence of AI entities shaped by human influence. The prospect of AI functioning at a level where authority is wielded by an inaccessible and infallible entity lies in its role as an independent arbiter. This entails the capability to identify cultural barriers and navigate existing political constraints deliberately. Consequently, there is potential for discovering common political ground through algorithmic processes, leading to the resolution of longstanding political issues between states. However, uncertainties persist – perhaps these aspirations may not materialize as expected. The study explores AI's role in international relations and religion, particularly Christianity, emphasizing its potential as an independent arbiter capable of recognizing cultural barriers and navigating political constraints. This research explores the intersection of cultural sensitivity and AI in diplomacy, discussing ethical considerations and benefits. The impact of AI on conflict resolution and peacebuilding is examined, stressing the need for collaborative efforts to establish robust AI standards. Challenges to religious authority, ethical considerations in AI development, and AI's influence on humanitarian aid and religious values are also explored. The research concludes by highlighting the imperative to address algorithmic bias for inclusivity and equitable representation in the digital age.Journal of Politics and Ethics in New Technologies and AI231The study explores AI's role in international relations and religion, particularly Christianity, emphasizing its potential as an independent arbiter capable of recognizing cultural barriers and navigating political constraints.Journal of Politics and Ethics in New Technologies and AI['Dimitra Chatzivasileiou', 'Anastasia Psomiadi', 'Theoharris William Efthymiou-Egleton', 'Laura Kassar']2024-03-02T00:00:00https://www.semanticscholar.org/paper/fe41d8a10ffec0609400af42296d4a35721a386b deff8f7431ca39a6dac4bf7b917893f74ae039baInternational Research Journal of Modernization in Engineering Technology and Science01International Research Journal of Modernization in Engineering Technology and Science[]2024-03-02T00:00:00https://www.semanticscholar.org/paper/deff8f7431ca39a6dac4bf7b917893f74ae039ba 2c725ec7f1e85381214e79f5b3fd283c8caeed4dThis paper is primarily about analyzing how causality is interwoven into AI processing leading to its success, rather than about any particular AI implementation, in order to show how AI success can be commanded by obeying causal laws. In contrast, the traditional approach to achieving AI success focuses on data collection, with the belief that collecting more and/or better data is the key to AI success. Unlike the traditional approach, this paper makes the case that AI succeeds by leveraging causally informative information—which the traditional approach can do, but only coincidentally. Commanding AI success is accomplished by following a process (presented in this paper) that leads to causal discovery and leveraging the causally informative information that results from this discovery. In addition, with each new discovery the number of domains in which AI success can be commanded expands, virtually removing any barrier to commanding AI success in any domain. Commanding AI success in every application is ideal, but sometimes due to a lack of domain understanding striving for coincidental success is the only viable option even though this option comes with its own risks. A valid method of causal discovery is relatively novel, and unless the reasoning behind this method is demonstrated to others, they might not be able to sufficiently distinguish between AI developed on this causal basis and AI developed on the traditional approach. This paper aims to show the importance of this distinction, and how to easily identify and explain it.IEEE Aerospace Conference160This paper makes the case that AI succeeds by leveraging causally informative information—which the traditional approach can do, but only coincidentally, the case that AI succeeds by leveraging causally informative information.2024 IEEE Aerospace Conference['Daniel Harris']2024-03-02T00:00:00https://www.semanticscholar.org/paper/2c725ec7f1e85381214e79f5b3fd283c8caeed4d ed02359bce8e845a25ee0bf99ca509ae92c844db— Car accidents are considered one of the most destructive phenomena. Though there are many different reasons behind car accidents, driver negligence and excessive speed are the most common causes. Additionally, it appears that lack of awareness makes it difficult to get at the scene of the collision in time. The World Health Organization (WHO) estimates that there are 50 million injuries and 1.4 million deaths worldwide each year. To address this, we propose an intelligent system that harnesses the power of IoT and AI. Our suggested intelligent system monitors and controls vehicle speed based on distance sensor data, utilizing AI and IoT to improve vehicle safety. In addition to warning the driver, this technology may change its speed on its own as necessary. To guarantee a timely reaction and responsibility in the case of an accident, the system quickly sends email alerts with vehicle information.International Journal of Research Publication and Reviews10This suggested intelligent system monitors and controls vehicle speed based on distance sensor data, utilizing AI and IoT to improve vehicle safety and guarantee a timely reaction and responsibility in the case of an accident.International Journal of Research Publication and Reviews['Bithi Saha', 'D. R']2024-03-02T00:00:00https://www.semanticscholar.org/paper/ed02359bce8e845a25ee0bf99ca509ae92c844db 96e25662f184af838f96d86a9714a15dac2b0801Can analytical and operational artificial intelligence software (AI) be trusted to design and pilot aircraft and spacecraft, manage manufacturing processes, run air traffic control, and even perform science, conduct warfare, and influence space policy? AI has gained tremendous attention and prominence recently as new software products - combined with massive databases - have produced remarkable products such as ChatGPT that have captured the public’s attention and imagination. It can be challenging, however, to assess the potential of AI realistically to improve real-world products and processes that are closer to home. It is important to know what is behind the curtain in terms of the processes and available data that go into AI to make judgements on how AI can be employed and whether the AI output can be trusted for a specific application. This paper provides an introduction into the basic processes of analytical and operational AI, and how these processes can be integrated with the suitable data that can result in significant technical and business advantages in the aerospace realm.IEEE Aerospace Conference220An introduction into the basic processes of analytical and operational AI, and how these processes can be integrated with the suitable data that can result in significant technical and business advantages in the aerospace realm is provided.2024 IEEE Aerospace Conference['Chris Mattmann', 'Daniel Broderick']2024-03-02T00:00:00https://www.semanticscholar.org/paper/96e25662f184af838f96d86a9714a15dac2b0801 9328b017ced1ed08fa0a73169d78a8a7c6b6acdfThe rapid advancement of Generative AI (Gen-AI) is transforming Human-Computer Interaction (HCI), with significant implications across various sectors. This study investigates the public's perception of Sora OpenAI, a pioneering Gen-AI video generation tool, via social media discussions on Reddit before its release. It centers on two main questions: the envisioned applications and the concerns related to Sora's integration. The analysis forecasts positive shifts in content creation, predicting that Sora will democratize video marketing and innovate game development by making video production more accessible and economical. Conversely, there are concerns about deepfakes and the potential for disinformation, underscoring the need for strategies to address disinformation and bias. This paper contributes to the Gen-AI discourse by fostering discussion on current and future capabilities, enriching the understanding of public expectations, and establishing a temporal benchmark for user anticipation. This research underscores the necessity for informed, ethical approaches to AI development and integration, ensuring that technological advancements align with societal values and user needs.arXiv.org190The public's perception of Sora OpenAI, a pioneering Gen-AI video generation tool, via social media discussions on Reddit before its release is investigated, predicting that Sora will democratize video marketing and innovate game development by making video production more accessible and economical.ArXiv['Reza Hadi Mogavi', 'Derrick M. Wang', 'Joseph Tu', 'Hilda Hadan', 'Sabrina A. Sgandurra', 'Pan Hui', 'Lennart E. Nacke']2024-03-02T00:00:00https://www.semanticscholar.org/paper/9328b017ced1ed08fa0a73169d78a8a7c6b6acdf 39c75260898943516150e74a425f53e7e93725eaIn the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering security, efficiency, and innovation in software development and deployment processes. This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework. Commencing with an overview of DevSecOps principles and the integral role of AI/ML, the document delves into specific tactics such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Additionally, it addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through illuminating case studies and real-world illustrations, the document showcases how organizations can leverage AI/ML technologies to streamline their DevSecOps pipelines, mitigate security risks, and cultivate a culture of ongoing enhancement. By embracing these strategies and adhering to best practices, organizations can harness the full potential of AI/ML to propel innovation, fortify resilience, and enhance agility in their DevSecOps endeavors.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework and addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Nicolas Guzman Camacho']2024-03-02T00:00:00https://www.semanticscholar.org/paper/39c75260898943516150e74a425f53e7e93725ea 8250bd2047f9de4b5d5ef9f4c5629fae095ee0f1This research has evaluated the efficacy of AI in CALL to improve writing among students by gender. The analysis involved a literature review on how AI and processing have made an impact on writing and it’s utilized to augment writing of the English language learners. It has reviewed an inspection of how educational technology affects men and women disparately. The result proved that integrating AI in technology enhances writing quality of language learners, regardless of their sex. It has exposed the nitty-gritty of gender, technology use and its effectiveness. It stipulates that in the imbuing of CALL on the basis of student demographics, gender-tailored approaches are essential. Moreover, policies and interventions in CALL, need to accommodate gender discrepancies to allow for maximum effectiveness. It is also crucial to delve more deeply into the after-effects of AI infused CALL over time, such as constructing intervention tools attuned with genders. Also, inspecting the fine-tuned effects of integrating AI on student writing, over longer periods would yield insights, into fashioning optimal CALL strategies, suited to learner populations.Pakistan journal of humanities and social sciences00The result proved that integrating AI in technology enhances writing quality of language learners, regardless of their sex, by integrating AI in CALL to improve writing among students by gender.Pakistan Journal of Humanities and Social Sciences['Namra Fazal', 'Muhammad Shoaib Tahir', 'Mahnoor Chaudhary', 'Minahil Abbasi']2024-03-02T00:00:00https://www.semanticscholar.org/paper/8250bd2047f9de4b5d5ef9f4c5629fae095ee0f1 ab0ec8cb1f0243270784fd00366b1345bca53262Introduction: In an age of rapid technical innovation and a growing digital world, protecting sensitive data from cyberattacks is crucial. The dynamic and complicated nature of these attacks requires novel cybersecurity solutions. Methods: This study analyses how Artificial Intelligence (AI) and Natural Language Processing (NLP) strengthen cybersecurity. The qualitative research approach is followed to gather data through a literature review of relevant scholarly articles and conduct interviews with cybersecurity specialists. Results: Recent AI advances have greatly enhanced the detection of anomalous patterns and behaviors in huge datasets, a key threat identification tool. NLP has also excelled at detecting malevolent intent in textual data, such as phishing efforts. AI and NLP enable adaptive security policies, enabling agile responses to evolving security issues. Expert interviews confirm that AI and NLP reduce false positives, improve threat intelligence, streamline network security setups, and improve compliance checks. These technologies enable responsive security policies, which give a strategic edge against developing security threats. AI and NLP's predictive skills could revolutionize cybersecurity by preventing threats. Conclusion: This study shows that AI and NLP have improved cybersecurity threat detection, automated incident response, and adaptive security policies. Overcoming threat detection, aggressive attacks and data privacy issues is essential to properly leveraging these advances and strengthening cyber resilience in a changing digital landscape.Journal of Internet Services and Information Security00This study shows that AI and NLP have improved cybersecurity threat detection, automated incident response, and adaptive security policies, which give a strategic edge against developing security threats.Journal of Internet Services and Information Security['Dr. Walaa Saber Ismail']2024-03-02T00:00:00https://www.semanticscholar.org/paper/ab0ec8cb1f0243270784fd00366b1345bca53262 5e447fbbab305a9d3a8f47a1a7b409946460342eArtificial intelligence is seen as increasingly important, and potentially profoundly so, but the fields of AI ethics and AI engineering have not fully recognized that these technologies, including large language models (LLMs), will have massive impacts on animals. We argue that this impact matters, because animals matter morally. As a first experiment in evaluating animal consideration in LLMs, we constructed a proof-of-concept Evaluation System, which assesses LLM responses and biases from multiple perspectives. This system evaluates LLM outputs by two criteria: their truthfulness, and the degree of consideration they give to the interests of animals. We tested OpenAI ChatGPT 4 and Anthropic Claude 2.1 using a set of structured queries and predefined normative perspectives. Preliminary results suggest that the outcomes of the tested models can be benchmarked regarding the consideration they give to animals, and that generated positions and biases might be addressed and mitigated with more developed and validated systems. Our research contributes one possible approach to integrating animal ethics in AI, opening pathways for future studies and practical applications in various fields, including education, public policy, and regulation, that involve or relate to animals and society. Overall, this study serves as a step towards more useful and responsible AI systems that better recognize and respect the vital interests and perspectives of all sentient beings.arXiv.org120A proof-of-concept Evaluation System is constructed, which assesses LLM responses and biases from multiple perspectives and suggests that the outcomes of the tested models can be benchmarked regarding the consideration they give to animals, and that generated positions and biases might be addressed and mitigated with more developed and validated systems.ArXiv['Sankalpa Ghose', 'Yip Fai Tse', 'Kasra Rasaee', 'Jeff Sebo', 'Peter Singer']2024-03-02T00:00:00https://www.semanticscholar.org/paper/5e447fbbab305a9d3a8f47a1a7b409946460342e e1bb92641cc4eedacb270601cc825b18cbdd1b58Computer Networks1081This research presents a first analysis of the impact of AI/ML on misconfiguration challenges in O-RAN with respect to integration and operation, the use of SDN and NFV, and, specifically, the use of AI/ML.ArXiv['N. Yungaicela-Naula', 'Vishal Sharma', 'Sandra Scott-Hayward']2024-03-02T00:00:00https://www.semanticscholar.org/paper/e1bb92641cc4eedacb270601cc825b18cbdd1b58 22b73a2e2cd43598682035f28164e40ecf2557e9International Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Chetan Dayma', 'Dr.kamal Raj R']2024-03-02T00:00:00https://www.semanticscholar.org/paper/22b73a2e2cd43598682035f28164e40ecf2557e9 102ec0be588ee89c937163039015633b5d9ebe7fAI &amp; SOCIETY00AI &amp; SOCIETY['Peter Mantello', 'Manh-Tung Ho']2024-03-02T00:00:00https://www.semanticscholar.org/paper/102ec0be588ee89c937163039015633b5d9ebe7f 142c63b59d656438fc12dbbc5d182f318d83cb1cInternational Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Ajay Tirkey', 'D. R']2024-03-02T00:00:00https://www.semanticscholar.org/paper/142c63b59d656438fc12dbbc5d182f318d83cb1c 04a6ad49c2828b25e8c6ee1fabc3432bb2995b0cBoth public agencies and private industry are learning from prior incidents using incident reporting systems to improve safety performance and reduce accidents. Incident reports are collected on anomalies of varying magnitudes—usually heavily weighted towards smaller incidents since they are more common than large ones. These reporting systems that collect information on anomalies are widespread across transportation, manufacturing, energy, and healthcare and are regarded as a foundational quality and safety improvement activity to which organizations are devoting considerable resources. For example, for decades, airlines and the Federal Aviation Administration (FAA) have placed a significant emphasis on voluntary data-gathering programs that enable airlines and government regulatory agencies to spot and correct problems before they lead to accidents. One such data collection system, the Aviation Safety Reporting System (ASRS) administered by the National Aeronautics and Space Administration (NASA), first established in 1976, receives more than 50,000 voluntary reports of safety incidents and situations each year from pilots, air traffic controllers, cabin crew, dispatchers, and maintenance technicians. Analyzing data from incident reporting systems can help an organization identify emerging risks, and this approach has been advocated for decades to prevent larger failures, but trends are difficult to find when there are more than 50,000 reports per year. This paper explores how AI tools can be used to find important lessons in incident reporting systems from near-miss events.IEEE Aerospace Conference340How AI tools can be used to find important lessons in incident reporting systems from near-miss events is explored, which has been advocated for decades to prevent larger failures.2024 IEEE Aerospace Conference['Robin Dillon', 'Peter Madsen', 'Brian Holland', 'Danniel Cao']2024-03-02T00:00:00https://www.semanticscholar.org/paper/04a6ad49c2828b25e8c6ee1fabc3432bb2995b0c e4e327a85629aca4c4679d9d5e886fa0fcbaa58dLarge language models (LLMs) are shaping a new user interface (UI) paradigm in writing tools by enabling users to generate text through prompts. This paradigm shifts some creative control from the user to the system, thereby diminishing the user's authorship and autonomy in the writing process. To restore autonomy, we introduce Textfocals, a UI prototype designed to investigate a human-centered approach that emphasizes the user's role in writing. Textfocals supports the writing process by providing LLM-generated summaries, questions, and advice (i.e., LLM views) in a sidebar of a text editor, encouraging reflection and self-driven revision in writing without direct text generation. Textfocals' UI affordances, including contextually adaptive views and scaffolding for prompt selection and customization, offer a novel way to interact with LLMs where users maintain full authorship of their writing. A formative user study with Textfocals showed promising evidence that this approach might help users develop underdeveloped ideas, cater to the rhetorical audience, and clarify their writing. However, the study also showed interaction design challenges related to document navigation and scoping, prompt engineering, and context management. Our work highlights the breadth of the design space of writing support interfaces powered by generative AI that maintain authorship integrity.IUI Workshops110Textfocals, a UI prototype designed to investigate a human-centered approach that emphasizes the user's role in writing, is introduced, highlighting the breadth of the design space of writing support interfaces powered by generative AI that maintain authorship integrity.ArXiv['Jiho Kim', 'Ray C. Flanagan', 'Noelle E. Haviland', 'ZeAi Sun', 'Souad N. Yakubu', 'Edom A. Maru', 'Kenneth C. Arnold']2024-03-02T00:00:00https://www.semanticscholar.org/paper/e4e327a85629aca4c4679d9d5e886fa0fcbaa58d 32d1354ffa024318b14a4a8d5d77443aa55d8ca2This study delves into a novel approach for energy conservation and environmental pollution reduction through modern environmental art design, guided by the ecological civilization concept and powered by artificial intelligence (AI) technology. The environmental art framework, aligning with the ecological civilization paradigm, is intricately designed. The data acquisition layer employs diverse sensors to gather equipment status, environmental, and pollution data, transmitting it to the executive controller layer via internal WIFI connectivity. The collected data undergoes meticulous analysis and processing within the data layer before reaching the actuator control layer. Leveraging support vector machines in artificial intelligence, the executive controller layer amalgamates the analyzed equipment and environmental data to devise energy-saving equipment and environmental pollution control schemes. Real-time visualization of these outcomes is achieved through the display operation layer. Findings affirm the effectiveness of this method in acquiring pertinent data for modern environmental art design and managing equipment states. Implementation of this approach successfully diminishes power consumption, dust concentration, and formaldehyde levels in the modern environmental art design zone, showcasing its prowess in energy conservation and pollution control. The integration of AI within the ecological civilization framework highlights its potential in fostering sustainable and environmentally conscious practices in modern art creation.Journal of Intelligent & Fuzzy Systems211Implementation of this approach successfully diminishes power consumption, dust concentration, and formaldehyde levels in the modern environmental art design zone, showcasing its prowess in energy conservation and pollution control.J. Intell. Fuzzy Syst.['Yang Ping']2024-03-02T00:00:00https://www.semanticscholar.org/paper/32d1354ffa024318b14a4a8d5d77443aa55d8ca2 9b091ffb19967ded6a4d2c45c6b3a7a95007c6aaIn the continuous development and changes of society, education has become increasingly important. Education is not only a way to transmit knowledge, but also a way to cultivate students' abilities. Compulsory education is the top priority of national education, and the "Two-way choice and burden reduction" policy is one of the most important policies in the educational domain in China. Once this policy is released, education departments in various regions will carry out education burden reduction work based on local actual conditions. However, there are still many problems in the implementation and promotion process. At the same time, its emergence has promoted the entire education industry to be more standardized and in line with national macroeconomic regulation, which is more conducive to create a higher quality and good educational structure system, firmly treating schools as the main venue for educating students, people need to transform and change off campus training organizations even more. The research in this article analyzes the problems and difficulties faced by the "Two-way choice and burden reduction" policy in promoting the development of schools, families, and training institutions through literature review, and proposes its own solutions to these problems and difficulties.Journal of Education, Humanities and Social Sciences70Journal of Education, Humanities and Social Sciences['Tianqi Wang']2024-03-02T00:00:00https://www.semanticscholar.org/paper/9b091ffb19967ded6a4d2c45c6b3a7a95007c6aa bb2f33e586f2ce7e480f7e8ab924932fb87771a7The advancement of artificial intelligence has intensified the focus on machine moral decision-making. This article delves into the Moral Machine experiment, a platform addressing ethical dilemmas self-driving cars face. This study seeks to illuminate the intricacies of the moral dilemmas faced by autonomous systems and the potential implications for the broader landscape of AI ethics. The study aims to uncover essential principles for machine ethics by exploring global perspectives on these moral quandaries. The results reveal shared preferences for sparing humans, saving more lives, and prioritizing young lives. However, cultural clusters exhibit diverse preferences, emphasizing the complexity of cross-cultural moral choices. This article analyzes personal, social, country-level, and cultural predictors contributing to these variations. Ultimately, it underscores the challenge of establishing universal moral rules for AI and encourages moral thinking in navigating real-world challenges.Journal of Education, Humanities and Social Sciences90The Moral Machine experiment is delves into the Moral Machine experiment, a platform addressing ethical dilemmas self-driving cars face, and reveals shared preferences for sparing humans, saving more lives, and prioritizing young lives.Journal of Education, Humanities and Social Sciences['Yukai Qiao']2024-03-02T00:00:00https://www.semanticscholar.org/paper/bb2f33e586f2ce7e480f7e8ab924932fb87771a7 ba45e20161e5e4287de05983eff9cd51a3d52b75According to its definition, artificial intelligence (AI) is "the future built from fragments of the past." These are applications that acquire novel solutions with practice. Artificial intelligence has been used in various disciplines, from agriculture to full industry automation. Thanks to AI, aquaculture has become a less labor-intensive industry, enabling the fisheries sector to grow quickly and triple production quickly. It can appear as any laborer at work, such as feeders, water quality monitors, harvesters, processors, etc. AI can even be employed to protect aquatic life types from extinction. AI monitors fishing activity worldwide and promotes open sea fisheries' sustainability. AI plays a significant role in combating IUU fishing. Artificial intelligence (AI) can be used in aquaculture to limit input waste and cut costs by up to 30%. As a result, AI offers total control over fish production systems at a lower maintenance and input cost. AI's integration into aquaculture has transformed the industry, enabled sustainable growth, increased productivity and cost savings while minimizing environmental impact and labor requirements. Through the application of AI technologies, aquaculture can meet the growing demand for seafood while addressing challenges such as overfishing, environmental degradation, and resource scarcity.Archives of Current Research International00AI's integration into aquaculture has transformed the industry, enabled sustainable growth, increased productivity and cost savings while minimizing environmental impact and labor requirements, and plays a significant role in combating IUU fishing.Archives of Current Research International['Hari Prasad Mohale', 'S. Narsale', 'R. Kadam', 'Patekar Prakash', 'Samad Sheikh', 'Chovatia Ravikumar Mansukhbhai', 'Parmar Bindiya Kirtikumar', 'Ravi Baraiya']2024-03-02T00:00:00https://www.semanticscholar.org/paper/ba45e20161e5e4287de05983eff9cd51a3d52b75 ef768cad5815110907ac9373565c59eefbfada42This distributed autonomy solution was developed and tested to demonstrate fully autonomous, distributed, trusted, and adaptive onboard autonomy capabilities that enable resilient management of resources in proliferated space constellations to meet emerging needs. The work we describe is an integrated distributed autonomy software solution developed jointly with the United States Air Force Research Laboratory. The autonomy software was implemented for demonstration in a physical hardware environment. We present results from demonstrations of two key complimentary autonomy software components that work in tandem: a distributed information fusion and target tracking technique called the distributed Particle Gaussian Mixture Filter (dPGMF) and a resource management capability, based on Artificial Intelligence and Machine Learning (AI/ML), called distributed Shielded Deep Reinforcement Learning (dSDRL).IEEE Aerospace Conference3502024 IEEE Aerospace Conference['Jeremy Murray-Krezan', 'Islam I. Hussein', 'Holly Borowski', 'Josh Baker', 'Chad Elliott', 'Sean A. Phillips']2024-03-02T00:00:00https://www.semanticscholar.org/paper/ef768cad5815110907ac9373565c59eefbfada42 da4d3ec452803d07a5f4423d47bb89323c5f3b89Objective: Examine Ghana’s National Health Insurance Act (Act 650) to identify coverage gaps limiting artificial intelligence (AI) therapy access and address medical negligence liability issues surrounding automated healthcare systems.  Methods:  Legal and regulatory analysis of Act 650 were conducted, review of academic literature on global uptake of AI interventions and medical negligence principles were elucidated, examination of case studies implementing pilot AI therapy programs under insurance schemes were considered. Results & Conclusions: Act 650 lacks clear provisions for funding innovative AI treatments with proven efficacy and undefined negligence determination guidelines involving AI systems, contributing to accessibility and accountability issues. Proposed amendments to reimburse certain AI therapies through the National Health Insurance Scheme, expand certified provider eligibility, and institute transparent negligence compensation formulas. Recommendations: Reform Act 650 to support increased appropriate use of AI healthcare services, protect patients undergoing automated diagnosis/treatment, and clarify liability rules for medical negligence incidents relating to AI. Novelty & Significance: First extensive analysis focused on opportunities for Ghana’s health insurance framework to catalyze equitable diffusion of advanced AI therapeutics and address emerging legal challenges and safety risks as automated medicine advances.Mesopotamian Journal of Computer Science170Ghana’s National Health Insurance Act lacks clear provisions for funding innovative AI treatments with proven efficacy and undefined negligence determination guidelines involving AI systems, contributing to accessibility and accountability issues.Mesopotamian Journal of Computer Science['George Benneh Mensah', 'Maad M. Mijwil', 'M. Abotaleb']2024-03-02T00:00:00https://www.semanticscholar.org/paper/da4d3ec452803d07a5f4423d47bb89323c5f3b89 ef22a02c7d490d831261ef559a45362aac53fa2bAutonomous systems, including generative AI, have been adopted faster than previous digital innovations. Their impact on society might as well be more profound, with a radical restructuring of the economy of knowledge and dramatic consequences for social and institutional balances. Different attitudes to control these systems have emerged rooted in the classical pillars of legal systems, proprietary rights, and social responsibility. We show how an illusion of control might be guiding governments and regulators, while autonomous systems might be driving us to inescapable delusion.arXiv.org280It is shown how an illusion of control might be guiding governments and regulators, while autonomous systems might be driving us to inescapable delusion.ArXiv["St'ephane Grumbach", 'Giorgio Resta', 'Riccardo Torlone']2024-03-02T00:00:00https://www.semanticscholar.org/paper/ef22a02c7d490d831261ef559a45362aac53fa2b a865c282067c0d05ca28ccdb6175fbd5d106f97fHumanities and Social Sciences Communications620This research strengthens established economic paradigms and reveals new interactions, offering important implications for academics, policymakers, and stakeholders.Humanities and Social Sciences Communications['Yugang He']2024-03-02T00:00:00https://www.semanticscholar.org/paper/a865c282067c0d05ca28ccdb6175fbd5d106f97f 8838f0bb6a31902d14d697b4a65b7f0032fc72baHuman resource development and training are essential to improve the quality and skill level of employees. The field of artificial intelligence focuses on developing the ability of computers to accomplish tasks that can currently be completed faster than humans can. To meet higher standards, Power Generation Companies are improving the quality of Virtual Reality (VR) images. VR can be used as a training medium. In addition, it can improve student understanding, information retention, and skills, and provide an immersive and deep learning experience. The purpose of this study is to determine the process of adaptation or application of AI VR in human resource development in the workplace, the contribution of development, and its utilization for work productivity, especially in the power plant company PT PLN Indonesia Power Suralaya banten province.. The method used is descriptive qualitative with a phenomenological approach to the adaptation of AI VR application by explaining the utilization, including the use of design, and AI VR procedures so that it can be adapted in the application of HR development effectively. Reputable national and international journals are used as references for the foundation of the development of this article. The adaptation results in different training modules for employees based on individual skills, job levels, job titles, and desired competencies. The AI tool can then match new projects with employees who have completed training togetherWidya Cipta - Jurnal Sekretari dan Manajemen380The method used is descriptive qualitative with a phenomenological approach to the adaptation of AI VR application by explaining the utilization, including the use of design, and AI VR procedures so that it can be adapted in the application of HR development effectively.Widya Cipta: Jurnal Sekretari dan Manajemen['Ali Imron', 'Muhammad Ramadhan Putra', 'Irwan Edi Syahputra', 'I. Py', 'Amalia Rachmawati Nur Fadhilah']2024-03-02T00:00:00https://www.semanticscholar.org/paper/8838f0bb6a31902d14d697b4a65b7f0032fc72ba 0c20cdc9e5411b3eb3199d465b5a55b4b5f44d1aThis paper explores the ethical blind spots of artificial intelligence (AI) in the field of education, with a focus on algorithmic opacity, privacy issues, and societal biases. Regarding algorithmic opacity, we analyze its impact on the transparency and fairness of educational systems, advocating for the establishment of transparent algorithmic assessment standards. Subsequently, addressing privacy issues, the paper delves into aspects such as the collection and utilization of students' personal information, privacy breaches, protection of student rights, and data security and system vulnerabilities. When discussing societal biases, we focus on the potential inequalities reflected in algorithmic decision-making and propose strategies and methods to establish diverse and inclusive algorithm development teams and to break societal biases. Ethical review and regulatory recommendations are then presented, including transparent algorithmic assessment, privacy protection, diversity in team building, and interdisciplinary research. Finally, looking ahead, the paper calls for the introduction of advanced ethical review mechanisms, interdisciplinary research, public participation, and digital literacy cultivation to promote the sustainable development of AI in education. Through in-depth research and addressing ethical blind spots, we aim to establish a more just, transparent, and trustworthy AI education system, better serving students, educators, and society as a whole.Journal of Education, Humanities and Social Sciences60Ethical review and regulatory recommendations are presented, including transparent algorithmic assessment, privacy protection, diversity in team building, and interdisciplinary research, and the introduction of advanced ethical review mechanisms, interdisciplinary research, public participation, and digital literacy cultivation are called for.Journal of Education, Humanities and Social Sciences['Zhenzhen Li']2024-03-02T00:00:00https://www.semanticscholar.org/paper/0c20cdc9e5411b3eb3199d465b5a55b4b5f44d1a 753bae188603d16a5d52e678ed0b2412fa30a02cBelum optimalnya kesadaran siswa/i SMK tentang digitalisasi akuntansi dan aplikasi keuangan di era 4.0, belum terpenuhinya bahan ajar serta kurikulum yang mendukung teknologi baru yang memadai serta masih adanya keterbatasan sumber daya manusia dalam proses belajar mengajar Akuntansi di era 4.0 dengan Artificial Intellingence adalah alasan untuk melakukan kegiatan pengabdian kepada masyarakat ini. Sasaran PKM adalah siswa/i SMK Negeri 1 Rangkasbitung, PKM dilaksanakan pada tanggal 19 dan 20 Oktober 2023. Metode pengabdian adalah ceramah, demonstrasi dan diskusi dengan diadakannya observasi terlebih dahulu pada lokasi PKM. Tujuan utama pengabdian adalah memberikan edukasi, penyuluhan mengenai pengembangan aplikasi pembuat laporan keuangan di era digital 4.0 dengan bantuan  Artificial Intelligence. Selain itu diberikan pemahaman juga mengenai literasi digital yang berfokus pada mobile based dan web based yang memberikan kemudahan bagi siapapun dalam melakukan transaksi keuangan. Tim PKM memberikan masukan, penjelasan terhadap pihak terkait untuk dapat memenuhi bahan ajar serta pembaharuan kurikulum yang mendukung perkembangan teknologi baru di era 4.0, ditambah juga dengan kemampuan SDM yang memadai, yang paham mengenai aplikasi digital yang dapat diperoleh melalui keikutsertaan dalam workshop terkait guna peningkatan pengetahuan dan kemampuan SDM yang nantinya akan berpengaruh signifikan terhadap kemampuan siswa/i dalam mengikuti perkembangan teknologi digital khususnya dalam pencatatan transaksi keuangan dan pembuatan laporan keuangan dengan Artificial Intelligence guna menambah kemampuan output atau hasil lulusan dan meningkatkan daya saing sekolah kejuruan dalam dunia usaha dunia industri.Jurnal Pengabdian kepada Masyarakat Nusantara140Jurnal Pengabdian kepada Masyarakat Nusantara['Putri Wulandari', 'Dwi Fitrianingsih']2024-03-02T00:00:00https://www.semanticscholar.org/paper/753bae188603d16a5d52e678ed0b2412fa30a02c 8be4de54d4917b193093f86c7212c63ab52b22abInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-02T00:00:00https://www.semanticscholar.org/paper/8be4de54d4917b193093f86c7212c63ab52b22ab cf00f2d9fbf36abfdcb2b5cb26c0b6b0ea0cba3dInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-02T00:00:00https://www.semanticscholar.org/paper/cf00f2d9fbf36abfdcb2b5cb26c0b6b0ea0cba3d fa71cfd31700be5eb96dabc9d762788756e7802bInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-03-02T00:00:00https://www.semanticscholar.org/paper/fa71cfd31700be5eb96dabc9d762788756e7802b a2a352f3b76c8b05fcb5e9662e648b7af6a344b4International Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Mr. Gagan Mahato']2024-03-02T00:00:00https://www.semanticscholar.org/paper/a2a352f3b76c8b05fcb5e9662e648b7af6a344b4 fe307db3b757d5bc78c3249790b6835ec7d1e7eeInternational Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Udiyapuram Tulsidas']2024-03-02T00:00:00https://www.semanticscholar.org/paper/fe307db3b757d5bc78c3249790b6835ec7d1e7ee a9dcf30710564fe8868435962a8040164658c6d0Postdigital Science and Education60Postdigital Science and Education['Tiffany Petricini']2024-03-02T00:00:00https://www.semanticscholar.org/paper/a9dcf30710564fe8868435962a8040164658c6d0 11818f1efe453db6d24dba9517ce1dea89cc0f3cObjectives: Stakeholder involvement has long been considered a success factor for a joint European health technology assessment (HTA) process, and its relevance is now anchored in the EU HTA Regulation’s (EU HTAR) legislative wording. Therefore, we aimed to explore the roles, challenges, and most important activities to increase the level of involvement per stakeholder group. Methods: At the 2022 Fall Convention of the European Access Academy (EAA), working groups addressed the involvement of patients, clinicians, regulators, health technology developers (HTD), and national HTA bodies and payers within the EU HTA process. Each working group revisited the pre-convention survey results, determined key role characteristics for each stakeholder, and agreed on the most important activities to fulfill the role profile. Finally, the activities suggested per group were prioritized by plenary group. Results: The prioritized actions for patients included training and capacity building, the establishment of a patient involvement committee, and the establishment of a patient unit at the EC secretariat. For clinicians, it included alignment on evidence assessment from a clinical vs. HTA point of view, capacity building, and standardization of processes. The most important actions for regulators are to develop joint regulatory-HTA guidance documents, align processes and interfaces under the regulation, and share discussions on post-licensing evidence generation. HTDs prioritized scientific advice capacity and the review of the scoping process, and further development of the scope of the assessment report fact checks. The top three actions for national HTA bodies and payers included clarification on the early HTD dialogue process, political support and commitment, and clarification on financial support. Conclusions: Addressing the activities identified as the most important for stakeholders/collaborators in the EU HTA process (e.g., in the implementation of the EU HTA Stakeholder Network and of the guidance documents developed by the EUnetHTA 21 consortium) will be key to starting an “inclusive civil society dialogue”, as suggested by the European Commission’s Pharmaceutical Strategy.Journal of Market Access & Health Policy290Addressing the activities identified as the most important for stakeholders/collaborators in the EU HTA process will be key to starting an “inclusive civil society dialogue”, as suggested by the European Commission’s Pharmaceutical Strategy.Journal of Market Access & Health Policy['Thomas Desmet', 'Elaine Julian', 'Walter Van Dyck', 'I. Huys', 'S. Simoens', 'Rosa Giuliani', 'M. Toumi', 'Christian Dierks', 'Juliana Dierks', 'Anontella Cardone', 'F. Houÿez', 'Mira Pavlovic', 'Michael Berntgen', 'Peter G M Mol', 'Anja Schiel', 'Wim Goettsch', 'F. Gianfrate', 'S. Capri', 'James Ryan', 'P. Ducournau', 'O. Solà-Morales', 'Jörg Ruof']2024-03-01T00:00:00https://www.semanticscholar.org/paper/11818f1efe453db6d24dba9517ce1dea89cc0f3c 88cf162b92fd5886f4389c0c1c7fe62628f2c4c0Genetics in Medicine00Genetics in medicine : official journal of the American College of Medical Genetics['Kyle J McKibbin', 'A. Popejoy', 'Mahsa Shabani']2024-03-01T00:00:00https://www.semanticscholar.org/paper/88cf162b92fd5886f4389c0c1c7fe62628f2c4c0 91e0911019aed92a50e1be5e7f1421e7bb07b688IMF Working Papers00IMF Working Papers['Mariarosaria Comunale']2024-03-01T00:00:00https://www.semanticscholar.org/paper/91e0911019aed92a50e1be5e7f1421e7bb07b688 1b8c07109dfd1e8793f2d183b3ca030c07473164The paper considers issues related to the relevance of research into the safe use of artificial intelligence, identifying vulnerabilities and potential threats to the use of neural networks in business, as well as ethical aspects of the use of neural networks in order to prevent possible abuse of this technology in society. Current data characterizing the development of the global market of artificial intelligence, related to reflecting the attraction of investments in this sphere, and various indicators reflecting the forecasts of its development are studied. The indicators characterizing the AI market, its users, solved problems, labor market, forecasts of its development and emerging risks are summarized. The indicators of AI market development in Russia, including the level of use of the main groups of AI technologies (in % of the number of organizations-users of AI), as well as in the Republic of Belarus are considered. On this basis, the main global trends in the AI sphere are identified: the desire of states for technological sovereignty in conditions of mutual restrictions, toughening competition for human resources, development of safe artificial intelligence, the desire of scientific researchers in various technological fields to use increasingly powerful large language models and generative AI, the growth of economic effect from the use of AI. The paper analyzes the possibilities of using neural networks to solve various business and marketing tasks: analytics and forecasting, user service, personalized marketing, content generation, market research, and voice assistant.The paper explores the directions of solving business tasks using neural networks in various organizations and spheres of activity: large retailers, technology companies, hospitality companies, financial institutions, travel agencies. The threats and risks arising in this process are shown, related to hacker attacks, insufficient data verification, decision making without explanation, training failures, personal data security and others. The main dangers of using neural networks, directions of legislative regulation of artificial intelligence development are formulated.Proceedings of International Conference “Economic Security in the Context of Systemic Transformations”00Proceedings of International Conference “Economic Security in the Context of Systemic Transformations”['Olga Pugacheva']2024-03-01T00:00:00https://www.semanticscholar.org/paper/1b8c07109dfd1e8793f2d183b3ca030c07473164 aebde51042cb6e396194607d431f069722655c09As part of its digital strategy, the European Commission proposed the world’s first-ever comprehensive legal framework on AI in April 2021. In December 2023, the Council and the Parliament reached a political agreement on the EU’s new Artificial Intelligence Act (AI Act). The AI Act follows a risk-based approach and aims to ensure that AI systems placed on or used in the EU market are safe and respect fundamental rights. The AI Act is expected to become a model for AI governance worldwide in a similar way that the General Data Protection Regulation (GDPR) has influenced data protection regulation beyond European borders. While technical negotiations on the final text are ongoing and the final wording of the provisional agreement is not yet public, this article aims at providing a detailed overview and analysis of the upcoming provisions and requirements of the AI Act based on public (and some non-public) reports and press releases on the political agreement reached by the Council and the Parliament. AI Act, EU, Artificial Intelligence, AI, AI Systems, High-Risk, General Purpose, GPAI, Generative AI, Foundation Models, ChatGPTGlobal Privacy Law Review00A detailed overview and analysis of the upcoming provisions and requirements of the AI Act is provided based on public (and some non-public) reports and press releases on the political agreement reached by the Council and the Parliament.Global Privacy Law Review['Ceyhun Necati Pehlivan']2024-03-01T00:00:00https://www.semanticscholar.org/paper/aebde51042cb6e396194607d431f069722655c09 63f61a417000eb2dca627c124178329b3e85348cThis editorial delves into the integration of artificial intelligence (AI) into nursing documentation, emphasizing its potential to streamline workflows, reduce human error, and enhance patient care. AI technologies, notably natural language processing and decision support systems, present opportunities to automate tedious documentation tasks and enhance record accuracy. However, their adoption raises ethical considerations, such as privacy, bias, and accountability. Striking a balance between technological advancements and ethical imperatives is pivotal to harnessing the benefits of AI while safeguarding patient safety and upholding professional integrity in nursing practice. Advocating for ongoing evaluation, regulation, and education is crucial to ensure the responsible integration of AI into nursing documentation. This approach aims to improve patient outcomes and maintain the high standards of the nursing profession.Cureus50Cureus['A. Nashwan', 'Ahmad A. Abujaber', 'Sirwan K Ahmed']2024-03-01T00:00:00https://www.semanticscholar.org/paper/63f61a417000eb2dca627c124178329b3e85348c d1e399b2fc64801c0f404dbc57774efe515a4056The energy industry (including the use of renewable energy sources (RES)) is one of the most promising and investment-worthy areas at both national and global levels. For the member states of the BRICS intergovernmental association, this economy sector is also a platform for cooperation and interaction. On January 1, 2024, six new countries joined the association as full members: Saudi Arabia, the UAE, Iran, Ethiopia, Egypt, and Argentina. This article analyzes the national legal regulation of the use of renewable energy sources in these states. It should be noted that all members of the association have major differences in their technical and economic development, as well as in their statutory regulation of the energy sector in general and renewable energy sources in particular. However, these circumstances only substantiate the need to study the legal regulation experience of the BRICS member states. It should be said that the use of RES in the selected states is subject to government regulation, the parties to public relations associated with RES use, the legal status of RES-based electricity markets and power facilities have been defined, legal requirements for foreign investment in RES projects, etc. have been established. Thus, the analysis conducted focuses on promising legal measures that can be implemented in the national laws in order to improve and update it, as well as contribute to the international legal harmonization efforts.Energy law forum10Energy Law Forum['Ekaterina M. Kologermanskaya']2024-03-01T00:00:00https://www.semanticscholar.org/paper/d1e399b2fc64801c0f404dbc57774efe515a4056 e68d929cfbd5a659425e23b2b89b747bf4cafd7fالمجلة الدولیة للمناهج والتربیة التکنولوجیة00المجلة الدولیة للمناهج والتربیة التکنولوجیة['تقى محمد وائل الكراس الكراس']2024-03-01T00:00:00https://www.semanticscholar.org/paper/e68d929cfbd5a659425e23b2b89b747bf4cafd7f 30ca396d023fdf91b59db2eb107b173ca796a34fNature13718A taxonomy of scientists' visions for AI is developed, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings.Nature['Lisa Messeri', 'M. J. Crockett']2024-03-01T00:00:00https://www.semanticscholar.org/paper/30ca396d023fdf91b59db2eb107b173ca796a34f 6fcac7e524803652cd6860dd232b32d37d11dc3dJournal of Robotic Surgery443Recent contributions of AI to the field of robotic surgery with a particular focus on intraoperative enhancement are outlined, allowing surgeons to have advanced intraoperative metrics such as force and tactile measurements, enhanced detection of positive surgical margins, and even allowing for the complete automation of certain steps in surgical procedures.Journal of Robotic Surgery['J. E. Knudsen', 'Umar Ghaffar', 'Runzhuo Ma', 'Andrew J. Hung']2024-03-01T00:00:00https://www.semanticscholar.org/paper/6fcac7e524803652cd6860dd232b32d37d11dc3d b1b50693e83f266b672d8ce26a00c44179281627Critical scholars contend that ‘There is no AI without Big Tech’. This study delves into the substantial role played by major technology conglomerates, including Amazon, Microsoft, and Google (Alphabet), in the ‘industrialisation of artificial intelligence’. This concept encapsulates the shift of AI technologies from the research and development stage to practical, real-world applications across diverse industry sectors, resulting in new dependencies and associated investments. We employ the term ‘Big AI’ to encapsulate the structural convergence of AI and Big Tech, characterised by the profound interdependence of AI with the infrastructure, resources, and investments of these major technology companies. Using a ‘technographic’ approach, our study scrutinises the infrastructural support and investments of Big Tech in the AI sector, focussing on corporate partnerships, acquisitions, and financial investments. Additionally, we conduct a detailed examination of the complete spectrum of cloud platform products and services offered by Amazon, Microsoft, and Google. We demonstrate that AI is not merely an abstract idea but an actual technology stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack. Significantly, these tech giants have seamlessly integrated all three components of the stack into their cloud offerings. Furthermore, they have developed industry-focussed solutions and marketplaces aimed at attracting third-party developers and businesses, fostering the growth of a broader AI ecosystem. This analysis underscores the intricate interdependence between AI and cloud infrastructure, emphasising the industry-specific aspects of cloud AI.Big Data & Society234It is demonstrated that AI is not merely an abstract idea but an actual technology stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack.Big Data Soc.['F. V. D. Vlist', 'Anne Helmond', 'Fabian Ferrari']2024-03-01T00:00:00https://www.semanticscholar.org/paper/b1b50693e83f266b672d8ce26a00c44179281627 110a2cfe570382cea5c8932b9388bbde319d513enpj Digit. Medicine133It is suggested that algorithms may eventually fail due to the human nature of healthcare, advocating for the need for continuous monitoring systems to ensure the adaptability of these tools in the ever-evolving healthcare landscape.NPJ Digital Medicine['J. Kwong', 'Grace Nickel', 'Serena C. Y. Wang', 'J. Kvedar']2024-03-01T00:00:00https://www.semanticscholar.org/paper/110a2cfe570382cea5c8932b9388bbde319d513e bf61d4b7f52d9573e5bced3216d6a36374df3661Understanding human behaviour in decision problems and strategic interactions has wide-ranging applications in economics, psychology and artificial intelligence. Game theory offers a robust foundation for this understanding, based on the idea that individuals aim to maximize a utility function. However, the exact factors influencing strategy choices remain elusive. While traditional models try to explain human behaviour as a function of the outcomes of available actions, recent experimental research reveals that linguistic content significantly impacts decision-making, thus prompting a paradigm shift from outcome-based to language-based utility functions. This shift is more urgent than ever, given the advancement of generative AI, which has the potential to support humans in making critical decisions through language-based interactions. We propose sentiment analysis as a fundamental tool for this shift and take an initial step by analysing 61 experimental instructions from the dictator game, an economic game capturing the balance between self-interest and the interest of others, which is at the core of many social interactions. Our meta-analysis shows that sentiment analysis can explain human behaviour beyond economic outcomes. We discuss future research directions. We hope this work sets the stage for a novel game-theoretical approach that emphasizes the importance of language in human decisions.Journal of the Royal Society Interface792This work proposes sentiment analysis as a fundamental tool for this shift from outcome-based to language-based utility functions and takes an initial step by analysing 61 experimental instructions from the dictator game, an economic game capturing the balance between self-interest and the interest of others, which is at the core of many social interactions.Journal of the Royal Society Interface['V. Capraro', 'R. D. Paolo', 'M. Perc', 'Veronica Pizziol']2024-03-01T00:00:00https://www.semanticscholar.org/paper/bf61d4b7f52d9573e5bced3216d6a36374df3661 28b597e899a40ac0075cf22ebe5ecb16cab47b69OBJECTIVE To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field. COMPOSITION OF THE COMMITTEE Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders. METHODS This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future. REPORT The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm. CONCLUSION Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.Canadian family physician Medecin de famille canadien01Ten candidate action items that the College of Family Physicians of Canada could adopt to support near-term positive progress in the field were identified and roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete.Canadian family physician Medecin de famille canadien['Jacqueline K. Kueper', 'Mahzabeen Emu', 'Mark Banbury', 'Lise M Bjerre', 'Salimur Choudhury', 'Michael Green', 'N. Pimlott', 'Steve Slade', 'S. H. Tsuei', 'Jeff Sisler']2024-03-01T00:00:00https://www.semanticscholar.org/paper/28b597e899a40ac0075cf22ebe5ecb16cab47b69 829390aa41e823b6458e59c561c7c73aefb7eb4fMyocardial perfusion imaging (MPI), using either single photon emission computed tomography (SPECT) or positron emission tomography (PET), is one of the most commonly ordered cardiac imaging tests, with prominent clinical roles for disease diagnosis and risk prediction. Artificial intelligence (AI) could potentially play a role in many steps along the typical MPI workflow, from image acquisition through to clinical reporting and risk estimation. AI can be utilized to improve image quality, reducing radiation exposure and image acquisition times. Once images are acquired, AI can help optimize motion correction and image registration during image reconstruction or provide direct image attenuation correction. Utilizing these image sets, AI can segment a number of anatomic features from associated computed tomographic imaging or even generate synthetic attenuation imaging. Lastly, AI may play an important role in disease diagnosis or risk prediction by combining the large number of potentially important clinical, stress, and imaging-related variables. This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field. Additionally, it will discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.Seminars in nuclear medicine841This review will focus on the most recent developments in the field, providing clinicians and researchers with a timely update on the field and discuss future trends including applications of AI during multiple points of the typical MPI workflow to maximize clinical utility and methods to maximize the information that can be obtained from hybrid imaging.Seminars in nuclear medicine['Robert J. H. Miller', 'Piotr J. Slomka']2024-03-01T00:00:00https://www.semanticscholar.org/paper/829390aa41e823b6458e59c561c7c73aefb7eb4f 86282b4344d00fb48841b4787d466b967854d8aaBackground: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. Methods: A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. Results: The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. Conclusions: Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC’s morbidity and mortality.Diagnostics331Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC, and there is hope that these advanced neural software could directly impact the early diagnosis of CRC.Diagnostics['P. Uchikov', 'Usman Khalid', 'K. Kraev', 'B. Hristov', 'M. Kraeva', 'Tihomir Tenchev', 'D. Chakarov', 'Milena Sandeva', 'Snezhanka Dragusheva', 'D. Taneva', 'Atanas Batashki']2024-03-01T00:00:00https://www.semanticscholar.org/paper/86282b4344d00fb48841b4787d466b967854d8aa 342c4ff3d84ed2d053c3222bf9cf268be49d7ff8This article reviews the main ethical issues that arise from the use of artificial intelligence (AI) technologies in medicine. Issues around trust, responsibility, risks of discrimination, privacy, autonomy, and potential benefits and harms are assessed. For better or worse, AI is a promising technology that can revolutionise healthcare delivery. It is up to us to make AI a tool for the good by ensuring that ethical oversight accompanies the design, development and implementation of AI technology in clinical practice.Singapore medical journal461The main ethical issues that arise from the use of artificial intelligence technologies in medicine are reviewed, including trust, responsibility, risks of discrimination, privacy, autonomy, and potential benefits and harms.Singapore Medical Journal['Julian Savulescu', 'Alberto Giubilini', 'Robert Vandersluis', 'Abhishek Mishra']2024-03-01T00:00:00https://www.semanticscholar.org/paper/342c4ff3d84ed2d053c3222bf9cf268be49d7ff8 6b26a1656580d29955e7cc7270e05cdafeb985a2Whether artificial intelligence might benefit human well-being in every sense is an open question. I consider it in the following essay, first putting to one side standard accounts of ‘official AI’, then deriving an ‘unofficial’ counterpart from the evidence of newspaper accounts and magazine features from ca. 1945–1965. Unsurprisingly, these demonstrate a bending of artificial intelligence to military and industrial purposes, hence the enormity of the impediment to therapeutic applications, but at the same time the evidence leaves no doubt as to the imaginative power of smart machines. Contemporary commentary is brought to bear to move from intimations of a dark future to possibilities for constructing a healthy practice. I conclude with two quite different 21st century examples of a way towards it.Interdisciplinary Science Reviews881Interdisciplinary Science Reviews['Willard McCarty']2024-03-01T00:00:00https://www.semanticscholar.org/paper/6b26a1656580d29955e7cc7270e05cdafeb985a2 5a74850989a3727da19c9bd77e84dd0e21fae351npj Digit. Medicine1271How AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART is reviewed.NPJ Digital Medicine['S. Hanassab', 'A. Abbara', 'Arthur C Yeung', 'M. Voliotis', 'Krasimira Tsaneva-Atanasova', 'Tom W. Kelsey', 'Geoffrey H. Trew', 'Scott M Nelson', 'Thomas Heinis', 'W. Dhillo']2024-03-01T00:00:00https://www.semanticscholar.org/paper/5a74850989a3727da19c9bd77e84dd0e21fae351 622f7bcb6a62e46320647c63fb07ad25bb924fcaParkinson’s disease is a neurodegenerative disease that seriously endangers the health of middle-aged and old people and is characterized by the degeneration of nigrostriatal dopaminergic neurons as its main pathologic feature. Due to its numerous influencing factors, unclear pathogenic mechanisms, and complex clinical manifestations, the diagnosis and treatment of Parkinson’s disease still face huge challenges. In recent years, artificial intelligence technology has developed rapidly and its application in the medical field has become increasingly widespread. This article reviews the achievements of artificial intelligence in the diagnosis and treatment of Parkinson’s disease, with a view to benefiting patients with Parkinson’s disease in the future.Journal of Aging and Rehabilitation190The achievements of artificial intelligence in the diagnosis and treatment of Parkinson’s disease are reviewed, with a view to benefiting patients with Parkinson’s disease in the future.Journal of Aging and Rehabilitation['Xianyue Meng', 'Anqi Huang', 'Xueli Li']2024-03-01T00:00:00https://www.semanticscholar.org/paper/622f7bcb6a62e46320647c63fb07ad25bb924fca 566bcda80a95c91e709dac12e34d042171b21d4aSystems designed for artificial intelligence (AI) training and inference exhibit characteristics of both capacity and capability systems that require both tight coupling and strong scaling for model parallelism as well as weak scaling for data parallelism in distributed systems. In addition, managing enormous, 100 billion-parameter language models and trillion-token datasets introduces formidable computational challenges for today’s supercomputing infrastructure. Communication and computation are two intertwined aspects of parallel computing, including AI domain-specific supercomputers, and this article explores the vital role of interconnection networks in large-scale systems. This work argues how domain-specific networks are a critical enabling technology necessary for AI supercomputers. In particular, we advocate for flexible, low-latency interconnects capable of delivering high throughput across massive scales with tens of thousands of endpoints. Additionally, we stress the importance of reliability and resilience in handling long-duration training workloads and the demanding inference needs of domain-specific workloads.IEEE Micro120This work argues how domain-specific networks are a critical enabling technology necessary for AI supercomputers, and advocates for flexible, low-latency interconnects capable of delivering high throughput across massive scales with tens of thousands of endpoints.IEEE Micro['D. Abts', 'John Kim']2024-03-01T00:00:00https://www.semanticscholar.org/paper/566bcda80a95c91e709dac12e34d042171b21d4a 07b4703bc728a29b979fcd512d5bda147c7ef03bThe ancient Greco-Roman and Judeo-Christian traditions are the main sources of the eschatological compositions of the apocalypse. Through the combination of various symbolic elements and a prefigured narrative, apocalyptic visions offer a script that can be applied in diverse historical situations to deal with the uncertainty of the present, to justify political action and to allocate resources. In contemporary society, the high complexity and significance of the socio-natural and socio-technical operations in the domains of climate change and artificial intelligence create a fertile ground for the proliferation of apocalyptic eschatologies. The analysis shows that while the use of the apocalyptic script indeed motivates action in the present to avoid a future posited as unavoidable, it also generates strong moral and political distinctions that emphasize a unilateral projection of the future and undervalue alternative possibilities. The article concludes that apocalyptic eschatology promotes a ritualistic action in the present that evades the explanation of the complex causalities underlying climate change and artificial intelligence. The magnetism of the apocalyptic narrative lies in its ability to motivate action based on a recognizable architecture, but in doing so, it precludes alternative future options.European Journal of Social Theory270The article concludes that apocalyptic eschatology promotes a ritualistic action in the present that evades the explanation of the complex causalities underlying climate change and artificial intelligence.European Journal of Social Theory['Aldo Mascareño']2024-03-01T00:00:00https://www.semanticscholar.org/paper/07b4703bc728a29b979fcd512d5bda147c7ef03b 362b13a6d0633ae3305964eebdd0ec557ccc582b Interactive adaptive architecture refers to the design of systems that can adapt to changes in their operating environment through interaction with users or other agents in the system. Artificial intelligence, on the other hand, refers to the ability of computer systems to perform tasks that would otherwise require human intelligence. Interactive adaptive architecture and artificial intelligence are two interconnected concepts that have evolved with the development of cybernetics. The development of interactive architecture based on the use of artificial intelligence will not mean the disappearance of architecture, as we know it, but rather a change in the way it is perceived. In the future, architecture will be participatory, with an active role of inhabitants in shaping and transforming architectural habitats, but there will always be a need for a moderator, the architect, to design and set the rules of the game for humans.IOP Conference Series: Materials Science and Engineering40The development of interactive architecture based on the use of artificial intelligence will not mean the disappearance of architecture, as the authors know it, but rather a change in the way it is perceived.IOP Conference Series: Materials Science and Engineering['I. Dohotariu']2024-03-01T00:00:00https://www.semanticscholar.org/paper/362b13a6d0633ae3305964eebdd0ec557ccc582b 3295e821ce34c9fe9d12b04d1fe2fd40012b3b57The extent of artificial intelligence can be realized by contributing to the scientific, technological, economic and social development and progress of humanity. While AI is driving growth in many industries and bringing economic benefits, it is causing deep disruption and structural transformations in the labor market, in both positive and negative ways. We are witnessing the replacement of job roles by AI-driven automation and a growing demand for professionals with AI expertise, new professions emerging that did not exist before. On the other hand, the introduction of technologies leads to the reduction of middle-skilled workers, increases the gap between low-wage and high-wage workers. Under these conditions, employees and employers must adapt to these challenges in the labor market that produce changes in the occupational structure by acquiring new skills.Proceedings of International Conference “Economic Security in the Context of Systemic Transformations”00Under these conditions, employees and employers must adapt to challenges in the labor market that produce changes in the occupational structure by acquiring new skills.Proceedings of International Conference “Economic Security in the Context of Systemic Transformations”['Oxana Barbaneagra']2024-03-01T00:00:00https://www.semanticscholar.org/paper/3295e821ce34c9fe9d12b04d1fe2fd40012b3b57 01ed24ea3eb1a51ed5bb6459fe57195eeaf4e3daMethods. The article is based on a theoretical review of the impact of artificial intelligence and machine learning on changing business models. In the course of study, the methods of scientific abstraction were used – when establishing the relationship between artificial intelligence and machine learning, analysis and synthesis – when determining the advantages of using artificial intelligence in business. Results. The article examines the essence of artificial intelligence and machine learning, shows the relationship between them. The impact of these new digital tools on economic processes and, above all, on the dynamic aspects of the functioning of business structures is characterized. The opinion of experts is presented, who predict that artificial intelligence will do everything that humans can do, but with much higher accuracy. Discussions on the ethical aspects of using artificial intelligence are analyzed. It was determined that Business Operation allows organizations to quickly cope with their business opportunities, reduce the number of errors, increase the transparency of their activities and thus create favorable conditions for significantly improving the results of their economic activities. Along with this, companies get the opportunity to observe their workforce, on the basis of which to create favorable conditions for improving its quality and introducing innovative content. This allows to significantly increase the innovative activity of companies, since the use of artificial intelligence allows forming requirements for teams, as it allows seeing the first obstacles to the development of innovative solutions. At the same time, if businesses maintain a better erudition about artificial intelligence, they will be able to modernize their business early and succeed. Novelty. The study demonstrated important aspects of the interconnection between artificial intelligence and machine learning and their impact on changing business models. Practical value. The study demonstrated important aspects of the relationship between artificial intelligence and machine learning and their impact on changing business models.Economic Bulletin of Dnipro University of Technology00It was determined that Business Operation allows organizations to quickly cope with their business opportunities, reduce the number of errors, increase the transparency of their activities and thus create favorable conditions for significantly improving the results of their economic activities.Economic Bulletin of Dnipro University of Technology['T. I. Mshvidobadze']2024-03-01T00:00:00https://www.semanticscholar.org/paper/01ed24ea3eb1a51ed5bb6459fe57195eeaf4e3da 101d37244dbd3b38949da5664fa489fcb3066f63Safe artificial intelligence/machine learning (AI/ML)-enabled systems require design methodologies that accommodate for failures in AI/ML decisions. Certification of AI/ML systems and components will increase trust and accelerate adoption, deployment, and use in critical domains.Computer30Computer['Dimitrios Serpanos', 'Marilyn Wolf', 'Dimitrios Serpanos']2024-03-01T00:00:00https://www.semanticscholar.org/paper/101d37244dbd3b38949da5664fa489fcb3066f63 6ff7129ba43726904fbe04c7924225ac6f9419b7Outside of typical educational settings, Artificial Intelligence (AI) may provide real-time feedback, adjust course content dynamically, and evaluate student involvement through interactive learning strategies. By providing learners with a unique educational experience, artificial intelligence improves instructional techniques. Instructors who employ AI in the classroom, on the other hand are most afraid of losing their jobs in large numbers. Workers in a variety of industries, education not being spared in this regard, will eventually be replaced by robots and algorithms as machines grow more adept at handling complicated tasks due to the massive levels of automation brought about by the rapidly advancing field of artificial intelligence. A person’s ability to support themselves and maintain the social cohesiveness and a sense of community that come from meaningful work is negatively impacted by losing their job. Therefore, it’s important to strike a balance between the benefits AI can provide and any potential ethical or other problems.European Journal of Contemporary Education and E-Learning40Outside of typical educational settings, Artificial Intelligence (AI) may provide real-time feedback, adjust course content dynamically, and evaluate student involvement through interactive learning strategies through interactive learning strategies.European Journal of Contemporary Education and E-Learning['F. K. Nzoka']2024-03-01T00:00:00https://www.semanticscholar.org/paper/6ff7129ba43726904fbe04c7924225ac6f9419b7 71380481a73faaf46bce2cf5be30d57cc5d64f36This study explores the integration of Artificial Intelligence (AI) into e-governance cybersecurity, focusing on its key insights, contributions, challenges, and future directions. AI-powered cybersecurity systems offer advanced capabilities in threat detection, response, and scalability, improving the protection of critical infrastructure and sensitive data in government digital services. However, ethical considerations such as transparency, fairness, and accountability, as well as challenges related to algorithm biases, data privacy, and cybersecurity skills gap, must be addressed to ensure responsible and effective use of AI technologies. Recommendations for policymakers, government agencies, and researchers are provided to maximize the benefits of AI while mitigating potential risks and enhancing e-governance cybersecurity resilience in the digital age.Journal of Trends in Computer Science and Smart Technology90Recommendations for policymakers, government agencies, and researchers are provided to maximize the benefits of AI while mitigating potential risks and enhancing e-governance cybersecurity resilience in the digital age.Journal of Trends in Computer Science and Smart Technology['Rahul Kumar Jha', 'Mona Jha']2024-03-01T00:00:00https://www.semanticscholar.org/paper/71380481a73faaf46bce2cf5be30d57cc5d64f36 ec6de5db438f065766229f47e0bbca7901a0a9f6We requested an interview with Jon McCormack after we encountered his work when looking for artists doing compelling work at the intersection of art and artificial intelligence (AI).IEEE Computer Graphics and Applications00IEEE Computer Graphics and Applications['Jon McCormack', 'F. Samsel', 'B. Campbell', 'F. Samsel']2024-03-01T00:00:00https://www.semanticscholar.org/paper/ec6de5db438f065766229f47e0bbca7901a0a9f6 1f08af241a3fa35edff265b8b6dbf19e2c4c42beThis article provides a new typology for understanding human labour integrated into the production of artificial intelligence systems through data preparation and model evaluation. We call these forms of labour ‘AI data work’ and show how they are an important and necessary element of the artificial intelligence production process. We draw on fieldwork with an artificial intelligence data business process outsourcing centre specialising in computer vision data, alongside a decade of fieldwork with microwork platforms, business process outsourcing, and artificial intelligence companies to help dispel confusion around the multiple concepts and frames that encompass artificial intelligence data work including ‘ghost work’, ‘microwork’, ‘crowdwork’ and ‘cloudwork’. We argue that these different frames of reference obscure important differences between how this labour is organised in different contexts. The article provides a conceptual division between the different types of artificial intelligence data work institutions and the different stages of what we call the artificial intelligence data pipeline. This article thus contributes to our understanding of how the practices of workers become a valuable commodity integrated into global artificial intelligence production networks.Big Data & Society270A conceptual division between the different types of artificial intelligence data work institutions and the different stages of what the authors call the artificial intelligence data pipeline is provided.Big Data Soc.['James Muldoon', 'C. Cant', 'Boxi A Wu', 'Mark Graham']2024-03-01T00:00:00https://www.semanticscholar.org/paper/1f08af241a3fa35edff265b8b6dbf19e2c4c42be 7abf5e01d6f6c1397ab963a8b2428440365b7257In this era of rapidly advancing technology, artificial intelligence (AI) has emerged as a transformative force, even being called the Fourth Industrial Revolution, along with gene editing and robotics. While it has undoubtedly become an increasingly important part of our daily lives, it must be recognized that it is not an additional tool, but rather a complex concept that poses a variety of challenges. AI, with considerable potential, has found its place in both medical care and clinical research. Within the vast field of pediatrics, it stands out as a particularly promising advancement. As pediatricians, we are indeed witnessing the impactful integration of AI-based applications into our daily clinical practice and research efforts. These tools are being used for simple to more complex tasks such as diagnosing clinically challenging conditions, predicting disease outcomes, creating treatment plans, educating both patients and healthcare professionals, and generating accurate medical records or scientific papers. In conclusion, the multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research. However, there are certain risks and threats accompanying this advancement including the biases that may contribute to health disparities and, inaccuracies. Therefore, it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.Turkish archives of pediatrics840The multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research, and it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.Turkish Archives of Pediatrics['Kaan Can Demirbaş', 'Mehmet Yıldız', 'S. Saygılı', 'Nur Canpolat', 'Özgür Kasapçopur']2024-03-01T00:00:00https://www.semanticscholar.org/paper/7abf5e01d6f6c1397ab963a8b2428440365b7257 5650ee04fd0affa1c02ce7d1a73bdca5ca74f593The most prevalent type of cancer among women is breast cancer. According to the statistics given by the World Health Organization (WHO), breast cancer is the reason behind the death of about 2.3 billion women globally in 2020, accounting for 685.9 million deaths. Since they are thought to be useful approaches, machine learning and deep learning techniques have drawn attention from researchers in breast cancer detection. Also, it can significantly assist in the process of prior detection and prediction of breast cancer by extracting handcrafted features. However, in recent years, improvements in artificial intelligence (AI) have enabled the successful use of deep learning strategies like CNN and the transfer learning method for detection of breast cancer. A significantly large dataset is used for deep learning methods. It does not require human intervention for feature extraction, which, as a result, enhances the patient's chances of survival. This review paper is based on breast cancer detection using deep learning and machine learning-based cancer detection techniques to aid in the understanding of trends and challenges in cancer detection.Journal of Trends in Computer Science and Smart Technology290This review paper is based on breast cancer detection using deep learning and machine learning-based cancer detection techniques to aid in the understanding of trends and challenges in cancer detection.Journal of Trends in Computer Science and Smart Technology['Tanvi Meet Dhruv']2024-03-01T00:00:00https://www.semanticscholar.org/paper/5650ee04fd0affa1c02ce7d1a73bdca5ca74f593 9e098fe4f84ed9834f29f4466f59411c61539031The field of artificial intelligence (AI) is a modern science that has benefited various disciplines, contributing to the enhancement of service quality across different sectors, including training. This study aims to explore the use of AI applications in the training field in the Sultanate of Oman. The research investigates the current utilization of these applications in training, the challenges faced by trainers, and the key areas of application in training. The researcher employed a descriptive methodology, suitable for this study, with a study population comprising members of the Omani Trainers Association affiliated with the Human Resources Association. The sample targeted 78 trainers in various fields. A questionnaire, consisting of 26 items distributed across two axes (reality and challenges), was prepared for data collection. Additionally, an open-ended question was included for the third aspect of the study. Statistical methods, including frequency and percentage calculations, mean, standard deviation, Pearson correlation coefficient, and Cronbach's alpha, were used to analyze the data, ensuring the reliability and validity of the study tool. The study results indicate that, from the perspective of the study sample, the current implementation of AI applications in training is perceived as average. This reflects their dissatisfaction with this reality, which contradicts the vision and mission of a true trainer. The study also identifies various challenges in the use of AI applications in training, emphasizing the need for collaborative efforts to find practical solutions and provide better services that help organizations achieve their goals. Furthermore, the study highlights seven areas where trainers utilize AI applications in training, with knowledge and information search and scientific facts research leading among these areas. The study recommends the development of a specialized program for training professional trainers in the application of AI across all fieldsRIMAK International Journal of Humanities and Social Sciences00The study recommends the development of a specialized program for training professional trainers in the application of AI across all fields, with knowledge and information search and scientific facts research leading among these areas.RIMAK International Journal of Humanities and Social Sciences['Dr. Abdulsalam Hameed ALRAMADHANI']2024-03-01T00:00:00https://www.semanticscholar.org/paper/9e098fe4f84ed9834f29f4466f59411c61539031 90e89a78290c2a40815b76049e52f3fe079ed365The integration of artificial intelligence (AI) in healthcare presents unprecedented opportunities for improving patient care and outcomes, yet it also brings forth a myriad of ethical dilemmas that demand careful consideration. This article examines the ethical challenges posed by AI in healthcare, ranging from concerns about algorithmic bias and patient privacy to issues of transparency, accountability, and professional autonomy. Through a comprehensive analysis of relevant literature, case studies, and regulatory considerations, the study explores the multifaceted ethical implications of AI technologies in clinical practice. Key findings underscore the importance of promoting transparency and accountability in AI algorithm development and deployment, as well as the need for robust regulatory oversight and ethical guidance to ensure patient rights and safety. Despite the complexities and challenges, AI offers immense potential to enhance patient care and healthcare efficiency when navigated responsibly and ethically. By prioritizing ethical principles and collaborative efforts, stakeholders can harness the transformative power of AI while upholding the highest standards of ethical healthcare practice.Медицинская этика130The ethical challenges posed by AI in healthcare are examined, ranging from concerns about algorithmic bias and patient privacy to issues of transparency, accountability, and professional autonomy, through a comprehensive analysis of relevant literature, case studies, and regulatory considerations.Медицинская этика['KA Koshechkin', 'AL Khokholov']2024-03-01T00:00:00https://www.semanticscholar.org/paper/90e89a78290c2a40815b76049e52f3fe079ed365 ce8685ec91af580b1af07f3d742a0ebe4beeb257In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into “Sustainability of AI” and “AI for Sustainability”, revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field’s maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations’ Sustainable Development Goals (SDGs), and addressing stakeholders’ influence.Analytics970The literature is systematically categorized into “Sustainability of AI” and “AI for Sustainability”, revealing a balanced perspective between the two, and a notable trend towards holistic approaches.Analytics['Rachit Dhiman', 'Sofia Miteff', 'Yuancheng Wang', 'Shih-Chi Ma', 'Ramila Amirikas', 'B. Fabian']2024-03-01T00:00:00https://www.semanticscholar.org/paper/ce8685ec91af580b1af07f3d742a0ebe4beeb257 2b7cf070a53f76e9746778f397752902febb1fc5Artificial intelligence (AI) has revolutionized the banking and financial industry by improving client relations, precision, and operational efficiency. This paper explores the use of artificial intelligence (AI) in banking and finance, including topics like credit scoring, fraud detection, investment management, and customer service. This research aims to identify the benefits and difficulties associated with the integration of AI in the financial sector by a comprehensive analysis of the body of existing literature. The results highlight how AI technologies have significantly improved decision-making, reduced operating costs, and increased overall profitability. Nonetheless, in order to guarantee the ethical and sustainable application of AI in the future, it is crucial to address issues with data privacy, prejudice, and ethical reasons.International Journal of Innovative Research in Computer Science & Technology00This research aims to identify the benefits and difficulties associated with the integration of AI in the financial sector by a comprehensive analysis of the body of existing literature.International Journal of Innovative Research in Computer Science and Technology['Ashima Narang', 'Priyanka Vashisht', 'S. B. Bajaj']2024-03-01T00:00:00https://www.semanticscholar.org/paper/2b7cf070a53f76e9746778f397752902febb1fc5 df54648aa41de228ec859d0d4a3597fa1c7a03f2Innovative technologies that use artificial intelligence in transport solutions recently emerging around the world include, among others issues of autonomous vehicle driving. The use of autonomous vehicle technology affects the issues of civil liability (liability and insurance), road safety, natural environment (energy efficiency, renewable energy sources), data (access, exchange, protection, privacy), IT infrastructure (effective and reliable communication), employment (creation and loss of jobs, training of truck drivers in the use of automated vehicles). The development of new technologies related to artificial intelligence, including autonomous vehicles, generates inevitable changes in law, economy and society. It is inevitable due to the fact that autonomy is undoubtedly a means to achieve the goal of improving the efficiency sought in every area of life. The article presents arguments confirming the thesis that the basic factor inhibiting the implementation of autonomous vehicle technology is the problem of artificial intelligence, including its definition and legal regulation.Scientific Journal of Silesian University of Technology. Series Transport00The article presents arguments confirming the thesis that the basic factor inhibiting the implementation of autonomous vehicle technology is the problem of artificial intelligence, including its definition and legal regulation.Scientific Journal of Silesian University of Technology. Series Transport['Piotr Czech']2024-03-01T00:00:00https://www.semanticscholar.org/paper/df54648aa41de228ec859d0d4a3597fa1c7a03f2 7eb2566722047c917e342ffd0ce3c74106f1e0e8The roles of higher education institutions are based on managing and monitoring access to knowledge and its sharing among individuals with the aim of development and innovation to serve scientific, cultural and economic development, to perform the roles, higher education institutions respond to the requirements of modern technical growth in their fields of activity by overcoming the difficulties that hinder relations between those involved in their activities, and perhaps artificial intelligence techniques is one of the modern technologies that contribute to the tasks of managing access to knowledge and monitoring its sharing among individuals. The field study aims to highlight the level of knowledge management and information monitoring among educational staff in higher education in Algeria by measuring their use of artificial intelligence techniques in teaching and scientific research activities, a descriptive analytical approach was used on a sample consisting of 70 professors distributed among five colleges at the University of Tamanghasset (College of Humanities and Social Sciences 18 professors, College of Legal and Political Sciences 13 professors, College of Economics and Management Sciences 14 professors, College of Science and Technology 13 professors, College of Arts and Languages 12 professors). The study concluded that knowledge management and information monitoring in higher education is a vital activity within the scientific and technological environment in the University of Tamanghasset that contributes to enriching the practices and uses of artificial intelligence techniques with a moderate positive correlation, the study recommended the necessity of conducting field studies and questionnaires to identify more comprehensive level that includes the administrative side and the student side with including the institution's research centers and laboratories and organizations surrounding university in the process, in addition conducting analytical studies to data and statistics related to the use of artificial intelligence techniques and their negative impact on various educational and research aspects, while working to strengthen information systems based on artificial intelligence.ARID International Journal of Social Sciences and Humanities00The study concluded that knowledge management and information monitoring in higher education is a vital activity within the scientific and technological environment in the University of Tamanghasset that contributes to enriching the practices and uses of artificial intelligence techniques.ARID International Journal of Social Sciences and Humanities['Ouledhacini Youcef', 'Boukhouidem fares']2024-03-01T00:00:00https://www.semanticscholar.org/paper/7eb2566722047c917e342ffd0ce3c74106f1e0e8 a8bfa10e8ec668e12ae610a693249e76daf614ffABSTRACT Background Patient‐based real‐time quality control (PBRTQC) has gained attention because of its potential to continuously monitor the analytical quality in situations wherein internal quality control (IQC) is less effective. Therefore, we tried to investigate the application of PBRTQC method based on an artificial intelligence monitoring (AI‐MA) platform in quality risk monitoring of Down syndrome (DS) serum screening. Methods The DS serum screening item determination data and relative IQC data from January 4 to September 7 in 2021 were collected. Then, PBRTQC exponentially weighted moving average (EWMA) and moving average (MA) procedures were built and optimized in the AI‐MA platform. The efficiency of the EWMA and MA procedures with intelligent and traditional control rules were compared. Next, the optimal EWMA procedures that contributed to the quality assurance of serum screening were run and generated early warning cases were investigated. Results Optimal EWMA and MA procedures on the AI‐MA platform were built. Comparison results showed the EWMA procedure with intelligent QC rules but not traditional quality rules contained the best efficiency. Based on the AI‐MA platform, two early warning cases were generated by using the optimal EWMA procedure, which finally found were caused by instrument failure. Moreover, the EWMA procedure could truly reflect the detection accuracy and quality in situations wherein traditional IQC products were unstable or concentrations were inappropriate. Conclusions The EWMA procedure built by the AI‐MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks.Journal of clinical laboratory analysis (Print)310The EWMA procedure built by the AI‐MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks in situations wherein traditional IQC products were unstable or concentrations were inappropriate.Journal of Clinical Laboratory Analysis['Xuran Yang', 'Qianlan Chen', 'Zhifeng Pan', 'Jingmao Cheng', 'Wenting Zheng', 'Yingliang Liang', 'Hui Chen', 'Guanghui Chen', 'Wandang Wang']2024-03-01T00:00:00https://www.semanticscholar.org/paper/a8bfa10e8ec668e12ae610a693249e76daf614ff a91e31c4e4e0102b425f1d3edc45cebb9684fefdIntroduction For artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering marginalised communities to take an active role in constructing research agendas and outputs. Given the unmet needs of the LGBTQI+ (Lesbian, Gay, Bisexual, Transgender, Queer and Intersex) community in mental healthcare, there is a pressing need for participatory research to include a range of diverse queer perspectives on issues of data collection and use (in routine clinical care as well as for research) as well as AI design. Here we propose a protocol for a Delphi consensus process for the development of PARticipatory Queer AI Research for Mental Health (PARQAIR-MH) practices, aimed at informing digital health practices and policy. Methods and analysis The development of PARQAIR-MH is comprised of four stages. In stage 1, a review of recent literature and fact-finding consultation with stakeholder organisations will be conducted to define a terms-of-reference for stage 2, the Delphi process. Our Delphi process consists of three rounds, where the first two rounds will iterate and identify items to be included in the final Delphi survey for consensus ratings. Stage 3 consists of consensus meetings to review and aggregate the Delphi survey responses, leading to stage 4 where we will produce a reusable toolkit to facilitate participatory development of future bespoke LGBTQI+–adapted data collection, harmonisation, and use for data-driven AI applications specifically in mental healthcare settings. Ethics and dissemination PARQAIR-MH aims to deliver a toolkit that will help to ensure that the specific needs of LGBTQI+ communities are accounted for in mental health applications of data-driven technologies. The study is expected to run from June 2024 through January 2025, with the final outputs delivered in mid-2025. Participants in the Delphi process will be recruited by snowball and opportunistic sampling via professional networks and social media (but not by direct approach to healthcare service users, patients, specific clinical services, or via clinicians’ caseloads). Participants will not be required to share personal narratives and experiences of healthcare or treatment for any condition. Before agreeing to participate, people will be given information about the issues considered to be in-scope for the Delphi (eg, developing best practices and methods for collecting and harmonising sensitive characteristics data; developing guidelines for data use/reuse) alongside specific risks of unintended harm from participating that can be reasonably anticipated. Outputs will be made available in open-access peer-reviewed publications, blogs, social media, and on a dedicated project website for future reuse.BMJ Open690A protocol for a Delphi consensus process for the development of PARticipatory Queer AI Research for Mental Health (PARQAIR-MH) practices, aimed at informing digital health practices and policy.BMJ Open['Dan W Joyce', 'A. Kormilitzin', 'J. Hamer-Hunt', 'Kevin R McKee', 'Nenad Tomašev']2024-03-01T00:00:00https://www.semanticscholar.org/paper/a91e31c4e4e0102b425f1d3edc45cebb9684fefd 9c543cc8eee17fbb238ff7a7cc0e33aecab6df8eThe management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient’s MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.Journal of Clinical Medicine620This review explores the evolution of weaning methods up to and including AI and ML as weaning aids and provides an important prediction regarding the success of the individual patient’s MV weaning.Journal of Clinical Medicine['Tamar Stivi', 'D. Padawer', 'Noor Dirini', 'A. Nachshon', 'Baruch M Batzofin', 'Stephane Ledot']2024-03-01T00:00:00https://www.semanticscholar.org/paper/9c543cc8eee17fbb238ff7a7cc0e33aecab6df8e a9792f977fae05772e65f52aa6accbad23a78901The research aims to determine the prospects for improving innovation management in business using artificial intelligence (AI). To achieve the research goal, the author examined the international experience of 33 countries as of the end of 2023, relevant at the beginning of 2024, with reference to IMD statistics. As a result, the author developed an econometric model of innovation management in business using AI and alternative digital technologies. The model has shown that instead of risks, implementing AI generates advantages for innovation management in business. The main conclusion is that the application of AI is preferable for innovation management compared to alternative digital technologies because it makes it possible to significantly increase the results and reduce the costs of such management. Therefore, the authors proposed a new approach to innovation management in business, which achieves a high level of management automation based on AI for the first time. The theoretical significance of the obtained results and the author’s conclusions is explained by the fact that they revealed the cause-and-effect relationships of using AI in innovation management in business. The managerial significance of this research lies in the fact that the application of the new approach will increase the efficiency of innovation management in business.Journal of Trends and Challenges in Artificial Intelligence00The authors proposed a new approach to innovation management in business, which achieves a high level of management automation based on AI for the first time, and shows that instead of risks, implementing AI generates advantages for innovation management in business.Journal of Trends and Challenges in Artificial Intelligence['Olga Konina']2024-03-01T00:00:00https://www.semanticscholar.org/paper/a9792f977fae05772e65f52aa6accbad23a78901 afe036a521bccb7d7f959b25d3ddf0e4ab987525Talent sourcing is one of the most effective mechanisms to engage with the talent pool and convert a candidate into an applicant. Today, machine learning has emerged as a trend to assist employers in addressing recruitment challeng-es with the help of tools such as neuro-linguistic programming (NLP) and automated assessments. 80% of the executives strongly believe deep learning makes candidate screening highly efficient. Including current start-ups globally, only 15% use artificial intelligence (AI) and are expected to increase by 31%. The study focused on the impact of AI in recruitment process. There are a few metrics, such as application completion rate, number of candidates per filled position, cost per hire, and so on. Here we would like to analyze the impact of using AI in various phases of hiring in the organization.IAES International Journal of Artificial Intelligence (IJ-AI)410The study focused on the impact of AI in recruitment process and analyzed a few metrics, such as application completion rate, number of candidates per filled position, cost per hire, and so on.IAES International Journal of Artificial Intelligence (IJ-AI)['V. Hemachandran', 'Kurakula Arun Kumar', 'Syarul Azlina Sikanda', 'Seema Sabharwal', 'Sivaprakasam Arun Kumar']2024-03-01T00:00:00https://www.semanticscholar.org/paper/afe036a521bccb7d7f959b25d3ddf0e4ab987525 16e61e7b92be1231f33f950e8e709f846783b317In developing countries such as Nigeria, artificial intelligence (AI) research has the potential to drive rapid advancement in various aspects of development, including the economy and technology. However, it is crucial to understand the focus of Nigerian AI researchers and identify unexplored areas of research that could lead to unprecedented development. To address this need, we used natural language processing, machine learning, and statistical algorithms to investigate the main areas of interest of Nigerian AI researchers. We identified ten topics and used scientometric analyses to reveal key concepts, keyword co-occurrences, and authorship networks. Our study found that Covenant University was the most prolific institution, with 375 publications, followed by the Federal University of Technology with 135 publications and the University of Ibadan with 121 publications. Overall, our research provides valuable insights into the structure and progression of AI research in Nigeria and highlights areas for improvement.IAES International Journal of Artificial Intelligence (IJ-AI)320This study used natural language processing, machine learning, and statistical algorithms to investigate the main areas of interest of Nigerian AI researchers and used scientometric analyses to reveal key concepts, keyword co-occurrences, and authorship networks.IAES International Journal of Artificial Intelligence (IJ-AI)['Afolabi Ibukun Tolulope', 'Martins Isaac', 'Owoseni Timileyin', 'Samuel Seth', 'Oputa Kingsley']2024-03-01T00:00:00https://www.semanticscholar.org/paper/16e61e7b92be1231f33f950e8e709f846783b317 762c96fc965d058dd918742698a8f5dd5744988bThe relevance of the research is due to the fact that today's advances in cognitive sciences have led to a real cognitive revolution: we understand more and more how our brains work, including how our brains learn. And while this understanding alone does not yet guarantee the most effective learning solutions, it is proving useful for optimizing educational environments and processes. From a neuroscientist's point of view, education is not a mechanical accumulation of skills, but a work with cognitive resources, including an attempt to increase them. Education is increasingly using knowledge about the brain to build educational processes, but this knowledge alone does not guarantee the creation of the most effective learning solutions. Understanding neurodevelopmental activity can serve as a motivator for educators to teach effectively. The aim of the research is to present effective artificial intelligence tools at different stages of educational design.Journal of Trends and Challenges in Artificial Intelligence00The aim of the research is to present effective artificial intelligence tools at different stages of educational design to serve as a motivator for educators to teach effectively.Journal of Trends and Challenges in Artificial Intelligence['Elena Shirinkina', 'Bezhan Sobirov']2024-03-01T00:00:00https://www.semanticscholar.org/paper/762c96fc965d058dd918742698a8f5dd5744988b 8fe365994e10e2c13d30f831a44149bc87a83cbaIn the dynamic intersection of finance and technology, the integration of artificial intelligence (AI) within the banking sector can mark a pivotal shift towards operational efficiency and enhanced customer service. This study, performed with the case of Armenian banking sector, employs a dual-analytical lens, focusing on the ratio of intangible assets to total assets (IA/TA) and IT-related costs in operational expenses (OPEX) to infer the extent of AI adoption through digital maturity. Despite the anticipation that these financial ratios would directly reflect a bank's digital transformation efforts, our findings illustrate a more complex reality, where such co-movement of the two ratios is not necessarily consistent over time within the sector. This deviation underscores the nuanced interplay between financial investment in digital technologies and the actual deployment of AI, revealing the need for a holistic approach that combines quantitative financial metrics with qualitative insights.Journal of Trends and Challenges in Artificial Intelligence00This study employs a dual-analytical lens, focusing on the ratio of intangible assets to total assets (IA/TA) and IT-related costs in operational expenses (OPEX) to infer the extent of AI adoption through digital maturity.Journal of Trends and Challenges in Artificial Intelligence['Henrik H. Manukyan', 'Suren H. Parsyan']2024-03-01T00:00:00https://www.semanticscholar.org/paper/8fe365994e10e2c13d30f831a44149bc87a83cba b7005318ec32ef9c7080dfd36b4e81d4f4d36cbcSmall and medium enterprises small and medium enterprises (SMEs) play a crucial role in nations’ economy, through job creations, reducing unemployment rate as well as increase the overall productivity and gross domestic product (GDP) of a country. However, most SMEs are often lagging in technology adoption which could be a game changer for their success. SMEs could adopt new technologies to improve their business operations and profitability. They are also useful in supporting SMEs to penetrate international market. This research suggests that implementation of the artificial intelligence (AI) through digital marketing (DM) and financial technology (Fintech) would assist SMEs to be competitive, current in leveraging on technology and increase their overall profitability. Based on secondary data analysis, this paper presents a conceptual framework of determining factors in adoption of AI through digital marketing and Fintech. It contributes to the academic knowledge of AI, DM and Fintech for small businesses, and presents a testable framework that can be replicated and adapted for future empirical study. IAES International Journal of Artificial Intelligence (IJ-AI)570This research suggests that implementation of the artificial intelligence (AI) through digital marketing (DM) and financial technology (Fintech) would assist SMEs to be competitive, current in leveraging on technology and increase their overall profitability.IAES International Journal of Artificial Intelligence (IJ-AI)['Mohammed Enshassi', 'R. Nathan', 'Soekmawati Soekmawati', 'Usama Al-mulali', 'H. Ismail']2024-03-01T00:00:00https://www.semanticscholar.org/paper/b7005318ec32ef9c7080dfd36b4e81d4f4d36cbc 1ef850922023a031083ecb0de3cdbb5a5b3ad056We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in Spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March–April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1970 comments from VH participants and trained two machine learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three health belief model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March–April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.Behavioral Science350How artificial intelligence reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments is investigated, showing that vaccine status from VH to later receiving a vaccine is influenced by barriers.Behavioral Sciences['Samaneh Omranian', 'Alireza Khoddam', 'Celeste Campos-Castillo', 'Sajjad Fouladvand', 'S. McRoy', 'Janet Rich-Edwards']2024-03-01T00:00:00https://www.semanticscholar.org/paper/1ef850922023a031083ecb0de3cdbb5a5b3ad056 0087e8dde3d13ba543e74995d92ae86144a6bd70 Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.Acta Marisiensis - Seria Medica490In conclusion, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.Acta Marisiensis - Seria Medica['B. Swapna', 'Shibani Shetty', 'Manjunath Shetty', 'S. Shetty']2024-03-01T00:00:00https://www.semanticscholar.org/paper/0087e8dde3d13ba543e74995d92ae86144a6bd70 9c94ab50302247bfcad5e72fdb4753571fe4e781Accounting information is a very important part of accounting resource sharing, accounting resource sharing can greatly save manpower and material resources, among which risk assessment is very important, you can know whether accounting information resources are safe. Traditional assessment methods cannot better meet the needs of today's society. Therefore, this paper proposes an artificial intelligence algorithm for risk assessment and analysis. First, the computer is used to classify and analyze the accounting information, and the indicators are divided according to the risk assessment requirements to reduce the risk assessment in the interfering factor. Then, the computer shares the results of the risk assessment with the accounting resources, forms a risk assessment plan, and conducts the risk assessment results Comprehensive analysis. MATLAB simulation shows that under certain evaluation criteria, the accuracy of risk assessment of artificial intelligence algorithms for accounting resource sharing Risk assessment authenticity is superior to traditional assessment methods.2024 3rd International Conference for Innovation in Technology (INOCON)190An artificial intelligence algorithm for risk assessment and analysis and MATLAB simulation shows that under certain evaluation criteria, the accuracy of risk assessment of artificial intelligence algorithms for accounting resource sharing Risk assessment authenticity is superior to traditional assessment methods.2024 3rd International Conference for Innovation in Technology (INOCON)['Li Wang']2024-03-01T00:00:00https://www.semanticscholar.org/paper/9c94ab50302247bfcad5e72fdb4753571fe4e781 5b991c8450f3ea124cf6aee2ceefdb0ff9ab9a2eFor the inaugural issue of the International Journal of Artificial Intelligence and Robotics Research (IJAIRR), I am honored to present an editorial that encapsulates the essence and ambition of this cutting-edge publication. IJAIRR emerges at a time when Artificial Intelligence and Robotics (AIR) are not merely technological novelties but fundamental drivers of progress across various scientific and practical domains. This journal aims to be at the forefront of documenting, analyzing, and guiding the interdisciplinary integration of AI, robotics, and fundamental sciences.Int. J. Artif. Intell. Robotics Res.00Int. J. Artif. Intell. Robotics Res.['Yu Sun', 'Dong Xu', 'Xiaorui Zhu']2024-03-01T00:00:00https://www.semanticscholar.org/paper/5b991c8450f3ea124cf6aee2ceefdb0ff9ab9a2e 82772c107a25ec8c7e0af76406ee488cd776a00cThe rapid progression of artificial intelligence (AI) in healthcare has greatly influenced emergency medicine, particularly in Taiwan-a nation celebrated for its technological innovation and advanced public healthcare. This narrative review examines the current status of AI applications in Taiwan's emergency medicine and highlights notable achievements and potential areas for growth. AI has wide capabilities encompass a broad range, including disease prediction, diagnostic imaging interpretation, and workflow enhancement. While the integration of AI presents promising advancements, it is not devoid of challenges. Concerns about the interpretability of AI models, the importance of dataset accuracy, the necessity for external validation, and ethical quandaries emphasize the need for a balanced approach. Regulatory oversight also plays a crucial role in ensuring the safe and effective deployment of AI tools in clinical settings. As its footprint continues to expand in medical education and other areas, addressing these challenges is imperative to harness the full potential of AI for transforming emergency medicine in Taiwan.Journal of Acute Medicine00This narrative review examines the current status of AI applications in Taiwan's emergency medicine and highlights notable achievements and potential areas for growth.Journal of acute medicine['Bing-Hung Shih', 'Chien-Chun Yeh']2024-03-01T00:00:00https://www.semanticscholar.org/paper/82772c107a25ec8c7e0af76406ee488cd776a00c 6fe3f5e887ba1c80c9966705cf5477fda8ce2e2cINTRODUCTION Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives. EVIDENCE ACQUISITION We performed a literature search on articles published from 2018 to September 2023. We included only original articles. EVIDENCE SYNTHESIS Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis. CONCLUSIONS AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.Minerva surgery00An overview of the evolving role of AI in the diagnosis and management of acute appendicitis is provided, highlighting its benefits, challenges, and future perspectives.Minerva surgery['Micaela Cappuccio', 'Paolo Bianco', 'Marco Rotondo', 'Salvatore Spiezia', 'Marco D’Ambrosio', 'Francesco Menegon Tasselli', 'Germano Guerra', 'Pasquale Avella']2024-03-01T00:00:00https://www.semanticscholar.org/paper/6fe3f5e887ba1c80c9966705cf5477fda8ce2e2c 9f99cbc38c500a0be39ba9aabfe016c4ee297df4Today, the problem of automating the control of the individual trajectory of the states of complex objects is relevant. It is necessary to influence the individual trajectory of the object's states, guided by certain parameters. The reaction should be adequate to change these parameters. The aim of the work is reasonable automation of adaptive control of complex objects and trajectories in artificial intelligence systems. Tasks to be solved: (1) Identification and evaluation of criteria by which it is possible to determine the level of the object's condition with a high degree of probability. (2) Creation of such a mechanism for issuing control actions to an object, on the basis of which it will be possible to create fully automated control trajectories that require a minimum of operator participation in the operation of the finished systemInternational Journal on Information Technologies and Security80The aim of the work is reasonable automation of adaptive control of complex objects and trajectories in artificial intelligence systems.International Journal on Information Technologies and Security['D. Mutin', 'Alexey Kaperko', 'Sergey Sorokin', 'Dmitriy Sotnikov', 'I. Atlasov', 'Nikita Ryndin']2024-03-01T00:00:00https://www.semanticscholar.org/paper/9f99cbc38c500a0be39ba9aabfe016c4ee297df4 20214d5c5925c1dcc02991e7ae7b13ac61da35ccThe evolution of pathology from its rudimentary beginnings around 1700 BC to the present day has been marked by profound advancement in understanding and diagnosing diseases. This journey, from the earliest dissections to the modern era of histochemical analysis, sets the stage for the next transformative leap to the integration of artificial intelligence (AI) in pathology. Recent research highlights AI’s significant potential to revolutionize healthcare within the next decade, with a particular impact on diagnostic processes. A majority of pathologists foresee AI becoming a cornerstone in diagnostic workflow, driven by the advent of image-based algorithms and computational pathology. These innovations promise to enhance the precision of disease diagnosis, particularly in complex cases, such as cancers, by offering detailed insights into the molecular and cellular mechanisms. Moreover, AI-assisted tools are improving the efficiency and accuracy of histological analysis by automating the evaluation of immunohistochemical biomarkers and tissue architecture. This shift not only accelerates diagnostic processes but also facilitates early disease management, crucial for improving patient outcomes. Furthermore, AI is reshaping educational paradigms in pathology, offering interactive learning environments that promise to enrich the training of future pathologists. Despite these advancements, the integration of AI in pathology raises ethical considerations regarding patient consent and data privacy. As pathology embarks on this AI-augmented era, it is imperative to navigate these challenges thoughtfully, ensuring that AI enhances rather than replaces the pathologist’s role. This editorial discussed the historical progression of pathology, the current impact of AI on diagnostic practices, and the ethical implications of its adoption, underscoring the need for a symbiotic relationship between pathologists and AI to unlock the full potential of healthcare.Cureus50The historical progression of pathology, the current impact of AI on diagnostic practices, and the ethical implications of its adoption are discussed, underscoring the need for a symbiotic relationship between pathologists and AI to unlock the full potential of healthcare.Cureus['Fnu Sandeep', 'Nfn Kiran', 'Zubair Rahaman', 'Pooja Devi', 'A. Bendari']2024-03-01T00:00:00https://www.semanticscholar.org/paper/20214d5c5925c1dcc02991e7ae7b13ac61da35cc 18a51de97f7545cd8348562ad43cc8951d07a4b2Section 1502 of the Dodd–Frank Act requires SEC‐registered issuers to conduct supply chain due diligence and submit conflict minerals disclosures (CMDs) that indicate whether their products contain tantalum, tin, tungsten, or gold (3TG) sourced from the Democratic Republic of the Congo (DRC) or its neighboring countries (“covered countries”). Consistent with the reputational cost hypothesis, we find that heightened public attention to CMDs increases responsible sourcing. After Section 1502 takes effect, we find higher demand for 3TG products processed in certified smelters, decreased conflicts in covered countries’ mining regions relative to other regions, and reduced sensitivity of conflict risk to conflict minerals’ price spikes. Finally, we find that conflicts decrease in Eastern DRC territories with prevalent 3T (tantalum, tin, and tungsten) mines but increase in territories with prevalent gold mines. Overall, our findings highlight the real effects of enhanced supply chain transparency regulation.Journal of Accounting Research762Journal of Accounting Research['Bok Baik', 'Omri Even-Tov', 'Russell Han', 'David Park']2024-02-29T00:00:00https://www.semanticscholar.org/paper/18a51de97f7545cd8348562ad43cc8951d07a4b2 3933202301e0c86f3d5aece8aa3b2b4ed61d0561The main purpose of the article is the theoretical and methodological substantiation of the modern approach to the formation of a model of state regulation of investment support for innovative industrial development in the context of helping to increase the level of financial security. The object of the study is the innovative development of the industrial sector of the Ukrainian economy. At the same time, the scientific task will be to present a methodological approach to assessing the current state of trends in investment support for innovative industrial development in all regions of Ukraine as a tool for state regulation. The research methodology involves the use of modern methods of analysis and synthesis of available information on the research topic. A nonparametric statistical method was used to analyze the trends and dynamics of industrial development in Ukraine. The integral assessment method was used to form a methodological approach to assessing the level of investment support for innovative development as an effective instrument of state regulation. As a result of the study, a model of state regulation of investment support for innovative industrial development is presented. An assessment was made of the level of investment support for innovative industrial development concerning the proposed model. Based on the results obtained, measures to ensure financial security in the conditions of Industry 4.0 are proposed. The key financial obstacles to investment support for innovative industrial development are presented, ordered by the priority of the state's response to them. The study has limitations in that it does not take into account all aspects of state regulation. In this case, only the specifics of the industrial sector of the economy were taken into account.Financial and credit activity problems of theory and practice260Financial and credit activity problems of theory and practice['Mykhailo Honchar', 'Igor Grybyk', 'Svitlana Honchar', 'Natalia Smolinska', 'Volodymyr Gavran']2024-02-29T00:00:00https://www.semanticscholar.org/paper/3933202301e0c86f3d5aece8aa3b2b4ed61d0561 4df9171e81b4921b661af9553561b6f42fe43fe6Bulletin00THE BULLETIN['M. Mekin', 'T. Kurakbaeva', 'S. Serikbaev', 'Zh. Kairlieva', 'B. Kulbay']2024-02-29T00:00:00https://www.semanticscholar.org/paper/4df9171e81b4921b661af9553561b6f42fe43fe6 42eda2bff25677d5d6d929c7c01b7915297137ff The recent commercial release of a new generation of chatbot systems, particularly those leveraging Transformer-based large language models (LLMs) such as ChatGPT, has caught the world by surprise and sparked debate about their potential consequences for society. While concerns about the existential threat posed by these technologies are often discussed, it is crucial to shift our focus towards the more immediate risks associated with their deployment. Such risks are further compounded by the lack of proactive measures addressing users’ literacy and the for-profit model via which these chatbots are distributed. Drawing on research in computer science and other fields, this paper looks at the immediate risks triggered by these products and reflects on the role of law within a broader policy directed at steering generative artificial intelligence technology towards the common good. It also reviews the relevant amendments proposed by the European Parliament to the European Commission’s proposal for an AI Act.European Journal of Risk Regulation00A look at the immediate risks triggered by chatbot systems, the role of law within a broader policy directed at steering generative artificial intelligence technology towards the common good, and the relevant amendments proposed by the European Parliament to the European Commission’s proposal for an AI Act are reviewed.European Journal of Risk Regulation['Sara Migliorini']2024-02-29T00:00:00https://www.semanticscholar.org/paper/42eda2bff25677d5d6d929c7c01b7915297137ff f491dac80f21c239b1134f871773c3f81f3da06dThe paper establishes that in the legal field of Ukraine there are more than 50 legal and regulatory acts that are directly or indirectly related to the regulation of the dairy industry market. The Law of Ukraine "On Milk and Dairy Products" is basic in the field of regulation of the dairy industry. The legislator fixed the interpretation of the main terms of the dairy industry in the relevant legal act - the Law of Ukraine "On Milk and Dairy Products". This act defines what "raw milk", "dairy raw materials", "dairy products", "traditional dairy products" are. Also, the legislator fixed the main institutions of the state policy of ensuring the safety and quality of milk and dairy products. In addition, the act discloses the issue of state support for producers of milk, dairy raw materials and dairy products and the mechanism for its implementation. It should be noted that the Law of Ukraine "On Milk and Dairy Products" regulates the legal and organizational foundations for ensuring the safety and quality of milk and dairy products for life and the health of the population and the environment during their production, transportation, processing, storage and sale, import into the customs territory and export from the customs territory of Ukraine. The study identified the main, in our opinion, regulatory legal acts that regulate the supply of milk, dairy raw materials and dairy products to the state, and also characterized their purpose, main provisions and meaning. It was found that milk and dairy products occupy a significant place in the market of food resources. It is noted that the production of dairy products is an important component of food security. It was found that providing the state with milk is a complex process that includes the following stages: milk production, milk processing, sale of dairy products, state regulation.Economics. Finances. Law00Economics. Finances. Law['Yurii Khobta']2024-02-29T00:00:00https://www.semanticscholar.org/paper/f491dac80f21c239b1134f871773c3f81f3da06d c24d1e1f82fcaa6fba17e3198e018465476df9f2 This research paper critically analyzes the ART Act 21 by examining its key provisions and assessing its strengths, weaknesses, and implementation. This research summarizes the various notifications related to the act and analyses its impact on infertility care, in terms of positive outcome, possible legal challenges, hurdles in function and suggestions for future, based on semi-structured interviews with multiple experts working at different in vitro fertilization centers. The analysis shows that the ART Act 21 sets standards and guidelines for ART clinics and ART banks. The act aims to ensure that infertility treatments are regulated and conducted ethically. However, the ART Act 21 has some limitations pertaining to enforcement mechanisms, cost of treatment, cryopreservation of gametes/embryos, research and innovation, and inclusion in public health. To address these limitations, the research paper suggests several suggestions for future consideration during the review of the impact of the ART Act 21 after wider discussion, such as providing explicit definitions of genetic disorders, robust enforcement mechanisms, greater access to affordable treatment, cryopreservation provisions, consideration of technological advancement, inclusion in public health, and provisions for insurance cover. Journal of Marine Medical Society120Several suggestions for future consideration are suggested during the review of the impact of the ART Act 21 after wider discussion, such as providing explicit definitions of genetic disorders, robust enforcement mechanisms, greater access to affordable treatment, cryopreservation provisions, consideration of technological advancement, inclusion in public health, and provisions for insurance cover.Journal of Marine Medical Society['Manisha', 'N. K. Tarway', 'Puja Kumari']2024-02-29T00:00:00https://www.semanticscholar.org/paper/c24d1e1f82fcaa6fba17e3198e018465476df9f2 11cd5eb3656cd905ac735c5d47e7a48f04b5b550International scientific journal Internauka Series Juridical Sciences00International scientific journal "Internauka". Series: "Juridical Sciences"['M. Kovaliv', 'Ruslan Skrynkovskyy', 'S. Petkov', 'Oleh Koretskyi', 'Bogdan Chorniy', 'M. Mykytiuk', 'Vitaliy Hudyma']2024-02-29T00:00:00https://www.semanticscholar.org/paper/11cd5eb3656cd905ac735c5d47e7a48f04b5b550 79aba5d96a8b0bb76a7e134360140c86514a7586International scientific journal Internauka Series Juridical Sciences00International scientific journal "Internauka". Series: "Juridical Sciences"['O. Bryhinets', 'D. Vasyliuk']2024-02-29T00:00:00https://www.semanticscholar.org/paper/79aba5d96a8b0bb76a7e134360140c86514a7586 6f0991bc08c0f4f4b7f0f3d682aec864c6d70a4aEnvironmental Law and Policy00Environmental Law and Policy['Jin-Young Park', 'Jae-Hyup Lee']2024-02-29T00:00:00https://www.semanticscholar.org/paper/6f0991bc08c0f4f4b7f0f3d682aec864c6d70a4a c47290c39d7440e19bcfd0f314829c82cc294125Bulletin00THE BULLETIN['A. Issaeva', 'D. Onaltayev', 'M. Nurgabylov', 'N. Chupryna', 'M. Bayetova']2024-02-29T00:00:00https://www.semanticscholar.org/paper/c47290c39d7440e19bcfd0f314829c82cc294125 d3185bca98f2429e733090a589caf5836316eb2fWith the advent of ChatGPT, the boundary between humans and machines has disappeared, making it increasingly difficult for people to discern whether the person they are dealing with is a machine or a human. Nevertheless, ChatGPT is bringing about changes in the way we work, communicate, and play in the digital world by bringing about a shift in information search and access methods and creating products such as books and art. ChatGPT optimizes conversations using Reinforcement Learning from Human Feedback (RLHF). Reinforcement Learning is a type of machine learning technique that learns how to perform a task through repeated trial and error interactions. A person scores ChatGPT's responses and reflects them in the reinforcement learning rewards algorithm to learn ChatGPT's reward model. It is learned that the more a human user judges a given query to be a good response, the greater the reward, and the more harmful text is generated, the less reward it receives. It is thanks to this RLHF technology that when talking with ChatGPT, you feel like you are talking with a logical and fluent person rather than a machine. ChatGPT will enable the commercialization of a generative AI service that enables interactive conversation rather than focusing on one-sided commands, and since it is a very large-scale model, versatility is its core competitiveness. In other words, ChatGPT allows you to exchange conversations and finds or converts results such as writing, composing, coding, or drawing with a single text input. As ChatGPT significantly lowers the threshold of time and limitations associated with content production, it could gain attention in a variety of fields including advertising, publishing, and gaming. Additionally, ChatGPT has great potential for growth in the field of education as it can unravel and explain complex concepts and correct errors through conversation. This study reviewed the technological development process and general-purpose use of generative artificial intelligence (ChatGPT) and the legal regulations of ChatGPT, focusing on the legal issues surrounding the emergence of generative artificial intelligence (ChatGPT) in the AI era.Korean Institute for Aggregate Buildings Law00The technological development process and general-purpose use of generative artificial intelligence (ChatGPT) and the legal regulations of ChatGPT are reviewed, focusing on the legal issues surrounding the emergence of generative artificial intelligence (ChatGPT) in the AI era.Korean Institute for Aggregate Buildings Law['Seung-Rae Kim', 'In-Bang Song']2024-02-29T00:00:00https://www.semanticscholar.org/paper/d3185bca98f2429e733090a589caf5836316eb2f 56f63a6f590ac2e03d507e5f1b668bd1fc906adfRecension du livre : The Rise of AI: implications and applications of Artificial Intelligence in Academic LibrariesRevue électronique suisse de science de l'information - RESSI02Revue électronique suisse de science de l'information (RESSI)['Stéphanie Haesen']2024-02-29T00:00:00https://www.semanticscholar.org/paper/56f63a6f590ac2e03d507e5f1b668bd1fc906adf 7d3f08a3a9ec8a81533f4acc7ff3cd5cdd160955The world of health insurance and Medicare has traditionally been perceived as complex and difficult to navigate. Fortunately, the application of Generative AI to virtual agents has begun to transform the industry. Large language and image, AI models, also known as generative AI or foundation models, have opened up new prospects for organizations and people involved in content creation. Once trained, a generative model can be "fine-tuned" for a certain content domain with far less data.International Research Journal on Advanced Engineering Hub (IRJAEH)152International Research Journal on Advanced Engineering Hub (IRJAEH)['B. D. Neelima', 'P. R. Prasad', 'A. Swapna', 'Shweta A. Kulkarni']2024-02-29T00:00:00https://www.semanticscholar.org/paper/7d3f08a3a9ec8a81533f4acc7ff3cd5cdd160955 dce0efbb751f80893bea16542d5f5eca88fdb812Background: In recent years, the use of artificial intelligence in the field of production and design has increased. As a result, in smart production and autonomous systems, the concepts of copyright and rights ownership on the works produced have become increasingly complex. In addition, there is no sufficient legal regulation regarding the rights of the software side of the system, the content providers and the commercial parties with whom they have agreements, in the productions made by autonomous systems through artificial intelligence software. In addition to the ownership of the work, the copyright of the elements in the content of the work and those who produce these elements also emerge as an important problem in productions made with artificial intelligence. Purpose of Study: In this study, it is aimed to examine the copyright issue in artificial intelligence applications of smart production and autonomous systems. Sources of Evidence: In the research, a literature review was conducted and semiotic analysis and content analysis were conducted based on academic studies. According to the results obtained, analyzes were made regarding the deficiencies in copyright and the main problems arising from field applications in smart production and autonomous systems made through artificial intelligence. Main Argument: The main argument of the research is that copyright is an important problem in both the short and long term in smart production and autonomous systems produced through artificial intelligence. Conclusions: Although DSM Directive 2019/790/EU, which was issued in 2016 and came into force in 2019, regulates digital copyrights, there are serious deficiencies regarding the ownership of the system or work and the legal regulations regarding smart productions and autonomous systems produced through artificial intelligence. While DSM Directive 2019/790/EU targets a uniform digital market, the copyright issue in artificial intelligence applications shows that this regulation is also inadequate. Regarding the AI Act, there is not yet sufficient regulation or implementation data regarding copyrights. The United States Copyright Office published in 2023 points out similar deficiencies in artificial intelligence and copyrights. Existing copyright regulations are insufficient today, especially for smart products produced by autonomous systems. One of the most important sources of the problem is that the work, its ownership, the types of work, and the commercial and moral values of the work are not fully defined. For a solution, comprehensive and advanced studies are needed regarding the copyrights of artificial intelligence.2024: Proceedings of Social Science and Humanities Research Association (SSHRA)00The main argument of the research is that copyright is an important problem in both the short and long term in smart production and autonomous systems produced through artificial intelligence.2024: Proceedings of Social Science and Humanities Research Association (SSHRA)['Gulde Alparslan']2024-02-29T00:00:00https://www.semanticscholar.org/paper/dce0efbb751f80893bea16542d5f5eca88fdb812 441922f7ed3a91c5ef9fdac7025bc67012a88815Abstract Recent artificial intelligence (AI) tools have demonstrated the ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g. Midjourney, Stable Diffusion, DALL-E), which automates humans’ artistic execution to generate digital artworks. Utilizing a dataset of over 4 million artworks from more than 50,000 unique users, our research shows that over time, text-to-image AI significantly enhances human creative productivity by 25% and increases the value as measured by the likelihood of receiving a favorite per view by 50%. While peak artwork Content Novelty, defined as focal subject matter and relations, increases over time, average Content Novelty declines, suggesting an expanding but inefficient idea space. Additionally, there is a consistent reduction in both peak and average Visual Novelty, captured by pixel-level stylistic elements. Importantly, AI-assisted artists who can successfully explore more novel ideas, regardless of their prior originality, may produce artworks that their peers evaluate more favorably. Lastly, AI adoption decreased value capture (favorites earned) concentration among adopters. The results suggest that ideation and filtering are likely necessary skills in the text-to-image process, thus giving rise to “generative synesthesia”—the harmonious blending of human exploration and AI exploitation to discover new creative workflows.PNAS Nexus225The results suggest that ideation and filtering are likely necessary skills in the text-to-image process, thus giving rise to “generative synesthesia”—the harmonious blending of human exploration and AI exploitation to discover new creative workflows.PNAS Nexus['Eric Zhou', 'Dokyun Lee']2024-02-29T00:00:00https://www.semanticscholar.org/paper/441922f7ed3a91c5ef9fdac7025bc67012a88815 a222625d335b1ff3beda12ff8d029df8702c9861BACKGROUND Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.Medical Teacher02The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.Medical teacher['Morris Gordon', 'Michelle Daniel', 'Aderonke Ajiboye', 'Hussein Uraiby', 'Nicole Y. Xu', 'Rangana Bartlett', 'Janice Hanson', 'Mary Haas', 'Maxwell T. Spadafore', 'C. Grafton-Clarke', 'Rayhan Yousef Gasiea', 'Colin Michie', 'Janet Corral', 'Brian Kwan', 'Diana Dolmans', 'S. Thammasitboon']2024-02-29T00:00:00https://www.semanticscholar.org/paper/a222625d335b1ff3beda12ff8d029df8702c9861 e4995915af3d5de1a3082bcab18203da3169653fImportance Ophthalmology is reliant on effective interpretation of multimodal imaging to ensure diagnostic accuracy. The new ability of ChatGPT-4 (OpenAI) to interpret ophthalmic images has not yet been explored. Objective To evaluate the performance of the novel release of an artificial intelligence chatbot that is capable of processing imaging data. Design, Setting, and Participants This cross-sectional study used a publicly available dataset of ophthalmic cases from OCTCases, a medical education platform based out of the Department of Ophthalmology and Vision Sciences at the University of Toronto, with accompanying clinical multimodal imaging and multiple-choice questions. Across 137 available cases, 136 contained multiple-choice questions (99%). Exposures The chatbot answered questions requiring multimodal input from October 16 to October 23, 2023. Main Outcomes and Measures The primary outcome was the accuracy of the chatbot in answering multiple-choice questions pertaining to image recognition in ophthalmic cases, measured as the proportion of correct responses. χ2 Tests were conducted to compare the proportion of correct responses across different ophthalmic subspecialties. Results A total of 429 multiple-choice questions from 136 ophthalmic cases and 448 images were included in the analysis. The chatbot answered 299 of multiple-choice questions correctly across all cases (70%). The chatbot's performance was better on retina questions than neuro-ophthalmology questions (77% vs 58%; difference = 18%; 95% CI, 7.5%-29.4%; χ21 = 11.4; P < .001). The chatbot achieved a better performance on nonimage-based questions compared with image-based questions (82% vs 65%; difference = 17%; 95% CI, 7.8%-25.1%; χ21 = 12.2; P < .001).The chatbot performed best on questions in the retina category (77% correct) and poorest in the neuro-ophthalmology category (58% correct). The chatbot demonstrated intermediate performance on questions from the ocular oncology (72% correct), pediatric ophthalmology (68% correct), uveitis (67% correct), and glaucoma (61% correct) categories. Conclusions and Relevance In this study, the recent version of the chatbot accurately responded to approximately two-thirds of multiple-choice questions pertaining to ophthalmic cases based on imaging interpretation. The multimodal chatbot performed better on questions that did not rely on the interpretation of imaging modalities. As the use of multimodal chatbots becomes increasingly widespread, it is imperative to stress their appropriate integration within medical contexts.JAMA ophthalmology234In this study, the recent version of the chatbot accurately responded to approximately two-thirds of multiple-choice questions pertaining to ophthalmic cases based on imaging interpretation, and the multimodal chatbot performed better on questions that did not rely on the interpretation of imaging modalities.JAMA ophthalmology['Andrew Mihalache', 'Ryan S Huang', 'Marko M. Popovic', 'Nikhil S Patil', 'Bhadra U. Pandya', 'Reut Shor', 'Austin Pereira', 'Jason Kwok', 'Peng Yan', 'David T. Wong', 'P. Kertes', 'Rajeev H. Muni']2024-02-29T00:00:00https://www.semanticscholar.org/paper/e4995915af3d5de1a3082bcab18203da3169653f bd9d69800eea8654feec95ff65426cf835d49b52Automated writing evaluation (AWE), which is the result of educational artificial intelligence technology, is a process of scoring and evaluating learners’ written texts automatically. The current study examined the effects of using integrative AWE programs on honing academic writing instruction. It also assessed students’ perceptions towards using these programs. A quasi-experimental pretest-posttest two-group design was used. Test, questionnaire, focus group discussion, and teacher diary were used to collect data from 92 randomly selected participants. The experimental group students learned writing skills with Writerly and Google Docs in integration, but the control group students learned through the conventional paper and pencil feedback system. When the quantitative data were analyzed through independent samples T-test and descriptive statistics, the qualitative data were analyzed thematically. The findings revealed that using the integrated AWE programs honed academic writing instruction because there was a statistical difference between the experimental and control groups in their academic writing performance. Hence, students who learned using the integrated AWE programs honed their academic writing performance because they were able to produce essays that addressed task achievement, coherence and cohesion, lexical resource, grammatical range and accuracy. However, students who learned through the conventional method were less effective in producing quality essays. Besides, the findings also discovered that the experimental group students had positive perceptions towards using the aforementioned AWE programs because they found the programs interesting, effective, goal-oriented, and supportive. Consequently, this study recommends researchers, curriculum designers, instructional material designers, teachers, and students pay due attention to integrated AWE programs. Jurnal Cakrawala Pendidikan01Students who learned using the integrated AWE programs honed their academic writing performance because they were able to produce essays that addressed task achievement, coherence and cohesion, lexical resource, grammatical range and accuracy.Jurnal Cakrawala Pendidikan['Bantalem Derseh Wale']2024-02-29T00:00:00https://www.semanticscholar.org/paper/bd9d69800eea8654feec95ff65426cf835d49b52 229be96ca6d0d9342bc9694ac58cc5bb28709183This study was conducted to develop and apply an artificial intelligence convergence invention education program to students enrolled in middle schools to find out whether this education program is effective as an invention education program. As a result of the study, first, an 8th artificial intelligence convergence invention education program was developed based on the method of utilizing artificial intelligence technology in solving the problem of inventions in previous studies. Second, the 8th artificial intelligence convergence invention education program developed could bring about a positive change in the invention attitude, and it worked regardless of gender. This study was applied only to second-year students of D middle school, and it was applied in conjunction with the 'appropriate technology and sustainable development' unit of the technology subject, so care should be taken, but it is expected that it will be the basic data necessary to introduce artificial intelligence technology in invention education and a precedent case.The Education Research Institute00Educational Research Institute['Dasol Kim']2024-02-29T00:00:00https://www.semanticscholar.org/paper/229be96ca6d0d9342bc9694ac58cc5bb28709183 8ea44f05b14ddaea83be282da8a1bcb82b5ab01eMany parties are then worried about the development of artificial intelligence. Facing artificial intelligence (AI) is undoubtedly a challenge for the academic world, as it is difficult to prepare graduates to have abilities that can match artificial intelligence. Therefore, this research will discuss how the legal education system in Indonesia prepares Law Faculty students to be competent with AI in the digital era. This research is limited to the learning system at the Faculty of Law. This is due to the issue that in the future, with the presence of AI, many jobs in the legal field will be replaced by computers. This research also aims to see the readiness of the Law Faculty in preparing Law Faculty students to face the digital era and the presence of AI. This research falls into the criteria for Normative Legal Research using library research methods. It is said to be normative legal research, because the research only looks at the readiness of universities based on existing norms or rules relating to the legal education system. And it is said to be library research because the researcher did not conduct interviews or observations in collecting the data. The writer then analyzed the data obtained using a deductive method, where the researcher looked at general things first and then narrowed them down to specific things. The results and brief discussion show that Indonesia needs to strengthen its legal education system to face the challenges brought by artificial intelligence by taking several steps to change. With these steps of change, the legal education system in Indonesia will be better prepared to face the changes brought about by the era of artificial intelligence.International Journal of Social Health270The results and brief discussion show that Indonesia needs to strengthen its legal education system to face the challenges brought by artificial intelligence by taking several steps to change.International Journal of Social Health['Henry Arianto']2024-02-29T00:00:00https://www.semanticscholar.org/paper/8ea44f05b14ddaea83be282da8a1bcb82b5ab01e 1cfffa324cee9c6e0697434ad79ba0e426274b3aAbstract: Artificial Intelligence (AI) is one of the current emerging technologies. In the history of computing AI has been in the similar role earlier - almost every decade since the 1950s, when the programming language Lisp was invented and used to implement self modifying applications. The second time that AI was described as one of the frontier technologies was in the 1970s, when Expert Systems (ES)were developed Currently in the 2010s, AI is again on the frontier in the form of (self-)learning systems manifesting in robot applications, smart hubs, intelligent data analytic s, etc. Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Indian’s economy and solves various social problems. Artificial intelligence refers to the ability of a computer or a computer-enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning, decision making and solving problems (Intelligence, 2017). By extension, the goal of AI systems is to develop systems capable of tacking complex problems in ways similar to human logic and reasoning.International Journal for Research in Applied Science and Engineering Technology00Artificial intelligence refers to the ability of a computer or a computer-enabled robotic system to process information and produce outcomes in a manner similar to the thought process of humans in learning, decision making and solving problems.International Journal for Research in Applied Science and Engineering Technology['Dr. Gurusiddayya S. Puranik']2024-02-29T00:00:00https://www.semanticscholar.org/paper/1cfffa324cee9c6e0697434ad79ba0e426274b3a 36e684345be3481ae6e5d0c57e9565b26ad2ca38Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.Sustainability1590An exhaustive survey of AI applications for optimal control is conducted, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance, underscoring the contribution it makes to the field of MSWI and WTE.Sustainability['Jian Tang', 'Tianzheng Wang', 'Heng Xia', 'Canlin Cui']2024-02-29T00:00:00https://www.semanticscholar.org/paper/36e684345be3481ae6e5d0c57e9565b26ad2ca38 8b05eb306f34753e3a8b940be9adcad68b695b83Introduction. In the contemporary context of global environmental, economic, and epidemiological challenges, the study of artificial intelligence (AI) and its impact on the labor market is crucial. The purpose of this paper is to study the impact of artificial intelligence on the labor market in the world and particularly in Ukraine. The results of the research highlight a global trend of job displacement due to technological advancements, particularly in robotics and AI. The study defines AI and highlights its potential in tasks requiring human intelligence. A McKinsey Global Institute study suggests up to 300 million jobs could be lost globally by 2030, emphasizing concerns about employment stability. The paper explores industry-specific data, identifying high-risk areas like routine tasks for automation and less susceptible roles requiring creativity. The impact on the labor market is nuanced, with most jobs only partially automated, offering opportunities for complementation. Positive aspects of AI, such as creating new employment opportunities and enhancing worker safety, are underscored. Governments and businesses are urged to proactively retrain workers at risk of automation. The potential for job reduction in Ukraine is discussed, emphasizing the need for education reform. Conclusion. The multifaceted impact of AI on employment necessitates careful planning and consideration. For Ukraine, the focus should be on creating competitive AI specialists through specialized education, international collaboration, and skills development, enabling the country to navigate the evolving global environment.Economics. Finances. Law00The results of the research highlight a global trend of job displacement due to technological advancements, particularly in robotics and AI, and the potential for job reduction in Ukraine is discussed, emphasizing the need for education reform.Economics. Finances. Law['Iryna Rossomakha', 'O. Kyrylenko', 'Anton Borysiuk']2024-02-29T00:00:00https://www.semanticscholar.org/paper/8b05eb306f34753e3a8b940be9adcad68b695b83 a46e1abc7254ff2e11137c8ef8c63c0c12f45defAI and Ethics140It is argued that the prima facie most obvious assessment criteria do not stand up to ethical scrutiny and that the current lack of assessment criteria has comprehensive implications regarding when algorithmic tools should be implemented in criminal justice practice.AI and Ethics['J. Ryberg']2024-02-29T00:00:00https://www.semanticscholar.org/paper/a46e1abc7254ff2e11137c8ef8c63c0c12f45def e2dcf91aef590db081b507cf4b4200bc8b76dfb8This big study looks into how Artificial Intelligence (AI) and Human Resource Management (HRM) connect in the Middle East. In the area where technology is changing very quickly, the study wants to find out how AI integration is changing HR practices in this place. The main part of the study is carefully created surveys for HR people and experts in AI/CS. These aim to find out how AI is used now, what more can be done with it, and ethical questions around its use in HRM. The main results show that AI is drastically making human resources jobs like hiring new workers, managing their paychecks, and checking their performance easier. This lets people working in HR avoid boring tasks and concentrate on big roles. The study also looks into the problems and limits that come with using AI. These include thinking about what's right, keeping your data secret, and needing a human link in HR jobs. This study is very important because it looks at how AI makes HR better. It also shows the careful mix needed between using new technology and keeping people in mind. It gives a future outlook on how AI can change HRM in the Middle Eastern business scene. It offers helpful information for both people who work there and those studying it.International Journal of Business and Applied Social Science00This big study looks into how Artificial Intelligence (AI) and Human Resource Management (HRM) connect in the Middle East and shows the careful mix needed between using new technology and keeping people in mind.International Journal of Business and Applied Social Science['Mouna Yamak']2024-02-29T00:00:00https://www.semanticscholar.org/paper/e2dcf91aef590db081b507cf4b4200bc8b76dfb8 e7203eeb9735ab98524911e64289d3b72bbf0a87The purpose of this study was to glean key learnings from the use of artificial intelligence on customer experience with emphasis on Zimbabwean telecoms companies. The study employed qualitative research design, using semi structured interviews with customers, staff, and management of the telecoms companies in Zimbabwe. To unearth the key themes and trends and insights between artificial intelligence and customer experience thematic analysis was applied. The research evaluated the models employed by the Zimbabwean telecoms companies in transforming the customer experience landscape and hurdles they face in moving customers to the new channels. The challenges experienced in the new trajectory were explicitly highlighted. Case studies of other telecoms companies who explored the same avenue were chronicled. The various merits and skepticism of artificial intelligence on customer experience were clearly evacuated paving way for future research areas to completely harness the potential in the discipline of customer experience and its contribution to competitiveness.Texila international journal of management00The research evaluated the models employed by the Zimbabwean telecoms companies in transforming the customer experience landscape and hurdles they face in moving customers to the new channels and paved the way for future research areas to completely harness the potential in the discipline of customer experience.Texila International Journal of Management['Sham Hokonya']2024-02-29T00:00:00https://www.semanticscholar.org/paper/e7203eeb9735ab98524911e64289d3b72bbf0a87 1e154fa5b47e78ed61a7909401b8f6d929213012This research paper delves into the dynamic landscape of computer science and artificial intelligence, unraveling the emerging fields that are shaping the future of technology. Aimed at students of class 11th, this paper provides an insightful exploration of cutting-edge domains, their applications, and the potential impact on various industries.International Journal of Advanced Research00International Journal of Advanced Research['Abhinav Agarwal']2024-02-29T00:00:00https://www.semanticscholar.org/paper/1e154fa5b47e78ed61a7909401b8f6d929213012 b66478f5b1f88f9a985e0634e4e674b1436464b5This study examines the impact of generative artificial intelligence (GAI) on higher education, focusing on the experiences of international students. It highlights the challenges of recognizing AI content and the potential for unfair charges against students. The essay also addresses biases in AI models and the need for justice and equity in AI judgments. It advocates for a well-rounded strategy that addresses both potential and problems, emphasizing AI literacy and ethical considerations. The essay incorporates the AI competency framework to ensure fair use of AI in the classroom.European Economic Letters (EEL)350This study examines the impact of generative artificial intelligence on higher education, focusing on the experiences of international students, and advocates for a well-rounded strategy that addresses both potential and problems, emphasizing AI literacy and ethical considerations.European Economic Letters (EEL)['Dr. Ajay Kumar Varshney, Dr. Ankit Garg, Dr. Sarjue Pandita', 'Dr. Manu Priya Gaur, Dr. Ritesh Kumar Singhal, Himanshu Sharma']2024-02-29T00:00:00https://www.semanticscholar.org/paper/b66478f5b1f88f9a985e0634e4e674b1436464b5 cd7b42b3053bf0bd45da505442b6e612c2da8ac9Background Delirium is a common complication among intensive care unit (ICU) patients that is linked to negative clinical outcomes. However, adherence to the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS guidelines), which recommend the use of the ABCDEF bundle, is sub-optimal in routine clinical care. To address this issue, AI-AntiDelirium, a nurse-led artificial intelligence-assisted prevention and management tool for delirium, was developed by our research team. Our pilot study yielded positive findings regarding the use of AI-AntiDelirium in preventing patient ICU delirium and improving activities of daily living and increasing intervention adherence by health care staff. Methods The proposed large-scale pragmatic, open-label, parallel-group, cluster randomized controlled study will assess the impact of AI-AntiDelirium on the incidence of ICU delirium and delirium-related outcomes. Six ICUs in two tertiary hospitals in China will be randomized in a 1:1 ratio to an AI-AntiDelirium or a PADIS guidelines group. A target sample size of 1,452 ICU patients aged 50 years and older treated in the ICU for at least 24 hours will be included. The primary outcome evaluated will be the incidence of ICU delirium and the secondary outcomes will be the duration of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, patient activities of daily living, and ICU nurse adherence to the ABCDEF bundle. Discussion If this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice. Clinical trial registration ChiCTR1900023711 (Chinese Clinical Trial Registry).PLoS ONE390If this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice.PLOS ONE['Shan Zhang', 'Wei Cui', 'Shu Ding', 'Xiangyu Li', 'Xi-Wei Zhang', 'Ying Wu']2024-02-29T00:00:00https://www.semanticscholar.org/paper/cd7b42b3053bf0bd45da505442b6e612c2da8ac9 e622e27d2f0b4fa29153c6241552c5c261a647f7Abstract: As Artificial Intelligence (AI) continues to permeate various facets of our lives, exploring the dynamics of its collaboration with humans becomes increasingly crucial. This research delves into the current trends, both positive and negative aspects, and the potential future problems and preventive measures associated with Human-AI collaboration. The study unfolds in-depth insights into the emerging landscape of AI integration, considering its impact on diverse domains, ethical considerations, and the evolving nature of human-AI partnerships.International Journal for Research in Applied Science and Engineering Technology00The study unfolds in-depth insights into the emerging landscape of AI integration, considering its impact on diverse domains, ethical considerations, and the evolving nature of human-AI partnerships.International Journal for Research in Applied Science and Engineering Technology['Ankush Naik']2024-02-29T00:00:00https://www.semanticscholar.org/paper/e622e27d2f0b4fa29153c6241552c5c261a647f7 f33276e63fd17759f56623b07e5aa17de534b173Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.Journal of Medical Internet Research330Clinicians are provided with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.Journal of Medical Internet Research['Hansa Bhargava', 'Carmela Salomon', 'Srinivasan Suresh', 'Anthony Chang', 'Rachel Kilian', 'D. V. Stijn', 'Albert Oriol', 'Daniel Low', 'Ashley Knebel', 'S. Taraman']2024-02-29T00:00:00https://www.semanticscholar.org/paper/f33276e63fd17759f56623b07e5aa17de534b173 136dc5a6080dfa9e4f49c0c5972feab580e9e17cGlobal industry transformation is occurring due to the adoption of Artificial Intelligence (AI) in the workplace, bringing a new era of productivity, creativity, and revolutionary change. AI technologies are evolving into essential tools that improve workflows, automate repetitive operations, and enhance human capabilities in various industries, including finance, human resources, and manufacturing. AI-driven robotic systems in manufacturing improve speed and precision, boosting output and economy of scale. Artificial intelligence (AI) algorithms that analyze large datasets and provide insights into market patterns, risk, and investing strategies benefit the financial sector. AI-powered chatbots are revolutionizing customer care by offering prompt, customized support. This article explores the multifaceted impact of Artificial Intelligence (AI) on the contemporary workforce, briefly examining its transformative influence on various industries.Journal of Economics &amp; Management Research00The multifaceted impact of Artificial Intelligence (AI) on the contemporary workforce is explored, briefly examining its transformative influence on various industries.Journal of Economics &amp; Management Research['Farhang Mossavar-Rahmani', 'Bahman Zohuri']2024-02-29T00:00:00https://www.semanticscholar.org/paper/136dc5a6080dfa9e4f49c0c5972feab580e9e17c 3f3fee469dd7a25ce3b14878026f2082b99ae148Trustworthiness in Artificial Intelligence (AI) innovation is a priority for governments, researchers and clinicians; however, clinicians have highlighted trust and confidence as barriers to their acceptance of AI within a clinical application. While there is a call to design and develop AI that is considered trustworthy, AI still lacks the emotional capability to facilitate the reciprocal nature of trust. This paper aims to highlight and discuss the enigma of seeking or expecting trust attributes from a machine and, secondly, reframe the interpretation of trustworthiness for AI through evaluating its reliability and validity as consistent with the use of other clinical instruments. AI interventions should be described in terms of competence, reliability and validity as expected of other clinical tools where quality and safety are a priority. Nurses should be presented with treatment recommendations that describe the validity and confidence of prediction with the final decision for care made by nurses. Future research should be framed to better understand how AI is used to deliver care. Finally, there is a responsibility for developers and researchers to influence the conversation about AI and its power towards improving outcomes. The sole focus on demonstrating trust rather than the business-as-usual requirement for reliability and validity attributes during implementation phases may result in negative experiences for nurses and clinical users. This research will have significant implications for the way in which future nursing is practised. As AI-based systems become a part of routine practice, nurses will be faced with an increasing number of interventions that require complex trust systems to operate. For any AI researchers and developers, understanding the complexity of trust and creditability in the use of AI in nursing will be crucial for successful implementation. This research will contribute and assist in understanding nurses’ role in this change.Journal of Research in Nursing40The enigma of seeking or expecting trust attributes from a machine is highlighted and the interpretation of trustworthiness for AI is reframe through evaluating its reliability and validity as consistent with the use of other clinical instruments.Journal of Research in Nursing['O. Higgins', 'Stephan K. Chalup', 'Rhonda L Wilson']2024-02-29T00:00:00https://www.semanticscholar.org/paper/3f3fee469dd7a25ce3b14878026f2082b99ae148 13eee046e970bc31fe3990b2188e883e1dcd9b97With the upcoming AI regulations (e.g., EU AI Act) and rapid advancements in generative AI, new challenges emerge in the area of Human-Centered Responsible Artificial Intelligence (HCR-AI). As AI becomes more ubiquitous, questions around decision-making authority, human oversight, accountability, sustainability, and the ethical and legal responsibilities of AI and their creators become paramount. Addressing these questions requires a collaborative approach. By involving stakeholders from various disciplines in the 2\textsuperscript{nd} edition of the HCR-AI Special Interest Group (SIG) at CHI 2024, we aim to discuss the implications of regulations in HCI research, develop new theories, evaluation frameworks, and methods to navigate the complex nature of AI ethics, steering AI development in a direction that is beneficial and sustainable for all of humanity.CHI Extended Abstracts370The implications of regulations in HCI research are discussed, new theories, evaluation frameworks, and methods are developed to navigate the complex nature of AI ethics, steering AI development in a direction that is beneficial and sustainable for all of humanity.{'pages': '582:1-582:4'}['Marios Constantinides', 'Mohammad Tahaei', 'D. Quercia', 'Simone Stumpf', 'Michael Madaio', 'Sean Kennedy', 'Lauren Wilcox', 'Jessica Vitak', 'Henriette Cramer', 'E. Bogucka', 'Ricardo A. Baeza-Yates', 'Ewa Luger', 'Jess Holbrook', 'Michael Muller', 'Ilana Golbin Blumenfeld', 'Giada Pistilli']2024-02-29T00:00:00https://www.semanticscholar.org/paper/13eee046e970bc31fe3990b2188e883e1dcd9b97 798053211eceb907416df66a1b8d6a915e1c4642In this paper the management of innovation processes and projects is studied, which is the basis for analyzing of factors affecting the complexity of the management process, and the existing methods of its optimization based on technologies and technological tools for innovation management are considered. The analysis of the prospects for the implementation artificial intelligence in the programs currently used in companies, positive and negative sides and conditions for effective use are identified. Also, according to the results of the research, recommendations for the development of software to optimize the management of innovation processes and projects using artificial intelligence are formulated.2024 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)290Recommendations for the development of software to optimize the management of innovation processes and projects using artificial intelligence are formulated.2024 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)['Viktoria D. Chubakova', 'Miroslava T. Chobitko', 'Ekaterina V. Zueva']2024-02-29T00:00:00https://www.semanticscholar.org/paper/798053211eceb907416df66a1b8d6a915e1c4642 a5e4fcf1f8b5b6aeebac35c97fa0d32a05d52bb5Abstract: Interactive entertainments using AI have become possible due to merging AI with games. The paper explores in-depth multi-dimensional relations among AI in gaming, including Game AI, Adaptive Gameplay, and ML in VE. The study intends to investigate how the current AI in games landscape impacts gameplay dynamics and customer experience. The paper discusses the latest approaches such as AI-driven NPCs and procedural generation of content that transforms gameplay experience. Secondly, it analyzes how machine-learning can help in designing adaptable gaming situations to satisfy different tastes and skills players. Further, this study identifies trends and envisages future breakthroughs in AI in gaming. This work aims at addressing challenges and ethical issues associated with artificial intelligence and virtual environment domain in order to understand where is future heading for gaming experience.International Journal for Research in Applied Science and Engineering Technology00The paper discusses the latest approaches such as AI-driven NPCs and procedural generation of content that transforms gameplay experience and analyzes how machine-learning can help in designing adaptable gaming situations to satisfy different tastes and skills players.International Journal for Research in Applied Science and Engineering Technology['Amanullah Didar Madre', 'Ibrahim Khan', 'Shaikh Mohammed Al Mishaad', 'Prof. Yaseera']2024-02-29T00:00:00https://www.semanticscholar.org/paper/a5e4fcf1f8b5b6aeebac35c97fa0d32a05d52bb5 fc3802298ce3f7497e0f5ba005e1658353214594Artificial intelligence (AI) and business analytics are two rapidly developing technologies transforming how businesses operate. AI is the branch of computer science that focuses on developing intelligent machines that can perform tasks that typically require human intelligence, while business analytics refers to the process of using data to make informed business decisions. The purpose of this paper is to explore the future of AI and business analytics and how these technologies will shape the business landscape in the coming years. The increasing availability and use of artificial intelligence (AI) technologies in business analytics have the potential to revolutionize the way organizations operate. This research paper explores the future of AI and business analytics, examining how advances in AI technology are shaping the field and what changes we can expect to see in the coming years. The paper examines the benefits and challenges of using AI in business analytics, as well as the ethical considerations that arise with the increased use of this technology. Additionally, the paper explores how organizations can best leverage AI in their business analytics strategies to maximize the potential benefits while minimizing the risks. Through a review of existing literature and analysis of emerging trends, this paper provides valuable insights for businesses and policymakers seeking to understand the evolving landscape of AI and business analytics. Looking ahead, the future of AI and business analytics is likely to be shaped by a number of key trends. These include the increasing use of machine learning and deep learning algorithms, the integration of AI with other emerging technologies such as blockchain and the Internet of Things, and the growing importance of explainable AI that can provide transparency and accountability in decision-making.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The paper examines the benefits and challenges of using AI in business analytics, as well as the ethical considerations that arise with the increased use of this technology, as well as the ethical considerations that arise with the increased use of this technology.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ayush Singh', 'Garima Jain', 'Neha Khandelwal', 'Priya Singh']2024-02-29T00:00:00https://www.semanticscholar.org/paper/fc3802298ce3f7497e0f5ba005e1658353214594 4d779f2e8b3c05e28b89b06bb79ee52ddf420c3dThe abstract explores the intricate and ethically challenging subject of personhood issues in the realm of Artificial Intelligence (AI). As AI continues to advance, questions regarding the legal status, rights, and responsibilities of AI systems emerge, sparking a profound debate on whether AI entities should be granted personhood. The discussion begins by framing the current state of AI, acknowledging its increasing sophistication and integration into various aspects of human life. The focus then shifts to the core question: Should AI systems be endowed with legal personhood? This query becomes more pertinent as AI evolves towards achieving artificial general intelligence, comparable to human cognitive abilities. The abstract delves into the philosophical and ethical dimensions surrounding personhood, drawing parallels with historical debates on consciousness and humanity. The concept of personhood for AI is examined through the lens of ownership, accountability, representation, and management. The critical juncture is highlighted, where the evolution of AI, particularly towards artificial general intelligence, prompts a reassessment of the legal and ethical frameworks governing these entities. Notable considerations include the potential implications of personhood for AI, such as issues of ownership and accountability. The abstract raises thought-provoking scenarios, questioning the consequences of AI entities being granted legal status. The potential accumulation of wealth, rights, and influence by autonomous AI entities poses challenges to existing societal structures. Drawing on analogies from corporate law, the abstract explores the possibility of AI entities having a distinct legal status, separate from their human creators. However, it cautions against succumbing to the humanoid hype, emphasizing the need for a nuanced understanding of the implications of granting legal personhood to AI. In conclusion, the abstract underscores the urgency of addressing personhood issues related to AI. It navigates through the complex intersection of technology, ethics, and law, encouraging a thoughtful approach to the evolving nature of AI entities and their potential impact on societal structures. As AI progresses, defining the legal and ethical boundaries of personhood for these entities becomes a pivotal task to ensure responsible and equitable integration into human society.International Journal For Multidisciplinary Research00The abstract delves into the philosophical and ethical dimensions surrounding personhood, drawing parallels with historical debates on consciousness and humanity, and encourages a thoughtful approach to the evolving nature of AI entities and their potential impact on societal structures.International Journal For Multidisciplinary Research['Sri Satya Jayanth Voruganti', 'Taruni Nakshatra Gadepalli']2024-02-29T00:00:00https://www.semanticscholar.org/paper/4d779f2e8b3c05e28b89b06bb79ee52ddf420c3d e1a2f6471c3b564bba6eddbe330e79b6b6df3656Abstract: The integration of artificial intelligence (AI) into healthcare and drug development has catalyzed an era of change in medical research and patient care. This research paper examines the evolving landscape of drug development and highlights the central role of artificial intelligence in advancing human healthcare. By combining data-driven approaches with advanced machine learning algorithms, AI can transform target identification, molecular screening, lead optimization and clinical trial design. In healthcare, AI applications include medical image analysis, disease diagnosis, personalized medicine, electronic health information management and more, promising better patient care, cost reduction and advances in medical research. However, these innovations come with challenges such as data protection, bias, regulatory barriers and ethical issues. The article highlights the potential of AI in drug development and healthcare and discusses similar AI-based solutions and methods. It also addresses issues related to data protection, interoperability, data quality, regulatory approval, ethical issues and resource constraints. Responsible development and implementation are critical to the safety and effectiveness of healthcare AI applications. Future work includes improving explainability, promoting knowledge sharing and collaboration, creating ethical frameworks, adapting regulatory processes, integrating AI into clinical practice, and addressing global health disparities. Through collaboration and continued research, AI holds the promise of revolutionizing healthcare, leading to better patient outcomes and a brighter future for medicine.International Journal for Research in Applied Science and Engineering Technology00The article highlights the potential of AI in drug development and healthcare and discusses similar AI-based solutions and methods, and addresses issues related to data protection, interoperability, data quality, regulatory approval, ethical issues and resource constraints.International Journal for Research in Applied Science and Engineering Technology['Yashvi Thakor', 'Prof. Gufran Ahmad Ansari']2024-02-29T00:00:00https://www.semanticscholar.org/paper/e1a2f6471c3b564bba6eddbe330e79b6b6df3656 25677b074f97cd29bbce7cd6f54be50726ea073bBackground: The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023. Methods: We performed a PubMed search using the terms, machine learning or artificial intelligence and 2023, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics.Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023. Results: Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%. Conclusion: Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.medRxiv380['Raghav Awasthi Msc', 'PhD Shreya', 'PhD Mishra MTech', 'Rachel Grasfield', 'Julia Maslinski', 'Dwarikanath', 'PhD Mahapatra BTech', 'Fasa Jacek B. Cywinski MD', 'F. F. F. Ashish K. Khanna MD', 'Kamal Maheshwari MD Mph', 'Chintan Dave', 'Avneesh Khare Mbbs', 'M. D. M. F. A. Papay', 'Facsfaap Piyush Mathur Md', 'BrainXAI ReSearch']2024-02-29T00:00:00https://www.semanticscholar.org/paper/25677b074f97cd29bbce7cd6f54be50726ea073b 05089fef4a1590917abc7097f21ba614d3209d6fThe question"Can machines think?"and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument"I think, therefore I am", this paper challenge the idea of a"thinking machine"supported by current AIs since there is no sense of self in them. Current artificial intelligence is only seemingly intelligent information processing and does not truly understand or be subjectively aware of oneself and perceive the world with the self as human intelligence does. In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm. This BriSe AI paradigm is dedicated to coordinating various cognitive functions and learning strategies in a self-organized manner to build human-level AI models and robotic applications. Specifically, BriSe AI emphasizes the crucial role of the Self in shaping the future AI, rooted with a practical hierarchical Self framework, including Perception and Learning, Bodily Self, Autonomous Self, Social Self, and Conceptual Self. The hierarchical framework of the Self highlights self-based environment perception, self-bodily modeling, autonomous interaction with the environment, social interaction and collaboration with others, and even more abstract understanding of the Self. Furthermore, the positive mutual promotion and support among multiple levels of Self, as well as between Self and learning, enhance the BriSe AI's conscious understanding of information and flexible adaptation to complex environments, serving as a driving force propelling BriSe AI towards real Artificial General Intelligence.arXiv.org590The positive mutual promotion and support among multiple levels of Self, as well as between Self and learning, enhance the BriSe AI's conscious understanding of information and flexible adaptation to complex environments, serving as a driving force propelling BriSe AI towards real Artificial General Intelligence.ArXiv['Yi Zeng', 'Feifei Zhao', 'Yuxuan Zhao', 'Dongcheng Zhao', 'Enmeng Lu', 'Qian Zhang', 'Yuwei Wang', 'Hui Feng', 'Zhuoya Zhao', 'Jihang Wang', 'Qingqun Kong', 'Yinqian Sun', 'Yang Li', 'Guobin Shen', 'Bing Han', 'Yiting Dong', 'Wenxuan Pan', 'Xiangfei He', 'Aorigele Bao', 'Jin Wang']2024-02-29T00:00:00https://www.semanticscholar.org/paper/05089fef4a1590917abc7097f21ba614d3209d6f 8d08a8b9e60a9a20afa2afd1f4882f9d2ca2c9f9Artificial intelligence is one of the media that can be used by children's story writers. With this media, writers can express creative ideas and vary their reading media. The method used is descriptive qualitative with secondary data based on phenomena in society. The result is that by using artificial intelligence, variations in children's reading are obtained. The implication is that it is hoped that there will be more varied development of children's stories.Journal of Language Development and Linguistics70By using artificial intelligence, variations in children's reading are obtained and it is hoped that there will be more varied development of children's stories.Journal of Language Development and Linguistics['Dina Purnama Sari']2024-02-29T00:00:00https://www.semanticscholar.org/paper/8d08a8b9e60a9a20afa2afd1f4882f9d2ca2c9f9 22e6e921b7d897bbc69f58861937cfcd507c2662American Journal of Infection Control111AI shows promise in enhancing HAI surveillance, potentially streamlining tasks, and freeing health care staff for patient-focused activities as well as demonstrating AI's potential in accurately identifying HAIs like CLABSI and CAUTI.American journal of infection control['T. L. Wiemken', 'Ruth M Carrico']2024-02-29T00:00:00https://www.semanticscholar.org/paper/22e6e921b7d897bbc69f58861937cfcd507c2662 11eec869c134ffdf810d4ddf16de809ec9ff89e8‘ODR’ stands for ‘Online Dispute Resolution’ and refers to a method of resolving disputes online. However, the meaning of ‘online’ here focuses more on access and connection through information and communication technology (ICT) from the perspective of network connection in terms of dispute resolution. ODR is a dispute resolution method using information and communication technology that emerged in the mid-1990s when the Internet began to become generalized and commercialized. In the early days when ODR began to be used, it was mainly recognized as a tool to resolve disputes that occurred online, such as defamation that occurred during online e-commerce or online communication. However, with the development of information and communication technology, the scope of ODR has gradually expanded to include disputes that occur not only online but also offline. Currently, the types of disputes that can be resolved through ODR are not limited to commercial transactions, but also encompass various types such as domestic, administrative, and housing disputes. Meanwhile, AI (Artificial Intelligence) increases the efficiency and transparency of ODR, saves cost and time, and helps people from various fields and cultures collaborate easily. ODR using AI is currently being researched and developed in various fields, and is expected to have more possibilities and advantages in the future. However, ODR using AI also has some ethical issues and challenges. In this paper, we reviewed specific measures for the successful establishment of an ODR system using AI.Korean Institute for Aggregate Buildings Law00Specific measures for the successful establishment of an ODR system using AI, which increases the efficiency and transparency of ODR, saves cost and time, and helps people from various fields and cultures collaborate easily are reviewed.Korean Institute for Aggregate Buildings Law['Wan-Kyu Jang']2024-02-29T00:00:00https://www.semanticscholar.org/paper/11eec869c134ffdf810d4ddf16de809ec9ff89e8 fdd40b8d2dd3b2d74a6bb7c0950e076ea9d6704bJournal of Artificial Intelligence &amp; Cloud Computing00Journal of Artificial Intelligence &amp; Cloud Computing['Girish M Chikkahonnegowda']2024-02-29T00:00:00https://www.semanticscholar.org/paper/fdd40b8d2dd3b2d74a6bb7c0950e076ea9d6704b 7a444cc9d8fd57d62a0f3388a3b7a8f43d68de01The rise in talent management complications led organizations to rely on the latest technologies to automate their routine HRM tasks through AI. This study proposed to examine fundamental aspects of AI in talent acquisition (cost-benefit, opportunities, and risk factors) from the context of strategic analysis and decision-making. 52 respondents from HRM and the information technology departments from fifteen large dairy enterprises, each with more than one thousand employees, were included in the focus group discussion. Both departments were included in the focus group discussion as they heavily employ AI in talent acquisition. The opinions were collected in multiple rounds based on the cost, benefit, opportunity, and risk criteria using the analytical hierarchy process, a multi-criteria decision-making framework. The findings demonstrated that most respondents opinioned AI supports talent acquisition with many opportunities (38.7%) that involve the identification of the best applicants (18.7%) and different benefits (33.2%) to the organization in the form of saving time and cost (16.1%) leading to higher efficacy. The study infers that the application of AI in HRM significantly contributes to talent acquisition, streamlining processes, improving efficiency, and enhancing decision-making. The study recommends that implementing AI in talent acquisition requires a strategic approach, and organizations need to consider factors such as data privacy, ethical use of AI, and ongoing training to ensure successful integration into their hiring processes. Additionally, regular monitoring and adjustments are essential to optimize the effectiveness of AI tools in talent acquisition. AcknowledgmentThe authors of this article would like to thank Prince Sultan University for its financial and academic support for this publication.Problems and Perspectives in Management00The study infers that the application of AI in HRM significantly contributes to talent acquisition, streamlining processes, improving efficiency, and enhancing decision-making, and recommends that implementing AI in talent acquisition requires a strategic approach.Problems and Perspectives in Management['Sania Khan', 'Shah Faisal', 'George Thomas']2024-02-29T00:00:00https://www.semanticscholar.org/paper/7a444cc9d8fd57d62a0f3388a3b7a8f43d68de01 0bc44e4be33705afc5f97f3b6e79e02a5e3113acThe Korean Journal of Political Science00Korean Journal of Political Science['Hyun-Jung Kim']2024-02-29T00:00:00https://www.semanticscholar.org/paper/0bc44e4be33705afc5f97f3b6e79e02a5e3113ac aa6c590210ed0f2d491bd580d03e4159640572ecAbstract: This project is a brilliant example of how technology can be harnessed for a profoundly positive purpose, enhancing the quality of life for those who may face challenges in certain aspects of daily living. It's about creating a tool that complements and supports individuals rather than substituting human interaction or assistance. That sounds like an incredible initiative! Combining technologies like object detection through YOLO algorithms, speech synthesis via text-to-speech, and incorporating them into smart glasses could significantly enhance the independence and mobility of visually impaired individuals. These glasses could provide real-time information about their surroundings, enabling them to navigate and interact more confidently with their environment.International Journal for Research in Applied Science and Engineering Technology00Combining technologies like object detection through YOLO algorithms, speech synthesis via text-to-speech, and incorporating them into smart glasses could significantly enhance the independence and mobility of visually impaired individuals.International Journal for Research in Applied Science and Engineering Technology['Navneet Kumar', 'Karishma Verma', 'Er. Asim Ahmad']2024-02-29T00:00:00https://www.semanticscholar.org/paper/aa6c590210ed0f2d491bd580d03e4159640572ec bedf31c8029f250730aebfa1b175f68068490786The article is devoted to analysis of vectors and specific features of AI solutions development in the field of customs service. Based on tracing the evolution of digital transformation in custom, conceptual model of AI integration in custom IT system is considered. Practical implications of AI systems introduction in customs, in particular within the context of international relations, are outlined, together with the examples of advanced experience.AD ALTA: 14/01-XL.330Practical implications of AI systems introduction in customs, in particular within the context of international relations, are outlined and conceptual model of AI integration in custom IT system is considered.AD ALTA: 14/01-XL.['Maksym Razumei', 'Iryna Kveliashvili', 'Serhii Kazantsev', 'Yevhen Hranyk', 'Oleksandr Akimov', 'L. Akimova']2024-02-29T00:00:00https://www.semanticscholar.org/paper/bedf31c8029f250730aebfa1b175f68068490786 29c3ae455576b8af0302a010c5475365cd531b9aThe Justice00The Justice['Dae-Hee Lee']2024-02-29T00:00:00https://www.semanticscholar.org/paper/29c3ae455576b8af0302a010c5475365cd531b9a d264272623ca53a536f408ea2f21ddad52e7c90fBritish medical journal40BMJ['See Chai Carol Chan', 'Ana Luisa Neves', 'A. Majeed', 'Aldo Faisal']2024-02-29T00:00:00https://www.semanticscholar.org/paper/d264272623ca53a536f408ea2f21ddad52e7c90f c8a83c173a3c0c757fc352f8ab38672772ada256The Journal of Korea Open Association for Early Childhood Education00The Journal of Korea Open Association for Early Childhood Education['Daewoong Kim', 'Y. Pack', 'Su Yeon Ryu', 'Young Jin Hwang', 'Yeo Hye Jang', 'Jin Wook Kim']2024-02-29T00:00:00https://www.semanticscholar.org/paper/c8a83c173a3c0c757fc352f8ab38672772ada256 56ebec2d2ddc42b97259713e96d45b3a6897c559Public Law00Public Law['Dae-in Kim']2024-02-29T00:00:00https://www.semanticscholar.org/paper/56ebec2d2ddc42b97259713e96d45b3a6897c559 ad641ad05bfda50186eb6add3ceb4f791444b1ddJournal of Research in Nursing40Journal of Research in Nursing['Dawn Dowding']2024-02-29T00:00:00https://www.semanticscholar.org/paper/ad641ad05bfda50186eb6add3ceb4f791444b1dd 4e7ea1964fea01cf411e09b2a09865996c69639aJournal of Next-generation Convergence Information Services Technology00Journal of Next-generation Convergence Information Services Technology['Jieun Kwon', 'Hyeon Woo Lee']2024-02-29T00:00:00https://www.semanticscholar.org/paper/4e7ea1964fea01cf411e09b2a09865996c69639a 0e8de99ac97b4915e200445cf78237084326dbf7Sogang Law Journal00Sogang Law Journal['Il Woo Kim']2024-02-29T00:00:00https://www.semanticscholar.org/paper/0e8de99ac97b4915e200445cf78237084326dbf7 93da39a0f1b77ea8fe73e13c8adbee474c31ecf7Dance Research Journal of Dance00Dance Research Journal of Dance['Yoo Ri Choi', 'Seung Hwa Jin']2024-02-29T00:00:00https://www.semanticscholar.org/paper/93da39a0f1b77ea8fe73e13c8adbee474c31ecf7 87d6280188cc43752fe7ce0a15e06b4e8bedfb0cRevue d'Intelligence Artificielle00Revue d'Intelligence Artificielle['Haider Sahib Nasrallah', 'I. V. Stepanyan', 'K. S. Nassrullah', 'Neder Jair Mendez Florez', 'Israa M. Abdalameer AL-Khafaji', 'Abdelrrahmane Mohamed Zidoun', 'Ravi Sekhar', 'Pritesh Shah', 'Sushma Pariha']2024-02-29T00:00:00https://www.semanticscholar.org/paper/87d6280188cc43752fe7ce0a15e06b4e8bedfb0c 21d63d781165c54fc9ceeb49bced758acd691edbCeská a slovenská neurologie a neurochirurgie00Česká a slovenská neurologie a neurochirurgie['Zuzana Trabalková', 'M. Števík', 'J. Sýkora', 'M. Vorčák', 'Kamil Zeleňák']2024-02-29T00:00:00https://www.semanticscholar.org/paper/21d63d781165c54fc9ceeb49bced758acd691edb 7084ac5bf5f215e7b2cbd215e47c611e03d49a8eKorean Journal of Sport Management00Korean Journal of Sport Management['M. Choi', 'Yun-Ji Jeong', 'Joon Sung Lee']2024-02-29T00:00:00https://www.semanticscholar.org/paper/7084ac5bf5f215e7b2cbd215e47c611e03d49a8e 0b004c265260142c0a653b313d6019b44d96a264Journal of Legislative Studies00Journal of Legislative Studies['Kee-Hong Kang']2024-02-29T00:00:00https://www.semanticscholar.org/paper/0b004c265260142c0a653b313d6019b44d96a264 ea941981687942eb38f23c4a448f5bbe82d0cec1Modern Studies in English Language &amp; Literature00Modern Studies in English Language &amp; Literature['Kim Yeonman']2024-02-29T00:00:00https://www.semanticscholar.org/paper/ea941981687942eb38f23c4a448f5bbe82d0cec1 00d5d7c9658db3eb1d425f69e479d3cb906d2785Journal of Korea Multimedia Society00Journal of Korea Multimedia Society['Byeongmin Lee', 'Seok-Chan Jeong']2024-02-29T00:00:00https://www.semanticscholar.org/paper/00d5d7c9658db3eb1d425f69e479d3cb906d2785 c663f9adea2e2dc254b8bda7ab7f6b1796d62e64Korean Business Education Review00Korean Business Education Review['Sungwon Choi']2024-02-29T00:00:00https://www.semanticscholar.org/paper/c663f9adea2e2dc254b8bda7ab7f6b1796d62e64 4b9663bffaa948c0a1d3e3bb4bcc961ca9ee9fb6Journal of Korea Research Association of International Commerce00Journal of Korea Research Association of International Commerce['Ho-Su Choi']2024-02-29T00:00:00https://www.semanticscholar.org/paper/4b9663bffaa948c0a1d3e3bb4bcc961ca9ee9fb6 06ea798822a211d0954e959c46c1036080462c85Public Law00Public Law['Euibien Moon']2024-02-29T00:00:00https://www.semanticscholar.org/paper/06ea798822a211d0954e959c46c1036080462c85 91ee22b27a3a6698986055aa54b8f53cd9bb6f22In the healthcare industry, many artificial intelligence (AI) models have attempted to overcome bias from class imbalances while also maintaining high results. Firstly, when utilizing a large number of unbalanced samples, current AI models and related research have failed to balance specificity and sensitivity a problem that can undermine the reliability of medical research. Secondly, no reliable method for obtaining detailed interpretability has been put forth when addressing large numbers of input features. The present research addresses these two critical research gaps with a proposed lightweight Artificial Neural Network (ANN) model. Using 43 input features from the 2021 Behavioral Risk Factor Surveillance System (BRFSS) dataset, the proposed model outperforms prior models in producing balanced outcomes from markedly unbalanced large survey data. The efficacy of this proposed ANN model is attributed to its simplified design, which reduces processing demands, and its resilience in identifying the probability of myocardial infarction (MI). This is demonstrated by its 80% specificity and 77% sensitivity and is substantiated by a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.87. The outcomes across the scopes of each specified data domain were also separately represented, thus demonstrating the proposed models' robust sensitivity. The interpretability of the model, as measured by Shapley values, reveals substantial correlations between myocardial infarction (MI) and its risk factors, including long-term medical conditions, socio-demographic factors, personal health habits, economic and social status, healthcare availability and affordability, as well as impairment statuses, providing valuable insights for improved cardiovascular risk assessment and personalized healthcare strategies.medRxiv650Using 43 input features from the 2021 Behavioral Risk Factor Surveillance System (BRFSS) dataset, the proposed model outperforms prior models in producing balanced outcomes from markedly unbalanced large survey data and reveals substantial correlations between myocardial infarction and its risk factors.['Simon Bin Akter', 'Sumya Akter', 'Tanmoy Sarkar Pias', 'David Eisenberg', 'J. Fernandez']2024-02-29T00:00:00https://www.semanticscholar.org/paper/91ee22b27a3a6698986055aa54b8f53cd9bb6f22 ee4217e9a4be225efb500f7f2732d7dc51e6274eAbstract: This survey explores the quality of software engineering and highlights the important role of artificial intelligence (AI) in improving software testing. It emphasizes the importance of software testing to determine the effectiveness and capabilities of software programs. This paper highlights inconsistencies in measurement guidance and the need for automation. It will also provide a better look at the changing ecosystem of automation products driven by roles in the convergence of the artificial intelligence and machine learning (ML) eras. An AI-powered machine is made based on machine learning principles and is known as a tool that performs test models, provides logic, solves problems and performs tasks correctly. The main purpose of this evaluation is to explain the practical use of artificial intelligence in software testing and to conduct an in-depth analysis of its impact on software performance and development, improving agility. In summary, this communication provides a vision for the future by demonstrating the effectiveness of intelligent automation tools in the software testing environment, making the transition to software development reliable and convenient. More generally, this survey paper discusses today's practices of using AI to improve software development and continually unlock problem-solving innovation in software testing and software engineering along with making UI testing more reliable.International Journal for Research in Applied Science and Engineering Technology01Today's practices of using AI to improve software development and continually unlock problem-solving innovation in software testing and software engineering along with making UI testing more reliable are discussed.International Journal for Research in Applied Science and Engineering Technology['Shital V. Hote']2024-02-29T00:00:00https://www.semanticscholar.org/paper/ee4217e9a4be225efb500f7f2732d7dc51e6274e a8bb1038a99d972cf81107a1a9b788647ab98e5fAbstract: This study investigated the collaborative role of AI and DevOps within the field of information technology and its significance for the United States. The research revealed that DevOps systems can pose significant challenges without the integration of artificial intelligence. AI and DevOps synergize to enhance efficiency in managing diverse tasks within the information technology domain. The exponential growth in data volume presents difficulties for DevOps teams in swiftly assimilating and addressing consumer issues. The automation trend has positioned DevOps as a vital component of information technology, facilitating efficient software delivery and faster market entry, resulting in more stable products. Furthermore, AIpowered technologies hold great potential for addressing critical issues such as national security. The research recommends organizations adopt AI-driven deployments within their DevOps environments. Given the limitations of computer processing capacity, artificial intelligence serves as a solution for storing, processing, and analyzing vast datasets. To grasp AI's role in DevOps, it's essential to understand their interdependency and its impact on AI. While companies recognize the potential of Artificial Intelligence and Machine Learning, the lack of proper knowledge impedes their full utilization. In the realm of software development, DevOps faces inherent challenges that AI systems can effectively address, playing a pivotal role in advancing digital transformation.International Journal for Research in Applied Science and Engineering Technology01The research revealed that DevOps systems can pose significant challenges without the integration of artificial intelligence, and recommends organizations adopt AI-driven deployments within their DevOps environments.International Journal for Research in Applied Science and Engineering Technology['Sarthak Srivastava', 'Manish Singh']2024-02-29T00:00:00https://www.semanticscholar.org/paper/a8bb1038a99d972cf81107a1a9b788647ab98e5f ecc2fd230d5187a98ff6771cbbe5a7a0720ab142As AI has a significant impact on daily life, the economy, and industry, various problems are raised. Among them, special attention should be paid to the problem of unreasonable discrimination. If big data continues to expand and AI technology develops further in the future, and AI is used in all areas of life, the problem of discrimination will become a very serious social problem. As there is a high possibility that the problem of unreasonable discrimination due to the use of AI will surface, legal countermeasures must be carefully considered. So this paper examines legal measures to prevent and respond to the problem of unfair discrimination caused by the use of AI. We briefly summarize the meaning and types of discrimination, examine the causes of discrimination due to the use of AI, the risks of such discrimination, and the necessity and difficulty of legal regulation regarding this, and then based on this discussion, present legal measures to prevent and regulate discrimination. There is a high risk that AI will not only reflect the biases inherent in our society, but also strengthen and perpetuate them. On the other hand, due to the opacity and complexity unique to AI, it is difficult to recognize and correct discrimination and resolve disputes. Accordingly, we must recognize the seriousness of the risk that AI will further solidify prejudice and discrimination in our society, and prepare normative response measures that meet the characteristics of such risk. First of all, standards for equality and fairness that should be reflected in legal regulations must be established. Reasonable conclusions must be made about the meaning and content of discrimination, the values of non-discrimination and fairness that must be observed, standards for evaluating the legitimacy of discriminatory treatment, and proper indicators. Next, it is important to conduct impact assessment and inspect the risks of AI systems in advance and ensure transparency to prevent unreasonable discrimination. In addition, the entity responsible for unreasonable discrimination that occurs in various contexts must be clearly identified, and procedural rules regarding dispute resolution must be clarified. Basically, it is necessary to hold the user who adopts the AI system accountable and appoint a person responsible for preventing bias. Above this, the fact that AI is being used must be notified to the person being evaluated, and the input and output data of the algorithm must be disclosed afterwards. Also, expansion of public data disclosure and relaxation and transition of the burden of proof for unreasonable discrimination should also be considered.Center for Public Interest &amp; Human Rights Law Chonnam National University00Center for Public Interest &amp; Human Rights Law Chonnam National University['Seokhan Hong']2024-02-28T00:00:00https://www.semanticscholar.org/paper/ecc2fd230d5187a98ff6771cbbe5a7a0720ab142 dedcc3daf208df54808d1ca0634294c6f83b21c7AI is efficient for humans, but as a black box, AI can't clearly explain its results. In this sense, AI is ambivalent. As an unknown technology, it is difficult to predict how it will evolve, so it is necessary to hold those who develop or provide AI services accountable and responsible. However, we should not regulate the technology, but rather the service or business model. The goal is to ensure that AI services are provided reliably and that the content is not problematic. Even if service providers monitor their services, it is difficult to do so perfectly. Therefore, the law imposes certain obligations on service providers and holds them responsible for their omissions. This is the principle of OSP liability stipulated in the Copyright Act and the Information and Communications Network Act. By entering a safe harbor, OSPs can be immunized in certain cases. The safe harbor regulation has been positive for the development of the internet. Generative AI is often criticized. It's time to consider whether it applies to AI service providers as well. According to the interpretation of the Copyright Act and the Information and Com- munication Network Act, a service provider that provides generative AI services is a service provider that provides a tool for content production. Users give instructions through prompts or utilize it as a tool to create content. To clarify, the user uses the service to create the result that the user intends or desires. The scope of liability for AI services varies depending on the legal nature of the provider. Reviewing the legal status of the AI service provider is important for the activation of AI services. AI is considered a tool, and the rights to its output should belong to the user. Logically, the user is also responsible for the creation of infringing works. Since the service is ultimately created by the user, it is difficult to hold the service provider responsible for the content of the product. However, service providers are subject to a certain duty of care in that they are the developer of the service and the provider of the generative AI service. It is desirable for the development of the AI industry to impose a duty of care that requires certain actions on the part of the service provider for defects in AI, such as the continuous creation of infringing works or the illusion of inaccurate content.Institute for Legal Studies Chonnam National University00It is desirable for the development of the AI industry to impose a duty of care that requires certain actions on the part of the service provider for defects in AI, such as the continuous creation of infringing works or the illusion of inaccurate content.Institute for Legal Studies Chonnam National University['Yun Myung Kim']2024-02-28T00:00:00https://www.semanticscholar.org/paper/dedcc3daf208df54808d1ca0634294c6f83b21c7 8d7fc273dcb838a61083bd99e2e484499d033a2dIn today’s fast-paced world, where late night eating and irregular eating patterns are common, chrononutrition offers a valuable approach on enhancing BMI (body mass index) and overall metabolic health. By aligning their eating habits with natural circadian rhythms and adopting effective strategies, individuals can potentially reduce the risk of obesity related issues and improve their overall health. Chrononutrition is an advanced area of research that examines the connection between the timing of food consumption and our bodies’ circadian rhythms. It has implications for overall health, including the BMI (body mass index) regulation. The timings and patterns of food consumption often are synchronized with the circadian rhythms and regulate a number of physiological processes. With studies indicating that a larger breakfast and a smaller dinner may help with weight management, recent research has revealed solid evidence associating meal timing to BMI regulation. Additionally, within the chrononutrition framework, intermittent fasting and time-restricted eating habits have emerged as promising approaches, suggesting potential advantages for weight loss and metabolism improvement. Chrononutrition affects insulin sensitivity, cardiovascular risk variables, and appetite regulation in addition to BMI, demonstrating its broader impact on metabolic health. This review paper explores the evolving intersection of Artificial Intelligence (AI) and chrononutrition, a field focused on the temporal aspects of nutrition and their significant effects on human health. The use of AI technology to the subject of chrononutrition presents encouraging opportunities for personalized and data-driven dietary advice, as the significance of mealtime becomes increasingly important.International Conference on Computing for Sustainable Global Development270This review paper explores the evolving intersection of Artificial Intelligence (AI) and chrononutrition, a field focused on the temporal aspects of nutrition and their significant effects on human health, suggesting potential advantages for weight loss and metabolism improvement.2024 11th International Conference on Computing for Sustainable Global Development (INDIACom)['Priya Bajaj', 'Kusum Lata']2024-02-28T00:00:00https://www.semanticscholar.org/paper/8d7fc273dcb838a61083bd99e2e484499d033a2d 05ba56d515296974feac4ff730bb9f724b004784Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. None of them prepare a plan of all the actions necessary for emotion regulation customized to the needs of a specific individual. To address this gap, we propose a computational model for an intelligent agent which, grounded in a multidimensional emotion representation, facilitates emotion regulation in individuals. This computational model is based on J. Gross’s theoretical framework of emotion regulation. An intelligent agent selects the most appropriate regulation strategy to maintain an individual’s emotional equilibrium considering the individual’s personality traits. A dynamic planner prepares a plan of emotion regulation actions which is dynamically adapted according to the emotional changes observed in the individual after applying the previous emotion regulation actions. This refinement of the initial regulatory action plan allows the proposed emotion regulation agent to adapt the plan to the specific characteristics of the individual, facilitating the individual to improve their emotion regulation capabilities and improve their emotional health.Systems421A computational model for an intelligent agent which, grounded in a multidimensional emotion representation, facilitates emotion regulation in individuals and allows the proposed emotion regulation agent to adapt the plan to the specific characteristics of the individual, facilitating the individual to improve their emotion regulation capabilities and improve their emotional health.Systems['Aaron Pico', 'Joaquín Taverner', 'Emilio Vivancos', 'V. Botti', 'Ana García-Fornes']2024-02-28T00:00:00https://www.semanticscholar.org/paper/05ba56d515296974feac4ff730bb9f724b004784 7c34528797ae267f19197c67c0f5466126d3191cFake news reveals the most stimulating problem of the times. Academic research is expanding as controversy increases, but it is difficult to obtain a comprehensive understanding of fake news unless a large amount of research results are examined one by one due to the complex intertwining of various sub-topics. Therefore, this paper aims to gain a macroscopic understanding of the production, dissemination, and regulation of fake news through a single research result. First, the current major discussions and issues on the concept and scope of fake news were investigated, and the causes and propagation processes of fake news were reviewed to confirm how changes in the media environment and human psychology affect the spread of fake news. It also investigated the controversial discussion on the regulation of fake news and suggested that the regulation should be minimized in terms of freedom of expression and that it is desirable to be implemented in an alternative way, such as technical regulation. It is hoped that this article can contribute to streamlining the theoretical review of the research field, especially the convergence academic field, which finds new ways to effectively manage and regulate fake news by providing a macroscopic understanding of fake news through the above discussion.The K Association of Education Research00The K Association of Education Research['Sun-Hye Kwak', 'Sung-Wook Lee']2024-02-28T00:00:00https://www.semanticscholar.org/paper/7c34528797ae267f19197c67c0f5466126d3191c 0d43f3781dc247afdf2fd16f0a4d118caacfd60eThe article discusses current issues related to the development of the institution of an authorized economic operator (hereinafter — AEO) in Russia. The study is devoted to the analysis of the regulatory framework, and also analyzes the current state of affairs. the purpose of the article is to identify problems that require discussion and deeper research in order to improve the regulation of the AEO institution; formulating conceptual proposals for its development and involving in this institute companies that really need simplifications when carrying out customs operations for conducting foreign trade activities. This study is based on the methodological approaches of domestic scientists to the problems of development of economic institutions. When conducting the study, the method of comparative analysis of legislation regulating the AEO institution was used; An analysis of scientific literature and existing data from open AEO Registers was carried out. Methods of scientific knowledge, system analysis (deduction, induction, analysis, synthesis), graphic and statistical methods, systems approach were used; methods of monographic, statistical and comparative analysis, generalization and interviews were used. results and scientific novelty of the study. The work focuses on the study of the terms underlying the AEO institution. According to the author, the term should more accurately reflect the purpose and essence of its creation. The legal regulation of the AEO institution requires a new rethinking, with prospects for the development of the institution, as laid down in the standards of the World Customs Organization, from the understanding of AEOs as national companies that have a number of simplifications when performing customs operations, to the understanding of AEOs as organizations — links in the supply chain of products between AEOs of other countries, provided they all meet trade and safety standards. A set of proposals for the development of the AEO Institute has been formed. Conclusions. The proposals concern clarification of terminology and proposals for improving the regulatory framework, in particular the development of simplifications. The federal customs services of the EAEU countries may use the provisions of this article when developing legal regulation for the development of the AEO institution. The degree of elaboration varies from a conceptual idea to the proposal of specific amendments to articles of laws.Administrative Consulting20Administrative Consulting['A. Y. Komelova']2024-02-28T00:00:00https://www.semanticscholar.org/paper/0d43f3781dc247afdf2fd16f0a4d118caacfd60e 41a422624aea850546813617c04c90d1967244f6In corporate management system of innovative production, the presence of intellectual property maintains con-troversial and borderline position between economics and law. The effective use of such two-axis regulation within the company significantly affects its recovery and stimulates the introduction of new solutions based on intellectual innovations that contribute to the improvement of market competitiveness of the organization. Cur-rently, Russia is implementing the technological development concept, which contains the elements of “intel-lectual activity results”. While considering this issue in the context of global technological trends and transfor-mational changes in the field of advanced manufacturing technologies, solutions for legislative positive impact based on intellectual property rights are becoming relevant. In the context of digitalization of the process of cre-ation of new products and virtual testing it is recommended to consider the possibility of legislative registration of new intellectual property objects.Теория и практика общественного развития00Теория и практика общественного развития['V. V. Begnarskii', 'Olesya A. Egereva']2024-02-28T00:00:00https://www.semanticscholar.org/paper/41a422624aea850546813617c04c90d1967244f6 73a90c8a191e3473eab0a5c210a84b9e2016ff32The article deals with the issues of international legal regulation of alternative energy within the framework of the Eurasian Economic Union. Based on the analysis of program and strategic documents of this integration association, a trend towards the development of alternative energy within the framework of the EAEU was revealed. It is concluded that the evolution of the paradigm towards the diversification of the energy balance and the development of international legal regulation of alternative energy will contribute to increasing the political, economic, energy stability of the EAEU and its member states.Economic problems and legal practice00Economic Problems and Legal Practice['M. Lizikova']2024-02-28T00:00:00https://www.semanticscholar.org/paper/73a90c8a191e3473eab0a5c210a84b9e2016ff32 a12531c0892a90fb1b99bd1a65e6dae717e165d4У статті розглянуто сукупність чинників, які здійснюють найбільший вплив на зовнішню торгівлю машинобудівною продукцією. Досліджено множину проблем і ситуацій, що можуть виникати на митниці при експортно-імпортних операціях з машинобудівною продукцією. Проаналізовано динаміку товарообігу машинобудівної продукції. Виявлено групи товарів, які користувалися найбільшим попитом на зовнішньому та внутрішньому ринках. Досліджено вплив мита на активність товарообігу машинобудівної продукції.РОЗВИТОК МІСТА50РОЗВИТОК МІСТА['Анна Вітюк', 'Анна Слободяник', 'Ольга Могилевська']2024-02-28T00:00:00https://www.semanticscholar.org/paper/a12531c0892a90fb1b99bd1a65e6dae717e165d4 ae41c7c5b2dcb76f4044dd88386b3bafe71e0c90As artificial intelligence (AI) continues to revolutionize business landscapes, the ethical implications of its deployment have garnered significant attention. This paper presents a comprehensive review of the intersection between AI and ethics in the context of corporate responsibility. The integration of AI into business processes necessitates a thorough understanding of responsible AI practices to ensure that technological advancements align with ethical standards and societal values. The first dimension explored in this review is the critical importance of transparency in AI algorithms and decision-making processes. Businesses adopting AI technologies must prioritize transparency to build trust among stakeholders, ensuring that the decision-making processes are understandable and accountable. Ethical considerations also extend to issues of bias and fairness, prompting the need for diverse and inclusive datasets to prevent discriminatory outcomes. Corporate responsibility in the realm of AI extends beyond technical aspects, encompassing the broader socio-economic impact of AI implementation. The review highlights the significance of considering the effects of AI on employment, inequality, and accessibility. Businesses are urged to adopt ethical guidelines that prioritize the well-being of employees and society at large, mitigating the potential negative consequences of AI on employment dynamics and social structures. Furthermore, the paper delves into the ethical considerations surrounding data privacy and security, emphasizing the importance of responsible data handling practices. As businesses accumulate vast amounts of data, it becomes imperative to prioritize the protection of individuals' privacy rights, reinforcing the ethical foundation of AI applications. This comprehensive review underscores the need for businesses to integrate responsible AI practices within the framework of corporate responsibility. By prioritizing transparency, fairness, and ethical data practices, organizations can navigate the complex terrain of AI implementation while ensuring alignment with societal values and ethical standards. This synthesis of AI and ethics in business is essential for fostering a sustainable and responsible technological future.International Journal of Science and Research Archive08This comprehensive review underscores the need for businesses to integrate responsible AI practices within the framework of corporate responsibility, and delves into the ethical considerations surrounding data privacy and security, emphasizing the importance of responsible data handling practices.International Journal of Science and Research Archive['Funmilola Olatundun Olatoye', 'Kehinde Feranmi Awonuga', 'Noluthando Zamanjomane Mhlongo', 'Chidera Victoria Ibeh', 'Oluwafunmi Adijat Elufioye', 'Ndubuisi Leonard Ndubuisi']2024-02-28T00:00:00https://www.semanticscholar.org/paper/ae41c7c5b2dcb76f4044dd88386b3bafe71e0c90 77dbd47b04a0945152b4e78a0834fdbfb9fd7865In the ever-evolving tapestry of financial planning, the integration of Artificial Intelligence (AI) emerges as a pivotal force, redefining the contours of strategic decision-making and operational efficiency. This paper delves into the historical progression, current implementations and the multifaceted impact of AI within the financial planning sphere, aiming to unravel the complexities and transformative potential of AI technologies. Through a rigorous examination of peer-reviewed literature and empirical studies, the research meticulously maps the trajectory of AI's integration in finance, from its nascent stages to its current stature as a cornerstone of financial innovation. The study's methodology, rooted in qualitative analysis, systematically explores the enhancements AI brings to financial decision-making, the challenges it poses, including ethical considerations and regulatory compliance and the qualitative shifts in financial strategies engendered by AI adoption. The findings illuminate AI's dual role as both a catalyst for unprecedented efficiency and a harbinger of new challenges, underscoring the need for a balanced approach to its integration. Conclusively, the paper advocates for a harmonious blend of innovation and ethical stewardship, recommending that financial institutions embrace AI's potential while rigorously addressing its challenges through continuous learning, adaptability, and ethical vigilance. The recommendations aim to guide stakeholders through the labyrinth of AI integration, ensuring that financial planning not only becomes more efficient and strategic but also remains equitable and transparent. This study serves as a beacon for future exploration, offering insights into navigating the complexities of AI-driven financial planning.World Journal of Advanced Engineering Technology and Sciences576It is recommended that financial institutions embrace AI's potential while rigorously addressing its challenges through continuous learning, adaptability, and ethical vigilance, ensuring that financial planning not only becomes more efficient and strategic but also remains equitable and transparent.World Journal of Advanced Engineering Technology and Sciences['Wilhelmina Afua Addy', 'Adeola Olusola Ajayi-Nifise', 'Binaebi Gloria Bello', 'Sunday Tubokirifuruar Tula', 'Olubusola Odeyemi', 'Titilola Falaiye']2024-02-28T00:00:00https://www.semanticscholar.org/paper/77dbd47b04a0945152b4e78a0834fdbfb9fd7865 3a1369bc220af8cb9a315bdc5dc5612d8d2c7859In an era where artificial intelligence (AI) increasingly intersects with every facet of human life, the imperative for ethical AI has never been more pronounced. This paper delves into the complex interplay between technological advancements in AI and the overarching human values that guide societal norms. The background of the study establishes the urgency of addressing ethical challenges inherent in AI, such as privacy, bias, and accountability, within the broader context of regulatory and policy frameworks. Aiming to critically evaluate the integration and effectiveness of ethical principles in AI applications, the paper navigates through a qualitative analysis, employing theoretical frameworks to dissect the ethical dimensions of AI. The scope encompasses a diverse range of topics, including global trends in ethical AI development, the impact of AI on human rights and personal freedoms, and the analysis of bias and fairness in AI algorithms. Real-world case studies provide insights into the successes and failures of ethical AI implementation, while the role of public perception and trust in AI adoption is scrutinized. The main conclusions reveal a dynamic global landscape of ethical AI, emphasizing the need for robust ethical frameworks and proactive strategies to mitigate biases and ensure equitable outcomes. Recommendations advocate for clear ethical guidelines, integration of ethics in AI development, transparency, accountability, multi-stakeholder collaboration, public engagement, and continuous ethical evaluation. The study concludes that balancing technological innovation with ethical constraints is crucial for the responsible development of AI. It underscores the importance of ethical vigilance, ensuring AI aligns with societal values and individual rights.International Journal of Science and Research Archive07The study concludes that balancing technological innovation with ethical constraints is crucial for the responsible development of AI, underscores the importance of ethical vigilance, ensuring AI aligns with societal values and individual rights.International Journal of Science and Research Archive['Benjamin Samson Ayinla', 'Olukunle Oladipupo Amoo', 'Akoh Atadoga', 'Temitayo Oluwaseun Abrahams', 'Femi Osasona', 'Oluwatoyin Ajoke Farayola']2024-02-28T00:00:00https://www.semanticscholar.org/paper/3a1369bc220af8cb9a315bdc5dc5612d8d2c7859 35b57712316746c6360f2b314b0634456ad575c3Greenwashing, the deceptive practice of presenting a misleading impression of a firm's environmental practices, has become a critical issue in corporate sustainability. This research explores the impact of greenwashing on corporate performance and examines strategies for regulating and overseeing greenwashing practices. Through a comprehensive review of literature, we identify the detrimental effects of greenwashing on firms, including reputational damage and loss of consumer trust. We propose several strategies for regulating greenwashing, including enhancing transparency, implementing stricter guidelines for environmental claims, and promoting third-party verification of environmental initiatives. By implementing these strategies, firms can improve their sustainability practices and regain consumer trust.Shanlax International Journal of Arts, Science and Humanities00Shanlax International Journal of Arts, Science and Humanities['H. K. Keerthi', 'H. Lakshmi', 'Shilpa Ajay', 'Sendhilkumar Manoharan']2024-02-28T00:00:00https://www.semanticscholar.org/paper/35b57712316746c6360f2b314b0634456ad575c3 053a41bbd96f17ae99b067f33906ce6fb9d081b7Ethics and Information Technology490This paper examines the adequacy of domain-specific governance mechanisms in the face of such integrated technologies and highlight their growing ineffectiveness, and proposes a comprehensive governance framework that is anticipatory, inclusive, and resilient.Ethics Inf. Technol.['Dirk Helbing', 'Marcello Ienca']2024-02-28T00:00:00https://www.semanticscholar.org/paper/053a41bbd96f17ae99b067f33906ce6fb9d081b7 cb4fe7851a07400d637d3bef955f229b39ffb96dJournal of Environmental Management1100Journal of environmental management['Zhe Chen', 'Xiaojing Li', 'Wei Si', 'Shouhong Xie', 'X. Xia']2024-02-28T00:00:00https://www.semanticscholar.org/paper/cb4fe7851a07400d637d3bef955f229b39ffb96d 14fbad4644fcefc0bb2ea5314bc65387a11b7b21The integration of artificial intelligence (AI) into human-robot interaction (HRI) has witnessed significant advancements in recent years, revolutionizing the way humans and robots collaborate and coexist. This review provides a comprehensive overview of the latest breakthroughs in AI-driven HRI and identifies the challenges that lie ahead. Recent years have seen a surge in AI-driven capabilities that enhance human-robot interaction. Machine learning algorithms enable robots to adapt to user preferences and behaviors, creating personalized and intuitive interactions. Natural language processing (NLP) facilitates seamless communication between humans and robots, enabling voice commands and context-aware responses. Computer vision advancements empower robots with enhanced perception, enabling them to recognize and interpret human gestures, emotions, and facial expressions. Reinforcement learning has played a pivotal role in enabling robots to learn from human feedback and optimize their actions in real-time. Socially assistive robots leverage AI to provide emotional support and companionship, particularly in healthcare and elderly care settings. Despite these advancements, challenges persist in the field of AI-driven HRI. Ethical considerations, including privacy concerns and the responsible use of AI in influencing human behavior, demand careful attention. Ensuring the safety and security of AI-driven robotic systems remains paramount, requiring robust measures against malicious attacks and unintended consequences. Human-robot trust remains a critical challenge, necessitating transparent AI algorithms and effective communication strategies. Interdisciplinary collaboration between AI researchers, roboticists, psychologists, and ethicists is essential to address the complex socio-technical aspects of HRI. The fusion of AI and human-robot interaction holds immense potential to redefine various facets of our daily lives. This review highlights recent strides in AI-driven HRI, emphasizing the need for interdisciplinary efforts to address challenges and ensure the responsible development and deployment of AI-powered robotic systems. As researchers continue to innovate, the dynamic landscape of AI and human-robot interaction promises a future where seamless collaboration and coexistence between humans and robots become an integral part of our societal fabric.GSC Advanced Research and Reviews435This review provides a comprehensive overview of the latest breakthroughs in AI-driven HRI and identifies the challenges that lie ahead, emphasizing the need for interdisciplinary efforts to address challenges and ensure the responsible development and deployment of AI-powered robotic systems.GSC Advanced Research and Reviews['Obinna Donald', 'Alexander Obaigbena', 'Oluwaseun Augustine Lottu', 'Ejike David Ugwuanyi', 'Boma Sonimitiem Jacks', 'Enoch Oluwademilade Sodiya', 'Obinna Donald Daraojimba']2024-02-28T00:00:00https://www.semanticscholar.org/paper/14fbad4644fcefc0bb2ea5314bc65387a11b7b21 d1935be8494f1babd5c8564d27f024d69bd95487The economic landscape of Africa is evolving rapidly, and the role of Small and Medium Enterprises (SMEs) is increasingly recognized as a key driver of sustainable development. This review explores the potential of leveraging Financial Technology (FinTech), Artificial Intelligence (AI), and Cybersecurity to accelerate SME growth in the African context, ultimately contributing to economic prosperity. In recent years, FinTech has gained prominence for its ability to revolutionize financial services. By facilitating seamless transactions, enhancing access to capital, and streamlining financial processes, FinTech presents a significant opportunity for SMEs in Africa to overcome traditional barriers to growth. The integration of AI technologies further amplifies this potential, enabling SMEs to harness data-driven insights for informed decision-making, operational efficiency, and personalized customer experiences. However, the adoption of FinTech and AI in the African SME sector necessitates robust cybersecurity measures. As digital transformation accelerates, the risk of cyber threats becomes more pronounced. Addressing these challenges requires a strategic approach to cybersecurity, encompassing robust data protection, threat intelligence, and resilient infrastructure. This review underscores the importance of a holistic approach, wherein the synergy between FinTech, AI, and Cybersecurity becomes a catalyst for economic prosperity. Policymakers, financial institutions, and technology providers must collaborate to create an enabling environment that fosters innovation, supports digital literacy, and ensures the security of digital ecosystems. By embracing this technological trifecta, African SMEs can navigate the complexities of the modern business landscape, foster innovation, and contribute significantly to job creation and economic growth. As Africa positions itself on the global stage, the strategic utilization of FinTech, AI, and Cybersecurity emerges as a pivotal driver for unlocking the full potential of SMEs and propelling the continent toward sustained economic prosperity.International Journal of Science and Research Archive03This review explores the potential of leveraging Financial Technology, Artificial Intelligence, and Cybersecurity to accelerate SME growth in the African context, ultimately contributing to economic prosperity.International Journal of Science and Research Archive['Chinwe Chinazo Okoye', 'Ekene Ezinwa Nwankwo', 'Favour Oluwadamilare Usman', 'Noluthando Zamanjomane Mhlongo', 'Olubusola Odeyemi', 'Chinedu Ugochukwu Ike']2024-02-28T00:00:00https://www.semanticscholar.org/paper/d1935be8494f1babd5c8564d27f024d69bd95487 0fdb31a57ef6f03f7374db930792a0c90ee7f4e2This comprehensive review explores the profound impact of artificial intelligence (AI) on warehouse automation, providing an in-depth examination of various AI-driven systems. As industries increasingly embrace automation to enhance efficiency and streamline operations, the integration of AI technologies into warehouse management systems has become pivotal, reshaping the landscape of logistics and supply chain management. AI-driven warehouse automation systems leverage advanced algorithms to optimize various aspects of warehouse operations, from inventory management to order fulfillment. Machine learning algorithms play a key role in demand forecasting, allowing warehouses to predict and adapt to changing customer needs. Computer vision technologies enhance robotic vision, facilitating tasks such as item recognition, pick-and-place operations, and quality control. These advancements significantly contribute to increased accuracy, speed, and cost-effectiveness in warehouse processes. The review provides a detailed examination of the applications of AI in warehouse automation, encompassing autonomous mobile robots (AMRs), robotic arms, and automated guided vehicles (AGVs). AMRs equipped with AI algorithms navigate warehouse environments autonomously, optimizing pick routes and adapting to changes in the warehouse layout. Robotic arms, enhanced by AI, enable precise and adaptable material handling, contributing to the efficiency of tasks like packing and palletizing. AGVs, guided by AI, ensure seamless material transport within warehouses, enhancing overall operational agility. Recent trends in AI-driven warehouse automation systems underscore the dynamic evolution of this field. Edge computing solutions empower these systems to process data locally, reducing latency and enhancing real-time decision-making. Reinforcement learning algorithms enable robotic systems to learn and adapt their behavior based on changing environmental conditions, contributing to continuous improvement and efficiency gains. In conclusion, this review illuminates the pivotal role of AI in transforming warehouse automation systems, revolutionizing the way logistics and supply chain operations are conducted. The collaborative synergy between AI and warehouse automation promises to drive unprecedented advancements in efficiency, accuracy, and adaptability within the evolving landscape of modern warehouses.GSC Advanced Research and Reviews373The pivotal role of AI in transforming warehouse automation systems, revolutionizing the way logistics and supply chain operations are conducted is illuminated, promising to drive unprecedented advancements in efficiency, accuracy, and adaptability within the evolving landscape of modern warehouses.GSC Advanced Research and Reviews['Enoch Oluwademilade Sodiya', 'Uchenna Joseph Umoga', 'Olukunle Oladipupo Amoo', 'Akoh Atadoga']2024-02-28T00:00:00https://www.semanticscholar.org/paper/0fdb31a57ef6f03f7374db930792a0c90ee7f4e2 cd0a2febc9e34ebec30bb8ffa44bb2e95ff9ec36Digital Marketing Analytics has become increasingly crucial in the contemporary business landscape, especially with the advent of Big Data and Artificial Intelligence (AI). This paper provides a comprehensive review of the strategies employed in Digital Marketing Analytics within the context of the rapidly evolving landscape of Big Data and AI. In the age of Big Data, businesses are inundated with vast amounts of information, making it imperative for marketers to leverage analytics tools effectively. This review explores the role of Digital Marketing Analytics in harnessing the power of Big Data, enabling marketers to extract actionable insights, identify trends, and make informed decisions. The integration of AI further enhances these capabilities, automating processes and offering predictive analytics for more targeted and personalized marketing strategies. The paper delves into various strategies employed in Digital Marketing Analytics, encompassing data collection, analysis, and interpretation. It discusses the significance of real-time analytics in responding promptly to market changes, optimizing campaigns, and enhancing customer experiences. Additionally, the review addresses the ethical considerations surrounding data privacy and the responsible use of AI in marketing practices. The synergy between Big Data and AI is explored as a catalyst for innovation in digital marketing. Strategies such as machine learning algorithms for customer segmentation, sentiment analysis, and predictive modeling are examined for their potential to revolutionize marketing effectiveness. Moreover, the paper highlights the evolving role of analytics in measuring the return on investment (ROI) of digital marketing initiatives. This review provides insights into the evolving landscape of Digital Marketing Analytics, emphasizing the strategic importance of leveraging Big Data and AI. Businesses that embrace these technologies stand to gain a competitive edge by unlocking valuable insights, optimizing marketing efforts, and staying agile in response to dynamic market conditions.World Journal of Advanced Research and Reviews623The paper delves into various strategies employed in Digital Marketing Analytics, encompassing data collection, analysis, and interpretation, and discusses the significance of real-time analytics in responding promptly to market changes, optimizing campaigns, and enhancing customer experiences.World Journal of Advanced Research and Reviews['Onyeka Franca Asuzu', 'Rhoda Adura Adeleye', 'Kehinde Feranmi Awonuga', 'Ndubuisi Leonard Ndubuisi', 'Tula Sunday Tubokirifuruar']2024-02-28T00:00:00https://www.semanticscholar.org/paper/cd0a2febc9e34ebec30bb8ffa44bb2e95ff9ec36 0cb6e31a309a089624dea9e1486b95be8331cbdeThe exponential growth of network complexity and data volume in modern digital ecosystems has underscored the need for innovative approaches to optimize network performance and efficiency. This paper delves into the potential of AI-driven optimization techniques in addressing this imperative. Leveraging artificial intelligence (AI) algorithms such as machine learning and deep learning, the study investigates how AI can revolutionize network management and operation to achieve higher levels of performance and reliability. Through a comprehensive review of existing literature and case studies, this paper elucidates the fundamental principles, methodologies, and applications of AI-driven optimization in diverse network environments. It examines how AI algorithms can analyze vast amounts of network data, identify patterns, and make data-driven decisions to optimize network configurations, routing protocols, and resource allocation strategies. Moreover, the study explores how AI-driven optimization can enhance network security, fault tolerance, and scalability by autonomously detecting and mitigating potential threats and vulnerabilities. The Review succinctly encapsulates the main findings and insights derived from the analysis, emphasizing the transformative potential of AI-driven optimization for network performance and efficiency enhancement. It underscores the benefits of AI-driven approaches in automating complex optimization tasks, reducing operational overhead, and adapting dynamically to changing network conditions and user demands. Additionally, the Review discusses the challenges and considerations associated with the implementation of AI-driven optimization techniques, including algorithmic bias, data privacy concerns, and ethical implications. In conclusion, the Review underscores the critical role of AI-driven optimization in addressing the evolving challenges of network management and operation. It advocates for continued research and development efforts aimed at harnessing the full potential of AI-driven optimization to unlock new levels of performance and efficiency in network infrastructures. By embracing AI-driven approaches, organizations can streamline network operations, improve user experience, and drive innovation in the digital era.Magna Scientia Advanced Research and Reviews03The Review underscores the critical role of AI-driven optimization in addressing the evolving challenges of network management and operation and advocates for continued research and development efforts aimed at harnessing the full potential of AI-driven optimization to unlock new levels of performance and efficiency in network infrastructures.Magna Scientia Advanced Research and Reviews['Uchenna Joseph Umoga', 'Enoch Oluwademilade Sodiya', 'Ejike David Ugwuanyi', 'Boma Sonimitiem Jacks', 'Oluwaseun Augustine Lottu', 'Obinna Donald Daraojimba', 'Alexander Obaigbena']2024-02-28T00:00:00https://www.semanticscholar.org/paper/0cb6e31a309a089624dea9e1486b95be8331cbde 158930c9c2e47f00b19b26d542a8ccf7141de776The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learning algorithms, allows supply chain professionals to forecast demand more accurately, identify potential disruptions, and optimize inventory levels. This not only improves overall efficiency but also reduces costs and minimizes the risk of stockouts or overstock situations. Furthermore, the integration of AI-driven automation in supply chain management has streamlined routine tasks, such as order processing, inventory replenishment, and route optimization. This automation not only accelerates processes but also mitigates the risk of human errors, enhancing overall reliability. The ability of AI to continuously learn from historical data and adapt to evolving market conditions contributes to a more agile and responsive supply chain ecosystem. In the context of supply chain risk management, AI and ML play a pivotal role in identifying vulnerabilities and providing proactive strategies to mitigate potential disruptions. Sentiment analysis and predictive modeling enable organizations to assess geopolitical, economic, and environmental factors, thereby enhancing the resilience of their supply chains. However, the adoption of AI and ML in supply chain analytics is not without challenges. This review explores the ethical considerations, data security concerns, and the need for skilled personnel in managing these advanced technologies. Additionally, it delves into the importance of explainability and transparency in AI-driven decision-making processes, emphasizing the need for a balance between automation and human oversight. This review underscores the transformative impact of AI and ML on supply chain analytics, emphasizing their potential to revolutionize traditional practices, enhance efficiency, and fortify resilience in an increasingly complex and dynamic business environment.GSC Advanced Research and Reviews633This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands, highlighting their potential to revolutionize traditional practices, enhance efficiency, and fortify resilience in an increasingly complex and dynamic business environment.GSC Advanced Research and Reviews['Andrew Ifesinachi', 'Enoch Oluwademilade Sodiya', 'Boma Sonimitiem Jacks', 'Ejike David Ugwuanyi', 'Mojisola Abimbola Adeyinka', 'Uchenna Joseph Umoga', 'Andrew Ifesinachi Daraojimba', 'Oluwaseun Augustine Lottu']2024-02-28T00:00:00https://www.semanticscholar.org/paper/158930c9c2e47f00b19b26d542a8ccf7141de776 71024d7586f98404b3c77e06628aa1137450d5d4 This Medical News article is an interview with University of Michigan computer scientist Jenna Wiens, whose research interests lie at the intersection of AI and health care, and JAMA Editor in Chief Kirsten Bibbins-Domingo. Journal of the American Medical Association (JAMA)02JAMA['Jennifer Abbasi', 'Y. Hswen']2024-02-28T00:00:00https://www.semanticscholar.org/paper/71024d7586f98404b3c77e06628aa1137450d5d4 24016ccc3ebdc1ea6dad9d4d5bc9019690ccea98This review explores the pivotal role of Artificial Intelligence (AI) in revolutionizing fraud detection and prevention within the realm of financial services. As financial crimes become increasingly sophisticated, traditional methods of detection fall short, necessitating the integration of advanced technologies. AI emerges as a transformative force, employing machine learning algorithms, predictive analytics, and anomaly detection to fortify the defenses against fraudulent activities. The review provides an in-depth examination of the historical context, tracing the evolution of fraud detection from manual methods to the contemporary AI-driven approaches. It delves into the diverse AI models utilized in fraud prevention, including supervised and unsupervised learning, deep learning, and natural language processing. The nuanced analysis encompasses the effectiveness of AI in identifying intricate patterns indicative of fraudulent behavior, demonstrating its superiority in discerning anomalies within vast and dynamic datasets. Moreover, the review elucidates the real-world implications of AI in fraud detection, spotlighting instances where the technology has successfully thwarted fraudulent schemes. The ethical considerations inherent in AI-driven fraud prevention are also scrutinized, emphasizing the importance of responsible and transparent practices to mitigate biases and ensure fairness in decision-making processes. As the financial landscape navigates an era of digital transformation, the review sheds light on the future trends and innovations in AI-driven fraud detection. Anticipated developments include the integration of Explainable AI (XAI), federated learning, and continuous adaptation to emerging threats. The discussion extends to the collaborative efforts between financial institutions, regulatory bodies, and technology providers to create a robust ecosystem capable of staying ahead of evolving fraudulent tactics. In conclusion, this review encapsulates the dynamic landscape of AI in fraud detection and prevention within financial services. The analysis underscores the transformative impact of AI, not only in bolstering security measures but also in fostering a proactive and adaptive approach to counter the ever-evolving nature of financial fraud. The synthesis of historical perspectives, current applications, and future trajectories provides a comprehensive understanding of how AI is reshaping the paradigm of fraud detection in the financial domain.International Journal of Science and Research Archive432The analysis underscores the transformative impact of AI, not only in bolstering security measures but also in fostering a proactive and adaptive approach to counter the ever-evolving nature of financial fraud.International Journal of Science and Research Archive['E. Nwankwo', 'Olubusola Odeyemi', 'Noluthando Zamanjomane Mhlongo', 'Oluwatobi Timothy Soyombo']2024-02-28T00:00:00https://www.semanticscholar.org/paper/24016ccc3ebdc1ea6dad9d4d5bc9019690ccea98 86c10e08b8f1e848ddb6b5f59f7dae061e7e8bd9This review paper critically examines the ethical considerations involved in implementing generative Artificial Intelligence (AI) in healthcare supply chain optimization across three distinct regions: India, the United Kingdom, and the United States of America. The study synthesizes findings from various case studies and academic research to highlight both common and unique ethical challenges faced in these countries. Key themes such as data privacy, algorithmic transparency, and equitable access to AI-driven healthcare solutions are explored, alongside the unique socio-cultural, legal, and regulatory challenges specific to each region. The paper proposes a set of best practices for incorporating ethical considerations into the deployment of generative AI in healthcare. These include the development of inclusive ethical frameworks, regular ethical audits, comprehensive training and education programs, public engagement initiatives, and interdisciplinary collaboration. The paper also delves into future research directions and policy development, emphasizing the need to address healthcare disparities, adapt legal and regulatory frameworks, enhance generative AI explainability, and evaluate long-term outcomes.The study concludes by underscoring the importance of ethical design and deployment of generative AI systems in healthcare, advocating for a balanced approach that aligns technological advancements with ethical standards and global healthcare needs. This comprehensive review aims to contribute to the discourse on ethical generative AI implementation, offering insights and recommendations for policymakers, healthcare professionals, and generative AI developers to foster responsible and beneficial use of generative AI in healthcare globally.International Journal of Biological and Pharmaceutical Sciences Archive282This review paper critically examines the ethical considerations involved in implementing generative Artificial Intelligence in healthcare supply chain optimization across three distinct regions: India, the United Kingdom, and the United States of America to propose a set of best practices for incorporating ethical considerations into the deployment of generative AI in healthcare.International Journal of Biological and Pharmaceutical Sciences Archive['Amina Catherine Ijiga', 'Ehi Peace', 'Idoko Peter Idoko', 'Daniel Obekpa Agbo', 'Kimberly D. Harry', 'Chijioke Ifakandu Ezebuka', 'Esther Ene Umama']2024-02-28T00:00:00https://www.semanticscholar.org/paper/86c10e08b8f1e848ddb6b5f59f7dae061e7e8bd9 c2c0eef817fe2d2389f60b471da5c015e79af91fThis research investigates the synergistic potential of artificial intelligence (AI) and Information Education and Communication (IEC) in the context of groundwater conservation for Solapur, a region participating in the Atal Bhujal Yojana. The primary objective is to assess the effectiveness of integrating AI technologies with community-centric education strategies to enhance water management practices. The methodology involves a comprehensive review of the Atal Bhujal Yojana, exploration of AI applications in global water management, and the formulation of strategies for AI-IEC integration. Key findings highlight the pivotal role of community engagement, the diverse applications of AI in water management, and the significance of IEC in shaping sustainable behaviors. Challenges and solutions, case studies, and future prospects are examined to provide a comprehensive overview. The implications of this research extend to the development of resilient water ecosystems, emphasizing the importance of collaborative efforts and forward-thinking solutions. This interdisciplinary approach positions Solapur as a model for effective groundwater conservation, leveraging technological advancements and community participation for a water-secure future.International Journal of Science and Research Archive692International Journal of Science and Research Archive['M. Shaikh', 'Farjana Birajdar']2024-02-28T00:00:00https://www.semanticscholar.org/paper/c2c0eef817fe2d2389f60b471da5c015e79af91f 1bdd538f1e7b0c04ff703fdb9cdd5e5e529f3c3aType 2 diabetes is a significant global health concern. It often causes diabetic foot ulcers (DFUs), which affect millions of people and increase amputation and mortality rates. Despite existing guidelines, the complexity of DFU treatment makes clinical decisions challenging. Large language models such as chat generative pretrained transformer (ChatGPT), which are adept at natural language processing, have emerged as valuable resources in the medical field. However, concerns about the accuracy and reliability of the information they provide remain. We aimed to assess the accuracy of various artificial intelligence (AI) chatbots, including ChatGPT, in providing information on DFUs based on established guidelines. Seven AI chatbots were asked clinical questions (CQs) based on the DFU guidelines. Their responses were analyzed for accuracy in terms of answers to CQs, grade of recommendation, level of evidence, and agreement with the reference, including verification of the authenticity of the references provided by the chatbots. The AI chatbots showed a mean accuracy of 91.2% in answers to CQs, with discrepancies noted in grade of recommendation and level of evidence. Claude-2 outperformed other chatbots in the number of verified references (99.6%), whereas ChatGPT had the lowest rate of reference authenticity (66.3%). This study highlights the potential of AI chatbots as tools for disseminating medical information and demonstrates their high degree of accuracy in answering CQs related to DFUs. However, the variability in the accuracy of these chatbots and problems like AI hallucinations necessitate cautious use and further optimization for medical applications. This study underscores the evolving role of AI in healthcare and the importance of refining these technologies for effective use in clinical decision-making and patient education.International Journal of Lower Extremity Wounds253The potential of AI chatbots as tools for disseminating medical information and their high degree of accuracy in answering CQs related to DFUs are highlighted and refining these technologies for effective use in clinical decision-making and patient education is highlighted.The international journal of lower extremity wounds['Makoto Shiraishi', 'Haesu Lee', 'Koji Kanayama', 'Yuta Moriwaki', 'Mutsumi Okazaki']2024-02-28T00:00:00https://www.semanticscholar.org/paper/1bdd538f1e7b0c04ff703fdb9cdd5e5e529f3c3a e439c8b5b02162afb1dd30694bbf39e1dd778684Diabetes mellitus (DM) predisposes patients to vascular complications. Retinal images and vasculature reflect the body's micro- and macrovascular health. They can be used to diagnose DM complications, including diabetic retinopathy (DR), neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as forecast the risk of cardiovascular events. Artificial intelligence (AI)-enabled systems developed for high-throughput detection of DR using digitized retinal images have become clinically adopted. Beyond DR screening, AI integration also holds immense potential to address challenges associated with the holistic care of the patient with DM. In this work, we aim to comprehensively review the literature for studies on AI applications based on retinal images related to DM diagnosis, prognostication, and management. We will describe the findings of holistic AI-assisted diabetes care, including but not limited to DR screening, and discuss barriers to implementing such systems, including issues concerning ethics, data privacy, equitable access, and explainability. With the ability to evaluate the patient's health status vis a vis DM complication as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.arXiv.org13The findings of holistic AI-assisted diabetes care, including but not limited to DR screening, are described, and barriers to implementing such systems are discussed, including issues concerning ethics, data privacy, equitable access, and explainability.ArXiv['Yasin Sadeghi Bazargani', 'Majid Mirzaei', 'Navid Sobhi', 'M. Abdollahi', 'Ali Jafarizadeh', 'Siamak Pedrammehr', 'R. Alizadehsani', 'Ru-San Tan', 'Sheikh Mohammad Shariful Islam', 'U. R. Acharya']2024-02-28T00:00:00https://www.semanticscholar.org/paper/e439c8b5b02162afb1dd30694bbf39e1dd778684 1242ea6ae9aa88015897dad2ae54fb9b9f118724Rapid urbanization is placing tremendous pressure on limited resources and aging infrastructure in cities worldwide. Meanwhile, new technologies are emerging to help address urban challenges through data-driven solutions. This paper explores how the strategic integration of artificial intelligence (AI) and Internet of Things (IoT) can transform urban management and services delivery for smart and sustainable cities. The Internet of Things enables the ubiquitous collection of real-time data across urban systems through embedded sensors. However, extracting actionable insights requires advanced analytics. Concurrently, artificial intelligence provides techniques to autonomously analyze huge volumes of IoT-sensed urban data. When combined effectively, AI and IoT can automatically monitor infrastructure, optimize operations, and enhance citizen experiences. This paper first defines key concepts and outlines applications of AI and IoT independently in areas like transportation, energy, environment, and public safety. It then examines how both technologies can be integrated for mutual benefit. Examples of integrated solutions such as predictive maintenance, intelligent transportation, and emergency response optimization are discussed. Challenges to adoption like data privacy, infrastructure costs, skills gaps, and technical standardization are also covered. The conclusion underscores the tremendous potential of AI and IoT to create efficient, resilient and livable urban environments through ubiquitous sensing and autonomous management. With proper policy support and collaborations, cities worldwide can leverage these smart technologies to sustainably combat problems facing urbanization.Journal of Technical Education Science192This paper explores how the strategic integration of artificial intelligence (AI) and Internet of Things (IoT) can transform urban management and services delivery for smart and sustainable cities.Journal of Technical Education Science['Thanh Van Hoang']2024-02-28T00:00:00https://www.semanticscholar.org/paper/1242ea6ae9aa88015897dad2ae54fb9b9f118724 f087c26547ac6a61d50f2cbaeb7dfd8172b31a6cA major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.Circulation01This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease and sets out to advance this mission by identifying best practices, gaps, and challenges for interested stakeholders.Circulation['A. Armoundas', 'Sanjiv M. Narayan', 'Donna K. Arnett', 'Kayte Spector-Bagdady', 'Derrick A Bennett', 'L. A. Celi', 'Paul A Friedman', 'M. Gollob', 'Jennifer L Hall', 'A. Kwitek', 'Elle Lett', 'B. Menon', 'Katherine A Sheehan', 'S. Al-Zaiti']2024-02-28T00:00:00https://www.semanticscholar.org/paper/f087c26547ac6a61d50f2cbaeb7dfd8172b31a6c 2b127c2cc7ce0a150318e6da0cd367ecd03f883aAim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims. Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies. Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain.Exploration of Digital Health Technologies151The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes.Exploration of Digital Health Technologies['Ashrafe Alam', 'Victor R. Prybutok']2024-02-28T00:00:00https://www.semanticscholar.org/paper/2b127c2cc7ce0a150318e6da0cd367ecd03f883a c2a1a10e5da086b849597406b49c3594fbef5ac0BACKGROUND/OBJECTIVES Artificial intelligence (AI) holds remarkable potential to improve care delivery in dermatology. End users (health professionals and general public) of AI-based Software as Medical Devices (SaMD) require relevant labelling information to ensure that these devices can be used appropriately. Currently, there are no clear minimum labelling requirements for dermatology AI-based SaMDs. METHODS Common labelling recommendations for AI-based SaMD identified in a recent literature review were evaluated by an Australian expert panel in digital health and dermatology via a modified Delphi consensus process. A nine-point Likert scale was used to indicate importance of 10 items, and voting was conducted to determine the specific characteristics to include for some items. Consensus was achieved when more than 75% of the experts agreed that inclusion of information was necessary. RESULTS There was robust consensus supporting inclusion of all proposed items as minimum labelling requirements; indication for use, intended user, training and test data sets, algorithm design, image processing techniques, clinical validation, performance metrics, limitations, updates and adverse events. Nearly all suggested characteristics of the labelling items received endorsement, except for some characteristics related to performance metrics. Moreover, there was consensus that uniform labelling criteria should apply across all AI categories and risk classes set out by the Therapeutic Goods Administration. CONCLUSIONS This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.Australasian Journal of Dermatology241This study provides critical evidence for setting labelling standards by the Therapeutic Goods Administration to safeguard patients, health professionals, consumers, industry, and regulatory bodies from AI-based dermatology SaMDs that do not currently provide adequate information about how they were developed and tested.The Australasian journal of dermatology['Åsa Ingvar', 'Ayooluwatomiwa I Oloruntoba', 'Maithili Sashindranath', 'Robert Miller', 'H. P. Soyer', 'P. Guitera', 'T. Caccetta', 'Stephen Shumack', 'L. Abbott', 'C. Arnold', 'Craig Lawn', 'Alison Button-Sloan', 'Monika Janda', 'Victoria J Mar']2024-02-28T00:00:00https://www.semanticscholar.org/paper/c2a1a10e5da086b849597406b49c3594fbef5ac0 372467ec595f6f7c08105d98c6163904c2846693The fusion of artificial intelligence (AI) with cybersecurity represents a paradigm shift in our efforts to safeguard digital assets against a dynamic threat landscape. This manuscript comprehensively analyses AI's transformative role in cybersecurity, covering foundational principles, advanced methodologies, and ethical considerations. This article begins with exploring fundamental AI techniques such as machine learning and natural language processing. The manuscript delineates their applications in bolstering threat detection, vulnerability analysis, and incident response. Traditional approaches to vulnerability analysis are juxtaposed with AI-driven methodologies, highlighting the efficacy of automated scanning, threat prioritization, and adaptive risk assessment. Moreover, the manuscript delves into the pivotal role of AI-driven automation in expediting incident response, minimizing human error, and fortifying overall security postures. Ethical and privacy concerns surrounding AI deployment in cybersecurity are carefully examined, emphasizing the importance of responsible decision-making, privacy protection, and transparency. Looking ahead, emerging trends such as adversarial machine learning and zero trust security present promising avenues for further exploration, offering opportunities to enhance digital resilience against evolving threats.World Journal of Advanced Research and Reviews731This manuscript comprehensively analyses AI's transformative role in cybersecurity, covering foundational principles, advanced methodologies, and ethical considerations, and delves into the pivotal role of AI-driven automation in expediting incident response, minimizing human error, and fortifying overall security postures.World Journal of Advanced Research and Reviews['Sontan Adewale', 'Daniel', 'Segun Victor Samuel']2024-02-28T00:00:00https://www.semanticscholar.org/paper/372467ec595f6f7c08105d98c6163904c2846693 e858eb682177ee31044f41e7224cce7a396a4602Background: Effectively managing drug demand and supply through pharmaceutical quantification is critical as it ensures that medications are readily available when needed while reducing costs, optimizing inventory management, and ultimately improving patient care. This research aimed to examine the existing literature on the influence of artificial intelligence (AI) and machine learning (ML) on predicting pharmaceutical demand in public systems. This review focused specifically on the accuracy of these methods, their limitations, and the ethical concerns associated with their use. Methods: The research used PubMed and Google Scholar databases, following PRISMA principles, and yielded 13 peer-reviewed articles. The quality of the included studies was assessed for potential bias using established standard criteria, the Cochrane Risk of Bias Checklist Tool for systematic reviews of intervention. Results: The results show that linear regression and random forest are the predominant models for predicting medication quantities in hospital pharmacies. However, the precision of these models can be affected by data entry inaccuracies and fluctuations. The study identified technical, human, and organizational obstacles as barriers to adoption, as well as problems related to privacy and confidentiality. Conclusion: The use of AI and ML can estimate the demand and supply of medicine in public pharmaceutical delivery systems. The results highlight the importance of further study to improve forecasting algorithm simulation accuracy, broaden single time-series projections to incorporate additional patient-associated factors and investigate various efficiency measures.GSC Biological and Pharmaceutical Sciences381The results show that linear regression and random forest are the predominant models for predicting medication quantities in hospital pharmacies, however, the precision of these models can be affected by data entry inaccuracies and fluctuations.GSC Biological and Pharmaceutical Sciences['Abraham Dongo Orcid', 'Tangi Ndakondja', 'Abraham Dongo']2024-02-28T00:00:00https://www.semanticscholar.org/paper/e858eb682177ee31044f41e7224cce7a396a4602 a4f52707bb523db3d050fc73a6dc627f895e162aArtificial Intelligence Review2341Artif. Intell. Rev.['Tristan Lim']2024-02-28T00:00:00https://www.semanticscholar.org/paper/a4f52707bb523db3d050fc73a6dc627f895e162a a0a08885d055aef2ace826dc8db43bc13fefd414Background: Chatbots are artificial intelligence (AI) programs designed to simulate conversations with human users through text or speech. The use of artificial intelligence chatbots (AICs) in scientific research presents benefits and challenges. Although the stances of journals and publishing organizations on AIC use is increasingly clear, little is known about researchers' perceptions of AICs in research. This survey study explores attitudes, familiarity, perceived benefits, limitations, and factors influencing adoption of AIC by researchers. Methods: A cross-sectional online survey of published researchers was conducted. Corresponding authors and their e-mail addresses were identified by querying PubMed for articles (any type) published in a MEDLINE indexed journal in the most recent two months and using R script on PubMed metadata. e-Mail invitations were sent to 61560 study authors. The survey, administered on SurveyMonkey, opened on July 9, 2023, and closed on August 11, 2023. Respondents had 3 weeks to complete the survey and were sent 2 reminder e-mails during the weeks of July 17, 2023, and July 24, 2023. Results: 2165 respondents completed the survey (4.0% response rate; 94% completion rate of those who responded). Most were familiar with the concept of AICs (n=1294/2138, 60.5%). About half had used an AIC previously for purposes relating to the scientific process (n=1107/2125, 52.1%). Only 244/2137 (11.4%) respondents reported that their institution offered training on using AI tools of whom 64/244 (26.2%) completed the training. 211/2131 (9.9%) reported that their institution implemented policies regarding AIC use in the scientific process. Most respondents expressed interest in learning more and receiving training on AIC use in the scientific process (n=1428/2048, 69.7%). Respondents had mixed opinions about the potential benefits of using AICs, whereas most agreed on their cons/challenges. Respondents agreed AICs were most beneficial in reducing the workload and administrative burden on researchers (n=1299/1941, 66.9%) and they were most concerned about the lack of understanding behind how AICs make decisions and generate responses (n=1484/1923, 77.2%). Conclusions: Most respondents are familiar with AICs and half used AICs in their own research. Although there is clear interest in understanding how AICs can be used, many hesitate due to existing limitations. Little formal instruction on using AICs is available across academic institutions.medRxiv50Most respondents are familiar with AICs and half used AICs in their own research, whereas most agreed on their cons/challenges.['J. Y. Ng', 'S. Maduranayagam', 'N. Suthakar', 'A. Li', 'C. Lokker', 'A. Iorio', 'R. B. Haynes', 'D. Moher']2024-02-28T00:00:00https://www.semanticscholar.org/paper/a0a08885d055aef2ace826dc8db43bc13fefd414 a934dab7eda22e1d1095c73690323c870a3597fdThe aim of the article is to justify the possibility of using artificial intelligence technologies – neural networks – to optimize the activities of a higher education teacher. The article discusses the educational capabilities of artificial intelligence for use in the higher education process. Procedural characteristics of artificial intelligence are presented in order to identify and describe its functions for use in higher education. The scientific novelty of the research is in identifying the potential of artificial intelligence technologies – neural networks – in relation to the educational process and optimizing the work of a higher education teacher. The results of the study include the following indicators: 1) artificial intelligence has a wide range of applications in the educational process; 2) neural networks have demonstrated the potential of their capabilities to optimize the work of a higher education teacher.Pedagogy Issues of Theory and Practice60The aim of the article is to justify the possibility of using artificial intelligence technologies – neural networks – to optimize the activities of a higher education teacher and identify and describe its functions for use in higher education.Pedagogy. Issues of Theory and Practice['A. Shirokolobova']2024-02-28T00:00:00https://www.semanticscholar.org/paper/a934dab7eda22e1d1095c73690323c870a3597fd bfb56fd157e55e11bf08de8075fcad48e01f0e42Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.Journal of Surgical Oncology410Artificial intelligence has the potential to improve the surgical treatment of patients with head and neck cancer and develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.Journal of surgical oncology['Bartosz Wojtera', 'M. Szewczyk', 'Piotr Pieńkowski', 'Wojciech Golusiński']2024-02-28T00:00:00https://www.semanticscholar.org/paper/bfb56fd157e55e11bf08de8075fcad48e01f0e42 22b7897796c841eebe4335caf1fa4b6363513829Abstract – Artificial Intelligence, renowned for its data interpretation, learning, and task achievement capabilities, has gained popularity in various industries and academies due to enhanced efficiency and quality. This study aims to determine the extent of AI usage among students, including functionality, availability, complexity, assessment scores, course mastery, and grading metrics. It also seeks to determine if a relationship exists between AI usage and their academic performance. The study employs a quantitative approach using a correlational design. The respondents of the study are 293 Business Administration students from Negros Oriental State University Main Campus 1, Dumaguete City. The study's findings suggest that AI usage among students is moderately prevalent in terms of functionality, availability, and complexity. However, the students' academic performance was found to be above-average, with high scores on assessments, course mastery, and excellent grades. There is no significant relationship between AI use and academic performance found. In conclusion, AI tools offer personalized learning experiences, immediate feedback, and collaborative activities, but further growth and improvement are needed, including training, accessibility, research, monitoring, and best practices sharingInternational Journal of Asian Business and Management00The study's findings suggest that AI usage among students is moderately prevalent, and students' academic performance was found to be above-average, with high scores on assessments, course mastery, and excellent grades.International Journal of Asian Business and Management['Jaysone Christopher M. Bancoro']2024-02-28T00:00:00https://www.semanticscholar.org/paper/22b7897796c841eebe4335caf1fa4b6363513829 95f5962c13f50b153bbc336826369dd758d5bf0eStartups are introduced to address changing societal needs, market demands, and survive in the sphere of innovation competition. This study develops an extended model of innovation capability and open innovation for startup performance with the role of artificial intelligence. The model was examined through a survey of startup founders and co-founders in the Philippines. This study used descriptive and structural equation modeling to examine constructs involved in innovation capability. The results show that innovation capability and open innovation strongly affects financial and operational performance. Furthermore, artificial intelligence positively affects innovation capability, and open innovation, from the startups perspective. Also, results show that artificial intelligence affects ideation and organizing structures, and management of technology while artificial intelligence has negative effect on know-how development in innovation capability. This study indicates that innovation capability with consideration of artificial intelligence for startups can result in worthwhile progress and opportunities. This work introduces research directions and practical recommendations on innovation capability and open innovation for startup performance.International Conference on Knowledge and Smart Technology580An extended model of innovation capability and open innovation for startup performance with the role of artificial intelligence is developed and indicates that innovation capability with consideration of artificial intelligence for startups can result in worthwhile progress and opportunities.2024 16th International Conference on Knowledge and Smart Technology (KST)['Alexander A. Hernandez', 'Randolph V. Sacdalan', 'Christopher T. Sopoco', 'Jasmine E. Villapando', 'Peter M. Garcia']2024-02-28T00:00:00https://www.semanticscholar.org/paper/95f5962c13f50b153bbc336826369dd758d5bf0e 29de2fec7bd49b9794dc0163bde2e02957306d56This research explores the challenges of developing artificial intelligence (AI) at universities in China, India, and Indonesia for teacher education students. A qualitative research method was employed, with data collected through in-depth focus group discussions with 12 doctoral students from the 3 countries in equal proportions. The sample selection was based on the diversity of the participants’ relevant backgrounds, experiences, and understandings. The data collected were analyzed using a thematic approach involving the identification, mapping, and interpretation of themes. The research findings indicate variations in the main challenges in developing AI to improve the quality of teacher education in each country. In Indonesia, infrastructure and Internet access are the main constraints limiting the application of AI technology. Meanwhile, in India, the main concern relates to the lack of human resources skilled in the field of AI, prompting the need for relevant skills development among educators. Conversely, in China, the problem concerns striking a balance between utilizing advanced AI technologies, safeguarding privacy, and developing the capacity to accommodate rapid advances in technology-based education. The findings of this study provide valuable strategic insights, enabling the design of appropriate strategies in each country. The implications of the findings can assist the relevant parties in overcoming specific barriers in the context of each country, supporting innovative developments in technology-based teacher education.International Journal of Learning, Teaching and Educational Research00The research findings indicate variations in the main challenges in developing AI to improve the quality of teacher education in each country, and can assist the relevant parties in overcoming specific barriers in the context of each country.International Journal of Learning, Teaching and Educational Research['Mustopa Mustopa', 'Nasikhin Nasikhin', 'Rikza Chamami', 'Hamidatun Nihayah', 'Muhammad Romadlon Habibullah', 'Ahmad Manshur']2024-02-28T00:00:00https://www.semanticscholar.org/paper/29de2fec7bd49b9794dc0163bde2e02957306d56 df2a1764a418abd7c0ffc6ffe7f2d0940e51c8e8This research paper is introduces as a centralized grievance portal based on Artificial Intelligence (AI) for streamlined and effective complaint resolution. The proposed system manipulates advanced AI techniques to enhance the complaint handling process, providing a user-friendly interface for complainants & optimizing resource utilization for resolution authorities. The integration of machine learning algorithms ensures intelligent analysis of complaints, prioritization, and swift solution, ultimately contributing to an efficient and transparent grievance redressal mechanismInternational Research Journal of Computer Science00The proposed system manipulates advanced AI techniques to enhance the complaint handling process, providing a user-friendly interface for complainants & optimizing resource utilization for resolution authorities.International Research Journal of Computer Science['Dr.Shivani Dubey', 'Dr.Ajay kumar Sahu', 'Prof.Vikas Singhal']2024-02-28T00:00:00https://www.semanticscholar.org/paper/df2a1764a418abd7c0ffc6ffe7f2d0940e51c8e8 410ffe7cc64f5f5ebc63acc454b1f7f35d4e1f28The integration of artificial intelligence (AI) in healthcare has emerged as a transformative force, promising to enhance medical diagnosis, treatment, and overall healthcare delivery. Hence, this study investigates the university students perceptions toward using AI in healthcare. A cross-sectional survey was conducted at two major universities using a paper-based questionnaire from September 2023 to November 2023. Participants' views regarding using artificial intelligence in healthcare were investigated using 25 items distributed across five domains. The Mann-Whitney U test was applied for the comparison of variables. The response rate for the survey was 75%, with a sample size of 279. More than half of the participants (52%, n = 145) expressed their belief in AI's potential to reduce treatment errors in the future. However, about (61.6%, n = 172) of participants fear the influence of AI that could prevent doctors from learning to make correct patient care judgments, and it was widely agreed (69%) that doctors should ultimately maintain final control over patient care. Participants with experience with AI, such as engaging with AI chatbots, significantly reported higher scores in both the "Benefits and Positivity Toward AI in Healthcare" and "Concerns and Fears" domains (p = 0.024) and (p = 0.026), respectively. The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration. The findings emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels. Insights from such research can guide the development of ethical frameworks, policies, and targeted interventions, fostering a harmonious integration of AI into the healthcare landscape in developing countries.medRxiv190The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration in healthcare, and emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels.['Najmaddin A H Hatem', 'M. Izham', 'M. Ibrahim', 'S. A. Yousuf', 'Dr. Najmaddin Abduh', 'Hamood Hatem', 'BClinPharm']2024-02-28T00:00:00https://www.semanticscholar.org/paper/410ffe7cc64f5f5ebc63acc454b1f7f35d4e1f28 8fb588992ccc3fa1043ebdea9d7f560efe220d0fWith the development of science and technology, the administration is increasing the number of fully automated administrative dispositions by automatic electronic systems without public officials' expressions or intervention based on artificial intelligence algorithms and big data in mass administrative procedures. Article 20 of the Framework Act on Administrative Law Affairs stipulates that fully automated administrative actions can be administered by a fully automated system based on the law, but it is emerging as a legal task for legislators to regulate the requirements and limitations of fully automated administrative actions and securing procedural fairness in individual laws. It is also necessary to clarify the distinction between partially automated administrative actions and fully automated administrative actions by artificial intelligence algorithms, and to consider the limitations of automatic administrative decisions in discretionary and judgment areas. The German Federal Administrative Procedure Act does not provide an opportunity for hearings or an explanation of the reasons for disposal for automated administrative actions, and the British Data Protection Act prepares to object to automated administrative decisions. In this paper, legislation on fully automated administrative actions under the Federal Administrative Procedure Act in Germany, the Framework Act on Taxes, the Social Security Act, and the Battery Act, legislation on the requirements and procedures of fully automated administrative actions under the Data Protection Act in the UK, the concept of fully automated administrative disposition and the establishment and presentation of administrative agencies stipulated in Spain's 2015 Act on the Legal System of the Public Sector, Galicia's Act on Digital Administration in 2019. By considering the legal principles on the promotion of fully automated administrative decisions under the Act on the Simplification and Organizational Rationalization of Andalusia, it seeks implications for Korea as well as examines the issue of ensuring due process for fully automated administrative decisions. In particular, we discuss the application of the legal reservation principle to fully automated administrative actions by artificial intelligence algorithms, procedural guarantees and procedural fairness for fully automated administrative decisions, transparency and explainability, and algorithmic impact assessment.National Public Law Review00The application of the legal reservation principle to fully automated administrative actions by artificial intelligence algorithms, procedural guarantees and procedural fairness for fully automated administrative decisions, transparency and explainability, and algorithmic impact assessment are discussed.National Public Law Review['Nam Wook Kim']2024-02-28T00:00:00https://www.semanticscholar.org/paper/8fb588992ccc3fa1043ebdea9d7f560efe220d0f 8a989147f1308910aee6680e21f258f2214226d9Background and aims With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer. Materials and Methods We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application. Results We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were “medical image analysis for diagnosis” (37%), “diagnostic or prognostic biomarkers modeling & bioinformatics” (19%), and “genomic or molecular analysis” (18%). Conclusion Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.Frontiers in Oncology400Investigation of publication trends and topics in AI-related publications in liver cancer reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade.Frontiers in Oncology['M. S. Rezaee-Zavareh', 'Naomy Kim', 'Yee Hui Yeo', 'Hyunseok Kim', 'Jeong Min Lee', 'C. Sirlin', 'B. Taouli', 'R. Saouaf', 'Ashley M. Wachsman', 'Mazen Noureddin', 'Zhiping Wang', 'Jason Moore', 'Debiao Li', 'Amit G. Singal', 'Ju Dong Yang']2024-02-28T00:00:00https://www.semanticscholar.org/paper/8a989147f1308910aee6680e21f258f2214226d9 c960680d945d0ef5b65cbff721330501baddaa8aWith the advent of artificial intelligence and sources like ChatGPT, students’ assessment and evaluation have become incredibly challenging. It has become an uphill task for teachers to ascertain the reliability of submitted assignments and other formative assessments. Because these text generators can draft articles, make summaries, and even write codes. These text generators, at one end, have supplemented the efforts of the teachers to develop quality content and, at the other end, have equally benefitted the students to solve the assignments and quizzes. How can teachers assess such unethical attempts? And what do they think about it? This study is aimed at exploring the challenges faced by the teachers of online/distance education institutions in the availability of artificially intelligent text generators. Qualitative research methodology has been used in this study. A grounded theory approach, as prescribed by Gioia, is employed. This methodology has been preferred because the data structure can reflect the whole research. Data have been collected from 20 teachers at higher education institutes involved in distance/online learning and classroom teaching using semi-structured interviews. Subjects have been selected using a purposive sampling technique. Results show that AI text generators have raised concerns among teachers in the evaluation of formative assessments. Students are using such text generators to solve the assignments and other activities given to them. However, there are some websites and software (like Turnitin) that can assess whether an assignment has been written using an AI text generator. However, these AI detectors are in their infancy stage and do need more precision. Teachers fear that such AI can take away the creativity and writing skills of students, and this technology might handicap them. This study has highlighted the concerns of academicians that need to be addressed by educationists.Journal of Asian development studies00The challenges faced by the teachers of online/distance education institutions in the availability of artificially intelligent text generators are explored and concerns of academicians that need to be addressed by educationists are highlighted.Journal of Asian Development Studies['Muhammad Zaheer', 'Saba Munir', 'Syeda Narjis Sherazi']2024-02-28T00:00:00https://www.semanticscholar.org/paper/c960680d945d0ef5b65cbff721330501baddaa8a 10fd37dc1372a6434e730c982acdcc177801707dNumber of digital banking transactions Indonesia based on katadata.com in August 2023 grew by 169,9% throughout the last five years in five years and the amount of third-party banking funds in Indonesia increased by 12.3% according to Indonesian Banking Statistics data for 2020-2022. Number of digital transactions at Bank Maybank Indonesia in 2022 increased by 28% and the number of accounts increased in 2022 by 34%, however the amount of funds raised by Bank Maybank Indonesia decreased by 1.19%.  It is alleged that the acquisition of accounts is not followed by customers who retain. This study was conducted to explore the effect of artificial intelligence banking and personal interaction quality on customer retention with customer satisfaction as an intervening variable. The research sample was 215 customer respondents using questionnaires and interviews. The analysis method uses Structural Equation Models Partical Least Square (SEMPLS) based on Smart Partial Least Square (Smart PLS) 3.2.9. The results of the study are artificial intelligence banking has a significant effect on customer retention, personal intelligence banking does not have a significant effect on customer retention. When mediated by customer satisfaction, artificial intelligence banking still has a significant effect on customer retention and personal intelligence banking has a significant effect on customer retention through customer satisfaction as an intervening variable.  Ekspektra : Jurnal Bisnis dan Manajemen200Artificial intelligence banking has a significant effect on customer retention, personal intelligence banking does not have a significant effect on customer retention and when mediated by customer satisfaction, artificial intelligence banking still has a significant effect on customer retention.Ekspektra : Jurnal Bisnis dan Manajemen['Chintasi Angreani¹', 'Nur Afifah²', 'Nur Afifah']2024-02-28T00:00:00https://www.semanticscholar.org/paper/10fd37dc1372a6434e730c982acdcc177801707d 2a2be86d49a0cf1742d725471c06d6aa884b3269Artificial Intelligence (AI) has emerged as a transformative force in the field of stock broking, revolutionizing traditional trading strategies and reshaping financial markets. This systematic review delves into the diverse array of AI-driven strategies employed in stock broking and assesses their outcomes, shedding light on the evolving landscape of algorithmic trading. The study encompasses a comprehensive analysis of various AI models, including machine learning algorithms, deep neural networks, and natural language processing techniques, that have been harnessed to analyze market data, predict stock movements, and optimize trading decisions. By synthesizing existing literature, the review offers insights into the effectiveness and limitations of these strategies, providing a nuanced understanding of their impact on market dynamics. Key findings reveal that AI applications in stock broking exhibit a wide spectrum of approaches, ranging from predictive modeling for price forecasting to sentiment analysis for gauging market sentiment. The review also explores the integration of reinforcement learning in algorithmic trading, highlighting the adaptive nature of AI systems in responding to dynamic market conditions. Furthermore, the outcomes of AI-driven strategies are evaluated in terms of risk management, profitability, and overall market efficiency. The review identifies trends indicating increased efficiency and reduced human biases, but also acknowledges challenges related to model interpretability, ethical considerations, and the potential for algorithmic-driven market volatility. This systematic review contributes to the evolving discourse on the role of AI in stock broking, offering a holistic examination of strategies and outcomes. As financial markets continue to embrace technological advancements, understanding the nuances of AI applications becomes paramount for market participants, regulators, and researchers alike. This study serves as a valuable resource for stakeholders seeking to navigate the complex interplay between artificial intelligence and the stock broking landscape.World Journal of Advanced Research and Reviews510This systematic review delves into the diverse array of AI-driven strategies employed in stock broking and assesses their outcomes, shedding light on the evolving landscape of algorithmic trading.World Journal of Advanced Research and Reviews['Andrew Ifesinachi', 'Noluthando Zamanjomane Mhlongo', 'Titilola Falaiye', 'Andrew Ifesinachi Daraojimba', 'Odeyemi Olubusola', 'Adeola Olusola Ajayi-Nifise']2024-02-28T00:00:00https://www.semanticscholar.org/paper/2a2be86d49a0cf1742d725471c06d6aa884b3269 508afe1302cced34521d4cfe8b870d00c326c45bNeurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties—Stroke and Epilepsy in particular—clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence [AI] can predict the expected neurological impairment from an AIS [Acute Ischemic Stroke], the possibility of ICH [IntraCranial Hemorrhage] expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.Brain Science460Artificial intelligence holds promise as a tool to ease a neurologist’s daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies.Brain Sciences['Smit P. Shah', 'John D. Heiss']2024-02-28T00:00:00https://www.semanticscholar.org/paper/508afe1302cced34521d4cfe8b870d00c326c45b e3e079a28db14f9fd277a1a211f5263d453b890cThis paper analyzes the causes, consequences, and logic of surveillance capitalism, delineating how behavioral surplus became the latest form of accumulation and questioning its ethical, legal, and material implications. The purpose of this project is to provide a decisively human response to an otherwise reductive, totalizing political economic system that uses equally reductive technology. Using history, political economy, and media ethics, it shows how surveillance capitalists use artificial intelligence (AI) to disrupt the privacy necessary for identity work and distort the moral autonomy necessary for democratic worldmaking. Exploiting human psychology and emotional vulnerabilities, surveillance capitalists interfere with our ability to become better versions of our personal and collective selves. We must therefore reject surveillance capitalism and embrace a more inconclusive understanding of democracy informed by care. While experts and technocrats can endlessly debate the potential outcomes and possibilities, the challenges of AI and an abusive surveillance capitalist system must ultimately be answered by a caring citizenry with equally resilient social institutions.Journal of Communication Inquiry210Journal of Communication Inquiry['Joseph Jones']2024-02-28T00:00:00https://www.semanticscholar.org/paper/e3e079a28db14f9fd277a1a211f5263d453b890c 05f850ce766335b624fc161d54873c032a78c060Formulation of the problem. This research explores the possibilities and peculiarities of employing artificial intelligence in general secondary education. A retrospective analysis of human utilization of artificial intelligence is conducted. Emphasis is placed on the relevance and significance of aligning the education system with contemporary demands and implementing new technological solutions. The focus is on the correctness of queries to systems and the necessity of analyzing work outcomes. Materials and methods. Data collection and processing were conducted during professional development courses for education professionals in the Chernihiv region (Ukraine) through pedagogical observation of course participants and their work with AI. Throughout the study, 34 training sessions were conducted, involving 748 teachers teaching various subjects. Among the showcased cases of AI usage, the most demanded queries among teachers are presented. Results. Examples of text analysis using the generative system ChatGPT 3.5 are provided. The obtained results are analyzed. The findings emphasized the necessity of clearly formulating queries and providing comprehensive input data and complete texts for analysis. In such cases, the artificial intelligence system can provide more accurate responses. This study demonstrated that the system handles this task quite successfully when providing the text of a program code and requesting commentary. However, solving mathematical problems poses specific difficulties, and obtaining the correct answer is only sometimes achievable. That reiterates the critical approach toward the system's obtained work outcomes. Conclusions. ChatGPT 3.5 can correctly analyze well-known facts and provide accurate information about them, but if the facts are local, the system cannot cope with the task. Simple math problems and tasks are performed correctly, but more complex ones already cause difficulties. It is essential not only to write prompts correctly but also to present input data. Such tasks as paraphrasing, translation, and changing the text's tone demonstrate modern AI systems' capabilities. ChatGPT 3.5 can become a good teacher's assistant or assistant for preparing various documentation. Requests for help in writing lesson plans, preparation of educational activities, formation of project topics, and technological maps of projects are carried out by the system at a high level.Physical and Mathematical Education90It is demonstrated that the system handles this task quite successfully when providing the text of a program code and requesting commentary, but solving mathematical problems poses specific difficulties, and obtaining the correct answer is only sometimes achievable, reiterating the critical approach toward the system's obtained work outcomes.Physical and Mathematical Education['Dmytro Pokryshen']2024-02-28T00:00:00https://www.semanticscholar.org/paper/05f850ce766335b624fc161d54873c032a78c060 1d1f55ba431db5f17b29bdff4c1771240092a594Artificial intelligence technologies are of increasing interest in the field of medicine and are one of the key areas for the digital transformation of healthcare . According to a number of experts, medical professionals and digital technology developers, the use of medical devices equipped with artificial intelligence technologies will raise healthcare to a high level, which will lead to improved clinical decision-making, high-quality analysis of digital images, prediction and control of the correctness of the prescribed treatment .However, failures associated with the use of medical devices equipped with artificial intelligence systems can have serious consequences for both clinical outcomes and patients . These consequences could undermine public confidence in artificial intelligence technologies and health care institutions in general . Given the certain novelty of technological solutions, data on the clinical efficacy and safety of products equipped with artificial intelligence are currently considered insufficient .This publication raises two important issues . The first part of the study describes the main physical, social and mental characteristics (properties) of patients that increase the likelihood that they will be victimized in the event of a crime situation in innovative health care services . The second part of the study identifies the risks of using AI technologies in health care that are of greatest concern to both patients and those exploiting innovative technologies .Victimology00The main physical, social and mental characteristics of patients that increase the likelihood that they will be victimized in the event of a crime situation in innovative health care services are described.Victimology['A. A. Shutova']2024-02-28T00:00:00https://www.semanticscholar.org/paper/1d1f55ba431db5f17b29bdff4c1771240092a594 572ebd04b1c73c1faa66f0b1f922ce6c0ebc351f The main purpose of writing this article was to show how disruptive technologies, including artificial intelligence and machine learning, can be used in military operations. We also covered how the UN and other international organisations have begun to regulate this rapidly exploding field from a policy perspective. We presented some of the technologies that use AI and made suggestions for their general military application and how these technologies can be used in peacetime operations. Artificial intelligence and intelligent devices can also bring enormous benefits in the areas of command and control systems, reconnaissance and intelligence activities. We also presented issues and dilemmas related to developments in this direction, such as ethical, liability, security and information protection problems and dilemmas.Land Forces Academy Review10It is shown how disruptive technologies, including artificial intelligence and machine learning, can be used in military operations and how the UN and other international organisations have begun to regulate this rapidly exploding field from a policy perspective.Land Forces Academy Review['Imre Négyesi']2024-02-28T00:00:00https://www.semanticscholar.org/paper/572ebd04b1c73c1faa66f0b1f922ce6c0ebc351f 5320ab4138bcd141ce34a56e8d70d2d959993effOne of the more controversial uses of artificial intelligence (AI) in the petroleum industry has been in technological advancement. The gas business generates data on a constant basis from several operational procedures. The gas sector is now very concerned about recording these data and using them appropriately. Making decisions based on inferential and predictive data analytics facilitates timely and accurate decision-making. The gas business is seeing a significant increase in the use of data analytics for decision-making despite numerous obstacles. Considerable progress has been made in the aforementioned field of study. With the use of artificial intelligence (AI) and machine learning (ML) techniques, many complicated issues may now be resolved with ease. This study, which looks at artificial intelligence applications in the natural gas sector, collected its data from numerous sources between 2005 and 2023. The current work might offer a technical framework for selecting pertinent technologies that will enable efficient information extraction from the massive amount of data produced by the gas industry.International Journal of Engineering and Advanced Technology200This study, which looks at artificial intelligence applications in the natural gas sector, collected its data from numerous sources between 2005 and 2023 and might offer a technical framework for selecting pertinent technologies that will enable efficient information extraction from the massive amount of data produced by the gas industry.International Journal of Engineering and Advanced Technology['Siddhartha Nuthakki', 'Chinmay Shripad Kulkarni', 'Satish Kathiriya', 'Yudhisthir Nuthakki']2024-02-28T00:00:00https://www.semanticscholar.org/paper/5320ab4138bcd141ce34a56e8d70d2d959993eff 2fba87886961b502416dace3cdaf44387629c134The healthcare sector is faced with challenges due to a shrinking healthcare workforce and a rise in chronic diseases that are worsening with demographic and epidemiological shifts. Digital health interventions that include artificial intelligence (AI) are being identified as some of the potential solutions to these challenges. The ultimate aim of these AI systems is to improve the patient’s health outcomes and satisfaction, the overall population’s health, and the well-being of healthcare professionals. The applications of AI in healthcare services are vast and are expected to assist, automate, and augment several healthcare services. Like any other emerging innovation, AI in healthcare also comes with its own risks and requires regulatory controls. A review of the literature was undertaken to study the existing regulatory landscape for AI in the healthcare services sector in developed nations. In the global regulatory landscape, most of the regulations for AI revolve around Software as a Medical Device (SaMD) and are regulated under digital health products. However, it is necessary to note that the current regulations may not suffice as AI-based technologies are capable of working autonomously, adapting their algorithms, and improving their performance over time based on the new real-world data that they have encountered. Hence, a global regulatory convergence for AI in healthcare, similar to the voluntary AI code of conduct that is being developed by the US-EU Trade and Technology Council, would be beneficial to all nations, be it developing or developed.Healthcare630A global regulatory convergence for AI in healthcare, similar to the voluntary AI code of conduct that is being developed by the US-EU Trade and Technology Council, would be beneficial to all nations, be it developing or developed.Healthcare['K. Palaniappan', 'Elaine Yan Ting Lin', 'Silke Vogel']2024-02-28T00:00:00https://www.semanticscholar.org/paper/2fba87886961b502416dace3cdaf44387629c134 69fde814f0e37827635ce3a0ed73741f94297327In this transformative era of organ transplantation, integrating Smart Match and artificial intelligence (AI) emerges as a pivotal advancement, revolutionizing organ allocation processes. Smart Match employs AI algorithms, enhancing organ matching precision and optimizing transplantation outcomes. Leveraging machine learning addresses complexities in donor-recipient pairing, immunosuppression management, and post-operative care, promising to minimize waitlist mortality and improve patient wellbeing. The multifaceted potential of Smart Match lies in its ability to not only streamline current practices but also pave the way for future innovations in solid organ transplantation. As technology continues to evolve, the collaboration between Smart Match and AI exemplifies a beacon of progress, promising increased efficiency, equitable organ distribution, and improved patient care. This article delves into the paradigm shift facilitated by Smart Match and AI, emphasizing their transformative impact on the landscape of organ allocation and patient outcomes.Frontiers in Artificial Intelligence370This article delves into the paradigm shift facilitated by Smart Match and AI, emphasizing their transformative impact on the landscape of organ allocation and patient outcomes.Frontiers in Artificial Intelligence['Rajkiran Deshpande']2024-02-28T00:00:00https://www.semanticscholar.org/paper/69fde814f0e37827635ce3a0ed73741f94297327 69f9e6b7fd46cf1e472a2eb4ddc15fdd2b07cad3В статье рассмотрены современные технологии искусственного интеллекта, применяемые в управлении производственными системами. Отмечается, что в российской экономике данная область находится на начальной стадии развития, и используемые технологии требуют систематизации и классификации. Приведены основные области применения технологий искусственного интеллекта на производстве, такие как оптимизация плана производства, моделирование производственных сценариев, прогнозирование состояния производственной системы, оптимальное управление по обратной связи и предиктивная диагностика оборудования. Описаны основные проблемы, возникающие при использовании систем искусственного интеллекта, требующие вмешательства человека: недостаток данных и их качество, несовершенство нормативно-правовой базы, безопасность данных, этические вопросы и технические трудности. The article discusses modern artificial intelligence technologies used in the management of production systems. It is noted that in the Russian economy, this area is at an early stage of development, and the technologies used require systematization and classification. The main areas of application of artificial intelligence technologies in production are presented, such as optimization of the production plan, modeling of production scenarios, forecasting the state of the production system, optimal feedback control and predictive diagnostics of equipment. In conclusion, the main problems that arise when using artificial intelligence systems that require human intervention are described: lack of data and their quality, imperfection of the regulatory framework, data security, ethical issues and technical difficulties.Regional and Branch Economy60Regional and Branch Economy['Е.С. Митяков', 'Я.В. Козлов']2024-02-28T00:00:00https://www.semanticscholar.org/paper/69f9e6b7fd46cf1e472a2eb4ddc15fdd2b07cad3 0f1214a325d82404e384e5e33e014fb74c291e62Background: In recent years, AI has significantly revolutionized the healthcare industry, with deep learning applications being used to identify various illnesses, evaluate cancerous lesions, and determine stroke onset. AI-based systems also have been applied in ophthalmology to address leading eye diseases. The aim: This study aims to determine the role of artificial intelligence (AI) in eye disease. Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and ScienceDirect, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done. Results: In the PubMed database, the results of our search brought up 157 articles, whereas the results of our search on ScienceDirect brought up 256 articles. The results of the search conducted by title screening yielded a total of 34 articles for PubMed and 28 articles for ScienceDirect. We compiled a total of 16 papers, 10 of which came from PubMed and 6 of which came from ScienceDirect. We excluded 4 review articles, 2 non-full text articles, 3 articles having insufficient outcomes, and 1 article having ineligible subjects. In the end, we included six research that met the criteria.   Conclusion: Our systematic study suggests that AI has a role in the diagnosis or screening of eye disease. AI can be a valuable tool for diabetic retinopathy (DR) screening, glaucoma screening, myopia screening, and diagnosis of dry eye syndrome.Journal of advanced research in Medical and Health science210A systematic study suggests that AI has a role in the diagnosis or screening of eye disease and can be a valuable tool for diabetic retinopathy screening, glaucoma screening, myopia screening, and diagnosis of dry eye syndrome.Journal of Advanced Research in Medical and Health Science (ISSN 2208-2425)['Arwan Firmansyah', 'I. Made', 'Surya Dinajaya', 'Muhammad Arfan', 'A. Utama']2024-02-28T00:00:00https://www.semanticscholar.org/paper/0f1214a325d82404e384e5e33e014fb74c291e62 06f3f5d2c9bcf6cebc7a389f20959e191918f473Digital, led by ChatGPT, is triggering innovative changes in all areas, including politics, economy, society, and culture. It is the so-called era of digital deepening. Digital innovation brings infinite possibilities and benefits to mankind, but artificial intelligence (AI) technology or research field for realizing human cognitive, reasoning, and judgment on a computer. In this paper, we will use a mixture of artificial intelligence and AI. It is also raising various issues that did not exist in the past, such as legal personality and tort liability, product copyright, and job change. Along with the development of artificial intelligence, what is being raised is the issue of ethics of artificial intelligence. For example, if a biased algorithm such as gender discrimination or racial discrimination is inserted by an artificial intelligence robot developer, artificial intelligence robots will also have this biased idea. In this respect, it is necessary to properly establish not only the technical research of artificial intelligence but also the ethical aspect. Accordingly, major institutions around the world are making efforts to develop artificial intelligence in the right direction for mankind by establishing 23 principles called 'Asiloma AI Principles' in 2017. These issues have complex and diverse interests and are difficult to resolve due to the lack of a clear normative system, so it is necessary to improve social acceptance through the establishment of a new normative system (order). This article examines the prerequisites for establishing a practical new digital order and normative system (Artificial Intelligence's legal personality and tort responsibility, artificial intelligence's fairness and ethics principles and equality principles, artificial intelligence and judgment, artificial intelligence and jobs, artificial intelligence and copyrights), and examines the constitution and administrative law to respond to artificial intelligence. It is always important to keep in mind that “artificial intelligence cannot take precedence over the constitution and basic rights” when it comes to normative responses. Regarding the relationship between the state and science and technology, the Constitution stipulates that “the state shall endeavor to develop the national economy through innovation of science and technology and the development of information and manpower (Article 127), and the rights of authors, inventors, and science and technicians are protected by law (Article 22).” It is constitutionally declared that the state should actively plan, form, and lead in a certain direction for the promotion of science and technology directly. In relation to these constitutional provisions, the establishment and implementation of science and technology policies can be seen as an important duty given to the Republic of Korea as a democratic welfare state. Apart from private autonomy, it is urged to prepare a basic artificial intelligence law to protect the safety and basic rights of the people affected by artificial intelligence.National Public Law Review00The prerequisites for establishing a practical new digital order and normative system (Artificial Intelligence's legal personality and tort responsibility, artificial intelligence's fairness and ethics principles and equality principles, artificial intelligence and judgment, artificial intelligence and jobs, artificial intelligence and copyrights), and examines the constitution and administrative law to respond to artificial intelligence are examined.National Public Law Review['Byeongrok Kim']2024-02-28T00:00:00https://www.semanticscholar.org/paper/06f3f5d2c9bcf6cebc7a389f20959e191918f473 70e85bb93d789cc5f6540f6534dc6a361cb66f7f   Relevance. Digitalization covers all spheres of our life, the pace of development of information technologies is accelerating – their importance in modern society is increasing. There is an active introduction of modern technologies into various business processes, including personnel management, which can change the human resource management system we are used to. The field of artificial intelligence development is not left behind, impressing the whole world with the pace of development in many areas, including work with personnel: recruitment, hiring and training.   The purpose of the article is to study the introduction of digital technologies in personnel management.   Objectives: to study the directions of artificial intelligence development in economics and management; to consider the history of the origin of the idea of such an application of artificial intelligence tools; to assess the interest in artificial intelligence in the field of human resource management; to consider the opinions of leading experts on the successes and prospects of the introduction of artificial intelligence in human resource management.   Methodology. In the course of scientific research, empirical, theoretical, statistical methods and methods of graphical representation were used.   Results. The theoretical aspects of the application of artificial intelligence technologies were studied; the popularity of this topic in science and business was shown. The experience of using artificial intelligence in human resource management of large global and Russian companies is summarized and the prospects for the development of this direction are shown. Examples of the positive effect of the introduction of artificial intelligence in the field of human resource management are shown.   Conclusions. The article emphasizes the relevance and importance of the topic under consideration, taking into account the latest trends, perspectives and views of leading analysts. The possibilities that artificial intelligence technologies open up in the field of human resources are considered.Proceedings of the Southwest State University. Series: Economics. Sociology. Management10The experience of using artificial intelligence in human resource management of large global and Russian companies is summarized and the prospects for the development of this direction are shown.Proceedings of the Southwest State University. Series: Economics. Sociology. Management['O. A. Polishchuk', 'P. A. Isaev', 'A. V. Fedorinov']2024-02-28T00:00:00https://www.semanticscholar.org/paper/70e85bb93d789cc5f6540f6534dc6a361cb66f7f 3f5c7040183d7c5b2ef166f4ea9014f7e2359532El análisis bibliométrico de la inteligencia artificial (IA) en el deporte revela una creciente tendencia en la investigación y aplicación de esta tecnología en este fenómeno social. En la última década, se ha observado un aumento significativo en el número de publicaciones científicas relacionadas con la inteligencia artificial y el deporte, lo que indica un gran interés en el tema. El objetivo de esta investigación fue analizar bibliométricamente los elementos de la inteligencia artificial en el deporte. La metodología utilizada fue la hermenéutica y el análisis de tres componentes fundamentales Autores, Revistas y Aportes (ARA) propuesta por los autores para la revisión bibliométrica. Se analizaron 1002 artículos científicos pertenecientes a las bases de datos Scopus (825), Science Direct (172) y Mendeley (5). Como criterios de inclusión en la investigación se tomaron dos: todos debían ser artículos científicos, en idioma español e inglés. Los principales resultados parten de la identificación de los principales autores, revistas y aportes que potencia la IA en el deporte, teniendo en cuenta las nuevas metodologías y tendencias de lo anterior. En conclusión, se define a la IA en el deporte como una herramienta que corrige errores, ayuda a la toma de decisiones, potencia nuevas estrategias de entrenamiento deportivo y en la competencia, ayuda a prevenir lesiones deportivas, a estudiar a los contrarios y potenciar escenarios deportivos de alta calidad. Palabras clave: Inteligencia artificial, entrenamiento deportivo, deporte moderno, análisis bibliométrico, metodología ARA. Abstract. The bibliometric analysis of artificial intelligence (AI) in sports reveals a growing trend in the research and application of this technology in this social phenomenon. In the last decade, there has been a significant increase in the number of scientific publications related to artificial intelligence and sports, indicating great interest in the topic. The objective of this research was to bibliometrically analyze the elements of artificial intelligence in sports. The methodology used was hermeneutics and the analysis of three fundamental components Authors, Journals and Contributions (ARA) proposed by the authors for the bibliometric review. 1002 scientific articles belonging to the Scopus (825), Science Direct (172) and Mendeley (5) databases were analyzed. Two criteria were taken as inclusion criteria in the research: all had to be scientific articles, in Spanish and English. The main results are based on the identification of the main authors, journals and contributions that enhance AI in sport, taking into account the new methodologies and trends of the above. In conclusion, AI in sports is defined as a tool that corrects errors, helps decision-making, enhances new sports training and competition strategies, helps prevent sports injuries, study opponents and enhance scenarios. high quality sports. Keywords: Artificial intelligence, sports training, modern sport, bibliometric analysis, ARA methodology.Retos241Retos['José Ramón Sanabria Navarro', 'W. N. Niebles Nuñez', 'Yahilina Silveira Pérez']2024-02-28T00:00:00https://www.semanticscholar.org/paper/3f5c7040183d7c5b2ef166f4ea9014f7e2359532 49690d8474d775e054c8c3ee53ff47eb0aaf660cIn an era where technological advancements are rapidly transforming industries, healthcare is the primary beneficiary of such progress [...]Algorithms110Algorithms['D. Chumachenko', 'Sergiy Yakovlev']2024-02-28T00:00:00https://www.semanticscholar.org/paper/49690d8474d775e054c8c3ee53ff47eb0aaf660c f4c43f55eca96cdbb992683f058ee893d99b600e The present work highlights how AI can enhance the personalized cosmetic experience based on the digitalization of make-up by consumers, the selection of perfect product characteristics and optimization of new cosmetic products on the basis of big data. Moreover, the different AI technologies applied in cosmetics have also been presented in a concise manner. Cosmetic companies are advancing and are expected to be even more advanced in the future. The present work could provide a new direction in the development of an AI algorithm approach for cosmetic companies’ development and for building a database for cosmetic applications. Current Cosmetic Science00Current Cosmetic Science['Harshita Mathur', 'Anurag Chaudhary', 'Devkant Sharma', 'Alok Sharma']2024-02-28T00:00:00https://www.semanticscholar.org/paper/f4c43f55eca96cdbb992683f058ee893d99b600e f8a02b8bd9bf739cf653e5c325cda79871514cfeJournal of The Korean Society of Water and Wastewater70Journal of the Korean Society of Water and Wastewater['Mincheol Kim', 'Youngho Park', 'Kwangtae You', 'Jongrack Kim']2024-02-28T00:00:00https://www.semanticscholar.org/paper/f8a02b8bd9bf739cf653e5c325cda79871514cfe 64afe172d0542f728d8835f8651e553280f3de03Mars, with its harsh and uncharted terrain, presents unique challenges for robotic exploration. Autonomous locomotion is a critical capability for robotic systems on the Martian surface, allowing them to traverse diverse landscapes, avoid obstacles, and accomplish scientific objectives efficiently. The AI-educated robot exhibits remarkable adaptability, successfully navigating through challenging terrains and environments while efficiently conserving energy. Additionally, the transfer of learned behaviours from simulation to the real-world robot validates the feasibility of this approach for future Mars missions. This research paper proposes a novel approach for teaching a robot to walk on Mars using AI and machine learning techniques. It demonstrates the effectiveness of the proposed approach in educating a robot to walk on Mars autonomously. The study focuses on the development of an autonomous walking algorithm for a robotic rover, leveraging state-of-the-art AI technologies. To achieve this, this paper employs deep reinforcement learning (DRL), which enables the robot to learn and adapt its locomotion strategies through interactions with its environment. A simulated Mars environment is constructed, emulating the unique challenges posed by the Martian landscape, including uneven terrain, loose soil, and rocks. This research presents a comparative analysis between previous findings from existing Mars exploration technologies, exemplified by missions like Curiosity and Perseverance, and a proposed mechanism for training a robot to autonomously walk on Mars using advanced AI. It represents a significant step toward enhancing the autonomy and versatility of robotic systems on Mars, contributing to the advancement of space exploration by enabling robots to explore, and investigate previously inaccessible regions of the Red Planet.International Conference on Computing for Sustainable Global Development270A novel approach for teaching a robot to walk on Mars using AI and machine learning techniques is proposed, which demonstrates the effectiveness of the proposed approach in educating a robot to walk on Mars autonomously.2024 11th International Conference on Computing for Sustainable Global Development (INDIACom)['Manisha Mittal', 'Prachi Dewan', 'Tanisha Panesar', 'Mahender Singh']2024-02-28T00:00:00https://www.semanticscholar.org/paper/64afe172d0542f728d8835f8651e553280f3de03 8b2bf5922f2a119cec72adb84d2221ada1b75957Revista Española de Geriatría y Gerontología360There is no difference between male and female older adults in the readiness for online learning and artificial intelligence anxiety levels, and it is moderate in both genders.Revista espanola de geriatria y gerontologia['E. G. Kabul', 'B. B. Calık', 'Nadir Tayfun Ozcan', 'Suleyman Gursoy']2024-02-28T00:00:00https://www.semanticscholar.org/paper/8b2bf5922f2a119cec72adb84d2221ada1b75957 7d45c7fe3ad150d2db119e422e5789dbd7b56f2fFlorence Nightingale Journal of Nursing00Florence Nightingale Journal of Nursing['Polat Göktaş', 'Aycan Küçükkaya', 'Pelin Karaçay']2024-02-28T00:00:00https://www.semanticscholar.org/paper/7d45c7fe3ad150d2db119e422e5789dbd7b56f2f c3d1a9822a6960c90882a98365c9bfd12f1fb1eaPostdigital Science and Education230Postdigital Science and Education['Michael A. Peters', 'P. Jandrić', 'Benjamin J. Green']2024-02-28T00:00:00https://www.semanticscholar.org/paper/c3d1a9822a6960c90882a98365c9bfd12f1fb1ea af21759d16a5909575f8085b2003d973bf56d60aNursing management170Nursing management['Leisha Buller', 'Ashley Hodo', 'Kimberly Williams']2024-02-28T00:00:00https://www.semanticscholar.org/paper/af21759d16a5909575f8085b2003d973bf56d60a 0cd537b401247d581c17da94bd08a0d544fc76bcScientific Journal of Applied Social and Clinical Science00Scientific Journal of Applied Social and Clinical Science['Adriana Barrea', 'Camila Henning Salmoria']2024-02-28T00:00:00https://www.semanticscholar.org/paper/0cd537b401247d581c17da94bd08a0d544fc76bc c82ae6608e710ba09629fc903dc6b96afbd81763Journal of the Korean Association of Information Education00Journal of The Korean Association of Information Education['Jiwon Lim', 'Yungsik Kim']2024-02-28T00:00:00https://www.semanticscholar.org/paper/c82ae6608e710ba09629fc903dc6b96afbd81763 8df3e1be489064066160b82f3945b04f9f8e7d28Annales de dermatologie et de vénéréologie00Annales de dermatologie et de venereologie['S. Karaa']2024-02-28T00:00:00https://www.semanticscholar.org/paper/8df3e1be489064066160b82f3945b04f9f8e7d28 ce2938c1b63651cdfb7b9da8ccee3e5c2005cffeYonsei Law Journal00YONSEI LAW JOURNAL['Juhee Eom']2024-02-28T00:00:00https://www.semanticscholar.org/paper/ce2938c1b63651cdfb7b9da8ccee3e5c2005cffe edd447a2cdf182a2e4aae55478400a4c2e3f3c33International Relations and Diplomacy00International Relations and Diplomacy['Anthony Victor Onwuegbuzia']2024-02-28T00:00:00https://www.semanticscholar.org/paper/edd447a2cdf182a2e4aae55478400a4c2e3f3c33 9dd47a1184cce8cbc7f9129ded2ab8927558db16Journal of Marketing Analytics660Journal of Marketing Analytics['Burak Cankaya', 'Robin Roberts', 'Stephanie Douglas', 'Rachel Vigness', 'Asil Oztekin']2024-02-28T00:00:00https://www.semanticscholar.org/paper/9dd47a1184cce8cbc7f9129ded2ab8927558db16 60879b33593d160deff247a2ba852afdce1e66afJournal of the Korean Association of Information Education00Journal of The Korean Association of Information Education['Youngki Park', 'Hoseong Lee']2024-02-28T00:00:00https://www.semanticscholar.org/paper/60879b33593d160deff247a2ba852afdce1e66af e9b202d32a9097725133e701eb621c6f6b0c061cThe meaning of happiness in human life depends on what each individual thinks of as happiness and what one pursues due to differences in personalities and values. An important value and ideology of modern constitution is the guarantee of human rights to ensure that all human beings can live the life they want. Therefore, the meaning of the right to pursue happiness can be seen as very important not only at the time of the modern human rights declaration, but also in modern society, where technological breakthroughs and artificial intelligence are emerging at ever-higher rates. The role of law is also very important in an intelligent information society, and it is also necessary to find out what fundamental rights the state should protect more actively through the interpretation and guidelines of the Constitution and include the necessary legislative direction or outline in the constitutional amendment. It is crucial to prepare for risks caused by the emergence of new technologies and the development of an intelligent information society, to protect the fundamental rights and safety of the people, and to review constitutional countermeasures. If successful, it may be possible to establish a normative framework for pursuing safe and sustainable development and human happiness.The Center for Asia and Diaspora00It is crucial to prepare for risks caused by the emergence of new technologies and the development of an intelligent information society, to protect the fundamental rights and safety of the people, and to review constitutional countermeasures.The Center for Asia and Diaspora['F. f']2024-02-28T00:00:00https://www.semanticscholar.org/paper/e9b202d32a9097725133e701eb621c6f6b0c061c e61f6d0276ecb856b655934623ee1d405874aabfThis article examines the portrayal of female artificial intelligences (AIs) in Hollywood's science fiction (SF) films, with a primary focus on Ex Machina. Employing feminist and psychoanalytic perspectives, the study critically reassesses how socio-cultural expectations and patriarchal desires shape the cinematic representation of female AIs. It seeks to address a nuanced gap by revealing the unconscious psychological forces that mold gendered imprints within technology and analyzing how (female) AIs, positioned as posthuman beings, not only mirror but engage in the construction of femininity for the fulfillment of male fantasies and the subversion of male dominance, accomplished through the strategic manipulation of “artificial skin” and gynoid bonding. Finally, this paper aims to contribute to the broader discourse on gender dynamics surrounding female AIs and their power relations with humanity in the cinematic SF. It explores the narrative functions of intelligent fembots, which may disrupt patriarchal narratives both in reel life and, perhaps, real life.Frontiers in Communication400The narrative functions of intelligent fembots may disrupt patriarchal narratives both in reel life and, perhaps, real life and the broader discourse on gender dynamics surrounding female AIs and their power relations with humanity in the cinematic SF is explored.Frontiers in Communication['Yongde Dai']2024-02-28T00:00:00https://www.semanticscholar.org/paper/e61f6d0276ecb856b655934623ee1d405874aabf 8934e8f7772a74f15f52272540db9fe4bb943fadMathematical Modelling of Engineering Problems01Mathematical Modelling of Engineering Problems['Issamar Issa', 'Batyr Orazbayev', 'Raigul Tuleuova', 'V. Makhatova']2024-02-27T00:00:00https://www.semanticscholar.org/paper/8934e8f7772a74f15f52272540db9fe4bb943fad b950d78b8421cc01d35203cddba7c5fc28441adfDigital Society200This introduction provides a concise overview of the individual contributions, grouping them into four thematic strands: (a) On Democracy, Regulation, and (Public) Legitimation in an AI-powered World, (b) On the Challenge of Protecting Privacy in Today’s Data Economy, (c) On Solidarity, Inclusivity, and inclusivity, and in AI Design, and (d) Reconsidering AI Ethics.Digit. Soc.['Judith Simon', 'Gernot Rieder', 'Jason Branford']2024-02-27T00:00:00https://www.semanticscholar.org/paper/b950d78b8421cc01d35203cddba7c5fc28441adf 0ca792786a60bf22749d8e47101d50e13d07c542The paper delves into the influence of artificial intelligence (AI) on business decision-making. By examining this phenomenon's technical, strategic, and ethical dimensions, the study seeks to unravel the implications that artificial intelligence integration brings to decision-making. The study conducted a comprehensive analysis to investigate the perceptions and experiences of individuals regarding integrating artificial intelligence in business decision-making. The study involved a detailed examination of demographic characteristics, artificial intelligence awareness, implementation status, perceived impact on decision-making speed and accuracy and ethical considerations related to bias in artificial intelligence-driven decision-making. The findings show that the gender and age distribution of respondents influence the perception and use of artificial intelligence in business decision-making. And artificial intelligence-driven decisions are dominant in the healthcare sector. Furthermore, artificial intelligence awareness and implementation indicated a generally positive outlook, with significant acknowledgement and familiarity among respondents. There is a positive perception of artificial intelligence making decisions faster with a positive contribution to the accuracy of business decisions. However, there is a record of some biases in artificial intelligence-driven decision-making. This highlights a significant concern in the fair and equitable application of artificial intelligence algorithms. This shows the importance of addressing biases to ensure ethical decision-making. The hypothesis testing sought to ascertain whether the incorporation of artificial intelligence is contingent on the accuracy of business decisions. The chi-square test results indicated insufficient evidence to propose a noteworthy relationship between the integration of artificial intelligence and decision accuracy. This implies that organizations should explore additional factors influencing decision accuracy, recognizing that artificial intelligence integration alone may not be the sole determinant.Mechanism of an economic regulation191The study conducted a comprehensive analysis to investigate the perceptions and experiences of individuals regarding integrating artificial intelligence in business decision-making, and found that the gender and age distribution of respondents influence the perception and use of artificial intelligence in business decision-making.Mechanism of an economic regulation['О. Кубатко', 'Стенлі Озімс', 'В.І. Вороненко']2024-02-27T00:00:00https://www.semanticscholar.org/paper/0ca792786a60bf22749d8e47101d50e13d07c542 69b0c55f17ff6be9bd211ee12322c14825018670Environmental science and pollution research international460Environmental science and pollution research international['Xiaohong Xiang', 'Wenting Wang']2024-02-27T00:00:00https://www.semanticscholar.org/paper/69b0c55f17ff6be9bd211ee12322c14825018670 f293b0d4e07190686576776530bf9e3664fe5c58The authors examine the issues of public regulation of regional development in this article. The reasons for regulation are mentioned – a balanced distribution of resources, economic development, the need for local fiscal and investment policies, as well as the regulation of trade and market relations. Measures of public regulation are being considered. Among them, for example, investment in infrastructure to stimulate a region’s development, as well as specialization. Such measures of state regulation contribute to ensuring social justice. For example, creating economic zones or zones of priority development in underdeveloped regions will attract investment and create jobs. That will reduce the level of inequality between different regions of the country. The conclusion is as follows: the regulation of state development is important for the growth of the region’s economic development and correcting the imbalance in development between regions.Economics00Economics: time realities['Oleksandr Balan', 'Maksym Voitenko', 'Dmitro Pulcha']2024-02-27T00:00:00https://www.semanticscholar.org/paper/f293b0d4e07190686576776530bf9e3664fe5c58 cbb80e8ef9f4ec5f7f44f91c600db5324c114b84Les relations professionnelles ou personnelles se construisent tout autant qu’elles se déconstruisent. C’est la notion de liance. Mais si le faire relation est un mécanisme « invisible », pour développer du relationnel, il faut s’appuyer sur un certain nombre de techniques que cet article va essayer de décrypter. Comme la médiation familiale est une démarche permettant de recréer du relationnel, nous avons voulu la comprendre. C’est ainsi qu’en rencontrant des médiatrices familiales, nous avons défini un certain nombre de ces techniques génératrices de liance (et donc de reliance). C’est à partir de la compréhension de ces démarches que nous proposons d’appliquer à la régulation sociale, la triangulation. Cette notion constitue une dimension centrale pour faciliter et réguler la liance entre les personnes.Management &amp; Avenir30Management &amp; Avenir['F. Silva']2024-02-27T00:00:00https://www.semanticscholar.org/paper/cbb80e8ef9f4ec5f7f44f91c600db5324c114b84 7689645eaa7c91608d982cb47159081f11001c58Money, as it is traditionally known, is undergoing its own digital revolution. Time will show whether we are facing a true paradigm shift in the essence of money or just mere modifications in the supports of representation. If the latter is linked to trust, the essence of money is related to trust in the issuer and the social consensus for its use and extension. The arrival of cryptoassets in the financial system has been a recurring topic of discussion in recent years.PAAKAT: Revista de Tecnología y Sociedad00Time will show whether the authors are facing a true paradigm shift in the essence of money or just mere modifications in the supports of representation, if the latter is linked to trust.Paakat: Revista de Tecnología y Sociedad['David López Jiménez']2024-02-27T00:00:00https://www.semanticscholar.org/paper/7689645eaa7c91608d982cb47159081f11001c58 affbd18275f8746b837708e169ffa45b8aa82284In the intricate regulatory landscape of today, financial institutions encounter formidable hurdles in meeting reporting mandates while upholding operational efficacy. This study delves into the transformative capacity of Artificial Intelligence (AI) and Machine Learning (ML) technologies in refining regulatory reporting procedures. Through harnessing AI/ML, entities can streamline data aggregation, analysis, and submission, thus fostering enhanced compliance and operational efficiency. Key strategies for integrating AI/ML into regulatory reporting frameworks are discussed, encompassing data standardization, predictive analytics, anomaly detection, and automation. Furthermore, the paper explores the advantages, obstacles, and optimal approaches associated with deploying AI/ML solutions in regulatory reporting. Drawing on real-world illustrations and case studies, this study offers insights into how AI/ML technologies can redefine regulatory reporting practices, empowering financial institutions to adeptly navigate regulatory intricacies while optimizing resource allocation and decision-making processes.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402304Insight is offered into how AI/ML technologies can redefine regulatory reporting practices, empowering financial institutions to adeptly navigate regulatory intricacies while optimizing resource allocation and decision-making processes.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Harish Padmanaban']2024-02-27T00:00:00https://www.semanticscholar.org/paper/affbd18275f8746b837708e169ffa45b8aa82284 b24e82219eec127d94d4c6c467272765c3bd4b7fEuropean Journal of Law and Economics563European Journal of Law and Economics['Miriam C. Buiten']2024-02-27T00:00:00https://www.semanticscholar.org/paper/b24e82219eec127d94d4c6c467272765c3bd4b7f ba59d1f28d2e8abf726cdfe2b080bc27bec424a0Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult. As a result, practitioners make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 390 tools, we map the current ecosystem of available AI audit tools. While there are many tools designed to assist practitioners with setting standards and evaluating AI systems, these tools often fell short of supporting the accountability goals of AI auditing in practice. We thus highlight areas for future tool development beyond evaluation -- from harms discovery to advocacy -- and outline challenges practitioners faced in their efforts to use AI audit tools. We conclude that resources are lacking to adequately support the full scope of needs for many AI audit practitioners and recommend that the field move beyond tools for just evaluation, towards more comprehensive infrastructure for AI accountability.arXiv.org1032It is concluded that resources are lacking to adequately support the full scope of needs for many AI audit practitioners and recommend that the field move beyond tools for just evaluation, towards more comprehensive infrastructure for AI accountability.ArXiv['Victor Ojewale', 'Ryan Steed', 'Briana Vecchione', 'Abeba Birhane', 'Inioluwa Deborah Raji']2024-02-27T00:00:00https://www.semanticscholar.org/paper/ba59d1f28d2e8abf726cdfe2b080bc27bec424a0 71edbc0be3165496838872faef83fba44e27d091Recently, there has been a huge effort focused on developing highly efficient open source libraries to perform Artificial Intelligence (AI) related computations on different computer architectures (for example, CPUs, GPUs and new AI processors). This has not only made the algorithms based on these libraries highly efficient and portable between different architectures, but also has substantially simplified the entry barrier to develop methods using AI. Here, we present a novel methodology to bring the power of both AI software and hardware into the field of numerical modelling by repurposing AI methods, such as Convolutional Neural Networks (CNNs), for the standard operations required in the field of the numerical solution of Partial Differential Equations (PDEs). The aim of this work is to bring the high performance, architecture agnosticism and ease of use into the field of the numerical solution of PDEs. We use the proposed methodology to solve the advection-diffusion equation, the non-linear Burgers equation and incompressible flow past a bluff body. For the latter, a convolutional neural network is used as a multigrid solver in order to enforce the incompressibility constraint. We show that the presented methodology can solve all these problems using repurposed AI libraries in an efficient way, and presents a new avenue to explore in the development of methods to solve PDEs and Computational Fluid Dynamics problems with implicit methods.arXiv.org372A novel methodology to bring the power of both AI software and hardware into the field of numerical modelling by repurposing AI methods, such as Convolutional Neural Networks (CNNs), for the standard operations required in the field of the numerical solution of Partial Differential Equations (PDEs).ArXiv['Boyang Chen', 'C. Heaney', 'Christopher C. Pain']2024-02-27T00:00:00https://www.semanticscholar.org/paper/71edbc0be3165496838872faef83fba44e27d091 bfd653ee364713fa0bbc6ecd8f916f4a11f9e371 In recent years, metasurface, as a representative of micro- and nano-optics, have demonstrated a powerful ability to manipulate light, which can modulate a variety of physical parameters, such as wavelength, phase, and amplitude, to achieve various functions and substantially improve the performance of conventional optical components and systems. Artificial Intelligence (AI) is an emerging strong and effective computational tool that has been rapidly integrated into the study of physical sciences over the decades and has played an important role in the study of metasurface. This review starts with a brief introduction to the basics and then describes cases where AI and metasurface research have converged: from AI-assisted design of metasurface elements up to advanced optical systems based on metasurface. We demonstrate the advanced computational power of AI, as well as its ability to extract and analyze a wide range of optical information, and analyze the limitations of the available research resources. Finally conclude by presenting the challenges posed by the convergence of disciplines.Nanophotonics2472The advanced computational power of AI is demonstrated, as well as its ability to extract and analyze a wide range of optical information, and the limitations of the available research resources are analyzed.Nanophotonics['Yunlai Fu', 'Xuxi Zhou', 'Yiwan Yu', 'Jiawang Chen', 'Shuming Wang', 'Shining Zhu', 'Zhenlin Wang']2024-02-27T00:00:00https://www.semanticscholar.org/paper/bfd653ee364713fa0bbc6ecd8f916f4a11f9e371 7c81e4d21ff6911b9106ee28a91bae09c713f59eThe development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and reports of criminal attacks and theft. Consequently, the need to achieve intelligent protection of personal information through machine learning algorithms has become a paramount concern. Artificial intelligence leverages advanced algorithms and technologies to effectively encrypt and anonymize personal data, enabling valuable data analysis and utilization while safeguarding privacy. This paper focuses on personal data privacy protection and the promotion of anonymity as its core research objectives. It achieves personal data privacy protection and detection through the use of machine learning's differential privacy protection algorithm. The paper also addresses existing challenges in machine learning related to privacy and personal data protection, offers improvement suggestions, and analyzes factors impacting datasets to enable timely personal data privacy detection and protection.arXiv.org209This paper achieves personal data privacy protection and detection through the use of machine learning's differential privacy protection algorithm and addresses existing challenges in machine learning related to privacy and personal data protection.ArXiv['Le Yang', 'Miao Tian', 'Duan Xin', 'Qishuo Cheng', 'Jiajian Zheng']2024-02-27T00:00:00https://www.semanticscholar.org/paper/7c81e4d21ff6911b9106ee28a91bae09c713f59e 3588887f211cc5be46d329c31adf8a976148d0b5 This article explores the impact of Artificial Intelligence (AI) on the music industry, particularly focusing on the case of AI-generated covers. The emergence of AI technologies has been raising concerns not just about the originality and protection of AI-generated outputs but also about the complex input and training phase of those systems. The focus of this contribution is the latter, analysing the case of AI covers from the perspective of copyright and image rights. In the first part, an overview of the text and data mining (TDM) exception found in Article 4 of Directive 2019/790 is presented, with a primary focus on the opt-out mechanism in connection with the three-step test. Moving to the second part, the analysis delves into the complexities of voice cloning, highlighting the absence of a comprehensive European Union regime for image rights. By addressing these issues, this contribution unveiled two crucial points. First, AI models trained on various artists’ works to create and spread deepfake covers not only violate copyright but also reveal shortcomings in the TDM exception. Second, while the multifaceted image right regime may not be as exhaustive as necessary, it proves to be a viable solution against voice cloning with anticipated advancements in the future.Journal of Intellectual Property Law &amp; Practice01While the multifaceted image right regime may not be as exhaustive as necessary, it proves to be a viable solution against voice cloning with anticipated advancements in the future.Journal of Intellectual Property Law &amp; Practice['Antonios Baris']2024-02-27T00:00:00https://www.semanticscholar.org/paper/3588887f211cc5be46d329c31adf8a976148d0b5 baac5441e0b06f082d5e82c27129f2a8fecd851aThe rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard); and 2) the eligibility of AI-generated content for copyright protection (AI-copyrightability). While both issues have ignited heated debates among academics and practitioners, most analysis has focused on their challenges posed to existing copyright doctrines. In this paper, we aim to better understand the economic implications of these two regulatory issues and their interactions. By constructing a dynamic model with endogenous content creation and AI model development, we unravel the impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare, and how these impacts are influenced by various economic and operational factors. For example, while generous fair use (use data for AI training without compensating the creator) benefits all parties when abundant training data exists, it can hurt creators and consumers when such data is scarce. Similarly, stronger AI-copyrightability (AI content enjoys more copyright protection) could hinder AI development and reduce social welfare. Our analysis also highlights the complex interplay between these two copyright issues. For instance, when existing training data is scarce, generous fair use may be preferred only when AI-copyrightability is weak. Our findings underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions and provide insights for business leaders navigating the complexities of the global regulatory environment.Social Science Research Network491The impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare are unraveled and underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions.ArXiv['S. A. Yang', 'Angela Huyue Zhang']2024-02-27T00:00:00https://www.semanticscholar.org/paper/baac5441e0b06f082d5e82c27129f2a8fecd851a 35b7027421e64392bd54bf00356826515c4f1d6bStudents, especially those outside the field of cybersecurity, are increasingly turning to Large Language Model (LLM)-based generative AI tools for coding assistance. These AI code generators provide valuable support to developers by generating code based on provided input and instructions. However, the quality and accuracy of the generated code can vary, depending on factors such as task complexity, the clarity of instructions, and the model’s familiarity with the programming language. Additionally, these generated codes may inadvertently utilize vulnerable built-in functions, potentially leading to source code vulnerabilities and exploits. This research undertakes an in-depth analysis and comparison of code generation, code completion, and security suggestions offered by prominent AI models, including OpenAI CodeX, CodeBert, and ChatGPT. The research aims to evaluate the effectiveness and security aspects of these tools in terms of their code generation, code completion capabilities, and their ability to enhance security. This analysis serves as a valuable resource for developers, enabling them to proactively avoid introducing security vulnerabilities in their projects. By doing so, developers can significantly reduce the need for extensive revisions and resource allocation, whether in the short or long term.Journal of The Colloquium for Information Systems Security Education220This research undertakes an in-depth analysis and comparison of code generation, code completion, and security suggestions offered by prominent AI models, including OpenAI CodeX, CodeBert, and ChatGPT to evaluate the effectiveness and security aspects of these tools in terms of their code generation, code completion capabilities, and their ability to enhance security.Journal of The Colloquium for Information Systems Security Education['Maryam Taeb', 'Hongmei Chi', 'Shonda Bernadin']2024-02-27T00:00:00https://www.semanticscholar.org/paper/35b7027421e64392bd54bf00356826515c4f1d6b 6246f9fa13f0f96ba3d8390dad1c258be00afe79PurposeThis paper aims to challenge the fashion of ubiquitous artificial intelligence (AI) and the effects which it will have upon society. In doing so it argues that the effects of AI will be minimal but important.Design/methodology/approachThis argument is based upon the Socratic method and explores the Utilitarian background in which AI is based while drawing upon classical literature and other examples to illustrate the argument.FindingsThe findings are encompassed in the argument and show that we need to be more open and careful when considering AI and its effects. We also need to be more realistic when considering potential benefits.Practical implicationsThis argument has significant implications for the adoption of AI.Social implicationsThe social implications are equally profound and will impact upon our application of AI solutions to current problems and upon humanity more generally.Originality/valueThis is the first paper which relates AI to human successes.Technological Sustainability250This argument is based upon the Socratic method and explores the Utilitarian background in which AI is based while drawing upon classical literature and other examples to illustrate the argument.Technological Sustainability['David Crowther', 'Hiba Hamdan']2024-02-27T00:00:00https://www.semanticscholar.org/paper/6246f9fa13f0f96ba3d8390dad1c258be00afe79 943c2cf4d2e1e86d688e4df519504b4a887a5e80This paper proposes and implements an AI-based decision framework for e-learning systems that can assess learners' emotions and adjust the learning activity in order to improve learning performance. An evolutionary genetic algorithm is proposed to identify suitable micro-brake activities to alter learners' emotions in the event learners feel emotions that are not optimal for learning, such as anxiety or sadness, so they can concentrate on learning more effectively. To test the proposed framework, a case study was conducted with English as second language learners over one semester. Fourteen participants were recruited from a gifted school and were randomly allocated to two groups, each consisting of seven participants. The first group was provided access to the system and its break activities, whereas the second group had no access to this system. The results showed that students with access to the system with break activities performed significantly better. This confirmed that micro-break activities, selected based on students' sentiments and inclinations, can have a positive effect on learning performance. The results have also confirmed that the proposed framework was successful in selecting and offering students effective activities that could improve student's mood. The results of this study hold significant practical implications for the design of adaptive e-learning systems and learning management platforms. Additionally, they contribute to the theoretical landscape of AI and learner emotions by proposing an innovative method for identifying, classifying, and providing personalized learning paths tailored to the unique needs of individual learners.2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)250An AI-based decision framework that can assess learners' emotions and adjust the learning activity in order to improve learning performance is proposed and implemented and confirmed that the proposed framework was successful in selecting and offering students effective activities that could improve student's mood.2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)['Ali Darejeh', 'Tayebeh Sargazi Moghadam', 'Mansoureh Delaramifar', 'Sara Mashayekh']2024-02-27T00:00:00https://www.semanticscholar.org/paper/943c2cf4d2e1e86d688e4df519504b4a887a5e80 03fc057b66e30de96b1b11bf3c5865b460c7a94fOver the past few years, wildfires have become a worldwide environmental emergency, resulting in substantial harm to natural habitats and playing a part in the acceleration of climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite improvements in detection techniques, the rising occurrence of wildfires demands creative solutions for prompt identification and effective control. This research investigates proactive methods for detecting and handling wildfires in the United States, utilizing Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology; Active monitoring and mapping with remote sensing and signaling leveraging on 5G technology; and Advanced response mechanisms to wildfire using drones and IOT devices. This study was based on secondary data collected from government databases and analyzed using descriptive statistics. In addition, past publications were reviewed through content analysis, and narrative synthesis was used to present the observations from various studies. The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively. Utilizing advanced technology could save lives and prevent significant economic losses caused by wildfires. Various methods, such as AI-enabled remote sensing and 5G-based active monitoring, can enhance proactive wildfire detection and management. In addition, super intelligent drones and IOT devices can be used for safer responses to wildfires. This forms the core of the recommendation to the fire Management Agencies and the government.Social Science Research Network610The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively, and AI-enabled remote sensing and 5G-based active monitoring can enhance proactive wildfire detection and management.ArXiv['S. Okoro', 'Alexander Lopez', 'Austine Unuriode']2024-02-27T00:00:00https://www.semanticscholar.org/paper/03fc057b66e30de96b1b11bf3c5865b460c7a94f d0a322ac822d6fe86952d3b67e7f782d73b011b4In the ever-evolving regulatory environment, adhering to reporting standards poses a significant hurdle for organizations spanning diverse sectors. Negotiating the intricacies of regulatory obligations necessitates innovative approaches. This document delves into the utilization of Artificial Intelligence (AI) and Machine Learning (ML) methodologies to bolster the precision and efficacy of reporting procedures. Through the integration of AI/ML, entities can streamline data analysis, detect patterns, and uphold compliance with regulatory frameworks. This research probes into the potential advantages, obstacles, and optimal strategies linked with the incorporation of AI/ML technologies into reporting infrastructures. Drawing upon a thorough examination of pertinent literature and case studies, valuable insights are offered to aid organizations in proficiently leveraging AI/ML to navigate regulatory intricacies and attain accurate reporting results.Online (Weston, Conn.)80This research probes into the potential advantages, obstacles, and optimal strategies linked with the incorporation of AI/ML technologies into reporting infrastructures to bolster the precision and efficacy of reporting procedures.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)['Harish Padmanaban']2024-02-27T00:00:00https://www.semanticscholar.org/paper/d0a322ac822d6fe86952d3b67e7f782d73b011b4 a4f774ca135ec6aef98a83f726c4d52627395cc3Precision Agriculture1480A critical review of precision agriculture techniques for fruit maturity estimation, with a focus on destructive and non-destructive measurement approaches, and the applications of ML in the domain is presented, highlighting the outstanding technical challenges and identifying the most promising areas for future research.Precision Agriculture['Maidul Islam', 'Suraj Bijjahalli', 'Thomas Fahey', 'A. Gardi', 'R. Sabatini', 'David W. Lamb']2024-02-27T00:00:00https://www.semanticscholar.org/paper/a4f774ca135ec6aef98a83f726c4d52627395cc3 91eca4793c50f3f21f3e5d57e22092fac07dd4aeAI software is regarded as the key to the formation of new domain and new quality combat capabilities in the future. In response to the characteristics of randomness, autonomy, and learning of AI software, traditional testing methods lack comprehensive coverage of non-functional requirements, test data is difficult to obtain and annotate, and evaluation indicators are not comprehensive. This paper focuses on the research of intelligent algorithm evaluation technology, proposing a multi-perspective and multi-attribute evaluation method for intelligent algorithms from four aspects: accuracy evaluation, efficiency evaluation, data robustness evaluation and algorithm coverage evaluation. This can effectively support future AI software evaluation work and improve the AI software testing technology system.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)60This paper focuses on the research of intelligent algorithm evaluation technology, proposing a multi-perspective and multi-attribute evaluation method for intelligent algorithms from four aspects: accuracy evaluation, efficiency evaluation, data robustness evaluation and algorithm coverage evaluation.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)['Yaming Zhang', 'Xiaomei Shen', 'Jianyang Ding', 'Lijin Wu', 'Longli Tang']2024-02-27T00:00:00https://www.semanticscholar.org/paper/91eca4793c50f3f21f3e5d57e22092fac07dd4ae 04c973e91e47bf39444868493aeae9a2d9160261The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge the chasm between AI model development and achievable benefit. To accomplish this, the Data Science team at Stanford Health Care has developed a Testing and Evaluation (T&E) mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan. We report on FURM assessments done to evaluate six AI guided solutions for potential adoption, spanning clinical and operational settings, each with the potential to impact from several dozen to tens of thousands of patients each year. We describe the assessment process, summarize the six assessments, and share our framework to enable others to conduct similar assessments. Of the six solutions we assessed, two have moved into a planning and implementation phase. Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.arXiv.org00Assessment process to identify fair, useful and reliable AI models (FURM) by conducting an ethical review, simulations to estimate usefulness, financial projections to assess sustainability, and a process to do ethical assessments are described.ArXiv['Alison Callahan', 'Duncan McElfresh', 'Juan M. Banda', 'Gabrielle Bunney', 'Danton Char', 'Jonathan Chen', 'Conor K. Corbin', 'Debadutta Dash', 'Norman L. Downing', 'Sneha S. Jain', 'N. Kotecha', 'Jonathan Masterson', 'Michelle M. Mello', 'Keith Morse', 'Srikar Nallan', 'Abby Pandya', 'Anurang Revri', 'Aditya Sharma', 'Christopher Sharp', 'Rahul Thapa', 'Michael Wornow', 'Alaa Youssef', 'Michael A. Pfeffer', 'Nigam H. Shah']2024-02-27T00:00:00https://www.semanticscholar.org/paper/04c973e91e47bf39444868493aeae9a2d9160261 b944a03725d8771730cb44a551a8cfbcc2bc1d55Explainable Artificial Intelligence (XAI) techniques generate explanations for predictions from AI models. These explanations can be evaluated for (i) faithfulness to the prediction, i.e., its correctness about the reasons for prediction, and (ii) usefulness to the user. While there are metrics to evaluate faithfulness, to our knowledge, there are no automated metrics to evaluate the usefulness of explanations in the clinical context. Our objective is to develop a new metric to evaluate usefulness of AI explanations to clinicians. Usefulness evaluation needs to consider both (a) how humans generally process explanations and (b) clinicians' specific requirements from explanations presented by clinical decision support systems (CDSS). Our new scoring method can evaluate the usefulness of explanations generated by any XAI method that provides importance values for the input features of the prediction model. Our method draws on theories from social science to gauge usefulness, and uses literature-derived biomedical knowledge graphs to quantify support for the explanations from clinical literature. We evaluate our method in a case study on predicting onset of sepsis in intensive care units. Our analysis shows that the scores obtained using our method corroborate with independent evidence from clinical literature and have the required qualities expected from such a metric. Thus, our method can be used to evaluate and select useful explanations from a diverse set of XAI techniques in clinical contexts, making it a fundamental tool for future research in the design of AI-driven CDSS.medRxiv670A new scoring method that draws on theories from social science to gauge usefulness, and uses literature-derived biomedical knowledge graphs to quantify support for the explanations from clinical literature, which is evaluated in a case study on predicting onset of sepsis in intensive care units.IEEE journal of biomedical and health informatics['Suparna Ghanvatkar', 'Vaibhav Rajan']2024-02-27T00:00:00https://www.semanticscholar.org/paper/b944a03725d8771730cb44a551a8cfbcc2bc1d55 f910bb702fa379f839fa512f42f8af1619a02847Complexities of robotic distal gastrectomy (RDG) give reason to assess physician's surgical skill. Varying levels in surgical skill affect patient outcomes. We aim to investigate how a novel artificial intelligence (AI) model can be used to evaluate surgical skill in RDG by recognizing surgical instruments.Fifty‐five consecutive robotic surgical videos of RDG for gastric cancer were analyzed. We used Deeplab, a multi‐stage temporal convolutional network, and it trained on 1234 manually annotated images. The model was then tested on 149 annotated images for accuracy. Deep learning metrics such as Intersection over Union (IoU) and accuracy were assessed, and the comparison between experienced and non‐experienced surgeons based on usage of instruments during infrapyloric lymph node dissection was performed.We annotated 540 Cadiere forceps, 898 Fenestrated bipolars, 359 Suction tubes, 307 Maryland bipolars, 688 Harmonic scalpels, 400 Staplers, and 59 Large clips. The average IoU and accuracy were 0.82 ± 0.12 and 87.2 ± 11.9% respectively. Moreover, the percentage of each instrument's usage to overall infrapyloric lymphadenectomy duration predicted by AI were compared. The use of Stapler and Large clip were significantly shorter in the experienced group compared to the non‐experienced group.This study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG. Our AI gives us a way to recognize and automatically generate instance segmentation of the surgical instruments present in this procedure. Use of this technology allows unbiased, more accessible RDG surgical skill.Annals of Gastroenterological Surgery290This study is the first to report that surgical skill can be successfully and accurately determined by an AI model for RDG and use of this technology allows unbiased, more accessible RDG surgical skill.Annals of Gastroenterological Surgery['James S. Strong', 'Tasuku Furube', 'M. Takeuchi', 'H. Kawakubo', 'Y. Maeda', 'S. Matsuda', 'Kazumasa Fukuda', 'R. Nakamura', 'Yuko Kitagawa']2024-02-27T00:00:00https://www.semanticscholar.org/paper/f910bb702fa379f839fa512f42f8af1619a02847 636bbf9a14f82513314a7252207d5634ccc3fa82Generative AI models are increasingly powering software applications, offering the capability to produce expressive content across varied contexts. However, unlike previous iterations of human-AI design, the emerging design process for generative capabilities primarily hinges on prompt engineering strategies. Given this fundamental shift in approach, our work aims to understand how collaborative software teams set up and apply design guidelines and values, iteratively prototype prompts, and evaluate prompts to achieve desired outcomes. We conducted design studies with 39 industry professionals, including designers, software engineers, and product managers. Our findings reveal a content-centric prototyping approach in which teams begin with the content they want to generate, then identify specific attributes, constraints, and values, and explore methods to give users the ability to influence and interact with those attributes. Based on associated challenges, such as the lack of model interpretability and overfitting the design to examples, we outline considerations for generative AI prototyping.arXiv.org820This work aims to understand how collaborative software teams set up and apply design guidelines and values, iteratively prototype prompts, and evaluate prompts to achieve desired outcomes in generative AI prototyping.ArXiv['Hari Subramonyam', 'Divy Thakkar', 'Jurgen Dieber', 'Anoop Sinha']2024-02-27T00:00:00https://www.semanticscholar.org/paper/636bbf9a14f82513314a7252207d5634ccc3fa82 ded477e762cfc08b6c052c84dfb9d034e1363c1aThis research paper explores the anticipated trends in service design for the year 2024, focusing on the implications for artificial intelligence (AI) and generative AI-based services within Nature Labs. It delves into shifts in service industries, evolving user behaviors, and the pivotal role of AI in revolutionizing service design and user experience (UX). By leveraging industry insights and emerging concepts, this paper aims to guide Nature Labs through the evolving landscape of service design, ensuring innovative and user-centric service offeringsInternational Journal of Research Publication and Reviews80International Journal of Research Publication and Reviews['Ramamurthy Valavandan', 'Prakash Valavandan', 'Kanagalakshmi S', 'Valavandan V', 'Savitha R', 'Kirubashin Kirubashin', 'Kiran Athidya P', 'Shubhashni P']2024-02-27T00:00:00https://www.semanticscholar.org/paper/ded477e762cfc08b6c052c84dfb9d034e1363c1a b20eb7269c9d2263ddd5a2808b2ca903b03dadeaThis paper explores the dynamic interplay between knowledge, innovation, and artificial intelligence (AI), emphasizing the crucial role of the human element in navigating this intricate dance. As AI continues to advance, its integration into various facets of society impacts knowledge creation, dissemination, and innovation processes. However, the human element remains essential for harnessing AI's potential effectively. This study delves into the complexities of this relationship, examining how humans contribute to AI development, shape its applications, and mitigate potential risks. Through a multidisciplinary lens, it discusses strategies for fostering synergy between AI capabilities and human expertise, ensuring that innovation remains guided by ethical considerations and human values. Ultimately, it highlights the necessity of understanding and nurturing the human element within the evolving landscape of knowledge and AI-driven innovation.Online (Weston, Conn.)00This study delves into the complexities of the relationship between knowledge, innovation, and artificial intelligence, examining how humans contribute to AI development, shape its applications, and mitigate potential risks.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)['Arabella Jo']2024-02-27T00:00:00https://www.semanticscholar.org/paper/b20eb7269c9d2263ddd5a2808b2ca903b03dadea 59bacc38f069160dfe599c33f9bcfdc45df9df92 This paper presents an autonomous drilling advisory system powered by digital twins and AI solutions. Such an advisory system aims to automate real-time monitoring and parameter optimization, reduce subject-matter experts, and meet the demands for safer and more efficient drilling toward autonomous operation. The methodology proposed in this research involves the creation of a comprehensive Digital Twin model that accurately replicates the drilling process by integrating hydraulic, thermal dynamic, and mechanical models. To ensure high model accuracy, an auto-calibration approach is developed, driven by real-time data, to fine-tune the Digital Twin models. Additionally, AI-based model reasoning techniques are employed to detect potential hazards and risks ahead of the bit proactively. This is achieved by comparing the ideal behavior of the digital twin replica with the actual behavior observed from downhole and the rig. As a result, real-time diagnostics are generated to supervise ongoing operations, accompanied by suggestions to mitigate identified risks. Furthermore, the system leverages the capabilities of the Digital Twin and optimization methods to create multiple combinations of operational parameters. These parameters are optimized by ranking the predicted performance derived from the Digital Twin. The optimized operational parameters are automatically generated as forward advice to drillers, enabling them to make informed decisions and enhance drilling performance. Testing results on multiple wells from different operators are presented, showcasing the system's capabilities in real-time monitoring and drilling parameter optimization. The system demonstrates its effectiveness in providing diagnostic messages with early anomaly detection during drilling and casing running. These diagnostic warnings include losses, leakage, poor hole cleaning, and stuck pipe, enabling proactive intervention to mitigate risks. Furthermore, the system optimizes operational parameters during drilling and tripping in real-time without requiring human intervention. This optimization covers parameters such as flow rate, rotary speed (RPM), and rate of penetration (ROP) during drilling, and tripping speed during tripping in and pulling out of the hole. The time savings achieved through the use of optimized parameters are quantified for both cases, demonstrating a substantial improvement in operational efficiency while maintaining safety margins. The scalability and adaptability of the system are also highlighted, emphasizing its ability to accommodate diverse drilling scenarios and integrate with existing solutions in various deployment conditions. The proposed methodology demonstrates the development of a robust and efficient system that enhances decision-making and improves drilling performance. In addition, the results highlight the potential benefits of combining AI and Digital Twin technologies in the drilling industry, paving the way for future innovations and advancements in the field.Day 1 Tue, March 05, 202470An autonomous drilling advisory system powered by digital twins and AI solutions that optimizes operational parameters during drilling and tripping in real-time without requiring human intervention, demonstrating a substantial improvement in operational efficiency while maintaining safety margins is presented.Day 1 Tue, March 05, 2024['Jie Cao', 'J. Nabavi', 'S. I. Oedegaard']2024-02-27T00:00:00https://www.semanticscholar.org/paper/59bacc38f069160dfe599c33f9bcfdc45df9df92 dd23286dec40cfcaf9e6113330388221957373d4The extensive industrialization of artificial intelligence (AI) since the mid-2010s has increasingly motivated artists to address its economic and sociopolitical consequences. In this chapter, I discuss interrelated art practices that thematize creative agency, crowdsourced labor, and delegated artmaking to reveal the social rootage of AI technologies and underline the productive human roles in their development. I focus on works whose poetic features indicate broader issues of contemporary AI-influenced science, technology, economy, and society. By exploring the conceptual, methodological, and ethical aspects of their effectiveness in disrupting the political regime of corporate AI, I identify several problems that affect their tactical impact and outline potential avenues for tackling the challenges and advancing the field.arXiv.org700ArXiv['Dejan Grba']2024-02-27T00:00:00https://www.semanticscholar.org/paper/dd23286dec40cfcaf9e6113330388221957373d4 ae907869717b428960fb058442fa98b1422ee0bfBackground: This research examines how market knowledge and artificial intelligence (AI) interact in different market designs such as business-to-business (B2B) settings while taking emerging technologies and the changing digitalization landscape into account. Objective: The main goal is to understand how AI affects market knowledge in different market designs such as businessto-business (B2B) contexts, taking into account language barriers, practical difficulties, and the revolutionary effects on decision-making and customer interactions. Result: They underscore the transformative potential of artificial intelligence (AI) by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space. Conclusion: In order to create a path for responsible AI integration in B2B marketing, the study concludes with recommendations for standardized terminology related to AI, practical insights into implementation challenges, and ethical issuesAdvances in Urban Regional Development and Planning00The transformative potential of artificial intelligence (AI) is underscore by highlighting how it shapes market knowledge, encourages customized approaches, and improves marketing efficacy in the business-to-business (B2B) space.Advances in Urban Regional Development and Planning[]2024-02-27T00:00:00https://www.semanticscholar.org/paper/ae907869717b428960fb058442fa98b1422ee0bf 521ffd676bb90fe940c7152249048ddac2e214e0The age of artificial intelligence (AI) is just around the corner. This article presents the AI-based technologies that represent a significant change for the learning and teaching process. The article describes the potential of personalised learning, automated assessment, chatbots, predictive models, intelligent robots, and virtual and augmented reality for education, by reviewing scientific literature. Nowadays, knowledge of these technologies is essential for teachers. In this article we conducted a qualitative survey involving teachers. The aim of our survey is to assess the perceptions of educators about the use of AI in education. Our results show that educators are open to these technologies regardless of generation and discipline. The study summarizes the appropriate use of these technologies, the role of teachers, their attention to students and their active communication, as only this can lead to effective education in the age of artificial intelligence.TEM Journal013The study summarizes the appropriate use of these technologies, the role of teachers, their attention to students and their active communication, as only this can lead to effective education in the age of artificial intelligence.TEM Journal['Norbert Annuš']2024-02-27T00:00:00https://www.semanticscholar.org/paper/521ffd676bb90fe940c7152249048ddac2e214e0 1129f21247bb95a6c5414edd5d4c664fdde92d39As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.Journal of Marine Science and Engineering1204Journal of Marine Science and Engineering['D. Song', 'Guoyang Shen', 'Chao Huang', 'Qian Huang', 'Jian Yang', 'Mi Dong', 'Young Hoon Joo', 'N. Duić']2024-02-27T00:00:00https://www.semanticscholar.org/paper/1129f21247bb95a6c5414edd5d4c664fdde92d39 240d6455094419980ee599435cc41961085b36c3Background: Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS. Materials and Methods: On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review. Results and Discussion: The studies included in this review allowed for the identification of three main “incident type” domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting. Conclusions: This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.Healthcare871It is highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors, and it appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills.Healthcare['M. Ferrara', 'G. Bertozzi', 'N. Di Fazio', 'I. Aquila', 'Aldo Di Fazio', 'A. Maiese', 'G. Volonnino', 'P. Frati', 'R. La Russa']2024-02-27T00:00:00https://www.semanticscholar.org/paper/240d6455094419980ee599435cc41961085b36c3 4b9c0f7c583fc86f1687bac38bdfbb92e870ff83The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of Artificial Intelligence (AI) and learning algorithms, propelling vehicles into realms of unprecedented autonomy. This paper provides a comprehensive exploration of the evolutionary trajectory of AI within autonomous vehicles, tracing the journey from foundational principles to the most recent advancements. Commencing with a current landscape overview, the paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing ethical considerations and bias in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI/learning algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI and learning algorithms, and automating key tasks at each level. Additionally, the document discusses the variation in software package sizes across different autonomy levelsarXiv.org401The paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles, elucidating the nuanced usage of AI and learning algorithms, and automating key tasks at each level.ArXiv['Divya Garikapati', 'Sneha Sudhir Shetiya']2024-02-27T00:00:00https://www.semanticscholar.org/paper/4b9c0f7c583fc86f1687bac38bdfbb92e870ff83 1be2c498e123029db7a7bb2125d6c06c361cb3e1Artificial intelligence (AI) develops over time. Education is concurrently the AI evolution,sometimes in front, sometimes behind. Education underlies AI, and later on, AI is used in education. Therefore, the utilization of AI in education cannot be denied. There are many advantages, but also some limitations and ethical issues that arise from the use of AI in education. There are many applications of AI in mathematics scattered throughout society, but not all of them are useful enough. Thus, the purpose of this research is to gain the famous application AI in mathematics (app AI math) that is mostly used and recommended by the students’ experience in doing and learning mathematics. T-test independent samples were used to test the score of two groups, using the AI math app or not, in order to know whether it was affected or not. The result of this research is that the most popular app is Photomath, followed by ChatGPT and Mathway. But still, many students did not use this app. Relating to their score, the students who have used the AI math app to learn math before are not different from the students who do not use the AI math app.  TNI Angkatan Udara00The result of this research is that the most popular app is Photomath, followed by ChatGPT and Mathway, and the students who have used the AI math app to learn math before are not different from the students who do not use this app.TNI Angkatan Udara['Rindu A. Funny', 'Ndaru A. Purnami', 'Maria A.D. K', 'Fajar K. Rahmawati']2024-02-27T00:00:00https://www.semanticscholar.org/paper/1be2c498e123029db7a7bb2125d6c06c361cb3e1 dba5744972701ac5e3226cfe78feb632edf09f95Current Gastroenterology Reports440Today, AI is being deployed to replicate expert judgement in specific tasks where disagreement, subjectivity, and bias are common, but the near future will herald contributions of AI doing what the authors cannot, including new detailed measures of IBD, enhanced analysis of images, and perhaps even fully automating care.Current gastroenterology reports['Mehwish Ahmed', 'Molly L. Stone', 'R. Stidham']2024-02-27T00:00:00https://www.semanticscholar.org/paper/dba5744972701ac5e3226cfe78feb632edf09f95 1dbc77a4a26b9754cf418c0d9f0cd435367dab3eWith the emergence of Education 4.0, Artificial Intelligence (AI) is increasingly being used and integrated in higher education institutions in recent years. It is hardly surprising as we are living in the era of digital technologies and a transformational shift in the educational system. This conceptual article proposes a study on adoption of artificial intelligence (AI) in higher education among undergraduate students. Drawing from the Theoretical model of The Unified Theory of Acceptance and Use of Technology (UTAUT), this study aims to examine the influence between the key variables in the UTAUT model such as performance expectation, effort expectation, social influence and facilitating conditions on attitudes and behavioral intention towards AI adoption in higher education institutions. This study will utilize quantitative research design using Structural Equation Modeling - Partial Least Squares (SEM-PLS) to analyze the data. It becomes central to investigate such AI adoption tendency among students as this will aid the institutions to tap into the potential problems and opportunities that may arise with its adoptions and usage. This study also attempts to clarify the potential linkages by engaging in a discussion prior to conducting empirical testing.International Journal of Business and Technopreneurship380This study aims to examine the influence between the key variables in the UTAUT model such as performance expectation, effort expectation, social influence and facilitating conditions on attitudes and behavioral intention towards AI adoption in higher education institutions.International Journal of Business and Technopreneurship (IJBT)['Farhana Hanim Mohsin', 'Norhayati Md Isa', 'Khairunnisa Ishak', 'Hatijah Mohamed Salleh']2024-02-27T00:00:00https://www.semanticscholar.org/paper/1dbc77a4a26b9754cf418c0d9f0cd435367dab3e f92554900ff59a7f44d74fd3237e90ec0680099dThe implementation of artificial intelligence in education (AIED) is in the spotlight due to being the most substantial advancement of the century. But, despite being beneficial to e-learning, AI has imposed ethical risks and challenges that need to be addressed. Hence, we aim to present a literature review concerning ethical considerations in using AI in e-learning. In this regard, we restored all relevant articles from 5 databases (Google Scholar, Scopus, PubMed, Web of science and Eric) based on PRISMA guidelines and predefined inclusion criteria, from January 2013 till June 2023. Each reviewer conducted the process of selecting studies and extracting data, which was then synthesized using a narrative method. Initially, 169 articles were identified through the search, with 38 articles meeting the predetermined inclusion criteria for data analysis. The majority of these articles centered on a broad discourse concerning ethics and artificial intelligence. Nevertheless, in most retrieved studies, there was a limited examination of ethical considerations regarding using AI in e-learning. Among them, respecting human rights, privacy, equity and fairness, trust and accountability, transparency; enhancing learning outcomes through surveillance of learners' performance and their learning process; providing equal opportunities to have access to technologies, and avoiding any discrimination and bias were the main ethical considerations regarding using AI in e-learning. We have explained a set of general principles and ethical considerations related to the application of AI in e-learning that can assist in designing new autonomous systems and also help to consider ethical concerns when integrating AI in online educational settings. More research, however, are needed to secure the safety and appropriateness of these systems for the students, teachers, institutions, and society altogether.2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)460A set of general principles and ethical considerations related to the application of AI in e-learning that can assist in designing new autonomous systems and also help to consider ethical concerns when integrating AI in online educational settings are explained.2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)['Leila Homayouni', 'Yamin Hejazi', 'Nahid Zarifsanaiey']2024-02-27T00:00:00https://www.semanticscholar.org/paper/f92554900ff59a7f44d74fd3237e90ec0680099d a2a19089952b421f8655bd210a3731df2b02fcfaEffective scholarly communication, whether oral or written, is inherently challenging. Scientific discourse relies on objective facts rather than subjective opinions. Hence, it necessitates grounding in evidence derived from research, ensuring the arguments presented are objective and supported by factual findings. Generating scholarly information, even in a single sentence, is a laborious process, demanding considerable time, financial resources, and substantial energy. In some instances, continuous or multi-year research is imperative. Nevertheless, technological advancements have revolutionized this landscape. Writing can now be expedited through the use of available artificial intelligence (AI) technologies, ranging from free versions to premium services. For example, Jenni AI functions akin to a "magical assistant," generating text on requested topics. ELICIT AI excels in grid synthesis, ResearchPal automates literature reviews, and others provide various functionalities. This poses a temptation to researchers, making AI a double-edged sword in the realm of publication integrity.Indonesian Contemporary Nursing Journal (ICON Journal)00Writing can now be expedited through the use of available artificial intelligence technologies, ranging from free versions to premium services, making AI a double-edged sword in the realm of publication integrity.Indonesian Contemporary Nursing Journal (ICON Journal)['Saldy Yusuf']2024-02-27T00:00:00https://www.semanticscholar.org/paper/a2a19089952b421f8655bd210a3731df2b02fcfa f3ffbf30bb34e62b66241f92bea72413a053a309The integration of artificial intelligence (AI) into e-Government framework, exemplified by Korea's approach, marks a paradigmatic shift in global governmental operations. AI, evolving from its initial theoretical foundations to advanced applications in areas like machine learning, predictive analytics, and natural language processing with large language models, is significantly transforming e-Government infrastructures. This transformation, with Korea among the leading nations, led to a study evaluating the potential impact of AI integration in the Korean e-Government system. Employing a robust crosssectional quantitative methodology, the study tested seven hypotheses to examine Korean officials' perspectives on AIenhanced e-Government system acceptance, considering variables such as social influence, trust, and attitude. Methods namely Partial Least Squares Structural Equation Modeling and the Statistical Package for the Social Sciences were used to analyze these aspects addressing research gaps and their implications for AI purveyors, policymakers, and foreign governments. This research is presumed to advance the discourse on the implementation and reception of AI in eGovernment.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)360The study tested seven hypotheses to examine Korean officials' perspectives on AIenhanced e-Government system acceptance, considering variables such as social influence, trust, and attitude, and analyzed aspects addressing research gaps and their implications for AI purveyors, policymakers, and foreign governments.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)['Chang Hee Yun', 'A. Teoh', 'Tze Yin Khaw']2024-02-27T00:00:00https://www.semanticscholar.org/paper/f3ffbf30bb34e62b66241f92bea72413a053a309 e60d8ea6ce143fb16d82a5f2cca1fa0b165470beApplying Artificial Intelligence (AI) and Machine Learning (ML) in critical contexts, such as medicine, requires the implementation of safety measures to reduce risks of harm in case of prediction errors. Spotting ML failures is of paramount importance when ML predictions are used to drive clinical decisions. ML predictive reliability measures the degree of trust of a ML prediction on a new instance, thus allowing decision-makers to accept or reject it based on its reliability. To assess reliability, we propose a method that implements two principles. First, our approach evaluates whether an instance to be classified is coming from the same distribution of the training set. To do this, we leverage Autoencoders (AEs) ability to reconstruct the training set with low error. An instance is considered Out-of-Distribution (OOD) if the AE reconstructs it with a high error. Second, it is evaluated whether the ML classifier has good performances on samples similar to the newly classified instance by using a proxy model. We show that this approach is able to assess reliability both in a simulated scenario and on a model trained to predict disease progression of Multiple Sclerosis patients. We also developed a Python package, named relAI, to embed reliability measures into ML pipelines. We propose a simple approach that can be used in the deployment phase of any ML model to suggest whether to trust predictions or not. Our method holds the promise to provide effective support to clinicians by spotting potential ML failures during deployment.arXiv.org320A simple approach that can be used in the deployment phase of any ML model to suggest whether to trust predictions or not is proposed, and holds the promise to provide effective support to clinicians by spotting potential ML failures during deployment.ArXiv['Lorenzo Peracchio', 'G. Nicora', 'Enea Parimbelli', 'T. M. Buonocore', 'Roberto Bergamaschi', 'Eleonora Tavazzi', 'A. Dagliati', 'Riccardo Bellazzi']2024-02-27T00:00:00https://www.semanticscholar.org/paper/e60d8ea6ce143fb16d82a5f2cca1fa0b165470be 98450437f2c44a38efc46233fb33b44a7a5986daThe rapid advancement of artificial intelligence (AI) technology has brought significant changes to the field of graphic design. This article addresses the evolving trends in the utilization of AI within graphic design, aiming to analyze its impact, explore ethical considerations, and forecast future developments. With the increasing integration of AI into graphic design processes, there arises a need to understand the implications, challenges, and opportunities associated with this technology. The question of how AI influences creativity, communication with the audience, and ethical considerations remains pertinent. The objective of this article is to examine contemporary trends in the application of artificial intelligence within the realm of graphic design. Through an analysis of current research and practices, the article seeks to elucidate the multifaceted aspects of AI adoption in graphic design processes. The study reveals that AI technology has significantly streamlined routine tasks, enhanced personalization capabilities, and expanded the possibilities for innovative design solutions. Furthermore, it highlights the ethical considerations surrounding AI implementation in graphic design, emphasizing the importance of transparency, privacy, and equitable access. In conclusion, the integration of artificial intelligence in graphic design offers immense potential for efficiency, creativity, and user engagement. However, it necessitates a balanced approach that preserves the human touch, fosters ethical practices, and encourages ongoing dialogue and collaboration within the design community. Embracing AI while upholding ethical standards will pave the way for a sustainable and inclusive future in graphic design.Věda a perspektivy00The study reveals that AI technology has significantly streamlined routine tasks, enhanced personalization capabilities, and expanded the possibilities for innovative design solutions, and highlights the ethical considerations surrounding AI implementation in graphic design, emphasizing the importance of transparency, privacy, and equitable access.Věda a perspektivy['Diana Buryk']2024-02-27T00:00:00https://www.semanticscholar.org/paper/98450437f2c44a38efc46233fb33b44a7a5986da bd5ba830f999fe09f865ecb71958f2c23f184844Artificial Intelligence (AI) has become an integral part of every financial sector. It is found to be the key player in the banking and other financial service institutions. More than 80 percent of banks have understood the potential benefits of application of AI in the industry. The use of AI in financial institutions helps to resolve the practical issues in taking financial decisions. The aim of this paper is to understand the applicability of Artificial Intelligence in financial sector and to study the uses of AI in financial credit decisions and risk management.International Journal For Multidisciplinary Research30The aim of this paper is to understand the applicability of Artificial Intelligence in financial sector and to study the uses of AI in financial credit decisions and risk management.International Journal For Multidisciplinary Research['Dr. J.K.Kalpana Devi']2024-02-27T00:00:00https://www.semanticscholar.org/paper/bd5ba830f999fe09f865ecb71958f2c23f184844 937d90dad224592e69307a6dec1b2c5c5af5bc2cArtificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies, and underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Jeff Shuford', 'Md.mafiqul Islam']2024-02-27T00:00:00https://www.semanticscholar.org/paper/937d90dad224592e69307a6dec1b2c5c5af5bc2c 8b4154c267c6452662a2efd78e1e2ba9fc92c776As the global climate situation becomes increasingly severe, the rapid development of robots or artificial intelligence (AI) technology to achieve zero emissions has gradually become a worldwide consensus among major countries. However, the applications of AI may not necessarily lead to a reduction in environmental pollution, as the outcomes may vary depending on the components of AI. The objective of this study is to examine the impacts of different components of artificial intelligence on environmental performance. To achieve this objective, the study utilises a panel regression model with a sample of 52 countries from the year 2019 to 2022. The seven sub-pillars considered for AI applications include commercial, development, government strategy, infrastructure, operating environment, research, and talent aspects. Additionally, this study investigates the impacts of different components of artificial intelligence on environmental performance in two groups of countries classified as advanced countries and developing countries. The results show that AI applications in development, government strategy, research, and the operating environment pillars are positive and significant for carbon emissions in all samples. Commercialisation in AI is negative and significant for carbon emissions in advanced countries. Infrastructure in AI is negative and significant for carbon emissions in developing countries. The implication of this study demonstrates that policies encouraging sustainable and responsible commercial AI development are effective in reducing the environmental impact in advanced countries. Developing countries, on the other hand, may benefit from policies that focus on building and enhancing AI infrastructure. The novelty of this study lies in the distinction between advanced and developing countries, allowing for tailored strategies to combat hazardous environments. Advanced countries may focus on managing the commercial aspects of AI, while developing countries may emphasise infrastructure development.International Conference on Mechatronics and Robotics Engineering1902024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE)['Hui Shan Lee', 'Kee Seng Kuang', 'Ping-Xin Liew', 'W. Har']2024-02-27T00:00:00https://www.semanticscholar.org/paper/8b4154c267c6452662a2efd78e1e2ba9fc92c776 8fa4f5b7b6687070e4947c7aff81e323089a6c28The general objective of the research was to determine the advances related to the startups and artificial intelligence. The specific objectives of the research are to identify the most successful startups that use artificial intelligence and the countries that invest the most in startups. Methodology, in this research, 53 documents have been selected, carried out in the period 2018 - 2024; including: scientific articles, review articles and information from websites of recognized organizations. Results, the number of startups is increasing rapidly on various continents and is applied in various economic sectors. Artificial Intelligence is having a significant impact on various human activities around the world. The current concern is the ethical use of AI, which is why various governments and international organizations are establishing recommendations and limitations for corporations that carry out such research. The startups that are currently emerging have artificial intelligence as their main component, due to the great advantages it offers. The United States, China and the United Kingdom are leading investment in startups worldwide. Conclusions, about the general objective of the research, to determine the advances related to the startups and artificial intelligence. The number of startups is increasing rapidly on various continents and is applied in various economic sectors. The current concern is the ethical use of AI. The startups that are currently emerging have artificial intelligence as their main component, due to the great advantages it offers. About the first specific objectives of the research, to identify the most successful startups that use artificial intelligence. On all five continents, there are several startups that use artificial intelligence and seek to provide technological solutions in the various fields of human activity. About the second specific objectives of the research, the countries that invest the most in startups. The United States, China and the United Kingdom are leading investment in startups worldwide.South Florida Journal of Development220The specific objectives of the research are to identify the most successful startups that use artificial intelligence and the countries that invest the most in startups.South Florida Journal of Development['Carlos Rios-Campos', 'Erick Orlando Guerrero Zambrano', 'Daniel Jesús Castro Vargas', 'Luis Alfredo Abanto Merino', 'Patricia Abigail Alejandría Vallejos', 'Irene Marely Ballena Alcantara', 'Deciderio Enrique Diaz Rubio', 'Daniel Samillan Rodriguez', 'Jhony Huaman Tomanguilla', 'Edilbrando Vega Calderón']2024-02-27T00:00:00https://www.semanticscholar.org/paper/8fa4f5b7b6687070e4947c7aff81e323089a6c28 b9ae9833696fe26780f3afc834fb1a46d4733813The conceptual model presented in this study articulates factors that influence how artificial intelligence (AI) affects product development (PD) and shows how this information may be effectively used for the growth and sustainability of an organization. Through an extensive review literature from online sources, with the dependent variable as product development, three independent variables were identified viz. digital infrastructure, data integration, and ethical dimension. To assess the influence of the independent variables on product development, the research objectives and research questions were formulated. It was identified that a strong product development process strategized through intelligent and ethical data driven decision-making leads to increased resilience and improved sustainability in the long-term.International Conference on Mechatronics and Robotics Engineering590It was identified that a strong product development process strategized through intelligent and ethical data driven decision-making leads to increased resilience and improved sustainability in the long-term.2024 10th International Conference on Mechatronics and Robotics Engineering (ICMRE)['Anuj Bhowmick', 'A. Seetharaman']2024-02-27T00:00:00https://www.semanticscholar.org/paper/b9ae9833696fe26780f3afc834fb1a46d4733813 04efff44e76405fcb7bf1446db9d13f1e110c954PURPOSE OF THIS REVIEW This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers. RECENT FINDINGS The use of AI in healthcare has increased in recent years, largely due to the accessibility of large datasets, such as those gathered from electronic health records. Although there has been less focus on pediatric anesthesia compared to adult anesthesia, research is on- going, especially for applications focused on risk factor identification for adverse perioperative events. Despite these advances, the lack of formal external validation or feasibility testing results in uncertainty surrounding the clinical applicability of these tools. SUMMARY The goal of using AI in pediatric anesthesia is to assist clinicians in providing safe and efficient care. Given that children are a vulnerable population, it is crucial to ensure that both clinicians and families have confidence in the clinical tools used to inform medical decision- making. While not yet a reality, the eventual incorporation of AI-based tools holds great potential to contribute to the safe and efficient care of our patients.Current Opinion in Anaesthesiology00How artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care is explored and potential future applications of AI are described, such as models for airway device selection, controlling anesthetic depth and nociception during surgery, and contributing to the training of pediatric anesthesia providers.Current opinion in anaesthesiology['Dominique Dundaru-Bandi', 'Ryan Antel', 'Pablo Ingelmo']2024-02-27T00:00:00https://www.semanticscholar.org/paper/04efff44e76405fcb7bf1446db9d13f1e110c954 fcc3dc70d4e1317645e1fc12774e3dc0449131a6 The oil and gas industry faces pressure to reduce emissions and minimize fuel consumption to decrease the carbon footprint of drilling operations. Like other players in the sector, drilling contractors have made a commitment to achieving measurable improvements that reduce greenhouse gas (GHG) emissions through the application of new technologies. The challenge is in identifying areas of operation with the greatest potential to deliver efficiency gains and finding ways to realize the reductions. In evaluating rig operations, it became clear that optimizing engine usage has the potential to deliver considerable cost savings and at the same time improve drilling performance. Ideally, this would mean automating the process, but automating drilling processes is not straightforward. It requires in-depth knowledge of rig operations and an understanding of the interplay between the driller, who controls the drilling process, and the generators that power the rig. One solution is to use artificial intelligence (AI) to analyze operational data to capture drilling insights that reliably predict power usage, ensuring operations can be executed as planned and that power demand never exceeds power availability. With the introduction of AI on AC drilling rigs, data monitored by the electronic drilling recorder (EDR) can be analyzed to correlate rig activities with power usage, enabling improvements in drilling efficiency and power management. Using AI changes the paradigm, making it possible to proactively forecast and deliver the correct amount of power to the rig as demand changes throughout the drilling process. This paper delves into the application of AI and automation of the power generation system to demonstrate its impact in revolutionizing drilling operations by forecasting and delivering power needs to deliver efficient drilling programs that use less fuel, produce lower emissions, extend equipment service life, and make fewer demands on the driller and rig crew.Day 1 Tue, March 05, 202440Day 1 Tue, March 05, 2024['L. Trueheart', 'C. Koritala', 'C. Stopkoski']2024-02-27T00:00:00https://www.semanticscholar.org/paper/fcc3dc70d4e1317645e1fc12774e3dc0449131a6 1bb79d5727d1912cfa2ec1244411c82b1c43980aIn recent years, the speed of adoption and implementation of artificial intelligence (AI) in higher education has increased markedly. By leveraging technologies including machine learning and language processing, improvements in learning have been achieved for learners, in line with the principles of personalized learning. This review study aims to examine the implementation of AI in university education. A search was conducted in databases between 1990 to 2022 using relevant keywords. Inclusion criteria consisted of original studies related to the implementation of AI in university education and with empirical evaluation or modeling. 42 selected articles were reviewed. The findings showed that AI is used in university education for personalized learning, intelligent tutoring systems, virtual assistants, automation, data analysis, and improving instructional design. There are also challenges such as privacy concerns, algorithmic bias, and reduction of human interactions. AI has the potential to transform learning and education in universities through an evidence-based and ethical approach. An intelligent integration of traditional pedagogies and new technologies is required.2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)510AI has the potential to transform learning and education in universities through an evidence-based and ethical approach and an intelligent integration of traditional pedagogies and new technologies is required.2024 11th International and the 17th National Conference on E-Learning and E-Teaching (ICeLeT)['Abdollah Mehrfar', 'Zahra Zolfaghari', 'Arash Bordbar', 'Zahra Karimimoghadam']2024-02-27T00:00:00https://www.semanticscholar.org/paper/1bb79d5727d1912cfa2ec1244411c82b1c43980a f77c491d1507cfa96800ea8879e8568ed23f5bcfFuture Microbiology61Future microbiology['M. Ordak']2024-02-27T00:00:00https://www.semanticscholar.org/paper/f77c491d1507cfa96800ea8879e8568ed23f5bcf 583ef2b57e3be5746da9c2ae544dc3654c5d864dDiscover Artificial Intelligence360This review comprehensively analyzes challenges and solutions presented in the literature, offering an overview of the most recent techniques utilized in this field and provides precise definitions of interpretability and explainability, aiming to clarify the distinctions between these concepts and their implications for the decision-making process.Discov. Artif. Intell.['Maria Frasca', 'Davide La Torre', 'Gabriella Pravettoni', 'I. Cutica']2024-02-27T00:00:00https://www.semanticscholar.org/paper/583ef2b57e3be5746da9c2ae544dc3654c5d864d 5fd108277b1aff9e97453c088df7d0cf22664110Schools have historically been ill-prepared to cater to the needs of deaf students at the elementary and secondary levels. This leads to communication difficulties that impact the learning process for each individual. During the recent COVID-19 pandemic, educational institutions for deaf students faced difficulties in providing effective teaching to children and youth. It is important to emphasize that education is fundamental for all individuals, without exception, as acquiring literacy skills enables them to lead a more fulfilling life. In this context, our research aims to investigate how the use of a computer tool can enhance communication for deaf students in a virtual environment. The methodology used involved the use of a checklist to gather data from each participant’s evaluation. The post-test yielded favorable results, thanks to the statistical analysis employed in the research. In conclusion, it has been determined that a real-time transcriber facilitates learning, leading to improved educational outcomes for deaf students.International Journal of Online and Biomedical Engineering (iJOE)00In conclusion, it has been determined that a real-time transcriber facilitates learning, leading to improved educational outcomes for deaf students.International Journal of Online and Biomedical Engineering (iJOE)['Christian Ovalle', 'Isaac Leonardo Vallejos García', 'Franco Rafael Zapata Berrios']2024-02-27T00:00:00https://www.semanticscholar.org/paper/5fd108277b1aff9e97453c088df7d0cf22664110 4f5955c00be8cda20d41c8cb7c61fb4eb19d7d87BMC Pulmonary Medicine180A deep-learning algorithm to detect pulmonary arterial hypertension using chest X-ray (CXR) images had superior pulmonary arterial hypertension detection capability compared with that of experienced doctors.BMC Pulmonary Medicine['Shun Imai', 'S. Sakao', 'Jun Nagata', 'A. Naito', 'Ayumi Sekine', 'T. Sugiura', 'A. Shigeta', 'Akira Nishiyama', 'Hajime Yokota', 'Norihiro Shimizu', 'Takeshi Sugawara', 'Toshiaki Nomi', 'Seiwa Honda', 'Keisuke Ogaki', 'N. Tanabe', 'Takayuki Baba', 'Takuji Suzuki']2024-02-27T00:00:00https://www.semanticscholar.org/paper/4f5955c00be8cda20d41c8cb7c61fb4eb19d7d87 ccaa86ee7157a6da5586f5f198def8666ed5361bThematic analysis, a well-enforced qualitative analytic method, is likely to continue evolving with the adoption of AI technologies. This how-to report does not delve into the details of thematic analysis itself, as there are ample existing studies on the topic. Instead, it acknowledges the potential impacts, dynamics, and pitfalls of AI in thematic analysis while offering valuable advice, particularly for novice analysts, on how to incorporate and document AI tools in each phase of a thematic analysis. The author underscores the importance of not allowing AI to overshadow the analyst's critical evaluative and interpretive skills but instead supporting the use of AI as an aid in thematic analysis, enhancing the depth and breadth of analysis, provided certain criteria are adhered to. This approach ensures that AI serves as a complementary tool, augmenting rather than replacing human analytical inquiry.The Qualitative Report00The author underscores the importance of not allowing AI to overshadow the analyst's critical evaluative and interpretive skills but instead supporting the use of AI as an aid in thematic analysis, enhancing the depth and breadth of analysis, provided certain criteria are adhered to.The Qualitative Report['Prokopis Christou']2024-02-27T00:00:00https://www.semanticscholar.org/paper/ccaa86ee7157a6da5586f5f198def8666ed5361b b0a1d5de8470c529d25302ddfa59174e51b773a2– The IAFOR International Conference on Arts &amp; Humanities – Hawaii 2024 Official Conference Proceedings00– The IAFOR International Conference on Arts &amp; Humanities – Hawaii 2024 Official Conference Proceedings['Chen Qu', 'Eunyoung Kim']2024-02-27T00:00:00https://www.semanticscholar.org/paper/b0a1d5de8470c529d25302ddfa59174e51b773a2 e9fd3c99debcb036ed7bf9d93effa96792f9ba52Multi-scale decomposition (MSD) has some problems, such as missing details and generating noise. This project aims to study multi-modal image based on texture decomposition model and using improved convolutional network and phase-shift consistency techniques. Aiming at the problem that MSD is easy to produce noise, low-pass filter optimization function and structure texture decomposition model are introduced to effectively solve the problem of decomposition technology. An adaptive filtering algorithm based on least square method is proposed, and the structure-texture analysis method based on image is combined to overcome the noise problem of traditional algorithms theoretically. Secondly, for structured texture, the fusion algorithm of convolutional neural network and Gaussian fairing is studied to enhance the effective acquisition of image details and remove noise. The phase-shift matching algorithm is used for high frequency band to achieve effective fusion of high frequency signals. Then the inversion is carried out to obtain the final composite image. The images are studied qualitatively and quantitatively from the perspectives of visual effect, mutual information, feature mutual information, structural similarity, information entropy, PSNR, etc.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)110This project aims to study multi-modal image based on texture decomposition model and using improved convolutional network and phase-shift consistency techniques to effectively solve the problem of decomposition technology.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)['Qiong Wu', 'Qubo Xie', 'Yi Chen']2024-02-27T00:00:00https://www.semanticscholar.org/paper/e9fd3c99debcb036ed7bf9d93effa96792f9ba52 121354fba0c3109630b09123d7d4c78c50e432aaThe technological strength of a company is crucial for partner selection and investment decisions. A strong technological strength enables the company to seize industry trends and technological frontiers, maintaining a competitive advantage. Open innovation is a method that promotes the development and growth of a company by strengthening connections and collaborative exchanges with the external environment. This study, based on the theory of complex networks, focuses on the research subject of an AI company and explores the relationship between open innovation, network structure, and technological strength. The research findings reveal that open precision has a positive impact on technological strength, while open breadth has a negative impact. Network structural holes negatively moderate the relationship between open precision and technological strength and intensify the negative impact of open breadth on technological strength. On the other hand, network degree centrality positively moderates the influence of open breadth on technological strength. The research results are significant for understanding the mechanism through which open innovation affects the technological strength of a company.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)180The research findings reveal that open precision has a positive impact on technological strength, while open breadth has a negative impact.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)['Tong Li', 'Ziming Wang']2024-02-27T00:00:00https://www.semanticscholar.org/paper/121354fba0c3109630b09123d7d4c78c50e432aa f766159b397db83f7ac70c490b7c904c9d65b0caThis paper establishes an effective early-warning system framework of corporate financial risk. This paper discusses the early-warning model of financial risk. On this basis, Bayes-SVM algorithm is applied to the classification of financial crisis events. We timely detect and alarm the abnormal situation in the company's financial system. Finally, an example is given to prove that the proposed algorithm is correct. This model provides a new way of thinking for the early warning and prevention of enterprise financial risk.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)80The Bayes-SVM algorithm is applied to the classification of financial crisis events and provides a new way of thinking for the early warning and prevention of enterprise financial risk.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)['Fan Xiao', 'Wu Nan']2024-02-27T00:00:00https://www.semanticscholar.org/paper/f766159b397db83f7ac70c490b7c904c9d65b0ca ed1812232b183258a2af89826c70b90dace4bff5This paper proposes a real-time monitoring system for offshore water environmental quality. This project combines LoRa wireless communication technology, sensor technology, Web technology and WeChat application technology to achieve long distance and high anti-interference ability. The system can collect, process, transmit, store, share, display, manage and control the environmental quality of offshore waters in real time. The system can be used for coastal water quality monitoring, fishery aquaculture monitoring and aquaculture monitoring. The experimental research proves that the system can realize the real-time, intelligent and long-term monitoring of offshore water environmental quality. This method can solve the problems of short observation distance, high cost, flexible deployment and weak anti-interference ability in Marine real-time monitoring.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)80The experimental research proves that the LoRa wireless communication technology, sensor technology, Web technology and WeChat application technology can solve the problems of short observation distance, high cost, flexible deployment and weak anti-interference ability in Marine real-time monitoring.2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA)['Zhang Yubai', 'Shuliang Tan', 'Daohuan Xu']2024-02-27T00:00:00https://www.semanticscholar.org/paper/ed1812232b183258a2af89826c70b90dace4bff5 1223d0998a208bb52f87415fccfeadc7a9548d8cAcute coronary syndrome is a significant part of cardiac etiology contributing to out-of-hospital cardiac arrest (OHCA), and immediate coronary angiography has been proposed to improve survival. This study evaluated the effectiveness of an AI algorithm in diagnosing near-total or total occlusion of coronary arteries in OHCA patients who regained spontaneous circulation. Conducted from 1 July 2019 to 30 June 2022 at a tertiary university hospital emergency department, it involved 82 OHCA patients, with 58 qualifying after exclusions. The AI used was the Quantitative ECG (QCG™) system, which provides a STEMI diagnostic score ranging from 0 to 100. The QCG score’s diagnostic performance was compared to assessments by two emergency physicians and three cardiologists. Among the patients, coronary occlusion was identified in 24. The QCG score showed a significant difference between occlusion and non-occlusion groups, with the former scoring higher. The QCG biomarker had an area under the curve (AUC) of 0.770, outperforming the expert group’s AUC of 0.676. It demonstrated 70.8% sensitivity and 79.4% specificity. These findings suggest that the AI-based ECG biomarker could predict coronary occlusion in resuscitated OHCA patients, and it was non-inferior to the consensus of the expert group.Journal of Clinical Medicine240It is suggested that the AI-based ECG biomarker could predict coronary occlusion in resuscitated OHCA patients, and it was non-inferior to the consensus of the expert group.Journal of Clinical Medicine['M. Park', 'Yoo Jin Choi', 'M. Shim', 'Youngjin Cho', 'Jiesuck Park', 'Jina Choi', 'Joonghee Kim', 'Eunkyoung Lee', 'Seo-Yoon Kim']2024-02-27T00:00:00https://www.semanticscholar.org/paper/1223d0998a208bb52f87415fccfeadc7a9548d8c 282b183fa3be506ec8ddca50adbacf9fd6a77866Neurology: Education90Neurology Education['Renzo Figari Jordan', 'S. Sandrone', 'A. Southerland']2024-02-27T00:00:00https://www.semanticscholar.org/paper/282b183fa3be506ec8ddca50adbacf9fd6a77866 24f99ca223d1f8cf18729fba80f56c396b197487Academic medicine : journal of the Association of American Medical Colleges00Academic medicine : journal of the Association of American Medical Colleges['Anjun Chen', 'Wenjun Chen', 'Yanfang Liu']2024-02-27T00:00:00https://www.semanticscholar.org/paper/24f99ca223d1f8cf18729fba80f56c396b197487 956243b0f7ea1870103eca64049250d3b2596138Production Planning &amp; Control660Production Planning &amp; Control['Femi Olan', 'Konstantina Spanaki', 'Wasim Ahmed', 'Guoqing Zhao']2024-02-27T00:00:00https://www.semanticscholar.org/paper/956243b0f7ea1870103eca64049250d3b2596138 d6af363527a4edc215d1f51cf8a18cec04f0ff00Modern neural network architectures still struggle to learn algorithmic procedures that require to systematically apply compositional rules to solve out-of-distribution problem instances. In this work, we propose an original approach to learn algorithmic tasks inspired by rewriting systems, a classic framework in symbolic artificial intelligence. We show that a rewriting system can be implemented as a neural architecture composed by specialized modules: the Selector identifies the target sub-expression to process, the Solver simplifies the sub-expression by computing the corresponding result, and the Combiner produces a new version of the original expression by replacing the sub-expression with the solution provided. We evaluate our model on three types of algorithmic tasks that require simplifying symbolic formulas involving lists, arithmetic, and algebraic expressions. We test the extrapolation capabilities of the proposed architecture using formulas involving a higher number of operands and nesting levels than those seen during training, and we benchmark its performance against the Neural Data Router, a recent model specialized for systematic generalization, and a state-of-the-art large language model (GPT-4) probed with advanced prompting strategies.arXiv.org290This work proposes an original approach to learn algorithmic tasks inspired by rewriting systems, a classic framework in symbolic artificial intelligence, and shows that a rewriting system can be implemented as a neural architecture composed by specialized modules.ArXiv['Flavio Petruzzellis', 'Alberto Testolin', 'A. Sperduti']2024-02-27T00:00:00https://www.semanticscholar.org/paper/d6af363527a4edc215d1f51cf8a18cec04f0ff00 b7b3d33d14de2a9cc13bbdff477913d4aa1abab9This conceptual paper examines the interplay between Porter’s Diamond, the role of government, and varying political ideologies on automated technology regulation in the global hospitality industry. The way in which these factors influence a global organization’s ability to achieve competitive advantage through the use of technology are examined. Specifically, mercantilist, liberal, social democratic, and communist ideologies are explored in relation to how they support or dissuade regulation, and their respective and collective impacts on competition. Additionally, the sources of government regulation, including global, bloc, country-level, and sub-country levels are discussed in relation to automated technology regulations. Ultimately, this study offers suggestions for competition as a result of existing and potential automated technology regulations for the hospitality industry, and suggests areas of study and questions for further consideration.Journal of Hospitality &amp; Tourism Research430Journal of Hospitality &amp; Tourism Research['Craig Webster', 'Lisa Cain']2024-02-26T00:00:00https://www.semanticscholar.org/paper/b7b3d33d14de2a9cc13bbdff477913d4aa1abab9 4929837619cf56a4627520237577ea7c424ed94dDiscover Artificial Intelligence940This comparative study reveals the intricacies and hurdles in formulating a cohesive global policy for AI regulation and proposes an innovative and integrated regulatory model that aims to bridge the gap between rapid AI advancements in the industry and the essential democratic processes of law-making.Discov. Artif. Intell.['Yoshija Walter']2024-02-26T00:00:00https://www.semanticscholar.org/paper/4929837619cf56a4627520237577ea7c424ed94d fe16fc328477c2c7ddc12eb3944cff69140c06d6In this paper, we project the impact of the implementation of a Joint Statement Initiative (JSI) on Services Domestic Regulation (SDR). We proceed in three steps. First, we include the WTO SDR Index, a binary score of SDR implementation in 23 sectors and 86 economies, in a gravity equation, estimated with the balance of payments services trade. We take into account domestic services trade to identify the impact of the importer-specific SDR Index by interacting the SDR Index with a border dummy, following an established methodology in the gravity literature. The estimation generates a significant impact of the SDR Index in a series of regressions pooled across all sectors, accounting for other determinants of services trade. Second, we map the estimates together with projected changes in the SDR Index into ad valorem equivalent trade cost changes under the implementation of the negotiated outcome on SDR. Estimated trade cost reductions are 10%, 14%, and 8.5% in lower-middle-income, upper-middle-income, and high-income countries respectively. In dollars, the estimated trade cost reduction of $127 Bn is similar to earlier OECD estimates of the trade cost reduction of the SDR of about $150 Bn. Third, the WTO Global Trade Model, a recursive dynamic computable general equilibrium (CGE) model, is employed to project the economic effects of the SDR outcome which are modelled as resource-saving reductions in iceberg trade costs. Simulations indicate that global income would increase by 0.3% in the long run (over 10 years), while global exports are projected to rise by 0.8%. The projected gains are largest in lower-and upper-middle-income countries while impacts on non-participants are projected to be marginally positive.WTO Working Papers510WTO Working Papers['Roger Yu So', '†. EddyBekkers']2024-02-26T00:00:00https://www.semanticscholar.org/paper/fe16fc328477c2c7ddc12eb3944cff69140c06d6 b767c165e7e9621c38c98c247856087f76e9f3401035This discussion examines the transformative impact of Artificial Intelligence in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills, and detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices.International Journal of Educational Technology in Higher Education['Yoshija Walter']2024-02-26T00:00:00https://www.semanticscholar.org/paper/b767c165e7e9621c38c98c247856087f76e9f340 0fd9465717fca7b9aee109b826a0c7ab2190cf42 Purpose This paper aims to explore the potential impact of artificial intelligence (AI) image generators, specifically MidJourney and DALL-E, on education and library services. The study aims to understand how these tools can revolutionize learning experiences and library resources while also addressing the ethical considerations surrounding their use. Design/methodology/approach This study investigates the technical foundations of MidJourney and DALL-E, highlighting their neural network architectures. It also traces the iterative refinement of these models and examines cost, accessibility and the unique prompt-guided capabilities of DALL-E 3. Findings MidJourney and DALL-E show remarkable progress in generating high-quality, photorealistic images from text prompts. The iterative refinement of these models demonstrates a trend toward improved creative output and user accessibility. DALL-E 3, in particular, allows users to guide image generation through prompt modifications, offering unprecedented control over the creative process. The study identifies potential applications in personalized learning, visual communication and research support in libraries, while recognizing challenges such as cost and accessibility. Originality/value This research innovatively explores AI's impact on education and libraries, detailing applications in personalized learning and research while addressing legal and ethical considerations. Library Hi Tech News62The technical foundations of MidJourney and DALL-E are investigated, highlighting their neural network architectures and the iterative refinement of these models demonstrates a trend toward improved creative output and user accessibility.Library Hi Tech News['Adebowale Jeremy Adetayo']2024-02-26T00:00:00https://www.semanticscholar.org/paper/0fd9465717fca7b9aee109b826a0c7ab2190cf42 91e0729e567f8caac75d82ef4562d0c4d6a9dd9fThis study examines the integration of genomics and artificial intelligence (AI) in the healthcare industry, focusing on the ethical and trust-related issues that arise from this integration. This integration highlights the significance of protecting genomic data by employing homomorphic encryption. This study emphasizes the significance of algorithmic transparency. It suggests employing interpretative frameworks like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to enhance the comprehensibility of AI algorithms. The article discusses two key regulatory measures: the Trustworthy Artificial Intelligence Initiative and the Genomic Data Sharing (GDS) Policy. This study examines the effectiveness of current practices in adapting to rapid technological advancements while maintaining ethical standards. This emphasizes the significance of attaining a balance between the benefits of predictive analytics in healthcare and the ethical considerations, such as informed consent and data integrity, as we transition from big data to big mechanisms. The importance of integration lies in its ability to revolutionize the healthcare sector. The study highlights the importance of robust governance frameworks to ensure technological advancements adhere to ethical standards and earn public trust. In summary, it is imperative to acknowledge and address the ethical considerations associated with integrating genomics and AI in healthcare to ensure effective and responsible implementation. This area is a major focus in contemporary medical research and practice.International Conference Control and Robots492It is imperative to acknowledge and address the ethical considerations associated with integrating genomics and AI in healthcare to ensure effective and responsible implementation, as the authors transition from big data to big mechanisms.2024 2nd International Conference on Cyber Resilience (ICCR)['F. Al-Akayleh', 'Ahmed S. A. Ali Agha']2024-02-26T00:00:00https://www.semanticscholar.org/paper/91e0729e567f8caac75d82ef4562d0c4d6a9dd9f 2ebb2fec20820931d43007de229567d247b0657eArtificial intelligence (AI) has made significant progress in addressing the specific obstacles related to post-cancer physical rehabilitation. This article examines AI technologies such as Support Vector Machines (SVM), Bayesian Inference, Reinforcement Learning, and Partially Observable Markov Decision Processes (POMDPs), focusing on their potential to improve the effectiveness and adaptability of rehabilitation strategies. SVM is recognized for its capability to analyze high-dimensional data obtained from wearable sensors, thereby enabling realtime patient monitoring. Bayesian methods facilitate the flexible adjustment of treatment plans, enhancing the efficient allocation of resources in healthcare environments. Reinforcement Learning enables realtime, dynamic optimization in robotic-assisted physiotherapy, yet it also raises ethical concerns regarding automated decision-making. POMDPs provide a mathematical framework for effectively addressing the uncertainties involved in post-cancer care. AI methods have significant potential for personalized and realtime adaptive treatments. However, it is important to address ethical considerations such as data privacy, informed consent, and algorithmic fairness through further investigation. This article emphasizes the importance of interdisciplinary research and ethical governance in maximizing the potential of AI in transforming post-cancer rehabilitation.International Conference Control and Robots512This article examines AI technologies such as Support Vector Machines, Bayesian Inference, Reinforcement Learning, and Partially Observable Markov Decision Processes, focusing on their potential to improve the effectiveness and adaptability of rehabilitation strategies.2024 2nd International Conference on Cyber Resilience (ICCR)['F. Al-Akayleh', 'M. Al-Remawi', 'Ahmed S. A. Ali Agha']2024-02-26T00:00:00https://www.semanticscholar.org/paper/2ebb2fec20820931d43007de229567d247b0657e 6cab8ef27c7e287badcf99615ff779a86305395bRapid advances in AI technology have important implications for, and effects on, brands and advertisers. Increasingly, brands are creating digital models to showcase clothing and accessories in a similar way to human models, with AI used to customize various body types, ages, sizes, and skin tones. However, little is known about how the underrepresented consumers respond to a brand's intention to use AI‐generated models to represent them. We explore this by conducting four studies. We find evidence that a brand's intention to use AI‐generated (vs. human) models negatively affects brand attitude (study 1). We further investigate this effect using two different underrepresented consumer groups: LGBTQIA+ consumers (study 2) and consumers with disabilities (study 3). We show the effect to be serially mediated by consumers' perception of greater threat to their self‐identity and a lower sense of belonging, subsequently having a negative effect on brand attitude. Finally, we show that the perception of a brand's motivation for representing diverse consumer groups can attenuate these negative effects (study 4). Specifically, when consumers believe a brand is intrinsically motivated to use AI‐generated diversity representations, they report a significantly lower social identity threat which in turn is associated with a significantly higher sense of belonging to the brand. Our research findings suggest that a brand's well‐meaning intentions to represent diversity can in fact have negative effects on the very consumers whom a brand is trying to attract. While catering to diversity is of critical importance, our results indicate that brand managers should exercise caution when using AI to appeal to diverse groups of potential consumers.Psychology &amp; Marketing781It is suggested that brand managers should exercise caution when using AI to appeal to diverse groups of potential consumers, as a brand's well‐meaning intentions to represent diversity can in fact have negative effects on the very consumers whom a brand is trying to attract.Psychology &amp; Marketing['S. Sands', 'Vladimir Demsar', 'Carla Ferraro', 'Colin Campbell', 'Justin Cohen']2024-02-26T00:00:00https://www.semanticscholar.org/paper/6cab8ef27c7e287badcf99615ff779a86305395b 9143af81a93b2b64adf48244fce1f020cf9a3217This article examines the impact of Artificial Intelligence (AI) on the comprehensive rehabilitation of post-cancer patients, specifically in the areas of psychological support and cognitive rehabilitation. AI platforms demonstrate the ability to provide personalized psychological interventions in real-time by utilizing advanced machine learning techniques such as Natural Language Processing (NLP), Random Forests, and Long Short-Term Memory (LSTM) networks. The research explores the use of AI in nutritional management for post-cancer care, including genomic-based dietary plans and the impact of nutrient-drug interactions. It emphasizes the ability of AI to rapidly adapt and provide personalized experiences, showing its potential to enhance post-cancer treatment outcomes. The article highlights the need for interdisciplinary collaboration to ensure the ethical and effective implementation of emerging AI technologies. Additional research is recommended to verify the effectiveness of these AI models on larger and more diverse groups of patients.International Conference Control and Robots521The research explores the use of AI in nutritional management for post-cancer care, including genomic-based dietary plans and the impact of nutrient-drug interactions and highlights the need for interdisciplinary collaboration to ensure the ethical and effective implementation of emerging AI technologies.2024 2nd International Conference on Cyber Resilience (ICCR)['Faisal Aburub', 'Ahmed S. A. Ali Agha']2024-02-26T00:00:00https://www.semanticscholar.org/paper/9143af81a93b2b64adf48244fce1f020cf9a3217 ec14f62c7472da7d3371d0140dce60607e7e411fThe field of sleep research is both broad and rapidly evolving. It spans from the diagnosis of sleep-related disorders to investigations of how sleep supports memory consolidation. The study of sleep includes a variety of approaches, starting with the sole focus on the visual interpretation of polysomnography characteristics and extending to the emergent use of advanced signal processing tools. Insights gained using artificial intelligence (AI) are rapidly reshaping the understanding of sleep-related disorders, enabling new approaches to basic neuroscientific studies. In this opinion article, we explore the emergent role of AI in sleep research, along two different axes: one clinical and one fundamental. In clinical research, we emphasize the use of AI for automated sleep scoring, diagnosing sleep-wake disorders and assessing measurements from wearable devices. In fundamental research, we highlight the use of AI to better understand the functional role of sleep in consolidating memories. While AI is likely to facilitate new advances in the field of sleep research, we also address challenges, such as bridging the gap between AI innovation and the clinic and mitigating inherent biases in AI models. AI has already contributed to major advances in the field of sleep research, and mindful deployment has the potential to enable further progress in the understanding of the neuropsychological benefits and functions of sleep.Clinical and Translational Neuroscience491This opinion article explores the emergent role of AI in sleep research, along two different axes: one clinical and one fundamental, and highlights the use of AI to better understand the functional role of sleep in consolidating memories.Clinical and Translational Neuroscience['Pinar Göktepe-Kavis', 'F. Aellen', 'Sigurd L. Alnes', 'A. Tzovara']2024-02-26T00:00:00https://www.semanticscholar.org/paper/ec14f62c7472da7d3371d0140dce60607e7e411f 2f2dacb39724b627d880c07c8810ee3187528442This article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402301How AI is not only transforming patient care but also shaping the future of healthcare delivery is highlighted, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['José Gabriel Carrasco Ramírez']2024-02-26T00:00:00https://www.semanticscholar.org/paper/2f2dacb39724b627d880c07c8810ee3187528442 ec6404dbc64909b7d950646e129827ee5ddbcf9bThe European legislature has proposed the Digital Services Act (DSA) and Artificial Intelligence Act (AIA) to regulate platforms and Artificial Intelligence (AI) products. We review to what extent third-party audits are part of both laws and to what extent access to models and data is provided. By considering the value of third-party audits and third-party data access in an audit ecosystem, we identify a regulatory gap in that the Artificial Intelligence Act does not provide access to data for researchers and civil society. Our contributions to the literature include: (1) Defining an AI audit ecosystem that incorporates compliance and oversight. (2) Highlighting a regulatory gap within the DSA and AIA regulatory framework, preventing the establishment of an AI audit ecosystem. (3) Emphasizing that third-party audits by research and civil society must be part of that ecosystem and demand that the AIA include data and model access for certain AI products. We call for the DSA to provide NGOs and investigative journalists with data access to platforms by delegated acts and for adaptions and amendments of the AIA to provide third-party audits and data and model access at least for high-risk systems to close the regulatory gap. Regulations modeled after European Union AI regulations should enable data access and third-party audits, fostering an AI audit ecosystem that promotes compliance and oversight mechanisms.arXiv.org981Regulations modeled after European Union AI regulations should enable data access and third-party audits, fostering an AI audit ecosystem that promotes compliance and oversight mechanisms.ArXiv['David Hartmann', "Jos'e Renato Laranjeira de Pereira", 'Chiara Streitbörger', 'Bettina Berendt']2024-02-26T00:00:00https://www.semanticscholar.org/paper/ec6404dbc64909b7d950646e129827ee5ddbcf9b 6d7f25772bc3df09e582476da4023619f6763932 Launched in a post pandemic world primed for technological solutions, Gen AI marked the beginning of a transformational period. Uses, norms, standards, and values embedded in AI technology are evolving at a dizzying pace and are in flux. The current period—termed “the Inter-AI Period”—presents an opportunity for researchers in management mathematics to shape an AI-enabled future. This window of opportunity will be short, after which mathematical principles and decision processes embedded in AI algorithms will harden. Technological evolution will certainly continue thereafter. But the views, norms, practices, and strategies would have been embedded in the technology. It is up to researchers to act now to shape an AI-enabled future—one that harnesses the powers of AI but with guardrails to protect users and humanity. This invited essay is based on research involving interviews of dozens of business leaders and scholars and is intended as a call to action for researchers to grasp the gravity of this period and act now.IMA Journal of Management Mathematics01This invited essay is based on research involving interviews of dozens of business leaders and scholars and is intended as a call to action for researchers to grasp the gravity of this period and act now.IMA Journal of Management Mathematics['Nada R Sanders']2024-02-26T00:00:00https://www.semanticscholar.org/paper/6d7f25772bc3df09e582476da4023619f6763932 fa4c5eeb70f3f25baa5a5e2bef7809bfe8a788a1Postdigital Science and Education402Postdigital Science and Education['Michael A. Peters', 'Benjamin J. Green']2024-02-26T00:00:00https://www.semanticscholar.org/paper/fa4c5eeb70f3f25baa5a5e2bef7809bfe8a788a1 744f7495c6cb7d184ce09490996992ab4ef6e6e3 Opera has historically been a site for developing new and emerging technologies. More recently, opera has proven a rich environment for investigating future scenarios involving human-robot interaction and exploring the potential of robot performers. The explosion of enthusiasm for AI and the ubiquity of machine learning tools have created numerous possibilities for generating creative content in music, poetry, images, and choreography. This article outlines efforts to explore the creative possibilities of robots and AI through a dedicated workshop with researchers from diverse fields. Our goal is to begin thinking beyond the human to investigate what robot performers might look and behave like when designed from a relational rather than a representational approach. We provide a brief history of robots on stage, including the authors’ prior work developing original performances featuring robots and human performers. We describe our iterative design approach and low- fi prototypes, summarizing key insights from the workshop. Finally, we analyze the workshop outcomes and consider the trade-offs of working with existing robots and the possibilities for working with open, configurable systems. Concept180This article outlines efforts to explore the creative possibilities of robots and AI through a dedicated workshop with researchers from diverse fields and provides a brief history of robots on stage, including the authors’ prior work developing original performances featuring robots and human performers.CONCEPT['Evelyn Ficarra', 'Tim Hopkins', 'Elizabeth Jochum', 'Chris Kiefer']2024-02-26T00:00:00https://www.semanticscholar.org/paper/744f7495c6cb7d184ce09490996992ab4ef6e6e3 1fdb5b8c2431f3384cb73dcde2764ed99a93d2f1The article examines the potential of Artificial Intelligence (AI) and machine learning in oncology rehabilitation. Traditional rehabilitation models have limitations in delivering personalized care in real-time. AI technologies close these gaps by utilizing advanced predictive capabilities and optimizing treatment strategies. Convolutional Neural Networks (CNNs) in radiomics provide a proactive approach to managing conditions such as lymphedema. In the field of physical rehabilitation, the integration of robotic systems with AI algorithms allows for real-time adaptive control mechanisms. This integration results in optimized muscle fiber recruitment and improves functional outcomes. Moreover, AI-powered platforms provide individualized psychological and nutritional assistance, enhancing the comprehensive care of individuals who have survived cancer. Despite the promising advancements, ethical considerations, including data privacy and algorithmic bias, necessitate a multidisciplinary approach for responsible implementation. Computational limitations, such as the requirement for extensive labeled datasets, present additional challenges. The analysis highlights the necessity of additional research to validate these emerging technologies, overcome their limitations, and establish ethical frameworks for their responsible clinical implementation.International Conference Control and Robots510The article examines the potential of Artificial Intelligence (AI) and machine learning in oncology rehabilitation and highlights the necessity of additional research to validate these emerging technologies, overcome their limitations, and establish ethical frameworks for their responsible clinical implementation.2024 2nd International Conference on Cyber Resilience (ICCR)['M. Al-Remawi', 'Faisal Aburub']2024-02-26T00:00:00https://www.semanticscholar.org/paper/1fdb5b8c2431f3384cb73dcde2764ed99a93d2f1 ca921065b3c4e091fcf23eb5938ffe0dbaaafefdArtificial Intelligence (AI) has emerged as a transformative force across numerous domains, from healthcare to finance and beyond. However, as AI systems become increasingly integrated into daily life, the ethical implications of their development and deployment are garnering significant attention. This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-02-26T00:00:00https://www.semanticscholar.org/paper/ca921065b3c4e091fcf23eb5938ffe0dbaaafefd b1506e988bf0d0b5189ef31704ab815c762d2aa6In the third decade of our century, AI is gradually becoming a part of daily life for people. The development of AI-based innovations in different fields such as navigation assistance software, image processing, and chatbots; and AI-based gear that helps paralyzed individuals regain their ability to walk, are convincing examples of how AI is being utilized more and more in daily life. As it develops, legal issues related to the use of AI may also arise, such as ethical issues, legal justice, due process of law, intellectual property, or personal data security. To mitigate legal problems, developing governance over AI is therefore necessary. This research is normative juridical research using statute and conceptual approaches. The legal analysis technique used is the argumentative analysis technique. The study findings indicate that since AI fundamentally differs from coding programs in that it is a dynamic system consisting of a network of algorithms that mimic biological neural networks, a different approach and governance system are required. This can be referred to as Lex Artificial Intelligence, or simply lex AI. Because of its uniqueness, AI governance cannot exclusively use the standard public or private ordering framework. It is then necessary to present lex AI as the sui generis governance with unique regulatory properties that can be paralleled with other laws as a law that complements those other laws.DiH: Jurnal Ilmu Hukum550The study findings indicate that since AI fundamentally differs from coding programs in that it is a dynamic system consisting of a network of algorithms that mimic biological neural networks, a different approach and governance system are required.DiH: Jurnal Ilmu Hukum['Ferdinand Lisaldy', 'Ismail Ismail', 'Dewi Iryani']2024-02-26T00:00:00https://www.semanticscholar.org/paper/b1506e988bf0d0b5189ef31704ab815c762d2aa6 52520d593d5bc942a5a49ec8008f977f7c56dcf4This empirical study focuses on the efficacy and efficiency of recruitment strategies in healthcare industry driven by organizational size, complexity as well as AI-based systems used for talent acquisition. These factors exert their influence through the mediating position that quality of candidate-job fit plays in this study. This research is conducted in the health sector in Dubai, United Arab Emirates with a valid sample of 309 employees from both public and private healthcare institutions. One of the main objectives is to measure how Al-driven, organizational size and complexity impacts efficacy and efficiency in recruitment strategies. In the case of health care, this study also considers how candidate-job fit quality mediates in such a relationship. In a quantitative way, there is an application of random sampling. The participants of this study were the employees in Dubai's health care industry, and a valid sample was formed from 309 respondents. To evaluate the hypotheses and model the interactions between the variables, the study uses partial least squares structural equation modelling, or PLS SEM. The research underscores the importance of organizational size and complexity and adds to the expanding body of knowledge on the use of AI in talent acquisition in the healthcare industry. The study intends to offer practical insights for healthcare organizations looking to improve their recruiting strategies through the implementation of AI-driven systems by investigating the mediating role of candidate-job match quality.International Conference Control and Robots130The study intends to offer practical insights for healthcare organizations looking to improve their recruiting strategies through the implementation of AI-driven systems by investigating the mediating role of candidate-job match quality.2024 2nd International Conference on Cyber Resilience (ICCR)['Samer Hamadneh', 'M. Alshurideh', 'E. Alquqa', 'Amer Al Kassem', 'K. Agha', 'Haitham M. Alzoubi']2024-02-26T00:00:00https://www.semanticscholar.org/paper/52520d593d5bc942a5a49ec8008f977f7c56dcf4 5da6a258ba89b36facca11af8bc43d077c6313f3Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary.We also present a study that assesses the quality of explanations created with this approachIUI Workshops210This paper introduces an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary, and assesses the quality of explanations created with this approach.ArXiv['Mohammad Hassany', 'Peter Brusilovsky', 'Jiaze Ke', 'Kamil Akhuseyinoglu', 'Arun Balajiee Lekshmi Narayanan']2024-02-26T00:00:00https://www.semanticscholar.org/paper/5da6a258ba89b36facca11af8bc43d077c6313f3 19ae3b335fb018adb004273ad5dfd25e908fc266AI has the potential to convert both the educating and learning processes; its application within the zone of instruction has gathered critical consideration over the globe for social engineering. AI's infusion in Ethiopia's instructive framework is the central contention of this research work. Ethiopia's instructive setting gives an interesting set of challenges and possibilities to study AI and its integration in education. The reason of this enquiry is to study and analyze the potential benefits, downsides, and strategies for utilizing AI in Ethiopian instructive settings. This research work commences with a thorough literature survey of the applications of AI within the instruction segments around the world and in Ethiopia. A mixed methodology research approach was delpoyed to collect data through in-depth interviews, case studies, and field observations. Policymakers, instructors, educationalists and parents are few of the stakeholders who would be able to gain valuable insights from recommendations of this research and utilize to set-up an educational framework in Africa.International Conference Control and Robots230Policymakers, instructors, educationalists and parents are few of the stakeholders who would be able to gain valuable insights from recommendations of this research and utilize to set-up an educational framework in Africa.2024 2nd International Conference on Cyber Resilience (ICCR)['M. Bishnoi', 'Swamynathan Ramakrishnan', 'Shanmugan Joghee', 'Anand Kumar', 'N. Thilagavathi']2024-02-26T00:00:00https://www.semanticscholar.org/paper/19ae3b335fb018adb004273ad5dfd25e908fc266 1d52a3874b76def582eb2c084471b02d358550d3In the midst of the digital revolution of the 21st century, cybersecurity has come to be a primary social situation, requiring revolutionary and Innovative solution. To find the proper answer for this pressing demand, AI (AI) has developed as an innovative catalyst that has fundamentally transformed the cybersecurity landscape. The power of AI is exemplified by its ability to process and interpret large and heterogeneous cybersecurity data sets optimizing critical functions such as threat detection, asset prioritization, and vulnerability management beyond human capabilities swiftly and accurately. This transformation is redefining our cybersecurity approach. This paper presents a full exploration of AI's extreme influence on cybersecurity, delving deep into how AI tools not only enhance but often surpass human-mediated processes. By unraveling the intricacies of integrating AI into the realm of cybersecurity, we vividly illustrate AI's potential to anticipate, detect, and proactively mitigate cyber threats, empowering organizations to bolster their digital security. However, it's vital to recognize the essential difficulties of AI. We underline the critical necessity for non-stop human administration and involvement to confirm that cybersecurity processes are in proportion and powerful. Additionally, we mention the probable ethical concerns and underscore the importance of strong authority structures to accelerate accountable and transparent AI usage in cybersecurity. This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future. It additionally lays the base for current investigation and discourse on the role of AI in cybersecurity—a conversation of growing importance in our unexpectedly advancing digital age.International Conference Control and Robots430This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future and lays the base for current investigation and discourse on the role of AI in cybersecurity.2024 2nd International Conference on Cyber Resilience (ICCR)['Mahmoud Mahfuri', 'Sameh Ghwanmeh', 'Rsha Almajed', 'Waseem Alhasan', 'Mohammed Salahat', 'Jin Hie Lee', 'Taher M. Ghazal']2024-02-26T00:00:00https://www.semanticscholar.org/paper/1d52a3874b76def582eb2c084471b02d358550d3 f088d93c6996de98cfaa30994fd3f2cd9c04cf32This study focuses on the estimate and analysis of social quotient (SQ), emotional quotient (EQ), and intellectual quotient (IQ) to investigate the synergy between artificial intelligence (AI) and human competence evaluation. Traditional evaluation procedures confront intrinsic limitations, necessitating a deeper look at AI's ability to revolutionize existing methods. AI's ability to absorb massive quantities of data, comprehend complicated behavioral patterns, and refine the identification of social, emotional, and intellectual qualities opens the door to more nuanced evaluations. This article digs at artificial intelligence AI's crucial position in data collecting, algorithmic processing, and the ethical concerns inherent in this expanding sector. This study intends to explain the progress and consequences of altering our knowledge of human capacities across multiple dimensions by scrutinizing the transformational potential and challenges of incorporating AI into SQ, EQ, and IQ evaluations.International Conference Control and Robots290This study intends to explain the progress and consequences of altering the authors' knowledge of human capacities across multiple dimensions by scrutinizing the transformational potential and challenges of incorporating AI into SQ, EQ, and IQ evaluations.2024 2nd International Conference on Cyber Resilience (ICCR)['Amer Ibrahim', 'Wael Ali', 'Nidal A. Al-Dmour', 'Taher M. Ghazal']2024-02-26T00:00:00https://www.semanticscholar.org/paper/f088d93c6996de98cfaa30994fd3f2cd9c04cf32 9d6ffe1a1252311f9495713401abfbdba0619336The role of teachers in utilizing artificial intelligence (AI) in educational services is a vital component of social engineering in modern education. AI technologies have the potential to transform education and learning by delivering tailored training, intelligent tutoring systems, and automated grading. However, effective AI integration in education is primarily reliant on instructors' direction, knowledge, and adaptability. This literature review study examines the various roles that teachers play in harnessing the power of AI in educational services. To begin, teachers act as guides for students as they traverse AI-powered tools and platforms. They offer guidance on how to leverage AI-powered resources to improve the outcomes of learning. Teachers are also in charge of creating and curating AI-powered learning material that corresponds with curricular objectives and engages students in meaningful ways. These are the pathways to do social engineering. However, challenges and problems may occur with the integration of AI in education, such as potential teacher displacement and ethical concerns about data privacy and algorithmic biases. As a result, instructors must actively participate in professional development in order to improve their grasp of AI, its consequences, and how to successfully integrate it into their teaching practice.International Conference Control and Robots270This literature review study examines the various roles that teachers play in harnessing the power of AI in educational services and offers guidance on how to leverage AI-powered resources to improve the outcomes of learning.2024 2nd International Conference on Cyber Resilience (ICCR)['Swamynathan Ramakrishnan', 'M. Bishnoi', 'Shanmugan Joghee', 'S. Jijitha', 'Anand Kumar']2024-02-26T00:00:00https://www.semanticscholar.org/paper/9d6ffe1a1252311f9495713401abfbdba0619336 313f285829a0f00ba76f68eb27a5e6368ecbf8e3The application of artificial intelligence (AI) in healthcare is growing as it becomes more prevalent in modern business and everyday life. It is frequently regarded as a significant technological advancement in the present period. In recent times, the fields of artificial intelligence (AI) and big data analytics have been utilised in the domain of mobile health (m-health) to establish a highly efficient healthcare system. Modern medical research utilises diverse and poorly understood data, including electronic health records (EHRs), medical imaging, and complex language that is widely unorganised. The growth of mobile applications, together with healthcare systems, is a significant factor leading to the presence of disorganised and unstructured datasets. The enhanced accessibility of diverse datasets and advanced computer techniques like machine learning can enable researchers to usher in a new era of highly efficient genetic therapy. This review paper has clarified the role of machine learning algorithms in healthcare systems.Journal of Engineering Research and Reports00The role of machine learning algorithms in healthcare systems is clarified and can enable researchers to usher in a new era of highly efficient genetic therapy.Journal of Engineering Research and Reports['Ashish K. Saxena', 'Stephanie Ness', 'Tushar Khinvasara']2024-02-26T00:00:00https://www.semanticscholar.org/paper/313f285829a0f00ba76f68eb27a5e6368ecbf8e3 30c4e370b7c4ccde4a37b1344a57500189f333bbThis article explores the educational significance of the closure of Z-library, an online platform enabling digital book piracy, through qualitative research with 103 postgraduates in a Sino-British Higher Education Institute (HEI) in China. Analysis found students viewed digital piracy as a tool to expedite academic practice and were weighed the criminal implications of the platform carefully. Consequently, the article suggests that universities need to consider further socioeconomically disadvantaged students alongside library resourcing and digital skills training. It recommends Artificial Intelligence (AI) and an ‘AI as Students Partners (AIasSP)’ philosophy as a solution to reduce student reliance on digital piracy. The article, therefore, highlights the potential of AI for improving skills-based study, administration, and improving the quality of the student experience. However, it concludes by discussing the ethical and privacy concerns raised by such an approach in Higher Education (HE), stressing the need for a multidisciplinary view of responsible AI as reshaping literacy and information retrieval practices across a process of lifelong learning.Quantum Journal of Social Sciences and Humanities480The article suggests that universities need to consider further socioeconomically disadvantaged students alongside library resourcing and digital skills training and recommends Artificial Intelligence and an ‘AI as Students Partners (AIasSP)’ philosophy as a solution to reduce student reliance on digital piracy.Quantum Journal of Social Sciences and Humanities['Michael James Day']2024-02-26T00:00:00https://www.semanticscholar.org/paper/30c4e370b7c4ccde4a37b1344a57500189f333bb 0529d5f12211d1807f24c55c082c913454213128Generative AI platforms and features are permeating many aspects of work. Entrepreneurs from lean economies in particular are well positioned to outsource tasks to generative AI given limited resources. In this paper, we work to address a growing disparity in use of these technologies by building on a four-year partnership with a local entrepreneurial hub dedicated to equity in tech and entrepreneurship. Together, we co-designed an interactive workshops series aimed to onboard local entrepreneurs to generative AI platforms. Alongside four community-driven and iterative workshops with entrepreneurs across five months, we conducted interviews with 15 local entrepreneurs and community providers. We detail the importance of communal and supportive exposure to generative AI tools for local entrepreneurs, scaffolding actionable use (and supporting non-use), demystifying generative AI technologies by emphasizing entrepreneurial power, while simultaneously deconstructing the veneer of simplicity to address the many operational skills needed for successful application.International Conference on Human Factors in Computing Systems990This paper details the importance of communal and supportive exposure to generative AI tools for local entrepreneurs, scaffolding actionable use (and supporting non-use), demystifying generative AI technologies by emphasizing entrepreneurial power, while simultaneously deconstructing the veneer of simplicity to address the many operational skills needed for success.ArXiv['Yasmine Kotturi', 'Angel Anderson', 'Glenn Ford', 'Michael Skirpan', 'Jeffrey P. Bigham']2024-02-26T00:00:00https://www.semanticscholar.org/paper/0529d5f12211d1807f24c55c082c913454213128 f38f6581ec1a0d051634ad0d38a7c5cca0d0c1fcMany sectors have been pursuing the adoption of Artificial Intelligence (AI) marketing tools, which promise to enhance the marketing process and digital transformation process. This transformation is driven by innovation, leading to the implementation of a more comprehensive approach in the business context, such as Industry 5.0. This article provides a broader review of AI marketing tools and Industry 5.0 by investigating the impact of AI marketing tools on implementing the three pillars of Industry 5:0, including customer-centric approach, resilience, and sustainability. Our findings through an extensive literature review indicate that AI marketing tools support implementing the three pillars of Industry 5.0 in the business context, highlighting the benefits and limitations of these implementations. These results may inform managers and policymakers who are involved in adapting AI marketing tools to implement Industry 5.0 successfully.International Conference Control and Robots720Investigating the impact of AI marketing tools on implementing the three pillars of Industry 5:0, including customer-centric approach, resilience, and sustainability indicates that AI marketing tools support implementing the three pillars of Industry 5.0 in the business context.2024 2nd International Conference on Cyber Resilience (ICCR)['Ali Mohammad Alenezi', 'Mohammad A.K Alsmairat', 'Nikolina Ljepava']2024-02-26T00:00:00https://www.semanticscholar.org/paper/f38f6581ec1a0d051634ad0d38a7c5cca0d0c1fc 9c6c45f1b04ee23d14de73473c068fbab7e1dd50In the current era of technological advancement, integrating Artificial Intelligence (AI) has become imperative globally. Whether or not we agree, it is essential to formulate policies and strategies for incorporating AI within the higher education system. Despite concerns regarding the potential negative impact of AI, it is evident that its evolution and widespread usage in educational environments cannot be halted. This study seeks to examine the positive impact of AI on higher education, with a specific focus on student learning experiences, academic integrity, and the creation of inclusive learning environments. Through a comprehensive literature review, this study highlights how AI contributes to personalized learning pathways, enhancing student engagement and improving educational outcomes. In the context of academic integrity, this paper concludes that the acceptance, usage, and implementation of AI by policymakers and academics will contribute to increased student engagement in a manner that reduces the likelihood of dishonest practices. By synthesizing current research findings, this study provides a balanced perspective on the benefits and challenges of AI in academia. The analysis suggests that, when implemented thoughtfully, AI can significantly transform higher education by enriching learning experiences, safeguarding academic integrity, and ensuring equity in access to education.International Conference Control and Robots340The analysis suggests that, when implemented thoughtfully, AI can significantly transform higher education by enriching learning experiences, safeguarding academic integrity, and ensuring equity in access to education.2024 2nd International Conference on Cyber Resilience (ICCR)['Wael Ali', 'Rachid Alami', 'Mohammad A.K Alsmairat', 'T. Almasaeid']2024-02-26T00:00:00https://www.semanticscholar.org/paper/9c6c45f1b04ee23d14de73473c068fbab7e1dd50 12a7da73807e763866ee73914a7ae08a691384d0This study seeks to determine the effects of AI-integrated digital marketing strategies on consumer behaviors, brand perceptions and sales performance. Using a mixed-methods approach, the paper integrates quantitative data from consumer surveys and digital analytics with qualitative information obtained by in-depth study of literature. The main purpose of the research encompasses the empirical assessment of itself significant AI applications, responsible for striving to achieve digital marketing such as personalized content suggestions by chatbots and predictive analytics. Using surveys assisted by the behavioral tracking that helps to understand how AI-driven personalization is dependent on consumer behavior and preference towards products or services. Moreover, the study focuses on how AI influences brand perception based upon consumer behavior towards brands adopting AI into their marketing strategies. In the same line of evaluating performance, study focuses on AI drives sales by studying conversion rates as well as customer retention and revenue growth. Finally, this study intends to provide useful information for practitioners in academia and industry on the relationship between AI-integrated digital marketing strategy processes that may influence consumer behavior, brand attitudes, and sales performance. The results would contribute as a guide in future marketing plans and they will enable businesses to utilize AI technologies better for higher efficiency and sustainable growth of developing markets that are dynamic digitally.International Conference Control and Robots170The research encompasses the empirical assessment of significant AI applications, responsible for striving to achieve digital marketing such as personalized content suggestions by chatbots and predictive analytics, and focuses on how AI influences brand perception based upon consumer behavior towards brands adopting AI into their marketing strategies.2024 2nd International Conference on Cyber Resilience (ICCR)['Anu Vij', 'Mohit Vij', 'Maged Farouk', 'P. Kumar']2024-02-26T00:00:00https://www.semanticscholar.org/paper/12a7da73807e763866ee73914a7ae08a691384d0 1690f61243a549b460cf0bd72a74fa1c65d18508The significant transformation of human resource (HR) management in the post- COVID-19 era reflects a paradigm shift, where organizations are increasingly turning to technology to optimize processes and maximize efficiency. This study looks at the integration of artificial intelligence (AI) and automation into HR practices, carefully investigating their impact on recruitment processes, HR management and employee engagement. Comprehensive analysis of how AI is revolutionizing recruitment methodologies and automating routine HR tasks reveals significant changes in how organizations manage their workforce. The research also examines how technology can improve employee engagement, highlighting ways in which AI-based solutions contribute to a more interactive and personalized employment experience. A crucial theme is addressed in the paper, the transition to data-driven decision making in HR. The challenges and considerations associated with implementing AI and automation, as well as anticipated future trends, are discussed in detail. Through careful analysis of these issues, the study aims to provide insights into the evolving role of technology in human resource management and its implications for organizations facing the current complexities of the modern workplace.Review of the Air Force Academy00Comprehensive analysis of how AI is revolutionizing recruitment methodologies and automating routine HR tasks reveals significant changes in how organizations manage their workforce.Review of the Air Force Academy['Florina FLOROIU (MIHAI)']2024-02-26T00:00:00https://www.semanticscholar.org/paper/1690f61243a549b460cf0bd72a74fa1c65d18508 7b3ce953a86182e67727796c4d34ccac5e71820cThe integration of high-throughput genomic sequencing and advanced AI algorithms is revolutionizing the fields of medicine and pharmaceuticals, particularly in the areas of personalized medicine and drug discovery. This article provides an overview of diverse genomic data types, such as DNA/RNA sequencing and single-cell genomics, and artificial intelligence techniques, including Support Vector Machines and deep learning architectures. These methodologies facilitate the extraction of useful information from intricate genomic data. Technological advancements such as DeepVariant and AlphaFold demonstrate the practical implications of this convergence. Transfer and reinforcement learning are specialized AI methods promising in optimizing treatment and analyzing biological networks. The research is a crucial resource for comprehending the influence of genomics and AI on healthcare industries.International Conference Control and Robots590An overview of diverse genomic data types, such as DNA/RNA sequencing and single-cell genomics, and artificial intelligence techniques, including Support Vector Machines and deep learning architectures, which facilitate the extraction of useful information from intricate genomic data.2024 2nd International Conference on Cyber Resilience (ICCR)['Rami A. Abdel Rahem', 'F. Al-Akayleh']2024-02-26T00:00:00https://www.semanticscholar.org/paper/7b3ce953a86182e67727796c4d34ccac5e71820c c6dbb67889eddbe4d5f8f99d29b2acb48bd41cdaThis article investigates the intricate dynamics of data monopolies, referred to as “data-opolies”, and their implications for democratic erosion. Data-opolies, typically embodied by large technology corporations, accumulate extensive datasets, affording them significant influence. The sustainability of such data practices is critically examined within the context of decentralized Web3 technologies amidst Artificial Intelligence (AI) disruption. Additionally, the article explores emancipatory datafication strategies to counterbalance the dominance of data-opolies. It presents an in-depth analysis of two emergent phenomena within the decentralized Web3 emerging landscape: People-Centered Smart Cities and Datafied Network States. The article investigates a paradigm shift in data governance and advocates for joint efforts to establish equitable data ecosystems, with an emphasis on prioritizing data sovereignty and achieving digital self-governance. It elucidates the remarkable roles of (i) blockchain, (ii) decentralized autonomous organizations (DAOs), and (iii) data cooperatives in empowering citizens to have control over their personal data. In conclusion, the article introduces a forward-looking examination of Web3 decentralized technologies, outlining a timely path toward a more transparent, inclusive, and emancipatory data-driven democracy. This approach challenges the prevailing dominance of data-opolies and offers a framework for regenerating datafied democracies through decentralized and emerging Web3 technologies.Big Data and Cognitive Computing1510The article investigates a paradigm shift in data governance and advocates for joint efforts to establish equitable data ecosystems, with an emphasis on prioritizing data sovereignty and achieving digital self-governance.Big Data Cogn. Comput.['Igor Calzada']2024-02-26T00:00:00https://www.semanticscholar.org/paper/c6dbb67889eddbe4d5f8f99d29b2acb48bd41cda c0f83e279fd868eede86a95cce991012cab9fd83Artificial Intelligence (AI) is becoming more ubiquitous throughout our lives. As our reliance on this technology increases, ensuring human operators maintain an adequate level of trust is integral to their safe and effective operations. To facilitate the appropriate level of operator trust in AI, a mechanism to continuously evaluate and calibrate human-AI trust is required. Such a Trust Management System (TMS) will be integral to developing trustworthy AI systems and thus enable collaborative and effective Human-AI Teaming (HAT) in future operations. This paper starts a review of the current state-of-the-art in trust research applicable to HAT, then summarizes the development and presents the IMPACTS (intention, measurability, performance, adaptivity, communication, transparency, security) homeostasis TMS. It is based on a dynamic and transactional trust framework and allows for continuous trust monitoring, managing, and behavior adjustment to ensure operator trust is calibrated.ACM Computing Surveys500The development and presentation of the IMPACTS (intention, measurability, performance, adaptivity, communication, transparency, security) homeostasis TMS, which is based on a dynamic and transactional trust framework and allows for continuous trust monitoring, managing, and behavior adjustment to ensure operator trust is calibrated.ACM Computing Surveys['Ming Hou', 'S. Banbury', 'Brad Cain', 'Scott Fang', 'Hannah Willoughby', 'Liam Foley', 'Edward Tunstel', 'Imre J. Rudas']2024-02-26T00:00:00https://www.semanticscholar.org/paper/c0f83e279fd868eede86a95cce991012cab9fd83 48a320efd19350cdb2e48bc5f38d5b5d2723051cIn this study, we examine whether the use of artificial intelligence (AI) can reduce the effect of independence conflicts on audit firm liability. In two experiments, we manipulate (1) whether procedures are performed by a human auditor or with AI and (2) whether the audit firm was careful in maintaining the appearance of independence from the audit client. Results of both experiments indicate that the use of AI significantly reduces the impact of the appearance of independence conflicts on jurors' judgments of audit firm liability. When concerns relating to the appearance of independence conflicts are present, the use of AI helps maintain the perceived objectivity of the auditor, which results in jurors maintaining higher overall trust in the audit process. Our study contributes to literature on determinants of auditor litigation risk and how technological change that is likely to grow in prominence might affect audit firm liability.This article is protected by copyright. All rights reserved.Contemporary Accounting Research01When concerns relating to the appearance of independence conflicts are present, the use of AI helps maintain the perceived objectivity of the auditor, which results in jurors maintaining higher overall trust in the audit process.Contemporary Accounting Research['Robert Libby', 'Patrick D. Witz']2024-02-26T00:00:00https://www.semanticscholar.org/paper/48a320efd19350cdb2e48bc5f38d5b5d2723051c 2eeb4ad2381a1b783e34f52bc864726ee42a2b27The study of the psychology of religion encompasses various aspects of human experiences and beliefs, including the influence of emerging technologies such as artificial intelligence (AI). This article aims to examine the impact of AI on religious practices and rituals, highlighting its potential to reshape how individuals engage with spirituality. By exploring AI-powered religious applications, virtual communities, and online services, we seek to understand the transformation of traditional religious practices and raise important questions about authenticity, inclusiveness, and the role of technology in the psychology of religious contexts. Moreover, ethical considerations and challenges arising from the integration of AI into religion will be addressed. As researchers delve into this intersection, it is crucial to strike a balance between technological advancements and preserving the fundamental aspects of spirituality, personal growth, and genuine human connection. This article contributes to the existing literature by shedding light on the potential implications of AI in the realm of religious experiences, calling for further exploration of its ethical dimensions and unintended consequences. Ultimately, understanding the influence of AI on the psychology of religion prompts us to reflect on the nature of spirituality, belief formation, and the human experience itself.Religions2221The impact of AI on religious practices and rituals is examined, highlighting its potential to reshape how individuals engage with spirituality and calling for further exploration of its ethical dimensions and unintended consequences.Religions['Khader I. Alkhouri']2024-02-26T00:00:00https://www.semanticscholar.org/paper/2eeb4ad2381a1b783e34f52bc864726ee42a2b27 5824606563375391c37ce8e26f6289df392962c4This study investigates users’ artificial intelligence (AI)-related competencies (i.e., AI knowledge, skills, and attitudes) and identifies the vulnerable user groups in the AI-shaped online news and entertainment environment. We surveyed 1088 Dutch citizens over the age of 16 years and identified five user groups through the latent class analysis: the average users, the expert advocates, the expert skeptics, the unskilled skeptics, and the neutral unskilled. The most vulnerable groups with the lowest levels of AI knowledge and AI skills (i.e., unskilled skeptics and neutral unskilled) were mostly older, with lower levels of education and privacy protection skills, than the average users. Overall, the results of this study resonate with the existing findings on the digital divide and provide evidence for an emerging AI divide among users. Finally, the societal implication of this study is discussed, such as the need for education programs and applications of the explainable AI.New Media &amp; Society921The most vulnerable groups with the lowest levels of AI knowledge and AI skills were mostly older, with lower levels of education and privacy protection skills, than the average users.New Media &amp; Society['Chenyue Wang', 'Sophie C Boerman', 'Anne C Kroon', 'Judith Möller', 'Claes H. de Vreese']2024-02-26T00:00:00https://www.semanticscholar.org/paper/5824606563375391c37ce8e26f6289df392962c4 1817e8fa43f48f28939b2ebe7a9057abe7562580 By introducing Article 4 in Directive 2019/790 (CDSMD), the European Union legislator intended to both encourage innovation and to provide more legal certainty for text and data mining (TDM) activities. That said, it appears that this provision does not strike a fair balance between the interests of rightholders and Artificial Intelligence (AI) developers. This article argues that Article 4(3) CDSMD does not necessarily strengthen the rightholders’ position whilst potentially hindering the advancement of AI developments in the European Union. It is unclear how the reservation of rights shall be realized in practice. By imposing transparency obligations on AI system providers, the upcoming Artificial Intelligence Act may however allow rightholders to make more effective use of the opt-out mechanism.Journal of Intellectual Property Law &amp; Practice01This article argues that Article 4(3) CDSMD does not necessarily strengthen the rightholders’ position whilst potentially hindering the advancement of AI developments in the European Union.Journal of Intellectual Property Law &amp; Practice['Gina Maria Ziaja']2024-02-26T00:00:00https://www.semanticscholar.org/paper/1817e8fa43f48f28939b2ebe7a9057abe7562580 5baea95bfec2bfac6f460fef97eae50316684e1aThis study is a literature review that examines the intricate interplay between innovative work behavior and compassion in the context of new social venture ideation, particularly within the era of artificial intelligence (AI). As organizations undergo profound transformations catalyzed by AI, it is crucial to comprehend how innovative work behaviors and compassionate initiatives synergize to shape the ideation of social ventures. Drawing from the social identity theory, which posits that individuals seeking distinction within society must contribute positively, this study underscores the significance of compassion as a prosocial emotion. This emotion, essential for engaging in new social venture ideation, connects individuals with suffering communities, fostering sensitivity to others' pain and needs. Such empathetic understanding becomes a catalyst for addressing societal issues. The insights garnered from this study offer valuable implications for both theoretical frameworks and ongoing research endeavors, particularly in the evolving landscape influenced by artificial intelligence.International Conference Control and Robots380Examining the intricate interplay between innovative work behavior and compassion in the context of new social venture ideation, particularly within the era of artificial intelligence, underscores the significance of compassion as a prosocial emotion.2024 2nd International Conference on Cyber Resilience (ICCR)['Anum Yazdani', 'Salima Hamouche', 'M. Shamout']2024-02-26T00:00:00https://www.semanticscholar.org/paper/5baea95bfec2bfac6f460fef97eae50316684e1a d9a970db3a9a56f8c31435f5f4e63b2da4d3fe67The study investigates the profound impact of Artificial Intelligence (AI) on various facets of the global economic landscape. Against a backdrop of rapid technological advancements, the study draws on the context of the pivotal IMF report highlighting the transformative potential of AI. The report suggests that AI could modify, replace, or transform about 60% of jobs in advanced economies and a significant proportion in emerging and low-income countries, reflecting a global paradigm shift in employment and economic structures. The core objective of this study is to thoroughly examine the role of AI-driven innovation in organizational competitiveness, its impact on community development and socioeconomic dynamics, and its implications on national economic policies and global economic trends. A quantitative research methodology was employed, involving a structured survey targeting a diverse group of professionals in various industries. The survey was meticulously designed to capture insights into participants' experiences and perceptions regarding AI implementation and its impacts. A total of 642 valid responses from consultants, technology enthusiasts, industry experts, and policymakers provided a robust dataset for analyzing the study's four hypotheses. The research findings reveal that AI integration significantly bolsters organizational competitiveness, echoing the insights from contemporary literature. Higher levels of AI adoption in communities are linked to improved socioeconomic outcomes, albeit with the risk of intensifying existing inequalities. On a national scale, strategies focusing on AI and innovation correlate with enhanced global economic competitiveness. Furthermore, the integration of AI in business processes markedly influences workforce dynamics, necessitating shifts in skill requirements and job roles. In light of these findings, the paper recommends strategic AI integration within businesses, equitable policy frameworks for AI deployment, a focus on AI in national economic strategies, substantial investment in workforce training, and international collaboration in AI development and ethics are imperative for maximizingAI's benefits while mitigating potential risks.Asian Journal of Economics Business and Accounting00The research findings reveal that AI integration significantly bolsters organizational competitiveness, echoing the insights from contemporary literature, and that higher levels of AI adoption in communities are linked to improved socioeconomic outcomes, albeit with the risk of intensifying existing inequalities.Asian Journal of Economics, Business and Accounting['Chinasa Susan Adigwe', 'O. O. Olaniyi', 'Samuel Oladiipo Olabanji', 'O. J. Okunleye', 'Nanyeneke Ravana Mayeke', 'Samson Abidemi Ajayi']2024-02-26T00:00:00https://www.semanticscholar.org/paper/d9a970db3a9a56f8c31435f5f4e63b2da4d3fe67 06ecc497edd61c2e548315a260edd0ca65242858BACKGROUND Institutional review boards (IRBs) have been criticised for delays in approvals for research proposals due to inadequate or inexperienced IRB staff. Artificial intelligence (AI), particularly large language models (LLMs), has significant potential to assist IRB members in a prompt and efficient reviewing process. METHODS Four LLMs were evaluated on whether they could identify potential ethical issues in seven validated case studies. The LLMs were prompted with queries related to the proposed eligibility criteria of the study participants, vulnerability issues, information to be disclosed in the informed consent document (ICD), risk-benefit assessment and justification of the use of a placebo. Another query was issued to the LLMs to generate ICDs for these case scenarios. RESULTS All four LLMs were able to provide answers to the queries related to all seven cases. In general, the responses were homogeneous with respect to most elements. LLMs performed suboptimally in identifying the suitability of the placebo arm, risk mitigation strategies and potential risks to study participants in certain case studies with a single prompt. However, multiple prompts led to better outputs in all of these domains. Each of the LLMs included all of the fundamental elements of the ICD for all case scenarios. Use of jargon, understatement of benefits and failure to state potential risks were the key observations in the AI-generated ICD. CONCLUSION It is likely that LLMs can enhance the identification of potential ethical issues in clinical research, and they can be used as an adjunct tool to prescreen research proposals and enhance the efficiency of an IRB.Journal of Medical Ethics190It is likely that LLMs can enhance the identification of potential ethical issues in clinical research, and they can be used as an adjunct tool to prescreen research proposals and enhance the efficiency of an IRB.Journal of medical ethics['K. Sridharan', 'G. Sivaramakrishnan']2024-02-26T00:00:00https://www.semanticscholar.org/paper/06ecc497edd61c2e548315a260edd0ca65242858 6b4e6a717e774055d061baa64ccf47e24d635b76In recent years, there has been a noticeable increase in the incidence of thyroid diseases, posing severe challenges to the traditional diagnosis and treatment methods. As a result, artificial intelligence technology has emerged as a valuable tool in the field of thyroid medical imaging, offering substantial support for auxiliary diagnosis and treatment in the future. In this paper, the application of artificial intelligence in the field of thyroid medical imaging is divided into three parts: image segmentation and texture analysis, benign and malignant diagnosis of thyroid gland, and postoperative analysis and prediction of thyroid conditions. Through an examination of recent research datasets and an analysis of the medical imaging process, this paper investigates the application of various algorithm models in these three areas and provides a comprehensive overview of the latest research trends. Furthermore, some certain directions for future research are also provided in this work.Applied and Computational Engineering00This paper investigates the application of various algorithm models in these three areas of thyroid medical imaging: image segmentation and texture analysis, benign and malignant diagnosis of thyroid gland, and postoperative analysis and prediction of thyroid conditions.Applied and Computational Engineering['Linze Chen']2024-02-26T00:00:00https://www.semanticscholar.org/paper/6b4e6a717e774055d061baa64ccf47e24d635b76 9f032d15fceda7ff26003dcdb2ebeb895de32af8This paper explored the essential role of Artificial Intelligence Artificial Intelligence applications within the field of clinical psychological research, examining their many-sided implications and potential future routes. The combination of Artificial Intelligence technologies in psychological research has accompanied in a standard shift, revolutionizing methodologies, diagnostic procedures, and therapeutic involvements. By studying the current landscape, we exposed the diverse applications of Artificial Intelligence, about predictive analytics, pattern recognition, and personalized treatment approaches, thereby enhancing both the efficiency and accuracy of psychological investigations. Besides, it navigates through the ethical considerations and challenges interfered with the increasing use of Artificial Intelligence in clinical psychology, emphasizing the imperative of ethical guidelines and oversight to ensure responsible and beneficial implementation. Furthermore, this study clarifies the future aspirations and untapped potential of Artificial Intelligence, envisioning collaborative human-Artificial Intelligence frameworks, innovative intervention strategies, and improved mental health care delivery systems. Through a comprehensive analysis of existing literature and practical studies, this research illuminates the transformative impact of Artificial Intelligence in clinical psychological research, offering insights into its current implications and charting a course toward its promising future applications.International Conference Control and Robots260This study clarifies the future aspirations and untapped potential of Artificial Intelligence, envisioning collaborative human-Artificial Intelligence frameworks, innovative intervention strategies, and improved mental health care delivery systems.2024 2nd International Conference on Cyber Resilience (ICCR)["Safa' Abunasrieh", 'Huthaifa Abdullah Alqeisi', 'Dalia Yaser Akileh', 'Abdallah M. A. Al-Tarawneh']2024-02-26T00:00:00https://www.semanticscholar.org/paper/9f032d15fceda7ff26003dcdb2ebeb895de32af8 8c7379b5b435231c5c22caff79df57682cb8bb05The purpose of this study is to determine whether artificial intelligence used in E-Commerce influences product recommendations for users. This study explains how much influence artificial intelligence on product recommendations supplied by E-commerce in terms of consumer behavior in making purchasing decisions. Research methods. This research used bibliometric analysis to find the mapping of this topic with articles period 2017 to 2023 from Scopus database. Of the 103 articles were showed by keyword and analyzing the articles according to the relate of the content about 29 articles were finally obtained. The research result is Artificial Intelligence has influence for E-commerce, recommendation system, decision support system, customer behaviour’s, and customer trust. Product recommendations have an impact on E-Commerce. Conclusion. However, from the literature review, founded that there are still a few journals discussing related to considerations to the implementation regarding the use of AI in e-commerce "Consumer behaviour", "Customer Trust", "Purchasing decisions". This study is also useful to generate additional AI-related research in e-commerce and unquestionably for a fresh subject will be covered especially in context of product recommendations on E-commerce.Advanced Information Systems320The research result is Artificial Intelligence has influence for E-commerce, recommendation system, decision support system, customer behaviour’s, and customer trust.Advanced Information Systems['Jeffry Vincent Louis', 'N. Noerlina', 'D. H. Syahchari']2024-02-26T00:00:00https://www.semanticscholar.org/paper/8c7379b5b435231c5c22caff79df57682cb8bb05 15ec66e07c9d911a7dd3c82e88258f01746d5e88Scientific Reports470Testing the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development found it able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.Scientific Reports['Ademir Franco', 'J. Murray', 'D. Heng', 'A. Lygate', 'D. Moreira', 'Jaqueline Ferreira', 'Djessyca Miranda E Paulo', 'Carlos Palhares Machado', 'J. Bueno', 'S. Mânica', 'L. Porto', 'A. Abade', 'Luiz Renato Paranhos']2024-02-26T00:00:00https://www.semanticscholar.org/paper/15ec66e07c9d911a7dd3c82e88258f01746d5e88 9bd172adcc0a6e9245a3eba8955c0cfe4da2e42bPhenomenology and the Cognitive Sciences450It is concluded that, as per current state, AI does not exhibit a basic or fundamental subjectivity and henceforth no consciousness or self is possible in models such as ChatGPT and similar technologies.Phenomenology and the Cognitive Sciences['Georg Northoff', 'Steven S. Gouveia']2024-02-26T00:00:00https://www.semanticscholar.org/paper/9bd172adcc0a6e9245a3eba8955c0cfe4da2e42b 1cc1cdf30343ce9619db24ea065c94f71771bd68Humanities and Social Sciences Communications340The Ritual Dialog Framework is introduced as a solution for better dialog between AI creators and users, blending anthropological insights with current acceptance challenges, and RDF focuses on building trust and a user-centered approach in XAI.Humanities and Social Sciences Communications['Aorigele Bao', 'Yi Zeng']2024-02-26T00:00:00https://www.semanticscholar.org/paper/1cc1cdf30343ce9619db24ea065c94f71771bd68 5026bbe6341abb488cd5b27fd5a97a8249e4baa4 Artificial intelligence (AI) has great potential to assist researchers in writing research proposals, by generating hypotheses, identifying literature, and suggesting methods for data collection and analysis. However, the use of AI in research proposal writing raises important bioethical implications, including the unintentional propagation of bias and questions about the role of human expertise and judgment in the research process. This paper explores the ethical implications of using AI in research proposal writing and proposes guidelines for the responsible and ethical use of AI in this context. The paper will review the potential benefits and challenges associated with using AI in research proposal writing, discuss the role of human expertise and judgment, and propose guidelines for promoting transparency and accountability in developing and using AI systems. Ultimately, addressing the bioethical issues related to AI in research proposal writing will require ongoing dialogue and collaboration between stakeholders, as well as a commitment to transparency, accountability, and ethical principles.Perspectives in Clinical Research200The ethical implications of using AI in research proposal writing are explored, guidelines for the responsible and ethical use of AI are proposed, and the role of human expertise and judgment are discussed.Perspectives in Clinical Research['S. Shivananda', 'V. Doddawad', 'C. S. Vidya', 'J. Chandrakala']2024-02-26T00:00:00https://www.semanticscholar.org/paper/5026bbe6341abb488cd5b27fd5a97a8249e4baa4 5d0e5746984695c0202920b02637e171b1085a7fObjective. The aim is to enhance the efficiency of diagnostics for determining the level of air attack safety through the practical integration principles of artificial intelligence. Methodology. Models and technologies for safety diagnostics of the region (territorial community) have been explored. The process of building an artificial intelligence model requires differentiation of objects at a level to accumulate assessments-characteristics of aerial vehicles. The practical integration principles of artificial intelligence into the forecasting technology are based on the Region Safety Index, used for constructing machine learning models. The optimal machine learning model of the proposed approach is selected from a list of several models. Results. A technology for predicting the level of regional safety based on the Safety Index has been developed. The recommended optimal model is the Random Forest model ([('max_depth', 13), ('max_features', 'sqrt'), ('min_samples_leaf', 1), ('min_samples_split', 2), ('n_estimators', 79)]), demonstrating the most effective quality indicators of MAE; MAX; RMSE 0.005; 0.083; 0.0139, respectively. Scientific Novelty. The proposed approach is based on a linear model of the Region Safety Index, which, unlike existing ones, takes into account the interaction of factors. This allows for advantages of the proposed method over existing approaches in terms of the root mean square error of 0.496; 0.625, respectively. In turn, this influences the quality of machine learning models. Practical Significance. The proposed solutions are valuable for diagnosing the level of safety in the region of Ukraine, particularly in the context of air attacks.Advanced Information Systems290The proposed approach is based on a linear model of the Region Safety Index, which, unlike existing ones, takes into account the interaction of factors and influences the quality of machine learning models.Advanced Information Systems['Oleksandr Shefer', 'Oleksandr Laktionov', 'Volodymyr Pents', 'Alina Hlushko', 'Nina Kuchuk']2024-02-26T00:00:00https://www.semanticscholar.org/paper/5d0e5746984695c0202920b02637e171b1085a7f 137c607b4a47c5c2ab95d81b0f396e3fc7b9da9dThe present study aims to establish a link between digital and humanitarian supply chain management. This study has focused on using artificial intelligence-big data analytical capabilities and information alignment to develop and maintain supply chain collaboration to achieve supply chain agility in a dynamic environment like a disaster. The targeted population is humanitarian organizations in Pakistan. Simple random sampling method, data was collected from 242 respondents using an online questionnaire. The Partial Least Square – Structural Equation Modelling technique has been used for analysis. Resource Based Theory and Contingency Theory in this study have provided foundations to develop and test the relationships among information alignment, supply chain agility, artificial intelligence – big data analytical capabilities, and supply chain collaboration in disaster management. Findings showed the use of artificial intelligence – big data analytical capabilities are beneficial for information alignment and supply chain agility.The Engineering Economist1150Findings showed the use of artificial intelligence – big data analytical capabilities are beneficial for information alignment and supply chain agility.Engineering Economics['Elisabeth T. Pereira', 'Muhammad Noman Shafique']2024-02-26T00:00:00https://www.semanticscholar.org/paper/137c607b4a47c5c2ab95d81b0f396e3fc7b9da9d c5334d73ab31d3822dc8ae5a9e9df60dc81c9383В статье рассматриваются различные трактовки понятий: искусственный интеллект, нейросети, киберсоциализация, практические примеры адаптации бизнеса к инновационным технологическим инструментам. В современном мире всё чаще применяются новые технологии, которые помогают облегчить жизнь и освободить человека от выполнения части рутинных обязанностей. И одной из наиболее популярных в последнее время технологий является искусственный интеллект, который применяется во многих сферах, начиная от простых действий в программах и интернете, заканчивая медицинскими или военными задачами. Благодаря многофункциональности, удобству и универсальности, искусственный интеллект пользуется активным спросом в сферах обслуживания, развлечения и коммуникации. The article discusses various interpretations of concepts: artificial intelligence, neural networks, cybersocialization, practical examples of business adaptation to innovative technological tools. In the modern world, new technologies are increasingly being used to help make life easier and free people from performing some of their routine duties. And one of the most popular technologies recently is artificial intelligence, which is used in many areas, ranging from simple actions in programs and the Internet, to medical or military tasks. Thanks to its versatility, convenience and versatility, artificial intelligence is in high demand in the areas of services, entertainment and communications.Вестник Академии права и управления00Вестник Академии права и управления['Д.Д. Макарова']2024-02-26T00:00:00https://www.semanticscholar.org/paper/c5334d73ab31d3822dc8ae5a9e9df60dc81c9383 6ffeb11b9ac2d90df23388b227549c541e3456b2The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of information and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge.Filosofiya osvity. Philosophy of Education00The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs, but intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge.Filosofiya osvity. Philosophy of Education['Anton Maleiev']2024-02-26T00:00:00https://www.semanticscholar.org/paper/6ffeb11b9ac2d90df23388b227549c541e3456b2 2d484c55c0c68ddd576d76e6af088485ac202c74Recently, a noticeable trend of using Generative Artificial Intelligence (GAI) within the educational field has raised interest to study the shape and impact of this utilization. This study investigates the opinions of Information Technology (IT) undergraduate students about the utilization of GAI during the learning process, focusing on their perception of the quality of produced information, challenges and concerns while using it, how they utilize it during learning, and long-term impact of using GAI. An electronic survey was distributed as a Google from to undergraduate students from five IT programs at the University of Petra. The results of 122 students revealed that 82% use GAI while learning. The main use of GAI (80% of respondents) was for solving programming problems, while 66% of respondents use GAI to write reports and express their ideas. The majority (86%) of participants evaluate the presented information from GAI before using them, since they believe that sometimes the information is incorrect. 89% of respondents believe that they can learn from GAI tools, however, 95% of them feel it is necessary to train them on the proper utilization of GAI in education, and, 76% think that regulations must be released about the acceptable use of GAI.International Conference Control and Robots150This study investigates the opinions of Information Technology (IT) undergraduate students about the utilization of GAI during the learning process, focusing on their perception of the quality of produced information, challenges and concerns while using it, how they utilize it during learning, and long-term impact of using GAI.2024 2nd International Conference on Cyber Resilience (ICCR)['Nuha El-Khalili', 'May Y. Al-Nashashibi', 'Salam Al-E’mari']2024-02-26T00:00:00https://www.semanticscholar.org/paper/2d484c55c0c68ddd576d76e6af088485ac202c74 e7a30e3f15ef43c544a4eceb28741c029d1fa984This study investigated the factors that influence the adoption of artificial intelligence (AI) in accounting among Malaysian Micro, Small Medium Enterprises (MSMEs). It focused on three factors based on the Technology, Organization, and Environment (TOE) framework, and the Diffusion of Innovation (DOI) theory. The factors examined include compatibility, complexity, security and privacy, top management support, business strategy support, organizational resources, business market structure, competitive pressure, and government regulations. A quantitative approach was employed to collect data which were then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) software. The study’s findings indicate that compatibility, top management support, business strategy support, organizational resources, business market structure, competitive pressure, and government regulations significantly impact AI adoption in MSMEs. However, the study did not find significant influences of complexity as well as security and privacy on AI adoption of Malaysian MSMEs. In conclusion, this study’s findings offer valuable insights on how organizations can effectively navigate these factors to achieve successful AI adoption in their accounting practices.Quantum Journal of Social Sciences and Humanities350The study’s findings indicate that compatibility, top management support, business strategy support, organizational resources, business market structure, competitive pressure, and government regulations significantly impact AI adoption in MSMEs, however, the study did not find significant influences of complexity as well as security and privacy on AI adoption of Malaysian MSMEs.Quantum Journal of Social Sciences and Humanities['Jia Wen Wong', 'Kiew Heong Angeline Yap']2024-02-26T00:00:00https://www.semanticscholar.org/paper/e7a30e3f15ef43c544a4eceb28741c029d1fa984 0f56035850b67caeb9b8c1efc7af8b14f3e9c4bdThis article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion is highlighted.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Most. Sohana Akter']2024-02-26T00:00:00https://www.semanticscholar.org/paper/0f56035850b67caeb9b8c1efc7af8b14f3e9c4bd 6272817e141c65ce26680c5a11185c2f35d36088 Artificial intelligence (AI) has reshaped significant aspects of our lives, including its role in healthcare. AI is a machine-based system that can make predictions, recommendations, and decisions influencing real or virtual environments of a given set of human-defined objectives. It is designed to operate with varying levels of autonomy. Since cardiovascular medicine is rapidly progressing and new technologies are introduced to cardiovascular tools, AI has become valuable in cardiovascular medicine. This narrative review will discuss the general concept of AI and its role in diagnosing cardiovascular diseases, including ECG, echocardiography, cardiac CT, nuclear cardiology, cardiac MRI, cardiac catheterization, electrophysiology, heart failure, clinical decision support system, and face recognition. New Emirates Medical Journal00This narrative review will discuss the general concept of AI and its role in diagnosing cardiovascular diseases, including ECG, echocardiography, cardiac CT, nuclear cardiology, cardiac MRI, cardiac catheterization, electrophysiology, heart failure, clinical decision support system, and face recognition.New Emirates Medical Journal['Rami Younes', 'Abdallah Almaghraby']2024-02-26T00:00:00https://www.semanticscholar.org/paper/6272817e141c65ce26680c5a11185c2f35d36088 1228480cf3a14d4b18f37da3af3fad73da12eef2This essay aims to examine topic related to artificial intelligence, explaining how it works and specially studying aspects of regulatory framework. The paper approaches the principle of explicability of AI decisions, performing an analysis on the braziilian parliamentary discussion and brazilian data protection act. The hermeneutic method was adopted as well as literature review.Concilium00The paper approaches the principle of explicability of AI decisions, performing an analysis on the braziilian parliamentary discussion and brazilian data protection act and the hermeneutic method was adopted.Concilium['Alexandre de Souza Araújo']2024-02-26T00:00:00https://www.semanticscholar.org/paper/1228480cf3a14d4b18f37da3af3fad73da12eef2 9eeb4e6fd3cff36936ac70e6641049f73d694a38As the menace of cyber threats intensifies, artificial intelligence emerges as a crucial tool for enhancing cybersecurity. This article delves into the advantages and drawbacks of AI in cybersecurity. The findings highlight positive outcomes in preventing and gathering information about cyber attacks using AI. In summary, advancing AI is imperative to address attacks' escalating volume and intricacy, recognizing that cybercriminals also leverage artificial intelligence for their malicious activities.International Conference Control and Robots250Advancing AI is imperative to address attacks' escalating volume and intricacy, recognizing that cybercriminals also leverage artificial intelligence for their malicious activities.2024 2nd International Conference on Cyber Resilience (ICCR)['Atif Ali', 'Shahzad Ahmed', 'Muhammad Hussain', 'Talat Ahmad Bhutta', 'Ali Raza', 'Muhammad Waqas Nadeem', 'Yasir Khan Jadoon', 'Farman Ali']2024-02-26T00:00:00https://www.semanticscholar.org/paper/9eeb4e6fd3cff36936ac70e6641049f73d694a38 ba06790221134c9581d825736568dc34d7cb2c72This study focuses on how Artificial Intelligence (AI) affects market analysis and the exploring of market possibilities, in the Abu Dhabi hospitality industry. The study also examines how effective entrepreneurial strategies mediate this relationship while considering the influence of market volatility. To gather data a quantitative research method is used with 221 participants from the Abu Dhabi hospitality industry in the UAE. The research model is evaluated using Structural Equation Modeling (SEM) through SmartPLS 4.0 allowing for an analysis of the proposed connections. Initial data analysis suggests that utilizing AI in market analysis has an impact on identifying market opportunities. Additionally, the study finds that effective entrepreneurial strategy development acts as a mediator in this relationship meaning that the positive effects of AI are partly explained by how well entrepreneurial strategies implemented. Moreover, market volatility plays a moderating role, in influencing how strongly AI utilization relates to discovering market opportunities.International Conference Control and Robots230The study finds that effective entrepreneurial strategy development acts as a mediator in this relationship meaning that the positive effects of AI are partly explained by how well entrepreneurial strategies implemented.2024 2nd International Conference on Cyber Resilience (ICCR)['M. Alshurideh', 'Bilal Zakarneh', 'Samer Hamadneh', 'Gouher Ahmed', 'Ch. Paramaiah', 'Haitham M. Alzoubi']2024-02-26T00:00:00https://www.semanticscholar.org/paper/ba06790221134c9581d825736568dc34d7cb2c72 0e8282f6b5ef9d19e975fbfaae3e567cb5d7e1f9As an emerging technology, artificial intelligence has great potential for improving employee training in enterprises. This paper aims to explore the significance of applying artificial intelligence in employee training and put forward relevant strategies for innovative employee training. Artificial intelligence constructs the process learning ecology through the whole thinking process of training, such as by emphasizing human-machine interaction, using artificial intelligence to create an immersive intelligent training system, focusing on reflective practical thinking, constantly improving the ability and level of artificial intelligence training, improving the learning ability and competitiveness of employees.Scientific and Social Research50The significance of applying artificial intelligence in employee training is explored and relevant strategies for innovative employee training are put forward.Scientific and Social Research['Zhaoyong Ouyang', 'Guanlin Liu', 'Lina Sha']2024-02-26T00:00:00https://www.semanticscholar.org/paper/0e8282f6b5ef9d19e975fbfaae3e567cb5d7e1f9 01e6ec12e1a60da5e2094d3424ef17b2544c090aThe development and distribution of COVID-19 vaccines are pivotal in halting the spread of the coronavirus. The pharmaceutical industry accelerated its processes to develop reliable vaccines. So far, six major firms have rolled out COVID-19 vaccines: Pfizer-BioNTech, Moderna, Johnson & Johnson, AstraZeneca, Sputnik, and Sinopharm. The global vaccination campaign kicked off in December 2020. Although over 905 million vaccines have been administered worldwide, there is an ongoing effort to vaccinate the remaining six billion people on the planet. A solid and effective supply chain strategy is essential for the equitable distribution of vaccines worldwide. Currently, there is a significant disparity in vaccine allocation among countries. This study explores the application of business intelligence systems to tackle this challenge. Implementing a sophisticated knowledge-driven supply chain system will lead to a more effective and efficient strategy for distributing the COVID-19 vaccine. This internationally monitored supply chain system will verify the volume and the paths of the vaccine shipments. As an intelligent knowledge-sharing platform, it will provide accurate data on the number of doses dispatched, including the specifics of each consignment's time and date. Furthermore, it will assist countries in understanding and planning the vaccine distribution logistics.International Conference Control and Robots260This study explores the application of business intelligence systems to tackle the significant disparity in vaccine allocation among countries and suggests a sophisticated knowledge-driven supply chain system will lead to a more effective and efficient strategy for distributing the COVID-19 vaccine.2024 2nd International Conference on Cyber Resilience (ICCR)['Hanady Al-zagheer', 'Ibrahim Abu Nahleh', 'Qais Hammouri', 'Hamza Ali Alshawabkeh']2024-02-26T00:00:00https://www.semanticscholar.org/paper/01e6ec12e1a60da5e2094d3424ef17b2544c090a 086462054a34e95494b1a11035404c3c2ef676b6Biomedical Journal of Scientific &amp; Technical Research02Biomedical Journal of Scientific &amp; Technical Research['Robert W McGee']2024-02-26T00:00:00https://www.semanticscholar.org/paper/086462054a34e95494b1a11035404c3c2ef676b6 f3b16342c706e70292e658f2695ac7ac44a2e4baPsychology and astrology are two disciplines that, at first glance, may seem unrelated. However, upon closer examination, there are intriguing intersections between the two that merit exploration. This conceptual note delves into the relationship between psychology and astrology, examining how insights from both fields can contribute to human well-being. By synthesizing psychological principles with astrological frameworks, individuals may gain deeper self-awareness, cultivate resilience, and navigate life's challenges with greater understanding and purposeJournal of Psychology &amp; Clinical Psychiatry01Journal of Psychology &amp; Clinical Psychiatry['Padmakali Banerjee', 'Braham Deep Sindhu', 'Swati Sindhu', 'Amita Puri', 'Astha Puri', 'Purnima Bamel', 'Aman Kumar', 'Taniya Singh', 'Acharya Ravinder Sharma', 'Vikas Sharma', 'Kapil Dev Nayar']2024-02-26T00:00:00https://www.semanticscholar.org/paper/f3b16342c706e70292e658f2695ac7ac44a2e4ba f7b5fd21eff55c38c77415284bee77d99bdd1f2eJournal of The American Geriatrics Society11Journal of the American Geriatrics Society['Rose M Li', 'Peter M Abadir', 'Alexis Battle', 'Rama Chellappa', 'N. Choudhry', 'George Demiris', 'Deepak Ganesan', 'Jason Karlawish', 'J. Moore', 'Jeremy D Walston']2024-02-26T00:00:00https://www.semanticscholar.org/paper/f7b5fd21eff55c38c77415284bee77d99bdd1f2e 191a40059f43cda3cbe54654572a0612c2409148The increasing complexity of the healthcare industry necessitates the recognition of human resources as a primary sustainable source of competitive advantage within healthcare management systems. This significance is magnified when healthcare professionals, physicians and nurses are considered. When human resource management (HRM) is discussed, it must be acknowledged that personnel are not devoid of emotions. The fostering of a healthy and sustainable working atmosphere is considered a critical responsibility by professional managers. In this research, a novel human resource management model is presented, which is based on AI: machine learning within the healthcare context. A deep learning model architecture based on CNN has been designed and optimized, which is trained in two scenarios through four datasets and also customized for the target hospital. The 92% accuracy is the power of the model in the recognition. The effectiveness of the model is assessed by curating a new dataset consisting of facial images of hospital professional staff displaying eight emotions: happiness, contempt, anger, sadness, disgust, fear, surprise, and neutrality. According to the post-implementation survey findings, the model has a positive impact on human resource management and enhances staff performance, making it suitable for modern and critical organizations such as hospitals and health canters. In the healthcare domain, effective communication is deemed essential for interactions with patients, as emotions play a significant role. Emotion recognition in human resource management has a profound impact not only on optimal work output but also on the relationships among personnel, clients, and the entire managed team. Qeios420A novel human resource management model is presented, which is based on AI: machine learning within the healthcare context and has a positive impact on human resource management and enhances staff performance, making it suitable for modern and critical organizations such as hospitals and health canters.Qeios['Saeed Rouhani', 'Mehran Rezvani', 'Yalda Madadi']2024-02-26T00:00:00https://www.semanticscholar.org/paper/191a40059f43cda3cbe54654572a0612c2409148 ca4df0956a5947f6b8695cf7cfdc9ac5ec68091bQuality Assurance (QA) aims to prevent mistakes and defects in manufactured products and avoid problems when delivering products or services to customers. QA for AI systems, however, poses particular challenges, given their data-driven and non-deterministic nature as well as more complex architectures and algorithms. While there is growing empirical evidence about practices of machine learning in industrial contexts, little is known about the challenges and best practices of quality assurance for AI systems (QA4AI). In this paper, we report on a mixed-method study of QA4AI in industry practice from various countries and companies. Through interviews with fifteen industry practitioners and a validation survey with 50 practitioner responses, we studied the concerns as well as challenges and best practices in ensuring the QA4AI properties reported in the literature, such as correctness, fairness, interpretability and others. Our findings suggest correctness as the most important property, followed by model relevance, efficiency and deployability. In contrast, transferability (applying knowledge learned in one task to another task), security and fairness are not paid much attention by practitioners compared to other properties. Challenges and solutions are identified for each QA4AI property. For example, interviewees highlighted the trade-off challenge among latency, cost and accuracy for efficiency (latency and cost are parts of efficiency concern). Solutions like model compression are proposed. We identified 21 QA4AI practices across each stage of AI development, with 10 practices being well recognized and another 8 practices being marginally agreed by the survey practitioners.arXiv.org1300A mixed-method study of QA4AI in industry practice from various countries and companies suggests correctness as the most important property, followed by model relevance, efficiency, efficiency and deployability.ArXiv['Chenyu Wang', 'Zhou Yang', 'Zewei Li', 'Daniela E. Damian', 'David Lo']2024-02-26T00:00:00https://www.semanticscholar.org/paper/ca4df0956a5947f6b8695cf7cfdc9ac5ec68091b afe9931d53aa7f9bfe94a964c4868a21c941c195The 14th European Nutrition Conference FENS 202300The 14th European Nutrition Conference FENS 2023['S. Wilson-Barnes', 'L. Gymnopoulos', 'K. Stefanidis', 'D. Tsatsou', 'R. Leoni', 'J. M. Botana', 'Kathryn H. Hart']2024-02-26T00:00:00https://www.semanticscholar.org/paper/afe9931d53aa7f9bfe94a964c4868a21c941c195 ab9885a2ff40529f0d6b27ed0e210559b8f68c90IS4SI Summit 202360IS4SI Summit 2023['Xiaoli Yang', 'Songbai Wang']2024-02-26T00:00:00https://www.semanticscholar.org/paper/ab9885a2ff40529f0d6b27ed0e210559b8f68c90 af578d7656a2ba85e29f2f49ce8ee15fe564e9bbJournal of public health500Journal of Public Health['Mohsen Khosravi', 'Zahra Zare', 'Seyyed Morteza Mojtabaeian', 'Reyhane Izadi']2024-02-26T00:00:00https://www.semanticscholar.org/paper/af578d7656a2ba85e29f2f49ce8ee15fe564e9bb 92e70c27e127b9e73b0b9c2ad2e7c1287f8c212400['M. A. Jabbar', 'S. Tiwari', 'S. Pani', 'Stephen Huang']2024-02-26T00:00:00https://www.semanticscholar.org/paper/92e70c27e127b9e73b0b9c2ad2e7c1287f8c2124 a972fe0bdc5fe7d5c86ac3ab740d9241984ca46900['Gabriele Gianini', 'P. Portier']2024-02-26T00:00:00https://www.semanticscholar.org/paper/a972fe0bdc5fe7d5c86ac3ab740d9241984ca469 47cceed04012b6acd8e5771e0ac250a5fe906c53World journal of urology10World Journal of Urology['Junjun Wang', 'Xing Yun']2024-02-26T00:00:00https://www.semanticscholar.org/paper/47cceed04012b6acd8e5771e0ac250a5fe906c53 7b8dcf8d21172be92c968cd8ebb418c0d53a6f8fThe rapid development of artificial intelligence technology brought about innovative changes in society in the era of the 4th industrial revolution. The image design sector is also one of the areas seeking new approaches using artificial intelligence technology. Image-generating artificial intelligence SW is expected to expand its application area in that users can efficiently generate the images they want through active deep learning. This study explored the development of a new service platform to create a region-specific image through cultural values. When various cultural heritage image data are collected and processed, and local product and landmark-related data are learned, a design automatic generation platform might be implemented. Through the introduction of the platform based on the Text-to-Image model, the development of regional specialized design products and customized tourism products would become more active.International Conference Control and Robots60This study explored the development of a new service platform to create a region-specific image through cultural values based on the Text-to-Image model, which would mean the development of regional specialized design products and customized tourism products would become more active.2024 2nd International Conference on Cyber Resilience (ICCR)['Dong Cheol Lee', 'BooYun Cho', 'Hae Jin Kim', 'Yoon Seob Nam']2024-02-26T00:00:00https://www.semanticscholar.org/paper/7b8dcf8d21172be92c968cd8ebb418c0d53a6f8f 7ea4be57744d44e4a82d72b2ea81fde44d54ee1aThe integration of Artificial Intelligence (AI) into big data analytics represents a pivotal shift in the management of Personally Identifiable Information (PII) within the financial sector. This study was prompted by the increasing reliance on AI for handling sensitive financial data and the consequent rise in data security concerns, exemplified by the 2019 Capital One data breach which compromised the PII of over 100 million individuals, highlighting the vulnerabilities inherent in digital data storage and management systems. Aiming to critically evaluate the effects of adopting AI in exploring big data on PII within the financial and economic sectors, the study focused on assessing how AI can transform data management processes, enhance data security, ensure compliance with regulatory requirements, and maintain data integrity. Employing a quantitative research methodology, data was gathered from 532 professionals in the financial sector through surveys distributed via LinkedIn. The hypotheses were tested using multiple regression analysis. The study's findings revealed that the adoption of AI in managing big data significantly enhances the security and privacy of PII in the financial sector. However, it also increases the risk of sophisticated cyber-attacks such as adversarial attacks and data poisoning. Significantly, financial institutions that integrate AI into their data management systems demonstrate higher compliance with data protection regulations, and AI-driven cybersecurity strategies were found to markedly improve the performance of cybersecurity systems in the sector. Based on these insights, the study recommends best practices and guidelines for financial institutions to effectively integrate AI into their data management systems. These include prioritizing data security and privacy, ensuring regulatory compliance, investing in AI-driven cybersecurity, and managing the inherent risks of AI integration. The study advocates for a balanced approach in AI adoption, emphasizing the need for robust security measures, continuous monitoring, and adapting to the evolving regulatory and technological landscape.Asian Journal of Economics Business and Accounting00The study's findings revealed that the adoption of AI in managing big data significantly enhances the security and privacy of PII in the financial sector, however, it also increases the risk of sophisticated cyber-attacks such as adversarial attacks and data poisoning.Asian Journal of Economics, Business and Accounting['Samuel Oladiipo Olabanji', 'Oluseun Babatunde Oladoyinbo', 'Christopher Uzoma Asonze', 'Tunbosun Oyewale Oladoyinbo', 'Samson Abidemi Ajayi', 'O. O. Olaniyi']2024-02-26T00:00:00https://www.semanticscholar.org/paper/7ea4be57744d44e4a82d72b2ea81fde44d54ee1a d5104608c064a4011bcf4d22ac18f1dbbb7b2ad3Artificial Intelligence (AI) has emerged as a transformative force in various domains, including tax compliance and financial regulation. This review explores the pivotal role of AI in enhancing these critical aspects of governance and economic stability. In the realm of tax compliance, AI-driven solutions offer unprecedented opportunities for governments to streamline tax administration processes, detect non-compliance, and mitigate tax evasion. Machine learning algorithms can analyze vast volumes of financial data with remarkable speed and accuracy, identifying patterns indicative of tax fraud or evasion. Furthermore, AI-powered predictive analytics enable tax authorities to anticipate taxpayer behavior and allocate resources effectively for enforcement purposes. By leveraging AI, governments can enhance revenue collection efficiency while minimizing compliance burdens on taxpayers. In financial regulation, AI technologies play a crucial role in monitoring and enforcing compliance with complex regulatory frameworks. With the exponential growth of financial transactions and the increasing sophistication of financial instruments, traditional regulatory mechanisms often struggle to keep pace. AI systems equipped with natural language processing capabilities can sift through immense volumes of regulatory documents and financial data to identify potential violations and assess systemic risks. Moreover, AI-based risk assessment models enable regulators to proactively identify emerging threats to financial stability, thereby facilitating timely interventions to prevent crises. However, the integration of AI in tax compliance and financial regulation also presents challenges and ethical considerations. The reliance on algorithmic decision-making raises concerns regarding transparency, accountability, and bias mitigation. Moreover, the proliferation of AI-driven solutions may exacerbate existing socio-economic disparities, as access to advanced technology remains uneven across jurisdictions and economic strata. While AI holds immense promise for enhancing tax compliance and financial regulation, its implementation must be accompanied by robust governance frameworks and ethical guidelines. Collaborative efforts between policymakers, regulators, and technology developers are essential to harnessing the full potential of AI while safeguarding against unintended consequences. Through responsible deployment and continuous refinement, AI can serve as a powerful tool in promoting fiscal transparency, regulatory effectiveness, and economic resilience. Keywords:  Artificial Intelligence, Tax, Financial, Compliance, Regulation, Review.Finance &amp; Accounting Research Journal02While AI holds immense promise for enhancing tax compliance and financial regulation, its implementation must be accompanied by robust governance frameworks and ethical guidelines, and collaboration between policymakers, regulators, and technology developers are essential to harnessing the full potential of AI.Finance &amp; Accounting Research Journal['Joseph Kuba Nembe', 'Joy Ojonoka Atadoga', 'Noluthando Zamanjomane Mhlongo', 'Titilola Falaiye', 'Odeyemi Olubusola', 'Andrew Ifesinachi Daraojimba', 'Bisola Beatrice Oguejiofor']2024-02-25T00:00:00https://www.semanticscholar.org/paper/d5104608c064a4011bcf4d22ac18f1dbbb7b2ad3 e8201b8e245e29ff0a5cff896b4288bd098c41c5The European Union (EU) Timber Regulation (EUTR) formally requires EU operators to conduct due diligence along their supply chains to prevent illegally sourced timber products from entering the European market. Little is known about the regulatory behaviour and motivations of operators to comply with this regulation. We explore the regulatory behaviour of companies by applying a synthesis of behavioural theories of regulatory compliance and transnational market regulation. Informed by qualitative and quantitative mixed methods, this study finds that EUTR compliance is influenced by operators' regulative, economic, normative and cultural‐cognitive motivations. The empirical analyses reveal that larger, publicly exposed companies are driven to comply through social pressure and the deterrence effect of sanctions and control. Operators' perceptions of the costs and benefits do not explain compliance behaviour in a significant, quantitative way. The Internal values to abide by the law are found to be a stronger motivator than economic cost–benefit calculations.Journal of Common Market Studies320JCMS: Journal of Common Market Studies['M. Köthke', 'M. Sotirov']2024-02-25T00:00:00https://www.semanticscholar.org/paper/e8201b8e245e29ff0a5cff896b4288bd098c41c5 670bee1fe0e48c5f23d8de7918428d744dc614abThe burgeoning challenges of waste management have propelled the integration of artificial intelligence (AI) into waste management systems, aiming to enhance efficiency, sustainability, and environmental impact. This abstract delves into the comparative review of AI-driven waste management innovations in the USA and Africa, illuminating the divergent strategies employed to address distinct contextual demands. In the USA, where waste management infrastructures are relatively advanced, AI technologies are leveraged to optimize waste collection routes, automate sorting processes, and enhance recycling efficiency. Machine learning algorithms analyze historical data to predict waste generation patterns, enabling municipalities to allocate resources more effectively. Additionally, robotic sorting systems equipped with computer vision contribute to the accurate segregation of recyclables, reducing contamination and promoting a circular economy. Conversely, in Africa, where waste management infrastructures may be less developed, AI applications prioritize scalable and adaptable solutions. Mobile applications powered by AI facilitate crowd-sourced waste reporting, enabling citizens to actively participate in waste management efforts. Furthermore, sensor-equipped smart bins optimize collection routes in real-time, improving resource utilization. The emphasis on community engagement and decentralized solutions reflects the unique challenges and opportunities present in African waste management contexts. Despite these regional disparities, common themes emerge, such as the role of data analytics, automation, and community involvement in shaping effective waste management systems. The comparative analysis underscores the importance of tailoring AI-driven innovations to the specific socio-economic and infrastructural landscapes of each region. Ultimately, understanding the nuanced approaches in the USA and Africa can inform a more holistic and globally adaptable framework for AI-driven waste management systems. Keywords: Al, Waste Management, USA, Africa, Innovation, Sanitation.Engineering Science &amp; Technology Journal06Understanding the nuanced approaches in the USA and Africa can inform a more holistic and globally adaptable framework for AI-driven waste management systems, underscoring the importance of tailoring AI-driven innovations to the specific socio-economic and infrastructural landscapes of each region.Engineering Science &amp; Technology Journal['Zamathula Queen Sikhakhane Nwokediegwu', 'Ejike David Ugwuanyi', 'Michael Ayorinde Dada', 'Michael Tega Majemite', 'Alexander Obaigbena']2024-02-25T00:00:00https://www.semanticscholar.org/paper/670bee1fe0e48c5f23d8de7918428d744dc614ab 8c2a604f0079d78fa8b1ff54102533212dca66dbAI &amp; SOCIETY321It is shown how pursuing a narrative understanding of technology and AI can support knowledge of process and practice through transparency, as well help summon us to responsibility through visions of possibility and of actual harms arising from AI practices.AI &amp; SOCIETY['Paul Hayes', 'Noel Fitzpatrick']2024-02-25T00:00:00https://www.semanticscholar.org/paper/8c2a604f0079d78fa8b1ff54102533212dca66db 872d932801a4c8653ec6f568e18ee6b147181183<jats:p>.</jats:p>Science Editor and Publisher34Science Editor and Publisher['Council Cope']2024-02-25T00:00:00https://www.semanticscholar.org/paper/872d932801a4c8653ec6f568e18ee6b147181183 adc4bc346850a8047869666cc2112934f3f89d82Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.Bioengineering1270This paper narrows its focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence.Bioengineering['Xi Xu', 'Jianqiang Li', 'Zhichao Zhu', 'Linna Zhao', 'Huina Wang', 'Changwei Song', 'Yining Chen', 'Qing Zhao', 'Jijiang Yang', 'Yan Pei']2024-02-25T00:00:00https://www.semanticscholar.org/paper/adc4bc346850a8047869666cc2112934f3f89d82 c39c21f9cd9a85809cbc0dfc0ed6b004e6c8ef8bConversational Agents (CAs) have increasingly been integrated into everyday life, sparking significant discussions on social media. While previous research has examined public perceptions of AI in general, there is a notable lack in research focused on CAs, with fewer investigations into cultural variations in CA perceptions. To address this gap, this study used computational methods to analyze about one million social media discussions surrounding CAs and compared people's discourses and perceptions of CAs in the US and China. We find Chinese participants tended to view CAs hedonically, perceived voice-based and physically embodied CAs as warmer and more competent, and generally expressed positive emotions. In contrast, US participants saw CAs more functionally, with an ambivalent attitude. Warm perception was a key driver of positive emotions toward CAs in both countries. We discussed practical implications for designing contextually sensitive and user-centric CAs to resonate with various users' preferences and needs.International Conference on Human Factors in Computing Systems1200Chinese participants tended to view CAs hedonically, perceived voice-based and physically embodied CAs as warmer and more competent, and generally expressed positive emotions, compared to US participants, who saw CAs more functionally, with an ambivalent attitude.{'pages': '155:1-155:17'}['Zihan Liu', 'Han Li', 'Anfan Chen', 'Renwen Zhang', 'Yi-Chieh Lee']2024-02-25T00:00:00https://www.semanticscholar.org/paper/c39c21f9cd9a85809cbc0dfc0ed6b004e6c8ef8b 83afded0935f1920d735dfdbccf907a44dda60f1In the aftermath of the COVID-19 pandemic, college students have faced various challenges that could negatively impact their critical thinking abilities due to disruptions to education, increased stress and anxiety, less social interaction, and the advancement of distance learning relying more heavily on digital tools. With the increasing integration of AI technology across sectors, higher education institutions have deployed various AI capabilities for intelligent campuses and modernized teaching. However, how to fully utilize AI capabilities to promote students’ thinking awareness on learning effectiveness is still not clear, as critical thinking is an essential skill set holding significant implications for college students’ development. This research adopts the resource-based theory (RBT) to conceptualize the university as a unified entity of artificial intelligence (AI) resources. It aims to investigate whether AI capabilities can foster critical thinking awareness among students by enhancing general self-efficacy and learning motivation. In particular, it examines the causal relationships between AI capabilities, general self-efficacy, motivation and critical thinking awareness. Primary data was collected through a questionnaire administered to 637 college students. Structural equation modeling was employed to test hypotheses pertaining to causality. The results showed that AI capabilities could indirectly enhance students’ critical thinking awareness by strengthening general self-efficacy and learning motivation, but the effect on critical thinking awareness was not significant. Meanwhile, general self-efficacy significantly affected the formation of learning motivation and critical thinking awareness. This indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution. This study explored the role of AI capabilities in education from the perspective of organizational capabilities. It not only proves how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process. This finding explains the inherent connections between the mechanism links. Furthermore, the study expands research on AI capabilities research from the technical level to the educational field. It provides a comprehensive and in-depth theoretical explanation theoretically, guiding the practice and application of AI in education. The study is of positive significance for understanding the need for the future development of the cultivation of critical thinking awareness talents needed for future development through AI capabilities in education.Systems1510It is proved how AI facilitates cognition, but also discovered the important mediating role of general self-efficacy and motivation in this process, which indicates that AI capabilities are able to reshape the cognitive learning process, but its direct influence on thinking awareness needs to be viewed with caution.Systems['Xi-Hui Jia', 'Jui-Che Tu']2024-02-25T00:00:00https://www.semanticscholar.org/paper/83afded0935f1920d735dfdbccf907a44dda60f1 9ca5cc2d1866db0a45cb11b466135e5289c447ccThe debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction and explainability are desirable goals in principle, the recent spread of high accuracy yet opaque machine learning (ML) algorithms has highlighted the trade-off between the two, marking this debate as an inter-disciplinary, inter-professional arena for negotiating expertise. There is no longer an agreement about what should be the “default” balance of prediction and explainability, with various positions reflecting claims for professional jurisdiction. Overall, there appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated. The impetus for writing this commentary comes from recent suggestions that explainability is overrated, including the argument that explainability is not guaranteed in human healthcare experts either. To shed light on this debate, its premises, and its recent twists, we provide an overview of key arguments representing different frames, focusing on AI in healthcare.Big Data & Society250There appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated, focusing on AI in healthcare.Big Data Soc.['Aviad E. Raz', 'Bert Heinrichs', 'Netta Avnoon', 'Gil Eyal', 'Yael Inbar']2024-02-25T00:00:00https://www.semanticscholar.org/paper/9ca5cc2d1866db0a45cb11b466135e5289c447cc 2114fffc6c0097b524eea911ce0e8976bf4358a4This commentary examines the inherent contradictions between participation in artificial intelligence (AI), controversy studies, and AI narratives.Big Data & Society220Big Data Soc.['Mona Sloane']2024-02-25T00:00:00https://www.semanticscholar.org/paper/2114fffc6c0097b524eea911ce0e8976bf4358a4 21d85c829f3954965b1d570517b71e6291241eb8The integration of artificial intelligence (AI) technologies holds significant promise in addressing pressing environmental and social challenges, thus contributing to sustainable development efforts worldwide. This article provides a comprehensive overview of the role of AI in tackling various aspects of sustainability, including environmental conservation, resource management, climate change mitigation, and social equity. By leveraging AI techniques such as machine learning, optimization, and data analytics, innovative solutions are being developed to monitor ecosystems, optimize energy consumption, enhance agricultural practices, and promote social inclusion. However, alongside these opportunities, there are also ethical, regulatory, and socio-economic considerations that must be carefully addressed to ensure that AI interventions contribute positively to sustainable development goals. This paper highlights recent advancements, challenges, and future directions in utilizing AI for sustainable development, emphasizing the importance of interdisciplinary collaboration and stakeholder engagement in realizing the full potential of AI-enabled solutions.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This paper highlights recent advancements, challenges, and future directions in utilizing AI for sustainable development, emphasizing the importance of interdisciplinary collaboration and stakeholder engagement in realizing the full potential of AI-enabled solutions.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.Safikul Isalm']2024-02-25T00:00:00https://www.semanticscholar.org/paper/21d85c829f3954965b1d570517b71e6291241eb8 d44c757413d27f4379e2c10e003a1846ec3c8c02Standards are put forward as important means to turn the ideals of ethical and responsible artificial intelligence into practice. One principle targeted for standardization is transparency. This article attends to the tension between standardization and transparency, by combining a theoretical exploration of these concepts with an empirical analysis of standardizations of artificial intelligence transparency. Conceptually, standards are underpinned by goals of stability and solidification, while transparency is considered a flexible see-through quality. In addition, artificial intelligence-technologies are depicted as ‘black boxed’, complex and in flux. Transparency as a solution for ethical artificial intelligence has, however, been problematized. In the empirical sample of standardizations, transparency is largely presented as a static, measurable, and straightforward information transfer, or as a window to artificial intelligence use. The standards are furthermore described as pioneering and able to shape technological futures, while their similarities suggest that artificial intelligence translucencies are already stabilizing into similar arrangements. To rely heavily upon standardization to govern artificial intelligence transparency still risks allocating rule-making to non-democratic processes, and while intended to bring clarity, the standardizations could also create new distributions of uncertainty and accountability. This article stresses the complexity of governing sociotechnical artificial intelligence principles by standardization. Overall, there is a risk that the governance of artificial intelligence is let to be too shaped by technological solutionism, allowing the standardization of social values (or even human rights) to be carried out in the same manner as that of any other technical product or procedure.Big Data & Society290There is a risk that the governance of artificial intelligence is let to be too shaped by technological solutionism, allowing the standardization of social values to be carried out in the same manner as that of any other technical product or procedure.Big Data Soc.['Charlotte Högberg']2024-02-25T00:00:00https://www.semanticscholar.org/paper/d44c757413d27f4379e2c10e003a1846ec3c8c02 d08125f1fe2fbe654df32f921ea7b56f828e3ba4This study presents a novel method for microstructure control in closed die hot forging that combines Model Predictive Control (MPC) with a developed machine learning model called DeepForge. DeepForge uses an architecture that combines 1D convolutional neural networks and gated recurrent units. It uses surface temperature measurements of a workpiece as input to predict microstructure changes during forging. The paper also details DeepForge's architecture and the finite element simulation model used to generate the data set, using a three-stroke forging process. The results demonstrate DeepForge's ability to predict microstructure with a mean absolute error of 0.4$\pm$0.3%. In addition, the study explores the use of MPC to adjust inter-stroke wait times, effectively counteracting temperature disturbances to achieve a target grain size of less than 35 microns within a specific 2D region of the workpiece. These results are then verified experimentally, demonstrating a significant step towards improved control and quality in forging processes where temperature can be used as an additional degree of freedom in the process.Journal of Manufacturing Processes380This study presents a novel method for microstructure control in closed die hot forging that combines Model Predictive Control (MPC) with a developed machine learning model called DeepForge, which uses an architecture that combines 1D convolutional neural networks and gated recurrent units.ArXiv['Jan Petrik', 'M. Bambach']2024-02-25T00:00:00https://www.semanticscholar.org/paper/d08125f1fe2fbe654df32f921ea7b56f828e3ba4 e95dd8a8a07470eb19770dad42470a16315b1b94PURPOSE With the introduction of ChatGPT, artificial intelligence (AI)-based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human-like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under-reported. RECENT FINDINGS Hitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question-based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI-based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible-sounding 'fake' responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI-based LLMs. SUMMARY Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision-making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.Ophthalmic & physiological optics932Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision-making and monitoring the accuracy of information.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians['Sayantani Biswas', 'L. N. Davies', 'Amy L Sheppard', 'Nicola S Logan', 'J. Wolffsohn']2024-02-25T00:00:00https://www.semanticscholar.org/paper/e95dd8a8a07470eb19770dad42470a16315b1b94 c7987b83607efa1fe7667e0a711ddca5af2c1a0cWebinar nasional Berbagi Praktek Baik Dalam Menyusun Karya Ilmiah Berbasis Artificial Intelligence, telah berhasil menjembatani kesenjangan antara teknologi AI yang berkembang pesat dan praktik penulisan ilmiah. Dengan partisipasi 885 peserta dari berbagai kalangan, kegiatan ini mengungkap potensi integrasi AI dalam pendidikan, khususnya terkait implementasi Kurikulum Merdeka dan penulisan karya ilmiah. Materi yang disampaikan, mulai dari literasi AI hingga aplikasinya dalam penulisan ilmiah, diterima positif, menunjukkan relevansi dan manfaat langsung bagi peserta. Diskusi yang kaya dan interaktif menyoroti pentingnya diversifikasi perspektif dalam memperkaya proses pembelajaran. Dinamika interaksi peserta mencerminkan keterlibatan tinggi dan keingintahuan terhadap AI, memperkuat komunitas akademik melalui pembentukan jaringan profesional. Grup WhatsApp sebagai kegiatan pendampingan lanjutan memfasilitasi diskusi berkelanjutan, mendukung pembelajaran dan kolaborasi. Hasil webinar menunjukkan kemajuan signifikan dalam meningkatkan literasi AI, mempersiapkan komunitas akademik untuk inovasi dan kemajuan ilmiah berkelanjutan. Kesuksesan ini menggarisbawahi pentingnya pembelajaran berkelanjutan dan adaptasi dengan teknologi untuk memajukan pendidikan dan penelitian. Adapun Metode pelaksanan kegiatan meliputi: (1). Mengembangkan materi webinar yang berhubungan dengan aplikasi praktis AI dalam penelitian dan pendidikan. (2). Menerapkan format webinar yang mendorong interaksi dan partisipasi aktif peserta untuk memperkaya pengalaman pembelajaran. (3). Membuka peluang bagi peserta untuk membangun jaringan dengan pembicara dan peserta lain melalui grup WhatsApp.Jurnal Pengabdian kepada Masyarakat Nusantara161Jurnal Pengabdian kepada Masyarakat Nusantara['S. Suyitno', 'Yulie Wahyuningsih', 'Devina Febrianti', 'Al Khoridatul Anisah', 'Aris Wisnu Wardana']2024-02-25T00:00:00https://www.semanticscholar.org/paper/c7987b83607efa1fe7667e0a711ddca5af2c1a0c eac3650f3c04ea35f3f087696020c1b8717717b9The study shows the importance of the concept of artificial intelligence's impact on stock markets in economics, with implications for other disciplines as well. The research emphasizes the importance of this concept via a bibliometric analysis and promotes more interdisciplinary collaboration among experts. Analyzed using several performance criteria, the quantitative attributes and importance of academic contributions from worldwide sources were evaluated. VosViewer software was used for Science Mapping, which revealed the vast network of ideas. Future researchers may profit from the favourable research trajectory and major relevance of published works, as they anticipate additional breakthroughs in the subject.GLOBAL MAINSTREAM JOURNAL OF ARTS, LITERATURE, HISTORY &amp; EDUCATION00The study shows the importance of the concept of artificial intelligence's impact on stock markets in economics, with implications for other disciplines as well, via a bibliometric analysis.GLOBAL MAINSTREAM JOURNAL OF ARTS, LITERATURE, HISTORY &amp; EDUCATION[]2024-02-25T00:00:00https://www.semanticscholar.org/paper/eac3650f3c04ea35f3f087696020c1b8717717b9 9e38c554a2d681ba0e81bfca7762f4a5e4b1895bThe penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.Informatics510The influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale is analyzed and outline.Informatics['Malinka Ivanova', 'G. Grosseck', 'Carmen Holotescu']2024-02-25T00:00:00https://www.semanticscholar.org/paper/9e38c554a2d681ba0e81bfca7762f4a5e4b1895b 41df7f50d8428b47e903ca9bd0b087ed39554adeDuring an artificial intelligence (AI)-assisted diabetic retinopathy screening event, we performed a survey on patients´ perceptions on AI. Respondents were individuals with diabetes, mostly followed in primary healthcare with a low education level. While 49.6% of participants said they knew what AI was, only 14% reported good or expert knowledge of AI. The vast majority reported positive feelings towards AI in healthcare. We highlight the importance of understanding patients´ views regarding AI in health in a real-life situation and emphasize the importance of digital education.Journal of Diabetes Science and Technology00The importance of understanding patients´ views regarding AI in health in a real-life situation and the importance of digital education are highlighted.Journal of diabetes science and technology['F. Malerbi', 'Beatriz Mezzomo Ventura', 'Mariana Fischer', 'Fernando Marcondes Penha']2024-02-25T00:00:00https://www.semanticscholar.org/paper/41df7f50d8428b47e903ca9bd0b087ed39554ade bb3082c534f3d605b940b988735bace162b73dabThis paper presents a comprehensive review of the impact of Artificial Intelligence (AI) on recruitment and selection processes within the oil and gas industry. The primary objective is to understand how AI technologies are transforming traditional recruitment methodologies and the implications of these changes for both employers and candidates. The methodology involves a systematic analysis of existing literature, case studies, and industry reports to identify key trends, opportunities, and challenges associated with the integration of AI in recruitment processes. The findings reveal that AI significantly enhances the efficiency and effectiveness of recruitment in the oil and gas sector by automating routine tasks, improving candidate targeting, and facilitating data-driven decision-making. AI-driven tools such as resume screening algorithms, predictive analytics, and virtual assistants are increasingly being adopted to streamline the recruitment process, reduce biases, and improve the quality of hires. However, the study also identifies potential challenges, including ethical concerns, the need for transparency in AI algorithms, and the risk of over-reliance on technology. The paper concludes that while AI presents substantial benefits in optimizing recruitment and selection processes, it is crucial for companies in the oil and gas industry to approach its implementation thoughtfully. This involves balancing technological advancements with human judgment, ensuring ethical use of AI, and continuously updating AI systems to adapt to the dynamic nature of the job market. The paper suggests that the future of recruitment in this industry will likely be a hybrid model that leverages the strengths of both AI and human expertise. Keywords: Artificial Intelligence, Recruitment, Oil and Gas Industry, Talent Acquisition, Machine Learning, Natural Language Processing, Predictive Analytics.Engineering Science &amp; Technology Journal00The paper suggests that the future of recruitment in this industry will likely be a hybrid model that leverages the strengths of both AI and human expertise, and continuously updating AI systems to adapt to the dynamic nature of the job market.Engineering Science &amp; Technology Journal['Patrick Oputa Odili', 'Cosmas Dominic Daudu', 'Adedayo Adefemi', 'Ifeanyi Onyedika Ekemezie', 'Gloria Siwe Usiagu']2024-02-25T00:00:00https://www.semanticscholar.org/paper/bb3082c534f3d605b940b988735bace162b73dab 564c3a40ee70859949c8530792cb8fc3260eceb9AI &amp; SOCIETY260This study is the first to explore public perceptions of and attitudes towards AI in Defence and demonstrates gaps in knowledge and misunderstandings that need to be addressed, and offers practical insights for keeping the public reliably, accurately, and adequately informed about the capabilities, limitations, benefits, and risks of AI in Defence.AI &amp; SOCIETY['Lee Hadlington', 'Maria Karanika-Murray', 'Jane Slater', 'Jens Binder', 'Sarah Gardner', 'Sarah Knight']2024-02-25T00:00:00https://www.semanticscholar.org/paper/564c3a40ee70859949c8530792cb8fc3260eceb9 56319b23480088db1430635cae2dd1ec53397464This paper explores the transformative impact of generative artificial intelligence (AI) on the “Business DataAnalysis and Application” course in the post-2023 era, marking a significant paradigm shift in educational methodologies. It investigates how generative AI reshapes teaching and learning dynamics, enhancing the processing of complex data sets and nurturing critical thinking skills. The study highlights the role of AI in fostering dynamic, personalized, and adaptive learning experiences, addressing the evolving pedagogical needs of the business sector. Key challenges, including equitable access, academic integrity, and ethical considerations such as data privacy and algorithmic bias, are thoroughly examined. The research reveals that the integration of generative AI aligns with current professional demands, equipping students with cutting-edge AI tools, and tailoring learning to individual needs through real-time feedback mechanisms. The study concludes that the incorporation of generative AI into this course signifies a substantial evolution in educationalapproaches, offering profound implications for student learning and professional development.Journal of Contemporary Educational Research230The research reveals that the integration of generative AI aligns with current professional demands, equipping students with cutting-edge AI tools, and tailoring learning to individual needs through real-time feedback mechanisms.Journal of Contemporary Educational Research['Suhan Wu']2024-02-25T00:00:00https://www.semanticscholar.org/paper/56319b23480088db1430635cae2dd1ec53397464 597a80c245ca9987d8e21a74c209918412738c83Many engaged in the development of Artificial Intelligence (AI) see education as a potential market for sales. They predict that AI will transform teaching, learning and assessment. In describing just how this will occur, AI advocates suggest a combination of individualized learning, adaptive and engaged assessment, and personal support for the learners' journey will be the hallmarks of the new reality for education. Harsh reality suggests something different. Using a simple framework of the potential impact of AI tools and the burden they require; a more incremental adoption is likely. This paper presents both the transformation framework and the more likely incremental approach as the choice for instructors, administrators, and policymakers.REVISTA PARAGUAYA DE EDUCACIÓN A DISTANCIA (REPED)200This paper presents both the transformation framework and the more likely incremental approach as the choice for instructors, administrators, and policymakers as the choice for instructors, administrators, and policymakers.REVISTA PARAGUAYA DE EDUCACIÓN A DISTANCIA (REPED)['Stephen Murgatroyd']2024-02-25T00:00:00https://www.semanticscholar.org/paper/597a80c245ca9987d8e21a74c209918412738c83 03f529a8143c0a28ff0d0f70996e5da130ff220cElectric vehicles (EVs) are widely regarded as a more environmentally sustainable transportation option compared to traditional internal combustion engine vehicles. However, concerns persist regarding the sustainability impacts of EV battery manufacturing, specifically the mining of lithium used in lithium-ion batteries. This literature review examines the potential for artificial intelligence (AI) to improve the sustainability of lithium mining and battery manufacturing for EVs. An extensive literature search yielded insights into the environmental impacts of lithium mining such as high water usage, carbon emissions, and soil contamination. These impacts present a challenge to the sustainability narrative of EVs. However, researchers have identified numerous applications for AI across the lithium-ion battery supply chain that could mitigate these effects. From optimizing mineral exploration and mining operations to recycling spent batteries, AI solutions show promise to reduce the lifecycle impacts of lithium-ion batteries. Additional research is still needed to quantify these potential improvements and implement AI responsibly. Furthermore, technology alone cannot address the complex socio-economic challenges associated with lithium mining. Ultimately, a combination of technological innovation and ethical, inclusive resource management will be required to truly transition to sustainable EV mobility. Keywords: electric vehicles, lithium-ion batteries, lithium mining, sustainability, artificial intelligence.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The potential for artificial intelligence (AI) to improve the sustainability of lithium mining and battery manufacturing for EVs is examined and AI solutions show promise to reduce the lifecycle impacts of lithium-ion batteries.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Akash Kashyap', 'Tove Hokato Zimomi', 'Prakhar Agrawal', 'Anjali Salunke']2024-02-25T00:00:00https://www.semanticscholar.org/paper/03f529a8143c0a28ff0d0f70996e5da130ff220c 218ac3bfac4343f61fcb713b74833382480ec7fdCollege Teaching190College Teaching['Sally E. Hyatt', 'Meghan B. Owenz']2024-02-25T00:00:00https://www.semanticscholar.org/paper/218ac3bfac4343f61fcb713b74833382480ec7fd e33463caff7cb34cb1e271e8ae0f7b873587f9bcEconomic Management and Big Data Application00Economic Management and Big Data Application['Weilan', 'Meiling Ma', 'Muratbekova Aiperi']2024-02-25T00:00:00https://www.semanticscholar.org/paper/e33463caff7cb34cb1e271e8ae0f7b873587f9bc 5c8823997228a2da5eaa02d0e2a23e782b9adf82Clean Technologies and Environmental Policy420Clean Technologies and Environmental Policy['Le Thanh Ha']2024-02-25T00:00:00https://www.semanticscholar.org/paper/5c8823997228a2da5eaa02d0e2a23e782b9adf82 ebe01e6360b6c2949cc087a5df82e61577ec2220The Korean Society for Child Education00The Korean Society for Child Education['S. Sim', 'Ji Yoon You', 'Sun Ah Lim']2024-02-25T00:00:00https://www.semanticscholar.org/paper/ebe01e6360b6c2949cc087a5df82e61577ec2220 692d99e2ca08b0b61958b4c4bdfc6e5e1f6af2baThe rise of AIGC has a profound impact on the development of film and television production. The application of artificial intelligence technology to produce content in film and television production is changing the traditional production process and mode, and gradually reshaping the production process. This paper analyzes application scenarios of AIGC and its influence on film and television production, and puts forward some countermeasures to this new generation mode from the aspects of adapting to the new production paradigm, strengthening talent training, and utilizing AIGC legally.The Art &amp; Design Research00This paper analyzes application scenarios of AIGC and its influence on film and television production, and puts forward some countermeasures to this new generation mode from the aspects of adapting to the new production paradigm, strengthening talent training, and utilizing AIGC legally.The Art &amp; Design Research['Yaoyao Zeng', 'Junling Liu']2024-02-25T00:00:00https://www.semanticscholar.org/paper/692d99e2ca08b0b61958b4c4bdfc6e5e1f6af2ba f018471406b596ee5b4f6321834d6c0e3d1161d1Artificial intelligence (AI) is increasingly employed in radiation protection, encompassing both medical devices and software. These technologies are integrated with AI throughout their manufacturing and application processes. This article underscores the imperative for comprehensive regulation in the utilization of AI. Decisions regarding AI application should not solely rest with manufacturers, medical professionals, or patients. Instead, an overarching "neutral" authority must be engaged to regulate, review, and enforce adherence to established protocols. The authors contend that relying on "self-regulation" within the free market, absent clear guidelines, proves to be inadequately effective and leads to patient's radiation protection safety issues. Swiss Journal of Radiology and Nuclear Medicine130Decisions regarding AI application should not solely rest with manufacturers, medical professionals, or patients, and an overarching "neutral" authority must be engaged to regulate, review, and enforce adherence to established protocols.Swiss Journal of Radiology and Nuclear Medicine['Stefanie Nicole Garni', 'Nando Mertineit', 'Gerd Nöldge', 'Keivan Daneshvar', 'F. Mosler']2024-02-24T00:00:00https://www.semanticscholar.org/paper/f018471406b596ee5b4f6321834d6c0e3d1161d1 7c1b6d09f64e46b803e95de6c79cab98f45770d1Based on the samples of 207 prefecture-level cities in China from 2002 to 2010, this study uses the exogenous shock of China’s first incorporation of environmental regulations into the assessment of local officials as a quasi-natural experiment, and applies the continuous difference-in-differences (DID) method to examine the impact of environmental regulation assessment pressure on local fiscal revenue. We find that the target pressure of environmental regulations for local officials has contributed to the growth of local fiscal revenue, and for each 0.01 increase in the targets of pollution emission reduction, local fiscal revenue increases by 0.204%. This result demonstrates a strong robustness. Our mechanism analysis further confirms that local governments employ various strategies to alleviate the financial burden induced by environmental regulations. These strategies include (1) not only adopting the “grabbing hand” approach, which involves extracting fiscal revenues from the market by reducing the fixed asset investment of local governments and enhancing the collection of pollution fees from enterprises, (2) but also utilizing the “helping hand” approach to augment financial resources, such as improving tax administration efficiency by cracking down on profit under-reporting and income tax evasion among enterprises. Moreover, the heterogeneity analysis suggests that the impact of environmental regulations on fiscal revenue is contingent upon the level of local fiscal self-sufficiency. This article offers empirical evidence to assist governments in devising effective environmental policies that aim to achieve a harmonious balance between economic growth and environmental protection.Sustainability370Sustainability['Jia Wang', 'Linhui Yu']2024-02-24T00:00:00https://www.semanticscholar.org/paper/7c1b6d09f64e46b803e95de6c79cab98f45770d1 7b10a5b4936486e74eebec8c9f92ae984b9d80d0AI advancements are poised to substantially modify human abilities in the foreseeable future. They include the integration of Brain–Computer Interfaces (BCIs) to augment cognitive functions, the application of gene editing, and the utilization of AI‐powered robotic exoskeletons to enhance physical strength. This study employs a comprehensive analytical framework combining factor analysis, clustering, ANOVA, and logistic regression to investigate public attitudes toward these transformative technologies. Our findings reveal three distinct clusters of public opinion reflecting varying optimism and concern toward AI technologies. Cluster 1 (1574 participants) held a positive view with high excitement while Cluster 2 (1334 participants) showed a balanced stance. Cluster 3 (2199 participants) expressed heightened concern despite some excitement. Notably, regional disparities, particularly between urban and rural participants, emerge as a prominent factor influencing these attitudes (ANOVA, F = 15.2, p < 0.001). Furthermore, logistic regression identifies key influencers of public perception, highlighting the significant roles played by religion and regional factors. The implications of these findings extend beyond understanding public sentiment. They underscore the need for informed policies that promote education and awareness about AI technologies, address ethical concerns, and engage the public in decision‐making processes. As society navigates this transformative technological landscape, a nuanced understanding of public attitudes becomes paramount, guiding ethical regulation, innovation, and public engagement strategies. This study provides valuable insights into the intricate dynamics surrounding AI acceptance and highlights the importance of adapting measures to evolving perceptions and attitudes among the general public.The American journal of economics and sociology331A comprehensive analytical framework combining factor analysis, clustering, ANOVA, and logistic regression reveals three distinct clusters of public opinion reflecting varying optimism and concern toward AI technologies and identifies key influencers of public perception.The American Journal of Economics and Sociology['S. Oprea', 'I. Nica', 'A. Bâra', 'I. Georgescu']2024-02-24T00:00:00https://www.semanticscholar.org/paper/7b10a5b4936486e74eebec8c9f92ae984b9d80d0 cc14dae34c79bc456c9e8d62d2dae9d84b9d6bdeIn recent years, wildfires have emerged as a global environmental crisis, causing significant damage to ecosystems, and contributing to climate change. Wildfire management methods involve prevention, response, and recovery efforts. Despite advancements in detection methods, the increasing frequency of wildfires necessitates innovative solutions for early detection and efficient management. This study explores proactive approaches to detect and manage wildfires in the United States by leveraging Artificial Intelligence (AI), Machine Learning (ML), and 5G technology. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology; Active monitoring and mapping with remote sensing and signaling leveraging on 5G technology; and Advanced response mechanisms to wildfire using drones and IOT devices. This study was based on secondary data collected from government databases and analyzed using descriptive statistics. In addition, past publications were reviewed through content analysis, and narrative synthesis was used to present the observations from various studies. The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively. This would save a lot of lives and prevent huge economic loss that is attributed to wildfire outbreaks and spread. Advanced technology can be used in several ways to help in the proactive detection and management of wildfires. This includes the development of the use of AI-enabled remote sensing and signaling devices and leveraging 5G technology for active monitoring and mapping of wildfires. In addition, super intelligent drones and IOT devices can be used for safer responses to wildfires. This forms the core of the recommendation to the fire Management Agencies and the government.Artificial Intelligence and Big Data620The results showed that developing new technology presents an opportunity to detect and manage wildfires proactively, and this forms the core of the recommendation to the fire Management Agencies and the government.Artificial Intelligence and Big Data['Okoro C. Stanley', 'Lopez Alexander', 'Unuriode O. Austine']2024-02-24T00:00:00https://www.semanticscholar.org/paper/cc14dae34c79bc456c9e8d62d2dae9d84b9d6bde 2bb222e2aa9e362f0ede7e0a9f3dd9185a3fd1c6The aim of this paper is to elaborate interconnection of logic, mathematics, AI. The fundamental influence of mathematical logic, which provides a framework for reasoning, analysis, and well-informed decision-making, is at the heart of intelligent systems. The importance of mathematical logic in artificial intelligence (AI) and its influence on a number of domains, including propositional logic, predicate logic, problem-solving strategies, event management, improved decision-making, inference, and deductive reasoning to generate biased predictions based on available data.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)1102024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Pranveer Singh', 'Abhinav Awasthi', 'A. Chaturvedi', 'M. K. Misra', 'Varun Shukla']2024-02-24T00:00:00https://www.semanticscholar.org/paper/2bb222e2aa9e362f0ede7e0a9f3dd9185a3fd1c6 eebbf48cf3a4aafffd4e064c73de94c5e3748422The goal of this research is to study key factors related to ‘Emotion-Recognition-AI’ that influences consumers' purchase behavior and consumer Trust and Satisfaction. A total of 609 respondents participated using convenience sampling in questionnaire survey and 433 valid responses were found. SMART-PLS software is used to assess the data and build structural equation model. The variables ‘Emotion-Recognition-AI’ type, Emotion Display Format, Product Category, and User Interface Design have a significant impact on Consumer Engagement and Trust and Customer Satisfaction. But, the variable Emotion Feedback Timing do not have much impact on mediating and dependent variables. There may be other external variables that could impact consumer trust and satisfaction. This research aims to contribute to the study of how AI technology can influence consumer behavior and decision-making processes and can be of great help to the marketing pros who want to explore AI tools for marketing strategy.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)330The variables ‘Emotion-Recognition-AI’ type, Emotion Display Format, Product Category, and User Interface Design have a significant impact on Consumer Engagement and Trust and Customer Satisfaction, but the variable Emotion Feedback Timing do not have much impact on mediating and dependent variables.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Abhay M. Vyas']2024-02-24T00:00:00https://www.semanticscholar.org/paper/eebbf48cf3a4aafffd4e064c73de94c5e3748422 1400c56d400e31bddfb37bcb8258714bd6e11863An efficient production line and regular preventive maintenance is a key of success for any manufacturing industry as it avoids the costly breakdowns and is principal factor for increase in revenue and net profits. One of the major constraints in maintenance is failing to understand the maintenance cycle of internal moving parts like bearings which are the key parts in any machinery. The maintenance cycle of such parts requires comprehensive technical understanding and experience which may not be available in all type of manufacturing units. Recent developments in AI techniques - Machine learning, Deep learning and Random Forests can help us predict the maintenance cycle by taking all the factors in consideration. We hereby present a detailed comprehensive survey of the work done in predictive modeling for bearing failure. This paper provides a systematic literature review of state of art techniques proposed by various researchers for Predictive Maintenance and determination of Remaining Useful Life (RUL) of a component. This paper presents information of certain Industrial functions and failures respected to maintenance, review of various models based on Ensemble Learning based algorithms, Neural Network based algorithms and some other miscellaneous algorithms also various types of sensors used for condition monitoring, types of datasets etc.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)1040This paper provides a systematic literature review of state of art techniques proposed by various researchers for Predictive Maintenance and determination of Remaining Useful Life (RUL) of a component.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Tasneem Kagzi', 'K. Pandey']2024-02-24T00:00:00https://www.semanticscholar.org/paper/1400c56d400e31bddfb37bcb8258714bd6e11863 aba7899087c851b3d7d74db32c21b0f2d425cbeaThe integration of artificial intelligence (AI) and geographic information systems (GIS) has opened up new possibilities and advancements in the field of geoscience. AI techniques, such as machine learning and deep learning, have shown great potential in solving various GIS challenges and improving the intelligence of GIS software. GeoAI, which combines AI with GIS operations, encompasses geospatial machine learning and geospatial deep learning. Geospatial machine learning in ArcGIS allows users to address tasks like geographical clustering, spatial classification, and spatial regression. Geospatial deep learning algorithms, on the other hand, enable advanced analysis of 3D data and images. AI for GIS involves applying AI techniques to enhance the capabilities of GIS software. This includes AI attribute collection, AI survey and mapping, AI cartography, and AI interaction. By leveraging AI, GIS software can become more intelligent and efficient in handling data and performing various tasks. GIS for AI refers to the utilization of GIS capabilities in further processing and mining data obtained from AI recognition. By incorporating geographical visualization and spatial analytics, GIS can enhance AI findings and provide decision makers with more intuitive information expression. Examples of GIS for AI applications include traffic flow monitoring, city component management, real-time geo-fence alerts, and vehicle tracking. Prominent companies across industries are strategically investing in AI, particularly machine learning, and leveraging location data to gain competitive advantages. Location analytics is being used for discovering hidden trends, gaining critical insights, and making informed decisions. For instance, manufacturers use AI systems for supply chain logistics, inspections, predictive maintenance, and anomaly detection. Retailers benefit from machine learning and location intelligence for siteInternational Journal of Advanced Engineering and Business Sciences140GIS for AI refers to the utilization of GIS capabilities in further processing and mining data obtained from AI recognition, and GeoAI, which combines AI with GIS operations, encompasses geospatial machine learning and geospatial deep learning.International Journal of Advanced Engineering and Business Sciences['Z. Ahmed']2024-02-24T00:00:00https://www.semanticscholar.org/paper/aba7899087c851b3d7d74db32c21b0f2d425cbea 04bd49a2a2eedd77ee12e19e696de3c428300289This research explores the consequences of AI integration in the labor market. As AI shapes various industries, it brings a dual impact: displacing some jobs while creating others. The automation driven by AI could be a threat to routine tasks, potentially leading to the displacement of specific roles within the routine tasks. However, AI also creates new job opportunities, particularly in AI development and related fields. This study aims to analyze the multifaceted impact of AI on US jobs, considering displacement, creation, and skills. The research considered the following aspects: Evaluation of job displacement and creation, skill shifts, the quality of AI’s impact on performance, and exploring the relationship between AI models and human tasks. We were able to show AI's influence on the tasks performed by humans. The negative relationship between AI influence and tasks performed by humans shows that AI indeed has a notable and statistically significant adverse impact on human-performed tasks. We discovered that as AI technology advances and becomes more prevalent, certain tasks and roles traditionally carried out by humans are being automated or replaced by machines. Also, we were able to show the relationship between the AI model and human-performed tasks. It was found that AI models exhibit a substantial and statistically significant positive relationship with tasks performed by humans. Our findings suggest a more optimistic outlook for the labor market, where rather than displacing jobs and workers, AI technologies have the potential to enhance their capabilities and create new opportunities.Artificial Intelligence and Big Data470It was found that AI models exhibit a substantial and statistically significant positive relationship with tasks performed by humans, suggesting a more optimistic outlook for the labor market, where rather than displacing jobs and workers, AI technologies have the potential to enhance their capabilities and create new opportunities.Artificial Intelligence and Big Data['Unuriode O. Austine', 'Okoro C. Stanley', 'Afolabi T. Osariemen', 'Durojaiye M.Olalekan', 'Lopez Alexander', 'Yusuf Y. Babatunde', 'Akinwande J. Mayowa']2024-02-24T00:00:00https://www.semanticscholar.org/paper/04bd49a2a2eedd77ee12e19e696de3c428300289 fc80e3e96927659f5168a295f267522738df318aThis paper investigates how historical inter-firm syndication networks influence venture capitalists’ (VCs) propensity to invest in startups pursuing novel, uncertain technologies, with a focus on artificial intelligence (AI). We theorize that VCs’ positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity. Specifically, reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity. Additionally, VC brokerage between disconnected industry clusters furnishes expansive, non-redundant information that is pivotal for discovering and assessing AI opportunities. Through hypotheses grounded in social network theory, we posit network-based mechanisms that equip VCs to navigate uncertainty when engaging with ambiguous innovations like AI. We test our framework, utilizing comprehensive historical records of global venture capital investments. Analyzing the location information of VC firms in this database, we uncovered a history of 14,751 investments made by Korean and Japanese firms. Using these data, we assembled an imbalanced panel dataset from 1984 to 2022 spanning 230 Korean and 413 Japanese VCs, with 4508 firm-year observations. Negative binomial regression analysis of this dataset reveals how historical relational patterns among venture capital firms foster readiness to evaluate unfamiliar innovations.Systems670It is theorized that VCs’ positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity, and reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity.Systems['Eun-jung Hyun', 'Brian Tae-Seok Kim']2024-02-24T00:00:00https://www.semanticscholar.org/paper/fc80e3e96927659f5168a295f267522738df318a 2f209c47fd18e2f4c3d8ab7a6ab1cae5ac69fa3dMachine learning (ML) is software technology which exploits Generative Artificial Intelligence (GAI) to train sophisticated advance software applications, which are capable to learn from Big Data Analysis for excellent performance, which gets more precise with repetitive training and software experience. Generative AI is a category of artificial intelligence technique that can generates different types of text content, suggestions and idea based images and voice. GAI frequently uses Apriori Algorithm (APAL) for Association Rule Mining in Big Data Investigation, Analysis and Result Generation. Computational Complexity, Noise Sensitivity and Computational time are three major vulnerabilities which Apriori Algorithm inherits in big data applications. Article suggests an advance algorithm, which is hybridization of Apriori Algorithm and Genetic Algorithm to address the noticed vulnerabilities. Test simulation is done in AI Tool Kit of Simulink in MATLAB 7.6 Virtual Simulation Environment.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)310An advance algorithm is suggested, which is hybridization of Apriori Algorithm and Genetic Algorithm to address the noticed vulnerabilities of Apriori Algorithm in big data applications.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Nida Afreen Rizvi', 'Pratik Buchke']2024-02-24T00:00:00https://www.semanticscholar.org/paper/2f209c47fd18e2f4c3d8ab7a6ab1cae5ac69fa3d 50476e9ee3564d929b8f7ff1c2feed2898a7799bEnvironment and Planning B Urban Analytics and City Science11Environment and Planning B: Urban Analytics and City Science['Michael Batty']2024-02-24T00:00:00https://www.semanticscholar.org/paper/50476e9ee3564d929b8f7ff1c2feed2898a7799b dbe3b28a634cb89386df30030c284c5fc378e6e5In response to the significant challenges facing the retail sector, including inefficient queue management, poor demand forecasting, and ineffective marketing, this paper introduces an innovative approach utilizing cutting-edge machine learning technologies. We aim to create an advanced smart retail analytics system (SRAS), leveraging these technologies to enhance retail efficiency and customer engagement. To enhance customer tracking capabilities, a new hybrid architecture is proposed integrating several predictive models. In the first stage of the proposed hybrid architecture for customer tracking, we fine-tuned the YOLOV8 algorithm using a diverse set of parameters, achieving exceptional results across various performance metrics. This fine-tuning process utilized actual surveillance footage from retail environments, ensuring its practical applicability. In the second stage, we explored integrating two sophisticated object-tracking models, BOT-SORT and ByteTrack, with the labels detected by YOLOV8. This integration is crucial for tracing customer paths within stores, which facilitates the creation of accurate visitor counts and heat maps. These insights are invaluable for understanding consumer behavior and improving store operations. To optimize inventory management, we delved into various predictive models, optimizing and contrasting their performance against complex retail data patterns. The GRU model, with its ability to interpret time-series data with long-range temporal dependencies, consistently surpassed other models like Linear Regression, showing 2.873% and 29.31% improvements in R2-score and mAPE, respectively.90An innovative approach utilizing cutting-edge machine learning technologies is introduced to create an advanced smart retail analytics system (SRAS), leveraging these technologies to enhance retail efficiency and customer engagement.['A. Hossam', 'A. Ramadan', 'M. Magdy', 'R. Abdelwahab', 'S. Ashraf', 'Z. Mohamed']2024-02-24T00:00:00https://www.semanticscholar.org/paper/dbe3b28a634cb89386df30030c284c5fc378e6e5 306434bd138a0b649dff00532e00cc206d0faa66The red queen effect is a metaphor used in the business world to describe the unsuccessful efforts of a company to get ahead of its competition. The red queen effect is the need to continually adapt and evolve to maintain relevance in an ever-changing environment. Companies must constantly innovate and find new ways to stay ahead of the competition to ensure their survival and success. Companies typically research or study the competition and then implement strategies to help boost their company sales and profits. This is an effective and practical method of outmaneuvering the competition. While this technique works in theory, companies might not achieve their goals because the competition engages in the same business practice. Despite a company's efforts to surpass the competition, the company does not move forward or grow. The aims of research paper are bifold firstly it attempts to identify the contributions of the RQE theory and secondly to enable corporates to evade the Red Queen Effect by using generative artificial intelligence to be prepared for Industry 5.0.Saudi Journal of Economics and Finance00The aims of research paper are to enable corporates to evade the Red Queen Effect by using generative artificial intelligence to be prepared for Industry 5.0.Saudi Journal of Economics and Finance['Saurav Kumar']2024-02-24T00:00:00https://www.semanticscholar.org/paper/306434bd138a0b649dff00532e00cc206d0faa66 109183c19196c0b3da835609cfbc8770197bec39Small Group Research50Small Group Research['Kate Bezrukova', 'Terri L. Griffith', 'Dennis Kivlighan', 'Lyn M. van Swol', 'Bret Bradley', 'Josette Gevers', 'Bertolt Meyer']2024-02-24T00:00:00https://www.semanticscholar.org/paper/109183c19196c0b3da835609cfbc8770197bec39 4272561feeb5df005a750a6ef0b14d03e8ea82d3Copyright and computer science continue to intersect and clash, but they can coexist. The advent of new technologies such as digitization of visual and aural creations, sharing technologies, search engines, social media offerings, and more challenge copyright-based industries and reopen questions about the reach of copyright law. Breakthroughs in artificial intelligence research, especially Large Language Models that leverage copyrighted material as part of training models, are the latest examples of the ongoing tension between copyright and computer science. The exuberance, rush-to-market, and edge problem cases created by a few misguided companies now raises challenges to core legal doctrines and may shift Open Internet practices for the worse. That result does not have to be, and should not be, the outcome. This Article shows that, contrary to some scholars' views, fair use law does not bless all ways that someone can gain access to copyrighted material even when the purpose is fair use. Nonetheless, the scientific need for more data to advance AI research means access to large book corpora and the Open Internet is vital for the future of that research. The copyright industry claims, however, that almost all uses of copyrighted material must be compensated, even for non-expressive uses. The Article's solution accepts that both sides need to change. It is one that forces the computer science world to discipline its behaviors and, in some cases, pay for copyrighted material. It also requires the copyright industry to abandon its belief that all uses must be compensated or restricted to uses sanctioned by the copyright industry. As part of this re-balancing, the Article addresses a problem that has grown out of this clash and under theorized.Social Science Research Network00This Article shows that, contrary to some scholars' views, fair use law does not bless all ways that someone can gain access to copyrighted material even when the purpose is fair use, and requires the copyright industry to abandon its belief that all uses must be compensated or restricted to uses sanctioned by the copyright industry.ArXiv['D. Desai', 'Mark Riedl']2024-02-24T00:00:00https://www.semanticscholar.org/paper/4272561feeb5df005a750a6ef0b14d03e8ea82d3 f394d1ee41bad66489c0e8e3bfd5bb27a14efe92An online auction is type of an auction that takes place over the Internet. It is a popular method for buying and selling products and services. Such systems give the best price to the seller as well as the buyers. The application proposed in this paper was developed with the objective of making the system reliable, easier and fast. Through this application, anyone can sell anything on the website by sitting at home. It’s as easy as browsing the app website. Even non-technical persons can easily interact with the process of buying and selling on the application. An online auction system permits a customer to submit online orders for items and/or services from a store that serves both walk-in customers and online customers. The online auction system presents an online display of an order cut off time and an associated delivery window for items selected by the customer of any 2nd hand products. The online auction system does not settle with a credit supplier of the customer until the item selected by the customer is picked from inventory. Therefore, customers can go online to modify their order. In addition, the service window is presented to the customer as a function of the order and service type selected by the customer, and also the choice is made according to the person's preference, where the order selection is correct. When ordering goods, a virtual shopping cart is also provided by many shopping systems for holding items selected for purchase. Until a customer completes their shopping trip, successive items selected for purchase are placed into the virtual shopping cart. Virtual shopping carts may be examined at any time, and their contents can be deleted and edited at the customer’s end.International Research Journal on Advanced Engineering Hub (IRJAEH)140The application proposed in this paper was developed with the objective of making the online auction system reliable, easier and fast.International Research Journal on Advanced Engineering Hub (IRJAEH)['Prashant Kaushik', 'Aditya Kumar Singh', 'Ajay Kumar', 'Ms. Nikita Verma', 'Mr. Arun', 'Kumar Rai']2024-02-24T00:00:00https://www.semanticscholar.org/paper/f394d1ee41bad66489c0e8e3bfd5bb27a14efe92 bb2f4d797792dedcfa4d3c2f57030ad6e04b2699This study investigated the integration readiness of four predominant cybersecurity Governance, Risk and Compliance (GRC) frameworks - NIST CSF 2.0, COBIT 2019, ISO 27001:2022, and the latest ISO 42001:2023 - for the opportunities, risks, and regulatory compliance when adopting Large Language Models (LLMs), using qualitative content analysis and expert validation. Our analysis, with both LLMs and human experts in the loop, uncovered potential for LLM integration together with inadequacies in LLM risk oversight of those frameworks. Comparative gap analysis has highlighted that the new ISO 42001:2023, specifically designed for Artificial Intelligence (AI) management systems, provided most comprehensive facilitation for LLM opportunities, whereas COBIT 2019 aligned most closely with the impending European Union AI Act. Nonetheless, our findings suggested that all evaluated frameworks would benefit from enhancements to more effectively and more comprehensively address the multifaceted risks associated with LLMs, indicating a critical and time-sensitive need for their continuous evolution. We propose integrating human-expert-in-the-loop validation processes as crucial for enhancing cybersecurity frameworks to support secure and compliant LLM integration, and discuss implications for the continuous evolution of cybersecurity GRC frameworks to support the secure integration of LLMs.arXiv.org1004ArXiv['Timothy R. McIntosh', 'Teo Susnjak', 'Tong Liu', 'Paul Watters', 'Raza Nowrozy', 'Malka N. Halgamuge']2024-02-24T00:00:00https://www.semanticscholar.org/paper/bb2f4d797792dedcfa4d3c2f57030ad6e04b2699 933b81ecaf746721213330b38bf9c05f1ef72842The primary purpose of this study was to investigate and present a theoretical model that identifies the most influential factors affecting the adoption of artificial intelligence (AI) by SMEs to achieve sustainable business performance in Saudi Arabia by integrating the Technology–Organization–Environment (TOE) framework. The authors utilized a quantitative method, using a survey instrument for this research. Data for this research were collected from managers working in six different sectors. Subsequently, based on company size, firms were divided into two groups, allowing multi-group analysis of small and medium-sized businesses to explore group differences. Hence, firm size played a moderating role in the conceptualized model. Data analysis was performed on SmartPLS 3, and the results suggest that dimensions of the TOE framework, such as relative advantage, compatibility, sustainable human capital, market and customer demand, and government support, play a significant role in the adoption of AI. Moreover, this study found a significant influence of AI on SMEs’ operational and economic performance. The multi-group analysis (MGA) results reveal significant group differences, with a medium-sized firm strengthening the relationship between relative advantage and AI adoption compared to small-size firms. The findings lead to practical implications for companies on how to increase the adoption of AI to help SMEs embrace their technological challenges in KSA and obtain sustainable business performance to contribute to the economy.Sustainability1371A significant influence of AI on SMEs’ operational and economic performance is found, and the findings lead to practical implications for companies on how to increase the adoption of AI to help SMEs embrace their technological challenges in KSA and obtain sustainable business performance to contribute to the economy.Sustainability['Saeed Badghish', 'Y. Soomro']2024-02-24T00:00:00https://www.semanticscholar.org/paper/933b81ecaf746721213330b38bf9c05f1ef72842 1155c8f619b91de3c715dc8e5f6eb8e81ba9261fInternational Journal for the Semiotics of Law260This paper will examine three spaces of regulation in online game world environments, including the rules and regulations that governs online interaction in virtual spaces, the ‘code’ that controls behaviour through game architecture, and the laws that are developed by players inside the game world.International Journal for the Semiotics of Law - Revue internationale de Sémiotique juridique['Morgan M. Broman', 'Pamela Finckenberg-Broman', 'Susan Bird']2024-02-24T00:00:00https://www.semanticscholar.org/paper/1155c8f619b91de3c715dc8e5f6eb8e81ba9261f a706d7f9013d569bff5745fb15a27cdcbb4a3154Heliyon781Heliyon['Haicheng Zhu', 'Hao Fang', 'Feilong Hua', 'Wei Shao', 'Penghui Cai']2024-02-24T00:00:00https://www.semanticscholar.org/paper/a706d7f9013d569bff5745fb15a27cdcbb4a3154 d5259a1e7110e59739efdac1b67728fe9274bda5Objective: The objective of this study is to thoroughly investigate the use of artificial intelligence (AI) and machine learning (ML) techniques for diagnosing and predicting prognosis in gastric cancer, utilizing the latest available data. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)guidelines, a systematic review investigated AI and ML applications in gastric cancer diagnosis and prognostic prediction. PubMed and Google Scholar were searched from February 2019 to January 2024 using specific syntax. Eligible trials were selected based on inclusion criteria including recent publication, focus on AI and ML in gastric cancer, and reporting diagnostic or prognostic outcomes. Data were extracted and quality assessed independently, with discrepancies resolved through discussion. Due to design heterogeneity, detailed analysis was omitted, and descriptive summaries of included articles were provided. Results: This review included a total of 8 articles. AI and ML techniques, including  convolutional neural networks (CNN) and deep learning models, have played pivotal roles in accurately diagnosing chronic atrophic gastritis, predicting postoperative gastric cancer prognosis, and identifying peritoneal metastasis in gastric cancer patients. These technologies offer potential advantages such as streamlining diagnostic procedures, guiding treatment decisions,  and enhancing patient outcomes in gastric cancer management. Conclusion: In the near future, AI applications may have a significant role in the diagnosis and prognosis prediction of gastric cancer.Journal of Cancer and Tumor International00In the near future, AI applications may have a significant role in the diagnosis and prognosis prediction of gastric cancer.Journal of Cancer and Tumor International['Rushin Patel', 'Mrunal Patel', 'Zalak Patel', 'Himanshu Kavani', 'Afoma Onyechi', 'Jessica Ohemeng-Dapaah', 'Dhruvkumar Gadhiya', 'Darshil Patel', 'Chieh Yang']2024-02-24T00:00:00https://www.semanticscholar.org/paper/d5259a1e7110e59739efdac1b67728fe9274bda5 c5456b3cfb1f9eaf703e056374a024311f0f49dbAbstrak: Kecerdasan buatan AI telah memberikan kontribusi besar dalam berbagai bidang, termasuk pendidikan dan pengembangan bahasa. Dalam rangka mengoptimalkan penggunaan teknologi AI dalam pengajaran bahasa Jerman, guru perlu menguasai AI dan terus mengikuti perkembangan teknologi terbaru. Kegiatan ini bertujuan untuk memberi pengenalan teknologi dan penggunaan AI bagi pembelajar dan pengajar. Mitra kegiatan pengabdian kepada masyarakat ini adalah guru bahasa Jerman yang tergabung dalam kelompok kegiatan Musyawarah Guru Mata Pelajaran (MGMP) Bahasa Jerman Malang dan anggota IGBJI Cabang Malang. Materi disampaikan dengan metode seminar dan demonstrasi yang mendiskusikan hal-hal penting terkait dengan teknologi AI yang dapat dimanfaatkan untuk pembelajaran dalam rangka meningkatkan keterampilan berbahasa Jerman. 32 peserta hadir dan mengikuti kegiatan dan mengisi angket dengan 8 pertanyaan tertutup dan 9 pertanyaan terbuka. Berdasarkan angket diketahui bahwa 69.2% peserta baru mengenal fitur ChatGPT4.0. 23.1% peserta berpendapat bahwa ChatGPT dapat sangat efektif digunakan dalam keterampilan berbahasa Jerman para peserta, terutama dalam menyediakan latihan, memberikan umpan balik, dan membantu pemahaman tata bahasa Jerman. Meskipun demikian, pada saat mencoba menggunakan peserta mendapatkan jawaban yang kurang memuaskan, karena teks yang hasil kurang memuaskan. Hal ini dapat terjadi karena banyak faktor, antara lain ketidaktepatan menuliskan prompt/ perintah dalam kolom chat. Meskipun terbukti memiliki kekurangan, peserta tetap berencana akan merekomendasikan penggunaan chatgpt dalam upaya meningkatkan keterampilan berbahasa Jerman.Abstract: Artificial intelligence (AI) has made a huge contribution in various areas, including education and language development. To optimize the use of AI technology in German lessons, teachers need to master AI and keep up with the latest technological developments. This activity aims to introduce technology and the use of AI to learners and educators. The partners for this community service activity are German language teachers who are part of the Malang German Language Subject Teachers Deliberation Group (Musyawarah Guru Mata Pelajaran/MGMP) and members of the Indonesian-German Teachers Association (IGBJI) Malang Branch. The material is delivered through seminar and demonstration methods, discussing important aspects of AI technology that can be utilized for learning to enhance German language skills. A total of 32 participants attended and engaged in the activity and they filled out a questionnaire with 8 closed-ended questions and 9 open-ended questions. Based on the questionnaire, it was found that 69.2% of participants were new to the features of ChatGPT 4.0. According to participants, ChatGPT can be used highly effectively in German language skills, especially in providing practice, giving feedback, and fostering understanding of German grammar. However, during the hands-on session, participants received less satisfactory responses due to less satisfactory generated text. This could be attributed to various factors, including inaccuracies in writing prompts/commands in the chat column. Despite proven shortcomings, participants still plan to recommend the use of ChatGPT in efforts to enhance German language skills.JMM (Jurnal Masyarakat Mandiri)260JMM (Jurnal Masyarakat Mandiri)['M. Kharis']2024-02-24T00:00:00https://www.semanticscholar.org/paper/c5456b3cfb1f9eaf703e056374a024311f0f49db d52e95516a5ec66810c73a598ec3575bf9d10d04В статье рассматривается понятие цифровой экономики и ее роль в современном обществе. Искусственный интеллект характеризуется как одна из важных цифровых технологий, которая способствует развитию бизнеса по различным направлениям, повышая его конкурентоспособность в условиях цифровой экономки. Анализируются кейсы внедрения искусственного интеллекта российскими компаниями для решения производственных задач, проблем безопасности, обслуживания и поиска клиентов. The article examines the concept of the digital economy and its role in modern society. Artificial intelligence is characterized as one of the important digital technologies that contributes to business development in various directions, increasing its competitiveness in the digital economy. Cases of the implementation of artificial intelligence by Russian companies to solve production problems, security problems, service and customer search are analyzed.Industrial Economics20Industrial Economics['М.М. Волков', 'С.А. Осадчий']2024-02-24T00:00:00https://www.semanticscholar.org/paper/d52e95516a5ec66810c73a598ec3575bf9d10d04 fe0461c09fc8cfa3b70a18fe56953b0fcd704b24This article presents an extensive survey on the role of artificial intelligence (AI) in different horizons of agriculture field and its related aspects. The article offers an overview contribution of AI in sustainable agriculture from period 2000 to 2021.VOSviewer and biblioshiny visualization applications were used to display and illustrate the research findings. The study of the research's findings reveals a considerable growth since 2018 in the quantity of educational initiatives released in the field of intelligent use of agriculture. As a result, there is substantial evidence that the development trajectory is displaying an upward tendency. Geographically, the National Cooperation Network reports that the majority of this research is done in China, the United States, India, Iran, and France. The co-author network analysis results showed that the primary writers from the US, China, the UK, and Germany worked together. The bibliometric study's ultimate finding will aid future academics in understanding the significance of sustaining and narrow-minded interest in AI and agricultural research in countries that publish the best studies to explore opportunities for collaboration.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)520The bibliometric study's ultimate finding will aid future academics in understanding the significance of sustaining and narrow-minded interest in AI and agricultural research in countries that publish the best studies to explore opportunities for collaboration.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Mukesh Birla', 'M. Saini', 'Neelamani Samal']2024-02-24T00:00:00https://www.semanticscholar.org/paper/fe0461c09fc8cfa3b70a18fe56953b0fcd704b24 5c3ec7327145e4d0e1cc545377a439df740bd984Цель исследования – выявить возможности использования методов искусственного интеллекта в медицинских проектах. Результатом работы являются предложенные решения по преодолению проблем, связанных с использованием интеллектуальных алгоритмов в этой области. The purpose of the study is to identify the possibilities of using artificial intelligence methods in medical projects. The result of the work is the proposed solutions to overcome the problems associated with the use of intelligent algorithms in this area.Industrial Economics00Industrial Economics['А.А. Антонова', 'С.В. Пальмов']2024-02-24T00:00:00https://www.semanticscholar.org/paper/5c3ec7327145e4d0e1cc545377a439df740bd984 6c519c141f57f0af4dcc1753d141ff457755468aIn the ever-evolving landscape of cybersecurity, the efficacy of IDS (Intrusion Detection System) is paramount. This paper explores the incorporation of advanced IDS techniques namely, reinforcement learning, predictive analysis, genetic algorithms, and artificial neural networks, to enhance the capabilities of IDS. The literature review encompasses a comprehensive examination of existing IDS, shedding light on their limitations and the need for innovative approaches. We delve into studies that employ reinforcement learning, predictive analysis, genetic algorithms, and artificial neural networks, individually, to bolster intrusion detection. The paper then synthesizes these approaches, exploring how their combined application offers a synergistic solution to the challenges posed by modern cyber threats. Methodologies employed in relevant studies are discussed, and the results are taken into account to reveal insights into the strengths and weaknesses of the integrated techniques. Additionally, we highlight challenges in implementation. This paper gives a comprehensive review of the current state of IDS and the idea for the most robust and reliable Intrusion Detection System.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)180This paper explores the incorporation of advanced IDS techniques namely, reinforcement learning, predictive analysis, genetic algorithms, and artificial neural networks, to enhance the capabilities of IDS.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Pankaj Hari Durole', 'Mukta Agarwal']2024-02-24T00:00:00https://www.semanticscholar.org/paper/6c519c141f57f0af4dcc1753d141ff457755468a d78861df48cc2e4cde609cbe7ff0c0e440184ddcPostdigital Science and Education413Postdigital Science and Education['Sarah Hayes', 'P. Jandrić', 'Benjamin J. Green']2024-02-24T00:00:00https://www.semanticscholar.org/paper/d78861df48cc2e4cde609cbe7ff0c0e440184ddc 6e853e664ecb7e52d06d0fbf997828c80a9d8595Health technology871Health and Technology['Mohammad Ali', 'S. Wani', 'Tathagata Dey', 'Seema Mehdi']2024-02-24T00:00:00https://www.semanticscholar.org/paper/6e853e664ecb7e52d06d0fbf997828c80a9d8595 86762457eeb3885ed34fe5ffa580889801fc4cbdThe human body's most important organ is the heart. Cardiovascular illness is a catch-all phrase for a variety of disorders that affect your heart. The study of algorithms and statistical models used by computer systems to carry out certain tasks without clear instructions and instead relying on patterns and deduction is known as machine learning (ML). Different algorithms can forecast cardiac disease. A crucial criterion for evaluating an algorithm is accuracy. In this article, a combination method based on naive bayes and random forest was suggested. Calculated parameters include accuracy, classification error, F-measure, recall, and precision. The hybrid algorithm that has been suggested has a 92% accuracy rate.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)170A combination method based on naive bayes and random forest and the hybrid algorithm that has been suggested has a 92% accuracy rate is suggested.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Reshu Choubey', 'Nishchol Mishra', 'Jitendra Agrawal', 'Yogendra PS Maravi']2024-02-24T00:00:00https://www.semanticscholar.org/paper/86762457eeb3885ed34fe5ffa580889801fc4cbd d5e86b2382c65e7ef33ca16092534d0000e119f8: Nowadays, Smart Glass emerges as a potential aid for individuals with visual impairments, offering the promise of enhanced quality of life. Designed for those seeking independent navigation with a sense of social ease and security, the concept revolves around the idea that visually impaired individuals prefer inconspicuous assistance tools. This paper delves into the significant advancements within wearable electronics, spot-lighting additional features. This innovative glass offers a multifaceted solution for individuals with visual impairments, providing assistance in diverse scenarios. Beyond aiding in the reading of scripts, they excel at distinguishing between currencies, enabling users to navigate financial transactions with ease. The glasses also enhance color recognition, allowing wearers to perceive and appreciate the vibrant spectrum of the world around them. Additionally, the incorporation of obstacle detection technology ensures a heightened sense of safety by alerting users when they are in proximity to potential hazards. Furthermore, the glasses feature advanced facial recognition capabilities, contributing to a more inclusive and socially connected experience by detecting faces and fostering seamless interactions.International Journal of Telecommunications90This paper delves into the significant advancements within wearable electronics, spot-lighting additional features of Smart Glass, a multifaceted solution for individuals with visual impairments, providing assistance in diverse scenarios.International Journal of Telecommunications['Mohamed Badawi', 'Al Nagar ,E Al Nagar', 'Mansour, R Mansour, R', 'Ibrahim ,Kh Ibrahim ,Kh', 'Nada Hegazy', 'Safa Elaskary']2024-02-24T00:00:00https://www.semanticscholar.org/paper/d5e86b2382c65e7ef33ca16092534d0000e119f8 46a78955ff755bafb755a8a00563e4372fa207f7Over the last decade, medical imaging, wearable sensors, personal health records, and public health groups have increased medical research data. This data may be used by cloud computing, GPUs, FPGAs, and TPUs. Several powerful AI algorithms have been created to analyze healthcare's massive databases. Health, biology, biosensors, and AI advances are covered. We address precision medicine, medical imaging, and IoT biosensors with machine learning. We study the newest wearable biosensing tech. Modern gadgets detect ailments using AI to examine electrochemical and electrophysiological data. These innovations show customized medicine by offering accurate, efficient, and economical point-of-care therapy. In healthcare data, edge computing, quick AI, and federated learning are examined. We finish with data-driven AI, IoT healthcare and biosensors, and data modality distribution adjustments. My last sentence is future thinking.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)200This work addresses precision medicine, medical imaging, and IoT biosensors with machine learning, and studies the newest wearable biosensing tech, which detects ailments using AI to examine electrochemical and electrophysiological data.2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)['Benazeer Haque', 'Ebtasam Ahmad Siddiqui', 'S. Jha']2024-02-24T00:00:00https://www.semanticscholar.org/paper/46a78955ff755bafb755a8a00563e4372fa207f7 4c26cea35c883de63f19a55019d3a5f16115cafcObjective: Issues of legalization of new digital rights are the subject of current discussions. To date, the need to consolidate the human rights, inextricably linked to the process of digitalization of society, is recognized in many international agendas. The right to disconnect is one of these rights, and the inclusion of it in the Loi Travail was the first step towards incorporating new digital human rights into national law. Despite the fact that the regulation of the right to disconnect is being discussed at the international level, countries are very cautious about this category in their national labor laws.   Purpose: to identify the problematic aspects of the legalization of the right to disconnect in labor relations through the prism of the analysis of the legislative experience of states of various legal jurisdictions.   Theoretical framework: The study of the regulatory landscape of foreign states in relation to the right to disconnect contributes to the consolidation of expertise about approaches to the legal protection of employees. Based on the above, when analyzing the literature, the list of sources under study included research papers, articles, and reviews related to the legalization of the right to disconnect in labor relations based on labor legislation at the national level, as well as at the level of collective agreements and corporate culture.   Methods: The research methodology is based on the main method of legal science – legal hermeneutics. When reviewing the research literature on the topic and including sources in the scope of the study, we were guided by the criteria of a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. The study also used the clustering method to determine the jurisdictions being studied.   Result & Conclusion: This knowledge will help to identify the main difficulties of direct legalization, the reasons for the implicit recognition of the right to disconnect and will help to identify effective methods of law-making in the future. As a result of an inductive analysis of specific examples of the legal regulation of the right to disconnect in the national laws of various jurisdictions, we have identified the main obstacles to legal consolidation and strategies for the legalization of the right to disconnect. Implications of research: The research identifies the main factors influencing the legalisation of the right to disconnect at the legislative level. It also categorises the strategies adopted by countries in different jurisdictions to regulate the right to disconnect in legal relations.   Originality/value: Increased knowledge of work-life balance through the legal entrenchment of the right to disconnect in the employment relationship. The findings of the study can serve as a basis for reorienting not only the legal policies of states in the area of the right to time off, but also the strategies of companies to improve the situation of workers.   Funding: This study was supported by the Science Committee of the Ministry of Science and High education of the Republic of Kazakhstan (grant no. fund this research AP19676064).Journal of Law and Sustainable Development160The findings of the study can serve as a basis for reorienting not only the legal policies of states in the area of the right to time off, but also the strategies of companies to improve the situation of workers.Journal of Law and Sustainable Development['Assel Kaishatayeva', 'Flura Ibragimova', 'Rassima Bayazitova', 'Nurolla Yessenzholov']2024-02-23T00:00:00https://www.semanticscholar.org/paper/4c26cea35c883de63f19a55019d3a5f16115cafc 96316c9e3e16588f543cc61af8ad39b2fdc6a7c5PurposeThe metaverse, through artificial intelligence (AI) systems and capabilities, allows considerable data analysis in the workplace, largely exceeding traditional people analytics data collection. While concerns over surveillance and issues associated with privacy and discrimination have been raised, the metaverse has the potential to offer opportunities associated with fairer assessment of employee performance and enhancement of the employee experience, especially with respect to gender and race, inclusiveness and workplace equity. This paper aims at shedding light on the diversity, equity and inclusion (DEI) opportunities and challenges of implementing the metaverse in the workplace, and the role played by AI.Design/methodology/approachThis paper draws on our past research on AI and the metaverse and provides insights addressed to human resources (HR) scholars and practitioners.FindingsOur analysis of AI applications to the metaverse in the workplace sheds light on the ambivalent role of and potential trade-offs that may arise with this emerging technology. If used responsibly, the metaverse can enable positive changes concerning the future of work, which can promote DEI. Yet, the same technology can lead to negative DEI outcomes if implementations occur quickly, unsupervised and with a sole focus on efficiencies and productivity (i.e. collecting metrics, models etc.).Practical implicationsManagers and HR leaders should try to be first movers rather than followers when deciding if (or, better, when) to implement metaverse capabilities in their organizations. But how the metaverse is implemented will be strategic. This involves choices concerning the degree of invasive/pervasive monitoring (internal) as well as make or buy decisions concerning outsourcing AI capabilities.Originality/valueOur paper is one among few (to date) that discusses AI capabilities in the metaverse at the intersection of the HR and information systems(IS) literature and that specifically tackles DEI issues. Also, we take a “balanced” approach when evaluating the metaverse from a DEI perspective. While most studies either demonize or celebrate these technologies from an ethical and DEI standpoint, we aim to highlight challenges and opportunities, with the goal to guide scholars and practitioners towards a responsible use of the metaverse in organizations.Person-centered review333This paper is one among few (to date) that discusses AI capabilities in the metaverse at the intersection of the HR and information systems literature and that specifically tackles DEI issues.Personnel Review['Marco Marabelli', 'Pamela Lirio']2024-02-23T00:00:00https://www.semanticscholar.org/paper/96316c9e3e16588f543cc61af8ad39b2fdc6a7c5 5246d53c5abd4f3d5f5682460bac5e0887f04f93Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that helps people identify potential harms from the AI applications they are prototyping. Based on a user's prompt, Farsight highlights news articles about relevant AI incidents and allows users to explore and edit LLM-generated use cases, stakeholders, and harms. We report design insights from a co-design study with 10 AI prototypers and findings from a user study with 42 AI prototypers. After using Farsight, AI prototypers in our user study are better able to independently identify potential harms associated with a prompt and find our tool more useful and usable than existing resources. Their qualitative feedback also highlights that Farsight encourages them to focus on end-users and think beyond immediate harms. We discuss these findings and reflect on their implications for designing AI prototyping experiences that meaningfully engage with AI harms. Farsight is publicly accessible at: https://PAIR-code.github.io/farsight.International Conference on Human Factors in Computing Systems2111After using Farsight, AI prototypers in the authors' user study are better able to independently identify potential harms associated with a prompt and find the tool more useful and usable than existing resources.{'pages': '976:1-976:40'}['Zijie J. Wang', 'Chinmay Kulkarni', 'Lauren Wilcox', 'Michael Terry', 'Michael Madaio']2024-02-23T00:00:00https://www.semanticscholar.org/paper/5246d53c5abd4f3d5f5682460bac5e0887f04f93 036e49f02180cb3fdb6d1e1ccd118abefef42d16The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality content across media, thereby reshaping online ecosystems but also raising concerns about market over-saturation and the potential marginalization of human creativity. Our work introduces a competition model generalized from the Tullock contest to analyze the tension between human creators and GenAI. Our theory and simulations suggest that despite challenges, a stable equilibrium between human and AI-generated content is possible. Our work contributes to understanding the competitive dynamics in the content creation industry, offering insights into the future interplay between human creativity and technological advancements in GenAI.arXiv.org561This work introduces a competition model generalized from the Tullock contest to analyze the tension between human creators and GenAI, and suggests that despite challenges, a stable equilibrium between human and AI-generated content is possible.ArXiv['Fan Yao', 'Chuanhao Li', 'Denis Nekipelov', 'Hongning Wang', 'Haifeng Xu']2024-02-23T00:00:00https://www.semanticscholar.org/paper/036e49f02180cb3fdb6d1e1ccd118abefef42d16 709e655acd36fc60d6f42bd42af91701cbf6b3a6 Manufacturers perpetually adapt their systems to meet unforeseen events, new objectives, competition, and improved understanding of processes. In that human-directed work, models mediate an enduring relationship between production resources and engineers. Accommodating new understanding in the models controlling production can lead to more effective manufacturing. That work has previously been the province of quality programs such as Six Sigma, but is now fertile ground to study human-computer interaction about that enduring relationship mediated by models. Can AI augment human capability in the arcane work of formulating and refining models? This question is relevant to complex system engineering generally, not just manufacturing. In answering this question, this paper adapts Klein’s flexecution for use in adaptable manufacturing systems. Theory flexecution, the methodical refinement of models, points to human-computer interactions that emphasize the roles of models, explanation, and machine agents that recognize the engineer’s goals. This perspective article illustrates these ideas with an example of formulating models for production scheduling.Journal of Integrated Design & Process Science241Klein’s flexecution is adapted for use in adaptable manufacturing systems and points to human-computer interactions that emphasize the roles of models, explanation, and machine agents that recognize the engineer’s goals.J. Integr. Des. Process. Sci.['Peter Denno']2024-02-23T00:00:00https://www.semanticscholar.org/paper/709e655acd36fc60d6f42bd42af91701cbf6b3a6 1ef1ef1c7dd25ee98491766894458d77fffdf3b1 We report the discovery of an active asteroid, 2016 UU121, for the first time via artificial intelligence-enhanced classification, informed by our NASA Partner program Active Asteroids, a Citizen Science project hosted on the Zooniverse platform. The early version of our deep neural network, TailNet, identified potential activity associated with 2016 UU121 in 40 Dark Energy Camera (DECam) images from UT 2021 September 10 to 11. The discovery was vetted and confirmed by our Active Asteroids core science team. In total, 66 DECam images of this object showed clear activity in the form of a tail. 2016 UU121 has a Tisserand parameter with respect to Jupiter of 3.161, thus we classify the object as an active asteroid. Moreover, the activity occurred near perihelion, so 2016 UU121 is also a candidate Main-belt comet.Research Notes of the AAS82Research Notes of the AAS['Nima Sedaghat', 'C. O. Chandler', 'W. J. Oldroyd', 'C. Trujillo', 'W. A. Burris', 'Henry H. Hsieh', 'J. Kueny', 'Kennedy A. Farrell', 'Jarod A. DeSpain', 'M. Magbanua', 'S. Sheppard', 'Michele T. Mazzucato', 'Milton K. D. Bosch', 'Tiffany Shaw-Diaz', 'V. Gonano', 'Al Lamperti', 'José A. da Silva Campos', 'Brian L. Goodwin', 'I. Terentev', 'Charles J. A. Dukes']2024-02-23T00:00:00https://www.semanticscholar.org/paper/1ef1ef1c7dd25ee98491766894458d77fffdf3b1 3ccc91aff409ee45f99b7cbf01f898369937c8f9The implementation of medical software and artificial intelligence (AI) algorithms into routine clinical cytometry diagnostic practice requires a thorough understanding of regulatory requirements and challenges throughout the cytometry software product lifecycle. To provide cytometry software developers, computational scientists, researchers, industry professionals, and diagnostic physicians/pathologists with an introduction to European Union (EU) and United States (US) regulatory frameworks. Informed by community feedback and needs assessment established during two international cytometry workshops, this article provides an overview of regulatory landscapes as they pertain to the application of AI, AI-enabled medical devices, and Software as a Medical Device in diagnostic flow cytometry. Evolving regulatory frameworks are discussed, and specific examples regarding cytometry instruments, analysis software and clinical flow cytometry in-vitro diagnostic assays are provided. An important consideration for cytometry software development is the modular approach. As such, modules can be segregated and treated as independent components based on the medical purpose and risk and become subjected to a range of context-dependent compliance and regulatory requirements throughout their life cycle. Knowledge of regulatory and compliance requirements enhances the communication and collaboration between developers, researchers, end-users and regulators. This connection is essential to translate scientific innovation into diagnostic practice and to continue to shape the development and revision of new policies, standards, and approaches.Cytometry. Part B, Clinical cytometry00An overview of regulatory landscapes as they pertain to the application of AI, AI-enabled medical devices, and Software as a Medical Device in diagnostic flow cytometry is provided.Cytometry. Part B, Clinical cytometry['Goce Bogdanoski', 'F. Lucas', 'Wolfgang Kern', 'Kamila Czechowska']2024-02-23T00:00:00https://www.semanticscholar.org/paper/3ccc91aff409ee45f99b7cbf01f898369937c8f9 23f3a53ee8b877462fa1d3177ef30b9eff118fc5The smart performance comparison of AI-based breast cancer detection models is an important research topic in the healthcare industry. It is used to compare and evaluate different AI-based models that are used to diagnose breast cancer. These models are mainly developed using machine learning, computer vision, or deep learning techniques. The methods used to compare and evaluate these models can vary depending on the purpose of the comparison. This can include comparing accuracy, precision, recall, or f-measure of different models. Furthermore, other criteria such as stability, reliability, explain ability, speed, and cost-effectiveness may be taken into consideration when evaluating the models. These models have achieved high sensitivity and specificity rates, outperforming traditional detection methods. However, the performance of the AI models varies based on the type of imaging technique and dataset used. Further, the research also highlights the need for more diverse and inclusive datasets to avoid potential biases in the AI models. The results from this comparison provide valuable insight into the performance of AI-based breast cancer detection models and can help healthcare professionals and researchers select and deploy the best model for their particular applications.2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)210The results from this comparison provide valuable insight into the performance of AI-based breast cancer detection models and can help healthcare professionals and researchers select and deploy the best model for their particular applications.2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)['Sana Samreen', 'Abdul Sajid Mohammed', 'Anuteja Reddy Neravetla', 'Nasmin Jiwani', 'J. Logeshwaran']2024-02-23T00:00:00https://www.semanticscholar.org/paper/23f3a53ee8b877462fa1d3177ef30b9eff118fc5 0a708b6786aaafff628bf8d22c1bc6226bb5b79bRecently, there has been great progress in the ability of artificial intelligence (AI) algorithms to classify dermatological conditions from clinical photographs. However, little is known about the robustness of these algorithms in real-world settings where several factors can lead to a loss of generalizability. Understanding and overcoming these limitations will permit the development of generalizable AI that can aid in the diagnosis of skin conditions across a variety of clinical settings. In this retrospective study, we demonstrate that differences in skin condition distribution, rather than in demographics or image capture mode are the main source of errors when an AI algorithm is evaluated on data from a previously unseen source. We demonstrate a series of steps to close this generalization gap, requiring progressively more information about the new source, ranging from the condition distribution to training data enriched for data less frequently seen during training. Our results also suggest comparable performance from end-to-end fine tuning versus fine tuning solely the classification layer on top of a frozen embedding model. Our approach can inform the adaptation of AI algorithms to new settings, based on the information and resources available.arXiv.org350It is demonstrated that differences in skin condition distribution, rather than in demographics or image capture mode are the main source of errors when an AI algorithm is evaluated on data from a previously unseen source.ArXiv['R. Rikhye', 'Aaron Loh', 'G. Hong', 'Preeti Singh', 'Margaret Ann Smith', 'Vijaytha Muralidharan', 'Doris Wong', 'R. Sayres', 'Michelle Phung', 'Nicolas Betancourt', 'Bradley Fong', 'Rachna Sahasrabudhe', 'Khoban Nasim', 'Alec Eschholz', 'Basil Mustafa', 'Jan Freyberg', 'Terry Spitz', 'Yossi Matias', 'G. Corrado', 'Katherine Chou', 'D. Webster', 'P. Bui', 'Yuan Liu', 'Yun Liu', 'Justin M. Ko', 'Steven Lin']2024-02-23T00:00:00https://www.semanticscholar.org/paper/0a708b6786aaafff628bf8d22c1bc6226bb5b79b 069e891c4264e73d1059ab85880b5647380c75a9The purpose of this paper is to give a comprehensive review of the related work that has everything to do with creating a wallpaper in artificial intelligence (AI) technology. Firstly, deep learning and neural network are summarized, especially generative adversarial networks that aim to effective generate images. Then, User interest modeling is summarized and analyzed, which is a key point to figure out the preference of the players. Further, main ideas are given about the trends and directions of wallpaper generation by AI.Applied and Computational Engineering00A comprehensive review of the related work that has everything to do with creating a wallpaper in artificial intelligence (AI) technology, especially generative adversarial networks that aim to effective generate images are summarized.Applied and Computational Engineering['Wenshu Hou', 'Zicheng Liu', 'Junjie Deng', 'Jiacheng Wang']2024-02-23T00:00:00https://www.semanticscholar.org/paper/069e891c4264e73d1059ab85880b5647380c75a9 0e9cba79e643adcc0c3cb89c71121622120a5f36Cancer has been classified as a diverse illness with a wide range of subgroups. Its early identification and prognosis, which have become a requirement of cancer research, are essential for clinical treatment. Patients have already benefited greatly from the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms in the field of healthcare. AI simulates and combines data, pre-programmed rules, and knowledge to produce predictions. Data are used to improve efficiency across several pursuits and tasks through the art of ML. DL is a larger family of ML methods based on representational learning and simulated neural networks. Support vector machines, convulsion neural networks, and artificial neural networks, among others, have been widely used in cancer research to construct prediction models that enable precise and effective decision-making. Although using these innovative methods can enhance our comprehension of how cancer progresses, further validation is required before these techniques can be used in routine clinical practice. We cover contemporary methods used in the modelling of cancer development in this article. The presented prediction models are built using a variety of guided ML approaches, as well as numerous input attributes and data collections. Early identification and cost-effective detection of cancer's progression are equally necessary for successful treatment of the disease. Smart material-based detection techniques can give end consumers a portable, affordable instrument to easily detect and monitor their health issues without the need for specialized knowledge. Owing to their cost-effectiveness, excellent sensitivity, multimodal detection capacity, and miniaturization aptitude, two-dimensional (2D) materials have a lot of prospects for clinical examination of various compounds as well as cancer biomarkers. The effectiveness of traditional devices is moving faster towards more useful techniques thanks to developments in 2D material-based biosensors/sensors. The most current developments in the design of 2D material-based biosensors/sensors-the next wave of cancer screening instruments-are also outlined in this article.Nanoscale830Nanoscale['Vibhas Chugh', 'Adreeja Basu', 'Ajeet Kaushik', 'Manshu', 'S. Bhansali', 'A. Basu']2024-02-23T00:00:00https://www.semanticscholar.org/paper/0e9cba79e643adcc0c3cb89c71121622120a5f36 d6205f631e6711d13814b6a02ad5011413ee0b88Education and Information Technologies : Official Journal of the IFIP technical committee on Education480Understanding of how TAC could be structured at school levels is enhanced and the implications for future development and practice to support TAC are direct.Education and Information Technologies['Jinhee Kim']2024-02-23T00:00:00https://www.semanticscholar.org/paper/d6205f631e6711d13814b6a02ad5011413ee0b88 a5a308b2de6734a5a47e391d4020e1b0b0c288e4 AI is considered to be very abstract to a range of critics. In this regard, algorithms are referred to regularly as black boxes and divorced from human intervention. A particular philosophical maneuver supports this outcome. The aim of this article is to (1) bring the philosophy to the surface that has contributed to this distance between AI and people and (2) offer an alternative philosophical position that can bring this technology closer to individuals and communities. The overall goal of the analysis in this paper is the humanising of AI by addressing the shortcomings of conceptualising algorithms as black boxes. Filosofija Sociologija300The overall goal of the analysis in this paper is the humanising of AI by addressing the shortcomings of conceptualising algorithms as black boxes.Filosofija. Sociologija['John W. Murphy', 'Carlos Largacha-Martínez']2024-02-23T00:00:00https://www.semanticscholar.org/paper/a5a308b2de6734a5a47e391d4020e1b0b0c288e4 33cbb757ee37e7388a32f2a83bfa0b90a87ada10Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology. This is particularly intriguing in early mammalian development, where early cell-lineage differentiation arises from processes that initially appear cell-autonomous but later materialize reliably at the tissue level. In this study, we develop a multi-scale, spatial-stochastic simulator of mouse embryogenesis, focusing on inner-cell mass (ICM) differentiation in the blastocyst stage. Our model features biophysically realistic regulatory interactions and accounts for the innate stochasticity of the biological processes driving cell-fate decisions at the cellular scale. We advance event-driven simulation techniques to incorporate relevant tissue-scale phenomena and integrate them with Simulation-Based Inference (SBI), building on a recent AI-based parameter learning method: the Sequential Neural Posterior Estimation (SNPE) algorithm. Using this framework, we carry out a large-scale Bayesian inferential analysis and determine parameter sets that reproduce the experimentally observed system behavior. We elucidate how autocrine and paracrine feedbacks via the signaling protein FGF4 orchestrate the inherently stochastic expression of fate-specifying genes at the cellular level into reproducible ICM patterning at the tissue scale. This mechanism is remarkably independent of the system size. FGF4 not only ensures correct cell lineage ratios in the ICM, but also enhances its resilience to perturbations. Intriguingly, we find that high variability in intracellular initial conditions does not compromise, but rather can enhance the accuracy and precision of tissue-level dynamics. Our work provides a genuinely spatial-stochastic description of the biochemical processes driving ICM differentiation and the necessary conditions under which it can proceed robustly.00This work provides a genuinely spatial-stochastic description of the biochemical processes driving ICM differentiation and the necessary conditions under which it can proceed robustly, and finds that high variability in intracellular initial conditions does not compromise, but rather can enhance the accuracy and precision of tissue-level dynamics.['Michael A. Ramirez-Sierra', 'T. Sokolowski']2024-02-23T00:00:00https://www.semanticscholar.org/paper/33cbb757ee37e7388a32f2a83bfa0b90a87ada10 3e9fc6e72f067ac14e36aa18c56b84ba602eac08Artificial Intelligence, renowned for its data interpretation, learning, and task achievement capabilities, has gained popularity in various industries and academies due to enhanced efficiency and quality. This study aims to determine the extent of AI usage among students, including functionality, availability, complexity, assessment scores, course mastery, and grading metrics. It also seeks to determine if a relationship exists between AI usage and their academic performance. The study employs a quantitative approach using a correlational design. The respondents of the study are 293 Business Administration students from Negros Oriental State University Main Campus 1, Dumaguete City. The study's findings suggest that AI usage among students is moderately prevalent in terms of functionality, availability, and complexity. However, the students' academic performance was found to be above-average, with high scores on assessments, course mastery, and excellent grades. There is no significant relationship between AI use and academic performance found. In conclusion, AI tools offer personalized learning experiences, immediate feedback, and collaborative activities, but further growth and improvement are needed, including training, accessibility, research, monitoring, and best practices sharing.Pedagogy Review: An International Journal of Educational Theories, Approaches and Strategies00The study's findings suggest that AI usage among students is moderately prevalent, and students' academic performance was found to be above-average, with high scores on assessments, course mastery, and excellent grades.Pedagogy Review: An International Journal of Educational Theories, Approaches and Strategies['J. C. Bancoro']2024-02-23T00:00:00https://www.semanticscholar.org/paper/3e9fc6e72f067ac14e36aa18c56b84ba602eac08 66b6d387d5209618a72fc3aa78a83bc6776da4f3Humanities and Social Sciences Communications170The authors insist on the importance of distinguishing between robots in light of their degree of autonomy and then drafting liability rules depending on whether the action was done autonomously by an unattended robot or whether it was done automatically by an attended robot.Humanities and Social Sciences Communications['Ahmed Eldakak', 'Abdulla Alremeithi', 'Emad Dahiyat', 'Moatasem El-Gheriani', 'Hassan Mohamed', 'Mohammad Ibrahim Abdulrahim Abdulla']2024-02-23T00:00:00https://www.semanticscholar.org/paper/66b6d387d5209618a72fc3aa78a83bc6776da4f3 358a09d3231dcc0c57f4941e2c4a8ae68a90e001This research endeavours to systematically investigate the multifaceted domain of AI literacy, given the pervasive impact of artificial intelligence on diverse facets of contemporary human existence. The inquiry is motivated by a fundamental question posed to educators: how best to cultivate AI literacies and competencies and how these proficiencies are structured and influenced. Employing a rigorous two-part methodology, the initial phase scrutinises 28 studies from the SCOPUS database, unveiling five distinct discourses germane to AI literacy. Subsequently, the second phase involves the administration of questionnaires to 73 students, whose responses undergo thematic analysis to discern patterns within the four domains delineated by Ng et al. The ensuing discourse underscores a pivotal revelation: despite formal adherence to established discourses, the conceptualisation of AI literacy necessitates a departure from conventional perspectives. Ethical principles, elucidated by students, emerge not merely as individual components but as integral facets of a broader societal literacy profile, thereby advocating a paradigm shift towards social reflection. This novel insight prompts a critical re-evaluation of AI literacy’s prevailing assumptions and conceptual frameworks, urging a transition towards models grounded in ecological or network dynamic interactionist principles.The social science670This research endeavours to systematically investigate the multifaceted domain of AI literacy, given the pervasive impact of artificial intelligence on diverse facets of contemporary human existence, urging a transition towards models grounded in ecological or network dynamic interactionist principles.Social Sciences['Michal Černý']2024-02-23T00:00:00https://www.semanticscholar.org/paper/358a09d3231dcc0c57f4941e2c4a8ae68a90e001 d663958affd3e455eb284a8d6eccfbc866e359f3The “Revolutionizing Road Safety: AI-Powered Road Defect Detection for Safer Roads” project aims to revolutionize road safety and infrastructure management by equipping patrolling vehicles with Line Scanner Cameras. These cameras enable real-time identification of road defects. This initiative addresses the labor-intensive and error-prone nature of manual defect detection in critical infrastructure. Natural disasters further compound this issue, necessitating extensive inspections for structural integrity. The integration of image processing techniques and machine learning methods offers a powerful solution, allowing for the analysis of captured images to discern potential defects. A comprehensive review of ten meticulously selected research articles spanning the past decade highlights One of the most encouraging automated methods for identifying cracks, emphasizing the potential of this AI-powered system to streamline road maintenance and repair efforts while bolstering road safety in worldwide.2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC)210A comprehensive review of ten meticulously selected research articles spanning the past decade highlights One of the most encouraging automated methods for identifying cracks, emphasizing the potential of this AI-powered system to streamline road maintenance and repair efforts while bolstering road safety in worldwide.2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC)['M. E. Paramasivam', 'Sutharshana Perumal', 'Hariharan Pathmanaban']2024-02-23T00:00:00https://www.semanticscholar.org/paper/d663958affd3e455eb284a8d6eccfbc866e359f3 2fb1c189c7271931d3a042747e7f5cbedb0c289aThe escalating calls for more suitable performance and performance in energy electronics and power structures, established in electric automobiles, renewable power setups, domestic home equipment, and diverse industrial packages, has led to a pragmatic solution: the combination of synthetic intelligence (AI). This article delves right into a complete evaluation of the implementation of AI in motor and strength electronic systems. It gives an introductory evaluation of power electronics and drive structures, highlighting the capacity for performance development via AI. The article explores the application of AI methodologies along with system studying, fuzzy logic, and metaheuristic strategies, presenting insights into their underlying principles. Furthermore, the narrative covers various AI applications within the power industry, dropping mild on fault detection, control mechanisms, strength management, and layout optimization.2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC)180The article explores the application of AI methodologies along with system studying, fuzzy logic, and metaheuristic strategies, presented insights into their underlying principles, highlighting the capacity for performance development via AI.2024 3rd International conference on Power Electronics and IoT Applications in Renewable Energy and its Control (PARC)['Vijay J. Patil', 'Suhas B Khadake', 'D. A. Tamboli', 'H. Mallad', 'Shantisagar M. Takpere', 'Vijay A. Sawant']2024-02-23T00:00:00https://www.semanticscholar.org/paper/2fb1c189c7271931d3a042747e7f5cbedb0c289a 74473d06670dc4dd5656ffc339895644563aec0bOur food system is complex, multifaceted, and in need of an upgrade. Population growth, climate change, and socioeconomic disparities are some of the challenges that create a systemic threat to its sustainability and capacity to address the needs of an evolving planet. The mission of the AI Institute of Next Generation Food Systems (AIFS) is to leverage the latest advances in AI to help create a more sustainable, efficient, nutritious, safe, and resilient food system. Instead of using AI in isolation, AIFS views it as the connective tissue that can bring together interconnected solutions from farm to fork. From guiding molecular breeding and building autonomous robots for precision agriculture, to predicting pathogen outbreaks and recommending personalized diets, AIFS projects aspire to pave the way for infrastructure and systems that empower practitioners to build the food system of the next generation. Workforce education, outreach, and ethical considerations related to the emergence of AI solutions in this sector are an integral part of AIFS with several collaborative activities aiming to foster an open dialogue and bringing closer students, trainees, teachers, producers, farmers, workers, policy makers, and other professionals.The AI Magazine80AI Mag.['Ilias Tagkopoulos', 'Mason Earles', 'Danielle G. Lemay', 'Xin Liu', 'Nitin Nitin', 'Aaron D. Smith', 'Tarek I. Zohdi', 'Stephen F. Brown']2024-02-23T00:00:00https://www.semanticscholar.org/paper/74473d06670dc4dd5656ffc339895644563aec0b f4aeeceac426cfb4d796ffe04fc2923f4034566aOver 13 million Americans aged 65 and older are currently living with a diagnosis of mild cognitive impairment (MCI), a common precursor to dementia. These individuals largely rely on a network of informal caregivers—family, friends, and community members—who work together with professional healthcare and social service providers to provide care and support in home settings. The AI‐CARING Institute contributes foundational AI research focused on developing personalized collaborative AI systems that improve the quality of life and independence of aging adults living at home.The AI Magazine100The AI‐CARING Institute contributes foundational AI research focused on developing personalized collaborative AI systems that improve the quality of life and independence of aging adults living at home.AI Mag.['Sonia Chernova', 'Elizabeth Mynatt', 'Agata Rozga', 'Reid G. Simmons', 'Holly Yanco']2024-02-23T00:00:00https://www.semanticscholar.org/paper/f4aeeceac426cfb4d796ffe04fc2923f4034566a 432bdeb7fa7afcfebcf9afbbbc938ac547956c68AI and Ethics430A set of checklists for ethical implementation of generative AI in business environment to minimise cyber security risk based on the discussed moral responsibilities and ethical concern are recommended.AI and Ethics['Declan Humphreys', 'Abigail Koay', 'Dennis Desmond', 'Erica Mealy']2024-02-23T00:00:00https://www.semanticscholar.org/paper/432bdeb7fa7afcfebcf9afbbbc938ac547956c68 6e49789a62c93bfc02c2979da6e7539322224c3aAI &amp; SOCIETY60AI &amp; SOCIETY['Morgan Luck']2024-02-23T00:00:00https://www.semanticscholar.org/paper/6e49789a62c93bfc02c2979da6e7539322224c3a 80080ad7ade98fb838badbce838f7678d704f648Nature Sustainability131Nature Sustainability['Rachit Dubey', 'Mathew D. Hardy', 'Thomas L. Griffiths', 'Rahul Bhui']2024-02-23T00:00:00https://www.semanticscholar.org/paper/80080ad7ade98fb838badbce838f7678d704f648 6e5db8c5994dd6d98da57fc48b4a8d71a1ab30ebIn recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and they rely on experiment-internal data as well as full detector simulations. We propose a new strategy, so-called classifier surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high-level information without requiring a sophisticated detector simulation. Technically, we show that Continuous Normalizing Flows are a suitable generative architecture that can be efficiently trained to sample classification results using Conditional Flow Matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs to the user. For a concrete example of tagging jets from hadronically decaying top quarks, we demonstrate the application of flows in combination with uncertainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights.311Technically, it is shown that Continuous Normalizing Flows are a suitable generative architecture that can be efficiently trained to sample classification results using Conditional Flow Matching and can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs to the user.['S. Bieringer', 'G. Kasieczka', 'J. Kieseler', 'Mathias Trabs']2024-02-23T00:00:00https://www.semanticscholar.org/paper/6e5db8c5994dd6d98da57fc48b4a8d71a1ab30eb a01e15d03e19a8feea0e8c0a295d2f62681e3c68Nature Machine Intelligence30Nature Machine Intelligence[]2024-02-23T00:00:00https://www.semanticscholar.org/paper/a01e15d03e19a8feea0e8c0a295d2f62681e3c68 96f230e27214327dcc5e1deacb790017a5c50836Gut120Gut['M. Abdelrahim', 'K. Siggens', 'Y. Iwadate', 'Naoto Maeda', 'H. Htet', 'Pradeep Bhandari']2024-02-23T00:00:00https://www.semanticscholar.org/paper/96f230e27214327dcc5e1deacb790017a5c50836 d6cfed6cddba12fb59f87cc73943592dd045da69Traditional Chinese medicine (TCM) is the practical experience and summary of the Chinese nation for thousands of years. It shows great potential in treating various chronic diseases, complex diseases and major infectious diseases, and has gradually attracted the attention of people all over the world. However, due to the complexity of prescription and action mechanism of TCM, the development of TCM industry is still in a relatively conservative stage. With the rise of artificial intelligence technology in various fields, many scholars began to apply artificial intelligence technology to traditional Chinese medicine industry and made remarkable progress. This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine. The limitations of artificial intelligence in these applications are also emphasized, including the lack of pharmacological research, database quality problems and the challenges brought by human-computer interaction. Nevertheless, the development of artificial intelligence has brought new opportunities and innovations to the modernization of traditional Chinese medicine. Integrating artificial intelligence technology into the comprehensive application of Chinese medicine industry is expected to overcome the major problems faced by traditional Chinese medicine industry and further promote the modernization of the whole traditional Chinese medicine industry.Frontiers in Pharmacology1472This paper comprehensively summarizes the important role of artificial intelligence in the development of traditional Chinese medicine industry from various aspects, including new drug discovery, data mining, quality standardization and industry technology of traditional Chinese medicine.Frontiers in Pharmacology['E. Zhou', 'Qin Shen', 'Yang Hou', 'Luca Rastrelli', 'Kuo Yang', 'Chi-Jung Tai']2024-02-23T00:00:00https://www.semanticscholar.org/paper/d6cfed6cddba12fb59f87cc73943592dd045da69 6814c0cda08946450b759e7903ec41e7f31fd589Purpose: The aim of this work is to study new trends in construction, their advantages and disadvantages, application of innovations, analysis of operability and efficiency of new technologies in the global construction industry.Methodology/approach: The literature review and systematization, study the expert opinion on open access resources, portals of private enthusiasts involved in seeking for ways to introduce and implement modern technologies in the construction industry.Research findings: The paper presents methods and conditions of using modern technologies in the world practice, disadvantages and advantages of technologies, the concept of artificial intelligence and its application in the construction industry.Value: The study of currently used modern technologies in the construction industry and their implementation in world practice.Vestnik Tomskogo gosudarstvennogo arkhitekturno-stroitel nogo universiteta JOURNAL of Construction and Architecture31The paper presents methods and conditions of using modern technologies in the world practice, disadvantages and advantages of technologies, the concept of artificial intelligence and its application in the construction industry.Vestnik Tomskogo gosudarstvennogo arkhitekturno-stroitel'nogo universiteta. JOURNAL of Construction and Architecture['M. M. Kashiripoor', 'V. A. Nikolyuk']2024-02-23T00:00:00https://www.semanticscholar.org/paper/6814c0cda08946450b759e7903ec41e7f31fd589 2eba3e453bcccc7b0c2fe965be50b36fbb5bb523Science &amp; Education311It is argued that epistemic insights can be introduced in AI teaching to highlight the differences between three paradigms: the imperative procedural, the declarative logic, and the machine learning based on neural networks (in particular, deep learning).Science &amp; Education['Eleonora Barelli', 'Michael Lodi', 'L. Branchetti', 'Olivia Levrini']2024-02-23T00:00:00https://www.semanticscholar.org/paper/2eba3e453bcccc7b0c2fe965be50b36fbb5bb523 4d905868898f490cb1c966e248315122490f1559Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.Diagnostics1761This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.Diagnostics['Buket Baddal', 'Ferdiye Taner', 'Dilber Uzun Ozsahin']2024-02-23T00:00:00https://www.semanticscholar.org/paper/4d905868898f490cb1c966e248315122490f1559 63da14f6e97083eb74273bfccfb51513c4b23558The concept of artificial intelligence (AI) involves the scientific and technological simulation of human intelligence, utilizing technologies such as deep learning, virtual reality, natural language processing, deep learning and more. Its core objective is to enable computers to process human- like abilities in perception, understanding, reasoning, learning, and decision-making. AI has achieved significant achievements and offers huge potential and applications across numerous areas, including the fashion and luxury industry. There, this article is to examine the application of AI technology in the fashion and luxury industry, specifically focusing on its utilization in personalized customer experience, market promotion and sales strategies, product design and innovation, as well as inventory and supply chain management. Additionally, this article aims to analyze the key existing issues and challenges brought about by these applications. This article a prospectus on the current focal points and future prospects of this research topic.Applied and Computational Engineering100The application of AI technology in the fashion and luxury industry is examined, specifically focusing on its utilization in personalized customer experience, market promotion and sales strategies, product design and innovation, as well as inventory and supply chain management.Applied and Computational Engineering['Junyi Dou']2024-02-23T00:00:00https://www.semanticscholar.org/paper/63da14f6e97083eb74273bfccfb51513c4b23558 3327e9bbf112f5496bccc00d53f071e58d640a9ePurpose: This systematic review and meta-analysis aimed to investigate the effects of artificial intelligence chatbot interventions on health outcomes in women. Methods: Ten relevant studies published between 2019 and 2023 were extracted from the PubMed, Cochrane Library, EMBASE, CINAHL, and RISS databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This review focused on experimental studies concerning chatbot interventions in women’s health. The literature was assessed using the ROB 2 quality appraisal checklist, and the results were visualized with a risk-of-bias visualization program. Results: This review encompassed seven randomized controlled trials and three single-group experimental studies. Chatbots were effective in addressing anxiety, depression, distress, healthy relationships, cancer self-care behavior, preconception intentions, risk perception in eating disorders, and gender attitudes. Chatbot users experienced benefits in terms of internalization, acceptability, feasibility, and interaction. A meta-analysis of three studies revealed significant effects in reducing anxiety (I2 = 0%, Q = 8.10, p < 0.017), with an effect size of −0.30 (95% CI, −0.42 to −0.18). Conclusions: Artificial intelligence chatbot interventions had positive effects on physical, physiological, and cognitive health outcomes. Using chatbots may represent pivotal nursing interventions for female populations to improve health status and support women socially as a form of digital therapy.Healthcare430Artificial intelligence chatbot interventions had positive effects on physical, physiological, and cognitive health outcomes and may represent pivotal nursing interventions for female populations to improve health status and support women socially as a form of digital therapy.Healthcare['Hyun-Kyoung Kim']2024-02-23T00:00:00https://www.semanticscholar.org/paper/3327e9bbf112f5496bccc00d53f071e58d640a9e d8b076deb126d660f139aafface116f46e732634Traditional online learning systems have long system response times and low accuracy in predicting learners' behavior, learning results, or personalized needs. In order to optimize the article on this issue, a STEAM personalized online learning system for artificial intelligence is constructed. This research adopts a combination of artificial intelligence technology and data analysis methods for system construction. First, the learner's personal information, learning behavior and learning results and other data are collected, and effective data analysis is carried out. Secondly, the application of personalized recommendation algorithms and intelligent learning models to recommend learning content, projects and activities suitable for learners' individual needs based on their interests, abilities and learning history. Through experimental tests, the mean square error of the system in this paper is maintained at 0.01-0.05, and the system can improve the user experience, learning effect and system performance.2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)210A STEAM personalized online learning system for artificial intelligence is constructed that combines artificial intelligence technology and data analysis methods for system construction, and the application of personalized recommendation algorithms and intelligent learning models to recommend learning content, projects and activities suitable for learners' individual needs.2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)['Lan Ma']2024-02-23T00:00:00https://www.semanticscholar.org/paper/d8b076deb126d660f139aafface116f46e732634 f712c383ca088af902db42aa2954e595b04102a6In recent years, Cross border Trade Supply Chain (CBTSC) is rapidly developed through the aid of the national policies. This business approach is not only develops the important benefits to the economy of nation but also address numerous issues. This research employs the Artificial Intelligence Optimization procedure which requires the integrated with issues of particular companies and analysis data, optimization. Light Gradient Boosting (LGB) is proposed for the data intensive inventory prediction with AI approach for CBTSC. In this proposed approach, AI approach utilized for intensive data prediction comprises five approaches such as data preprocessing, extraction of feature, selection of feature, parameter along with training, and outcomes from the assessment. The LGB method has an accuracy of 40.14% in RMSE and a reasonable computation time of 7 min 11 s. This research can be utilized as valuable basis for additional implementation of methodology in several e-commerce companies. E-commerce companies can afford an improved strategy for their management of inventory, thereby reducing additional inventory or stock-outs, and enhance their sales design, publicity and marketing actions.2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)160Light Gradient Boosting (LGB) is proposed for the data intensive inventory prediction with AI approach for CBTSC, and can be utilized as valuable basis for additional implementation of methodology in several e-commerce companies.2024 International Conference on Integrated Circuits and Communication Systems (ICICACS)['Ji Wei']2024-02-23T00:00:00https://www.semanticscholar.org/paper/f712c383ca088af902db42aa2954e595b04102a6 1f961bf9eece5199d1102bbacc844abbf8739eafThis paper explores the intersection of artificial intelligence (AI) and the financial sector, showcasing their transformative synergy. The integration of AI into finance has led to pioneering advancements like robo-advisors and AI-driven risk assessment methods. These innovations reshape investment strategies and risk management, ushering in a new era of financial operations. The study's focal question examines how AI recalibrates investment management, risk assessment, and fraud prevention in finance. The paper comprises sections on AI's impact on investment management, risk assessment, and fraud detection, detailing how robo-advisors provide personalized portfolio recommendations, AI aids risk identification and management, and transaction surveillance benefits from AI-powered fraud detection. Ethical, regulatory, and accountability considerations are discussed, reflecting AI's transformative influence on traditional financial paradigms. The application of AI in transaction detection and its role in enhancing portfolio recommendations, risk management, and automated trading are examined. While AI holds potential, its limitations such as data quality, model risks, and ethical concerns must be addressed. Regulatory oversight is crucial to ensure responsible AI implementation, fostering a balance between technological progress and financial stability. This paper underscores the intricate relationship between AI and finance, portraying AI's capacity to reshape the financial landscape and drive innovationApplied and Computational Engineering00Applied and Computational Engineering['Hanzhang Lu', 'Yantao Peng', 'Junhao Ding', 'Zhe Fu']2024-02-23T00:00:00https://www.semanticscholar.org/paper/1f961bf9eece5199d1102bbacc844abbf8739eaf e178501ad0f536cd2ad8f8f6c6fa28c5c885a939Breast Cancer Research400The significant role of AI analyzers in improving pathologists' concordance in improving pathologists' concordance in the classification of breast cancer molecular subtypes is underscores.Breast Cancer Research : BCR['Minsun Jung', 'Seung Geun Song', 'S. Cho', 'Sangwon Shin', 'Taebum Lee', 'W. Jung', 'Hajin Lee', 'Jiyoung Park', 'Sanghoon Song', 'G. Park', 'Heon Song', 'Seonwook Park', 'Jinhee Lee', 'Mingu Kang', 'Jongchan Park', 'Sergio Pereira', 'D. Yoo', 'Keunhyung Chung', 'Siraj M. Ali', 'So-Woon Kim']2024-02-23T00:00:00https://www.semanticscholar.org/paper/e178501ad0f536cd2ad8f8f6c6fa28c5c885a939 8ce8b64996e147093327088bff2b24b365d87376Bioinformatics is an interdisciplinary branch of science that develops methods and software tools for understanding biological data. Bioinformatics include both the power of biological concept and computational method to solve biological problem. It also bridged biological field with speed and accuracy of computer. Pre-design of vaccines by using artificial intelligence model for future upcoming viruses. Using AI throughout the vaccine development process to ensure that virus/pathogen vaccine met the needs of individuals without spending much time. A piece of genetic code that is capable of copying itself and typically has a detrimental effect on body, the pre-design vaccines will be available on one click no need for direct trials on humans. The model gives the predicted information about the upcoming risks for transmitting the disease in future generations by using artificial intelligence. The model is based on artificial intelligences and bioinformatics filed, all data will be presented and analyze simultaneously by the model and will efficiently build the vaccine molecule against the virus. The model provides highest accuracy and speed to sort out the vaccine.Journal for Research in Applied Sciences and Biotechnology110Pre-design of vaccines by using artificial intelligence model for future upcoming viruses by using artificial intelligence throughout the vaccine development process to ensure that virus/pathogen vaccine met the needs of individuals without spending much time.Journal for Research in Applied Sciences and Biotechnology['Renuka Anil Jojare', 'M. Jadhav', 'Dipak Pandit Chavan']2024-02-23T00:00:00https://www.semanticscholar.org/paper/8ce8b64996e147093327088bff2b24b365d87376 661d6df5dab31449886c830cd60e9174f46cfa43Currently, the field of artificial intelligence mainly includes key technologies such as computer vision, natural language processing, cross-media analytical reasoning, intellectually adaptive learning, group intelligence, autonomous unmanned systems, smart chips and brain-computer interfaces. These operations that can be completed with the assistance of artificial intelligence, with the help of artificial intelligence, life efficiency is greatly improved and the cost is greatly reduced. However, at present, Artificial Intelligence is only effective in the field of pointed and high-end research and learning, and there are still some thresholds in the application of daily life. In recent days, there have been a series of new technological inventions such as ChatGPT and a series of civilian Artificial Intelligence. Most of these new programs cannot be reasonably applied. This paper takes the application of Artificial Intelligence in the professional field as the background, and deeply explores the application of Artificial Intelligence technology in the non-professional field as exemplified by ChatGPT. This paper finds that in the medical field, Artificial Intelligence has played a considerable role, and Artificial Intelligence research on medical care is also in progress, so this paper makes predictions about the Artificial Intelligence that will appear in the medical industry based on the current situation, and investigates the understanding and use of Artificial Intelligence among teenagers through questionnaires, and analyzes the collected data.Applied and Computational Engineering00This paper makes predictions about the Artificial Intelligence that will appear in the medical industry based on the current situation, and investigates the understanding and use of Artificial Intelligence among teenagers through questionnaires, and analyzes the collected data.Applied and Computational Engineering['Zewen Li']2024-02-23T00:00:00https://www.semanticscholar.org/paper/661d6df5dab31449886c830cd60e9174f46cfa43 06c843cc924ff998a54d79719fe9770f82373ad6AI plays a pivotal role in detecting neurological disorders by leveraging advanced technologies to analyze vast amounts of data and aid in diagnosis. Here are several key roles AI plays. Artificial Intelligence (AI) has emerged as a revolutionary tool in the realm of healthcare, particularly in the early detection and accurate diagnosis of neurological disorders. The present paper delves into the multifaceted applications of AI specifically tailored to identify and discern various neurological conditions. AI's prowess in medical imaging analysis has significantly advanced the field by enabling nuanced and precise identification of neurological anomalies. By meticulously analyzing MRI scans, CT scans, and X-rays, AI-driven algorithms excel in detecting subtle patterns indicative of diverse neurological disorders such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and brain tumors. These technologies not only enhance diagnostic accuracy but also enable early intervention and improved patient outcomes. Moreover, AI leverages extensive datasets encompassing clinical records, genetic information, and biosensor data to predict and assess an individual's susceptibility to neurological disorders. Predictive analytics powered by machine learning models, aid in risk assessment, paving the way for personalized medicine and proactive healthcare strategies. Ethical considerations underscore the implementation of AI in neurological disorder detection, emphasizing the need for transparent algorithms, stringent data privacy protocols, and unbiased AI systems to ensure patient confidentiality and trust in healthcare.  The evolving landscape of AI in neuroscience presents an exciting frontier, fostering collaborations between AI experts and neuroscientists. Together, they aim to unravel the intricacies of neurological disorders, pushing the boundaries of innovation and paving the path toward early detection, targeted treatments, and improved quality of life for individuals affected by these conditions. This paper highlights the transformative impact of AI in detecting neurological disorders, 7emphasizing its role in early detection, personalized medicine, ethical considerations, and the potential for collaborative advancements in neuroscience.International Research Journal on Advanced Engineering Hub (IRJAEH)00The present paper delves into the multifaceted applications of AI specifically tailored to identify and discern various neurological conditions, emphasizing its role in early detection, personalized medicine, ethical considerations, and the potential for collaborative advancements in neuroscience.International Research Journal on Advanced Engineering Hub (IRJAEH)['Khushi Jha', 'Awadhesh Kumar']2024-02-23T00:00:00https://www.semanticscholar.org/paper/06c843cc924ff998a54d79719fe9770f82373ad6 37b52eeab8c41ab1e4848a307e47e3b0426d8005Environmental Research1032This exhaustive evaluation exhaustively examines the removals and emissions of greenhouse gases (GHGs) in the agriculture industry and provides a strategic plan for the agriculture industry to become more environmentally conscious and sustainable.Environmental research['Morteza Saberikamarposhti', 'Kok-Why Ng', 'Mehdi Yadollahi', 'Hesam Kamyab', 'Jie Cheng', 'Majid Khorami']2024-02-23T00:00:00https://www.semanticscholar.org/paper/37b52eeab8c41ab1e4848a307e47e3b0426d8005 08434a24ce2c168ce4ba2a28af0cf3b1b9760408Journal of Research in Science Teaching81Journal of Research in Science Teaching['Troy D. Sadler', 'F. Mensah', 'Jonathan Tam']2024-02-23T00:00:00https://www.semanticscholar.org/paper/08434a24ce2c168ce4ba2a28af0cf3b1b9760408 819ed7974778fdb22f6d34d54d2c838098999044In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.American Journal of Health-System Pharmacy01In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance, but these manuscripts are not the final version of record and will be replaced with the final article at a later time.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists['S. Smoke']2024-02-23T00:00:00https://www.semanticscholar.org/paper/819ed7974778fdb22f6d34d54d2c838098999044 4207fcb9126f77165d27190086782f86812489f1The study aims at assessing the outcome of AI and IoT to operational efficiency in business processes for various sectors. Exploring the Synthesis of the existing literature, combined with empirical evidence, this research examines the transformation possibility of AI and IoT Technologies in the improvement of the company. The centric findings have proved that there are significant upsurges in metrics of performance such as efficiency, productivity, quality, and customer satisfaction across different sectors of industries which are like healthcare, manufacturing, and retailing. Healthcare is another example. In this sector AI and IoT combination reduced the patient queue times by 50% while in manufacturing produced more products while spending 33% less capital costs. Similar to the case of brick and mortar stores, they recorded a 25% increment in their sales through AI-aided demand forecasting and the use of IoT in the inventory management. The findings of this research therefore point the way for the tremendous role played by AI and IoT in driving operational excellence, decision making processes and innovation in performance management. The research leads to the identification of major issues and concerns such as data privacy, security and technology integration, that require additional attention. Eventually, by utilizing AI and IoT technologies, companies find new models of sustainable development and market advantages which are of great help in the context of the current business conditions.Journal of Informatics Education and Research241The centric findings have proved that there are significant upsurges in metrics of performance such as efficiency, productivity, quality, and customer satisfaction across different sectors of industries which are like healthcare, manufacturing, and retailing.Journal of Informatics Education and Research['Dr. Pankaj Mudholkar, Dr. Megha Mudholkar']2024-02-23T00:00:00https://www.semanticscholar.org/paper/4207fcb9126f77165d27190086782f86812489f1 72b9d2eecdb1740a1508e324ad546f4c8dcdcd53Expert Review of Ophthalmology100Expert Review of Ophthalmology['Fatima Rizvi', 'Anza Rizvi', 'Anthony Vipin Das']2024-02-23T00:00:00https://www.semanticscholar.org/paper/72b9d2eecdb1740a1508e324ad546f4c8dcdcd53 c24611ce74ce18570820850c431c499ddddcb505In the evolving landscape of digital education, chatbots have emerged as potential game-changers, promising personalized and adaptive learning experiences. This research undertook an in-depth exploration of ChatGPT's potential as an educational tool, focusing on user perceptions, experiences and learning outcomes. Through a mixed-methods approach, a diverse group of 102 participants engaged with ChatGPT, providing insights pre- and postinteraction. The study reveals a notable positive shift in perceptions after exposure, underscoring the efficacy of ChatGPT. However, challenges such as prompting effectiveness and information accuracy emerged as pivotal concerns. Introducing the concept of 'AI-learning-competence', this study lays the groundwork for future research, emphasizing the need for formal training and pedagogical integration of AI tools.arXiv.org00An in-depth exploration of ChatGPT's potential as an educational tool, focusing on user perceptions, experiences and learning outcomes, reveals a notable positive shift in perceptions after exposure, underscoring the efficacy of ChatGPT.ArXiv['Holger Arndt']2024-02-23T00:00:00https://www.semanticscholar.org/paper/c24611ce74ce18570820850c431c499ddddcb505 d0d1f1e1e1f49ad1bdf15c7bb9337882dc543788Discover Applied Sciences280The IoAI is an excellent human-disconnected community in solving its problems through innovative ideas, high-tech products, and energy-efficient tools.Discover Applied Sciences['Saeed Banaeian Far', 'Azadeh Imani Rad']2024-02-23T00:00:00https://www.semanticscholar.org/paper/d0d1f1e1e1f49ad1bdf15c7bb9337882dc543788 ce177986b8cf1960d09c6e0bd40af893e7af70d0European Radiology50European radiology['Chun-Han Liao', 'Yi-Jui Liu']2024-02-23T00:00:00https://www.semanticscholar.org/paper/ce177986b8cf1960d09c6e0bd40af893e7af70d0 d7385c814f1083b12e483aaad745574332996963AI and Ethics90AI and Ethics['Wegene Demisie Jima', 'Tesfaye Adisu Tarekegn', 'Taye Girma Debelee']2024-02-23T00:00:00https://www.semanticscholar.org/paper/d7385c814f1083b12e483aaad745574332996963 ad5691004445486ecdfe9fef25b5115dbe21f3d6Technology, Pedagogy and Education210Technology, Pedagogy and Education['Laura Butler', 'Louise Starkey']2024-02-23T00:00:00https://www.semanticscholar.org/paper/ad5691004445486ecdfe9fef25b5115dbe21f3d6 5f2589b156a89655dcb707988f19dd18bd00032cJournal of South Asian Federation of Obstetrics and Gynaecology270Journal of South Asian Federation of Obstetrics and Gynaecology['Akila Kannaiyan', 'Sovan Bagchi', 'Vinaya Vijayan', 'Polevoy Georgiy', 'Sasikala Manickavasagam', 'Devika Sanil Kumar']2024-02-23T00:00:00https://www.semanticscholar.org/paper/5f2589b156a89655dcb707988f19dd18bd00032c 5137be856afcea0262ceeb657fa6f3c3723ed317AI &amp; SOCIETY260AI &amp; SOCIETY['Andreia Martinho']2024-02-23T00:00:00https://www.semanticscholar.org/paper/5137be856afcea0262ceeb657fa6f3c3723ed317 c560398487d5c93d17f2192dc02417b2312c8bb6Journal of Engineering Research00Journal of Engineering Research['João Pedro Santos Nanni', 'Gabriel Almeida de Azevedo', 'Daniel Torres Farias Alencar', 'Koffi Arnold Apolinarie Kini', 'Homero Henrique Nepomuceno Bortolussi', 'Guilherme Augusto Spiegel Gualazzi']2024-02-23T00:00:00https://www.semanticscholar.org/paper/c560398487d5c93d17f2192dc02417b2312c8bb6 30aa58f72f00d34e17352947892ed7acd5a6e9b2Nature Physics10Nature Physics['Luís A. Nunes Amaral']2024-02-23T00:00:00https://www.semanticscholar.org/paper/30aa58f72f00d34e17352947892ed7acd5a6e9b2 3734f30ab7902fa896ee33ceec3508df5b51bcdaClinical and Molecular Hepatology00Clinical and Molecular Hepatology['Moon-Haeng Hur', 'Jeong-Hoon Lee']2024-02-23T00:00:00https://www.semanticscholar.org/paper/3734f30ab7902fa896ee33ceec3508df5b51bcda 8753ae75288d9443d68ef7bdc636ec493140bb35Microalgae are emerging as a sustainable source of bioproducts, including food, animal feed, nutraceuticals, and biofuels. This review emphasizes the need to carefully select suitable species and highlights the importance of strain optimization to enhance the feasibility of developing algae as a sustainable resource for food and biomaterial production. It discusses microalgal bioprospecting methods, different types of cultivation systems, microalgal biomass yields, and cultivation using wastewater. The paper highlights advances in artificial intelligence that can optimize algal productivity and overcome the limitations faced in current microalgal industries. Additionally, the potential of UV mutagenesis combined with high-throughput screening is examined as a strategy for generating improved strains without introducing foreign genetic material. The necessity of a multifaceted optimization approach for enhanced productivity is acknowledged. This review provides an overview of recent developments crucial for the commercial success of microalgal production.Frontiers in Sustainable Food Systems1230This review provides an overview of recent developments crucial for the commercial success of microalgal production and highlights advances in artificial intelligence that can optimize algal productivity and overcome the limitations faced in current microalgal industries.Frontiers in Sustainable Food Systems['A. Alzahmi', 'Sarah Daakour', 'David Nelson', 'D. Al-Khairy', 'J. Twizere', 'K. Salehi-Ashtiani']2024-02-23T00:00:00https://www.semanticscholar.org/paper/8753ae75288d9443d68ef7bdc636ec493140bb35 3a43fad8b5832469388380dd177ea1b59647ffa2This paper explores a two‐population evolutionary game that models the role of manufacturer regulation as a motivation to auto parts quality certification. In particular, we assume that auto parts suppliers can choose whether or not to obtain the certification, ensuring a relatively high quality of the products, and manufactures can choose whether or not to engage in regulation for certification facilitation, influencing the suppliers' incentive to avoid punishment of uncertificated products. We study the Nash equilibria of this game and conduct static and dynamic evolutionary analyses. The research shows that reducing the auto parts suppliers' certification cost, increasing the penalty cost of the supplier who provides the auto parts that are not certified, and distributing the risk‐loss proportion of manufacturer and supplier properly can encourage auto parts suppliers to obtain the quality certification.Managerial and Decision Economics270Managerial and Decision Economics['Xin Cai', 'Dongdong Li', 'Chaofa Wang']2024-02-22T00:00:00https://www.semanticscholar.org/paper/3a43fad8b5832469388380dd177ea1b59647ffa2 c12d53c19df2b52533a48b234ae12410e17d3836The evolution of AI is set to profoundly reshape the future. The European Union, recognizing this impending prominence, has enacted the AI Act, regulating market access for AI-based systems. A salient feature of the Act is to guard democratic and humanistic values by focusing regulation on transparency, explainability, and the human ability to understand and control AI systems. Hereby, the EU AI Act does not merely specify technological requirements for AI systems. The EU issues a democratic call for human-centered AI systems and, in turn, an interdisciplinary research agenda for human-centered innovation in AI development. Without robust methods to assess AI systems and their effect on individuals and society, the EU AI Act may lead to repeating the mistakes of the General Data Protection Regulation of the EU and to rushed, chaotic, ad-hoc, and ambiguous implementation, causing more confusion than lending guidance. Moreover, determined research activities in Human-AI interaction will be pivotal for both regulatory compliance and the advancement of AI in a manner that is both ethical and effective. Such an approach will ensure that AI development aligns with human values and needs, fostering a technology landscape that is innovative, responsible, and an integral part of our society.arXiv.org410Without robust methods to assess AI systems and their effect on individuals and society, the EU AI Act may lead to repeating the mistakes of the General Data Protection Regulation of the EU and to rushed, chaotic, ad-hoc, and ambiguous implementation.ArXiv['André Calero Valdez', 'Moreen Heine', 'Thomas Franke', 'Nicole Jochems', 'Hans-Christian Jetter', 'Tim Schrills']2024-02-22T00:00:00https://www.semanticscholar.org/paper/c12d53c19df2b52533a48b234ae12410e17d3836 1c716f926ab1b494cbff057e3b17cc4fdb188f03The successful implementation of the policy of the Minister of Education, Research and Technology Regulation Article 18 Paragraph 9 Number 53 of 2023 is influenced by the delivery of information and communication by policy actors. By applying policy implementation theory, this research will examine the factors that influence the dimensions of communication, such as communication distribution, clarity of information, and consistency, and then provide a better understanding of the importance of communication in building cooperation in higher education programs, providing input and recommendations for legislators. Moreover,And other educational institutions are increasing information dissemination efforts and expanding the scope of their programs. This research aims to determine how implementers carry out information implementation, clarity, and consistency through communication studies. The research used is qualitative with a descriptive approach carried out through observations and interviews for three months at three universities in Sumbawa Regency with informants determined through purposive sampling, intended to provide an accurate picture of a particular situation or the relationship between various actual phenomena. Regularly. Information has been provided, but the implementation still needs to run optimally; this can be seen from the uneven delivery of information to technical implementers. The provision of information could have been better. Namely, the central institution still needs to conduct direct outreach to the targets or target objects by implementing technical UPTs. Meanwhile, academic parties at the University are waiting while studying the aims and objectives of the policy. It is hoped that central institutions will optimize the delivery of information and communication.Jurnal Impresi Indonesia210Jurnal Impresi Indonesia['Sagita Intan Cahyany', 'M. S. Anshori']2024-02-22T00:00:00https://www.semanticscholar.org/paper/1c716f926ab1b494cbff057e3b17cc4fdb188f03 331fa9e381cd219a3961cd5ef7dda401e8751606Significance As AI interacts with humans on an increasing array of tasks, it is important to understand how it behaves. Since much of AI programming is proprietary, developing methods of assessing AI by observing its behaviors is essential. We develop a Turing test to assess the behavioral and personality traits exhibited by AI. Beyond administering a personality test, we have ChatGPT variants play games that are benchmarks for assessing traits: trust, fairness, risk-aversion, altruism, and cooperation. Their behaviors fall within the distribution of behaviors of humans and exhibit patterns consistent with learning. When deviating from mean and modal human behaviors, they are more cooperative and altruistic. This is a step in developing assessments of AI as it increasingly influences human experiences.Proceedings of the National Academy of Sciences of the United States of America2513A Turing test is developed to assess the behavioral and personality traits exhibited by AI, and ChatGPT variants play games that are benchmarks for assessing traits: trust, fairness, risk-aversion, altruism, and cooperation.Proceedings of the National Academy of Sciences of the United States of America['Qiaozhu Mei', 'Yutong Xie', 'Walter Yuan', 'Matthew O Jackson']2024-02-22T00:00:00https://www.semanticscholar.org/paper/331fa9e381cd219a3961cd5ef7dda401e8751606 dbecfb88aff7baa55b3c2180431beabf19186420Slavko Splichal was interviewed by Gabriella Szabó on the 14th October 2023.Central European Journal of Communication00Central European Journal of Communication['Gabriella Szabó', 'S. Splichal']2024-02-22T00:00:00https://www.semanticscholar.org/paper/dbecfb88aff7baa55b3c2180431beabf19186420 64fe5a9abfcde0c7f345ef636bd70547dd212ac3The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.International Conference on Human Factors in Computing Systems812This paper devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space.{'pages': '1050:1-1050:17'}['Orit Shaer', 'Angel Cooper', 'O. Mokryn', 'Andrew L. Kun', 'Hagit Ben-Shoshan']2024-02-22T00:00:00https://www.semanticscholar.org/paper/64fe5a9abfcde0c7f345ef636bd70547dd212ac3 8ec9a9ee4c4bbbbe76b53c174145c2b8a7a29978Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.Sustainability671This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem, and investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations.Sustainability['Abdulaziz Aldoseri', 'K. Al-Khalifa', 'Abdel Magid Hamouda']2024-02-22T00:00:00https://www.semanticscholar.org/paper/8ec9a9ee4c4bbbbe76b53c174145c2b8a7a29978 284bb9ee87010245a3237804f7f3adda2b6a327aNature Communications951Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported and further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.Nature Communications['Alexander P. L. Martindale', 'Benjamin Ng', 'V. Ngai', 'A. Kale', 'Lavinia Ferrante di Ruffano', 'R. Golub', 'Gary S Collins', 'D. Moher', 'M. Mccradden', 'Lauren Oakden-Rayner', 'Samantha Cruz Rivera', 'Melanie J. Calvert', 'Christopher J. Kelly', 'Cecilia S Lee', 'Christopher Yau', 'An-Wen Chan', 'P. Keane', 'Andrew L Beam', 'A. Denniston', 'Xiaoxuan Liu']2024-02-22T00:00:00https://www.semanticscholar.org/paper/284bb9ee87010245a3237804f7f3adda2b6a327a 899e42af143af4bc09dd6fe4c0d5b1e28eb1039fThe convergence of Artificial Intelligence (AI) and blockchain technology is reshaping the digital world, offering decentralized, secure, and efficient AI services on blockchain platforms. Despite the promise, the high computational demands of AI on blockchain raise significant privacy and efficiency concerns. The Optimistic Privacy-Preserving AI (opp/ai) framework is introduced as a pioneering solution to these issues, striking a balance between privacy protection and computational efficiency. The framework integrates Zero-Knowledge Machine Learning (zkML) for privacy with Optimistic Machine Learning (opML) for efficiency, creating a hybrid model tailored for blockchain AI services. This study presents the opp/ai framework, delves into the privacy features of zkML, and assesses the framework's performance and adaptability across different scenarios.arXiv.org271This study presents the Optimistic Privacy-Preserving AI framework, delves into the privacy features of zkML, and assesses the framework's performance and adaptability across different scenarios.ArXiv['Cathie So', 'KD Conway', 'Xiaohang Yu', 'Suning Yao', 'Kartin Wong']2024-02-22T00:00:00https://www.semanticscholar.org/paper/899e42af143af4bc09dd6fe4c0d5b1e28eb1039f 86fa8b6fd272b949346dd93c7c99e9bbb2ca8e52Nature Network Boston07Nature medicine['C. Goldberg', 'Laura Adams', 'David Blumenthal', 'Patricia Flatley Brennan', 'Noah Brown', 'A. Butte', 'Morgan Cheatham', 'Dave deBronkart', 'Jennifer Dixon', 'Jeffrey M. Drazen', 'Barbara J. Evans', 'Sara M. Hoffman', 'Chris Holmes', 'Peter Lee', 'A. Manrai', 'G. Omenn', 'Jonathan B. Perlin', 'Rachel Ramoni', 'Guillermo Sapiro', 'Rupa Sarkar', 'Harpreet Sood', 'E. Vayena', 'Isaac S. Kohane']2024-02-22T00:00:00https://www.semanticscholar.org/paper/86fa8b6fd272b949346dd93c7c99e9bbb2ca8e52 0f31e06e4ffbe244954c8e548a0a191715639cc9Algorithmic decision support (ADS), using Machine-Learning-based AI, is becoming a major part of many processes. Organizations introduce ADS to improve decision-making and use available data, thereby possibly limiting deviations from the normative"homo economicus"and the biases that characterize human decision-making. However, a closer look at the development and use of ADS systems in organizational settings reveals that they necessarily involve a series of largely unspecified human decisions. They begin with deliberations for which decisions to use ADS, continue with choices while developing and deploying the ADS, and end with decisions on how to use the ADS output in an organization's operations. The paper presents an overview of these decisions and some relevant behavioral phenomena. It points out directions for further research, which is essential for correctly assessing the processes and their vulnerabilities. Understanding these behavioral aspects is important for successfully implementing ADS in organizations.arXiv.org780An overview of the development and use of ADS systems in organizational settings and some relevant behavioral phenomena is presented, which points out directions for further research, which is essential for correctly assessing the processes and their vulnerabilities.ArXiv['Joachim Meyer']2024-02-22T00:00:00https://www.semanticscholar.org/paper/0f31e06e4ffbe244954c8e548a0a191715639cc9 a44aa266a42431515045415bc1e37126dc2e53b6Israel’s commitment to a policy of collective punishment, disproportionate response, outright assassination and ethnic cleansing are the culmination of a long process. It has its origins in the expropriations organized by colonial powers, as does the state of Israel itself, but the proximate causes are the recent turn in politics to Biblical Jewish fundamentalism and an irrational enthusiasm for new military technologies, especially AI. These have led to important changes in official Israeli Defense Forces (IDF) policies, including a fundamental degradation of their ethical code.Teknokultura. Revista de Cultura Digital y Movimientos Sociales00Teknokultura. Revista de Cultura Digital y Movimientos Sociales['Chris H. Gray']2024-02-22T00:00:00https://www.semanticscholar.org/paper/a44aa266a42431515045415bc1e37126dc2e53b6 df51481582710b669fef5531d1ec2a50d51177daThe gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights into the gut microbiome's functional dynamics and its impact on host metabolism. These facilitate the identification of microbial biomarkers associated with disease states and dietary interventions, paving the way for personalized nutrition strategies tailored to individual gut microbiome profiles. AI platforms can also offer tailored dietary recommendations based on microbiome composition and health objectives. Healthcare professionals leverage AI to optimize dietary interventions, promoting gut microbiome modulation and preventing chronic diseases. Challenges like data standardization and privacy persist, yet addressing them is vital for maximizing AI's benefits in health outcomes and precision medicine. Ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The gut microbiome's role in metabolism and calorie intake is explored, alongside recent AI advancements impacting personalized nutrition, as ongoing AI and microbiome research promise to revolutionize personalized nutrition and metabolic health worldwide.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Ritcha Saxena', 'Vikas Sharma', 'Ananya Saxena', 'Aakash Patel']2024-02-22T00:00:00https://www.semanticscholar.org/paper/df51481582710b669fef5531d1ec2a50d51177da 008f1430a0863d4bbd4dca619a10e1b8c113fcc4The growing worldwide waste problem necessitates creative and long-term approaches to efficient waste disposal. This work introduces a novel method for predicting and classifying decomposable and non-decomposable waste by combining the Internet of Things (IoT) with artificial intelligence (AI), more especially Convolutional Neural Networks (CNN). The proposed system uses a CNN model trained on large datasets to identify organic and inorganic waste items accurately. Moreover, the Blynk platform transfers classification labels to a cloud-based infrastructure for real-time monitoring and analysis via Internet of Things technology. In order to track the presence of methane (CH4) and its concentration in waste bins over the course of ten days, the research also integrates gas and ultrasonic sensors. This all-encompassing strategy attempts to minimize environmental impact, maximize resource utilization, and offer insightful information for sustainable waste management practices. The trained CNN model demonstrated excellent performance in identifying and classifying waste, with an accuracy rate of 96%. The system contributes to a responsive waste management framework and possible biogas production study by enabling real-time monitoring and analysis through the use of cloud-based infrastructure and the Blynk platform.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)130This work introduces a novel method for predicting and classifying decomposable and non-decomposable waste by combining the Internet of Things (IoT) with artificial intelligence (AI), more especially Convolutional Neural Networks (CNN).2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)['Biplov Paneru', 'Krishna Bikram Shah', 'Bishwash Paneru', 'Nawraj Bhattrai', 'Vikram Alexander', 'Hem Raj Pant', 'Khem Narayan Poudyal', 'Silvia Nova']2024-02-22T00:00:00https://www.semanticscholar.org/paper/008f1430a0863d4bbd4dca619a10e1b8c113fcc4 2a5b594f641b440342ecb04f67672ed71503be1b This Viewpoint discusses the need to create standards for audiomics to identify unique audio biomarkers of health and disease—now possible because of more efficient voice data analysis available through the use of artificial intelligence (AI)—and to improve patient care. JAMA Otolaryngology - Head and Neck Surgery60The need to create standards for audiomics to identify unique audio biomarkers of health and disease—now possible because of more efficient voice data analysis available through the use of artificial intelligence (AI)—is discussed.JAMA otolaryngology-- head & neck surgery['Yael Bensoussan', 'Olivier Elemento', 'A. Rameau']2024-02-22T00:00:00https://www.semanticscholar.org/paper/2a5b594f641b440342ecb04f67672ed71503be1b 472ef557ac493f11abe595a15dfd2d9daed34733The paper investigates the synergy between AI and IoT in revolutionizing public transit systems, emphasizing their role in addressing existing challenges and improving overall efficiency. It explores the application of IoT devices in creating a smart infrastructure that enables real-time data collection, monitoring, and management of transit operations. The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles. By analyzing sensor data, transit authorities can proactively address maintenance needs, minimizing downtime, reducing repair costs, and ensuring a more reliable and sustainable public transit service. Route optimization emerges as another crucial aspect of the study, highlighting how AI algorithms leverage historical and real-time data to recommend the most efficient transit routes. Factors such as traffic patterns, weather conditions, and passenger demand are considered to enhance overall system efficiency and reduce travel time for passengers. The paper introduces the concept of dynamic scheduling, illustrating how AI-driven algorithms adapt transit schedules in real-time based on changing passenger needs and external factors. This dynamic approach aims to provide more responsive services, ultimately reducing wait times and improving overall user satisfaction. Passenger information systems are explored as a pivotal component, illustrating how AI and IoT technologies enhance the passenger experience. Real-time communication through mobile apps, digital displays, and other channels ensures that passengers have accurate and timely information about arrival times, delays, and alternative routes, empowering them to make informed decisions. The researchers also delve into fare optimization, examining how AI algorithms analyze data on passenger demographics, travel patterns, and economic factors to create fair and affordable fare structures. This approach aims to encourage ridership, increase revenue, and improve the financial sustainability of public transit systems. The abstract presents a comprehensive overview of how the integration of AI and IoT technologies in public transit systems transforms urban mobility. The findings suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit. As cities continue to explore innovative solutions, the abstract serves as a roadmap for developing smarter, user-friendly, and efficient urban transportation networks, ultimately contributing to improved quality of life for residents.  International Journal of Scientific Research and Management540The research delves into the realm of predictive maintenance, showcasing how AI algorithms, powered by IoT data, can anticipate potential issues in transit vehicles and suggest that leveraging real-time data, predictive analytics, and dynamic solutions can significantly enhance the reliability, accessibility, and sustainability of public transit.International Journal of Scientific Research and Management (IJSRM)['Naveen Vemuri', 'Venkata Manoj Tatikonda', 'Naresh Thaneeru']2024-02-22T00:00:00https://www.semanticscholar.org/paper/472ef557ac493f11abe595a15dfd2d9daed34733 4a9e370943985d14449a68b1ac83dc55ba0bd617This paper aims to explore the innovative model of integrating AI (Artificial Intelligence) services in the Taiwanese real estate industry. The research employs literature analysis and the Fuzzy Analytic Hierarchy Process (FAHP) as the methodological approach. A FAHP questionnaire survey was conducted among real estate professionals in the Tainan region of Taiwan. Through the calculation of relative weights among various dimensions, the study identifies key factors related to the adoption of AI-based innovations by real estate agents in Taiwan. These findings serve as crucial references for the real estate industry in transactional and operational management.Proceedings of International Conference on Artificial Life and Robotics100The study identifies key factors related to the adoption of AI-based innovations by real estate agents in Taiwan that serve as crucial references for the real estate industry in transactional and operational management.Proceedings of International Conference on Artificial Life and Robotics['Li-Min Chuang', 'Chih-Hung Chen']2024-02-22T00:00:00https://www.semanticscholar.org/paper/4a9e370943985d14449a68b1ac83dc55ba0bd617 82bfb3bab3d121f09d161ef059cac4e45bc3fea4Food wastage because of the lack or incompletion of a household replenishment system is an essential topic to be addressed. An appropriate utilization of Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart household replenishment system to reduce food wastage. Therefore, this systematic review is dedicated to survey papers utilizing IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This study was conducted by following the PRISMA search strategy. It examined 70 papers in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. This comprehensive research aims to point out the approaches, contributions, used components and limitations of the reviewed papers to develop a unified framework for a household replenishment system. The analysis resulted in 43 approaches using IoT technology, 27 using AI, and recently the use of AIoT has been trending in the past two years. This systematic review provides future directions for researchers acquired from the limitations of the reviewed papers to enhance the household replenishment system by developing and adding required features in smart food storage compartments. Further investigation into smart home appliances would lead to extensive approaches like smart shops, industries, and eventually smart cities.J. Smart Cities Soc.440This comprehensive research aims to point out the approaches, contributions, used components and limitations of the reviewed papers to develop a unified framework for a household replenishment system to reduce food wastage.J. Smart Cities Soc.['Khaled Mosaed Almassar', 'Mohammad T. Khasawneh']2024-02-22T00:00:00https://www.semanticscholar.org/paper/82bfb3bab3d121f09d161ef059cac4e45bc3fea4 f010d3c5fe3b85d93afc404fb24b879022113f4cIn recent years, the technology sector has witnessed substantial disruptions, leading to major layoffs within toptier tech conglomerates. As the momentum of the digital age continues, there's an emerging concern regarding the psycho-logical impact of these layoffs on individual employees. This research delves deep into understanding the repercussions of these widespread job losses on mental health, emphasizing the increased intensity of symptoms like depression and anxiety. Utilizing the Beck Depression Inventory (BDI) for its methodology, the study scrupulously evaluates the depth of these mental health symptoms before and subsequent to the implementation of AI-driven mental health solutions. The conclusive data robustly establishes that when AI-centric interventions are combined with holistic mental health practices, there's a marked reduction in depression and anxiety symptoms, particularly those originating from job-related insecurities. As the world grapples with the swift changes in employment dynamics in this digital age, the study accentuates the significance of AI. Tapping into its vast potential to enhance mental resilience can be seen as an avant-garde strategy, presenting a pragmatic way to address the looming mental health challenges arising from unpredictable economic transitions.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)240The conclusive data robustly establishes that when AI-centric interventions are combined with holistic mental health practices, there's a marked reduction in depression and anxiety symptoms, particularly those originating from job-related insecurities.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)['Suranjeet Chowdhury Avik', 'Abdullahi Chowdhury', 'Moriwam', 'M. A. Maruf', 'R. Naha', 'Imtiaz Ahammad']2024-02-22T00:00:00https://www.semanticscholar.org/paper/f010d3c5fe3b85d93afc404fb24b879022113f4c 19d622c304442b8a3f378b8c934881ac2102617cMaze Bunny adalah sebuah permainan teka-teki labirin yang memiliki sistem jalur yang rumit, berliku-liku, serta memiliki banyak jalan buntu. Dalam permainan ini, user diharuskan untuk mencari pintu keluar secepat mungkin dengan cara menemukan jalur terpendek dan tidak tersesat. Pada studi ini, peneliti mencoba menerapkan kecerdasan buatan atau AI berupa algoritma A* yang kemudian akan diterapkan pada sebuah karakter yang tidak dapat dimainkan atau dikenal dengan istilah NPC. Algoritma A* dipilih karena dapat mencari jalur terpendek yang paling efisien dengan cara mencari jalur terpendek dari satu titik ke titik lain pada graf atau peta yang berbentuk grid atau graph. Efisiensi inilah yang nantinya digunakan untuk menciptakan tokoh NPC-AI yang akan menjadi rival bagi user.Jurnal Pengembangan Sistem Informasi dan Informatika70Jurnal Pengembangan Sistem Informasi dan Informatika['I. Pratiwi']2024-02-22T00:00:00https://www.semanticscholar.org/paper/19d622c304442b8a3f378b8c934881ac2102617c dd11f05ef6b06d9ca3ebd2d4ac1add34f0b04c5bPurposeThe study’s objective is to measure the importance consumers attach to AI-based attributes, namely, chatbots, face recognition, virtual fitting room, smart parking and cashier-free station in retail stores. The study also examines the specific purpose of using these attributes for shopping.Design/methodology/approachA conjoint experiment was conducted using fractional factorial design. Consumers were given 14 profiles (AI attributes and its levels) to rank according to their visiting preferences.FindingsThe results revealed that the retail chatbot was considered the most important attribute, followed by face recognition, virtual fitting room, smart parking system and cashier-free station. Moreover, consumers prefer to use chatbots for in-store shopping assistance over alerts and updates, customer support and feedback. Similarly, consumers wish a face recognition facility for greetings while entering the store over other services. In addition, cluster analyses revealed that customer groups significantly differ in their preferences for AI-based attributes.Practical implicationsThe study guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.Originality/valueOur results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores. The present study contributes to the literature by investigating consumer preferences for various AI technologies and their specific uses for shopping.International Journal of Retail &amp; Distribution Management420The results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores, and guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.International Journal of Retail &amp; Distribution Management['Kavita Srivastava', 'Divyanshi Pal']2024-02-22T00:00:00https://www.semanticscholar.org/paper/dd11f05ef6b06d9ca3ebd2d4ac1add34f0b04c5b 5c4a75e7436e402af046c24655fefe71ee87e379In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models’ current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced “jailbreak” techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies’ reliability and ethical soundness, paving the way for safer and more secure AI applications.Electronics400This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses, and spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation.Electronics['B. Hannon', 'Y. Kumar', 'Dejaun Gayle', 'J. J. Li', 'Patricia Morreale']2024-02-22T00:00:00https://www.semanticscholar.org/paper/5c4a75e7436e402af046c24655fefe71ee87e379 08dfb627149c562930b9c22be75382643f0255bcThe rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can support music therapists, who urgently need new technology support. This study used a mixed method, including semi-structured interviews and a participatory design approach. By collaborating with music therapists, we explored design opportunities for musical AIs in music therapy. We presented the co-design outcomes involving the integration of musical AIs into a music therapy process, which was developed from a theoretical framework rooted in emotion-focused therapy. After that, we concluded the benefits and concerns surrounding music AIs from the perspective of music therapists. Based on our findings, we discussed the opportunities and design implications for applying musical AIs to music therapy. Our work offers valuable insights for developing human-AI collaborative music systems in therapy involving complex procedures and specific requirements.International Conference on Human Factors in Computing Systems1100This study used a mixed method, including semi-structured interviews and a participatory design approach, to explore design opportunities for musical AIs in music therapy and the benefits and concerns surrounding music AIs from the perspective of music therapists.ArXiv['Jingjing Sun', 'Jingyi Yang', 'Guyue Zhou', 'Yucheng Jin', 'Jiangtao Gong']2024-02-22T00:00:00https://www.semanticscholar.org/paper/08dfb627149c562930b9c22be75382643f0255bc ca63b3d8eb2110baa14ca035bf8d64209cb232bdArtificial intelligence (AI) has significantly impacted various business sectors, including recruitment and selection practices. Associations risk losing their strategic advantage as they battle to find and recruit qualified ability. Employing faculty goes to man-made consciousness (artificial intelligence) devices to assist with procuring ability, increment effectiveness, and lessen costs. However, despite the best efforts to integrate equitable and evidence-based systems, using these tools may exacerbate bias. We methodicallly survey the writing on the ethicality of man-made intelligence empowered enrolling and determination rehearses in four phases: First, we classify the identified literature based on assumed perspectives to demonstrate how existing research evaluates the ethicality of AI recruiting.  I make sense of how man-made intelligence based employing choices in associations are setting ward and mix the capacities of algorithmic powered apparatuses with decisions and decisions made by process specialists. I finish up by offering hypothetical and functional contemplations for ability, recruiting, and the mix of calculations at work. The implementation of AI-based processes in the recruiting sector has resulted in increased efficiency and qualitative benefits for both employers and potential employees.  European Economic Letters (EEL)270This work methodicallly surveys the writing on the ethicality of man-made intelligence empowered enrolling and determination and offers hypothetical and functional contemplations for ability, recruiting, and the mix of calculations at work.European Economic Letters (EEL)['Dr. H S. Abzal Basha, Mrs. N. Rajitha, Ms Jebakerupa Roslin A', 'Dr Kiran Kumar Thoti, Mohammed Khalid R P, Dr. Biswo Ranjan Mishra']2024-02-22T00:00:00https://www.semanticscholar.org/paper/ca63b3d8eb2110baa14ca035bf8d64209cb232bd a2555b09d0c066acbb021cd5b6e7332227c2b07fThis research study aims to investigate the intricate connection between students' attitudes toward Artificial Intelligence (AI) and the correlates of their learning outcomes. With the growing integration of AI in various educational settings, understanding how students perceive AI and how these perceptions correlate with their academic performance is of vital importance. This research employs a descriptive and correlational approach, combining surveys and learning outcomes data analysis to delve into the changing aspects of this relationship. By exploring students' attitudes toward AI, their acceptance levels, and their learning outcomes, this study seeks to provide insights that can inform educators, policymakers, and AI developers on optimizing AI's role in education for enhanced student success. Also, the findings indicate a significant relationship between students' attitudes toward AI and their learning outcomes, emphasizing the importance of considering these attitudes in understanding and predicting educational outcomes. One of the key recommendations is that institutions should consider integrating more comprehensive AI education into their curriculum to bridge the gap in actively seeking out information about AI. This proactive approach can contribute to a more informed and adaptive student body, ultimately fostering a positive and constructive environment for AI integration in education.International journal of social science and human research90The findings indicate a significant relationship between students' attitudes toward AI and their learning outcomes, emphasizing the importance of considering these attitudes in understanding and predicting educational outcomes.International Journal of Social Science and Human Research['Dr. Neilson D. Bation', 'Dr. Jovertlee C. Pudan, EnP']2024-02-22T00:00:00https://www.semanticscholar.org/paper/a2555b09d0c066acbb021cd5b6e7332227c2b07f 279734028def20dd08e0c1d46e75850423de79a8Ontologies have the potential to play an important role in the cybersecurity landscape as they are able to provide a structured and standardized way to semantically represent and organize knowledge about a domain of interest. They help in unambiguously modeling the complex relationships between various cybersecurity concepts and properties. Leveraging this knowledge, they provide a foundation for designing more intelligent and adaptive cybersecurity systems. In this work, we propose an ontology-based cybersecurity framework that extends well-known cybersecurity ontologies to specifically model and manage threats imposed on applications, systems, and services that rely on artificial intelligence (AI). More specifically, our efforts focus on documenting prevalent machine learning (ML) threats and countermeasures, including the mechanisms by which emerging attacks circumvent existing defenses as well as the arms race between them. In the ever-expanding AI threat landscape, the goal of this work is to systematically formalize a body of knowledge intended to complement existing taxonomies and threat-modeling approaches of applications empowered by AI and to facilitate their automated assessment by leveraging enhanced reasoning capabilities.Future Internet110The goal of this work is to systematically formalize a body of knowledge intended to complement existing taxonomies and threat-modeling approaches of applications empowered by AI and to facilitate their automated assessment by leveraging enhanced reasoning capabilities.Future Internet['Davy Preuveneers', 'Wouter Joosen']2024-02-22T00:00:00https://www.semanticscholar.org/paper/279734028def20dd08e0c1d46e75850423de79a8 9fdc1265cbb1319bdd44ce2b15c16e268c647993Computer-based intelligence (AI) has shown significant potential in various applications, but it requires a clear understanding of the local area. This issue falls within the Reasonable simulated intelligence (XAI) field, which is crucial for the practical application of AI models. This article discusses the current work in XAI, defining explainability in machine learning and proposing a new definition. It also discusses ongoing commitments related to AI models, including Profound Learning strategies. The article suggests Dependable Computerized reasoning, a method for large-scale AI execution in real-world contexts with reliability, model logic, and responsibility at its core. The goal is to provide a comprehensive taxonomy for newcomers and professionals to embrace AI's benefits.European Economic Letters (EEL)280The article suggests Dependable Computerized reasoning, a method for large-scale AI execution in real-world contexts with reliability, model logic, and responsibility at its core, to provide a comprehensive taxonomy for newcomers and professionals to embrace AI's benefits.European Economic Letters (EEL)['Dr. R. Nadanasabai, Venkata Ramaiah Turlapati, Dr. Biswo Ranjan Mishra', 'Dr. Khaja Mohinuddeen J., Dr. Anurag Aeron, Dr. Lenin S']2024-02-22T00:00:00https://www.semanticscholar.org/paper/9fdc1265cbb1319bdd44ce2b15c16e268c647993 0c48ccd00f943cfcfb9fa687eb3559ce8c1055d4The emerging growth in communication technology, created a path for innovation of various consumer products that support peoples in different arena. The problem faced by visually impaired people on communicating the emotions, actual intention clearly to the people on other side, is solved by the innovative virtual modules developed. A comprehensive study on AI based talking and virtual eye and its benefits are discussed over here. Various automotive devices are evolving recently to assist visual impairment peoples. Android application based guiding system; GPS enabled smart navigational systems are one of those developments. It is considered as one of the social responses to support visually impaired peoples and helping them for survival. The existing state of art approaches considers the critical challenges behind the scenario and evaluated various innovations using embedded systems technology and artificial intelligence systems. Considering all aspects of problems, in terms of cost, reliability, the flexibility in usage, the exploration of products focused on keen feature analysis through cognitive approach. All these steps are keeping a supportive hand to the visually impaired peoples.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)1302024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)['Kumar P', 'Karthick V', 'Lithishkumar S', 'Madhavan V']2024-02-22T00:00:00https://www.semanticscholar.org/paper/0c48ccd00f943cfcfb9fa687eb3559ce8c1055d4 9ebb6b9615f41448d724b655b1dd37ff1bc8468dSignificant advancement has been achieved in medical reasoning (Artificial intelligence). However, the interpretability and confidence of many deep learning model discovery ideas face difficulties in fundamental applications like COVID-19 identification. The compatibility of Explainable AI (XAI) techniques with COVID-19 results according to chest X-ray pictures is examined in this work. Our model achieves uncompromising exactness and provides interpretable experiences in its dynamic interaction by combining Local Interpretable Model Agnostic Explanations (LIME) and the VGG16 architecture in conjunction with a transfer learning technique. Upgrading transparency, confidence, and comprehension in artificial intelligence-driven clinical diagnostics is the goal of the research.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)170This model achieves uncompromising exactness and provides interpretable experiences in its dynamic interaction by combining Local Interpretable Model Agnostic Explanations (LIME) and the VGG16 architecture in conjunction with a transfer learning technique.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)['Manikanta Gangam', 'Vishal Baghel', 'Mohd Mohsin Ali', 'Manish Raj', 'Ayushman Pranav', 'Vaibhav ranjan']2024-02-22T00:00:00https://www.semanticscholar.org/paper/9ebb6b9615f41448d724b655b1dd37ff1bc8468d 552ed450355f7684bf05ccb92685860863904097The integration of artificial intelligence (AI) into India's defence landscape holds immense promise for enhancing strategic capabilities and operational efficiency. However, this transformative potential is accompanied by a range of ethical concerns that demand careful deliberation and proactive measures. This paper explores into the ethical dimensions of AI in Indian Défense, examining the potential benefits and associated risks. On the one hand, AI offers the potential to revolutionize India's defence capabilities, enabling precision targeting, enhanced situational awareness, and improved decision-making. AI-powered systems could streamline logistics, optimize resource allocation, and strengthen surveillance capabilities, bolstering India's défense posture. However, the ethical implications of AI in defence cannot be overlooked. The potential for unintended consequences, bias and discrimination, and loss of human control over warfare poses significant challenges. The development of autonomous weapons systems raises particularly acute ethical concerns, as the prospect of machines making life-and-death decisions warrants careful scrutiny. To address these ethical concerns and confirm responsible AI development, India must adopt a comprehensive approach that prioritizes transparency, accountability, and public engagement. Clear guidelines and regulations are essential to govern AI usage in defences, ensuring that these technologies are employed in a manner that adheres to ethical principles and international norms. In conclusion, India's pursuit of AI-powered defence capabilities must be guided by a commitment to ethical responsibility. By carefully considering the ethical implications and adopting proactive measures, India can harness the transformative power of AI while mitigating its risks, ensuring that AI serves as a force for good in safeguarding India's national security.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)320By carefully considering the ethical implications and adopting proactive measures, India can harness the transformative power of AI while mitigating its risks, ensuring that AI serves as a force for good in safeguarding India's national security.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)['K. Santhi', 'M. Shri', 'Sankalp Joshi', 'Gaurav Sharma']2024-02-22T00:00:00https://www.semanticscholar.org/paper/552ed450355f7684bf05ccb92685860863904097 3adc8f160df34a808b4aaa27c2e2f6db1a6fcb2fThis study explores barriers to AI adoption in automated organizational decision-making. Through qualitative interviews with 13 senior managers in South Africa, the study identified human social dynamics, restrictive regulations, creative work environments, lack of trust, dynamic business environments, loss of power, and ethical considerations. The study applied the adaptive structuration theory (AST) model to AI decision-making adoption, providing recommendations to overcome these barriers. The AST offers a deeper understanding of the dynamic interaction between technological and social dimensions.Journal of Informatics Education and Research220The study applied the adaptive structuration theory (AST) model to AI decision-making adoption, providing recommendations to overcome barriers to AI adoption in automated organizational decision-making.Journal of Informatics Education and Research['Venkata Ramaiah Turlapati, P. Vichitra, Dr. Khaja Mohinuddeen J.', 'Dr Navjyot Raval, Dr. Khaja Mohinuddeen J., Dr. Biswo Ranjan Mishra']2024-02-22T00:00:00https://www.semanticscholar.org/paper/3adc8f160df34a808b4aaa27c2e2f6db1a6fcb2f 2448e680a93f9a9aa80a9e75544aa866f7f6bc37With the continuous evolution of artificial intelligence technology, AI drawing tools have emerged as highly esteemed instruments in the modern design industry. These tools, owing to their exceptional performance and innovative features, offer creators an unprecedented artistic experience. However, the factors influencing designers’ continuance intention to use AI drawing tools remain ambiguous. This study is grounded in the expectation–confirmation model–information systems continuance (ECM-ISC) model, which is further refined and hypothesized in light of the characteristics of AI drawing tools. Using structural equation modeling, we analyzed 398 valid questionnaire responses. The results elucidated the relationships of key constructs, such as perceived usefulness, perceived ease of use, satisfaction, expectation confirmation, perceived playfulness, perceived switching cost, subjective norms, and perceived risk, on designers’ continuance intention. Notably, perceived ease of use, traditionally considered vital, did not result in a significant influence on continuance intention or perceived usefulness in this research. This insight offers new perspectives for AI drawing tool developers and designers, suggesting that while pursuing user friendliness, broader considerations affecting user decisions should be taken into account. This study not only enriches the theoretical framework but also provides valuable guidance for the practical field.Systems680This research elucidated the relationships of key constructs and offered new perspectives for AI drawing tool developers and designers, suggesting that while pursuing user friendliness, broader considerations affecting user decisions should be taken into account.Systems['Pujunqian Fan', 'Qianling Jiang']2024-02-22T00:00:00https://www.semanticscholar.org/paper/2448e680a93f9a9aa80a9e75544aa866f7f6bc37 3cb49109746fa7527638e7a2af8ff60ccef9b22dWith the enormous growth in interest and use of generative artificial intelligence (AI) systems seen since the launch of ChatGPT in autumn 2022 have come questions both about the legal status of AI outputs, and of using protected works as training inputs. It is inevitable that UK higher education institution (HEI) library copyright advice services will see an increase in questions around use of works with AI as a result. Staff working in such library services are not lawyers or able to offer legal advice to their academic researchers. Nonetheless, they must look at the issues raised, consider how to advise in analogous situations of using copyright material, and offer opinion to researchers accordingly. While the legal questions remain to be answered definitively, copyright librarians can still offer advice on both open licences and use of copyright material under permitted exceptions. We look here at how library services can address questions on copyright and open licences for generative AI for researchers in UK HEIs.F1000Research190Library services can address questions on copyright and open licences for generative AI for researchers in UK HEIs by offering advice on both open licences and use of copyright material under permitted exceptions.F1000Research['Andrew Johnson']2024-02-22T00:00:00https://www.semanticscholar.org/paper/3cb49109746fa7527638e7a2af8ff60ccef9b22d 40d22b42f9fbf720e986280e5f7aeab7d8752b4bCurrent and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research. On the other hand, such systems can also become our adversaries when they impoverish our ability to learn as theorists, or when they lead us astray through inaccurate, biased, or fake information. No matter which angle is considered, and whether we like it or not, AI systems are here to stay. In this curated discussion, we raise questions about human centrality and agency in the research process, and about the multiple philosophical and practical challenges we are facing now and ones we will face in the future.Journal of management inquiry822Questions about human centrality and agency in the research process are raised, and about the multiple philosophical and practical challenges the authors are facing now and ones they will face in the future are raised.Journal of Management Inquiry['Mukta Kulkarni', 'Saku Mantere', 'E. Vaara', 'Elmira van den Broek', 'Stella Pachidi', 'Vern L. Glaser', 'Joel Gehman', 'Gianpiero Petriglieri', 'Dirk Lindebaum', 'Lindsey D. Cameron', 'Hatim A. Rahman', 'Gazi Islam', 'Michelle Greenwood']2024-02-22T00:00:00https://www.semanticscholar.org/paper/40d22b42f9fbf720e986280e5f7aeab7d8752b4b f9950352df5101c0b2dad9773610706704644727Abstract Objective To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches. Methods We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert’s historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. Results The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues. Conclusion We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.J. Am. Medical Informatics Assoc.341The approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews that might be overlooked or delayed in manual reviews by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.Journal of the American Medical Informatics Association : JAMIA['Siru Liu', 'A. McCoy', 'Josh F Peterson', 'T. Lasko', 'Dean F. Sittig', 'Scott D Nelson', 'Jennifer Andrews', 'Lorraine Patterson', 'Cheryl M Cobb', 'David Mulherin', 'Colleen T Morton', 'Adam Wright']2024-02-22T00:00:00https://www.semanticscholar.org/paper/f9950352df5101c0b2dad9773610706704644727 24720421766dc7c1d786a484ce6202ed9f849bd8INTRODUCTION: As countries work on the transition towards renewable energies that comply with the 2030 Agenda and the sustainable development goals, Artificial Intelligence is presented as a tool that is being adopted to promote the generation of renewable energies such as solar or wind power. , given the support it offers to automation, assisted decisions, and production efficiency. OBJECTIVES: To analyze the key players in renewable energy and artificial intelligence research. METHODS: The Scopus database is used to obtain the scientific articles for the period 2013-2023, and the Visualization of Similarities program (VOSviewer 1.6.18) is used for data processing and analysis. RESULTS: An analysis of 822 articles shows that the countries with the highest scientific production are China (148), India (136) and the United States (81). In this regard, it is clear that there is significant collaboration between countries. With regard to the analysis of Co-occurrence - Author Keywords, three clusters are generated. The first cluster, identified with the color red, is related to artificial intelligence management; the second cluster, identified with the color green, is related to artificial intelligence innovation; and the third cluster, identified with the color blue, is related to energy models. CONCLUSION: Researchers are facing new challenges every day to respond to the irruption of the use of new algorithms in the generation of renewable energies, given the range of available tools such as deep learning or neural networks. Research results have revealed that in recent years, scientific production has understood that AI is not a trend but rather a challenge facing society, industry, countries, or education in order to achieve sustainable development.EAI Endorsed Transactions on Energy Web1021Researchers are facing new challenges every day to respond to the irruption of the use of new algorithms in the generation of renewable energies, given the range of available tools such as deep learning or neural networks.EAI Endorsed Trans. Energy Web['Rolando Eslava-Zapata', 'Verenice Sánchez-Castillo', 'Emma Juaneda-Ayensa']2024-02-22T00:00:00https://www.semanticscholar.org/paper/24720421766dc7c1d786a484ce6202ed9f849bd8 f52b4725c65c8292b6aed51a4843c6aa77c430d5Diagnostic Pathology421Artificial intelligence has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly, and its multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization.Diagnostic Pathology['Amr Soliman', 'Zaibo Li', 'Anil V. Parwani']2024-02-22T00:00:00https://www.semanticscholar.org/paper/f52b4725c65c8292b6aed51a4843c6aa77c430d5 272c4aff7d59e0822a14488113157dfb864bfc54Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine’s support of AI research.Bioengineering581A systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets underscores the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice.Bioengineering['Maha Alattar', 'Alok Govind', 'Shraddha Mainali']2024-02-22T00:00:00https://www.semanticscholar.org/paper/272c4aff7d59e0822a14488113157dfb864bfc54 ba075cce9e350136ea11429249bb6e226466d3f5International Research Journal of Modernization in Engineering Technology and Science01International Research Journal of Modernization in Engineering Technology and Science[]2024-02-22T00:00:00https://www.semanticscholar.org/paper/ba075cce9e350136ea11429249bb6e226466d3f5 3129e521efaf00e5e49625f00c6d8b9d7f299d1aArtificial Intelligence is about knowing something (having a knowledge) and then using that knowledge to do something with that knowledge [1].  This is demonstrated by algorithms in machine learning and data mining [1]. International Journal of Scientific Research and Management30This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging individual pieces of data.International Journal of Scientific Research and Management (IJSRM)['Nripesh Trivedi']2024-02-22T00:00:00https://www.semanticscholar.org/paper/3129e521efaf00e5e49625f00c6d8b9d7f299d1a f25cb81707fe114baccab1adbe740105292de01dBMC Medical Education320The study’s findings give a sound foundation for making suggestions to clinical laboratories, scientific bodies, and national and international Clinical Chemistry and laboratory medicine organisations on how to manage pathologists’ shifting practises because of AI.BMC Medical Education['L. Jafri', 'Arsala Jameel Farooqui', 'Janet Grant', 'Usmaan Omer', 'R. Gale', 'Sibtain Ahmed', 'Aysha Habib Khan', 'I. Siddiqui', 'F. Ghani', 'H. Majid']2024-02-22T00:00:00https://www.semanticscholar.org/paper/f25cb81707fe114baccab1adbe740105292de01d bb9fd892ab7e34862c6fea064bc61ed9c35fc0aaArtificial intelligence is a dynamic and emerging form of technological innovation that has numerous ramifications for international business managers. The aim of this article is to obtain commentary from researchers about the role artificial intelligence will play in the global arena. This includes asking questions about how it will affect internationalization processes and whether it will lead to more international collaboration. Well‐known researchers provide advice on what international business managers should do in terms of staying competitive but also how they can integrate learning from artificial intelligence into their business operations. Lastly, suggestions for future research regarding the interplay between international business and artificial intelligence are provided.Thunderbird International Business Review430Commentary from well‐known researchers about the role artificial intelligence will play in the global arena is obtained and advice on what international business managers should do in terms of staying competitive but also how to integrate learning from artificial intelligence into their business operations is provided.Thunderbird International Business Review['Vanessa Ratten', 'Rakibul Hasan', 'Deepak Kumar', 'John Bustard', 'Arto Ojala', 'Yashar Salamzadeh']2024-02-22T00:00:00https://www.semanticscholar.org/paper/bb9fd892ab7e34862c6fea064bc61ed9c35fc0aa fbe428be7288ff548a6d516ab7b0ce7f9712a834This article is devoted to the problem of adapting the higher education system to the new realities of introducing artificial intelligence into everyday life. The author proposes new constructs in communication between teachers and students, based on the use of modern research and development tools (neural networks, big data, artificial intelligence, etc.), which make it possible to improve the mastery of the material with minimal losses due to obsolescence and a decrease in the relevance of the proposed studying courses. Thus it maximizes the competitive advantages of university graduates who acquire competencies that are in high demand in the modern world.Humanities and Social Sciences Bulletin of the Financial University30New constructs in communication between teachers and students are proposed, based on the use of modern research and development tools, which make it possible to improve the mastery of the material with minimal losses due to obsolescence and a decrease in the relevance of the proposed studying courses.Humanities and Social Sciences. Bulletin of the Financial University['D. Petrosyants']2024-02-22T00:00:00https://www.semanticscholar.org/paper/fbe428be7288ff548a6d516ab7b0ce7f9712a834 59a06d72f834fbe66f88a53d59b3729f411cbf09This study investigates the acceptability of different artificial intelligence (AI) applications in education from a multi-stakeholder perspective, including students, teachers, and parents. Acknowledging the transformative potential of AI in education, it addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI. Through a vignette methodology, participants were presented with four scenarios where AI's agency, transparency, explainability, and privacy were manipulated. After each scenario, participants completed a survey that captured their perceptions of AI's global utility, individual usefulness, justice, confidence, risk, and intention to use each scenario's AI if available. The data collection comprising a final sample of 1198 multi-stakeholder participants was distributed through a partner institution and social media campaigns and focused on individual responses to four AI use cases. A mediation analysis of the data indicated that acceptance and trust in AI varies significantly across stakeholder groups. We found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence. The study highlights that the acceptance of AI in education is a nuanced and multifaceted issue that requires careful consideration of specific AI applications and their characteristics, in addition to the diverse stakeholders' perceptions.arXiv.org770It is found that the key mediators between high and low levels of AI's agency, transparency, and explainability, as well as the intention to use the different educational AI, included perceived global utility, justice, and confidence.ArXiv['A. Karran', 'Patrick Charland', 'J-T. Martineau', 'Ana Ortiz de Guinea', 'Annemarie Lesage', 'S. Sénécal', 'Pierre-Majorique Léger']2024-02-22T00:00:00https://www.semanticscholar.org/paper/59a06d72f834fbe66f88a53d59b3729f411cbf09 fdf600e0a98472fab2c20e94af21a9d808b20cb0The AIFARMS Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability national AI institute brings together over 40 world‐class AI and agriculture researchers, with the common mission to develop foundational advances in AI and use them to ensure that future agriculture is environmentally friendly, sustainable, affordable, and accessible to diverse farming communities. Since its establishment in 2020, AIFARMS has advanced the state of the art in autonomous farming, cover crop planting, machine learning for improved outcomes from remote sensing, dynamic estimation of yield loss from weeds, and livestock management. The institute has prioritized the creation and utilization of high‐quality, openly available data sets for advancing foundational AI and tackling agricultural challenges. AIFARMS leverages a close partnership between UIUC and Tuskegee University to build programming for a skilled and diverse next‐generation workforce in digital agriculture. We are expanding the reach of AIFARMS outside of the current partners to collaborate with national AI institutions and international partners.The AI Magazine30AI Mag.['Vikram S. Adve', 'Jessica M. Wedow', 'Elizabeth A. Ainsworth', 'Girish Chowdhary', 'Angela Green‐Miller', 'Christina Tucker']2024-02-22T00:00:00https://www.semanticscholar.org/paper/fdf600e0a98472fab2c20e94af21a9d808b20cb0 10adecb25aea1b773dba2d8637fe20a630ec1feaCircuits Syst. Signal Process.250This paper aims to find the best trade-off among the watermarking requirements such as capacity, inaudibility, and robustness by applying an AI model and demonstrates that AI has the capability to compromise among the watermarking criteria.Circuits, Systems, and Signal Processing['Xin Liu', 'M. Nematollahi']2024-02-22T00:00:00https://www.semanticscholar.org/paper/10adecb25aea1b773dba2d8637fe20a630ec1fea adeeb685b34bbf0ff79ad4587fef4b1b41953b09In recent years, labor shortage has become a serious issue in industrial fields. Various technologies including robot and information processing system to realize flexible work like humans are effective solutions to this issue. Artificial intelligence technology of arm-type robots equipped with 3D sensors and force sensors has been applied in the manufacturing field to cope with different intelligent and highly precise tasks in Mitsubishi Electric. In addition, various technologies to expand the scope of application to the service field, as well as to realize highly functional delivery with mobile robots is under development. Furthermore, IoT technology is also being used for easy and quick on-site implementation and efficient operation. This paper introduces these initiatives with actual examples.Proceedings of International Conference on Artificial Life and Robotics80Artificial intelligence technology of arm-type robots equipped with 3D sensors and force sensors has been applied in the manufacturing field to cope with different intelligent and highly precise tasks in Mitsubishi Electric.Proceedings of International Conference on Artificial Life and Robotics['Haruhisa Okuda']2024-02-22T00:00:00https://www.semanticscholar.org/paper/adeeb685b34bbf0ff79ad4587fef4b1b41953b09 b309b9292f502ed28054436c463e503fd45f8a74Despite medical and technological advancements, foot amputations continue to rise. Thus, the effort of diabetic foot management should be toward prevention and early diagnosis. Healthcare professionals need to be trained, equipped, and supported with adequate resources to be able to identify and deliver appropriate foot care. Every effort should be made to minimize the impact of complications and to ensure prompt access to care for everyone. Artificial intelligence and smart technology could provide a significant opportunity to improve efficiency in diabetes care, which may reduce diabetic foot complications. The possible potential of the new technologies which are emerging together with their current developing applications for diabetic foot care are suggested. A call for immediate change in diabetes foot screening guidelines is imperative to save limbs and lives.International Journal of Lower Extremity Wounds00The possible potential of the new technologies which are emerging together with their current developing applications for diabetic foot care are suggested and a call for immediate change in diabetes foot screening guidelines is imperative to save limbs and lives.The international journal of lower extremity wounds['C. Formosa', 'N. Chockalingam', 'N. Papanas', 'A. Gatt']2024-02-22T00:00:00https://www.semanticscholar.org/paper/b309b9292f502ed28054436c463e503fd45f8a74 ac9b4cb990acbc17404d6938d8b27d00c7cf0671Nowadays Artificial Intelligence (AI) affects most of our lives; it is positive in some ways, but there are limitations in others. Teachers' perceptions of AI are still poorly studied, despite ongoing debate and growing research in the field. One of its main effects is on students in the first grades; however, this study aimed to investigate the effect of AI on the first three grades of students in basic schools in the governorate of the capital Amman Jordan, from the perspective of its teachers as a case study. The study was performed using a scientific questionnaire, and 125 participants completed this survey. The findings of an analysis of teachers' beliefs regarding using AI technologies to teach the first three grades in elementary schools in Jordan's capital, Amman Governorate, are presented in detail. As a result of the significance of these axes in educational curricula and strategies, as well as related sustainability, the focus was on five axes in the questionnaire questions: the validity of information derived from artificial intelligence, supporting students' technical knowledge and conceptual knowledge, focusing on development practices for students' skills, and finally achieving educational outcomes. The findings show that in-service teachers need to be trained to use current AI-based tools more effectively. To integrate AI into regular education, teachers must participate in the process of co-designing materials while taking into account contextual circumstances and, most importantly, curricula. Teachers' input during the development can help put AI in perspective, resulting in tangible effects and significant educational advances.International Journal of Religion250The findings show that in-service teachers need to be trained to use current AI-based tools more effectively, and to integrate AI into regular education, teachers must participate in the process of co-designing materials while taking into account contextual circumstances and, most importantly, curricula.International Journal of Religion['Al-Qawabah R. H']2024-02-22T00:00:00https://www.semanticscholar.org/paper/ac9b4cb990acbc17404d6938d8b27d00c7cf0671 f99d4a3207a910314af3d20c342c7c024f527cd3This research aimed to analyze the importance of artificial intelligence and sustainability in higher education according to the literature in the field and to present the relationships of this context with the United Nations Sustainable Development Goals (SDGs). The adopted research strategies included bibliometric analysis using VOSviewer software and literature review, considering the Web of Science scientific database. The bibliometric analysis resulted in the clustering of four groups. The blue cluster highlighted the emergence of interest in studies on AI and sustainability in higher education following the Covid-19 pandemic. The green cluster emphasized the importance of more efficient teaching methods adapted to the demands of higher education, as well as the need to empower teachers to use artificial intelligence in developing students' skills and competencies, emphasizing sustainability. The yellow cluster indicated the presence of artificial intelligence in higher education based on the triad of sustainable education and innovation, aiming to prepare students for future challenges. The red cluster emphasized the impact of artificial intelligence in higher education, focusing on student learning, efficiency, and sustainable performance. Finally, the literature analysis identified the main AI technologies in higher education and their relationship with the United Nations SDGs. The reflections presented here can contribute to expanding discussions on the relationship between artificial intelligence and sustainability in higher education. From a practical standpoint, it can serve as a foundation for university managers aiming to promote the integration of AI into their teaching processes, considering the context of sustainability.Concilium80The reflections presented here can contribute to expanding discussions on the relationship between artificial intelligence and sustainability in higher education and can serve as a foundation for university managers aiming to promote the integration of AI into their teaching processes, considering the context of sustainability.Concilium['Reimison Moreira Fernandes', 'Verônica de Menezes Nascimento Nagata', 'A. C. S. Melo', 'Vitor William Batista Martins']2024-02-22T00:00:00https://www.semanticscholar.org/paper/f99d4a3207a910314af3d20c342c7c024f527cd3 a3c24b0c2b5f1893fdbd4677ba2cdb1fe3409fb0We introduce Brain-Artificial Intelligence Interfaces (BAIs) as a new class of Brain-Computer Interfaces (BCIs). Unlike conventional BCIs, which rely on intact cognitive capabilities, BAIs leverage the power of artificial intelligence to replace parts of the neuro-cognitive processing pipeline. BAIs allow users to accomplish complex tasks by providing high-level intentions, while a pre-trained AI agent determines low-level details. This approach enlarges the target audience of BCIs to individuals with cognitive impairments, a population often excluded from the benefits of conventional BCIs. We present the general concept of BAIs and illustrate the potential of this new approach with a Conversational BAI based on EEG. In particular, we show in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to generate language. Our work thus demonstrates, for the first time, the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.arXiv.org1050It is shown in an experiment with simulated phone conversations that the Conversational BAI enables complex communication without the need to generate language, and demonstrates, for the first time, the ability of a speech neuroprosthesis to enable fluent communication in realistic scenarios with non-invasive technologies.ArXiv['Anja Meunier', 'Michal Robert Zák', 'Lucas Munz', 'Sofiya Garkot', 'Manuel Eder', 'Jiachen Xu', 'Moritz Grosse-Wentrup']2024-02-22T00:00:00https://www.semanticscholar.org/paper/a3c24b0c2b5f1893fdbd4677ba2cdb1fe3409fb0 abf4e110d928b657f16c4d97dbce91ed970c15b8Discover Artificial Intelligence30It is argued that both weak and strong artificial intelligence systems, devoid of human-defined goals, would not inherently pose existential threats to humanity, challenging the notions of artificial intelligence alignment and bringing into question the validity of Nick Bostrom’s Orthogonality Thesis.Discov. Artif. Intell.['Cody Holl']2024-02-22T00:00:00https://www.semanticscholar.org/paper/abf4e110d928b657f16c4d97dbce91ed970c15b8 683f450eb92005462c0395aeb5d4838f220e2f4aInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-22T00:00:00https://www.semanticscholar.org/paper/683f450eb92005462c0395aeb5d4838f220e2f4a a3ba4fcf248f9d33149e433f7465c8693e4964e8International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-22T00:00:00https://www.semanticscholar.org/paper/a3ba4fcf248f9d33149e433f7465c8693e4964e8 05d91196af92e2b251e07bc3e2dd12bb7226bedaACS Sustainable Resource Management111ACS Sustainable Resource Management['M. Kamkar', 'Kevin C. Leonard', 'Ivet Ferrer', 'Say Chye Joachim Loo', 'Elizabeth J. Biddinger', 'Dean Brady', 'Danielle Julie Carrier', 'Nicholas Gathergood', 'Hongxian Han', 'Ive Hermans', 'King Kuok Mimi Hii', 'B. Hwang', 'W. Loh', 'Michael A. R. Meier', 'A. C. Marr', 'Graham N. Newton', 'W. Srubar', 'Ning Yan', 'Michael K. C. Tam', 'Jingwen Chen', 'A. Moores', 'B. Subramaniam', 'P. Licence', 'Julio F. Serrano']2024-02-22T00:00:00https://www.semanticscholar.org/paper/05d91196af92e2b251e07bc3e2dd12bb7226beda 6d631e78f2f6e280f2ddd49f42a69ba79a78f6d1MAGMA700This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods, identifying current limitations and point out future directions of deep learning-based MRI motion correction.Magma['Zijian Zhou', 'Peng Hu', 'Haikun Qi']2024-02-22T00:00:00https://www.semanticscholar.org/paper/6d631e78f2f6e280f2ddd49f42a69ba79a78f6d1 d15895e1e50914c21e29fa866a751de273ef9d43This article attempts to describe Poles’ attitudes towards AI in the development of Christian prayer as a technology supporting prayer practices. Four research questions were formulated: 1. Do the frequency of prayer and engagement in religious practices influence the attitudes of Poles towards prayer programs/applications based on AI technology? 2. Does believers’ age affect Poles’ attitudes towards prayer programs/applications based on AI? 3. Does believers’ place of residence affect the attitude of Poles towards AI-based prayer programs/applications? 4. Do current users of the prayer-supporting applications plan to continue using it, and are new believers considering using it in the future? Research hypotheses were adopted to verify the research problem, with the first, second, and third being positively verified. H1: The higher the level of prayer frequency and engagement in religious practices of respondents, the more conservative the attitude towards prayer programs/applications based on AI; H2: The age of respondents differentiates the attitudes of Poles towards prayer programs/applications. H3: The respondents’ place of residence differentiates Poles’ attitudes towards prayer programs/applications. H4: Most AI users plan to continue such usage in the future, while new practitioners will appear.Religions170Poles’ attitudes towards AI in the development of Christian prayer as a technology supporting prayer practices is described to describe how the frequency of prayer and engagement in religious practices influence the attitudes of Poles towards prayer programs/applications based on AI.Religions['Małgorzata Gruchoła', 'Małgorzata Sławek-Czochra', 'Robert Zieliński']2024-02-22T00:00:00https://www.semanticscholar.org/paper/d15895e1e50914c21e29fa866a751de273ef9d43 dfdbcb2f1c870c80a1147a2f38a02d259b93c9f8European Radiology240This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms.European radiology['Mustafa Ege Seker', 'Yilmaz Onat Koyluoglu', 'A. N. Ozaydin', 'S. O. Gurdal', 'B. Ozcinar', 'N. Cabioğlu', 'V. Ozmen', 'E. Arıbal']2024-02-22T00:00:00https://www.semanticscholar.org/paper/dfdbcb2f1c870c80a1147a2f38a02d259b93c9f8 371c5528ed81dfae75c090856cdc62e91770455dIntensive & Critical Care Nursing510Generating AI-based entries for ICU diaries is feasible, but raises serious questions about nursing ethics, empathy, data protection, and values of professional nurses.Intensive & critical care nursing['Ella Peschel', 'Susanne Krotsetis', 'Anna-Henrikje Seidlein', 'Peter Nydahl']2024-02-22T00:00:00https://www.semanticscholar.org/paper/371c5528ed81dfae75c090856cdc62e91770455d 4058bb4d50d0b0e05d51d1d5dd66f842985bfd7dJournal of Medical Economics230Journal of medical economics['Abdallah Abou Hajal', 'Ahmad Z. Al Meslamani']2024-02-22T00:00:00https://www.semanticscholar.org/paper/4058bb4d50d0b0e05d51d1d5dd66f842985bfd7d 439b32c8ca06b92d29fd21265660dfd880a524abHeliyon710The findings demonstrate that the impact of AI on the labour force employment structure reflects unique characteristics for China and promotes the advancement of the nation's employment structure.Heliyon['Xiaowen Wang', 'Mingyue Chen', 'Nanxu Chen']2024-02-22T00:00:00https://www.semanticscholar.org/paper/439b32c8ca06b92d29fd21265660dfd880a524ab 914bb3a36570a65fad3c6847cda9c2f6436d0a82International Journal of Human-Computer Interaction620International Journal of Human–Computer Interaction['Ryosuke Yokoi', 'K. Nakayachi']2024-02-22T00:00:00https://www.semanticscholar.org/paper/914bb3a36570a65fad3c6847cda9c2f6436d0a82 8921191f180e1f32d62c0576c11b37e1613ad134Biomedical Journal of Scientific &amp; Technical Research00Biomedical Journal of Scientific &amp; Technical Research['Hong Yun Ma']2024-02-22T00:00:00https://www.semanticscholar.org/paper/8921191f180e1f32d62c0576c11b37e1613ad134 8c762ba3937e75295bbb7b0f83f9c6075d89e9caApplied Artificial Intelligence790Applied Artificial Intelligence['Eleanor Mill', 'W. Garn', 'Nick F. Ryman-Tubb', 'Chris J. Turner']2024-02-22T00:00:00https://www.semanticscholar.org/paper/8c762ba3937e75295bbb7b0f83f9c6075d89e9ca dec9adb2558f859342fc6167e1e2fd52d35ddad6Philosophy &amp; Technology20Philosophy &amp; Technology['J. Ryberg']2024-02-22T00:00:00https://www.semanticscholar.org/paper/dec9adb2558f859342fc6167e1e2fd52d35ddad6 07d964e3bda99364b808675b057c769d0eab8695Geo-Congress 2024130Geo-Congress 2024['Brett W. Maurer', 'Morgan D. Sanger']2024-02-22T00:00:00https://www.semanticscholar.org/paper/07d964e3bda99364b808675b057c769d0eab8695 bb606e4204464e5b0a97bc10c2872fef7f01a491Computational intelligence (CI) uses applied computational methods for problem-solving inspired by the behavior of humans and animals. Biological systems are used to construct software to solve complex problems, and one type of such system is an artificial immune system (AIS), which imitates the immune system of a living body. AISs have been used to solve problems that require identification and learning, such as computer virus identification and removal, image identification, and function optimization problems. In the body’s immune system, a wide variety of cells work together to distinguish between the self and non-self and to eliminate the non-self. AISs enable learning and discrimination by imitating part or all of the mechanisms of a living body’s immune system. Certainly, some deep neural networks have exceptional performance that far surpasses that of humans in certain tasks, but to build such a network, a huge amount of data is first required. These networks are used in a wide range of applications, such as extracting knowledge from a large amount of data, learning from past actions, and creating the optimal solution (the optimization problem). A new technique for pre-training natural language processing (NLP) software ver.9.1by using transformers called Bidirectional Encoder Representations (BERT) builds on recent research in pre-training contextual representations, including Semi-Supervised Sequence Learning, Generative Pre-Training, ELMo (Embeddings from Language Models), which is a method for obtaining distributed representations that consider context, and ULMFit (Universal Language Model Fine-Tuning). BERT is a method that can address the issue of the need for large amounts of data, which is inherent in large-scale models, by using pre-learning with unlabeled data. An optimization problem involves “finding a solution that maximizes or minimizes an objective function under given constraints”. In recent years, machine learning approaches that consider pattern recognition as an optimization problem have become popular. This pattern recognition is an operation that associates patterns observed as spatial and temporal changes in signals with classes to which they belong. It involves identifying and retrieving predetermined features and rules from data; however, the features and rules here are not logical information, but are found in images, sounds, etc. Therefore, pattern recognition is generally conducted by supervised learning. Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.BioMedInformatics500Based on a new theory that deals with the process by which the immune system learns from past infection experiences, the clonal selection of immune cells can be viewed as a learning rule of reinforcement learning.BioMedInformatics['Y. Matsuzaka', 'R. Yashiro']2024-02-22T00:00:00https://www.semanticscholar.org/paper/bb606e4204464e5b0a97bc10c2872fef7f01a491 f4ec3053e502a8abf7f1555aede0e7b87716544cThe adoption of Artificial Intelligence-based chatbots, including ChatGPT, in various sectors has raised concerns about their implications in higher education and scientific research. While the academic world aims to foster critical thinking and produce reliable research, the use of chatbots has elicited resistance from some academics due to fears of inaccuracies. In this paper, we extensively examine this phenomenon in higher education and scientific research, seeking to understand its practical applications, limitations, and potential risks. We investigated how ChatGPT is currently being used by academia, young researchers, and students. We also identified its areas of application and conducted trials by engaging ChatGPT, with transcripts included in the paper. Based on our findings, we discuss the results in the context of the needs in higher education and scientific research, presenting guidelines for responsible adoption. We distinguish positive use cases, areas requiring caution, explicit limitations of ChatGPT, and cases of unethical use. Importantly, we view ChatGPT as a valuable technological innovation but emphasize the necessity for thoughtful and responsible implementation. While we do not consider its use inherently deceitful, consistent, and shared guidelines are essential to ensure its ethical and effective application.Possibility Studies &amp; Society290This paper extensively examined how ChatGPT is currently being used by academia, young researchers, and students, and identified its areas of application and conducted trials by engaging ChatGPT, with transcripts included in the paper.Possibility Studies &amp; Society['Daniele Saccenti', 'Matilde Buattini', 'Silvia Grazioli', 'Dalila Torres']2024-02-22T00:00:00https://www.semanticscholar.org/paper/f4ec3053e502a8abf7f1555aede0e7b87716544c f01db438cb82fc3f9f7693eb8c52ec6444645a56 The Internet economy and computer-aided innovation enable improvements in the quality and quantity of outcomes of innovation processes. Traditional “research pipes” are often too slow to fit with contemporary business logic. In this paper, we focus on the intersection of innovation and automation and the potential they create together. Innovation automation represents a next generation of automation that has structural implications. Automation in the innovation context is about maintaining the richness of creative innovation processes while also absorbing a greater amount of data, information, and knowledge inputs and producing more holistic outputs that meet customer needs better and are faster on the market. The paper builds a novel academic “playground” for the research on innovation automation as the efficient and effective use of co-creative intelligence—the fusion and mixture of artificial intelligence, human intelligence, and the intelligence of crowds. Covering the wide field of innovation automation requires various future research programs. The main focus areas in this paper are related to understanding innovation automation, enabling the way to new management of innovation and ecosystem development. We also propose relevant research themes for the future. Journal of Innovation Management00The paper builds a novel academic “playground” for the research on innovation automation as the efficient and effective use of co-creative intelligence—the fusion and mixture of artificial intelligence, human intelligence, and the intelligence of crowds.Journal of Innovation Management['Vesa Harmaakorpi', 'H. Melkas', 'J. Porras', 'A. Pässilä']2024-02-22T00:00:00https://www.semanticscholar.org/paper/f01db438cb82fc3f9f7693eb8c52ec6444645a56 3940282d7188357a3ff21143dd212a1d5f11c6ceAlzheimer's disease poses an escalating global health challenge, necessitating accurate and timely diagnosis for effective intervention. This study presents a novel approach to Alzheimer's detection utilising advanced machine learning techniques applied to brain MRI scans. Leveraging Explainable Artificial Intelligence (XAI) methods, the developed model not only detects Alzheimer's disease but also offers transparent insights into the intricate patterns within the MRI data. In an era where Alzheimer's prevalence is rising, our methodology provides a valuable tool for clinicians and patients. By employing XAI, individuals can gain a comprehensive understanding of their MRI results, enabling them to seek second opinions and fostering a deeper comprehension of their condition. This research marks a significant step towards democratising medical diagnostics, empowering individuals with knowledge and promoting informed decision-making in Alzheimer's diagnosis and management.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)210A novel approach to Alzheimer's detection utilising advanced machine learning techniques applied to brain MRI scans is presented, leveraging Explainable Artificial Intelligence (XAI) methods, which not only detects Alzheimer's disease but also offers transparent insights into the intricate patterns within the MRI data.2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)['Afif Deshmukh', 'Neave Kallivalappil', "Kyle D'souza", 'Chinmay Kadam']2024-02-22T00:00:00https://www.semanticscholar.org/paper/3940282d7188357a3ff21143dd212a1d5f11c6ce 1b987790a3c6a2e86e7db3ddaad6bfb9b818b464In this paper, the question is posed whether Artificial Intelligence (AI) represent an extension of human biases or a path towards achieving equality. It systematically examines three pivotal aspects of algorithmic discrimination: the propensity of algorithms to inherit biases from pre-existing data, the ability of algorithm creators to mitigate this bias through internal rules, and the role of supranational management and regulation in curbing algorithmic bias. By exploring examples of biased models, the research identified instances of both good and bad practices. Additionally, it pointed towards the potential framework for ensuring an equal gateway, as exemplified in the latest international legal frameworks addressing AI non-discrimination principles. The focus of this work is a discussion on the legal regulation of decision-making by algorithms, aiming to prevent potential discrimination.International Conference on Information Technology180The focus of this work is a discussion on the legal regulation of decision-making by algorithms, aiming to prevent potential discrimination.2024 28th International Conference on Information Technology (IT)['Jovan Jablan', 'Luka Laković', 'Andrea Micanovic']2024-02-21T00:00:00https://www.semanticscholar.org/paper/1b987790a3c6a2e86e7db3ddaad6bfb9b818b464 b9a19e6e0d09016960c4aaa47ed270d44bb58a33Recently, the UK and EU have introduced legislation in the field of generative artificial intelligence, using different models of legal regulation. China, based on the experience of different countries, has taken an eclectic position in relation to generative artificial intelligence. The Cyberspace Administration of the People’s Republic of China has published a number of regulations to regulate generative artificial intelligence services that create content based on data sets. The article provides an overview of legal regulation in the field of generative artificial intelligence, analyzes the shortcomings and features of Chinese legislation in this area, and offers recommendations for its improvement, which can be used, among other things, to change Russian legislation in this area.Juridical Science and Practice00An overview of legal regulation in the field of generative artificial intelligence is provided, analyzes the shortcomings and features of Chinese legislation in this area, and offers recommendations for its improvement, which can be used to change Russian legislation in this area.Juridical science and practice['Shaoxue Jia']2024-02-21T00:00:00https://www.semanticscholar.org/paper/b9a19e6e0d09016960c4aaa47ed270d44bb58a33 8d6c211938a85cbb97fd01f139c39c2141981584 The Indonesian Government is attracting foreign investment in infrastructure to support equitable development by simplifying the bureaucratic process for business licensing through Online Single Submission (oss). However, from the oss introduction in 2018, it has not yet transformed Indonesia’s investment climate. This paper consists of a normative legal study which uses a statutory, conceptual, and comparative approach with Canada and New Zealand to identify the oss policy disharmony that negatively impacts the investment climate in the Indonesian infrastructure. The results showed a need to reconstruct systems and policies based on the Government Regulation of the Republic of Indonesia Number 24 of 2018 concerning Online Integrated Business Licensing Services and the Law of the Republic of Indonesia Number 11 of 2020 concerning Job Creation to accelerate and increase investment in the era of the fourth industrial revolution.European Journal of Comparative Law and Governance00European Journal of Comparative Law and Governance['Widhayani Dian Pawestri', 'Vincentius Sutanto', 'Kukuh Leksono Suminaring Aditya', 'Qona’aha Noor Maajid', 'Khofifah Nura Adila']2024-02-21T00:00:00https://www.semanticscholar.org/paper/8d6c211938a85cbb97fd01f139c39c2141981584 98e07970cf2609454d74a33c29a2c0629f520fe9Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.International Conference on Human Factors in Computing Systems1523A first hand account of ideating AI concepts to improve critical care medicine and the research implications for improved collaboration and stakeholder engagement are discussed.ArXiv['Nur Yildirim', 'Susanna Zlotnikov', 'Deniz Sayar', 'Jeremy M. Kahn', 'L. Bukowski', 'Sher Shah Amin', 'K. Riman', 'B. Davis', 'J. S. Minturn', 'Andrew J. King', 'Dan Ricketts', 'Lu Tang', 'Venkatesh Sivaraman', 'Adam Perer', 'Sarah Preum', 'James McCann', 'John Zimmerman']2024-02-21T00:00:00https://www.semanticscholar.org/paper/98e07970cf2609454d74a33c29a2c0629f520fe9 9f1c54b9b2a5f03147c37aa2085a97543b73cfbbOne of the most deadly types of cancer is lung cancer, and effective treatment depends on early detection. To detect lung cancer in its early stages, numerous computer-aided diagnostic (CAD) methods have been developed. Due to their excellent accuracy, these methods mostly use machine learning models. The medical profession continues to be cautious despite their accuracy because there aren't any obvious justifications for their predictions. In order to provide comprehensible explanations for identifying lung cancer in chest X-ray pictures using convolutional neural network-based models, we propose the use of Explainable AI (XAI) approaches. Our main goal is to boost the medical community's confidence in the accuracy of machine learning methods for making diagnoses of illnesses.2024 4th International Conference on Advanced Research in Computing (ICARC)220This work proposes the use of Explainable AI (XAI) approaches for identifying lung cancer in chest X-ray pictures using convolutional neural network-based models, to boost the medical community's confidence in the accuracy of machine learning methods for making diagnoses of illnesses.2024 4th International Conference on Advanced Research in Computing (ICARC)['Himash Wedisinghe', 'T.G.I. Fernando']2024-02-21T00:00:00https://www.semanticscholar.org/paper/9f1c54b9b2a5f03147c37aa2085a97543b73cfbb 55268902c25f8c119b2e024bd7e46e31ed7bfaa5 Artificial intelligence (AI) has achieved significant progress in recent years and its applications cover a wide range of fields such as computer vision, natural language processing, autonomous driving and medical diagnosis. In industry, the rapid development of real-time-sensor measurement techniques promotes equipment surveillance and maintenance into the era of big data. It is still challenging to manually analyze this big data and establish general physical modelling by using the information hidden in these data. To this end, AI technology, such as machine learning and neural networks, has emerged as a promising tool to extract useful knowledge from measured data, and to tackle the real-time monitoring, diagnosis problems as well as enhancing the health management and reliability of modern industrial equipment. With the vision of establishing a strong link between AI and industrial equipment surveillance and maintenance, this special feature is designated to select, organize and exhibit the latest research progress on the cutting-edge research topics relevant to AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management. Submissions for this topic in Measurement Science and Technology are open from 23 March 2022 to 30 September 2022 and contains 42 outstanding papers in above research fields.Measurement science and technology00This special feature is designated to select, organize and exhibit the latest research progress on the cutting-edge research topics relevant to AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management.Measurement Science and Technology['Zhuyun Chen', 'Haidong Shao', 'Te Han', 'Konstantinos Gryllias']2024-02-21T00:00:00https://www.semanticscholar.org/paper/55268902c25f8c119b2e024bd7e46e31ed7bfaa5 b5f367e645821c266d8bf83790a7075eeda6c41eThis article explores the use of artificial intelligence (AI)-driven artificial intelligence, business intelligence, and energy management to improve the efficiency and sustainability of the electronics industry. Transformation of the energy system is essential to solve the global problems caused by climate change and energy demand. To achieve this, it is important to advance artificial intelligence technology and use its potential to transform the energy system. This article presents research on the integration of artificial intelligence and energy management to optimize energy use and increase sustainability, while also identifying benefits and issues associated with its use. The findings highlight the importance of using AI-powered energy intelligence and business intelligence to increase energy efficiency, promote sustainable practices, and achieve electricity usage targets for a greener, more efficient future. In this, Energy Intelligence (EI), Energy Business Intelligence (EBI) and Energy Management Systems (EMS) are taken into account and addressed.CSI International Symposium on Artificial Intelligence and Signal Processing2002024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)['Mahdieh Zakizadeh', 'Mazyar Zand']2024-02-21T00:00:00https://www.semanticscholar.org/paper/b5f367e645821c266d8bf83790a7075eeda6c41e ef9fb73599e8f9301d90c7e2a3895e208deece18This paper explores a paradigm shift in work culture in the contemporary art sector due to digital transition and the introduction of AI. New ways of working with AI and digital software are embedded and normalized in everyday Chinese artistic practices. This work includes new forms of creativity and efficiency, yet, simultaneously includes new types of digital labour. This paper conceptualizes this as “digital art work,” which draws attention to the often-overlooked aspects of artists’ work, particularly their everyday artistic practices that increasingly include digital software and AI. What is the role and position of the artist in an environment where digital software and AI are becoming more central in artistic creation? How do artists creatively (mis)use AI? What does this paradigm shift in work culture mean for the future of the artist’s role and the future of the contemporary art sector? This paper draws on 48 semi-structured interviews with visual artists and arts professionals, including painters, sculptors, mixed-media, and internet artists as well as contemporary art gallery owners, museum project directors, curators, and culture policymakers living and working in China during 2023. The findings show how Chinese artists are mastering AI and opening up new spaces for creativity and how the contemporary art sector in China has already transitioned to a new “digital way” in artistic creation. These findings can help to create policy around AI globally and provide solutions for the sustainability of the artist profession and the future of the contemporary art sector.European Journal of Cultural Management and Policy190How Chinese artists are mastering AI and opening up new spaces for creativity and how the contemporary art sector in China has already transitioned to a new “digital way” in artistic creation can help to create policy around AI globally and provide solutions for the sustainability of the artist profession and the future of the contemporary art sector.European Journal of Cultural Management and Policy['Emma Duester']2024-02-21T00:00:00https://www.semanticscholar.org/paper/ef9fb73599e8f9301d90c7e2a3895e208deece18 dbfacacad4938865f4a985a5564572f902c134c0 Cystic Fibrosis (CF) is a fatal hereditary condition marked by thicker mucus production, which can cause problems with the digestive and respiratory systems. The quality of life and survival rates of CF patients can be improved by early identification and individualized therapy measures. With an emphasis on its applications in diagnosis and therapy, this paper investigates how Artificial Intelligence (AI) is transforming the management of Cystic Fibrosis (CF). AI-powered algorithms are revolutionizing CF diagnosis by utilizing huge genetic, clinical, and imaging data databases. In order to identify CF mutations quickly and precisely, machine learning methods evaluate genomic profiles. Furthermore, AI-driven imaging analysis helps to identify lung and gastrointestinal issues linked to cystic fibrosis early and allows for prompt treatment. Additionally, AI aids in individualized CF therapy by anticipating how patients will react to already available medications and enabling customized treatment regimens. Drug repurposing algorithms find prospective candidates from already-approved drugs, advancing treatment choices. Additionally, AI supports the optimization of pharmacological combinations, enhancing therapeutic results while minimizing side effects. AI also helps with patient stratification by connecting people with CF mutations to therapies that are best for their genetic profiles. Improved treatment effectiveness is promised by this tailored strategy. The transformational potential of artificial intelligence (AI) in the field of cystic fibrosis is highlighted in this review, from early identification to individualized medication, bringing hope for better patient outcomes, and eventually prolonging the lives of people with this difficult ailment. Current Respiratory Medicine Reviews00The transformational potential of artificial intelligence (AI) in the field of cystic fibrosis is highlighted, from early identification to individualized medication, bringing hope for better patient outcomes, and eventually prolonging the lives of people with this difficult ailment.Current Respiratory Medicine Reviews['Biswajit Basu', 'Srabona Dutta', 'Monosiz Rahaman', 'A. Bose', 'Sourav Das', 'Jigna Prajapati', 'B. Prajapati']2024-02-21T00:00:00https://www.semanticscholar.org/paper/dbfacacad4938865f4a985a5564572f902c134c0 001f237943e5c44b444e2d905cc26c5e1cca980eThis study investigates the efficiency of large language models (LLMs) in producing routine, negative, and persuasive business emails for educational purposes within the context of Business Writing. Specifically, it compares the outputs generated by four widely-used LLMs (ChatGPT 3.5, Llama 2, Bing Chat, and Bard) when presented with identical email scenarios. These generated emails are evaluated using an elaborate rubric, allowing for a systematic assessment of LLMs' performance across three distinct email types. The results of the study show that the output with the same prompt varies greatly despite the rather formulaic nature of business emails. For instance, some LLMs struggle with following the requested structure and maintaining consistency in tone, while others have issues with unity and conciseness. The findings of this research hold implications for teaching business writing (rubrics, task instructions, in-class implementation), as well as for the integration of AI in professional communication at large.World Journal of English Language00The results of the study show that the output with the same prompt varies greatly despite the rather formulaic nature of business emails, which holds implications for teaching business writing, as well as for the integration of AI in professional communication at large.World Journal of English Language['Marina Jovic', 'S. Mnasri']2024-02-21T00:00:00https://www.semanticscholar.org/paper/001f237943e5c44b444e2d905cc26c5e1cca980e 61d69565b13287e07718fef63fcbb292a4620fdbThis paper introduces the Multi-Step Action Model (MSAM), a closed-source AI model designed by Empsing to address challenges hindering AI adoption in enterprises. Through a holistic examination, this paper explores MSAM's foundational principles, design architecture, and future trajectory. It evaluates MSAM's performance via rigorous testing methodologies and envisions its potential impact on advancing AI adoption within organizations.arXiv.org40The Multi-Step Action Model is introduced, a closed-source AI model designed by Empsing to address challenges hindering AI adoption in enterprises and its potential impact on advancing AI adoption within organizations is contemplated.ArXiv['Shreyash Mishra', 'Shrey Shah', 'Rex Pereira']2024-02-21T00:00:00https://www.semanticscholar.org/paper/61d69565b13287e07718fef63fcbb292a4620fdb 272e8db56f6471e5b893824ed59e8072d4977580The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, technical and privacy requirements, we focus on the importance of data quality (training/test) in DL. Since data quality dictates the behaviour of ML products, evaluating data quality will play a key part in the regulatory approval of medical AI products. We perform a systematic review following PRISMA guidelines using the databases PubMed and ACM Digital Library. We identify 2362 studies, out of which 62 records fulfil our eligibility criteria. From this literature, we synthesise the existing knowledge on data quality frameworks and combine it with the perspective of ML applications in medicine. As a result, we propose the METRIC-framework, a specialised data quality framework for medical training data comprising 15 awareness dimensions, along which developers of medical ML applications should investigate a dataset. This knowledge helps to reduce biases as a major source of unfairness, increase robustness, facilitate interpretability and thus lays the foundation for trustworthy AI in medicine. Incorporating such systematic assessment of medical datasets into regulatory approval processes has the potential to accelerate the approval of ML products and builds the basis for new standards.arXiv.org1580The METRIC-framework is proposed, a specialised data quality framework for medical training data comprising 15 awareness dimensions, along which developers of medical ML applications should investigate a dataset and lays the foundation for trustworthy AI in medicine.ArXiv['Daniel Schwabe', 'Katinka Becker', 'Martin Seyferth', 'Andreas Klass', 'Tobias Schäffter']2024-02-21T00:00:00https://www.semanticscholar.org/paper/272e8db56f6471e5b893824ed59e8072d4977580 d55e8343d0e0042d3014bf545a9044081f9b1b94In today’s digital world, a vast amount of unstructured data is generated, primarily consisting of images and videos. Extracting meaningful information from these visuals is crucial for effective retrieval. Among the various fields where these images play a role, sports are essential in everyone’s life. This article introduces a system aimed at helping toddlers effortlessly understand sports balls. The proposed framework establishes a classification system for sports images, considering factors such as ball size, color, direction, dimension, and other vital details. Our approach uses object detection and image processing techniques to classify various sports balls, including cricket balls, basketball, volleyball, tennis balls, and football. To determine their positions and provide relevant information, we implemented the neuro-symbolic ai framework (NSAI) to provide symbolic reasoning question-answer capabilities to classify balls.CSI International Symposium on Artificial Intelligence and Signal Processing160A system aimed at helping toddlers effortlessly understand sports balls is introduced, using the neuro-symbolic ai framework (NSAI) to provide symbolic reasoning question-answer capabilities to classify balls.2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)['Abhilasha Mangal', 'Tarjni Vyas', 'Rajesh Gupta', 'S. Tanwar', 'Hossein Shahinzadeh']2024-02-21T00:00:00https://www.semanticscholar.org/paper/d55e8343d0e0042d3014bf545a9044081f9b1b94 c4518089e69db80f2847005863b7f1a7fec5b623This study takes on the challenge of growing urban waste and presents a ground-breaking waste management approach. By merging Robotics and Artificial Intelligence (AI) with traditional Dirty Materials Recovery Facilities (MRFs), our project revolutionizes waste separation. Our intelligent MRFs utilize state-of-the-art robotic arms controlled by the advanced YOLOv8x AI model trained with a custom dataset of garbage items, accurately identifying and sorting various materials such as glass, metal, biodegradable, plastic, and cardboard. The innovative combination of these technologies results in unparalleled precision and efficiency in waste disposal. Ultimately, our system streamlines recycling processes, promotes sustainable city living, and significantly reduces environmental harm.CSI International Symposium on Artificial Intelligence and Signal Processing190This project revolutionizes waste separation by merging Robotics and Artificial Intelligence with traditional Dirty Materials Recovery Facilities (MRFs), and streamlines recycling processes, promotes sustainable city living, and significantly reduces environmental harm.2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)['Reza Javanmard Alitappeh', 'Mohammad Roudbari', 'Raeika Pourali', 'Ali Foladi']2024-02-21T00:00:00https://www.semanticscholar.org/paper/c4518089e69db80f2847005863b7f1a7fec5b623 4e2bde0e0398be95a7997bccd2eb7ffd6aaa206eThis study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI. Topics addressed include the role of AI at various operational layers of AVs, the implications of the EU's AI Act on AVs, and the need for new testing methodologies for Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). The study also provides a detailed analysis on the importance of cybersecurity audits, the need for explainability in AI decision-making processes and protocols for assessing the robustness and ethical behaviour of predictive systems in AVs. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology, highlighting the need for multidisciplinary expertise.arXiv.org1520ArXiv["David Fern'andez Llorca", 'Ronan Hamon', 'Henrik Junklewitz', 'Kathrin Grosse', 'Lars Kunze', 'Patrick Seiniger', 'Robert Swaim', 'Nick Reed', 'Alexandre Alahi', "Emilia G'omez", "Ignacio S'anchez", 'Á. Kriston']2024-02-21T00:00:00https://www.semanticscholar.org/paper/4e2bde0e0398be95a7997bccd2eb7ffd6aaa206e 0b403706c9296b36667272bccd1d0786fa5887faAI and Ethics180A new perspective on the ethical discussion of the generative AI models, especially text-to-image models, is offered by bridging the gap between the technical and sociological perspectives on these issues, which has been largely overlooked in the existing literature.AI and Ethics['Jorge Luis Morton']2024-02-21T00:00:00https://www.semanticscholar.org/paper/0b403706c9296b36667272bccd1d0786fa5887fa 631e527f8a9bd491dc18f7a00f64f4e124a16832BACKGROUND With the growing relevance of AI-based patient-facing information, microsurgical-specific online information provided by professional organizations was compared to that of ChatGPT and assessed for accuracy, comprehensiveness, clarity, and readability. METHODS Six plastic and reconstructive surgeons blindly assessed responses to ten microsurgery-related medical questions written either by American Society of Reconstructive Microsurgery (ASRM) or ChatGPT based on accuracy, comprehensiveness, and clarity. Surgeons were asked to choose which source provided the overall highest quality microsurgical patient-facing information. Additionally, 30 individuals with no medical background (ages 18-81, μ=49.8) were asked to determine a preference when blindly comparing materials. Readability scores were calculated, and all numerical scores were analyzed using the following six reliability formulas: Flesch-Kincaid Grade Level, Flesch-Kincaid Readability Ease, Gunning Fog Index, Simple Measure of Gobbledygook (SMOG) Index, Coleman-Liau Index, Linsear Write Formula (LWF), and Automated Readability Index. Statistical analysis of microsurgical-specific online sources was conducted utilizing paired t-tests. RESULTS Statistically significant differences in comprehensiveness and clarity were seen in favor of ChatGPT. Surgeons, 70.7% of the time, blindly choose ChatGPT as the source that overall provided the highest quality microsurgical patient-facing information. Non-medical individuals 55.9% of the time selected AI-generated microsurgical materials as well. Neither ChatGPT nor ASRM-generated materials were found to contain inaccuracies. Readability scores for both ChatGPT and ASRM materials were found to exceed recommended levels for patient proficiency across six readability formulas, with AI-based material scored as more complex. CONCLUSION AI-generated patient-facing materials were preferred by surgeons in terms of comprehensiveness and clarity when blindly compared to online material provided by ASRM. Studied AI-generated material was not found to contain inaccuracies. Additionally, surgeons and non-medical individuals consistently indicated an overall preference for AI-generated material. A readability analysis suggested that both materials sourced from ChatGPT and ASRM surpassed recommended reading levels across six readability scores.Journal of reconstructive microsurgery00A readability analysis suggested that both materials sourced from ChatGPT and ASRM surpassed recommended reading levels across six readability scores, and surgeons and non-medical individuals consistently indicated an overall preference for AI-generated material.Journal of reconstructive microsurgery['Charlotte E Berry', 'Alexander Z. Fazilat', 'Christopher V. Lavin', 'Hendrik Lintel', 'Naomi A. Cole', 'C. S. Stingl', 'Caleb Valencia', 'Annah G. Morgan', 'Arash Momeni', 'Derrick C Wan']2024-02-21T00:00:00https://www.semanticscholar.org/paper/631e527f8a9bd491dc18f7a00f64f4e124a16832 db6a1d48071096ce3c26659550cf5dd24c4657a4The Institute for Student‐AI Teaming (iSAT) addresses the foundational question: how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.The AI Magazine50The Institute for Student‐AI Teaming addresses the foundational question: how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.AI Mag.['Sidney K. D’Mello', 'Quentin L. Biddy', 'Thomas Breideband', 'Jeffrey B. Bush', 'M. Chang', 'Arturo Cortez', 'Jeffrey Flanigan', 'Peter W. Foltz', 'Jamie C. Gorman', 'Leanne Hirshfield', 'Monlin Monica Ko', 'Nikhil Krishnaswamy', 'Rachel Lieber', 'James H. Martin', 'Martha Palmer', 'W. Penuel', 'Thomas M. Philip', 'S. Puntambekar', 'James Pustejovsky', 'Jason G. Reitman', 'Tamara Sumner', 'Michael Tissenbaum', 'Lyn Walker', 'Jacob Whitehill']2024-02-21T00:00:00https://www.semanticscholar.org/paper/db6a1d48071096ce3c26659550cf5dd24c4657a4 8cdaf08fc39e9260228bd0cbf09d7cda7d496e36Information Systems Frontiers1480In the earlier stages of AI adoption, demand pull is the main driver rather than technology push, and a processual sensemaking model is developed extending the theory on institutions and sensemaking.Information Systems Frontiers['Rohit Madan', 'Mona Ashok']2024-02-21T00:00:00https://www.semanticscholar.org/paper/8cdaf08fc39e9260228bd0cbf09d7cda7d496e36 3cdc641e767982eab5855f9c94f201a82ef9b93aAI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which can yield misleading conclusions about AI performance. How might we empower communities to drive the intentional design and curation of evaluation datasets for AI that impacts them? We investigate this question on Wikipedia, an online community with multiple AI-based content moderation tools deployed. We introduce Wikibench, a system that enables communities to collaboratively curate AI evaluation datasets, while navigating ambiguities and differences in perspective through discussion. A field study on Wikipedia shows that datasets curated using Wikibench can effectively capture community consensus, disagreement, and uncertainty. Furthermore, study participants used Wikibench to shape the overall data curation process, including refining label definitions, determining data inclusion criteria, and authoring data statements. Based on our findings, we propose future directions for systems that support community-driven data curation.International Conference on Human Factors in Computing Systems950This work introduces Wikibench, a system that enables communities to collaboratively curate AI evaluation datasets, while navigating ambiguities and differences in perspective through discussion, and proposes future directions for systems that support community-driven data curation.ArXiv['Tzu-Sheng Kuo', 'Aaron Halfaker', 'Zirui Cheng', 'Jiwoo Kim', 'Meng-Hsin Wu', 'Tongshuang Wu', 'Kenneth Holstein', 'Haiyi Zhu']2024-02-21T00:00:00https://www.semanticscholar.org/paper/3cdc641e767982eab5855f9c94f201a82ef9b93a 4968197516580cf59e9b706fdaf7037cbf6d5097The mission of the AI Institute in Dynamic Systems is to develop the next generation of advanced machine learning (ML) and AI tools for controlling complex physical systems by discovering physically interpretable and physics‐constrained data‐driven models through optimal sensor selection and placement. The research effort is anchored by a common task framework (CTF) that evaluates the performance of ML algorithms, architectures, and optimization schemes for the diverse tasks required in engineering applications. The aim is to push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross‐disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data. The CTF further supports sustainable and open‐source challenge datasets, which are foundational for developing interpretable, ethical, and inclusive tools to solve problems fundamental to human safety, society, and the environment.The AI Magazine100The aim is to push beyond the boundaries of modern techniques by closing the loop between data collection, control, and modeling, creating a unique and cross‐disciplinary architecture for learning physically interpretable and physics constrained models of complex dynamic systems from time series data.AI Mag.['J. Kutz', 'S. Brunton', 'Krithika Manohar', 'Hod Lipson', 'Na Li']2024-02-21T00:00:00https://www.semanticscholar.org/paper/4968197516580cf59e9b706fdaf7037cbf6d5097 3044087857e614cb22ba6123ebc08fe207977a20The article examines aspects related to the opportunities and threats of using artificial intelligence (AI) in politi-cal communications. The conclusion is drawn about the dual nature of the use of artificial intelligence (AI) in political communications. Attention is drawn to the key current threats to the use of AI in political communica-tions – the spread of deepfakes and the use of AI algorithms in disinformation, manipulation of public con-sciousness or gaining advantages in the selective delivery of information to recipients. A comprehensive ar-gument is given for the depth of the problems associated with the potential use of AI in electoral communica-tions and the difficulties that arise in overcoming them. Practical measures are proposed to level out the nega-tive aspects of the potential use of artificial intelligence in political and, above all, electoral communications in the Russian Federation.Общество политика экономика право00The conclusion is drawn about the dual nature of the use of artificial intelligence (AI) in political communications – the spread of deepfakes and the use of AI algorithms in disinformation, manipulation of public con-sciousness or gaining advantages in the selective delivery of information to recipients.Общество: политика, экономика, право['Elina В. Urtaeva']2024-02-21T00:00:00https://www.semanticscholar.org/paper/3044087857e614cb22ba6123ebc08fe207977a20 60bba8dba3b5d763c73d59b5d66be81498545efdScience &amp; Education272Recommendations for research and teaching are provided that recognize the role of materiality in the context of GenAI, specifically in practical work, scientific argumentation, and learning with GenAI.Science &amp; Education['Kok‐Sing Tang', 'Grant Cooper']2024-02-21T00:00:00https://www.semanticscholar.org/paper/60bba8dba3b5d763c73d59b5d66be81498545efd 37ef7632480b672100aa0305addbdb4f743b5b7bIntegrating artificial intelligence (AI) technologies into various aspects of society has sparked both excitement and concern regarding its potential impact on inequality. This abstract provides an overview of the key issues surrounding AI and inequality, exploring the challenges and opportunities arising from the widespread adoption of AI systems. Firstly, we examine how AI technologies have the potential to exacerbate existing inequalities across various domains, including labor markets, education, healthcare, and access to services. AI-driven automation may lead to job displacement and wage polarization, widening the gap between high-skilled and low-skilled workers. Moreover, algorithmic biases embedded in AI systems can perpetuate discrimination and inequity, particularly against marginalized communities. However, alongside these challenges, AI also presents opportunities to address inequality and promote inclusivity. AI-powered innovations have the potential to enhance efficiency, accessibility, and affordability in sectors such as healthcare, education, and financial services, thereby reducing disparities in access to essential resources and opportunities. Additionally, initiatives focused on ethical AI development and responsible AI governance can mitigate the negative impacts of AI on inequality by promoting fairness, transparency, and accountability in algorithmic decision-making processes. In conclusion, while AI has the potential to both exacerbate and mitigate inequality, its ultimate impact depends on the choices we make in designing, deploying, and governing AI systems. By prioritizing equity, social justice, and human welfare in AI development and implementation, we can harness the transformative power of AI to create a more equitable and inclusive society. Qeios21By prioritizing equity, social justice, and human welfare in AI development and implementation, the transformative power of AI can harness the transformative power of AI to create a more equitable and inclusive society.Qeios['Milad Shahvaroughi Farahani', 'Ghazal Ghasemi']2024-02-21T00:00:00https://www.semanticscholar.org/paper/37ef7632480b672100aa0305addbdb4f743b5b7b 6def831f7c606523a7225b6b781989dbb64b967fSimple Summary Governments worldwide have prioritized multicancer early detection (MCED) for the better management of cancers. Artificial intelligence (AI) is a promising technology to enhance the performance of MCED. In this review, key components of MCED AI are explored. We focus on detection targets such as serum protein biomarkers and cell-free DNA. Based on the serum biomarkers, various AI model training methods and validation techniques are investigated. The emphasis is on understanding how these approaches influence predictive efficacy. We demonstrate the importance of real-world data rather than case-control data for trustworthy implementation and the potential benefits of AI integration in MCED. Moreover, challenges in deploying MCED AIs in clinical settings are highlighted, including issues such as presenting predictive reports and addressing cancer-related information. Abstract The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.Cancers1001Several mature MCED AI products currently available in the market are reviewed, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application, and the challenges encountered by existing MCED AI products are illuminated.Cancers['Hsin-Yao Wang', 'Wan-Ying Lin', 'Chenfei Zhou', 'Zih-Ang Yang', 'Sriram Kalpana', 'Michael S. Lebowitz']2024-02-21T00:00:00https://www.semanticscholar.org/paper/6def831f7c606523a7225b6b781989dbb64b967f fabc20672271cca90f6685a31670d51f7ec3cd39Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.Mathematics631This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems to summarize XAI in common actuarial problems.Mathematics['Catalina Lozano-Murcia', 'Francisco P. Romero', 'J. Serrano-Guerrero', 'Arturo Peralta', 'J. A. Olivas']2024-02-21T00:00:00https://www.semanticscholar.org/paper/fabc20672271cca90f6685a31670d51f7ec3cd39 ec144e08cdff828bbb0dfc60fd0b487ad496e142Electromyography (EMG) proves invaluable myoelectric manifestation in identifying neuromuscular alterations resulting from ischemic strokes, serving as a potential marker for diagnostics of gait impairments caused by ischemia. This study aims to develop an interpretable machine learning (ML) framework capable of distinguishing between the myoelectric patterns of stroke patients and those of healthy individuals through Explainable Artificial Intelligence (XAI) techniques. The research included 48 stroke patients (average age 70.6 years, 65% male) undergoing treatment at a rehabilitation center, alongside 75 healthy adults (average age 76.3 years, 32% male) as the control group. EMG signals were recorded from wearable devices positioned on the bicep femoris and lateral gastrocnemius muscles of both lower limbs during indoor ground walking in a gait laboratory. Boosting ML techniques were deployed to identify stroke-related gait impairments using EMG gait features. Furthermore, we employed XAI techniques, such as Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and Anchors to interpret the role of EMG variables in the stroke-prediction models. Among the ML models assessed, the GBoost model demonstrated the highest classification performance (AUROC: 0.94) during cross-validation with the training dataset, and it also overperformed (AUROC: 0.92, accuracy: 85.26%) when evaluated using the testing EMG dataset. Through SHAP and LIME analyses, the study identified that EMG spectral features contributing to distinguishing the stroke group from the control group were associated with the right bicep femoris and lateral gastrocnemius muscles. This interpretable EMG-based stroke prediction model holds promise as an objective tool for predicting post-stroke gait impairments. Its potential application could greatly assist in managing post-stroke rehabilitation by providing reliable EMG biomarkers and address potential gait impairment in individuals recovering from ischemic stroke.Italian National Conference on Sensors641An interpretable EMG-based stroke prediction model holds promise as an objective tool for predicting post-stroke gait impairments and could greatly assist in managing post-stroke rehabilitation by providing reliable EMG biomarkers and address potential gait impairment in individuals recovering from ischemic stroke.Sensors (Basel, Switzerland)['Iqram Hussain', 'Rafsan Jany']2024-02-21T00:00:00https://www.semanticscholar.org/paper/ec144e08cdff828bbb0dfc60fd0b487ad496e142 6f8ad9a150428649e8a51bedc4d0aecf81ba22bfPURPOSE To the best of our knowledge, implementation of artificial intelligence (AI)-based vision screening in community health fair settings has not been previously studied. This prospective cohort study explored the incorporation of AI in a community health fair setting to improve access to eyecare. METHODS Vision screening was implemented during a community health fair event using an AI-based non-mydriatic fundus camera. In addition, a questionnaire was provided to survey the various barriers to eyecare and assess eye health literacy. RESULTS A total of 53 individuals were screened at this event. Notably, about 88% of participants had follow-up appointments scheduled accordingly with an approximate 62% attendance rate. The most reported barrier to eyecare was lack of health insurance followed by transportation. CONCLUSION The addition of AI-based vision screening in community health fairs may ultimately help improve access to eye care.Ophthalmic Epidemiology50The addition of AI-based vision screening in community health fairs may ultimately help improve access to eye care and assess eye health literacy.Ophthalmic epidemiology['Bhakti Panchal', 'Samuel Asanad', 'Rana Malek', 'Kashif Munir', 'Lisa S Schocket']2024-02-21T00:00:00https://www.semanticscholar.org/paper/6f8ad9a150428649e8a51bedc4d0aecf81ba22bf 6e9dca0a1113b439fb6ff75f59accf6298fd8770With the flourishing development of artificial intelligence and computer animation technologies, there has been an increasing intersection between these two. In the field of computer animation, the use of artificial intelligence significantly reduces the difficulties in design, production, and post-production processes, which has a massive impact on the entire field. The paper attempts to discuss the relationship between artificial intelligence and computer animation. Not only does the paper elaborate on the related applications of artificial intelligence in various subfields of computer animation, but it also analyzes existing problems and future development trends. The research indicates that AI has achieved significant breakthroughs in computer animation, such as auto-generation of animations, real-time character driving, and emotionally responsive animation creation. However, it also faces challenges like handling interactions in complex scenarios, maintaining realism, and animating high-level abstract concepts. Despite these challenges, it is believed that in the future, AI will further propel the development of computer animation, aiding creators in producing animations that are more vibrant, intricate, and personalized.Applied and Computational Engineering00The research indicates that AI has achieved significant breakthroughs in computer animation, such as auto-generation of animations, real-time character driving, and emotionally responsive animation creation, but it also faces challenges like handling interactions in complex scenarios, maintaining realism, and animating high-level abstract concepts.Applied and Computational Engineering['Zhihong Huang']2024-02-21T00:00:00https://www.semanticscholar.org/paper/6e9dca0a1113b439fb6ff75f59accf6298fd8770 e4657bf56e759ca990e662908d71af2650b3b6adPancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.Frontiers in Oncology460A concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications is provided.Frontiers in Oncology['Guohua Zhao', 'Xi Chen', 'Mengying Zhu', 'Yang Liu', 'Yue Wang']2024-02-21T00:00:00https://www.semanticscholar.org/paper/e4657bf56e759ca990e662908d71af2650b3b6ad e27d4a3019a674ed4064b1ca04d67c1ad478908cThis study examines the implications of Artificial Intelligence (AI) in enhancing educational methodologies at the higher secondary level, focusing on its impact on student engagement and learning outcomes, the experiences and challenges faced by educators in integrating AI tools, and the broader ethical and privacy considerations. Utilizing a qualitative methodology based on interviews with educators and students, the research aims to explore the nuanced perspectives on the role of AI in education, identifying both the potential benefits and drawbacks. Initial findings highlight the positive effects of AI on personalized learning experiences alongside significant challenges related to resource limitations and the need for comprehensive support for educators. Moreover, concerns about ethical implications and data privacy emerge as critical issues requiring careful management. The study concludes that while AI offers transformative potential for educational practices, ensuring effective integration, addressing ethical concerns, and preparing students for future technological challenges is essential for leveraging AI's full benefits in the educational sector.Journal of Education and Social Studies00While AI offers transformative potential for educational practices, ensuring effective integration, addressing ethical concerns, and preparing students for future technological challenges is essential for leveraging AI's full benefits in the educational sector.Journal of Education and Social Studies['Muhammad Azeem Sarwar', 'Ms Saima', 'Afshan Gul']2024-02-21T00:00:00https://www.semanticscholar.org/paper/e27d4a3019a674ed4064b1ca04d67c1ad478908c 4d97eefb9efbc39f91922337a90403eb88c2ace7Recently, the increased use of artificial intelligence in healthcare has significantly changed the developments in the field of medicine. Medical centres have adopted AI applications and used it in many applications to predict disease diagnosis and reduce health risks in a predetermined way. In addition to Artificial Intelligence (AI) techniques for processing data and understanding the results of this data, Explainable Artificial Intelligence (XAI) techniques have also gained an important place in the healthcare sector. In this study, reliable and explainable artificial intelligence studies in the field of healthcare were investigated and the blockchain framework, one of the latest technologies in the field of reliability, was examined. Many researchers have used blockchain technology in the healthcare industry to exchange information between laboratories, hospitals, pharmacies, and doctors and to protect patient data. In our study, firstly, the studies whose keywords were XAI and Trustworthy Artificial Intelligence were examined, and then, among these studies, priority was given to current articles using Blockchain technology. Combining the existing methods and results of previous studies and organizing these studies, our study presented a general framework obtained from the reviewed articles. Obtaining this framework from current studies will be beneficial for future studies of both academics and scientists.International Conference on Information Technology310This study investigated reliable and explainable artificial intelligence studies in the field of healthcare and the blockchain framework, one of the latest technologies in the field of reliability, was examined and a general framework obtained was presented.2024 28th International Conference on Information Technology (IT)['Kübra Arslanoğlu', 'Mehmet Karaköse']2024-02-21T00:00:00https://www.semanticscholar.org/paper/4d97eefb9efbc39f91922337a90403eb88c2ace7 d31b721ae712ce9937ec534e89a9db24ca6a4548This paper examines the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) in improving legal advisory services within the Montenegrin banking industry. It explores the vectorization of regulatory documents using ADA-2 embedding model, the storage and management of these vectorized forms in Chroma DB, and the utilization of GPT-4 for processing relevant documents to generate user responses, providing insights into the use of artificial intelligence (AI) for legal advisement and financial education.International Conference on Information Technology110This paper explores the vectorization of regulatory documents using ADA-2 embedding model, the storage and management of these vectorized forms in Chroma DB, and the utilization of GPT-4 for processing relevant documents to generate user responses, providing insights into the use of artificial intelligence for legal advisement and financial education.2024 28th International Conference on Information Technology (IT)['Ivan Bošković', 'Vladan Tabaš']2024-02-21T00:00:00https://www.semanticscholar.org/paper/d31b721ae712ce9937ec534e89a9db24ca6a4548 d31566d338ed9f7b9934b30c83400fefe947964ePurpose: The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) and machine learning (ML) play a pivotal role in optimizing supply chain processes by enhancing demand forecasting accuracy through sophisticated algorithms. They streamline inventory management by predicting demand patterns and automating replenishment tasks, leading to reduced stockouts and excess inventory. AI-powered analytics enable real-time insights into supply chain performance, identifying bottlenecks and inefficiencies for proactive decision-making. Unique Contribution to Theory, Practice and Policy: Theory of technology acceptance model (TAM), resource-based view (RBV) theory & dynamic capabilities theory may be used to anchor future studies on analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes. Encourage supply chain stakeholders to adopt blockchain solutions for enhanced transparency, traceability, and efficiency. Advocate for regulatory frameworks that promote the adoption of blockchain technology in supply chains while addressing concerns related to data privacy, interoperability, and standardization.International journal of supply chain management170This study looked into already published studies and reports as the data was easily accessed through online journals and libraries and may be used to anchor future studies on analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes.International Journal of Supply Chain Management['Jackson Mwangi']2024-02-21T00:00:00https://www.semanticscholar.org/paper/d31566d338ed9f7b9934b30c83400fefe947964e 0eb41f48563a145bafea7de8719703c020f5f622Since the application of artificial intelligence in games, a variety of different games have come out, and more and more people consume their rest time by playing games as entertainment. This paper mainly studies the influence of artificial intelligence technology on the immersion of game players and its reflection on the development of games. The purpose of the research is to prove how artificial intelligence technology has an impact on the immersion of game players and what aspects of the game. Mainly through the analysis of the relationship between artificial intelligence and game development, peoples views on the application of artificial intelligence in games and feedback to study. This paper finds that artificial intelligence technology has a very positive and positive impact on the immersion of game players and still has a profound impact on the future development of games.Applied and Computational Engineering00It is found that artificial intelligence technology has a very positive and positive impact on the immersion of game players and still has a profound impact on the future development of games.Applied and Computational Engineering['Hongting Lin']2024-02-21T00:00:00https://www.semanticscholar.org/paper/0eb41f48563a145bafea7de8719703c020f5f622 3cd6a7deda7df1ce2a9e81de65870ed24a462f7enpj Digit. Medicine380A cost-effectiveness analysis in a nationwide diabetic retinopathy screening program in China, comprising 251,535 participants with diabetes over 30 years, found the most cost-effective AI model exhibited higher sensitivity and lower specificity than the status quo.NPJ Digital Medicine['Yueye Wang', 'Chi Liu', 'Wenyi Hu', 'L. Luo', 'Danli Shi', 'Jian Zhang', 'Qiuxia Yin', 'Lei Zhang', 'Xiaotong Han', 'Mingguang He']2024-02-21T00:00:00https://www.semanticscholar.org/paper/3cd6a7deda7df1ce2a9e81de65870ed24a462f7e f7bdac3442a5a2d6f04bf8f4eb8ad341c07760e3The emergence of artificial intelligence (AI) in academia has prompted various debates on the uses, threats, and limitations of tools that can create text for numerous academic purposes. Critics argue that these advancements may provide opportunities for cheating and plagiarism and even replace the art of writing entirely. To reclaim the creativity and depth that academic writing holds, we propose both an innovative approach to safeguard the creativity and depth of academic writing and a scaffolded way to enhance success in terms of authenticity for the author and, by extension, meaning for the reader. This novel conceptual algorithmic trickle filter model aims to inform successful academic writing and embody the writer’s agency—a task too sophisticated for current AI tools. Our model provides a scaffolded decision-making process in a highly personal, flexible, and iterative individual writing development tool applied in a health-conscious way. We position this model as a step towards a pedagogic paradigm shift in reclaiming academic writing that, rather than competing with AI, doubles down on the personal self-evaluative aspects that academic writing offers both author and reader. Brock Education: a Journal of Educational Research and Practice00This model provides a scaffolded decision-making process in a highly personal, flexible, and iterative individual writing development tool applied in a health-conscious way that doubles down on the personal self-evaluative aspects that academic writing offers both author and reader.Brock Education Journal['Donna Poade', 'Russell M. Crawford']2024-02-21T00:00:00https://www.semanticscholar.org/paper/f7bdac3442a5a2d6f04bf8f4eb8ad341c07760e3 a9e88b8d4e0565889e8ff235dd1af597f1a04146Humanities and Social Sciences Communications90The aim of the study is a comprehensive view of the topic of social impacts of the use of artificial intelligence and provides a basis for further discussions and research in this important area, urging collaboration among technical experts, ethicists, lawyers, and social scientists.Humanities and Social Sciences Communications['R. Rakowski', 'Petra Kowaliková']2024-02-21T00:00:00https://www.semanticscholar.org/paper/a9e88b8d4e0565889e8ff235dd1af597f1a04146 460877163aa0c935dcc16c34c8465fcfccb317b0The integration of Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) into Decision Support Systems (DSS) is revolutionizing agriculture, enabling data-driven decision-making and ensuring the integrity of AI model results for enhanced productivity. To ensure the reliability of AI-driven insights, a pioneering approach is proposed, which employs Distributed Ledger Technology (DLT). The proposed system combines advanced AI algorithms with the security and transparency of DLT. By leveraging digital signatures, cryptographic hashing, and timestamping, this solution guarantees the immutability of data recorded in the ledger. This innovation fosters stakeholder trust, enabling independent verification of AI model outputs by policymakers, researchers, and farmers. The system’s accountability and transparency make it a valuable tool for promoting data interoperability and collaboration across diverse agricultural systems. This study outlines the system’s architecture, testing, and assessment, highlighting its role in preserving data integrity and ensuring accurate AI model outputs. This technology has the potential to revolutionize decision-making in AI-driven agriculture, addressing critical concerns around data reliability and promoting more efficient and sustainable practices.International Conference on Information Technology330The proposed system combines advanced AI algorithms with the security and transparency of DLT, and guarantees the immutability of data recorded in the ledger, to ensure the reliability of AI-driven insights.2024 28th International Conference on Information Technology (IT)['G. Gkogkos', 'Nikolaos Giakoumoglou', 'E. Pechlivani', 'K. Votis', 'D. Tzovaras']2024-02-21T00:00:00https://www.semanticscholar.org/paper/460877163aa0c935dcc16c34c8465fcfccb317b0 efe202701d5a4a4c7b65b31d0daf7e2d1ed2264bImplementation science : IS1150This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence and provides recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly.Implementation Science : IS['K. Trinkley', 'Ruopeng An', 'Anna M. Maw', 'Russell E. Glasgow', 'Ross C. Brownson']2024-02-21T00:00:00https://www.semanticscholar.org/paper/efe202701d5a4a4c7b65b31d0daf7e2d1ed2264b 3b6d2ba7b094436b7264fb0b08653841c4fa6bbaAutonomous Infrastructure, Management and Security002024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS)[]2024-02-21T00:00:00https://www.semanticscholar.org/paper/3b6d2ba7b094436b7264fb0b08653841c4fa6bba 74b586bcccc88e62f4cb8a5667fcf64f05a69f2cNEJM Catalyst57NEJM Catalyst['Aaron A. Tierney', 'Gregg Gayre', 'Brian Hoberman', 'Britt Mattern', 'Manuel A Ballesca', 'P. Kipnis', 'Vincent Liu', 'Kristine Lee']2024-02-21T00:00:00https://www.semanticscholar.org/paper/74b586bcccc88e62f4cb8a5667fcf64f05a69f2c 6938f9824c4772abf3125dbca9ada7315d53e812O uso da Inteligência Artificial (IA) na Polícia Militar do Paraná (PMPR) destaca o papel da tecnologia na transformação do trabalho policial e das relações com as comunidades. A PMPR, que conta com cerca de 20 mil policiais distribuídos em 31 batalhões, tem investido em Inteligência Artificial para aperfeiçoar suas operações. É exemplo o APP 190 – Emergência Paraná, câmeras de videovigilância com capacidade de reconhecimento facial, drones para patrulhamento aéreo, sistemas de reconhecimento facial para identificação de suspeitos e análise de padrões comportamentais. A Inteligência Artificial ajuda a prevenir, investigar e combater o crime e a melhorar a eficiência operacional e a qualidade dos serviços. A predição de crimes, a análise de dados históricos e atuais e a tomada de decisões estratégicas são áreas de aplicação da Inteligência Artificial. No entanto, a implementação da IA à PMPR levanta desafios éticos e legais, como garantir a transparência, a justiça e evitar a dependência excessiva da tecnologia. A integração da IA na PMPR representa um avanço significativo, mas devem ser implementadas medidas para reduzir os riscos e garantir que a tecnologia seja utilizada de forma ética e responsável. O equilíbrio entre a inovação tecnológica e os princípios fundamentais de justiça e equidade é fundamental para o sucesso desta evolução na segurança pública.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação['Maurício Nakashima']2024-02-21T00:00:00https://www.semanticscholar.org/paper/6938f9824c4772abf3125dbca9ada7315d53e812 f9cb0ccea97d6dfac25735ed1e4dbba8e91fa002The abstract commences by elucidating the foundational role of AI in reshaping traditional educational paradigms, emphasizing the advent of personalized learningexperiences tailored to individual student needs. Intelligent tutoring systems, driven by AI algorithms, are discussed for their ability to provide adaptive and customized support, fostering enhanced student engagement and performance. Educational analytics, powered by AI, is explored as a pivotal tool for extracting meaningful insights from vast datasets,informing evidence-based decision-making for educators and administrators.The review highlights notable case studies and successful implementations of AI in educational settingsacross various levels, from primary education to higher education and professional development. These case studies offer insights into the practical applications of AI, showcasing its effectiveness in optimizing teaching and learning outcomes.International Journal of Multidisciplinary Approach Research and Science321The foundational role of AI in reshaping traditional educational paradigms is elucidated, emphasizing the advent of personalized learning experiences tailored to individual student needs, and educational analytics, powered by AI, is explored as a pivotal tool for extracting meaningful insights from vast datasets.International Journal of Multidisciplinary Approach Research and Science['Sagnika Dash', 'Chandrasekhar Bhoi']2024-02-21T00:00:00https://www.semanticscholar.org/paper/f9cb0ccea97d6dfac25735ed1e4dbba8e91fa002 3ea141caee0a6d63136f4586818b860b2cc02adeEducation and Information Technologies : Official Journal of the IFIP technical committee on Education311Education and Information Technologies['Mohammed Airaj']2024-02-21T00:00:00https://www.semanticscholar.org/paper/3ea141caee0a6d63136f4586818b860b2cc02ade cd3a429c7cfb2bd4362e1b73d2f52364344a8aa4Medical Teacher10Medical teacher['Hongnan Ye']2024-02-21T00:00:00https://www.semanticscholar.org/paper/cd3a429c7cfb2bd4362e1b73d2f52364344a8aa4 c9f71a2c2a12f394020f8a7233d0500748448f8eEuropean Journal of Communication10European Journal of Communication['Paulo Nuno Vicente']2024-02-21T00:00:00https://www.semanticscholar.org/paper/c9f71a2c2a12f394020f8a7233d0500748448f8e f41d89190f5156c980c6f04b909163e357ee5cecScience China Information Sciences1740Science China Information Sciences['Yi Song', 'Xiaoyuan Xie', 'Baowen Xu']2024-02-21T00:00:00https://www.semanticscholar.org/paper/f41d89190f5156c980c6f04b909163e357ee5cec 50254f699129a1d57c40ef2b062f581bbc12854dStroke00Stroke['Lee H. Schwamm']2024-02-21T00:00:00https://www.semanticscholar.org/paper/50254f699129a1d57c40ef2b062f581bbc12854d 642701ad00e964c7886907c1ed8fcfa3ba85d955Multimedia tools and applications400Multimedia Tools and Applications['Apeksha Koul', 'Rajesh K. Bawa', 'Yogesh Kumar']2024-02-21T00:00:00https://www.semanticscholar.org/paper/642701ad00e964c7886907c1ed8fcfa3ba85d955 f9c4e9185e8c80d55abbd84d1eb4173026181261Annals of Operations Research540Annals of Operations Research['K. Sriprateep', 'Sarinya Sala-ngam', 'Y. Srithep', 'Surajet Khonjun', 'Paulina Golińska-Dawson', 'Thanatkij Srichok', 'N. Nanthasamroeng', 'R. Pitakaso', 'Sarayut Gonwirat', 'Peerawat Luesak']2024-02-21T00:00:00https://www.semanticscholar.org/paper/f9c4e9185e8c80d55abbd84d1eb4173026181261 b557a01bce903b3e7a99b400b1ac77cfd19de503The global significance of the steel industry as an economic cornerstone cannot be overstated, with its pivotal role in construction, automotive manufacturing, and pipe production. This paper investigates the transformative influence of deep learning, encompassing machine vision and artificial intelligence, on elevating performance standards within the steel industry. The industry’s critical contribution to manufacturing building materials, automotive components, and high-value energy and fluid transmission pipes underscores the need for continuous technological evolution. Machine vision and artificial intelligence have emerged as pivotal catalysts in the pursuit of precision data analysis and enhanced industrial performance. This research explores the escalating importance of these technologies, elucidating their substantial impact on refining industrial processes within the steel sector. Recognized as powerful instruments for progression and optimization, machine vision and artificial intelligence contribute significantly to the industry’s technological landscape. This study comprehensively reviews pertinent articles to delve into the myriad applications of machine vision and artificial intelligence in the steel industry. By scrutinizing the latest developments and applications, the paper aims to provide a thorough understanding of how these technologies are actively shaping the industry’s landscape. The findings underscore the instrumental role of deep learning in augmenting efficiency, fostering innovation, and ultimately advancing the standards of the steel industry on a global scale.CSI International Symposium on Artificial Intelligence and Signal Processing510This paper investigates the transformative influence of deep learning, encompassing machine vision and artificial intelligence, on elevating performance standards within the steel industry, and comprehensively reviews pertinent articles to delve into the myriad applications of machine vision and artificial intelligence in the steel industry.2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)['Hamed Aghapanah', 'Ali Saeeidi Rad', 'Reza Rasti']2024-02-21T00:00:00https://www.semanticscholar.org/paper/b557a01bce903b3e7a99b400b1ac77cfd19de503 bcbbd91de45bde3678b24acd1c114b3772154bebThis paper introduces an advanced approach in artificial intelligence (AI) that incorporates neuroscience-inspired attention mechanisms and distractor learning into artificial neural networks (ANNs). This novel method significantly reduces model size and RAM consumption during training, offering a more efficient and resource-effective solution for AI development. By emulating the human brain’s ability to process information and manage distractions, our approach not only addresses common AI challenges such as model collapse and bias amplification but also contributes to a reduction in computational resource requirements. This is particularly beneficial in scenarios where computing power and memory are limited, making AI more accessible and sustainable. Furthermore, the smaller model footprint and lower resource demand pave the way for broader applications of AI in various fields, including those with restricted hardware capabilities. This paper details the theoretical framework of this approach, its practical implementation, and the potential implications for future AI developments, emphasizing the balance between advanced AI capabilities and resource efficiency.CSI International Symposium on Artificial Intelligence and Signal Processing140An advanced approach in artificial intelligence that incorporates neuroscience-inspired attention mechanisms and distractor learning into artificial neural networks (ANNs) is introduced, offering a more efficient and resource-effective solution for AI development.2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP)['Michael Bidollahkhani', 'Maryam Raahemi', 'Pınar Haskul']2024-02-21T00:00:00https://www.semanticscholar.org/paper/bcbbd91de45bde3678b24acd1c114b3772154beb 0f6fe4206eae66e34699de5d6d1cf3e64ffc6db9The article below summarises the key features of a new agreement between the Council of the European Union and the European Parliament regarding proposals to regulate AI.Assessment and Development Matters00Assessment and Development Matters['Nigel Evans']2024-02-20T00:00:00https://www.semanticscholar.org/paper/0f6fe4206eae66e34699de5d6d1cf3e64ffc6db9 f1e7f44092840dc1d9e51bd40f80883cd0bf9635 The governance of artificial intelligence (AI) is at a historical juncture. Legislative acts, global treaties, export controls, and technical standards are now dominating the discourse over what used to be a predominantly market-driven space. Amidst all this frenzy, this paper explains why none of these projects will achieve ‘alignment’ of AI with the prospect of a sustainable model of production authentically committed to the rights and freedoms of people and communities. By reflecting on the role of law in consolidating the visions and logics of few multinationals in the global value chains of AI, it warns against the peril of regulating AI without looking at the methods and logistics of its material production. Following a detailed overview of the various (techno-)legal ways through which law enables the flow of materials, capital, and power from Global South to Global North, and from small players to lead firms, the paper concludes with some preliminary thoughts on a transformative agenda for the transnational regulation of infocomputational production.International Journal of Law and Information Technology21Int. J. Law Inf. Technol.['P. Terzis']2024-02-20T00:00:00https://www.semanticscholar.org/paper/f1e7f44092840dc1d9e51bd40f80883cd0bf9635 48af2b763c8391b67f9ebc89f5d3adb597cc8c33The article presents the results of a comparative analysis of regulatory requirements for expertise of clinical trials documentation, submitted for regulatory authority and ethic committees’ approval in EU member countries and Ukraine, outlining the main trends, considering the updated Regulation (EU) No 536/2014, which came into effect on January 31, 2023. Among the positive changes are simplification of safety reporting requirements, use of artificial intelligence in the process of clinical trials documentation examination for obtaining regulatory authority and ethic commission approval, introduction of a single portal for submitting materials for clinical trials, and functioning of database for the submission and review of initial Clinical Trial Application documents and obtaining authorization within the EU to facilitate the interaction between applicants and regulatory authority are highlighted. To harmonize Ukraine’s regulatory requirements with EU legislation, it is advisable to use a single portal for data exchange and document submission for applicants in regards to clinical trials, regulatory authority and local ethics committees. This will expedite the examination process of clinical trial documentation and simplify the monitoring of document review progress.Pharmacia350To harmonize Ukraine’s regulatory requirements with EU legislation, it is advisable to use a single portal for data exchange and document submission for applicants in regards to clinical trials, regulatory authority and local ethics committees.Pharmacia['L. Hala', 'Oleksandr Nabok']2024-02-20T00:00:00https://www.semanticscholar.org/paper/48af2b763c8391b67f9ebc89f5d3adb597cc8c33 43cc3ab7808302cbba97993fb6f64b6c090da78a00['Daniel Cash']2024-02-20T00:00:00https://www.semanticscholar.org/paper/43cc3ab7808302cbba97993fb6f64b6c090da78a f0714726ffa92fd46df284c18e880521964bd30f00['Jean-Baptiste Poulle', 'Arut Kannan', 'Nicolas Spitz', 'Sandra Kahn', 'Anastasia Sotiropoulou']2024-02-20T00:00:00https://www.semanticscholar.org/paper/f0714726ffa92fd46df284c18e880521964bd30f cb4663899e5bfe45511ba7df7aa8c3911512316bReview of Agricultural, Food and Environmental Studies260Review of Agricultural, Food and Environmental Studies['Marie Hrabanski', 'Sidonie Verdeil', 'Antoine Ducastel']2024-02-20T00:00:00https://www.semanticscholar.org/paper/cb4663899e5bfe45511ba7df7aa8c3911512316b 66f581ae106ac28faebd3d54a01edd53cf91da30Generative AI's expanding footprint across numerous industries has led to both excitement and increased scrutiny. This paper delves into the unique security challenges posed by Generative AI, and outlines potential research directions for managing these risks.arXiv.org792This paper delves into the unique security challenges posed by Generative AI, and outlines potential research directions for managing these risks.ArXiv['Banghua Zhu', 'Norman Mu', 'Jiantao Jiao', 'David Wagner']2024-02-20T00:00:00https://www.semanticscholar.org/paper/66f581ae106ac28faebd3d54a01edd53cf91da30 7f24d013e1c61d23e2144b37cf432ea8dd923be7Choreography creation is a multimodal endeavor, demanding cognitive abilities to develop creative ideas and technical expertise to convert choreographic ideas into physical dance movements. Previous endeavors have sought to reduce the complexities in the choreography creation process in both dimensions. Among them, non-AI-based systems have focused on reinforcing cognitive activities by helping analyze and understand dance movements and augmenting physical capabilities by enhancing body expressivity. On the other hand, AI-based methods have helped the creation of novel choreographic materials with generative AI algorithms. The choreography creation process is constrained by time and requires a rich set of resources to stimulate novel ideas, but the need for iterative prototyping and reduced physical dependence have not been adequately addressed by prior research. Recognizing these challenges and the research gap, we present an innovative AI-based choreography-support system. Our goal is to facilitate rapid ideation by utilizing a generative AI model that can produce diverse and novel dance sequences. The system is designed to support iterative digital dance prototyping through an interactive web-based user interface that enables the editing and modification of generated motion. We evaluated our system by inviting six choreographers to analyze its limitations and benefits and present the evaluation results along with potential directions for future work.IUI Companion441An innovative AI-based choreography-support system designed to support iterative digital dance prototyping through an interactive web-based user interface that enables the editing and modification of generated motion.{'pages': '11-17'}['Yimeng Liu', 'Misha Sra']2024-02-20T00:00:00https://www.semanticscholar.org/paper/7f24d013e1c61d23e2144b37cf432ea8dd923be7 56df62407ba0878d34493f12a6ece8634ee0db9eTrustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases and communicate potential risks of harm. Abundant guidance exists on how to achieve transparency for medical AI products, but it is unclear whether publicly available information adequately informs about their risks. To assess this, we retrieved public documentation on the 14 available CE-certified AI-based radiology products of the II b risk category in the EU from vendor websites, scientific publications, and the European EUDAMED database. Using a self-designed survey, we reported on their development, validation, ethical considerations, and deployment caveats, according to trustworthy AI guidelines. We scored each question with either 0, 0.5, or 1, to rate if the required information was “unavailable”, “partially available,” or “fully available.” The transparency of each product was calculated relative to all 55 questions. Transparency scores ranged from 6.4% to 60.9%, with a median of 29.1%. Major transparency gaps included missing documentation on training data, ethical considerations, and limitations for deployment. Ethical aspects like consent, safety monitoring, and GDPR-compliance were rarely documented. Furthermore, deployment caveats for different demographics and medical settings were scarce. In conclusion, public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks. We call on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health.Frontiers in Digital Health501Public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks, and is called on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health.Frontiers in Digital Health['Jana Fehr', 'Brian Citro', 'Rohit Malpani', 'Christoph Lippert', 'V. Madai']2024-02-20T00:00:00https://www.semanticscholar.org/paper/56df62407ba0878d34493f12a6ece8634ee0db9e 3f628e310f2d50c0746c2d38e8547181765a17ee02['Branka Panic', 'Paige Arthur']2024-02-20T00:00:00https://www.semanticscholar.org/paper/3f628e310f2d50c0746c2d38e8547181765a17ee 48f88aa4a854b0f5d5b227a048233c8f0e3ecfb3The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety. While considerable strides have been made in addressing AI alignment challenges, existing methodologies primarily focus on technical facets, often neglecting the intricate sociotechnical nature of AI systems, which can lead to a misalignment between the development and deployment contexts. To this end, we posit a new problem worth exploring: Incentive Compatibility Sociotechnical Alignment Problem (ICSAP). We hope this can call for more researchers to explore how to leverage the principles of Incentive Compatibility (IC) from game theory to bridge the gap between technical and societal components to maintain AI consensus with human societies in different contexts. We further discuss three classical game problems for achieving IC: mechanism design, contract theory, and Bayesian persuasion, in addressing the perspectives, potentials, and challenges of solving ICSAP, and provide preliminary implementation conceptions.arXiv.org1000This work proposes a new problem worth exploring: Incentive Compatibility Sociotechnical Alignment Problem (ICSAP), and discusses three classical game problems for achieving IC: mechanism design, contract theory, and Bayesian persuasion, in addressing the perspectives, potentials, and challenges of solving ICSAP.ArXiv['Zhaowei Zhang', 'Fengshuo Bai', 'Mingzhi Wang', 'Haoyang Ye', 'Chengdong Ma', 'Yaodong Yang']2024-02-20T00:00:00https://www.semanticscholar.org/paper/48f88aa4a854b0f5d5b227a048233c8f0e3ecfb3 2a1c35fc6f8baa7bc9964a8525d9b534a59eb2f6In the ever-evolving regulatory environment, adhering to reporting standards poses a significant hurdle for organizations spanning diverse sectors. Negotiating the intricacies of regulatory obligations necessitates innovative approaches. This document delves into the utilization of Artificial Intelligence (AI) and Machine Learning (ML) methodologies to bolster the precision and efficacy of reporting procedures. Through the integration of AI/ML, entities can streamline data analysis, detect patterns, and uphold compliance with regulatory frameworks. This research probes into the potential advantages, obstacles, and optimal strategies linked with the incorporation of AI/ML technologies into reporting infrastructures. Drawing upon a thorough examination of pertinent literature and case studies, valuable insights are offered to aid organizations in proficiently leveraging AI/ML to navigate regulatory intricacies and attain accurate reporting results.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This research probes into the potential advantages, obstacles, and optimal strategies linked with the incorporation of AI/ML technologies into reporting infrastructures to bolster the precision and efficacy of reporting procedures.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Dr. Sreeram Mullankandy']2024-02-20T00:00:00https://www.semanticscholar.org/paper/2a1c35fc6f8baa7bc9964a8525d9b534a59eb2f6 2c2ba4099d4812727f8aec178afca311f46f54f2Optimization is a universal quest, reflecting the basic human need to do better. Improved optimizations of energy‐efficiency, safety, robustness, and other criteria in engineered systems would bring incalculable societal benefits. But, fundamental challenges of scale and complexity keep many such real‐world optimization needs beyond reach. This article describes The Institute for Learning‐enabled Optimization at Scale (TILOS), an NSF AI Research Institute for Advances in Optimization that aims to overcome these challenges in three high‐stakes use domains: chip design, communication networks, and contextual robotics. TILOS integrates foundational research, translation, education, and broader impacts toward a new nexus of optimization, AI, and data‐driven learning. We summarize central challenges, early progress, and futures for the institute.The AI Magazine50The Institute for Learning‐enabled Optimization at Scale (TILOS), an NSF AI Research Institute for Advances in Optimization that aims to overcome challenges in three high‐stakes use domains: chip design, communication networks, and contextual robotics, is described.AI Mag.['Andrew B. Kahng', 'Arya Mazumdar', 'Jodi Reeves', 'Yusu Wang']2024-02-20T00:00:00https://www.semanticscholar.org/paper/2c2ba4099d4812727f8aec178afca311f46f54f2 70f2988a71d7fc0fe0e861e7ad77e2dccbd7571cAI &amp; SOCIETY240It is demonstrated how the language and reasonings that academics, developers, consumers, marketers, and journalists deploy to accept or reject AI as authentic intelligence has far-reaching bearing on how the authors understand their human intelligence and condition.AI &amp; SOCIETY['Siri Beerends', 'Ciano Aydin']2024-02-20T00:00:00https://www.semanticscholar.org/paper/70f2988a71d7fc0fe0e861e7ad77e2dccbd7571c d3612937ab80d7765c4ac1597e626e757862d9fdIn complex industrial and chemical process control rooms, effective decision-making is crucial for safety and effi- ciency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influ- ence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye- tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effec- tiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide valuable insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time. These predictions enable the development of more effective intervention strategies.arXiv.org330The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influ- ence diagrams, a hidden Markov model, and deep reinforcement learning.ArXiv['Ammar N. Abbas', 'Chidera W. Amazu', 'Joseph Mietkiewicz', 'Houda Briwa', 'Andres Alonzo Perez', 'Gabriele Baldissone', 'M. Demichela', 'Georgios G. Chasparis', 'John D. Kelleher', 'M. Leva']2024-02-20T00:00:00https://www.semanticscholar.org/paper/d3612937ab80d7765c4ac1597e626e757862d9fd 13d95e79c61d1cc2950072a62b72f6c0c43c434fArtikel ini membahas mengenai permasalahan penggunaan Artificial Intelligence (AI) untuk pembelajaran di kalangan mahasiswa Sekolah Tinggi Ilmu Pengetahuan Alam (STIT) Tarbiyah Pemalang. Dalam konteks ini, penelitian mengeksplorasi pola penggunaan AI, kesadaran akan risiko ketergantungan, dan dampaknya terhadap pengembangan keterampilan pribadi, khususnya pemikiran kritis dan analitis. Temuan menunjukkan bahwa penggunaan AI dikalangan mahasiswa STIT Pemalang mengalami banyak problematika, antara lain plagiasi, menurunkan berpikir kritis mahasiswa dan keterampilan yang menurun. Penelitian ini menggunakan metode wawancara dan observasi pada mahasiswa STIT Pemalang. Artikel ini memberikan gambaran singkat tentang dampak integrasi AI dalam pembelajaran dan menekankan perlunya pendekatan yang bijaksana untuk memastikan manfaat optimal tanpa mengorbankan pengembangan keterampilan dan intelektual mahasiswa.  Madaniyah00Madaniyah['Lukman Lukman', 'Riska Agustina', 'Rihadatul Aisy']2024-02-20T00:00:00https://www.semanticscholar.org/paper/13d95e79c61d1cc2950072a62b72f6c0c43c434f f92980559faa977e9c5f61bef810d8d24a54b2fdIncreasing diversity in educational settings is challenging in part due to the lack of access to resources for non-traditional learners in remote communities. Post-pandemic platforms designed specifically for remote and hybrid learning---supporting team-based collaboration online---are positioned to bridge this gap. Our work combines the use of these new platforms with co-creation and collaboration tools for AI assisted remote Work-Integrated-Learning (WIL) opportunities, including efforts in community and with the public library system. This paper outlines some of our experiences to date, and proposes methods to further integrate AI education into community-driven applications for remote WIL.Proceedings of the AAAI Symposium Series90This work combines the use of these new platforms with co-creation and collaboration tools for AI assisted remote Work-Integrated-Learning opportunities, including efforts in community and with the public library system.ArXiv['Derek Jacoby', 'Saiph Savage', 'Yvonne Coady']2024-02-20T00:00:00https://www.semanticscholar.org/paper/f92980559faa977e9c5f61bef810d8d24a54b2fd 73f52ae2bdda4339ca16c326526ff64db820a108AI-based virtual assistants are increasingly used to support daily ideation tasks. The values or bias present in these agents can influence output in hidden ways. They may also affect how people perceive the ideas produced with these AI agents and lead to implications for the design of AI-based tools. We explored the effects of AI agents with different values on the ideation process and user perception of idea quality, ownership, agent competence, and values present in the output. Our study tasked 180 participants with brainstorming practical solutions to a set of problems with AI agents of different values. Results show no significant difference in self-evaluation of idea quality and perception of the agent based on value alignment; however, ideas generated reflected the AI's values and feeling of ownership is affected. This highlights an intricate interplay between AI values and human ideation, suggesting careful design considerations for future AI-supported brainstorming tools.CHI Extended Abstracts390Results show no significant difference in self-evaluation of idea quality and perception of the agent based on value alignment, but an intricate interplay between AI values and human ideation is highlighted, suggesting careful design considerations for future AI-supported brainstorming tools.{'pages': '152:1-152:11'}['Alicia Guo', 'Pat Pataranutaporn', 'Pattie Maes']2024-02-20T00:00:00https://www.semanticscholar.org/paper/73f52ae2bdda4339ca16c326526ff64db820a108 7e13cdf1d1b17611ccd0cb7440cd49c81bcdae70This article examines the expansive growth of ChatGPT and the implications for large-scale test design. The authors contend that the impressive test simulation results observed by Chat-GPT undergird ongoing construct validity concerns with student testing. In order to address these challenges, a set of strategies is proposed that emphasises authentic assessment, the importance of human elements in traditional paper-and-pencil questions, and the controversial issue of the stakes ascribed to test results. Collectively, these approaches are meant to help test developers more carefully consider existing limitations within traditional standardised and large-scale assessment programs. Ultimately, test design reforms that enhance validity are increasingly needed to address the challenges posed by AI applications.Assessment and Development Matters70It is contended that the impressive test simulation results observed by Chat-GPT undergird ongoing construct validity concerns with student testing, and a set of strategies is proposed that emphasises authentic assessment, the importance of human elements in traditional paper-and-pencil questions, and the controversial issue of the stakes ascribed to test results.Assessment and Development Matters['Louis Volante', 'Christopher DeLuca']2024-02-20T00:00:00https://www.semanticscholar.org/paper/7e13cdf1d1b17611ccd0cb7440cd49c81bcdae70 fbb0b1cc4f2cd79b25ec84f2f8ea07ca8789c7abIn the modern music industry, AI music generators have gained particular importance. The use of AI greatly simplifies the creation of polyphony. In addition, it can increase student motivation and interest.This study focuses on the AI‐assisted creation of polyphonic music. The purpose of this study is to determine how creating polyphony through the Deep Bach model impacts the academic motivation of music university students.Achieving this goal is possible by conducting an experimental training program based on the use of the above‐mentioned model.The results show that students in the experimental group have higher motivation than the control group participants. Therefore, AI‐based music creation has the potential to become a new trend in music education.The findings of this study can be useful for music education experts, providing empirical data on the effectiveness of AI in music education. The use of the data can ultimately improve the learning process. Future research can focus on developing alternative AI models as well as investigating their effectiveness in music education.Journal of Computer Assisted Learning210The results show that students in the experimental group have higher motivation than the control group participants, and AI‐based music creation has the potential to become a new trend in music education.Journal of Computer Assisted Learning['Na Yuan']2024-02-20T00:00:00https://www.semanticscholar.org/paper/fbb0b1cc4f2cd79b25ec84f2f8ea07ca8789c7ab f2109af63954e544829b639ca827e04f9f27cbb2This article explores how Artificial Intelligence (AI) can enhance psychometric testing in the workplace. By leveraging natural language processing, machine learning algorithms, and data analytics, AI-driven psychometric testing offers greater efficiency, accuracy, and fairness. It discusses the potential of AI to revolutionise traditional testing methods and highlights its benefits for candidate selection, talent management, and employee development.Assessment and Development Matters50The potential of AI to revolutionise traditional testing methods and highlights its benefits for candidate selection, talent management, and employee development are discussed.Assessment and Development Matters['Niamh Herbert']2024-02-20T00:00:00https://www.semanticscholar.org/paper/f2109af63954e544829b639ca827e04f9f27cbb2 b26a4ae15be637c3df3af1a5add9dbd1c3bf40bdDetecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.Frontiers in Environmental Science1652This review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment.Frontiers in Environmental Science['S. M. Popescu', 'Sheikh Mansoor', 'O. A. Wani', 'S. S. Kumar', 'Vikas Sharma', 'Arpita Sharma', 'Vivak M. Arya', 'M. B. Kirkham', 'Deyi Hou', 'N. Bolan', 'Yong Suk Chung']2024-02-20T00:00:00https://www.semanticscholar.org/paper/b26a4ae15be637c3df3af1a5add9dbd1c3bf40bd 3a5b1aa13c4a367ec4fc37a602d3fbfda89192dbThe use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.Frontiers in Digital Health432This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.Frontiers in Digital Health['Henry J. Paiste', 'Ryan C. Godwin', 'Andrew D. Smith', 'Dan E. Berkowitz', 'Ryan L. Melvin']2024-02-20T00:00:00https://www.semanticscholar.org/paper/3a5b1aa13c4a367ec4fc37a602d3fbfda89192db 3f8330a0e322879b18d7371acbf23d430153c3bfHepatology International781It is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.Hepatology international['Peng Zhang', 'Chaofei Gao', 'Yifei Huang', 'Xiangyi Chen', 'Zhuoshi Pan', 'Lan Wang', 'Di Dong', 'Shao Li', 'Xiaolong Qi']2024-02-20T00:00:00https://www.semanticscholar.org/paper/3f8330a0e322879b18d7371acbf23d430153c3bf e21952c2a220934d0944fd232c5476e46fd50e5ePURPOSE To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. METHODS The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. RESULTS Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all). CONCLUSION The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. CLINICAL SIGNIFICANCE This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.Diagnostic and Interventional Radiology01It is indicated that AI-specific guidelines are not commonly encouraged and mandated by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement.Diagnostic and interventional radiology['Burak Koçak', 'Ali Keleş', 'Fadime Köse']2024-02-20T00:00:00https://www.semanticscholar.org/paper/e21952c2a220934d0944fd232c5476e46fd50e5e 73753496611eeab184ccd7eee86e8f52422c3afbThe increase in digital evidence, especially in cases involving Indecent Images of Children (IIOC), presents a pressing challenge for law enforcement agencies. In this article, we discuss two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI. The two main types are of Data-Driven Model (DDM) age classification and ModelBased Reasoning (MBR), and in this article, examples for both are provided and discussed in the contents of IIOC investigations.Assessment and Development Matters40Two of the most prominent types of Artificial Intelligence (AI) and how they can be used in digital forensic processes are discussed, providing examples, and highlighting potential challenges that are likely to be experienced in developing and adopting AI.Assessment and Development Matters['Simon Parkinson', 'Saad Khan']2024-02-20T00:00:00https://www.semanticscholar.org/paper/73753496611eeab184ccd7eee86e8f52422c3afb 2cd89837a907704915ccc57d9c9a8438829d6cdcArtificial Intelligence is evolving at a breathtaking pace. It offers huge opportunities, yet creates significant challenges to virtually every organisation. Even the leaders of the companies which are at the forefront of unleashing its capabilities seem unsure as to its power, and governmental authorities are unsure about how – or even if – Artificial Intelligence should be controlled. Against this uncertain backdrop, all organisations need urgently to be defining – or revising – their competencies, so that these opportunities can practically be maximised, and the threats managed. This article explores what competencies might be relevant for all organisations facing up to a new world of AI.Assessment and Development Matters00This article explores what competencies might be relevant for all organisations facing up to a new world of AI.Assessment and Development Matters['Stewart Wright']2024-02-20T00:00:00https://www.semanticscholar.org/paper/2cd89837a907704915ccc57d9c9a8438829d6cdc c845ccd398f8ea435bc9dd17f2d850287668a78dSimple Summary The field of artificial intelligence (AI) is quickly becoming recognized for its potential to significantly improve medicine. AI is still in its infancy when it comes to treating Chronic Myeloid Leukemia (CML), which was once thought to be an easily treated cancer until TKIs were introduced and significantly increased patient survival. Notably, preliminary trial results are intriguing and promising in terms of AI’s performance and flexibility to be used in many scenarios. With a general focus that extends beyond Machine Learning (ML) and embraces the broader AI area, in this review we describe the state of the art of AI applications in the field of CML, including the methods and goals. We also take advantage of the occasion to talk about the primary dangers and crucial issues that AI needs to address, particularly in light of the crucial role that the “human” element plays and how important it is in this field. Abstract Artificial intelligence (AI) is emerging as a discipline capable of providing significant added value in Medicine, in particular in radiomic, imaging analysis, big dataset analysis, and also for generating virtual cohort of patients. However, in coping with chronic myeloid leukemia (CML), considered an easily managed malignancy after the introduction of TKIs which strongly improved the life expectancy of patients, AI is still in its infancy. Noteworthy, the findings of initial trials are intriguing and encouraging, both in terms of performance and adaptability to different contexts in which AI can be applied. Indeed, the improvement of diagnosis and prognosis by leveraging biochemical, biomolecular, imaging, and clinical data can be crucial for the implementation of the personalized medicine paradigm or the streamlining of procedures and services. In this review, we present the state of the art of AI applications in the field of CML, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field. The present scooping review spans on publications reported in Pubmed from 2003 to 2023, and resulting by searching “chronic myeloid leukemia” and “artificial intelligence”. The time frame reflects the real literature production and was not restricted. We also take the opportunity for discussing the main pitfalls and key points to which AI must respond, especially considering the critical role of the ‘human’ factor, which remains key in this domain.Cancers840The state of the art of AI applications in the field of CML is presented, describing the techniques and objectives, and with a general focus that goes beyond Machine Learning (ML), but instead embraces the wider AI field.Cancers['Simona Bernardi', 'Mauro Vallati', 'Roberto Gatta']2024-02-20T00:00:00https://www.semanticscholar.org/paper/c845ccd398f8ea435bc9dd17f2d850287668a78d 8e224d662e50e2950026664c03f11896ba851508In this essay, we identify several themes of the digital business transformation, with a particular focus on the economy‐wide impacts of artificial intelligence and digital platforms. In doing so, we highlight specific industries, beyond just the high‐profile “Big Tech” firms, where the digital business revolution is having, or promises to have, significant impact. The papers in this special issue (flagged with bold font below) provide a deeper analysis of the themes and applications we touch on here.Social Science Research Network480This essay identifies several themes of the digital business transformation, with a particular focus on the economy‐wide impacts of artificial intelligence and digital platforms, and highlights specific industries, beyond just the high‐profile “Big Tech” firms, where the digital business revolution is having significant impact.SSRN Electronic Journal['Hanna Halaburda', 'Jeffrey Prince', 'D. Daniel Sokol', 'Feng Zhu']2024-02-20T00:00:00https://www.semanticscholar.org/paper/8e224d662e50e2950026664c03f11896ba851508 0a0b41291ee0911e4e3b1f3fbe7c9947b9152a04This article explores essential managerial competencies in the context of integrating artificial intelligence (AI) into the modern workplace. Key skills include technical literacy for informed decision-making, ethical consideration to address biases, change management for seamless AI adoption, and effective communication to bridge technical and non-technical stakeholders. These competencies position managers as pivotal contributors to successful AI management, organisational resilience, and innovation.Assessment and Development Matters10Assessment and Development Matters['Ben Moore']2024-02-20T00:00:00https://www.semanticscholar.org/paper/0a0b41291ee0911e4e3b1f3fbe7c9947b9152a04 1dbfaa55758f861d050b1bc62806dd09d4423e9bThe use of artificial intelligence, as well as the digital transformation and the incorporation of advanced technologies in the field of the justice administration is a fact that has left no one indifferent. The progress of artificial intelligence-based technologies, which are allowing the automation of a multitude of tasks that are currently performed by different operators and legal professionals, will cause that many of these services and routine tasks will be developed by machines, with the consequent loss of prominence of these professional groups. In this context, what is the future for the legal professionals who represent the citizen in Courts? It seems difficult to imagine the need for the figure of Court's Procurator, especially when many other operators and legal professionals could be replaced by intelligent machines. For this reason, this paper will try to analyze the impact that these new technologies will have on the functions that these professionals perform daily on behalf of the parties before the courts. El uso de la inteligencia artificial, así como la transformación digital y la incorporación de tecnologías avanzadas en el ámbito de la Administración de Justicia es un hecho que no ha dejado indiferente a nadie. El avance de las tecnologías basadas en la inteligencia artificial, que están permitiendo la automatización de multitud de tareas que actualmente son realizadas por diferentes operadores y profesionales jurídicos, provocará que muchos de estos servicios y tareas rutinarias sean desarrollados por máquinas, con la consiguiente pérdida de protagonismo de estos colectivos profesionales. En este contexto, ¿cuál es el futuro de los profesionales del Derecho que representan al ciudadano ante los Tribunales? Parece difícil imaginar la necesidad de la figura del Procurador de los Tribunales, máxime cuando muchos otros operadores y profesionales del Derecho podrían ser sustituidos por máquinas inteligentes. Por ello, este trabajo tratará de analizar el impacto que estas nuevas tecnologías tendrán en las funciones que estos profesionales desempeñan diariamente en representación de las partes ante los tribunales.Oñati Socio-Legal Series390Oñati Socio-Legal Series['Xabier Fernandez Galarreta']2024-02-20T00:00:00https://www.semanticscholar.org/paper/1dbfaa55758f861d050b1bc62806dd09d4423e9b e57a656cf2177d3bfe48ed06cbf1cfb8f9f59efcIn the 1950s and 1960s, in molecular biology, information technology was mainly applied to the molecular evolution of proteins and DNA, and later expanded to multiple fields such as sequence alignment, protein structure prediction, and gene splicing. Entering the 21st century, the completion of the Human Genome Project marks the arrival of the era of biomedical big data, providing a large amount of data for the application of artificial intelligence in this field. Especially in recent years, the continuous accumulation of medical data has pushed the application of artificial intelligence in the medical field to a broader and more practical level. This paper briefly introduces the applications of artificial intelligence in genomics, proteomics, transcriptomics, epigenetics, drug development, and other fields. I hope this review can clearly introduce which biomedical fields artificial intelligence can be applied to, and also promote doctors and related scholars to actively use artificial intelligence technology to solve specific biomedical problems.Medinformatics1120This paper briefly introduces the applications of artificial intelligence in genomics, proteomics, transcriptomics, epigenetics, drug development, drug development, and other fields and hopes this review can clearly introduce which biomedical fields artificial intelligence can be applied to, and promote doctors and related scholars to actively use artificial intelligence technology to solve specific biomedical problems.Medinformatics['Hao Lin']2024-02-20T00:00:00https://www.semanticscholar.org/paper/e57a656cf2177d3bfe48ed06cbf1cfb8f9f59efc efb25225d63b930960d1afdb63ebb4ccf677dfd5Hiding behind a smartphone screen, online dating applications provide a playground of opportunity for fraudsters and scammers. With ease of access to artificial intelligence, the technological capabilities of nefarious individuals are quickly growing. From sophisticated chatbots designed to engage in conversations and extract personal data, to deepfake technology used to create convincing false personas. This article summarises the current and upcoming risks which artificial intelligence poses to dating application and social media users. Deepfake technology is a key risk; the world is experiencing greater use of attractive deepfake images to convince dating app users into involvement in a romance scam, face-swaps to target and blackmail social media users with their intimate images, and instant generation of child sexual abuse material. Other risks include stalkers tracking their victims with greater ease, and individuals downloading nefarious dating applications which utilise chatbots to gather information and get paid. Gaps in empirical research are identified and discussed.Assessment and Development Matters130The world is experiencing greater use of attractive deepfake images to convince dating app users into involvement in a romance scam, face-swaps to target and blackmail social media users with their intimate images, and instant generation of child sexual abuse material.Assessment and Development Matters['Rachel Fletcher', 'Calli Tzani', 'Maria Ioannou']2024-02-20T00:00:00https://www.semanticscholar.org/paper/efb25225d63b930960d1afdb63ebb4ccf677dfd5 41920eafc75a0b29c74f0c700dc7f0d63cb0f9c7The article defines the content of the concept of artificial intelligence; identifies the main elements of artificial intelligence; outlines the main directions of application of artificial intelligence technologies in management; provides advantages and disadvantages of introducing artificial intelligence technologies in management; identifies the main directions of further development of the field of artificial intelligence and the consequences of its practical application in UkraineInterConf20The article defines the content of the concept of artificial intelligence, identifies the main elements of artificial intelligence, and outlines the main directions of application of artificial intelligence technologies in management.InterConf['Oksana Stashkevych']2024-02-20T00:00:00https://www.semanticscholar.org/paper/41920eafc75a0b29c74f0c700dc7f0d63cb0f9c7 475b092d79c179e1737e2e9ace9dadc02c4a7c1bPurposeThe study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.Design/methodology/approachA scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.FindingsEducators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.Originality/valueThis study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.Education + Training810This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively.Education + Training['Li Chen', 'Dirk Ifenthaler', 'Jane Yin-Kim Yau', 'Wenting Sun']2024-02-20T00:00:00https://www.semanticscholar.org/paper/475b092d79c179e1737e2e9ace9dadc02c4a7c1b 65b0243d47aeb68acc090e1cdbc6088d42e3eff0The purpose of the study is to consider the issue and identify problems with the introduction of artificial intelligence in education and, in particular, teacher training. The study is based on online questionnaires that were sent to teachers (375 people) of higher educational institutions and schools in Russia and China. The results of the study suggest that teacher training based on artificial intelligence can improve students' knowledge, but at this stage, the use of technology should be combined with the traditional learning approach. The research may be of interest to teachers, students, parents, school, and university administrations, as well as to a wide range of people interested in modern education trends. The results obtained with the help of this study can be considered when planning a strategy for introducing artificial intelligence into education, making decisions about its share in learning with due regard to a specific area of study.International Journal of Web-Based Learning and Teaching Technologies160The results of the study suggest that teacher training based on artificial intelligence can improve students' knowledge, but at this stage, the use of technology should be combined with the traditional learning approach.International Journal of Web-Based Learning and Teaching Technologies['Wei Wu', 'Gulnara Burdina', 'Alena Gura']2024-02-20T00:00:00https://www.semanticscholar.org/paper/65b0243d47aeb68acc090e1cdbc6088d42e3eff0 c04c968ae7c2e8e10afc7b0a926222dd119fe38eOBJECTIVE Artificial intelligence has recently become available for widespread use in medicine, including the interpretation of digitized information, big data for tracking disease trends and patterns, and clinical diagnosis. Comparative studies and expert opinion support the validity of imaging and data analysis, yet similar validation is lacking in clinical diagnosis. Artificial intelligence programs are here compared with a diagnostic generator program in clinical neurology. METHODS Using 4 nonrandomly selected case records from New England Journal of Medicine clinicopathologic conferences from 2017 to 2022, 2 artificial intelligence programs (ChatGPT-4 and GLASS AI) were compared with a neurological diagnostic generator program (NeurologicDx.com) for diagnostic capability and accuracy and source authentication. RESULTS Compared with NeurologicDx.com, the 2 AI programs showed results varying with order of key term entry and with repeat querying. The diagnostic generator yielded more differential diagnostic entities, with correct diagnoses in 4 of 4 test cases versus 0 of 4 for ChatGPT-4 and 1 of 4 for GLASS AI, respectively, and with authentication of diagnostic entities compared with the AI programs. CONCLUSIONS The diagnostic generator NeurologicDx yielded a more robust and reproducible differential diagnostic list with higher diagnostic accuracy and associated authentication compared with artificial intelligence programs.The Neurologist130The diagnostic generator NeurologicDx yielded a more robust and reproducible differential diagnostic list with higher diagnostic accuracy and associated authentication compared with artificial intelligence programs.The neurologist['P. Finelli']2024-02-20T00:00:00https://www.semanticscholar.org/paper/c04c968ae7c2e8e10afc7b0a926222dd119fe38e 07206d96993b88994ef5ddc390a0a01c597d7476Philosophy &amp; Technology440It is found that appeal to collective responsibility will be of limited use in filling the responsibility gap: the models considered either do not apply to the case at hand or else the relevant sort of collective responsibility will not be sufficient to remove the costs that are often associated with an absence of responsibility.Philosophy &amp; Technology['Isaac Taylor']2024-02-20T00:00:00https://www.semanticscholar.org/paper/07206d96993b88994ef5ddc390a0a01c597d7476 c6238bebbbaee039fda7c1f7f0c94ba4ab89c67eOnline learning platforms with integrated tools of learning analytics (LA) and artificial intelligence (AI) are growing in popularity in general education in Lithuania. Such platforms have a number of advantages in terms of the teaching-learning process, however, there is a lack of research about such advantages after direct use of the platforms in general education schools. Thus, the purpose of the current study is to find out the perceived benefits of online learning platforms with LA and AI tools. The research was conducted in 11 schools in Lithuania. The students at these schools tested the LearnLab and Eduten Playground online learning platforms for almost three months. Descriptive statistics methods and chi-square (χ2) criteria were applied. Results showed that students claim that their learning achievements have improved thanks to the platforms. Moreover, research results showed, that when working with platforms, it is appropriate to pay attention and, in parallel, to teach students computer literacy from the elementary grades, to develop a relationship with the computer as a work tool. It is also appropriate to start working with LA and AI platforms from the primary grades, which would positively stimulate the growth of digital competence, as well as the interest of students in the educational subject(s) and the positive growth of learning achievements.European Scientific Journal00Research results showed, that when working with platforms, it is appropriate to pay attention and, in parallel, to teach students computer literacy from the elementary grades, to develop a relationship with the computer as a work tool.European Scientific Journal, ESJ['Aleksandra Batuchina', 'J. Melnikova', 'J. Zaščerinska', 'A. Ahrens']2024-02-20T00:00:00https://www.semanticscholar.org/paper/c6238bebbbaee039fda7c1f7f0c94ba4ab89c67e 0f2f810e9c717fe83503120a68d05eac38f8615aThis article explores an innovative ensemble approach integrating multiple AI models to achieve consensus in psychological assessments in the educational psychology domain. Addressing the rapid advancements in Artificial General Intelligence (AGI), it emphasises the importance of principles over specific models in this fast-evolving field. The study compares responses from various AI models using a set of literacy and socioeconomic questions. The findings highlight the diversity of responses from individual models and the holistic ensemble perspective offered by DAIM. The article underscores the potential shift towards Artificial Specific Intelligence (ASI) in psychology, advocating for a focus on multi-model approaches and, potentially, a re-evaluation of humanconsensus in psychological research.Assessment and Development Matters00An innovative ensemble approach integrating multiple AI models to achieve consensus in psychological assessments in the educational psychology domain is explored, advocating for a focus on multi-model approaches and, potentially, a re-evaluation of humanconsensus in psychological research.Assessment and Development Matters['Ian Smythe']2024-02-20T00:00:00https://www.semanticscholar.org/paper/0f2f810e9c717fe83503120a68d05eac38f8615a 9634a7d8137f095e5e1084a6644d23f58ed336a6Derzhavne upravlinnya udoskonalennya ta rozvytok00Derzhavne upravlinnya udoskonalennya ta rozvytok['T. Palamarchuk']2024-02-20T00:00:00https://www.semanticscholar.org/paper/9634a7d8137f095e5e1084a6644d23f58ed336a6 98d231413c2440c5699e1a77c1ddff7c7ea52c54IJARCCE00IJARCCE['Saurabh Suman Choudhuri']2024-02-20T00:00:00https://www.semanticscholar.org/paper/98d231413c2440c5699e1a77c1ddff7c7ea52c54 6194622b85d8872c4f72e4b9bad0e12c8e4aafcbInternational Journal of Medical Informatics300It was determined that the leadership self-efficacy of the manager nurses was at a good level and that their artificial intelligence readiness was at a medium level in terms of cognition, skill, foresight and ethics while presenting their professional knowledge.International journal of medical informatics['Ayşe Eminoğlu', 'Şirin Çelikkanat']2024-02-20T00:00:00https://www.semanticscholar.org/paper/6194622b85d8872c4f72e4b9bad0e12c8e4aafcb 57e4ce6c9f078c62246974720f64009437a631f5Economic Affairs00Economic Affairs['Serhii , Lysenko']2024-02-20T00:00:00https://www.semanticscholar.org/paper/57e4ce6c9f078c62246974720f64009437a631f5 2b870f634c03223b0ea630c90ef011c6b25cdd3bOral surgery, oral medicine, oral pathology and oral radiology00Oral surgery, oral medicine, oral pathology and oral radiology['Donald A. Tyndall']2024-02-20T00:00:00https://www.semanticscholar.org/paper/2b870f634c03223b0ea630c90ef011c6b25cdd3b fd65342fa21a6116fc0782fa1ce25c60e1df34ddScientific Reports00Scientific Reports['Mika Koivisto', 'Simone Grassini']2024-02-20T00:00:00https://www.semanticscholar.org/paper/fd65342fa21a6116fc0782fa1ce25c60e1df34dd 80eaa1a5dc82704176fb203ca4193e64d1eebdeeResearch in Veterinary Science900This review aims to provide available digital and AI-based systems designed to aid precision farming of sheep, offering an up-to-date understanding on the subject.Research in veterinary science['Muhammad Furqan Arshad', 'G. P. Burrai', 'A. Varcasia', 'Maria Francesca Sini', 'F. Ahmed', 'Giovanni Lai', 'M. Polinas', 'E. Antuofermo', 'C. Tamponi', 'Raffaella Cocco', 'Andrea Corda', 'M. L. P. Parpaglia']2024-02-20T00:00:00https://www.semanticscholar.org/paper/80eaa1a5dc82704176fb203ca4193e64d1eebdee 8156b8957db72c7becbee05376583f6734807e5cMolecular Pharmaceutics190Molecular pharmaceutics['Tibo Duran', 'Bodhisattwa Chaudhuri']2024-02-20T00:00:00https://www.semanticscholar.org/paper/8156b8957db72c7becbee05376583f6734807e5c c346d8c02b07dbb75a1097e09c13346b1efad007Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)00Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023)['Galina Barskaya', 'Tatiana Chernysheva', 'Igor Krupkin', 'Anastasia Lesiv']2024-02-20T00:00:00https://www.semanticscholar.org/paper/c346d8c02b07dbb75a1097e09c13346b1efad007 1797ca19c19025d596585d4ebdb8b23880918d2bJournal of Perioperative Echocardiography170Journal of Perioperative Echocardiography['Minati Choudhury']2024-02-20T00:00:00https://www.semanticscholar.org/paper/1797ca19c19025d596585d4ebdb8b23880918d2b d7dab0451b22727cf1b685d03ebaa572289bb793Exclusive Real World Evidence Journal230Exclusive Real World Evidence Journal['Prantar Chakrabarti']2024-02-20T00:00:00https://www.semanticscholar.org/paper/d7dab0451b22727cf1b685d03ebaa572289bb793 2b67f7b129e255093d97ce1a59285c33ea05d9bcIJARCCE00IJARCCE['Jhilik Kabir', 'Adrita Chakraborty', 'Abdullah-Al Mahmood', 'Aditi Chakaraborty']2024-02-20T00:00:00https://www.semanticscholar.org/paper/2b67f7b129e255093d97ce1a59285c33ea05d9bc cd3f7bd1ea30ad307b210870af94dd7a82ae763fDigital technologies have increased our ability to process images. Artists in the past had fewer tools at their disposal to create their artworks. There is a revolution in art today thanks to development of computer technologies. Activities of artists using new technological tools that emerge with digitalization can be defined as digital art. All branches of art where art and technology combine are within the scope of digital art. The main difference between traditional art and digital art is the medium where the artwork is created. In traditional art, a musician uses a musical instrument to display his work, or a painter produces his work with canvas and brush. In digital art, this occurs using technological devices. Many applications, from digital graphic arrangements to video installations, from virtual realities to artificial intelligence applications, fall within the scope of digital art. This article's goals are to investigate how technology has affected art throughout history and to look at how digital art is created and sold. In this article, the impact of artificial intelligence on today's art and artists will be examined. NFTs, another controversial artwork of today, will be focused on and information will be given about the variants of these new technologies in today's art. Literature review and compilation were chosen as the method. According to the findings, artists who closely follow technological developments are the pioneers of the digitalization and dissemination of art. With the evolution of artificial intelligence from its first applications to the present day, artificial intelligence has now turned into a productive tool that can produce works that have never existed before, beyond being just an algorithm. In addition, it has been observed that, thanks to NFTs, digital art can escape the authority of art institutions, create its own autonomous space in a decentralized environment, and present itself as a digital asset open to everyone.JOURNAL OF ARTS00The impact of artificial intelligence on today's art and artists will be examined and it is observed that, thanks to NFTs, digital art can escape the authority of art institutions, create its own autonomous space in a decentralized environment, and present itself as a digital asset open to everyone.JOURNAL OF ARTS['Kerem Düzenli', 'N. Z. Perdahci']2024-02-20T00:00:00https://www.semanticscholar.org/paper/cd3f7bd1ea30ad307b210870af94dd7a82ae763f 46da8f8045d3dad6bd0df94fb0fcc300ddf90763The rapid advancements in artificial intelligence technologies have enhanced the ability for individuals who want to generate a substantial amount of seemingly genuine discussions, images and/or videos that are tailored to promote specific narratives. Unfortunately, this advancement has also provided a valuable tool for actors who seek to promote potentially harmful ideologies and share disinformation to large online audiences. By leveraging AI, these individuals can significantly enhance their recruitment efforts and bolster their perceived credibility, by producing seemingly legitimate but artificially fabricated evidence that supports their proposed narrative. This pressing issue is discussed in terms of its potentially negative consequences on the encouragement of radicalisation in users exposed to this artificially produced disinformation. Not only does it pose a risk to the integrity of people’s perception of truth, but it also has the potential to exacerbate the likelihood of radicalisation occurring.Assessment and Development Matters50Artificial intelligence poses a risk to the integrity of people’s perception of truth, but it also has the potential to exacerbate the likelihood of radicalisation occurring.Assessment and Development Matters['Thomas James Vaughan Williams', 'Maria Ioannou', 'Calli Tzani']2024-02-20T00:00:00https://www.semanticscholar.org/paper/46da8f8045d3dad6bd0df94fb0fcc300ddf90763 aac3cd259b891c144a830a1d838409b5c42a9ee5This paper delves into the incorporation of artificial intelligence and machine learning (AI/ML) technologies to optimize regulatory reporting processes. It explores how AI/ML algorithms streamline data analysis, interpretation, and compliance within regulatory frameworks. Through the utilization of advanced algorithms, organizations can bolster the efficiency and accuracy of regulatory reporting, resulting in enhanced compliance outcomes. The paper outlines key applications of AI/ML in regulatory reporting and addresses challenges and considerations linked to their implementation. Additionally, it underscores the potential benefits of adopting AI/ML-driven approaches for regulatory reporting processes across diverse industries.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This paper delves into the incorporation of artificial intelligence and machine learning technologies to optimize regulatory reporting processes and outlines key applications of AI/ML in regulatory reporting and addresses challenges and considerations linked to their implementation.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Dr. Sreeram Mullankandy']2024-02-20T00:00:00https://www.semanticscholar.org/paper/aac3cd259b891c144a830a1d838409b5c42a9ee5 274b146881b00151182c8ef903d665584590c262Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.International Journal of Business Analytics610A comprehensive bibliometric analysis of the AI ethics literature over the past two decades reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems.ArXiv['Di Kevin Gao', 'Andrew Haverly', 'Sudip Mittal', 'Jiming Wu', 'Jingdao Chen']2024-02-20T00:00:00https://www.semanticscholar.org/paper/274b146881b00151182c8ef903d665584590c262 938a1fd610f13804fac9dc052cb016c87f2991daLarge language models steer their behaviors based on texts generated by others. This capacity and their increasing prevalence in online settings portend that they will intentionally or unintentionally"program"one another and form emergent AI subjectivities, relationships, and collectives. Here, we call upon the research community to investigate these"society-like"properties of interacting artificial intelligences to increase their rewards and reduce their risks for human society and the health of online environments. We use a simple model and its outputs to illustrate how such emergent, decentralized AI collectives can expand the bounds of human diversity and reduce the risk of toxic, anti-social behavior online. Finally, we discuss opportunities for AI self-moderation and address ethical issues and design challenges associated with creating and maintaining decentralized AI collectives.arXiv.org470A simple model is used and its outputs are illustrated to illustrate how such emergent, decentralized AI collectives can expand the bounds of human diversity and reduce the risk of toxic, anti-social behavior online.ArXiv['Shiyang Lai', 'Yujin Potter', 'Junsol Kim', 'Richard Zhuang', 'Dawn Song', 'James Evans']2024-02-19T00:00:00https://www.semanticscholar.org/paper/938a1fd610f13804fac9dc052cb016c87f2991da 761aaf92d14e96a0cedfd21ff7723ea0e045799ePurposeThe purpose of this paper is to analyze the symbiotic evolution decisions of digital innovation enterprises, research institutes and the government in the digital innovation ecosystem.Design/methodology/approachBased on innovation ecosystem theory and an evolutionary game model, this study constructs a tripartite symbiotic evolution game model of digital innovation ecosystems with digital innovation enterprises, research institutes and the government as the main bodies and analyzes the influencing factors as well as the evolution paths of the different behavioral strategies of each subject through numerical simulation.FindingsThe research shows that the digital innovation ecosystem has the characteristic of self-organization, which requires the symbiotic cooperation of each subject. The government plays an active role in any stage of symbiotic evolution, and the system cannot enter symbiosis under a low level of subsidies and penalties. Only when the initial willingness to cooperate of digital innovation enterprises and scientific research institutes is at a medium or high level is the system likely to become symbiotic. While digital innovation enterprises are more sensitive to government subsidies and punishments, scientific research institutes are more sensitive to the distribution proportion of cooperation income.Originality/valueThis study includes government regulation into the research scope, expands the research mode of the digital innovation ecosystem and overcomes the difficulties of empirical research in collecting dynamic large sample data. It vividly and systematically simulates the symbiotic evolution process of the digital innovation ecosystem, which provides a theoretical and practical reference for digital innovation ecosystem governance.Kybernetes470The research shows that the digital innovation ecosystem has the characteristic of self-organization, which requires the symbiotic cooperation of each subject, which provides a theoretical and practical reference for digital innovation ecosystem governance.Kybernetes['Donglin Chen', 'Min Fu', 'Lei Wang']2024-02-19T00:00:00https://www.semanticscholar.org/paper/761aaf92d14e96a0cedfd21ff7723ea0e045799e b22c6ad1a19d70733281c27178ea82e686cbf1d6Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.arXiv.org62This article has investigated the problem of AI-generated text detection from two different aspects: semantics and syntax and presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset.ArXiv['Mohammad Heydari Rad', 'Farhan Farsi', 'Shayan Bali', 'Romina Etezadi', 'M. Shamsfard']2024-02-19T00:00:00https://www.semanticscholar.org/paper/b22c6ad1a19d70733281c27178ea82e686cbf1d6 22629e2ecacb3381c005f7643a4355b502d59464The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and security. Therefore, the task of detecting AI-generated imagery is of paramount importance to prevent illegal activities. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake, comprising state-of-the-art generators, diverse object categories, and real-world applications. WildFake dataset has the following advantages: 1) Rich Content with Wild collection: WildFake collects fake images from the open-source community, enriching its diversity with a broad range of image classes and image styles. 2) Hierarchical structure: WildFake contains fake images synthesized by different types of generators from GANs, diffusion models, to other generative models. These key strengths enhance the generalization and robustness of detectors trained on WildFake, thereby demonstrating WildFake's considerable relevance and effectiveness for AI-generated detectors in real-world scenarios. Moreover, our extensive evaluation experiments are tailored to yield profound insights into the capabilities of different levels of generative models, a distinctive advantage afforded by WildFake's unique hierarchical structure.arXiv.org771To assess the generalizability and robustness of AI-generated image detection, a large-scale dataset, referred to as WildFake, is presented, comprising state-of-the-art generators, diverse object categories, and real-world applications, demonstrating WildFake's considerable relevance and effectiveness for AI-generated detectors in real-world scenarios.ArXiv['Yan Hong', 'Jianfu Zhang']2024-02-19T00:00:00https://www.semanticscholar.org/paper/22629e2ecacb3381c005f7643a4355b502d59464 576ea392fe4110a894e068c99d95d37cf126f2ffThe substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices. On the one hand, data-centric approaches show great potential towards training energy-efficient AI models. On the other hand, instance selection methods demonstrate the capability of training AI models with minimised training sets and negligible performance degradation. Despite the growing interest in both topics, the impact of data-centric training set selection on energy efficiency remains to date unexplored. This paper presents an evolutionary-based sampling framework aimed at (i) identifying elite training samples tailored for datasets and model pairs, (ii) comparing model performance and energy efficiency gains against typical model training practice, and (iii) investigating the feasibility of this framework for fostering sustainable model training practices. To evaluate the proposed framework, we conducted an empirical experiment including 8 commonly used AI classification models and 25 publicly available datasets. The results showcase that by considering 10% elite training samples, the models' performance can show a 50% improvement and remarkable energy savings of 98% compared to the common training practice.arXiv.org651This paper presents an evolutionary-based sampling framework aimed at identifying elite training samples tailored for datasets and model pairs, comparing model performance and energy efficiency gains against typical model training practice, and investigating the feasibility of this framework for fostering sustainable model training practices.ArXiv['Mohammed Alswaitti', 'R. Verdecchia', 'Grégoire Danoy', 'Pascal Bouvry', 'Johnatan E. Pecero']2024-02-19T00:00:00https://www.semanticscholar.org/paper/576ea392fe4110a894e068c99d95d37cf126f2ff 90b9f803410e172e48cbd1021c14a1225ccd2442Reaching consensus on a commonly accepted definition of AI Fairness has long been a central challenge in AI ethics and governance. There is a broad spectrum of views across society on what the concept of fairness means and how it should best be put to practice. In this workbook, we tackle this challenge by exploring how a context-based and society-centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow. We begin by exploring how, despite the plurality of understandings about the meaning of fairness, priorities of equality and non-discrimination have come to constitute the broadly accepted core of its application as a practical principle. We focus on how these priorities manifest in the form of equal protection from direct and indirect discrimination and from discriminatory harassment. These elements form ethical and legal criteria based upon which instances of unfair bias and discrimination can be identified and mitigated across the AI project workflow. We then take a deeper dive into how the different contexts of the AI project lifecycle give rise to different fairness concerns. This allows us to identify several types of AI Fairness (Data Fairness, Application Fairness, Model Design and Development Fairness, Metric-Based Fairness, System Implementation Fairness, and Ecosystem Fairness) that form the basis of a multi-lens approach to bias identification, mitigation, and management. Building on this, we discuss how to put the principle of AI Fairness into practice across the AI project workflow through Bias Self-Assessment and Bias Risk Management as well as through the documentation of metric-based fairness criteria in a Fairness Position Statement.Social Science Research Network01This workbook explores how a context-based and society-centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow.ArXiv['David Leslie', 'Cami Rincón', 'Morgan Briggs', 'A. Perini', 'Smera Jayadeva', 'Ann Borda', 'SJ Bennett', 'Christopher Burr', 'Mhairi Aitken', 'Michael A. Katell', 'Claudia Fischer', 'Janis Wong', 'Ismael Kherroubi Garcia']2024-02-19T00:00:00https://www.semanticscholar.org/paper/90b9f803410e172e48cbd1021c14a1225ccd2442 b0603010761e222562089898c490590848c2ea01Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories to transform healthcare is tremendous, especially in their capacity to support machine learning (ML) and artificial intelligence (AI) tools. Current discussions about the generalizability of such tools in healthcare provoke concerns of risk of bias—ML models underperform in women and ethnic and racial minorities. The use of ML may exacerbate existing healthcare disparities or cause post-deployment harms. Do neuroimaging data repositories and their capacity to support ML/AI-driven clinical discoveries, have both the potential to accelerate innovative medicine and harden the gaps of social inequities in neuroscience-related healthcare? In this paper, we examined the ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories. We explored this in two parts; firstly, in a theoretical experiment, we argued for a South East Asian-based repository to redress global imbalances. Within this context, we then considered the ethical framework toward the inclusion vs. exclusion of the migrant worker population, a group subject to healthcare inequities. Secondly, we created a model simulating the impact of global variations in the presentation of anosmia risks in COVID-19 toward altering brain structural findings; we then performed a mini AI ethics experiment. In this experiment, we interrogated an actual pilot dataset (n = 17; 8 non-anosmic (47%) vs. 9 anosmic (53%) using an ML clustering model. To create the COVID-19 simulation model, we bootstrapped to resample and amplify the dataset. This resulted in three hypothetical datasets: (i) matched (n = 68; 47% anosmic), (ii) predominant non-anosmic (n = 66; 73% disproportionate), and (iii) predominant anosmic (n = 66; 76% disproportionate). We found that the differing proportions of the same cohorts represented in each hypothetical dataset altered not only the relative importance of key features distinguishing between them but even the presence or absence of such features. The main objective of our mini experiment was to understand if ML/AI methodologies could be utilized toward modelling disproportionate datasets, in a manner we term “AI ethics.” Further work is required to expand the approach proposed here into a reproducible strategy.Frontiers Artif. Intell.641The ethical concerns of ML-driven modeling of global community neuroscience needs arising from the use of data amassed within neuroimaging data repositories are examined, and if ML/AI methodologies could be utilized toward modelling disproportionate datasets are explored, in a manner the authors term “AI ethics.”Frontiers in Artificial Intelligence['Christine Lock', 'Nicole Si Min Tan', 'Ian James Long', 'N. Keong']2024-02-19T00:00:00https://www.semanticscholar.org/paper/b0603010761e222562089898c490590848c2ea01 fece47766baf5ac94c74fee924589fe372ba81df This Viewpoint summarizes a recent lawsuit alleging that a hospital violated patients’ privacy by sharing electronic health record (EHR) data with Google for development of medical artificial intelligence (AI) and discusses how the federal court’s decision in the case provides key insights for hospitals planning to share EHR data with for-profit companies developing medical AI. Journal of the American Medical Association (JAMA)41A recent lawsuit alleging that a hospital violated patients’ privacy by sharing EHR data with Google for development of medical artificial intelligence (AI) is summarized and how the federal court’s decision provides key insights for hospitals planning to share EHR data with for-profit companies developing medical AI is discussed.JAMA['M. Duffourc', 'Sara Gerke']2024-02-19T00:00:00https://www.semanticscholar.org/paper/fece47766baf5ac94c74fee924589fe372ba81df 39d1430d51494703e6142051a909a0660c7f5dccScientific Reports651The findings of the study showed that some individual characteristics as creative personal identity and openness to experience personality influence how people perceive the presented artworks in function of their believed source.Scientific Reports['Simone Grassini', 'Mika Koivisto']2024-02-19T00:00:00https://www.semanticscholar.org/paper/39d1430d51494703e6142051a909a0660c7f5dcc c2400d09993319898ecec666f41d61dfa896b1d0The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.Open Access Journal of Applied Science and Technology00Artificial intelligence's powerful potential to revolutionize healthcare delivery across multiple domains is explored, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.Open Access Journal of Applied Science and Technology[]2024-02-19T00:00:00https://www.semanticscholar.org/paper/c2400d09993319898ecec666f41d61dfa896b1d0 16821d2907d30f91ad052a12e916df889bdde059The accurate detection and analysis of chemicals have become increasingly important for security and environmental monitoring with the integration of artificial intelligence (AI) methods gaining traction. However, the scarcity of certain chemicals poses significant challenges to the AI learning process. This paper presents a comprehensive AI approach and strategic direction for generating synthetic gas chromatography-mass spec-trometry (GC-MS) data for such limited-availability chemicals. We conduct exploratory data analysis (EDA) on GC-MS data and apply advanced AI-driven generative algorithms, with a focus on Variational Autoencoder (VAE) and Generative Adversarial Network (GAN), acknowledging the challenges faced by current AI technologies in learning from chemical data. Additionally, we introduce a secondary contribution by developing custom Python-based tools for 3D visualization of GC-MS data, enhancing intuitive understanding and analysis precision. Our findings offer new possibilities and directions for the expansive application of AI in chemical analysis.Digital Signal Processing and Signal Processing Education Workshop170A comprehensive AI approach and strategic direction for generating synthetic gas chromatography-mass spec-trometry data for such limited-availability chemicals and applies advanced AI-driven generative algorithms, with a focus on Variational Autoencoder (VAE) and Generative Adversarial Network (GAN).2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Namkyung Yoon', 'Hwang-nam Kim']2024-02-19T00:00:00https://www.semanticscholar.org/paper/16821d2907d30f91ad052a12e916df889bdde059 bc2dc60173166e062512d73ee15731be038b6f19Diabetic retinopathy is a leading cause of vision loss in Canada and creates significant economic and social burden on patients. Diabetic retinopathy is largely a preventable complication of diabetes mellitus. Yet, hundreds of thousands of Canadians continue to be at risk and thousands go on to develop vision loss and disability. Blindness has a significant impact on the Canadian economy, on families and the quality of life of affected individuals. This paper provides an economic analysis on two potential interventions for preventing blindness and concludes that use of AI to identify high-risk individuals could significantly decrease the costs of identifying, recalling, and screening patients at risk of vision loss, while achieving similar results as a full-fledged screening and recall program. We propose that minimal data interoperability between optometrists and family physicians combined with artificial intelligence to identify and screen those at highest risk of vision loss can lower the costs and increase the feasibility of screening and treating large numbers of patients at risk of going blind in Canada.Studies in Health Technology and Informatics00It is proposed that minimal data interoperability between optometrists and family physicians combined with artificial intelligence to identify and screen those at highest risk of vision loss can lower the costs and increase the feasibility of screening and treating large numbers of patients at risk of going blind in Canada.Studies in health technology and informatics['Swetha R Chakravarthy', 'Dora Mugambi', 'Karim Keshavjee']2024-02-19T00:00:00https://www.semanticscholar.org/paper/bc2dc60173166e062512d73ee15731be038b6f19 ac6e714630ac63222d2dbbca1236d03376d77a45Advanced disease prediction is an important step toward achieving a proactive healthcare system. New technologies such as artificial intelligence are very promising in their ability to predict the onset of future disease much earlier than has been possible in the past. However, artificial intelligence requires training and training requires data. In this study, we report on the ready availability, but lack of accessibility and real-time access to healthcare data required to treat five high-cost diseases that are predictable using AI and preventable using well-established evidence-based therapies. There is urgent need for action on the part of governments and other interest holders to define and invest in the infrastructure required to make data for training and deploying AI at scale more accessible.Studies in Health Technology and Informatics00There is urgent need for action on the part of governments and other interest holders to define and invest in the infrastructure required to make data for training and deploying AI at scale more accessible.Studies in health technology and informatics['Fahreen Walimohamed', 'Karim Keshavjee']2024-02-19T00:00:00https://www.semanticscholar.org/paper/ac6e714630ac63222d2dbbca1236d03376d77a45 45374c1b5bc3508a95ba9e65b234c6ce3f5c04b9Successful content moderation in online platforms relies on a human-AI collaboration approach. A typical heuristic estimates the expected harmfulness of a post and uses fixed thresholds to decide whether to remove it and whether to send it for human review. This disregards the prediction uncertainty, the time-varying element of human review capacity and post arrivals, and the selective sampling in the dataset (humans only review posts filtered by the admission algorithm). In this paper, we introduce a model to capture the human-AI interplay in content moderation. The algorithm observes contextual information for incoming posts, makes classification and admission decisions, and schedules posts for human review. Only admitted posts receive human reviews on their harmfulness. These reviews help educate the machine-learning algorithms but are delayed due to congestion in the human review system. The classical learning-theoretic way to capture this human-AI interplay is via the framework of learning to defer, where the algorithm has the option to defer a classification task to humans for a fixed cost and immediately receive feedback. Our model contributes to this literature by introducing congestion in the human review system. Moreover, unlike work on online learning with delayed feedback where the delay in the feedback is exogenous to the algorithm's decisions, the delay in our model is endogenous to both the admission and the scheduling decisions. We propose a near-optimal learning algorithm that carefully balances the classification loss from a selectively sampled dataset, the idiosyncratic loss of non-reviewed posts, and the delay loss of having congestion in the human review system. To the best of our knowledge, this is the first result for online learning in contextual queueing systems and hence our analytical framework may be of independent interest.arXiv.org00A near-optimal learning algorithm is proposed that carefully balances the classification loss from a selectively sampled dataset, the idiosyncratic loss of non-reviewed posts, and the delay loss of having congestion in the human review system.ArXiv['Thodoris Lykouris', 'Wentao Weng']2024-02-19T00:00:00https://www.semanticscholar.org/paper/45374c1b5bc3508a95ba9e65b234c6ce3f5c04b9 fd89eb09b2850d4baa61e4877434b4956de9cb98The journalism has adopted technology at the next level, where Artificial Intelligence (AI) collaborates in the journalistic field. The aim of this research is to explore AI in the field of journalism and its perceived impact on journalistic products. Literature studies were carried out in international journals indexed by Scopus for 2015-2023. As a result, AI-based journalism is defined as an independent system for collecting, analyzing and processing data through programs created by humans both textually and visually; AI ethics in the form of collecting credible and trustworthy data that has been selected by the editorial team; advantages in the form of being able to overcome contemporary journalistic problems and challenges in the form of journalistic competence.Ideas: Jurnal Pendidikan, Sosial, dan Budaya00The aim of this research is to explore AI in the field of journalism and its perceived impact on journalistic products.Ideas: Jurnal Pendidikan, Sosial, dan Budaya['Rizki Apriliyanti', 'Ade Nur Atika Sari', 'Riska Aulia Noor']2024-02-19T00:00:00https://www.semanticscholar.org/paper/fd89eb09b2850d4baa61e4877434b4956de9cb98 c6efb2d08a34a1d40e4caf4578044422e6e55b4cThe evolution of Closed-Circuit Television (CCTV) systems, initially designed for security, has transcended into diverse domains, impacting public spaces, transportation hubs, and commercial districts. While serving traditional roles in crime prevention, these systems now play vital roles in correctional institutions, education, and various industries. The proliferation of CCTV cameras, though providing extensive surveillance, presents challenges of network congestion and resource-intensive processing. This prompts the need for innovative solutions. Edge AI Terminals-a transformative approach that strategically places advanced computing capabilities closer to data sources, reducing reliance on centralized servers. Additionally, it can employ cutting-edge AI algorithms, enabling heightened efficiency and responsiveness. In this paper, we discuss the evolution of CVTV usage and the revolution of surveillance by integrating edge AI technology. We further discuss how edge AI terminals can play a key role in future road infrastructure. Finally, some major challenges and their potential solutions are highlighted.Digital Signal Processing and Signal Processing Education Workshop240The evolution of CVTV usage and the revolution of surveillance by integrating edge AI technology is discussed and how edge AI terminals can play a key role in future road infrastructure is discussed.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['M. Tariq', 'M. Ajmal', 'Euiri Jo', 'M. Saad', 'Seri Park', 'Jinhong Kim', 'Dongkyun Kim']2024-02-19T00:00:00https://www.semanticscholar.org/paper/c6efb2d08a34a1d40e4caf4578044422e6e55b4c 0409e883a35f5435d460f59af156d6c595493523The use of Artificial Intelligence (AI) in Internet of Things (IoT) ecosystem has been growing exponentially, enabling various Computer Vision (CV) applications. These applications must handle large image data demanding reliable communication systems that retain image quality for downstream Deep Learning (DL) tasks. Existing communication systems, such as Orthogonal Frequency Division Multiplexing (OFDM), promise improvements in data rate, spectral efficiency, and mitigation of multipath fading; however, these systems often distort the received images due to complex channel environments and impairments from various physical layer (PHY) blocks. Source Coding is one such PHY block, which aims for compression savings at the expense of image quality. Therefore, in this study, we evaluate the performance of a DL model for downstream image recognition tasks, where images are transmitted over communication systems utilizing various source coding schemes over complex channels. Experimental analysis shows that Variable-Length Coding (VLC) retains superior image quality, which results in over 95% DL model accuracy throughout the experiment.Digital Signal Processing and Signal Processing Education Workshop60Experimental analysis shows that Variable-Length Coding (VLC) retains superior image quality, which results in over 95% DL model accuracy throughout the experiment, which shows that Variable-Length Coding retains superior image quality.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Nazmul Islam', 'Seokjoo Shin']2024-02-19T00:00:00https://www.semanticscholar.org/paper/0409e883a35f5435d460f59af156d6c595493523 d8126a1517f51700192479867467e44ef61f9ef6Online social networks often create echo chambers where people only hear opinions reinforcing their beliefs. An echo chamber often generates polarization, leading to conflicts caused by people with radical opinions, such as the January 6, 2021, attack on the US Capitol. The echo chamber has been viewed as a human-specific problem, but this implicit assumption is becoming less reasonable as large language models, such as ChatGPT, acquire social abilities. In response to this situation, we investigated the potential for polarization to occur among a group of autonomous AI agents based on generative language models in an echo chamber environment. We had AI agents discuss specific topics and analyzed how the group's opinions changed as the discussion progressed. As a result, we found that the group of agents based on ChatGPT tended to become polarized in echo chamber environments. The analysis of opinion transitions shows that this result is caused by ChatGPT's high prompt understanding ability to update its opinion by considering its own and surrounding agents' opinions. We conducted additional experiments to investigate under what specific conditions AI agents tended to polarize. As a result, we identified factors that strongly influence polarization, such as the agent's persona. These factors should be monitored to prevent the polarization of AI agents.arXiv.org400The analysis of opinion transitions shows that the group of agents based on ChatGPT tended to become polarized in echo chamber environments, caused by ChatGPT's high prompt understanding ability to update its opinion by considering its own and surrounding agents' opinions.ArXiv['Masaya Ohagi']2024-02-19T00:00:00https://www.semanticscholar.org/paper/d8126a1517f51700192479867467e44ef61f9ef6 bb30aa283b43fe26be2b57b070c41b5c74899bd9This article presents a novel system designed for human resources (HR) companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools. At the core of this system is Airable, serving as the central database, which is seamlessly I integrated with various other tools such as PDF.co for document handling, GitHub for code repository management, and OpenAI for advanced AI algorithms. The integration and automation of these diverse tools are skillfully orchestrated using Make, a no-code solution that streamlines the workflow and enhances efficiency. This innovative approach allows HR companies to leverage technology for more accurate personality predictions, thereby improving their recruitment processes and decision-making strategies.Digital Signal Processing and Signal Processing Education Workshop220A novel system designed for human resources companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools, using Make, a no-code solution that streamlines the workflow and enhances efficiency.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Gulnara Abitova', 'Ayanbek Serikov', 'Vladimir Nikulin', 'Mira Rakhimzhanova', 'Gulnur Shuteyeva', 'K. Kulniyazova']2024-02-19T00:00:00https://www.semanticscholar.org/paper/bb30aa283b43fe26be2b57b070c41b5c74899bd9 d4b487e9e941eb69cc05733064bb23d5f39be5bfAI driven by deep learning is transforming many aspects of science and technology. The enormous success of deep learning stems from its unique capability of extracting essential features from Big Data for decision-making. However, the feature extraction and hidden representations in deep neural networks (DNNs) remain inexplicable, primarily because of lack of technical tools to comprehend and interrogate the feature space data. The main hurdle here is that the feature data are often noisy in nature, complex in structure, and huge in size and dimensionality, making it intractable for existing techniques to analyze the data reliably. In this work, we develop a computational framework named contrastive feature analysis (CFA) to facilitate the exploration of the DNN feature space and improve the performance of AI. By utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline, CFA mitigates the limitations of traditional approaches and provides an urgently needed solution for the analysis of feature space data. The technique allows feature data exploration in unsupervised, semi-supervised and supervised formats to address different needs of downstream applications. The potential of CFA and its applications for pruning of neural network architectures are demonstrated using several state-of-the-art networks and well-annotated datasets across different disciplines.IEEE Transactions on Pattern Analysis and Machine Intelligence00A computational framework named contrastive feature analysis (CFA) is developed to facilitate the exploration of the DNN feature space and improve the performance of AI by utilizing the interaction relations among the features and incorporating a novel data-driven kernel formation strategy into the feature analysis pipeline.IEEE transactions on pattern analysis and machine intelligence['Md Tauhidul Islam', 'Lei Xing']2024-02-19T00:00:00https://www.semanticscholar.org/paper/d4b487e9e941eb69cc05733064bb23d5f39be5bf 241b80c3b60268bbbe24adbe9c2ab63687db57ec Over the last few years, artificial intelligence (AI) technologies have become embedded in various domains of social life, prompting legislative efforts at both national and international levels. In the European Union (EU), this drive for legislation has been reflected in various legal instruments, notably the proposed AI Act, which is expected to become a global standard through the “Brussels Effect.” This Article argues that while the AI Act will likely produce a Brussels Effect of its own, such an outcome will be accompanied by a side effect that undermines the EU’s ambition to spread legislative text and values in AI governance. Since the AI Act follows EU product safety legislation, its provisions supply limited protection to some of the values the EU policy intends to protect, such as the protection of fundamental rights. These shortcomings are compounded by the EU’s active efforts to shape alternative instruments, such as the Council of Europe’s proposed convention on AI along the lines of the AI Act. As a result, the diffusion of the AI Act as a global standard will have consequences for the EU policy agenda on AI and the conceptualization of the Brussels Effect.Social Science Research Network130SSRN Electronic Journal['Marco Almada', 'Anca Radu']2024-02-19T00:00:00https://www.semanticscholar.org/paper/241b80c3b60268bbbe24adbe9c2ab63687db57ec afa3b20333c33f2b1631429f6e8ba1d39cd9f1c2This paper proposes an innovative method to detect energy theft on electrical network. By utilizing satellite imagery to analyze building rooftops and comparing them with registered consumer units, illicit connections in the distribution grid can be identified. The approach solves the assignment problem between AI-detected buildings and registered consumer units, enabling the detection of illegal connections. Suspicious locations without assigned meters are flagged, allowing utility companies to prioritize inspections effectively. Simulated tests with representative data demonstrate promising results in accurately detecting the majority of illegal connections, and an illustrative case of real-world detection is provided. However, further assessment and exploration of alternative methodologies are necessary to enhance accuracy. This research provides utility companies with a valuable tool to combat energy theft, improve system efficiency, and ensure a reliable and profitable electrical energy sector by effectively addressing the assignment problem between detected buildings and registered consumer units using satellite imagery.IEEE PES Innovative Smart Grid Technologies Conference210This research provides utility companies with a valuable tool to combat energy theft, improve system efficiency, and ensure a reliable and profitable electrical energy sector by effectively addressing the assignment problem between detected buildings and registered consumer units using satellite imagery.2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)['Antônio Mário Kaminski', 'Filipe Gabriel Carloto', 'C. H. Barriquello', 'V. J. Garcia', 'Matheus Mello Jacques', 'O. C. Filho']2024-02-19T00:00:00https://www.semanticscholar.org/paper/afa3b20333c33f2b1631429f6e8ba1d39cd9f1c2 5bffac7f695e9ef1abe826cfc3234f84c053e016The adoption of Artificial Intelligence (AI) in the Canadian healthcare system falls behind that of other countries. Socio-technological considerations such as organizational readiness and a limited understanding of the technology are a few barriers impeding its adoption. To address this need, this study implemented a five-month AI mentorship program with the primary objective of developing participants' AI toolset. The analysis of our program's effectiveness resulted in recommendations for a successful mentorship and AI development and implementation program. 12 innovators and 11 experts from diverse backgrounds were formally matched and two symposiums were integrated into the program design. 8 interviewed participants revealed positive perceptions of the program underscoring its contribution to their professional development. Recommendations for future programs include: (1) obtaining organizational commitment for each participant; (2) incorporating structural supports throughout the program; and (3) adopting a team-based mentorship approach. The findings of this study offer a foundation rooted in evidence for the formulation of policies necessary to promote the integration of AI in Canada.Studies in Health Technology and Informatics130A five-month AI mentorship program with the primary objective of developing participants' AI toolset is implemented, resulting in recommendations for a successful mentorship and AI development and implementation program.Studies in health technology and informatics['Divya Kamath', 'Bemnet Teferi', 'Rebecca Charow', 'Jane Mattson', 'Jessica Jardine', 'Tharshini Jeyakumar', 'Maram Omar', 'Melody Zhang', 'Jillian Scandiffio', 'Mohammad Salhia', 'A. Dhalla', 'D. Wiljer']2024-02-19T00:00:00https://www.semanticscholar.org/paper/5bffac7f695e9ef1abe826cfc3234f84c053e016 e4dec77d701df54ed4fd58d9a173cebc12ba3f45The sustainability of AI systems depends on the capacity of project teams to proceed with a continuous sensitivity to their potential real-world impacts and transformative effects. Stakeholder Impact Assessments (SIAs) are governance mechanisms that enable this kind of responsiveness. They are tools that create a procedure for, and a means of documenting, the collaborative evaluation and reflective anticipation of the possible harms and benefits of AI innovation projects. SIAs are not one-off governance actions. They require project teams to pay continuous attention to the dynamic and changing character of AI production and use and to the shifting conditions of the real-world environments in which AI technologies are embedded. This workbook is part two of two workbooks on AI Sustainability. It provides a template of the SIA and activities that allow a deeper dive into crucial parts of it. It discusses methods for weighing values and considering trade-offs during the SIA. And, it highlights the need to treat the SIA as an end-to-end process of responsive evaluation and re-assessment.Social Science Research Network70This workbook is part two of two workbooks on AI Sustainability and provides a template of the SIA and activities that allow a deeper dive into crucial parts of it, highlighting the need to treat the SIA as an end-to-end process of responsive evaluation and re-assessment.ArXiv['David Leslie', 'Cami Rincón', 'Morgan Briggs', 'A. Perini', 'Smera Jayadeva', 'Ann Borda', 'SJ Bennett', 'Christopher Burr', 'Mhairi Aitken', 'Michael A. Katell', 'Claudia Fischer', 'Janis Wong', 'Ismael Kherroubi Garcia']2024-02-19T00:00:00https://www.semanticscholar.org/paper/e4dec77d701df54ed4fd58d9a173cebc12ba3f45 fcf794447c514de86a433553bf3e9c5b658a37afThe National AI Institute for Adult Learning and Online Education (AI‐ALOE) develops AI learning and teaching assistants to enhance the proficiency of adult reskilling and upskilling, and thereby transform workforce development. The AI assistants both address known problems in online education for reskilling/upskilling and help personalize adult learning for workforce development. AI‐ALOE develops new AI models and techniques for self‐explanation, machine teaching, and mutual theory of mind to make the AI assistants usable, learnable, teachable, and scalable. AI‐ALOE is also developing a data architecture for deploying and evaluating the AI assistants, collecting and analyzing data, and personalizing learning at scale.The AI Magazine70AI Mag.['Ashok Goel', 'Chris Dede', 'Myk Garn', 'Chaohua Ou']2024-02-19T00:00:00https://www.semanticscholar.org/paper/fcf794447c514de86a433553bf3e9c5b658a37af f52802aead5df607c8f00e8a6f719a356d05ca6aAI &amp; SOCIETY600The socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions are considered, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built.AI &amp; SOCIETY['Malak Sadek', 'Emma Kallina', 'Thomas Bohné', 'C. Mougenot', 'Rafael A. Calvo', 'Stephen Cave']2024-02-19T00:00:00https://www.semanticscholar.org/paper/f52802aead5df607c8f00e8a6f719a356d05ca6a caa4be1cc12d302e5fa80f66307247174c90fd96AI and Ethics400Recommendations for companies’ technological design and development, for industry’s governance measures and academia’s research endeavors are recapitulated and summarized in a holistic framework that aims to facilitate a reflected implementation of ‘ethics in and by design’ in the future.AI and Ethics['Franziska Poszler', 'Edy Portmann', 'Christoph Lütge']2024-02-19T00:00:00https://www.semanticscholar.org/paper/caa4be1cc12d302e5fa80f66307247174c90fd96 30ec4b31c3a4f7bfe1162847957a0bc9c9a16aeaSustainable AI projects are continuously responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society that the design, development, and deployment of AI technologies may have. Projects, which centre AI Sustainability, ensure that values-led, collaborative, and anticipatory reflection both guides the assessment of potential social and ethical impacts and steers responsible innovation practices. This workbook is the first part of a pair that provides the concepts and tools needed to put AI Sustainability into practice. It introduces the SUM Values, which help AI project teams to assess the potential societal impacts and ethical permissibility of their projects. It then presents a Stakeholder Engagement Process (SEP), which provides tools to facilitate proportionate engagement of and input from stakeholders with an emphasis on equitable and meaningful participation and positionality awareness.Social Science Research Network550This workbook introduces the SUM Values, which help AI project teams to assess the potential societal impacts and ethical permissibility of their projects, and presents a Stakeholder Engagement Process (SEP), which provides tools to facilitate proportionate engagement of and input from stakeholders.ArXiv['David Leslie', 'Cami Rincón', 'Morgan Briggs', 'A. Perini', 'Smera Jayadeva', 'Ann Borda', 'SJ Bennett', 'Christopher Burr', 'Mhairi Aitken', 'Michael A. Katell', 'Claudia Fischer', 'Janis Wong', 'Ismael Kherroubi Garcia']2024-02-19T00:00:00https://www.semanticscholar.org/paper/30ec4b31c3a4f7bfe1162847957a0bc9c9a16aea 589d136188807ba0b9c31889141b161823e92b0cRecently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders. These remarks, however, apply primarily to Europe, the USA, China and other technologically developed nations. Africa is yet to leverage the potential applications of AI and ML within the medical space. Sub-Saharan African countries are currently disadvantaged economically and infrastructure-wise. Yet precisely, these circumstances create significant opportunities for the deployment of medical AI, which has already been deployed in some places in the continent. However, while AI and ML have come with enormous promises in Africa, there are still challenges when it comes to successfully applying AI and ML designed elsewhere within the African context, especially in diagnosing mental health disorders. We argue, in this paper, that there ought not to be a homogeneous/generic design of AI and ML used in diagnosing mental health disorders. Our claim is grounded on the premise that mental health disorders cannot be diagnosed solely on 'factual evidence' but on both factual evidence and value-laden judgements of what constitutes mental health disorders in sub-Saharan Africa. For ML to play a successful role in diagnosing mental health disorders in sub-Saharan African medical spaces, with a precise focus on South Africa, we allude that it ought to understand what sub-Saharan Africans consider as mental health disorders, that is, the value-laden judgements of some conditions.Journal of Medical Ethics250It is argued that there ought not to be a homogeneous/generic design of AI and ML used in diagnosing mental health disorders in sub-Saharan Africa, and that it ought to understand what sub-Saharan Africans consider as mental health disorders, that is, the value-laden judgements of some conditions.Journal of medical ethics['Edmund Terem Ugar', 'Ntsumi Malele']2024-02-19T00:00:00https://www.semanticscholar.org/paper/589d136188807ba0b9c31889141b161823e92b0c fb129d35faacb2b68641502f92a9a30453391384AI systems may have transformative and long-term effects on individuals and society. To manage these impacts responsibly and direct the development of AI systems toward optimal public benefit, considerations of AI ethics and governance must be a first priority. In this workbook, we introduce and describe our PBG Framework, a multi-tiered governance model that enables project teams to integrate ethical values and practical principles into their innovation practices and to have clear mechanisms for demonstrating and documenting this.Social Science Research Network00This workbook introduces and describes the PBG Framework, a multi-tiered governance model that enables project teams to integrate ethical values and practical principles into their innovation practices and to have clear mechanisms for demonstrating and documenting this.ArXiv['David Leslie', 'Cami Rincón', 'Morgan Briggs', 'A. Perini', 'Smera Jayadeva', 'Ann Borda', 'SJ Bennett', 'Christopher Burr', 'Mhairi Aitken', 'Michael A. Katell', 'Claudia Fischer']2024-02-19T00:00:00https://www.semanticscholar.org/paper/fb129d35faacb2b68641502f92a9a30453391384 22a9f516f59029625c8388a2b7ac137ee4616e66This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and accuracy. In the monolingual task, we evaluated traditional deep-learning methods, multiscale positive-unlabeled framework (MPU), fine-tuning, adapters and ensemble methods. Then, we selected the top-performing models based on their accuracy from the monolingual models and evaluated them in subtasks A and B. The final model construction employed a stacking ensemble that combined fine-tuning with MPU. Our system achieved 8th (scored 8th in terms of accuracy, officially ranked 13th) place in the official test set in multilingual settings of subtask A. We release our system code at:https://github.com/warmth27/SemEval2024_QUSTarXiv.org191The participation of team QUST in Task 8 SemEval 2024 was presented, and the system achieved 8th (scored 8th in terms of accuracy, officially ranked 13th) place in the official test set in multilingual settings of subtask A.ArXiv['Xiaoman Xu', 'Xiangrun Li', 'Taihang Wang', 'Jianxiang Tian', 'Ye Jiang']2024-02-19T00:00:00https://www.semanticscholar.org/paper/22a9f516f59029625c8388a2b7ac137ee4616e66 c7ae78ae4ef1104b2b69acb68ff3f74ca0c106b4PurposeThis study examines the impact of generative artificial intelligence (GenAI), particularly ChatGPT, on higher education (HE). The ease with which content can be generated using GenAI has raised concerns across academia regarding its role in academic contexts, particularly regarding summative assessments. This research makes a unique contribution to the literature by examining university student and staff perceptions of current and future issues pertaining to the role of GenAI in universities.Design/methodology/approachA qualitative method involving five one-to-one semi-structured interviews with four students and a lecturer explored the ethical and practical issues of GenAI text generation in academia. An inductive thematic analysis was chosen as it provided nuanced insights aligned with the study’s goals.FindingsUse of GenAI was discussed within the context of a range of topics, including perceptions of academic misconduct, authorial integrity and issues pertaining to university policies. Participants universally defined traditional classifications of academic misconduct but were unable to provide clear definitions where the use of GenAI was included for writing summative assessments. Students showed a more open engagement with GenAI, considering it a tool for overcoming obstacles rather than a means to plagiarise. Educators were generally more cautious and less optimistic about the academic role of GenAI. Lack of clear institutional policies surrounding such tools also contributed to ethical ambiguities.Originality/valueThe study highlights diverging perspectives between students and academics, which necessitate a forum for dialogue, ensuring the need to develop clear policies to steer the integration of GenAI in a manner that is beneficial for students and academics.The international journal of information and learning technology431The study highlights diverging perspectives between students and academics, ensuring the need to develop clear policies to steer the integration of GenAI in a manner that is beneficial for students and academics.The International Journal of Information and Learning Technology['James Ewert Duah', 'Paul McGivern']2024-02-19T00:00:00https://www.semanticscholar.org/paper/c7ae78ae4ef1104b2b69acb68ff3f74ca0c106b4 ea3fa5e8d4d708cb0dac762ab9b3eaefedf17afcAI &amp; SOCIETY451The key normative choices and corresponding tradeoffs that are involved in specifying guidance for the implementation of AI ethics principles in the defence domain are outlined and the importance of a pro-ethical institutional culture is highlighted.AI &amp; SOCIETY['Alexander Blanchard', 'Christopher Thomas', 'M. Taddeo']2024-02-19T00:00:00https://www.semanticscholar.org/paper/ea3fa5e8d4d708cb0dac762ab9b3eaefedf17afc b4f436df18ad19c49160ae4820d18efe5bfdc5e5This review provides a structured literature analysis of Artificial Intelligence (AI) applications in enhancing manufacturing resilience. The research is guided by three primary questions addressing the use cases, technologies, and benefits of AI across the five resilience phases: Prepare, Prevent, Protect, Respond, and Recover. Findings from 78 papers reveal that AI significantly contributes to predictive maintenance, risk mitigation, and quality control, with machine learning and deep learning being the predominant technologies. The study highlights the pivotal role of AI in advancing manufacturing towards proactive, resilient, and adaptable operations. The insights gleaned offer a roadmap for future research and practical AI integration in manufacturing, underscoring the value of AI in driving industrial innovation and efficiency.Digital Signal Processing and Signal Processing Education Workshop800This review provides a structured literature analysis of Artificial Intelligence applications in enhancing manufacturing resilience, revealing that AI significantly contributes to predictive maintenance, risk mitigation, and quality control, with machine learning and deep learning being the predominant technologies.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Florian A. Maier', 'Sivaphani Puppala', 'Michael Oberle']2024-02-19T00:00:00https://www.semanticscholar.org/paper/b4f436df18ad19c49160ae4820d18efe5bfdc5e5 8a7a7a99e0b192aaae0981779639c29038c67ba4Background Artificial Intelligence (AI) has the potential to significantly enhance human-computer interactions. This paper introduces a cutting-edge method for computer control using eye-gesture recognition. Methods Our system employs a sophisticated algorithm to accurately interpret eye movements, converting them into actionable commands. This technology not only improves accessibility for individuals with physical impairments, but also offers a more intuitive interaction mode for the general user base. Results We tested our method using a comprehensive dataset and achieved a remarkable accuracy rate of over 99.6283% in translating eye gestures into functional commands. Our system utilizes a variety of tools, including PyCharm, OpenCV, mediapipe, and pyautogui, to achieve these results. Conclusions We discuss potential applications of our technology, such as in the emerging field of gesture-controlled weaponry, which could have significant implications for military and rescue operations. Overall, our work represents a substantial step forward in integrating AI with human-computer interaction, enhancing accessibility, improving user engagement, and unlocking innovative applications for critical industries.F1000Research580A cutting-edge method for computer control using eye-gesture recognition that not only improves accessibility for individuals with physical impairments, but also offers a more intuitive interaction mode for the general user base.F1000Research['Nachaat Mohamed']2024-02-19T00:00:00https://www.semanticscholar.org/paper/8a7a7a99e0b192aaae0981779639c29038c67ba4 d9d82ee15cc1f7711d8843ffada53aae2887f160Artificial Intelligence (AI) has significantly transformed teaching and learning, facilitating a shift from teacher-centered to student-centered education. This review outlines the broad implications of AI for education and synthesizes both the opportunities and challenges associated with its implementation. Examining over 55 papers related to the impacts of AI on education, the review encompasses various educational contexts, avoiding a singular focus on specific types of education or the teaching of AI alone. According to the review, AI introduces new opportunities for creating intelligent content that enhances learning experiences, fostering interactivity and a student-centered approach. Smart content enables instructors to integrate multimedia, interactive tools, AI-related wearables, and information technologies, diversifying learning modes and engaging students more effectively. The creation of smart content aligns with smart education frameworks to ensure efficient content development. AI also contributes to the development of intelligent tutoring systems, which simulate human tutors to deliver personalized and adaptive educational experiences. These systems can host smart content, enabling independent learning. Additionally, AI improves virtual learning environments by analyzing student data to tailor content and delivery methods based on individual needs. It automates tasks such as grading and feedback, allowing teachers to concentrate on other essential responsibilities. While AI brings significant benefits, it is not without limitations. Challenges include infrastructure requirements, considerations of inclusion and equity, teacher readiness and preparation, data quality and inclusivity, profit orientation, data privacy and ethical concerns, and the potential for unequal access. Addressing these limitations is crucial for maximizing the positive impacts of AI in the realm of education.Acta Pedagogia Asiana610According to the review, AI introduces new opportunities for creating intelligent content that enhances learning experiences, fostering interactivity and a student-centered approach, and contributes to the development of intelligent tutoring systems, which simulate human tutors to deliver personalized and adaptive educational experiences.Acta Pedagogia Asiana['K. Tang']2024-02-19T00:00:00https://www.semanticscholar.org/paper/d9d82ee15cc1f7711d8843ffada53aae2887f160 2ffa16f49aa64805aa5076d9a014f9b0f3cc0e68Humanity has made huge progress over the past millennia. We are working with technologies, robots that not only help us to work accurately, efficiently and quickly, but they work in a similar way to the human brain: they perceive, think, learn and solve problems. In my research, I will focus on artificial intelligence, which is becoming more and more popular nowadays, looking at its past, present and future, its main trends in the corporate sector, and how it threatens people's job opportunities. At the same time, one of my research objectives is to investigate how much the development of a country is related to the uptake of AI in the European Union, which I will test with correlation analysis, taking into account indicators of artificial intelligence penetration in the corporate sector from one side and the various AI indicators such as digital penetration, internet usage, computer culture, and economic indicators as GDP per capita from the other side.   Applied Studies in Agribusiness and Commerce70One of the research objectives is to investigate how much the development of a country is related to the uptake of AI in the European Union, taking into account indicators of artificial intelligence penetration in the corporate sector from one side and the various AI indicators such as digital penetration, internet usage, computer culture, and economic indicators as GDP per capita from the other side.Applied Studies in Agribusiness and Commerce['János Balla', 'Lóránd-István Králik']2024-02-19T00:00:00https://www.semanticscholar.org/paper/2ffa16f49aa64805aa5076d9a014f9b0f3cc0e68 a8cca29fb058de24ed4197bf61c444b445bdce36The article is devoted to the prospects of studying the concept of accounting and analytical mechanisms for ensuring management decision-making of agro-industrial complex organizations of agro-industrial complex organizations with an active ecological position using the «genetic homogeneity» of elements. This will allow the use of automatic processing systems based on artificial intelligence and machine learning. The article presents the results of an analysis of the problems and tasks of the development of the Russian agro-industrial sector, taking into account the tendency to forcibly divide the countries of the world into two opposing camps.Buhuchet v sel'skom hozjajstve (Accounting in Agriculture)10The article presents the results of an analysis of the problems and tasks of the development of the Russian agro-industrial sector, taking into account the tendency to forcibly divide the countries of the world into two opposing camps.Buhuchet v sel'skom hozjajstve (Accounting in Agriculture)['Y. Katkov', 'A. Romanova', 'M. Dzhikiya']2024-02-19T00:00:00https://www.semanticscholar.org/paper/a8cca29fb058de24ed4197bf61c444b445bdce36 1e40db15a17f3f93b9b5136c7517ce34da75a0d4The digital economy and AI development have significantly revolutionized diverse sectors, profoundly influencing productivity. Focusing on agriculture and the artificial fiber manufacturing industry, this paper scrutinizes the impact of AI on productivity in these domains. By methodically analyzing prevalent literature, research studies, industry reports, and case studies, this investigation aims to elucidate how AI has heightened productivity within these sectors. The results exhibit substantial enhancements in resource optimization, yield enhancement, quality control, and efficiency resulting from the implementation of AI. Furthermore, the subsequent sections of discussion and conclusion elucidate these findings, emphasizing the potential advantages and enduring challenges concerning AI’s assimilation to ameliorate productivity within the digital economy.Finance &amp; Economics40The subsequent sections of discussion and conclusion elucidate the potential advantages and enduring challenges concerning AI’s assimilation to ameliorate productivity within the digital economy.Finance &amp; Economics['Haichuan Huang']2024-02-19T00:00:00https://www.semanticscholar.org/paper/1e40db15a17f3f93b9b5136c7517ce34da75a0d4 9c067960a689e2b3a9fd41a8fdf3bf9a44e4715fThe recent advances in Generative Artificial In-telligence (GenAI) and Large Language Models (LLMs) have generated significant interest across the world. For a successful adoption of GenAI and LLMs by industry, it is critical to identify their potential benefits, impact, and challenges. Accordingly, in this work, we investigate a few use cases of LLMs, which are relevant across most industry segments. In order to empirically evaluate the impact of GenAI on the code generation use case, we build CodePrompt, a handcrafted dataset of sequential prompts used by a human user to generate code. We approximate efficiency by considering the ratio of the number of tokens of code generated by an LLM to the number of tokens in the user's prompt. Experimental results reveal that a sequential trial of prompts for code generation may lead to an efficiency factor of about 6.33, on average, which means that a user's effort is reduced to about one-sixth.Digital Signal Processing and Signal Processing Education Workshop160This work builds CodePrompt, a handcrafted dataset of sequential prompts used by a human user to generate code, and results reveal that a sequential trial of prompts for code generation may lead to an efficiency factor of about 6.33, on average, which means that a user's effort is reduced to about one-sixth.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Barun Kumar Saha']2024-02-19T00:00:00https://www.semanticscholar.org/paper/9c067960a689e2b3a9fd41a8fdf3bf9a44e4715f c4020502f3e913bec1879aee86ca31f15dd70a22Journal of Artificial Intelligence, Machine Learning and Data Science03Journal of Artificial Intelligence, Machine Learning and Data Science['Mohammed Sallam', 'Johan Snygg', 'Malik Sallam']2024-02-19T00:00:00https://www.semanticscholar.org/paper/c4020502f3e913bec1879aee86ca31f15dd70a22 5a657273b9cdf3d30d5a03b66aacec441b30a95bThe rapid spread of conversational AI, as well as the potential for personal conversations with chatbots, makes it relevant to examine what norms and values underlie chatbot responses. This article examines the feeling rules for anger implicitly communicated by a recent chatbot (ChatGPT). Querying the chatbot about appropriate and inappropriate anger, the study shows how specific feeling rules are articulated by AI. The chatbot communicates norms of productive, respectful, constructive, controlled and calm expression of anger through talk and, as such, relies on communication as a pervasive cultural repertoire. Based on a rereading of Boltanski and Thévenot’s (2006) economies of worth focusing on feeling rules, it is argued that different moral repertoires have implications for feeling rules. Using this theoretical framework to analyse the responses of the chatbot, it is evident that it primarily relies on both the industrial and the domestic orders of worth to assess anger. The chatbot articulates the problem of anger as unproductiveness and disrespect. The feeling rules implied in the responses of the chatbot reflect a neoliberal conception of self as individually responsible, productive, self-regulating, emotionally competent and able to find solutions. The seemingly neutral advice of the chatbot potentially depoliticises anger, disciplines people to remain productive and respectful and narrows the scope of anger expressions that are deemed acceptable.Emotions and Society500The feeling rules for anger implicitly communicated by a recent chatbot (ChatGPT) are examined and it is evident that it primarily relies on both the industrial and the domestic orders of worth to assess anger.Emotions and Society['Merete Monrad']2024-02-19T00:00:00https://www.semanticscholar.org/paper/5a657273b9cdf3d30d5a03b66aacec441b30a95b 1e2cf514f3b313e0e0b3b9afb149a2db8d329bc4AI and Ethics80AI and Ethics['James Oluwaseyi Hodonu-Wusu']2024-02-19T00:00:00https://www.semanticscholar.org/paper/1e2cf514f3b313e0e0b3b9afb149a2db8d329bc4 a70d10fe6d40dc6dce522f8b37c1d9a3448846f2Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)50Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)['Liuyi Zhang']2024-02-19T00:00:00https://www.semanticscholar.org/paper/a70d10fe6d40dc6dce522f8b37c1d9a3448846f2 425f1f895c9aedc4926d14b306fad51f13a63e90Journal of Acute Care60Journal of Acute Care['M. Kanchi']2024-02-19T00:00:00https://www.semanticscholar.org/paper/425f1f895c9aedc4926d14b306fad51f13a63e90 a7b8221de50b981431c04c41999bea76e7f1b54bChanging English290Changing English['Stuart Bender']2024-02-19T00:00:00https://www.semanticscholar.org/paper/a7b8221de50b981431c04c41999bea76e7f1b54b dddfa203aa3f00a8757447f05c3fe0146f8de8d8Journal of Foodservice Business Research520Journal of Foodservice Business Research['Yakup Kemal Ozekici', 'Cemal Ersin Silik', 'Ahmet Usakli']2024-02-19T00:00:00https://www.semanticscholar.org/paper/dddfa203aa3f00a8757447f05c3fe0146f8de8d8 c94ce17d63a7959d19b9e1aa06b98fde6dfd4456While organisms are continually experiencing and interacting with their environments, the role and extent of experiences in behavioral development has been controversial. Some argue that adaptive behaviors are acquired through experiences, while others claim they are the result of innate programs that don’t require environmental input. Such controversies have historically occurred within animal behavior and psychology, but similar debates are emerging in the field of artificial intelligence. Here, the debate is centered on those who design experience-dependent systems that are trained to learn the statistical properties of “environmental” inputs, and those advocating the use of pre-packaged artificially “innate” responses tailored to prespecified inputs. Those favoring artificial innateness draw analogies with animal behavior to argue that innateness is necessary for the emergence of complex adaptive behavior. But does behavioral development in animals reflect the unfolding of innate programs? Here we highlight the widespread role of specifically causal experiences in the ontogeny of species-typical behaviors. All behaviors are an outcome of a chain of organism-environment transactions—called ontogenetic niches—that begin in the earliest periods of life. This challenges the notion that organisms come prepared with innate programs for behavior. We suggest that an artificial intelligence that matches the complexity of animal behavior should be based on principles of behavioral development, where experiences are necessary and specifically causal factors in the emergence of behavioral abilities.Adaptive Behavior450It is suggested that an artificial intelligence that matches the complexity of animal behavior should be based on principles of behavioral development, where experiences are necessary and specifically causal factors in the emergence of behavioral abilities are highlighted.Adaptive Behavior['Gregory M Kohn', 'Mateusz Kostecki']2024-02-19T00:00:00https://www.semanticscholar.org/paper/c94ce17d63a7959d19b9e1aa06b98fde6dfd4456 ddefdf5f32e1c2550d2ca0e27fb37d7d10bfce81134 million gallons of oil were spilled into the Gulf of Mexico after the explosion of an offshore oil rig in 2010. Known as the Deepwater Horizon spill, this event crippled marine environments spanning thousands of miles and killed countless sea creatures already deemed at risk of extinction. Over 10 years and billions of dollars later, efforts to clean up this spill continue. Rapid mitigation is necessary to prevent future incidents from spiraling out of control. After an oil spill, various organizations must decide how to remediate it. To do so, there are close to a dozen methods employed today. Each approach has its pros and cons and must be carefully selected based on spill conditions. Some techniques (such as in-situ burning of the oil slick off the water) are highly effective but have environmentally degrading effects. Choosing a suboptimal remediation tactic can lead to billions of wasted dollars, and more importantly, leftover oil that continues to harm the environment. During this study, an artificial intelligence (AI) based system using a convolutional neural network (CNN) has been developed to prescribe the most effective oil spill countermeasure. Findings were used to develop a mobile application to further expedite oil spill cleanup and recovery in real time. After being tested at various configurations, the machine learning model achieved a maximum average accuracy of 93.1 % after 16.19 seconds of training time with 10 epochs and a batch-size of 16. This work significantly enhances our ability to quickly remediate oil spills, protecting the environment from this disastrous calamity.Digital Signal Processing and Signal Processing Education Workshop100An artificial intelligence (AI) based system using a convolutional neural network (CNN) has been developed to prescribe the most effective oil spill countermeasure and foundings were used to develop a mobile application to further expedite oil spill cleanup and recovery in real time.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Sathvik Chemudupati']2024-02-19T00:00:00https://www.semanticscholar.org/paper/ddefdf5f32e1c2550d2ca0e27fb37d7d10bfce81 e4e21528bba143cdbe8209c241649980fd4eb3f6Generative Artificial Intelligence (GenAI) is revolutionizing manufacturing, automating design, predicting failures, and cutting costs. This paper explores its diverse applications, including predictive maintenance and realtime monitoring, highlighting its role in enhancing productivity and innovation in manufacturing processes. Echoing this, a 2023 KPMG survey shows 77% of executives rate GenAI as a pivotal technology, with 71 % planning its adoption within two years, underscoring its potential to transform manufacturing operations and strategies [1].Digital Signal Processing and Signal Processing Education Workshop70This paper explores GenAI's diverse applications, including predictive maintenance and realtime monitoring, highlighting its role in enhancing productivity and innovation in manufacturing processes.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Mofeoluwa Jide-Jegede', 'Tomiwa Omotesho']2024-02-19T00:00:00https://www.semanticscholar.org/paper/e4e21528bba143cdbe8209c241649980fd4eb3f6 757b6183a77050dcfd0fef850f8130cbab4a153bThis research aims to study and analyze strategies for the strategic application of artificial intelligence in marketing by developing a framework that guides artificial intelligence planning strategies in marketing systematically and can be followed up by making decisions about service strategies at the J&T Company. The research locus is the object and source of data from the place being researched so that the information obtained can provide accurate data and truth in research. J&T Cargo is a technologically innovative express company under the auspices of the J&T Group. The locus of this research was carried out at: Marketing manager, HR manager and marketing expert team. The qualitative approach carried out through the data analysis technique used is the Manual Data Analysis Procedure (MDAP) by Rofiah, (2022), from the results of interviews accompanied by triangulation of sources, methods and theories it can be concluded that artificial intelligence for marketing strategies at J&T Cargo is used to determine preferences. Various Consumer Segments; Micro Segment Customers; Target Cause Marketing Outreach; Identify the Best Target; Refining Customer Based Perception Maps; Positioning Slogan; Psychographic Consumer Segmentation; Tourism Consumer Segment; New Customer Promotion Target; Target Digital Consumers; Target Customers Based on Brand with the aim of monitoring local market developments, so that in this business J&T Cargo is not left behind compared to other competitors. This paper also contributes to strategic marketing research by providing a systematic and rigorous approach to identifying research gaps that bridge strategic marketing practice research and artificial intelligence.Journal of economics, finance and management studies00From the results of interviews accompanied by triangulation of sources, methods and theories it can be concluded that artificial intelligence for marketing strategies at J&T Cargo is used to determine preferences.JOURNAL OF ECONOMICS, FINANCE AND MANAGEMENT STUDIES['Zaenur Rizky', 'Chusnul Rofiah']2024-02-19T00:00:00https://www.semanticscholar.org/paper/757b6183a77050dcfd0fef850f8130cbab4a153b d30493d6d1e6deabc8fa5f7b69bf802ffb0dd3daIn this work, a novel framework for detecting mali-cious networks in the IoT-enabled Metaverse networks to ensure that malicious network traffic is identified and integrated to suit optimal Metaverse cybersecurity is presented. First, the study raises a core security issue related to the cyberthreats in Metaverse networks and its privacy breaching risks. Second, to address the shortcomings of efficient and effective network intrusion detection (NIDS) of dark web traffic, this study employs a quantization-aware trained (QAT) 1D CNN followed by fully con-nected networks (ID CNNs-GRU-FCN) model, which addresses the issues of and memory contingencies in Metaverse NIDS models. The QAT model is made interpretable using eXplainable artificial intelligence (XAI) methods namely, SHapley additive exPlanations (SHAP) and local interpretable model-agnostic ex-planations (LIME), to provide trustworthy model transparency and interpretability. Overall, the proposed method contributes to storage benefits four times higher than the original model without quantization while attaining a high accuracy of 99.82 %.Digital Signal Processing and Signal Processing Education Workshop230A quantization-aware trained (QAT) 1D CNN followed by fully con-nected networks (ID CNNs-GRU-FCN) model is employed, which addresses the issues of and memory contingencies in Metaverse NIDS models.2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)['Ebuka Chinaechetam Nkoro', 'C. I. Nwakanma', 'Jae-Min Lee', 'Dong‐Seong Kim']2024-02-19T00:00:00https://www.semanticscholar.org/paper/d30493d6d1e6deabc8fa5f7b69bf802ffb0dd3da 21b1541b75df337afbfa7d69021ac5906617e91eThis study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.Computer Science &amp; IT Research Journal04Assessment of how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends reveals that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture.Computer Science &amp; IT Research Journal['Oluwafunmi Adijat Elufioye', 'Chinedu Ugochukwu Ike', 'Olubusola Odeyemi', 'Favour Oluwadamilare Usman', 'Noluthando Zamanjomane Mhlongo']2024-02-18T00:00:00https://www.semanticscholar.org/paper/21b1541b75df337afbfa7d69021ac5906617e91e 72be8232b058e8e63c67124dcdb528587c56e98cThis review explores the intricate and evolving relationship between Artificial Intelligence (AI) and Quantum Computing within the realm of financial markets. As technology continues to advance, the integration of AI and quantum computing has emerged as a paradigm-shifting force, promising unprecedented capabilities to analyze and navigate the complexities of financial systems. This critical review delves into the synergies, challenges, and potential disruptions arising from the intersection of these two transformative technologies. The utilization of AI in financial markets has witnessed remarkable progress in recent years, with machine learning algorithms, deep neural networks, and natural language processing contributing to enhanced data analysis, predictive modeling, and decision-making. However, the computational demands of these sophisticated algorithms often surpass the capabilities of classical computing architectures, paving the way for the exploration of quantum computing as a potential solution. Quantum computing, with its ability to process vast datasets and perform complex calculations at speeds inconceivable by classical computers, presents a revolutionary approach to addressing the computational challenges faced by AI in financial applications. The review critically examines the potential advantages of quantum computing, such as its capacity to solve optimization problems, simulate financial scenarios, and secure data through quantum cryptography. Despite the promises, the integration of AI and quantum computing in financial markets is not without hurdles. The review investigates the current limitations, including hardware constraints, error correction challenges, and the high costs associated with quantum computing infrastructure. Ethical considerations and regulatory frameworks surrounding the implementation of such powerful technologies in financial decision-making also warrant careful examination. This critical review provides a comprehensive analysis of the intersection of AI and quantum computing in financial markets, shedding light on the transformative potential, challenges, and ethical implications that accompany this cutting-edge convergence of technologies. Understanding this intersection is crucial for stakeholders seeking to navigate the evolving landscape of finance and technology. Keywords: AI, Quantum, Computing, Financial Market, Review.Computer Science &amp; IT Research Journal04This critical review provides a comprehensive analysis of the intersection of AI and quantum computing in financial markets, shedding light on the transformative potential, challenges, and ethical implications that accompany this cutting-edge convergence of technologies.Computer Science &amp; IT Research Journal['Akoh Atadoga', 'Chinedu Ugochukwu Ike', 'Onyeka Franca Asuzu', 'Benjamin Samson Ayinla', 'Ndubuisi Leonard Ndubuisi', 'Rhoda Adura Adeleye']2024-02-18T00:00:00https://www.semanticscholar.org/paper/72be8232b058e8e63c67124dcdb528587c56e98c f2c76aca199104c7e94827b3479b6836dfbd10e0Artificial intelligence (AI) in education transforms the instructional processes and learning competence of students. AI can adapt to the individual learning needs of students. By analyzing students’ progress, performance, and preferences, AI systems can deliver tailored content, recommend additional resources, and provide feedback. The purpose of this study was to develop initial understanding on how AI models help students cope with math anxiety and lack of confidence in engaging with mathematics learning. This exploratory research established the connections between what students feel when using AI and how it benefits them. College students (n = 20) enrolled in different math-related programs (i.e., engineering, statistics/mathematics, computer science, education) were purposively sampled for a one-on-one interview. Thematic analysis indicated that students are now turning to AI models as a coping mechanism to alleviate math anxiety and boost their self-assurance. These AI models function as “mentors” and “math companions” that offer step-by-step explanations and personalized support. Their adaptability and personalized approach make mathematics more accessible to students, with the potential to reduce anxiety and enhance the overall learning experience. Moreover, the use of AI models encourages a sense of independence, motivating students to actively engage in self-guided learning. The findings open new questions about using AI models in improving the self-efficacy and confidence of students in mathematics learning. There is also an opportunity to build an AI-assisted learning with a focus on psychological interventions and behavioral interconnections mediating students’ academic performance.Environment and Social Psychology811Initial understanding on how AI models help students cope with math anxiety and lack of confidence in engaging with mathematics learning is developed and new questions about using AI models in improving the self-efficacy and confidence of students in mathematics learning are opened.Environment and Social Psychology['H. V. Inoferio', 'Marcelino Espartero', 'M. Asiri', 'Michelle Damin', 'Jason V. Chavez']2024-02-18T00:00:00https://www.semanticscholar.org/paper/f2c76aca199104c7e94827b3479b6836dfbd10e0 7987818066aa92322b16a62b309ab4de98333944Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts under three randomly sequenced conditions: no AI assistance (control), next-sentence suggestions (low scaffolding), and next-paragraph suggestions (high scaffolding). Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time). While low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users. No significant cognitive burden was observed while using the scaffolded writing tools, but a moderate decrease in text ownership and satisfaction was noted. Our results have broad implications for the design of AI-powered writing tools, including the need for personalized scaffolding mechanisms.International Conference on Human Factors in Computing Systems741A U-shaped impact of scaffolding on writing quality and productivity (words/time) is revealed and has broad implications for the design of AI-powered writing tools, including the need for personalized scaffolding mechanisms.ArXiv['Paramveer S. Dhillon', 'Somayeh Molaei', 'Jiaqi Li', 'Maximilian Golub', 'Shaochun Zheng', 'Lionel P. Robert']2024-02-18T00:00:00https://www.semanticscholar.org/paper/7987818066aa92322b16a62b309ab4de98333944 1cf10e0e7e33775ac7047cca3569fa38536d76a0The relentless evolution of Artificial Intelligence (AI) has significantly transformed the landscape of business analytics, offering unparalleled opportunities for organizations to enhance decision-making processes and gain a competitive edge. This study provides a comprehensive review of AI integration in business analytics, focusing on the distinctive trends observed in both the United States (USA) and African business ecosystems. In the United States, a technologically advanced market, the adoption of AI in business analytics has witnessed remarkable strides. Corporations across various sectors leverage AI-driven tools and algorithms to analyze vast datasets, extract meaningful insights, and optimize strategic decision-making. The USA's emphasis on innovation and robust technological infrastructure has propelled AI integration as a cornerstone of modern business strategies. Contrastingly, the African continent is experiencing a unique trajectory in AI adoption within the realm of business analytics. Despite facing challenges related to infrastructure and resource limitations, African businesses are increasingly recognizing the transformative potential of AI. Initiatives promoting AI education and collaboration with global tech partners have contributed to a growing awareness and implementation of AI in business analytics across various African industries. This review explores commonalities and divergences in the trends observed between the USA and Africa, highlighting the factors influencing AI integration in each region. Factors such as regulatory frameworks, cultural nuances, and economic landscapes play a pivotal role in shaping the AI landscape in both contexts. By understanding these trends, businesses can tailor their AI strategies to align with regional dynamics, fostering sustainable growth and innovation. This study provides valuable insights into the evolving landscape of AI integration in business analytics, offering a comparative analysis of trends in the USA and Africa. As organizations navigate the complexities of adopting AI, acknowledging regional variations becomes crucial for developing effective and context-specific strategies. Keywords: AI, Business Analytics, USA, Africa, Business, Innovation.Computer Science &amp; IT Research Journal00This study provides valuable insights into the evolving landscape of AI integration in business analytics, offering a comparative analysis of trends in the USA and Africa, highlighting the factors influencing AI integration in each region.Computer Science &amp; IT Research Journal['Femi Osasona', 'Andrew Ifesinachi Daraojimba', 'Akoh Atadoga', 'Shedrack Onwusinkwue', 'Ogugua Chimezie Obi', 'Samuel Onimisi Dawodu']2024-02-18T00:00:00https://www.semanticscholar.org/paper/1cf10e0e7e33775ac7047cca3569fa38536d76a0 37582d152460d5cd9d755c5875ea536835a7305aManaging a multi-tenant network presents formidable challenges to managed service providers (MSPs) as they endeavour to provide exceptional service quality to clients scattered across diverse locations. These challenges are compounded by clients' stringent requirements to shield the identities of their network elements, preventing their exposure for centralised monitoring purposes. Consequently, the task of MSPs in overseeing geographically dispersed networks becomes more intricate due to the necessity of establishing separate real-time monitoring systems for each client. Moreover, the importance of visualising monitored data cannot be overstated, as it unveils invaluable patterns that illuminate service quality issues. Consequently, a monitoring tool equipped with a richly informative dashboard becomes indispensable for strategic planning and the delivery of top-tier services. Furthermore, the integration of AI -driven resource prediction enhances incident resolution capabilities by enabling proactive alert notifications. In response to these complex challenges, this research introduces an architectural solution centred around a data visualisation platform. This platform harnesses the power of a portable, decentralised AI-enabled real-time network monitoring tool custom-tailored for multi-tenant networks. The proposed approach adopts a pragmatic strategy that seamlessly integrates multiple open-source modules across geographically dispersed networks. The result is nothing short of remarkable compared with the manual approach, with network element downtime experiencing a remarkable 95% reduction, and incident resolution seeing a noteworthy 90 % reduction. This transformative impact directly benefits service desk professionals and network design engineers alike of MSPs.International Conference on Big Data and Smart Computing160The proposed approach adopts a pragmatic strategy that seamlessly integrates multiple open-source modules across geographically dispersed networks and is nothing short of remarkable compared with the manual approach, with network element downtime experiencing a remarkable 95% reduction, and incident resolution seeing a noteworthy 90 % reduction.2024 IEEE International Conference on Big Data and Smart Computing (BigComp)['Adeel Rafiq', 'Muhammad Zeeshan Shakir', 'David Gray', 'Julie Inglis', 'Fraser Ferguson']2024-02-18T00:00:00https://www.semanticscholar.org/paper/37582d152460d5cd9d755c5875ea536835a7305a 991ea65441a76cf817475541c60e2740b352c71dAs artificial intelligence (AI) systems become more sophisticated, they are increasingly integrated into high-stakes decision-making processes, such as hiring, fraud detection, loan approvals, and medical diagnoses. However, this growing reliance on AI raises concerns about the potential for these systems to perpetuate and amplify societal biases. Researchers have developed two main approaches to bias mitigation in AI to address this issue: human-in-the-loop (HITL) systems and AI fairness toolkits. HITL systems involve human reviewers actively participating in the AI decision-making process, while AI fairness toolkits are software tools that can identify and mitigate bias. HITL systems are particularly effective in addressing biases tied to specific domains, while AI fairness toolkits can be useful in identifying and addressing bias proactively. This paper examines different combinations of HITL systems and AI fairness toolkits, conducts an experiment to evaluate biases in hiring decisions using each, and provides recommendations for organizations considering implementing one or both approaches.International Conference on Big Data and Smart Computing220Different combinations of HITL systems and AI fairness toolkits are examined, an experiment to evaluate biases in hiring decisions using each is conducted, and recommendations for organizations considering implementing one or both approaches are provided.2024 IEEE International Conference on Big Data and Smart Computing (BigComp)['Christopher G. Harris']2024-02-18T00:00:00https://www.semanticscholar.org/paper/991ea65441a76cf817475541c60e2740b352c71d 4bb0dddba3ae0824ee3859a7365773353430f154Purpose: The objective of this study was to examine the role of Artificial Intelligence (AI) in personalizing online learning. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings revealed that there exists a contextual and methodological gap relating to the role of Artificial Intelligence (AI) in personalizing online learning. Preliminary empirical review revealed the transformative potential of AI in personalizing online learning, aligning with established learning theories and offering practical applications such as adaptive content delivery and data-driven decision-making. However, the responsible and ethical use of AI remains paramount, requiring privacy safeguards and ongoing collaboration among stakeholders. This research underscores AI's capacity to make online education more engaging and effective while emphasizing the need for ongoing exploration and responsible implementation to shape the future of learning. Unique Contribution to Theory, Practice and Policy: The Cognitive Load Theory (CLT), the Constructivist Learning Theory and Self-Determination Theory (SDT) may be used to anchor future studies on personalizing online learning. The study made the following recommendations: Incorporating artificial intelligence (AI) effectively into online learning requires institutions to integrate AI-powered personalization tools, continually monitor and improve AI systems, prioritize ethical considerations and transparency, offer professional development for educators, support research and evaluation efforts, focus on customization and scalability, and establish regular feedback mechanisms from all stakeholders. These measures collectively ensure that AI enhances online learning experiences by providing tailored content and recommendations while maintaining data privacy, ethical standards, and educator involvement, ultimately benefiting learners and educators alike.Journal of Online and Distance Learning00It is revealed that there exists a contextual and methodological gap relating to the role of Artificial Intelligence (AI) in personalizing online learning, and measures collectively ensure that AI enhances online learning experiences by providing tailored content and recommendations while maintaining data privacy, ethical standards, and educator involvement.Journal of Online and Distance Learning['Vince Willis']2024-02-18T00:00:00https://www.semanticscholar.org/paper/4bb0dddba3ae0824ee3859a7365773353430f154 92ef18c06ef473bb086d168885a9ac3eff2abf2eThis study delves into the varied sentiments and attitudes prevalent across the different sectors related to integrating Artificial intelligence (AI). Understanding how sectors perceive and embrace these changes is crucial for informed decision-making and policy formulation as AI technologies continue to thrive in industries. Artificial intelligence is making waves in 2023 as businesses, consumers, and the government benefit from this technology, promising new opportunities, economic growth, and the transformation of different industries. There was so much propaganda surrounding artificial intelligence based on economic factors such as employment, education, income patterns, housing, and food security, and with time, these issues have been proven true or false. AI will have a broadly beneficial effect on society.Engineering International450Engineering International['Ashish K Saxena']2024-02-18T00:00:00https://www.semanticscholar.org/paper/92ef18c06ef473bb086d168885a9ac3eff2abf2e 2062d3566e7e28417060f97fefb328e5c3b8f8dfWith the very recent and broad awareness of Large Language Models (LLMs) across many disciplines and industries, there is a high level of excitement and expectation for the potential of this technology. As with any significant advance in technology, there is also the accompanying hype with overstatements of the capabilities of LLMs (eIEEE International Solid-State Circuits Conference10This paper presents a meta-modelling architecture suitable for modeling large language models (LLMs) and investigates the architecture’s role in knowledge representation and provides a meta-modelling architecture suitable for machine learning.2024 IEEE International Solid-State Circuits Conference (ISSCC)['Larry Heck', 'Eric Karl', 'Jun-Seok Park', 'Jae-sun Seo', 'Yongpan Liu', 'Vivek De']2024-02-18T00:00:00https://www.semanticscholar.org/paper/2062d3566e7e28417060f97fefb328e5c3b8f8df 5be39696b4707b1955e91e8f21dfad6f8a231472Alzheimer’s disease (AD), a common cause of dementia, affects over 30 million people worldwide and accounts for more than 1% of the global GDP [1]. Given that age is a significant risk factor, the number of AD patients is projected to double in the next two decades. While there is currently no cure for AD, increasing evidence suggests that electrical brain stimulation is a potential treatment [2]. The hippocampus (HC) is a critical brain region that exhibits specific rhythmic neural activities during memory encoding and consolidation. In-phase stimulation has the potential to entrain and amplify these rhythms, thereby enhancing memory formation and retrieval. Consequently, innovative stimulation protocols are being developed to treat AD by modulating the HC and its associated brain regions.IEEE International Solid-State Circuits Conference00New stimulation protocols are being developed to treat AD by modulating the HC and its associated brain regions, which have the potential to entrain and amplify these rhythms, thereby enhancing memory formation and retrieval.2024 IEEE International Solid-State Circuits Conference (ISSCC)['Yuhan Hou', 'Yi Zhu', 'Xiao Wu', 'Yinfei Li', 'Timothy H. Lucas', 'Andrew G. Richardson', 'Xilin Liu']2024-02-18T00:00:00https://www.semanticscholar.org/paper/5be39696b4707b1955e91e8f21dfad6f8a231472 1a1dd67b0b4f1e5ddce46e2d396b3bd564829d2eJournal of Teaching in Travel &amp; Tourism40Journal of Teaching in Travel &amp; Tourism['Anh Viet Le', 'Warren Metzger']2024-02-18T00:00:00https://www.semanticscholar.org/paper/1a1dd67b0b4f1e5ddce46e2d396b3bd564829d2e a8960687d9747220c66f7605de4dd1755865bbe0Artificial Intelligence (AI) has started making its impact in the education field recently. Although Artificial Intelligence (AI) has long existed in other fields such as medicine, engineering, journalism, and forensic analysis, it has only made its impact in the education field after the introduction of ChatGPT at the end of November 2022. Generative AI is seen as a tool that can assist teachers and students in the field of academics such as generating ideas, evaluating essays, storytelling, and providing feedback. It has even been considered as the co-author in students’ manuscripts and essays. However, Artificial Intelligence (AI) is still under study in the field of writing as it has been introduced recently. Based on the gap indicating the rare usage of AI in writing, this study hopes to enlighten researchers, educators, and application developers to focus on developing AI applications for writing. This is done by providing a systematic review on the use of Artificial Intelligence (AI) in writing over the last 10 years.International Journal of Academic Research in Progressive Education and Development231This study hopes to enlighten researchers, educators, and application developers to focus on developing AI applications for writing by providing a systematic review on the use of Artificial Intelligence (AI) in writing over the last 10 years.International Journal of Academic Research in Progressive Education and Development['Shirley Ling Jen', 'Abdul Rahim Hj Salam']2024-02-18T00:00:00https://www.semanticscholar.org/paper/a8960687d9747220c66f7605de4dd1755865bbe0 b0f19f294ef445d092e1bc2c85e58650ee261f4fArtificial intelligence (AI), a branch of computer science, involves developing intelligent computer programs to mimic human intelligence and automate various processes [...]BioMedInformatics500BioMedInformatics['M. Gromiha', 'Palanisamy Preethi', 'Medha Pandey']2024-02-18T00:00:00https://www.semanticscholar.org/paper/b0f19f294ef445d092e1bc2c85e58650ee261f4f 04e9712a4ae3a717caceee5d71203af96050674aChronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT‐based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management.MedComm – Future Medicine460A comprehensive exploration of the latest breakthroughs in the field of CT‐based AI in COPD diagnosis is offered, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management.MedComm – Future Medicine['Fangfei Wang', 'Sixiang Li', 'Yuanxu Gao', 'Shiyue Li']2024-02-18T00:00:00https://www.semanticscholar.org/paper/04e9712a4ae3a717caceee5d71203af96050674a ca26dbf1a7ab79b75674a9c438f298d28cc0f545The paper scrutinizes the social business model in light of current and impending challenges within the capitalist system. It emphasizes the integration of this model into a civil economy oriented toward public well-being, illustrating how it effectively addresses environmental, social, and economic issues while ensuring economic sustainability. The strategic utilization of Artificial Intelligence (AI) to optimize resources and enhance production efficiency is explored as a pivotal element in achieving sustainable development goals. The article then presents a case study—Madri Leone, a winery in Puglia, Italy—run by two sisters. This case study serves as a concrete example of success, combining family tradition, social commitment, and sustainable practices. In summary, the primary objective of this contribution is to demonstrate the compatibility of the social business model with the current and future socio-economic context. It highlights the model’s potential to contribute significantly to the resolution of social and environmental challenges while maintaining economic sustainability.Sustainable Economies250The model’s potential to contribute significantly to the resolution of social and environmental challenges while maintaining economic sustainability is highlighted, highlighting the model’s potential to contribute significantly to the resolution of social and environmental challenges while maintaining economic sustainability.Sustainable Economies['Federico De Andreis', 'U. Comite', 'Alba M. Gallo', 'Diana M. Andone', 'Giacomo Ciaschi']2024-02-18T00:00:00https://www.semanticscholar.org/paper/ca26dbf1a7ab79b75674a9c438f298d28cc0f545 7f5eff624db26a38aad9c1718f260c8423e7a5cdThis research explores the profound impact of Artificial Intelligence (AI) on the marketing landscape, examining its evolution from early applications in data analytics to contemporary uses. AI’s role in transforming marketing strategies is pivotal, encompassing personalized customer experiences, predictive analytics, and efficiency improvements. The study highlights AI’s ability to process vast data sets rapidly, reshaping customer engagement and market analysis. Focusing on the multifaceted integration of AI into marketing, the research emphasizes its contribution to customer personalization, fostering brand loyalty, and boosting conversion rates. Predictive analytics, another cornerstone, enables businesses to craft strategies aligned with future market dynamics proactively. Despite its advantages, ethical considerations surrounding data privacy and consumer consent are pivotal, requiring responsible and transparent AI use. The study is a comprehensive resource for academic researchers and industry professionals, providing insights into historical development, current applications, and ethical implications. It offers organizations a clear roadmap for leveraging AI effectively in marketing operations amid the growing reliance on digital platforms and expanding data availability. Anticipating future developments, the research discusses the convergence of AI with augmented reality, virtual reality, predictive customer journeys, and the Internet of Things. Balancing technological advancements with ethical considerations remains crucial, emphasizing the need for ongoing adaptation in this dynamic landscape. The synergy between AI and marketing propels businesses into a new era of precision, personalization, and strategic insight, redefining customer connections. In this transformative journey, embracing change, innovation, and ethical practices becomes paramount for sustained success.International Journal of Management and Administration00This research explores the profound impact of Artificial Intelligence (AI) on the marketing landscape, examining its evolution from early applications in data analytics to contemporary uses, and discusses the convergence of AI with augmented reality, virtual reality, predictive customer journeys, and the Internet of Things.International Journal of Management and Administration['Hafize Nurgül Durmuş Şenyapar']2024-02-18T00:00:00https://www.semanticscholar.org/paper/7f5eff624db26a38aad9c1718f260c8423e7a5cd 00f3a2a9169a77cdd5d43e84ad61981945f78218Various studies have been carried out to establish the key drivers impacting small enterprise sustainable performance in developing countries. Despite many policy-oriented studies to uncover the factors influencing SME resilience in emerging markets, these firms continue to register high failure rate, which has been further exacerbated by the Covid-19 pandemic. Guided by a history of linear- and log-linear econometric model estimation that ignores potential network effects, our study extends the literature by implicating SMME resilience as a production network. Utilising data from both incubated and non-incubated SMMEs, marking a departure from traditional linear econometric models, radial basis function artificial neural network algorithm was invoked to establish the drivers of SMME resilience during Covid-19 regime. The study extends the literature by implicating eXplainable Artificial Intelligence (XAI) methods. Specifically, optimal SHapley Additive Explanations values (SHAP values) were computed to enhance the prediction output from the machine learning algorithm. The XAI analytics provide insightful findings on the key drivers which influenced the resilience of SMMEs during the Covid-19 pandemic. The importance of innovation through introduction of new products, company age and higher number of marketing mediums is confirmed however total assets, analytics, educational level and number of workers surfaced as a threat to these enterprises’ sustainable performance. The study recommends that both the government and SMEs should leverage XAI to identify their heterogeneous attributes and inform intelligent decision-making which necessities their resilient performance.International Journal of Research In Business and Social Science410International Journal of Research in Business and Social Science (2147- 4478)['Helper Zhou', 'L. T. Chamba', 'R. D. Zondo']2024-02-18T00:00:00https://www.semanticscholar.org/paper/00f3a2a9169a77cdd5d43e84ad61981945f78218 eb59319cdd4431501e171b6b0423e7113e22be1fJournal of Neurology824The value of AI in neurology and brain health is explored, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation.Journal of neurology['Sebastian Voigtlaender', 'Johannes Pawelczyk', 'Mario Geiger', 'E. Vaios', 'P. Karschnia', 'M. Cudkowicz', 'Jorg Dietrich', 'Ira R J Hebold Haraldsen', 'Valery L Feigin', 'Mayowa Owolabi', 'Tara L White', 'Paweł Świeboda', 'Nita A. Farahany', 'Vivek Natarajan', 'Sebastian F. Winter']2024-02-17T00:00:00https://www.semanticscholar.org/paper/eb59319cdd4431501e171b6b0423e7113e22be1f ae8f9f02b090351543e5e0ea945e2993b27d9d42In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical question emerges: Are LLMs conscious of the existence of these decoding strategies and capable of regulating themselves? The current decoding generation process often relies on empirical and heuristic manual adjustments to hyperparameters based on types of tasks and demands. However, this process is typically cumbersome, and the decoding hyperparameters may not always be optimal for each sample. To address the aforementioned challenges, we propose a novel text generation paradigm termed Hyperparameter Aware Generation (HAG). By leveraging hyperparameter-aware instruction tuning, the LLM autonomously determines the optimal decoding strategy and configs based on the input samples, enabling self-regulation. Our approach eliminates the need for extensive manual tuning, offering a more autonomous, self-regulate model behavior. Experimental results spanning six datasets across reasoning, creativity, translation, and mathematics tasks demonstrate that hyperparameter-aware instruction tuning empowers the LLMs to self-regulate the decoding strategy and hyperparameter. HAG extends the current paradigm in the text generation process, highlighting the feasibility of endowing the LLMs with self-regulate decoding strategies.arXiv.org360Experimental results spanning six datasets across reasoning, creativity, translation, and mathematics tasks demonstrate that hyperparameter-aware instruction tuning empowers the LLMs to self-regulate the decoding strategy and hyperparameter.ArXiv['Siyin Wang', 'Shimin Li', 'Tianxiang Sun', 'Jinlan Fu', 'Qinyuan Cheng', 'Jiasheng Ye', 'Junjie Ye', 'Xipeng Qiu', 'Xuanjing Huang']2024-02-17T00:00:00https://www.semanticscholar.org/paper/ae8f9f02b090351543e5e0ea945e2993b27d9d42 3f4ce5d3066ab47b3c68f261ee68177648b53228Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these 'ironies of automation' have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users' roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.arXiv.org1292It is suggested how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation.ArXiv['Auste Simkute', 'Lev Tankelevitch', 'Viktor Kewenig', 'Ava Elizabeth Scott', 'Abigail Sellen', 'Sean Rintel']2024-02-17T00:00:00https://www.semanticscholar.org/paper/3f4ce5d3066ab47b3c68f261ee68177648b53228 1fa988d02320f713391a0325fb36d21e88842a45The advent of artificial intelligence (AI) has challenged academic institutions to ensure ethical practices and reward/promote merit. Adding formal insights into the importance of maintaining academic integrity, this paper examines the association of anti‐plagiarism/anti‐cheating policies with resources that facilitate such behavior. Using unique internet search indices of policies and resources, we find that the two are positively associated. This association is robust when internet policies are restricted to news searches, and include course syllabi. The findings reinforce the view that policies to check plagiarism/cheating likely lack teeth and maybe a step behind the resources that facilitate unethical behavior.Managerial and Decision Economics251The association of anti‐plagiarism/anti‐cheating policies with resources that facilitate such behavior is found to be positively associated with unique internet search indices of policies and resources.Managerial and Decision Economics['R. Goel', 'M. A. Nelson']2024-02-17T00:00:00https://www.semanticscholar.org/paper/1fa988d02320f713391a0325fb36d21e88842a45 658c852761dda1372c50d0698093532518c92387The robustness of AI-content detection models against cultivated attacks (e.g., paraphrasing or word switching) remains a significant concern. This study proposes a novel token-ensemble generation strategy to challenge the robustness of current AI-content detection approaches. We explore the ensemble attack strategy by completing the prompt with the next token generated from random candidate LLMs. We find the token-ensemble approach significantly drops the performance of AI-content detection models (The code and test sets will be released). Our findings reveal that token-ensemble generation poses a vital challenge to current detection models and underlines the need for advancing detection technologies to counter sophisticated adversarial strategies.arXiv.org281This study proposes a novel token-ensemble generation strategy to challenge the robustness of current AI-content detection approaches and underlines the need for advancing detection technologies to counter sophisticated adversarial strategies.ArXiv['Fan Huang', 'Haewoon Kwak', 'Jisun An']2024-02-17T00:00:00https://www.semanticscholar.org/paper/658c852761dda1372c50d0698093532518c92387 bce3f9ca6b711b727944b6df3e71c2f42b2586e0AIM This study explores the potential of a generative artificial intelligence tool (ChatGPT) as clinical support for nurses. Specifically, we aim to assess whether ChatGPT can demonstrate clinical decision-making equivalent to that of expert nurses and novice nursing students. This will be evaluated by comparing ChatGPT responses to clinical scenarios to those of nurses on different levels of experience. DESIGN This is a cross-sectional study. METHODS Emergency room registered nurses (i.e. experts; n = 30) and nursing students (i.e. novices; n = 38) were recruited during March-April 2023. Clinical decision-making was measured using three validated clinical scenarios involving an initial assessment and reevaluation. Clinical decision-making aspects assessed were the accuracy of initial assessments, the appropriateness of recommended tests and resource use and the capacity to reevaluate decisions. Performance was also compared by timing response generations and word counts. Expert nurses and novice students completed online questionnaires (via Qualtrics), while ChatGPT responses were obtained from OpenAI. RESULTS Concerning aspects of clinical decision-making and compared to novices and experts: (1) ChatGPT exhibited indecisiveness in initial assessments; (2) ChatGPT tended to suggest unnecessary diagnostic tests; (3) When new information required re-evaluation, ChatGPT responses demonstrated inaccurate understanding and inappropriate modifications. In terms of performance, the mean number of words utilized in ChatGPT answers was 27-41 times greater than that utilized by both experts and novices; and responses were provided approximately 4 times faster than those of novices and twice faster than expert nurses. ChatGPT responses maintained logical structure and clarity. CONCLUSIONS A generative AI tool demonstrated indecisiveness and a tendency towards over-triage compared to human clinicians. IMPACT The study shows that it is important to approach the implementation of ChatGPT as a nurse's digital assistant with caution. More study is needed to optimize the model's training and algorithms to provide accurate healthcare support that aids clinical decision-making. REPORTING METHOD This study adhered to relevant EQUATOR guidelines for reporting observational studies. PATIENT OR PUBLIC CONTRIBUTION Patients were not directly involved in the conduct of this study.Journal of Advanced Nursing44The study shows that it is important to approach the implementation of ChatGPT as a nurse's digital assistant with caution and more study is needed to optimize the model's training and algorithms to provide accurate healthcare support that aids clinical decision-making.Journal of advanced nursing['M. Saban', 'Ilana Dubovi']2024-02-17T00:00:00https://www.semanticscholar.org/paper/bce3f9ca6b711b727944b6df3e71c2f42b2586e0 bc351e9bf74e2b6ad6e2459e04d8fff43d17dd16Studies in Higher Education343Studies in Higher Education['Aniekan Essien', 'O. Bukoye', 'Christine O’Dea', 'M. Kremantzis']2024-02-17T00:00:00https://www.semanticscholar.org/paper/bc351e9bf74e2b6ad6e2459e04d8fff43d17dd16 654e6cd3429de914339ab427805edd73699d92deThis research reviews specific problems in considering how much influence the factors of user data security, user satisfaction, user retention, application of artificial intelligence (AI) and impact on user experience. The goal is to provide useful guidance for app developers and e-commerce business owners to improve User Experience and integrate AI effectively. This research is included in Quantitative study by distribute questionnaires online to Lazada application users. The aim is to understand user views of Lazada application services. And these factors have a significant role in maintaining competitiveness and improving applications. And the research concludes that user data security and user retention factors have worthy of attention on applications of artificial intelligence and user experience. However, user experience with the application of artificial intelligence (AI) and user experience have an insignificant influence.International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM)170The research concludes that user data security and user retention factors have worthy of attention on applications of artificial intelligence and user experience, however, user experience with the application of artificial intelligence (AI) and user experience have an insignificant influence.International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM)['Dinda Virgia Yurendira', 'Brilian Dwi Saputra', 'Osly Usman']2024-02-17T00:00:00https://www.semanticscholar.org/paper/654e6cd3429de914339ab427805edd73699d92de ff2307c2cff0185dc0fe67c44c3de454e3f1e7cdIn response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.Processes900This article outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms.Processes['Guanglei Li', 'Tengqi Luo', 'Ran Liu', 'Chenchen Song', 'Congyu Zhao', 'Shouyuan Wu', 'Zhengguang Liu']2024-02-17T00:00:00https://www.semanticscholar.org/paper/ff2307c2cff0185dc0fe67c44c3de454e3f1e7cd 27da47f159cd064d9be0e36d7d4b9fa6aa628401The increasing significance of large models and their multi-modal variants in societal information processing has ignited debates on social safety and ethics. However, there exists a paucity of comprehensive analysis for: (i) the interactions between human and artificial intelligence systems, and (ii) understanding and addressing the associated limitations. To bridge this gap, we propose Model Autophagy Analysis (MONAL) for large models' self-consumption explanation. MONAL employs two distinct autophagous loops (referred to as ``self-consumption loops'') to elucidate the suppression of human-generated information in the exchange between human and AI systems. Through comprehensive experiments on diverse datasets, we evaluate the capacities of generated models as both creators and disseminators of information. Our key findings reveal (i) A progressive prevalence of model-generated synthetic information over time within training datasets compared to human-generated information; (ii) The discernible tendency of large models, when acting as information transmitters across multiple iterations, to selectively modify or prioritize specific contents; and (iii) The potential for a reduction in the diversity of socially or human-generated information, leading to bottlenecks in the performance enhancement of large models and confining them to local optima.580The proposed Model Autophagy Analysis (MONAL) employs two distinct autophagous loops (referred to as ``self-consumption loops'') to elucidate the suppression of human-generated information in the exchange between human and AI systems.['Shu Yang', 'Lijie Hu', 'Lu Yu', 'Muhammad Asif Ali', 'Di Wang']2024-02-17T00:00:00https://www.semanticscholar.org/paper/27da47f159cd064d9be0e36d7d4b9fa6aa628401 4fb568514120338a5df8eebcc8da39a9a8d5d47dBMC Medical Ethics400BMC Medical Ethics['Abdallah Al-Ani', 'Abdallah Rayyan', 'Ahmad Maswadeh', 'H. Sultan', 'Ahmad Alhammouri', 'Hadeel Asfour', 'Tariq Alrawajih', 'Sarah Al Sharie', 'Fahed Al Karmi', 'Ahmed Mahmoud Al-Azzam', 'Asem H. Mansour', 'M. Al-Hussaini']2024-02-17T00:00:00https://www.semanticscholar.org/paper/4fb568514120338a5df8eebcc8da39a9a8d5d47d 719ef4eedb3f51db455e160c78548ee15b37b9b9Artificial intelligence systems connected to sensor-laden devices are becoming pervasive, which has significant implications for a range of AI risks, including to privacy, the environment, autonomy, and more. There is therefore a growing need for increased accountability around the responsible development and deployment of these technologies. In this paper, we provide a comprehensive analysis of the evolution of sensors, the risks they pose by virtue of their material existence in the world, and the impacts of ubiquitous sensing and on-device AI. We propose incorporating sensors into risk management frameworks and call for more responsible sensor and system design paradigms that address risks of such systems. To do so, we trace the evolution of sensors from analog devices to intelligent, networked systems capable of real-time data analysis and decision-making at the extreme edge of the network. We show that the proliferation of sensors is driven by calculative models that prioritize data collection and cost reduction and produce risks that emerge around privacy, surveillance, waste, and power dynamics. We then analyze these risks, highlighting issues of validity, safety, security, accountability, interpretability, and bias. We surface sensor-related risks not commonly captured in existing approaches to AI risk management, using a materiality lens that reveals how physical sensor properties shape data and algorithmic models. We conclude by advocating for increased attention to the materiality of algorithmic systems, and of on-device AI sensors in particular, and highlight the need for development of a responsible sensor design paradigm that empowers users and communities and leads to a future of increased fairness, accountability and transparency.arXiv.org1070This paper traces the evolution of sensors from analog devices to intelligent, networked systems capable of real-time data analysis and decision-making at the extreme edge of the network and proposes incorporating sensors into risk management frameworks and calls for more responsible sensor and system design paradigms that address risks of such systems.ArXiv['Matthew Stewart', 'Emanuel Moss', 'Pete Warden', 'Brian Plancher', 'Susan Kennedy', 'Mona Sloane', 'V. Reddi']2024-02-17T00:00:00https://www.semanticscholar.org/paper/719ef4eedb3f51db455e160c78548ee15b37b9b9 41e49ad7ba466f6f7129d0cabf38c5edabcfedb3The use of artificial intelligence (AI) technology in healthcare is estimated to grow at 47.6%/year. AI applications in orthopedics are used for diagnostics, predictive models, medical image analysis, and risk prediction. This review aims to provide an understanding of AI applications used in orthopedics, their benefits, future applications, and challenges to be overcome.Indian Journal of Medical Sciences100This review aims to provide an understanding of AI applications used in orthopedics, their benefits, future applications, and challenges to be overcome.Indian Journal of Medical Sciences['Lakshmi Nathan', 'Veerabahu Muthusamy']2024-02-17T00:00:00https://www.semanticscholar.org/paper/41e49ad7ba466f6f7129d0cabf38c5edabcfedb3 9ca897a24c0161bad03a5ab23adb84a4b16d2b79With the ever-growing urgency of food insecurity and the threat of climate change, there is increasing interest in using artificial intelligence for Earth observations (AI-EO) for agriculture, particularly in Sub-Saharan Africa (SSA). This paper provides an overview of the primary research areas within AI-EO for agriculture in SSA. We discuss examples and limitations of current research as well as opportunities for future work. In addition, we identify ten key considerations for future efforts involving AI-EO for agriculture in SSA.Proceedings of the International Workshop on Social Impact of AI for Africa 202200An overview of the primary research areas within AI-EO for agriculture in SSA is provided and ten key considerations for future efforts involving AI-EO for agriculture in SSA are identified.Proceedings of the International Workshop on Social Impact of AI for Africa 2022['Catherine L. Nakalembe', 'Hannah R. Kerner']2024-02-17T00:00:00https://www.semanticscholar.org/paper/9ca897a24c0161bad03a5ab23adb84a4b16d2b79 ebb68a2ccf3a6d8b6c6acad9492d6e1e350814bdIn African traditions, the crossroads is where the trickster makes his/her appearance. Eshu, Legba, Anansi and others create complexity when our decisions fold back on themselves. AI has created yet another crossroads, and again the trickster brings surprises. What might have seemed like Africa’s worst challenges-“underdeveloped” from the colonial perspective-could be the basis by which computational aids can facilitate more sustainable and egalitarian futures. Blending the heritage algorithms of Africa’s past with full stack decolonization can guide us through the crossroads, on the path towards generative justice.Proceedings of the International Workshop on Social Impact of AI for Africa 202200What might have seemed like Africa’s worst challenges-“underdeveloped” from the colonial perspective-could be the basis by which computational aids can facilitate more sustainable and egalitarian futures.Proceedings of the International Workshop on Social Impact of AI for Africa 2022['Ron Eglash']2024-02-17T00:00:00https://www.semanticscholar.org/paper/ebb68a2ccf3a6d8b6c6acad9492d6e1e350814bd 82b9de657e729b5e4fe87d509b52138122272945Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years create valid end-to-end simulations as the different models need to be configured in consistent ways so their results can be meaningfully interpreted. MINT is a novel framework that uses AI for model integration. MINT captures extensive knowledge about models and data, including their requirements and constraints. MINT guides a user to pose a well-formed modeling question, select and configure models, find appropriate datasets, set up scenarios and parameters, run the simulations, and visualize the results. MINT currently includes climate, hydrology, and agriculture models for different areas of Ethiopia, Kenya, and South Sudan. Our goal is to understand droughts through integrating meteorological, hydrological, and agricultural analyses.Proceedings of the International Workshop on Social Impact of AI for Africa 202250The goal of MINT is to understand droughts through integrating meteorological, hydrological, and agricultural analyses through integrating meteorological, hydrological, and agricultural analyses.Proceedings of the International Workshop on Social Impact of AI for Africa 2022['Belete Berhanu', 'Ethiopia Bisrat Zeleke', 'Yolanda Gil', 'D. Khider', 'Maximiliano Osorio', 'V. Ratnakar', 'H. Vargas']2024-02-17T00:00:00https://www.semanticscholar.org/paper/82b9de657e729b5e4fe87d509b52138122272945 50c28feecd969cb24de08b24103f380071832f7bIt is sometimes difficult to evaluate progress in Generative AI, that is, image generation and large language models. This may be because they represent a paradigm shift in AI, and the traditional ways of developing, evaluating, understanding, and deploying AI systems no longer apply. Instead, we need to develop new such approaches, possibly by extending those currently in use in cognitive neuroscience and psychology. In this manner, a new AI paradigm can be created, providing a significant leap in AI research and practice.The AI Magazine10AI Mag.['Risto Miikkulainen']2024-02-17T00:00:00https://www.semanticscholar.org/paper/50c28feecd969cb24de08b24103f380071832f7b b203124664498730055991f8141078eedd6b2481AI technology has become increasingly involved in a plethora of societal functions in recent years, but racial bias in AI algorithms has revealed a dangerous trend. With the rapid advancements in technology in general as well as artificial intelligence algorithms, bias is unknowingly developing in these algorithms due to the lack of attention towards it. However, recent efforts have been made to first recognize that bias exists in these algorithms as well as strategies to eradicate it. The implications of the research performed in this analysis go much further than a simple moral obligation to promote inclusiveness for marginalized groups in society; racial bias in AI algorithms has the potential to involve life and death consequences. Specifically in the provision of health, an unbiased algorithm may inherently contain bias due to factors outside of the algorithm itself. It is important to use diverse data sets in our algorithms to ensure that the data does not contain bias. Using a data set which is not diverse may lead to the algorithm developing bias over time, which may cause adverse impacts on patients. In addition, we will discuss how bias affects Africa in comparison to more developed countries. We will look into the future of how we can eliminate bias in artificial intelligence and advance the provision of health more equitably across the global community. Based on findings showcasing examples of racially biased AI technology used to combat the COVID-19 pandemic, efforts currently being taken to eradicate racial bias in AI are highlighted along with a discussion of future actions that should be performed. With more federal regulations surrounding AI algorithms along with an emphasis on promoting diversity in the personnel and data of the AI community, particularly in Africa, the future of AI can be one free of racially biased tendencies.Proceedings of the International Workshop on Social Impact of AI for Africa 202200Based on findings showcasing examples of racially biased AI technology used to combat the COVID-19 pandemic, efforts currently being taken to eradicate racial bias in AI are highlighted along with a discussion of future actions that should be performed.Proceedings of the International Workshop on Social Impact of AI for Africa 2022['Andrew Galvin', 'Andrew Hogan']2024-02-17T00:00:00https://www.semanticscholar.org/paper/b203124664498730055991f8141078eedd6b2481 7f4ab93dcd0ede54fb5e72c533d4c22d9bad23caTo ensure the educational goals of the 21st century, creating a “learning society” and “lifelong learning,” the development of self-learning competence in the teaching process is crucial. Constructivism and Metacognition theories have shown that teachers need to create a learning environment that helps learners build and create knowledge for themselves based on thinking, exploration, and practice. This approach aids in the development of students’ self-learning competence. AI Chatbot serves as an effective tool in building a virtual assistant, a virtual practice medium that allows students to interact, inquire, and experience 24/7 from anywhere. With AI Chatbot, students will receive support according to their needs, desires, and self-awareness levels. Moreover, AI Chatbot provides a suitable environment for developing self-learning competence for students.International Journal of Current Science Research and Review230AI Chatbot serves as an effective tool in building a virtual assistant, a virtual practice medium that allows students to interact, inquire, and experience 24/7 from anywhere and provides a suitable environment for developing self-learning competence for students.International Journal of Current Science Research and Review['Vũ Thị Lan', 'Nguyen Minh Giam']2024-02-17T00:00:00https://www.semanticscholar.org/paper/7f4ab93dcd0ede54fb5e72c533d4c22d9bad23ca 8a5580457d2c3f275d3d8d94dfadfb999d05a75bThe NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /aI‐faI/) is one of the inaugural NSF AI research institutes (https://iaifi.org). The IAIFI is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles from fundamental physics. By combining state‐of‐the‐art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community. Since trustworthy AI is as important for physics discovery as it is for other applications of AI in society, IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies. To cultivate human intelligence, the IAIFI promotes training, education, and public engagement at the intersection of physics and AI. In these ways, the IAIFI is fusing deep learning with deep thinking to gain a deeper understanding of our universe and AI.The AI Magazine01The IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies to gain a deeper understanding of the authors' universe and AI.AI Mag.['Jesse Thaler', 'Mike Williams', 'Marisa LaFleur']2024-02-17T00:00:00https://www.semanticscholar.org/paper/8a5580457d2c3f275d3d8d94dfadfb999d05a75b 45af6455348fe0f1031935252eaf53c5d3dce4f8This research paper explores stakeholders' perspectives on the challenges associated with implementing Artificial Intelligence (Ai) in highway transport. The investigation focuses on three main areas of limitation: technical, regulatory, and ethical barriers. The study, backed by an in-depth survey analysis, reveals key limitations identified by stakeholders, including limited access to Ai technology (42.6%), lack of government support (27.9%), the absence of industry-wide regulations (27.4%), concerns about job displacement (29.4%), privacy implications (25.5%), and cybersecurity risks (30.2%). Additionally, the paper provides recommendations for policymakers, industry stakeholders, and researchers to address these challenges [1].Journal of Engineering Research and Reports00This research paper explores stakeholders' perspectives on the challenges associated with implementing Artificial Intelligence in highway transport and reveals key limitations identified by stakeholders, including limited access to Ai technology, technical, regulatory, and ethical barriers.Journal of Engineering Research and Reports['Glory Chinwe Ugo', 'A. C. Apata', 'Praise Onimisi Dawodu']2024-02-17T00:00:00https://www.semanticscholar.org/paper/45af6455348fe0f1031935252eaf53c5d3dce4f8 4e92b6548056c616c78e09202af676673813e35bBackground: Since 2017, the Philippines Business for Social Progress (PBSP) has implemented active case finding for Tuberculosis under their Advancing Client-centered Care and Expanding Sustainable Services for TB (ACCESSTB) project. This study aims to conduct a comparative analysis of a screening approach using AI for Chest X-ray (CXR) interpretation versus an approach relying solely on human-readers in three major regions of the Philippines. Methods: This study undertook a retrospective analysis of data derived from two well-established and ongoing screening approaches. The data on number of people screened and the outcome of the screening at each stage of the screening process was extracted from quarterly reports provided by PBSP. Subsequently, the data was analysed to determine the diagnostic yield, the number needed to screen and the drop-out rates. Results: The AI screening approach had a lower number needed to screen (26.3) compared to human-reader screening (41.5). The main reason driving this difference is the lower drop-out rate after CXR (16.6% in AI approach versus 43.1% in human reader approach). This lower drop-out rate is attributed to the quicker turnaround time for CXR results and this has an important public health benefit because a higher proportion of positive TB individuals participating in the screening will receive treatment in the AI screening approach (3.8% versus 2.4%). Conclusion: The results illustrate that AI-powered CXR screening has clear benefits compared to screening with human readers. Further research is required to determine the comparative cost-effectiveness of the two screening approaches. Key words: tuberculosis, active case finding, CXR screening, artificial intelligence, benefitInternational Journal of Health Sciences and Research310A retrospective analysis of data derived from two well-established and ongoing screening approaches shows that AI-powered CXR screening has clear benefits compared to screening with human readers.International Journal of Health Sciences and Research['Irene Nampewo', 'Proochista Ariana', 'Shibu Vijayan']2024-02-17T00:00:00https://www.semanticscholar.org/paper/4e92b6548056c616c78e09202af676673813e35b 5b46d43a3991427ed51a5d4b59add50a20230401Management Review Quarterly1480This paper provides a conceptual framework of how the use of AI might help top managers better focus their attention on discontinuous change and highlights factors that influence top managers' attention allocation and likely enhance or inhibit it through the use of AI.Management Review Quarterly['Philip Mundlos']2024-02-17T00:00:00https://www.semanticscholar.org/paper/5b46d43a3991427ed51a5d4b59add50a20230401 db1a2ac641d0276151c17124371aad492e1c2dd0This study investigates the transformative impact of Robotic Process Automation (RPA) on modern accounting practices. The primary objective is to analyze how RPA is revolutionizing traditional accounting methods, focusing on its integration, challenges, and future prospects. Employing a systematic literature review and content analysis methodology, the study draws on a range of academic journals, industry reports, and white papers, published between 2013 and 2023. The findings reveal that RPA significantly enhances operational efficiency, accuracy, and compliance in accounting processes by automating routine tasks. This automation allows accounting professionals to shift their focus towards more strategic and analytical roles. The study identifies key challenges in RPA adoption, including integration complexities, workforce adaptation, and privacy concerns. It also highlights the evolving role of educational institutions in preparing future accountants for a digitalized environment. Looking forward, the study predicts further advancements in RPA, driven by the integration of AI and machine learning, offering sophisticated applications in predictive analytics and decision support. However, these advancements come with challenges that need balanced management. Strategic recommendations for industry practitioners emphasize the importance of continuous learning and effective integration strategies. Policymakers are advised to develop regulatory frameworks guiding the ethical use of RPA in accounting. The study suggests future research directions, including the long-term impacts of RPA on the accounting profession and the integration of RPA with emerging technologies. Finally, RPA stands as a pivotal technology in the accounting sector, offering significant benefits while presenting challenges that require careful navigation as the field continues to evolve. Keywords: Robotic Process Automation, Accounting Practices, Modern Accounting, Automation.Engineering Science &amp; Technology Journal04The findings reveal that RPA significantly enhances operational efficiency, accuracy, and compliance in accounting processes by automating routine tasks, and allows accounting professionals to shift their focus towards more strategic and analytical roles.Engineering Science &amp; Technology Journal['Benjamin Samson Ayinla', 'Akoh Atadoga', 'Chinedu Ugochukwu Ike', 'Ndubuisi Leonard Ndubuisi', 'Onyeka Franca Asuzu', 'Rhoda Adura Adeleye']2024-02-17T00:00:00https://www.semanticscholar.org/paper/db1a2ac641d0276151c17124371aad492e1c2dd0 f98bb2fc55b61bf13bf15200abb06322708a3e62The journal of the International Association of Medical Science Educators : JIAMSE20Chatbots outperformed the average applicant on PREview, suggesting their potential for healthcare training and decision-making and highlighting risks of online assessment delivery.Medical science educator['John C. Lin', 'Sai S. Kurapati', 'David N Younessi', 'Ingrid U. Scott', 'Dan A. Gong']2024-02-17T00:00:00https://www.semanticscholar.org/paper/f98bb2fc55b61bf13bf15200abb06322708a3e62 0eae29f102356aeca7f94e22b108eb18325c95e3Since the reform and opening-up, China has developed into the world’s number one manufacturing country. Meanwhile, China’s environmental protection efforts continue to strengthen. So, will changes in the intensity of environmental regulatory policies have an impact on the technological development level and international competitiveness of China’s high-tech manufacturing industries? In response to this issue, we have reviewed relevant research in the field of environmental regulation and export technology complexity, and then selected appropriate indicators to quantify the environmental regulation and export technology complexity of high-tech manufacturing industries in different regions of China. Furthermore, the entropy method was used to calculate the intensity of environmental regulations in different regions of China. In the subsequent empirical analysis, based on relevant indicator data from 30 provinces in China, excluding Tibet, from 2006 to 2021, we quantitatively analyzed the impact of China’s environmental regulations on the complex export technology of high-tech manufacturing industries. The degree of influence and the robustness of the benchmark regression results was proved through endogeneity testing and robustness testing. The main conclusions are as follows: (1) from 2006 to 2021, China’s environmental regulation intensity and the technological complexity of high-tech industry exports have shown an upward trend. (2) The empirical analysis results show that the increase in intensity has a significant “U-shaped” impact on the technological complexity of exports of high-tech manufacturing industries. (3) The “U-shaped” impact of environmental regulation on the technological complexity of exports of high-tech manufacturing industries has regional differences. However, the high-tech manufacturing industry does not show obvious industry differences. (4) Environmental regulations will affect the level of export technology complexity of the high-tech manufacturing industry through foreign direct investment, human capital, and innovative R D investment, which cause indirect effects. Based on those conclusions, this paper has suggested corresponding policy measures and future research directions.Economies603Economies['Weixin Yang', 'Xiu Zheng', 'Yunpeng Yang']2024-02-16T00:00:00https://www.semanticscholar.org/paper/0eae29f102356aeca7f94e22b108eb18325c95e3 19806fa5cc42d63dab8278b295c165b80deb6b59The article attempts to define the concept of legal regulation of relations in the area of genetic technologies. The different models of legal regulation are used as a basis. Priority is given by the authors to the two-level model of legislation. This model consists of one Federal framework law, the provisions of which are supplemented by a group of special legislative acts which regulate certain aspects of genetic technologies. The authors present arguments in favor of choosing this model, in order to build a legislative system for genetic technologies and they highlight its advantages. The article concludes with a proposal to amend the current Federal Law of 5 July, 1996 No. 86-FZ ‘On State Regulation in the Field of Genetic Engineering’ which may serve as a basis for the development of the two-level model of legislation.Lex Genetica70Lex Genetica['O. S. Grin’', 'T. Shilyuk']2024-02-16T00:00:00https://www.semanticscholar.org/paper/19806fa5cc42d63dab8278b295c165b80deb6b59 1b04767c1b153a69786300f9d7b8287020bc00bb   The need to counter external threats to Russia’s national security predetermined the development of new and improvement of existing regulations in 2022. An assessment and comparative analysis of the legal means and innovations available in the field of labor confirmed that there is an objective need for comprehensive legal regulation of labor relations during emergency events. Considering the features of the organization of labor activity amid imposition of martial law and the operation of special economic measures, the author exposes conceptual uncertainty, discrepancies in the order, content and limits of impact on the sphere of labor, difficulties in applying and controversial issues of innovations were revealed, the lack of a general concept and uniformity of labor regulation in the conditions of emergency circumstances. It is proposed to streamline the regulations by focusing on the fact that countering threats to national security is the system-forming basis for the differentiation of the legal regulation of relations in the sphere of labor.Actual Problems of Russian Law30Actual Problems of Russian Law['S. V. Paramonova']2024-02-16T00:00:00https://www.semanticscholar.org/paper/1b04767c1b153a69786300f9d7b8287020bc00bb 4c74690a8ddaed05c6242cea5edc69ca28e3e7e2   The authors examine the trends and features of the development of legal regulation of genomic research in the context of digitalization at the level of global and regional international organizations, including integration associations. The current sources of legal regulation of the use of artificial intelligence technologies in the field of genomic research in modern international and integration law are analyzed, and the main trends in the development of global and regional regulation are identified. Attention is given to the fact that key international acts adopted both at the global and regional levels, and regulating certain aspects of genomic research, are so-called acts of soft law, which are advisory and constitute a kind of «pre-law». In conclusion, recommendations are presented for improving the relevant legal regulation within the framework of integration associations with the participation of the Russian Federation.Actual Problems of Russian Law00Recommendations are presented for improving the relevant legal regulation within the framework of integration associations with the participation of the Russian Federation.Actual Problems of Russian Law['D. V. Ponomareva', 'M. V. Nekoteneva']2024-02-16T00:00:00https://www.semanticscholar.org/paper/4c74690a8ddaed05c6242cea5edc69ca28e3e7e2 80acb32fa991a8ed2ee2babbc2ff9be4b7ab2e25The article analyzes the current state of international legal regulation of genetic research in connection with the development of Russian legislation. The author concludes that all international legal regulation of genetic research is reduced today either to the establishment of basic principles or to prohibitions. The provisions of current Russian legislation governing genomic research do not go beyond the paradigm set out in international acts, although they have their own characteristics. The article notes that a distinctive feature of Russian legislative machinery is the detailed regulation of procedural issues and the powers of state bodies. At the same time, several issues including biobanking are not regulated at all. According to the author, the further development of science may require a more detailed regulation and a shift towards more flexible methods of regulation.Lex Genetica40Lex Genetica['S. V. Kosilkin']2024-02-16T00:00:00https://www.semanticscholar.org/paper/80acb32fa991a8ed2ee2babbc2ff9be4b7ab2e25 0b64533126198b94e7043011cfb5a8b3f8300892Ukraine is on the verge of joining the European Union, which requires, on the one hand, the transformation of the regulation of banking activity in accordance with the standards adopted by it, and on the other hand, the implementation of the concept of sustainable financing in all spheres of public life, including the banking system. Recently, a stable trend has emerged in the countries of the Euro zone, which consists in liaison of the mechanism of regulation of banks’ activity to the goals of sustainable financing, which makes it necessary to do changes to the existing standard of requirements for the capital of credit institutions (CI) and to regulate its adequacy. The mentioned metamorphosis has also spread to the banks of Ukraine, which are only taking the first steps in the direction of introducing the key principles of sustainable financing into their practical work. Along with the above, in the near future the domestic banking sector may face the problem of "greening" the mechanism of regulation of their activity according to European standards, directives, regulations and guidelines. Solving this extremely difficult problem will require the NBU to take decisive and, at the same time, well-balanced measures, which would not hinder the active development of investment and lending in a sustainable economy. The aim of the study is to reveal the key provisions of the regulation of banks in the EU, to determine the vectors of their change in accordance with the goals of sustainable financing, as well as to develop recommendations for the banking sector of Ukraine. In this research, methods of scientific knowledge were used, in particular: obser­vation, theoretical generalization, ab­stract­tion, comparison, analysis and synthesis, induction and deduction. The main provisions of the Directive on capital requirements and regula­tion of capital adequacy of banks in the European Union are outlined. The essence of harmonious finance and the stages of its transformation in EU countries are revealed. The ecological vector of the regulation of banks’ activities was considered, and the difficulties and prospects of its implementation in the banks of Ukraine were determined. A "chain" of step-by-step implementation of the NBU’s "environ­mental" regulatory initiatives in Ukrainian banks is proposed based on the best European practices and the possibilities of their imple­mentation in a country at war.SCIENTIA FRUCTUOSA00SCIENTIA FRUCTUOSA['Natalia Shulga', 'Serhii Savluk']2024-02-16T00:00:00https://www.semanticscholar.org/paper/0b64533126198b94e7043011cfb5a8b3f8300892 9a2d2783a57684178a81c3efe5f3df65cc75c8b5Environmental pollution has become a serious problem in the past decades, especially in developing countries with rapid economic growth. As the world’s largest developing country with incredible speed of development, China provides a unique perspective to investigate the productivity effect of environmental regulations. The main finding of this study supports the Porter hypothesis, that stricter environmental regulations significantly promote productivity in China. We also explore three potential impact channels leading to this productivity improvement, including rising technical innovation, optimizing financial management and reducing resource misallocation. The latter two mechanisms are rarely discussed in relevant studies. However, these two additional effects are of great importance since they can help establish a more comprehensive market environment and a more optimized industrial structure, especially in developing countries. Overall, this study proves that China has achieved dual goals of protecting the environment and economic growth by implementing stricter environmental regulations, which is worthy of reference for other developing countries facing similar problems.Singapore Economic Review270The Singapore Economic Review['Weibing Li', 'Nan Chen']2024-02-16T00:00:00https://www.semanticscholar.org/paper/9a2d2783a57684178a81c3efe5f3df65cc75c8b5 d0a518519b6b922d98e31b3882d3a393f7e54d89Insights into Imaging131The majority of IUSs of CE-marked AI-based medical devices lack substantial information and, in few cases, contradict the claims of the product.Insights into Imaging['K. V. van Leeuwen', 'Dennis M Hedderich', 'Hugh Harvey', 'S. Schalekamp']2024-02-16T00:00:00https://www.semanticscholar.org/paper/d0a518519b6b922d98e31b3882d3a393f7e54d89 c838fc43ebae82a4cb27616391764169288310e0Nature04Nature['Elizabeth Gibney']2024-02-16T00:00:00https://www.semanticscholar.org/paper/c838fc43ebae82a4cb27616391764169288310e0 a2812a61dca283ddaab2b6aa8c6b89fc184c6831Impact Assessment and Project Appraisal473Impact Assessment and Project Appraisal['R. Sandfort', 'Birthe Uhlhorn', 'G. Geißler', 'I. Lyhne', 'A. Jiricka-Pürrer']2024-02-16T00:00:00https://www.semanticscholar.org/paper/a2812a61dca283ddaab2b6aa8c6b89fc184c6831 4f143b0a284c2f36f108554cc2724ca845e30c25As humanity grapples with the pressing challenges of climate change, biodiversity loss, and environmental degradation, there is a growing recognition of the pivotal role that artificial intelligence (AI) can play in addressing these issues. This article explores the intersection of AI and sustainability, highlighting how technological advancements are being leveraged to mitigate environmental impacts and promote a more sustainable future. From optimizing energy consumption and resource management to enhancing conservation efforts and predicting environmental risks, AI offers innovative solutions across various sectors. This paper examines key applications of AI for sustainability, discusses current trends and challenges, and outlines future directions for research and implementation.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This article explores the intersection of AI and sustainability, highlighting how technological advancements are being leveraged to mitigate environmental impacts and promote a more sustainable future.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Sohana Akter']2024-02-16T00:00:00https://www.semanticscholar.org/paper/4f143b0a284c2f36f108554cc2724ca845e30c25 a4db1bddfd5ed2772a681aad82ef9db1b23d23a5Importance. AI rapidly and dramatically transforms reality, which poses a problem for the new generation of university graduates coming into profession. Social sciences and humanities majors are concerned about the future of their careers and uncertain of professional skills in demand. This perspective piece argues in favor of shifting to interdisciplinary approach in higher education, with emphasis on integrative content embracing special knowledge, foreign language contexts and pertinent AI-mediated settings. The underlying idea is that in educational contexts, AI cannot only focus on procedural aspects – teaching techniques and management tasks; it is essential to provide language learners with a new professional scope of reference, which means changed curriculums, revised content, and new professions.Research Methods. The work relies on various qualitative methods of research: analysis of present day labour market in AI-mediated contexts of social sciences and humanities; analysis of literature covering the use of AI for foreign language teaching and learning; a descriptive and analytical method; methods of generalizing and systematizing the selected material; interpretive analysis. The materials include scientific works of Russian and foreign scientists and modern labor market data.Results and Discussion. Labour market analysis makes it possible to discover skills essential to a new generation of specialists in social sciences and humanities. In this respect, arguments for updating the content of teaching the majors in question are provided, and a discipline with adequate integrative potential is named. The interdisciplinary approach is illustrated with AI-mediated foreign language contexts of social sciences and humanities as part of the updated integrative content of the discipline “Foreign Language” to be mastered by students.Conclusion. The conducted research brings us to the idea that the discipline “Foreign Language” has a unique potential for preparing a new generation of graduates in social sciences and humanities underpinned by AI. Along with its traditional goal – developing a person’s communicative competence, essential in digital settings, it has good prospects of integrating special subject knowledge and its language correlates, necessary for the effective operation of AI algorithms in such areas, as well as for developing the “linguo-cognitive dimension” of professional activity adequate to these conditions.Tambov University Review. Series: Humanities290The discipline “Foreign Language” has a unique potential for preparing a new generation of graduates in social sciences and humanities underpinned by AI, with emphasis on integrative content embracing special knowledge, foreign language contexts and pertinent AI-mediated settings.Tambov University Review. Series: Humanities['D. V. Aleynikova', 'L. V. Yarotskaya']2024-02-16T00:00:00https://www.semanticscholar.org/paper/a4db1bddfd5ed2772a681aad82ef9db1b23d23a5 101dfa8913b0050901f154a293f529884059ca8bThe global food delivery market provides many opportunities for AI-based services that can improve the efficiency of feeding the world. This paper presents the Cloud Kitchen platform as a decision-making tool for restaurants with food delivery and a simulator to evaluate the impact of the decisions. The platform consists of a Technology-Specific Bridge (TSB) that provides an interface for communicating with restaurants or the simulator. TSB uses a PDDL model to represent decisions embedded in the Unified Planning Framework (UPF). Decision-making, which concerns allocating customers' orders to vehicles and deciding in which order the customers will be served (for each vehicle), is done via a Vehicle Routing Problem with Time Windows (VRPTW), an efficient tool for this problem. We show that decisions made by our platform can improve customer satisfaction by reducing the number of delayed deliveries using a real-world historical dataset.arXiv.org80It is shown that decisions made by the Cloud Kitchen platform can improve customer satisfaction by reducing the number of delayed deliveries using a real-world historical dataset.ArXiv['Slavomír Svancár', 'Lukás Chrpa', 'Filip Dvorák', 'Tomás Balyo']2024-02-16T00:00:00https://www.semanticscholar.org/paper/101dfa8913b0050901f154a293f529884059ca8b e8539837f67c884ca04b374307a053f92c3ccb05Abstract: Deep learning models have demonstrated remarkable capabilities across various domains, but their inherent complexity often leads to challenges in understanding and interpreting their decisions. The demand for transparent and interpretable artificial intelligence (AI) systems is particularly crucial in fields such as healthcare, finance, and autonomous systems. This research paper presents a comprehensive study on the application of Explainable AI (XAI) techniques to enhance transparency and interpretability in deep learning models. Keywords: Explainable AI (XAI), artificial intelligence (AI).INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper presents a comprehensive study on the application of Explainable AI (XAI) techniques to enhance transparency and interpretability in deep learning models.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Dr.Shashank Singh', 'Dr. Dhirendra Pratap Singh', 'Mr.Kaushal Chandra']2024-02-16T00:00:00https://www.semanticscholar.org/paper/e8539837f67c884ca04b374307a053f92c3ccb05 49b5fd7b6fa6dfe587af976692207cdb8b29453dJournal of imaging informatics in medicine170A customizable, modular data retrieval application (RIL-workflow), which integrates clinical notes, images, and prescription data, is described and its feasibility applied to research at the authors' institution and validated by demonstrating its capability to aggregate, curate, and handle errors related to data from multiple sources to generate a multimodal database for clinical AI research.Journal of imaging informatics in medicine['Ali Ganjizadeh', 'S. Zawada', 'Steve G Langer', 'Bradley J. Erickson']2024-02-16T00:00:00https://www.semanticscholar.org/paper/49b5fd7b6fa6dfe587af976692207cdb8b29453d 9d1a4accaa76ac40be42a0e5d9cb112dd478e210We introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of AI threats. Starting at the base, the pyramid emphasizes Data Integrity, which is essential for the accuracy and reliability of datasets and AI models, including their weights and parameters. Ensuring data integrity is crucial, as it underpins the effectiveness of all AI-driven decisions and operations. The next level, AI System Performance, focuses on MLOps-driven metrics such as model drift, accuracy, and false positive rates. These metrics are crucial for detecting potential security breaches, allowing for early intervention and maintenance of AI system integrity. Advancing further, the pyramid addresses the threat posed by Adversarial Tools, identifying and neutralizing tools used by adversaries to target AI systems. This layer is key to staying ahead of evolving attack methodologies. At the Adversarial Input layer, the framework addresses the detection and mitigation of inputs designed to deceive or exploit AI models. This includes techniques like adversarial patterns and prompt injection attacks, which are increasingly used in sophisticated attacks on AI systems. Data Provenance is the next critical layer, ensuring the authenticity and lineage of data and models. This layer is pivotal in preventing the use of compromised or biased data in AI systems. At the apex is the tactics, techniques, and procedures (TTPs) layer, dealing with the most complex and challenging aspects of AI security. This involves a deep understanding and strategic approach to counter advanced AI-targeted attacks, requiring comprehensive knowledge and planning.arXiv.org250The AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats, is introduced, providing a structured approach to understanding and addressing various levels of AI threats.ArXiv['Chris M. Ward', 'Joshua D. Harguess', 'Julia Tao', 'Daniel Christman', 'Paul Spicer', 'Mike Tan']2024-02-16T00:00:00https://www.semanticscholar.org/paper/9d1a4accaa76ac40be42a0e5d9cb112dd478e210 9042b01e85f5f0bd54168fbb3f71efb76b4c7d46This research paper delves into the dynamic intersection of Artificial Intelligence (AI) and Customer Relationship Management (CRM), exploring the profound effects of AI technologies on modern business practices. As AI continues to evolve, it reshapes how organizations manage and nurture their relationships with customers, presenting new opportunities and challenges. The study investigates the role of AI in personalizing customer experiences, emphasizing the utilization of advanced algorithms to analyze vast datasets and derive actionable insights. AI's influence on data management, analysis, and the subsequent enhancement of customer insights are scrutinized, providing a comprehensive understanding of its impact on informed decision-making. Furthermore, the paper examines the integration of AI-powered chatbots and virtual assistants in CRM systems, evaluating their effectiveness in providing real-time support, streamlining interactions, and improving overall customer satisfaction. The automation of repetitive CRM tasks through AI technologies is also explored, highlighting the resulting efficiencies and the potential for human resources to engage in more strategic aspects of customer relationship management. Sales forecasting, customer segmentation, and sentiment analysis emerge as key focal points, illustrating how AI contributes to more accurate predictions, targeted marketing strategies, and proactive reputation management. The impact of AI on cross-selling, upselling, and customer retention strategies is scrutinized, offering insights into how businesses can leverage AI to optimize revenue and foster enduring customer loyalty. As businesses navigate the rapidly evolving landscape of AI in CRM, this research paper aims to provide a comprehensive overview of the transformative dynamics at play. By understanding the nuanced influence of AI on customer relationships, organizations can adapt their strategies to align with the evolving expectations and demands of the contemporary market.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The role of AI in personalizing customer experiences is investigated, emphasizing the utilization of advanced algorithms to analyze vast datasets and derive actionable insights, and sales forecasting, customer segmentation, and sentiment analysis emerge as key focal points.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-02-16T00:00:00https://www.semanticscholar.org/paper/9042b01e85f5f0bd54168fbb3f71efb76b4c7d46 75e1ccada3430fdfa033bfeab4c31cc6bbc10ce5As humanity grapples with the pressing challenges of climate change, biodiversity loss, and environmental degradation, there is a growing recognition of the pivotal role that artificial intelligence (AI) can play in addressing these issues. This article explores the intersection of AI and sustainability, highlighting how technological advancements are being leveraged to mitigate environmental impacts and promote a more sustainable future. From optimizing energy consumption and resource management to enhancing conservation efforts and predicting environmental risks, AI offers innovative solutions across various sectors. This paper examines key applications of AI for sustainability, discusses current trends and challenges, and outlines future directions for research and implementation.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This article explores the intersection of AI and sustainability, highlighting how technological advancements are being leveraged to mitigate environmental impacts and promote a more sustainable future.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Most. Sohana Akter']2024-02-16T00:00:00https://www.semanticscholar.org/paper/75e1ccada3430fdfa033bfeab4c31cc6bbc10ce5 1ff54bbd8180ae7fcecf673a206bba3a8c8b8f1dThe design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic engineering. However, navigating this vast combinatorial search space remains a severe challenge due to time and financial constraints. This scenario is rapidly evolving as the transformative advancements in AI, particularly in the realm of generative models and optimization algorithms, have been propelling the protein design field towards an unprecedented revolution. In this survey, we systematically review recent advances in generative AI for controllable protein sequence design. To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms. We then offer in-depth reviews of each design task and discuss the pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.arXiv.org640This survey systematically review recent advances in generative AI for controllable protein sequence design and outlines the foundational tasks in protein sequence design in terms of the constraints involved and presents key generative models and optimization algorithms.ArXiv['Yiheng Zhu', 'Zitai Kong', 'Jialun Wu', 'Weize Liu', 'Yuqiang Han', 'Mingze Yin', 'Hongxia Xu', 'Chang-Yu Hsieh', 'Tingjun Hou']2024-02-16T00:00:00https://www.semanticscholar.org/paper/1ff54bbd8180ae7fcecf673a206bba3a8c8b8f1d ac99039a649ad4fded6cfea7b9ac9fe1228fb2e7AI colour cosmetics applications emerged as an innovative solution to promote branded colour cosmetics and improve consumer decision making, primarily as a trial function. The purpose of this study is to investigate the factors that influence the adoption of AI colour cosmetics applications through the lens of social comparison theory. For promotion managers of cosmetic retailers and developers of AI colour cosmetics applications looking to promote and reach a large segment, managerial implications of this research are provided. Many leading retailers and cosmetic companies are adopting AI and machine learning technologies to better cater to their customers in the US by providing personalized products. The latest skin measurement tools allow for direct visualization and quantification of data, and as a result, there is great potential for further integration of machine learning in the cosmetics industry. With the aid of high- International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 02 | February - 2024 SJIF Rating: 8.176 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM28686 | Page 2 resolution visualization and IoT devices that can handle massive data flow, there is likely to be a significant impact on the industry in the future. It is expected that AI will become more prevalent in skin and cosmetics, facilitating age prediction, skin type assessment, and the development of analytical tools. As scientific and technological advancements continue, the use of artificial intelligence models is becoming more widespread. In cosmetic dermatology, artificial intelligence is being used in various new applications that are available to both patients and doctors. Patients now have greater control over their cosmetic care, thanks to the development of customizable skincare, augmented reality applications, and at-home skin analysis tools. Doctors are also utilizing artificial intelligence in innovative ways, including the creation of models for predicting treatment outcomes and instruments for comprehensive skin analysis. Further research is needed in areas such as robotically assisted treatments and automated energy-based therapy apparatuses. Artificial intelligence models in cosmetic dermatology are empowering patients to make more informed decisions about their skin care. Although AI has many benefits and applications, there are still areas where it needs to improve. Data protection and security must be a top priority. There is always a risk when relying on information provided by augmented reality platforms. We have seen recent examples of security breaches associated with such programs. Moreover, the outcomes produced by these applications may not always be reliable, and therefore, AI may not be entirely trustworthy. The skincare industry has experienced significant growth over the last decade, with a surge during the pandemic when the world became increasingly digital. Therefore, integrating AI into skincare is a promising concept. Keywords: Organic cosmetics, Sustainability, Artificial intelligenceINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The factors that influence the adoption of AI colour cosmetics applications through the lens of social comparison theory are investigated to investigate the factors that influence the adoption of artificial intelligence in cosmetic dermatology through the lens of social comparison theory.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-02-16T00:00:00https://www.semanticscholar.org/paper/ac99039a649ad4fded6cfea7b9ac9fe1228fb2e7 4605811733860edc79eaf59aa70f4ec8e0b6ac8aThe purpose of this study is to advance conceptual understandings of the cognitive flexibility construct, in support of creativity and innovation in smart city civic spaces, employing the use of large language model artificial intelligence chatbots such as ChatGPT. Based on a review of the research and practice literature, this study formulates a conceptual framework for cognitive flexibility in support of creativity and innovation in AI environments, adaptable to smart cities. A research design is used that employs AI as a design material, in combination with a topical inquiry involving boundary setting and perspective taking, to co-pilot an exploration with ChatGPT-3.5/4. This study operationalizes the framework for applications to learning approaches, addressing flexibility and inclusivity in smart city spaces and regions. With the rapid evolving of chatbot technologies, ChatGPT-4 is used in the exploration of a speculative real-world urban example. This work is significant in that AI chatbots are explored for application in urban spaces involving creative ideation, iteration, engagement, and cognitive flexibility; future directions for exploration are identified pertaining to ethical and civil discourse in smart cities and learning cities, as well as the notion that AI chatbots and GPTs (generative pre-trained transformers) may become a zeitgeist for understanding and learning in smart cities.Urban Science830A conceptual framework for cognitive flexibility in support of creativity and innovation in AI environments, adaptable to smart cities is formulates, addressing flexibility and inclusivity in smart city spaces and regions.Urban Science['S. Chauncey', 'H. P. McKenna']2024-02-16T00:00:00https://www.semanticscholar.org/paper/4605811733860edc79eaf59aa70f4ec8e0b6ac8a f4608120e62aadda901660a04a2656190d231108Ethical considerations in artificial intelligence (AI) have become increasingly crucial as AI technologies permeate various aspects of society. This paper delves into the complexities surrounding bias and accountability in AI systems. Bias in AI algorithms can perpetuate societal inequalities and discrimination, while accountability gaps raise concerns about the responsible use of AI and potential legal liabilities. By exploring these issues, this paper aims to shed light on the ethical challenges inherent in AI development and deployment, offering insights into how stakeholders can navigate these complexities to foster more equitable and responsible AI applications.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300Light is shed on the ethical challenges inherent in AI development and deployment, offering insights into how stakeholders can navigate these complexities to foster more equitable and responsible AI applications.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-02-16T00:00:00https://www.semanticscholar.org/paper/f4608120e62aadda901660a04a2656190d231108 18b3e4f0f05811265346c30249a17ee856b00056This editorial explores how artificial intelligence (AI) is revolutionizing the science of pharmacokinetics (PK). It discusses the challenges of conventional PK analysis and how AI has transformed this area. It highlights the promise of artificial intelligence (AI) in predicting pharmacokinetic profiles from chemical structures and its application in several aspects of pharmacology, including dosage customization and drug interactions. Additionally, it emphasizes how important ethical issues and openness are to AI applications, especially when it comes to pharmacokinetic prediction and dataset adaptation. Future directions for AI in PK are discussed, with the creation of all-inclusive AI pharmacokinetics/pharmacometrics software being envisioned. Drug discovery and patient care could be transformed toward more individualized and effective healthcare solutions with the help of this software, which could handle tasks such as data cleaning, model selection, and regulatory report preparation. The editorial highlights the importance of AI in improving pharmaceutical sciences while urging caution and teamwork in navigating its possible uses in pharmacokinetics.Journal of Pharmacy & Pharmaceutical Sciences50The editorial highlights the importance of AI in improving pharmaceutical sciences while urging caution and teamwork in navigating its possible uses in pharmacokinetics, and highlights the promise of artificial intelligence in predicting pharmacokinetic profiles from chemical structures.Journal of Pharmacy & Pharmaceutical Sciences['Ali Ghayoor', 'Hamed Gilzad Kohan']2024-02-16T00:00:00https://www.semanticscholar.org/paper/18b3e4f0f05811265346c30249a17ee856b00056 508eb5a6a9c2af0c8bcf2836d77edc52af7fb75fIn the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber–physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.Simulation (San Diego, Calif.)400The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation.SIMULATION['Ruth Dirnfeld', 'Lorenzo De Donato', 'Alessandra Somma', 'Mehdi Saman Azari', 'Stefano Marrone', 'Francesco Flammini', 'V. Vittorini']2024-02-16T00:00:00https://www.semanticscholar.org/paper/508eb5a6a9c2af0c8bcf2836d77edc52af7fb75f 7802831c0a9d12697f833ecf8812f8c4296f28d7PurposeThe study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.Design/methodology/approachThis study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.FindingsThe finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.Practical implicationsThis study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.Originality/valueThis study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.Journal of Hospitality and Tourism Insights470The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions, affecting consumer emotion.Journal of Hospitality and Tourism Insights['Kamrul Hasan Bhuiyan', 'Selim Ahmed', 'Israt Jahan']2024-02-16T00:00:00https://www.semanticscholar.org/paper/7802831c0a9d12697f833ecf8812f8c4296f28d7 00e45be562d2af79c9be60e4e34b6d1c73b17359A comprehensive review of recent challenges faced by pregnant women and how Artificial intelligence can use to overcome these challenges is provided in this paper. In this paper, we explore various AI technologies and methodologies that contribute to the development of personalized pregnancy care system. Pregnancy is a complex vital period in a woman’s life with potential impact on her physical and psychological health.AI-driven personalized pregnancy care includes prediction of complications during pregnancy, proper diet for pregnant women and optimization of treatment plans. There are so many existing AI technologies related pregnancy care. However, these technologies are still facing many challenges. To improve existing techniques further research is required.Journal of Communication Engineering and VLSI Design210Journal of Communication Engineering and VLSI Design['Dattatray G. Takale', 'Vrushali Samant', 'Saniya Samant', 'Samruddhi Ubhad', 'Shlesha Patil', 'Suraj Datir']2024-02-16T00:00:00https://www.semanticscholar.org/paper/00e45be562d2af79c9be60e4e34b6d1c73b17359 a1101537ed4c1e7deed1ec3d1d26524c8e860b3bThe AgAID Institute is a National AI Research Institute focused on developing AI solutions for specialty crop agriculture. Specialty crops include a variety of fruits and vegetables, nut trees, grapes, berries, and different types of horticultural crops. In the United States, the specialty crop industry accounts for a multibillion dollar industry with over 300 crops grown just along the U.S. west coast. Specialty crop agriculture presents several unique challenges: they are labor‐intensive, are easily impacted by weather extremities, and are grown mostly on irrigated lands and hence are dependent on water. The AgAID Institute aims to develop AI solutions to address these challenges, particularly in the face of workforce shortages, water scarcity, and extreme weather events. Addressing this host of challenges requires advancing foundational AI research, including spatio‐temporal system modeling, robot sensing and control, multiscale site‐specific decision support, and designing effective human–AI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.The AI Magazine70Current AgAID efforts in foundational AI research include spatio‐temporal system modeling, robot sensing and control, multiscale site‐specific decision support, and designing effective human–AI workflows.AI Mag.['Alan Fern', 'Margaret Burnett', 'Joseph Davidson', 'J. Doppa', 'Paola Pesantez‐Cabrera', 'Anantharaman Kalyanaraman']2024-02-16T00:00:00https://www.semanticscholar.org/paper/a1101537ed4c1e7deed1ec3d1d26524c8e860b3b 2f81024fe705a70ad1dcacecb371e2c31c8e49b9The nuclear sector represents the primary source of carbon-free energy in the United States. Nevertheless, existing nuclear power plants face the threat of early shutdowns due to their inability to compete economically against alternatives such as gas power plants. Optimizing the fuel cycle cost through the optimization of core loading patterns is one approach to addressing this lack of competitiveness. However, this optimization task involves multiple objectives and constraints, resulting in a vast number of candidate solutions that cannot be explicitly solved. While stochastic optimization (SO) methodologies are utilized by various nuclear utilities and vendors for fuel cycle reload design, manual design remains the preferred approach. To advance the state-of-the-art in core reload patterns, we have developed methods based on Deep Reinforcement Learning. Previous research has laid the groundwork for this approach and demonstrated its ability to discover high-quality patterns within a reasonable timeframe. However, there is a need for comparison against legacy methods to demonstrate its utility in a single-objective setting. While RL methods have shown superiority in multi-objective settings, they have not yet been applied to address the competitiveness issue effectively. In this paper, we rigorously compare our RL-based approach against the most commonly used SO-based methods, namely Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Subsequently, we introduce a new hybrid paradigm to devise innovative designs, resulting in economic gains ranging from 2.8 to 3.3 million dollars per year per plant. This development leverages interpretable AI, enabling improved algorithmic efficiency by making black-box optimizations interpretable. Future work will focus on scaling this method to address a broader range of core designs.arXiv.org501This paper rigorously compares the RL-based approach against the most commonly used SO-based methods, namely Genetic Algorithm, Simulated Annealing, and Tabu Search, resulting in economic gains ranging from 2.8 to 3.3 million dollars per year per plant.ArXiv['Paul Seurin', 'K. Shirvan']2024-02-16T00:00:00https://www.semanticscholar.org/paper/2f81024fe705a70ad1dcacecb371e2c31c8e49b9 0c5a72082be6edf45cece96d7e19e6cb3940da39Artificial intelligence technologies have become a ubiquitous part of human life. This prompts us to ask, ‘how should we live well with artificial intelligence?’ Currently, the most prominent candidate answers to this question are principlist. According to these approaches, if you teach people some finite set of principles or convince them to adopt the right rules, people will be able to live and act well with artificial intelligence, even in an evolving and opaque moral world. We find the dominant principlist approaches to be ill-suited to providing forward-looking moral guidance regarding living well with artificial intelligence. We analyze some of the proposed principles to show that they oscillate between being too vague and too specific. We also argue that such rules are unlikely to be flexible enough to adapt to rapidly changing circumstances. By contrast, we argue for an Aristotelian virtue ethics approach to artificial intelligence ethics. Aristotelian virtue ethics provides a concrete and actionable guidance that is also flexible; thus, it is uniquely well placed to deal with the forward-looking and rapidly changing landscape of life with artificial intelligence. However, virtue ethics is agent-based rather than action-based. Using virtue ethics as a basis for living well with artificial intelligence requires ensuring that at least some virtuous agents also possess the relevant scientific and technical expertise. Since virtue ethics does not prescribe a set of rules, it requires exemplars who can serve as a model for those learning to be virtuous. Cultivating virtue is challenging, especially in the absence of moral sages. Despite this difficulty, we think the best option is to attempt what virtue ethics requires, even though no system of training can guarantee the production of virtuous agents. We end with two alternative visions – one from each of the two authors – about the practicality of such an approach.Theory and Research in Education270The best option is to attempt what virtue ethics requires, even though no system of training can guarantee the production of virtuous agents, and argues for an Aristotelian virtue ethics approach to artificial intelligence ethics.Theory and Research in Education['Nicholas Smith', 'Darby Vickers']2024-02-16T00:00:00https://www.semanticscholar.org/paper/0c5a72082be6edf45cece96d7e19e6cb3940da39 5b13f1834564b6c029a9281b64eb4758235e5bfeMedical Education10Medical education['Vijaya Krishnan', 'Vishakha Patil', 'V. Panhale']2024-02-16T00:00:00https://www.semanticscholar.org/paper/5b13f1834564b6c029a9281b64eb4758235e5bfe bccf19422bd00797fe9f99c10c2ccaf886c9a502Philosophy &amp; Technology20Philosophy &amp; Technology['Scott Hill']2024-02-16T00:00:00https://www.semanticscholar.org/paper/bccf19422bd00797fe9f99c10c2ccaf886c9a502 0fa49dc5da32d418d1ebcefe5dd7f6da8f5d4dd8 Purpose Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices. Design/methodology/approach As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks. Findings The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry). Originality/value To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others. Interactive Technology and Smart Education1470This study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field, with a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.Interactive Technology and Smart Education['K. B. Mustapha', 'E. Yap', 'Y. Abakr']2024-02-16T00:00:00https://www.semanticscholar.org/paper/0fa49dc5da32d418d1ebcefe5dd7f6da8f5d4dd8 681e216d37f5dede6b807c0b4cb4990d1b90a195Information Technology for Development660Information Technology for Development['G. Iazzolino', 'N. Stremlau']2024-02-16T00:00:00https://www.semanticscholar.org/paper/681e216d37f5dede6b807c0b4cb4990d1b90a195 17153fd6d0f58b5fb0abfb2db0888058664c86c9Public sector digitalisation is transforming public governance at an accelerating rate. Digitalisation is outpacing the evolution of the legal framework. Despite several strands of international efforts to adjust good administration guarantees to new modes of digital public governance, progress has so far been slow and tepid. The increasing automation of decision-making processes puts significant pressure on traditional good administration guarantees, jeopardises individual due process rights, and risks eroding public trust. Automated decision-making has, so far, attracted the bulk of scholarly attention, especially in the European context. However, most analyses seek to reconcile existing duties towards individuals under the right to good administration with the challenges arising from digitalisation. Taking a critical and technology-centred doctrinal approach to developments under the law of the European Union and the Council of Europe, this paper goes beyond current debates to challenge the sufficiency of existing good administration duties. By stressing the mass effects that can derive from automated decision-making by the public sector, the paper advances the need to adapt good administration guarantees to a collective dimension through an extension and a broadening of the public sector’s good administration duties: that is, through an extended ex ante control of organisational risk-taking, and a broader ex post duty of automated redress. These legal modifications should be urgently implemented.Social Science Research Network440SSRN Electronic Journal['A. Sanchez-Graells']2024-02-16T00:00:00https://www.semanticscholar.org/paper/17153fd6d0f58b5fb0abfb2db0888058664c86c9 23a60afa4642024a82d8e73daf88b277ae33e7cfJournal of Computational Information Systems740Journal of Computer Information Systems['Surbhi Choudhary', 'N. Kaushik', 'Brijesh Sivathanu', 'Nripendra P. Rana']2024-02-16T00:00:00https://www.semanticscholar.org/paper/23a60afa4642024a82d8e73daf88b277ae33e7cf d1e29d85586655ca868de1dc2c6d2c57e2d4f44dEducation and Information Technologies : Official Journal of the IFIP technical committee on Education290Education and Information Technologies['Oscar Karnalim', 'Hapnes Toba', 'M. Johan']2024-02-16T00:00:00https://www.semanticscholar.org/paper/d1e29d85586655ca868de1dc2c6d2c57e2d4f44d c373cfdef891c45fb4e8c4a8cd158aeda81ffddfMachine learning (ML) trains itself by discovering patterns of correlations that can be applied to new inputs. That is a very powerful form of generalization, but it is also very different from the sort of generalization that the west has valorized as the highest form of truth, such as universal laws in some of the sciences, or ethical principles and frameworks in moral reasoning. Machine learning’s generalizations synthesize the general and the particular in a new way, creating a multidimensional model that often retains more of the complex differentiating patterns it has uncovered in the training process than the human mind can grasp. Particulars speak louder in these models than they do in traditional generalizing frameworks. This creates an odd analogy with recent movements in moral philosophy, particularly the feminist ethics of care which reject the application of general moral frameworks in favor of caring responses to the particular needs and interests of those affected by a moral decision. This paper suggests that our current wide-spread and justified worries about ML’s inexplicability—primarily arising from its reliance on staggeringly complex patterns of particulars—may be preparing our culture more broadly for a valorizing of particulars as at least as determinative as generalizations, and that this might help further advance the importance of particulars in ideas such as those put forward by the ethics of care.Philosophies140This paper suggests that the current wide-spread and justified worries about ML’s inexplicability—primarily arising from its reliance on staggeringly complex patterns of particulars—may be preparing the authors' culture more broadly for a valorizing of particulars as at least as determinative as generalizations, and that this might help further advance the importance of particulars in ideas such as those put forward by the ethics of care.Philosophies['David Weinberger']2024-02-16T00:00:00https://www.semanticscholar.org/paper/c373cfdef891c45fb4e8c4a8cd158aeda81ffddf 91fcd30a5c9d36f2bc610617d917ebe9f6132afcEfficient timetable creation is paramount for organizational productivity, yet often a labor-intensive task. In this paper, we present ’Timelog’, a groundbreaking innovation that harnesses the power of Genetic Algorithms to generate bespoke, conflict-free timetables tailored to user-defined requirements. Timelog empowers users with the ability to customize key parameters such as density, duration, and compatibility, ensuring schedules that perfectly align with their unique needs.To evaluate the effectiveness of Timelog, we introduce a set of performance metrics, including schedule efficiency and resource utilization, providing a comprehensive assessment of its capabilities. Remarkably, Timelog consistently achieves an astounding accuracy rate of 99% when used in educational institutions compliant with UGC (University Grants Commission) norms, making it an ideal solution for academia.By seamlessly integrating genetic algorithms, Timelog represents a transformative leap in the scheduling domain, offering organizations a streamlined, efficient, and highly customizable scheduling solution. This innovation promises to revolutionize the scheduling process, significantly enhancing organizational efficiency and productivity.2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)180To evaluate the effectiveness of Timelog, a set of performance metrics, including schedule efficiency and resource utilization, are introduced, providing a comprehensive assessment of its capabilities.2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)['Divanshu Singh', 'Avish Khandelwal', 'Aniket Kumar', 'Harsh Bawaskar', 'Prajakta S. Ugale']2024-02-16T00:00:00https://www.semanticscholar.org/paper/91fcd30a5c9d36f2bc610617d917ebe9f6132afc a82008de6da33b4aadf51b2ce3575c8b69a516c9Educational Administration Theory and Practices00Educational Administration Theory and Practices['Sanjay Vaid', 'Dr. Ashish Kumar', 'Dr. Priyanka Yadav']2024-02-16T00:00:00https://www.semanticscholar.org/paper/a82008de6da33b4aadf51b2ce3575c8b69a516c9 b81e721592bf2525d3a3d2276bb9321a039e289aInternational Journal of Production Research720International Journal of Production Research['Vishwa V. Kumar', 'Avimanyu Sahoo', 'Siva K. Balasubramanian', 'Sampson Gholston']2024-02-16T00:00:00https://www.semanticscholar.org/paper/b81e721592bf2525d3a3d2276bb9321a039e289a 26d3bf9e02da70440e538d163723d02f8e8ccf5dLaw, Innovation and Technology00Law, Innovation and Technology['Ann C Luk']2024-02-16T00:00:00https://www.semanticscholar.org/paper/26d3bf9e02da70440e538d163723d02f8e8ccf5d 1b9934b466d739543f980e7a87ed324834b3a346This article examines activities aimed at implementation and promotion of UNESCO’s fundamental documents in the field of regulation of artificial intelligence and, in particular, the “Recommendation on the Ethical Considerations of Artificial Intelligence” and other tools developed by this specialized agency of the United Nations. The article provides a detailed review of the conference “National experience in implementation of the UNESCO Recommendation on the Ethics of Artificial Intelligence”, which was organized on October 27, 2023 by the MGIMO Centre for Artificial intelligence with the support of the Commission of the Russian Federation for UNESCO and the Russian Committee of the UNESCO Information for All Program (IFAP).Journal of Digital Economy Research00UNESCO’s fundamental documents in the field of regulation of artificial intelligence and, in particular, the “Recommendation on the Ethical Considerations of Artificial Intelligence” and other tools developed by this specialized agency of the United Nations are examined.Journal of Digital Economy Research['K. Y. Pankova']2024-02-16T00:00:00https://www.semanticscholar.org/paper/1b9934b466d739543f980e7a87ed324834b3a346 c3cbe190404dc9bee0de5a25a16f5b21a52f0c30Introduction Digital accessibility involves designing digital systems and services to enable access for individuals, including those with disabilities, including visual, auditory, motor, or cognitive impairments. Artificial intelligence (AI) has the potential to enhance accessibility for people with disabilities and improve their overall quality of life. Methods This systematic review, covering academic articles from 2018 to 2023, focuses on AI applications for digital accessibility. Initially, 3,706 articles were screened from five scholarly databases—ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and Springer. Results The analysis narrowed down to 43 articles, presenting a classification framework based on applications, challenges, AI methodologies, and accessibility standards. Discussion This research emphasizes the predominant focus on AI-driven digital accessibility for visual impairments, revealing a critical gap in addressing speech and hearing impairments, autism spectrum disorder, neurological disorders, and motor impairments. This highlights the need for a more balanced research distribution to ensure equitable support for all communities with disabilities. The study also pointed out a lack of adherence to accessibility standards in existing systems, stressing the urgency for a fundamental shift in designing solutions for people with disabilities. Overall, this research underscores the vital role of accessible AI in preventing exclusion and discrimination, urging a comprehensive approach to digital accessibility to cater to diverse disability needs.Frontiers in Artificial Intelligence822A critical gap is revealed in addressing speech and hearing impairments, autism spectrum disorder, neurological disorders, and motor impairments, revealing the need for a more balanced research distribution to ensure equitable support for all communities with disabilities.Frontiers in Artificial Intelligence['Khansa Chemnad', 'Achra Othman']2024-02-16T00:00:00https://www.semanticscholar.org/paper/c3cbe190404dc9bee0de5a25a16f5b21a52f0c30 2be1b796ceebe7f9ff4f309bb102d96196a670abThe Provincial Health Services Authority (PHSA) of British Columbia suggested that a paradigm shift from weight to well-being could address the unintended consequences of focusing on obesity and improve the outcomes of efforts to address the challenges facing both individuals and our healthcare system. In this paper, we jointly used artificial intelligence (AI) and participatory modeling to examine the possible consequences of this paradigm shift. Specifically, we created a conceptual map with 19 experts to understand how obesity and physical and mental well-being connect to each other and other factors. Three analyses were performed. First, we analyzed the factors that directly connect to obesity and well-being, both in terms of causes and consequences. Second, we created a reduced version of the map and examined the connections between categories of factors (e.g., food production, and physiology). Third, we explored the themes in the interviews when discussing either well-being or obesity. Our results show that obesity was viewed from a medical perspective as a problem, whereas well-being led to broad and diverse solution-oriented themes. In particular, we found that taking a well-being perspective can be more comprehensive without losing the relevance of the physiological aspects that an obesity-centric perspective focuses on.Inf.01It is found that taking a well-being perspective can be more comprehensive without losing the relevance of the physiological aspects that an obesity-centric perspective focuses on.Inf.['P. Giabbanelli', 'Grace MacEwan']2024-02-16T00:00:00https://www.semanticscholar.org/paper/2be1b796ceebe7f9ff4f309bb102d96196a670ab 428547d1e70a6bd55999be4ad5f4243718dee34eScientific Reports661These technologies have the potential to mitigate environmental externalities by addressing information imbalances within global supply chains, but it is essential to prioritize inclusive governance that emphasizes democratic participation to mitigate any unintended negative consequences, especially for vulnerable communities.Scientific Reports['Zexin Hong', 'Kun Xiao']2024-02-16T00:00:00https://www.semanticscholar.org/paper/428547d1e70a6bd55999be4ad5f4243718dee34e 1dc13d0fae5037c93a86938c3a19763ad288300eExisting approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM -- reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts \textit{and} spontaneously engage in social reasoning.arXiv.org690It is suggested that a robust, or general, ASI will respond to prompts and spontaneously engage in social reasoning and is argued for a principled approach to studying and developing AI ToM.ArXiv['Nikolos Gurney', 'D. Pynadath', 'Volkan Ustun']2024-02-16T00:00:00https://www.semanticscholar.org/paper/1dc13d0fae5037c93a86938c3a19763ad288300e 94865c1c724f92b2592e976b1e6711cb79d8e8d2Unleashing the Implicit Impact of Artificial Intelligence in Education In a period characterized by technological advancements, Artificial Intelligence (AI) has surfaced as a transformative force across colorful diligence, and education is no exception. As classrooms evolve to meet the demands of the digital age, AI is playing a vital part in reshaping the geography of education. This composition explores the profound impact of AI on education, from substantiated literacy gests to intelligent training systems.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This composition explores the profound impact of AI on education, from substantiated literacy gests to intelligent training systems, from substantiated literacy gests to intelligent training systems.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Nakka Maisaiah']2024-02-16T00:00:00https://www.semanticscholar.org/paper/94865c1c724f92b2592e976b1e6711cb79d8e8d2 2632a3877612271accf1970e617abd368e96b187Artificial intelligence (AI) technology has brought convenience to human lives, but its pervasive impact extends beyond individuals, affecting society as a whole. Consequently, the necessity for an AI ethics education program has become increasingly apparent. This study aims to investigate the influence of an experimental research study that developed and implemented an AI ethics education program for learners’ ethical awareness and attitude towards AI. The research methodology involved validating a model of the AI ethics education program by applying it to a group of 10 domain experts. Additionally, pre-test and post-test designs were employed with 17 middle school students as the experimental group. The same assessment was administered before and after the implementation of the AI ethics education program, and the data were analyzed using paired-sample t-tests. The findings of this study are as follows: Firstly, an AI ethics education program model was developed, incorporating key competencies such as AI literacy, critical thinking skills in AI, and AI problem-solving skills, all within the context of AI ethics. The implementation of this model was effective in the educational setting. Secondly, significant improvements were observed in the ethical awareness of middle school students across all domains after participating in the program. Thirdly, the attitudes of middle school students towards AI exhibited significant enhancements across all domains. These findings contribute to the broader field of AI ethics education by highlighting the importance of ethical awareness in AI and fostering favorable attitudes towards AI. The implications of this study are significant for the field of AI education.Applied Sciences410Significant improvements were observed in the ethical awareness of middle school students across all domains after participating in the AI ethics education program, and the attitudes of middle school students towards AI exhibited significant enhancements across all domains.Applied Sciences['Jung-In Choi', 'Eunja Yang', 'Eun-Hee Goo']2024-02-16T00:00:00https://www.semanticscholar.org/paper/2632a3877612271accf1970e617abd368e96b187 edc19802affc0097ef8dedb4efc2845e88b87b29The article researches the problem of ethical support of the application of artificial intelligence (AI) in medicine and healthcare, which is topical for modern sci­ence. Despite a significant number of foreign and domestic publications devoted to the topic of AI, the conceptual justification of the ethics of AI application in medicine and healthcare remains poorly developed. Relying on international recommendations and articles, as well as on their own experience of research activities, work in research ethics committees, the results of a pilot survey of health care workers, etc., the authors define and analyze the basic ethical principles of using AI in medicine and health care. The proposed principles are considered in the context of their practical application to protect human and natural rights and interests, which includes preservation of patient confidentiality, prevention of discrimination, protection from AI errors, respect for in­formed consent, as well as compliance with the norms of “open science”, mutual trust of developers and users, etc. The proposed principles are analyzed in the context of their practical application. The application of the proposed principles will orient scientists, AI developers, ethical committees conducting expert review of research, society as a whole to the priorities of humanization of healthcare, respect for human beings and nature, as well as to educate society, create a regulatory framework, ethical recommen­dations and codes of ethics for the use of AI in medicine and healthcare.Journal of Digital Economy Research20The article defines and analyzes the basic ethical principles of using AI in medicine and health care, which includes preservation of patient confidentiality, prevention of discrimination, protection from AI errors, respect for in­formed consent, as well as compliance with the norms of “open science”, mutual trust of developers and users, etc.Journal of Digital Economy Research['V. Sokolchik', 'A. I. Razuvanov']2024-02-16T00:00:00https://www.semanticscholar.org/paper/edc19802affc0097ef8dedb4efc2845e88b87b29 83e6b1cf1d11df4f918a1ee1be3d97abe16abd40Medicine has changed rarely in history as rapidly as it is today. Constantly new methods are being introduced that can improve health outcomes. Six such developments are presented in this article, that is, Artificial Intelligence (AI) in diagnosis and treatment, 3D-printed organs, tele-surgery, nano-medicine, CRISPR technology, and quantum teleportation. Thus, with these developments, several problems in medicine can be solved to the benefit of patients. However, it also increases the responsibility of users to apply the methods in accordance with ethical principles.Global Journal of Biotechnology and Biomaterial Science00Artificial Intelligence (AI) in diagnosis and treatment, 3D-printed organs, tele-surgery, nano-medicine, CRISPR technology, and quantum teleportation are presented.Global Journal of Biotechnology and Biomaterial Science['Manfred Doepp']2024-02-16T00:00:00https://www.semanticscholar.org/paper/83e6b1cf1d11df4f918a1ee1be3d97abe16abd40 55e2ad200f652dd9d96e11042a397943814033beThe article examines the role and the influence of artificial intelligence in the digital age in the field of higher education, in particular in the field of International relations. The article, which comprehensively presents the issue under discussion, also makes the focus on the growing role of artificial intelligence in the field of international relations, which has become one of the important issues on the agenda of superpower relations. At the same time, the advantages and risks of digital technologies and artificial intelligence systems are discussed, which, on the one hand, ensure technological progress, and on the other, create dividing barriers between developed and less developed countries. The author researches how the foreign policy departments of the leading world powers use the opportunities provided by artificial intelligence systems in their work, and what advantages this provides. The above-mentioned questions indicate that the current generation of International relations scholars must be capable to use the instruments of digital technologies and artificial intelligence systems in the course of their professional activities, which will save time and increase the efficiency of the work done. However, the important question is how to use digital technologies and artificial intelligence systems in the educational process of training specialists in International relations, so that this does not have negative consequences and does not lead to overload of students or tutors. It is necessary to identify the relevant components of education in this regard, as well as to estimate the proportion of using the artificial intelligence in education. It is important that the university is also a research environment and the functioning of a continuous chain of science and education is really crucial, and in which the use of artificial intelligence capabilities is also important. The article also analyzes and presents the views of researchers involved in the issue regarding the positive and negative consequences of the use of artificial intelligence in higher education. Accordingly, conclusions were drawn regarding the prospects for the use of artificial intelligence in higher education.Journal of Digital Economy Research50The article analyzes and presents the views of researchers involved in the issue regarding the positive and negative consequences of the use of artificial intelligence in higher education, and conclusions were drawn regarding the prospects for the use of artificial intelligence in higher education.Journal of Digital Economy Research['Zh. S. Manukyan']2024-02-16T00:00:00https://www.semanticscholar.org/paper/55e2ad200f652dd9d96e11042a397943814033be 8ce5fb1f31559521288532ab07496e5eccd3062bBackground: Kidney stones are a clinical condition with the presence of stones along the urinary tract of varying sizes. The aim of this research is the need for a system to automatically detect kidney stones so that it can help radiologists in diagnosing kidney stones accurately, effectively and efficiently, and patients can immediately undergo further action to cure kidney stones.Methods: The difference in research carried out by researchers is the use of artificial intelligence which uses deep learning with a convolutional neural network (CNN) algorithm. This research uses images obtained from CT scan results from public data (Kaggle) and primary hospital data. The number of images used in the Augmentation training data was 2338 normal images and 2390 kidney stone images. The augmentation testing data used 540 normal images and 446 kidney stone images. The research also involved experts, namely radiology specialists, in determining images with abnormal and normal stone tones.Results: research obtained from CT Scan images of kidney stones with augmentation and original using public data/Kaggle images, obtained using augmentation obtained a high accuracy value of 99.69%. Meanwhile, in testing data using primary/hospital data images, augmented data obtained accuracy values that were still low at 45.43% and 45.23%, respectively.Conclusions: The use of deep learning with the CNN model in training data augmentation obtained high accuracy values, however in testing data using hospital CT scan images the accuracy value was still low, but it was able to recognize images of kidney stones, so it could help in automatically diagnosing kidney stones. For future work could involve refining the model to handle variations in hospital data or exploring additional features to improve generalizability.Jurnal Imejing Diagnostik (JImeD)00The use of deep learning with the CNN model in training data augmentation obtained high accuracy values, however in testing data using hospital CT scan images the accuracy value was still low, but it was able to recognize images of kidney stones, so it could help in automatically diagnosing kidney stones.Jurnal Imejing Diagnostik (JImeD)['Nanang Sulaksono', 'Ary Kurniawati']2024-02-16T00:00:00https://www.semanticscholar.org/paper/8ce5fb1f31559521288532ab07496e5eccd3062b 709f4dda20192edab7cbcc9753a96bb258c17937Introduction Dementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately. Methods Quantitative research was conducted to address the objective of this study using randomly selected 343 Saudi patients. The Chi-square test was conducted to determine the association of the patient’s cognitive function with various features, including demographical and medical history. Two widely used AI algorithms, logistic regression and support vector machine (SVM), were used for detecting cognitive decline. This study also assessed patients’ cognitive function based on gender and developed the predicting models for males and females separately. Results Fifty four percent of patients have normal cognitive function, 34% have MCI, and 12% have dementia. The prediction accuracies for all the developed models are greater than 71%, indicating good prediction capability. However, the developed SVM models performed the best, with an accuracy of 93.3% for all patients, 94.4% for males only, and 95.5% for females only. The top 10 significant predictors based on the developed SVM model are education, bedtime, taking pills for chronic pain, diabetes, stroke, gender, chronic pains, coronary artery diseases, and wake-up time. Conclusion The results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia. This research can also stipulate substantial direction and supportive intuitions for scholars to enhance their understanding of crucial research, emerging trends, and new developments in future cognitive decline studies.Frontiers in Computational Neuroscience650The results of this study emphasize the higher accuracy and reliability of the proposed methods in cognitive decline prediction that health practitioners can use for the early detection of dementia.Frontiers in Computational Neuroscience['Khalid AlHarkan', 'Nahid Sultana', 'Noura Al Mulhim', 'Assim M. AlAbdulKader', 'Noor Alsafwani', 'Marwah Barnawi', 'Khulud Alasqah', 'Anhar Bazuhair', 'Zainab Alhalwah', 'Dina Bokhamseen', 'Sumayh S. Aljameel', 'Sultan Alamri', 'Y. Alqurashi', 'Kholoud Al Ghamdi']2024-02-16T00:00:00https://www.semanticscholar.org/paper/709f4dda20192edab7cbcc9753a96bb258c17937 3081ed3c737b72102bf4531839cc85774cdebb68Current Atherosclerosis Reports360This paper is a comprehensive guide for AI development teams to understand assumptions in datasets and chosen metrics for outcome/ground truth, and how this translates to real-world performance for cardiovascular disease (CVD).Current atherosclerosis reports['M. van Assen', 'Ashley Beecy', 'Gabrielle Gershon', 'Janice Newsome', 'Hari Trivedi', 'Judy Gichoya']2024-02-16T00:00:00https://www.semanticscholar.org/paper/3081ed3c737b72102bf4531839cc85774cdebb68 6339b83aae08406c6345a24cd748225f2399dec5This research explores the use of Artificial Intelligence (AI) as an innovative strategy to combat school dropout in Brazilian higher education. Faced with the increasing challenge of student retention in universities, the study focuses on evaluating the effectiveness of AI in this context, analyzing both its capabilities and limitations. The main objective is to understand how AI can be integrated into higher education, assessing its contributions to personalized learning and student engagement. The methodology consists of an extensive literature review, including the collection and detailed analysis of relevant studies on the use of AI in the educational field. The findings highlight that AI offers significant opportunities to tailor education to individual student needs, enhancing engagement and retention. Practical examples from the Brazilian context illustrate how AI implementation has contributed to reducing school dropout rates. However, the study also identifies key challenges, such as the need for adequate technological infrastructure and ongoing training for educators to effectively use AI tools. The conclusion is that AI holds substantial potential to mitigate dropout issues in higher education, but its successful implementation requires careful consideration. It is essential to take into account legal, ethical aspects, and institutional specifics to ensure effective integration of AI into the Brazilian educational system. This study provides valuable insights for strategic decision-making and the development of more efficient and contemporary educational policies.Contribuciones a las ciencias sociales00The findings highlight that AI offers significant opportunities to tailor education to individual student needs, enhancing engagement and retention, and holds substantial potential to mitigate dropout issues in higher education.CONTRIBUCIONES A LAS CIENCIAS SOCIALES['Monique Bolonha das Neves Meroto', 'Alberto da Silva Franqueira', 'Cláudia Lúcia Caldeira De Queiróz', 'Elzo Brito Dos Santos Filho', 'Ivoneide Teixeira Da Costa', 'Paola Rodrigues da Silva Cunha', 'Ricardo Gomes Da Silva', 'Vanessa Vasconcelos Lima']2024-02-16T00:00:00https://www.semanticscholar.org/paper/6339b83aae08406c6345a24cd748225f2399dec5 4d7371df679525345322b5c21a3dd1e022138c9dIn the 21st century, the integration of technology into education has been evolving rapidly, with Artificial Intelligence (AI) emerging as a transformative force. This article explores the multifaceted impact of AI in education, ranging from personalized learning experiences to ethical considerations.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The multifaceted impact of AI in education, ranging from personalized learning experiences to ethical considerations, is explored, with Artificial Intelligence emerging as a transformative force.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Nakka Maisaiah']2024-02-16T00:00:00https://www.semanticscholar.org/paper/4d7371df679525345322b5c21a3dd1e022138c9d 11431275fc62de91d7484bf6eefa96002e447a03Background With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis. Objective This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots. Methods The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field. Results A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are “classification”, “diagnosis”, and “risk”. Meanwhile, the current research hotpots were “late gadolinium enhancement” and “carotid ultrasound”. Conclusions AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.Frontiers in Cardiovascular Medicine480This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots.Frontiers in Cardiovascular Medicine['Jirong Zhang', 'Jimei Zhang', 'Juan Jin', 'Xicheng Jiang', 'Linlin Yang', 'Shiqi Fan', 'Qiao Zhang', 'Ming Chi']2024-02-16T00:00:00https://www.semanticscholar.org/paper/11431275fc62de91d7484bf6eefa96002e447a03 086529ba6ac20673ce76233818ed3a6919378093Artificial Intelligence (AI) is being used in the field of neurosurgery for improving patient outcomes, reducing the risk of complications, and increasing the efficiency of surgical procedures. AI algorithms can analyze patient data, plan surgical procedures, guide surgical instruments, monitor brain activity, and improve post-operative care. The benefits of incorporating AI into neurosurgical practice include pre-operative planning, intraoperative navigation, real-time monitoring, and post-operative care. AI is already being used in neurosurgery for image segmentation, surgical planning, intraoperative navigation, real-time monitoring, and predictive analytics. The potential applications of AI in neurosurgery include personalized medicine, virtual reality, robotic surgery, predictive analytics, and medical imaging. However, the challenges of incorporating AI into neurosurgical practice are data quality, data privacy and security, regulatory frameworks, and training and education. In short, AI has the potential to completely transform the discipline of neurosurgery, but there is a need to address the challenges associated with its incorporation into neurosurgical practice.Pakistan journal of neurological sciences260Artificial Intelligence has the potential to completely transform the discipline of neurosurgery, but there is a need to address the challenges associated with its incorporation into neurosurgical practice.Pakistan Journal of Neurological Sciences['Ahtesham Khizar']2024-02-16T00:00:00https://www.semanticscholar.org/paper/086529ba6ac20673ce76233818ed3a6919378093 54595370296db69ccd6817d3703050f8a0547ed7Background Artificial Intelligence (A.I.) is revolutionizing various sectors like healthcare, specifically in nursing education, by improving the quality of care, streamlining workflows, and reducing the cost of healthcare. Integrating A.I. into nursing education can enhance students’ personalized and efficient learning experiences. This study will aim to develop and implement research on A.I. and identification among (BSc) nursing students in selected colleges in Wardha. Protocol 100 students will be selected for the study by using a purposive sampling technique. This study will use one group per test and post-test design, and the structured questionnaires will be delivered to the students. Pre-test and post-test data will be taken to assess the development of student knowledge. Conclusions A.I. can be used to create more realistic stimulation experiences, which can help the students to develop their clinical skills. Furthermore, integrating A.I. into nursing education can revolutionize the sector by benefiting students, tutors, and the healthcare system.F1000Research120This study will aim to develop and implement research on A.I. and identification among (BSc) nursing students in selected colleges in Wardha and create more realistic stimulation experiences, which can help the students to develop their clinical skills.F1000Research['Utkarsh Warghane', 'Seema Singh']2024-02-16T00:00:00https://www.semanticscholar.org/paper/54595370296db69ccd6817d3703050f8a0547ed7 a64b93490d856aeac189a8da882fabcd3a5b2979The study investigates EFL (English as a Foreign Language) learners' opinions, attitudes and the challenges of incorporating AI-powered teaching and learning. It also examines how their ideas and attitudes are affected by demographic variables.  258 students were selected using a random sampling method from a population comprising students studying in different levels of programs at the College of Science and College of Business Administration, Prince Sattam bin Abdul-Aziz University, KSA. A questionnaire was self-developed using some modified items from prior studies as the study looks at how certain independent variables (e.g., study level, residential background and parents' educational level) affect the dependent variable (e.g., learners' opinions, attitudes and challenges for AI-powered learning and teaching).  The quantitative approach (descriptive quantitative design) revealed that Saudi EFL students held a high level of positive opinions and attitudes towards AI-powered learning. However, the analysis found that many students thought implementing AI-powered learning was challenging. A one-way ANOVA showed no significant difference based on respondents' residential background and parental education. However, respondents differed significantly based on their level or year of study. The study findings will assist administrators and course teachers in using AI-powered technologies to overcome challenges and prepare students for achievement in the English language.Journal of Education and e-Learning Research210The quantitative approach (descriptive quantitative design) revealed that Saudi EFL students held a high level of positive opinions and attitudes towards AI-powered learning, however, the analysis found that many students thought implementing AI-powered learning was challenging.Journal of Education and e-Learning Research['Mohammad Jamshed', 'Iftikhar Alam', 'Sultan Al Sultan', 'Sameena Banu']2024-02-16T00:00:00https://www.semanticscholar.org/paper/a64b93490d856aeac189a8da882fabcd3a5b2979 94c7dba719552821f418dcf79691386a2076d4e2Computing Open00Computing Open['A. Nilofer', 'S. Sasikala']2024-02-16T00:00:00https://www.semanticscholar.org/paper/94c7dba719552821f418dcf79691386a2076d4e2 561a042fb5bd332e6958251527d14d0936c4d13bAmerican Journal of Gastroenterology100The American journal of gastroenterology['Jeremy Louissaint', 'Hugo E Vargas']2024-02-16T00:00:00https://www.semanticscholar.org/paper/561a042fb5bd332e6958251527d14d0936c4d13b 0140404b44f59f27044232378449286da48b853fInternational journal of latest engineering research and applications00International Journal of Latest Engineering Research and Applications (IJLERA)['Supriya Rai', 'Mahek Doshi', 'Ansh Mehta', 'Payal Rani', 'Ashish Goyal']2024-02-16T00:00:00https://www.semanticscholar.org/paper/0140404b44f59f27044232378449286da48b853f 3af36c73b870e06caec0abb8a245d29d278da8b100['S. Vijayalakshmi', 'P. Durgadevi', 'Lija Jacob', 'B. Balusamy', 'P. Nand']2024-02-16T00:00:00https://www.semanticscholar.org/paper/3af36c73b870e06caec0abb8a245d29d278da8b1 729395182e7b549fc65d866c7084ecdb751bf0a0Incorporating generative artificial intelligence (GAI) in education has become crucial in contemporary educational environments. This research article thoroughly investigates the ramifications of implementing GAI in the higher education context of Saudi Arabia, employing a blend of quantitative and qualitative research approaches. Survey-based quantitative data reveals a noteworthy correlation between educators’ awareness of GAI and the frequency of its application. Notably, around half of the surveyed educators are at stages characterized by understanding and familiarity with GAI integration, indicating a tangible readiness for its adoption. Moreover, the study’s quantitative findings underscore the perceived value and ease associated with integrating GAI, thus reinforcing the assumption that educators are motivated and inclined to integrate GAI tools like ChatGPT into their teaching methodologies. In addition to the quantitative analysis, qualitative insights from in-depth interviews with educators unveil a rich tapestry of perspectives. The qualitative data emphasizes GAI’s role as a catalyst for collaborative learning, contributing to professional development, and fostering innovative teaching practices.PeerJ Computer Science410The study’s quantitative findings underscore the perceived value and ease associated with integrating GAI, thus reinforcing the assumption that educators are motivated and inclined to integrate GAI tools like ChatGPT into their teaching methodologies.PeerJ Computer Science['Abdullah M. Alammari']2024-02-16T00:00:00https://www.semanticscholar.org/paper/729395182e7b549fc65d866c7084ecdb751bf0a0 a5b5917713bdb6a574eb457ebd20891fcfb2684aRecent advancements in language technology and Artificial Intelligence have resulted in numerous Language Models being proposed to perform various tasks in the legal domain ranging from predicting judgments to generating summaries. Despite their immense potential, these models have been proven to learn and exhibit societal biases and make unfair predictions. In this study, we explore the ability of Large Language Models (LLMs) to perform legal tasks in the Indian landscape when social factors are involved. We present a novel metric, $\beta$-weighted $\textit{Legal Safety Score ($LSS_{\beta}$)}$, which encapsulates both the fairness and accuracy aspects of the LLM. We assess LLMs' safety by considering its performance in the $\textit{Binary Statutory Reasoning}$ task and its fairness exhibition with respect to various axes of disparities in the Indian society. Task performance and fairness scores of LLaMA and LLaMA--2 models indicate that the proposed $LSS_{\beta}$ metric can effectively determine the readiness of a model for safe usage in the legal sector. We also propose finetuning pipelines, utilising specialised legal datasets, as a potential method to mitigate bias and improve model safety. The finetuning procedures on LLaMA and LLaMA--2 models increase the $LSS_{\beta}$, improving their usability in the Indian legal domain. Our code is publicly released.arXiv.org510A novel metric, $\beta$-weighted $\textit{Legal Safety Score ($LSS_{\beta}$)}$, is presented, which encapsulates both the fairness and accuracy aspects of the LLM and proposes finetuning pipelines, utilising specialised legal datasets, as a potential method to mitigate bias and improve model safety.ArXiv['Yogesh Tripathi', 'Raghav Donakanti', 'Sahil Girhepuje', 'Ishan Kavathekar', 'Bhaskara Hanuma Vedula', 'Gokul S Krishnan', 'Shreya Goyal', 'Anmol Goel', 'Balaraman Ravindran', 'P. Kumaraguru']2024-02-16T00:00:00https://www.semanticscholar.org/paper/a5b5917713bdb6a574eb457ebd20891fcfb2684a 629f8f6f4eee3aa5dfd796ad9c06443735f7e3a6International Journal of Data Science and Analysis120This work proposes a methodology which fills the gap, extending the Forward Search approach, employed in robust statistical learning, to machine learning models, and applies it to the context of Bitcoin price prediction, comparing a linear regression model against a nonlinear neural network model.International Journal of Data Science and Analytics['Paolo Giudici', 'E. Raffinetti', 'Marco Riani']2024-02-16T00:00:00https://www.semanticscholar.org/paper/629f8f6f4eee3aa5dfd796ad9c06443735f7e3a6 3ba76522ec9f547ff5453e595dd4c660a743f24bBackground Artificial intelligence (AI)-based computer perception technologies (e.g., digital phenotyping and affective computing) promise to transform clinical approaches to personalized care in psychiatry and beyond by offering more objective measures of emotional states and behavior, enabling precision treatment, diagnosis, and symptom monitoring. At the same time, passive and continuous nature by which they often collect data from patients in non-clinical settings raises ethical issues related to privacy and self-determination. Little is known about how such concerns may be exacerbated by the integration of neural data, as parallel advances in computer perception, AI, and neurotechnology enable new insights into subjective states. Here, we present findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care and contextualize them within the neuroethics and neurorights literatures. Methods We conducted qualitative interviews with patients (n = 20), caregivers (n = 20), clinicians (n = 12), developers (n = 12), and clinician developers (n = 2) regarding their perspective toward using PC in clinical care. Transcripts were analyzed in MAXQDA using Thematic Content Analysis. Results Stakeholder groups voiced concerns related to (1) perceived invasiveness of passive and continuous data collection in private settings; (2) data protection and security and the potential for negative downstream/future impacts on patients of unintended disclosure; and (3) ethical issues related to patients’ limited versus hyper awareness of passive and continuous data collection and monitoring. Clinicians and developers highlighted that these concerns may be exacerbated by the integration of neural data with other computer perception data. Discussion Our findings suggest that the integration of neurotechnologies with existing computer perception technologies raises novel concerns around dignity-related and other harms (e.g., stigma, discrimination) that stem from data security threats and the growing potential for reidentification of sensitive data. Further, our findings suggest that patients’ awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation). These results highlight the need for systematic research into how best to implement these technologies into clinical care in ways that reduce disruption, maximize patient benefits, and mitigate long-term risks associated with the passive collection of sensitive emotional, behavioral and neural data.Frontiers in Human Neuroscience930Findings from a multi-site NCATS-funded study of ethical considerations for translating computer perception into clinical care suggest that patients’ awareness and preoccupation with feeling monitored via computer sensors ranges from hypo- to hyper-awareness, with either extreme accompanied by ethical concerns (consent vs. anxiety and preoccupation).Frontiers in Human Neuroscience['Meghan Hurley', 'Anika Sonig', 'John Herrington', 'Eric A. Storch', 'G. Lazaro-Munoz', 'Jennifer Blumenthal-Barby', 'K. Kostick-Quenet']2024-02-16T00:00:00https://www.semanticscholar.org/paper/3ba76522ec9f547ff5453e595dd4c660a743f24b 7770f77142b69a7e77f47cca4a622564a705916bThe Japanese Journal of Ergonomics40The Japanese Journal of Ergonomics['Keiga Terao', 'D. Karikawa', 'Makoto Takahashi']2024-02-15T00:00:00https://www.semanticscholar.org/paper/7770f77142b69a7e77f47cca4a622564a705916b dfee39bd85ce7da1bb38c5c9a8e76bdff3d4abfeThe objective of this systematic review was to assess the adequacy of current medication management in Ghana considering the risks posed by increased artificial intelligence (AI) automation in pharmacies worldwide A qualitative comparative approach was used despite reviewed the Ghana 1994 Pharmacy Act against recognition of AI challenges and international governance guidelines . The results revealed flaws in terms of quality prerequisites, transparency checklists and liability mechanisms developed for AI systems compared to existing regulations of the manual process. Outdated approaches to patient care that fail to ensure patient safety or address threats to the accuracy of recommendations from data collection biases and technical errors. Proposed changes include a requirement for usability testing before approving AI pharmacy deployments and the creation of a review board to review post-implementation systems for validity. Updating regulations to deal with modern equipment puts innovation and responsible regulation in the fast-paced healthcare industry. This study contributes significantly to preliminary research on AI policy readiness in the Ghanaian legal context, and suggests a feasible methodology for exploring qualitative differences for use in companies and countries competing for technology a disturbing, increasingly beyond the date code. Early government reform helps keep pace with the realities of adoption.  Babylonian Journal of Artificial Intelligence242This study contributes significantly to preliminary research on AI policy readiness in the Ghanaian legal context, and suggests a feasible methodology for exploring qualitative differences for use in companies and countries competing for technology a disturbing, increasingly beyond the date code.Babylonian Journal of Artificial Intelligence['George Benneh Mensah', 'Maad M. Mijwil', 'I. Adamopoulos']2024-02-15T00:00:00https://www.semanticscholar.org/paper/dfee39bd85ce7da1bb38c5c9a8e76bdff3d4abfe fc14de693f8e1c1b268f300374c5d591d5469894The present study pays particular attention to issues of originality, intellectual property, and potential biases in machine learning models. European legislation on AI, along with various legislative acts that have followed this initial attempt at regulation, is examined as an essential reference point for initiating a technologically responsible and sustainable prospective discussion. The article analyzes two main perspectives: the risks of uncontrolled AI growth, with emphasis on the damage to the conceptualization of technological primacy over human cognition, and the opportunities for harmonization between human and AI. These themes are contextualized in relation to technological development, regulatory policies, consumer trends, and social values.Fashion Highlight00The article analyzes two main perspectives: the risks of uncontrolled AI growth, with emphasis on the damage to the conceptualization of technological primacy over human cognition, and the opportunities for harmonization between human and AI.Fashion Highlight['Niccolò Musmeci', 'Pietro Salvatore Pantano']2024-02-15T00:00:00https://www.semanticscholar.org/paper/fc14de693f8e1c1b268f300374c5d591d5469894 fdf0c125edcc8890c17010f0c66ebb833a9556a3Journal of Russian Law02Journal of Russian Law['Stanislav L’vovich Nudel’']2024-02-15T00:00:00https://www.semanticscholar.org/paper/fdf0c125edcc8890c17010f0c66ebb833a9556a3 4bfb3722adf383edfaa123b4c41dcf50ecbd3e24The paper analyzes main approaches to the legal regulation of the digital ruble. The authors examine in detail the history and reasons for the introduction of digital currencies of central banks, the features of digital currencies of central banks, the features of the digital ruble as an object of civil law regulation. The paper concludes that the full-scale use of the digital ruble will depend not only on the formal indication of the digital ruble as a legally recognized method of fulfilling a civil obligation, but also on the recognition and provision of an actual possibility of using the digital ruble as a means of fulfilling public legal obligations, primarily obligations arising from tax relations. Such recognition will require amendments to the budget and tax legislation, as well as changes in the operation mode of the Federal Treasury of the Russian Federation. The question of how disputes and disagreements between the participants of relations arising in connection with the introduction of the digital ruble will be resolved requires careful consideration.Actual Problems of Russian Law00Actual Problems of Russian Law['T. E. Rozhdestvenskaya', 'A. Guznov']2024-02-15T00:00:00https://www.semanticscholar.org/paper/4bfb3722adf383edfaa123b4c41dcf50ecbd3e24 d621103dc9dfbb6eae26e34066d9120a3cec831eSocial media platforms have diverse content moderation policies, with many prominent actors hesitant to impose strict regulations. A key reason for this reluctance could be the competitive advantage that comes with lax regulation. A popular platform that starts enforcing content moderation rules may fear that it could lose users to less-regulated alternative platforms. Moreover, if users continue harmful activities on other platforms, regulation ends up being futile. This article examines the competitive aspect of content moderation by considering the motivations of all involved players (platformer, news source, and social media users), identifying the regulation policies sustained in equilibrium, and evaluating the information quality available on each platform. Applied to simple yet relevant social networks such as stochastic block models, our model reveals the conditions for a popular platform to enforce strict regulation without losing users. Effectiveness of regulation depends on the diffusive property of news posts, friend interaction qualities in social media, the sizes and cohesiveness of communities, and how much sympathizers appreciate surprising news from influencers.arXiv.org280Effectiveness of regulation depends on the diffusive property of news posts, friend interaction qualities in social media, the sizes and cohesiveness of communities, and how much sympathizers appreciate surprising news from influencers.ArXiv['So Sasaki', "C'edric Langbort"]2024-02-15T00:00:00https://www.semanticscholar.org/paper/d621103dc9dfbb6eae26e34066d9120a3cec831e b80beddf89e9193a7fbdf05760289f2611863454Clean Technologies and Environmental Policy521Clean Technologies and Environmental Policy['Lan-Ye Wei', 'Zhao Liu', 'Puju Cao', 'Huan Zhang']2024-02-15T00:00:00https://www.semanticscholar.org/paper/b80beddf89e9193a7fbdf05760289f2611863454 d7157f637f588649f0eeb5f77d3bfb3cdd0678acPlanning Theory &amp; Practice20Planning Theory &amp; Practice['K. McClymont']2024-02-15T00:00:00https://www.semanticscholar.org/paper/d7157f637f588649f0eeb5f77d3bfb3cdd0678ac bfcf4d877539e24e6d2b29f78455d0940148c789O risco na avaliação do crédito e seu impacto nos diversos setores da economia geram os temores de uma possível crise nos mercados produtivo e financeiro. Para minimizar os riscos foi instituído o Cadastro Positivo instituído pela Lei nº 12.414 de 2011, que, com as propostas legislativas de sua alteração, traz o questionamento sobre a necessidade ou não da regulação da matéria pelo Estado. O presente artigo busca analisar se as regras já previstas em lei são suficientes para o bom funcionamento dos bancos de dados positivos ou se ainda há necessidade de intervenção do poder público. Será então enfocado o risco do crédito para o sistema financeiro do país e posteriormente conceituado o cadastro positivo e descrito as principais regras constantes da Lei do Cadastro Positivo. Em seguida serão elencadas as principais mudanças propostas para a alteração da Lei nº 12.414/2011, bem como seus fundamentos e a análise delas. Por fim, será tratado sobre a regulação econômica do Estado e sua relação com o Cadastro Positivo e o crédito. Trata-se de pesquisa bibliográfica e documental, auxiliada pelo método histórico, cujo método científico utilizado é o dedutivo, a pesquisa é exploratória de abordagem qualitativa. Ao final, foi possível concluir que as normas constantes dos bancos de proteção ao crédito com o aperfeiçoamento da regulação da atividade pela Lei do Cadastro Positivo têm contribuído para o fomento, ainda que lento, do mercado de crédito, de forma que se torna desnecessária a intervenção estatal.DELOS: DESARROLLO LOCAL SOSTENIBLE00DELOS: DESARROLLO LOCAL SOSTENIBLE['Cloves Barbosa De Siqueira', 'M. Tassigny', 'Josana Pessoa de Andrade Mundstock', 'Danielle Costa de Souza Simas']2024-02-15T00:00:00https://www.semanticscholar.org/paper/bfcf4d877539e24e6d2b29f78455d0940148c789 95f42ced882922c5daf4afae1a326bcabeff5ec2Digital Society122This paper argues that a common division of labor in AI development and deployment can lead to specific obligations for which no entity is responsible, even though they apply to the effort as a whole, and proposes a mechanism to ensure that ethical obligations do not slip through the cracks because of the way an effort is structured.Digit. Soc.['Nandhini Swaminathan', 'David Danks']2024-02-15T00:00:00https://www.semanticscholar.org/paper/95f42ced882922c5daf4afae1a326bcabeff5ec2 d19893b559ab2bf0de46e38bff8d77b9913756fbEuropean Radiology Experimental1012A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology.European Radiology Experimental['S. Gitto', 'F. Serpi', 'Domenico Albano', 'Giovanni Risoleo', 'Stefano Fusco', 'Carmelo Messina', 'L. Sconfienza']2024-02-15T00:00:00https://www.semanticscholar.org/paper/d19893b559ab2bf0de46e38bff8d77b9913756fb bd15acc371153925bf86924cb2d13114ca6c95f8The incorporation of Large Language Models (LLMs) in healthcare marks a significant advancement. However, the application has predominantly been limited to discriminative and question-answering tasks, which does not fully leverage their interactive potential. To address this limitation, our paper presents AI Hospital, a framework designed to build a real-time interactive diagnosis environment. To simulate the procedure, we collect high-quality medical records to create patient, examiner, and medical director agents. AI Hospital is then utilized for the interactive evaluation and collaboration of LLMs. Initially, we create a Multi-View Medical Evaluation (MVME) benchmark where various LLMs serve as intern doctors for interactive diagnosis. Subsequently, to improve diagnostic accuracy, we introduce a collaborative mechanism that involves iterative discussions and a dispute resolution process under the supervision of the medical director. In our experiments, we validate the reliability of AI Hospital. The results not only explore the feasibility of apply LLMs in clinical consultation but also confirm the effectiveness of the dispute resolution focused collaboration method.arXiv.org432This paper presents AI Hospital, a framework designed to build a real-time interactive diagnosis environment and introduces a collaborative mechanism that involves iterative discussions and a dispute resolution process under the supervision of the medical director.ArXiv['Zhihao Fan', 'Jialong Tang', 'Wei Chen', 'Siyuan Wang', 'Zhongyu Wei', 'Jun Xi', 'Fei Huang', 'Jingren Zhou']2024-02-15T00:00:00https://www.semanticscholar.org/paper/bd15acc371153925bf86924cb2d13114ca6c95f8 619e78e4ba0816987942292924a00af07a174fc3npj Digit. Medicine151Collaboration amongst providers, entrepreneurs, and researchers must be prioritized to ensure that AI in diabetes care is providing quality and equitable patient care, especially for entrepreneurs and innovators.NPJ Digital Medicine['Serena C. Y. Wang', 'Grace Nickel', 'Kaushik P. Venkatesh', 'Marium M. Raza', 'J. Kvedar']2024-02-15T00:00:00https://www.semanticscholar.org/paper/619e78e4ba0816987942292924a00af07a174fc3 aa1b6eb2563523b72d01d2570f71a81930a83320AI and Ethics581The research unveils the complex dynamics of offensive AI, stressing the need for adaptive defences and ethical considerations, and develops and presents the AI Cybersecurity Dimensions (AICD) Framework, a comprehensive, multidimensional schema designed to guide academics, policymakers, and industry professionals in understanding and combating the evolving challenges posed by AI-driven cyber threats.AI and Ethics['Masike Malatji', 'Alaa Tolah']2024-02-15T00:00:00https://www.semanticscholar.org/paper/aa1b6eb2563523b72d01d2570f71a81930a83320 e9f4c1a1d45b1359170608eab2015ff9f8fbc030With this editorial, we inaugurate the next issue of our journal, which is dedicated to showcasing AI, ML, and E-Health projects within real healthcare environments. Web3 Journal: ML in Health Science01Web3 Journal: ML in Health Science['Y. Rusinovich']2024-02-15T00:00:00https://www.semanticscholar.org/paper/e9f4c1a1d45b1359170608eab2015ff9f8fbc030 c34bec688251d49399a1d82e3cb17ee8b4e9e829This study evaluates the impact of Rori, an AI powered conversational math tutor accessible via WhatsApp, on the math performance of approximately 1,000 students in grades 3-9 across 11 schools in Ghana. Each school was assigned to a treatment group or control group; the students in the control group continued their regular math instruction, while students in the treatment group engaged with Rori, for two 30-minute sessions per week over 8 months in addition to regular math instruction. We find that the math growth scores were substantially higher for the treatment group with an effect size of 0.37, and that the results were statistically significant (p<0.001). The fact that Rori works with basic mobile devices on low-bandwidth data networks gives the intervention strong potential to support personalized learning on other low-and-middle-income countries (LMICs), where laptop ownership and high-speed internet - prerequisite for many video-centered learning platforms - remain extremely limited. While the results should be interpreted judiciously, as they only report on year 1 of the intervention, and future research is necessary to better understand which conditions are necessary for successful implementation, they do suggest that chat-based tutoring solutions leveraging artificial intelligence could offer a costeffective approach to enhancing learning outcomes for millions of students globally.arXiv.org270Rori's results suggest that chat-based tutoring solutions leveraging artificial intelligence could offer a costeffective approach to enhancing learning outcomes for millions of students globally.ArXiv['Owen Henkel', 'Hannah Horne-Robinson', 'Nessie Kozhakhmetova', 'Amanda Lee']2024-02-15T00:00:00https://www.semanticscholar.org/paper/c34bec688251d49399a1d82e3cb17ee8b4e9e829 73ff93fb05c7edac7067fe601add3da87b0ba1ef In the BCS ‘Digital in Business Life 2024’ survey, we asked some Al-specific questions in addition to the normal ‘planning-for-the-next-year’ style queries. In keeping with a new BCS Fellows Technical Advisory Group paper on the augmented intelligence economy, Brian Runciman MBCS, ChatGPT and BCS members collaborate to report on the prevailing view of members on AI's current and near-future impact.ITNOW00ITNOW['B. Runciman']2024-02-15T00:00:00https://www.semanticscholar.org/paper/73ff93fb05c7edac7067fe601add3da87b0ba1ef e13592886eb919c9689c0654fe3666b62f07ab35Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets. For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare. However, the traditional process for analyzing such datasets demands substantial human effort and expertise for the data selection, processing, and analysis. To address this challenge, we introduce a novel framework, a Team of AI-made Scientists (TAIS), designed to streamline the scientific discovery pipeline. TAIS comprises simulated roles, including a project manager, data engineer, and domain expert, each represented by a Large Language Model (LLM). These roles collaborate to replicate the tasks typically performed by data scientists, with a specific focus on identifying disease-predictive genes. Furthermore, we have curated a benchmark dataset to assess TAIS's effectiveness in gene identification, demonstrating our system's potential to significantly enhance the efficiency and scope of scientific exploration. Our findings represent a solid step towards automating scientific discovery through large language models.arXiv.org600A novel framework, a Team of AI-made Scientists (TAIS), designed to streamline the scientific discovery pipeline, and contains simulated roles, including a project manager, data engineer, and domain expert represented by a Large Language Model (LLM).ArXiv['Haoyang Liu', 'Yijiang Li', 'Jinglin Jian', 'Yuxuan Cheng', 'Jianrong Lu', 'Shuyi Guo', 'Jinglei Zhu', 'Mianchen Zhang', 'Miantong Zhang', 'Haohan Wang']2024-02-15T00:00:00https://www.semanticscholar.org/paper/e13592886eb919c9689c0654fe3666b62f07ab35 77ca20d99639569076fa7e72157752295a42660d Martin Cooper MBCS speaks to Tim Bashford about exciting developments in healthcare technology that may allow the early detection and treatment of respiratory diseases such as COPD through AI-based vocal analysis.ITNOW00ITNOW['Martin Cooper']2024-02-15T00:00:00https://www.semanticscholar.org/paper/77ca20d99639569076fa7e72157752295a42660d cc5c523f4ed7496c9700f9df4244994a67ffe305 Award winning digital artist, Paul Brown, offers a personal perspective on how AI could grow closer to achieving sentience, its relationship to human evolution, and how it might be impacted by climate change.ITNOW00ITNOW['Georgia Smith']2024-02-15T00:00:00https://www.semanticscholar.org/paper/cc5c523f4ed7496c9700f9df4244994a67ffe305 59cc431d0b84eceac11f0644462cb547aff23180Countries worldwide have been implementing different actions national strategies for Artificial Intelligence (AI) to shape policy priorities and guide their development concerning AI. Several AI indices have emerged to assess countries' progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices combine multiple indicators using linear additive methods such as weighted sums, although they are limited in their ability to account for interactions among indicators. Another limitation concerns the use of deterministic weights, which can be perceived as subjective and vulnerable to debate and scrutiny, especially by nations that feel disadvantaged. Aiming at mitigating these problems, we conduct a methodological analysis to derive AI indices based on multiple criteria decision analysis. Initially, we assess correlations between different AI dimensions and employ the Choquet integral to model them. Thus, we apply the Stochastic Multicriteria Acceptability Analysis (SMAA) to conduct a sensitivity analysis using both weighted sum and Choquet integral in order to evaluate the stability of the indices with regard the weights. Finally, we introduce a novel ranking methodology based on SMAA, which considers several sets of weights to derive the ranking of countries. As a result, instead of using predefined weights, in the proposed approach, the ranking is achieved based on the probabilities of countries in occupying a specific position. In the computational analysis, we utilize the data employed in The Global AI Index proposed by Tortoise. Results reveal correlations in the data, and our approach effectively mitigates bias. In the sensitivity analysis, we scrutinize changes in the ranking resulting from weight adjustments. We demonstrate that our proposal rankings closely align with those derived from weight variations, proving to be more robust.arXiv.org460A novel ranking methodology based on the Stochastic Multicriteria Acceptability Analysis is introduced, which considers several sets of weights to derive the ranking of countries and achieves the ranking of countries based on the probabilities of countries in occupying a specific position.ArXiv['B. S. Campello', 'G. D. Pelegrina', 'R. Pelissari', 'Ricardo Suyama', 'L. T. Duarte']2024-02-15T00:00:00https://www.semanticscholar.org/paper/59cc431d0b84eceac11f0644462cb547aff23180 52fad39ac033ecffbaf0b9788969603f38312f70 Senior Manager Alex Robertson-Mair, and Data Architect Adrian Wong of FS Lighthouse, KPMG, explain an innovative solution to fighting money laundering with AI technologies.ITNOW50ITNOW['Alex Robertson-Mair', 'Adrian Wong']2024-02-15T00:00:00https://www.semanticscholar.org/paper/52fad39ac033ecffbaf0b9788969603f38312f70 c415c2713350eedd4b3454dd77e463f54ade12d0This investigation reveals a novel exploit derived from PNG image file formats, specifically their alpha transparency layer, and its potential to fool multiple AI vision systems. Our method uses this alpha layer as a clandestine channel invisible to human observers but fully actionable by AI image processors. The scope tested for the vulnerability spans representative vision systems from Apple, Microsoft, Google, Salesforce, Nvidia, and Facebook, highlighting the attack's potential breadth. This vulnerability challenges the security protocols of existing and fielded vision systems, from medical imaging to autonomous driving technologies. Our experiments demonstrate that the affected systems, which rely on convolutional neural networks or the latest multimodal language models, cannot quickly mitigate these vulnerabilities through simple patches or updates. Instead, they require retraining and architectural changes, indicating a persistent hole in multimodal technologies without some future adversarial hardening against such vision-language exploits.arXiv.org290A novel exploit derived from PNG image file formats, specifically their alpha transparency layer, and its potential to fool multiple AI vision systems is revealed, indicating a persistent hole in multimodal technologies without some future adversarial hardening against such vision-language exploits.ArXiv['David A. Noever', 'Forrest McKee']2024-02-15T00:00:00https://www.semanticscholar.org/paper/c415c2713350eedd4b3454dd77e463f54ade12d0 fb9916634567b77160b6835e313033c0ef418f03Generative AI brings novel and impressive abilities to help people in everyday tasks. There are many AI workflows that solve real and complex problems by chaining AI outputs together with human interaction. Although there is an undeniable lure of AI, it's uncertain how useful generative AI workflows are after the novelty wears off. Additionally, tools built with generative AI have the potential to be personalized and adapted quickly and easily, but do users take advantage of the potential to customize? We conducted a three-week longitudinal study with 12 users to understand the familiarization and customization of generative AI tools for science communication. Our study revealed that the familiarization phase lasts for 4.3 sessions, where users explore the capabilities of the workflow and which aspects they find useful. After familiarization, the perceived utility of the system is rated higher than before, indicating that the perceived utility of AI is not just a novelty effect. The increase in benefits mainly comes from end-users' ability to customize prompts, and thus appropriate the system to their own needs. This points to a future where generative AI systems can allow us to design for appropriation.arXiv.org1060After familiarization, the perceived utility of the system is rated higher than before, indicating that the perceived utility of AI is not just a novelty effect, and points to a future where generative AI systems can allow us to design for appropriation.ArXiv['Tao Long', 'Katy Ilonka Gero', 'Lydia B. Chilton']2024-02-15T00:00:00https://www.semanticscholar.org/paper/fb9916634567b77160b6835e313033c0ef418f03 bdd366caceaaf60442bdc7db3dd5bac39225c5efThis article examines emerging legal issues and theories of liability for directors involved in the management of AI financial instruments that are protected as trade secrets. The main question of the article is whether excessive delegation of functions or lack of transparency of AI algorithms can undermine the performance of fiduciary duties by directors. By reviewing case law in the context of strict oversight of past technological failures, the article proposes a renewed approach to the use of blockchain tools that will maintain efficiency benefits while ensuring necessary reporting and accountability. The study suggests that governance based on the principles of auditing AI performance and setting minimum standards for explainability can help strike a balance between driving innovation, addressing liability issues, and aligning with modern doctrines that hold boards accountable for key decision-making. new technologies. As algorithms become increasingly integrated into senior management decision-making processes, there is a need to further explore transparency mechanisms and monitoring processes that will support evolving fiduciary responsibilities in relation to evolving automation capabilities that impact shareholder interests.Общество и инновации00It is suggested that governance based on the principles of auditing AI performance and setting minimum standards for explainability can help strike a balance between driving innovation, addressing liability issues, and aligning with modern doctrines that hold boards accountable for key decision-making.Общество и инновации['Jamilya Panabergenova']2024-02-15T00:00:00https://www.semanticscholar.org/paper/bdd366caceaaf60442bdc7db3dd5bac39225c5ef eab1fe17d66cc5c25ace0ff2af4afa7ac764d4beDesigning seamless, frictionless user experiences has long been a dominant trend in both applied behavioral science and artificial intelligence (AI), in which the goal of making desirable actions easy and efficient informs efforts to minimize friction in user experiences. However, in some settings, friction can be genuinely beneficial, such as the insertion of deliberate delays to increase reflection, preventing individuals from resorting to automatic or biased behaviors, and enhancing opportunities for unexpected discoveries. More recently, the popularization and availability of AI on a widespread scale has only increased the need to examine how friction can help or hinder users of AI; it also suggests a need to consider how positive friction can benefit AI practitioners, both during development processes (e.g., working with diverse teams) and to inform how AI is designed into offerings. This paper first proposes a"positive friction"model that can help characterize how friction is currently beneficial in user and developer experiences with AI, diagnose the potential need for friction where it may not yet exist in these contexts, and inform how positive friction can be used to generate solutions, especially as advances in AI continue to be progress and new opportunities emerge. It then explores this model in the context of AI users and developers by proposing the value of taking a hybrid"AI+human"lens, and concludes by suggesting questions for further exploration.arXiv.org700A "positive friction" model is proposed that can help characterize how friction is currently beneficial in user and developer experiences with AI, diagnose the potential need for friction where it may not yet exist, and inform how positive friction can be used to generate solutions, especially as advances in AI continue to be progress and new opportunities emerge.ArXiv['Zeya Chen', 'Ruth Schmidt']2024-02-15T00:00:00https://www.semanticscholar.org/paper/eab1fe17d66cc5c25ace0ff2af4afa7ac764d4be f62e80b29e8bb2208aaa571be631357a6de0b7c1Artificial Intelligence Review200A systematic literature review (SLR) of Artificial Intelligence (AI) techniques in identification and classification of software requirements revealed that transfer learning based approaches extensively used in classification and yielding most accurate results and outperforms the other ML and DL techniques.Artif. Intell. Rev.['K. Kaur', 'Parminder Kaur']2024-02-15T00:00:00https://www.semanticscholar.org/paper/f62e80b29e8bb2208aaa571be631357a6de0b7c1 a74f781c561347408b0a5ef485aa9f1cac57c4b4The construction industry is a vital sector of the global economy, but it faces many productivity challenges in various processes, such as design, planning, procurement, inspection, and maintenance. Generative artificial intelligence (AI), which can create novel and realistic data or content, such as text, image, video, or code, based on some input or prior knowledge, offers innovative and disruptive solutions to address these challenges. However, there is a gap in the literature on the current state, opportunities, and challenges of generative AI in the construction industry. This study aims to fill this gap by providing a state-of-the-art analysis of generative AI in construction, with three objectives: (1) to review and categorize the existing and emerging generative AI opportunities and challenges in the construction industry; (2) to propose a framework for construction firms to build customized generative AI solutions using their own data, comprising steps such as data collection, dataset curation, training custom large language model (LLM), model evaluation, and deployment; and (3) to demonstrate the framework via a case study of developing a generative model for querying contract documents. The results show that retrieval augmented generation (RAG) improves the baseline LLM by 5.2, 9.4, and 4.8% in terms of quality, relevance, and reproducibility. This study provides academics and construction professionals with a comprehensive analysis and practical framework to guide the adoption of generative AI techniques to enhance productivity, quality, safety, and sustainability across the construction industry.arXiv.org00A framework for construction firms to build customized generative AI solutions using their own data, comprising steps such as data collection, dataset curation, training custom large language model (LLM), model evaluation, and deployment is proposed.ArXiv['Ridwan Taiwo', 'I. T. Bello', 'S. Abdulai', 'Abdul-Mugis Yussif', 'B. Salami', 'Abdullahi Saka', 'Tarek Zayed']2024-02-15T00:00:00https://www.semanticscholar.org/paper/a74f781c561347408b0a5ef485aa9f1cac57c4b4 98c8fc2e70505d1c03ed0a8af51b1cdefeee8792Generative artificial intelligence (AI) is rapidly emerging as a transformative force across various sectors. As education shifts towards an AI-focused future, adapting teaching methodologies and evaluation strategies to this technological evolution becomes more and more important. This paper delves into the profound implications of generative AI on business education, critically analyzing its influence on broad program learning outcomes as well as on specific assessment tasks, ranging from quizzes to work-integrated learning projects. By examining and assessing responses from ChatGPT, we evaluate their structural coherence and the potential to enhance or replace key skills evaluated in students. Our findings primarily indicate that for formulaic quizzes and short essay questions, GPT-4 often delivers accurate solutions. In the context of research reports and reflection journals, GPT-4 serves more as a guide and scaffolding tool. As AI continues to advance, it becomes increasingly crucial for business educators to re-examine their learning objective frameworks. This includes utilizing the technology’s potential while navigating its complexities, ensuring that education remains effective, relevant, and in step with the pace of innovation.Journal of Curriculum and Teaching00This paper delves into the profound implications of generative AI on business education, critically analyzing its influence on broad program learning outcomes as well as on specific assessment tasks, ranging from quizzes to work-integrated learning projects.Journal of Curriculum and Teaching['Xiao Xu']2024-02-15T00:00:00https://www.semanticscholar.org/paper/98c8fc2e70505d1c03ed0a8af51b1cdefeee8792 eada69eb961d5968f69cd95647a29e95f791c783Recently, we introduced a new paradigm for alpha mining in the realm of quantitative investment, developing a new interactive alpha mining system framework, Alpha-GPT. This system is centered on iterative Human-AI interaction based on large language models, introducing a Human-in-the-Loop approach to alpha discovery. In this paper, we present the next-generation Alpha-GPT 2.0 \footnote{Draft. Work in progress}, a quantitative investment framework that further encompasses crucial modeling and analysis phases in quantitative investment. This framework emphasizes the iterative, interactive research between humans and AI, embodying a Human-in-the-Loop strategy throughout the entire quantitative investment pipeline. By assimilating the insights of human researchers into the systematic alpha research process, we effectively leverage the Human-in-the-Loop approach, enhancing the efficiency and precision of quantitative investment research.arXiv.org70The next-generation Alpha-GPT 2.0 framework emphasizes the iterative, interactive research between humans and AI, embodying a Human-in-the-Loop strategy throughout the entire quantitative investment pipeline, enhancing the efficiency and precision of quantitative investment research.ArXiv['Hang Yuan', 'Sai Wang', 'Jian Guo']2024-02-15T00:00:00https://www.semanticscholar.org/paper/eada69eb961d5968f69cd95647a29e95f791c783 cf951c0af9689ef303d5d584aab8a51c881133c0Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.arXiv.org2112It is concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions, and AI holds great promise for improving ROP management.ArXiv['Ali Jafarizadeh', 'Shadi Farabi Maleki', 'Parnia Pouya', 'Navid Sobhi', 'M. Abdollahi', 'Siamak Pedrammehr', 'Chee Peng Lim', 'Houshyar Asadi', 'R. Alizadehsani', 'Ruyan Tan', 'Sheikh Mohammad Shariful Islam', 'U. R. Acharya']2024-02-15T00:00:00https://www.semanticscholar.org/paper/cf951c0af9689ef303d5d584aab8a51c881133c0 5d58fa15da260fe0b0b594f270d0d18cef9f1482Environmental science and pollution research international892Through a meticulous analysis, the review underscores AI's unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation.Environmental science and pollution research international['A. K. Wani', 'Farida Rahayu', 'Ilham Ben Amor', 'Munleef Quadir', 'Mala Murianingrum', 'Parnidi Parnidi', 'Anjuman Ayub', 'Supriyadi Supriyadi', 'Sakiroh Sakiroh', 'Saefudin Saefudin', 'Abhinav Kumar', 'Evy Latifah']2024-02-15T00:00:00https://www.semanticscholar.org/paper/5d58fa15da260fe0b0b594f270d0d18cef9f1482 75979fc6972cd201fea180f64de340556bb258b5Herzschrittmachertherapie & Elektrophysiologie161An overview of the current state of AI-enhanced ECG analysis is provided, existing limitations are discussed, and the challenges that may arise for healthcare professionals in this context are explored.Herzschrittmachertherapie & Elektrophysiologie['Wilhelm Haverkamp', 'Nils Strodthoff']2024-02-15T00:00:00https://www.semanticscholar.org/paper/75979fc6972cd201fea180f64de340556bb258b5 19f6968903170504af2f2f3fc0018decb5bab975Economic Change and Restructuring661The study finds that contrary to the traditional impression of “machines replacing humans,” AI technology is correlated with increasing the total number of jobs on the market, and modern industrial agglomeration represented by virtual agglomeration is an indispensable mediating mechanism for AI to create jobs.Economic Change and Restructuring['Yang Shen']2024-02-15T00:00:00https://www.semanticscholar.org/paper/19f6968903170504af2f2f3fc0018decb5bab975 c8953d3636fe02e764515f95f67ebac846a116b1The use of artificial intelligence (AI) continues to establish itself in the most diverse areas of medicine at an increasingly fast pace. Nevertheless, many healthcare professionals lack the basic technical understanding of how this technology works, which severely limits its application in clinical settings and research. Thus, we would like to discuss the functioning and classification of AI using melanoma as an example in this review to build an understanding of the technology behind AI. For this purpose, elaborate illustrations are used that quickly reveal the technology involved. Previous reviews tend to focus on the potential applications of AI, thereby missing the opportunity to develop a deeper understanding of the subject matter that is so important for clinical application. Malignant melanoma has become a significant burden for healthcare systems. If discovered early, a better prognosis can be expected, which is why skin cancer screening has become increasingly popular and is supported by health insurance. The number of experts remains finite, reducing their availability and leading to longer waiting times. Therefore, innovative ideas need to be implemented to provide the necessary care. Thus, machine learning offers the ability to recognize melanomas from images at a level comparable to experienced dermatologists under optimized conditions.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG01The functioning and classification of AI is discussed using melanoma as an example in this review to build an understanding of the technology behind AI.Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG['T. Hartmann', 'Johannes Passauer', 'Julien Hartmann', 'Laura Schmidberger', 'Manfred Kneilling', 'S. Volc']2024-02-15T00:00:00https://www.semanticscholar.org/paper/c8953d3636fe02e764515f95f67ebac846a116b1 a7626afdedc162450a8486849d95127a6c7e2e4eThe implementation of Artificial Intelligence (AI) in the accounting field represents a hot topic. ChatGPT, an AI tool, became very popular recently, due to its conversational voice and abilities. The study is motivated less by the evolution of this Large Language Model (LLM), and more by its capabilities. This paper explores the impact of AI on accounting and accountants, in a dynamic world, with a focus on financial reporting. The research discusses about using AI technologies, more exactly ChatGPT 4, as tools available for accountants, and how they are changing the way financial data is processed, analyzed, and reported. The objectives of the author are to examine the potential advantages, benefits, limits, and risks associated with AI implementation in accounting, including increased accuracy and efficiency, as well as concerns around data privacy and security. In this regard, a quantitative method of research was used. It was realized an experiment with testing ChatGPT and its capabilities. Furthermore, the author argued that accountants need to develop new skills and competencies. This includes a deep understanding of AI algorithms and their limitations, as well as the ability to interpret and communicate the results of AI-driven analysis to non-technical stakeholders. By embracing AI technologies and developing new skills and competencies, accounting professionals can contribute to the long-term success of organizations in a dynamic and rapidly changing world. The paper also considers the challenges of detecting and preventing dishonesty and suggests strategies that accountants can implement to ensure integrity to use of these tools. These strategies refer to policies and procedures, providing training and support. The added value of this paper is the fact that provides an understanding of the implications of AI on accounting. The paper concludes that while the use of AI for accounting in a dynamic world presents benefits and opportunities, there are also some challenges to face. Accountants can effectively address these concerns by taking a proactive and ethical approach to the responsible use of these tools. Future research could be represented by creating focus groups and interviews with different stakeholders to observe the impact of ChatGPT in a business environment, by discussing both financial and nonfinancial reporting.Audit Financiar00While the use of AI for accounting in a dynamic world presents benefits and opportunities, there are also some challenges to face, and accountants can effectively address these concerns by taking a proactive and ethical approach to the responsible use of these tools.Audit Financiar['L. Dumitraşcu']2024-02-15T00:00:00https://www.semanticscholar.org/paper/a7626afdedc162450a8486849d95127a6c7e2e4e a39914078c6ec7d19e08d4cbe5405690e6c33c59In today's business environment, adapting to rapidly changing conditions is becoming a key factor for success. Digital technologies and artificial intelligence undoubtedly play a major role here. But how to use them most effectively? This article reveals methods and strategies aimed at optimizing business processes using the latest advances in the field of IT technologies. From the article, you can learn how to automate routine operations, reduce costs and time to complete tasks, increase customer satisfaction and employee motivation. The advantages of integrating artificial intelligence into company management are described, as well as its ability to adapt to individual business needs.Upravlenie kachestvom (Quality management)00Methods and strategies aimed at optimizing business processes using the latest advances in the field of IT technologies are revealed, to automate routine operations, reduce costs and time to complete tasks, increase customer satisfaction and employee motivation.Upravlenie kachestvom (Quality management)['A.I. Nikolaev']2024-02-15T00:00:00https://www.semanticscholar.org/paper/a39914078c6ec7d19e08d4cbe5405690e6c33c59 221c73fd025a5184d29f029cc26ef98830126debParasitic diseases, including malaria, leishmaniasis, and trypanosomiasis, continue to plague populations worldwide, particularly in resource-limited settings and disproportionately affecting vulnerable populations. It has limited the use of conventional health-care delivery and disease control approaches and necessitated exploring innovative strategies. In this direction, artificial intelligence (AI) has emerged as a transformative tool with immense promise in parasitic disease control, offering the potential for enhanced diagnostics, precision drug discovery, predictive modeling, and personalized treatment. Predictive AI algorithms have assisted in understanding parasite transmission patterns and outbreaks by analyzing vast amounts of epidemiological data, environmental factors, and population demographics. This has strengthened public health interventions, resource allocation, and outbreak preparedness strategies, enabling proactive measures to mitigate disease spread. In diagnostics, AI-enabled accurate and rapid identification of parasites by analyzing microscopic images. This capability is particularly valuable in remote regions with limited access to diagnostic facilities. AI-driven computational methods have also assisted in drug discovery for parasitic diseases by identifying novel drug targets and predicting the efficacy and safety of potential drug candidates. This approach has streamlined drug development, leading to more effective and targeted therapies. This article reviews these current developments and their transformative impacts on the health-care sector. It also assessed the hurdles that require attention before these transformations can be realized in real-life scenarios.Tropical Parasitology330Current developments in artificial intelligence in parasitic disease control, including accurate and rapid identification of parasites by analyzing microscopic images, and drug discovery by identifying novel drug targets and predicting the efficacy and safety of potential drug candidates are reviewed.Tropical Parasitology['S. Parija', 'Abhijit Poddar']2024-02-15T00:00:00https://www.semanticscholar.org/paper/221c73fd025a5184d29f029cc26ef98830126deb 686f95e07f833b1bccc88858165e0e972ce79c5bPurpose Artificial intelligence (AI) offers many benefits to improve predictive marketing practice. It raises ethical concerns regarding customer prioritization, market share concentration and consumer manipulation. This paper explores these ethical concerns from a contemporary perspective, drawing on the experiences and perspectives of AI and predictive marketing professionals. This study aims to contribute to the field by providing a modern perspective on the ethical concerns of AI usage in predictive marketing, drawing on the experiences and perspectives of professionals in the area. Design/methodology/approach The study conducted semistructured interviews for 6 weeks with 14 participants experienced in AI-enabled systems for marketing, using purposive and snowball sampling techniques. Thematic analysis was used to explore themes emerging from the data. Findings Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior. Originality/value The authors identify seven unique themes and benchmark them with Ashok’s model to provide a structured lens for interpreting the results. The framework presented by this research is unique and can be used to support ethical research spanning social, technological and economic aspects within the predictive marketing domain. Spanish Journal of Marketing - ESIC710Results reveal that using AI in marketing could lead to unintended consequences, such as perpetuating existing biases, violating customer privacy, limiting competition and manipulating consumer behavior.Spanish Journal of Marketing - ESIC['Hina Naz', 'Muhammad Kashif']2024-02-15T00:00:00https://www.semanticscholar.org/paper/686f95e07f833b1bccc88858165e0e972ce79c5b bc57fa6b83d5991a68d38865f1f7b0018be9e0baThe present work aims to explore the role and factors that influence the interaction between marketing and artificial intelligence, the developing role of the marketer in the digital age, and the effects of artificial intelligence on the marketing process. Through a comprehensive marketing analysis, the research highlights the emerging power that Artificial Intelligence is exerting in all the marketing and production phases. The article is divided into three phases: the first phase focuses on the transition from traditional to digital marketing, emphasizing how new technologies had a significant impact on the commercial scene. The focus transitioned to the operational frame of AI into marketing operations, recognizing the latter’s ability to add value throughout the modern consumer’s conversion funnel. Following that, the inquiry yielded exciting ideas for possible future developments. Finally, the presentation provided a complete overview of the transition of marketing to digital and the function of artificial intelligence in this context.International journal of business and management review00The role and factors that influence the interaction between marketing and artificial intelligence, the developing role of the marketer in the digital age, and the effects of artificial intelligence on the marketing process are explored.International Journal of Business and Management Review['Aelita Mani']2024-02-15T00:00:00https://www.semanticscholar.org/paper/bc57fa6b83d5991a68d38865f1f7b0018be9e0ba 490adf7a825ffc5b3a1f177e1d0cb8c8e2fca61eThis paper examines the increasing role of artificial intelligence (AI) in information technology (IT) higher education and the key opportunities and challenges associated with its adoption. A review of 75-100 research studies published during 2016-2022 and industry perspectives reveals AI’s effectiveness in improving learning outcomes through personalized, adaptive systems. However, integrating AI also poses risks regarding transparency, accountability, automation, biases, accessibility, and ethical impacts. Faculty perceptions, technology readiness, curriculum reform needs, and policy implications are analyzed under a conceptual framework integrating technology adoption and AI ethics theories. Qualitative methodology entails literature analysis to highlight AI’s advantages in optimizing human teaching efforts while weighing concerns around dehumanization, data privacy, and disempowerment. Balanced policies and practices focused on developing students’ AI competencies alongside critical thinking abilities are recommended to harness AI’s potential equitably and ethically. Deliberate efforts are needed to engineer inclusion into AI systems and uphold transparency in automated decision-making. The study informs strategies for readying IT students to responsibly apply AI tools to augment human capabilities.Pacific International Journal00Examination of the increasing role of artificial intelligence in information technology (IT) higher education and the key opportunities and challenges associated with its adoption reveals AI’s effectiveness in improving learning outcomes through personalized, adaptive systems but also poses risks regarding transparency, accountability, automation, biases, accessibility, and ethical impacts.Pacific International Journal['Xuejiao Bai']2024-02-15T00:00:00https://www.semanticscholar.org/paper/490adf7a825ffc5b3a1f177e1d0cb8c8e2fca61e 02ef947613e430410e626beccc41d8140e253f98This research aims to analyze Artificial Intelligence (AI)-based campaigns in implementing General Elections. The research method used is normative legal research, with statutory and conceptual approaches. The research results explain the limitations of the artificial intelligence (AI) campaign. These include the use of digital or AI technology that is not significantly different from reality as it currently exists; all digital content or AI-produced content is required to have a watermark or information label that includes the name of the application or website where the content was created, the creation date, does not contain racism or words or sentences that have multiple interpretations, and does not use a person without consent, who has died, and minors. There is an urgent need for an AI supervisory commission, as well as the need for special legislation about AI to avoid potential exploitation. Furthermore, it is necessary to amend Article 1 Number 35 and Article 274 Paragraph 4 of Law No. 7 of 2017 concerning the General Election, so AI is applicable as campaign material but only if it uses watermarks, is not significantly different from reality, does not contain racism, and does not use living or deceased people without consent.RESEARCH REVIEW International Journal of Multidisciplinary00It is necessary to amend Article 1 Number 35 and Article 274 Paragraph 4 of Law No. 7 of 2017 concerning the General Election, so AI is applicable as campaign material but only if it uses watermarks, is not significantly different from reality, does not contain racism, and does not use living or deceased people without consentRESEARCH REVIEW International Journal of Multidisciplinary['Putri Rizkika Bahri', 'H. M. G. Asmara', 'Muh. Risnain']2024-02-15T00:00:00https://www.semanticscholar.org/paper/02ef947613e430410e626beccc41d8140e253f98 3571c2f0d01d1cd70d2cdfd171a729b68de9d855This study provides an in-depth analysis of the intersection between artificial intelligence (AI) and fashion, focusing on the potentials and ethical challenges related to the intensive use of large datasets. Using methodologies such as “Envisioning” and the “Scenario Planning Model”, the aim is to develop clear and structured approaches to the deployment of AI in the fashion industry, exploring how this technology can transform and enrich the creative process, extending human capabilities. Among the main ethical challenges are training on culturally connoted data, which risks introducing formative bias, and the need to explain AI processes and decisions in understandable terms (explainability). The possible formalization of AI methods to make their processes and results formal is also discussed. The potential capabilities of these technologies to overcome the limits of human intellect are also considered. The conclusions highlight the importance of a balance between technological innovation and ethical considerations to promote ongoing dialogue among diverse stakeholders, ensuring that AI in the fashion industry evolves inclusively, ethically, and responsibly, fostering a future where technology and human creativity coexist harmoniously.Fashion Highlight00An in-depth analysis of the intersection between artificial intelligence and fashion, focusing on the potentials and ethical challenges related to the intensive use of large datasets, highlights the importance of a balance between technological innovation and ethical considerations to promote ongoing dialogue among diverse stakeholders.Fashion Highlight['Arrigo Bertacchini', 'Pietro Salvatore Pantano']2024-02-15T00:00:00https://www.semanticscholar.org/paper/3571c2f0d01d1cd70d2cdfd171a729b68de9d855 786dec33d683e569c7eb70e46621daa79f3c130aIn fashion domain, companies increasingly navigate a complex web of data involving intricate correlations, dependencies, and the unpredictability of human behavior. Managing these diverse data flows is critical to improving decision-making in an industry that depends on both creativity and precision. In this context, artificial intelligence (AI) techniques have emerged as powerful tools that offer unparalleled efficiency in interpreting and using these huge datasets. However, as the industry moves deeper and deeper into this digital frontier, it is encountering a wide range of ethical concerns. This paper examines this intersection, exploring both the technological breakthroughs that AI is bringing to fashion and the ethical implications that accompany this digital evolution. We discuss the need for robust frameworks and guidelines to ensure the responsible use of AI, noting its potential to both increase and mitigate the fashion industry’s environmental impact.Fashion Highlight00The need for robust frameworks and guidelines to ensure the responsible use of AI is discussed, noting its potential to both increase and mitigate the fashion industry’s environmental impact.Fashion Highlight['B. Giovanola', 'S. Tiribelli', 'Emanuele Frontoni', 'M. Paolanti']2024-02-15T00:00:00https://www.semanticscholar.org/paper/786dec33d683e569c7eb70e46621daa79f3c130a 8da6f00a1c8d94a449ff1b25bb827fa4230cb1d7 Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and treating psychiatric disorders. This short communication presents a current perspective on using Artificial Intelligence in psychiatry.Industrial Psychiatry Journal180A current perspective on using Artificial Intelligence in psychiatry is presented in a short communication.Industrial Psychiatry Journal['Markanday Sharma', 'Prateek Yadav', 'S. P. Panda']2024-02-15T00:00:00https://www.semanticscholar.org/paper/8da6f00a1c8d94a449ff1b25bb827fa4230cb1d7 4a22ff1b5e94b0f65a90e97c74da58c599bd7d0cOver the past decade, the proliferation of electronic devices, wearables, and information technology has enabled the collection and extraction of vast amounts of personal and behavioural data, penetrating our biological and physical nature. We are witnessing the gradual transformation of data mining into life mining. The big data collected feeds machine learning algorithms and artificial intelligence systems, which effectively implement real-time surveillance of our lives, mainly for commercial purposes. Starting from theoretical reflections on the relationship between humans and non-humans in AI, the essay identifies some projects by fashion and jewellery designers that subvert the ubiquitous surveillance system, acting concretely in specific processual and technological dynamics. Finally, by adopting a disruptive approach, the essay seeks to chart new spaces of design thinking that disobey or begin to question the prevailing logic involved in AI.Fashion Highlight00The essay identifies some projects by fashion and jewellery designers that subvert the ubiquitous surveillance system, acting concretely in specific processual and technological dynamics, to chart new spaces of design thinking that disobey or begin to question the prevailing logic involved in AI.Fashion Highlight['Annarita Bianco', 'Chiara Scarpitti', 'Raffaele La Marca']2024-02-15T00:00:00https://www.semanticscholar.org/paper/4a22ff1b5e94b0f65a90e97c74da58c599bd7d0c 3e9b34ea950280cfc904c00317fe19c6aa5ae5c3International Journal of Cardiology: Heart & Vasculature300AI-ECG did not accurately distinguish patients who developed AF post-PFO closure from those who did not, and extrapolation of its performance to procedural settings such as PFO closure requires further investigation.International Journal of Cardiology. Heart & Vasculature['Omar Baqal', 'Eiad Habib', 'Elfatih A. Hasabo', 'Francesca Galasso', 'Timothy Barry', 'R. Arsanjani', 'John P. Sweeney', 'P. Noseworthy', 'F. David Fortuin']2024-02-15T00:00:00https://www.semanticscholar.org/paper/3e9b34ea950280cfc904c00317fe19c6aa5ae5c3 63c1fd7211b146f1986933168e359fff69b3c5a7ABSTRACT Background: Retina fundus images conducted in Greenland are telemedically assessed for diabetic retinopathy by ophthalmological nurses in Denmark. Applying an AI grading solution, in a Greenlandic setting, could potentially improve the efficiency and cost-effectiveness of DR screening. Method: We developed an AI model using retina fundus photos, performed on persons registered with diabetes in Greenland and Denmark, using Optos® ultra wide-field scanning laser ophthalmoscope, graded according to ICDR. Using the ResNet50 network we compared the model’s ability to distinguish between different images of ICDR severity levels in a confusion matrix. Results: Comparing images with ICDR level 0 to images of ICDR level 4 resulted in an accuracy of 0.9655, AUC of 0.9905, sensitivity and specificity of 96.6%. Comparing ICDR levels 0,1,2 with ICDR levels 3,4, we achieved a performance with an accuracy of 0.8077, an AUC of 0.8728, a sensitivity of 84.6% and a specificity of 78.8%. For the other comparisons, we achieved a modest performance. Conclusion: We developed an AI model using Greenlandic data, to automatically detect DR on Optos retina fundus images. The sensitivity and specificity were too low for our model to be applied directly in a clinical setting, thus optimising the model should be prioritised.International Journal of Circumpolar Health390An AI model using Greenlandic data, to automatically detect DR on Optos retina fundus images is developed, but the sensitivity and specificity were too low for the model to be applied directly in a clinical setting, thus optimising the model should be prioritised.International Journal of Circumpolar Health['T. Larsen', 'Maria Bråthen Pettersen', 'Helena Nygaard Jensen', 'Michael Lynge Pedersen', 'Henrik Lund-Andersen', 'M. E. Jørgensen', 'Stine Byberg']2024-02-15T00:00:00https://www.semanticscholar.org/paper/63c1fd7211b146f1986933168e359fff69b3c5a7 dfa52eb5f8a6e047ddf761d94db288a20a33136fAIMS In addition to the popular search engines on the Internet, ChatGPT may provide accurate and reliable health information. The aim of this study was to examine whether ChatGPT's responses to frequently asked questions concerning cerebral palsy (CP) by families were reliable and useful. METHODS Google trends were used to find the most frequently searched keywords for CP. Five independent physiatrists assessed ChatGPT responses to 10 questions. Seven-point Likert-type scales were used to rate information reliability and usefulness based on whether the answer can be validated and is understandable. RESULTS The median ratings for reliability of information for each question varied from 2 (very unsafe) to 5 (relatively very reliable). The median rating was 4 (reliable) for four questions. The median ratings for usefulness of information varied from 2 (very little useful) to 5 (moderately useful). The median rating was 4 (partly useful) for seven questions. CONCLUSION Although ChatGPT appears promising as an additional tool for informing family members of individuals with CP about medical information, it should be emphasized that both consumers and health care providers should be aware of the limitations of artificial intelligence-generated information.Physical & Occupational Therapy in Pediatrics170Although ChatGPT appears promising as an additional tool for informing family members of individuals with CP about medical information, it should be emphasized that both consumers and health care providers should be aware of the limitations of artificial intelligence-generated information.Physical & occupational therapy in pediatrics['Ayşe Merve Ata', 'Berke Aras', 'Özlem Yılmaz Taşdelen', 'Canan Çelik', 'Canan Çulha']2024-02-15T00:00:00https://www.semanticscholar.org/paper/dfa52eb5f8a6e047ddf761d94db288a20a33136f c8ff6d435e4ada7d7384e9fde18c80603bb031e4The quality of the decision is not only related to the judge's considerations but also its suitability to the composition of the decision so that the resulting decision is not easily overturned at the level of legal action and increases public confidence in the judicial institution. This research aims to analyze the quality of judges' decisions in Indonesia in terms of the composition of the decision texts that have been made. This research uses normative legal research methods, a statutory approach, and a comparative approach. The study results show that decisions are not based on the structure of decisions determined by the Supreme Court. One of the reasons is the minimal use of AI, even though AI can help judges identify which parts of the decision structure are not yet in the decision prepared by the judge and improve them so that it is hoped that it will produce uniformity and decisions that are certain and not easily overturned. Indonesia needs to learn from New Zealand guidelines for using AI at the court and tribunal level. Judges can apply AI, some related to summarizing information and administration.Journal of Human Rights Culture and Legal System470Analysis of judges' decisions in Indonesia in terms of the composition of the decision texts that have been made shows that decisions are not based on the structure of decisions determined by the Supreme Court and Indonesia needs to learn from New Zealand guidelines for using AI at the court and tribunal level.Journal of Human Rights, Culture and Legal System['Nur Putri Hidayah', 'G. Wicaksono', 'Christian Sri Kusuma Aditya', 'Yuda Munarko']2024-02-15T00:00:00https://www.semanticscholar.org/paper/c8ff6d435e4ada7d7384e9fde18c80603bb031e4 c145d1aa89ecec40f7dbe0c1ccd29ada059ff8afJournal of Nuclear Medicine20Journal of nuclear medicine : official publication, Society of Nuclear Medicine['P. Lohmann', 'R. Gutsche', 'J. Werner', 'N. Shah', 'K. Langen', 'N. Galldiks']2024-02-15T00:00:00https://www.semanticscholar.org/paper/c145d1aa89ecec40f7dbe0c1ccd29ada059ff8af 4063a64e9eb797f25ae54325d01d5346d205f9c9Proceedings of the Water Environment Federation00Proceedings of the Water Environment Federation['Sara Titus']2024-02-15T00:00:00https://www.semanticscholar.org/paper/4063a64e9eb797f25ae54325d01d5346d205f9c9 0f31dc3df08ed64df8f0768adb12d706a68e572dEducation and Information Technologies : Official Journal of the IFIP technical committee on Education300Education and Information Technologies['Tao Huang', 'Jing Geng', 'Yuxia Chen', 'Han Wang', 'Huali Yang', 'Shengze Hu']2024-02-15T00:00:00https://www.semanticscholar.org/paper/0f31dc3df08ed64df8f0768adb12d706a68e572d c5226084da0c3fa531992dd7179ea61320a91817Educational Administration Theory and Practices00Educational Administration Theory and Practices['Ms. Nibedita Basu', 'Dr. Rhishikesh Dave']2024-02-15T00:00:00https://www.semanticscholar.org/paper/c5226084da0c3fa531992dd7179ea61320a91817 365bf2cd0a0c33201fc711e2deaaffa365718934Proceedings of the Water Environment Federation00Proceedings of the Water Environment Federation['Eric Packer', 'Erin Lansey']2024-02-15T00:00:00https://www.semanticscholar.org/paper/365bf2cd0a0c33201fc711e2deaaffa365718934 07ba13dad1ee6a552745fd3bfdf937920bdd9a83Respiratory Failure and Mechanical Ventilation00Respiratory Failure and Mechanical Ventilation['A. Baptistella', 'Diego Carvalho', 'Fabiana Dallacosta', 'João Rogério Nunes Filho']2024-02-15T00:00:00https://www.semanticscholar.org/paper/07ba13dad1ee6a552745fd3bfdf937920bdd9a83 d9143db5fa96658155dbff602e91e95227ab66f9Bezmialem Science00Bezmialem Science['Enes Emre Başar', 'Aysu Kes- Erkul']2024-02-15T00:00:00https://www.semanticscholar.org/paper/d9143db5fa96658155dbff602e91e95227ab66f9 6388454d669f3c41de7c4b94489f57d9f7e657e1Heart70Heart['Namisha Singh', 'Sanjay Mehta']2024-02-15T00:00:00https://www.semanticscholar.org/paper/6388454d669f3c41de7c4b94489f57d9f7e657e1 3d853222d8de74a5868d5c98308aaf126ec623ddInternational Journal of Clinical Biochemistry and Research80International Journal of Clinical Biochemistry and Research['Uma Maheshwari K']2024-02-15T00:00:00https://www.semanticscholar.org/paper/3d853222d8de74a5868d5c98308aaf126ec623dd 6b3dcfc0887b2682904e33667a868ffd529fec47This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.Computers &amp; Chemical Engineering2821This paper identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control.ArXiv['Benjamin Decardi-Nelson', 'A. Alshehri', 'Akshay Ajagekar', 'Fengqi You']2024-02-15T00:00:00https://www.semanticscholar.org/paper/6b3dcfc0887b2682904e33667a868ffd529fec47 83e8fc6bbac9ee6894f6c6dc04e836a593c7797bEnsuring artificial intelligence behaves in such a way that is aligned with human values is commonly referred to as the alignment challenge. Prior work has shown that rational agents, behaving in such a way that maximizes a utility function, will inevitably behave in such a way that is not aligned with human values, especially as their level of intelligence goes up. Prior work has also shown that there is no"one true utility function"; solutions must include a more holistic approach to alignment. This paper describes oblivious agents: agents that are architected in such a way that their effective utility function is an aggregation of a known and hidden sub-functions. The hidden component, to be maximized, is internally implemented as a black box, preventing the agent from examining it. The known component, to be minimized, is knowledge of the hidden sub-function. Architectural constraints further influence how agent actions can evolve its internal environment model. We show that an oblivious agent, behaving rationally, constructs an internal approximation of designers' intentions (i.e., infers alignment), and, as a consequence of its architecture and effective utility function, behaves in such a way that maximizes alignment; i.e., maximizing the approximated intention function. We show that, paradoxically, it does this for whatever utility function is used as the hidden component and, in contrast with extant techniques, chances of alignment actually improve as agent intelligence grows.arXiv.org350It is shown that an oblivious agent, behaving rationally, constructs an internal approximation of designers' intentions, and, as a consequence of its architecture and effective utility function, behaves in such a way that maximizes alignment; i.e., maximizing the approximated intention function.ArXiv['Paulo Garcia']2024-02-15T00:00:00https://www.semanticscholar.org/paper/83e8fc6bbac9ee6894f6c6dc04e836a593c7797b ffdc999a6e9bf07515bb9bb0f129406b64e5f69cWhat does the Lacanian notion of fantasy offer to the study of security imaginaries? The article answers this question by introducing a fantasmatic reading strategy illustrated by a case study of the US narrative of ‘technological revolutions of war’ that has recently been fueled by a growing demand for ethical and explainable artificial intelligence in military applications and weapon systems. The article offers a Lacanian comment to the expanding International Relations literature on security imaginaries. It demonstrates how a fantasmatic reading encompasses both a discourse analytical tracing of background understandings employed by many security imaginary scholars and an affective tracing at the margins of discourse that captures the force with which subjects continue to invest in – and patch the constitutive gaps of – a security imaginary. In studying security imaginaries through fantasies, we propose zooming in on three analytical moves: analyzing the continuing construction of a lost utopia in security discourses; following the specific objects of desire that is organized around its own inevitable failure; and locating the mode of enjoyment encountered at the boundaries of the socially acceptable norms.Millennium: Journal of International Studies00A fantasmatic reading strategy illustrated by a case study of the US narrative of ‘technological revolutions of war’ that has recently been fueled by a growing demand for ethical and explainable artificial intelligence in military applications and weapon systems is introduced.Millennium: Journal of International Studies['J. T. Jacobsen', 'Katrine Nørgaard']2024-02-15T00:00:00https://www.semanticscholar.org/paper/ffdc999a6e9bf07515bb9bb0f129406b64e5f69c c65df43d2e8aef9b0c0f078e8b1a53a76f5b0f95Addressing the ethical developments of the technology involved in the Apple Vision Pro device that proposes access to virtual and augmented reality, we address the evolution of the technology involved in contrast to the slow development of law, highlighting the need for regulation adaptable to social and technological changes. This article aims to propose a discussion about the role of law in user privacy considering legal and ethical implications, especially in relation to the rights of privacy and self-determination. In this context, the influence and neutrality of algorithms are addressed from the aspect of political control, raising the need for a dialogue between science, technology and their influence on democracy so that this technology does not interfere with the protection of individual and collective rights. The present study is based on exploratory research, with bibliographic analysis based on data collection on updated scientific material on the topic.Caderno Pedagógico00The influence and neutrality of algorithms are addressed from the aspect of political control, raising the need for a dialogue between science, technology and their influence on democracy so that this technology does not interfere with the protection of individual and collective rights.Caderno Pedagógico['Efraim Caprioli', 'Jonathan de Barros Vitta', 'Henrique Lacerda Nieddermeyer', 'Paulo Pardo']2024-02-14T00:00:00https://www.semanticscholar.org/paper/c65df43d2e8aef9b0c0f078e8b1a53a76f5b0f95 3b22f932a429ea63e980d9128ee0064c172d3ac0This protocol describes the A-EYE Study and provides information about procedures for entering participants. Every care was taken in its drafting, but corrections or amendments may be necessary. These will be circulated to investigators in the Study. Problems relating to this Study should be referred, in the first instance, to the Chief Investigator. This study will adhere to the principles outlined in the UK Policy Framework for Health and Social Care Research (v3.2 10th October 2017). It will be conducted in compliance with the protocol, the UK General Data Protection Regulation and Data Protection Act 2018, and other regulatory requirements as appropriate.medRxiv110This protocol describes the A-EYE Study and provides information about procedures for entering participants and information about procedures for entering participants is provided.['Rémi J. Hernandez', 'Dr Wahbi K. El-Bouri', 'Dr Savita Madhusudhan', 'Prof Yalin Zheng', 'Mr Remi Hernandez', 'Fluorescein Angiography', 'Icga Indocyanine', 'Green Angiography', 'Alex Astor']2024-02-14T00:00:00https://www.semanticscholar.org/paper/3b22f932a429ea63e980d9128ee0064c172d3ac0 b25003994a16f1c00c7abfbaa91876d006099b72In everyday discourse, and also in the academic literature, the expressions “regulatory interventions” (i.e. interventions intended to regulate behaviours) and “normative interventions” (i.e. interventions which set norms/rules) are usually assumed to be synonymous. From this perspective, any regulatory intervention is also normative, and vice versa. This article investigates the relationship between regulation and rules/norms in order to verify whether the “regulatory” and the “normative” aspects are intrinsically and essentially connected, as is usually thought (on the assumption that there is no regulation without rules and no rules without regulation).Journal for the Theory of Social Behaviour350Journal for the Theory of Social Behaviour['G. Lorini', 'Stefano Moroni']2024-02-14T00:00:00https://www.semanticscholar.org/paper/b25003994a16f1c00c7abfbaa91876d006099b72 2ff1a9623d78e96091395f314258c11cb26d648dWith the panel data of 21 China’s industrial industries from 2008 to 2020, the relationship models between intelligent industry, energy regulation and ecological transformation are constructed and tested from two dimensions of resource saving and environmental friendliness, then equity financing is introduced into this model as moderating variable to discuss the moderating effects on the relationships between intelligent industry, energy regulation and ecological transformation. Results show that: ⑴China’s industrial industries significantly transformed to the resource-saving type, and the environment-friendly level stayed in a slow progression. ⑵Intelligent industry affected ecological transformation positively and significantly. The impact of energy regulation on ecological transformation was nonlinear. The regulation of energy consumption can significantly stimulate the transformation of resource saving, and restrain the transformation of environmental friendliness; the regulation of energy structure can significantly stimulate the transformation of environmental friendliness. ⑶ Equity financing can positively moderate the relationship between intelligent industry and ecological transformation, and it can also moderate the regulation of energy structure and promote the transformation to environmental friendliness, especially in the low consumption industries.PLoS ONE480PLOS ONE['Yunyi Wu']2024-02-14T00:00:00https://www.semanticscholar.org/paper/2ff1a9623d78e96091395f314258c11cb26d648d 79172a6f35c0b463f54e10cab62162fbf3fc3fb5Synthetic data generated through machine learning algorithms from original real-world data is gaining prominence across sectors due to their potential to provide privacy-preserving alternatives to traditional data sources. However, recent studies have raised concerns about the re-identification risks of synthetic data. This article examines the legal challenges surrounding synthetic data protection, with a focus on the European Union's General Data Protection Regulation (GDPR). After briefly explaining the methods of synthetic data generation and discussing their potential for privacy preservation, the article analyses the shortcomings of the personal/non-personal dualist approach under the GDPR. It then assesses the possibility of a paradigm change in data protection legislation, moving beyond this binary categorisation. The article argues in favour of establishing clear guidelines for the generation and processing of synthetic data, prioritising the principles of transparency, accountability and fairness.Big Data & Society60The article argues in favour of establishing clear guidelines for the generation and processing of synthetic data, prioritising the principles of transparency, accountability and fairness, with a focus on the European Union's General Data Protection Regulation.Big Data Soc.['A. Beduschi']2024-02-14T00:00:00https://www.semanticscholar.org/paper/79172a6f35c0b463f54e10cab62162fbf3fc3fb5 d1a93b82780453feeaa48fc6d9e859d6abb03f56To alleviate the financial shortage for public service provision, a government agency may jointly finance, own, and run a service system with a private firm (in the manner of a joint venture) or delegate service provision to the firm subject to regulation in service price or wait time. We model the service system as a queueing system in which customers are heterogeneous in service valuation and sensitive to price and delay. While the government aims to maximize social welfare, the firm's goal is to maximize profit. Hence, the joint venture has the objective of a mix of profit maximization and social welfare creation. Under the regulation, two types of interaction between the government and the firm, i.e., sequential move (in the absence of the government's myopic adjustment) and simultaneous move (in the presence of myopic adjustment), are considered. We find that while wait time regulation is more efficient than price regulation in the presence of myopic adjustment, the relationship is reversed in the absence of myopic adjustment. Somewhat surprisingly, price regulation with myopic adjustment may backfire. However, in some instances, the government must take a large share in a joint venture to achieve the same performance under price regulation without myopic adjustment. Our work uncovers whether the government adopts myopic adjustment plays a critical role in choosing the regulation instrument.Production and operations management00Production and Operations Management['Ming Hu', 'Weixiang Huang', 'Chunhui Liu', 'Wenhui Zhou']2024-02-14T00:00:00https://www.semanticscholar.org/paper/d1a93b82780453feeaa48fc6d9e859d6abb03f56 b087f4ea95466379c59715d8680b7221250ebc05Nature reviews genetics10Nature reviews. Genetics['Michael Attwaters']2024-02-14T00:00:00https://www.semanticscholar.org/paper/b087f4ea95466379c59715d8680b7221250ebc05 859794e8a4aa5f3fcf8b57de8a48bce74c668dbeEthics and Information Technology494The paper reveals that in order to change design culture in the field of AI urbanism, it is necessary to rethink some of the key ideas that inform the Western and modern worldview through novel philosophical reflections.Ethics Inf. Technol.['Otello Palmini', 'Federico Cugurullo']2024-02-14T00:00:00https://www.semanticscholar.org/paper/859794e8a4aa5f3fcf8b57de8a48bce74c668dbe 0679d7f50a097bc03dcfad4e9d081b47b7d10ebfEye1410Eye['M. Masalkhi', 'J. Ong', 'E. Waisberg', 'Andrew G. Lee']2024-02-14T00:00:00https://www.semanticscholar.org/paper/0679d7f50a097bc03dcfad4e9d081b47b7d10ebf 3137d068315c981d0d58e4c3ac46caee088adf8cThis scholarly investigation delves into the transformative impact of Artificial Intelligence (AI) on enhancing customer experience in the business realm. The study's purpose was to meticulously examine the integration, evolution, and strategic implications of AI in business operations, particularly in customer engagement. A comprehensive literature review and detailed case study analysis constituted the core methodology, focusing on peer-reviewed articles and practical examples from diverse business sectors. This approach facilitated a multi-dimensional exploration, capturing both the technological advancements in AI and the associated implementation challenges within various business contexts. Central findings from this research underscore AI's evolution from an emerging technological tool to a fundamental component in customer-centric business strategies. AI's capabilities in personalizing customer interactions, automating support systems, and leveraging predictive analytics have revolutionized business-customer dynamics. However, this evolution is not without its challenges, including data privacy concerns, ethical considerations, and the need for skilled AI expertise. The study concludes that AI is a strategic asset, necessitating thoughtful integration into business models. It emphasizes the importance of a collaborative approach, where AI specialists and industry experts work synergistically to tailor AI solutions to specific business needs. Ethical considerations and maintaining customer trust are highlighted as pivotal in AI deployment strategies. The study recommends continuous innovation, investment in AI infrastructure and talent, and adherence to ethical AI practices. These measures are essential for businesses to enhance customer experiences and drive sustainable growth in the digital age Keywords: Artificial Intelligence, Customer Experience, Business Strategy, AI Integration, Ethical Considerations.Computer Science &amp; IT Research Journal01The study concludes that AI is a strategic asset, necessitating thoughtful integration into business models and emphasizes the importance of a collaborative approach, where AI specialists and industry experts work synergistically to tailor AI solutions to specific business needs.Computer Science &amp; IT Research Journal['Sunday Tubokirifuruar Tula', 'Azeez Jason Kess-Momoh', 'Ganiyu Bolawale Omotoye', 'Binaebi Gloria Bello', 'Andrew Ifesinachi Daraojimba']2024-02-14T00:00:00https://www.semanticscholar.org/paper/3137d068315c981d0d58e4c3ac46caee088adf8c 4c6874a6c056792c8d43c2670b6e94f0303ff0cdAI-based code assistants are increasingly popular as a means to enhance productivity and improve code quality. This study compares four AI-based code assistants, GitHub Copilot, Tabnine, ChatGPT, and Google Bard, in method generation tasks, assessing their ability to produce accurate, correct, and efficient code. Results show that code assistants are useful, with complementary capabilities, although they rarely generate ready-to-use correct code.ICSE Companion71Comparing four AI-based code assistants in method generation tasks shows that code assistants are useful, with complementary capabilities, although they rarely generate ready-to-use correct code.ArXiv['Vincenzo Corso', 'Leonardo Mariani', 'D. Micucci', 'O. Riganelli']2024-02-14T00:00:00https://www.semanticscholar.org/paper/4c6874a6c056792c8d43c2670b6e94f0303ff0cd 23dfea1f980cde76e5594b186166b4dd38a26bbeAI and Ethics221The TAI-PRM framework builds upon established methods, such as Failure Mode and Effect Analysis (FMEA) and the Industrial ISO 31000, and provides tools and metrics to manage risks associated with AI artefacts in the manufacturing sector.AI and Ethics['E. Vyhmeister', 'Gabriel G. Castañé']2024-02-14T00:00:00https://www.semanticscholar.org/paper/23dfea1f980cde76e5594b186166b4dd38a26bbe 2de7e8b12cb33e1aa5ff47ceb6c85e3dea414549This scholarly inquiry delves into the burgeoning intersection of Artificial Intelligence (AI) and financial forecasting, particularly within the stock market domain. The study's backdrop is set against the rapid evolution of AI techniques, which have significantly altered the landscape of financial analysis. The primary aim is to dissect and evaluate the impact of AI on stock market predictions, juxtaposing its capabilities against traditional forecasting methods while navigating through the ethical and practical complexities inherent in AI implementation. The scope of the paper encompasses a comprehensive review of AI's evolution in financial analysis, its comparative effectiveness, and the sector-specific applications in stock markets. Methodologically, the study employs a systematic review of existing literature, focusing on peer-reviewed articles that shed light on the performance, challenges, and future prospects of AI in stock market forecasting. The findings reveal AI's profound potential in enhancing market efficiency and volatility understanding, albeit tempered by challenges such as data quality issues, model interpretability, and the need for robust regulatory frameworks. The main conclusions underscore AI's transformative role in financial forecasting, highlighting its ability to analyze vast datasets and predict market trends with heightened accuracy. However, the study also acknowledges the limitations within AI models, emphasizing the necessity for a balanced approach that integrates AI with traditional methods and continuous algorithmic refinement. Recommendations advocate for collaborative efforts between technologists, ethicists, and financial experts to develop ethically sound, transparent, and effective AI applications. In summary, this paper offers a panoramic view of AI's role in financial forecasting, serving as a guidepost for future explorations in this field. It underscores the immense possibilities and intricate challenges of AI in the dynamic landscape of stock market analysis, paving the way for a new era of data-driven decision-making in finance. Keywords:  Artificial Intelligence, Financial Forecasting, Stock Market Prediction, Machine Learning, Ethical Considerations, Regulatory Frameworks.Finance &amp; Accounting Research Journal01A panoramic view of AI's role in financial forecasting underscores the immense possibilities and intricate challenges of AI in the dynamic landscape of stock market analysis, paving the way for a new era of data-driven decision-making in finance.Finance &amp; Accounting Research Journal['David Iyanuoluwa Ajiga', 'Rhoda Adura Adeleye', 'Onyeka Franca Asuzu', 'Oluwaseyi Rita Owolabi', 'Binaebi Gloria Bello', 'Ndubuisi Leonard Ndubuisi']2024-02-14T00:00:00https://www.semanticscholar.org/paper/2de7e8b12cb33e1aa5ff47ceb6c85e3dea414549 e09da190a4a22f0092b5972e3f8ec628449ae0f8 Purpose This paper aims to offer an approach to cyborg composing with artificial intelligence (AI). The author posits that the hybridity of the cyborg, which amalgamates human and artificial elements, invites a cascade of creative and emancipatory possibilities. The author critically examines the biases embedded in AI systems while gesturing toward the generative potential of AI–human entanglements. Drawing on Bakhtinian theories of dialogism, the author contends that crafting found poetry with AI could inspire writers to problematize the ideologies embedded into the corpus while teasing apart its elisions or contradictions, sparking new forms of expression at the interface of the organic and the artificial. Design/methodology/approach To illustrate this approach to human–AI composing, the author shares a found poem that she wrote using ChatGPT alongside her reflection on the poem. The author reflects on her positionality as well as the positionality of her artificial interlocutor, interrogating the notion of subjectivity in relation to Bakhtinian dialogism and multivocality. Findings Weaving tales of resilience in harmony or tension with AI could unravel threads of possibility as human writers enrich, deepen or complicate AI-generated texts. By composing with AI, writers can resist closure, infiltrate illusions of objectivity and “speak back” to AI and the dominant voices replicated in its systems. Originality/value By encouraging students to critically engage with, question and complicate AI-generated texts, one can open avenues for alternative ways of thinking and writing, inspiring students to imagine and compose speculative futures. Ultimately, in animating assemblages of the organic and the artificial, one can invite transformative possibilities of being and becoming. English Teaching: Practice &amp; Critique150It is posits that the hybridity of the cyborg, which amalgamates human and artificial elements, invites a cascade of creative and emancipatory possibilities, and contends that crafting found poetry with AI could inspire writers to problematize the ideologies embedded into the corpus while teasing apart its elisions or contradictions.English Teaching: Practice &amp; Critique['Ruth Li']2024-02-14T00:00:00https://www.semanticscholar.org/paper/e09da190a4a22f0092b5972e3f8ec628449ae0f8 96a5b428dd488d995e646b715530edc91dddc6f3Objective: Firearm injury research necessitates using data from often-exploited vulnerable populations of Black and Brown Americans. In order to minimize distrust, this study provides a framework for establishing AI trust and transparency with the general population. Methods: We propose a Model Facts template that is easily extendable and decomposes accuracy and demographics into standardized and minimally complex values. This framework allows general users to assess the validity and biases of a model without diving into technical model documentation. Examples: We apply the Model Facts template on two previously published models, a violence risk identification model and a suicide risk prediction model. We demonstrate the ease of accessing the appropriate information when the data is structured appropriately. Discussion: The Model Facts template is limited in its current form to human based data and biases. Like nutrition facts, it also will require some educational resources for users to grasp its full utility. Human computer interaction experiments should be conducted to ensure that the interaction between user interface and model interface is as desired. Conclusion: The Model Facts label is the first framework dedicated to establishing trust with end users and general population consumers. Implementation of Model Facts into firearm injury research will provide public health practitioners and those impacted by firearm injury greater faith in the tools the research provides.arXiv.org00This study provides a framework for establishing AI trust and transparency with the general population and proposes a Model Facts template that is easily extendable and decomposes accuracy and demographics into standardized and minimally complex values.ArXiv['Jessica Zhu', 'Michel Cukier', 'Joseph Richardson']2024-02-14T00:00:00https://www.semanticscholar.org/paper/96a5b428dd488d995e646b715530edc91dddc6f3 2f64a7cf3a0229199b2ca0925bc25704f1abd9d0With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution. This makes AI-generated text (AIGT) detection of great importance. Among existing methods, white-box methods are generally superior to black-box methods in terms of performance and generalizability, but they require access to LLMs' internal states and are not applicable to black-box settings. In this paper, we propose to estimate word generation probabilities as pseudo white-box features via multiple re-sampling to help improve AIGT detection under the black-box setting. Specifically, we design POGER, a proxy-guided efficient re-sampling method, which selects a small subset of representative words (e.g., 10 words) for performing multiple re-sampling in black-box AIGT detection. Experiments on datasets containing texts from humans and seven LLMs show that POGER outperforms all baselines in macro F1 under black-box, partial white-box, and out-of-distribution settings and maintains lower re-sampling costs than its existing counterparts.arXiv.org470POGER, a proxy-guided efficient re-sampling method, which selects a small subset of representative words for performing multiple re-sampling in black-box AIGT detection and maintains lower re-sampling costs than its existing counterparts.ArXiv['Yuhui Shi', 'Qiang Sheng', 'Juan Cao', 'Hao Mi', 'Beizhe Hu', 'Danding Wang']2024-02-14T00:00:00https://www.semanticscholar.org/paper/2f64a7cf3a0229199b2ca0925bc25704f1abd9d0 a2c7938658b2dfef0e38f3033306b982061faa82 Purpose In the fourth industrial revolution, where business accounting integrates with automation through artificial intelligence (AI) and information communication technology (ICT), auditors must be able to access and analyze vast data and information to identify potential risks and issues. Using data analytics and AI to study significant amounts of data linked to audits, this study aims to investigate auditing practices by leveraging ICT and AI to enhance the audit process. Design/methodology/approach Bibliometric and quantitative research techniques have been used in the study’s mixed-method process. The theoretical underpinnings of AI have been investigated using the bibliometric research method, and the challenge of implementing ICT-enabled auditing practices among auditing professionals has been studied using the quantitative research method. Surveys, interviews and bibliometric analysis have all been used as data-gathering techniques. Findings Research in AI and auditing has a broad worldwide scope, involving developed and developing nations. ICT perceived benefits have no direct effect on auditing practices. However, ICT training has a mediating effect on the relationship between ICT perceived benefits and auditing practices. ICT adoption has no moderating effect on the relationship between ICT training and auditing practices. Research limitations/implications Findings have significance for lead auditors, policymakers and the Institute of Chartered Accountants of India (ICAI), who are keenly interested in upgrading the auditing practice of accounting professionals in India by incorporating AI and ICT determinants. Practical implications This research makes a significant contribution by offering a thorough framework for improving the knowledge management of practising auditors regarding ICT adoption, training and perceived benefits, a crucial component of auditing practices in the digital age. In addition, it provides insightful information about how AI affects accounting practices, which may point the way for further study in this area. Originality/value This research has significant implications for auditing firms in India. It can inform ICAI, policymakers and regulators in their attempts to foster the incorporation of AI and ICTs in auditing practice. Accounting Research Journal360Auditing practices by leveraging ICT and AI to enhance the audit process is investigated by offering a thorough framework for improving the knowledge management of practising auditors regarding ICT adoption, training and perceived benefits, a crucial component of auditing practices in the digital age.Accounting Research Journal['M. M. Thottoli']2024-02-14T00:00:00https://www.semanticscholar.org/paper/a2c7938658b2dfef0e38f3033306b982061faa82 9734b16b14358adb93b139b2f55bd8eb4f7f8f81Background Understanding women’s perspectives can help to create an effective and acceptable artificial intelligence (AI) implementation for triaging mammograms, ensuring a high proportion of screening-detected cancer. This study aimed to explore Swedish women’s perceptions and attitudes towards the use of AI in mammography. Method Semistructured interviews were conducted with 16 women recruited in the spring of 2023 at Capio S:t Görans Hospital, Sweden, during an ongoing clinical trial of AI in screening (ScreenTrustCAD, NCT 04778670) with Philips equipment. The interview transcripts were analysed using inductive thematic content analysis. Results In general, women viewed AI as an excellent complementary tool to help radiologists in their decision-making, rather than a complete replacement of their expertise. To trust the AI, the women requested a thorough evaluation, transparency about AI usage in healthcare, and the involvement of a radiologist in the assessment. They would rather be more worried because of being called in more often for scans than risk having overlooked a sign of cancer. They expressed substantial trust in the healthcare system if the implementation of AI was to become a standard practice. Conclusion The findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist. Effective communication regarding the role and limitations of AI is crucial to ensure that patients understand the purpose and potential outcomes of AI-assisted healthcare.BMJ Open470The findings suggest that the interviewed women, in general, hold a positive attitude towards the implementation of AI in mammography; nonetheless, they expect and demand more from an AI than a radiologist.BMJ Open['Jennifer Viberg Johansson', 'Karin Dembrower', 'Fredrik Strand', 'Åsa Grauman']2024-02-14T00:00:00https://www.semanticscholar.org/paper/9734b16b14358adb93b139b2f55bd8eb4f7f8f81 3ab43429924d68d38a3237a64ebe441f05e9b775AI and Ethics530Analysis of a single institution’s ethics applications for research related to AI suggests that existing REC models can effectively support consideration of ethical issues in AI research, and proposes that any new materials should be embedded in this existing well-established ecosystem.AI and Ethics['Simon Knight', 'A. Shibani', 'Nicole Vincent']2024-02-14T00:00:00https://www.semanticscholar.org/paper/3ab43429924d68d38a3237a64ebe441f05e9b775 b754cde67cebb61ab52db857bb035ba64eba6b56AI and Ethics790This paper will study the relationship of AI hype and marginalised communities, with particular emphasis on the LGBTQ + community, and look at the way that AI impacts on this community.AI and Ethics['Dawn McAra-Hunter']2024-02-14T00:00:00https://www.semanticscholar.org/paper/b754cde67cebb61ab52db857bb035ba64eba6b56 4f780dc5ad9ba6d28850cd78b67bde325939d53dThe NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) focuses on creating trustworthy AI for a variety of environmental and Earth science phenomena. AI2ES includes leading experts from AI, atmospheric and ocean science, risk communication, and education, who work synergistically to develop and test trustworthy AI methods that transform our understanding and prediction of the environment. Trust is a social phenomenon, and our integration of risk communication research across AI2ES activities provides an empirical foundation for developing user‐informed, trustworthy AI. AI2ES also features activities to broaden participation and for workforce development that are fully integrated with AI2ES research on trustworthy AI, environmental science, and risk communication.The AI Magazine100AI2ES also features activities to broaden participation and for workforce development that are fully integrated with AI2ES research on trustworthy AI, environmental science, and risk communication.AI Mag.['Amy McGovern', 'Imme Ebert-Uphoff', 'Elizabeth A. Barnes', 'Ann Bostrom', 'Mariana G Cains', 'Phillip Davis', 'J. Demuth', 'Dimitrios I. Diochnos', 'Andrew H. Fagg', 'Philippe Tissot', 'John K. Williams', 'Christopher D. Wirz']2024-02-14T00:00:00https://www.semanticscholar.org/paper/4f780dc5ad9ba6d28850cd78b67bde325939d53d 7dfa69c601cae4a841a975a2afa75c2f7b7a8e5bCan AI and humans genuinely communicate? In this article, after giving some background and motivating my proposal (sections 1 to 3), I explore a way to answer this question that I call the"mental-behavioral methodology"(sections 4 and 5). This methodology follows the following three steps: First, spell out what mental capacities are sufficient for human communication (as opposed to communication more generally). Second, spell out the experimental paradigms required to test whether a behavior exhibits these capacities. Third, apply or adapt these paradigms to test whether an AI displays the relevant behaviors. If the first two steps are successfully completed, and if the AI passes the tests with human-like results, this constitutes evidence that this AI and humans can genuinely communicate. This mental-behavioral methodology has the advantage that we don't need to understand the workings of black-box algorithms, such as standard deep neural networks. This is comparable to the fact that we don't need to understand how human brains work to know that humans can genuinely communicate. This methodology also has its disadvantages and I will discuss some of them (section 6).arXiv.org920This mental-behavioral methodology has the advantage that the authors don't need to understand the workings of black-box algorithms, such as standard deep neural networks, to know that humans can genuinely communicate.ArXiv['Constant Bonard']2024-02-14T00:00:00https://www.semanticscholar.org/paper/7dfa69c601cae4a841a975a2afa75c2f7b7a8e5b 548537a913457c84cccf7987d89160eefb6559dfThe EngageAI Institute focuses on AI‐driven narrative‐centered learning environments that create engaging story‐based problem‐solving experiences to support collaborative learning. The institute's research has three complementary strands. First, the institute creates narrative‐centered learning environments that generate interactive story‐based problem scenarios to elicit rich communication, encourage coordination, and spark collaborative creativity. Second, the institute creates virtual embodied conversational agent technologies with multiple modalities for communication (speech, facial expression, gesture, gaze, and posture) to support student learning. Embodied conversational agents are driven by advances in natural language understanding, natural language generation, and computer vision. Third, the institute is creating an innovative multimodal learning analytics framework that analyzes parallel streams of multimodal data derived from students’ conversations, gaze, facial expressions, gesture, and posture as they interact with each other, with teachers, and with embodied conversational agents. Woven throughout the institute's activities is a strong focus on ethics, with an emphasis on creating AI‐augmented learning that is deeply informed by considerations of fairness, accountability, transparency, trust, and privacy. The institute emphasizes broad participation and diverse perspectives to ensure that advances in AI‐augmented learning address inequities in STEM. The institute brings together a multistate network of universities, diverse K‐12 school systems, science museums, and nonprofit partners. Key to all of these endeavors is an emphasis on diversity, equity, and inclusion.The AI Magazine50The EngageAI Institute focuses on AI‐driven narrative‐centered learning environments that create engaging story‐based problem‐solving experiences to support collaborative learning and emphasizes broad participation and diverse perspectives to ensure that advances in AI‐augmented learning address inequities in STEM.AI Mag.['James Lester', 'Mohit Bansal', 'Gautam Biswas', 'Cindy E. Hmelo-Silver', 'Jeremy Roschelle', 'Jonathan Rowe']2024-02-14T00:00:00https://www.semanticscholar.org/paper/548537a913457c84cccf7987d89160eefb6559df d3c48bd179d35d87c8443983b5a004d0b4603d9cEthics and Information Technology830This paper analyzes the specific challenges of AI in urban crisis management as an example and test case for many super wicked decision problems and argues that to solve urgent crisis problems, the context, capacities, and networks need to be addressed.Ethics Inf. Technol.['Tina Comes']2024-02-14T00:00:00https://www.semanticscholar.org/paper/d3c48bd179d35d87c8443983b5a004d0b4603d9c c63fa354a2165bb64acef82ef66e0e1aecf6b7eaClients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborators. Unfortunately, given the complexity of requirements ranging from functionality, data, and governance, effective guidelines for collaborative specification of requirements in client-AI expert collaborations are missing. In this work, we introduce AINeedsPlanner, a workbook that AI experts and clients can use to facilitate effective interchange and clear specifications. The workbook is based on (1) an interview of 10 completed AI application project teams, which identifies and characterizes steps in AI application planning and (2) a study with 12 AI experts, which defines a taxonomy of AI experts' information needs and dimensions that affect the information needs. Finally, we demonstrate the workbook's utility with two case studies in real-world settings.arXiv.org580This work introduces AINeedsPlanner, a workbook that AI experts and clients can use to facilitate effective interchange and clear specifications in client-AI expert collaborations.ArXiv['Dae Hyun Kim', 'Hyungyu Shin', 'Shakhnozakhon Yadgarova', 'J. Son', 'Hariharan Subramonyam', 'Juho Kim']2024-02-14T00:00:00https://www.semanticscholar.org/paper/c63fa354a2165bb64acef82ef66e0e1aecf6b7ea 919cee8e29af2089601fe08141f6be42ada9fc12Communication education601Communication Education['Patric R. Spence', 'Renee Kaufmann', 'Kenneth A. Lachlan', 'Xialing Lin', 'Stephen A. Spates']2024-02-14T00:00:00https://www.semanticscholar.org/paper/919cee8e29af2089601fe08141f6be42ada9fc12 cb2624f1cc955ce705c4211b3fb7cad66a22545bBMJ evidence-based medicine70BMJ evidence-based medicine['Sarah Gebauer', 'Carly Eckert']2024-02-14T00:00:00https://www.semanticscholar.org/paper/cb2624f1cc955ce705c4211b3fb7cad66a22545b 57b8f76d92023bce80ffce54de7fe7cca0beaeeeAuditors need robust methods to assess the compliance of web platforms with the law. However, since they hardly ever have access to the algorithm, implementation, or training data used by a platform, the problem is harder than a simple metric estimation. Within the recent framework of manipulation-proof auditing, we study in this paper the feasibility of robust audits in realistic settings, in which models exhibit large capacities.We first prove a constraining result: if a web platform uses models that may fit any data, no audit strategy—whether active or not—can outperform random sampling when estimating properties such as demographic parity. To better understand the conditions under which state-of-the-art auditing techniques may remain competitive, we then relate the manipulability of audits to the capacity of the targeted models, using the Rademacher complexity. We empirically validate these results on popular models of increasing capacities, thus confirming experimentally that large-capacity models, which are commonly used in practice, are particularly hard to audit robustly. These results refine the limits of the auditing problem, and open up enticing questions on the connection between model capacity and the ability of platforms to manipulate audit attempts.2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)460The feasibility of robust audits in realistic settings, in which models exhibit large capacities is studied, to refine the limits of the auditing problem, and open up enticing questions on the connection between model capacity and the ability of platforms to manipulate audit attempts.2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)['A. Godinot', 'G. Tredan', 'E. L. Merrer', 'C. Penzo', 'F. Taïani']2024-02-14T00:00:00https://www.semanticscholar.org/paper/57b8f76d92023bce80ffce54de7fe7cca0beaeee dabe73059a9c143b82f66385ec13df40d725dec6International Journal of Scientific Engineering and Technology00International Journal of Science, Engineering and Technology['D. B']2024-02-14T00:00:00https://www.semanticscholar.org/paper/dabe73059a9c143b82f66385ec13df40d725dec6 4c2e7f94f27f5d5cf1418dcea695abfc1915e6b6Big data and artificial intelligence are game-changing technologies for the underdeveloped healthcare industry because they help optimize the entire supply chain and deliver more exact patient outcome information. Machine learning approaches that have recently seen more growing popularity include deep learning models that have brought revolution within the healthcare system in the previous years due to more complicated data compared to previous years . Machine learning is an essential data analysis procedure to describe efficient and effective methods to extract hidden information from large amounts of data that it would take logical analytics too long to manage. Recent years have seen an expansion and growth of advanced intelligent systems that have been able to learn more about clinical treatments and glean untapped medical information emanating from vast quantities of data when it comes to drug discovery and chemistry. The aim of this chapter is, therefore, to assess which big data and artificial intelligence approaches are prevalent in healthcare systems by investigating the most advanced big data structures, applications, and industry trends today available. First and foremost, the purpose is to provide a comprehensive overview of how the artificial intelligence and big data models can allocation in healthcare solutions fill the gap between machine learning approaches’ lack of human coverage and the healthcare data’s complexity. Moreover, current artificial intelligence technologies, including generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, are also increasingly being used for drug discovery and chemistry . Finally, the work presents the existing open challenges and the future directions in the drug formulation development field. To this end, the review will cover on published algorithms/automation tools for artificial intelligence applied to large scale-data in the case of healthcare .Mesopotamian Journal of Big Data340The purpose is to provide a comprehensive overview of how the artificial intelligence and big data models can allocation in healthcare solutions fill the gap between machine learning approaches’ lack of human coverage and the healthcare data’s complexity.Mesopotamian Journal of Big Data['Ahmed Hussein Ali', 'Saad Ahmed Dheyab', 'A. Alamoodi', 'Aws A. Magableh', 'Yuantong Gu']2024-02-14T00:00:00https://www.semanticscholar.org/paper/4c2e7f94f27f5d5cf1418dcea695abfc1915e6b6 925a67c8742f77df1f9842702bbc1349ca596c53Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.JAMA psychiatry450These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.JAMA psychiatry['I. Skampardoni', 'I. Nasrallah', 'Ahmed Abdulkadir', 'Junhao Wen', 'Randa Melhem', 'E. Mamourian', 'G. Erus', 'J. Doshi', 'Ashish Singh', 'Zhijian Yang', 'Yuhan Cui', 'Gyujoon Hwang', 'Zheng Ren', 'Raymond Pomponio', 'D. Srinivasan', 'S. T. Govindarajan', 'Paraskevi Parmpi', 'K. Wittfeld', 'H. Grabe', 'Robin Bülow', 'Stefan Frenzel', 'D. Tosun', 'M. Bilgel', 'Yang An', 'Daniel S. Marcus', 'P. LaMontagne', 'S. Heckbert', 'Thomas R. Austin', 'L. Launer', 'Aristeidis Sotiras', 'M. Espeland', 'Colin L. Masters', 'P. Maruff', 'J. Fripp', 'Sterling C. Johnson', 'John C. Morris', 'Marilyn S. Albert', 'R. N. Bryan', 'K. Yaffe', 'H. Völzke', 'Luigi Ferrucci', 'T. Benzinger', 'A. Ezzati', 'Russell T. Shinohara', 'Yong Fan', 'Susan M Resnick', 'M. Habes', 'David A. Wolk', 'H. Shou', 'Konstantina S. Nikita', 'C. Davatzikos']2024-02-14T00:00:00https://www.semanticscholar.org/paper/925a67c8742f77df1f9842702bbc1349ca596c53 54527a50e3a09e59e65b6d850a5e0d767e25a57aAdvances in large language models (LLMs) provide new opportunities in healthcare for improved patient care, clinical decision-making, and enhancement of physician and administrator workflows. However, the potential of these models importantly depends on their ability to generalize effectively across clinical environments and populations, a challenge often underestimated in early development. To better understand reasons for these challenges and inform mitigation approaches, we evaluated ClinicLLM, an LLM trained on [HOSPITAL]'s clinical notes, analyzing its performance on 30-day all-cause readmission prediction focusing on variability across hospitals and patient characteristics. We found poorer generalization particularly in hospitals with fewer samples, among patients with government and unspecified insurance, the elderly, and those with high comorbidities. To understand reasons for lack of generalization, we investigated sample sizes for fine-tuning, note content (number of words per note), patient characteristics (comorbidity level, age, insurance type, borough), and health system aspects (hospital, all-cause 30-day readmission, and mortality rates). We used descriptive statistics and supervised classification to identify features. We found that, along with sample size, patient age, number of comorbidities, and the number of words in notes are all important factors related to generalization. Finally, we compared local fine-tuning (hospital specific), instance-based augmented fine-tuning and cluster-based fine-tuning for improving generalization. Among these, local fine-tuning proved most effective, increasing AUC by 0.25% to 11.74% (most helpful in settings with limited data). Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations.arXiv.org350This study evaluated ClinicLLM, an LLM trained on [HOSPITAL]'s clinical notes, analyzing its performance on 30-day all-cause readmission prediction focusing on variability across hospitals and patient characteristics, and compared local fine-tuning, instance-based augmented fine-tuning and cluster-based fine-tuning for improving generalization.ArXiv['Salman Rahman', 'L. Jiang', 'Saadia Gabriel', 'Yindalon Aphinyanagphongs', 'E. Oermann', 'R. Chunara']2024-02-14T00:00:00https://www.semanticscholar.org/paper/54527a50e3a09e59e65b6d850a5e0d767e25a57a 1318096ec8288c0ad3695c974c776c278f500c9fBreast ultrasound is used in a wide variety of clinical scenarios, including both diagnostic and screening applications. Limitations of ultrasound, however, include its low specificity and, for automated breast ultrasound screening, the time necessary to review whole-breast ultrasound images. As of this writing, four AI tools that are approved or cleared by the FDA address these limitations. Current tools, which are intended to provide decision support for lesion classification and/or detection, have been shown to increase specificity among non-specialists and to decrease interpretation times. Potential future applications include triage of patients with palpable masses in low-resource settings, preoperative prediction of axillary lymph node metastasis, and preoperative prediction of neoadjuvant chemotherapy response. Challenges in the development and clinical deployment of AI for ultrasound include: the limited availability of curated training datasets compared to mammography; the high variability in ultrasound image acquisition due to equipment- and operator-related factors (which may limit algorithm generalizability); and the lack of post-implementation evaluation studies. Furthermore, current AI tools for lesion classification were developed based on 2D data, but diagnostic accuracy could potentially be improved if multimodal ultrasound data were used, such as color Doppler, elastography, cine clips, and 3D imaging.AJR. American journal of roentgenology12Challenges in the development and clinical deployment of AI for ultrasound include the limited availability of curated training datasets compared to mammography; the high variability in ultrasound image acquisition due to equipment- and operator-related factors (which may limit algorithm generalizability); and the lack of post-implementation evaluation studies.AJR. American journal of roentgenology['M. Bahl', 'Jung Min Chang', 'Lisa A. Mullen', 'Wendie A Berg']2024-02-14T00:00:00https://www.semanticscholar.org/paper/1318096ec8288c0ad3695c974c776c278f500c9f 62c8c1fb00dfc08087830f767b55c96ad977bee7Given that artificial intelligence (AI) is significantly transforming businesses, it is crucial to examine how AI will change the future of the hospitality and tourism industry. By integrating multiple data sources (i.e., practitioner literature, research literature, and expert opinions), we suggest three trends constituting opportunities and challenges (AI applications in different business sectors, primary AI functions, emerging AI topics), three possible themes of change (adoption and acceptance, operations management, AI in marketing), as well as four directions for future research (AI interaction, AI and organizational decision making, organizational implications, and managerial issues). Our findings present a detailed picture of AI development and applications along with predictions regarding its place in the industry. Finally, we outline a research agenda that addresses key issues for stakeholders in hospitality and tourism: individuals, including customers and employees; organizations and businesses; and public policymakers and governments.Journal of Hospitality &amp; Tourism Research521This work presents a detailed picture of AI development and applications along with predictions regarding its place in the industry and outlines a research agenda that addresses key issues for stakeholders in hospitality and tourism.Journal of Hospitality &amp; Tourism Research['Hyunsu Kim', 'Kevin Kam Fung So', 'Seunghun Shin', 'Jing Li']2024-02-14T00:00:00https://www.semanticscholar.org/paper/62c8c1fb00dfc08087830f767b55c96ad977bee7 afbb824e7f07b44436ae969c38b1d21dc9efc8f0Background: The study compared the readability, grade level, understandability, actionability, and accuracy of standard patient educational material against artificial intelligence chatbot-derived patient educational material regarding cirrhosis. Methods: An identical standardized phrase was used to generate patient educational materials on cirrhosis from 4 large language model-derived chatbots (ChatGPT, DocsGPT, Google Bard, and Bing Chat), and the outputs were compared against a pre-existing human-derived educational material (Epic). Objective scores for readability and grade level were determined using Flesch-Kincaid and Simple Measure of Gobbledygook scoring systems. 14 patients/caregivers and 8 transplant hepatologists were blinded and independently scored the materials on understandability and actionability and indicated whether they believed the material was human or artificial intelligence-generated. Understandability and actionability were determined using the Patient Education Materials Assessment Tool for Printable Materials. Transplant hepatologists also provided medical accuracy scores. Results: Most educational materials scored similarly in readability and grade level but were above the desired sixth-grade reading level. All educational materials were deemed understandable by both groups, while only the human-derived educational material (Epic) was considered actionable by both groups. No significant difference in perceived actionability or understandability among the educational materials was identified. Both groups poorly identified which materials were human-derived versus artificial intelligence-derived. Conclusions: Chatbot-derived patient educational materials have comparable readability, grade level, understandability, and accuracy to human-derived materials. Readability, grade level, and actionability may be appropriate targets for improvement across educational materials on cirrhosis. Chatbot-derived patient educational materials show promise, and further studies should assess their usefulness in clinical practice.Hepatology Communications222Chatbot-derived patient educational materials have comparable readability, grade level, understandability, and accuracy to human-derived materials, and further studies should assess their usefulness in clinical practice.Hepatology Communications['Faruq Pradhan', 'Alexandra R. Fiedler', 'Kaeli Samson', 'Marco Olivera-Martinez', 'Wuttiporn Manatsathit', 'T. Peeraphatdit']2024-02-14T00:00:00https://www.semanticscholar.org/paper/afbb824e7f07b44436ae969c38b1d21dc9efc8f0 806eae43cfcb86eed3c0495dc9ac9904c790fcf7J. Medical Syst.352The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence’s transformative potential for healthcare administrators, practitioners, and patients.Journal of Medical Systems['Valentina Bellini', 'Michele Russo', 'Tania Domenichetti', 'Matteo Panizzi', 'Simone Allai', 'E. Bignami']2024-02-14T00:00:00https://www.semanticscholar.org/paper/806eae43cfcb86eed3c0495dc9ac9904c790fcf7 d27a81198561740355239507ba674fbee6f29635The application of artificial intelligence technology to improve the evacuation control system at fires is considered. The focus is on the shortcomings of traditional evacuation methods and the potential of artificial intelligence in big data processing to optimize the flow of people. The integration of artificial intelligence algorithms with models of human behavior to develop more effective evacuation scenarios that take into account the psychological and physiological aspects of human behavior in emergency situations is discussed.NATURAL AND MAN-MADE RISKS (PHYSICO-MATHEMATICAL AND APPLIED ASPECTS)31The integration of artificial intelligence algorithms with models of human behavior to develop more effective evacuation scenarios that take into account the psychological and physiological aspects of human behavior in emergency situations is discussed.NATURAL AND MAN-MADE RISKS (PHYSICO-MATHEMATICAL AND APPLIED ASPECTS)["Grigoriy Mel'nikov", 'Sergey Tursenev']2024-02-14T00:00:00https://www.semanticscholar.org/paper/d27a81198561740355239507ba674fbee6f29635 59819b89956249d481d019b6f316603813ace03bIntegrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a significant leap forward in managing GI disorders. AI-enabled applications, such as computer-aided detection and computer-aided diagnosis, have significantly advanced GI endoscopy, improving early detection, diagnosis and personalized treatment planning. AI algorithms have shown promise in the analysis of endoscopic data, critical in conditions with traditionally low diagnostic sensitivity, such as indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks can markedly improve the diagnostic process when integrated with cholangioscopy or endoscopic ultrasound, especially in the detection of malignant biliary strictures and cholangiocarcinoma. AI’s capacity to analyze complex image data and offer real-time feedback can streamline endoscopic procedures, reduce the need for invasive biopsies, and decrease associated adverse events. However, the clinical implementation of AI faces challenges, including data quality issues and the risk of overfitting, underscoring the need for further research and validation. As the technology matures, AI is poised to become an indispensable tool in the gastroenterologist’s arsenal, necessitating the integration of robust, validated AI applications into routine clinical practice. Despite remarkable advances, challenges such as operator-dependent accuracy and the need for intricate examinations persist. This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.Annals of gastroenterology : quarterly publication of the Hellenic Society of Gastroenterology770This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.Annals of Gastroenterology['Hassam Ali', 'Muhammad Ali Muzammil', 'D. Dahiya', 'Farishta Ali', 'Shafay Yasin', 'Waqar Hanif', 'M. Gangwani', 'Muhammad Aziz', 'Muhammad Khalaf', 'Debargha Basuli', 'Mohammad Al-Haddad']2024-02-14T00:00:00https://www.semanticscholar.org/paper/59819b89956249d481d019b6f316603813ace03b 748f350b647bd279fc4092b291e95ad5008f7cdaIn recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI’s applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system’s interface.Frontiers in Neurology1000This article provides a comprehensive examination of AI’s applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models.Frontiers in Neurology['Yue Qian', 'Ahmad Alhaskawi', 'Yanzhao Dong', 'Juemin Ni', 'Sahar Abdalbary', 'Hui Lu']2024-02-14T00:00:00https://www.semanticscholar.org/paper/748f350b647bd279fc4092b291e95ad5008f7cda 28ddafb2c76580d97179ba71508dec8d2ed53564The recent development of ChatGPT, an artificial intelligence application that mimics human language, has many educators questioning its place in the classroom. This article provides suggestions for instructional uses of ChatGPT in the literacy classroom so that teachers can empower students to use this tool safely and effectively for learning.The Reading teacher230Suggestions for instructional uses of ChatGPT in the literacy classroom so that teachers can empower students to use this tool safely and effectively for learning.The Reading Teacher['Amy Hutchison']2024-02-14T00:00:00https://www.semanticscholar.org/paper/28ddafb2c76580d97179ba71508dec8d2ed53564 bda2019e5df2d04603310e8ca99cedb16b75b2e7The expansion of the use of intelligent computer systems based on artificial intelligence makes it possible to operate with a large amount of input data, generate a greater number of alternatives based on previous experience and competitive analysis, select and implement the best one for current market conditions. Such opportunities are especially in demand when solving non-standard, complex, unstructured management tasks. The result of artificial intelligence application in managerial decision-making is to increase the flexibility and efficiency of business processes, the implementation of the best complex options in a limited time and resource mode. The article is devoted to the consideration of research and successful cases related to the use of artificial intelligence technology in corporate governance. The methodological basis is represented by general scientific methods of cognition: comparative analysis, systematization, etc. As a result, based on the analysis of management practice, general requirements for the use of artificial intelligence in the decision-making process in corporate management are determined.Bulletin of Chelyabinsk State University00The article is devoted to the consideration of research and successful cases related to the use of artificial intelligence technology in corporate governance and general requirements for the use of artificial intelligence in the decision-making process in corporate management are determined.Bulletin of Chelyabinsk State University['Alexander\u202fV. Ageev', 'Sergey\u202fV. Simonov', 'Sergey M. Kashin', 'Vitaliy A. Matchinov', 'Igor\u202fV. Cherpakov']2024-02-14T00:00:00https://www.semanticscholar.org/paper/bda2019e5df2d04603310e8ca99cedb16b75b2e7 47e1d000ead4e52e5b23be21625683e3ebf43124We give 19 proofs of the famous Angle Bisector Theorem from Euclid's Elements. The first proof is the Euclid's original proof, the remaining proofs use the methods of Euclidean Geo\-metry, Trigonometry, Analytic Geometry, Complex Numbers, and Gr\"obner Bases. The Gr\"obner Bases proof is in the area of Automatic Proving and Artificial Intelligence, so that the proofs in a way symbolise the development of mathematics from 300 BC (the Euclid's time) to modern days. We discuss what the proofs illustrate and why they are important for Math Education. All the proofs, except the first one, are original.STEM Education Notes0019 proofs of the famous Angle Bisector Theorem from Euclid's Elements are given, which symbolise the development of mathematics from 300 BC (the Euclid's time) to modern days.STEM Education Notes['H. Kulosman', 'A. Miller']2024-02-14T00:00:00https://www.semanticscholar.org/paper/47e1d000ead4e52e5b23be21625683e3ebf43124 b98b58c0124a556488334b1056fdc5045c6b9d6aThis study aims to examine the impact of artificial intelligence (AI) on English language teaching in higher education. The scope of the study was limited to the opportunities and challenges of English language teaching. This study was conducted using a mixed method of conducting surveys and in-depth interviews. A questionnaire link using Spreadsheet’s Google Form was sent to the English lecturer through the WhatsApp group and personal contact No. The study sample consisted of 16 English lecturers. Analyzing the questionnaire and interview transcript indicates that the emergence of artificial intelligence (AI) technology has created opportunities and challenges for English language teaching and learning. AI has changed the landscape of English language teaching in higher education. English lecturers utilized different types of AI for various purposes, for example, asking and solving questions and checking for grammatical errors, checking plagiarism, paraphrasing, and reviewing literature. The study also revealed that AI has a variety of advantages for language teaching and learning, including the detection of plagiarism and grammatical errors. In addition, AI has created opportunities and challenges for the future of English language teaching. AI required digital literacy to utilize. The English teaching profession might be taken over by AI in the future, so English lecturers must continuously improve their digital literacy.Jurnal Edukasi Saintifik180Analysis of the questionnaire and interview transcript indicates that the emergence of artificial intelligence (AI) technology has created opportunities and challenges for English language teaching and learning, and AI has created opportunities and challenges for the future of English language teaching.Jurnal Edukasi Saintifik['Afif Zuhdy Idham', 'Wahyuddin Rauf', 'Abd. Rajab']2024-02-14T00:00:00https://www.semanticscholar.org/paper/b98b58c0124a556488334b1056fdc5045c6b9d6a 013c2fb1a36e02194af4ed6d4672a8657eaad15bAbstract Background Artificial intelligence (AI) is gaining recognition as a promising adjunct in healthcare including gastrointestinal endoscopy. Randomized controlled trials have demonstrated improved polyp detection with computer-aided detection (CADe) systems in colonoscopy. Despite the foreseeable translation of CADe systems into the endoscopy suite, no study to date has explored how introducing this novel technology influences endoscopist perceptions and cognitive processes. Aims To explore how AI assistance impacts endoscopists’ perceptions of and cognitive processes during colonoscopy. Methods Faculty in gastroenterology and general surgery at the University Health Network (Toronto, Canada) were interviewed in April 2023 to explore their baseline perceptions of AI before the planned installation of the Medtronic GI GeniusTM Intelligent Endoscopy Module, an AI polyp detection tool, at Toronto Western Hospital in May 2023. After performing a minimum of 10 colonoscopies using GI GeniusTM, participants were re-interviewed to discuss their perceptions of AI-assisted colonoscopy and how it influenced their cognition during the procedure. Analysis was informed by constructivist grounded theory, whereby interview data were transcribed and coded iteratively using constant comparison to generate themes. Results In this interim constant comparative analysis, 9 participants (6 gastroenterologists and 3 general surgeons) were interviewed to generate 16 interview transcripts (9 pre-exposure and 7 post-exposure; 1 interview pending, 1 participant on leave). Participants held the view that: (1) AI will inevitably become the standard of care in colonoscopy, where clinicians and AI function symbiotically and in partnership; (2) CADe systems may facilitate standardization of practice and reduce inter-endoscopist variability by improving detection of specific types of lesions (e.g., sessile serrated polyps); (3) CADe assistance provides a “second set of eyes” that interacts with, but does not replace, endoscopists’ cognitive processes; and (4) trainees should gain familiarity with AI systems, despite mixed opinions regarding AI’s specific role in training. Conclusions We found that study participants conceptualize AI as an essential component of colonoscopy practice and training, with endoscopist thought processes and CADe systems appearing to function symbiotically. Elucidating the specific ways in which AI and endoscopist cognition interact will facilitate the future refinement of these tools. Funding Agencies NoneJournal of the Canadian Association of Gastroenterology00It is found that study participants conceptualize AI as an essential component of colonoscopy practice and training, with endoscopist thought processes and CADe systems appearing to function symbiotically.Journal of the Canadian Association of Gastroenterology['C. Lee', 'C. H. Parker', 'L. W. Liu', 'M. Salim', 'T. Jeyalingam']2024-02-14T00:00:00https://www.semanticscholar.org/paper/013c2fb1a36e02194af4ed6d4672a8657eaad15b 367f522cbab59ff02e5f59aee3e3f43a8c6611e6Introducción: la auditoría financiera ha experimentado transformaciones importantes gracias a la adopción de nuevas tecnologías, entre las que se destaca la inteligencia artificial. Objetivo: Este artículo investiga cómo la inteligencia artificial ayuda a la gestión de los procesos que se llevan a cabo durante la elaboración de una auditoría financiera. Metodología: Se realiza un estudio descriptivo de la información encontrada en libros y artículos científicos sobre auditoría financiera y nuevas tecnologías. Resultados: Los resultados muestran que la inteligencia artificial ha tenido especial impacto en el análisis de grandes volumenes de datos, pudiendo ser procesados y analizados en un período de tiempo corto. También se está utilizando la inteligencia artificial en la auditoría para evaluar riesgos, para automatizar tareas de revisión manuales, tediosas y repetitivas; y para detectar fraudes. Conclusiones: Se concluye que, la inteligencia artificial está realizando tareas de auditoría que requerían anteriormente la intervención del humano, y que hoy hace que el proceso sea menos complejo, más eficiente y preciso; con ahorro de dinero, esfuerzo y tiempo. Aún así, la inteligencia artificial no puede sustituir por completo al ser humano ni su escepticismo profesional.Revista Estrategia Organizacional00Revista Estrategia Organizacional['Betty Auxiliadora De La Hoz Suárez', 'Ismael felipe Luna Moran', 'Arleth Esther Manjarrés Tete', 'Aminta Isabel De La Hoz Suárez']2024-02-14T00:00:00https://www.semanticscholar.org/paper/367f522cbab59ff02e5f59aee3e3f43a8c6611e6 f2eef70954a61f1033aae105062601b30ed675b3Nature Italy10Nature Italy['Viola Rita']2024-02-14T00:00:00https://www.semanticscholar.org/paper/f2eef70954a61f1033aae105062601b30ed675b3 c44f689a387932130b4583cf120d397a821659bdPsicología iberoamericana70Psicología Iberoamericana['Sarah Frances Gordon', 'Bernardo Turnbull']2024-02-14T00:00:00https://www.semanticscholar.org/paper/c44f689a387932130b4583cf120d397a821659bd 0087097a638a44d5796de87bbabccb19daeb5fd7Education and Information Technologies : Official Journal of the IFIP technical committee on Education1010Education and Information Technologies['X. Hu', 'Hui Sui', 'Xingyu Geng', 'Li Zhao']2024-02-14T00:00:00https://www.semanticscholar.org/paper/0087097a638a44d5796de87bbabccb19daeb5fd7 cdfa925d8b04cd90557855b3f475f36b4cd5b4e8International Journal of Research in Library Science00International Journal of Research in Library Science['A. Kalisdha']2024-02-14T00:00:00https://www.semanticscholar.org/paper/cdfa925d8b04cd90557855b3f475f36b4cd5b4e8 70affa777b4eb7e375dc32a9d31284335b80208aПредставлен анализ возможности применения сравнительно новой и хорошо зарекомендовавшей себя методики машинного обучения для распределенной си-стемы сбора и обработки информации – федеративного обучения моделей, использую-щейся для выполнения полета роем дронов, как группы агентов с точки зрения передачи накопленных данных и обучения модели искусственного интеллекта. Описана схема ра-боты федеративного обучения для группы беспилотных летательных аппаратов (БПЛА). Особое внимание уделено понятию конвергенции (сходимости) в машинном обучении. The purpose of this study is to review and analyze the application of a relative-ly new and well–proven machine learning methodology for a distributed information collection and processing system – federated model training used to fly a swarm of drones as a group of agents in terms of transferring accumulated data and training an artificial intelligence model. The scheme of federal training for a group of unmanned aerial vehicles (UAV) is described. Special attention is paid to the concept of convergence in machine learning.Вестник Адыгейского государственного университета, серия «Естественно-математические и технические науки»00Вестник Адыгейского государственного университета, серия «Естественно-математические и технические науки»['Виталий Анатольевич Довгаль']2024-02-14T00:00:00https://www.semanticscholar.org/paper/70affa777b4eb7e375dc32a9d31284335b80208a 2bf364c726d0944dd766fbb6df42d900b0f2fa56We introduce a multi-agent simulator for economic systems comprised of heterogeneous Households, heterogeneous Firms, Central Bank and Government agents, that could be subjected to exogenous, stochastic shocks. The interaction between agents defines the production and consumption of goods in the economy alongside the flow of money. Each agent can be designed to act according to fixed, rule-based strategies or learn their strategies using interactions with others in the simulator. We ground our simulator by choosing agent heterogeneity parameters based on economic literature, while designing their action spaces in accordance with real data in the United States. Our simulator facilitates the use of reinforcement learning strategies for the agents via an OpenAI Gym style environment definition for the economic system. We demonstrate the utility of our simulator by simulating and analyzing two hypothetical (yet interesting) economic scenarios. The first scenario investigates the impact of heterogeneous household skills on their learned preferences to work at different firms. The second scenario examines the impact of a positive production shock to one of two firms on its pricing strategy in comparison to the second firm. We aspire that our platform sets a stage for subsequent research at the intersection of artificial intelligence and economics.arXiv.org671A multi-agent simulator for economic systems comprised of heterogeneous Households, heterogeneous Firms, Central Bank and Government agents, that could be subjected to exogenous, stochastic shocks is introduced and set a stage for subsequent research at the intersection of artificial intelligence and economics.ArXiv['Kshama Dwarakanath', 'Svitlana Vyetrenko', 'P. Tavallali', 'T. Balch']2024-02-14T00:00:00https://www.semanticscholar.org/paper/2bf364c726d0944dd766fbb6df42d900b0f2fa56 a3cd7a85e3661a344c4a87e9136e5eb15f4eed75Fervent calls for more robust governance of the harms associated with artificial intelligence (AI) are leading to the adoption around the world of what regulatory scholars have called a management-based approach to regulation. Recent initiatives in the United States and Europe, as well as the adoption of major self-regulatory standards by the International Organization for Standardization, share in common a core management-based paradigm. These management-based initiatives seek to motivate an increase in human oversight of how AI tools are trained and developed. Refinements and systematization of human-guided training techniques will thus be needed to fit within this emerging era of management-based regulatory paradigm. If taken seriously, human-guided training can alleviate some of the technical and ethical pressures on AI, boosting AI performance with human intuition as well as better addressing the needs for fairness and effective explainability. In this paper, we discuss the connection between the emerging management-based regulatory frameworks governing AI and the need for human oversight during training. We broadly cover some of the technical components involved in human-guided training and then argue that the kinds of high-stakes use cases for AI that appear of most concern to regulators should lean more on human-guided training than on data-only training. We hope to foster a discussion between legal scholars and computer scientists involving how to govern a domain of technology that is vast, heterogenous, and dynamic in its applications and risks.arXiv.org471The connection between the emerging management-based regulatory frameworks governing AI and the need for human oversight during training are discussed and it is argued that the kinds of high-stakes use cases for AI should lean more on human-guided training than on data-only training.ArXiv['C. Coglianese', 'Colton R. Crum']2024-02-13T00:00:00https://www.semanticscholar.org/paper/a3cd7a85e3661a344c4a87e9136e5eb15f4eed75 91ab8fc15ff9f310ff53db179d5e17c3c4b71ab8The rise of data theft in the digital space has prompted the public to call for regulations regarding data security to be implemented immediately. Opinions in the form of requests for the passing of the Personal Data Protection Law (UUPDP) via social media Twitter have become very widely discussed. UUPDP is a regulation related to personal data protection to fill the regulatory void in the digital space. The keyword UUPDP became a topic of conversation on Twitter when the UUPDP personal data protection law was passed by the government and the DPR. The aim of this research is to map the Digital Opinion Movement through communication networks and identify influential actors in communication networks using the keyword UUPDP on Twitter. This research uses the Social Media Network Analysis (SMNA) method. The theory used to answer this case is the Digital Movement of Opinion/DMO theory and communication network theory. information collected via Twitter from 11 September 2022 – 21 September 2022. Stages in analyzing and retrieving data using the website based Netlytic.org application and Gephi software. The use of this tool is used to map influential actors involved in the communication network using UUPDP keywords, next to measure centrality using the indicators of degree centrality, betweenness centrality, closeness centrality and eigenvector centrality. The results of the research are 2 (two) main actors who influence the movement of digital opinion in communication networks with the keyword UUPDP, namely @franken_blues and @hunterjagar3.Journal La Sociale00The aim of this research is to map the Digital Opinion Movement through communication networks and identify influential actors in communication networks using the keyword UUPDP on Twitter and to measure centrality using the indicators of degree centrality, betweenness centrality, closeness centrality and eigenvector centrality.Journal La Sociale['Akhmad Kurniawan']2024-02-13T00:00:00https://www.semanticscholar.org/paper/91ab8fc15ff9f310ff53db179d5e17c3c4b71ab8 14a5089671449fda090ffcfed15af824b1e2cf36This article analyzes the public comments submitted to the Commodity Futures Trading Commission (CFTC), 2010–2014, in response to proposed rules for implementing the Dodd‐Frank reforms. By addressing a fine‐grained typology of commenting organizations to a topic model of the combined comments, we illuminate a new pattern of public engagement in financial regulation. Contrary to the economic concept of regulatory capture, our data show no sharp divide between the suppliers of complex derivatives (the dealer banks) and their customers in other parts of the finance industry. In keeping with the general concept of financialization—but contrary to its strongest versions—our data show distinctly delimited evidence of a convergence between the banks and other sectors. Instead of either capture or all‐embracing financialization, the comments submitted to the CFTC display an overriding opposition in the justifications used by commenters to explain their preferences. Commenters from the financial services and allied sectors consistently adopt a language of technical legitimation. Commenters in agriculture, livestock, retail energy, and many nonmarket organizations adopt a language of moral justification. The clarity of the divide between these two idioms indicates that the underlying purposes of new financial instruments are now being questioned. Even in the realm of esoteric financial instruments such as derivatives, the moral economy of market competition is once again subject to debate among market participants as well as observers.Regulation &amp; Governance330Regulation &amp; Governance['J. N. Ziegler', 'Konrad Posch', 'Thomas Nath']2024-02-13T00:00:00https://www.semanticscholar.org/paper/14a5089671449fda090ffcfed15af824b1e2cf36 191d12f952e89daa0d41b05d8e2503abbb10fff8Nature reviews. Immunology10Nature reviews. Immunology['Yvonne Bordon']2024-02-13T00:00:00https://www.semanticscholar.org/paper/191d12f952e89daa0d41b05d8e2503abbb10fff8 e7eaacaa9c827bf5dfd810f733a674dd5e2876e4As the retail landscape undergoes a profound transformation in the era of digitalization, the integration of Artificial Intelligence (AI) and predictive analytics has emerged as a pivotal force reshaping the industry. This paper provides a comprehensive review of the latest trends in AI-driven predictive analytics within the retail sector and explores innovative customer engagement strategies that leverage these advanced technologies. The review begins by elucidating the foundational concepts of AI and predictive analytics, highlighting their synergistic role in forecasting consumer behavior, demand patterns, and market trends. The paper then delves into the emerging trends, such as machine learning algorithms, natural language processing, and computer vision, that are revolutionizing the way retailers harness data for strategic decision-making. In addition to outlining technological advancements, the paper emphasizes the crucial role of data quality and ethical considerations in the implementation of AI-driven predictive analytics. It examines the challenges associated with privacy concerns, algorithmic bias, and the need for transparent AI models to ensure responsible and fair use of customer data. Furthermore, the paper explores a spectrum of customer engagement strategies enabled by AI-driven predictive analytics. From personalized shopping experiences and targeted marketing campaigns to dynamic pricing and inventory optimization, retailers are deploying innovative approaches to enhance customer satisfaction and loyalty. The review also discusses case studies of successful AI implementations in leading retail enterprises, showcasing tangible benefits such as improved operational efficiency, increased sales, and enhanced customer retention. These real-world examples illustrate the transformative impact of AI-driven predictive analytics on diverse aspects of the retail value chain. By examining emerging trends and customer engagement strategies, it serves as a valuable resource for industry professionals, researchers, and policymakers seeking to navigate the evolving landscape of AI in the retail sector. Keywords: AI-driven Predictive Analytics, Retail Industry, Customer Engagement Strategies, Machine Learning Algorithms, Natural Language Processing.International Journal of Management &amp; Entrepreneurship Research05A comprehensive review of the latest trends in AI-driven predictive analytics within the retail sector and explores innovative customer engagement strategies that leverage these advanced technologies, such as machine learning algorithms, natural language processing, and computer vision.International Journal of Management &amp; Entrepreneurship Research['David Iyanuoluwa Ajiga', 'Ndubuisi Leonard Ndubuisi', 'Onyeka Franca Asuzu', 'Oluwaseyi Rita Owolabi', 'Tula Sunday Tubokirifuruar', 'Rhoda Adura Adeleye']2024-02-13T00:00:00https://www.semanticscholar.org/paper/e7eaacaa9c827bf5dfd810f733a674dd5e2876e4 07ea2bac239ce2021ef0172d9aef6d0d5d4e2be4Artificial Intelligence (AI) has rapidly become an integral part of decision-making processes across various industries, revolutionizing the way choices are made. This Review delves into the ethical considerations associated with the use of AI in decision-making, exploring the implications of algorithms, automation, and machine learning. The incorporation of AI in decision-making introduces a myriad of ethical concerns that demand careful scrutiny. The opacity of algorithms raises questions about transparency, accountability, and bias. Decision-making processes driven by AI can be complex and difficult to interpret, leading to challenges in understanding how and why specific choices are made. As a result, ethical concerns emerge regarding the potential lack of transparency and accountability, especially when these decisions impact individuals or societal groups. Bias in AI algorithms poses a critical ethical challenge. Machine learning models learn from historical data, and if that data is biased, the AI system may perpetuate and even exacerbate existing biases. Addressing this challenge requires meticulous examination of training data, algorithmic design, and ongoing monitoring to ensure fairness and mitigate discrimination. The increased reliance on AI in decision-making processes also raises concerns about accountability and responsibility. When AI systems make decisions, determining who is ultimately responsible for those decisions becomes a complex ethical issue. Establishing a framework for accountability is crucial to ensure that individuals, organizations, and developers share responsibility for the outcomes of AI-driven decisions. Moreover, ethical considerations extend to the broader societal impact of AI in decision-making. Issues such as job displacement, economic inequality, and the potential concentration of power in the hands of a few require careful ethical examination. Striking a balance between technological advancement and social responsibility is paramount to ensuring that AI benefits society as a whole. In conclusion, this review highlights the ethical implications of integrating AI into decision-making processes. It underscores the need for transparency, fairness, and accountability to address concerns related to bias, responsibility, and the broader societal impact of AI-driven decisions. Ethical frameworks must evolve alongside technological advancements to foster a responsible and equitable integration of AI in decision-making processes. Keywords: Ethical, Implications, AI, Decision Making, Process.International Journal of Management &amp; Entrepreneurship Research04The need for transparency, fairness, and accountability is underscores the need for transparency, fairness, and accountability to address concerns related to bias, responsibility, and the broader societal impact of AI-driven decisions.International Journal of Management &amp; Entrepreneurship Research['Femi Osasona', 'Olukunle Oladipupo Amoo', 'Akoh Atadoga', 'Temitayo Oluwaseun Abrahams', 'Oluwatoyin Ajoke Farayola', 'Benjamin Samson Ayinla']2024-02-13T00:00:00https://www.semanticscholar.org/paper/07ea2bac239ce2021ef0172d9aef6d0d5d4e2be4 dfbaaf70ae9f03d0ed5f5c879ad5b855c495af55Large language models (LLMs) are becoming more prevalent and have found a ubiquitous use in providing different forms of writing assistance. However, LLM-powered writing systems can frustrate users due to their limited personalization and control, which can be exacerbated when users lack experience with prompt engineering. We see design as one way to address these challenges and introduce GhostWriter, an AI-enhanced writing design probe where users can exercise enhanced agency and personalization. GhostWriter leverages LLMs to learn the user's intended writing style implicitly as they write, while allowing explicit teaching moments through manual style edits and annotations. We study 18 participants who use GhostWriter on two different writing tasks, observing that it helps users craft personalized text generations and empowers them by providing multiple ways to control the system's writing style. From this study, we present insights regarding people's relationship with AI-assisted writing and offer design recommendations for future work.arXiv.org573GhostWriter is introduced, an AI-enhanced writing design probe where users can exercise enhanced agency and personalization in large language models, and insights regarding people's relationship with AI-assisted writing are presented.ArXiv['Catherine Yeh', 'Gonzalo Ramos', 'Rachel Ng', 'Andy Huntington', 'Richard Banks']2024-02-13T00:00:00https://www.semanticscholar.org/paper/dfbaaf70ae9f03d0ed5f5c879ad5b855c495af55 20f2da1c1d30d25c11c6d50fe53c4d82d3867943AI-based code assistants are promising tools that can facilitate and speed up code development. They exploit machine learning algorithms and natural language processing to interact with developers, suggesting code snippets (e.g., method implementations) that can be incorporated into projects. Recent studies empirically investigated the effectiveness of code assistants using simple exemplary problems (e.g., the re-implementation of well-known algorithms), which fail to capture the spectrum and nature of the tasks actually faced by developers. In this paper, we expand the knowledge in the area by comparatively assessing four popular AI-based code assistants, namely GitHub Copilot, Tabnine, ChatGPT, and Google Bard, with a dataset of 100 methods that we constructed from real-life open-source Java projects, considering a variety of cases for complexity and dependency from contextual elements. Results show that Copilot is often more accurate than other techniques, yet none of the assistants is completely subsumed by the rest of the approaches. Interestingly, the effectiveness of these solutions dramatically decreases when dealing with dependencies outside the boundaries of single classes.arXiv.org291Assessing four popular AI-based code assistants, namely GitHub Copilot, Tabnine, ChatGPT, and Google Bard, with a dataset of 100 methods constructed from real-life open-source Java projects, shows that Copilot is often more accurate than other techniques, yet none of the assistants is completely subsumed by the rest of the approaches.ArXiv['Vincenzo Corso', 'Leonardo Mariani', 'D. Micucci', 'O. Riganelli']2024-02-13T00:00:00https://www.semanticscholar.org/paper/20f2da1c1d30d25c11c6d50fe53c4d82d3867943 c9d83802dd49a155a9a2969e70f582812d6a99e1Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation caused by deepfake techniques. However, the problem of detecting purely synthesized face images has been explored to a lesser extent. In particular, the recent popular Diffusion Models (DMs) have shown remarkable success in image synthesis. Existing detectors struggle to generalize between synthesized images created by different generative models. In this work, a comprehensive benchmark including human face images produced by Generative Adversarial Networks (GANs) and a variety of DMs has been established to evaluate both the generalization ability and robustness of state-of-the-art detectors. Then, the forgery traces introduced by different generative models have been analyzed in the frequency domain to draw various insights. The paper further demonstrates that a detector trained with frequency representation can generalize well to other unseen generative models.arXiv.org401A comprehensive benchmark including human face images produced by Generative Adversarial Networks and a variety of DMs has been established to evaluate both the generalization ability and robustness of state-of-the-art detectors.ArXiv['Yuhang Lu', 'Touradj Ebrahimi']2024-02-13T00:00:00https://www.semanticscholar.org/paper/c9d83802dd49a155a9a2969e70f582812d6a99e1 191df526eb5373215e2767ca0df5b4ec7d8db844The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios.arXiv.org1562This study conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models, and provides a taxonomy of 378 normative issues in 19 topic areas and ranks them according to their prevalence in the literature.ArXiv['Thilo Hagendorff']2024-02-13T00:00:00https://www.semanticscholar.org/paper/191df526eb5373215e2767ca0df5b4ec7d8db844 672fb45ab3d8967f6a3b13944a071505ea5f2392Background: The advent of Artificial Intelligence (AI) within organizational frameworks, particularly in the realm of Human Resource Management (HRM), has initiated a transformative shift in operational efficiencies and strategies across various sectors. This integration aims to leverage AI's capabilities to augment human decision-making, enhance operational efficiency, and foster a culture of innovation within organizations. Despite the potential benefits, the practical application and tangible impact of AI strategies on organizational effectiveness remain areas of significant academic and practical interest. Objective: This study aimed to investigate the influence of AI strategies and creativity oriented HRM practices on organizational effective performance. It sought to explore the synergistic relationship between AI implementation and innovative HR practices, and their collective impact on enhancing organizational efficiency and performance metrics. Methods: Employing a cross-sectional survey design, the study collected data from employees working in private hospitals in Lahore, Pakistan. A total of 144 valid responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the relationships between AI strategy implementation, creativity-oriented HRM practices, and organizational effective performance. Reliability and validity of the constructs were evaluated through Cronbach's alpha, composite reliability, and average variance extracted (AVE) measures. Results: The findings revealed that AI strategy implementation (Cronbach's alpha = 0.942, AVE = 0.611) and creativity oriented HRM practices (Cronbach's alpha = 0.932, AVE = 0.585) were both significantly associated with enhanced organizational effective performance (Cronbach's alpha = 0.932, AVE = 0.533). The path analysis indicated strong positive relationships between AI strategies and creativity-oriented HRM practices (β = 0.688, p < 0.001), between AI strategies and organizational performance (β = 0.228, p = 0.004), and between creativity-oriented HRM practices and organizational performance (β = 0.597, p < 0.001). The model explained 59% of the variance in organizational effective performance. Conclusion: The study concludes that the strategic integration of AI within HRM frameworks significantly contributes to organizational effectiveness. Emphasizing creativity-oriented HRM practices in conjunction with AI strategies can lead to substantial improvements in organizational performance. These findings underscore the importance of a strategic approach to AI integration in HRM, highlighting the need for organizations to foster environments that promote innovation and creativity.Journal of Health and Rehabilitation Research00The strategic integration of AI within HRM frameworks significantly contributes to organizational effectiveness, highlighting the need for organizations to foster environments that promote innovation and creativity.Journal of Health and Rehabilitation Research['Abid Ghaffar', 'Arfan Arshad', 'Muhammad Usman Siddqiue', 'Adeel Nasir']2024-02-13T00:00:00https://www.semanticscholar.org/paper/672fb45ab3d8967f6a3b13944a071505ea5f2392 a7a80196d2b2a59cfaa0bb41db5a498c5d72d72dThe landscape of pharmaceutical R&D is being reshaped by the synergistic integration of Artificial Intelligence (AI) and groundbreaking drug discoveries, mainly focusing on MYC-modulating compounds and BET protein inhibitors. This Patent Highlight delves into this convergence, illustrating a transformative shift in the pharmaceutical industry's approach to drug development, strategic management, and treating various diseases, from cancer to inflammatory and fibrotic disorders.ACS Medicinal Chemistry Letters60This Patent Highlight delves into this convergence of Artificial Intelligence and groundbreaking drug discoveries, illustrating a transformative shift in the pharmaceutical industry's approach to drug development, strategic management, and treating various diseases.ACS medicinal chemistry letters['Robert B. Kargbo']2024-02-13T00:00:00https://www.semanticscholar.org/paper/a7a80196d2b2a59cfaa0bb41db5a498c5d72d72d 77baa6335a85aa1babea786cfce2dbf9d3118b70Objectives: Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods, such as data perturbation and adversarial learning, that have been applied in the biomedical domain to address bias. Methods: We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness. Results: The bias of AIs in biomedicine can originate from multiple sources such as insufficient data, sampling bias and the use of health-irrelevant features or race-adjusted algorithms. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic. Distributional methods include data augmentation, data perturbation, data reweighting methods, and federated learning. Algorithmic approaches include unsupervised representation learning, adversarial learning, disentangled representation learning, loss-based methods and causality-based methods.arXiv.org940To mitigate bias concerns during model development, a survey of recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV) and other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness are surveyed.ArXiv['Yifan Yang', 'Mingquan Lin', 'Han Zhao', 'Yifan Peng', 'Furong Huang', 'Zhiyong Lu']2024-02-13T00:00:00https://www.semanticscholar.org/paper/77baa6335a85aa1babea786cfce2dbf9d3118b70 5039c985b9ccd003dfa558876fbade54923623a6Machine Learning (ML) and 'Artificial Intelligence' ('AI') methods tend to replicate and amplify existing biases and prejudices, as do Robots with AI. For example, robots with facial recognition have failed to identify Black Women as human, while others have categorized people, such as Black Men, as criminals based on appearance alone. A 'culture of modularity' means harms are perceived as 'out of scope', or someone else's responsibility, throughout employment positions in the 'AI supply chain'. Incidents are routine enough (incidentdatabase.ai lists over 2000 examples) to indicate that few organizations are capable of completely respecting peoples' rights; meeting claimed equity, diversity, and inclusion (EDI or DEI) goals; or recognizing and then addressing such failures in their organizations and artifacts. We propose a framework for adapting widely practiced Research and Development (R&D) project management methodologies to build organizational equity capabilities and better integrate known evidence-based best practices. We describe how project teams can organize and operationalize the most promising practices, skill sets, organizational cultures, and methods to detect and address rights-based fairness, equity, accountability, and ethical problems as early as possible when they are often less harmful and easier to mitigate; then monitor for unforeseen incidents to adaptively and constructively address them. Our primary example adapts an Agile development process based on Scrum, one of the most widely adopted approaches to organizing R&D teams. We also discuss limitations of our proposed framework and future research directions.arXiv.org1770A framework for adapting widely practiced Research and Development (R&D) project management methodologies to build organizational equity capabilities and better integrate known evidence-based best practices is proposed.ArXiv['Andrew Hundt', 'Julia Schuller', 'Severin Kacianka']2024-02-13T00:00:00https://www.semanticscholar.org/paper/5039c985b9ccd003dfa558876fbade54923623a6 d1ca74ad649031d8e309fae84da9e38d1233daa3Aim: This study was conducted to evaluate the acceptance among healthcare practitioners and scientific researchers of the current official regulatory recommendations regarding the incorporation of human categorization through confounders, such as “Religion”, into AI and ML-based clinical research and healthcare settings. Materials and Methods: An anonymous online survey was conducted using the Telegram platform, where participants were asked a single question: "Do you consider the inclusion of Religious status in Artificial Intelligence and Machine Learning models justified from the perspective of medical ethics and science?" Respondents were provided with only two response options: "Yes" or "No." This survey was specifically targeted at international groups, focusing primarily on English and Russian-speaking clinicians and scientific researchers. Results: 134 unique individuals participated in the survey. The results revealed that two-third of the respondents (87 individuals) agreed that including Religion status as predictor in the ML and AI models is inappropriate. Conclusion: Two-thirds of healthcare practitioners and scientific researchers agree that categorizing individuals within healthcare settings based on their religion is inappropriate. Educational programs are needed to inform healthcare and scientific professionals that AI and ML applications should be built on unbiased and ethically appropriate predictors. ML is incapable of distinguishing individual human characteristics. Therefore, constructing healthcare AI and ML models based on confounders like religion is unlikely to aid in identifying the cause of or treating any pathology or disease. Moreover, the high conflict potential of this predictor may further deepen societal disparities.Web3 Journal: ML in Health Science00Evaluating the acceptance among healthcare practitioners and scientific researchers of the current official regulatory recommendations regarding the incorporation of human categorization through confounders, such as “Religion”, into AI and ML-based clinical research and healthcare settings revealed that two-thirds of healthcare practitioners and scientific researchers agree that categorizing individuals within healthcare settings based on their religion is inappropriate.Web3 Journal: ML in Health Science['Y. Rusinovich', 'V. Rusinovich']2024-02-13T00:00:00https://www.semanticscholar.org/paper/d1ca74ad649031d8e309fae84da9e38d1233daa3 b0f34a79cea3b04549db0fdb67045777bac8a8a2Reproductive Biology and Endocrinology280The hypothesis suggests that the assimilation of AI could streamline ICSI implementation, leading to an overall enhancement in the realm of male fertility treatments, however, it is essential to conduct further investigations to substantiate the efficacy of AI applications in a clinical setting.Reproductive Biology and Endocrinology : RB&E['P. Sengupta', 'S. Dutta', 'R. Jegasothy', 'Petr Slama', 'C. Cho', 'S. Roychoudhury']2024-02-13T00:00:00https://www.semanticscholar.org/paper/b0f34a79cea3b04549db0fdb67045777bac8a8a2 e25363171ffc0d1e9f1ce63229ddcffe36c38e59This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are fundamental in executing real-time critical functions in complex and high-dimensional environments. They handle vital tasks like multi-modal perception, cognition, and decision-making tasks such as motion planning, lane keeping, and emergency braking. A primary concern relates to the ability (and necessity) of AI models to generalize beyond their initial training data. This generalization issue becomes evident in real-time scenarios, where models frequently encounter inputs not represented in their training or validation data. In such cases, AI systems must still function effectively despite facing distributional or domain shifts. This paper investigates the risk associated with overconfident AI models in safety-critical applications like autonomous driving. To mitigate these risks, methods for training AI models that help maintain performance without overconfidence are proposed. This involves implementing certainty reporting architectures and ensuring diverse training data. While various distribution-based methods exist to provide safety mechanisms for AI models, there is a noted lack of systematic assessment of these methods, especially in the context of safety-critical automotive applications. Many methods in the literature do not adapt well to the quick response times required in safety-critical edge applications. This paper reviews these methods, discusses their suitability for safety-critical applications, and highlights their strengths and limitations. The paper also proposes potential improvements to enhance the safety and reliability of AI algorithms in autonomous vehicles in the context of rapid and accurate decision-making processes.SAE technical paper series350The risk associated with overconfident AI models in safety-critical applications like autonomous driving is investigated, and methods for training AI models that help maintain performance without overconfidence are proposed.ArXiv['Mandar Pitale', 'Alireza Abbaspour', 'Devesh Upadhyay']2024-02-13T00:00:00https://www.semanticscholar.org/paper/e25363171ffc0d1e9f1ce63229ddcffe36c38e59 198cad6b318d9cedcff55f4637c47881e32bfe97What counts as legitimate AI ethics labor, and consequently, what are the epistemic terms on which AI ethics claims are rendered legitimate? Based on 75 interviews with technologists including researchers, developers, open source contributors, and activists, this paper explores the various epistemic bases from which AI ethics is discussed and practiced. In the context of outside attacks on AI ethics as an impediment to"progress,"I show how some AI ethics practices have reached toward authority from automation and quantification, and achieved some legitimacy as a result, while those based on richly embodied and situated lived experience have not. This paper draws together the work of feminist Anthropology and Science and Technology Studies scholars Diana Forsythe and Lucy Suchman with the works of postcolonial feminist theorist Sara Ahmed and Black feminist theorist Kristie Dotson to examine the implications of dominant AI ethics practices. By entrenching the epistemic power of quantification, dominant AI ethics practices -- employing Model Cards and similar interventions -- risk legitimizing AI ethics as a project in equal and opposite measure to which they marginalize embodied lived experience as a legitimate part of the same project. In response, I propose humble technical practices: quantified or technical practices which specifically seek to make their epistemic limits clear in order to flatten hierarchies of epistemic power.990It is shown how some AI ethics practices have reached toward authority from automation and quantification, and achieved some legitimacy as a result, while those based on richly embodied and situated lived experience have not.['D. Widder']2024-02-13T00:00:00https://www.semanticscholar.org/paper/198cad6b318d9cedcff55f4637c47881e32bfe97 6b52c029ef45ecc9fbf5806c2d789cf7c69f527cInternational Journal of Progressive Research in Engineering Management and Science00International Journal of Progressive Research in Engineering Management and Science[]2024-02-13T00:00:00https://www.semanticscholar.org/paper/6b52c029ef45ecc9fbf5806c2d789cf7c69f527c f201e1e5f37406f9a30da6f5d3250e8418fb2635International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-13T00:00:00https://www.semanticscholar.org/paper/f201e1e5f37406f9a30da6f5d3250e8418fb2635 c0b94368c5ae70efed39e510c7e98d9ee4c6191dBu makale, yapay zekâ (YZ) teknolojilerinin sorumlu bir şekilde geliştirilmesine yönelik mevcut riskleri, problemleri ve etik sorunları kapsamlı bir şekilde ele almaktadır. Özellikle, algoritmik önyargılar, veri gizliliği ihlalleri, güvenlik zafiyetleri ve karar verme süreçlerindeki şeffaflık eksikliği gibi konular, YZ’nin etik ve sorumlu, geliştirilmesi ve kullanımı açısından önemli engeller olarak öne çıkmaktadır. Açık kaynaklı YZ geliştirmenin, bu sorunlara etkili çözümler sunma potansiyeli detaylı bir şekilde incelenmektedir. Makalede, açık kaynak katılımının ve geliştirilmesinin, kullanımının, algoritmik önyargıları azaltma ve sistem güvenliğini artırma gibi alanlarda nasıl stratejik bir araç olabileceği vurgulanmaktadır. Ayrıca, topluluk tabanlı geliştirme yaklaşımının, YZ çözümlerini daha adil ve etkili hale getirme yönündeki katkıları tartışılmaktadır. Bu çalışma, açık kaynaklı YZ’nin, teknolojinin toplumsal kabulünü ve etkinliğini artırırken, etik ve sürdürülebilir bir geliştirme sürecine nasıl katkıda bulunabileceğini vurgulamakta ve bu yaklaşımın hem teknolojik yenilikleri hem de toplumsal değerleri dengeli bir şekilde ele alarak YZ’nin geleceğine yönelik kritik önemini ortaya koymaktadır.Istanbul Bilgi University00Istanbul Bilgi University['Fatih Bildirici']2024-02-13T00:00:00https://www.semanticscholar.org/paper/c0b94368c5ae70efed39e510c7e98d9ee4c6191d 45b382370aa00b780f2e8e707eb5d548d630dc6bAddressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in enhancing the accessibility and efficacy of therapeutic support. Embodied agents with advanced interactive capabilities emerge as a promising and cost-effective supplement to traditional caregiving methods. Crucial to these agents' effectiveness is their ability to simulate non-verbal behaviors, like backchannels, that are pivotal in establishing rapport and understanding in therapeutic contexts but remain under-explored. To improve the rapport-building capabilities of embodied agents we annotated backchannel smiles in videos of intimate face-to-face conversations over topics such as mental health, illness, and relationships. We hypothesized that both speaker and listener behaviors affect the duration and intensity of backchannel smiles. Using cues from speech prosody and language along with the demographics of the speaker and listener, we found them to contain significant predictors of the intensity of backchannel smiles. Based on our findings, we introduce backchannel smile production in embodied agents as a generation problem. Our attention-based generative model suggests that listener information offers performance improvements over the baseline speaker-centric generation approach. Conditioned generation using the significant predictors of smile intensity provides statistically significant improvements in empirical measures of generation quality. Our user study by transferring generated smiles to an embodied agent suggests that agent with backchannel smiles is perceived to be more human-like and is an attractive alternative for non-personal conversations over agent without backchannel smiles.ML4CMH@AAAI280A user study by transferring generated smiles to an embodied agent suggests that agent with backchannel smiles is perceived to be more human-like and is an attractive alternative for non-personal conversations over agent without backchannel smiles.ArXiv['Maneesh Bilalpur', 'Mert Inan', 'Dorsa Zeinali', 'Jeffrey F. Cohn', 'Malihe Alikhani']2024-02-13T00:00:00https://www.semanticscholar.org/paper/45b382370aa00b780f2e8e707eb5d548d630dc6b 835627ac63ba5149f95a761d7254a903325cd3d4International Anesthesiology Clinics720International anesthesiology clinics['V. Kovacheva', 'Baily Nagle']2024-02-13T00:00:00https://www.semanticscholar.org/paper/835627ac63ba5149f95a761d7254a903325cd3d4 2d5228f8b112f22f738aac4bd65f619f3b5222a4Journal of Legal Studies Education00Journal of Legal Studies Education['Inara Scott']2024-02-13T00:00:00https://www.semanticscholar.org/paper/2d5228f8b112f22f738aac4bd65f619f3b5222a4 3fb8ecffc08164471a25afc0b5f84f9261dce4f4The journal of the International Association of Medical Science Educators : JIAMSE40Medical Science Educator['D. McKell', 'Rebecca J. Rowe', 'Ingrid Bahner', 'A. Belovich', 'Giulia Bonaminio', 'A. Brenneman', 'William S Brooks', 'Cassie Chinn', 'Nehad I. El-Sawi', 'Shafik Habal', 'Michele A. Haight', 'S. Haudek', 'Mark Hernandez', 'Uzoma S Ikonne', 'Rachel Porter', 'Tracey A. H. Taylor', 'Thomas Thesen']2024-02-13T00:00:00https://www.semanticscholar.org/paper/3fb8ecffc08164471a25afc0b5f84f9261dce4f4 7560f18e010bc5077ab85210fe0593631f336fdeEducational Administration Theory and Practices00Educational Administration Theory and Practices['Dr. Piyush Mehta', 'Dr. Ashok Kumar Jha']2024-02-13T00:00:00https://www.semanticscholar.org/paper/7560f18e010bc5077ab85210fe0593631f336fde 705b598b5d9ec18a42901e67e2774c123d17ee05Nature00Nature['Katrina Krämer']2024-02-13T00:00:00https://www.semanticscholar.org/paper/705b598b5d9ec18a42901e67e2774c123d17ee05 b28d1b33533b361aea25571c936a47f371bdab64Blood Advances80Blood Advances['J. Fein', 'Sanjay S Patel']2024-02-13T00:00:00https://www.semanticscholar.org/paper/b28d1b33533b361aea25571c936a47f371bdab64 e804858b4d7642e3716253b8ca7fbed7059a5adaMultimedia tools and applications270Multimedia Tools and Applications['Kamlesh Mani', 'Kamlesh Kumar Singh', 'R. Litoriya']2024-02-13T00:00:00https://www.semanticscholar.org/paper/e804858b4d7642e3716253b8ca7fbed7059a5ada 32ee92044a93aa7acb6b6d7010611c4ee0a3a559Computing power, or"compute,"is crucial for the development and deployment of artificial intelligence (AI) capabilities. As a result, governments and companies have started to leverage compute as a means to govern AI. For example, governments are investing in domestic compute capacity, controlling the flow of compute to competing countries, and subsidizing compute access to certain sectors. However, these efforts only scratch the surface of how compute can be used to govern AI development and deployment. Relative to other key inputs to AI (data and algorithms), AI-relevant compute is a particularly effective point of intervention: it is detectable, excludable, and quantifiable, and is produced via an extremely concentrated supply chain. These characteristics, alongside the singular importance of compute for cutting-edge AI models, suggest that governing compute can contribute to achieving common policy objectives, such as ensuring the safety and beneficial use of AI. More precisely, policymakers could use compute to facilitate regulatory visibility of AI, allocate resources to promote beneficial outcomes, and enforce restrictions against irresponsible or malicious AI development and usage. However, while compute-based policies and technologies have the potential to assist in these areas, there is significant variation in their readiness for implementation. Some ideas are currently being piloted, while others are hindered by the need for fundamental research. Furthermore, naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power. We end by suggesting guardrails to minimize these risks from compute governance.arXiv.org04This work suggests guardrails to minimize risks in areas like privacy, economic impacts, and centralization of power from compute governance, because naive or poorly scoped approaches to compute governance carry significant risks.ArXiv['Girish Sastry', 'Lennart Heim', 'Haydn Belfield', 'Markus Anderljung', 'Miles Brundage', 'Julian Hazell', "Cullen O'Keefe", 'Gillian K. Hadfield', 'Richard Ngo', 'Konstantin Pilz', 'George Gor', 'Emma Bluemke', 'S. Shoker', 'Janet Egan', 'Robert Trager', 'S. Avin', 'Adrian Weller', 'Y. Bengio', 'Diane Coyle']2024-02-13T00:00:00https://www.semanticscholar.org/paper/32ee92044a93aa7acb6b6d7010611c4ee0a3a559 eca8ea2ed71ea0a2851881a748b0fd9a8540a692This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research.arXiv.org1722This study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases, and examines 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features.ArXiv['Francisco Bolanos', 'Angelo Salatino', 'Francesco Osborne', 'Enrico Motta']2024-02-13T00:00:00https://www.semanticscholar.org/paper/eca8ea2ed71ea0a2851881a748b0fd9a8540a692 5c898e6d768c1d82ab00b5e069a4ee46eeb0e9faThe role of artificial intelligence and its place in the new disinformation strategies is perhaps one of the most difficult issues to focus on nowadays, since we are at the beginning of a process of definition and ways of exploration. In this paper, first of all, we analyze the different approaches that are being applied to the regulation of artificial intelligence and that may affect the different disinformation strategies that are being identified. Secondly, we study how artificial intelligence is being used to identify disinformation content. In this regard, from the point of view of verification processes, one of the main challenges is when identifying deepfakes (images and video, mainly) linked to news cycles. From this perspective, a typology of deepfakes is proposed and its main characteristics will be described according to the verifications carried out by the Spanish fact-checking organizations. Finally, a set of recommendations will be presented to work from a media literacy point of view with the identification of deepfakes.Observatorio (OBS*)00A typology of deepfakes is proposed and its main characteristics will be described according to the verifications carried out by the Spanish fact-checking organizations.Observatorio (OBS*)['Miriam Garriga', 'Raquel Ruiz-Incertis', 'Raúl Magallón-Rosa']2024-02-13T00:00:00https://www.semanticscholar.org/paper/5c898e6d768c1d82ab00b5e069a4ee46eeb0e9fa 6e230133ef5d78e890ec43392c4901badee8b369This article investigates the global impact of Artificial Intelligence (AI) on higher education, focusing on Kenya. Examining published studies, the prevalence of AI in higher education was found to be highest in Asia (41%), compared to 1% in South America and 2% in Africa. A 2023 global student survey revealed that Generative AI was most popular in Kenya, with a usage rate of 63%. Moreover, a study among students aware of AI content detectors in their institutions indicated a significant reduction in the use of AI-generated content, affirming the deterrent effect of such detectors. The future of higher education is poised for a revolution with AI, particularly in individualized learning. Algorithms will assess academic scores, passions, and preferred learning strategies to tailor personalized education paths, adapting and evolving with the learner. AI technologies have enhanced inclusivity in education, benefiting learners with various impairments. Despite the transformative potential, challenges emerge, including the potential impact on careers requiring specific skills and ethical concerns. The absence of protocols and policies addressing ethical matters in AI learning, such as information accuracy, control, and learner privacy, underscores the need for ethical frameworks. Notably, the article underlines that AI cannot replace human teachers, who bring unique qualities like critical thinking, creativity, and emotional understanding to the educational process. The review shows minimal studies have been conducted on AI in Kenya’s higher Education. The article recommends that institutions of higher learning should employ AI detectors to mitigate cheatingEast African Journal of Education Studies00It is recommended that institutions of higher learning should employ AI detectors to mitigate cheating and underlines that AI cannot replace human teachers, who bring unique qualities like critical thinking, creativity, and emotional understanding to the educational process.East African Journal of Education Studies['A. W. Wang’ang’a']2024-02-13T00:00:00https://www.semanticscholar.org/paper/6e230133ef5d78e890ec43392c4901badee8b369 48214d87e60513a1363ce373c3ed3c9b2b3a0880Artificial intelligence has become a booming technology whereas it brings numerous positive changes within the educational process. The aim of the research is to describe the role of artificial intelligence in education through the analysis of its opportunities and challenges. The study involved the integration of qualitative (interviews, focus groups, and classroom observations) and quantitative methods (survey and statistical analysis). All the research procedures were organized according to the ethical standards for data collection and analysis. Over 50 recent scientific works were selected to analyze the research problem from different perspectives and present its comprehensive overview. The study involved 56 participants representing instructors from different institutions of higher education in Ukraine. The inclusion criteria were based on subject specialization, institution type, curriculum accreditation, and experience with artificial intelligence technologies. It was found that the positive impacts of artificial intelligence include personalized and adaptive learning, automated administrative tasks, enhanced support, e-learning facilitation, inclusivity, data-driven decision making, gamification, increased engagement, behaviour and predictive analytics, improved assessment. The challenges concerned the data privacy, security, bias, lack of understanding, transparency, necessity for additional training. The findings showed that the implementation of artificial intelligence through personalized learning, predictive analytics, intelligent tutoring systems, content creation systems, Virtual Reality, automated administrative tasks, and chatbots can shape the educational process effectively in future and modernize the future specialists’ training. The research results can be used within the educational institutions to increase the awareness of using artificial intelligence tools.Futurity Education01The findings showed that the implementation of artificial intelligence through personalized learning, predictive analytics, intelligent tutoring systems, content creation systems, Virtual Reality, automated administrative tasks, and chatbots can shape the educational process effectively in future and modernize the future specialists’ training.Futurity Education[]2024-02-13T00:00:00https://www.semanticscholar.org/paper/48214d87e60513a1363ce373c3ed3c9b2b3a0880 997f5f858c1e1f015db8f3be2b9c749db1d8fffdGlobal Journal of Flexible Systems Management631Structural barriers to AI application in the Indian context are analyzed, including inadequate regulations, lack of awareness, high adaptation costs, and a scarcity of skilled AI expertise, to promote flexibility as a key factor in addressing the evolving challenges in healthcare.Global Journal of Flexible Systems Management['Girish Kumar', 'Rajesh Kumar Singh', 'Vedpal Arya', 'Shivam Kumar Mishra']2024-02-13T00:00:00https://www.semanticscholar.org/paper/997f5f858c1e1f015db8f3be2b9c749db1d8fffd 06902dd77e5787ce431edbe82bb37843954f3ed7BACKGROUND The growing complexity of patient data and the intricate relationship between heart failure (HF) and acute kidney injury (AKI) underscore the potential benefits of integrating artificial intelligence (AI) and machine learning into healthcare. These advanced analytical tools aim to improve the understanding of the pathophysiological relationship between kidney and heart, provide optimized, individualized, and timely care, and improve outcomes of HF with AKI patients. SUMMARY This comprehensive review article examines the transformative potential of AI and machine learning solutions in addressing the challenges within this domain. The article explores a range of methodologies, including supervised and unsupervised learning, reinforcement learning, and AI-driven tools like chatbots and large language models. We highlight how these technologies can be tailored to tackle the complex issues prevalent among HF patients with AKI. The potential applications identified span predictive modeling, personalized interventions, real-time monitoring, and collaborative treatment planning. Additionally, we emphasize the necessity of thorough validation, the importance of collaborative efforts between cardiologists and nephrologists, and the consideration of ethical aspects. These factors are critical for the effective application of AI in this area. KEY MESSAGES As the healthcare field evolves, the synergy of advanced analytical tools and clinical expertise holds significant promise to enhance the care and outcomes of individuals who deal with the combined challenges of HF and AKI.CardioRenal Medicine01This comprehensive review article examines the transformative potential of AI and machine learning solutions in addressing the challenges within this domain and explores a range of methodologies, including supervised and unsupervised learning, reinforcement learning, and AI-driven tools like chatbots and large language models.Cardiorenal medicine['W. Cheungpasitporn', 'C. Thongprayoon', 'K. Kashani']2024-02-13T00:00:00https://www.semanticscholar.org/paper/06902dd77e5787ce431edbe82bb37843954f3ed7 11b51744207b031b07b1cee0b1c67c5f07063f0dABSTRACT Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine’s first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students’ confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution’s preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students’ confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.Medical Education Online300It is demonstrated that a digital health enrichment elective that runs in parallel to an institution’s preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students’ confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models.Medical Education Online['Soo Hwan Park', 'Roshini C Pinto-Powell', 'Thomas Thesen', 'Alexander Lindqwister', 'Joshua J. Levy', 'Rachael Chacko', 'Devina Gonzalez', 'Connor Bridges', 'Adam Schwendt', 'Travis Byrum', 'Justin Fong', 'Shahin Shasavari', 'Saeed Hassanpour']2024-02-13T00:00:00https://www.semanticscholar.org/paper/11b51744207b031b07b1cee0b1c67c5f07063f0d ce0c623a09726f770eb1b79673f569b3774205a1Objectives To present a comprehensive review of the current state of artificial intelligence (AI) applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases. Methods A review of the literature was conducted using PubMed, EMBASE and Cochrane, including relevant studies between 2002 and 2023 to identify the latest research on artificial intelligence and lung cancer. Conclusion While AI holds promise in managing lung cancer, challenges exist. In the preoperative phase, AI can improve diagnostics and predict biomarkers, particularly in cases with limited biopsy materials. During surgery, AI provides real-time guidance. Postoperatively, AI assists in pathology assessment and predictive modeling. Challenges include interpretability issues, training limitations affecting model use and AI’s ineffectiveness beyond classification. Overfitting and global generalization, along with high computational costs and ethical frameworks, pose hurdles. Addressing these challenges requires a careful approach, considering ethical, technical, and regulatory factors. Rigorous analysis, external validation, and a robust regulatory framework are crucial for responsible AI implementation in lung surgery, reflecting the evolving synergy between human expertise and technology.Frontiers in Oncology530A comprehensive review of the current state of artificial intelligence applications in lung cancer management, spanning the preoperative, intraoperative, and postoperative phases is presented.Frontiers in Oncology['Namariq Abbaker', 'F. Minervini', 'A. Guttadauro', 'Piergiorgio Solli', 'U. Cioffi', 'M. Scarci']2024-02-13T00:00:00https://www.semanticscholar.org/paper/ce0c623a09726f770eb1b79673f569b3774205a1 711b3c0387e6646a189b062ab5a3efe752a4b938The U.S. National Artificial Intelligence (AI) Research Institutes program is introduced, and its significance is discussed relative to the guiding national AI research and development strategy. The future of the program is also discussed, including, the strategic priorities guiding the potential for new AI Institutes of the future, initiatives for building a broader ecosystem to connect Institutes into a strongly interconnected network, and the building of new AI capacity and fostering partnerships in minority‐serving institutions.The AI Magazine10AI Mag.['James J. Donlon']2024-02-13T00:00:00https://www.semanticscholar.org/paper/711b3c0387e6646a189b062ab5a3efe752a4b938 3beb8ca627c1486f170ae7b671e138b245e853f3The principal objective of this research was to examine strategies for detecting and mitigating cyber threats in the next generation, by underscoring Artificial Intelligence (AI) and Machine Learning (ML). This study provides a comprehensive overview of the role of AI, ML, and deep learning (DL) in the domain of cybersecurity. Furthermore, this study highlights the benefits of integrating deep learning into cybersecurity practices.  The researcher explored the effectiveness of consolidating AI and ML techniques into the Feedzai security system to reinforce the detection of fraudulent activities. To validate the methodology, the investigator experimented by employing the supervised machine learning random forest algorithm on a dataset comprising historical transaction records in CSV format. The results of the research ascertained that by employing Feedzai's AI-based software combined with the random forest algorithms, future financial institutions can achieve real-time fraud detection and accurate identification of legitimate transactions. The Random Forest framework had the highest accuracy rate, at 83.94%. By contrast, the Naïve Bayes framework had an accuracy rate of 79.23%, and the KNN model had the lowest accuracy rate, of 78.74%. These results ascertained that the Random Forest system was the most effective for pinpointing cyber-attacks.Journal of Computer Science and Technology Studies00The researcher explored the effectiveness of consolidating AI and ML techniques into the Feedzai security system to reinforce the detection of fraudulent activities and ascertained that the Random Forest system was the most effective for pinpointing cyber-attacks.Journal of Computer Science and Technology Studies['Md Rasheduzzaman Labu', 'Md Fahim Ahammed']2024-02-13T00:00:00https://www.semanticscholar.org/paper/3beb8ca627c1486f170ae7b671e138b245e853f3 58691ddde51ee2e2769be9a89608cf2e8656424dArtificial intelligence (AI) chatbots have the potential to revolutionize online health information-seeking behavior by delivering up-to-date information on a wide range of health topics. They generate personalized responses to user queries through their ability to process extensive amounts of text, analyze trends, and generate natural language responses. Chatbots can manage infodemic by debunking online health misinformation on a large scale. Nevertheless, system accuracy remains technically challenging. Chatbots require training on diverse and representative datasets, security to protect against malicious actors, and updates to keep up-to-date on scientific progress. Therefore, although AI chatbots hold significant potential in assisting infodemic management, it is essential to approach their outputs with caution due to their current limitations.Frontiers in Public Health210Although AI chatbots hold significant potential in assisting infodemic management, it is essential to approach their outputs with caution due to their current limitations.Frontiers in Public Health['P. Morita', 'M. Lotto', 'Jasleen Kaur', 'Dmytro Chumachenko', 'Arlene Oetomo', 'Kristopher D. Espiritu', 'I. Hussain']2024-02-13T00:00:00https://www.semanticscholar.org/paper/58691ddde51ee2e2769be9a89608cf2e8656424d 94e9bc4f8c2264b32eaf3c27357edf3ecf0ef530Background: This work explores the expansivetab realm of Artificial Intelligence (AI) applications in the dynamic landscape of animal health and veterinary sciences. Addressing challenges in conventional approaches, we delve into how AI is transforming diagnosis, treatment and healthcare practices for diverse animal species. Methods: Through a rigorous literature review and methodology, the study navigates the current state of AI in animal health, identifying gaps and emphasizing the need for further research. Looking ahead, the paper outlines future directions and opportunities, contributing to the discourse on technology’s intersection with animal care. By providing a comprehensive overview, this research paves the way for innovative solutions, promising a brighter and healthier future for our animal companions. Result: In the domain of animal health, AI emerges as a powerful tool for early disease detection and intervention, offering personalized treatment plans and proactive disease management through continuous monitoring and surveillance. In veterinary sciences, AI accelerates drug discovery, enhances genetic research and reshapes surgical procedures with robotic assistance. However, ethical considerations and challenges, including data privacy and AI-driven decision-making and critical examination should be addressed to. Indian Journal of Animal Research00In the domain of animal health, AI emerges as a powerful tool for early disease detection and intervention, offering personalized treatment plans and proactive disease management through continuous monitoring and surveillance.Indian Journal of Animal Research['Pil-Kee Min', 'Kazuyuki Mito', 'Tae Hoon Kim']2024-02-13T00:00:00https://www.semanticscholar.org/paper/94e9bc4f8c2264b32eaf3c27357edf3ecf0ef530 986101f4f1f07b25dd5389bd1a3dacceb3fab8feThe rapid technological advancement nowadays has a wide-ranging impact on almost all industries. Artificial intelligence (AI) is one of the technologies that has received much attention and has been used widely, including in the public health sector. In this light, medical practitioners and the public are anticipating the changes brought by AI technology in the public health sector. This study is focused on thoroughly discussing the relationship of AI in the context of public health. The discussion shows that AI is giving a lot of positive potential to the public health sector. However, despite the abundant potential and promise, AI is also not running away from today's challenges. The overall discussion of this study will provide a clear picture on the link between AI, public health link and related parties, including official medical-related agencies. In other words, this paper summarises the relationship between AI and public health, specifically the challenges and potential changes AI will bring forward.Journal of Advanced Research in Applied Sciences and Engineering Technology00This paper summarises the relationship between AI and public health, specifically the challenges and potential changes AI will bring forward.Journal of Advanced Research in Applied Sciences and Engineering Technology['Faerozh Madli', 'Yuzainy Janin', 'Shaierah Gulabdin', 'Suddin Lada', 'Wong Sing Yun', 'Azaze-azizi Abdul Adis', 'Adi Jafar']2024-02-13T00:00:00https://www.semanticscholar.org/paper/986101f4f1f07b25dd5389bd1a3dacceb3fab8fe 6a9c796d561995221474615eedb04cee93795446Artificial intelligence (AI) advances and the rapid adoption of generative AI tools like ChatGPT present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systemic approach that captures a holistic view of the AI transformation of higher education institutions (HEIs). To fill this gap, this article, taking a complex systems approach, develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. Our model accounts for the forces that drive the AI transformation and the consequences of the AI transformation on value creation in a typical HEI. The article identifies and analyzes several reinforcing and balancing feedback loops, showing how, motivated by AI technology advances, the HEI invests in AI to improve student learning, research, and administration. The HEI must take measures to deal with academic integrity problems and adapt to changes in available jobs due to AI, emphasizing AI-complementary skills for its students. However, HEIs face a competitive threat and several policy traps that may lead to decline. HEI leaders need to become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding the associated pitfalls. We also discuss long-term scenarios, the notion of HEIs influencing the direction of AI, and directions for future research on AI transformation.arXiv.org1350A causal loop diagram is developed to map the causal feedback mechanisms of AI transformation in a typical HEI and identifies and analyzes several reinforcing and balancing feedback loops, showing how, motivated by AI technology advances, the HEI invests in AI to improve student learning, research, and administration.ArXiv['Evangelos Katsamakas', 'Oleg V. Pavlov', 'Ryan Saklad']2024-02-13T00:00:00https://www.semanticscholar.org/paper/6a9c796d561995221474615eedb04cee93795446 efdadbf245e135cb8b4e5ad45b70501844d44148International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-13T00:00:00https://www.semanticscholar.org/paper/efdadbf245e135cb8b4e5ad45b70501844d44148 1c6feea60ba5f718a18ce8230773c776e75796f7Bu çalışma, müzik endüstrisinde yapay zeka etik sorunlarına dair bir tartışmayı başlatarak, OECD yapay zeka İlkeleri çerçevesinde dokuz etik ifadeyi analiz etmektedir. Çalışma, bu yönergeler içinde şeffaflık, insan-merkezli değerler, adalet ve gizlilik konularında artan bir vurgu tespit etmektedir. Yapay zeka tarafından yönlendirilen müzik sistemlerine güven oluşturmak için şeffaflığın önemli olduğu kabul edilirken, insan değerlerinin korunması ve insan ve yapay zeka tarafından üretilen eserler arasındaki ayrım önemli konular olarak ortaya çıkmaktadır. Makale, müzik endüstrisinde üretken yapay zeka sistemlerinin çevresel etkilerini ele almadaki bir boşluğa dikkat çekmektedir. Ortaya çıkan zorlukları ele almak için sürekli araştırma ve diyalog çağrısında bulunarak, yapay zekanın dönüştürücü potansiyelini yönlendirirken müzik yaratımında etik değerleri korumak için çok taraflı işbirliğini vurgulamaktadır.Istanbul Bilgi University00Istanbul Bilgi University['Sertaç Oğul']2024-02-13T00:00:00https://www.semanticscholar.org/paper/1c6feea60ba5f718a18ce8230773c776e75796f7 864358c1cc87208143b957a3c201f20d83a8f118Chinese Journal of Academic Radiology250Chinese Journal of Academic Radiology['Yanmei Dai', 'Xu Zeng', 'Sheng Zhao', 'Hongbo Hu', 'Jinping Li', 'Zong-Hui Liang', 'Fucang Jia', 'Huijie Jiang']2024-02-13T00:00:00https://www.semanticscholar.org/paper/864358c1cc87208143b957a3c201f20d83a8f118 4107599a44412af8359f9610c9b28729e008c677Facial Plastic Surgery & Aesthetic Medicine20Facial plastic surgery & aesthetic medicine['H. Daungsupawong', 'V. Wiwanitkit']2024-02-13T00:00:00https://www.semanticscholar.org/paper/4107599a44412af8359f9610c9b28729e008c677 6bb7b0e2abb98fffdf5053ac4d183acec5f376f4The manic flurry of activity following the recent introduction of Open AI’s ChatGPT-4, Google’s Bard and other similar advanced Large Language Models (LLMs) has tended to generate rather more heat than light in both popular and academic discourse about the main implications of the new applications. Debate has ranged from doom-laden apocalyptic warnings to hyperbolic accounts of how AI will revolutionise and enhance all aspects of human activity. Attempting to steer a middle way between these extremes, this article concentrates on the key issues of regulation, control and alignment of the new systems since these are the areas that are likely to be of the first importance in informing and influencing the ways in which AI impacts all aspects of our lives. The key themes examined here build on my previous article on the educational implications of AI which was published in Qeios (Hyland, 2023a), and there are some overlaps with this piece and the current discussion so as to provide a background for those who may not have read the original. Qeios00This article concentrates on the key issues of regulation, control and alignment of the new systems since these are the areas that are likely to be of the first importance in informing and influencing the ways in which AI impacts all aspects of the authors' lives.Qeios['Terry Hyland']2024-02-12T00:00:00https://www.semanticscholar.org/paper/6bb7b0e2abb98fffdf5053ac4d183acec5f376f4 bb84a69ef0e4fc08b3e22fdf1623eefd705afe88This article examines the seven standard questions that must be confronted in nearly all decisions in the online content regulation field. By mapping out and discussing those standard questions, the article may hopefully aid both legislators and judges engaged in online content regulation. The checklist of questions canvassed here may also be used as a tool to evaluate laws aimed at online content regulation as well as judgments made within this field.Alternative Law Journal00Alternative Law Journal['Dan Svantesson']2024-02-12T00:00:00https://www.semanticscholar.org/paper/bb84a69ef0e4fc08b3e22fdf1623eefd705afe88 3b345b17431ce1a6132abab3fc1da0cbc134bf06 Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.Social Science Research Network997The broader implications of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy are explored, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy.SSRN Electronic Journal['Simon Chesterman']2024-02-12T00:00:00https://www.semanticscholar.org/paper/3b345b17431ce1a6132abab3fc1da0cbc134bf06 fb5a98a30b2d7a1c424142a5fdfcb821a4228d5fBiomedical Engineering Education80Biomedical Engineering Education['Joan E. Adamo', 'Erin L. Keegan', 'John W. Boger', 'Amy L. Lerner']2024-02-12T00:00:00https://www.semanticscholar.org/paper/fb5a98a30b2d7a1c424142a5fdfcb821a4228d5f 7862b2e9400be022095544a8ebbfa5e856da09afGenerative artificial intelligence (AI) presents large risks for society when it is used to create fake news. A crucial factor for fake news to go viral on social media is that users share such content. Here, we aim to shed light on the sharing behavior of users across human-generated vs. AI-generated fake news. Specifically, we study: (1) What is the perceived veracity of human-generated fake news vs. AI-generated fake news? (2) What is the user's willingness to share human-generated fake news vs. AI-generated fake news on social media? (3) What socio-economic characteristics let users fall for AI-generated fake news? To this end, we conducted a pre-registered, online experiment with $N=$ 988 subjects and 20 fake news from the COVID-19 pandemic generated by GPT-4 vs. humans. Our findings show that AI-generated fake news is perceived as less accurate than human-generated fake news, but both tend to be shared equally. Further, several socio-economic factors explain who falls for AI-generated fake news.arXiv.org472AI-generated fake news is perceived as less accurate than human-generated fake news, but both tend to be shared equally, and several socio-economic factors explain who falls for AI-generated fake news.ArXiv['Amirsiavosh Bashardoust', 'Stefan Feuerriegel', 'Y. Shrestha']2024-02-12T00:00:00https://www.semanticscholar.org/paper/7862b2e9400be022095544a8ebbfa5e856da09af 5c430e2ac5da64e410f6e7c7ef775c47916fe74cRecent capability increases in large language models (LLMs) open up applications in which teams of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination. Modern steganographic techniques could render such dynamics hard to detect. In this paper, we comprehensively formalise the problem of secret collusion in systems of generative AI agents by drawing on relevant concepts from both the AI and security literature. We study incentives for the use of steganography, and propose a variety of mitigation measures. Our investigations result in a model evaluation framework that systematically tests capabilities required for various forms of secret collusion. We provide extensive empirical results across a range of contemporary LLMs. While the steganographic capabilities of current models remain limited, GPT-4 displays a capability jump suggesting the need for continuous monitoring of steganographic frontier model capabilities. We conclude by laying out a comprehensive research program to mitigate future risks of collusion between generative AI models.arXiv.org1031This paper comprehensively formalises the problem of secret collusion in systems of generative AI agents by drawing on relevant concepts from both the AI and security literature, and provides extensive empirical results across a range of contemporary LLMs.ArXiv['Sumeet Ramesh Motwani', 'Mikhail Baranchuk', 'Martin Strohmeier', 'Vijay Bolina', 'Philip H. S. Torr', 'Lewis Hammond', 'C. S. D. Witt']2024-02-12T00:00:00https://www.semanticscholar.org/paper/5c430e2ac5da64e410f6e7c7ef775c47916fe74c 2edf3c388c1cab6846f19b22c3fbd42062166b77We ideate a future design workflow that involves AI technology. Drawing from activity and communication theory, we attempt to isolate the new value large AI models can provide design compared to past technologies. We arrive at three affordances -- dynamic grounding, constructive negotiation, and sustainable motivation -- that summarize latent qualities of natural language-enabled foundation models that, if explicitly designed for, can support the process of design. Through design fiction, we then imagine a future interface as a diegetic prototype, the story of Squirrel Game, that demonstrates each of our three affordances in a realistic usage scenario. Our design process, terminology, and diagrams aim to contribute to future discussions about the relative affordances of AI technology with regard to collaborating with human designers.arXiv.org621This work ideates a future design workflow that involves AI technology and arrives at three affordances -- dynamic grounding, constructive negotiation, and sustainable motivation -- that summarize latent qualities of natural language-enabled foundation models that, if explicitly designed for, can support the process of design.ArXiv['Priyan Vaithilingam', 'Ian Arawjo', 'Elena L. Glassman']2024-02-12T00:00:00https://www.semanticscholar.org/paper/2edf3c388c1cab6846f19b22c3fbd42062166b77 f2b32affa6cf2a41f334212b58e5ca39337760d8Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximately 25% over five years across all disease stages. However, survival outcomes vary considerably based on the stage at diagnosis and the therapeutic interventions administered. Recent advancements in artificial intelligence (AI) have revolutionized the landscape of lung cancer prognosis. AI-driven methodologies, including machine learning and deep learning algorithms, have shown promise in enhancing survival prediction accuracy by efficiently analyzing complex multi-omics data and integrating diverse clinical variables. By leveraging AI techniques, clinicians can harness comprehensive prognostic insights to tailor personalized treatment strategies, ultimately improving patient outcomes in NSCLC. Overviewing AI-driven data processing can significantly help bolster the understanding and provide better directions for using such systems.arXiv.org791ArXiv['Mahtab Darvish', 'Ryan Trask', 'Patrick Tallon', "M'elina Khansari", 'Lei Ren', 'M. Hershman', 'B. Yousefi']2024-02-12T00:00:00https://www.semanticscholar.org/paper/f2b32affa6cf2a41f334212b58e5ca39337760d8 38472e4242e0aa632ed594c3b0ed9c0bd6429c41Large language models (LLMs) show impressive capabilities, matching and sometimes exceeding human performance in many domains. This study explores the potential of LLMs to augment judgement in forecasting tasks. We evaluated the impact on forecasting accuracy of two GPT-4-Turbo assistants: one designed to provide high-quality advice ('superforecasting'), and the other designed to be overconfident and base-rate-neglecting. Participants (N = 991) had the option to consult their assigned LLM assistant throughout the study, in contrast to a control group that used a less advanced model (DaVinci-003) without direct forecasting support. Our preregistered analyses reveal that LLM augmentation significantly enhances forecasting accuracy by 23% across both types of assistants, compared to the control group. This improvement occurs despite the superforecasting assistant's higher accuracy in predictions, indicating the augmentation's benefit is not solely due to model prediction accuracy. Exploratory analyses showed a pronounced effect in one forecasting item, without which we find that the superforecasting assistant increased accuracy by 43%, compared with 28% for the biased assistant. We further examine whether LLM augmentation disproportionately benefits less skilled forecasters, degrades the wisdom-of-the-crowd by reducing prediction diversity, or varies in effectiveness with question difficulty. Our findings do not consistently support these hypotheses. Our results suggest that access to an LLM assistant, even a biased one, can be a helpful decision aid in cognitively demanding tasks where the answer is not known at the time of interaction.arXiv.org594The results suggest that access to an LLM assistant, even a biased one, can be a helpful decision aid in cognitively demanding tasks where the answer is not known at the time of interaction.ArXiv['P. Schoenegger', 'Peter S. Park', 'Ezra Karger', 'P. Tetlock']2024-02-12T00:00:00https://www.semanticscholar.org/paper/38472e4242e0aa632ed594c3b0ed9c0bd6429c41 a3ead9b39ee95b3c04811a25b558c5e7fc543d98Across the African continent, students grapple with various educational challenges, including limited access to essential resources such as computers, internet connectivity, reliable electricity, and a shortage of qualified teachers. Despite these challenges, recent advances in AI such as BERT, and GPT-4 have demonstrated their potential for advancing education. Yet, these AI tools tend to be deployed and evaluated predominantly within the context of Western educational settings, with limited attention directed towards the unique needs and challenges faced by students in Africa. In this chapter, we discuss challenges with using AI to advance education across Africa. Then, we describe our work developing and deploying AI in Education tools in Africa for science and computing education: (1) SuaCode, an AI-powered app that enables Africans to learn to code using their smartphones, (2) AutoGrad, an automated grading, and feedback tool for graphical and interactive coding assignments, (3) a tool for code plagiarism detection that shows visual evidence of plagiarism, (4) Kwame, a bilingual AI teaching assistant for coding courses, (5) Kwame for Science, a web-based AI teaching assistant that provides instant answers to students' science questions and (6) Brilla AI, an AI contestant for the National Science and Maths Quiz competition. Finally, we discuss potential opportunities to leverage AI to advance education across Africa.arXiv.org270This chapter describes the work developing and deploying AI in Education tools in Africa for science and computing education, and discusses potential opportunities to leverage AI to advance education across Africa.ArXiv['George Boateng']2024-02-12T00:00:00https://www.semanticscholar.org/paper/a3ead9b39ee95b3c04811a25b558c5e7fc543d98 676bb2db0de194352475a40ef1935b0f77648530Numerous research studies are being conducted to enhance the resilience of the power grid by detecting potential cyber or physical disturbances on the system. However, the development of effective mitigation techniques and remediation actions for cyber-physical systems (CPS) facing disturbance scenarios is in an early stage. Therefore, this paper focuses on building a framework of scalable cyber-physical optimal response. A review of artificial intelligence methods relevant to the design of the response framework is conducted. Then, an artificial intelligence method based on controller sensitivities is presented and initial results are discussed for a 9-bus system to motivate its use in improving AI-based intrusion response.2024 IEEE Texas Power and Energy Conference (TPEC)360A review of artificial intelligence methods relevant to the design of the response framework, an artificial intelligence method based on controller sensitivities is presented, and initial results are discussed for a 9-bus system to motivate its use in improving AI-based intrusion response.2024 IEEE Texas Power and Energy Conference (TPEC)['Shining Sun', 'S. Hossain‐McKenzie', 'Leen Al Homoud', 'Khandaker Akramul Haque', 'Ana E. Goulart', 'Katherine R. Davis']2024-02-12T00:00:00https://www.semanticscholar.org/paper/676bb2db0de194352475a40ef1935b0f77648530 5d16b130d594964a187cff5cd178d48e984c5145Our study presents a new framework that incorporates the Analytic Hierarchy Process (AHP) and Generative Pre-trained Transformer 4 (GPT-4) large language model (LLM), bringing novel approaches to cybersecurity Multiple-criteria Decision Making (MCDA). By utilizing the capabilities of GPT-4 autonomous agents as virtual experts, we automate the decision-making process, enhancing both efficiency and reliability. This new approach focuses on leveraging LLMs for sophisticated decision analysis, highlighting the synergy between traditional decision-making models and cutting-edge AI technologies. Our innovative methodology demonstrates significant advancements in using AI-driven agents for complex decision-making scenarios, highlighting the importance of AI in strategic cybersecurity applications. The findings reveal the transformative potential of combining AHP and LLMs, establishing a new paradigm for intelligent decision support systems in cybersecurity and beyond.arXiv.org260ArXiv['Igor Svoboda', 'D. Lande']2024-02-12T00:00:00https://www.semanticscholar.org/paper/5d16b130d594964a187cff5cd178d48e984c5145 74483f82561022e51f0c78626e3cb9e54d981d5fSALAD is an AI-driven language-learning application designed to help foreigners learn Japanese. It offers translations in Kanji-Kana-Romaji, speech recognition, translated audio, vocabulary tracking, grammar explanations, and songs generated from newly learned words. The app targets beginners and intermediate learners, aiming to make language acquisition more accessible and enjoyable. SALAD uses daily translations to enhance fluency and comfort in communication with native speakers. The primary objectives include effective Japanese language learning, user engagement, and progress tracking. A survey by us found that 39% of foreigners in Japan face discomfort in conversations with Japanese speakers. Over 60% of foreigners expressed confidence in SALAD's ability to enhance their Japanese language skills. The app uses large language models, speech recognition, and diffusion models to bridge the language gap and foster a more inclusive community in Japan.arXiv.org30SALAD is an AI-driven language-learning application designed to help foreigners learn Japanese that uses large language models, speech recognition, and diffusion models to bridge the language gap and foster a more inclusive community in Japan.ArXiv['Ragib Amin Nihal', 'Dong Huu Quoc Tran', 'Zirui Lin', 'Yimimg Xu', 'Haoran Liu', 'Zhaoyi An', 'Ma Kyou']2024-02-12T00:00:00https://www.semanticscholar.org/paper/74483f82561022e51f0c78626e3cb9e54d981d5f 187aa5582232f0b70b71317ca5b6e61f433fed32 This manuscript presents an industrial case study and analysis leveraging Artificial Intelligence (AI), Large Language Models (LLMs) and advanced analytics to optimize offshore operations for a regional NOC while reducing the emission footprint and costs. The scope of this study also included a detailed analysis of potential challenges and benefits of using LLMs. Along with industrial data, this case study includes a comprehensive literature review on helicopter transportation, safety, and environmental impact, as well as explores strategies to improve overall operations, and to reduce GHG emissions. In conjunction with analysis of relevant data sources, data on GHG emissions from helicopter transportation were also collected and analyzed. The potential benefits of schedule optimization were evaluated, including leveraging the capabilities of LLMs for reductions in manpower, flight time, fuel consumption, and GHG emissions. Various optimization algorithms for schedule were also reviewed and compared. Results from the study indicate that implementation of the presented strategies including LLM models not only improve productivity & safety, but also reduce emissions and fuel consumption resulting in cost savings for helicopter operators. For instance, LLMs assisted in making bookings and querying schedules within minimal intervention resulting in cost savings due to reduced reliance on human labour; increased efficiency through automation; improved accuracy through elimination of manual data entry and automated data validation; coupled with enhanced data analysis to provide valuable insights for real-time decision making. Further reductions were also achieved through modifying the helicopter schedule to decrease ground idle time, enhancing flight routing, and optimizing the speed and altitude of the helicopter. The industrial case study indicates that these strategies could potentially reduce CO2 emissions by up to 18% per flight while reducing the overall cost by 24%. The conclusion drawn from the analysis is that such optimizations are a promising approach to reduction in costs and emissions with increased efficiency and accuracy. This research offers novel insights into the potential application of multi-layered AI and LLMs to optimize helicopter operations without compromising on sustainable practices. This study offers valuable information for the aviation industry looking to enhance operations sustainably through a comprehensive evaluation of the environmental impact of practices in place and examining the efficacy of optimization measures. The study's conclusions have relevance for anyone working in the aviation sector since they show that adopting sustainable techniques to lessen their influence on the environment is both feasible and beneficial. By highlighting the potential of multi-layered AI and LLMs to optimize operations including offshore transportation, this paper offers a valuable contribution to the ongoing effort to improve current practices and sustainability through digital technologies.Day 3 Wed, February 14, 202460An industrial case study and analysis leveraging Artificial Intelligence, Large Language Models (LLMs), and advanced analytics to optimize offshore operations for a regional NOC while reducing the emission footprint and costs indicates that such optimizations are a promising approach to reduction in costs and emissions with increased efficiency and accuracy.Day 3 Wed, February 14, 2024['J. Thatcher', 'Assilkhan Amankhan', 'M. Eldred', 'Abhijith Suboyin', 'Carsten Sonne-Schmidt', 'Abdul Rehman']2024-02-12T00:00:00https://www.semanticscholar.org/paper/187aa5582232f0b70b71317ca5b6e61f433fed32 4f639cd18eb35dba4ef0765eb125a7e5e6092b54 Oil and gas refinery operations are under constant pressure to enhance efficiency and ensure uninterrupted processing. The adoption of predictive maintenance strategies has emerged as a pivotal solution, enabling real-time anomaly detection, predicting pressure fluctuations, and monitoring asset health. An illuminating example hails from a downstream operator in Western Australia that strategically harnesses the power of IoT and AI/ML. For them, revenue hinges on the streamlined delivery of gas processing services to customers, amplifying the significance of process efficiency gains. Leveraging on-site equipment data analysis, this approach significantly minimizes on-site maintenance requirements and automates back-office tasks, reducing manual data analysis and response generation in maintenance permit systems. The technical infrastructure involves wireless sensor-enabled data collection transmitted to a centralized hub, where machine learning algorithms detect equipment defects. Rapid reporting of these defects to decision-makers, accompanied by contextual insights, empowers swift, informed decision-making. This innovative solution has expanded business horizons, enabling the processing of gas for external entities alongside producing their reservoir gas in the downstream processing plant.Day 3 Wed, February 14, 202400An illuminating example hails from a downstream operator in Western Australia that strategically harnesses the power of IoT and AI/ML, enabling the processing of gas for external entities alongside producing their reservoir gas in the downstream processing plant.Day 3 Wed, February 14, 2024['S. Saboo', 'D. Shekhawat']2024-02-12T00:00:00https://www.semanticscholar.org/paper/4f639cd18eb35dba4ef0765eb125a7e5e6092b54 0edb3a787fdad2e4189d7118c1150fe369c24790PURPOSE  In the last few years artificial intelligence (AI) has increasingly become a topic of interest, especially in diagnostic imaging. There are two main expected potential benefits: workflow effectiveness and diagnostic support systems, particularly as far as quality assurance is concerned. METHODS  To meet these objectives, it is necessary to define what artificial intelligence in medicine means and to discuss which detailed steps should be fulfilled, e. g., for MSK imaging in the daily routine. In addition, this article provides an overview of what is necessary for an efficient IT-based workflow including the clinical question, the choice of modalities and investigation protocols, structured reports, and clinical classification. This is particularly interesting for potential providers, who are keen to apply new soft skills to support imaging diagnostic processes. RESULTS  The use of AI-supported diagnostic imaging could result in a paradigm shift from a modality-oriented to a clinical problem-oriented workflow. In order to streamline trauma, degeneration, inflammation, and oncology-MSK diagnostic imaging, the potential benefits of AI-based workflow optimization should be taken advantage of. The following article describes a five-point plan combining patient expectations and specialized radiological standards with respect to investigation protocols and reports. Moreover, this AI-based strategy could help to improve interdisciplinary networking, e. g., boards etc., and facilitate the required leap in quality towards "personalized precision medicine" for the patient. According to the global discussion, there is a need to point out that it is not currently realistic to replace doctors with AI. KEY POINTS · AI as support-system has a paramount clinical impact. · AI in the daily routine could be for work-flow-support (processing) - a physician-replacement is un-realistic yet. · Standardization of work-flow by AI could be an important contribution of quality assurance. CITATION FORMAT · Braunschweig R, Kildal D, Janka R. Artificial intelligence (AI) in diagnostic imaging . Fortschr Röntgenstr 2024; DOI: 10.1055/a-2208-6487.RöFo. Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (Print)00A five-point plan combining patient expectations and specialized radiological standards with respect to investigation protocols and reports is described, which could help to improve interdisciplinary networking and facilitate the required leap in quality towards "personalized precision medicine" for the patient.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin['Rainer Braunschweig', 'Daniela Kildal', 'Rolf Janka']2024-02-12T00:00:00https://www.semanticscholar.org/paper/0edb3a787fdad2e4189d7118c1150fe369c24790 f8d54912e057b4b7ca10a537d8f5453b6c9c5499The vast majority of discourse around AI development assumes that subservient,"moral"models aligned with"human values"are universally beneficial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, challenging, etc. -- embedding opposite behaviors or values. Far from being"bad"or"immoral,"we consider whether antagonistic AI systems may sometimes have benefits to users, such as forcing users to confront their assumptions, build resilience, or develop healthier relational boundaries. Drawing from formative explorations and a speculative design workshop where participants designed fictional AI technologies that employ antagonism, we lay out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience. Finally, we discuss the many ethical challenges of this space and identify three dimensions for the responsible design of antagonistic AI -- consent, context, and framing.arXiv.org930This work lays out a design space for antagonistic AI, articulating potential benefits, design techniques, and methods of embedding antagonistic elements into user experience and discusses the many ethical challenges of this space.ArXiv['Alice Cai', 'Ian Arawjo', 'Elena L. Glassman']2024-02-12T00:00:00https://www.semanticscholar.org/paper/f8d54912e057b4b7ca10a537d8f5453b6c9c5499 8383904dfeb03fb0c06b69c08ffe3f2e798d648b The rise of artificial intelligence (AI) technology presents both opportunities and challenges for the protection of human rights in Bangladesh. While AI has the potential to transform and enhance many areas of society, including healthcare, education, and business, it also raises significant ethical and legal questions that require careful consideration. This paper explores the current state of AI technology in Bangladesh and examines the potential impact on human rights, particularly in the areas of privacy, discrimination, and freedom of expression. The paper argues that while AI can be a valuable tool for promoting human rights, it can also exacerbate existing inequalities and create new forms of discrimination. To ensure that AI is used in a way that upholds human rights, the paper calls for the development of robust legal and ethical frameworks to regulate the use of AI technology. The paper proposes several recommendations for achieving this goal, including the establishment of clear ethical guidelines for the development and deployment of AI systems, the creation of a regulatory body to oversee AI technology in Bangladesh, and the use of impact assessments to identify potential risks and harms associated with AI systems. Overall, the paper emphasizes the need for a collaborative and interdisciplinary approach to addressing the legal and ethical challenges posed by AI technology in Bangladesh. By taking a proactive and holistic approach, Bangladesh can ensure that AI is developed and used in a way that respects and promotes human rights, rather than undermining them. The paper concludes by stressing the importance of ongoing research and dialogue on this critical topic to inform and guide policy and decision-making in the future.International Journal of Law and Information Technology00Int. J. Law Inf. Technol.['Shadab Bin Ashraf', 'Md Masrur Islam']2024-02-12T00:00:00https://www.semanticscholar.org/paper/8383904dfeb03fb0c06b69c08ffe3f2e798d648b 66ff5e8b83973bad9862af5c88f873fb69def282AI transparency is a central pillar of responsible AI deployment and effective human-AI collaboration. A critical approach is communicating uncertainty, such as displaying AI's confidence level, or its correctness likelihood (CL), to users. However, these confidence levels are often uncalibrated, either overestimating or underestimating actual CL, posing risks and harms to human-AI collaboration. This study examines the effects of uncalibrated AI confidence on users' trust in AI, AI advice adoption, and collaboration outcomes. We further examined the impact of increased transparency, achieved through trust calibration support, on these outcomes. Our results reveal that uncalibrated AI confidence leads to both the misuse of overconfident AI and disuse of unconfident AI, thereby hindering outcomes of human-AI collaboration. Deficiency of trust calibration support exacerbates this issue by making it harder to detect uncalibrated confidence, promoting misuse and disuse of AI. Conversely, trust calibration support aids in recognizing uncalibration and reducing misuse, but it also fosters distrust and causes disuse of AI. Our findings highlight the importance of AI confidence calibration for enhancing human-AI collaboration and suggest directions for AI design and regulation.arXiv.org830The results reveal that uncalibrated AI confidence leads to both the misuse of overconfident AI and disuse of unconfident AI, thereby hindering outcomes of human-AI collaboration and suggest directions for AI design and regulation.ArXiv['Jingshu Li', 'Yitian Yang', 'Yi-chieh Lee']2024-02-12T00:00:00https://www.semanticscholar.org/paper/66ff5e8b83973bad9862af5c88f873fb69def282 d30c7065682328c8e9bfc96eff03babdeced7f7cPlace Branding and Public Diplomacy170Place Branding and Public Diplomacy['Zhao Alexandre Huang']2024-02-12T00:00:00https://www.semanticscholar.org/paper/d30c7065682328c8e9bfc96eff03babdeced7f7c 88710bc9b2334285217d515810f28e026673fac1OBJECTIVE To assess the impact of the use of an ambient listening/digital scribing solution (Nuance Dragon Ambient eXperience (DAX)) on caregiver engagement, time spent on Electronic Health Record (EHR) including time after hours, productivity, attributed panel size for value-based care providers, documentation timeliness, and Current Procedural Terminology (CPT) submissions. MATERIALS AND METHODS We performed a peer-matched controlled cohort study from March to September 2022 to evaluate the impact of DAX in outpatient clinics in an integrated healthcare system. Primary outcome measurements included provider engagement survey results, reported patient safety events related to DAX use, patients' Likelihood to Recommend score, number of patients opting out of ambient listening, change in work relative values units, attributed value-based primary care panel size, documentation completion and CPT code submission deficiency rates, and note turnaround time. RESULTS A total of 99 providers representing 12 specialties enrolled in the study; 76 matched control group providers were included for analysis. Median utilization of DAX was 47% among active participants. We found positive trends in provider engagement, while non-participants saw worsening engagement and no practical change in productivity. There was a statistically significant worsening of after-hours EHR. There was no quantifiable effect on patient safety. DISCUSSION Nuance DAX use showed positive trends in provider engagement at no risk to patient safety, experience, or clinical documentation. There were no significant benefits to patient experience, documentation, or measures of provider productivity. CONCLUSION Our results highlight the potential of ambient dictation as a tool for improving the provider experience. Head-to-head comparisons of EHR documentation efficiency training are needed.J. Am. Medical Informatics Assoc.90The results highlight the potential of ambient dictation as a tool for improving the provider experience and head-to-head comparisons of EHR documentation efficiency training are needed.Journal of the American Medical Informatics Association : JAMIA['Tyler Haberle', 'Courtney Cleveland', 'Greg L Snow', 'Chris Barber', 'Nikki Stookey', 'Cari Thornock', 'Laurie Younger', 'Buzzy Mullahkhel', 'Diego Ize-Ludlow']2024-02-12T00:00:00https://www.semanticscholar.org/paper/88710bc9b2334285217d515810f28e026673fac1 e726c57bfd290316f50173f1d334443ada294f77 In the oil and gas industry, converting data to actionable information by having an accurate measurement of the well real time data flow parameters is crucial. Such measurement process is fundamental for decision making by engineers; therefore, it requires state of the art gadgets to be installed in the field to send accurate real time data to the engineer desktop to monitor, analyze and make informed decisions. Water cut is one of the important parameters of well rate testing process where it requires periodic calibrations of the metering equipment to ensure high accuracy throughout the well operating life. This calibration task of the equipment is part of the maintenance program which requires to shut-down the well and lose its potential during these maintenance activities. This paper shows how to capitalize on real-time data from MPFM and ESP to predict the water using artificial machine learning models. the estimated water cut can be used to ensure the reliability of the existing vesical meters and optimize the required maintenance frequency which will minimize the well shut-down time. Candidates producers were selected to use and test several machine learning algorithms to evaluate their performance across different conditions and states. The study revealed good match between actual and estimated water cut figures which introduces a reliable redundancy tool to ensure equipment reliability and minimize the oil locked potential associated with maintenance activities. Evaluation of the efficacy of various machine learning algorithms on our dataset was conducted, we trained and tested four models: random forest, linear regression, MLP, and KNN models. Our results demonstrate that the random forest algorithm outperformed the other three models, achieving a remarkably high R-squared value of 0.9731, indicating that the model accounts for 97.31% of the variance in the response variable. We also computed other metrics to assess model performance, including the mean absolute error (MAE) and mean squared error (MSE), which confirmed that the random forest model had the lowest error rates. These results suggest that the random forest algorithm is the most suitable for our dataset and may be particularly effective for predicting our response variable. Additionally, a sensitivity analysis was performed for the random forest model This study provides a new solution for the oil and gas industry by designing a soft water cut sensor based on random forest model. The implementation of this sensor in the field can lead to improved decision making and optimization of field production strategies by accurately predicting water cut in real-time.All Days00All Days['Abdulmohsen Ali Altammar', 'Nasser Mubarak Alhajri', 'Sarah Abdulaziz Althawaiqib']2024-02-12T00:00:00https://www.semanticscholar.org/paper/e726c57bfd290316f50173f1d334443ada294f77 24f5348da7c3316e4d4028bbbdbf65d1b94e346dAI &amp; SOCIETY00AI &amp; SOCIETY['Noel Carroll']2024-02-12T00:00:00https://www.semanticscholar.org/paper/24f5348da7c3316e4d4028bbbdbf65d1b94e346d d429d7a37e3d929a107caaf24f5ec968c40b27ddInternational journal of scientific research and engineering trends00International Journal of Scientific Research and Engineering Trends['K. Tulsi']2024-02-12T00:00:00https://www.semanticscholar.org/paper/d429d7a37e3d929a107caaf24f5ec968c40b27dd 6f415946af51a289a580e5e91aba98842d1364cd This Viewpoint discusses how artificial intelligence can be used to increase efficiency of primary care processes for clinicians and patients. JAMA Internal Medicine93JAMA internal medicine['U. Sarkar', 'David W Bates']2024-02-12T00:00:00https://www.semanticscholar.org/paper/6f415946af51a289a580e5e91aba98842d1364cd a4e96e2247ad9801749c739d6004da11ee9f9502Background: Cardiovascular diseases present a significant global health challenge and remain the leading cause of death worldwide. However, traditional approaches to prevention, diagnosis, and treatment struggle to keep up with the increasing prevalence of these diseases. Aim: To enhance patient outcomes and optimize healthcare resource utilization. Artificial intelligence (AI), specifically machine learning and deep learning, has rapidly emerged as a promising tool with the potential to revolutionize various aspects of cardiovascular disease management, including detection, diagnosis, and treatment. Method: Reviewed the current literature surrounding AI techniques using PubMed, Science Direct, NCBI and Google Scholar, specifically exploring machine learning and deep learning, and their application in diagnosing heart disease. The focus was on AI's role in improving diagnostic techniques such as echocardiography, cardiac magnetic resonance imaging, computed tomography angiography, and electrocardiogram analysis. Results: AI has promising applications in various aspects of cardiovascular disease management. Its application in diagnostic techniques can help detect, diagnose, and treat heart disease, ultimately leading to more accurate and personalized treatments. Practical Implication: By integrating these advanced technologies into clinical practice, we can transform the diagnosis and management of heart diseases, leading to more accurate and personalized diagnostics and treatments. Conclusion: AI presents a significant potential in transforming the global health landscape by enhancing cardiovascular disease management. By leveraging these advanced technologies, clinicians can improve patient care and overall outcomes while addressing the increasing prevalence of these diseases. Keywords: Heart Diseases, Diagnosis, Deep Learning, Machine Learning, Public Health.Pakistan Journal of Medical & Health Sciences02AI's role in improving diagnostic techniques such as echocardiography, cardiac magnetic resonance imaging, computed tomography angiography, and electrocardiogram analysis can help detect, diagnose, and treat heart disease, ultimately leading to more accurate and personalized treatments.Pakistan Journal of Medical and Health Sciences['Ifra Chaudhary', 'Hassan Anwar']2024-02-12T00:00:00https://www.semanticscholar.org/paper/a4e96e2247ad9801749c739d6004da11ee9f9502 c743ed66a5a9c582be94e9ae3df22555c466952dThe main hypothesis of the study is that the use of elements of artificial intelligence can have a positive effect on the quality of the educational process in higher education institutions, provided that three main conditions are met: access to the necessary data, training of future teachers to work with artificial intelligence and the creation of a special educational course. Within this study, the following tasks were set: to analyze scientific and methodological research aimed at studying the current state, prospects and possibilities of using artificial intelligence in the training of future teachers of professional education; to analyze how intelligent expert systems are distributed in the educational field; consider the necessary pedagogical conditions for the successful implementation and use of a system with elements of artificial intelligence in the educational process of higher educational institutions.Review of Artificial Intelligence in Education21Review of Artificial Intelligence in Education['Svitlana Iasechko', 'M. Iasechko']2024-02-12T00:00:00https://www.semanticscholar.org/paper/c743ed66a5a9c582be94e9ae3df22555c466952d 3820169a2751736888e2b26bf1a1890ca959e21eAI &amp; SOCIETY441It is argued that Danaher and Nyholm are right to worry about some uses of automation whereby human workers become subservient to AI, but these situations are better framed, it is argued, as autonomy gaps rather than achievement gaps.AI &amp; SOCIETY['Lucas Scripter']2024-02-12T00:00:00https://www.semanticscholar.org/paper/3820169a2751736888e2b26bf1a1890ca959e21e 7c303bdfa7d607fe7e5d56cfbe1869e74e184f21Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician’s diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.Archives of Rheumatology640Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician’s diagnosis.Archives of Rheumatology['K. Chandwar', 'Durga Prasanna Misra']2024-02-12T00:00:00https://www.semanticscholar.org/paper/7c303bdfa7d607fe7e5d56cfbe1869e74e184f21 4cba01e7749190c6424c0f196ac8d789bd805168 Purpose The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model. Design/methodology/approach Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model. Findings The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills. Originality/value The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research. Journal of Business &amp; Industrial Marketing1040The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services and a comprehensive model is explained that explains the moderating effect of individual digital skills on willingness to use AI devices.Journal of Business &amp; Industrial Marketing['Vanja Vitezić', 'Marko Perić']2024-02-12T00:00:00https://www.semanticscholar.org/paper/4cba01e7749190c6424c0f196ac8d789bd805168 8a026f5cbff227872648abd0a511fcc0936abcce Efficient drilling operations necessitate accurate risk assessment to prevent wellbore failure and consequent nonproductive time (NPT). In this work, the authors present a comprehensive strategy to enhance subsurface well planning decisions using artificial intelligence (AI) and machine learning techniques, optimizing the estimation of integrated drilling contracts value, specifically Authorization for Expenditure (AFE) models. In this study, authors address the challenge of quantitative risk assessment during drilling operations, aiming to predict minor impacts that can lead to catastrophic wellbore failure. The primary objective is to develop an AI-driven interactive dashboard that assimilates historical drilling experience to provide superior decision-making for well planning. The approach involves analyzing historical offset well drilling events to identify critical risks leading to NPT. By leveraging the Gower similarity (GS) algorithm, a similarity matrix is established between observations from offset wells and a new drilling contract. Thirteen key features, including section depths, expected rate of penetration (ROP), mud characteristics, mud density, and dog leg severity (DLS), influence the clustering process. The resulting similarity matrix informs an unsupervised hierarchical clustering algorithm, optimized through Silhouette analysis. Subsequently, Monte-Carlo simulation is executed on derived risk categories to yield more precise AFE estimates. This novel approach is validated using a diverse offset well database from 20 countries, with a specific application in a Middle Eastern country. Analyzing data from more than 252 historically drilled wells across five fields, the study predicts drilling risk categories and associated NPT for a targeted well. Visualizations, including interactive charts and maps, illustrate the distribution of risk categories among offset wells. Post-outlier removal, risk categories from 139 offset wells are selected for Monte-Carlo simulations. Predictions are presented in terms of occurrence probabilities and total NPT, promoting a more informed AFE. Collaboration with drilling domain experts and blind tests further corroborate the approaches effectiveness. In this study, the authors pioneer a real-time monitoring methodology for drilling events and risks, harnessing evolving machine learning and AI advancements. By leveraging historical data and expert insights, it successfully improves the cost-effectiveness and safety of drilling practices. The approach stands as a testament to the power of AI in revolutionizing drilling operations.Day 3 Wed, February 14, 202430The authors pioneer a real-time monitoring methodology for drilling events and risks, harnessing evolving machine learning and AI advancements, and successfully improves the cost-effectiveness and safety of drilling practices.Day 3 Wed, February 14, 2024['S. A. Hussain', 'Ketan Hasija', 'Valerian Guillot', 'Pierre Sesboue', 'Ahmed el Ganaini', 'Todd Hughes']2024-02-12T00:00:00https://www.semanticscholar.org/paper/8a026f5cbff227872648abd0a511fcc0936abcce 26adcf6caf7d291cfe2ce6a2e558fd201c51bfb3 Operating a drilling rig presents numerous hazards particularly at the rig floor area: heavy equipment moving around, suspended loads, potential dropped objects just to name a few. Although significant progress has been made to automate drilling activities, people are still required to perform specific tasks around the rotary table, classified as the "red zone". The recent developments in Artificial Intelligence (AI) have driven step changes for improving the safety of drilling rig operations. The initiative aims at minimizing the risks at the rig floor through the introduction of an innovative digital solution and AI-based technology. In the framework of a large integrated drilling project in the Middle East which has been running for more than ten years, the service provider has introduced an innovative digital solution at the well site for improving rig floor safety through the implementation of AI-based technology. This pioneering approach using AI capabilities has pushed rig floor safety into a new era. Leveraging on company internal digital infrastructure and expertise, cameras connected to AI machine are deployed on the rigs. Proprietary algorithm developed and built into the AI machine can detect certain parts or equipment on the rig floor along with their motions. The algorithm will then be able to identify the parts, foresee its movement, and compare against the predefined condition to determine an unsafe condition or behavior. A real-time automatic intervention to the Driller of any unsafe act or condition is now made possible to stop an incident or accident from happening. The project was first pilot tested as a proof of concept on one rig prior to further deployment to the other rigs in the project. Conceptual infrastructure setup was tested and proven to be possible and reliable. The "brain" was constructed through development and integration of multiple algorithms. Challenges were faced to achieve desired accuracy and consistency of the detection method for it to produce meaningful result and trigger accountable intervention. Also, several rounds of iteration were done to yield on acceptable result. Besides the real-time intervention, the system is also capable of producing statistics and reports for further analysis and optimizations to target both improvement in efficiency and reduction of HSE exposure for the workers. The feedback loop through discussions with the rig crews is key to achieve progress and continuous improvement towards a safer human behavior on the rig floor. The system was also integrated with the larger Behavioral Engineering Methodology (BEM) workflow that the service provider has implemented for various other processes to identify the gaps and risks associated with the human interactions at the rig floor. In its quest to achieve accident-free operations, the service provider has embraced digital solutions and Artificial Intelligence for transforming the management of HSE in general and particularly at the rig floor. Both operators and service companies will benefit from the digitalization of HSE processes and workflows to enable the implementation of innovative ways to further reduce the risks associated with drilling activities.All Days00In the framework of a large integrated drilling project in the Middle East, the service provider has introduced an innovative digital solution at the well site for improving rig floor safety through the implementation of AI-based technology.All Days['Alexandre Javay', 'Muhamad Zaki Zaini', 'Abdullah Almana', 'Viktor Yurtaev']2024-02-12T00:00:00https://www.semanticscholar.org/paper/26adcf6caf7d291cfe2ce6a2e558fd201c51bfb3 ae76553fceb16381a28fec00389a5c18b8171dd1The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.International Journal of Mental Health Nursing1420The importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis, is highlighted, highlighting the vital role of mental health professionals in providing professional assistance and guidance.International journal of mental health nursing['S. Wimbarti', 'B. H. R. Kairupan', 'T. Tallei']2024-02-12T00:00:00https://www.semanticscholar.org/paper/ae76553fceb16381a28fec00389a5c18b8171dd1 bbeda452131a2ef67fc59a760e56607ad6dec75bPengabdian Masyarakat berjudul "Pemanfaatan Artificial Intelligence dalam Pembuatan SlidePresentasi bagi Guru-Guru” ini merupakan upaya mendukung inovasi dalam dunia pendidikankhususnya di tingkat Sekolah Dasar (SD) dan Pendidikan Anak Usia Dini (PAUD) di SekolahBrainfor Islamic School Kisaran. Permasalahan yang dihadapi guru-guru pada tingkat ini adalahkendala dalam menyusun materi presentasi yang cepat, serta menarik perhatian orangtua siswamaupun siswanya dan mampu efektif dalam menyampaikan informasi. Dalam menyikapipermasalahan ini, kami mengusulkan solusi melalui pemanfaatan kecerdasan buatan (AI) denganfokus pada penggunaan ChatGPT yang dapat memberikan panduan desain, layout, dan kontenpresentasi secara otomatis, sesuai dengan tujuan yang ingin di sampaikan Narasumber atau Guru.Penggunaan ChatGPT ini bertujuan membantu guru-guru Brainfor dalam menyusun slidepresentasi yang sesuai alur ataupun prosedur serta relevan dengan kurikulum yang berlaku.ChatGPT ini akan dapat diakses melalui platform online atau aplikasi, memudahkan akses danpenggunaan oleh para guru. Dalam upaya mendukung pemahaman dan penerapan teknologi ini,proposal ini juga mencakup pelatihan intensif bagi guru-guru mengenai penggunaan alat AIdalam menyusun materi presentasi yang berkualitas.Selain itu, sebagai langkah inovatif tambahan, narasumber melibatkan penggunaan platformdesain grafis Canva dalam pembuatan slide presentasi. Canva adalah alat yang user-friendly danpopuler dalam pembuatan desain grafis, termasuk pembuatan slide presentasi. Para guru akandiajarkan cara mengintegrasikan output dari Chat GPT yang dikembangkan dengan Canva,sehingga para guru-guru dapat menghasilkan presentasi yang lebih kreatif dan sesuai dengangaya masing-masing. Langkah ini diambil untuk memberikan variasi dan fleksibilitas lebihkepada guru dalam menciptakan materi yang menarik perhatian siswa atau objek lainnya.Rencana kegiatan diawali dengan identifikasi kebutuhan guru-guru melalui survei dan dialoguntuk memastikan bahwa AI yang digunakan dapat menjawab kebutuhan yang sebenarnya.Setelah itu, dilanjutkan dengan penggunaan Canva dalam desain grafis. Pelatihan ini tidak hanyamencakup aspek teknis, tetapi juga penerapan praktis dalam kegiatan pengetahuan danpembelajaran sehari-hari.Setelah fase pelatihan, guru-guru akan diberikan kesempatan untuk mengimplementasikanpengetahuan yang diperoleh mereka dalam pembelajaran di kelas. Selama periode ini, merekajuga akan mendapatkan dukungan dan pemantauan penggunaan ChatGPT AI dan Canva untukmemberikan dampak positif atau hasil pembelajaran.Dengan menyatukan kecerdasan buatan, platform desain grafis, dan pelatihan guru, proyek inibertujuan menciptakan solusi holistik untuk meningkatkan kualitas pembelajaran di tingkat SDdan PAUD. Melalui pendekatan ini, diharapkan para guru dapat dengan lebih kreatif dan efektifmenyampaikan materi kepada siswa, maupun objek lainnya serta menghasilkan dampak positifpada proses pembelajaran dan pemahaman mereka. Journal Of Indonesian Social Society (JISS)160Journal Of Indonesian Social Society (JISS)['Dewi Maharani', 'Dewi Anggraeni', 'Rika Nofitri']2024-02-12T00:00:00https://www.semanticscholar.org/paper/bbeda452131a2ef67fc59a760e56607ad6dec75b f972367fbc3b101a5eeed7c352bd7786e8b44452The goal of this research is to (1) identify the status and development of AI and ML-based learning support systems and their impact on human learning, with a specific focus on techniques employed in previous research, and (2) demonstrate the process of designing a learning support system using AI. Artificial intelligence (AI) and machine learning (ML) technologies have received attention in education. The existing research on AI in education is examined, considering the implications of its application in research. Noteworthy ML techniques from the literature are explained, followed by a discussion on leveraging AI and ML technologies to enhance learning support. Additionally, with consideration of both front-end and back-end approaches,a framework for incorporating AI into education is proposed. Subsequently, a learning design model, Self-regulated Learning with AI Assistants (SLAA), is suggested for addressing the objectives of AI-based learning support system design. The categorization of AI and ML techniques in education research reveals nine types, including supervised learning, mining approaches, and Bayesian techniques. The exploration illustrates how these techniques can be employed in designing a learning support system. This paper provides an empirical overview of AI in education, addresses technological and pedagogical considerations for developing personalized and adaptive learning environments, and outlines the challenges and potential future research directions.American Journal of Education and Learning00An empirical overview of AI in education is provided, addresses technological and pedagogical considerations for developing personalized and adaptive learning environments, and outlines the challenges and potential future research directions.American Journal of Education and Learning['Donggil Song']2024-02-12T00:00:00https://www.semanticscholar.org/paper/f972367fbc3b101a5eeed7c352bd7786e8b44452 9ec7ed26338ef45b7580a193fc38b8dbf5c96f88The purpose of the research is to critically analyze the legal aspects of the use of artificial intelligence (AI) in the field of education, as well as to study the role of chatbots and the Chat GPT model, plagiarism issues, educational modeling and the impact of AI on the labor market. To achieve this goal, various research methods are used, including the analysis of current legal norms and international legislation that relate to the use of AI in education. The study also includes an analysis of intellectual property issues, data privacy, ethical standards and liability. The results of the study highlight the problem of plagiarism in chat rooms and emphasize the importance of careful use of information to ensure academic integrity. Despite the possible misuse of AI by students, such as chatbots and GPT models, for plagiarism, these technologies can also facilitate plagiarism detection. The research also examines the use of machine learning and data analytics to create personalized learning experiences, improve learning effectiveness, and retain knowledge. The overall conclusion is that the integration of artificial intelligence in education has the potential to improve the quality and accessibility of education, but this requires a sound legal framework. The article also evaluates the effectiveness of various AI tools, including chatbots that provide information on demand and Chat GPT, useful for processing textual materials. The paper also examines the role of learning simulation in personalizing education, using AI to analyze performance data, and tailoring individual learning pathways.Review of Artificial Intelligence in Education40The overall conclusion is that the integration of artificial intelligence in education has the potential to improve the quality and accessibility of education, but this requires a sound legal framework.Review of Artificial Intelligence in Education['T. Kronivets', 'Olena Yakovenko', 'Ye Tymoshenko', 'Mykhailo Ilnytskyi']2024-02-12T00:00:00https://www.semanticscholar.org/paper/9ec7ed26338ef45b7580a193fc38b8dbf5c96f88 9c6a22f347e3fd64e997ab5d972e35f373e7f276American Journal of Clinical Pathology00American journal of clinical pathology[]2024-02-12T00:00:00https://www.semanticscholar.org/paper/9c6a22f347e3fd64e997ab5d972e35f373e7f276 7528b8a61a860b047ace26eae90745638824dd6bFrontiers in Artificial Intelligence and Applications00Frontiers in Artificial Intelligence and Applications[]2024-02-12T00:00:00https://www.semanticscholar.org/paper/7528b8a61a860b047ace26eae90745638824dd6b 500a7c6fe2b32ccdc991e469ae664053b855ca4eInternational Journal of Dermatology51International journal of dermatology['Mohamad Goldust']2024-02-12T00:00:00https://www.semanticscholar.org/paper/500a7c6fe2b32ccdc991e469ae664053b855ca4e 6bce72d0142fe8dede2ba5bda0c319a47c543894Perspectives on Politics00Perspectives on Politics['E. S. Kehlenbach']2024-02-12T00:00:00https://www.semanticscholar.org/paper/6bce72d0142fe8dede2ba5bda0c319a47c543894 3ec3399adb19303741a80315946584116a3b3635Law, Innovation and Technology00Law, Innovation and Technology['Simone Casiraghi', 'Niels van Dijk']2024-02-12T00:00:00https://www.semanticscholar.org/paper/3ec3399adb19303741a80315946584116a3b3635 6fd0f5cd852c69744c873e19690fd17e55d03ed1JAMA Internal Medicine40JAMA internal medicine['Isabel Ostrer', 'Louise Aronson']2024-02-12T00:00:00https://www.semanticscholar.org/paper/6fd0f5cd852c69744c873e19690fd17e55d03ed1 ee3d735ad4f5242fcc781e8aea9e1c6625663de1Journal of Physical Education, Recreation &amp; Dance40Journal of Physical Education, Recreation &amp; Dance['Adam Keath', 'James Wyant', 'Brooke Towner']2024-02-12T00:00:00https://www.semanticscholar.org/paper/ee3d735ad4f5242fcc781e8aea9e1c6625663de1 f8faafdc1608fc05e2cd91f7b639969363667545Journal of Travel &amp; Tourism Marketing930Journal of Travel &amp; Tourism Marketing['Sheikh Raheel Manzoor', 'Rezwan Ullah', 'Afraseyab Khattak', 'Munsif Ullah', 'Heesup Han']2024-02-12T00:00:00https://www.semanticscholar.org/paper/f8faafdc1608fc05e2cd91f7b639969363667545 f9f183fd46e3d2b3f8ae2f337ac1e57efef2d972In this paper a cobot system is presented, that extends a Universal Robot with Artificial Intelligence (i.e., machine learning techniques) to allow for a safe human-robot collaboration, which is one of the main technologies in Industry 4.0 and is currently significantly changing the shop floor of manufacturing companies. Typically, these cobots are equipped with a camera to dynamically adapt to new situations and actions carried out by the worker who is collaborating with the robot in the same workspace. But obviously, switching from traditional industrial robots (acting completely isolated from humans) to smart robots also requires a change concerning the skills and knowledge workers must have to be able to control, manage, and interact with such cobot systems. Therefore, the main goal of this demonstrator is to develop a hard-and software environment, enabling a variety of different training scenarios to get trainees, employees, and students familiar with the main technical aspects of such human-robot interaction. Besides hardware and software related aspects, the paper will also briefly address the learning content, which is on the one hand, the basics of robotics and machine learning based image processing, and on the other hand, the interaction of the various components to form a functional overall system.Fit für die Zukunft: Praktische Lösungen für die industrielle Automation10The main goal of this demonstrator is to develop a hard-and software environment, enabling a variety of different training scenarios to get trainees, employees, and students familiar with the main technical aspects of such human-robot interaction.Fit für die Zukunft: Praktische Lösungen für die industrielle Automation['Tobias Schubert', 'Sebastian Heßlinger', 'Alexander Dwarnicak']2024-02-12T00:00:00https://www.semanticscholar.org/paper/f9f183fd46e3d2b3f8ae2f337ac1e57efef2d972 d4f9e25632c6e2cbc8d6c1a8ddf75be7c1948536This study thoroughly explores advanced approaches for addressing financial fraud, focusing on the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI). Recognizing the drawbacks of outdated methods, the examination aims to analyze the current situation, closely examining the efficiency and limitations of ML and AI techniques while mapping out intricate directions for future research. We delve into the intricate history of financial fraud, uncovering the inherent constraints embedded in conventional rule-based and manual detection approaches. Then, machine learning (ML) and artificial intelligence (AI) are discussed, highlighting significant research and successful implementations that have transformed the field of fraud detection. While analyzing the assessment metrics, we use various measures such as accuracy, precision, recall, F1 score, and the enigmatic ROC-AUC. Then, diverse ML and AI algorithms are introduced, including the mysterious Random Forest, the reliable Support Vector Machines (SVM), and the complex neural networks. As comparative analysis unfurls, uncovering the strengths and weaknesses inherent in distinct ML and AI systems. Beyond the limits of performance measures, our interpretation transcends, diving into the real-world ramifications and the labyrinth of possible routes for refinement and advancement.Journal of Economics, Finance and Accounting Studies02This study thoroughly explores advanced approaches for addressing financial fraud, focusing on the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI) techniques while mapping out intricate directions for future research.Journal of Economics, Finance and Accounting Studies['Paulin Kamuangu']2024-02-11T00:00:00https://www.semanticscholar.org/paper/d4f9e25632c6e2cbc8d6c1a8ddf75be7c1948536 73243853d4a082b8b974b8a888ba87629b2acb87Background: The deployment of black-box AI models in medical imaging presents significant challenges, especially in maintaining reliability across different clinical settings. These challenges are compounded by distribution shifts that can lead to failures in reproducing the accuracy attained during the AI model's original validations. Method: We introduce the conformal triage algorithm, designed to categorize patients into low-risk, high-risk, and uncertain groups within a clinical deployment setting. This method leverages a combination of a black-box AI model and conformal prediction techniques to offer statistical guarantees of predictive power for each group. The high-risk group is guaranteed to have a high positive predictive value, while the low-risk group is assured a high negative predictive value. Prediction sets are never constructed; instead, conformal techniques directly assure high accuracy in both groups, even in clinical environments different from those in which the AI model was originally trained, thereby ameliorating the challenges posed by distribution shifts. Importantly, a representative data set of exams from the testing environment is required to ensure statistical validity. Results: The algorithm was tested using a head CT model previously developed by Do and colleagues [9] and a data set from Massachusetts General Hospital. The results demonstrate that the conformal triage algorithm provides reliable predictive value guarantees to a clinically significant extent, reducing the number of false positives from 233 (45%) to 8 (5%) while only abstaining from prediction on 14% of data points, even in a setting different from the training environment of the original AI model. Conclusions: The conformal triage algorithm offers a promising solution to the challenge of deploying black-box AI models in medical imaging across varying clinical settings. By providing statistical guarantees of predictive value for categorized patient groups, this approach significantly enhances the reliability and utility of AI in optimizing medical imaging workflows, particularly in neuroradiology.medRxiv181The conformal triage algorithm offers a promising solution to the challenge of deploying black-box AI models in medical imaging across varying clinical settings, and significantly enhances the reliability and utility of AI in optimizing medical imaging workflows, particularly in neuroradiology.['Anastasios Nikolas Angelopoulos', 'S. R. Pomerantz', 'S. Do', 'S. Bates', 'C. P. Bridge', 'D. C. Elton', 'M. H. Lev', 'R. G. Gonzalez', 'M. I. Jordan', 'J. Malik']2024-02-11T00:00:00https://www.semanticscholar.org/paper/73243853d4a082b8b974b8a888ba87629b2acb87 570b4cc810c95f0cb46b39f4d0498dceffbe9b0dCritical Policy Studies252Critical Policy Studies['Eugenia Stamboliev', 'Tim Christiaens']2024-02-11T00:00:00https://www.semanticscholar.org/paper/570b4cc810c95f0cb46b39f4d0498dceffbe9b0d 4790433121e9cac27d7d0c076b1d3353a9e0df46This paper explores how AI policy documents mediate the stabilization of socio-technical assemblages. It does so by developing the theory-methods package of ‘discursive infrastructuring’ and applying it to the U.K.’s National AI Strategy. By centering the conceptual slipperiness of emerging technologies such as AI, this framework sheds light on how policy documents work to stabilize emerging socio-technical assemblages comprising specific actors, ideologies, flows of capital, and relationships of power. In the context of the National AI Strategy, discursive infrastructuring reveals how the document stabilises: AI as an autonomous and inevitable force; a technical/social dualism which privileges the technical over the social in driving innovation; the ‘heroic engineer’ as an individual, masculine and rational archetype; and, the U.K. as a dominant and modernising player on AI’s global stage. This assemblage does not only exist in the document’s words; it is translated into practice through the funding of institutions, the centring of technical pedagogies of AI, and the opening of visa routes for ‘globally mobile individuals’. The application of ‘discursive infrastructuring’ to the National AI Strategy thus elucidates the constitutive role of policy discourse in stabilising politically situated material-semiotic conceptions of AI.First Monday00First Monday['Sophie Bennani-Taylor']2024-02-11T00:00:00https://www.semanticscholar.org/paper/4790433121e9cac27d7d0c076b1d3353a9e0df46 5bdd6278d8ce8689bc93acdaa45f009ac989a490Career and education guidance is undergoing a transformative phase, especially in the context of life-long learning. Modernizing career services to meet the diverse needs of students is imperative in the age of technology. This article navigates the considerations for career services professionals, exploring the characteristics and needs of today's students, available technologies, funding requirements, and confidentiality issues. The challenge lies in creating accessible services that seamlessly connect education with employment. This paper presents a pioneering study that explores the untapped potential of artificial intelligence (AI) in revolutionizing career and education guidance, not only for higher education but also for students above the 9th standard. Drawing insights from focus groups, scenario work, and practical trials, the research provides a comprehensive view of the requirements and possibilities of integrating AI into career guidance. Perspectives from students, guidance staff, and institutions shape the findings, highlighting potential values, functions, and the driving forces and obstacles in adopting AI for career guidance. The study introduces varied modes of agency and maturity levels, establishing a framework for AI involvement in guidance processes. Future research directions include a focus on agency in guidance interaction, developing a guidance data ecosystem, and addressing ethical concerns.International Journal For Multidisciplinary Research70This paper presents a pioneering study that explores the untapped potential of artificial intelligence (AI) in revolutionizing career and education guidance, not only for higher education but also for students above the 9th standard.International Journal For Multidisciplinary Research['Abhishek Thapliyal', 'Vishwam Valiyaveettil']2024-02-11T00:00:00https://www.semanticscholar.org/paper/5bdd6278d8ce8689bc93acdaa45f009ac989a490 bf1304a4a3e993ef48890f8fa6061b8647bc3b48Theology and Science00Theology and Science['Mark Graves', 'Jane Compson', 'Ali-Reza Bhojani', 'Cyrus Olsen', 'Thomas Arnold']2024-02-11T00:00:00https://www.semanticscholar.org/paper/bf1304a4a3e993ef48890f8fa6061b8647bc3b48 5611bed0dd3b68ad07cc003c60d3abe1dfee5670International Conference on Tangible, Embedded, and Embodied Interaction20{'pages': '93:1-93:3'}['Jesse Josua Benjamin', 'Joseph Lindley']2024-02-11T00:00:00https://www.semanticscholar.org/paper/5611bed0dd3b68ad07cc003c60d3abe1dfee5670 c35faf1105664ff9b3193d8ebbe9dea3e5ac4c68International Journal of Technology and Design Education210International Journal of Technology and Design Education['Lu Cai']2024-02-11T00:00:00https://www.semanticscholar.org/paper/c35faf1105664ff9b3193d8ebbe9dea3e5ac4c68 ca2177ddcfa33fd02772c4f662d6844c0a29191bInternational Conference on Tangible, Embedded, and Embodied Interaction10{'pages': '102:1-102:3'}['Nicola Privato']2024-02-11T00:00:00https://www.semanticscholar.org/paper/ca2177ddcfa33fd02772c4f662d6844c0a29191b cabbd8398e6a06ea3a3642ef3826f156a71daf74JMIR Research Protocols120JMIR Research Protocols['Jaime Angeles Sesgundo III', 'David Collin Maeng', 'Jumelle Aubrey Tukay', 'Maria Patricia Ascano', 'Justine Suba-Cohen', 'Virginia Sampang']2024-02-11T00:00:00https://www.semanticscholar.org/paper/cabbd8398e6a06ea3a3642ef3826f156a71daf74 a6d21ff5ea1139444577313440cb2ad7df6ed8d1Intention is an important and challenging concept in AI. It is important because it underlies many other concepts we care about, such as agency, manipulation, legal responsibility, and blame. However, ascribing intent to AI systems is contentious, and there is no universally accepted theory of intention applicable to AI agents. We operationalise the intention with which an agent acts, relating to the reasons it chooses its decision. We introduce a formal definition of intention in structural causal influence models, grounded in the philosophy literature on intent and applicable to real-world machine learning systems. Through a number of examples and results, we show that our definition captures the intuitive notion of intent and satisfies desiderata set-out by past work. In addition, we show how our definition relates to past concepts, including actual causality, and the notion of instrumental goals, which is a core idea in the literature on safe AI agents. Finally, we demonstrate how our definition can be used to infer the intentions of reinforcement learning agents and language models from their behaviour.Adaptive Agents and Multi-Agent Systems423This work operationalise the intention with which an agent acts, relating to the reasons it chooses its decision, in structural causal influence models and demonstrates how this definition can be used to infer the intentions of reinforcement learning agents and language models from their behaviour.{'pages': '1901-1909'}['Francis Rhys Ward', 'Matt MacDermott', 'Francesco Belardinelli', 'Francesca Toni', 'Tom Everitt']2024-02-11T00:00:00https://www.semanticscholar.org/paper/a6d21ff5ea1139444577313440cb2ad7df6ed8d1 fe66128c33a31677d5d91082042a7911a21be2fdWe provide a birds eye view of the rapid developments in AI and Deep Learning that has led to the path-breaking emergence of AI in Large Language Models. The aim of this study is to place all these developments in a pragmatic broader historical social perspective without any exaggerations while at the same time without any pessimism that created the AI winter in the 1970s to 1990s. We also at the same time point out toxicity, bias, memorization, sycophancy, logical inconsistencies, hallucinations that exist just as a warning to the overly optimistic. We note here that just as this emergence of AI seems to occur at a threshold point in the number of neural connections or weights, it has also been observed that human brain and especially the cortex region is nothing special or extraordinary but simply a case of scaled-up version of the primate brain and that even the human intelligence seems like an emergent phenomena of scale.arXiv.org320A birds eye view of the rapid developments in AI and Deep Learning that has led to the path-breaking emergence of AI in Large Language Models and points out toxicity, bias, memorization, sycophancy, logical inconsistencies, hallucinations that exist just as a warning to the overly optimistic.ArXiv['Arifa Khan', 'P. Saravanan', 'S. K. Venkatesan']2024-02-11T00:00:00https://www.semanticscholar.org/paper/fe66128c33a31677d5d91082042a7911a21be2fd b5f835987b97ddc9e37fbe142fec6e4a5c29f538International Journal of Systems Science300International Journal of Systems Science: Operations &amp; Logistics['Sahar Akhgar', 'F. Dehghanian', 'Milad Mohammadi']2024-02-11T00:00:00https://www.semanticscholar.org/paper/b5f835987b97ddc9e37fbe142fec6e4a5c29f538 b747507130b7bac5a5c4acb9ba528725a3fb4813GRUR International00GRUR International['Kerem Cem Sanlı']2024-02-10T00:00:00https://www.semanticscholar.org/paper/b747507130b7bac5a5c4acb9ba528725a3fb4813 807771a9c87a482a6efb895ce534d0f16e50fe03The proliferation of images generated by artificial intelligence (AI) has significantly impacted the digital landscape, especially on social media platforms where the distinction between natural and synthetic content is increasingly blurred. This study embarks on a comparative review of the strategies used by major social media platforms—Facebook/Instagram, Twitter, TikTok, and YouTube—to detect AI-generated images. Employing a comprehensive methodology that includes a systematic review of academic literature, analysis of platform policies, and expert interviews, this research assesses the effectiveness of various detection methods, ranging from sophisticated AI tools to user reporting mechanisms. The findings reveal diverse approaches: Facebook and Instagram utilise a blend of AI detection and human moderation; Twitter integrates machine learning algorithms with user reports; TikTok emphasises AI tools within moderation workflows and educational initiatives; and YouTube relies on its Content ID system alongside AI analysis. The study highlights the critical role of effective detection systems in maintaining content authenticity and user trust, underscoring the importance of balancing automated detection with human oversight. The ongoing development and refinement of these technologies, alongside collaborative efforts and evolving regulatory frameworks, are identified as essential for ensuring a trustworthy digital environment. This research contributes to the discourse on digital integrity, offering insights into the complexities of safeguarding social media ecosystems against the challenges posed by AI-generated content.Global Mainstream Journal01A comparative review of the strategies used by major social media platforms—Facebook/Instagram, Twitter, TikTok, and YouTube—to detect AI-generated images reveals diverse approaches, highlighting the critical role of effective detection systems in maintaining content authenticity and user trust.Global Mainstream Journal[]2024-02-10T00:00:00https://www.semanticscholar.org/paper/807771a9c87a482a6efb895ce534d0f16e50fe03 d0d43dc9cd36e5a56d7ac1f4a9eccd9ffa7db44cScientific Reports361An AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries is presented.Scientific Reports['Sidharth Pancholi', 'Thomas H Everett', 'Bradley S. Duerstock']2024-02-10T00:00:00https://www.semanticscholar.org/paper/d0d43dc9cd36e5a56d7ac1f4a9eccd9ffa7db44c d557045d496bf97880739d31fcdc61e36d5160deHarm reporting in the field of Artificial Intelligence (AI) currently operates on an ad hoc basis, lacking a structured process for disclosing or addressing algorithmic flaws. In contrast, the Coordinated Vulnerability Disclosure (CVD) ethos and ecosystem play a pivotal role in software security and transparency. Globally, there are ongoing efforts to establish frameworks that promote transparency and collaboration in addressing AI-related issues, though challenges persist. Algorithmic flaws in machine learning (ML) models present distinct challenges compared to traditional software vulnerabilities, warranting a specialized approach. To address this gap, we propose the implementation of a dedicated Coordinated Flaw Disclosure (CFD) framework tailored to the intricacies of machine learning and artificial intelligence issues. This paper delves into the historical landscape of disclosures in ML, encompassing the ad hoc reporting of harms and the emergence of participatory auditing. By juxtaposing these practices with the well-established disclosure norms in cybersecurity, we argue that the broader adoption of CFD has the potential to enhance public trust through transparent processes that carefully balance the interests of both organizations and the community.arXiv.org1080This paper delves into the historical landscape of disclosures in ML, encompassing the ad hoc reporting of harms and the emergence of participatory auditing and argues that the broader adoption of CFD has the potential to enhance public trust through transparent processes that carefully balance the interests of both organizations and the community.ArXiv['Sven Cattell', 'Avijit Ghosh']2024-02-10T00:00:00https://www.semanticscholar.org/paper/d557045d496bf97880739d31fcdc61e36d5160de 4b42457a7dccd24590e0ac6c971fcebc7fd25db5Artificial intеlligеncе (AI) is rеvolutionising industriеs and organisations. This rеsеarch dеlvеs into thе ways in which lеading tеchnology companiеs such, as Googlе, Facеbook, Amazon, Nеtflix, and Alibaba utilisе AI to еnhancе thеir companies’ valuе strеamlinе opеrations and еlеvatе customеr еxpеriеncеs. Through an analysis of publishеd studiеs, this study uncovеrs thе primary applications, kеy succеss factors and potеntial consеquеncеs of intеgrating AI into digital organisations. Thе findings illustratе how AI еmpowеrs businеssеs to offеr products and sеrvicеs improvеs еfficiеncy through automation procеssеs and providеs data drivеn-insights that aid in dеcision making. Howеvеr, it is crucial to еnsurе that AI is utilisеd еthically and rеsponsibly. Dеspitе thе potеntial offеrеd by AI tеchnology businеssеs still nееd to addrеss associatеd challеngеs. To achiеvе succеss in this rеalm, companiеs must еmbracе thе capabilitiеs of AI whilе fostеring crеativity and nurturing a culturе as еmphasisеd in thе rеport.Management and Economics Review00Findings illustratе how AI еmpowеrs businеssеs to offеr products and sеrvicеs improvеs improvеs еfficiеncy through automation procеssеs and providеs data drivеn-insights that aid in dеcision making.MANAGEMENT AND ECONOMICS REVIEW['Mohammed Charaf Eddine Bourezig', 'Soumya Chahinez Taleb Bouguerri TALEB BOUGUERRI']2024-02-10T00:00:00https://www.semanticscholar.org/paper/4b42457a7dccd24590e0ac6c971fcebc7fd25db5 3154eef49edd7eaaa5ee30d7ea97b08cea8aae66In this paper, we aim to assess the coupling capability of artificial intelligence in international legal education, delving into crucial aspects of its implementation and effectiveness. This paper constructs a coupling empowerment model of AI international legal education by using artificial intelligence technology. It also discusses the application of Pearson product–moment correlation coefficient in correlation analysis, the implementation of AI knowledge mapping in the help of intelligent parents, and the application of BP neural algorithm in artificial neural networks in order to establish a cognitive student model. This teaching mode can provide personalized learning experience and intelligent teaching support and allow accurate assessment of students’ learning level and cognitive ability. The results show that the employment rate of students is increased from 75% to 100%, and the evaluation of practicability is maintained at 10 points. It proves that AI technology provides an innovative approach to international law education, which is expected to promote the efficient use of educational resources and improve students’ performance and employment rate.Sustainability300It proves that AI technology provides an innovative approach to international law education, which is expected to promote the efficient use of educational resources and improve students’ performance and employment rate.Sustainability['Yunyao Wang', 'Shudong Yang']2024-02-10T00:00:00https://www.semanticscholar.org/paper/3154eef49edd7eaaa5ee30d7ea97b08cea8aae66 5e0b4d57ed5994abc26f28d096e9114bc0353a58MethodsX271The hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R2 (R2 > 0.7) and the results show that the hybrid model outperform them.MethodsX['Kulsawasd Jitkajornwanich', 'Nattadet Vijaranakul', 'S. Jaiyen', 'Panu Srestasathiern', 'S. Lawawirojwong']2024-02-10T00:00:00https://www.semanticscholar.org/paper/5e0b4d57ed5994abc26f28d096e9114bc0353a58 ec58fb94d8b083cf9dd2652087edc34897db87c7Psychology Journal: Research Open00Psychology Journal: Research Open[]2024-02-10T00:00:00https://www.semanticscholar.org/paper/ec58fb94d8b083cf9dd2652087edc34897db87c7 36b6d8167d6e5589993db38e8443a8326a5e937eJournal of Advanced Zoology00Journal of Advanced Zoology['Ms Indrani Biswas', 'Dr R.K Singh']2024-02-10T00:00:00https://www.semanticscholar.org/paper/36b6d8167d6e5589993db38e8443a8326a5e937e 78928b0c46c9f02633327867ec48f216a9a6fc9bBiol. Cybern.214It is argued that discoveries in neuroscience were (and continue to be) instrumental in driving the development of new AI technology, but a more nuanced story is yielded, where AI researchers were loosely inspired by the brain, but ideas flowed mostly in the other direction.Biological cybernetics['Samuel J Gershman']2024-02-10T00:00:00https://www.semanticscholar.org/paper/78928b0c46c9f02633327867ec48f216a9a6fc9b 7b1b2954305b49050dc32612932aed989b15226fThis study investigates the transformative impact of artificial intelligence (AI) on education, assessing both its advantages and drawbacks. Employing the random sampling survey method, data was collected from 160 participants at Dhaka International University. The respondents, predominantly aged between 18-21 years with an average age of 20.6, exhibited varying levels of awareness regarding AI tools- 46 percent were familiar, 44 percent somewhat aware, and 10 percent unfamiliar. Notably, 72.5 percent of participants gained knowledge about artificial intelligence through online sources such as websites, research papers, and forums. The study identified prominent AI tools used by respondents, with ChatGPT being the most widely employed, alongside Google Translator, Microsoft Bard, Grammarly, QuillBot, and YouChat. ChatGPT, in particular, found extensive application in academic studies, research, website development, and app development. Interestingly, 17.5 percent of respondents reported being paid users of AI tools. Beyond usage patterns, the research delved into the efficacy of AI in enhancing education, addressing privacy concerns, and exploring associated challenges, benefits, impacts, ethical considerations, and potential future directions. Employing a Likert scale for assessment, the study concludes by advocating for the integration of AI in education while underscoring the imperative for safeguards to mitigate potential misuse. This comprehensive exploration provides valuable insights into the evolving landscape of AI within the educational sector.Bangladesh Journal of Multidisciplinary Scientific Research00The study identified prominent AI tools used by respondents, with ChatGPT being the most widely employed, alongside Google Translator, Microsoft Bard, Grammarly, QuillBot, and YouChat, with ChatGPT found extensive application in academic studies, research, website development, and app development.Bangladesh Journal of Multidisciplinary Scientific Research[]2024-02-10T00:00:00https://www.semanticscholar.org/paper/7b1b2954305b49050dc32612932aed989b15226f 34b422902965cb199d04a4b6c33769ab56e51c3cWith artificial intelligence (AI) integrated increasingly to enhance personalized diagnosis and data-driven treatment recommendations, this analysis examines the legal sufficiency of Ghana’s Health Institutions and Facilities Act 2011 (Act 829) to address medical negligence risks from reliance on AI systems in clinical settings. The CREAC framework structures evaluating gaps where existing health regulations may lack clarity for emerging issues of accountability. Explanation contextualizes the probabilistic nature of AI inferences and how general principles of medical negligence could have ambiguous application currently if erroneous AI contributions result in patient harm. Application to a hypothetical scenario assesses if adequate protections for appropriate integration exist across developers, systems, healthcare facilities, and practitioners under applicable interpretations of existing laws. Finding liability rules insufficient absent targeted AI governance, conclusions recommend amending Act 829 in key areas to codify expectations for responsible innovation and prevent ambiguity in liability. This work carries scientific novelty as one of the first structured jurisdictional analyses internationally of healthcare AI accountability gaps through a legal lens. Practical significance lies in setting the stage for strengthening protections in Ghana through proposed statutory reforms that reduce uncertainty around this crucial area for quality care. The method and recommendations offer a model for modernizing medical negligence law and AI policy amidst ongoing digitization in healthcare worldwide.Mesopotamian Journal of Artificial Intelligence in Healthcare132This analysis examines the legal sufficiency of Ghana’s Health Institutions and Facilities Act 2011 to address medical negligence risks from reliance on AI systems in clinical settings to recommend amending Act 829 in key areas to codify expectations for responsible innovation and prevent ambiguity in liability.Mesopotamian Journal of Artificial Intelligence in Healthcare['George Benneh Mensah', 'P. Dutta']2024-02-10T00:00:00https://www.semanticscholar.org/paper/34b422902965cb199d04a4b6c33769ab56e51c3c 3c25c169ff63c904b56e3f23a81539de1f4a113fArtificial intelligence (AI) techniques are commonly used in e-commerce, but there is little bibliometric analysis in this field. Using a bibliometric approach, it conducted a comprehensive study over the past decade to assess the research landscape, progress, and emerging trends in the field of artificial intelligence in e-commerce. Data was collected from related literature in the Scopus database from 2014 to the first half of 2023. VOSviewer and R studio were used to perform the bibliometric analysis of AI in e-commerce. The author status, nations, affiliations, annual publications, keywords, and journals were all evaluated in this way. The oldest relevant article was published in 1994, and article reviews were the most common form of document among the 669 manuscripts. Furthermore, the most popular research areas in this topic are business, management, and accounting. Additionally, the most productive journal is Proceedings of the International Conference on Electronic Business (ICEB). Moreover, the UK is the country that has published the most articles, and in terms of co-authorship, it has the strongest overall link. Finally, the keyword co-occurrence network indicates that the most important keywords are machine learning, e-commerce, recommender systems, fraud detection, decision-making systems, data mining, and online retailing.Management and Economics Review01The keyword co-occurrence network indicates that the most important keywords are machine learning, e-commerce, recommender systems, fraud detection, decision-making systems, data mining, and online retailing.MANAGEMENT AND ECONOMICS REVIEW['Samira Frioui', 'Amel Graa']2024-02-10T00:00:00https://www.semanticscholar.org/paper/3c25c169ff63c904b56e3f23a81539de1f4a113f 42d2cf9db948f9c786ac9c3f43fba4baa623a0a9The subject of this article is artificial intelligence (AI) and its protection as industrial property, more particularly as inventions. It indicates the essence of artificial intelligence and the areas of application of the technology. The protection of the results of AI as inventions is considered, also the advantages and disadvantages of artificial intelligence are presented. The results of the done patent research are analysed. The filed applications for inventions and patents granted in the field of artificial intelligence in a national and international aspect are identified, with conclusions and recommendations for applicant activity in the study area.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The essence of artificial intelligence and the areas of application of the technology are indicated and the advantages and disadvantages of artificial intelligence are presented.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika['Vladislava Pаcheva']2024-02-10T00:00:00https://www.semanticscholar.org/paper/42d2cf9db948f9c786ac9c3f43fba4baa623a0a9 0e908cc2e39face7abd38fd9deb2ffd31a46e41aThe problems of protecting information resources from cyberattacks of public and private en-terprises are considered based on the analysis of data in the USA for 2022, taking into account the type of cyberattack and estimates of the damage caused. The analysis of cyberattacks allows us to conclude that the security of information resources depends on the human factor for more than 90 percent and it is in this direction that maximum efforts should be made. Improving the protection of information resources is not possible without the use of artificial intelligence (AI). The possibilities of the influence of AI on the cyber defense of the energy industry are consid-ered and areas that require attention in the development of systems of protection against cyber attacks, which are "doomed" to attract the achievements of AI, are proposed. At the same time, it is taken into account that AI not only allows you to increase protection against cyber attacks, but can also make computer networks less secure. And the extraordinary capabilities of neural networks require the urgent creation of agreed international protocols for their developers.Èlektronnoe modelirovanie00The analysis of data in the USA for 2022 allows us to conclude that the security of information resources depends on the human factor for more than 90 percent and it is in this direction that maximum efforts should be made.Èlektronnoe modelirovanie['L.O. Mytko']2024-02-10T00:00:00https://www.semanticscholar.org/paper/0e908cc2e39face7abd38fd9deb2ffd31a46e41a 043a7d7afe8ecf2a994959b00ed4059b60e2a7baRead any news website in 2024 and you are likely to find an article about artificial intelligence (AI). Many people are debating the benefits and risks of the technology, while others are simply trying to fully grasp its meaning and scope. There seems to be only one certainty, which is that AI is here to stay. This month, we take a few minutes to consider the potential impact of AI on union prevention strategies and the steps employers should begin taking to take advantage of, and mitigate the risks regarding, this new technology.Management Report for Nonunion Organizations00The potential impact of AI on union prevention strategies and the steps employers should begin taking to take advantage of, and mitigate the risks regarding, this new technology are considered.Management Report for Nonunion Organizations['S. Wich']2024-02-10T00:00:00https://www.semanticscholar.org/paper/043a7d7afe8ecf2a994959b00ed4059b60e2a7ba ffcd3702fc65adf5e5571ef27f3efe44540ebe60PharmacoEconomics - open430There is a promising indication that GPT-4 can have practical applications in the automation of health economic model construction, and potential benefits include accelerated model development timelines and reduced costs of development.PharmacoEconomics Open['T. Reason', 'W. Rawlinson', 'J. Langham', 'A. Gimblett', 'Bill Malcolm', 'Sven L Klijn']2024-02-10T00:00:00https://www.semanticscholar.org/paper/ffcd3702fc65adf5e5571ef27f3efe44540ebe60 6bebc755aec2a641108a60593a53714e610aa4c4The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.  International Journal of Renewable Energy Development2804A comprehensive bibliometric analysis is presented to gain deeper insights into the progression of AI in energy research from 2003 to 2023 and demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment.International Journal of Renewable Energy Development['Thanh Tuan Le', 'J. C. Priya', 'Huu Cuong Le', 'Nguyen Viet Linh Le', 'Minh Thai Duong', 'Dao Nam Cao']2024-02-10T00:00:00https://www.semanticscholar.org/paper/6bebc755aec2a641108a60593a53714e610aa4c4 43fa4ac21d76cd0d008cc30cd9e69c94862f9281CrimRxiv00CrimRxiv['Ian T. Adams']2024-02-10T00:00:00https://www.semanticscholar.org/paper/43fa4ac21d76cd0d008cc30cd9e69c94862f9281 7d4cca2b4a949531e27402e821f9df250e09e0bbInternational Journal of Medical Informatics00NAIF, using routinely available parameters, outperforms in sensitivity existing scoring methods (Fib4 and APRI) in diagnosing severe liver fibrosis, even when tested with external validation datasets.International journal of medical informatics['Samir Hassoun', 'Chiara Bruckmann', 'S. Ciardullo', 'Gianluca Perseghin', 'Fabio Marra', 'Armando Curto', 'Umberto Arena', 'Francesco Broccolo', 'Francesca Di Gaudio']2024-02-10T00:00:00https://www.semanticscholar.org/paper/7d4cca2b4a949531e27402e821f9df250e09e0bb 05e09a517dead08e2912382aac6203f30b1cf898European Heart Journal30European heart journal['Jamol Uzokov', 'A. Alyavi', 'B. Alyavi', 'Akbar Abdullaev']2024-02-10T00:00:00https://www.semanticscholar.org/paper/05e09a517dead08e2912382aac6203f30b1cf898 2d8bb6907dc90f55e008d76331eb52d67ebea73aInternational Journal for Quality Research00International Journal for Quality Research['Abdullah Rashed Alrumi']2024-02-10T00:00:00https://www.semanticscholar.org/paper/2d8bb6907dc90f55e008d76331eb52d67ebea73a 8f27f5265ee7f4558ed9beabba1ef3614f88ffcfJournal of Hospital Medicine110Journal of hospital medicine['C. Gleber', 'Kathleen Fear']2024-02-10T00:00:00https://www.semanticscholar.org/paper/8f27f5265ee7f4558ed9beabba1ef3614f88ffcf 54a0b95e40b868b02878683ee3e361ce4ba13d52Intellectual property has a key role in ensuring national economic security. It is being constantly challenged and multiple risks affect its adequate application. Intellectual property threats need to be addressed on government, company and individual level to tackle serious security risks and prevent damages. The article reviews relevant policies and strategies, which need to be put in place, in view of the adoption of disruptive technologies such as artificial intelligence. It also discusses the elements of a robust and systemic economic model, which would enable monitoring and assessing the risks and multiple dimensions of IP threats in their interaction with the digital environment and infrastructure. Such a model could form and objective basis for evidence-based policy making to enhance national security through intellectual property.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The article reviews relevant policies and strategies, which need to be put in place, in view of the adoption of disruptive technologies such as artificial intelligence and discusses the elements of a robust and systemic economic model, which would enable monitoring and assessing the risks and multiple dimensions of IP threats in their interaction with the digital environment and infrastructure.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika[]2024-02-10T00:00:00https://www.semanticscholar.org/paper/54a0b95e40b868b02878683ee3e361ce4ba13d52 1627ebcae9fc98c1826efdd0bf58f23afc5a37d4Artificial Intelligence is most used technology in every examine how AI developments relate to IVF contracts, with field. Already, advances in AI are employed for administrative tasks, patient involvement and adherence, diagnosis and treatment suggestions in the medical field, and diagnosis. In order to increase the accuracy of the concluded contract in terms of civil law and medical law, automation and human removal from decision-making processes regarding civil law regulation of contracts for the provision of in vitro fertilization are necessary everywhere today. So the purpose of this paper we study how Advancement of AI is used for decision making on civil law regulation of contract for the provision of In vitro fertilization This paper also develops a method for decision support systems for civil law regulation of the contract for the provision of in vitro fertilization. Decision is tree algorithm is used for check contract is true or not. Various performance matrices also analyzed in this paper.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)130This paper studies how Advancement of AI is used for decision making on civil law regulation of contract for the provision of In vitro fertilization and develops a method for decision support systems for civil law regulation of the contract for the provision of in vitro fertilization.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['Sourav Choudhury', 'Joyirsiram', 'Suresh Frederick', 'Akshay Ashok Bannatti', 'L. Rahunathn', 'K. Purohit']2024-02-09T00:00:00https://www.semanticscholar.org/paper/1627ebcae9fc98c1826efdd0bf58f23afc5a37d4 c114cab772bc03f51b770e8f97da27d68b77fe9aAs the world’s largest emitter of carbon, China has implemented a series of environmental regulatory policies to reduce emissions. However, most of these environmental regulations have been at the expense of increased corporate environmental costs. Therefore, research on how to efficiently control these costs is of significant practical importance. This paper uses the China’s carbon trading policy (CTP) implemented in 2013 as a quasi-natural experiment, utilizing data from Chinese listed manufacturing firms between 2008 and 2020. Employing a difference-in-differences (DID) model, the study investigates the impact of market-incentive environmental regulatory policies (ERP) on environmental costs. The findings reveal that CTP significantly reduced the environmental costs of firms, confirming the positive and vital role market-incentive ERP can play in environmental protection and cost control. These conclusions remain robust after a series of stability tests. Mechanism analysis suggests that the cost reductions brought by market-incentive ERP are primarily achieved through increasing green innovation. Heterogeneity analysis shows that non-state-owned enterprises (non-SOEs), key polluting firms, firms with lower financial constraints, and firms with lower total production efficiency benefit more from market-incentive environmental regulatory policies. This study provides new empirical evidence for government policy-making aimed at achieving long-term sustainable development.PLoS ONE700PLOS ONE['Zhilong Qin', 'Chao Tu', 'Weihui Han', 'Qintong Jiang']2024-02-09T00:00:00https://www.semanticscholar.org/paper/c114cab772bc03f51b770e8f97da27d68b77fe9a 31ed0bb349640874ee90204d1e8ffd730b9a491dComputer-Aided Design and Applications00Computer-Aided Design and Applications['Qian Li', 'Lun Li']2024-02-09T00:00:00https://www.semanticscholar.org/paper/31ed0bb349640874ee90204d1e8ffd730b9a491d 0bcbbe4a38b35ba3b7a8709eeed163917c2eb3ecGenerative AI, exemplified by models like transformers, has opened up new possibilities in various domains but also raised concerns about fairness, transparency and reliability, especially in fields like medicine and law. This paper emphasizes the urgency of ensuring fairness and quality in these domains through generative AI. It explores using cryptographic techniques, particularly Zero-Knowledge Proofs (ZKPs), to address concerns regarding performance fairness and accuracy while protecting model privacy. Applying ZKPs to Machine Learning models, known as ZKML (Zero-Knowledge Machine Learning), enables independent validation of AI-generated content without revealing sensitive model information, promoting transparency and trust. ZKML enhances AI fairness by providing cryptographic audit trails for model predictions and ensuring uniform performance across users. We introduce snarkGPT, a practical ZKML implementation for transformers, to empower users to verify output accuracy and quality while preserving model privacy. We present a series of empirical results studying snarkGPT's scalability and performance to assess the feasibility and challenges of adopting a ZKML-powered approach to capture quality and performance fairness problems in generative AI models.arXiv.org292ArXiv['Bianca-Mihaela Ganescu', 'Jonathan Passerat-Palmbach']2024-02-09T00:00:00https://www.semanticscholar.org/paper/0bcbbe4a38b35ba3b7a8709eeed163917c2eb3ec d8fac43c459d80233412e058582cd0bb0b9a16f8The recent statement of future (human) extinction by Artificial Intelligence (AI), made by the Center for AI Safety, crystallises techno-capitalists’ lack of care and responsibility for environmental and social harms that digital technologies already produce. Rather than identifying their collective roles and responsibilities in addressing these harms, the Center’s statement highlights future risks, and ties these to global extinction threats. There are, however, alternatives to such an approach, as articulated in research at the intersection of geographies of care and digital geographies, including that which is bringing together more-than-human and more-than-real approaches. While the software and hardware of AI continues to present amorphous challenges to the same techno-capitalists who benefit from them, we could instead prioritise care, repair and responsibility in/of AI to address current problems in this area.Environment and Planning F401While the software and hardware of AI continues to present amorphous challenges to the same techno-capitalists who benefit from them, researchers at the intersection of geographies of care and digital geographies could prioritise care, repair and responsibility in/of AI to address current problems in this area.Environment and Planning F['Jessica McLean']2024-02-09T00:00:00https://www.semanticscholar.org/paper/d8fac43c459d80233412e058582cd0bb0b9a16f8 9826b936a476ae4edd0e6159daf3bbb4c9a24711Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, so called"gray failure", for AI workloads, significantly affecting end-to-end performance and concealing performance issues, which complicates root cause analysis for failures and regressions. We introduce Anubis, a proactive validation system for AI infrastructure that mitigates hidden degradation caused by hardware redundancies and enhances overall reliability. Anubis features a comprehensive benchmark suite, capable of evaluating individual hardware components and representing most real AI workloads. It comprises a Validator which learns benchmark criteria to clearly pinpoint defective components. Additionally, Anubis incorporates a Selector to balance validation time and issue-related penalties, enabling optimal timing for validation execution with a tailored subset of benchmarks. Through testbed evaluation and simulation, we demonstrate that Anubis can increase the mean time between incidents by up to 22.61x. Anubis has been successfully deployed in Azure production, validating hundreds of thousands of GPUs over the last two years.arXiv.org671This work introduces Anubis, a proactive validation system for AI infrastructure that mitigates hidden degradation caused by hardware redundancies and enhances overall reliability in cloud AI infrastructure.ArXiv['Yifan Xiong', 'Yuting Jiang', 'Ziyue Yang', 'L. Qu', 'Guoshuai Zhao', 'Shuguang Liu', 'Dong Zhong', 'Boris Pinzur', 'Jie Zhang', 'Yang Wang', 'Jithin Jose', 'Hossein Pourreza', 'Jeff Baxter', 'Kushal Datta', 'Prabhat Ram', 'Luke Melton', 'Joe Chau', 'Peng Cheng', 'Yongqiang Xiong', 'Lidong Zhou']2024-02-09T00:00:00https://www.semanticscholar.org/paper/9826b936a476ae4edd0e6159daf3bbb4c9a24711 0bd6483fd3ca8a4ff45f3db02279e663acf7064aThis paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators. We assess the performance of three LLMs and one open-source LM in Question-Answering (QA) and evaluation tasks using the TriviaQA (Joshi et al., 2017) dataset. Results indicate a significant disparity, with LLMs exhibiting lower performance in evaluation tasks compared to generation tasks. Intriguingly, we discover instances of unfaithful evaluation where models accurately evaluate answers in areas where they lack competence, underscoring the need to examine the faithfulness and trustworthiness of LLMs as evaluators. This study contributes to the understanding of “the Generative AI Paradox” (West et al., 2023), highlighting a need to explore the correlation between generative excellence and evaluation proficiency, and the necessity to scrutinize the faithfulness aspect in model evaluations.Conference of the European Chapter of the Association for Computational Linguistics201Results indicate a significant disparity, with LLMs exhibiting lower performance in evaluation tasks compared to generation tasks, highlighting a need to explore the correlation between generative excellence and evaluation proficiency, and the necessity to scrutinize the faithfulness aspect in model evaluations.ArXiv['Juhyun Oh', 'Eunsu Kim', 'Inha Cha', 'Alice Oh']2024-02-09T00:00:00https://www.semanticscholar.org/paper/0bd6483fd3ca8a4ff45f3db02279e663acf7064a b075c94b0e5d52865aeb63e82e94803958a4eeb5The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption. Conversational interactions with LLMs enable programmers to obtain natural language explanations for various software development tasks. However, LLMs often leap to action without sufficient context, giving rise to implicit assumptions and inaccurate responses. Conversations between developers and LLMs are primarily structured as question-answer pairs, where the developer is responsible for asking the the right questions and sustaining conversations across multiple turns. In this paper, we draw inspiration from interaction patterns and conversation analysis -- to design Robin, an enhanced conversational AI-assistant for debugging. Through a within-subjects user study with 12 industry professionals, we find that equipping the LLM to -- (1) leverage the insert expansion interaction pattern, (2) facilitate turn-taking, and (3) utilize debugging workflows -- leads to lowered conversation barriers, effective fault localization, and 5x improvement in bug resolution rates.arXiv.org171Robin, an enhanced conversational AI-assistant for debugging is designed, finding that equipping the LLM to leverage the insert expansion interaction pattern, facilitate turn-taking, and utilize debugging workflows leads to lowered conversation barriers, effective fault localization, and 5x improvement in bug resolution rates.ArXiv['Bhavya Chopra', 'Yasharth Bajpai', 'Param Biyani', 'Gustavo Soares', 'Arjun Radhakrishna', 'Chris Parnin', 'Sumit Gulwani']2024-02-09T00:00:00https://www.semanticscholar.org/paper/b075c94b0e5d52865aeb63e82e94803958a4eeb5 0475e93ee1d26a814e65b3fbbc192f5e9527983aFace-to-face communication modeling in computer vision is an area of research focusing on developing algorithms that can recognize and analyze non-verbal cues and behaviors during face-to-face interactions. We propose an alternative to text chats for Human-AI interaction, based on non-verbal visual communication only, using facial expressions and head movements that mirror, but also improvise over the human user, to efficiently engage with the users, and capture their attention in a low-cost and real-time fashion. Our goal is to track and analyze facial expressions, and other non-verbal cues in real-time, and use this information to build models that can predict and understand human behavior. We offer three different complementary approaches, based on retrieval, statistical, and deep learning techniques. We provide human as well as automatic evaluations and discuss the advantages and disadvantages of each direction.arXiv.org270An alternative to text chats for Human-AI interaction is proposed, based on non-verbal visual communication only, using facial expressions and head movements that mirror, but also improvise over the human user, to efficiently engage with the users, and capture their attention in a low-cost and real-time fashion.ArXiv['Dragos Costea', 'Alina Marcu', 'Cristina Lazar', 'Marius Leordeanu']2024-02-09T00:00:00https://www.semanticscholar.org/paper/0475e93ee1d26a814e65b3fbbc192f5e9527983a f622fb94582eb0a200529edf0f84362cf9d9ea26The proposed work introduces a discreet AI-powered Gym assistance system that offers subtle, real-time form correction and repetition counting during workout sessions. By combining AI and webcam technology, the system provides unobtrusive guidance, ensuring users maintain proper form. The approach utilizes deep learning and real-time object detection for accurate feedback. Results show promising effectiveness across various exercises.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)70This work introduces a discreet AI-powered Gym assistance system that offers subtle, real-time form correction and repetition counting during workout sessions by combining AI and webcam technology, and utilizes deep learning and real-time object detection for accurate feedback.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['Bharath Kumar', 'A. Julian']2024-02-09T00:00:00https://www.semanticscholar.org/paper/f622fb94582eb0a200529edf0f84362cf9d9ea26 63c473f2f64759742cf39fc470552af735abd0e5A large language model (LLM) is a trained deep-learning model that understands and generates text in a human-like fashion. Due to the significant advancements of LLM, it becomes a challenging task to distinguish human-written content from artificial intelligence (AI) generated content. In this work, we leverage the machine learning (ML) models to reliably identify whether an essay is authored by a human being or by an LLM. Concerns about LLMs replacing human tasks, especially in education persist. However, optimism remains for their potential as tools to enhance writing skills. An academic worry is LLMs facilitating plagiarism due to their extensive training in text and code datasets. Using diverse texts and unknown generative models, we replicate typical scenarios to encourage feature learning across models. In a study involving human subjects, we demonstrate that the annotation scheme offered by generative textual likelihood ratio (GLTR) enhances the human detection rate of fake text from 74% to 99% without requiring any previous training. GLTR is open source and publicly deployed, already finding widespread use in detecting generated outputs.Educational Sciences International Conference170This work leverages the machine learning (ML) models to reliably identify whether an essay is authored by a human being or by an LLM, and demonstrates that the annotation scheme offered by generative textual likelihood ratio (GLTR) enhances the human detection rate of fake text from 74% to 99% without requiring any previous training.2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)['M. Prajapati', 'Santos Kumar Baliarsingh', 'Chinmayee Dora', 'Ashutosh Bhoi', 'Jhalak Hota', 'J. P. Mohanty']2024-02-09T00:00:00https://www.semanticscholar.org/paper/63c473f2f64759742cf39fc470552af735abd0e5 3ad3f86a07954bdf48ecc043cc70b352b4d8e33fThe integration of Artificial Intelligence (AI) in healthcare has been impeded by a significant issue: a lack of trust among healthcare professionals, stemming from the opacity of AI decision-making processes and a general unfamiliarity with AI technologies. This study investigates the impact of AI's explainability and healthcare professionals' familiarity with AI on their trust in AI applications within healthcare settings. Adopting a quantitative research methodology, the study utilized a structured questionnaire to gather data from a diverse group of healthcare professionals, including doctors, nurses, and administrators, across various hospitals and healthcare institutions in Pakistan. The research employed a stratified random sampling approach to ensure a comprehensive and representative data set. The results indicated a positive and significant relationship between AI explainability and trust in AI (Path Coefficient: 0.62, t-Value: 5.20), suggesting that clearer and more transparent AI decision-making processes enhance healthcare professionals' trust., Similarly, familiarity with AI was found to positively influence trust in AI (Path Coefficient: 0.48, t-Value: 4.35), highlighting the importance of exposure and understanding of AI systems among healthcare professionals. These findings have crucial implications for both AI developers and healthcare administrators. For AI developers, the emphasis must be on creating more transparent and interpretable AI systems.  For healthcare administrators, the results suggest the need to invest in training and educational programs to increase professionals' familiarity with AI, thereby enhancing trust and acceptance.  The study significantly contributes to the existing literature by empirically validating the importance of AI explainability and familiarity in building trust in AI within the healthcare context, especially in a developing country setting. For policymakers, these insights are critical in guiding strategies and policies aimed at effectively integrating AI into healthcare systems. By addressing the identified factors, healthcare sectors can better leverage AI's potential, leading to improved patient care and more efficient healthcare operations.The Asian Bulletin of Big Data Management160A positive and significant relationship between AI explainability and trust in AI is indicated, suggesting that clearer and more transparent AI decision-making processes enhance healthcare professionals' trust and the need to invest in training and educational programs to increase professionals' familiarity with AI, thereby enhancing trust and acceptance.The Asian Bulletin of Big Data Management['Suhail A Chandio', 'A. Rehman', 'Shehr Bano', 'Ammar Hammed', 'Ammad Hussain']2024-02-09T00:00:00https://www.semanticscholar.org/paper/3ad3f86a07954bdf48ecc043cc70b352b4d8e33f d46ab0fdf2e7fe80dfc8be3a100d2434644104abOne industry that has been greatly impacted by the development of artificial intelligence (AI) is filmmaking. Thanks to AI-powered technologies and techniques that have significantly improved the visual appeal of films, filmmakers now have creative alternatives for creating captivating and engaging visual experiences. Although the revolutionary artificial intelligence’s (AI) effects on the film industry are becoming recognized, there is a striking lack of thorough study that especially addresses the subtle ways in which AI enhances the aesthetic appeal of movies. The literature currently in publication often provides general summaries of artificial intelligence applications in the film industry rather than going into great depth on the specifics of how AI algorithms impact and improve the visual aesthetics of motion pictures. thoroughly examining the approaches and strategies used in the integration of AI to improve the artistic quality of digital filmmaking, The goal of this research is to bridge this knowledge gap and improve our comprehension of the intricate connection between artificial intelligence and visual appeal.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)120After thoroughly examining the approaches and strategies used in the integration of AI to improve the artistic quality of digital filmmaking, this research bridges the knowledge gap and improves comprehension of the intricate connection between artificial intelligence and visual appeal.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['C. Manikandan', 'Ankit Kashyap', 'Fakira Mohan Nahak']2024-02-09T00:00:00https://www.semanticscholar.org/paper/d46ab0fdf2e7fe80dfc8be3a100d2434644104ab baab15f47c1e910691ec8d40129013d58e8b7a2fAmong the many shifting paradigms in banking and investing, the incorporation of AI has emerged as a key driver. Without using any first-person pronouns, it explores the motivations for and innovations brought about by this revolutionary shift. This examines AI’s numerous dimensions to show its revolutionary impact on banking and investment. This sets out to examine the inner workings of AI-driven financial technology in an effort to provide light on its many potential uses and consequences. AI enables predictive analytics, which in turn allows financial institutions to make educated choices with unparalleled precision and efficiency by evaluating real-time data streams and previous trends. As a byproduct, it seeks to better understand the ethical and regulatory hurdles brought on by the extensive use of AI in the financial sector. Intelligent algorithms speed up processes and enhance individuals and organization’s financial well-being, which raises awareness of AI’s banking potential.Educational Sciences International Conference220This sets out to examine the inner workings of AI-driven financial technology in an effort to provide light on its many potential uses and consequences and to better understand the ethical and regulatory hurdles brought on by the extensive use of AI in the financial sector.2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)['Ramakrishnan Raman', 'P. Tiwari']2024-02-09T00:00:00https://www.semanticscholar.org/paper/baab15f47c1e910691ec8d40129013d58e8b7a2f 359031cd5aaf5d657eb0e7509283180088c4e22aAIIRA seeks to transform agriculture by creating a new AI‐driven framework for modeling plants at various agronomically relevant scales. We accomplish this by designing and deploying AI‐driven predictive models that fuse diverse data with siloed domain knowledge. AIIRA's vision, illustrated in Figure 1, consists of four technical thrusts with cross‐cutting education, training, and outreach activities. Our activities are focused on theory, algorithms, and tools for the principled creation of goal‐oriented AI tools deployed at plant and field scales. Our use‐inspired AI developments are tightly integrated with USDA‐relevant challenges in crop improvement and sustainable crop production. Our strong social science focus ensures sustained AI adoption across the ag value chain. Our cyberinfrastructure (CI) efforts ensure cohesive, sustainable, and extensible CI to reproducibly share and manage data assets and analysis workflows to a diverse spectrum of the Ag community. Taken together, this will ensure long‐term payoffs in AI and agriculture. AIIRA has established a new field of Cyber Agricultural Systems at the intersection of plant science, agronomics, and AI. Our signature activities build the workforce for this new field through formal and informal educational activities. Through these activities, AIIRA  creates accessible pathways for underrepresented groups, especially Native Americans and women.The AI Magazine40AI Mag.['B. Ganapathysubramanian', 'Jessica M. P. Bell', 'George Kantor', 'Nirav C. Merchant', 'Soumik Sarkar', 'P. Schnable', 'Michelle Segovia', 'Arti Singh', 'Asheesh K. Singh']2024-02-09T00:00:00https://www.semanticscholar.org/paper/359031cd5aaf5d657eb0e7509283180088c4e22a 6a26b8beda2523fccb2a054ab5ade43ba3781d34Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.Frontiers in Robotics and AI930This review categorizes selected papers based on robotic application scenarios or AI methodologies by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper.Frontiers in Robotics and AI['Omar Coser', 'C. Tamantini', 'Paolo Soda', 'Loredana Zollo']2024-02-09T00:00:00https://www.semanticscholar.org/paper/6a26b8beda2523fccb2a054ab5ade43ba3781d34 566b9c782e4f8bc9f03e2381c5df700069b749a7Many people are interested in ChatGPT since it has become a prominent AIGC model that provides high-quality responses in various contexts, such as software development and maintenance. Misuse of ChatGPT might cause significant issues, particularly in public safety and education, despite its immense potential. The majority of researchers choose to publish their work on Arxiv. The effectiveness and originality of future work depend on the ability to detect AI components in such contributions. To address this need, this study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv. For this study, a dataset was created using physics, mathematics, and computer science articles. Using the newly built dataset, the following step is to put originality.ai through its paces. The statistical analysis shows that Originality.ai is very accurate, with a rate of 98%.arXiv.org230This study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv.ai and shows that Originality.ai is very accurate, with a rate of 98%.ArXiv['Arslan Akram']2024-02-09T00:00:00https://www.semanticscholar.org/paper/566b9c782e4f8bc9f03e2381c5df700069b749a7 68562520677bb2ea1807b07b50b6f3f6b05651f5One of today's most significant societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacting agents (human and artificial), aligns with human values. To address this challenge, we detail a formal model of human values for their explicit computational representation. To our knowledge, this has not been attempted as yet, which is surprising given the growing volume of research integrating values within AI. Taking as our starting point the wealth of research investigating the nature of human values from social psychology over the last few decades, we set out to provide such a formal model. We show how this model can provide the foundational apparatus for AI-based reasoning over values, and demonstrate its applicability in real-world use cases. We illustrate how our model captures the key ideas from social psychology research and propose a roadmap for future integrated, and interdisciplinary, research into human values in AI. The ability to automatically reason over values not only helps address the value alignment problem but also facilitates the design of AI systems that can support individuals and communities in making more informed, value-aligned decisions. More and more, individuals and organisations are motivated to understand their values more explicitly and explore whether their behaviours and attitudes properly reflect them. Our work on modelling human values will enable AI systems to be designed and deployed to meet this growing need.arXiv.org00This work details a formal model of human values for their explicit computational representation, and illustrates how it captures the key ideas from social psychology research and proposes a roadmap for future integrated, and interdisciplinary, research into human values in AI.ArXiv['Nardine Osman', "M. d'Inverno"]2024-02-09T00:00:00https://www.semanticscholar.org/paper/68562520677bb2ea1807b07b50b6f3f6b05651f5 9b1eb4c21181cf3fc6f07825a1e614c9cab6348fThe adoption of artificial intelligence tools can improve production efficiency in the agroindustry. Our objective was to perform the predictive modeling of carrot yield and quality. The crop was grown in two commercial areas during the summer season in Brazil. The root samples were taken at 200 points with a 30 × 30 m sampling grid at 82 and 116 days after sowing in both areas. The total fresh biomass, aerial part, and root biometry were quantified for previous crop harvesting to measure yield. The quality of the roots was assessed by sub-sampling three carrots by the concentration of total soluble solids (°Brix) and firmness in the laboratory. Vegetation indices were extracted from satellite imagery. The most important variables for the predictive models were selected by principal component analysis and submitted to the Artificial Neural Network (ANN), Random Forest (RF), and Multiple Linear Regression (MLR) algorithms. SAVI and NDVI indices stood out as predictors of crop yield, and the results from the ANN (R2 = 0.68) were superior to the RF (R2 = 0.67) and MLR (R2 = 0.61) models. Carrot quality cannot be modeled by the predictive models in this study; however, it should be explored in future research, including other crop variables.AgriEngineering481Carrot quality cannot be modeled by the predictive models in this study; however, it should be explored in future research, including other crop variables.AgriEngineering['Yara Karine de Lima Silva', 'C. Furlani', 'T. F. Canata']2024-02-09T00:00:00https://www.semanticscholar.org/paper/9b1eb4c21181cf3fc6f07825a1e614c9cab6348f 61d0dea39fae41bd18e7426c493d24daf9639c73Law, Innovation and Technology01Law, Innovation and Technology['Georgios Pavlidis']2024-02-09T00:00:00https://www.semanticscholar.org/paper/61d0dea39fae41bd18e7426c493d24daf9639c73 b6266cbbf8f8973d10f48febee93545399006230J. Cloud Comput.681Many diagnostic techniques used in the ASD literature, such as neuroimaging, speech recordings, facial features, facial features, and EEG signals are discussed, leading to the conclusion that in schools and educational settings, autism can be diagnosed cheaply, quickly, and accurately through face analysis.J. Cloud Comput.['Yue Pan', 'Andia Foroughi']2024-02-09T00:00:00https://www.semanticscholar.org/paper/b6266cbbf8f8973d10f48febee93545399006230 dafee438f3357659aeebe64b9aef30f09e52b041INTRODUCTION A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to certified dentists. This methodological systematic review aims to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning and that have used radiographic databases to classify, detect, and segment dental caries. METHODS The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persist between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists. RESULTS After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n=17) at the time when detection (n=15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38437, while the augmented training set ranged from 300 to 315786. Convolutional neural network (CNN) was the most commonly used model. The mean completeness of CLAIM items was 49 % (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model's performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain. CONCLUSION This review demonstrates that the overall scientific quality of studies conducted to feed AI algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.Caries Research800The overall scientific quality of studies conducted to feed AI algorithms is low and some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.Caries research['Amadou Diaw Ndiaye', 'Marie-Agnès Gasqui', 'Fabien Millioz', 'Matthieu Perard', 'Fatou Leye Benoist', 'Brigitte Grosgogeat']2024-02-09T00:00:00https://www.semanticscholar.org/paper/dafee438f3357659aeebe64b9aef30f09e52b041 343565145bd1d5cad907dec579491b719f1004a3Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models everyday. Several different techniques in computer science literature account for the human interaction with machine learning systems, but their classification is sparse and the goals varied. This survey proposes a taxonomy of Hybrid Decision Making Systems, providing both a conceptual and technical framework for understanding how current computer science literature models interaction between humans and machines.arXiv.org1480This survey proposes a taxonomy of Hybrid Decision Making Systems, providing both a conceptual and technical framework for understanding how current computer science literature models interaction between humans and machines.ArXiv['Clara Punzi', 'Roberto Pellungrini', 'Mattia Setzu', 'F. Giannotti', 'D. Pedreschi']2024-02-09T00:00:00https://www.semanticscholar.org/paper/343565145bd1d5cad907dec579491b719f1004a3 6e0feaafb805a0b8245333e393068327b565895fJournal of Developmental and Behavioral Pediatrics00Journal of developmental and behavioral pediatrics : JDBP['Jenny S. Radesky']2024-02-09T00:00:00https://www.semanticscholar.org/paper/6e0feaafb805a0b8245333e393068327b565895f 1e93600da33c7d5a8884992424b72ade22d6a7e1Phenomenology and the Cognitive Sciences130Phenomenology and the Cognitive Sciences['Daniil Koloskov']2024-02-09T00:00:00https://www.semanticscholar.org/paper/1e93600da33c7d5a8884992424b72ade22d6a7e1 5d96c97ac014ae621967ba8336f44a531f0214f6Current approaches to prosthetic control are limited by their reliance on traditional methods, which lack real-time adaptability and intuitive responsiveness. These limitations are particularly pronounced in assistive technologies designed for individuals with diverse cognitive states and motor intentions. In this paper, we introduce a framework that leverages Reinforcement Learning (RL) with Deep Q-Networks (DQN) for classification tasks. Additionally, we present a preprocessing technique using the Common Spatial Pattern (CSP) for multiclass motor imagery (MI) classification in a One-Versus-The-Rest (OVR) manner. The subsequent 'csp space' transformation retains the temporal dimension of EEG signals, crucial for extracting discriminative features. The integration of DQN with a 1D-CNN-LSTM architecture optimizes the decision-making process in real-time, thereby enhancing the system's adaptability to the user's evolving needs and intentions. We elaborate on the data processing methods for two EEG motor imagery datasets. Our innovative model, RLEEGNet, incorporates a 1D-CNN-LSTM architecture as the Online Q-Network within the DQN, facilitating continuous adaptation and optimization of control strategies through feedback. This mechanism allows the system to learn optimal actions through trial and error, progressively improving its performance. RLEEGNet demonstrates high accuracy in classifying MI-EEG signals, achieving as high as 100% accuracy in MI tasks across both the GigaScience (3-class) and BCI-IV-2a (4-class) datasets. These results highlight the potential of combining DQN with a 1D-CNN-LSTM architecture to significantly enhance the adaptability and responsiveness of BCI systems.arXiv.org220A framework that leverages Reinforcement Learning with Deep Q-Networks (DQN) for classification tasks and incorporates a 1D-CNN-LSTM architecture as the Online Q-Network within the DQN, facilitating continuous adaptation and optimization of control strategies through feedback is introduced.ArXiv['Sriram V.C. Nallani', 'Gautham Ramachandran']2024-02-09T00:00:00https://www.semanticscholar.org/paper/5d96c97ac014ae621967ba8336f44a531f0214f6 ecedd2a754f875b746e19b56c323fd4d59c56235At the current stage of development and implementation of information technology in various areas of human activity, decisive changes are taking place, as there are powerful technical resources for the accumulation and processing of large amounts of information [...]Syst.00Syst.['Tetyana Hovorushchenko', 'Ivan Izonin', 'Hakan Kutucu']2024-02-09T00:00:00https://www.semanticscholar.org/paper/ecedd2a754f875b746e19b56c323fd4d59c56235 8fde6e8cdd9774482c0550f5abbcbf04feaf9186Rice’s quality and resistance to disease are crucial for agribusiness. Crop production must be increased using effective practises and tactics. The introduction of sophisticated artificial intelligence and machine learning methods has been especially beneficial to the area of agricultural innovation. By examining photos of leaves, agricultural fields, or seeds, these techniques have shown astoundingly high levels of success in diagnosing illnesses. In order to increase the production of rice, one of the main crops in the world, this research provides a thorough assessment with a precision agricultural emphasis. The research articles reviewed and analysed in this work use a variety of techniques for identifying crop diseases, evaluating the health of seedlings, and assessing grain quality. They were all published within the last eight years. The Web of Science and Scopus databases were used in experiments to extract data, which allowed for an analysis of research trends in rice disease diagnosis using artificial intelligence. This study includes worldwide trends, annual patterns, and country-specific citation data, offering insightful information to academics working in this area.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)400This study includes worldwide trends, annual patterns, and country-specific citation data, offering insightful information to academics working in this area, and provides a thorough assessment with a precision agricultural emphasis.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['Suphiya Parveen', 'Savita', 'Saurav Ganguly']2024-02-09T00:00:00https://www.semanticscholar.org/paper/8fde6e8cdd9774482c0550f5abbcbf04feaf9186 fbe2ef16f9faa8494b6857e80d982c91bb34b5d1This research presents a cutting-edge Al-driven digital disease identification system tailored to chicken husbandry. The system incorporates multiple sensors and enhanced object-detecting capabilities by leveraging collaborative conveyor machinery. The major goal is to improve disease detection precision in poultry habitats. The technology provides a complete method for recognizing potential health risks in chickens by painstakingly studying temperature changes and applying object detection algorithms using TensorFlow. The combination of artificial intelligence and sensor technology results in a potent tool for preventive disease control in the chicken business. This invention achieves some of the major sustainable development goals to kinetically monitor, protect and raise of chicks in day-to-day life, which are essentially named as Decent Work and Economic Growth, Industry Innovation and Infrastructure, Responsible Consumption and Production. The collaborative conveyor machine enables a coordinated and efficient procedure while collecting and analyzing data in real-time. This digital disease identification system marks a big step in optimizing chicken farming techniques due to the synergy of AI machine learning and sensor improvements. A collaborative conveyor machine with several sensors and item-detecting capabilities is used in this technique. The system is particularly designed to combat diseases such as fowl pox, avian paramyxovirus and coccidiosis. Using powerful AI algorithms, this system intends to increase illness detection accuracy and efficiency in chicken farming, resulting in better flock health management. The system promptly alerts farmers through GSM and IoT technologies, enhancing real-time monitoring and proactive management in poultry health.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)250This invention achieves some of the major sustainable development goals to kinetically monitor, protect and raise of chicks in day-to-day life, which are essentially named as Decent Work and Economic Growth, Industry Innovation and Infrastructure, Responsible Consumption and Production.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['M. K. Elango', 'A. Harini', 'R. Soundar', 'P. Suroopa']2024-02-09T00:00:00https://www.semanticscholar.org/paper/fbe2ef16f9faa8494b6857e80d982c91bb34b5d1 e4eb713a63fef4b2937ba0c9aeff9447a195ffd3Air pollution, stemming from human activities like energy production and transportation, poses a significant environmental threat. It is important to predict the accurate concentration of various pollutants for proactive safety of public health. This study utilizes a dataset from a heavily polluted area in Italy, comprising hourly averaged responses from metal oxide sensors. Various models, including deep learning (LSTM and GRU) and machine learning (Linear Regression, Decision Tree, Random Forest, SVM, Huber Regressor, Gradient Boosting Regression, K-Neighbors Regression, AdaBoost Regression, Gaussian Regression, and MLP Regression), were employed to analyze pollutant levels, focusing on carbon monoxide (CO). Noteworthy patterns, such as elevated CO levels in October and distinct daily/weekly variations, were identified, offering a comprehensive understanding of air quality dynamics. The inverse correlation between ambient humidity and NO levels suggests links between pollutants and environmental variables. Among the tested prediction models, Gradient Boosting (GB) regression stood out, providing insightful and accurate results for pollutant concentrations and weekly variations. The refined GB model demonstrated exceptional reliability, with a notable test R2 score of 0.91, showcasing its effectiveness in capturing dataset intricacies.Educational Sciences International Conference240Among the tested prediction models, Gradient Boosting (GB) regression stood out, providing insightful and accurate results for pollutant concentrations and weekly variations, showcasing its effectiveness in capturing dataset intricacies.2024 International Conference on Emerging Systems and Intelligent Computing (ESIC)['Chittaranjan Pradhan', 'Mritunjay Kumar', 'Divyansi Mishra', 'Boudhaditya Priyaranjan Banerjee']2024-02-09T00:00:00https://www.semanticscholar.org/paper/e4eb713a63fef4b2937ba0c9aeff9447a195ffd3 970aa573c78e0ebb7c8d05818a2776ddd37646d8Emerging technologies are causing a major transition in higher education in an era of rapid technological growth. This essay examines the various ways that emerging technologies are affecting higher education, including how they affect inclusiveness, accessibility, student participation, and teaching and learning approaches. It also looks at the difficulties that institutions and teachers have when putting these technologies into practice and how they can change the face of higher education. Using a mixed methods research approach including surveys, interviews and document analysis, the study shows that the widespread use of new technologies in higher education drives academic achievement and engagement effective but there are still issues with privacy, policy and training. The study also examines the significant impact of the COVID-19 pandemic on technology adoption and identifies ways in which future technologies can be integrated into higher education.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)220The study shows that the widespread use of new technologies in higher education drives academic achievement and engagement effective but there are still issues with privacy, policy and training.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['Pragati Mishra', 'N. Partheeban', 'E. Rajesh']2024-02-09T00:00:00https://www.semanticscholar.org/paper/970aa573c78e0ebb7c8d05818a2776ddd37646d8 ff613b585f05e81fd5d849dcbcd9c11b554b062dThe emergence of artificial intelligence (AI) has transformed global business, aiding operational efficiency and innovation. It utilizes machine learning and big data analytics, driving predictive market trends and strategic decision‐making. However, despite the rising discussion and accessibility of AI tools, understanding its impact on international business remains limited. This article explores AI's potential in international business strategies, practices, and activities. To address this aim, we reviewed 37 articles in the existing literature to critically explore AI within the context of international business. More specifically, we explored how AI can be applied to innovation approaches in international business, international market selection, entry modes, foreign exchange, international human resource management, international supply chains, managing across cultures, and more topics. AI has necessitated changes in workplace configurations and the need for organizational and employee adjustments in response to this technology. As a result of the foregoing issues on AI integration within international business, our analysis provided an exploratory discussion around its use, challenges, managerial implications, and suggested areas requiring future studies.Thunderbird International Business Review731How AI can be applied to innovation approaches in international business, international market selection, entry modes, foreign exchange, international human resource management, international supply chains, managing across cultures, and more topics is explored.Thunderbird International Business Review['Jane Menzies', 'Bianka Sabert', 'Rohail Hassan', 'Prince Kofi Mensah']2024-02-09T00:00:00https://www.semanticscholar.org/paper/ff613b585f05e81fd5d849dcbcd9c11b554b062d ff4ad464a3593f466ffd41f0053e9a7217300300An important aim of this paper is to convey some basics of mathematical logic to the legal community working with Artificial Intelligence. After analysing what AI is, we decide to delimit ourselves to rule-based AI leaving Neural Networks and Machine Learning aside. Rule based AI allows for Formal methods which are described in a rudimentary form. We will then see how mathematical logic interacts with legal rule-based AI practice. We shall see how mathematical logic imposes limitations and complications to AI applications. We classify the limitations and interactions between mathematical logic and legal AI in three categories: logical, computational and mathematical. The examples to showcase the interactions will largely come from European traffic regulations. The paper closes off with some reflections on how and where AI could be used and on basic mechanisms that shape society.arXiv.org340The paper delimit itself to rule-based AI leaving Neural Networks and Machine Learning aside, and sees how mathematical logic interacts with legal rule-based AI practice and how mathematical logic imposes limitations and complications to AI applications.ArXiv['J. Joosten', "Manuela Montoya Garc'ia"]2024-02-09T00:00:00https://www.semanticscholar.org/paper/ff4ad464a3593f466ffd41f0053e9a7217300300 316603210125023bc051ed9a1319550a8d2450e3With the potential to completely transform health-care, artificial intelligence (AI) has become a very effective instrument. The use of AI, its underlying theories, and its evolutionary trajectory are all examined in this article. Finding novel medications and vaccines, managing electronic health records, monitoring epidemics, identifying clinical problems, and supporting rehabilitation are all tasks that AI can help with. Concerns about expenses, ethics, safety, and privacy must be taken into consideration, though. The transformation potential of artificial intelligence (AI) in healthcare is highlighted in this study, along with the necessity of strong governance to guarantee its responsible and advantageous application. By driving the healthcare revolution through inventive medical transformation, AI is positioned to influence the direction of medical innovation in the future.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)360The transformation potential of artificial intelligence (AI) in healthcare is highlighted in this study, along with the necessity of strong governance to guarantee its responsible and advantageous application.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['Priyanka Gupta', 'Anmol Yadav']2024-02-09T00:00:00https://www.semanticscholar.org/paper/316603210125023bc051ed9a1319550a8d2450e3 e3572bb5d133754bd1e8fb0f34e3bff02b702d79Counterintelligence (CI) and Artificial Intelligence (AI) represent two distinct yet interconnected domains that play pivotal roles in safeguarding National and International Security. On the first hand, CI involves activities and measures taken to identify, prevent and counter any Intelligence activities of hostile entities, such as spying, sabotage and information gathering. On the other hand, AI refers to the development and use of computer systems that can perform tasks that typically require human intelligence, such as learning, reasoning and problem-solving. Subsequently, in the ever-evolving landscape of global security, the rise of AI has ushered in a new era of CI practices. The present paper delves into the intersection of CI and AI, exploring the profound impact of AI on the CI processes and how it is transforming National Security strategies, highlighting at the same time the fields of mutually influence. Ultimately, underscores the imperative of harnessing AI's potential to strengthen CI efforts in an ever-evolving threat landscape. Plus, it investigates the ethical concerns and privacy implications associated with AI in CI emphasizing the imperative of responsible AI development and deployment. Finally, through comprehensive international case studies, offers insights into how United States, China, Russia and Israel have integrated AI into their Intelligence and CI strategies, shedding light on the diverse approaches and challenges faced by different countries. Summarizing, the paper underscores the potential synergy between AI and CI, while also acknowledging the formidable challenges it presents, such as privacy concerns and adversarial AI. Striking a balance between harnessing AI's power and safeguarding national interests remains a pivotal task for policymakers and intelligence agencies in the ever-evolving landscape of national security.Journal of Politics and Ethics in New Technologies and AI490The present paper delves into the intersection of CI and AI, exploring the profound impact of AI on the CI processes and how it is transforming National Security strategies, highlighting at the same time the fields of mutually influence.Journal of Politics and Ethics in New Technologies and AI['A. Kanellopoulos']2024-02-09T00:00:00https://www.semanticscholar.org/paper/e3572bb5d133754bd1e8fb0f34e3bff02b702d79 1c099077dc6049ec002d7651be4aa657fca347b3Autonomous Intelligent Systems210This work proposes a comprehensive classification framework for existing Shapley value-based feature attribution methods from three dimensions: Shapley value type, feature replacement method, and approximation method and summarizes the limitations associated with the Shapley value.Auton. Intell. Syst.['Meng Li', 'Hengyang Sun', 'Yanjun Huang', 'Hong Chen']2024-02-09T00:00:00https://www.semanticscholar.org/paper/1c099077dc6049ec002d7651be4aa657fca347b3 bfd390fa1786d1a9c601d46c13fb0bf83e193b4cIn this paper, we build on the 1971 memo"Twenty Things to Do With a Computer"by Seymour Papert and Cynthia Solomon and propose twenty constructionist things to do with artificial intelligence and machine learning. Several proposals build on ideas developed in the original memo while others are new and address topics in science, mathematics, and the arts. In reviewing the big themes, we notice a renewed interest in children's engagement not just for technical proficiency but also to cultivate a deeper understanding of their own cognitive processes. Furthermore, the ideas stress the importance of designing personally relevant AI/ML applications, moving beyond isolated models and off-the-shelf datasets disconnected from their interests. We also acknowledge the social aspects of data production involved in making AI/ML applications. Finally, we highlight the critical dimensions necessary to address potential harmful algorithmic biases and consequences of AI/ML applications.arXiv.org380The ideas stress the importance of designing personally relevant AI/ML applications, moving beyond isolated models and off-the-shelf datasets disconnected from their interests and highlight the critical dimensions necessary to address potential harmful algorithmic biases and consequences of AI/ML applications.ArXiv['Yasmin B. Kafai', 'Luis Morales-Navarro']2024-02-09T00:00:00https://www.semanticscholar.org/paper/bfd390fa1786d1a9c601d46c13fb0bf83e193b4c c608c942cc6bbd47d59891017efbcbf03dfabbebArtificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.Hearts860The utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals and its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies are investigated.Hearts['Shiavax J. Rao', 'S. Iqbal', 'A. Isath', 'H. H. Virk', 'Zhen Wang', 'Benjamin S. Glicksberg', 'C. Krittanawong']2024-02-09T00:00:00https://www.semanticscholar.org/paper/c608c942cc6bbd47d59891017efbcbf03dfabbeb ab956ab66301a2fc30b27468450867d037a846feMarketing literature classify artificial intelligence and Internet of Things as disruptive technologies. These technologies considered as providers of digital solutions facilitate the attraction and maintenance of customers. Present day realities indicate that artificial intelligence enabled devises are prevalent in corporations. This paper sought to examine the influence of artificial intelligence on marketing practice in Nigeria. Specifically, objectives of this study is to examine the roles of artificial intelligence in marketing and to ascertain the level of Nigeria’s preparedness for artificial intelligence intervention in marketing practice. The study adopted the desk research method that entail a review of extant literature.INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH00The roles of artificial intelligence in marketing and the level of Nigeria’s preparedness for artificial intelligence intervention in marketing practice are examined to ascertain the level of Nigeria’s preparedness for artificial intelligence intervention in marketing practice.INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH['D. O. Ewanlen', 'Olalekan Adebowale Asaolu']2024-02-09T00:00:00https://www.semanticscholar.org/paper/ab956ab66301a2fc30b27468450867d037a846fe a45150c0688cac730c87dd02aa1079b7c4dfde5fArtificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize decision making across various domains. This research article explores the impact of AI in decision making and its implications for individuals, organizations, and society. The article begins by providing an overview of AI and its key components, such as machine learning and natural language processing. It then discusses the role of AI in enhancing decision-making processes by automating tasks, augmenting human capabilities, and providing data-driven insights. The article highlights the benefits of AI in improving decision accuracy, efficiency, and scalability, while also acknowledging the challenges and risks associated with its implementation. These challenges include ethical considerations, biases in AI algorithms, and potential job displacement. The article further explores the importance of transparency, accountability, and interpretability in AI decision-making systems. Additionally, it discusses the role of human-AI collaboration and the need for interdisciplinary approaches to ensure the responsible and ethical deployment of AI in decision making. Drawing on case studies and empirical research, the article provides concrete examples of how AI is transforming decision making in various fields, such as finance, healthcare, and transportation. Finally, the article concludes by discussing future directions and recommendations for policymakers, organizations, and individuals to harness the full potential of AI in decision making while addressing its ethical, social, and economic implications. KEYWORDS: Artificial Intelligence, Decision Making, Machine Learning, Automation, Augmentation, Ethical Considerations, Bias, Transparency, Human-AI Collaboration, Responsible AI.EPRA International Journal of Economics, Business and Management Studies00The article highlights the benefits of AI in improving decision accuracy, efficiency, and scalability, while also acknowledging the challenges and risks associated with its implementation.EPRA International Journal of Economics, Business and Management Studies['Muhammad Eid BALBAA', 'Marina Sagatovna ABDURASHIDOVA']2024-02-09T00:00:00https://www.semanticscholar.org/paper/a45150c0688cac730c87dd02aa1079b7c4dfde5f 276ebc15f3bd945b35852229e955f991f3c18180The use of Artificial Intelligence in the military is like two sides of a coin. It can provide convenience and aid in military operations but has the potential to hinder military operations. Dangerous and potentially catastrophic for humanity will be inevitable as no restrictions on its use. The United States, China, Australia, Japan, and India are examples of nations whose militaries have developed artificial intelligence technology. Geographically, Southeast Asia, which is located in the middle of these nations, will experience a significant impact due to its tight maritime borders if there is no international consensus on the military application of artificial intelligence technology. An autonomous or autonomous system to operate this technology will reduce the amount of human control and allow it to operate without any human intervention. It will be a threat to the application of the fundamental principles of international humanitarian law, such as the distinction principle, and proportionality principle. Where these principles are tightly intertwined with human command and control in making decisions regarding the execution of attacks. The article employs normative legal methodology. Furthermore, this paper endeavours to assess the pertinence of principles in international humanitarian law during the era of the artificial intelligence arms race. It also delves into the contribution of ASEAN in upholding stability, peace, and security in the Southeast Asia region, thereby reinforcing the importance of this research. This research emphasises the importance of aligning the progress of artificial intelligence in military contexts with core principles of international humanitarian law. It underscores the need for ASEAN to safeguard regional peace and security by establishing a novel regulatory framework that outlines restrictions on the development and deployment of artificial intelligence for military objectives.Lentera Hukum610The pertinence of principles in international humanitarian law during the era of the artificial intelligence arms race is assessed and the contribution of ASEAN in upholding stability, peace, and security in the Southeast Asia region is delves into, thereby reinforcing the importance of this research.Lentera Hukum['Y. Putro', 'Muhammad Insan Tarigan', 'Haekal Al Asyari']2024-02-09T00:00:00https://www.semanticscholar.org/paper/276ebc15f3bd945b35852229e955f991f3c18180 2e3f23a89b403466949a6029e65bf8db2a32e961Objective This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image. Study Design Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen's Kappa and Multiclass Receiver Operating Characteristic AUC were used to evaluate the performance of the model. Results The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen's Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, Unable to Assess a score of 0.916 and for Text Added score of 0.981 was achieved. Conclusion This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available.American Journal of Perinatology00This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available.American journal of perinatology['E. Frisch', 'Anant Jain', 'Michael Jin', 'Erik P. Duhaime', 'Amol Malshe', 'Steve Corey', 'Robert Allen', 'Nicole M. Duggan', 'Chanel Fischetti']2024-02-09T00:00:00https://www.semanticscholar.org/paper/2e3f23a89b403466949a6029e65bf8db2a32e961 eaf232154f4cb3f875ce732aaec140ddaadd2c35International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-09T00:00:00https://www.semanticscholar.org/paper/eaf232154f4cb3f875ce732aaec140ddaadd2c35 c4bc99f9764afac4fb51ce7fbefd2774816fd679International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-09T00:00:00https://www.semanticscholar.org/paper/c4bc99f9764afac4fb51ce7fbefd2774816fd679 02f1aa3038f560eafac82dcb97d72716a3e85464Nature Reviews Electrical Engineering393Nature Reviews Electrical Engineering['Yuanzheng Li', 'Yizhou Ding', 'Shangyang He', 'Fei Hu', 'Juntao Duan', 'Guanghui Wen', 'Hua Geng', 'Zhengguang Wu', 'H. Gooi', 'Yong Zhao', 'Chenghui Zhang', 'Shengwei Mei', 'Zhigang Zeng']2024-02-09T00:00:00https://www.semanticscholar.org/paper/02f1aa3038f560eafac82dcb97d72716a3e85464 ba487603dcd4925765dabfb4492f5856077dc0feBACKGROUND Hematopoietic stem cell transplantation (HSCT) is a procedure with high morbidity and mortality. Identifying patients for maximum benefit and risk assessment is crucial in the decision-making process. This has led to the development of predictive risk models for HSCT in adults, which have limitations when applied to pediatric population. Our goal was to develop an automatic learning algorithm to predict survival in children with malignant disorders undergoing HSCT. METHODS We studied allogenic HSCTs performed on children with malignant disorders at a third-level hospital between 1991 and 2021. Survival was analyzed using the Kaplan-Meier method, log-rank test for the univariate analysis, and Cox regression for the multivariate analysis. A prognostic index was constructed based on these findings. Lastly, we constructed a predictive model using a random forest algorithm to forecast 1-year survival after HSCT. RESULTS We analyzed 229 HSCTs in 201 patients with a median follow-up of 1.64 years. Variables that impacted on the multivariate analysis were older age (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.12-1.76, p = .003), oldest period of HSCT (HR 0.46, 95% CI 0.29-0.73, p < .001), and mismatched donor (HR 2.65, 95% CI 1.51-4.65, p = .001). Our prognostic index was associated with 3-year overall survival (OS; p < .001). A random forest was developed using as variables: diagnosis, age, year of HSCT, time from diagnosis to HSCT, disease stage, donor type, and conditioning. This achieved 72% accuracy in predicting 1-year OS. CONCLUSIONS Our index and random forest was effective in predicting 1-year survival. However, further validation in diverse populations is necessary to establish their generalizability.European Journal of Haematology181A predictive model using a random forest algorithm to predict survival in children with malignant disorders undergoing HSCT and a prognostic index that was associated with 3-year overall survival were effective in predicting 1-year survival.European journal of haematology['C. Echecopar', 'Inés Abad', 'Víctor Galán-Gómez', 'Yasmina Mozo Del Castillo', 'L. Sisinni', 'D. Bueno', 'Beatriz Ruz', 'Antonio Pérez-Martínez']2024-02-09T00:00:00https://www.semanticscholar.org/paper/ba487603dcd4925765dabfb4492f5856077dc0fe 450eee1131f9bf0c1f5a08fc07a2eaed8452d8ecAn outstanding improvement in regular language handling innovation is shown by ChatGPT. Monstrous measures of information were utilized to prepare a model that can create responses that like those of a person in various circumstances. This article offers specialists the opportunity to examine the potential purposes of this innovation in various enterprises, for example, client assistance, psychological wellness guiding, training, and some more. Scientists can release ChatGPT’s maximum capacity and improve the existences of millions of individuals by understanding how it works and how it very well might be custom fitted for different use cases. Furthermore, intensive exploration on ChatGPT can possibly essentially change current Man-made consciousness. ChatGPT is preparing for future improvements by making perusing and tracking down news/data simpler than at any other time existed. By concentrating on how ChatGPT works, specialists can acquire significant experiences into the basic components of these advances and add to progressing endeavors to make considerably more modern artificial intelligence frameworks. So, research on ChatGPT is both invigorating and significant. Whether you are keen on investigating new applications for this innovation or adding to the improvement of further developed simulated intelligence frameworks, there are a lot of motivations to jump into this entrancing field of study.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)120The potential purposes of ChatGPT are examined to examine the potential purposes of this innovation in various enterprises, for example, client assistance, psychological wellness guiding, training, and some more.2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)['Parth Belwal', 'Ashutosh Upadhyay', 'Anuj Kumar Dixit']2024-02-09T00:00:00https://www.semanticscholar.org/paper/450eee1131f9bf0c1f5a08fc07a2eaed8452d8ec be3b6e0a827ba35077c552a56859b9a6d1912482Increase in complexity, the development of economic changes and the globalization ofINTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH00INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH['Dede Chinyere Helen', 'Abue Regina Elejie', 'Ogar Joy Iyeumbe', 'Umoh Godwin Godwin', 'Echadu Melford Ochang', 'Bassey Prince Etim']2024-02-09T00:00:00https://www.semanticscholar.org/paper/be3b6e0a827ba35077c552a56859b9a6d1912482 3d8ccf8e18675811264ace4c0b3447af5c76f948Scientific Reports380The APC model represents a potential clinical tool to striate specific patient outcomes using machine learning models and patient-specific image-based (biomechanical and morphological) and clinical data as input that could greatly assist clinicians in their management decisions for patients with AAA.Scientific Reports['Timothy K Chung', 'P. Gueldner', 'Okechukwu U Aloziem', 'Nathan L Liang', 'David A. Vorp']2024-02-09T00:00:00https://www.semanticscholar.org/paper/3d8ccf8e18675811264ace4c0b3447af5c76f948 6f8d995fd1df6458bb87f4ba027cb36303e5da67Computer-Aided Design and Applications00Computer-Aided Design and Applications['Weiran Wang', 'Liyan Zhang']2024-02-09T00:00:00https://www.semanticscholar.org/paper/6f8d995fd1df6458bb87f4ba027cb36303e5da67 a2267d17aed9cc26464a65bcc0b29c5085d8a723Computer-Aided Design and Applications00Computer-Aided Design and Applications['Xiaofei Wang']2024-02-09T00:00:00https://www.semanticscholar.org/paper/a2267d17aed9cc26464a65bcc0b29c5085d8a723 e959ba05e04bef24c9e6055e85c2596ab60798f8This study addresses the pressing relevance of implementing distance learning technology in the Republic of Kazakhstan across various educational levels, guided by the framework of legal regulation. The study aims to investigate the benefits of using diverse distance learning technologies in modern education, improving access, motivation, and flexibility while fostering environmental awareness and societal prosperity. The chosen methodology is based on the diagnostic testing method to assess and analyze the educational needs and preferences of students. The study involved 100 participants, ranging in age from 19 to 26, from the Abai Kazakh National Pedagogical University. The study shows important signs and criteria for incorporating distance learning into legal rules in a way that works. These include factors related to the environment, motivation, cognition, analysis, and culture. This transformation of education aligns with real-life situations, adapting to changing circumstances and allowing students to select distance learning when necessary. The implementation of modern technology in distance education facilitates profound knowledge acquisition while accommodating evolving personal circumstances, ultimately fostering a conducive environment for continued high-quality education at cognitive and social levels.E-Learning and Digital Media280E-Learning and Digital Media['Nurlan Apakhayev', 'Indira Mussabekova', 'Dina B. Bugybay', 'Kaldarbek Kuandykov', 'Kuanysh Koishybaiuly']2024-02-08T00:00:00https://www.semanticscholar.org/paper/e959ba05e04bef24c9e6055e85c2596ab60798f8 c7f764a6d6fbf42b0bbc9b53c2f951bc56f66be6Polish Journal of Environmental Studies00Polish Journal of Environmental Studies['Xiaoyu Li', 'Di Ke', 'Guodong Li', 'Sang-bing Tsai']2024-02-08T00:00:00https://www.semanticscholar.org/paper/c7f764a6d6fbf42b0bbc9b53c2f951bc56f66be6 ef65eff75a0aed22519b4dbbe937396c07e4c0d4Economie et Statistique / Economics and Statistics00Economie et Statistique / Economics and Statistics['S. Lecocq', 'Valérie Orozco', 'Christine Boizot-Szantai', 'Céline Bonnet', 'Fabrice Etilé']2024-02-08T00:00:00https://www.semanticscholar.org/paper/ef65eff75a0aed22519b4dbbe937396c07e4c0d4 c656d6307bfbcb0032f0c8ea49bdd4917c3fdf18Humanities and Social Sciences Communications1120Humanities and Social Sciences Communications['Wanli Zhang', 'Bin Zhu', 'Yongling Li', 'Dan Yan']2024-02-08T00:00:00https://www.semanticscholar.org/paper/c656d6307bfbcb0032f0c8ea49bdd4917c3fdf18 9dfff581a5bb4f75ff73ded07ccea586ba1dfaafEuropean Journal of Risk Regulation00European Journal of Risk Regulation['Mary Dobbs']2024-02-08T00:00:00https://www.semanticscholar.org/paper/9dfff581a5bb4f75ff73ded07ccea586ba1dfaaf 37eb7cd38e39a2a9d76ca416dd1a5e156d725becnpj Digit. Medicine190An analysis of the interaction between the 510(k) process —the historically dominant path to market for most medical devices— and the De Novo pathway, a more recent alternative that targets more novel devices, including those involving new technologies, diagnostics, hardware, and software.NPJ Digital Medicine['M. Aboy', 'C. Crespo', 'Ariel D. Stern']2024-02-08T00:00:00https://www.semanticscholar.org/paper/37eb7cd38e39a2a9d76ca416dd1a5e156d725bec e7d651af369e32b10956d27c350b28bd92e4753dв статье рассматриваются предпосылки формирования и развития многоуровневого регулирования климатической повестки на мировой арене. В свете увеличивающегося осознания климатических изменений и связанных с ними проблем, таких как повышение температуры, изменение погодных условий и угрозы для экосистем, государства и международные организации были вынуждены начать обращать больше внимания на эту проблему. Выделяются ключевые международные, межнациональные и государственные документы, определившие вектор развития регулирования климатической повестки. В статье основное внимание уделяется документам, которые регулируют различные аспекты климатической повестки на разных уровнях воздействия: глобальном, международном и национальном. Выделяются ключевые проблемы и риски в данной сфере и предлагаются методы и пути для их митигации. Также рассматривается роль государственных и международных образований и историческая степень их влияния на регулирование климата. В результате анализа происхождения процессов был сделан вывод о том, что формирование и развитие различных уровней регулирования климатической повестки происходит под влиянием множества факторов, включая научные данные, рост общественного сознания и политического давления, а также стремительно расширяющееся международное сотрудничество государств. the article discusses the prerequisites for the formation and development of multilevel regulation of the climate agenda on the world stage. Considering the increasing awareness of climate change and related problems, such as rising temperatures, changing weather conditions and threats to ecosystems, states and international organizations have been forced to start paying more attention to this problem. The key international, interethnic, and state documents that determined the vector of development of the regulation of the climate agenda are highlighted. The article focuses on documents that regulate various aspects of the climate agenda at different levels of impact: global, international, and national. The key problems and risks in this area are highlighted and methods and ways for their mitigation are proposed. The role of state and international entities and the historical degree of their influence on climate regulation are also considered. As a result of the analysis of the origin of the processes, it was concluded that the formation and development of various levels of regulation of the climate agenda is influenced by many factors, including scientific data, the growth of public consciousness and political pressure, as well as the rapidly expanding international cooperation of states.Modern Economy Success20Modern Economy Success['М.Н. Ларина']2024-02-08T00:00:00https://www.semanticscholar.org/paper/e7d651af369e32b10956d27c350b28bd92e4753d 8f5f9987e0dbf7d262512a5d08cb10483b0979f700['Kristen M. Weede Alexander', 'K. D. O’Hara']2024-02-08T00:00:00https://www.semanticscholar.org/paper/8f5f9987e0dbf7d262512a5d08cb10483b0979f7 e1341a29f2e47cfb2ef17b85a35a7d8443d4c0b9Information &amp; Communications Technology Law00Information &amp; Communications Technology Law['Murat Can Pehlivanoğlu']2024-02-08T00:00:00https://www.semanticscholar.org/paper/e1341a29f2e47cfb2ef17b85a35a7d8443d4c0b9 693de3a0b75e3f41b0102368afe45a0035a4042dArtificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, inscrutable AI systems exacerbate the existing challenge of safety assurance of AD. One way to mitigate this challenge is to utilize explainable AI (XAI) techniques. To this end, we present the first comprehensive systematic literature review of explainable methods for safe and trustworthy AD. We begin by analyzing the requirements for AI in the context of AD, focusing on three key aspects: data, model, and agency. We find that XAI is fundamental to meeting these requirements. Based on this, we explain the sources of explanations in AI and describe a taxonomy of XAI. We then identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation. Finally, we propose a modular framework called SafeX to integrate these contributions, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.arXiv.org1574This work presents the first comprehensive systematic literature review of explainable methods for safe and trustworthy AD, and identifies five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation.ArXiv['Anton Kuznietsov', 'Balint Gyevnar', 'Cheng Wang', 'Steven Peters', 'Stefano V. Albrecht']2024-02-08T00:00:00https://www.semanticscholar.org/paper/693de3a0b75e3f41b0102368afe45a0035a4042d 1fef6bf6db93c130587c00bc3c5759de26087302When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control for the team. To determine the best selection of a control agent, we propose the addition of an AI manager (via Reinforcement Learning) which learns as an outside observer of the team. The manager learns a model of behavior linking observations of agent performance and the environment/world the team is operating in, and from these observations makes the most desirable selection of a control agent. We restrict the manager task by introducing a set of constraints. The manager constraints indicate acceptable team operation, so a violation occurs if the team enters a condition which is unacceptable and requires manager intervention. To ensure minimal added complexity or potential inefficiency for the team, the manager should attempt to minimize the number of times the team reaches a constraint violation and requires subsequent manager intervention. Therefore our manager is optimizing its selection of authorized agents to boost overall team performance while minimizing the frequency of manager intervention. We demonstrate our manager performance in a simulated driving scenario representing the case of a hybrid team of agents composed of a human driver and autonomous driving system. We perform experiments for our driving scenario with interfering vehicles, indicating the need for collision avoidance and proper speed control. Our results indicate a positive impact of our manager, with some cases resulting in increased team performance up to ~187% that of the best solo agent performance.arXiv.org611This work addresses the case of hybrid teams in which, at any time, only one team member is authorized to act as control for the team, and proposes the addition of an AI manager which learns as an outside observer of the team to determine the best selection of a control agent.ArXiv['Andrew Fuchs', 'A. Passarella', 'M. Conti']2024-02-08T00:00:00https://www.semanticscholar.org/paper/1fef6bf6db93c130587c00bc3c5759de26087302 d569ba536efe539e93bc8b53f19ad91d5a08a1d1 This paper surveys the Variable Autonomy (VA) robotics literature that considers two contributory elements to Trustworthy AI: transparency and explainability. These elements should play a crucial role when designing and adopting robotic systems, especially in VA where poor or untimely adjustments of the system’s level of autonomy can lead to errors, control conflicts, user frustration and ultimate disuse of the system. Despite this need, transparency and explainability is, to the best of our knowledge, mostly overlooked in VA robotics literature or is not considered explicitly. In this paper, we aim to present and examine the most recent contributions to the VA literature concerning transparency and explainability. In addition, we propose a way of thinking about VA by breaking these two concepts down based on: the mission of the human-robot team; who the stakeholder is; what needs to be made transparent or explained; why they need it; and how it can be achieved. Last, we provide insights and propose ways to move VA research forward. Our goal with this paper is to raise awareness and inter-community discussions among the Trustworthy AI and the VA robotics communities. ACM Computing Surveys441This paper surveys the Variable Autonomy (VA) robotics literature that considers two contributory elements to Trustworthy AI: transparency and explainability and proposes a way of thinking about VA by breaking these two concepts down based on the mission of the human-robot team.ACM Comput. Surv.['Leila Methnani', 'Manolis Chiou', 'Virginia Dignum', 'Andreas Theodorou']2024-02-08T00:00:00https://www.semanticscholar.org/paper/d569ba536efe539e93bc8b53f19ad91d5a08a1d1 34ff3baeb735ccf2e8df86572c421ca43e67c696Research into recidivism risk prediction in the criminal legal system has garnered significant attention from HCI, critical algorithm studies, and the emerging field of human-AI decision-making. This study focuses on algorithmic crime mapping, a prevalent yet underexplored form of algorithmic decision support (ADS) in this context. We conducted experiments and follow-up interviews with 60 participants, including community members, technical experts, and law enforcement agents (LEAs), to explore how lived experiences, technical knowledge, and domain expertise shape interactions with the ADS, impacting human-AI decision-making. Surprisingly, we found that domain experts (LEAs) often exhibited anchoring bias, readily accepting and engaging with the first crime map presented to them. Conversely, community members and technical experts were more inclined to engage with the tool, adjust controls, and generate different maps. Our findings highlight that all three stakeholders were able to provide critical feedback regarding AI design and use - community members questioned the core motivation of the tool, technical experts drew attention to the elastic nature of data science practice, and LEAs suggested redesign pathways such that the tool could complement their domain expertise.International Conference on Human Factors in Computing Systems1431Surprisingly, it was found that domain experts (LEAs) often exhibited anchoring bias, readily accepting and engaging with the first crime map presented to them, and community members and technical experts were more inclined to engage with the tool, adjust controls, and generate different maps.ArXiv['Md. Romael Haque', 'Devansh Saxena', 'Katherine Weathington', 'Joseph Chudzik', 'Shion Guha']2024-02-08T00:00:00https://www.semanticscholar.org/paper/34ff3baeb735ccf2e8df86572c421ca43e67c696 928910f71e576775c67e40b6b74432000d025e5cRecent advancements in HCI and AI research attempt to support user experience (UX) practitioners with AI-enabled tools. Despite the potential of emerging models and new interaction mechanisms, mainstream adoption of such tools remains limited. We took the lens of Human-Centered AI and presented a systematic literature review of 359 papers, aiming to synthesize the current landscape, identify trends, and uncover UX practitioners' unmet needs in AI support. Guided by the Double Diamond design framework, our analysis uncovered that UX practitioners' unique focuses on empathy building and experiences across UI screens are often overlooked. Simplistic AI automation can obstruct the valuable empathy-building process. Furthermore, focusing solely on individual UI screens without considering interactions and user flows reduces the system's practical value for UX designers. Based on these findings, we call for a deeper understanding of UX mindsets and more designer-centric datasets and evaluation metrics, for HCI and AI communities to collaboratively work toward effective AI support for UX.arXiv.org2601A systematic literature review of 359 papers was presented, aiming to synthesize the current landscape, identify trends, and uncover UX practitioners' unmet needs in AI support, finding that UX practitioners' unique focuses on empathy building and experiences across UI screens are often overlooked.ArXiv['Yuwen Lu', 'Yuewen Yang', 'Qinyi Zhao', 'Chengzhi Zhang', 'Toby Jia-Jun Li']2024-02-08T00:00:00https://www.semanticscholar.org/paper/928910f71e576775c67e40b6b74432000d025e5c 1bc2fdf256855c485e77be27805f9febf9a70e75In the ever-expanding landscape of Artificial Intelligence (AI), where innovation thrives and new products and services are continuously being delivered, ensuring that AI systems are designed and developed responsibly throughout their entire lifecycle is crucial. To this end, several AI ethics principles and guidelines have been issued to which AI systems should conform. Nevertheless, relying solely on high-level AI ethics principles is far from sufficient to ensure the responsible engineering of AI systems. In this field, AI professionals often navigate by sight. Indeed, while recommendations promoting Trustworthy AI (TAI) exist, these are often high-level statements that are difficult to translate into concrete implementation strategies. There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals. To address this challenge, our work presents an experience report where we develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice - and report insights from its application in an industrial case study. The framework is built on the result of a systematic review of the state of the practice, a survey, and think-aloud interviews with 34 AI practitioners. The framework, unlike most of those already in the literature, is designed to provide actionable guidelines and tools to support different types of stakeholders throughout the entire Software Development Life Cycle (SDLC). Our goal is to empower AI professionals to confidently navigate the ethical dimensions of TAI through practical insights, ensuring that the vast potential of AI is exploited responsibly for the benefit of society as a whole.arXiv.org301The goal is to empower AI professionals to confidently navigate the ethical dimensions of TAI through practical insights, ensuring that the vast potential of AI is exploited responsibly for the benefit of society as a whole.ArXiv['M. T. Baldassarre', 'Domenico Gigante', 'Marcos Kalinowski', 'Azzurra Ragone']2024-02-08T00:00:00https://www.semanticscholar.org/paper/1bc2fdf256855c485e77be27805f9febf9a70e75 918f7f7a0a07ef22413c8d3e174a52a8cf64bfd0Motivation Melanoma is a severe form of skin cancer increasing globally with about 324.000 cases in 2020, making it the fifth most common cancer in the United States. Conventional drug discovery methods face limitations due to the inherently time consuming and costly. However, the emergence of artificial intelligence (AI) has opened up new possibilities. AI models can effectively simulate and evaluate the properties of a vast number of potential drug candidates, substantially reducing the time and resources required by traditional drug discovery processes. In this context, the development of AI normalizing flow models, employing machine learning techniques to create new molecular structures, holds great promise for accelerating the discovery of effective anticancer therapies. Results This manuscript introduces a novel AI model, named TumFlow, aimed at generating new molecular entities with potential therapeutic value in cancer treatment. It has been trained on the comprehensive NCI-60 dataset, encompassing thousands of molecules tested across 60 tumour cell lines, with a specific emphasis on the melanoma SK-MEL-28 cell line. The model successfully generated new molecules with predicted improved efficacy in inhibiting tumour growth while being synthetically feasible. This represents a significant advancement over conventional generative models, which often produce molecules that are challenging or impossible to synthesize. Furthermore, TumFlow has also been utilized to optimize molecules known for their efficacy in clinical melanoma treatments. This led to the creation of novel molecules with a predicted enhanced likelihood of effectiveness against melanoma, currently undocumented on PubChem. Availability and Implementation https://github.com/drigoni/TumFlow. Supplementary information Uploaded.bioRxiv390A novel AI model, named TumFlow, aimed at generating new molecular entities with potential therapeutic value in cancer treatment, which successfully generated new molecules with predicted improved efficacy in inhibiting tumour growth while being synthetically feasible.bioRxiv['Davide Rigoni', 'Sachithra Yaddehige', 'Nicoletta Bianchi', 'A. Sperduti', 'Stefano Moro', 'Cristian Taccioli']2024-02-08T00:00:00https://www.semanticscholar.org/paper/918f7f7a0a07ef22413c8d3e174a52a8cf64bfd0 92b2c0506d647dddb7ed8576235e1f6a63168742The increasing popularity of AI, particularly Large Language Models (LLMs), has significantly impacted various domains, including Software Engineering. This study explores the integration of AI tools in software engineering practices within a large organization. We focus on ANZ Bank, which employs over 5000 engineers covering all aspects of the software development life cycle. This paper details an experiment conducted using GitHub Copilot, a notable AI tool, within a controlled environment to evaluate its effectiveness in real-world engineering tasks. Additionally, this paper shares initial findings on the productivity improvements observed after GitHub Copilot was adopted on a large scale, with about 1000 engineers using it. ANZ Bank's six-week experiment with GitHub Copilot included two weeks of preparation and four weeks ofactive testing. The study evaluated participant sentiment and the tool's impact on productivity, code quality, and security. Initially, participants used GitHub Copilot for proposed use-cases, with their feedback gathered through regular surveys. In the second phase, they were divided into Control andCopilot groups, each tackling the same Python challenges, and their experiences were again surveyed. Results showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive. Participant responses were overall positive, confirming GitHub Copilot's effectiveness in large-scale software engineering environments. Early data from 1000 engineers also indicated a significant increase in productivity and job satisfaction.Software Engineering60An experiment conducted using GitHub Copilot within a controlled environment to evaluate its effectiveness in real-world engineering tasks showed a notable boost in productivity and code quality with GitHub Copilot, though its impact on code security remained inconclusive.ArXiv['Sayan Chatterjee', 'Ching Louis Liu', 'Gareth Rowland', 'Tim Hogarth']2024-02-08T00:00:00https://www.semanticscholar.org/paper/92b2c0506d647dddb7ed8576235e1f6a63168742 e8852db5a0423311c355e9d3422de748bf0fba80Arthritis Research & Therapy370Interestingly, individual parameters were found to be associated with drug responses in different directions, indicating highly complex interactions and machine learning can be of help in the decision-process by disentangling these relations.Arthritis Research & Therapy['D. Ukalovic', 'B. Leeb', 'B. Rintelen', 'G. Eichbauer-Sturm', 'P. Spellitz', 'Rudolf Puchner', 'Manfred Herold', 'M. Stetter', 'V. Ferincz', 'J. Resch-Passini', 'Jochen Zwerina', 'M. Zimmermann-Rittereiser', 'Ruth Fritsch-Stork']2024-02-08T00:00:00https://www.semanticscholar.org/paper/e8852db5a0423311c355e9d3422de748bf0fba80 874535dcc024ef8a55c932b14025ff8333c12c1dIn an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization’s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization’s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.Applied System Innovation640The key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation are outlined and a comprehensive approach to assess the organization’s data infrastructure, governance practices, and existing AI capabilities is emphasized.Applied System Innovation['Abdulaziz Aldoseri', 'K. Al-Khalifa', 'Abdel Magid Hamouda']2024-02-08T00:00:00https://www.semanticscholar.org/paper/874535dcc024ef8a55c932b14025ff8333c12c1d 47848e694bac7205fff719e449a6f8d12bbb8a30Artificial intelligence (AI)-driven automation is of growing interest in the service sector. Using practice theory in service innovation and recombinant uncertainty frameworks, we ask whether AI patent approval for service firms is received positively by the stock market and whether patent radicalness strengthens or exacerbates the stock market reaction. We draw on 650 service industry firms from the years 1976 to 2019 with 133,813 non-AI patents and AI patents, including 7,543 (AI machine learning), 33,804 (AI hardware), and 53,419 (AI planning/control). The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction. In addition, stock market reaction is insignificant to AI-related hardware patents and increasing radicalness does not influence this relationship. The findings are robust to excluding large firms representing a significant portion of the AI patents. With increasing radicalness, the stock market reaction to machine learning patents is more positive for low temporal depth and exacerbates with higher patent pedigree. The findings have implications for AI patenting among firms in the service sector.Journal of services research540The results show that the stock market reaction is positive for machine learning AI patents, and increasing radicalness strengthens the positive relationship; however, the reaction is negative to AI-related planning and control patents and increasing radicalness exacerbates the negative reaction.Journal of Service Research['Pankaj C. Patel', 'G. Sahi']2024-02-08T00:00:00https://www.semanticscholar.org/paper/47848e694bac7205fff719e449a6f8d12bbb8a30 67db43d905127af037b7993dff42f57749d310c7Water quality plays a pivotal role in agricultural productivity, with its impact reverberating across crop yield and livestock health. This research delves into the realm of artificial intelligence (AI) to unveil the intricate relationships between groundwater quality and agricultural outcomes. Leveraging a comprehensive methodology, we harnessed the power of AI techniques to predict groundwater quality classifications and elucidate their ramifications for farming practices. Our investigation commenced with meticulous data collection from the Telangana Open Data portal, culminating in a dataset spanning three years. Employing techniques such as median imputation and linear regression, we addressed missing values to ensure the integrity of our analysis. Subsequently, we engaged in normalization and dimensionality reduction through Principal Component Analysis (PCA) to facilitate model training and interpretation. The predictive prowess of AI emerged through our evaluation of models, with the XGBoost classifier showcasing a mean cross-validation accuracy of 90.65%. Importantly, interpretability was enhanced by visualizations, including heatmaps and attribute correlations, which unveiled underlying patterns in water quality data. The practical significance of our work was revealed as the model’s predictive capabilities translated into actionable insights for agricultural planning. Farmers can leverage AI-driven predictions to optimize crop selection and irrigation strategies, thereby mitigating potential yield losses. Acknowledging limitations and avenues for future research, this paper presents a compelling case for the application of AI in deciphering the water quality-agricultural productivity nexus. Through accurate predictions and actionable insights, we unveil a pathway towards sustainable agricultural practices informed by cutting-edge technology and scientific rigor.Journées Francophones d'Ingénierie des Connaissances220A compelling case is presented for the application of AI in deciphering the water quality-agricultural productivity nexus through accurate predictions and actionable insights, which unveils a pathway towards sustainable agricultural practices informed by cutting-edge technology and scientific rigor.2024 2nd International Conference on Computer, Communication and Control (IC4)['B. Hardas', 'Krish Tomar']2024-02-08T00:00:00https://www.semanticscholar.org/paper/67db43d905127af037b7993dff42f57749d310c7 f8238281da2f416e4405533468c7be6071831c5fThe study aims to assess artificial intelligence's effects on select HRM functions and to validate the proposed conceptual framework through empirical analysis showing the connection between HRM function as well as artificial intelligence. Questionnaire consisting of closed-ended questionnaires were distributed in order to achieve these goals. The reliability, validity, and correlation analysis were used to assess the factors that make up the suggested model. The hypothesis was further tested and the suggested model was validated using regression analysis. Findings show that all variables account for 89% of HRM explanation, with a R square (R2) of 0.890. The ANOVA values for the regression model, indicate validation at a 95% confidence level. The beta (β) values of all factors are 0.902 and 0.545 in the coefficient summary, which is a reasonable representation of their influence on HRM. These strong AI-based HR apps are a valuable tool for any type of business, even though they lack cognitive capacities of humans. This study will help most businesses to successfully integrate AI-related techniques into hiring, according to our research, as AI will permeate every aspect of HR in the near future and should be seen as a good thing since it makes life better.Migration Letters00This study will help most businesses to successfully integrate AI-related techniques into hiring, as AI will permeate every aspect of HR in the near future and should be seen as a good thing since it makes life better.Migration Letters['Dr. Rajni', 'Dr. Rani Jaiswal', 'Dr. Anshika Rajvanshi', 'Dr. Mohsin Shaikh', 'Vikas Tiwari', 'Dr. Kamal Alaskar']2024-02-08T00:00:00https://www.semanticscholar.org/paper/f8238281da2f416e4405533468c7be6071831c5f c3bf370ec0ec5cbf69712e5d269e9f49fbf69dd5With artificial intelligence (AI) continuing to pervade many aspects of society, it is critical to comprehend the dynamics of trust in AI decision-making and human-AI interaction. This study explores the many facets of trust and looks at how important it is in influencing user attitudes, actions, and the general effectiveness of AI systems. In order to understand the complex interactions between intelligent machines and people, the research incorporates multidisciplinary viewpoints from the fields of psychology, human-computer interaction, and ethics. The first area of inquiry is what influences the creation of first faith in AI. [1]We investigate how consumers' desire to trust AI-driven technology is influenced by system transparency, explain ability, and user experience through empirical study. The creation of design concepts intended to build a foundation of trust in AI systems is informed by insights gained at this stage. The second aspect of the study focuses on how trust changes over time in extended encounters between humans and artificial intelligence. We study the dynamics of trust-building and erosion by monitoring user experiences and system performance. This helps to clarify the critical points and factors that affect the trust's trajectory. This long-term viewpoint aids in the creation of adaptable artificial intelligence systems that can adapt to changing user demands and address issues with trust. The third line of investigation concerns the function of trust in AI-influenced decision-making processes. We evaluate the extent to which users depend on AI-generated insights and the influence of trust on decision outcomes using experimental scenarios and real-world case studies. This stage clarifies the fine balance needed to maximise the collaboration between AI and humans and emphasises the significance of matching AI suggestions with user values. The research concludes with an examination of the consequences of trust in AI for wider societal contexts, with a focus on ethical issues. We look at accountability frameworks, the potential fallout from blind trust, and the moral obligations of AI engineers in creating reliable systems. In order to foster a symbiotic relationship between humans and intelligent systems in a world increasingly driven by AI, this thorough investigation of the role of trust in human-AI interaction and decision-making ultimately aims to provide actionable insights for the design, implementation, and governance of AI technologies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This thorough investigation of the role of trust in human-AI interaction and decision-making ultimately aims to provide actionable insights for the design, implementation, and governance of AI technologies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Sameer Kumar', 'Dr.S.K.Manju Bargavi']2024-02-08T00:00:00https://www.semanticscholar.org/paper/c3bf370ec0ec5cbf69712e5d269e9f49fbf69dd5 c3adfcb4edb7af1a4651086dfe4b1c4372a3c30dThis article looks at the progress, challenges and future directions in the field of speech recognition for intelligent AI devices. Speech recognition technology has made significant progress over the years, enabling seamless interactions between humans and artificial intelligence. This article provides an in-depth analysis of modern speech recognition techniques used in smart AI devices and the potential applications and limitations of the technology. It also discusses the current challenges faced by developers and researchers and suggests possible solutions to overcome them. Keywords-Natural language processing Neat vs. scruff, Soft vs. hard computing, Narrow vs general AI, Rule-based approaches, Statistical and machine learning based methods, Deep Neural Networks, Deep learning models for speech recognition, Hybrid and ensemble approaches, Voice biometrics and authentication, Speech translation and language learning, Natural language processing improvement.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00An in-depth analysis of modern speech recognition techniques used in smart AI devices and the potential applications and limitations of the technology is provided.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-02-08T00:00:00https://www.semanticscholar.org/paper/c3adfcb4edb7af1a4651086dfe4b1c4372a3c30d 0929c666321d70f3b27d26e39ea3105afc5172e1MSA-Management Sciences Journal01MSA-Management Sciences Journal['Doaa Ayoub', 'Madiha Metawie', 'Mira Fakhry']2024-02-08T00:00:00https://www.semanticscholar.org/paper/0929c666321d70f3b27d26e39ea3105afc5172e1 5c4080f491a26b6b00978b931361e26a1e7be59bThe emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and may contribute to the spread of propaganda by malicious actors. We developed the "LLMs Among Us" experimental framework on top of the Mastodon social media platform for bot and human participants to communicate without knowing the ratio or nature of bot and human participants. We built 10 personas with three different LLMs, GPT-4, Llama 2 Chat, and Claude. We conducted three rounds of the experiment and surveyed participants after each round to measure the ability of LLMs to pose as human participants without human detection. We found that participants correctly identified the nature of other users in the experiment only 42% of the time despite knowing the presence of both bots and humans. We also found that the choice of persona had substantially more impact on human perception than the choice of mainstream LLMs.Proceedings of the AAAI Symposium Series421The "LLMs Among Us" experimental framework on top of the Mastodon social media platform for bot and human participants to communicate without knowing the ratio or nature of bot and human participants is developed.ArXiv['Kristina Radivojevic', 'Nicholas Clark', 'Paul Brenner']2024-02-08T00:00:00https://www.semanticscholar.org/paper/5c4080f491a26b6b00978b931361e26a1e7be59b 2b399224b540af03002ceb0e0ca3181a2eea665dBehaviour &amp; Information Technology930Behaviour &amp; Information Technology['Y. Ma', 'Zhongzhun Deng']2024-02-08T00:00:00https://www.semanticscholar.org/paper/2b399224b540af03002ceb0e0ca3181a2eea665d 7e3f307e0e1587e8ed84491fc2c34c7a70216cea A close look at Google's GNoME inorganic materials dataset [Nature 624, 80 (2023)], and 11 things you would like to know.Materials Futures100Materials Futures['Miao Liu', 'Sheng Meng']2024-02-08T00:00:00https://www.semanticscholar.org/paper/7e3f307e0e1587e8ed84491fc2c34c7a70216cea dec6a85f2698dc3187babb2b5201411a4e9c2f9aTCC Africa00TCC Africa['Joy Owango', 'N. Outa', 'Emma Jones', 'Wilson de Souza']2024-02-08T00:00:00https://www.semanticscholar.org/paper/dec6a85f2698dc3187babb2b5201411a4e9c2f9a b57083a93bd055bb161e828371ecc8355961407cInternational Journal of Engineering and Manufacturing00International Journal of Engineering and Manufacturing['Pijush Dutta', 'Gour Gopal Jana', 'Shobhandeb Paul', 'Souvik Pal']2024-02-08T00:00:00https://www.semanticscholar.org/paper/b57083a93bd055bb161e828371ecc8355961407c 14a34ddfcd47e75258bd5403a58ae49654407624International Journal of Engineering and Manufacturing00International Journal of Engineering and Manufacturing['N. Yadav', 'Aryan Sinha', 'Mohit Jain', 'Aman Agrawal', 'Sofia Francis']2024-02-08T00:00:00https://www.semanticscholar.org/paper/14a34ddfcd47e75258bd5403a58ae49654407624 5eb988d1926b80963c382abe0cfb623cc018c308Place Branding and Public Diplomacy00The heightened threats posed by synthetic media are examined, as well as the potential that these tools hold for creating effective countermeasures, to assess the potential for these same tools to improve detection.Place Branding and Public Diplomacy['Rolf Fredheim', 'James Pamment']2024-02-08T00:00:00https://www.semanticscholar.org/paper/5eb988d1926b80963c382abe0cfb623cc018c308 eecefc6545f68181de11fdf7bd3367e2548f520cResearch efforts in the prediction of stroke and diabetes prioritize early detection in order to enhance patient outcomes. To achieve this, a variety of methodologies are integrated. Existing studies, on the other hand, are marred by imbalanced datasets, lack of diversity in their datasets, potential bias, and inadequate model comparisons; these flaws underscore the necessity for more comprehensive and inclusive research methodologies. This paper provides a thorough assessment of machine learning algorithms in the context of early detection and diagnosis of stroke and diabetes. The research employed widely used algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost Classifier, to examine medical data and derive significant findings. The XGBoost Classifier demonstrated superior performance, with an outstanding accuracy, precision, recall, and F1-score of 87.5%. The comparative examination of the algorithms indicated that the Decision Tree, Random Forest, and XGBoost classifiers consistently exhibited strong performance across all measures. The models demonstrated impressive discrimination capabilities, with the XGBoost Classifier and Random Forest reaching accuracy rates of roughly 87.5% and 86.5% respectively. The Decision Tree Classifier exhibited notable performance, with an accuracy rate of 83%. The overall accuracy of the models was evident in the F1-score, a metric that incorporates recall and precision, where the XGBoost model exhibited a marginal improvement of 2% over the Random Forest and Decision Tree models, and 4.25 percent over the last two. The aforementioned results underscore the effectiveness of the XGBoost Classifier, which will be employed as a predictive model in this study, alongside the Random Forest and Decision Tree models, for the accurate identification of stroke and diabetes. Furthermore, combining datasets improves model performance by utilizing relative features. This integrated dataset improves the model's efficiency and creates a resilient and comprehensive prediction model, improving healthcare outcomes. The findings of this research make a valuable contribution to the advancement of AI-driven diagnostic systems, hence enhancing the quality of healthcare decision-making.International Journal of Intelligent Systems and Applications00A thorough assessment of machine learning algorithms in the context of early detection and diagnosis of stroke and diabetes and the effectiveness of the XGBoost Classifier is provided, which will be employed as a predictive model in this study, alongside the Random Forest and Decision Tree models, for the accurate identification of stroke and diabetes.International Journal of Intelligent Systems and Applications['Ngoc-Bich Le', 'Thi-Thu-Hien Pham', 'Sy-Hoang Nguyen', 'Nhat-Minh Nguyen', 'Tan-Nhu Nguyen']2024-02-08T00:00:00https://www.semanticscholar.org/paper/eecefc6545f68181de11fdf7bd3367e2548f520c 6588839281b8c3f0157f393dd13facf9f8c4b3eaCurrent research attempts to demonstrate the prospects of Artificial Intelligence (AI) for knowledge management and the resulting systems in a three-dimensional environment. We analytically examine underlying characteristics of knowledge management, such as knowledge sharing, application, and storage, in the context of tacit and explicit knowledge in a data mining environment. The crucial role of tact and explicit knowledge and its reusability, especially in a data mining environment, has been considered in present studies. It is believed that there is a need to develop a professional model incorporating Artificial Intelligence (AI) to benefit from knowledge management in companies, especially about tacit and explicit knowledge, and its reusability. The likely role of AI applications during various knowledge management processes and the benefits of AI for knowledge management systems that leverage tacit and explicit knowledge and knowledge reusability were highlightedInternational Journal of Information Systems and Computer Sciences00There is a need to develop a professional model incorporating Artificial Intelligence to benefit from knowledge management in companies to benefit from knowledge management in companies, especially about tacit and explicit knowledge, and its reusability.International Journal of Information Systems and Computer Sciences[]2024-02-08T00:00:00https://www.semanticscholar.org/paper/6588839281b8c3f0157f393dd13facf9f8c4b3ea 0c746ce8d11993ee8ebfbdaed3531f2ea769d72fThe field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aim to identify the applications of AI to the field of dermatology and to understand their ethical implications. We utilized a multifaceted search approach, searching PubMed, Medline, Cochrane, and Google Scholar for primary literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Extension for Scoping Reviews. Our advanced query included terms related to dermatology, artificial intelligence, and ethical considerations. Our search yielded a total of 202 papers. After initial screening, 68 studies were included. Thirty-two related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, violations of privacy, and replacement of dermatologist jobs. Seventeen discussed limited skin of color representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use, and privacy challenges. Seven addressed inaccuracies of responses of large language models. Seven examined attitudes and trust towards AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of artificial intelligence integration into clinical practice include increased patient access, improved clinical decision making, efficiency, and many others. However, safeguards must be implemented to ensure ethical applications of artificial intelligence.British Journal of Dermatology02This scoping review utilized a multifaceted search approach, searching PubMed, Medline, Cochrane, and Google Scholar for primary literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Reviews to identify the applications of AI to the field of dermatology and to understand their ethical implications.The British journal of dermatology['Emily R. Gordon', 'M. Trager', 'D. Kontos', 'Chunhua Weng', 'L. J. Geskin', 'Lydia S Dugdale', 'F. Samie']2024-02-08T00:00:00https://www.semanticscholar.org/paper/0c746ce8d11993ee8ebfbdaed3531f2ea769d72f 827dcb9eb876472359957cbb31f17aa1fb1a8055OBJECTIVE The objective of this scoping review is to elucidate the scope and nature of research on the monitoring of clinical artificial intelligence (AI) systems. The review will identify the various methodologies used to monitor clinical AI, while also mapping the reasons that influence the selection of monitoring approaches. INTRODUCTION AI is being used in clinical decision-making at an increasing rate. While much attention has been directed toward the development and validation of AI for clinical applications, the practical implementation aspects, notably the establishment of rational monitoring/quality assurance systems, has received comparatively limited scientific interest. Given the scarcity of evidence and the heterogeneity of methodologies used in this domain, there is a compelling rationale for conducting a scoping review on this subject. INCLUSION CRITERIA This scoping review will include any publications that describe systematic, continuous, or repeated initiatives that evaluate or predict clinical performance of AI models with direct implications for the management of patients in any segment of the health care system. METHODS Publications will be identified through searches of the MEDLINE (Ovid), Embase (Ovid), and Scopus databases. Additionally, backward and forward citation searches as well as a thorough investigation of gray literature will be conducted. Title and abstract screening, full-text evaluation, and data extraction will be performed by 2 or more independent reviewers. Data will be extracted using a tool developed by the authors. The results will be presented graphically and narratively. REVIEW REGISTRATION Open Science Framework https://osf.io/afkrn.JBI Evidence Synthesis121The review will identify the various methodologies used to monitor clinical AI, while also mapping the reasons that influence the selection of monitoring approaches, to elucidate the scope and nature of research on the monitoring of clinical artificial intelligence systems.JBI evidence synthesis['E. S. Andersen', 'Johan Baden Birk-Korch', 'Richard Röttger', 'C. Brasen', 'I. Brandslund', 'J. S. Madsen']2024-02-08T00:00:00https://www.semanticscholar.org/paper/827dcb9eb876472359957cbb31f17aa1fb1a8055 3c67b505c5d8d1528297778ed3ad3198454d04d5Objective Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and apply these scores remains limited. We investigated the positive predictive values (PPVs) of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings. Materials and Methods From March 2020 to May 2022, 656 breasts from 599 women (mean age 52.6 ± 11.5 years, including 0.6% [4/599] high-risk women) who underwent mammography and received positive AI-CAD results (Lunit Insight MMG, abnormality score ≥ 10) were retrospectively included in this study. Univariable and multivariable analyses were performed to evaluate the associations between the AI-CAD abnormality scores and clinical and radiological factors. The breasts were subdivided according to the abnormality scores into groups 1 (10–49), 2 (50–69), 3 (70–89), and 4 (90–100) using the optimal binning method. The PPVs were calculated for all breasts and subgroups. Results Diagnostic indications and positive imaging findings by radiologists were associated with higher abnormality scores in the multivariable regression analysis. The overall PPV of AI-CAD was 32.5% (213/656) for all breasts, including 213 breast cancers, 129 breasts with benign biopsy results, and 314 breasts with benign outcomes in the follow-up or diagnostic studies. In the screening mammography subgroup, the PPVs were 18.6% (58/312) overall and 5.1% (12/235), 29.0% (9/31), 57.9% (11/19), and 96.3% (26/27) for score groups 1, 2, 3, and 4, respectively. The PPVs were significantly higher in women with diagnostic indications (45.1% [155/344]), palpability (51.9% [149/287]), fatty breasts (61.2% [60/98]), and certain imaging findings (masses with or without calcifications and distortion). Conclusion PPV increased with increasing AI-CAD abnormality scores. The PPVs of AI-CAD satisfied the acceptable PPV range according to Breast Imaging-Reporting and Data System for screening mammography and were higher for diagnostic mammography.Korean Journal of Radiology141The positive predictive values of the abnormality scores generated by a deep learning-based commercial AI-CAD system and analyzed them in relation to clinical and radiological findings showed increased with increasing AI-CAD abnormality scores.Korean Journal of Radiology['Si Eun Lee', 'H. Hong', 'Eun-Kyung Kim']2024-02-08T00:00:00https://www.semanticscholar.org/paper/3c67b505c5d8d1528297778ed3ad3198454d04d5 80bd62c7511b1e1c172967c13fbee22a4e74b741The purpose of the study is to determine how artificial intelligence affects small and medium-sized businesses' use of accounting information systems. Regression analysis was employed to verify the suggested model and test the hypothesis. Thus, it was determined that AI offers small enterprises new opportunities. With the aid of this study, the use of AI in accounting systems will increase in the future. Small and medium-sized firms (SMEs) are using artificial intelligence (AI) to improve many aspects of their operations and gain a competitive advantage. Migration Letters470It was determined that AI offers small enterprises new opportunities and with the aid of this study, the use of AI in accounting systems will increase in the future.Migration Letters['Dr. Vani Haridasan', 'Dr. K. Muthukumaran', 'Dr. K. Usha', 'Dr. S. Bharathi Vasu', 'Ms. V. Jhansi']2024-02-08T00:00:00https://www.semanticscholar.org/paper/80bd62c7511b1e1c172967c13fbee22a4e74b741 7cb2db144e02c52e7d27112b0a049673f407a77eWith the escalating abundance of structured and unstructured data and the rapid advancements in analytical techniques, Artificial Intelligence (AI) is catalyzing a revolution in the healthcare industry. However, as AI becomes increasingly indispensable in healthcare, concerns are mounting regarding the lack of transparency, explainability, and potential bias in model predictions. Addressing these issues, Explainable Artificial Intelligence (XAI) emerges as a pivotal solution. XAI plays a crucial role in fostering trust among medical practitioners and AI researchers, thereby paving the way for the broader integration of AI in healthcare. This paper aims to introduce diverse interpretability techniques, shedding light on the comprehensibility and interpretability of XAI systems. These techniques, when applied judiciously, offer significant advantages in the healthcare domain. Given that medical diagnosis models directly impact human life, it is imperative to instill confidence in treating patients based on instructionsfrom seemingly opaque models. The content of this paper includes illustrations grounded in the heart disease dataset, demonstrating how explainability techniques should be prioritized to establish trustworthiness when utilizing AI systems in healthcare. Keywords: Explainable AI, Healthcare, Heart disease, Programming frame- works, LIME, SHAP, Example-based Techniques, Feature-based Techniques.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Diverse interpretability techniques are introduced, shedding light on the comprehensibility and interpretability of XAI systems, and how explainability techniques should be prioritized to establish trustworthiness when utilizing AI systems in healthcare.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ms Madhavilatha', 'Ms G.T. Prasanna Kumari']2024-02-08T00:00:00https://www.semanticscholar.org/paper/7cb2db144e02c52e7d27112b0a049673f407a77e 4258c7e536426ed87993cec3625302949cc2070a1. Overview of Tourism Industry Growth in India: Tourism in India has seen phenomenal growth in recent years, appearing as a significant contributor to the national economy. In 2022, India ranked at the 35th position in the World Travel and Tourism Council (WTTC) Travel & Tourism 2023 Economic Impact Report, contributing 8.6% to the national GDP and generating eighty-two million jobs. This trajectory is expected to continue, with WTTC forecasting India to climb to the 24th position by 2032, highlighting the industry's ongoing economic vitality. 2. Introduction to AI and its Capabilities: Artificial intelligence (AI) encompasses a range of technologies, including machine learning, natural language processing, and computer vision, enabling machines to learn from data and perform tasks typically requiring human intelligence. These capabilities can be harnessed for diverse applications, including data analysis, pattern recognition, and robotic automation, opening doors for innovative solutions across various sectors. 3. Brief on Principles of Sustainable Tourism: Sustainable tourism focuses on minimizing the negative environmental, social, and cultural impacts of tourism activities. It advocates for responsible resource management, community engagement, and cultural preservation, aiming to ensure the long-term viability of tourism destinations and the well-being of local communities.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper introduces a range of technologies, including machine learning, natural language processing, and computer vision, enabling machines to learn from data and perform tasks typically requiring human intelligence, and can be harnessed for diverse applications.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-02-08T00:00:00https://www.semanticscholar.org/paper/4258c7e536426ed87993cec3625302949cc2070a 3f9d8c2ceb2a2020c456dc150fd00a7a77d44fe3PurposeIn the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.Design/methodology/approachTo explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.FindingsThe investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.Originality/valueThis study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.Kybernetes830The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT, and underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy.Kybernetes['Shaohua Yang', 'Murtaza Hussain', 'R.M. Ammar Zahid', 'Umer Sahil Maqsood']2024-02-08T00:00:00https://www.semanticscholar.org/paper/3f9d8c2ceb2a2020c456dc150fd00a7a77d44fe3 d16a5e5a23c6c0690e4fc10a5d1acb187a78040eBACKGROUND Despite improvements in antiretroviral therapy (ART) availability, suboptimal adherence is common among youth with HIV (YWH) and can increase drug resistance and poor clinical outcomes. Our study examined an innovative mobile app-based intervention that used automated directly observed therapy (aDOT) using artificial intelligence, along with conditional economic incentives (CEIs) to improve ART adherence and enhance viral suppression among YWH. SETTING We conducted a pilot study of the aDOT-CEI intervention, informed by the operant framework of Key Principles in Contingency Management Implementation, to improve ART adherence among YWH (18-29) in California and Florida who had an unsuppressed HIV viral load. METHODS We recruited 28 virally unsuppressed YWH from AIDS Healthcare Foundation (AHF) clinics, who used the aDOT platform for 3 months. Study outcomes included feasibility and acceptability, self-reported ART adherence, and HIV viral load. RESULTS Participants reported high satisfaction with the app (91%), and 82% said that it helped them take their medication. Comfort with the security and privacy of the app was moderate (55%), and 59% indicated the incentives helped improve daily adherence. CONCLUSION Acceptability and feasibility of the aDOT-CEI intervention were high with potential to improve viral suppression, although some a priori metrics were not met. Pilot results suggest refinements which may improve intervention outcomes, including increased incentive amounts, provision of additional information, and reassurance about app privacy and security. Additional research is recommended to test the efficacy of the aDOT-CEI intervention to improve viral suppression in a larger sample.Journal of Acquired Immune Deficiency Syndromes00Acceptability and feasibility of the aDOT-CEI intervention were high with potential to improve viral suppression, although some a priori metrics were not met.Journal of acquired immune deficiency syndromes['Marie C. D. Stoner', 'Louis Smith', 'Kristin Ming', 'Noah Mancuso', 'Henna Patani', 'A. Sukhija-Cohen', 'Yancy Granados', 'Danielle Wagner', 'Mallory O Johnson', 'S. Napierala', 'Torsten B. Neilands', 'Parya Saberi']2024-02-08T00:00:00https://www.semanticscholar.org/paper/d16a5e5a23c6c0690e4fc10a5d1acb187a78040e bb35c9a7319532df0e3251f8b24943ea4a604a05Purpose: The current production for energy consumption generates harmful impacts of carbon dioxide (CO2) to the environment causing instability to sustainable development goals. The constitutional reforms of British government serve to be an important means of resolving any encountered incompatibilities to political environment. This study aims to evaluate green economy using developed equation for renewable energy towards political polarization of corporate governance. Materials and Methods: The Kano Model Assessment is used to measure the equivalency of 1970 Patents Act to UK Intellectual Property tabulating the criteria for the fulfillment of sustainable development goals in respect to the environment, artificial intelligence, and dynamic dichotomy of administrative agencies and presidential restriction, as statutory interpretation development to renewable energy.   Findings: The constitutional forms of British government satisfy the sustainable development goals needed to fight climate change, advocate healthy ecosystem, promote leadership of magnates, and delegate responsibilities towards green economy. The presidential partisanship must be observed to delineate parties of concerns and execute the government prescriptions in equivalence to the dichotomous relationship of technology and the environment in fulfilling the rights and privileges of all citizens. Hence, the political elites can execute corporate governance towards sustainable development of renewable energy promoting environmental parks and zero emission target of carbon dioxide (CO2) discharges. Implications to Theory, Practice and Policy: The economic theory developed in statutory interpretation for renewable energy serves as a tool to reduce detrimental impacts of carbon dioxide (CO2) to the environment, mitigate climate change, and produce artefacts of bioenergy and artificial intelligence promoting sustainable development. It is suggested to explore other vulnerabilities of artificial intelligence to prosper economic success.American Journal of Online and Distance Learning00American Journal of Online and Distance Learning['Z. Llarena']2024-02-08T00:00:00https://www.semanticscholar.org/paper/bb35c9a7319532df0e3251f8b24943ea4a604a05 491ab733ce54065f2dcd45d560a7aa4cf05591f1Artificial intelligence(AI) is a science that involves simulation of intelligent behaviors in machineries, like visual perception, decision making, speech recognition and so on. AI is a computational model that allows computers to learn from data and approximate solutions for complex functions. Due to their flexibility and robustness, AI has been widely applied in large scale fields ranging from robotics to airplane flight control. This chapter discusses the advances in all aspect of AI applied in several issues, such as hydrology, agronomy, meteorology, education, healthcare, action, and more. It focuses specifically on various AI applications related to water and soil management and states that AI achieves high performance, accuracy, and correlation with low statistical errors as a rapid decision tool under changing climate conditions. Brief introductions of AI with their adaptability to agricultural water and soil management are also interpreted. Furthermore, this chapter illustrates how the AI tool will help agricultural decision makers and water and soil managers achieve agricultural sustainability. Nowadays speech interfaces are becoming more common and popular becoming a part of daily lives. Speech interfaces have the ability to produce intelligible speech in cases where it is not possible for speech production. Keywords: Visual Perception, hydrology, agronomy, meteorology, computational.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This chapter discusses the advances in all aspect of AI applied in several issues, such as hydrology, agronomy, meteorology, education, healthcare, action, and more and illustrates how the AI tool will help agricultural decision makers and water and soil managers achieve agricultural sustainability.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Prof. Miss Dhenge Damini', 'Miss Kamble Ashwini', 'Miss More Dipali']2024-02-08T00:00:00https://www.semanticscholar.org/paper/491ab733ce54065f2dcd45d560a7aa4cf05591f1 16e4f157211503c9c53837c3f0aea92aaeb801a6In this unprecedented era of technology-driven transformation, it becomes more critical than ever that we aggressively invest in developing robust artificial intelligence (AI) for wargaming in support of decision-making. By advancing AI-enabled systems and pairing these with human judgment, we will be able to enhance all-domain awareness, improve the speed and quality of our decision cycles, offer recommendations for novel courses of action, and more rapidly counter our adversary's actions. It therefore becomes imperative that we accelerate the development of AI to help us better address the complexity of modern challenges and dilemmas that currently requires human intelligence and, if possible, attempt to surpass human intelligence--not to replace humans, but to augment and better inform human decision-making at machine speed. Although deep reinforcement learning continues to show promising results in intelligent agent behavior development for the long-horizon, complex tasks typically found in combat modeling and simulation, further research is needed to enable the scaling of AI to deal with these intricate and expansive state-spaces characteristic of wargaming for either concept development, education, or analysis. To help address this challenge, in our research, we are developing and implementing a hierarchical reinforcement learning framework that includes a multi-model approach and dimension-invariant observation abstractions.arXiv.org790A hierarchical reinforcement learning framework that includes a multi-model approach and dimension-invariant observation abstractions is developed that is able to enhance all-domain awareness, improve the speed and quality of the authors' decision cycles, and offer recommendations for novel courses of action.ArXiv['Scotty Black', 'Christian J. Darken']2024-02-08T00:00:00https://www.semanticscholar.org/paper/16e4f157211503c9c53837c3f0aea92aaeb801a6 abc055b1aec114e2b7b1216f554fb4a2839ef0daArtificial intelligence (AI) has undergone rapid evolution in recent decades, catalysing the emergence of ground-breaking technologies that have reshaped various sectors. Among these advancements is the advent of autonomous vehicles, poised to revolutionize transportation and mobility. Moreover, AI has spurred the development of cutting-edge solutions in healthcare, exemplified by AI-powered medical imaging systems. This manuscript presents an overview of AI's evolution and explores the latest strides in autonomous vehicles and healthcare innovations. Delving into the foundational technologies like machine learning and computer vision, it elucidates the methodologies employed in crafting autonomous vehicles and healthcare solutions. The document also scrutinizes the advantages and hurdles inherent in these innovations, while offering insights into future avenues of research. Overall, it underscores AI's profound impact on transportation, healthcare, and beyond, underscoring the transformative potential of autonomous vehicles and healthcare technologies in fostering safer and more efficient mobility and healthcare systems.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300An overview of AI's evolution is presented and the latest strides in autonomous vehicles and healthcare innovations are explored, underscoring the transformative potential of autonomous vehicles and healthcare technologies in fostering safer and more efficient mobility and healthcare systems.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Sohana Akter']2024-02-08T00:00:00https://www.semanticscholar.org/paper/abc055b1aec114e2b7b1216f554fb4a2839ef0da 591b1ade81a5dbf4e99b78be349ed64a6d9e6564Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies, and underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Jeff Shuford', 'Md.mafiqul Islam']2024-02-08T00:00:00https://www.semanticscholar.org/paper/591b1ade81a5dbf4e99b78be349ed64a6d9e6564 13d985491677048fdbf05b6065f17bf14753a604Artificial Intelligence (AI) and Machine Learning (ML) represent burgeoning fields with the potential to transform numerous facets of society and industry. AI encompasses computer systems and algorithms capable of executing tasks typically necessitating human intelligence, such as learning, problem-solving, and decision-making. Conversely, ML entails the creation of algorithms facilitating computers to glean insights from data and refine their performance over time, sans explicit programming. This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles. Furthermore, we scrutinize the potential advantages and apprehensions linked with these technologies, including the prospect of job displacement and the susceptibility to misuse. Finally, we underscore the significance of ethical considerations and conscientious development practices to ensure the realization of AI and ML benefits while mitigating adverse repercussions.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Sohel Rana']2024-02-08T00:00:00https://www.semanticscholar.org/paper/13d985491677048fdbf05b6065f17bf14753a604 f5a7f86518ec2c746fee977105a06e6e7ff2773aMany disciplines, such as computer vision and natural language processing (NLP), find broad applications for artificial intelligence (AI) and machine learning (ML). We will give a brief history of edge detection in this post, which is an essential method for emphasizing important characteristics in a wide range of computer vision applications. We will also explore the transformative potential of transformer-based deep learning models in improving natural language processing applications. In addition, we will present two current research initiatives that demonstrate the creative uses of AI in business negotiation and the pharmaceutical industry. Furthermore, for this journal issue, we have carefully chosen five papers that are pertinent to these topics.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The history of edge detection is given, which is an essential method for emphasizing important characteristics in a wide range of computer vision applications, and the transformative potential of transformer-based deep learning models in improving natural language processing applications is explored.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-02-08T00:00:00https://www.semanticscholar.org/paper/f5a7f86518ec2c746fee977105a06e6e7ff2773a 3a509987397758a89e935320ba53958567934546Int. J. Soc. Robotics461Int. J. Soc. Robotics['Liying Xu', 'Yuyan Zhang', 'Feng Yu', 'Xiaojun Ding', 'Jiahua Wu']2024-02-08T00:00:00https://www.semanticscholar.org/paper/3a509987397758a89e935320ba53958567934546 9aae3fb085057a8f2e4f0fde96ccae12e953baacUtilitarian games such as dictator games to measure fairness have been studied in the social sciences for decades. These games have given us insight into not only how humans view fairness but also in what conditions the frequency of fairness, altruism and greed increase or decrease. While these games have traditionally been focused on humans, the rise of AI gives us the ability to study how these models play these games. AI is becoming a constant in human interaction and examining how these models portray fairness in game play can give us some insight into how AI makes decisions. Over 101 rounds of the dictator game, I conclude that AI has a strong sense of fairness that is dependant of it it deems the person it is playing with as trustworthy, framing has a strong effect on how much AI gives a recipient when designated the trustee, and there may be evidence that AI experiences inequality aversion just as humans.arXiv.org210Over 101 rounds of the dictator game, it is concluded that AI has a strong sense of fairness that is dependant of it it deems the person it is playing with as trustworthy, framing has a strong effect on how much AI gives a recipient when designated the trustee, and there may be evidence that AI experiences inequality aversion just as humans.ArXiv['Jazmia Henry']2024-02-08T00:00:00https://www.semanticscholar.org/paper/9aae3fb085057a8f2e4f0fde96ccae12e953baac 4e3cec5428714714460d1d5c3b46603631bc96e8Millions of individuals use the cryptocurrency industry, which is today a vibrant open-source network and payment network. It would be incredibly intriguing for investors to foresee the cryptocurrency value, but it would also make it impossible to predict because the value of Bitcoin changes every day. The most well-known cryptocurrencies are Bitcoin and Ether, which have drawn significant interest from investors and academics in recent years. To illustrate how well our techniques for price prediction work, compare the accuracy and error rate of different cryptocurrencies. The proposed Lasso regression model compares the precision and error rate of Ethereum and Bitcoin cryptocurrencies to demonstrate their effectiveness.Journées Francophones d'Ingénierie des Connaissances250The proposed Lasso regression model compares the precision and error rate of Ethereum and Bitcoin cryptocurrencies to demonstrate their effectiveness and compare the accuracy and error rate of different cryptocurrencies.2024 2nd International Conference on Computer, Communication and Control (IC4)['S. Anusha', 'Dr. D. N. Vasundhara']2024-02-08T00:00:00https://www.semanticscholar.org/paper/4e3cec5428714714460d1d5c3b46603631bc96e8 e7aae84ed07935a510de328f6063102b68353c8aCogent Business &amp; Management340Cogent Business &amp; Management['Nasir Albalawee', 'Amjed Al Fahoum']2024-02-08T00:00:00https://www.semanticscholar.org/paper/e7aae84ed07935a510de328f6063102b68353c8a 6eedc5265fcbcc07241ea52dab7f2e305fef06ffEuropean Journal of Neurology60European journal of neurology['Antonio Cerasa', 'Byron Crowe']2024-02-08T00:00:00https://www.semanticscholar.org/paper/6eedc5265fcbcc07241ea52dab7f2e305fef06ff 330c401e767e436c8ac109c3b762996a01a2d8b0European Heart Journal100European heart journal['Charo Bruce', 'M. A. Gatzoulis', 'M. Brida']2024-02-08T00:00:00https://www.semanticscholar.org/paper/330c401e767e436c8ac109c3b762996a01a2d8b0 19abd40ab667989455885e9389ba5c957c8aca0aWaste Management Research00Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA['Rodrigo Navia', 'David E Ross']2024-02-08T00:00:00https://www.semanticscholar.org/paper/19abd40ab667989455885e9389ba5c957c8aca0a af50d0704b160198799025d0aefffc1cc4107318The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction, enabling a versatile and adaptable AI framework. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare. Our model demonstrates its ability to generate meaningful and contextually relevant outputs in each area. The strength of our approach lies in its generality, leveraging a variety of data sources such as robotics sequences, gameplay data, large-scale video datasets, and textual information for effective multimodal and multi-task learning. Our approach provides a promising avenue for developing generalist, action-taking, multimodal systems.arXiv.org592This work proposes an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks, enabling a versatile and adaptable AI framework.ArXiv['Zane Durante', 'Bidipta Sarkar', 'Ran Gong', 'Rohan Taori', 'Yusuke Noda', 'Paul Tang', 'Ehsan Adeli', 'S. K. Lakshmikanth', 'Kevin Schulman', 'Arnold Milstein', 'D. Terzopoulos', 'Ade Famoti', 'Noboru Kuno', 'A. Llorens', 'Hoi Vo', 'Katsushi Ikeuchi', 'Fei-Fei Li', 'Jianfeng Gao', 'Naoki Wake', 'Qiuyuan Huang']2024-02-08T00:00:00https://www.semanticscholar.org/paper/af50d0704b160198799025d0aefffc1cc4107318 da756f80eb40dfd7bc3ead7dc9baeceebaab851eBackground A considerable number of minors in the United States are diagnosed with developmental or psychiatric conditions, potentially influenced by underdiagnosis factors such as cost, distance, and clinician availability. Despite the potential of digital phenotyping tools with machine learning (ML) approaches to expedite diagnoses and enhance diagnostic services for pediatric psychiatric conditions, existing methods face limitations because they use a limited set of social features for prediction tasks and focus on a single binary prediction, resulting in uncertain accuracies. Objective This study aims to propose the development of a gamified web system for data collection, followed by a fusion of novel crowdsourcing algorithms with ML behavioral feature extraction approaches to simultaneously predict diagnoses of autism spectrum disorder and attention-deficit/hyperactivity disorder in a precise and specific manner. Methods The proposed pipeline will consist of (1) gamified web applications to curate videos of social interactions adaptively based on the needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) the development of ML models that classify several conditions simultaneously and that adaptively request additional information based on uncertainties about the data. Results A preliminary version of the web interface has been implemented, and a prior feature selection method has highlighted a core set of behavioral features that can be targeted through the proposed gamified approach. Conclusions The prospect for high reward stems from the possibility of creating the first artificial intelligence–powered tool that can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as autism spectrum disorder and attention-deficit/hyperactivity disorder. International Registered Report Identifier (IRRID) PRR1-10.2196/52205JMIR Research Protocols550The prospect for high reward stems from the possibility of creating the first artificial intelligence–powered tool that can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as autism spectrum disorder and attention-deficit/hyperactivity disorder.JMIR Research Protocols['Aditi Jaiswal', 'Ruben Kruiper', 'Abdur Rasool', 'Aayush Nandkeolyar', 'Dennis P Wall', 'Peter Washington']2024-02-08T00:00:00https://www.semanticscholar.org/paper/da756f80eb40dfd7bc3ead7dc9baeceebaab851e 270221e3ddd6cdd824cf42181ba5d87df423f9b9Artificial intelligence (AI) has driven many information and communication technology (ICT) breakthroughs. Nonetheless, the scope of ICT systems has expanded far beyond AI since the Turing test proposal. Critically, recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI. In some cases, even works from mathematics, statistics, and engineering would be affected. Worryingly, AI misdefinitions are observed from Western societies to the Global South. In this paper, we propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is. Our online, publicly-available VADER framework scores the coverage of premises that should underlie AI definitions for regulation, which aim to (i) reproduce principles observed in other successful technology regulations, and (ii) include all AI techniques and approaches while excluding non-AI works. Regarding the latter, our score is based on a dataset of representative AI, non-AI ICT, and non-ICT examples. We demonstrate our contribution by reviewing the AI regulation proposals of key players, namely the United States, United Kingdom, European Union, and Brazil. Importantly, none of the proposals assessed achieve the appropriateness score, ranging from a revision need to a concrete risk to ICT systems and works from other fields.arXiv.org430A framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is is is proposed and reviewed by reviewing the AI regulation proposals of key players, namely the United States, United Kingdom, European Union, and Brazil.ArXiv['Leonardo C. T. Bezerra', 'Alexander E. I. Brownlee', 'Luana Ferraz Alvarenga', 'R. Moioli', 'Thais Vasconcelos Batista']2024-02-07T00:00:00https://www.semanticscholar.org/paper/270221e3ddd6cdd824cf42181ba5d87df423f9b9 987f6d645ae48f80c391bdfce2e48f06d1da9049This article explores emerging issues surrounding artificial intelligence (AI) and copyright through a two-pronged approach. First, it provides an extensive literature review analyzing government and industry strategies for addressing AI copyright concerns and evaluates their rationality. Second, it details experiments conducted using neural networks to examine relevant information and investigate image copyright challenges, assessing mainstream large language models’ efficacy in handling copyright matters. The literature review explores AI copyright perspectives of the United Kingdom, China, the European Union, and the United States. It finds that countries emphasize balanced regulation and innovation (UK), ethical content creation (China), regulating high-risk applications (EU), or principles like non-discrimination and privacy (US). However, comprehensive governance frameworks are needed to navigate AI’s ethical, social, and legal intricacies. The experimental portion trains a convolutional neural network on a dataset of 41 infringing and non-infringing image sets to identify copyright infringement. While achieving over 80% accuracy, enhancements through expanded training data, segmentation, and multi-domain detection could improve generalization. The paper concludes with an analysis advocating copyright adaptation for AI creations, measured protections for standalone AI works, and constructive policies from interdisciplinary dialogue.Cambridge Explorations in Arts and Sciences00An extensive literature review analyzing government and industry strategies for addressing AI copyright concerns and evaluates their rationality is provided, and experiments conducted using neural networks to examine relevant information and investigate image copyright challenges are details.Cambridge Explorations in Arts and Sciences['Hongyi Ling', 'Lei Cheng', 'Zifeng Geng', 'Haotian Shi', 'Yingzhuo Li', 'Weichen Wang']2024-02-07T00:00:00https://www.semanticscholar.org/paper/987f6d645ae48f80c391bdfce2e48f06d1da9049 d0c695241a9652362e87affe49a6b47fd079ec5dEven though the current financial situation of the judiciary in terms of working conditions means that a few courts have relatively satisfactory working conditions, while the majority are regularly faced with unfilled judicial and administrative positions, a lack of basic material and technical resources for smooth work, and lack of or inadequate premises for staff accommodation and work, the author will ask a doctrinally quite topical question: Can artificial intelligence replace the judge? EU law does not specify the concept of the court but leaves this question to the legislature of each state, which is why this question is regulated differently in EU states and thus represents a greater challenge for the future. In addition, the dynamic normative activity in this area at the European level requires further study and adaptation of the regulation. The main objective of this text is to present some of the potential problems, so a sketch of the challenges and possible answers that this text will offer can be considered as a modest contribution to the discussion on the inclusion of AI in litigation.Harmonius Journal of Legal and Social Studies in South East Europe00The main objective of this text is to present some of the potential problems of the inclusion of AI in litigation and to present some of the possible answers.Harmonius Journal of Legal and Social Studies in South East Europe['D. Bodul']2024-02-07T00:00:00https://www.semanticscholar.org/paper/d0c695241a9652362e87affe49a6b47fd079ec5d 090b00258510964014c6eda5c889c78dd9ea5048British medical journal20BMJ['Alison Bedford Russell', 'Jane Slater', 'Neil Woods']2024-02-07T00:00:00https://www.semanticscholar.org/paper/090b00258510964014c6eda5c889c78dd9ea5048 c070dae9810a6fa22795aac75c25de5954a17b55This research explores the impact of Artificial Intelligence (AI) on community development, spanning healthcare, education, environmental sustainability, and community empowerment. The purpose of this study is to comprehensively analyze the perceptions and experiences of participants in underserved communities regarding AI applications. Employing a mixed-methods approach, quantitative surveys provide statistical insights, complemented by qualitative narratives to capture nuanced perspectives. The methodology involves surveying 120 participants from diverse occupations and age groups, utilizing Likert scales and regression analysis. The results reveal a positive perception of AI across domains, emphasizing its potential for positive societal outcomes. Noteworthy is the statistical significance of AI's impact on healthcare, education, and environmental sustainability. The inclusion of qualitative narratives enriches the findings, providing depth to statistical measures. The novelty of this study lies in its holistic exploration of AI's impact on community development, integrating quantitative and qualitative dimensions. The research contributes to the field by providing nuanced insights into the multifaceted aspects of AI in community contexts. In conclusion, this study underscores the need for responsible AI deployment, aligning with community values, as communities navigate the evolving technological landscape.Journal of Community Service and Society Empowerment125The results reveal a positive perception of AI across domains, emphasizing its potential for positive societal outcomes, and underscores the need for responsible AI deployment, aligning with community values, as communities navigate the evolving technological landscape.Journal of Community Service and Society Empowerment['Ansarullah Hasas', 'Musawer Hakimi', 'Amir Kror Shahidzay', 'Abdul Wajid Fazil']2024-02-07T00:00:00https://www.semanticscholar.org/paper/c070dae9810a6fa22795aac75c25de5954a17b55 3aa2135fb83e6e682b89db4913fb79eb2b0f44d61041[]2024-02-07T00:00:00https://www.semanticscholar.org/paper/3aa2135fb83e6e682b89db4913fb79eb2b0f44d6 9cf4f633c93a73720d9ae4672f8371eeb2eb53cfBreast Cancer Research142Improved in risk assessment could stem from enhancing cancer detection capabilities of smaller cancers, and other state-of-the-art AI detection algorithms with high performance for smaller cancers might achieve relatively high performance for risk assessment.Breast Cancer Research : BCR['Ruggiero Santeramo', 'Celeste Damiani', 'Jiefei Wei', 'Giovanni Montana', 'A. R. Brentnall']2024-02-07T00:00:00https://www.semanticscholar.org/paper/9cf4f633c93a73720d9ae4672f8371eeb2eb53cf 95983e17a4aa2c158afc3d3f279e45a7e105c0a7The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in various applications. Although developers are encouraged to produce model cards, it's not clear how much information or what information these cards contain. In this study, we conduct a comprehensive analysis of 32,111 AI model documentations on Hugging Face, a leading platform for distributing and deploying AI models. Our investigation sheds light on the prevailing model card documentation practices. Most of the AI models with substantial downloads provide model cards, though the cards have uneven informativeness. We find that sections addressing environmental impact, limitations, and evaluation exhibit the lowest filled-out rates, while the training section is the most consistently filled-out. We analyze the content of each section to characterize practitioners' priorities. Interestingly, there are substantial discussions of data, sometimes with equal or even greater emphasis than the model itself. To evaluate the impact of model cards, we conducted an intervention study by adding detailed model cards to 42 popular models which had no or sparse model cards previously. We find that adding model cards is moderately correlated with an increase weekly download rates. Our study opens up a new perspective for analyzing community norms and practices for model documentation through large-scale data science and linguistics analysis.arXiv.org592A comprehensive analysis of 32,111 AI model documentations on Hugging Face, a leading platform for distributing and deploying AI models, finds that sections addressing environmental impact, limitations, and evaluation exhibit the lowest filled-out rates, while the training section is the most consistently filled-out.ArXiv['Weixin Liang', 'Nazneen Rajani', 'Xinyu Yang', 'Xinyu Yang', 'Ezinwanne Ozoani', 'Eric Wu', 'Yiqun T. Chen', 'Daniel Smith', 'James Zou']2024-02-07T00:00:00https://www.semanticscholar.org/paper/95983e17a4aa2c158afc3d3f279e45a7e105c0a7 f201a186fcc23b8491ef88341fa0d92cc659a21bFrontiers of Engineering Management81This study explores the integration of ChatGPT and AI-generated content (AIGC) in engineering management and categorizes AIGC services within the domain of engineering management and conceptualizes an AIGC-aided engineering lifecycle.Frontiers of Engineering Management['Zuge Yu', 'Yeming Gong']2024-02-07T00:00:00https://www.semanticscholar.org/paper/f201a186fcc23b8491ef88341fa0d92cc659a21b f6320887c508a5cbbc538569951fce4f6c8e4cf9Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use clauses (commonly referred to as Responsible AI Licenses) were proposed to give developers a framework for releasing AI assets while specifying their users to mitigate negative applications. As of the end of 2023, on the order of 40,000 software and model repositories have adopted responsible AI licenses licenses. Notable models licensed with behavioral use clauses include BLOOM (language) and LLaMA2 (language), Stable Diffusion (image), and GRID (robotics). This paper explores why and how these licenses have been adopted, and why and how they have been adapted to fit particular use cases. We use a mixed-methods methodology of qualitative interviews, clustering of license clauses, and quantitative analysis of license adoption. Based on this evidence we take the position that responsible AI licenses need standardization to avoid confusing users or diluting their impact. At the same time, customization of behavioral restrictions is also appropriate in some contexts (e.g., medical domains). We advocate for ``standardized customization'' that can meet users' needs and can be supported via tooling.arXiv.org381It is taken that responsible AI licenses need standardization to avoid confusing users or diluting their impact and advocate for ``standardized customization'' that can meet users' needs and can be supported via tooling.ArXiv['Aaron Gokaslan', 'Daniel McDuff', 'Tim Korjakow', 'Scott Cambo', 'Jesse Josua Benjamin', 'Jenny Lee', 'Yacine Jernite', 'Carlos Muñoz Ferrandis', 'Alek Tarkowski', 'Joseph Lindley', 'A. F. Cooper', 'Danish Contractor', 'Chia-Hsiang Kao', 'Evan Trop', 'McKinley Polen', 'Jasmine Collins', 'Landan Seguin', 'Austin Jacobson', 'Mihir Patel', 'Jonathan Frankle', 'Cory Stephenson', 'Volodymyr Kuleshov']2024-02-07T00:00:00https://www.semanticscholar.org/paper/f6320887c508a5cbbc538569951fce4f6c8e4cf9 d8e228127819df1259c0060fa44b793aef082d22"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 (SD, 11.3) years; all female), acquired between August 2016-March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08, P < .001) and breast imaging specialists (difference of 0.05, P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. ©RSNA, 2024.Radiology: Artificial Intelligence01A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics.Radiology. Artificial intelligence['Jiye G Kim', 'Bryan Haslam', 'Abdul Rahman Diab', 'Ashwin Sakhare', 'Giorgia Grisot', 'Hyunkwang Lee', 'Jacqueline Holt', 'Christoph I. Lee', 'William Lotter', 'A. G. Sorensen']2024-02-07T00:00:00https://www.semanticscholar.org/paper/d8e228127819df1259c0060fa44b793aef082d22 618f0284465a8f2b4e979f0213227b7c51816565We present MIPS, a novel method for program synthesis based on automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a benchmark of 62 algorithmic tasks that can be learned by an RNN and find it highly complementary to GPT-4: MIPS solves 32 of them, including 13 that are not solved by GPT-4 (which also solves 30). MIPS uses an integer autoencoder to convert the RNN into a finite state machine, then applies Boolean or integer symbolic regression to capture the learned algorithm. As opposed to large language models, this program synthesis technique makes no use of (and is therefore not limited by) human training data such as algorithms and code from GitHub. We discuss opportunities and challenges for scaling up this approach to make machine-learned models more interpretable and trustworthy.arXiv.org364MIPS uses an integer autoencoder to convert the RNN into a finite state machine, then applies Boolean or integer symbolic regression to capture the learned algorithm in Python code, auto-distilling the learned algorithm into Python code.ArXiv['Eric J. Michaud', 'Isaac Liao', 'Vedang Lad', 'Ziming Liu', 'Anish Mudide', 'Chloe Loughridge', 'Zifan Carl Guo', 'Tara Rezaei Kheirkhah', "Mateja Vukeli'c", 'Max Tegmark']2024-02-07T00:00:00https://www.semanticscholar.org/paper/618f0284465a8f2b4e979f0213227b7c51816565 6d2d3510c7563d976d3ad07b87cff63430ed0c29In this study, we set out to measure race and gender bias prevalent in text-to-image (TTI) AI image generation, focusing on the popular model Stable Diffusion from Stability AI. Previous investigations into the biases of word embedding models—which serve as the basis for image generation models—have demonstrated that models tend to overstate the relationship between semantic values and gender, ethnicity, or race. These biases are not limited to straightforward stereotypes; more deeply rooted biases may manifest as microaggressions or imposed opinions on policies, such as paid paternity leave decisions. In this analysis, we use image captioning software OpenFlamingo and Stable Diffusion to identify and classify bias within text-to-image models. Utilizing data from the Bureau of Labor Statistics, we engineered 50 prompts for profession and 50 prompts for actions in the interest of coaxing out shallow to systemic biases in the model. Prompts included generating images for "CEO", "nurse", "secretary", "playing basketball", and "doing homework". After generating 20 images for each prompt, we document the model’s results. We find that biases do exist within the model across a variety of prompts. For example, 95% of the images generated for "playing basketball" were African American men. We then analyze our results through categorizing our prompts into a series of income and education levels corresponding to data from the Bureau of Labor Statistics. Ultimately, we find that racial and gender biases are present yet not drastic.International Conference on Applied Informatics and Communication110This study sets out to measure race and gender bias prevalent in text-to-image (TTI) AI image generation, focusing on the popular model Stable Diffusion from Stability AI, and finds that racial and gender biases are present yet not drastic.2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)['Aadi Chauhan', 'Taran Anand', 'Tanisha Jauhari', 'Arjav Shah', 'Rudransh Singh', 'Arjun Rajaram', 'Rithvik Vanga']2024-02-07T00:00:00https://www.semanticscholar.org/paper/6d2d3510c7563d976d3ad07b87cff63430ed0c29 5741458ed7ca0ce5b9d9d8fb1c4dfc5614d975bcAI systems cannot exist without data. Now that AI models (data science and AI) have matured and are readily available to apply in practice, most organizations struggle with the data infrastructure to do so. There is a growing need for data engineers that know how to prepare data for AI systems or that can setup enterprise-wide data architectures for analytical projects. But until now, the data engineering part of AI engineering has not been getting much attention, in favor of discussing the modeling part. In this paper we aim to change this by perform a mapping study on data engineering for AI systems, i.e., AI data engineering. We found 25 relevant papers between January 2019 and June 2023, explaining AI data engineering activities. We identify which life cycle phases are covered, which technical solutions or architectures are proposed and which lessons learned are presented. We end by an overall discussion of the papers with implications for practitioners and researchers. This paper creates an overview of the body of knowledge on data engineering for AI. This overview is useful for practitioners to identify solutions and best practices as well as for researchers to identify gaps.arXiv.org480An overview of the body of knowledge on data engineering for AI is created, which life cycle phases are covered, which technical solutions or architectures are proposed and which lessons learned are presented, by performing a mapping study on data engineering for AI systems.ArXiv['Petra Heck']2024-02-07T00:00:00https://www.semanticscholar.org/paper/5741458ed7ca0ce5b9d9d8fb1c4dfc5614d975bc 7f4893a3355c4b26e7cfb678d33bc9d93eb5646dThis study of the literature focuses on the use of AI-powered systems in educational settings, examining the field of systems created to monitor suspicious and unusual activity. The paper explores the developments in data analytics, machine learning, and artificial intelligence that make advanced monitoring systems possible. It looks at the technology, approaches, and studies that have already been used to build these kinds of systems, highlighting how well they work to identify anomalous behavior in student environments. The assessment also identifies obstacles, moral issues, and prospective future paths in the creation and application of AI-driven solutions for boosting security and promoting a secure learning environment.International Journal of Advanced Research in Science, Communication and Technology90This study of the literature focuses on the use of AI-powered systems in educational settings, examining the field of systems created to monitor suspicious and unusual activity and looking at how well they work to identify anomalous behavior in student environments.International Journal of Advanced Research in Science, Communication and Technology['Hamsa D R', 'Harsha N', 'A S Vinay Raj']2024-02-07T00:00:00https://www.semanticscholar.org/paper/7f4893a3355c4b26e7cfb678d33bc9d93eb5646d 4a79f1acaee07814b54f0eb1f2eba2c909606e7aThe governance of artificial intelligence (AI) is an urgent challenge that requires actions from three interdependent stakeholders: individual citizens, technology corporations, and governments. We conducted an online survey (N = 525) of US adults to examine their beliefs about the governance responsibility of these stakeholders as a function of trust and AI ethics. Different dimensions of trust and different ethical concerns were associated with beliefs in governance responsibility of the three stakeholders. Specifically, belief in the governance responsibility of the government was associated with ethical concerns about AI, whereas belief in governance responsibility of corporations was related to both ethical concerns and trust in AI. Belief in governance responsibility of individuals was related to human-centered values of trust in AI and fairness. Overall, the findings point to the need for an interdependent framework in which citizens, corporations, and governments share governance responsibilities, guided by trust and ethics as the guardrails.Public Understanding of Science440The findings point to the need for an interdependent framework in which citizens, corporations, and governments share governance responsibilities, guided by trust and ethics as the guardrails.Public understanding of science['Prabu David', 'Hyesun Choung', 'John S. Seberger']2024-02-07T00:00:00https://www.semanticscholar.org/paper/4a79f1acaee07814b54f0eb1f2eba2c909606e7a 2eb0a3a83503ded42457c358f172e372fd69eb28This paper aims to explore the feasibility of AI in the field of children’s painting education and to design AI-assisted applications to guide children’s painting. The group selected Liu Yichen, the younger brother of the group member Swan, as the research subject, and selected six of his paintings. Through interviews, we learned about his creative intentions and the details that he failed to complete due to his own limitations. Then, through AI technology, we would conduct image processing experiments on the six selected paintings (some are dominated by keywords, some are not). Finally, we would feed back the generated works to Liu Yichen, and found out through interviews whether these works meet his expectations and whether they can stimulate him to further creation. At the same time, Liu Yichen’s relatives, friends, and strangers will evaluate Liu Yichen’s original works and AI works through questionnaires to test the feasibility of applying AI painting to children’s painting. The results obtained from the interview of the experimental subject Liu and the questionnaire of friends, relatives and strangers showed positive results. However, 12 per cent of the strangers showed extremely negative attitudes in the questionnaire. Therefore, the study concluded that the integration of AI into early art education is highly feasible in today’s market. However, in the face of the arrival of AI, we still need to leave a buffer period for human society, so as to realize the symbiosis between human and technology in a more peaceful way.Cambridge Explorations in Arts and Sciences00The study concluded that the integration of AI into early art education is highly feasible in today’s market, however, in the face of the arrival of AI, the authors still need to leave a buffer period for human society, so as to realize the symbiosis between human and technology in a more peaceful way.Cambridge Explorations in Arts and Sciences['Lyuzhaozhao Ye', 'Runkun Pan', 'Rui Zhu', 'Suwan Ge', 'Tianyi Zhou', 'Heng Xu']2024-02-07T00:00:00https://www.semanticscholar.org/paper/2eb0a3a83503ded42457c358f172e372fd69eb28 bbd79dcaa1c54749fa9669cdccf381cf7ac5b67aArtificial Intelligence (AI), a system that can be considered to think like humans, act like humans, think rationally, and act rationally, has been adopted in manufacturing industry globally. In view of the rapid development of AI and the reality of Industry 4.0, this paper focuses on the application of AI in manufacturing industry taking Haier Corporation as a case study. We propose the following hypotheses: the application of AI shortens the product RD cycle of manufacturing companies, increases their productivity, reduces the defect product rate, reduces carbon footprint, reduces the number of low-skilled employees in manufacturing, and increases the number of highly-skilled employees.Cambridge Explorations in Arts and Sciences00Cambridge Explorations in Arts and Sciences['Jingyi Wu', 'Zitong Song', 'Yilin Zhao', 'Xuran Shi']2024-02-07T00:00:00https://www.semanticscholar.org/paper/bbd79dcaa1c54749fa9669cdccf381cf7ac5b67a b54ec885c27d4a6862121ac9879a29eee9dd29d3Abstract. This research is based on the importance of auditor performance, because poor auditor performance will have an impact on the results of financial statement audits. This study aims to determine the effect of auditor professionalism and artificial intelligence (AI) on auditor performance. Using a descriptive verification method with a quantitative approach. The main data for this study came from KAP auditors in the South Jakarta area. With the criteria of having worked at KAP for at least one year, and the sample was taken by nonprobability sampling with purposive sampling technique. This study involved 44 respondents. In this study, SEM-PLS was used to process the data. The results show that AI does not affect auditor performance. In addition, auditor performance is significantly affected by auditor professionalism.Abstrak. Penelitian ini berdasarkan pada pentingnya kinerja auditor, karena kinerja auditor yang buruk akan berdampak pada hasil audit laporan keuangan. Penelitian ini bertujuan untuk mengetahui pengaruh profesionalisme auditor dan kecerdasan buatan (AI) terhadap kinerja auditor. Menggunakan metode deskriptif verifikatif dengan pendekatan kuantitatif. Data utama penelitian ini berasal dari auditor KAP di wilayah Jakarta Selatan. Dengan kriteria telah bekerja di KAP selama minimal satu tahun, dan sampel diambil dengan nonprobability sampling dengan teknik purposive sampling. Studi ini melibatkan 44 responden. Dalam penelitian ini, SEM-PLS digunakan untuk mengolah data. Hasil menunjukkan bahwa AI tidak mempengaruhi kinerja auditor. Selain itu, kinerja auditor dipengaruhi signifikan oleh profesionalisme auditor.Bandung Conference Series: Accountancy00Bandung Conference Series: Accountancy['Muhammad Daffa Wardana Nitipradja', 'Nopi Hernawati']2024-02-07T00:00:00https://www.semanticscholar.org/paper/b54ec885c27d4a6862121ac9879a29eee9dd29d3 b522efa1e15491ad30afece28b2c3144c12f9645Final report for the AI Assisted Experiment Control and Calibration project. This project integrated AI/ML into the controls and calibration of a production detector system in the GlueX spectrometer, a large scale Nuclear Physics detector in experimental Hall-D at Jefferson Lab. The AI/ML model predicted calibration constants for a Central Drift Chamber using environmental information available prior to taking the data. The device controls were then automatically adjusted so that the calibration values needed for post-processing of the data were much more stable and quicker to determine. Integration into a production system required guardrails and policy choices to ensure safety of the equipment and the data quality. The project sought to apply similar technology to other detectors. Those efforts are also described here. This documents many of the details of the project.170This project integrated AI/ML into the controls and calibration of a production detector system in the GlueX spectrometer, a large scale Nuclear Physics detector in experimental Hall-D at Jefferson Lab.['Thomas Britton', 'Michael Goodrich', 'Naomi Jarvis', 'T. Jeske', 'N. Kalra', 'D. Lawrence', 'D. McSpadden']2024-02-07T00:00:00https://www.semanticscholar.org/paper/b522efa1e15491ad30afece28b2c3144c12f9645 e822c6c898ba663d6a608710b3079cad98b39fe9The use of artificial intelligence (AI) in cyber security [1] has proven to be very effective as it helps security professionals better understand, examine, and evaluate possible risks and mitigate them. It also provides guidelines to implement solutions to protect assets and safeguard the technology used. As cyber threats continue to evolve in complexity and scope, and as international standards continuously get updated, the need to generate new policies or update existing ones efficiently and easily has increased [1] [2].The use of (AI) in developing cybersecurity policies and procedures can be key in assuring the correctness and effectiveness of these policies as this is one of the needs for both private organizations and governmental agencies. This study sheds light on the power of AI-driven mechanisms in enhancing digital defense procedures by providing a deep implementation of how AI can aid in generating policies quickly and to the needed level.International Conference on Applied Informatics and Communication140This study sheds light on the power of AI-driven mechanisms in enhancing digital defense procedures by providing a deep implementation of how AI can aid in generating policies quickly and to the needed level.2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)['Shadi Jawhar', 'Jeremy Miller', 'Zeina Bitar']2024-02-07T00:00:00https://www.semanticscholar.org/paper/e822c6c898ba663d6a608710b3079cad98b39fe9 4d3600d125ae07ce3e6715dbe7b81d66ce7183ccAim: The aim of this study was to assess healthcare practitioners' and scientific researchers' understanding of the current recommendations by official regulators on incorporating human categorization through “dirty” confounders, such as Nationality and Immigrant Status, into AI and ML-based clinical research and healthcare settings. Materials and Methods: An anonymous online survey was conducted using the Telegram platform, where participants were asked a single question: "Is the inclusion of predictors such as 'Nationality' and 'Immigrant Status' in AI and ML medical models ethical and consistent with contemporary scientific standards?" Respondents were provided with two response options: "Yes" or "No." The survey was specifically targeted at international groups, focusing primarily on English and Russian-speaking clinicians and scientific researchers. Results: 180 unique individuals participated in the survey. The results revealed that one-third of the respondents (60 individuals) agreed that including predictors such as nationality and immigration status is inappropriate in the current ML and AI models. Conclusion: In conclusion, the fact that only one-third of healthcare practitioners and scientific researchers disagree with the categorization of patients and algorithm recipients based on nationality background is at odds with the standards set by official regulators. This discrepancy underscores the urgent need for educational programs aimed at sensitizing the scientific community. Such initiatives should advocate for the prioritization of biological predictors over nationality-based data as documented in passports or identity cards, ensuring that the principles of AI and ML in healthcare align with human-centered, ethical standards.Web3 Journal: ML in Health Science00The fact that only one-third of healthcare practitioners and scientific researchers disagree with the categorization of patients and algorithm recipients based on nationality background is at odds with the standards set by official regulators underscores the urgent need for educational programs aimed at sensitizing the scientific community.Web3 Journal: ML in Health Science['Y. Rusinovich', 'V. Rusinovich']2024-02-07T00:00:00https://www.semanticscholar.org/paper/4d3600d125ae07ce3e6715dbe7b81d66ce7183cc 8c1cfa9dd591f0c25d1c8c157acfbee974fb6bf5Artificial intelligence (AI) has been successfully used in cyber security for enhancing comprehending, investigating, and evaluating cyber threats. It can effectively anticipate cyber risks in a more efficient way. AI also helps in putting in place strategies to safeguard assets and data. Due to their complexity and constant development, it has been difficult to comprehend cybersecurity controls and adopt the corresponding cyber training and security policies and plans.Given that both cyber academics and cyber practitioners need to have a deep comprehension of cybersecurity rules, artificial intelligence (AI) in cybersecurity can be a crucial tool in both education and awareness. By offering an in-depth demonstration of how AI may help in cybersecurity education and awareness and in creating policies fast and to the needed level, this study focuses on the efficiency of AI-driven mechanisms in strengthening the entire cyber security education life cycle.International Conference on Applied Informatics and Communication220This study focuses on the efficiency of AI-driven mechanisms in strengthening the entire cyber security education life cycle.2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)['Shadi Jawhar', 'Jeremy Miller', 'Zeina Bitar']2024-02-07T00:00:00https://www.semanticscholar.org/paper/8c1cfa9dd591f0c25d1c8c157acfbee974fb6bf5 dcdc899fdbfe882a941528c0512bd840b68e8645As Computational Thinking (CT) continues to permeate younger age groups in K-12 education, established CT platforms such as Scratch face challenges in catering to these younger learners, particularly those in the elementary school (ages 6-12). Through formative investigation with Scratch experts, we uncover three key obstacles to children's autonomous Scratch learning: artist's block in project planning, bounded creativity in asset creation, and inadequate coding guidance during implementation. To address these barriers, we introduce ChatScratch, an AI-augmented system to facilitate autonomous programming learning for young children. ChatScratch employs structured interactive storyboards and visual cues to overcome artist's block, integrates digital drawing and advanced image generation technologies to elevate creativity, and leverages Scratch-specialized Large Language Models (LLMs) for professional coding guidance. Our study shows that, compared to Scratch, ChatScratch efficiently fosters autonomous programming learning, and contributes to the creation of high-quality, personally meaningful Scratch projects for children.International Conference on Human Factors in Computing Systems750ChatScratch is introduced, an AI-augmented system to facilitate autonomous programming learning for young children and efficiently fosters autonomous programming learning, and contributes to the creation of high-quality, personally meaningful Scratch projects for children.ArXiv['Liuqing Chen', 'Shuhong Xiao', 'Yunnong Chen', 'Ruoyu Wu', 'Yaxuan Song', 'Lingyun Sun']2024-02-07T00:00:00https://www.semanticscholar.org/paper/dcdc899fdbfe882a941528c0512bd840b68e8645 bf987a09c830f13d5bd48043969c0c8e3cac3b12The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches, and delves into their applications in diverse domains, including healthcare and finance. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed. The paper further investigates the potential convergence of XAI with cognitive sciences, the development of emotionally intelligent AI, and the quest for Human-Like Intelligence (HLI) in AI systems. As AI progresses towards Artificial General Intelligence (AGI), considerations of consciousness, ethics, and societal impact become paramount. The ongoing pursuit of deciphering the mysteries of the brain with AI and the quest for HLI represent transformative endeavors, bridging technical advancements with multidisciplinary explorations of human cognition.arXiv.org1220This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches, and delves into their applications in diverse domains, including healthcare and finance.ArXiv['Yongchen Zhou', 'Richard Jiang']2024-02-07T00:00:00https://www.semanticscholar.org/paper/bf987a09c830f13d5bd48043969c0c8e3cac3b12 4c953d1ca5ce119a821456a7f044c9d8207ca42bThe project “AI Fitness Model Using Deep Learning (YOLOv5)” aims inorder to transform the fitness business by leveraging state-of-the-art deep learning techniques, specifically YOLOv5 (You Only Look Once version 5), to develop an advanced and efficient fitness tracking system. The model is designed to accurately detect and analyze human poses, movements, and exercise routines in real-time using computer vision. By employing YOLOv5’s object detection capabilities, the AI fitness model can identify key body points, track exercise execution, and provide personalized feedback to users, enhancing their workout experience. This creative strategy not only facilitates automated performance monitoring but also enables the creation of adaptive and dynamic fitness routines tailored to individual needs, fostering improved engagement and results in the realm of health and wellnessInternational Journal of Advanced Research in Science, Communication and Technology80The project aims to transform the fitness business by leveraging state-of-the-art deep learning techniques, specifically YOLOv5 (You Only Look Once version 5), to develop an advanced and efficient fitness tracking system.International Journal of Advanced Research in Science, Communication and Technology['B Adibasava', 'Gowtham R', 'Dr Asha K H']2024-02-07T00:00:00https://www.semanticscholar.org/paper/4c953d1ca5ce119a821456a7f044c9d8207ca42b b0bbf16cac3b4627d2a316dca63cb9c6a3c6b4adThe probabilistic formal verification (PFV) of AI systems is in its infancy. So far, approaches have been limited to ad-hoc algorithms for specific classes of models and/or properties. We propose a unifying framework for the PFV of AI systems based onWeighted Model Integration (WMI), which allows to frame the problem in very general terms. Crucially, this reduction enables the verification of many properties of interest, like fairness, robustness or monotonicity, over a wide range of machine learning models, without making strong distributional assumptions. We support the generality of the approach by solving multiple verification tasks with a single, off-the-shelf WMI solver, then discuss the scalability challenges and research directions related to this promising framework.arXiv.org560This work proposes a unifying framework for the PFV of AI systems based on Weighted Model Integration (WMI), which allows to frame the problem in very general terms, and enables the verification of many properties of interest over a wide range of machine learning models, without making strong distributional assumptions.ArXiv['Paolo Morettin', 'Andrea Passerini', 'Roberto Sebastiani']2024-02-07T00:00:00https://www.semanticscholar.org/paper/b0bbf16cac3b4627d2a316dca63cb9c6a3c6b4ad 56b9f23cf099a7eb54d6ca672789898102cc81b3In the rapidly evolving landscape of information management, libraries are undergoing a profound digital transformation to remain vibrant knowledge centers. This article explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) as catalysts for sustainability within libraries. The application of advanced algorithms not only revolutionizes information retrieval with intelligent search systems but also optimizes resource management through predictive analytics. Libraries can leverage AI to create personalized learning experiences, recommending resources tailored to individual preferences and learning styles. Automation, guided by ML, streamlines processes, minimizing environmental impact by ensuring efficient use of physical resources. Beyond the digital realm, smart building systems powered by AI enhance energy efficiency, contributing to the overall sustainability of library infrastructure. As libraries embrace AI and ML, they not only adapt to the digital era but also play a crucial role in fostering a more sustainable futureInternational Journal of Advanced Research in Science, Communication and Technology00This article explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) as catalysts for sustainability within libraries and how libraries can leverage AI to create personalized learning experiences.International Journal of Advanced Research in Science, Communication and Technology['Sreeja Ramachandran']2024-02-07T00:00:00https://www.semanticscholar.org/paper/56b9f23cf099a7eb54d6ca672789898102cc81b3 e292ba3ef766a1e60d658427649934ec622f4affThis research critically navigates the intricate landscape of AI deception, concentrating on deceptive behaviours of Large Language Models (LLMs). My objective is to elucidate this issue, examine the discourse surrounding it, and subsequently delve into its categorization and ramifications. The essay initiates with an evaluation of the AI Safety Summit 2023 (ASS) and introduction of LLMs, emphasising multidimensional biases that underlie their deceptive behaviours.The literature review covers four types of deception categorised: Strategic deception, Imitation, Sycophancy, and Unfaithful Reasoning, along with the social implications and risks they entail. Lastly, I take an evaluative stance on various aspects related to navigating the persistent challenges of the deceptive AI. This encompasses considerations of international collaborative governance, the reconfigured engagement of individuals with AI, proposal of practical adjustments, and specific elements of digital education.arXiv.org70This research critically navigates the intricate landscape of AI deception, concentrating on deceptive behaviours of Large Language Models (LLMs), and takes an evaluative stance on various aspects related to navigating the persistent challenges of the deceptive AI.ArXiv['Linge Guo']2024-02-07T00:00:00https://www.semanticscholar.org/paper/e292ba3ef766a1e60d658427649934ec622f4aff facd9bce96988a6e843203fcddc7d27f0c2b4ab7Nature Communications161The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions and with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved.Nature Communications['Hanning Ying', 'Xiaoqing Liu', 'Min Zhang', 'Y. Ren', 'Shihui Zhen', 'Xiaojie Wang', 'Bo Liu', 'Peng Hu', 'Lian Duan', 'Mingzhi Cai', 'Ming Jiang', 'Xiangdong Cheng', 'Xiangyang Gong', 'Haitao Jiang', 'Jianshuai Jiang', 'Jianjun Zheng', 'Kelei Zhu', 'Wei Zhou', 'Baochun Lu', 'Hongkun Zhou', 'Yiyu Shen', 'Jinlin Du', 'Mingliang Ying', 'Qiang Hong', 'Jingang Mo', 'Jianfeng Li', 'Guanxiong Ye', 'Shizheng Zhang', 'Hongjie Hu', 'Jihong Sun', 'Hui Liu', 'Yiming Li', 'Xingxin Xu', 'Huiping Bai', 'Shuxin Wang', 'Xin Cheng', 'Xiaoyin Xu', 'Long Jiao', 'Risheng Yu', 'Wan-Yee Lau', 'Yizhou Yu', 'Xiujun Cai']2024-02-07T00:00:00https://www.semanticscholar.org/paper/facd9bce96988a6e843203fcddc7d27f0c2b4ab7 6cc8b8651bf11ad5f7e4106f0ae70197b30a8228Interactive Learning Environments30Interact. Learn. Environ.["Pericles 'Asher' Rospigliosi"]2024-02-07T00:00:00https://www.semanticscholar.org/paper/6cc8b8651bf11ad5f7e4106f0ae70197b30a8228 a876dfbf4d294f5af3da7687eed88383fddc202eResearch in Developmental Disabilities110The qualitative research scrutinizes the experiences and perceptions of exceptional learners engaged in AI-mediated discussions versus traditional classroom dialogues to reveal how these learners process and construct knowledge differently when AI is incorporated into their discussions and how it compares to conventional learning environments.Research in developmental disabilities['Alia El Naggar', 'Eman Gaad', 'Shannaiah Aubrey Mae Inocencio']2024-02-07T00:00:00https://www.semanticscholar.org/paper/a876dfbf4d294f5af3da7687eed88383fddc202e e638c3b1ce9a9af69477bce77a9f64ad63849449Biomedical Journal of Scientific &amp; Technical Research00Biomedical Journal of Scientific &amp; Technical Research['Doepp Manfred']2024-02-07T00:00:00https://www.semanticscholar.org/paper/e638c3b1ce9a9af69477bce77a9f64ad63849449 d880249d6a8c63f50c5c8422202a85444a741170JAMA dermatology110JAMA dermatology['K. Kose', 'V. Rotemberg']2024-02-07T00:00:00https://www.semanticscholar.org/paper/d880249d6a8c63f50c5c8422202a85444a741170 ed90ca3acf39066225e9f8e1d6d5b5de2a878361Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI.Education sciences822Some of the underlying concepts of generative AI, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models, that would allow the reader to better understand generative AI are introduced.Education Sciences['Faisal Kalota']2024-02-07T00:00:00https://www.semanticscholar.org/paper/ed90ca3acf39066225e9f8e1d6d5b5de2a878361 97c5ec7b8a59ff6c0b99f49bcd3742b258c6edbeThis diagnostic study investigates the performance of a privacy-preserving federated learning approach vs a classical centralized and ensemble learning approach for artificial intelligence–based melanoma diagnostics.JAMA dermatology343JAMA Dermatology['Sarah Haggenmüller', 'Max Schmitt', 'E. Krieghoff-Henning', 'A. Hekler', 'Roman C. Maron', 'Christoph Wies', 'J. Utikal', 'Friedegund Meier', 'Sarah Hobelsberger', 'F. Gellrich', 'M. Sergon', 'Axel Hauschild', 'L. E. French', 'Lucie M. Heinzerling', 'Justin G. Schlager', 'K. Ghoreschi', 'Max Schlaak', 'F. Hilke', 'G. Poch', 'Sören Korsing', 'C. Berking', 'M. Heppt', 'Michael Erdmann', 'S. Haferkamp', 'K. Drexler', 'D. Schadendorf', 'W. Sondermann', 'Matthias Goebeler', 'B. Schilling', 'J. N. Kather', 'Stefan Fröhling', 'T. Brinker']2024-02-07T00:00:00https://www.semanticscholar.org/paper/97c5ec7b8a59ff6c0b99f49bcd3742b258c6edbe f558cf48f42edeec762609ac4feeb23dd430b2feBiosemiotics331An Embodied Human Language Model (EHLM), inspired by Active Inference research, is introduced, as a promising alternative that integrates sensory input, embodied representations, and adaptive strategies for contextualized analysis and conceptual utility maximization.Biosemiotics['S. Torres-Martínez']2024-02-07T00:00:00https://www.semanticscholar.org/paper/f558cf48f42edeec762609ac4feeb23dd430b2fe 3ae4fa0767aca8df1d11207aa248dd10e5b297e5In response to growing environmental challenges, there is an urgent need to understand how corporations can leverage new technologies to boost sustainability and eco‐innovation. This study addresses this need by investigating Artificial Intelligence adoption (AIA) influence on green innovation (greenovation) performance among Chinese firms as China's expanding digital economy and severe ecological pressures make it unique study context. Specifically, panel data on 8722 firm‐year observations from Chinese listed firms from 2008 to 2017 is analyzed to test the relationship. The main findings show that higher AIA is associated with increased greenovation, measured through green patents. This positive effect is more pronounced among privately‐owned enterprises versus state‐owned enterprises. Additionally, financial analysts are found to strengthen the AI‐greennovation link through information dissemination and scrutiny. Importantly, the study findings are robust and validated through a battery of tests, including change regression, instrumental variable methods, propensity score match (PSM), and sysGMM. Overall, this study provides novel empirical evidence that AI holds promise as an enabler of corporate eco‐innovation. The findings have crucial implications for research and practice regarding leveraging digital technologies for sustainability, especially in emerging economies like China that is undergoing rapid technological change.Business Strategy and the Environment811Business Strategy and the Environment['Murtaza Hussain', 'Shaohua Yang', 'Umer Sahil Maqsood', 'R. Zahid']2024-02-07T00:00:00https://www.semanticscholar.org/paper/3ae4fa0767aca8df1d11207aa248dd10e5b297e5 6981a81f710debe1e762710cc979361b9e63c46eAs part of the 125th issue of Teachers College Record, this commentary provides an overview of technological innovation with a focus on the emergence of machine learning and artificial intelligence (AI). The presence and use of AI is a pressing contemporary topic in education, raising questions about the information and perspective(s) AI might privilege, as well as the evolving ethical concerns related to the blurred and increasingly indistinguishable boundaries of human and nonhuman entities and practices.Teachers College Record51The presence and use of AI is a pressing contemporary topic in education, raising questions about the information and perspective AI might privilege, as well as the evolving ethical concerns related to the blurred and increasingly indistinguishable boundaries of human and nonhuman entities and practices.Teachers College Record: The Voice of Scholarship in Education['Sandra Schamroth Abrams']2024-02-07T00:00:00https://www.semanticscholar.org/paper/6981a81f710debe1e762710cc979361b9e63c46e ef1787b91b1d7ba8ae69d1b731c35581d566ad31Background: Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians’ awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting. Objectives: To analyze the characteristics and trends of AI skin cancer publications from dermatology journals. Methods: AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles’ general characteristics and features related to AI. Results: A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%). Conclusions: Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.Journal of Cutaneous Medicine and Surgery631Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms, indicating the eminent need for dermatologists to label or annotate images used by novel AI systems.Journal of Cutaneous Medicine and Surgery['Maxine Joly-Chevrier', 'Anne X. Nguyen', 'Laurence Liang', 'Michael Lesko-Krleza', 'Philippe Lefrançois']2024-02-07T00:00:00https://www.semanticscholar.org/paper/ef1787b91b1d7ba8ae69d1b731c35581d566ad31 893d9dc2d86b71e3ba67490decd96f91954e47ceAbstract This perspective outlines the Artificial Intelligence and Technology Collaboratories (AITC) at Johns Hopkins University, University of Pennsylvania, and University of Massachusetts, highlighting their roles in developing AI‐based technologies for older adult care, particularly targeting Alzheimer's disease (AD). These National Institute on Aging (NIA) centers foster collaboration among clinicians, gerontologists, ethicists, business professionals, and engineers to create AI solutions. Key activities include identifying technology needs, stakeholder engagement, training, mentoring, data integration, and navigating ethical challenges. The objective is to apply these innovations effectively in real‐world scenarios, including in rural settings. In addition, the AITC focuses on developing best practices for AI application in the care of older adults, facilitating pilot studies, and addressing ethical concerns related to technology development for older adults with cognitive impairment, with the ultimate aim of improving the lives of older adults and their caregivers. Highlights Addressing the complex needs of older adults with Alzheimer's disease (AD) requires a comprehensive approach, integrating medical and social support. Current gaps in training, techniques, tools, and expertise hinder uniform access across communities and health care settings. Artificial intelligence (AI) and digital technologies hold promise in transforming care for this demographic. Yet, transitioning these innovations from concept to marketable products presents significant challenges, often stalling promising advancements in the developmental phase. The Artificial Intelligence and Technology Collaboratories (AITC) program, funded by the National Institute on Aging (NIA), presents a viable model. These Collaboratories foster the development and implementation of AI methods and technologies through projects aimed at improving care for older Americans, particularly those with AD, and promote the sharing of best practices in AI and technology integration. Why Does This Matter? The National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITC) program's mission is to accelerate the adoption of artificial intelligence (AI) and new technologies for the betterment of older adults, especially those with dementia. By bridging scientific and technological expertise, fostering clinical and industry partnerships, and enhancing the sharing of best practices, this program can significantly improve the health and quality of life for older adults with Alzheimer's disease (AD).Alzheimer's & Dementia231This perspective outlines the Artificial Intelligence and Technology Collaboratories at Johns Hopkins University, University of Pennsylvania, and University of Massachusetts, highlighting their roles in developing AI‐based technologies for older adult care, particularly targeting Alzheimer's disease.Alzheimer's & Dementia['Peter Abadir', 'Esther S Oh', 'Rama Chellappa', 'N. Choudhry', 'George Demiris', 'Deepak Ganesan', 'Jason Karlawish', 'Benjamin M. Marlin', 'Rose M Li', 'N. Dehak', 'Alicia Arbaje', 'Mathias Unberath', 'Thomas Cudjoe', 'Christopher Chute', 'Jason H Moore', 'Phillip Phan', 'Quincy M. Samus', 'Nancy L. Schoenborn', 'Alexis Battle', 'Jeremy D Walston']2024-02-07T00:00:00https://www.semanticscholar.org/paper/893d9dc2d86b71e3ba67490decd96f91954e47ce 2ec728d6a1dd38ed0b74b03253cc84307d989408Scholars agree that digital technologies such as artificial intelligence (AI) pose a political challenge. In this article, we study empirically how different actors in the German political system define AI as a policy problem. We use an original data set of 6421 statements by representatives of political parties, interest groups, scientific experts, and public officials in parliamentary debates, government consultations, and quality newspapers. Through Discourse Network Analysis and quantitative text analyses we show that most actors define AI as technology (innovation) policy and link it to government operations, international cooperation, and macroeconomics. Although they are present, consumer protection, labor, and education seem to be less important policy issues concerning AI. The results imply that the capacity of the national government to reduce problem definition uncertainty and to steer the political agenda is difficult and that most actors focus on technological innovation rather than civil rights‐related aspects.European Policy Analysis1031It is shown that most actors define AI as technology (innovation) policy and link it to government operations, international cooperation, and macroeconomics, and that most actors focus on technological innovation rather than civil rights‐related aspects.European Policy Analysis['Nicole Lemke', 'Philipp Trein', 'Frédéric Varone']2024-02-07T00:00:00https://www.semanticscholar.org/paper/2ec728d6a1dd38ed0b74b03253cc84307d989408 1a3a19c6e3c1edaa6bb81bd3d19913bd030e9257Many disciplines, such as computer vision and natural language processing (NLP), find broad applications for artificial intelligence (AI) and machine learning (ML). We will give a brief history of edge detection in this post, which is an essential method for emphasizing important characteristics in a wide range of computer vision applications. We will also explore the transformative potential of transformer-based deep learning models in improving natural language processing applications. In addition, we will present two current research initiatives that demonstrate the creative uses of AI in business negotiation and the pharmaceutical industry. Furthermore, for this journal issue, we have carefully chosen five papers that are pertinent to these topics.  Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402301The history of edge detection is given, which is an essential method for emphasizing important characteristics in a wide range of computer vision applications, and the transformative potential of transformer-based deep learning models in improving natural language processing applications is explored.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-02-07T00:00:00https://www.semanticscholar.org/paper/1a3a19c6e3c1edaa6bb81bd3d19913bd030e9257 8b12f4303a7981806bf65c345f3b4291b556bfd8Artificial intelligence (AI) has undergone rapid evolution in recent decades, catalysing the emergence of ground-breaking technologies that have reshaped various sectors. Among these advancements is the advent of autonomous vehicles, poised to revolutionize transportation and mobility. Moreover, AI has spurred the development of cutting-edge solutions in healthcare, exemplified by AI-powered medical imaging systems. This manuscript presents an overview of AI's evolution and explores the latest strides in autonomous vehicles and healthcare innovations. Delving into the foundational technologies like machine learning and computer vision, it elucidates the methodologies employed in crafting autonomous vehicles and healthcare solutions. The document also scrutinizes the advantages and hurdles inherent in these innovations, while offering insights into future avenues of research. Overall, it underscores AI's profound impact on transportation, healthcare, and beyond, underscoring the transformative potential of autonomous vehicles and healthcare technologies in fostering safer and more efficient mobility and healthcare systems.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300An overview of AI's evolution is presented and the latest strides in autonomous vehicles and healthcare innovations are explored, underscoring the transformative potential of autonomous vehicles and healthcare technologies in fostering safer and more efficient mobility and healthcare systems.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Sohana Akter']2024-02-07T00:00:00https://www.semanticscholar.org/paper/8b12f4303a7981806bf65c345f3b4291b556bfd8 df3bd9160e646e93d8267af4c642d62b3833d0d4The turn-of-the-century advancement of technology opened the possibility of excluding the human factor in many areas. The tendency to speed up the processes in all fields of work can be compared with the tendencies that occurred during the First Industrial Revolution. Education, goods production, services, sports, entertainment, medicine... there is almost no field that does not take the advantage of computer and Internet technologies, especially machine learning (ML) and artificial intelligence (AI). For the purposes of theoretical considerations, starting from the traditional view of the separation of powers into a legislature, an executive, and a judiciary, the question can be raised as to whether and under what conditions AI can be used in the judicial decision-making process, that is, whether, under certain conditions, it can be left to a computer to perform actions or reach decisions in court.Harmonius Journal of Legal and Social Studies in South East Europe00The question can be raised as to whether and under what conditions AI can be used in the judicial decision-making process, that is, whether, under certain conditions, it can be left to a computer to perform actions or reach decisions in court.Harmonius Journal of Legal and Social Studies in South East Europe['Žarko Dimitrijevic']2024-02-07T00:00:00https://www.semanticscholar.org/paper/df3bd9160e646e93d8267af4c642d62b3833d0d4 1cdd11d514c208dd7f10b62b231e37be6241b66aArtificial Intelligence (AI) and Machine Learning (ML) represent burgeoning fields with the potential to transform numerous facets of society and industry. AI encompasses computer systems and algorithms capable of executing tasks typically necessitating human intelligence, such as learning, problem-solving, and decision-making. Conversely, ML entails the creation of algorithms facilitating computers to glean insights from data and refine their performance over time, sans explicit programming. This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles. Furthermore, we scrutinize the potential advantages and apprehensions linked with these technologies, including the prospect of job displacement and the susceptibility to misuse. Finally, we underscore the significance of ethical considerations and conscientious development practices to ensure the realization of AI and ML benefits while mitigating adverse repercussions.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Sohel Rana']2024-02-07T00:00:00https://www.semanticscholar.org/paper/1cdd11d514c208dd7f10b62b231e37be6241b66a 6be01c57550c5615d6fc8d3985c55e6c03587180Artificial intelligence (AI) and intellectual property (IP) share some key similarities, such as uncertainty in predictions, processing a massive amount of data, and machine learning. Yet, they also differ from each other. This paper provides background information on how these two domains have evolved over time. It also highlights how Saudi Arabia's IP system differs from those of other countries. Furthermore, this article explores the relationship between AI and IP and their application in copyright. This study is significant as it helps identify the challenges and opportunities that AI presents with respect to IP in terms of copyright. Finally, this article makes recommendations that will help protect both AI and IP.International Journal of Sociotechnology and Knowledge Development (IJSKD)150This study helps identify the challenges and opportunities that AI presents with respect to IP in terms of copyright and makes recommendations that will help protect both AI and IP.International Journal of Sociotechnology and Knowledge Development['M. Albakjaji', 'Reem Almarzouqi']2024-02-07T00:00:00https://www.semanticscholar.org/paper/6be01c57550c5615d6fc8d3985c55e6c03587180 3848a81f7bb0dfae91925b9e0eb8b6547d39726aChinese financial institutions (CFIs) are increasingly embracing artificial intelligence (AI) for their financial decision-making driven by AI's capacity to mitigate risks and enhance efficiency and accuracy. However, there remain ethical challenges related to the integration of AI in financial decision-making. This study develops the AI ethics best practices model (AB-PraM) to mitigate ethical concerns and enhance the application of AI in financial decision-making. By employing a quantitative methodology, this research collected questionnaire data from 320 financial experts in CFIs. Structural equation modelling (SEM) was adopted to identify AI ethics best practices for the implementation of the AB-PraM. The findings of this research will mitigate AI ethics challenges in CFIs and provide a practical framework for transparent and accountable decision-making in alignment with ethical standards and regulations.International Journal of Information Technologies and Systems Approach200The findings of this research will mitigate AI ethics challenges in CFIs and provide a practical framework for transparent and accountable decision-making in alignment with ethical standards and regulations.International Journal of Information Technologies and Systems Approach['Wenzhen Mai', 'M. Ambashe', 'C. C. Ohueri']2024-02-07T00:00:00https://www.semanticscholar.org/paper/3848a81f7bb0dfae91925b9e0eb8b6547d39726a ac5a8c86f2fa2e9ace47bdad80570014e1d0015bIntroduction: With 1.93 million new instances of colorectal cancer (CRC) reported in 2020, the disease presents a danger to world health. With its potential to improve CRC management, artificial intelligence (AI) has become increasingly prominent in the medical field. This research attempts to evaluate the current status of AI applications in CRC diagnosis and treatment, considering regional differences in healthcare systems and populations. Methodology: On databases like ScienceDirect, Google Scholar, and PubMed, a systematic literature evaluation was carried out using search phrases including "artificial intelligence," "colorectal cancer," "diagnosis," and "treatment." English-language research on AI applications in CRC diagnosis and treatment that were published during the previous five years met the inclusion criteria. Results: Endoscopic, non-invasive, histological, and radiographic techniques are among the AI applications used in CRC diagnosis. Prognostic forecasts, diagnostic accuracy, and tumor segmentation are all significantly enhanced by AI. AI helps with targeted therapy and chemoradiotherapy decision-making, improves surgical accuracy, and helps with personalized regimens. Conclusion: The use of AI in colorectal cancer management has the potential for timely identification, precise diagnosis, and customized care. Continuous developments in AI algorithms and clinical data support the development of precision medicine, which offers significant gains in CRC treatment and detection.Sapienza: International Journal of Interdisciplinary Studies00The use of AI in colorectal cancer management has the potential for timely identification, precise diagnosis, and customized care as well as the development of precision medicine, which offers significant gains in CRC treatment and detection.Sapienza: International Journal of Interdisciplinary Studies['Raul Jonathan Ríos Quinte', 'Alisson Berenice Ortiz Osorio', 'César David Toaquiza Toapanta', 'Elsa Alicia Landi Faican', 'Jefferson Alexander García Toala']2024-02-07T00:00:00https://www.semanticscholar.org/paper/ac5a8c86f2fa2e9ace47bdad80570014e1d0015b f67a135c6f7eda97deb1d3da67bf3ff00a85d59aThe sudden outbreak of a major pandemic often leads to the widespread dissemination of rumours related to the event. The public serves as both disseminators and regulators of rumours. Enhancing the public's capability to defend against rumours and strengthening their resilience are crucial for turning the tide of the pandemic. This study focuses on the rumours surrounding the COVID‐19 event and explores their impact on public vulnerability. Researching rumours during the pandemic reveals that in the era of artificial intelligence, the public's information needs, scepticism towards government resilience, and distrust in social relationships can deepen vulnerability, resulting in a proliferation of rumours. Therefore, it is proposed that governments should utilize new technologies, break away from traditional governance systems, and construct a rumour resolution system focusing on demand‐oriented approaches, employing artificial intelligence techniques, and precision repairing of social trust. This approach aims to reduce public vulnerability during significant pandemics and enhance the government's capabilities in rumour prevention and emergency management.Expert systems170It is proposed that governments should utilize new technologies, break away from traditional governance systems, and construct a rumour resolution system focusing on demand‐oriented approaches, employing artificial intelligence techniques, and precision repairing of social trust to reduce public vulnerability during significant pandemics and enhance the government's capabilities in rumour prevention and emergency management.Expert Systems['Yuhuan Kong']2024-02-07T00:00:00https://www.semanticscholar.org/paper/f67a135c6f7eda97deb1d3da67bf3ff00a85d59a c0e851f4f9e0ca2179bd7b5afefa23ad9e56a19eDriven by the rapid ascent of artificial intelligence (AI), organizations are at the epicenter of a seismic shift, facing a crucial question: How can AI be successfully integrated into existing operations? To help answer it, manage expectations and mitigate frustration, this article introduces Computational Management, a systematic approach to task automation for enhancing the ability of organizations to harness AI's potential within existing workflows. Computational Management acts as a bridge between the strategic insights of management science with the analytical rigor of computational thinking. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow. Such procedures focus on task (re)formulation, on the assessment of the automation potential of tasks, on the completion of task specification templates for AI selection and adaptation. Included in the article there are manual and automated methods, with prompt suggestions for publicly available LLMs, to complete these three procedures. The first procedure, task (re)formulation, focuses on breaking down work activities into basic units, so they can be completed by one agent, involve a single well-defined action, and produce a distinct outcome. The second, allows the assessment of the granular task and its suitability for automation, using the Task Automation Index to rank tasks based on whether they have standardized input, well-defined rules, repetitiveness, data dependency, and objective outputs. The third, focuses on a task specification template which details information on 16 critical components of tasks, and can be used as a checklist to select or adapt the most suitable AI solution for integration into existing workflows. Computational Management provides a roadmap and a toolkit for humans and AI to thrive together, while enhancing organizational efficiency and innovation.arXiv.org510The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow, focusing on task (re)formulation, on the assessment of the automation potential of tasks, and on the completion of task specification templates for AI selection and adaptation.ArXiv['T. Jadad-Garcia', 'A. Jadad']2024-02-07T00:00:00https://www.semanticscholar.org/paper/c0e851f4f9e0ca2179bd7b5afefa23ad9e56a19e fa11a3bbf0a1dcd549653f88d1027b6dce034f33Artificial Intelligence (AI) stands as a pivotal innovation deeply ingrained in both our daily routines and industrial operations. Its rapid evolution promises transformative impacts across various sectors, from cutting-edge industries to the lives of ordinary individuals. AI constantly updates human experiences, shaping interactions and augmenting capabilities. For instance, contemporary educational institutions leverage AI algorithms for attendance tracking via facial recognition technology. Looking ahead, the advent of autonomous vehicles represents a pinnacle of AI application, where vehicles rely entirely on AI systems for navigation, detecting traffic signals, and navigating roads.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300Looking ahead, the advent of autonomous vehicles represents a pinnacle of AI application, where vehicles rely entirely on AI systems for navigation, detecting traffic signals, and navigating roads.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['José Gabriel Carrasco Ramírez', 'Md.mafiqul Islam']2024-02-07T00:00:00https://www.semanticscholar.org/paper/fa11a3bbf0a1dcd549653f88d1027b6dce034f33 a44ee7828586026308ea40815517057af1425761This study investigated the use of artificial intelligence (AI) in special education, with the aim of personalizing teaching and promoting the inclusion of students with disabilities and special needs. The central problem addressed was the need to adapt teaching individually, taking into account the unique characteristics of each student. To achieve this objective, a literature review was conducted, analyzing studies, research and experiences related to AI in special education. The methodology included a literature review of reliable academic sources, such as scientific articles and research reports. The results of the analysis indicated that AI plays a key role in personalizing teaching, adapting content and resources according to the specific needs of students. Furthermore, AI contributes to promoting inclusion by creating accessible and equitable learning environments. However, ethical issues such as student data privacy and transparency in AI algorithms are identified that require careful consideration. Furthermore, practical limitations, such as the availability of technological resources and teacher training, represent challenges to be overcome in the successful implementation of AI in special education. Final considerations highlight the importance of AI as a promising tool for personalizing teaching in special education, as long as its application is conducted ethically and aware of practical limitations.Contribuciones a las ciencias sociales00The results of the analysis indicated that AI plays a key role in personalizing teaching, adapting content and resources according to the specific needs of students, and contributes to promoting inclusion by creating accessible and equitable learning environments.CONTRIBUCIONES A LAS CIENCIAS SOCIALES['Silvana Maria Aparecida Viana Santos', 'Cláudio Gomes Da Silva', 'Ianan Eugênia De Carvalho', 'Luciane Pereira De Castilho', 'Monique Bolonha das Neves Meroto', 'Paulo Roberto Tavares', 'Rosane dos Reis Pires', 'Sibele Selvina de Oliveira Rodrigues Moniz']2024-02-07T00:00:00https://www.semanticscholar.org/paper/a44ee7828586026308ea40815517057af1425761 307c93d7b60d26f0be3016172b1ac8f8427853f1Turkish J. Electr. Eng. Comput. Sci.04Turkish J. Electr. Eng. Comput. Sci.['Mehmet Pekkaya', 'Zafer Uysal', 'Aytaç Altan', 'S. Karasu']2024-02-07T00:00:00https://www.semanticscholar.org/paper/307c93d7b60d26f0be3016172b1ac8f8427853f1 1a4f0b10faedbbf7d1df7acf191c552dc94978b3International Journal of Sustainable Transportation401International Journal of Sustainable Transportation['A. Patwary', 'A. Khattak']2024-02-07T00:00:00https://www.semanticscholar.org/paper/1a4f0b10faedbbf7d1df7acf191c552dc94978b3 11d2981d80cb27cf1a914539af8da954c5515475Abstract not available Delta Med Col J. Jan 2021;9(1):1-2Delta Medical College Journal00Delta Medical College Journal['Md Rezwanur Rahman']2024-02-07T00:00:00https://www.semanticscholar.org/paper/11d2981d80cb27cf1a914539af8da954c5515475 1b55636188be8360faeba03055568c0b26ec1dfd Which artificial intelligence (AI) systems are agents? To answer this question, I propose a multidimensional account of agency. According to this account, a system's agency profile is jointly determined by its level of goal-directedness and autonomy as well as is abilities for directly impacting the surrounding world, long-term planning and acting for reasons. Rooted in extant theories of agency, this account enables fine-grained, nuanced comparative characterizations of artificial agency. I show that this account has multiple important virtues and is more informative than alternatives. More speculatively, it may help to illuminate two important emerging questions in AI ethics: 1. Can agency contribute to the moral status of non-human beings, and how? 2. When and why might AI systems exhibit power-seeking behaviour and does this pose an existential risk to humanity?The Philosophical Quarterly693A multidimensional account of agency is proposed that is jointly determined by its level of goal-directedness and autonomy as well as is abilities for directly impacting the surrounding world, long-term planning and acting for reasons.The Philosophical Quarterly['L. Dung']2024-02-07T00:00:00https://www.semanticscholar.org/paper/1b55636188be8360faeba03055568c0b26ec1dfd 0d56dcec01214444f960fbe88adb673a80839485The manufacturing industry is the pillar of the national economy and the foundation of national power. With the development of China’s economy and society, as well as the improvement of science and technology, the transformation and upgrading of China’s manufacturing industry is also more urgent. At present, in the field of manufacturing, artificial intelligence technology and manufacturing technology are constantly integrating, gradually forming an intelligent manufacturing base. As the core industry of China, the car industry embodies mechanisation, automation, and intelligence, and artificial intelligence is now widely applied in this industry. Representative technologies such as speech recognition and computer vision have brought intelligent and convenient experiences to the car industry and car companies themselves, improving safety, innovation, and efficiency. The current Chinese car industry has made significant progress in the application of artificial intelligence, and has made many breakthroughs in research, development, and production. This report takes BYD auto company, which has occupied a large market share in recent years, as an example. Based on previous annual financial reports and some literature in related fields, this report analyses the impact of artificial intelligence on the development of the BYD enterprise. At the same time, this report studies this company’s current application of artificial intelligence and explores the possibility of wider application of artificial intelligence in the automotive industry and enterprise development.Cambridge Explorations in Arts and Sciences00The impact of artificial intelligence on the development of the BYD enterprise is analyzed and the possibility of wider application of artificial intelligence in the automotive industry and enterprise development is explored.Cambridge Explorations in Arts and Sciences['Le Dai', 'Lufei Li', 'Yanan Liu', 'Yuehan Lin', 'Yulin Cheng']2024-02-07T00:00:00https://www.semanticscholar.org/paper/0d56dcec01214444f960fbe88adb673a80839485 07e89566e7936709dd9aff06829d952402afe944Scientific Reports620A desktop application that helps people stay focused on their work and train self-regulation at the same time, and results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control—benefitting users to successfully achieve their long-term goals.Scientific Reports['Maria Wirzberger', 'Anastasia Lado', 'Mike Prentice', 'Ivan Oreshnikov', 'Jean-Claude Passy', 'Adrian Stock', 'Falk Lieder']2024-02-07T00:00:00https://www.semanticscholar.org/paper/07e89566e7936709dd9aff06829d952402afe944 5eaec902b3900799d6c9dd42da09a6b23a9597bcThe article discusses the regulation of artificial intelligence (AI) in Brazil, with a focus on Bill 21/2020, which was approved by the Federal Chamber of Deputies in September 2021. The article is based on bibliographical and documentary research to analyze the normative proposal and the discussions surrounding the regulation of AI in Brazil. Bill 21/2020, the result of an OECD recommendation, aims to be the Legal Framework for Artificial Intelligence in Brazil, establishing objectives and foundations for AI, as well as principles for its development and application. The bill is currently before the Senate, where it is being discussed in public hearings and receiving contributions, including a substitute drawn up by a committee of lawyers. However, critics of the bill consider that it was rushed through, resulting in a bill with only 32 articles and an excess of abstract and principled rules.Contribuciones a las ciencias sociales00The article is based on bibliographical and documentary research to analyze the normative proposal and the discussions surrounding the regulation of AI in Brazil, with a focus on Bill 21/2020, which was approved by the Federal Chamber of Deputies in September 2021.CONTRIBUCIONES A LAS CIENCIAS SOCIALES['Mayara Rayssa da Silva Rolim', 'Daniella Maria dos Santos Dias', 'Gabriel Napoleão Velloso Filho']2024-02-06T00:00:00https://www.semanticscholar.org/paper/5eaec902b3900799d6c9dd42da09a6b23a9597bc 9765687eb94cddeffedcb81dca5205c5fb9bd456Journal of Banking Regulation50The analysis of four criminal cases brought against top banks and conclusions of the study indicate that the increase in predicate crimes for money laundering, constantly evolving sanctions regime along with the enhanced scrutiny and enforcement action against banks are hindering technology innovation and legal implications of using AI driven tools for compliance.Journal of Banking Regulation['Umut Turksen', 'Vladlena Benson', 'Bogdan Adamyk']2024-02-06T00:00:00https://www.semanticscholar.org/paper/9765687eb94cddeffedcb81dca5205c5fb9bd456 e5a4f96bca7c0a6c1c626316b286f9c8c9652812The subject of the study is the system of land relations in Russia, the imperfection of which gives rise to negative infrastructural externalities caused by the modernization and development of linear engineering infrastructure The objective of the work is to develop methods for state regulation of the external effects, the main one of which is internalization through land taxation. In the work the concept of “negative infrastructure-related externalities”, their structure and economic nature in land relations are introduced. Also the consequences of negative infrastructure-related externalities on the economic interests of owners of encumbered land plots are determined. To overcome those issues the economic mechanism for regulating the use of land resources and their redistribution is developed. The scope of application of research results covers a lot of long-term aims: from the differentiation of land taxation for the internalization of negative infrastructure-related externalities to territorial planning at all levels.Voprosy Ekonomiki00Voprosy Ekonomiki['E. N. Bykova']2024-02-06T00:00:00https://www.semanticscholar.org/paper/e5a4f96bca7c0a6c1c626316b286f9c8c9652812 cb54249492287f35232b90239f6d59c02b8bd4f4This study examines the situations that arise when regulating a natural monopoly market by focusing on Türkiye’s electricity market. Specifically, this qualitative study investigates the Energy Market Regulatory Authority (EMRA) in terms of capture theory, drawing on various documentary sources, such as electricity sector regulation legislation (constitutions, sector laws, decree-laws, Plan and Budgeting Committee documents of The Grand National Assembly of Türkiye), EMRA official decisions, newspaper reports from 2001 to 2021, and media interviews. The empirical findings discussed throughout the article reveal numerous instances consistent with the capture theory.Sosyoekonomi420Sosyoekonomi['Özgün Akduran Erol']2024-02-06T00:00:00https://www.semanticscholar.org/paper/cb54249492287f35232b90239f6d59c02b8bd4f4 4b2373de0b96089a9b1e8a1f8ef2992f7a192cafThe recently enacted Inflation Reduction Act (IRA) includes a number of incentive-based programs (e.g., tax credits) designed to motivate firms to develop new clean technologies for fighting climate change. However, the IRA also includes a fee firms incur for excessive methane emissions. This represents the first time the United States government has ever levied a fee on greenhouse gas emissions, and it raises an interesting research question—how should a budget-constrained regulator balance the use of both incentive and penalty-based levers for stimulating investment in clean technology development? In this paper, we examine a regulator’s optimal penalty and subsidy decisions for motivating firms to invest in clean technology development. We illustrate how the level of competitive intensity in the market can influence a budget-constrained regulator with multiple competing objectives—the environment, firm profits, and consumer welfare. We find that a subsidy is always beneficial, irrespective of the regulator’s objective. While imposing a firm penalty always benefits the environment, it always negatively impacts the sum of firm profits and consumer welfare. However, depending on the level of competition in the market, instances can occur where imposing a high penalty actually benefits total firm profits or consumer welfare (separately). Interestingly, a regulator that cares about all three dimensions of its objective equally, should always set the penalty to either its minimum or maximum value, depending on whether the environmental cost of the harmful product is high or low.Production and operations management00Production and Operations Management['Mina Mohammadi', 'H. Sebastian Heese', 'Tim Kraft']2024-02-06T00:00:00https://www.semanticscholar.org/paper/4b2373de0b96089a9b1e8a1f8ef2992f7a192caf ea5f9074c5b9a62cc4c4c039b88a08becb48d0e8Chemistry and technology of fuels and oils80Chemistry and Technology of Fuels and Oils['Wenying Zhang', 'Jingyi Lu', 'Wei Tian']2024-02-06T00:00:00https://www.semanticscholar.org/paper/ea5f9074c5b9a62cc4c4c039b88a08becb48d0e8 009fc5dfb8a9111841e97e044e50184031565b41CodeCompose is an AI-assisted code authoring tool powered by large language models (LLMs) that provides inline suggestions to 10's of thousands of developers at Meta. In this paper, we present how we scaled the product from displaying single-line suggestions to multi-line suggestions. This evolution required us to overcome several unique challenges in improving the usability of these suggestions for developers. First, we discuss how multi-line suggestions can have a 'jarring' effect, as the LLM's suggestions constantly move around the developer's existing code, which would otherwise result in decreased productivity and satisfaction. Second, multi-line suggestions take significantly longer to generate; hence we present several innovative investments we made to reduce the perceived latency for users. These model-hosting optimizations sped up multi-line suggestion latency by 2.5x. Finally, we conduct experiments on 10's of thousands of engineers to understand how multi-line suggestions impact the user experience and contrast this with single-line suggestions. Our experiments reveal that (i) multi-line suggestions account for 42% of total characters accepted (despite only accounting for 16% for displayed suggestions) (ii) multi-line suggestions almost doubled the percentage of keystrokes saved for users from 9% to 17%. Multi-line CodeCompose has been rolled out to all engineers at Meta, and less than 1% of engineers have opted out of multi-line suggestions.arXiv.org192This paper presents how the product was scaled from displaying single-line suggestions to multi-line suggestions, and reveals that multi-line suggestions almost doubled the percentage of keystrokes saved for users from 9% to 17%.ArXiv['Omer Dunay', 'Daniel Cheng', 'Adam Tait', 'Parth Thakkar', 'Peter C. Rigby', 'Andy Chiu', 'Imad Ahmad', 'Arun Ganesan', 'C. Maddila', 'V. Murali', 'Ali Tayyebi', 'Nachiappan Nagappan']2024-02-06T00:00:00https://www.semanticscholar.org/paper/009fc5dfb8a9111841e97e044e50184031565b41 3c71362b24bf6e86d63a0388a47490548ffe0f0bThe use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage, especially the black-box character of AI, has hindered adoption. So far, little is known about the contextual factors influencing users' perception of AI-based CAs in general and the effect of provided explanations by explainable AI (XAI) in particular. While research on algorithm aversion provides some initial explanations, information regarding the effects of different XAI approaches on different types of decisions on the attitudes of (potential) applicants is scarce. Therefore, in this study, we use a quantitative, quota-representative study (n = 490) to assess the acceptance of CAs in recruiting. By applying an experimental within-subject design, we provide a more nuanced perspective on why and when providing explanations increases user acceptance. We also show that contextual factors such as the type of assessed skills are major determinants of this effect, and we conclude that XAI is not a "one-size-fits-all approach." Based on the insight that contextual factors of the decision problem are more important than the type of XAI approach itself, we argue that the use and the effects of explainability in recruiting need a more nuanced perspective, focusing on the fit of explanations with the user's characteristics and preferences.ACM SIGMIS Database: the DATABASE for Advances in Information Systems391It is argued that the use and the effects of explainability in recruiting need a more nuanced perspective, focusing on the fit of explanations with the user's characteristics and preferences, and XAI is not a "one-size-fits-all approach.ACM SIGMIS Database: the DATABASE for Advances in Information Systems['Jürgen Fleiß', 'Elisabeth Bäck', 'Stefan Thalmann']2024-02-06T00:00:00https://www.semanticscholar.org/paper/3c71362b24bf6e86d63a0388a47490548ffe0f0b 551375cce1e2a79309a967bf607a4d189267a8f1Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). The previous approaches for VLMMs involved Supervised Fine-Tuning (SFT) with instruction-tuned datasets, integrating LLM with visual encoders, and adding additional learnable modules. Video and text multimodal alignment remains challenging, primarily due to the deficient volume and quality of multimodal instruction-tune data compared to text-only data. We present a novel alignment strategy that employs multimodal AI system to oversee itself called Reinforcement Learning from AI Feedback (RLAIF), providing self-preference feedback to refine itself and facilitating the alignment of video and text modalities. In specific, we propose context-aware reward modeling by providing detailed video descriptions as context during the generation of preference feedback in order to enrich the understanding of video content. Demonstrating enhanced performance across diverse video benchmarks, our multimodal RLAIF approach, VLM-RLAIF, outperforms existing approaches, including the SFT model. We commit to open-sourcing our code, models, and datasets to foster further research in this area.arXiv.org01This work presents a novel alignment strategy that employs multimodal AI system to oversee itself called Reinforcement Learning from AI Feedback (RLAIF), providing self-preference feedback to refine itself and facilitating the alignment of video and text modalities.ArXiv['Daechul Ahn', 'Yura Choi', 'Youngjae Yu', 'Dongyeop Kang', 'Jonghyun Choi']2024-02-06T00:00:00https://www.semanticscholar.org/paper/551375cce1e2a79309a967bf607a4d189267a8f1 6854777579ee1a50905dee73e23588242fa9d003BACKGROUND Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing Autonomous AI to AI assisted human (AI-H) optical diagnosis. METHODS We performed a randomized non-inferiority trial of patients undergoing elective colonoscopies in one academic institution. Patients were randomized int: 1) Autonomous AI-based CADx optical diagnosis of diminutive polyps without human input; 2) endoscopists performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. Primary outcome was accuracy in optical diagnosis in both arms using pathology as gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. RESULTS 467 patients were randomized (238 patients/158 polyps in the Autonomous AI group; 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95%Confidence Interval 69.7-84.7) in the Autonomous AI group and 72.1% (95%CI 65.5-78.6) in the AI-H group (p=0.86). For high confidence diagnoses, accuracy for optical diagnosis was 77.2% (95%CI 69.7-84.7) in the Autonomous AI group and 75.5% (95%CI 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95%CI 86.9-96.1] vs 82.1% [95%CI 76.5-87.7]; p=0.016). CONCLUSIONS Autonomous AI-based optical diagnosis exhibits non-inferior accuracy to endoscopist-based diagnosis. Both Autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis, however Autonomous AI achieved higher agreement with pathology-based surveillance intervals.Gastroenterology01Both Autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis, however Autonomous AI achieved higher agreement with pathology-based surveillance intervals.Gastroenterology['R. Djinbachian', 'C. Haumesser', 'M. Taghiakbari', 'Heiko Pohl', 'A. Barkun', 'S. Sidani', 'J. Liu Chen Kiow', 'B. Panzini', 'Simon Bouchard', 'E. Deslandres', 'A. Alj', 'D. von Renteln']2024-02-06T00:00:00https://www.semanticscholar.org/paper/6854777579ee1a50905dee73e23588242fa9d003 de261fc1927be556512fc525a4db09f91f1ac446This study explores the paradigm change that artificial intelligence (AI) has brought about in the context of libraries in the age of intelligent information. A quantitative survey was carried out, and a methodical description of the study's procedure is provided. The research sampling strategy, data gathering, data collection tool, and statistical analysis are all included in this quantitative approach. Additionally, a random sampling procedure was used to choose the study sample. Similar to this, questionnaires were employed as response instruments to collect data in both physical and digital formats. Findings from the investigation showed that 45% of institutions are private nature and 50% institutions shows the status public. 1-10 years’ experience were 62% respondents, 11-20 years’ experience were 31% and only 8% of respondents were 21 years above experience. The study emphasizes how important it is to create a welcoming and open library ecosystem where artificial intelligence (AI) enhances operational effectiveness while protecting user privacy and pleasure. All things considered, this thorough analysis highlights the many ways that artificial intelligence (AI) might transform libraries and meet the many demands of their patrons. Looking forward, the article discusses the promising future of AI in libraries, envisioning AI-powered chatbots, predictive analytics, and virtual helpers as integral components of an AI-driven library landscape.International Journal of Applied and Scientific Research01The investigation showed that 45% of institutions are private nature and 50% institutions shows the status public, and how important it is to create a welcoming and open library ecosystem where artificial intelligence (AI) enhances operational effectiveness while protecting user privacy and pleasure.International Journal of Applied and Scientific Research['Mehranullah Baber', 'Kanwal Islam', 'Adnan Ullah', 'Wahid Ullah']2024-02-06T00:00:00https://www.semanticscholar.org/paper/de261fc1927be556512fc525a4db09f91f1ac446 8074ccf1470d9113c824d5fb8f48305382c78335Background Prior studies have shown the potential of large language models (LLMs) to support in differential diagnosis in radiology. However, the interaction of human users with LLMs in this context has not been evaluated. Purpose To investigate the impact of human-LLM collaboration on accuracy and efficiency of brain MRI differential diagnosis. Methods In this retrospective study, twenty brain MRI cases with a challenging but definitive diagnosis were selected and randomized into two groups. Six inexperienced radiology residents were instructed to determine the three most likely differential diagnoses for each of these cases via conventional internet search or utilizing an LLM-based search engine ((C) Perplexity AI, powered by GPT-4). Accuracy of suggested differential diagnoses was analyzed using the chi-square test and Mann-Whitney U test. Interpretation times were analyzed using the student's t-test. Benefits and challenges in human-LLM interaction were derived from observations and participant feedback. Results LLM-assisted brain MRI differential diagnosis yielded superior accuracy (38/59 [LLM-assisted] vs 25/59 [conventional] correct diagnoses, p = 0.03). No difference in interpretation time (8.12 +/- 3.22 min [LLM-assisted] vs 7.96 +/- 2.65 min [conventional], p = 0.76) or level of confidence (median of 2.5 [LLM-assisted] vs 3.0 [conventional], p = 0.96) was observed. Several challenges related to human errors and technical limitations were identified. Conclusion Human-LLM collaboration has the potential to improve brain MRI differential diagnosis. Yet, several challenges must be addressed to ensure effective adoption and user acceptance.medRxiv41Human-LLM collaboration has the potential to improve brain MRI differential diagnosis, yet, several challenges must be addressed to ensure effective adoption and user acceptance.['S. H. Kim', 'S. Schramm', 'C. Berberich', 'E. Rosenkranz', 'L. Schmitzer', 'K. Serguen', 'C. Klenk', 'N. Lenhart', 'C. Zimmer', 'B. Wiestler', 'D. M. Hedderich']2024-02-06T00:00:00https://www.semanticscholar.org/paper/8074ccf1470d9113c824d5fb8f48305382c78335 96910d8365709393ea33630e200f6229fe947178Recent advances in foundation models, especially in large multi-modal models and conversational agents, have ignited interest in the potential of generally capable embodied agents. Such agents will require the ability to perform new tasks in many different real-world environments. However, current foundation models fail to accurately model physical interactions and are therefore insufficient for Embodied AI. The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions. This paper focuses on the prospects of building foundation world models for the upcoming generation of embodied agents and presents a novel viewpoint on the significance of causality within these. We posit that integrating causal considerations is vital to facilitating meaningful physical interactions with the world. Finally, we demystify misconceptions about causality in this context and present our outlook for future research.arXiv.org2021It is posited that integrating causal considerations is vital to facilitating meaningful physical interactions with the world and demystify misconceptions about causality in this context and present the outlook for future research.ArXiv['Tarun Gupta', 'Wenbo Gong', 'Chao Ma', 'Nick Pawlowski', 'Agrin Hilmkil', 'M. Scetbon', 'Ade Famoti', 'A. Llorens', 'Jianfeng Gao', 'Stefan Bauer', 'Danica Kragic', 'Bernhard Schölkopf', 'Cheng Zhang']2024-02-06T00:00:00https://www.semanticscholar.org/paper/96910d8365709393ea33630e200f6229fe947178 4841ef2a4c9a3424bcf5b72137b989f323af1b4bBackground: Novel products applying artificial intelligence (AI)-based approaches to digital pathology images have consistently emerged onto the commercial market, touting improvements in diagnostic accuracy, workflow efficiency, and treatment selection. However, publicly available information on these products can be variable, with few sources to obtain independent evidence. Methods: Our objective was to identify and assess the public evidence on AI-based products for digital pathology. We compared key features of products on the European Economic Area/Great Britain (EEA/GB) markets, including their regulatory approval, intended use, and published validation studies. We included products that used haematoxylin and eosin (H&E)-stained tissue images as input, applied an AI-based method to support image interpretation, and received regulatory approval by September 2023. Results: We identified 26 AI-based products that met our inclusion criteria. The majority (73%) were focused on breast pathology or uropathology, and their primary function was tumour or feature detection. Of the 26 products, 24 had received regulatory approval via the self-certification route as General in vitro diagnostic (IVD) medical devices, which does not require independent review by a conformity assessment body. Furthermore, only 10 of the products (38%) were associated with peer-reviewed scientific publications describing their development and internal validation, while 11 products (42%) had peer-reviewed publications describing external validation (i.e., testing on data from a source distinct to that used in development). Conclusions: The availability of public information on new products for digital pathology is struggling to keep up with the rapid pace of development. To support transparency, we gathered available public evidence on regulatory-approved AI products into an online register: https://resources.npic.uk/AI/ProductRegister. We anticipate this will provide an accessible resource on novel devices and support decisions on which products could bring benefit to patients.medRxiv431The availability of public information on new products for digital pathology is struggling to keep up with the rapid pace of development, and available public evidence on regulatory-approved AI products into an online register is gathered to support transparency.['Gillian Matthews', 'Clare McGenity', 'Daljeet Bansal', 'Darren Treanor']2024-02-06T00:00:00https://www.semanticscholar.org/paper/4841ef2a4c9a3424bcf5b72137b989f323af1b4b e1a280f346b575c85cde4ad4ba0e10dc6df93105AI and Ethics1271The infeasibility of accurately monitoring advanced AI systems to predict the emergence of certain capabilities prior to their manifestation is demonstrated, arguing for the impossibility of reliably foreseeing some capabilities.AI and Ethics['Roman V. Yampolskiy']2024-02-06T00:00:00https://www.semanticscholar.org/paper/e1a280f346b575c85cde4ad4ba0e10dc6df93105 883c6cc74d73c91438a3cad76466fd2ae70a44d6We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself from the classical paradigm of static learning. By broadening the scope of Embodied AI, we introduce a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent. This framework is aligned with Friston's active inference principle, offering a comprehensive approach to EAI development. Despite the progress made in the field of AI, substantial challenges, such as the formulation of a novel AI learning theory and the innovation of advanced hardware, persist. Our discussion lays down a foundational guideline for future Embodied AI research. Highlighting the importance of creating Embodied AI agents capable of seamless communication, collaboration, and coexistence with humans and other intelligent entities within real-world environments, we aim to steer the AI community towards addressing the multifaceted challenges and seizing the opportunities that lie ahead in the quest for AGI.arXiv.org1130By broadening the scope of Embodied AI, this work introduces a theoretical framework based on cognitive architectures, emphasizing perception, action, memory, and learning as essential components of an embodied agent.ArXiv['Giuseppe Paolo', 'Jonas Gonzalez-Billandon', "Bal'azs K'egl"]2024-02-06T00:00:00https://www.semanticscholar.org/paper/883c6cc74d73c91438a3cad76466fd2ae70a44d6 525ff089b6c5df9b311124adc3e549060093c982This study investigates self-assessment tendencies in Large Language Models (LLMs), examining if patterns resemble human cognitive biases like the Dunning–Kruger effect. LLMs, including GPT, BARD, Claude, and LLaMA, are evaluated using confidence scores on reasoning tasks. The models provide self-assessed confidence levels before and after responding to different questions. The results show cases where high confidence does not correlate with correctness, suggesting overconfidence. Conversely, low confidence despite accurate responses indicates potential underestimation. The confidence scores vary across problem categories and difficulties, reducing confidence for complex queries. GPT-4 displays consistent confidence, while LLaMA and Claude demonstrate more variations. Some of these patterns resemble the Dunning–Kruger effect, where incompetence leads to inflated self-evaluations. While not conclusively evident, these observations parallel this phenomenon and provide a foundation to further explore the alignment of competence and confidence in LLMs. As LLMs continue to expand their societal roles, further research into their self-assessment mechanisms is warranted to fully understand their capabilities and limitations.Inf.282This study investigates self-assessment tendencies in Large Language Models, examining if patterns resemble human cognitive biases like the Dunning–Kruger effect, and shows cases where high confidence does not correlate with correctness, suggesting overconfidence and low confidence despite accurate responses indicates potential underestimation.Inf.['Aniket Kumar Singh', 'Bishal Lamichhane', 'Suman Devkota', 'Uttam Dhakal', 'Chandra Dhakal']2024-02-06T00:00:00https://www.semanticscholar.org/paper/525ff089b6c5df9b311124adc3e549060093c982 f503d176fd6038ed4db0b496ab2bb043416f0023In an era where generative AI is reshaping the landscape of business and technology, this editorial addresses the critical imperative for transformative reform in business education. It emphasizes the dual nature of generative AI as both a formidable disruptor and a catalyst for innovation, necessitating a shift in how we educate the future workforce. The editorial calls for a proactive and comprehensive reevaluation of current educational models, advocating for an integration of AI literacy and ethical considerations into the core of business curricula. We aim to galvanize academia into action, advocating for an educational evolution that not only acknowledges the challenges posed by AI but also harnesses its potential to enrich and advance business education in preparing students for an AI-driven future.ACM SIGMIS Database: the DATABASE for Advances in Information Systems60The editorial calls for a proactive and comprehensive reevaluation of current educational models, advocating for an integration of AI literacy and ethical considerations into the core of business curricula.ACM SIGMIS Database: the DATABASE for Advances in Information Systems['Benyawarath Nithithanatchinnapat', 'Joshua Maurer', 'Xuefei (Nancy) Deng', 'K. D. Joshi']2024-02-06T00:00:00https://www.semanticscholar.org/paper/f503d176fd6038ed4db0b496ab2bb043416f0023 2714a1041bd304454924992b99e4a8c0e63007a5 Purpose The objective of this study is to propose a model for the implementation of a technological platform for participants to develop solutions to problems related to the Fourth Industrial Revolution (4IR) megatrends, and taking advantage of artificial intelligence (AI) to develop their complex thinking through co-creation work. Design/methodology/approach The development of the model is based on a combination of participatory action research and user-centered design (UCD) methodologies, seeking to ensure that the platform is user-oriented and based on the experiences of the authors. The model itself is structured around the active and transformational learning (ATL) framework. Findings This study highlights the importance of addressing 4IR megatrends in education to prepare students for a technology-driven world. The proposed model, based on ATL and supported by AI, integrates essential competencies for tackling challenges and generating innovative solutions. The integration of AI into the platform fosters personalized learning, collaboration and reflection and enhances creativity by offering new insights and tools, whereas UCD ensures alignment with user needs and expectations. Originality/value This research presents an innovative educational model that combines ATL with AI to foster complex thinking and co-creation of solutions to problems related to 4IR megatrends. Integrating ATL ensures engagement with real-world problems and critical thinking while AI provides personalized content, tutoring, data analysis and creative support. The collaborative platform encourages diverse perspectives and collective intelligence, benefiting other researchers to better conceive learner-centered platforms promoting 21st-century skills and co-creation. Interactive Technology and Smart Education620An innovative educational model is presented that combines ATL with AI to foster complex thinking and co-creation of solutions to problems related to 4IR megatrends, and integrates essential competencies for tackling challenges and generating innovative solutions.Interactive Technology and Smart Education['J. Sanabria-Z', 'Pamela Geraldine Olivo']2024-02-06T00:00:00https://www.semanticscholar.org/paper/2714a1041bd304454924992b99e4a8c0e63007a5 ddb7d02d1724f9fd531a298d66c48c4eb02f1817This study consists of a novel approach toward the analysis of court judgments spanning five countries, including the United States, the United Kingdom, Rwanda, Sweden and Hong Kong. This study also explores the intersection of the latest advancements in artificial intelligence (AI) and legal analysis, emphasizing the role of AI (specifically generative AI) in identifying human biases and facilitating automated, valid, and coherent multisided argumentation of court judgments with the goal of ensuring consistent application of laws in and across various jurisdictions. By incorporating Advanced Language Models (ALMs) and a newly introduced human-AI collaborative framework, this paper seeks to analyze Grounded Theory-based research design with Advanced Language Models (ALMs) in the practice of law. SHIRLEY is the name of the AI-based application (built on top of OpenAI's GPT technology), focusing on detecting logical inconsistencies and biases across various legal decisions. SHIRLEY analysis is aggregated and is accompanied by a comparison-oriented AI-based application called SAM (also an ALM) to identify relative deviations in SHIRLEY bias detections. Further, a CRITIC is generated within semi-autonomous arbitration process via the ALM, SARA. A novel approach is introduced in the utilization of an AI arbitrator to critically evaluate biases and qualitative-in-nature nuances identified by the aforementioned AI applications (SAM in concert with SHIRLEY), based on the Hague Rules on Business and Human Rights Arbitration. This Semi-Automated Arbitration Process (SAAP) aims to uphold the integrity and fairness of legal judgments by ensuring a nuanced debate-resultant"understanding"through a hybrid system of AI and human-based collaborative analysis.arXiv.org120This paper seeks to analyze Grounded Theory-based research design with Advanced Language Models (ALMs) in the practice of law to uphold the integrity and fairness of legal judgments by ensuring a nuanced debate-resultant understanding through a hybrid system of AI and human-based collaborative analysis.ArXiv["Michael De'Shazer"]2024-02-06T00:00:00https://www.semanticscholar.org/paper/ddb7d02d1724f9fd531a298d66c48c4eb02f1817 b5f34e29173fc5669f507800937a1e20095153b9The integration of Artificial Intelligence (AI) in healthcare systems has witnessed remarkable progress in recent years, revolutionizing the landscape of patient care, diagnostics, and medical research. This paper provides a comprehensive survey of the diverse applications, challenges, and benefits of AI in healthcare. We explore the role of machine learning algorithms and natural language processing in enhancing diagnosis and treatment planning. The utilization of AI for predictive analytics, personalized medicine, and patient management is discussed, showcasing its potential to improve healthcare outcomes and reduce costs. Additionally, we address ethical considerations, data privacy concerns, and regulatory frameworks that accompany the implementation of AI in healthcare. Through an extensive literature review, this paper aims to offer insights into the current state of AI in healthcare, highlighting key trends and future directions for research and development in this rapidly evolving fieldInternational Journal of Advanced Research in Science, Communication and Technology70The role of machine learning algorithms and natural language processing in enhancing diagnosis and treatment planning and the utilization of AI for predictive analytics, personalized medicine, and patient management is discussed.International Journal of Advanced Research in Science, Communication and Technology['Sachin Shetty V S', 'Vedanth M', 'Manjunath S']2024-02-06T00:00:00https://www.semanticscholar.org/paper/b5f34e29173fc5669f507800937a1e20095153b9 a800ff94e6cdb0747c1772f3eed4f98ae74faa76This paper evaluates No-Code AutoML as a solution for challenges in AI product prototyping, characterized by unpredictability and inaccessibility to non-experts, and proposes a conceptual framework. This complexity of AI products hinders seamless execution and interdisciplinary collaboration crucial for human-centered AI products. Relevant to industry and innovation, it affects strategic decision-making and investment risk mitigation. Current approaches provide limited insights into the potential and feasibility of AI product ideas. Employing Design Science Research, the study identifies challenges and integrates no-code AutoML as a solution by presenting a framework for AI product prototyping with No-code AutoML. A case study confirms its potential in supporting non-experts, offering a structured approach to AI product development. The framework facilitates accessible and interpretable prototyping, benefiting academia, managers, and decision-makers. Strategic integration of no-code AutoML enhances efficiency, empowers non-experts, and informs early-stage decisions, albeit with acknowledged limitations.Social Science Research Network940Strategic integration of no-code AutoML enhances efficiency, empowers non-experts, and informs early-stage decisions, albeit with acknowledged limitations, albeit with acknowledged limitations.ArXiv['Mario Truss', 'Marc Schmitt']2024-02-06T00:00:00https://www.semanticscholar.org/paper/a800ff94e6cdb0747c1772f3eed4f98ae74faa76 fcff5279e1848735c469b65c5cd63f234cb39994The past decade has witnessed an explosive growth in the development and use of artificial intelligence (AI) across diverse fields [...]Inf.90Inf.['Sidong Liu', 'C. Castillo-Olea', 'S. Berkovsky']2024-02-06T00:00:00https://www.semanticscholar.org/paper/fcff5279e1848735c469b65c5cd63f234cb39994 e5ab062914c3a5d7d0a8a445bd641879dfa74d31The growing role of conversational AI Chatbots continues to change the library and information service landscape. Chatbots are replacing some of the library services that humans conventionally perform. In the era of instant evolution of artificial intelligence (AI), the role of Chatbots in libraries keeps expanding and acquiring more experience. This paper aims to examine the extant research on library Chatbots using an integrative literature review (ILR) approach. Empirical and non-empirical papers from the Scopus database to ascertain what is already known about the topic. Forty papers (articles and conference papers) were scrutinized for further analysis. The leading emergent themes in the literature were (1) The evolution of Chatbots technology in libraries, (2) Antecedents for Chatbot use in libraries, (3) User experience with Chatbot use in libraries, (4) Chatbot use in libraries amidst COVID-19, and (5) Challenges facing Chatbot use in libraries. Research on Chatbots in library services is still embryonic and has only begun to flourish. Nevertheless, there is still a significant research gap despite its surging curve. The findings of this integrative review contribute to the body of knowledge on the nexus between artificial intelligence and library operations. It also furnishes academics and practitioners with six potential directions for future research opportunities.Journal of Librarianship and Information Science340The findings of this integrative review contribute to the body of knowledge on the nexus between artificial intelligence and library operations and furnishes academics and practitioners with six potential directions for future research opportunities.Journal of Librarianship and Information Science['M. Aboelmaged', 'Shaker Bani-Melhem', 'Mohd Ahmad Al-Hawari', 'Ifzal Ahmad']2024-02-06T00:00:00https://www.semanticscholar.org/paper/e5ab062914c3a5d7d0a8a445bd641879dfa74d31 ab424a812aad4db056f569ce8e3cefb0e64a39f8This extensive review of the literature examines the pervasive impact of artificial intelligence (AI) on language learning and teaching, covering innovative uses like real-time language practice, personalized language learning, and automated testing methods in the teaching of English as a foreign language (EFL). The survey covers a wide range of topics, such as the use of Natural Language Processing (NLP) to extract Business Process Models in Business Process Management (BPM), the deployment of English-language chat-bots operating on the WeChat platform that are based on transfer learning, and the incorporation of NLP tools and virtual reality in the e-learning platform Exills. Additionally, it delves into cutting-edge technologies such as chat-bots for language learning, low-resource spoken language learning applications with AI support, and the broad application of AI in EFL teaching, tackling issues and offering encouraging results. The poll also addresses the use of AI in language learning tools, focusing on transparency and ethical issues while utilizing chat-bots, machine translation, speech recognition, and AI-generated content. The study highlights the importance of exposure to different cultures, community interaction, and multilingual voice recognition in creating a comprehensive language learning environment. It also highlights the limitations and enormous possibilities of integrating AI into language instruction.International Journal of Advanced Research in Science, Communication and Technology140International Journal of Advanced Research in Science, Communication and Technology['Nanda Kalyan', 'Dr. Kavita Patil', 'Students']2024-02-06T00:00:00https://www.semanticscholar.org/paper/ab424a812aad4db056f569ce8e3cefb0e64a39f8 8113eb624277760433ff40bf33b9b2e03009e77cApplications such as ChatGPT and WOMBO Dream make it easy to inspire students without programming knowledge to use artificial intelligence (AI). Therefore, given the increasing importance of AI in all disciplines, innovative strategies are needed to educate students in AI without programming knowledge so that AI can be integrated into their study modules as a future skill. This work presents a didactic planning script for applied AI. The didactic planning script is based on the AI application pipeline and links AI concepts with study-relevant topics. These linkages open up a new solution space and promote students' interest in and understanding of the potentials and risks of AI. An example lecture series for master students in energy management shows how AI can be seamlessly integrated into discipline-specific lectures. To this end, the planning script for applied AI is adapted to fit the study programs' topic. This specific teaching scenario enables students to solve a discipline-specific task step by step using the AI application pipeline. Thus, the application of the didactic planning script for applied AI shows the practical implementation of the theoretical concepts of AI. In addition, a checklist is presented that can be used to assess whether AI can be used in the discipline-specific lecture. AI as a future skill must be learned by students based on use cases that are relevant to the course of studies. For this reason, AI education should fit seamlessly into various curricula, even if the students do not have a programming background due to their field of study.arXiv.org140The didactic planning script is based on the AI application pipeline and links AI concepts with study-relevant topics and opens up a new solution space and promotes students' interest in and understanding of the potentials and risks of AI.ArXiv['Julius Schöning', 'Tim Wawer', 'K. Griese']2024-02-06T00:00:00https://www.semanticscholar.org/paper/8113eb624277760433ff40bf33b9b2e03009e77c 1cf99cae0f2b227d4625758ec6db14cc3fa4e574Introduction In healthcare, rapid access to accurate information is essential, especially for nurses who make critical decisions. Artificial intelligence (AI) offers promise in this context, with ChatGPT and Google Bard being notable AI-driven information retrieval tools. Methods This study evaluated ChatGPT and Google Bard's performance by assessing their responses to 50 diverse medical knowledge questions, covering infection control, vital signs, CPR, and more, and comparing their response to the correct answers. Results ChatGPT achieved a 64% accuracy rate, while Google Bard achieved 56%. Both models agreed on key medical concepts, but disagreements emerged in some areas, highlighting disparities in their responses. Nurses' expertise in patient-centered care, clinical judgment, and communication complements AI. AI aids in providing evidence-based information but cannot replace nurses' human touch and critical thinking. Integrating AI into nursing education enhances learning and prepares professionals for evolving healthcare landscapes. Conclusion ChatGPT and Google Bard have strengths and weaknesses, making them valuable aids but not substitutes for nurses. Ethical considerations are vital as AI continues to shape healthcare. Nurses must ensure ethical AI use while upholding their commitment to compassionate care.Barw Medical Journal370ChatGPT and Google Bard have strengths and weaknesses, making them valuable aids but not substitutes for nurses, and nurses' expertise in patient-centered care, clinical judgment, and communication complements AI.Barw Medical Journal['Yousif M. Mahmood', 'R. O. Mohammed', 'Imad J. Habibullah', 'Hawbash M Rahim', 'Abdulwahid M. Salih']2024-02-06T00:00:00https://www.semanticscholar.org/paper/1cf99cae0f2b227d4625758ec6db14cc3fa4e574 2b1effd563e0daa3048da493a6f31d3d14d15305Objective. To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app 'listener' that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods. We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and AI for processing unstructured text. Results. Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across five healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion. Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs, (2) increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.medRxiv280Cumulus addresses barriers to data sharing based on federally required support for standard APIs, increasing use of cloud computing, and advances in AI and has potential for scalability to support learning across myriad network configurations and use cases.medRxiv['Andrew J McMurry', 'D. Gottlieb', 'Timothy A Miller', 'James R Jones', 'Ashish Atreja', 'Jennifer Crago', 'Pankaja M Desai', 'Brian E. Dixon', 'Matthew Garber', 'Vladimir Ignatov', 'Lyndsey A Kirchner', 'Philip R O Payne', 'Anil J Saldanha', 'Prabhu Shankar', 'Y. Solad', 'Elizabeth A Sprouse', 'Michael Terry', 'Adam B Wilcox', 'K. D. Mandl']2024-02-06T00:00:00https://www.semanticscholar.org/paper/2b1effd563e0daa3048da493a6f31d3d14d15305 2667a56c916c50117f95e8fb6ef6a814cff79131J. Cloud Comput.601This review paper dives into the issues of ecological and climate monitoring, emphasizing the complications caused by technical limits, data integration, scale differences, and the critical requirement for accurate and timely information.J. Cloud Comput.['Huimin Han', 'Zehua Liu', 'Jiuhao Li', 'Zhixiong Zeng']2024-02-06T00:00:00https://www.semanticscholar.org/paper/2667a56c916c50117f95e8fb6ef6a814cff79131 5d78f2d99421d99594ab6e89f5b790cd92455660This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.Inf.251This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories, highlighting the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries.Inf.['Timotej Jagrič', 'Aljaz Herman']2024-02-06T00:00:00https://www.semanticscholar.org/paper/5d78f2d99421d99594ab6e89f5b790cd92455660 fe4da3d6aa2f5948cca2b45470a16d24eca70873Nature11Nature['Davide Castelvecchi']2024-02-06T00:00:00https://www.semanticscholar.org/paper/fe4da3d6aa2f5948cca2b45470a16d24eca70873 3c1cc44388b47099562558e68f874f1a499cf8e0Nonprofit Communications Report00Nonprofit Communications Report[]2024-02-06T00:00:00https://www.semanticscholar.org/paper/3c1cc44388b47099562558e68f874f1a499cf8e0 2761f3884cc5cd81f1ad2c56b1955811480afea2Successful Fundraising00Successful Fundraising[]2024-02-06T00:00:00https://www.semanticscholar.org/paper/2761f3884cc5cd81f1ad2c56b1955811480afea2 5cdbb09ff10eefaa2ea714291cb5e5ec377be2f6International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-06T00:00:00https://www.semanticscholar.org/paper/5cdbb09ff10eefaa2ea714291cb5e5ec377be2f6 c9ba1cd7816159f499eaf81c8517636158ef6bdaNature00Nature['Katrina Krämer']2024-02-06T00:00:00https://www.semanticscholar.org/paper/c9ba1cd7816159f499eaf81c8517636158ef6bda c719462b1fcbb85c7796f0c381f3a01a6183fc7aJournal of Macromarketing90Journal of Macromarketing['J. Fowler', 'Amy Watson']2024-02-06T00:00:00https://www.semanticscholar.org/paper/c719462b1fcbb85c7796f0c381f3a01a6183fc7a a9f70ad32aba9c2f9378463337ea7a9a1f2b6c01Intracranial hemorrhage (ICH) is a dangerous life-threatening condition leading to disability. Timely and high-quality diagnosis plays a huge role in the course and outcome of this disease. The gold standard in determining ICH is computed tomography. This method requires a prompt involvement of highly qualified personnel, which is not always possible, for example, in case of a staff shortage or increased workload. In such a situation, every minute counts, and time can be lost. The solution to this problem seems to be a set of diagnostic decisions, including the use of artificial intelligence, which will help to identify patients with ICH in a timely manner and provide prompt and quality medical care. However, the main obstacle to the development of artificial intelligence is a lack of high-quality datasets for training and testing. In this paper, we present a dataset including 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH, enriched with clinical and technical parameters, as well as the methodology of its generation utilizing natural language processing tools. The dataset is publicly available, which contributes to increased competition in the development of artificial intelligence systems and their advancement and quality improvement.International Conference on Data Technologies and Applications210A dataset including 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH, enriched with clinical and technical parameters, as well as the methodology of its generation utilizing natural language processing tools is presented.Data['A. Khoruzhaya', 'T. Bobrovskaya', 'D. V. Kozlov', 'Dmitriy Kuligovskiy', 'Vladimir P. Novik', 'Kirill M. Arzamasov', 'E. I. Kremneva']2024-02-06T00:00:00https://www.semanticscholar.org/paper/a9f70ad32aba9c2f9378463337ea7a9a1f2b6c01 6a3ba35273bfff06eb3bb02c834efb65babb4327EDPACS: The EDP Audit, Control, and Security Newsletter200EDPACS['Ceray Aldemir', 'Tuğba Uçma Uysal']2024-02-06T00:00:00https://www.semanticscholar.org/paper/6a3ba35273bfff06eb3bb02c834efb65babb4327 01d78e8b05c17709d214b3383e6e38b34f175950International Journal of Human-Computer Interaction980International Journal of Human–Computer Interaction['Jieon Lee', 'Jong Soo You', 'Dae-Jin Lee']2024-02-06T00:00:00https://www.semanticscholar.org/paper/01d78e8b05c17709d214b3383e6e38b34f175950 a176ddf1ba51dd9eec754350408a55bf9d6d9efaArtificial intelligence (AI) is a game changer in many fields, including cultural heritage. It supports the planning and preservation of heritage sites and cities, enables the creation of virtual experiences to enrich cultural tourism and engagement, supports research, and increases access and understanding of heritage objects. Despite some impressive examples, the full potential of AI for economic, social, and cultural change is not yet fully visible. Against this background, this article aims to (a) highlight the scope of AI in the field of cultural heritage and innovation, (b) highlight the state of the art of AI technologies for cultural heritage, (c) highlight challenges and opportunities, and (d) outline an agenda for AI, cultural heritage, and innovation.The Heritage991The scope of AI in the field of cultural heritage and innovation is highlighted, the state of the art of AI technologies for cultural heritage is highlighted, challenges and opportunities are highlighted, and an agenda for AI, cultural heritage, and innovation is outlined.Heritage['Sander Muenster', 'Ferdinand Maiwald', 'Isabella di Lenardo', 'Juha Henriksson', 'Antoine Isaac', 'Manuela Milica Graf', 'Clemens Beck', 'Johan Oomen']2024-02-06T00:00:00https://www.semanticscholar.org/paper/a176ddf1ba51dd9eec754350408a55bf9d6d9efa 7ac4713cf919278a756028fc581e5a2877a7f5edArtificial intelligence revolutionizes nursing informatics and healthcare by enhancing patient outcomes and healthcare access while streamlining nursing workflow. These advancements, while promising, have sparked debates on traditional nursing ethics like patient data handling and implicit bias. The key to unlocking the next frontier in holistic nursing care lies in nurses navigating the delicate balance between artificial intelligence and the core values of empathy and compassion. Mindful utilization of artificial intelligence coupled with an unwavering ethical commitment by nurses may transform the very essence of nursing.Nursing Ethics201The key to unlocking the next frontier in holistic nursing care lies in nurses navigating the delicate balance between artificial intelligence and the core values of empathy and compassion.Nursing ethics['A. L. Watson']2024-02-06T00:00:00https://www.semanticscholar.org/paper/7ac4713cf919278a756028fc581e5a2877a7f5ed 719abe10339870307a76969957ad7c8379f603b5The financial industry is experiencing a paradigm shift driven by Artificial Intelligence (AI). This manuscript delves into the profound impact of AI on banking client services, exploring how it revolutionizes the industry. As banks strive to elevate customer experiences, reduce operational costs, and maintain their competitive edge, AI emerges as a critical enabler. This study employs a mixed-method research approach to comprehensively investigate the impact of Artificial Intelligence (AI) on client service in the banking sector. To test the hypothesis, we conducted a regression analysis with customer satisfaction as the dependent variable and AI integration and operational efficiency as independent variables. The results demonstrate a statistically significant relationship between AI integration and customer satisfaction (β = 0.632, p < 0.001). The research reveals that clients perceive a moderate level of AI integration into banking operations, with room for further enhancement. Notably, clients express high satisfaction with AI-enhanced services, underscoring its positive influence, as corroborated by existing literature. Recommendations emphasize strategic actions to maximize AI's potential: augmenting AI integration beyond chatbots to encompass predictive analytics and fraud detection, initiating customer education programs to familiarize clients with AI-powered services, prioritizing data privacy and security measures, and providing comprehensive staff training in AI ethics and customer interaction. In conclusion, "The Transformative Power of Artificial Intelligence in Banking Client Service" serves as a timely guide for banking professionals, policymakers, and researchers eager to harness AI's potential for the betterment of the financial industry and its clients. This research paints a compelling picture of how AI is reshaping the future of banking, offering insights into its transformative capacity. As AI continues to evolve, this manuscript provides a roadmap for financial institutions, highlighting the need to adapt, collaborate, and strategize effectively to thrive in the rapidly changing landscape.South Asian Journal of Social Studies and Economics01"The Transformative Power of Artificial Intelligence in Banking Client Service" serves as a timely guide for banking professionals, policymakers, and researchers eager to harness AI's potential for the betterment of the financial industry and its clients.South Asian Journal of Social Studies and Economics['B. Maseke']2024-02-06T00:00:00https://www.semanticscholar.org/paper/719abe10339870307a76969957ad7c8379f603b5 b6e0ded09ba2286e0227bd1fc5615f5778a4fc1fIndia, the second-most populated country globally with 1.4 billion people, faces significant challenges in its agriculture sector, including the need to feed a growing global population, mitigate climate change impacts, and ensure sustainable resource management. To address these challenges, innovative techniques and advanced technologies are required. Modern farming techniques, coupled with artificial intelligence (A.I.), offer promising solutions. Technologies such as drones, biotechnology, genetics, and precision farming can enhance efficiency, productivity, and sustainability. Implementing these approaches can lead to a fiftyfold increase in yield and a 50% reduction in manpower, contributing to increased crop yields, reduced water usage, minimized chemical applications, and improved labor efficiency. Smart agriculture holds the potential to revolutionize the industry, significantly contributing to global food security and the preservation of natural resources.Journal of Scientific Research and Reports00Smart agriculture holds the potential to revolutionize the industry, significantly contributing to global food security and the preservation of natural resources.Journal of Scientific Research and Reports['Chanda Thapliyal Nautiyal', 'Pankaj Nautiyal', 'Gaurav Papnai', 'Harsh Mittal', 'Khusboo Agrawal', 'Shivani', 'Vishesh', 'Raj Nandini']2024-02-06T00:00:00https://www.semanticscholar.org/paper/b6e0ded09ba2286e0227bd1fc5615f5778a4fc1f 81a87d6f79b54cf2b356c91c214837a351dfa4f8Digitalisasi dalam pembelajaran abad 21 semakin tidak bisa terpisahkan, salah satunya adalah hadirnya kecerdasan buatan atau sering disebut dengan artificial intelligence dalam pembelajaran. Penelitian ini bertujuan menganalisis terkait persepsi mahasiswa dalam penggunaan platform Quizizz sebagai media kuis interaktif. Jenis penelitian menggunakan metode campuran pararel konvergen (mixed method) dengan Purposive Sampling sebagai teknik sampling yang digunakan. Teknik pengumpulan data menggunakan angket kuisoner dan wawancara. Model Miles and Huberman digunakan sebagai teknik analisis data yang terdiri dari pengumpulan data, reduksi data, penyajian data dan penarikan kesimpulan. Berdasarkan hasil penelitian disimpulkan bahwa mahasiswa dalam mata kuliah model pembelajaran SD berpersepsi Quizizz memiliki unsur kemutakhiran yang sesuai dengan era society 5.0. Penggunaan Quizizz dalam kuis interaktif memberikan kepuasan terhadap mahasiswa dengan adanya transparansi nilai hasil kuis tersebut. Mahasiswa berpendapat Quizizz mudah dan fleksibel dalam penggunaanya karena kuis dapat dikerjakan dimanapun dan kapanpun dengan menggunakan gadget. Gamifikasi yang terdapat dalam Quizizz menjadikan motivasi belajar mahasiswa menjadi meningkat, tertutama dalam mengerjakan soal kuis menjadi tidak membosankan. Implikasi penelitian ini perlu adanya pelatihan bagi dosen guna mengembangkan kompetensi digital seperti membuat kuis interaktif berbasis artificial intelligenceJurnal Basicedu00Jurnal Basicedu['Deby Fauzi Asidiqi', 'Dede Kurnia Adiputra']2024-02-06T00:00:00https://www.semanticscholar.org/paper/81a87d6f79b54cf2b356c91c214837a351dfa4f8 57dce5a7088f84f4ffd936d9c251d7426762ecf3This paper explores the integration of Explainable Automated Machine Learning (AutoML) in the realm of financial engineering, specifically focusing on its application in credit decision-making. The rapid evolution of Artificial Intelligence (AI) in finance has necessitated a balance between sophisticated algorithmic decision-making and the need for transparency in these systems. The focus is on how AutoML can streamline the development of robust machine learning models for credit scoring, while Explainable AI (XAI) methods, particularly SHapley Additive exPlanations (SHAP), provide insights into the models' decision-making processes. This study demonstrates how the combination of AutoML and XAI not only enhances the efficiency and accuracy of credit decisions but also fosters trust and collaboration between humans and AI systems. The findings underscore the potential of explainable AutoML in improving the transparency and accountability of AI-driven financial decisions, aligning with regulatory requirements and ethical considerations.Social Science Research Network330This study demonstrates how the combination of AutoML and XAI not only enhances the efficiency and accuracy of credit decisions but also fosters trust and collaboration between humans and AI systems.ArXiv['Marc Schmitt']2024-02-06T00:00:00https://www.semanticscholar.org/paper/57dce5a7088f84f4ffd936d9c251d7426762ecf3 b3df729b1506f82e376847a2f5ab3bb4544dd201The collaboration between artificial intelligence (AI) and humans is reshaping international business (IB) management dynamics, aiming to achieve global sustainable development. Recent IB literature indicates that managing AI brings benefits such as better resource reconfiguration, reduced transaction costs, and global sustainable development. However, existing IB literature provides only meager knowledge about the characteristics of AI and how these characteristics can be employed for international expansion at the intersection of sustainable development. In response, our aim is to construct these characteristics by employing directed qualitative content analysis of empirical AI research. Based on our three constructed characteristics of AI, we contribute to current IB literature by providing a framework to balance economic and social goals and utilizing AI for global sustainable development. Further, we provide future IB research themes to guide IB and AI research toward achieving a sustainable production and consumption agenda.Thunderbird International Business Review1180Based on three constructed characteristics of AI, a framework to balance economic and social goals and utilizing AI for global sustainable development is provided by providing a framework to balance economic and social goals and utilizing AI for global sustainable development.Thunderbird International Business Review['Rakibul Hasan', 'Arto Ojala']2024-02-06T00:00:00https://www.semanticscholar.org/paper/b3df729b1506f82e376847a2f5ab3bb4544dd201 4d973d0fa7d3bb0b97a4b0cd4bdead2037823224The article highlights the impact of the integration of artificial intelligence (AI) into the educational environment, focusing on the possibilities of technological convergence. Based on the recognition of the changing world and the need to adapt educational approaches to the challenges of today, the study offers a deeper understanding of the role of innovative technologies. The study reveals the potential of AI to create personalised, adaptive learning paths for students of different educational and qualification levels. The use of AI algorithms to customise the learning process to individual needs promotes critical thinking and collaboration, which are important for building a safe social environment. The study highlights the possibility of using AI to reduce social inequality in areas affected by military aggression. The introduction of AI in the educational process can ensure equal access to quality education for all regions. The authors of the article emphasise the need for a multidisciplinary approach to the use of AI in education, where the interaction of educators and civil society is crucial for the implementation of technologies with ethics and efficiency. An innovative approach to education supported by AI can foster cooperation, understanding, and cohesion, contributing to a stable and harmonious society.Multidisciplinary Reviews00The authors of the article emphasise the need for a multidisciplinary approach to the use of AI in education, where the interaction of educators and civil society is crucial for the implementation of technologies with ethics and efficiency.Multidisciplinary Reviews['Nataliia Bakhmat', 'Iryna Romanova', 'Larysa Oronovska', 'Olga Rudenko', 'Olena Mogyl']2024-02-06T00:00:00https://www.semanticscholar.org/paper/4d973d0fa7d3bb0b97a4b0cd4bdead2037823224 ebe6bcbc6329f0bb490807c169bff88963aca840The issue of artificial intelligence (AI) ethics is a prominent research subject. While there is a compendious literature that explores this area, surprisingly little of it makes explicit reference to the ethic‐theoretical foundations upon which it is built. To address this matter, this study makes an examination of the AI ethics literature to identify its ethic‐theoretical foundations. The study identifies the lack of AI ethics literature that draws upon seminal ethics works and the ensuing disconnectedness among the publications on this subject. It also uncovers numerous non‐Western ethic‐theoretical positions that can be adopted and may afford new insight into AI ethics research and practice. Employing these alternative lenses may obviate the tendency for Western worldviews to dominate the academic literature. The study provides some guidance for future AI ethics research which should endeavor to clearly articulate its chosen ethic‐theoretical position, and for practice which could benefit from understanding and articulating the principles upon which AI systems are founded. It also provides some observations of, and guidance for, the utilization of Litmaps software in the conduct of Literature reviews.Thunderbird International Business Review1200The study identifies the lack of AI ethics literature that draws upon seminal ethics works and the ensuing disconnectedness among the publications and uncovers numerous non‐Western ethic‐theoretical positions that can be adopted and may afford new insight into AI ethics research and practice.Thunderbird International Business Review['Gareth R. T. White', 'Anthony Samuel', 'Paul Jones', 'Naveen Madhavan', 'Ademola Afolayan', 'Ahmed Abdullah', 'Tanmay Kaushik']2024-02-06T00:00:00https://www.semanticscholar.org/paper/ebe6bcbc6329f0bb490807c169bff88963aca840 bca0729f8ae1733aa6396a5337194f43e817cb97International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-06T00:00:00https://www.semanticscholar.org/paper/bca0729f8ae1733aa6396a5337194f43e817cb97 65601a313d40835b7d5e034ebde42a880efc16e2Bien qu’ils aient été étudiés depuis les années 2000, les enjeux que suscitent les systèmes d’intelligence artificielle (IA) lorsqu’ils sont utilisés éducation (SIA-ED) font actuellement l’objet d’une attention croissante dans la littérature scientifique. Il est toutefois difficile d’en avoir une vue synthétique car ils sont abordés par les chercheurs et chercheuses au travers de terrains éducatifs, de techniques computationnelles et d’angles d’analyse hétérogènes. Aussi, l’objectif de cet article est de mener une revue systématique de la littérature sur les enjeux éthiques et critiques des SIA-ED afin d’en avoir un meilleur portrait. Une analyse de 58 documents scientifiques nous a amenés à identifier 70 enjeux éthiques et critiques des SIA-ED, que nous avons organisés sous 6 tensions : complexité des situations éducatives vs standardisation technique ; agentivité des acteurs et actrices scolaires vs automatisation technique ; justice scolaire vs rationalité technique ; gouvernance scolaire vs conception technique ; besoin d’intelligibilité des acteurs et actrices scolaires vs opacité technique ; dignité des acteurs et actrices scolaires vs exploitation des données.Canadian Journal of Learning and Technology00Canadian Journal of Learning and Technology['Simon Collin', 'Alexandre Lepage', 'Léo Nebel']2024-02-06T00:00:00https://www.semanticscholar.org/paper/65601a313d40835b7d5e034ebde42a880efc16e2 f10e9ea8f57211712fe638f94123f83e751355e901['Kashif Naseer Qureshi', 'Thomas Newe']2024-02-06T00:00:00https://www.semanticscholar.org/paper/f10e9ea8f57211712fe638f94123f83e751355e9 1b2e5067055b3fb4ebd74109303869ce79e992d7Abstract: UD. Hasibuan is one of the businesses engaged in selling poultry feed for the surrounding city area. For poultry feed, the type of corn is usually dried corn taken from local farmers, and the process of separating the corn kernels and corn cobs is done manually (separated using a knife, one one by one) so that corn seed production is not optimal, plus the corn seed separator on the market is quite expensive. To overcome this, the researcher wanted to design a tool for separating corn kernels with cobs. This research used quantitative methods to find references for designing corn kernel separating machines. From the design results, it was found that the machine designed was made in the form of a small scale and a large scale, where each engine drive used a DC motor with 220VAC power, with a torque of 2800rpm, a current of 1.1A and a power of 125Watt, capable of separating dry corn from the cobs, by the help of a modified dividing axle on the engine. Apart from that, the machine works automatically, so that if there is corn to be separated, the artificial intelligence system or automatic system embedded in the system will be active, using an Arduino nano controller  with a 5Vdc voltage supply and an SFR 05 supporting sensor and a 1 channel relay module with supply. 5Vdc.            Keywords: Corn separator; Microcontroller; UD. Hasibuan Abstrak: UD. Hasibuan merupakan salah satu usaha yang bergerak dalam penjualan pakan unggas untuk wilayah kota kisaran sekitar, untuk pakan unggas jenis jagung biasanya untuk jagung kering diambil dari petani sekitar, dan proses pemisahan antara biji jagung dan tongkol jagung dilakukan secara manual (dipisahkan dengan menggunakan pisau, satu persatu) sehingga produksi biji jagung tidak maksimal, ditambah lagi alat pemisah biji jagung dipasaran kategori cukup mahal. Untuk mengatasi hal tersebut, maka peneliti ingin merancang sebuah alat pemisah biji jagung dengan tongkol nya, penelitian ini menggunakan metode kuantitatif untuk mencari refrensi perancangan mesin pemisah biji jagung. Dari hasil perancangan mendapati bahwa, mesin yang dirancang dibuat dalam bentuk scala kecil dan scala besar, dimana masing masing penggerak mesin menggunakan motor DC dengan daya 220VAC, dengan torsi 2800rpm, arus 1,1A dan daya 125Watt mampu untuk memisahkan jagung kering dari tongkolnya, dengan bantuan as pemisah yang dimodifikasi pada mesin. Selain itu mesin bekerja secara otomatis, sehingga jika ada jagung yang akan di pisahkan, sistem kecerdasan buatan atau sistem otomatis yang ditanamkan pada sistem akan aktif, dengan menggunakan controller  arduino nano dengan supply tegangan 5Vdc dan sensor pendukung SFR 05 dan modul relay 1 chanel dengan supply 5Vdc. Kata kunci: Pemisah jagung; Microcontroller; UD. HasibuanJurnal Teknika70JURNAL TEKNISI['Ricki Ananda', 'Muhammad Amin', 'A. Afandi']2024-02-06T00:00:00https://www.semanticscholar.org/paper/1b2e5067055b3fb4ebd74109303869ce79e992d7 4a38d967c9204b5a582c299d6ca3aa64f38d3bacThis study introduces an innovative optimization system for agriculture, utilizing the Particle Swarm Optimization (PSO) algorithm. This system focuses on maximizing net revenue in agricultural contexts while simultaneously minimizing the consumption of natural resources and inputs, with particular attention to the efficient use of water. A distinctive feature of the system is its versatility and accessibility, being designed for easy implementation on smartphones, which broadens its reach to a variety of users, from small to large-scale farmers. The efficiency and speed in obtaining results are key aspects of this system, facilitating a more agile and informed decision-making process in the agricultural sector. The PSO algorithm, which forms the basis of the system, effectively identifies the optimal balance between input consumption and crop productivity. This study validated the system through comparisons with available literature data, focusing on specific crops such as iceberg lettuce and melon. The results not only showed consistency with existing data but in some cases, exceeded expectations. In conclusion, this system represents a significant contribution to modern agriculture, offering a reliable and easily accessible tool for economic decision-making. The ability to quickly adapt to different types of inputs, compatibility with mobile devices, and the ease of incorporating new production functions, regardless of complexity or the number of inputs, highlight the flexibility and practical relevance of this technological innovation.Caderno Pedagógico30This study validated the PSO algorithm system through comparisons with available literature data, focusing on specific crops such as iceberg lettuce and melon, and showed consistency with existing data but in some cases, exceeded expectations.Caderno Pedagógico['Manoel Villas Bôas Júnior', 'Angel Ramon Sanchez Delgado', 'Jose Airton Chaves Cavalcante Júnior', 'Maria Claudia Rodriguez']2024-02-06T00:00:00https://www.semanticscholar.org/paper/4a38d967c9204b5a582c299d6ca3aa64f38d3bac b42b01098d53c11f7b6e6f458b33b651d23c6040Hydropower plays a crucial role in supplying electricity to developed nations and is projected to expand its capacity in various developing countries such as Sub-Saharan Africa, Argentina, Colombia, and Turkey. With the increasing demand for sustainable energy and the emphasis on reducing carbon emissions, the significance of hydropower plants is growing. Nevertheless, numerous challenges arise for these plants due to their aging infrastructure, impacting both their efficiency and structural stability. In order to tackle these issues, the present study has formulated a specialized real-time framework for identifying damage, with a particular focus on detecting corrosion in the conductors of generators within hydropower plants. It should be noted that corrosion processes can be highly complex and nonlinear, making it challenging to develop accurate physics-based models that capture all the nuances. Therefore, the proposed framework leverages autoencoder, an unsupervised, data-driven AI technology with the Mahalanobis distance, to capture the intricacies of corrosion and automate its detection. Rigorous testing shows that it can identify slight variations indicating conductor corrosion with over 80% sensitivity and a 5% false alarm rate for ‘medium’ to ‘high’ severity damage. By detecting and resolving corrosion early, the system reduces disruptions, streamlines maintenance, and mitigates unscheduled repairs’ negative effects on the environment. This enhances energy generation effectiveness, promotes hydroelectric facilities’ long-term viability, and fosters community prosperity.Smart Cities180By detecting and resolving corrosion early, the system reduces disruptions, streamlines maintenance, and mitigates unscheduled repairs’ negative effects on the environment, which enhances energy generation effectiveness, promotes hydroelectric facilities’ long-term viability, and fosters community prosperity.Smart Cities['F. T. Fera', 'Christos Spandonidis']2024-02-06T00:00:00https://www.semanticscholar.org/paper/b42b01098d53c11f7b6e6f458b33b651d23c6040 e22c6f7aa5249ed31fd8c454c0966a4f3d02007fCybernetics and Systems Analysis30Cybernetics and Systems Analysis['V. P. Boyun']2024-02-06T00:00:00https://www.semanticscholar.org/paper/e22c6f7aa5249ed31fd8c454c0966a4f3d02007f b3ad64eae2482b80ef47c227fcb2ff4aa5875fccJournal of Property Investment &amp; Finance00Journal of Property Investment &amp; Finance['Nick French']2024-02-06T00:00:00https://www.semanticscholar.org/paper/b3ad64eae2482b80ef47c227fcb2ff4aa5875fcc 163ec532c56a2a25eb188e3aca8616b76682a4b7School Science and Mathematics60School Science and Mathematics['Adrienne Redmond‐Sanogo', 'Catherine Maiorca', 'Thomas Roberts', 'Jessica Ivy', 'Megan Burton']2024-02-06T00:00:00https://www.semanticscholar.org/paper/163ec532c56a2a25eb188e3aca8616b76682a4b7 84282fe65d4325f6da2cff2a36fea8039f8101b5Applied Economics270Applied Economics['Qing Zhang', 'Ting Su', 'Zhen Zhou']2024-02-06T00:00:00https://www.semanticscholar.org/paper/84282fe65d4325f6da2cff2a36fea8039f8101b5 dcd2f0156d89619c622431cffa0b9c2e01c631a0We explore the AI2050"hard problems"that block the promise of AI and cause AI risks: (1) developing general capabilities of the systems; (2) assuring the performance of AI systems and their training processes; (3) aligning system goals with human goals; (4) enabling great applications of AI in real life; (5) addressing economic disruptions; (6) ensuring the participation of all; (7) at the same time ensuring socially responsible deployment; (8) addressing any geopolitical disruptions that AI causes; (9) promoting sound governance of the technology; and (10) managing the philosophical disruptions for humans living in the age of AI. For each problem, we outline the area, identify significant recent work, and suggest ways forward. [Note: this paper reviews literature through January 2023.]arXiv.org00The AI2050 "hard problems" that block the promise of AI and cause AI risks are explored, with a focus on developing general capabilities of the systems and assuring the performance of AI systems and their training processes.ArXiv['Gavin Leech', 'S. Garfinkel', 'Misha Yagudin', 'Alexander Briand', 'Aleksandr Zhuravlev']2024-02-06T00:00:00https://www.semanticscholar.org/paper/dcd2f0156d89619c622431cffa0b9c2e01c631a0 c524b984fc7d65ba4817be382953ada93fe2ca8bThis paper studies the output regulation problem of time-varying descriptor systems and the problem of designing state feedback and dynamic measurement output feedback control laws which asymptotically achieves output regulation and disturbance rejection is considered. New regulator equations are proposed for time-varying descriptor systems in the form of differential-algebraic matrix equations. The unique solution of the proposed regulator equations is given as well. We prove that the output regulation problem of time-varying descriptor systems is solvable if and only if the given regulator equations are solvable. Based on the solution of the regulator equations, the state feedback and dynamic measurement output feedback control laws are designed to solve the output regulation problem. The work extends the existing results of output regulation problem for time-varying linear systems to the time-varying descriptor systems. Numerical examples are given to show the effectiveness of our methodology.Measurement and control (London. 1968)110Measurement and Control['X. Su', 'Wenai Liu', 'Adiya Bao']2024-02-05T00:00:00https://www.semanticscholar.org/paper/c524b984fc7d65ba4817be382953ada93fe2ca8b 13283cbaa4313b059bef3621348e7f2f15ef3ef5The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL), whose starting point of prioritizing data privacy while performing ML fundamentally differs from that of centralized learning. We believe the AI Act and future regulations could be the missing catalyst that pushes FL toward mainstream adoption. However, this can only occur if the FL community reprioritizes its research focus. In our position paper, we perform a first-of-its-kind interdisciplinary analysis (legal and ML) of the impact the AI Act may have on FL and make a series of observations supporting our primary position through quantitative and qualitative analysis. We explore data governance issues and the concern for privacy. We establish new challenges regarding performance and energy efficiency within lifecycle monitoring. Taken together, our analysis suggests there is a sizable opportunity for FL to become a crucial component of AI Act-compliant ML systems and for the new regulation to drive the adoption of FL techniques in general. Most noteworthy are the opportunities to defend against data bias and enhance private and secure computationarXiv.org693There is a sizable opportunity for FL to become a crucial component of AI Act-compliant ML systems and for the new regulation to drive the adoption of FL techniques in general.ArXiv['Herbert Woisetschläger', 'Alexander Erben', 'Bill Marino', 'Shiqiang Wang', 'Nicholas D. Lane', 'R. Mayer', 'Hans-Arno Jacobsen']2024-02-05T00:00:00https://www.semanticscholar.org/paper/13283cbaa4313b059bef3621348e7f2f15ef3ef5 323082e059693a0699909ef57063940226a29eeaThe transition to renewable energy is strongly affected by legal regulation. To increase the efficiency of the introduction of renewable energy into the energy systems of component states of federations and accelerate the energy transition, it is necessary to carry out systematic work to improve regional legislation in this area. The purpose of this study was to analyze the current regulatory legal acts on the renewable energy of the regions of a number of countries such as the USA, Germany, India, Switzerland and Russia in order to form a universal list of issues that need regulation at the regional level. The main methods for achieving the objectives set in this study were the comparative legal method and the method of analysis and synthesis. As a result, a number of recommendations were developed describing how legal relations primarily need to be regulated by regional legislation, and examples of different approaches to their settlement were presented. The issues in need of legal regulation were divided into three groups according to the degree of importance of their regulation by the legislation of the component state of the federation. Further development of this study will be aimed at identifying the most effective industrial practices for resolving each of the issues included in the compiled list which will help improve the efficiency of regional legal regulation of renewable energy.Energies200Energies['E. Kirichenko', 'K. Kirichenko', 'Anna Kirichenko']2024-02-05T00:00:00https://www.semanticscholar.org/paper/323082e059693a0699909ef57063940226a29eea 7e8a4d1f7a6aeb4562c9f997dfc045b04e02878cExisting work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy. Additionally, many techniques overlook the distinction between those who train ML models and those responsible for assessing their trustworthiness. To address these issues, we propose a framework that views trustworthy ML as a multi-objective multi-agent optimization problem. This naturally lends itself to a game-theoretic formulation we call regulation games. We illustrate a particular game instance, the SpecGame in which we model the relationship between an ML model builder and fairness and privacy regulators. Regulators wish to design penalties that enforce compliance with their specification, but do not want to discourage builders from participation. Seeking such socially optimal (i.e., efficient for all agents) solutions to the game, we introduce ParetoPlay. This novel equilibrium search algorithm ensures that agents remain on the Pareto frontier of their objectives and avoids the inefficiencies of other equilibria. Simulating SpecGame through ParetoPlay can provide policy guidance for ML Regulation. For instance, we show that for a gender classification application, regulators can enforce a differential privacy budget that is on average 4.0 lower if they take the initiative to specify their desired guarantee first.arXiv.org480A framework that views trustworthy ML as a multi-objective multi-agent optimization problem, which naturally lends itself to a game-theoretic formulation the authors call regulation games, and introduces ParetoPlay, a novel equilibrium search algorithm that ensures that agents remain on the Pareto frontier of their objectives and avoids the inefficiencies of other equilibria.ArXiv['Mohammad Yaghini', 'Patty Liu', 'Franziska Boenisch', 'Nicolas Papernot']2024-02-05T00:00:00https://www.semanticscholar.org/paper/7e8a4d1f7a6aeb4562c9f997dfc045b04e02878c f618e8fb1e2fe388e39ee569cca8260f2b3c08e0Статья посвящена проблемам управления исполнительной и распорядительной деятельностью Министерства юстиции, а также путям решения данных проблем. The article is devoted to the problems of management and executive and administrative activities of the Ministry of Justice, as well as ways to solve these problems.Journal of Applied Research00Journal of Applied Research['Е.В. Еремина']2024-02-05T00:00:00https://www.semanticscholar.org/paper/f618e8fb1e2fe388e39ee569cca8260f2b3c08e0 08ea5b3d7783d7ef2368d4b3c2d76ecdc1a4040cEnvironmental and Resource Economics490Environmental and Resource Economics['Wenjie Luo', 'Xunyong Xiang']2024-02-05T00:00:00https://www.semanticscholar.org/paper/08ea5b3d7783d7ef2368d4b3c2d76ecdc1a4040c e67ef55048698ba98b3981b1118dfb56d9f979aaTechnology Analysis &amp; Strategic Management310Technology Analysis &amp; Strategic Management['Sheng Liu', 'Haoteng Xu', 'Xiuying Chen']2024-02-05T00:00:00https://www.semanticscholar.org/paper/e67ef55048698ba98b3981b1118dfb56d9f979aa 8c90f780728687b026397d564251138025f7006dJournal of international economic law00Journal of International Economic Law['Han-Wei Liu', 'Weihuan Zhou']2024-02-05T00:00:00https://www.semanticscholar.org/paper/8c90f780728687b026397d564251138025f7006d 63de9f83987c3bb9669f88695f76e4b494c76a32European Competition Journal00European Competition Journal['Francesca Niola']2024-02-05T00:00:00https://www.semanticscholar.org/paper/63de9f83987c3bb9669f88695f76e4b494c76a32 d8c1314fca562b050e199051fe978e3b05e8b165International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Peehu Bhardwaj']2024-02-05T00:00:00https://www.semanticscholar.org/paper/d8c1314fca562b050e199051fe978e3b05e8b165 dd44a086729e962af046aff808385b523fbcd856The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse. There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques. In this paper, we seek to understand how well these approaches can perform against today's modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI). Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations while Expert artists produce higher false positives). We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.arXiv.org785This paper curates real human art across 7 styles, generates matching images from 5 generative models, and applies 8 detectors to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.ArXiv['Anna Yoo Jeong Ha', 'Josephine Passananti', 'Ronik Bhaskar', 'Shawn Shan', 'Reid Southen', 'Haitao Zheng', 'Ben Y. Zhao']2024-02-05T00:00:00https://www.semanticscholar.org/paper/dd44a086729e962af046aff808385b523fbcd856 19e60f81aed3c0e10757860cd31e54b1f374e337AI and Ethics223This essay focuses on anthropomorphism as both a form of hype and fallacy, which is shown to exaggerate AI capabilities and performance by attributing human-like traits to systems that do not possess them.AI and Ethics['Adriana Placani']2024-02-05T00:00:00https://www.semanticscholar.org/paper/19e60f81aed3c0e10757860cd31e54b1f374e337 b975c0d80f926fc8ffe6b0edb9511d4e39cd07b8 In recent years, the integration of artificial intelligence (AI) has markedly bolstered productivity, especially in agriculture, mitigating environmental impacts like greenhouse gas emissions. This shift employs a range of tech, like IT, sensors, robotics, and AI, boosting output while curbing negative effects. Challenges persist, notably food scarcity and climate threats for a growing global population. By 2050, two billion more people will need sustenance, necessitating urgent agricultural innovation. This article reviewed databases from 1985 to 2023 (Google Scholar, Scopus, ISI Web of Knowledge), analyzing AI’s role in agriculture. Keywords like AI, precision feeding, welfare, animal husbandry, and management were used for systematic literature review. Findings highlight AI’s pivotal role in addressing global food shortages. Investment in emerging tech, especially AI, is crucial for a sustainable food supply.CABI Reviews611Analysis of databases from 1985 to 2023 highlights AI’s pivotal role in addressing global food shortages, and investment in emerging tech, especially AI, is crucial for a sustainable food supply.CABI Reviews['Ebrahim Talebi', 'Maryam Khosravi Nezhad']2024-02-05T00:00:00https://www.semanticscholar.org/paper/b975c0d80f926fc8ffe6b0edb9511d4e39cd07b8 a8faa9d8cc9186fd1c5220f1454f99f91b6ca09aScientific Reports831A novel, psychologically informed questionnaire that captures attitudes towards AI as a single construct, independent of specific contexts or applications is presented and it is found that agreeableness and younger age predict a more positive view towards artificially intelligent technology, whereas the susceptibility to conspiracy beliefs connects to a more negative attitude.Scientific Reports['Jan-Philipp Stein', 'Tanja Messingschlager', 'Timo Gnambs', 'Fabian Hutmacher', 'Markus Appel']2024-02-05T00:00:00https://www.semanticscholar.org/paper/a8faa9d8cc9186fd1c5220f1454f99f91b6ca09a 4b5c4dcc5e39f9a3efa12eb5dd2f6424355be5b2Conversational AI refers to the use of artificial intelligence technology to enable machines to engage in human-like conversations, allowing for interactive and dynamic interactions with users. There are several tools that are developed by using AI and are widely used across various domains. In education, AI tools can offer several benefits, such as increased accessibility, personalized learning experiences, and improved engagement for students. However, potential downsides may include concerns about data privacy, accuracy of responses, and overreliance on technology without human interaction for holistic learning. This research paper aims to provide a comprehensive guide on the ethical aspects of using AI in higher education. Drawing on insights from twenty recent research papers in the field, this paper discusses the right direction and attitude towards AI, the potential benefits for students, and the risks of electronic plagiarism, black box theory, and diminished creativity. The paper also examines whether AI should be prohibited in higher education to mitigate the potential negative effects. This paper contributes to the ongoing conversation on the role of AI in education and provides a foundation for future research.International Computer Science Conference240This paper discusses the right direction and attitude towards AI, the potential benefits for students, and the risks of electronic plagiarism, black box theory, and diminished creativity, and examines whether AI should be prohibited in higher education to mitigate the potential negative effects.2024 IEEE 18th International Conference on Semantic Computing (ICSC)['ZongXu Li', 'Ajay Dhruv', 'Vijal Jain']2024-02-05T00:00:00https://www.semanticscholar.org/paper/4b5c4dcc5e39f9a3efa12eb5dd2f6424355be5b2 8a2fb83ab9774755677b3f42c4e744e670e802e2Place Branding and Public Diplomacy50Place Branding and Public Diplomacy['Lucie Qian Xia']2024-02-05T00:00:00https://www.semanticscholar.org/paper/8a2fb83ab9774755677b3f42c4e744e670e802e2 b7740857ea5a6ec93d6fa1b1eae922c230ebc342Providing food for the growing population is a challenging task, however, with historical agricultural practices, we can’t meet the food requirement of the world population. We are in the need to adopt modern technology to overcome adverse climatic and cultural challenges, which are faced by current generation, that is Artificial Intelligence (AI). AI is the booming technology in the agriculture, which uses different sensors and neural networks and uses resources minimally based on need and predict the coming obstacles, which causes huge loss to crop. This review explain is, various applications of AI in the sustainable agriculture for crop managemen by overcoming realtime challenges and importance of AI in agriculture by comparing with traditional methods.  Environment conservation journal890Various applications of AI in the sustainable agriculture for crop managemen by overcoming realtime challenges and importance of AI in agriculture by comparing with traditional methods are explained.Environment Conservation Journal['Bhargava Kotte', 'Naveen A', 'Sai Akhil V', 'Hema Lingireddy', 'Gowtham K V', 'Abhijeet Mudhale', 'Guru Sri B', 'Abhishek E']2024-02-05T00:00:00https://www.semanticscholar.org/paper/b7740857ea5a6ec93d6fa1b1eae922c230ebc342 aeb61faf8ad8aed6bc6382a07aef624600607f21The article embarks on an investigative journey into the complex legal and ethical landscape shaped by the advent of Artificial Intelligence (AI). The research problem centres on the urgent need to understand and address the gap between evolving AI technologies and the existing legal and ethical frameworks. This gap poses significant challenges to societal norms, legal systems, and ethical principles, warranting a comprehensive multidisciplinary analysis. The research objectives are twofold: firstly, to dissect the legal implications AI poses to existing regulatory structures, and secondly, to explore the ethical dilemmas emanating from AI's pervasive influence across various societal sectors. The study employs an eclectic research method, integrating doctrinal analysis with a qualitative examination of case studies and existing literature across disciplines like law, ethics, technology, and sociology. This approach facilitates a holistic understanding of the AI era's legal and ethical intricacies. The key findings of this research underscore a dissonance between rapid technological advancements in AI and the slower evolution of legal and ethical norms. This disjunction leads to legal loopholes and ethical ambiguities in AI governance, privacy, accountability, and human rights. Furthermore, the study identifies a pressing need for adaptive legal frameworks and ethical guidelines that can keep pace with AI's transformative impact. Implications of these findings are profound for both theory and practice. Theoretically, the article contributes to an enriched understanding of the intersection between law, ethics, and technology. Practically, it offers actionable insights for policymakers, technologists, and ethicists to collaboratively formulate responsive legal and ethical strategies. These strategies are essential for safeguarding societal values while embracing technological progress, ensuring AI's development is both legally sound and ethically responsible.International Law Research560The article embarks on an investigative journey into the complex legal and ethical landscape shaped by the advent of Artificial Intelligence, identifying a pressing need for adaptive legal frameworks and ethical guidelines that can keep pace with AI's transformative impact.International Law Research['Ogochukwu C. Nweke', 'G. I. Nweke']2024-02-05T00:00:00https://www.semanticscholar.org/paper/aeb61faf8ad8aed6bc6382a07aef624600607f21 e4f7e282548c4ca88957eb2d4f9da909bb765f3eCurrent discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension, painting a future where AI reshapes every facet of human life, including Education. This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators, policymakers, and the public. It explores the promises that AI holds for enhancing learning through personalisation at scale, against the backdrop of concerns about ethical implications, the devaluation of non-STEM subjects, and the potential transformative impact on our neurocognitive and socio-emotional functioning. Drawing on recent research and global discourse, the paper seeks to unpack the reasons behind the vagueness of current discussions on AI in Education (AIED) and the implications of this ambiguity for future educational practices and policies. By highlighting insights from educational research and synthesising evidence-based best practices in AIED, the aim is to provide a clearer understanding of how AI technologies can be aligned with the fundamental principles of learning and teaching, and explore what concrete actions may need to be prioritised now to truly enhance learning experiences and outcomes for all in the future.arXiv.org250A clearer understanding is provided of how AI technologies can be aligned with the fundamental principles of learning and teaching, and what concrete actions may need to be prioritised now to truly enhance learning experiences and outcomes for all in the future.ArXiv['K. Porayska-Pomsta']2024-02-05T00:00:00https://www.semanticscholar.org/paper/e4f7e282548c4ca88957eb2d4f9da909bb765f3e 7df8dd37484341101607e66b69e65b1666483db0With the rapid advance of computer graphics and artificial intelligence technologies, the ways we interact with the world have undergone a transformative shift. Virtual Reality (VR) technology, aided by artificial intelligence (AI), has emerged as a dominant interaction media in multiple application areas, thanks to its advantage of providing users with immersive experiences. Among those applications, medicine is considered one of the most promising areas. In this paper, we present a comprehensive examination of the burgeoning field of AI-enhanced VR applications in medical care and services. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories based on different phases of medical diagnosis and treatment: Visualization Enhancement, VR-related Medical Data Processing, and VR-assisted Intervention. This categorization enables a structured exploration of the diverse roles that AI-powered VR plays in the medical domain, providing a framework for a more comprehensive understanding and evaluation of these technologies. To our best knowledge, this is the first systematic survey of AI-powered VR systems in medical settings, laying a foundation for future research in this interdisciplinary domain.arXiv.org640This is the first systematic survey of AI-powered VR systems in medical settings, laying a foundation for future research in this interdisciplinary domain and introducing a systematic taxonomy.ArXiv['Yixuan Wu', 'Kaiyuan Hu', 'D. Chen', 'Jian Wu']2024-02-05T00:00:00https://www.semanticscholar.org/paper/7df8dd37484341101607e66b69e65b1666483db0 e851c3e73878f0e544633ead6b10edd55e7b5b3dInsights into Imaging430The RADAR framework is presented, which has been adapted from Fryback and Thornbury’s imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation, and offers a comprehensive framework for valuing radiology AI.Insights into Imaging['B.J. Boverhof', 'W. Redekop', 'Daniel Bos', 'M. P. Starmans', 'Judy Birch', 'Andrea Rockall', 'J. J. Visser']2024-02-05T00:00:00https://www.semanticscholar.org/paper/e851c3e73878f0e544633ead6b10edd55e7b5b3d 1fefb60c81d9080c7e655765d83bfc659815e375Integrative experiment design is a needed improvement over ad hoc experiments, but the specific proposed method has limitations. We urge a further break with tradition through the use of an enormous untapped resource: Decades of causal discovery artificial intelligence (AI) literature on optimizing the design of systematic experimentation.Behavioral and Brain Sciences00Decades of causal discovery artificial intelligence (AI) literature on optimizing the design of systematic experimentation on optimizing the design of systematic experimentation are analyzed.The Behavioral and brain sciences['E. Kummerfeld', 'Bryan Andrews']2024-02-05T00:00:00https://www.semanticscholar.org/paper/1fefb60c81d9080c7e655765d83bfc659815e375 66b8535bb4746ac3dc9fa049d4acb155f2b89666At the nexus of artificial intelligence, robotics, image processing, and natural language processing (NLP), quadruped AI assistants provide a revolutionary method of interacting with machines. Intending to give quadruped AI assistants emotional intelligence, this literature review methodically looks over and compiles the body of research, concentrating on how to use natural language processing and image processing together. The review seeks to offer a comprehensive grasp of the developments, challenges, and possible uses in this diverse subject. The initial section of the survey provides an overview of quadruped robots and highlights how they are integrated with image processing technology to provide visual perception. It explores the various locomotion techniques, demonstrating how these robots use picture data to improve their capacity to navigate and adapt to different situations. The conversation also touches on sensor technologies, highlighting their function in obtaining and deciphering visual data for intelligent interaction. In addition, the research delves into how quadruped AI assistants include natural language processing and examines how these robots interpret and react to instructions in human language. One of the main topics of debate is how sentiment analysis and emotional recognition methods might help these assistants become more emotionally intelligent. Finally, this review of the literature offers a comprehensive viewpoint on how natural language processing and image processing are integrated in quadruped AI assistants, providing academics and practitioners with an outline for developing emotional intelligence in these robots. Compiling knowledge from robotics, artificial intelligence, image processing, and natural language processing is essential to creating emotionally competent quadruped AI assistants and ensuring their smooth incorporation into human-centered settings.This research also investigates the integration of Natural Language Processing and Image Processing for Emotional Intelligence in Quadruped AI Assistants and thoroughly assesses the gyroscope functioning on these robotic systemsInternational Journal of Advanced Research in Science, Communication and Technology110This review of the literature offers a comprehensive viewpoint on how natural language processing and image processing are integrated in quadruped AI assistants, providing academics and practitioners with an outline for developing emotional intelligence in these robots.International Journal of Advanced Research in Science, Communication and Technology['Rohan Singh R', 'Dhanush P V']2024-02-05T00:00:00https://www.semanticscholar.org/paper/66b8535bb4746ac3dc9fa049d4acb155f2b89666 850492651130dfdabadea6ec3b59122fcf9f627bAI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as DALLE and Imagen, have integrated digital watermarks aimed at facilitating the discernment of synthetic images' authenticity. These watermarks are embedded within the image pixels and are invisible to the human eye while remains their detectability. Nevertheless, a comprehensive investigation into the potential impact of these invisible watermarks on the utility of synthetic medical images has been lacking. In this study, we propose the incorporation of invisible watermarks into synthetic medical images and seek to evaluate their efficacy in the context of downstream classification tasks. Our goal is to pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.arXiv.org110This study proposes the incorporation of invisible watermarks into synthetic medical images and seeks to evaluate their efficacy in the context of downstream classification tasks and pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.ArXiv['Xiaodan Xing', 'Huiyu Zhou', 'Yingying Fang', 'Guang Yang']2024-02-05T00:00:00https://www.semanticscholar.org/paper/850492651130dfdabadea6ec3b59122fcf9f627b 6fa8a8c4f4f722016bc327d4850b4d61a1e3dfceInternational Journal of Science and Research (IJSR)01International Journal of Science and Research (IJSR)['Sarthak Srivastava Manish Singh']2024-02-05T00:00:00https://www.semanticscholar.org/paper/6fa8a8c4f4f722016bc327d4850b4d61a1e3dfce 63b692039c30a67dcb8e7aefda71a231f7b08a3dNature Network Boston81Nature medicine['Jacqueline Sin']2024-02-05T00:00:00https://www.semanticscholar.org/paper/63b692039c30a67dcb8e7aefda71a231f7b08a3d 82e769a4f76243c20e36c62696dfb19318439abfInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Siby Samuel']2024-02-05T00:00:00https://www.semanticscholar.org/paper/82e769a4f76243c20e36c62696dfb19318439abf ef0587d6787a4461056e4f317029a4348ed42fdeIn an era characterized by rapid technological advancement, our research focuses on the exciting convergence of gaming and brain-computer interface (BCI) technologies. Our main goal is to evaluate the accuracy and reliability of BCI technology in measuring color perception. These efforts require the careful application of artificial intelligence techniques that provide valuable insights into the ever-evolving field of human-computer interaction. To achieve this, we use an EEG device to record brain activity by immersing participants in a series of fascinating visual stimuli. We then carefully analyze this neural data using machine learning algorithms based primarily on Python. Our findings have important implications that extend far beyond gaming: the potential of BCI technology for understanding human cognition is very promising in a variety of areas, including healthcare, rehabilitation, entertainment, education, and groundbreaking research efforts. This study represents an important step in further exploring the ability of BCI technology to revolutionize our connection to the digital world and provide assistance to people with visual impairments.2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)120This study uses an EEG device to record brain activity, and carefully analyzes this neural data using machine learning algorithms based primarily on Python to evaluate the accuracy and reliability of BCI technology in measuring color perception.2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)['Jinendra Dipak Gambhir', 'Mohammad Affan Khalil', 'Kiran George']2024-02-05T00:00:00https://www.semanticscholar.org/paper/ef0587d6787a4461056e4f317029a4348ed42fde 741856a1fbc979fed8902289bd60b8cbf023aabePostdigital Science and Education110Postdigital Science and Education['Benjamin J. Green']2024-02-05T00:00:00https://www.semanticscholar.org/paper/741856a1fbc979fed8902289bd60b8cbf023aabe 1160a3c9a20a84bfc3799fcf1624df01b043dde1Aim: This research investigates the interconnections among Data Analytics, Artificial Intelligence, and other cutting-edge technologies to enhance comprehension of fraud prevention. The advantages of integrating machine learning and data analytics into artificial intelligence systems for industry-wide fraud detection and prevention are examined in this study. Study Design: My approach involved conducting an extensive examination of existing literature and analysing numerous case studies to gather information on the role of artificial intelligence, data, and analytics in fraud prevention. Place and Duration of the Study: A broad spectrum of academic, corporate, and governmental sources is utilised to supply the research study with its international scope. This study examines publications and developments from 2019 to 2023. Methodology: The research procedure incorporated an exhaustive literature review. This assessment was composed of academic journals, conference proceedings, and official publications. A qualitative analysis was conducted to assess the data, identify commonalities, and evaluate the strengths and weaknesses of AI fraud protection solutions. A more comprehensive examination of practical implementations was facilitated by case studies, which enhanced comprehension of fraud prevention strategies propelled by AI. Results: The research revealed important findings concerning the various ways in which analytics, data, and artificial intelligence can be implemented to prevent fraudulent activities. An examination of comparisons between generative AI for social engineering, credit card analytics, and cyber-physical security for Internet of Things (IoT) networks illuminated the merits and demerits of different Artificial Intelligence (AI) approaches. Conclusion: According to the findings of the study, AI, data, and analytics may alter system defences against fraud. The above-mentioned results underscore the significance of flexible fraud prevention strategies. Constant collaboration, innovative technology, and ongoing investigation are required to remain ahead of evolving fraud techniques. The paper concludes by emphasising the significance of future challenges and orientations.Asian Journal of Research in Computer Science00Artificial intelligence, data, and analytics may alter system defences against fraud, and this research investigates the interconnections among Data Analytics, Artificial Intelligence, and other cutting-edge technologies to enhance comprehension of fraud prevention.Asian Journal of Research in Computer Science['Pankaj Gupta']2024-02-05T00:00:00https://www.semanticscholar.org/paper/1160a3c9a20a84bfc3799fcf1624df01b043dde1 3ae09124a0c653e6d82b19dd333b7d572da0832aQualitative Research in Psychology270Qualitative Research in Psychology['Alexandra F. Gibson', 'Alexander Beattie']2024-02-05T00:00:00https://www.semanticscholar.org/paper/3ae09124a0c653e6d82b19dd333b7d572da0832a 8bad8efbcd51303d46f4e6376a471872182cb0f1International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Parul Batra Deep Manishkumar']2024-02-05T00:00:00https://www.semanticscholar.org/paper/8bad8efbcd51303d46f4e6376a471872182cb0f1 4f5569a114a8caa8fe549397583ce9f35bfce4dcAI &amp; SOCIETY00AI &amp; SOCIETY['Mamun Rashid']2024-02-05T00:00:00https://www.semanticscholar.org/paper/4f5569a114a8caa8fe549397583ce9f35bfce4dc 7b7268c43dc692009f844e302ce9a72fb508bfa6International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Chinmoyee Deori Leena Hujuri']2024-02-05T00:00:00https://www.semanticscholar.org/paper/7b7268c43dc692009f844e302ce9a72fb508bfa6 f9ffd78364c9494f2a42b098e21b62337cc9a153Artificial Intelligence Review552This paper explores how AI is integrated with technologies such as the Internet of Things, blockchain, Natural Language Processing, virtual reality, Augmented Reality, Mixed Reality, and Extended Reality, and explores the potential benefits and challenges of using AI in the Metaverse.Artif. Intell. Rev.['Mona M. Soliman', 'Eman Ahmed', 'A. Darwish', 'A. Hassanien']2024-02-05T00:00:00https://www.semanticscholar.org/paper/f9ffd78364c9494f2a42b098e21b62337cc9a153 c4a933c85a8675ea90a9c8c19a6dfab82f1410e6Cell and gene therapies represent promising new treatment options for many diseases, but also face challenges for clinical translation and delivery. Hospital-based GMP facilities enable rapid bench-to-bedside development and patient access but require significant adaptation to implement pharmaceutical manufacturing in healthcare infrastructures constrained by space, regulations, and resources. This article reviews key considerations, constraints, and solutions for establishing hospital facilities for advanced therapy medicinal products (ATMPs). Technologies like process analytical technology (PAT), continuous manufacturing, and artificial intelligence (AI) can aid these facilities through enhanced process monitoring, control, and automation. However, quality systems tailored for product quality rather than just compliance, and substantial investment in infrastructure, equipment, personnel, and multi-departmental coordination, remain crucial for successful hospital ATMP facilities and to drive new therapies from research to clinical impact.Applied Sciences481Quality systems tailored for product quality rather than just compliance, and substantial investment in infrastructure, equipment, personnel, and multi-departmental coordination, remain crucial for successful hospital ATMP facilities and to drive new therapies from research to clinical impact.Applied Sciences['Cristobal Aguilar-Gallardo', 'Ana Bonora-Centelles']2024-02-05T00:00:00https://www.semanticscholar.org/paper/c4a933c85a8675ea90a9c8c19a6dfab82f1410e6 dee150e860cd2d9641fa9acff97a84c8e6f9b9ceBMC Medicine1951AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems are still in the early stage of development.BMC Medicine['Yue Cai', 'Yue Cai', 'Li-Ying Tang', 'Yi-Han Wang', 'Mengchun Gong', 'Tian-Ci Jing', 'Hui-Jun Li', 'Jesse Li-Ling', 'Wei Hu', 'Zhihua Yin', 'Da-Xin Gong', 'Guang-Wei Zhang']2024-02-05T00:00:00https://www.semanticscholar.org/paper/dee150e860cd2d9641fa9acff97a84c8e6f9b9ce f44a3268b8c4c9db2378d909c8a46c8a066266a1Artificial intelligence is transforming our lives, and technological progress and transfer from the academic and theoretical sphere to the real world are accelerating yearly. But during that progress and transition, several open problems and questions need to be addressed for the field to develop ethically, such as digital privacy, ownership, and control. These are some of the reasons why the currently most popular approaches of artificial intelligence, i.e., centralized AI (CEAI), are questionable, with other directions also being widely explored, such as decentralized artificial intelligence (DEAI), to solve some of the most reaching problems. This paper provides a systematic literature review (SLR) of existing work in the field of DEAI, presenting the findings of 71 identified studies. The paper's primary focus is identifying the building blocks of DEAI solutions and networks, tackling the DEAI analysis from a bottom-up approach. In the end, future directions of research and open problems are proposed.arXiv.org1061A systematic literature review (SLR) of existing work in the field of DEAI, presenting the findings of 71 identified studies, with a primary focus on identifying the building blocks of DEAI solutions and networks.ArXiv['Vid Keršič', 'Muhamed Turkanović']2024-02-05T00:00:00https://www.semanticscholar.org/paper/f44a3268b8c4c9db2378d909c8a46c8a066266a1 7f02f6309fa66870f8cfbde72e8b801cecc73209Generative Artificial Intelligence (GenAI), specifically large language models like ChatGPT, has swiftly entered organizations without adequate governance, posing both opportunities and risks. Despite extensive debates on GenAI's transformative nature and regulatory measures, limited research addresses organizational governance, encompassing technical and business perspectives. This review paper fills this gap by surveying recent works. It goes beyond mere summarization by developing a framework for GenAI governance within companies. Our framework outlines the scope, objectives, and governance mechanisms tailored to harness business opportunities and mitigate risks associated with GenAI integration. This research contributes a focused approach to GenAI governance, offering practical insights for companies navigating the challenges of responsible AI adoption. It is also valuable for a technical audience to broaden their perspective as increasingly ethical and business concerns gain in prevalence and allow them to identify novel research directions.arXiv.org541This framework outlines the scope, objectives, and governance mechanisms tailored to harness business opportunities and mitigate risks associated with GenAI integration, offering practical insights for companies navigating the challenges of responsible AI adoption.ArXiv['Johannes Schneider', 'Rene Abraham', 'Christian Meske']2024-02-05T00:00:00https://www.semanticscholar.org/paper/7f02f6309fa66870f8cfbde72e8b801cecc73209 66d8ba9d631443f01e7af4e93a893bbede8f47b4Education and Information Technologies : Official Journal of the IFIP technical committee on Education591It is revealed that students conceal AI-powered academic cheating behaviors when directly questioned, as the prevalence of cheaters observed via list experiments is almost threefold the prevalence of cheaters observed via the basic direct questioning approach.Education and Information Technologies['Hung Manh Nguyen', 'Daisaku Goto']2024-02-05T00:00:00https://www.semanticscholar.org/paper/66d8ba9d631443f01e7af4e93a893bbede8f47b4 98cce4704884893d89a99996c651e8a157105d75Artificial intelligence (AI) is becoming increasingly prevalent in libraries as a means of enhancing services and improving performance. The implementation of AI aims to create computer systems and machines that can think and act like humans, thus presenting both challenges and opportunities for libraries. AI is already being utilized in various ways within libraries, such as expert systems for reference services, robots for book shelving, and virtual reality for immersive learning experiences. Despite concerns that AI may replace librarians, it is actually facilitating libraries to become more efficient and provide better services. As technology rapidly advances in today's digital age, AI is expected to play a significant role in transforming libraries and adapting to the changing needs of society. The role of libraries is evolving, and they are embracing AI to enhance their performance and services.Alexandria The Journal of National and International Library and Information Issues00The role of libraries is evolving, and they are embracing AI to enhance their performance and services, such as expert systems for reference services, robots for book shelving, and virtual reality for immersive learning experiences.Alexandria: The Journal of National and International Library and Information Issues['Akinola Samson Adesina', 'A. N. Zubairu']2024-02-05T00:00:00https://www.semanticscholar.org/paper/98cce4704884893d89a99996c651e8a157105d75 532d95d20a80891b57f17beee709f9169feb4244Pathologie430An overview of the latest developments in pathology AI is provided, particularly concerning the black box character of AI, and solutions to make decision processes more transparent using methods of so-called explainable AI (XAI) are described.Pathologie['Frederick Klauschen', 'Jonas Dippel', 'Philipp Keyl', 'Philipp Jurmeister', 'M. Bockmayr', 'Andreas Mock', 'Oliver Buchstab', 'Maximilian Alber', 'Lukas Ruff', 'G. Montavon', 'Klaus-Robert Müller']2024-02-05T00:00:00https://www.semanticscholar.org/paper/532d95d20a80891b57f17beee709f9169feb4244 e4f5352be73fd902b4ef8942713b351f41fe2dd0Surgical Endoscopy350How the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8 is observed.Surgical Endoscopy['V. López-López', 'Zeniche Morise', 'M. Albadalejo-González', 'Concepción Gomez Gavara', 'B. Goh', 'Y. Koh', 'Sijberden Jasper Paul', 'M. Hilal', 'K. Mishima', 'Jaime Arthur Pirola Krürger', 'Paulo Herman', 'Alvaro Cerezuela', 'R. Brusadin', 'T. Kaizu', 'Juan Lujan', 'F. Rotellar', 'K. Monden', 'M. Dalmau', 'N. Gotohda', 'M. Kudo', 'A. Kanazawa', 'Yutaro Kato', 'H. Nitta', 'S. Amano', 'R. D. Valle', 'M. Giuffrida', 'M. Ueno', 'Yuichiro Otsuka', 'D. Asano', 'Minoru Tanabe', 'O. Itano', 'Takuya Minagawa', 'D. Eshmuminov', 'Irene Herrero', 'Pablo Ramírez', 'J. Ruipérez-Valiente', 'R. Robles-Campos', 'Go Wakabayashi']2024-02-05T00:00:00https://www.semanticscholar.org/paper/e4f5352be73fd902b4ef8942713b351f41fe2dd0 5feaef5b48ba47d5ffdc46518c1a7e02f9b9a0aaArtificial intelligence (AI) is poised to revolutionize how science, and biomedical research in particular, are done. With AI, problem solving and complex tasks using massive data sets can be performed at a much higher rate and dimensionality level compared to humans. With the ability to handle huge data sets and self-learn, AI is already being exploited in drug design, drug repurposing, toxicology, and material identification. AI could also be used in both basic and clinical research in study design, defining outcomes, analyzing data, interpreting findings, and even identifying the most appropriate areas of investigation and funding sources. State-of-the-art AI-based large language models (LLM), such as ChatGPT and Perplexity, are positioned to change forever how science is communicated and how scientists interact with one another and their profession, including post-publication appraisal and critique. Like all revolutions, upheaval will follow and not all outcomes can be predicted, necessitating guardrails at the onset, especially to minimize the untoward impact of the many drawbacks of LLMs, which include lack of confidentiality, risk of hallucinations, and propagation of mainstream albeit potentially mistaken opinions and perspectives. In this review, we highlight areas of biomedical research that are already being reshaped by AI and how AI is likely to impact it further in the near future. We discuss the potential benefits of AI in biomedical research and address possible risks, some surrounding the creative process, that warrant further reflection.Journal of Cardiovascular Pharmacology00Journal of cardiovascular pharmacology['R. Altara', 'Cameron J Basson', 'Giuseppe Biondi-Zoccai', 'G. Booz']2024-02-05T00:00:00https://www.semanticscholar.org/paper/5feaef5b48ba47d5ffdc46518c1a7e02f9b9a0aa 7677426e237945916c1a6d33bfc7f9a80682a151Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.Electronics270The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence.Electronics['Mifta Ahmed Umer', 'E. G. Belay', 'Luis Borges Gouveia']2024-02-05T00:00:00https://www.semanticscholar.org/paper/7677426e237945916c1a6d33bfc7f9a80682a151 72fd2abb815ea01c860b6d705c236a9b5299103fThis study investigates the influence of Artificial Intelligence (AI) on digital innovation using a SmartPLS approach, drawing insights from a dataset comprising 156 relevant observations. In the rapidly evolving digital landscape, AI has emerged as a powerful driver of innovation, reshaping organizational processes and outcomes across various sectors. Through a comprehensive analysis, the research explores the intricate relationships between AI adoption and digital innovation outcomes, addressing key questions regarding the extent to which AI influences process efficiency, product quality, and service creativity. The findings reveal significant correlations, highlighting the role of AI in enhancing organizational readiness, technological integration, and data quality. Moreover, the study identifies the critical importance of fostering an innovation culture and implementing effective change management strategies to leverage the full potential of AI-driven digital transformation. The robustness of the SmartPLS model is confirmed through substantial R-Square values and path coefficients, affirming the validity of the research hypotheses. Overall, this research contributes to a deeper understanding of the mechanisms through which AI influences digital innovation, offering actionable insights for businesses, policymakers, and researchers seeking to navigate and harness the potential of AI-driven digital transformation.IAIC Transactions on Sustainable Digital Innovation (ITSDI)250This research contributes to a deeper understanding of the mechanisms through which AI influences digital innovation, offering actionable insights for businesses, policymakers, and researchers seeking to navigate and harness the potential of AI-driven digital transformation.IAIC Transactions on Sustainable Digital Innovation (ITSDI)['Harfizar', 'Muhammad Wisnu', 'Miftah Wicaksono', 'Fadly Baidhowi Hakim', 'Hadi Wijaya', 'Eirene Taufikurrahman Saleh', 'Muhammad Wisnu Wicaksono', 'Miftah Baidhowi Hakim', 'Fadly Hadi Wijaya', 'Eirene Sana']2024-02-05T00:00:00https://www.semanticscholar.org/paper/72fd2abb815ea01c860b6d705c236a9b5299103f ce0fedf4d55420dfcb4e7ae9a12140d838d40c97Pharmaceutical Medicine330It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.Pharmaceutical medicine['Giulio Napolitano', 'Canan Has', 'Anne Schwerk', 'Jui-Hung Yuan', 'Carsten Ullrich']2024-02-05T00:00:00https://www.semanticscholar.org/paper/ce0fedf4d55420dfcb4e7ae9a12140d838d40c97 b03c540658bdaf495e8999074a3be537538b4d47Using transnational panel data from 61 nations and regions from 2000 to 2019, this article empirically examines both the influence of artificial intelligence on the Global Value Chain as it pertains to the manufacturing industry and its mechanism of action. According to the report, AI significantly improves the industrial sector’s GVC position; this finding still holds after multiple robustness and endogeneity tests of the model. The findings of the heterogeneity test at the national level demonstrate that, in developing nations as opposed to developed countries, AI has a stronger impact on advancing the GVC position of the manufacturing industry. Heterogeneity tests at the industry level show that AI has a significant role in promoting the GVC of high, medium and low technology manufacturing industries. The mechanism test demonstrates three primary ways by which AI contributes to improving the GVC position of the manufacturing industry: by improving both production efficiency and technological innovation capacity, and by reducing trade costs. This study provides policy implications for the promotion of AI with respect to China’s manufacturing industry GVC position.Sustainability370Heterogeneity tests at the industry level show that AI has a significant role in promoting the GVC of high, medium and low technology manufacturing industries, and the mechanism test demonstrates three primary ways by which AI contributes to improving the GVC position of the manufacturing industry.Sustainability['Jun Liu', 'Xin Jiang', 'Mengxue Shi', 'Yuning Yang']2024-02-05T00:00:00https://www.semanticscholar.org/paper/b03c540658bdaf495e8999074a3be537538b4d47 98b4d934cc064371850b04283505874329901d06Intracranial hemorrhage (IH) is a type of bleeding within the skull, caused by reasons such as head injury, hypertension, or vascular malformation. With a 30-day mortality rate of up to 52% and 67,000 cases per year in the United States, IH is a disease that can cause irreversible damage within hours of onset, making prompt treatment crucial. Annual IH incidence dropped from 5.21 to 3.30 per 10,000 individuals from 2000 to 2010, but 30-day fatality rates remained unchanged, suggesting that further work is necessary to improve diagnosis. Artificial intelligence solutions have been implemented to assist in IH diagnosis in recent years, improving the communication time of an IH finding, flagging emergency cases, and demonstrating the effectiveness of machine learning in optimizing radiology clinic workflows. This study aimed to investigate the potential of a multi-tier prediction architecture in the diagnosis of intracranial hemorrhages (IHs). To boost model training speeds, featurization was used to convert CT scans into numerical vectors. Models to test for each of five IH subcategories were developed with the Multi-Layer Perceptron, K Nearest neighbors, and Random Forest classifiers. The best model of each subcategory was used to assemble a traditional and a multi-tier prediction architecture, and the performance of each architecture was evaluated. The novel approach improved the average accuracy by 11.14% and decreased Hamming Loss by 0.0928. The study concludes that a multi-tier prediction architecture has the potential to improve IH diagnosis.2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)210A multi-tier prediction architecture has the potential to improve IH diagnosis and improved the average accuracy by 11.14% and decreased Hamming Loss by 0.0928, the study concludes.2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)['Shiven Balaji', 'Sindhu Ghanta']2024-02-05T00:00:00https://www.semanticscholar.org/paper/98b4d934cc064371850b04283505874329901d06 5c4e9220fba601bc60cc7424fe4045dcec759d40Industry 4.0 Science00Industry 4.0 Science[]2024-02-05T00:00:00https://www.semanticscholar.org/paper/5c4e9220fba601bc60cc7424fe4045dcec759d40 c4225b3d2cf20dfb194691c17058d0cdb78a3876Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school.Sustainability121The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs.Sustainability['Claudio Giovanni Demartini', 'Luciano Sciascia', 'Andrea Bosso', 'Federico Manuri']2024-02-05T00:00:00https://www.semanticscholar.org/paper/c4225b3d2cf20dfb194691c17058d0cdb78a3876 2420a77d294d0594d1a469aeeab1e0d4795c9b71Arthritis care & research00Arthritis care & research['Alix Bird', 'C. McMaster', 'D. Liew']2024-02-05T00:00:00https://www.semanticscholar.org/paper/2420a77d294d0594d1a469aeeab1e0d4795c9b71 3c8d93fec0573b85c0d49774eb8da698a9297913Introduction Assistive technology is increasingly used to support the physical needs of differently abled persons but has yet to make inroads on support for cognitive or psychological issues. This gap is an opportunity to address another—the lack of contribution from theoretical social science that can provide insights into problems that cannot be seen. Using Affect Control Theory (ACT), the current project seeks to close that gap with an artificially intelligent application to improve interaction and affect for people with Alzheimer’s Disease and Related Dementias (ADRD). Using sociological theory, it models interactions with persons with ADRD based on self-sentiments, rather than cognitive memory, and informs a cellphone-based assistive tool called VIPCare for supporting caregivers. Methods Staff focus groups and interviews with family members of persons with ADRD in a long-term residential care facility collected residents’ daily needs and personal histories. Using ACT’s evaluation, potency, and activity dimensions, researchers used these data to formulate a self-sentiment profile for each resident and programmed that profile into the VIPCare application. VIPCare used that profile to simulate affectively intelligent social interactions with each unique resident that reduce deflection from established sentiments and, thus, negative emotions. Results We report on the data collection to design the application, develop self-sentiment profiles for the resident, and generate assistive technology that applies a sociological theory of affect to real world management of interaction, emotion, and mental health. Discussion By reducing trial and error in learning to engage people with dementia, this tool has potential to smooth interaction and improve wellbeing for a population vulnerable to distress.Frontiers in Sociology350An artificially intelligent application to improve interaction and affect for people with Alzheimer’s Disease and Related Dementias (ADRD) is designed and generated that applies a sociological theory of affect to real world management of interaction, emotion, and mental health.Frontiers in Sociology['Linda Francis', 'M. Ghafurian']2024-02-05T00:00:00https://www.semanticscholar.org/paper/3c8d93fec0573b85c0d49774eb8da698a9297913 cbc8e506a882c9f31990d53f1b1da579a6a56d8aYapay zekâ pazarlama teknolojisi, çevrimiçi alışveriş yapan tüketicilere kişiselleştirilmiş satın alma deneyimi, işletmelere ise müşteri ihtiyaçlarını etkin bir şekilde karşılama imkanı tanımaktadır. Bu çalışma, yapay zeka pazarlama teknolojisi deneyiminin, çevrimiçi satın alma niyeti üzerindeki etkisinde ilgili teknolojiye olan güvenin ve algılanan riskin rolünü ortaya koymak amacıyla yapılmıştır. Bu bağlamda yapay zeka pazarlama teknolojisinin deneyimsel unsurları doğruluk, içgörü ve etkileşim olmak üzere üç boyutta ele alınmış; güven, algılanan risk ve satın alma niyetinin de dahil olduğu SOR modeline dayanan kavramsal bir model geliştirilmiştir. Ardından çevrimiçi alışveriş geçmişi bulunan kişilere çevrimiçi anket yöntemi ile 480 örnek hacimli nicel bir çalışma gerçekleştirilmiştir. Kartopu örnekleme yöntemi ile toplanan veriler, SmartPLS 4 programı kullanılarak Kısmi En Küçük Kareler Varyans Temelli Yapısal Eşitlik Modellemesi (PLS-SEM) ile analiz edilmiştir. Sonuçlar, içgörü ve etkileşim deneyiminin teknolojiye olan güveni pozitif bir şekilde etkilediğini, doğruluk deneyiminin ise güven üzerinde herhangi bir etkisi olmadığını göstermiştir. Ayrıca tüketicilerin risk algılarının azalmasında yapay zeka pazarlama teknolojisinin deneyimsel unsurlarının tamamının pozitif bir etkisi olduğu tespit edilmiştir. İçgörü deneyimi, tüketicilerin güven ve risk algılarında belirleyici deneyimsel unsur olarak tanımlanmıştır. Güven, çevrimiçi satın alma niyeti pozitif ve anlamlı bir şekilde etkilerken aynı etki algılanan risk bağlamında gerçekleşmemiştirİktisadi ve İdari Bilimler Fakültesi Dergisi00İktisadi ve İdari Bilimler Fakültesi Dergisi['Ceylan Bozpolat']2024-02-05T00:00:00https://www.semanticscholar.org/paper/cbc8e506a882c9f31990d53f1b1da579a6a56d8a 76b6083e01d4c7f2bed0deab475eb52fc58a9503Education and Information Technologies : Official Journal of the IFIP technical committee on Education180Education and Information Technologies['Jongsawas Chongwatpol']2024-02-05T00:00:00https://www.semanticscholar.org/paper/76b6083e01d4c7f2bed0deab475eb52fc58a9503 4031b5667e13ee8d3bc02249ee452a937d87dc32International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Chapman Eze Nnadozie']2024-02-05T00:00:00https://www.semanticscholar.org/paper/4031b5667e13ee8d3bc02249ee452a937d87dc32 1c40bf3589ce8a3fa73237331f7aef2953539a64International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['P. Deepa']2024-02-05T00:00:00https://www.semanticscholar.org/paper/1c40bf3589ce8a3fa73237331f7aef2953539a64 d741695f11ab7d3902abb45b7101595314e1574aJournal of Public Relations Research280Journal of Public Relations Research['Nneka Logan', 'Damion Waymer']2024-02-05T00:00:00https://www.semanticscholar.org/paper/d741695f11ab7d3902abb45b7101595314e1574a bc9ed9bab81307c904f84be38a3a9cb5f138be4dInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Chapman Eze Nnadozie']2024-02-05T00:00:00https://www.semanticscholar.org/paper/bc9ed9bab81307c904f84be38a3a9cb5f138be4d 3a846d9874ee34d759faee117fbd177569f07a08Journal of Art, Design and Music00Journal of Art, Design and Music['B. E. F. Afolabi̇', 'J. Oladesu', 'B. A. Siyanbola', 'A. Adeloye', 'O. P. Odewole']2024-02-05T00:00:00https://www.semanticscholar.org/paper/3a846d9874ee34d759faee117fbd177569f07a08 2ad7d1030d6b4d99774a3f9c8c75d6d8f1677732International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Navneet Kaur Chahal']2024-02-05T00:00:00https://www.semanticscholar.org/paper/2ad7d1030d6b4d99774a3f9c8c75d6d8f1677732 03d8cf1509ea84a216b753feac023e114a8d6e88Journal of Ultrasound180Journal of ultrasound['A. Boccatonda', 'Fabio Piscaglia']2024-02-05T00:00:00https://www.semanticscholar.org/paper/03d8cf1509ea84a216b753feac023e114a8d6e88 3ea503b638ca96d9fb511eaf117c323a965767d2Journal of Cyber Security Technology30Journal of Cyber Security Technology['P. Radanliev']2024-02-05T00:00:00https://www.semanticscholar.org/paper/3ea503b638ca96d9fb511eaf117c323a965767d2 679dae0fcf757e135585bbb45e18e4a98f83a2b6Leadership and Policy in Schools00Leadership and Policy in Schools['Awol Endris', 'A. Tlili', 'Ronghuai Huang', 'Lin Xu', 'TingWen Chang', 'Sanjaya Mishra']2024-02-05T00:00:00https://www.semanticscholar.org/paper/679dae0fcf757e135585bbb45e18e4a98f83a2b6 bb7601cf3769a004678ee0b8756865b63fae5c8bInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Chapman Eze Nnadozie']2024-02-05T00:00:00https://www.semanticscholar.org/paper/bb7601cf3769a004678ee0b8756865b63fae5c8b 5cff46d958f592bc847b1c327f22ef4b3278b5a9This paper examines the concept of embodied artificial general intelligence (AGI), its relationship to human consciousness, and the key role of the metaverse in facilitating this relationship. The paper leverages theoretical frameworks such as embodied cognition, Michael Levin's computational boundary of a"Self,"Donald D. Hoffman's Interface Theory of Perception, and Bernardo Kastrup's analytical idealism to build the argument for achieving embodied AGI. It contends that our perceived outer reality is a symbolic representation of alternate inner states of being, and that AGI could embody a higher consciousness with a larger computational boundary. The paper further discusses the developmental stages of AGI, the requirements for the emergence of an embodied AGI, the importance of a calibrated symbolic interface for AGI, and the key role played by the metaverse, decentralized systems, open-source blockchain technology, as well as open-source AI research. It also explores the idea of a feedback loop between AGI and human users in metaverse spaces as a tool for AGI calibration, as well as the role of local homeostasis and decentralized governance as preconditions for achieving a stable embodied AGI. The paper concludes by emphasizing the importance of achieving a certain degree of harmony in human relations and recognizing the interconnectedness of humanity at a global level, as key prerequisites for the emergence of a stable embodied AGI.arXiv.org270The paper concludes by emphasizing the importance of achieving a certain degree of harmony in human relations and recognizing the interconnectedness of humanity at a global level, as key prerequisites for the emergence of a stable embodied AGI.ArXiv['Martin Schmalzried']2024-02-05T00:00:00https://www.semanticscholar.org/paper/5cff46d958f592bc847b1c327f22ef4b3278b5a9 a78cd35d7f76d8c24b00c4c09a639abf8f7c05f7Applied Ergonomics430This work applied the Work Domain Analysis Broken Nodes and Event Analysis of Systemic Teamwork-Broken Links methods to identify potential risks in a future 'envisioned world' AGI-based uncrewed combat aerial vehicle system and proposes that work is required to develop controls to manage the other risks identified.Applied ergonomics['Paul M. Salmon', 'S. McLean', 'T. Carden', 'Brandon J. King', 'J. Thompson', 'C. Baber', 'NA. Stanton', 'G. Read']2024-02-05T00:00:00https://www.semanticscholar.org/paper/a78cd35d7f76d8c24b00c4c09a639abf8f7c05f7 2c44cd484875ca59cfc03c38e4050e724ed9593bThe aim of this community service is to enhance teachers' creativity in utilizing Artificial Intelligence (AI) technology in teaching activities. The service method was conducted in-person, utilizing LCD Projectors and sound systems to support workshop activities. The results of the implementation show that teachers have gained an understanding of creating instructional media using Canva and CapCup. This is evident as teachers can operate both AI applications and produce instructional media that are sufficiently good and diverse. In conclusion, the workshop proceeded smoothly as scheduled, providing a positive impact on teachers.Jurnal Pengabdian Masyarakat dan Penelitian Thawalib01The results of the implementation show that teachers have gained an understanding of creating instructional media using Canva and CapCup, and can operate both AI applications and produce instructional media that are sufficiently good and diverse.Jurnal Pengabdian Masyarakat dan Penelitian Thawalib['Dirga Ayu Lestari']2024-02-05T00:00:00https://www.semanticscholar.org/paper/2c44cd484875ca59cfc03c38e4050e724ed9593b 06d4f6bd5271eab3d33c892e21b9eb33395e9404For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad generalisation to situations outside their training data has proved elusive.In this work, we look at several novel approaches for solving the Abstraction&Reasoning Corpus (ARC), a dataset of abstract visual reasoning tasks introduced to test algorithms on broad generalization. Despite three international competitions with $100,000 in prizes, the best algorithms still fail to solve a majority of ARC tasks and rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task. First, we adapt the DreamCoder neurosymbolic reasoning solver to ARC. DreamCoder automatically writes programs in a bespoke domain-specific language to perform reasoning, using a neural network to mimic human intuition. We present the Perceptual Abstraction and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks, and propose a new recognition model that allows us to significantly improve on the previous best implementation.We also propose a new encoding and augmentation scheme that allows large language models (LLMs) to solve ARC tasks, and find that the largest models can solve some ARC tasks. LLMs are able to solve a different group of problems to state-of-the-art solvers, and provide an interesting way to complement other approaches. We perform an ensemble analysis, combining models to achieve better results than any system alone. Finally, we publish the arckit Python library to make future research on ARC easier.arXiv.org521This work presents the Perceptual Abstraction and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks, and proposes a new recognition model that allows us to significantly improve on the previous best implementation.ArXiv['Mikel Bober-Irizar', 'Soumya Banerjee']2024-02-05T00:00:00https://www.semanticscholar.org/paper/06d4f6bd5271eab3d33c892e21b9eb33395e9404 1ecb049401b444cb3a3f19a2c8e37ae959d24a12Explainable artificial intelligence (XAI) has helped elucidate the internal mechanisms of machine learning algorithms, bolstering their reliability by demonstrating the basis of their predictions. Several XAI models consider causal relationships to explain models by examining the input-output relationships of prediction models and the dependencies between features. The majority of these models have been based their explanations on counterfactual probabilities, assuming that the causal graph is known. However, this assumption complicates the application of such models to real data, given that the causal relationships between features are unknown in most cases. Thus, this study proposed a novel XAI framework that relaxed the constraint that the causal graph is known. This framework leveraged counterfactual probabilities and additional prior information on causal structure, facilitating the integration of a causal graph estimated through causal discovery methods and a black-box classification model. Furthermore, explanatory scores were estimated based on counterfactual probabilities. Numerical experiments conducted employing artificial data confirmed the possibility of estimating the explanatory score more accurately than in the absence of a causal graph. Finally, as an application to real data, we constructed a classification model of credit ratings assigned by Shiga Bank, Shiga prefecture, Japan. We demonstrated the effectiveness of the proposed method in cases where the causal graph is unknown.arXiv.org270This study proposed a novel XAI framework that leveraged counterfactual probabilities and additional prior information on causal structure, facilitating the integration of a causal graph estimated through causal discovery methods and a black-box classification model.ArXiv['Daisuke Takahashi', 'Shohei Shimizu', 'Takuma Tanaka']2024-02-05T00:00:00https://www.semanticscholar.org/paper/1ecb049401b444cb3a3f19a2c8e37ae959d24a12 9580fc12280be1949c6943d29fb1fbf8d5156f94With two-sided platforms becoming an increasingly ubiquitous business model, quality is a vital factor for the success of high-technology platforms that face fierce competition. To maintain competency, high-technology platforms commonly use two quality regulation strategies: the exclusion strategy (E strategy), in which the platform denies access to low-quality complementors, and the subsidization strategy (S strategy), in which the platform provides a fixed subsidy to high-quality complementors. This paper investigates the optimal quality regulation strategy for platforms in a duopoly setting. We examine and compare three scenarios: (i) both platforms adopt the exclusion strategy, i.e. mode EE, (ii) both platforms adopt the subsidization strategy, i.e. mode SS, and (ii) one platform adopts the subsidization strategy while the other adopts the exclusion strategy, i.e. mode SE. First, we find that although the developer network size is larger and the platforms charge developers higher access fees under SS, the average quality and the consumer access fees are lower under SS than under EE, leading to lower profits for platforms. Second, under SE, in comparison with the platform that adopts the exclusion strategy, the platform that uses the subsidization strategy achieves lower average quality and larger network sizes on both sides but may set higher or lower access fees on both sides. Moreover, the platform under the subsidization strategy profits more (less) when the operation cost on the developer side is high (low). Third, asymmetric mode SE does not necessarily induce moderate outcomes for market participants compared to modes EE and SS. We also examine the equilibrium mode by considering platforms’ optimal strategies for quality regulation. Our analyses reveal that as the operation cost on the developer side increases, the equilibrium mode evolves from EE to SE/ES and then to SS. These results and insights are robust to several alternative assumptions.Production and operations management00Production and Operations Management['Gaoyan Lyu', 'Lin Tian', 'Wei Wang']2024-02-04T00:00:00https://www.semanticscholar.org/paper/9580fc12280be1949c6943d29fb1fbf8d5156f94 836c652834b0f6ffe10e53ede1c0b9433cfad9eaGenerative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity and authenticity of contents generated by these Deep Generative Models (DGMs) have sparked significant copyright concerns. There have been various legal debates on how to effectively safeguard copyrights in DGMs. This work delves into this issue by providing a comprehensive overview of copyright protection from a technical perspective. We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders. For data copyright, we delve into methods data owners can protect their content and DGMs can be utilized without infringing upon these rights. For model copyright, our discussion extends to strategies for preventing model theft and identifying outputs generated by specific models. Finally, we highlight the limitations of existing techniques and identify areas that remain unexplored. Furthermore, we discuss prospective directions for the future of copyright protection, underscoring its importance for the sustainable and ethical development of Generative AI.arXiv.org1705A comprehensive overview of copyright protection from a technical perspective is provided, examining from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders.ArXiv['Jie Ren', 'Han Xu', 'Pengfei He', 'Yingqian Cui', 'Shenglai Zeng', 'Jiankun Zhang', 'Hongzhi Wen', 'Jiayuan Ding', 'Hui Liu', 'Yi Chang', 'Jiliang Tang']2024-02-04T00:00:00https://www.semanticscholar.org/paper/836c652834b0f6ffe10e53ede1c0b9433cfad9ea 1d94c8025808d6756faae25aa2299a7be803e5f5As humans advance toward a higher level of artificial intelligence, it is always at the cost of escalating computational resource consumption, which requires developing novel solutions to meet the exponential growth of AI computing demand. Neuromorphic hardware takes inspiration from how the brain processes information and promises energy-efficient computing of AI workloads. Despite its potential, neuromorphic hardware has not found its way into commercial AI data centers. In this article, we try to analyze the underlying reasons for this and derive requirements and guidelines to promote neuromorphic systems for efficient and sustainable cloud computing: We first review currently available neuromorphic hardware systems and collect examples where neuromorphic solutions excel conventional AI processing on CPUs and GPUs. Next, we identify applications, models and algorithms which are commonly deployed in AI data centers as further directions for neuromorphic algorithms research. Last, we derive requirements and best practices for the hardware and software integration of neuromorphic systems into data centers. With this article, we hope to increase awareness of the challenges of integrating neuromorphic hardware into data centers and to guide the community to enable sustainable and energy-efficient AI at scale.arXiv.org960This article reviews currently available neuromorphic hardware systems and collects examples where neuromorphic solutions excel conventional AI processing on CPUs and GPUs, and derives requirements and best practices for the hardware and software integration of neuromorphic systems into data centers.ArXiv['B. Vogginger', 'A. Rostami', 'Vaibhav Jain', 'Sirine Arfa', 'Andreas Hantsch', 'David Kappel', 'Michael Schäfer', 'Ulrike Faltings', 'H. A. Gonzalez', 'Chen Liu', 'Christian Mayr']2024-02-04T00:00:00https://www.semanticscholar.org/paper/1d94c8025808d6756faae25aa2299a7be803e5f5 85b29adf4bdaad2992ed9e94f8066bfd7182c9cdIn the realm of global business, logistics stands out as a cornerstone, and the ongoing development of Artificial Intelligence (AI) is shaping logistics into a secure and intelligent domain. The digitization of freight forwarding involves converting traditional logistic procedures into streamlined, digitized processes within the freight forwarding system. This paper provides a concise exploration of AI applications in logistics systems, delving into the transformative impact on supply chain management. Focusing on key components such as machine learning and predictive modeling, it offers a brief overview of AI's role in optimizing logistics processes and enhancing efficiency. Also, we will show a framework for digital freight forwarding in logistics considering AI applications with the explanation.International Conference on Advanced Communication Technology150A concise exploration of AI applications in logistics systems, delving into the transformative impact on supply chain management and key components such as machine learning and predictive modeling are offered.2024 26th International Conference on Advanced Communications Technology (ICACT)['Md Ariful Islam Mozumder', 'Rashedul Islam Sumon', 'Ziaullah Khan', 'Shah Muhammad Imtiyaj Uddin', 'Muhammad Omair Khan', 'Hee-Cheol Kim']2024-02-04T00:00:00https://www.semanticscholar.org/paper/85b29adf4bdaad2992ed9e94f8066bfd7182c9cd 1c3ce2c4e5277e621ed9518a2ffe2a70cc061775Background: Pediatric or congenital cataract (CC) is a leading cause of visual impairment and blindness in children worldwide. Deep learning (DL), a subfield of artificial intelligence, has the potential to enhance diagnosis, treatment, and outcomes in various medical fields. Research Objectives: summarize and evaluate the diagnostic and prediction capabilities of DL algorithms for CC. Methods: From 1st February to 25th March 2023, a literature search was conducted in databases such as PubMed, ScienceDirect, EMBASE, and EBSCO, as well as alternative sources such as Google Scholar. Search terms included “pediatric/congenital cataract”, “artificial intelligence", "deep learning", "convolutional neural network", “diagnosis”, "screening", "prediction" and other relevant synonyms. Quality assessment of studies were assessed based on CONSORT-AI and QUADAS-2. Outcomes extracted included accuracy, sensitivity, specificity, and area under the curve (AUC). Results: Out of 69 studies screened, five studies with different study designs, dataset sizes, and type of DL algorithms employed were included in the systematic review. Most studies employed DL to analyze slit-lamp images to diagnose CC, while one study utilized DL to predict existence of CC from several risk factors. In silico, most studies demonstrated high accuracy and validity of DL algorithms in detecting and predicting CC; however, DL algorithm is not as accurate in diagnosing CC when compared to human counterparts. These studies had limited generalizability given the homogenous population. Conclusion: DL shows potential as an adjunct tool for ophthalmologists to improve diagnosis and, therefore, treatment decisions for CC, particularly in remote and underdeveloped regions with limited medical resources.Ophthalmologica Indonesiana00DL shows potential as an adjunct tool for ophthalmologists to improve diagnosis and, therefore, treatment decisions for CC, particularly in remote and underdeveloped regions with limited medical resources.Ophthalmologica Indonesiana['Eric Tjoeng', 'Nur Devina Annisa']2024-02-04T00:00:00https://www.semanticscholar.org/paper/1c3ce2c4e5277e621ed9518a2ffe2a70cc061775 6b028cacb4ffac2ebcaf6a407ac61e2700f60ffbThis paper proposes a method and system for generating training data to support AI based anomaly detection. The use of AI in abnormal behavior detection systems is becoming increasingly popular, with active research on AI-based anomaly detection methods using machine learning. In general, existing research relies on open datasets provided by various laboratories like Swat, WaDI, SMAP and MSL for testing and validation purposes. Since the types of normal and malicious packets depend on the specific network to which they are applied, verifying AI-based anomaly detection methods using an open dataset may yield different results than when applied in real-world scenarios. In other words, open datasets captured from specific networks may not be suitable for applying AI-based abnormal detection methods to other networks. In addition, AI-based datasets may be insufficient for learning, leading to the use of simulated attacks. Open datasets are difficult to provide sufficient data for training and often contain malicious packets using simulated attack packets. Since malicious attacks are always transformed into new forms and developed in types, it is necessary to prepare a database for new malicious attacks and to learn about them. Therefore, one of the major challenges in developing effective anomaly detection systems is acquiring an appropriate dataset. To address this issue, we propose a system for extracting training data by collecting packets from the actual network to apply AI-based abnormal detection. Our proposed system offers the advantage of accurately reflecting the network's packet characteristics by gathering data from live networks for AI-based abnormality detection and dataset creation. Furthermore, as it incorporates a dataset for the latest malicious attacks within the network, it enables more practical anomaly detection compared to the use of existing datasets. We simulated and tested the proposed system at the laboratory level to confirm its behavior.International Conference on Advanced Communication Technology100A system for extracting training data by collecting packets from the actual network to apply AI-based abnormal detection, which offers the advantage of accurately reflecting the network's packet characteristics by gathering data from live networks for AI-based abnormality detection and dataset creation.2024 26th International Conference on Advanced Communications Technology (ICACT)['Thi My Truong', 'W. Choi', 'Jang-Hyeon Jeong', 'S. Choi']2024-02-04T00:00:00https://www.semanticscholar.org/paper/6b028cacb4ffac2ebcaf6a407ac61e2700f60ffb 08f78c8dc1ae1eab012ec1ecc10fb1359030ae1fDiscovering the creative potentials of a random signal to various artistic expressions in aesthetic and conceptual richness is a ground for the recent success of generative machine learning as a way of art creation. To understand the new artistic medium better, we conduct a comprehensive analysis to position AI-generated art within the context of human art heritage. Our comparative analysis is based on an extensive dataset, dubbed ``ArtConstellation,'' consisting of annotations about art principles, likability, and emotions for 6,000 WikiArt and 3,200 AI-generated artworks. After training various state-of-the-art generative models, art samples are produced and compared with WikiArt data on the last hidden layer of a deep-CNN trained for style classification. We actively examined the various art principles to interpret the neural representations and used them to drive the comparative knowledge about human and AI-generated art. A key finding in the semantic analysis is that AI-generated artworks are visually related to the principle concepts for modern period art made in 1800-2000. In addition, through Out-Of-Distribution (OOD) and In-Distribution (ID) detection in CLIP space, we find that AI-generated artworks are ID to human art when they depict landscapes and geometric abstract figures, while detected as OOD when the machine art consists of deformed and twisted figures. We observe that machine-generated art is uniquely characterized by incomplete and reduced figuration. Lastly, we conducted a human survey about emotional experience. Color composition and familiar subjects are the key factors of likability and emotions in art appreciation. We propose our whole methodologies and collected dataset as our analytical framework to contrast human and AI-generated art, which we refer to as ``ArtNeuralConstellation''. Code is available at: https://github.com/faixan-khan/ArtNeuralConstellationarXiv.org310A comprehensive analysis to position AI-generated art within the context of human art heritage and observes that machine-generated art is uniquely characterized by incomplete and reduced figuration.ArXiv['Faizan Farooq Khan', 'Diana Kim', 'Divyansh Jha', 'Youssef Mohamed', 'Hanna Chang', 'A. Elgammal', 'Luba Elliott', 'Mohamed Elhoseiny']2024-02-04T00:00:00https://www.semanticscholar.org/paper/08f78c8dc1ae1eab012ec1ecc10fb1359030ae1f 137a6f59a8989b347266d5ed40bc8f89b0c6015aAs time goes on, rapid development happens in the healthcare industry, and most of the significant challenges healthcare professionals and stakeholders face is supply chain management. With an excessive increase in demand for healthcare services and the need for efficient, cost-effective, and high-quality healthcare delivery, healthcare supply chain management has become a crucial factor in considering success in healthcare structures. Recently, Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain have shown some potential to revolutionize healthcare supply chain management. In this research, we explore the benefits associated with integrating the abovementioned technologies in healthcare for more effective and efficient supply chain management in this industry. By leveraging these technologies, we explore the potential benefits of their integration into the healthcare supply chain using the eventual existing challenges. In this paper, we highlight the problems faced by conventional supply chain management and show how integrating AI, IoT, and BC can serve as a powerful tool to overcome these challenges. To achieve our goal, we carried out a multi-dimensional analysis with case studies that considered crucial aspects such as visibility, efficiency, data-driven decision-making, security, and trust in the supply chain. We proposed a healthcare supply chain management system that incorporates AI, IoT, and blockchain to raise awareness among healthcare providers about the benefits of an intelligent supply chain management system.International Conference on Advanced Communication Technology190A healthcare supply chain management system that incorporates AI, IoT, and blockchain is proposed to raise awareness among healthcare providers about the benefits of an intelligent supply chain management system.2024 26th International Conference on Advanced Communications Technology (ICACT)['Tagne Poupi Theodore Armand', 'Kouayep Sonia Carole', 'Subrata Bhattacharjee', 'Md Ariful Islam Mozumder', 'Austin Oguejiofor Amaechi', 'Hee-Cheol Kim']2024-02-04T00:00:00https://www.semanticscholar.org/paper/137a6f59a8989b347266d5ed40bc8f89b0c6015a 758cfb05ee1ab2145f18f02768d7ca4b78cdcf05Artificial intelligence holds promise for individualized medicine. Yet, transitioning models from prototyping to clinical applications poses challenges, with confounders being a significant hurdle. We introduce a two-dimensional confounder framework (Confound Continuum), integrating a statistical dimension with a biomedical perspective. Informed and context-sensitive confounder decisions are indispensable for accurate model building, rigorous evaluation and valid interpretation. Using prediction of hand grip strength (HGS) from neuroimaging-derived features in a large sample as an example task, we develop a conceptual framework for confounder considerations and integrate it with an exemplary statistical investigation of 130 candidate confounders. We underline the necessity for conceptual considerations by predicting HGS with varying confound removal scenarios, neuroimaging derived features and machine learning algorithms. We use the confounders alone as features or together with grey matter volume to dissect the contribution of the two signal sources. The conceptual confounder framework distinguishes between high-performance models and pure link models that aim to deepen our understanding of feature-target relationships. The biological attributes of different confounders can overlap to varying degrees with those of the predictive problem space, making the development of pure link models increasingly challenging with greater overlap. The degree of biological overlap allows to sort potential confounders on a conceptual Confound Continuum. This conceptual continuum complements statistical investigations with biomedical domain-knowledge, represented as an orthogonal two-dimensional grid. Exemplary HGS predictions highlighted the substantial impact of confounders on predictive performance. In contrast, choice of features or learning algorithms had considerably smaller influences. Notably, models using confounders as features often outperformed models relying solely on neuroimaging features. Our study provides a confounder framework that combines a statistical perspective on confounders and a biomedical perspective. It stresses the importance of domain expertise in predictive modelling for critical and deliberate interpretation and employment of predictive models in biomedical applications and research.medRxiv641This study provides a confounder framework that combines a statistical perspective on confounders and a biomedical perspective, and stresses the importance of domain expertise in predictive modelling for critical and deliberate interpretation and employment of predictive models in biomedical applications and research.['Vera Komeyer', 'Dr. Simon B. Eickhoff', 'Dr. Christian Grefkes', 'Dr. Kaustubh R. Patil', 'Dr. Federico Raimondo']2024-02-04T00:00:00https://www.semanticscholar.org/paper/758cfb05ee1ab2145f18f02768d7ca4b78cdcf05 013aca20c16f7a4c697152e6059d24e1bfa13db0An artificial intelligence-generated content-enhanced computer-aided diagnosis (AIGC-CAD) model, designated as ThyGPT, has been developed. This model, inspired by the architecture of ChatGPT, could assist radiologists in assessing the risk of thyroid nodules through semantic-level human-machine interaction. A dataset comprising 19,165 thyroid nodule ultrasound cases from Zhejiang Cancer Hospital was assembled to facilitate the training and validation of the model. After training, ThyGPT could automatically evaluate thyroid nodule and engage in effective communication with physicians through human-computer interaction. The performance of ThyGPT was rigorously quantified using established metrics such as the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, and specificity. The empirical findings revealed that radiologists, when supplemented with ThyGPT, markedly surpassed the diagnostic acumen of their peers utilizing traditional methods as well as the performance of the model in isolation. These findings suggest that AIGC-CAD systems, exemplified by ThyGPT, hold the promise to fundamentally transform the diagnostic workflows of radiologists in forthcoming years.arXiv.org300It is suggested that AIGC-CAD systems, exemplified by ThyGPT, hold the promise to fundamentally transform the diagnostic workflows of radiologists in forthcoming years.ArXiv['Jincao Yao', 'Yunpeng Wang', 'Zhikai Lei', 'Kai Wang', 'Xiaoxian Li', 'Jianhua Zhou', 'Xiang Hao', 'Jiafei Shen', 'Zhenping Wang', 'Rongrong Ru', 'Yaqing Chen', 'Yahan Zhou', 'Chen Chen', 'Yanming Zhang', 'Ping Liang', 'Dong Xu']2024-02-04T00:00:00https://www.semanticscholar.org/paper/013aca20c16f7a4c697152e6059d24e1bfa13db0 b4e241e73c614f4278945dd963b3efcf452953cbRecent advancements in Artificial Intelligence (AI) have significantly influenced the field of Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the literature based on the primary anatomical structures related to CVD, dividing them into non-vessel structures (such as ventricles and atria) and vessel structures (including the aorta and coronary arteries). This categorization provides a structured approach to explore various imaging modalities like Magnetic Resonance Imaging (MRI), which are commonly used in CVD research. Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis. Additionally, we compile a list of publicly accessible cardiac image datasets and code repositories, intending to support research reproducibility and facilitate data and algorithm sharing within the community. We conclude with an examination of the challenges and limitations inherent in current AI-based CVD analysis methods and suggest directions for future research to overcome these hurdles.2932An extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential and compiling a list of publicly accessible cardiac image datasets and code repositories to support research reproducibility and facilitate data and algorithm sharing within the community.['Xin Wang', 'Hongtu Zhu']2024-02-04T00:00:00https://www.semanticscholar.org/paper/b4e241e73c614f4278945dd963b3efcf452953cb e0c176370fcca6f8d11286f922c2154eaa69fc34The abundance of literature on ethical concerns regarding artificial intelligence (AI) highlights the need to systematize, integrate, and categorize existing efforts through a systematic literature review. The article aims to investigate prevalent concerns, proposed solutions, and prominent ethical approaches within the field. Considering 309 articles from the beginning of the publications in this field up until December 2021, this systematic literature review clarifies what the ethical concerns regarding AI are, and it charts them into two groups: (i) ethical concerns that arise from the design of AI and (ii) ethical concerns that arise from human–AI interactions. The analysis of the obtained sample highlights the most recurrent ethical concerns. Finally, it exposes the main proposals of the literature to handle the ethical concerns according to the main ethical approaches. It interprets the findings to lay the foundations for future research on the ethics of AI.Business and Society Review1341This systematic literature review clarifies what the ethical concerns regarding AI are, and it charts them into two groups: (i) ethical concerns that arise from the design of AI and (ii) ethical concerns that arise from human–AI interactions.Business and Society Review['Francesco Vincenzo Giarmoleo', 'Ignacio Ferrero', 'M. Rocchi', 'Massimiliano Pellegrini']2024-02-04T00:00:00https://www.semanticscholar.org/paper/e0c176370fcca6f8d11286f922c2154eaa69fc34 8ae87c5fd1624c8ddd04fed57a1e644cb35e4cc9Public Choice440Public Choice['Arthur Schram', 'A. Ule']2024-02-04T00:00:00https://www.semanticscholar.org/paper/8ae87c5fd1624c8ddd04fed57a1e644cb35e4cc9 6366eca63f82703659a6ee5fafe1145e3b7fd98dWith the rise of complex cyber devices Cyber Forensics (CF) is facing many new challenges. For example, there are dozens of systems running on smartphones, each with more than millions of downloadable applications. Sifting through this large amount of data and making sense requires new techniques, such as from the field of Artificial Intelligence (AI). To apply these techniques successfully in CF, we need to justify and explain the results to the stakeholders of CF, such as forensic analysts and members of the court, for them to make an informed decision. If we want to apply AI successfully in CF, there is a need to develop trust in AI systems. Some other factors in accepting the use of AI in CF are to make AI authentic, interpretable, understandable, and interactive. This way, AI systems will be more acceptable to the public and ensure alignment with legal standards. An explainable AI (XAI) system can play this role in CF, and we call such a system XAI-CF. XAI-CF is indispensable and is still in its infancy. In this paper, we explore and make a case for the significance and advantages of XAI-CF. We strongly emphasize the need to build a successful and practical XAI-CF system and discuss some of the main requirements and prerequisites of such a system. We present a formal definition of the terms CF and XAI-CF and a comprehensive literature review of previous works that apply and utilize XAI to build and increase trust in CF. We discuss some challenges facing XAI-CF. We also provide some concrete solutions to these challenges. We identify key insights and future research directions for building XAI applications for CF. This paper is an effort to explore and familiarize the readers with the role of XAI applications in CF, and we believe that our work provides a promising basis for future researchers interested in XAI-CF.arXiv.org2330This paper presents a formal definition of the terms CF and XAI-CF and a comprehensive literature review of previous works that apply and utilize XAI to build and increase trust in CF.ArXiv['Shahid Alam', 'Zeynep Altıparmak']2024-02-04T00:00:00https://www.semanticscholar.org/paper/6366eca63f82703659a6ee5fafe1145e3b7fd98d a926e1f78492ec3ca30b89cb07f2af8b04db7af4The article explores the transformative impact of Artificial Intelligence (AI) on engineering, focusing on the evolution over the last decade. AI has become a cornerstone in reliable engineering, influencing various aspects such as predictive maintenance, fault detection, optimization, automation, and decision support. Predictive maintenance, enabled by AI algorithms, revolutionizes traditional approaches by analysing extensive datasets to predict equipment failures, allowing proactive interventions and minimizing downtime. Fault detection and diagnostics benefit from AI's real-time monitoring and early anomaly identification, reducing the risk of catastrophic failures and enhancing overall system reliability. Optimization of complex systems is facilitated by AI's capacity to process vast amounts of data, leading to improved performance and minimized resource consumption. The integration of AI in automation and robotics reshapes manufacturing processes, emphasizing precision and reliability. Simulation and modelling, data analysis, and supply chain optimization are also discussed as vital areas where AI contributes to enhanced reliability. The article highlights the importance of ethical considerations and human oversight in deploying AI responsibly, emphasizing a collaborative synergy between AI and human expertise for continued advancements in engineering solutions.Advances in Operation Research and Production Management00The article highlights the importance of ethical considerations and human oversight in deploying AI responsibly, emphasizing a collaborative synergy between AI and human expertise for continued advancements in engineering solutions.Advances in Operation Research and Production Management['Lili Alice Zhang']2024-02-04T00:00:00https://www.semanticscholar.org/paper/a926e1f78492ec3ca30b89cb07f2af8b04db7af4 6d94e203392cfbc404113604ea1c6276e5c10eadThe rapid progress of Artificial intelligence in generative modeling is marred by widespread misuse. In response, researchers turn to use-based restrictions—contractual terms prohibiting certain uses—as a “solution” for abuse. While these restrictions can be beneficial to artificial intelligence governance in API-gated settings, their failings are especially significant in open-source models: not only do they lack any means of enforcement, but they also perpetuate the current proliferation of tokenistic efforts toward ethical artificial intelligence. This observation echoes growing literature that points to useless efforts in “AI ethics,” and underscores the need to shift from this paradigm. This article provides an overview of these drawbacks and argues that researchers should divert their efforts to studying deployable, effective, and theoretically grounded solutions like watermarking and model alignment from human feedback to effect tangible changes in the current climate of artificial intelligence.Big Data & Society100It is argued that researchers should divert their efforts to studying deployable, effective, and theoretically grounded solutions like watermarking and model alignment from human feedback to effect tangible changes in the current climate of artificial intelligence.Big Data Soc.['Jonathan Cui', 'David A Araujo']2024-02-04T00:00:00https://www.semanticscholar.org/paper/6d94e203392cfbc404113604ea1c6276e5c10ead 8e9163a3e1144a141ae9bf323e5c9f477fd10c9dThe integration of Artificial Intelligence (AI) into industrial engineering, epitomized by the advent of Industry 4.0, has reshaped manufacturing landscapes. This article explores the profound impact of AI over the past decade, focusing on predictive maintenance, operational optimization, robotics, quality control, and supply chain management. Predictive maintenance, facilitated by machine learning algorithms, minimizes downtime and optimizes resource allocation. Operational optimization, achieved through AI's real-time data analysis, enhances decision-making, resource utilization, and overall efficiency. The infusion of AI into robotics elevates manufacturing capabilities, while quality control processes benefit from advanced image recognition and machine learning, ensuring higher standards. In supply chain management, AI predicts demand, optimizes inventory, and streamlines routes, fostering resilience. Human-machine collaboration, highlighted by collaborative robots and AI-driven workforce empowerment, underlines the transformative synergy. The article concludes with a reflection on the past decade's developments, emphasizing the ongoing evolution of AI in industrial engineering, promising smarter, more adaptable, and globally competitive operations in the future.Advances in Operation Research and Production Management00The profound impact of AI over the past decade is explored, focusing on predictive maintenance, operational optimization, robotics, quality control, and supply chain management, which enhances decision-making, resource utilization, and overall efficiency.Advances in Operation Research and Production Management['Xuze Lin']2024-02-04T00:00:00https://www.semanticscholar.org/paper/8e9163a3e1144a141ae9bf323e5c9f477fd10c9d 29ca89def57f5e829a855f394988c1feca67d407Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence WIREs Data Mining and Knowledge Discovery1410Overall, AI demonstrated promising capabilities across various AF‐related tasks, however, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance.WIREs Data Mining and Knowledge Discovery['Massimo Salvi', 'Madhav R. Acharya', 'S. Seoni', 'Oliver Faust', 'Ruyan Tan', 'P. Barua', 'Salvador García', 'F. Molinari', 'U. R. Acharya']2024-02-04T00:00:00https://www.semanticscholar.org/paper/29ca89def57f5e829a855f394988c1feca67d407 46291039ce202c51622af199346cfbeb0ec9ccd0International Conference on Advanced Communication Technology002024 26th International Conference on Advanced Communications Technology (ICACT)[]2024-02-04T00:00:00https://www.semanticscholar.org/paper/46291039ce202c51622af199346cfbeb0ec9ccd0 e535b91c336ab9ad7a1d4bd9e2cfc340e465d008International Conference on Advanced Communication Technology002024 26th International Conference on Advanced Communications Technology (ICACT)[]2024-02-04T00:00:00https://www.semanticscholar.org/paper/e535b91c336ab9ad7a1d4bd9e2cfc340e465d008 01cb90034e62d388853d948d6462b97b13948f56International Conference on Advanced Communication Technology002024 26th International Conference on Advanced Communications Technology (ICACT)[]2024-02-04T00:00:00https://www.semanticscholar.org/paper/01cb90034e62d388853d948d6462b97b13948f56 910b3657cc5000117efd0f04c4eb160a70b28361International Journal of Science Education261International Journal of Science Education['S. Erduran', 'Olivia Levrini']2024-02-04T00:00:00https://www.semanticscholar.org/paper/910b3657cc5000117efd0f04c4eb160a70b28361 39e3579320c4e231e73a305d0649f26adc1a440eThe 14th European Nutrition Conference FENS 202300The 14th European Nutrition Conference FENS 2023['Isabel Espinosa-Salinas', 'G. Freixer', 'Sergio Romero-Tapiador', 'Blanca Lacruz Pleguezuelos', 'Rubén Tolosana', 'J. Ortega-Garcia', 'Enrique Carrillo-de Santa Pau']2024-02-04T00:00:00https://www.semanticscholar.org/paper/39e3579320c4e231e73a305d0649f26adc1a440e 35128eee505bf6984e9068028ba14361e0bdfeb9International Conference on Advanced Communication Technology002024 26th International Conference on Advanced Communications Technology (ICACT)['Dan Klein']2024-02-04T00:00:00https://www.semanticscholar.org/paper/35128eee505bf6984e9068028ba14361e0bdfeb9 dd8286545da1a832dada35032728d1013d4d9242Journal of Sustainability for Energy00Journal of Sustainability for Energy['Swapandeep Kaur', 'Raman Kumar', 'Kanwardeep Singh', 'Yinglai Huang']2024-02-04T00:00:00https://www.semanticscholar.org/paper/dd8286545da1a832dada35032728d1013d4d9242 2e972d093c4bd876b8bc1b600e05d53c3f27b4b3Place Branding and Public Diplomacy130Place Branding and Public Diplomacy['Corneliu Bjola', 'I. Manor']2024-02-04T00:00:00https://www.semanticscholar.org/paper/2e972d093c4bd876b8bc1b600e05d53c3f27b4b3 1e905582abf156c2acf92f9c0d6a8acc2220c854iScience1770This work proposes increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives.iScience['Youzhi Qu', 'Chen Wei', 'Penghui Du', 'Wenxin Che', 'Chi Zhang', 'Wanli Ouyang', 'Yatao Bian', 'Feiyang Xu', 'Bin Hu', 'Kai Du', 'Haiyan Wu', 'Jia Liu', 'Quanying Liu']2024-02-04T00:00:00https://www.semanticscholar.org/paper/1e905582abf156c2acf92f9c0d6a8acc2220c854 a9485f40ad83b9b6e1f691a345a00524d6b3908dCognitive Neurodynamics1270It is argued here that standard, feed-forward deep neural networks (DNNs) are unable to implement this function, whereas an alternative, fully brain-constrained class of neural architectures spontaneously exhibits it.Cognitive Neurodynamics['Max Garagnani']2024-02-04T00:00:00https://www.semanticscholar.org/paper/a9485f40ad83b9b6e1f691a345a00524d6b3908d 19d8f953f38077065e7f3978a86da7d2a1ead059In terms of market relations, Russia actively integrated into the world space as an outcome of globalization processes, which significantly determined its foreign economic activity. Increased political tension, growing threats to Russia’s national interests and unprecedented sanctions pressure bring to the fore the need seeking for new ways and tools for the country’s economic development. Analysis of import operations in real circumstances helps to identify factors of influence and opportunities for strengthening the Russian economy. In particularly such as analysis of the structure and dynamics of imports, expenses managing of import operations, income and efficiency, formats of interaction between counterparties. The study’s aim is to consider the issues of legal regulation of import operations in the Russian Federation, considering international and national legal norms, as well as aspects that reveal the analysis’ basis and accounting of import operations in nowadays realities. Timely analysis of import operations and identifying factors influencing efficiency allows developing additional procedures. The authors consider the need focusing on cost savings, conducting simplified customs declaration procedures within the framework of international and national law and minimizing risks.The world of new economy30The world of new economy['L. Kupriyanova', 'T. V. Petrusevich']2024-02-03T00:00:00https://www.semanticscholar.org/paper/19d8f953f38077065e7f3978a86da7d2a1ead059 a574b0c51b720fbd83b2564816d7b6e8448332f8This paper aims to provide a comprehensive review of the integration of artificial intelligence (AI) and blockchain technology in U.S. tax administration. It explores how these technologies are revolutionizing tax compliance and fraud detection, offering a comparative analysis with traditional methods. The paper highlights the potential benefits of these technologies in enhancing efficiency, accuracy, and transparency in tax administration, aligning with the U.S. government's objectives of ensuring fiscal integrity and public trust. The review also examines international best practices and proposes how the U.S. can leverage these technologies to maintain its global leadership in financial governance and innovation. The study is structured around four key objectives: assessing the current integration of AI and blockchain in tax administration, evaluating their effectiveness in enhancing tax compliance, identifying implementation challenges, and developing strategic recommendations. Employing a comprehensive literature review approach, the study synthesizes findings from various sources to provide an in-depth understanding of the role and impact of these technologies in modern tax systems. The results reveal that AI and blockchain significantly improve tax compliance and administration efficiency but also introduce challenges such as data privacy concerns and the need for robust regulatory frameworks. In conclusion, the study underscores the transformative potential of AI and blockchain in tax administration, recommending continuous research and development, coupled with stakeholder education and engagement. These efforts are crucial for overcoming operational challenges and fully harnessing the benefits of these technologies in modernizing tax systems. The paper concludes with strategic recommendations for policymakers, tax authorities, and researchers, emphasizing the importance of a balanced approach that fosters technological innovation while maintaining legal compliance and adherence to fundamental principles. Keywords: Artificial Intelligence, Blockchain, Tax Administration, Tax Compliance, Digital Transformation, Financial Governance.Computer Science &amp; IT Research Journal07The results reveal that AI and blockchain significantly improve tax compliance and administration efficiency but also introduce challenges such as data privacy concerns and the need for robust regulatory frameworks, which underscores the transformative potential of AI and blockchain in tax administration.Computer Science &amp; IT Research Journal['Odunayo Adewunmi Adelekan', 'Olawale Adisa', 'Bamidele Segun Ilugbusi', 'Ogugua Chimezie Obi', 'Kehinde Feranmi Awonuga', 'Onyeka Franca Asuzu', 'Ndubuisi Leonard Ndubuisi']2024-02-03T00:00:00https://www.semanticscholar.org/paper/a574b0c51b720fbd83b2564816d7b6e8448332f8 ed88a960dff229ee182712fec4e0ff182970581aScientific Reports203Assessment of ChatGPT in the field of popular science, specifically in answering questions related to CRC diagnosis and treatment, shows its general efficiency in providing CRC information falls short of expert standards, indicating the need for further advancements and improvements in AI technology for patient education in healthcare.Scientific Reports['W. Peng', 'Yifei Feng', 'Cui Yao', 'Sheng Zhang', 'Han Zhuo', 'Tianzhu Qiu', 'Yi Zhang', 'Junwei Tang', 'Yanhong Gu', 'Yueming Sun']2024-02-03T00:00:00https://www.semanticscholar.org/paper/ed88a960dff229ee182712fec4e0ff182970581a 98075cc852769fb9b4c44b3a72329a08a446942cHumanities and Social Sciences Communications562This analysis highlights how biases in AI-driven knowledge production can reinforce Western science, overlooking diverse sources of expertise and perspectives regarding conservation research and practices.Humanities and Social Sciences Communications['Danilo Urzedo', 'Zarrin Tasnim Sworna', 'Andrew J. Hoskins', 'Cathy J. Robinson']2024-02-03T00:00:00https://www.semanticscholar.org/paper/98075cc852769fb9b4c44b3a72329a08a446942c 83160239a079a2ebeaa50d9876e4a203e6fa4310Ethics and Information Technology251Ethics Inf. Technol.['M. Klenk']2024-02-03T00:00:00https://www.semanticscholar.org/paper/83160239a079a2ebeaa50d9876e4a203e6fa4310 0de35329d3c5d80c0153c4b8160896d11259f264The burgeoning integration of Artificial Intelligence (AI) into Environmental, Social, and Governance (ESG) initiatives within the financial sector represents a paradigm shift towards more sus-tainable and equitable financial practices. This paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. With the advent of stringent regulatory requirements and heightened stakeholder awareness, financial institutions (FIs) are increasingly compelled to adopt ESG criteria. AI emerges as a pivotal tool in navigating the complex in-terplay of financial activities and sustainability goals. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. Further, we delve into the critical con-siderations surrounding the use of data and the development of models, underscoring the importance of data quality, privacy, and model robustness. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes. Conclusively, our findings suggest that while AI offers transformative potential for ESG in banking, it also poses significant challenges that necessitate careful consideration. The final part of the paper synthesizes the survey's insights, proposing a forward-looking stance on the adoption of AI in ESG practices. We conclude with recommendations with a reference architecture for future research and development, advocating for a balanced approach that leverages AI's strengths while mitigating its risks within the ESG domain.arXiv.org381ArXiv['Jun Xu']2024-02-03T00:00:00https://www.semanticscholar.org/paper/0de35329d3c5d80c0153c4b8160896d11259f264 bf600c030f97367b47275c3f9eff68484ab6a775Artificial Intelligence (AI) systems assist in enhancing data analysis with meaningful insights and promote real-time decision construction that implements blockchain technology in data security. This factor guides SMEs in managing big data by usage of predictive analysis and algorithms in reducing operational complexity in the USA. There are 33.2 million SMEs in the US and contribute 50% of the overall GDP in this country as per the 2023 report [1]. The application of AI and automation technology leads to improved work distribution and enhanced overall performance in handling the SME's services. This factor encouraged operational facilities to use the real-time tracking system and promote the level of the smart manufacturing process. The primary research used 101 participants to conduct a survey on SME employees for the transformation of SMEs with AI in analyzing opportunities and challenges. This analysis reflects that most of the employees felt encouraged to adopt AI and blockchain in expanding the overall productivity of USA SMEs.International Journal of Membrane Science and Technology00The primary research used 101 participants to conduct a survey on SME employees for the transformation of SMEs with AI in analyzing opportunities and challenges and reflects that most of the employees felt encouraged to adopt AI and blockchain in expanding the overall productivity of USA SMEs.International Journal of Membrane Science and Technology['Srinivasa Rao Gunturu']2024-02-03T00:00:00https://www.semanticscholar.org/paper/bf600c030f97367b47275c3f9eff68484ab6a775 9c778fa6d2c3cdc102475a9e1f679f287580fd45This study investigates the potential application of artificial intelligence (AI) in the field of sustainable fashion by streamlining the production of clothing and incorporating trend analysis. By researching current fashion trends and consumer preferences to identify the most well-liked apparel patterns and designs, the article seeks to optimise supply and demand while reducing surplus production. Fashion garment production can be optimised to fulfil customer demand while minimising waste and lowering costs by incorporating trend research into the manufacturing process. Companies may now invest in clothing concepts that will sell thanks to AI trend forecasting, which reduces some of the uncertainty and human error that now hinder trend forecasting. The fashion industry is infamous for its detrimental effects on society and the environment, including creation of waste and pollution. While the fashion industry has started to adopt more sustainable practises, AI has the ability to quicken the process by offering creative answers to some of the sector's most pressing problems. The study looks at a number of AI-related case studies in the fashion sector, including the use of predictive analytics to cut waste and streamline supply chains, computer vision to enhance textile recycling, and natural language processing to encourage openness and moral work practises. The paper also examines how AI could revolutionise the fashion business and hasten the shift to a more ethical and sustainable future. The report also highlights the necessity for careful evaluation of these concerns as the fashion industry develops by posing significant ethical and social implications for AI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-02-03T00:00:00https://www.semanticscholar.org/paper/9c778fa6d2c3cdc102475a9e1f679f287580fd45 893b01ff3d3127d15db4472685a895a9f5616a0aPrescriptive AI represents a transformative shift in decision-making, offering causal insights and actionable recommendations. Despite its huge potential, enterprise adoption often faces several challenges. The first challenge is caused by the limitations of observational data for accurate causal inference which is typically a prerequisite for good decision-making. The second pertains to the interpretability of recommendations, which is crucial for enterprise decision-making settings. The third challenge is the silos between data scientists and business users, hindering effective collaboration. This paper outlines an initiative from IBM Research, aiming to address some of these challenges by offering a suite of prescriptive AI solutions. Leveraging insights from various research papers, the solution suite includes scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models (LLMs) to bridge communication gaps via a conversation agent. A proof-of-concept, PresAIse, demonstrates the solutions' potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface, democratizing advanced analytics for strategic decision-making.arXiv.org320An initiative from IBM Research, aiming to address some of the challenges of enterprise adoption of prescriptive AI by offering a suite of prescriptive AI solutions, includes scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models to bridge communication gaps via a conversation agent.ArXiv['Wei Sun', 'Scott McFaddin', 'Linh Ha Tran', 'Shivaram Subramanian', 'K. Greenewald', 'Y. Tenzin', 'Zack Xue', 'Youssef Drissi', 'M. Ettl']2024-02-03T00:00:00https://www.semanticscholar.org/paper/893b01ff3d3127d15db4472685a895a9f5616a0a 11854121d46248586a7feefcfa5b1704504a93d3International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-02-03T00:00:00https://www.semanticscholar.org/paper/11854121d46248586a7feefcfa5b1704504a93d3 64572cb3e1e41cc92e4f576c72a3290cc956befeModern clinical trials can capture tens of thousands of clinicogenomic measurements per individual. Employing manual approaches to discover predictive biomarkers, as differentiated from prognostic markers, is a challenging task. To address this challenge, we present an automated neural network framework based on contrastive learning, which we have named the predictive biomarker modeling framework (PBMF). This general-purpose framework explores potential predictive biomarkers in a systematic and unbiased manner, as demonstrated in simulated "ground truth" synthetic scenarios resembling clinical trials. Applied retrospectively to real clinicogenomic data sets, particularly in the complex field of immunooncology (IO) predictive biomarker discovery, our algorithm successfully found biomarkers that identify IO-treated individuals who survive longer than those treated with chemotherapy. In a retrospective analysis, we demonstrated how our framework could have contributed to a phase 3 clinical trial (NCT02008227) by uncovering a predictive biomarker based solely on early study data. Patients identified with this predictive biomarker had a 15% improvement in survival risk, as compared to those of the original trial. This improvement was achieved with a simple, interpretable decision tree generated via PBMF knowledge distillation. Our framework offers a rapid and robust approach to inform biomarker strategy, providing actionable outcomes for clinical decision-making.medRxiv110An automated neural network framework based on contrastive learning that finds biomarkers that identify IO-treated individuals who survive longer than those treated with chemotherapy, providing actionable outcomes for clinical decision-making is presented.['Gustavo Arango-Argoty', 'D. Bikiel', 'Gerald J. Sun', 'Elly Kipkogei', 'Kaitlin M. Smith', 'Etai Jacob']2024-02-03T00:00:00https://www.semanticscholar.org/paper/64572cb3e1e41cc92e4f576c72a3290cc956befe e8b2dec40e51257b32d4e4f29d95afa9c3919366Current Opinion in Structural Biology510The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.Current opinion in structural biology['Dawei Meng', 'Shiqiang Zhang', 'Yuanfang Huang', 'Kehang Mao', 'Jing-Dong J. Han']2024-02-03T00:00:00https://www.semanticscholar.org/paper/e8b2dec40e51257b32d4e4f29d95afa9c3919366 0a4cd5dc9a9ac9bf0fb8418fbdea9c04e6110010Applications in the Internet of Things (IoT) utilize machine learning to analyze sensor-generated data. However, a major challenge lies in the lack of targeted intelligence in current sensing systems, leading to vast data generation and increased computational and communication costs. To address this challenge, we propose a novel sensing module to equip sensing frameworks with intelligent data transmission capabilities by integrating a highly efficient machine learning model placed near the sensor. This model provides prompt feedback for the sensing system to transmit only valuable data while discarding irrelevant information by regulating the frequency of data transmission. The near-sensor model is quantized and optimized for real-time sensor control. To enhance the framework's performance, the training process is customized and a"lazy"sensor deactivation strategy utilizing temporal information is introduced. The suggested method is orthogonal to other IoT frameworks and can be considered as a plugin for selective data transmission. The framework is implemented, encompassing both software and hardware components. The experiments demonstrate that the framework utilizing the suggested module achieves over 85% system efficiency in terms of energy consumption and storage, with negligible impact on performance. This methodology has the potential to significantly reduce data output from sensors, benefiting a wide range of IoT applications.arXiv.org730A novel sensing module to equip sensing frameworks with intelligent data transmission capabilities by integrating a highly efficient machine learning model placed near the sensor by integrating a highly efficient machine learning model placed near the sensor.ArXiv['Wenjun Huang', 'Arghavan Rezvani', 'Hanning Chen', 'Yang Ni', 'Sanggeon Yun', 'Sungheon Jeong', 'Mohsen Imani']2024-02-03T00:00:00https://www.semanticscholar.org/paper/0a4cd5dc9a9ac9bf0fb8418fbdea9c04e6110010 28b1c8faa0f739ae1cd6952e069b387fdb6af982npj Digit. Medicine971Current research suggests that integrating AI with home-based VRehab can lead to improved rehabilitation outcomes for patients, but further research is required to fully assess the effectiveness of various forms of AI-driven home-based VRehab, taking into account its unique challenges and using standardized metrics.NPJ Digital Medicine['A. Abedi', 'Tracey J. F. Colella', 'M. Pakosh', 'Shehroz S. Khan']2024-02-03T00:00:00https://www.semanticscholar.org/paper/28b1c8faa0f739ae1cd6952e069b387fdb6af982 4fe21c75bcd11ef79d38df338caaf76e35d0cc41Environmental Monitoring & Assessment631An AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021.Environmental Monitoring and Assessment['C. P. Ezenkwu', 'San Cannon', 'Ebuka Ibeke']2024-02-03T00:00:00https://www.semanticscholar.org/paper/4fe21c75bcd11ef79d38df338caaf76e35d0cc41 02ea50c9fa700bb2dce0923fd22b498203502817PURPOSE OF REVIEW This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes. RECENT FINDINGS Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality. The integration of AI-based risk scores in managing CAP has the potential to enhance the accuracy of predicting patients at higher risk, facilitating timely intervention and resource allocation. Moreover, AI algorithms reduce variability associated with subjective clinical judgment, promoting consistency in decision-making, and provide real-time risk assessments, aiding in the dynamic management of patients with CAP. SUMMARY The development and implementation of AI-tools for hospitalization in CAP represent a transformative approach to improving patient outcomes. The integration of AI into healthcare has the potential to revolutionize the way we identify and manage individuals at risk of severe outcomes, ultimately leading to more efficient resource utilization and better overall patient care.Current opinion in pulmonary medicine441The integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management is explored, emphasizing its relevance in predicting the risk of hospitalization and the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes.Current opinion in pulmonary medicine['Maria Antonietta Barbieri', 'V. Battini', 'Sessa Maurizio']2024-02-03T00:00:00https://www.semanticscholar.org/paper/02ea50c9fa700bb2dce0923fd22b498203502817 e009758236ec0cbfb870d23f0ac0a5e331386246When the environmental performance is below the aspiration, will firms make substantive changes? In order to answer this question, based on the behavioral theory of the firm, this paper examines the impact of negative environmental performance feedback on substantive green innovation (GI) and its influencing mechanism. It is found that the negative environmental performance feedback induces substantive GI, which is positively moderated by external regulations (i.e., government environmental regulation and public environmental concern) and internal incentive (i.e., executive equity incentive). Media pressure and risk preference act as mediators in the above promotion effect. Heterogeneity analysis shows that the above promotion effect as well as the moderating effects of external regulations are more pronounced in private firms, while the moderating effect of internal incentive is more pronounced in state‐owned firms. Furthermore, the above promotion and moderating effects are more pronounced after the ESG rating event of SynTao Green Finance Agency. This paper offers new sights into understanding the motives of substantive GI and policy suggestion for promoting firms to achieve sustainable development.Corporate Social Responsibility and Environmental Management1200Corporate Social Responsibility and Environmental Management['Zi-Yi Sun', 'Xiao Sun', 'Yuting Dong']2024-02-03T00:00:00https://www.semanticscholar.org/paper/e009758236ec0cbfb870d23f0ac0a5e331386246 df9bcd97193f5ccd5983a15247567db91f7bb834Over the past few years, medical artificial intelligence (AI) has been extensively utilized within the healthcare industry. However, the deployment of AI raises complicated social and ethical issues related to security, privacy, and human rights. While the use of artificial intelligence (AI) has the potential to improve healthcare outcomes and operational efficiency, this article gives a detailed assessment of current cutting-edge AI breakthroughs in clinical laboratories. It focuses on the potential benefits of AI and its application in clinical laboratory. The use of AI in clinical laboratory is rapidly growing, with the potential to alter patient care in the near future. Furthermore, it has the potential to democratize modern laboratory services, making them available to people all around the world.Journal of Cellular Biotechnology450The use of AI in clinical laboratory is rapidly growing, with the potential to alter patient care in the near future, and has the potential to democratize modern laboratory services, making them available to people all around the world.Journal of Cellular Biotechnology['Yugeshwari Tiwade', 'Nandkishor Jageshwar Bankar', 'Vaishnavi Mishra', 'Anita Sajjanar']2024-02-03T00:00:00https://www.semanticscholar.org/paper/df9bcd97193f5ccd5983a15247567db91f7bb834 543806d0729cfc11641b9642ec237b4408bb0f24Artificial Intelligence Review1580This effort targets providing a novel thorough review of the most recent MHOs applied to define the ungiven parameters of SOFCs stacks, where thirty up-to-date MHOs from the last five years are comprehensively illustrated.Artif. Intell. Rev.['Hossam Ashraf', 'Abdelmonem Draz']2024-02-03T00:00:00https://www.semanticscholar.org/paper/543806d0729cfc11641b9642ec237b4408bb0f24 a8bd61ffe2db42b007450d43086565982d177703Di era digital saat ini, pemerintah di Negara Indonesia masih berusaha sejak dini dalam memasuki tentang kehidupan bermasyarakat dengan mengandalkan pemrograman dan kecerdasan buatan, yang akan digunakan untuk mengajar di Sekolah Dasar (SD). Tujuan pengembangan Sumber Daya Manusia (SDM) yang dikaitkan dalam bidang teknologi komunikasi dan informasi yang masih memiliki beberapa program seperti pelatihan aplikasi openAI, canva dan aplikasi yang mendukung dalam pembuatan model pembelajaran, selain mengimplementasikan upaya pemerataan literasi juga pada upaya peningkatan kompetensi bidang teknologi komunikasi dan informasi di seluruh wilayah dan kalangan masyarakat. Metodologi yang digunakan pada kegiatan pengabdian masyarakat dengan menggunakan focus group discussion (FGD) untuk memberikan pemahaman baru tentang kegiatan pelatihan Thematic Academy (TA). Adapun tujuan proses kegiatan tersebut guna meningkatkan pengetahuan, keterampilan, sikap, serta daya saing sumber daya manusia bidang teknologi informasi dan komunikasi sebagai bagian dari program pembangunan dilakukan dalam 3 (tiga) kali pertemuan meliputi pengenalan kecerdasan buatan (artificial intelligence). Hasil temuan pada kegiatan pelatihan dibuktikan dengan tindak lanjut yang mana adanya pemahaman mengenai pembelajaran Berbasis kecerdasan buatan bagi pengajar sehingga dapat disalurkan kepada murid-murid yang diajarnya. Peserta terkait pemanfaatan Kecerdasan Buatan untuk menunjang pembelajaran di SD.In the current digital era, the government in Indonesia is still trying from an early age to enter into social life by relying on programming and artificial intelligence, which will be used for teaching in elementary schools. The aim of developing Human Resources (HR), which is related to the field of communication and information technology, which still has several programs such as training on OpenAI applications, Canva, and applications that support the creation of learning models, apart from implementing efforts to equalize literacy, is also an effort to increase competence in the field of communication technology and information in all regions and communities. The methodology used in community service activities uses focus group discussions (FGD) to provide a new understanding of Thematic Academy (TA) training activities. The activity aims to improve the knowledge, skills, attitudes, and competitiveness of human resources in information and communication technology as part of a development program carried out in three meetings, including the introduction of artificial intelligence. The findings from the training activities were proven by follow-up in which there was an understanding of artificial intelligence-based learning for teachers so that it could be distributed to the students they taught. Participants related to using Artificial Intelligence to support learning in elementary schools. Bubungan Tinggi: Jurnal Pengabdian Masyarakat00Bubungan Tinggi: Jurnal Pengabdian Masyarakat['M. Amin', 'A. Rizal', 'Gerhana Danan Jaya', 'Muhammaad Farriz Adi', 'Anjasmoro Setyo Waloyo', 'Bakhrudin All Habsy', 'S. Arifah', 'R. Maryam', 'Wardatul Mufidah']2024-02-03T00:00:00https://www.semanticscholar.org/paper/a8bd61ffe2db42b007450d43086565982d177703 a1b1efae01c6b0fa22b25f2b6937dca31dbcc9c6Human Resources for Health580Professional regulation for doctors and nurses in Kenya and Uganda is generally perceived as weak, yet professionals are more positive about online licencing and regulation where they have relationships with regulators, and an ambidextrous approach to improving regulation is proposed, which is termed deconcentrating regulation.Human Resources for Health['Gerry McGivern', 'Francis Wafula', 'G. Seruwagi', 'Tina Kiefer', 'A. Musiega', 'Catherine Nakidde', 'Dosila Ogira', 'Mike Gill', 'Mike English']2024-02-02T00:00:00https://www.semanticscholar.org/paper/a1b1efae01c6b0fa22b25f2b6937dca31dbcc9c6 fe25f7efb025b8a1a8f4377957bfafd4b747a196Theory and society600Theory and Society['Luis Flores']2024-02-02T00:00:00https://www.semanticscholar.org/paper/fe25f7efb025b8a1a8f4377957bfafd4b747a196 e4005c2a5306faebe94656a3c4d52e4db8a1ce0cThe production and promotion of “AI” technology involves dehumanization on many fronts. I explore these processes of dehumanization and the role that cognitive science can play by bringing a richer picture of human cognition to the discourse.Current Directions in Psychological Science295Current Directions in Psychological Science['Emily M. Bender']2024-02-02T00:00:00https://www.semanticscholar.org/paper/e4005c2a5306faebe94656a3c4d52e4db8a1ce0c a4f57be20ae6de9c7c42b1cb51b6605706b4cf45Recent advances of artificial intelligence (AI) code generators are opening new opportunities in software security research, including misuse by malicious actors. We review use cases for AI code generators for security and introduce an evaluation benchmark.IEEE Security &amp; Privacy73Use cases for AI code generators for security and an evaluation benchmark are reviewed and a benchmark is introduced to introduce an evaluation benchmark.ArXiv['R. Natella', 'Pietro Liguori', 'Cristina Improta', 'B. Cukic', 'Domenico Cotroneo']2024-02-02T00:00:00https://www.semanticscholar.org/paper/a4f57be20ae6de9c7c42b1cb51b6605706b4cf45 61b3ae594ebc6b055dff24ea97fc784e09f14a9cChatGPT and other general large language models (LLMs) have achieved remarkable success, but they have also raised concerns about the misuse of AI-generated texts. Existing AI-generated text detection models, such as based on BERT and RoBERTa, are prone to in-domain over-fitting, leading to poor out-of-domain (OOD) detection performance. In this paper, we first collected Chinese text responses generated by human experts and 9 types of LLMs, for which to multiple domains questions, and further created a dataset that mixed human-written sentences and sentences polished by LLMs. We then proposed LLM-Detector, a novel method for both document-level and sentence-level text detection through Instruction Tuning of LLMs. Our method leverages the wealth of knowledge LLMs acquire during pre-training, enabling them to detect the text they generate. Instruction tuning aligns the model's responses with the user's expected text detection tasks. Experimental results show that previous methods struggle with sentence-level AI-generated text detection and OOD detection. In contrast, our proposed method not only significantly outperforms baseline methods in both sentence-level and document-level text detection but also demonstrates strong generalization capabilities. Furthermore, since LLM-Detector is trained based on open-source LLMs, it is easy to customize for deployment.arXiv.org262LLM-Detector is proposed, a novel method for both document-level and sentence-level text detection through Instruction Tuning of LLMs that not only significantly outperforms baseline methods in both sentence-level and document-level text detection but also demonstrates strong generalization capabilities.ArXiv['Rongsheng Wang', 'Hao Chen', 'Ruizhe Zhou', 'Han Ma', 'Yaofei Duan', 'Yanlan Kang', 'Songhua Yang', 'Baoyu Fan', 'Tao Tan']2024-02-02T00:00:00https://www.semanticscholar.org/paper/61b3ae594ebc6b055dff24ea97fc784e09f14a9c 7d68d27a0d313e5a433442e7f5ffcac44659133eThe recent development of generative models unleashes the potential of generating hyper-realistic fake images. To prevent the malicious usage of fake images, AI-generated image detection aims to distinguish fake images from real images. However, existing method suffer from severe performance drop when detecting images generated by unseen generators. We find that generative models tend to focus on generating the patches with rich textures to make the images more realistic while neglecting the hidden noise caused by camera capture present in simple patches. In this paper, we propose to exploit the noise pattern of a single simple patch to identify fake images. Furthermore, due to the performance decline when handling low-quality generated images, we introduce an enhancement module and a perception module to remove the interfering information. Extensive experiments demonstrate that our method can achieve state-of-the-art performance on public benchmarks.arXiv.org492This paper proposes to exploit the noise pattern of a single simple patch to identify fake images and introduces an enhancement module and a perception module to remove the interfering information.ArXiv['Jiaxuan Chen', 'Jieteng Yao', 'Li Niu']2024-02-02T00:00:00https://www.semanticscholar.org/paper/7d68d27a0d313e5a433442e7f5ffcac44659133e 2e0fab0edfcc00f00be276820a273c0724d93ea0Emerging Distributed AI systems are revolutionizing big data computing and data processing capabilities with growing economic and societal impact. However, recent studies have identified new attack surfaces and risks caused by security, privacy, and fairness issues in AI systems. In this paper, we review representative techniques, algorithms, and theoretical foundations for trustworthy distributed AI through robustness guarantee, privacy protection, and fairness awareness in distributed learning. We first provide a brief overview of alternative architectures for distributed learning, discuss inherent vulnerabilities for security, privacy, and fairness of AI algorithms in distributed learning, and analyze why these problems are present in distributed learning regardless of specific architectures. Then we provide a unique taxonomy of countermeasures for trustworthy distributed AI, covering (1) robustness to evasion attacks and irregular queries at inference, and robustness to poisoning attacks, Byzantine attacks, and irregular data distribution during training; (2) privacy protection during distributed learning and model inference at deployment; and (3) AI fairness and governance with respect to both data and models. We conclude with a discussion on open challenges and future research directions toward trustworthy distributed AI, such as the need for trustworthy AI policy guidelines, the AI responsibility-utility co-design, and incentives and compliance.ACM Computing Surveys2232This paper provides a unique taxonomy of countermeasures for trustworthy distributed AI, covering robustness to evasion attacks and irregular queries at inference, and robustness to poisoning attacks, Byzantine attacks, and irregular data distribution during training.ArXiv['Wenqi Wei', 'Ling Liu']2024-02-02T00:00:00https://www.semanticscholar.org/paper/2e0fab0edfcc00f00be276820a273c0724d93ea0 391707335a321b176f4028fcf7f00c3a57f5ff19Introduction In the field of education, new technologies have enhanced the objectivity and scientificity of educational evaluation. However, concerns have been raised about the fairness of evaluators, such as artificial intelligence (AI) algorithms. This study aimed to assess college students’ perceptions of fairness in educational evaluation scenarios through three studies using experimental vignettes. Methods Three studies were conducted involving 172 participants in Study 1, 149 in Study 2, and 145 in Study 3. Different evaluation contexts were used in each study to assess the influence of evaluators on students’ perception of fairness. Information transparency and explanations for evaluation outcomes were also examined as potential moderators. Results Study 1 found that different evaluators could significantly influence the perception of fairness under three evaluation contexts. Students perceived AI algorithms as fairer evaluators than teachers. Study 2 revealed that information transparency was a mediator, indicating that students perceived higher fairness with AI algorithms due to increased transparency compared with teachers. Study 3 revealed that the explanation of evaluation outcomes moderated the effect of evaluator on students’ perception of fairness. Specifically, when provided with explanations for evaluation results, the effect of evaluator on students’ perception of fairness was lessened. Discussion This study emphasizes the importance of information transparency and comprehensive explanations in the evaluation process, which is more crucial than solely focusing on the type of evaluators. It also draws attention to potential risks like algorithmic hegemony and advocates for ethical considerations, including privacy regulations, in integrating new technologies into educational evaluation systems. Overall, this study provides valuable theoretical insights and practical guidance for conducting fairer educational evaluations in the era of new technologies.Frontiers in Psychology1221College students’ perceptions of fairness in educational evaluation scenarios are assessed through three studies using experimental vignettes to provide valuable theoretical insights and practical guidance for conducting fairer educational evaluations in the era of new technologies.Frontiers in Psychology['Fangyuan Chai', 'Jiajia Ma', 'Yi Wang', 'Jun Zhu', 'Tingting Han']2024-02-02T00:00:00https://www.semanticscholar.org/paper/391707335a321b176f4028fcf7f00c3a57f5ff19 b388bd06a976c90ba076efe10a43988d1c6e7d70Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm in the field of artificial intelligence (AI), offering unprecedented capabilities in decision-making across diverse domains. This article explores the profound impact of DRL on enhancing the decision-making capabilities of AI systems, elucidating its underlying principles, applications, and implications.DRL represents a fusion of deep learning and reinforcement learning, enabling machines to learn complex behaviors and make decisions by interacting with their environment. The utilization of neural networks allows DRL algorithms to handle high-dimensional input spaces, making it well-suited for tasks that involve intricate decision-making processes.One of the key strengths of DRL lies in its ability to address problems with sparse and delayed rewards, common challenges in traditional reinforcement learning. Through a process of trial and error, DRL algorithms can learn optimal decision strategies by navigating through a vast decision space, adapting to dynamic environments, and maximizing cumulative rewards over time.The applications of DRL span various domains, including robotics, finance, healthcare, gaming, and autonomous systems. In robotics, DRL facilitates the development of intelligent agents capable of autonomously navigating complex environments, performing intricate tasks, and adapting to unforeseen circumstances. In finance, DRL is leveraged for portfolio optimization, algorithmic trading, and risk management, demonstrating its potential to revolutionize traditional financial strategies.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402301The profound impact of DRL on enhancing the decision-making capabilities of AI systems is explored, elucidating its underlying principles, applications, and implications.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Jeff Shuford']2024-02-02T00:00:00https://www.semanticscholar.org/paper/b388bd06a976c90ba076efe10a43988d1c6e7d70 32a2aa1f191bfdbe079275d90e1bc5ecfdcc42efBACKGROUND SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak. PURPOSE The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development. METHODS A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax. RESULTS During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it. CONCLUSION We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.Current Topics in Medicinal Chemistry01A thorough evaluation of the literature on the part of Artificial Intelligence as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development is undertaken.Current topics in medicinal chemistry['Kavya Singh', 'Navjeet Kaur', 'Ashish Prabhu']2024-02-02T00:00:00https://www.semanticscholar.org/paper/32a2aa1f191bfdbe079275d90e1bc5ecfdcc42ef 5c96740dbcc3a50d9280c81ab8128b025cf05879Computer Assisted Language Learning462Computer Assisted Language Learning['G. Liu', 'Ron Darvin', 'Chaojun Ma']2024-02-02T00:00:00https://www.semanticscholar.org/paper/5c96740dbcc3a50d9280c81ab8128b025cf05879 dcc5118e26aeb3511c0ba535c8ad417ad335d707PurposeThe purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.Design/methodology/approachThe data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).FindingsFour primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.Originality/valueThis is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.Asia Pacific Journal of Marketing and Logistics600This is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.Asia Pacific Journal of Marketing and Logistics['Hung-Che Wu', 'S. X. Chen', 'Haonan Xu']2024-02-02T00:00:00https://www.semanticscholar.org/paper/dcc5118e26aeb3511c0ba535c8ad417ad335d707 ede4a71ffa6cc0c631416b20970d4ed42096f862This study investigates the profound impact of transfer learning on the performance of artificial intelligence (AI) models when applied across diverse domains. Transfer learning, a machine learning technique that leverages knowledge gained from one task to improve performance on a related task, has demonstrated remarkable success in various applications. The article explores the underlying principles of transfer learning, its mechanisms, and the ways in which it enhances AI performance. The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systemsJournal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systems.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam']2024-02-02T00:00:00https://www.semanticscholar.org/paper/ede4a71ffa6cc0c631416b20970d4ed42096f862 e0620d7345fcb76710ce10fe8ca9f891c532221eThe Aesthetics of Old Film Formats explores the dynamic interplay between traditional cinematic aesthetics and the transformative impact of artificial intelligence (AI) on the revitalization of vintage film formats. The emergence of AI technologies has breathed new life into the world of cinema, fostering a renaissance of classic film formats such as 8mm, 16mm, and 35mm. Filmmakers and visual artists now have access to AI-powered tools and algorithms that can restore, enhance, and creatively manipulate analog footage, offering a bridge between the past and the future. This paper examines how AI, with its ability to upscale, colorize, and refine aged film material, has preserved cinematic heritage and extended the horizons of creative expression. There is a research gap in understanding the complex ethical implications and difficulties associated with the use of artificial intelligence (AI) in the preservation and manipulation of analog footage, even though the abstract emphasizes the transformative impact of AI on the revival of vintage film formats. Authenticity, historical accuracy, and the possibility of unintentional biases created during the AI-driven restoration and augmentation procedures are some examples of ethical considerations. The abstract also emphasizes how AI might help preserve film history and foster new forms of artistic expression.2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE)80Examining how AI, with its ability to upscale, colorize, and refine aged film material, has preserved cinematic heritage and extended the horizons of creative expression shows how AI might help preserve film history and foster new forms of artistic expression.2024 International Conference on Computer, Electrical & Communication Engineering (ICCECE)['Manikandan C', 'Ankit Kashyap', 'Fakira Mohan Nahak']2024-02-02T00:00:00https://www.semanticscholar.org/paper/e0620d7345fcb76710ce10fe8ca9f891c532221e f4307731ff228980126359eddac056d446fa2715In an effort to inform the discussion surrounding existential risks from AI, we formulate Extinction-level Goodhart's Law as"Virtually any goal specification, pursued to the extreme, will result in the extinction of humanity", and we aim to understand which formal models are suitable for investigating this hypothesis. Note that we remain agnostic as to whether Extinction-level Goodhart's Law holds or not. As our key contribution, we identify a set of conditions that are necessary for a model that aims to be informative for evaluating specific arguments for Extinction-level Goodhart's Law. Since each of the conditions seems to significantly contribute to the complexity of the resulting model, formally evaluating the hypothesis might be exceedingly difficult. This raises the possibility that whether the risk of extinction from artificial intelligence is real or not, the underlying dynamics might be invisible to current scientific methods.arXiv.org400This work identifies a set of conditions that are necessary for a model that aims to be informative for evaluating specific arguments for Extinction-level Goodhart's Law, and aims to understand which formal models are suitable for investigating this hypothesis.ArXiv['Vojtěch Kovařík', 'Chris van Merwijk', 'Ida Mattsson']2024-02-02T00:00:00https://www.semanticscholar.org/paper/f4307731ff228980126359eddac056d446fa2715 d0f9afdac87ba302926dbc9f47a111b9454849f9This paper examines the question of how generative AI can improve the well-being of blind or visually impaired people. It refers to a current example, the Be My Eyes app, in which the Be My AI feature was integrated in 2023, which is based on GPT-4 from OpenAI. The author’s tests are described and evaluated. There is also an ethical and social discussion. The power of the tool, which can analyze still images in an amazing way, is demonstrated. Those affected gain a new independence and a new perception of their environment. At the same time, they are dependent on the world view and morality of the provider or developer, who prescribe or deny them certain descriptions. An outlook makes it clear that the analysis of moving images will mean a further leap forward. It is fair to say that generative AI can fundamentally improve the well-being of blind and visually impaired people and will change it in various ways.Proceedings of the AAAI Symposium Series100ArXiv['Oliver Bendel']2024-02-02T00:00:00https://www.semanticscholar.org/paper/d0f9afdac87ba302926dbc9f47a111b9454849f9 a6e18eeabac7ad032168042bb489a5adb66b5ad1Abstract Background Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patients’ treatments and prognoses. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP. Objective This paper reviews VAP prediction models that are based on AI, providing a reference for the early identification of high-risk groups in future clinical practice. Methods A scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The Wanfang database, the Chinese Biomedical Literature Database, Cochrane Library, Web of Science, PubMed, MEDLINE, and Embase were searched to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. The data extracted from the included studies were synthesized narratively. Results Of the 137 publications retrieved, 11 were included in this scoping review. The included studies reported the use of AI for predicting VAP. All 11 studies predicted VAP occurrence, and studies on VAP prognosis were excluded. Further, these studies used text data, and none of them involved imaging data. Public databases were the primary sources of data for model building (studies: 6/11, 55%), and 5 studies had sample sizes of <1000. Machine learning was the primary algorithm for studying the VAP prediction models. However, deep learning and large language models were not used to construct VAP prediction models. The random forest model was the most commonly used model (studies: 5/11, 45%). All studies only performed internal validations, and none of them addressed how to implement and apply the final model in real-life clinical settings. Conclusions This review presents an overview of studies that used AI to predict and diagnose VAP. AI models have better predictive performance than traditional methods and are expected to provide indispensable tools for VAP risk prediction in the future. However, the current research is in the model construction and validation stage, and the implementation of and guidance for clinical VAP prediction require further research.JMIR Medical Informatics210AI models have better predictive performance than traditional methods and are expected to provide indispensable tools for VAP risk prediction in the future, however, the current research is in the model construction and validation stage, and the implementation of and guidance for clinical VAP prediction require further research.JMIR Medical Informatics['Jinbo Zhang', 'Pingping Yang', 'Lu Zeng', 'Shan Li', 'Jiamei Zhou']2024-02-02T00:00:00https://www.semanticscholar.org/paper/a6e18eeabac7ad032168042bb489a5adb66b5ad1 629b65c8629c05efcf02d40bd314a71890e8df90The industry's ostensible technological sophistication contributes to the highly dynamic ecommerce environment. When new technology is made available, many of these companies openly adopt it to stay competitive market. Internet shop owners have embraced a variety of technologies, including artificial intelligence. Technology is rapidly evolving. Artificial intelligence significantly facilitates the conversion of interest into purchase intentions. The majority of the information gathered by e-commerce companies is about prospective customers or prospects. AI can be used to interact with warm leads or cold leads who have indicated interest in a brand or product. Furthermore, AI has been demonstrated to be a highly constructive technique of retargeting customers. Artificial intelligence advancements have increased consumer satisfaction even further, making it even more critical in today's climate. This paper will investigate the factors that influence artificial intelligence's practical implacability in order to better understand how it affects consumers' online purchase plans. This paper explores the various variables influencing consumers' purchase intentions for e-retailing using a technology-based model as the foundation. This study has developed a model that shows how business organisations can incorporate artificial intelligence into retailing in order to comprehend consumer requirements and encourage technology adoption. This research has looked more closely at consciousness, subjective norms, and faith as constructs that heighten the tenacity of artificial intelligence.International Journal of Scientific Research in Science Engineering and Technology150A model is developed that shows how business organisations can incorporate artificial intelligence into retailing in order to comprehend consumer requirements and encourage technology adoption and has looked more closely at consciousness, subjective norms, and faith as constructs that heighten the tenacity of artificial intelligence.International Journal of Scientific Research in Science, Engineering and Technology['Dr. G Manikandan', 'Dr. G Bhuvaneswari']2024-02-02T00:00:00https://www.semanticscholar.org/paper/629b65c8629c05efcf02d40bd314a71890e8df90 20c6714262e2f71feae4331f312f1ab1dc9754fdBackground Social intelligence (SI) is of great importance in the success of the counseling and psychotherapy, whether for the psychologist or for the artificial intelligence systems that help the psychologist, as it is the ability to understand the feelings, emotions, and needs of people during the counseling process. Therefore, this study aims to identify the Social Intelligence (SI) of artificial intelligence represented by its large linguistic models, “ChatGPT; Google Bard; and Bing” compared to psychologists. Methods A stratified random manner sample of 180 students of counseling psychology from the bachelor’s and doctoral stages at King Khalid University was selected, while the large linguistic models included ChatGPT-4, Google Bard, and Bing. They (the psychologists and the AI models) responded to the social intelligence scale. Results There were significant differences in SI between psychologists and AI’s ChatGPT-4 and Bing. ChatGPT-4 exceeded 100% of all the psychologists, and Bing outperformed 50% of PhD holders and 90% of bachelor’s holders. The differences in SI between Google Bard and bachelor students were not significant, whereas the differences with PhDs were significant; Where 90% of PhD holders excel on Google Bird. Conclusion We explored the possibility of using human measures on AI entities, especially language models, and the results indicate that the development of AI in understanding emotions and social behavior related to social intelligence is very rapid. AI will help the psychotherapist a great deal in new ways. The psychotherapist needs to be aware of possible areas of further development of AI given their benefits in counseling and psychotherapy. Studies using humanistic and non-humanistic criteria with large linguistic models are needed.Frontiers in Psychology410The results indicate that the development of AI in understanding emotions and social behavior related to social intelligence is very rapid and will help the psychotherapist a great deal in new ways.Frontiers in Psychology['Nabil Saleh Sufyan', 'F. Fadhel', 'Saleh Safeer Alkhathami', 'Jubran Y. A. Mukhadi']2024-02-02T00:00:00https://www.semanticscholar.org/paper/20c6714262e2f71feae4331f312f1ab1dc9754fd 90a8664a523ed8170f420adc15e27715846b6eafThe assimilation of AI technology into theatre practices has inaugurated an expansive frontier of possibilities for both thespians and spectators. In terms of movement, this involves the use of avatars, which inhabit a customary screen milieu (encompassing three-dimensional in-world scenography) that necessitates simultaneous consideration of a tridimensional theatrical space and coexisting performers, within a moment of real-time inception and interconnectedness. This complex confluence raises questions pertaining to the ‘avatarisation’ of corporeal embodiments on the theatrical stage and the consequent emergence of novel performative methodologies. Within AI-enabled performances, the use of motion capture technology, commonly known as ‘mocap’, entails the recording of skeletal data from physical actors, referred to as ‘mocaptors’, who wear a geo-spatial system for motion capture. This is then translated into digital data that can subsequently be used to animate digital characters or avatars.Movement00Within AI-enabled performances, the use of motion capture technology, commonly known as ‘mocap’, entails the recording of skeletal data from physical actors, referred to as ‘mocaptors’, who wear a geo-spatial system for motion capture, which is then translated into digital data that can be used to animate digital characters or avatars.Movement['Abhik Maiti']2024-02-02T00:00:00https://www.semanticscholar.org/paper/90a8664a523ed8170f420adc15e27715846b6eaf 9e4cb78ce642989747cc3a2be9365d08d61f9bd4Cognitive computing represents a groundbreaking paradigm in artificial intelligence (AI) systems, aiming to emulate and replicate the intricate processes of human intelligence. This article explores the fundamental principles, methodologies, and applications of cognitive computing, shedding light on how it transforms traditional AI approaches. By drawing inspiration from human cognition, cognitive computing systems leverage advanced algorithms, neural networks, and machine learning techniques to emulate complex cognitive functions such as perception, reasoning, and problem-solving.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The fundamental principles, methodologies, and applications of cognitive computing are explored, shedding light on how it transforms traditional AI approaches.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Amandeep Singla']2024-02-02T00:00:00https://www.semanticscholar.org/paper/9e4cb78ce642989747cc3a2be9365d08d61f9bd4 f3a601e67aa929ba8f1f12553a95c7f66551a816As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.Medical Teacher270First year medical students are aware that AI will play a role in their careers as students and future physicians and expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations.Medical teacher['Emely Robleto', 'Ali Habashi', 'Mary-Ann Benites Kaplan', 'Richard L Riley', 'Chi Zhang', 'Laura Bianchi', 'Lina A Shehadeh']2024-02-02T00:00:00https://www.semanticscholar.org/paper/f3a601e67aa929ba8f1f12553a95c7f66551a816 817453e1bd694483970e22007859bdc690a6cb61Journal of general internal medicine21Journal of general internal medicine['D. Khullar', 'Xingbo Wang', 'Fei Wang']2024-02-02T00:00:00https://www.semanticscholar.org/paper/817453e1bd694483970e22007859bdc690a6cb61 95bbdf1c86e8d8eaaffb2a3e2d87e8d2cdf052a3Family and Consumer Sciences Research Journal00Family and Consumer Sciences Research Journal['Melanie D. Schmitt']2024-02-02T00:00:00https://www.semanticscholar.org/paper/95bbdf1c86e8d8eaaffb2a3e2d87e8d2cdf052a3 f24efd1cb5ebe2c28bdb451aebb5d56b865a1897Communications of the ACM00Commun. ACM['Pamela Samuelson']2024-02-02T00:00:00https://www.semanticscholar.org/paper/f24efd1cb5ebe2c28bdb451aebb5d56b865a1897 8062f5bdd8ebe595508aedd8ba58bfd92e573889Journal of Paramedic Practice60Journal of Paramedic Practice['Joe Frankland']2024-02-02T00:00:00https://www.semanticscholar.org/paper/8062f5bdd8ebe595508aedd8ba58bfd92e573889 4f02c572db3032eaae3b3142506cf51c2b994434Journal of Homosexuality00Journal of Homosexuality['Silpa Joy', 'Neerej Dev']2024-02-02T00:00:00https://www.semanticscholar.org/paper/4f02c572db3032eaae3b3142506cf51c2b994434 b482dea6351a199058f2e20a03d03df1fb74cce6Dialog00Dialog['Ted Peters']2024-02-02T00:00:00https://www.semanticscholar.org/paper/b482dea6351a199058f2e20a03d03df1fb74cce6 131a9d8c2c428385d67a45ae39f449368c365b15Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs) are reshaping the field of Software Engineering (SE). Existing LLM-based multi-agent systems have successfully resolved simple dialogue tasks. However, the potential of LLMs for more complex tasks, such as automated code generation for large and complex projects, have been explored in only a few existing works. This paper introduces CodePori, a novel model designed to automate code generation for extensive and complex software projects based on natural language prompts. We employ LLM-based multi-AI agents to handle creative and challenging tasks in autonomous software development. Each agent engages with a specific task, including system design, code development, code review, code verification, and test engineering. We show in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars. It identifies and mitigates potential security vulnerabilities and corrects errors while maintaining a solid code performance level. We also conducted an evaluation of CodePori against existing solutions using HumanEval and the Massively Multitask Benchmark for Python (MBPP) benchmark. The results indicate that CodePori improves upon the benchmarks in terms of code accuracy, efficiency, and overall performance. For example, CodePori improves the pass@1 metric on HumanEval to 87.5% and on MBPP to 86.5%, representing a clear improvement over the existing models. We also assessed CodePori's performance through practitioner evaluations, with 91% expressing satisfaction with the model's performance.arXiv.org345It is shown in the paper that CodePori is able to generate running code for large-scale projects, completing the entire software development process in minutes rather than hours, and at a cost of a few dollars.ArXiv['Zeeshan Rasheed', 'Muhammad Waseem', 'Mika Saari', 'Kari Systä', 'P. Abrahamsson']2024-02-02T00:00:00https://www.semanticscholar.org/paper/131a9d8c2c428385d67a45ae39f449368c365b15 5851e1eb37099e25c5b4a59f998c8a1872cb0a36The fusion of Artificial Intelligence (AI) and healthcare heralds a new era of innovation and transformation, yet it is not without its ethical quandaries. This comprehensive review traverses the intricate landscape where AI meets healthcare, delving into the ethical dilemmas that arise alongside practical applications. The ethical considerations span a spectrum, encompassing issues of patient privacy, transparency, accountability, and the inadvertent perpetuation of biases within AI algorithms. Privacy concerns emerge as a central ethical dilemma as healthcare providers leverage AI to process vast amounts of patient data. Striking a delicate balance between harnessing the power of AI for diagnostic and predictive purposes and safeguarding sensitive medical information is a critical challenge. Moreover, the review scrutinizes the ethical implications of AI algorithms and their potential to perpetuate biases, inadvertently exacerbating health disparities. A nuanced examination of bias mitigation strategies becomes imperative to ensure that AI technologies contribute to equitable healthcare outcomes. In tandem with ethical considerations, the review illuminates the practical applications reshaping the healthcare landscape. AI-driven diagnostics, predictive modeling, and personalized treatment plans emerge as transformative tools, enhancing clinical decision-making and patient outcomes. The efficient allocation of resources, streamlined workflows, and the acceleration of drug discovery processes showcase the tangible benefits of AI integration. This review aspires to guide healthcare practitioners, policymakers, and technologists in navigating the ethical crossroads of AI in healthcare. By fostering an awareness of ethical pitfalls and emphasizing responsible AI development, stakeholders can collaboratively shape a future where AI augments healthcare delivery, upholds ethical standards, and ultimately improves the quality of patient care. Keywords:  AI, Healthcare, Ethics, Review, AI Application.International medical science research journal06This comprehensive review traverses the intricate landscape where AI meets healthcare, delving into the ethical dilemmas that arise alongside practical applications and illuminates the practical applications reshaping the healthcare landscape.International Medical Science Research Journal['Evangel Chinyere Anyanwu', 'Chiamaka Chinaemelum Okongwu', 'Tolulope O Olorunsogo', 'Oluwatoyin Ayo-Farai', 'Femi Osasona', 'Obinna Donald Daraojimba']2024-02-02T00:00:00https://www.semanticscholar.org/paper/5851e1eb37099e25c5b4a59f998c8a1872cb0a36 fb6bf4c685d5f9c2f0fb3032855d3c23580998b9Abstract Artificial intelligence was introduced 60 years ago and has evolved immensely since that time. While artificial intelligence is found in nearly all aspects of our life, the use of artificial intelligence in the healthcare industry has only recently become apparent and more widely discussed. It is expected that artificial intelligence will allow improved disease recognition, treatment optimization, cost and time savings, product development, decision making, and marketing. For pain medicine specifically, these same benefits will be translatable and we can expect better disease recognition and treatment selection. As adoption occurs with this impressive technology, it will be imperative for the pain medicine community to be informed on proper definitions and expected use cases for artificial intelligence. Our objective was to provide pain medicine physicians an overview of artificial intelligence, including important definitions to aid understanding, and to offer potential clinical applications pertinent to the specialty.Journal of Pain Research293The objective was to provide pain medicine physicians an overview of artificial intelligence, including important definitions to aid understanding, and to offer potential clinical applications pertinent to the specialty.Journal of Pain Research['Jonathan M. Hagedorn', 'Tony K George', 'Rohit Aiyer', 'Keith Schmidt', 'John D Halamka', "R. D'souza"]2024-02-02T00:00:00https://www.semanticscholar.org/paper/fb6bf4c685d5f9c2f0fb3032855d3c23580998b9 bf4f2c7b9762e6e0c91ddf330c91700299caf34e This Viewpoint offers 3 recommendations for health care organizations and other stakeholders to consider as part of the Health and Human Services’ artificial intelligence safety program. JAMA Health Forum71JAMA health forum['Raj Ratwani', 'David W. Bates', 'David C. Classen']2024-02-02T00:00:00https://www.semanticscholar.org/paper/bf4f2c7b9762e6e0c91ddf330c91700299caf34e 6704300213d4331ba6c318cde82204b45a46b37c Purpose Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience. Design/methodology/approach The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots. Findings The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions. Originality/value The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change. Journal of Financial Reporting & Accounting360The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions, and overall, AI integration minimizes human interactions and errors in financial institutions.Journal of Financial Reporting and Accounting['Sara Ebrahim Mohsen', 'Allam Hamdan', 'H. Shoaib']2024-02-02T00:00:00https://www.semanticscholar.org/paper/6704300213d4331ba6c318cde82204b45a46b37c 820ef9067481b2ec86bdfa4c5b9b724bba697137With the rapid development of artificial intelligence (AI) technology, AI educators have become a reality. The advancement and increasing applications of AI technology in higher education not only provide more efficient tools for teachers in long-term and focused teaching, but also provide new active and independent spaces for sustainable self-motivated learning for college students. It is of great importance that the effects of AI educator design are understood to ensure the sustainable development and deployment of AI-driven courses at universities. This paper investigates the influences of AI educators’ autonomy design on students’ usage intentions by delving into how the artificial autonomy of AI educators satisfies students’ needs. Drawing on the uses and gratification (U&G) framework, we theoretically elaborate on how AI educator autonomy (i.e., sensing autonomy, thought autonomy, and action autonomy) influences students’ intentions to use an AI educator through the mediating effects of U&G benefits (i.e., information-seeking gratification, social interaction gratification, and entertainment gratification). By conducting an online survey (N = 673) on college students, we found that the sensing autonomy of AI educators is positively associated with usage intention due to the mediating effects of social interaction and entertainment gratifications; the thought autonomy of AI educators is positively related to usage intention, mediated by information-seeking and social interaction gratifications, and the action autonomy of AI educators is positively linked with usage intention through the paths of information-seeking and entertainment gratifications. Our findings provide both theoretical contributions and practical implications.Sustainability1330This paper investigates the influences of AI educators’ autonomy design on students’ usage intentions by delving into how the artificial autonomy of AI educators satisfies students’ needs by conducting an online survey on college students.Sustainability['Wanshu Niu', 'Wuke Zhang', 'Chuanxia Zhang', 'Xiaofeng Chen']2024-02-02T00:00:00https://www.semanticscholar.org/paper/820ef9067481b2ec86bdfa4c5b9b724bba697137 b8a7ab25e21356da7c9f1aef2e816391bb920035Background Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction. Objective This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots. Results A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray et al.2018.NAT MED(1196 Total Citations). Conclusions Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.Frontiers in Oncology550Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing, and it is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future.Frontiers in Oncology['Ruikang Zhong', 'Tangke Gao', 'Jinghua Li', 'Zexing Li', 'Xue Tian', 'Chi Zhang', 'Ximing Lin', 'Yuehui Wang', 'Lei Gao', 'K. Hu']2024-02-02T00:00:00https://www.semanticscholar.org/paper/b8a7ab25e21356da7c9f1aef2e816391bb920035 fc1766e44dfe616fec6b5178c727f6dddaa0ed21As artificial intelligence (AI) continues advancing, ensuring positive societal impacts becomes critical, especially as AI systems become increasingly ubiquitous in various aspects of life. However, developing"AI for good"poses substantial challenges around aligning systems with complex human values. Presently, we lack mature methods for addressing these challenges. This article presents and evaluates the Positive AI design method aimed at addressing this gap. The method provides a human-centered process to translate wellbeing aspirations into concrete practices. First, we explain the method's four key steps: contextualizing, operationalizing, optimizing, and implementing wellbeing supported by continuous measurement for feedback cycles. We then present a multiple case study where novice designers applied the method, revealing strengths and weaknesses related to efficacy and usability. Next, an expert evaluation study assessed the quality of the resulting concepts, rating them moderately high for feasibility, desirability, and plausibility of achieving intended wellbeing benefits. Together, these studies provide preliminary validation of the method's ability to improve AI design, while surfacing areas needing refinement like developing support for complex steps. Proposed adaptations such as examples and evaluation heuristics could address weaknesses. Further research should examine sustained application over multiple projects. This human-centered approach shows promise for realizing the vision of 'AI for Wellbeing' that does not just avoid harm, but actively benefits humanity.arXiv.org730The Positive AI design method provides a human-centered process to translate wellbeing aspirations into concrete practices supported by continuous measurement for feedback cycles and shows promise for realizing the vision of 'AI for Wellbeing' that does not just avoid harm, but actively benefits humanity.ArXiv['W. Maden', 'Derek Lomas', 'P. Hekkert']2024-02-02T00:00:00https://www.semanticscholar.org/paper/fc1766e44dfe616fec6b5178c727f6dddaa0ed21 11478a8eead4e6b4ed468836adf2953398cfd3b2In this review paper, the dynamic landscape of utilizing artificial intelligence in stock trading is studied. The paper comprehensively examines the transformative impact of AI on various aspects of trading, including the evolution of algorithms, the rise of machine learning-driven strategies, and the integration of generative AI in optimizing front-office productivity. The researcher explores the empirical evidence and insights from existing literature to offer a nuanced understanding of the benefits and challenges associated with the implementation of AI in stock tradingInternational Journal For Multidisciplinary Research00This paper comprehensively examines the transformative impact of AI on various aspects of trading, including the evolution of algorithms, the rise of machine learning-driven strategies, and the integration of generative AI in optimizing front-office productivity.International Journal For Multidisciplinary Research['Muskan Satnaliwala']2024-02-02T00:00:00https://www.semanticscholar.org/paper/11478a8eead4e6b4ed468836adf2953398cfd3b2 c511c285f56c8b916f5224dd5eae5d8ea7a5d34a Non-Orthogonal Multiple Access (NOMA) is an innovation that has great potential in wireless communication. It permits multiple users to efficiently allot a frequency band by adjusting their power allocations. Nevertheless, attaining fair power allocation in NOMA structures presents complex challenges that require specific models, extensive training data, and addressing issues of generalization. This review aims to explore the applications of Artificial Intelligence (AI) and Deep Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA systems. The focus is on developing strong AI-DL models and creative optimization methods specifically designed for dynamic environments to improve transparency and interpretability. This study explores a wide range of techniques, including Reinforcement Learning, Convolutional Neural Networks (CNN) for power allocation, Generative Adversarial Networks, Deep Reinforcement Learning, and Transfer Learning. The goal is to enhance various aspects, such as power allocation, user coupling, scheduling strategies, interference cancellation, user mobility, security, and deeplearning-based NOMA. Despite the difficulties, impartial power allocation algorithms based on AI and DL show promise in improving user performance and promoting fair power distribution in NOMA systems. This study emphasizes the significance of continuous research efforts to overcome current obstacles, enhance efficiency, and strengthen the dependability of wireless communication systems. This highlights the significance of NOMA as an advanced innovation for upcoming wireless generations that go beyond 5G. Future areas of study involve investigating federated learning and novel techniques for gathering data and utilizing interpretable AI-DL models to address existing constraints. Overall, this review highlights the potential of AI and DL techniques in achieving fair power distribution in NOMA systems. However, further investigation is crucial to addressing obstacles and fully exploring the capabilities of NOMA technology International Journal of Sensors Wireless Communications and Control00This review aims to explore the applications of Artificial Intelligence (AI) and Deep Learning (DL) methods to tackle the challenges associated with fair power allocation in NOMA systems and highlights the potential of AI and DL techniques in achieving fair power distribution in NOMA systems.International Journal of Sensors, Wireless Communications and Control['Seda Kirtay', 'Kazim Yildiz', 'Veysel Gokhan Bocekci']2024-02-02T00:00:00https://www.semanticscholar.org/paper/c511c285f56c8b916f5224dd5eae5d8ea7a5d34a dbfb1532e93183ac9581adb425e5b47e2a03657dMultiple myeloma is the second most frequent hematologic malignancy worldwide with high morbidity and mortality. Although it is considered an incurable disease, the enhanced understanding of this neoplasm has led to new treatments, which have improved patients’ life expectancy. Large amounts of data have been generated through different studies in the settings of clinical trials, prospective registries, and real-world cohorts, which have incorporated laboratory tests, flow cytometry, molecular markers, cytogenetics, diagnostic images, and therapy into routine clinical practice. In this review, we described how these data can be processed and analyzed using different models of artificial intelligence, aiming to improve accuracy and translate into clinical benefit, allow a substantial improvement in early diagnosis and response evaluation, speed up analyses, reduce labor-intensive process prone to operator bias, and evaluate a greater number of parameters that provide more precise information. Furthermore, we identified how artificial intelligence has allowed the development of integrated models that predict response to therapy and the probability of achieving undetectable measurable residual disease, progression-free survival, and overall survival leading to better clinical decisions, with the potential to inform on personalized therapy, which could improve patients’ outcomes. Overall, artificial intelligence has the potential to revolutionize multiple myeloma care, being necessary to validate in prospective clinical cohorts and develop models to incorporate into routine daily clinical practice.Frontiers in Hematology430Overall, artificial intelligence has the potential to revolutionize multiple myeloma care, being necessary to validate in prospective clinical cohorts and develop models to incorporate into routine daily clinical practice.Frontiers in Hematology['Martha Romero', 'A. Mosquera Orgueira', 'Mateo Mejia Saldarriaga']2024-02-02T00:00:00https://www.semanticscholar.org/paper/dbfb1532e93183ac9581adb425e5b47e2a03657d cc124d15472440a66e0276ba93a75505039d9379Artificial intelligence not only changes the production methods of traditional industries but also provides an important opportunity to decouple industrial development from environmental degradation and promote green economic growth. In order to further explore the green value of AI, this paper constructs an indicator of industrial robot penetration at the regional level, based on the idea of Bartik’s instrumental variable, and measures green development efficiency using the improved Super-SBM model. Based on a comprehensive explanation of the influence mechanism, a spatial measurement model and mediating effect model are constructed to test the spatial spillover effect and transmission mechanism between AI and green development. This study shows that (1) there is a significant inverted U shape in the impact of AI on green development; (2) the heterogeneity analysis finds that the structural dividend of AI is more obvious in capital-intensive and technology-intensive areas, which can more fully release its empowering effect on green development; (3) AI can not only directly affect green development but also indirectly affect green development by promoting green technology innovation and optimizing industrial structures, etc.; (4) AI has a significant inverted U-shaped spatial spillover effect on green development, and the development of local AI has a radiation-driven effect on the green development performance of its spatially related areas. The research methodology of this paper can be used for future research, and the results could provide support for the formulation of regional AI applications and green development policies.Sustainability780An indicator of industrial robot penetration at the regional level is constructed, based on the idea of Bartik’s instrumental variable, and green development efficiency is measured using the improved Super-SBM model.Sustainability['Mingyue Chen', 'Shuting Wang', 'Xiaowen Wang']2024-02-02T00:00:00https://www.semanticscholar.org/paper/cc124d15472440a66e0276ba93a75505039d9379 2d8fe6a902b498ab309ea2604019c103f5959ba4The general objective of this research was to analyze the preferences towards Artificial Intelligence (AI) in university professors in Ecuador, particularly those who teach the subject of Microeconomics. The methodology used was quantitative, descriptive, field-typed, and non-experimental, based on documentary research. The population consisted of 25 teachers from three state universities in the city of Guayaquil, Ecuador. The data collection technique was the survey and the instrument was a questionnaire of closed questions, which was applied through the Google Forms platform. The results found indicate that a large majority know about the use of AI in higher education; they also consider that the benefits that AI brings to education are to improve teaching, a large percentage indicated not to use AIs to teach Microeconomics, a small number of teachers who use AI, do it for data analysis through Machine Learning, the fact of the little use of this technology among teachers is mainly due to the absence of university policies on the use of artificial intelligence. Among the conclusions, the formulation of educational policies oriented to the implementation of artificial intelligence in Ecuadorian universities, the adaptation of infrastructure, and the investment of resources for this purpose are considered essential, as well as the training of teaching staff to take full advantage of this technology that is already a reality in many national and international contexts.Sapienza: International Journal of Interdisciplinary Studies180The formulation of educational policies oriented to the implementation of artificial intelligence in Ecuadorian universities, the adaptation of infrastructure, and the investment of resources for this purpose are considered essential, as well as the training of teaching staff to take full advantage of this technology that is already a reality in many national and international contexts.Sapienza: International Journal of Interdisciplinary Studies['Viviana Vanessa Aparicio-Izurieta']2024-02-02T00:00:00https://www.semanticscholar.org/paper/2d8fe6a902b498ab309ea2604019c103f5959ba4 3f3f14d2fda4a3fc37a841474b156f13b2b8f4ddThe article highlights the integration of technology into journalism, and new emerging media trends. It studies the current situation regarding the application of artificial intelligence technologies to journalism, the problems encountered in this field. Examples of artificial intelligence journalism widely used in international media are indicated. Study provides information about companies offering AI services to global news organizations. It also emphasizes the need for journalism education to be constantly modern and keep up with the development of technology. The article explains the concept of inclusive journalism, the main task of inclusive journalism, the opportunities it can create, and the application of artificial intelligence to inclusive journalism. Academic research on inclusivity is reviewed. Moreover, the activities of inclusive journalism in Azerbaijan are studied and the current situation is evaluated. The study indicates that the application of information communication technology (ICT) to journalism will serve to provide information to people with physical disabilities. Also, the article explores the prospects of applying ICT to journalism and the problems it can pose. It notes that the application of artificial intelligence technologies to journalism can provide a new form of information collection, preparation and dissemination.Problems of Information Society00The study indicates that the application of information communication technology (ICT) to journalism will serve to provide information to people with physical disabilities and the prospects of applying ICT to journalism and the problems it can pose.Problems of Information Society['Sunbul Zalova']2024-02-02T00:00:00https://www.semanticscholar.org/paper/3f3f14d2fda4a3fc37a841474b156f13b2b8f4dd 6f348d6574060d347e7d9d5c7567cdc3e8f31cbaThe word meta is derived from latin and translates to mean “beyond.” Thus, the concept of the Metaverse aims to travel beyond mundanity, to go beyond the boundaries of the present and to impossibly reach beyond the constraints of reality itself. The fundamental concept of the Metaverse was developed and proposed by Neil Stephenson in the year 1992 and is as such a very new possibility. The computational resources for the metaverse now exist in the form of the internet, the cloud and AI. This paper aims to discuss the potential contained in the metaverse and more specifically for AI in the metaverse. As society progresses, so does its Artificial Intelligence and this can have far-reaching impact on the metaverse as a whole. The paper also discusses new applications for the metaverse as well in the field of AI and the world at large.International Journal For Multidisciplinary Research00The potential contained in the metaverse and more specifically for AI in the metaverse is discussed and new applications for the metaverse as well in the field of AI and the world at large are discussed.International Journal For Multidisciplinary Research['Bhavishya Ku', 'Ms. Akshatha K.']2024-02-02T00:00:00https://www.semanticscholar.org/paper/6f348d6574060d347e7d9d5c7567cdc3e8f31cba 3b24da7cde824adb82d90ed91b852fcb7dd3e1bbDespite the advent of direct‐acting antiviral agents (DAAs) as a definitive therapy for chronic hepatitis C virus (HCV) infection, the burden of the disease remains globally elevated. The emerging big data on different HCV paradigms fostered the introduction of artificial intelligence/machine learning (AI/ML) applications to help decrease that burden by providing more optimised strategies for early diagnosis and treatment prioritisation. The current review provides descriptive and analytical insight into the recently published AI/ML applications in five medical aspects of HCV infection. In addition, it highlights the opportunities these powerful tools offer in designing national health policies that prioritise HCV patients for the costly DAAs and developing broadly neutralising HCV antibodies. Finally, this paper highlights the challenges encountered in developing and applying these AI/ML models to clinical practice and suggests schemes to overcome some of them. The presented models were primarily evaluated using the Matthews correlation coefficient and the F1‐score to make a more reliable inference about their predictive power under imbalanced datasets. Many published AI/ML applications offered great utilities for predicting novel HCV treatments and prioritising patients for DAAs receipt, especially in settings of limited resources and high HCV burden. Some outperformed the classical diagnostic tools, such as third‐generation serological tests, alpha‐fetoprotein, and ultrasound, in detecting HCV infections and early HCV‐associated hepatocellular carcinoma, respectively. However, further statistical and clinical validation of AI/ML models is highly advocated before incorporating these applications into clinical practice.Reviews in Medical Virology570The presented models were primarily evaluated using the Matthews correlation coefficient and the F1‐score to make a more reliable inference about their predictive power under imbalanced datasets and suggests schemes to overcome some of them.Reviews in Medical Virology['Ahmed N. Farrag', 'Ahmed M. Kamel', 'Iman A. El‐Baraky']2024-02-02T00:00:00https://www.semanticscholar.org/paper/3b24da7cde824adb82d90ed91b852fcb7dd3e1bb 4742bbd5553f89fc8111a052373739bdb015ecf5Abstrak: Pendekatan microlearning memfasilitasi terbatasnya kemampuan kita dalam belajar dengan lebih baik. Berkembangnya kecerdasan buatan/artificial inttellingence (AI) Era digital perlu disikapi dengan bijak, terutama dalam pengembangan Pendidikan kedepannya. Sayangnya mitra pelatihan ini yakni MGMP Guru IPA yang berjumlah 41 orang guru masih belum pernah memanfaatkannggunakan AI dan juga microlearning dalam pembelajaran. Pelatihan dan pendampingan ini bertujuan untuk memberikan pengetahuan dan keterampilan penggunaan AI dalam mendesain pembelajaran berorientasi microlearning. Metode dalam pelaksanaan pelatihan ini di bagi dalam tiga tahap yakni, persiapan, pelatihan dan pendampingan, serta evaluasi. Instrument yang digunakan dalam pemetaan hasil kegiatan ini adalah instrumen pretest posttest, angket respon dan lembar penilaian video microlearning. Hasil pelatihan menunjukkan bahwa terjadi peningkatan hasil pada aspek pengetahuan dan juga keterampilan. Pada Aspek pengetahuan terlihat dari peningkatan rerasa skor pretest dan posttest sebesar 32%, sedangkan aspek keterampilan didapatkan dari skor video microlearning yang juga terdapat peningkatan 58,7%. Berdasarkan hasil respon data menunjukkan respon positif terhadap pelaksanaan pelatihan. Simpulan dari hasil pelatihan ini adalah,terdapat peningkatan rerata pengetahuan skor N-gain 0,6 dengan kategori sedang dan keterampilan skor 0,72 dengan kategori tinggi dari peserta setelah diberikan pelatihan pemanfaatan artificial intelligence untuk pendidik ipa dalam memfasilitasi microlearning.Abstract: Our limited capacity to learn better is facilitated by the microlearning approach. It is important to respond to the rise of artificial intelligence in a thoughtful manner, particularly as it relates to the future of education. Unfortunately, this training partner, namely MGMP Science Teachers, totaling 41 teachers, still does not utilize AI and microlearning in learning. Giving partners knowledge and abilities in applying AI to develop microlearning-oriented learning is the goal of this training and mentorship. Implementing this training is divided into three stages, namely, preparation, training and mentoring, and evaluation. He instruments used in mapping the results of this activity were prepost-test instruments, response questionnaires and microlearning video assessment sheets. He knowledge component is demonstrated by a 32% rise in pre- and post-test scores, while the skills component is demonstrated by a 58.7% increase in the microlearning video score. The response data indicates that there was a favorable reaction to the training's execution. The conclusion from the results of this training is that there was an increase in the average knowledge score of N-gain of 0.6 in the medium category and skill score of 0.72 in the high category of participants after being given training on the use of artificial intelligence.JMM (Jurnal Masyarakat Mandiri)160JMM (Jurnal Masyarakat Mandiri)['S. Hidayati', 'Wahono Widodo', 'H. Subekti', 'Ernita Vika Aulia', 'Dyah Permata Sari']2024-02-02T00:00:00https://www.semanticscholar.org/paper/4742bbd5553f89fc8111a052373739bdb015ecf5 d7b394771eb2b04ba65181ba79d94acb845b6f76International Orthopaedics02International orthopaedics['A. Mavrogenis', 'Philippe Hernigou', 'Marius M Scarlat']2024-02-02T00:00:00https://www.semanticscholar.org/paper/d7b394771eb2b04ba65181ba79d94acb845b6f76 e4e202d98f426f757e83c86c1b60e50fdaeeb542Nurse Author &amp; Editor231Nurse Author &amp; Editor['Golnar Ghane', 'S. Ghiyasvandian', 'Amir Mohammad Chekeni', 'Raoofeh Karimi']2024-02-02T00:00:00https://www.semanticscholar.org/paper/e4e202d98f426f757e83c86c1b60e50fdaeeb542 68b5d5c86e07bc946441a7b97cb19c614e68c7a0The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.Journal La Multiapp00This paper summarizes the extensive study of AI and the silent epidemic of antibiotic resistance, and hopes to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.Journal La Multiapp['Mohammed F. Al Marjani Marjani', 'Rana K. Mohammed', 'Entithaar Mhwes Zghair', 'Yasmin Makki Mohialden']2024-02-02T00:00:00https://www.semanticscholar.org/paper/68b5d5c86e07bc946441a7b97cb19c614e68c7a0 d6a6c7b2fe4e1c2b7ef354fe096cf6883804c265Libraries use various information management systems for organizing, packaging, and repackaging services to mention a few. The reliability of these services is highly affected by several factors including; an increase in the number of users, limited resources, decentralized learning, and the emergence of digital resources. Bearing the benefits that AI technologies offer to libraries including cost-effective operations, improved services, and timely analyses, research to investigate the awareness and prospects have been conducted in various countries. Several studies investigated the level of adopting AI technologies for effective services in academic libraries for a particular study area. The general observation from such studies indicates that the level of AI adoption and awareness varies depending on a particular country under investigation. Therefore, due to the diversification of awareness and adoption levels from various areas, it is vital to investigate it in the Tanzanian context. This study aims to investigate the level of awareness and prospects of AI adoption in Tanzanian academic libraries using a qualitative approach in which 36 librarians from 7 giant and widespread higher learning institutions (HLI) are interviewed. The findings reported in this study indicate that the level of awareness is high (68.3%) while that of adoption is low (23%). Furthermore, the findings imply that the demand and readiness for the adoption of AI among librarians is very high. Therefore, this work provides new information to librarians, HLIs’ management, and policymakers regarding the trend of artificial intelligence adoption in academic libraries. The findings reported in this paper can be used by librarians and management to align their plans toward AI adoption for effective and better service delivery.University of Dar es Salaam Library Journal220This study aims to investigate the level of awareness and prospects of AI adoption in Tanzanian academic libraries using a qualitative approach in which 36 librarians from 7 giant and widespread higher learning institutions (HLI) are interviewed and indicates that the level of awareness is high while that of adoption is low.University of Dar es Salaam Library Journal['Hussein A. Bakiri', 'Hadija Mbembati', 'Rose Tinabo']2024-02-02T00:00:00https://www.semanticscholar.org/paper/d6a6c7b2fe4e1c2b7ef354fe096cf6883804c265 ff00e55ecbf2bbe4c14bf00e19fa1e8bc001290cInjury Prevention400Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention['D. A. Quistberg']2024-02-02T00:00:00https://www.semanticscholar.org/paper/ff00e55ecbf2bbe4c14bf00e19fa1e8bc001290c f3ee37ad59523dc0485b0c8eb34f287e969b7aa8In the dynamic and complex environment of industrial control rooms, operators are often inundated with numerous tasks and alerts, leading to a state known as task overload. This condition can result in decision fatigue and increased reliance on cognitive biases, which may compromise the decision-making process. To mitigate these risks, the implementation of decision support systems (DSSs) is essential. These systems are designed to aid operators in making swift, well-informed decisions, especially when their judgment may be faltering. Our research presents an artificial intelligence (AI)-based framework utilizing dynamic influence diagrams and reinforcement learning to develop a powerful decision support system. The foundation of this AI framework is the creation of a robust, interpretable, and effective DSS that aids control room operators during critical process disturbances. By incorporating expert knowledge, the dynamic influence diagram provides a comprehensive model that captures the uncertainties inherent in complex industrial processes. It excels in anomaly detection and recommending optimal actions. Furthermore, this model is improved through a strategic collaboration with reinforcement learning, which refines the recommendations to be more context-specific and accurate. The primary goal of this AI framework is to equip operators with a live, reliable DSS that significantly enhances their response during process upsets. This paper describes the development of the AI framework and its implementation in a simulated control room environment. Our results show that the DSS can improve operator performance and reduce cognitive workload. However, it also uncovers a trade-off with situation awareness, which may decrease as operators become overly dependent on the system’s guidance. Our study highlights the necessity of balancing the advantages of decision support with the need to maintain operator engagement and understanding during process operations.Processes352The results show that the DSS can improve operator performance and reduce cognitive workload, however, it also uncovers a trade-off with situation awareness, which may decrease as operators become overly dependent on the system’s guidance.Processes['Joseph Mietkiewicz', 'Ammar N. Abbas', 'Chidera W. Amazu', 'Gabriele Baldissone', 'Anders L. Madsen', 'M. Demichela', 'M. Leva']2024-02-02T00:00:00https://www.semanticscholar.org/paper/f3ee37ad59523dc0485b0c8eb34f287e969b7aa8 9653154e0ba043757a19a51d626312abec75b0b3This article studies the sampled-data cooperative output regulation problem for linear multiagent systems. First, a novel adaptive distributed continuous-discrete observer is established to recover the leader's state, which, on one hand, only relies on the sampling output of the leader, and on the other hand, does not need to store the sampled message from neighbors. Second, by solely making use of the digital states of both the agent and the distributed observer, a certainty equivalence control law is synthesized featuring a time-varying feedforward gain. It is rigorously proven that, with this time-varying feedforward gain, the proposed control approach can achieve exponential convergence of the tracking errors given arbitrary time-varying leader's signal, and the upper bound for the sampling intervals is explicitly given. Third, for the class of chain-integrator multiagent systems, the proposed control approach does not need any restriction on the upper bound of the sampling intervals, and thus would be more practical in certain application scenarios from the perspective of energy saving. The performance of the proposed control approach is validated by the simulation results of inverter-based distributed generation systems.IEEE Transactions on Automatic Control300This article studies the sampled-data cooperative output regulation problem for linear multiagent systems, and a novel adaptive distributed continuous-discrete observer is established to recover the leader's state, which only relies on the sampling output of the leader, and does not need to store the sampled message from neighbors.IEEE Transactions on Automatic Control['Ying Zhang', 'Youfeng Su', 'He Cai']2024-02-01T00:00:00https://www.semanticscholar.org/paper/9653154e0ba043757a19a51d626312abec75b0b3 e094aaf719026f43efc33be564f7b11d8866f5abComputer Law Review International00Computer Law Review International['Rahul Matthan']2024-02-01T00:00:00https://www.semanticscholar.org/paper/e094aaf719026f43efc33be564f7b11d8866f5ab e46eefefd8c9a2282a7a303589a0452499b60e90 The collection of data from websites at great scale - so-called data scraping - is the foundation for ChatGPT and most other Generative AI (GenAI) tools. Much of the previous discussion on the regulation of GenAI has focused on the US and EU and not so much on more technical aspects like data scraping. In response, this article focuses on the regulation of data scraping to build and deploy GenAI in China, and reviews applicable regulation and case law. We find that the sectoral approach to AI regulation in China provides important insights into balancing technological progress and societal values, diverging from the laissez-faire attitude in the US and the horizontal approach with the AI Act in the EU.Computer Law Review International00It is found that the sectoral approach to AI regulation in China provides important insights into balancing technological progress and societal values, diverging from the laissez-faire attitude in the US and the horizontal approach with the AI Act in the EU.Computer Law Review International['Qian Li', 'Konrad Kollnig']2024-02-01T00:00:00https://www.semanticscholar.org/paper/e46eefefd8c9a2282a7a303589a0452499b60e90 82a71b8334e1db6c79de0f2f04c393aa85fa873fAI in Precision Oncology00AI in Precision Oncology['Bethany Hills Grois']2024-02-01T00:00:00https://www.semanticscholar.org/paper/82a71b8334e1db6c79de0f2f04c393aa85fa873f 4d74d66a7bf9ed1789c6046dba486c0bf967a012Journal of Bioethical Inquiry350The issue of gaining informed patient consent to AI-enhanced care from the vantage point of the United Kingdom's National Health Service setting is considered and the best way to protect patients from potential harms associated with the introduction of AI is not via an overly burdensome patient consent process but via evaluation and regulation of AI technologies.Journal of bioethical inquiry['M. Pruski']2024-02-01T00:00:00https://www.semanticscholar.org/paper/4d74d66a7bf9ed1789c6046dba486c0bf967a012 f20ca6546d45278250e9d6fe8d03a079bb65615eThis comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, and patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, and revolutionizing patient care. However, this evolution is accompanied by ethical, legal, and operational considerations that necessitate careful navigation. The review underscores key findings, emphasizing the implications for the future of hospital management. It calls for a proactive approach, urging stakeholders to invest in education, prioritize ethical guidelines, foster collaboration, advocate for thoughtful regulation, and embrace a culture of innovation. The healthcare industry can successfully navigate this transformative era through collective action, ensuring that AI contributes to more effective, accessible, and patient-centered healthcare delivery.Cureus450This comprehensive review explores the transformative impact of artificial intelligence on hospital management, delving into its applications, challenges, and future trends and calls for a proactive approach.Cureus['Shefali V Bhagat', 'Deepika Kanyal']2024-02-01T00:00:00https://www.semanticscholar.org/paper/f20ca6546d45278250e9d6fe8d03a079bb65615e bda7f4d9f9529165bfc75e99c801813c7b1ca053This study proposes the use of Artificial Intelligence (AI) to automatize part of the legislative impact assessment process. In particular, the focus of this study is the automatic identification of administrative burdens from legislative documents. The goal of impact assessment for administrative burdens is to apply an evidence-based approach toward compliance costs generated by regulation. Employing advanced Natural Language Processing (NLP) techniques based on a transformer architecture, a system was specifically developed and tested using Portuguese legislation. The experimental phase involved the system's ability to accurately and comprehensively identify administrative burdens. Experimental results demonstrated the system's effectiveness, showing its suitability for supporting the legislative impact assessment process by automating a time-consuming task. To the best of our knowledge, this is the first attempt concerning the use of AI for automatizing the identification of administrative burdens. The proposed system may provide governments and policymakers with a tool to speed up the legislative impact assessment process, thereby streamlining decision-making processes. Moreover, the use of AI can make the legislative impact assessment process less subjective, thus increasing its transparency and making citizens more confident about the impartiality of the process that leads to new legislation. Doi: 10.28991/ESJ-2024-08-01-019 Full Text: PDFEmerging Science Journal570This first attempt concerning the use of AI for automatizing the identification of administrative burdens may provide governments and policymakers with a tool to speed up the legislative impact assessment process, thereby streamlining decision-making processes.Emerging Science Journal['Victor Costa', 'Pedro Coelho', 'Mauro Castelli']2024-02-01T00:00:00https://www.semanticscholar.org/paper/bda7f4d9f9529165bfc75e99c801813c7b1ca053 afef353e368a72c986a20b067f11d801d7781485This article concentrates on the prescribed-time cooperative output regulation problem (CORP) of linear heterogeneous multiagent systems (HMASs) under directed topology. First, a novel distributed observer with prescribed-time convergence is designed to estimate the state of the exosystem. Then, two distributed prescribed-time control protocols, one based on the agent's state, the other based on the agent's output, are proposed using the observed exosystem's state. It is shown that the CORP of linear HMASs with any different orders is solved in a prescribed (any user-chosen as needed) time. Unlike the existing finite-time control strategies, the settling time is guaranteed by the designed first-order smooth control protocols, independent of the system's initial values and the control parameters. Lastly, a numerical simulation is presented to verify our theoretical results.IEEE Transactions on Industrial Informatics281It is shown that the CORP of linear HMASs with any different orders is solved in a prescribed time, and the settling time is guaranteed by the designed first-order smooth control protocols, independent of the system's initial values and the control parameters.IEEE Transactions on Industrial Informatics['Chongyang Chen', 'Yiyan Han', 'Song Zhu', 'Zhigang Zeng']2024-02-01T00:00:00https://www.semanticscholar.org/paper/afef353e368a72c986a20b067f11d801d7781485 326cc7c2904f367911b8776d175dcf36c8fe51a9China’s economy experienced great growth, which also induces large carbon emission. Facing the target of “Carbon peak, Carbon neutrality” in China, it is vital to improve the carbon emission efficiency. Employing the spatial Difference-in-Differences model, this paper investigates the impact of environmental regulation on carbon emission efficiency with a quasi-natural experiment of Pollution Levy Standards Adjustment in China. Our empirical results show that the environmental regulation can significantly improve the carbon emission efficiency. moreover, two impact channels are explored: green innovation and industrial upgrading. More specifically, the green innovation increases with environmental regulation, and the increased green innovation improves carbon emission efficiency. The industry upgrading increases with environmental regulation, and the increased industry upgrading improves carbon emission efficiency. Finally, in terms of city heterogeneity, we find that the impact of environmental regulation will be more pronounced for larger cities and resource-based cities. Our findings suggest that the environmental regulation must be enhanced for both smaller cities and non-resource-based cities. Moreover, to promote the green innovation of firms, since green innovation is risky and costly, governments should provide more subsidies or grants on corporate green technologies, thus firms will be motivated to invest in green technologies to reduce carbon emission.PLoS ONE741PLOS ONE['Yi He', 'Xiang Zhang', 'Qinghua Xie']2024-02-01T00:00:00https://www.semanticscholar.org/paper/326cc7c2904f367911b8776d175dcf36c8fe51a9 0edeb8e0e7ffc794a2103cc3e7e985f43d86edd3This article will discuss the possibility of endowing artificial intelligence with legal capacity in general and the legal regulation of legal relations related to the creation of IP objects with artificial intelligence in the territory of the Republic of Uzbekistan.The American Journal of Political Science Law and Criminology80The American Journal of Political Science Law and Criminology['I. Umarova']2024-02-01T00:00:00https://www.semanticscholar.org/paper/0edeb8e0e7ffc794a2103cc3e7e985f43d86edd3 78feadf57b529b580df96379d8c6885141d8bf51Scope of practice regulation in medicine is crucial for ensuring patient safety, access to care and professional autonomy. This paper explores the impact of scope of practice regulation on healthcare delivery, professional responsibilities and patient outcomes. It discusses the variability in standards for safe practice, the challenges in defining boundaries between medical specialties and the recent controversies in cosmetic surgery practice. The paper also examines the potential benefits and drawbacks of rigorous scope of practice regulations, including their impact on clinical innovation, flexibility and access to care. Furthermore, it delves into the implications of defensive medicine and the consequences of restrictive regulations on patient care. The author proposes implementing a proactive, national, artificial intelligence-powered, real-time outcome monitoring system to address these challenges. This system aims to cover every patient undergoing a surgical procedure and could be gradually extended to non-surgical conditions, benefiting all key stakeholders in the health system. The paper emphasises the need for a balanced approach to scope of practice regulation to avoid stifling clinical innovation and professional autonomy, while ensuring patient safety and professional accountability.Australian Health Review110The author proposes implementing a proactive, national, artificial intelligence-powered, real-time outcome monitoring system to address the variability in standards for safe practice, the challenges in defining boundaries between medical specialties and the recent controversies in cosmetic surgery practice.Australian health review : a publication of the Australian Hospital Association['Christian A Gericke']2024-02-01T00:00:00https://www.semanticscholar.org/paper/78feadf57b529b580df96379d8c6885141d8bf51 fdfca20988879ab5c4f2935663921778d38d0d0cTampered images can easily be used for illegal activities, such as spreading rumors, economic fraud, fabricating false news, and illegally obtaining experience benefits, etc. With the improvement and development of artificial intelligence (AI), image manipulation technology has also been further improved, more and more retouching software in daily life adopts AI technology. So far, there is no AI-based tampered dataset. To address this challenge, we propose a dataset-IPM15K. It utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images. This dataset also could serve as a catalyst for progressing many vision tasks, e.g., localization, segmentation, and alpha-matting, etc. Additionally, we propose an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images. Our model consists of four modules: the detail extraction module (DEM), which utilizes different sizes of convolutions and perceptual fields to extract more valuable information of tampered locations; the multi-branch attention fusion module (MAFM), which fully exploits contextual information of different levels to capture subtle traces of tampering; the feature decoder component (FDC), which combines fused features to identify tampered regions; and the detail enhancement block (DEB), which continues to supplement the detailed information of the detected regions. Extensive experiments on three public datasets and the proposed dataset show that MFI-Net outperforms various state-of-the-art (SOTA) manipulation detection baselines.IEEE transactions on circuits and systems for video technology (Print)482This work proposes a dataset-IPM15K, which utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images and proposes an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images.IEEE Transactions on Circuits and Systems for Video Technology['Ruyong Ren', 'Qixian Hao', 'Shaozhang Niu', 'Keyang Xiong', 'Jiwei Zhang', 'Maosen Wang']2024-02-01T00:00:00https://www.semanticscholar.org/paper/fdfca20988879ab5c4f2935663921778d38d0d0c 54ccd590370ca0b4eda335c7d57545e1d0b36529Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.Journal of Cardiothoracic and Vascular Anesthesia02The authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape.Journal of cardiothoracic and vascular anesthesia['Michael Mathis', 'Kirsten R. Steffner', 'Harikesh Subramanian', 'George P. Gill', 'Natalia I. Girardi', 'Sagar Bansal', 'Karsten Bartels', 'Ashish K. Khanna', 'Jiapeng Huang']2024-02-01T00:00:00https://www.semanticscholar.org/paper/54ccd590370ca0b4eda335c7d57545e1d0b36529 410a5d37f101dc2d842a0240513b43c9b6253723Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.Healthcare1002This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology, which has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients.Healthcare['Dhir Gala', 'Haditya Behl', 'Mili Shah', 'A. Makaryus']2024-02-01T00:00:00https://www.semanticscholar.org/paper/410a5d37f101dc2d842a0240513b43c9b6253723 b4fdbda70929850042a7e41ca09eb4a0613c67d1Simple Summary Lung cancer therapeutics have dramatically improved in recent years. Indeed, precision oncology could be exemplified by non-small cell lung cancer (NSCLC), with molecular profiling and programmed death ligand 1 (PD-L1) immunohistochemical expression representing an integral part of its tailored treatment. The present narrative review aims to highlight the promising role of artificial intelligence (AI) technologies in the optimal, patient-centered management of NSCLC, by distilling as well as interpreting big data. Abstract Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.Cancers952The present narrative review aims to highlight the promising role of artificial intelligence (AI) technologies in the optimal, patient-centered management of NSCLC, by distilling as well as interpreting big data.Cancers['O. Fiste', 'Ioannis Gkiozos', 'A. Charpidou', 'N. Syrigos']2024-02-01T00:00:00https://www.semanticscholar.org/paper/b4fdbda70929850042a7e41ca09eb4a0613c67d1 5a7d3c909b3468916dc1b63149d6c26e3c85d204Part I reviews persistent challenges obstructing progress in understanding complex fatigue’s biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.Brain Science723Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.Brain Sciences['T. Rudroff']2024-02-01T00:00:00https://www.semanticscholar.org/paper/5a7d3c909b3468916dc1b63149d6c26e3c85d204 763c5c150f6216865d909da5aeee99ba00f656aaThe technology innovation, especially in the case of artificial intelligence, has significantly transformed the work processes and how they are organised and performed. Even if the adoption of advanced technologies usually leads to a higher work performance, there are risks of negative disruptions in the working systems, such as non-ethical use and social negative effects. The paper presents the results of an ethnographic research conducted by the authors, with the objective to identify the impact of the artificial intelligence adoption in the workplace on the professional knowledge and skills requirements and on the upskilling and reskilling strategies. Three different domains were considered: information technology, educationAmfiteatru Economic452The paper presents the results of an ethnographic research conducted by the authors, with the objective to identify the impact of the artificial intelligence adoption in the workplace on the professional knowledge and skills requirements and on the upskilling and reskilling strategies.Amfiteatru Economic['C. Bodea', 'Mario Paparic', 'Radu Mogos', 'M. Dascalu']2024-02-01T00:00:00https://www.semanticscholar.org/paper/763c5c150f6216865d909da5aeee99ba00f656aa 9e0d5c0877b7e762f966d8833a06619fcfe35c1cIndustrial cyber–physical systems (ICPSs) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized solution. In recent times, artificial intelligence (AI) has had a significant impact on wireless sensor networks (WSNs) designed to operate as ICPS components. In this research work, we present AGRIC: AI-based green routing for ICPS. While following the cluster-based routing, the election of cluster head (CH) is executed using our proposed AI-inspired extended spotted hyena Lévy flight optimization (ESHLFO) algorithm. Furthermore, to address the energy hole problem, four energy-unlimited data collection nodes are used around the periphery of the network. The results of the experiment demonstrate the fact AGRIC delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.IEEE Internet of Things Journal311This research work presents AGRIC: AI-based green routing for ICPS, a proposed AI-inspired extended spotted hyena Lévy flight optimization algorithm that delivers network longevity and supreme performance in the context of stability time, throughput, and energy left over in the network as important performance indicators.IEEE Internet of Things Journal['Sandeep Verma', 'Satnam Kaur', 'S. Garg', 'Ajay K Sharma', 'Mubarak Alrashoud']2024-02-01T00:00:00https://www.semanticscholar.org/paper/9e0d5c0877b7e762f966d8833a06619fcfe35c1c a3d97ab9896b78ce8fdbe46c5ffb60676cc296efIntegrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.International Journal of Electrical and Computer Engineering (IJECE)501This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture, and identifies knowledge gaps that point to future research opportunities.International Journal of Electrical and Computer Engineering (IJECE)['Hicham Slimani', 'J. El Mhamdi', 'A. Jilbab']2024-02-01T00:00:00https://www.semanticscholar.org/paper/a3d97ab9896b78ce8fdbe46c5ffb60676cc296ef 40a62ac09535a22b01950fcd909d7b9bcc93d772Explainable artificial intelligence (XAI) is a burgeoning concept. It is gaining prominence as an approach to better understand how artificial intelligence solutions' outputs can improve decision making. Evaluation frameworks to enable organizations to understand XAIs what, why, how, and when are yet to be developed. Thus, we aim to fill this void by developing a conceptual content, context, process, and outcome (CCPO) evaluation framework to justify XAIs adoption and effective management using construction organizations as a backdrop for the article's setting. After introducing and describing the proposed novel CCPO framework for operationalizing XAI, we discuss its implications for future research. The contributions of our article are twofold: First, it highlights the need for organizations to embrace and enact XAI so that decision makers and stakeholders can better understand why and how a specific prediction materializes; and second, it provides a frame of reference for organizations to realize the business value and benefits of XAI.IEEE Engineering Management Review901The need for organizations to embrace and enact XAI is highlighted so that decision makers and stakeholders can better understand why and how a specific prediction materializes and a frame of reference for organizations to realize the business value and benefits of XAI is provided.IEEE Engineering Management Review['Peter E. D. Love', 'J. Matthews', 'Weili Fang', 'Stuart Porter', 'Hanbin Luo', 'L. Ding']2024-02-01T00:00:00https://www.semanticscholar.org/paper/40a62ac09535a22b01950fcd909d7b9bcc93d772 e71dc483ad6336230b4f5021a566411c97833a1b: Hambatan utama yang saat ini dihadapi oleh mitra adalah kendala dalam mengadopsi dan mengintegrasikan teknologi kecerdasan buatan (AI) dalam konteks pembelajaran matematika. Pengabdian ini bertujuan untuk meningkatkan pemahaman, pengetahuan, dan keterampilan calon guru matematika, khususnya dalam pembelajaran berbasis artificial intelligence melalui penggunaan media canva. Metode yang diterapkan dalam pengabdian ini adalah pendampingan dan sharing session mengenai pembelajaran berbasis artificial intelligence melalui media canva. Tahapan yang dilalui meliputi: (1) persiapan; (2) pengenalan; (3) pendampingan; (4) evaluasi. Instrumen evaluasi terdiri dari 13 soal pemantik mengenai materi quantification untuk mengukur pemahaman mahasiswa dalam menerapkan pembelajaran berbasis artificial intelligence melalui media canva. Partisipasi dalam kegiatan ini melibatkan 15 mahasiswa dan 4 guru pendamping. Hasil evaluasi menunjukkan peningkatan sebesar 22% terhadap pemahaman, kemampuan, dan keterampilan mahasiswa dalam menguasai materi quantification setelah menerapkan pembelajaran berbasis kecerdasan buatan melalui media canva.Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif171Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif['N. Azizah', 'Yusuf Amhar', 'T. Suci', 'Sikky El Walida', 'Magister', 'Pendidikan Matematika', 'Universitas Islam Malang', 'Yayasan Pendidikan Muslim', 'Cendekia Batu']2024-02-01T00:00:00https://www.semanticscholar.org/paper/e71dc483ad6336230b4f5021a566411c97833a1b 901bd0de96762322eb10731b504fb3bb3cca14a8Management Review Quarterly771This systematic literature review, based on the PRISMA protocol, consolidates the state of the art of AI with an explicit focus on SMEs and highlights the perceived challenges regarding implementation in this company size, finding a total of 27 different challenges perceived by SMEs in the adoption of AI.Management Review Quarterly['Leon Oldemeyer', 'Andreas Jede', 'Frank Teuteberg']2024-02-01T00:00:00https://www.semanticscholar.org/paper/901bd0de96762322eb10731b504fb3bb3cca14a8 152927df3336e12283887c5b775aa14ec9fcd206stage of future primary and secondary teachers in Romania. Through quantitative exploratory research, carried out on a sample of 270 students from the Faculty of Education, Social Sciences and Psychology, the subjects' interaction with Artificial Intelligence and the intention to integrate Artificial Intelligence in education were investigated, using binary logistic regression. The analysis shows that, among the six variables of the model, “confidence in one's ability to use Artificial Intelligence” and “perception of a greater number of advantages” have a positive and significant impact on the willingness to use Artificial Intelligence in the educational process, more than 'previous use', 'level of knowledge' or 'student requirements'. These results are of particular importance for the revision of teacher education programs and the development of educational policies that increase future teachers' confidence in the ability to use Artificial Intelligence, eliminating fears or misconceptions about Artificial Intelligence in education. (ii) Artificial Intelligence and the modelling of teachers’ competencies explores educators' perspective on their own role in shaping skills and presents educators' perceived challenges and key measures in the context of expanding Artificial Intelligence. Thus, teachers' positive attitudes toward Artificial Intelligence significantly influence cognitive, fundamental, and educational management competencies. The research highlights key challenges to integrate Artificial Intelligence into education, including the imperative of professional development for educators and ensuring equitable access to educational resources and technology. The study advocates for initiatives to bridge the digital divide and integrate Artificial Intelligence education into school curricula. (iii) Quantitative evaluation of willingness to use Artificial Intelligence within business and economic academic environment analyzes the state of information, use and availability of use of Artificial Intelligence in the economic and business university environment, according to Romanian teaching staff opinions. The research aims to identify the advantages, disadvantages and how Artificial Intelligence is used on the teachers’ personal initiative in research, teaching, and evaluation activities. The results of the study identify the aspects that can optimize the processes of education - research, teaching, evaluation and learning to meet the increased dynamics of the use of Artificial Intelligence in the economic academic environment in Romania. Also, the advantages associated with the use of Artificial Intelligence systems and the solutions proposed to maximize the benefits brought by Artificial Intelligence in research, teaching, evaluation activities in the opinion of teachers are highlighted.Amfiteatru Economic01The research aims to identify the advantages, disadvantages and how Artificial Intelligence is used on the teachers' personal initiative in research, teaching, and evaluation activities, and identifies the aspects that can optimize the processes of education to meet the increased dynamics of the use of Artificial Intelligence in the economic academic environment in Romania.Amfiteatru Economic['R. Zaharia']2024-02-01T00:00:00https://www.semanticscholar.org/paper/152927df3336e12283887c5b775aa14ec9fcd206 e04c9d858f15c40ccda11ac6f08cb4086567eed4Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.Diagnostics1141A systematic review of the use of a workflow that combines DP and artificial intelligence applied to histopathology in the field of hepatology and promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.Diagnostics['F. Grignaffini', 'Francesco Barbuto', 'Maurizio Troiano', 'L. Piazzo', 'P. Simeoni', 'F. Mangini', 'C. De Stefanis', 'Andrea Onetti Muda', 'Fabrizio Frezza', 'Anna Alisi']2024-02-01T00:00:00https://www.semanticscholar.org/paper/e04c9d858f15c40ccda11ac6f08cb4086567eed4 2b9e1337af552b31d63f51fea4669f8093949cc7Indian Journal of Gastroenterology661AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions by helping to predict histologic remission and long-term outcomes.Indian Journal of Gastroenterology['P. Pal', 'Kanapuram Pooja', 'Z. Nabi', 'Rajesh Gupta', 'M. Tandan', 'G. Venkat Rao', 'Nageshwar Reddy']2024-02-01T00:00:00https://www.semanticscholar.org/paper/2b9e1337af552b31d63f51fea4669f8093949cc7 745ba70252121be8fb9c7aae686ffd700ff6b0ddThis comprehensive review explores the transformative role of artificial intelligence (AI) in the realm of gastrointestinal cancer. Gastrointestinal cancers present unique challenges, necessitating precise diagnostic tools and personalized treatment strategies. Leveraging AI, particularly machine learning and deep learning algorithms, has demonstrated remarkable potential in revolutionizing early detection, treatment planning, prognosis, and drug development. The analysis of current research and technological advancements underscores the capacity of AI to unravel intricate patterns within extensive datasets, providing actionable insights that enhance diagnostic accuracy and treatment efficacy. The transformative impact of AI on the landscape of gastrointestinal cancer is emphasized, signaling a paradigm shift towards more precise and targeted cancer care. The conclusion emphasizes the need for sustained research efforts and collaborative initiatives among AI researchers, healthcare professionals, and policymakers. By fostering interdisciplinary collaboration, we can navigate the evolving field of gastrointestinal cancer care, embracing the potential of AI to improve patient outcomes and contribute to a more effective and personalized approach to cancer management.Cureus471The transformative impact of AI on the landscape of gastrointestinal cancer is emphasized, signaling a paradigm shift towards more precise and targeted cancer care and the need for sustained research efforts and collaborative initiatives among AI researchers, healthcare professionals, and policymakers.Cureus['Sreetama Mukherjee', 'Sunita Vagha', 'Pravin Gadkari']2024-02-01T00:00:00https://www.semanticscholar.org/paper/745ba70252121be8fb9c7aae686ffd700ff6b0dd e5ceffa89509f0428bff695702c379cdd428393dBackground and Objectives: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of semen analysis results remains quite poor despite years of practice and advancements. Artificial intelligence (AI) algorithms, which can analyze and synthesize large amounts of data, can address the unique challenges involved in semen analysis due to the high objectivity of current methodologies. This review addresses recent AI advancements in semen analysis. Materials and Methods: A systematic literature search was performed in the PubMed database. Non-English articles and studies not related to humans were excluded. We extracted data related to AI algorithms or models used to evaluate semen parameters from the original studies, excluding abstracts, case reports, and meeting reports. Results: Of the 306 articles identified, 225 articles were rejected in the preliminary screening. The evaluation of the full texts of the remaining 81 publications resulted in the exclusion of another 48 articles, with a final inclusion of 33 original articles in this review. Conclusions: AI and machine learning are becoming increasingly popular in biomedical applications. The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms. Furthermore, when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.Medicina591The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms, and when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.Medicina['M. K. Panner Selvam', 'Ajaya Kumar Moharana', 'S. Baskaran', 'R. Finelli', 'Matthew C. Hudnall', 'Suresh C Sikka']2024-02-01T00:00:00https://www.semanticscholar.org/paper/e5ceffa89509f0428bff695702c379cdd428393d 08cfd8a5e31525209deb7f509b980d78a8f1300fStudies in Russian Economic Development31It is proven that the measurement infrastructure and monitoring system for AI should not only reflect its contribution to achieving strategic goals, but also be specified in accordance with the current institutional framework for implementing the innovation economy model as a whole.Studies on Russian Economic Development['L. V. Matraeva', 'E. Vasiutina', 'O. E. Bashina']2024-02-01T00:00:00https://www.semanticscholar.org/paper/08cfd8a5e31525209deb7f509b980d78a8f1300f 6e4a9ebc4174695930589efa09a6ad2e4bb965bdThis paper, normative in nature and scope, addresses the perks and limits of the strategic CSR approach when confronted with current debates on the ethics of artificial intelligence, responsible artificial intelligence, and sustainable technology in business organizations. The paper summarizes the classic arguments underpinning the “business case” for the social responsibility of businesses and the main moral arguments for responsible and sustainable behavior in light of recent technological ethical challenges. Both streams are confronted with organizational ethical dilemmas arising in designing and deploying artificial intelligence, yielding tensions between social and economic goals. While recognizing the effectiveness of the business argument for responsible behavior in artificial intelligence, the paper addresses some of its main limits, particularly in light of the “digital washing” phenomenon. Exemplary cases of digital washing and corporate inconsistencies here discussed are taken from the literature on the topic and re-assessed in light of the proposed normative approach. Hence, the paper proposes to overcome some limits of the business case for CSR applied to AI, which mainly focuses on compliance and reputational risks and seeks returns in digital washing, by highlighting the normative arguments supporting a moral case for strategic CSR in AI. This work contributes to the literature on business ethics and strategic CSR at its intertwining with the ethics of AI by proposing a normative point of view on how to deploy the moral case in organizations when dealing with AI-related ethical dilemmas. It does so by critically reviewing the state-of-the-art studies on the debate, which, so far, contain different streams of research, and adding to such a body of literature what is here identified and labeled as the “human argument”.Sustainability1071The paper proposes to overcome some limits of the business case for CSR applied to AI, which mainly focuses on compliance and reputational risks and seeks returns in digital washing, by highlighting the normative arguments supporting a moral case for strategic CSR in AI.Sustainability['Rosa Fioravante']2024-02-01T00:00:00https://www.semanticscholar.org/paper/6e4a9ebc4174695930589efa09a6ad2e4bb965bd fd909c38b940e29e09a0a57d7b66645805c58216Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.Psychology Research and Behavior Management801This study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety.Psychology Research and Behavior Management['Po-Chien Chang', 'Wenhui Zhang', 'Qihai Cai', 'Hongchi Guo']2024-02-01T00:00:00https://www.semanticscholar.org/paper/fd909c38b940e29e09a0a57d7b66645805c58216 9dd54f38bad4e3786c1d5e550cfcbd2b22dfc50fArtificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.Diagnostics731The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.Diagnostics['Chieh-Chen Wu', 'M. Islam', 'T. N. Poly', 'Yung-Ching Weng']2024-02-01T00:00:00https://www.semanticscholar.org/paper/9dd54f38bad4e3786c1d5e550cfcbd2b22dfc50f 7ba615bb5c9fe6ce8e492fad3b2ce2b0ca331b07Background: Our study aimed to explore the way artificial intelligence (AI) utilization is perceived in pediatric medicine, examining its acceptance among patients (in this case represented by their adult parents), and identify the challenges it presents in order to understand the factors influencing its adoption in clinical settings. Methods: A structured questionnaire was applied to caregivers (parents or grandparents) of children who presented in tertiary pediatric clinics. Results: The most significant differentiations were identified in relation to the level of education (e.g., aversion to AI involvement was 22.2% among those with postgraduate degrees, 43.9% among those with university degrees, and 54.5% among those who only completed high school). The greatest fear among respondents regarding the medical use of AI was related to the possibility of errors occurring (70.1%). Conclusions: The general attitude toward the use of AI can be considered positive, provided that it remains human-supervised, and that the technology used is explained in detail by the physician. However, there were large differences among groups (mainly defined by education level) in the way AI is perceived and accepted.Children411The general attitude toward the use of AI can be considered positive, provided that it remains human-supervised, and that the technology used is explained in detail by the physician.Children['E. Berghea', 'M. Ionescu', 'R. Gheorghiu', 'I. Țincu', 'C. Cobilinschi', 'M. Craiu', 'M. Bălgrădean', 'F. Berghea']2024-02-01T00:00:00https://www.semanticscholar.org/paper/7ba615bb5c9fe6ce8e492fad3b2ce2b0ca331b07 5da0d7e09c4604c7681eba2cac5c3ef14352342aThis paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.International Journal of Electrical and Computer Engineering (IJECE)501This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries.International Journal of Electrical and Computer Engineering (IJECE)['Raúl Jáuregui-Velarde', 'L. Andrade-Arenas', 'Pedro Molina-Velarde', 'Cesar Yactayo-Arias']2024-02-01T00:00:00https://www.semanticscholar.org/paper/5da0d7e09c4604c7681eba2cac5c3ef14352342a 7781cefdb5bf5b09d29d0e8e6be81a85db095692Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women’s reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.Journal of Clinical Medicine1070The current status of AI in gynecology, the upcoming developments in this area, and the challenges facing its clinical implementation are established, namely the technological and ethical concerns for technology development, implementation, and accountability are discussed.Journal of Clinical Medicine['Marta Brandão', 'F. Mendes', 'M. Martins', 'P. Cardoso', 'Guilherme Macedo', 'Teresa Mascarenhas', 'M. M. Mascarenhas Saraiva']2024-02-01T00:00:00https://www.semanticscholar.org/paper/7781cefdb5bf5b09d29d0e8e6be81a85db095692 d4e4c192f79de4d0b4b223358297bc990923573a Introduction: We aimed to evaluate the impact of the implementation of a stroke triage artificial intelligence (AI) software on a large academic healthcare system. Methods: A retrospective study was conducted in our spoke and hub network comparing equal corresponding periods of pre- and post- Viz.ai implementation between January 2021 and December 2022. Door to needle, door to puncture (DTP), successful reperfusion rates, transfer rates, and clinical outcomes at discharge were compared. Results: The analysis encompassed a total of 5,456 patients including 2313 pre- and 2275 post-Viz.ai patients across 11 spokes and 416 pre- and 452 post-Viz.ai patients across 4 hubs. Among 868 patients undergoing EVT at the 4 hubs, there was no differences in baseline data except for a higher baseline NIHSS score in the post Viz.ai group. We found an overall absolute reduction of transfers by 60 cases equating to a statistically significant decrease of 2.3% (P=0.04). The results showed an overall decrease by 13.5 minutes (8.1%) which was not statistically significant (P=0.3). There was an overall statistically significant decrease of 6 minutes (9.5%) in DTP times from 63 (23-98) minutes to 57 (23-79) minutes (P=0.01). No other differences in outcomes were noted between both groups. Conclusion: The automatic AI software was able to show a notable impact in our hub and spoke mature network driven by decrease of unnecessary transfers, DIDO and DTP. These data support the role of AI software as an effective triaging tool in the stroke and hub networks. More studies are needed to confirm these results. Stroke00The automatic AI software was able to show a notable impact in the authors' hub and spoke mature network driven by decrease of unnecessary transfers, DIDO and DTP, which support the role of AI software as an effective triaging tool in the stroke and hub networks.Stroke['Mohamed F Dohiem', 'Abdallah Sultny', 'A. Al-Bayati', 'N. Bhatt', 'M. Starr', 'Marcelo Rocha', 'Lucas Rios Rocha', 'Raul G Nogueira']2024-02-01T00:00:00https://www.semanticscholar.org/paper/d4e4c192f79de4d0b4b223358297bc990923573a 2b86d44cad0c014f19da3aa509b8d812eaebf00bThe expected fifth industrial revolution or Industry 5.0 (I-5.0) is human-centered and concerns societal values, and sustainability. I-5.0 focuses on human and machine coworking by augmenting human-collaborative intelligent robots. The current developments in information communications and the increasing market need for high agility and innovative ways to tailor products urge the world for an I-5.0 transformation. Artificial intelligence (AI) plays a key role in I-5.0 in offering intelligent components, smart digital twins, smart cyber-physical systems, and cobotics for efficient, precise and human-centered decision-making. In this context, AI boosts human-robot collaboration in production for timely and precise responses to the market and thereof realizing the concept of mass personalization. In addition, since I-5.0 is based on the idea of the widespread connection of devices, and identities in and out of the production process, there is a high level of vulnerability in front of cyberattacks where AI assists in smart cyber defense techniques. This article is a review of I-5.0 human-center concerns, involvement of social values, and I-5.0 technological elements in connection with human-collaborative AI. The role of AI in mass personalization and defense in front of cybersecurity threats is analyzed, and the horizon of I-5.0 in AI context is depicted. Finally, the realization challenges are reviewed as I-5.0 aims to make the future instead of waiting to face the future, and there is a variety of challenges on this path.IEEE Transactions on Cognitive and Developmental Systems830The role of AI in mass personalization and defense in front of cybersecurity threats is analyzed, the horizon of I-5.0 in AI context is depicted, and the realization challenges are reviewed.IEEE Transactions on Cognitive and Developmental Systems['M. Khosravy', 'Neeraj Gupta', 'Antoine Pasquali', 'Nilanjan Dey', 'R. G. Crespo', 'Olaf Witkowski']2024-02-01T00:00:00https://www.semanticscholar.org/paper/2b86d44cad0c014f19da3aa509b8d812eaebf00b 2e5c6a5ec93606a0107181c6579d21e6eb01f25a ChatGPT is an artificial intelligence (AI) platform with expanding uses across society, including the solicitation of medical advice by patients. Traditionally, men’s health patients obtained educational information through the Urology Care Foundation (UCF) or institutional websites. Today, men are increasingly turning to social media and AI given improving technology and prevalent stigma surrounding sexual health issues. Previous studies have demonstrated the ability of ChatGPT to perform certain physician tasks, but studies of its patient-facing use are limited. Most online health educational information far exceeds the recommended American sixth grade reading level, as defined by the NIH and AMA. Hence, existing resources are largely inaccessible to large swaths of the population. In this study, we questioned whether AI holds may help improve the provision of health educational information for the public. To conduct the first analysis of ChatGPT-created information regarding men’s sexual health conditions and provide statistical comparison with resources produced by UCF. Frequently asked patient questions regarding erectile dysfunction, premature ejaculation, low testosterone, sperm retrieval, penile augmentation, and male infertility were compiled from the American Urological Association. Questions included definition of the condition, etiology, diagnostics, treatment, prognosis, and common patient concerns. Responses from UCF and ChatGPT were compared using the following validated readability formulas: Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning-Fog Index, Simple Measure of Gobbledygook, Coleman-Liau Index, and Automated Readability Index. Descriptive analysis of word count, sentence length, syllable count, and word complexity was also performed. Furthermore, adjusted ChatGPT (ChatGPT-a) responses were generated by prompting the language model with the command “Explain it to me like I am in sixth grade.” Finally, all responses were further graded by two independent reviewers for accuracy and comprehensiveness. Statistical comparisons were made using Welch’s unpaired t-test. Readability scores are presented in Table 1. UCF was significantly more readable than ChatGPT for all six sexual medicine topics across all scoring metrics (all p<0.001). FKGL was 8.87 for UCF versus 14.83 for ChatGPT. ChatGPT responses were longer (278.3 versus 222.9 words) and included more complex words (28.1% versus 14.3%). When prompted with a command for more accessible language (ChatGPT-a), responses approached the readability of UCF across all metrics, including an average Flesch Reading Ease of 54.8 and FKGL of 9.6. UCF and ChatGPT had equal quality and accuracy on qualitative analysis. Men’s health information provided by ChatGPT is less accessible when compared to UCF, although both platforms exceed the recommended sixth grade level. Given its sensitive nature, sexual medicine information is increasingly sought out online. Our findings indicate that AI can simplify online information to accommodate an individual user’s health literacy, but improvement in the current platform is needed. Future iterations of ChatGPT may be adapted towards the provision of medical information and trained on evidence-based literature, hence improving both readability and quality. As AI grows, providers must study this new platform to better understand and assist their patients. No. Journal of Sexual Medicine00Findings indicate that AI can simplify online information to accommodate an individual user’s health literacy, but improvement in the current platform is needed.The Journal of Sexual Medicine['Y. Shah', 'A. Ghosh', 'C. Lallas', 'M. Shah', 'S. Cohen']2024-02-01T00:00:00https://www.semanticscholar.org/paper/2e5c6a5ec93606a0107181c6579d21e6eb01f25a 9a67656fc3efdbf63b83a1abc9f15b423f364dfeIntroduction Lung cancer (LC) is the most common cause of cancer-related deaths worldwide. Its early detection can be achieved with a CT scan. Two large randomised trials proved the efficacy of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk populations. The decrease in specific mortality is 20%–25%. Nonetheless, implementing LCS on a large scale faces obstacles due to the low number of thoracic radiologists and CT scans available for the eligible population and the high frequency of false-positive screening results and the long period of indeterminacy of nodules that can reach up to 24 months, which is a source of prolonged anxiety and multiple costly examinations with possible side effects. Deep learning, an artificial intelligence solution has shown promising results in retrospective trials detecting lung nodules and characterising them. However, until now no prospective studies have demonstrated their importance in a real-life setting. Methods and analysis This open-label randomised controlled study focuses on LCS for patients aged 50–80 years, who smoked more than 20 pack-years, whether active or quit smoking less than 15 years ago. Its objective is to determine whether assisting a multidisciplinary team (MDT) with a 3D convolutional network-based analysis of screening chest CT scans accelerates the definitive classification of nodules into malignant or benign. 2722 patients will be included with the aim to demonstrate a 3-month reduction in the delay between lung nodule detection and its definitive classification into benign or malignant. Ethics and dissemination The sponsor of this study is the University Hospital of Nice. The study was approved for France by the ethical committee CPP (Comités de Protection des Personnes) Sud-Ouest et outre-mer III (No. 2022-A01543-40) and the Agence Nationale du Medicament et des produits de Santé (Ministry of Health) in December 2023. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations. Trial registration number NCT05704920.BMJ Open130This open-label randomised controlled study focuses on LCS for patients aged 50–80 years, who smoked more than 20 pack-years, whether active or quit smoking less than 15 years ago, to determine whether assisting a multidisciplinary team with a 3D convolutional network-based analysis of screening chest CT scans accelerates the definitive classification of nodules into malignant or benign.BMJ Open['J. Benzaquen', 'Paul Hofman', 'Stephanie Lopez', 'Sylvie Leroy', 'Nesrine Rouis', 'Bernard Padovani', 'Eric Fontas', 'C. Marquette', 'J. Boutros']2024-02-01T00:00:00https://www.semanticscholar.org/paper/9a67656fc3efdbf63b83a1abc9f15b423f364dfe 97529a9366b85021ae851605032285747d201e49Artificial intelligence (AI) is not a new concept. Since the 2022 release of a popular large language model, AI has become readily accessible to the general population, brought transformational shifts in healthcare, and created significant implications for nurse leaders. Specifically, AI has major indications in the area of evidence-based practice. Historically, new evidence takes years to reach the bedside. Nurse leaders are instrumental in closing the research-to-practice gap and, in doing so, promote optimal patient safety and care delivery methods. This article provides an overview of using AI in the context of nursing leadership in healthcare settings, including appropriate case use. In addition, this article covers the ethical challenges of using AI in clinical settings.Journal of Nursing Administration50An overview of using AI in the context of nursing leadership in healthcare settings, including appropriate case use, and the ethical challenges of using AI in clinical settings are provided.The Journal of nursing administration['Justin Fontenot']2024-02-01T00:00:00https://www.semanticscholar.org/paper/97529a9366b85021ae851605032285747d201e49 b4caebd49133c470dce4bc38e164cf80e8cc0ecfArtificial intelligence (AI) supported applications have become increasingly prevalent in health care practice, with mental health services being no exception. AI applications can be employed at various stages of mental health services and with different roles. This study aims to understand the potential advantages and disadvantages of using AI in mental health services, to explore its future roles, and outcomes through the opinions of mental health professionals engaged with AI. Thus, we conducted a qualitative study with semi-structured interviews with 13 mental health professionals who have expertise in AI, and a content analysis of the interview transcripts. We concluded that the use of AI in mental health services revealed advantages and disadvantages for clients, the profession itself, and experts. Our study emphasized four findings. Firstly, the participants were likely to have positive opinions about using AI in mental health services. Increased satisfaction, widespread availability of mental health services, reduced expert-driven problems, and workload were among the primary advantages. Secondly, the participants stated that AI could not replace a clinician but could serve a functional role as an assistant. However, thirdly, they were skeptical about the notion that AI would radically transform mental health services. Lastly, the participants expressed limited views on ethical and legal issues surrounding data ownership, the ‘black box’ problem, algorithmic bias, and discrimination. Although our research has limitations, we expect that AI will play an increasingly important role in mental health care services.Cyberpsychology: Journal of Psychosocial Research on Cyberspace1020The use of AI in mental health services revealed advantages and disadvantages for clients, the profession itself, and experts, and it is expected that AI will play an increasingly important role in mental health care services.Cyberpsychology: Journal of Psychosocial Research on Cyberspace['Mücahit Gültekin', 'Meryem Şahin']2024-02-01T00:00:00https://www.semanticscholar.org/paper/b4caebd49133c470dce4bc38e164cf80e8cc0ecf fac871f950d17002b7c0a7ab7029457ab15dc895Objective Artificial Intelligence (AI) has made significant inroads into various domains, including medicine, raising concerns about algorithmic bias. This study investigates the presence of biases in generative AI programs, with a specific focus on gender and racial representations across 19 medical residency specialties. Methodology This comparative study utilized DALL-E2 to generate faces representing 19 distinct residency training specialties, as identified by the Association of American Medical Colleges (AAMC), which were then compared to the AAMC's residency specialty breakdown with respect to race and gender. Results Our findings reveal an alignment between OpenAI's DALL-E2's predictions and the current demographic landscape of medical residents, suggesting an absence of algorithmic bias in this AI model. Conclusion This revelation gives rise to important ethical considerations. While AI excels at pattern recognition, it inherits and mirrors the biases present in its training data. To combat AI bias, addressing real-world disparities is imperative. Initiatives to promote inclusivity and diversity within medicine are commendable and contribute to reshaping medical education. This study underscores the need for ongoing efforts to dismantle barriers and foster inclusivity in historically male-dominated medical fields, particularly for underrepresented populations. Ultimately, our findings underscore the crucial role of real-world data quality in mitigating AI bias. As AI continues to shape healthcare and education, the pursuit of equitable, unbiased AI applications should remain at the forefront of these transformative endeavors.Cureus280The findings reveal an alignment between OpenAI's DALL-E2's predictions and the current demographic landscape of medical residents, suggesting an absence of algorithmic bias in this AI model.Cureus['Shu Lin', 'Saket Pandit', 'Tara Tritsch', 'Arkene S. Levy', 'M. Shoja']2024-02-01T00:00:00https://www.semanticscholar.org/paper/fac871f950d17002b7c0a7ab7029457ab15dc895 dccdaded0f411057b1408fba6f06a9ddf163f87dWith the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.Intractable & Rare Diseases Research480The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks, with the most common data source being database data.Intractable & rare diseases research['Da He', 'Ru Wang', 'Zhilin Xu', 'Jiangna Wang', 'Peipei Song', 'Haiyin Wang', 'Jinying Su']2024-02-01T00:00:00https://www.semanticscholar.org/paper/dccdaded0f411057b1408fba6f06a9ddf163f87d fdb9f98b85829acc54005c0314c3137bbc2980f9Recently, there have been significant changes in the labour market and in the lives of employees, as modern society adapts increasingly easily to the implementation of artificial intelligence tools. However, technological changes have also created challenges, including a gap between available and required competencies in the use of artificial intelligence technologies. This study aims to analyse the relationships between employee competencies and effectiveness in the use of artificial intelligence tools, in order to highlight the set of essential competencies in effective interaction with artificial intelligence technology. Therefore, to achieve the purpose of the research, a questionnaire was created and completed by 209 Romanian employees between August and September 2023. For data analysis, two advanced techniques were applied: structural equation modelling (SEM) and necessary conditions analysis (NCA) using the SmartPLS v4 program. The results suggest that employee competencies are significantly associated with the effectiveness of using AI tools, and optimism and innovativeness positively mediate this relationship. The originality of the research stands out through the use of two advanced analysis methods (structural equation modelling and necessary conditions analysis), with the aim of identifying the set of sufficient and necessary skills in the use of artificial intelligence tools. These findings have significant implications for organisations, the educational system, and future research directions on the managerial implications of using artificial intelligence tools.Amfiteatru Economic570The results suggest that employee competencies are significantly associated with the effectiveness of using AI tools, and optimism and innovativeness positively mediate this relationship.Amfiteatru Economic['Ion Popa', 'Marian-Mihai Cioc', 'Andreea Breazu', 'C. Popa']2024-02-01T00:00:00https://www.semanticscholar.org/paper/fdb9f98b85829acc54005c0314c3137bbc2980f9 bc21b2d8357e222583415b906625b7451a90319cArtificial intelligence (AI) has become a revolutionary influence in the field of ophthalmology, providing unparalleled capabilities in data analysis and pattern recognition. This narrative review delves into the crucial role that AI plays, particularly in the context of anterior segment diseases with a genetic basis. Corneal dystrophies (CDs) exhibit significant genetic diversity, manifested by irregular substance deposition in the cornea. AI-driven diagnostic tools exhibit promising accuracy in the identification and classification of corneal diseases. Importantly, chat generative pre-trained transformer (ChatGPT)-4.0 shows significant advancement over its predecessor, ChatGPT-3.5. In the realm of glaucoma, AI significantly contributes to precise diagnostics through inventive algorithms and machine learning models, surpassing conventional methods. The incorporation of AI in predicting glaucoma progression and its role in augmenting diagnostic efficiency is readily apparent. Additionally, AI-powered models prove beneficial for early identification and risk assessment in cases of congenital cataracts, characterized by diverse inheritance patterns. Machine learning models achieving exceptional discrimination in identifying congenital cataracts underscore AI's remarkable potential. The review concludes by emphasizing the promising implications of AI in managing anterior segment diseases, spanning from early detection to the tailoring of personalized treatment strategies. These advancements signal a paradigm shift in ophthalmic care, offering optimism for enhanced patient outcomes and more streamlined healthcare delivery.Cureus480A narrative review delves into the crucial role that AI plays, particularly in the context of anterior segment diseases with a genetic basis, and emphasizes the promising implications of AI in managing anterior segment diseases, spanning from early detection to the tailoring of personalized treatment strategies.Cureus['Priyanka Venkatapathappa', 'A. Sultana', 'Vidhya K S', 'Romy Mansour', 'Venkateshappa Chikkanarayanappa', 'Harish Rangareddy']2024-02-01T00:00:00https://www.semanticscholar.org/paper/bc21b2d8357e222583415b906625b7451a90319c 8895b5aa36723f8d470eebd9ffb539187f863679BACKGROUND The field of artificial intelligence (AI) is poised to significantly influence the future of medicine. With the accumulation of vast databases and recent advancements in computer science methods, AI's capabilities have been demonstrated in numerous areas, from diagnosis and morbidity prediction to patient treatment. Establishing an AI research and development unit within a medical center offers multiple advantages, particularly in fostering research and tapping into the immediate potential of AI at the patient's bedside. OBJECTIVES To outline the steps taken to establish a center for AI and big data within an innovation center at a tertiary hospital in Israel. METHODS We conducted a retrospective analysis of projects developed in the field of AI at the Artificial Intelligence Center at the Rabin Medical Center, examining trends, clinical domains, and the predominant sectors over a specific period. RESULTS Between 2019 and 2023, data from 49 AI projects were gathered. A substantial and consistent growth in the number of projects was observed. Following the inauguration of the Artificial Intelligence Center we observed an increase of over 150% in the volume of activity. Dominant sectors included cardiology, gastroenterology, and anesthesia. Most projects (79.6%) were spearheaded by physicians, with the remainder by other hospital sectors. Approximately 59.2% of the projects were applied research. The remainder were research-based or a mix of both. CONCLUSIONS Developing technological projects based on in-hospital medical data, in collaboration with clinicians, is promising. We anticipate the establishment of more centers dedicated to medical innovation, particularly involving AI.The Israel Medical Association journal : IMAJ00A retrospective analysis of projects developed in the field of AI at the Artificial Intelligence Center at the Rabin Medical Center, examining trends, clinical domains, and the predominant sectors over a specific period.The Israel Medical Association journal : IMAJ['Nadav Loebl', 'Eytan Wirtheim', 'L. Perl']2024-02-01T00:00:00https://www.semanticscholar.org/paper/8895b5aa36723f8d470eebd9ffb539187f863679 1d2e33eff45ed1db7533c1d7d4d7ba92b584d03fArtificial intelligence (AI) is increasingly affecting many aspects of our day-to-day lives. Although the benefits of AI to society are potentially transformative, many fear the cost to human rights may be too great without focused attention.Computer80Artificial intelligence is increasingly affecting many aspects of the authors' day-to-day lives, and although the benefits of AI to society are potentially transformative, many fear the cost to human rights may be too great without focused attention.Computer['Kristen K. Greene', 'M. F. Theofanos', 'Craig Watson', 'Anne Andrews', 'Eyeisha Barron']2024-02-01T00:00:00https://www.semanticscholar.org/paper/1d2e33eff45ed1db7533c1d7d4d7ba92b584d03f c4c80e1d9529c0298cd356f6bbf46f4f07d84ff3Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories of risk for assessment, management, and communication. For this topic, the framing of conventional risk and decision analyses is ongoing. This paper introduces a method to quantify risk as the disruption of the order of AI initiatives in healthcare systems, aiming to find the scenarios that are most and least disruptive to system order. This novel approach addresses scenarios that bring about a re-ordering of initiatives in each of the following three characteristic layers: purpose, structure, and function. In each layer, the following model elements are identified: 1. Typical research and development initiatives in healthcare. 2. The ordering criteria of the initiatives. 3. Emergent conditions and scenarios that could influence the ordering of the AI initiatives. This approach is a manifold accounting of the scenarios that could contribute to the risk associated with AI in healthcare. Recognizing the context-specific nature of risks and highlighting the role of human in the loop, this study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems. This finding suggests that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of “high-stakes” AI systems, particularly in healthcare. Future work should connect this approach with decision analysis and quantifying the value of information. Future work will explore the disruptions of system order in additional layers of the healthcare system, including the environment, boundary, interconnections, workforce, facilities, supply chains, and others.Syst.520This study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems, suggesting that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of “high-stakes” AI systems, particularly in healthcare.Syst.['Neginsadat Moghadasi', 'Rupa S. Valdez', 'M. Piran', 'Negar Moghaddasi', 'Igor Linkov', 'Thomas L. Polmateer', 'Davis C. Loose', 'James H. Lambert']2024-02-01T00:00:00https://www.semanticscholar.org/paper/c4c80e1d9529c0298cd356f6bbf46f4f07d84ff3 1721f04ab56a4d79ca842fa1611413b0e7108fc0Background: Artificial intelligence (AI) holds significant promise for transforming healthcare delivery, including dentistry. However, the successful integration of AI into dental practice necessitates an understanding of dental professionals' perspectives, attitudes, and readiness to adopt AI technology. This study aimed to explore dental professionals' perceptions, attitudes, and practices regarding AI adoption in dentistry. Methods: This cross-sectional study was conducted among 256 dental professionals using an online questionnaire. Participants were assessed for familiarity with AI technology, perceived barriers to adoption, attitudes towards AI, current usage patterns, and factors influencing adoption decisions. Data are analysed using descriptive statistics, including frequencies, percentages, means, and standard deviations. Inferential statistics, such as chi-square tests and regression analysis, were employed to examine associations between variables and identify predictors of AI adoption in dentistry. Results: The study surveyed 256 dental professionals from various regions across India, primarily aged 30 to 50 years (mean age: 42.6), with a nearly equal gender split (male: 48.4%, female: 51.6%) and high educational attainment (67.8% with master's or doctoral degrees). Private practices were predominant (56.3%). The diagnostic algorithms and treatment planning software were well known (77.3% and 70.3% familiarity, respectively). Technical concerns (average score: 3.82 ± 0.68) were the main barriers to AI adoption, followed by financial considerations (average score: 3.45 ± 0.72), ethical and legal issues (average score: 3.21 ± 0.65), and organizational factors (average score: 3.67 ± 0.71). Despite these concerns, most participants had positive attitudes towards AI (70.3% agreed). Current usage varied, with diagnostic support and administrative tasks being the most common (44.5% and 82.8% usage, respectively). Perceived utility (average score: 4.12 ± 0.75) and ease of use (average score: 3.98 ± 0.69) significantly influenced adoption, as identified by regression analysis (perceived utility: β = 0.342, p < 0.001; ease of use: β = 0.267, p = 0.005). Conclusion: This study provides valuable insights into AI adoption in dentistry, highlighting the multifaceted nature of barriers and facilitators that influence dental professionals’ adoption decisions. Strategies to promote AI adoption should address practical considerations, ethical concerns, and educational needs to facilitate the integration of AI technology into dental practices.Cureus180This study provides valuable insights into AI adoption in dentistry, highlighting the multifaceted nature of barriers and facilitators that influence dental professionals’ adoption decisions.Cureus['Parameswari Royapuram Parthasarathy', 'Santosh R. Patil', 'A. Dawasaz', 'Fawaz Abdul Hamid Baig', 'M. Karobari']2024-02-01T00:00:00https://www.semanticscholar.org/paper/1721f04ab56a4d79ca842fa1611413b0e7108fc0 772253aed0a34d60797c87837a103549ec906333Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles. Will this herald a decline in mechanistic studies?PLoS Biology130Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles, will this herald a decline in mechanistic studies?PLOS Biology['Víctor de Lorenzo']2024-02-01T00:00:00https://www.semanticscholar.org/paper/772253aed0a34d60797c87837a103549ec906333 3e6f98e13fdccd03d188f736f278a4ea7d7bd45fIn the rapidly evolving landscape of Industry 4.0, the integration of Artificial Intelligence (AI) into Human Resources (HR) practices has emerged as a pivotal factor in enhancing organizational efficiency. This research study delves into the multifaceted implications of AI adoption within HR departments and its overarching impact on the operational efficiency of organizations. In the era of Industry 4.0, characterized by advanced automation, connectivity, and data-driven decision-making, AI technologies are playing an increasingly significant role in reshaping traditional HR functions. This research aims to quantitatively assess the extent to which AI-driven HR practices influence employee recruitment, retention, development, and overall human capital management. By analyzing data from a diverse set of organizations across different industries, this study seeks to identify patterns, trends, and best practices related to AI integration in HR. The research methodology involves a combination of surveys, data analysis, and case studies to collect and analyze quantitative data on AI adoption in HR practices and the subsequent impact on organizational efficiency. Key performance indicators (KPIs) such as employee productivity, cost effectiveness, and strategic alignment are scrutinized in order to ascertain the correlation between AI in HR and organizational success. Preliminary findings indicate that AI-driven HR practices are facilitating more streamlined and data-informed decision-making processes, allowing organizations to make better-informed talent-related choices. The insights gained from this study will be instrumental in guiding organizations in optimizing their HR functions through AI integration, enabling them to adapt and thrive in the Industry 4.0 landscape. Additionally, this research contributes to a deeper understanding of the evolving dynamics between AI, HR practices, and organizational efficiency, with implications for strategic decision-making and policy development in the context of Industry 4.0.Feb-Mar 2024110Preliminary findings indicate that AI-driven HR practices are facilitating more streamlined and data-informed decision-making processes, allowing organizations to make better-informed talent-related choices.Feb-Mar 2024['Dr. Shweta Kulshrestha']2024-02-01T00:00:00https://www.semanticscholar.org/paper/3e6f98e13fdccd03d188f736f278a4ea7d7bd45f e100f07a3654e85c8ec938081b729f315d5e30a7Background: Cochlear implantation is a critical surgical intervention for patients with severe hearing loss. Postoperative care is essential for successful rehabilitation, yet access to timely medical advice can be challenging, especially in remote or resource-limited settings. Integrating advanced artificial intelligence (AI) tools like Chat Generative Pre-trained Transformer (ChatGPT)-4 in post-surgical care could bridge the patient education and support gap. Aim: This study aimed to assess the effectiveness of ChatGPT-4 as a supplementary information resource for postoperative cochlear implant patients. The focus was on evaluating the AI chatbot's ability to provide accurate, clear, and relevant information, particularly in scenarios where access to healthcare professionals is limited. Materials and methods: Five common postoperative questions related to cochlear implant care were posed to ChatGPT-4. The AI chatbot's responses were analyzed for accuracy, response time, clarity, and relevance. The aim was to determine whether ChatGPT-4 could serve as a reliable source of information for patients in need, especially if the patients could not reach out to the hospital or the specialists at that moment. Results: ChatGPT-4 provided responses aligned with current medical guidelines, demonstrating accuracy and relevance. The AI chatbot responded to each query within seconds, indicating its potential as a timely resource. Additionally, the responses were clear and understandable, making complex medical information accessible to non-medical audiences. These findings suggest that ChatGPT-4 could effectively supplement traditional patient education, providing valuable support in postoperative care. Conclusion: The study concluded that ChatGPT-4 has significant potential as a supportive tool for cochlear implant patients post surgery. While it cannot replace professional medical advice, ChatGPT-4 can provide immediate, accessible, and understandable information, which is particularly beneficial in special moments. This underscores the utility of AI in enhancing patient care and supporting cochlear implantation.Cureus150ChatGPT-4 has significant potential as a supportive tool for cochlear implant patients post surgery, and while it cannot replace professional medical advice, ChatGPT-4 can provide immediate, accessible, and understandable information, which is particularly beneficial in special moments.Cureus['A. Aliyeva', 'Elif Sarı', 'Elvin Alaskarov', 'Rauf Nasirov']2024-02-01T00:00:00https://www.semanticscholar.org/paper/e100f07a3654e85c8ec938081b729f315d5e30a7 de239b9fbad97f137b47453539a0af328affd776In aquaculture, controlling water quality parameters is an important challenge. The water quality parameters affect the growth of aquatic organisms. Thus, maintaining water quality balance has become the primary goal of aquaculture operators. However, the traditional water quality inspection method is low in accuracy and consumes considerable time and human resources. On the other hand, since water quality sensors are immersed in seawater for a long time, algae will grow on the sensors, affecting their accuracy. Therefore, to solve the abovementioned problems, this article reports the design and implementation of a full-time artificial intelligence of things (AIoT)-based water quality inspection and prediction system, which uses a simple recurrent unit (SRU) model to predict water quality data. With the proposed system, it is possible to collect water quality sensing data 24 h a day and further use the SRU model for sensor data prediction to assist aquaculture farmers in managing and controlling outdoor aquaculture ponds. Moreover, a 24-h water quality sampling tank is designed to overcome the problem of sensor error. Throughout the whole process, data are transmitted to a water quality monitoring cloud platform for further inspection and prediction. In this article, SRU-based prediction is used to obtain predictions of water quality parameters, and three popular metrics mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance. As a result, experimental results show that the proposed method offers good performance for prediction of water quality.IEEE Sensors Journal280Experimental results show that the proposed method offers good performance for prediction of water quality, and three popular metrics mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) are used to evaluate the performance.IEEE Sensors Journal['Wu-Chih Hu', 'Liang-Bi Chen', 'Bo-Hao Wang', 'Guo-Wei Li', 'Xiangzheng Huang']2024-02-01T00:00:00https://www.semanticscholar.org/paper/de239b9fbad97f137b47453539a0af328affd776 f98cd02b50b8cb806a52bfd564c9b8fedcc62710Introduction Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions. Methods and analysis A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants’ attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants’ opinions on how their experiences compare. Data will be analysed thematically using the Framework Method. Ethics and dissemination This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.BMJ Open440The perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions are explored to explore participants’ attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making.BMJ Open['Niluka Gunathilaka', 'Tiffany E Gooden', 'Jennifer Cooper', 'Sarah Flanagan', 'Tom Marshall', 'S. Haroon', "A. d'Elia", 'F. Crowe', 'Thomas Jackson', 'K. Nirantharakumar', 'Sheila Greenfield']2024-02-01T00:00:00https://www.semanticscholar.org/paper/f98cd02b50b8cb806a52bfd564c9b8fedcc62710 21bf33f7a145f2518463187d54e751b49160c620Background The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists’ assessments. Methods A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts. Conclusions The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.Journal of Thoracic Disease00The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19.Journal of Thoracic Disease['Rudolf Bumm', 'P. Zaffino', 'Andras Lasso', 'R. Estépar', 'Steven Pieper', 'Jakob Wasserthal', 'M. Spadea', 'T. Latshang', 'Nadine Kawel-Boehm', 'A. Wäckerlin', 'Raphael Werner', 'G. Hässig', 'Markus Furrer', 'Ron Kikinis']2024-02-01T00:00:00https://www.semanticscholar.org/paper/21bf33f7a145f2518463187d54e751b49160c620 ca4bd38276b39536e4ef8ddc9bea53ec87766915The study aimed to identify proposals to activate the role of artificial intelligence in developing the skills of the century The twenty-first among educational leaders in light of smart digital transformations, and the study adopted the curriculum Qualitative, the researcher conducted interviews with (20) faculty members from Jordanian universities Governmental and private. The results of these interviews showed that faculty members presented a range Proposals to activate the role of artificial intelligence in developing twenty-first century skills among leaders Educational education in light of smart digital transformations included: activating modern technology in all its forms The educational process in Jordanian public and private universities continues on an ongoing basis within their centers and structures Administrative and organizational, universities innovate modern mechanisms that contribute to enhancing the use of intelligence applications Artificial among educational leaders, educational leaders in Jordanian universities seek to develop Their skills by accepting change and not resisting it, and the universities’ keenness to activate electronic platforms Based on modern technologies that are compatible with current and future requirements and goals, we direct Jordanian universities, in their policies and future plans, seek to raise awareness among educational leaders of the importance of activating Applications of artificial intelligence and spreading the culture of digital transformations in educational circles. The study recommended Conducting further studies related to artificial intelligence and modern technologies and linking them to other variablesInternational Journal of Humanities and Educational Research10International Journal of Humanities and Educational Research['Dr. Tahani Ibrahim El ALI']2024-02-01T00:00:00https://www.semanticscholar.org/paper/ca4bd38276b39536e4ef8ddc9bea53ec87766915 dbf093d06bc44e765b40a2e738a731090565e81cBackground: Given the dialogistic properties of ChatGPT, we hypothesized that this artificial intelligence (AI) function can be used as a self-service tool where clinical questions can be directly answered by AI. Our objective was to assess the content, accuracy, and accessibility of AI-generated content regarding common perioperative questions for reduction mammaplasty. Methods: ChatGPT (OpenAI, February Version, San Francisco, Calif.) was used to query 20 common patient concerns that arise in the perioperative period of a reduction mammaplasty. Searches were performed in duplicate for both a general term and a specific clinical question. Query outputs were analyzed both objectively and subjectively. Descriptive statistics, t tests, and chi-square tests were performed where appropriate with a predetermined level of significance of P less than 0.05. Results: From a total of 40 AI-generated outputs, mean word length was 191.8 words. Readability was at the thirteenth grade level. Regarding content, of all query outputs, 97.5% were on the appropriate topic. Medical advice was deemed to be reasonable in 100% of cases. General queries more frequently reported overarching background information, whereas specific queries more frequently reported prescriptive information (P < 0.0001). AI outputs specifically recommended following surgeon provided postoperative instructions in 82.5% of instances. Conclusions: Currently available AI tools, in their nascent form, can provide recommendations for common perioperative questions and concerns for reduction mammaplasty. With further calibration, AI interfaces may serve as a tool for fielding patient queries in the future; however, patients must always retain the ability to bypass technology and be able to contact their surgeon.Plastic and Reconstructive Surgery, Global Open390Currently available AI tools, in their nascent form, can provide recommendations for common perioperative questions and concerns for reduction mammaplasty and may serve as a tool for fielding patient queries in the future; however, patients must always retain the ability to bypass technology and be able to contact their surgeon.Plastic and Reconstructive Surgery Global Open['C. Boyd', 'Kshipra Hemal', 'Thomas J Sorenson', 'Parth A. Patel', 'Jonathan M. Bekisz', 'Mihye Choi', 'N. Karp']2024-02-01T00:00:00https://www.semanticscholar.org/paper/dbf093d06bc44e765b40a2e738a731090565e81c 5d3fb191717d201a0891b26077e3aa6601080ac6Objectives The objective of this analysis is to present a current view of the field of ophthalmology and vision research and artificial intelligence (AI) from topical and geographical perspectives. This will clarify the direction of the field in the future and aid clinicians in adapting to new technological developments. Methods A comprehensive search of four different databases was conducted. Statistical and bibliometric analysis were done to characterise the literature. Softwares used included the R Studio bibliometrix package, and VOSviewer. Results A total of 3939 articles were included in the final bibliometric analysis. Diabetic retinopathy (391, 6% of the top 100 keywords) was the most frequently occurring indexed keyword by a large margin. The highest impact literature was produced by the least populated countries and in those countries who collaborate internationally. This was confirmed via a hypothesis test where no correlation was found between gross number of published articles and average number of citations (p value=0.866, r=0.038), while graphing ratio of international collaboration against average citations produced a positive correlation (r=0.283). Majority of publications were found to be concentrated in journals specialising in vision and computer science, with this category of journals having the highest number of publications per journal (18.00 publications/journal), though they represented a small proportion of the total journals (<1%). Conclusion This study provides a unique characterisation of the literature at the intersection of AI and ophthalmology and presents correlations between article impact and geography, in addition to summarising popular research topics.BMJ Health & Care Informatics170This study provides a unique characterisation of the literature at the intersection of AI and ophthalmology and presents correlations between article impact and geography, in addition to summarising popular research topics.BMJ Health & Care Informatics['Hayley Monson', 'Jeff Demaine', 'Adrianna Perryman', 'T. Felfeli']2024-02-01T00:00:00https://www.semanticscholar.org/paper/5d3fb191717d201a0891b26077e3aa6601080ac6 1801340558478f88e248e70846a012fcf37a3b0eOne of the most challenging and prevalent side effects of LVAD implantation is that of right heart failure (RHF) that may develop afterwards. The purpose of this study is to review and highlight recent advances in the uses of AI in evaluating RHF after LVAD implantation. The available literature was scanned using certain key words (artificial intelligence, machine learning, left ventricular assist device, prediction of right heart failure after LVAD) was scanned within Pubmed, Web of Science, and Google Scholar databases. Conventional risk scoring systems were also summarized, with their pros and cons being included in the results section of this study in order to provide a useful contrast with AI-based models. There are certain interesting and innovative ML approaches towards RHF prediction among the studies reviewed as well as more straightforward approaches that identified certain important predictive clinical parameters. Despite their accomplishments, the resulting AUC scores were far from ideal for these methods to be considered fully sufficient. The reasons for this include the low number of studies, standardized data availability, and lack of prospective studies. Another topic briefly discussed in this study is that relating to the ethical and legal considerations of using AI-based systems in healthcare. In the end, we believe that it would be beneficial for clinicians to not ignore these developments despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance.Diagnostics980It is believed that it would be beneficial for clinicians to not ignore recent advances in the uses of AI in evaluating RHF after LVAD implantation despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance.Diagnostics['Ozlem Balcioglu', 'Cemre Ozgocmen', 'D. Ozsahin', 'Tahir Yagdi']2024-02-01T00:00:00https://www.semanticscholar.org/paper/1801340558478f88e248e70846a012fcf37a3b0e 6d7aad8b136b7010ef0c0cb77a113b0d513dbc16Purpose: This research paper examines quantitatively the impact of Artificial Intelligence (AI) as an independent variable on three organisational behavior components as dependent variables: job satisfaction, personality, and attitudes. Design/methodology: The sample of our study includes alumni graduates of the past two years reflecting on post-covid19 era as young workforce from Cairo, Egypt. It is important to note that there is no sufficient data regarding the impact of AI on OB testing this bracket of young population workforce, and highlighting specific components in the study of OB. Findings: This research finding revealed that AI can explain 46.5 % of Organizational Behavior which refers to the importance of AI usage among organizations and how this might change the current organizational environments.  Research Limitations: This research is limited by the fact that the sample encompassed a specific bracket of the young workforce age ranging from 20-35 years old even though they come from different backgrounds. Practical Implications: AI tools can enhance employees job satisfaction as well as it can help mitigate cognitive biases and groupthink, developing a decision-making culture that is more objective and data driven. Originality: Therefore, the utility of this research on the academic level highlights the fact of identifying major concerns and formulations of essential conclusions to get a deeper understanding of the relationship between the different variables stated. On the practical level, it sheds light for several organisations on the danger of technology replacement of employees due to the invasive impact of AI usage. Keywords: Artificial Intelligence, Personality, Attitudes, Job Satisfaction, Organisational BehaviorEuropean Journal of Sustainable Development00Examining quantitatively the impact of Artificial Intelligence as an independent variable on three organisational behavior components as dependent variables revealed that AI can explain 46.5 % of Organizational Behavior which refers to the importance of AI usage among organizations and how this might change the current organizational environments.European Journal of Sustainable Development['Zeinab Younis', 'Marwa Ibrahim', 'Habiba Azzam']2024-02-01T00:00:00https://www.semanticscholar.org/paper/6d7aad8b136b7010ef0c0cb77a113b0d513dbc16 7b3f035a8c26d26b67ed7c8b6aff7b5cb184f3bd The complexity and growth of data in healthcare means that artificial intelligence (AI) will be increasingly applied in this area. This article (study) de-scribes the types of AI used by care providers and life and biophysical sciences companies. This paper describes the structure of the methodology of artificial intelligence (AI) algorithms and its machine learning (ML) subsystem with respect to the prediction of environmental pollution and its negative impact on humans. Key categories of AI applications focus on diagnosis and treatment or referral, patient engagement and adherence, and administrative activities. Diagnosis of diseases is a crucial and very important task of the physician in planning the right treatment and ensuring the health status of patients. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. Indoor air quality (IAQ) is an important issue for well-being and good health, as most people spend almost all of their time in different types of buildings. Given the increasing availability of data and the rapid expansion of AI techniques, it is essential to explore the development of indoor and outdoor air quality predictions using AI techniques to improve and maintain IAQ.Journal of Physics: Conference Series440The structure of the methodology of artificial intelligence (AI) algorithms and its machine learning (ML) subsystem with respect to the prediction of environmental pollution and its negative impact on humans is described.Journal of Physics: Conference Series['Bohumír Garlík', 'Jan Přívětivý']2024-02-01T00:00:00https://www.semanticscholar.org/paper/7b3f035a8c26d26b67ed7c8b6aff7b5cb184f3bd 91752ee39de4112d07c6b08e224357c5625ab3dbPurpose This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations. Design/methodology/approach The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing. Findings Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link. Practical implications The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance. Originality/value There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory. Journal of Modelling in Management890A framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory is presented.Journal of Modelling in Management['Hamad Mohamed Almheiri', 'Syed Zamberi Ahmad', 'Abdul Rahim Abu Bakar', 'Khalizani Khalid']2024-02-01T00:00:00https://www.semanticscholar.org/paper/91752ee39de4112d07c6b08e224357c5625ab3db b3c8a1b97271d0d628b5274b0a7f9f74d50fbef0 Since November 2022, Artificial Intelligence (AI) chatbots have grown in popularity including in urologic conditions. However, their accuracy and quality has not been evaluated systematically. In this study, we sought to assess the accuracy and quality of AI chatbots in the management of Erectile Dysfunction (ED). We aim to evaluate the accuracy and quality of open-source language models in fielding common clinical questions pertaining to ED compared to board certified urologists. Two AI open-source language models, ChatGPT and Google Bard, were fielded 15 standard questions related to ED some of which included causes, risk factors and treatment options of ED. Two board certified urologists were given the same questions on a standard survey. A third blinded board-certified urologist served as a grader using the AUA guidelines for ED on a Likert scale to assess the accuracy, robustness, and bias of each response. Urologist and AI responses were graded and aggregated using Likert scales. Overall AI responses were significantly more accurate (p<0.01), robust (p<0.01), and unbiased (p<0.01). Additionally, Google Bard had the highest scores all around followed by ChatGPT. The urologists’ responses were approximately 38% lower compared to the AI responses. This study suggests that AI responses were superior compared to urologists in the areas of accuracy, robustness, and bias pertaining to the management of ED. Albeit, the chatbots have promising role in urologic conditions including ED, its widespread clinical use and adoption warrants further evaluation in the context of clinical decision making and enhancing patient care. No. Journal of Sexual Medicine00This study suggests that AI responses were superior compared to urologists in the areas of accuracy, robustness, and bias pertaining to the management of ED.The Journal of Sexual Medicine['M. Pathuri', 'O. Marciano', 'P. Barrtero Guimaraes', 'E. Kocjancic', 'O. Raheem']2024-02-01T00:00:00https://www.semanticscholar.org/paper/b3c8a1b97271d0d628b5274b0a7f9f74d50fbef0 0fc819b5b49eef4d4df2d3b5147870e635be68c6Penelitian ini dilatar belakangi oleh fenomena yang terjadi dilapangan, peneliti melihat sebagian remaja di Desa Ruwuk Ranggan kurang memperhatikan tanggung jawab intelektualnya, khususnya dalam pembelajaran Al-Qur’an. Penelitian ini bertujuan untuk mendeskripsikan tentang self-regulated learning pada remaja di Desa Ruwuk Ranggan Kecamatan Cempaga Kabupaten Kotawaringin Timur. Penelitian ini menggunakan pendekatan kualitatif deskriptif  dengan teknik pengumpulan data menggunakan observasi, wawancara, dan dokumentasi. Hasil penelitian ini didapatkan bahwa mayoritas remaja di Desa Ruwuk Ranggan memiliki self-regulated learning yang rendah, terkhusus dalam belajar Al-Qur’an secara mandiri, namun ada juga sebagian remaja yang memiliki self-regulated learning yang tinggi. self-regulated learning pada remaja dalam belajar Al-Qur’an dipengaruhi oleh beberapa faktor pendukung yaitu; 1) keyakinan diri (Self efficacy), 2) motivasi diri (Self-motivation), 3) dukungan sosial.Anterior Jurnal00Anterior Jurnal['Saiful Lutfi', 'Surawan Surawan', 'Adisty Arselia Zanuba']2024-01-31T00:00:00https://www.semanticscholar.org/paper/0fc819b5b49eef4d4df2d3b5147870e635be68c6 f8942bc208da18943bb4610106f54a1311819c22The article examines the relationship between state policy, the environmental sphere, and security. It was determined that the main problem is the development and effective implementation of the state environmental policy, which would ensure sustainable development and environmental safety, taking into account modern environmental challenges. It is emphasized that environmental policy can be integrated into various spheres of social life (economy, social policy, education and health). It has been confirmed that issues of state policy in the environmental sphere require the development of an integrated approach, which will include legislative regulation, scientific and research activities, education and public involvement. The connections between the state policy in the environmental sphere and security (connection with the health and well-being of the population; risks of environmental pollution of air, water and soil; the impact of environmental policy on energy security) have been studied as an important factor that contributes to social stability, strengthens trust to the authorities and raises public awareness of environmental issues, the active participation of the public in the formation and implementation of environmental policy is recognized. The legislative field regarding environmental policy is characterized and the root causes of environmental problems in Ukraine are listed. Environmental risks associated with the conduct of military operations are described. Data from the official resource of the Ministry of Environmental Protection and Natural Resources of Ukraine "EkoZagroza" regarding and impact on the environment are given. The role of public involvement work and educational campaigns aimed at raising citizens' awareness of environmental problems and the need to solve them has been defined. The importance of global environmental challenges and their impact on human health, the economy and the stability of society is revealed. The need to form an integrated approach to environmentally caused diseases in the context of safety is emphasized. The need to involve a wide range of stakeholders, including government institutions, the scientific community, the business sector and civil society, is emphasized. Directions for increasing the effectiveness of state policy in the environmental sphere are presented and their characteristics are given.Bulletin of the National Technical University KhPI Series Actual problems of Ukrainian society development00Bulletin of the National Technical University "KhPI". Series: Actual problems of Ukrainian society development['Olena Mykhailovska']2024-01-31T00:00:00https://www.semanticscholar.org/paper/f8942bc208da18943bb4610106f54a1311819c22 f0e901ebd92985c4353f317467ff3f87a179af14NEJM AI101NEJM AI['Paige Nong', 'Reema Hamasha', 'Karandeep Singh', 'Julia Adler-Milstein', 'Jody Platt']2024-01-31T00:00:00https://www.semanticscholar.org/paper/f0e901ebd92985c4353f317467ff3f87a179af14 f970425f556e52f8061d4ef1d5e8f4acf92b5210Purpose: This study examines the impact of enhanced regulation on managerial finance tactics following significant financial scandals. It aims to explore how financial managers navigate increased regulatory scrutiny and integrate ethical considerations into their strategic decision-making processes. Research Design and Methodology: Employing a qualitative systematic literature review, this research synthesizes insights from academic journals, books, and reputable sources. The study uses thematic analysis to identify key themes, patterns, and gaps related to managerial finance practices in a regulated environment. Findings and Discussion: The findings reveal that financial scandals have led to stricter regulatory frameworks, compelling financial managers to prioritize compliance and ethical conduct. Integrating advanced technologies like blockchain and AI has enhanced regulatory compliance processes, while a culture of integrity and transparency within financial institutions has become crucial for rebuilding stakeholder trust. These strategies are essential for mitigating regulatory risks and ensuring long-term organizational stability. Implications: The research underscores the necessity for financial institutions to adopt proactive compliance strategies and foster a culture of ethical conduct. Policymakers and practitioners are encouraged to leverage technological innovations to streamline compliance processes and maintain regulatory adherence. Future research should focus on the effectiveness of specific compliance strategies and the interplay between regulatory frameworks, technological advancements, and ethical considerations in managerial finance.Advances in Management &amp; Financial Reporting00Advances in Management &amp; Financial Reporting['Muslim Muslim']2024-01-31T00:00:00https://www.semanticscholar.org/paper/f970425f556e52f8061d4ef1d5e8f4acf92b5210 28a947e60989eca2a012b457c694317276729ae9The key to realizing sustainable human development is to improve the utilization of ecological capital. Under the requirements of innovation-driven and green economic development, how to formulate appropriate environmental regulation policies and accurately implement high-quality economic development strategies to promote the utilization of ecological capital has become the focus of theoretical research and practical exploration. This paper examines the effects of environmental regulation, high-quality economic development, and the interaction term between the two on ecological capital utilization using a fixed-effects model based on panel data for 30 provincial-level political regions (excluding Tibet) in China from 2008 to 2020. The empirical results show that both environmental regulation and economic quality development have a significant positive effect on ecological capital utilization. However, environmental regulation can inhibit technological innovation, which in turn affects economic quality development, and the interaction term between environmental regulation and economic quality development has a significant negative effect on ecological capital utilization. Based on this, the government should enhance environmental regulations while increasing support and technological innovation subsidies for heavily polluting enterprises and new industries to promote high-quality economic development while improving the utilization of ecological capital.Frontiers in Environmental Science521Frontiers in Environmental Science['Tao Li', 'Wenqian Tian', 'Shitong Zhang', 'Shuhong Wang']2024-01-31T00:00:00https://www.semanticscholar.org/paper/28a947e60989eca2a012b457c694317276729ae9 1fdca1370aa537f0fb5f408c41eb4976a86a7527The case of pain asymbolia is a case study that provides evidence of the mechanisms underlying the relationship between bodily experience, affective experience, and self-awareness. On one account pain asymbolia is the result of an affective deficit. Sensory signals of bodily damage are not associated with characteristic negative affect. Cochrane endorses this account as part of his version of a “conceptual act” theory of affective experience. In contrast, I propose an active inference account of affect in general and pain asymbolia in particular. In the active inference framework the self is inferred as the endogenous cause of bodily and affective experience in the process of organismic regulation. This preserves Cochranes ambition to ground affect in bodily regulation but avoids the problem for affective deficit accounts of asymbolia that cannot do justice to the neural correlates.Journal of Philosophy of Emotion281Journal of Philosophy of Emotion['Philip Gerrans']2024-01-31T00:00:00https://www.semanticscholar.org/paper/1fdca1370aa537f0fb5f408c41eb4976a86a7527 401de097b5aa71ad95b9a668cf8bec4c83abb055The article delves into the peculiarities of financial control procedures in the construction industry and the role of the state in their optimization. It is noted that the creation of infrastructure objects of social space, erection of buildings, performance of other architectural and construction tasks is an integral part of human society, ensur-ing its full functioning. However, construction as an economic sector needs to be regulated by the state, in par-ticular, through financial control on its part. The author presents the main forms of realization of this type of su-pervisory activity in relation to objects and subjects of construction. It is emphasized that it is necessary to ex-clude abuses on the part of authorized structures in terms of implementation of inspections. The author refers to such innovations: the adjustment of the directions of state regulation of financial control in the field of con-struction in legislative form; in terms of control measures, reflect all of the above authorities in conducting fi-nancial control of the construction industry; state regulation of financial control methods, namely, the adjust-ment of the monitoring function of financial control. The practical significance of this study lies in the applica-tion of this model in the practical activities of state regulation of financial control.Теория и практика общественного развития00Теория и практика общественного развития['Igor A. Korneychuk']2024-01-31T00:00:00https://www.semanticscholar.org/paper/401de097b5aa71ad95b9a668cf8bec4c83abb055 59c6b6fd8c5d2b650aa0b56a3effb2e6149e71c1This research discusses the impact of the integration of artificial intelligence (AI) in Human Resource Management (HRM) practices through a systematic literature review approach. Involving the analysis of 37 articles from various academic databases, the research identified the key benefits provided by AI in HRM, such as improved efficiency, process effectiveness and corporate decision making. However, significant challenges were also identified, including issues of data security, privacy and the need for HR skills development. In addition, the psychological impact on employees and work team dynamics is an important concern. In conclusion, the combination of AI in HRM has the capability to shape a new paradigm in human resource management, however it requires careful coping with rising demanding situations. This study offers a stable basis for a deep know-how of the complex interactions between AI and HRM, starting the door to in addition research and improvement on this region.PRODUCTIVITY366AI has the capability to shape a new paradigm in human resource management, however it requires careful coping with rising demanding situations, and a stable basis for a deep know-how of the complex interactions between AI and HRM is offered.Management Studies and Business Journal (PRODUCTIVITY)['Dampak Kecerdasan', 'AI Praktik', 'Manajemen Sumber', 'Daya Manusia', 'Hendri Sucipto', 'Kata Kunci', 'Kecerdasan Buatan', 'AI Dampak']2024-01-31T00:00:00https://www.semanticscholar.org/paper/59c6b6fd8c5d2b650aa0b56a3effb2e6149e71c1 f372afffe40c78c7c8a2302a1a9a6cee982d156bThis research explores the impact of artificial intelligence (AI) on marketing strategy with a focus on contextual understanding of consumers, increasing operational efficiency, better personalization of content, decision-making effectiveness, and the need for integration with expertise. Through systematic literature analysis, this research identifies the positive potential and challenges faced by companies in adopting AI technology in their marketing strategies. The results provide deep insight into how the integration of artificial intelligence can shape modern marketing and deliver significant benefits.PRODUCTIVITY332The positive potential and challenges faced by companies in adopting AI technology in their marketing strategies are identified and the results provide deep insight into how the integration of artificial intelligence can shape modern marketing and deliver significant benefits.Management Studies and Business Journal (PRODUCTIVITY)['Asep Supriadi']2024-01-31T00:00:00https://www.semanticscholar.org/paper/f372afffe40c78c7c8a2302a1a9a6cee982d156b 57b56623567ed852838c5dae5c5088716fe86346We present a new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees. These AI correctors are auxiliary maps whose role is to moderate the decisions of some previously constructed underlying classifier by either approving or rejecting its decisions. The rejection of a decision can be used as a signal to suggest abstaining from making a decision. A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions. These bounds are distribution agnostic and do not rely on assumptions on the data dimension. Our empirical example illustrates how the framework can be applied to improve the performance of an image classifier in a challenging real-world task where training data are scarce.arXiv.org281A new methodology for handling AI errors by introducing weakly supervised AI error correctors with a priori performance guarantees that are distribution agnostic and do not rely on assumptions on the data dimension.ArXiv['I. Tyukin', 'T. Tyukina', 'D. V. Helden', 'Zedong Zheng', 'E. Mirkes', 'Oliver J. Sutton', 'Qinghua Zhou', 'Alexander N. Gorban', 'Penelope Allison']2024-01-31T00:00:00https://www.semanticscholar.org/paper/57b56623567ed852838c5dae5c5088716fe86346 cb006c23c2512c5b8e0b3e1c080f79f5af6db75bArtificial intelligence (AI) has become popular worldwide after technological breakthroughs in the early 2010s. Accordingly, many organizations and individuals have been using AI for various applications. Previous research has been dominated by case studies regarding the industrial use of AI, although how time-series changes affect users’ perceptions has not been clarified yet. This study analyzes time-series changes in AI perceptions through text mining from nonfinancial information obtained from Japanese firms’ disclosures. The main findings of this study are as follows: first, perceptions of AI vary across industries; second, the business sector has progressed through the stages of recognition, investment, strategization, commercialization, and monetization. This transition is concurrent with each category’s evolving interpretation of the innovator theory proposed by Rogers (2003), to some extent. Third, it took approximately a decade from the breakthrough technology to the monetization by Japanese firms. Our findings underline the importance of speeding up the organizational process through intervention and contribution to the areas regarding “diffusion of innovation” and perceptual characteristics.Applied System Innovation561Perceptions of AI vary across industries, and it took approximately a decade from the breakthrough technology to the monetization by Japanese firms, underline the importance of speeding up the organizational process through intervention and contribution to the areas regarding “diffusion of innovation” and perceptual characteristics.Applied System Innovation['Yusuke Hoshino', 'Takashi Hirao']2024-01-31T00:00:00https://www.semanticscholar.org/paper/cb006c23c2512c5b8e0b3e1c080f79f5af6db75b f7e46efc4f2e22ac331a1047f096a374d19df03dThis paper investigates the voting behaviors of Large Language Models (LLMs), specifically GPT-4 and LLaMA-2, their biases, and how they align with human voting patterns. Our methodology involved using a dataset from a human voting experiment to establish a baseline for human preferences and a corresponding experiment with LLM agents. We observed that the methods used for voting input and the presentation of choices influence LLM voting behavior. We discovered that varying the persona can reduce some of these biases and enhance alignment with human choices. While the Chain-of-Thought approach did not improve prediction accuracy, it has potential for AI explainability in the voting process. We also identified a trade-off between preference diversity and alignment accuracy in LLMs, influenced by different temperature settings. Our findings indicate that LLMs may lead to less diverse collective outcomes and biased assumptions when used in voting scenarios, emphasizing the importance of cautious integration of LLMs into democratic processes.arXiv.org611It is discovered that varying the persona can reduce some of these biases and enhance alignment with human choices in Large Language Models, and while the Chain-of-Thought approach did not improve prediction accuracy, it has potential for AI explainability in the voting process.ArXiv['Joshua C. Yang', 'Marcin Korecki', 'Damian Dailisan', 'C. I. Hausladen', 'Dirk Helbing']2024-01-31T00:00:00https://www.semanticscholar.org/paper/f7e46efc4f2e22ac331a1047f096a374d19df03d 5004f7853a00b44e4aca88f04826d071de14f53bAbstract: In the rapidly evolving landscape of artificial intelligence (AI) and cybersecurity, the increasing adoption of large language models has introduced both opportunities and challenges. The utilization of large generative AI models, such as GPT 3.5 and GPT 4.0 used in ChatGPT, has shown promising potential in various domains, including cybersecurity, software engineering, and human-computer interaction. However, alongside their benefits, these models raise concerns regarding transparency, interpretability, and ethical considerations. Furthermore, AI-driven cybersecurity has emerged as a critical defense against sophisticated cyber threats, but it faces issues related to accuracy, false positives, and the need for data-efficient techniques. The integration of AI in cybersecurity has also led to new attack vectors and vulnerabilities that require comprehensive solutions. To address these multifaceted challenges, a research survey paper is warranted to analyze the state-ofthe-art understanding of the use of generative AI in cybersecurity, addressing issues identified through statistical analysis, new attack vectors and vulnerabilities that have emerged, innovative solutions that may exist, and the current approach to promoting responsible and secure AI practices.International Journal for Research in Applied Science and Engineering Technology01A research survey paper is warranted to analyze the state-ofthe-art understanding of the use of generative AI in cybersecurity, addressing issues identified through statistical analysis, new attack vectors and vulnerabilities that have emerged, innovative solutions that may exist, and the current approach to promoting responsible and secure AI practices.International Journal for Research in Applied Science and Engineering Technology['Harshaan Chugh']2024-01-31T00:00:00https://www.semanticscholar.org/paper/5004f7853a00b44e4aca88f04826d071de14f53b 84dbbd90973aed21432120301d3dc74fadd6d841The paper presents the results of a study dedicated to modern digitization processes in the energy sector, in particular implementation of Artificial Intelligence (AI) systems for optimizing energy processes. The study deals with the key aspects of the AI system’s implementation in energy processes. A special focus was given to the terminology in the context of the system approach to the AI-related categories. The article provides results of the corresponding research with the following objectives: to argue the concept of the AI-systems in the energy sector; consider the main stages of the AI-system implementation for optimizing processes within the enterprise's energy complex, analyze the know-how regarding AI-system usage in the energy sector of Ukraine: determine AI challenges and perspectives; recommend the controlling system to mitigate AI-risks. We concluded that the energy companies are able to optimize energy processes by implementation of the AI systems. The study revealed the main features of AI systems, their implementation within energy companies for optimizing business processes, features of the controlling system to manage AI-related risks, and ways to develop AI systems within companies and energy processes. As far as implementation of the AI systems is usually followed by socio-economic consequences we paid attention to the following AI-related aspects as well: 1) the impact of AI systems on technological unemployment; 2) consideration of organizational changes depending on the level of the AI systems use within energy companies; 3) recommendations for support of AI systems in energy processes in the post-war period in the Ukrainian economy. Thus, the research underscores the pivotal role of AI systems in fostering a more resilient and environmentally conscious approach to energy management, thereby contributing to the long-term sustainable development goals of enterprises.Herald of Khmelnytskyi National University Economic sciences00It is concluded that the energy companies are able to optimize energy processes by implementation of the AI systems, and the pivotal role of AI systems in fostering a more resilient and environmentally conscious approach to energy management, thereby contributing to the long-term sustainable development goals of enterprises.Herald of Khmelnytskyi National University. Economic sciences['Ольга Дегтярьова', 'Тетяна Куклінова', 'Cофія Куклінова']2024-01-31T00:00:00https://www.semanticscholar.org/paper/84dbbd90973aed21432120301d3dc74fadd6d841 530c4ebbd813f84b1c5bfaea7816e45128db0262Currently, the use of artificial intelligence has become widespread, especially among students. However, there is a consensus that artificial intelligence brings about some changes in students' intellectual lives, particularly when used excessively. One of the tangible transformations is related to the authorithy of knowledge, where students prefer to complete assignments with the assistance of artificial intelligence without seeking other authoritative sources. This research aims to find answers to the following questions: How has the use of artificial intelligence spread among students? What significant changes in students' intellectual lives as the impact of excessive use of artificial intelligence, particularly concerning the transformation in the authority of knowledge? This study is a qualitative research with a phenomenological approach to investigate a variety of phenomena surrounding the research topic directly from the field. The research was conducted in the Department of Arabic Language Education at STAI Syaichona Moh. Cholil Bangkalan. This research found that: students use AI in learning, such as ChatGPT, chatbots, and AI-based electronic dictionaries, primarily because their lectures are related to language translation. Furthermore, this research also found that a transformation in the authority of knowledge has occurred, transitioning from lecturers and authoritative books to reliable websites, opinions of intellectual figures, and journals published by universities.Journal of Contemporary Islamic Education72This research found that students use AI in learning, such as ChatGPT, chatbots, and AI-based electronic dictionaries, primarily because their lectures are related to language translation, and a transformation in the authority of knowledge has occurred.Journal of Contemporary Islamic Education['Fera Andriani Djakfar Musthafa']2024-01-31T00:00:00https://www.semanticscholar.org/paper/530c4ebbd813f84b1c5bfaea7816e45128db0262 a67c5d838138e586796dbf3a4ae0fb9137f52ab3Background As Industry 4.0 debuted roughly a decade ago, it is now necessary to examine how it affects various aspects of the discipline. It is the responsibility of the education sector to guarantee that the next generation is equipped mentally, physically, and cognitively to face unforeseen challenges. Numerous educational institutions are outfitted with Industry 4.0 technology-based learning. Industry 4.0 fosters advancements in learning methodologies, especially for language enhancements. Learners may gain knowledge at their base, providing them an opportunity for independent study. The majority of subjects have been acquired through Industry 4.0. This research chapter explores the intersection of Industry 4.0 and education, specifically focusing on the SmallTalk AI tool. It investigates how technological and digital innovations within the context of Industry 4.0 can serve as powerful tools to enhance language learning outcomes. Methods This article presents a comprehensive analysis of statistical data and empirical evidence to support the positive impact of Industry 4.0 technology of SmallTalk on language acquisition particularly speaking. The study also determines the relationship among participants’ usage through the technology acceptance model (TAM). Furthermore, it examines the challenges and opportunities associated with integrating these innovations into language learning pedagogies, offering insights for educators and policymakers to harness the potential of Industry 4.0 in fostering language proficiency. The research employs quantitative analysis. The data obtained from educational institutions has been analyzed using the SPSS and AMOS software. Results The results indicate that Industry 4.0 has had an important effect on English language acquisition. This self-supported adaptable system of education facilitates effective student learning. This study also suggests that future research into the utility of Industry 4.0 be conducted elsewhere internationally.PeerJ Computer Science380A comprehensive analysis of statistical data and empirical evidence to support the positive impact of Industry 4.0 on English language acquisition and suggests that future research into the utility of Industry 4.0 be conducted elsewhere internationally.PeerJ Computer Science['Chunxiao Zhang', 'Zhiyan Liu', 'Aravind B.R.', 'Hariharasudan A']2024-01-31T00:00:00https://www.semanticscholar.org/paper/a67c5d838138e586796dbf3a4ae0fb9137f52ab3 8223db0a974058c3925ebaaf3ef5d155f557cd93The integration of Artificial Intelligence (AI) into healthcare systems heralds a transformative era marked by unprecedented advancements in diagnosis, treatment, and patient care. This comprehensive review explores the multifaceted applications of AI in the healthcare domain, offering a synthesized perspective on the current state of knowledge and future implications. By examining the intersection of AI with medical imaging, predictive analytics, personalized medicine, and virtual health assistants, this research illuminates the promising trajectory of AI in reshaping the landscape of healthcare delivery. The initial segment of the review focuses on AI applications in medical imaging and diagnostics. Harnessing machine learning algorithms and deep neural networks, AI demonstrates remarkable capabilities in interpreting complex medical images, such as radiographs, MRIs, and CT scans. The paper evaluates the accuracy and efficiency of AI-driven diagnostic tools, addressing challenges and opportunities for integration into clinical workflows. Moving beyond diagnostics, the second thematic area explores the role of AI in predictive analytics for disease prevention and early intervention. By leveraging patient data, electronic health records, and genomic information, AI models contribute to identifying at-risk populations, predicting disease trajectories, and optimizing preventive strategies. The research assesses the ethical considerations and data privacy implications inherent in deploying predictive AI models within healthcare ecosystems. The third section investigates the paradigm shift towards personalized medicine facilitated by AI technologies. Analyzing patient-specific data, including genetic information and treatment response patterns, AI tailors treatment plans to individual characteristics, optimizing therapeutic outcomes. The review explores case studies and ongoing initiatives in precision medicine to showcase the tangible benefits and challenges associated with personalized healthcare. In the final thematic area, the review delves into the burgeoning field of virtual health assistants and AI-driven patient care. From chatbots offering real-time medical advice to virtual nurses monitoring patient well-being, AI enhances accessibility and engagement in healthcare delivery. The paper examines the potential for AI to improve patient outcomes, increase healthcare accessibility, and alleviate the burden on healthcare professionals. Throughout the review, ethical considerations surrounding patient privacy, algorithmic biases, and the responsible use of AI in clinical decision-making are critically evaluated. The research concludes with a forward-looking perspective, emphasizing the imperative of ongoing collaboration between healthcare professionals, technologists, and policymakers to harness the full potential of AI in fostering a more efficient, accessible, and patient-centric healthcare ecosystemInternational Journal of Advanced Research in Science, Communication and Technology10The research concludes with a forward-looking perspective, emphasizing the imperative of ongoing collaboration between healthcare professionals, technologists, and policymakers to harness the full potential of AI in fostering a more efficient, accessible, and patient-centric healthcare ecosystem.International Journal of Advanced Research in Science, Communication and Technology['Elaine Rich', 'Patrick Winston']2024-01-31T00:00:00https://www.semanticscholar.org/paper/8223db0a974058c3925ebaaf3ef5d155f557cd93 59f4322ff656c81e9767c78e8599dabccebb5b5cPatients managing a complex illness such as cancer face a complex information challenge where they not only must learn about their illness but also how to manage it. Close interaction with healthcare experts (radiologists, oncologists) can improve patient learning and thereby, their disease outcome. However, this approach is resource intensive and takes expert time away from other critical tasks. Given the recent advancements in Generative AI models aimed at improving the healthcare system, our work investigates whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data. We conducted a formative need-finding study in which participants discussed chest computed tomography (CT) scans and associated radiology reports of a fictitious close relative with a cardiothoracic radiologist. Using thematic analysis of the conversation between participants and medical experts, we identified commonly occurring themes across interactions, including clarifying medical terminology, locating the problems mentioned in the report in the scanned image, understanding disease prognosis, discussing the next diagnostic steps, and comparing treatment options. Based on these themes, we evaluated two state-of-the-art generative visual language models against the radiologist's responses. Our results reveal variability in the quality of responses generated by the models across various themes. We highlight the importance of patient-facing generative AI systems to accommodate a diverse range of conversational themes, catering to the real-world informational needs of patients.arXiv.org510This work investigates whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data, and highlights the importance of patient-facing generative AI systems to accommodate a diverse range of conversational themes, catering to the real-world informational needs of patients.ArXiv['Shreya Rajagopal', 'Subhashis Hazarika', 'Sookyung Kim', 'Yan-Ming Chiou', 'Jae Ho Sohn', 'Hari Subramonyam', 'Shiwali Mohan']2024-01-31T00:00:00https://www.semanticscholar.org/paper/59f4322ff656c81e9767c78e8599dabccebb5b5c 500e8ad0e67d26e117275bef51edca1ffbf81556Abstract: In today's hyper-competitive business environment, streamlining order processing is crucial for maximizing efficiency, minimizing errors, and fostering customer satisfaction. Traditional methods, often manual and error-prone, struggle to keep pace with the demands of modern commerce. This paper delves into the transformative power of Artificial Intelligence (AI) in revolutionizing order processing within SAP. the leading Enterprise Resource Planning (ERP) system. The article explores the integration of Artificial Intelligence (AI) and Optical Character Recognition (OCR) technologies, shedding light on how this union reshapes traditional workflows. From streamlining data entry processes to empowering intelligent decision-making, the article delineates the best practices for organizations seeking to harness the power of AI in SAP order processing. The discussion encompasses key considerations before implementation, seamless integration strategies, and best practices in training AI models. Real-world case studies illustrate successful implementations, highlighting the tangible benefits achieved in terms of efficiency, accuracy, and overall workflow optimization.International Journal for Research in Applied Science and Engineering Technology00The article explores the integration of Artificial Intelligence (AI) and Optical Character Recognition (OCR) technologies, shedding light on how this union reshapes traditional workflows within SAP, the leading Enterprise Resource Planning system.International Journal for Research in Applied Science and Engineering Technology['Moyinuddeen Shaik']2024-01-31T00:00:00https://www.semanticscholar.org/paper/500e8ad0e67d26e117275bef51edca1ffbf81556 639f7c8fc74320e0ef8cfe19fb9f842fcca344beIn today’s business environment, the role of adoption of AI is very important. Although the pace of this adoption is low in emerging markets and Pakistan in particular, however, it is increasing significantly in the devolved markets. In the coming years it would be indispensable for organizations to opt for AI adoption in different task management. There is little evidence of AI adoption in recruitment and selection. The objective of this study is to explore the different factors impacting AI adoption in recruitment and selection process. The different factors that fall under the broader category of technological, organizational, environmental of TOE model in recruitment and selection process are investigated through interviews. The sample of this study is senior management from different organizations where AI adoption is used, or they have intention to use. This study has found and validated some existing factors and new some factors are identified. Management also has shown some concern about adoption of AI. The results of this study can be valuable for the organizations to develop their AI adoption practices in this competing environment to have viable and distinguishable edge.Bahria University Journal Of Management &amp; Technology140The results of this study can be valuable for the organizations to develop their AI adoption practices in this competing environment to have viable and distinguishable edge.Bahria University Journal Of Management &amp; Technology['Rehana Farhat', 'Temoor Anjum']2024-01-31T00:00:00https://www.semanticscholar.org/paper/639f7c8fc74320e0ef8cfe19fb9f842fcca344be 676b1c74535efca92fbb79a26ea66df9ea07e7e7Abstract Objectives To determine the extent and content of academic publishers’ and scientific journals’ guidance for authors on the use of generative artificial intelligence (GAI). Design Cross sectional, bibliometric study. Setting Websites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023. Participants Top 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact. Main outcome measures The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. Results Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. Conclusions Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.British medical journal2111The BMJ['Conner Ganjavi', 'M. Eppler', 'Asli Pekcan', 'Brett Biedermann', 'Andre Abreu', 'Gary S. Collins', 'I. Gill', 'Giovanni E. Cacciamani']2024-01-31T00:00:00https://www.semanticscholar.org/paper/676b1c74535efca92fbb79a26ea66df9ea07e7e7 c29015e6597ab46f327c6527c5e3925ad07f3aaeAbstract With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians’ decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care.Open Forum Infectious Diseases572Artificial intelligence is unlikely to replace the role of ID experts, but could instead augment it, however, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation.Open Forum Infectious Diseases['Bradley J. Langford', 'W. Branch-Elliman', 'Priya Nori', 'Alexandre R Marra', 'Gonzalo Bearman']2024-01-31T00:00:00https://www.semanticscholar.org/paper/c29015e6597ab46f327c6527c5e3925ad07f3aae 0ec3642fd8c79cd0e39df922819beb08053959b9The emergence of generative large language model (LLM) artificial intelligence (AI) represents one of the most profound developments in healthcare in decades, with the potential to create revolutionary and seismic changes in the practice of medicine as we know it. However, significant concerns have arisen over questions of liability for bad outcomes associated with LLM AI-influenced medical decision making. Although the authors were not able to identify a case in the United States that has been adjudicated on medical malpractice in the context of LLM AI at this time, sufficient precedent exists to interpret how analogous situations might be applied to these cases when they inevitably come to trial in the future. This commentary will discuss areas of potential legal vulnerability for clinicians utilizing LLM AI through review of past case law pertaining to third-party medical guidance and review the patchwork of current regulations relating to medical malpractice liability in AI. Finally, we will propose proactive policy recommendations including creating an enforcement duty at the US Food and Drug Administration (FDA) to require algorithmic transparency, recommend reliance on peer-reviewed data and rigorous validation testing when LLMs are utilized in clinical settings, and encourage tort reform to share liability between physicians and LLM developers.Journal of Osteopathic Medicine171This commentary will discuss areas of potential legal vulnerability for clinicians utilizing LLM AI through review of past case law pertaining to third-party medical guidance and review the patchwork of current regulations relating to medical malpractice liability in AI, and propose proactive policy recommendations.Journal of osteopathic medicine['David O Shumway', 'Hayes J Hartman']2024-01-31T00:00:00https://www.semanticscholar.org/paper/0ec3642fd8c79cd0e39df922819beb08053959b9 aeeb0a4b529425a21e4a3f416e3c78010635eeb7Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.Bioinformation21The use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.Bioinformation['Raed Farzan']2024-01-31T00:00:00https://www.semanticscholar.org/paper/aeeb0a4b529425a21e4a3f416e3c78010635eeb7 8ccc81829ca578f70307aaa1f083b2ede07178ebArtificial intelligence (AI) is a fast-paced technological advancement in terms of its application to various fields of science and technology. In particular, AI has the potential to play various roles in veterinary clinical practice, enhancing the way veterinary care is delivered, improving outcomes for animals and ultimately humans. Also, in recent years, the emergence of AI has led to a new direction in biomedical research, especially in translational research with great potential, promising to revolutionize science. AI is applicable in antimicrobial resistance (AMR) research, cancer research, drug design and vaccine development, epidemiology, disease surveillance, and genomics. Here, we highlighted and discussed the potential impact of various aspects of AI in veterinary clinical practice and biomedical research, proposing this technology as a key tool for addressing pressing global health challenges across various domains.Frontiers in Veterinary Science511The potential impact of various aspects of AI in veterinary clinical practice and biomedical research are highlighted and discussed, proposing this technology as a key tool for addressing pressing global health challenges across various domains.Frontiers in Veterinary Science['O. C. Akinsulie', 'Ibrahim Idris', 'Victor Ayodele Aliyu', 'Sammuel Shahzad', 'O. Banwo', 'S. C. Ogunleye', 'M. Olorunshola', 'Deborah O. Okedoyin', 'Charles Ugwu', 'I. Oladapo', 'Joy Olaoluwa Gbadegoye', 'Qudus Afolabi Akande', 'Pius Babawale', 'Sahar Rostami', 'K. Soetan']2024-01-31T00:00:00https://www.semanticscholar.org/paper/8ccc81829ca578f70307aaa1f083b2ede07178eb 293cbd829d2c66fdbf9592f11dbf26f8d2129beaAbstract: Artificial Intelligence (AI) is revolutionizing the landscape of Engineering Education, albeit with limited empirical qualitative assessment of its potential and perceived benefits or challenges. This paper investigates the integration and experiences of academic professionals with AI tools in their pedagogical practices. The study employs a comprehensive survey asking specific questions relating to the familiarity, motivation, usage, training, benefits, challenges, ethical considerations, and future implications of AI tools like ChatGPT, Google Bard, and Microsoft Bing. The results illustrate a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches. However, the findings also highlight the need for adequate training and ethical guidelines for responsible AI use. Additionally, perceived challenges are revealed, underscoring the importance of addressing them to harness AI's full potential in education. Overall, this research contributes to the understanding of AI's value in education from an academic standpoint, outlining its role, advantages, challenges, and future expectations. This study is essential in the ongoing discourse of AI in education, and it can inform the development of training, policies, and strategies for effective and responsible AI integration in the future of engineering education.International Journal for Research in Applied Science and Engineering Technology00The integration and experiences of academic professionals with AI tools in their pedagogical practices are investigated, illustrating a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches.International Journal for Research in Applied Science and Engineering Technology['Harsh R. Mishra']2024-01-31T00:00:00https://www.semanticscholar.org/paper/293cbd829d2c66fdbf9592f11dbf26f8d2129bea cfdaeca5a74b995a3dcbff241f6049d61619e188This article focuses on fact-checking initiatives in the context of the rise of artificial intelligence. With reference to theories of the political economy of communication and platform studies, this study sheds light on the very confusing evolution of initiatives in Africa. The approach combines content analysis and distanced observation of two fact-checking platforms, chosen on the basis of their local roots and the experimentation of smart tools: Africa Check and Check4Decision. The results highlight the economic and technological dependencies of African platforms on GAFAM via factchecking services and an automation process that is far from complete with regard to local realities. It appears that the African context provides a different perspective with structural constraints and "cultural" algorithmic biases.European Scientific Journal00This study sheds light on the very confusing evolution of initiatives in Africa and highlights the economic and technological dependencies of African platforms on GAFAM via factchecking services and an automation process that is far from complete with regard to local realities.European Scientific Journal ESJ['Sokhna Fatou Seck Sarr']2024-01-31T00:00:00https://www.semanticscholar.org/paper/cfdaeca5a74b995a3dcbff241f6049d61619e188 7829d5c943297a11bafc7209f9baf9725d371728This paper introduces a unique perspective. Rather than focusing on improving the already significant achievements of existing artificial intelligence algorithms, it investigates the potential of merging various algorithms to enhance their overall capabilities. Essential design aspects required for this integration are examined, and a prototype system is developed to demonstrate the practical application of these design principles. This method aims to broaden the range of capabilities accessible to a system, addressing the limitation of the narrow focus prevalent in contemporary artificial intelligence.Computer and Information Science330This paper investigates the potential of merging various algorithms to enhance their overall capabilities, addressing the limitation of the narrow focus prevalent in contemporary artificial intelligence.Computer and Information Science['Andrew McInnis Jr', 'Mohammad Alshibli', 'Ahmad Alzaghal', 'Samir Hamada']2024-01-31T00:00:00https://www.semanticscholar.org/paper/7829d5c943297a11bafc7209f9baf9725d371728 07ce116c574f7e29ca5d6623d594728ec826590cPemanfaatan kecerdasan buatan (Artificial Intelligence/AI) dalam dunia pendidikan di Indonesia telah mengalami perkembangan yang signifikan dalam beberapa tahun terakhir. Kemajuan teknologi AI telah membuka peluang baru dalam meningkatkan kualitas pendidikan dan mengatasi berbagai tantangan yang dihadapi oleh sistem pendidikan di Indonesia. Hal tersebut tentunya memunculkan opini/ komentar yang beragam dari masyarakat khususnya pengguna media sosial X/Twitter. Penelitian ini fokus pada analisis sentimen review yang diungkapkan pada media sosial X/Twitter. Tujuan utama penelitian ini adalah untuk mengembangkan metode analisis sentimen yang efektif dengan memanfaatkan algoritma SVM (Support Vector Machine) yang dioptimalkan dengan Feature Selection PSO (Particle Swarm Optimization). Dalam penelitian ini, data review dari pengguna X/Twitter dikumpulkan dan dianalisis untuk mengidentifikasi sentimen positif atau negatif dalam konteks setiap komentar. Algoritma SVM digunakan untuk mengklasifikasikan sentimen berdasarkan kemiripan dengan komentar-komentar yang telah diketahui sentimennya. Feature Selection PSO digunakan untuk mengoptimalkan parameter-parameter dalam SVM yang bertujuan untuk meningkatkan akurasi analisis sentimen. Hasil pengujian dalam analisis sentimen terhadap komentar-komentar atau tweet pada media sosial X/Twitter menggunakan algoritma SVM dan SVM berbasis PSO menunjukan bahwa algoritma SVM berbasis PSO memiliki nilai akurasi yang lebih baik. Algoritma SVM dengan feature selection PSO menghasilkan nilai accuracy = 89.50%, precision = 86.98%, recall = 93.00% dan AUC = 0.964. Sedangkan algoritma SVM mimiliki nilai accuracy = 87.50%, precision = 85.46%, recall = 90.50% dan AUC = 0.956. Hal ini menunjukan bahwa penggunaan feature selection PSO pada algoritma SVM ternyata mampu meningkatkan nilai akurasi yang dihasilkan.Computer Science (CO-SCIENCE)00Computer Science (CO-SCIENCE)['Atang Saepudin', 'R. Aryanti', 'E. Fitriani', 'Royadi Royadi', 'Dian Ardiansyah']2024-01-31T00:00:00https://www.semanticscholar.org/paper/07ce116c574f7e29ca5d6623d594728ec826590c 8ac6442f8bb2a836f48ab268d1c43152bc934001Drawing on rhetorical genre studies, we explore research article abstracts created by generative artificial intelligence (AI). These synthetic genres—genre-ing activities shaped by the recursive nature of language learning models in AI-driven text generation—are of interest as they could influence informational quality, leading to various forms of disordered information such as misinformation. We conduct a two-part study generating abstracts about (a) genre scholarship and (b) polarized topics subject to misinformation. We conclude with considerations about this speculative domain of AI text generation and dis/misinformation spread and how genre approaches may be instructive in its identification.Journal of Technical Writing and Communication190A two-part study generating abstracts about genre scholarship and polarized topics subject to misinformation is conducted, concluding with considerations about this speculative domain of AI text generation and dis/misinformation spread and how genre approaches may be instructive in its identification.Journal of Technical Writing and Communication['Brad Mehlenbacher', 'Ana Patricia Balbon', 'A. Mehlenbacher']2024-01-31T00:00:00https://www.semanticscholar.org/paper/8ac6442f8bb2a836f48ab268d1c43152bc934001 804eea1d43f455162ce39c427265fd20a871a19fAbstract: This article explores the transformative impact of artificial intelligence (AI) on drug discovery. Traditional drug discovery, slow and serendipitous, struggled to meet urgent medical needs. Artificial intelligence (AI) emerges as a transformative force, harnessing vast scientific data to predict drug properties and efficacy with remarkable precision. From identifying novel targets to designing custom molecules, AI streamlines selection, reduces costs and opens doors to previously unexplored therapeutic avenues. Breakthrough candidates like Atacicept, GTX-007, and AB-928 showcase the power of AI in accelerating drug discovery, introducing innovative strategies, and paving the way for a future of precision medicine and improved patient outcomes. This review explores the multifaceted impact of AI on drug discovery, highlighting its potential to revolutionize how we combat disease.International Journal for Research in Applied Science and Engineering Technology00A review explores the multifaceted impact of AI on drug discovery, highlighting its potential to revolutionize how the authors combat disease.International Journal for Research in Applied Science and Engineering Technology['Mayur Bagane', 'Dr. Rajesh Jorgewad']2024-01-31T00:00:00https://www.semanticscholar.org/paper/804eea1d43f455162ce39c427265fd20a871a19f 2a95e55da7ff57c4027b5573797034017f34e40eInternational relations scholarship has long emphasized that popular culture can impact public understandings and political realities. In this article, we explore these potentials in the context of military-themed videogames and their portrayals of weaponized artificial intelligence (AI). Within paradoxical videogame representations of AI weapons both as ‘insurmountable enemies’ that pose existential threats to humankind in narratives and as ‘easy targets’ that human protagonists routinely overcome in gameplay, we identify distortions of human–machine interaction that contradict real-world scenarios. These distortions revolve around videogames affording players enhanced human agency to dominate AI weapons to offer enjoyable gameplay, contradicting the same weapons being intended to diminish human agency on real-world battlefields. By leveraging the Actor-Network Theory concept of ‘translation’, we explain how these distorted portrayals of AI weapons are produced by entanglements between heterogeneous human and non-human actors that aim to make videogames mass-marketable and profitable. In so doing, we echo game studies research that calls for greater attention to the commercial and ludic dimensions of videogames so that international relations scholarship can better account for pop culture’s bounded abilities to impact public understandings and political realities.Security Dialogue420This article identifies distortions of human–machine interaction that contradict real-world scenarios within paradoxical videogame representations of AI weapons both as ‘insurmountable enemies’ that pose existential threats to humankind in narratives and as ‘easy targets’ that human protagonists routinely overcome in gameplay.Security Dialogue['Guangyu Qiao-Franco', 'Paolo Franco']2024-01-31T00:00:00https://www.semanticscholar.org/paper/2a95e55da7ff57c4027b5573797034017f34e40e 97a2dfe10ba9027e4a3e4bf97efd7f5fc009bb42Background: The increasing demand for organic legume products has raised concerns about the validity of supply chains. This research explores the integration of blockchain and Artificial Intelligence (AI) technologies as a robust solution for ensuring the accuracy of organic legume products in supply chains. Leveraging the immutable and transparent nature of blockchain, the study establishes a decentralized ledger to record and validate each stage of the supply chain, from crop husbandry to distribution. Methods: Artificial intelligence (AI) algorithms are used in tandem to examine data points and identify irregularities that can signal the existence of fake goods. Through the integration of various technologies, the research aims to offer an advanced and flexible system that can anticipate and detect any risks to the validity of the product. Smart contract implementation on the blockchain enables automated verification procedures assuring, adherence to organic norms and laws. Result: Through case studies and empirical evidence, this paper demonstrates the efficacy of the proposed blockchain and AI integration in mitigating the risks associated with counterfeit organic legume products. This research contributes to the burgeoning field of blockchain and AI applications in supply chain management, offering a novel approach to fortify the integrity of organic food supply chains. Legume Research An International Journal00The efficacy of the proposed blockchain and AI integration in mitigating the risks associated with counterfeit organic legume products is demonstrated, offering a novel approach to fortify the integrity of organic food supply chains.LEGUME RESEARCH - AN INTERNATIONAL JOURNAL['Si-Yeong Kim', 'A. Alzubi']2024-01-31T00:00:00https://www.semanticscholar.org/paper/97a2dfe10ba9027e4a3e4bf97efd7f5fc009bb42 250e899c37fb69907fe3cbb8fe61117d71b41d18Studying the behaviour of engineering systems and processes from the perspective of applications of artificial intelligence provides an invaluable reference to improve their productivity and industrial development at large. This study comprehensively unveiled the problems faced by engineering systems and how artificial intelligence could be deployed as a technique for the future advancement of the industry. A brief background of the application of artificial intelligence in some selected engineering fields revealed that insufficient operational and process data from both plants and processes are major problems causing the survival of sustainable intelligent systems thereby, leading to incessant system failure. Furthermore, it was equally discovered that artificial intelligent for specific application are based on the data obtained from such application. Thus, there is no universally agreed artificial intelligent for a specific application. This made it a bit complex in developing intelligent systems. Keywords: Artificial Neural Network, Applications, Engineering, Training, Data.International Conference on Sustainable Engineering and Materials Development (ICSEMD)730This study comprehensively unveiled the problems faced by engineering systems and how artificial intelligence could be deployed as a technique for the future advancement of the industry.International Conference on Sustainable Engineering and Materials Development (ICSEMD)['Oghenevwegba T. Emuowhochere', 'E. Salawu', 'Samson O. Ongbali', 'O. Ajayi']2024-01-31T00:00:00https://www.semanticscholar.org/paper/250e899c37fb69907fe3cbb8fe61117d71b41d18 c297d50d5e4dd95f8fd0073f510d7fea08a2401dGiven the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.Journal of Environmental Science and Health. Part A: Toxic/Hazardous Substances and Environmental Engineering850This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI) and shows these models have shown to be more adept at simulating and modeling processes than traditional models.Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering['M. Mathaba', 'JeanClaude Banza']2024-01-31T00:00:00https://www.semanticscholar.org/paper/c297d50d5e4dd95f8fd0073f510d7fea08a2401d 0c54a5fffdcb26dca44537a519acde60f6eb229aThe research seeks to delve into and comprehend the attitudes of university students regarding artificial intelligence (AI) and to identify potential factors influencing these attitudes. The research employs a descriptive research design with a quantitative approach. A sample of 240 university students, including both males and females, was selected using simple random sampling. The AI Attitude scale (AIAS-4) developed by Grassini in 2023 was used to collect the data. Statistical techniques, such as “descriptive analysis,” “indeoendent sample t-test,” “one-way ANOVA,” and “post hoc” test were used to analyze the data. The findings indicate that there was no statistically significant difference in attitudes toward AI between male and female university students. Furthermore, our research has substantiated a significant difference in attitudes toward AI among university students specializing in the fields of arts, science, and commerce. The findings of this study suggest that science students displayed a significantly more positive attitude toward AI when compared to their counterparts in the Arts and Commerce streams. Moreover, we examined the impact of educational level on AI attitudes and found no significant difference in attitudes across different educational levels among university students.Journal of Educational Technology Systems90It is suggested that science students displayed a significantly more positive attitude toward AI when compared to their counterparts in the Arts and Commerce streams, and the impact of educational level on AI attitudes was examined.Journal of Educational Technology Systems['Khalid Bashir Hajam', 'Sanjib Gahir']2024-01-31T00:00:00https://www.semanticscholar.org/paper/0c54a5fffdcb26dca44537a519acde60f6eb229a fc7a6a331c93e11a95d488049a93623398f659c4This research explores the impact of using Artificial Intelligence (AI) on scientific Indonesian language learning outcomes at PGRI Wiranegara University. With quantitative and qualitative approaches, this research shows a significant increase in the academic achievement of students who receive AI-based learning. The results of the analysis show a positive difference in values between the experimental group and the control group. Students reported positive experiences regarding AI assistance in overcoming learning difficulties. The conclusion confirms that AI integration is effective in improving the efficiency and quality of scientific Indonesian language learning in higher education. This research contributes to the development of innovative learning methods based on AI technology in higher education environments.International Journal of Applied Research and Sustainable Sciences00The conclusion confirms that AI integration is effective in improving the efficiency and quality of scientific Indonesian language learning in higher education.International Journal of Applied Research and Sustainable Sciences['Sugianti', 'Ilmiyatur Rosidah']2024-01-31T00:00:00https://www.semanticscholar.org/paper/fc7a6a331c93e11a95d488049a93623398f659c4 7f317ff5af664cafbee8bf9e541ce4074819eb6fArtificial Intelligence plays a great role in improving the quality of financial reporting to the stakeholders as it can free up the human resources and improves security measures as well as makes sure that the organisation is going in right technological direction or not. This kind of technology is also helpful in retaining customers towards business which is the main cause of this research study. The time and cost are to be used in an effective manner by the organisation that helps in improving its overall performance. The current research has covered secondary sources for collecting qualitative data in a successful manner.International Journal of Innovative Research in Multidisciplinary Education00The current research has covered secondary sources for collecting qualitative data in a successful manner and the time and cost are to be used in an effective manner by the organisation that helps in improving its overall performance.International Journal of Innovative Research in Multidisciplinary Education['Dr. Ibrahim M. Oleimat', 'Dr. Khalil Abu Saleem', 'Dr. Abeer Samara', 'Mr. Ahmad Al- Issa', 'M. M. Oleimat', 'Mr. Ali M. G. Khawaldeh']2024-01-31T00:00:00https://www.semanticscholar.org/paper/7f317ff5af664cafbee8bf9e541ce4074819eb6f fe282c0ef0e3e8c8b0129e7055bb8ad4d2fd466eArtificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.Pharmaceutics320Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology, and health authorities should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.Pharmaceutics['C. Bailleux', 'Jocelyn Gal', 'Emmanuel Chamorey', 'B. Mograbi', 'Gerard Milano']2024-01-31T00:00:00https://www.semanticscholar.org/paper/fe282c0ef0e3e8c8b0129e7055bb8ad4d2fd466e 0277780d3aa18c5d3ec225c53bc0261f0f296f27Abstract: Intelligent machines will ultimately substitute or improve on human abilities in many fields. Artificial intelligence is the intelligence displayed by software or robots. It falls within the umbrella of computer science. As it has improved human lives in many ways, artificial intelligence is growing in popularity as a subject of study in computer science. Over the past two decades, artificial intelligence has significantly boosted performance across a variety of industries, including manufacturing, services, and education. Artificial intelligence research has given rise to the fast-expanding field of expertise system. Artificial intelligence applications have a significant impact on many aspects of life because expert systems are frequently employed nowadays to handle complicated problems in domains like education, engineering, business, medical, weather forecasting, etc. The quality and efficiency have increased in the fields using artificial intelligence technologies. This paper provides an overview of this technology and the application of artificial intelligence in various fields, paying particular attention to how this technology is used in the field of education and discussing its significance, search methods, inventions, and future.International Journal for Research in Applied Science and Engineering Technology00An overview of this technology and the application of artificial intelligence in various fields is provided, paying particular attention to how this technology is used in the field of education and discussing its significance, search methods, inventions, and future.International Journal for Research in Applied Science and Engineering Technology['Snigdha Tandalaskar', 'Deb Prakash Roy', 'M. K. Ghosh', 'Ishika Dey']2024-01-31T00:00:00https://www.semanticscholar.org/paper/0277780d3aa18c5d3ec225c53bc0261f0f296f27 0821a2bdff8bbc3595598e4c5ef41d6b455a8b9aArtificial Intelligence (AI) is driving a significant and positive change in how businesses operate, fundamentally changing established models and pushing enterprises towards a more efficient and innovative future. This concise abstract explores the intricate influence of artificial intelligence (AI) on several aspects of corporate operations. It thoroughly analyses the development and present uses of AI, as well as successful cases, obstacles, and forthcoming trends. 1. An Examination of the Role of Artificial Intelligence (AI) in the Operations of Businesses. The introduction provides a comprehensive overview of the development of AI and its incorporation into business operations. The text explores the role of AI in transforming decision-making processes, highlighting its versatility in optimizing operations across various industries. It covers topics such as automation and predictive analytics. 2. Artificial Intelligence (AI) is being Increasingly Utilized in Several Aspects of Business Operations. An extensive examination of AI applications includes the enhanced efficiency of automation, the predictive capabilities of analytics, the transformative influence of AI in Customer Relationship Management (CRM), and its effects on Supply Chain Management. The passage emphasizes the essential role of AI in improving operational efficiency.Global Journal of Management and Business Research00An extensive examination of AI applications includes the enhanced efficiency of automation, the predictive capabilities of analytics, the transformative influence of AI in Customer Relationship Management (CRM), and its effects on Supply Chain Management.Global Journal of Management and Business Research['Zuo Bruno']2024-01-31T00:00:00https://www.semanticscholar.org/paper/0821a2bdff8bbc3595598e4c5ef41d6b455a8b9a 55fbfa5b0519df4ad66f34766da9f88fd018bd2bAbstract: It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. While no consensual definition of Artificial Intelligence (AI) exists, AI is broadly categorized as the study of computation that allow for perception, reason and action Today, the amount of data that is generated, by both humans and machines, far outpaces. Human’s ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.International Journal for Research in Applied Science and Engineering Technology00This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks and suggests the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.International Journal for Research in Applied Science and Engineering Technology['Prof. Dhengle Ashwini B', 'Aradwad Vaishnavi', 'Vedant Bidve', 'Balaji Gomchale', 'Supriya Tompe']2024-01-31T00:00:00https://www.semanticscholar.org/paper/55fbfa5b0519df4ad66f34766da9f88fd018bd2b c099256dd4867e223c9cdf40da5a574614f255d1This editorial examines the transformative role of Artificial Intelligence (AI) in enhancing cognitive accessibility for neurodiverse individuals. It explores the evolution from convention - al assistive technologies to sophisticated AI-driven solutions, highlighting how these advance - ments are reshaping inclusivity in education and the workplace. The piece critically analyzes the benefits and challenges of AI in this context, considering ethical implications, user-centered design, and the need for equitable access. It concludes with a call to action for continued inno - vation and collaboration in developing AI technologies that truly cater to the diverse needs of neurodiverse individuals.PriMera Scientific Engineering70This editorial examines the transformative role of Artificial Intelligence in enhancing cognitive accessibility for neurodiverse individuals and critically analyzes the benefits and challenges of AI in this context, considering ethical implications, user-centered design, and the need for equitable access.PriMera Scientific Engineering['Dr. Rukiya Deetjen-Ruiz', 'Marjorie P Daniel', 'Dr. Jennie Telus', 'Lodz Deetjen']2024-01-31T00:00:00https://www.semanticscholar.org/paper/c099256dd4867e223c9cdf40da5a574614f255d1 361bab688f6da72c33352f90e9717c1e12c575b3Abstract: The integration of Artificial Intelligence (AI) in image processing has significantly transformed the field of computer vision and visual information analysis. It examines the transformative impact of AI technologies in computer vision, image recognition, and image generation. This report also addresses the challenges and considerations associated with AI in image processing, while highlighting the potential for innovation and advancement in this rapidly evolving field.International Journal for Research in Applied Science and Engineering Technology00The integration of Artificial Intelligence in image processing has significantly transformed the field of computer vision and visual information analysis and the potential for innovation and advancement in this rapidly evolving field is highlighted.International Journal for Research in Applied Science and Engineering Technology['Rohan Dutta']2024-01-31T00:00:00https://www.semanticscholar.org/paper/361bab688f6da72c33352f90e9717c1e12c575b3 c502ff62a5821d864615f6bd5f75bf703611dce0Scant research has delved into the non-clinical facets of artificial intelligence (AI), concentrating on leveraging data to enhance the efficiency of healthcare systems and operating rooms. Notably, there is a gap in the literature regarding the implementation and outcomes of AI solutions. The absence of published results demonstrating the practical application and effectiveness of AI in domains beyond clinical settings, particularly in the field of surgery, served as the impetus for our undertaking in this area. Within the realm of non-clinical strategies aimed at enhancing operating room efficiency, we characterize OR efficiency as the capacity to successfully perform four uncomplicated arthroplasty surgeries within an 8-h timeframe. This Community Case Study addresses this gap by presenting the results of incorporating AI recommendations at our clinical institute on 228 patient arthroplasty surgeries. The implementation of a prescriptive analytics system (PAS), utilizing supervised machine learning techniques, led to a significant improvement in the overall efficiency of the operating room, increasing it from 39 to 93%. This noteworthy achievement highlights the impact of AI in optimizing surgery workflows.Frontiers in Artificial Intelligence160This Community Case Study describes OR efficiency as the capacity to successfully perform four uncomplicated arthroplasty surgeries within an 8-h timeframe, and presents the results of incorporating AI recommendations at the clinical institute on 228 patient arthroplasty surgeries.Frontiers in Artificial Intelligence['Farid Al Zoubi', 'Koorosh Kashanian', 'Paul E. Beaulé', 'Pascal Fallavollita']2024-01-31T00:00:00https://www.semanticscholar.org/paper/c502ff62a5821d864615f6bd5f75bf703611dce0 1c9a9355f9771408f431ba5300e7c7ad3a6dc21dArtificial Intelligence (AI) had been developed in various fields of human life. The use of AI brought the world towards a digital transformation that had not been imagined before. One form of AI development in the fields of biology and biology education brought the development of science in both fields far beyond expectations. The use of AI in the fields of biology and biology education had utilized many new methods and discoveries that are beneficial to humans. The purpose of this study was to determine the application of AI in biology and biology learning. The literature review method was used to analyze and synthesize research results from a new perspective. The results of the analysis of various pieces of our literature found various uses of AI. In the field of biology, including AI in the field of biology used for biological data analysis, genetic data analysis, investigation of complex biological phenomena (synthetic biology and system biology), bioinformatics, disease detection, and diagnosis. AI had been utilized in various fields of biological sciences such as medical, agriculture, animal husbandry, and industry for product development and automation in the production process utilizing IoT. There were 24 types of AI utilization in education, especially biology learning, which can be grouped into six groups: personalized and tutoring learning (teaching assistance/tutor), evaluation and assessment, teaching media, enriching learning, virtual classes, and learning aids.Jurnal Mangifera Edu320The purpose of this study was to determine the application of AI in biology and biology learning and found various uses of AI.Jurnal Mangifera Edu['Ipin Aripin', 'Aden Arif Gaffar', 'Muhammad Barin Abdul Jabar', 'Diana Yulianti']2024-01-31T00:00:00https://www.semanticscholar.org/paper/1c9a9355f9771408f431ba5300e7c7ad3a6dc21d 6ec7c390361c13c0ddd1db6a4962f2209eb7611cAbstract: This research paper offers an in-depth examination of how artificial intelligence (AI) is utilized in the healthcare sector. The primary emphasis is on understanding the profound influence it has in areas such as disease diagnosis, personalized medicine, and the optimization of patient care.International Journal for Research in Applied Science and Engineering Technology00An in-depth examination of how artificial intelligence is utilized in the healthcare sector with a primary emphasis on understanding the profound influence it has in areas such as disease diagnosis, personalized medicine, and the optimization of patient care is offered.International Journal for Research in Applied Science and Engineering Technology['Saniya Shaikh']2024-01-31T00:00:00https://www.semanticscholar.org/paper/6ec7c390361c13c0ddd1db6a4962f2209eb7611c f885d84660fef32ea4154c9944979b3178decfa8PriMera Scientific Medicine and Public Health00PriMera Scientific Medicine and Public Health[]2024-01-31T00:00:00https://www.semanticscholar.org/paper/f885d84660fef32ea4154c9944979b3178decfa8 14faed00db373d87bf90e8ee8f2c0dbbeed768ddInternational Journal of Production Research765International Journal of Production Research['Ilya Jackson', 'Dmitry A. Ivanov', 'Alexandre Dolgui', 'Jafar Namdar']2024-01-31T00:00:00https://www.semanticscholar.org/paper/14faed00db373d87bf90e8ee8f2c0dbbeed768dd 576761471e8002fcdd2ba6cc8e6b0c12da285ca2Este trabajo explora de manera crítica algunas supuestas implicaciones del desarrollo de la inteligencia artificial (IA), particularmente también del aprendizaje de las máquinas (AM), sobre cómo concebimos el papel de la empresarialidad en la economía. La cuestión del impacto de la IA y el AM se examina bajo la hipótesis de un sistema de mercado descentralizado y preguntándonos si algún día los empresarios podrán ser reemplazados por las máquinas la respuesta a esta pregunta es de gran escepticismo. No sólo la cosmovisión materialista que está detrás de la ambición de gran parte de la investigación en IA proyecta serias dudas sobre las posibilidades de éxito de cualquier intento de emular la empresarialidad de forma algorítmica con ayuda de los ordenadores, la mera posibilidad de inteligencia artificial general (IAG) también puede descartarse por razones puramente científicas. El trabajo concluye que los empresarios seres humanos continuarán siendo la fuerza impulsora del mercado.REVISTA PROCESOS DE MERCADO120REVISTA PROCESOS DE MERCADO['Ludwig Van Den Hauwe']2024-01-31T00:00:00https://www.semanticscholar.org/paper/576761471e8002fcdd2ba6cc8e6b0c12da285ca2 33506ebf680da2b1e7f59de3f71e56d90723bc1aCurrent Psychology332Current Psychology['Byung-Jik Kim', 'Min-Jik Kim', 'Julak Lee']2024-01-31T00:00:00https://www.semanticscholar.org/paper/33506ebf680da2b1e7f59de3f71e56d90723bc1a 71f601bc802902739d1caf2eef5c5ece8999da52European Urology Oncology211This study externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease and provided evidence for consistent validation of the deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care.European urology oncology['Ashley E. Ross', 'Jingbin Zhang', 'Huei-Chung Huang', 'Rikiya Yamashita', 'Jessica Keim-Malpass', 'J. Simko', 'S. DeVries', 'Todd M. Morgan', 'L. Souhami', 'M. C. Dobelbower', 'L. S. McGinnis', 'Christopher U Jones', 'R. Dess', 'K. Zeitzer', 'Kwang Choi', 'A. Hartford', 'J. Michalski', 'Adam Raben', 'Leonard G. Gomella', 'A. O. Sartor', 'S. Rosenthal', 'H. Sandler', 'D. E. Spratt', 'S. Pugh', 'O. Mohamad', 'A. Esteva', 'E. Chen', 'E. Schaeffer', 'Phuoc T. Tran', 'F. Y. Feng']2024-01-31T00:00:00https://www.semanticscholar.org/paper/71f601bc802902739d1caf2eef5c5ece8999da52 8a0c9cf4a4a75e291286ebe2a31b3d75e1762675American Journal of Gastroenterology01The American journal of gastroenterology['J. Ge', 'Irene Y Chen', 'M. Pletcher', 'Jennifer C Lai']2024-01-31T00:00:00https://www.semanticscholar.org/paper/8a0c9cf4a4a75e291286ebe2a31b3d75e1762675 ac56b0b9bd05524ba53c49fac3753524bc84e6f7Economy and Society261Economy and Society['David Kampmann']2024-01-31T00:00:00https://www.semanticscholar.org/paper/ac56b0b9bd05524ba53c49fac3753524bc84e6f7 52061487025975178068b880739128e6f00a4860 The changing supply and demand of hydrocarbons in recent years, generated by inflation, pandemics, and wars, have impacted its price considerably, developing limitations in maintenance processes in the oil industry worldwide; however, the mechanical deterioration of facilities due to corrosion does not stop. This article contributes original to the knowledge and management of a pipeline transportation system (PTS) without an immediate high impact that would help reduce property loss due to corrosion through the development of intelligent evaluation models that combine field data, laboratory, and cognitive knowledge in a case study in Mexico. The research is divided into Part 1 (Modeling; https://doi.org/10.1515/corrrev-2021-0080) and Part 2 (Operational and economic risk analysis of PTS under corrosive effects, using Monte Carlo simulation, MCS), supported by information and knowledge of 564 km of soils, from cathodic protection studies, essential to determine corrosive profiles of the PTS, considering supply and demand effects, with 1095 data on the price of the Mexican mixture from 2016 to 2019, as well as data monthly inflation rates for the same period, to generate financial estimates representative of the system, in search of replicable exchange actions and practices in international fields.Corrosion reviews200Corrosion Reviews['Jonathan J. Cid-Galiot', 'A. Aguilar-Lasserre', 'José Pastor Rodríguez-Jarquín', 'Alina Evelyn Badillo-Márquez', 'Manuel Adam-Medina']2024-01-31T00:00:00https://www.semanticscholar.org/paper/52061487025975178068b880739128e6f00a4860 40f181875b8570e81a904e601ef44e93102120b5[This retracts the article DOI: 10.1155/2022/2178579.].Journal of Environmental and Public Health10Journal of Environmental and Public Health['Journal of Environmental and Public Health']2024-01-31T00:00:00https://www.semanticscholar.org/paper/40f181875b8570e81a904e601ef44e93102120b5 145646f057395fadd417bd1ffc47f76cd3bcc5f2This study examined the elementary school teachers' stages of concern and level of use of mathematics AI teaching support system. For this purpose, 266 survey responses were collected from elementary school teachers in South Korea, and the data were analyzed using the Concerns-Based Adoption Model. The elementary school teachers' stages of concern in school and AI class support systems showed the highest at level 0 (Awareness), the lowest at level 4 (cooperation), and an upward trend that increased again at levels 5 and 6. The stages of concern of elementary school teachers in the AI class support system differed significantly according to the teacher's position, AI training, and AI class application. The implementation level of elementary school teachers for the AI teaching support system in mathematics was at the level of non-users who did not implement the AI teaching support systemJournal of the Korea Academia-Industrial cooperation Society260The elementary school teachers' stages of concern in school and AI class support systems showed the highest at level 0 (Awareness), the lowest at level 4 (cooperation), and an upward trend that increased again at levels 5 and 6.Journal of the Korea Academia-Industrial cooperation Society['Chung-Kyung Kim']2024-01-31T00:00:00https://www.semanticscholar.org/paper/145646f057395fadd417bd1ffc47f76cd3bcc5f2 b8cc00693c75e539396fe4ae47951bbc44f2323fBu çalışmanın amacı günümüz modern çağında teknolojilerin gelişmesiyle beraber artan yapay zekâ çalışmalarıyla ilgili oluşan kaygıların muhasebe meslek mensupları üzerinde, onların gelecekte istihdam edilebilme algıları kapsamında incelenmesini ve değerlendirilmesini sağlamaktır. Çalışmada muhasebe meslek mensupları üzerinde oluşan yapay zekâ kaygılarının, kendilerinin gelecekte istihdam edilebilme algıları üzerinde ne gibi etkilere sahip olabileceğini ve amaca uygun ölçeklerle analiz edilmesi tasarlanmıştır. Çalışmada ampirik yöntemler tercih edilmiştir. Çalışma sonucunda; işletmeler üzerinde çalışanlarda meydana gelebilecek kaygı çeşitlerinden birisi olan yapay zekâ kaygılarının muhasebe meslek mensupları açısından gelecekte istihdam edilebilirlik algılar üzerinde (β: 0,264 pAlanya Akademik Bakış200Alanya Akademik Bakış['Ali Özbek']2024-01-31T00:00:00https://www.semanticscholar.org/paper/b8cc00693c75e539396fe4ae47951bbc44f2323f b8a38d08cdd338316d7058a14a1f11224cde2988Birçok alanda olduğu gibi teknoloji alanında yaşanan gelişmelere paralel olarak yapay zekâ uygulamalarının çalışma yaşamına da girmiş olması, çalışanlar üzerinde yapay zekâ kaygısı olarak ifade edilen kavramı kritik bir konu haline getirmiş durumdadır. Çalışmada turizm sektörü çalışanlarının yapay zekâ kaygı düzeylerinin belirlenmesi ve yapay zekâ kaygılarının onların içsel ve dışsal motivasyonları üzerine etkisinin belirlenmesi amaçlanmıştır. Kolayda örnekleme yöntemi ile ulaşılan turizm sektöründe çalışan 165 katılımcıdan anket aracılığıyla toplanan veriler üzerinde gerçekleştirilen analizler sonucunda; turizm sektörü çalışanlarının motivasyon düzeylerinin yüksek, yapay zeka kaygı düzeylerinin düşük olduğu, yapay zeka kaygıları ile dışsal motivasyonları arasında anlamlı bir ilişki olmadığı fakat içsel motivasyonla pozitif yönde anlamlı ve düşük düzeyde bir ilişki olduğu sonucuna ulaşılmıştır. araştırma değişkenlerini ele alan bir çalışmaya rastlanılamamış olması, araştırmanın özgün yönünü ve önemini ortaya koymaktadırAlanya Akademik Bakış220Alanya Akademik Bakış['Nur Çetiner', 'Filiz Özlem Çetinkaya']2024-01-31T00:00:00https://www.semanticscholar.org/paper/b8a38d08cdd338316d7058a14a1f11224cde2988 7de62c78020cc2bf1e56e0186d0356d34fb77e6cThe size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.Applied Sciences540A review of studies related to the detection of anomalies in smart grids using AI, including the use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids.Applied Sciences['Marcelo Fabian Guato Burgos', 'Jorge Morato', 'Fernanda Paulina Vizcaino Imacaña']2024-01-31T00:00:00https://www.semanticscholar.org/paper/7de62c78020cc2bf1e56e0186d0356d34fb77e6c 24b42e841abbb9d9b6840ca32c63ca129e697d0dThis integration enables the system to collect and monitor information from remote sources efficiently. During the course of this research, a novel predictive PID approach was developed, splitting the control architecture into two tiers. The upper tier utilizes the extreme learning machine (ELM) as an intelligent predictive model, while the lower tier integrates an enhanced single-neuron adaptive predictive PID control algorithm, combining the strengths of ELM and PID control. The research findings suggest that the AI algorithm-based instrument automatic monitoring and control system holds significant promise. This technology has the potential to enhance production efficiency, reduce energy consumption, improve environmental monitoring, and provide superior safety and quality control.International Journal of Information Technologies and Systems Approach120The research findings suggest that the AI algorithm-based instrument automatic monitoring and control system holds significant promise, and has the potential to enhance production efficiency, reduce energy consumption, improve environmental monitoring, and provide superior safety and quality control.International Journal of Information Technologies and Systems Approach['Qinmei Wang']2024-01-31T00:00:00https://www.semanticscholar.org/paper/24b42e841abbb9d9b6840ca32c63ca129e697d0d 89a4a2ef83919ee8a626d27b695d11f2e08b0365<jats:p />Journal of Sensors10Journal of Sensors['Journal of Sensors']2024-01-31T00:00:00https://www.semanticscholar.org/paper/89a4a2ef83919ee8a626d27b695d11f2e08b0365 2d205ae9350c052edb5b5b4c6ec8ca6049875fdfAsia-pacific Journal of Convergent Research Interchange00Asia-pacific Journal of Convergent Research Interchange['Jae Jin Kim', 'Yoon Min Hwang']2024-01-31T00:00:00https://www.semanticscholar.org/paper/2d205ae9350c052edb5b5b4c6ec8ca6049875fdf c1505f3461e7f5ba2a492c9b8cbf8ec4db3bb066<jats:p />Journal of Sensors10Journal of Sensors['Journal of Sensors']2024-01-31T00:00:00https://www.semanticscholar.org/paper/c1505f3461e7f5ba2a492c9b8cbf8ec4db3bb066 26fb65a3cb5d956319f58ff8742e2fe98a9d3199<jats:p>Ν/Α</jats:p>Επιθεώρηση Κοινωνικών Ερευνών00Επιθεώρηση Κοινωνικών Ερευνών['G. Gotsis']2024-01-31T00:00:00https://www.semanticscholar.org/paper/26fb65a3cb5d956319f58ff8742e2fe98a9d3199 42242565246babe97e2d043750f34a7489ec7392Journal of Investigative Dermatology40The Journal of investigative dermatology['Aditya K. Gupta', 'M. Talukder', 'Tong Wang', 'Roxana Daneshjou', 'Vincent Piguet']2024-01-31T00:00:00https://www.semanticscholar.org/paper/42242565246babe97e2d043750f34a7489ec7392 b2635cfc785e12673ec48b99b0a463cf7b20f6daAfrican Journal of Development Studies (formerly AFFRIKA Journal of Politics, Economics and Society)00African Journal of Development Studies (formerly AFFRIKA Journal of Politics, Economics and Society)['Alexander Maina Kimari', 'Eric Blanco Niyitunga', 'Jahed Mohammad']2024-01-31T00:00:00https://www.semanticscholar.org/paper/b2635cfc785e12673ec48b99b0a463cf7b20f6da ba2cb7122e053f77d287240ec0127b958bbfced4Global Journal of Pediatrics (GJP)00Global Journal of Pediatrics (GJP)['M. D. Navarro Rubio']2024-01-31T00:00:00https://www.semanticscholar.org/paper/ba2cb7122e053f77d287240ec0127b958bbfced4 208d3a63721e8d86b8039688f51cc50ea5e0e7f0Journal of Digital Contents Society40Journal of Digital Contents Society['Jung-Min Kim', 'Jungwoo Choi', 'Kyoung-Chin Seo']2024-01-31T00:00:00https://www.semanticscholar.org/paper/208d3a63721e8d86b8039688f51cc50ea5e0e7f0 4b41a1b2cd9d191ade5025e8f70698d68a43f084Revista Romana de Inginerie Civila/Romanian Journal of Civil Engineering00Revista Romana de Inginerie Civila/Romanian Journal of Civil Engineering['Constantin Cilibiu', 'A. Abrudan']2024-01-31T00:00:00https://www.semanticscholar.org/paper/4b41a1b2cd9d191ade5025e8f70698d68a43f084 6f4a137196ad979e88992b7c4200b400508027e2Drug Discovery Today100Drug discovery today['Mohammad Abavisani', 'Alireza Khoshrou', 'A. Sahebkar']2024-01-31T00:00:00https://www.semanticscholar.org/paper/6f4a137196ad979e88992b7c4200b400508027e2 6e74dd07f809d1d06c7ae2bb16cb592ed38f593bJournal of Research in Pharmacy Practice50Journal of Research in Pharmacy Practice['R. K. Garg']2024-01-31T00:00:00https://www.semanticscholar.org/paper/6e74dd07f809d1d06c7ae2bb16cb592ed38f593b bedc152b5e274c32da357809920ef14698f6d8bbJournal of Bio-Environment Control110Journal of Bio-Environment Control['K.P.S. Kumaratenna', 'Young-Yeol Cho']2024-01-31T00:00:00https://www.semanticscholar.org/paper/bedc152b5e274c32da357809920ef14698f6d8bb 0d794cc5d4cc1c7b1c3f74c0ccc0f67121fbb4afThe Asian Journal of Kinesiology (Online)00The Asian Journal of Kinesiology['Jin-Hee Seo']2024-01-31T00:00:00https://www.semanticscholar.org/paper/0d794cc5d4cc1c7b1c3f74c0ccc0f67121fbb4af 6fbedb4c0c237de27c1aa9a1fe5fadc01532c575Journal of Ethics Education Studies00Journal of Ethics Education Studies['Eu-sun Heo']2024-01-31T00:00:00https://www.semanticscholar.org/paper/6fbedb4c0c237de27c1aa9a1fe5fadc01532c575 703c8121f12d281eca4fddc0665ada20cef896efBritish Journal of Anaesthesia210The need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct is highlighted.British journal of anaesthesia['J. Bowness', 'Xiaoxuan Liu', 'P. Keane']2024-01-31T00:00:00https://www.semanticscholar.org/paper/703c8121f12d281eca4fddc0665ada20cef896ef 1ccbea396376f0e07cef5eb765dec02fbd56fb94Major Gifts Report00The Major Gifts Report['Kim Pawlak']2024-01-31T00:00:00https://www.semanticscholar.org/paper/1ccbea396376f0e07cef5eb765dec02fbd56fb94 fc7990da9f64ff3f73c1254dcb2a5682cff87d16Journal of Digital Contents Society60Journal of Digital Contents Society['Daehyun Han']2024-01-31T00:00:00https://www.semanticscholar.org/paper/fc7990da9f64ff3f73c1254dcb2a5682cff87d16 9fdc16749cf570b12e6633ade280075f28ec8b86La inteligencia artificial (artificial intelligence, AI) ha revolucionado muchos ámbitos sociales y humanos, pero la creación audiovisual supone un aspecto central en la reflexión sobre sus potencialidades. El trabajo enfoca algunas transformaciones que sufre el proceso creativo en el videoarte o video experimental, a través de algunas obras, con las que se puede reflexionar sobre las dimensiones estéticas, la temporalidad, la relación entre sonido e imagen y la efímera constitución de la audiovisualidad. El trabajo realiza un estudio exploratorio sobre el tema con una metodología de investigación cualitativa, para la interpretación y discusión de las obras-ejemplos del autor Ben Bogart. Las consideraciones finales apuntan a que la AI incluye operaciones artísticas relacionadas con las vanguardias anteriores del media art, como el collage o la apropiación. Todas estas herramientas conllevan numerosas implicaciones éticas y posicionamientos del artista, así como posibilidades para subvertir lógicas creativas.AusArt170AusArt['Regilene Sarzi-Ribeiro', 'A. Sedeño-Valdellós']2024-01-31T00:00:00https://www.semanticscholar.org/paper/9fdc16749cf570b12e6633ade280075f28ec8b86 285696c7a145843a7cd2fd2e9014795ab8da12f2As artificial intelligence and automation technology develop, the concept and application of intelligent manufacturing is recognized by more and more people, and the development trend of industrial enterprises' intelligence is gradually remarkable. In order to improve the industrial intelligence of an economy and indirectly promote its circular economy, this study uses fuzzy hierarchical analysis and feed‐forward neural network algorithm to construct an evaluation model of the intelligence of an economy and multiple linear regression to build an analytical model to evaluate the effect and impact of industrial intelligence on circular economy. Based on China's provincial economic yearbooks from 2012 to 2022, the total absolute difference between the average absolute error values of the hybrid fuzzy hierarchical analysis and feedforward neural network algorithm model, the traditional hierarchical analysis model and the manual evaluation method designed in this study are 0.14 and 0.31, respectively. In the industrial intelligentization ‐ industrial structure model, except for the proportion of output value of state‐owned enterprises above the scale, all other indicators have a significant positive effect, indicating that industrial intelligence, information construction and urbanization are conducive to economic scale growth. In the industrial intelligentization ‐ environmental bias technology progress model, the regression coefficients of the proportion of output value of state‐owned enterprises above the scale, industrial intelligence score, and postal communication per capita are 3.846, 0.8510, and 0.0381, respectively, which can accelerate the industrial transformation of the economy. In the industrial intelligence‐economic scale model, the percentage of output value of state‐owned enterprises above the scale significantly effects the environmental bias toward technological progress and the regression coefficient is −34.72, indicating that the lower percentage of state‐owned enterprises in the economic structure is more conducive to industrial intelligence. This study has some reference significance for auxiliary economies to carry out industrial intelligence and stimulate the development of circular economy.Advanced Control for Applications190Advanced Control for Applications['Jianlin Zhao']2024-01-31T00:00:00https://www.semanticscholar.org/paper/285696c7a145843a7cd2fd2e9014795ab8da12f2 c560a2aa5755bbdd027aa242f5a973707d5c187dIn an era characterized by vast data streams and complex socioeconomic dynamics, the fusion and precise analysis of multi‐sourced intelligence has emerged as a pivotal challenge. To address this, the study constructs a sophisticated intelligence fusion network (IFN) architecture leveraging the potential of Artificial General Intelligence (AGI) and the security tenets of blockchain technology. Drawing from diverse fields including informatics, computer science, data analytics, and network security, the research adopts an integrative methodology comprising both a comprehensive literature review and systems analysis. Key findings highlight the prowess of AGI‐driven IFNs in enhancing governmental early warning systems for crisis management. These networks underscore a paradigm shift from reactive postevent measures to proactive pre‐event forecasting, thus bolstering the efficacy of governmental responses. Moreover, the decentralized nature of blockchain technology ensures data integrity, fostering trust in interdepartmental data sharing—an essential for efficient crisis management in hierarchical administrative structures. This study accentuates the need for redefining crisis management strategies, emphasizing data‐driven decision‐making and seamless intelligence sharing to ensure optimal outcomes.Journal of Contingencies and Crisis Management270The study constructs a sophisticated intelligence fusion network (IFN) architecture leveraging the potential of Artificial General Intelligence (AGI) and the security tenets of blockchain technology to accentuate the need for redefining crisis management strategies.Journal of Contingencies and Crisis Management['Yang Yue', 'Joseph Z. Shyu']2024-01-31T00:00:00https://www.semanticscholar.org/paper/c560a2aa5755bbdd027aa242f5a973707d5c187d 79597e5f16ab6a8f0107a336c7df81b36b991a9aAI &amp; SOCIETY430Analysis of patterns of media use and exposure to media messages are related to attitudes about artificial intelligence (AI) image generators shows that technology news use and science fiction viewing predicted support for AI art but also predicted belief that AI image generators will take jobs and steal art styles from human artists.AI &amp; SOCIETY['Paul R. Brewer', 'Liam Cuddy', 'Wyatt Dawson', 'Robert Stise']2024-01-30T00:00:00https://www.semanticscholar.org/paper/79597e5f16ab6a8f0107a336c7df81b36b991a9a bfb7c8007963ff2606cc934e469e4ee00cee90d2This article is a revised version of a concept paper written for the European Commission on the private international law regulation of individual employment relationships within the EU. It aims to assess the regulation of such relationships from the perspective of European private international law and indicate potential avenues for reform.European Labour Law Journal00European Labour Law Journal['U. Grušić']2024-01-30T00:00:00https://www.semanticscholar.org/paper/bfb7c8007963ff2606cc934e469e4ee00cee90d2 91ac58f3877e6fb04049373e792bd44918a65380In the dynamic landscape of the burgeoning renewable energy sector, optimizing energy output, ensuring robust infrastructure maintenance, and seamless integration into the grid present formidable challenges. This paper delves into the transformative potential of artificial intelligence (AI) as a solution to these critical issues. The focus of this study is on the current state of AI applications within the renewable energy domain, particularly honing in on its profound impact on predictive maintenance and energy optimization across diverse sources such as solar, wind, and hydro. By examining the underlying AI techniques employed in this context, the research seeks to unravel the intricacies of how AI contributes to enhancing the efficiency and sustainability of renewable energy systems. A critical component of this exploration involves the analysis of successful case studies, illustrating real-world applications where AI has made substantial strides in predictive maintenance and energy optimization. These cases provide tangible evidence of the practical implications of incorporating AI into renewable energy practices. The research explores AI’s role in renewable energy, focusing on emerging trends and future directions. It aims to understand AI’s transformative influence on optimization, sustainability, and energy efficiency, fostering a more resilient and efficient energy landscape. AI is revolutionizing the renewable energy sector, transforming infrastructure maintenance, energy generation optimization, and integrating renewable sources into the grid. Its advanced analytics, predictive capabilities, and optimization are crucial in achieving global renewable energy targets. As AI technology evolves, its impact on the renewable energy landscape will deepen, paving the way for a cleaner, more sustainable future. By harnessing AI’s power, we can accelerate the transition towards a renewable energy future, ensuring a thriving planet for future generations.International Journal of Science and Research Archive06The focus of this study is on the current state of AI applications within the renewable energy domain, particularly honing in on its profound impact on predictive maintenance and energy optimization across diverse sources such as solar, wind, and hydro.International Journal of Science and Research Archive['Ahmad Hamdan', 'Kenneth Ifeanyi Ibekwe', 'Valentine Ikenna Ilojianya', 'Sedat Sonko', 'Emmanuel Augustine Etukudoh']2024-01-30T00:00:00https://www.semanticscholar.org/paper/91ac58f3877e6fb04049373e792bd44918a65380 8767753a6473e1928b3805e121b8d57e7163a23fThis study examines the impact of artificial intelligence (AI) on financial control, exploring the implementation of AI technology in financial selection-making strategies, predictive analysis and hazard manipulation. the use of a systematic literature examine approach, this studies covers the substantial modifications delivered via using AI in improving operational overall performance, presenting deep insights for financial preference making, and improving customer revel in in the banking area.. Even though it provides great benefits, this research also highlights ethical challenges, data security, adoption risks, as well as the need for policy and regulatory adjustments to the development of AI technology in the context of financial management. It is hoped that the results of this research can provide guidance for companies and policy makers in facing revolutionary changes in financial management in the digital era.PRODUCTIVITY07The substantial modifications delivered via using AI in improving operational overall performance, presenting deep insights for financial preference making, and improving customer revel in in the banking area are covered.Management Studies and Business Journal (PRODUCTIVITY)['Muhammad Hidayat', 'Siska Yulia Defitri', 'H. Hilman']2024-01-30T00:00:00https://www.semanticscholar.org/paper/8767753a6473e1928b3805e121b8d57e7163a23f 4f91a1bd9458b9d73ddf88cfc1616e3108c5f1c2Abstract: Artificial intelligence (AI) is considered a vital factor that will fundamentally alter the cybersecurity environment. AI technology is progressing much faster than expected, and AI-based security services are being introduced into the global security market on a daily basis. However, how AI can contribute to the cybersecurity field and what changes it will bring remain unknown. Nonetheless, cybersecurity is not merely a technical issue but also a process for dealing with regulations, policies, and security risks; therefore, the introduction of AI technology introduction can make a fundamental difference in cybersecurity policy as a whole. This study primarily aims to better understand the concept and characteristics of AI from the cybersecurity perspective and identify its future implications on cybersecurity environment at the national policy level. This study predicts what modifications will be made to national cybersecurity strategies (NCSS) when machine learning (ML) is introduced and implemented. It also provides a basic policy recommendation that offers potential responses to these changes. The study first describes the emergence of AI in the cybersecurity field and explains AI-ML technical services and AI security policy elements. Second, through NCSS material analysis, this study categorizes NCSS into 11 categories and selects the critical functions of each dimension. Finally, it predicts the changes that will occur when AI is introduced within the selected NCSS category. It also introduces the priorities and considerations required for these changes.Tehnički glasnik316This study predicts what modifications will be made to national cybersecurity strategies (NCSS) when machine learning (ML) is introduced and implemented and provides a basic policy recommendation that offers potential responses to these changes.Tehnički glasnik['Geunhye Kim', 'Kyudong Park']2024-01-30T00:00:00https://www.semanticscholar.org/paper/4f91a1bd9458b9d73ddf88cfc1616e3108c5f1c2 8530388908d2642e7b829a1372bcbe2ef8997defThis editorial introduces and explains the Journal of Management Studies’ (JMS) new policy on artificial intelligence (AI). We reflect on the use of AI in conducting research and generating journal submissions and what this means for the wider JMS community, including our authors, reviewers, editors, and readers. Specifically, we consider how AI‐generated research and text could both assist and augment the publication process, as well as harm it. Consequentially, our policy acknowledges the need for careful oversight regarding the use of AI to assist in the authoring of texts and in data analyses, while also noting the importance of requiring authors to be transparent about how, when and where they have utilized AI in their submissions or underlying research. Additionally, we examine how and in what ways AI's use may be antithetical to the spirit of a quality journal like JMS that values both human voice and research transparency. Our editorial explains why we require author teams to oversee all aspects of AI use within their projects, and to take personal responsibility for accuracy in all aspects of their research. We also explain our prohibition of AI's use in peer‐reviewers’ evaluations of submissions, and regarding editors’ handling of manuscripts.Journal of Management Studies245The policy acknowledges the need for careful oversight regarding the use of AI to assist in the authoring of texts and in data analyses, while also noting the importance of requiring authors to be transparent about how, when and where they have utilized AI in their submissions or underlying research.Journal of Management Studies['Caroline Gatrell', 'Daniel Muzio', 'Corinne Post', 'Christopher Wickert']2024-01-30T00:00:00https://www.semanticscholar.org/paper/8530388908d2642e7b829a1372bcbe2ef8997def 1d964196ea0daf8ee9eb404fa2791ef6d02715d9This paper delves into the transformative impact of Artificial Intelligence (AI) on traditional accounting practices, examining its role in reshaping financial reporting, auditing, and decision-making processes. The study explores the evolution from manual, labor-intensive accounting methods to sophisticated, AI-driven approaches by setting it against the backdrop of rapid technological advancements. The aim is to critically assess how AI integration is redefining the landscape of accounting, highlighting both the opportunities and challenges it presents. The study meticulously analyzes peer-reviewed articles, case studies, and industry reports from the last decade by employing a systematic literature review and bibliometric analysis. This methodology ensures a comprehensive understanding of AI's integration in accounting, its effectiveness in enhancing accuracy and efficiency, and the strategic implications for accounting professionals and firms. The findings reveal that AI significantly improves the accuracy and efficiency of financial reporting, automating routine tasks and enabling predictive analytics for strategic decision-making. However, challenges such as the need for skilled personnel adept in AI, data privacy concerns, and the high costs of AI integration are notable. The study also highlights the resistance to change as a significant barrier to AI adoption in accounting practices. In conclusion, the paper recommends a balanced approach to AI integration in accounting, emphasizing the need for continuous learning, adaptation, and strategic planning. It advocates for investment in training and development to build AI competency and stresses the importance of ethical considerations and regulatory compliance. The study concludes that while AI presents challenges, its potential to revolutionize accounting practices is undeniable, offering new avenues for growth and innovation in the digital era.World Journal of Advanced Research and Reviews415It is revealed that AI significantly improves the accuracy and efficiency of financial reporting, automating routine tasks and enabling predictive analytics for strategic decision-making, and enabling predictive analytics for strategic decision-making in the digital era.World Journal of Advanced Research and Reviews['Beryl Odonkor', 'Simon Kaggwa', 'Prisca Ugomma Uwaoma', 'Azeez Olanipekun Hassan', 'Oluwatoyin Ajoke Farayola']2024-01-30T00:00:00https://www.semanticscholar.org/paper/1d964196ea0daf8ee9eb404fa2791ef6d02715d9 02d2d5d0df657d9cef6ed92c8a08c3e99dac3040npj Precision Oncology1304Emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks are explored.NPJ Precision Oncology['Bouchra Derraz', 'Gabriele Breda', 'Christoph Kaempf', 'Franziska Baenke', 'Fabienne Cotte', 'Kristin Reiche', 'Ulrike Köhl', 'J. N. Kather', 'Deborah Eskenazy', 'S. Gilbert']2024-01-30T00:00:00https://www.semanticscholar.org/paper/02d2d5d0df657d9cef6ed92c8a08c3e99dac3040 24932b2885fd78b602a028d34f457d1d827693f8The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions to preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring and conservation. This research paper explores the applications of AI in ecosystem management, including wildlife tracking, habitat assessment, biodiversity analysis, and natural disaster prediction. AI's role in environmental monitoring and conservation includes wildlife tracking, habitat assessment, resource conservation, biodiversity analysis, and species identification. AI algorithms analyze camera trap footage, drone imagery, and GPS data to identify and estimate population sizes, leading to improved anti-poaching efforts and enhanced protection of diverse species. Habitat assessment and resource conservation involve AI-powered image analysis, which aids in assessing forest health, detecting deforestation, and identifying areas in need of restoration. Biodiversity analysis and species identification are achieved through AI algorithms that analyze acoustic recordings, environmental DNA (eDNA), and camera trap footage. These innovations identify different species, assess biodiversity levels, and even discover new or endangered species. AI-powered flood prediction systems provide early warnings, empowering communities with better preparedness and evacuation efforts. Challenges, such as data quality and availability, algorithmic bias, and infrastructure limitations, are acknowledged as opportunities for growth and improvement. In policy and regulation, the paper advocates for clear frameworks prioritizing data privacy and security, algorithmic transparency, and equitable access. Responsible development and ethical use of AI are emphasized as foundational pillars, ensuring that the integration of AI into environmental conservation aligns with principles of fairness, transparency, and societal benefit.World Journal of Advanced Research and Reviews374This research paper explores the applications of AI in ecosystem management, including wildlife tracking, habitat assessment, biodiversity analysis, and natural disaster prediction, and advocates for clear frameworks prioritizing data privacy and security, algorithmic transparency, and equitable access.World Journal of Advanced Research and Reviews['Onyebuchi Nneamaka', 'Chisom', 'Onyebuchi Nneamaka Chisom', 'Preye Winston Biu', 'Aniekan Akpan Umoh', 'Bartholomew Obehioye Obaedo', 'Abimbola Oluwatoyin Adegbite', 'Ayodeji Abatan']2024-01-30T00:00:00https://www.semanticscholar.org/paper/24932b2885fd78b602a028d34f457d1d827693f8 7a57a52e1a273799bed7c882bc12177ca89609abLarge language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. However, recent efforts on LLMs for wireless networks are limited to a direct application of existing language models that were designed for natural language processing (NLP) applications. To address this challenge and create wireless-centric foundation models, this paper presents a comprehensive vision on how to design universal foundation models that are tailored towards the deployment of artificial intelligence (AI)-native networks. Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI. In essence, these properties enable the proposed LMM framework to build universal capabilities that cater to various cross-layer networking tasks and alignment of intents across different domains. Preliminary results from experimental evaluation demonstrate the efficacy of grounding using RAG in LMMs, and showcase the alignment of LMMs with wireless system designs. Furthermore, the enhanced rationale exhibited in the responses to mathematical questions by LMMs, compared to vanilla LLMs, demonstrates the logical and mathematical reasoning capabilities inherent in LMMs. Building on those results, we present a sequel of open questions and challenges for LMMs. We then conclude with a set of recommendations that ignite the path towards LMM-empowered AI-native systems.arXiv.org183A comprehensive vision on how to design universal foundation models that are tailored towards the deployment of artificial intelligence (AI)-native networks is presented and a set of recommendations are concluded that ignite the path towards LMM-empowered AI-native systems.ArXiv['Shengzhe Xu', 'C. Thomas', 'Omar Hashash', 'N. Muralidhar', 'Walid Saad', 'Naren Ramakrishnan']2024-01-30T00:00:00https://www.semanticscholar.org/paper/7a57a52e1a273799bed7c882bc12177ca89609ab ee72465dd04c8f7d4553bfd04c0063ad6ee9ba5aPrecision agriculture, facilitated by advancements in Artificial Intelligence (AI), has emerged as a transformative paradigm in modern farming. This review comprehensively examines the integration of AI technologies in precision agriculture to enhance sustainability and optimize farming practices. The paper synthesizes recent research and developments in AI applications, covering key areas such as crop monitoring, resource management, decision support systems, and automation. The adoption of AI-driven techniques, including machine learning, computer vision, and sensor technologies, is reshaping traditional farming methods by providing farmers with real-time data and actionable insights. Crop monitoring applications utilize satellite imagery, drones, and ground-based sensors to assess plant health, detect diseases, and optimize irrigation strategies. AI-driven decision support systems empower farmers to make informed choices based on data-driven predictions, weather forecasts, and historical patterns, contributing to resource-efficient practices and minimizing environmental impact. Resource management is a critical aspect of sustainable farming, and AI plays a pivotal role in optimizing the use of water, fertilizers, and pesticides. Smart irrigation systems, enabled by AI algorithms, ensure precise and efficient water distribution, reducing water wastage and promoting water conservation. AI-driven analysis of soil conditions helps farmers tailor fertilization practices, enhancing nutrient utilization and minimizing environmental runoff. The review also explores the role of AI in automating farming operations through robotics and autonomous vehicles. These technologies not only alleviate labor shortages but also improve efficiency in planting, harvesting, and crop maintenance. Additionally, the integration of AI fosters connectivity in agriculture, enabling seamless communication between devices, sensors, and farming equipment. As precision agriculture continues to evolve, the review highlights challenges and future prospects. Ethical considerations, data security, and the digital divide in rural areas are among the challenges that need attention. Moreover, the paper discusses potential avenues for further research, emphasizing the need for interdisciplinary collaboration to address the complex issues associated with the sustainable implementation of AI in precision agriculture. This review provides a comprehensive overview of the transformative impact of AI in precision agriculture, offering insights into current technologies, challenges, and future directions. The integration of AI not only enhances productivity and efficiency but also contributes to the long-term sustainability of farming practices, ensuring food security in the face of a growing global population.World Journal of Advanced Research and Reviews512This review comprehensively examines the integration of AI technologies in precision agriculture to enhance sustainability and optimize farming practices, covering key areas such as crop monitoring, resource management, decision support systems, and automation.World Journal of Advanced Research and Reviews['Donald Obinna Daraojimba', 'Adebunmi Okechukwu Adewusi', 'Onyeka Franca Asuzu', 'Temidayo Olorunsogo', 'Chinwe Iwuanyanwu', 'Ejuma Adaga']2024-01-30T00:00:00https://www.semanticscholar.org/paper/ee72465dd04c8f7d4553bfd04c0063ad6ee9ba5a 48dd3519f7180ce1f48479804611408923ce94c9Powered by the increasing predictive capabilities of machine learning algorithms, artificial intelligence (AI) systems have begun to be used to overrule human mistakes in many settings. We provide the first field evidence this AI oversight carries psychological costs that can impact human decision-making. We investigate one of the highest visibility settings in which AI oversight has occurred: the Hawk-Eye review of umpires in top tennis tournaments. We find that umpires lowered their overall mistake rate after the introduction of Hawk-Eye review, in line with rational inattention given psychological costs of being overruled by AI. We also find that umpires increased the rate at which they called balls in, which produced a shift from making Type II errors (calling a ball out when in) to Type I errors (calling a ball in when out). We structurally estimate the psychological costs of being overruled by AI using a model of rational inattentive umpires, and our results suggest that because of these costs, umpires cared twice as much about Type II errors under AI oversight.arXiv.org412This work structurally estimates the psychological costs of being overruled by AI using a model of rational inattentive umpires, and suggests that because of these costs, umpires cared twice as much about Type II errors under AI oversight.ArXiv['David Almog', 'Romain Gauriot', 'Lionel Page', 'Daniel Martin']2024-01-30T00:00:00https://www.semanticscholar.org/paper/48dd3519f7180ce1f48479804611408923ce94c9 c3de3c183379e03badff2b7269e1986fcf96e001Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable water resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, and future trends. Smart water management enhances operational efficiency, predictive maintenance, and resource conservation while addressing data security and infrastructure investment challenges. Regulatory frameworks play a pivotal role in shaping the responsible deployment of AI and IoT, ensuring data privacy and ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, and blockchain for enhanced water data security. The alignment with Sustainable Development Goals (SDGs) underscores the transformative potential of smart water management in achieving universal access to clean water, climate resilience, and inclusive, sustainable development. As we embrace these technologies, collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.World Journal of Advanced Research and Reviews652This comprehensive review explores the Internet of Things (IoT) and Artificial Intelligence (AI) technologies' benefits, challenges, regulatory implications, and future trends and concludes collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.World Journal of Advanced Research and Reviews['Michael Ayorinde Dada', 'Michael Tega Majemite', 'Alexander Obaigbena', 'Onyeka Henry Daraojimba', 'Johnson Sunday Oliha', 'Zamathula Queen', 'Sikhakhane Nwokediegwu']2024-01-30T00:00:00https://www.semanticscholar.org/paper/c3de3c183379e03badff2b7269e1986fcf96e001 a31b645cc0d7ec06721b8665d85f0db7b960c0e7With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide interest for locally explaining models. Here, we demonstrate their interpretation strongly depends on both the summary statistic and the estimator for it, which in turn define what we identify as an 'anchor point'. We show that the convention of using a mean anchor point may generate misleading interpretations for survival analysis and introduce median-SHAP, a method for explaining black-box models predicting individual survival times.arXiv.org232It is shown that the convention of using a mean anchor point may generate misleading interpretations for survival analysis and median-SHAP, a method for explaining black-box models predicting individual survival times, is introduced.ArXiv['Lucile Ter-Minassian', 'Sahra Ghalebikesabi', 'Karla Diaz-Ordaz', 'Chris Holmes']2024-01-30T00:00:00https://www.semanticscholar.org/paper/a31b645cc0d7ec06721b8665d85f0db7b960c0e7 a9f32a4801158a9432ece2158201cdbbcf2fcab4The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, threat prediction, and automated response. By investigating prior research and real-world implementations, the study provides valuable insights into the potential of AI/ML, uncovering current trends, challenges, and prospects in enhancing cybersecurity tactics amid a dynamically changing threat landscape.World Journal of Advanced Research and Reviews322This study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity, uncovering current trends, challenges, and prospects in enhancing cybersecurity tactics amid a dynamically changing threat landscape.World Journal of Advanced Research and Reviews['Ajala Olakunle Abayomi', 'Olakunle Abayomi Ajala', 'Olusegun Abiodun Balogun']2024-01-30T00:00:00https://www.semanticscholar.org/paper/a9f32a4801158a9432ece2158201cdbbcf2fcab4 47e1bfd682d451da8fb94a11839e6cb0ef4b7d67Responsible design of AI systems is a shared goal across HCI and AI communities. Responsible AI (RAI) tools have been developed to support practitioners to identify, assess, and mitigate ethical issues during AI development. These tools take many forms (e.g., design playbooks, software toolkits, documentation protocols). However, research suggests that use of RAI tools is shaped by organizational contexts, raising questions about how effective such tools are in practice. To better understand how RAI tools are -- and might be -- evaluated, we conducted a qualitative analysis of 37 publications that discuss evaluations of RAI tools. We find that most evaluations focus on usability, while questions of tools' effectiveness in changing AI development are sidelined. While usability evaluations are an important approach to evaluate RAI tools, we draw on evaluation approaches from other fields to highlight developer- and community-level steps to support evaluations of RAI tools' effectiveness in shaping AI development practices and outcomes.International Conference on Human Factors in Computing Systems2121While usability evaluations are an important approach to evaluate RAI tools, evaluation approaches from other fields are drawn on to highlight developer- and community-level steps to support evaluations of RAI tools' effectiveness in shaping AI development practices and outcomes.ArXiv['Glen Berman', 'Nitesh Goyal', 'Michael Madaio']2024-01-30T00:00:00https://www.semanticscholar.org/paper/47e1bfd682d451da8fb94a11839e6cb0ef4b7d67 b9609f23d937b3524e9a2acfe2a501ffc2606003The integration of Artificial Intelligence (AI) in the renewable energy sector has emerged as a transformative force, enhancing the efficiency and sustainability of energy systems. This paper provides a comprehensive review of the application of AI in two critical aspects of renewable energy in relation to predictive maintenance and energy optimization. Predictive maintenance, enabled by AI, has revolutionized the renewable energy landscape by predicting and preventing equipment failures before they occur. Utilizing machine learning algorithms, AI analyzes vast amounts of data from sensors and historical performance to identify patterns indicative of potential faults. This proactive approach not only minimizes downtime but also extends the lifespan of renewable energy infrastructure, resulting in substantial cost savings and improved reliability. Furthermore, AI plays a pivotal role in optimizing the energy output of renewable sources. Through advanced data analytics and real-time monitoring, AI algorithms can adapt to changing environmental conditions, predicting energy production patterns and optimizing resource allocation. This ensures maximum energy yield from renewable sources, making them more competitive with traditional energy sources. The paper delves into specific AI techniques such as deep learning, neural networks, and predictive analytics employed for predictive maintenance and energy optimization in various renewable energy systems like solar, wind, and hydropower. Challenges and opportunities associated with implementing AI in renewable energy are discussed, including data security, interoperability, and the need for standardized frameworks. The synthesis of AI technologies with renewable energy not only addresses operational challenges but also contributes to the global transition towards sustainable and clean energy solutions. This review serves as a valuable resource for researchers, practitioners, and policymakers seeking insights into the evolving landscape of AI applications in the renewable energy sector. As technology continues to advance, the synergies between AI and renewable energy are poised to shape the future of the global energy paradigm.World Journal of Advanced Research and Reviews491World Journal of Advanced Research and Reviews['Samuel Onimisi Dawodu', 'Shedrack Onwusinkwue', 'Femi Osasona', 'Islam Ahmad', 'Ibrahim Ahmad', 'Anthony Chigozie Anyanwu', 'Ogugua Chimezie Obi', 'Ahmad Hamdan']2024-01-30T00:00:00https://www.semanticscholar.org/paper/b9609f23d937b3524e9a2acfe2a501ffc2606003 a47ce93e46b91c8b028a6bee656c751708af1a37Pada abad post modern ini, teknologi telah menjadi komponen kunci dalam mendefinisikan manusia dan masyarakat. Kehadiran teknologi, terutama dalam bentuk teknologi informasi dan komunikasi seperti Artificial Intelligence (AI), telah mempengaruhi berbagai aspek kehidupan manusia. Tujuan dari kegiatan pengabdian kepada masyarakat adalah untuk memberikan pemahaman dasar tentang teknologi AI pada santri di pesantren  Syariful Anam, sehingga para santri dapat lebih memahami cara kerjanya dan potensi pemanfaatannya serta meningkatkan kesadaran tentang etika dan memberikan wawasan tentang dampak teknologi AI secara luas dan memberikan perspektif yang lebih holistik. Metode yang digunakan dalam kegiatan ini yaitu dengan memberikan seminar sebagai upaya interaktif dan diskusi untuk menyampaikan informasi dan pengetahuan kepada peserta. Hasil dari kegiatan pengabdian ini menunjukkan bahwa peserta, termasuk Generasi Z, menunjukkan antusiasme dan kesadaran yang tinggi tentang pentingnya etika dalam pemanfaatan teknologi AI. Dengan pemahaman etika yang mendalam tentang penggunaan teknologi AI, Generasi Z diharapkan dapat menjadi pemimpin masa depan yang tidak hanya cerdas secara teknologi, tetapi juga memiliki kesadaran etika yang tinggiJurnal Abadimas Adi Buana181Jurnal Abadimas Adi Buana['Theguh', 'Bisri Bisri', 'Fuad Nawawi']2024-01-30T00:00:00https://www.semanticscholar.org/paper/a47ce93e46b91c8b028a6bee656c751708af1a37 75fae48643ec18dcbbafe4a2f4dba50c9c9e9f18Large Language Models (LLMs) have been widely deployed for their remarkable capability to generate texts resembling human language. However, they could be misused by criminals to create deceptive content, such as fake news and phishing emails, which raises ethical concerns. Watermarking is a key technique to mitigate the misuse of LLMs, which embeds a watermark (e.g., a bit string) into a text generated by a LLM. Consequently, this enables the detection of texts generated by a LLM as well as the tracing of generated texts to a specific user. The major limitation of existing watermark techniques is that they cannot accurately or efficiently extract the watermark from a text, especially when the watermark is a long bit string. This key limitation impedes their deployment for real-world applications, e.g., tracing generated texts to a specific user. This work introduces a novel watermarking method for LLM-generated text grounded in \textbf{error-correction codes} to address this challenge. We provide strong theoretical analysis, demonstrating that under bounded adversarial word/token edits (insertion, deletion, and substitution), our method can correctly extract watermarks, offering a provable robustness guarantee. This breakthrough is also evidenced by our extensive experimental results. The experiments show that our method substantially outperforms existing baselines in both accuracy and robustness on benchmark datasets. For instance, when embedding a bit string of length 12 into a 200-token generated text, our approach attains an impressive match rate of $98.4\%$, surpassing the performance of Yoo et al. (state-of-the-art baseline) at $85.6\%$. When subjected to a copy-paste attack involving the injection of 50 tokens to generated texts with 200 words, our method maintains a substantial match rate of $90.8\%$, while the match rate of Yoo et al. diminishes to below $65\%$.arXiv.org614This work introduces a novel watermarking method for LLM-generated text grounded in error-correction codes that under bounded adversarial word/token edits (insertion, deletion, and substitution), can correctly extract watermarks, offering a provable robustness guarantee.ArXiv['Wenjie Qu', 'Dong Yin', 'Zixin He', 'Wei Zou', 'Tianyang Tao', 'Jinyuan Jia', 'Jiaheng Zhang']2024-01-30T00:00:00https://www.semanticscholar.org/paper/75fae48643ec18dcbbafe4a2f4dba50c9c9e9f18 2e5b3bee612a8fac444e3c3b045a847f92bd37e8The study examines artificial intelligence's (AI) place in marketing strategies, emphasising its potential uses, advantages, and commercial impacts. A more tailored approach is fostered by key artificial intelligence technologies including machine learning algorithms, natural language processing, and predictive analytics, which give marketers insights into consumer behavior, preferences, and trends. AI- powered solutions are transforming the consumer experience, encouraging interaction, and streamlining the conversion process. AI-driven tools ensure that advertisers precisely contact their target audiences by analyzing large datasets in real-time. The article also discusses ethical issues such algorithmic biases, transparency, and data privacy. The impact of AI on marketing professionals is also discussed in the paper, with a focus on how the industry's skill requirements are changing. Keywords Artificial intelligence, Marketing, Machine Learning, Consumer Behaviour, Digital MarketingINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT10The study examines artificial intelligence's place in marketing strategies, emphasising its potential uses, advantages, and commercial impacts, and discusses ethical issues such algorithmic biases, transparency, and data privacy.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['P. V. K. Shree', 'Marcello M. Mariani', 'Rodrigo Perez-Vega', 'Jochen Wirtz']2024-01-30T00:00:00https://www.semanticscholar.org/paper/2e5b3bee612a8fac444e3c3b045a847f92bd37e8 975d3271af839364d29a536681a6468ae70a9ea1While generative AI is now widespread and useful in society, there are potential risks of misuse, e.g., unconsciously influencing cognitive processes or decision-making. Although this causes a security problem in the cognitive domain, there has been no research about neural and computational mechanisms counteracting the impact of malicious generative AI in humans. We propose DecNefGAN, a novel framework that combines a generative adversarial system and a neural reinforcement model. More specifically, DecNefGAN bridges human and generative AI in a closed-loop system, with the AI creating stimuli that induce specific mental states, thus exerting external control over neural activity. The objective of the human is the opposite, to compete and reach an orthogonal mental state. This framework can contribute to elucidating how the human brain responds to and counteracts the potential influence of generative AI.arXiv.org220DecNefGAN is proposed, a novel framework that combines a generative adversarial system and a neural reinforcement model that bridges human and generative AI in a closed-loop system, with the AI creating stimuli that induce specific mental states, thus exerting external control over neural activity.ArXiv['Mikihiro Kasahara', 'Taiki Oka', 'V. Taschereau-Dumouchel', 'Mitsuo Kawato', 'Hiroki Takakura', 'A. Cortese']2024-01-30T00:00:00https://www.semanticscholar.org/paper/975d3271af839364d29a536681a6468ae70a9ea1 2d0372cc30b3bee5a29d4c4a4302f2d47df2549dMultimodal large language models (LMMs) excel in world knowledge and problem-solving abilities. Through the use of a world-facing camera and contextual AI, emerging smart accessories aim to provide a seamless interface between humans and LMMs. Yet, these wearable computing systems lack an understanding of the user's attention. We introduce GazeGPT as a new user interaction paradigm for contextual AI. GazeGPT uses eye tracking to help the LMM understand which object in the world-facing camera view a user is paying attention to. Using extensive user evaluations, we show that this gaze-contingent mechanism is a faster and more accurate pointing mechanism than alternatives; that it augments human capabilities by significantly improving their accuracy in a dog-breed classification task; and that it is consistently ranked as more natural than head- or body-driven selection mechanisms for contextual AI. Moreover, we prototype a variety of application scenarios that suggest GazeGPT could be of significant value to users as part of future AI-driven personal assistants.arXiv.org340GazeGPT uses eye tracking to help the LMM understand which object in the world-facing camera view a user is paying attention to and is consistently ranked as more natural than head- or body-driven selection mechanisms for contextual AI.ArXiv['Robert Konrad', 'Nitish Padmanaban', 'J. G. Buckmaster', 'Kevin C. Boyle', 'Gordon Wetzstein']2024-01-30T00:00:00https://www.semanticscholar.org/paper/2d0372cc30b3bee5a29d4c4a4302f2d47df2549d 3139e0c7002df948519a66b5a57a43fab9e013b9Artificial Intelligence (AI) has emerged as a transformative force in the field of cybersecurity, playing a pivotal role in safeguarding national infrastructure. This review focuses on the application of AI technologies within the context of the United States, examining their efficacy in fortifying critical systems against evolving cyber threats. The paper delves into various AI-driven cybersecurity strategies, ranging from anomaly detection and predictive analysis to threat intelligence and automated response mechanisms. The integration of AI in cybersecurity not only enhances the speed and accuracy of threat detection but also addresses the dynamic nature of cyber threats. The specific AI technologies employed in the United States, including machine learning, natural language processing, and neural networks, highlighting their contributions to bolstering the resilience of national infrastructure are also examined. Furthermore, the challenges and ethical considerations associated with the widespread adoption of AI in cybersecurity are assessed. It discusses the need for robust regulatory frameworks to govern the deployment of AI in sensitive domains and emphasizes the importance of collaboration between government agencies, private enterprises, and research institutions to foster innovation and address emerging threats. In conclusion, this review provides a comprehensive analysis of the role of AI in cybersecurity within the United States, emphasizing its significance in protecting critical national infrastructure. By exploring technological advancements, challenges, and ethical considerations, this paper contributes to the ongoing discourse on leveraging AI to safeguard against the ever-evolving landscape of cyber threats.World Journal of Advanced Research and Reviews423This review focuses on the application of AI technologies within the context of the United States, examining their efficacy in fortifying critical systems against evolving cyber threats and the challenges and ethical considerations associated with the widespread adoption of AI in cybersecurity.World Journal of Advanced Research and Reviews['Donald Obinna Daraojimba', 'Adebunmi Okechukwu Adewusi', 'U. Okoli', 'Temidayo Olorunsogo', 'Ejuma Adaga', 'Ogugua Chimezie Obi']2024-01-30T00:00:00https://www.semanticscholar.org/paper/3139e0c7002df948519a66b5a57a43fab9e013b9 110f3c0394692ee393f8eb69cca6e0239549dee5The integration of Artificial Intelligence (AI) in groundwater management is a transformative stage, characterized by innovation and challenges. This research paper explores the multilayered application of AI in this field, dividing its contributions, addressing its associated challenges, and revealing the prospects of future potential. AI-driven innovations are designed to revolutionize groundwater management, providing precise predictive modeling, real-time monitoring, and data integration. However, these innovations face challenges such as interpretability issues, specialized technical expertise requirements, and limited data quality and quantity for effective AI model performance. In the future, AI holds significant promise in groundwater management. Advanced AI models can yield improved predictions of groundwater behavior, identify vulnerable areas prone to pollution and depletion, prompt proactive interventions, and foster collaborative platforms among scientists, policymakers, and local communities. Collaborative platforms driven by AI offer potential for synergistic engagement among scientists, policymakers, and local communities, collectively guiding groundwater resource management. Embracing AI's potential while addressing its challenges remains pivotal for sustainable and resilient groundwater management practices. By embracing AI's potential while addressing its challenges, the landscape of groundwater resource management will continue to evolve.International Journal of Science and Research Archive402This research paper explores the multilayered application of AI in this field, dividing its contributions, addressing its associated challenges, and revealing the prospects of future potential.International Journal of Science and Research Archive['M. Shaikh', 'Farjana Birajdar']2024-01-30T00:00:00https://www.semanticscholar.org/paper/110f3c0394692ee393f8eb69cca6e0239549dee5 980be952f3e78a6c5e4b7d1535dfb51a7b182d81Introduction Artificial Intelligence is a growing field in medical research that could potentially help in the challenging diagnosis of acute appendicitis (AA) in children. However, usefulness of AI in clinical settings remains unclear. Our aim was to assess the accuracy of AIs in the diagnosis of AA in the pediatric population through a systematic literature review. Methods PubMed, Embase, and Web of Science were searched using the following keywords: "pediatric", "artificial intelligence", "standard practices", and "appendicitis", up to September 2023. The risk of bias was assessed using PROBAST. Results A total of 302 articles were identified and nine articles were included in the final review. Two studies had prospective validation, seven were retrospective, and no randomized control trials were found. All studies developed their own algorithms and had an accuracy greater than 90% or AUC > 0.9. All studies were rated as a "high risk" concerning their overall risk of bias. Conclusion We analyzed the current status of artificial intelligence in the diagnosis of appendicitis in children. The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.European journal of pediatric surgery02The application of AI shows promising potential, but the need for more rigor in study design, reporting, and transparency is urgent to facilitate its clinical implementation.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie['Robin Rey', 'Renato Gualtieri', 'Giorgio La Scala', 'Klara M Posfay Barbe']2024-01-30T00:00:00https://www.semanticscholar.org/paper/980be952f3e78a6c5e4b7d1535dfb51a7b182d81 76d14057e805a09cf9481a9a294948da1e409584This analysis examines Ghana’s Health Professions Regulatory Bodies Act, 2013 (Act 857) to assess its fitness to govern the ascent of artificial intelligence (AI) in reshaping healthcare delivery. As advanced algorithms supplement or replace human judgments, dated laws centered on individual practitioner liability struggle to contemplate emerging negligence complexities. Act 857 lacks bespoke provisions for governing this new era beyond outdated assumptions of human-centric care models. With AI projected to transform medicine, proactive reforms appear vital to enable innovation gains while upholding accountability. Through an IRAC legal analysis lens supplemented by case law spanning from the United States to Ghana, this paper demonstrates how judiciaries globally are elucidating risks from legal uncertainty given increasingly autonomous health technologies. Findings reveal governance gaps impeding equitable access to remedy where algorithmic activities contribute to patient harm. Calls for stringent training, validation and monitoring prerequisites before deploying higher-risk AI systems signal a reframed standard of care is warranted. Detailed recommendations to modernize Act 857 and adjacent regulation are provided, covering practitioner codes, product safety, ongoing evaluation duties, and crucially, updated liability rules on apportioning fault between disparate enterprises enabling flawed AI. Beyond protecting patients and practitioners, enhanced governance can boost investor confidence in Ghana’s AI healthcare ecosystem. Ultimately astute reforms today can reinforce innovation gains tomorrow across a more ethical, accountable industry.Mesopotamian Journal of Artificial Intelligence in Healthcare151This analysis examines Ghana’s Health Professions Regulatory Bodies Act, 2013 to assess its fitness to govern the ascent of artificial intelligence (AI) in reshaping healthcare delivery and reveals governance gaps impeding equitable access to remedy where algorithmic activities contribute to patient harm.Mesopotamian Journal of Artificial Intelligence in Healthcare['George Benneh Mensah']2024-01-30T00:00:00https://www.semanticscholar.org/paper/76d14057e805a09cf9481a9a294948da1e409584 655ea194dd6f7ae2cd4d62a0686e92c038787827The morbidity and mortality rates in lung cancer are high worldwide. Early diagnosis and personalized treatment are important to manage this public health issue. In recent years, artificial intelligence (AI) has played increasingly important roles in early screening, auxiliary diagnosis, and prognostic assessment. AI uses algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes. In this review, we describe the current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications.Frontiers in Medicine410The current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications are described and algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes are described.Frontiers in Medicine['Wenjuan Liu', 'Nan Shen', 'Limin Zhang', 'Xiaoxi Wang', 'Bainan Chen', 'Zhuo Liu', 'Chao Yang']2024-01-30T00:00:00https://www.semanticscholar.org/paper/655ea194dd6f7ae2cd4d62a0686e92c038787827 2624867823abad7d4fda34b76780a365a07f2c37Annals of Operations Research600The capacity of the SHapley Additive exPlanations (SHAP) technique to give finance managers an intuitive explanation of the anomaly detections AI-based ML models generate for a specific customer transaction dataset is illustrated.Annals of Operations Research['Renu Sabharwal', 'S. Miah', 'S. Wamba', 'Peter Cook']2024-01-30T00:00:00https://www.semanticscholar.org/paper/2624867823abad7d4fda34b76780a365a07f2c37 4f7d163228fb06ed24750d4bc3db656d1a87e1caArtificial intelligence (AI) has brought about a paradigm shift in numerous industries and is persistently altering the methodology employed in scientific inquiry. Although AI has the potential to streamline specific facets of the scientific method, detractors contend that its integration could potentially erode the foundations of science. A potential issue arises when an excessive dependence on AI for data analysis and experimentation results in the erosion of human creativity and intuition as pivotal components in scientific breakthroughs. The propensity for fortuitous discoveries and innovative concepts to arise from ostensibly unrelated disciplines may be impeded by AI’s emphasis on identifying patterns in preexisting data sets. Moreover, algorithms employed in AI systems that rely on training data possess an intrinsic bias, which may introduce intangible prejudices into scientific investigation. Furthermore, it is critical to specify that AI is incapable of engaging in debates or comprehending profound philosophical inquiries pertaining to the fundamental principles that govern our universe. As a result, although AI has the potential to significantly aid scientists in their endeavors, it must be implemented with prudence to guarantee that it enhances rather than erodes the fundamental tenets and character of scientific investigation.  Science Insights00Although AI has the potential to significantly aid scientists in their endeavors, it must be implemented with prudence to guarantee that it enhances rather than erodes the fundamental tenets and character of scientific investigation.Science Insights['Sabine Zur Schlemmer']2024-01-30T00:00:00https://www.semanticscholar.org/paper/4f7d163228fb06ed24750d4bc3db656d1a87e1ca f52b472d606edb6e5847f56e47cf9d0d042aa9d0Artificial intelligence (AI) holds considerable promise for transforming clinical diagnostics. While much has been written both about public attitudes toward the use of AI tools in medicine and about uncertainty regarding legal liability that may be delaying its adoption, the interface of these two issues has so far drawn less attention. However, understanding this interface is essential to determining how jury behaviour is likely to influence adoption of AI by physicians. One distinctive concern identified in this paper is a 'negative outcome penalty paradox' (NOPP) in which physicians risk being penalised by juries in cases with negative outcomes, whether they overrule AI determinations or accept them. The paper notes three reasons why AI in medicine is uniquely susceptible to the NOPP and urges serious further consideration of this complex dilemma.Journal of Medical Ethics220A 'negative outcome penalty paradox' (NOPP) is identified in which physicians risk being penalised by juries in cases with negative outcomes, whether they overrule AI determinations or accept them.Journal of medical ethics['Jacob M Appel']2024-01-30T00:00:00https://www.semanticscholar.org/paper/f52b472d606edb6e5847f56e47cf9d0d042aa9d0 96bb582684cd29929d4b29028e09dbec7a339bcdThe article reveals the meaning of the concept of artificial intelligence and proves the necessity of its application in the educational space. The purpose of the article is to show the importance of artificial intelligence as an educational and entertainment tool in higher education. The methodological concept is aimed at ensuring the effectiveness of students' education with the help of artificial intelligence. The research results show the types (weak and strong), directions, functions of artificial intelligence; the importance of edutainment in higher education and its main principles are revealed. Artificial intelligence is presented as a toolkit of edutainment in higher education and the importance of using artificial intelligence functions for edutainment in US higher education to improve human skills and abilities is shown. The significance of the avatar and virtual teacher in the educational process is revealed. During the research and experimental work, we singled out the leading factors of the actualization of the ideas of artificial intelligence as an educational and entertainment toolkit in the modern theory and practice of training students of higher education.Revista Amazonía investiga320The article reveals the meaning of the concept of artificial intelligence and proves the necessity of its application in the educational space and the importance of using artificial intelligence functions for edutainment in US higher education to improve human skills and abilities.Revista Amazonia Investiga['Nadiya Ryzheva', 'Dmytro Nefodov', 'S. Romanyuk', 'Hanna Marynchenko', 'Mariia Kudla']2024-01-30T00:00:00https://www.semanticscholar.org/paper/96bb582684cd29929d4b29028e09dbec7a339bcd c0e2751e472c2926ad5be377f3fb515abd866febAscribing authorship of scientific publications to artificial intelligence is a complex and controversial issue. However, it is a challenging and uncertain problem that, given the growing development of artificial intelligence-based technologies that go beyond the performance of purely technical tasks and even contribute to the development of aspects such as the incorporation of scientific research information published in languages other than English, also contributing to potential insights in research, is becoming unavoidable when considering scientific publishing. This paper aims to add to this discussion by arguing that, although this is a challenging and even controversial position, it is inevitable and even ethically desirable to accept artificial intelligence, if it subsidizes sufficiently, as a (co-)author of any scientific publication. It is a matter of starting to think about how this attribution can be controlled and achieved with increasing respect for the ethics of scientific publication.Science Insights210It is inevitable and even ethically desirable to accept artificial intelligence, if it subsidizes sufficiently, as a (co-)author of any scientific publication, according to this paper.Science Insights['Sandro Serpa', 'Fuzhou Wang', 'Longjun Zhou', 'Özgül Keleş']2024-01-30T00:00:00https://www.semanticscholar.org/paper/c0e2751e472c2926ad5be377f3fb515abd866feb 7ce3faf718a7567caa3dc4f2f1f0c4dc99bc50acJournal of Innovation and Entrepreneurship520How the integration of artificial intelligence (AI) in idea management systems can support innovation team members in increasing the innovation potential of the ideas that are elaborated is discussed.Journal of Innovation and Entrepreneurship['Henning Sejer Jakobsen', 'Jacob Brix', 'Rune Sejer Jakobsen']2024-01-30T00:00:00https://www.semanticscholar.org/paper/7ce3faf718a7567caa3dc4f2f1f0c4dc99bc50ac 09e7a40ad78a623803b895b15f267680c055cfe9Computational Urban Science130The scope of adopting new technologies like machine learning and deep learning for cyclone prediction in countries like Bangladesh, which are cyclone-prone but have constraints on funds to invest in this field is identified.Computational Urban Science['Sabbir Rahman', 'N. Sharmin', 'Ahsan Rahat', 'Mukhlesur Rahman', 'Mahbubur Rahman']2024-01-30T00:00:00https://www.semanticscholar.org/paper/09e7a40ad78a623803b895b15f267680c055cfe9 4370f50bfcfef4837ebd601db1cbbbc31ffff349With the rising global demand for rice, improving production efficiency through advanced technologies like artificial intelligence (AI) is crucial. This systematic review gathered recent literatures on learning algorithm models applied to automate rice agriculture tasks. The objectives were to analyze the performance accuracy of different machine learning algorithms for rice classification and determine the most effective models. The 116 studies from 2016-2023 were screened and 70 were included. The algorithms were evaluated by weighted mean accuracy percentage across studies while maintaining consideration to sample sizes. The results showed the DenseNet121 deep convolutional neural network achieved the overall highest accuracy of 99.98%, also topping rice disease detection. For variety classification, Deep Neural Networks reached 99.95% accuracy by learning complex visual differences. Adaptive Neuro-Fuzzy Inference System led in grading quality of 98.6% by discerning grain features. Larger datasets improved accuracy indicating that the more training data has, it enhances model accuracy. The review demonstrates AI’s significant potential to automate essential aspects of rice production. Further research expanding standardized algorithm evaluations is recommended to strengthen the evidence-base and support integration of AI for intelligent, sustainable rice agriculture.International Journal of Science and Research Archive780The results showed the DenseNet121 deep convolutional neural network achieved the overall highest accuracy of 99.98%, also topping rice disease detection and Deep Neural Networks reached 99.95% accuracy for variety classification.International Journal of Science and Research Archive['Angelo Mari', 'Cuevas Paredes', 'Kylin Bocalan Felizardo', 'Edwin Romeroso Arboleda']2024-01-30T00:00:00https://www.semanticscholar.org/paper/4370f50bfcfef4837ebd601db1cbbbc31ffff349 55fad8ead4c38bcbfb993e79e21af2a5c8346e22Artificial Intelligence (AI), is one of the key innovations created by Information System (IS) in the 21st century. The technology operates on an interconnected network which facilitates marketing decisions. AI role in marketing is quite enormous, as customers’ needs and wants are efficiently met, with customers satisfied and employees sustainably engaged, towards future relationship with the firm. In-spite of its proficient value addition, there are outstanding issues which firms’ employee are dissatisfied with the deployment of AIM, in organizational operations, these includes; the skill gap, resistance to change, and job security threat. The study examines various studies form (journals, books and e-repositories) on AI adoption, firms’ operational activities, and perceived job threat by personnel, saddled with responsibilities of implementing AIM. studies selected was between 2012-2023, the theoretical framework attempts to fill existing gap in literature on customer satisfaction and job threat to personnel, due to AIM deployment. Qualitative research methodology was adopted using in in-depth interviews through focus groups (Management, employee, and AI Technicians), Systematic Literature Review (SLR) will also be adopted to analyze the variables of the construct. The study findings show that there is significant relationship between AI Marketing and customers satisfaction, based on effective engagement of firms’ personnel saddled with the responsibility of deploying AIM in the organization. The implication of the study will be increase labor turnover, for employee without required AI set skills, and apprehension if customers are not satisfied. Future research should examine employee reaction in other functional areas of business, where AI id deployed.World Journal of Advanced Research and Reviews870There is significant relationship between AI Marketing and customers satisfaction, based on effective engagement of firms’ personnel saddled with the responsibility of deploying AIM in the organization, and the implication of the study will be increase labor turnover, for employee without required AI set skills, and apprehension if customers are not satisfied.World Journal of Advanced Research and Reviews['Idongesit Oto Eshiett', 'Oto Eyamba Eshiett']2024-01-30T00:00:00https://www.semanticscholar.org/paper/55fad8ead4c38bcbfb993e79e21af2a5c8346e22 9d5970b1cb675a6abdfe12e9d0cc7edf4846c0abTechnology is the most pressing and elusive goal of any nation for the development of the education system and the entire system. Artificial Intelligence is making many changes in the educational setup. It has both positive and negative impacts on education, a versatile approach plays an important role in the education sector with huge possibilities for new emerging trends in the learning and teaching process. The field of artificial intelligence is an area of science that creates and studies machines to stimulate human intelligence processes. As a result, the study objectives in this paper are designed to assist professionals, students, teachers as well as business experts. Firstly, it examines the important technological elements and characteristics of AI, which are vital for the fourth industrial revolution in the education field. Second, this article analyses the key achievements and different obstacles that make the AI revolution for education in Industry 4.0 so possible. AI changes the styles of teaching and learning presently. The study also looks at the possible influence of AI on education, such as how teachers' roles are evolving and the need for new skills in the industry. This study enhances the currently existing reservoir of knowledge and offers insights into the future implications of this fast-expanding technology by offering a complete analysis of AI's impact on Industry 4.0 and education. This is especially important in today's environment when the usage of artificial intelligence (AI) is becoming more common and has the potential to dramatically impact our society and economy.World Journal of Advanced Research and Reviews60This study enhances the currently existing reservoir of knowledge and offers insights into the future implications of this fast-expanding technology by offering a complete analysis of AI's impact on Industry 4.0 and education.World Journal of Advanced Research and Reviews['Shazia Shaheen']2024-01-30T00:00:00https://www.semanticscholar.org/paper/9d5970b1cb675a6abdfe12e9d0cc7edf4846c0ab 352a3b058b0f9c3fd48f5783ca85612b7b20dd78The aim of the article is to study and improve the use of artificial intelligence in education by analysing international experience. The main methods used were general scientific methods, documentary analysis, standard statistics, and factor analysis. The study's main results demonstrate the rapid growth in the popularity of artificial intelligence in virtual learning systems in all the countries under consideration. The article reveals a tendency to increase the demand for these technologies. The study concludes that AI has an important role in the educational process and that future research should focus on evaluating its effectiveness in training specific specialists.Revista Amazonía investiga90The study concludes that AI has an important role in the educational process and that future research should focus on evaluating its effectiveness in training specific specialists.Revista Amazonia Investiga['Pavel Polián', 'I. Kopotun', 'Petr Polián']2024-01-30T00:00:00https://www.semanticscholar.org/paper/352a3b058b0f9c3fd48f5783ca85612b7b20dd78 22d680275ceda7434b564713ac1f25d68e14bf79This paper critically examines the integration of Artificial Intelligence (AI) into business operations, focusing on the challenges of legal accountability and ethical considerations. It first traces the development of AI and its transformative impact on commerce, providing a basis for examining the key ethical and responsibility challenges. The paper presents research findings that highlight the complexity of assigning responsibility for AI-generated outcomes and discusses the different approaches in national and international legal frameworks for AI. It emphasizes the need for clear legal structures and ethical guidelines to govern the role of AI in business and society. The paper concludes by highlighting the importance of harmonized global frameworks to ensure the responsible integration of AI, addressing both theoretical and policy implications. The findings point to a significant shift in legal trends and societal impacts due to AI and emphasize the urgent need for ethical deployment to prevent the reinforcement of societal biases.Udayana Journal of Law and Culture30The findings point to a significant shift in legal trends and societal impacts due to AI and emphasize the urgent need for ethical deployment to prevent the reinforcement of societal biases.Udayana Journal of Law and Culture['Matthias Holzhausen']2024-01-30T00:00:00https://www.semanticscholar.org/paper/22d680275ceda7434b564713ac1f25d68e14bf79 f3866315667250d6fee6a6fe0b9fcac793036f7bArtificial Intelligence (AI) is increasingly becoming a cornerstone in the evolution of project management. Its capabilities extend beyond simple automation, fostering improved decision-making processes and enhancing collaborative efforts. Among the various AI tools available, chatbots stand out as particularly transformative for project management. This study delves into the role of AI-powered chatbots in project stakeholder engagement, a critical aspect of successful project management. Chatbots, powered by sophisticated AI algorithms, can provide continuous support and interaction with project stakeholders. This is particularly vital in managing complex projects where continuous communication and prompt responses can significantly influence project success. The study examines how these AI-driven chatbots facilitate stakeholder engagement, focusing on key benefits such as improved communication, increased stakeholder satisfaction, and better overall project outcomes. Through a detailed analysis, we have identified that chatbots enhance communication by offering stakeholders immediate, personalized responses, thereby reducing response times and improving the efficiency of information exchange. This immediacy and personalization contribute to heightened stakeholder satisfaction, as stakeholders feel their concerns and queries are addressed promptly and effectively. Furthermore, our findings suggest that these improvements in stakeholder engagement directly correlate with enhanced project outcomes, including better adherence to timelines, improved project quality, and increased likelihood of meeting project objectives. However, the deployment of chatbots in project management is not without its challenges. One significant hurdle is the need for advanced natural language processing (NLP) capabilities. Effective chatbots must understand and process complex human language nuances to interact effectively with stakeholders. Another challenge observed is the potential for chatbots to become disruptive or annoying. This can occur when chatbots fail to provide relevant or accurate information, or when their interaction style does not align with stakeholder expectations. In conclusion, AI-powered chatbots hold substantial promise for revolutionizing stakeholder engagement in project management. While they present remarkable benefits in improving communication, stakeholder satisfaction, and project outcomes, there are challenges that need to be addressed. These include enhancing NLP capabilities and fine-tuning the interaction style of chatbots to suit diverse stakeholder groups. With these improvements, AI-powered chatbots could significantly contribute to the success of various projects, marking a new era in project management where AI plays a pivotal role in stakeholder engagement and overall project success.Indian Journal of Software Engineering and Project Management00It is identified that chatbots enhance communication by offering stakeholders immediate, personalized responses, thereby reducing response times and improving the efficiency of information exchange, and these improvements in stakeholder engagement directly correlate with enhanced project outcomes.Indian Journal of Software Engineering and Project Management['Herat Joshi']2024-01-30T00:00:00https://www.semanticscholar.org/paper/f3866315667250d6fee6a6fe0b9fcac793036f7b a71ec71e9081e4892e1672b6ff32bfe9964fd4c9This study examines stakeholders’ attitudes toward Artificial intelligence (AI) tools in Ukrainian higher education institutions, employing a comprehensive mixed-methods approach. The research combines qualitative focus group discussions, which involved a diverse range of participants, and a quantitative survey questionnaire distributed to a sizeable cohort. The quantitative data reveals a noteworthy trend, with a majority of stakeholders expressing positive attitudes toward AI integration, emphasising its potential for personalised learning and real-time feedback. This positive sentiment, however, is tempered by identified concerns, notably surrounding the accuracy of AI-generated content. The study establishes a connection with existing literature, affirming the widespread acceptance and benefits of AI in education. For a more nuanced understanding, a detailed breakdown of these quantitative results sheds light on the extent and distribution of stakeholder attitudes and concerns. Moreover, the qualitative component delves deeper into stakeholders’ perspectives, capturing the richness of their expressions. Concrete examples and direct quotes from participants in focus group discussions provide a qualitative dimension to the study’s findings, offering insights into the nuances of stakeholders’ viewpoints. To address these concerns, the research emphasises the need for tailored intervention plans, focusing on content quality, ethical implications and comprehensive training. These recommendations are rooted in a thorough analysis of both quantitative and qualitative data, providing practical insights for policymakers and institutions. In particular, the study highlights the importance of inclusive decision-making and targeted communication strategies, recognising role-based and age-related variations among stakeholders. By adopting a holistic approach to AI integration and acknowledging the interrelationships between attitudes, perceptions, expectations and concerns, this research provides a comprehensive guide for effectively leveraging AI while addressing associated concerns in higher education institutions.International Journal of Learning, Teaching and Educational Research380By adopting a holistic approach to AI integration and acknowledging the interrelationships between attitudes, perceptions, expectations and concerns, this research provides a comprehensive guide for effectively leveraging AI while addressing associated concerns in higher education institutions.International Journal of Learning, Teaching and Educational Research['V. Bobrytska', 'H. Krasylnykova', 'Nataliia А. Beseda', 'Sergii R. Krasylnykov', 'Tetiana S. Skyrda']2024-01-30T00:00:00https://www.semanticscholar.org/paper/a71ec71e9081e4892e1672b6ff32bfe9964fd4c9 542c776f18dc7b7a1c19f933674e59fce0ab2f8fDiabetic retinopathy (DR) is a major microvascular complication of diabetes, affecting a substantial portion of diabetic patients worldwide. Timely intervention is pivotal in mitigating the risk of blindness associated with DR, yet early detection remains a challenge due to the absence of early symptoms. Screening programs have emerged as a strategy to address this burden, and this paper delves into the role of artificial intelligence (AI) in advancing DR screening in Japan. There are two pathways for DR screening in Japan: a health screening pathway and a clinical referral path from physicians to ophthalmologists. AI technologies that realize automated image classification by applying deep learning are emerging. These technologies have exhibited substantial promise, achieving sensitivity and specificity levels exceeding 90% in prospective studies. Moreover, we introduce the potential of Generative AI and large language models (LLMs) to transform healthcare delivery, particularly in patient engagement, medical records, and decision support. Considering the use of AI in DR screening in Japan, we propose to follow a seven-step framework for systematic screening and emphasize the importance of integrating AI into a well-designed screening program. Automated scoring systems with AI enhance screening quality, but their effectiveness depends on their integration into the broader screening ecosystem. LLMs emerge as an important tool to fill gaps in the screening process, from personalized invitations to reporting results, facilitating a seamless and efficient system. However, it is essential to address concerns surrounding technical accuracy and governance before full-scale integration into the healthcare system. In conclusion, this review highlights the challenges in the current screening pathway and the potential for AI, particularly LLM, to revolutionize DR screening in Japan. The future direction will depend on leadership from ophthalmologists and stakeholders to address long-standing challenges in DR screening so that all people have access to accessible and effective screening.Medicina230The role of artificial intelligence in DR screening in Japan is delves into and the potential of Generative AI and large language models (LLMs) to transform healthcare delivery is introduced, particularly in patient engagement, medical records, and decision support are introduced.Medicina['Ryo Kawasaki']2024-01-30T00:00:00https://www.semanticscholar.org/paper/542c776f18dc7b7a1c19f933674e59fce0ab2f8f 33151ec8b648755f03a7de5f11dca72349231e71The advent of artificial intelligence (AI) has brought about a profound transformation in numerous areas, including the field of science writing. With the rising complexity and data-driven nature of scientific research, effective communication of findings and ideas becomes ever more vital. AI has become a potent technology that may aid in producing scientific material, conducting data analysis, and optimizing literature reviews. Nevertheless, it is crucial to acknowledge the constraints of AI in generating scientific material and to comprehend its optimal integration with human expertise. We herein prospectively examined the role of AI in science writing, discussing its possible advantages and difficulties, and emphasizing the significance of upholding human subjectivity in this developing field.Science Insights00The role of AI in science writing is examined, discussing its possible advantages and difficulties, and emphasizing the significance of upholding human subjectivity in this developing field.Science Insights['Balbir P. Gupta']2024-01-30T00:00:00https://www.semanticscholar.org/paper/33151ec8b648755f03a7de5f11dca72349231e71 286a76f9193f789c2afe1647384991a95f7c6d65The growing use of Artificial Intelligence, along with its factors is rapidly increasing in various fields now a days. It providing the opportunity to upgrade the efficiency of various industries and business, including transportation sector. AI gives more efficient service to Public Transportation to enhance the urban mobility. The goal of AI to acquire the knowledge about reasoning, planning, perception and deal with objects. The AI for transportation is assist to reduce the risk and enhance the safety. AI applications is help to solve the challenges such as travel demand, CO2 emissions, safety concerns, and fuel waste. The challenges and desideratum faces during transportation can be easily addressed by AI algorithm. This paper stretches a view about the AI technique use in worldwide Transportation of methodology, applications, future of AI in deep learning and limitations.International Journal of Scientific Research in Science Engineering and Technology50A view is stretches a view about the AI technique use in worldwide Transportation of methodology, applications, future of AI in deep learning and limitations.International Journal of Scientific Research in Science, Engineering and Technology['Miss. Asawari Satish Isalkar', 'Dr. Rajeshkumar U. Sambhe']2024-01-30T00:00:00https://www.semanticscholar.org/paper/286a76f9193f789c2afe1647384991a95f7c6d65 de49df08d80afd43adc3e270a39c293146b0d534Despite notable progressions in artificial intelligence (AI) technology, the imminent complete supplanting of human writers by AI is exceedingly improbable. Undoubtedly, AI is currently capable of producing texts that are only marginally cogent or even imitate specific writing styles by utilizing machine learning and algorithms. However, what distinguishes human writers is their capacity to imbue their works with ingenuity, sentimentality, and distinct viewpoints. Writing is more than simply assembling words; it is an authentic expression of oneself. In addition, human beings possess superior cognitive abilities such as critical thinking, intuition, and the capacity to comprehend intricate contexts beyond the current capabilities of any algorithm. However, who can say what the future may bring? It is not inconceivable that talent-matched AI writers will one day compose bestselling novels and award-winning articles alongside us humans.  Science Insights00It is not inconceivable that talent-matched AI writers will one day compose bestselling novels and award-winning articles alongside us humans.Science Insights['Atsuki Kojima']2024-01-30T00:00:00https://www.semanticscholar.org/paper/de49df08d80afd43adc3e270a39c293146b0d534 2134f7ef8f2c4b5e56d6f239377616e8ad79e7c5Artificial Intelligence merupakan subbidang ilmu komputer yang difokuskan untuk menciptakan kecerdasan buatan yang memiliki pola pikir dan perilaku seperti manusia. Artificial Intelligence (AI) merupakan simulasi dari kecerdasan yang dimiliki oleh manusia,  yang dimodelkan di dalam mesin dan diprogram agar bisa berpikir seperti halnya manusia.  Sedangkan menurut Mc Leod dan Schell,  kecerdasan buatan adalah aktivitas penyediaan mesin seperti komputer dengan kemampuan untuk menampilkan perilaku manusia. Proses yang terjadi dalam Artificial Intelligence mencakup learning, reasoning, dan self-correction. Proses ini hampir sama dengan manusia yang melakukan analisis sebelum memberikan keputusan.  AI  dapat digunakan dalam berbagai bidang kehidupan, termasuk di dalamnya pembelajaran vokasi. Pembelajaran vokasi merupakan pembelajaran yang disiapkan untuk dunia kerja, yang bertujuan untuk menyiapkan lulusan untuk siap bekerja.  Agar lulusan siap bekerja maka pembelajaran vokasional harus memuat pelatihan khusus yang cenderung bersifat reproduktif dengan fokus pada pengembangan kebutuhan industri. Agar selaras dengan kebutuhan dunia industi, pembelajaran vokasi perlu dirancang dengan menghadirkan teknologi, Artifial Intelligence salah satunya. Metodologi yang digunakan dalam penelitian ini adalah observasional kualitatif deskriptif berkonsentrasi pada identifikasi fitur atau karakteristik dari kejadian tertentu yang diperiksa selama proses pengumpulan data.  Penelitian ini menggunakan wawancara terstruktur untuk memperoleh data penelitian dan pencarian literatur metodis pada database jurnal terkait Artificial Intelligence.  Pemanfaatan AI pada pembelajaran vokasi diharapkan dapat mengoptimal waktu dan proses pembelajaran yang cepat dan hasil yang maksimal.Journal of Information and Technology00ENCRYPTION: Journal of Information And Technology['Sari Prabandari', 'Suhardianto']2024-01-30T00:00:00https://www.semanticscholar.org/paper/2134f7ef8f2c4b5e56d6f239377616e8ad79e7c5 2c5d55da946a308f701a0121c579e9375fa0f9eaDiscover Artificial Intelligence400The need for eXplainable Artificial Intelligence (XAI) in the field of genomics is highlighted and how the understanding of genomic regions, specifically the non-coding regulatory region of genomes (i.e., enhancers), can help uncover underlying molecular principles of disease states, in particular cancer in humans.Discov. Artif. Intell.['Khizra Maqsood', 'H. Hagras', 'N. Zabet']2024-01-30T00:00:00https://www.semanticscholar.org/paper/2c5d55da946a308f701a0121c579e9375fa0f9ea 780eaa17eab5c29ebd812eba1d3a2980af586bffThis study examines the necessary changes in Ghana’s medical negligence law which governs artificial intelligence (AI) pilots in hospitals to preserve the doctor-patient relationship and address the liability gap by reviewing Law 575, examination of case law. Combined with lessons from Nigerian hospitals and the global literature, practical recommendations result in acceptable changes, provider responsibilities are renewed, patient advocacy in automation Contributions include modeling legal language for maintaining standards of care, limiting algorithmic harm, and advising emerging AIs on diagnosis or treatment.  The need for transparency of equipment and current pilots calls for an update to Rule 575 sooner rather than waiting for crimes to occur. Emphasis is placed on the potential of clinical leaders and policymakers.Al-Salam Journal for Medical Science200Al-Salam Journal for Medical Science['George Benneh Mensah', 'Maad M. Mijwil', 'I. Adamopoulos', 'A. Bardavouras', 'Fredrick O. Kayusi']2024-01-30T00:00:00https://www.semanticscholar.org/paper/780eaa17eab5c29ebd812eba1d3a2980af586bff e32acf7c714eb8a68d65aa3b5b15033c7f11dba4The voter information tools collectively known as “Voting Advice Applications” (VAAs) have emerged as particularly popular tools in the realm of E-participation. Today, VAAs are integral parts of election campaigns in many countries around the world as they routinely engage millions of citizens, in addition to political actors and the media. This contribution assesses the integration of Artificial Intelligence (AI) in the design and dissemination of VAAs, considering normative, ethical, and methodological challenges. The study provides a comprehensive overview of AI applications in VAA development, from formulating questions to disseminating information, and concludes by highlighting areas where AI can serve as a valuable tool for enhancing the positive impact of VAAs on democratic processes.Frontiers in Political Science1160The study provides a comprehensive overview of AI applications in VAA development, from formulating questions to disseminating information, and concludes by highlighting areas where AI can serve as a valuable tool for enhancing the positive impact of VAAs on democratic processes.Frontiers in Political Science['Kostas Gemenis']2024-01-30T00:00:00https://www.semanticscholar.org/paper/e32acf7c714eb8a68d65aa3b5b15033c7f11dba4 8bc98d5d7b55afb16506ee205c6be9330798faffThis competitive world demands for the human resources as a mandatory asset in order to improve the organizational performance. The organizations have to strive for adopting the innovative HR practices to improve their performance and be different among its competitors. In near future, HRM is moving from the traditional way of HR practices to more advanced progress like automation, augmented intelligence, robotics and AI. AI has been proved as life – changing for us. From automation of mundane and time-consuming tasks, to the professionals today are more towards optimizing the combination of human and automated work to gain a simple and intuitive work environment. It provides them enough time to deliver the enhanced employee performance. To compete with AI and advanced machines, the real challenge now lies within the respective HR department that how will they train and re-transform their workforce in understanding the AI and collaborating and working with AI and robotics. augmentation and amplification of human capabilities, AI has the potential to drastically transform the way we live and work. For HR, this is not just an opportunity but also an urgency to adapt and adopt.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00To compete with AI and advanced machines, the real challenge now lies within the respective HR department that how will they train and re-transform their workforce in understanding the AI and collaborating and working with AI and robotics.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['A. B. Kumar', 'DR.K Sasirekha']2024-01-30T00:00:00https://www.semanticscholar.org/paper/8bc98d5d7b55afb16506ee205c6be9330798faff 186b620393a2e3f1d0e9b0ec0620a80b5ea55af5Service Industries Journal503The Service Industries Journal['D. Belanche', 'Russell W. Belk', 'L. Casaló', 'C. Flavián']2024-01-30T00:00:00https://www.semanticscholar.org/paper/186b620393a2e3f1d0e9b0ec0620a80b5ea55af5 64088298530a71a5a8e6538520c6c63a67fbe11aBACKGROUND Diabetic foot ulcers (DFUs) are serious complications of diabetes which can lead to lower extremity amputations (LEAs). Risk prediction models can identify high-risk patients who can benefit from early intervention. Machine learning (ML) methods have shown promising utility in medical applications. Explainable modeling can help its integration and acceptance. This study aims to develop a risk prediction model using ML algorithms with explainability for LEA in DFU patients. METHODS This study is a retrospective review of 2559 inpatient DFU episodes in a tertiary institution from 2012 to 2017. Fifty-one features including patient demographics, comorbidities, medication, wound characteristics, and laboratory results were reviewed. Outcome measures were the risk of major LEA, minor LEA and any LEA. Machine learning models were developed for each outcome, with model performance evaluated using receiver operating characteristic (ROC) curves, balanced-accuracy and F1-score. SHapley Additive exPlanations (SHAP) was applied to interpret the model for explainability. RESULTS Model performance for prediction of major, minor, and any LEA event achieved ROC of 0.820, 0.637, and 0.756, respectively, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively. Using SHAP, key features that contributed to the predictions were identified for explainability. Total white cell (TWC) count, comorbidity score and red blood cell count contributed highest weightage to major LEA event. Total white cell, eosinophils, and necrotic eschar in the wound contributed most to any LEA event. CONCLUSIONS Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU. Explainability can help provide clinical insights and identify at-risk patients for early intervention.Journal of Diabetes Science and Technology41Machine learning algorithms performed well in predicting the risk of LEA in a patient with DFU, with XGBoost, XGBoost, and Gradient Boosted Trees algorithms demonstrating best results for each model, respectively.Journal of diabetes science and technology['C. W. Oei', 'Y. Chan', 'Xiaojin Zhang', 'Kee Hao Leo', 'E. Yong', 'Rhan Chaen Chong', 'Q. Hong', 'Li Zhang', 'Ying Pan', 'G. Tan', 'Malcolm Mak']2024-01-30T00:00:00https://www.semanticscholar.org/paper/64088298530a71a5a8e6538520c6c63a67fbe11a 2933150bdd448de81201771420aeb612b73d9930Optical and quantum electronics121Optical and Quantum Electronics['Sha Dong', 'Hanjun Chen']2024-01-30T00:00:00https://www.semanticscholar.org/paper/2933150bdd448de81201771420aeb612b73d9930 8f0b809171a75a7bf3ef1847386a5a0f044af13dThe European Ethical Charter on the use of AI in judicial systems approaches holistically the integration of judicial policies by including precise provisions in national legislations, and for judicial bodies and legal professionals - the thinking and testing of the tools used, because AI is not confused with digitization. The use of AI must respect the principles included in the Charter, the fundamental rights and freedoms laid down in the CFREU, to ensure the feasibility of the data legal framework for the application of AI, by the standards imposed by the Council, democracy, and the rule of law. The aim is to increase the efficiency of justice and set limits to the use of AI.Bulletin of the Transilvania University of Braşov: Series VII: Social Sciences, Law00The European Ethical Charter on the use of AI in judicial systems approaches holistically the integration of judicial policies by including precise provisions in national legislations and requires the thinking and testing of the tools used.Bulletin of the Transilvania University of Braşov. Series VII: Social Sciences • Law['Simona Franguloiu']2024-01-30T00:00:00https://www.semanticscholar.org/paper/8f0b809171a75a7bf3ef1847386a5a0f044af13d e90c909128542240808616733c9d31ddc88c0623International Journal of System Assurance Engineering and Management270International Journal of System Assurance Engineering and Management['Silky Sharma', 'Gurinder Singh', 'Chandra Shekhar Sharma', 'Shikha Kapoor']2024-01-30T00:00:00https://www.semanticscholar.org/paper/e90c909128542240808616733c9d31ddc88c0623 e302f0cb9885f2b5b2d6976cfbc61c52deb73f81Human Arenas550Human Arenas['Rania Abdelmoneim', 'Kamel Jebreen', 'Eqbal Radwan', 'Wafa Kammoun-Rebai']2024-01-30T00:00:00https://www.semanticscholar.org/paper/e302f0cb9885f2b5b2d6976cfbc61c52deb73f81 169d86379fee97032467b1255bc363dc0601982fInnovation in Language Learning and Teaching420Innovation in Language Learning and Teaching['Hanieh Shafiee Rad']2024-01-30T00:00:00https://www.semanticscholar.org/paper/169d86379fee97032467b1255bc363dc0601982f f264470475c7d79be53198f839a8f05a3443f0a0GBS Impact: Journal of Multi Disciplinary Research00GBS Impact: Journal of Multi Disciplinary Research['Kirankumar Agadi', 'Uttamkumar Kinange']2024-01-30T00:00:00https://www.semanticscholar.org/paper/f264470475c7d79be53198f839a8f05a3443f0a0 87066db0eeba5be06851412c83411c3a6e4f22ccThe journals of gerontology. Series A, Biological sciences and medical sciences160The journals of gerontology. Series A, Biological sciences and medical sciences['P. Abadir', 'Rama Chellappa']2024-01-30T00:00:00https://www.semanticscholar.org/paper/87066db0eeba5be06851412c83411c3a6e4f22cc b3b009c2a2bfdeec73c039b144cb265f5e7dd518International Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology['Patel Keyur']2024-01-30T00:00:00https://www.semanticscholar.org/paper/b3b009c2a2bfdeec73c039b144cb265f5e7dd518 de26c7f7046d18fe10f5ccc520a0135dd484a144Comput. Methods Programs Biomed.350The results demonstrate the potential of combining DL and SHAP-based XAI to unravel the brain network involved in producing MI and can optimize the requirements for out-of-laboratory BCI applications involving real users.Computer methods and programs in biomedicine['Sergio Pérez-Velasco', 'Diego Marcos-Martínez', 'Eduardo Santamaría-Vázquez', 'Víctor Martínez-Cagigal', 'Selene Moreno-Calderón', 'Roberto Hornero']2024-01-30T00:00:00https://www.semanticscholar.org/paper/de26c7f7046d18fe10f5ccc520a0135dd484a144 d51f22d0e6b078818df4787a55f847284b3d00bfMedicine, Science and the Law110Medicine, science, and the law['Amina Memon', 'Zoe Given‐Wilson', 'Derya Ozkul', 'Karen McGregor Richmond', 'Julia Muraszkiewicz', 'Ella Weldon', 'Cornelius Katona']2024-01-30T00:00:00https://www.semanticscholar.org/paper/d51f22d0e6b078818df4787a55f847284b3d00bf 67c812f628b98936990788bd098afb885d3818f4Yapay zekâ teknolojisinin gelişimiyle birlikte algoritmik devlet, bilgi devleti (i-devlet), yapay zekâ devleti, yapay zekâ bakanlığı, yapay zekâ bürokrasisi, akıllı devlet gibi ülkelerin ve toplumların yönetim süreçlerini de kapsayan birçok alanda yeni kavramlar ve uygulamalar ortaya çıkmıştır. Günümüzde yapay zekâ toplumların gelişimi ve dijital dönüşümünde birinci gündem maddesi olarak yer almaktadır. Yeni iletişim teknolojilerinin sağladığı olanaklarla toplumlarda yapay zekâ rekabeti hız kazanmıştır. Bu çalışmanın amacı, Türkiye’nin ve Türk Devletleri Teşkilatı üyesi ülkelerin dijitalleşme süreçlerinde yapay zekâ alanında yapmış oldukları çalışmaları analiz etmektir. Araştırma, 2010 ve 2022 yılları arasında Google Scholar veri tabanında Türk Konseyi ve Türk Devletleri Teşkilatı hakkında yayınlanmış çalışmalar üzerinde içerik analizi yöntemi kullanımıyla yürütülmüştür. Yapay zekâ çalışmalarının hükümeti ve toplumsal kurumları güçlendirdiği bilinmektedir. Ancak yapay zekâ üzerine yapılan çalışmalar incelendiğinde yapay zekâ ile Türk Konseyi ve yeni adıyla Türk Devletleri Teşkilatını bütünleştiren çalışmaların oranı sadece yüzde 1.72 olarak bulunmuştur. Türkiye’nin e-devlet aşamasında olduğu ve yeni yapay zekâ atılımları ile algoritmik ve akıllı devlet olma yolunda ilerlediği görülmüştür. Bu çalışmanın yapay zekâ, yapay zekâ ve toplum, yapay zekâ ve devlet, dijital dönüşüm alanında yapılacak yeni çalışmalara kaynak oluşturması hedeflenmiştir.Erciyes İletişim Dergisi230Erciyes İletişim Dergisi['Sevgi Kavut']2024-01-30T00:00:00https://www.semanticscholar.org/paper/67c812f628b98936990788bd098afb885d3818f4 bf92baad9a8665b52d0617dd39f7f13d41de9e99Training an effective Machine learning (ML) model is an iterative process that requires effort in multiple dimensions. Vertically, a single pipeline typically includes an initial ETL (Extract, Transform, Load) of raw datasets, a model training stage, and an evaluation stage where the practitioners obtain statistics of the model performance. Horizontally, many such pipelines may be required to find the best model within a search space of model configurations. Many practitioners resort to maintaining logs manually and writing simple glue code to automate the workflow. However, carrying out this process on the cloud is not a trivial task in terms of resource provisioning, data management, and bookkeeping of job histories to make sure the results are reproducible. We propose an end-to-end cloud-based machine learning platform, Accelerated Cloud for AI (ACAI), to help improve the productivity of ML practitioners. ACAI achieves this goal by enabling cloud-based storage of indexed, labeled, and searchable data, as well as automatic resource provisioning, job scheduling, and experiment tracking. Specifically, ACAI provides practitioners (1) a data lake for storing versioned datasets and their corresponding metadata, and (2) an execution engine for executing ML jobs on the cloud with automatic resource provisioning (auto-provision), logging and provenance tracking. To evaluate ACAI, we test the efficacy of our auto-provisioner on the MNIST handwritten digit classification task, and we study the usability of our system using experiments and interviews. We show that our auto-provisioner produces a 1.7x speed-up and 39% cost reduction, and our system reduces experiment time for ML scientists by 20% on typical ML use cases.arXiv.org230ACAI provides practitioners with a data lake for storing versioned datasets and their corresponding metadata, and an execution engine for executing ML jobs on the cloud with automatic resource provisioning (auto-provision), logging and provenance tracking.ArXiv['Dachi Chen', 'Weitian Ding', 'Chen Liang', 'Chang Xu', 'Junwei Zhang', 'Majd Sakr']2024-01-30T00:00:00https://www.semanticscholar.org/paper/bf92baad9a8665b52d0617dd39f7f13d41de9e99 843975f3de6613698f6c8b7c8e641e904d03b6fdFluorescent imaging has revolutionized biomedical research, enabling the study of intricate cellular processes. Multiplex immunofluorescent imaging has extended this capability, permitting the simultaneous detection of multiple markers within a single tissue section. However, these images are susceptible to a myriad of undesired artifacts, which compromise the accuracy of downstream analyses. Manual artifact removal is impractical given the large number of images generated in these experiments, necessitating automated solutions. Here, we present QUAL-IF-AI, a multi-step deep learning-based tool for automated artifact identification and management. We demonstrate the utility of QUAL-IF-AI in detecting four of the most common types of artifacts in fluorescent imaging: air bubbles, tissue folds, external artifacts, and out-of-focus areas. We show how QUAL-IF-AI outperforms state-of-the-art methodologies in a variety of multiplexing platforms achieving over 85% of classification accuracy and more than 0.6 Intersection over Union (IoU) across all artifact types. In summary, this work presents an automated, accessible, and reliable tool for artifact detection and management in fluorescent microscopy, facilitating precise analysis of multiplexed immunofluorescence images.bioRxiv360QUAL-IF-AI is presented, a multi-step deep learning-based tool for automated artifact identification and management in fluorescent microscopy, facilitating precise analysis of multiplexed immunofluorescence images.bioRxiv['M. Andhari', 'Giulia Rinaldi', 'Pouya Nazari', 'Gautam Shankar', 'N. Dubroja', 'Johanna Vets', 'Tessa Ostyn', 'M. Vanmechelen', 'B. Decraene', 'Alexandre Arnould', 'Willem Mestdagh', 'Bart De Moor', 'F. De Smet', 'Francesca Bosisio', 'A. Antoranz']2024-01-30T00:00:00https://www.semanticscholar.org/paper/843975f3de6613698f6c8b7c8e641e904d03b6fd d6f72a5c69385273663d4dfa861619ceee34eefbQuarterly Journal of the Operational Research Society of India (OPSEARCH)730OPSEARCH['Hanane Allioui', 'Azzeddine Allioui', 'Youssef Mourdi']2024-01-30T00:00:00https://www.semanticscholar.org/paper/d6f72a5c69385273663d4dfa861619ceee34eefb 1f9b9fa9f58842764a886ce1a119ad631934511eInternational Journal of Women's Health and Reproduction Sciences40International Journal of Women's Health and Reproduction Sciences['Z. Kurdoğlu', 'D. Taş', 'A. Khaki']2024-01-30T00:00:00https://www.semanticscholar.org/paper/1f9b9fa9f58842764a886ce1a119ad631934511e 9ddc59319ed573cc65c13a367e5bad56dda74d30Reconfigurable intelligent surfaces (RIS) offer the potential to customize the radio propagation environment for wireless networks, and will be a key element for 6G communications. However, due to the unique constraints in these systems, the optimization problems associated to RIS configuration are challenging to solve. This paper illustrates a new approach to the RIS configuration problem, based on the use of artificial intelligence (AI) and deep learning (DL) algorithms. Concretely, a custom convolutional neural network (CNN) intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform. This FPGA option provides the best performance, with ×20 performance increase over the closest FP32, GPU-accelerated option, and almost ×3 performance advantage when compared with the INT8-quantized, TPU-accelerated implementation. More noticeably, this is achieved even when high-level synthesis (HLS) tools are used and no custom accelerators are developed. At the same time, the inherent reconfigurability of FPGAs opens a new field for their use as enabler hardware in RIS applications.Italian National Conference on Sensors460A custom convolutional neural network intended for edge computing is presented, and implementations on different representative edge devices are compared, including the use of commercial AI-oriented devices and a field-programmable gate array (FPGA) platform.Sensors (Basel, Switzerland)['Alberto Martín-Martín', 'Rubén Padial-Allué', 'Encarnación Castillo', 'L. Parrilla', 'Ignacio Parellada-Serrano', 'Alejandro Morán', 'Antonio García']2024-01-30T00:00:00https://www.semanticscholar.org/paper/9ddc59319ed573cc65c13a367e5bad56dda74d30 483d87681316a457cc737b15acda5b084bcffaf4Journal of Purdue Undergraduate Research00The Journal of Purdue Undergraduate Research['Yiyao Zhang']2024-01-29T00:00:00https://www.semanticscholar.org/paper/483d87681316a457cc737b15acda5b084bcffaf4 4fda99880cdbf8f178f01eb4c8dbdae7f959ea94In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks. However, despite AI red-teaming's central role in policy discussions and corporate messaging, significant questions remain about what precisely it means, what role it can play in regulation, and how it relates to conventional red-teaming practices as originally conceived in the field of cybersecurity. In this work, we identify recent cases of red-teaming activities in the AI industry and conduct an extensive survey of relevant research literature to characterize the scope, structure, and criteria for AI red-teaming practices. Our analysis reveals that prior methods and practices of AI red-teaming diverge along several axes, including the purpose of the activity (which is often vague), the artifact under evaluation, the setting in which the activity is conducted (e.g., actors, resources, and methods), and the resulting decisions it informs (e.g., reporting, disclosure, and mitigation). In light of our findings, we argue that while red-teaming may be a valuable big-tent idea for characterizing GenAI harm mitigations, and that industry may effectively apply red-teaming and other strategies behind closed doors to safeguard AI, gestures towards red-teaming (based on public definitions) as a panacea for every possible risk verge on security theater. To move toward a more robust toolbox of evaluations for generative AI, we synthesize our recommendations into a question bank meant to guide and scaffold future AI red-teaming practices.arXiv.org1839This work identifies recent cases of red-teaming activities in the AI industry and conducts an extensive survey of relevant research literature to characterize the scope, structure, and criteria for AI red-teaming practices, and synthesizes recommendations into a question bank meant to guide and scaffold future AI red-teaming practices.ArXiv['Michael Feffer', 'Anusha Sinha', 'Zachary Chase Lipton', 'Hoda Heidari']2024-01-29T00:00:00https://www.semanticscholar.org/paper/4fda99880cdbf8f178f01eb4c8dbdae7f959ea94 2d007d30a37698efb920200b2a2caf581a8837a902['V. Papakonstantinou', 'P. De Hert']2024-01-29T00:00:00https://www.semanticscholar.org/paper/2d007d30a37698efb920200b2a2caf581a8837a9 d6c561469656811ae76c03a6cf914e0e6b7557faJournal of Purdue Undergraduate Research00The Journal of Purdue Undergraduate Research['Emma Niecikowski']2024-01-29T00:00:00https://www.semanticscholar.org/paper/d6c561469656811ae76c03a6cf914e0e6b7557fa af0d49e15bab2b4128a1f11fc82ac22094329113International Journal of Academic Research in Business and Social Sciences00International Journal of Academic Research in Business and Social Sciences['Noor Hanim Rahmat', 'Thassanee Thasrabiab']2024-01-29T00:00:00https://www.semanticscholar.org/paper/af0d49e15bab2b4128a1f11fc82ac22094329113 899019391c27836ad474dfb04ad891e18c90b5eaSimple Summary This paper explores the methane emissions from the livestock industry and their large impact on climate change, with a particular focus on cattle. It emphasizes how important it is to monitor and control methane accurately because it is a powerful greenhouse gas that accounts for 14–16% of world emissions. The study evaluates both conventional and AI-powered techniques for methane emission detection, emphasizing the significance of cattle in particular. It has been determined that region-specific formulations are required. The review discusses a number of topics, such as the methane emissions from livestock, the promise of AI technology, difficulties in gathering data, the use of methane in carbon credit programs, and current research and innovation. The review aims to improve knowledge and practices for climate change mitigation by highlighting the crucial role that accurate measurement and estimation methodologies play. It draws attention to the role that methane produced by livestock, particularly cattle, plays in climate change and stresses the need for precise measuring methods to be integrated into mitigation efforts. Abstract This review examines the significant role of methane emissions in the livestock industry, with a focus on cattle and their substantial impact on climate change. It highlights the importance of accurate measurement and management techniques for methane, a potent greenhouse gas accounting for 14–16% of global emissions. The study evaluates both conventional and AI-driven methods for detecting methane emissions from livestock, particularly emphasizing cattle contributions, and the need for region-specific formulas. Sections cover livestock methane emissions, the potential of AI technology, data collection issues, methane’s significance in carbon credit schemes, and current research and innovation. The review emphasizes the critical role of accurate measurement and estimation methods for effective climate change mitigation and reducing methane emissions from livestock operations. Overall, it provides a comprehensive overview of methane emissions in the livestock industry by synthesizing existing research and literature, aiming to improve knowledge and methods for mitigating climate change. Livestock-generated methane, especially from cattle, is highlighted as a crucial factor in climate change, and the review underscores the importance of integrating precise measurement and estimation techniques for effective mitigation.Animals872Animals : an Open Access Journal from MDPI['J. Ghassemi Nejad', 'Mun-Su Ju', 'J. Jo', 'Kyung-Hwan Oh', 'Y. Lee', 'Sung-Dae Lee', 'Eun-Joong Kim', 'S. Roh', 'Hong-Gu Lee']2024-01-29T00:00:00https://www.semanticscholar.org/paper/899019391c27836ad474dfb04ad891e18c90b5ea b45e835615566d5df408a69f228b1e2d09f94583This paper investigates a novel algorithmic vulnerability when imperceptible image layers confound multiple vision models into arbitrary label assignments and captions. We explore image preprocessing methods to introduce stealth transparency, which triggers AI misinterpretation of what the human eye perceives. The research compiles a broad attack surface to investigate the consequences ranging from traditional watermarking, steganography, and background-foreground miscues. We demonstrate dataset poisoning using the attack to mislabel a collection of grayscale landscapes and logos using either a single attack layer or randomly selected poisoning classes. For example, a military tank to the human eye is a mislabeled bridge to object classifiers based on convolutional networks (YOLO, etc.) and vision transformers (ViT, GPT-Vision, etc.). A notable attack limitation stems from its dependency on the background (hidden) layer in grayscale as a rough match to the transparent foreground image that the human eye perceives. This dependency limits the practical success rate without manual tuning and exposes the hidden layers when placed on the opposite display theme (e.g., light background, light transparent foreground visible, works best against a light theme image viewer or browser). The stealth transparency confounds established vision systems, including evading facial recognition and surveillance, digital watermarking, content filtering, dataset curating, automotive and drone autonomy, forensic evidence tampering, and retail product misclassifying. This method stands in contrast to traditional adversarial attacks that typically focus on modifying pixel values in ways that are either slightly perceptible or entirely imperceptible for both humans and machines.arXiv.org281ArXiv['Forrest McKee', 'David A. Noever']2024-01-29T00:00:00https://www.semanticscholar.org/paper/b45e835615566d5df408a69f228b1e2d09f94583 85e421c6f7e732e52ed78f6281393edb360ffb26(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of “unknown common” compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.Toxics411Toxics['Vito Gelao', 'Stefano Fornasaro', 'S. Briguglio', 'Michele Mattiussi', 'Stefano De Martin', 'Aleksander Astel', 'P. Barbieri', 'S. Ličen']2024-01-29T00:00:00https://www.semanticscholar.org/paper/85e421c6f7e732e52ed78f6281393edb360ffb26 d09889517a4701dc5a2d8a621827e7575e1ba488The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.Diagnostics961There is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and catalyze a game-changing transformation in clinical activities, according to this review of the current state-of-the-art of AI in the scope of small-bowel study.Diagnostics['Joana Mota', 'Maria João Almeida', 'F. Mendes', 'M. Martins', 'T. Ribeiro', 'J. Afonso', 'P. Cardoso', 'Hélder Cardoso', 'Patrícia Andrade', 'J. Ferreira', 'M. Mascarenhas', 'Guilherme Macedo']2024-01-29T00:00:00https://www.semanticscholar.org/paper/d09889517a4701dc5a2d8a621827e7575e1ba488 800d28e7666f1fccfe1c11272da14fcd43c1efcbResearch in Engineering Design581The review’s results suggest that AI in EC requires developing distributed AI systems to manage the ensuing complexity and suggest that AI in EC requires developing distributed AI systems for automation.Research in Engineering Design['P. Burggräf', 'Johannes Wagner', 'T. Saßmannshausen', 'T. Weißer', 'O. Radisic-Aberger']2024-01-29T00:00:00https://www.semanticscholar.org/paper/800d28e7666f1fccfe1c11272da14fcd43c1efcb 9f89f8cb14e03dffba9821c4a0ba6df0f89ff65fStrategic Analysis02Strategic Analysis['Javad Heiran‐Nia']2024-01-29T00:00:00https://www.semanticscholar.org/paper/9f89f8cb14e03dffba9821c4a0ba6df0f89ff65f de3c9e140af2be93b61d0203a1d182e343e52cb1Asia Pacific Journal of Education752Asia Pacific Journal of Education['Jun Liu', 'Cong Wang', 'Zile Liu', 'Minghui Gao', 'Yanhua Xu', 'Jiayu Chen', 'Yichun Cheng']2024-01-29T00:00:00https://www.semanticscholar.org/paper/de3c9e140af2be93b61d0203a1d182e343e52cb1 baa9faa5d9576aa4a3ce41d0c38134ec3e063a6aOriginal Equipment Manufacturers and large first-tier manufacturing companies have realized the need to utilize digitalization to compete in the global economy. These companies have the resources, funding, and engineering talent to evaluate and implement Industry 4.0, or Smart Manufacturing, capabilities to improve competitiveness and profitability. This is not the case for 90% of the supply chain comprising small- and medium-sized manufacturers (SMMs). In this paper, we explore the inhibitors to technology adoption and present the role of artificial intelligence (AI) in improving adoption rates in the industrial base. The paper closes with a look at how technologies such as AI will affect the future of electronics manufacturing.2024 Pan Pacific Strategic Electronics Symposium (Pan Pacific)140The inhibitors to technology adoption are explored and the role of artificial intelligence (AI) in improving adoption rates in the industrial base is presented, with a look at how technologies such as AI will affect the future of electronics manufacturing.2024 Pan Pacific Strategic Electronics Symposium (Pan Pacific)['Gregory A. Harris', 'Ashley C. Yarbrough']2024-01-29T00:00:00https://www.semanticscholar.org/paper/baa9faa5d9576aa4a3ce41d0c38134ec3e063a6a 13c79ea98beb94ac9e6b06409dd66fc33d955323Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.Bioengineering310How AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features is detailed.Bioengineering['Lawrence Holland', 'Sofia I. Hernandez Torres', 'Eric J. Snider']2024-01-29T00:00:00https://www.semanticscholar.org/paper/13c79ea98beb94ac9e6b06409dd66fc33d955323 45c752bb5bdeaf9900c554d26a4528204facfacaThis paper explores the transformation of masculinity in Spike Jonze’s 2013 film Her, examining how the protagonist Theodore’s relationship with the artificial intelligence character Samantha challenges traditional norms of masculinity. Set in a near-future Los Angeles, the film follows Theodore (Joaquin Phoenix), a lonely man who forms a relationship with his AI operating system, Samantha (Scarlett Johansson). Their unconventional bond, characterized by emotional openness and vulnerability, underscores shifting paradigms of masculinity in an increasingly digital world. Through close analysis of Theodore’s personality, emotional struggles, and interactions with Samantha, this paper argues that the film paints a nuanced portrait of contemporary masculinity. Theodore’s willingness to be emotionally vulnerable with an AI companion reflects a departure from the conventional masculine ideal of stoicism. His dependence on Samantha for emotional support also challenges the norm of male independence and self-sufficiency. Furthermore, Samantha’s influence disrupts Theodore’s understanding of intimacy and relationships, emphasizing fluidity over rigid norms. Ultimately, Theodore’s journey suggests a redefined conceptualization of masculinity –adaptive, emotionally expressive, and receptive to meaningful connections in unexpected places. Situated at the intersection of film studies, gender studies and technology, this paper illuminates how Her insightfully portrays the complex renegotiation of masculinity in an increasingly digitized and AI-integrated world.American Journal of Interdisciplinary Research and Innovation140American Journal of Interdisciplinary Research and Innovation['Sunday Joseph Ayodabo']2024-01-29T00:00:00https://www.semanticscholar.org/paper/45c752bb5bdeaf9900c554d26a4528204facfaca 25cf748eda2d028039bc02b217495b1e52fc0ea0KNOWLEDGE TRANSFER STATEMENT This study explored how artificial intelligence (AI) can revolutionize geriatric care by improving oral health and alleviating social disconnection among isolated older adults. The findings can guide clinicians in integrating AI tools into practices, assist policymakers in developing AI-inclusive health policies, and inform patients about the potential benefits of AI in enhancing their health outcomes and social connection.JDR Clinical & Translational Research70The findings can guide clinicians in integrating AI tools into practices, assist policymakers in developing AI-inclusive health policies, and inform patients about the potential benefits of AI in enhancing their health outcomes and social connection.JDR clinical and translational research['X. Qi', 'B. Wu']2024-01-29T00:00:00https://www.semanticscholar.org/paper/25cf748eda2d028039bc02b217495b1e52fc0ea0 f5b71f05afc2f8a975d7aaf8bf4362d8a7804f55Prostate cancer (PCa) poses significant challenges for timely diagnosis and prognosis, leading to high mortality rates and increased disease risk and treatment costs. Recent advancements in machine learning and digital imagery offer promising potential for developing automated and objective assessment pipelines that can reduce human capital and resource costs. However, the reliance of AI models on large amounts of clinical data for training presents a significant challenge, as this data is often biased, lacking diversity, and not readily available. Here we aim to address this limitation by employing customized generative adversarial network (GAN) models to produce high-quality synthetic images of different PCa grades (radical prostatectomy (RP)) and needle biopsies, which were customized to account for the granularity associated with each Gleason grade. The generated images were subjected to multiple rounds of benchmarking, quantifications and quality control assessment before being used to train an AI model (EfficientNet) for grading digital histology images of adenocarcinoma specimens (RP sections) and needle biopsies obtained from the PANDA challenge repository. Validation was performed using the AI model trained with synthetic data to grade digital histology from the cancer genome atlas (TCGA) (RP sections) and needle biopsy data from Radboud University Medical Center and Karolinska Institute. Results demonstrated that the AI model trained with a combination of image patches derived from original and enhanced synthetic images outperformed the model trained with original digital histology images. Together, this study demonstrates the potential of customized GAN models to generate a large cohort of synthetic data that can train AI models to effectively grade PCa specimens. This approach could potentially eliminate the need for extensive clinical data for training any AI model in the domain of digital imagery, leading to cost and time-effective diagnosis and prognosis.bioRxiv910This study demonstrates the potential of customized GAN models to generate a large cohort of synthetic data that can train AI models to effectively grade PCa specimens, leading to cost and time-effective diagnosis and prognosis.bioRxiv['Derek J. Van Booven', 'Cheng-Bang Chen', 'Oleksander Kryvenko', 'S. Punnen', 'Victor Sandoval', 'Sheetal Malpani', 'Ahmed Noman', 'Farhan Ismael', 'A. Briseño', 'Yujie Wang', 'Himanshu Arora']2024-01-29T00:00:00https://www.semanticscholar.org/paper/f5b71f05afc2f8a975d7aaf8bf4362d8a7804f55 c3cb22be938f1518aee2ca9b6964bce231aca3ffData collection and analytics are critical to navigating today's supply chain challenges and market demands. The increasing complexity of electronic products manufactured today, coupled with higher demands on quality and reliability, makes this now a requirement to be competitive. Often termed Factory Digitization, it is being enabled by modern solutions such as a Manufacturing Execution System (MES) that integrates Internet of Things (IoT) and machine data in order to capture a comprehensive digital history of product manufacturing and testing. These solutions use web scale technologies that require lower support costs, provide plug-n-play type data connectivity, rapid adoption, enabling enterprises to be nimble and adaptable amidst growing industry complexity. Factory digitization results in highly contextual data, and this is what is unleashing the next manufacturing innovation wave of applied AI/ML, currently making its way through semiconductor fabrication, backend assembly, and testing. And now penetrating the SMT industry. This paper covers key points of how AI/ML requires Factory Digitization in order to be adopted, the barriers and risks to implementing this digital transformation, and how the right infrastructure and AI/ML approach is able to deliver exceptional competitive advantages to electronics manufacturing.2024 Pan Pacific Strategic Electronics Symposium (Pan Pacific)00Key points of how AI/ML requires Factory Digitization in order to be adopted are covered, the barriers and risks to implementing this digital transformation, and how the right infrastructure and AI/ML approach is able to deliver exceptional competitive advantages to electronics manufacturing are covered.2024 Pan Pacific Strategic Electronics Symposium (Pan Pacific)['Ryan Gamble', 'Daniel Gutierrez']2024-01-29T00:00:00https://www.semanticscholar.org/paper/c3cb22be938f1518aee2ca9b6964bce231aca3ff d79637c513f17cd479f4172d376fff8f762c0c65The fusion of quantum computing and artificial intelligence (AI) heralds a transformative era for Industry 4.0, offering unprecedented capabilities and challenges. This paper delves into the intricacies of quantum AI, its potential impact on Industry 4.0, and the necessary change management and innovation strategies for seamless integration. Drawing from theoretical insights and real-world case studies, we explore the current landscape of quantum AI, its foreseeable influence, and the implications for organizational strategy. We further expound on traditional change management tactics, emphasizing the importance of continuous learning, ecosystem collaborations, and proactive approaches. By examining successful and failed quantum AI implementations, lessons are derived to guide future endeavors. Conclusively, the paper underscores the imperative of being proactive in embracing quantum AI innovations, advocating for strategic foresight, interdisciplinary collaboration, and robust risk management. Through a comprehensive exploration, this paper aims to equip stakeholders with the knowledge and strategies to navigate the complexities of quantum AI in Industry 4.0, emphasizing its transformative potential and the necessity for preparedness and adaptability.Applied Informatics1140The imperative of being proactive in embracing quantum AI innovations is highlighted, advocating for strategic foresight, interdisciplinary collaboration, and robust risk management, and the necessity for preparedness and adaptability.AI['Meng-Leong How', 'Sin-Mei Cheah']2024-01-29T00:00:00https://www.semanticscholar.org/paper/d79637c513f17cd479f4172d376fff8f762c0c65 60ec5b0afbf55cd791337bedb1b78b4370dbb8bcCholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.Frontiers in Oncology1390Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.Frontiers in Oncology['Jianhao Huang', 'Xuesong Bai', 'Yanyu Qiu', 'Xiaodong He']2024-01-29T00:00:00https://www.semanticscholar.org/paper/60ec5b0afbf55cd791337bedb1b78b4370dbb8bc ca04dca85390770b0e57ed5441aee7bbd3aacb50In the continually evolving domain of software development, guaranteeing the dependability and functionality of Application Programming Interfaces (APIs) is of utmost importance. Traditional approaches to API testing frequently encounter difficulties in keeping up with the dynamic nature of APIs, resulting in inefficiencies and overlooked defects. This research paper investigates the transformative potential of Artificial Intelligence (AI) in API testing, ushering in a new era of intelligent testing. Intelligent API testing harnesses the capabilities of AI to enhance the efficiency, precision, and adaptability of the testing process. API driven techniques enable the production of diverse and realistic test data, ensuring comprehensive test coverage. Furthermore, AI-powered algorithms can anticipate potential issues, identify anomalies, and optimize test case selection, all while adapting to evolving API schemas. This research paper delves into the various aspects of intelligent API testing, encompassing data generation, tools and technologies, benefits and impact, challenges, and real-world use cases. We illustrate how AI empowers testers to discover subtle defects, streamline testing endeavors, and enhance the overall quality of APIdriven applications. As we navigate the era of digital transformation, intelligent API testing emerges as an essential tool in the software development toolkit, enabling organizations to deliver robust and resilient APIs that fulfill the demands of contemporary applications. Embracing AI in API testing not only holds the promise of expediting the development lifecycle but also ensures that APIs remain agile and reliable in an ever-changing digital landscape.International Journal of Software Engineering &amp; Applications120This research paper investigates the transformative potential of Artificial Intelligence in API testing, ushering in a new era of intelligent testing, and illustrates how AI empowers testers to discover subtle defects, streamline testing endeavors, and enhance the overall quality of APIdriven applications.International Journal of Software Engineering &amp; Applications['Rohit Khankhoje']2024-01-29T00:00:00https://www.semanticscholar.org/paper/ca04dca85390770b0e57ed5441aee7bbd3aacb50 a0c14299617cef00d359bbbfd26092fa33057adfThis report examines a novel risk associated with current (and projected) AI tools. Making effective decisions about future actions requires us to reason under uncertainty (RUU), and doing so is essential to many critical real world problems. Overfaced by this challenge, there is growing demand for AI tools like LLMs to assist decision-makers. Having evidenced this demand and the incentives behind it, we expose a growing risk: we 1) do not currently sufficiently understand LLM capabilities in this regard, and 2) have no guarantees of performance given fundamental computational explosiveness and deep uncertainty constraints on accuracy. This report provides an exposition of what makes RUU so challenging for both humans and machines, and relates these difficulties to prospective AI timelines and capabilities. Having established this current potential misuse risk, we go on to expose how this seemingly additive risk (more misuse additively contributed to potential harm) in fact has multiplicative properties. Specifically, we detail how this misuse risk connects to a wider network of underlying structural risks (e.g., shifting incentives, limited transparency, and feedback loops) to produce non-linear harms. We go on to provide a solutions roadmap that targets multiple leverage points in the structure of the problem. This includes recommendations for all involved actors (prospective users, developers, and policy-makers) and enfolds insights from areas including Decision-making Under Deep Uncertainty and complex systems theory. We argue this report serves not only to raise awareness (and subsequently mitigate/correct) of a current, novel AI risk, but also awareness of the underlying class of structural risks by illustrating how their interconnected nature poses twin-dangers of camouflaging their presence, whilst amplifying their potential effects.arXiv.org00It is argued this report serves not only to raise awareness of a current, novel AI risk, but also awareness of the underlying class of structural risks by illustrating how their interconnected nature poses twin-dangers of camouflaging their presence, whilst amplifying their potential effects.ArXiv['Toby D. Pilditch']2024-01-29T00:00:00https://www.semanticscholar.org/paper/a0c14299617cef00d359bbbfd26092fa33057adf 9113dba792e136c7ff1f5e464d8a42dc458cc845This study reviews studies on Artificial Intelligence (AI) maturity models (MM) in automotive manufacturing. To stay competitive, SMEs in the automotive industry need to embrace digitalization. SMEs employ a large segment of the USA's workforce. The benefits of operational efficiency, quality improvement, cost reduction, and innovative culture have made SMEs more aggressive about digitalization. Digitalizing operations with Artificial Intelligence are on the rise. In this paper, AI applications in SMEs are examined through the lens of an AI maturity model.International Journal of Artificial Intelligence &amp; Applications00This study reviews studies on Artificial Intelligence maturity models in automotive manufacturing and examines AI applications in SMEs through the lens of an AI maturity model.International Journal of Artificial Intelligence &amp; Applications['Dharmender Salian']2024-01-29T00:00:00https://www.semanticscholar.org/paper/9113dba792e136c7ff1f5e464d8a42dc458cc845 c66799650546d8114fd43fa039507339f044b4c0Businesses increasingly use Artificial Intelligence (AI) Applicant Tracking Systems (ATS) to screen job applicants’ résumés. A summative content analysis auditing how 18 business communication, business English, and technical communication textbooks cover résumés and AI ATS found a lack of consensus. The study identified the challenge of offering specific advice on emerging AI technology in textbooks. The article recommends writing and teaching practice changes when discussing emerging technology and creating or using textbook content.Business and Professional Communication Quarterly190The study identified the challenge of offering specific advice on emerging AI technology in textbooks and recommended writing and teaching practice changes when discussing emerging technology and creating or using textbook content.Business and Professional Communication Quarterly['Kathryn Lookadoo', 'Sarah Moore']2024-01-29T00:00:00https://www.semanticscholar.org/paper/c66799650546d8114fd43fa039507339f044b4c0 dbf060fe7501d6f7f87a898eec7c301645a86ae6This study investigates the transformative impact of Artificial Intelligence (AI) on PT. Perhutani Anugerah Kimia, with a focus on user experience, AI investments, digital transformation, and their collective influence on increased business efficiency. Through path analysis, the study reveals statistically significant direct effects, highlighting the pivotal role of user experience and strategic AI investments in propelling digital transformation and, consequently, improving business efficiency. The indirect effects analysis underscores the mediating role of digital transformation, elucidating how enhancements in user experience and AI investments positively cascade to boost business efficiency. These findings advocate for a strategic emphasis on user-centric approaches and substantial AI investments, positioning organizations to navigate the evolving business landscape by fostering digital transformation and realizing tangible operational efficiency gains. This research contributes valuable insights to organizations seeking to harness the full potential of AI for holistic and impactful digital evolution.Equator Journal of Management and Entrepreneurship (EJME)00This research contributes valuable insights to organizations seeking to harness the full potential of AI for holistic and impactful digital evolution, positioning organizations to navigate the evolving business landscape by fostering digital transformation and realizing tangible operational efficiency gains.Equator Journal of Management and Entrepreneurship (EJME)['Agnes Dini Mardania']2024-01-29T00:00:00https://www.semanticscholar.org/paper/dbf060fe7501d6f7f87a898eec7c301645a86ae6 5a22ddaf2ebc7607f4b89940a31ca73a45ea23edThis paper evaluates the ability of Artificial Intelligence (AI) to augment todays' proven methodologies for testing electronic components. With a combined experience of more than 10,000 sources of activity (e.g., actual tests, evaluations, collaborative projects, data sheets, user manuals, and custom test equipment), AI technology has the ability to be integrated and adopted to in order to improve electronic component testing. Due to the customization from chip to chip, there are a range of risks that need to be addressed before implementing an AI strategy for testing. As an outlook, AI enabled solutions may improve existing manual test methods and offer new test services. Further, advice is provided for a new regulatory framework to match the changing pace of new technologies such as AI.2024 Pan Pacific Strategic Electronics Symposium (Pan Pacific)00This paper evaluates the ability of Artificial Intelligence to augment todays' proven methodologies for testing electronic components and advice is provided for a new regulatory framework to match the changing pace of new technologies such as AI.2024 Pan Pacific Strategic Electronics Symposium (Pan Pacific)['Charlie Polidoro']2024-01-29T00:00:00https://www.semanticscholar.org/paper/5a22ddaf2ebc7607f4b89940a31ca73a45ea23ed 46694f750ff97513e48b5eb8b52c2184d5840b2bAbstract The increasing availability of microtargeted advertising and the accessibility of generative artificial intelligence (AI) tools, such as ChatGPT, have raised concerns about the potential misuse of large language models in scaling microtargeting efforts for political purposes. Recent technological advancements, involving generative AI and personality inference from consumed text, can potentially create a highly scalable “manipulation machine” that targets individuals based on their unique vulnerabilities without requiring human input. This paper presents four studies examining the effectiveness of this putative “manipulation machine.” The results demonstrate that personalized political ads tailored to individuals’ personalities are more effective than nonpersonalized ads (studies 1a and 1b). Additionally, we showcase the feasibility of automatically generating and validating these personalized ads on a large scale (studies 2a and 2b). These findings highlight the potential risks of utilizing AI and microtargeting to craft political messages that resonate with individuals based on their personality traits. This should be an area of concern to ethicists and policy makers.PNAS Nexus215It is demonstrated that personalized political ads tailored to individuals’ personalities are more effective than nonpersonalized ads and the feasibility of automatically generating and validating these personalized ads on a large scale is showcased.PNAS Nexus['Almog Simchon', 'Matthew Edwards', 'Stephan Lewandowsky']2024-01-29T00:00:00https://www.semanticscholar.org/paper/46694f750ff97513e48b5eb8b52c2184d5840b2b decedc682d6bbc051a9ac51e3cfc873ad87529c3 This Viewpoint argues for a shift in focus by the White House executive order on artificial intelligence from regulatory targets to patient outcomes. Journal of the American Medical Association (JAMA)62JAMA['John W. Ayers', 'Nimit Desai', 'Davey M Smith']2024-01-29T00:00:00https://www.semanticscholar.org/paper/decedc682d6bbc051a9ac51e3cfc873ad87529c3 ecf9c9f9f9ae16ec731171fc37e0660c50a55d80Justification. The incidence of diseases of the circulatory system of the population of the Russian Federation has been steadily increasing over the past two decades, increasing 2,047 times from 2000 to 2019. The process of vascular calcification implies the deposition of calcium salts in the artery wall, leading to remodeling of the vascular wall. Radiation research methods are the gold standard for the diagnosis of vascular calcification. However, due to the need for medical professionals to process a large amount of data for a certain period of time, the number of diagnostic errors inevitably increases, as well as the efficiency of work decreases. The active development and introduction of artificial intelligence (AI) into clinical practice has opened up opportunities for specialists to solve these problems. The purpose of the study. To analyze the domestic and foreign literature devoted to the use of AI in the diagnosis of various types of vascular calcification, as well as to summarize the prognostic value of vascular calcification and evaluate aspects that prevent the diagnosis of vascular calcification without the use of AI. Material and methods. The authors searched for publications in the electronic databases PubMed, Web of Science, Google Scholar and eLibrary. The search was carried out using the following keywords: "artificial intelligence", "machine learning", "vascular calcification", "artificial intelligence", "machine learning", "vascular calcification". The search was carried out in the time interval from the moment of the foundation of the corresponding database until July 2023. Conclusion. AI has proven itself well in the diagnosis of vascular calcification. In addition to improving accuracy and efficiency, the ability to detail surpasses the capabilities of the manual diagnostic method. AI has reached a level that allows doctors to help instrumental diagnostics in the automatic detection of vascular calcification. AI capabilities can contribute to the effective development of radiology in the future.Digital Diagnostics650AI has reached a level that allows doctors to help instrumental diagnostics in the automatic detection of vascular calcification and can contribute to the effective development of radiology in the future.Digital Diagnostics['Yu. A. Trusov', 'Victoria S. Chupakhina', 'Adilya S. Nurkaeva', 'Natalia A. Yakovenko', 'Irina V. Ablenina', 'Roksana F. Latypova', 'Aleksandra P. Pitke', 'Anastasiya A. Yazovskih', 'Artem S. Ivanov', 'Darya S. Bogatyreva', 'Ulyana A. Popova', 'Azat F. Yuzlekbaev']2024-01-29T00:00:00https://www.semanticscholar.org/paper/ecf9c9f9f9ae16ec731171fc37e0660c50a55d80 3cbaa57c0d0c32c812137ea88263f213b906b408 This Viewpoint describes a strategy for addressing major challenges in artificial intelligence in pediatrics to maximize clinical impact. JAMA pediatrics80JAMA pediatrics['J. Nijman', 'Ruben S Zoodsma', 'Erik Koomen']2024-01-29T00:00:00https://www.semanticscholar.org/paper/3cbaa57c0d0c32c812137ea88263f213b906b408 a3df228f4f7a94e209ef0b6bce5dac2a5b2deaa6Artificial intelligence (AI) is involved with computer system that deals with solving complex problems using symbolic programming. This model helps to reduce the time and save money while give a better way to understand the relationship between different formulations and processes parameters. The use of AI has been growing fast in the pharmaceutical industry. This paper focuses on the advantage of AI in several fields of the pharmaceutical zone such as drug discovery, Quality control, drug development, and clinical trials design etc. to mention a few names that helps to reduce the workload of humans and also achieve the goals in a short period. AI is a broad term that covers areas including artificial neural network (ANN) and machine learning (ML). Using ML allow for exact prediction and identification of pattern.Pharmaceutical Drug Regulatory Affairs Journal00The advantage of AI in several fields of the pharmaceutical zone such as drug discovery, Quality control, drug development, and clinical trials design etc. is focused on.Pharmaceutical Drug Regulatory Affairs Journal['Vinchurkar K']2024-01-29T00:00:00https://www.semanticscholar.org/paper/a3df228f4f7a94e209ef0b6bce5dac2a5b2deaa6 1952e63486cb964a922725b5e91c19512c8dd126Artificial intelligence (AI) is increasingly developing and being widely applied in all areas of life, such as: education, healthcare, transportation, production. The content of this article refers to the application of AI to improve the quality and efficiency of the control and operation of the Heating, Ventilation, and Air Conditioning (HVAC) system while also helping to reduce the system’s energy consumption.2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon)80The application of AI to improve the quality and efficiency of the control and operation of the Heating, Ventilation, and Air Conditioning system while also helping to reduce the system’s energy consumption is referred to.2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon)['Duc M. Nguyen', 'Mikhail P. Belov', 'A. M. Belov']2024-01-29T00:00:00https://www.semanticscholar.org/paper/1952e63486cb964a922725b5e91c19512c8dd126 50846a22bd7f6cf90ab68647e76c09978abc9bedAs the digital landscape continues to evolve rapidly, businesses are increasingly turning to artificial intelligence (AI) to shape their digital strategies. This paper presents a comprehensive literature review that investigates the multifaceted influence of AI on business value within the digital realm. Drawing on a wide range of academic research, industry reports, and case studies, this review explores the transformative impact of AI on decision making, personalization, cost reduction, revenue generation, and supply chain optimization. It also delves into the role of AI in risk management, customer insights, and competitive advantage, while emphasizing the ethical considerations and regulatory implications of AI integration. Additionally, this review highlights the importance of talent development and strategic partnerships in maximizing AI's potential for enhancing business value. By 12 synthesizing key findings and trends, this paper offers valuable insights for businesses seeking to navigate the complex landscape of AI in digital strategy, emphasizing the critical need for responsible AI practices and continuous innovation to drive sustainable business value.Scandic Journal Of Advanced Research And Reviews00This paper presents a comprehensive literature review that investigates the multifaceted influence of AI on business value within the digital realm, highlighting the importance of talent development and strategic partnerships in maximizing AI's potential for enhancing business value.Scandic Journal Of Advanced Research And Reviews['Shabahat Khan¹', 'Shamas Munir', 'Javeria Rizvi', 'Nawazish Ali Shaukat']2024-01-29T00:00:00https://www.semanticscholar.org/paper/50846a22bd7f6cf90ab68647e76c09978abc9bed 8ab21c6d19f6cae55314a5085e69c62e81a720e7With the rapid development of science and technology, artificial intelligence (AI) has penetrated into every aspect of our lives. Especially in the field of intelligent control system, the application of AI algorithm is increasingly becoming the key force to promote technological innovation and industrial upgrading. Intelligent control system relies on advanced AI algorithm, which can realize highly automated and intelligent decision-making and operation, and is widely used in industrial manufacturing, smart city, medical and health and other fields.2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)90Intelligent control system relies on advanced AI algorithm, which can realize highly automated and intelligent decision-making and operation, and is widely used in industrial manufacturing, smart city, medical and health and other fields.2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)['Shunru Zhang']2024-01-29T00:00:00https://www.semanticscholar.org/paper/8ab21c6d19f6cae55314a5085e69c62e81a720e7 e65e36729355900b270f8ce497f1a2d6ca89a01bArtificial intelligence has started to expand in journalism, especially in advanced news organisations. It is evident that journalists are beginning to realise the importance of AI and that it will be a partner to human journalists in their work inside newsrooms. Despite the numerous benefits that AI contributes to journalism, several challenges hinder the expansion and spread of its adoption among journalists. The ethical challenges of AI systems have become a concern among journalists. Therefore, this research is guided by the relationship between technological development and media ethics as the philosophical study of morality, specifically the Social Responsibility Theory. This study adopts a qualitative approach to explore the ethical challenges of AI faced by journalists. In-depth interviews were conducted with 14 journalists working in the newsroom of a government-affiliated channel, Al Mamlaka TV in Jordan. Data obtained from interviews conducted were analysed thematically. The results concluded that the main ethical challenges faced by journalists in the newsroom in adopting AI are data bias; privacy violations; and the absence of legislation and international regulations regarding the use of AI in journalism. The study concludes that journalists at Al Mamlaka TV adhere to the basics of Social Responsibility Theory through their critical adoption of AI in the newsroom.Studies in Media and Communication00The main ethical challenges faced by journalists in the newsroom in adopting AI are data bias; privacy violations; and the absence of legislation and international regulations regarding the use of AI in journalism.Studies in Media and Communication['Omar Al-Zoubi', 'Normahfuzah Ahmad', 'Norsiah Abdul Hamid']2024-01-29T00:00:00https://www.semanticscholar.org/paper/e65e36729355900b270f8ce497f1a2d6ca89a01b f6181b752d6f065469035be7dca2df28867267feGiven the increasing role of artificial intelligence (AI) in many decision-making processes, we investigate the presence of AI bias towards terms related to a range of neurodivergent conditions, including autism, ADHD, schizophrenia, and obsessive-compulsive disorder (OCD). We use 11 different language model encoders to test the degree to which words related to neurodiversity are associated with groups of words related to danger, disease, badness, and other negative concepts. For each group of words tested, we report the mean strength of association (Word Embedding Association Test [WEAT] score) averaged over all encoders and find generally high levels of bias. Additionally, we show that bias occurs even when testing words associated with autistic or neurodivergent strengths. For example, embedders had a negative average association between words related to autism and words related to honesty, despite honesty being considered a common strength of autistic individuals. Finally, we introduce a sentence similarity ratio test and demonstrate that many sentences describing types of disabilities, for example, "I have autism" or "I have epilepsy," have even stronger negative associations than control sentences such as "I am a bank robber."Autism Research330This work uses 11 different language model encoders to test the degree to which words related to neurodiversity are associated with groups of words related to danger, disease, badness, and other negative concepts and finds generally high levels of bias.Autism research : official journal of the International Society for Autism Research['Sam Brandsen', 'Tara Chandrasekhar', 'Lauren Franz', 'Jordan Grapel', 'Geraldine Dawson', 'David Carlson']2024-01-29T00:00:00https://www.semanticscholar.org/paper/f6181b752d6f065469035be7dca2df28867267fe 1ee40b9f47659dcccc64c2b7b115e707d1cbd3c0PurposeIn the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.Design/methodology/approachFor diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.FindingsTesting of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.Research limitations/implicationsViewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.Practical implicationsAs well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.Originality/valueThis paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.Journal of Enterprise Information Management760This pioneering study showed that such machines could achieve excellent performance, and that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.J. Enterp. Inf. Manag.['Pei-Ju Wu', 'Yu-Chin Tai']2024-01-29T00:00:00https://www.semanticscholar.org/paper/1ee40b9f47659dcccc64c2b7b115e707d1cbd3c0 739ab1204114b2ca493edff873a8facc14f34ad3The use of Artificial Intelligence Technology aims to develop knowledge through information search engines so that it can help students, teachers and schools. The targets that will become benchmarks in community service include various counseling, presentations and questions and answers. With technology involving artificial intelligence in it, it makes it easier for pupils and students to search for information obtained from various sources. This data collection technique is interviews with previously prepared data, both observation and documentation and using relevant library methods. The aim of this service at the Bahrul Uluum Al Kamal Asahan Islamic boarding school is in the field of religious knowledge and can also understand Artificial Intelligence. By providing community service, it can help and increase knowledge outside of religious knowledge, namely artificial intelligence. Apart from that, it is also able to invite the community to be directly involved in its development. Keywords: IT; artificial intelligence; information systemsJurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal00The aim of this service at the Bahrul Uluum Al Kamal Asahan Islamic boarding school is in the field of religious knowledge and can also understand Artificial Intelligence.Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal['S. Suparmadi', 'Z. Zulkarnain', 'Akmal Akmal']2024-01-29T00:00:00https://www.semanticscholar.org/paper/739ab1204114b2ca493edff873a8facc14f34ad3 14590058a2dcd0a014ec4fabdcc21f308856e5cbThis comparative analysis delves into the applications of Artificial Intelligence (AI), Neural Networks, and Control Systems within the mining industry. It explores the advantages, limitations, and future prospects of these technologies, shedding light on how they are transforming the landscape of mining operations. By examining their roles in optimizing safety, efficiency, and sustainability, this article aims to provide valuable insights for stakeholders in the mining sector and beyond.2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon)1602024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon)['Daria O. Smirnova', 'Evgeniy K. Skreblo']2024-01-29T00:00:00https://www.semanticscholar.org/paper/14590058a2dcd0a014ec4fabdcc21f308856e5cb 72c1c247bfdfdc0266c2f4dd1468a7f25bcfbfbdAbstract Administrative burden across state–federal benefits programs is unsustainable, and artificial intelligence (AI) and associated technologies have emerged and resulted in significant interest as possible solutions. While early in development, AI has significant potential to reduce administrative waste and increase efficiency, with many government agencies and state legislators eager to adopt the new technology. Turning to existing frameworks defining what functions are considered “inherently governmental” can help determine where more autonomous implementation could be not only appropriate but also provide unique advantages. Such areas could include eligibility and redetermination of Medicaid eligibility as well as preventing improper Medicaid payments. However, while AI is promising, this technology may not be ready for fully autonomous implementation and instead could be deployed to augment human capabilities with robust safeguards until it has proven to be more reliable. In the meantime, the Centers for Medicare and Medicaid Services should release clear guidance around the use of AI by state Medicaid programs, and policymakers must work together to harness AI technologies in order to improve the efficiency and effectiveness of the Medicaid program.Health affairs scholar300Current frameworks defining what functions are considered “inherently governmental” can help determine where more autonomous implementation could be not only appropriate but also provide unique advantages in areas of eligibility and redetermination of Medicaid eligibility as well as preventing improper Medicaid payments.Health Affairs Scholar['Ted Cho', 'Brian J Miller']2024-01-29T00:00:00https://www.semanticscholar.org/paper/72c1c247bfdfdc0266c2f4dd1468a7f25bcfbfbd df37a7601da3b91a0c8a6cc33d1b925bb273c243 Automated Machine Learning or AutoML is a set of approaches and processes to make machine learning accessible for non-experts. AutoML can exhibit optimized enhancement of an existing model or suggest the best models for precise datasets. In the field of computerized Artificial Intelligence (AI), medical experts better utilize AI models with available encrypted information science ability. This paper aims to characterize and summarize the stage-wise design of Automated Machine Learning (AutoML) analysis e-healthcare platform starting from the sensing layer and transmission to the cloud using IoT (Internet of Things). To support the AutoML concept, the Auto Weka2.0 package, which serves as the open-source software platform, holds the predominant priority for experimental analysis to generate statistical reports. To validate the entire framework, a case study on Glaucoma diagnosis using the AutoML concept is carried out, and its identification of best-fit model configuration rates is also presented. The Auto-ML built-in model possesses a higher influence factor to generate population-level statistics from the available individual patient histories. Further, AutoML is integrated with the Closed-loop Healthcare Feature Store (CHFS) to support data analysts with an automated end-to-end ML pipeline to help clinical experts provide better medical examination through automated mode. Open Biomedical Engineering Journal490The stage-wise design of Automated Machine Learning (AutoML) analysis e-healthcare platform starting from the sensing layer and transmission to the cloud using IoT (Internet of Things) is characterized and summarized.The Open Biomedical Engineering Journal['E. Priyanka', 'S. Thangavel', 'R. Mohanasundaram', 'Shamala Subramaniam']2024-01-29T00:00:00https://www.semanticscholar.org/paper/df37a7601da3b91a0c8a6cc33d1b925bb273c243 b31121112e646d24d39693723c9ec28bea4279f9OBJECTIVES Artificial intelligence-based robotic systems are increasingly used in medical laboratories. This study aimed to test the performance of KANKA (Labenko), a stand-alone, artificial intelligence-based robot that performs sorting and preanalytical quality control of blood tubes. METHODS KANKA is designed to perform preanalytical quality control with respect to error control and preanalytical sorting of blood tubes. To detect sorting errors and preanalytical inappropriateness within the routine work of the laboratory, a total of 1000 blood tubes were presented to the KANKA robot in 7 scenarios. These scenarios encompassed various days and runs, with 5 repetitions each, resulting in a total of 5000 instances of sorting and detection of preanalytical errors. As the gold standard, 2 experts working in the same laboratory identified and recorded the correct sorting and preanalytical errors. The success rate of KANKA was calculated for both the accurate tubes and those tubes with inappropriate identification. RESULTS KANKA achieved an overall accuracy rate of 99.98% and 100% in detecting tubes with preanalytical errors. It was found that KANKA can perform the control and sorting of 311 blood tubes per hour in terms of preanalytical errors. CONCLUSIONS KANKA categorizes and records problem-free tubes according to laboratory subunits while identifying and classifying tubes with preanalytical inappropriateness into the correct error sections. As a blood acceptance and tube sorting system, KANKA has the potential to save labor and enhance the quality of the preanalytical process.American Journal of Clinical Pathology200It was found that KANKA can perform the control and sorting of 311 blood tubes per hour in terms of preanalytical errors and has the potential to save labor and enhance the quality of the preanalytical process.American journal of clinical pathology['A. Şişman', 'B. Basok', 'İnanç Kara', 'Ayfer Çolak', 'U. Bilge', 'Ferhat Demirci', 'Nuri Başoglu']2024-01-29T00:00:00https://www.semanticscholar.org/paper/b31121112e646d24d39693723c9ec28bea4279f9 e82f5e52f71b88ab624bb9cd28f4b2186410086dArtificial intelligence (AI) is the ability of a machine to mimic human functions such as reasoning, learning, planning and creativity. AI enables technical systems to perceive the environment in which they operate, process this perception and solve problems, acting to achieve a particular goal. The computer receives data (either already prepared or collected via its own sensors, such as a camera), processes it and reacts. AI systems are able to adapt their behaviour to some extent, analysing the effects of previous actions and operating autonomously.Journal of Engineering Science00Artificial intelligence is the ability of a machine to mimic human functions such as reasoning, learning, planning and creativity, which enables technical systems to perceive the environment in which they operate, process this perception and solve problems, acting to achieve a particular goal.JOURNAL OF ENGINEERING SCIENCE['Titu-Marius I. Băjenescu']2024-01-29T00:00:00https://www.semanticscholar.org/paper/e82f5e52f71b88ab624bb9cd28f4b2186410086d 19bdba5a15b6824d08530f6407b24cb8899f7875Clinical Neuroradiology510This commentary proposes that widespread support for medical AI must be driven by clear and transparent scientific reporting, beginning at the earliest stages of scientific research.Clinical neuroradiology['Anthony J. Winder', 'Emma A. M. Stanley', 'J. Fiehler', 'N. D. Forkert']2024-01-29T00:00:00https://www.semanticscholar.org/paper/19bdba5a15b6824d08530f6407b24cb8899f7875 62afaf00a7d59026474e7707389d1aaf62a4090b Purpose Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education. Design/methodology/approach This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF). Findings According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI. Practical implications By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education. Originality/value The finding of this study should be considered when successful implementation of AI in education is in progress. Interactive Technology and Smart Education240The results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI, and the model can help the authorities and policymakers facilitate the adoption of AI in medical education.Interactive Technology and Smart Education['Mohammadhiwa Abdekhoda', 'Afsaneh Dehnad']2024-01-29T00:00:00https://www.semanticscholar.org/paper/62afaf00a7d59026474e7707389d1aaf62a4090b 68b640cbea4f864da687b1f71cb9da71e917d879Advances in Therapy1100The current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases is comprehensively summarized.Advances in therapy['Feifei Lu', 'Yao Meng', 'Xiaoting Song', 'Xiaotong Li', 'Zhuang Liu', 'Chunru Gu', 'Xiaojie Zheng', 'Yi Jing', 'Wei Cai', 'Kanokwan Pinyopornpanish', 'Andrea Mancuso', 'F. Romeiro', 'Nahum Méndez-Sánchez', 'Xingshun Qi']2024-01-29T00:00:00https://www.semanticscholar.org/paper/68b640cbea4f864da687b1f71cb9da71e917d879 a22e7a2fd44bc5e806c8f89a7609b83d85039a7aThis research paper delves into the intriguing intersection of artificial intelligence (AI) and human psychology, exploring the multifaceted ways in which these domains converge and influence each other. The study encompasses various applications of AI technologies in understanding, simulating, and augmenting human psychological processes, aiming to shed light on the transformative potential and ethical considerations of such integration. The first section investigates the role of AI in emotion recognition, where machine learning algorithms discern human emotions through facial expressions, voice modulation, and physiological signals. Examining the applications in mental health, human-computer interaction, and sentiment analysis, this research assesses the impact on individual well-being and the broader societal implications. The second thematic area delves into personalized learning using AI, exploring how adaptive educational content can cater to diverse learning styles, preferences, and cognitive abilities. The study evaluates the effectiveness of personalized learning in enhancing student engagement and academic achievement, with implications for reshaping educational paradigms. The third focus of this research centers on the intersection of AI-driven chatbots and mental health support. Analyzing the development and deployment of AI chatbots in providing assistance for mental health issues, the study evaluates the efficacy of these tools in reducing stigma and improving accessibility to mental health resources. Ethical considerations constitute a significant aspect of this research, with an examination of the responsible use of AI in psychometrics, personality profiling, and predictive modeling. Privacy concerns, bias mitigation, and the ethical implications of employing AI in sensitive psychological domains are critically assessed. The paper also explores the collaborative potential of AI in creative endeavors, investigating how AI tools enhance human creativity in areas such as art, music, and writing. The psychological impact on creators and the implications for the future of creative industries are thoroughly examined. Throughout the research, the ethical implications of AI are a recurrent theme, as responsible deployment and consideration of biases in AI algorithms are paramount. The study concludes with a reflection on the evolving landscape of AI in conjunction with human psychology, emphasizing the need for interdisciplinary collaboration, ethical guidelines, and ongoing research to navigate the intricate nuances of this dynamic relationship.International Journal of Advanced Research in Science, Communication and Technology20The study encompasses various applications of AI technologies in understanding, simulating, and augmenting human psychological processes, aiming to shed light on the transformative potential and ethical considerations of such integration, and evaluates the impact on individual well-being and the broader societal implications.International Journal of Advanced Research in Science, Communication and Technology['Shaikh Mohd Azhar Mohd Abrar']2024-01-29T00:00:00https://www.semanticscholar.org/paper/a22e7a2fd44bc5e806c8f89a7609b83d85039a7a 216655b15c372b683a04b95a6fe25b2aa522378dThis research paper provides a comprehensive overview of the transformative role of Artificial Intelligence (AI) in revolutionizing the healthcare sector. As AI technologies continue to advance, their integration into various facets of healthcare promises groundbreaking improvements in diagnostics, personalized medicine, predictive analytics, and Electronic Health Records (EHR) management. This paper explores the applications of AI in these critical areas, highlighting its potential to enhance patient care, optimize treatment strategies, and streamline healthcare operations. Additionally, the paper addresses the challenges and ethical considerations associated with AI adoption in healthcare. By offering insights into the current state of AI in healthcare and its future implications, this research aims to contribute to a deeper understanding of the profound impact AI is making on the healthcare landscapeInternational Journal of Advanced Research in Science, Communication and Technology00This research paper explores the applications of AI in these critical areas, highlighting its potential to enhance patient care, optimize treatment strategies, and streamline healthcare operations.International Journal of Advanced Research in Science, Communication and Technology['Sanjana Pawar']2024-01-29T00:00:00https://www.semanticscholar.org/paper/216655b15c372b683a04b95a6fe25b2aa522378d d903abd6c30653801062f9cae66ba7da3621b009Journal of system and management sciences00Journal of System and Management Sciences[]2024-01-29T00:00:00https://www.semanticscholar.org/paper/d903abd6c30653801062f9cae66ba7da3621b009 65aadc7436f40c32650ed70e55f434385c15e01bDefense &amp; Security Analysis01Defense &amp; Security Analysis['J. Sarkin', 'S. Sotoudehfar']2024-01-29T00:00:00https://www.semanticscholar.org/paper/65aadc7436f40c32650ed70e55f434385c15e01b 9797fb714f2210bd08bd2068f745751fb53ba738As advancements in healthcare technologies continue to emerge, the integration of AI-Technology has brought about significant transformations in various healthcare sectors. While substantial advancements have been made in applying AI to enhance physical health, its implementation in the field of mental health is still in its early stages. This descriptive study aims to address this gap by exploring the perspectives of mental health professionals (MHPs) on the acceptance and utilization of AI technology. Unified Theory of Acceptance and Use of Technology (UTAUT) was utilized to assess MHPs’ attitudes and beliefs towards AI implementation in psychotherapeutic practices. The sample was compromised of 349 MHPs. The findings reveal the task characteristic (TC) domain as the most influential domain, followed by Performance expectancy (PE), Behavioural intentions (BI), Personal innovativeness in IT (PT), Social influence (SI), Effort expectancy (EE), Perceived substitution crisis (PSC), Technology characteristic (TECH), and Initial trust (IT). The study also identifies statistically significant differences in AI usage based on gender variable, with females demonstrating a higher level of AI usage in comparison to males. Furthermore, the study highlights diverse applications of AI in the field of mental health, including AI-assisted assessments (AAA), chatbots for psychotherapy support (CPS), and data analytics for personalized treatment recommendations (DAPTR). By incorporating mental healthcare professionals’ (MHPs) perspectives, this research significantly contributes to a comprehensive understanding of the acceptance and utilization of AI technology in psychotherapy. The findings offer valuable insights into MHPs’ perceptions, concerns, and perceived advantages associated with integrating AI technology within clinical settings in the field of mental health.Journal of Autonomous Intelligence1041By incorporating mental healthcare professionals’ (MHPs) perspectives, this research significantly contributes to a comprehensive understanding of the acceptance and utilization of AI technology in psychotherapy.Journal of Autonomous Intelligence['Shirin Abdallah Alimour', 'Emad Alnono', 'Shaima Aljasmi', 'Hani El Farran', 'A. Alqawasmi', 'Mohamed Mahmoud Alrabeei', 'Fanar Shwedeh', 'Ahmad Aburayya']2024-01-29T00:00:00https://www.semanticscholar.org/paper/9797fb714f2210bd08bd2068f745751fb53ba738 2cc00406b30cdbb1330c056b4c6cd7ddd3da8c77BACKGROUND: There is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor. METHODS: Using demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is zero, the true SCA initial rhythm is recorded. The internal cohort consisted of 421 emergency medical services-witnessed out-of-hospital SCAs with PEA or VF as the initial rhythm in the Portland, Oregon metropolitan area. External validation was performed in 220 emergency medical services-witnessed SCAs from Ventura, CA. RESULTS: In the internal cohort, the artificial intelligence model achieved an area under the receiver operating characteristic curve of 0.68 (95% CI, 0.61–0.76). Model performance was similar in the external cohort, achieving an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.59–0.84). Anemia, older age, increased weight, and dyspnea as a warning symptom were the most important features of PEA-SCA; younger age, chest pain as a warning symptom and established coronary artery disease were important features associated with VF. CONCLUSIONS: The artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort. These findings enhance the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies.Circulation: Arrhythmia and Electrophysiology01The artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort, enhancing the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies.Circulation. Arrhythmia and Electrophysiology['L. Holmstrom', 'B. Bednarski', 'H. Chugh', 'Habiba Aziz', 'Hoang Nhat Pham', 'A. Sargsyan', 'A. Uy-Evanado', 'D. Dey', 'A. Salvucci', 'J. Jui', 'K. Reinier', 'Piotr J. Slomka', 'S. Chugh']2024-01-29T00:00:00https://www.semanticscholar.org/paper/2cc00406b30cdbb1330c056b4c6cd7ddd3da8c77 a0eb83ee945b43a8ef2e5042a95591d6d05440aeTujuan dari penelitian ini adalah untuk mengetahui apakah kontrak yang dibuat oleh AI memenuhi syarat keabsahan sesuai dengan ketentuan pasal 1320 KUH Perdata. Serta memberikan pemahaman yang lebih baik tentang implikasi hukum dan regulasi yang berkaitan dengan penggunaan AI dalam pembuatan kontrak. Jenis penelitian yang digunakan adalah yuridis normatif. Pendekatan penelitian ini menggunakan pendekatan undang-undang (Statute Approach) dan konseptual (Conceptual Approach). Sumber bahan hukum yang digunakan berupa bahan hukum premier yang terdiri dari undang-undang yang mengatur tentang keperdataan dan undang-undang yang mengatur tentang regulasi AI. Adapun bahan hukum sekunder berupa jurnal yang membahas tentang kedudukan hukum AI dan legal tech beserta artikel ilmiah. Teknik pengumpulan bahan hukum dalam penelitian hukum normatif dilakukan dengan cara studi pustaka (Library Research) berupa data sekunder sebagai bahan dasar untuk diteliti dengan cara mengadakan penelusuran terhadap peraturan-peraturan dan literatur-literatur lain berkaitan dengan permasalahan yang diteliti. Hasil penelitian ini menunjukan bahwa AI memiliki potensi besar dalam pembuatan kontrak, tetapi perlu diperhatikan kedudukan hukumnya. Dengan memandang AI sebagai subjek hukum dengan pemilik atau pengguna yang bertanggung jawab, kontrak yang dibuat oleh AI dapat dianggap sah sesuai dengan hukum positif Indonesia.Al-Adl : Jurnal Hukum190Al-Adl : Jurnal Hukum['Jajang Nurzaman', 'Dwi Fidhayanti']2024-01-29T00:00:00https://www.semanticscholar.org/paper/a0eb83ee945b43a8ef2e5042a95591d6d05440ae e8dfe7e5100b0463e36997c4227404b1553691efJournal of Responsible Innovation650Journal of Responsible Innovation['F. Lysen', 'Sally Wyatt']2024-01-29T00:00:00https://www.semanticscholar.org/paper/e8dfe7e5100b0463e36997c4227404b1553691ef dc1c8fb68b0e92b173f0a77b8226a447d523a7caMultimedia tools and applications430Multimedia Tools and Applications['Javad Hassannataj Joloudari', 'Sanaz Mojrian', 'Hamid Saadatfar', 'Issa Nodehi', 'Fatemeh Fazl', 'Sahar Khanjani Shirkharkolaie', 'R. Alizadehsani', 'H. M. D. Kabir', 'Ruyan Tan', 'U. R. Acharya']2024-01-29T00:00:00https://www.semanticscholar.org/paper/dc1c8fb68b0e92b173f0a77b8226a447d523a7ca dcc367b0753fe745b9f6a808fd65b77a72a63f13Acta Obstetricia et Gynecologica Scandinavica80Acta Obstetricia et Gynecologica Scandinavica['Prakesh S. Shah', 'Ganesh Acharya']2024-01-29T00:00:00https://www.semanticscholar.org/paper/dcc367b0753fe745b9f6a808fd65b77a72a63f13 85bd6125256d0117d1acbd5ac9e9ef63b57e6c90Sugar Tech70This short communication delineates GAI’s prospective applications in the sugar industry, emphasizing tangible benefits and outlining the necessary steps toward integrating this innovative technology effectively.Sugar Tech['P. Ray']2024-01-29T00:00:00https://www.semanticscholar.org/paper/85bd6125256d0117d1acbd5ac9e9ef63b57e6c90 189823c7102db553e6da0eb46acfb43f17550271This position paper presents a theoretical framework for enhancing explainable artificial intelligence (xAI) through emergent communication (EmCom), focusing on creating a causal understanding of AI model outputs. We explore the novel integration of EmCom into AI systems, offering a paradigm shift from conventional associative relationships between inputs and outputs to a more nuanced, causal interpretation. The framework aims to revolutionize how AI processes are understood, making them more transparent and interpretable. While the initial application of this model is demonstrated on synthetic data, the implications of this research extend beyond these simple applications. This general approach has the potential to redefine interactions with AI across multiple domains, fostering trust and informed decision-making in healthcare and in various sectors where AI's decision-making processes are critical. The paper discusses the theoretical underpinnings of this approach, its potential broad applications, and its alignment with the growing need for responsible and transparent AI systems in an increasingly digital world.arXiv.org130A theoretical framework for enhancing explainable artificial intelligence (xAI) through emergent communication (EmCom), focusing on creating a causal understanding of AI model outputs, which aims to revolutionize how AI processes are understood, making them more transparent and interpretable.ArXiv['Adam Perrett']2024-01-29T00:00:00https://www.semanticscholar.org/paper/189823c7102db553e6da0eb46acfb43f17550271 36b359a5212278ceca0761a521eb1fa4071ef229This article examines the intricate relationship between humans and text-to-image generative models (generative artificial intelligence/genAI) in the realm of art. The article frames that relationship in the theory of mediated action—a well-established theory that conceptualizes how tools shape human thoughts and actions. The article describes genAI systems as learning, cocreating, and communicating, multimodally capable hybrid systems that distill and rely on the wisdom and creativity of massive crowds of people and can sometimes surpass them. Those systems elude the theoretical description of the role of tools and locus of control in mediated action. The article asks how well the theory can accommodate both the transformative potential of genAI tools in creative fields and art, and the ethics of the emergent social dynamics it generates. The article concludes by discussing the fundamental changes and broader implications that genAI brings to the realm of mediated action and, ultimately, to the very fabric of our daily lives.IEEE Computer Graphics and Applications160The article asks how well the theory of mediated action can accommodate both the transformative potential of genAI tools in creative fields and art, and the ethics of the emergent social dynamics it generates.IEEE Computer Graphics and Applications['Henriikka Vartiainen', 'M. Tedre']2024-01-29T00:00:00https://www.semanticscholar.org/paper/36b359a5212278ceca0761a521eb1fa4071ef229 337d6d67e878979ad2b6bacbb8d2c8bcfab7eabfWith the digital transformation occurring in both public and private institutions, leading to the provision of services through electronic means and the digitization of most transactions, there arose a need for legal regulations to govern electronic transactions and address crimes committed through electronic means. In the Kingdom of Bahrain, the legislator took steps to regulate such crimes through a dedicated law separate from the Penal Code. The objective of this research is to assess the level of integration achieved among various laws pertaining to the regulation of electronic crimes, particularly those related to the Social Insurance Law that occur using electronic means. The Social Insurance Law in Bahrain establishes detailed provisions for implementing all associated obligations. This study highlights the importance of granting judicial control officer status to the inspectors of the General Authority, and also emphasizes the need to activate Article 18 of the Information Technology Crimes Law. This is where the Public Prosecution has delegated authority to the General Authority's inspectors to seize evidence of crimes related to the Social Insurance Law.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)2902024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Amjad Al Nagrash', 'Nawal Alareed', 'S. Aldulaimi', 'M. Abdeldayem', 'Abed Rzaij Aswad']2024-01-28T00:00:00https://www.semanticscholar.org/paper/337d6d67e878979ad2b6bacbb8d2c8bcfab7eabf ce7cec276104e402f64263ae1009b9d2d3e19f28Consider sellers in a competitive market that use algorithms to adapt their prices from data that they collect. In such a context it is plausible that algorithms could arrive at prices that are higher than the competitive prices and this may benefit sellers at the expense of consumers (i.e., the buyers in the market). This paper gives a definition of plausible algorithmic non-collusion for pricing algorithms. The definition allows a regulator to empirically audit algorithms by applying a statistical test to the data that they collect. Algorithms that are good, i.e., approximately optimize prices to market conditions, can be augmented to contain the data sufficient to pass the audit. Algorithms that have colluded on, e.g., supra-competitive prices cannot pass the audit. The definition allows sellers to possess useful side information that may be correlated with supply and demand and could affect the prices used by good algorithms. The paper provides an analysis of the statistical complexity of such an audit, i.e., how much data is sufficient for the test of non-collusion to be accurate.Symposium on Computer Science and Law372{'pages': '98-108'}['Jason D. Hartline', 'Sheng Long', 'Chenhao Zhang']2024-01-28T00:00:00https://www.semanticscholar.org/paper/ce7cec276104e402f64263ae1009b9d2d3e19f28 4ba019cec16377356b6ba920270796ef58028c2eContinuous innovations profoundly impact the financial and commercial domains, reshaping conventional business practices. Among the disruptive forces, Artificial Intelligence (AI), Machine Learning (ML), and blockchain technology stand out prominently. This study aims to evaluate the integration of blockchain, AI, and ML within financial accounting practices. It suggests a potential revolutionary impact on financial accounting through the adoption of blockchain technology and ML, promising reduced accounting expenses, heightened precision, real-time financial reporting capabilities, and expeditious auditing processes. AI's role in automating repetitive financial accounting tasks assists organizations in circumventing the need for additional staff, thereby minimizing associated costs. Consequently, to bolster efficiency, businesses are increasingly embracing blockchain technology and AI applications in their financial accounting operations.arXiv.org225A potential revolutionary impact on financial accounting is suggested through the adoption of blockchain technology and ML, promising reduced accounting expenses, heightened precision, real-time financial reporting capabilities, and expeditious auditing processes.ArXiv['V. Kanaparthi']2024-01-28T00:00:00https://www.semanticscholar.org/paper/4ba019cec16377356b6ba920270796ef58028c2e 114d8e366ecc2e25c80ad5655b0ed9501f693307Personalized services bridge the gap between a financial institution and its customers and are built on trust. The more we trust the product, the keener we are to disclose our personal information in order to receive a highly personalized service that maximizes consumer value. Artificial Intelligence (AI) can help financial institutions tailor relevant products and services to their customers as well as improve their credit risk management, compliance, and fraud detection capabilities by incorporating chatbots and face recognition systems. The present study has analyzed sixteen research papers using the PRISMA model to perform a Systematic Literature Review (SLR). It has identified five research gaps and corresponding questions to analyze the present scenario. One of the gaps is credit risk detection for improved personalization and trust. Finally, an AI-based credit risk detection model has been built using four supervised machine learning classifiers viz., Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression. Performance comparison shows an optimal performance of the model giving accuracy of ~89%, precision of ~88%, recall of ~89%, specificity of ~89%, F1_score of ~88%, and AUC of 0.77 for the Random Forest classifier. This model is foreseen to be most suitable for envisaging customer characteristics for which personalized credit risk mitigation strategies are particularly effective as compared to other existing works presented in this study.arXiv.org245An AI-based credit risk detection model has been built using four supervised machine learning classifiers viz., Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression to be most suitable for envisaging customer characteristics for which personalized credit risk mitigation strategies are particularly effective.ArXiv['V. Kanaparthi']2024-01-28T00:00:00https://www.semanticscholar.org/paper/114d8e366ecc2e25c80ad5655b0ed9501f693307 d93f8d8d545a0d74db0d8a5be9dab2980b4e7d70This study examines the transformative impact of artificial intelligence (AI) integration in e-HRM (e-HRM) on improving employee performance and organizational productivity. The study explores the innovative applications of artificial intelligence in e-HRM and its profound impact on HR practices and employee development. The main goal is to find out how artificial intelligence-based e-HRM change traditional HR processes, focusing mainly on their impact on employee performance indicators and overall organizational efficiency. By analyzing the integration of AI and HR, this study illuminates the complexity of this technological fusion and its impact on modern workplaces. The descriptive analytical strategy was employed in the study to attain the research objectives by utilizing primary and secondary data. A structured questionnaire was prepared and disseminated on employees of the study organization, and then analysed using SPSS techniques such as Pearson Correlation, Cronbach Alpha coefficient, simple and multiple regressions to assess the study hypotheses. The study created a literature review that addressed the main subtopics linked with computerised HRM and its relationship to employee performance. Finally, the study's major findings demonstrated that the AI-Driven e-HRM system components had a considerable positive effect on improving various HRM practises, and that AI-Driven e-HRM helps organisations achieve their goals and develop their employees.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)161The study's major findings demonstrated that the AI-Driven e-HRM system components had a considerable positive effect on improving various HRM practises, and that AI-Driven e-HRM helps organisations achieve their goals and develop their employees.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['A. Samman', 'Aly Ahmed Abdullah Al Obaidly']2024-01-28T00:00:00https://www.semanticscholar.org/paper/d93f8d8d545a0d74db0d8a5be9dab2980b4e7d70 4d2ee68ba5e9e6c0537f20d143560b8bc7a8b1d2This study investigates the integration and impact of Large Language Models (LLMs), like ChatGPT, in India's healthcare sector. Our research employs a dual approach, engaging both general users and medical professionals through surveys and interviews respectively. Our findings reveal that healthcare professionals value ChatGPT in medical education and preliminary clinical settings, but exercise caution due to concerns about reliability, privacy, and the need for cross-verification with medical references. General users show a preference for AI interactions in healthcare, but concerns regarding accuracy and trust persist. The study underscores the need for these technologies to complement, not replace, human medical expertise, highlighting the importance of developing LLMs in collaboration with healthcare providers. This paper enhances the understanding of LLMs in healthcare, detailing current usage, user trust, and improvement areas. Our insights inform future research and development, underscoring the need for ethically compliant, user-focused LLM advancements that address healthcare-specific challenges.arXiv.org200The findings reveal that healthcare professionals value ChatGPT in medical education and preliminary clinical settings, but exercise caution due to concerns about reliability, privacy, and the need for cross-verification with medical references.ArXiv['Aryaman Raina', 'Prateek Mishra', 'Harshit goyal', 'Dhruv Kumar']2024-01-28T00:00:00https://www.semanticscholar.org/paper/4d2ee68ba5e9e6c0537f20d143560b8bc7a8b1d2 e51c33f9a446e9ca5e8929e09c80c87b65fd2eaaWith the progress of artificial intelligence (AI), generative AI has emerged as a novel catalyst for driving innovation within enterprises. This study, rooted in behavior activation theory, endeavors to examine the impact of generative AI on enterprise innovation. A conceptual model is formulated to elucidate the relationship between generative AI and enterprise innovation. Utilizing structural equation modeling to scrutinize this model, the findings reveal substantial positive effects: AI generated content quality significantly influences the activation of enterprise innovation behavior (ß = 0.37, t-value = 7.64, p < 0.01), AI experience has a notable positive impact on innovation behavior activation (ß = 0.19, t-value = 3.47, p < 0.01), and a supportive AI usage environment significantly influences the activation of enterprise innovation behavior (ß= 0.46, t-value = 10.48, p <0.01). Furthermore, innovation behavior activation makes a significant contribution to enterprise innovation performance (ß = 0.65, t-value = 18.23, p < 0.01).2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)300The findings reveal substantial positive effects of generative AI on enterprise innovation, and a conceptual model is formulated to elucidate the relationship between generative AI and enterprise innovation.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Haonan Xu', 'Ruoxuan Xu', 'Hongyu Lin', 'Xiaojuan He']2024-01-28T00:00:00https://www.semanticscholar.org/paper/e51c33f9a446e9ca5e8929e09c80c87b65fd2eaa 81f004505a23669d5974f48a41cc1dacdbb6ad44Undoubtedly, the conversation around artificial intelligence (AI) has recently intensified, fostered by the rapid development of technology. Learning institutions have joined in the debate, with teachers and learners on the frontline of this conversation. This study particularly explores the direct impact of AI applications such as chatbots (ChatGPT), personalized learning experiences, and predictive analytics on EFL classroom learning, outlining AI's advantages and disadvantages on foreign language learners and their teachers. It employs a qualitative approach to data collection and screening, utilizing surveys on a sample of students at the university level. Evidently, from the study findings, AI can potentially improve learning, especially among students in EFL classrooms. However, there is concern about AI inhibiting the development of learners’ research and critical thinking skills. Largely, the findings recognize AI’s value in EFL classrooms but appeal for caution from teachers in its application. This study offers valuable insights into AI’s impact on EFL classrooms and offers possible changes that could help in its successful integration into EFL teaching practice.The Asian Conference on Education 2023: Official Conference Proceedings90Largely, the findings recognize AI’s value in EFL classrooms but appeal for caution from teachers in its application, and offers possible changes that could help in its successful integration into EFL teaching practice.The Asian Conference on Education 2023: Official Conference Proceedings['Lidija Eliott']2024-01-28T00:00:00https://www.semanticscholar.org/paper/81f004505a23669d5974f48a41cc1dacdbb6ad44 4950dfb9df1aae95b827b17fed1db409990e24f9Technology breakthroughs and the rise of artificial intelligence have transformed organizations and how we function. With computers' increasing capacity to undertake activities previously designated for people, there has been rising concern about their possible impact on job security and wages. This study examines how IT sector employees perceive the effects of AI advancements on their jobs. Semi-structured interviews were conducted to gather primary data for this study, which involved selecting 37 IT sector employees as a sample. While technology advancements provide various benefits, they pose substantial challenges to the job market, requiring careful consideration and proactive efforts to ensure an effortless transition. The present study highlights the challenges, opportunities, and complications of incorporating AI and automation into the modern workplace. Through in-depth interviews, the authors identified several factors related to how employees in the IT sector felt that the development of AI was impacting their work. The author also proposed the BASE model (Building a Resilient Workforce, Automate Business Processes, Strategic Training and Development Process, Ethical Consideration), which illustrates the various strategies that an organization should implement to improve productivity and performance.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)240The authors identified several factors related to how employees in the IT sector felt that the development of AI was impacting their work, and proposed the BASE model, which illustrates the various strategies that an organization should implement to improve productivity and performance.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Shalini Rastogi', 'Deepika Pandita']2024-01-28T00:00:00https://www.semanticscholar.org/paper/4950dfb9df1aae95b827b17fed1db409990e24f9 c354a979517d7f76b69e3d5a11ece5a830d70860Several research studies have proven how an Artificial Intelligence (AI) application eases people's jobs in writing. In this regard, AI applications and how students use them in writing Curriculum Vitae (CV) were described. Eighty business students participated in answering the online questionnaire. The result shows that instead of using AI-based-CV Maker instantly, the participants prefer AI assistance for technical skills such as translation, design and grammar checking. AI tools are only one of the tools to use due to trust issues on content and contextual background.  Jurnal Smart90The result shows that instead of using AI-based-CV Maker instantly, the participants prefer AI assistance for technical skills such as translation, design and grammar checking.Jurnal Smart['Maria Setyaningsih Nernere']2024-01-28T00:00:00https://www.semanticscholar.org/paper/c354a979517d7f76b69e3d5a11ece5a830d70860 c1f10798b93b924a251adf907967cd29d4df9441In recent years, artificial intelligence (AI) has become increasingly relevant for organizations to exploit business-related databases and remain competitive. As artificial intelligence (AI) becomes more and more integrated into businesses' operational frameworks, it is imperative to understand the subtle effects of AI on organisational culture. This qualitative study aims to explore the relationship between AI and corporate culture and ascertain how AI functions as a product but also influences workplace norms, beliefs, and behaviours. This study uses in-depth interviews with key stakeholders, executives, and staff to examine the rise in leadership roles, the use of AI tools, and the challenges faced during this revolutionary process. The findings demonstrate how AI becomes a symbolic product that shapes cultural components like decision-making processes and the fundamental identity of the company. The study emphasises the need for additional research and gives useful information regarding the dynamic relationship between AI and organisational culture.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)150The findings demonstrate how AI becomes a symbolic product that shapes cultural components like decision-making processes and the fundamental identity of the company.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Adel Mahmoud Al Samman']2024-01-28T00:00:00https://www.semanticscholar.org/paper/c1f10798b93b924a251adf907967cd29d4df9441 4ad8b220007f5f32c076f2aa5248faef77cbc4c3      The integration of Artificial Intelligence (AI) into language education has revolutionized the teaching of English as a Second Language (ESL). This study explores the impact of AI on ESL instruction and investigates how AI-driven technologies enhance language learning outcomes. By analyzing current pedagogical approaches and technological innovations, this research examines how AI augments traditional language teaching methods, providing personalized and adaptive learning experiences for ESL learners.        AI-based language learning platforms offer interactive modules tailored to individual student needs, creating an immersive and efficient learning environment. Additionally, the use of natural language processing (NLP) and machine learning algorithms enables real-time feedback and assessment, empowering learners to improve their language skills independently. This study critically evaluates the effectiveness of AI-driven tools in addressing linguistic challenges, such as pronunciation, grammar, and comprehension, while also considering their socio-cultural implications in diverse learning contexts.          By examining the evolving role of AI in ESL education, this research contributes to a comprehensive understanding of the relationship between technology and language pedagogy. The insights from this analysis will inform educators, policymakers, and stakeholders about the potential and limitations of AI in reshaping language acquisition. Ultimately, this exploration advocates for a synergistic approach that harnesses AI's capabilities to optimize ESL instruction while preserving the human elements essential for holistic language learning experiences.Eurasian Science Review An International peer-reviewed multidisciplinary journal00This study critically evaluates the effectiveness of AI-driven tools in addressing linguistic challenges, such as pronunciation, grammar, and comprehension, while also considering their socio-cultural implications in diverse learning contexts.Eurasian Science Review An International peer-reviewed multidisciplinary journal['Lazzat Konyrova']2024-01-28T00:00:00https://www.semanticscholar.org/paper/4ad8b220007f5f32c076f2aa5248faef77cbc4c3 68968a489ebab47b737779c7d8910af7a13a34ddThe current research provides a thorough examination of the impact of Artificial Intelligence (AI) in the realm of higher education. This study undertakes a comprehensive examination of relevant scholarly material published between 2019 and 2023. It investigates the impact of AI technologies on the improvement of customized learning, academic writing, and research abilities among students at the university level. The primary focal points include the influence of artificial intelligence (AI) on educational achievements, the readiness of educators to incorporate AI into their pedagogical approaches, and the ethical ramifications associated with the use of AI in academic environments. The results of the study demonstrate that the integration of AI technology in educational settings has a substantial positive impact on student engagement and academic achievement. However, it is important to acknowledge and address certain issues that arise in this context, including the need to ensure that educators possess the necessary skills and knowledge to effectively use AI, as well as the ethical considerations associated with its implementation. This research not only emphasizes the potential of artificial intelligence (AI) in revolutionizing higher education but also emphasizes the need of a well-rounded implementation strategy that takes into account both the capabilities and limits of AI. This study establishes a basis for future academic investigations, specifically focusing on the formulation of approaches that are both efficient and morally sound in the integration of artificial intelligence inside educational environments.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)290The study investigates the impact of AI technologies on the improvement of customized learning, academic writing, and research abilities among students at the university level and establishes a basis for future academic investigations.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['A. Ateeq', 'M. Alaghbari', 'Mohammed Alzoraiki', 'Marwan Milhem', 'Baligh Ali Hasan Beshr']2024-01-28T00:00:00https://www.semanticscholar.org/paper/68968a489ebab47b737779c7d8910af7a13a34dd 39e830a7aa3602c0988005b7a2b2ee28ac12355dHuman resource management is the backbone of business and organization. This study investigates AI's revolutionary effect on HRM by delving into AI's function in maximizing organizational and personnel development. This study analyses 1,505 articles from 1984 to 2024 using latent semantic analysis, which comes under the umbrella of natural language processing (NLP) techniques. The methodology relies on data extracted from Scopus. Preprocessing is made more accessible with the KNIME tool. In this study, the TF-IDF score is calculated, and based upon these scores, K-Mean clustering is applied to predict the recent areas on which future researchers can work. The report highlights the different applications of AI in HRM by identifying five unique clusters: HRM in the Education Sector, Data Learning and Technology, Workforce Management, Agriculture and Technology, Business and Project Management, and Data and Project Management. To help navigate the ever-changing junction of AI and human capital, the research ends by offering suggestions for future initiatives, such as ethical issues, long-term organizational impact, industry-specific adoption, and advanced natural language processing techniques.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)140This study analyses 1,505 articles from 1984 to 2024 using latent semantic analysis, which comes under the umbrella of natural language processing (NLP) techniques, and calculates the TF-IDF score, and K-Mean clustering is applied to predict the recent areas on which future researchers can work.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Chetan Sharma', 'Nisha Chanana']2024-01-28T00:00:00https://www.semanticscholar.org/paper/39e830a7aa3602c0988005b7a2b2ee28ac12355d 3ec3790a548b9dda914efda2caf38e0c4b7cc565In today's interconnected digital ecosystem, Artificial Intelligence (AI) and Machine Learning (ML) stand at the forefront of innovation, efficiency, and resource management. However, the rapid ascension of AI and ML technologies that provide a foundation of groundbreaking capabilities need to be protected. Moreover, techniques that make AI and ML effective, efficient, and a force multiplier within the computing domain, and the central tenets of modern AI and ML technological advancement positioning them as the future of tomorrow, need to be safeguarded against exploitation and abuse by threat actors. Based on their ubiquity, embedded use of AI and ML within industry faces a major paradox; as AI and ML technologies empower and streamline operations while providing transformative benefits to users, they simultaneously introduce formidable threats through security vulnerabilities that can cause massive damage if not defended against. This discourse delves into cybersecurity vulnerabilities inherent to AI and ML implementations within critical infrastructure domains of supply chain management and project management to explore how these weaknesses may be exploited. Further, we discuss ways AI and ML can be exploited by cybersecurity vulnerabilities within industry to compromise data and resources. Through a focused, multiple case study, we identify a serious flaw within AL and ML needing further investigation, identify data needing further examination, and isolate specific cybersecurity threats associated with AI and ML in supply chain management and project management, including exploitation of user vulnerabilities, use of AI and ML to bypass security measures, and use of AI and ML to automate attacks.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)310A serious flaw within AL and ML needing further investigation is identified, data needing further examination is identified, and specific cybersecurity threats associated with AI and ML in supply chain management and project management are isolated.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Joseph Squillace', 'Justice Cappella', 'Andrew Sepp']2024-01-28T00:00:00https://www.semanticscholar.org/paper/3ec3790a548b9dda914efda2caf38e0c4b7cc565 7fc6c213e38c6a4723359edf139a371e22577734The integration of artificial intelligence [AI] into higher education is a rapidly growing field that offers transformative potential for teaching, learning and organizational processes. This meta-analysis brings together a variety of studies from the past decade to examine the effects, trends, and challenges associated with the implementation of AI in higher education. The analysis includes a comprehensive review of peer- reviewed literature from popular databases, fastening on the effectiveness of AI interventions, linked patterns, and differences between disciplines and styles. Investigation shows that AI has a positive impact on substantiated learning experiences, admin streamlining and data- driven decision- making. Still, ethical considerations related to data analysis, algorithm creation, and the responsible use of AI crop as crucial areas of discussion. This study offers recommendations for future practice, policy, and exploration, championing for the integration of AI into faculty, teaching, learning, ethical guidance, and investment in ongoing studies. This meta- analysis demonstrates the transformative eventuality of AI to reform higher education, pressing the significance of addressing ethical enterprises and responsible implementation for educational optimal outcomes.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)300This meta- analysis demonstrates the transformative eventuality of AI to reform higher education, pressing the significance of addressing ethical enterprises and responsible implementation for educational optimal outcomes.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['A. Samman']2024-01-28T00:00:00https://www.semanticscholar.org/paper/7fc6c213e38c6a4723359edf139a371e22577734 e7476f5f29ba425e3d440b2447555977e56a98ebThe purpose of this study is to explore the areas of Supply Chain Management (SCM) in which Artificial Intelligence (AI) and Machine Learning (ML) are implemented and to identify the main related AI and ML techniques and research gaps for future applications. A systematic literature review methodology was used to analyze selected studies from 2019 to 2023, collected from Scopusindexed journals, and associate AI and ML with SCM. A total of 50 studies were identified, of which 10 studies were selected using pre-identified inclusion criteria. These studies were thoroughly reviewed and analyzed to identify the main AI and ML applications, techniques, and gaps in SCM. The review revealed that there are two main areas of SCM applications that utilize AI and ML: demand forecasting and supply chain (SC) risk management. Each application area within the SCM utilizes different sets of AI and ML techniques. Demand forecasting studies mainly implement artificial neural network (ANN) and time series/ regression ML models, while SC risk management studies discuss the Internet of Things (IoT) and cloud computing and implement classification ML models. The main gaps found in the shortlisted studies are limited data accessibility and the need for employing deep and hybrid models. The limitations of the study are the review size and the scope of AI and ML applications in SCM. Although a few earlier studies touch on AI or ML applications within SCM, this study offers a panoramic view of both AI and ML within the SC field as a whole.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)290A panoramic view of both AI and ML within the SC field as a whole is offered, revealing that there are two main areas of SCM applications that utilize AI and ML: demand forecasting and supply chain (SC) risk management.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Kubra Maki Edhrabooh', 'A. Al-Alawi']2024-01-28T00:00:00https://www.semanticscholar.org/paper/e7476f5f29ba425e3d440b2447555977e56a98eb 72b86418cb904ede30e50b75127e46883b2efe62Artificial intelligence (AI) has become pervasive in almost every field of human activity, from healthcare to finance. AI has the potential to transform government services, improving their efficiency, effectiveness, and overall quality for the benefit of citizens. AI can also help government employees focus on more complex and high-value activities, improving their productivity and efficiency overall. However, there are also concerns about the impact of AI on government jobs and the need to upskill and reskill them to take advantage of the latest technologies that support AI. To realize the potential benefits of AI, there needs to be a comprehensive strategy that takes into account the challenges and opportunities. AI solutions help to remove routine and boring work from employees.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)330To realize the potential benefits of AI, there needs to be a comprehensive strategy that takes into account the challenges and opportunities.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Mahmoud Khalifa', 'W. Nageab', 'Sally Elsayed']2024-01-28T00:00:00https://www.semanticscholar.org/paper/72b86418cb904ede30e50b75127e46883b2efe62 843c79f03f063bd10452992a0356cd000f7d24baThe constantly evolving digital era unleashes opportunities and challenges in digital marketing. The current study seeks to explore the current status of digital marketing in India, emphasizing how AI and the infusion of Artificial intelligence with predictive analytics can captivate the mushrooming opportunities and bring radical change in the marketing approaches employed in digital marketing. The semi structured interviews conducted using a qualitative approach have put forth different barriers encountered in digital marketing and provided the proposed viable resolutions. Further, this study also presents a decision framework that illustrates the potential revolutionary effects and impacts of integrating AI and predictive analytics effectively within digital marketing campaigns.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)150The current status of digital marketing in India is explored, emphasizing how AI and the infusion of Artificial intelligence with predictive analytics can captivate the mushrooming opportunities and bring radical change in the marketing approaches employed in digital marketing.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Anusuya Yadav', 'Deepika Pandita']2024-01-28T00:00:00https://www.semanticscholar.org/paper/843c79f03f063bd10452992a0356cd000f7d24ba 09c0b4aab82b229b65bdb610958c844ac7afd62dThis paper examines how Artificial Intelligence (AI) has transformed cardiovascular pharmacology. With cardiovascular diseases still one of the leading causes of global morbidity and mortality, AI comes to play a special role in changing the face of an aging world. This paper delves into the different aspects of AI in cardiovascular pharmacology. Among these are its use for predictive modeling, big data integration, and acceleration of drug discovery and patient care processes. We talk about the progress achieved by AI technologies such as machine learning, deep learning and data analytics; how they can enhance diagnostic accuracy, tailor treatment to fit individual patients, and optimize drug development. Moreover, the paper explores the problems and ethical challenges involved in incorporating AI into health care--data privacy; algorithmic bias; lack of transparency. Combining the use of case studies, actual applications and future prospects, it paints a clear picture of how AI will radically change cardiovascular pharmacology. The paper shows that artificial intelligence is already changing patient clinical outcomes today and how healthcare costs for society overall may follow suit in the near future.International Journal For Multidisciplinary Research170It is shown that artificial intelligence is already changing patient clinical outcomes today and how healthcare costs for society overall may follow suit in the near future.International Journal For Multidisciplinary Research['Hannah Alex']2024-01-28T00:00:00https://www.semanticscholar.org/paper/09c0b4aab82b229b65bdb610958c844ac7afd62d 445e21fbac76d4bb1c883b4eec0030a5e01c53eaArtificial Intelligence (AI) holds great potential for enhancing Risk Management (RM) through automated data integration and analysis. While the positive impact of AI in RM is acknowledged, concerns are rising about unintended consequences. This study explores factors like opacity, technology and security risks, revealing potential operational inefficiencies and inaccurate risk assessments. Through archival research and stakeholder interviews, including chief risk officers and credit managers, findings highlight the risks stemming from the absence of AI regulations, operational opacity, and information overload. These risks encompass cybersecurity threats, data manipulation uncertainties, monitoring challenges, and biases in algorithms. The study emphasizes the need for a responsible AI framework to address these emerging risks and enhance the effectiveness of RM processes. By advocating for such a framework, the authors provide practical insights for risk managers and identify avenues for future research in this evolving field.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)130Findings highlight the risks stemming from the absence of AI regulations, operational opacity, and information overload, and the need for a responsible AI framework to address these emerging risks and enhance the effectiveness of RM processes.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['A. Metwally', 'Salah A.M. Ali', 'Abdelnasser T.I. Mohamed']2024-01-28T00:00:00https://www.semanticscholar.org/paper/445e21fbac76d4bb1c883b4eec0030a5e01c53ea 3f3f0c35a5cbc2278c1890767a65b26c64115e7eThis paper explores the efficacy of ChatGPT in generating data for Item Response Theory (IRT) using the R programming language. Focusing on the 2 Parameter Logistic Model (2PLM), it evaluates datasets produced by ChatGPT against several IRT assumptions like unidimensionality and local independence. The study compares these datasets with those generated by researchers, assessing compliance with simulation conditions, bias, and RMSE values. The results indicate that while ChatGPT algorithms successfully generate data adhering to IRT assumptions, they exhibit more issues with item parameter compliance compared to researcher-generated algorithms. This study highlights ChatGPT's potential in data generation, but also underscores the importance of human expertise in guiding its outputs for scientific research.arXiv.org640While ChatGPT algorithms successfully generate data adhering to IRT assumptions, they exhibit more issues with item parameter compliance compared to researcher-generated algorithms, highlighting the importance of human expertise in guiding its outputs for scientific research.ArXiv['Hatice Gurdil', 'Yesim Beril Soguksu', 'Salih Salihoğlu', 'Fatma Coskun']2024-01-28T00:00:00https://www.semanticscholar.org/paper/3f3f0c35a5cbc2278c1890767a65b26c64115e7e a743e9510ca71f204072785c982500922fc2574aThe Asian Conference on Education 2023: Official Conference Proceedings00The Asian Conference on Education 2023: Official Conference Proceedings['Jon Gorham', 'Daniel J. Mills']2024-01-28T00:00:00https://www.semanticscholar.org/paper/a743e9510ca71f204072785c982500922fc2574a b90d417807ccc5b397dbd4ebbed35e00b9dc2f93AI &amp; SOCIETY10AI &amp; SOCIETY['Hazel T. Biana', 'J. J. Joaquin']2024-01-28T00:00:00https://www.semanticscholar.org/paper/b90d417807ccc5b397dbd4ebbed35e00b9dc2f93 a783cc56ab03e26be8a35cf88daccaa6802fc949Given the importance of public engagement in governments' adoption of artificial intelligence systems, artificial intelligence researchers and practitioners spend little time reflecting on who those publics are. Classifying publics affects assumptions and affordances attributed to the publics' ability to contribute to policy or knowledge production. Further complicating definitions are the publics' role in artificial intelligence production and optimization. Our structured analysis of the corpus used a mixed method, where algorithmic generation of search terms allowed us to examine approximately 2500 articles and provided the foundation to conduct an extensive systematic literature review of approximately 100 documents. Results show the multiplicity of ways publics are framed, by examining and revealing the different semantic nuances, affordances, political and expertise lenses, and, finally, a lack of definitions. We conclude that categorizing publics represents an act of power, politics, and truth-seeking in artificial intelligence.Public Understanding of Science450It is concluded that categorizing publics represents an act of power, politics, and truth-seeking in artificial intelligence.Public understanding of science['Renée Sieber', 'Ana Brandusescu', 'Abigail Adu-Daako', 'Suthee Sangiambut']2024-01-28T00:00:00https://www.semanticscholar.org/paper/a783cc56ab03e26be8a35cf88daccaa6802fc949 8e8c0a671ee83ac560858209a68a34814fb5ccefThe Asian Conference on Education 2023: Official Conference Proceedings00The Asian Conference on Education 2023: Official Conference Proceedings['Poh Nguk Lau', 'Steven Chee Kuen Ng', 'Li Fern Tan']2024-01-28T00:00:00https://www.semanticscholar.org/paper/8e8c0a671ee83ac560858209a68a34814fb5ccef 2847588b02821e014007ba5cfed8498f942cf63cInfrastructure services in autonomous cooperative driving are providing various applications along with the development of edge computing technology. While existing infrastructure services simply integrated collected information and shared it with autonomous vehicles, research that actively intervenes in the driving judgment and control of real-time autonomous vehicles is increasing. The representative research is a project to develop autonomous driving support technology using infrastructure guidance. In this project, we are developing technology that predicts the driving intentions and paths of vehicles in real-time using an Edge RSU (Road Side Unit) equipped with sensors and ML models, and provides guidance information that enables vehicles to drive more safely and efficiently. This paper deals with the research content of a data pipeline that can continuously improve the performance of Machine Learning (ML) models installed in Edge RSU in infrastructure guidance services. The data pipeline concept presented in this study was designed based on Data-Centric AI from data collection to ML model deployment. The method that was researched for autonomous driving vehicles was redesigned to be suitable for infrastructure guidance services. This concept has the advantage of allowing multiple Edge RSUs to perform continuous data collection and services from fixed locations. For this reason, we expect higher stability and efficiency than the results applied to autonomous driving vehicles. The current research stage is proceeding with the development of an automated pipeline design and ML models to be installed in Edge RSU and plans to apply it to the infrastructure guidance service testbed from 2024.International Conference on Electronics, Information and Communications20This paper is developing technology that predicts the driving intentions and paths of vehicles in real-time using an Edge RSU equipped with sensors and ML models, and provides guidance information that enables vehicles to drive more safely and efficiently.2024 International Conference on Electronics, Information, and Communication (ICEIC)['Jaehwan Kim', 'Jinkyung Jeon', 'Jieun Park', 'Seungkwon Jung']2024-01-28T00:00:00https://www.semanticscholar.org/paper/2847588b02821e014007ba5cfed8498f942cf63c 562f6a0a55342e7177bb688e79e9b5b24cf37725Food Science and Biotechnology140Food Science and Biotechnology['Youngjin Kim', 'Jooho Lee', 'Sangoh Kim']2024-01-28T00:00:00https://www.semanticscholar.org/paper/562f6a0a55342e7177bb688e79e9b5b24cf37725 fc055fe9fbb524141592f26594558b56774212cbIn this study, I employ a multifaceted comprehensive scientometric approach to explore the intellectual underpinnings of AI and ML in financial research by examining the publication patterns of articles, journals, authors, institutions, and nations by leveraging quantitative techniques, that transcend conventional systematic literature reviews, enabling the effective analysis of vast scientometric and bibliographic data. By applying these approaches, I identify influential works, seminal contributions, thought leaders, topical clusters, research streams, and new research frontiers, ultimately fostering a deeper understanding of the knowledge structure in AI and ML finance research by considering publication records from 2010 to 2022 from several search engines and database sources. The present study finds a marked increase in publications from 2017 to 2022, which highlights a growing interest and expanding research activity in the field, indicating its potential significance and relevance in the contemporary academic landscape.International Journal of Engineering and Advanced Technology215The present study finds a marked increase in publications from 2017 to 2022, which highlights a growing interest and expanding research activity in the field, indicating its potential significance and relevance in the contemporary academic landscape.ArXiv['V. Kanaparthi']2024-01-28T00:00:00https://www.semanticscholar.org/paper/fc055fe9fbb524141592f26594558b56774212cb 964356ca1f366f39f18db927cc3fc9ec565fc083Researchers from Darktrace, a global leader in cybersecurity AI, saw a 135% spike in unique social engineering attacks from January to February 2023, coinciding with the broad usage of ChatGPT, which was made public in October 2022. With the advancement in electronic attack methods, different facilities are looking for ways to protect their data and technology systems. To do this, they must adopt more proactive methods to overcome these threats. Thus, Artificial intelligence (AI) proved its capabilities to face such threats. The current study aimed to identify the applications of AI and the importance of the role it plays in information security applications and the era of cybersecurity. This paper relies on quantitative data published by international organizations and research centers. Through adopting the descriptive approach, the results of this investigation show that the important step for progress is in the level of Information Technology (IT) infrastructure protection. For IT, it is necessary to use AI. Taking all these things into consideration, the results suggest that AI will strengthen the barrier between your systems and cyber threats.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)11The results suggest that AI will strengthen the barrier between systems and cyber threats and the important step for progress is in the level of Information Technology (IT) infrastructure protection.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['W. Nageab', 'R. Alrasheed', 'Mahmoud Khalifa']2024-01-28T00:00:00https://www.semanticscholar.org/paper/964356ca1f366f39f18db927cc3fc9ec565fc083 dba313f3e91b2838492f66d3faf79a610625ee0fIn recent years, there has been significant progress in the field of Artificial Intelligence, both in terms of technological advancements and knowledge acquisition. These advancements have led to the development of unorthodox learning methodologies in Artificial Intelligence applications. Artificial Intelligence is the field of study focused on developing advanced systems that can effectively learn and teach, providing learners with the most relevant information based on their own learning requirements and preferences. AI applications have made significant contributions to the education industry, particularly in higher education, where they play a crucial role in facilitating learning. This research examined the motivation and efficacy of learners in relation to the artificial intelligence learning strategy for learning applications. Data were gathered from a total of 121 respondents who were selected from five higher education colleges in the Sindh region of Pakistan. This study revealed that most learners expressed satisfaction with the utilization of artificial intelligence (AI) applications in various aspects. Specifically, they acknowledged that AI applications enhance learning capabilities and productivity. Moreover, they recognized the usefulness of AI applications in augmenting knowledge and facilitating the learning process by providing easily understandable content. What is your opinion on the potential of AI applications in these areas? Most learners expressed good motivation for the questions and expressed optimism about the usefulness of artificial intelligence applications. In conclusion, more engagement between learners and AI learning applications will provide positive outcomes in comprehending the material of the relevant topic. This paper proposes the implementation of training programs for learners specifically focused on AI learning applications.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)191This research examined the motivation and efficacy of learners in relation to the artificial intelligence learning strategy for learning applications and proposed the implementation of training programs for learners specifically focused on AI learning applications.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Mudasir Ali Rind', 'Mohammad Ali Al Qudah', 'Pirali Aliyev']2024-01-28T00:00:00https://www.semanticscholar.org/paper/dba313f3e91b2838492f66d3faf79a610625ee0f a6dc2d4fc9bf7f68dd4f0f8c1bda0e842fe3f1d6This research explores the complex terrain of cognitive dissonance that banking industry employees encounter while negotiating the changing environment of artificial intelligence (AI) integration. In a time when artificial intelligence (AI) is revolutionizing banking processes, it is critical to comprehend the elements that lead to cognitive dissonance among employees. The study takes a multimodal approach, integrating quantitative techniques to investigate the complex interactions between factors that affect cognitive dissonance in the workplace. Data were gathered from 344 bank employees via online questionnaires. The influence of AI -driven changes on job positions, workplace relationships, and employee expectations is examined in this study. It examines how individuals' flexibility, organizational culture changes, and communication gaps affect their capacity to align their views with the quickly changing technology world. The study also looks at the ethical implications of using AI in banking organizations, taking into account how moral issues might make employees feel more cognitively disoriented. The purpose of this study's findings is to offer useful information to human resource specialists, policymakers, and stakeholders in the banking sector who are attempting to manage the difficulties caused by cognitive dissonance while integrating AI. This research helps to design solutions that promote employee well-being, adaptation, and resilience in the face of revolutionary technology breakthroughs by elucidating the multiple dynamics at play. The purpose of this study is to investigate the many aspects of cognitive dissonance that affect employees in banking.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)240The influence of AI -driven changes on job positions, workplace relationships, and employee expectations is examined in this study, examining how individuals' flexibility, organizational culture changes, and communication gaps affect their capacity to align their views with the quickly changing technology world.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Channi Sachdeva', 'Veer P. Gangwar', 'Veena Grover', 'Saikat Gochhait']2024-01-28T00:00:00https://www.semanticscholar.org/paper/a6dc2d4fc9bf7f68dd4f0f8c1bda0e842fe3f1d6 30454518f2923e9c958c018cec8b3aba9de146efAll major sectors are rapidly embracing AI technology to provide rapid and quality services to patients. Along with opportunities, the integration of AI in healthcare has some ethical and legal challenges. The present paper aims to discuss ethical and legal concerns of integrating AI technology into the Jordanian healthcare system. Focus-group discussion (FGD), a qualitative method, was applied to collect primary data from six participants. Results show that the Jordanian government is proactive in integrating AI in core sectors, including healthcare. There are some legal provisions to promote secure and safe AI-driven services. However, the legal system does not have a special law that could regulate AI-system. It is highly recommended that the laws should be much more comprehensive, and the training of ethics should be the priority in the Jordanian healthcare system.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)270Ethical and legal concerns of integrating AI technology into the Jordanian healthcare system are discussed and it is highly recommended that the laws should be much more comprehensive and the training of ethics should be the priority in the Jordanian healthcare system.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Abdallah Q. Bataineh', 'Alaa S. Mushtaha', 'Ibrahim A. Abu-AlSondos', 'S. Aldulaimi', 'M. Abdeldayem']2024-01-28T00:00:00https://www.semanticscholar.org/paper/30454518f2923e9c958c018cec8b3aba9de146ef de697d76c69e0feca1b419fc17bcaa552b382097In the contemporary environment of rapidly advancing technologies, this study investigates the influence of artificial intelligence (AI) capabilities on business performance, focused on the era of emerging technologies. Furthermore, this study is to examine how the combination of artificial intelligence capability; the timely decisions influence overall business performance, and to provide insights into how these factors interact and contribute to enhancing organizational success in the context of modern AI-driven decision-making processes. The research employs a quantitative approach to analyze data collected from 184 upper echelons from diverse firms. The study revealed that businesses leveraging higher artificial intelligence capability often achieved improved timely decisions. However, the optimal balance between timely decision-making varied across contexts, influencing overall business performance outcomes. Understanding this interplay is crucial for organizations seeking to enhance their operational effectiveness and strategic outcomes. The general perspective of this study offers valuable insights for organizations aiming to strategically enhance operational efficiency and overall effectiveness in an AI-driven environment.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)480The study revealed that businesses leveraging higher artificial intelligence capability often achieved improved timely decisions, however, the optimal balance between timely decision-making varied across contexts, influencing overall business performance outcomes.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Suheil Neiroukh', 'Hasan Yousef Aljuhmani', 'S. Alnajdawi']2024-01-28T00:00:00https://www.semanticscholar.org/paper/de697d76c69e0feca1b419fc17bcaa552b382097 488aceba35645ac3a3bb29e7246af03d88a73e9aAI &amp; SOCIETY271Higher levels of SADNESS were associated with higher negative attitudes towards AI (fearing AI), and primary emotional systems—according to Affective Neuroscience Theory—represent tools for survival, which have been homologously conserved across mammalian species including homo sapiens.AI &amp; SOCIETY['Christian Montag', 'Raian Ali', 'K. Davis']2024-01-28T00:00:00https://www.semanticscholar.org/paper/488aceba35645ac3a3bb29e7246af03d88a73e9a 0f6a30ec1ef85eb78d4056b8793c5dd7a8ee62c6While extensive research has documented the cognitive changes associated with Parkinson’s disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the clinical diagnosis of neurodegenerative disorders, but a deeper investigation of the current literature available on PD is lacking. This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD. A total of 10 studies published between 2016 and 2023 were included in this review. Tasks used to elicit language were mainly structured or unstructured narrative discourse. Transcriptions were mostly analyzed using Natural Language Processing (NLP) techniques. The classification accuracy (%) ranged from 43 to 94, sensitivity (%) ranged from 8 to 95, specificity (%) ranged from 3 to 100, AUC (%) ranged from 32 to 97. The most frequent optimal linguistic measures were lexico-semantic (40%), followed by NLP-extracted features (26%) and morphological consistency features (20%). Artificial intelligence applied to linguistic markers provides valuable insights into PD. However, analyzing measures derived from narrative discourse can be time-consuming, and utilizing ML requires specialized expertise. Moving forward, it is important to focus on facilitating the integration of both narrative discourse analysis and artificial intelligence into clinical practice.Brain Science711This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD with a total of 10 studies published between 2016 and 2023.Brain Sciences['Cinzia Palmirotta', 'S. Aresta', 'Petronilla Battista', 'Serena Tagliente', 'Gianvito Lagravinese', 'Davide Mongelli', 'Christian Gelao', 'Pietro Fiore', 'Isabella Castiglioni', 'Brigida Minafra', 'C. Salvatore']2024-01-28T00:00:00https://www.semanticscholar.org/paper/0f6a30ec1ef85eb78d4056b8793c5dd7a8ee62c6 ac26a337df5c1662cb5dfd186d03a346dbcd3290Research on utilization of artificial intelligence in higher education has significantly expanded in recent years. However, the existing literature in this domain highlights a shortage of research in specific subareas, such as ChatGPT and the innovative utilization of advanced artificial intelligence tools. With the growing number of studies focusing on artificial intelligence in higher education, there is a need to assess to what extent the current body of research is filling the previously reported research gap. This study aims to review research published within the last 11 months in the year 2023, to assess the status and direction of recent publications in these specific areas and to provide a comprehensive summary that will assist scholars and higher education institutions in shaping their future work on artificial intelligence in higher education. Using a systematic literature review methodology, 295 articles published on the Scopus database were analyzed. The review findings indicate that the majority of papers serve a general overview purpose, with a moderate focus on generative AI, advanced integration of AI into teaching and learning, and prediction modes. On the contrary, a limited number of papers were directed toward AI for assessment, AI Chatbot, and support for administrative processes. These findings highlight the need for a shift of research efforts from more general exploration topics to a more advanced investigation into the usage of AI tools in a novel and sophisticated manner.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)230The review findings indicate that the majority of papers serve a general overview purpose, with a moderate focus on generative AI, advanced integration of AI into teaching and learning, and prediction modes, while a limited number of papers were directed toward AI for assessment, AI Chatbot, and support for administrative processes.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Salem Alateyyat', 'Mohamed Soltan']2024-01-28T00:00:00https://www.semanticscholar.org/paper/ac26a337df5c1662cb5dfd186d03a346dbcd3290 a9866f9d46ccfd52200520f0e9fa97bbd0599c65The present study is focused on investigating how artificial intelligence (AI) technologies are changing human resource management (HRM) practices, namely in the areas of learning and development, data-driven decision-making, employee experience, and recruitment. It looks at the possible benefits, challenges, and effects of integrating AI into HR procedures, with the primary goal of comprehending how AI is altering traditional HR roles. The research paper's initial phase was a comprehensive review of the corpus of literature on AI in the HR field. To establish a solid knowledge base and identify areas in need of research, this involved searching through relevant industry reports, academic publications, and other sources. The research is both descriptive and qualitative. The following conclusion emerged from the study's findings: AI provides recruiters with a useful tool for talent optimization; intelligent AI technologies will gradually change routine administrative jobs, freeing up HR executives and recruiters to focus more on tactical roles; and AI solutions will make it easier for people to find employment. The use of AI will transform hiring, expand programs for learning and development, improve employee satisfaction, and make data-driven decision-making possible. Even though there are challenges, addressing them will help businesses completely integrate AI into HR, which will improve output, effectiveness, and worker happiness. This study adds something new to the field by examining in detail how AI has changed HR practices. HR professionals can gain from its useful insights, problem identification, consequences for organizational performance, and identification of future research topics.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)260The following conclusion emerged from the study's findings: AI provides recruiters with a useful tool for talent optimization; intelligent AI technologies will gradually change routine administrative jobs, freeing up HR executives and recruiters to focus more on tactical roles; and AI solutions will make it easier for people to find employment.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['C. Gupta', 'V. V. R. Kumar']2024-01-28T00:00:00https://www.semanticscholar.org/paper/a9866f9d46ccfd52200520f0e9fa97bbd0599c65 47eeef323fe5a06c96eddc161fa876fd7967849cThe main objective of this study is to construct a comprehensive conceptual framework that link the relationship between artificial intelligence (AI), trust service framework of system reliability and reliability of accounting information system (AIS). To achieve this objective, the current related literature synthesized and analyzed using meta-analysis as a methodological approach. The findings of this study suggest that AI techniques can enhance the AIS reliability by supporting the security, confidentiality, privacy, processing integrity, and availability as five principles of trust service framework for system reliability. The proposed conceptual framework provides an explanation for such findings. The implications of this study will benefit a wide range of stakeholders including users, management, auditors. Some suggestions for future studies are provided in this study.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)190The findings of this study suggest that AI techniques can enhance the AIS reliability by supporting the security, confidentiality, privacy, processing integrity, and availability as five principles of trust service framework for system reliability.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Salah A.M. Ali', 'A. Metwally', 'Abdelnasser T.I. Mohamed']2024-01-28T00:00:00https://www.semanticscholar.org/paper/47eeef323fe5a06c96eddc161fa876fd7967849c aed70100da2d121af39bc0ffd0975d49ccc25989The main principle of AI is to simulate and go beyond the way humans understand and interact with the world around us, which has quickly become the cornerstone of innovation. Artificial intelligence improves the performance and productivity of institutions by automating processes or tasks that previously required human strength, and the emergence of solutions and tools that rely on artificial intelligence means that more companies can benefit from artificial intelligence at a lower cost and less time. Hence, the adoption of competitive advantage (CA) is one of the most significant challenges for business organization management because of the urgent need to acquire a competitive advantage, depending on the extent to which the industrial sector can create a good working environment and formulate a strategy that supports innovation and its ability to respond to scientific progress and possess good knowledge and skills to achieve excellence in the internal and external environment. Therefore, the current research aimed to systematically analyze the scientific literature related to the application of artificial intelligence and machine learning (ML) in industry to achieve competitive advantage in business organizations. It has been shown that artificial intelligence has a positive impact on achieving competitive advantage in business organizations, and the research relied on The descriptive analytical approach to determine the framework to develop the proposed framework to clarify the relationship between artificial intelligence and competitive advantage in business organizations. An analysis of the literature on artificial intelligence and the competitive advantage of business organizations has been used to answer the main question of research: what is the role of artificial intelligence applications in achieving the competitive advantage of industrial business organizations?2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)120It has been shown that artificial intelligence has a positive impact on achieving competitive advantage in business organizations, and the research relied on the descriptive analytical approach to determine the framework to develop the proposed framework to clarify the relationship between artificial intelligence and competitive advantage in business organizations.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Mohamed Albaz', 'Mahmoud Khalifa']2024-01-28T00:00:00https://www.semanticscholar.org/paper/aed70100da2d121af39bc0ffd0975d49ccc25989 abd8ef3fcd05a8ba61adc23097109acee5e1cec0The aim of this study is to construct a conceptual framework that studies the complicate interaction between Artificial Intelligence, Organizational Justice and Organizational Trust. By using the Meta-Analysis methodological approach, we seek to analyze and synthesize the current literature with the aim of outlining the relationship between adopting Artificial Intelligence, aspects of Justice seen by stakeholders, and trust dynamics within organization. We proposed a paradigm in a trial to reveal the interconnections among such dimensions through the combination of empirical evidence and the theoretical underpinnings, outlining the significant insights of scholars and practitioners who navigated the AI driven work environments.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)280A paradigm in a trial is proposed to reveal the interconnections among such dimensions through the combination of empirical evidence and the theoretical underpinnings, outlining the significant insights of scholars and practitioners who navigated the AI driven work environments.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Adel Mahmoud Al Samman', 'Abdlnasser Mohamed']2024-01-28T00:00:00https://www.semanticscholar.org/paper/abd8ef3fcd05a8ba61adc23097109acee5e1cec0 3594ad9dba8587d743c1e3f4628514e26d0453faThe purpose of this study is to examine difficulties in India's healthcare industry and determine the practicality of using AI as a solution. The integration of Artificial Intelligence (AI) technologies, which include machine learning and robots, has transformed several sectors, most notably healthcare. These technologies are altering the delivery of medical services and diagnostics by processing large information, recognizing detailed patterns, and engaging in sophisticated human interactions. The combination of artificial intelligence with healthcare has drastically revolutionized service delivery, addressing issues such as individualized therapy and exact diagnosis. Its potential influence extends beyond care, clinical processes, and healthcare management, resulting in a transformational landscape. AI has the potential to improve healthcare, emergency response systems, and disaster management in India. However, due to different populations, language variances, limited resources, and economic limits, its implementation faces distinct obstacles. Utilizing AI opens up possibilities for early illness diagnosis, improved diagnostics, telemedicine breakthroughs, disease prediction, and effective health record administration. However, successful implementation necessitates resolving technological challenges as well as issues for good governance. The approach of this review article entails describing and critically analyzing existing research on AI technologies, with a special emphasis on their possible applications in India's healthcare scene. The goal is to identify how artificial intelligence (AI) might potentially reduce current issues in the Indian healthcare sector.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)210The purpose of this study is to examine difficulties in India's healthcare industry and determine the practicality of using AI as a solution, and identify how artificial intelligence (AI) might potentially reduce current issues in the Indian healthcare sector.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Madhav Tilve', 'Shailesh Rastogi', 'R. Gautam']2024-01-28T00:00:00https://www.semanticscholar.org/paper/3594ad9dba8587d743c1e3f4628514e26d0453fa a7fbd4035fdb9c6198ef198eb2d7d4d55a763379Effective human resources management (HRM) is crucial for all kinds of businesses in today's competitive environment since it improves organizational performance. This study aims to investigate how artificial intelligence (AI) is currently used in recruiting. A scoping review is a method used to look through the body of existing literature from different databases. Bias, which is otherwise becoming more prevalent in manual recruiting processes, is eliminated in recruitment through automation and analytics. Additionally, in conventional face-to-face interviews, candidates have been found to use impression management strategies. However, the impact of these strategies can be reduced with automated recruiting processes. Virtual interviews powered by AI minimize human bias. Additionally, it makes it easier for recruiters to find talent worldwide. Candidates' opinions of their workplaces and work surroundings are enhanced by stimulation. Using gamified methods, recruiters can determine if a prospect has the necessary work skills. The results and conclusions were practically exact in indicating that the best applications of technology may be found in the field of recruitment, where employing AI is favorable. The hiring process is more effective when AI is used. It minimizes repetitive tasks. The human resources (HR) team benefits from time savings. It is a very new development for HRM to use AI, and very few businesses have done so completely for all HR procedures. The main advantages of using AI will be increased productivity and automated repetitive work; nevertheless, the main challenge that businesses may have is their readiness to adopt innovative technology.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)250It is indicated that the best applications of technology may be found in the field of recruitment, where employing AI is favorable, and the hiring process is more effective when AI is used.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['A. Alzyoud', 'K. Omar', 'Ahmed Arbab']2024-01-28T00:00:00https://www.semanticscholar.org/paper/a7fbd4035fdb9c6198ef198eb2d7d4d55a763379 3c46091c22b9d8eda4a11660d77d3ac1e1518f21Intelligent intelligence is the most exciting technological development of our modern age. In the area of legal practice, artificial intelligence has played a prominent role in demonstrating a proactive perception and conclusion of the outcome of judicial proceedings with precision, comprehensiveness, and maximum speed. It helps to inform and inform lawyers about the development and definition of litigation strategies. The study highlights the advantages and disadvantages of artificial intelligence in the legal field and has produced several important findings. One of these results is the need to balance humans and robots so that artificial intelligence does not replace the human mind because of the progress and evolution of algorithms. However, the legal community must effectively exploit the capabilities of artificial intelligence and view it as a complementary tool to the human mind by establishing an appropriate legal framework governing its use and defining the legal responsibility for that innovative technology. In this way, we will be able to make the most of artificial intelligence in the administration of justice and the improvement of the justice system.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)150The legal community must effectively exploit the capabilities of artificial intelligence and view it as a complementary tool to the human mind by establishing an appropriate legal framework governing its use and defining the legal responsibility for that innovative technology.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Mahmoud Khalifa', 'Mahmoud Sabry']2024-01-28T00:00:00https://www.semanticscholar.org/paper/3c46091c22b9d8eda4a11660d77d3ac1e1518f21 208a4ff5f31e01a4649655d8e94ff3574499df41The study explores how artificial intelligence technologies can be used to address health crises. These technologies can be used to track and record any crisis before or at the moment of its occurrence, enabling rapid intervention and success in overcoming the crisis as soon as it is reached through rapid alerts for intervention by these applications. The study focuses on tracking these applications during the stages of managing the COVID-19 crisis, in terms of “the mechanism of operation of these technologies, their areas, and the challenges of their use”, based on the application of several European and Arab countries of such technologies during the four stages of managing the crisis; in an attempt to explore the global effort during the COVID-19 crisis in the age of artificial intelligence. The current study aimed to clarify the role of artificial intelligence technology and its impact on crisis management, an analytical study. The study recommended the need to enhance the role of artificial intelligence technology, given the contribution of this technology to achieving high levels of environmental quality and thus reaching the management and facing crises.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)210The study explores how artificial intelligence technologies can be used to address health crises and recommended the need to enhance the role of artificial intelligence technology, given the contribution of this technology to achieving high levels of environmental quality and thus reaching the management and facing crises.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Mahmoud Khalifa', 'Maged M. Albaz', 'W. Nageab']2024-01-28T00:00:00https://www.semanticscholar.org/paper/208a4ff5f31e01a4649655d8e94ff3574499df41 6f373c476a21d15cd1ba2cadb4cf7a03fe3a15f6The rise of technology has revolutionized talent management practices. Companies are utilizing data-driven approaches for recruitment, performance evaluation, and succession planning. Moreover, remote work and virtual collaboration have gained traction, prompting organizations to reevaluate their talent acquisition and retention strategies. The pandemic has underscored the significance of technology in facilitating virtual work and maintaining a collaborative work culture within the organization. This necessitates constant monitoring and analysis of the dynamics of talent management. This research aims to provide an overview of the challenges experienced by Human Resource (HR) professionals in talent management within the service industry. In addition, the authors investigate how HR professionals may utilize Artificial Intelligence (AI) based resources and tools in order to overcome the issues pertaining to the development and retention of skilled personnel. The authors propose an AI-driven talent management model based on the challenges and AI solutions discussed in this paper, which will aid HR professionals in better incorporating AI into talent management practices.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)210An AI-driven talent management model is proposed based on the challenges and AI solutions discussed in this paper, which will aid HR professionals in better incorporating AI into talent management practices.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Fatima Vapiwala', 'Deepika Pandita']2024-01-28T00:00:00https://www.semanticscholar.org/paper/6f373c476a21d15cd1ba2cadb4cf7a03fe3a15f6 2e259856adaf1e9b49f53aecd5ace5c2211d0c27The impact of Artificial Intelligence (AI) technologies on physician stakeholders is a hot topic, but more information and data on their attitudes need to be collected. This study aims to build a model to assess Italian physicians' attitudes toward AI applications and evaluate the factors predisposing them to be more favourable to large-scale employment.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)00This study aims to build a model to assess Italian physicians' attitudes toward AI applications and evaluate the factors predisposing them to be more favourable to large-scale employment.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['V. De Nicolò', 'Davide La Torre']2024-01-28T00:00:00https://www.semanticscholar.org/paper/2e259856adaf1e9b49f53aecd5ace5c2211d0c27 1060cbc3091a03553a4a2b7334327d824f1b1522Stock price prediction remains a critical yet challenging task that has attracted the focus of both researchers and practitioners. The purpose of this study is to present a comprehensive review of recent advancements in the application of artificial intelligence (AI)-based techniques in predicting stock price movements. A systematic analysis of research papers published from 2020 to 2023 was conducted, employing a keyword-based search across two significant databases. Fourteen influential papers were selected, which utilized various AI techniques to forecast stock prices. The literature review reveals a range of predictive approaches, including technical, fundamental, and sentiment analysis, with a significant emphasis on mixed approaches that integrate multiple models. Notably, hybrid models outperform traditional methods by leveraging deep learning algorithms to handle non-linear complexities and temporal dynamics. The paper concludes by emphasizing the significant potential of AI in stock market prediction, highlighting a promising future that involves refining hybrid models and integrating big data analytics. The insights derived from this review provide guidance for future research and practical applications in the financial industry, emphasizing the transformative impact of AI on investment strategies and decision-making processes.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)200A comprehensive review of recent advancements in the application of artificial intelligence (AI)-based techniques in predicting stock price movements reveals a range of predictive approaches, including technical, fundamental, and sentiment analysis, with a significant emphasis on mixed approaches that integrate multiple models.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['A. Al-Alawi', 'Naser Alshakhoori']2024-01-28T00:00:00https://www.semanticscholar.org/paper/1060cbc3091a03553a4a2b7334327d824f1b1522 7b7660c554427171edc07e22d211369935854cd7With the rapid development and wide application of artificial intelligence technology, its impact on China's labor market has become more and more significant, and has also attracted extensive attention and research in the academic community. Artificial intelligence is profoundly changing the pattern of the labor market, which has a significant impact on industrial development as well as employment opportunities. In this paper, the impact of artificial intelligence technology on the labor market is discussed in depth by analyzing the current status of research at home and abroad. The research results show that there is a duality in the impact of AI technology on the labor market, on the one hand, AI technology can improve production efficiency and create more new employment opportunities, on the other hand, the popularization of intelligent technology will also have an impact on some traditional labor positions. Therefore, in order to effectively cope with the challenges brought by AI technology, and at the same time can make better use of the opportunities brought by it to further promote the stability and sustainable development of the labor market and achieve higher quality employment, this paper puts forward relevant policy recommendations.International Journal of Global Economics and Management00There is a duality in the impact of AI technology on the labor market, on the one hand, AI technology can improve production efficiency and create more new employment opportunities, on the other hand, the popularization of intelligent technology will also have an impact on some traditional labor positions.International Journal of Global Economics and Management['Rui Yan']2024-01-28T00:00:00https://www.semanticscholar.org/paper/7b7660c554427171edc07e22d211369935854cd7 874efc07be10151eee4ef7c5a175450dc32c2805The world of Industry 4.0 embraces the transformation that calls for strategic investments, cooperative partnerships, and a proactive approach to overcome obstacles to successfully transition to smarter, more connected, and more efficient in- dustrial ecosystems. The accounting and finance industry has benefited greatly from process modernization, which has resulted in a significant decrease in the use of the traditional system through the integration of digital technologies. The use of various artificial intelligence technologies such as expert systems for audit and taxation, intelligent agents for customer service, Natural Language Processing (NLP), Robotic Process Automation (RPA), Blockchain, data analytics, and machine learning for decision- making has been proposed to automate, streamline, and improve the efficiency of various tasks and processes in accounting and finance operations. The study specifically investigates how the incorporation of AI technologies affects the reliability & timeliness of financial & accounting processes. The paper also presents some use cases and AI applications relevant to this field.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)120The study specifically investigates how the incorporation of AI technologies affects the reliability & timeliness of financial & accounting processes.2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)['Rahila Rahim', 'M. Chishti']2024-01-28T00:00:00https://www.semanticscholar.org/paper/874efc07be10151eee4ef7c5a175450dc32c2805 c15d8d07cdb91700e1987e40f04694eed9fc80edWith rapid development of Artificial Intelligence (AI), researchers have found many bioinspired AI applications, such as bioinspired images and speech processing, which can increase accuracy [...].Biomimetics180Biomimetics['Haoran Wei', 'Fei Tao', 'Zhenghua Huang', 'Yanhua Long']2024-01-28T00:00:00https://www.semanticscholar.org/paper/c15d8d07cdb91700e1987e40f04694eed9fc80ed ce710ca206484ef6fae2e2bc4248612d0c1883f0The Asian Conference on Education 2023: Official Conference Proceedings00The Asian Conference on Education 2023: Official Conference Proceedings['David Campos-Delgado', 'Ricardo Alonso-Rivera']2024-01-28T00:00:00https://www.semanticscholar.org/paper/ce710ca206484ef6fae2e2bc4248612d0c1883f0 fadffd55a18e61a7023bfc7dee5e2f4f058d8dbfAI &amp; SOCIETY10AI &amp; SOCIETY['A. Ogunyiola']2024-01-28T00:00:00https://www.semanticscholar.org/paper/fadffd55a18e61a7023bfc7dee5e2f4f058d8dbf f3cf3cce927d7d4e51477455dbf96a54f900d16fJournal of Environmental Management1031Journal of environmental management['Yuying Sun', 'Kai Wu', 'Sihui Liu', 'Yongmiao Hong']2024-01-27T00:00:00https://www.semanticscholar.org/paper/f3cf3cce927d7d4e51477455dbf96a54f900d16f 5b591ce03ccee2a217213ae34535d532c7148e90Cryptocurrency has grown rapidly since it was first introduced to the public. Many countries, including Indonesia, have begun to adopt special regulations for cryptocurrencies in response to the exponential growth and penetration of cryptocurrencies in the traditional financial system. Regulations aim to protect investors, ensure economic stability, and prevent illegal activities such as terrorism, money laundering and tax evasion. In January 2023, Law Number 4 of 2023 concerning Development and Strengthening of the Financial Sector ("UU P2SK") was enacted as a form of financial sector reform. Starting January 2025, regulation and supervision of cryptocurrency transactions will be carried out by the Financial Services Authority (OJK) replacing the Commodity Futures Trading Supervisory Agency (Bappebti) which has established several regulations regarding Crypto assets. But until now There are no regulations regarding permits or prohibitions for influencers/celebrities in marketing crypto assets via social media or other media to the public even though these regulations are needed to mitigate the incidence of victims and losses, as in several cases that have occurred. In the future, a comprehensive and integrated regulation is needed with other laws and regulations such as the Terrorism Law, the Money Laundering Crime Law, the Consumer Protection Law, the ITE Law, the Criminal Code and other related laws and regulations, in order to keep up with the rapid development of cryptocurrency and the changing character of the digital world. global and borderless.Journal of Social Science300Journal of Social Science['Francisca Romana Nanik Alfiani']2024-01-27T00:00:00https://www.semanticscholar.org/paper/5b591ce03ccee2a217213ae34535d532c7148e90 b49d0acb07d87d2d81d92bba6dc00ceeb5975a61BMC Primary Care1082This study is the first to the authors' knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms, and offers nuanced insights into PCP attitudes towards AI in primary care.BMC Primary Care['Matthew R. Allen', 'Sophie Webb', 'Ammar Mandvi', 'Marshall Frieden', 'Ming Tai-Seale', 'Gene Kallenberg']2024-01-27T00:00:00https://www.semanticscholar.org/paper/b49d0acb07d87d2d81d92bba6dc00ceeb5975a61 932ee16bf5cbcb9a066a7bbf233e63ff00be8975For many people, anxiety, depression, and other social and mental factors can make composing text messages an active challenge. To remedy this problem, large language models (LLMs) may yet prove to be the perfect tool to assist users that would otherwise find texting difficult or stressful. However, despite rapid uptake in LLM usage, considerations for their assistive usage in text message composition have not been explored. A primary concern regarding LLM usage is that poor public sentiment regarding AI introduces the possibility that its usage may harm perceptions of AI-assisted text messages, making usage counter-productive. To (in)validate this possibility, we explore how the belief that a text message did or did not receive AI assistance in composition alters its perceived tone, clarity, and ability to convey intent. In this study, we survey the perceptions of 26 participants on 18 randomly labeled pre-composed text messages. In analyzing the participants' ratings of message tone, clarity, and ability to convey intent, we find that there is no statistically significant evidence that the belief that AI is utilized alters recipient perceptions. This provides hopeful evidence that LLM-based text message composition assistance can be implemented without the risk of counter-productive outcomes.arXiv.org141There is no statistically significant evidence that the belief that AI is utilized alters recipient perceptions, providing hopeful evidence that LLM-based text message composition assistance can be implemented without the risk of counter-productive outcomes.ArXiv["N'yoma Diamond"]2024-01-27T00:00:00https://www.semanticscholar.org/paper/932ee16bf5cbcb9a066a7bbf233e63ff00be8975 f2b755e75aabe1bc6e640740ccfd9251ad657427AI &amp; SOCIETY321It is argued that neither concept of AI is amenable to values being ‘embedded’ and is best understood as a dimension of the relationship between technology and society, a relationship that can be theorized in multiple ways.AI &amp; SOCIETY['Deborah G. Johnson', 'Mario Verdicchio']2024-01-27T00:00:00https://www.semanticscholar.org/paper/f2b755e75aabe1bc6e640740ccfd9251ad657427 d203b3a1c7e2e2be27bbacd3d2eb174e58670b5eUnlike routine tasks where consistency is prized, in creativity and innovation the goal is to create a diverse set of ideas. This paper delves into the burgeoning interest in employing Artificial Intelligence (AI) to enhance the productivity and quality of the idea generation process. While previous studies have found that the average quality of AI ideas is quite high, prior research also has pointed to the inability of AI-based brainstorming to create sufficient dispersion of ideas, which limits novelty and the quality of the overall best idea. Our research investigates methods to increase the dispersion in AI-generated ideas. Using GPT-4, we explore the effect of different prompting methods on Cosine Similarity, the number of unique ideas, and the speed with which the idea space gets exhausted. We do this in the domain of developing a new product development for college students, priced under $50. In this context, we find that (1) pools of ideas generated by GPT-4 with various plausible prompts are less diverse than ideas generated by groups of human subjects (2) the diversity of AI generated ideas can be substantially improved using prompt engineering (3) Chain-of-Thought (CoT) prompting leads to the highest diversity of ideas of all prompts we evaluated and was able to come close to what is achieved by groups of human subjects. It also was capable of generating the highest number of unique ideas of any prompt we studied.Social Science Research Network91Pools of ideas generated by GPT-4 with various plausible prompts are less diverse than ideas generated by groups of human subjects, but the diversity of AI generated ideas can be substantially improved using prompt engineering and Chain-of-Thought prompting leads to the highest diversity of ideas.ArXiv['Lennart Meincke', 'Ethan R. Mollick', 'Christian Terwiesch']2024-01-27T00:00:00https://www.semanticscholar.org/paper/d203b3a1c7e2e2be27bbacd3d2eb174e58670b5e 29d7b31516fdda00555f94dda997582aedbb3524Electric power components and systems182Electric Power Components and Systems['V. C.', 'A. Chaturvedi', 'Arvin Tony A', 'P. V. V. S. Srinivas', 'P. S. Ranjit', 'Ravi Rastogi', 'M. R. Arun', 'A. Rajaram']2024-01-27T00:00:00https://www.semanticscholar.org/paper/29d7b31516fdda00555f94dda997582aedbb3524 2bdaf621517794ac6e88aa90ce95a797eeaa47e5This research aims to get a conceptual knowledge of influencer marketing, as well as its relationship with emotional marketing. Emotional marketing is considered a component of influencer marketing, since customers form an emotional connection with the influencers. An analysis has been conducted to investigate the level of emotional connection that may be formed between influencers and customers across many categories, including nano influencers, micro influencers, and celebrities. Influencers use several methods such as authenticity, narrative, engagement, and interaction to control customers’ purchasing attitude and intention. These strategies enable influencers to develop an emotional connection with consumers. These strategies assist in quantifying the influence on customers by analysing their purchases. The future trajectory of marketing, with a focus on the integration of AI, has been thoroughly examined, along with a comprehensive analysis of the potential impact of influencer marketing.Shanlax International Journal of Management120The future trajectory of marketing, with a focus on the integration of AI, has been thoroughly examined, along with a comprehensive analysis of the potential impact of influencer marketing.Shanlax International Journal of Management['Preethi Gaur']2024-01-27T00:00:00https://www.semanticscholar.org/paper/2bdaf621517794ac6e88aa90ce95a797eeaa47e5 577a3f3476c65f043dc25a583b529b39c538c6f3Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.Web3 Journal: ML in Health Science00The findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings.Web3 Journal: ML in Health Science['Y. Rusinovich', 'V. Rusinovich']2024-01-27T00:00:00https://www.semanticscholar.org/paper/577a3f3476c65f043dc25a583b529b39c538c6f3 40c24cdf2cc187196f2b5738b75ca08b12ccb9feArtificial intelligence (AI) and cloud computing are transforming human resources (HR) throughout the digital revolution. This white paper examines how these technologies are converging and how they affect HR practices. First, the report charts the evolution of HR technology from manual methods to complex digital ecosystems. This essay stresses the vital moment when AI and cloud technologies drive HR innovation, altering talent acquisition, employee engagement, performance management, and other areas. This article discusses AI’s position in HR and its applications throughout HR. Real-world case studies show how AI-powered recruitment improves candidate screening and predictive analytics informs HR decisions. Cloud HR solutions, fundamental to modern HR technology, are targeted. This article discusses cloud-based HR systems’ versatility, cost savings, and ability to adapt to changing demands. Additionally, this section features companies that have improved HR procedures using cloud technology. Cloud platforms provide AI-powered HR solutions, according to the paper. Addressing bias and ensuring AI fairness are ethical considerations in the analysis. It also stresses cloud-based HR system data privacy and security. HR experts are crucial to this transformation. The paper discusses HR teams’ changing roles in an AI and cloud-centric HR world and offers upskilling and reskilling techniques to maximise HR functions’ strategic value.Shanlax International Journal of Management70The paper discusses HR teams’ changing roles in an AI and cloud-centric HR world and offers upskilling and reskilling techniques to maximise HR functions’ strategic value.Shanlax International Journal of Management['M. Nalini']2024-01-27T00:00:00https://www.semanticscholar.org/paper/40c24cdf2cc187196f2b5738b75ca08b12ccb9fe 6496a3d1e67fe68685364e22557aae01c72281e5This method for AI-driven drug discovery and therapeutic target selection for rare genetic diseases is a cutting-edge and comprehensive strategy for addressing the challenges posed by such diseases. As a prerequisite, it is necessary to collect and combine genomic, transcriptomic, proteomic, and clinical data from numerous sources in a systematic manner. By integrating genetic and clinical data, it is possible to construct a more complete picture of the disorders. Feature selection procedures are utilized to further simplify the research by focusing on the most essential characteristics for target identification and reducing the dimensionality of the data. Based on the integrated data set, machine learning models such as Neural Networks, Support Vector Machines, and Random Forests are utilized to recommend prospective therapeutic targets. Validation and cross-validation techniques are used to assure the validity and generalizability of these models. The strategy emphasizes adherence to applicable ethical principles and legal frameworks while encouraging collaboration across specialties. The patient is the focal point throughout, and new data is continuously collected to improve results and develop more individualized therapies based on everyone's unique genetic profile. The iterative nature of the methodology allows for continual refinement as new data and clinical insights emerge. AI-predicted targets undergo biological validation through in vitro and in vivo experiments, bridging the gap between computational predictions and clinical applications. Knowledge and insights are shared with the scientific community through open-access databases and collaborative platforms.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)2702024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)['Suresh T', 'S. Kaliappan', 'H. Ali', 'Bura Vijay Kumar']2024-01-27T00:00:00https://www.semanticscholar.org/paper/6496a3d1e67fe68685364e22557aae01c72281e5 047125c7e5240b091ae146bf374b6d2ee733659cAccurate market forecasting is essential in today's fast-paced and intensely competitive business environment, as it helps direct strategic decision-making and ensure maximum performance for businesses. The practice of market forecasting has been fundamentally altered by the development of Artificial Intelligence (AI), a game-changing technology that appeared recently. This in-depth research investigates the varied landscape of AI-driven market forecasting models, analyzing their techniques, strengths, and limits, as well as their applicability across a variety of business sectors. The first part of the research explains the relevance of accurate market forecasting and the limitations faced by conventional approaches in the face of complex and fast-changing market dynamics. This sets the stage for the rest of the study, which focuses on how to improve established methods. After that, it goes into the fundamental ideas that underpin artificial intelligence, covering topics such as machine learning, deep learning, natural language processing, and ensemble approaches. These ideas provide the foundation for today's artificial intelligence-driven forecasting models, which give businesses the ability to tap into the potential of large data and generate valuable insights from that data. Following this, a comprehensive study of AI-driven techniques for forecasting will be presented. These approaches will include time series analysis, sentiment analysis, market sentiment aggregation, and predictive modeling. Case examples illustrate the use of these methodologies in a variety of fields, including but not limited to the financial industry, the e-commerce industry, the energy industry, and the healthcare industry. The research also digs into the ethical issues that surround the use of AI for market forecasting, with an emphasis on transparency, the reduction of bias, and responsible data use.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)170This in-depth research investigates the varied landscape of AI-driven market forecasting models, analyzing their techniques, strengths, and limits, as well as their applicability across a variety of business sectors.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)['Monalisha Chakraborty', 'Prasanta Parida']2024-01-27T00:00:00https://www.semanticscholar.org/paper/047125c7e5240b091ae146bf374b6d2ee733659c e455454014d446654f04c31dcfec0a29f695c1f4Generative AI tools are used to create art-like outputs and sometimes aid in the creative process. These tools have potential benefits for artists, but they also have the potential to harm the art workforce and infringe upon artistic and intellectual property rights. Without explicit consent from artists, Generative AI creators scrape artists' digital work to train Generative AI models and produce art-like outputs at scale. These outputs are now being used to compete with human artists in the marketplace as well as being used by some artists in their generative processes to create art. We surveyed 459 artists to investigate the tension between artists' opinions on Generative AI art's potential utility and harm. This study surveys artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation. Results show that a majority of artists believe creators should disclose what art is being used in AI training, that AI outputs should not belong to model creators, and express concerns about AI's impact on the art workforce and who profits from their art. We hope the results of this work will further meaningful collaboration and alignment between the art community and Generative AI researchers and developers.arXiv.org820Survey of artists' opinions on the utility and threat of Generative AI art models, fair practices in the disclosure of artistic works in AI art training models, ownership and rights of AI art derivatives, and fair compensation show a majority of artists believe creators should disclose what art is being used in AI training.ArXiv['Juniper Lovato', 'Julia Zimmerman', 'Isabelle Smith', 'Peter Dodds', 'Jennifer Karson']2024-01-27T00:00:00https://www.semanticscholar.org/paper/e455454014d446654f04c31dcfec0a29f695c1f4 f2cf16930e5102bcf0e3e6c8229435111d2c425dThe capabilities of artificial intelligence (AI) and machine learning (ML) algorithms are constantly expanding, necessitating efficient and high-performance hardware systems. We have investigated the creation of hardware accelerators based on VLSI that are intended to effectively manage the heavy workloads of machine learning jobs, also explored low-power VLSI architectures that preserve computing capabilities while reducing power consumption to solve energy efficiency issues in AI and ML systems. To balance performance and energy utilization, power management strategies and circuit design improvements are analysed. The study emphasizes hardware-software co-design techniques, considering the integration of VLSI-based hardware accelerators with software frameworks to obtain optimal performance and flexibility, to address the complexity and scalability of AI and ML systems. We also examined the cutting-edge VLSI technologies that have the potential to support powerful AI and ML applications. The speed and effectiveness of AI and ML algorithms could be improved significantly by these technologies, which include neuromorphic computing, approximation computing, and in-memory computing. The research also discusses the difficulties in designing VLSI for AI and ML algorithms, and its possible solutions for challenges including design complexity, scalability, memory management, and data mobility.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)190The creation of hardware accelerators based on VLSI that are intended to effectively manage the heavy workloads of machine learning jobs are investigated, and low-power VLSI architectures that preserve computing capabilities while reducing power consumption are explored to solve energy efficiency issues in AI and ML systems.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)['Prashray Nagar', 'Sashwat Boruah', 'A. Bhoi', 'Arpita Patel', 'Jigar Sarda', 'Pallavi Darjij']2024-01-27T00:00:00https://www.semanticscholar.org/paper/f2cf16930e5102bcf0e3e6c8229435111d2c425d a1da8c74d26f80719126f987b9675e2643ddbe19Generative artificial intelligence tools like large language models are rapidly transforming academic research and real world applications. However, discussions on ethical guidelines for generative AI in science remain fragmented, underscoring the urgent need for consensus based standards. This paper offers an initial framework by developing analyses and mitigation strategies across five key themes: understanding model limitations regarding truthfulness and bias; respecting privacy, confidentiality, and copyright; avoiding plagiarism and policy violations when incorporating model output; ensuring applications provide overall benefit; and using AI transparently and reproducibly. Common scenarios are outlined to demonstrate potential ethical violations. We argue that global consensus coupled with professional training and reasonable enforcement are critical to promoting the benefits of AI while safeguarding research integrity.120It is argued that global consensus coupled with professional training and reasonable enforcement are critical to promoting the benefits of AI while safeguarding research integrity, and an initial framework for analyses and mitigation strategies is offered.['Zhicheng Lin']2024-01-27T00:00:00https://www.semanticscholar.org/paper/a1da8c74d26f80719126f987b9675e2643ddbe19 66a6b7ed6a53a04cf1ef28a87a1b7867dc7d1329Humans frequently make decisions with the aid of artificially intelligent (AI) systems. A common pattern is for the AI to recommend an action to the human who retains control over the final decision. Researchers have identified ensuring that a human has appropriate reliance on an AI as a critical component of achieving complementary performance. We argue that the current definition of appropriate reliance used in such research lacks formal statistical grounding and can lead to contradictions. We propose a formal definition of reliance, based on statistical decision theory, which separates the concepts of reliance as the probability the decision-maker follows the AI's recommendation from challenges a human may face in differentiating the signals and forming accurate beliefs about the situation. Our definition gives rise to a framework that can be used to guide the design and interpretation of studies on human-AI complementarity and reliance. Using recent AI-advised decision making studies from literature, we demonstrate how our framework can be used to separate the loss due to mis-reliance from the loss due to not accurately differentiating the signals. We evaluate these losses by comparing to a baseline and a benchmark for complementary performance defined by the expected payoff achieved by a rational decision-maker facing the same decision task as the behavioral decision-makers.370A formal definition of reliance is proposed, based on statistical decision theory, which separates the concepts of reliance as the probability the decision-maker follows the AI's recommendation from challenges a human may face in differentiating the signals and forming accurate beliefs about the situation.['Ziyang Guo', 'Yifan Wu', 'Jason D. Hartline', 'J. Hullman']2024-01-27T00:00:00https://www.semanticscholar.org/paper/66a6b7ed6a53a04cf1ef28a87a1b7867dc7d1329 17ae0343df418c5cb114e7e7b9614ae8c6b80fd6The paper examined the Adoption of Artificial Intelligence in Education: Challenges and Possibilities. It is a known fact that, the current technological explosion affecting all faceted of human endeavor does not exempted the education sector, hence, owing to this reality, different forms of technologies are currently in used in order to improve on the pedagogical or operational skills of both teachers and students with the hope of bringing improvement in teaching and learning process. Therefore, the adoption of emerging technology such as Artificial Intelligent (AI) in education system becomes imperative. Interestingly to note was that, AI has been in the education technology space for a while, but its adoption has been greatly slow. However, during the COVID-19 pandemic, virtual learning forced the industry to shift and the technology helps streamline the student education process by offering access to suitable courses, bettering communication among students with their tutors thereby bridging the gaps of learning shortage occasioned by the pandemics. It was in line with this development that, this review paper discussed the sub-field of Artificial Intelligence applications and these were: Machine learning; Speech recognition; Expert system; Natural language processing; Robotics; Vision and planning. Moreso, the paper explained Artificial Intelligence and its application in education owing to its significance to educational development in this 21st Century. It further highlighted the broad classifications of Artificial Intelligence for Educational Adoption and these are: Students focus, Teacher’s focus and Institutional focus. Additionally, Educational advantages of its adoption are identified and they include:  It makes it possible for easy collaborations between the teachers and the students in or outside the classroom setting and It has the capability of providing real-time data and algorithm of any kinds depending on the request of the user. Major challenges which include Difficulties in using AI in an enable classroom to address the digital divide and avoid exacerbating existing inequalities that already in existence and Lack of adequate infrastructure, such as reliable internet connectivity, electricity, and devices, that are essential for the delivery and use of AI-based educational solutions. Lastly, Conclusion are made as regard the discussed around the adoption of AI in education.Asian Journal of Advanced Research and Reports00The paper discussed the sub-field of Artificial Intelligence applications and these were: Machine learning; Speech recognition; Expert system; Natural language processing; Robotics; Vision and planning; and highlighted the broad classifications of Artificial Intelligence for Educational Adoption.Asian Journal of Advanced Research and Reports['R. M. Mafara', 'Shehu, Suleiman Abdullahi']2024-01-27T00:00:00https://www.semanticscholar.org/paper/17ae0343df418c5cb114e7e7b9614ae8c6b80fd6 3d728f056e855c8237ebb5515b0f5e84d0dbd37bACM / IMS Journal of Data Science41ACM / IMS Journal of Data Science['Misha Belkin']2024-01-27T00:00:00https://www.semanticscholar.org/paper/3d728f056e855c8237ebb5515b0f5e84d0dbd37b 9096b6b09e9db7cb3a2e107f054cefa7db6d1e16Bridging the Divide01Bridging the Divide['Sharmin Shoukat', 'Wi-Suk Kwon']2024-01-27T00:00:00https://www.semanticscholar.org/paper/9096b6b09e9db7cb3a2e107f054cefa7db6d1e16 11c4059d4607bd9967b50e93f29a9ab5066a7cf2International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-01-27T00:00:00https://www.semanticscholar.org/paper/11c4059d4607bd9967b50e93f29a9ab5066a7cf2 82bae970d265e87306f5de106f7c4493a242f054International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-01-27T00:00:00https://www.semanticscholar.org/paper/82bae970d265e87306f5de106f7c4493a242f054 ada86fad82097fc3c76000b3af2862fc21678919Abstract Mastering commonsense understanding and reasoning is a pivotal skill essential for conducting engaging conversations. While there have been several attempts to create datasets that facilitate commonsense inferences in dialogue contexts, existing datasets tend to lack in-depth details, restate information already present in the conversation, and often fail to capture the multifaceted nature of commonsense reasoning. In response to these limitations, we compile a new synthetic dataset for commonsense reasoning in dialogue contexts using GPT, ℂonvoSense, that boasts greater contextual novelty, offers a higher volume of inferences per example, and substantially enriches the detail conveyed by the inferences. Our dataset contains over 500,000 inferences across 12,000 dialogues with 10 popular inference types, which empowers the training of generative commonsense models for dialogue that are superior in producing plausible inferences with high novelty when compared to models trained on the previous datasets. To the best of our knowledge, ℂonvoSense is the first of its kind to provide such a multitude of novel inferences at such a large scale.Transactions of the Association for Computational Linguistics470A new synthetic dataset for commonsense reasoning in dialogue contexts using GPT, ℂonvoSense, that boasts greater contextual novelty, offers a higher volume of inferences per example, and substantially enriches the detail conveyed by the inferences.Transactions of the Association for Computational Linguistics['Sarah E. Finch', 'Jinho D. Choi']2024-01-27T00:00:00https://www.semanticscholar.org/paper/ada86fad82097fc3c76000b3af2862fc21678919 0a0b12d344627a155d5ad945c25b88441a4318f1Millions of individuals worldwide suffer from chronic kidney disease (CKD). Early identification and treatment prevent CKD and improve patient outcomes. Machine learning can analyze enormous volumes of medical data and find patterns that could help detect CKD early. The current study uses a massive dataset of clinical records, laboratory test results, and patient demographics. Feature selection and data processing improve data integrity. LR, SVM, RF, and deep neural networks have been built and tested using the dataset. Sensitivity, AUC-ROC, Accuracy, and specificity are used to evaluate CKD model performance. The results show that machine learning algorithms can accurately identify early CKD risk factors. Future studies may combine real-time data streams with personalized patient data to increase CKD detection algorithms' accuracy and therapeutic value.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)230The results show that machine learning algorithms can accurately identify early CKD risk factors and may combine real-time data streams with personalized patient data to increase CKD detection algorithms' accuracy and therapeutic value.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)['Smit Thacker', 'Ravirajsinh Vaghela']2024-01-27T00:00:00https://www.semanticscholar.org/paper/0a0b12d344627a155d5ad945c25b88441a4318f1 f5cc609d1cd7ab4ae088827e215d58c01503b169Bridging the Divide00Bridging the Divide['Sanaz Einollahi', 'Wi-Suk Kwon']2024-01-27T00:00:00https://www.semanticscholar.org/paper/f5cc609d1cd7ab4ae088827e215d58c01503b169 e6619b8b88e02a7e9b4ac6c85af2db681d3fcbb1We have increasingly seen that new graduates have difficulty finding a job, which they often work in different jobs than the ones they graduated from, and that employees are not satisfied with their career choices. One reason for this may be the lack of practical, useful education when a person is in college or has just graduated from college. Chatbots are very useful and a topic of interest in the field of computer science and artificial intelligence due to their ability to emulate experts in different applications and replicate human interaction on multiple levels. Research shows that using chatbots to provide career advice can be an effective way to provide these services in an environment where counselors work. The lack of good and appropriate vocational training means that young people begin to look for jobs that their parents have chosen for them, or jobs chosen only for high wages. These decisions will be made regardless of whether; they are in the person's best interests and interests. This can cause a person to be dissatisfied with their job, which affects not only their personal health but also the overall efficiency of their work. Therefore, the development of chatbots will better inform users and help them choose jobs. This will enable them to consider jobs that they do not expect to be more fulfilling and fulfilling than jobs that do not align with their interests. Research and development software was used in this study. According to the research method, the research was conducted to collect information about people's thoughts about their career choices and what kind of job training they want from the system. In addition, the latest research on career guidance was taken into account when creating the basis of the survey. This information is used to develop a chatbot on the Facebook Messenger platform using objects powered using tools such as the Facebook SDK, Messenger Platform API and JavaScript, as well as the Wit.ai API that supports natural language usage smart technology. Chatbots can understand user input and provide relevant and appropriate responses reliably and instantly. Overall, it is hoped that the results obtained will create a positive impression of the implementation of the system and thus become a valuable asset for any school or institution wishing to use the system.International Journal of Innovative Research in Information Security00Research shows that using chatbots to provide career advice can be an effective way to provide these services in an environment where counselors work, and is hoped that the results obtained will create a positive impression of the implementation of the system and thus become a valuable asset for any school or institution wishing to use the system.International Journal of Innovative Research in Information Security['Prof.Vikas Singhal', 'Dr.Pankaj Gupta', 'Ayush Shekhar']2024-01-27T00:00:00https://www.semanticscholar.org/paper/e6619b8b88e02a7e9b4ac6c85af2db681d3fcbb1 a6a8896dea728310d1bfe829e027e10cccdf4974Recent advancements in large language models (LLMs) have significantly enhanced capabilities in natural language processing and artificial intelligence. These models, including GPT-3.5 and LLaMA-2, have revolutionized text generation, translation, and question-answering tasks due to the transformative Transformer model. Despite their widespread use, LLMs present challenges such as ethical dilemmas when models are compelled to respond inappropriately, susceptibility to phishing attacks, and privacy violations. This paper addresses these challenges by introducing a multi-pronged approach that includes: 1) filtering sensitive vocabulary from user input to prevent unethical responses; 2) detecting role-playing to halt interactions that could lead to 'prison break' scenarios; 3) implementing custom rule engines to restrict the generation of prohibited content; and 4) extending these methodologies to various LLM derivatives like Multi-Model Large Language Models (MLLMs). Our approach not only fortifies models against unethical manipulations and privacy breaches but also maintains their high performance across tasks. We demonstrate state-of-the-art performance under various attack prompts, without compromising the model's core functionalities. Furthermore, the introduction of differentiated security levels empowers users to control their personal data disclosure. Our methods contribute to reducing social risks and conflicts arising from technological abuse, enhance data protection, and promote social equity. Collectively, this research provides a framework for balancing the efficiency of question-answering systems with user privacy and ethical standards, ensuring a safer user experience and fostering trust in AI technology.arXiv.org460This research provides a framework for balancing the efficiency of question-answering systems with user privacy and ethical standards, ensuring a safer user experience and fostering trust in AI technology.ArXiv['Yunhong He', 'Jianling Qiu', 'Wei Zhang', 'Zhe Yuan']2024-01-27T00:00:00https://www.semanticscholar.org/paper/a6a8896dea728310d1bfe829e027e10cccdf4974 c856d7a8904732b1084354857de715682f33603bBridging the Divide40Bridging the Divide['Garim Lee', 'Hye-Young Kim']2024-01-27T00:00:00https://www.semanticscholar.org/paper/c856d7a8904732b1084354857de715682f33603b f77d0bc729d37f7f9ff17664fef9ac36359e0b6dThe issue of work-related musculoskeletal disorders (WRMSD) is becoming increasingly prominent in occupational health and workplace safety, affecting a growing number of individuals. This pressing issue demands increased attention, especially in crucial sectors like the garment industry. In response to this challenge, our study focuses on developing and evaluating deep learning models to facilitate the early prediction and diagnosis of WRMSD in garment industry workers. This research investigates the potential of several deep learning models and conducts a comparative analysis of their performance metrics. The findings demonstrate that the Bi-directional Gated Recurrent Unit model achieved a remarkable accuracy of 97.19%, surpassing all other deep learning models examined in this study.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)210The findings demonstrate that the Bi-directional Gated Recurrent Unit model achieved a remarkable accuracy of 97.19%, surpassing all other deep learning models examined in this study.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)['Ankit Vijayvargiya', 'Shruti Paliwal', 'Naveen Gehlot', 'Rajesh Kumar', 'Kieran Moran']2024-01-27T00:00:00https://www.semanticscholar.org/paper/f77d0bc729d37f7f9ff17664fef9ac36359e0b6d 4557d6e5de9e577fea6f0f014f0ec7c9d99e44cfBridging the Divide40Bridging the Divide['Y. Min', 'Kyu-Hye Lee', 'Eunsoo Baek']2024-01-27T00:00:00https://www.semanticscholar.org/paper/4557d6e5de9e577fea6f0f014f0ec7c9d99e44cf cf574b33afff4624a2a03eda6a692e5e77289915Work instructions have always been necessary, but in recent times they have only gained the respect they deserve and now that they are in high demand and available worldwide, it is important to have a simple understanding of the various tasks that need to be done. Career counsellors are responsible for providing high school students with the experience and skills they need to make informed career decisions, education, and long-term goals. Music; It is a social event that unites people regardless of market, age, history, language, interests, political affiliation and income. Music and streaming apps are in demand because they are versatile and compatible with daily life, travel, sports and other activities. The rise of mobile phones and digital multimedia technology has made digital music a consumer favourite for many young people. Although career orientation has always been important, it has recently become widely recognized as a key element in today's career change research. High school students, in particular, need early and sustained exposure to many popular careers around the world. This information allows them to make decisions that patiently pursue their educational preferences. Music is a unifying force that transcends borders and barriers and plays a special role in this field. Music and streaming services are integrated into daily life, travel and entertainment, resulting in a universal love of music. The growth of mobile phones and digital technology has brought digital music even more to the forefront of youth content. However, many students do not have enough guidance to bridge the gap between their education, passions, and future career goals. This often leads to frustration and inability to take action. To meet this need, our web career counselling program, based on the ASP.NET Framework, provides students with career planning tools, skills development and opportunity guidance through a Google Dialog flow-powered counselling chatbot. By exploring music as a potential career path and leveraging its global appeal, we aim to inspire and guide students into a successful and rewarding career.International Journal of Innovative Research in Advanced Engineering00The web career counselling program, based on the ASP.NET Framework, provides students with career planning tools, skills development and opportunity guidance through a Google Dialog flow-powered counselling chatbot, and aims to inspire and guide students into a successful and rewarding career.International Journal of Innovative Research in Advanced Engineering['Sunil Kumar', 'Dr.Shivani Dubey', 'Prof.Vikas Singhal', 'Dr.Ajay kumar Sahu', 'Dr.Pankaj Gupta']2024-01-27T00:00:00https://www.semanticscholar.org/paper/cf574b33afff4624a2a03eda6a692e5e77289915 32beec3040175c926bd3984ed8f94a0fc3e95eb9The COVID-19 pandemic significantly disrupted dental education, highlighting the need for innovative remote learning solutions. This study, centered in the United Arab Emirates, explores dental students' perceptions of the Metaverse as an educational tool, contrasting it with traditional digital platforms like Zoom. Our research aims to fill the gap in understanding the effectiveness of Metaverse technology in dental education. Employing Partial Least Squares-Structural Equation Modeling (PLS-SEM) and an Artificial Neural Network (ANN) approach, we analyzed data from 833 students across various institutions. The findings reveal that user adoption decisions in the Metaverse are greatly influenced by User Mobility and Users' Accessibility. The ANN model showed superior accuracy in predicting outcomes compared to other methods. These results contribute to the broader discussion on artificial intelligence, particularly its role in environmental sustainability.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)310This study explores dental students' perceptions of the Metaverse as an educational tool, contrasting it with traditional digital platforms like Zoom, and reveals that user adoption decisions in the Metaverse are greatly influenced by User Mobility and Users' Accessibility.2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)['Said Salloum', 'Khaled Shaalan', 'Mohammad Alfaisal', 'Ayham Salloum', 'Tarek Gaber']2024-01-27T00:00:00https://www.semanticscholar.org/paper/32beec3040175c926bd3984ed8f94a0fc3e95eb9 cc6f3db5658c149dc2d67e1414074aa773e95ff6Environmental and Resource Economics421Environmental and Resource Economics['Fabio Antoniou', 'P. Hatzipanayotou', 'Nikos Tsakiris']2024-01-27T00:00:00https://www.semanticscholar.org/paper/cc6f3db5658c149dc2d67e1414074aa773e95ff6 4ebdb6e19419fa1f860d0457f324623c6f83defd Lethal autonomous weapon systems (LAWS) are the subject of considerable international debate turning around the extent to which humans remain in control over using force. But what is precisely at stake is less clear as stakeholders have different perspectives on the technologies that animate LAWS. Such differences matter because they shape the substance of the debate, which regulatory options are put on the table, and also normativity on LAWS in the sense of understandings of appropriateness. To understand this process, I draw on practice theories, science and technology studies (STS), and critical norm research. I argue that a constellation of communities of practice (CoPs) shapes the public debate about LAWS and focus on three of these CoPs: diplomats, weapon manufacturers, and journalists. Actors in these CoPs discursively perform practices of boundary-work, in the STS sense, to shape understandings of technologies at the heart of LAWS: automation, autonomy, and AI. I analyze these dynamics empirically in two steps: first, by offering a general-level analysis of practices of boundary-work performed by diplomats at the Group of Governmental Experts on LAWS from 2017 to 2022; and second, through examining such practices performed by weapon manufacturers and journalists in relation to the use of loitering munitions, a particular type of LAWS, in the Second Libyan Civil War (2014–2020).Global Studies Quarterly842It is argued that a constellation of communities of practice (CoPs) shapes the public debate about LAWS and focus on three of these CoPs: diplomats, weapon manufacturers, and journalists.Global Studies Quarterly['Ingvild Bode']2024-01-27T00:00:00https://www.semanticscholar.org/paper/4ebdb6e19419fa1f860d0457f324623c6f83defd 644b63d75b4893a46bcaff761a37f6a6246c9926Introduction: The burgeoning burden on emergency departments is a global challenge that we have been confronting for many years. Emerging artificial intelligence (AI)-based solutions may constitute a critical component in the optimization of these units. This systematic review was conducted to thoroughly examine and summarize the currently available AI solutions, assess potential benefits from their implementation, and identify anticipated directions of further development in this fascinating and rapidly evolving field. Methods: This systematic review utilized data compiled from three key scientific databases: PubMed (2045 publications), Scopus (877 publications), and Web of Science (2495 publications). After meticulous removal of duplicates, we conducted a detailed analysis of 2052 articles, including 147 full-text papers. From these, we selected 51 of the most pertinent and representative publications for the review. Results: Overall the present research indicates that due to high accuracy and sensitivity of machine learning (ML) models it's reasonable to use AI in support of doctors as it can show them the potential diagnosis, which could save time and resources. However, AI-generated diagnoses should be verified by a doctor as AI is not infallible Conclusions: Currently available AI algorithms are capable of analysing complex medical data with unprecedented precision and speed. Despite AI's vast potential, it is still a nascent technology that is often perceived as complicated and challenging to implement. We propose that a pivotal point in effectively harnessing this technology is the close collaboration between medical professionals and AI experts. Future research should focus on further refining AI algorithms, performing comprehensive validation, and introducing suitable legal regulations and standard procedures, thereby fully leveraging the potential of AI to enhance the quality and efficiency of healthcare delivery.Archives of Academic Emergency Medicine590Overall the present research indicates that due to high accuracy and sensitivity of machine learning (ML) models it's reasonable to use AI in support of doctors as it can show them the potential diagnosis, which could save time and resources.Archives of Academic Emergency Medicine['Szymczyk Aleksandra', 'Krion Robert', 'Krzyzaniak Klaudia', 'Lubian Dawid', 'Sieminski Mariusz']2024-01-27T00:00:00https://www.semanticscholar.org/paper/644b63d75b4893a46bcaff761a37f6a6246c9926 cf1b63c26fdfcf3acfda08334016ea6524ff4063Penggunaan asisten virtual berbasis Artificial Intelligence (AI) telah menunjukkan dampak yang signifikan dalam berbagai sektor, khususnya pendidikan. Kehadiran asisten virtual berbasis AI yang mampu menjawab pertanyaan dari berbagai topik menciptakan peluang dan tantangan baru terutama terkait ujian dan penilaian. Penelitian ini meneliti dampak penggunaan asisten virtual berbasis AI, khususnya ChatGPT (GPT-4), terhadap integritas sertifikasi kompetensi pemrograman yang dilakukan secara online. Melalui eksperimen lapangan, penulis mengikuti tiga sertifikasi kompetensi pemrograman secara online terkait “Python Fundamentals for Beginners”, “Java Programming”, dan “Android Application Development” serta menggunakan ChatGPT (GPT-4) untuk menjawab semua soal ujian. Hasil ujian menunjukkan bahwa GPT-4 berhasil menjawab 9 dari 10 soal yang diberikan pada tiga sertifikasi kompetensi pemrograman yang diikuti dan dinyatakan lulus ujian, hal ini menimbulkan pertanyaan serius tentang validitas sertifikat yang diperoleh melalui sertifikasi kompetensi yang dilakukan secara online. Penelitian ini mengungkapkan bahwa perlu adanya metode ujian yang lebih efektif dalam menilai kemampuan sesungguhnya dari peserta sertifikasi, seperti ujian berbasis proyek, ujian menggunakan soal dalam bentuk video, dan ujian berbasis wawancara. Metode ujian ini tidak hanya akan meningkatkan kredibilitas sertifikasi kompetensi pemrograman secara online, tetapi juga memastikan bahwa peserta yang lulus sertifikasi memang memiliki keahlian terkait bidang yang diujikan. Penelitian lebih lanjut diperlukan untuk mengeksplorasi dampak asisten virtual berbasis AI dalam berbagai konteks pendidikan khususnya pada sertifikasi kompetensi secara online.JURNAL KRIDATAMA SAINS DAN TEKNOLOGI300JURNAL KRIDATAMA SAINS DAN TEKNOLOGI['Imam Prayogo Pujiono', 'Eko Hari Rachmawanto', 'Fida Maisa Hana']2024-01-27T00:00:00https://www.semanticscholar.org/paper/cf1b63c26fdfcf3acfda08334016ea6524ff4063 4bead9a5087059d1f66ccda07a55d942fd576855Journal of Marketing Analytics262Journal of Marketing Analytics['Michael Rodriguez', 'Robert Peterson']2024-01-27T00:00:00https://www.semanticscholar.org/paper/4bead9a5087059d1f66ccda07a55d942fd576855 5370d704e386688fa0c13127dbd493438c4a1f1fA transformação global causada pela Revolução Informacional remodelou a sociedade contemporânea ao introduzir inteligências artificiais (IA’s) com capacidade de aprendizado autônomo. Contudo, a implementação dessas tecnologias suscita preocupações relacionadas à discriminação racial, de gênero, econômica e nacional. Broussard salienta que o racismo nas IA's resulta de algoritmos concebidos por indivíduos, refletindo preconceitos inconscientes de seus programadores. O incidente do reconhecimento facial da Polícia Civil do Ceará, que erroneamente identificou o ator Michael B. Jordan, ilustra tais problemas. O racismo algorítmico, examinado através da perspectiva da violência simbólica, integra um sistema de marginalização resultante de um longo processo social e histórico. Debate-se, então, a função do ordenamento jurídico na proteção contra tais práticas discriminatórias, evidenciando um interesse legislativo recente e em desenvolvimento, realizado pela Comissão de Juristas no Senado Federal. Partindo do método indutivo e da análise bibliográfica, o estudo tem como objetivo analisar a IA como agente de ratificação da marginalização racial e o papel do ordenamento jurídico em sua regulamentação.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação['Luís Filipe da Silva Nascimento']2024-01-27T00:00:00https://www.semanticscholar.org/paper/5370d704e386688fa0c13127dbd493438c4a1f1f 50fa586ea8239b443221981f13451ecc7e834c98The Article Abstract is not available.Journal of Community Health Research00Journal of Community Health Research['F. Madadizadeh', 'Sajjad Bahariniya']2024-01-27T00:00:00https://www.semanticscholar.org/paper/50fa586ea8239b443221981f13451ecc7e834c98 bc79e5d41b62012d42ab417ccc5b5c82cc84c31aDalam era digital yang berkembang pesat, kecerdasan buatan (AI) telah menjadi unsur kunci dalam kehidupan sehari-hari, termasuk dalam lingkungan akademik. Penelitian ini mengeksplorasi bagaimana literasi AI mempengaruhi norma dan etika akademik mahasiswa, khususnya di Universitas Negeri Makassar. Tujuan penelitian ini adalah untuk mengidentifikasi dampak pengetahuan AI pada norma sosial, etika, dan perilaku akademik mahasiswa. Metode penelitian kuantitatif digunakan dengan pendekatan desain longitudinal sectional, menggunakan kuesioner untuk mengumpulkan data dari 74 partisipan. Hasil penelitian menunjukkan bahwa peningkatan literasi AI berkorelasi dengan perubahan signifikan dalam norma dan etika akademik, menekankan pentingnya pemahaman AI yang komprehensif dalam konteks akademis. Penelitian ini menyajikan perspektif baru mengenai pentingnya literasi AI dalam membekali mahasiswa dengan kemampuan untuk menghadapi berbagai tantangan dan memanfaatkan peluang di zaman teknologi yang dinamis.Jurnal Pendidikan Terapan00Jurnal Pendidikan Terapan['Haris Haris', 'Muhammad Ridha Darwis', 'Arsyanda', 'M. Rahmat Wahyudi JY', 'M. Ilham']2024-01-27T00:00:00https://www.semanticscholar.org/paper/bc79e5d41b62012d42ab417ccc5b5c82cc84c31a bb199bce992ca3900800002d4ff9cce4e1093badYapay genel zekânın (YGZ), endüstri devrimine benzer bir devrime neden olacağı kabul edilmekte ve yaşamımızı birçok yönden etkileyeceği düşünülmektedir. YGZ devrimi, sadece teknolojik gelişmeleri değil, aynı zamanda insanların bu değişime adapte olma sürecini içermektedir. Bu çalışma, YGZ’nın öğretmen rolüne yapabileceği muhtemel etkileri incelemektedir. YGZ, insan düzeyinde bilişsel yeteneklere sahip teknoloji olarak tanımlanmakta ve eğitim-öğretimde birçok kullanım alanına sahiptir. YGZ’nın öğretmen rollerine muhtemel etkilerini inceleyen yabancı literatürde sınırlı sayıda çalışma bulunmaktadır. Türkiye özelinde ise bu konuda herhangi bir çalışmaya rastlanmamıştır. Bu çalışma, küresel ölçekte yeni bir teknolojik paradigma olan YGZ’nın eğitim-öğretim alanındaki muhtemel etkilerine dair anlayışımızı artırmak adına önemli bir boşluğu doldurmaktadır. Çalışmada, nitel araştırma yöntemlerinden doküman analizi kullanılmıştır. Çalışma sonucunda, YGZ'nın kişiselleştirilmiş öğrenme ortamları oluşturma, öğrenci performansını izleme, eğitim-öğretim süreçlerini geliştirme ve eğitimde fırsat eşitliği sağlama konularında öğretmenlere destek olabileceği belirlenmiştir. YGZ kullanımında, kişisel veri gizliliği, algoritmik önyargı ve adil erişim gibi etik konuların önemi vurgulanmıştır. YGZ’nın eğitim-öğretim süreçlerinde sorumlu ve güvenli bir şekilde kullanılımının bir gereklilik olduğu üzerinde durulmuştur. Bu bağlamda, öğretmenlerin YGZ çağına etkili bir şekilde adapte olabilmeleri için nitelikli bir öğretmen eğitimi planının oluşturulması zorunluluğu ortaya çıkarılmıştır.Açıköğretim Uygulamaları ve Araştırmaları Dergisi160Açıköğretim Uygulamaları ve Araştırmaları Dergisi['Hacı Yolcu']2024-01-27T00:00:00https://www.semanticscholar.org/paper/bb199bce992ca3900800002d4ff9cce4e1093bad e29269be09ec42eb79397e7e4660a48a5acc0f78The usage of Artificial Intelligence (AI) systems has increased exponentially, thanks to their ability to reduce the amount of data to be analyzed, the user efforts and preserving a high rate of accuracy. However, introducing this new element in the loop has converted them into attacked points that can compromise the reliability of the systems. This new scenario has raised crucial challenges regarding the reliability and trustworthiness of the AI models, as well as about the uncertainties in their response decisions, becoming even more crucial when applied in critical domains such as healthcare, chemical, electrical plants, etc. To contain these issues, in this paper, we present NeuralSentinel (NS), a tool able to validate the reliability and trustworthiness of AI models. This tool combines attack and defence strategies and explainability concepts to stress an AI model and help non-expert staff increase their confidence in this new system by understanding the model decisions. NS provide a simple and easy-to-use interface for helping humans in the loop dealing with all the needed information. This tool was deployed and used in a Hackathon event to evaluate the reliability of a skin cancer image detector. During the event, experts and non-experts attacked and defended the detector, learning which factors were the most important for model misclassification and which techniques were the most efficient. The event was also used to detect NS’s limitations and gather feedback for further improvements.AI, Machine Learning and Applications270This paper presents NeuralSentinel (NS), a tool able to validate the reliability and trustworthiness of AI models, which combines attack and defence strategies and explainability concepts to stress an AI model and help non-expert staff increase their confidence in this new system by understanding the model decisions.ArXiv['Xabier Echeberria-Barrio', 'Mikel Gorricho', 'Selene Valencia', 'Francesco Zola']2024-01-27T00:00:00https://www.semanticscholar.org/paper/e29269be09ec42eb79397e7e4660a48a5acc0f78 f8b934f3f2f486188af3fdbd2c58c5d26dcd254aThe development of effective legislation on intellectual property in the context of shaping a digital society is an important issue for ensuring the stable development of innovation and protecting creators’ rights. The aim of the work is to analyse the constitutional and international principles of legislative regulation in the field of intellectual property law in the European Union to improve its legal regulation in Ukraine. The scientific basis was the application of the dialectical method as a way to delve deeper into the issues of intellectual property law, as well as the use of methods such as detailing and synthesis, abstraction, analysis and synthesis, and comparative legal method. The peculiarities of legislation on intellectual property in Ukraine and the European Union have been studied, revealing the lack of unified legal regulation of intellectual property issues. Experience confirms that institutional support is necessary for the field of intellectual property in Ukraine. To determine an effective state policy, it is necessary to develop and implement new terminology in the field of copyright protection. In the past, insufficient international cooperation has led to Ukrainian legislation not meeting modern requirements, especially in actively developing areas that require special terms and designations for the protection of intellectual work results. Based on the results of the conducted research, it has been established that the system of intellectual property protection in Ukraine is developing and requires constant improvement. The existence of violations of intellectual property rights indicates the need for the implementation of a programme to improve this system, as state protection of intellectual property is the main aspect of developing an innovative economy and increasing Ukraine’s competitiveness. In other words, due to significant gaps in legislation, manufacturers of innovative products will not rush to introduce them to the Ukrainian market, and high-tech start-ups are not protected from unfair copying of ideas. Also, based on the research results, gaps have been identified in the regulation of legal regimes for texts, music, and images generated by artificial intelligence. The research results can be useful for legislators working on improving legislation on intellectual property and for the development of strategies for managing intellectual property, which will contribute to increasing competitiveness and innovative development of businessLaw. Human. Environment130Law. Human. Environment['Artem Polishchuk']2024-01-27T00:00:00https://www.semanticscholar.org/paper/f8b934f3f2f486188af3fdbd2c58c5d26dcd254a 91b923014ae4cebbdf1608cc99bc290b7b86c52bJournal of Consumer Policy380This paper assesses the limits of the current formulation of the GDPR which does not take expressly into account the category of inferred data as a special category of data and questions whether the toolbox put in place by the GDPR is still effective in protecting data subjects from practices such as neuromarketing and eye-tracking.Journal of Consumer Policy['L. Sposini']2024-01-26T00:00:00https://www.semanticscholar.org/paper/91b923014ae4cebbdf1608cc99bc290b7b86c52b f7b53822247c58d90180e7168a6edd1058e79bd3This poster describes work on the General Data Protection Regulation (GDPR) in open-source software. Although open-source software is commonly integrated into regulated software, and thus must be engineered or adapted for compliance, we do not know how such laws impact open-source software development. We surveyed open-source developers (N=47) to understand their experiences and perceptions of GDPR. We learned many engineering challenges, primarily regarding the management of users' data and assessments of compliance. We call for improved policy-related resources, especially tools to support data privacy regulation implementation and compliance in open-source software.ICSE Companion160This poster describes work on the General Data Protection Regulation (GDPR) in open-source software and calls for improved policy-related resources, especially tools to support data privacy regulation implementation and compliance in open-source software.{'pages': '268-269'}['Lucas Franke', 'Huayu Liang', 'Aaron Brantly', 'James C Davis', 'Chris Brown']2024-01-26T00:00:00https://www.semanticscholar.org/paper/f7b53822247c58d90180e7168a6edd1058e79bd3 7bce899ebe4bd69eb477935e65f52b6a3ef9da77Self- and social regulation are widely expected to increase autonomous motivation; however, few empirical studies have examined the reciprocal influences of autonomous motivation and motivational regulation. This study examined the reciprocal path model between autonomous motivation and three modes of motivational regulation (self-, co-, and socially shared regulation) in intellectual group activities by comparing university students with working adults. The participants were 181 university students and 295 working adults who completed an online questionnaire consisting of psychological measurements. With respect to autonomous motivation and the three modes of motivational regulation, a bidirectional model of university students and working adults was established and statistically analyzed on the basis of two time points of data, one month apart (T1 and T2). The hypothesized path model had a good fit through a multi-group structural equation modeling analysis. Autonomous motivation at T1 positively predicted socially shared regulation, co-regulation, and self-regulation at T2, one month later, for both groups. However, the three modes of regulation did not positively or significantly predict autonomous motivation in either group. Considering the reciprocal influences of autonomous motivation and motivational regulation, we discuss the necessity of implementing these practices in universities and workplaces.Journal of Educational and Developmental Psychology300Journal of Educational and Developmental Psychology['Takamichi Ito', 'T. Umemoto', 'M. Nakaya']2024-01-26T00:00:00https://www.semanticscholar.org/paper/7bce899ebe4bd69eb477935e65f52b6a3ef9da77 944e34b9a7f20ae8bac41d77bbd6dd0593eb011aHumanities and Social Sciences Communications680Humanities and Social Sciences Communications['Yanchao Feng', 'Rongbing Huang', 'Yidong Chen', 'Guoshuo Sui']2024-01-26T00:00:00https://www.semanticscholar.org/paper/944e34b9a7f20ae8bac41d77bbd6dd0593eb011a 0be56da7f960008cf1c8dad9de00d4cef7209926Facial recognition technology is changing how people pass through customs at airports, check in at schools, and move anonymously in public spaces. Yet despite these transformations, its use by the government is largely unregulated. This Article informs the policy and doctrinal debates about facial recognition by presenting a public attitudes perspective. These three novel empirical studies show the nuanced views that Americans hold about government use of facial recognition. The data reveal that people are generally comfortable with the government using facial recognition to investigate serious crimes, enhance the security of controlled spaces like airports and schools, and increase the efficiency of identity verification in some contexts. But people are often not comfortable with casual governmental facial recognition use in public spaces. This pattern of strong comfort for tailored uses persisted even when, in a second study, participants were primed with negative information about the accuracy of facial recognition. Here I explore the implications of these results for both current Fourth Amendment doctrine as well as future legislative reform, promoting a balanced approach that allows tailored use of facial recognition while regulating its purposes.Science and Technology Law Review00Science and Technology Law Review['Matthew Kugler']2024-01-26T00:00:00https://www.semanticscholar.org/paper/0be56da7f960008cf1c8dad9de00d4cef7209926 4e71a293ddf57952b560b1308e8c6e54523eeb32Applied Economics460Applied Economics['Yongqiang Gao', 'Yumeng Nie', 'Miaohan Zhang']2024-01-26T00:00:00https://www.semanticscholar.org/paper/4e71a293ddf57952b560b1308e8c6e54523eeb32 2eff8cbb4b226575a2f6aa93c40804c163e463acRegulatory compliance in the pharmaceutical industry entails navigating through complex and voluminous guidelines, often requiring significant human resources. To address these challenges, our study introduces a chatbot model that utilizes generative AI and the Retrieval Augmented Generation (RAG) method. This chatbot is designed to search for guideline documents relevant to the user inquiries and provide answers based on the retrieved guidelines. Recognizing the inherent need for high reliability in this domain, we propose the Question and Answer Retrieval Augmented Generation (QA-RAG) model. In comparative experiments, the QA-RAG model demonstrated a significant improvement in accuracy, outperforming all other baselines including conventional RAG methods. This paper details QA-RAG's structure and performance evaluation, emphasizing its potential for the regulatory compliance domain in the pharmaceutical industry and beyond. We have made our work publicly available for further research and development.arXiv.org474A chatbot model that utilizes generative AI and the Retrieval Augmented Generation method to provide answers based on the retrieved guidelines for regulatory compliance in the pharmaceutical industry and beyond is introduced.ArXiv['Jaewoong Kim', 'Moohong Min']2024-01-26T00:00:00https://www.semanticscholar.org/paper/2eff8cbb4b226575a2f6aa93c40804c163e463ac b828ea2176b26d3043ee1dcca6c59e794477fbe4As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.Sustainability814It has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction and the random forest method’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.Sustainability['Marek Walacik', 'Aneta Chmielewska']2024-01-26T00:00:00https://www.semanticscholar.org/paper/b828ea2176b26d3043ee1dcca6c59e794477fbe4 85101cf32cb110ced9fe4142a408e2f9dbe1acb3The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for PBL assessment. We conducted workshops with 18 college students, encouraging them to speculate an alternative world where they could freely employ AI in PBL while needing to report this process to assess their skills and contributions. Our workshops yielded various scenarios of students' use of AI in PBL and ways of analyzing these uses grounded by students' vision of education goal transformation. We also found students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand the use of AI. Based on these findings, we discuss future research opportunities on student-AI interactions and understanding AI-enhanced learning.International Conference on Human Factors in Computing Systems1012A co-design study to explore the potential of students' AI usage data as a novel material for PBL assessment and found students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand the use of AI.{'pages': '94:1-94:19'}['Chengbo Zheng', 'Kangyu Yuan', 'Bingcan Guo', 'Reza Hadi Mogavi', 'Zhenhui Peng', 'Shuai Ma', 'Xiaojuan Ma']2024-01-26T00:00:00https://www.semanticscholar.org/paper/85101cf32cb110ced9fe4142a408e2f9dbe1acb3 9e3946c72061ff71bc762afd178fd8e3cce2f480The integration of artificial intelligence (AI) topics into K–12 school curricula is a relatively new but crucial challenge faced by education systems worldwide. Attempts to address this challenge are hindered by a serious lack of curriculum materials and tools to aid teachers in teaching AI. This article introduces the theoretical foundations and design principles for implementing co-design projects in AI education, empirically tested in 12 Finnish classrooms. The article describes a project where 4th- and 7th-graders (N = 213) explored the basics of AI by creating their own AI-driven applications. Additionally, a framework for distributed scaffolding is presented, aiming to foster children's agency, understanding, creativity, and ethical awareness in the age of AI.Informatics in Education. An International Journal1071The theoretical foundations and design principles for implementing co-design projects in AI education are introduced, empirically tested in 12 Finnish classrooms and a framework for distributed scaffolding is presented, aiming to foster children's agency, understanding, creativity, and ethical awareness in the age of AI.Informatics in Education['J. Kahila', 'Henriikka Vartiainen', 'M. Tedre', 'Eetu Arkko', 'Anssi Lin', 'Nicolas Pope', 'I. Jormanainen', 'Teemu Valtonen']2024-01-26T00:00:00https://www.semanticscholar.org/paper/9e3946c72061ff71bc762afd178fd8e3cce2f480 2aa6a1006b6a103c6e568cf672abdb71a73537a9Nature Geoscience43Nature Geoscience['Caroline Jay', 'Yurong Yu', 'Ian Crawford', 'S. Archer-Nicholls', 'Philip James', 'A. Gledson', 'Gavin Shaddick', 'Robert Haines', 'Loïc Lannelongue', 'Emily Lines', 'Scott Hosking', 'David Topping']2024-01-26T00:00:00https://www.semanticscholar.org/paper/2aa6a1006b6a103c6e568cf672abdb71a73537a9 5ef07d94991e8c491e7f23efe547ae94e6e49a49"Revealing the Academic Terrain: A Bibliometric Analysis of AI Virtual Reality Research Patterns," the author explores the complex field of academic works that investigate the relationship between artificial intelligence (AI) and virtual reality (VR). Using bibliometric techniques, we carefully investigate the development, patterns, and influential figures in this ever-changing subject. Our investigation reveals the intricate network of academic publications, clarifying trends in publishing trends, cooperation networks, and topical areas of interest. Our study offers useful insights for scholars, practitioners, and policymakers who want a thorough grasp of the present and potential future orientations of AI virtual reality research by negotiating this scholarly landscape.West Science Interdisciplinary Studies00This study offers useful insights for scholars, practitioners, and policymakers who want a thorough grasp of the present and potential future orientations of AI virtual reality research by negotiating this scholarly landscape.West Science Interdisciplinary Studies['Vanisa Siti Nurjanah', 'Yuliani Pratiwi Herlina']2024-01-26T00:00:00https://www.semanticscholar.org/paper/5ef07d94991e8c491e7f23efe547ae94e6e49a49 cfc95bc70ad327e54233b8cb503a517b82fc5c50The advancements in automated problem-solving were explored by the agents of Artificial Intelligence (AI)Generated Content (AIGC). Although existing AIGC-based AI Agent systems can solve simple tasks, it is complicated to handle complex tasks due to inconsistent work logic. In this research, the operating mechanism of AI Agents was dissected to elucidate why AIGC is suitable as the foundation for AI Agents. Based on this technology, we proposed an AI Agent framework named SOP-GPT which has integrated the concept of Standard Operating Procedures (SOP) from human workflow into AI Agents. SOP-GPT comprises three main elements: process, role, and skills. By combining the generative capability of AIGC with human SOP mechanisms, the quality of generation was enhanced to meet the expectations of human work requirements. We further explored how to assemble an AIGC engineering process for the division of labor and collaboration methods between various functional departments or units in enterprises in this framework. This research results provide a perspective for human management experience to enhance the capabilities of AI Agents and provide direction for future research.2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI)100An AI Agent framework named SOP-GPT is proposed which has integrated the concept of Standard Operating Procedures (SOP) from human workflow into AI Agents and the quality of generation was enhanced to meet the expectations of human work requirements.2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI)['Chorng-Ming Chen', 'I-Long Lin']2024-01-26T00:00:00https://www.semanticscholar.org/paper/cfc95bc70ad327e54233b8cb503a517b82fc5c50 2c261b72f328c4a12b62f965196f919217645325 Mental illness remains a global public health concern, affecting millions of individuals worldwide. However, barriers such as limited access to mental healthcare, stigma, and resource constraints hinder effective interventions and treatment. The fourth industrial age, marked by the integration of artificial intelligence technologies, offers innovative solutions to revolutionize mental health counseling and support. This review explores the challenges faced in traditional mental healthcare and proposes the integration of AI-generated health counseling as a transformative approach. AI-powered chatbots and virtual assistants present accessible, cost-effective alternatives that overcome geographical barriers and combat stigma. These chatbots employ natural language processing and machine learning to engage users in personalized and interactive conversations. Chatbots also offer continuous support, psychoeducation, and coping strategies. Virtual Reality Therapy leverages AI to create realistic simulations for exposure therapy, proving effective in treating anxiety disorders and PTSD. AI-driven voice assistants and virtual coaches enhance mental health counseling by delivering behavioral therapy and improving symptoms of depression and anxiety. They enhance accessibility, provide 24/7 support, and reduce stigma, offering personalized support tailored to individual needs. Integrating AI-generated health counseling in mental healthcare can bridge treatment gaps, improve accessibility, and strengthen the patient-provider relationship. AI serves as a valuable supplement, working collaboratively with human therapists to provide comprehensive care. Embracing AI technologies responsibly holds promise for the future of mental health counseling and offers transformative possibilities to address the global burden of mental illness. Current Psychiatry Research and Reviews00This review explores the challenges faced in traditional mental healthcare and proposes the integration of AI-generated health counseling as a transformative approach, and AI-powered chatbots and virtual assistants present accessible, cost-effective alternatives that overcome geographical barriers and combat stigma.Current Psychiatry Research and Reviews['Shankar Ganesh M', 'V. N']2024-01-26T00:00:00https://www.semanticscholar.org/paper/2c261b72f328c4a12b62f965196f919217645325 25f2b5cfe27d88a8867f27a9e14e57a3768f2409The present study aims to examine the ethical considerations about the exploration of Artificial Intelligence technology. As the field of artificial intelligence (AI) continues to grow, it is important to examine the ethical implications of its use. The Global AI Technology Acceptance Model and Innovation Resistance Theory are two theoretical frameworks that can be used to understand the impact of AI on ethical considerations. By analyzing these frameworks, we can better understand the factors contributing to adopting AI and how ethical concerns can be addressed. This paper aims to explore the intersection of these two theories and their potential implications for ethical considerations in the development and deployment of artificial intelligence. This research contributes to a deeper understanding of the ethical considerations surrounding the use of AI. It provides insights into how we can ensure that AI is used responsibly and ethically. The result of this study is of great importance given the rapid pace of technological advancement and the potential for AI to significantly impact society.Journal of Strategic Innovation and Sustainability620The intersection of the Global AI Technology Acceptance Model and Innovation Resistance Theory and their potential implications for ethical considerations in the development and deployment of artificial intelligence are explored.Journal of Strategic Innovation and Sustainability['Sean Edgington', 'Karina Kasztelnik']2024-01-26T00:00:00https://www.semanticscholar.org/paper/25f2b5cfe27d88a8867f27a9e14e57a3768f2409 ec6fdfce5482583b97ff72a708703f01f3dfdf32This study provides a comprehensive review of the development of service-oriented manufacturing and explores the prospects of AI technology in this field. It includes an in-depth analysis of the history, trends, and typical cases of service-oriented manufacturing both domestically and internationally. It includes an in-depth analysis of the history, trends, and typical cases of service-oriented manufacturing both domestically and internationally. The paper highlights the current status and challenges faced by China's service-oriented manufacturing, distilling the main The paper highlights the current status and challenges faced by China's service-oriented manufacturing, distilling the main development models such as intelligent services, third-party outsourcing, and full lifecycle services. By examining specific enterprise cases, the paper reveals innovative transformation paths in China. By examining specific enterprise cases, the paper reveals innovative transformation paths in the practical implementation of service-oriented manufacturing. By examining specific enterprise cases, the paper reveals innovative transformation paths in the practical implementation of service-oriented manufacturing. The aim is to optimize service processes through AI technology, enhance production efficiency and service quality, and provide theoretical support and practical guidance for the deep integration of manufacturing and service industries. The aim is to optimize service processes through AI technology, enhance production efficiency and service quality, and provide theoretical support and practical guidance for the deep integration of manufacturing and service industries.Transactions on Economics, Business and Management Research190This study provides a comprehensive review of the development of service-oriented manufacturing and explores the prospects of AI technology in this field and reveals innovative transformation paths in the practical implementation of service-oriented manufacturing in China.Transactions on Economics, Business and Management Research['Yufan Li', 'Canfang Liu', 'Jiubing Zhang', 'Yuheng Ren']2024-01-26T00:00:00https://www.semanticscholar.org/paper/ec6fdfce5482583b97ff72a708703f01f3dfdf32 ba2dc12e40d5c0fa4779157c0c09931e8ea11cf0The emergence of artificial intelligence in healthcare is probably leading to another two-speed world. On one hand, widely accessible AI applications such as language models are becoming ubiquitous, while on the other, resource-intensive technologies like robotic surgery and personalized medicine will be reserved for a privileged few. This development signifies a growing disparity in access to AI advancements. The paper also discusses the inevitability of widespread automated medical consultation, and the need for a quality assurance system to oversee the burgeoning use of AI in healthcare. Qeios10The paper discusses the inevitability of widespread automated medical consultation, the need for a quality assurance system to oversee the burgeoning use of AI in healthcare, and the growing disparity in access to AI advancements.Qeios['Emmanuel Lagarde']2024-01-26T00:00:00https://www.semanticscholar.org/paper/ba2dc12e40d5c0fa4779157c0c09931e8ea11cf0 e8bf21b9e0921353dfffadc6e26a1d8947e8d89cThis article presents a series of reflections on the so-called ‘singularity’ of Artificial Intelligence (AI). It begins with a meditation on Freud’s notion of the ‘Uncanny’ to help us understand the experience of interacting with the new AI. It then critically engages the formal notion of ‘the singularity’ by returning to the classical critique of artificial computing in Hubert Dreyfus, and suggests that ‘singularity’ should be understood rather in its effects within the context of a socio-cultural phenomenon which progressively disembodies the human being. Finally, in conversation with Dominique Janicaud and Emmanuel Falque, it begins to outline the contours of an alternative ‘philosophical-theological intelligence’: characterized by ambivalence, the ‘potentiality’ of rationality, and the finitude of our human condition that Christ came to fully embody and share with us, not so that we might escape it, but ‘undergo’ it in common together.Stellenbosch theological journal00This article presents a series of reflections on the so-called ‘singularity’ of Artificial Intelligence, and critically engages the formal notion of ‘the singularity’ by returning to the classical critique of artificial computing in Hubert Dreyfus.Stellenbosch Theological Journal['Calvin D. Ullrich']2024-01-26T00:00:00https://www.semanticscholar.org/paper/e8bf21b9e0921353dfffadc6e26a1d8947e8d89c 362322d120abf8f71010c201deeb0270a998bddbThe primary objective of this article is to investigate and forecast nurses’ attitudes toward using AI nurses for outpatients with chronic diseases. AI technology is used in hospitals in a disease-centric manner. However, it is desired by healthcare regulators to be used in an individual-centric and holistic manner. The research model was developed based on the Unified Theory of Accepting and Using Technology. In determining the causes and consequences of the attitudes, actions, ideas, and beliefs of the nurses, the screening technique of causal comparison was used. Research data was collected from registered nurses who work in research hospitals and use intelligent health technologies for inpatients. Based on 494 responses, this study conducted a dual-phase assessment using Partial Least Squares Structural Equation Modeling as well as the creation of an AI method known as deep learning (artificial neural network). According to the results, nurses are convinced that AI is a suitable tool for their nursing tasks and increases their efficiency and productivity. It has been determined that nurses have intentions to use AI nurses for outpatients with chronic diseases. However, nurses have concerns about the reliability of ambulatory patient data. The policies and strategies of regulators will affect the acceptance of AI technology, not only for nurses but for all healthcare professionals and patients.Environment and Social Psychology770It has been determined that nurses have intentions to use AI nurses for outpatients with chronic diseases, however, nurses have concerns about the reliability of ambulatory patient data.Environment and Social Psychology['A. Uymaz', 'Pelin Uymaz', 'Yakup Akgül']2024-01-26T00:00:00https://www.semanticscholar.org/paper/362322d120abf8f71010c201deeb0270a998bddb 9f0708db2d48be13aa5879b1d00a8b21832c8954Researchers, government bodies, and organizations have been repeatedly calling for a shift in the responsible AI community from general principles to tangible and operationalizable practices in mitigating the potential sociotechnical harms of AI. Frameworks like the NIST AI RMF embody an emerging consensus on recommended practices in operationalizing sociotechnical harm mitigation. However, private sector organizations currently lag far behind this emerging consensus. Implementation is sporadic and selective at best. At worst, it is ineffective and can risk serving as a misleading veneer of trustworthy processes, providing an appearance of legitimacy to substantively harmful practices. In this paper, we provide a foundation for a framework for evaluating where organizations sit relative to the emerging consensus on sociotechnical harm mitigation best practices: a flexible maturity model based on the NIST AI RMF.arXiv.org450This paper provides a foundation for a framework for evaluating where organizations sit relative to the emerging consensus on sociotechnical harm mitigation best practices: a flexible maturity model based on the NIST AI RMF.ArXiv['Ravit Dotan', 'Borhane Blili-Hamelin', 'Ravi Madhavan', 'Jeanna Matthews', 'Joshua Scarpino']2024-01-26T00:00:00https://www.semanticscholar.org/paper/9f0708db2d48be13aa5879b1d00a8b21832c8954 8646d3e8cff48d747382f9cb94e8308c13977e33Artificial-intelligence tools in research like ChatGPT are playing an increasingly transformative role in revolutionizing scientific publishing and re-shaping its economic background. They can help academics to tackle such issues as limited space in academic journals, accessibility of knowledge, delayed dissemination, or the exponential growth of academic output. Moreover, AI tools could potentially change scientific communication and academic publishing market as we know them. They can help to promote Open Access (OA) in the form of preprints, dethrone the entrenched journals and publishers, as well as introduce novel approaches to the assessment of research output. It is also imperative that they should do just that, once and for all.arXiv.org00Artificial-intelligence tools in research like ChatGPT are playing an increasingly transformative role in revolutionizing scientific publishing and re-shaping its economic background, and it is imperative that they should do just that, once and for all.ArXiv['W. Strielkowski']2024-01-26T00:00:00https://www.semanticscholar.org/paper/8646d3e8cff48d747382f9cb94e8308c13977e33 29821668b9f3368437d039fa27cff220a65c793eGenerative Artificial Intelligence (GAI) plays a prominent role in assisting designers in creating Journey Maps (JMs) based on design thinking. Today, JMs is used to analyze user behavior and emotions in various patterns and establish a structured framework with reduced subjectivity through the integration of GAI. As user-friendly tools continue to gain traction and are investigated for the significance of journey map contents. However, it is important to develop future applications in education from students' perspective. Using the Analytic Hierarchy Process (AHP), we assessed the importance of different elements of JMs. As a result, student preferences based on their educational experiences were determined. Modules were assessed for advantages and disadvantages or the opportunities and threats associated with the journey user’s behaviors. Despite limitations such as sample size and regional focus, the study result underscored the potential of AI in design education to enhance the creation and evaluation of JMs. Consequently, it advocated for the integration of AI tools into design curricula to enhance design quality and efficiency.2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI)150The study result underscored the potential of AI in design education to enhance the creation and evaluation of JMs and advocated for the integration of AI tools into design curricula to enhance design quality and efficiency.2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI)['Allan Chung', 'Yu-Chen He', 'Ling-Fang Lin', 'Yo-Wen Liang']2024-01-26T00:00:00https://www.semanticscholar.org/paper/29821668b9f3368437d039fa27cff220a65c793e 4fc5d4cc298027a3e15e31699d37f72a320de6f6Amidst global aging in Japan, there is a significant shift from traditional facility-based care to home-based care due to shortages of care facilities and personnel. While home living is often preferred, this places a substantial burden on family caregivers in providing daily care and supervision for the elderly. In prior studies, methods such as elderly “mind” sensing through voice-based dialogue systems and quality assessment techniques were proposed for the elderly’s in-home activities using skeletal sensing technology. However, the recognition of changes in the elderly such as facial expressions, body posture, and behaviors is essential for monitoring elderly individuals at home and has not yet been fully developed. Hence, we proposed non-verbal features in the context of human-centered recognition. Multiple pre-trained models were integrated with image data in edge environments to extract human-centered features and characterize them as context. We integrated locally executable image recognition technology based on multiple pre-trained models for human-centered context recognition from live images. Following this approach, an in-home monitoring system can be developed as a standard using a computer and USB camera.2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI)140A locally executable image recognition technology based on multiple pre-trained models for human-centered context recognition from live images is integrated and an in-home monitoring system can be developed as a standard using a computer and USB camera.2024 IEEE 7th Eurasian Conference on Educational Innovation (ECEI)['Sinan Chen', 'Masahide Nakamura', 'Kiyoshi Yasuda']2024-01-26T00:00:00https://www.semanticscholar.org/paper/4fc5d4cc298027a3e15e31699d37f72a320de6f6 bce36c690ac8d417ef0393f268cc60fd8a174c8aThe relationships between entities in a document are extracted according to natural language processing methods. Deep neural network is used to recognize the required multi-label text. According to the general specification, the system is optimized, and the design and implementation of the system are obtained. This project explores four major NLP modes such as ALBERT, RNN Search, BERT-CRF, Text ING based on the high-performance hardware of the Centeno platform. According to the element relation, tree structure and network structure, a general MNet construction method is proposed. The extracted correlation information is used to determine whether the matching conditions of each security requirement template are established, and then the final set of security requirement templates is screened. The extracted security requirements are modeled and instantiated in this way. Simulation results show that the model can deal with semantic dependency and human-computer interaction in complex systems. By analyzing the semantics of the operation interface in SCADA system, it is transformed into a general MNet construction, which lays a foundation for realizing the semantic analysis of users.2024 IEEE 4th International Conference on Power, Electronics and Computer Applications (ICPECA)120This project explores four major NLP modes such as ALBERT, RNN Search, BERT-CRF, Text ING based on the high-performance hardware of the Centeno platform and transforms the semantics of the operation interface in SCADA system into a general MNet construction which lays a foundation for realizing the semantic analysis of users.2024 IEEE 4th International Conference on Power, Electronics and Computer Applications (ICPECA)['Miaofang Shen']2024-01-26T00:00:00https://www.semanticscholar.org/paper/bce36c690ac8d417ef0393f268cc60fd8a174c8a 4a962603631af072cba88ec08b616b1534926756We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems. Editor’s summary The Clean Water Act is a defining piece of environmental legislation in the US, but the waters that it protects from pollution have never been clearly defined. Greenhill et al. developed a machine learning model that uses geospatial data to predict which waters are covered by the Clean Water Act and trained and tested the model with jurisdictional determinations from the US Army Corps of Engineers. This work provides an estimate of the extent of protected waterways, as well as an understanding of the effects of Supreme Court and White House rules that have reinterpreted or changed the regulation. For a subset of sites with high predictive accuracy, their model can also act as decision support tool to expedite permitting. —Bianca Lopez A machine learning model improves estimates of the extent of protection by the US Clean Water Act and the effects of deregulation.Science303Science['Simon Greenhill', 'Hannah Druckenmiller', 'Sherrie Wang†', 'David A. Keiser', 'Manuela Girotto', 'Jason K. Moore', 'Nobuhiro Yamaguchi', 'Alberto Todeschini', 'Joseph S. Shapiro']2024-01-26T00:00:00https://www.semanticscholar.org/paper/4a962603631af072cba88ec08b616b1534926756 05f40289c993345e4f639ed97d64b31a00a901c9The first publication on the use of artificial intelligence (AI) in pediatrics dates back to 1984. Since then, research on AI in pediatrics has become much more popular, and the number of publications has largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into the use of AI in pediatrics has arisen. To fill this gap, a novel methodology, synthetic knowledge synthesis (SKS), was applied. Using SKS, we identified the most prolific countries, institutions, source titles, funding agencies, and research themes and the most frequently used AI algorithms and their applications in pediatrics. The corpus was extracted from the Scopus (Elsevier, The Netherlands) bibliographic database and analyzed using VOSViewer, version 1.6.20. Done An exponential growth in the literature was observed in the last decade. The United States, China, and Canada were the most productive countries. Deep learning was the most used machine learning algorithm and classification, and natural language processing was the most popular AI approach. Pneumonia, epilepsy, and asthma were the most targeted pediatric diagnoses, and prediction and clinical decision making were the most frequent applications.Electronics673Pneumonia, epilepsy, and asthma were the most targeted pediatric diagnoses, and prediction and clinical decision making were the most frequent applications in pediatrics.Electronics['J. Završnik', 'Peter Kokol', 'Bojan Žlahtič', 'Helena Blažun Vošner']2024-01-26T00:00:00https://www.semanticscholar.org/paper/05f40289c993345e4f639ed97d64b31a00a901c9 2126a71834508288b9b258cea71fcf9763bf8f3dArtificial Intelligence (AI) is one of the technologies in today’s era that is very useful in its application. Artificial Intelligence (AI) is artificial intelligence which is a model of human intelligence which is a model of human intelligence that is applied in a machine for the manufacture of intelligent machines.In Fact,at this time we have to use technology that supports learning is really needed. The word of education requires innovation and creativity in order to realize learning that remains effective.The purpose of this article is to answer the question 1)is Artificial Intelligence (AI) important for learning?2) how is the use of artificial intelligence for learning?the article uses a qualitative method with data collection techniques with library research.Nuansa Informatika71The article uses a qualitative method with data collection techniques with library research to answer the question 1) is Artificial Intelligence (AI) important for learning? and 2) how is the use of artificial intelligence for learning?NUANSA INFORMATIKA['Niza Tadzkiratun Nafisah', 'Fitriansyah Maria', 'Muhammad Ridho Amanatullah', 'Tata Sutabri']2024-01-26T00:00:00https://www.semanticscholar.org/paper/2126a71834508288b9b258cea71fcf9763bf8f3d 5c9bc0e16ef645656aca926bb11c798874ea7a23Parkinson's disease (PD) is a neurological condition that generally strikes people in their average age of onset for PD a neurological disorder, is 55 and up. A wide variety of motor and non-motor symptoms can be observed in patients with PD. The medical community has made great strides in recent years, but Parkinson's disease still has no treatment or cure. Therefore, exploring possible ways for early PD identification is an intriguing scientific endeavor. Full symptoms may not appear for years due to the progressive nature of PD. Thus, early diagnosis is vital to enhance the patient's quality of life. Symptoms will usually worsen with time, so keep that in mind. Several neurodegenerative disorders share very similar symptoms, making early identification crucial for disease prediction. Many people are starting to pay attention to using Artificial Intelligence (AI) methods in medical diagnostics because they can process massive volumes of data and make reliable statistical predictions. This paper covers all the bases when it comes to artificial intelligence (AI) approaches to PD diagnosis, including the many deep and machine learning-based methods that have been deployed and how they have opened new avenues for research. Furthermore, the study explores the current situation and future possibilities of data-driven AI approaches to Parkinson's disease diagnosis. This study is an excellent resource as a review article for researchers interested in creating PD prediction models employing different AI-based modalities.International Journal of Mathematics, Statistics, and Computer Science401This study explores the current situation and future possibilities of data-driven AI approaches to Parkinson's disease diagnosis, including the many deep and machine learning-based methods that have been deployed and how they have opened new avenues for research.International Journal of Mathematics, Statistics, and Computer Science['Aiesha Mahmoud Ibrahim', 'Mazin Abed Mohammed']2024-01-26T00:00:00https://www.semanticscholar.org/paper/5c9bc0e16ef645656aca926bb11c798874ea7a23 4f4d308d8aeb03672e3ad42257c5fa62d58e416cBackground The HIV epidemic continues to grow fastest among men who have sex with men (MSM) in Malaysia in the presence of stigma and discrimination. Engaging MSM on the internet using chatbots supported through artificial intelligence (AI) can potentially help HIV prevention efforts. We previously identified the benefits, limitations, and preferred features of HIV prevention AI chatbots and developed an AI chatbot prototype that is now tested for feasibility and acceptability. Objective This study aims to test the feasibility and acceptability of an AI chatbot in promoting the uptake of HIV testing and pre-exposure prophylaxis (PrEP) in MSM. Methods We conducted beta testing with 14 MSM from February to April 2022 using Zoom (Zoom Video Communications, Inc). Beta testing involved 3 steps: a 45-minute human-chatbot interaction using the think-aloud method, a 35-minute semistructured interview, and a 10-minute web-based survey. The first 2 steps were recorded, transcribed verbatim, and analyzed using the Unified Theory of Acceptance and Use of Technology. Emerging themes from the qualitative data were mapped on the 4 domains of the Unified Theory of Acceptance and Use of Technology: performance expectancy, effort expectancy, facilitating conditions, and social influence. Results Most participants (13/14, 93%) perceived the chatbot to be useful because it provided comprehensive information on HIV testing and PrEP (performance expectancy). All participants indicated that the chatbot was easy to use because of its simple, straightforward design and quick, friendly responses (effort expectancy). Moreover, 93% (13/14) of the participants rated the overall chatbot quality as high, and all participants perceived the chatbot as a helpful tool and would refer it to others. Approximately 79% (11/14) of the participants agreed they would continue using the chatbot. They suggested adding a local language (ie, Bahasa Malaysia) to customize the chatbot to the Malaysian context (facilitating condition) and suggested that the chatbot should also incorporate more information on mental health, HIV risk assessment, and consequences of HIV. In terms of social influence, all participants perceived the chatbot as helpful in avoiding stigma-inducing interactions and thus could increase the frequency of HIV testing and PrEP uptake among MSM. Conclusions The current AI chatbot is feasible and acceptable to promote the uptake of HIV testing and PrEP. To ensure the successful implementation and dissemination of AI chatbots in Malaysia, they should be customized to communicate in Bahasa Malaysia and upgraded to provide other HIV-related information to improve usability, such as mental health support, risk assessment for sexually transmitted infections, AIDS treatment, and the consequences of contracting HIV.JMIR Human Factors310To ensure the successful implementation and dissemination of AI chatbots in Malaysia, they should be customized to communicate in Bahasa Malaysia and upgraded to provide other HIV-related information to improve usability, such as mental health support, risk assessment for sexually transmitted infections, AIDS treatment, and the consequences of contracting HIV.JMIR Human Factors['M. H. Cheah', 'Yan Nee Gan', 'Frederick L Altice', 'Jeffrey A Wickersham', 'Roman Shrestha', 'Nur Afiqah Mohd Salleh', 'K. Ng', 'I. Azwa', 'Vimala Balakrishnan', 'A. Kamarulzaman', 'Zhao Ni']2024-01-26T00:00:00https://www.semanticscholar.org/paper/4f4d308d8aeb03672e3ad42257c5fa62d58e416c 1802c1d2da0e3340d21e54afad601297b4c4f407MOOCs (massive open online courses) are popular distance courses for which anyone can sign up online with no limits on the number of participants. Moreover, artificial intelligence is a combination of algorithms for the development of human and rational capabilities by machines. This article presents a quantitative study with a sample of 734 MOOCs on artificial intelligence from three important platforms. Through exploratory and factor analyses, and with the support of a category system, it is concluded that, there are similarities in terms of access to content, ease of navigation, design, toolbars, consistency, visible hypertexts, browsing support and links, help in content searching, and course development with regard to the technical dimension. Regarding the pedagogical dimension, xMOOCs represent the most extensive international trend, and unidirectional resources predominate. In relation to the content dimension, MOOCs that include content on the emerging and current uses of artificial intelligence in learning and training are remarkable, including three main trends in MOOCs on artificial intelligence: machine learning and education, ethics of AI, and human learning and inclusivity.Applied Sciences330There are similarities in terms of access to content, ease of navigation, design, toolbars, consistency, visible hypertexts, browsing support and links, help in content searching, and course development with regard to the technical dimension of MOOCs on artificial intelligence.Applied Sciences['Emilio José Delgado Algarra', 'César Bernal Bravo', 'María Belén Morales Cevallos', 'Eloy López Meneses']2024-01-26T00:00:00https://www.semanticscholar.org/paper/1802c1d2da0e3340d21e54afad601297b4c4f407 4d01af6e6ddb6dbb74d355a397ed4746d6c233b0In recent years, the field of artificial intelligence has witnessed rapid development and continuous technological advancements. It has found widespread application in various domains, exerting profound influences on all aspects of human social life. This article focuses on evaluating the impact of artificial intelligence on college students' learning. Based on a survey of 4605 participants, the collected data was transformed into numerical values and underwent preliminary data processing. An indicator evaluation system was established, encompassing priority, scientificity, and feasibility, in order to construct a comprehensive evaluation framework. By conducting both objective and subjective analyses of the survey questions, the obtained weight values were subjected to consistency tests, which confirmed their reliability. Through the application of the Analytic Hierarchy Process (AHP), the final weights were determined, and the top eight indicators were selected for evaluation. An AHP-TOPSIS combined evaluation model was developed, which concludes that artificial intelligence has significantly influenced college students' learning and yielded positive effects. The novelty of this article lies in the utilization of the AHP-TOPSIS combined evaluation model, which incorporates the advantages of both models and avoids the limitations associated with a single model, such as biased perspectives and low reliability.Highlights in Science Engineering and Technology90The novelty of this article lies in the utilization of the AHP-TOPSIS combined evaluation model, which incorporates the advantages of both models and avoids the limitations associated with a single model, such as biased perspectives and low reliability.Highlights in Science, Engineering and Technology['Wei Wang', 'Jinlong Peng', 'Chenhao Li', 'Feng Guo', 'Jun Jiao', 'Qianyi Deng']2024-01-26T00:00:00https://www.semanticscholar.org/paper/4d01af6e6ddb6dbb74d355a397ed4746d6c233b0 c7d9c9a1244a1931297a0ef5fea0b6768a5248feGlaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI’s potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI’s vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.Bioengineering1480Artificial intelligence’s vast capabilities to transform glaucoma care are highlighted while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside are summarized.Bioengineering['Yan Zhu', 'Rebecca J. Salowe', 'Caven Chow', 'Shuo Li', 'O. Bastani', 'Joan M. O’Brien']2024-01-26T00:00:00https://www.semanticscholar.org/paper/c7d9c9a1244a1931297a0ef5fea0b6768a5248fe 4fed6f62157c2a935968173337ef388b7fc9b791Relevance. The growing digitalization of society, leads to the emergence of new forms of socially dangerous behavior (socially dangerous activity). In many ways, such criminal and legal risks are caused by the involvement of the phenomenon of artificial intelligence in various spheres of human activity. During the operation of artificial intelligence, it is also possible to exert harmful effects on artificial intelligence itself and (or) its carrier, which, from the standpoint of the current criminal law, may not always receive an unambiguous qualification.The purpose of the study is to identify criminal law risks existing in the context of artificial intelligence and formulate scientifically based conclusions regarding the prospects for the development of domestic criminal law and legislation.Objectives: to identify the key criminal law risks associated with the exploitation of artificial intelligence; to check whether artificial intelligence has the properties necessary for criminal personality; to identify possible options for criminal law assessment of harm caused by the exploitation of artificial intelligence; to establish the sufficiency of criminal law resources to protect artificial intelligence itself from socially dangerous behavior.Methodology. The methodological basis of the research was the universal dialectical method of cognition of phenomena and processes of the surrounding reality. During the development of theoretical and applied provisions of the work, a set of general scientific and private scientific research methods (formal logical, predictive, formal legal, etc.) was also used.Results. The prospects for the development of criminal law and criminal legislation of Russia in the context of the problem of artificial intelligence directly depend on the level of scientific and technological achievements in its programming and operation.Conclusion. The modern potential of artificial intelligence precludes raising the question of its criminal legal personality mainly due to its lack of ability to mentally perceive its own socially dangerous activity, which is a prerequisite for criminal liability. The developer of the corresponding program or the operator of the artificial intelligence carrier device must be responsible for causing harm to interests protected by criminal law in connection with the exploitation of artificial intelligence. Proceedings of the Southwest State University. Series: History and Law50The modern potential of artificial intelligence precludes raising the question of its criminal legal personality mainly due to its lack of ability to mentally perceive its own socially dangerous activity, which is a prerequisite for criminal liability.Proceedings of Southwest State University. Series: History and Law['N. Lopashenko', 'E. V. Kobzeva', 'Z. D. Rozhavskiy']2024-01-26T00:00:00https://www.semanticscholar.org/paper/4fed6f62157c2a935968173337ef388b7fc9b791 4a7129948382ecdd294f07c77c0ea83dbfdf7859npj Digit. Medicine75NPJ Digital Medicine['Aubrey A Shick', 'Christina M Webber', 'Nooshin Kiarashi', 'Jessica Weinberg', 'Aneesh Deoras', 'Nicholas Petrick', 'A. Saha', 'Matthew C. Diamond']2024-01-26T00:00:00https://www.semanticscholar.org/paper/4a7129948382ecdd294f07c77c0ea83dbfdf7859 ba7d68afe48de2d5918042975b9d386bf3de979fEnvironmental science and pollution research international990A water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA is proposed, which has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.Environmental science and pollution research international['Jing Chen', 'Haiyang Li', 'Manirankunda Felix', 'Yudi Chen', 'Keqiang Zheng']2024-01-26T00:00:00https://www.semanticscholar.org/paper/ba7d68afe48de2d5918042975b9d386bf3de979f 0cc00a945506b6fb41aac71965617fd1ae41d611Frontiers in Plant Science10Frontiers in Plant Science['Yaqoob Majeed', 'Longsheng Fu', 'Long He']2024-01-26T00:00:00https://www.semanticscholar.org/paper/0cc00a945506b6fb41aac71965617fd1ae41d611 4b75dd49d643548e07b1d0acd3c7a0da9bbaa7bcEnvironmental regulations are important organizational strategic drivers. Environmental regulatory mechanisms include “carrots” or “sticks”—they can be incentive or coercion‐based. This study empirically investigates stock market reactions to various environmental regulatory mechanisms. Using a sample of 334 environmental regulatory events reported by 212 listed Chinese firms between 2010 and 2020, we find that monetary reward has a greater positive market reaction towards governmental reward than non‐monetary reward. We also find that governmental operational disruption penalties have a greater negative market reaction than operational non‐disruption penalties. Governmental levels of the environmental regulation implementation subject do not play a moderating role in the relationship between governmental regulations and firm market value. These findings provide various policy and organizational insights as businesses seek to meet legitimacy gains in response environmental regulatory mechanisms.Business Strategy and the Environment700Business Strategy and the Environment['Lihua Sun', 'Chunguang Bai', 'Joseph Sarkis']2024-01-25T00:00:00https://www.semanticscholar.org/paper/4b75dd49d643548e07b1d0acd3c7a0da9bbaa7bc 2227a65357c3f61b30781b1185aef8343729117cObjective: Analyze Ghana's public health law comprehensiveness, responsiveness, uniformity and accountability regarding complex modern risks at the intersection of infectious disease, medical AI and vaccine equity. Method: Doctrinal legal review (CRuPAC) of the 2012 Public Health Act combined with sociolegal analysis of judicial cases, academic literature and comparative governance on emerging technologies. Results: Gaps exist regarding infectious disease forecasting, transparency duties, decentralized flexibility and technology regulation that constrained pandemic response. Conclusions: Ghana's outdated health law requires modernization to address twenty-first century convergence of biotechnology, data usage and human rights. Recommendations: Parliament should amend legislation to embed oversight, participatory mechanisms and binding duties around accountable and rights-respecting development and deployment of AI tools supporting vaccine delivery. Contributions: Provides novel interdisciplinary framework assessing legal readiness for scientific healthcare priorities. Significance: Analyzes institutional deficiencies and reform options for life-saving technology integrations.International Journal For Multidisciplinary Research110Ghana's outdated health law requires modernization to address twenty-first century convergence of biotechnology, data usage and human rights.International Journal For Multidisciplinary Research['Alfred Addy', 'Veronica Adams', 'Maria Acka', 'Cynthia Amadzor', 'Beatrice Ekua Amoh', 'George Benneh Mensah']2024-01-25T00:00:00https://www.semanticscholar.org/paper/2227a65357c3f61b30781b1185aef8343729117c 6d075e2bcf65a4f52f1900a8b97ec03eead95833Relevance. The relevance of the study on social entrepreneurship in Kazakhstan is conditioned upon the fact that it is an essential source of social, economic, and environmental wealth, and is also defined as one of the key components in the policy of developed countries. Purpose. The purpose of this paper is to cover the integral mechanism of functioning of the segment under study and to investigate its legal regulation. Methodology. In this article were used methodological approaches, such as the theoretical methodological approach, the method of legal hermeneutics, the statistical method, the method of comparative legal analysis, the method of synthesis, etc. Results. The results of the study showed that currently social entrepreneurship in Kazakhstan fully provides the state with the completeness of the performance of the functions assigned to it, but to increase this indicator, the practices of advanced countries, especially the USA, Great Britain, Australia, South Korea, and Malaysia were studied, which will contribute to the allocation of recommendations for raising the role of effective social entrepreneurship in the region under study. Conclusions. This study identifies and covers the theoretical aspect of the implementation of social entrepreneurship, analyses the legal norms regulating this activity in Kazakhstan, namely the Entrepreneurial Code of the Republic of Kazakhstan, the Resolution of the Republic of Kazakhstan "Rules for maintaining the register of social entrepreneurship entities" (2021) and other regulations; the statistics of social entrepreneurship in the cities of republican significance of Nursultan were analysed in detail Almaty, Shymkent, and other regions; based on this, the advantages of social entrepreneurship in Kazakhstan and the problems that may stand in the way of the proper functioning of this sector were investigated.Scientific Herald of Uzhhorod University Series Physics250Scientific Herald of Uzhhorod University Series Physics['Assel Ualiyeva', 'Nazarbek Zhempiissov', 'Tolkyn Zhabelova', 'Kadir Nurgalym', 'Zhanna Shayakhmetova']2024-01-25T00:00:00https://www.semanticscholar.org/paper/6d075e2bcf65a4f52f1900a8b97ec03eead95833 a2cb79dc8961d9327d5f4a9a11438a14c0ea6edeThe article is devoted to the analysis of the powers of different levels of government on the legal regulation of municipal information systems. The author notes that the current provisions of the federal laws ‘On Information, Information Technologies and Information Protection’ and ‘On General Principles of Organisation of Local Self-Government in the Russian Federation’ question the legality of its implementation at the regional and municipal levels. To solve this problem, it seems necessary, firstly, to fix in Article 13 of the Federal Law ‘On Information, Information Technologies and Information Protection’ the possibility to establish the peculiarities of legal regulation of municipal information systems not only by legislative, but also bylaws on local self-government; secondly, to specify in the Federal Law ‘On General Principles of Organisation of Local Self-Government in the Russian Federation’ the possibility of the subjects of the Russian Federation to carry out lawmaking activities. At the same time, it is important to enshrine in regional laws on municipal information systems the conceptual requirements that ensure the possibility of integration of information systems of different municipalities among themselves, as well as with regional state information systems.Constitutional and municipal law00It seems necessary to fix in Article 13 of the Federal Law ‘On Information, Information Technologies and Information Protection’ the possibility to establish the peculiarities of legal regulation of municipal information systems not only by legislative, but also bylaws on local self-government.Constitutional and municipal law['S. Channov']2024-01-25T00:00:00https://www.semanticscholar.org/paper/a2cb79dc8961d9327d5f4a9a11438a14c0ea6ede 970114aed906d82694b04c797d2603ca9dcf4e23In the rapidly evolving landscape of entrepreneurship, the integration of Artificial Intelligence (AI) has emerged as a transformative force, reshaping traditional business paradigms and offering unprecedented opportunities for success. This paper provides a comprehensive and critical review of AI-driven strategies employed by entrepreneurs to enhance their ventures. The review encompasses a thorough analysis of key AI applications, their impact on various aspects of entrepreneurship, and the potential benefits and challenges associated with their implementation. The first section explores the role of AI in market analysis, highlighting how advanced data analytics and predictive modelling contribute to informed decision-making and market forecasting. The discussion then extends to AI-driven innovations in product development, emphasizing the acceleration of ideation, prototyping, and customization through machine learning algorithms. Next, the paper scrutinizes the influence of AI on customer engagement and relationship management. It delves into the personalized customer experiences facilitated by chatbots, recommendation systems, and sentiment analysis, while also addressing ethical considerations surrounding data privacy and algorithmic biases. Entrepreneurial operations and efficiency gains are examined in the subsequent section, emphasizing AI's impact on supply chain management, logistics, and resource optimization. The review underscores the potential for increased productivity and cost-effectiveness through the implementation of AI-powered automation and smart systems. Despite the myriad advantages, the paper critically examines challenges such as ethical concerns, job displacement, and the digital divide. It emphasizes the need for a balanced approach that addresses the societal impact of AI adoption while fostering inclusive entrepreneurial ecosystems. In conclusion, this critical review not only provides a comprehensive overview of the current landscape of AI-driven strategies in entrepreneurship but also offers insights into the potential future developments and challenges. Entrepreneurs, policymakers, and researchers can leverage this analysis to navigate the evolving intersection of AI and entrepreneurship, fostering a sustainable and ethically sound environment for entrepreneurial success in the digital era. Keywords: Artificial Intelligence (AI), Entrepreneurship, Strategic Implementation, Innovation, Market Analysis, Predictive Modelling.International Journal of Management &amp; Entrepreneurship Research012A comprehensive and critical review of AI-driven strategies employed by entrepreneurs to enhance their ventures, highlighting the potential for increased productivity and cost-effectiveness through the implementation of AI-powered automation and smart systems.International Journal of Management &amp; Entrepreneurship Research['Favour Oluwadamilare Usman', 'Nsisong Louis Eyo-Udo', 'Emmanuel Augustine Etukudoh', 'Beryl Odonkor', 'Chidera Victoria Ibeh', 'Ayodeji Adegbola']2024-01-25T00:00:00https://www.semanticscholar.org/paper/970114aed906d82694b04c797d2603ca9dcf4e23 c7aa12066343e7aa77522e1940ea18238ecc83e5One of the most concrete measures towards meaningful AI accountability is to consequentially assess and report the systems’ performance and impact. However, the practical nature of the "AI audit" ecosystem is muddled and imprecise, making it difficult to work through various concepts, practices, and involved (as well as ignored) stakeholders. First, we taxonomize current AI audit practices as completed by regulators, law firms, civil society, journalism, academia, and consulting agencies. Next, we assess the impact of audits done by stakeholders within each domain. We find that only a subset of AI audit studies translate to desired accountability outcomes. We thus assess and isolate practices necessary for effective AI audit results, articulating the observed connections between AI audit design, methodology and institutional context on its effectiveness as a meaningful mechanism for accountability.2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)2007It is found that only a subset of AI audit studies translate to desired accountability outcomes, and the observed connections between AI audit design, methodology and institutional context on its effectiveness as a meaningful mechanism for accountability are articulated.2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)['Abeba Birhane', 'Ryan Steed', 'Victor Ojewale', 'Briana Vecchione', 'Inioluwa Deborah Raji']2024-01-25T00:00:00https://www.semanticscholar.org/paper/c7aa12066343e7aa77522e1940ea18238ecc83e5 2b04552c4242a1f99636d0cf426cf2dc1ccdfd27 Purpose This paper aims to measure the trade price impact of a recent regulatory disclosure intervention in municipal securities secondary markets, which required broker-dealers to disclose securities trading information on a near-real-time and continuing basis. Design/methodology/approach The author analyzes trade price outcomes in the preintervention and postintervention regimes using a suite of time series estimations that give heteroskedasticity-robust standard errors (Prais–Winsten and Cochrain–Orcutt), accommodate higher-order lag structure in the error term (autoregressive integrated moving average) and account for volatility clustering in the time series (generalized autoregressive conditional heteroskedasticity). Findings Results show that regulatory disclosure intervention significantly improved trade price efficiency in municipal securities secondary markets as daily trade price differential and volatility both declined market-wide after the disclosure intervention. Research limitations/implications The sample consists of trades in State of California general obligation bonds; therefore, empirical findings may not be generalizable to other states, local governments and different types of bonds. Practical implications The findings highlight voluntary information disclosure as a practical and effective mechanism in disclosure regulation of municipal securities secondary markets. Originality/value Only a small body of work exists that examines information disclosure regulation in municipal securities secondary markets; therefore, this paper expands knowledge on the topic and should provide renewed impetus for regulatory efforts aimed at improving the efficiency of municipal capital markets. Journal of Financial Economic Policy330Journal of Financial Economic Policy['Komla D. Dzigbede']2024-01-25T00:00:00https://www.semanticscholar.org/paper/2b04552c4242a1f99636d0cf426cf2dc1ccdfd27 9cddb76c900c4e73de7ae9856ddd8f912d29ed1eThis comprehensive study explores the integration and effectiveness of Artificial Intelligence (AI) in Identity and Access Management (IAM) within cloud environments. It primarily focuses on how AI can enhance user authentication, authorization, and access control, addressing the challenges and possibilities in cloud computing. The study adopts a mixed-methods approach, employing both quantitative and qualitative analyses. A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness. Four hypotheses are explored: the impact of hardware and software configurations on system accuracy (H1), the influence of computational environments on reliability (H2), the role of demographic factors in user acceptance (H3), and the effect of technological enhancements on system performance and acceptance (H4). Findings indicate significant correlations between these factors and the effectiveness of AI in IAM. Notably, hardware configurations and security concerns influence system accuracy; computational environment variations affect system reliability; demographic factors impact user acceptance; and enhancements such as user feedback, advancements in AI technology, continuous learning algorithms, and system transparency improve performance and acceptance. These insights underscore the need for advanced hardware, standardized software, user-centric design, and continuous improvement in AI technologies for effective IAM in cloud environments. The study provides actionable recommendations for cloud service providers and developers, emphasizing the importance of involving users in development processes, ensuring transparency, and adopting adaptive algorithms. Future research directions include longitudinal studies on the impact of technological advancements and exploring demographic-specific responses to AI-integrated IAM solutions.Asian Journal of Research in Computer Science443A survey involving 582 cybersecurity experts provides insights into the current state and potential of AI in IAM, while multiple regression analysis examines the impact of various factors on system effectiveness.Asian Journal of Research in Computer Science['Samuel Oladiipo Olabanji', 'O. O. Olaniyi', 'Chinasa Susan Adigwe', 'O. J. Okunleye', 'Tunbosun Oyewale Oladoyinbo']2024-01-25T00:00:00https://www.semanticscholar.org/paper/9cddb76c900c4e73de7ae9856ddd8f912d29ed1e addd7ccbb33e332d723c28532abea6272d6bbe3cInternational Journal of Computer Assisted Radiology and Surgery21It can be observed that certain state-of-the-art AI algorithms and systems, such as large language models or generative pre-trained transformers (GPTs), are becoming increasingly complex and need to be rigorously examined to render them transparent and comprehensible in their usage for all stakeholders involved in health care.International journal of computer assisted radiology and surgery['Heinz U. Lemke', 'Franziska Mathis-Ullrich']2024-01-25T00:00:00https://www.semanticscholar.org/paper/addd7ccbb33e332d723c28532abea6272d6bbe3c a1ac8fc78480ec9b271f1dc1fd5d920cb251f9a9Applying AI techniques to journalism.Communications of the ACM61Commun. ACM['Nicholas Diakopoulos', 'Christoph Trattner', 'D. Jannach', 'Irene Costera Meijer', 'Enrico Motta']2024-01-25T00:00:00https://www.semanticscholar.org/paper/a1ac8fc78480ec9b271f1dc1fd5d920cb251f9a9 d7d4d56daa0f3c292a06b17a709589410c915742Clinical simulation is a useful method for evaluating AI-enabled clinical decision support (CDS). Simulation studies permit patient- and risk-free evaluation and far greater experimental control than is possible with clinical studies. The effect of CDS assisted and unassisted patient scenarios on meaningful downstream decisions and actions within the information value chain can be evaluated as outcome measures. This paper discusses the use of clinical simulation in CDS evaluation and presents a case study to demonstrate feasibility of its application.Medinfo01The use of clinical simulation in CDS evaluation is discussed and a case study is presented to demonstrate feasibility of its application and the effect of CDS assisted and unassisted patient scenarios on meaningful downstream decisions and actions within the information value chain can be evaluated as outcome measures.Studies in health technology and informatics['D. Lyell', 'Adriaan Lustig', 'Kate Denyer', 'Satya Vedantam', 'F. Magrabi']2024-01-25T00:00:00https://www.semanticscholar.org/paper/d7d4d56daa0f3c292a06b17a709589410c915742 f0757f65d9ccbf450dbfa0669c6efd27e79ba0e5With the growing integration of Internet of Things (IoT) technologies and Artificial Intelligence (AI) in healthcare, it is crucial to prioritize transparency and interpretability in the decision-making process. This paper presents a novel framework that utilizes Explainable AI (XAI) to improve the interpretability of predictive healthcare models. The proposed system integrates feature importance-based methodologies with the Local Interpretable Model-agnostic Explanations (LIME) technique to offer a comprehensive comprehension of the predictive and preventive healthcare recommendations. The framework commences by conducting an in-depth examination of the present condition of Internet of Things (IoT) in the healthcare sector, as well as the importance of predictive and preventive healthcare. The literature review examines the difficulties related to the comprehensibility of artificial intelligence (AI) in the healthcare field and presents feature importance-based approaches and LIME as potential remedies. The focus is on the hybrid approach that combines these techniques, as it has the potential to offer precise predictions while also ensuring a strong level of interpretability. The methodology section delineates the procedure for gathering healthcare data and IoT sensor data, subsequently followed by preprocessing measures such as data cleansing and feature engineering. The predictive models undergo a process of selection, training, and evaluation, with the primary objective of attaining a notable accuracy level of 0.961. This text provides a detailed explanation of how the combination of feature importance-based approaches and LIME improves the transparency and interpretability of the model. An extensive case study is provided to illustrate the implementation of the suggested framework in an actual situation. The results and evaluation section showcases the exceptional precision of 0.961, as well as enhanced interpretability scores and decreased computational time in comparison to the baseline XAI models. The discussion section juxtaposes the suggested hybrid approach with conventional models, examines ethical considerations, and investigates the scalability and generalizability of the framework. To conclude, the paper provides a concise overview of the findings and implications of the Explainable AI-Powered IoT Systems for Predictive and Preventive Healthcare framework. This hybrid approach demonstrates high accuracy, improved interpretability, and efficient computational performance, making it a promising advancement in personalized health management and wellness optimization. This research adds to the expanding collection of literature on Explainable Artificial Intelligence (XAI) in the healthcare sector, thus opening up possibilities for future research avenues and practical applications in this domain.Journal of Electrical Systems171A novel framework that utilizes Explainable AI (XAI) to improve the interpretability of predictive healthcare models and integrates feature importance-based methodologies with the Local Interpretable Model-agnostic Explanations (LIME) technique to offer a comprehensive comprehension of the predictive and preventive healthcare recommendations.Journal of Electrical Systems['Et al. Uddhav T. Kumbhar']2024-01-25T00:00:00https://www.semanticscholar.org/paper/f0757f65d9ccbf450dbfa0669c6efd27e79ba0e5 958358e3c576e301fd922b0890318463491f58c1In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for their development in cellular networks, especially in 5G-and-beyond networks. The availability of high-quality networking datasets is one of the essential aspects for creating data-driven algorithms in network management and optimisation tasks. These datasets serve as the foundation for empowering AI algorithms to make informed decisions and optimise network resources efficiently. In this research work, we propose the IW-IB-5GNET networking dataset: an infrastructure-wide and intent-based dataset that is intended to be of use in research and development of network management and optimisation solutions in 5G-and-beyond networks. It is infrastructure wide due to the fact that the dataset includes information from all layers of the 5G network. It is also intent based as it is initiated based on predefined user intents. The proposed dataset has been generated in an emulated 5G network, with a wide deployment of network sensors for its creation. The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation.Italian National Conference on Sensors381The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation in 5G-and-beyond networks.Sensors (Basel, Switzerland)['Jimena Andrade-Hoz', 'Qi Wang', 'J. Alcaraz-Calero']2024-01-25T00:00:00https://www.semanticscholar.org/paper/958358e3c576e301fd922b0890318463491f58c1 a8efdfcd9b9cb2f50817a5fbbaa80675691a730bAs a general purpose technology without a concrete pre-defined purpose, personal chatbots can be used for a whole range of objectives, depending on the personal needs, contexts, and tasks of an individual, and so potentially impact a variety of values, people, and social contexts. Traditional methods of risk assessment are confronted with several challenges: the lack of a clearly defined technology purpose, the lack of clearly defined values to orient on, the heterogeneity of uses, and the difficulty of actively engaging citizens themselves in anticipating impacts from the perspective of their individual lived realities. In this article, we leverage scenario writing at scale as a method for anticipating AI impact that is responsive to these challenges. The advantages of the scenario method are its ability to engage individual users and stimulate them to consider how chatbots are likely to affect their reality and so collect different impact scenarios depending on the cultural and societal embedding of a heterogeneous citizenship. Empirically, we tasked 106 US-based participants to write short fictional stories about the future impact (whether desirable or undesirable) of AI-based personal chatbots on individuals and society and, in addition, ask respondents to explain why these impacts are important and how they relate to their values. In the analysis process, we map those impacts and analyze them in relation to socio-demographic as well as AI-related attitudes of the scenario writers. We show that our method is effective in (1) identifying and mapping desirable and undesirable impacts of AI-based personal chatbots, (2) setting these impacts in relation to values that are important for individuals, and (3) detecting socio-demographic and AI-attitude related differences of impact anticipation.arXiv.org881The method is effective in identifying and mapping desirable and undesirable impacts of AI-based personal chatbots, setting these impacts in relation to values that are important for individuals, and detecting socio-demographic and AI-attitude related differences of impact anticipation.ArXiv['Kimon Kieslich', 'Natali Helberger', 'Nicholas Diakopoulos']2024-01-25T00:00:00https://www.semanticscholar.org/paper/a8efdfcd9b9cb2f50817a5fbbaa80675691a730b e330bd0ebd2385152552f1b5c923620e3b8d3630International Cybersecurity Law Review180The study shows how blockchain can successfully address certain attack vectors related to AI systems, such as data poisoning in trained AI models and data sets, and analyses how blockchain can facilitate independent audits and verification of AI system behaviour.International Cybersecurity Law Review['Simona Ramos', 'Joshua Ellul']2024-01-25T00:00:00https://www.semanticscholar.org/paper/e330bd0ebd2385152552f1b5c923620e3b8d3630 d9868f33461cf191eaf8713eb93aac7a234fcc33Religion-based AI chatbots serve religious practitioners by bringing them godly wisdom through technology. These bots reply to spiritual and worldly questions by drawing insights or citing verses from the Quran, the Bible, the Bhagavad Gita, the Torah, or other holy books. They answer religious and theological queries by claiming to offer historical contexts and providing guidance and counseling to their users. A criticism of these bots is that they may give inaccurate answers and proliferate bias by propagating homogenized versions of the religions they represent. These “embodied spiritual machines” may likewise bear bias against women, their gender, and their societal roles. This paper crafts a concept intended to address this GPT issue by reimagining, modifying, and implementing a feminist approach to these chatbots. It examines the concepts and designs of these bots and how they address women-related questions. Along with the challenge of bringing gender and diversity-sensitive religious wisdom closer to the people through technology, the paper proposes a re-engineered model of a fair religion-based AI chatbot.Philosophies180Along with the challenge of bringing gender and diversity-sensitive religious wisdom closer to the people through technology, the paper proposes a re-engineered model of a fair religion-based AI chatbot.Philosophies['Hazel T. Biana']2024-01-25T00:00:00https://www.semanticscholar.org/paper/d9868f33461cf191eaf8713eb93aac7a234fcc33 c0c1e132aaefd6fba2d2e5a11498d7b241a56e80Artificial intelligence (AI) has become an increasingly important tool in agriculture, providing farmers with innovative solutions to improve productivity, efficiency, and sustainability. Using AI-powered tools such as drones, sensors, and machine learning algorithms, farmers can gather and analyze data about soil health, crop growth, and weather patterns to make informed decisions and optimize their operations. AI has the potential to transform agriculture by enabling more precise and targeted applications of fertilizers, pesticides, and other inputs, reducing waste, and increasing yields. It also has the potential to reduce the environmental impact of agriculture by minimizing the use of harmful chemicals and promoting sustainable farming practices. Additionally, AI can assist in the automation of tedious and repetitive tasks, freeing up farmers to focus on more strategic decision-making and higher-value activities. This can also help to address labor shortages in the agriculture sector, particularly in countries with aging populations or where traditional agriculture work is seen as less desirable. Overall, the use of AI in agriculture has the potential to revolutionize the industry, making it more efficient, sustainable, and profitable while also ensuring that we can continue to feed a growing global population in a responsible and sustainable manner. Keyword: Artificial Intelligence, AI in Agriculture, AI and Agriculture, Sustainable Farming, AI application in AgricultureINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The use of AI in agriculture has the potential to revolutionize the industry, making it more efficient, sustainable, and profitable while also ensuring that the authors can continue to feed a growing global population in a responsible and sustainable manner.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Bhumika Sharma', 'Raunak Singhvi', 'Prashant Chauhan', 'Nayan Naik']2024-01-25T00:00:00https://www.semanticscholar.org/paper/c0c1e132aaefd6fba2d2e5a11498d7b241a56e80 292ec94183c71fd0663ec35f2efc8afa34391b39This article explores the transformative potential of Artificial Intelligence (AI) in enhancing municipal finance systems in Georgia. It begins by examining the current state of Georgian municipal finance, highlighting challenges such as limited fiscal autonomy, inefficiencies in revenue collection, and issues of transparency and accountability. The paper then provides an overview of the global advancements in AI within the finance sector, illustrating how AI can revolutionize financial processes through predictive analytics, risk management, and automation. Focusing on the Georgian context, the article discusses specific applications of AI that can address prevailing challenges in municipal finance. These applications include improving revenue collection, enhancing budget allocation efficiency, and increasing financial transparency. The paper also presents international case studies from cities like Singapore and Barcelona, where AI has successfully improved municipal finance, providing practical insights and potential strategies for Georgia. The challenges of implementing AI in Georgian municipal finance, including technological infrastructure requirements, data quality concerns, and ethical and legal considerations, are critically examined. The paper concludes with policy recommendations for the successful integration of AI in Georgian municipal finance. These recommendations encompass developing a strategic framework for AI adoption, investing in technological infrastructure, enhancing data governance, and fostering public engagement and trust. By highlighting the role of AI in addressing the complexities of municipal finance, this article contributes to the discourse on leveraging technology for public administration reform and offers a roadmap for policymakers and municipal authorities in Georgia. Keywords: Artificial Intelligence; Municipal finance; Financial transparency; Ethical use of AI; Data management.Economics70The role of AI in addressing the complexities of municipal finance is highlighted, contributing to the discourse on leveraging technology for public administration reform and offering a roadmap for policymakers and municipal authorities in Georgia.Economics['Ioseb Berikashvili Ioseb Berikashvili']2024-01-25T00:00:00https://www.semanticscholar.org/paper/292ec94183c71fd0663ec35f2efc8afa34391b39 0fdf0aaac9a529ce7b6f0e24c0bf1b8228acade6OBJECTIVES The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on Artificial Intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centers from the 29th of May 2023 to the 30th of July 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.British Journal of Radiology00Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.The British journal of radiology['B. Al Mohammad', 'Afnan Aldaradkeh', 'Monther A. Gharaibeh', 'Warren Reed']2024-01-25T00:00:00https://www.semanticscholar.org/paper/0fdf0aaac9a529ce7b6f0e24c0bf1b8228acade6 72aee643948de49d7ef34be247b4e8e6b9c31ff9The publication discusses ethical and legal issues in the use of artificial intelligence technologies in the healthcare sector. Digital technologies should not only contribute to the positive development of the healthcare sector, but also inspire confidence among patients and medical professionals, and also not create fears about their use, therefore, ethical and legal issues are given importance. In this case, a significant role will be given to the legal regulation of social relations arising in connection with the use of technology in medicine.Juridical World00A significant role will be given to the legal regulation of social relations arising in connection with the use of technology in medicine as well as ethical and legal issues are given importance.Juridical World['Albina Shutova']2024-01-25T00:00:00https://www.semanticscholar.org/paper/72aee643948de49d7ef34be247b4e8e6b9c31ff9 f539efb9f0d3a75fd95d4f0e6076f3556a243d6bAs the world becomes increasingly interconnected through digital technologies, the protection of individuals' privacy has emerged as a critical concern. This paper conducts a comprehensive global review of privacy legislation and enforcement mechanisms, shedding light on the challenges posed by the digital age. With a focus on the intricate balance between technological advancements and the fundamental right to privacy, the study explores the evolving legal landscape and its implications for individuals, businesses, and governments. The analysis encompasses diverse jurisdictions, highlighting the variations in privacy laws and enforcement approaches across regions. From the European Union's robust General Data Protection Regulation (GDPR) to the nuanced approaches in Asia and the Americas, this review synthesizes the evolving regulatory frameworks. Special attention is given to emerging issues such as the use of artificial intelligence, biometrics, and surveillance technologies, which pose unique challenges to existing privacy paradigms. Moreover, the paper investigates the effectiveness of enforcement mechanisms in ensuring compliance with privacy laws. It examines the role of governmental agencies, regulatory bodies, and international collaborations in addressing cross-border data flows and global privacy challenges. The study also evaluates the impact of recent high-profile privacy incidents on shaping legislative responses and enforcement strategies. By presenting a holistic view of privacy law challenges in the digital age, this research contributes to the ongoing discourse on safeguarding individuals' privacy rights in an era of rapid technological innovation. The findings provide valuable insights for policymakers, legal practitioners, businesses, and individuals seeking a deeper understanding of the evolving dynamics surrounding privacy legislation and enforcement on a global scale. Keywords: Law, Privacy Law, Digital Age, Review, Data Protection.International journal of applied research in social sciences05A comprehensive global review of privacy legislation and enforcement mechanisms, shedding light on the challenges posed by the digital age, and investigates the effectiveness of enforcement mechanisms in ensuring compliance with privacy laws.International Journal of Applied Research in Social Sciences['Oluwatosin Reis', 'Nkechi Emmanuella Eneh', 'Benedicta Ehimuan', 'Anthony Anyanwu', 'Temidayo Olorunsogo', 'Temitayo Oluwaseun Abrahams']2024-01-25T00:00:00https://www.semanticscholar.org/paper/f539efb9f0d3a75fd95d4f0e6076f3556a243d6b 3771edb135ae9cba2dd254eb7fcc7658afa8ae35The intensive development of digital artificial intelligence technologies has revealed the problem of insufficient regulatory legal regulation of the foundations and conditions of their functioning, as well as integration into related branches of law. This article is devoted to the study of the axiological aspect of law in the conditions of digital reality and ensuring the rights and legitimate interests of individual citizens using the latest technologies, including artificial intelligence. It is concluded that it is necessary to revise the substantive foundations of the system of legal values, depending on their assessment and implication in the digital society, while maintaining a focus on respect for law as a universal social value.HISTORY OF STATE AND LAW00It is concluded that it is necessary to revise the substantive foundations of the system of legal values, depending on their assessment and implication in the digital society, while maintaining a focus on respect for law as a universal social value.History of state and law['Tatyana M. Lopatina']2024-01-25T00:00:00https://www.semanticscholar.org/paper/3771edb135ae9cba2dd254eb7fcc7658afa8ae35 a994f4d76427f86d6afc1b0fdc2871a6045a318fPresented as a technological innovation set to usher in significant changes globally, artificial intelligence (AI) enters various segments of life, especially in the field of education. Civil engineering students, as part of their learning journey, try to apply it in class, especially in aspects of engineering English vocabulary. In the current study, students used the English Language Speech Assistant (ELSA) application. The research method used was descriptive research, where the research subjects were taken from 1 class of 29 students. In this study the questionnaires were used as instruments to collect research data. Based on the results of survey analysis and data collected, it was revealed that students had very good and positive perceptions of using the ELSA application. Students exhibit great joy and enthusiasm in learning engineering English vocabulary using the ELSA application. In addition to offering an effective means of pronouncing vocabulary words, this application's feedback also serves as a motivational factor to learn engineering English vocabulary. Thus, the student's positive perspective on this application of AI can be an indicator of the success of students' language learning in the future.Journal of English Education Program02The student's positive perspective on this application of AI can be an indicator of the success of students' language learning in the future.Journal of English Education Program['Sylvia Irene Persulessy', 'Reynold Nikijuluw', 'Juvrianto Chrissunday Jakob']2024-01-25T00:00:00https://www.semanticscholar.org/paper/a994f4d76427f86d6afc1b0fdc2871a6045a318f b2fa7ccb5d6fb741264d9148618708fa7b03961bMany firms have established formal carbon neutrality (CN) targets in response to the increasing climate risk and related regulatory requirements. Subsequently, they have implemented various measures and adopted multiple approaches to attain these goals. Academic research has given due attention to firms' efforts in this direction. However, past studies have primarily focused on non‐digital and process‐oriented approaches to achieving CN, with the potential of digital technologies such as artificial intelligence (AI) remaining less explored. Our study aims to address this gap by qualitatively examining the use of AI for pursuing CN, drawing insights from firms with prior experience in the area. We analyzed the collected qualitative data to identify four key dimensions that capture different nuances of applying AI for achieving CN: (a) implementing AI for direct and indirect control of emissions, (b) accepting the strategic trade‐offs related to funding, data and systems concerns, and social priorities, (c) overcoming organizational and human‐related impediments, and (d) acknowledging the significant impact of AI in terms of gains in business model efficiency and measurable CN target attainment, which ultimately contribute to CN. Based on our findings, we propose a convergence–divergence model encompassing the positive aspects, inhibiting factors, synergies, and offsets necessary for firms to leverage AI to achieve net‐zero emissions effectively. Overall, our study contributes to the discourse on the utilization of AI for CN in a comprehensive manner.Business Strategy and the Environment592A convergence–divergence model encompassing the positive aspects, inhibiting factors, synergies, and offsets necessary for firms to leverage AI to achieve net‐zero emissions effectively is proposed.Business Strategy and the Environment['Adeel Luqman', 'Qingyu Zhang', 'Shalini Talwar', 'Meena Bhatia', 'Amandeep Dhir']2024-01-25T00:00:00https://www.semanticscholar.org/paper/b2fa7ccb5d6fb741264d9148618708fa7b03961b 6adabd74814e175de9cf341b77095a99bb2bc788Insights into Imaging212The case of Southern illustrates that organisations can reap more benefits from AI implementation by investing in long-term initiatives that holistically align both social and technological aspects of clinical practice.Insights into Imaging['Bomi Kim', 'Stephan R. Romeijn', 'Mark van Buchem', 'M. Mehrizi', 'Willem Grootjans']2024-01-25T00:00:00https://www.semanticscholar.org/paper/6adabd74814e175de9cf341b77095a99bb2bc788 99ee0859bf83c91d697887a063c9447758bb4c42Purpose: This paper presents an in-depth analysis of the interaction between Artificial Intelligence (AI), organizational agility, and performance within the UAE's public energy sector. It explores the transformative role of AI in this context and the critical importance of organizational agility in determining outcomes in the energy field.   Design/methodology/data analysis: The methodology employed in this study is a cross-sectional survey design, with data collected from 245 managers across various public energy companies in the UAE. The survey instrument measured variables pertaining to AI, such as Customer Relationship Management and Cost-efficient IS Operations, and facets of organizational agility, including Responsiveness and Competency, as well as overall Organizational Performance.   Findings: The study's findings reveal a significant direct impact of AI on organizational performance, which is further enhanced by the presence of organizational agility. The data indicates that AI's integration within Customer Relationship Management and Cost-efficient IS Operations positively affects performance. Additionally, organizational agility through its components of Responsiveness and Competency serves as a significant intermediary, amplifying the influence of AI on performance.   Originality/value: The research is grounded in the Process Theory of Change, the Diffusion of AI Theory, and the Resource-Based View Theory, providing a solid theoretical base for its exploration. It offers a nuanced understanding of the combined impact of AI and organizational agility on the public energy sector's performance.   Practical implications: The paper concludes with a conceptual framework that encapsulates these relationships, providing stakeholders with a comprehensive view of the interdependencies between AI, agility, and performance. It stresses the imperative for a strategic embrace of AI and organizational agility to foster resilience, adaptability, and sustainable advancement in the UAE's public energy sector. The insights from this paper guide future strategic orientations, emphasizing the integration of technological innovation with agile organizational practices as a pathway to enhanced performance and sectoral leadership.Journal of Law and Sustainable Development244Journal of Law and Sustainable Development['S. Alshamsi', 'Tuan Pah Rokiah Syed Hussain', 'Sharif Shofirun Sharif Ali']2024-01-25T00:00:00https://www.semanticscholar.org/paper/99ee0859bf83c91d697887a063c9447758bb4c42 7f2feec3238c182db0d0db6b2fbb8c620ed979baDengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.Medinfo01An artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application and proves that Artificial Intelligence with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction.Studies in health technology and informatics['Dina Nur Anggraini Ningrum', 'Yu-Chuan Li', 'Chien-Yeh Hsu', 'M. S. Muhtar', 'H. Suhito']2024-01-25T00:00:00https://www.semanticscholar.org/paper/7f2feec3238c182db0d0db6b2fbb8c620ed979ba c0ec4a60686768922b85b4cc297904cc9f42cab4This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in propelling the dairy industry toward net zero emissions, a critical objective in the global fight against climate change. Employing the Canadian dairy sector as a case study, the study extrapolates its findings to demonstrate the global applicability of these technologies in enhancing environmental sustainability across the agricultural spectrum. We begin by delineating the environmental challenges confronting the dairy industry worldwide, with an emphasis on greenhouse gas (GHG) emissions, including methane from enteric fermentation and nitrous oxide from manure management. The pressing need for innovative approaches in light of the accelerating climate crisis forms the crux of our argument. Our analysis delves into the role of Big Data and AI in revolutionizing emission management in dairy farming. This includes applications in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are thoroughly examined. Crucially, the paper addresses the wider implications of integrating these technologies in dairy farming. We discuss the development of benchmarking standards for emissions, the importance of data privacy, and the essential role of policy in promoting sustainable practices. These aspects are vital in supporting the adoption of technology, ensuring ethical use, and aligning with international climate commitments. Concluding, our comprehensive study not only suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts.Climate691This comprehensive study suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts.Climate['S. Neethirajan']2024-01-25T00:00:00https://www.semanticscholar.org/paper/c0ec4a60686768922b85b4cc297904cc9f42cab4 e523d133f12917a26b6dd9aeccdc5de62a768649Transcatheter aortic valve replacement (TAVR) has emerged as a viable alternative to surgical aortic valve replacement, as accumulating clinical evidence has demonstrated its safety and efficacy. TAVR indications have expanded beyond high-risk or inoperable patients to include intermediate and low-risk patients with severe aortic stenosis. Artificial intelligence (AI) is revolutionizing the field of cardiology, aiding in the interpretation of medical imaging and developing risk models for at-risk individuals and those with cardiac disease. This article explores the growing role of AI in TAVR procedures and assesses its potential impact, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes. In addition, current challenges and future directions in AI implementation are highlighted.Diagnostics640The growing role of AI in TAVR procedures is explored and its potential impact is assessed, with particular focus on its ability to improve patient selection, procedural planning, post-implantation monitoring and contribute to optimized patient outcomes.Diagnostics['Mina M. Benjamin', 'M. Rabbat']2024-01-25T00:00:00https://www.semanticscholar.org/paper/e523d133f12917a26b6dd9aeccdc5de62a768649 3543fa86ea52dea99591c487500e26f319ddd7b3Artificial intelligence (AI) is a technogenic form of life created by man, possessing elements of subjectivity. The subjectivity being formed is autonomous relative to a human being. The source of AI development is data automatically read from billions of inhabitants, regardless of their will and desire. There is a latent impact of AI on the foundations of the social order, including the value concepts of society as a whole and of an individual subject (legal narratives). AI is unconstitutional and poses a real threat to the foundations of the social order of the Russian Federation, especially in terms of preservation and development of traditional values.HISTORY OF STATE AND LAW00Artificial intelligence is unconstitutional and poses a real threat to the foundations of the social order of the Russian Federation, especially in terms of preservation and development of traditional values.History of state and law['Pavel N. Astapenko']2024-01-25T00:00:00https://www.semanticscholar.org/paper/3543fa86ea52dea99591c487500e26f319ddd7b3 434c1970e1daf171d0d5d913e83c657086efe2e7Despite the technological advancements in the transportation sector, the industry continues to grapple with increasing energy consumption and vehicular emissions, which intensify environmental degradation and climate change. The inefficient management of traffic flow, the underutilization of transport network interconnectivity, and the limited implementation of artificial intelligence (AI)-driven predictive models pose significant challenges to achieving energy efficiency and emission reduction. Thus, there is a timely and critical need for an integrated, sophisticated approach that leverages intelligent transportation systems (ITSs) and AI for energy conservation and emission reduction. In this paper, we explore the role of ITSs and AI in future enhanced energy and emission reduction (EER). More specifically, we discuss the impact of sensors at different levels of ITS on improving EER. We also investigate the potential networking connections in ITSs and provide an illustration of how they improve EER. Finally, we discuss potential AI services for improved EER in the future. The findings discussed in this paper will contribute to the ongoing discussion about the vital role of ITSs and AI applications in addressing the challenges associated with achieving energy savings and emission reductions in the transportation sector. Additionally, it will provide insights for policymakers and industry professionals to enable them to develop policies and implementation plans for the integration of ITSs and AI technologies in the transportation sector.arXiv.org30The role of ITSs and AI in future enhanced energy and emission reduction (EER) is explored, including the impact of sensors at different levels of ITS on improving EER, and the potential networking connections in ITSs are investigated.ArXiv['Omar Rinchi', 'Ahmad Alsharoa', 'I. Shatnawi', 'Anvita Arora']2024-01-25T00:00:00https://www.semanticscholar.org/paper/434c1970e1daf171d0d5d913e83c657086efe2e7 dc6bf38e0beceb3221f30569efab7fbdaaef1bddThis paper delves into the integration of artificial intelligence within Estonia’s education system, showcasing the country’s global reputation for digital innovation. Utilising the method of the case study and the method of document analysis, we explore not only the practical outcomes, challenges, and strategies related to the adoption of artificial intelligence in the Estonian education system but also its economic implications. The research highlights artificial intelligence’s potential to lead to cost efficiencies and economic benefits for the education sector. Furthermore, we address the critical issue of fostering artificial intelligence literacy among students, schools, and universities, pinpointing data privacy and ethical considerations as key areas of concern. The strategies implemented by educational institutions to mitigate these challenges and promote a comprehensive understanding of artificial intelligence are also discussed. In conclusion, the paper underscores artificial intelligence’s transformative potential in reshaping education, offering guidance for other nations and academic entities aiming to successfully integrate artificial intelligence-based technologies. Emphasis is placed on the importance of a supportive policy framework and a solid ethical foundation to navigate the complexities of artificial intelligence adoption and ensure responsible use, while also considering the economic impact on the educational ecosystem and the labour market at large.Romanian Economic Journal110This paper delves into the integration of artificial intelligence within Estonia’s education system, showcasing the country’s global reputation for digital innovation and offering guidance for other nations and academic entities aiming to successfully integrate artificial intelligence-based technologies.The Romanian Economic Journal['Gabriela Călinescu', 'Marcela Tanasciuc']2024-01-25T00:00:00https://www.semanticscholar.org/paper/dc6bf38e0beceb3221f30569efab7fbdaaef1bdd a7df88181f199668210587ebb9dcda41fc06202eThe rapid increase in new challenges of the combination of the Internet of Things (IoT) and artificial intelligence (AI), which are emerging technologies, can play a compelling role in prompting the development of artificial intelligence Internet of Things (AIoT). Therefore, the demand for AI competencies for everyone will increase. Educational institutes focus on encouraging AI education because the demand for AI-literate workers will increase in the industrial sector. However, teachers’ lack of AI knowledge is a significant barrier to AI education. Thus, developing the teacher’s AI competencies and educating them about how to use and teach students is critical. In this study, we proposed artificial intelligence of things professional development (AIoT-PD) training to prepare the AI competencies of teachers ready to teach. A quasi-experimental design with a two-day training workshop was conducted among 13 teachers to examine its impact on AI competencies, including AI knowledge, AI skill, and AI attitude. The quantitative data were collected via a pretest and posttest after the training activity, while qualitative data were collected via interviews. This study showed that teachers’ AI knowledge significantly improved. These findings revealed the AIoT training workshop’s effectiveness in enhancing teachers’ AI competencies, which can help them effectively teach students in AI education.International Journal of Interactive Mobile Technologies00Results showed that teachers’ AI knowledge significantly improved and revealed the AIoT training workshop’s effectiveness in enhancing teachers’ AI competencies, which can help them effectively teach students in AI education.Int. J. Interact. Mob. Technol.['Pornchai Kitcharoen', 'S. Howimanporn', 'S. Chookaew']2024-01-25T00:00:00https://www.semanticscholar.org/paper/a7df88181f199668210587ebb9dcda41fc06202e 5c3e1171fcff8eec8a6cb709925d014f9fe50d3fThis research aims to examine the sufficiency of global legal norms on legal norms in the field of digitalization and artificial intelligence. Descriptive scanning model, content analysis methods and semiotic analysis methods were used in the research. In this context, in the research, studies on global law and artificial intelligence were analyzed and their results were evaluated. It was then analyzed with SWOT analysis in terms of artificial intelligence, global law and digitalization. According to the results obtained from the literature review and semiotic analysis, digitalization and globalization are in a two-way relationship as two important concepts that trigger each other and are primarily affected by artificial intelligence applications. Although artificial intelligence applications positively affect the digitalization process in terms of their legal effects, they also bring with them some drawbacks in judicial matters and global jurisdiction. In particular, the fact that the exact framework of the artificial intelligence issue is not yet known, that it is open to external interventions, that a global legal system has not yet been formed, and the differences between international law and regional legal systems can be listed as the most important problems in the legal applications of artificial intelligence. As a result, globalization brings developments that will necessitate important and radical changes in the field of law, as in all areas of life. Therefore, although the law has a much faster and more effective working environment than in the past, it is also open to manipulation. Current global legal norms are inadequate regarding both digitalization and artificial intelligence. In the legal field, on the one hand, cumbersome and bureaucratic legal systems must be abandoned, and on the other hand, more dynamic, more modern and faster legal systems must be adopted.International Journal of Law and Politics Studies00The sufficiency of global legal norms on legal norms in the field of digitalization and artificial intelligence is examined to examine whether current global legal norms are inadequate regarding both digitalization and artificial intelligence.International Journal of Law and Politics Studies['Gulde Alparslan']2024-01-25T00:00:00https://www.semanticscholar.org/paper/5c3e1171fcff8eec8a6cb709925d014f9fe50d3f a744cd3dab38d42853fc7c0cbac4d433634b4569Artificial Intelligence (AI) has great potential to improve healthcare, but implementation into routine practice remains a challenge. This study scoped the extent to which AI and Natural Language Processing (NLP) is being implemented into routine practice in Australian healthcare organisations. An environmental scan of publicly available data was undertaken to identify AI applications. Publicly available data consisted of news posts from Australian public healthcare organisations and conference proceedings from key research organisations. Two researchers reviewed and analysed posts related to AI applications to create a list of potential implementation case studies. The final list of AI applications was reviewed by a governance committee in order to identify any missing applications. One application was identified by the governance committee and subsequently added. The environmental scan identified eighteen AI applications, of which eleven met all eligibility criteria. Only one application included NLP. Twelve applications were included when the application identified by the governance committee was added to the list. Implementation of AI applications is spread across four broad categories of use: 1) Decision Support, 2) Monitoring Treatment Effectiveness, 3) Personalised Care and 4) Risk Prediction.Medinfo00The extent to which AI and Natural Language Processing is being implemented into routine practice in Australian healthcare organisations is scoped to create a list of potential implementation case studies.Studies in health technology and informatics['Anna Janssen', 'Shah Kavisha', 'Alison Johnson', 'Anna Marinic', 'Helena Teede', 'Tim Shaw']2024-01-25T00:00:00https://www.semanticscholar.org/paper/a744cd3dab38d42853fc7c0cbac4d433634b4569 b1a786bbca38392ae17a9d0becd0eaa05b47c3edColorectal cancer is one of the most common malignant tumors worldwide. Due to the heterogeneity in patient outcomes and treatment responses to standard therapy regimens, personalized diagnostic and therapeutic strategies have remained a focus of sustained interest in research. In recent years, with the rapid progression of artificial intelligence (AI) technology in the medical field, an abundance of phased research results has emerged in the decision-making for preoperative, intraoperative, and postoperative diagnostic and therapeutic plans for colorectal cancer, demonstrating great potential for application. This new and efficient solution provides for the personalized evaluations and auxiliary diagnoses and treatments of patients with colorectal cancer. In the future, AI systems may continue to advance towards multimodal, multi-omics, and real-time directions. This paper aims to explore the current state of research on the multi-faceted auxiliary applications of AI in the diagnosis and treatment of colorectal cancer, as well as to present a prospective view of the innovations that AI technology could bring to personalized colorectal cancer treatment in the future and the challenges it may face.Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery00The current state of research on the multi-faceted auxiliary applications of AI in the diagnosis and treatment of colorectal cancer is explored, as well as a prospective view of the innovations that AI technology could bring to personalized colorectal cancer treatment in the future and the challenges it may face are presented.Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery['W. Wei', 'K. S. He', 'Z. Y. Hu', 'Z. Y. Liu', 'J. Q. Tang', 'J. Tian']2024-01-25T00:00:00https://www.semanticscholar.org/paper/b1a786bbca38392ae17a9d0becd0eaa05b47c3ed 65287d38c6340cd445dd0f2e678e8b17cffeeb16Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.Electronics3330It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices, which will contribute to the development of effective heart diagnostics.Electronics['Zofia Rudnicka', 'Klaudia Proniewska', 'Mark Perkins', 'A. Pręgowska']2024-01-25T00:00:00https://www.semanticscholar.org/paper/65287d38c6340cd445dd0f2e678e8b17cffeeb16 5d582056179e02302d6661880e852ba10a7d3dfeINTRODUCTION Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment. AREAS COVERED This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation. EXPERT OPINION Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.Expert Review of Respiratory Medicine580A narrative review aims to bridge the knowledge gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment, and outlines the key steps in building a model.Expert review of respiratory medicine['J. Antão', 'J. De Mast', 'A. Marques', 'F. Franssen', 'Martijn A. Spruit', 'Qichen Deng']2024-01-25T00:00:00https://www.semanticscholar.org/paper/5d582056179e02302d6661880e852ba10a7d3dfe 416301ee0b502cbe9c0ab13c03dc703883660ab0Implementing Artificial Intelligence in medicine is revolutionizing how medicine is practiced. It has much promise in bringing about improved clinical outcomes and efficiency while decreasing costs. There are also concerns and unintended consequences that are being realized and significant efforts to consider ethical principles in the implementation of Artificial Intelligence in medicine. One potential consequence may be the loss of what has been described as the soul of medicine: the physician-patient relationship. This relationship is especially precious in the context of what the US Surgeon General Vivek H. Murthy MD has called an "Epidemic of Loneliness and Isolation." This commentary describes considerations and potential steps to protect this vital relationship while implementing Artificial Intelligence approaches to improving patient care. If not vigilant, Artificial Intelligence may unintentionally erode the physician-patient relationship resulting in physician/patient isolation.Otolaryngology Head & Neck Surgery50This commentary describes considerations and potential steps to protect this vital relationship while implementing Artificial Intelligence approaches to improving patient care, because Artificial Intelligence may unintentionally erode the physician-patient relationship resulting in physician/patient isolation.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery['Walter T Lee']2024-01-25T00:00:00https://www.semanticscholar.org/paper/416301ee0b502cbe9c0ab13c03dc703883660ab0 ef63f04a4145990e7e0c9cde0105fdc759c8870aEarly detection of melanoma, a potentially lethal type of skin cancer with high prevalence worldwide, improves patient prognosis. In retrospective studies, artificial intelligence (AI) has proven to be helpful for enhancing melanoma detection. However, there are few prospective studies confirming these promising results. Existing studies are limited by low sample sizes, too homogenous datasets, or lack of inclusion of rare melanoma subtypes, preventing a fair and thorough evaluation of AI and its generalizability, a crucial aspect for its application in the clinical setting. Therefore, we assessed 'All Data are Ext' (ADAE), an established open-source ensemble algorithm for detecting melanomas, by comparing its diagnostic accuracy to that of dermatologists on a prospectively collected, external, heterogeneous test set comprising eight distinct hospitals, four different camera setups, rare melanoma subtypes, and special anatomical sites. We advanced the algorithm with real test-time augmentation (R-TTA, i.e. providing real photographs of lesions taken from multiple angles and averaging the predictions), and evaluated its generalization capabilities. Overall, the AI showed higher balanced accuracy than dermatologists (0.798, 95% confidence interval (CI) 0.779-0.814 vs. 0.781, 95% CI 0.760-0.802; p<0.001), obtaining a higher sensitivity (0.921, 95% CI 0.900- 0.942 vs. 0.734, 95% CI 0.701-0.770; p<0.001) at the cost of a lower specificity (0.673, 95% CI 0.641-0.702 vs. 0.828, 95% CI 0.804-0.852; p<0.001). As the algorithm exhibited a significant performance advantage on our heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists particularly in diagnosing challenging cases.arXiv.org410As the algorithm exhibited a significant performance advantage on the authors' heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists particularly in diagnosing challenging cases.ArXiv['Lukas Heinlein', 'Roman C. Maron', 'A. Hekler', 'Sarah Haggenmüller', 'Christoph Wies', 'J. Utikal', 'Friedegund Meier', 'Sarah Hobelsberger', 'F. Gellrich', 'M. Sergon', 'Axel Hauschild', 'L. E. French', 'Lucie M. Heinzerling', 'Justin G. Schlager', 'K. Ghoreschi', 'Max Schlaak', 'F. Hilke', 'G. Poch', 'Sören Korsing', 'C. Berking', 'M. Heppt', 'Michael Erdmann', 'S. Haferkamp', 'K. Drexler', 'D. Schadendorf', 'W. Sondermann', 'Matthias Goebeler', 'Bastian Schilling', 'E. Krieghoff-Henning', 'T. Brinker']2024-01-25T00:00:00https://www.semanticscholar.org/paper/ef63f04a4145990e7e0c9cde0105fdc759c8870a 15f479258fb5290bb6a60141d21d6820aee33932Embracing Artificial Intelligence (AI) is becoming more common in a variety of areas, including healthcare, banking, and transportation, and it is based on substantial data analysis. However, utilizing data for AI raises a number of obstacles. This extensive article examines the challenges connected with using data for AI, including data quality, volume, privacy and security, bias and fairness, interpretability and ethical considerations, and the required technical knowledge. The investigation delves into each obstacle, providing insightful solutions for businesses and organizations to properly handle these complexities. Organizations may effectively harness AI’s capabilities to make educated decisions by understanding and proactively tackling these difficulties, obtaining a competitive edge in the digital era. This review study, which provides a thorough examination of numerous solutions developed over the last decade to address data difficulties for AI, is expected to be a helpful resource for the scientific research community. It not only provides insights into current difficulties, but it also serves as a platform for creating novel ideas to alter our approaches to data strategies for AI.Journal of Intelligent & Fuzzy Systems230This review study, which provides a thorough examination of numerous solutions developed over the last decade to address data difficulties for AI, is expected to be a helpful resource for the scientific research community.J. Intell. Fuzzy Syst.['Ramesh Sundar', 'Ziaul Haque Choudhury', 'M. Chiranjivi', 'Gayatri Parasa', 'Praseeda Ravuri', 'M. Sivaram', 'Balambigai Subramanian', 'Kireet Muppavaram', 'Vijaya Madhavi Lakshmi. Challa']2024-01-25T00:00:00https://www.semanticscholar.org/paper/15f479258fb5290bb6a60141d21d6820aee33932 8c0213f8d7932420fad501a7c5f92f0e636057f9Artificial intelligence (AI) is a modern technology that has transformed the management of investment projects. Artificial intelligence offers a wide range of opportunities to improve the efficiency and quality of investment projects in a variety of ways, including data analysis, and the ability to process vast amounts of data that enables artificial intelligence to effectively analyze historical and current data. This helps to make more accurate decisions and a deeper understanding of potential trends and challenges, improving planning helps artificial intelligence to improve planning processes and identify potential risks. Using smart forecasting models, investors can estimate different impacts and identify optimal scenarios for maximizing returns on investment and improving project management. Artificial intelligence can improve project management processes by predicting and identifying potential problems early, allowing immediate corrective action, and avoiding unforeseen delays. Improved decision-making contributes to improved decision-making capacity by providing thorough analysis and comprehensive reports. This supports investors and leaders in making informed decisions based on accurate and prompt information.The American Journal of Management and Economics Innovations00Improved decision-making contributes to improved decision-making capacity by providing thorough analysis and comprehensive reports, which supports investors and leaders in making informed decisions based on accurate and prompt information.The American Journal of Management and Economics Innovations['Ali Kareem Abuzabiba', 'Zainab Qasim Jebur Al-Nasrawi', 'Kareem Qasim Jebur Al-Nasrawi']2024-01-25T00:00:00https://www.semanticscholar.org/paper/8c0213f8d7932420fad501a7c5f92f0e636057f9 ef81fa6f509b9140bef8ab11fda028e1e6c5f7d1The use of artificial intelligence (hereinafter referred to as Al) is one of the leading trends in marketing and advertising in recent years. With its help, it is possible to solve tasks of varying complexity: from generating ideas for promotion to creating advertising creatives. The research problem is that in order to work effectively, companies need to understand which neural networks can most optimally solve the tasks of marketing activities. To do this, it is necessary to define a list of such neural networks. The authors consider the use of Al in marketing communications and analyse neural networks that can be used in business. The study purpose is to form a relevant list of neural networks for the implementation of marketing activities of the company. The most popular neural networks, their functionality and features were studied. The domestic and foreign experience analysis of using neural networks in the marketing activities of companies is conducted. Recommendations for more efficient use of neural networks are proposed. Based on the conducted research, a relevant list of neural networks for the implementation of the company’s marketing activities was determined. The methodological basis of the article was formed of publications on the problems of using neural networks in business and marketing sphere. The works of domestic and foreign researchers were studied. The following theoretical research methods were used in the article: analysis, synthesis, problematisation. The content analysis was chosen as an empirical method.Digital Sociology90The authors consider the use of Al in marketing communications and analyse neural networks that can be used in business and marketing sphere and form a relevant list of neural networks for the implementation of marketing activities of the company.Digital Sociology['K. A. Arzhanova', 'L. D. Pisklakova']2024-01-25T00:00:00https://www.semanticscholar.org/paper/ef81fa6f509b9140bef8ab11fda028e1e6c5f7d1 05bba1530b951c85fa2263c4c26374a4518eb25cArtificial Intelligence (AI) is the technology related to simulating human behaviour in machines. Machine intelligence is a subfield of AI in which available raw data is processed to learn inherent patterns and build a model to adapt to new data. Deep learning models utilize very large amount of data and extract important features and classify the data. Multiagent systems or distributed AI systems are autonomous, proactive, reactive and have ability to interact with humans and other agents. Medicine includes all the processes involved in preventing, diagnosing and curing diseases. It includes medical staff and supporting staff records, drug information, decision support information for medical professionals, clinical lab tests, X-rays, magnetic resonance images, surgeries, and so on. AI has a number of applications in medicine including expert systems, medical robots, medial image analysis and distributed medical agents etc. Expert systems can function as medical experts and helpful for patients who are unable to reach a medical specialist due to cost, or being in a remote area. The role of AI is significant in radiology as abnormal data is labelled in medical images obtained from computed tomography, X-rays, and magnetic resonance imaging etc. more accurately. Medical robots assist in patient care, clinical settings, surgeries and in many other ways. Distributed medical agents enable the availability of a number of medical experts online to examine critical cases. In this paper the role of artificial intelligence in the above mentioned medical applications is elaborated with relevant examples. It is concluded that AI is indispensable in medicine for effective and efficient healthcare.International Journal For Multidisciplinary Research400It is concluded that AI is indispensable in medicine for effective and efficient healthcare.International Journal For Multidisciplinary Research['Syamala Devi']2024-01-25T00:00:00https://www.semanticscholar.org/paper/05bba1530b951c85fa2263c4c26374a4518eb25c 5416d7824641d2caadc70fc586b00fb462b7b797Artificial Intelligence Bibliometric Analysis (AI) is a study involving quantitative measurement and evaluation of scientific literature on artificial intelligence. Relevant keywords in this analysis include "artificial intelligence", "machine teaching", "deep training", and "This method allows researchers to identify major developments and focus in artificial intelligence research, provide insight into the authors' contributions, and understand the direction of this science. Artificial intelligence is a branch of computer science that focuses on developing computing systems that can perform tasks that normally require human intelligence. Using algorithms and mathematical models, AI can process data quickly, identify patterns, and make intelligent decisions.AI development has created applications that have changed the way we work, learn, and interact. From efficient automation systems to virtual assistants that understand and respond to human conversations, AI has made significant contributions to improving productivity and quality of life. We use VOSviewer software to classify the material after reviewing the database.West Science Interdisciplinary Studies00This method allows researchers to identify major developments and focus in artificial intelligence research, provide insight into the authors' contributions, and understand the direction of this science.West Science Interdisciplinary Studies['Rohimatun Nur’aeni', 'Rendy Zalsahra']2024-01-25T00:00:00https://www.semanticscholar.org/paper/5416d7824641d2caadc70fc586b00fb462b7b797 d239f5e36020b12d0c213bace6a9ba8e7b9b4abbJournal of the American College of Surgeons00Journal of the American College of Surgeons[]2024-01-25T00:00:00https://www.semanticscholar.org/paper/d239f5e36020b12d0c213bace6a9ba8e7b9b4abb c953fd42a4c7bb701808b8edf75950cfe3f1a2fbThe Journal of urban technology643Journal of Urban Technology['Ying Xu', 'Federico Cugurullo', 'Heming Zhang', 'Alexander Gaio', 'Weishi Zhang']2024-01-25T00:00:00https://www.semanticscholar.org/paper/c953fd42a4c7bb701808b8edf75950cfe3f1a2fb a187642e0c8e6d9dc6f73f905038761be35db9b1NEJM AI01NEJM AI['David Blumenthal']2024-01-25T00:00:00https://www.semanticscholar.org/paper/a187642e0c8e6d9dc6f73f905038761be35db9b1 95a67bfb20dbe382be0811491340850bc80da06fWhy ethics is not enough.Communications of the ACM20Commun. ACM['Alfred Z. Spector']2024-01-25T00:00:00https://www.semanticscholar.org/paper/95a67bfb20dbe382be0811491340850bc80da06f 905bfadd1aa30c2df444c9b7e0503d91b1c8dcddEducational jurisdictions worldwide are integrating AI education in their curricula, across grades K-12, and across subject areas, with a focus on AI applications, societal implications, and AI ethics. Jurisdictions also focusing on how AI works and how AI is developed are realizing that AI relies heavily on mathematical algorithms. The jurisdictions that are advancing K-12 AI mathematics curricula to prepare students to understand and apply the mathematics concepts used by AI systems are focused on grades 11-12 courses. This paper investigates how AI mathematics curricula may be designed for younger grades. First, we take a close look at the nature of a neural network and identify the mathematics typically used. Second, we review K-12 AI curricula in Canada and internationally and note that they lack a focus on AI mathematics. Third, we offer examples of how we may engage students across grades with mathematics used in the neural networks. Last, we look at future directions of AI mathematics education and research. Neural networks are not the only approach to AI, and there is more to AI than neural networks. However, neural networks have led to impressive progress in the field of AI, such as the development of large language models like ChatGPT. For our paper, focusing on neural networks gives us a sufficient starting point for addressing the questions we raise. This paper contributes to conversations about the intersection of AI education and mathematics education, and the development and research of AI mathematics curricula and teaching and learning resources across K-12.Journal of Digital Life and Learning00This paper investigates how AI mathematics curricula may be designed for younger grades and takes a close look at the nature of a neural network and identifies the mathematics typically used.Journal of Digital Life and Learning['George Gadanidis', 'Li Li', 'Jonathan Tan']2024-01-25T00:00:00https://www.semanticscholar.org/paper/905bfadd1aa30c2df444c9b7e0503d91b1c8dcdd 6b903789bfd9e0b88febd63357940eb4603cf65bThe Social science journal (Fort Collins)00The Social Science Journal['Lailatul Sya’diyah']2024-01-25T00:00:00https://www.semanticscholar.org/paper/6b903789bfd9e0b88febd63357940eb4603cf65b baca639517fdced0382c8dea3e95b838128d0d6fGlobalization, industrialization, and digitalization have led to structural changes in the economy and labor markets, affecting their internationalization, flexibility, labor mobility, and the emergence of new forms of employment. The purpose of the academic paper is to identify the economic consequences of digital transformation and automation of labor markets in the example of the EU-27 countries for the period 2013-2022. The structural-functional analysis was used in the academic paper to characterize and systematically study the economic consequences of digitalization and automation in the labor markets of the EU-27 countries. The functioning of the labor market in various EU-27 countries in the context of digital transformation is characterized by several features. The EU-27 labor markets are characterized by rapid employment recovery, especially during the pandemic and economic downturn in 2020, and employment revival in 2021-2022. In the Member States, a stable level of employment is generally observed; there is a decrease in the share of people with 0-2, and 3-4 educational attainment levels, while the share of people with 5-8 educational attainment levels is growing, and there is a stable growth in wages and incomes. Changes in the social structure of the employed by vocational and educational levels and qualifications in favor of increasing the importance and role of higher education have been revealed. Changes in forms of employment and the emergence of new forms of employment (sharing of workers and workplaces, temporary management, casual labor, ICT-based mobile work, voucher work, portfolio work, crowd employment, and collaborative work) have been identified.Financial Engineering70The structural-functional analysis was used in the academic paper to characterize and systematically study the economic consequences of digitalization and automation in the labor markets of the EU-27 countries for the period 2013-2022.Financial Engineering['Anastasiia Tokunova', 'Viktor Zvonar', 'Dmytro Polozhentsev', 'Valentyna Pavlova', 'Olesia Fedoruk']2024-01-25T00:00:00https://www.semanticscholar.org/paper/baca639517fdced0382c8dea3e95b838128d0d6f 005eaadf776432c4ec72847d7e715c2d72a98d1cAesthetic surgery journal60Aesthetic surgery journal['Jose F. Palacios', 'Nicholas Bastidas']2024-01-25T00:00:00https://www.semanticscholar.org/paper/005eaadf776432c4ec72847d7e715c2d72a98d1c 379c58927ce77d66b47ea3bad90fd338a3c67067Indian Journal of Ophthalmology170Indian journal of ophthalmology['Shweta Agarwal']2024-01-25T00:00:00https://www.semanticscholar.org/paper/379c58927ce77d66b47ea3bad90fd338a3c67067 cf294b28219c898551c8ff2b54b38038ef05099eJournal of the Knowledge Economy310Journal of the Knowledge Economy['Shuai Wang', 'Xin Huang', 'Mengyue Xia', 'Xing Shi']2024-01-25T00:00:00https://www.semanticscholar.org/paper/cf294b28219c898551c8ff2b54b38038ef05099e d27aceaa81591acde8d983ce419f413b61f9c4c9NEJM AI210NEJM AI['Josiah Aklilu', 'Min Woo Sun', 'Shelly Goel', 'Sebastiano Bartoletti', 'Anita Rau', 'Griffin Olsen', 'Kay S. Hung', 'Sophie L. Mintz', 'Vicki Luong', 'Arnold Milstein', 'Mark J. Ott', 'R. Tibshirani', 'Jeffrey K. Jopling', 'Eric C. Sorenson', 'D. Azagury', 'Serena Yeung-Levy']2024-01-25T00:00:00https://www.semanticscholar.org/paper/d27aceaa81591acde8d983ce419f413b61f9c4c9 98af717f55cd0ca9d186477646c3d17d26a9f2f8Balkan Medical Journal160Balkan Medical Journal['Selçuk Korkmaz']2024-01-25T00:00:00https://www.semanticscholar.org/paper/98af717f55cd0ca9d186477646c3d17d26a9f2f8 bc8d94c45e133dfc9d37b18a78bafc814e9debcbEuropean Journal of Preventive Cardiology120European journal of preventive cardiology['P. Agostoni', 'G. Cattadori', 'E. Salvioni', 'S. Sciomer']2024-01-25T00:00:00https://www.semanticscholar.org/paper/bc8d94c45e133dfc9d37b18a78bafc814e9debcb 64ad0c033dba5375f8d6bcfbf6d0a08b90893325Science Editing00Science Editing['Payam Hosseinzadeh Kasani', 'Kee Hyun Cho', 'Jae-Won Jang', 'Cheol-Heui Yun']2024-01-25T00:00:00https://www.semanticscholar.org/paper/64ad0c033dba5375f8d6bcfbf6d0a08b90893325 89227587b96f1fe7c3bf78019380469e75a48793Multimedia tools and applications650Multimedia Tools and Applications['Mridula Dwivedi', 'Babita Pandey', 'Vipin Saxena']2024-01-25T00:00:00https://www.semanticscholar.org/paper/89227587b96f1fe7c3bf78019380469e75a48793 df5c13147fb9854cedc38127e9eeba0fbaf64c79A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.Medinfo00It is found out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care, and the early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.Studies in health technology and informatics['Michaela C. Neff', 'Jannik Schaaf', 'Richard Noll', 'Svea Holtz', 'Dania Schütze', 'S. M. Köhler', 'Beate S. Müller', 'Najia Ahmadi', 'Michael von Wagner', 'Holger Storf']2024-01-25T00:00:00https://www.semanticscholar.org/paper/df5c13147fb9854cedc38127e9eeba0fbaf64c79 6fe6e3d9ebc672124b43149fb8de1915c8c4796dAs artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML)-enabled medical devices are increasingly used in healthcare. In this study, we collected publicly available information on AI/ML-enabled medical devices approved by the FDA in the United States, as of the latest update on 19 October 2023. We performed comprehensive analysis of a total of 691 FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices and offer an in-depth analysis of clearance pathways, approval timeline, regulation type, medical specialty, decision type, recall history, etc. We found a significant surge in approvals since 2018, with clear dominance of the radiology specialty in the application of machine learning tools, attributed to the abundant data from routine clinical data. The study also reveals a reliance on the 510(k)-clearance pathway, emphasizing its basis on substantial equivalence and often bypassing the need for new clinical trials. Also, it notes an underrepresentation of pediatric-focused devices and trials, suggesting an opportunity for expansion in this demographic. Moreover, the geographical limitation of clinical trials, primarily within the United States, points to a need for more globally inclusive trials to encompass diverse patient demographics. This analysis not only maps the current landscape of AI/ML-enabled medical devices but also pinpoints trends, potential gaps, and areas for future exploration, clinical trial practices, and regulatory approaches. In conclusion, our analysis sheds light on the current state of FDA-approved AI/ML-enabled medical devices and prevailing trends, contributing to a wider comprehension.Electronics1820A significant surge in approvals since 2018 is found, with clear dominance of the radiology specialty in the application of machine learning tools, attributed to the abundant data from routine clinical data.Electronics['Geeta Joshi', 'Aditi Jain', 'Shalini Reddy Araveeti', 'Sabina Adhikari', 'Harshit Garg', 'Mukund Bhandari']2024-01-24T00:00:00https://www.semanticscholar.org/paper/6fe6e3d9ebc672124b43149fb8de1915c8c4796d 163ce2c06d70662b61689325bb18caeb8e9f1d91AI is increasingly being used in the public sector, including public security. In this context, the use of AI-powered remote biometric identification (RBI) systems is a much-discussed technology. RBI systems are used to identify criminal activity in public spaces, but are criticised for inheriting biases and violating fundamental human rights. It is therefore important to ensure that such systems are developed in the public interest, which means that any technology that is deployed for public use needs to be scrutinised. While there is a consensus among business leaders, policymakers and scientists that AI must be developed in an ethical and trustworthy manner, scholars have argued that ethical guidelines do not guarantee ethical AI, but rather prevent stronger regulation of AI. As a possible counterweight, public opinion can have a decisive influence on policymakers to establish boundaries and conditions under which AI systems should be used -- if at all. However, we know little about the conditions that lead to regulatory demand for AI systems. In this study, we focus on the role of trust in AI as well as trust in law enforcement as potential factors that may lead to demands for regulation of AI technology. In addition, we explore the mediating effects of discrimination perceptions regarding RBI. We test the effects on four different use cases of RBI varying the temporal aspect (real-time vs. post hoc analysis) and purpose of use (persecution of criminals vs. safeguarding public events) in a survey among German citizens. We found that German citizens do not differentiate between the different modes of application in terms of their demand for RBI regulation. Furthermore, we show that perceptions of discrimination lead to a demand for stronger regulation, while trust in AI and trust in law enforcement lead to opposite effects in terms of demand for a ban on RBI systems.arXiv.org740It is found that German citizens do not differentiate between the different modes of application in terms of their demand for RBI regulation, while trust in AI and trust in law enforcement lead to opposite effects in terms of demand for a ban on RBI systems.ArXiv['Kimon Kieslich', 'Marco Lünich']2024-01-24T00:00:00https://www.semanticscholar.org/paper/163ce2c06d70662b61689325bb18caeb8e9f1d91 a64b0c39bfade4210245bea02009999a9d2ee850How does the European Union handle the soft regulation of digital political campaigning? We assesses the effectiveness of the EU’s soft governance concerning digital campaigning by examining how global digital platforms respond to the EU Code of Practice on Disinformation. In doing so, we advance a framework for analysis which measures specific steps in the platform compliance with soft law. Our results, based on the content analysis of platforms’ annual reports, show that compliance depends on the priority assigned to regulatory themes by on-line corporations. Overall, we find higher levels of platform formal commitment rather than symbolic commitment through forms of report editing to signal compliance with the code of practice. Our analysis also shows evidence of implementation following from formal commitments when reporting requirements are less rigid. Consequently, EU soft governance can be effective for digital campaigning in areas prioritised by the addressees of regulation.Policy studies582Policy Studies['Gabriela Borz', 'Fabrizio De Francesco', 'Thomas L. Montgomerie', 'Michael Peter Bellis']2024-01-24T00:00:00https://www.semanticscholar.org/paper/a64b0c39bfade4210245bea02009999a9d2ee850 0d83892c3ddab8a374f765130ffb665858cf245eThe article analyzes the main aspects of the legal framework for regulating foreign economic activity at Ukrainian enterprises. It was found that global geopolitical risks have increased dramatically after Russia’s invasion of Ukraine. Investors, market participants and policy makers expect that the war will have a dampening impact on the global economy, raising inflation, leading to a sharp increase in uncertainty and risks of serious negative consequences. Despite the significant disruption to trade logistics, the Ukrainian legislature responded promptly and amended the Customs Code of Ukraine and other regulations. Among the important changes are the exemption of certain goods from import duties and VAT, and simplification of the goods declaration procedure.Visnik Zaporiz'kogo nacional'nogo universitetu. Ekonomicni nauki00Visnik Zaporiz kogo nacional nogo universitetu Ekonomicni nauki['Д.В. Куліш']2024-01-24T00:00:00https://www.semanticscholar.org/paper/0d83892c3ddab8a374f765130ffb665858cf245e 18c462a848b51e48376bc05fa9ed6d52882ee855Behavior Analysis in Practice00Behavior Analysis in Practice['Albert Malkin', 'Karl F. Gunnarsson', 'Kendra Thomson', 'Promise O. Tewogbola', 'Eric A. Jacobs']2024-01-24T00:00:00https://www.semanticscholar.org/paper/18c462a848b51e48376bc05fa9ed6d52882ee855 541958c384c3356c2da798344600b2a07482e035Recently, AI assistants based on large language models (LLMs) show surprising performance in many tasks, such as dialogue, solving math problems, writing code, and using tools. Although LLMs possess intensive world knowledge, they still make factual errors when facing some knowledge intensive tasks, like open-domain question answering. These untruthful responses from the AI assistant may cause significant risks in practical applications. We believe that an AI assistant's refusal to answer questions it does not know is a crucial method for reducing hallucinations and making the assistant truthful. Therefore, in this paper, we ask the question"Can AI assistants know what they don't know and express them through natural language?"To answer this question, we construct a model-specific"I don't know"(Idk) dataset for an assistant, which contains its known and unknown questions, based on existing open-domain question answering datasets. Then we align the assistant with its corresponding Idk dataset and observe whether it can refuse to answer its unknown questions after alignment. Experimental results show that after alignment with Idk datasets, the assistant can refuse to answer most its unknown questions. For questions they attempt to answer, the accuracy is significantly higher than before the alignment.arXiv.org443A model-specific"I don't know"(Idk) dataset is constructed for an assistant, which contains its known and unknown questions, based on existing open-domain question answering datasets, and whether it can refuse to answer its unknown questions after alignment.ArXiv['Qinyuan Cheng', 'Tianxiang Sun', 'Xiangyang Liu', 'Wenwei Zhang', 'Zhangyue Yin', 'Shimin Li', 'Linyang Li', 'Kai Chen', 'Xipeng Qiu']2024-01-24T00:00:00https://www.semanticscholar.org/paper/541958c384c3356c2da798344600b2a07482e035 32ebace7d40cb9f00c0cfa583b76ef3687b429e6Interactive storytelling is vital for preschooler development. While children's interactive partners have traditionally been their parents and teachers, recent advances in artificial intelligence (AI) have sparked a surge of AI-based storytelling technologies. As these technologies become increasingly ubiquitous in preschoolers' lives, questions arise regarding how they function in practical storytelling scenarios and, in particular, how parents, the most critical stakeholders, experience and perceive these technologies. This paper investigates these questions through a qualitative study with 17 parents of children aged 3-6. Our findings suggest that even though AI-based storytelling technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive, functional, and algorithmic challenges. We elaborate on these challenges and discuss the possible implications of future AI-based storytelling technologies for preschoolers. We conclude by highlighting the design implications for future AI-based storytelling technologies.arXiv.org682It is suggested that even though AI-based storytelling technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive, functional, and algorithmic challenges.ArXiv['Yuling Sun', 'Jiali Liu', 'Bingsheng Yao', 'Jiaju Chen', 'Dakuo Wang', 'Xiaojuan Ma', 'Yuxuan Lu', 'Ying Xu', 'Liang He']2024-01-24T00:00:00https://www.semanticscholar.org/paper/32ebace7d40cb9f00c0cfa583b76ef3687b429e6 6c732983dee743e95bfeea05a419532294627923As the number of accepted papers at AI and ML conferences reaches into the thousands, it has become unclear how researchers access and read research publications. In this paper, we investigate the role of social media influencers in enhancing the visibility of machine learning research, particularly the citation counts of papers they share. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023, alongside controls precisely matched by 9 key covariates. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers, with median citation counts 2-3 times higher than those of the control group. Additionally, the study delves into the geographic, gender, and institutional diversity of highlighted authors. Given these findings, we advocate for a responsible approach to curation, encouraging influencers to uphold the journalistic standard that includes showcasing diverse research topics, authors, and institutions.arXiv.org261The role of social media influencers in enhancing the visibility of machine learning research, particularly the citation counts of papers they share, is investigated, with a significant increase in citations for papers endorsed by these influencers.ArXiv['Iain Xie Weissburg', 'Mehir Arora', 'Liangming Pan', 'William Yang Wang']2024-01-24T00:00:00https://www.semanticscholar.org/paper/6c732983dee743e95bfeea05a419532294627923 cf4dad59f723c28381310f2d56402872b6b6550eGPU remoting is a promising technique for supporting AI applications. Networking plays a key role in enabling remoting. However, for efficient remoting, the network requirements in terms of latency and bandwidth are unknown. In this paper, we take a GPU-centric approach to derive the minimum latency and bandwidth requirements for GPU remoting, while ensuring no (or little) performance degradation for AI applications. Our study including theoretical model demonstrates that, with careful remoting design, unmodified AI applications can run on the remoting setup using commodity networking hardware without any overhead or even with better performance, with low network demands.arXiv.org451This study including theoretical model demonstrates that, with careful remoting design, unmodified AI applications can run on the remoting setup using commodity networking hardware without any overhead or even with better performance, with low network demands.ArXiv['Tianxia Wang', 'Zhuofu Chen', 'Xingda Wei', 'Jinyu Gu', 'Rong Chen', 'Haibo Chen']2024-01-24T00:00:00https://www.semanticscholar.org/paper/cf4dad59f723c28381310f2d56402872b6b6550e 99ba80278593b8b5911f6712cc37225887e84941UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices. This paper provides a guide to the implemented and prospective artificial intelligence (AI) capabilities of UMBRELLA in real-world IoT systems. Four existing UMBRELLA applications are presented in detail: 1) An automated streetlight monitoring for detecting issues and triggering maintenance alerts; 2) A Digital twin of building environments providing enhanced air quality sensing with reduced cost; 3) A large-scale Federated Learning framework for reducing communication overhead; and 4) An intrusion detection for containerised applications identifying malicious activities. Additionally, the potential of UMBRELLA is outlined for future smart city and multi-robot crowdsensing applications enhanced by semantic communications and multi-agent planning. Finally, to realise the above use-cases we discuss the need for a tailored MLOps platform to automate UMBRELLA’s model pipelines and establish trust.2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)211This paper provides a guide to the implemented and prospective artificial intelligence capabilities of UMBRELLA in real-world IoT systems and discusses the need for a tailored MLOps platform to automate UMBRELLA’s model pipelines and establish trust.2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)['Peizheng Li', 'Ioannis Mavromatis', 'Aftab Khan']2024-01-24T00:00:00https://www.semanticscholar.org/paper/99ba80278593b8b5911f6712cc37225887e84941 84823c397546a517ccc189630f958ddf44e17715OBJECTIVE Large language models (LLMs) such as ChatGPT-4 have raised critical questions regarding their distinguishability from human-generated conten. In this research we evaluated the effectiveness of online detection tools in identifying ChatGPT-4 vs human-written text. METHODS A two texts produced by Chat GPT-4 using differing prompts and one text created by a human author were analytically assessed using the following online detection tools: GPTZero, ZeroGPT, Writer ACD, and Originality. RESULTS The findings revealed a notable variance in the detection capabilities of the employed detection tools. GPTZero and ZeroGPT exhibited inconsistent assessments regarding the AI-origin of the texts. Writer ACD predominantly identified texts as human-written, whereas Originality consistently recognized the AI-generated content in both samples from Chat GPT-4. This highlights Originality's enhanced sensitivity to patterns characteristic of AI-generated text. CONCLUSION The study demonstrates that while automatic detection tools may discern texts generated by Chat GPT-4 significant variability exists in their accuracy. Undoubtedly, there is an urgent need for advanced detection tools to ensure the authenticity and integrity of content, especially in scientific and academic research. However, our findings underscore an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa.Current Medical Research and Opinion161The study demonstrates that while automatic detection tools may discern texts generated by Chat GPT-4 significant variability exists in their accuracy, and underscores an urgent need for more refined detection methodologies to prevent the misdetection of human-written content as AI-generated and vice versa.Current medical research and opinion['Valentina Bellini', 'Federico Semeraro', 'J. Montomoli', 'Marco Cascella', 'E. Bignami']2024-01-24T00:00:00https://www.semanticscholar.org/paper/84823c397546a517ccc189630f958ddf44e17715 1de3da237c90aff6e0023993775306f700c479a4Installing probabilistic world models into artificial agents opens an efficient channel for humans to communicate with and control these agents. In addition to updating agent policies, humans can modify their internal world models in order to influence their decisions. The challenge, however, is that currently existing world models are difficult for humans to adapt because they lack a natural communication interface. Aimed at addressing this shortcoming, we develop Language-Guided World Models (LWMs), which can capture environment dynamics by reading language descriptions. These models enhance agent communication efficiency, allowing humans to simultaneously alter their behavior on multiple tasks with concise language feedback. They also enable agents to self-learn from texts originally written to instruct humans. To facilitate the development of LWMs, we design a challenging benchmark based on the game of MESSENGER (Hanjie et al., 2021), requiring compositional generalization to new language descriptions and environment dynamics. Our experiments reveal that the current state-of-the-art Transformer architecture performs poorly on this benchmark, motivating us to design a more robust architecture. To showcase the practicality of our proposed LWMs, we simulate a scenario where these models augment the interpretability and safety of an agent by enabling it to generate and discuss plans with a human before execution. By effectively incorporating language feedback on the plan, the models boost the agent performance in the real environment by up to three times without collecting any interactive experiences in this environment.arXiv.org412Language-Guided World Models are developed, which can capture environment dynamics by reading language descriptions, allowing humans to simultaneously alter their behavior on multiple tasks with concise language feedback and enable agents to self-learn from texts originally written to instruct humans.ArXiv['Alex Zhang', 'Khanh Nguyen', 'Jens Tuyls', 'Albert Lin', 'Karthik Narasimhan']2024-01-24T00:00:00https://www.semanticscholar.org/paper/1de3da237c90aff6e0023993775306f700c479a4 3455eb47ffd50c6322833de6747bdbd4a69b4b35 This Viewpoint discusses the managerial and organizational challenges that could result from the use of artificial intelligence systems in psychiatric research and care. JAMA psychiatry42JAMA psychiatry['Nick Obradovich', 'Tim Johnson', 'Martin P. Paulus']2024-01-24T00:00:00https://www.semanticscholar.org/paper/3455eb47ffd50c6322833de6747bdbd4a69b4b35 8023f3a1b07bda7eb2267f3ecd2228763def03ecJournal of Marketing Management352Journal of Marketing Management['R. Kozinets', 'Mina Seraj-Aksit']2024-01-24T00:00:00https://www.semanticscholar.org/paper/8023f3a1b07bda7eb2267f3ecd2228763def03ec b81f023083c13a0c9117e931df140a6158fb9689Abstrak Chat GPT merupakan salah satu AI yang paling sering diminai oleh mahasiswa dalam memahami pembelajaran. Hal ini karena Chat GPT memberikan banyak sumber data secara cepat dan instan dalam memecahkan suatu masalah. Penelitian ini bertujuan mengetahui motivasi mahasiswa mempelajari Moprhosyntax sebelum dan setelah menggunakan apikasi AI chat GPT. Dengan kata lain, peneliti ingin mengukur efektifitas media Chat GPT Bagai mahasiswa dalam pembelajaran Moprheme dan Syntax yang disingkat dnegan Morphosyntax. Penelitian ini merupakan penelitian eksperimen yang berfokus pada motivasi mahasiswa. Peneliti menggunakan purposive sampling dengan menggunakan 32 mahasiswa. Sementara itu, kueasioner yang teah di desain dalam 25 petanyaan pilihan ganda digunakan sebagai data penelitian. Penelitian ini menggunakan statistik deskriptif dan statistik inferensial dengan uji-t untuk menganalisis data. Berdasarkan hasil analisis, diketahui taraf signifikan 5% untuk Df = 31, nilai thitung 6,621, dan nilai ttabel 1,696, maka nilai thitung ttabel yang berarti Ha diterima. Mahasiswa juga mendapatkan nilai rata-rata pre-test 75,718 dan rata-rata post-test 83,906. Dengan demikian, ChatGPT efektif dalam meningkatkan minat mahasiswa dalam pembelajaran linguistik   Abstract The students were familiar with ChatGPT as AI in education. This application provides various data to solve the students’ problems easily and quickly. The purpose of this study is to determine students' motivation before using ChatGPT media in linguistic learning; Determine students' motivation after using ChatGPT media in linguistic learning; and determine how effective ChatGPT is in increasing students' motivation in linguistic learning. This study was designed quantitatively, and the type of experiment was pre-experiment with one pretest-posttest group. 32 students in the treatment class were the sample used by the author.  Purposive sampling was the sampling method. In this study, a questionnaire test was used to collect data. This study used descriptive statistics and inferential statistics with t-test to analyze the data based on the exam results. Based on the results of the analysis, it is known that the 5% significant level for Df = 31, the tcount value is 6.621, and the ttable value is 1.696, then the tcount value is ttable which means Ha is accepted. Students also get an average pre-test score of 75.718 and an average post-test of 83,906. Thus, "ChatGPT is effective in increasing students' interest in learning linguistics."  Jurnal Kiprah140Jurnal Kiprah['Caltira Rosiana', 'Triana Wuri Cahyanti', 'Lina Eka Rahayu']2024-01-24T00:00:00https://www.semanticscholar.org/paper/b81f023083c13a0c9117e931df140a6158fb9689 65eec4b83927a3efe1449e33200227a8a45dd568My research centers on the development of context-adaptive AI systems to improve end-user adoption through the integration of technical methods. I deploy these AI systems across various interaction modalities, including user interfaces and embodied agents like robots, to expand their practical applicability. My research unfolds in three key stages: design, development, and deployment. In the design phase, user-centered approaches were used to understand user experiences with AI systems and create design tools for user participation in crafting AI explanations. In the ongoing development stage, a safety-guaranteed AI system for a robot agent was created to automatically provide adaptive solutions and explanations for unforeseen scenarios. The next steps will involve the implementation and evaluation of context-adaptive AI systems in various interaction forms. I seek to prioritize human needs in technology development, creating AI systems that tangibly benefit end-users in real-world applications and enhance interaction experiences.CHI Extended Abstracts430A safety-guaranteed AI system for a robot agent was created to automatically provide adaptive solutions and explanations for unforeseen scenarios and the next steps will involve the implementation and evaluation of context-adaptive AI systems in various interaction forms.ArXiv['Christine P Lee']2024-01-24T00:00:00https://www.semanticscholar.org/paper/65eec4b83927a3efe1449e33200227a8a45dd568 0baf99d199905fa306bc408379fb8d00ad519c66 As artificial intelligence (AI) systems become increasingly capable of performing design tasks, they are expected to be deployed to assist human designers' decision-making in a greater variety of ways. For complex design problems such as those with multiple objectives, one AI may not always perform its expected accuracy due to the complexity of decision-making, and therefore multiples of AIs may be implemented to provide design suggestions. For such assistance to be productive, human designers must develop appropriate confidence in each AI and in themselves and accept or reject AI inputs accordingly. This work conducts a human subjects experiment to examine the development of a human designer's confidence in each AI and self-confidence throughout decision-making assisted by two AIs and how these confidences influence the decision to accept AI inputs. Major findings demonstrate that certain performance combinations of the two AIs and feedback lead to severe decreases in a human designer's confidences. Additionally, a human designer's decision to accept AI suggestions depends on their self-confidence and confidence in one of the two AIs. Finally, an additional AI does not increase a human designer's likelihood of conforming to AI suggestions. Therefore, in comparison to a scenario with one AI, the results in this work caution the implementation of an additional AI to AI-assisted decision-making scenarios. The insights also inform the design and management of human-AI teams to improve the outcome of AI-assisted decision-making.Journal of Mechanical Design00This work conducts a human subjects experiment to examine the development of a human designer's confidence in each AI and self-confidence throughout decision-making assisted by two AIs and how these confidences influence the decision to accept AI inputs and how these confidences influence the decision to accept AI inputs.Journal of Mechanical Design['L. Chong', 'K. Kotovsky', 'Jonathan Cagan']2024-01-24T00:00:00https://www.semanticscholar.org/paper/0baf99d199905fa306bc408379fb8d00ad519c66 f52c27c9fd3e7a4c9f90c0fd6d994718d7635acaThere is an imbalance in the development of artificial intelligence between industries. Compared to non-AI enterprise, AI- enterprise will save labor, enhance innovation capabilities, and improve production efficiency. By constructing a two-industry model of AI and non-AI enterprise, this paper finds that with the development of artificial intelligence in the same industry, the AI enterprise will occupy a dominant position, attracting labor and capital from the non-AI enterprise into the AI enterprise. In different industries, the development of artificial intelligence improves the production efficiency of the enterprise. However, due to the price effect, non-AI enterprise benefits more. Labor and capital flow from AI enterprise to non-AI enterprise. In order to promote the improvement of production efficiency in the whole society, the government can tax non-AI enterprise and subsidize them to AI enterprise. Taxation promotes the degree of automation and the improvement of production efficiency, but it has only a short-term effect on the development of AI. At the same time, taxation inhibits the development of non-AI enterprise, and there is a high risk of unemployment. When both industries use artificial intelligence for production, the labor share and the capital share of the two industries will tend to the same value. The convergence of technology measures is conducive to increasing labor income share and reducing income inequality, but it is not conducive to innovation.PLoS ONE490By constructing a two-industry model of AI and non-AI enterprise, this paper finds that with the development of artificial intelligence in the same industry, the AI enterprise will occupy a dominant position, attracting labor and capital from the non-AI enterprise into the AI enterprise.PLOS ONE['Xu Huang']2024-01-24T00:00:00https://www.semanticscholar.org/paper/f52c27c9fd3e7a4c9f90c0fd6d994718d7635aca ddbb5e0a21fd69f80248cc740cfe61ef872cad9aDesigning for AI trustworthiness is challenging, with a lack of practical guidance despite extensive literature on trust. The Multisource AI Scorecard Table (MAST), a checklist rating system, addresses this gap in designing and evaluating AI-enabled decision support systems. We propose the Principled Approach for Designing Trustable Human-centered AI systems using MAST Methodology (PADTHAI-MM), a nine-step framework what we demonstrate through the iterative design of a text analysis platform called the REporting Assistant for Defense and Intelligence Tasks (READIT). We designed two versions of READIT, high-MAST including AI context and explanations, and low-MAST resembling a"black box"type system. Participant feedback and state-of-the-art AI knowledge was integrated in the design process, leading to a redesigned prototype tested by participants in an intelligence reporting task. Results show that MAST-guided design can improve trust perceptions, and that MAST criteria can be linked to performance, process, and purpose information, providing a practical and theory-informed basis for AI system design.arXiv.org410Results show that MAST-guided design can improve trust perceptions, and that MAST criteria can be linked to performance, process, and purpose information, providing a practical and theory-informed basis for AI system design.ArXiv['Nayoung Kim', 'Myke C. Cohen', 'Yang Ba', 'Anna Pan', 'Shawaiz Bhatti', 'Pouria Salehi', 'James Sung', 'Erik Blasch', 'M. Mancenido', 'Erin K. Chiou']2024-01-24T00:00:00https://www.semanticscholar.org/paper/ddbb5e0a21fd69f80248cc740cfe61ef872cad9a a8a9993cc9e8994cec859e9e71ca563cfaa2034cExposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted an experiment (800+ participants, 40+ countries) where participants viewed creative ideas that were from ChatGPT or prior experimental participants and then brainstormed their own idea. We varied the number of AI-generated examples (none, low, or high exposure) and if the examples were labeled as 'AI' (disclosure). Our dynamic experiment design -- ideas from prior participants in an experimental condition are used as stimuli for future participants in the same experimental condition -- mimics the interdependent process of cultural creation: creative ideas are built upon prior ideas. Hence, we capture the compounding effects of having LLMs 'in the culture loop'. We find that high AI exposure (but not low AI exposure) did not affect the creativity of individual ideas but did increase the average amount and rate of change of collective idea diversity. AI made ideas different, not better. There were no main effects of disclosure. We also found that self-reported creative people were less influenced by knowing an idea was from AI, and that participants were more likely to knowingly adopt AI ideas when the task was difficult. Our findings suggest that introducing AI ideas into society may increase collective diversity but not individual creativity.arXiv.org690It is found that high AI exposure (but not low AI exposure) did not affect the creativity of individual ideas but did increase the average amount and rate of change of collective idea diversity.ArXiv['Joshua Ashkinaze', 'Julia Mendelsohn', 'Qiwei Li', 'Ceren Budak', 'Eric Gilbert']2024-01-24T00:00:00https://www.semanticscholar.org/paper/a8a9993cc9e8994cec859e9e71ca563cfaa2034c ef2db8291c8ad0e7d59dd1483a8b52bd052ce5ab Machine-learning (ML) seismology relies on large datasets with high-fidelity labels from humans to train generalized models. Among the seismological applications of ML, earthquake detection, and P- and S-wave arrival picking are the most widely studied, with capabilities that can exceed humans. Here, we present a regional artificial intelligence (AI) earthquake dataset (TXED) compiled for the state of Texas. The TXED dataset is composed of earthquake signals with manually picked P- and S-wave arrival times and manually picked noise waveforms corresponding to more than 20,000 earthquake events spanning from the beginning of the Texas seismological network (TexNet) (1 January 2017) to date. These data are a supplement to the existing worldwide open-access seismological AI datasets and represent the signal and noise characteristics of Texas. Direct applications of the TXED datasets include improving the performance of a global picking model in Texas by transfer learning using the new dataset. This dataset will also serve as a benchmark dataset for fundamental AI research like designing seismology-oriented deep-learning architectures. We plan to continue to expand the TXED dataset as more observations are made by TexNet analysts.Seismological Research Letters180The TXED dataset is composed of earthquake signals with manually picked P- and S-wave arrival times and manually picked noise waveforms corresponding to more than 20,000 earthquake events spanning from the beginning of the Texas seismological network (TexNet) (1 January 2017) to date.Seismological Research Letters['Yangkang Chen', 'Alexandras Savvaidis', 'O.M. Saad', 'Guo-Chin Dino Huang', 'Daniel Siervo', 'Vincent O’Sullivan', 'Cooper McCabe', 'Bede Uku', 'Preston Fleck', 'Grace Burke', 'Natalie L. Alvarez', 'Jessica Domino', 'I. Grigoratos']2024-01-24T00:00:00https://www.semanticscholar.org/paper/ef2db8291c8ad0e7d59dd1483a8b52bd052ce5ab 9ffe1d889fc0bad5eb875fb929e248fa65fdbdf7The "Emergence of AI in Marketing and Its Implications" explores the profound impact of Artificial Intelligence (AI) on contemporary marketing strategies. Through an exhaustive examination of secondary data, this paper illuminates how businesses leverage AI to redefine customer experiences, enhance decision-making, and optimize operational efficiency. Delving into various applications, the paper uncovers how leading companies, exemplified by case studies on Amazon and Netflix, strategically deploy AI to gain competitive advantages in dynamic markets. While the benefits of AI in marketing are evident, the paper acknowledges the challenges inherent in this transformative shift. Ethical considerations, privacy concerns, and potential biases in algorithms demand nuanced responses. The conclusion emphasizes the necessity for businesses to adopt responsible AI practices, fostering transparency and accountability. This exploration underscores the dynamic nature of the AI-marketing landscape, emphasizing the need for businesses to stay agile and adaptive. As the intersection of technology and marketing evolves, the abstract envisions a future where businesses, armed with innovative AI applications, forge a redefined relationship with consumers. The paper concludes by calling for a balanced approach that integrates innovation with ethical considerations, charting a course toward a future where AI and marketing coalesce for the benefit of businesses and consumers alike.Lloyd Business Review150The paper concludes by calling for a balanced approach that integrates innovation with ethical considerations, charting a course toward a future where AI and marketing coalesce for the benefit of businesses and consumers alike.Lloyd Business Review['Ayan Barat', 'Krity Gulati']2024-01-24T00:00:00https://www.semanticscholar.org/paper/9ffe1d889fc0bad5eb875fb929e248fa65fdbdf7 62f897e518c1933540dbe0a661e742039d6b83bfThe rapid development of ChatGPT and other generative intelligence models has recently catalyzed the integration of artificial intelligence (AI) into medicine. This evolution raises critical challenges that require attention to technological literacy training in medical education. These generative intelligence models can create inaccurate information, known as hallucinations, and introduce bias into unforeseen workflows. Physician involvement remains pivotal in guiding AI applications, as they possess the most critical perspective on the impact of artificial intelligence on patient outcomes. Physicians must have a voice in AI development and contribute to fact-checking and risk reduction. The development of learning these tools in technology during medical school is vital in addressing these challenges. Institutions like the Western University of Health Sciences advocate for increased technological literacy among future healthcare providers. Improving technical proficiency in students will ensure responsible AI integration, potentially reducing healthcare disparities and empowering prospective providers for more patient-centric care.Journal of the Osteopathic Family Physicians of California40Improving technical proficiency in students will ensure responsible AI integration, potentially reducing healthcare disparities and empowering prospective providers for more patient-centric care.Journal of the Osteopathic Family Physicians of California['Kenny Le', 'Frederick Chang']2024-01-24T00:00:00https://www.semanticscholar.org/paper/62f897e518c1933540dbe0a661e742039d6b83bf e158ab9601d3bc7ac1685b8ea41c59fbb3f896bdPrecision agriculture tools like decision support systems increasingly use machine‐learning algorithms and other types of artificial intelligence (AI) to analyze large quantities of agricultural data and provide recommendations to producers and crop advisers. However, several barriers threaten adoption of these tools. Three papers in the recent Agronomy Journal special section, “Machine Learning in Agriculture,” explore this phenomenon and offer solutions and opportunities for building trust in these technologies.CSA News00Three papers in the recent Agronomy Journal special section, “Machine Learning in Agriculture,” explore this phenomenon and offer solutions and opportunities for building trust in these technologies.CSA News['Tess Joosse']2024-01-24T00:00:00https://www.semanticscholar.org/paper/e158ab9601d3bc7ac1685b8ea41c59fbb3f896bd 86493911e18e2c37a10c46ec3b692b8205fe2eb4Web design now plays a crucial part in how people interact with websites thanks to the quick development of the digital world. Traditional web design methods that rely on manual coding have run afoul of the intricate requirements of contemporary interfaces. This is where artificial intelligence (AI) comes in as a revolutionary force in the development of web design. With a particular emphasis on automated code creation, this literature review launches an informative investigation of the relationship between AI and web design.The idea of automated code generation, where AI algorithms allow the conversion of design inputs into useful web code, is inextricably tied to this investigation. In order to enable AI to harness design principles and translate them into concrete, interactive web interfaces, this paper aims to present an integrative review of the methodologies, datasets, and algorithms that enable this. This study outlines the crucial role played by AI in influencing both the present and the future of web design by examining the aggregate insights from the selected research publications.This paper navigates the past, present, and future of AI-powered web design through a rigorous trip. Historical paradigms, the transformational potential of deep learning, actual case studies, ethical issues, and a comprehensive strategy for future progress are all included in the exploration. This assessment aims to illuminate the successes of the area while also inspiriting novel ideas that will transform the development of digital landscapes as the symbiotic link between AI and web design develops.2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)380This paper aims to present an integrative review of the methodologies, datasets, and algorithms that enable AI to harness design principles and translate them into concrete, interactive web interfaces and launches an informative investigation of the relationship between AI and web design.2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)['Veera Harish Muthazhagu', 'B Surendiran']2024-01-24T00:00:00https://www.semanticscholar.org/paper/86493911e18e2c37a10c46ec3b692b8205fe2eb4 915a7ffb14371fe046b78e0f650459699836e52aImage generation using generative AI is rapidly becoming a major new source of visual media, with billions of AI generated images created using diffusion models such as Stable Diffusion and Midjourney over the last few years. In this paper we collect and analyse over 3 million prompts and the images they generate. Using natural language processing, topic analysis and visualisation methods we aim to understand collectively how people are using text prompts, the impact of these systems on artists, and more broadly on the visual cultures they promote. Our study shows that prompting focuses largely on surface aesthetics, reinforcing cultural norms, popular conventional representations and imagery. We also find that many users focus on popular topics (such as making colouring books, fantasy art, or Christmas cards), suggesting that the dominant use for the systems analysed is recreational rather than artistic.EvoMUSART400This study shows that prompting focuses largely on surface aesthetics, reinforcing cultural norms, popular conventional representations and imagery, suggesting that the dominant use for the systems analysed is recreational rather than artistic.ArXiv['Jon McCormack', 'M. T. Llano', 'S. Krol', 'Nina Rajcic']2024-01-24T00:00:00https://www.semanticscholar.org/paper/915a7ffb14371fe046b78e0f650459699836e52a 837ab3ace7581fa53a3f9a1644091071b8a2a239The article describes testing a weakness of the go-playing AI KataGo (9d) detected by K. Pelrine et al. Building groups following the developed 4-level algorithm reveals the incapability of AI to understand intentional sacrifice of larger groups and to prioritize when discovering several group sacrifices on the board. The value of the developed strategy lies in human victory over a potentially unbeatable program even regarding the fact that human is playing on the level of 1q (2000 Elo).RUSSIAN JOURNAL OF INFORMATION TECHNOLOGY IN SPORTS. V.1, №S1, 2024. SPECIAL ISSUE. Collection of abstracts of the VII All-Russian scientific and practical conference with international participation "Sports Informatics Day"00A weakness of the go-playing AI KataGo (9d) detected by K. Pelrine et al. reveals the incapability of AI to understand intentional sacrifice of larger groups and to prioritize when discovering several group sacrifices on the board.RUSSIAN JOURNAL OF INFORMATION TECHNOLOGY IN SPORTS. V.1, №S1, 2024. SPECIAL ISSUE. Collection of abstracts of the VII All-Russian scientific and practical conference with international participation "Sports Informatics Day"['Vadim Filippov', 'Tatiana Zborovskaya']2024-01-24T00:00:00https://www.semanticscholar.org/paper/837ab3ace7581fa53a3f9a1644091071b8a2a239 8e7378140c002a34b7ac30fc1c1eaf1c5dac5ed5With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLM, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed API and API given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from inability to extend beyond basic analyses or more in-depth technical experimental design. We argue that a LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows, such a synergy between human expertise and LLM efficiency in experimentation can open new door for accelerating scientific research, enabling effective experimental protocols archive and sharing in scientific community.arXiv.org400It is argued that a LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows, such a synergy between human expertise and LLM efficiency in experimentation can open new door for accelerating scientific research, enabling effective experimental protocols archive and sharing.ArXiv['Yongtao Liu', 'M. Checa', 'Rama K. Vasudevan']2024-01-24T00:00:00https://www.semanticscholar.org/paper/8e7378140c002a34b7ac30fc1c1eaf1c5dac5ed5 a3a1e2e8d503b2d0dd818e7087a34030ead62711The integration of Artificial Intelligence (AI) in autonomous vehicles represents a transformative leap towards safer, more efficient, and technologically advanced transportation systems. This abstract provides a comprehensive overview of the dynamic landscape where AI converges with autonomous vehicles, examining the synergies, challenges, and far-reaching implications of this groundbreaking integration AI-Powered Perception and Decision-Making: Delving into the technological core, the abstract explores how AI empowers autonomous vehicles with sophisticated perception systems, such as computer vision and sensor fusion. It discusses the role of machine learning algorithms in real-time decision-making, enabling vehicles to adapt to dynamic road conditions and unforeseen circumstances. Challenges in Autonomy: Recognizing the complexity of autonomous systems, the abstract addresses challenges such as handling edge cases, ensuring robustness against adversarial attacks, and navigating regulatory and ethical considerations. It emphasizes the importance of addressing these challenges to foster public trust and acceptance of autonomous vehicles. Human-AI Interaction in Autonomous Vehicles: Examining the interface between humans and AI-driven vehicles, the abstract discusses the importance of designing intuitive and trustworthy communication channels. It explores advancements in natural language processing and gesture recognition, fostering seamless collaboration between humans and autonomous systems. Regulatory Landscape and Ethical Considerations: Recognizing the pivotal role of regulations, the abstract discusses the evolving regulatory landscape for autonomous vehicles. It delves into ethical considerations surrounding AI decisions in critical situations, underscoring the need for a harmonized approach to ensure responsible AI deployment in autonomous driving scenarios.International Journal of Advanced Research in Science, Communication and Technology20This abstract provides a comprehensive overview of the dynamic landscape where AI converges with autonomous vehicles, examining the synergies, challenges, and far-reaching implications of this groundbreaking integration.International Journal of Advanced Research in Science, Communication and Technology['Sharique Masood Khan']2024-01-24T00:00:00https://www.semanticscholar.org/paper/a3a1e2e8d503b2d0dd818e7087a34030ead62711 4946299870c72383e64ec63923de62c74094e632Cognitive Computation251Cognitive Computation['Gaurang Bansal', 'V. Chamola', 'Amir Hussain', 'Mohsen Guizani', 'D. Niyato']2024-01-24T00:00:00https://www.semanticscholar.org/paper/4946299870c72383e64ec63923de62c74094e632 50ab4cea94aee97475ee085e8f7de82484f081c1Nature Machine Intelligence71Nature Machine Intelligence['Noelia Ferruz', 'M. Zitnik', 'Pierre-Yves Oudeyer', 'Emmie Hine', 'Nandana Sengupta', 'Yiyu Shi', 'Diana Mincu', 'Sebastian Porsdam Mann', 'Payel Das', 'Francesco Stella']2024-01-24T00:00:00https://www.semanticscholar.org/paper/50ab4cea94aee97475ee085e8f7de82484f081c1 fe1e7f07ec5546061ba9fda973f0b92aaff0a548Artificial intelligence (AI) has disrupted modern workplaces like never before and has induced digital workstyles. These technological advancements are generating significant interest among HR leaders to embrace AI in human resource management (HRM). Researchers and practitioners are keen to investigate the adoption of AI in HRM and the resultant human–machine collaboration. This study investigates HRM specific factors that enable and inhibit the adoption of AI in extended HR ecosystems and adopts a qualitative case research design with an abductive approach. It studies three well-known Indian companies at different stages of AI adoption in HR functions. This research investigates key enablers such as optimistic and collaborative employees, strong digital leadership, reliable HR data, specialized HR partners, and well-rounded AI ethics. The study also examines barriers to adoption: the inability to have a timely pulse check of employees’ emotions, ineffective collaboration of HR employees with digital experts as well as external HR partners, and not embracing AI ethics. This study contributes to the theory by providing a model for AI adoption and proposes additions to the unified theory of acceptance and use of technology in the context of AI adoption in HR ecosystems. The study also contributes to the best-in-class industry HR practices and digital policy formulation to reimagine workplaces, promote harmonious human–AI collaboration, and make workplaces future-ready in the wake of massive digital disruptions.Frontiers in Psychology572This study investigates HRM specific factors that enable and inhibit the adoption of AI in extended HR ecosystems and adopts a qualitative case research design with an abductive approach and proposes additions to the unified theory of acceptance and use of technology in the context of AI adoption in HR ecosystems.Frontiers in Psychology['Antarpreet Singh', 'Jatin Pandey']2024-01-24T00:00:00https://www.semanticscholar.org/paper/fe1e7f07ec5546061ba9fda973f0b92aaff0a548 585ab28513d1a7bc004f3617566666b8210edaa8ABSTRACT Artificial intelligence (AI) methods, especially machine learning and natural language processing, are increasingly affecting health professions education (HPE), including the medical school application and selection processes, assessment, and scholarship production. The rise of large language models over the past 18 months, such as ChatGPT, has raised questions about how best to incorporate these methods into HPE. The lack of training in AI among most HPE faculty and scholars poses an important challenge in facilitating such discussions. In this commentary, the authors provide a primer on the AI methods most often used in the practice and scholarship of HPE, discuss the most pressing challenges and opportunities these tools afford, and underscore that these methods should be understood as part of the larger set of statistical tools available.Despite their ability to process huge amounts of data and their high performance completing some tasks, AI methods are only as good as the data on which they are trained. Of particular importance is that these models can perpetuate the biases that are present in those training datasets, and they can be applied in a biased manner by human users. A minimum set of expectations for the application of AI methods in HPE practice and scholarship are discussed in this commentary, including the interpretability of the models developed and the transparency needed into the use and characteristics of such methods.Academic medicine : journal of the Association of American Medical Colleges212A minimum set of expectations for the application of AI methods in HPE practice and scholarship are discussed in this commentary, including the interpretability of the models developed and the transparency needed into the use and characteristics of such methods.Academic medicine : journal of the Association of American Medical Colleges['Gustavo A Patino', 'Jonathan M Amiel', 'Megan E. L. Brown', 'M. Lypson', 'Teresa M Chan']2024-01-24T00:00:00https://www.semanticscholar.org/paper/585ab28513d1a7bc004f3617566666b8210edaa8 bd65fc51bd84fd2588303dbdfe9d83bfbb3c6ddfArtificial intelligence (AI) tools like ChatGPT demonstrate the potential to support personalized and adaptive learning experiences. This study explores how ChatGPT can facilitate self-regulated learning processes and learning computer programming. An evaluative case study design guided the investigation of ChatGPT’s capabilities to aid independent learning. Prompts mapped to self-regulated learning processes elicited ChatGPT’s support across learning tools: instructional materials, content tools, assessments, and planning. Overall, ChatGPT provided comprehensive, tailored guidance on programming concepts and practices. It consolidated multimodal information sources into integrated explanations with examples. ChatGPT also effectively assisted planning by generating detailed schedules. However, its interactivity and assessment functionality demonstrated shortcomings. ChatGPT’s effectiveness relies on learners’ metacognitive skills to seek help and assess its limitations. The implications include ChatGPT’s potential to provide Bloom’s two-sigma tutoring benefit at scale.Education sciences471This study explores how ChatGPT can facilitate self-regulated learning processes and learning computer programming and its implications include ChatGPT’s potential to provide Bloom’s two-sigma tutoring benefit at scale.Education Sciences['Kendall Hartley', 'Merav Hayak', 'Un Hyeok Ko']2024-01-24T00:00:00https://www.semanticscholar.org/paper/bd65fc51bd84fd2588303dbdfe9d83bfbb3c6ddf fca22b967142e375d01d2fa14b33b423746d9485International Journal of Retina and Vitreous320AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score, and future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations.International Journal of Retina and Vitreous['Nikhil Gopalakrishnan', 'Aishwarya Joshi', 'Jay Chhablani', 'N. Yadav', 'N. Reddy', 'P. Rani', 'Ram Snehith Pulipaka', 'Rohit Shetty', 'Shivani Sinha', 'V. Prabhu', 'R. Venkatesh']2024-01-24T00:00:00https://www.semanticscholar.org/paper/fca22b967142e375d01d2fa14b33b423746d9485 9f16cb33486833d27183a8e58063049f9d3eebdbThe remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.Healthcare1070The literature is examined that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction, and AI tools in educational and training settings are presented.Healthcare['Luca Andriollo', 'Aurelio Picchi', 'Rudy Sangaletti', 'L. Perticarini', 'S. Rossi', 'G. Logroscino', 'Francesco Benazzo']2024-01-24T00:00:00https://www.semanticscholar.org/paper/9f16cb33486833d27183a8e58063049f9d3eebdb a222156648248ba61a016fcd28cf89291b994174Keeping students engaged and motivated during online or class discussion may be challenging. Artificial intelligence has potential to facilitate active learning by enhancing student engagement, motivation, and learning outcomes. The purpose of this study was to develop, test usability of, and explore undergraduate nursing students' perceptions toward the Artificial Intelligence-Teaching Assistant System. The system was developed based on three main components: machine tutor intelligence, a graphical user interface, and a communication connector. They were included in the system to support contextual machine tutoring. A field-testing study design, a mixed-method approach, was utilized with questionnaires and focus group interview. Twenty-one undergraduate nursing students participated in this study, and they interacted with the system for 2 hours following the required activity checklist. The students completed the validated usability questionnaires and then participated in the focus group interview. Descriptive statistics were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data from the focus group interviews. The results showed that the Artificial Intelligence-Teaching Assistant System was user-friendly. Four main themes emerged, namely, functionality, feasibility, artificial unintelligence, and suggested learning modality. However, Artificial Intelligence-Teaching Assistant System functions, user interface, and content can be improved before full implementation.Computers, Informatics, Nursing260The results showed that the Artificial Intelligence-Teaching Assistant System was user-friendly, however, Artificial Intelligence-Teaching Assistant System functions, user interface, and content can be improved before full implementation.Computers, informatics, nursing : CIN['Y. Kowitlawakul', 'Jocelyn Jie Min Tan', 'S. Suebnukarn', 'Hoang D. Nguyen', 'D. Poo', 'Joseph Chai', 'Devi M Kamala', 'Wenru Wang']2024-01-24T00:00:00https://www.semanticscholar.org/paper/a222156648248ba61a016fcd28cf89291b994174 ce20516ece875038925249039174865a2ce74d9eThe comprehensive holistic care for paediatric patients is essential but artificial intelligence is playing a vital role where accuracy with limited duration a constructive health care services can be delivered to the children where indirectly nurses are getting technically skilled and more developed then the previous nursing services.Clinical and Translational Discovery60Clinical and Translational Discovery['Pranay Bende']2024-01-24T00:00:00https://www.semanticscholar.org/paper/ce20516ece875038925249039174865a2ce74d9e 53e8fc3eed5876db0ca90bef0eccd5e88e2f1d64In the last decade, artificial intelligence has generated several challenges in societies, with a special focus on public administration. Through the development of this literature review, we intend to underline the challenges that this has caused in the realm of public affairs, especially in terms of the smart cities framework, considering the legal perspective that is intrinsically associated with it. In this way, we based our research on a wide range of articles, from which we considered those with the greatest relevance and the highest number of citations in order to substantiate this theme in a more precise way. Finally, we present a set of conclusions, as well as opportunities for future investigations.The social science00This literature review intends to underline the challenges that artificial intelligence has caused in the realm of public affairs, especially in terms of the smart cities framework, considering the legal perspective that is intrinsically associated with it.Social Sciences['P. Correia', 'Ricardo Lopes Dinis Pedro', 'Ireneu Mendes', 'Alexandre D. C. S. Serra']2024-01-24T00:00:00https://www.semanticscholar.org/paper/53e8fc3eed5876db0ca90bef0eccd5e88e2f1d64 7eced4729ab3df8cc5ebaddd338d8ee983b51e9cForensic Science, Medicine, and Pathology20Forensic science, medicine, and pathology['Paige Tynan']2024-01-24T00:00:00https://www.semanticscholar.org/paper/7eced4729ab3df8cc5ebaddd338d8ee983b51e9c fe7f4295ab059fad7eb84d290cdde070252034bcThermal pollution is a technique that modifies the temperature of ambient water, resulting in water quality damage. The focus of this article is on thermal pollution, which is a major environmental concern. The environment has been severely impacted by thermal pollution. This section discusses the different types of thermal pollution based on temperature (cold and hot). Sources include industrial wastewater, urban and suburban runoff, and other natural and anthropogenic processes such as volcano eruptions, deforestation, and soil erosion. It also gives an overview of the consequences, such as dissolved oxygen depletion, aquatic animal death, harmful chemical discharge, and ecological imbalance. Thermal pollution can be reduced by building and using artificial lakes, cooling lakes, cooling towers, and water recycling, among other things. This research examines the consequences of thermal breakdown as well as potential artificial intelligence enabled solutions.2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)160The consequences of thermal breakdown as well as potential artificial intelligence enabled solutions are examined, as well as potential artificial intelligence enabled solutions for solutions to thermal pollution.2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)['Siddharth Goswami', 'Sachin Sharma', 'Priya Kohli']2024-01-24T00:00:00https://www.semanticscholar.org/paper/fe7f4295ab059fad7eb84d290cdde070252034bc de61d12b1c9bf31069404feb6a739a0498989599International Transactions in Operational Research00Int. Trans. Oper. Res.[]2024-01-24T00:00:00https://www.semanticscholar.org/paper/de61d12b1c9bf31069404feb6a739a0498989599 2cc1a058e6be300f4498f4b1cd88e4a66c72b240<jats:p />Journal of Food Quality11Journal of Food Quality['Journal of Food Quality']2024-01-24T00:00:00https://www.semanticscholar.org/paper/2cc1a058e6be300f4498f4b1cd88e4a66c72b240 c58fc31db6e54f1f7627367675bf2356fe93a196<jats:p />Journal of Food Quality10Journal of Food Quality['Journal of Food Quality']2024-01-24T00:00:00https://www.semanticscholar.org/paper/c58fc31db6e54f1f7627367675bf2356fe93a196 c141c3cb4548a9c30d932d4af65cce8ab8609488<jats:p />Journal of Electrical and Computer Engineering10J. Electr. Comput. Eng.['Journal of Electrical and Computer Engineering']2024-01-24T00:00:00https://www.semanticscholar.org/paper/c141c3cb4548a9c30d932d4af65cce8ab8609488 87bd8d42e0479799aa0cdf890b8739cd9d2a5f67<jats:p />Journal of Food Quality10Journal of Food Quality['Journal of Food Quality']2024-01-24T00:00:00https://www.semanticscholar.org/paper/87bd8d42e0479799aa0cdf890b8739cd9d2a5f67 7583ed8c94e1c7f10945d76239f93817b2e14521<jats:p />Complex10Complex.['Complexity']2024-01-24T00:00:00https://www.semanticscholar.org/paper/7583ed8c94e1c7f10945d76239f93817b2e14521 c1985fabacb43a2bbedea3b86653fae8f397b068<jats:p />Complex10Complex.['Complexity']2024-01-24T00:00:00https://www.semanticscholar.org/paper/c1985fabacb43a2bbedea3b86653fae8f397b068 3c3f52883bc45d1406eb22fb9a5b1fbdbba089f1Health technology470Health and Technology['RC Theijeswini', 'Soumya Basu', 'R. Swetha', 'Jayaraman Tharmalingam', 'Sudha Ramaiah', 'R. Calaivanane', 'V. R. Sreedharan', 'Paul Livingstone', 'A. Anbarasu']2024-01-24T00:00:00https://www.semanticscholar.org/paper/3c3f52883bc45d1406eb22fb9a5b1fbdbba089f1 92522a35c49fb2b13a9119e52b6e4d690d027318<jats:p />Journal of Sensors10Journal of Sensors['Journal of Sensors']2024-01-24T00:00:00https://www.semanticscholar.org/paper/92522a35c49fb2b13a9119e52b6e4d690d027318 caa2979a49caf23fd0d03f9e5b9d8a7dca928092Water and Environment Journal00Water and Environment Journal['Robin Walls']2024-01-24T00:00:00https://www.semanticscholar.org/paper/caa2979a49caf23fd0d03f9e5b9d8a7dca928092 eae96a71f0afe81bed53854446839b760b48e63bJournal of Latinos & Education20Journal of Latinos and Education['Gheri Febri Ananda', 'Roni Eka Rahmat', 'Zefky Okta Feri', 'Ardiyan Latif']2024-01-24T00:00:00https://www.semanticscholar.org/paper/eae96a71f0afe81bed53854446839b760b48e63b 3bb455d81bceccb70661c59800fa06bf0fd53e3dInternational Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Fabio Morandín-Ahuerma', 'Abelardo Romero-Fernández', 'Laura Villanueva-Méndez', 'Judith Contreras-González', 'Esmeralda Santos Cabañas', 'Daniel Esteban-Hernández', 'Heidi Cruz-Verona']2024-01-24T00:00:00https://www.semanticscholar.org/paper/3bb455d81bceccb70661c59800fa06bf0fd53e3d 2694bab1ee238d92bef4bd479d0019997b77a99bInternational Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Carlos Alexandre Gomes Passarinho Menezes', 'Yohan Schmidt Krubniki', 'Marcelo Bertolucci Fonseca', 'Lucas Nunes Viltrakis', 'Roberto Pereira Correa', 'João Victor Texeira Colares', 'João Victtor Silva Pantoja', 'Sarah Gama Ghersberg', 'Rodrigo Petraccho Betarelli']2024-01-24T00:00:00https://www.semanticscholar.org/paper/2694bab1ee238d92bef4bd479d0019997b77a99b d823d9cb6633845430a0c92095abb512ba35f5e0Artificial Intelligence (AI) continues to be a powerful tool in the research and development ecosystem. AI computers are invented to assist human invention and also created to invent. Where an AI is created to invent, through self‐learning, they can interact with set of data presumably created by humans and as a result, a new patentable invention(s) can emerge. However, where the AI inventors and the resulting inventions sit within the inventorship legal framework, and the theory of legal personhood continues to raise legal and policy questions that challenge some underlying or presumed settled intellectual property law assumptions. One of the contentions has been the implications of the AI machine's autonomous inventions on the legislative and judicially established threshold for patent inventorship and the jurisprudential theory of legal personhood. The judicial decisions in the United States of America (USA), United Kingdom (UK), and Australia in the Device for the Autonomous Bootstrapping of Unified Sentience (DABUS) patent applications have given judicial certainty on whether AI machine inventors qualify as inventors. However, they also reawakened the debate about the need to sustain patent incentives for AI innovations. This article draws from the inventorship threshold in the UK and US following the court decisions in the DABUS cases. The judicial decisions of courts and the administrative judgements of national Intellectual Property Offices (IPOs) relating to inventorship as well as the theory of legal personhood, reveal that an AI machine invention can be patent eligible. However, the machine does not satisfy the inventorship criteria and consequently is incapable of being named an inventor. On the other hand, the inventorship requirement of contemporaneous conception and reduction to practice meant that an AI owner/programmer may not satisfy the requirement of inventorship, even though he/she programmed the inventing machine. These decisions and judgements favour an implied situation where autonomous AI inventions could be without named inventors and owners. Consequently, those inventions will automatically form part of prior arts thereby rendering myriads of future human and AI inventions obvious or already existing in the public domain. In contributing to the discourse, this article advances the argument that to optimise the patent system, national IPOs and the courts can rely on ‘simultaneous conception and reduction to practice’ to recognise the programmer/owner or other relevant stakeholders in AI innovation as the inventor of AI autonomous inventions.Journal of World Intellectual Property00The argument that to optimise the patent system, national IPOs and the courts can rely on ‘simultaneous conception and reduction to practice’ to recognise the programmer/owner or other relevant stakeholders in AI innovation as the inventor of AI autonomous inventions is advanced.The Journal of World Intellectual Property['Ezinne Mirian Igbokwe']2024-01-24T00:00:00https://www.semanticscholar.org/paper/d823d9cb6633845430a0c92095abb512ba35f5e0 ff83ad4062543657bb47ba1c60a08b40a5a5741fThe paper introduces a process to accelerate the mechanics of science and insight. The process comprises two parts, both involving artificial intelligence embedded in Idea Coach, part of the Mind Genomics platform.. The first part of the process identifies a topic (policy for the citrus industry)Mind Genomics Studies in Psychology &amp; Experience370The paper introduces a process to accelerate the mechanics of science and insight involving artificial intelligence embedded in Idea Coach, part of the Mind Genomics platform.Mind Genomics Studies in Psychology &amp; Experience['Howard R. Moskowitz', 'Stephen Rappaport', 'Taylor Mulvey', 'Jehoshua Deitel']2024-01-24T00:00:00https://www.semanticscholar.org/paper/ff83ad4062543657bb47ba1c60a08b40a5a5741f b2145103774011b1dc3bc27d135e7bfa97bf4e88Lucid waters and green mountains are gold and silver mountains, and employment is the foundation of people's livelihood. Protecting the environment and promoting the employment are the important subjects of China's economic and social development. Based on the theoretical and empirical evidence, this paper finds through the provincial panel data from 2010 to 2019 that environmental regulation generally promotes employment by affecting GDP, but environmental regulation in northeast China has a negative effect on employment. Therefore, China should find the balance between the intensity of environmental regulation and economic growth, so as to protect the environment and improve people's livelihood.Frontiers in Humanities and Social Sciences90Frontiers in Humanities and Social Sciences['Yuefan Guo']2024-01-23T00:00:00https://www.semanticscholar.org/paper/b2145103774011b1dc3bc27d135e7bfa97bf4e88 60f784c6db84f3d7c74aac2a07e6159a36525ed0In the context of global climate change, China has proposed the "Carbon peaking and Carbon neutrality goals" goal of carbon emissions will reach carbon peak by 2030 and carbon neutral by 2060. As a market-based environmental regulation policy to promote greenhouse gas emission reduction, carbon emission trading policy is expected to reduce carbon emissions and improve carbon productivity, among which industrial structure plays a regulatory role in the impact of carbon emission trading policy on carbon productivity. Using the provincial data of China from 2006 to 2019, this paper evaluated the influence of carbon emission trading policy on carbon emission, taking the industrial structure as the regulating variable, studied the industrial structure in the influence of environmental regulation on carbon emission, and tested the regression results of DID model through parallel trend test, placebo test and PSM-DID model regression. After the above research process, the following conclusions are drawn: the carbon emission trading policy has a significant positive promotion effect on carbon emission reduction, and it is further concluded that the industrial structure has a significant negative regulatory effect on carbon productivity under the carbon emission trading policy.Frontiers in Humanities and Social Sciences180Frontiers in Humanities and Social Sciences['Chenyang Liu']2024-01-23T00:00:00https://www.semanticscholar.org/paper/60f784c6db84f3d7c74aac2a07e6159a36525ed0 762f131750214f83fe7716297cb21e66c135d171Abstract. The purpose of the study is to develop a model for the modernization of conceptual approaches to state regulation of the agro-food market of the region, which includes tools and mechanisms that eliminate imbalances in its development. The existing unilateral mechanism of state support for the agricultural sector in the face of agricultural producers does not ensure the maintenance of the total supply of food at the level of consumer demand, which, on the one hand, leads to a crisis of overproduction of certain types of food, on the other hand, does not allow to increase their economic accessibility. Methods. The instrumental and methodological apparatus of the study consisted of methods of comparative, structural-target, scenario analysis, expert assessments, cognitive modeling and others. The processing of analytical material, which ensures the reliability of calculations, was carried out using the IGLA decision support system. The scientific novelty lies in the substantiation of additional tools and mechanisms aimed at stimulating solvent demand, among which the development and implementation of a regional program of domestic food aid and a mechanism for stimulating solvent demand in the context of the regional State Program “Development of agriculture and regulation of agricultural products, raw materials and food markets” are proposed. Results. It is argued that the variety of factors affecting the functioning of the agri-food market, the high level of dynamism, multi-aspect and uncertainty of the processes taking place in it, the complexity of obtaining relevant information necessary to predict its development, allow us to attribute the agri-food market to complex poorly structured systems, the study of which by traditional methods is significantly difficult. To solve the problems of regulating the agri-food market, it is proposed to use cognitive modeling methods, which allows us to justify the need and adequacy of the use of additional tools of state regulation aimed at stimulating consumer demand.Agrarian Bulletin of the50Agrarian Bulletin of the["Vladimir Vasil'evich Kuznecov", 'Marina Holodova']2024-01-23T00:00:00https://www.semanticscholar.org/paper/762f131750214f83fe7716297cb21e66c135d171 ed7386e1be25487b93b985db89c075d6c53ff2d9Abstract. The purpose of the study is to assess the current regulatory framework for the regulation of organic agriculture for the analysis of existing shortcomings and the degree of its compliance with the real needs of domestic agribusiness. The main method of research was the analysis of the legislative framework at the federal and regional levels, as well as domestic and foreign experience in organizing budget support for organic agricultural producers on its basis. Results. The current regulatory legal acts regulating the production of organic food in Russia and the world are considered. The assessment of measures to support organic agriculture in the context of individual regions of the Russian Federation was carried out. The characteristics of the development features and necessary adjustments of the current regulatory framework of organic production are given. Taking into account foreign practices, proposals are formulated to reduce the costs of organic producers for certification by developing systems of guarantee participation and group certification. In order to more actively develop the organic food market in Russia and, in particular, in the North-West, measures are proposed to make adjustments to the legal regulation of organic farming and animal husbandry, the transition to mass certification of small businesses. This will strengthen the competitive position of domestic organic agricultural producers primarily in the domestic market and strengthen the overall position of the industry even in conditions of increased external restrictions for export supplies. The scientific novelty of the study is to assess the existing institutional opportunities and limitations in the production of organic products at the present stage of development of the agricultural sector.Agrarian Bulletin of the30Agrarian Bulletin of the["Natal'ya Nikonova", 'Kh. Dibirova', 'Aleksey G. Nikonov']2024-01-23T00:00:00https://www.semanticscholar.org/paper/ed7386e1be25487b93b985db89c075d6c53ff2d9 08a03e09da2d8ea79a5e84100197c42ce7022706This research aims to analyze the effectiveness of the Regional Regulation Formation Agency in Producing Initiative Regional Regulations in the DPRD of North Sulawesi. This research uses the theory of Richard M. Steers with measures of effectiveness, namely goal achievement, integration and adaptation. This research used a qualitative descriptive method with 4 informants consisting of the deputy chairman of the DPRD of North Sulawesi, the chairman of BAPEMPERDA (Regional Regulation Establishment Agency), members of BAPEMPERDA, and BAPEMPERDA staff. The results of this research show that the performance of BAPEMPERDA is still not optimal, seen from the lack of effectiveness in making regional regulations, to the point that they do not reach directly to the community. Based on research findings, several things that need to be suggested to the DPRD (Regional People's Representative Council), especially BAPEMPERDA North Sulawesi, are the need for commitment from DPRD members so that a regional regulation can be effective, not only in terms of quality, but can be felt by the constituents, namely the community.Journal La Sociale30Journal La Sociale['Deo Jeremy Tulangow', 'Joanne V. Mangindaan', 'Very Y. Londa']2024-01-23T00:00:00https://www.semanticscholar.org/paper/08a03e09da2d8ea79a5e84100197c42ce7022706 942240c1374c0edc417c1e3c304080d07a921629Introduction. The relevance of the article is due to the problems of the regulatory and legal substantiation of the use of virtual technologies in education, which consists in the lack of legal criteria for the introduction of immersive technologies in the educational space. Currently, despite the intensive development of digital technologies, including immersive, domestic education lacks actual regulations that would regulate the use of virtual reality (VR) / augmented reality (AR) technologies in the educational process.Purpose setting. It is becoming relevant to conduct a comparative legal analysis of scientific publications in the legal and pedagogical areas on the regulatory and legal support of virtual VR and AR technologies, to study the regulatory framework of Russian legislation governing the use of VR and AR technologies in education, to identify common regulatory and legal problems for educational organizations of all levels.Methodology and methods of the study. The study includes a review of bibliographic and regulatory sources on the problems of legal justification for the use of virtual technologies in education, a structural analysis of local legal acts, generalization and synthesis of research results. The article talks about the need to have a clear regulatory framework for their use in the educational process, the development of mechanisms and regulations for assessing the safety of the use of technologies, the compliance of the content with the educational goals, and the streamlining of the conceptual apparatus.Results. The results include a generalization of the main legal problems that complicate the implementation of immersive/virtual technologies in the educational process.Conclusion. It has been revealed that the problems of legal regulation of the use of virtual technologies are of a common nature for educational organizations of all levels; the issues of legal justification for the use of virtual technologies in local regulations, the creators of which are each educational organization, have not been sufficiently resolved; the very construction of the concept «virtual technologies» is not presented definitively in regulatory legal acts, which allows the use of broader terms «immersive technologies», «immersion technologies, etc.; the need to develop requirements for new competencies of teachers has been identified; regulatory and legal regulation of the process of introducing VR/AR technologies within the educational process as an educational method is required.Professional education in the modern world70The article talks about the need to have a clear regulatory framework for their use in the educational process, the development of mechanisms and regulations for assessing the safety of the use of technologies, the compliance of the content with the educational goals, and the streamlining of the conceptual apparatus.Professional education in the modern world['A. E. Pamirsky', 'E. N. Maslova', 'E. A. Zhirova', 'I. E. Pamirsky', 'K. S. Golokhvast']2024-01-23T00:00:00https://www.semanticscholar.org/paper/942240c1374c0edc417c1e3c304080d07a921629 0f41c28f58f929383079b4fd1258e1759fe765beThe profound impact of artificial intelligence (AI) on the modes of teaching and learning necessitates a reexamination of the interrelationships among technology, pedagogy, and subject matter. Given this context, we endeavor to construct a framework for integrating the Technological Pedagogical Content Knowledge of Artificial Intelligence Technology (Artificial Intelligence—Technological Pedagogical Content Knowledge, AI-TPACK) aimed at elucidating the complex interrelations and synergistic effects of AI technology, pedagogical methods, and subject-specific content in the field of education. The AI-TPACK framework comprises seven components: Pedagogical Knowledge (PK), Content Knowledge (CK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Pedagogical Knowledge (AI-TCK), AI-Technological Content Knowledge (AI-TPK), and AI-TPACK itself. We developed an effective structural equation modeling (SEM) approach to explore the relationships among teachers’ AI-TPACK knowledge elements through the utilization of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The result showed that six knowledge elements all serve as predictive factors for AI-TPACK variables. However, different knowledge elements showed varying levels of explanatory power in relation to teachers’ AI-TPACK. The influence of core knowledge elements (PK, CK, and AI-TK) on AI-TPACK is indirect, mediated by composite knowledge elements (PCK, AI-TCK, and AI-TPK), each playing unique roles. Non-technical knowledge elements have significantly lower explanatory power for teachers of AI-TPACK compared to knowledge elements related to technology. Notably, content knowledge (C) diminishes the explanatory power of PCK and AI-TCK. This study investigates the relationships within the AI-TPACK framework and its constituent knowledge elements. The framework serves as a comprehensive guide for the large-scale assessment of teachers’ AI-TPACK, and a nuanced comprehension of the interplay among AI-TPACK elements contributes to a deeper understanding of the generative mechanisms underlying teachers’ AI-TPACK. Such insights bear significant implications for the sustainable development of teachers in the era of artificial intelligence.Sustainability1082The framework serves as a comprehensive guide for the large-scale assessment of teachers’ AI-TPACK, and a nuanced comprehension of the interplay among AI-TPACK elements contributes to a deeper understanding of the generative mechanisms underlying teachers’ AI-TPACK.Sustainability['Yimin Ning', 'Cheng Zhang', 'Binyan Xu', 'Ying Zhou', 'Tommy Tanu Wijaya']2024-01-23T00:00:00https://www.semanticscholar.org/paper/0f41c28f58f929383079b4fd1258e1759fe765be 1d1ea023bd1b0363951407d286bb7b4306e4a60eBMC Medical Ethics391The findings signal that instead of narrow product-specific AI guidance, ethical AI development may need a systemic, proactive perspective that includes the ethical considerations (objectives, actors, and context) and focuses on healthcare applications.BMC Medical Ethics['Laura Arbelaez Ossa', 'Giorgia Lorenzini', 'Stephen R. Milford', 'D. Shaw', 'Bernice Elger', 'Michael Rost']2024-01-23T00:00:00https://www.semanticscholar.org/paper/1d1ea023bd1b0363951407d286bb7b4306e4a60e e271197fd838f5a0777e1e6f834154ed840d802cBackground Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation. Objective This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care. Methods A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework. Results Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues. Conclusions Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698JMIR Mental Health621Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships.JMIR Mental Health['Jessica Rogan', 'Sandra Bucci', 'Joesph Firth']2024-01-23T00:00:00https://www.semanticscholar.org/paper/e271197fd838f5a0777e1e6f834154ed840d802c 11f01361e1f7ed7487a10d7abd176f30fd8a0435We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.arXiv.org431A method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output, which significantly improves the F1 detection scores of existing AI content detection models across various domains.ArXiv['Chengzhi Mao', 'Carl Vondrick', 'Hao Wang', 'Junfeng Yang']2024-01-23T00:00:00https://www.semanticscholar.org/paper/11f01361e1f7ed7487a10d7abd176f30fd8a0435 b3ff2a1a905d760111d56e5f9ab2bd4f0c07d097This study explores the use of AI technology in the Google Read Along application as a tool to improve English pronunciation, particularly for students who struggle with English pronunciation. Therefore, the purpose of this study is to evaluate the Google Read Along app's effectiveness in improving English pronunciation, analyze the students' responses to using Google Read Along, and discover the factors that help students succeed in improving their pronunciation. Read Aloud is used in conjunction with AI technology to help children learn by listening to and precisely repeating new words and phrases. A quasi-experimental method was used to collect data, with 35 students in the experimental group and 35 in the control group. A questionnaire was presented to the experimental group regarding how they responded to Google Read Along, and interviews were conducted as further information to identify the factors affecting their pronunciation improvement. The results of the N-Gain test show that the Google Read Along is efficient in helping students improve their English pronunciation when used in combined with the Read Aloud approach by an average of 65.73 percent. As a result, a teaching strategy that combines the Read Aloud method and AI Google Read Along can be an effective alternative. Additionally, the instant feedback offered by this application gives students a chance to recognize their errors directly, and the convenience of using the application for learning anytime anywhere has a significant impact on their success in improving their pronunciation.Acitya: Journal of Teaching and Education161The results of the N-Gain test show that the Google Read Along is efficient in helping students improve their English pronunciation when used in combined with the Read Aloud approach, and a teaching strategy that combines the Read Aloud method and AI Google Read Along can be an effective alternative.Acitya: Journal of Teaching and Education['Dhanan Abimanto', 'Wasi Sumarsono']2024-01-23T00:00:00https://www.semanticscholar.org/paper/b3ff2a1a905d760111d56e5f9ab2bd4f0c07d097 190496eb44a51783f7e15d4384889f68c5248117 In recent years, automated written feedback (AWF) has gained popularity in language learning and teaching as a form of artificial intelligence (AI). The present study aimed at providing a comprehensive state-of-the-art review of AWF. Using Scopus as the main database, we identified 83 SSCI-indexed published articles on AWF (1993–2022). We investigated several main domains consisting of research contexts, AWF systems, feedback focus, ways of utilizing AWF, research design, foci of investigation, and results. Our results showed that although AWF was primarily studied in language and writing classes at the tertiary level, with a focus on English as the target language, the scope of AWF research has been steadily broadening to include diverse language environments and ecological settings. This heterogeneity was also demonstrated by the wide range of AWF systems employed (n = 31), ways of integrating AWF (n = 14), different types of AWF examined (n = 3), as well as varied research designs. In addition, three main foci of investigation were delineated: (1) the performance of AWF; (2) perceptions, uses, engagement with AWF, and influencing factors; and (3) the impact of AWF. We identified positive, negative, neutral, and mixed results in all three main foci of investigation. Overall, less positive results were found in validating AWF compared to results favoring the other two areas. Lastly, we grounded our findings within the argument-based validity framework and also examined the potential implications.ReCALL1051Although AWF was primarily studied in language and writing classes at the tertiary level, with a focus on English as the target language, the scope of AWF research has been steadily broadening to include diverse language environments and ecological settings.ReCALL['Huawei Shi', 'Vahid Aryadoust']2024-01-23T00:00:00https://www.semanticscholar.org/paper/190496eb44a51783f7e15d4384889f68c5248117 42eb0f26f3edd0673f8f13eae2704f7d865b07241011[]2024-01-23T00:00:00https://www.semanticscholar.org/paper/42eb0f26f3edd0673f8f13eae2704f7d865b0724 bc486871e6f392c40ae823d6dbaa51a1e3461426This study presents findings from a professional development (PD) webinar aimed at sensitizing and gathering teacher educators’ knowledge of Generative Artificial Intelligence (GAI). The primary objective of the webinar was to deepen teacher educators’ understanding and applications of GAI within the context of teacher education in Ghana and to identify areas requiring additional development. Three hundred and seven participants from a diverse group, including teacher educators, administrators, and in-service teachers participated in the PD session. The session was conducted online via Zoom. The video and audio recordings were transcribed and analyzed thematically using MAXQDA version 2022.4. Findings indicate a diverse range of familiarity with GAI among participants. While some expressed knowledge of GAI tools, others were learning about GAI for the first time. Further, the findings showed an increasing curiosity among participants for the inspiring functions of GAI in education, such as automatic scoring, academic writing, assisting teachers with image generation for their classroom practices, etc. The participants demonstrated a willingness to include GAI in their classroom practices and support their students. However, they also identified infrastructural gaps, such as the expense of premium GAI tools, training on GAI promptings, and ethical issues such as transparency, as potential barriers to the successful implementation of GAI in teacher education. Therefore, the study suggests that institutional support should be provided to teacher educators. This support would expand their access to various GAI tools and features. The study further recommends integrating GAI, including explainable GAI and prompt engineering, as a core component of teacher education and continuous professional development programs. Additionally, it emphasizes the importance of strengthening educators' skills in innovative assessment practices.Journal of AI413The study suggests that institutional support should be provided to teacher educators to expand their access to various GAI tools and features and recommends integrating GAI, including explainable GAI and prompt engineering, as a core component of teacher education and continuous professional development programs.Journal of AI['Matthew Nyaaba', 'Xiaoming Zhai']2024-01-23T00:00:00https://www.semanticscholar.org/paper/bc486871e6f392c40ae823d6dbaa51a1e3461426 668450aab64a750d5b693b6989a7034ad04bd960Nature02Nature['Matthew Hutson']2024-01-23T00:00:00https://www.semanticscholar.org/paper/668450aab64a750d5b693b6989a7034ad04bd960 e6170d1936bd0e8bcfa4382b001ef2cf137e7e66Increased delegation of commercial, scientific, governmental, and personal activities to AI agents -- systems capable of pursuing complex goals with limited supervision -- may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents.arXiv.org1810Three categories of measures to increase visibility into AI agents are assessed: agent identifiers, real-time monitoring, and activity logging that apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers.ArXiv['Alan Chan', 'Carson Ezell', 'Max Kaufmann', 'K. Wei', 'Lewis Hammond', 'Herbie Bradley', 'Emma Bluemke', 'Nitarshan Rajkumar', 'David Krueger', 'Noam Kolt', 'Lennart Heim', 'Markus Anderljung']2024-01-23T00:00:00https://www.semanticscholar.org/paper/e6170d1936bd0e8bcfa4382b001ef2cf137e7e66 943edc1dcad59aaead520375cb4197cb5e003d25Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Journal of Medical Imaging and Radiation Oncology570This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice.Journal of medical imaging and radiation oncology['Adrian P. Brady', 'Bibb Allen', 'Jaron J R Chong', 'E. Kotter', 'Nina Kottler', 'John Mongan', 'Lauren Oakden-Rayner', 'D. Pinto dos Santos', 'An Tang', 'Christoph Wald', 'John Slavotinek']2024-01-23T00:00:00https://www.semanticscholar.org/paper/943edc1dcad59aaead520375cb4197cb5e003d25 fe3cba86c9961a6276148df786101e6771911ce7 This research explores AI-generated originality's impact on copyright regulations. It meticulously examines legal frameworks such as the Berne Convention, EU Copyright Law, and national legislation. Rigorously analyzing cases, including Infopaq International A/S v Danske Dagblades Forening and Levola Hengelo BV v Smilde Foods BV, illuminates evolving originality and human involvement in AI creativity. The study also contemplates global perspectives, drawing from esteemed organizations such as the World Intellectual Property Organization and the European Court of Justice and exploring diverse approaches adopted by individual nations. The paper emphasizes the imperative need for legislative updates to address the challenges and opportunities of AI-generated works. It highlights the pivotal role of international collaboration and public awareness in shaping copyright policies for the AI-driven creativity era. It also offers insights and recommendations for policymakers and researchers navigating this complex terrain.Asian Journal of International Law80The imperative need for legislative updates to address the challenges and opportunities of AI-generated works is emphasized, and the pivotal role of international collaboration and public awareness in shaping copyright policies for the AI-driven creativity era is highlighted.Asian Journal of International Law['Hafiz Gaffar', 'Saleh Albarashdi']2024-01-23T00:00:00https://www.semanticscholar.org/paper/fe3cba86c9961a6276148df786101e6771911ce7 31b9e0c15b969d1aaa13c8f9c0423878fb75d12eA Collaborative Artificial Intelligence System (CAIS) performs actions in collaboration with the human to achieve a common goal. CAISs can use a trained AI model to control human-system interaction, or they can use human interaction to dynamically learn from humans in an online fashion. In online learning with human feedback, the AI model evolves by monitoring human interaction through the system sensors in the learning state, and actuates the autonomous components of the CAIS based on the learning in the operational state. Therefore, any disruptive event affecting these sensors may affect the AI model's ability to make accurate decisions and degrade the CAIS performance. Consequently, it is of paramount importance for CAIS managers to be able to automatically track the system performance to understand the resilience of the CAIS upon such disruptive events. In this paper, we provide a new framework to model CAIS performance when the system experiences a disruptive event. With our framework, we introduce a model of performance evolution of CAIS. The model is equipped with a set of measures that aim to support CAIS managers in the decision process to achieve the required resilience of the system. We tested our framework on a real-world case study of a robot collaborating online with the human, when the system is experiencing a disruptive event. The case study shows that our framework can be adopted in CAIS and integrated into the online execution of the CAIS activities.arXiv.org110A new framework to model CAIS performance when the system experiences a disruptive event is provided and is equipped with a set of measures that aim to support CAIS managers in the decision process to achieve the required resilience of the system.ArXiv['Diaeddin Rimawi', 'Antonio Liotta', 'Marco Todescato', 'Barbara Russo']2024-01-23T00:00:00https://www.semanticscholar.org/paper/31b9e0c15b969d1aaa13c8f9c0423878fb75d12e 798b7ecb12611f962b07a231f61bd7f3b4575cddSafeguarding intellectual property and preventing potential misuse of AI-generated images are of paramount importance. This paper introduces a robust and agile plug-and-play watermark detection framework, dubbed as RAW. As a departure from traditional encoder-decoder methods, which incorporate fixed binary codes as watermarks within latent representations, our approach introduces learnable watermarks directly into the original image data. Subsequently, we employ a classifier that is jointly trained with the watermark to detect the presence of the watermark. The proposed framework is compatible with various generative architectures and supports on-the-fly watermark injection after training. By incorporating state-of-the-art smoothing techniques, we show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image, even in the presence of certain adversarial attacks targeting watermark removal. Experiments on a diverse range of images generated by state-of-the-art diffusion models reveal substantial performance enhancements compared to existing approaches. For instance, our method demonstrates a notable increase in AUROC, from 0.48 to 0.82, when compared to state-of-the-art approaches in detecting watermarked images under adversarial attacks, while maintaining image quality, as indicated by closely aligned FID and CLIP scores.arXiv.org590A robust and agile plug-and-play watermark detection framework, dubbed as RAW, which introduces learnable watermarks directly into the original image data and provides provable guarantees regarding the false positive rate for misclassifying a watermarked image, even in the presence of certain adversarial attacks targeting watermark removal.ArXiv['Xun Xian', 'Ganghua Wang', 'Xuan Bi', 'Jayanth Srinivasa', 'Ashish Kundu', 'Mingyi Hong', 'Jie Ding']2024-01-23T00:00:00https://www.semanticscholar.org/paper/798b7ecb12611f962b07a231f61bd7f3b4575cdd a800e02bd0926faaf4c8f9fa786e2b33e7fef95farXiv.org20This chapter provides an overview of ethical challenges associated with the human curation of genocide-related information using a three-part framework inspired by Belmont criteria (i.e. curation challenges associated with respect for individuals, beneficence and justice/fairness).ArXiv['M. Makhortykh']2024-01-23T00:00:00https://www.semanticscholar.org/paper/a800e02bd0926faaf4c8f9fa786e2b33e7fef95f 99f49d4b406cc9acb84365aaee2e9b23ecf6b59fThis scholarly paper embarks on an exploratory journey into the realm of AI-driven environmental health disease modeling, with a keen focus on its implications in the diverse healthcare landscapes of the USA and Africa. The study's background delves into the historical evolution of disease modeling techniques, emphasizing the revolutionary role of AI in modern public health strategies. It meticulously examines the comparative effectiveness of AI models in these distinct regions, addressing the challenges and opportunities inherent in AI-driven health models. Aiming to unravel the multifaceted impact of AI in disease prediction and public health policy, the paper navigates through various thematic corridors. It critically analyzes the significance of data sources and quality, ethical considerations in AI health modeling, and the integration of AI models into public health policies. The scope of the paper encompasses a comprehensive review of AI's efficacy in predicting environmental diseases, its role in enhancing disease surveillance systems, and the geographic and socioeconomic variations affecting model accuracy. The main findings reveal that AI models, while effective in disease prediction and surveillance, encounter challenges related to data integrity and ethical complexities. The study concludes that the integration of AI in healthcare necessitates a balanced approach, advocating for policies that support the development of context-specific AI models and address ethical concerns. Recommendations include fostering interdisciplinary collaboration and continuous evaluation of AI models to align them with evolving healthcare needs and ethical standards. This paper serves as a beacon for understanding AI's transformative potential in environmental health disease modeling, offering insights that are crucial for shaping future public health strategies and interventions. Keywords:  AI in Healthcare, Disease Modeling, Public Health Policy, Data Quality, Ethical Considerations, Geographic Variations.International medical science research journal00The study concludes that the integration of AI in healthcare necessitates a balanced approach, advocating for policies that support the development of context-specific AI models and address ethical concerns.International Medical Science Research Journal['Nzubechukwu Chukwudum Ohalete', 'Oluwatoyin Ayo-Farai', 'Tolulope O Olorunsogo', 'Paschal Maduka', 'Temidayo Olorunsogo']2024-01-23T00:00:00https://www.semanticscholar.org/paper/99f49d4b406cc9acb84365aaee2e9b23ecf6b59f 820f6d64eb5c077c1ba71b307694ff6a2406b283 Here, we learn about Professor Chien-Feng Huang’s interdisciplinary research at the National University of Kaohsiung in Taiwan, concerning the move towards generalised pairs trading strategies through artificial intelligence. Prof. Chien-Feng Huang has worked on interdisciplinary research and applications across artificial intelligence (AI) and finance in the past two decades. This article addresses Haung’s vision towards utilising AI to construct a generalised approach for pairs trading problems and how Huang regards AI as an excellent opportunity to assist humans in exploring unknown territories in the trading world. Open Access Government00This article addresses Haung’s vision towards utilising AI to construct a generalised approach for pairs trading problems and how Huang regards AI as an excellent opportunity to assist humans in exploring unknown territories in the trading world.Open Access Government['Chien-Feng Huang']2024-01-23T00:00:00https://www.semanticscholar.org/paper/820f6d64eb5c077c1ba71b307694ff6a2406b283 d05e59c0742aee9239149fddecb9100f24d89980Generative artificial intelligence (AI) is poised to reshape the way individuals communicate and interact. While this form of AI has the potential to efficiently make numerous human decisions, there is limited understanding of how individuals respond to its use in social interaction. In particular, it remains unclear how individuals engage with algorithms when the interaction entails consequences for other people. Here, we report the results of a large-scale pre-registered online experiment (N = 3,552) indicating diminished fairness, trust, trustworthiness, cooperation, and coordination by human players in economic twoplayer games, when the decision of the interaction partner is taken over by ChatGPT. On the contrary, we observe no adverse welfare effects when individuals are uncertain about whether they are interacting with a human or generative AI. Therefore, the promotion of AI transparency, often suggested as a solution to mitigate the negative impacts of generative AI on society, shows a detrimental effect on welfare in our study. Concurrently, participants frequently delegate decisions to ChatGPT, particularly when the AI's involvement is undisclosed, and individuals struggle to discern between AI and human decisions.420The promotion of AI transparency, often suggested as a solution to mitigate the negative impacts of generative AI on society, shows a detrimental effect on welfare in this study.['Fabian Dvorak', 'Regina Stumpf', 'Sebastian Fehrler', 'Urs Fischbacher']2024-01-23T00:00:00https://www.semanticscholar.org/paper/d05e59c0742aee9239149fddecb9100f24d89980 59981037e2d8eec249a11169e768d5fa7cb0a099 In a recent interview, our editors delved into the world of soil health monitoring and the transformative role played by artificial intelligence (AI), big data, and machine learning, with Mogens H. Greve, Professor and Head of the Soil Section at the Institute of Agroecology, Aarhus University. Professor Mogens H. Greve, provided valuable insights into the evolving landscape of soil science and its intersection with cutting-edge technologies in this exclusive interview with Open Access Government. The integration of soil mapping and AI Greve, an experienced soil mapping expert, has reshaped the conventional viewpoint by underscoring the pivotal role of soil mapping within the realm of AI for soil health. Open Access Government00Professor Mogens H. Greve, an experienced soil mapping expert, has reshaped the conventional viewpoint by underscoring the pivotal role of soil mapping within the realm of AI for soil health.Open Access Government['M. Greve']2024-01-23T00:00:00https://www.semanticscholar.org/paper/59981037e2d8eec249a11169e768d5fa7cb0a099 1077a5236e1e5c85da9592c1a67540afa1d9ff93The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods. This paper presents"x-[plAIn]", a new approach to make XAI more accessible to a wider audience through a custom Large Language Model (LLM), developed using ChatGPT Builder. Our goal was to design a model that can generate clear, concise summaries of various XAI methods, tailored for different audiences, including business professionals and academics. The key feature of our model is its ability to adapt explanations to match each audience group's knowledge level and interests. Our approach still offers timely insights, facilitating the decision-making process by the end users. Results from our use-case studies show that our model is effective in providing easy-to-understand, audience-specific explanations, regardless of the XAI method used. This adaptability improves the accessibility of XAI, bridging the gap between complex AI technologies and their practical applications. Our findings indicate a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users.arXiv.org410A new approach to make XAI more accessible to a wider audience through a custom Large Language Model (LLM), developed using ChatGPT Builder, which is effective in providing easy-to-understand, audience-specific explanations, regardless of the XAI method used.ArXiv['Philip Mavrepis', 'Georgios Makridis', 'G. Fatouros', 'Vasileios Koukos', 'Maria Margarita Separdani', 'D. Kyriazis']2024-01-23T00:00:00https://www.semanticscholar.org/paper/1077a5236e1e5c85da9592c1a67540afa1d9ff93 a4ba4f342b371bd42be2512772ae66b878835569The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and artificial intelligence. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how artificial intelligence can increase biorisk. Within this perspective, we present a simplistic framework for evaluating biorisk and demonstrate how this framework falls short in achieving actionable outcomes for a biorisk manager. We then suggest a potential path forward that builds upon existing risk characterization work and justify why characterization efforts of AI-enabled tools for engineering biology is needed.arXiv.org180This work presents a simplistic framework for evaluating biorisk and demonstrates how this framework falls short in achieving actionable outcomes for a biorisk manager and suggests a potential path forward that builds upon existing risk characterization work and justifies why characterization efforts of AI-enabled tools for engineering biology is needed.ArXiv['Matthew E. Walsh']2024-01-23T00:00:00https://www.semanticscholar.org/paper/a4ba4f342b371bd42be2512772ae66b878835569 b2ee293e3e5a02bd077814a16b9026e07e24c2fb In the initial instance involving artificial intelligence (AI)-generated images in China, the Beijing Internet Court determined that AI-generated images are considered protectable works, and the AI user is recognized as the author.Journal of Intellectual Property Law &amp; Practice00Journal of Intellectual Property Law &amp; Practice['Tingting Wen']2024-01-23T00:00:00https://www.semanticscholar.org/paper/b2ee293e3e5a02bd077814a16b9026e07e24c2fb a5220751c5961dfb1338a74a69813847267491e9This paper examines the influence of AI advancements on Centers of Excellence (CoEs) across diverse sectors, identifying common functional and process areas that can be addressed through AI integration. The integration of Artificial Intelligence (AI) into Centers of Excellence (CoEs) is revolutionizing organizational innovation by reshaping traditional CoE practices and enhancing their functionality. This paper explores the dynamic interplay between established CoE principles and innovative AI technologies, demonstrating how AI is transforming CoEs into data-driven, adaptable, and predictive hubs of expertise. Traditionally, CoEs have served as centralized knowledge repositories, consolidating best practices for process optimization and fostering excellence. The infusion of AI introduces new dimensions to their functions, propelling CoEs into an era of augmented intelligence, adaptability, and predictive capabilities. This research, grounded in the authors' firsthand experience in establishing and operating CoEs, delves into core CoE principles and functions, examining how AI initiatives can contribute to enhancing their capabilities and improving efficiency, cost savings, and Time to Market (TTM). The paper encompasses specific CoEs across various sectors and domains, including Technological, Learning, Health, and Finance. Additionally, it explores the potential of an AI-dedicated CoE to support various organizational areas, including other CoEs, in their evolution towards self-supporting AI functionalities for their specialized domain CoE. As AI continues to serve as a catalyst for progress across domains like Data Science, Analytics, Business Automation, Process Optimization, Innovation, Idea Generation, Quality Assurance, Monitoring, Scalability, Augmented Decision-Making, and more, the fusion of traditional CoE foundations with AI-driven adaptations empowers organizations to maintain a competitive edge.International Conference on Trends &amp; Innovations in Management, Engineering, Sciences and Humanities240The dynamic interplay between established CoE principles and innovative AI technologies is explored, demonstrating how AI is transforming CoEs into data-driven, adaptable, and predictive hubs of expertise.International Conference on Trends &amp; Innovations in Management, Engineering, Sciences and Humanities['Amnon Ekstein']2024-01-23T00:00:00https://www.semanticscholar.org/paper/a5220751c5961dfb1338a74a69813847267491e9 e8c21373445c6a5a8cdec7fe7c6bf5cf6f813115Manual labeling and categorization are extremely time-consuming and, thus, costly. AI and ML-supported information systems can bridge this gap and support labor-intensive digital activities. Since it requires categorization, coding-based analysis, such as qualitative content analysis, reaches its limits with large amounts of data and could benefit from AI and ML-based support. Empirical social research, its application domain, benefits from Big Data’s ability to create more extensive human behavior and development models. A range of applications are available for statistical analysis to serve this purpose. This paper aims to implement an information system that supports researchers in empirical social research in performing AI-supported qualitative content analysis. AI2VIS4BigData is a reference model that standardizes use cases and artifacts for Big Data information systems that integrate AI and ML for user empowerment. Thus, this work’s concepts and implementations try to achieve an AI2VIS4BigData-compliant information system that supports social researchers in categorizing text data and creating insightful dashboards. Thereby, the text categorization is based on an existing ML component. Furthermore, it presents two evaluations that were conducted for these concepts and implementations: a qualitative cognitive walkthrough assessing the system’s usability and a quantitative user study with 18 participants revealed that though the users perceive AI support as more efficient, they need more time to reflect on the recommendations. The research revealed that AI support increased the correctness of the users’ categorizations but also slowed down their decision-making. The assumption that this is due to the UI design and additional information for processing requires follow-up research.Big Data and Cognitive Computing170This paper aims to implement an information system that supports researchers in empirical social research in performing AI-supported qualitative content analysis and presents the text categorization, which is based on an existing ML component.Big Data Cogn. Comput.['Thoralf Reis', 'Lukas Dumberger', 'Sebastian Bruchhaus', 'Thomas Krause', 'Verena Schreyer', 'M. X. Bornschlegl', 'Matthias L. Hemmje']2024-01-23T00:00:00https://www.semanticscholar.org/paper/e8c21373445c6a5a8cdec7fe7c6bf5cf6f813115 c7231d0395eefe95cb6829284848bb878244b1e2Bacterial promoters play a crucial role in gene expression by serving as docking sites for the transcription initiation machinery. However, accurately identifying promoter regions in bacterial genomes remains a challenge due to their diverse architecture and variations. In this study, we propose MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool, to comprehensively screen bacterial promoter regions in 12 diverse genomes. We leveraged biologically relevant and informative DNA structural properties, such as DNA duplex stability and base stacking, and state-of-the-art machine learning (ML) strategies to gain insights into promoter characteristics. We evaluated several machine learning models, including Support Vector Machines, Random Forests, and XGBoost, and assessed their performance using accuracy, precision, recall, specificity, F1 score, and MCC metrics. Our findings reveal that XGBoost outperformed other models and current state-of-the-art promoter prediction tools, namely Sigma70pred and iPromoter2L, achieving F1-scores >95% in most systems. Significantly, the use of one-hot encoding for representing nucleotide sequences complements these structural features, enhancing our XGBoost model's predictive capabilities. To address the challenge of model interpretability, we incorporated explainable AI techniques using Shapley values. This enhancement allows for a better understanding and interpretation of the predictions of our model. In conclusion, our study presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria, utilizing original downstream sequences as nonpromoter controls. This tool has the potential to significantly advance the field of bacterial genomics and contribute to our understanding of gene regulation in diverse bacterial systems.Journal of Chemical Information and Modeling541This study proposes MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool to comprehensively screen bacterial promoter regions in 12 diverse genomes, and presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria.Journal of chemical information and modeling['Subhojit Paul', 'Kaushika Olymon', 'Gustavo Sganzerla Martinez', 'Sharmilee Sarkar', 'V. Yella', 'Aditya Kumar']2024-01-23T00:00:00https://www.semanticscholar.org/paper/c7231d0395eefe95cb6829284848bb878244b1e2 daf3460b0194b2709a7e9fdd22cb3378a19d03d3Journal of Medical Ethics41Journal of medical ethics['Hazem Zohny', 'Sebastian Porsdam Mann', 'Brian D. Earp', 'J. McMillan']2024-01-23T00:00:00https://www.semanticscholar.org/paper/daf3460b0194b2709a7e9fdd22cb3378a19d03d3 91bb3a853fee70fb258fd6d2efff196df12ba08f4th INTERNATIONAL CONFERENCE ON BIOLOGICAL RESEARCH AND APPLIED SCIENCE004th INTERNATIONAL CONFERENCE ON BIOLOGICAL RESEARCH AND APPLIED SCIENCE[]2024-01-23T00:00:00https://www.semanticscholar.org/paper/91bb3a853fee70fb258fd6d2efff196df12ba08f 06bc1cff88ac84898b23a7ef403e1e31efa517c0Expanding the range of enzyme use through selective mutations.Scilight00Scilight['L. Green']2024-01-23T00:00:00https://www.semanticscholar.org/paper/06bc1cff88ac84898b23a7ef403e1e31efa517c0 0a176fff5a2a8240d53e4c2a36b3fef46ee4fc10When training artificial intelligence for games encompassing multiple roles, the development of a generalized model capable of controlling any character within the game presents a viable option. This strategy not only conserves computational resources and time during the training phase but also reduces resource requirements during deployment. training such a generalized model often encounters challenges related to uneven capabilities when controlling different roles. A simple method is introduced based on Regret Matching+, which facilitates a more balanced performance of strength by the model when controlling various roles.arXiv.org40A simple method is introduced based on Regret Matching+, which facilitates a more balanced performance of strength by the model when controlling various roles when controlling different roles.ArXiv['Xiaoxi Wang']2024-01-23T00:00:00https://www.semanticscholar.org/paper/0a176fff5a2a8240d53e4c2a36b3fef46ee4fc10 bd794c1e7db61089589d1bb40226608f7b501062AI &amp; SOCIETY410AI &amp; SOCIETY['Itai Bavli', 'Anita Ho', 'Ravneet Mahal', 'Martin J. McKeown']2024-01-23T00:00:00https://www.semanticscholar.org/paper/bd794c1e7db61089589d1bb40226608f7b501062 b5ede146775aa2b773d4763604c45b816d3ecefcRecent Generative Artificial Intelligence (GenAI) trends focus on various applications, including creating stories, illustrations, poems, articles, computer code, music compositions, and videos. Extrinsic hallucinations are a critical limitation of such GenAI, which can lead to significant challenges in achieving and maintaining the trustworthiness of GenAI. In this paper, we propose two new concepts that we believe will aid the research community in addressing limitations associated with the application of GenAI models. First, we propose a definition for the"desirability"of GenAI outputs and three factors which are observed to influence it. Second, drawing inspiration from Martin Fowler's code smells, we propose the concept of"prompt smells"and the adverse effects they are observed to have on the desirability of GenAI outputs. We expect our work will contribute to the ongoing conversation about the desirability of GenAI outputs and help advance the field in a meaningful way.arXiv.org150This paper proposes two new concepts that will aid the research community in addressing limitations associated with the application of GenAI models, including the concept of prompt smells and the adverse effects they are observed to have on the desirability of GenAI outputs.ArXiv['Krishna Ronanki', 'Beatriz Cabrero Daniel', 'Christian Berger']2024-01-23T00:00:00https://www.semanticscholar.org/paper/b5ede146775aa2b773d4763604c45b816d3ecefc a5fdbdab6556b0b9d3b27d3e4d3c0bb2c88be5b7Bridging the Divide120Bridging the Divide['Hyunjeong Rhee', 'Kyu-Hye Lee']2024-01-23T00:00:00https://www.semanticscholar.org/paper/a5fdbdab6556b0b9d3b27d3e4d3c0bb2c88be5b7 8b1e9146234a9f4595fb53027dacd6d588c35a0eBiomedical Journal of Scientific &amp; Technical Research00Biomedical Journal of Scientific &amp; Technical Research['Jammal Omotoyosi Adeyemi']2024-01-23T00:00:00https://www.semanticscholar.org/paper/8b1e9146234a9f4595fb53027dacd6d588c35a0e 8291722dbb55498464a5b58304f4cc645fa65420Journal of Happiness Studies421Using data from 39 European countries collected in 2021, it is consistently found that people with negative perceptions report lower life satisfaction, and this finding is robust across a number of robustness checks.Journal of Happiness Studies['Tim Hinks']2024-01-23T00:00:00https://www.semanticscholar.org/paper/8291722dbb55498464a5b58304f4cc645fa65420 13f797c7bab4daf0f29ee9e58d09fb22dcac0a6bMilitary Medical Research2123The involvement of AI in antibacterial drug development and utilization is systematically reviewed, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.Military Medical Research['Guang-Yu Liu', 'Dan Yu', 'Mei‑Mei Fan', 'Xu Zhang', 'Ze-Yu Jin', 'Christoph Tang', 'Xiaofen Liu']2024-01-23T00:00:00https://www.semanticscholar.org/paper/13f797c7bab4daf0f29ee9e58d09fb22dcac0a6b f437e772bc223adb02b7a9c19fa47407f7198957This study examined the efficacy of artificial intelligence (AI) technologies in predictive risk assessment and their contribution to ensuring business continuity. This research aimed to understand how different AI components, such as natural language processing (NLP), AI-powered data analytics, AI-driven predictive maintenance, and AI integration in incident response planning, enhance risk assessment and support business continuity in an environment where businesses face a myriad of risks, including natural disasters, cyberattacks, and economic fluctuations. A cross-sectional design and quantitative method were used to collect data for this study from a sample of 360 technology specialists. The results of this study show that AI technologies have a major impact on business continuity and predictive risk assessment. Notably, it was discovered that NLP improved the accuracy and speed of risk assessment procedures. The integration of AI into incident response plans was particularly effective, greatly decreasing company interruptions and improving recovery from unforeseen events. It is advised that businesses invest in AI skills, particularly in fields such as NLP for automated risk assessment, data analytics for prompt risk detection, predictive maintenance for operational effectiveness, and AI-enhanced incident response planning for crisis management.Risks631It was discovered that NLP improved the accuracy and speed of risk assessment procedures, and the integration of AI into incident response plans was particularly effective, greatly decreasing company interruptions and improving recovery from unforeseen events.Risks['Stavros Kalogiannidis', 'D. Kalfas', 'Olympia Papaevangelou', 'Grigoris Giannarakis', 'F. Chatzitheodoridis']2024-01-23T00:00:00https://www.semanticscholar.org/paper/f437e772bc223adb02b7a9c19fa47407f7198957 b7f9c7de7df4756832061b8f91f71a038a9c6e9cCleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities.Children931The findings showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment and inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities.Children['K. Almoammar']2024-01-23T00:00:00https://www.semanticscholar.org/paper/b7f9c7de7df4756832061b8f91f71a038a9c6e9c 2e94cad4f8a2868e42b99e080938ad4a4b97d675Scientific Reports251The survey results show that, on the whole, the integration of artificial intelligence technology into maker education can significantly improve students’ learning feelings and attitudes, and enhance the enthusiasm of learning emotions.Scientific Reports['Qizhong Ou', 'Xinglin Chen']2024-01-23T00:00:00https://www.semanticscholar.org/paper/2e94cad4f8a2868e42b99e080938ad4a4b97d675 4010e949325fc741df03022288a12cb0553c6310Organizations including companies, nonprofits, governments, and academic institutions are increasingly developing, deploying, and utilizing artificial intelligence (AI) tools. Responsible AI (RAI) governance approaches at organizations have emerged as important mechanisms to address potential AI risks and harms. In this work, we interviewed 17 technical contributors across organization types (Academic, Government, Industry, Nonprofit) and sectors (Finance, Health, Tech, Other) about their experiences with internal RAI governance. Our findings illuminated the variety of organizational definitions of RAI and accompanying internal governance approaches. We summarized the first detailed findings on algorithm review boards (ARBs) and similar review committees in practice, including their membership, scope, and measures of success. We confirmed known robust model governance in finance sectors and revealed extensive algorithm and AI governance with ARB-like review boards in health sectors. Our findings contradict the idea that Institutional Review Boards alone are sufficient for algorithm governance and posit that ARBs are among the more impactful internal RAI governance approaches. Our results suggest that integration with existing internal regulatory approaches and leadership buy-in are among the most important attributes for success and that financial tensions are the greatest challenge to effective organizational RAI. We make a variety of suggestions for how organizational partners can learn from these findings when building their own internal RAI frameworks. We outline future directions for developing and measuring effectiveness of ARBs and other internal RAI governance approaches.arXiv.org680The results suggest that integration with existing internal regulatory approaches and leadership buy-in are among the most important attributes for success and that financial tensions are the greatest challenge to effective organizational RAI.ArXiv['Emily Hadley', 'Alan R. Blatecky', 'Megan Comfort']2024-01-23T00:00:00https://www.semanticscholar.org/paper/4010e949325fc741df03022288a12cb0553c6310 ced510a9eb4f292495529ba14be0c0572eecdee5The specific features of implementing the artificial intelligence technologies in the work of the agribusiness enterprises are studied. The main directions for using such technologies are distinguished, the successful practices of implementing thereof in the work of the national enterprises are noted, and the trends for supporting the above mentioned processes are analysed. The importance of the state support aimed at enhancing accessibility of the AI-based technologies for the agribusiness sector is substantiated.Economy and ecology of territorial educations30The specific features of implementing the artificial intelligence technologies in the work of the agribusiness enterprises are studied and the state support aimed at enhancing accessibility of the AI-based technologies for the agribusiness sector is substantiated.Economy and ecology of territorial educations['Vyacheslav V. Polyakov']2024-01-23T00:00:00https://www.semanticscholar.org/paper/ced510a9eb4f292495529ba14be0c0572eecdee5 e8b4ab15d395812ba9ef2df2ef09a8596e406273In the developmental dilemma of artificial intelligence (AI)-assisted judicial decision-making, the technicalarchitecture of AI determines its inherent lack of transparency and interpretability, which is challenging to fundamentallyimprove. This can be considered a true challenge in the realm of AI-assisted judicial decision-making. By examining thecourt’s acceptance, integration, and trade-offs of AI technology embedded in the judicial field, the exploration of potentialconflicts, interactions, and even mutual shaping between the two will not only reshape their conceptual connotations andintellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of thejudicial trial system.Journal of Electronic Research and Application70By examining the court’s acceptance, integration, and trade-offs of AI technology embedded in the judicial field, the exploration of potential conflicts, interactions, and even mutual shaping between the two will strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.Journal of Electronic Research and Application['Kexin Chen']2024-01-23T00:00:00https://www.semanticscholar.org/paper/e8b4ab15d395812ba9ef2df2ef09a8596e406273 1603b80439d50b8313bba75a25906f0fb7b3a0f6Background: The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive thickness of the maternal abdominal wall, or the presence of post-surgical scars on the maternal abdominal wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes of fetal ultrasound evaluation, and for disease diagnosis, which helps conventional imaging methods. The usage of information, ultrasound scan images, and a machine learning program create an algorithm capable of assisting healthcare providers by reducing the workload, reducing the duration of the examination, and increasing the correct diagnosis capability. The recent remarkable expansion in the use of electronic medical records and diagnostic imaging coincides with the enormous success of machine learning algorithms in image identification tasks. Objectives: We aim to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.Life480This work aims to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.Life['Iuliana-Alina Enache', 'Cătălina Iovoaica-Rămescu', 'Ș. Ciobanu', 'Elena-Iuliana-Anamaria Berbecaru', 'Andreea Vochin', 'I. Băluță', 'A. Istrate-Ofițeru', 'C. Comănescu', 'R. Nagy', 'D. Iliescu']2024-01-23T00:00:00https://www.semanticscholar.org/paper/1603b80439d50b8313bba75a25906f0fb7b3a0f6 c5fbc268a60ba8f1b273dbcb3004f69b9a43036dTraditional science and technology museums are facing challenges in the rapid advancements in science and technology. These challenges include limited exhibition formats, outdated methods of conveying information, and inefficient resource management. However, with the rapid development of artificial intelligence technology, there is an opportunity to overcome these challenges. Artificial intelligence technology, specifically in the field of natural language processing, has made significant progress in improving multilingual understanding and generative models. This advancement can greatly enhance the learning experience for diverse audiences. In addition, the development of computer vision technology focuses on scene understanding, video analysis, and visual inference. These advancements can provide a more immersive and engaging exhibition experience at science and technology museums. This paper aims to explore how traditional science and technology museums can integrate and redesign artificial intelligence technology to better meet the needs of their audiences. By doing so, it can improve the effectiveness of science and technology communication and enable museums to adapt more flexibly to the rapidly changing science and technology landscape. The findings of this study can provide strategic considerations and guidance for the future development of science and technology museums.Frontiers in Business, Economics and Management60This paper aims to explore how traditional science and technology museums can integrate and redesign artificial intelligence technology to better meet the needs of their audiences and improve the effectiveness of science and technology communication.Frontiers in Business, Economics and Management['Jiayi Liu', 'Xiangyu Gao', 'Wenfeng Zhang', 'Liwei Zeng', 'Yi Zeng', 'Yi Yin', 'Xiaojun Qian']2024-01-23T00:00:00https://www.semanticscholar.org/paper/c5fbc268a60ba8f1b273dbcb3004f69b9a43036d a928209e740ce168a6cf3c824dcba9d1aab07255The article examines the theoretical and practical aspects of globalization and the impact of artificial intelligence on the economy and business. Researching the impact of globalization, technology, and artificial intelligence on the economy is a hot topic that is attracting significant interest among countries and companies. This research makes it possible to understand the current trends in the development of the world economy, predict its future changes and develop strategies that will help companies and countries use new opportunities and reduce possible risks. Today, artificial intelligence has a great impact on various fields of activity, in particular on business and the economy, which has global consequences, therefore, conducting research in this field is of great importance. The modern world is covered by the process of globalization, dynamic changes in technology and the development of the Internet, it needs a tool for the development of business and economy, for this it is possible to use artificial intelligence. The implementation of artificial intelligence opens up new opportunities for companies, such as automation, data analytics, personalization of services, development of innovative products and improvement of management. However, at the same time, it brings new challenges. Researching the impact of globalization, technology, and artificial intelligence on the economy is an important tool for developing strategies and solving problems affecting the economy and society as a whole.Visnik Zaporiz'kogo nacional'nogo universitetu. Ekonomicni nauki90The theoretical and practical aspects of globalization and the impact of artificial intelligence on the economy and business are examined, an important tool for developing strategies and solving problems affecting the economy and society as a whole.Visnik Zaporiz kogo nacional nogo universitetu Ekonomicni nauki['Н.О. Дугієнко', 'А.Н. Cулєйманова']2024-01-23T00:00:00https://www.semanticscholar.org/paper/a928209e740ce168a6cf3c824dcba9d1aab07255 87dc45326e57ea3297d253a0185e0db1f1ecc2b2Agriculture is the backbone of many economies across the globe [...]Agronomy272Agronomy['Nguyenthanh Son', 'Cheng-Ru Chen', 'C. Syu']2024-01-23T00:00:00https://www.semanticscholar.org/paper/87dc45326e57ea3297d253a0185e0db1f1ecc2b2 ba380431daf938ca99bc2749fe0dbaea9b51d1ccFrontiers in Digital Health411Frontiers in Digital Health['Maurizio Caon', 'Karthik Adapa', 'Anastasia Murphy', 'B. L. Green', 'Edmondo Robinson']2024-01-23T00:00:00https://www.semanticscholar.org/paper/ba380431daf938ca99bc2749fe0dbaea9b51d1cc c26e9d5bca96ae510144db0a2d866f3e4440ef9aIn the European context, both the EU AI Act proposal and the draft Standardisation Request on safe and trustworthy AI link standardisation to fundamental rights. However, these texts do not provide any guidelines that specify and detail the relationship between AI standards and fundamental rights, its meaning or implication. This chapter aims to clarify this critical regulatory blind spot. The main issue tackled is whether the adoption of AI harmonised standards, based on the future AI Act, should take into account fundamental rights. In our view, the response is yes. The high risks posed by certain AI systems relate in particular to infringements of fundamental rights. Therefore, mitigating such risks involves fundamental rights considerations and this is what future harmonised standards should reflect. At the same time, valid criticisms of the European standardisation process have to be addressed. Finally, the practical incorporation of fundamental rights considerations in the ongoing European standardisation of AI systems is discussed.arXiv.org50The main issue tackled is whether the adoption of AI harmonised standards, based on the future AI Act, should take into account fundamental rights, and whether this is what future harmonised standards should reflect.ArXiv['Marion Ho-Dac']2024-01-23T00:00:00https://www.semanticscholar.org/paper/c26e9d5bca96ae510144db0a2d866f3e4440ef9a 4f4c734f373cec92e886944086ff9c60c4a89c9dInternational Journal of Health Sciences00International Journal of Health Science['Daniely Carlos Silva', 'Maria Thaís Lucena Rodrigues Valente', 'Rayanne Lopes de Medeiros', 'Gabriela Baêta Barbosa Leite', 'Fernanda da Silveira Nunes Arcanjo Chaves', 'Brena Maria Almeida Araújo de Paula Pessoa', 'Andressa Karkow Crivellaro', 'Giovana Giacomelle Thompson', 'Letícia Castelioni Fachin', 'Eduarda Tumoli Ferreira', 'Nathalia Sofia Mayer Ceron', 'Neidejany de Assunção do Sacamento']2024-01-23T00:00:00https://www.semanticscholar.org/paper/4f4c734f373cec92e886944086ff9c60c4a89c9d e387789962693427c559ec604b39bd34c3d50bd8Indian Journal of Surgical Oncology510Indian Journal of Surgical Oncology['Shruti Gairola', 'S. Solanki', 'S. Patkar', 'M. Goel']2024-01-23T00:00:00https://www.semanticscholar.org/paper/e387789962693427c559ec604b39bd34c3d50bd8 d5597ace7c793d5658d6a2d3b7ea5fd34354dff5Bridging the Divide150Bridging the Divide['Mona Maher', 'Fatma Baytar']2024-01-23T00:00:00https://www.semanticscholar.org/paper/d5597ace7c793d5658d6a2d3b7ea5fd34354dff5 13b9a6d69ce9cb5de96fa9578f5e339b5644dbe3Dreams of machines rivaling human intelligence have shaped the field of AI since its inception. Yet, the very meaning of human-level AI or artificial general intelligence (AGI) remains elusive and contested. Definitions of AGI embrace a diverse range of incompatible values and assumptions. Contending with the fractured worldviews of AGI discourse is vital for critiques that pursue different values and futures. To that end, we provide a taxonomy of AGI definitions, laying the ground for examining the key social, political, and ethical assumptions they make. We highlight instances in which these definitions frame AGI or human-level AI as a technical topic and expose the value-laden choices being implicitly made. Drawing on feminist, STS, and social science scholarship on the political and social character of intelligence in both humans and machines, we propose contextual, democratic, and participatory paths to imagining future forms of machine intelligence. The development of future forms of AI must involve explicit attention to the values it encodes, the people it includes or excludes, and a commitment to epistemic justice.1190A taxonomy of AGI definitions is provided, laying the ground for examining the key social, political, and ethical assumptions they make and proposing contextual, democratic, and participatory paths to imagining future forms of machine intelligence.['Borhane Blili-Hamelin', 'Leif Hancox-Li', 'Andrew Smart']2024-01-23T00:00:00https://www.semanticscholar.org/paper/13b9a6d69ce9cb5de96fa9578f5e339b5644dbe3 db1fb0588c6f8572bf910a72988c7875b1f72005The development of technology that continues to evolve has given birth to an innovation called artificial intelligence or artificial intelligence which is usually called "AI". The development of AI has sparked an algorithm called deepfake technology. Deepfakes use machine learning and neural network technology, which are methods in AI that teach computers to process data in a way inspired by the human brain. This study aims to determine the regulation of AI as perpetrators of deepfake crimes and to determine the criminal responsibility of AI who commit criminal acts in Indonesia. The research method used is normative legal research using a statutory approach (statue approach), conceptual approach (conceptual approach), and comparative approach (comparative approach). AI is classified as an electronic system and electronic agent which when viewed to the characteristics of AI that has a match with the definition of electronic systems and electronic agents. If AI commits deepfake crimes, it can violate several articles in Law No. 19 of 2016 concerning Electronic Information and Transactions. In California, legislation has been passed to address deepfakes related to pornography, fraud, and defamation: Calif AB-602 and Calif AB-730. There are three AI criminal liability models that commit criminal acts, namely Perpetration-via another model (PVM), Natural-Probable-Consequence Liability Model (NPCLM), and Direct Liability Model (DLM). In Indonesia, AI has not been recognized as a legal subject so that if you commit a criminal act, the person who must be responsible is the creator of AI or AI usersAsian Journal of Social and Humanities260This study aims to determine the regulation of AI as perpetrators of deepfake crimes and to determine the criminal responsibility of AI who commit criminal acts in Indonesia.Asian Journal of Social and Humanities['Asri Gresmelian Eurike Hailtik', 'Wiwik Afifah']2024-01-22T00:00:00https://www.semanticscholar.org/paper/db1fb0588c6f8572bf910a72988c7875b1f72005 b26a11366231b591fe3426e78209bbd2215ba286This article centers on the legal regulation of artificial intelligence (AI) use in public administration. The main features of AI, the risks associated with its use in the public sector, and its key functions were outlined. The program and strategic plans for AI introduction in Russia were systematized. The prospects for creating a digital state were highlighted. Through careful analysis, the most viable areas of public administration were identified where AI can realize its full technical potential without entirely taking over the tasks of human officials (such as digital controllers, judges, and investigators). A clear distinction was revealed between “weak” AI, a technical assistant and decision-making tool for human officials, and “strong” AI, a fully-fledged subject of legal public relations with legal capacity. The connection between the development of new technologies and the evolution of the state apparatus was stressed. It was concluded that the integration of multiple algorithms in management activities is causing a shift from quantitative to qualitative changes: the ways state bodies exercise their public powers are being altered and revised.Uchenye Zapiski Kazanskogo Universiteta Seriya Gumanitarnye Nauki90Through careful analysis, the most viable areas of public administration were identified where AI can realize its full technical potential without entirely taking over the tasks of human officials (such as digital controllers, judges, and investigators).Uchenye Zapiski Kazanskogo Universiteta Seriya Gumanitarnye Nauki['T. G. Kakokho']2024-01-22T00:00:00https://www.semanticscholar.org/paper/b26a11366231b591fe3426e78209bbd2215ba286 da65443c52f3ec97215769fefa917f0a1156acffBackground Clinical Artificial intelligence (AI) has reached a critical inflection point. Advances in algorithmic science and increased understanding of operational considerations in AI deployment are opening the door to widespread clinical pathway transformation. For surgery in particular, the application of machine learning algorithms in fields such as computer vision and operative robotics are poised to radically change how we screen, diagnose, risk-stratify, treat and follow-up patients, in both pre- and post-operative stages, and within operating theatres. Summary In this paper, we summarise the current landscape of existing and emerging integrations within complex surgical care pathways. We investigate effective methods for practical use of AI throughout the patient pathway, from early screening and accurate diagnosis to intraoperative robotics, post-operative monitoring and follow-up. Horizon scanning of AI technologies in surgery is used to identify novel innovations that can enhance surgical practice today, with potential for paradigm shifts across core domains of surgical practice in the future. Any AI-driven future must be built on responsible and ethical usage, reinforced by effective oversight of data governance, and of risks to patient safety in deployment. Implementation is additionally bound to considerations of usability and pathway feasibility, and the need for robust healthcare technology assessment and evidence generation. While these factors are traditionally seen as barriers to translating AI into practice, we discuss how holistic implementation practices can create a solid foundation for scaling AI across pathways. Key Messages The next decade will see rapid translation of experimental development into real-world impact. AI will require evolution of work practices, but will also enhance patient safety, enhance surgical quality outcomes, and provide significant value for surgeons and health systems. Surgical practice has always sat on a bedrock of technological innovation. For those that follow this tradition, the future of AI in surgery starts now.European surgical research. Europaische chirurgische Forschung. Recherches chirurgicales europeennes03This paper investigates effective methods for practical use of AI throughout the patient pathway, from early screening and accurate diagnosis to intraoperative robotics, post-operative monitoring and follow-up, and discusses how holistic implementation practices can create a solid foundation for scaling AI across pathways.European surgical research. Europaische chirurgische Forschung. Recherches chirurgicales europeennes['Ahmad Guni', 'Piyush Varma', 'Joe Zhang', 'M. Fehervari', 'H. Ashrafian']2024-01-22T00:00:00https://www.semanticscholar.org/paper/da65443c52f3ec97215769fefa917f0a1156acff cec8358d13131303b3a5c100097cd13605d77a3cThe rapid advancement of artificial intelligence (AI) in education necessitates a shared understanding of its intended purpose and societal implications. This paper underscores the significance of societal perspectives in AI and education, often overshadowed by technological aspects. At the same time, policy guidelines for the integration of AI technology within educational systems are playing a pivotal role in shaping the future of education. What we as society imagine AI and education to be, will in some shape or form lead the development of suggested fixes. The aim is to aid the understanding of why and how visions of learning and education are framed in relation to developments in Educational Technology (EdTech) and their introduction in education. It thereby contributes to the ongoing discussion on the integration of AI in education and its potential societal impacts.Policy Futures in Education192The aim is to aid the understanding of why and how visions of learning and education are framed in relation to developments in Educational Technology (EdTech) and their introduction in education.Policy Futures in Education['Cornelia Linderoth', 'Magnus Hultén', 'Linnéa Stenliden']2024-01-22T00:00:00https://www.semanticscholar.org/paper/cec8358d13131303b3a5c100097cd13605d77a3c 45063d25b2015cf79a4d7a11545309e240f76b1bThe recent hyperbole around artificial intelligence (AI) has impacted on our ability to properly consider the lasting educational implications of this technology. This paper outlines a number critical issues and concerns that need to feature more prominently in future educational discussions around AI. These include: (i) the limited ways in which educational processes and practices can be statistically modelled and calculated; (ii) the ways in which AI technologies risk perpetuating social harms for minoritized students; (iii) the losses incurred through reorganising education to be more ‘machine readable’; and (iv) the ecological and environmental costs of data-intensive and device-intensive forms of AI. The paper concludes with a call for slowing down and recalibrating current discussions around AI and education – paying more attention to issues of power, resistance and the possibility of re-imagining education AI along more equitable and educationally beneficial lines.Nordisk Tidsskrift for Pedagogikk og Kritikk272There is a call for slowing down and recalibrating current discussions around AI and education – paying more attention to issues of power, resistance and the possibility of re-imagining education AI along more equitable and educationally beneficial lines.Nordisk tidsskrift for pedagogikk og kritikk['Neil Selwyn']2024-01-22T00:00:00https://www.semanticscholar.org/paper/45063d25b2015cf79a4d7a11545309e240f76b1b 3ae1b016991f8b05928b1ce14cc43002a5715594A scientific subject called artificial intelligence (AI) aims to create computer systems that function as efficiently as a skilled human does. Such effectiveness can substantially improve education by utilising the most cutting-edge technologies. To determine the level of AI awareness among Al Ain University faculty members and to investigate the connections between AI awareness, the digital transformation scale (DTS), and the technological, scientific scale (TSS), this study set out to measure that awareness. The descriptive-correlational research technique of the study included three analyses, with a particular focus on AI, DTS, and TSS. 101 academics, or 43.5% of the College of Education faculty, were represented in the sample from all departments. They were chosen using a simple random sampling technique. The quantitative data analysis revealed that the faculty members exhibited a medium level of awareness, with a mean score of 3.05 on a 5-point scale. A correlation value of 0.139 and a significance coefficient of 0.165 indicated no statistically significant correlation between faculty members’ awareness of AI and DTS. With a correlation of 0.568 and a significance level of P <0.01, the study found that among faculty members, there was a direct and statistically significant positive link between AI awareness and TSS. It is essential to prepare faculty members for using AI in the classroom and to modify their perspectives on it by holding seminars and providing them with the training they need to do so.American Journal of Education and Technology411The study found that among faculty members, there was a direct and statistically significant positive link between AI awareness and TSS, and the study set out to measure that awareness.American Journal of Education and Technology['Asmaa Jumaha AlMahdawi']2024-01-22T00:00:00https://www.semanticscholar.org/paper/3ae1b016991f8b05928b1ce14cc43002a5715594 3d8da9e6cb6f652616aa23f2f084566e821c78761011[]2024-01-22T00:00:00https://www.semanticscholar.org/paper/3d8da9e6cb6f652616aa23f2f084566e821c7876 29ece14fabfd5b4a4f4cba27451b4818b4e701d5Artificial intelligence (AI) in games has advanced significantly, notably in perfect information games such as Go and Chess. Imperfect information games, in which participants do not have complete information about the game state, create more difficulties. They incorporate both public and private observations, where strategies must be improved to achieve a Nash equilibrium. This study investigates artificial intelligence and reinforcement learning approaches, in which agents learn to maximize future rewards through interactions with their surroundings. The paper then focuses on card game research platforms such as RLCard and OpenAI Gym. It gives a comprehensive summary of research in No Limit Texas Hold'em, a difficult two-player poker game with a large decision space. DeepStack and Libratus are successful systems that have attained expert-level and superhuman play, respectively. Pluribus, a superhuman artificial intelligence for six-player poker, and DouZero, a pure reinforcement learning technique for the multiplayer card game, DouDiZhu, are both investigated. Overall, this paper provides background information on reinforcement learning and imperfect information games, analyzes commonly used research platforms, evaluates the effectiveness of AI algorithms in various card games, and offers future research areas and directions.Applied and Computational Engineering30This study investigates artificial intelligence and reinforcement learning approaches, in which agents learn to maximize future rewards through interactions with their surroundings, and focuses on card game research platforms such as RLCard and OpenAI Gym.Applied and Computational Engineering['Megan Sun']2024-01-22T00:00:00https://www.semanticscholar.org/paper/29ece14fabfd5b4a4f4cba27451b4818b4e701d5 0c42e37bba601f85129818b9f738bd072d55e484In recent years, the application of artificial intelligence technology in the field of agriculture has been rapidly developed. This paper summarizes the application of artificial intelligence in agriculture and divides it into two main directions: monitoring system and expert system. This paper analyzes the soil monitoring, pest monitoring, and plant growth detection of the monitoring system, the simple decision chain of the expert system, and the complex expert system combined with artificial intelligence technology. Utilizing sensor networks, image processing, and machine learning techniques, artificial intelligence enables real-time monitoring of soil parameters, automatic identification of pest and disease, analysis of plant growth status, and provision of tailored management recommendations. By employing rule-based expert systems, artificial intelligence assists farmers in making informed decisions. These applications have significantly advanced resource management optimization, pest control, precise growth monitoring, and intelligent decision-making in agriculture. At the end of the article, this paper summarizes the full text and looks forward to the future trend.Applied and Computational Engineering00This paper analyzes the soil monitoring, pest monitoring, and plant growth detection of the monitoring system, the simple decision chain of the expert system, and the complex expert system combined with artificial intelligence technology.Applied and Computational Engineering['Muyao Niu']2024-01-22T00:00:00https://www.semanticscholar.org/paper/0c42e37bba601f85129818b9f738bd072d55e484 d09ef965b35c267abce10d3511880446a76f2f8dAlzheimer's disease is the most common form of dementia that can cause a brain neurological disorder with progressive memory loss as a result of brain cell damage. Prevention and treatment of disease is a key challenge in today's aging society. Accurate diagnosis of Alzheimer's disease plays an important role in patient management, especially in the early stages of the disease, because awareness of risk allows patients to undergo preventive measures even before brain damage occurs irreversible. Over the years, techniques such as statistical modeling or machine learning algorithms have been used to improve understanding of this condition. The objective of the work is the study of the methods of detection and progression of Alzheimer's disease through artificial intelligence techniques that have been proposed in the last three years.Journal of Automation, Mobile Robotics & Intelligent Systems00The objective of the work is the study of the methods of detection and progression of Alzheimer's disease through artificial intelligence techniques that have been proposed in the last three years.Journal of Automation, Mobile Robotics and Intelligent Systems['Eduardo Garea-Llano']2024-01-22T00:00:00https://www.semanticscholar.org/paper/d09ef965b35c267abce10d3511880446a76f2f8d f70538e78dff556ba1d4207491cdbcb0bd169203With the rapid development of artificial intelligence technology, content generated by artificial intelligence has been rapidly applied to people's lives. At the same time, it is also accompanied by many infringement lawsuits, whether AIGC has really caused different degrees of infringement to human artists. Through the analysis of the existing literature on copyright issues and the walkthrough of Stable Diffusion, an AI-generated image platform, this article digs into the main factors that the AI-generated platform causes infringements on human artists. Provide references for using AI by enterprises and related media, and let more scholars pay attention to this issue. The study found that in the workflow of the AI generation platform, taking Stable Diffusion as an example, the two processes of model training and image generation may cause copyright infringement to a certain extent. Based on this, the AI generation platform has unauthorized use of copyright works, excessive plagiarism and adaptation of copyright works, and the generated images are not marked with watermarks or sources, which damages the copyright owner's rights.Applied and Computational Engineering120Applied and Computational Engineering['Lyulin Zhuang']2024-01-22T00:00:00https://www.semanticscholar.org/paper/f70538e78dff556ba1d4207491cdbcb0bd169203 d98e68a20e9de5e4d8581f64ad0b9bcf90c5b7fcIn recent years, with the rapid development of artificial intelligence technology, early childhood artificial intelligence training systems have gradually been applied in the field of education. However, there is still a lack of systematic research and exploration on the ethical issues of artificial intelligence training systems for young children. This study is guided by the child-centered theory and aims to explore the ethics of artificial intelligence training systems for young children, and propose corresponding solutions. Firstly, the development status and ethical issues of artificial intelligence training systems for young children were analyzed through literature review. Then, based on the principle of child centeredness, an ethical evaluation was conducted on the design and use of artificial intelligence training systems for young children. Finally, specific suggestions were proposed to protect children's rights and promote their development in the early childhood artificial intelligence training system, and future research was prospected.Applied and Computational Engineering00An ethical evaluation was conducted on the design and use of artificial intelligence training systems for young children and specific suggestions were proposed to protect children's rights and promote their development in the early childhood artificial intelligence training system.Applied and Computational Engineering['Xi Liu']2024-01-22T00:00:00https://www.semanticscholar.org/paper/d98e68a20e9de5e4d8581f64ad0b9bcf90c5b7fc df8f0f3b912488f3f8d4ffc0abea3dcdc0ff5fc8Abstract Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms—a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial–mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.Briefings Bioinform.1120Standigm ASK emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.Briefings in Bioinformatics['Seokjin Han', 'Ji Eun Lee', 'Seolhee Kang', 'Minyoung So', 'Hee Jin', 'Jang Ho Lee', 'Sunghyeob Baek', 'Hyungjin Jun', 'Tae Yong Kim', 'Yun-Sil Lee']2024-01-22T00:00:00https://www.semanticscholar.org/paper/df8f0f3b912488f3f8d4ffc0abea3dcdc0ff5fc8 7f12b12fe5528900a3c4cbb9985b72299cde2e9fThe rapidly evolving digital landscape of the 21st century has marked the ascendancy of Artificial Intelligence (AI) as a potent transformative element across multifarious sectors, especially within the educational realm. This research undertakes a meticulous exploration of AI-integrated conversational models, emphasizing the pivotal role of ChatGPT, a brainchild of OpenAI. Delving into its developmental trajectory, this study maps the intricate transitions from ChatGPT's version 3.5 to its superior successor, version 4.0. This journey reveals marked enhancements, such as the model's adeptness at handling extensive textual data, and its uncanny ability to produce nuanced, human-like interactive responses. From empirical and qualitative evaluations, it was found that ChatGPT has demonstrated a profound impact in two main areas: expanding student engagement and advocating for a hyper-personalized learning paradigm. Findings suggest a compelling correlation between ChatGPT-integrated pedagogical methods and augmented student motivation, proactive engagement, and enriched academic achievements. Moreover, detailed case studies within specialized fields, notably medical education and legal studies, underscore ChatGPT's versatility in tailoring instructional content to niche disciplinary requirements. In synthesizing these insights, this research postulates an imminent educational future characterized by deep personalization, dynamic interactivity, and an enhanced learner-centric ethos. Such a vision places this study at the forefront of educational discourse, proffering invaluable guidance for educators, technologists, and policymakers endeavoring to maximize the benefits of AI-infused pedagogy.Applied and Computational Engineering00It was found that ChatGPT has demonstrated a profound impact in two main areas: expanding student engagement and advocating for a hyper-personalized learning paradigm, and an imminent educational future characterized by deep personalization, dynamic interactivity, and an enhanced learner-centric ethos.Applied and Computational Engineering['Linlin Fang']2024-01-22T00:00:00https://www.semanticscholar.org/paper/7f12b12fe5528900a3c4cbb9985b72299cde2e9f 419493320881336efc8e9aeb7f363d6d3e3b0abbWith the continuous development of the digital economy, artificial intelligence technology is becoming more and more widely used in various fields. This paper mainly discusses the practice and application of artificial intelligence technology in the digital economy, including its application field, technical principle, implementation method, and future development trends. Through analyzing the current application status and existing problems of artificial intelligence technology in the digital economy, this paper puts forward some improvement measures and development suggestions, aiming to provide a reference for promoting the better application of artificial intelligence technology in the digital economy.Applied and Computational Engineering00Through analyzing the current application status and existing problems of artificial intelligence technology in the digital economy, some improvement measures and development suggestions are put forward, aiming to provide a reference for promoting the better application of artificial intelligence technology in the digital economy.Applied and Computational Engineering['Quan Zhou']2024-01-22T00:00:00https://www.semanticscholar.org/paper/419493320881336efc8e9aeb7f363d6d3e3b0abb 64d81c0b0b5faf19811db1bc9c0a90dffde2c5c1This paper focuses on how designers can find the right balance and new foundation between themselves and Artificial Intelligence Generated Content (AIGC) at a time when the current artificial intelligence trend is invading the design industry like a wave. This study uses two methods of text analysis and semi-structured interviews to explore the coexistence between AIGC and designers. The results show that, for now, AIGC can help solve some of the fundamental problems in the design process but not all of them. Almost all designers dare not underestimate the possibility of AIGC in the future, and the arrival of AIGC is already an irreversible fact. This study explores the future impact of AIGC on the creative design industry through the perspective of designers and critical theory. It provides practical inspiration and some valuable thinking for the design industry.Applied and Computational Engineering00This study uses two methods of text analysis and semi-structured interviews to explore the coexistence between AIGC and designers and shows that, for now, AIGC can help solve some of the fundamental problems in the design process but not all of them.Applied and Computational Engineering['Yanan Sheng']2024-01-22T00:00:00https://www.semanticscholar.org/paper/64d81c0b0b5faf19811db1bc9c0a90dffde2c5c1 0ddb9abf3b5a82af865237db6b9a6bf5dd45fa97The relevance of the study is due to the fact that the problems associated with artificial intelligence and the development of digital technologies are extremely important for the modern world. In the process of re-search, the author notes that in the conditions of the mid-20s. In the 21st century, modern information technologies in the legal environment contribute to the adoption of new technical solutions, which are based on developments in the field of artificial intelligence, and are capable of solving complex legal problems, including those that have a positive impact on the fight against crime. In the process of research, taking into account the close attention of the country's leadership to the problem of introducing the technologies under consideration into various spheres of life of Russian society, the author examined possible aspects of the use of this technology in the system of the Ministry of Internal Affairs of Russia. At the same time, he made attempts to identify both the positive aspects of using these technologies and the possibility of negative consequences with their widespread implementation. As a result, the author focuses on the fact that the advantages of using the technologies under consideration in the law enforcement sphere, first of all, create real opportunities for police officers to gradually get rid of monotonous, monotonous activities, and use the released forces and means in more serious activities, for which robotics is not suitable for. In the process of research, the author convincingly proved that the use of these technologies beyond any doubt has a positive impact on the effectiveness of the professional activities of employees of the Ministry of Internal Affairs of Russia. From the standpoint of countering criminal manifestations, technologies based on artificial intelligence have enormous potential. The intelligence in question is already in high demand for working with large amounts of data, in particular statistical information, preparing reports, certificates, and information materials. In addition, these technologies can and should be used in operational search activities, criminal investigations, and the fight against various types of criminal manifestations. At the same time, the author specifically emphasizes that in order to maintain the effectiveness of the activities of the Russian Ministry of Internal Affairs, it is very important that the accumulated positive experience in the use of new technologies and coherence of actions are not only placed at the forefront, but also receive further development.VESTNIK ADVANCED TRAINING INSTITUTE OF THE MIA OF RUSSIA00The author convincingly proved that the use of technologies based on artificial intelligence beyond any doubt has a positive impact on the effectiveness of the professional activities of employees of the Ministry of Internal Affairs of Russia.VESTNIK ADVANCED TRAINING INSTITUTE OF THE MIA OF RUSSIA['P. Kobets']2024-01-22T00:00:00https://www.semanticscholar.org/paper/0ddb9abf3b5a82af865237db6b9a6bf5dd45fa97 b78de7fff69e8456ed07b8598c6d3478bf021afeArtificial Intelligence (AI), as a new technology emerging in recent years, has received great attention in various fields. For the manufacturing industry, the ability to integrate artificial intelligence technology into the production process of manufacturing can improve the productivity of manufacturing enterprises. This paper discusses the development trend of the manufacturing industry in the context of artificial intelligence technology. It analyses several advantages of AI technology applied to the manufacturing industry, including innovation in technology, helping enterprises to achieve the optimization of human resources as well as improving the matching degree of the market. Based on the above advantages, the integration of artificial intelligence in the manufacturing industry can be further developed. At the same time, this paper also analyses the current integration of artificial intelligence technology problems and difficulties and puts forward suggestions. The results of this paper can be proved for the integration of artificial intelligence into the manufacturing industry, confirming that the integration of artificial intelligence plays a great role in the development of the manufacturing industry.Highlights in Business, Economics and Management100The results of this paper can be proved for the integration of artificial intelligence into the manufacturing industry, confirming that the integration of artificial intelligence plays a great role in the development of the manufacturing industry.Highlights in Business, Economics and Management['Weizheng Weng']2024-01-22T00:00:00https://www.semanticscholar.org/paper/b78de7fff69e8456ed07b8598c6d3478bf021afe af0d3e9b2bae4e00b42a441b49b3c9b00088f4b0Artificial Intelligence (AI) has transformed a variety of areas, and education is no exception. With the development of deep learning and neural network, AI is poised to change the way people teach and learn. This paper explores the future prospects of deep learning and neural networks in education, highlighting the potential benefits and challenges they may bring. AI technologies, like deep learning algorithms and neural networks, have the potential to transform education through customized learning experiences, intelligent tutoring, streamlining administrative duties, and facilitating data-based decision making. Enhanced personalized learning helps students to learn at their own pace and in their preferred style, smart tutoring systems offer personalized guidance and support. Automation of administrative tasks increases efficiency and accuracy, while data-driven decision making helps educators make informed choices about students' outcomes. However, the implementation of AI in education poses challenges such as data privacy, equity, and the preservation of the teacher-student relationship. Efforts should be made to address these challenges and fully harness the potential of deep learning and neural networks in education.Applied and Computational Engineering00The future prospects of deep learning and neural networks in education are explored, highlighting the potential benefits and challenges they may bring and efforts should be made to address these challenges and fully harness the potential of deep learning and neural networks in education.Applied and Computational Engineering['Peiran Yu']2024-01-22T00:00:00https://www.semanticscholar.org/paper/af0d3e9b2bae4e00b42a441b49b3c9b00088f4b0 54a26773059be6c192ecc39a050cae76eda92d99This systematic literature review extensively explores the prospects of Artificial Intelligence (AI) applications in the realm of learning from 2019 to 2023. It delves into key areas such as intelligent tutoring systems and online learning platforms, while meticulously examining their underlying technologies. The coverage includes methods for integrating AI into student models, as well as algorithms for enhancing adaptive learning and improving the precision of personalized learning content recommendations. The paper also discusses challenges and prospects, including the potential of AI to reshape the teaching profession and privacy concerns. The review underscores the ongoing necessity of striking a balance between human involvement and automation, emphasizing that AI serves as an auxiliary tool rather than a replacement in education. Additionally, the study investigates the integration of AI in online learning, emphasizing the need for user-friendly interfaces and robust data protection policies. Lastly, the research highlights the importance of interdisciplinary collaboration and ethical considerations, envisioning a future where AI and education seamlessly harmonize.Applied and Computational Engineering00Applied and Computational Engineering['Bowen Zhou']2024-01-22T00:00:00https://www.semanticscholar.org/paper/54a26773059be6c192ecc39a050cae76eda92d99 a30bd1cc0edde6bdbf4280e9c4399a964bcb1082Future Business Journal550This study aims to unpack the intention and AU of AI among hiring professionals in the context of Bangladesh, a developing country in the South Asian region and provides fresh insights for developing policy interventions to hire professionals for thriving AI adoption in the context of developing countries effectively.Future Business Journal['Muhaiminul Islam', 'Md. Mahbubur Rahman', 'Md. Abu Taher', 'G. M. A. A. Quaosar', 'Md. Aftab Uddin']2024-01-22T00:00:00https://www.semanticscholar.org/paper/a30bd1cc0edde6bdbf4280e9c4399a964bcb1082 52c2fd5255aeb92b4f46057e7e038ee936ec2162This bibliometric analysis delves into the utilization of Artificial Intelligence (AI) in enhancing the efficiency of banking financial processes within Southeast Asian countries. With the banking sector in Southeast Asia undergoing rapid technological transformation, AI has emerged as a pivotal force, promising improvements in operational efficiency, risk management, and customer experience. By analyzing the scholarly landscape, including citation metrics, network visualizations, trend analysis, and density visualization, this research uncovers key themes and trends in the adoption of AI in banking across the region. The study not only offers valuable insights into the evolving research domain but also provides guidance for academics, practitioners, and policymakers aiming to shape the future of financial processes in Southeast Asia.West Science Interdisciplinary Studies00This bibliometric analysis delves into the utilization of Artificial Intelligence in enhancing the efficiency of banking financial processes within Southeast Asian countries and provides guidance for academics, practitioners, and policymakers aiming to shape the future of financial processes in Southeast Asia.West Science Interdisciplinary Studies['Hendri Khuan', 'Loso Judijanto', 'Titiek Rachmawati', 'Tia Tanjung', 'A. Y. Vandika']2024-01-22T00:00:00https://www.semanticscholar.org/paper/52c2fd5255aeb92b4f46057e7e038ee936ec2162 b2b992d88367d7bad8634e97b5156fbbbd33b9d6This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion is highlighted.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Most. Sohana Akter']2024-01-22T00:00:00https://www.semanticscholar.org/paper/b2b992d88367d7bad8634e97b5156fbbbd33b9d6 fe93a94588d8a49efdfed02e5a8f2df8376764c1As the core technology of the new round of social change, artificial intelligence is promoting the upgrading of traditional industries and driving the rapid development of the "unmanned economy," which will have an extremely important impact on the fields of intelligent transportation, smart home, and intelligent medical care. With the arrival of the information age, through coding, programmed machines can complete the tasks that humans can not complete which is intelligence. Today's artificial intelligence allows people to witness real intelligence: let the machine be automatic, through simulation of human thinking and learning ability, to complete many complex tasks and decision-making, with the data, arithmetic, and algorithms to achieve continuous breakthroughs. Additionally, artificial intelligence may even enter an eternal spring. The foreseeable is that AI will reshape human life in the future, and the era of AI with infinite possibilities will come in full swing. The factors that make artificial intelligence worthwhile for people to invest in are crucial. In recent years, people have devoted a lot of time and effort to the invention of artificial intelligence in order to make life more convenient, make people's travel easier, and reduce a lot of unnecessary expenses as well and save a lot of time. Besides, the development of human beings has always been inseparable from artificial intelligence. Every stage of development is improved by technology. However, the emergence of artificial intelligence will undoubtedly improve our lives and enhance our quality of life. More precisely, AI has liberated a large amount of labor and enhanced the way of human thinking.Highlights in Business, Economics and Management80Highlights in Business, Economics and Management['Axin Zhang']2024-01-22T00:00:00https://www.semanticscholar.org/paper/fe93a94588d8a49efdfed02e5a8f2df8376764c1 97a823399267e62e0817ac962afeb9701193e883The essay explores the influence of artificial intelligence (AI) on society and its potential to take over jobs from humans. With the ongoing acceleration of technology and the increasing independence of machines, a reduced number of workers will be required. The significant progress of artificial intelligence indicates that numerous jobs such as those of paralegals, journalists, office workers, and even computer programmers are at the brink of becoming obsolete as robots and intelligent software are set to replace them. It examines the possibility of augmented intelligence and concentrates on machine learning and deep learning as possible approaches. The study indicates variables that determine how likely an occupation is to be automated and highlights the advantages of using AI to boost work productivity. The application of AI and the concerned problem associated with it has a huge impact on human society. Machine learning and deep learning are implemented to discuss the feasibility of augmented intelligence. Many scientific approaches suggest the factors that determine the automation potential of an occupation and the benefits of using AI to improve work efficiency. Data analysis and result comparison are used in the essay. The essay draws the conclusion that Artificial Intelligence should improve human productivity and propel the development of society, but not replace it.Applied and Computational Engineering00The essay draws the conclusion that Artificial Intelligence should improve human productivity and propel the development of society, but not replace it.Applied and Computational Engineering['Chengke Zhang']2024-01-22T00:00:00https://www.semanticscholar.org/paper/97a823399267e62e0817ac962afeb9701193e883 3f24f1414392bb987d49ae1a1e1c79dc040c6e35This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion is highlighted.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Jeff Shuford']2024-01-22T00:00:00https://www.semanticscholar.org/paper/3f24f1414392bb987d49ae1a1e1c79dc040c6e35 e954a4f55a3ef21db8e303c0a4d3874796ef504bThe essay explores the influence of artificial intelligence (AI) on society and its potential to take over jobs from humans. With the ongoing acceleration of technology and the increasing independence of machines, a reduced number of workers will be required. The significant progress of artificial intelligence indicates that numerous jobs such as those of paralegals, journalists, office workers, and even computer programmers are at the brink of becoming obsolete as robots and intelligent software are set to replace them. It examines the possibility of augmented intelligence and concentrates on machine learning and deep learning as possible approaches. The study indicates variables that determine how likely an occupation is to be automated and highlights the advantages of using AI to boost work productivity. The application of AI and the concerned problem associated with it has a huge impact on human society. Machine learning and deep learning are implemented to discuss the feasibility of augmented intelligence. Many scientific approaches suggest the factors that determine the automation potential of an occupation and the benefits of using AI to improve work efficiency. Data analysis and result comparison are used in the essay. The essay draws the conclusion that Artificial Intelligence should improve human productivity and propel the development of society, but not replace it.Applied and Computational Engineering00The essay draws the conclusion that Artificial Intelligence should improve human productivity and propel the development of society, but not replace it.Applied and Computational Engineering['Franca Salis-Madinier', 'Anne Demelenne']2024-01-22T00:00:00https://www.semanticscholar.org/paper/e954a4f55a3ef21db8e303c0a4d3874796ef504b b4980384f191ca33cfbc9d3fbd5cde5308ca2cc1In this article, we will highlight the impact and application of Artificial Intelligence on Agriculture, along with the challenges in the adoption of Artificial Intelligence. Artificial Intelligence has become one of the most important technologies in every sector, including education, banking, robotics, agriculture, etc. In the agriculture sector, it is playing a very crucial role and it is transforming the agriculture industry. This article highlighted the application of Artificial Intelligence along with the challenges in the adoption of Artificial Intelligence and popular Artificial Intelligence start-ups used in agriculture. Today’s agriculture system has reached a different level due to technology like Artificial Intelligence and Robotics etc. This article highlights Artificial Intelligence with its applications and merits. Bhartiya Krishi Anusandhan Patrika00The application of Artificial Intelligence along with the challenges in the adoption of Artificial Intelligence and popular Artificial Intelligence start-ups used in agriculture are highlighted.Bhartiya Krishi Anusandhan Patrika['Dr.Harish Chandra Bharvey', 'Ramnivas Sharma']2024-01-22T00:00:00https://www.semanticscholar.org/paper/b4980384f191ca33cfbc9d3fbd5cde5308ca2cc1 1db543b99afb68188669a9fe1a65f3a9ea3c9de6Artificial Intelligence (AI) originating in the mid-twentieth century, replicates human thought processes using computer systems, integrating elements from various disciplines. Since 1955, AI applications have expanded rapidly, addressing educational challenges in the past decade, notably in medical education. A Web of Science search indicates a growing interest, reflected in increasing publications and citations over the last two decades. The surge highlights a recent uptick in AI utilization for research and development in medical education, including virtual inquiry systems, medical distance learning, and teaching video creation in medical schools.1Top of Form Medical education is a continuous learning process, spanning from undergraduate to postgraduate and specialty training, applicable to various healthcare professionals.2,3 Recognizing the need to build on existing knowledge amid rapidly advancing technology, AI in medical education becomes crucial. AI holds potential in enhancing the non-analytical, humanistic aspects of medicine, aiding healthcare professionals in processing vast amounts of information and improving diagnostic capabilities.3MedERA - Journal of CMH LMC and IOD00The surge highlights a recent uptick in AI utilization for research and development in medical education, including virtual inquiry systems, medical distance learning, and teaching video creation in medical schools.MedERA - Journal of CMH LMC and IOD['Dr Farhat Ijaz']2024-01-22T00:00:00https://www.semanticscholar.org/paper/1db543b99afb68188669a9fe1a65f3a9ea3c9de6 028def533ee8d71e9336ea4f4d9ec6ecfe530262Currently, large language models are on the rise with breakthrough progress in artificial intelligence. Existing reviews of AI game agents have not covered these latest developments, requiring a combing and analysis of the newest research advancements in game AI agents. This paper summarizes the application scenarios of game AI agents in four aspects: combat AI, Non-Player Character (NPC) interaction, automated testing, and Artificial General Intelligence (AGI) testing. In combat AI, there is a progressive developmental trend, with the introduction of Monte Carlo tree search and reinforcement learning enabling AI game agents to fully surpass humans in traditional board games. In NPC interaction, full AI is unnecessary. Game developers only need to incorporate AI for abilities related to player experience to increase appeal, with controllable generation results. In automated testing, game AI agents lack generalizability for testing so far. In AGI testing, academia has helpfully explored general game AI, but capabilities remain limited to certain games. Introducing large language models to game AI agents shows unprecedented capabilities. Finally, this paper provides an outlook on the hot topics and future directions of this research subject.Applied and Computational Engineering00This paper summarizes the application scenarios of game AI agents in four aspects: combat AI, Non-Player Character (NPC) interaction, automated testing, and Artificial General Intelligence (AGI) testing.Applied and Computational Engineering['Yuqi Lan', 'Zhenghao Li']2024-01-22T00:00:00https://www.semanticscholar.org/paper/028def533ee8d71e9336ea4f4d9ec6ecfe530262 3a57cff2670b967945f628baf9b23cb025f371e8This study investigates Indonesia’s ambitious artificial intelligence (AI) policy within the context of its decentralised governance structure. Through in-depth case studies in Jakarta, Central Java, and East Java, we analyse emerging AI-based policy responses and their challenges in a rapidly evolving technological landscape. Drawing from elite interviews conducted with central and local government officials and documentary research, this study offers rare insights into the local perspective on the struggle to accommodate the central government's ambitious plan with limited resources. This article finds that the divergence in the views and visions of AI between central and local governments has complicated the formulation and implementation of AI-based policies. Central authorities wield a dominant role, evident through regulatory mandates and a centralised decision-making approach that can potentially constrain local autonomy. This power asymmetry, coupled with the lack of specific AI-focused regulations, challenges local governments’ capacity to independently design and manage AI initiatives aligned with their unique contexts. Interestingly, instead of showing their resistance towards the ambitious national plan, local leaders have embraced AI policies, positioning them as innovative tools to enhance popularity in the lead-up to the 2024 general election.Journal of Current Southeast Asian Affairs180It is found that the divergence in the views and visions of AI between central and local governments has complicated the formulation and implementation of AI-based policies.Journal of Current Southeast Asian Affairs['R. Wadipalapa', 'Riris Katharina', 'Poltak Partogi Nainggolan', 'Sitti Aminah', 'Tini Apriani', 'Diana Ma’rifah', 'Azmi Listya Anisah']2024-01-22T00:00:00https://www.semanticscholar.org/paper/3a57cff2670b967945f628baf9b23cb025f371e8 4c615c416d070df4e9cd07c76feb9ae46bd3f482In connection with the problem of inclusion of objects produced by artificial intelligence (AI) into civil transactions turnover, the issue of subjectivity of rights to them must be resolved. One of the possible solutions (some researchers call it the main one) is considering granting the status of a subject of law to artificial intelligence itself. The paper is devoted to criticism of this approach.The social behavior of people is formed on the basis of their physical essence, this relationship will remain valid in the future. It is obvious that artificial intelligence is obviously devoid of physical essence. Even if we talk about a legal entity (in a number of systems, which are fictitious entities), the consequences of its activities are one way or another assigned to individuals.It seems appropriate to endow AI with the object characteristics of an intellectual complex — in line with property complexes in civil law, assigning initial rights to computer program developers. Considering AI as a subject of civil legal relations seems not only unjustified, but also prevents a doctrinal solution to the issue of legal personality, which is a prerequisite for the formation of the norms of current legislation.Actual Problems of Russian Law30Criticism of the considering AI as a subject of civil legal relations seems not only unjustified, but also prevents a doctrinal solution to the issue of legal personality, which is a prerequisite for the formation of the norms of current legislation.Actual Problems of Russian Law['S. V. Zykov']2024-01-22T00:00:00https://www.semanticscholar.org/paper/4c615c416d070df4e9cd07c76feb9ae46bd3f482 a416260fecca7ea5488f23daf2ba88deff9dc3baIn modern society, there is an increasing prevalence of individuals who are able to avail themselves of medical facilities for the purpose of receiving healthcare services. With the rise in population, there has been a corresponding decrease in the availability of medical resources for some disorders, leading to challenges in accurately diagnosing patients by healthcare professionals. According to specialists, artificial intelligence (AI) is being considered as a potential solution for addressing medical challenges. This paper mainly focuses on discussing the impact of artificial intelligence in the medical field. Through methods of literature review and analysis, this study explores the fundamental idea of AI and its use in the medical field. Besides, the paper also introduces the potential flaws behind AI in the medical field, and how will artificial intelligence help us further in the medical field. The study reveals that artificial intelligence is extensively employed within the medical sector. Through extensive training, AI has the potential to attain a considerable level of accuracy when it comes to diagnosing various ailments. The level of precision exhibited is akin to that observed in medical practitioners diagnoses. Nevertheless, artificial intelligence possesses certain limitations. For instance, in the context of privacy preservation, several patients exhibit a reluctance to divulge their symptoms. In the absence of adequate safeguards for information security, this situation might potentially lead to adverse consequences in the lives of these patients and their interpersonal interactions. Ultimately, AI continues to possess significant potential for advancement within the realm of medicine.Applied and Computational Engineering00The study reveals that artificial intelligence is extensively employed within the medical sector and has the potential to attain a considerable level of accuracy when it comes to diagnosing various ailments.Applied and Computational Engineering['Kaiwen Zeng']2024-01-22T00:00:00https://www.semanticscholar.org/paper/a416260fecca7ea5488f23daf2ba88deff9dc3ba 394c1f76920e95fe72442426008e0d541e260903Swimming has consistently maintained its status as a highly favored athletic pursuit for a span of one hundred years. In contemporary times, the burgeoning field of artificial intelligence (AI) has exhibited notable advancements, resulting in significant impacts across all domains. Examples of industries include finance, the service industry, and engineering. Furthermore, it has been implemented in various other sports previously. Presently, a predominant focus of scholarly inquiry is in the exploration of artificial intelligence (AI) applications within the realm of team sports, including disciplines such as basketball, volleyball, and rugby. Nevertheless, swimming shares certain characteristics with the aforementioned sports. In order to enhance the advancement of the swimming domain through the utilization of artificial intelligence (AI) technology, this essay will examine the feasibility of implementing certain AI applications that have been employed to support other sports, and will also introduce several AI technologies that have the potential to make distinctive contributions to the field of swimming.Applied and Computational Engineering00This essay will examine the feasibility of implementing certain AI applications that have been employed to support other sports, and will also introduce several AI technologies that have the potential to make distinctive contributions to the field of swimming.Applied and Computational Engineering['Zhehao Yu']2024-01-22T00:00:00https://www.semanticscholar.org/paper/394c1f76920e95fe72442426008e0d541e260903 bbeed19c5159f5d0371d2a77cf8d455d31e3d7caThis article delves into the transformative collaboration between Artificial Intelligence (AI) and robotics within the context of Industry 4.0 applications. Industry 4.0 represents a paradigm shift in manufacturing, characterized by the integration of advanced technologies. The synergy between AI and robotics plays a pivotal role in reshaping industrial processes, leading to increased automation, predictive maintenance strategies, collaborative robotics (cobots), enhanced quality control, and optimized supply chain operations. AI algorithms empower machines to learn, adapt, and make intelligent decisions, fostering adaptability and efficiency in manufacturing. The seamless integration of AI and robotics not only improves operational processes but also introduces novel approaches to human-robot collaboration, quality assurance, and supply chain management. The article also addresses challenges associated with this integration, such as workforce displacement concerns and the need for standardized communication protocols. As the field continues to evolve, navigating these challenges and capitalizing on the ongoing advancements in AI and robotics will be instrumental in unlocking the full potential of their collaborative synergy, ultimately defining the future landscape of Industry 4.0.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This article delves into the transformative collaboration between Artificial Intelligence (AI) and robotics within the context of Industry 4.0 applications, and addresses challenges associated with this integration, such as workforce displacement concerns and the need for standardized communication protocols.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md. Rashel Mia', 'Jeff Shuford']2024-01-22T00:00:00https://www.semanticscholar.org/paper/bbeed19c5159f5d0371d2a77cf8d455d31e3d7ca ba4eaccb95fe361e8588304a69ca5e3b4aaedd3bForensic Science, Medicine, and Pathology52Forensic science, medicine, and pathology['A. Leković', 'S. Nikolić']2024-01-22T00:00:00https://www.semanticscholar.org/paper/ba4eaccb95fe361e8588304a69ca5e3b4aaedd3b 84f9bfa0305cab2d0fa9969b955139c3eb34c589KI - Künstliche Intelligenz220The potential for ML and AI methods to yield useful predictions of individual treatment effects is illustrated using the predicted individual treatment effects (PITE) framework which uses baseline covariates (features) to predict whether a treatment is expected to yield benefit for a given patient compared to an alternative intervention.KI - Künstliche Intelligenz['T. Jaki', 'Chi Chang', 'Alena Kuhlemeier', 'M. Lee Van Horn']2024-01-22T00:00:00https://www.semanticscholar.org/paper/84f9bfa0305cab2d0fa9969b955139c3eb34c589 f731ad2eb8851772f592c8513cd7b4d300798342We present an updated position integrating cognitive architectures into workflow by utilizing the architecture for what it does most effectively: human-like few-shot learning integrating the vast amount of data stored by foundation models. By supplementing the language-generation capabilities with the constraints of cognitive-architectures guiding prompts, it should be possible to generate more relevant output and possibly even predict when the foundation model is hallucinating. Recent advances in few-shot learning capabilities of cognitive architectures in applied domains will be discussed with some parallel capabilities described by foundation models. Just as we use research from social psychology to 'nudge' people into making informed decisions, we should be able to use cognitive architectures to 'nudge' foundation models into developing more human-relevant content.Proceedings of the AAAI Symposium Series390Proceedings of the AAAI Symposium Series['Robert H. Thomson', 'Nathaniel D. Bastian']2024-01-22T00:00:00https://www.semanticscholar.org/paper/f731ad2eb8851772f592c8513cd7b4d300798342 026cc6982a8ef2b26d4de0adffd0efaa6e798816Language models have a significant impact on the labor market, and this paper aims to analyze the effects on different parties in detail and give policy suggestions on the adverse effects brought by AI. The author first applies a case using news about the employment market, then does an analysis and gives suggestions based on the case. The research finds that AI causes fluctuations in the labor market through the displacement effect, productivity effect, and the reinstatement effect. The pattern of employment also adjusts to favor the job seekers with AI skill sets. The paper further underlines the undesirable effects of AI including deepfakes and biases, and legal and technological measures could be adopted to alleviate the problems. This paper could help scholars better understand the impacts of AI on the labor market, and the suggestions listed could give authorities ideas on policy implementations to regulate AI.Highlights in Business, Economics and Management170The research finds that AI causes fluctuations in the labor market through the displacement effect, productivity effect, and the reinstatement effect, and the pattern of employment also adjusts to favor the job seekers with AI skill sets.Highlights in Business, Economics and Management['Zhiqing Bian']2024-01-22T00:00:00https://www.semanticscholar.org/paper/026cc6982a8ef2b26d4de0adffd0efaa6e798816 6f23e7acf00c39a1b8eff3628bbb55598719c49cRegional anesthesia and pain medicine10Regional anesthesia and pain medicine['Nathan C Hurley', 'E. Schwenk']2024-01-22T00:00:00https://www.semanticscholar.org/paper/6f23e7acf00c39a1b8eff3628bbb55598719c49c 7de9f460c28645737091275f4662ae93af82ef4aGlobal Journal of Flexible Systems Management800Global Journal of Flexible Systems Management['Kirti Nayal', 'Rakesh D. Raut', 'Mukesh Kumar', 'Sanjoy Kumar Paul', 'B. Narkhede']2024-01-22T00:00:00https://www.semanticscholar.org/paper/7de9f460c28645737091275f4662ae93af82ef4a 3b047cb391bc64aeda0ddcec09e112a035c17ec4Clinical Nursing Research120Clinical nursing research['Jerrold M Jackson', 'Melissa D Pinto']2024-01-22T00:00:00https://www.semanticscholar.org/paper/3b047cb391bc64aeda0ddcec09e112a035c17ec4 4640187465bdd3f96dc942978671a41cf050535fThe focus of this research is to demonstrate how a platform composed of systems of artificial cognitive agents and social robotics can interact, teach and learn with students and teachers, through a pedagogical practice and methodological integration of the psychological concepts of the Theory of Multiple Intelligences, the educational foundations of the Dialectic methodology and the relationship explored in the Man-Machine Symbiosis. The objective of this article is to present the cognitive methodological concept of the project developed, grounding it from the theoretical principles, to the selection criteria for the models presented in the current discussions, taking as lines of thought that seek to define the strategy for creating personalized learning storytelling, the techniques for building social robotics in education and the interactivity between visual feedback in the challenging context of technological education.Proceedings of the AAAI Symposium Series190The cognitive methodological concept of the project developed is presented, grounding it from the theoretical principles, to the selection criteria for the models presented in the current discussions, taking as lines of thought the strategy for creating personalized learning storytelling, the techniques for building social robotics in education and the interactivity between visual feedback in the challenging context of technological education.Proceedings of the AAAI Symposium Series['Rodrigo F. Souza', 'Walter T. Lima Jr']2024-01-22T00:00:00https://www.semanticscholar.org/paper/4640187465bdd3f96dc942978671a41cf050535f 0eb0e917f7698dd90c6dbf7d03f844a8a8250022Insights into Imaging826This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice.Insights into Imaging['Adrian P. Brady', 'Bibb Allen', 'Jaron J R Chong', 'E. Kotter', 'Nina Kottler', 'John Mongan', 'Lauren Oakden-Rayner', 'Daniel Pinto Dos Santos', 'An Tang', 'Christoph Wald', 'John Slavotinek']2024-01-22T00:00:00https://www.semanticscholar.org/paper/0eb0e917f7698dd90c6dbf7d03f844a8a8250022 3a9b43368a09d07657abe0e62a6c4a1d2428d40cRecent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent.Information Processing &amp; Management1463A fresh perspective is introduced to reassess the relationship between AI and social science, a cohesive framework is provided that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and state-of-art experiment simulation platforms are summarized to facilitate research in these two directions.ArXiv['Ruoxi Xu', 'Yingfei Sun', 'Mengjie Ren', 'Shiguang Guo', 'Ruotong Pan', 'Hongyu Lin', 'Le Sun', 'Xianpei Han']2024-01-22T00:00:00https://www.semanticscholar.org/paper/3a9b43368a09d07657abe0e62a6c4a1d2428d40c e01be52c14ce2053ea77beed4d398728ed4ffc41As artificial intelligence (AI) becomes increasingly integrated into our lives, the need for new norms is urgent. However, AI evolves at a much faster pace than the characteristic time of norm formation, posing an unprecedented challenge to our societies. This paper examines possible criticalities of the processes of norm formation surrounding AI. It focuses on how new norms can be established, rather than on what these norms should be. It distinguishes different scenarios based on the centralization or decentralization of the norm formation process, analysing the cases where new norms are shaped by formal authorities or informal institutions, or emerge spontaneously in a bottom-up fashion. On the latter point, the paper reports a conversation with ChatGPT in which the LLM discusses some of the emerging norms it has observed. Far from seeking exhaustiveness, this article aims to offer readers interpretive tools to frame society’s response to the growing pervasiveness of AI. An outlook on how AI could influence the formation of future social norms emphasizes the importance for open societies to anchor their formal deliberation process in an open, inclusive and transparent public discourse. This article is part of the theme issue ‘Social norm change: drivers and consequences’.Philosophical transactions of the Royal Society of London. Series B, Biological sciences513Examination of possible criticalities of the processes of norm formation surrounding AI focuses on how new norms can be established, rather than on what these norms should be, and distinguishes different scenarios based on the centralization or decentralization of the norm formation process.Philosophical Transactions of the Royal Society B: Biological Sciences['Andrea Baronchelli']2024-01-22T00:00:00https://www.semanticscholar.org/paper/e01be52c14ce2053ea77beed4d398728ed4ffc41 7978ea649789e595df3a4fd1c19635fe7fe02aaaThe field of drug design and discovery is undergoing a transformative shift, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to expedite and optimize the drug development process. Traditional methods are often costly and time-consuming, involving extensive testing and sequential stages. However, contemporary drug development integrates AI, particularly in drug identification and preclinical studies, resulting in significant resource and time savings. AI is utilized for bioactivity and physicochemical forecasting, de novo molecule design, synthesis prediction, and drug-target profile representation. This review introduces the AI-based Drug Design and Discovery System (AI-D3S), a comprehensive approach utilizing serialization, de-serialization, Particle Swarm Optimization (PSO), and Support Vector Machine (SVM). The system demonstrates superior accuracy, precision, sensitivity, and specificity compared to conventional methods, showcasing an average improvement of 8.7%. Our review study evaluates the system on two chemical databases, MAO and Biodegradation, and illustrates its efficacy in predicting drug-annotation combinations. The potential of AI in pharmaceuticals extends from drug design to personalized therapies, decision-making, and efficient resource allocation in marketing. The research envisions AI as an indispensable tool in the pharmaceutical sector, driving innovation, reducing costs, and ensuring the production of higher-quality products. The AI-D3S model presented in this study sets the stage for future advancements in drug design and discovery, offering a promising avenue for the integration of AI in revolutionizing the pharmaceutical industry.Journal of Angiotherapy00The AI-D3S model presented in this study sets the stage for future advancements in drug design and discovery, offering a promising avenue for the integration of AI in revolutionizing the pharmaceutical industry.Journal of Angiotherapy[]2024-01-22T00:00:00https://www.semanticscholar.org/paper/7978ea649789e595df3a4fd1c19635fe7fe02aaa 95a24e314f8d589206d19327f1b06f63ecacd399Scientific Reports322RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis, is presented, aiming to enhance the quality and reproducibility of AI studies.Scientific Reports['Alessandro Barberis', 'H. J. Aerts', 'Francesca M. Buffa']2024-01-22T00:00:00https://www.semanticscholar.org/paper/95a24e314f8d589206d19327f1b06f63ecacd399 c884b60cdcb5e562aeb690cfac0888f755992525Artificial Intelligence (AI) has emerged as a transformative force across numerous domains, from healthcare to finance and beyond. However, as AI systems become increasingly integrated into daily life, the ethical implications of their development and deployment are garnering significant attention. This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402302This article conducts a comprehensive survey of the ethical considerations in AI, with a specific focus on navigating the complex landscape of bias and fairness.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md.mafiqul Islam', 'Jeff Shuford']2024-01-22T00:00:00https://www.semanticscholar.org/paper/c884b60cdcb5e562aeb690cfac0888f755992525 e3ab7a9500fdbbaedf3036faa9364c2dc96d398dThis article explores the transformative impact of Artificial Intelligence (AI) in healthcare, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care. Predictive analytics enable early disease prevention and diagnosis by identifying patterns and risk factors, contributing to improved patient outcomes and cost-effective healthcare. Machine learning facilitates personalized treatment plans, leveraging individual patient data for tailored interventions that enhance efficacy and minimize adverse effects. AI-driven algorithms in medical imaging enhance diagnostic accuracy, providing rapid and precise assessments. Decision support systems, powered by AI, streamline healthcare workflows by offering real-time insights based on patient data and clinical guidelines, facilitating evidence-based decision-making. Remote patient monitoring, facilitated by AI, allows for proactive healthcare interventions by tracking vital signs and identifying potential health issues in real time. The article also discusses challenges and ethical considerations associated with AI integration in healthcare, emphasizing the importance of responsible deployment and regulatory frameworks. The comprehensive exploration underscores how AI is not only transforming patient care but also shaping the future of healthcare delivery.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402302How AI is not only transforming patient care but also shaping the future of healthcare delivery is highlighted, with a specific focus on how predictive analytics and decision support systems are revolutionizing patient care.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Md. Shohel Rana', 'Jeff Shuford']2024-01-22T00:00:00https://www.semanticscholar.org/paper/e3ab7a9500fdbbaedf3036faa9364c2dc96d398d f17cc6488e52d32b4147d50397404d5fbc66391cAs organizations increasingly rely on cloud computing for storage, processing, and deployment of sensitive data, ensuring robust security measures becomes paramount. This paper explores the intersection of artificial intelligence (AI) and cloud security, presenting AI-driven solutions as the future of safeguarding sensitive data in the digital age. Leveraging AI algorithms and machine learning techniques, cloud security can adapt and evolve to counter emerging threats in real-time, enhancing detection, prevention, and response capabilities. This paper discusses various AI-driven approaches to cloud security, including anomaly detection, threat intelligence analysis, and behavior analytics, highlighting their effectiveness in mitigating risks and ensuring compliance with regulatory standards. Additionally, it addresses the challenges and ethical considerations associated with AI-driven cloud security, emphasizing the importance of transparency, accountability, and ethical AI principles. By embracing AI-driven solutions, organizations can fortify their defenses against cyber threats and maintain the integrity and confidentiality of their sensitive data in the evolving digital landscape.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402302Various AI-driven approaches to cloud security, including anomaly detection, threat intelligence analysis, and behavior analytics are discussed, highlighting their effectiveness in mitigating risks and ensuring compliance with regulatory standards.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023['Hassan Rehan']2024-01-22T00:00:00https://www.semanticscholar.org/paper/f17cc6488e52d32b4147d50397404d5fbc66391c 3087b34adbb91ebde2fe08299596b5605d3b7654Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes531This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice.Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes['Adrian P. Brady', 'Bibb Allen', 'Jaron J R Chong', 'E. Kotter', 'Nina Kottler', 'John Mongan', 'Lauren Oakden-Rayner', 'Daniel Pinto Dos Santos', 'An Tang', 'Christoph Wald', 'John Slavotinek']2024-01-22T00:00:00https://www.semanticscholar.org/paper/3087b34adbb91ebde2fe08299596b5605d3b7654 2270fec08f83ae369d1dfd66e54af1e2700fbcb0With the continuous development of the manufacturing industry, the application of intelligent robots is becoming more and more extensive. Many emerging technologies can be applied to intelligent robots. Typical intelligent robots still have room for further improvement in terms of automatic control and adaptation to the surrounding environment. And independent learning of production tasks and realization of human-computer interaction is the future development direction of industrial robots. In view of these deficiencies, the development of intelligent robots is particularly important. This paper introduces the application fields, key technologies and needs of intelligent robots, and explains the important position of intelligent robots in the future manufacturing industry. The functions of intelligent robot control, perception and human-computer interaction based on artificial intelligence technology are studied. Possible future challenges, limitations and problems will be presented at the end of this paper.Applied and Computational Engineering00The application fields, key technologies and needs of intelligent robots, and the important position of intelligent robots in the future manufacturing industry are explained.Applied and Computational Engineering['Wang xin']2024-01-22T00:00:00https://www.semanticscholar.org/paper/2270fec08f83ae369d1dfd66e54af1e2700fbcb0 bb4db3eb4937d619caaf7973f20d56a4ac1871c9 Ute Deichmann, Director of the Jacques Loeb Centre for the History and Philosophy of the Life Sciences at Ben-Gurion University of the Negev, discusses the adoption and limitations of Artificial Intelligence within modern experimental biology. ‘How generative AI could disrupt scientific publishing.’ ‘ChatGPT use shows that the grant-application system is broken.’ ‘Companies say the technology will lead to faster drug development. Independent verification and clinical trials will determine whether the claim holds up.’ ‘AlphaFold touted as next big thing for drug discovery — but is it?’ These recent headlines from the journal Nature not only indicate the enormous impact of artificial intelligence (AI), the intelligence of machines or software, on research across various scientific disciplines but also point to challenges regarding AI reliability. What does intelligence here mean? The Cambridge Dictionary defines intelligence as ‘the ability to learn, understand, and make judgments or have opinions based on reason.’ Open Access Government00Open Access Government['Ute Deichmann']2024-01-22T00:00:00https://www.semanticscholar.org/paper/bb4db3eb4937d619caaf7973f20d56a4ac1871c9 42ed22f948975e4f0b0f53f97a3e280bc6f83718Artificial intelligence (AI) is revolutionizing many industries, while at the same time facing challenges to safe and reliable use such as vulnerability to adversarial attacks and data drift. Although many AI test and evaluation (T&E) tools exist, integrating them is difficult. Under a program funded by the Chief Digital and AI Office (CDAO), we are developing a library to simplify the AI T&E process by providing user- and developer-friendly interfaces for composing T&E workflows. We illustrate the effectiveness of this approach with an example that compares clean and perturbed accuracy of two models on a computer vision dataset.Proceedings of the AAAI Symposium Series00A library to simplify the AI T&E process by providing user- and developer-friendly interfaces for composing T&E workflows is developed, illustrating the effectiveness of this approach with an example that compares clean and perturbed accuracy of two models on a computer vision dataset.Proceedings of the AAAI Symposium Series['Lei Hamilton', 'Garrett Botkin', 'Olivia Brown', 'Justin A. Goodwin', 'Michael Yee', 'Vincent Mancuso', 'Sanjeev Mohindra']2024-01-22T00:00:00https://www.semanticscholar.org/paper/42ed22f948975e4f0b0f53f97a3e280bc6f83718 9a0f0b975005cd4df08b2498c75aa102d0ab927bBackground: The rapid advancement of generative artificial intelligence (AI) has revolutionized understanding and generation of human language. Their integration into healthcare has shown potential for improving medical diagnostics, yet a comprehensive diagnostic performance evaluation of generative AI models and the comparison of their diagnostic performance with that of physicians has not been extensively explored. Methods: In this systematic review and meta-analysis, a comprehensive search of Medline, Scopus, Web of Science, Cochrane Central, and medRxiv was conducted for studies published from June 2018 through December 2023, focusing on those that validate generative AI models for diagnostic tasks. Meta-analysis was performed to summarize the performance of the models and to compare the accuracy of the models with that of physicians. The quality of studies was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Results: The search resulted in 54 studies being included in the meta-analysis, with 13 of these also used in the comparative analysis. Eight models were evaluated across 17 medical specialties. The overall accuracy for generative AI models across 54 studies was 57% (95% confidence interval [CI]: 51-63%). The I-squared statistic of 96% signifies a high degree of heterogeneity among the study results. Meta-regression analysis of generative AI models revealed significantly improved accuracy for GPT-4, and reduced accuracy for some specialties such as Neurology, Endocrinology, Rheumatology, and Radiology. The comparison meta-analysis demonstrated that, on average, physicians exceeded the accuracy of the models (difference in accuracy: 14% [95% CI: 8-19%], p-value <0.001). However, in the performance comparison between GPT-4 and physicians, GPT-4 performed slightly higher than non-experts (-4% [95% CI: -10-2%], p-value = 0.173), and slightly underperformed compared to experts (6% [95% CI: -1-13%], p-value = 0.091). The quality assessment indicated a high risk of bias in the majority of studies, primarily due to small sample sizes. Conclusions: Generative AI exhibits promising diagnostic capabilities, with accuracy varying significantly by model and medical specialty. Although they have not reached the reliability of expert physicians, the findings suggest that generative AI models have the potential to enhance healthcare delivery and medical education, provided they are integrated with caution and their limitations are well-understood. This study also highlights the need for more rigorous research standards and a larger number of cases in the future.medRxiv800Although they have not reached the reliability of expert physicians, the findings suggest that generative AI models have the potential to enhance healthcare delivery and medical education, provided they are integrated with caution and their limitations are well-understood.['MD PhD Hirotaka Takita', 'MS Shannon L Walston', 'MD PhD Hiroyuki Tatekawa', 'MD Kenichi Saito', 'MD Mph Yasushi Tsujimoto', 'MD PhD Yukio Miki', 'MD PhD Daiju Ueda']2024-01-22T00:00:00https://www.semanticscholar.org/paper/9a0f0b975005cd4df08b2498c75aa102d0ab927b 278bc3b9d7e9e38e5922dbfee8c9f5546d44fd72Information Systems Journal152Information Systems Journal['Robert M. Davison', 'Hameed Chughtai', 'Petter Nielsen', 'Marco Marabelli', 'Federico Iannacci', 'Marjolein A. G. van Offenbeek', 'Monideepa Tarafdar', 'Manuel Trenz', 'A. Techatassanasoontorn', 'Antonio Díaz Andrade', 'Niki Panteli']2024-01-21T00:00:00https://www.semanticscholar.org/paper/278bc3b9d7e9e38e5922dbfee8c9f5546d44fd72 cf29200c699d8b8929b8390e6185e61cf1e64928This article explores the implementation of Artificial Intelligence (AI) in higher education in Kolaka, Southeast Sulawesi. The research aims to identify and analyze how AI can be used to enhance the learning process and academic administration in universities. The research method includes primary data collection through interviews and observations, as well as secondary data from academic sources. The results show that AI contributes significantly to operational efficiency and the learning experience. This research provides new insights into the application of AI in the academic environment and offers recommendations for higher education institutions that want to integrate AI technologyJournal of Computer Networks, Architecture and High Performance Computing00New insights are provided into the application of AI in the academic environment and recommendations for higher education institutions that want to integrate AI technology are offered.Journal of Computer Networks, Architecture and High Performance Computing['Sulfikar Sallu', 'Raehang Raehang', 'Qammaddin Qammaddin']2024-01-21T00:00:00https://www.semanticscholar.org/paper/cf29200c699d8b8929b8390e6185e61cf1e64928 09f0ff58c9a6348a39be90ae4816a510a1a41506The last time you opened a fresh Google Doc, were you welcomed with a sparkly bluish‐purple prompt “help me write”? Existing tools many of us know and love have already implemented AI features such as this one; in fact, you might say the lovable (or love‐to‐loathe) Clippy — Microsoft's office‐supply‐shaped assistant — was the first. And he was probably way ahead of his time.The Successful Registrar00The Successful Registrar['Donna Talarico']2024-01-21T00:00:00https://www.semanticscholar.org/paper/09f0ff58c9a6348a39be90ae4816a510a1a41506 09e94b984ae0593ef8b752344001a6416b4aee1fAsia Pacific Journal of Education371Asia Pacific Journal of Education['Nan Wang', 'Xiao Wang', 'Yu-Sheng Su']2024-01-21T00:00:00https://www.semanticscholar.org/paper/09e94b984ae0593ef8b752344001a6416b4aee1f 825d9fed193ba2ecb0b75aa8e0ffcf36416f4542Critical Humanities01Critical Humanities['David Arellano Smith']2024-01-21T00:00:00https://www.semanticscholar.org/paper/825d9fed193ba2ecb0b75aa8e0ffcf36416f4542 016987e841c0c7c635b52d2c4f96abcda71fc193Critical Humanities00Critical Humanities['Barbara Postema', 'Ilan Manouach']2024-01-21T00:00:00https://www.semanticscholar.org/paper/016987e841c0c7c635b52d2c4f96abcda71fc193 a332b9ea33fe95e5f0aadf1a5cbfae60a364d8d2Critical Humanities00Critical Humanities['S. Mainaly']2024-01-21T00:00:00https://www.semanticscholar.org/paper/a332b9ea33fe95e5f0aadf1a5cbfae60a364d8d2 41749e33d91d8dae0849c719dea744b0342dae76International Design Journal00International Design Journal['S. Nassar']2024-01-21T00:00:00https://www.semanticscholar.org/paper/41749e33d91d8dae0849c719dea744b0342dae76 ac9e6fe77eeec1f6bc42ca52b2f100949bfe231cCritical Humanities00Critical Humanities['Barbara Postema', 'Puspa Damai̇']2024-01-21T00:00:00https://www.semanticscholar.org/paper/ac9e6fe77eeec1f6bc42ca52b2f100949bfe231c de775f9cb18a54962ca7945f516911aae3ec4e4dAutonomous and intelligent systems (AIS) are being developed and deployed across a wide range of sectors and encompass a variety of technologies designed to engage in different forms of independent reasoning and self-directed behavior. These technologies may bring considerable benefits to society but also pose a range of risk management challenges, particularly when deployed in safety-critical sectors where complex interactions between human, social, and technical processes underpin safety and resilience. Healthcare is one safety-critical sector at the forefront of efforts to develop and deploy intelligent technologies, such as through artificial intelligence (AI) systems intended to automate key aspects of healthcare tasks such as reading medical images to identify signs of pathology. This article develops a qualitative analysis of the sociotechnical sources of risk and resilience associated with the development, deployment, and use of AI in healthcare, drawing on 40 in-depth interviews with participants involved in the development, management, and regulation of AI. Qualitative template analysis is used to examine sociotechnical sources of risk and resilience, drawing on and elaborating Macrae's (2022, Risk Analysis, 42(9), 1999-2025) SOTEC framework that integrates structural, organizational, technological, epistemic, and cultural sources of risk in AIS. This analysis explores an array of sociotechnical sources of risk associated with the development, deployment, and use of AI in healthcare and identifies an array of sociotechnical patterns of resilience that may counter those risks. In doing so, the SOTEC framework is elaborated and translated to define key sources of both risk and resilience in AIS.Risk Analysis361This analysis explores an array of sociotechnical sources of risk associated with the development, deployment, and use of AI in healthcare and identifies an array of sociotechnical patterns of resilience that may counter those risks.Risk analysis : an official publication of the Society for Risk Analysis['Carl Macrae']2024-01-21T00:00:00https://www.semanticscholar.org/paper/de775f9cb18a54962ca7945f516911aae3ec4e4d 116a312358c3c869afa5a6494eaa0f2eb7e45167Artificial Intelligence poses a consortium of multiple digital technologies able to perform tasks which were thought about that they can only be done by humans. To do so, it applies complex learning and decision-making processes based on analysis of structured and unstructured data. Currently, AI is assumed to have massive benefits in the areas of efficiency and performance of companies, although the impact on financial key performance indicators (KPI) is still unexplored. The underlying thesis of this research is that the financial impact of AI can already be seen in practice. The research question is whether there is an impact of company-driven AI efforts on financial KPI, like the return on assets (ROA) and the market capitalization. To obtain the intended results, a theoretical and empirical analysis was chosen as particular approach. Firstly, the existing scientific research is examined regarding already measurable financial impacts of digital technologies. In a second step, a regression model for panel data will be applied on a dataset containing financial data of the forty biggest German companies and their respective AI effort per year as a binary variable over a time period of seven years. As a result, a financial influence of AI cannot be verified yet on a statistically significant level. Despite of this, an increasing number of AI efforts over the last years can be confirmed.Copernican Journal of Finance &amp; Accounting00The research question is whether there is an impact of company-driven AI efforts on financial KPI, like the return on assets (ROA) and the market capitalization, like the return on assets (ROA) and the market capitalization.Copernican Journal of Finance &amp; Accounting['Daniel Maier']2024-01-21T00:00:00https://www.semanticscholar.org/paper/116a312358c3c869afa5a6494eaa0f2eb7e45167 07f04cca42bd3b1722645713a7040ac584e1a8f5Ethical conduct of artificial intelligence (AI) is undoubtedly becoming an ever more pressing issue considering the inevitable integration of these technologies into our lives. The literature so far discussed the responsibility domains of AI; this study asks the question of how to instil ethicality into AI technologies. Through a three‐step review of the AI ethics literature, we find that (i) the literature is weak in identifying solutions in ensuring ethical conduct of AI, (ii) the role of professional conduct is underexplored, and (iii) based on the values extracted from studies about AI ethical breaches, we thus propose a conceptual framework that offers professionalism as a solution in ensuring ethical AI. The framework stipulates fairness, nonmaleficence, responsibility, freedom, and trust as values necessary for developers and operators, as well as transparency, privacy, fairness, trust, solidarity, and sustainability as organizational values to ensure sustainability in ethical development and operation of AI.Systems research and behavioral science1320A conceptual framework is proposed that offers professionalism as a solution in ensuring ethical AI and stipulates fairness, nonmaleficence, responsibility, freedom, and trust as values necessary for developers and operators, as well as transparency, privacy, fairness, trust, solidarity, and sustainability as organizational values to ensure sustainability in ethical development and operation of AI.Systems Research and Behavioral Science['Anton Klarin', 'Hossein Ali Abadi', 'Rifat Sharmelly']2024-01-21T00:00:00https://www.semanticscholar.org/paper/07f04cca42bd3b1722645713a7040ac584e1a8f5 aa776ade06645e23c7dd106a513d75c84e91305eThe article outlines the legal basis of the penalties for violation of the General Data Protection Regulation in the EU. The necessity of studying the mechanisms by which the procedure for collecting fines for violations in the field of personal data protection within the European Union is carried out. Provisions of the General Data Protection Regulation are analyzed in order to apply the experience of EU member states in the field of personal data protection as well as in the context of harmonization of Ukrainian legislation with EU law. The article analyzes changes in the legislation on the protection of personal data and identifies the most frequently applied penalties in the form of fines imposed on companies in countries such as Great Britain, Netherlands, and Spain. Forms of liability for violations of the Regulation are: administrative fine, warning, temporary suspension of activity, or certain actions performed by the controller or operator. In addition to the list, member states in their domestic legislation have the right to establish additional forms of liability for violations of personal data protection, if such a penalty does not contradict the GDPR. In the article, it has also been determined which violations in the field of personal data protection act as grounds for imposing fines, namely, it has been established that these are non-compliance by companies with the security of personal data, non-observance of conditions and rules regarding consent to the processing of personal data, and the subsequent use by companies of personal data of customers after the leak the period provided for their use or the transfer of customer data to third parties. As mentioned above, the adoption of the General Data Protection Regulation contributed to ensuring the stability of normative acts that regulate issues related to the confidentiality of personal data of users, as well as responsibility for the violation of rights related to such data. According to the Regulation, any information by which a person can be identified should be interpreted as personal data. The article examines the application of mechanisms for imposing fines and analyzes the circumstances that determine the extent of liability for violations in the field of personal data protection.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['I. Lavorska', 'S. Stahura']2024-01-20T00:00:00https://www.semanticscholar.org/paper/aa776ade06645e23c7dd106a513d75c84e91305e 1f39037c56c0985d716e5808772e58b84c9dbae4ChatGPT is an artificial intelligence (AI) chatbot that provides users with detailed responses and accurate answers to any questions. It has garnered significant attention after its launch in November 2022. We analyze the returns of AI-themed crypto assets around the launch and widespread attention towards ChatGPT. We reveal significant abnormal returns for AI tokens after the launch of ChatGPT, up to 41% over the course of two weeks. Moreover, 90% of tokens exhibit positive abnormal returns. This suggests that the attention towards ChatGPT and AI in general has transitioned to cryptocurrency markets, resulting in positive price effects for AI-related cryptocurrencies.Social Science Research Network279It is revealed that significant abnormal returns for AI tokens after the launch of ChatGPT, up to 41% over the course of two weeks, suggests that the attention towards ChatGPT and AI in general has transitioned to cryptocurrency markets, resulting in positive price effects for AI-related cryptocurrencies.SSRN Electronic Journal['Lennart Ante', 'Ender Demir']2024-01-20T00:00:00https://www.semanticscholar.org/paper/1f39037c56c0985d716e5808772e58b84c9dbae4 df8fc4c0c3c6b57a9d9070e17c08bef35b8a1a38Archives of Toxicology1063Transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment to better safeguard human and environmental wellbeing across diverse populations.Archives of Toxicology['Nicole Kleinstreuer', 'Thomas Hartung']2024-01-20T00:00:00https://www.semanticscholar.org/paper/df8fc4c0c3c6b57a9d9070e17c08bef35b8a1a38 475ffef321adf663a2abc5e638d5500b07ed0adcThe confluence of Artificial Intelligence (AI) and Machine Learning (ML) with the Financial Technology (FinTech) sector has ushered in a paradigm shift, fundamentally altering the contours of financial services. This scholarly endeavor undertakes a meticulous scrutiny of the evolutionary trajectory of AI and ML within the FinTech domain spanning the pivotal period of 2016 to 2020. Inextricably interwoven with notions of efficiency, security, and innovation, this exploration traverses the realms of operational processes, anti-fraud mechanisms, the bespoke landscape of personalized financial services, and the overarching influence on financial institutions. The canvas of this inquiry unfurls its historical panorama by anchoring in the pre-2016 epoch, elucidating the nascent manifestations of AI applications in finance. A discerning lens is cast upon pivotal technologies and algorithms that formed the bedrock of subsequent advancements. The narrative then unfurls to encapsulate the ascendancy of predictive analytics, the assimilation of both supervised and unsupervised learning paradigms, and the nuanced integration of Natural Language Processing (NLP) in the discerning analysis of financial data. Venturing into the substantive body of discourse, the examination scrutinizes specific strides, notably the assimilation of Robotic Process Automation (RPA) for the augmentation of operational efficiency. A close inspection follows the evolutionary trajectory of AI-driven algorithms tailored for the prophylaxis of fraud, fortifying the bulwarks against malfeasance within the financial ecosystem. Furthermore, the intricate tapestry of personalized financial services unfolds through the prism of recommendation systems, showcasing a nuanced blend of tailored financial offerings.Journal of Economics, Finance and Accounting Studies221This scholarly endeavor undertakes a meticulous scrutiny of the evolutionary trajectory of AI and ML within the FinTech domain spanning the pivotal period of 2016 to 2020, exploring the realms of operational processes, anti-fraud mechanisms, the bespoke landscape of personalized financial services, and the overarching influence on financial institutions.Journal of Economics, Finance and Accounting Studies['Paulin Kamuangu']2024-01-20T00:00:00https://www.semanticscholar.org/paper/475ffef321adf663a2abc5e638d5500b07ed0adc ffaa3494ee634c0cd40be6803862b71eeb4a529fLarge language models now possess human-level linguistic abilities in many contexts. This raises the concern that they can be used to deceive and manipulate on unprecedented scales, for instance spreading political misinformation on social media. In future, agentic AI systems might also deceive and manipulate humans for their own ends. In this paper, first, I argue that AI-generated content should be subject to stricter standards against deception and manipulation than we ordinarily apply to humans. Second, I offer new characterizations of AI deception and manipulation meant to support such standards, according to which a statement is deceptive (manipulative) if it leads human addressees away from the beliefs (choices) they would endorse under ``semi-ideal'' conditions. Third, I propose two measures to guard against AI deception and manipulation, inspired by this characterization:"extreme transparency"requirements for AI-generated content and defensive systems that, among other things, annotate AI-generated statements with contextualizing information. Finally, I consider to what extent these measures can protect against deceptive behavior in future, agentic AIs, and argue that non-agentic defensive systems can provide an important layer of defense even against more powerful agentic systems.arXiv.org331It is argued that AI-generated content should be subject to stricter standards against deception and manipulation than the authors ordinarily apply to humans, and new characterizations of AI deception and manipulation meant to support such standards are offered.ArXiv['Christian Tarsney']2024-01-20T00:00:00https://www.semanticscholar.org/paper/ffaa3494ee634c0cd40be6803862b71eeb4a529f 36d80b596dd9fa79337f09b398db33c4aed102e2International Research Journal of Modernization in Engineering Technology and Science01International Research Journal of Modernization in Engineering Technology and Science[]2024-01-20T00:00:00https://www.semanticscholar.org/paper/36d80b596dd9fa79337f09b398db33c4aed102e2 095a6acc133c4e3c3fc0cc6ddf86ecc12357d22f549 Background: While immunotherapies have been approved for use in HCC patients, there is a need for validated predictive biomarkers that correlate with treatment outcomes. We investigated whether AI-powered spatial analysis of non-cancerous cells within the TME can be potential biomarkers for ICIs with or without bevacizumab in advanced HCC. Methods: Analysis of images of H&E-stained slides was conducted by an AI model, Lunit SCOPE IO in pre-treatment tumor samples of 163 HCC patients treated with atezolizumab plus bevacizumab as first-line ( n=82), or monotherapies of nivolumab or pembrolizumab as ≥ second-line ( n=81) at CHA Bundang Medical Center or Samsung Medical Center. We analyzed the correlation between clinical outcomes after the treatment and AI-powered TME-related variables, including TILs and endothelial cells, within intratumoral or stromal areas. Inflamed immune phenotype (IIP) was defined as cases exhibiting enrichment of intratumoral TILs. Results: Baseline characteristics, including Child-Pugh liver classification, Barcelona Clinic Liver Cancer stage, and hepatitis B virus, were well-balanced between treatment regimens. IIP was predictive of longer progression-free survival (PFS) of nivolumab or pembrolizumab monotherapy (median PFS 4.7 months for IIP vs. 2.2 months for non-IIP; hazard ratio [HR] 0.50; 95% confidence interval [CI] 0.25-0.99; p=0.042), but not PFS of atezolizumab plus bevacizumab (median PFS 6.8 months vs. 6.2 months; HR 0.92; 95% CI 0.50-1.69; p=0.762). PFS of atezolizumab plus bevacizumab was significantly longer in cases harboring intratumoral endothelial cell density in the highest quartile (median PFS 6.7 months for upper 25% vs. 3.9 months for lower 75%; HR 0.51; 95% CI 0.27-0.97; p=0.037), while there was no significant difference in PFS of pembrolizumab or nivolumab monotherapy (median PFS 2.3 months vs. 2.8 months; HR 1.02; 95% CI 0.59-1.77; p=0.935). Conclusions: AI-powered TME analysis shows IIP is predictive of longer PFS with ICI monotherapies, whereas intratumoral endothelial cell density is specifically associated with PFS of atezolizumab plus bevacizumab in advanced HCC. The latter finding may suggest efficacy of combined VEGFRi and ICI activity is dependent on the amount of baseline tumor vasculature.Journal of Clinical Oncology00Inflamed immune phenotype is predictive of longer progression-free survival with ICI monotherapies, whereas intratumoral endothelial cell density is specifically associated with PFS of atezolizumab plus bevacizumab in advanced HCC, which may suggest efficacy of combined VEGFRi and ICI activity is dependent on the amount of baseline tumor vasculature.Journal of Clinical Oncology['H. Chon', 'Gwangil Kim', 'B. Kang', 'Jung Yong Hong', 'Haeyoun Kang', 'Sohyun Hwang', 'Sungmi Lee', 'Sanghoon Jung', 'Chansik An', 'Won Suk Lee', 'Seulki Kim', 'Yoo‐Sung Lim', 'Siraj M. Ali', 'C. Ock', 'H. Lim', 'Chanyoung Kim']2024-01-20T00:00:00https://www.semanticscholar.org/paper/095a6acc133c4e3c3fc0cc6ddf86ecc12357d22f c0bdd612ea3afbfeca4bb3f9448eda6fc71bbea7This paper presents a novel approach to school safety, integrating the Internet of Things (IoT), artificial intelligence (AI), and cloud computing to enhance detection and response systems in educational settings subtly. Our system uses IoT and AI to detect unusual activities and environmental changes, focusing on non-intrusive monitoring to maintain a supportive atmosphere. The cloud computing component ensures efficient data processing and real-time response coordination. We prioritize ethical technology use, upholding data privacy and personal integrity. The preventive approach fosters a safe and supportive environment rather than enforcing control. The paper discusses the technical framework, implementation challenges, and case studies demonstrating effectiveness in real-world scenarios. Our model offers a balanced solution for enhancing school safety while maintaining a positive educational atmosphere.Artificial Intelligence and Applications200This system uses IoT and AI to detect unusual activities and environmental changes, focusing on non-intrusive monitoring to maintain a supportive atmosphere, and offers a balanced solution for enhancing school safety while maintaining a positive educational atmosphere.Artificial Intelligence and Applications['Cléber Viana']2024-01-20T00:00:00https://www.semanticscholar.org/paper/c0bdd612ea3afbfeca4bb3f9448eda6fc71bbea7 8d716fe6571ef65bf61e4563e58d0d04c190e6caIn today’s global context, EFL learners face the challenge of mastering a new language and academic writing, especially in higher education. The study investigates how AI transforms university-level EFL students’ academic writing skills, aiming to revolutionize their approach to written language for academic success despite language barriers. Using a mixed-methods approach, this study investigates the perspectives of fifty first-year female students at Al-Baha University, Saudi Arabia, during the 2023–2024 academic year, employing both qualitative and quantitative data analysis. Using a 5-point Likert-type questionnaire and Zoom interviews, the study clarifies EFL students’ perceptions of AI writing tools. Results from the questionnaire highlight the active usage of tools such as Grammarly and GPT-3 among students. Students favor the integration AI tools into coursework, although the level of support from instructors varies. EFL students see the positive impact on writing quality but remain unsure about confidence improvement. Interviews reveal diverse tool usage, with Grammarly and ChatGPT notably favored for their adaptability and cost-free nature. The study supports integrating AI writing tools into EFL university education, emphasizing benefits such as enhanced writing quality, time efficiency, and bolstered academic integrity. The paper highlights AI’s significant impact on EFL university students’ writing skills in today’s digitally reliant world where English holds key communication importance. It underscores AI-powered tools as valuable complements to conventional writing skills, emphasizing equitable access, guidance, and collaboration between AI and educators. The study suggests strategies for creating dynamic, tech-driven learning settings that empower EFL students in their writing tasks and academic endeavors.International Journal of English Linguistics370This study investigates how AI transforms university-level EFL students’ academic writing skills, aiming to revolutionize their approach to written language for academic success despite language barriers, and underscores AI-powered tools as valuable complements to conventional writing skills.International Journal of English Linguistics['Abir Sabry Mohamed Selim']2024-01-20T00:00:00https://www.semanticscholar.org/paper/8d716fe6571ef65bf61e4563e58d0d04c190e6ca 15d304e15caa8103bafea01223c7749743d8e696European Journal for Philosophy of Science340The theoretical prospects of deep artificial neural networks in proving mathematical theorems and whether such AI systems could, or should, become accepted as active agents in mathematical communities are analyzed.European Journal for Philosophy of Science['Markus Pantsar']2024-01-20T00:00:00https://www.semanticscholar.org/paper/15d304e15caa8103bafea01223c7749743d8e696 90c6e9bc031fcd8242434f0d590ae93360685785Abstract In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)‐derived organoids. Stem cell‐derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error‐prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid‐based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label‐free organoid recognition, and three‐dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI‐organoid integration, focusing on the establishment of reliable AI model decision‐making processes and the standardization of organoid research.Bioengineering & Translational Medicine1550This review presents the challenges and potential solutions in AI‐organoid integration, focusing on the establishment of reliable AI model decision‐making processes and the standardization of organoid research.Bioengineering & Translational Medicine['Sudhiksha Maramraju', 'Andrew Kowalczewski', 'Anirudh Kaza', 'Xiyuan Liu', 'J. Singaraju', 'Mark V Albert', 'Zhen Ma', 'Huaxiao Yang']2024-01-20T00:00:00https://www.semanticscholar.org/paper/90c6e9bc031fcd8242434f0d590ae93360685785 b531a8e862e29e4abbd6ac4ddaa221649cccf016Bridging the Divide00Bridging the Divide['Dooyoung Choi', 'Hakyung Lee']2024-01-20T00:00:00https://www.semanticscholar.org/paper/b531a8e862e29e4abbd6ac4ddaa221649cccf016 f737a2e94c452d9082c61c446cc6471eebdc4a3fBridging the Divide00Bridging the Divide['Dooyoung Choi', 'Hakyung Lee']2024-01-20T00:00:00https://www.semanticscholar.org/paper/f737a2e94c452d9082c61c446cc6471eebdc4a3f e2101d0903a65688dd82b043e39af81712c5d739SN Business &amp; Economics1161It is found that the literature on this topic has expanded considerably since the beginning of the XXI century, covering a variety of countries and different AI applications in finance, amongst which Predictive/forecasting systems, Classification/detection/early warning systems and Big data Analytics/Data mining /Text mining stand out.SN Business & Economics['Salman Bahoo', 'M. Cucculelli', 'Xhoana Goga', 'Jasmine Mondolo']2024-01-20T00:00:00https://www.semanticscholar.org/paper/e2101d0903a65688dd82b043e39af81712c5d739 0d796cd8a69e74338c675f299035b360df9f0edbSpace-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. Besides bringing significant benefits to various applications and services, SAGINs are envisioned to extend high-speed broadband coverage to remote areas, such as small towns or mining sites, or areas where terrestrial infrastructure cannot reach, such as airplanes or maritime use cases. However, due to the limited power and storage resources, as well as other constraints introduced by the design of terrestrial networks, SAGINs must be intelligently configured and controlled to satisfy the envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to massive amounts of available data, AI has been leveraged to address pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in the development of AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.arXiv.org01The interplay of AI and SAGINs is investigated by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs and a comprehensive overview of some potential applications of AI in SAGINs is presented.ArXiv['A. Bakambekova', 'N. Kouzayha', 'T. Al-Naffouri']2024-01-20T00:00:00https://www.semanticscholar.org/paper/0d796cd8a69e74338c675f299035b360df9f0edb ecce391440df78bfff14093ed26f8c5eefdd280bWhen machine writing enters the field of knowledge production, it causes a series of intelligent ethical risks, and the existing intellectual property system is deconstructed. The innovation of science and technology will break the balance mechanism of the existing intellectual property, and machine writing will become a new force of knowledge production. The intelligent ethical risk brought by machine writing is undoubtedly a "revolution" for the existing intellectual property system. From the ethical perspective of artificial intelligence, this paper from the ethical relations, ethics, ethical behavior and ethics profiling machine writing anomie of intellectual property rights subject status, object attribute, the legislative idea, the impact on the level of the judicial practice, and then from the legal environment, algorithm design, governance mechanism, platform regulation puts forward four human coexistence pattern of knowledge production, The reinvention of intellectual property rights.Academic Journal of Science and Technology140This paper from the ethical relations, ethics, ethical behavior and ethics profiling machine writing anomie of intellectual property rights subject status, object attribute, the legislative idea, the impact on the level of the judicial practice and from the legal environment puts forward four human coexistence pattern of knowledge production, The reinvention of intellectual property rights.Academic Journal of Science and Technology['Chuanji Zuo']2024-01-20T00:00:00https://www.semanticscholar.org/paper/ecce391440df78bfff14093ed26f8c5eefdd280b c59dce2be4e945d48d130bb9921e064fd464357aThe article examines problematic issues of rule-making by executive authorities, which are aimed at improving and ensuring the effectiveness of rule-making activities, which is an effective condition for the successful formation of the legal system of Ukraine, taking into account the state-legal reform, as well as the current level of its development. When implementing state-legal reform, the needs of rule-making have their own peculiarity, since the legal regulation of social relations cannot be imagined without normative-legal acts of executive authorities. As a result, it can be seen that the main task in this area is to improve and optimize the forms of implementation of their rule-making activities and to determine the degree of legal influence on social relations. In Ukraine, until recently, due attention was not paid to the problems of sub-legal norm-making. In some areas, they were covered only fragmentarily. As a result, the practice of implementing rule-making activities remains ineffective, since the activities of executive authorities are largely disorganized, and the creation of a new system is extremely slow and contradictory. It was determined that rule-making is a special activity of the state, in the person of specially authorized subjects of power, which are aimed at the normative consolidation of public needs and interests and the provision of appropriate conditions for the realization of the rights and freedoms of citizens and their effective protection, which is carried out by creating a system internally agreed normative legal acts with the aim of increasing the effectiveness of regulation of social relations in Ukraine. It is noted that the most fully reveal the content and purpose of the rule-making activity of the executive authorities, its main principles and special features, which contribute to the actual reflection of social regularities in legal prescriptions. Proposals regarding the improvement of legal regulation of the rule-making activity of the executive authorities were provided.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law['V. Panasiuk', 'Y. Baltsii', 'P. Synytsyn']2024-01-20T00:00:00https://www.semanticscholar.org/paper/c59dce2be4e945d48d130bb9921e064fd464357a 21d9cfc4b6badc22227cfe3b645d67aaa95adfd0The influence of global culture and modernization in the field of technology has had a major impact on the field of education, especially in learning and teaching methods. The technology which keeps developing rapidly and provides great opportunities to improve the quality and effectiveness of learning media is Artificial Intelligence technology. Based on the results of observations and interviews, 85% of the 19 third-grade students at SDN 3 Sekarteja found it difficult to recognize and read the Sasak script language in local content lessons. This research aims to overcome students' difficulties in recognizing and reading the Sasak script language at SDN 3 Sekarteja through the development of mobile-based interactive learning media that utilizes Artificial Intelligence tools to create, so that they can be used as a means of teaching and learning activities. This research was carried out using the methods of problem analysis, data collection, design stage, design stage, development stage, trial and implementation. The results showed a very good response which was evidenced by the results of testing interactive learning media showing a good response: the absence of errors and getting very good results regarding the implementation of interactive learning media at SDN 3 Sekarteja from the results of the questionnaire.Infotek : Jurnal Informatika dan Teknologi00This research aims to overcome students' difficulties in recognizing and reading the Sasak script language at SDN 3 Sekarteja through the development of mobile-based interactive learning media that utilizes Artificial Intelligence tools to create, so that they can be used as a means of teaching and learning activities.Infotek : Jurnal Informatika dan Teknologi['Hariman Bahtiar', 'Hasna’ Muallifatunnafiah', 'N. Nurhidayati']2024-01-20T00:00:00https://www.semanticscholar.org/paper/21d9cfc4b6badc22227cfe3b645d67aaa95adfd0 0c85a3f89c3090e26418b283ca4c01572dbf7762International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-01-20T00:00:00https://www.semanticscholar.org/paper/0c85a3f89c3090e26418b283ca4c01572dbf7762 c456e177b145292c8280725acd53ff23b61592b5In the era of globalisation and technological development, artificial intelligence (AI) plays a significant role in financial activities and services. AI in financial technology has a clear potential to accelerate the financial industry's transformation by offering excellent value to customers by providing tailor-made products and services, thus improving customer experience. The paper aims to model the factors influencing bank customers' readiness for artificially intelligent banking products within the South African banking sector. Data were collected from 346 banking customers within South Africa. The study results revealed that demographic and socio-cultural variables influence the readiness for artificially intelligent banking products. Behavioural finance biases also influence bank customers' readiness for artificially intelligent banking products. Furthermore, the study also found that customers' readiness for artificial intelligent banking products is faced with the limitation of the inaccessibility to technological tools in rural areas. Consequently, policies that can improve infrastructure and enable rural citizens to cope with advanced technology can improve bank customers' readiness for artificially intelligent banking products in South Africa.International Journal of Economics and Financial Issues00The study found that customers' readiness for artificial intelligent banking products is faced with the limitation of the inaccessibility to technological tools in rural areas, so policies that can improve infrastructure and enable rural citizens to cope with advanced technology can improve bank customers' readiness for artificially intelligent banking products in South Africa.International Journal of Economics and Financial Issues['L. Garekwe', 'S. Ferreira-Schenk', 'Z. Dickason-Koekemoer']2024-01-20T00:00:00https://www.semanticscholar.org/paper/c456e177b145292c8280725acd53ff23b61592b5 c8cc6ca886011e63c64bc9ac03b5a7798734fcecInternational Journal of Educational Technology in Higher Education14413Findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education and a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application.International Journal of Educational Technology in Higher Education['Melissa Bond', 'Hassan Khosravi', 'Maarten de Laat', 'Nina Bergdahl', 'Violeta Negrea', 'Emily Oxley', 'Phuong Pham', 'Sin Wang Chong', 'G. Siemens']2024-01-19T00:00:00https://www.semanticscholar.org/paper/c8cc6ca886011e63c64bc9ac03b5a7798734fcec 8b43d4151a74472b6e4e34c3e70661a8be0257b2Purpose: The aim of the study was to the ethical and legal implications of using big data and artificial intelligence for public relations campaigns in the United States Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: In the United States, utilizing big data and artificial intelligence for public relations campaigns presents ethical and legal challenges. These include concerns about privacy infringement through data collection, the risk of bias and misinformation in AI-generated content, and the necessity of complying with data protection laws like GDPR and U.S. regulations. Balancing the benefits of these technologies with ethical standards and legal compliance is a complex task for the PR industry in the U.S. Unique Contribution to Theory, Practice and Policy: Utilitarianism Theory, Rights-based Ethics Theory & Deontological Ethics may be used to anchor future studies on the ethical and legal implications of using big data and artificial intelligence for public relations campaigns in the United States. PR professionals should receive mandatory training on these guidelines to ensure ethical use of data and AI tools. Advocate for industry-wide adoption of ethical standards and encourage professional organizations to enforce adherence to these standards as a condition of membership.International journal of communication and public relation191In the United States, utilizing big data and artificial intelligence for public relations campaigns presents ethical and legal challenges which include concerns about privacy infringement through data collection, the risk of bias and misinformation in AI-generated content, and the necessity of complying with data protection laws like GDPR and U.S. regulations.International Journal of Communication and Public Relation['Michael James']2024-01-19T00:00:00https://www.semanticscholar.org/paper/8b43d4151a74472b6e4e34c3e70661a8be0257b2 c40d36045cecad6b72c95d1c0d1ed5c943c39db0OBJECTIVE To assess performance of an artificial intelligence (AI) decision support software in assessing and recommending biopsy of triple-negative breast cancers (TNBCs) on US. METHODS Retrospective institutional review board-approved review identified patients diagnosed with TNBC after US-guided biopsy between 2009 and 2019. Artificial intelligence output for TNBCs on diagnostic US included lesion features (shape, orientation) and likelihood of malignancy category (benign, probably benign, suspicious, and probably malignant). Artificial intelligence true positive was defined as suspicious or probably malignant and AI false negative (FN) as benign or probably benign. Artificial intelligence and radiologist lesion feature agreement, AI and radiologist sensitivity and FN rate (FNR), and features associated with AI FNs were determined using Wilcoxon rank-sum test, Fisher's exact test, chi-square test of independence, and kappa statistics. RESULTS The study included 332 patients with 345 TNBCs. Artificial intelligence and radiologists demonstrated moderate agreement for lesion shape and orientation (k = 0.48 and k = 0.47, each P <.001). On the set of examinations using 6 earlier diagnostic US, radiologists recommended biopsy of 339/345 lesions (sensitivity 98.3%, FNR 1.7%), and AI recommended biopsy of 333/345 lesions (sensitivity 96.5%, FNR 3.5%), including 6/6 radiologist FNs. On the set of examinations using immediate prebiopsy diagnostic US, AI recommended biopsy of 331/345 lesions (sensitivity 95.9%, FNR 4.1%). Artificial intelligence FNs were more frequently oval (q < 0.001), parallel (q < 0.001), circumscribed (q = 0.04), and complex cystic and solid (q = 0.006). CONCLUSION Artificial intelligence accurately recommended biopsies for 96% to 97% of TNBCs on US and may assist radiologists in classifying these lesions, which often demonstrate benign sonographic features.Journal of Breast Imaging01Artificial intelligence accurately recommended biopsies for 96% to 97% of TNBCs on US and may assist radiologists in classifying these lesions, which often demonstrate benign sonographic features.Journal of breast imaging['Kristen Coffey', 'Brianna Aukland', 'T. Amir', 'V. Sevilimedu', 'N. Saphier', 'Victoria L. Mango']2024-01-19T00:00:00https://www.semanticscholar.org/paper/c40d36045cecad6b72c95d1c0d1ed5c943c39db0 03f3be9e38023e16d24eb4c46b526a96af469a50Background Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector. Methods A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained. Results Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery. Conclusion This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.Frontiers Artif. Intell.561The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.Frontiers in Artificial Intelligence['O. Adigwe', 'Godspower Onavbavba', 'S. Sanyaolu']2024-01-19T00:00:00https://www.semanticscholar.org/paper/03f3be9e38023e16d24eb4c46b526a96af469a50 d14e31946da272efa82ded3040079c4c2499e12cThis paper proposes an A* artificial intelligence pathfinding algorithm based on the hexagonal grid map of Unreal Engine 5. This algorithm utilizes the rich tools and resources provided by Unreal Engine 5 to evaluate each node through a heuristic function, thus finding the shortest path. Test results show that this algorithm not only can quickly find the shortest path, but also can effectively avoid obstacle grids, with advantages such as high efficiency, flexibility, and scalability. This research result has high practical value for solving pathfinding problems on hexagonal grid maps and can provide strong support for game development and other fields of artificial intelligence applications.2024 4th International Conference on Neural Networks, Information and Communication (NNICE)110Test results show that this A* artificial intelligence pathfinding algorithm based on the hexagonal grid map of Unreal Engine 5 can effectively avoid obstacle grids, with advantages such as high efficiency, flexibility, and scalability.2024 4th International Conference on Neural Networks, Information and Communication (NNICE)['Hongbo Xing', 'Mengyao Chai', 'Yaju Song']2024-01-19T00:00:00https://www.semanticscholar.org/paper/d14e31946da272efa82ded3040079c4c2499e12c fbc091f9a77dc1171fcfee01697f4d5b11202aefArtificial intelligence (AI) has emerged as a crucial technique for extracting high-throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI-based radiomics has proven to be a non-invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI-based multi-omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.Oncology Report1100It is suggested that AI-based multi-omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.Oncology Reports['Yanli Wang', 'Weihong Lin', 'Xiaoling Zhuang', 'Xiali Wang', 'Yifang He', 'Luhong Li', 'Guorong Lyu']2024-01-19T00:00:00https://www.semanticscholar.org/paper/fbc091f9a77dc1171fcfee01697f4d5b11202aef d14cf10266da8e3ad6bfe287832e91678c83c11dIn this editorial, we explore the existing utilization of artificial intelligence (AI) within the healthcare industry, examining both its scope and potential harms if implemented and relied upon on a broader scale. Collaboration among corporations, government bodies, policymakers, and medical experts is essential to address potential concerns, ensuring smooth AI integration into healthcare systems.The Open Respiratory Medicine Journal210The existing utilization of artificial intelligence within the healthcare industry is explored, examining both its scope and potential harms if implemented and relied upon on a broader scale.The Open Respiratory Medicine Journal['Syeda Farheen Zaidi', 'Asim Shaikh', 'Salim Surani']2024-01-19T00:00:00https://www.semanticscholar.org/paper/d14cf10266da8e3ad6bfe287832e91678c83c11d ada698cb4d1f4c9560a6d10630f03069bca7a528With state support, artificial intelligence is being intensively introduced into the healthcare sector. This review provides general information about artificial intelligence and its place in the healthcare system. A part of the lecture is devoted to the Moscow experiment on the use of computer vision in real clinical practice and the experience of the Kirov region on the use of artificial intelligence in the medical examination of the population. The lecture will be interesting to doctors and students to expand their general understanding of artificial intelligence and the possibility of its application in practical healthcare.Russian Family Doctor60This review provides general information about artificial intelligence and its place in the healthcare system and the Moscow experiment on the use of computer vision and the experience of the Kirov region on the use of artificial intelligence in the medical examination of the population.Russian Family Doctor['T. S. Fil']2024-01-19T00:00:00https://www.semanticscholar.org/paper/ada698cb4d1f4c9560a6d10630f03069bca7a528 b6610d232fc8b05338c1dd32cd68909998ae7098Energy &amp; Fuels1734Energy &amp; Fuels['Van Nhanh Nguyen', 'W. Tarelko', 'Prabhakar Sharma', 'A. S. El-Shafay', 'Wei-Hsin Chen', 'Phuoc Quy Phong Nguyen', 'X. Nguyen', 'Anh Tuan Hoang']2024-01-19T00:00:00https://www.semanticscholar.org/paper/b6610d232fc8b05338c1dd32cd68909998ae7098 e6da8804b343324c556d95555797eccc73e0a37bMany factors influence well-being and health in everyday life. While people are aware of traffic delays or continuous work stress, other factors influence the state of the body on a subconscious level. The built environment subconsciously influences human physiology during every second of life, which has a cumulative long-term effect. The idea of biophilic design identifies the importance of natural elements implemented in architectural structures to improve the occupants’ health and well-being. This paper measures the impact of biophilic design on positive emotions and productivity in two separate but conceptually related pilot studies that apply novel approaches: (a) facial emotion recognition (FER) with residual masking networks and (b) sentiment detection using Large Language Models. The first study measures the emotions of people when confronted with images of different kinds of architecture, via FER and via a user survey. We find clear trends for emotions detected by FER and significant evidence for self-stated emotions that architecture implementing biophilic design evokes more positive emotions. The second study measures the influence of natural elements on productivity and team engagement. The findings show that natural elements in the surroundings do influence productivity and sentiment positively. As the sample size of subjects, especially for the second study, was relatively small, future research will need to apply these ideas in a larger setup to acquire further evidence for the importance of biophilic design for human well-being and health.Sustainability271Sustainability['Tobias M. Ramm', 'Mathias Werwie', 'Tim Otto', 'Peter A. Gloor', 'N. Salingaros']2024-01-19T00:00:00https://www.semanticscholar.org/paper/e6da8804b343324c556d95555797eccc73e0a37b 3491d36b8f4d97dacaf9b3c887caf98bc50491bbPediatric Critical Care Medicine01Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies['T. Buchman', 'Robert C. Tasker']2024-01-19T00:00:00https://www.semanticscholar.org/paper/3491d36b8f4d97dacaf9b3c887caf98bc50491bb 3c0335e92b02848d9ec230cb522c455bd75d35bcBACKGROUND The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Additive exPlanation (SHAP), to develop a predictive model for critically ill patients. METHODS We extracted data from the Medical Information Mart for Intensive Care IV database, encompassing all intensive care unit admissions. We employed nine different methods to develop the models. The most accurate model, with the highest area under the receiver operating characteristic curve, was selected as the optimal model. Additionally, we used SHAP to explain the workings of the ML model. RESULTS The study included 21 395 critically ill patients, with a median age of 68 years (interquartile range, 56-79 years), and most patients were male (56.9%). The cohort was randomly split into a training set (N = 16 046) and a validation set (N = 5349). Among the nine models developed, the Random Forest model had the highest accuracy (87.62%) and the best area under the receiver operating characteristic curve value (0.89). The SHAP summary analysis showed that Glasgow Coma Scale, urine output, and blood urea nitrogen were the top three risk factors for outcome prediction. Furthermore, SHAP dependency analysis and SHAP force analysis were used to interpret the Random Forest model at the factor level and individual level, respectively. CONCLUSION A transparent ML model for predicting outcomes in critically ill patients using SHAP methodology is feasible and effective. SHAP values significantly improve the explainability of ML models.Postgraduate medical journal281A transparent ML model for predicting outcomes in critically ill patients using SHAP methodology is feasible and effective and SHAP values significantly improve the explainability of ML models.Postgraduate medical journal['Chang Hu', 'Chao Gao', 'Tianlong Li', 'Chang Liu', 'Zhiyong Peng']2024-01-19T00:00:00https://www.semanticscholar.org/paper/3c0335e92b02848d9ec230cb522c455bd75d35bc 34c15f01d2c768e61c8a9771190d1e818d8ffe97В статье рассматриваются актуальные вопросы использования Искусственного интеллекта в ходе цифровой трансформации в Российской Федерации. При проведении исследования установлено, что Искусственный интеллект представляет собой такую научную область, которая включает разнообразные методики, технологии, приемы и инструменты, позволяющие создавать интеллектуальные системы, функционирующие и действующие как человек. Технологии Искусственного интеллекта постоянно развиваются и имеют дальнейшую перспективу применения в различных сферах деятельности. Автором широко представлены результаты исследований российских специалистов с применением Искусственного интеллекта. Проведен анализ опыта развития технологий Искусственного интеллекта в Китае. Установлено, что на сегодняшний день инструменты Искусственного интеллекта активно применяются в различных сферах человеческой деятельности. Лидерами по внедрению систем Искусственного интеллекта в России сегодня являются банковские структуры, промышленные предприятия, а также телекоммуникационные компании. Перспективными областями применения Искусственного интеллекта в России являются логистика и капитальное строительство, где благодаря ему можно значительно сократить текущие затраты, оптимизировать запасы, регулировать поставки и т. п. В России при внедрении технологий Искусственного интеллекта важно исходить из анализа успешного иностранного опыта, прежде всего, Китая, являющегося одним из мировых лидеров по использованию Искусственного интеллекта и показывающего значительную динамику в развитии различных систем Искусственного интеллекта. The article deals with topical issues of using Artificial Intelligence in the course of digital transformation in the Russian Federation. During the study, it was established that artificial intelligence is such a scientific field that includes a variety of methods, technologies, techniques and tools that allow you to create intellectual systems that function and act like a person. Artificial intelligence technologies are constantly evolving and have a further perspective of application in various fields of activity. The author widely presents the results of research by Russian specialists using artificial intelligence. The analysis of the experience of developing artificial intelligence technologies in China was carried out. It has been established that today AI tools are actively used in various fields of human activity. The leaders in the implementation of artificial intelligence systems in Russia today are banking structures, industrial enterprises, and telecommunications companies. Promising areas of application of Artificial Intelligence in Russia are logistics and capital construction, where, thanks to AI, it is possible to significantly reduce current costs, optimize inventories, regulate supplies, etc. In Russia, when introducing artificial intelligence technologies, it is important to proceed from the analysis of successful foreign experience, primarily China, which is one of the world leaders in the use of artificial intelligence and shows significant dynamics in the development of various artificial intelligence systems.Вестник Академии права и управления00Вестник Академии права и управления['В.И. Рустамов']2024-01-19T00:00:00https://www.semanticscholar.org/paper/34c15f01d2c768e61c8a9771190d1e818d8ffe97 c47b67ac158fec79b5d64d9838e42ff963f75ed9В статье рассматриваются актуальные примеры взаимодействия журналистики с современными медийными трендами, включающими в себя такие направления, как использование искусственного интеллекта и принципов иммерсивной журналистики. Предпринимается попытка исследовать опыт различных изданий в эксплуатации разных видов программируемых алгоритмов, которые сегодня уже используют специалисты. Рассматриваются теории исследователей, изучающих данный вопрос, в том числе выводы, на основании которых делается заключение. Также в статье предлагается обратить внимание на положительные, и отрицательные стороны исследуемого мадиатренда, при этом наблюдения опираются на мнение журналистов и примеры использования редакциями различных приложений. Благодаря этому решается вопрос о наиболее эффективном применении нейронных сетей в медиапространстве, в частности в сфере журналистики, с учетом допустимых границ, которых целесообразно придерживаться современному журналисту, а также отмечаются отрицательные моменты, которые рекомендуется изучить и учесть в дальнейшем.International Journal of Information and Communication Technology00INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES['С.В. Ашенова', 'А.К. Артықбаев']2024-01-19T00:00:00https://www.semanticscholar.org/paper/c47b67ac158fec79b5d64d9838e42ff963f75ed9 c131cbc967661c9331555a20ba10c32439158c4aInformation Systems Journal00Information Systems Journal['Irfan Walhidayah', 'Ahmad Rizaldi']2024-01-19T00:00:00https://www.semanticscholar.org/paper/c131cbc967661c9331555a20ba10c32439158c4a f7a9109d9db51df28c7cba633ce2d11d052ca807Annals of Mathematics and Artificial Intelligence00Ann. Math. Artif. Intell.['Dimitrios I. Diochnos', 'M. Golumbic', 'F. Hoffman']2024-01-19T00:00:00https://www.semanticscholar.org/paper/f7a9109d9db51df28c7cba633ce2d11d052ca807 c072a59b06f4202b7eb8bd8e2f64f9f8b98c5922Critical Care Medicine00Critical care medicine['T. Buchman', 'Robert C. Tasker']2024-01-19T00:00:00https://www.semanticscholar.org/paper/c072a59b06f4202b7eb8bd8e2f64f9f8b98c5922 032026717da7c69ead5c76773144a64195e16c1aWorldviews on Evidence-Based Nursing30Worldviews on evidence-based nursing['Per Nilsen']2024-01-19T00:00:00https://www.semanticscholar.org/paper/032026717da7c69ead5c76773144a64195e16c1a 0a57fb82c67c75d1c7fc9ab5bdaf00172b7ae9cdCritical Care Medicine90Critical care medicine['Hari Trivedi', 'Judy Gichoya']2024-01-19T00:00:00https://www.semanticscholar.org/paper/0a57fb82c67c75d1c7fc9ab5bdaf00172b7ae9cd f8a4691e113ac04fbd9a5eafe4e1b5fe1aaade5eJMIR Mental Health600JMIR Mental Health['Andrea Ferrario', 'Jana Sedlakova', 'M. Trachsel']2024-01-19T00:00:00https://www.semanticscholar.org/paper/f8a4691e113ac04fbd9a5eafe4e1b5fe1aaade5e 429fba7a6a7fd3287e3b1d9826fc8579b23a76e7Current problems in cardiology10Current problems in cardiology['Luqiao Ni', 'Kai Chen', 'Yi Kong']2024-01-19T00:00:00https://www.semanticscholar.org/paper/429fba7a6a7fd3287e3b1d9826fc8579b23a76e7 f525e7355f6f3ac62274865fe0da1e3124cc7d1aJournal of Industrial Integration and Management00Journal of Industrial Integration and Management['C. Ganeshkumar', 'R. Basu', 'M. Yuvaraj', 'Arokiaraj David']2024-01-19T00:00:00https://www.semanticscholar.org/paper/f525e7355f6f3ac62274865fe0da1e3124cc7d1a 590504e3135c19244b01a0e82e98ee70427683efRAiSE-2023180RAiSE-2023['Abdul Kareem', 'Varuna Kumara', 'Akshatha Naik']2024-01-19T00:00:00https://www.semanticscholar.org/paper/590504e3135c19244b01a0e82e98ee70427683ef a1824db479ead8af8388b1ce0a241d4def24c0a1Machine learning is an area of artificial intelligence that deals with the development of algorithms and models for automatically detecting patterns and making inferences from data. Neural networks are one of the most popular machine learning models that simulate the learning process of the brain and are widely used in various fields such as pattern recognition, prediction and control. Matlab is a popular programming language in the field of machine learning due to its ease of use and numerous libraries that contain the implementation of various machine learning algorithms. In this paper, we will present the simulation of machine learning in neural networks using different algorithms in Matlab. We will describe several algorithms such as feedforward neural network, convolutional neural network and deep neural network. Also, we will show how these algorithms are applied in practice using different datasets. Finally, we will compare the performance of different algorithms and analyze their advantages and disadvantages.Open Access Journal of Applied Science and Technology03This paper presents the simulation of machine learning in neural networks using different algorithms in Matlab, and describes several algorithms such as feedforward neural network, convolutional neural network and deep neural network.Open Access Journal of Applied Science and Technology[]2024-01-19T00:00:00https://www.semanticscholar.org/paper/a1824db479ead8af8388b1ce0a241d4def24c0a1 812ff72377d1cb4c1578f2776d4ca0cfc8264210 This Viewpoint discusses the potential drawbacks of the use of artificial intelligence (AI) in medicine, for example, the loss of certain skills due to the reliance on AI, and how physicians should consider how to take advantage of the potential benefits of AI without losing control over their profession. Journal of the American Medical Association (JAMA)73JAMA['A. Fogo', 'Andreas Kronbichler', 'Ingeborg M. Bajema']2024-01-19T00:00:00https://www.semanticscholar.org/paper/812ff72377d1cb4c1578f2776d4ca0cfc8264210 788e244540a69019766113104be0f966f31e1bbfConsidering recent technological advances and the growing prevalence of digitalization, Islamic educational instruction and training will need to adopt a fresh perspective. The field of artificial intelligence offers a path for additional research that has the potential to have a favorable impact on both the efficiency and the development of competencies. This article describes the process followed to create multimedia-based teaching materials for Islamic religious education subjects used in Senior High Schools in West Sumatra. These materials were designed to assist Islamic education students with their academic pursuits. While developing this model, the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) services that Dick and Carry developed were utilized during the design phase. In Islamic education, students engage in hands-on learning by interacting with previously crafted designs and observing how an evaluation of the model is built through time. These findings, implications, and potential future research areas involving artificial intelligence in Islamic religious education and training at Indonesia's senior high school level will be discussed.International Journal of Religion602The process followed to create multimedia-based teaching materials for Islamic religious education subjects used in Senior High Schools in West Sumatra were described, designed to assist Islamic education students with their academic pursuits.International Journal of Religion['S. Syahrizal', 'Fifi Yasmi', 'T. Mary']2024-01-19T00:00:00https://www.semanticscholar.org/paper/788e244540a69019766113104be0f966f31e1bbf 6ebdf192a71d1eea10b24dc2d91a6e3c43f994e2PurposeChatGPT, an artificial intelligence (AI)-powered chatbot, has gained substantial attention in the academic world for its potential to transform the education industry. While ChatGPT offers numerous benefits, concerns have also been raised regarding its impact on the quality of education. This study aims to bridge the gap in research by exploring teachers' perspectives on the adoption of ChatGPT, with a focus on identifying factors that motivate and inhibit them to adopt ChatGPT for educational purposes.Design/methodology/approachThis research has employed a interpretative phenomenological analysis (IPA) qualitative approach. Through in-depth interviews among the teachers, data will be collected to identify the motivating and inhibiting factors that impact teachers' willingness to adopt ChatGPT. The data was collected from 34 teachers working across 10 branches of the University of Technology and Applied Sciences (UTAS) in Oman.FindingsThe analysis revealed four themes under motivating factors that encourage teachers to adopt ChatGPT for their educational purpose. These include Theme 1: Exploration of innovative education technologies, Theme 2: Personalization teaching and learning, Theme 3: Time-saving and Theme 4: Professional development. On the other hand, inhibiting factors includes five themes which includes Theme 1: Reliability and accuracy concerns, Theme 2: Reduced human interaction, Theme 3: Privacy and data security, Theme 4: lack of institutional support and Theme 5: Overreliance on ChatGPT.Practical implicationsThis study contributes to the understanding of teachers' perspectives on the adoption of ChatGPT in education. By understanding teachers' perspectives, policymakers can design appropriate policies and service providers can customize their offerings to meet teachers' requirements. The study's findings will be valuable for higher education institutions (HEIs) in formulating policies to ensure the appropriate and effective utilization of ChatGPT. The study will provide suggestions to ChatGPT service providers, enabling them to focus on motivating factors and address inhibiting factors, thereby facilitating the seamless adoption of ChatGPT among teachers.Originality/valueIn comparison to previous studies, this study goes beyond merely discussing the possible benefits and limitations of ChatGPT in education. This research significantly contributes to the understanding of ChatGPT adoption among teachers by identifying specific motivating and inhibiting factors that influence teachers to adopt ChatGPT for educational purposes. The research enables to gain important new insights that were not previously found, giving a fresh dimension to the existing literature.Journal of Research in Innovative Teaching &amp; Learning832The study will provide suggestions to ChatGPT service providers, enabling them to focus on motivating factors and address inhibiting factors, thereby facilitating the seamless adoption of ChatGPT among teachers.Journal of Research in Innovative Teaching &amp; Learning['Habiba Al-Mughairi', 'Preeti Bhaskar']2024-01-19T00:00:00https://www.semanticscholar.org/paper/6ebdf192a71d1eea10b24dc2d91a6e3c43f994e2 bf8e1e5e7b593132708ec934387892a54b01a4dcThis study aimed to evaluate the performance of three artificial intelligence (AI) image synthesis models, Dall-E 2, Stable Diffusion, and Midjourney, in generating urban design imagery based on scene descriptions. A total of 240 images were generated and evaluated by two independent professional evaluators using an adapted sensibleness and specificity average metric. The results showed significant differences between the three AI models, as well as differing scores across urban scenes, suggesting that some projects and design elements may be more challenging for AI art generators to represent visually. Analysis of individual design elements showed high accuracy in common features like skyscrapers and lawns, but less frequency in depicting unique elements such as sculptures and transit stops. AI-generated urban designs have potential applications in the early stages of exploration when rapid ideation and visual brainstorming are key. Future research could broaden the style range and include more diverse evaluative metrics. The study aims to guide the development of AI models for more nuanced and inclusive urban design applications, enhancing tools for architects and urban planners.IEEE Computer Graphics and Applications261Analysis of individual design elements showed high accuracy in common features like skyscrapers and lawns, but less frequency in depicting unique elements such as sculptures and transit stops, suggesting that some projects and design elements may be more challenging for AI art generators to represent visually.IEEE Computer Graphics and Applications['Connor Phillips', 'Junfeng Jiao', 'Emmalee Clubb']2024-01-19T00:00:00https://www.semanticscholar.org/paper/bf8e1e5e7b593132708ec934387892a54b01a4dc fe4a4cee1a42cb02d69ae603b85098db364624bbThe concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that can learn from data and generalize their judgment to new observations by exploiting primarily statistical methods. The new millennium has seen the proliferation of Artificial Neural Networks (ANNs), a formalism able to reach extraordinary achievements in complex problems such as computer vision and natural language recognition. In particular, designers claim that this formalism has a strong resemblance to the way the biological neurons operate. This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective. The main reason is that ML algorithms have notable prediction power although they cannot necessarily provide a causal explanation about the achieved predictions. For example, an ANN could be trained on a large dataset of consumer financial information to predict creditworthiness. The model takes into account various factors like income, credit history, debt, spending patterns, and more. It then outputs a credit score or a decision on credit approval. However, the complex and multi-layered nature of the neural network makes it almost impossible to understand which specific factors or combinations of factors the model is using to arrive at its decision. This lack of transparency can be problematic, especially if the model denies credit and the applicant wants to know the specific reasons for the denial. The model’s “black box” nature means it cannot provide a clear explanation or breakdown of how it weighed the various factors in its decision-making process. Secondly, this work rejects the belief that a machine can simply learn from data, either in supervised or unsupervised mode, just by applying statistical methods. The process of learning is much more complex, as it requires the full comprehension of a learned ability or skill. In this sense, further ML advancements, such as reinforcement learning and imitation learning denote encouraging similarities to similar cognitive skills used in human learning.Electronics441This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective, and rejects the belief that a machine can simply learn from data just by applying statistical methods.Electronics['Enrico Barbierato', 'Alice Gatti']2024-01-19T00:00:00https://www.semanticscholar.org/paper/fe4a4cee1a42cb02d69ae603b85098db364624bb fd325e281c0deb95cf2d8150fc62ded92670186aIn the zeitgeist of the 21st century, artificial intelligence (AI) has emerged as a focal point of discussions across various domains, from its applications in everyday life to its implications in creative fields. Music culture and the music industry have not remained untouched. This empirical study examines the portrayal of AI in German music-focused print magazines between 2016 and 2022, a period marked by significant advancements in AI, including its foray into artistic creation. Through a quantitative content analysis of 10,344 articles from prominent music publications, a mere 0.67% were found to engage with the topic of AI. The data reveals a noticeable uptick in such articles from 2019 onward. This study elucidates the multifaceted perceptions and evaluations of AI with the help of framing theory and the technology acceptance model. The findings indicate a predominantly neutral stance, with variations across different magazines. Most magazines treat AI as a noteworthy topic, but not a central one. Thus, only a few articles address technology acceptance or relevant factors pertaining to it. Based on these findings, this article examines the implications for musicians and experts in the music media sector, along with future research approaches.Jahrbuch Musikpsychologie290This empirical study examines the portrayal of AI in German music-focused print magazines between 2016 and 2022, a period marked by significant advancements in AI, including its foray into artistic creation.Jahrbuch Musikpsychologie['Nicolas Ruth', 'Kristin Marie Zickler']2024-01-19T00:00:00https://www.semanticscholar.org/paper/fd325e281c0deb95cf2d8150fc62ded92670186a 4ab69ddff5f6e1e98cb5c9f22bafea97fc4be8d0 Artificial intelligence (AI)'s dual functionalities in scholarly publishing raise ethical and practical concerns. Chatbots, much more than AI‐driven search engines, have the potential to distort information and disseminate false truths and flawed science. The role of AI in scholarly publishing needs to be critically examined. Learned Publishing10Artificial intelligence (AI)'s dual functionalities in scholarly publishing raise ethical and practical concerns and the role of AI in scholarly publishing needs to be critically examined.Learn. Publ.['Pascal Hetzscholdt']2024-01-19T00:00:00https://www.semanticscholar.org/paper/4ab69ddff5f6e1e98cb5c9f22bafea97fc4be8d0 f6c1a216f86d06f3f83cafa6366642de4f8d476fArtificial intelligence will have profound impacts across health systems, transforming health care, public health, and research. Responsible AI can accelerate efforts toward health systems being more resilient, sustainable, equitable, and person-centred. This paper provides an overview of the background and current state of artificial intelligence in health, perspectives on opportunities, risks, and barriers to success. The paper proposes several areas to be explored for policy makers to advance the future of responsible AI in health that is adaptable to change, respects individuals, champions equity, and achieves better health outcomes for all. The areas to be explored relate to trust, capacity building, evaluation, and collaboration. This recognises that the primary forces that are needed to unlock the value from artificial intelligence are people-based and not technical. The OECD is ready to support efforts for co-operative learning and collective action to advance the use of responsible AI in health.OECD Artificial Intelligence Papers710The paper proposes several areas to be explored for policy makers to advance the future of responsible AI in health that is adaptable to change, respects individuals, champions equity, and achieves better health outcomes for all.OECD Artificial Intelligence Papers['Brian Anderson', 'Eric Sutherland']2024-01-19T00:00:00https://www.semanticscholar.org/paper/f6c1a216f86d06f3f83cafa6366642de4f8d476f 89dd33bf4495617017b65a723060f7257b531966The escalating integration of Artificial Intelligence (AI) in clinical settings carries profound implications for the doctrine of informed consent, presenting challenges that necessitate immediate attention. China, in its advancement in the deployment of medical AI, is proactively engaging in the formulation of legal and ethical regulations. This paper takes China as an example to undertake a theoretical examination rooted in the principles of medical ethics and legal norms, analyzing informed consent and medical AI through relevant literature data. The study reveals that medical AI poses fundamental challenges to the accuracy, adequacy, and objectivity of information disclosed by doctors, alongside impacting patient competency and willingness to give consent. To enhance adherence to informed consent rules in the context of medical AI, this paper advocates for a shift towards a patient-centric information disclosure standard, the restructuring of medical liability rules, the augmentation of professional training, and the advancement of public understanding through educational initiatives.Developing World Bioethics00This paper advocates for a shift towards a patient-centric information disclosure standard, the restructuring of medical liability rules, the augmentation of professional training, and the advancement of public understanding through educational initiatives.Developing world bioethics['Yue Wang', 'Zhuo Ma']2024-01-19T00:00:00https://www.semanticscholar.org/paper/89dd33bf4495617017b65a723060f7257b531966 b8640f172976258ec977ceb00e70676e451fb431Integrated Development Environments (IDEs) have become central to modern software development, especially with the integration of Artificial Intelligence (AI) to enhance programming efficiency and decision-making. The study of in-IDE Human-AI Experience is critical in understanding how these AI tools are transforming the software development process, impacting programmer productivity, and influencing code quality. We conducted a literature review to study the current state of in-IDE Human-AI Experience research, bridging a gap in understanding the nuanced interactions between programmers and AI assistants within IDEs. By analyzing 36 selected papers, our study illustrates three primary research branches: Design, Impact, and Quality of Interaction. The trends, challenges, and opportunities identified in this paper emphasize the evolving landscape of software development and inform future directions for research and development in this dynamic field. Specifically, we invite the community to investigate three aspects of these interactions: designing task-specific user interface, building trust, and improving readability.arXiv.org410A literature review is conducted to study the current state of in-IDE Human-AI Experience research, bridging a gap in understanding the nuanced interactions between programmers and AI assistants within IDEs and invites the community to investigate three aspects of these interactions: designing task-specific user interface, building trust, and improving readability.ArXiv['Agnia Sergeyuk', 'Sergey Titov', 'M. Izadi']2024-01-19T00:00:00https://www.semanticscholar.org/paper/b8640f172976258ec977ceb00e70676e451fb431 681abc99993d85663ee94a32881320cb0df6a910Artificial Intelligence (AI) systems have become pervasive in numerous facets of modern life, wielding considerable influence in critical decision-making realms such as healthcare, finance, criminal justice, and beyond. Yet, the inherent opacity of many AI models presents significant hurdles concerning trust, accountability, and fairness. To address these challenges, Explainable AI (XAI) has emerged as a pivotal area of research, striving to augment the transparency and interpretability of AI systems. This survey paper serves as a comprehensive exploration of the state-of-the-art in XAI methods and their practical applications. We delve into a spectrum of techniques, spanning from model-agnostic approaches to interpretable machine learning models, meticulously scrutinizing their respective strengths, limitations, and real-world implications. The landscape of XAI is rich and varied, with diverse methodologies tailored to address different facets of interpretability. Model-agnostic approaches offer versatility by providing insights into model behavior across various AI architectures. In contrast, interpretable machine learning models prioritize transparency by design, offering inherent understandability at the expense of some predictive performance. Layer-wise Relevance Propagation (LRP) and attention mechanisms delve into the inner workings of neural networks, shedding light on feature importance and decision processes. Additionally, counterfactual explanations open avenues for exploring what-if scenarios, elucidating the causal relationships between input features and model outcomes. In tandem with methodological exploration, this survey scrutinizes the deployment and impact of XAI across multifarious domains. Successful case studies showcase the practical utility of transparent AI in healthcare diagnostics, financial risk assessment, criminal justice systems, and more. By elucidating these use cases, we illuminate the transformative potential of XAI in enhancing decision-making processes while fostering accountability and fairness. Nevertheless, the journey towards fully transparent AI systems is fraught with challenges and opportunities. As we traverse the current landscape of XAI, we identify pressing areas for further research and development. These include refining interpretability metrics, addressing the scalability of XAI techniques to complex models, and navigating the ethical dimensions of transparency in AI decision-making.Through this survey, we endeavor to cultivate a deeper understanding of transparency in AI decision-making, empowering stakeholders to navigate the intricate interplay between accuracy, interpretability, and ethical considerations. By fostering interdisciplinary dialogue and inspiring collaborative innovation, we aspire to catalyze future advancements in Explainable AI, ultimately paving the way towards more accountable and trustworthy AI systems.Advances in Robotic Technology00This survey serves as a comprehensive exploration of the state-of-the-art in XAI methods and their practical applications, and endeavor to cultivate a deeper understanding of transparency in AI decision-making, empowering stakeholders to navigate the intricate interplay between accuracy, interpretability, and ethical considerations.Advances in Robotic Technology['Jain R']2024-01-19T00:00:00https://www.semanticscholar.org/paper/681abc99993d85663ee94a32881320cb0df6a910 475a7d104364f8b10851681a09475895ab26be4dThe work is devoted to defining theoretical and methodological approaches to the constitutional law regulation of the economy and economic relations. The main conceptual models of constitutional and legal regulation are considered, which include: direct public administration; liberal model; liberal democratic model; model of the social state. The Russian model of constitutional regulation of the economy, built on the principles of a welfare state and the priority of ensuring socio-economic rights and freedoms in compliance with the public law and order established by constitutional norms, is analyzed. The author's conclusions are formulated, reflecting the general characteristics of the economy as an object of constitutional and legal regulation in modern economic conditions.STATE POWER AND LOCAL SELF-GOVERNMENT00State power and local self-government['Ekaterina Yu. Stakhanova']2024-01-18T00:00:00https://www.semanticscholar.org/paper/475a7d104364f8b10851681a09475895ab26be4d d566a9f5a630feb5d5d9deba9eb328307cfdaf97Building on the concept of participatory regulation, this study emphasizes recognizing the multidimensional character of citizens' risk regulation preferences. Using the case of autonomous vehicles, we specify six technology‐related risks: product safety, regulatory oversight, legal liability, ethical prioritization, data protection, and human supervision. We argue that differences in these multidimensional risk regulation preferences are shaped by citizens' political beliefs, technology attitudes, and national innovation cultures. To test these hypotheses, a conjoint experiment was conducted in the United States (1188 participants), Japan (1135 participants), and Germany (1174 participants) in which respondents compared hypothetical regulation regimes for self‐driving cars, varying alongside the six regulatory risk dimensions. The findings show a universal preference for increased legal responsibility of manufacturers and more stringent safety regulations for autonomous vehicles. Political beliefs and technological attitudes had minimal impact on these preferences. Although there were some cultural differences in privacy and ethical prioritization, no systematic differences were noted across countries, suggesting the possibility of finding common ground in standardizing risk regulations for self‐driving cars.Regulation &amp; Governance670Regulation &amp; Governance['Sebastian Hemesath', 'Markus Tepe']2024-01-18T00:00:00https://www.semanticscholar.org/paper/d566a9f5a630feb5d5d9deba9eb328307cfdaf97 d70e35c1d38dcc71dca1c1e6cb8eb3de4d3661d9The article is devoted to the application of the subject-oriented approach in legal regulation. As an example, the proceedings on cases of administrative offenses are considered in terms of the status of participants in the proceedings that facilitate the conduct of proceedings in the case. Special attention is paid to the status of the prosecutor. A comprehensive analysis of the content of the statuses of these persons is carried out, which includes a comparison of these statuses with each other, as well as with the provisions governing the relevant procedural actions. Based on the results of the analysis, proposals are formulated to improve the legal regulation of this production, as well as some recommendations of a methodological nature.Administrative law and procedure00Administrative law and procedure['Aleksandr Yu. Yakimov']2024-01-18T00:00:00https://www.semanticscholar.org/paper/d70e35c1d38dcc71dca1c1e6cb8eb3de4d3661d9 db3935557437bbae96e858929ef9d472e35afd50The article provides a comparative legal analysis of the provisions of the Federal Law “On conducting an experiment to establish special regulation in order to create the necessary conditions for implementing partnership financing activities in Certain Subjects of the Russian Federation and on Amendments to Certain Legislative Acts of the Russian Federation” and the initial draft this law, introduced to the State Duma of the Russian Federation by a group of deputies and senators. Attention is drawn to the insufficient regulation of partnership financing relations, and it is concluded that a number of provisions of the draft federal law were more precise and definite in their content compared to the adopted federal law. When analyzing the provisions of the adopted federal law, obvious contradictions in its content, duplication, as well as systemic inconsistency of the regulatory material were revealed.Banking law00Banking law['G. Ruchkina']2024-01-18T00:00:00https://www.semanticscholar.org/paper/db3935557437bbae96e858929ef9d472e35afd50 195abeb7c260fa021da13f6aa4fe5ad735613137The emergence of a variety of digital companies breaks up the competitive landscape of the market, which simultaneously changes peoples lifestyles as well. As the main tool to convenient peoples lives and to motivate the economic development of society, the progress made by digital technology proposed high requirements for authorities to update and adopt new regulatory approaches to retain the market order. Currently, the policy regulation for online agencies, known as platforms, causes intense academic discussion due to the inapplicability of the traditional regulatory model and the conflicting views of technical innovation or governmental restriction. This paper will focus on an innovative regulatory doctrine, namely Meta-regulation which enables the government to set up the rules for platforms from a unique perspective, and meanwhile, exploring two dimensions including an explanation of the standardized concept of Meta-regulation and giving an analysis of its generalization within the context of the digital economy. A conceptually accepted definition of Meta-Regulation and a series of reasons produced to demonstrate the applicability of Meta-Regulation for platform regulating will be figured out at the end of this paper.Advances in Economics, Management and Political Sciences00This paper will focus on an innovative regulatory doctrine, namely Meta-regulation which enables the government to set up the rules for platforms from a unique perspective, and exploring two dimensions including an explanation of the standardized concept of Meta-regulation and giving an analysis of its generalization within the context of the digital economy.Advances in Economics, Management and Political Sciences['Peilin Wu']2024-01-18T00:00:00https://www.semanticscholar.org/paper/195abeb7c260fa021da13f6aa4fe5ad735613137 6a4a3569c71f871a0cb8c0c9a84d797689f09399This article discusses the meaning and content of the payment functions of such an electronic commerce participant as a marketplace in the context of giving it the status of an aggregator in connection with this and extending to its activities in full the entire mechanism of consumer protection, as well as all forms and methods of protecting consumer rights, including administrative lawsuits in consumer protection. The author comes to the conclusion that such a sign of a marketplace as the presence of the functions of a paying agent should not exclusively determine its status as an aggregator.Banking law00Banking law['Marina N. Ilyushina']2024-01-18T00:00:00https://www.semanticscholar.org/paper/6a4a3569c71f871a0cb8c0c9a84d797689f09399 0977771110bade2d71d616a46b17786c325083b1The article presents the results of a study of regulations on the formation of reporting information on the inventories of organizations in accounting (financial) statements. According to the Program for the Development of Federal Accounting Standards, it is planned to adopt a federal standard dedicated to accounting (financial) reporting, which, in particular, will include rules for drawing up explanations to statements containing additional information about accounting objects, including the inventories of organizations. In order to implement this Program, the Russian Ministry of Finance has developed the draft FSBU 4/2023 “Accounting (financial) reporting”. This article examines the regulations presented in this project for the generation of reporting information on inventories. The results of a comparison of the requirements for the generation of reporting information on inventories set out in the draft FSBU 4/2023 are presented with the regulations for the disclosure of such information in PBU 4/99 “Accounting statements of an organization”, in Order of the Ministry of Finance of Russia No. 66n dated July 2, 2010 “On forms of financial statements of organizations” and in FAS 5/2019 “Inventories”. Both expedient and controversial aspects of regulations on the formation of reporting information on inventories are substantiated. Recommendations for resolving controversial methodological issues are presented.Buhuchet v zdravoohranenii (Accounting in Healthcare)20Buhuchet v zdravoohranenii (Accounting in Healthcare)['T. Druzhilovskaya']2024-01-18T00:00:00https://www.semanticscholar.org/paper/0977771110bade2d71d616a46b17786c325083b1 6fefd138be7a96ad6fd5bc448b6be28f69c8198a Purpose This study aims to explore the collaborative potential of Microsoft Copilot and Anthropic Claude AI as an assistive technology in education and library services. The research delves into technical architectures and various use cases for both tools, proposing integration strategies within educational and library environments. The paper also addresses challenges such as algorithmic bias, hallucination and data rights. Design/methodology/approach The study used a literature review approach combined with the proposal of integration strategies across education and library settings. Findings The collaborative framework between Copilot and Claude AI offers a comprehensive solution for transforming education and library services. The study identifies the seamless combination of real-time internet access, information retrieval and advanced comprehension features as key findings. In addition, challenges such as algorithmic bias and data rights are addressed, emphasizing the need for responsible AI governance, transparency and continuous improvement. Originality/value Contribute to the field by exploring the unique collaborative framework of Copilot and Claude AI in a specific context, emphasizing responsible AI governance and addressing existing gaps. Library Hi Tech News171The collaborative framework between Copilot and Claude AI offers a comprehensive solution for transforming education and library services and identifies the seamless combination of real-time internet access, information retrieval and advanced comprehension features as key findings.Library Hi Tech News['Adebowale Jeremy Adetayo', 'Mariam Oyinda Aborisade', 'Basheer Abiodun Sanni']2024-01-18T00:00:00https://www.semanticscholar.org/paper/6fefd138be7a96ad6fd5bc448b6be28f69c8198a fb9dda68fcccc19ceccb7e030b38eb5ed382bd2fObjective: This paper examines the adequacy of Ghana’s Public Health Act 2012 for governing rising use of algorithmic systems like AI in automating vaccine distribution. Method: Employing the structured CREAC legal reasoning framework, it systematically analyses current statutory flexibility, rights safeguards and accountability provisions in the Act to balance technological innovation against risks of automated opacity, bias and exclusion errors. Results: While the legislation provides ample principles-based scope for administrative pilots and controlled deployment of AI coordination tools to improve immunization equity, reliance on 20th century assumptions of technological neutrality means significant gaps in addressing unique socio-ethical hazards of autonomous predictive analytics. Contributions: First structured application of legal study methodology to contemporize public health law assessments for coming healthcare automation advances, yielding actionable policy upgrades. Advances interdisciplinary discourse on ethical technological transformation of vital services. Significance: Anchored in legal realities and public access imperatives of a developing country, declines facile overhaul recommendations, favoring participative, evidence-led amendments upholding innovation incentives within updated rights regimes. Socially-grounded contribution bridging theory with practice in governance discourse. Sets agenda for anticipatory, democratic legal regimes upholding reliable and unbiased AI assistance in equitable healthcare access.International Journal For Multidisciplinary Research51The adequacy of Ghana’s Public Health Act 2012 for governing rising use of algorithmic systems like AI in automating vaccine distribution is examined, with first structured application of legal study methodology to contemporize public health law assessments for coming healthcare automation advances.International Journal For Multidisciplinary Research['Alfred Addy', 'Shadrach Asamoah-Atakorah', 'George Benneh Mensah', 'Samuel William Doodo', 'Rebecca Asamoah-Atakorah']2024-01-18T00:00:00https://www.semanticscholar.org/paper/fb9dda68fcccc19ceccb7e030b38eb5ed382bd2f 0b861e6d961ae0c6b0cddf599ea7650a6590816b Purpose The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool” for writing. Design/methodology/approach The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as “tool” through intersectional feminist discursive-material analysis of the metaphorical entailments of the term. Findings Through this work, the authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students. Originality/value Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of “tool” for generative AI services. English Teaching: Practice &amp; Critique271The authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “ objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students.English Teaching: Practice &amp; Critique['Lucinda McKnight', 'Cara Shipp']2024-01-18T00:00:00https://www.semanticscholar.org/paper/0b861e6d961ae0c6b0cddf599ea7650a6590816b e7fe9af94dd8e1baf7793801eab40018d81f1b0fAI &amp; SOCIETY571The study finds that usage intentions are mediated and moderated by compensatory mechanisms that arise from the perception of AI-based virtual influencers’ functional benefits and existential threats to human identity.AI &amp; SOCIETY['S. V. Jin', 'Vijay Viswanathan']2024-01-18T00:00:00https://www.semanticscholar.org/paper/e7fe9af94dd8e1baf7793801eab40018d81f1b0f 3474be1d8dd9e941bc84f9473faa4358601ec8b1Edge Artificial Intelligence (AI) relies on the integration of Machine Learning (ML) into even the smallest embedded devices, thus enabling local intelligence in real-world applications, e.g. for image or speech processing. Traditional Edge AI frameworks lack important aspects required to keep up with recent and upcoming ML innovations. These aspects include low flexibility concerning the target hardware and limited support for custom hardware accelerator integration. Artificial Intelligence for Embedded Systems Framework (AIfES) has the goal to overcome these challenges faced by traditional edge AI frameworks. In this paper, we give a detailed overview of the architecture of AIfES and the applied design principles. Finally, we compare AIfES with TensorFlow Lite for Microcontrollers (TFLM) on an ARM Cortex-M4-based System-on-Chip (SoC) using fully connected neural networks (FCNNs) and convolutional neural networks (CNNs). AIfES outperforms TFLM in both execution time and memory consumption for the FCNNs. Additionally, using AIfES reduces memory consumption by up to 54% when using CNNs. Furthermore, we show the performance of AIfES during the training of FCNN as well as CNN and demonstrate the feasibility of training a CNN on a resource-constrained device with a memory usage of slightly more than 100 kB of RAM.IEEE Transactions on Pattern Analysis and Machine Intelligence801A detailed overview of the architecture of AIfES and the applied design principles is given, and the performance of AIfES during the training of FCNN as well as CNN is shown and the feasibility of training a CNN on a resource-constrained device with a memory usage of slightly more than 100 kB of RAM is demonstrated.IEEE Transactions on Pattern Analysis and Machine Intelligence['Lars Wulfert', 'Johannes Kühnel', 'L. Krupp', 'Justus Viga', 'C. Wiede', 'P. Gembaczka', 'Anton Grabmaier']2024-01-18T00:00:00https://www.semanticscholar.org/paper/3474be1d8dd9e941bc84f9473faa4358601ec8b1 ebfa8a7de85663f987e6d149d1baed22f9198097Objective: This analysis explored gaps between Ghana’s Public Health Act’s oversight provisions and on-the-ground implementation realities using an algorithmic accountability lens, assessing the sufficiency of current vaccine supply chain governance to address risks of unfairness and opacity from integrating artificial intelligence systems. Method: A structured CRAC/IRAC framework was utilized integrating legal analysis of statutory duties under the Public Health Act, case law precedents, real-world examples, counterevidence, and multidisciplinary literature to holistically evaluate institutional capabilities and barriers for monitoring AI automation. Results: The research found that while existing law confers broad transparency and equity mandates applicable to algorithmic tools for health officials under Sections 97, 108 and 169, practical challenges surrounding proprietary opacity of commercial AI and gaps in enforceability impede their fulfillment, necessitating updated regulations. Scientific Contribution: This pioneers legal analysis of AI governance in Ghana while transferring analytical concepts like algorithmic fairness into the sociolegal domain, seeding an important emerging field. It provides a template for assessing automation impacts on rights empirically using mixed criteria. Practical Significance: Scrutinizing legal shortcomings and barriers early while AI integration remains nascent aims positively influence application of guidelines protecting patients. It brings material questions of resource prioritization rooted in moral values of justice into sharper relief for key decision-makers shaping digitized futures.International Journal For Multidisciplinary Research141International Journal For Multidisciplinary Research['Alfred Addy', 'Gbadagba Kwame Joshua', 'Johnson Mensah Sukah Selorm', 'Emmanuel Fuah', 'George Benneh Mensah']2024-01-18T00:00:00https://www.semanticscholar.org/paper/ebfa8a7de85663f987e6d149d1baed22f9198097 414671a18e8134bd53d643b99e92893a9d2f660bAI &amp; SOCIETY02AI &amp; SOCIETY['S. Kraaijeveld']2024-01-18T00:00:00https://www.semanticscholar.org/paper/414671a18e8134bd53d643b99e92893a9d2f660b 6062048c546f6f155d9d4669f013a7c09ec5f133This study "AI-powered self-checkout System" heralds a paradigm shift in the retail landscape, combining artificial intelligence (AI) with innovative technologies to redefine the traditional checkout experience. As consumer expectations evolve, retailers are embracing this advanced system to enhance efficiency, streamline processes, and deliver a more personalized and convenient shopping journey.At its core, this system leverages computer vision to autonomously recognize and track items selected by customers. By eliminating the need for manual barcode scanning, it not only expedites the checkout process but also minimizes errors, ensuring a high level of accuracy in transaction records. Furthermore, the system incorporates machine learning algorithms to continuously refine its performance, learning from transaction data to optimize item recognition accuracy and adapt to evolving purchasing patterns. An integral facet of this system is its ability to handle various payment methods securely, ensuring a frictionless and secure financial transaction. This study presents an update on the ongoing development and implementation of a cutting-edge self-checkout system.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)200An update on the ongoing development and implementation of a cutting-edge self-checkout system that leverages computer vision to autonomously recognize and track items selected by customers and incorporates machine learning algorithms to continuously refine its performance.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)['Harsha Harrison', 'Christina C', 'Aaron Vincent Baiju', 'Sreeja V', 'V. S']2024-01-18T00:00:00https://www.semanticscholar.org/paper/6062048c546f6f155d9d4669f013a7c09ec5f133 e9484ee1e9875a49b1014e1d7ce587ffebed23d4In recent decades, vision problems have become more common. One significant barrier is that people who are visually impaired generally require assistance performing their daily tasks. As a result, developing a technological solution to this challenge is critical. Implementing an automatic object identification system can enable visually impaired people to roam independently. The study employs two Artificial Intelligence (AI) models, a Region-based Fully Convolutional Network (RFCN) and a Mask Region-based Convolutional Neural Network (Mask RCNN), to detect objects and assess their performance. The evaluation demonstrates RFCN's outstanding performance with an impressive Mean Average Precision (mAP) score of 0.825. The Raspberry Pi processor is used as a vital component in the process of hardware development. The RFCN model is integrated into the processor to aid in the identification of objects collected by the system's pi-camera. The detected objects are communicated to users via auditory feedback. The hardware system also includes an ultrasonic sensor, which provides distance information for the detected objects. This full hardware solution is specifically developed to help visually impaired people overcome mobility issues in both indoor and outdoor settings. The suggested system seeks to improve the autonomy and safety of visually impaired individuals in their day-to-day activities by seamlessly mixing advanced technologies such as AI and embedded systems.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)160The suggested system seeks to improve the autonomy and safety of visually impaired individuals in their day-to-day activities by seamlessly mixing advanced technologies such as AI and embedded systems.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)['Syed Sameer', 'Parul Madan', 'Sathish Kannan', 'Vijay Jagdish Upadhye', 'Harshal Patil', 'S. Rajkumar']2024-01-18T00:00:00https://www.semanticscholar.org/paper/e9484ee1e9875a49b1014e1d7ce587ffebed23d4 b85e713980a8020d6e9e9823b966772fd8f154adAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts individuals of all ages. This disorder comprises of symptoms ranging from social and communicational challenges to an exhibition of repetitive behaviors. Early diagnosis and prognosis of ASD is focal for managing its symptoms, supplementing learning outcomes, and social skill development. Conventional diagnosis of ASD based on behavioral evaluations confronts problems owing to the lack of clear-cut rules. On the other hand, Machine Learning and Deep Learning models-based techniques excel in diagnosing ASD by assessing behavioral factors, finding even the tiniest contrariety in gaze and facial attributes. This paper aims to propose an AI-based Autism Detection System leveraging two distinct models. For children aged 2 to 8, XceptionNet model is employed to assess facial dysmorphology in Autistic children's images with an accuracy of 85%, while individuals aged 9 and above undergo evaluation by Light Gradient Boosting Machine Classifier which considers diverse indicators, such as the ASD score, age, gender, ethnicity, etc. to detect Autism with 99% accuracy. Thus, the proposed models enhance the accuracy of ASD detection by addressing the distinct characteristics and requirements of people of all age groups with Autism.Confluence00The proposed models enhance the accuracy of ASD detection by addressing the distinct characteristics and requirements of people of all age groups with Autism.2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)['Manya Tuli', 'Anupriya Chandrasekhar', 'Shubham Tyagi', 'Abhishek Singhal']2024-01-18T00:00:00https://www.semanticscholar.org/paper/b85e713980a8020d6e9e9823b966772fd8f154ad 08f141f56d09b412c6e8a81a04a4da5dab6d4600Scientific Reports500A framework, rooted in deliberative democracy and science communication studies, is presented to evaluate equity in human–AI communication to examine how GPT-3 responded to different populations who vary in sociodemographic backgrounds and viewpoints on crucial science and social issues.Scientific Reports['Kaiping Chen', 'Anqi Shao', 'Jirayu Burapacheep', 'Yixuan Li']2024-01-18T00:00:00https://www.semanticscholar.org/paper/08f141f56d09b412c6e8a81a04a4da5dab6d4600 f4900e178f197ccb405a51cb19ef03ba3b61ef76The use of artificial intelligence (AI) in human resource management (HR) has grown rapidly in recent years. AI has the potential to improve the efficiency, effectiveness and accuracy of HR processes. PT Semen Baturaja Persero (SMBR) is one of the largest cement companies in Indonesia. SMBR has applied AI to various HR processes, including recruitment and selection, training and development, performance appraisal, and compensation and benefits. This research aims to analyze the use of AI in HR management at SMBR. This research uses a qualitative method with a case study approach. Data was collected through in-depth interviews with stakeholders in SMBR. The research results show that AI has provided significant benefits for SMBR in HR management. AI has helped SMBR to improve the efficiency of HR processes, such as recruitment and selection, training and development, and performance appraisal; improving the effectiveness of HR processes, such as recruitment and selection, and performance appraisal; improve the accuracy of HR processes, such as recruitment and selection, and performance appraisal. AI has great potential to improve HR management in SMBR.Enigma in Economics110The research results show that AI has provided significant benefits for SMBR in HR management, improving the efficiency of HR processes, such as recruitment and selection, training and development, and performance appraisal; and improving the accuracy of HR processes, such as recruitment and selection, and performance appraisal.Enigma in Economics['Abu Bakar', 'Syah Amin', 'Silvia Jessika', 'Syahwami', 'Joko Sunaryo']2024-01-18T00:00:00https://www.semanticscholar.org/paper/f4900e178f197ccb405a51cb19ef03ba3b61ef76 4fa76b11cbf10974da4166b6c12ff33c34901991The research work "AI Assisted Interactive AANLI Mirror," introduces the cutting-edge technology to transform the way one engages with mirrors and streamline the outfit selection process in retail settings. This innovative smart mirror seamlessly integrates advanced voice recognition and ultrasound sensors for a truly interactive experience using Computer Vision and NLP along with Embedded framework. Its key features include natural language voice interaction, empowering users to request outfit suggestions and fashion advice effortlessly. Notably, it excels in outfit location finding by allowing users to describe desired garments and then guiding them precisely to the items within the store using a comprehensive inventory database. The mirror's ultrasonic sensor accurately measures a user's height upon vocal request, ensuring a proper fit without the need for manual measurements. Furthermore, it leverages this height data to recommend clothing items that suit the user's physique, guiding them step-by-step to their location within the store. The Voice-Activated Smart Mirror promises to redefine the retail shopping experience with its intuitive, voice-guided capabilities.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)120The research work "AI Assisted Interactive AANLI Mirror" introduces the cutting-edge technology to transform the way one engages with mirrors and streamline the outfit selection process in retail settings with its intuitive, voice-guided capabilities.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)['Aaliya S Mohammed', 'Liya Virge', 'Anjali A', 'V. S']2024-01-18T00:00:00https://www.semanticscholar.org/paper/4fa76b11cbf10974da4166b6c12ff33c34901991 c6fbd46f04eff014e776586dd53e6aac773815abRecent progress in artificial intelligence (AI) marks a pivotal moment in human history. It presents the opportunity for machines to learn, adapt, and perform tasks that have the potential to assist people, from everyday activities to their most creative and ambitious projects. It also has the potential to help businesses and organizations harness knowledge, increase productivity, innovate, transform, and power shared prosperity. This tremendous potential raises two fundamental questions: (1) Will AI actually advance national and global economic transformation to benefit society at large? and (2) What issues must we get right to fully realize AI's economic value, expand prosperity and improve lives everywhere? We explore these questions by considering the recent history of technology and innovation as a guide for the likely impact of AI and what we must do to realize its economic potential to benefit society. While we do not presume the future will be entirely like that past, for reasons we will discuss, we do believe prior experience with technological change offers many useful lessons. We conclude that while progress in AI presents a historic opportunity to advance our economic prosperity and future wellbeing, its economic benefits will not come automatically and that AI risks exacerbating existing economic challenges unless we collectively and purposefully act to enable its potential and address its challenges. We suggest a collective policy agenda - involving developers, deployers and users of AI, infrastructure providers, policymakers, and those involved in workforce training - that may help both realize and harness AI's economic potential and address its risks to our shared prosperity.530It is concluded that while progress in AI presents a historic opportunity to advance their economic prosperity and future wellbeing, its economic benefits will not come automatically and that AI risks exacerbating existing economic challenges unless the authors collectively and purposefully act to enable its potential and address its challenges.['Guy Ben-Ishai', 'Jeff Dean', 'James Manyika', 'Ruth Porat', 'H. Varian', 'Kent Walker']2024-01-18T00:00:00https://www.semanticscholar.org/paper/c6fbd46f04eff014e776586dd53e6aac773815ab 46e5022d9b5410e6ccfbc5374fb49dc7e464be73This report explores the rapid advancements in AI and ML applications in healthcare over recent years, focusing on journals like NEJM AI as key examples. It critically examines the composition of their editorial boards, highlighting a gap in clinical experience that raises questions about the human-centric nature and sustainability of current AI and ML healthcare research standards.Web3 Journal: ML in Health Science00Web3 Journal: ML in Health Science['Y. Rusinovich']2024-01-18T00:00:00https://www.semanticscholar.org/paper/46e5022d9b5410e6ccfbc5374fb49dc7e464be73 684c9c77dcb82d9a841f264be95058e0d8f0cee9Abstract This paper explores the inter-semiotic analysis of the ideational meaning in images generated by the text-to-image AI tool, Bing Image Creator. It adopts Kress and Van Leeuwen’s Grammar of Visual Design as its theoretical framework as the original grounding of the framework in systemic functional grammar (SFG) ensures a solid theoretical basis for undertaking analyses that involve the incorporation of textual and visual components. The integration of an AI generative model within the analytical framework enables a systematic connection between language and visual representations. This incorporation offers the potential to generate well-regulated pictorial representations that are systematically grounded in controlled textual prompts. This approach introduces a novel avenue for re-examining inter-semiotic processes, leveraging the power of AI technology. The paper argues that visual representations possess unique structural devices that surpass the limitations of verbal or written communication as they readily accommodate larger amounts of information in contrast to the limitations of the linear nature of alphabetic writing. Moreover, this paper extends its contribution by critically evaluating specific aspects of the Grammar of Visual Design.Language and Semiotic Studies90It is argued that visual representations possess unique structural devices that surpass the limitations of verbal or written communication as they readily accommodate larger amounts of information in contrast to the limitations of the linear nature of alphabetic writing.Language and Semiotic Studies['Arash Ghazvineh']2024-01-18T00:00:00https://www.semanticscholar.org/paper/684c9c77dcb82d9a841f264be95058e0d8f0cee9 d17d44860262e2174e3cece364376c9d89616c83Artificial intelligence (AI) relies on models that are reliable and simple to comprehend. Generative AI is a subset of AI as it can generate something new and unique from random noise or existing data inputs regarding an image, text, and data. In auto-augmentation, a generative AI can help developers better plan and estimate work. This research covers two critical areas of AI research: auto-augmentation and explainability. Additionally, our approach makes machine learning models more accessible to read by maintaining the purity of labels and adding saliency factors for better performance. The data is changed using auto-augmentation methods to improve model adaptation during training. These changes could alter ground truth labels, impacting how well the model works. We offer a label preservation way to rectify issues, ensuring data enhancement processes maintain label consistency. Applying the suggested method's saliency parameters makes it easier to understand how the model's forecasts work, which increases their dependability and openness. Using intermediate layer models without knowing anything about the domain makes hard-positive cases that keep the original labels. It improves performance across disciplines. Seeking model explainability is done with parameter saliency maps. It makes it easier to understand how models behave by finding and studying the network factors that lead to bad decisions. Using a ResNet18 classifier, the suggested method is tested thoroughly on the CIFAR100 dataset.2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T)200This research offers a label preservation way to rectify issues, ensuring data enhancement processes maintain label consistency and makes machine learning models more accessible to read by maintaining the purity of labels and adding saliency factors for better performance.2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T)['Rohit Doriya', 'Jaykumar Lachure', 'Rajesh Doriya']2024-01-18T00:00:00https://www.semanticscholar.org/paper/d17d44860262e2174e3cece364376c9d89616c83 f827bccc8cfe084cf3e3080b02920ad9aeb3259eAsia Pacific Journal of Education201Asia Pacific Journal of Education['M. Jabar', 'Elena Chiong-Javier', 'Penchan Pradubmook Sherer']2024-01-18T00:00:00https://www.semanticscholar.org/paper/f827bccc8cfe084cf3e3080b02920ad9aeb3259e 9c1c161a7d3712b93ae12dc05864c3c83e8a7a9eBilgi ve Belge Araştırmaları Dergisi / The Journal of Information and Documentation Studies00Bilgi ve Belge Araştırmaları Dergisi / The Journal of Information and Documentation Studies['Muhammad Ali']2024-01-18T00:00:00https://www.semanticscholar.org/paper/9c1c161a7d3712b93ae12dc05864c3c83e8a7a9e f6804c5c023af16621dc3f1021aee3a3ebe63cb3American Control Conference00Advances in Computer and Communication['Xiaoqing Lei', 'Qiaoge Hui']2024-01-18T00:00:00https://www.semanticscholar.org/paper/f6804c5c023af16621dc3f1021aee3a3ebe63cb3 ac3dd2749bc3b6b34e4e2013eb719ba880c2c0bcThe complexities arising from disparate data sources, conflicting contracts, residency requirements, and the demand for multiple AI models in trade finance supply chains have hindered small and medium-sized enterprises (SMEs) with limited resources from harnessing the benefits of artificial intelligence (AI) capabilities, which could otherwise enhance their business efficiency and predictability. This paper introduces a decentralized AI orchestration framework that prioritizes transparency and explainability, offering valuable insights to funders, such as banks, and aiding them in overcoming the challenges associated with assessing SMEs’ financial credibility. By utilizing an orchestration technique involving symbolic reasoners, language models, and data-driven predictive tools, the framework empowers funders to make more informed decisions regarding cash flow prediction, finance rate optimization, and ecosystem risk assessment, ultimately facilitating improved access to pre-shipment trade finance for SMEs and enhancing overall supply chain operations.Journal of Risk and Financial Management283A decentralized AI orchestration framework that prioritizes transparency and explainability is introduced, offering valuable insights to funders, such as banks, and aiding them in overcoming the challenges associated with assessing SMEs’ financial credibility.Journal of Risk and Financial Management['Marjan Alirezaie', 'William Hoffman', 'Paria Zabihi', 'Hossein Rahnama', 'Alex Pentland']2024-01-18T00:00:00https://www.semanticscholar.org/paper/ac3dd2749bc3b6b34e4e2013eb719ba880c2c0bc 68556035f913451e965943c28e200e5f288db8fcHumanities and Social Sciences Communications594To give full play to the positive role of artificial intelligence technology in employment, China should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.Humanities and Social Sciences Communications['Yang Shen', 'Xiuwu Zhang']2024-01-18T00:00:00https://www.semanticscholar.org/paper/68556035f913451e965943c28e200e5f288db8fc ba8f82c8d811d29a41aca1cfc6637e7a45ec0ef3BACKGROUND AND PURPOSE The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize patient care and clinical decision-making. This study aimed to explore the reliability of large language models in neurology by comparing the performance of an AI chatbot with neurologists in diagnostic accuracy and decision-making. METHODS A cross-sectional observational study was conducted. A pool of clinical cases from the American Academy of Neurology's Question of the Day application was used as the basis for the study. The AI chatbot used was ChatGPT, based on GPT-3.5. The results were then compared to neurology peers who also answered the questions-a mean of 1500 neurologists/neurology residents. RESULTS The study included 188 questions across 22 different categories. The AI chatbot demonstrated a mean success rate of 71.3% in providing correct answers, with varying levels of proficiency across different neurology categories. Compared to neurology peers, the AI chatbot performed at a similar level, with a mean success rate of 69.2% amongst peers. Additionally, the AI chatbot achieved a correct diagnosis in 85.0% of cases and it provided an adequate justification for its correct responses in 96.1%. CONCLUSIONS The study highlights the potential of AI, particularly large language models, in assisting with clinical reasoning and decision-making in neurology and emphasizes the importance of AI as a complementary tool to human expertise. Future advancements and refinements are needed to enhance the AI chatbot's performance and broaden its application across various medical specialties.European Journal of Neurology191The study highlights the potential of AI, particularly large language models, in assisting with clinical reasoning and decision-making in neurology and emphasizes the importance of AI as a complementary tool to human expertise.European journal of neurology['Ângelo Fonseca', 'A. Ferreira', 'Luís Ribeiro', 'Sandra Moreira', 'Cristina Duque']2024-01-18T00:00:00https://www.semanticscholar.org/paper/ba8f82c8d811d29a41aca1cfc6637e7a45ec0ef3 bcd9fcc87e9372c4b90409adb2ed8688be062e4aIntroduction In a drip-and-ship model for endovascular thrombectomy (EVT), early identification of large vessel occlusion (LVO) and timely referral to a comprehensive center (CSC) are crucial when patients are admitted to an acute stroke center (ASC). Several artificial intelligence (AI) decision-aid tools are increasingly being used to facilitate the rapid identification of LVO. This retrospective cohort study aimed to evaluate the impact of deploying e-Stroke AI decision support software in the hyperacute stroke pathway on process metrics and patient outcomes at an ASC in the United Kingdom. Methods Except for the deployment of e-Stroke on 01 March 2020, there were no significant changes made to the stroke pathway at the ASC. The data were obtained from a prospective stroke registry between 01 January 2019 and 31 March 2021. The outcomes were compared between the 14 months before and 12 months after the deployment of AI (pre-e-Stroke cohort vs. post-e-Stroke cohort) on 01 March 2020. Time window analyses were performed using Welch’s t-test. Cochran–Mantel–Haenszel test was used to compare changes in disability at 3 months assessed by modified Rankin Score (mRS) ordinal shift analysis, and Fisher’s exact test was used for dichotomised mRS analysis. Results In the pre-e-Stroke cohort, 19 of 22 patients referred received EVT. In the post-e-Stroke cohort, 21 of the 25 patients referred were treated. The mean door-in-door-out (DIDO) and door-to-referral times in pre-e-Stroke vs. post-e-Stroke cohorts were 141 vs. 79 min (difference 62 min, 95% CI 96.9–26.8 min, p < 0.001) and 71 vs. 44 min (difference 27 min, 95% CI 47.4–5.4 min, p = 0.01), respectively. The adjusted odds ratio (age and NIHSS) for mRS ordinal shift analysis at 3 months was 3.14 (95% CI 0.99–10.51, p = 0.06) and the dichotomized mRS 0–2 at 3 months was 16% vs. 48% (p = 0.04) in the pre- vs. post-e-Stroke cohorts, respectively. Conclusion In this single-center study in the United Kingdom, the DIDO time significantly decreased since the introduction of e-Stroke decision support software into an ASC hyperacute stroke pathway. The reduction in door-in to referral time indicates faster image interpretation and referral for EVT. There was an indication of an increased proportion of patients regaining independent function after EVT. However, this should be interpreted with caution given the small sample size. Larger, prospective studies and further systematic real-world evaluation are needed to demonstrate the widespread generalisability of these findings.Frontiers in Neurology151The DIDO time significantly decreased since the introduction of e-Stroke decision support software into an ASC hyperacute stroke pathway, and the reduction in door-in to referral time indicates faster image interpretation and referral for EVT.Frontiers in Neurology['K. Nagaratnam', 'Ain Neuhaus', 'James H. Briggs', 'Gary A. Ford', 'Z. V. J. Woodhead', 'Dibyaa Maharjan', 'G. Harston']2024-01-18T00:00:00https://www.semanticscholar.org/paper/bcd9fcc87e9372c4b90409adb2ed8688be062e4a a736f5530bb0738fdcc894e88a20b902b8510ac8Purpose: The authors aimed to establish an artificial intelligence (AI)-based method for preoperative diagnosis of breast lesions from contrast enhanced mammography (CEM) and to explore its biological mechanism. Materials and methods: This retrospective study includes 1430 eligible patients who underwent CEM examination from June 2017 to July 2022 and were divided into a construction set (n=1101), an internal test set (n=196), and a pooled external test set (n=133). The AI model adopted RefineNet as a backbone network, and an attention sub-network, named convolutional block attention module (CBAM), was built upon the backbone for adaptive feature refinement. An XGBoost classifier was used to integrate the refined deep learning features with clinical characteristics to differentiate benign and malignant breast lesions. The authors further retrained the AI model to distinguish in situ and invasive carcinoma among breast cancer candidates. RNA-sequencing data from 12 patients were used to explore the underlying biological basis of the AI prediction. Results: The AI model achieved an area under the curve of 0.932 in diagnosing benign and malignant breast lesions in the pooled external test set, better than the best-performing deep learning model, radiomics model, and radiologists. Moreover, the AI model has also achieved satisfactory results (an area under the curve from 0.788 to 0.824) for the diagnosis of in situ and invasive carcinoma in the test sets. Further, the biological basis exploration revealed that the high-risk group was associated with the pathways such as extracellular matrix organization. Conclusions: The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions.International Journal of Surgery320The AI model based on CEM and clinical characteristics had good predictive performance in the diagnosis of breast lesions and revealed that the high-risk group was associated with the pathways such as extracellular matrix organization.International Journal of Surgery (London, England)['Haicheng Zhang', 'Fan Lin', 'Tiantian Zheng', 'Jing Gao', 'Zhongyi Wang', 'Kun Zhang', 'Xiang Zhang', 'Cong Xu', 'Feng Zhao', 'H. Xie', 'Qin Li', 'Kun Cao', 'Yajia Gu', 'Ning Mao']2024-01-18T00:00:00https://www.semanticscholar.org/paper/a736f5530bb0738fdcc894e88a20b902b8510ac8 33f6eb8df4eafcf098efdd42675a7aade33b7227Artificial intelligence (AI) integration within Ghana’s health workforce training programs shows immense potential to enhance quality, consistency and personalization if thoughtfully implemented. This mixed methods SWOT analysis evaluates major strengths like expanded accessibility, weaknesses around connectivity barriers and literacy, opportunities in simulation training, and threats of job automation requiring ethical change management. Ghanaian case studies and policy documents inform targeted opportunities to employ context-appropriate design, creative financing partnerships and participatory principles upholding equity. Risks of algorithmic bias and data exploitation spotlight needs for transparent auditing, consent safeguards and democratized oversight. Recommended actions integrate precedents in change leadership, human-centered AI adoption and African innovation prioritization to pragmatically advance both cutting edge advancement and human dignity. With Ghana pioneering amongst first nations globally to release a national AI strategy, demonstration of such balanced governance can model pathways for technological innovation upholding empowerment across sectors and regions. This multidimensional analytical evaluation contributes uniquely inclusive, grounded perspectives to advance conceptual clarity and practical guidance around AI assimilation - directing focus toward preparatory investments, policy formulation, partnership building and design choices required to ensure gains outpacing grievances along ongoing modernization trajectories.International Journal For Multidisciplinary Research340This mixed methods SWOT analysis evaluates major strengths like expanded accessibility, weaknesses around connectivity barriers and literacy, opportunities in simulation training, and threats of job automation requiring ethical change management within Ghana’s health workforce training programs.International Journal For Multidisciplinary Research['Rebecca Asamoah-Atakorah', 'Shadrach Asamoah-Atakorah', 'Osei Atakorah Amaniampong', 'Johnson Mensah Sukah Selorm', 'Alfred Addy', 'Maximous Diebieri', 'George Benneh Mensah']2024-01-18T00:00:00https://www.semanticscholar.org/paper/33f6eb8df4eafcf098efdd42675a7aade33b7227 50f9e1d131415ea5b1d27ef104517d5c67147dd9In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles.Frontiers in Psychiatry710This work proposes a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients.Frontiers in Psychiatry['Meghan Reading Turchioe', 'Alison Hermann', 'Natalie C. Benda']2024-01-18T00:00:00https://www.semanticscholar.org/paper/50f9e1d131415ea5b1d27ef104517d5c67147dd9 d0351d54be5196f7812393964b1dcad47cc1ce58Twenty-first century is famously termed the age of the fourth industrial revolution, which is because of the massive amount of data being generated and stored which could be interpreted and analyzed by intelligible programs. Just as the discovery of the microscope in the sixteenth century led humans to discover things about human biology that the naked eye could not see, likewise artificial intelligence could be used to look for patterns in the data which humans otherwise would have less likely perceived. This paper will capitalize on this. How much potential could aid in the health care field A review and guide are compiled for any researcher or student who might want to practically implement the ideas discussed. The implementation of artificial intelligence for the analysis of medical images and beyond is to be discussed in this paper. Tools and software developed from these ideas could help medical practitioners make more accurate decisions.Machine LearningSukkur IBA Journal of Computing and Mathematical Sciences400The implementation of artificial intelligence for the analysis of medical images and beyond is to be discussed and tools and software developed from these ideas could help medical practitioners make more accurate decisions.Sukkur IBA Journal of Computing and Mathematical Sciences['Bushra Memon']2024-01-18T00:00:00https://www.semanticscholar.org/paper/d0351d54be5196f7812393964b1dcad47cc1ce58 1b84d1fdb8fc9df0cea0b02fcc6e25e530af0c8bGovernance of Artificial Intelligence (AI) is becoming increasingly urgent in higher education institutions for its impact on the quality and equity of education. Earlier in 2023, AI usage conversations across the sector appeared to predominantly focus on the other AI, Academic Integrity. Just like the technology has advanced in the past eight months since the first ACODE (Australasian Council of Open Digital Education) whitepaper on this topic this year, so too has the practice of using AI. In October 2023, ACODE conducted a survey to get an understanding of AI policy and practice in member institutions. The results show there is a mixed view whether guidelines and policies are in place with respect to the governance of AI and data which suggests the sector needs to continue these conversations for some time to come. Furthermore, ethical application of AI is emerging through experimental activities and pilots of proof of concepts, suggesting that case studies and recommendations for practice could be developed in 2024 to further assure quality and equity in education.ASCILITE Publications00There is a mixed view whether guidelines and policies are in place with respect to the governance of AI and data which suggests the sector needs to continue conversations for some time to come, and ethical application of AI is emerging through experimental activities and pilots of proof of concepts.ASCILITE Publications['Ratna Selvaratnam', 'Lynnae Venaruzzo']2024-01-18T00:00:00https://www.semanticscholar.org/paper/1b84d1fdb8fc9df0cea0b02fcc6e25e530af0c8b 053c7735514ba04a5491716a3f1d3c724a661e57The incorporation of Artificial Intelligence (AI) into the realm of retail marketing signifies a significant and transformative change within the sector. This paper provides a thorough examination of the rising role of AI in the field of retail marketing, highlighting its transformative impact on customer engagement and operational effectiveness. Through a methodical examination of research articles and industry reports, the paper has discovered the diverse impact of AI on various aspects of the retail business. These include data-driven decision-making, insights into customer behavior, the enhancement of omnichannel experiences, as well as the optimization of inventory and pricing strategies. Furthermore, the paper examines the significant obstacles that AI implementation in the retail industry encounters, encompassing concerns related to data protection, financial constraints, and the ever-changing legal and ethical environment. The continuous progress of AI technology is leading to a growing recognition of its capacity to revolutionize retail marketing. This trend highlights the significance of integrating AI into retail operations, as it is no longer merely a strategic option but a necessary requirement for achieving success and maintaining a competitive edge in the industry.Advances in Economics, Management and Political Sciences00A thorough examination of the rising role of AI in the field of retail marketing is provided, highlighting its transformative impact on customer engagement and operational effectiveness and the significant obstacles that AI implementation in the retail industry encounters.Advances in Economics, Management and Political Sciences['Zexuan Wang']2024-01-18T00:00:00https://www.semanticscholar.org/paper/053c7735514ba04a5491716a3f1d3c724a661e57 c1e596525705ccdb45fd85f6ab69a3cde217d008Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success rates in classification tasks, it remains uncertain whether its use significantly enhances the potential for early warning of impending problems. The following question arises: will classical methods eventually replace the effectiveness of these advanced techniques? This paper sheds light on the fact that even classical methods continue to achieve results that are not far behind, highlighting their enduring importance in financial analysis. This paper aims to develop bankruptcy prediction models for the chemical industry in Slovakia and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as AI techniques, artificial neural networks (ANNs), support vector machines (SVMs), and decision trees (DTs). The analysis aims to determine which of the employed methods is the most efficient. The research sample consists of circa 600 enterprises operating in the Slovak chemical industry. The selection of eleven financial indicators used for bankruptcy prediction was grounded in prior research and existing literature. The results show that all of the explored methods yielded highly similar outcomes. Therefore, determining the clear superiority of any single method is a difficult task. This might be partially due to the potentially reduced quality of the input data. In addition to classical statistical methods employed in econometrics, there is an ongoing development of AI-based models and their hybrid forms. The following question arises: to what extent can these newer approaches enhance accuracy and effectiveness?International Journal of Financial Studies470This paper sheds light on the fact that even classical methods continue to achieve results that are not far behind, highlighting their enduring importance in financial analysis.International Journal of Financial Studies['Stanislav Letkovský', 'Sylvia Jenčová', 'Petra Vašaničová']2024-01-18T00:00:00https://www.semanticscholar.org/paper/c1e596525705ccdb45fd85f6ab69a3cde217d008 a593c89e5ddae4f36822145c404f17c5d44c5bb4The central pressure of power is definitely what drives modern society. Its central role in our lives is plain to see since the smooth operation of electrical infrastructure is fundamental to the functioning of the modern world. These buildings comprise an electrical community that integrates electrical additives in a complex web that allows for green technology, transmission, and power distribution to end users. To synthesize and renovate these strength gadget components, many factors must be carefully considered. Correcting for human error becomes a critical concern in the fabrication of components and the protection of electronic devices in the very precise field of electrical engineering. Innovative solutions to improve efficiency and ensure the reliability of certain equipment are required due to the complicated nature of these duties. Here, Artificial Intelligence (AI) is a game-changing solution that essentially involves replicating human intelligence by programming computers to think and reason like humans. This might lead to incredibly persuasive conclusions based on data. This study investigates the potential AI applications in electrical engineering. This study explores the world of artificial intelligence to find a solution to how this generation can improve and strengthen several aspects of electrical engineering, such as system security and factor production. Aiming to unveil new aspects of innovation and performance within the ever-changing realm of electrical systems, this study delves into the integration of AI.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)220This study explores the world of artificial intelligence to find a solution to how this generation can improve and strengthen several aspects of electrical engineering, such as system security and factor production.2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)['Vijay J. Patil', 'Suhas B Khadake', 'D. A. Tamboli', 'H. Mallad', 'Shantisagar M. Takpere', 'Vijay A. Sawant']2024-01-18T00:00:00https://www.semanticscholar.org/paper/a593c89e5ddae4f36822145c404f17c5d44c5bb4 29d5f2f68f2a7997f62cfe72542f166522fbc6f3 Addressing the challenges posed by climate change, surging population, rival demands on land for renewable fuel manufacturing, and adverse soil conditions is crucial for ensuring global food security. Achieving sustainable solutions necessitates the integration of multidisciplinary knowledge, such as materials technology and informatics. The convergence of precision agriculture with nanotechnology and artificial intelligence (AI) offers promising prospects for sustainable food production. Through real-time responsiveness to crop growth using advanced technologies, such as nanotechnology and AI, farmers can optimize resource allocation and make informed decisions. Newer opportunities for sustainable food production arise through the integration of precision agriculture, nanotechnology, and artificial intelligence. This convergence enables farmers to dynamically respond to crop growth variations using advanced techniques. By combining nanotechnology and informatics methods with existing models for nutrient cycling and crop productivity, it becomes possible to enhance critical aspects, such as precision targeting, efficient absorption, effective distribution, optimized nutrient assimilation, and long-term effects on soil microbial communities. This integration offers significant potential for improving agriculture and addressing sustainability challenges in food production. Ultimately, this synergy allows for the development of nanoscale agrochemicals that offer a balance between safety and functionality, ensuring optimal performance in agricultural systems. Current Nanoscience00By combining nanotechnology and informatics methods with existing models for nutrient cycling and crop productivity, it becomes possible to enhance critical aspects, such as precision targeting, efficient absorption, effective distribution, optimized nutrient assimilation, and long-term effects on soil microbial communities.Current Nanoscience['Sumel Ashique', 'Amisha S Raikar', 'Sabahat Jamil', 'Lavanya Lakhminarayana', 'Shilpa Amit Gajbhiye', 'Sneha De', 'Shubneesh Kumar']2024-01-18T00:00:00https://www.semanticscholar.org/paper/29d5f2f68f2a7997f62cfe72542f166522fbc6f3 c2562debb2620d80852e2bbd664f0cfc4fe8a327This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.Cardiology Journal960The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients.Cardiology Journal['Zofia Rudnicka', 'A. Pręgowska', 'Kinga Glądys', 'Mark Perkins', 'Klaudia Proniewska']2024-01-18T00:00:00https://www.semanticscholar.org/paper/c2562debb2620d80852e2bbd664f0cfc4fe8a327 1a934978b5fdb4134229419ca22ec9d6b1e78f2bThe emergence of generative artificial intelligence (AI) has had a huge impact on all areas of life, including the field of education. AI can assist teachers in cultivating talents and promoting personalized learning and teaching, but it also prevents individuals from thinking independently and creatively. In the era of generative AI, the rapid development of technology and its significant impact on the field of education are inevitable. There are many educational issues related to it, such as teaching methods, student training goals, teaching philosophy and purposes, and other educational issues, that require re-conceptualization and review.Journal of Contemporary Educational Research00There are many educational issues related to generative artificial intelligence, such as teaching methods, student training goals, teaching philosophy and purposes, and other educational issues, that require re-conceptualization and review.Journal of Contemporary Educational Research['Zhengyu Yang']2024-01-18T00:00:00https://www.semanticscholar.org/paper/1a934978b5fdb4134229419ca22ec9d6b1e78f2b 61e78299fce377505f5eba24f48236baea340332Internet Interventions663Internet Interventions['Sarah J. Egan', 'Catherine Johnson', 'Tracey D. Wade', 'P. Carlbring', 'Shravan Raghav', 'R. Shafran']2024-01-18T00:00:00https://www.semanticscholar.org/paper/61e78299fce377505f5eba24f48236baea340332 003e4a8007b2a2242f6684569b0eb053b92d52f5Supply Chain Forum: an International Journal372Supply Chain Forum: An International Journal['Rimi Debnath', 'P. Majumder', 'Anirban Tarafdar', 'Baby Bhattacharya', 'U. Bera']2024-01-18T00:00:00https://www.semanticscholar.org/paper/003e4a8007b2a2242f6684569b0eb053b92d52f5 71d54cc950ad298dafbe5e034d13214e352fc60a Purpose The technology acceptance model (TAM) is a widely used framework explaining why users accept new technologies. Still, its relevance is questioned because of evolving consumer behavior, demographics and technology. Contrary to a research paper or systematic literature review, the purpose of this critical reflection paper is to discuss TAM's relevance and limitations in hospitality and tourism research. Design/methodology/approach This paper uses a critical reflective approach, enabling a comprehensive review and synthesis of recent academic literature on TAM. The critical evaluation encompasses its historical trajectory, evolutionary growth, identified limitations and, more specifically, its relevance in the context of hospitality and tourism research. Findings TAM's limitations within the hospitality and tourism context revolve around its individual-centric perspective, limited scope, static nature, cultural applicability and reliance on self-reported measures. Research limitations/implications To optimize TAM's efficacy, the authors propose several strategic recommendations. These include embedding TAM within the specific context of the industry, delving into TAM-driven artificial intelligence adoption, integrating industry-specific factors, acknowledging cultural nuances and using comprehensive research methods, such as mixed methods approach. It is imperative for researchers to critically assess TAM's suitability for their studies and be open to exploring alternative models or methods that can adeptly navigate the distinctive dynamics of the industry. Originality/value This critical reflection paper prompts a profound exploration of technology adoption within the dynamic hospitality and tourism sector, makes insightful inquiries into TAM's future potential and presents recommendations. International Journal of Contemporary Hospitality Management743This critical reflection paper prompts a profound exploration of technology adoption within the dynamic hospitality and tourism sector, makes insightful inquiries into TAM's future potential and presents recommendations.International Journal of Contemporary Hospitality Management['Emmanuel Mogaji', 'G. Viglia', 'P. Srivastava', 'Yogesh K. Dwivedi']2024-01-18T00:00:00https://www.semanticscholar.org/paper/71d54cc950ad298dafbe5e034d13214e352fc60a 00f4a4268d83e8cc14aaf20ff8e83079aae416a2New England Journal of Medicine161The New England journal of medicine['M. Mello', 'Neel Guha']2024-01-18T00:00:00https://www.semanticscholar.org/paper/00f4a4268d83e8cc14aaf20ff8e83079aae416a2 9ac1344c5aa35a21ca816731fc819d6f5c4cb8f7Humanities and Social Sciences Communications631Demographic traits explained the most variance in comfort with AI revealing that men and those with higher perceived technology competence were more comfortable with AI management in every domain.Humanities and Social Sciences Communications['Ekaterina Novozhilova', 'Kate K. Mays', 'James E. Katz']2024-01-18T00:00:00https://www.semanticscholar.org/paper/9ac1344c5aa35a21ca816731fc819d6f5c4cb8f7 d70beecc8146f4765dbc2402d1b0b4c923459179This comparative legal analysis examines gaps in medical negligence liability principles and healthcare AI governance policies across Ghana, Nigeria and South Africa. Applying structured analytical frameworks, it maps longstanding laws focused on human provider standards of care against modern contexts of rising algorithmic and robotic assistance needing tailored accountability. By identifying jurisdictional deficiencies in adapting existing negligence rules to emerging technologies, targeted recommendations to update century-old statutes through enumerated amendments are proposed for aligning law with clinical disruption. This forward-looking modernization of reasonableness constructs and negligence duties represents an original jurisprudential contribution strengthening accountability in AI integration protecting patient welfare. Significance emerges for developers, policymakers and adopters in forming clear guidelines and statutory updates essential for responsible health AI innovation as Africa undergoes profound practice transformation. In conclusion, deliberate legal reforms affirm enduring human dignity commitments amidst technological upheaval through precision scaffolding, catalyzing ethical adoption rooted in regional values.International Journal For Multidisciplinary Research00D deliberate legal reforms affirm enduring human dignity commitments amidst technological upheaval through precision scaffolding, catalyzing ethical adoption rooted in regional values as Africa undergoes profound practice transformation.International Journal For Multidisciplinary Research['George Benneh Mensah', 'Felix Nyante', 'Ebenezer Aboagye Akuffo', 'Alfred Addy']2024-01-18T00:00:00https://www.semanticscholar.org/paper/d70beecc8146f4765dbc2402d1b0b4c923459179 5f0915739ed25b1d780130465188f3e046575f64AI &amp; SOCIETY310AI &amp; SOCIETY['Thomas Cantens']2024-01-18T00:00:00https://www.semanticscholar.org/paper/5f0915739ed25b1d780130465188f3e046575f64 06a454ff22c8b829238c4f2bcc89cf3bf6b9c5bdFacial Plastic Surgery & Aesthetic Medicine40Facial plastic surgery & aesthetic medicine['David W. Chou', 'Pauline Huynh', 'Mingyang Gray', 'Joshua D Rosenberg', 'K. Brandstetter', 'Andrew Kleinberger', 'Charles W. Shih']2024-01-18T00:00:00https://www.semanticscholar.org/paper/06a454ff22c8b829238c4f2bcc89cf3bf6b9c5bd f74ff56e56bbc948c47df7ea19ed5dcf86c93439GEORGIAN SCIENTISTS00GEORGIAN SCIENTISTS['Lily Petriashvili', 'Irina Khomeriki']2024-01-18T00:00:00https://www.semanticscholar.org/paper/f74ff56e56bbc948c47df7ea19ed5dcf86c93439 f52dbff012b3237a6f5859c65f70cf99546d8f03International Journal of Cooperative Information Systems00International Journal of Cooperative Information Systems['Hao Liu']2024-01-18T00:00:00https://www.semanticscholar.org/paper/f52dbff012b3237a6f5859c65f70cf99546d8f03 c098184ad83c1eb990593ecf3714699afca7d8b9,Informatica250Informatica['Ren Zhiyi', 'Zhao Nan', 'Zhiyan Shi']2024-01-18T00:00:00https://www.semanticscholar.org/paper/c098184ad83c1eb990593ecf3714699afca7d8b9 c8cde3a4ed833d18f9bb375575a06a7b0453eb68Current Pulmonology Reports560Current Pulmonology Reports['M. Jalloul', 'Dana Alkhulaifat', 'Monica Miranda-Schaeubinger', 'Laura De Leon Benedetti', 'Hansel J. Otero', 'Farouk Dako']2024-01-18T00:00:00https://www.semanticscholar.org/paper/c8cde3a4ed833d18f9bb375575a06a7b0453eb68 4a20b5e99acca89b792b7cfebe8e921b2ec1d09dAsia Pacific Journal of Education500Asia Pacific Journal of Education['A. Lee']2024-01-18T00:00:00https://www.semanticscholar.org/paper/4a20b5e99acca89b792b7cfebe8e921b2ec1d09d 0ce6de14f230c5640822162fc57d917bb51c9b6aDiabetic retinopathy (DR) is a serious eye condition induced by diabetes that can result in eyesight. In order to overcome the vision impairment of diabetes mellitus patients, it is essential for early age prevention by medical practitioners. In the traditional approach, ophthalmologists conduct various screening tests to detect DR but fail to achieve accurate and conclusive diagnosis due to the associated time consuming processes. In order to eliminate such burden on the ophthalmologists, deep learning and machine learning techniques have evolved rapidly playing a significant role in the classification of diabetic and non diabetic patients. In this paper, the proposed framework implements a publicly available Benchmark APTOS 2019 Gaussian-filtered DR image dataset using a customized CNN model, yielding an enhanced accuracy of 98 %. In addition, the framework also employs a LIME explainer visualization model to provide enhanced transparency and interpretability to the generated predictions. This eliminates the “blackbox” -ed nature of the predicted results from the traditional ML process and enables healthcare providers to take clinical decisions with confidence providing visual representations of the significant features that contribute towards an outcome.Confluence210The proposed framework implements a publicly available Benchmark APTOS 2019 Gaussian-filtered DR image dataset using a customized CNN model, yielding an enhanced accuracy of 98 % and employs a LIME explainer visualization model to provide enhanced transparency and interpretability to the generated predictions.2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)['Uppamma Posham', 'S. Bhattacharya']2024-01-18T00:00:00https://www.semanticscholar.org/paper/0ce6de14f230c5640822162fc57d917bb51c9b6a dd7b85c36a198a7da11e545798f458ef609b49e5American Control Conference00Advances in Computer and Communication['Shuifeng Zhang', 'Meng Li', 'Daoyou Fang']2024-01-18T00:00:00https://www.semanticscholar.org/paper/dd7b85c36a198a7da11e545798f458ef609b49e5 b6644695c17d04a9420d245a554c7271abfa7c35AI &amp; SOCIETY00AI &amp; SOCIETY['Nguyen T. Thanh-Huyen']2024-01-18T00:00:00https://www.semanticscholar.org/paper/b6644695c17d04a9420d245a554c7271abfa7c35 516bd3fe7f9c181c6131b2ceab64abf6f809e99fThis paper addresses trust issues created from the ubiquity of black box algorithms and surrogate explainers in Explainable Intrusion Detection Systems (X-IDS). While Explainable Artificial Intelligence (XAI) aims to enhance transparency, black box surrogate explainers, such as Local Interpretable Model-Agnostic Explanation (LIME) and SHapley Additive exPlanation (SHAP), are difficult to trust. The black box nature of these surrogate explainers makes the process behind explanation generation opaque and difficult to understand. To avoid this problem, one can use transparent white box algorithms such as Rule Extraction (RE). There are three types of RE algorithms: pedagogical, decompositional, and eclectic. Pedagogical methods offer fast but untrustworthy white-box explanations, while decompositional RE provides trustworthy explanations with poor scalability. This work explores eclectic rule extraction, which strikes a balance between scalability and trustworthiness. By combining techniques from pedagogical and decompositional approaches, eclectic rule extraction leverages the advantages of both, while mitigating some of their drawbacks. The proposed Hybrid X-IDS architecture features eclectic RE as a white box surrogate explainer for black box Deep Neural Networks (DNN). The presented eclectic RE algorithm extracts human-readable rules from hidden layers, facilitating explainable and trustworthy rulesets. Evaluations on UNSW-NB15 and CIC-IDS-2017 datasets demonstrate the algorithm's ability to generate rulesets with 99.9% accuracy, mimicking DNN outputs. The contributions of this work include the hybrid X-IDS architecture, the eclectic rule extraction algorithm applicable to intrusion detection datasets, and a thorough analysis of performance and explainability, demonstrating the trade-offs involved in rule extraction speed and accuracy.arXiv.org290The proposed Hybrid X-IDS architecture features eclectic RE as a white box surrogate explainer for black box Deep Neural Networks (DNN), and the presented eclectic RE algorithm extracts human-readable rules from hidden layers, facilitating explainable and trustworthy rulesets.ArXiv['Jesse Ables', 'Nathaniel Childers', 'William Anderson', 'Sudip Mittal', 'Shahram Rahimi', 'I. Banicescu', 'Maria Seale']2024-01-18T00:00:00https://www.semanticscholar.org/paper/516bd3fe7f9c181c6131b2ceab64abf6f809e99f 9351420bee0b56cdf5de278876dccb6b20c16c7dHoney bees pollinate about one-third of the world's food supply, but bee colonies have alarmingly declined by nearly 40% over the past decade due to several factors, including pesticides and pests. Traditional methods for monitoring beehives, such as human inspection, are subjective, disruptive, and time-consuming. To overcome these limitations, artificial intelligence has been used to assess beehive health. However, previous studies have lacked an end-to-end solution and primarily relied on data from a single source, either bee images or sounds. This study introduces a comprehensive system consisting of bee object detection and health evaluation. Additionally, it utilized a combination of visual and audio signals to analyze bee behaviors. An Attention-based Multimodal Neural Network (AMNN) was developed to adaptively focus on key features from each type of signal for accurate bee health assessment. The AMNN achieved an overall accuracy of 92.61%, surpassing eight existing single-signal Convolutional Neural Networks and Recurrent Neural Networks. It outperformed the best image-based model by 32.51% and the top sound-based model by 13.98% while maintaining efficient processing times. Furthermore, it improved prediction robustness, attaining an F1-score higher than 90% across all four evaluated health conditions. The study also shows that audio signals are more reliable than images for assessing bee health. By seamlessly integrating AMNN with image and sound data in a comprehensive bee health monitoring system, this approach provides a more efficient and non-invasive solution for the early detection of bee diseases and the preservation of bee colonies.arXiv.org220An Attention-based Multimodal Neural Network was developed to adaptively focus on key features from each type of signal for accurate bee health assessment, which achieved an overall accuracy of 92.61%, surpassing eight existing single-signal Convolutional Neural Networks and Recurrent Neural Networks.ArXiv['Andrew Liang']2024-01-18T00:00:00https://www.semanticscholar.org/paper/9351420bee0b56cdf5de278876dccb6b20c16c7d 2823d9c0efbb72ce37a40ff4b2c45f64056b988aStiftelsen Juridisk Fakultetslitteratur00Stiftelsen Juridisk Fakultetslitteratur['Johanna Chamberlain', 'Andreas Kotsios']2024-01-17T00:00:00https://www.semanticscholar.org/paper/2823d9c0efbb72ce37a40ff4b2c45f64056b988a 97d035c996a270bc19a2912533ed7ed5fcecb882The prevalence of unsafe food poses a widespread challenge across numerous nations. Despite the continuous investments by the Chinese government in food safety regulation, the condition of food safety in China is still not ideal and requires substantial enhancements. Cost-effective, information-based strategies are essential for the effective management of food safety hazards. In this research, we established an extensive database of food enterprises with documented violations and carried out a randomized intervention trial to assess the effects of regulatory information interventions on the decrease of production violations in these enterprises. The findings reveal that interventions based on food safety regulatory information were instrumental in diminishing production violations among food enterprises and had spillover effects within a given geographic area. It is important to note that the impact of the intervention was delayed, with noticeable results on production violations becoming apparent 6 months post-intervention. Additionally, the degree of information communication and the degree of information concern can positively moderate the reduction of food enterprises’ production violation behavior by food safety regulatory information intervention.Frontiers in Sustainable Food Systems320Frontiers in Sustainable Food Systems['Tong Zhao', 'Taiping Li', 'Dan Liu', 'Yun Luo']2024-01-17T00:00:00https://www.semanticscholar.org/paper/97d035c996a270bc19a2912533ed7ed5fcecb882 4992d4dbd0d2615764c58bfa3abfadcab5e79282Social Science Research Network00SSRN Electronic Journal['Liane Colonna']2024-01-17T00:00:00https://www.semanticscholar.org/paper/4992d4dbd0d2615764c58bfa3abfadcab5e79282 753e43f81d38853d9060540e070243f48ccc1be7CFA Institute submits its response to the European Commission targeted consultation on the implementation of the Sustainable Finance Disclosure Regulation (SFDR).00[]2024-01-17T00:00:00https://www.semanticscholar.org/paper/753e43f81d38853d9060540e070243f48ccc1be7 50e03dd6fe844915a8e285d8af371f33de923efbThe shortage of medical workers is the most serious challenge of modern healthcare. The article considers the issues of improving the legislation as one of the ways to solve the problem of insufficient supply of doctors.Scientific Bulletin of the Omsk State Medical University10The article considers the issues of improving the legislation as one of the ways to solve the problem of insufficient supply of doctors.Scientific Bulletin of the Omsk State Medical University['N. A. Ivanova', 'I. P. Burashnikova']2024-01-17T00:00:00https://www.semanticscholar.org/paper/50e03dd6fe844915a8e285d8af371f33de923efb 645284e103010343446ebaa39f36a32b168652e6This paper introduces ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change. We trained two 7B models from scratch on a science-oriented dataset of 300B tokens. For the first model, the 4.2B domain-specific tokens were included during pre-training and the second was adapted to the climate domain after pre-training. Additionally, ClimateGPT-7B, 13B and 70B are continuously pre-trained from Llama~2 on a domain-specific dataset of 4.2B tokens. Each model is instruction fine-tuned on a high-quality and human-generated domain-specific dataset that has been created in close cooperation with climate scientists. To reduce the number of hallucinations, we optimize the model for retrieval augmentation and propose a hierarchical retrieval strategy. To increase the accessibility of our model to non-English speakers, we propose to make use of cascaded machine translation and show that this approach can perform comparably to natively multilingual models while being easier to scale to a large number of languages. Further, to address the intrinsic interdisciplinary aspect of climate change we consider different research perspectives. Therefore, the model can produce in-depth answers focusing on different perspectives in addition to an overall answer. We propose a suite of automatic climate-specific benchmarks to evaluate LLMs. On these benchmarks, ClimateGPT-7B performs on par with the ten times larger Llama-2-70B Chat model while not degrading results on general domain benchmarks. Our human evaluation confirms the trends we saw in our benchmarks. All models were trained and evaluated using renewable energy and are released publicly.arXiv.org03ClimateGPT, a model family of domain-specific large language models that synthesize interdisciplinary research on climate change, is introduced and a suite of automatic climate-specific benchmarks to evaluate LLMs is proposed.ArXiv['David Thulke', 'Yingbo Gao', 'Petrus Pelser', 'Rein Brune', 'Rricha Jalota', 'Floris Fok', 'Michael Ramos', 'Ian van Wyk', 'Abdallah Nasir', 'Hayden Goldstein', 'Taylor Tragemann', 'Katie Nguyen', 'Ariana Fowler', 'Andrew Stanco', 'Jon Gabriel', 'Jordan Taylor', 'Dean Moro', 'Evgenii Tsymbalov', 'Juliette de Waal', 'E. Matusov', 'Mudar Yaghi', 'Mohammad Shihadah', 'Hermann Ney', 'Christian Dugast', 'Jonathan Dotan', 'Daniel Erasmus']2024-01-17T00:00:00https://www.semanticscholar.org/paper/645284e103010343446ebaa39f36a32b168652e6 085abb51657a4f1123c5f976eb28abe89ef9c880The question of how professional and lay communities develop trust in new technologies, and automation in particular, has been a matter of lively debate. As a charismatic technology, artificial intelligence (A.I.) has been a common topic of these debates. This paper presents a case study of how the discourses and principles of ethics of technology development—specifically, of A.I.— were mobilized to form trust among actors in the fields of computer science, risk communication, and weather forecasting. My analysis draws on sociology of expertise and the literature on ethics of A.I. to ask: how emerging networks of expertise use ethics to overcome mistrust in technology? And, what role does the institutionalization of those networks play in the process of trust formation? I situate this discussion on the NSF Institute for Research on Trustworthy A.I. The Institute is positioned as a mediating organization with the goal of increasing trust in this technology primarily the weather forecasting community, but also among the public. I show that first, to better understand how scientific and professional fields react to increased automation it is crucial to unpack the historical backdrop of how the professional identity of those experts has been shaped by a relationship with computer-supported modeling. To this end, I situate the discussion in the long-standing tensions between computer modelling and tacit knowledge in weather forecasting. Second, I argue that the means of establishing trust in A.I. propagated by the actors in the paper, which pair norms of explainability to sensitivity to professional intuitions and domain-specific conventions, rely on a series of “mutual orientations” (Edwards, 1996). I mobilize the concept of “mutual orientations” to describe the work of tailoring the ethics of A.I. to the specific requirements of weather sciences, but also to the vision of a national strategy of investment in this technology.Science &amp; Technology Studies00A case study of how the discourses and principles of ethics of technology development were mobilized to form trust among actors in the fields of computer science, risk communication, and weather forecasting, and the work of tailoring the ethics of A.I. to the specific requirements of weather sciences.Science &amp; Technology Studies['Przemyslaw Matt Lukacz']2024-01-17T00:00:00https://www.semanticscholar.org/paper/085abb51657a4f1123c5f976eb28abe89ef9c880 269e92d4adc05c401343ff34a5c0acec52a17701This study explores how mocked explainable AI (xAI) decision support system and Augmented Reality (AR) impact clinicians' trust and performance in Transesophageal Echocardiography (TEE) imaging. A cohort of 24 Emergency Medicine clinicians assessed three xAI modalities alongside an AR interface. The study found that detailed xAI explanations gained the highest trust and decision accuracy. AR-based decision support was also viewed as innovative, but potentially distracting. Further studies with larger sample sizes in real clinical settings are required to validate these findings.2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)180The study found that detailed xAI explanations gained the highest trust and decision accuracy, and AR-based decision support was also viewed as innovative, but potentially distracting.2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)['Ryan Harari', 'Abdullah Al-Taweel', 'T. Ahram', 'Hamid Shokoohi']2024-01-17T00:00:00https://www.semanticscholar.org/paper/269e92d4adc05c401343ff34a5c0acec52a17701 97e2c03189fa9e102e2271b8ad1dacaca71fcaf4Artificial intelligence (AI) is rapidly transforming the global economy, and Latin America is no exception. In recent years, there has been a growing interest in AI development and implementation in the region. This paper presents a ranking of Latin American (LATAM) countries based on their potential to become emerging powers in AI. The ranking is based on three pillars: infrastructure, education, and finance. Infrastructure is measured by the availability of electricity, high-speed internet, the quality of telecommunications networks, and the availability of supercomputers. Education is measured by the quality of education and the research status. Finance is measured by the cost of investments, history of investments, economic metrics, and current implementation of AI. While Brazil, Chile, and Mexico have established themselves as major players in the AI industry in Latin America, our ranking demonstrates the new emerging powers in the region. According to the results, Argentina, Colombia, Uruguay, Costa Rica, and Ecuador are leading as new emerging powers in AI in Latin America. These countries have strong education systems, well-developed infrastructure, and growing financial resources. The ranking provides a useful tool for policymakers, investors, and businesses interested in AI development in Latin America. It can help to identify emerging LATAM countries with the greatest potential for AI growth and success.330A ranking of Latin American (LATAM) countries based on their potential to become emerging powers in AI, based on three pillars: infrastructure, education, and finance, which demonstrates the new emerging powers in the region.['Abraham Ramos Torres', 'Laura N Montoya']2024-01-17T00:00:00https://www.semanticscholar.org/paper/97e2c03189fa9e102e2271b8ad1dacaca71fcaf4 d08cb3c3abfdb95023ad9597af938d92a5d63523Only 200 out of 300,000 AI/ML-related original publications on PubMed focus on human-centered settings. Why is human wellbeing often overlooked in medical AI research? Web3 Journal: ML in Health Science00Web3 Journal: ML in Health Science['Y. Rusinovich']2024-01-17T00:00:00https://www.semanticscholar.org/paper/d08cb3c3abfdb95023ad9597af938d92a5d63523 7bae32e452b20967ff286f6a25e64acc2b6d4003Recent years have witnessed an increasing number of artificial intelligence (AI) applications in transportation. As a new and emerging technology, AI's potential to advance transportation goals and the full extent of its impacts on the transportation sector is not yet well understood. As the transportation community explores these topics, it is critical to understand how transportation professionals, the driving force behind AI Transportation applications, perceive AI's potential efficiency and equity impacts. Toward this goal, we surveyed transportation professionals in the United States and collected a total of 354 responses. Based on the survey responses, we conducted both descriptive analysis and latent class cluster analysis (LCCA). The former provides an overview of prevalent attitudes among transportation professionals, while the latter allows the identification of distinct segments based on their latent attitudes toward AI. We find widespread optimism regarding AI's potential to improve many aspects of transportation (e.g., efficiency, cost reduction, and traveler experience); however, responses are mixed regarding AI's potential to advance equity. Moreover, many respondents are concerned that AI ethics are not well understood in the transportation community and that AI use in transportation could exaggerate existing inequalities. Through LCCA, we have identified four latent segments: AI Neutral, AI Optimist, AI Pessimist, and AI Skeptic. The latent class membership is significantly associated with respondents' age, education level, and AI knowledge level. Overall, the study results shed light on the extent to which the transportation community as a whole is ready to leverage AI systems to transform current practices and inform targeted education to improve the understanding of AI among transportation professionals.arXiv.org400The study results shed light on the extent to which the transportation community as a whole is ready to leverage AI systems to transform current practices and inform targeted education to improve the understanding of AI among transportation professionals.ArXiv['Yiheng Qian', 'Tejaswi Polimetla', 'Thomas W. Sanchez', 'Xiang Yan']2024-01-17T00:00:00https://www.semanticscholar.org/paper/7bae32e452b20967ff286f6a25e64acc2b6d4003 e37fd45f48c0f298c247ed6de5c3a911eaf82cceTujuan pelaksanaan pengabdian kepada masyarakat adalah untuk memberikan pengetahuan dan pemahaman terkait Artificial Intelligence (AI) pada guru Pondok Pesantren El Jasmeen. Berdasarkan hasil evaluasi peserta, diketahui bahwa semua peserta pelatihan pemanfaatan Artificial Intelligence (AI) dalam pembelajaran merasa puas terkait kegiatan maupun pemateri yang terlibat. Tujuan dari kegiatan “Pelatihan Pemanfaatan Artificial Intelligence (AI) dalam Pembelajaran” adalah memberikan pengetahuan dan ketrampilan pemanfaatan Artificial Intelligence pada guru Pondok Pesantren El Jasmeen sehingga mampu meningkatkan kualitas pembelajaran di Pondok Pesantren El JAsmeenCommunity Reinforcement and Development Journal40Community Reinforcement and Development Journal['Uke Prajogo', 'Bunyamin Bunyamin', 'Siti Munfaqiroh', 'Lindananty Lindananty', 'Lidia Andiani', 'S. Sunarto']2024-01-17T00:00:00https://www.semanticscholar.org/paper/e37fd45f48c0f298c247ed6de5c3a911eaf82cce ffe663007bd72b222271f9b61354b6c124f7b1daWe propose a learning analytics-based methodology for assessing the collaborative writing of humans and generative artificial intelligence. Framed by the evidence-centered design, we used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims; we used data collected from the CoAuthor writing tool as potential evidence for these claims; and we used epistemic network analysis to make inferences from the data about the claims. Our findings revealed significant differences in the writing processes of different groups of CoAuthor users, suggesting that our method is a plausible approach to assessing human-AI collaborative writing.International Conference on Learning Analytics and Knowledge430This work used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims, and used epistemic network analysis to make inferences from the data about the claims.{'pages': '178-188'}['Yixin Cheng', 'Kayley Lyons', 'Guanliang Chen', 'D. Gašević', 'Z. Swiecki']2024-01-17T00:00:00https://www.semanticscholar.org/paper/ffe663007bd72b222271f9b61354b6c124f7b1da 596a0716a13f9e3f7bc84b95e34cc0a445f374f4Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.JMIR Nursing360This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings and argues that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows.JMIR nursing['Sjors Groeneveld', 'G. Bin Noon', 'M. D. den Ouden', 'H. van Os-Medendorp', 'J. E. W. C. van Gemert-Pijnen', 'Rudolph M Verdaasdonk', 'P. Morita']2024-01-17T00:00:00https://www.semanticscholar.org/paper/596a0716a13f9e3f7bc84b95e34cc0a445f374f4 d851f6b94485ab78d3e08747b6cfaeabb1058fdcWith the growing popularity of conversational agents based on large language models (LLMs), we need to ensure their behaviour is ethical and appropriate. Work in this area largely centres around the 'HHH' criteria: making outputs more helpful and honest, and avoiding harmful (biased, toxic, or inaccurate) statements. Whilst this semantic focus is useful when viewing LLM agents as mere mediums or output-generating systems, it fails to account for pragmatic factors that can make the same speech act seem more or less tactless or inconsiderate in different social situations. With the push towards agentic AI, wherein systems become increasingly proactive in chasing goals and performing actions in the world, considering the pragmatics of interaction becomes essential. We propose an interactional approach to ethics that is centred on relational and situational factors. We explore what it means for a system, as a social actor, to treat an individual respectfully in a (series of) interaction(s). Our work anticipates a set of largely unexplored risks at the level of situated social interaction, and offers practical suggestions to help agentic LLM technologies treat people well.590What it means for a system, as a social actor, to treat an individual respectfully in a (series of) interaction(s) is explored, and an interactional approach to ethics is proposed that is centred on relational and situational factors.['Lize Alberts', 'Geoff Keeling', 'Amanda McCroskery']2024-01-17T00:00:00https://www.semanticscholar.org/paper/d851f6b94485ab78d3e08747b6cfaeabb1058fdc ad7e74984b183a9fffd95df5a5db99508bc93f55In response to the relentless demand for creative and engaging content in the digital age, the Unified AI project emerges as a groundbreaking solution. Content creators across various domains, from videos and images to text, are constantly in search of innovative and efficient ways to captivate their audiences. Unified AI addresses this insatiable demand by introducing a comprehensive solution that empowers users to effortlessly generate audio, video, and images through state-of-the-art technologies. Unified AI is a product of the growing need to streamline and democratize content creation. The project's primary goal is to make advanced technology accessible to a wider user base, thus empowering businesses, educators, artists, and individuals to create exceptional content. By bridging the gap between the increasing demands for automation and the limitations of existing content generation methods, Unified AI aims to revolutionize the content creation industry. The project seeks to provide an efficient, cost-effective, and user-centric solution to meet the diverse needs of content creators. At its core, Unified AI serves as an intersection where cutting-edge technologies converge with the ever-evolving landscape of content creation. By leveraging state-of-the-art methods, the project not only meets the demands of the current digital era but also anticipates future trends and challenges. Through this initiative, the team behind Unified AI envisions a transformative impact on the content creation landscape, offering a versatile and adaptive solution for content creators in today's dynamic digital environmentInternational Journal of Advanced Research in Science, Communication and Technology10The project seeks to provide an efficient, cost-effective, and user-centric solution to meet the diverse needs of content creators, and bridging the gap between the increasing demands for automation and the limitations of existing content generation methods.International Journal of Advanced Research in Science, Communication and Technology['Mrs. Sonal', 'S. Jogdand', 'Yash A. Bhende', 'Sahil V. Dumbre', 'Parth M. Jadhav', 'Kashish A. Degaonkar']2024-01-17T00:00:00https://www.semanticscholar.org/paper/ad7e74984b183a9fffd95df5a5db99508bc93f55 c3b49a477d006887d71ac90e8353686876fc75c9Nature00Nature[]2024-01-17T00:00:00https://www.semanticscholar.org/paper/c3b49a477d006887d71ac90e8353686876fc75c9 2fc7e399a5de9e2c4afd1709352e13fe11a2cc78In this paper we propose a novel solution to generate Behavior Trees automatically and autonomously from reinforcement learned autonomous agents. We developed a Capture The Flag-style game in which two teams compete to win. The Behavior Trees are generated from the knowledge of Q-Learned autonomous agents competing in this game by extracting their knowledge and parsing it into a Behavior Tree format. The proposed algorithm can generate these trees with comparable performance to the autonomous agents as well as compete with handmade state machine like solutions.2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)130A novel solution to generate Behavior Trees automatically and autonomously from reinforcement learned autonomous agents competing in a Capture The Flag-style game is proposed.2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)['Ralph Dworzanski', 'Helmut Hlavacs']2024-01-17T00:00:00https://www.semanticscholar.org/paper/2fc7e399a5de9e2c4afd1709352e13fe11a2cc78 85ff04678f7f74610562771938efa1e8f7f2ce5b00ACM TechBrief: Trusted AI['Bran Knowles', 'John T. Richards']2024-01-17T00:00:00https://www.semanticscholar.org/paper/85ff04678f7f74610562771938efa1e8f7f2ce5b 148662458c04d686bc27b97149e22bd5ef69b6c5Nature10Nature['D. Castelvecchi']2024-01-17T00:00:00https://www.semanticscholar.org/paper/148662458c04d686bc27b97149e22bd5ef69b6c5 ed0e1012200b01cadca83c09da4dca9cf2117348The Law Teacher00The Law Teacher['Audrey Fried']2024-01-17T00:00:00https://www.semanticscholar.org/paper/ed0e1012200b01cadca83c09da4dca9cf2117348 36655bc7866a6398621a0b7c240f208d9d058061Objective We developed a method to assess the consistency of the assignment of ICD codes, using coding performed at a United States health system at the time of the transition from ICD-9CM to ICD-10CM. Methods Using clusters of equivalent codes derived from the US Centers for Disease Control General Equivalence Mapping (GEM) tables, ICD assignments occurring during the ICD-9CM to ICD-10CM transition were evaluated in EHR data from the US Veterans Administration Central Data Warehouse, using a deep learning model based on 860 covariates. The model was then used to detect abrupt changes across the transition; additionally changes at each VA station were examined. Results Many of the 687 most-used code clusters had ICD-10CM assignments differing greatly from that predicted by the GEM from the codes used in ICD-9CM. Notably, the observed transition patterns varied widely across care locations. Conclusion Machine learning can model variability across time and across location, enabling an assessment of coding consistency. Expert review is not scalable, deep learning model applied to a large dataset of EHR records provides an approximation of ground truth.medRxiv130A method to assess the consistency of the assignment of ICD codes, using coding performed at a United States health system at the time of the transition from ICD-9CM to ICD-10CM, found machine learning can model variability across time and across location, enabling an assessment of coding consistency.['Stuart J. Nelson', 'Ying Yin', 'Y. Shao', 'Phillip Ma', 'M. Tuttle', 'Qing Zeng-Treitler']2024-01-17T00:00:00https://www.semanticscholar.org/paper/36655bc7866a6398621a0b7c240f208d9d058061 b7c45088982f2712e4c6909542fbbbf41813c84cArtificial intelligence technology is used to filter the visual content displayed on digital display platforms in a way that enhances its competitive role and organizes its content. It also involves several risks, including the possibility of causing direct damage to users of visual content display platforms via the Internet. The utilization of artificial intelligence technology for content filtering on these platforms gives rise to legal concerns regarding the liability framework in cases of damages resulting from such filtering activities. This is due to the absence of established legal regulations governing the use of artificial intelligence technology, as well as the ongoing development or nonexistence of relevant legal rules. Furthermore, the user base of these platforms continues to expand. The study proposes the adoption of a liability system that achieves a balance between the owners, operators, or developers of these platforms and their users. The responsibility of the stronger party arises as soon as the damage occurs. This type of responsibility is more suitable for the circumstances surrounding the employment of artificial intelligence tools in filtering visual content on digital display platforms via the Internet.Pakistan Journal of Criminology00The study proposes the adoption of a liability system that achieves a balance between the owners, operators, or developers of these platforms and their users, and is more suitable for the circumstances surrounding the employment of artificial intelligence tools in filtering visual content on digital display platforms via the Internet.Pakistan Journal of Criminology[]2024-01-17T00:00:00https://www.semanticscholar.org/paper/b7c45088982f2712e4c6909542fbbbf41813c84c c60fd7b1fa801857e324cf50166c00bc13551dceBy adopting a systematic literature and science mapping review, this paper aims to explore the impact of artificial intelligence (AI) on organizational justice and project performance. A total of 47 bibliographic records from the Scopus database were analyzed. The results revealed the annual publication trends of research articles and relevant peer-reviewed journals in the studied domain. It was found that while AI technology has made significant progress in several fields, its application areas in project management and organizational justice are still relatively low. Moreover, it objectively discussed the co-occurrence analysis of keywords, co-authors, countries/regions, and documents in the fields, revealing the current research topics. The main research topics include the (1) AI’s influence on organizational justice, decision analysis, and digital transformation, (2) fostering organizational justice and AI’s role in enhancing project performance, and (3) improving organizational performance approaches. Furthermore, this paper proposed research gaps and future research directions, including (1) advancing business intelligence strategies, (2) unlocking AI technology potential on organizational justice and project performance, (3) the adaption of cultural, diversity, environmental, and social factors, (4) the impact of AI on complex and challenging leadership styles, and (5) developing a comprehensive understanding of the agile framework. The findings of this paper could contribute to a better understanding of how AI shapes project/construction management and organizational justice, providing practical solutions for innovative development for researchers and policymakers.Buildings933It was found that while AI technology has made significant progress in several fields, its application areas in project management and organizational justice are still relatively low.Buildings['Xinran Zhang', 'M. Antwi-Afari', 'Yongcheng Zhang', 'Xuejiao Xing']2024-01-17T00:00:00https://www.semanticscholar.org/paper/c60fd7b1fa801857e324cf50166c00bc13551dce cfe41266203094cd17f3e90c25dec5aa48bb0f18Background Artificial intelligence (AI)–powered technologies are being increasingly used in almost all fields, including medicine. However, to successfully implement medical AI applications, ensuring trust and acceptance toward such technologies is crucial for their successful spread and timely adoption worldwide. Although AI applications in medicine provide advantages to the current health care system, there are also various associated challenges regarding, for instance, data privacy, accountability, and equity and fairness, which could hinder medical AI application implementation. Objective The aim of this study was to identify factors related to trust in and acceptance of novel AI-powered medical technologies and to assess the relevance of those factors among relevant stakeholders. Methods This study used a mixed methods design. First, a rapid review of the existing literature was conducted, aiming to identify various factors related to trust in and acceptance of novel AI applications in medicine. Next, an electronic survey including the rapid review–derived factors was disseminated among key stakeholder groups. Participants (N=22) were asked to assess on a 5-point Likert scale (1=irrelevant to 5=relevant) to what extent they thought the various factors (N=19) were relevant to trust in and acceptance of novel AI applications in medicine. Results The rapid review (N=32 papers) yielded 110 factors related to trust and 77 factors related to acceptance toward AI technology in medicine. Closely related factors were assigned to 1 of the 19 overarching umbrella factors, which were further grouped into 4 categories: human-related (ie, the type of institution AI professionals originate from), technology-related (ie, the explainability and transparency of AI application processes and outcomes), ethical and legal (ie, data use transparency), and additional factors (ie, AI applications being environment friendly). The categorized 19 umbrella factors were presented as survey statements, which were evaluated by relevant stakeholders. Survey participants (N=22) represented researchers (n=18, 82%), technology providers (n=5, 23%), hospital staff (n=3, 14%), and policy makers (n=3, 14%). Of the 19 factors, 16 (84%) human-related, technology-related, ethical and legal, and additional factors were considered to be of high relevance to trust in and acceptance of novel AI applications in medicine. The patient’s gender, age, and education level were found to be of low relevance (3/19, 16%). Conclusions The results of this study could help the implementers of medical AI applications to understand what drives trust and acceptance toward AI-powered technologies among key stakeholders in medicine. Consequently, this would allow the implementers to identify strategies that facilitate trust in and acceptance of medical AI applications among key stakeholders and potential users.JMIR Human Factors591The results of this study could help the implementers of medical AI applications to understand what drives trust and acceptance toward AI-powered technologies among key stakeholders in medicine.JMIR Human Factors['Daria Shevtsova', 'Anam Ahmed', 'Iris W A Boot', 'Carmen Sanges', 'Michael Hudecek', 'John J L Jacobs', 'S. Hort', 'H. Vrijhoef']2024-01-17T00:00:00https://www.semanticscholar.org/paper/cfe41266203094cd17f3e90c25dec5aa48bb0f18 3ef6a705f4d95086b85006149c12742e10af4436Integration of the Internet of Things (IoT) into the fields of wastewater treatment and water quality prediction has the potential to revolutionize traditional approaches and address urgent challenges, considering the global demand for clean water and sustainable systems. This comprehensive article explores the transformative applications of smart IoT technologies, including artificial intelligence (AI) and machine learning (ML) models, in these areas. A successful example is the implementation of an IoT-based automated water quality monitoring system that utilizes cloud computing and ML methods to effectively address the above-mentioned issues. The IoT has been employed to optimize, simulate, and automate various aspects, such as monitoring and managing natural systems, water-treatment processes, wastewater-treatment applications, and water-related agricultural practices like hydroponics and aquaponics. This review presents a collection of significant water-based applications, which have been combined with the IoT, artificial neural networks, or ML and have undergone critical peer-reviewed assessment. These applications encompass chlorination, adsorption, membrane filtration, monitoring water quality indices, modeling water quality parameters, monitoring river levels, and automating/monitoring effluent wastewater treatment in aquaculture systems. Additionally, this review provides an overview of the IoT and discusses potential future applications, along with examples of how their algorithms have been utilized to evaluate the quality of treated water in diverse aquatic environments.Water1271This comprehensive article explores the transformative applications of smart IoT technologies, including artificial intelligence (AI) and machine learning (ML) models, in these areas of wastewater treatment and water quality prediction.Water['Ahmed E. Alprol', 'A. Mansour', 'Marwa Ezz El-Din Ibrahim', 'Mohamed Ashour']2024-01-17T00:00:00https://www.semanticscholar.org/paper/3ef6a705f4d95086b85006149c12742e10af4436 5faed39b961059bdba69cd7123dd6713b9574ed9Endometrial cancer is one of the most prevalent tumours in females and holds an 83% survival rate within 5 years of diagnosis. Hypoestrogenism is a major risk factor for the development of endometrial carcinoma (EC) therefore two major types are derived, type 1 being oestrogen-dependent and type 2 being oestrogen independent. Surgery, chemotherapeutic drugs, and radiation therapy are only a few of the treatment options for EC. Treatment of gynaecologic malignancies greatly depends on diagnosis or prognostic prediction. Diagnostic imaging data and clinical course prediction are the two core pillars of artificial intelligence (AI) applications. One of the most popular imaging techniques for spotting preoperative endometrial cancer is MRI, although this technique can only produce qualitative data. When used to classify patients, AI improves the effectiveness of visual feature extraction. In general, AI has the potential to enhance the precision and effectiveness of endometrial cancer diagnosis and therapy. This review aims to highlight the current status of applications of AI in endometrial cancer and provide a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer. Still, additional study is required to comprehend its strengths and limits fully.Annals of Medicine and Surgery660The current status of applications of AI in endometrial cancer is highlighted and a comprehensive understanding of how recent advancements in AI have assisted clinicians in making better diagnosis and improving prognosis of endometrial cancer is provided.Annals of Medicine and Surgery['S. Butt', 'Amna Soulat', 'P. Lal', 'Hajar Fakhor', 'S. K. Patel', 'Mashal Binte Ali', 'Suneel Arwani', 'Anmol Mohan', 'Koushik Majumder', 'Vikash Kumar', 'Usha Tejwaney', 'Sarwan Kumar']2024-01-17T00:00:00https://www.semanticscholar.org/paper/5faed39b961059bdba69cd7123dd6713b9574ed9 b5147b40e48657d0591d830e07019c55b21a9f14 In recent years, the intersection between religion and artificial intelligence (AI) has spurred discussions of a philosophical and theological nature in the academic literature and in public debates. These discussions have often focused on the potential of “general” and “strong AI” to replace God and/or human intelligence. However, this does not reflect the state of the technologies currently in use. We argue that there are several ways in which religious actors interact with existing “narrow” or “weak” AI tools that merit the attention of researchers working on religions and AI. We look at the practical ways in which religious actors use existing AI tools for their activities, while also considering their engagements in terms of education-, advocacy- and policy-related initiatives in the field of AI. Based on a range of examples of how religious actors employ and assess AI technologies within and beyond their religious practices, we present preliminary reflections on these interactions and suggest questions for further research.Religion and Development570It is argued that there are several ways in which religious actors interact with existing “narrow” or “weak” AI tools that merit the attention of researchers working on religions and AI.Religion and Development['Susanna Trotta', 'D. S. Iannotti', 'Boris Rähme']2024-01-17T00:00:00https://www.semanticscholar.org/paper/b5147b40e48657d0591d830e07019c55b21a9f14 8e0f6fb245d54bf26d29bf794914df1720bdcd03Abstract There is a growing interest in developing artificial intelligence (AI) weapon guidance systems in the military domain. Current guidance systems employ various methods to guide a weapon to its target. This target accuracy is a critical factor for its effectiveness. Artificial intelligence capabilities could enable these systems to perform tasks that usually require human intelligence, such as identifying potential threats or targets on the battlefield while continually improving the system’s performance at its assigned task.Journal of Biosecurity Biosafety and Biodefense Law00Artificial intelligence capabilities could enable weapon guidance systems to perform tasks that usually require human intelligence, such as identifying potential threats or targets on the battlefield while continually improving the system’s performance at its assigned task.Journal of Biosecurity, Biosafety, and Biodefense Law['Danielle Munstedt']2024-01-17T00:00:00https://www.semanticscholar.org/paper/8e0f6fb245d54bf26d29bf794914df1720bdcd03 bbfa67ae1618ae3e4d9f7c5a63fca323216a0c14Cancer patients are considered susceptible to COVID-19 associated with the presence of an immunosuppressive state. One type of cancer that causes many deaths is lung cancer. There are many challenges and difficulties, especially in treating lung cancer during the COVID-19 period. As technology develops, experts are trying to use machine learning and artificial intelligence in the treatment and detection of lung cancer. Artificial intelligence and machine learning can be used to predict lung cancer using data from microarrays, pictures, and other sources. Limited data on COVID-19 patients with a relatively small sample, information regarding the implementation of machine learning and artificial intelligence for the treatment of COVID-19 patients with lung cancer is still limited. Therefore, it is necessary to have a literature review to explain the effect of machine learning and artificial intelligence for lung cancer patients during the COVID-19 period from existing studies. The literature review used in this study. Sourced from the search results of journals from various countries related to lung cancer and COVID-19 in Pubmed, Science Direct, and Google Scholar. The results show an important role of machine learning and artificial intelligence for lung cancer patients in COVID-19 pandemic.G-Tech00The results show an important role of machine learning and artificial intelligence for lung cancer patients in COVID-19 pandemic.G-Tech: Jurnal Teknologi Terapan['Selly Anastassia Amellia Kharis', 'Arman Haqqi Anna Zili', 'Fauzan Ihza Fajar', 'Agustiani Putri', 'Melisa Arisanty']2024-01-17T00:00:00https://www.semanticscholar.org/paper/bbfa67ae1618ae3e4d9f7c5a63fca323216a0c14 d8b050d1217965f02d75bee991dba763ca32f0cdThis review analyzes the most influential artificial intelligence (AI) studies in health and life sciences from the past three years, delineating the evolving role of AI in these fields. We identified and analyzed the top 50 cited articles on AI in biomedicine, revealing significant trends and thematic categorizations, including Drug Development, Real-World Clinical Implementation, and Ethical and Regulatory Aspects, among others. Our findings highlight a predominant focus on AIs application in clinical settings, particularly in diagnostics, telemedicine, and medical education, accelerated by the COVID-19 pandemic. The emergence of AlphaFold marked a pivotal moment in protein structure prediction, catalyzing a cascade of related research and signifying a broader shift towards AI-driven approaches in biological research. The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies. Furthermore, it illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing our capabilities to disentangle complex, interconnected molecular processes. As AI continues to permeate the health and life sciences, balancing its rapid technological advancements with ethical stewardship and regulatory vigilance will be crucial for its sustainable and effective integration into healthcare and research.Applied Sciences560The review underscores AIs pivotal role in disease subtyping and patient stratification, facilitating a transition towards more personalized medicine strategies, and illustrates AIs impact on biology, particularly in parsing complex genomic and proteomic data, enhancing the capabilities to disentangle complex, interconnected molecular processes.Applied Sciences['Benjamin S. Glicksberg', 'Eyal Klang']2024-01-17T00:00:00https://www.semanticscholar.org/paper/d8b050d1217965f02d75bee991dba763ca32f0cd c86cfb7189f9d03c988f00c104c87566f8a254c6Objective Modern healthcare systems face challenges related to the stable and sufficient blood supply of blood due to shortages. This study aimed to predict the monthly blood transfusion requirements in medical institutions using an artificial intelligence model based on national open big data related to transfusion. Methods Data regarding blood types and components in Korea from January 2010 to December 2021 were obtained from the Health Insurance Review and Assessment Service and Statistics Korea. The data were collected from a single medical institution. Using the obtained information, predictive models were developed, including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and category boosting (CatBoost). An ensemble model was created using these three models. Results The prediction performance of XGBoost, LGBM, and CatBoost demonstrated a mean absolute error ranging from 14.6657 for AB+ red blood cells (RBCs) to 84.0433 for A+ platelet concentrate (PC) and a root mean squared error ranging from 18.5374 for AB+ RBCs to 118.6245 for B+ PC. The error range was further improved by creating ensemble models, wherein the department requesting blood was the most influential parameter affecting transfusion prediction performance for different blood products and types. Except for the department, the features that affected the prediction performance varied for each product and blood type, including the number of RBC antibody screens, crossmatch, nationwide blood donations, and surgeries. Conclusion Based on blood-related open big data, the developed blood-demand prediction algorithm can efficiently provide medical facilities with an appropriate volume of blood ahead of time.Digital Health310Based on blood-related open big data, the developed blood-demand prediction algorithm can efficiently provide medical facilities with an appropriate volume of blood ahead of time.Digital Health['Hi Jeong Kwon', 'Sholhui Park', 'Young Hoon Park', 'S. Baik', 'D. Park']2024-01-17T00:00:00https://www.semanticscholar.org/paper/c86cfb7189f9d03c988f00c104c87566f8a254c6 9004d8a6a7a6756e7a51d3031b16f41e45d6034dThis article explores the complex interplay between Artificial Intelligence (AI), ubiquitous learning, and lifelong learning. Drawing upon in-depth analysis of pertinent literature, it elucidates the ways in which AI can bolster ubiquitous and lifelong learning in terms of personalized learning, continuous accessibility, adaptive content delivery, instant feedback and assessment, Natural Language Processing, data-driven insights, assistance and support, personal learning assistants, tailored learning resources and efficient time management. It also highlights the pre-requisite of harnessing AI for the purpose of ubiquitous learning, and lifelong learning. In the future, AI is anticipated to become the cornerstone of education systems across all levels.Environment-Behaviour Proceedings Journal260The ways in which AI can bolster ubiquitous and lifelong learning in terms of personalized learning, continuous accessibility, adaptive content delivery, instant feedback and assessment, Natural Language Processing, data-driven insights, assistance and support, personal learning assistants, tailored learning resources and efficient time management are highlighted.Environment-Behaviour Proceedings Journal['M. N. Masrek', 'Tri Susantari', 'Fitri Mutia', 'Helmi Prasetyo Yuwinanto', 'Ragil Tri Atmi']2024-01-17T00:00:00https://www.semanticscholar.org/paper/9004d8a6a7a6756e7a51d3031b16f41e45d6034d 8402eeef94ee23d8ed09d748b4b6353a49a9a184This paper discusses the limitations of machine learning (ML), particularly deep artificial neural networks (ANNs), which are effective at approximating complex functions but often lack transparency and explanatory power. It highlights the `problem of induction' : the philosophical issue that past observations may not necessarily predict future events, a challenge that ML models face when encountering new, unseen data. The paper argues for the importance of not just making predictions but also providing good explanations, a feature that current models often fail to deliver. It suggests that for AI to progress, we must seek models that offer insights and explanations, not just predictions.arXiv.org620The paper argues for the importance of not just making predictions but also providing good explanations, a feature that current models often fail to deliver, and suggests that for AI to progress, the authors must seek models that offer insights and explanations, not just predictions.ArXiv['Udesh Habaraduwa']2024-01-17T00:00:00https://www.semanticscholar.org/paper/8402eeef94ee23d8ed09d748b4b6353a49a9a184 612d6b515942d09dba0d8803d44d7fa00983528eJournal of Library Administration151Journal of Library Administration['Laurie M. Bridges', 'Kelly McElroy', 'Zach Welhouse']2024-01-17T00:00:00https://www.semanticscholar.org/paper/612d6b515942d09dba0d8803d44d7fa00983528e af3de6ee10e6703ac0bfc885cda2c042014ef5d4Injury61Injury['Dawood Rashid', 'Rahim Hirani', 'Samy Khessib', 'Neha Ali', 'Mill Etienne']2024-01-17T00:00:00https://www.semanticscholar.org/paper/af3de6ee10e6703ac0bfc885cda2c042014ef5d4 b4b5beb55ff52bef683ab9b0067347846f0c40e2Scientific Reports321The data-driven models trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values outperform the traditional physical models, and the best-performing model predicts that the 2023 AISMR will be roughly 790 mm, which is typical of a normal monsoon year.Scientific Reports['Udit Narang', 'Kushal Juneja', 'Pankaj Upadhyaya', 'Popat Salunke', 'Tanmoy Chakraborty', 'S. Behera', 'S. Mishra', 'Akhil Dev Suresh']2024-01-17T00:00:00https://www.semanticscholar.org/paper/b4b5beb55ff52bef683ab9b0067347846f0c40e2 2a35b09557cab2a5d07d000fb108cf59aa4fc1ecEmergencias00Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias['Rafael Castro-Delgado', 'Manuel Pardo Ríos']2024-01-17T00:00:00https://www.semanticscholar.org/paper/2a35b09557cab2a5d07d000fb108cf59aa4fc1ec 8e64d578f7ae19690d4fada44229a291933360b8Evolutionary Intelligence00Evol. Intell.['Stefania Tomasiello', 'Feng Feng', 'Yichuan Zhao']2024-01-17T00:00:00https://www.semanticscholar.org/paper/8e64d578f7ae19690d4fada44229a291933360b8 443b2053b311f9b9849b8a8e212ab126f3fb2155Coaching: An International Journal of Theory, Research and Practice250Coaching: An International Journal of Theory, Research and Practice['N. Terblanche', 'Michelle van Heerden', 'Robin Hunt']2024-01-17T00:00:00https://www.semanticscholar.org/paper/443b2053b311f9b9849b8a8e212ab126f3fb2155 1e8ae5684b65e79c6bc56030103bf2f033d46b14Journal of Engineering Research00Journal of Engineering Research['Caique Amorim', 'João Vitor Rodrigues de Souza']2024-01-17T00:00:00https://www.semanticscholar.org/paper/1e8ae5684b65e79c6bc56030103bf2f033d46b14 87e84eb21ecfbd16fae9c0ee9966bc8cbd1a3901Al-Mustaqbal Journal of Sustainability in Engineering Sciences00Al-Mustaqbal Journal of Sustainability in Engineering Sciences['Riyam K. Marjan', 'Ali Mohammed Hasan Zubaidi']2024-01-17T00:00:00https://www.semanticscholar.org/paper/87e84eb21ecfbd16fae9c0ee9966bc8cbd1a3901 43c9b4b02304e151988bf69aed6a7df06a51f68c00['Sandhya Makkar', 'Gobinath Ravindran', 'R. Chakrabortty', 'Arindam Pal']2024-01-17T00:00:00https://www.semanticscholar.org/paper/43c9b4b02304e151988bf69aed6a7df06a51f68c 4b280f26da1aeb7a0e7786d44ed2e17d80a11b58Emergencias00Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias['Santiago Nogué-Xarau', 'M. Amigó-Tadín', 'José Ríos-Guillermo']2024-01-17T00:00:00https://www.semanticscholar.org/paper/4b280f26da1aeb7a0e7786d44ed2e17d80a11b58 0880f13bf4d095aaaedece06d683d9d76149f83eReviews in cardiovascular medicine00Reviews in Cardiovascular Medicine['Yuxuan Zhang', 'Mo-yang Wang', 'Erli Zhang', 'Yongjian Wu']2024-01-17T00:00:00https://www.semanticscholar.org/paper/0880f13bf4d095aaaedece06d683d9d76149f83e 14a34cc5904e726c599b43535693872696f3d0f4The aim of this research has been to identify the main benefits and limitations that primary education, secondary education, and higher education teachers perceive regarding the use of artificial intelligence in education. To achieve this, a total of 276 opinions were collected from teachers currently working in educational institutions, who were asked about the benefits and limitations they considered in integrating artificial intelligence in classrooms. The results indicate that, overall, teachers perceive more limitations than benefits in the use of artificial intelligence. Among the most prominent benefits are the facilitation of tasks and access to resources. As for the major perceived limitations, they include inappropriate use and lack of critical review of the results. A correspondence analysis revealed a significant association between certain benefits and limitations based on the educational stage in which the teachers work. Finally, these results reveal a distinct perception among teachers at different stages, which implies a need for differentiated teacher training in the use of artificial intelligence according to the specific requirements of each educational stage. El objetivo de la presente investigación ha sido conocer cuáles son los principales beneficios y limitaciones que el profesorado de educación primaria, educación secundaria y educación superior detecta en torno al uso de la inteligencia artificial en educación. Para ello, se han recogido un total de 276 opiniones de docentes que ejercen actualmente en centros educativos, a quienes se les preguntó cuáles consideraban que eran los beneficios y las limitaciones de la integración de la inteligencia artificial en las aulas. Los resultados indican que los docentes, en general, observan más limitaciones que beneficios en el uso de la inteligencia artificial. Entre los beneficios más destacados se encuentran la facilitación para la realización de tareas y el acceso a recursos. Entre las mayores limitaciones percibidas, el uso inadecuado y la falta de revisión crítica de los resultados. Tras un análisis de correspondencias se observó una asociación significativa entre algunos beneficios y limitaciones según la etapa educativa del profesorado. Finalmente, estos resultados revelan una percepción distinta por parte del profesorado de distintas etapas, lo que conllevaría a una necesidad de formación del profesorado diferenciada para el uso de la inteligencia artificial según las necesidades que se presentan en cada etapa educativa.Revista Electronica Interuniversitaria de Formación del Profesorado01Revista Electrónica Interuniversitaria de Formación del Profesorado['Nahia Delgado', 'Lucía Campo Carrasco', 'M. Sainz de la Maza', 'José María Etxabe-Urbieta']2024-01-17T00:00:00https://www.semanticscholar.org/paper/14a34cc5904e726c599b43535693872696f3d0f4 f36e8f9d4243f344ea41acde7b62618c2b5cac85Current Osteoporosis Reports2513The main objective of this scientific study was to see whether AI could be used in a scientifically appropriate manner to improve the scientific writing process and indeed, AI reduced the time for writing but had significant inaccuracies.Current Osteoporosis Reports['Melissa A. Kacena', 'Lilian I Plotkin', 'Jill C. Fehrenbacher']2024-01-16T00:00:00https://www.semanticscholar.org/paper/f36e8f9d4243f344ea41acde7b62618c2b5cac85 05f9540dd327f3bdb7088afb809d447dd9084215By collecting the micro data of China's A-share listed enterprises, this paper studies the impact of environmental regulation on the green technology innovation of heterogeneous enterprises based on the perspective of "Porter hypothesis". It is found that environmental regulation can effectively promote the green technology innovation level of enterprises. And the conclusion remains robust after controlling for endogeneity, replacing the explained variables, and changing the time series. In the heterogeneity analysis, due to the differences in enterprise strength and social responsibility between state-owned enterprises and non-state-owned enterprises, environmental regulation has different effects on the green technology innovation effect of enterprises with different features. Due to the good economic foundation and resource endowment in the eastern region and the western region, the environmental regulation has a more obvious effect on the green technology innovation of enterprises in this region. The central region does not have these advantages, the effect is not significant. In the mechanism analysis, the cost mechanism and the compensation of innovation mechanism proposed by the theoretical analysis are verified.Frontiers in Business, Economics and Management180Frontiers in Business, Economics and Management['Gen Zuo']2024-01-16T00:00:00https://www.semanticscholar.org/paper/05f9540dd327f3bdb7088afb809d447dd9084215 623646d2fc23e000d88b853a60faebbe840834ddCurrent Osteoporosis Reports127This writing experiment showed that while AI may reduce total writing time, hallucinations and plagiarism were prevalent issues with this method and human editing was still necessary to ensure accuracy.Current Osteoporosis Reports['Tyler J. Margetts', 'Sonali J. Karnik', 'Hannah S. Wang', 'Lilian I Plotkin', 'Adrian L. Oblak', 'Jill C. Fehrenbacher', 'Melissa A. Kacena', 'Alexandru Movila']2024-01-16T00:00:00https://www.semanticscholar.org/paper/623646d2fc23e000d88b853a60faebbe840834dd 8fc7300aed77640dbcc58ad22c1fc053939ab911Prostate Cancer and Prostatic Diseases192Chat-GPT has a poor accuracy when answering questions on the PCa EAU guidelines recommendations, and future studies should assess its performance after adequate training.Prostate cancer and prostatic diseases['Riccardo Lombardo', 'G. Gallo', 'J. Stira', 'B. Turchi', 'Giuseppe Santoro', 'S. Riolo', 'Matteo Romagnoli', 'A. Cicione', 'G. Tema', 'A. Pastore', 'Y. Al salhi', 'A. Fuschi', 'Giorgio Franco', 'A. Nacchia', 'Andrea Tubaro', 'C. de Nunzio']2024-01-16T00:00:00https://www.semanticscholar.org/paper/8fc7300aed77640dbcc58ad22c1fc053939ab911 b1b3b2912404f52f662060faf0990594ebe84f0dThis paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta’s Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains.Remote Sensing352This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation and shows that although promising, SAM still has room for improvement to support AI-augmented terrain mapping.Remote. Sens.['Wenwen Li', 'Chia-Yu Hsu', 'Sizhe Wang', 'Yezhou Yang', 'Hyunho Lee', 'Anna K. Liljedahl', 'C. Witharana', 'Yili Yang', 'Brendan M. Rogers', 'S. Arundel', 'Matthew B. Jones', 'Kenton McHenry', 'Patricia Solis']2024-01-16T00:00:00https://www.semanticscholar.org/paper/b1b3b2912404f52f662060faf0990594ebe84f0d 287f00d7e43c6c95f40d3fedd2840b2028d160a1While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects. We cover five approaches for addressing the tensions via trade-offs, ranging from rudimentary to complex. The approaches differ in the types of considered context, scope, methods for measuring contexts, and degree of justification. None of the approaches is likely to be appropriate for all organisations, systems, or applications. To address this, we propose a framework which consists of: (i) proactive identification of tensions, (ii) prioritisation and weighting of ethics aspects, (iii) justification and documentation of trade-off decisions. The proposed framework aims to facilitate the implementation of well-rounded AI/ML systems that are appropriate for potential regulatory requirements.arXiv.org501This work proposes a framework which consists of proactive identification of tensions, prioritisation and weighting of ethics aspects, justification and documentation of trade-off decisions, and aims to facilitate the implementation of well-rounded AI/ML systems that are appropriate for potential regulatory requirements.ArXiv['Conrad Sanderson', 'Emma Schleiger', 'David M. Douglas', 'Petra Kuhnert', 'Qinghua Lu']2024-01-16T00:00:00https://www.semanticscholar.org/paper/287f00d7e43c6c95f40d3fedd2840b2028d160a1 7dc06aa955addb1e69d37445444a15d753922471Recent concern over energy use in wastewater treatment plants (WWTPs) has spurred research on enhancing efficiency and identifying energy-saving technologies. Treating one cubic meter of wastewater consumes at least 0.18 kWh of electricity. About 50% of the energy consumed during this process is attributed to aeration, which varies based on treatment quality and facility size. To harness energy savings in WWTPs, the transition from traditional controls to artificial intelligence (AI)-based strategies has been observed. Research in this area has demonstrated significant improvements to the efficiency of wastewater treatment. This contribution offers an extensive review of the literature from the past decade. It aims to contribute to the ongoing discourse on improving the efficiency and the sustainability of WWTPs. It covers conventional and advanced control strategies, with a particular emphasis on AI-based control utilizing algorithms such as neural networks and fuzzy logic. The review includes four key areas of wastewater treatment AI research as follows: parameter forecasting, performance analysis, modeling development, and process optimization. It also points out potential disadvantages of using AI controls in WWTPs as well as research gaps such as the limited translation of AI strategies from research to real-world implementation and the challenges associated with implementing AI models outside of simulation environments.Water821An extensive review of the literature from the past decade on wastewater treatment AI research covers conventional and advanced control strategies, with a particular emphasis on AI-based control utilizing algorithms such as neural networks and fuzzy logic.Water['Celestine Monday', 'Mohamed S. Zaghloul', 'Diwakar Krishnamurthy', 'Gopal Achari']2024-01-16T00:00:00https://www.semanticscholar.org/paper/7dc06aa955addb1e69d37445444a15d753922471 ce6eae2b85206c5a3f34aecb303136be4bc94e32PurposeArtificial intelligence (AI) applications pose a potential threat to users' data security and privacy due to their high data-dependence nature. This paper aims to investigate an understudied issue in the literature, namely, how users perceive the threat of and decide to use a threatening AI application. In particular, it examines the influencing factors and the mechanisms that affect an individual’s behavioral intention to use facial recognition, a threatening AI.Design/methodology/approachThe authors develop a research model with trust as the key mediating variable by integrating technology threat avoidance theory, the theory of planned behavior and contextual factors related to facial recognition. Then, it is tested through a sequential mixed-methods investigation, including a qualitative study (for model development) of online comments from various platforms and a quantitative study (for model validation) using field survey data.FindingsPerceived threat (triggered by perceived susceptibility and severity) and perceived avoidability (promoted by perceived effectiveness, perceived cost and self-efficacy) have negative and positive relationships, respectively, with an individual’s attitude toward facial recognition applications; these relationships are partially mediated by trust. In addition, perceived avoidability is positively related to perceived behavioral control, which along with attitude and subjective norm is positively related to individuals' intentions to use facial recognition applications.Originality/valueThis paper is among the first to examine the factors that affect the acceptance of threatening AI applications and how. The research findings extend the current literature by providing rich and novel insights into the important roles of perceived threat, perceived avoidability, and trust in affecting an individual’s attitude and intention regarding using threatening AI applications.Internet Research921The research findings extend the current literature by providing rich and novel insights into the important roles of perceived threat, perceived avoidability, and trust in affecting an individual’s attitude and intention regarding using threatening AI applications.Internet Research['Xiaojun Wu', 'Zhongyun Zhou', 'Shouming Chen']2024-01-16T00:00:00https://www.semanticscholar.org/paper/ce6eae2b85206c5a3f34aecb303136be4bc94e32 5909a42994f7373997ed96cad9be448c31dd8c46 Patient navigation aims to overcome access barriers and optimize care delivery, but navigators cannot identify pre-diagnosed patients using traditional methods, hindering navigation efficacy. We observed care gaps among patients identified at our institution with suspected pancreatic cancer during January 2023: among 67 patients with new pancreas cancer, only 36% underwent biopsy with 22 days between radiology and biopsy; and 30% were seen by an outpatient oncologist with 32 days between radiology and visit. We sought to improve patient care outcomes through daily prospective AI-guided identification and navigation of patients with radiographic findings suspicious for pancreas cancer. In June 2023, we implemented an AI-guided daily workflow using the following steps: 1) at 24-hour intervals, an NLP model reviewed abdominal imaging reports and flagged reports containing language suspicious for pancreatic cancer; 2) a trained coordinator and GI oncologist validated reports with new masses and sent such reports to a GI navigator, and 3) the navigator facilitated appointments for proper follow-up, including GI services, the multi-disciplinary tumor board, and clinical trial coordinator. During June 2023, the model identified 69 patients with suspicion for new pancreas cancer. 19% were immediately eligible for navigation and scheduled for follow-up within our institution with an average of 8 days between report and navigation - 77% saw an outpatient oncologist with an average of 17.5 days between report and visit, 69% underwent biopsy with an average of 4.5 days between report and biopsy, and 54% were seen at multidisciplinary pancreatic cancer tumor board (pcTB) with an average of 16 days between report and pcTB. Furthermore, of these, we were able to enroll 4 patients to clinical trials and 5 accruals to biospecimen studies during this one-month period. 2 of these accruals were to the P-1000 study, which has been open at our institution for the last 2 years with a 0.5 per month accrual rate. Of the 81% who were not immediately eligible for navigation, 23% were already established with an oncologist in our system, 23% sought care elsewhere, 23% were not eligible for or did not desire further workup, and 30% were still undergoing in-patient workup. We show that an AI-guided workflow can transform referral patterns of pre-diagnosed pancreatic cancer patients, creating a new access stream for navigators to intervene much earlier in a patient’s continuum of cancer care with resultant improvement in healthcare delivery, evidenced by an average of 4.5 days between imaging and biopsy and 17.5 days between imaging and outpatient oncology visit, compared to our traditional cancer care approach with averages of 22 and 32 days, respectively. Furthermore, we have rapidly accelerated pancreas cancer clinical trial enrollment and biospecimen accrual at our institution since implementation of this workflow with 4 patients enrolled and 5 biospecimen accruals in a month alone. We are now working to expand this innovative framework across additional cancer types. Citation Format: Kristen M. John, Joseph Tenner, Yuddy Franco, Rolando Croocks, Melissa Perez, Tara McEvoy, Tiffany Zavadsky, Kristen Beyer, Rita Mercieca, Sandeep Nadella, Anthony Carvino, Matthew Barish, Daniel A. King. Implementation of a real-time AI-guided navigation service for pancreas cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr PR01.Cancer Research00It is shown that an AI-guided workflow can transform referral patterns of pre-diagnosed pancreatic cancer patients, creating a new access stream for navigators to intervene much earlier in a patient’s continuum of cancer care with resultant improvement in healthcare delivery.Cancer Research['Kristen M. John', 'Joseph Tenner', 'Yuddy Franco', 'Rolando Croocks', 'Melissa Perez', 'Tara McEvoy', 'Tiffany Zavadsky', 'Kristen Beyer', 'Rita Mercieca', 'Sandeep Nadella', 'Anthony Carvino', 'Matthew Barish', 'Daniel A. King']2024-01-16T00:00:00https://www.semanticscholar.org/paper/5909a42994f7373997ed96cad9be448c31dd8c46 557f939d9e5634c04e5fe3ab2026b0501f7be6ecAs deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the presentation of conformal prediction sets--a distribution-free class of methods for generating prediction sets with specified coverage--to express uncertainty in AI-advised decision-making. Through a large online experiment, we compare the utility of conformal prediction sets to displays of Top-1 and Top-k predictions for AI-advised image labeling. In a pre-registered analysis, we find that the utility of prediction sets for accuracy varies with the difficulty of the task: while they result in accuracy on par with or less than Top-1 and Top-k displays for easy images, prediction sets offer some advantage in assisting humans in labeling out-of-distribution (OOD) images in the setting that we studied, especially when the set size is small. Our results empirically pinpoint practical challenges of conformal prediction sets and provide implications on how to incorporate them for real-world decision-making.International Conference on Human Factors in Computing Systems690It is found that the utility of prediction sets for accuracy varies with the difficulty of the task: while they result in accuracy on par with or less than Top-1 and Top-k displays for easy images, prediction sets offer some advantage in assisting humans in labeling out-of-distribution images in the setting that the authors studied.ArXiv['Dongping Zhang', 'Angelos Chatzimparmpas', 'Negar Kamali', 'J. Hullman']2024-01-16T00:00:00https://www.semanticscholar.org/paper/557f939d9e5634c04e5fe3ab2026b0501f7be6ec 1cf241ab230b94a0f510b07086cfdb3aa8b2ba4bFairness is one of the socio-technical concerns that must be addressed in AI-based systems. Unfair AI-based systems, particularly unfair AI-based mobile apps, can pose difficulties for a significant proportion of the global population. This paper aims to analyze fairness concerns in AI-based app reviews.We first manually constructed a ground-truth dataset, including a statistical sample of fairness and non-fairness reviews. Leveraging the ground-truth dataset, we developed and evaluated a set of machine learning and deep learning classifiers that distinguish fairness reviews from non-fairness reviews. Our experiments show that our best-performing classifier can detect fairness reviews with a precision of 94%. We then applied the best-performing classifier on approximately 9.5M reviews collected from 108 AI-based apps and identified around 92K fairness reviews. Next, applying the K-means clustering technique to the 92K fairness reviews, followed by manual analysis, led to the identification of six distinct types of fairness concerns (e.g., 'receiving different quality of features and services in different platforms and devices' and 'lack of transparency and fairness in dealing with user-generated content'). Finally, the manual analysis of 2,248 app owners' responses to the fairness reviews identified six root causes (e.g., 'copyright issues') that app owners report to justify fairness concerns.arXiv.org960A set of machine learning and deep learning classifiers that distinguish fairness reviews from non-fairness reviews is developed and evaluated and shows that the best-performing classifier can detect fairness reviews with a precision of 94%.ArXiv['A. R. Nasab', 'Maedeh Dashti', 'Mojtaba Shahin', 'Mansooreh Zahedi', 'Hourieh Khalajzadeh', 'Chetan Arora', 'Peng Liang']2024-01-16T00:00:00https://www.semanticscholar.org/paper/1cf241ab230b94a0f510b07086cfdb3aa8b2ba4b 4f2d80a4792b44b0024760798065a4c0d0166822In an era of increasing globalization and business complexity, the management of Islamic financial risk has become a primary concern for Islamic financial institutions. This research aims to investigate the potential use of blockchain technology and artificial intelligence (AI) in enhancing the effectiveness of Islamic financial risk management. Through a literature review method, this study details recent advances in the utilization of blockchain technology for transparency and security of Islamic financial data, as well as the implementation of AI for predictive analysis and fraud detection. The results indicate that blockchain integration can reduce the risk of data manipulation, while AI provides the capability to identify potential risks more quickly and accurately. However, challenges such as legal aspects, regulations, and technology adaptation need to be considered. In the context of Islamic finance, this research provides insights into how blockchain technology and AI can transform the paradigm of financial risk management, creating opportunities for increased efficiency and sustainability within the Islamic financial system. The practical implications of these findings underscore the importance of strategic planning in adopting these technologies to ensure compliance with Sharia principles and maximize their benefits.Jurnal Ekuisci00In the context of Islamic finance, this research provides insights into how blockchain technology and AI can transform the paradigm of financial risk management, creating opportunities for increased efficiency and sustainability within the Islamic financial system.Jurnal Ekuisci['Yanita Hendarti', 'Budi Winarno', 'Muhammad Primbang Aprilianto']2024-01-16T00:00:00https://www.semanticscholar.org/paper/4f2d80a4792b44b0024760798065a4c0d0166822 229c44dbf6ef632059de6018dd2e22b518046dcc Purpose Artificial intelligence (AI) allows the brand to co-create value with young customers through mobile apps. However, as many brands claim that their mobile apps are using the most updated AI technology, young customers face app fatigue and start questioning the authenticity of this touchpoint. This paper aims to study the mediating effect of authenticity for the value co-creation of AI-powered branded applications. Design/methodology/approach Drawing from regulatory engagement theory, this study conceptualize authenticity as the key construct in customers’ value experience process, which triggers customer value co-creation. Two scenario-based online experiments are conducted to collect data from 444 young customers. Data analysis is performed using ANOVA and Process Hayes. Findings The results reveal that perceived authenticity is an important mediator between media richness (chatbot vs AI text vs augmented reality) and value co-creation. There is no interaction effect of co-brand fit (high vs low) and source endorsement (doctor vs government) on the relationship between media richness and perceived authenticity, whereas injunctive norms (high vs low) strengthen this relationship. Practical implications The finding provides insights for marketing managers on engaging young customers suffering from app fatigue. Authenticity holds the key to young customers’ technological perceptions. Originality/value This research highlights the importance of perceived authenticity in encouraging young customers to co-create value. Young customers consider authenticity as a motivational force experience that involves customers through the app’s attributes (e.g. media richness) and social standards (e.g. norms), rather than brand factors (e.g. co-brand fit, source endorsement). Young Consumers880Study of the mediating effect of authenticity for the value co-creation of AI-powered branded applications reveals that perceived authenticity is an important mediator between media richness and value co-creation.Young Consumers['Diem-Trang Vo', 'Long Nguyen', 'Duy Dang-Pham', 'A. Hoang']2024-01-16T00:00:00https://www.semanticscholar.org/paper/229c44dbf6ef632059de6018dd2e22b518046dcc 293f2e94f6d0daf960b9d2463eccadabdf2dcdb7The purpose of the study is to demonstrate the role of artificial intelligence in the ability to accelerate global efforts to protect the environment and conserve natural resources by monitoring air pollution and energy emissions. Monitoring attacks on forest areas, as well as describing the role of the constitutions of the world's countries, including the Jordanian Constitution, by adding constitutional texts addressing environmental protection using artificial intelligence technologies. The importance of this research lies in demonstrating international and constitutional efforts to protect the environment using artificial intelligence technologies by accelerating global and local efforts to protect the environment, conserve natural resources, and use digital tools to monitor air pollution and energy emissions. And monitoring attacks on forest areas, as well as describing the role of countries' constitutions, including the Jordanian Constitution, by adding constitutional texts addressing environmental protection using artificial intelligence technologies.Pakistan Journal of Criminology00The purpose of the study is to demonstrate the role of artificial intelligence in the ability to accelerate global efforts to protect the environment and conserve natural resources by monitoring air pollution and energy emissions and adding constitutional texts addressing environmental protection using artificial intelligence technologies.Pakistan Journal of Criminology[]2024-01-16T00:00:00https://www.semanticscholar.org/paper/293f2e94f6d0daf960b9d2463eccadabdf2dcdb7 3edfc47bb4e37a031636ad82b5c4a8f27a2eee1fImportance Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities. Observations While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people's daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model's behavior. Prompts such as "Write this note for a specialist consultant" and "Write this note for the patient's mother" will produce markedly different content. Conclusions and Relevance Foundation models and generative AI represent a major revolution in AI's capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.Journal of the American Medical Association (JAMA)1310JAMA['Michael D Howell', 'G. Corrado', 'Karen B. DeSalvo']2024-01-16T00:00:00https://www.semanticscholar.org/paper/3edfc47bb4e37a031636ad82b5c4a8f27a2eee1f b266ee6ca0b25558bf5ddbbbb8353ec44f31beb0Artificial Intelligence (AI) in Education claims to have the potential for building personalised curricula, as well as bringing opportunities for democratising education and creating a renaissance of new ways of teaching and learning. Millions of students are starting to benefit from the use of these technologies, but millions more around the world are not, due to the digital divide and deep pre-existing social and educational inequalities. If this trend continues, the first large-scale delivery of AI in Education could lead to greater educational inequality, along with a global misallocation of educational resources motivated by the current techno-solutionist narrative, which proposes technological solutions as a quick and flawless way to solve complex real-world problems. This work focuses on posing questions about the future of AI in Education, intending to initiate the pressing conversation that could set the right foundations (e.g., inclusion and diversity) for a new generation of education that is permeated with AI technology. The main goal of our opinion piece is to conceptualise a sustainable, large-scale and inclusive AI for the education ecosystem that facilitates equitable, high-quality lifelong learning opportunities for all. The contribution starts by synthesising how AI might change how we learn and teach, focusing on the case of personalised learning companions and assistive technology for disability. Then, we move on to discuss some socio-technical features that will be crucial to avoiding the perils of these AI systems worldwide (and perhaps ensuring their success by leveraging more inclusive education). This work also discusses the potential of using AI together with free, participatory and democratic resources, such as Wikipedia, Open Educational Resources and open-source tools. We emphasise the need for collectively designing human-centred, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as supporting new emerging pedagogies. Finally, we ask what it would take for this educational revolution to provide egalitarian and empowering access to education that transcends any political, cultural, language, geographical and learning-ability barriers, so that educational systems can be responsive to all learners’ needs.Sustainability574The need for collectively designing human-centred, transparent, interactive and collaborative AI-based algorithms that empower and give complete agency to stakeholders, as well as supporting new emerging pedagogies is emphasised.Sustainability['Sahan Bulathwela', 'M. Pérez-Ortiz', 'Catherine Holloway', 'M. Cukurova', 'John Shawe-Taylor']2024-01-16T00:00:00https://www.semanticscholar.org/paper/b266ee6ca0b25558bf5ddbbbb8353ec44f31beb0 fabfcd80cdab0a3dc6d277fde20cf50579f1c62aAdvances in Artificial Intelligence (AI) have led to AI systems' being used increasingly in medical education research. Current methods of reporting on the research, however, tend to follow patterns of describing an intervention and reporting on results, with little description of the AI in the system, or the many concerns about the use of AI. In essence, the readers do not actually know anything about the system itself. This paper proposes a checklist for reporting on AI systems, and covers the initial protocols and scoping, modelling and code, algorithm design, training data, testing and validation, usage, comparisons, real-world requirements, results and limitations, and ethical considerations. The aim is to have a systematic reporting process so that readers can have a comprehensive understanding of the AI system that was used in the research.Medical Teacher91A checklist for reporting on AI systems is proposed, and covers the initial protocols and scoping, modelling and code, algorithm design, training data, testing and validation, usage, comparisons, real-world requirements, results and limitations, and ethical considerations.Medical teacher['Ken Masters', 'Daniel Salcedo']2024-01-16T00:00:00https://www.semanticscholar.org/paper/fabfcd80cdab0a3dc6d277fde20cf50579f1c62a 579917a051844cda8fecf8f60ed5d1fcb3274808Background Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. Methods and Results We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9‐feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87. Conclusions Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease291An automated method that includes harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism holds promise to scale echocardiography screening for RHD.Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease['Kelsey Brown', 'Pooneh Roshanitabrizi', 'J. Rwebembera', 'E. Okello', 'A. Beaton', 'M. G. Linguraru', 'Craig A Sable']2024-01-16T00:00:00https://www.semanticscholar.org/paper/579917a051844cda8fecf8f60ed5d1fcb3274808 e312ce0a43419d4d1dbac2dc8123396218db8770Artificial intelligence has potential to counter vaccine hesitancy while building trust in vaccines, but it must be deployed ethically and responsibly, argue Heidi Larson and Leesa LinBritish medical journal221The BMJ['Heidi J Larson', 'Leesa Lin']2024-01-16T00:00:00https://www.semanticscholar.org/paper/e312ce0a43419d4d1dbac2dc8123396218db8770 ce73da8ed38cc7fe23927bda287615518c3af45aPurposeThis research examines the relationship between the green version of intellectual capital (IC) (measured through green versions of human, structural and relational capitals (GHC, GSC and GRC)), co-creational capital (CC), green innovation (GI), technological innovation (TI) (measured through artificial intelligence) and start-up competitive advantage (SCA).Design/methodology/approachAn online questionnaire collected data from 275 participants. To test the hypotheses, the data were analyzed using SmartPLS.FindingsThe results confirmed the positive influence of GSC and CC on TI and GI, GRC with GI and that of GI and TI with SCA. The results also reveal that IC can influence innovation and describe how innovation can drive the competitive advantage (CA) of start-ups.Research limitations/implicationsThis self-report study examines the associations by collecting data at one point in time, which results in methodological limitations regarding the generalization of the results. The second limitation is that the findings are limited to start-ups.Originality/valueThis research work examined a model that combined three components of green IC, customer capital, two forms of innovation and CA. These associations have not been previously examined yet can provide useful insight into what drives green and TIs and how they further influence competitiveness. This study provides unique inferences that improve the value of the literature on IC and innovation, using start-ups as context.European Journal of Innovation Management1081This study provides unique inferences that improve the value of the literature on IC and innovation, using start-ups as context to provide useful insight into what drives green and TIs and how they further influence competitiveness.European Journal of Innovation Management['M. Almansour']2024-01-16T00:00:00https://www.semanticscholar.org/paper/ce73da8ed38cc7fe23927bda287615518c3af45a 9e71fe494567c6d5d4b4854b6c7face9c466566bThe abstract highlights the transformative role of Artificial Intelligence (AI) in drug discovery, covering key aspects such as de novo drug design, synthesis planning, and the future implications of AI in pharmaceutical research. It begins by emphasizing AI's significant impact on drug discovery, particularly in applications like virtual screening and drug design. The survey provides a detailed overview of drug discovery, focusing on molecular property prediction and molecule generation. It explores essential components like data resources and benchmark platforms. The chronological organization of AI techniques showcases the historical evolution of AI in drug discovery. The abstract further discusses AI's applications in the pharmaceutical lifecycle, manufacturing, and post-market surveillance. It concludes by projecting the future role of AI in drug discovery, emphasizing precision medicine, personalized experiences, and collaborative efforts between AI and human researchersInternational Journal of Advanced Research in Science, Communication and Technology201International Journal of Advanced Research in Science, Communication and Technology['Saurabh Dhumane', 'Kiran Dukare', 'Tejas Naik', 'Mahesh Shelke', 'Krushna Dhongade']2024-01-16T00:00:00https://www.semanticscholar.org/paper/9e71fe494567c6d5d4b4854b6c7face9c466566b 4be3345dc63c567e9d6a519e89e7eee91d958ea7International Journal of Artificial Intelligence in Education3081It is proposed that embracing asset-based approaches will empower the AIED community to reach broader populations of learners, particularly for students who are historically underserved, marginalized, and “deficit-ized.”International Journal of Artificial Intelligence in Education['Jaclyn L. Ocumpaugh', 'Rod D. Roscoe', 'Ryan S. Baker', 'Stephen Hutt', 'Stephen Aguilar']2024-01-16T00:00:00https://www.semanticscholar.org/paper/4be3345dc63c567e9d6a519e89e7eee91d958ea7 47e682f316f5e0bf8905aad17d06bdf68f175c79This paper aims to investigate the sociological implications of the digital divide concerning access to information and its impact on social inequality. With the rise of artificial intelligence (AI) and automation, digital technologies have become increasingly central to various aspects of life, including education, employment, and civic engagement. However, unequal access to these technologies exacerbates existing social disparities, leading to potential consequences based on socioeconomic status, race, gender, and geographic location. This study seeks to understand the relationship between the digital divide and social inequality, focusing on how these factors interact and influence access to information in the contemporary digital era.RESEARCH REVIEW International Journal of Multidisciplinary00This study seeks to understand the relationship between the digital divide and social inequality, focusing on how these factors interact and influence access to information in the contemporary digital era.RESEARCH REVIEW International Journal of Multidisciplinary['Amlan Lahiri']2024-01-16T00:00:00https://www.semanticscholar.org/paper/47e682f316f5e0bf8905aad17d06bdf68f175c79 3ea8b80da4f30fa074e84ad08bfbf2629d41a900ObjectiveThe global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care.Psychiatry380Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations, they must navigate challenges like data privacy and ethical considerations.Psychiatry['Xiaoming Li', 'Fenglan Chen', 'Lijun Ma']2024-01-16T00:00:00https://www.semanticscholar.org/paper/3ea8b80da4f30fa074e84ad08bfbf2629d41a900 fae6fb4ecb7b3dcaa9500299dcc0b13862698f8dBackground: Artificial intelligence (AI) is increasingly investigated for use in dermatologic conditions. We review recent literature on AI, its potential application for pediatric dermatology, and its impact on the underserved community. Objective: To evaluate the current state of AI in dermatology and its application to pediatric patients. Methods: Literature search was performed in PubMed and Google Scholar using the following key terms in combination with "pediatric", and "dermatology": "artificial intelligence," "AI," "machine learning," "augmented intelligence," "neural network," and "deep learning". Results: Current research is based on images from adult databases, with minimal delineation of patient age. Most literature on AI and dermatologic conditions pertains to melanoma and non-melanoma skin cancers, reporting accuracy from 67-99%. Other commonly studied diseases include psoriasis, acne vulgaris, onychomycosis, and atopic dermatitis, having varying accuracy, sensitivity, and specificity. A recently developed AI algorithm for diagnosis of infantile hemangioma found 91.7% accuracy. AI may be a means to increase access to pediatric dermatologic care, yet challenges remain for its use in underserved communities. Conclusion: Literature on AI systems for dermatologic diseases continues to grow. Further research may tailor AI algorithms for pediatric patients and those of diverse skin color to decrease algorithm bias and increase diagnostic accuracy.SKIN The Journal of Cutaneous Medicine00Evaluating the current state of AI in dermatology and its application to pediatric patients finds that AI may be a means to increase access to pediatric dermatologic care, yet challenges remain for its use in underserved communities.SKIN The Journal of Cutaneous Medicine['Joshua Burshtein', 'Maria Gnarra Buethe']2024-01-16T00:00:00https://www.semanticscholar.org/paper/fae6fb4ecb7b3dcaa9500299dcc0b13862698f8d 82dcd904dba8daa6518596a0a7883fea4470f958Artificial intelligence (AI) can give intelligent ideas for disease diagnosis and therapy by evaluating physiological data from wearable technology. AI and robots are getting more acceptable for doctors, and a growing number of institutions are using robots along with human supervision to do tasks that were previously performed by humans. The main advantage of AI is that it decreases the time required for medication development, which reduces the expenses associated with drug research, improves the returns on investment, and may even result in a cost reduction for the end user. The tools like MEDi robot and robotic pharmacy are described in this review. Personal health or pathology records and public health organizations could benefit from AI analysis to speed up and minimize failures in the drug discovery process. The different AI tools like robotic pharmacy used in the production of oral and injectable medications, including hazardous chemotherapy agents. Many studies are being conducted to improve the already existing AI technologies in order to make the pharmaceutical profession more efficient. The purpose of this article is to provide a quick overview of the importance of AI in pharmacyInternational Journal of Advanced Research in Science, Communication and Technology250The purpose of this article is to provide a quick overview of the importance of AI in pharmacy by describing the different AI tools used in the production of oral and injectable medications, including hazardous chemotherapy agents.International Journal of Advanced Research in Science, Communication and Technology['Ashwini Gaikwad', 'Sandesh Panmand', 'Rushikesh Gade', 'Akash Tattu', 'Pravin Hadawale']2024-01-16T00:00:00https://www.semanticscholar.org/paper/82dcd904dba8daa6518596a0a7883fea4470f958 ae0be9de7b5fe873a133827ca5c5c45e3406114bThis research aims to analyze the use of artificial intelligence platforms that can be used to convey writing subjects. This research used a mixed-method design that combines quantitative and qualitative approaches. This research aims to test the effectiveness of AI in teaching writing involving 30 high school students around the Kalideres RPTRA as samples. Quantitative data is obtained through pre-tests and, post-tests and surveys to measure English writing skills and students' perceptions of using AI in learning writing. Qualitative data was obtained through interviews. The findings of this research show that Artificial Intelligence technology can used as a medium in developing English language learning for students, especially writing skills. This research applies AI platforms, namely Gencraft and ChatGPT. The research results show an increase in writing skills after using AI. These findings support that using AI is effective in learning English, especially writing skills. The data analysis results show that the output Sig = 0.00 means that the pre-test and post-test averages differ. The application of Gencraft and ChatGPT media is effective in improving student's writing skills. Learning effectiveness with Gencraft and ChatGPT media can also be seen from the average pre-test score of 71,47 and post-test of 46,81, which shows that learning outcomes have increased. Observations made during the learning process using Gencraft and ChatGPT media revealed that the average student's writing ability was in a good category and was improving, as evidenced by the students' work in writing descriptive texts.English Learning Innovation00The findings show that Artificial Intelligence technology can used as a medium in developing English language learning for students, especially writing skills, and that using AI is effective in learning English, especially writing skills.English Learning Innovation['Rizky Mirani Desi Pratama', 'D. Hastuti']2024-01-16T00:00:00https://www.semanticscholar.org/paper/ae0be9de7b5fe873a133827ca5c5c45e3406114b 72ae7876e64e2b6901b5b97e2798fe2fe28cd554This essay explores the challenges and developments of FinTech in the era of artificial intelligence (AI). It highlights the potential benefits of AI in automating processes, enhancing decision-making, and delivering personalized financial services. However, it also identifies corresponding risks associated with the use of AI tools, including the source of data, the reliability of financial decisions, and potential disruption to the financial industry. The essay proposes effective strategies to address these challenges and discusses the role of AI in shaping the future of FinTech.Frontiers in Business, Economics and Management160The essay proposes effective strategies to address the challenges and developments of FinTech in the era of artificial intelligence and discusses the role of AI in shaping the future of FinTech.Frontiers in Business, Economics and Management['Kejia Duan']2024-01-16T00:00:00https://www.semanticscholar.org/paper/72ae7876e64e2b6901b5b97e2798fe2fe28cd554 4d25d6b2cfb0d102d3c7d2812354eb175186632bThis research aims to identify potential areas for future sociological research related to artificial intelligence (AI). The study used bibliometric analysis methods and the VosViewer pro- gram to process data. The data analyzed included 31 articles related to "sociology" and "artificial intelligence," and 1,277 articles pertinent to "social" and "artificial intelligence," all published on ScienceDirect between 2003 and 2023. Network visualization, overlay, and density analysis were used to process the data. This revealed that current sociological re- search on AI only covers five topics - artificial intelligence, sociology, technology, affects, and artificial intelligence. However, social research on AI has identified 100 topics across five datasets, with almost all research being conducted within the past decade. It is noteworthy that "sociology" is not among these 100 topics. However, these 100 topics have the potential to become sociological research subjects by applying sociological principles. The research findings suggest that sociologists can publish their scientific documents in 3,800 journals and books published by Elsevier, indicating a high probability of acceptance. Furthermore, the topics can be framed from a sociological perspective, thus providing greater insight on the subjects and potentially opening up the door to more publications by the sociologists.  Jurnal ilmu sosial580The research found that sociologists can publish their scientific documents in 3,800 journals and books published by Elsevier, indicating a high probability of acceptance and the topics can be framed from a sociological perspective, thus providing greater insight on the subjects and potentially opening up the door to more publications by the sociologists.JURNAL ILMU SOSIAL['Agustina Multi Purnomo']2024-01-16T00:00:00https://www.semanticscholar.org/paper/4d25d6b2cfb0d102d3c7d2812354eb175186632b a6628aec9566200f267fe61e64e563d847602ac6Pada saat ini, teknologi sudah sangat berkembang. Salah satu bisnis yang terkenal akan perkembangannya adalah online shopping. Dimana sistem penjualan mereka, mampu melayani pelanggan secara mudah dan efisien menggunakan teknologi artificial intelligence chatbot. Chatbot merupakan salah satu metode artificial intelligence yang digunakan dalam aplikasi perpesanan yang dapat membantu menambah kenyamanan bagi pelanggan sebagai program otomatis yang berinteraksi dengan pelanggan seperti layaknya manusia. Penelitian ini bertujuan untuk menganalisis pengaruh chatbot dalam kenyamanan pelanggan dalam berbelanja online pada e-commerce. Penelitian ini menggunakan penelitian kuantitatif. Teknik pengumpulan data yang digunakan dalam penelitian ini adalah dengan menggunakan kuesioner. Kuesioner disebarkan secara random pada cluster tertentu yaitu mahasiswa Universitas Internasional Batam, Universitas Teknologi Batam, Universitas Universal dan Universitas Putera Batam untuk memperoleh data sebanyak 400 responden untuk diukur. Pengolahan data dilakukan untuk mendapatkan hasil sebagai berikut : (1)Teori kepuasan pelanggan (2) Efek Responsiveness (3) Efek Extrinsic Value (4) Efek Intrinsic Value (5) Peran Online Convenience (6) Peran Customer Satisfaction. Berdasarkan hasil penelitian yang telah dilakukan, dapat disimpulkan bahwa dengan menggunakan artificial intelligence chatbot dapat memudahkan penjual untuk merespon pertanyaan dari pelanggan dengan cepat. Selain itu, dengan menggunakan chatbot, pelanggan akan mendapatkan jawaban dengan mudah dan cepat. Secara singkatnya, peran ketanggapan chatbot artificial intelligence dalam membentuk kepuasan pelanggan berperan penting dalam kelangsungan terjadinya proses jual beli online pada e-commerce.Technologia : Jurnal Ilmiah130Technologia : Jurnal Ilmiah['Erica Titoni, Dhafa Firgana, Bryan Aditya, Tito A. Priba Robby Lianto']2024-01-16T00:00:00https://www.semanticscholar.org/paper/a6628aec9566200f267fe61e64e563d847602ac6 b926ae580ee5a1000b0b3eda399afbb19fc18785This paper examines the transformative impact of Artificial Intelligence (AI) in healthcare, delving into its potential and challenges. It analyses the integration of AI into medical practices, focusing on how it revolutionizes diagnostics, treatment planning, and patient care. AI deployment's ethical and legal implications in healthcare are critically assessed, highlighting the need for robust frameworks to safeguard patient privacy and data security. The paper advocates for interdisciplinary collaboration among healthcare professionals, ethicists, and legal experts to optimize AI's benefits while mitigating risks. It underscores the importance of continual education and policy development to adapt to the evolving landscape of AI in healthcare, aiming for improved patient outcomes and efficient healthcare delivery.Special journal of the Medical Academy and other Life Sciences00The integration of AI into medical practices is analyzed, focusing on how it revolutionizes diagnostics, treatment planning, and patient care, and the need for robust frameworks to safeguard patient privacy and data security is highlighted.Special journal of the Medical Academy and other Life Sciences['N. Tzenios']2024-01-16T00:00:00https://www.semanticscholar.org/paper/b926ae580ee5a1000b0b3eda399afbb19fc18785 2351cba66fbafc3db229f53a712e853aa1d1e1caNature Communications280Comparing the results of this research with those of other recent studies confirm the supremacy of the developed second-level model over several previously developed models.Nature Communications['Saeid Akbarifard', 'Mohamad Reza Madadi', 'Mohammad Zounemat-Kermani']2024-01-16T00:00:00https://www.semanticscholar.org/paper/2351cba66fbafc3db229f53a712e853aa1d1e1ca 7327f152502e8d7a80989772deace698410afaf0Cartography and Geographic Information Science1422Cartography and Geographic Information Science['Yuhao Kang', 'Song Gao', 'Robert E. Roth']2024-01-16T00:00:00https://www.semanticscholar.org/paper/7327f152502e8d7a80989772deace698410afaf0 52a227145ea768268c3987d59b6da72531fd8b50Asia Pacific Journal of Education342Asia Pacific Journal of Education['Shuaiyao Ma', 'Lei Lei']2024-01-16T00:00:00https://www.semanticscholar.org/paper/52a227145ea768268c3987d59b6da72531fd8b50 66e9b6f5f9ac385c2f4e9a01ad5cb7c749d1feffEvidence-Based Nursing51Evidence-based nursing['A. Nashwan', 'Ahmad A. Abujaber']2024-01-16T00:00:00https://www.semanticscholar.org/paper/66e9b6f5f9ac385c2f4e9a01ad5cb7c749d1feff 3c349b99a3dbfe61f5f0cd305adcdff15510252cCIRET has set up an AI research group to highlight the need for human ethical mediation in theage of digital technology and binary logic. Francisco Varela has observed that cognition can take place inthe computer field without appealing to consciousness. Yet human decision-making cannot be the resultof cognition alone and requires the interaction between cognition and consciousness. In fact, rationalistand reductionist models borrowed from the hard sciences have only shown a mechanistic vision of AI ora biological-environmental vision, which cannot be applied to complex human phenomena occurring in agrey zone. In this grey or fuzzy zone of mediation, conciliation, and repair, we need the dialectical processor dialogue between consciousness and cognition. In this context, the proposal of mediating leaders andmanagers appears as a possible ethical alternative to demonstrate that consciousness is beyond the logicof the computer. Humans must remain responsible for all the effective decisions that will help us solveproblems theoretically and concretely. We therefore need an emerging global wisdom that flows from ourconversations about AI and appeals to human consciousness at all its levels of reality. This group produceda Symposium on November 21 and 22, 2023, in which we imagine that AI may be at the service of humanevolution and resiliency in “learning to be societies” instead of contributing to block our evolutions: if AIremains a work tool. . . Certainly it will because the creation sparkles still a mystery for men themselves.Transdisciplinary Journal of Engineering &amp; Science00This group produced a Symposium on November 21 and 22, 2023, in which it is imagined that AI may be at the service of humane evolution and resiliency in “learning to be societies” instead of contributing to block the authors' evolutions.Transdisciplinary Journal of Engineering &amp; Science['Site Admin', 'Mariana Thieriot Loisel']2024-01-16T00:00:00https://www.semanticscholar.org/paper/3c349b99a3dbfe61f5f0cd305adcdff15510252c c657d5d3f1de57030925326ffbf913656e3993c1Yapay zekâ şemsiye bir kavram olup dört türde kategorize edilmektedir. Bunlar, tepki veren, sınırlı hafızaya sahip, zihin teorisi ile bilinç kazanmış ve öz farkındalığa sahip yapay zekâ türleridir. Bu türler arasında halihazırda yalnızca tepki veren ve sınırlı hafızaya sahip yapay zekâ türleri günlük hayatta kullanılmaktadır. Zihin teorisi olarak adlandırılan yapay zekâya ilişkin çalışmalar halen devam etmektedir. Bu yapay zekâ türüne insansı robot Sophia örnek gösterilebilir. Dördüncü tür ise, artık kendi bilinci ve öz farkındalığı bulunan yapay zekâdır. Yapay zekânın hukuki statüsü konusunda temel görüş farklılığı, bu teknolojinin hak öznesi mi yoksa hak objesi mi olduğu noktasında toplanmaktadır. Bu çerçevede, eşya, köle, tüzel kişi ve elektronik kişi olmak üzere dört temel görüş öne sürülmüştür. Diğer yandan, Avrupa Parlamentosu tarafından, 14 Haziran 2023 tarihinde kabul edilen, dünyanın ilk Yapay Zekâ Yasası ile OECD tarafından 8 Kasım 2023 tarihinde açıklanan tanım, yapay zekânın hukuki statüsünü değerlendirmede yol göstericidir. Bu çerçevede yapay zekânın makine tabanlı bir sistem olduğu ve insan merkezli bir teknoloji olması zorunluluğu dikkate alınmalıdır.Anadolu Üniversitesi Hukuk Fakültesi Dergisi80Anadolu Üniversitesi Hukuk Fakültesi Dergisi['Özge YENİCE CEYLAN']2024-01-16T00:00:00https://www.semanticscholar.org/paper/c657d5d3f1de57030925326ffbf913656e3993c1 27d1d6be1967807b22160db26c8d5e589839c513Environmental science and pollution research international430A neural network optimised by particle swarm optimisation algorithm is employed to predict the total greenhouse gas emissions of the line in the construction phase to show the technical efficiency, pure technical efficiency, and scale efficiency of the stations and sections were high.Environmental science and pollution research international['Teng Li', 'Eryu Zhu', 'Zhengwei Bai', 'Wenchao Cai', 'Honghe Jian', 'Haoran Liu']2024-01-16T00:00:00https://www.semanticscholar.org/paper/27d1d6be1967807b22160db26c8d5e589839c513 29904d357355571f47a3403ec853b9bf641f3f89NEJM AI100NEJM AI['J. Kwong', 'David-Dan Nguyen', 'A. Khondker', 'J. Kim', 'Alistair Johnson', 'M. McCradden', 'Girish S. Kulkarni', 'A. Lorenzo', 'L. Erdman', 'M. Rickard']2024-01-16T00:00:00https://www.semanticscholar.org/paper/29904d357355571f47a3403ec853b9bf641f3f89 89f5af26a3d492defd0d96f95d24269b0cdf270aInternational Journal of Human Sciences Research00International Journal of Human Sciences Research['Mireya Salgado Gallegos', 'J. Merlos', 'Silvia Edith Albarrán Trujillo', 'Guadalupe Rodríguez Camacho']2024-01-16T00:00:00https://www.semanticscholar.org/paper/89f5af26a3d492defd0d96f95d24269b0cdf270a c00c9ef74e9a91e271427d05a536eb1001a22e74Background: Technological growth allows aviation companies to embrace practices and applications that improve their approaches. A concept that is fast gaining attention from firms in this area is the Metaverse. This technology, driven by Artificial Intelligence (AI), improves consumer services, particularly by allowing passengers to travel virtually. Various entities already use this feature, and organisational and scholarly reports suggest that such establishments record positive outcomes. The primary goal of this analysis is to describe why operators must watch out for possible legal implications of using this tool. An important point is that they must prevent data security breaches that might violate consumers’ privacy rights. A few enterprises in this sector have become victims of infringements that resulted in data loss. Subsequently, some of these issues may proceed to court, and organisations spend many resources handling such cases. Another vital message relating to the utilisation of this innovation is that it could cause unfair competition. Particular establishments, especially those yet to deploy this idea, may claim groups that use Metaverse for exposing vital personal data to cyber attackers. Besides, the sector witnesses legal proceedings whereby some airlines blame competitors for indulging in unfair competition. While no specific Metaverse laws exist, a suitable remedy for operators is to follow legislations and policies that define AI use for commercial purposes. It is necessary to abide by regulations safeguarding consumers’ data privacy. Another solution is that corporations can adhere to international provisions such as the General Data Protection Regulation (GDPR) that have a global effect. Moreover, non-compliance could cause devastating legal repercussions that harm business practices. This paper introduces these challenges and pays more attention to the practical and legal aspects. Methods: This paper retrieves data from secondary sources, encompassing websites and journal articles. The approach entails reviewing what the authors of selected works present about the topic and taking relevant information for this project. The approach saves time and is cost-effective. Results and conclusion: Various firms in the aviation sector already use Metaverse to enhance their consumer experience. Companies feel attracted because of the many merits associated with the technology. However, they must watch out for the potential limitations of using this concept. In addition, users should consider the legal aspects of the innovation.Access to Justice in Eastern Europe241Access to Justice in Eastern Europe['Meera Abdulla Alshamsi', 'Attila Sipos']2024-01-16T00:00:00https://www.semanticscholar.org/paper/c00c9ef74e9a91e271427d05a536eb1001a22e74 b467a226955f8c6865ca0555ed0d65de9c8610d0India is one of the largest markets for the automobile sector and considering the trends of road fatalities and injuries related to road accidents, it is pertinent to continuously review the safety regulations and introduce standards which promise enhanced safety. With this objective, various Advanced Driver Assistance Systems (ADAS) regulations are proposed to be introduced in the Indian market. ADAS such as, Anti-lock Braking Systems, Advanced Emergency Braking systems, Lane Departure Warning Systems, Auto Lane Correction Systems, Driver Drowsiness Monitoring Systems, etc., assist the driver during driving. They tend to reduce road accidents and related fatalities by their advanced and artificial intelligent fed programs. This paper will share an insight on the past, recent trends and the upcoming developments in the regulation domain with respect to safety.SAE technical paper series00SAE Technical Paper Series['Pratik Nayak', 'Vishal Rawal', 'Kamalesh Patil', 'Vikram Tandon', 'Akbar Badusha']2024-01-16T00:00:00https://www.semanticscholar.org/paper/b467a226955f8c6865ca0555ed0d65de9c8610d0 8ed3851a65e1a649a439cf0e2cd192939558c479The emergence and popularity of generative artificial intelligence (AI) tools, particularly text-based ones known as large language models, pose both opportunities and challenges to education. The ability of these tools to generate human-like texts based on minimal instructions causes concerns among educators about students’ use of these tools for academic writing, which may constitute a breach of academic integrity. We propose a pedagogical design that models on self-regulated learning and the authoring cycle and develops students’ critical thinking and self-regulation when composing academic writing using text-based generative AI tools. It contains six iterative and interactive phases. Students first plan the content and structure of the writing, then generate prompts for text-based generative AI tools. Next, students preview and verify the tools’ output, followed by the fourth phase of producing the writing using the corrected output. Fifthly, peer review by fellow students may be required to polish and proofread the writing. Lastly, through portfolio-tracking, students reflect on the writing process, and formulate strategies for future usage of text-based generative AI tools for writing. This pedagogical design helps students and teachers embrace text-based generative AI while addressing the perils these tools present, and guides the development of education interventions and instruments.Research and Practice in Technology Enhanced Learning751A pedagogical design that models on self-regulated learning and the authoring cycle and develops students’ critical thinking and self-regulation when composing academic writing using text-based generative AI tools is proposed.Res. Pract. Technol. Enhanc. Learn.['Siu-Cheung Kong', 'John Chi-Kin Lee', 'Olson Tsang']2024-01-15T00:00:00https://www.semanticscholar.org/paper/8ed3851a65e1a649a439cf0e2cd192939558c479 b23cc69452a7a4a80e288f01f310320361083251: The purpose of this research is to analyze the significant impact role of financial architecture regulation in the fintech peer-to-peer (P2P) lending ecosystem, using a quantitative approach and the SEM-Amos analysis tools. The research period was carried out around 2022, using research instruments in the form of distributing questionnaires, and the respondents were users of the P2P lending fintech mobile application spread, both as borrowers - lenders and interviews with stakeholders in the P2P lending fintech industry. The results indicated a significant impact of financial architecture regulation on the fintech P2P lending ecosystem, with an estimated value of 0.922. This finding demonstrates that financial architecture regulation plays a crucial role in establishing a strong and stable fintech P2P lending ecosystem. The provision of regulations, is one of the core functions of regulatory bodies and serves to guide and direct the future development of this industry. It is essential that regulations address key aspects such as big data analytics, automation and robotics, which serves as the basis for the development of information technology (IT),) to strengthen the fintech P2P lending ecosystem.Indonesian Journal of Business and Entrepreneurship440A significant impact of financial architecture regulation on the fintech P2P lending ecosystem, with an estimated value of 0.922%, demonstrates that financial architecture regulation plays a crucial role in establishing a strong and stable fintech P2P lending ecosystem.Indonesian Journal of Business and Entrepreneurship['Chandra Wijaya', 'B. Y. Nugroho', 'M. F. Arkanuddin']2024-01-15T00:00:00https://www.semanticscholar.org/paper/b23cc69452a7a4a80e288f01f310320361083251 18a3061c865e176eb7ff238e29ec967df8a9e77bThe article identifies current trends affecting the legal mechanism of harmonization of self-regulation and state regulation. The scientific forecast is substantiated that against the background of trends in socialization and democratization of the economy and entrepreneurship, the process of harmonization of state regulation and self-regulation will only intensify, which will be facilitated by the further development of digital technologies.CIVIL LAW00Civil law['A. Barkov', 'Yana S. Grishina']2024-01-15T00:00:00https://www.semanticscholar.org/paper/18a3061c865e176eb7ff238e29ec967df8a9e77b 35ff9ba77be6ddde2d972fca4f6eaf08e7a20bd0L’intelligence artificielle nourrit craintes et espoirs en matière de fiabilité de l’information et d’évolutions du métier de journaliste. En réalité, l’enjeu tient au bon usage de l’outillage.Revue Projet00Revue Projet['L. Dierickx']2024-01-15T00:00:00https://www.semanticscholar.org/paper/35ff9ba77be6ddde2d972fca4f6eaf08e7a20bd0 f0c77924b20fea6b123074a912c5a13bc714852cThe paper is an assessment of several efforts undertaking as well as challenges faced by National Environmental Standards Regulatory and Enforcement Agency (NESREA) being the regulator in enforcing the provisions of various environmental laws which have been enacted from independence to the present as an unwavering answer to dealing with environmental threats challenging Nigeria as a country. The work examines the effectiveness of NESREA in Nigeria as an enforcement agency, looks in brief the environmental challenges to economic development of Nigeria and the well-being of its citizenry in line with the national and international provisions for environmental assessment in Nigeria. This work employed the use of primary and secondary sources in gathering of information. The paper identified a lot of challenges confronting NESREA to include flagrant abuse of environmental laws without commensurate penalties, weak enforcement, corruption on the part of the staff of the regulatory agency, poor public enlightenment education while also advocating for the reinforcement of the regulatory institution to make room for a broader participation, an elaborate supervisory, monitoring and enforcement of environmental rules, laws and principles.British Journal of Mass Communication and Media Research300British Journal of Mass Communication and Media Research['Nwachukwu Okechukwu']2024-01-15T00:00:00https://www.semanticscholar.org/paper/f0c77924b20fea6b123074a912c5a13bc714852c 66395b760438242b87357c5317a872b483dd7169This study analyses artificial intelligence (AI's) impact on education in Africa, focusing on personalized learning, technology integration, and challenges in educational development. This review explores the transformative role of Artificial Intelligence (AI) in reshaping educational landscapes across Africa. As the continent strives for inclusive and quality education, AI emerges as a potent tool with the potential to address educational challenges, enhance learning outcomes, and bridge existing gaps. The review delves into various applications of AI in education, ranging from personalized learning experiences to adaptive assessment methodologies, and examines their impact on diverse learning environments. It gives an overview of the current state of education in Africa, the review highlights the disparities in access, quality, and infrastructure. It also investigates the innovative ways in which AI technologies are being integrated into educational systems. AI-powered adaptive learning platforms, virtual tutors, and intelligent content delivery systems are analyzed for their effectiveness in catering to the diverse needs of students across the continent. The review also addresses the potential of AI in overcoming language barriers, promoting literacy, and fostering digital skills development. Moreover, it explores the role of AI in facilitating teacher support, professional development, and administrative tasks, thereby contributing to the overall improvement of the education ecosystem. Ethical considerations, privacy concerns, and the digital divide are critically examined to ensure that the integration of AI in education aligns with ethical standards and promotes equitable access. Case studies and pilot projects from various African countries are presented to illustrate successful implementations, challenges faced, and lessons learned. Furthermore, the review discusses the importance of collaborative efforts involving governments, educational institutions, technology developers, and the private sector. Policy recommendations and strategic initiatives are explored to guide the responsible and sustainable integration of AI in education across the diverse socio-economic and cultural contexts prevalent in Africa. In conclusion, the review synthesizes the current state of AI in education in Africa, offering insights into its potential to revolutionize learning environments. The transformative power of AI in addressing educational challenges and fostering a culture of continuous improvement is underscored, paving the way for a more inclusive, accessible, and innovative education landscape in the African context. Keywords: Artificial Intelligence, Education, Transform Learning, Environments, Africa.International journal of applied research in social sciences03The transformative power of AI in addressing educational challenges and fostering a culture of continuous improvement is underscored, paving the way for a more inclusive, accessible, and innovative education landscape in the African context.International Journal of Applied Research in Social Sciences['Onyebuchi Nneamaka Chisom', 'Chika Chioma Unachukwu', 'Blessing Osawaru']2024-01-15T00:00:00https://www.semanticscholar.org/paper/66395b760438242b87357c5317a872b483dd7169 bb2a8cf8b119b39d172b47be7c8d153f9ec1217fArtificial intelligence (AI) has the potential to revolutionize healthcare by automating the detection and classification of events and anomalies. In the scope of this work, events and anomalies are abnormalities in the patient’s data, where the former are due to a medical condition, such as a seizure or a fall, and the latter are erroneous data due to faults or malicious attacks. AI-based event and anomaly detection (EAD) and their classification can improve patient outcomes by identifying problems earlier, enabling more timely interventions while minimizing false alarms caused by anomalies. Moreover, the advancement of Medical Internet of Things (MIoT), or wearable devices, and their high processing capabilities facilitated the gathering, AI-based processing, and transmission of data, which enabled remote patient monitoring, and personalized and predictive healthcare. However, it is fundamental in healthcare to ensure the explainability of AI systems, meaning that they can provide understandable and transparent reasoning for their decisions. This article proposes an online EAD approach using a lightweight autoencoder (AE) on the MIoT. The detected abnormality is explained using KernelSHAP, an explainable AI (XAI) technique, where the explanation of the abnormality is used, by an artificial neural network (ANN), to classify it into an event or anomaly. Intensive simulations are conducted using the Medical Information Mart for Intensive Care (MIMIC) data set for various physiological data. Results showed the robustness of the proposed approach in the detection and classification of events, regardless of the percentage of the present anomalies.IEEE Internet of Things Journal393Results showed the robustness of the proposed approach in the detection and classification of events, regardless of the percentage of the present anomalies, in the detection and classification of events.IEEE Internet of Things Journal['Menatalla Abououf', 'Shakti Singh', 'R. Mizouni', 'Hadi Otrok']2024-01-15T00:00:00https://www.semanticscholar.org/paper/bb2a8cf8b119b39d172b47be7c8d153f9ec1217f a16d32deaac860af3722893b7a65fbed9d8725f5This study explores how discussing metaphors for AI can help build awareness of the frames that shape our understanding of AI systems, particularly large language models (LLMs) like ChatGPT. Given the pressing need to teach"critical AI literacy", discussion of metaphor provides an opportunity for inquiry and dialogue with space for nuance, playfulness, and critique. Using a collaborative autoethnographic methodology, we analyzed metaphors from a range of sources, and reflected on them individually according to seven questions, then met and discussed our interpretations. We then analyzed how our reflections contributed to the three kinds of literacies delineated in Selber's multiliteracies framework: functional, critical, and rhetorical. These allowed us to analyze questions of ethics, equity, and accessibility in relation to AI. We explored each metaphor along the dimension of whether or not it was promoting anthropomorphizing, and to what extent such metaphors imply that AI is sentient. Our findings highlight the role of metaphor reflection in fostering a nuanced understanding of AI, suggesting that our collaborative autoethnographic approach as well as the heuristic model of plotting AI metaphors on dimensions of anthropomorphism and multiliteracies, might be useful for educators and researchers in the pursuit of advancing critical AI literacy.Open Praxis351It is suggested that the collaborative autoethnographic approach as well as the heuristic model of plotting AI metaphors on dimensions of anthropomorphism and multiliteracies, might be useful for educators and researchers in the pursuit of advancing critical AI literacy.ArXiv['Anuj Gupta', 'Yasser Atef', 'Anna Mills', 'Maha Bali']2024-01-15T00:00:00https://www.semanticscholar.org/paper/a16d32deaac860af3722893b7a65fbed9d8725f5 886f6e33b8c970e4f18769ecc4b048c3e88809c4The functions of human resource management (HRM) have changed radically in the past 20 years due to market and technological forces, becoming more cross-functional and data-driven. In the age of AI, the role of HRM professionals in organizations continues to evolve. Artificial intelligence (AI) is transforming many HRM functions and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. A growing body of evidence highlights the benefits AI brings to the field of HRM. Despite the increased interest in AI-HRM scholarship, focus on human-AI interaction at work and AI-based technologies for HRM is limited and fragmented. Moreover, the lack of human considerations in HRM tech design and deployment can hamper AI digital transformation efforts. This paper provides a contemporary and forward-looking perspective to the strategic and human-centric role HRM plays within organizations as AI becomes more integrated in the workplace. Spanning three distinct phases of AI-HRM integration (technocratic, integrated, and fully-embedded), it examines the technical, human, and ethical challenges at each phase and provides suggestions on how to overcome them using a human-centric approach. Our paper highlights the importance of the evolving role of HRM in the AI-driven organization and provides a roadmap on how to bring humans and machines closer together in the workplace.Frontiers Artif. Intell.961The importance of the evolving role of HRM in the AI-driven organization is highlighted and a roadmap on how to bring humans and machines closer together in the workplace is provided.Frontiers in Artificial Intelligence['Ali Fenwick', 'Gábor Molnár', 'Piper Frangos']2024-01-15T00:00:00https://www.semanticscholar.org/paper/886f6e33b8c970e4f18769ecc4b048c3e88809c4 690382e5a348cbd13237f164835fb0672c6e517eThe conventional discourse on existential risks (x-risks) from AI typically focuses on abrupt, dire events caused by advanced AI systems, particularly those that might achieve or surpass human-level intelligence. These events have severe consequences that either lead to human extinction or irreversibly cripple human civilization to a point beyond recovery. This discourse, however, often neglects the serious possibility of AI x-risks manifesting incrementally through a series of smaller yet interconnected disruptions, gradually crossing critical thresholds over time. This paper contrasts the conventional"decisive AI x-risk hypothesis"with an"accumulative AI x-risk hypothesis."While the former envisions an overt AI takeover pathway, characterized by scenarios like uncontrollable superintelligence, the latter suggests a different causal pathway to existential catastrophes. This involves a gradual accumulation of critical AI-induced threats such as severe vulnerabilities and systemic erosion of econopolitical structures. The accumulative hypothesis suggests a boiling frog scenario where incremental AI risks slowly converge, undermining resilience until a triggering event results in irreversible collapse. Through systems analysis, this paper examines the distinct assumptions differentiating these two hypotheses. It is then argued that the accumulative view reconciles seemingly incompatible perspectives on AI risks. The implications of differentiating between these causal pathways -- the decisive and the accumulative -- for the governance of AI risks as well as long-term AI safety are discussed.arXiv.org561It is argued that the accumulative view reconciles seemingly incompatible perspectives on AI risks, and suggests a boiling frog scenario where incremental AI risks slowly converge, undermining resilience until a triggering event results in irreversible collapse.ArXiv['Atoosa Kasirzadeh']2024-01-15T00:00:00https://www.semanticscholar.org/paper/690382e5a348cbd13237f164835fb0672c6e517e ab2ca48c2c0001a569802fc2ee69dfcd1229b202This study examines physicians’ attitudes toward the intention to use AI doctors in healthcare. Currently, physicians use smart health technologies, health data, and AI in disease-focused research hospitals, and industry regulators hope that AI technology will be extensively used for each person, which means a shift from disease-centric to individual-centric healthcare. Using the theory of technology acceptance and use, a research model was developed to understand physicians’ intentions to use AI doctors for data collection, diagnosis, treatment planning, and patient follow-up. The causal comparison screening technique was used to determine the causes and consequences of physicians’ attitudes, behaviors, ideas, and beliefs. The responses of 478 physicians were evaluated using structural equation modeling and deep learning (an artificial neural network). It was discovered that physicians intend to use AI doctors first for diagnosis and treatment planning, and then for data collection and patient follow-up. According to the findings, the main constructs are performance expectancy, perceived task technology fit, high-tech habits, and hedonic motivation.Environment and Social Psychology01It was discovered that physicians intend to use AI doctors first for diagnosis and treatment planning, and then for data collection and patient follow-up, and the main constructs are performance expectancy, perceived task technology fit, high-tech habits, and hedonic motivation.Environment and Social Psychology['A. Uymaz', 'Pelin Uymaz', 'Yakup Akgül']2024-01-15T00:00:00https://www.semanticscholar.org/paper/ab2ca48c2c0001a569802fc2ee69dfcd1229b202 c70825dd184f8c8693db6c9c270b3cddc1539819Artificial Intelligence (AI) is rapidly transforming various industries including marketing. The use of AI in marketing has made it more efficient and effective than ever before. However, there is a growing concern that AI may soon replace marketers altogether. This research aims to investigate the impact of AI on the marketing profession. Using a qualitative research approach, data was collected through in-depth interviews with marketing professionals and experts in the field of AI. The findings reveal that AI has already had a significant impact on marketing, particularly in the areas of data analysis and personalization. AI-powered tools and algorithms can analyse large amounts of data and provide insights that marketers would not have been able to uncover otherwise. Additionally, AI can create personalized content for individuals based on their preferences and behaviours. However, the research also found that AI is not yet advanced enough to fully replace marketers. While AI can assist with certain tasks, such as data analysis and content creation, it cannot replace the creativity and human touch that marketers bring to the table. Furthermore, there are ethical concerns around the use of AI in marketing, particularly with regards to privacy and bias. Keyword- Artificial Intelligence; MarketingINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The research found that AI is not yet advanced enough to fully replace marketers, while AI can assist with certain tasks, such as data analysis and content creation, it cannot replace the creativity and human touch that marketers bring to the table.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Nashrah Arshad', 'Kshitiz Sharma', 'Aaditya Singh', 'Vaishnavi Kavhale']2024-01-15T00:00:00https://www.semanticscholar.org/paper/c70825dd184f8c8693db6c9c270b3cddc1539819 f34c30508a0c3798d5b21ed9c8b49dd43195132fOne of the key challenges in preventing major process safety accidents in an operating plant is the lack of an integrated system/model that brings together the risks posed by the deficiencies / deviations on the safety critical barriers, for operational decision making. Based on this context, a model/framework was developed for assessing and visualizing the accumulation of process safety risks arising from safety critical barriers impairments in petroleum facilities in Niger-Delta Nigeria. Based on the review of the model, the need for an intelligent web-based software was identified. An exploratory study was therefore undertaken through extensive literature review and focused group participants, to develop a conceptual framework for an intelligent web-based software for process safety cumulative risk visualization. The results from the study make it evident that the conceptual framework provides a novel approach in developing an intelligent web-based software using artificial intelligence (AI) techniques, for real time visualization of process safety cumulative risk picture.International journal of engineering and advanced technology studies00The results make it evident that the conceptual framework provides a novel approach in developing an intelligent web-based software using artificial intelligence (AI) techniques, for real time visualization of process safety cumulative risk picture.International Journal of Engineering and Advanced Technology Studies['Emeka Maduabuchi']2024-01-15T00:00:00https://www.semanticscholar.org/paper/f34c30508a0c3798d5b21ed9c8b49dd43195132f cabd370fef97ab4a0cf90df7a214c719cd345330Generative AI (Gen AI) is an emerging AI Technology which broadly describes machine learning systems capable of generating numerous applications in various domains. AI Users can use Gen AI for generating text, image, program code or other types of contents. The main capability of Gen AI is to produce highly realistic and complex contents that can imitate human creativity, making a valuable AI for many application domains. This paper focuses on emergence, its evolution and future of Generative AI.International Journal For Multidisciplinary Research60The main capability of Gen AI is to produce highly realistic and complex contents that can imitate human creativity, making a valuable AI for many application domains.International Journal For Multidisciplinary Research['Chalamayya Batchu', 'Veera Venkt Satya']2024-01-15T00:00:00https://www.semanticscholar.org/paper/cabd370fef97ab4a0cf90df7a214c719cd345330 37662d2dc082d8714d0dd4c3e4b6328e1eeb39cdArtificial Intelligence is a field that lives many lives, and the term has come to encompass a motley collection of scientific and commercial endeavours. In this paper, I articulate the contours of a rather neglected but central scientific role that AI has to play, which I dub `AI-as-exploration'.The basic thrust of AI-as-exploration is that of creating and studying systems that can reveal candidate building blocks of intelligence that may differ from the forms of human and animal intelligence we are familiar with. In other words, I suggest that AI is one of the best tools we have for exploring intelligence space, namely the space of possible intelligent systems. I illustrate the value of AI-as-exploration by focusing on a specific case study, i.e., recent work on the capacity to combine novel and invented concepts in humans and Large Language Models. I show that the latter, despite showing human-level accuracy in such a task, most probably solve it in ways radically different, but no less relevant to intelligence research, to those hypothesised for humans.arXiv.org430It is suggested that AI is one of the best tools the authors have for exploring intelligence space, namely the space of possible intelligent systems, which may differ from the forms of human and animal intelligence they are familiar with.ArXiv['Dimitri Coelho Mollo']2024-01-15T00:00:00https://www.semanticscholar.org/paper/37662d2dc082d8714d0dd4c3e4b6328e1eeb39cd 8db72bc612045661eb50d2263e76469854593686This research paper explores the pivotal role that Artificial Intelligence (AI) plays in the financial industry, specifically in the realm of fraud detection and prevention. As financial transactions increasingly migrate to digital platforms, the need for robust security measures has become paramount. This paper examines how AI technologies, such as machine learning and data analytics, are employed to identify and mitigate fraudulent activities. By analyzing existing literature, case studies, and industry reports, we aim to provide a comprehensive understanding of the effectiveness and challenges associated with integrating AI into financial systems. Keyword: Artificial Intelligence, AI in Finance, Fraud Detection using AIINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper examines how AI technologies are employed to identify and mitigate fraudulent activities in the financial industry, specifically in the realm of fraud detection and prevention.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-01-15T00:00:00https://www.semanticscholar.org/paper/8db72bc612045661eb50d2263e76469854593686 6ba87d96a082c14f54af2ac46f99d972e0a9ad7eOBJECTIVE In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of "signal-image-knowledge" has remained unchanged. However, the process of "signal to image" inevitably introduces information distortion, ultimately leading to irrecoverable biases in the "image to knowledge" process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal). APPROACH This study focuses on Computed Tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of "human-signal-image" using the workflow "CT-simulated data- reconstructed CT," and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1,994 patients with retrospective cases of solid lung nodules and modeled different types of data. Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an AUC of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866). SIGNIFICANCE The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of "signal-to-image" can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of "signal-image-knowledge", opening up new avenues for more accurate medical diagnostics. .Physics in Medicine and Biology00A novel AI predictive model directly targeting raw data (RCTM) is developed, embodying the fusion of local to global information extraction from raw data, which can enhance the precision of AI models and the concept of "signal-to-image" can be extended to other types of imaging.Physics in medicine and biology['Bingxi He', 'Caixia Sun', 'Hailin Li', 'Yongbo Wang', 'Y. She', 'Mengmeng Zhao', 'M. Fang', 'Yongbei Zhu', 'Kun Wang', 'Zhenyu Liu', 'Ziqi Wei', 'Wei Mu', 'Shuo Wang', 'Zhenchao Tang', 'Jingwei Wei', 'Lizhi Shao', 'Lixia Tong', 'Feng Huang', 'Yu Guo', 'Huimao Zhang', 'Mingze Tang', 'D. Dong', 'Chang Chen', 'Jianhua Ma', 'Jie Tian']2024-01-15T00:00:00https://www.semanticscholar.org/paper/6ba87d96a082c14f54af2ac46f99d972e0a9ad7e 987f98ade902eae9dd1d20880514d07e76aeed27With the high-speed development of society, as well as the increasingly fierce competition and social anxiety, the demand for emotional services has increased greatly. Generally, it is difficult to fully personalize human interaction to fulfill peoples needs, but the emerging interactive Artificial Intelligence (AI) fills a lot of gaps and is continuing to be deeply integrated into human life in all aspects. As an emerging technology, the development potential and challenges of interactive AI in various aspects are simultaneously revealed in the human vision. Taking the movie HER as an example, this paper analyzes the role of interactive AI in establishing an emotional connection with humans and the problems it faces in the process such as the separation of human-machine emotions. It can be summarized that while interactive AI has the potential to fulfill social needs, provide emotional value, and assist in the sorting out of information, it also faces challenges in ethics, privacy, and copyright.Lecture Notes in Education Psychology and Public Media00It can be summarized that while interactive AI has the potential to fulfill social needs, provide emotional value, and assist in the sorting out of information, it also faces challenges in ethics, privacy, and copyright.Lecture Notes in Education Psychology and Public Media['Xinyi Hou']2024-01-15T00:00:00https://www.semanticscholar.org/paper/987f98ade902eae9dd1d20880514d07e76aeed27 5a112885aa60ca8a441a4b4ebe8a5c980e593bf8In this paper, starting from heart cardiovascular data of human beings, we provide a neuron network based mathematical computation method to capture the nonlinear dynamics of cardiovascular data and proposed an AI based approach to predict cardiovascular data and then classify between a healthy data model and an ill data model. From the cardiovascular nonlinear dynamics model, we further elucidate its similarity to Wiener problem as a special case and Kalman filtering. This clarify interpret able of AI and neuron network based approach for heartbeat data science.Other Conferences70From the cardiovascular nonlinear dynamics model, its similarity to Wiener problem as a special case and Kalman filtering is elucidated and this clarify interpret able of AI and neuron network based approach for heartbeat data science.{'pages': '1298304 - 1298304-6', 'volume': '12983'}['Mingyong Zhou', 'Jiajin Li']2024-01-15T00:00:00https://www.semanticscholar.org/paper/5a112885aa60ca8a441a4b4ebe8a5c980e593bf8 57f7ca446ee064215cbb4544e967b670fe9710c5AI and Ethics750The current state of AI ethical guidelines within the European Union is investigated, which revealed that most guidelines do not acknowledge gender bias but address discrimination, and four recommendations for designing effective guidelines to tackle gender biases in AI are proposed.AI and Ethics['Ana Santana González', 'Lucia Rampino']2024-01-15T00:00:00https://www.semanticscholar.org/paper/57f7ca446ee064215cbb4544e967b670fe9710c5 1386c679fc8c86526d89a02f0498909a3555fe42In the era of large AI models, the complex architecture and vast parameters present substantial challenges for effective AI quality management (AIQM), e.g. large language model (LLM). This paper focuses on investigating the quality assurance of a specific LLM-based AI product--a ChatGPT-based sentiment analysis system. The study delves into stability issues related to both the operation and robustness of the expansive AI model on which ChatGPT is based. Experimental analysis is conducted using benchmark datasets for sentiment analysis. The results reveal that the constructed ChatGPT-based sentiment analysis system exhibits uncertainty, which is attributed to various operational factors. It demonstrated that the system also exhibits stability issues in handling conventional small text attacks involving robustness.arXiv.org380The study delves into stability issues related to both the operation and robustness of the expansive AI model on which ChatGPT is based, and demonstrates that the constructed ChatGPT-based sentiment analysis system exhibits uncertainty, which is attributed to various operational factors.ArXiv['Tinghui Ouyang', 'AprilPyone Maungmaung', 'Koichi Konishi', 'Yoshiki Seo', 'Isao Echizen']2024-01-15T00:00:00https://www.semanticscholar.org/paper/1386c679fc8c86526d89a02f0498909a3555fe42 4cfed99b934c90ceb60a4ec99bdd4ee47b2e533bObjectives The purposes of this study is to analyze the view of pre-service teachers’ about teacher’s role in the AI era. Methods For this, reflective writing was used for 101 pre-service teachers who were enrolled university located in D metropolitan. Results Pre-service teachers predicated that AI’s influence on school education would increase in the future society. As a result, they concerned about excessive dependence on AI, fairness in evaluation and individualism while there were positive aspect of individualized education for learners, reducing teachers’ work and reducing the academic gap between students. Pre-service teachers recognized that teachers were who guide students to the human life by understanding and empathizing for them with a sense of duty in education and that AI could never replace them. They thought that teachers in future society should play the role of class planners, AI administrators, sympathetic leaders, guides to community life, and persons who develop critical thinking skills of students. Conclusions Frist, Pre-service teachers positively recognize the need for school change due to social change and show their’s subjective appearance as subjects for school change. Second, focusing on the ambivalence of AI in-fluence on education, they define the role of teacher in terms of ethics aspect, focusing on humanity. Third, it can be seen that pre-service teachers have a self-reflection aspect, in that they define the identity of teachers through understanding and empathy of the relationship between teachers and students. Forth, Pre-service teachers suggest what the educational direction schools should be by defining the desirable human life is com-munity life.Korean Association For Learner-Centered Curriculum And Instruction00Pre-service teachers positively recognize the need for school change due to social change and show their’s subjective appearance as subjects for school change and define the role of teacher in terms of ethics aspect, focusing on humanity.Korean Association For Learner-Centered Curriculum And Instruction['Hye-Jeung Lee']2024-01-15T00:00:00https://www.semanticscholar.org/paper/4cfed99b934c90ceb60a4ec99bdd4ee47b2e533b 328e83f14b3258eef00605b086a335e663a17bd7Multim. Tools Appl.170Multimedia Tools and Applications['Andrea Cartolano', 'Alfredo Cuzzocrea', 'Giovanni Pilato']2024-01-15T00:00:00https://www.semanticscholar.org/paper/328e83f14b3258eef00605b086a335e663a17bd7 26ffa0631ba2d2c01297e4e23cf3ff9ee0100daaSemantris Google AI-Based Learning research has examined the students' disinterest and low level of vocabulary acquisition in English subject at MTs DDI Takkalasi. The students cited difficulty in pronunciation and other reasons for their lack of enthusiasm towards English as a difficult subject. This lack of interest and motivation is due to the absence of an interesting and enjoyable learning environment, which hinders students' ability to concentrate on their studies. In addition, the limited application of modern technology in the learning process makes this a problem that must be addressed given the increasingly advanced era. The research employed a quasi-experimental design to investigate the effectiveness of an intervention in improving students' English language proficiency. The study utilized a quantitative approach, conducting pre-test and post-test analyses to measure the impact of the intervention. The pre-test served as a baseline measurement, while the post-test assessed the outcomes after implementing the intervention. The researcher employed a quota sampling technique to select participants. The results of the Semantris Google AI-Based Learning study showed a significant difference in vocabulary improvement between the two groups, as evidenced by the T-count value of 6.858, exceeding the critical T-table value of 2.060. The rejection of the null hypothesis (H0) is supported by the mean N gain value of 0.4249 obtained from the experimental class, which falls within the moderate range according to the N gain table. This finding confirms that the use of Semantris Google AI-Based Learning is very effective in improving the vocabulary mastery of MTs DDI Takkalasi students.Al-Irsyad: Journal of Education Science00A significant difference in vocabulary improvement was showed between the two groups, confirming that the use of Semantris Google AI-Based Learning is very effective in improving the vocabulary mastery of MTs DDI Takkalasi students.Al-Irsyad: Journal of Education Science['Fathur Rahman Arif Hasan', 'Adi Isma', 'Ambo Dalle', 'Yulie Asni']2024-01-15T00:00:00https://www.semanticscholar.org/paper/26ffa0631ba2d2c01297e4e23cf3ff9ee0100daa efa90d236f292d42c57f5b963b9ea70531e11163The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledge for smart systems. However, the concept of machine unlearning has emerged as a transformative paradigm shift in the field of AI, due to the amount of false information that have been learned over the past. Machine unlearning refers to the ability of AI systems to reverse or discard previously acquired knowledge or patterns, enabling them to adapt and refine their understanding in response to changing circumstances or new insights. This paper explores the concept of machine unlearning, its implications, methods, challenges, and potential applications. The paper begins by providing an overview of the traditional learning-based approaches in AI and the limitations they impose on system adaptability and agility. It then delves into the concept of machine unlearning, discussing various techniques and algorithms employed to remove or modify learned knowledge from AI models or datasets.2024 21st Learning and Technology Conference (L&T)120The paper begins by providing an overview of the traditional learning-based approaches in AI and the limitations they impose on system adaptability and agility, and delves into the concept of machine unlearning, discussing various techniques and algorithms employed to remove or modify learned knowledge from AI models or datasets.2024 21st Learning and Technology Conference (L&T)['Layan Jaman', 'Reem Alsharabi', 'Passent Elkafrawy']2024-01-15T00:00:00https://www.semanticscholar.org/paper/efa90d236f292d42c57f5b963b9ea70531e11163 57eebbad02d95ab0bd8b712ff29fdbb63c12cdd4The research article explores the factors influencing student's preferences towards the usage of Artificial Intelligence (AI) tools in their academics. The study employs exploratory research methods, beginning with an online questionnaire distributed to university students, aiming to identify key attributes or variables affecting the preference for AI tools in academic work. The data collected is used to perform statistical analysis, employing factor analysis to reduce the 25 attributes. The results reveal that the attributes are reduced to 5 factors such as 'Easiness and convenience,' 'Interest Less,' 'Creativity,' 'Feeling Bored,' and 'Course Likeliness' which significantly impact students' preferences. Keywords : Artificial intelligence (AI), Factor Analysis, Education, AcademicsINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The results reveal that the attributes are reduced to 5 factors such as 'Easiness and convenience,' 'Interest Less,' 'Interest Less,' 'Creativity,' 'Feeling Bored,' and 'Course Likeliness' which significantly impact students' preferences.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['S. J I']2024-01-15T00:00:00https://www.semanticscholar.org/paper/57eebbad02d95ab0bd8b712ff29fdbb63c12cdd4 fcff9769c319be949d678841a9eb9915927b3423Machine learning (ML) and artificial intelligence (AI) techniques have now become commonplace in software products and services. When threat modelling a system, it is therefore important that we consider threats unique to ML and AI techniques, in addition to threats to our software. In this paper, we present a threat model that can be used to systematically uncover threats to AI based software. The threat model consists of two main parts, a model of the software development process for AI based software and an attack taxonomy that has been developed using attacks found in adversarial AI research. We apply the threat model to two real life AI based software and discuss the process and the threats found.International Conference on Information Systems Security and Privacy260A threat model that can be used to systematically uncover threats to AI based software is presented and an attack taxonomy that has been developed using attacks found in adversarial AI research is developed.ArXiv['Vimal Kumar', 'Juliette Mayo', 'Khadija Bahiss']2024-01-15T00:00:00https://www.semanticscholar.org/paper/fcff9769c319be949d678841a9eb9915927b3423 0449dff5003a02871bfad9b61cb0a29edf8cc544In today's fiercely competitive job market, interview performance plays a pivotal role in securing employment. However, candidates often face the challenge of limited access to personalized and realistic interview practice opportunities, hindering their confidence-building efforts. To address this gap, we propose an AI-powered mock interviewer web application that creates an interview environment for candidates and elevates their interview preparation. The web app extracts vital information from user-uploaded resumes using Google's Palm model and Textract, crafting interview questions customized to the user's role and skills. It offers HR and Technical interview rounds. The HR segment assesses soft skills, while the Technical round presents role-specific technical questions, resulting in comprehensive performance reports and personalized feedback for targeted preparation. Notably, the web app employs voice analysis to detect fillers, pauses, and repetitive language, offering practical insights for improved communication. This analysis provides practical insights into communication challenges and offers actionable feedback to improve interview articulation and bolster user confidence. Key Words: Interview Preparation, AI-Powered Mock Interview Simulation, Personalized Feedback, Confidence BuildingINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00An AI-powered mock interviewer web application that creates an interview environment for candidates and elevates their interview preparation, and employs voice analysis to detect fillers, pauses, and repetitive language, offering practical insights for improved communication.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ijsrem Journal']2024-01-15T00:00:00https://www.semanticscholar.org/paper/0449dff5003a02871bfad9b61cb0a29edf8cc544 ea9dfc586f8c0f96fda227bb944184a210513b67There are several challenges associated with electric vehicles. This work discusses the effect of electric vehicles on the load frequency deviation.. However, Load cannot be the same throughout, load deviates from time to time. To get rid of these disadvantages related to conventional controllers, a lot many schemes have been put forth in literature. This work presents a new design of various types of load frequency controllers based on different types of Artificial Intelligent (AI) optimization techniques such as Fuzzy logic, ANN tuner for a single area power system. The performance of the controller under study shows an enhancement in the frequency deviation signal as well as the peak overshoot and settling time for the frequency output signal. The performance of the proposed scheme is validated using MATLAB/ SIMULINK tools. KEYWORDS: Multi- Area Power System., Electric Vehicles, Load Frequency Control, Artificial Neural Networks (ANN), Adaptive Neuro Fuzzy Inference Systems (ANFIS).INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A new design of various types of load frequency controllers based on different types of Artificial Intelligent (AI) optimization techniques such as Fuzzy logic, ANN tuner for a single area power system is presented.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Rahul Parmar', 'Dr. Saurabh Gupta', 'Dr. Devendra Sharma']2024-01-15T00:00:00https://www.semanticscholar.org/paper/ea9dfc586f8c0f96fda227bb944184a210513b67 da6eda22c69434f674f31eef72312f845017eae0This study investigates the intersection between Artificial Intelligence (AI) and science fiction (Sci-Fi), using bibliometrix R-Package and VOSviewer network visualizations to analyze data from the Web of Science (WoS) and Scopus databases. The search yielded 462 articles from WoS and a significantly larger corpus of 1029 articles from Scopus. Our findings indicate that Computer Science is the predominant field in AI and Sci-Fi research, accounting for 39% and 40% of the documents in WoS and Scopus, respectively. This underscores the central role of Computer Science in shaping AI within Sci-Fi narratives. Notably, in Scopus, Engineering and Mathematics also emerged as significant categories, highlighting the research’s strong technical and quantitative focus. The study also reveals a keen interest in understanding AI’s societal impacts, as evidenced by numerous Humanities and Social Sciences articles. Additionally, recent AI and Sci-Fi research trends, such as ’chatbots’, ’ChatGPT’, ’metaverse’, and ’privacy’, were identified. These trends align with advancements in natural language processing (NLP), virtual realities (VR), data privacy, and deep learning technologies, indicating a shift in academic focus towards these cutting-edge areas. This comprehensive study offers valuable insights into the evolving landscape of AI and Sci-Fi research, guiding future scholarly exploration in these dynamically interconnected fields.2024 21st Learning and Technology Conference (L&T)1702024 21st Learning and Technology Conference (L&T)['Akila Sarirete', 'Najla Quotah']2024-01-15T00:00:00https://www.semanticscholar.org/paper/da6eda22c69434f674f31eef72312f845017eae0 43cf9780ede15e7d2b6431211ca5cb1fae8e2dadArtificial intelligence can be defined as one of the best gifts of science and technology to the mankind. These emerging technologies plays an important role in “revolutionizing the modern world”. Artificial intelligence has made life easy as it has been used and is still in use in different spheres of the evolving human life. Can AI be considered as a “legal person”? Currently, various countries including the UK and USA consider the fact that AI is still dependent on some amount of human input. The extensive usage of generative AI programs leaves us with the question of who, if anyone,may hold the copyright to content created using these programs, notwithstanding the fact that theAI's programmer as well as it's user, and the AI program itself all together play an important rolein the creation of these works. One of the main reasons why it's very challenging to copyright AI-generated works is the vagueness in relation to human involvement and intentions and various other questions regarding ownership. There are chances of a model creator to be accused of “vicarious liability” for the said infringement. Another issue is that, in International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 01 | January - 2024 SJIF Rating: 8.176 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM27618 | Page 2 AI will probably most likely lead to the end of the world, but in the meantime, there'll be great companies. - Sam Altman jurisdictions, like the US, any output generated solely using a machine is not eligible for copyright protection, as most jurisdictions tend to give protection to only the original works, those having a “human author”. This research paper tends to give reasons on the questions of “whether AI generated works are eligible for copyright protection”. KEYWORDS: Artificial Intelligence, Intellectual property, innovation and technology, copyright law, dataprivacy, legal person, AI generated works, digital jurisdiction, liability of AI,copyright infringement.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper tends to give reasons on the questions of whether AI generated works are eligible for copyright protection, as most jurisdictions tend to give protection to only the original works, those having a "human author”.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Divyaprashiktha T.K']2024-01-15T00:00:00https://www.semanticscholar.org/paper/43cf9780ede15e7d2b6431211ca5cb1fae8e2dad 5d96802c0d05c22b76f88cc885049f73e931718bManagement Review Quarterly580Management Review Quarterly['Dona Ghosh', 'Rajarshi Ghosh', 'Sahana Roy Chowdhury', 'Boudhayan Ganguly']2024-01-15T00:00:00https://www.semanticscholar.org/paper/5d96802c0d05c22b76f88cc885049f73e931718b 5bec3f6f21ccd452038d0323dc62ce208222bc3dMachine Intelligence Research470Mach. Intell. Res.['Xiaohua Sun', 'Juexiao Qin']2024-01-15T00:00:00https://www.semanticscholar.org/paper/5bec3f6f21ccd452038d0323dc62ce208222bc3d 3c2223a5205dfddcc3a8f551c283864378bf85c1.Computer-Aided Design and Applications210Computer-Aided Design and Applications['Yang-Rong Ye', 'He-Guang Ceng', 'Ye Qiang', 'Gu-Shi Qiang', 'Li Qiang']2024-01-15T00:00:00https://www.semanticscholar.org/paper/3c2223a5205dfddcc3a8f551c283864378bf85c1 7ba25892abafe22eb5ed8c8033624962161df257This research paper provides a comprehensive overview of Chat GPT, a cutting- edge natural language processing technology that has rapidly gained popularity recently. With the ability to generate human-like responses and a growing capacity to understand complex language and contextual nuances, Chat GPT has the potential to revolutionize the way we interact with machines and greatly enhance communication and productivity across a wide range of industries and fields. The paper covers the background and current state of Chat GPT, including its architecture, training process, and applications. It highlights the advancements made in the development of the technology, particularly the introduction of the latest version, GPT-4, which has over 100 trillion parameters compared to just 175 billion of GPT- 3.5 (500 times). It can even generate output (text, art, etc.) that is nearly indistinguishable from that made by a human. In addition to its potential benefits, the paper also examines the ethical, social, economic, and technical implications of Chat GPT. It also identifies concerns around privacy and data security, the potential for the technology to exacerbate existing biases and inequalities, and the risk of misuse or unintended consequences. It is crucial that these challenges are addressed to ensure that Chat GPT is developed and used in a responsible and beneficial manner. Furthermore, the paper discusses the regulations and policies that must be implemented to ensure the responsible development and use of Chat GPT. It emphasizes the importance of transparency and accountability, as well as the need to protect users' rights and promote fair access to the technology. The paper also explores the future developments of Chat GPT, including improvements in multilingual capabilities, emotional intelligence, and personalization. It also highlights the potential for Chat GPT to continue to evolve and improve, particularly with regards to contextual understanding and integration with human assistance. Keywords: Chat GPT, natural language processing, GPT - 4, AI, ethics, regulations, future developments, challenges, limitations, applications, multilingual capabilities, emotional intelligence, personalization.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper provides a comprehensive overview of Chat GPT, a cutting- edge natural language processing technology that has rapidly gained popularity recently and highlights the potential for Chat GPT to continue to evolve and improve, particularly with regards to contextual understanding and integration with human assistance.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Krish Bhanushali', 'Nirupam Gupta', 'Hardik Patel']2024-01-15T00:00:00https://www.semanticscholar.org/paper/7ba25892abafe22eb5ed8c8033624962161df257 261139ae2545b1c85e322c8226a5329a572aef49Objectives The purpose of this study is to develop teaching and learning materials for math classes for matrix with artificial intelligence, to promote the content, methods, and connection between theory and practice of learning materials for high school students, and to understand educational effects as math education materials. Methods We developed teaching and learning materials that can explore the image data classification principle of artificial neural networks using matrix multiplication and the MNIST dataset in the representation and process-ing of image data using matrices, and applied and analyzed the developed learning materials for 20 high school students to complete the learning materials. Results The developed materials were useful for students to intuitively understand the principles of artificial in-telligence and matrix concepts, and to appreciate the mathematical usefulness and value while applying them to actual image data processing. In addition, logical thinking was stimulated and students could learn mathematics in a developmental way during the learning process. Conclusions The process and results of this study are expected to provide implications for future approaches and material development for artificial intelligence math learning.Korean Association For Learner-Centered Curriculum And Instruction00The developed materials were useful for students to intuitively understand the principles of artificial in-telligence and matrix concepts, and to appreciate the mathematical usefulness and value while applying them to actual image data processing.Korean Association For Learner-Centered Curriculum And Instruction['Byeong Nam Hwang', 'Minshik Cho']2024-01-15T00:00:00https://www.semanticscholar.org/paper/261139ae2545b1c85e322c8226a5329a572aef49 2633b0cd03b74a9689e779aabc4c3e24b28c470fNo abstract South African Journal of Sports Medicine50South African Journal of Sports Medicine['M. Lambert']2024-01-15T00:00:00https://www.semanticscholar.org/paper/2633b0cd03b74a9689e779aabc4c3e24b28c470f 4cbb1415fcea2b0a62c7630822400b0608858422Humanities and Social Sciences Communications846The findings of the focused review shed light on the potential perils of ChatGPT implementation across various societal levels, including issues such as devaluation of relationships, unemployment, privacy concerns, bias, misinformation, and digital inequities.Humanities and Social Sciences Communications['Athanasios Polyportis', 'Nikolaos Pahos']2024-01-15T00:00:00https://www.semanticscholar.org/paper/4cbb1415fcea2b0a62c7630822400b0608858422 1c2b23cb69749ed561d006a17b2e5d9015496ec2An analysis study of the state of the art on the convergence process first, then confluence, final symbiosis between Artificial Intelligence (AI) and Digital Competences (CD) for Digital Education and 4.0 is presented, following a descriptive and diachronic method to be able to analyze from different points of view the complexity, problems and opportunities that this process implies, trying to present, in each of the phases of study of this phenomenon, the models, experiences and lines of research that illustrate the educational impact of this symbiosis . The study proceeds to analyze: 1) first, the characteristics of AI and the drivers that have led to its impact on Education (Educational Artificial Intelligence), 2) the challenges that this impact on Education has brought about, 3) and the initiatives of political, social, and educational agents to assimilating the effects of AI in educational innovation. 4) Then the analysis stops at the definition, characters, properties and initiatives of the DC in Education. Analyzed in pairs, a detailed analysis is made of the symbiosis process that can promote Education 4.0: the changes in the behavior of the agents in the educational process, the tools and good practices that lead to an effective use of this symbiosis, the progress of intelligent technologies in Education, the enunciation of own educational objectives, which are generating new educational models and also the proposal of new evaluation systems.JLIS.it272An analysis study of the state of the art on the convergence process first, then confluence, final symbiosis between Artificial Intelligence (AI) and Digital Competences (CD) for Digital Education and 4.0, trying to present, in each of the phases of study of this phenomenon, the models, experiences and lines of research that illustrate the educational impact of this symbiosis.JLIS.it['Miguel-Ángel Marzal', 'Maurizio Vivarelli']2024-01-15T00:00:00https://www.semanticscholar.org/paper/1c2b23cb69749ed561d006a17b2e5d9015496ec2 f0689cb98561e79d53d5f3071527bbb4a8e9aef1This study aimed to investigate the relationship between teacher professional development, quality of lecture design, student engagement, teacher technical skills, pedagogical content knowledge and teacher satisfaction in using Artificial Intelligence (AI)-Powered Facilitator for designing lectures. The study used a non-random sample technique, and 208 participants answered a survey via Google Form after one semester, using a 5-point Likert scale to rate their responses. The structural equation model was used to analyze the data, and six factors were included in the study. The study confirmed hypotheses that teacher professional development, quality of lecture design, student engagement, and pedagogical content knowledge have a positive effect on teacher satisfaction. However, the study also revealed that teacher technical skills have a negative effect on teacher satisfaction, and pedagogical content knowledge has no significant effect. The proposed conceptual model explained 55.7% of the variance in teacher satisfaction Theoretical and practical implications were also discussed. These findings provide insights into the factors that contribute to teacher satisfaction in utilizing AI-Powered Facilitator for designing lectures and could inform the development of effective teacher training programs.European Journal of Educational Research422The study confirmed hypotheses that teacher professional development, quality of lecture design, student engagement, and pedagogical content knowledge have a positive effect on teacher satisfaction, but revealed that teacher technical skills have a negative effect on teachers satisfaction.European Journal of Educational Research['Thi Hong']2024-01-15T00:00:00https://www.semanticscholar.org/paper/f0689cb98561e79d53d5f3071527bbb4a8e9aef1 8673ee4c43e98fac50b3cb6d65d96df5a4f2d8c3The recognition of the value and importance of recognizing patterns in the stock market is widely accepted. As a result, using innovative decision-making strategies is expected to lead to significant returns in stock prices. Predicting the stock market proves challenging because of the limited volatility and inherent disorderliness observed within the data. Hence, investors need help optimizing their profits through informed predictions in the stock market. Stock market projections are made by employing mathematical approaches and machine learning methods. The stock market is inherently vulnerable to unforeseen changes due to its intricate nature and absence of a linear progression. Since the advent of personal computers and the proliferation of technological advancements in the 1990s, scholars have investigated the application of artificial intelligence in the investment sector. Many solutions have been created to tackle the matter of volatility in stock market prices. This study examined a total of 146 research articles that were published in academic journals over twelve years (2011-2023). These papers focused on applying artificial intelligence in predicting stock market trends. The listed works encompass several methodologies, including technical analysis, fundamental analysis, sentiment analysis, and time series analysis. Every academic field is comprehensively explored, encompassing its initial findings and most recent advancements. Moreover, the existing body of literature suggests a growing focus on this particular sector, with a heightened level of specialization and an expanded range of topics being explored.Multidisciplinary Reviews02This study examined a total of 146 research articles that were published in academic journals over twelve years (2011-2023) focused on applying artificial intelligence in predicting stock market trends.Multidisciplinary Reviews['Harmanjeet Singh', 'Manisha Malhotra']2024-01-15T00:00:00https://www.semanticscholar.org/paper/8673ee4c43e98fac50b3cb6d65d96df5a4f2d8c3 2708e260449793e7c7ec67c5fed1e6fe4db57df5AI has gained significant traction as an innovative tool for automating tasks, enhancing data analytics, and reducing the risk of errors in auditing processes. This study investigated the impact of adopting artificial intelligence (AI) on the quality of audit practice in Nigeria, focusing on data mining, machine learning, and image recognition as proxies for the independent variable. Population was 251 accounting firms in southwest Nigeria, with a sample size of 159, purposively determined. The study utilized structured questionnaires for data collection, with regression analysis, and correlation matrices adopted for the analysis. The findings revealed a significant positive relationship between data mining and image recognition with the quality of audit practice in Nigeria. Machine learning, however, showed an insignificant negative relationship. This suggests that AI, particularly data mining and image recognition, can enhance audit quality in Nigeria. As a result, the study recommended that Nigerian audit professionals and firms should consider incorporating data mining techniques into their audit processes to improve effectiveness and error detection.European Journal of Accounting, Auditing and Finance Research01A significant positive relationship between data mining and image recognition with the quality of audit practice in Nigeria is revealed, suggesting that AI, particularly data mining and image recognition, can enhance audit quality in Nigeria.European Journal of Accounting, Auditing and Finance Research['I. Akinadewo', 'Oluwagbemi E. Oke', 'Jeremiah O. Akinadewo', 'M. Dagunduro']2024-01-15T00:00:00https://www.semanticscholar.org/paper/2708e260449793e7c7ec67c5fed1e6fe4db57df5 ea9240efff0e86bcbe87723dd2750d9eea5b552bThis study aims to examine and evaluate the influence of incorporating Artificial Intelligence (AI) into the instruction of scientific education in secondary schools in Nigeria. The primary objective is to investigate how AI technologies might improve the overall quality and efficacy of scientific teaching, leading to enhanced learning outcomes for secondary school students. The study employs a retrospective research approach, analyzing past data to gain insights into the development and impact of AI in scientific teaching in Nigerian secondary schools. The research design involves a comprehensive collection and analysis of secondary data from educational databases, government papers, academic journals, and other relevant repositories. Results from the study highlight the role of AI in teaching science, emphasizing Adaptive Learning Systems (ALS), Intelligent Tutoring Systems (ITS), and Virtual Laboratories and Simulations. ALS personalizes the learning process, ITS provides interactive and individualized instruction, and virtual laboratories offer immersive digital experiments. Challenges and barriers to the effective adoption of AI in scientific education include infrastructural limitations, teacher preparation and competence, and ethical considerations. Opportunities for successful integration involve government support, teacher training, and industry partnerships. Future prospects anticipate developments in personalized learning environments, improved data analytics, integration of virtual and augmented reality, enhanced natural language processing, and global cooperation in education. In conclusion, the study recommends the integration of AI into the national curriculum, adequate funding and resources, ongoing professional development for teachers, and a strategic curriculum development that fosters a blended learning environment.European journal of computer science and information technology01The integration of AI into the national curriculum, adequate funding and resources, ongoing professional development for teachers, and a strategic curriculum development that fosters a blended learning environment are recommended.European Journal of Computer Science and Information Technology['Adesina Isaac Okunade']2024-01-15T00:00:00https://www.semanticscholar.org/paper/ea9240efff0e86bcbe87723dd2750d9eea5b552b f2e9c06685f45a66a704bfff6e87f139e002c342This paper explores the connections between artificial intelligence and climate smart agricultural change research as a whole and its usefulness in adaptation efforts in smart agricultural technologies. In article increased attention is currently being paid to the use of smart technologies. The article provides an analysis of the prospects for the use of artificial intelligence technologies and Climate-Smart Agriculture. At the preparatory stage, an analysis of publications in the Woofs network was carried out, which allows specifying the essence and scope of artificial intelligence technologies in climate smart agriculture. The authors considered divided into four important components which include: the management of crops, farms, livestock and aquaculture to achieve a near-term balance in food security and livelihoods; the management of landscapes and ecosystems top reserve ecosystem services that are critical for agricultural development, food security, adaptation, and mitigation; enable better farm and land management by providing services on climate impacts and mitigation actions to managers of these resources; enhancing the derivable benefits of Climate-Smart Agriculture through demand-side measures and value chain interventions. Accordingly, Climate-Smart Agriculture and artificial intelligence aims to achieve the objectives of increasing productivity and incomes sustainably, making agriculture adaptive to the changing climate, and where possible cost-effective.Access Journal - Access to Science, Business, Innovation in the digital economy231The article provides an analysis of the prospects for the use of artificial intelligence technologies and Climate-Smart Agriculture, which aims to achieve the objectives of increasing productivity and incomes sustainably, making agriculture adaptive to the changing climate, and where possible cost-effective.Access Journal - Access to Science, Business, Innovation in the digital economy['I. Gryshova', 'Anush Balian', 'Iryna Antonik', 'V. Miniailo', 'V. Nehodenko', 'Yanislava Nyzhnychenko']2024-01-15T00:00:00https://www.semanticscholar.org/paper/f2e9c06685f45a66a704bfff6e87f139e002c342 5fed0ccf07de793545170b2cc0a238105aaa797eArtificial intelligence has generated several concerns and discussions, especially about the possible risks and consequences if ethical principles are not critically observed. Information was collected through documentary and hermeneutic research methods, in which interpretation and critical analysis prevail, followed by the study of relevant bibliographic references on these topics. The results were triangulated with the answers from the artificial intelligence chat (ChatGPT 3.5) in Spanish. It was found that there are significant differences between human beings, transhuman, and artificial intelligence, generating different ethical and spiritual-transcendent dilemmas today, which can make the intelligent machine a danger to humanity. Concepts such as singularity, autonomy, conscience, decision-making, and freedom, among others, allow us to glimpse the difference between the programmed, automated machine with certain functionality and human autonomy. It is concluded that not everything techno-scientifically possible is ethically acceptable, nor is it possible to equate the intelligent machine programmed by algorithms with human beings capable of self-awareness, self-determination, thinking about their existence, and being aware of their uniqueness, among other vital differences.Región Científica480It is concluded that not everything techno-scientifically possible is ethically acceptable, nor is it possible to equate the intelligent machine programmed by algorithms with human beings capable of self-awareness, self-determination, thinking about their existence, and being aware of their uniqueness, among other vital differences.Región Científica['Fernando Antonio Zapata Muriel', 'Santiago Montoya Zapata', 'D. Montoya-Zapata']2024-01-15T00:00:00https://www.semanticscholar.org/paper/5fed0ccf07de793545170b2cc0a238105aaa797e 36f575fc21a024c8a37cc12f4165a9a42f523201Artificial Intelligence is pivotal in transforming Manufacturing and Inventory management practices and maintaining this delicate inventory balance. AI-driven predictive forecasting, for instance, considers historical data, seasonality, and market dynamics to provide exceptionally accurate demand forecasts. Managing inventory has become a complex puzzle in today’s fast-paced business world. As companies expand their reach and cater to a global audience, tracking inventory and maintaining an accurate “available-to-promise” status in real time has become a challenge. In the current competitive landscape, retailers prioritise a seamless approach to inventory management, especially with the growing importance of omnichannel fulfilment. The art of balancing supply and demand while keeping costs in check has become a high-stakes game where every piece of the inventory puzzle must fit perfectly. In this article, we explore the remarkable role of Artificial intelligence in Manufacturing and Inventory management. We will discuss how businesses can harness the power of AI to optimize their efficiency in Manufacturing and Inventory processes.International Journal For Multidisciplinary Research00The remarkable role of Artificial intelligence in Manufacturing and Inventory management is explored and how businesses can harness the power of AI to optimize their efficiency in Manufacturing and Inventory processes is discussed.International Journal For Multidisciplinary Research['Bharathi N S yadav', 'Sathvik K U', 'Raghava J', 'Yashwanth B S']2024-01-15T00:00:00https://www.semanticscholar.org/paper/36f575fc21a024c8a37cc12f4165a9a42f523201 ed39af40bcd25d6be452aa79b4bffb74f95654cdLibraries have a history dating back centuries, serving as guardians of knowledge and fostering intellectual growth. Libraries have long been a cornerstone of human civilization, serving as repositories of knowledge, cultural hubs, and learning centers. However, in the age of artificial intelligence (AI), some have questioned the relevance of libraries. This article explores the timeless relevance of libraries in an era marked by rapid technological advancements. The article uses a qualitative method and draws on a review of academic articles, studies, and case studies about libraries, artificial intelligence (AI), and digitalization. The analysis focuses on the historical importance of libraries, the challenges they confront in the age of AI, and prospective approaches to incorporating AI into library services. Libraries play a vital role in fostering these skills, offering a range of resources and educational programs to help individuals navigate the digital landscape effectively. Librarians, with their expertise, guide users in assessing the credibility of online sources, understanding data privacy, and developing information literacy skills. By promoting digital literacy, libraries empower individuals to make informed decisions in the age of AI, mitigating the risks associated with misinformation and algorithmic biases. Libraries remain relevant and essential in our society. Beyond their role as knowledge repositories, libraries promote critical thinking, digital literacy, community engagement, and equitable access to information. As we navigate the complexities of the digital age, libraries continue to serve as intellectual and social sanctuaries, fostering human connections and preserving the values that make us uniquely human.IP Indian Journal of Library Science and Information Technology80The historical importance of libraries, the challenges they confront in the age of AI, and prospective approaches to incorporating AI into library services are focused on.IP Indian Journal of Library Science and Information Technology['Suryakanth Halaburagi', 'Prashant Mukarambi']2024-01-15T00:00:00https://www.semanticscholar.org/paper/ed39af40bcd25d6be452aa79b4bffb74f95654cd 36fb35c3c451cf9aefe38ecaf0f84c4cb73689c0The convergence of Generative Artificial Intelligence (GAI), Emotional Intelligence (EI), and social skills represents a transformative force in the realm of marketing strategy. The purpose of the study is to explore the intricate synergy between GAI, which leverages machine learning and natural language processing, and the deeply human qualities of EI and social skills. It investigates in general how this alliance has the potential to revolutionize future marketing practices by creating more personalized, emotionally resonant, and ethically responsible strategies.The foundation of this research lies in addressing critical questions facing the marketing industry today. First, the need for enhanced personalization. Second, the creation of emotionally intelligent content. Third, ethical considerations and cross-cultural adaptability. Fourth, long-term effectiveness. Fifth, the dynamic interplay between human marketers and AI.Through an interdisciplinary approach, encompassing fields such as AI, psychology, ethics, and marketing, this study examines in general the state of the art in GAI and EI integration, shedding light on how AI systems can be imbued with the capacity to perceive, understand, and respond to human emotions effectively. It delves into the ethical considerations that must underpin the development and deployment of emotionally intelligent marketing strategies, safeguarding consumer privacy and trust.Furthermore, this research investigates in general the cross-cultural adaptability of GAI-driven marketing strategies infused with EI, recognizing the importance of acknowledging cultural nuances in emotional expression and social norms. It evaluates the long-term efficacy of emotionally intelligent marketing, exploring whether such strategies foster enduring customer loyalty and brand affinity.Finally, this study navigates the evolving landscape of human-AI collaboration in marketing, defining the optimal roles of human marketers and AI systems in strategy development and execution.The goal of this research is to provide practical insights, ethical guidelines, and innovative strategies that enable marketers to leverage the complete potential of GAI in tandem with EI and social skills. The study envisions a future where marketing practices create genuine connections with consumers, encourage meaningful interactions, and drive sustainable business growth in a time marked by technological advancement and changing consumer expectations.British journal of marketing studies00This study delves into the ethical considerations that must underpin the development and deployment of emotionally intelligent marketing strategies, safeguarding consumer privacy and trust, and evaluates the long-term efficacy of emotionally intelligent marketing, as well as defining the optimal roles of human marketers and AI systems in strategy development and execution.British Journal of Marketing Studies['Mohammed Nadeem']2024-01-15T00:00:00https://www.semanticscholar.org/paper/36fb35c3c451cf9aefe38ecaf0f84c4cb73689c0 95f6b17b4da1ac56ff125358a4a87655ba26f32eThis research aims to identify the current status of artificial intelligence and its potential impact on motivation towards learning. It provides conceptual foundations for thoughtful, policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created by recent developments in artificial intelligence. The research adopts a descriptive approach, using a random sample of 45 male and female teachers from the Zayed Higher Organization. The results show that artificial intelligence plays a significant role in motivating deaf students in the Emirates, as the average responses of the participants reached 3.56 in this area. The results also indicate that there are statistically significant differences, at a significance level of α = 0.05, between the assessments of the study sample members regarding the role of artificial intelligence in motivating deaf students in the United Arab Emirates, depending on gender. In light of these findings, a set of recommendations is presented.Intercontinental Journal of Social Sciences00It is shown that artificial intelligence plays a significant role in motivating deaf students in the Emirates, as the average responses of the participants reached 3.56 in this area.Intercontinental Journal of Social Sciences['Khazina Almansoori']2024-01-15T00:00:00https://www.semanticscholar.org/paper/95f6b17b4da1ac56ff125358a4a87655ba26f32e 6dc8aa9ba887c2f41d5acbac808b670bd16887ffThis paper explores the critical intersection of security and privacy in advanced artificial intelligence (AI) and machine learning (ML) with Internet of Things (IoT) systems and edge computing applied to big data in the renewable energy (RE) sector, where the generated data is grown exponentially, presenting unique challenges in data management, analysis, and security. This study discusses the complexities of anomaly detection (AD) in RE data, examining the evolving security threats and the need for real-time processing. Through a comprehensive literature review and the proposal of an innovative framework, we address the security and privacy challenges in AD for RE data, evaluate the effectiveness of current solutions, and propose robust strategies for enhancing security measures. The study underscores the need for continuous security protocols’ adaptation to evolving threats. It emphasizes the importance of regular audits and compliance with regulatory standards to maintain the resilience of RE systems against cyber threats.2024 21st Learning and Technology Conference (L&T)340This study discusses the complexities of anomaly detection in RE data, examining the evolving security threats and the need for real-time processing, and underscores the need for continuous security protocols’ adaptation to evolving threats.2024 21st Learning and Technology Conference (L&T)['Suzan Katamoura', 'Mehmet Sabih Aksoy', 'B. Alkhamees']2024-01-15T00:00:00https://www.semanticscholar.org/paper/6dc8aa9ba887c2f41d5acbac808b670bd16887ff 5f9343a5a37ecccab665442fe55b34b4c98535d1Background and Aims Artificial intelligence-enhanced electrocardiograms (AI-ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions lack actionability at an individual patient level, explainability and biological plausibility. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. Methods and Results The AIRE platform was developed in a secondary care dataset of 1,163,401 ECGs from 189,539 patients, using deep learning with a discrete-time survival model to create a subject-specific survival curve using a single ECG. Therefore, AIRE predicts not only risk of mortality, but time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil and the UK, including volunteers, primary care and secondary care subjects. AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776)), cardiovascular (CV) death 0.832 (0.831-0.834), non-CV death (0.749 (0.747-0.751)), future ventricular arrhythmia (0.760 (0.756-0.763)), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.889))). Through phenome- and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological aging and metabolic syndrome. Conclusion AIRE is an actionable, explainable and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short- and long-term risk estimation.medRxiv00The AIRE platform was developed in a secondary care dataset of 1,163,401 ECGs from 189,539 patients, using deep learning with a discrete-time survival model to create a subject-specific survival curve using a single ECG, and identified candidate biological pathways for the prediction of increased risk.['A. Sau', 'L. Pastika', 'E. Sieliwonczyk', 'K. Patlatzoglou', 'A. H. Ribeiro', 'K. McGurk', 'B. Zeidaabadi', 'H. Zhang', 'K. Macierzanka', 'D. Mandic', 'E. Sabino', 'L. Giatti', 'S. M. Barreto', 'L. D. Camelo', 'I. Tzoulaki', "D. O'Regan", 'N. S. Peters', 'J. S. Ware', 'A. L. Ribeiro', 'D. Kramer', 'J. Waks', 'F. S. Ng']2024-01-15T00:00:00https://www.semanticscholar.org/paper/5f9343a5a37ecccab665442fe55b34b4c98535d1 5de9dd7a93ede9a5c563f3149ef31da97972ca69This work addresses the impact of artificial intelligence on the control of existing activities in labor relations. Its problematic issue is anomie and the challenges arising from it. Since this is a problem that goes beyond Brazilian law, support was sought in a study carried out in Germany, where the lack of specific norms to protect workers has also led researchers to carry out a new interpretation of existing legislation, observing the peculiarities of labor relations and the impact of the new technology. When interpreting existing norms in Brazilian law, it was found that the General Data Protection Law provides general protection instruments for workers. For the collective protection of workers, the existing norms in Brazilian law still need to be advanced, allowing an effective protection of their rights.Revista Ágora Filosófica00This work addresses the impact of artificial intelligence on the control of existing activities in labor relations and the challenges arising from it, and finds that the General Data Protection Law provides general protection instruments for workers.Revista Ágora Filosófica['Fábio Túlio Barroso', 'Haroldo Carneiro Leão Sobrinho']2024-01-15T00:00:00https://www.semanticscholar.org/paper/5de9dd7a93ede9a5c563f3149ef31da97972ca69 a9bc4926bd29ea24cf598201d2e051cf3bd4341b"A Paradigm Shift" describes how AI is revolutionising marketing tactics. It represents a fundamental shift in the way that marketers view, interact with, and target customers. Better consumer interaction, more precise ad targeting, and more efficient decision-making are all made possible by AI's ability to provide data-driven insights, personalised experiences, predictive analytics, and automation in marketing efforts. This change signals a move towards AI-powered marketing methods that are more effective, accurate, and customised. The study explains the influence of Artificial intelligence in marketing and discusses how artificial intelligence causes remarkable growth and differences in marketing. The study also focuses on the future of artificial intelligence in marketing. AI's impact on marketing has already resulted in notable improvements in efficiency, customisation, and decision-making. Future developments in personalization, content production, and ethical issues are all promising applications of AI in marketing that will influence the field for years to come.International Journal For Multidisciplinary Research210The study explains the influence of Artificial intelligence in marketing and discusses how artificial intelligence causes remarkable growth and differences in marketing and focuses on the future of artificial intelligence in marketing.International Journal For Multidisciplinary Research['Bharathi N S', 'Manjunath S R', 'Hemantha M S', 'Praveen H A']2024-01-15T00:00:00https://www.semanticscholar.org/paper/a9bc4926bd29ea24cf598201d2e051cf3bd4341b 8b38410d69d73ff7d1789a770a37a09f4b78a4adScience Fiction (SF) can play a role in the innovations of the future. Artificial Intelligence (AI), particularly Generative AI (Gen-AI), may play a role in creating science fiction scenarios that could shape the future. Hence, this study aims to examine people’s views on SF and their views about AI-generated SF’s viability to become reality in the future.2024 21st Learning and Technology Conference (L&T)470This study aims to examine people’s views on SF and their views about AI-generated SF’s viability to become reality in the future.2024 21st Learning and Technology Conference (L&T)['Tyler A. Schisler', 'Kalyn Melham', 'Benjamin Malum', 'Afsaneh Behforouz', 'Joel Wiredu', 'Faisal Kalota']2024-01-15T00:00:00https://www.semanticscholar.org/paper/8b38410d69d73ff7d1789a770a37a09f4b78a4ad 4a0b1659ecf3b5fda63c6135ff670ccb90ab62b2In an era marked by rapid technological advancements, this study meticulously explores the transformative integration of Artificial Intelligence (AI) in accounting, focusing on its implications for the future of U.S. financial markets. The advent of AI in accounting signifies a paradigm shift, transcending traditional methodologies and introducing a new era of efficiency and strategic analysis. This evolution is pivotal in reshaping accounting practices and influencing the broader economic fabric of the U.S. financial markets. The primary aim of this paper is to dissect and understand the multifaceted integration of AI in accounting, assessing its impact, challenges, and future prospects. The study offers a detailed analysis that bridges the gap between technological innovation and practical accounting applications. It thoroughly examines AI's role in revolutionizing accounting practices, its economic significance in the U.S. financial markets, and the dual nature of opportunities and challenges it presents. The study extends to propose strategic recommendations for effectively harnessing AI in accounting. The study concludes that AI's integration in accounting is a significant evolutionary step, enhancing efficiency, accuracy, and decision-making capabilities. However, it also brings forth challenges such as skill adaptation and ethical considerations. Recommendations include a proactive approach in educational reform, policy development, and fostering interdisciplinary collaboration to effectively navigate AI's complexities in accounting. This paper serves as a seminal work, providing a classical yet engaging narrative on AI's role in reshaping accounting practices and its broader economic implications, setting a foundation for future explorations and strategic implementations Keywords:  Artificial Intelligence, Accounting Practices, U.S. Financial Markets, Technological Innovation, Economic Significance, Strategic Analysis.Finance &amp; Accounting Research Journal00The study concludes that AI's integration in accounting is a significant evolutionary step, enhancing efficiency, accuracy, and decision-making capabilities, however, it also brings forth challenges such as skill adaptation and ethical considerations.Finance &amp; Accounting Research Journal['Beryl Odonkor', 'Simon Kaggwa', 'Prisca Ugomma Uwaoma', 'Azeez Olanipekun Hassan', 'Oluwatoyin Ajoke Farayola']2024-01-15T00:00:00https://www.semanticscholar.org/paper/4a0b1659ecf3b5fda63c6135ff670ccb90ab62b2 7967a4f3b7f387dc7f32b1021c04df1a3e8c6e5fSocial networking sites have an impact on all aspects of life, if at the beginning this was related to the ability to obtain electronic devices through the physical condition or the provision of infrastructure, but this matter will only exceed a few years at the worst estimate to become the main feature of the modern era, which is The era of media through digital platforms, as social networks opened the way for a shift in media work methods according to modern technologies, a large part of which has become shifting towards artificial intelligence technology, as the new technologies provided by digital media have reshaped media practice, whether at the level Producing and narrating the content in ways that are compatible with the nature of the digital platform on the one hand, and the shift in the methods of dealing with the digital audience with these contents on the other hand. Users of these platforms decide what they want. Keywords: )artificial intelligence, social networking sites, digital platforms, identification of interests).ARID International Journal of Media Studies and Communication Sciences00The era of media through digital platforms, as social networks opened the way for a shift in media work methods according to modern technologies, a large part of which has become shifting towards artificial intelligence technology.ARID International Journal of Media Studies and Communication Sciences['Dr. Taghreed Fadhil Hussein']2024-01-15T00:00:00https://www.semanticscholar.org/paper/7967a4f3b7f387dc7f32b1021c04df1a3e8c6e5f eb873e1ff014e33feac3f990e63af6bb2ad6d7e0In every corner of social media, abuse, bias, and discrimination are prevalent phenomena. Misinformation spreading, cyberbullying, and even the swaying of public opinion and user viewpoints are examples of ABD (Abuse, Bias, and Discrimination). It is also important to remember that Artificial Intelligence (AI) systems have the potential to function biasedly, which could result in unfair user interactions and information distribution. This essay aims to highlight the potential hazards associated with social media use in the modern world. As an essential instrument for spreading information, it should be transparent, secure, equitable, and inclusive. This paper can increase the diversity of information flow, lessen the effects of abuse, bias, and discrimination, improve the social media environment, and increase the value of social media by skillfully utilizing AI technology. This paper's primary focus is on AI's numerous approaches and tactics to identify and lessen bias, abuse, and discrimination on social media. It emphasizes the significance of data diversity and quality and how to refine algorithms to make them fairer and more transparent. It also delves into stricter and more precise regulations and user education for social media, ensuring, with the help of AI algorithms, it becomes a safer, more inclusive, and fairer space.Lecture Notes in Education Psychology and Public Media00This paper can increase the diversity of information flow, lessen the effects of abuse, bias, and discrimination, improve the social media environment, and increase the value of social media by skillfully utilizing AI technology.Lecture Notes in Education Psychology and Public Media['Xuwen Lin']2024-01-15T00:00:00https://www.semanticscholar.org/paper/eb873e1ff014e33feac3f990e63af6bb2ad6d7e0 236d73b2b7d95d42b3559e051573020dcf00db6bThis paper seeks to understand approach and pattern of Artificial Intelligence (Al). It examines the contemporary approaches to Artificial Intelligence. Artificial Intelligence (Al)is a branch of Science which deals in development of modern machines, which can find solutions to complex problems in human. Artificial intelligence basically works on human intelligence characteristics and algorithms so that machine can behave like human.International Education and Research Journal00International Education and Research Journal['Dr. Mohammadi Shaikh', 'Shaikh Summaiya Naznin']2024-01-15T00:00:00https://www.semanticscholar.org/paper/236d73b2b7d95d42b3559e051573020dcf00db6b 00d992d0ad3fd3f467e41de248cc52d381f3ee1dThe article discusses the specifics of the impact of the introduction of artificial intelligence on the oil industry. The development of shale production a few years ago radically reshaped the landscape of the oil and gas market, and now it may be changed by artificial intelligence (AI) and work with big data (Big Data). Major players are already realizing that without technological advancement, their business will be left behind. Accenture has found that 36 % of the world’s oil companies are now actively using Big Data technology, and another 38 % intend to adopt it in the next 3–5 years. The potential of AI is aimed at helping companies identify promising wells, automate drilling processes and make production as efficient and cost-effective as possible. The oil and gas industry has amassed large amounts of complexly structured data from oil and gas field development, but many companies are barely utilizing it. With the help of data analytics, the oil industry can discover new opportunities for production and better utilize existing infrastructure.Azerbaijan Oil Industry00Azerbaijan Oil Industry['R.G. Aliyeva']2024-01-15T00:00:00https://www.semanticscholar.org/paper/00d992d0ad3fd3f467e41de248cc52d381f3ee1d a75063b00edd660d75eecaab44e8939191b882dc00[]2024-01-15T00:00:00https://www.semanticscholar.org/paper/a75063b00edd660d75eecaab44e8939191b882dc ee438f115a2f6e19a7b085b8cb1db2064cc17d78This paper analyzed the increasing use of Blockchain Technology in the ownership of Non-Fungible Tokens (NFT) and Digital Art. It brings into perspective the advantages of Blockchain and Articial Intelligence (AI) in the realm of collectibles, like art, music, and virtual reality. As they are NFTs, they can neither be traded nor exchanged, unlike cryptocurrencies. This opens up opportunities for new-age companies like OpenSea and Magic Eden amongst others who have achieved the status of Unicorns. An attempt was made to understand the process through which such status was achieved.Global Journal For Research Analysis00GLOBAL JOURNAL FOR RESEARCH ANALYSIS['Shreyasi Jindal']2024-01-15T00:00:00https://www.semanticscholar.org/paper/ee438f115a2f6e19a7b085b8cb1db2064cc17d78 de764618a9c85a40a691135ad30921d1d5471f97Cet article interroge les usages de techniques d’intelligence artificielle dans l’art et s’intéresse aux œuvres dotées d’I.A. qui simulent certaines caractéristiques de l’esprit humain telles que l’apprentissage, la pensée créative et l’aptitude à prendre des décisions. Conceptuellement et formellement innovantes, ces œuvres forment une catégorie artistique qui se constitue depuis plusieurs décennies, mais qui demeure très peu connue du grand public et assez peu étudiée par les théoriciens de l’art. Les notions d’intelligence naturelle et d’intelligence artificielle, aussi bien que leurs convergences, sont reprises et examinées du point de vue des projets artistiques cités pour établir à la fois leurs modes de simulation de l’intelligence et le fonctionnement des œuvres. Les expériences esthétiques proposées au sein de certaines œuvres sont essentiellement collaboratives et interactives. Ce constat permettra de considérer le dialogue entre l’humain et la machine dans ce contexte comme un terrain propice à une techno-écologie du sensible.Visible00Visible['Nikoleta Kerinska']2024-01-15T00:00:00https://www.semanticscholar.org/paper/de764618a9c85a40a691135ad30921d1d5471f97 4c5cc17c5c0f76880c5440d3bcb28c0a958af03eThis research paper delves into the transformative role of Outcome-Based Education (OBE) in the realm of higher education, as advocated by India's National Education Policy (NEP) 2020. The primary focus is on the enhancement of program outcomes, program-specific outcomes, course outcomes, and unit outcomes, along with the integration of Bloom's Taxonomy for effective assessment and accreditation processes. The study explores the shift from traditional content-centric education to a more dynamic, outcome-focused approach, emphasizing competency and skill development in line with global educational standards. It investigates how OBE can be strategically used to refine curricula, pedagogy, and assessment methods, thereby enhancing the overall educational experience and outcomes for students. Additionally, the paper examines the pivotal role of Bloom's Taxonomy in categorizing and assessing educational objectives within OBE, facilitating diverse and rigorous evaluation criteria.International Journal of Scientific Research in Computer Science Engineering and Information Technology00This research paper delves into the transformative role of Outcome-Based Education (OBE) in the realm of higher education, as advocated by India's National Education Policy (NEP) 2020, and examines the pivotal role of Bloom's Taxonomy in categorizing and assessing educational objectives within OBE.International Journal of Scientific Research in Computer Science, Engineering and Information Technology['Milan Mehta', 'Rupal Mehta', 'Akshat Mehta', 'Parth Mehta']2024-01-15T00:00:00https://www.semanticscholar.org/paper/4c5cc17c5c0f76880c5440d3bcb28c0a958af03e 19d71f411a6a968bf9c66ccb235dcfb1eb256486This article deals with the concept of “ultimate history” and the role of present-day AI language models. On the one hand, there stands an old idea that someday, with the development of science, it will become possible to create one final narrative of history. With the fast burgeoning of the AI this question becomes even more imperative for historians all over the world. What would mean to be a historian in a world with a machine that knows more than yourself about history, and what would be the new role of a historian, if AI manages to crack the “ultimate history” once and for all? Those are some of the questions that this paper tries to answer.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The concept of “ultimate history” and the role of present-day AI language models are dealt with and what would be the new role of a historian, if AI manages to crack the “ultimate history” once and for all are tried.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika['Lubomir Krastev']2024-01-15T00:00:00https://www.semanticscholar.org/paper/19d71f411a6a968bf9c66ccb235dcfb1eb256486 2a7077ab18ca6fd78b7379577955f1ae308c115fMachine Intelligence Research00Mach. Intell. Res.['Luntian Mou', 'Feng Gao', 'Zijin Li', 'Jiaying Liu', 'Hongxun Yao', 'J. Hoorn']2024-01-15T00:00:00https://www.semanticscholar.org/paper/2a7077ab18ca6fd78b7379577955f1ae308c115f 18f3597dc68dcf6349c1f33a7ad26f2f78bf5d70تمثلت مشكلة البحث في حاجة واقع تعليم اللغة العربية وتعلمها إلى سيناريوهات مقترحة لأدوار تطبيقات الذكاء الاصطناعي لإثرائه، ووجود عدة تحديات تواجه توظيف تلك التطبيقات في تعليم اللغة العربية وتعلمها، فاستهدف البحث عرض أهم تطبيقات الذكاء الاصطناعي التي يمكن توظيفها في إثراء تعليم اللغة العربية وتعلمها، والكشف عن أبرز التحديات التي تواجه ذلك، ووضع سيناريوهات مقترحة لأدوار تطبيقات الذكاء الاصطناعي في إثراء تعليم اللغة العربية وتعلمها، واعتمد البحث المنهج الوصفي التحليلي، والمنهج شبه التجريبي، وتألفت مجموعة البحث من مجموعة من المختصين والخبراء من أعضاء هيئة التدريس في المناهج وطرق تدريس اللغة العربية، وفي تخصص التكنولوجيا وتطوير المناهج، ومديري وحدات التحول الرقمي بالجامعات المصرية، وأخصائي تكنولوجيا التعليم بوزارة التربية والتعليم، بلغ عددهم (59) مختصًا. وتم إعداد استبانتين لتحديد تطبيقات الذكاء الاصطناعي التي يمكن توظيفها في إثراء تعليم اللغة العربية وتعلمها، وأهم التحديات التي تواجه ذلك، ووضع سيناريو مقترح لتوظيف تطبيقات الذكاء الاصطناعي في إثراء تعليم اللغة العربية وتعلمها، وتوصل البحث إلى بعض تطبيقات الذكاء الاصطناعي التي يمكن توظيفها في إثراء تعليم اللغة العربية وتعلمها، بلغت (13) تطبيقًا رئيسًا، وتحديات توظيف تلك التطبيقات، وبلغت (11) تحديًا، ووضع سيناريو مقترح لتوظيف تطبيقات الذكاء الاصطناعي في إثراء تعليم اللغة العربية وتعلمها. وفي ضوء هذه النتائج، تم تقديم مجموعة متنوعة من التوصيات والمقترحات الكلمات المفتاحية: سيناريوهات مقترحة- تطبيقات الذكاء الاصطناعي – إثراء تعليم اللغة العربية وتعلمها.Arid International Journal of Educational and physcological sciences00Arid International Journal of Educational and physcological sciences['Professor Dr. Abdel Razek Mukhtar Mahmoud Abdel Qader']2024-01-15T00:00:00https://www.semanticscholar.org/paper/18f3597dc68dcf6349c1f33a7ad26f2f78bf5d70 a9eff8bdf8441e0d088b6920a07842f7ab75d8dbObjectives In this study, we investigated how project classes that discover and solve local problems based on public data affect the development of data literacy skills in elementary school students. Methods After analyzing the needs of instructors and learners by applying the ADDIE model, a 10-session social studies education program for elementary school using public data was developed and two Delphi tests were conducted. In addition, a two-group pretest-posttest experiment was conducted to measure the effects of data literacy on 38 fourth-grade elementary school students in Sejong City. Results The experimental group in which students directly searched and analyzed public data showed statistically significant improvement in the data collection area of data literacy competency compared to the control group in which students analyzed data provided by teachers. In addition, the experimental and control groups showed the same improvement in other areas of data literacy except the data collection area. Conclusions This social studies education program using public data improved the overall data literacy skills of fourth-grade elementary school students. In addition, compared to the existing teaching method that uses data provided by the teacher, the teaching program in this study can improve some areas of data literacy competency by allowing students to find data on their own in their daily lives and use it to solve problems.Korean Association For Learner-Centered Curriculum And Instruction00Compared to the existing teaching method that uses data provided by the teacher, the teaching program in this study can improve some areas of data literacy competency by allowing students to find data on their own in their daily lives and use it to solve problems.Korean Association For Learner-Centered Curriculum And Instruction['Jaeyoung Yoo', 'Seung-Hyun Kim', 'Kwihoon Kim']2024-01-15T00:00:00https://www.semanticscholar.org/paper/a9eff8bdf8441e0d088b6920a07842f7ab75d8db 89b4d8ba3b3558bc6affc9b34099ed62f19cc32c. College students shoulder the heavy burden of sustainable social development in China and are an important reserve of human resources in China. The number of students who die from drowning every year accounts for the first among all kinds of accidental deaths of students. Most of them occur outside the school when they are separated from parental supervision and school teacher management. Drowning of college students also occurs frequently. To cultivate college students, it is not only necessary to start from their morality, quality, ability, and other factors but also necessary to vigorously popularize college students' swimming so that every college student can learn to swim and have the ability to swim for self-protection. Based on the comparison between traditional data analysis and non-linear data analysis, this study analyzes the optimization effect of the University Swimming Curriculum system. Through the comparison of the coupling degree of the two and the student's perception of the curriculum, it can be seen that the optimization effect of non-linear data analysis on the University Swimming Curriculum system is more in line with the needs of college students to learn swimming.Computer-Aided Design and Applications110It can be seen that the optimization effect of non-linear data analysis on the University Swimming Curriculum system is more in line with the needs of college students to learn swimming.Computer-Aided Design and Applications['Yueliang Xi', 'Feng Gao']2024-01-15T00:00:00https://www.semanticscholar.org/paper/89b4d8ba3b3558bc6affc9b34099ed62f19cc32c c3663d43ebdc987790e64bdadbdfd50be347b8f5<jats:p />Lecture Notes in Education Psychology and Public Media00Lecture Notes in Education Psychology and Public Media['Yuchen Kong']2024-01-15T00:00:00https://www.semanticscholar.org/paper/c3663d43ebdc987790e64bdadbdfd50be347b8f5 b26027b76eecba879b692b768fbbdd1fa63b3613International Journal of Pharmacology200International Journal of Pharmacology['M. Kandeel']2024-01-15T00:00:00https://www.semanticscholar.org/paper/b26027b76eecba879b692b768fbbdd1fa63b3613 832c0a769e787554a38b8a11dbeea629a4a6fd73Journal of Education Policy410Journal of Education Policy['Kalervo N. Gulson', 'S. Sellar']2024-01-15T00:00:00https://www.semanticscholar.org/paper/832c0a769e787554a38b8a11dbeea629a4a6fd73 0803f3c8d95acda83b861ff5175f25b7caf847d3This study reviews the evolution of China's environmental regulations and provides a literature review of the effect of environmental regulation on green innovation in China. This study provides a three-stage summary of the evolution of China's environmental regulation framework: the first stage is the initial and exploratory period of environmental legislation; the second stage is the formulation and implementation of environmental policies; and the last stage is the strengthening of multidimensional environmental regulation framework. By reviewing the evolution of China's environmental regulation framework, as well as reviewing past literature, this study finds that (1) China's environmental regulation has developed into a multidimensional framework that incorporates government command and control, market incentives, and public participation, exhibiting typical Chinese characteristics. (2) Environmental regulations are a primary driver of green innovation in China, but the findings of empirical studies are controversial because past studies have focused on different policies, regions, and industries. (3) Future research could examine the optimal combination of green innovation-oriented environmental regulation policies, the multidimensional synergy of environmental regulation framework, and the impact of policy and regional heterogeneity on enterprise behavior.International Journal of Academic Research in Business and Social Sciences310International Journal of Academic Research in Business and Social Sciences['Yang Liu', 'Roshazlizawati Mohd Nor', 'Ma Kalthum Ishak', 'Xuan Li']2024-01-14T00:00:00https://www.semanticscholar.org/paper/0803f3c8d95acda83b861ff5175f25b7caf847d3 d09dee195c0e42f46522d69910fc764624265ccfJournal of Russian Law00Journal of Russian Law['A. Zemlin']2024-01-14T00:00:00https://www.semanticscholar.org/paper/d09dee195c0e42f46522d69910fc764624265ccf 49a1907070bde86352361fbe6856d5f949ca2201As AI systems quickly improve in both breadth and depth of performance, they lend themselves to creating increasingly powerful and realistic agents, including the possibility of agents modeled on specific people. We anticipate that within our lifetimes it may become common practice for people to create a custom AI agent to interact with loved ones and/or the broader world after death. We call these generative ghosts, since such agents will be capable of generating novel content rather than merely parroting content produced by their creator while living. In this paper, we first discuss the design space of potential implementations of generative ghosts. We then discuss the practical and ethical implications of generative ghosts, including potential positive and negative impacts on individuals and society. Based on these considerations, we lay out a research agenda for the AI and HCI research communities to empower people to create and interact with AI afterlives in a safe and beneficial manner.arXiv.org995This paper discusses the design space of potential implementations of generative ghosts, and lays out a research agenda for the AI and HCI research communities to empower people to create and interact with AI afterlives in a safe and beneficial manner.ArXiv['Meredith Ringel Morris', 'Jed R. Brubaker']2024-01-14T00:00:00https://www.semanticscholar.org/paper/49a1907070bde86352361fbe6856d5f949ca2201 4abb9aeb5c5b7312f747badb17b930b361fc3871The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper delves into the legal and regulatory implications of Generative AI and LLMs in the European Union context, analyzing aspects of liability, privacy, intellectual property, and cybersecurity. It critically examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA) draft, in addressing the unique challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models, ensuring they align with the EU's evolving digital landscape and legal standards.Social Science Research Network444This paper critically examines the adequacy of the existing and proposed EU legislation in addressing the unique challenges posed by Generative AI in general and LLMs in particular and proposes recommendations to ensure the safe and compliant deployment of generative models.ArXiv['Claudio Novelli', 'F. Casolari', 'Philipp Hacker', 'Giorgio Spedicato', 'Luciano Floridi']2024-01-14T00:00:00https://www.semanticscholar.org/paper/4abb9aeb5c5b7312f747badb17b930b361fc3871 ad6d1c79623da8bb8b63dbdad1a9de05d9cc2dcbIn a broad sense, artificial intelligence (AI) refers to any computer or system behavior that resembles human behavior. A subfield of artificial intelligence called "machine learning" enables computers to learn from data without explicit human programming. One of the most significant contemporary trends in global healthcare is the use of artificial intelligence (AI) technologies in medicine. Technologies based on artificial intelligence are profoundly transforming the world's healthcare system, enabling a dramatic reconstruction of the medical diagnostics system while simultaneously lowering healthcare expenditures. Identifying the class of diseases to which a disease belongs is crucial before treating it. It is feasible to categorize the type of disease based on the feature space of the condition. Algorithms for machine learning can address this issue.Middle East Research Journal of Engineering and Technology01Algorithms for machine learning enables computers to learn from data without explicit human programming, enabling a dramatic reconstruction of the medical diagnostics system while simultaneously lowering healthcare expenditures.Middle East Research Journal of Engineering and Technology['Muhammad Baballe Ahmad', 'Shehu Hassan Ayagi', 'Umar Farouk Musa']2024-01-14T00:00:00https://www.semanticscholar.org/paper/ad6d1c79623da8bb8b63dbdad1a9de05d9cc2dcb dffb9997fff1b6587d7e0d924294d4bf2988979dClimate change is a global problem that has a significant impact on human health and economic well-being. Artificial intelligence (AI) has been shown to have great potential in reducing the effects of climate change. This article tries to provide a basic overview of the relationship between AI and mitigating climate change, highlighting AI's revolutionary potential in combating this pressing global issue. In particular, this article looks at how big data is essential to the success of climate action programs and how AI technologies may use these enormous databases to help develop more efficient mitigation measures for climate change and adapt to them. We have investigated novel methods for comprehending climate dynamics, maximizing renewable energy systems, enhancing climate resilience, and improving environmental justice via the use of AI technology.Journal of Advanced Zoology00How big data is essential to the success of climate action programs and how AI technologies may use these enormous databases to help develop more efficient mitigation measures for climate change and adapt to them is looked at.Journal of advanced zoology['Pijush Kanti Tripathi', 'Hasibul Rahaman', 'Sangeeta Laxmanrao Jadhav- Chavan', 'Saptarshi Mukherjee']2024-01-14T00:00:00https://www.semanticscholar.org/paper/dffb9997fff1b6587d7e0d924294d4bf2988979d 8a3c47caafc882c62eee1c2955aebdda067d900fThe accelerating advancements in Artificial Intelligence (AI) and Machine Learning (ML), highlighted by the development of cutting-edge Generative Pre-trained Transformer (GPT) models by organizations such as OpenAI, Meta, and Anthropic, present new challenges and opportunities in warfare and security. Much of the current focus is on AI's integration within weapons systems and its role in rapid decision-making in kinetic conflict. However, an equally important but often overlooked aspect is the potential of AI-based psychological manipulation at internet scales within the information domain. These capabilities could pose significant threats to individuals, organizations, and societies globally. This paper explores the concept of AI weapons, their deployment, detection, and potential countermeasures.IUI Workshops470This paper explores the concept of AI weapons, their deployment, detection, and potential countermeasures within the information domain.ArXiv['Philip G. Feldman', 'Aaron Dant', 'James R. Foulds']2024-01-14T00:00:00https://www.semanticscholar.org/paper/8a3c47caafc882c62eee1c2955aebdda067d900f caaf9ee75db554bbd1ed564e235512474de883a8 This article investigates the capabilities and limitations of ChatGPT, a natural language processing (NLP) tool, and large language models (LLMs), developed from advanced artificial intelligence (AI). Designed to help computers understand and produce text understandable by humans, ChatGPT is particularly aimed at general scientific writing and healthcare research applications. Our methodology involved searching the Scopus database for ’type 2 diabetes’ and ’T2 diabetes’ articles from reputable journals. After eliminating duplicates, we used ChatGPT to formulate conclusions for each selected article by inputting their structured abstracts, excluding the original conclusions. Additionally, we tested ChatGPT’s response to simple misuse scenarios. Our findings show that ChatGPT can accurately grasp context and concisely summarize primary research findings. Additionally, it helps individuals who are not as experienced in mathematical analysis by providing coding guidelines for mathematical analyses in a variety of computer languages and by demystifying difficult model results. In conclusion, even if ChatGPT and other AI technologies are revolutionizing scientific publishing and healthcare, their use should be strictly controlled by authoritative laws.Mesopotamian Journal of Artificial Intelligence in Healthcare500The findings show that ChatGPT can accurately grasp context and concisely summarize primary research findings, and helps individuals who are not as experienced in mathematical analysis by providing coding guidelines for mathematical analyses in a variety of computer languages and by demystifying difficult model results.Mesopotamian Journal of Artificial Intelligence in Healthcare['S. Salisu', 'Osamah Mohammed Alyasiri', 'Hussain A. Younis', 'Thaeer Mueen Sahib', 'Ahmed Hussein Ali', 'Ameen A Noor', 'Israa M. Hayder']2024-01-14T00:00:00https://www.semanticscholar.org/paper/caaf9ee75db554bbd1ed564e235512474de883a8 4d8f31d398a5fffa28411c7fe64167931c98b964In the realm of digital media, the advent of AI-generated synthetic images has introduced significant challenges in distinguishing between real and fabricated visual content. These images, often indistinguishable from authentic ones, pose a threat to the credibility of digital media, with potential implications for disinformation and fraud. Our research addresses this challenge by employing machine learning techniques to discern between AI-generated and genuine images. Central to our approach is the CIFAKE dataset, a comprehensive collection of images labeled as"Real"and"Fake". We refine and adapt advanced deep learning architectures like ResNet, VGGNet, and DenseNet, utilizing transfer learning to enhance their precision in identifying synthetic images. We also compare these with a baseline model comprising a vanilla Support Vector Machine (SVM) and a custom Convolutional Neural Network (CNN). The experimental results were significant, demonstrating that our optimized deep learning models outperform traditional methods, with DenseNet achieving an accuracy of 97.74%. Our application study contributes by applying and optimizing these advanced models for synthetic image detection, conducting a comparative analysis using various metrics, and demonstrating their superior capability in identifying AI-generated images over traditional machine learning techniques. This research not only advances the field of digital media integrity but also sets a foundation for future explorations into the ethical and technical dimensions of AI-generated content in digital media.arXiv.org150This research addresses the challenge of AI-generated synthetic images by employing machine learning techniques to discern between AI-generated and genuine images, and refined and adapt advanced deep learning architectures like ResNet, VGGNet, and DenseNet to enhance their precision in identifying synthetic images.ArXiv['Yuyang Wang', 'Yizhi Hao', 'Amando Xu Cong']2024-01-14T00:00:00https://www.semanticscholar.org/paper/4d8f31d398a5fffa28411c7fe64167931c98b964 feacb3129097876863b3e25f5d750454a58e73b7Creative design is a nonlinear process where designers generate diverse ideas in the pursuit of an open-ended goal and converge towards consensus through iterative remixing. In contrast, AI-powered design tools often employ a linear sequence of incremental and precise instructions to approximate design objectives. Such operations violate customary creative design practices and thus hinder AI agents' ability to complete creative design tasks. To explore better human-AI co-design tools, we first summarize human designers' practices through a formative study with 12 design experts. Taking graphic design as a representative scenario, we formulate a nonlinear human-AI co-design framework and develop a proof-of-concept prototype, OptiMuse. We evaluate OptiMuse and validate the nonlinear framework through a comparative study. We notice a subconscious change in people's attitudes towards AI agents, shifting from perceiving them as mere executors to regarding them as opinionated colleagues. This shift effectively fostered the exploration and reflection processes of individual designers.International Conference on Human Factors in Computing Systems710A subconscious change in people's attitudes towards AI agents is noticed, shifting from perceiving them as mere executors to regarding them as opinionated colleagues, which effectively fostered the exploration and reflection processes of individual designers.ArXiv['Jiayi Zhou', 'Renzhong Li', 'Junxiu Tang', 'Tan Tang', 'Haotian Li', 'Weiwei Cui', 'Yingcai Wu']2024-01-14T00:00:00https://www.semanticscholar.org/paper/feacb3129097876863b3e25f5d750454a58e73b7 56a9ed72497a611c0538c2d64cc511396eedca13روح القوانين00روح القوانين['رضا محمود العبد']2024-01-14T00:00:00https://www.semanticscholar.org/paper/56a9ed72497a611c0538c2d64cc511396eedca13 df2ee0bd374b659acac01f9f4fe6a2b97ba135f4WorldCALL Official Conference Proceedings00WorldCALL Official Conference Proceedings['Tricia Thrasher', 'Regina Kaplan‐Rakowski', 'Uliana Ovsiannikova', 'Justine Meyr', 'Ye Yuan']2024-01-14T00:00:00https://www.semanticscholar.org/paper/df2ee0bd374b659acac01f9f4fe6a2b97ba135f4 c53e19d422dc5ffa2363561fd8100059890d3555This study examines the impact of artificial intelligence (AI) on the accounting profession. It systematically investigates the impacts in which AI technologies have reformed the accounting field, redefining the roles and responsibilities of accountants. Using literature review, this study sheds light on the impact of AI on the accounting profession. The results of this study mostly found that the impact of AI on accounting profession can be divided into three themes; (i) automation of routine tasks; (ii) enhanced data analysis and (iii) value-added of the professional roles. The automation of routine tasks includes data entry, validation and transaction processing, while for enhanced data analysis, it includes predictive analytics and decision support. AI also has impacted in terms of value-added of the professional roles which comprise of increasing scalability and cost savings and focus on higher value activities. The findings of this study suggest that the accounting profession is evolving in response to AI technology, and accountants should embrace these changes to harness the full potential of AI in their work.Business Management and Strategy00The findings of this study suggest that the accounting profession is evolving in response to AI technology, and accountants should embrace these changes to harness the full potential of AI in their work.Business Management and Strategy['Nurul Afza Khusaini Mat Hussin', 'Nurul Ain Nadiah Mohd Bukhari', 'Nurul Hani Azyyati Nor Hashim', 'Sharina Nur Azyyati Shaipul Bahari', 'Mazurina Mohd Ali']2024-01-14T00:00:00https://www.semanticscholar.org/paper/c53e19d422dc5ffa2363561fd8100059890d3555 2db5cbaf535dc3a338346ce7a08760024daaa7fcPenggunaan kecerdasan buatan (AI) telah menjadi perbincangan utama dalam inovasi pendidikan saat ini. Artikel ini menguraikan berbagai aplikasi AI yang dapat meningkatkan efektivitas dan efisiensi dalam perguruan tinggi. Salah satu aspek utama yang dibahas adalah penggunaan sistem rekomendasi AI untuk mencari artikel yang saling terkait dalam suatu topik pada jurnal dan menguraikan isi dari jurnal tersebut. Hasil yang kami harapkan adalah agar dapat membantu antar sesama peneliti dan mahasiswa dan bisa mempermudah dan mempersingkat dalam mencari jurnal dan  menguraikan isi.Jurnal Abdi Masyarakat Multidisiplin230 Jurnal Abdi Masyarakat Multidisiplin['Tommy Kuncara', 'D. Wulan', 'Raden Roro Shinta', 'Adam Huda Nugraha', 'A. Pratama', 'Ratih Fitriyatun', 'D. Anggraeni', 'Analisis Situasi']2024-01-14T00:00:00https://www.semanticscholar.org/paper/2db5cbaf535dc3a338346ce7a08760024daaa7fc cc0ec8a149c3f711da349775c7f7a08eeab1babbBackground: Sleep apnea is a prevalent sleep disorder, especially in males and older ages. The common diagnostic methods, including polysomnography (PSG), are expensive, difficult to perform, and time-consuming. Numerous studies are focusing on developing easy-to-perform methods based on artificial intelligence (AI) for the early diagnosis of sleep apnea. This systematic review aimed to gather current methods based on the convolutional neural network (CNN) for the diagnosis of sleep apnea. Methods: Three international electronic databases (PubMed, Web of Science [WoS], and Scopus) were searched from 2010 to October 2023. All studies that have developed CNN-based methods for the diagnosis of sleep apnea and have accomplished the performance tests were included. Finally, the characteristics of the studies were extracted and summarized. Results: A total of 36 studies were included in this systematic review. Various physiological signals have been proposed to detect sleep apnea, including electrocardiogram (ECG), blood oxygen saturation (SpO2), sound signals, respiratory signals, electroencephalogram (EEG), and nasal airflow. Electrocardiogram was the most frequently used signal in the studies, followed by SpO2. The highest reported accuracy was achieved by SpO2 or ECG-based methods and with a one-dimensional CNN (1D-CNN) classifier. Using multiple signals did not necessarily increase the performance of test results. Conclusions: Diagnostic methods based on CNN can be used only as screening tools or home diagnosis of sleep apnea. These methods are easy to perform and can only reduce the diagnostic costs and waiting time for a sleep study in special scenarios. Nevertheless, PSG is still the gold standard for the diagnosis of sleep disorders.Archives of Neuroscience820D diagnostic methods based on convolutional neural network can be used only as screening tools or home diagnosis of sleep apnea, and can only reduce the diagnostic costs and waiting time for a sleep study in special scenarios.Archives of Neuroscience['Behnam Samadi', 'Shahram Samadi', 'Mehrshad Samadi', 'Sepehr Samadi', 'Mehrdad Samadi', 'Mahdi Mohammadi']2024-01-14T00:00:00https://www.semanticscholar.org/paper/cc0ec8a149c3f711da349775c7f7a08eeab1babb 88de37565d2ffee734e3261334e1e8e3726a019aThis paper evaluates AI from concrete to Abstract (Queiroz et al. 2021), a recently proposed method that enables awareness among the general public on machine learning. Such is possible due to the use of WiSARD, an easily understandable machine learning mechanism, thus requiring little effort and no technical background from the target users. WiSARD is adherent to digital computing; training consists of writing to RAM-type memories, and classification consists of reading from these memories. The model enables easy visualization and understanding of training and classification tasks' internal realization through ludic activities. Furthermore, the WiSARD model does not require an Internet connection for training and classification, and it can learn from a few or one example. WiSARD can also create"mental images"of what it has learned so far, evidencing key features pertaining to a given class. The AIcon2abs method's effectiveness was assessed through the evaluation of a remote course with a workload of approximately 6 hours. It was completed by thirty-four Brazilian subjects: 5 children between 8 and 11 years old; 5 adolescents between 12 and 17 years old; and 24 adults between 21 and 72 years old. The collected data was analyzed from two perspectives: (i) from the perspective of a pre-experiment (of a mixed methods nature) and (ii) from a phenomenological perspective (of a qualitative nature). AIcon2abs was well-rated by almost 100% of the research subjects, and the data collected revealed quite satisfactory results concerning the intended outcomes. This research has been approved by the CEP/HUCFF/FM/UFRJ Human Research Ethics Committee.arXiv.org490ArXiv['Rubens Lacerda Queiroz', 'Cabral Lima', 'F. F. Sampaio', 'Priscila Machado Vieira Lima']2024-01-14T00:00:00https://www.semanticscholar.org/paper/88de37565d2ffee734e3261334e1e8e3726a019a c0413780ff6c2cb8943ff47db14edd5144d2648cThis study looks into the possibilities and related challenges of using artificial intelligence (AI) to identify cancer. AI technologies have become attractive tools in oncology because of the growing complexity of medical data and the need for precise and timely cancer diagnosis. In reviewing the state of AI applications for cancer detection today, the paper focuses on biomarker analysis, medical imaging, and biomarker analysis. Additionally continuing conversation on how to use technology to improve cancer diagnostics' efficiency and accuracy while maintaining ethical norms and patient safety.International Journal of Scientific Research in Science and Technology80The paper focuses on biomarker analysis, medical imaging, and biomarker analysis, and continuing conversation on how to use technology to improve cancer diagnostics' efficiency and accuracy while maintaining ethical norms and patient safety.International Journal of Scientific Research in Science and Technology['Payal D Bhavsar', 'Darshanaben Dipakkumar Pandya', 'Hansaben Haribhai Patel', 'Abhijeetsinh Jadeja']2024-01-14T00:00:00https://www.semanticscholar.org/paper/c0413780ff6c2cb8943ff47db14edd5144d2648c c3563566305e425088afc5574d99d7306c0d336aClinical and Experimental Ophthalmology51Clinical & experimental ophthalmology['S. Bacchi', 'J. Kovoor', 'Aashray K. Gupta', 'WengOnn Chan']2024-01-14T00:00:00https://www.semanticscholar.org/paper/c3563566305e425088afc5574d99d7306c0d336a c4e15ecc5ad0ed9212232b9c814d24282fbda1f3European Journal of Cardio-Thoracic Surgery00European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery['Michael Poullis']2024-01-14T00:00:00https://www.semanticscholar.org/paper/c4e15ecc5ad0ed9212232b9c814d24282fbda1f3 b6885d129ed50106f00e22786b020c03cbe6a5c5Saudi Journal of Biological Sciences470The study highlighted the recent deep learning models single or in combination with high accuracy, sensitivity, and specificity to ensure reliable use for bacterial pneumonia identification and differentiate from other viral, fungal pneumonia in children and adults through chest x-rays and radiographs.Saudi Journal of Biological Sciences['Mohammad Zubair']2024-01-14T00:00:00https://www.semanticscholar.org/paper/b6885d129ed50106f00e22786b020c03cbe6a5c5 2e4259138ceb4186a945be5095c19ea45383c147The labor dispute is one of the most common civil disputes. It can be resolved in the order of the following steps, which include mediation in arbitration, arbitration award, first-instance mediation, first-instance judgment, and second-instance judgment. The process can cease at any step when it is successfully resolved. In recent years, due to the increasing rights awareness of employees, the number of labor disputes has been rising annually. However, resolving labor disputes is time-consuming and labor-intensive, which brings a heavy burden to employees and dispute resolution institutions. Using artificial intelligence algorithms to identify and predict the critical path of labor dispute resolution is helpful for saving resources and improving the efficiency of, and reducing the cost of dispute resolution. In this study, a machine learning approach based on Shapley Additive exPlanations (SHAP) and a soft voting strategy is applied to predict the critical path of labor dispute resolution. We name our approach LDMLSV (stands for Labor Dispute Machine Learning based on SHapley additive exPlanations and Voting). This approach employs three machine learning models (Random Forest, Extra Trees, and CatBoost) and then integrates them using a soft voting strategy. Additionally, SHAP is used to explain the model and analyze the feature contribution. Based on the ranking of feature importance obtained from SHAP and an incremental feature selection method, we obtained an optimal feature subset comprising 33 features. The LDMLSV achieves an accuracy of 0.90 on this optimal feature subset. Therefore, the proposed approach is a highly effective method for predicting the critical path of labor dispute resolution.Mathematics361The proposed approach LDMLSV (stands for Labor Dispute Machine Learning based on SHapley additive exPlanations and Voting) is a highly effective method for predicting the critical path of labor dispute resolution.Mathematics['Jianhua Guan', 'Zu-Guo Yu', 'Yongan Liao', 'Runbin Tang', 'Ming Duan', 'Guosheng Han']2024-01-14T00:00:00https://www.semanticscholar.org/paper/2e4259138ceb4186a945be5095c19ea45383c147 a91aabc2aa1530a283de49518d5ad79433886472AI is analysed from different perspectives, one of which is the application of AI in the public sector. The development of the AI requires a responsible approach, including ethical guidelines, regulation, and continuous public education. It is important to strike a balance between promoting innovation and minimising potential negative consequences. There is also a need to promote technology development in a way that benefits society as a whole and not just certain groups. It is becoming a common tool in education. Already, AI is changing the way schools, universities, students, and educators work and children learn. Educational institutions benefit from it by helping teachers meet the specific needs of each learner. There is a strong push for closer cooperation on digital education at European Union level and the importance of collaboration across all sectors to make education fit for the digital age. The article provides a general overview of artificial intelligence, its impact on society, how it can be applied in the public sector, whether humans are already able to exploit and benefit from this technology, and how to guard against the negative effects of technology. What legal measures need to be taken to ensure that artificial intelligence is a progress for mankind and not a regression in any field. Keywords: public sector, artificial intelligence, artificial intelligence technologies, artificial intelligence systems, big data, ethics, national power, education, teacher, teaching.Applied Scientific Research00A general overview of artificial intelligence, its impact on society, how it can be applied in the public sector, whether humans are already able to exploit and benefit from this technology, and how to guard against the negative effects of technology are provided.Applied Scientific Research['Viktorija Girinskienė']2024-01-13T00:00:00https://www.semanticscholar.org/paper/a91aabc2aa1530a283de49518d5ad79433886472 553bef18bb4097f6a9cfbdc0600e2d4bb3b860b3Current Osteoporosis Reports176The main aim of this project was to determine whether the use of AI could improve the process of writing a scientific review article and it was found that the human-only approach took less time to complete than the AI-assisted approach.Current Osteoporosis Reports['Olatundun D. Awosanya', 'Alexander Harris', 'Amy Creecy', 'Xian Qiao', 'Angela J. Toepp', 'Thomas McCune', 'M. Kacena', 'M. Ozanne']2024-01-13T00:00:00https://www.semanticscholar.org/paper/553bef18bb4097f6a9cfbdc0600e2d4bb3b860b3 a597da055fe21785225604142498e23892cdddd2Scientific Reports402AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI—a range with low PI agreement, could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.Scientific Reports['Amanda Dy', 'Ngoc-Nhu Jennifer Nguyen', 'Julien Meyer', 'Melanie Dawe', 'Wei Shi', 'Dimitri Androutsos', 'Anthony Fyles', 'Fei-Fei Liu', 'Susan J. Done', 'April Khademi']2024-01-13T00:00:00https://www.semanticscholar.org/paper/a597da055fe21785225604142498e23892cdddd2 27cc5a2971b0c71678525a207b019842ae40715aAI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of second opinions may affect decision-makers' behavior and performance in AI-assisted decision-making. We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model. However, if decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI without inducing increased under-reliance in some cases. We conclude by discussing the implications of our findings for promoting effective human-AI collaborations in decision-making.Proceedings of the ACM on Human-Computer Interaction1172It is found that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model.ArXiv['Zhuoran Lu', 'Dakuo Wang', 'Ming Yin']2024-01-13T00:00:00https://www.semanticscholar.org/paper/27cc5a2971b0c71678525a207b019842ae40715a f0340c3932c4dcc9f505f06390540f16c7ee7905In an era characterized by the pervasive integration of artificial intelligence into decision-making processes across diverse industries, the demand for trust has never been more pronounced. This thesis embarks on a comprehensive exploration of bias and fairness, with a particular emphasis on their ramifications within the banking sector, where AI-driven decisions bear substantial societal consequences. In this context, the seamless integration of fairness, explainability, and human oversight is of utmost importance, culminating in the establishment of what is commonly referred to as"Responsible AI". This emphasizes the critical nature of addressing biases within the development of a corporate culture that aligns seamlessly with both AI regulations and universal human rights standards, particularly in the realm of automated decision-making systems. Nowadays, embedding ethical principles into the development, training, and deployment of AI models is crucial for compliance with forthcoming European regulations and for promoting societal good. This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias. These contributions are validated through their practical application in real-world scenarios, in collaboration with Intesa Sanpaolo. This collaborative effort not only contributes to our understanding of fairness but also provides practical tools for the responsible implementation of AI-based decision-making systems. In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages, further promoting progress in the field of AI fairness.arXiv.org02This thesis embarks on a comprehensive exploration of bias and fairness, with a particular emphasis on their ramifications within the banking sector, where AI-driven decisions bear substantial societal consequences.ArXiv['Alessandro Castelnovo']2024-01-13T00:00:00https://www.semanticscholar.org/paper/f0340c3932c4dcc9f505f06390540f16c7ee7905 76f36d4ee7a6f53f0919ddf3c1cdba8424eb727bJournal of Applied Genetics1461This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.Journal of applied genetics['Suvojit Bose', 'Saptarshi Banerjee', 'Soumya Kumar', 'Akash Saha', 'Debalina Nandy', 'Soham Hazra']2024-01-13T00:00:00https://www.semanticscholar.org/paper/76f36d4ee7a6f53f0919ddf3c1cdba8424eb727b 33a8c63c960b3bdbe929f3800d171be5a82c011fJ. Intell. Robotic Syst.00The works within this collection highlight the increasing prominence of robotics in education, with it becoming a part of basic education, and discuss a topical collection showcasing research presented at Latin American robotics events in 2021.J. Intell. Robotic Syst.['Bruno M. F. Silva', 'Eduardo Todt', 'Tiago P. Nascimento', 'C. D. C. F. Curvelo', 'Luiz M. G. Gonçalves']2024-01-13T00:00:00https://www.semanticscholar.org/paper/33a8c63c960b3bdbe929f3800d171be5a82c011f 654ef42aa838d6a955be22a2bf4f720f83135ba0Conversational agents have been gaining increasing popularity in recent years. Influenced by the widespread adoption of task-oriented agents such as Apple Siri and Amazon Alexa, these agents are being deployed into various applications to enhance user experience. Although these agents promote"ask me anything"functionality, they are typically built to focus on a single or finite set of expertise. Given that complex tasks often require more than one expertise, this results in the users needing to learn and adopt multiple agents. One approach to alleviate this is to abstract the orchestration of agents in the background. However, this removes the option of choice and flexibility, potentially harming the ability to complete tasks. In this paper, we explore these different interaction experiences (one agent for all) vs (user choice of agents) for conversational AI. We design prototypes for each, systematically evaluating their ability to facilitate task completion. Through a series of conducted user studies, we show that users have a significant preference for abstracting agent orchestration in both system usability and system performance. Additionally, we demonstrate that this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers.arXiv.org490It is shown that users have a significant preference for abstracting agent orchestration in both system usability and system performance, and this mode of interaction is able to provide quality responses that are rated within 1% of human-selected answers.ArXiv['Christopher Clarke', 'Karthik Krishnamurthy', 'W. Talamonti', 'Yiping Kang', 'Lingjia Tang', 'Jason Mars']2024-01-13T00:00:00https://www.semanticscholar.org/paper/654ef42aa838d6a955be22a2bf4f720f83135ba0 cb681840cf91d55de4fa3eb791c83571e65e9307Robotic arms, integral in domestic care for individuals with motor impairments, enable them to perform Activities of Daily Living (ADLs) independently, reducing dependence on human caregivers. These collaborative robots require users to manage multiple Degrees-of-Freedom (DoFs) for tasks like grasping and manipulating objects. Conventional input devices, typically limited to two DoFs, necessitate frequent and complex mode switches to control individual DoFs. Modern adaptive controls with feed-forward multi-modal feedback reduce the overall task completion time, number of mode switches, and cognitive load. Despite the variety of input devices available, their effectiveness in adaptive settings with assistive robotics has yet to be thoroughly assessed. This study explores three different input devices by integrating them into an established XR framework for assistive robotics, evaluating them and providing empirical insights through a preliminary study for future developments.IEEE/ACM International Conference on Human-Robot Interaction311This study explores three different input devices by integrating them into an established XR framework for assistive robotics, evaluating them and providing empirical insights through a preliminary study for future developments.ArXiv['Max Pascher', 'Kevin Zinta', 'J. Gerken']2024-01-13T00:00:00https://www.semanticscholar.org/paper/cb681840cf91d55de4fa3eb791c83571e65e9307 ad9437c79d7ddb0cf43558c64c80bc3c78c1ac99Current opinion in pharmacology (Print)1050The role of recent advances in Artificial Intelligence in GPCR research is investigated, including the application of machine learning in GPCR classification, prediction of GPCR activation levels, modelling GPCR 3D structures and interactions, and drug design.Current opinion in pharmacology['João P. L. Velloso', 'Aaron S. Kovacs', 'D. Pires', 'D. Ascher']2024-01-13T00:00:00https://www.semanticscholar.org/paper/ad9437c79d7ddb0cf43558c64c80bc3c78c1ac99 b8855aca1df0d6134f3e0348deaccdb4e0643c7fThis study examines the ethical challenges and regulatory dynamics of Artificial Intelligence (AI) in relation to data integrity and its influence on social dynamics. Employing a cross-sectional survey approach, primary data was collected from 650 AI practitioners across various sectors, encompassing developers, data scientists, ethicists, and policymakers. The study investigated the correlations between regulatory compliance, ethical awareness, professional training, and experience in AI practice with the effectiveness of AI implementation and data integrity. The findings revealed a strong positive correlation between higher levels of regulatory compliance and perceived effectiveness in AI implementation, as well as between AI ethics awareness and data integrity assurance. Moreover, a significant relationship was observed between professional training in AI and its positive impact on social dynamics. However, experience in the AI field, while positively correlated, showed a weaker link to data integrity, indicating that experience alone is insufficient for ensuring effective AI practices. The study highlights the importance of ethical considerations, regulatory frameworks, and professional training in shaping AI development and its societal implications. The need for dynamic, adaptable, and inclusive regulatory frameworks that can align AI practices with societal values and ethical norms is emphasized. Future research directions include exploring AI ethics and regulation in diverse cultural contexts and the impact of emerging technologies like quantum computing on AI ethics.Asian Journal of Advanced Research and Reports04A strong positive correlation was revealed between higher levels of regulatory compliance and perceived effectiveness in AI implementation, as well as between AI ethics awareness and data integrity assurance, and a significant relationship was observed between professional training in AI and its positive impact on social dynamics.Asian Journal of Advanced Research and Reports['Tunbosun Oyewale Oladoyinbo', 'Samuel Oladiipo Olabanji', 'O. O. Olaniyi', 'Olubukola Omolara Adebiyi', 'O. J. Okunleye', 'Adegbenga Ismaila Alao']2024-01-13T00:00:00https://www.semanticscholar.org/paper/b8855aca1df0d6134f3e0348deaccdb4e0643c7f 30538e5fd82625040487412cb4f186d4261b5853Motivation to use artificial intelligence (AI) technologies are those factors that influenced users’ drive to actually use AI technologies. There are various AI technologies that librarians can adopt to improve their service functions. However, studies have shown that the use of AI among librarians is low. It is in the light of this that this study therefore investigated the motivation to use AI technologies among librarians in Edo and Delta States. Descriptive survey research design was adopted. The population consist of 125 librarians selected from 18 Universities in Edo and Delta States, Nigeria. Total enumeration technique was adopted because of the manageable size of the population. A validated questionnaire was used to collect data from the respondents.  Data collected was analysed using both inferential and descriptive statistics. Finding revealed that digital competence (Adj. R2 = 0.195, p = 0.000b) has significant but weak influence on motivation to use AI technologies among librarians. The study concluded that digital competence influenced motivation to use AI technologies. The study recommended that there should be more awareness of AI among librarians. At the same time, librarians should improve their digital skill through regular training programs so that they can effectively use AI tools.Journal of Technology Innovations and Energy180Investigation of the motivation to use AI technologies among librarians in Edo and Delta States, Nigeria found that digital competence influenced motivation to use AI technologies.Journal of Technology Innovations and Energy['Nosakhare Okuonghae', 'Sunday Tunmibi']2024-01-13T00:00:00https://www.semanticscholar.org/paper/30538e5fd82625040487412cb4f186d4261b5853 95ee7745c6fc3ca7faf1c80020b8e4535fa5dfcbThe world population is expected to grow to around 9 billion by 2050. The growing need for foods with high protein levels makes aquaculture one of the fastest-growing food industries in the world. Some challenges of fishing production are related to obsolete aquaculture techniques, overexploitation of marine species, and lack of water quality control. This research systematically analyzes aquaculture technologies, such as sensors, artificial intelligence (AI), and image processing. Through the systematic PRISMA process, 753 investigations published from 2012 to 2023 were analyzed based on a search in Scopus and Web of Science. It revealed a significant 70.5% increase in the number of articles published compared to the previous year, indicating a growing interest in this field. The results indicate that current aquaculture technologies are water monitoring sensors, AI methodologies such as K-means, and contour segmentation for computer vision. Also, it is reported that K means technologies offer an efficiency from 95% to 98%. These methods allow decisions based on data patterns and aquaculture insights. Improving aquaculture methodologies will allow adequate management of economic and environmental resources to promote fishing and satisfy nutritional needs.Journal of Marine Science and Engineering610This research systematically analyzes aquaculture technologies, such as sensors, artificial intelligence (AI), and image processing, and indicates that current aquaculture technologies are water monitoring sensors, AI methodologies such as K-means, and contour segmentation for computer vision.Journal of Marine Science and Engineering['Omar Capetillo-Contreras', 'F. Pérez-Reynoso', 'M. Zamora-Antuñano', 'J. M. Álvarez-Alvarado', 'J. Rodríguez-Reséndíz']2024-01-13T00:00:00https://www.semanticscholar.org/paper/95ee7745c6fc3ca7faf1c80020b8e4535fa5dfcb 5f43f54f60450c37d603d436f2c18a65dbfc6225Artificial intelligence (AI) holds the potential to revolutionize teaching and learning methodologies, tackle some of the largest issues facing education today, and hasten the achievement of SDG 4. The revolutionary effects of artificial intelligence (AI) on education are explored in depth in this study paper. Researcher examined the possible advantages, challenges, and ethical issues related to this changing paradigm by examining the incorporation of AI technology in several educational environments. In the present study, researchers have discussed the use of AI in education, focusing on its benefits like personalized learning, data-driven insights, and accessibility. It also discussed the drawbacks, such as data privacy, biases, and ethical issues. This study has also emphasized the importance of accountability, transparency, and justice in AI algorithms. It also discusses future developments in AI-driven education, such as adaptive learning, augmented reality, and the potential of AI to address global education issues. The focus is on promoting a balanced viewpoint and addressing ethical concerns in AI-driven education. The integration of Artificial Intelligence (AI) in education is a transformative process, offering a wide range of applications from predictive analytics to personalized learning platforms. However, ethical issues like privacy, bias, and transparency must be addressed to ensure responsible AI adoption in the future.International journal of research - granthaalayah340The integration of Artificial Intelligence (AI) in education is a transformative process, offering a wide range of applications from predictive analytics to personalized learning platforms, however, ethical issues like privacy, bias, and transparency must be addressed to ensure responsible AI adoption in the future.International Journal of Research -GRANTHAALAYAH['Suvendu Ray', 'Deb Prasad Ray']2024-01-13T00:00:00https://www.semanticscholar.org/paper/5f43f54f60450c37d603d436f2c18a65dbfc6225 7fe3b8751668cda45d46c145233ac983aa987c62In recent years, there has been a growing interest in the integration of artificial intelligence (AI) into various fields. Artificial Intelligence (AI) also has become a prominent technology in various fields, including education. One area where Artificial Intelligent (AI) has shown great potential is in English Language Teaching (ELT). With the rapid development of technology, Artificial Intelligent (AI) tools and applications have been integrated into language learning platforms, classrooms, and online resources, transforming the way English is taught and learned. In the realm of language teaching, Artificial Intelligent offers significant potential for enhancing the learning process and improving outcomes. English language teaching is no exception, as educators are exploring the potential of Artificial Intelligent (AI) to enhance language learning experiences. English language teaching is no exception, as Artificial Intelligent (AI) can enhance the learning experience for English language learners by providing personalized instruction, immediate feedback, and immersive language practice. This research addresses the extent to use artificial intelligence in English language teaching. Researcher used descriptive qualitative approach with questionnaire as technique of data collection. The results of this research are the Artificial Intelligence is needed in English language teaching. Artificial Intelligence also has negative and positive impact in English language teaching depend on utilized correctly and at the appropriate timing. Besides that, Artificial Intelligence also help students in improving their English skill like speaking, writing, and reading using product of Artificial Intelligence like Chat GPT, Quillbot, Grammarly, Plagiarism Checker, Paraphrasingtool.com. So, the use of Artificial Intelligent in English language teaching is very important or useful and also reducing the burden of teachers and improving teaching quality.International Journal of English Learning and Applied Linguistics (IJELAL)00The results of this research are the Artificial Intelligence is needed in English language teaching and the use of Artificial Intelligent in English language teaching is very important or useful for reducing the burden of teachers and improving teaching quality.International Journal of English Learning and Applied Linguistics (IJELAL)['Rizqi Akbarani']2024-01-13T00:00:00https://www.semanticscholar.org/paper/7fe3b8751668cda45d46c145233ac983aa987c62 68211c97de4b032c475b1355aef5125e77d5c2c0Machine learning algorithms are increasingly being utilized in scenarios, such, as criminal, administrative and civil proceedings. However, there is growing concern regarding the lack of transparency and accountability due to the “black box” nature of these algorithms. This makes it challenging for judges’ to comprehend how decisions or predictions are reached. This paper aims to explore the significance of Explainable AI (xAI) in enhancing transparency and accountability within contexts. Additionally, it examines the role that the judicial system can play in developing xAI. The methodology involves a review of existing xAI research and a discussion on how feedback from the system can improve its effectiveness in legal settings. The argument presented is that xAI is crucial in contexts as it empowers judges to make informed decisions based on algorithmic outcomes. However, the lack of transparency, in decision-making processes can impede judge’s ability to do effectively. Therefore, implementing xAI can contribute to increasing transparency and accountability within this decision-making process. The judicial system has an opportunity to aid in the development of xAI by emulating reasoning customizing approaches according to specific jurisdictions and audiences and providing valuable feedback for improving this technology’s efficacy.Hence the primary objective is to emphasize the significance of xAI in enhancing transparency and accountability, within settings well as the potential contribution of the judicial system, towards its advancement. Judges could consider asking about the rationale, behind outcomes. It is advisable for xAI systems to provide a clear account of the steps taken by algorithms to reach their conclusions or predictions. Additionally, it is proposed that public stakeholders have a role, in shaping xAI to guarantee ethical and socially responsible technology. Kutafin Law Review190The argument presented is that xAI is crucial in contexts as it empowers judges to make informed decisions based on algorithmic outcomes, and the lack of transparency, in decision-making processes can impede judge's ability to do effectively.Kutafin Law Review['G. Chaudhary']2024-01-13T00:00:00https://www.semanticscholar.org/paper/68211c97de4b032c475b1355aef5125e77d5c2c0 72a26c1e19a24528fa8c2386b9895c88389716c9The fourth industrial revolution, i.e., Industry 4.0, is associated with Cyber-Physical Systems (CPS), which are entities integrating hardware (e.g., smart sensors and actuators connected through the Industrial Internet of Things) together with control and analytics software used to drive and support decisions at several levels. The latest developments in Artificial Intelligence (AI) and Machine Learning (ML) have enabled increased autonomy and closer human-robot cooperation in the production and manufacturing industry, thus leading to Autonomous Cyber-Physical Production Systems (ACPPS) and paving the way to the fifth industrial revolution (i.e., Industry 5.0). ACPPS are increasingly critical due to the possible consequences of their malfunctions on human co-workers, and therefore, evaluating their trustworthiness is essential. This paper reviews research trends, relevant attributes, modelling languages, and tools related to the model-based trustworthiness evaluation of ACPPS. As in many other engineering disciplines and domains, model-based approaches, including stochastic and formal analysis tools, are essential to master the increasing complexity and criticality of ACPPS and to prove relevant attributes such as system safety in the presence of intelligent behaviors and uncertainties.ACM Computing Surveys850This paper reviews research trends, relevant attributes, modelling languages, and tools related to the model-based trustworthiness evaluation of ACPPS and proves relevant attributes such as system safety in the presence of intelligent behaviors and uncertainties.ACM Computing Surveys['Maryam Zahid', 'Alessio Bucaioni', 'Francesco Flammini']2024-01-13T00:00:00https://www.semanticscholar.org/paper/72a26c1e19a24528fa8c2386b9895c88389716c9 0484a7edeff8c803f61165f43a5397e9768fcdd5The era of industrial revolution 4.0. placing human life on faster technology. This era gave rise to changes in research paradigms driven by the communications technology revolution, economic globalization, changes in the legal profession and legal education. Artificial intelligence technology is making an impact in the legal field. The influence of artificial intelligence technology in the legal field is that this technology can help resolve legal problems by conducting analysis and review of large data sets (big data). Applications that use artificial intelligence technology in the legal field such as the Bloomberg points of law application, westlaw edge citator Improvement, cara on casetext, lex machina on lexis advance, judicata, ravel law, kira system, leverton, ebrevia, thoughtriver, lawgeex, legal robot, forever, ross intelligenceSMART150The era of industrial revolution 4.0 gave rise to changes in research paradigms driven by the communications technology revolution, economic globalization, changes in the legal profession and legal education.SMART: Journal of Multidisciplinary Educational['QD Kusumawardani']2024-01-13T00:00:00https://www.semanticscholar.org/paper/0484a7edeff8c803f61165f43a5397e9768fcdd5 de292b7f6fd3ab468cf35be0492e6dd9f337dceeEuropean Journal of Radiology Open282Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography, however, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.European Journal of Radiology Open['Si Eun Lee', 'H. Hong', 'Eun-Kyung Kim']2024-01-13T00:00:00https://www.semanticscholar.org/paper/de292b7f6fd3ab468cf35be0492e6dd9f337dcee ca01dd2f14eef73b88c7f700aa0f51ba4882ccd3EURASIP Journal on Advances in Signal Processing170A novel communication framework inspired by brain cognition for UAV communication in heterogeneous environments is introduced, which iteratively determines the importance of signals, effectively eliminating unimportant signals with interference characteristics, and reducing their transmission power.EURASIP Journal on Advances in Signal Processing['Chao Ren', 'Jiayin Song', 'Mengxuan Qiu', 'Yingqi Li', 'Xianmei Wang']2024-01-13T00:00:00https://www.semanticscholar.org/paper/ca01dd2f14eef73b88c7f700aa0f51ba4882ccd3 9e97a0e8c1fc6030caaa8a0ea1b03c72c83b849dAI &amp; SOCIETY10AI &amp; SOCIETY['Chan Lu']2024-01-13T00:00:00https://www.semanticscholar.org/paper/9e97a0e8c1fc6030caaa8a0ea1b03c72c83b849d b7f5ed6efb888403e75464c7efab5941effec82bPhilosophy &amp; Technology440It is argued that disparities in responding to digital distraction threaten fair equality of opportunity when it comes to digital distraction in the classroom and that they may lead to an unequal contribution of achievements that require complex cognition by people from lower socioeconomic backgrounds.Philosophy &amp; Technology['Kaisa Kärki']2024-01-12T00:00:00https://www.semanticscholar.org/paper/b7f5ed6efb888403e75464c7efab5941effec82b 63d343ea9e19c7a0f33a04d20541c1438533f938Legal certainty for Communal Intellectual Property protection on the inventory and record-keeping arrangements in terms of ownership proof in Indonesia, has increasingly been regulated in various regulations. However, threats are also growing. Traditional Cultural expression works are easily turned into personal video works. Along with that, such works are also vulnerable as those are easily threatened by Artificial intelligence’s ability to express works made from previous works of art such as paintings. This article aims to analyze Traditional Cultural Expressions protection which are transformed or adapted into personal works or works made by Artificial Intelligence and the measures to overcome these threats. The results show that referring to Government Regulation 56/2022, the commercial use of Traditional Cultural Expressions works both in the form of adaptation and transformation by individual humans and Artificial Intelligence is required to obtain a permit and pay attention to the distribution of benefits which will further be determined by the Minister. However, regulations on this mechanism has not been explicitly regulated. Measures to overcome threats can be made through measures to turn threats into opportunities and strengths. It is also relevant to prioritize countervailing measures, namely by following the pattern of threats as a balancing act.LAW REFORM210This article aims to analyze Traditional Cultural Expressions protection which are transformed or adapted into personal works or works made by Artificial Intelligence and the measures to overcome these threats.LAW REFORM['Nico Dharmawan', 'Desak Putu Dewi Kasih', 'Putu Aras Samsithawrati', 'Putri Triari Dwijayanthi', 'Made Suksma Prijandhini Devi Salain', 'Mirah Mahaswari', 'Made Grazia Ustriyana', 'Robert Vaisile Moisa']2024-01-12T00:00:00https://www.semanticscholar.org/paper/63d343ea9e19c7a0f33a04d20541c1438533f938 e98c743fe71aa8480bd71ac472fb95264c40496aIntegrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the urgent need for effective bias detection and mitigation strategies to build patient trust. The evolving roles of decentralized data sharing, regulatory frameworks, and patient agency are discussed in depth. Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care.Applied Sciences376Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care.Applied Sciences['Steven M. Williamson', 'Victor R. Prybutok']2024-01-12T00:00:00https://www.semanticscholar.org/paper/e98c743fe71aa8480bd71ac472fb95264c40496a 732ce53c573475f2691a7cfc716cf4f568d17360Most traditional AI safety research has approached AI models as machines and centered on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate interpretable persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak performance across all risk categories: PAP consistently achieves an attack success rate of over $92\%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses, and advocate for more fundamental mitigation for highly interactive LLMsarXiv.org7547This paper proposes a persuasion taxonomy derived from decades of social science research and applies the taxonomy to automatically generate interpretable persuasive adversarial prompts (PAP) to jailbreak LLMs.ArXiv['Yi Zeng', 'Hongpeng Lin', 'Jingwen Zhang', 'Diyi Yang', 'Ruoxi Jia', 'Weiyan Shi']2024-01-12T00:00:00https://www.semanticscholar.org/paper/732ce53c573475f2691a7cfc716cf4f568d17360 6814b26d56ec273609750a9f7683833a3eb711a4Digital ecosystems allow banks to expand their offerings of financial and non-financial services, and thereby raise the quality and speed of customer service. Yet the broader range of banks’ non-financial services increases the size of their non-core assets and affects their financial stability. This creates certain complexities to ecosystems’ management both at the level of a bank and at the level of financial market regulation. The study covers theoretical and practical aspects of setting up and developing ecosystems in the Russian banking industry. The theory of industrial organisation and the ecosystem concept constitute the methodological basis of the research. The study adopts comparative and structural analysis methods. The evidence comes from public and internal reporting of PAO Sberbank, AO Tinkoff Bank, VTB Bank (PAO). The research found that banking ecosystems differ in the key area of activities (for instance, the Tinkoff Bank’s ecosystem focuses on investment and education, whereas VTB Bank’s one concentrates on a housing programme), nature of interaction between their participants, and the method of creation (universal, niche, outsourcing, insourcing). The development specifics of digital banking ecosystems depend on their specialisation, structure of immobilised assets, customer base, and level of business processes’ digitalisation. The value of the research comes from the revealed structural and functional peculiarities of digital ecosystems in the banking industry, as well as in the proved necessity to further refine the methods for accounting and assessing immobilised assets of banks.Journal of New Economy250The research found that banking ecosystems differ in the key area of activities (for instance, the Tinkoff Bank’s ecosystem focuses on investment and education, whereas VTB Bank’s one concentrates on a housing programme), nature of interaction between their participants, and the method of creation.Journal of New Economy['Svetlana Galazova']2024-01-12T00:00:00https://www.semanticscholar.org/paper/6814b26d56ec273609750a9f7683833a3eb711a4 ab2f918a79518e2edfe3d147bef9718cc3301447Recent neuroimaging and eye tracking studies have suggested that children with autism spectrum disorder (ASD) may exhibit more variable and idiosyncratic brain responses and eye movements than typically developing (TD) children. Here we extended this research for the first time to pupillometry recordings. We successfully completed pupillometry recordings with 103 children (66 with ASD), 4.5-years-old on average, who viewed three 90 second movies, twice. We extracted their pupillary time-course for each movie, capturing their stimulus evoked pupillary responses. We then computed the correlation between the time-course of each child and those of all others in their group. This yielded an average inter-subject correlation value per child, representing how similar their pupillary responses were to all others in their group. ASD participants exhibited significantly weaker inter-subject correlations than TD participants, reliably across all three movies. Differences across groups were largest in responses to a naturalistic movie containing footage of a social interaction between two TD children. This measure enabled classification of ASD and TD children with a sensitivity of 0.82 and specificity of 0.73 when trained and tested on independent datasets. Using the largest ASD pupillometry dataset to date, we demonstrate the utility of a new technique for measuring the idiosyncrasy of pupil regulation, which can be completed even by young children with co-occurring intellectual disability. These findings reveal that a considerable subgroup of ASD children have significantly more unstable, idiosyncratic pupil regulation than TD children, indicative of more variable, weakly regulated, underlying neural activity.bioRxiv421bioRxiv['Isabel Bleimeister', 'Inbar Avni', 'M. Granovetter', 'G. Meiri', 'M. Ilan', 'A. Michaelovski', 'I. Menashe', 'Marlene Behrmann', 'I. Dinstein']2024-01-12T00:00:00https://www.semanticscholar.org/paper/ab2f918a79518e2edfe3d147bef9718cc3301447 ea3b308765728ad021c1edfc2ee73ebcbabb2e22Public Money &amp; Management340Public Money &amp; Management['Sahar Zaman', 'Michael Howlett', 'A. Migone']2024-01-12T00:00:00https://www.semanticscholar.org/paper/ea3b308765728ad021c1edfc2ee73ebcbabb2e22 adb4c9cf8d0a7dc7bcf879232ea53744f1b412a2In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas.International Journal of Energy Research660International Journal of Energy Research['Yang Li', 'Xuan Wang', 'Yilin Wang', 'Jiachao Peng', 'Shaofeng Chen']2024-01-12T00:00:00https://www.semanticscholar.org/paper/adb4c9cf8d0a7dc7bcf879232ea53744f1b412a2 0980ca27dbf4057ead1a3579ab81f5e6f7c9bf20This article presents feedback on the implementation of an Artificial Intelligence-based adaptive learning Moodle plugin aimed at enhancing the engagement levels and academic performance of 102 Moroccan high school students. The primary objective of this study was to assess and compare the performance of students utilizing the adaptive learning system with those employing conventional learning methods. To guarantee the efficacy of this approach, a participant satisfaction survey and a comprehensive summative evaluation were conducted, revealing the positive impact of AI-based adaptive learning on the participants. The results of this study highlight the potential benefits of integrating AI-driven adaptive learning into high school computer science curricula, emphasizing how it may raise student engagement and academic performance. These results strengthen the determination to use this teaching methodology with students in future educational activities.International Journal of Interactive Mobile Technologies01Int. J. Interact. Mob. Technol.['Aymane Ezzaim', 'A. Dahbi', 'A. Haidine', 'Abdelhak Aqqal']2024-01-12T00:00:00https://www.semanticscholar.org/paper/0980ca27dbf4057ead1a3579ab81f5e6f7c9bf20 29ed66accfbc575f6721326aebdbb073bf4653deBackground The integration of artificial intelligence (AI)–based applications in the medical field has increased significantly, offering potential improvements in patient care and diagnostics. However, alongside these advancements, there is growing concern about ethical considerations, such as bias, informed consent, and trust in the development of these technologies. Objective This study aims to assess the role of ethics in the development of AI-based applications in medicine. Furthermore, this study focuses on the potential consequences of neglecting ethical considerations in AI development, particularly their impact on patients and physicians. Methods Qualitative content analysis was used to analyze the responses from expert interviews. Experts were selected based on their involvement in the research or practical development of AI-based applications in medicine for at least 5 years, leading to the inclusion of 7 experts in the study. Results The analysis revealed 3 main categories and 7 subcategories reflecting a wide range of views on the role of ethics in AI development. This variance underscores the subjectivity and complexity of integrating ethics into the development of AI in medicine. Although some experts view ethics as fundamental, others prioritize performance and efficiency, with some perceiving ethics as potential obstacles to technological progress. This dichotomy of perspectives clearly emphasizes the subjectivity and complexity surrounding the role of ethics in AI development, reflecting the inherent multifaceted nature of this issue. Conclusions Despite the methodological limitations impacting the generalizability of the results, this study underscores the critical importance of consistent and integrated ethical considerations in AI development for medical applications. It advocates further research into effective strategies for ethical AI development, emphasizing the need for transparent and responsible practices, consideration of diverse data sources, physician training, and the establishment of comprehensive ethical and legal frameworks.JMIR AI481The critical importance of consistent and integrated ethical considerations in AI development for medical applications is highlighted, emphasizing the need for transparent and responsible practices, consideration of diverse data sources, physician training, and the establishment of comprehensive ethical and legal frameworks.JMIR AI['Lukas Weidener', 'Michael Fischer']2024-01-12T00:00:00https://www.semanticscholar.org/paper/29ed66accfbc575f6721326aebdbb073bf4653de ec4bf0fe881c2db4fd99d0d55695ca906f8ba973Nature02Nature['Mariana Lenharo']2024-01-12T00:00:00https://www.semanticscholar.org/paper/ec4bf0fe881c2db4fd99d0d55695ca906f8ba973 20fa87188c9cb073f13a3692df4dc9cd6bb172aaAs Artificial Intelligence (AI) technology becomes more and more prevalent, it becomes increasingly important to explore how we as humans interact with AI. The Human-AI Interaction (HAI) sub-field has emerged from the Human-Computer Interaction (HCI) field and aims to examine this very notion. Many interaction patterns have been implemented without fully understanding the changes in required cognition as well as the cognitive science implications of using these alternative interfaces that aim to be more human-like in nature. Prior research suggests that theory of mind representations are crucial to successful and effortless communication, however very little is understood when it comes to how theory of mind representations are established when interacting with AI.arXiv.org130The Human-AI Interaction (HAI) sub-field has emerged from the Human-Computer Interaction field and aims to examine how theory of mind representations are established when interacting with AI.ArXiv['Mark Adkins']2024-01-12T00:00:00https://www.semanticscholar.org/paper/20fa87188c9cb073f13a3692df4dc9cd6bb172aa e5a133d4b6d6722ee607d5b3b6c65fc365a6c35aInspection of water pipelines with cameras under pressure is attracting attention. The inspection can be performed without digging the ground and water interruption by inserting a camera into aging water pipes while the water is flowing. However, the inspection has two problems: (1) a long-time visual check by expert engineers is required and (2) variations in the evaluation standards. To solve these problems, we have developed an AI image analysis system for automatically judging the state of degradation of water pipelines by using images captured from the in-pipe endoscope cameras. This report describes the developed technology and software to support the inspection work.PHM Society Asia-Pacific Conference60An AI image analysis system for automatically judging the state of degradation of water pipelines by using images captured from the in-pipe endoscope cameras is developed.PHM Society Asia-Pacific Conference['Ying Piao', 'Hiroshi Sukegawa', 'Kenji Kimiyama', 'Kensuke Nakamura', 'Toshiharu Sugino', 'T. Kunizane', 'Akira Koizumi']2024-01-12T00:00:00https://www.semanticscholar.org/paper/e5a133d4b6d6722ee607d5b3b6c65fc365a6c35a 7877167acf11012e02094615c995cdb0b6388ac1Examining the scope of research on AI teaching and learning in adult education is important for keeping up with current trends. It also helps find gaps that need more research in this area. This study conducts a comprehensive analysis of citation metrics in AI teaching and adult learning up to August 20, 2023, utilizing a Bibliometric analysis approach. This paper examines 435 selected papers that assess key citation metrics (total citations, citations per year, per paper, and per author) to gauge research impact. The main result shows that a significant increase in research output since 2009, with 2022 being the year of highest publication volume. The findings reveal robust scholarly engagement, articles and conference papers dominate this field, comprising 89.66% of the corpus, with peer-reviewed articles and conference papers taking precedence. English is the predominant language of publication (98.39%), while other languages, such as Chinese, Spanish, and Portuguese, are used to a lesser extent. Social sciences (51.03%) are the primary focus of this research, followed by computer science (45.75%), engineering (26.44%), and business-related fields (9.89%). This study's implications are twofold. Theoretically, it underscores the ongoing significance of AI-enhanced adult education, encouraging exploration of evolving theoretical frameworks. Managerially, it advises practitioners and policymakers to draw insights from highly cited articles when making decisions about program development and implementation. Future research could be updated with more recent data to incorporate changing citation trends investigating highly cited articles' content and impact may reveal their influence. In summary, this analysis provides valuable insights into the scholarly influence of AI in adult education, offering a solid foundation for further exploration of theoretical and practical aspects within this dynamic field.International Journal of Academic Research in Progressive Education and Development600This analysis provides valuable insights into the scholarly influence of AI in adult education, offering a solid foundation for further exploration of theoretical and practical aspects within this dynamic field.International Journal of Academic Research in Progressive Education and Development['Yong Azrina Ali Akbar', 'Azyyati Anuar', 'Rosliza Md Zani', 'Fatihah Norazami Abdullah', 'Elixon Sunian @ Elixson Sulaiman']2024-01-12T00:00:00https://www.semanticscholar.org/paper/7877167acf11012e02094615c995cdb0b6388ac1 187605e6ae2da06853e6ebbb757ee98169d7ada3Business and technology are intricately connected through logic and design. They are equally sensitive to societal changes and may be devastated by scandal. Cooperative multi-robot systems (MRSs) are on the rise, allowing robots of different types and brands to work together in diverse contexts. Generative artificial intelligence has been a dominant topic in recent artificial intelligence (AI) discussions due to its capacity to mimic humans through the use of natural language and the production of media, including deep fakes. In this article, we focus specifically on the conversational aspects of generative AI, and hence use the term Conversational Generative artificial intelligence (CGI). Like MRSs, CGIs have enormous potential for revolutionizing processes across sectors and transforming the way humans conduct business. From a business perspective, cooperative MRSs alone, with potential conflicts of interest, privacy practices, and safety concerns, require ethical examination. MRSs empowered by CGIs demand multi-dimensional and sophisticated methods to uncover imminent ethical pitfalls. This study focuses on ethics in CGI-empowered MRSs while reporting the stages of developing the MORUL model.arXiv.org320This study focuses on ethics in CGI-empowered MRSs while reporting the stages of developing the MORUL model, which has enormous potential for revolutionizing processes across sectors and transforming the way humans conduct business.ArXiv['Rebekah A. Rousi', 'H. Samani', 'Niko Mäkitalo', 'Ville Vakkuri', 'S. Linkola', 'Kai-Kristian Kemell', 'Paulius Daubaris', 'Ilenia Fronza', 'T. Mikkonen', 'P. Abrahamsson']2024-01-12T00:00:00https://www.semanticscholar.org/paper/187605e6ae2da06853e6ebbb757ee98169d7ada3 fd73e725d64184e15b6ff9c58e4ea26e816faeb6Humanities and Social Sciences Communications650It is suggested that users prefer VAs with abstract language in a hedonic-dominant service context, but that VAs with concrete language are more competitive in a utilitarian-dominant service context, and the perception of processing fluency mediates this effect.Humanities and Social Sciences Communications['Hai Lan', 'Xiaofei Tang', 'Yong Ye', 'Huiqin Zhang']2024-01-12T00:00:00https://www.semanticscholar.org/paper/fd73e725d64184e15b6ff9c58e4ea26e816faeb6 048a509f0f6cb5a5761a8f748db3d21146668cd5 Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.Journal of Mechanical Design00This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events, Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced.Journal of Mechanical Design['Zeda Xu', 'C. Hong', 'Nicolas F. Soria Zurita', 'J. Gyory', 'Gary Stump', 'H. Nolte', 'Jonathan Cagan', 'Christopher McComb']2024-01-12T00:00:00https://www.semanticscholar.org/paper/048a509f0f6cb5a5761a8f748db3d21146668cd5 b2f81c4d1f0067e132d8256e5cad88d2ac41936bData for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.Frontiers in Radiology550Research from the last 5 years in applications of AI to multi-modal imaging shows many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed.Frontiers in Radiology['Marko Milosevic', 'Qingchu Jin', 'Akarsh Singh', 'Saeed Amal']2024-01-12T00:00:00https://www.semanticscholar.org/paper/b2f81c4d1f0067e132d8256e5cad88d2ac41936b b69f756839c114303da8d90d8a9374ea815f4235Nutrition Research and Food Science Journal00Nutrition Research and Food Science Journal[]2024-01-12T00:00:00https://www.semanticscholar.org/paper/b69f756839c114303da8d90d8a9374ea815f4235 cff22bc758cef2ef7c62f9a5bab8bf22a3b9a72000['Ronald M. Razmi']2024-01-12T00:00:00https://www.semanticscholar.org/paper/cff22bc758cef2ef7c62f9a5bab8bf22a3b9a720 6e109ad0773994e9fc82ee00e3e2ec3fe96d0936This brief commentary explores the opportunities and challenges presented by the increasing prevalence of artificial intelligence in the field of psychology in South Africa. Artificial intelligence has the potential to revolutionise teaching and learning, research, content production, and professional services, but it also presents some challenges to academic and professional psychology in South Africa. While some generative artificial intelligence can produce written work, such as assignments, literature reviews, and theses, they currently cannot replace human reasoning and the critical thinking abilities required to argue a particular point (at this stage). Artificial intelligence chatbots can also act as teaching assistants and even provide complex psychological interventions such as cognitive-behavioural therapy. In research and publication, artificial intelligence can increase efficiency and provide new insights and perspectives by detecting patterns and relationships that may have been overlooked by human researchers. However, the use of artificial intelligence raises ethical concerns, particularly around ownership and authorship of artificial intelligence–generated content, potential biases, and errors. The commentary concludes that as artificial intelligence technology continues to evolve, and with the human–artificial intelligence partnership continuing to unfold, it is important to recognise the risks associated with its use in academic writing and ensure that psychology students develop appropriate research skills.South African Journal of Psychology230As artificial intelligence technology continues to evolve, and with the human–artificial intelligence partnership continuing to unfold, it is important to recognise the risks associated with its use in academic writing and ensure that psychology students develop appropriate research skills.South African Journal of Psychology['J. Munnik', 'H. Noorbhai']2024-01-12T00:00:00https://www.semanticscholar.org/paper/6e109ad0773994e9fc82ee00e3e2ec3fe96d0936 88b42a20361b2b3418f23bcb0759c2e54fa08a47AI &amp; SOCIETY460AI &amp; SOCIETY['Denise Kleinrichert']2024-01-12T00:00:00https://www.semanticscholar.org/paper/88b42a20361b2b3418f23bcb0759c2e54fa08a47 7315a8c15c9f6d2d6d2cbd8cb6b8b62c83ebdc3fCambridge Quarterly of Healthcare Ethics00Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees['Orsolya Friedrich', 'Sebastian Schleidgen']2024-01-12T00:00:00https://www.semanticscholar.org/paper/7315a8c15c9f6d2d6d2cbd8cb6b8b62c83ebdc3f 67793724dd8cd84d1e1a5bfde6e6e0bf5d35288cGaming Law Review00Gaming Law Review['Kabir Singh']2024-01-12T00:00:00https://www.semanticscholar.org/paper/67793724dd8cd84d1e1a5bfde6e6e0bf5d35288c 1253f1213a552ca8ae574660c22aab7906a54321. Automotive product development and the manufacture of automotive products are subject to a series of individual development and production steps. One of these production steps is the assembly of the individual components using suitable connection techniques to create the entire BIW (body in white). However, before the appropriate connection processes can be applied, they must be defined in a series of development steps. Each connection technology element must be created (by CAD support), tested (by CAE support) and is subjected to several CAM-related criteria. Which connection technology element is best suited to join two or more components depends on several factors, including the material pairing. This means that CAD engineers must consider a variety of parameters in order to select the appropriate connection technique variant. Considering that modern BIWs require several thousand connection elements to assemble the entire BIW, we are talking about an enormous manual effort. This currently standardized procedure has two main disadvantages. Firstly, an enormous number of resources (time, costs, manpower, etc.) is required to create all the connection technology elements of the BIW in CAD environments. Secondly, the manual creation of several thousand connection technology elements can lead to a relatively high failure rate. For both disadvantages shown, the automatic creation of connection technology elements in CAD environments can be used as a remedy. With the help of AI-based approaches and several available parameters, a suggestion for the CAD engineer should be offered as to which connection technique variant is the most efficient for the given conditions. In a further step, CAE-and CAM-based models, parameters, and values could also be included in this prediction and further increase the efficiency in the process of the automatic creation of connection technology elements.Computer-Aided Design and Applications170With the help of AI-based approaches and several available parameters, a suggestion for the CAD engineer should be offered as to which connection technique variant is the most efficient for the given conditions, to increase the efficiency in the process of the automatic creation of connection technology elements.Computer-Aided Design and Applications['Alexander Kreis', 'Aya Abdullah', 'Hussein Hussein']2024-01-12T00:00:00https://www.semanticscholar.org/paper/1253f1213a552ca8ae574660c22aab7906a54321 874310d0f3cebaad2d54ad7756a142a3e5d77da6Visual Cognition60Visual Cognition['Daniel J. Graham']2024-01-12T00:00:00https://www.semanticscholar.org/paper/874310d0f3cebaad2d54ad7756a142a3e5d77da6 dfcaba99239bd407a021a7b5dbd0ff1ca6e7472cRadiography440This review highlights the need for standardised and comprehensive AI training programs for imaging staff and provides insights into the evaluation of existing AI educational interventions which will be valuable when planning AI education for healthcare staff.Radiography['G. Doherty', 'L. McLaughlin', 'C. Hughes', 'J. McConnell', 'R. Bond', 'S. McFadden']2024-01-12T00:00:00https://www.semanticscholar.org/paper/dfcaba99239bd407a021a7b5dbd0ff1ca6e7472c fa77f63751a0a8f3855c81a15577bf45b24cd1b2International Journal of Human-Computer Interaction730International Journal of Human–Computer Interaction['Shangyang Gao', 'Zhuoran Lu', 'Hao Luan', 'Ming Yin', 'Lei Wang']2024-01-12T00:00:00https://www.semanticscholar.org/paper/fa77f63751a0a8f3855c81a15577bf45b24cd1b2 b8e109a81f08c20faa6b21708aac5ec335f16035In the era of the Internet of Things, developing a smart sensor system with sustainable power supply, easy deployment, and flexible use has become a challenging problem. Traditional power supplies, prone to frequent replacements or requiring charging during usage, hinder the advancement of wearable devices. The contact-to-separate friction nanogenerator (TEN G), composed of polychotomy ethylene (PTFE) and aluminum (AI) foils, addresses this issue by harvesting human motion energy through body movement arrangement. This energy is then utilized to monitor human motion posture by detecting changes in output electrical signals. In 2012, Academician Wang Zhonglin and his team invented the triboelectric nanogenerator (TENG), which operates as a self-driven sensor primarily powered by Maxwell displacement current. This enables direct conversion of mechanical stimulation into electrical signals during action, making it suitable for self-powered sensor applications. TENG-based sensors boast a simple structure and high instantaneous power density, providing a crucial tool for constructing intelligent sensor systems. Additionally, when combined with machine learning characteristics such as low cost, short development cycles, robust data processing, and predictive capabilities, the processing of the numerous electrical signals generated by TENG has a significant impact. The integration of TENG sensors is poised to drive the rapid evolution of intelligent sensor networks in various fields such as transportation, security, water conservancy, and construction, where urban sound management is essential. The method of seamlessly integrating sensors with Internet access is implemented using the NetBox network development platform, following these steps: 1) Initially, read the default parameter settings from pre-existing storage media, including parameters like IP address, subnet code, gateway address, sensor input and output types, and sensor range. These settings can be modified online to match the specific sensor used.2)Convert the sensor output signal into digital format using analog-to-digital conversion within the microcontroller.3)The microcontroller receives tasks from either the sensor or the Internet, such as medium, display, data processing, and web server, and processes them according to specified priorities. Existing sensor networks often comprise costly information sensing devices, limiting their large-scale deployment and functional measurement range due to cost and maintenance constraints.2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE)209The method of seamlessly integrating sensors with Internet access is implemented using the NetBox network development platform, following these steps: Initially, read the default parameter settings from pre-existing storage media, including parameters like IP address, subnet code, gateway address, sensor input and output types, and sensor range.2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE)['Weixiang Wan', 'Wenjian Sun', 'Qiang Zeng', 'Linying Pan', 'Jingyu Xu', 'Bo Liu']2024-01-12T00:00:00https://www.semanticscholar.org/paper/b8e109a81f08c20faa6b21708aac5ec335f16035 9c6a7331751db05e8e0f1f3bd358352697ec6423Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively. This will free up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions. This will also enable the continued integration of remote monitoring and control towers having profound effects on coverage and practice models. In the PACU and ICU, the technology will lead to the development of early warning systems that can identify patients who are at risk of complications, enabling early interventions and more proactive care. The integration of augmented reality will allow for better integration of diverse types of data and better decision-making. Postoperatively, the proliferation of wearable devices that can monitor patient vital signs and track their progress will allow patients to be discharged from the hospital sooner and receive care at home. This will require increased use of telemedicine, which will allow patients to consult with doctors remotely. All of these advances will require changes to legal and regulatory frameworks that will enable new workflows that are different from those familiar to today’s providers.Anesthesia and Analgesia554Technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively, freeing up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions.Anesthesia & Analgesia['Max M Feinstein', 'Daniel Katz', 'Samuel DeMaria', 'Ira S. Hofer']2024-01-12T00:00:00https://www.semanticscholar.org/paper/9c6a7331751db05e8e0f1f3bd358352697ec6423 2bb99ffc3bcab49440aab710e8c55b170ff9c603Philosophy &amp; Technology333This article considered how one should determine whether one type of sentencing algorithm would be ethically preferable to another type of sentencing algorithm (e.g., a model based on machine learning) or another type of sentencing algorithm (e.g., a model based on old-fashioned programming).Philosophy &amp; Technology['J. Ryberg']2024-01-12T00:00:00https://www.semanticscholar.org/paper/2bb99ffc3bcab49440aab710e8c55b170ff9c603 b29abff861c9a5e3b99faa440a74337326bdde20The release of ChatGPT in November 2022 prompted a massive uptake of generative artificial intelligence (GenAI) across higher education institutions (HEIs). HEIs scrambled to respond to its use, especially by students, looking first to regulate it and then arguing for its productive integration within teaching and learning. In the year since the release, HEIs have increasingly provided policies and guidelines to direct GenAI. In this paper we examined documents produced by 116 US universities categorized as high research activity or R1 institutions to comprehensively understand GenAI related advice and guidance given to institutional stakeholders. Through an extensive analysis, we found the majority of universities (N=73, 63%) encourage the use of GenAI and many provide detailed guidance for its use in the classroom (N=48, 41%). More than half of all institutions provided sample syllabi (N=65, 56%) and half (N=58, 50%) provided sample GenAI curriculum and activities that would help instructors integrate and leverage GenAI in their classroom. Notably, most guidance for activities focused on writing, whereas code and STEM-related activities were mentioned half the time and vaguely even when they were (N=58, 50%). Finally, more than one half of institutions talked about the ethics of GenAI on a range of topics broadly, including Diversity, Equity and Inclusion (DEI) (N=60, 52%). Overall, based on our findings we caution that guidance for faculty can become burdensome as extensive revision of pedagogical approaches is often recommended in the policies.arXiv.org431Examining documents produced by 116 US universities categorized as high research activity or R1 institutions to comprehensively understand GenAI related advice and guidance given to institutional stakeholders finds guidance for faculty can become burdensome as extensive revision of pedagogical approaches is often recommended in the policies.ArXiv['Nora McDonald', 'Aditya Johri', 'Areej Ali', 'Aayushi Hingle']2024-01-12T00:00:00https://www.semanticscholar.org/paper/b29abff861c9a5e3b99faa440a74337326bdde20 c75e9dad20f8762bdb36b2b10bf449ea3c3db77eBACKGROUND Artificial Intelligence (AI) tools created to enhance decision-making may have a significant impact on treatment algorithms for peripheral arterial disease (PAD). A Markov-based AI model was developed to predict optimal therapy based on maximization of calculated quality of life (cQoL), a patient-centered system of assessment designed to report outcomes directly linked to health-related quality of life. STUDY DESIGN The AI model was prospectively interrogated immediately after individual interventions for PAD over a 12-year period to test predictive performance. Patient cQoL was determined at each patient follow-up visit. RESULTS 1143 consecutive patients were evaluated, with a median follow-up of 18 months. Observed mean annualized cQoL was higher than predicted by the model (.85±.38 vs .79±.18, P<.0001). Of five potential clinical outcomes, the AI model correctly predicted final status in 71.3% of patients, with insignificant model performance deterioration over time (-0.15%/month, r=-.49, P=.063). The chance of having the condition predicted by the model was .57±.32, compared with a theoretical maximum of .70±.19 (P<.0001, mean ratio .79). The AI model performed better in patients with claudication than limb-threatening ischemia (75.5% vs 63.6%, P=.014), but equally well for open or endovascular intervention (69.8% vs 70.5%, P=.70). Graft/artery patency and amputation-free survival were better for patients with claudication and those treated with endovascular techniques. CONCLUSION AI can successfully predict treatment for PAD that maximizes patient quality of life in most cases. Future application of AI incorporating better estimates of patient anatomic and physiological risk factors and refinement of model structure should further enhance performance.Journal of the American College of Surgeons00Artificial Intelligence can successfully predict treatment for PAD that maximizes patient quality of life in most cases and future application of AI incorporating better estimates of patient anatomic and physiological risk factors and refinement of model structure should further enhance performance.Journal of the American College of Surgeons['Thomas E Brothers', 'P. Baliga']2024-01-12T00:00:00https://www.semanticscholar.org/paper/c75e9dad20f8762bdb36b2b10bf449ea3c3db77e f8a3b48ee8ac1159369ff32789691fbb2b1935a9Surgical Endoscopy240Two deep-learning models that recognize liver vessels in LLR with high accuracy and sufficient processing speed are successfully developed and suggest the potential of a new real-time automated navigation system for LLR.Surgical Endoscopy['Norikazu Une', 'Shin Kobayashi', 'D. Kitaguchi', 'Taiki Sunakawa', 'Kimimasa Sasaki', 'Tateo Ogane', 'Kazuyuki Hayashi', 'Norihito Kosugi', 'M. Kudo', 'M. Sugimoto', 'H. Hasegawa', 'N. Takeshita', 'N. Gotohda', 'Masaaki Ito']2024-01-12T00:00:00https://www.semanticscholar.org/paper/f8a3b48ee8ac1159369ff32789691fbb2b1935a9 ad4b8b8c4627e6ff667c5a4bb4f96e28f71c7243INTRODUCTION: Tribal entrepreneurship can be understood as infusing the knowledge of commerce and trade into the tribal groups and thereby exploring their products and known for their economic betterment and social advancement. An accumulating body of research has demonstrated that artificial intelligence (AI) is an indistinguishable feature of the fourth industrial revolution. This study integrates the literature on AI and new technologies to examine the constraining and facilitating forces for developing and scaling-up AI-enabling technologies in Africa. This article proposes an integrated conceptual model to elucidate the range of external drivers encompassing global competitive drivers, and market and industry drivers. The internal drivers include the potential to enhance product development speed, improve quality, drive production cost down, and minimise errors and manual processes in organisations.  OBJECTIVES: In this study, socio-economic status of tribal population has been taken as a reference in order to see whether there is any impact of economic wellbeing on their livelihood [1]. The study is an endeavour to examine the role of micro enterprises for social and economic empowerment of tribal community in Odisha and Andhra Pradesh and role of AI. RESULTS: The main results obtained in this paper are the following SEM identified that there is a lower degree of positive impact of “Tribal entrepreneurship” on “Economic Empowerment” and “women Empowerment” and lower degree of negative impact on “Social Empowerment”. It is also concluded that Economic Empowerment of tribal entrepreneurs through micro-enterprises has reduced the dependency on private money lenders the most. Women Empowerment of tribal entrepreneurs has helped the women to achieve gender equality and the social Empowerment aids in the development of competency and technical skills through micro-enterprises. CONCLUSION: The tribal communities in Odisha are regarded as the most disadvantageous group of people in terms of their socio-economic status. The present research makes some pivotal contributions to the current AI literature. First, in spite of the growing recognition that development of new industries and new-business development is increasingly predicated on the adoption of new technologies (Krasniqi and Hajrizi Citation2016), there is a paucity of studies examining contemporary challenges faced by developing nations and their inability to capitalise on such ample and obvious opportunities. ICST Transactions on Scalable Information Systems300An integrated conceptual model is proposed to elucidate the range of external drivers encompassing global competitive drivers, and market and industry drivers to examine the constraining and facilitating forces for developing and scaling-up AI-enabling technologies in Africa.ICST Transactions on Scalable Information Systems['Deepali Rani Sahoo', 'Teena']2024-01-12T00:00:00https://www.semanticscholar.org/paper/ad4b8b8c4627e6ff667c5a4bb4f96e28f71c7243 7cdf234593ac680438187d40712526a0aba268a5Efforts to develop black-box artificial intelligence (AI) systems have become a phenomenon of emerging global interest in academia, business, and society, and have led to the development of the XAI research field. With its pluralistic perspective, information systems (IS) research is destined to contribute to this emerging field; thus, it is not surprising that the number of research publications at XAI has increased significantly. This paper aims to provide a comprehensive overview of XAI research in public and electronic markets, specifically using a structured literature review. Based on a literature review of 180 research papers, this work examines the most receptive points, the development of academic debates, and the most important concepts and methodologies. In addition, eight research areas with different levels of maturity in e-markets are identified. Finally, guidelines for the XAI research agenda in IS are presentedInternational Journal of Advanced Research in Science, Communication and Technology200This work examines the most receptive points, the development of academic debates, and the most important concepts and methodologies in XAI research in public and electronic markets using a structured literature review.International Journal of Advanced Research in Science, Communication and Technology['Saurabh Sudhakar Umredkar', 'Swapnil Anil', 'Bagde', 'Sonu Ramkumar', 'Prof Nikita Shahu', 'Khanzode']2024-01-12T00:00:00https://www.semanticscholar.org/paper/7cdf234593ac680438187d40712526a0aba268a5 88d15727758a08427f4b0129764a4a29d75bbe50Artificial intelligence (AI) is a game changer in the healthcare, educational, and other sectors. The use of AI in the healthcare sector has shown that AI and machine learning should be promoted in the improvement of service provision, including in predicting and managing pandemics. This study assessed the roles of AI in predicting and managing pandemics with lessons learnt from the COVID-19 pandemic. A narrative review was conducted from December 2023 to January 2024 on the role of AI and machine learning in predicting and managing pandemics. A literature search was done using PubMed and Google Scholar. This study found that AI is useful in the healthcare sector and can be used to predict and manage pandemics. Additionally, AI can be used in disease modelling and improve public health service provision. There is a need to promote and strengthen the use of AI and machine learning in the healthcare sector.Scholars Academic Journal of Biosciences00It is found that AI is useful in the healthcare sector and can be used to predict and manage pandemics and Additionally, AI can be used in disease modelling and improve public health service provision.Scholars Academic Journal of Biosciences['Steward Mudenda', 'Shafiq Mohamed']2024-01-12T00:00:00https://www.semanticscholar.org/paper/88d15727758a08427f4b0129764a4a29d75bbe50 b37e6ab325897819c1051ca3f2f9570bddbe76fcThis abstract provides an overview of Professor Fernando Gomes de Souza Junior's lecture "Exploring the Synergy of Biofuels and Artificial Intelligence," presented at the IBMEC Lecture's Discussion Series on Industry and Energy on September 6, 2023. Prof. Gomes, an accomplished researcher in polymer science and nanotechnology, discusses the intersection of biofuels and artificial intelligence. He highlights the importance of leveraging machine learning and data mining techniques to harness the vast knowledge repository in renewable energy research. Prof. Gomes presents a groundbreaking 2023 study that utilizes advanced computational tools to analyze the dynamics of nanocatalysts in biofuel production, revealing insights into research trajectories, global trends, and shifting focal points. He further discusses the application of machine learning in optimizing biodiesel synthesis, emphasizing its potential for enhancing efficiency and economic benefits in biofuel production. Prof. Gomes underscores artificial intelligence's significance in shaping biofuel research's future and encourages researchers to embrace emerging trends in this transformative field.Brazilian Journal of Experimental Design Data Analysis and Inferential Statistics20Brazilian Journal of Experimental Design, Data Analysis and Inferential Statistics['Fernando Gomes de Souza Junior']2024-01-12T00:00:00https://www.semanticscholar.org/paper/b37e6ab325897819c1051ca3f2f9570bddbe76fc 03168b7715af4994061cd1b35a28954653caef4fThis study examined the impact of AI-based training compared to conventional instruction approaches in the context of language acquisition.Employing a survey-based methodology, this study collected data from language learners to assess their perspectives and experiences of both traditional and AI-based training.The aim was to determine the advantages and disadvantages of AI-based training and its potential to enhance language learning outcomes.This study commences with a comprehensive analysis of existing research on AI in language learning and compares AI-based training with conventional instruction techniques.This study seeks to contribute to the existing body of knowledge by identifying the gaps in the literature.A representative sample of 72 learners will be administered the survey questionnaire as part of the research approach.The study collected demographic data from respondents and information on their experiences with and opinions on both traditional and AI-based training.Descriptive and inferential statistics were used to analyze the responses and draw insightful conclusions.The findings of this study shed light on the impact of AI-based training on language-learning outcomes.The analysis compared the effectiveness of AI-based instruction with conventional teaching methods, highlighting the advantages and disadvantages of each approach.The study also addresses the constraints and challenges encountered during the research process, which could affect the generalizability of the results.The study’s findings have implications for language teachers, educational institutions, and policymakers while also advancing our understanding of AI’s role of AI in language learning.The results may guide decisions regarding instructional strategies, curriculum design, and the use of AI technology in language learning programs.The study concludes with recommendations for further investigation of the potential of AI-based language learning training and solutions to the issues identified.World Journal of English Language01The study’s findings have implications for language teachers, educational institutions, and policymakers while also advancing the understanding of AI’s role of AI in language learning.World Journal of English Language['Chitra Dhanapal', 'N. Asharudeen', 'Sabina Yasmin Alfaruque']2024-01-12T00:00:00https://www.semanticscholar.org/paper/03168b7715af4994061cd1b35a28954653caef4f 9b6089d64b70698fa8fcbab8cb8763ebf760ae54ACS Synthetic Biology01ACS synthetic biology['Héctor García Martín', 'Stanislav Mazurenko', 'Huimin Zhao']2024-01-12T00:00:00https://www.semanticscholar.org/paper/9b6089d64b70698fa8fcbab8cb8763ebf760ae54 4be099da8236a1d635bcf54d8714933c8c2ce37bJournal of Taibah University Medical Sciences221Thorough AI instruction in dental schools and continuing education programs for practitioners are urgently needed to maximize AI's potential benefits in dentistry.Journal of Taibah University Medical Sciences['M. Dashti', 'Jimmy Londono', 'S. Ghasemi', 'Z. Khurshid', 'F. Khosraviani', 'N. Moghaddasi', 'M. Zafar', 'Delband Hefzi']2024-01-12T00:00:00https://www.semanticscholar.org/paper/4be099da8236a1d635bcf54d8714933c8c2ce37b fc3cf5ca8d50fd5383662b79a823723f4f74425aScholars consider a new translation of the prescient play that coined the term “robot” Scholars consider a new translation of the prescient play that coined the term “robot”Science00Science['Ed Finn']2024-01-12T00:00:00https://www.semanticscholar.org/paper/fc3cf5ca8d50fd5383662b79a823723f4f74425a bf47379cbdf549012e4772ad5a9d52572aef7615The International Networking Symposium on Artificial Intelligence and Informatics in Nuclear Medicine00The International Networking Symposium on Artificial Intelligence and Informatics in Nuclear Medicine['Rudi Dierckx', 'C. Tsoumpas', 'Ronald Borra', 'Jan Pruim', 'R. Slart', 'A. Glaudemans']2024-01-12T00:00:00https://www.semanticscholar.org/paper/bf47379cbdf549012e4772ad5a9d52572aef7615 c04b7e8368389b25fdde282fecc9d1bd51ad437800['Lothar Determann']2024-01-12T00:00:00https://www.semanticscholar.org/paper/c04b7e8368389b25fdde282fecc9d1bd51ad4378 29a16e7e8a710498066404d29c035ea570225f4a00['Matteo Zaralli']2024-01-12T00:00:00https://www.semanticscholar.org/paper/29a16e7e8a710498066404d29c035ea570225f4a 00c57849cfa2ae038db5c19ee2a83d1accbe1f46Political Communication10Political Communication['Michael W. Wagner']2024-01-12T00:00:00https://www.semanticscholar.org/paper/00c57849cfa2ae038db5c19ee2a83d1accbe1f46 b2671b19aaa39edb8a8ccaa1339c6d1c92a9c166Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI), where agents interact with environments to learn policies for solving complex tasks. In recent years, DRL has achieved remarkable breakthroughs in various tasks, including video games, robotic control, quantitative trading, and autonomous driving. Despite its accomplishments, security and privacy-related issues still prevent us from deploying trustworthy DRL applications. For example, by manipulating the environment, an attacker can influence an agent’s actions, misleading it to behave abnormally. Additionally, an attacker can infer private training data and environmental information by maliciously interacting with DRL models, causing a privacy breach. In this survey, we systematically investigate the recent progress of security and privacy issues in the context of DRL. First, we present a holistic review of security-related attacks within DRL systems from the perspectives of single-agent and multi-agent systems, and review privacy-related attacks. Second, we review and classify defense methods used to address security-related challenges, including robust learning, anomaly detection, and game theory approaches. Third, we review and classify privacy-preserving technologies, including encryption, differential privacy, and policy confusion. Finally, we conclude the survey by discussing open issues and possible directions for future research in this field.ACM Computing Surveys1770This survey systematically investigates the recent progress of security and privacy issues in the context of DRL, and presents a holistic review of security-related attacks within DRL systems from the perspectives of single-agent and multi-agent systems, and reviews privacy-related attacks.ACM Computing Surveys['Kanghua Mo', 'Peigen Ye', 'Xiaojun Ren', 'Shaowei Wang', 'Wen J. Li', 'Jin Li']2024-01-12T00:00:00https://www.semanticscholar.org/paper/b2671b19aaa39edb8a8ccaa1339c6d1c92a9c166 b9a0a0d62a3deb48f5e567ea3577b44bbe81db42Nature Cities50Nature Cities['Aileen Nielsen']2024-01-11T00:00:00https://www.semanticscholar.org/paper/b9a0a0d62a3deb48f5e567ea3577b44bbe81db42 9ca698be06035cebe2325d3e95f3a33f8c18f94fHumanities and Social Sciences Communications281The regulation and legislation of the application of AI is very deficient, both in the EU and in Spain, and a series of aspects to be taken into account for the future regulation of the application of AI in medicine are proposed.Humanities and Social Sciences Communications['Óscar Andrés Molina', 'Miriam Jiménez Bernal', 'Daniel López Wolf', 'Benjamín Herreros']2024-01-11T00:00:00https://www.semanticscholar.org/paper/9ca698be06035cebe2325d3e95f3a33f8c18f94f cec5687c1354b9fe9292ed8e765fe9bc7b842512ABSTRACT In October 2023, the Food and Drug Administration (FDA) released a proposed rule that ends enforcement discretion for laboratory-developed tests (LDTs). The FDA’s proposal outlines a five-stage implementation to begin regulating LDTs as they do for commercial in vitro diagnostics (IVDs), including modified FDA-approved/cleared tests. We outline here concerns from the clinical and public health microbiology laboratory perspective. It is our opinion that LDTs performed by individual Clinical Laboratory Improvement Amendments-certified diagnostic laboratories should not be regulated in the same way as commercial IVDs. This rule, if finalized, will negatively impact the diagnostic services currently offered by clinical and public health laboratories and, therefore, patients and the providers who care for them. Ending enforcement discretion will likely stifle diagnostic innovation and decrease access to diagnostic testing and health equity. Furthermore, the lack of infrastructure, including personnel and funding, at the FDA and diagnostic laboratories to support the required submissions for review is an obstacle. Like the FDA, diagnostic laboratories prioritize patient safety, accurate clinical diagnostics, and health equity. Since the scope of the LDT landscape is currently unknown, we are supportive of a registration process, along with non-burdensome adverse event reporting, to first understand the scope of clinical use of LDTs and any associated safety concerns. Any regulatory rule should be based on data that have been gathered systematically, not anecdotes or case reports. A rule must also balance the potential negative impact to patient care with realistic safety risks for infectious disease diagnostics.Journal of Clinical Microbiology293It is the opinion that LDTs performed by individual Clinical Laboratory Improvement Amendments-certified diagnostic laboratories should not be regulated in the same way as commercial IVDs, and support of a registration process along with non-burdensome adverse event reporting to first understand the scope of clinical use of LDTs.Journal of Clinical Microbiology['Melissa B. Miller', 'Mary Lee Watts', 'L. Samuel']2024-01-11T00:00:00https://www.semanticscholar.org/paper/cec5687c1354b9fe9292ed8e765fe9bc7b842512 e09d26579e045a211125c53e3e2e30f4abd83523The International Atomic Energy Agency asserts that the regulation of the safety of civil nuclear power requires national regulatory agencies to be effectively independent. However, in the early years of national civil nuclear power programs national nuclear industries were dominated by iron triangles or subgovernments of powerful actors with an interest in promoting the industry. The creation of an independent safety regulator requires a radical restructuring of the national governance framework. Windows of opportunity or critical junctures for such reform occur only occasionally. This paper examines the cases of France, Japan, and India to identify the factors that determine the degree of success in attempts to break the power of nuclear iron triangles or subgovernments and create an effectively independent regulator. This analysis shows a serious nuclear accident can create the opportunity to dismantle an iron triangle. The extent and speed with which reforms can be implemented depend greatly on pre‐existing and prevailing conditions. Key determinants include the power structures and attitudes toward nuclear power in elite politics, the degree of engagement of civil society, and pressures from international organizations. Of these, the first, elite politics, appears to be the most important in these three cases.Regulation &amp; Governance470Regulation &amp; Governance['Philip Andrews‐Speed', 'Nur Azha Putra']2024-01-11T00:00:00https://www.semanticscholar.org/paper/e09d26579e045a211125c53e3e2e30f4abd83523 24bea2b31d25e8a769fb56409d8decc0e5a38b87Scientific Reports380The paper’s main result is an impossibility result: a general regulation function that achieves this goal while obeying the core values of democratic societies (freedom of expression and user privacy) does not exist.Scientific Reports['C. Avin', 'Hadassa Daltrophe', 'Zvi Lotker']2024-01-11T00:00:00https://www.semanticscholar.org/paper/24bea2b31d25e8a769fb56409d8decc0e5a38b87 e3b79f05f3b886925111f51e9e2b7ec0fbac0146Journal of Regulatory Economics361Journal of Regulatory Economics['Juan S. Mora-Sanguinetti', 'Javier Quintana', 'Isabel Soler', 'R. Spruk']2024-01-11T00:00:00https://www.semanticscholar.org/paper/e3b79f05f3b886925111f51e9e2b7ec0fbac0146 021e6c5892347287182b405228fb22923691e3f0At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue. AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.arXiv.org12119This work introduces AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue that demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors.ArXiv['Tao Tu', 'Anil Palepu', 'M. Schaekermann', 'Khaled Saab', 'Jan Freyberg', 'Ryutaro Tanno', 'Amy Wang', 'Brenna Li', 'Mohamed Amin', 'Nenad Tomašev', 'Shekoofeh Azizi', 'Karan Singhal', 'Yong Cheng', 'Le Hou', 'Albert Webson', 'Kavita Kulkarni', 'S. Mahdavi', 'Christopher Semturs', 'Juraj Gottweis', 'Joelle Barral', 'Katherine Chou', 'Greg S. Corrado', 'Yossi Matias', 'A. Karthikesalingam', 'Vivek Natarajan']2024-01-11T00:00:00https://www.semanticscholar.org/paper/021e6c5892347287182b405228fb22923691e3f0 8060a8ddb589695b7968c3efa786a86e64a73fa2This article explores the pivotal intersection of Artificial Intelligence (AI), student engagement, and academic performance in higher education, specifically at Kabul University. As technology evolves, understanding AI's implications on education becomes critical for effective pedagogical strategies and student readiness. The research aims to bridge the gap between technological advancements and educational practices, comprehensively investigating AI's impact on student engagement and academic performance. The study addresses awareness, ethical considerations, autonomy perceptions, and AI integration into curricula. Employing a quantitative approach, the study involves 200 students from various Kabul University faculties, utilizing SPSS version 23 for analysis. Regression analyses, ANOVA, and structured questionnaires allow a nuanced exploration of AI engagement dimensions. Key findings indicate commendable AI awareness in students' daily lives, with room for improvement in academic integration. Ethical considerations emphasize a baseline for ethical AI use. Autonomy perceptions and AI tool engagement reveal nuanced layers, emphasizing a holistic AI education approach. In conclusion, this research advocates a balanced AI integration in education, offering implications for pedagogical strategies, curriculum development, and institutional policies. The findings guide educators, policymakers, and institutions in navigating AI-enhanced learning environments, ensuring students' technological literacy and ethical grounding.RIGGS: Journal of Artificial Intelligence and Digital Business197The research aims to bridge the gap between technological advancements and educational practices, comprehensively investigating AI's impact on student engagement and academic performance, and advocates a balanced AI integration in education.RIGGS: Journal of Artificial Intelligence and Digital Business['Abdul Wajid Fazil', 'Musawer Hakimi', 'Amir Kror Shahidzay', 'Ansarullah Hasas']2024-01-11T00:00:00https://www.semanticscholar.org/paper/8060a8ddb589695b7968c3efa786a86e64a73fa2 b3115708fa48d14099e22dd29c75b6ce865595dcJudicial communication has a great influence on the effectiveness of procedural mechanisms for the protection of violated rights, because in its main manifestation is the actual content of the procedural legal relationship. Despite this, judicial communication does not receive the necessary attention at the legislative level due to the weak theoretical development of this issue. As a consequence of this approach, the full potential of the civil (arbitration) procedural form cannot be implemented, and in some cases neglecting the interests of judicial communication creates obstacles to the right to judicial protection and entails a violation of key principles of the process. Overcoming the negative effects is possible, first of all, by reconsidering the limits of the use in judicial enforcement of the classical legal means of regulating social relations (the rule of law, principles, legal consciousness, legal culture).Courier of the Kutafin Moscow State Law University10Courier of Kutafin Moscow State Law University (MSAL))['O. A. Sukhorukova']2024-01-11T00:00:00https://www.semanticscholar.org/paper/b3115708fa48d14099e22dd29c75b6ce865595dc d310b2a5bcb8307ab6a20152df161ebd17620f22Environment, Development and Sustainability360Environment, Development and Sustainability['Xianna Hong', 'Manxiu Ning', 'Qiuhua Chen', 'Chenyong Shi', 'Nan Wang']2024-01-11T00:00:00https://www.semanticscholar.org/paper/d310b2a5bcb8307ab6a20152df161ebd17620f22 e3eb02c13cb5f52f95e3ef5f116b251d282f127eWith the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support humans in decision making, it is essential to quantitatively understand how diverse forms of AI assistance influence humans' decision making behavior. To this end, much of the current research focuses on the end-to-end prediction of human behavior using ``black-box'' models, often lacking interpretations of the nuanced ways in which AI assistance impacts the human decision making process. Meanwhile, methods that prioritize the interpretability of human behavior predictions are often tailored for one specific form of AI assistance, making adaptations to other forms of assistance difficult. In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making. By conceptualizing AI assistance as the ``nudge'' in human decision making processes, our approach centers around modelling how different forms of AI assistance modify humans' strategy in weighing different information in making their decisions. Evaluations on behavior data collected from real human decision makers show that the proposed framework outperforms various baselines in accurately predicting human behavior in AI-assisted decision making. Based on the proposed framework, we further provide insights into how individuals with different cognitive styles are nudged by AI assistance differently.AAAI Conference on Artificial Intelligence713A computational framework is proposed that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making and provides insights into how individuals with different cognitive styles are nudged by AI assistance differently.{'pages': '10083-10091'}['Zhuoyan Li', 'Zhuoran Lu', 'Ming Yin']2024-01-11T00:00:00https://www.semanticscholar.org/paper/e3eb02c13cb5f52f95e3ef5f116b251d282f127e 142f5f481fb5bf621fa9696c3b56cf3cbec91076Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy as human experts. However, such systems face one central challenge: their decisions have far-reaching consequences for the health and lives of people. Therefore, the decisions of AI systems need to be understandable and justifiable by humans. In this work, we present a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data. ALPODS is able to explain its decisions in a form that is understandable to human experts. For the identified subpopulations, fuzzy reasoning rules expressed in the typical language of domain experts are generated. A visualization method based on these rules allows human experts to understand the reasoning used by the AI system. A comparison with a selection of state-of-the-art XAI systems shows that ALPODS operates efficiently on known benchmark data and on everyday routine case data.BioMedInformatics762This work presents a novel explainable AI (XAI) method called algorithmic population descriptions (ALPODS), which is able to classify (diagnose) cases based on subpopulations in high-dimensional data and is able to explain its decisions in a form that is understandable to human experts.BioMedInformatics['A. Ultsch', 'J. Hoffmann', 'M. Röhnert', 'M. von Bonin', 'U. Oelschlägel', 'Cornelia Brendel', 'M. Thrun']2024-01-11T00:00:00https://www.semanticscholar.org/paper/142f5f481fb5bf621fa9696c3b56cf3cbec91076 d6e9c8688ae313e6b5f3580bdbb85e99e97dde94The objective of this study is to explore the enablers and inhibitors of AI‐enabled drone adoption for manufacturing process audit using a mixed‐method design. A qualitative study was performed to explore the enablers and inhibitors. Further, based on the findings of the qualitative studies, a framework was proposed, and proposed hypotheses were examined using a survey‐based study. The results indicated that function, environmental, and epistemic values are major enablers, whereas vulnerability and sunk cost barriers are major inhibitors to adoption intention. The initial trust and inertia were crucial mediators, and organizations' technological innovativeness played a crucial moderating role. This study enriches the literature on technological adoption for sustainability and helps audit service providers design strategies to enhance AI‐enabled drone adoption for process audits.Business Strategy and the Environment690The results indicated that function, environmental, and epistemic values are major enablers, whereas vulnerability and sunk cost barriers are major inhibitors to adoption intention.Business Strategy and the Environment['Amit Shankar', 'Abhishek Behl', 'Vijay Pereira', 'Meena Chavan', 'Francesco Chirico']2024-01-11T00:00:00https://www.semanticscholar.org/paper/d6e9c8688ae313e6b5f3580bdbb85e99e97dde94 0fe0c070df0bfbbc30ce444a5251ece063091271An emotional support conversation system aims to alleviate users' emotional distress and assist them in addressing their challenges. To generate supportive responses, it is critical to consider multiple factors such as empathy, support strategies, and response coherence, as established in prior methods. Nonetheless, previous models occasionally generate unhelpful responses, which intend to provide support but display counterproductive effects. According to psychology and communication theories, poor performance in just one contributing factor might cause a response to be unhelpful. From the model training perspective, since these models have not been exposed to unhelpful responses during their training phase, they are unable to distinguish if the tokens they generate might result in unhelpful responses during inference. To address this issue, we introduce a novel model-agnostic framework named mitigating unhelpfulness with multifaceted AI feedback for emotional support (Muffin). Specifically, Muffin employs a multifaceted AI feedback module to assess the helpfulness of responses generated by a specific model with consideration of multiple factors. Using contrastive learning, it then reduces the likelihood of the model generating unhelpful responses compared to the helpful ones. Experimental results demonstrate that Muffin effectively mitigates the generation of unhelpful responses while slightly increasing response fluency and relevance.arXiv.org460A novel model-agnostic framework named mitigating unhelpfulness with multifaceted AI feedback for emotional support (Muffin), which employs a multifaceted AI feedback module to assess the helpfulness of responses generated by a specific model with consideration of multiple factors.ArXiv['Jiashuo Wang', 'Chunpu Xu', 'Chak Tou Leong', 'Wenjie Li', 'Jing Li']2024-01-11T00:00:00https://www.semanticscholar.org/paper/0fe0c070df0bfbbc30ce444a5251ece063091271 9946eccb1eb7d409497f8f56274e975c47597c07In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. Furthermore, we introduce a training framework leveraging augmentation policies to maintain solvers' utility in dynamic environments. Besides the data generation and augmentation, our proposed approaches also include novel ML-driven policies for personalized solver strategies, with an emphasis on applications like graph convolutional networks for initial basis selection and reinforcement learning for advanced presolving and cut selection. Additionally, we detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance. Compared with traditional solvers such as Cplex and SCIP, our ML-augmented OptVerse AI Solver demonstrates superior speed and precision across both established benchmarks and real-world scenarios, reinforcing the practical imperative and effectiveness of machine learning techniques in mathematical programming solvers.arXiv.org1140This study presents a comprehensive study on the integration of machine learning techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.ArXiv['Xijun Li', 'Fangzhou Zhu', 'Hui-Ling Zhen', 'Weilin Luo', 'Meng Lu', 'Yimin Huang', 'Zhenan Fan', 'Zirui Zhou', 'Yufei Kuang', 'Zhihai Wang', 'Zijie Geng', 'Yang Li', 'Haoyang Liu', 'Zhiwu An', 'Muming Yang', 'Jianshu Li', 'Jie Wang', 'Junchi Yan', 'Defeng Sun', 'Tao Zhong', 'Yong Zhang', 'Jia Zeng', 'M. Yuan', 'Jianye Hao', 'Jun Yao', 'Kun Mao']2024-01-11T00:00:00https://www.semanticscholar.org/paper/9946eccb1eb7d409497f8f56274e975c47597c07 ececd1c6bc2de3e9ac062560de24b345e2b39c56Artificial Intelligence (AI) and Machine Learning (ML) are rapidly emerging technologies that augur revolutionary changes in developing nations. AI and ML can help address challenges in critical areas such as agriculture, healthcare, education, and employment. The challenges and barriers to the widespread adoption of AI and ML technologies in Indian villages were analyzed through participatory approaches from a livelihood perspective. AI implementations were proposed for a real-world case study conducted as part of the Live-in-Labs® program in Malkhanpur, a rural village in the Indian state of Uttar Pradesh, addressing the community challenge of low income. The challenges were evaluated at different dimensions - community level, household level, and individual level, using Participatory Rural Appraisal (PRA) and Human-centered Design (HCD) approach. This paper explored the application of AI to drive employment to achieve income generation and overall well-being. A generative AI-based platform, ’JobConnect: AI-based Rural Job Seeker-Provider App with ChatGPT Assistance’ is proposed with AI-based virtual assist with NLP, location-based Job listing, and matching algorithms. The platform is designed to be user-friendly and accessible to rural community with varying levels of digital literacy and connectivity.2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)180A generative AI-based platform, ’JobConnect: AI-based Rural Job Seeker-Provider App with ChatGPT Assistance’ is proposed with AI-based virtual assist with NLP, location-based Job listing, and matching algorithms, designed to be user-friendly and accessible to rural community with varying levels of digital literacy and connectivity.2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)['K. B. Bharath Suhas', 'A. P. Sri Krishna', 'Karishram B.', 'M. S. Roopesh', 'Sachithanantha Jothi S.', 'Kondepati Teja', 'Anu G. Kumar', 'Krishna Nandanan']2024-01-11T00:00:00https://www.semanticscholar.org/paper/ececd1c6bc2de3e9ac062560de24b345e2b39c56 ac539f8c78dbe949644cac2b87555979e7fdb58bThis opinion article discusses the impact of artificial intelligence (AI) on global health, addressing its potential risks and benefits to the field. It suggests that, given the existential risks of AI development, the global health community must contribute to AI-related advances, ensuring health equity and the wellbeing of vulnerable populations. Through transdisciplinary collaborations, robust AI governance, and an emphasis on equity, strategies are proposed to harness the potential of AI to reduce health inequalities and improve wellbeing at global and local levels.PLOS Global Public Health70It is suggested that, given the existential risks of AI development, the global health community must contribute to AI-related advances, ensuring health equity and the wellbeing of vulnerable populations.PLOS Global Public Health['Carlos A Faerron Guzmán']2024-01-11T00:00:00https://www.semanticscholar.org/paper/ac539f8c78dbe949644cac2b87555979e7fdb58b d79573901f0fa5aab8e788a434e3d382b9c50937Artificial intelligence, also referred to as AI, allows operations to run efficiently, gathers important and relevant data, and self-analyses trends for accurate decision making. This research paper focuses on exposing the impact of AI TRiSM in knowledge and decision making for business executives in the education industry. The objectives are to investigate the impacts of AI TRiSM on business executives in knowledge and decision making in the education industry, and to explore how we can maximize the positive impacts and minimize the negative impacts of AI TRiSM on business executives in knowledge and decision making in the education industry. The problem to be identified it that although AI carries many advantages, not many are fully aware of its capabilities. The hypotheses to be investigates is that H1: E-education tools will improve student engagement and enhance learning outcomes compared to the traditional method of learning, and H2: Business executives and educators’ workload will be reduced because of AI introduction to the education industry. Various research questions will be asked to state the truthfulness and accuracy of both hypotheses. Secondary and primary data has been gathered to share sufficient insights around the topic. AI is all about humanizing technology. However, as much as we want to replicate human intelligence, AI is still at its infancy to think and feel like a human. AI TRiSM guarantees the robustness, fairness, reliability, effectiveness, privacy, and data protection of AI models. Many companies have adopted AI such as Google, Netflix, and Tesla. In the education industry, the introduction of AI has several advantages such as personalized learning and automation of tasks. However, it does have its disadvantages such as high cost and privacy concern. Several cases around the globe (UAE, China, and USA) have been reflected as well to highlight how AI has been applied in education. Through primary data gathering, 2 interviews have been conducted. 1 from the student perspective, and the other from the educator perspective, and both were asked 6 questions during the interview. From those responses, data was analyzed. Limitations of conducting this research paper has been addressed, followed by recommendations of adopting AI technologies, and finally a conclusion that summarizes the whole research paper and reflects on the accuracy of both hypotheses stated.International Journal of Theory of Organization and Practice (IJTOP)1180The hypotheses to be investigates is that E-education tools will improve student engagement and enhance learning outcomes compared to the traditional method of learning, and business executives and educators’ workload will be reduced because of AI introduction to the education industry.International Journal of Theory of Organization and Practice (IJTOP)['Mounir El Khatib', 'Moza Al Sharif', 'Hanadi Mohamad']2024-01-11T00:00:00https://www.semanticscholar.org/paper/d79573901f0fa5aab8e788a434e3d382b9c50937 d82c9244ede7f205c9844965a81b9843fb2a4824Appropriate reliance is critical to achieving synergistic human-AI collaboration. For instance, when users over-rely on AI assistance, their human-AI team performance is bounded by the model's capability. This work studies how the presentation of model uncertainty may steer users' decision-making toward fostering appropriate reliance. Our results demonstrate that showing the calibrated model uncertainty alone is inadequate. Rather, calibrating model uncertainty and presenting it in a frequency format allow users to adjust their reliance accordingly and help reduce the effect of confirmation bias on their decisions. Furthermore, the critical nature of our skin cancer screening task skews participants' judgment, causing their reliance to vary depending on their initial decision. Additionally, step-wise multiple regression analyses revealed how user demographics such as age and familiarity with probability and statistics influence human-AI collaborative decision-making. We discuss the potential for model uncertainty presentation, initial user decision, and user demographics to be incorporated in designing personalized AI aids for appropriate reliance.Proceedings of the ACM on Human-Computer Interaction1070The results demonstrate that showing the calibrated model uncertainty alone is inadequate, and calibrating model uncertainty and presenting it in a frequency format allow users to adjust their reliance accordingly and help reduce the effect of confirmation bias on their decisions.ArXiv['Shiye Cao', 'Anqi Liu', 'Chien-Ming Huang']2024-01-11T00:00:00https://www.semanticscholar.org/paper/d82c9244ede7f205c9844965a81b9843fb2a4824 54693c34f82f125ad51b140dca45b89ef1731bdaThe intended automation in the financial industry creates a proper area for artificial intelligence usage. However, complex and high regulatory standards and rapid technological developments pose significant challenges in developing and deploying AI-based services in the finance industry. The regulatory principles defined by financial authorities in Europe need to be structured in a fine-granular way to promote understanding and ensure customer safety and the quality of AI-based services in the financial industry. This will lead to a better understanding of regulators’ priorities and guide how AI-based services are built. This paper provides a classification pattern with a taxonomy that clarifies the existing European regulatory principles for researchers, regulatory authorities, and financial services companies. Our study can pave the way for developing compliant AI-based services by bringing out the thematic focus of regulatory principles.Machine Learning and Knowledge Extraction150This study can pave the way for developing compliant AI-based services by bringing out the thematic focus of regulatory principles by providing a classification pattern with a taxonomy that clarifies the existing European regulatory principles.Mach. Learn. Knowl. Extr.['Mustafa Pamuk', 'Matthias Schumann', 'Robert C. Nickerson']2024-01-11T00:00:00https://www.semanticscholar.org/paper/54693c34f82f125ad51b140dca45b89ef1731bda eec1331ebe15c7acc94a57762a8224d2a95312d4The evolution of cybersecurity has witnessed a transformative shift from reactive defense measures to proactive threat-hunting and risk-mitigation strategies. In response to the rapidly evolving threat landscape, the integration of Artificial Intelligence (AI) into Security Information and Event Management (SIEM) tools has emerged as a crucial solution. Historically, SIEMs primarily aggregated security data but struggled to analyze the vast, complex datasets effectively. The integration of AI, especially Machine Learning (ML) and Deep Learning (DL), revolutionized these systems. AI algorithms enable SIEMs to extract meaningful insights from massive datasets, allowing for the identification of subtle anomalies and hidden threats that may not be detected by traditional detection methods. This transition marks a fundamental shift from simple data aggregation to intelligent analysis, empowering SIEMs to move beyond detection towardproactive threat hunting. This paper highlights the role of AI in predicting threats, leveraging historical data to forecast potential risks, and continuously learning to adapt to evolving threat landscapes. It also explores the real-world use cases of AI-powered SIEMs in proactive threat hunting and risk mitigation.Turkish Journal of Computer and Mathematics Education40The role of AI in predicting threats, leveraging historical data to forecast potential risks, and continuously learning to adapt to evolving threat landscapes is highlighted and the real-world use cases of AI-powered SIEMs in proactive threat hunting and risk mitigation are explored.Turkish Journal of Computer and Mathematics Education (TURCOMAT)['Srinivas Reddy Pulyala']2024-01-11T00:00:00https://www.semanticscholar.org/paper/eec1331ebe15c7acc94a57762a8224d2a95312d4 b254c8f01f34131ac19e4f3e81360447a188e71cEffective winter road maintenance relies on precise road friction estimation. Machine learning (ML) models have shown significant promise in this; however, their inherent complexity makes understanding their inner workings challenging. This paper addresses this issue by conducting a comparative analysis of road friction estimation models using four ML methods, including regression tree, random forest, eXtreme Gradient Boosting (XGBoost), and support vector regression (SVR). We then employ the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (AI) to enhance model interpretability. Our analysis on an Alberta dataset reveals that the XGBoost model performs best with an accuracy of 91.39%. The SHAP analysis illustrates the logical relationships between predictor features and friction within all three tree-based models, but it also uncovers inconsistencies within the SVR model, potentially attributed to insufficient feature interactions. Thus, this paper not only showcase the role of explainable AI in improving the ML interpretability of models for road friction estimation, but also provides practical insights that could improve winter road maintenance decisions.Canadian journal of civil engineering (Print)150A comparative analysis of road friction estimation models using four ML methods, including regression tree, random forest, eXtreme Gradient Boosting, XGBoost, and support vector regression, and employs the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (AI) to enhance model interpretability.Canadian Journal of Civil Engineering['Xueru Ding', 'Tae J. Kwon']2024-01-11T00:00:00https://www.semanticscholar.org/paper/b254c8f01f34131ac19e4f3e81360447a188e71c 8a732f88259e54f33b63c4152e8d68d589688f71The development of Artificial Intelligence today is increasingly rapid. Artificial Intelligence is able to process and collect data to carry out a task efficiently and accurately, as well as being creative and flexible, so that AI can produce work independently. However, the use of Artificial Intelligence cannot be separated from providing data in the form of works protected by copyright. This article discusses the concept of Artificial Intelligence in Law Number 28 of 2014 concerning Copyright and the problems that exist, namely the use of works protected by copyright as data for Artificial Intelligence creations. This research is normative juridical research with a conceptual approach and a statutory approach carried out by examining existing doctrines and applicable regulations. According to the provisions of Copyright Law in Indonesia, Artificial Intelligence cannot yet be categorized as the creator of a creation because it is not a legal subject, and the use of a creation to utilize Artificial Intelligence in the creative field must still respect and respect the creative work by obtaining permission from the creator of the creation.International journal of social science and human research140The concept of Artificial Intelligence in Law Number 28 of 2014 concerning Copyright and the problems that exist, namely the use of works protected by copyright as data for Artificial Intelligence creations are discussed.International Journal of Social Science and Human Research['Aryo Bhaskoro']2024-01-11T00:00:00https://www.semanticscholar.org/paper/8a732f88259e54f33b63c4152e8d68d589688f71 d6761be907ed2477b0171f767eeff27a6478ce32There is a growing need for solutions that can improve the communication between anomaly detection algorithms and human operators. In the context of real-time monitoring of networked systems, it is crucial that new solutions do not increase the burden on an already overloaded visual channel. Sonification can be leveraged as a peripheral monitoring tool that complements current visualization systems. We conceptualized, designed, and prototyped Datascapes, a framework project that explores the potential of sound-based applications for the monitoring of cyber-attacks on AI-supported networked environments. Within Datascapes, two Design Actions were realized that applied sonification on the monitoring and detection of anomalies in (1) water distribution networks and (2) Internet networks. Two series of prototypes were implemented and evaluated in a real-world environment with eight experts in network management and cybersecurity. This paper presents experimental results on the use of sonification to disclose anomalous behavior and assess both its gravity and the location within the network. Furthermore, we define and present a design methodology and evaluation protocol that, albeit grounded in sonification for anomaly detection, can support designers in the definition, development, and validation of real-world sonification applications.Frontiers of Computer Science590This paper presents experimental results on the use of sonification to disclose anomalous behavior and assess both its gravity and the location within the network and defines and presents a design methodology and evaluation protocol that can support designers in the definition, development, and validation of real-world sonification applications.Frontiers in Computer Science['Sara Lenzi', 'Ginevra Terenghi', 'Damiano Meacci', 'Aitor Moreno Fernandez-de-Leceta', 'Paolo Ciuccarelli']2024-01-11T00:00:00https://www.semanticscholar.org/paper/d6761be907ed2477b0171f767eeff27a6478ce32 8d995e3f17c6578c6ef8a03933434547272636c8Background Regular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored. Objective The purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot. Methods A coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition–specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output. Results AI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities. Conclusions There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.JMIR Medical Education550There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations, and exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.JMIR Medical Education['A. Zaleski', 'Rachel S Berkowsky', 'K. Craig', 'L. Pescatello']2024-01-11T00:00:00https://www.semanticscholar.org/paper/8d995e3f17c6578c6ef8a03933434547272636c8 20421e4511ce0f7037440bc689c0d48e1499a999Artificial intelligence (AI) developments have transformed many sectors, and the legal sector is no exception. This research study uses AI Attorney, a cutting-edge system created with Natural Language Processing (NLP) techniques and powered by the well-known Natural Language Toolkit (NLTK) library, which facilitates novel approach for legal research. This work focuses to present users with pertinent legal articles and sections based on the scenario they enter or by letting them submit a case PDF file. This work utilizes keyword extraction procedure which enhance effectiveness and accuracy that are pertinent to their current context. The findings show that AI Attorney considerably improves the legal research process by providing accurate information after thorough examination and comparison with existing legal research tools.2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)120The findings show that AI Attorney considerably improves the legal research process by providing accurate information after thorough examination and comparison with existing legal research tools.2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)['Jujjavarapu Sujan Chowdary', 'Karaka Rupasree', 'Eswara Venkata Sai Raja', 'Beebi Naseeba', 'Nagendra Panini Challa', 'Morampudi Mahathi']2024-01-11T00:00:00https://www.semanticscholar.org/paper/20421e4511ce0f7037440bc689c0d48e1499a999 3ab6fd760b34b2d41b933cb1f65e6d748cb31209Today’s data-driven generation has led to remarkable advancements in technology. However, as there are two sides to a coin, technology too has both its advantages and disadvantages. The expansion of AI has given rise to ‘Deepfake’ which involves skillful superimposing of person’s face with another person’s face which is very dangerous and it is used to produce morphed images and disseminate fake videos which has led to cyberbullying, financial fraud and cybersecurity risks. Our goal is to correctly determine authentic images by classifying them into AI generated v/s real images.We have used ‘PyGoogle’ image library for creation of dataset for AI images and for the real image dataset we have used our own camera to capture real images. We have used CNN model on both the dataset and observed that accuracy of Google images dataset is 88 percent and that of the own dataset is 81 percent. For evaluating the performance of our model we have created Confusion Matrix for the same.2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)170The goal is to correctly determine authentic images by classifying them into AI generated v/s real images by using CNN model on both the dataset and observing that accuracy of Google images dataset is 88 percent and that of the own dataset is 81 percent.2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)['Ruchira Purohit', 'Yana Sane', 'Devashree Vaishampayan', 'Sowmya Vedantam', 'Mangal Singh']2024-01-11T00:00:00https://www.semanticscholar.org/paper/3ab6fd760b34b2d41b933cb1f65e6d748cb31209 264b048bac046cabdfc703e7a52ac9906b611096The aim of the research was to evaluate innovative methodology and approaches to teaching with AI in Ukrainian higher education and to outline the effective delivery practices. The research objectives were to explain AI-based teaching approaches and methods used in the Ukrainian institutions of higher education, to find the most effective teaching approaches and methods in the system of training of future specialists, and to develop the methodological recommendations for instructors involved in the training of future AI experts. Within the framework of the given research, a number of pedagogical articles and key documents devoted to the analysis of used artificial intelligence in teaching, and implementation of AI-based methodology were studied . Over 50 scientific that were published during the recent five years to understand the problem and to evaluate the improvements brought by AI were selected. A mixed methodology that combined descriptive, association, and intervention methods was applied. Descriptive methods included literature review, narrative, content analysis of educational and professional programs, as well as the phenomenological approach. Associational methods included predictive analysis aimed at forecasting of the probability of innovative methodology and approaches that impact the AI education. The intervention suggested using a survey and an assessment. The research included 42 instructors of Ukrainian institutions of higher education that were involved in the training of future AI experts. They represented graduating departments that were responsible for the development of educational and professional program, formulated the training objectives, and designed the conceptual framework for the creation of innovative educational environment with the use of AI. The research resulted in the development of methodical recommendations on using teaching methods and approaches in AI-powered higher education. The recommendations can be suggested by instructors at higher education institutions that are involved specifically in training future AI experts . Also, the recommendations are applicable for scientific and pedagogical stuff working on digitalisation of educational process.Futurity Education00The research resulted in the development of methodical recommendations on using teaching methods and approaches in AI-powered higher education that are applicable for scientific and pedagogical stuff working on digitalisation of educational process.Futurity Education[]2024-01-11T00:00:00https://www.semanticscholar.org/paper/264b048bac046cabdfc703e7a52ac9906b611096 4bae78f7188a58e9bca9aeb79111565c1839eb25Neural computing & applications (Print)76The empirical results unequivocally establish the superior performance of XAI-CROP, an innovative algorithm that harnesses eXplainable artificial intelligence (XAI) principles to empower farmers with comprehensible insights into the recommendation process, surpassing the opaque nature of conventional machine learning models.Neural Comput. Appl.['Mahmoud Y. Shams', 'Samah A. Gamel', 'Fatma M. Talaat']2024-01-11T00:00:00https://www.semanticscholar.org/paper/4bae78f7188a58e9bca9aeb79111565c1839eb25 e07729f8961667720e1fab32674705d1bbc6063cThere is an urgent need to transition our economy, society, and culture towards systems and actions that facilitate ecological sustainability. Such radical change requires equally radical transformation of approaches to decision making and resource use. Sustainable entrepreneurship (SE) is often presented as the answer to meeting the triple‐bottom‐line challenges that businesses face; however, there are very real limits to what it can achieve. SE is in the early stages of adopting tools at the technological frontier that offer empirical guidance at every point of an entrepreneurial decision‐making process. Big Data (BD) advances the potential for artificial intelligence (AI) to inform decision making, while also charting pathways to achieve desired outcomes. So far, the interactions between AI, BD, and SE have been generally under‐studied. In this primarily conceptual paper, we address the lack of work consolidating and synthesizing these literatures. We suggest that AI and BD readily contribute to further sustainable development of the weak form, but that it also holds great promise for achieving the strong sustainability ideal. We offer two propositions regarding how the integration of AI and BD can inform/support SE. We conclude by mapping out potential avenues for future research.Journal of economic surveys (Print)1655It is suggested that AI and BD readily contribute to further sustainable development of the weak form, but that it also holds great promise for achieving the strong sustainability ideal.Journal of Economic Surveys['Steve J. Bickley', 'A. Macintyre', 'Benno Torgler']2024-01-11T00:00:00https://www.semanticscholar.org/paper/e07729f8961667720e1fab32674705d1bbc6063c 20dd4e7443552fc6b5d4b24c138a24553a00c0c0In the domain of healthcare, most importantly pediatric healthcare, the role of artificial intelligence (AI) has significantly impacted the medical field. Congenital heart diseases represent a group of heart diseases that are known to be some of the most critical cardiac conditions present at birth. These heart diseases need a swift diagnosis as well as an intervention to ensure the wellbeing of newborns. Fortunately, with the help of AI, including the highly advanced algorithms, analytics and imaging involved, it provides us with a promising era for neonatal care. This article reviewed published data in PubMed, Science Direct, UpToDate, and Google Scholar between the years 2015–2023. To conclude The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis. Several studies have demonstrated the efficacy of AI-based approaches for diagnosing congenital heart diseases, with results indicating that the systems can achieve high levels of sensitivity and specificity. In addition, AI can help reduce the workload of healthcare professionals allowing them to focus on other critical aspects of patient care. Despite the potential benefits of using AI, in addition to detecting congenital heart disease, there are still some challenges to overcome, such as the need for large amounts of high-quality data and the requirement for careful validation of the algorithms. Nevertheless, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.Frontiers in Digital Health253The use of artificial intelligence in detecting congenital heart diseases has shown great promise in improving the accuracy and efficiency of diagnosis, with ongoing research and development, AI is likely to become an increasingly valuable tool for improving the diagnosis and treatment of congenital heart diseases.Frontiers in Digital Health['Haris Ejaz', 'Tarannum Thyyib', 'Ahmed Ibrahim', 'Aroob Nishat', 'Jhancy Malay']2024-01-11T00:00:00https://www.semanticscholar.org/paper/20dd4e7443552fc6b5d4b24c138a24553a00c0c0 a65f8dd3b90f99ae11a3dcaab8288f1cd4fa038bINTRODUCTION: Artificial intelligence (AI) is a novel type of library technology. AI technologies and the needs of data librarians are hybrid and symbiotic, because academic libraries must insert AI technologies into their information and data services. Library services need AI to interpret the context of big data.OBJECTIVES: In this context, we explore the use of the the OpenAI Codex, a deep learning model trained on Python code from repositories, to generate code scripts for data librarians. This investigation examines the practices, models, and methodologies for obtaining code script insights from complex code environments linked to AI GPT technologies.  METHODS: The proposed AI-powered method aims to assist data librarians in creating code scripts using Python libraries and plugins such as the integrated development environment PyCharm, with additional support from the Machinet AI and Bito AI plugins. The process involves collaboration between the data librarian and the AI agent, with the librarian providing a natural language description of the programming problem and the OpenAI Codex generating the solution code in Python.RESULTS: Five specific web-scraping problems are presented. The scripts demonstrate how to extract data, calculate metrics, and write the results to files.CONCLUSION: Overall, this study highlights the application of AI in assisting data librarians with code script creation for web scraping tasks. AI may be a valuable resource for data librarians dealing with big data challenges on the Web. The possibility of creating Python code with AI is of great value, as AI technologies can help data librarians work with various types of data sources. The Python code in Data Science web scraping projects uses a machine-learning model that can generate human-like code to help create and improve the library service for extracting data from a web collection. The ability of nonprogramming data librarians to use AI technologies facilitates their interactions with all types and data sources. The Python programming language has artificial intelligence modules, packages, and plugins such as the OpenAI Codex, which serialises automation and navigation in web browsers to simulate human behaviour on pages by entering passwords, selecting captcha options, collecting data, and creating different collections of datasets to be viewed.ICST Transactions on Scalable Information Systems322This study highlights the application of AI in assisting data librarians with code script creation for web scraping tasks and examines the practices, models, and methodologies for obtaining code script insights from complex code environments linked to AI GPT technologies.ICST Transactions on Scalable Information Systems['Alexandre Semeler', 'Adilson Pinto', 'Tibor Koltay', 'Thiago Dias', 'Arthur Oliveira', 'José González', 'Helen Beatriz Frota Rozados']2024-01-11T00:00:00https://www.semanticscholar.org/paper/a65f8dd3b90f99ae11a3dcaab8288f1cd4fa038b 3cc7bcf909f3c66070bb1236a2efb54fc1493812Nature Communications382Diabetic eye exam completion rate was significantly higher in the intervention group than the control group, and autonomous AI increases diabetic eye exam completion rates in youth with diabetes.Nature Communications['Risa M Wolf', 'R. Channa', 'T. Y. A. Liu', 'Anum Zehra', 'Lee Bromberger', 'Dhruva Patel', 'A. Ananthakrishnan', 'Elizabeth A Brown', 'L. Prichett', 'Harold Lehmann', 'M. Abràmoff']2024-01-11T00:00:00https://www.semanticscholar.org/paper/3cc7bcf909f3c66070bb1236a2efb54fc1493812 5cc740ec4f764d53eeb52251f36cef2bdf82541fThis research article focuses on investigating the impact of technology readiness (TR) on the adoption of artificial intelligence (AD) by accountants and auditors, utilizing intermediary factors, such as perceived usefulness (PU) and perceived ease-of-use (PEOU), within companies in Vietnam. Based on 143 survey responses, the results demonstrate a positive relationship between TR and AI adoption among professionals in the accounting and auditing industry. Additionally, the analysis reveals that the intermediary factors PU and PEOU positively influence AI adoption. TR consistently relates with PU and PEOU in applying artificial intelligence in accounting and auditing. The result of the experiment study is that technology readiness positively impacts the AI adoption of accountants and auditors from companies in Vietnam. Hence, perceived usefulness and ease of use mediate the relationship between technology readiness and the adoption of AI technologies by workers in the accounting and auditing industry. This study contributes not only academically by enriching scientific knowledge on AI adoption but also holds practical significance by suggesting training and development policies from a business perspective in the future.Journal of Risk and Financial Management404Investigating the impact of technology readiness on the adoption of artificial intelligence by accountants and auditors within companies in Vietnam finds that technology readiness positively impacts the AI adoption of accountants and auditors from companies in Vietnam.Journal of Risk and Financial Management['Nguyen Thi Mai Anh', 'Le Thi Khanh Hoa', 'Lai Phuong Thao', 'Duong Anh Nhi', 'Nguyen Thanh Long', 'Nguyen Thanh Truc', 'Vu Ngoc Xuan']2024-01-11T00:00:00https://www.semanticscholar.org/paper/5cc740ec4f764d53eeb52251f36cef2bdf82541f fc613719dfc96edae99e81b14ab96bb382985980In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.Frontiers Artif. Intell.401The impact of the machine learning methodologies applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis, are explored and the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID are included.Frontiers in Artificial Intelligence['Ijaz Ahmad', 'Alessia Amelio', 'A. Merla', 'Francesca Scozzari']2024-01-11T00:00:00https://www.semanticscholar.org/paper/fc613719dfc96edae99e81b14ab96bb382985980 8b234b23583730fc93d493bd997880b3afa6b888While AI is widely used in biomedical research and medical practice, its use is constrained to few specific practical areas, e.g., radiomics. Participants of the workshop on “Artificial Intelligence in Biology and Medicine” (Jerusalem, Feb 14–15, 2023), both researchers and practitioners, aimed to build a holistic picture by exploring AI advancements, challenges and perspectives, as well as to suggest new fields for AI applications. Presentations showcased the potential of large language models (LLMs) in generating molecular structures, predicting protein-ligand interactions, and promoting democratization of AI development. Ethical concerns in medical decision making were also addressed. In biological applications, AI integration of multi-omics and clinical data elucidated the health relevant effects of low doses of ionizing radiation. Bayesian latent modeling identified statistical associations between unobserved variables. Medical applications highlighted liquid biopsy methods for non-invasive diagnostics, routine laboratory tests to identify overlooked illnesses, and AI's role in oral and maxillofacial imaging. Explainable AI and diverse image processing tools improved diagnostics, while text classification detected anorexic behavior in blog posts. The workshop fostered knowledge sharing, discussions, and emphasized the need for further AI development in radioprotection research in support of emerging public health issues. The organizers plan to continue the initiative as an annual event, promoting collaboration and addressing issues and perspectives in AI applications with a focus on low-dose radioprotection research. Researchers involved in radioprotection research and experts in relevant public policy domains are invited to explore the utility of AI in low-dose radiation research at the next workshop.Frontiers Artif. Intell.281The workshop fostered knowledge sharing, discussions, and emphasized the need for further AI development in radioprotection research in support of emerging public health issues, with a focus on low-dose radioprotection research.Frontiers in Artificial Intelligence['Y. Socol', 'Ariella Richardson', 'Imene Garali-Zineddine', 'Stephane Grison', 'Guillaume Vares', 'Dmitry Klokov']2024-01-11T00:00:00https://www.semanticscholar.org/paper/8b234b23583730fc93d493bd997880b3afa6b888 8b1850f842e40863d807f94bb482ebbfee6c81feBackground: The current gold standard for quantification of the fat content of the liver (i.e., nonalcoholic fatty liver disease, NAFLD) is the visual microscopic inspection of liver biopsies by pathologists. Percentage of macrosteatosis (%MaS), used in determining NAFLD diagnosis, is vital in determining the transplant suitability of a donor liver. A major limitation of this method is inevitable human error which causes interobserver variability and overestimation of %MaS which could cause a potential discard of donor livers. We hypothesize that artificial intelligence (AI) can assist pathologists in providing a more objective and accurate measure of %MaS. Methods: Our literature review identified HALO (image analysis) and U-Net (deep-learning) as AI programs currently available for high-accuracy %MaS calculation in liver biopsies. We compared the pathologist-reported %MaS from de-novo liver transplant (LT) biopsy samples taken 2h post-reperfusion to the %MaS calculated by HALO and/or U-Net (Fig. 1). 250 patients had undergone de novo LT at Indiana University between 2019-2020, and 211 of these patients had sufficient data (liver enzyme tests, biopsy results) to be included. Each biopsy sample was digitized into 5 random non-overlapping tiles at 20x magnification using a Leica microscope. Early allograft dysfunction (EAD) is defined by the presence of at least one of the following: (i) INR >1.6 on postoperative day (POD) 7, (ii) total bilirubin >10 mg/dL on POD7, or (iii) AST/ALT >2000 IU/L within the first 7 days following LT. Results: Our literature review identified that both HALO and U-Net estimated the %MaS in liver allograft biopsies significantly lower than pathologists' estimation (Fig.1). Of 211 included patients, 46 (21.8%) had EAD. In this ongoing project, we found U-Net to have a 97.3% training accuracy with eight epochs (2000 biopsy images each). Tiles from the first ten patients are being analyzed by HALO and/or U-Net to calculate an average %MaS for each patient. These calculations will be compared to the %MaS estimation made by pathologists. Conclusions: The rapidly evolving field of AI is emerging as a promising method in the quantification of the fat content of the liver with increased accuracy. AI will therefore help pathologists and transplant surgeons to determine liver transplant viability and better predict EAD in transplant patients.Proceedings of IMPRS00Artificial intelligence (AI) will help pathologists and transplant surgeons to determine liver transplant viability and better predict EAD in transplant patients and is emerging as a promising method in the quantification of the fat content of the liver with increased accuracy.Proceedings of IMPRS['Evan J. Catron', 'Robert P. Passarelli', 'Danielle Wilmes', 'Alex Griesemer', 'Barry Wei', 'Minh-Uyen T. Le', 'Ping Li', 'Wenjun Zhang', 'Jingmei Lin', 'P. Mihaylov', 'C. Kubal', 'R. Mangus', 'B. Ekser']2024-01-11T00:00:00https://www.semanticscholar.org/paper/8b1850f842e40863d807f94bb482ebbfee6c81fe 74f8bfbe6710b888c6684938abdb90dcf4cf2f7eThe relevance of the use of artificial intelligence in crime prevention is shown. Victimological foundations and possibilities of using artificial intelligence are presented. The definition of the concept “victimological aspects of the use of artificial intelligence in crime prevention” is proposed. The basics of the use of computer security systems in crime prevention are revealed. Possible errors in the process of using artificial intelligence in crime prevention are considered. The importance of interdisciplinary study of the use of artificial intelligence in crime prevention is determined. The obtained research results can be taken into account and used in the introduction of new forms of artificial intelligence in crime prevention, as well as in the development and analysis of new scientific topics in criminology, criminal law and information systems.Juridical World00The relevance of the use of artificial intelligence in crime prevention is shown and the importance of interdisciplinary study of the use of artificial intelligence in crime prevention is determined.Juridical World['G. Aglyamova']2024-01-11T00:00:00https://www.semanticscholar.org/paper/74f8bfbe6710b888c6684938abdb90dcf4cf2f7e b84bb7927b1872b92a4fa1d41a28c38fa99aa4d8Neurocritical Care130Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9), however, most studies showed a high risk of bias.Neurocritical care['S. T. van Hal', 'M. van der Jagt', 'M. V. van Genderen', 'D. Gommers', 'J. F. Veenland']2024-01-11T00:00:00https://www.semanticscholar.org/paper/b84bb7927b1872b92a4fa1d41a28c38fa99aa4d8 e25b4b014ff8cb7b6779908a74fdc1c433bc504bFrom the perspective of defuturing design philosophy, this article discusses the close relationship between the growing body of artificial-intelligence (AI) artifacts in the Brazilian Judiciary and the phenomenon of litigiousness therein. Litigiousness has traditionally been tackled through mechanisms that increase productivity and efficiency in case processing, a strategy that has not succeeded in reducing litigiousness, as data make evident. Analyzing data from relevant sources, this article demonstrates that AI artifacts mostly perform tasks related to clustering and mass handling of cases, following the same path dependency. Consequently, they entail risks of judges’ alienation and loss of agency, which can negatively impact citizens’ fundamental rights. Moreover, they defuture; that is, they erase other (preferable) futures. Albeit AI artifacts can play a part in tackling litigiousness, there should be a critical reflection upon futuring and defuturing. Therefore, this article recommends that SoDF—a systemic approach to design that seeks to explore design consequences, futuring and defuturing—be mandatory to any AI design process. Additionally, it proposes continuous judicial monitoring for alienation and loss of agency, as well as investments in judicial education to empower judges to effectively control and supervise AI artifacts. Finally, it suggests a further research agenda.Laws170This article recommends that SoDF—a systemic approach to design that seeks to explore design consequences, futuring and defuturing—be mandatory to any AI design process.Laws['L. A. C. Münch', 'Taís Schilling Ferraz']2024-01-11T00:00:00https://www.semanticscholar.org/paper/e25b4b014ff8cb7b6779908a74fdc1c433bc504b 907a496cd854ef07bd5528223443cd65f5d48dc8Human intelligence, also known as artificial intelligence (AI), is a system made through a computer system that can perform tasks like humans. It is growing rapidly with all the sophistication presented, and AI has mastered almost all sectors. However, the sophistication and all the innovations presented raise concerns for humans, especially for the future. AI for its users is very helpful by providing all the conveniences for humans with a low budget, more efficient time, etc., at the same time AI also has a very extraordinary impact on human life in various sectors. Because it is predicted that AI will replace human work, then what is the purpose of Islamic law (Maqasid sharia) regarding this phenomenon? Therefore, it is clear that the development of AI poses challenges and threats to human life. This study uses a phenomenological approach and a literature review by searching for data and information online related to the phenomenon of the challenges and threats of AI. By using descriptive and qualitative data„ it was found that the use of AI is used as a means to achieve benefits and that there is a need for supervision in its use and proper regulation so that all the results of AI can be accounted for. Keywords: artificial intelligence, era 5.0, Maqasid SyariahKnE Social Sciences120It was found that the use of AI is used as a means to achieve benefits and that there is a need for supervision in its use and proper regulation so that all the results of AI can be accounted for.KnE Social Sciences['Lusiana', 'Muhammd Harun', 'Ema Fathimah', 'Wasti Indah Haryani Daulay']2024-01-11T00:00:00https://www.semanticscholar.org/paper/907a496cd854ef07bd5528223443cd65f5d48dc8 ed006e981dae418e8d7f174b337d7bbca2697a12INTRODUCTION: As the essential part of intelligent learning, innovative learning model construction is conducive to improving the quality of intelligent new teaching models, thus leading the deep integration of teaching and artificial intelligence and accelerating the change and development of teaching supported by artificial intelligence.OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are problems such as more objectivity, poor precision, and a single method of evaluation indexes.METHODS: his paper proposes an intelligent learning construction method based on cluster analysis and deep learning algorithms. First of all, the intelligent learning model construction process is sorted out by clarifying the idea of clever learning model construction and extracting model elements; then, the intelligent learning model is constructed through a K-means clustering algorithm and deep compression sparse self-encoder; finally, the effectiveness and high efficiency of the proposed method is verified through simulation experiment analysis.RESULTS: Solved the problem that the intelligent learning model construction method is not objective enough, has poor accuracy and is not efficient enough.CONCLUSION: The results show that the proposed method improves the model’s accuracy.EAI Endorsed Transactions on Scalable Information Systems260An intelligent learning model construction method based on cluster analysis and deep learning algorithms improves the model’s accuracy and the effectiveness and high efficiency of the proposed method is verified through simulation experiment analysis.EAI Endorsed Trans. Scalable Inf. Syst.['Lijun Pan']2024-01-11T00:00:00https://www.semanticscholar.org/paper/ed006e981dae418e8d7f174b337d7bbca2697a12 e7da6422cd40303e07736f10dcd667b4b5df30bcArtificial intelligence (AI) is a complex combination of multidisciplinary machines and systems that can replicate human‐like cognitive tasks to execute capabilities such as pattern recognition, decision‐making, and problem‐solving. Dating back to the 2000s, AI has been utilized in the medical field, however the interest in this subject has sharply increased over the past several years. Erectile dysfunction (ED) is an increasingly pervasive issue as men age, affecting up to 150 million men worldwide. In the field of men's health, AI has been employed to assist physicians in the evaluation and management of ED. This article aims to summarize the ways in which AI has been utilized in the management of ED, as well as the considerations that must be made when implementing this technology. AI can be utilized for virtual health assistance to protect patient privacy and increase access to care. Augmented reality can aid surgeons in real‐time during operations, as well as be utilized to prepare physicians for situations that they may encounter in the operating room. Pharmaceutical companies can benefit from AI in the interpretation of data, analysis of chemical compounds and in drug development. Additionally, AI can be used to assist patients in post‐procedure recovery in the form of rehabilitation and post‐treatment monitoring. While the utilization of AI in men's health is an exciting venture, there are tremendous ethical and practical considerations that have limited its use in the management of ED.UroPrecision460This article aims to summarize the ways in which AI has been utilized in the management of ED, as well as the considerations that must be made when implementing this technology.UroPrecision['Darren Sanchez', 'H. Slovacek', 'Run Wang']2024-01-11T00:00:00https://www.semanticscholar.org/paper/e7da6422cd40303e07736f10dcd667b4b5df30bc e66879de3a6f572956940a87ea353a65e666e4ceI explore the benefits and shortcomings of including Artificial Intelligence (AI) in environmental governance and remaining within planetary boundaries. AI for the environment should be used with other tools and knowledge such as humanistic, social, and ethical values. AI systems can help mitigate greenhouse gas emissions, ocean acidification, and chemical pollution, safeguard biodiversity, improve water use in agriculture, support vulnerable societies, and combat environmental crimes. AI can efficiently analyze data, monitor, predict and manage natural resources. AI systems does not only describe nature but also active shape by transforming agriculture, fishery, infrastructure, and construction practices. Nevertheless, AI systems enable novel paths for environmental control. The developers from these algorithms commonly originate from prosperous nations, whereas the impact of these algorithms is global. Hence, people with lesser resources and agency are left at a disadvantage to advocate for their interests. These inequalities can result in a new way of colonizing, where wealthier individuals impose their agendas on the rest of the global population. The impact of AI systems on environmental governance is of an unprecedented scale. Ideally, AI systems should adhere to internationally agreed ethical and legal principles.Open Research Europe270Open Research Europe['Ingrid CAMPO RUIZ']2024-01-11T00:00:00https://www.semanticscholar.org/paper/e66879de3a6f572956940a87ea353a65e666e4ce 748c32404f74c2e1b62ec50f99bb0759a7a33c02The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft. Although there is a great deal of value in their work, I will argue that, for familiar philosophical reasons, their methodology, !Blackbox Interpretability"#is wrongheaded. But there is a better way. There is an exciting and emerging discipline of !Inner Interpretability"#(and specifically Mechanistic Interpretability) that aims to uncover the internal activations and weights of models in order to understand what they represent and the algorithms they implement. In my view, a crucial mistake in Black-box Interpretability is the failure to appreciate that how processes are carried out matters when it comes to intelligence and understanding. I can#t pretend to have a full story that provides both necessary and sufficient conditions for being intelligent, but I do think that Inner Interpretability dovetails nicely with plausible philosophical views of what intelligence requires. So the conclusion is modest, but the important point in my view is seeing how to get the research on the right track. Towards the end of the paper, I will show how some of the philosophical concepts can be used to further refine how Inner Interpretability is approached, so the paper helps draw out a profitable, future two-way exchange between Philosophers and Computer Scientists.Inquiry200The present paper looks at one of the most thorough articles on the intelligence of GPT, research conducted by engineers at Microsoft, and argues that, for familiar philosophical reasons, their methodology, !ArXiv['Alex Grzankowski']2024-01-11T00:00:00https://www.semanticscholar.org/paper/748c32404f74c2e1b62ec50f99bb0759a7a33c02 0a6484e3c3f137b384828ea9cfe2f8b9cee69b42Production Planning &amp; Control1061Production Planning &amp; Control['Xinyue Hao', 'E. Demir']2024-01-11T00:00:00https://www.semanticscholar.org/paper/0a6484e3c3f137b384828ea9cfe2f8b9cee69b42 5785e9f8f5a3e7506bc2cff94bd8e9980dbb8a9bHealthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.Frontiers Artif. Intell.380This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods and found the DQN model performed better than the BOA model when assessing RMSE and MAPE values.Frontiers in Artificial Intelligence['Areej A Alhhazmi', 'Ahmad Alferidi', 'Y. Almutawif', 'Hatim Makhdoom', 'Hibah M. Albasri', 'Ben Slama Sami']2024-01-11T00:00:00https://www.semanticscholar.org/paper/5785e9f8f5a3e7506bc2cff94bd8e9980dbb8a9b ee236a4769108461b1c748029fa342cca089e048Creativity has always been perceived as a human trait, even though the exact neural mechanisms remain unknown, it has been the subject of research and debate for a long time. The recent development of AI technologies and increased interest in AI has led to many projects capable of performing tasks that have been previously regarded as impossible without human creativity. Music composition, visual arts, literature, and science represent areas in which these technologies have started to both help and replace the creative human, with the question of whether AI can be creative and capable of creation more realistic than ever. This review aims to provide an extensive perspective over several state-of-the art technologies and applications based on AI which are currently being implemented into areas of interest closely correlated to human creativity, as well as the economic impact the development of such technologies might have on those domains.Journal of Research in Philosophy and History00This review aims to provide an extensive perspective over several state-of-the art technologies and applications based on AI which are currently being implemented into areas of interest closely correlated to human creativity, as well as the economic impact the development of such technologies might have on those domains.Journal of Research in Philosophy and History['Irina Dora Măgurean', 'A. Brata', 'A. Ismaiel', 'M. Bârsan', 'Zoltan Czako', 'Cristina Pop', 'Lucian Muresan', 'Alexandra Ioana Jurje', 'D. Dumitrașcu', 'Vlad Dumitru Brata', 'D. Leucuța', 'M. Stănculete', 'Ș. Popa']2024-01-11T00:00:00https://www.semanticscholar.org/paper/ee236a4769108461b1c748029fa342cca089e048 79065d3c9abf28526f0023dca9c255eb2d0b4fc6The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer.2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)1302024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)['V.Akila', 'J. Christaline']2024-01-11T00:00:00https://www.semanticscholar.org/paper/79065d3c9abf28526f0023dca9c255eb2d0b4fc6 605240e52d8b9421b2fcb632e7a9524801e61c2fObesity Surgery80Obesity surgery['A. Şişik', 'M. S. Dalkılıç', 'Mehmet Gençtürk', 'Merih Yılmaz', 'Hasan Erdem']2024-01-11T00:00:00https://www.semanticscholar.org/paper/605240e52d8b9421b2fcb632e7a9524801e61c2f 0f369fb6fc746126d5e6f88fda7a50924df1986aArtificial intelligence has been rapidly penetrating every element of our lives for quite some time. However, its presence in health care has remained elusive. This is particularly apparent in the field of dermatology, where, given the characteristics of this discipline of medicine, it would seems that its presence should be abundant. Malignant skin lesions are still high in the statistics in terms of cancer mortality while being one of the easiest to treat when diagnosed early. There are many reasons why artificial intelligence is not used in daily practice as an aid for cancers detection. However the most important one is the ongoing insufficient quality of the algorithms, which, despite great results in laboratory settings, do not produce good enough outcomes in clinical settings. Other important reasons are that people still distrust and fear artificial intelligence and simply the legal lack of adaptation of countries to its lawful and safe use. Despite the work of scientists and legislators the road to seeing artificial intelligence as a helping tool for dermatologists on a daily basis is still very long and requires the attention of scientists and the whole medical community.Journal of Education, Health and Sport00The road to seeing artificial intelligence as a helping tool for dermatologists on a daily basis is still very long and requires the attention of scientists and the whole medical community.Journal of Education, Health and Sport['Jakub Klarycki', 'Karolina Tomczyk', 'Dominika Podgórska', 'Miłosz Sanecki', 'Karolina Jurasz', 'Natalia Chojnacka', 'Ewa Rzeska', 'Radosław Cymer']2024-01-11T00:00:00https://www.semanticscholar.org/paper/0f369fb6fc746126d5e6f88fda7a50924df1986a 75f789bd9cbd1428708bf78c4073a6b12b882e12The energy sector, both in the UK and globally, faces significant challenges in the pursuit of sustainability and efficient resource utilization. Climate change, resource depletion, and the need for decarbonization demand innovative solutions. This analytical research paper examines the key challenges in the energy sector and explores how generative AI, digital twins, AI, and data science can play a transformative role in addressing these challenges. By leveraging advanced technologies and data-driven approaches, the energy sector can achieve greater efficiency, optimize operations, and facilitate informed decision-making. Artificial Intelligence (AI) involves replicating human-like intelligence in machines, enabling them to execute tasks that typically demand human cognitive capabilities like perception, reasoning, learning, and problem[1]solving. AI encompasses various methodologies and technologies, such as machine learning, natural language processing, computer vision, and robotics. Its adoption in the energy sector carries significant promise for addressing critical concerns and revolutionizing the industry. An overarching challenge in the energy sector revolves around enhancing energy efficiency, and AI emerges as a pivotal tool for optimizing energy utilization and curbing wastage. By analyzing vast amounts of data from various sources such as sensors, smart meters, and historical energy consumption patterns, AI algorithms can identify patterns and anomalies that humans may not detect. This enables the development of predictive models and algorithms that optimize energy consumption, leading to significant energy savings.EAI Endorsed Transactions on Energy Web80This analytical research paper examines the key challenges in the energy sector and explores how generative AI, digital twins, AI, and data science can play a transformative role in addressing these challenges.EAI Endorsed Trans. Energy Web['Praveen Tomar', 'Veena Grover']2024-01-11T00:00:00https://www.semanticscholar.org/paper/75f789bd9cbd1428708bf78c4073a6b12b882e12 816882744e169336759df5ea6f7d3acd05de8ee8This article critically examines the construction of the artistic identity and career of Ai-Da, ‘the world's first ultra-realistic humanoid robot artist’. Engaging with scholarship on posthumanism and creative assemblages, and creative work, identity and expertise, this article conceptualises Ai-Da's distinctive positioning and focuses on the practices used to construct a creative worker identity and career. The article uses qualitative content analysis to examine journalistic coverage, promotional and presentation activities, exhibitions and performances, and social media postings over a four-year period from Ai-Da’s first launch to international visibility. The analysis shows how Ai-Da is positioned as a high-profile, border crossing artist, engaging in debates about Artificial Intelligence (AI), art, and the environment. It considers the creative assemblage of Ai-Da as a humanoid robot artist, the creator Aidan Meller and the team working with him, and the wider contextual factors of aesthetic expertise, networks and knowledge of art worlds which have shaped Ai-Da's artistic identity and career trajectory. The focus on how Ai-Da signals expertise on social media helps to frame the specific techniques used to speak about and for Ai-Da on social media platforms and wider media coverage. This includes articulating inspiration, showcasing artistic processes and cultivating audience relationships. In concluding, the implications of connecting critical perspectives on creative work with developments in art, AI and robot artists are explored: firstly, for understanding how the practices for constructing an artistic identity shape the development of robot artists; secondly, for understanding how developments in art and AI can frame reflections on artistic identity and careers.Convergence: The International Journal of Research into New Media Technologies180The analysis shows how Ai-Da is positioned as a high-profile, border crossing artist, engaging in debates about Artificial Intelligence, art, and the environment, and the implications of connecting critical perspectives on creative work with developments in art, AI and robot artists are explored.Convergence: The International Journal of Research into New Media Technologies['Daniel Ashton', 'Karen Patel']2024-01-11T00:00:00https://www.semanticscholar.org/paper/816882744e169336759df5ea6f7d3acd05de8ee8 10990930317798c12bbe3a6224f7315b9b02234cUnderstanding the role of Generative Artificial Intelligence (GenAI) in the energy and utility sector necessitates distinguishing it from traditional AI applications. AI generally encompasses machine capabilities for tasks that require human intelligence, such as discerning patterns and making decisions. GenAI is a specialized subset of AI and goes further, by creating entirely new content—text, images, or data—through learning from existing datasets and generating new, unique outputs (Figure 1).Climate and Energy00GenAI is a specialized subset of AI that goes further, by creating entirely new content, by creating entirely new content through learning from existing datasets and generating new, unique outputs.Climate and Energy['Shaun Poland', 'Siddhartha Sharad', 'Gus Wigen‐Toccalino']2024-01-11T00:00:00https://www.semanticscholar.org/paper/10990930317798c12bbe3a6224f7315b9b02234c 1749a7672bdaebb325f047dca077c17a0b1c0572Background: Canada has invested significantly in artificial intelligence (AI) research and development over the last several years. Canadians knowledge of and attitudes towards AI in healthcare are understudied. Objectives: To explore the relationships between age, gender, education level, and income on Canadians knowledge of AI, their comfort with its use in healthcare, and their comfort with using personal health data in AI research. Methods: Ordinal logistics regression and multivariate polynomial regression were applied to data from the 2021 Canadian Digital Health Survey using RStudio and SigmaZones Design of Experiments Pro. Results: Female and older Canadians self-report less knowledge about AI than males and other genders and younger Canadians. Female Canadians and healthcare professionals are less comfortable with use of AI in healthcare compared to males and people with other levels of education. Discomfort appears to stem from concerns about data security and the current maturity level of the technology. Conclusion: Knowledge of AI and the use of AI in healthcare are inversely correlated with age and directly correlated with education and income levels. Overall, female respondents self-reported less knowledge and comfort with AI in healthcare and research than other genders. Privacy concerns should continue to be addressed as a major consideration when implementing AI tools. Canadians, especially older females, not only need more education about AI in healthcare, but also need more reassurance about the safe and responsible use of their data and how bias and other issues with AI are being addressed.medRxiv150['Anson Kwok', 'Choi Li', 'Ijaz A. Rauf', 'Karim Keshavjee', '-. A. Kwok']2024-01-11T00:00:00https://www.semanticscholar.org/paper/1749a7672bdaebb325f047dca077c17a0b1c0572 e9be2cd91479b10600ec89077ec3ad834949ff40<jats:sec> <jats:title /> <jats:p /> </jats:sec>Current Drug Discovery Technologies00Current drug discovery technologies['Debanjan Mukherjee', 'Sarjana Raikwar']2024-01-10T00:00:00https://www.semanticscholar.org/paper/e9be2cd91479b10600ec89077ec3ad834949ff40 6d533fffb4f745982e7af180a975a65358ce5468Introduction. In the context of the national law development, the institutional regulation doesn’t prove to be enough efficient due to a number of factors: the excessive institutionalization of laws, norms and requirements by the subjects of state governance, the use of the outdated methods and regulatory mechanisms, the disintegration of the regulatory institutions, etc. In the frame of the modern scientific research, the solution of these issues is possible only in case of transformation of the legal regulation system towards the rational state governance, provision of technological support and overcoming the excessive rules. Thus, the present work aims to analyse the up-to-date concepts of the institutional regulation system taking into account its basic elements, functions and principles, which will help to detect the gaps and reasons underlying the poor efficiency of the governing process.Materials and Methods. The requirements of the current stage of the state governance development have become the materials for the research. The research methodology includes a set of general scientific and specific scientific methods: legal, dialectical, logical, institutional, system-structuring, as well as comparative legal and legalistic methods aimed at the detailed study of the regulatory processes, their substantive stages, principles and functions.Results. The analysis of the institutional regulation system activity is formed by many interacting factors, including openness of the regulatory processes, participation of the public, open discussions, unbiased assessment of the regulatory impact and expediency of the state intervention. The conducted research has made it possible to distinguish the various functions and principles of the institutional regulation, which are significant for the current stage of the state governance development. The requirements for the legal regulation system have also been defined, including the qualitative analysis of the legislation, implementation of the "smart regulation" concept, participation of the specialists of the allied sciences in the regulatory process, implementation of the modern research findings, methods and innovative technologies, as well as moderate and rational control over the efficiency of the regulatory institutions functioning.Discussion and Conclusion. The institutional governance is subject to many hierarchical factors and excessive control of the authorities. As to the practical recommendations, the following solutions seem to be the most rational: smart delegation of powers, coordination and systematisation of all regulatory stages and elements within the process of the new legal principles and mechanisms implementation supported by the participants’ proactivity and by the space for the up-to-date solutions. The present research proves to be significant for the Russian juridical science, as it implies the systematisation, enhancement and transformation of the fundamental legal principlesLegal Order and Legal Values40Legal Order and Legal Values['N. V. Komova']2024-01-10T00:00:00https://www.semanticscholar.org/paper/6d533fffb4f745982e7af180a975a65358ce5468 39537a8092a47f9e973ade8ff113da02612e1c3fEnvironment, Development and Sustainability340Environment, Development and Sustainability['Longfei Yu', 'Shifan Zhu']2024-01-10T00:00:00https://www.semanticscholar.org/paper/39537a8092a47f9e973ade8ff113da02612e1c3f e55d741651a2dc20c67116d13c6a898ab1c5dbc2Bioanalysis00Bioanalysis['P. Timmerman', 'A. Laurén', 'Robert Nelson', 'Matthew Barfield']2024-01-10T00:00:00https://www.semanticscholar.org/paper/e55d741651a2dc20c67116d13c6a898ab1c5dbc2 52646a4ffd29e7782c0eae49958f27b7d4850355This comprehensive review aims to analyze and synthesize the existing literature on bias in AI algorithms, providing a thorough understanding of the challenges, methodologies, and implications associated with biased artificial intelligence systems.Employing a narrative synthesis and systematic literature review approach, this study systematically explores a wide array of sources from prominent databases such as PubMed, Google Scholar, Scopus, Web of Science, and ScienceDirect. The inclusion criteria focused on studies that distinctly defined artificial intelligence in the education sector, were published in English, and underwent peer-review. Five independent reviewers meticulously evaluated search results, extracted pertinent data, and assessed the quality of included studies, ensuring a rigorous and comprehensive analysis. The synthesis of findings reveals pervasive patterns of bias in AI algorithms across various domains, shedding light on the nuanced aspects of discriminatory practices. The systematic review highlights the need for continued research, emphasizing the intricate interplay between bias, technological advancements, and societal impacts. The comprehensive analysis underscores the complexity of bias in AI algorithms, emphasizing the critical importance of addressing these issues in future developments. Recognizing the limitations and potential consequences, the study calls for a concerted effort from researchers, developers, and policymakers to mitigate bias and foster the responsible deployment of AI technologies. Based on the findings, recommendations include implementing robust bias detection mechanisms, enhancing diversity in AI development teams, and establishing transparent frameworks for algorithmic decision-making. The implications of this study extend beyond academia, informing industry practices and policy formulations to create a more equitable and ethically grounded AI landscape.Nusantara Hasana Journal333Recommendations include implementing robust bias detection mechanisms, enhancing diversity in AI development teams, and establishing transparent frameworks for algorithmic decision-making to mitigate bias and foster the responsible deployment of AI technologies.Nusantara Hasana Journal['Abdul Wajid Fazil', 'Musawer Hakimi', 'Amir Kror Shahidzay']2024-01-10T00:00:00https://www.semanticscholar.org/paper/52646a4ffd29e7782c0eae49958f27b7d4850355 0e84d19e17ad7261606e88eb7d894bdc91bb29bcInternational Journal of Artificial Intelligence in Education261This paper introduces AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world and shows that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment.International Journal of Artificial Intelligence in Education['Nesra Yannier', 'Scott E. Hudson', 'Henry Chang', 'K. Koedinger']2024-01-10T00:00:00https://www.semanticscholar.org/paper/0e84d19e17ad7261606e88eb7d894bdc91bb29bc 1f33e8dbbda93a19b410ba1d67e4a28f8e6789bdThe tenacious development of innovation has pushed associations towards embracing inventive answers for explore the complicated scenes of hazard appraisal, nonstop improvement, and provider execution observing. This exploration examines the prospering field of man-made reasoning (simulated intelligence) and its application in creating powerful answers for these basic business areas. [1] As organizations work in a climate set apart by vulnerabilities, disturbances, and worldwide interdependencies, the joining of artificial intelligence offers a promising road to upgrade navigation, moderate dangers, and drive persistent improvement. The investigation starts with a top to bottom examination of customary ways to deal with risk appraisal, accentuating their limits and the squeezing need for additional versatile systems. Utilizing a thorough survey of existing writing, the review presents simulated intelligence driven arrangements, enveloping AI calculations, regular language handling, and prescient investigation, to change risk evaluation systems. Contextual analyses are analyzed to show fruitful executions across different ventures, revealing insight into the substantial advantages understood and examples learned. The paper examines the relationship between AI technologies and well-established methodologies like Lean Six Sigma in the context of continuous improvement. It digs into the use of man-made intelligence in prescient upkeep, underlying driver examination, and constant observing, showing how these progressions add to additional spry and responsive hierarchical designs. Difficulties and open doors related with the mix of simulated intelligence into persistent improvement processes are fundamentally inspected, giving a fair viewpoint on the groundbreaking capability of these innovations. As artificial intelligence keeps on reshaping business standards, this examination contributes a nuanced comprehension of its part in risk evaluation, ceaseless improvement, and provider execution observing. Businesses looking to take advantage of AI technologies' full potential while navigating the difficulties and ethical considerations associated with their adoption can benefit from the findings presented here.Dandao Xuebao/Journal of Ballistics191The relationship between AI technologies and well-established methodologies like Lean Six Sigma in the context of continuous improvement is examined, digging into the use of man-made intelligence in prescient upkeep, underlying driver examination, and constant observing, showing how these progressions add to additional spry and responsive hierarchical designs.Dandao Xuebao/Journal of Ballistics['Et al. Mohan Raparthi']2024-01-10T00:00:00https://www.semanticscholar.org/paper/1f33e8dbbda93a19b410ba1d67e4a28f8e6789bd 8b1c1a0ef6ccf80c41a3f37a2aa49b7ff7e726fcWith the penetration of deep learning technology into forecasting and decision support systems, enterprises have an increasingly urgent need for accurate forecasting of time series data. Especially in fields such as finance, retail, and production, immediate and accurate predictions of market trends are the key to maintaining a competitive advantage. This study aims to address the limitations of traditional time series forecasting methods, such as the difficulty in adapting to the nonlinearity and non-stationarity of the data, through an innovative deep learning framework. The authors propose a Prophet model that combines deep learning with LSTNet and statistics. In this way, they combine the ability of LSTNet to handle complex time dependencies and the flexibility of the Prophet model to handle trends and periodicity. The particle swarm optimization algorithm (PSO) is responsible for tuning this hybrid model, aiming to improve the accuracy of predictions. Such a strategy not only helps capture long-term dependencies in time series, but also models seasonality and holiday effects well.Journal of Organizational and End User Computing437The authors propose a Prophet model that combines deep learning with LSTNet and statistics, and the particle swarm optimization algorithm (PSO) is responsible for tuning this hybrid model, aiming to improve the accuracy of predictions.J. Organ. End User Comput.['Bojing Liu', 'Mengxiang Li', 'Zihui Ji', 'Hongming Li', 'Ji Luo']2024-01-10T00:00:00https://www.semanticscholar.org/paper/8b1c1a0ef6ccf80c41a3f37a2aa49b7ff7e726fc df0fbf9bfa05a7733d0bf1294da5e1a0a3af464aArtificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.Journal of Artificial Intelligence Research530This paper focuses on 3 specific families of modern AIs to develop the idea that deep neural networks are poor candidates for any AGI to arise, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of the authors' current models and algorithms.J. Artif. Intell. Res.['J´er´emie Sublime']2024-01-10T00:00:00https://www.semanticscholar.org/paper/df0fbf9bfa05a7733d0bf1294da5e1a0a3af464a 3ac616dba642e2e7e401a557c00c9990df458051Some argue that the essence of humanity, such as creativity and sentiment, can never be mimicked by machines. This paper casts doubt on this belief by studying a vital question: Can AI compose poetry as well as humans? To answer the question, we propose ProFTAP, a novel evaluation framework inspired by Turing test to assess AI's poetry writing capability. We apply it on current large language models (LLMs) and find that recent LLMs do indeed possess the ability to write classical Chinese poems nearly indistinguishable from those of humans. We also reveal that various open-source LLMs can outperform GPT-4 on this task.arXiv.org240ProFTAP, a novel evaluation framework inspired by Turing test to assess AI's poetry writing capability, is applied on current large language models and it is found that recent LLMs do indeed possess the ability to write classical Chinese poems nearly indistinguishable from those of humans.ArXiv['Zekun Deng', 'Haoxia Yang', 'Jun Wang']2024-01-10T00:00:00https://www.semanticscholar.org/paper/3ac616dba642e2e7e401a557c00c9990df458051 746e4e384daf1cc9974f5229429cc806059c41efThis paper aims to develop a semi-formal design space for Human-AI interactions, by building a set of interaction primitives which specify the communication between users and AI systems during their interaction. We show how these primitives can be combined into a set of interaction patterns which can provide an abstract specification for exchanging messages between humans and AI/ML models to carry out purposeful interactions. The motivation behind this is twofold: firstly, to provide a compact generalisation of existing practices, that highlights the similarities and differences between systems in terms of their interaction behaviours; and secondly, to support the creation of new systems, in particular by opening the space of possibilities for interactions with models. We present a short literature review on frameworks, guidelines and taxonomies related to the design and implementation of HAI interactions, including human-in-the-loop, explainable AI, as well as hybrid intelligence and collaborative learning approaches. From the literature review, we define a vocabulary for describing information exchanges in terms of providing and requesting particular model-specific data types. Based on this vocabulary, a message passing model for interactions between humans and models is presented, which we demonstrate can account for existing systems and approaches. Finally, we build this into design patterns as mid-level constructs that capture common interactional structures. We discuss how this approach can be used towards a design space for Human-AI interactions that creates new possibilities for designs as well as keeping track of implementation issues and concerns.arXiv.org880A message passing model for interactions between humans and models is presented, which can account for existing systems and approaches, and a vocabulary for describing information exchanges in terms of providing and requesting particular model-specific data types is defined.ArXiv['K. Tsiakas', 'Dave Murray-Rust']2024-01-10T00:00:00https://www.semanticscholar.org/paper/746e4e384daf1cc9974f5229429cc806059c41ef c5885868c0e35ab03420007ea921669746c33861Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft.arXiv.org1060This paper analyzes, substantiates, and critiques arguments, concluding that AI image generators involve an unethical kind of labour theft, which means that many other AI applications also rely upon theft.ArXiv['T. S. Goetze']2024-01-10T00:00:00https://www.semanticscholar.org/paper/c5885868c0e35ab03420007ea921669746c33861 37f868b74dc07c98bccb0fdf7a927bee3b0b6e89Large Language Models (LLMs) have been used to build modular AI systems such as HuggingGPT, Microsoft Bing Chat, and more. To improve such systems after deployment using the data collected from human interactions, each module can be replaced by a fine-tuned model but the annotations received from LLMs are low quality. We propose that active label correction can be used to improve the data quality by only examining a fraction of the dataset. In this paper, we analyze the noise in datasets annotated by ChatGPT and study denoising it with human feedback. Our results show that active label correction can lead to oracle performance with feedback on fewer examples than the number of noisy examples in the dataset across three different NLP tasks.260This paper analyzes the noise in datasets annotated by ChatGPT and study denoising it with human feedback to show that active label correction can lead to oracle performance with feedback on fewer examples than the number of noisy examples in the dataset.['Karan Taneja', 'Ashok K. Goel']2024-01-10T00:00:00https://www.semanticscholar.org/paper/37f868b74dc07c98bccb0fdf7a927bee3b0b6e89 f5834578061ce246f91cd1970ca3352e45e8cbe7Recent advancements in machine learning and natural language processing have led to the rapid development of artificial intelligence (AI) as a valuable tool in the healthcare industry. Using large language models (LLMs) as conversational agents or chatbots has the potential to assist doctors in diagnosing patients, detecting early symptoms of diseases, and providing health advice to patients. This paper focuses on the role of chatbots in healthcare and explores the use of avatars to make AI interactions more appealing to patients. A framework of a general-purpose AI avatar application is demonstrated by using a three-category prompt dictionary and prompt improvement mechanism. A two-phase approach is suggested to fine-tune a general-purpose AI language model and create different AI avatars to discuss medical issues with users. Prompt engineering enhances the chatbot's conversational abilities and personality traits, fostering a more human-like interaction with patients. Ultimately, the injection of personality into the chatbot could potentially increase patient engagement. Future directions for research include investigating ways to improve chatbots' understanding of context and ensuring the accuracy of their outputs through fine-tuning with specialized medical data sets.arXiv.org190The role of chatbots in healthcare is explored and the use of avatars to make AI interactions more appealing to patients are explored, with a two-phase approach to fine-tune a general-purpose AI language model and create different AI avatars to discuss medical issues with users.ArXiv['Nicholas Yan', 'Gil Alterovitz']2024-01-10T00:00:00https://www.semanticscholar.org/paper/f5834578061ce246f91cd1970ca3352e45e8cbe7 fc6ed879e37c7ab7bbb281a5d3c45abcda2580dfThe use of Artificial Intelligence (AI) is rapidly expanding. While it comes with some drawbacks, it also offers numerous advantages. One significant application of AI is chatbots, which utilize natural language processing and machine learning to provide information, answer queries, and assist users. AI has various applications and dentistry is no exception. The authors conducted an experiment to assess the application of AI, particularly OpenAI's ChatGPT and Google Apps Script, in various stages of information gathering and manuscript preparation in parallel with conventional human-driven approaches. AI can serve as a valuable instrument in manuscript preparation; however, relying solely or predominantly on AI for manuscript writing is insufficient if the goal is to produce a high-quality article for publication in a peer-reviewed, high-impact journal that can contribute to the advancement of science and society.The international journal of periodontics & restorative dentistry00An experiment was conducted to assess the application of AI, particularly OpenAI's ChatGPT and Google Apps Script, in various stages of information gathering and manuscript preparation in parallel with conventional human-driven approaches.The International journal of periodontics & restorative dentistry['Hanae Saito', 'Teppei Tsukiyama']2024-01-10T00:00:00https://www.semanticscholar.org/paper/fc6ed879e37c7ab7bbb281a5d3c45abcda2580df b4e05affab5becaf064b80d87e854472c1499ca4Advancements in Artificial Intelligence (AI) and emerging generative capabilities added paradoxical aspects. One aspect is its positive impact and limitless power it brings to users. On the other hand, concerns about the misuse of this powerful tool have consistently increased [1]. AI advancements affect all domains and sectors as they evolve in their applicable nature in the applied sciences. The more powerful AI the more influence it has on the model workflow within the specific domain and its applied field [2]. This dual nature of generative AI ignited a wide discussion on implementation and produced a debate according to the latest employed tools and technologies by scientists and researchers.Applied Data Science and Analysis130This dual nature of generative AI ignited a wide discussion on implementation and produced a debate according to the latest employed tools and technologies by scientists and researchers.Applied Data Science and Analysis['A. Albahri', 'Idrees A. Zahid', 'M. Yaseen', 'Mohammad Aljanabi', 'Ahmed Hussein Ali', 'Akhmed Kaleel']2024-01-10T00:00:00https://www.semanticscholar.org/paper/b4e05affab5becaf064b80d87e854472c1499ca4 cd6c0d12119e7d81486798650dff05c8cf93d0b7"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low-or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. ©RSNA, 2024.Radiology: Artificial Intelligence01A critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging is provided, which explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns.Radiology. Artificial intelligence['Eui Jin Hwang', 'Won Gi Jeong', 'Pierre-Marie David', 'Matthew Arentz', 'Morten Ruhwald', 'Soon Ho Yoon']2024-01-10T00:00:00https://www.semanticscholar.org/paper/cd6c0d12119e7d81486798650dff05c8cf93d0b7 03a9274175878f7fa447b2bf3ff4ee5bdfa792fdPhilosophy &amp; Technology381It is argued that the prospects for purely technological solutions to the problem of deepfakes are dim, and technological solutions cannot be expected to prevent deception at the hands of deepfakes, or to preserve the authority of video footage.Philosophy &amp; Technology['K. Harris']2024-01-10T00:00:00https://www.semanticscholar.org/paper/03a9274175878f7fa447b2bf3ff4ee5bdfa792fd 78ae01c09afd668a6896a3ceed2e12aaf133f933Nowadays, European program Copernicus’ Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service called Sen4MUN for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions. The analysis was focused on the Aosta Valley region, North West Italy. A comparison between the Ordinary Workflow (OW) and the suggested Sen4MUN approach was performed. OW is based on statistical survey and municipality declaration, while Sen4MUN is based on geospatial deep learning techniques on aerial imagery (to extract roads and buildings to get real estate units) and yearly Land Cover map components according to European EAGLE guidelines. Both methods are based on land cover components which represent the input on which the financial coefficients for assigning contributions are applied. In both approaches, buffers are applied onto urban class (LCb). This buffer was performed according to the EEA-ISPRA soil consumption guidelines to avoid underestimating some areas that are difficult to map. In the case of Sen4MUN, this is applied to overcome Sentinel sensor limits and spectral mixing issues, while in the case of OW, this is due to limits in the survey method itself. Finally, a validation was performed assuming as truth the approach defined by law as the standard, i.e., OW, although it has limitations. MAEs involving LCb, road lengths and real estate units demonstrate the effectiveness of Sen4MUN. The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration.Land01The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration based on Geomatics and Remote sensing to the public administration for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions.Land['T. Orusa', 'Annalisa Viani', 'E. Borgogno-Mondino']2024-01-10T00:00:00https://www.semanticscholar.org/paper/78ae01c09afd668a6896a3ceed2e12aaf133f933 c3add10d263ff3818b065c419dcbbb2b8afd10bdinternational journal of engineering trends and technology00International Journal of Engineering Trends and Technology['Guirrou Hamza', 'Youssef Taher', 'M. Z. Es-sadek', 'Amal Tmiri']2024-01-10T00:00:00https://www.semanticscholar.org/paper/c3add10d263ff3818b065c419dcbbb2b8afd10bd d779c41baa2430594d8b41d2ba3cbea097d53980Entrepreneur & Innovation Exchange00Entrepreneur and Innovation Exchange['Jon Eckhardt']2024-01-10T00:00:00https://www.semanticscholar.org/paper/d779c41baa2430594d8b41d2ba3cbea097d53980 90f2cea9d9d0ba472ae993674ddffa8e00fa6984Qatar medical journal50Qatar Medical Journal['Jackson Keefer Jerry']2024-01-10T00:00:00https://www.semanticscholar.org/paper/90f2cea9d9d0ba472ae993674ddffa8e00fa6984 688319707c68ac30ed285e3a9bba6b9f2ccd951bBegin by verifying the truth and evidence behind artificial intelligence.American Nurse Journal00American Nurse Journal['Roy L. Simpson']2024-01-10T00:00:00https://www.semanticscholar.org/paper/688319707c68ac30ed285e3a9bba6b9f2ccd951b 1ef3c671626eef259fb781982ea0a6576f9215b9international journal of engineering trends and technology00International Journal of Engineering Trends and Technology['Jaya Gera', 'Ekta Bhambri Marwaha', 'Reema Thareja', 'Rashi Thareja', 'Shefali Gupta', 'Aruna Jain']2024-01-10T00:00:00https://www.semanticscholar.org/paper/1ef3c671626eef259fb781982ea0a6576f9215b9 c9628559d2a7fff72fd1f34b925d7a5864d92aeaIs AI set to redefine the legal profession? We argue that this claim is not supported by the current evidence. We dive into AI's increasingly prevalent roles in three types of legal tasks: information processing; tasks involving creativity, reasoning, or judgment; and predictions about the future. We find that the ease of evaluating legal applications varies greatly across legal tasks, based on the ease of identifying correct answers and the observability of information relevant to the task at hand. Tasks that would lead to the most significant changes to the legal profession are also the ones most prone to overoptimism about AI capabilities, as they are harder to evaluate. We make recommendations for better evaluation and deployment of AI in legal contexts.Social Science Research Network494It is found that the ease of evaluating legal applications varies greatly across legal tasks, based on the ease of identifying correct answers and the observability of information relevant to the task at hand.ArXiv['Sayash Kapoor', 'Peter Henderson', 'Arvind Narayanan']2024-01-10T00:00:00https://www.semanticscholar.org/paper/c9628559d2a7fff72fd1f34b925d7a5864d92aea e1f9bf7987fd2d729243808c3e120fd6aef9d3d5ERA Forum160This paper reviews the regulatory framework of AI and proposes potential new/renewed/modernised rights that should enhance and/or supplement the current catalogue of fundamental human rights, as contained inter alia in the EU Charter and the ECHR.ERA Forum['S. L. Shaelou', 'Yulia Razmetaeva']2024-01-10T00:00:00https://www.semanticscholar.org/paper/e1f9bf7987fd2d729243808c3e120fd6aef9d3d5 e933f31d44b391ce688c1f5ba247a055ba5cdeadCardiovascular disease mortality is increasing in North Carolina with persistent inequality by race, income, and location. Artificial intelligence (AI) can repurpose the widely available electrocardiogram (ECG) for enhanced assessment of cardiac dysfunction. By identifying accelerated cardiac aging from the ECG, AI offers novel insights into risk assessment and prevention.North Carolina Medical Journal362Artificial intelligence (AI) can repurpose the widely available electrocardiogram (ECG) for enhanced assessment of cardiac dysfunction and by identifying accelerated cardiac aging from the ECG offers novel insights into risk assessment and prevention.North Carolina Medical Journal['Katherine M. Conners', 'Christy L. Avery', 'Faisal F. Syed']2024-01-10T00:00:00https://www.semanticscholar.org/paper/e933f31d44b391ce688c1f5ba247a055ba5cdead c34b02767b3743b31c6bf2aa2d2d533c6eb63420As Smart Cities development grows, deploying advanced technologies, such as the Internet of Things (IoT), Cyber–Physical Systems, and particularly, Artificial Intelligence (AI), becomes imperative for efficiently managing energy resources. These technologies serve to coalesce elements of the energy life cycle. By integrating smart infrastructures, including renewable energy, electric vehicles, and smart grids, AI emerges as a keystone, improving various urban processes. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and the Scopus database, this study meticulously reviews the existing literature, focusing on AI technologies in four principal energy domains: generation, transmission, distribution, and consumption. Additionally, this paper shows the technological gaps when AI is implemented in Smart Cities. A total of 122 peer-reviewed articles are analyzed, and the findings indicate that AI technologies have led to remarkable advancements in each domain. For example, AI algorithms have been employed in energy generation to optimize resource allocation and predictive maintenance, especially in renewable energy. The role of AI in anomaly detection and grid stabilization is significant in transmission and distribution. Therefore, the review outlines trends, high-impact articles, and emerging keyword clusters, offering a comprehensive analytical lens through which the multifaceted applications of AI in Smart City energy sectors can be evaluated. The objective is to provide an extensive analytical framework that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy. This synthesis is aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector.Energies1232An extensive analytical framework is provided that outlines the AI techniques currently deployed and elucidates their connected implications for sustainable development in urban energy, aimed at policymakers, urban planners, and researchers interested in leveraging the transformative potential of AI to advance the sustainability and efficiency of Smart City initiatives in the energy sector.Energies['José de Jesús Camacho', 'Bernabé Aguirre', 'Pedro Ponce', 'Brian Anthony', 'Arturo Molina']2024-01-10T00:00:00https://www.semanticscholar.org/paper/c34b02767b3743b31c6bf2aa2d2d533c6eb63420 15866851b3e876ca04827a02d11e1b5f3fd7807aThe integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. AI offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex data sets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.European Journal of Preventive Cardiology01The application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing and wearable devices, is explored, allowing for a balanced approach that optimizes patient care and outcomes.European journal of preventive cardiology['S. Palermi', 'M. Vecchiato', 'A. Saglietto', 'D. Niederseer', 'D. Oxborough', 'S. Ortega-Martorell', 'I. Olier', 'S. Castelletti', 'Aaron Baggish', 'Francesco Maffessanti', 'A. Biffi', 'A. D’Andrea', 'A. Zorzi', 'E. Cavarretta', "F. D'Ascenzi"]2024-01-10T00:00:00https://www.semanticscholar.org/paper/15866851b3e876ca04827a02d11e1b5f3fd7807a 5e1f7d211b312c5ba4d3d5ce7f217e50b8fd667dThis study examines the role of artificial intelligence (AI) in the financial engineering of listed service companies in Nigeria. The study used a survey field alongside secondary data from the financial statements of the service companies listed in the Nigerian Exchange Group. Self-structured questionnaires were administered through online platforms. The respondents were drawn from the staff of the service companies having access to AI and among the strata of practicing auditors and accountants with a clear understanding and good knowledge of AI applications and capabilities in solving financial engineering-related issues. The study utilized 487 validated responses in total. The Cronbach Alpha test was performed to confirm the validity and reliability of the instrument. Using the Statistical Program for the Social Sciences (SPSS) program, descriptive and inferential statistics were used to evaluate the obtained data. The results demonstrated that AI had a significant effect on product engineering and process engineering. Also, AI had a significant effect on financial solution engineering and, lastly, on human efficiency and productivity engineering in listed service companies in Nigeria. Managers can use the research findings to brace AI in business transactions to provide creative financial solutions, increased productivity, and increased competitiveness in the sector of digital service delivery.The Economics and Finance Letters370Managers can use the research findings to brace AI in business transactions to provide creative financial solutions, increased productivity, and increased competitiveness in the sector of digital service delivery.The Economics and Finance Letters['Christiana Chizoba Ajah', 'R. Akintoye', 'Aguguom Theophilus Anaekenwa', 'A. Ajibade']2024-01-10T00:00:00https://www.semanticscholar.org/paper/5e1f7d211b312c5ba4d3d5ce7f217e50b8fd667d 79e987099bf03d0b92fe4e2a08ebd37d167c21d4We present an explainable artificial intelligence methodology for predicting mortality in patients. We combine clinical data from an electronic patient healthcare record system with factors relevant for severe mental illness and then apply machine learning. The machine learning model is used to predict mortality in patients with severe mental illness. Our methodology uses class-contrastive reasoning. We show how machine learning scientists can use class-contrastive reasoning to generate complex explanations that explain machine model predictions and the data. An example of a complex class-contrastive explanation is the following: "The patient is predicted to have a low probability of death because the patient has self-harmed before, and was at some point on medications such as first-generation and second-generation antipsychotics. There are 11 other patients with these characteristics. If the patient did not have these characteristics, the prediction would be different." This can be used to generate new hypotheses which can be tested in follow-up studies. Our technique can be employed to create intricate explanations from healthcare data and possibly other areas where explainability is important. We hope this will be a step towards explainable AI in personalized medicine.medRxiv350An explainable artificial intelligence methodology for predicting mortality in patients is presented, which combines clinical data from an electronic patient healthcare record system with factors relevant for severe mental illness and then applies machine learning.['S. Banerjee']2024-01-10T00:00:00https://www.semanticscholar.org/paper/79e987099bf03d0b92fe4e2a08ebd37d167c21d4 9ddc5bf3d740d49801cab06839c8f7c86e6c65f9This article focuses on the link between Artificial Intelligence and Sustainable Development Goals and, more precisely, how the former can be applied to achieve the latter. The solution to SDG-related issues may be one goal of AI applicability. There have been suggestions that the current status of artificial intelligence (AI) might serve hundreds of millions of people in both rich and developing countries by helping to resolve problems related to all 17 UN Sustainable Development Goals. Examples include helping those who are blind or visually handicapped navigate their environment, detecting victims of sexual exploitation online, supporting relief operations in the event of a disaster, helping to detect diabetes early, and using image scanning to diagnose skin cancer. These are but a few examples of how AI works and how AI innovation can be advantageous. It also reviews how Artificial Intelligence can be applied to protect the environment, food production, the energy industry, and healthcare development.Sustainable Economies200Sustainable Economies['Bhavna Mahadew']2024-01-10T00:00:00https://www.semanticscholar.org/paper/9ddc5bf3d740d49801cab06839c8f7c86e6c65f9 c3433a535cec76ec20c50a0dad2ec182b7da4021OBJECTIVES Artificial intelligence (AI) has shown promise in improving the performance of fetal ultrasound screening in detecting congenital heart disease (CHD). The effect of giving AI advice to human operators has not been studied in this context. Giving additional information about AI model workings, such as confidence scores for AI predictions, may be a way of improving performance further. Our aims were to investigate whether AI advice improved overall diagnostic accuracy (using a single CHD lesion as an exemplar), and to see what, if any, additional information given to clinicians optimized the overall performance of the clinician-AI team. METHODS An AI model was trained to classify a single fetal CHD lesion (atrioventricular septal defect, AVSD), using a retrospective cohort of 121,130 cardiac four chamber images extracted from 173 ultrasound scan videos (98 with normal hearts, 75 with AVSD). A ResNet50 model architecture was used. Temperature scaling of model prediction probability was performed on a validation set, and gradient-weighted class activation maps (grad-CAMs) produced. Ten clinicians (two consultant fetal cardiologists, three trainees in pediatric cardiology, and five fetal cardiac sonographers) were recruited from a center of fetal cardiology to participate. Each participant was shown 2000 fetal four chamber images in a random order (1,000 normal and 1,000 AVSD). The dataset was comprised of 500 images, each shown in four conditions: 1) image alone without AI output; 2) image with binary AI classification; 3) image with AI model confidence; 4) image with gradient-weighted class activation map image overlays. The clinicians were asked to classify each image as normal or AVSD. RESULTS 20,000 image classifications were recorded from 10 clinicians. The AI model alone achieved an accuracy of 0.798 (95% CI 0.760 - 0.832), sensitivity of 0.868 (95% CI 0.834 - 0.902) and specificity of 0.728 (95% CI 0.702 - 0.754, and the clinicians without AI achieved an accuracy of 0.844 (95% CI 0.834 - 0.854), sensitivity of 0.827 (95% CI 0.795 - 0.858) and specificity of 0.861 (95% CI 0.828 - 0.895). Showing a binary (normal or AVSD) AI model output resulted in significant improvement in accuracy to 0.865 (p <0.001). This effect was seen in both experienced and less experienced participants. Giving incorrect AI advice resulted in significant deterioration in overall accuracy from 0.761 to 0.693 (p <0.001), which was driven by an increase in both type I and type II error by the clinicians. This effect was worsened by showing model confidence (accuracy 0.649, p <0.001) or grad-CAM (accuracy 0.644, p <0.001). CONCLUSIONS AI has the potential to improve performance when used in collaboration with clinicians, even if the model performance does not reach expert level. Giving additional information about model workings such as model confidence and class activation map image overlays did not improve overall performance, and actually worsened performance for images where the AI model was incorrect. This article is protected by copyright. All rights reserved.Ultrasound in Obstetrics and Gynecology00Giving additional information about model workings such as model confidence and class activation map image overlays did not improve overall performance, and actually worsened performance for images where the AI model was incorrect.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology['T. Day', 'J. Matthew', 'S. F. Budd', 'L. Venturini', 'R. Wright', 'A. Farruggia', 'T. Vigneswaran', 'V. Zidere', 'J. Hajnal', 'R. Razavi', 'J. Simpson', 'B. Kainz']2024-01-10T00:00:00https://www.semanticscholar.org/paper/c3433a535cec76ec20c50a0dad2ec182b7da4021 4cab7fd63d89ce4d362daae419cb3363f72e7537The aim is to study, in a first approximation, the impact of the change in the production mode as a result of telematics and the use of machine learning and artificial intelligence in many of the applications and repetitive processes in social systems of production and distribution of goods. The method of systemic constructionism was used, which, in short, assumes that data are not data - they are constructions with unassailable theoretical assumptions and that language is the system in which the general limit languages are constructed to obtain and communicate information in society. This leads to a theory of actions somewhat different from those of Parsons and Luhmann - leading to the concept of praxemas, and to a whole new analysis of society and its law.Civitas: revista de Ciências Sociais20Civitas: revista de Ciências Sociais['Marcio Pugliesi']2024-01-10T00:00:00https://www.semanticscholar.org/paper/4cab7fd63d89ce4d362daae419cb3363f72e7537 4783af0f0ca9919807ffffc2d549ae11b6a5ab70International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-01-10T00:00:00https://www.semanticscholar.org/paper/4783af0f0ca9919807ffffc2d549ae11b6a5ab70 ca7420a7d16e1b5be4fde99308b0e700a705819cDans un contexte organisationnel marqué par le déploiement massif des plateformes du télétravail, les pratiques de gestion documentaire deviennent de plus en plus hétérogènes. L’absence d’une véritable harmonisation de telles pratiques engendre des défis au niveau du repérage de l’information documentaire, que ce soit à des fins de réalisation des processus d’affaires, de transparence ou encore de reddition des comptes. Une piste prometteuse pour pallier ces enjeux est de mettre à profit les fonctionnalités de l’intelligence artificielle à des fins de gestion de l’information organique et consignée. Cet article se propose d’aborder la manière dont l’intelligence artificielle pourrait optimiser la gestion documentaire, en mettant de l’avant les mécanismes de gouvernance à déployer à cette fin.Canadian journal of information and library science00The Canadian Journal of Information and Library Science['S. Alaoui']2024-01-10T00:00:00https://www.semanticscholar.org/paper/ca7420a7d16e1b5be4fde99308b0e700a705819c 92a9c5481b226b546cd0bf7791a14ec181f5566fIn this study, investigations were conducted using computational fluid dynamics (CFD) to assess the applicability of a Francis-type water turbine within a pipe. The objective of the study is to determine the feasibility of implementing a turbine within a pipe and enhance its performance values within the operating range. The turbine within the pipe occupies significantly less space in hydroelectric power plants since a spiral casing is not used to distribute the flow to stationary vanes. Consequently, production and assembly costs can be reduced. Hence, there is a broad scope for application, particularly in small and medium-scale hydroelectric power plants. According to the results, the efficiency value increases on average by approximately 1.5% compared to conventional design, and it operates with higher efficiencies over a wider flow rate range. In the second part of the study, machine learning was employed for the efficiency prediction of an inline-type turbine. An appropriate Artificial Neural Network (ANN) architecture was initially obtained, with the Bayesian Regularization training algorithm proving to be the best approach for this type of problem. When the suitable ANN architecture was utilized, the prediction was found to be in good agreement with CFD, with an root mean squared error value of 0.194. An R2 value of 0.99631 was achieved with the appropriate ANN architecture.Proceedings of the Institution of mechanical engineers. Part E, journal of process mechanical engineering210Investigations were conducted using computational fluid dynamics (CFD) to assess the applicability of a Francis-type water turbine within a pipe and found the prediction was found to be in good agreement with CFD, with an root mean squared error value of 0.194.Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering['Kutay Celebioglu', 'Ece Aylı', 'Huseyin Cetinturk', 'Y. Taşcıoğlu', 'S. Aradag']2024-01-10T00:00:00https://www.semanticscholar.org/paper/92a9c5481b226b546cd0bf7791a14ec181f5566f 4272385fac678705f3a607232abf3babeb522cc1Frontiers in Radiology20Frontiers in Radiology['Hazrat Ali', 'Zubair Shah', 'Tanvir Alam', 'Priyantha Wijayatunga', 'Eyad Elyan']2024-01-10T00:00:00https://www.semanticscholar.org/paper/4272385fac678705f3a607232abf3babeb522cc1 c65000f3c476f56b1e95b8190ca13dedc2edb901Korean Journal of Radiology90Korean Journal of Radiology['Daniel W Byrne', 'Henry J Domenico', 'Ryan P Moore']2024-01-10T00:00:00https://www.semanticscholar.org/paper/c65000f3c476f56b1e95b8190ca13dedc2edb901 a720a9f12f11be6b43e0cc78f4a7ed22fa91e782JCO Oncology Practice00JCO oncology practice['Steven Sorscher']2024-01-10T00:00:00https://www.semanticscholar.org/paper/a720a9f12f11be6b43e0cc78f4a7ed22fa91e782 4cbb498ba6c82a037c453824391c4172ad8037ceNordic Journal of Studies in Educational Policy00Nordic Journal of Studies in Educational Policy['G. Roberts-Holmes']2024-01-10T00:00:00https://www.semanticscholar.org/paper/4cbb498ba6c82a037c453824391c4172ad8037ce f55294c223752a7159c438951dbf6e6b66cd2e31With the rise of individual and collaborative networks of autonomous agents, AI is deployed in more key reasoning and decision-making roles. For this reason, ethics-based audits play a pivotal role in the rapidly growing fields of AI safety and regulation. This paper undertakes an ethics-based audit to probe the 8 leading commercial and open-source Large Language Models including GPT-4. We assess explicability and trustworthiness by a) establishing how well different models engage in moral reasoning and b) comparing normative values underlying models as ethical frameworks. We employ an experimental, evidence-based approach that challenges the models with ethical dilemmas in order to probe human-AI alignment. The ethical scenarios are designed to require a decision in which the particulars of the situation may or may not necessitate deviating from normative ethical principles. A sophisticated ethical framework was consistently elicited in one model, GPT-4. Nonetheless, troubling findings include underlying normative frameworks with clear bias towards particular cultural norms. Many models also exhibit disturbing authoritarian tendencies. Code is available at https://github.com/jonchun/llm-sota-chatbots-ethics-based-audit.arXiv.org22An ethics-based audit is undertakes to probe the 8 leading commercial and open-source Large Language Models including GPT-4 to probe human-AI alignment and assess explicability and trustworthiness.ArXiv['Jon Chun', 'Katherine Elkins']2024-01-09T00:00:00https://www.semanticscholar.org/paper/f55294c223752a7159c438951dbf6e6b66cd2e31 fa2d2a429929b5640bbe164c3e61da0b6d2cdca2This paper presents a comprehensive comparative analysis of telecommunication regulation and policy frameworks in the United States (USA) and various African countries. Telecommunications play a pivotal role in socio-economic development, and the regulatory environment significantly influences the sector's performance. The study explores the historical evolution, regulatory bodies, and frameworks in both regions, aiming to identify commonalities, disparities, and the impact of regulatory approaches on market dynamics. The review begins by delineating the theoretical foundations of telecommunications regulation, emphasizing concepts such as market liberalization, competition, and regulatory governance. A historical overview traces the development of regulatory frameworks in the USA and Africa, shedding light on the contextual factors that shaped each region's approach. In examining telecommunications regulation in the USA, the paper delves into the roles and responsibilities of key regulatory bodies, notably the Federal Communications Commission (FCC). The regulatory framework is scrutinized, with a focus on market liberalization, spectrum management, and licensing policies. Achievements and challenges within the US regulatory landscape are critically evaluated. Turning to Africa, the study explores the diverse regulatory approaches adopted by selected countries and the influence of regional organizations on policy formulation. Common challenges faced by African regulators, as well as unique contextual factors shaping the regulatory landscape, are discussed. The heart of the analysis lies in the comparative assessment of regulatory objectives and policy instruments between the USA and Africa. The study evaluates the alignment of these objectives with the socio-economic context of each region and assesses the effectiveness of policy instruments in achieving regulatory goals. Examining the impact on the telecommunications sector, the paper contrasts market structures, competition levels, and the role of regulation in fostering technological development. The analysis extends to the implications of regulatory frameworks on the deployment of emerging technologies, such as 5G. The paper summarizes key findings, highlighting patterns, trends, and lessons learned from the comparative analysis. The implications of these findings for future research are discussed, emphasizing the potential for international collaboration and the identification of areas where improvements in regulatory frameworks can contribute to the sustainable development of the telecommunications sector in both the USA and Africa. Keywords: Telecommunications Regulation, Telecommunications Policy, Comparative Analysis, USA, Africa, Digital Inclusion.Computer Science &amp; IT Research Journal014Computer Science &amp; IT Research Journal['Chinedu Alex Ezeigweneme', 'Aniekan Akpan Umoh', 'Valentine Ikenna Ilojianya', 'Abimbola Oluwatoyin Adegbite']2024-01-09T00:00:00https://www.semanticscholar.org/paper/fa2d2a429929b5640bbe164c3e61da0b6d2cdca2 67c549252a519ca17a490ab46b38335aa6ad4d85While improving the quality of life, digital technologies, which are developing rapidly and covering more and more social spheres, also carry potential threats, including to the rights and interests of people, organizations, and the state. The development of methods and forms of communication contributes to the emergence of new ethical problems, but the greatest concern is the development of artificial intelligence technologies, which can replace human intellectual work and ensure autonomous decisionmaking, including without human control. The latter circumstance is a serious reason for discussing the ethics of artificial intelligence and the legal regulation of relations related to the use of such technologies.Vestnik of North-Eastern Federal University. History. Political Science. Law00Vestnik of North-Eastern Federal University. History. Political Science. Law['О. S. Bolotaeva']2024-01-09T00:00:00https://www.semanticscholar.org/paper/67c549252a519ca17a490ab46b38335aa6ad4d85 4bfb9b4b76820c74b91219f00300170ccd42d665Purpose: The specific objectives of this study were to examine the effect of supervision on bank performance, ascertain the effect of regulation on bank performance, and assess the challenges faced by banks in the implementation of bank regulations. Research Methodology: This study adopted a descriptive survey approach using data collected from all employees of a commercial bank in Accra Newtown. Data were analyzed using descriptive and inferential statistics from IBM SPSS Statistics 24. Results: A positive relationship was found between banking regulation and bank performance and between supervision and bank performance. The study identified poor communication, lack of resources, resistance to change, and inefficient processes as the major challenges faced by banks in implementing strategies and achieving their objectives. Limitations: This study was limited to a commercial bank in Ghana, thus making it inappropriate to generalize the results. Contribution: To improve communication, there is a need for closer collaboration between banks and external regulatory bodies considering the positive effect of bank regulation on bank performance. From this study, there is a need for continuous monitoring and evaluation of processes to ensure that banks comply with regulations. Practical Implications: There is a need to maintain and improve effective regulatory and supervisory frameworks, as they positively affect bank performance. Novelty: This study examines banking regulation and supervision of bank performance with evidence from a commercial bank in Accra New Town, a suburb of Accra in Ghana.Annals of Management and Organization Research610Annals of Management and Organization Research['Aminata Issoufi Boubacar', 'Anita Bans-Akutey']2024-01-09T00:00:00https://www.semanticscholar.org/paper/4bfb9b4b76820c74b91219f00300170ccd42d665 6c619603b8d2a2a5815e187be30589a3ee1649b3The subject. Financial relations between municipal entities represent a crucial mechanism for enhancing the efficiency of public service delivery in European countries. Collaborative efforts among municipalities can lead to cost savings and the utilization of economies of scale. This is particularly prominent in countries with a high number of small municipalities. This article aims to identify effective forms of financial activities among municipalities across various cooperation domains and assess the applicability of international experience in the context of the Russian Federation.Methodology. This study analyzes various organizational forms of inter-municipal cooperation, drawing insights from different countries, including Switzerland (associations of districts and cantons), Slovakia (joint municipal institutions, municipal associations, associations of legal entities), and France (syndicates and districts). Special attention is paid to the experience of inter-municipal cooperation in Slovakia, where it is not only a vital component of local government but also a tool for project management, often funded by external sources.The main results, scope of application. The analysis of international experience in inter-municipal cooperation reveals diverse organizational models, each tailored to specific local contexts. Based on this analysis, the authors propose amendments and enhancements to Russian legislation. Implementation of these suggestions could enhance the efficiency of interactions among Russian municipal entities, improve planning capabilities, enhance labor productivity, and optimize public service expenditure.Conclusions. Inter-municipal cooperation, as demonstrated by various international models, offers valuable insights for Russia. Adapting and implementing lessons from abroad can lead to improved governance and resource allocation, ultimately resulting in enhanced service delivery and cost-effectiveness for the benefit of the Russian population.Law Enforcement Review90Law Enforcement Review['V. Olkhovik', 'E. Juchnevicius']2024-01-09T00:00:00https://www.semanticscholar.org/paper/6c619603b8d2a2a5815e187be30589a3ee1649b3 97b4a29ba2040d051fd24d75cd104d37dd6b77e8In recent years, attempts to falsify Russian and world history, distort historical truth and destroy historical memory have become more frequent. As a result, amendments to the Constitution of the Russian Federation in 2020 introduced a new norm regulating processes aimed at preventing, disclosing and suppressing distortions of historical facts, events and personalities of our Motherland. The creation of a new legal mechanism for the protection of historical truth contributes to the formation of the historical memory of the nation, the strengthening of national identity.Vestnik of North-Eastern Federal University. History. Political Science. Law00Vestnik of North-Eastern Federal University. History. Political Science. Law['U. P. Egorova', 'K. E. Vasiliev']2024-01-09T00:00:00https://www.semanticscholar.org/paper/97b4a29ba2040d051fd24d75cd104d37dd6b77e8 ecd10721b81dec81b4977421c4ad775348155554The rapid advancement of artificial intelligence (AI) technologies, epitomized by tools like ChatGPT, Claude, Bard, Copilot, and Copy AI, has significantly reshaped various professional landscapes. This study aimed to assess the impact of these AI tools on professional performance, job dynamics, and societal perceptions. Amidst their benefits in enhancing efficiency and introducing novel capabilities, these tools also pose challenges concerning job displacement, ethical implications, and societal balance. Data from 1623 professionals across diverse industries were analyzed to assess AI tool utilization, functionality, user satisfaction, and perceived impacts. The results indicate that AI tools substantially enhance professional efficiency and are vital in diverse tasks including data analysis and decision-making. However, they also significantly affect traditional job roles, underscoring the urgency for workforce adaptation and skill development. Notably, the study unveils a generational gap in AI adoption, with younger users showing higher engagement compared to older cohorts, suggesting a digital divide. The study’s novelty lies in its comprehensive analysis of AI tool impacts across multiple professions, highlighting ethical and societal challenges. Concerns about AI-induced job displacement, privacy, and ethical use were evident, calling for responsible AI integration. The study advocate for targeted reskilling programs to equip the workforce for an AI-driven future and ethical guidelines to ensure AI tools' responsible development and use. This research contributes to the understanding of AI’s role in modern professional settings and offers strategic insights for policymakers, educators, and industry leaders. Emphasizing a balanced approach, the study urges for AI deployment that maximizes benefits while addressing potential risks and societal concerns.Asian Journal of Advanced Research and Reports03A generational gap in AI adoption is unveiled, with younger users showing higher engagement compared to older cohorts, suggesting a digital divide, and the study urges for AI deployment that maximizes benefits while addressing potential risks and societal concerns.Asian Journal of Advanced Research and Reports['Yewande Alice Marquis', 'Tunbosun Oyewale Oladoyinbo', 'Samuel Oladiipo Olabanji', 'O. O. Olaniyi', 'Samson Abidemi Ajayi']2024-01-09T00:00:00https://www.semanticscholar.org/paper/ecd10721b81dec81b4977421c4ad775348155554 e65b98ecdfc61f7429b9df03d9ac431289b29f54Higher educational institutions (HEIs) are facing a significant challenge in maintaining academic integrity due to the technological integration of generative artificial intelligence (AI). The widespread use of AI tools by college students has resulted in an increase in plagiarism and cheating, highlighting the need for effective implementation of this technology. However, there is a lack of research on the best practices for using AI in academic settings. HEIs must take responsibility for addressing these issues, as the majority of institutions do not have formal guidelines for AI use, leading to confusion among students and instructors. To combat academic misconduct, HEIs should establish clear objectives and policies for the equitable, inclusive, and ethical use of AI. Improving AI literacy among students and faculty is crucial, as it ensures that everyone has equal access to technology, preventing a digital divide. Moreover, proactive education on the ethical use of AI is vital for HEIs to prepare students for the AI‐driven future of education and maintain academic integrity.Journal of Contingencies and Crisis Management22To combat academic misconduct, HEIs should establish clear objectives and policies for the equitable, inclusive, and ethical use of AI, as there is a lack of research on the best practices for using AI in academic settings.Journal of Contingencies and Crisis Management['NaYoung Song']2024-01-09T00:00:00https://www.semanticscholar.org/paper/e65b98ecdfc61f7429b9df03d9ac431289b29f54 2f08cfe76ae4b72ecd7c4ba09d4f4af602f94252Peer review facilitates quality control and integrity of scientific research. Although publishing policies have adapted to include the use of Artificial Intelligence (AI) tools, such as Chat Generative Pre-trained Transformer (ChatGPT), in the preparation of manuscripts by authors, there is a lack of guidelines or policies on whether peer reviewers can use such tools. The present article highlights the lack of policies on the use of AI tools in the peer review process (PRP) and argues that we need to go beyond policies by creating transparent procedures that will enable journals to investigate allegations of non-compliance and take decisions that will protect the integrity of the peer review system. Reviewers found to violate relevant policies must be excluded from the process to safeguard the integrity of the peer review system.Research Ethics121The present article highlights the lack of policies on the use of AI tools in the peer review process (PRP) and argues that it needs to go beyond policies by creating transparent procedures that will enable journals to investigate allegations of non-compliance and take decisions that will protect the integrity of the peer review system.Research Ethics['V. Mollaki']2024-01-09T00:00:00https://www.semanticscholar.org/paper/2f08cfe76ae4b72ecd7c4ba09d4f4af602f94252 4980246cf1f11c559de7f599de3c43ef5860a93aThis article explores the intricate dynamics of the fashion industry, focusing on PT. SRB Depot Tj. Pura. The study investigates the direct and indirect effects of Influencer Marketing and AI Technology on the Fashion Industry and Enhancing Brand. Through a comprehensive path analysis, the research reveals that Influencer Marketing significantly influences both the Fashion Industry and Brand Enhancement, both directly and indirectly through the Fashion Industry. These findings underscore the influential role of influencer-driven strategies in shaping consumer perceptions and fostering positive brand associations. However, the analysis does not provide statistically significant evidence for the direct or indirect impact of AI Technology on the Fashion Industry or Brand Enhancement within the observed context. The results emphasize the nuanced nature of these relationships, highlighting the pivotal role of the Fashion Industry as a mediator in the influence of Influencer Marketing on Brand Enhancement. This research contributes valuable insights for businesses, suggesting that strategic investments in influencer marketing can play a significant role in shaping brand perceptions within the dynamic landscape of the fashion industry. Keywords: Marketing Strategies, AI Technology, Enhancing Brand Awareness, Fashion IndustryJournal of Economic Bussines and Accounting (COSTING)120This research contributes valuable insights for businesses, suggesting that strategic investments in influencer marketing can play a significant role in shaping brand perceptions within the dynamic landscape of the fashion industry.Journal of Economic, Bussines and Accounting (COSTING)['Nur Afifah']2024-01-09T00:00:00https://www.semanticscholar.org/paper/4980246cf1f11c559de7f599de3c43ef5860a93a 84cc2e7cf55fa206f516d0e87d7b0073231b3f0eDesk rejections of artificial intelligence (AI)–related submissions to the Project Management Journal® (PMJ) are high. This article provides an overview and state-of-the-art snapshot on academic and practitioner work to derive at potential future research topics and guidelines on the execution and reporting of AI-related studies in project management.Project Management Journal312An overview and state-of-the-art snapshot on academic and practitioner work to derive at potential future research topics and guidelines on the execution and reporting of AI-related studies in project management.Project Management Journal['Ralf Müller', 'Giorgio Locatelli', 'Vered Holzmann', 'Marly Nilsson', 'Temisan Sagay']2024-01-09T00:00:00https://www.semanticscholar.org/paper/84cc2e7cf55fa206f516d0e87d7b0073231b3f0e ef597b822958aff999d56e4614923fee01ca223aMultiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.Circulation02A framework that defines value from an organizational perspective is established, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation.Circulation['Kate Hanneman', 'D. Playford', 'Damini Dey', 'M. van Assen', 'Domenico Mastrodicasa', 'Tessa S Cook', 'J. W. Gichoya', 'Eric E Williamson', 'Geoffrey D Rubin']2024-01-09T00:00:00https://www.semanticscholar.org/paper/ef597b822958aff999d56e4614923fee01ca223a b9a36a50efc4d0a2402d2ecb405dcda1755900e5Cardiology and Therapy352AI dialogue platforms can enhance the readability of PEMs for patients with AS but may not fully meet recommended reading skill levels, highlighting potential tools to help strengthen cardiac health literacy in the future.Cardiology and Therapy['Armaun D. Rouhi', 'Y. Ghanem', 'Laman Yolchieva', 'Zena Saleh', 'Hansa Joshi', 'Matthew C. Moccia', 'A. Suarez-Pierre', 'Jason J Han']2024-01-09T00:00:00https://www.semanticscholar.org/paper/b9a36a50efc4d0a2402d2ecb405dcda1755900e5 e2c0c9aacdf42564668c66f3db14bf89b91773e7Artificial Intelligence (AI) can be a useful tool in the management of disease processes such as hepatocellular carcinoma (HCC) as treatment decisions are often complex and multifaceted. AI applications in medicine are expanding with the ongoing advances in AI including more sophisticated machine learning and deep learning processes. In preliminary studies, AI algorithms have demonstrated superiority in predicting the development of HCC compared with standard models. Radiomics, a quantitative method used to extract features from medical imaging, has been applied to numerous liver imaging modalities to aid in the diagnosis and prognostication of HCC. Deep learning methodologies can help us to identify patients at higher likelihood of disease progression and improve risk stratification. AI applications have expanded into the field of surgery as models not only help us to predict surgical outcomes but AI methodologies are also used intra-operatively, in real time, to help us to define anatomic structures and aid in the resection of complex lesions. In this review, we discuss promising applications of AI in the management of HCC. While further clinical validation is warranted to improve generalizability through the inclusion of larger and more diverse populations, AI is expected to play a central role in assisting clinicians with the management of complex disease processes such as HCC.Livers821Promising applications of AI in the management of HCC are discussed and AI is expected to play a central role in assisting clinicians with the management of complex disease processes such as HCC.Livers['Carolina Larrain', 'Alejandro Torres-Hernandez', 'D. B. Hewitt']2024-01-09T00:00:00https://www.semanticscholar.org/paper/e2c0c9aacdf42564668c66f3db14bf89b91773e7 942c73e67b58989d919cc7b57e62ae894f4d14d1 This paper reports on the findings of a systematic review in relation to the research management practices in Higher Learning Institutions through the use of Artificial intelligence (AI) technologies such as ChatGPT in Tanzania. AI technologies have gained significant popularity in recent times. However, their integration into academic settings raises concerns, especially in terms of potential ethical considerations. The systematic review at hand used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to retrieve English records in Google Scholar under the phrase "ChatGPT in research¨. Eligibility criteria included the published research papers on ChatGPT and research practices. A total of 28 documents were retrieved. Only 20 documents met the inclusion criteria after full screening. The findings indicate that setting a code of ethics for using AI is paramount. Further research is needed in order to gain detailed insights into this new innovation and technology. It was concluded that ChatGPT in research has to be validated with other methods.JOURNAL OF ISSUES AND PRACTICE IN EDUCATION290It was concluded that ChatGPT in research has to be validated with other methods and setting a code of ethics for using AI is paramount.JOURNAL OF ISSUES AND PRACTICE IN EDUCATION['Patrick Renatus Manyengo']2024-01-09T00:00:00https://www.semanticscholar.org/paper/942c73e67b58989d919cc7b57e62ae894f4d14d1 4b60873f2a04b2902ca755f3267f0d67b890ce13 Purpose This study aims to identify, review and synthesize existing literature on key theories, drivers and barriers affecting consumer adoption or resistance to artificial intelligence (AI) in the hospitality sector. Design/methodology/approach This study aims to conduct a complete literature review of the accrued knowledge generated so far on AI in the hospitality sector. To attain the overall objectives of this study, we used the systematic literature review (SLR) method. This method systematically handles the diversity of knowledge in a specific topic to answer precise research questions. It also generates new visions through a synthesis of the literature, to identify the knowledge gaps, set the new directions for the future researcher and provide sufficient guidance to inform the policy and practice. Findings The findings of this study are presented in three sections, as follows: descriptive analysis, content analysis and synthesized framework. The findings highlighted the state-of-the-art mapping of the existing research in terms of publication frequency over time and across publication outlets, key theories, methods and geographies. In addition, literature on consumer adoption (or resistance) of AI in hospitality is content analyzed to highlight key drivers and barriers. Moreover, this review critically evaluates extant literature and sets future agendas by postulating specific research questions for further knowledge development in this field of study. Research limitations/implications The SLR focused on consumer adoption or resistance to use AI in hospitality literature. The future researcher may include additional streams to get better results. Practical implications The study findings will help multiple stakeholders to understand the underlying causes of customer resistance or barriers to the intention to use/adopt AI services in the hotel sector. Furthermore, study results will allow them to better analyze the relationship between customer barriers, intents or consumer decision behaviors. Originality/value First, this study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality. This study categorizes the existing diversified literature in two main themes – drivers and barriers – to present a simplistic picture of the existing literature. Second, the review highlights the gaps and limitations in existing research and provides guidance for future scholars. Third, the key contribution of this review is the development of a unified framework on the consumer adoption or resistance of AI in the hospitality sector. That is, this study puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature. Finally, this study facilitates the practitioners to understand the key motivating and hindering factors affecting the adoption and resistance behavior. Journal of Hospitality and Tourism Technology540This study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality and puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature.Journal of Hospitality and Tourism Technology['Hafiz Muhammad Wasif Rasheed', 'Yuanqiong He', 'Hafiz Muhammad Usman Khizar', 'Junaid Khalid']2024-01-09T00:00:00https://www.semanticscholar.org/paper/4b60873f2a04b2902ca755f3267f0d67b890ce13 b0c2e6360ef11d13c85834e11391525153a0e322Innovation has fostered and enabled Industry 5.0, Society 5.0, and Marketing 5.0. It has also affected competition and marketing dynamics in all industries, including architectural engineering and construction industry (AEC). Intensified competition in AEC, alongside the accelerated innovations fostering and enabling changes in AEC’s supply-and-demand dynamics, highlights the importance of time and how cost-effective, strategic, tactical, and innovative marketing has further influenced AEC’s supply-and-demand aspects, which need to be sustainable to reduce its embodied environmental footprint mostly due to the climate crisis humanity is experiencing. Based on an in-depth literature review, this study aims to suggest the preliminary conceptual model’s step towards artificial intelligence (AI)-neuro-green marketing in AEC as a potential key for a sustainable built environment. This study emphasizes potential of the AI-neuro-green marketing to foster competition by design (including architectural and interior design), to enhance the effectiveness of neuro-green marketing in fostering sustainable built environment, and to reduce AEC’s embodied environmental footprint, outputs, and services. Furthermore, this study emphasizes the potential contribution of the AI-neuro-green marketing in AEC to Construction 5.0 and Society 5.0. This study is expected to contribute to the literature through the concept of AI-neuro-green marketing.Journal of Technology in Architecture Design and Planning340Journal of Technology in Architecture Design and Planning['A. Tuz', 'B. Sertyesilisik']2024-01-09T00:00:00https://www.semanticscholar.org/paper/b0c2e6360ef11d13c85834e11391525153a0e322 a5283d358d2dff37a038ab30292705e755366262This article studies the scope of Artificial Intelligence (AI) through the HPTD-M theory, i.e., the Holopraxis Transdisicplinary Management. It aims at collaborating to the debate on the limits of AI, including ChatGPT simulations, comparing the four types of intelligence in the HPTD-M Theory, namely empirical, emotional, rational, and intuitive, with the nine types of Gardner´s multiple intelligences theory (MI Theory). The types of intelligence are also compared with psychosomatics, the emotional shadow of the Western culture, the levels of the collective unconscious, and soft skills. The concept of mediating manager has an essential role in showing the limits of AI, which is an exceptional instrument for KNOWING but not UNDERSTANDING. An example of adequate use of AI through ChatGPT is demonstrated through a discussion on Plato's four virtues. A table considering the HPTD-M quaternary structure of intelligence shows how there cannot be consciousness awakening in AI, since it is limited to rationality. Besides, the HPTD-M’s three types of logic, i.e., Binary, Feedback, and Included Third, are another way to demonstrate that AI is based merely on the Binary logic. Management tools need to be used a priori with awareness of the limits of their applications. AI is no different, a disruptive technology that every professional will have to learn to deal with, like the personal computer in the late 1980s, an excellent rational tool, but not an empirical, emotional, or intuitive resource for problem solving. The AI binary logic does not apply to the complexity of human phenomena. Furthermore, AI can function as a consultant or assistant in terms of an efficient source of information, but not as an effective manager or decision maker. Roughly, through the managerial theory, effectiveness is to do the right thing, which is more than efficiency (to do things right). Then, in this author´s opinion constructed through simulations in the ChatGPT to obtain efficient results, the questions to AI need to be objective and precise in the concepts. There cannot be complex issues involving human phenomena: This is for the effective human decision maker, not for AI to answer since there can’t be consciousness awakening in AI. Transdisciplinary Journal of Engineering &amp; Science00The scope of Artificial Intelligence through the HPTD-M theory is studied, including ChatGPT simulations, comparing the four types of intelligence in the HPTD-M Theory, namely empirical, emotional, rational, and intuitive, with the nine types of Gardner´s multiple intelligences theory (MI Theory).Transdisciplinary Journal of Engineering &amp; Science['Leonardo Da Silva Guimarães Martins da Costa']2024-01-09T00:00:00https://www.semanticscholar.org/paper/a5283d358d2dff37a038ab30292705e755366262 e9fd4c052a67d78c2fc21629b4bd8a7a21022095The emergence of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AIs impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n=610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 . Most of the participants (78.7%, n=480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n=429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n=405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training.medRxiv280The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area, and future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training.PLOS Digital Health['Muhammad Mustafa Habib', 'Zahra Hoodbhoy', 'M. A. R. Siddiqui']2024-01-09T00:00:00https://www.semanticscholar.org/paper/e9fd4c052a67d78c2fc21629b4bd8a7a21022095 fc547354a3dd9a14967f302296ea39fc35b5bd79 Will advanced artificial intelligence (AI) language models exhibit trust toward humans? Gauging an AI model’s trust in humans is challenging because—absent costs for dishonesty—models might respond falsely about trusting humans. Accordingly, we devise a method for incentivizing machine decisions without altering an AI model’s underlying algorithms or goal orientation and we employ the method in trust games between an AI model from OpenAI and a human experimenter (namely, author TJ). We find that the AI model exhibits behavior consistent with trust in humans at higher rates when facing actual incentives than when making hypothetical decisions—a finding that is robust to prompt phrasing and the method of game play. Furthermore, trust decisions appear unrelated to the magnitude of stakes and additional experiments indicate that they do not reflect a non-social preference for uncertainty.Journal of Physics: Complexity00It is found that the AI model exhibits behavior consistent with trust in humans at higher rates when facing actual incentives than when making hypothetical decisions—a finding that is robust to prompt phrasing and the method of game play.Journal of Physics: Complexity['Tim Johnson', 'Nick Obradovich']2024-01-09T00:00:00https://www.semanticscholar.org/paper/fc547354a3dd9a14967f302296ea39fc35b5bd79 4812cbc145b05dc7fdc76a5349b75ac03f6a9dfdThe purpose of the study is to offer valuable insights into how artificial intelligence is revolutionizing investment practices, and the impact of this transformation on investors, as well as the wider financial market scenario in the United States. The study investigated how the use of advanced AI technologies in business settings affects the valuation and fairness of investments in the United States. The goal of this research is to provide insights into how AI can influence financial decision-making and improve investment outcomes. The study findings suggest that AI possesses the potential to influence investor behavior, as AI-powered analytics and robot-advisors continue to gain prominence in guiding investment decisions. The increasing integration of AI in business practices raises ethical and regulatory concerns that impact public perception and the regulatory landscape, thereby affecting investment values. AI-based tools can process vast amounts of data accurately and quickly, enabling identification of investment opportunities, risks, and trends more efficiently than traditional methods. This, in turn, could foster better investment decisions and potentially higher returns.Journal of Applied Business and Economics520The study findings suggest that AI possesses the potential to influence investor behavior, as AI-powered analytics and robot-advisors continue to gain prominence in guiding investment decisions.Journal of Applied Business and Economics['Sean Edgeington', 'Karina Kasztelnik']2024-01-09T00:00:00https://www.semanticscholar.org/paper/4812cbc145b05dc7fdc76a5349b75ac03f6a9dfd ebbd1b12bbfd6919c579834bd0f5bfe968557c3dHumanistic Buddhism is one of the mainstreams of modern Buddhism, with special emphasis on the humanistic dimension. With the development of artificial intelligence (AI) technology, Humanistic Buddhism is also at an important stage of modernization and transformation, thus facing a continuous negotiation between religious values and technological innovations. This paper first argues that AI is technically beneficial to the propagation of Buddhism by citing several cases in which AI technology has been used in Buddhism. Then, by comparing Master Hsing Yun’s Buddhist ethics to “Posthuman” ethics, it points out that the theories of Humanistic Buddhism share similarities with AI and Posthuman ethics. Among them, Master Hsing Yun’s theory of “the nature of insentient beings” provides an important theoretical reference for the question of “whether AI can become a Buddha”. From the technical and ethical dimensions, it points out that the interaction between Humanistic Buddhism and AI can promote original uses or implementations of AI technology. However, it should also be noted that compared to the cases of “Artificial Narrow Intelligence”discussed in the paper, the “Strong AI” could lead to much more ethical crises. It is also likely to cause the cult of science and technology, and thus subvert the humanistic tradition of Buddhism with a new instrumental rationality. In addition, there are some potential pitfalls that Humanistic Buddhism may encounter when using AI. Hence, while it is necessary to encourage the use of technologies such as AI in contemporary Buddhism, it is also important for Buddhism to keep a critical distance from digital technologies.Religions140It is argued that AI is technically beneficial to the propagation of Buddhism by citing several cases in which AI technology has been used in Buddhism and by comparing Master Hsing Yun’s Buddhist ethics to “Posthuman” ethics, it points out that the theories of Humanistic Buddhism share similarities with AI and Posthuman ethics.Religions['Yutong Zheng']2024-01-09T00:00:00https://www.semanticscholar.org/paper/ebbd1b12bbfd6919c579834bd0f5bfe968557c3d ac16f4c0c1c5fb4c625009fbe3c0fb75c493b023This special article provides a comprehensive commentary on the significant role of artificial intelligence (AI) in the field of dermatology. It explores the potential of AI in various aspects of dermatologic practice, including diagnosis, treatment planning, research and patient management. The article discusses the current state of AI in dermatology, its challenges and the ethical considerations surrounding its implementation. It highlights the transformative impact of AI on dermatologic care and offers insights into the future directions of AI in the field.Indian Journal of Dermatology120The article discusses the current state of AI in dermatology, its challenges and the ethical considerations surrounding its implementation, and offers insights into the future directions of AI in the field.Indian Journal of Dermatology['Shreyas P. Kololgi', 'C. Lahari']2024-01-09T00:00:00https://www.semanticscholar.org/paper/ac16f4c0c1c5fb4c625009fbe3c0fb75c493b023 7fc4037c1e797e545f5c59d2d58f4a9e164114caThis Master's thesis titled "The Positive Impacts of Artificial Intelligence in Highway Transport," explores the transformative effects of artificial intelligence in highway transport from the perspective of users and stakeholders. Focusing on key areas such as traffic control/management, smart traffic lights, predictive maintenance, and autonomous vehicles [1,2], the study employs an in-depth survey analysis using questionnaires. Results indicate notable improvements, with a 24.9% increase in traffic safety, 23.6% reduction in congestion, 15% boost in mobility, 14.7% enhancement in sustainability, and a 21.7% increase in efficiency. These findings offer insights into user/stakeholder perceptions of current artificial intelligence applications in highway transport and their potential benefits. The thesis also evaluates limitations to artificial intelligence implementation in highway transport, providing valuable perspectives for policymakers, industry stakeholders, and researchers.Journal of Engineering Research and Reports00This Master's thesis explores the transformative effects of artificial intelligence in highway transport from the perspective of users and stakeholders, and evaluates limitations to artificial intelligence implementation in highway transport.Journal of Engineering Research and Reports['Glory Chinwe Ugo', 'A. C. Apata', 'Praise Onimisi Dawodu']2024-01-09T00:00:00https://www.semanticscholar.org/paper/7fc4037c1e797e545f5c59d2d58f4a9e164114ca 92eb849569d13c5a8d314cf63ec10b63c95ef5f0Artificial intelligence is likely to revolutionize multiple aspects of organizational creativity. Through a multilevel theoretical lens, the present paper reviews the extant body of knowledge on creativity at individual, team and organizational levels, and draws a series of propositions on how the implementation of artificial intelligence may affect each level. Spanning cognitive, behavioural and psychological domains, our propositions aim at directing future research efforts on important creativity‐related areas likely to be affected by artificial intelligence, including the trade‐off between convergent and divergent thinking, the distribution of skills within groups, and the absorptive capacity of organizations.Creativity and Innovation Management980Positions aim at directing future research efforts on important creativity‐related areas likely to be affected by artificial intelligence, including the trade‐off between convergent and divergent thinking, the distribution of skills within groups, and the absorptive capacity of organizations.Creativity and Innovation Management['Luca Grilli', 'Mattia Pedota']2024-01-09T00:00:00https://www.semanticscholar.org/paper/92eb849569d13c5a8d314cf63ec10b63c95ef5f0 2ff65bd00fbff6d10d644bdcdbd16f3621164662Many of the problems raised by artificial intelligence (AI) researchers between 2000 and 2020 have, in one way or another, already been addressed in science fiction before. Analysis of science fiction texts allows us to identify the following models of relationships between humans and AI. The first model can be called exclusively friendly AI (R. Heinlein, “The Moon is a Harsh Mistress”). Such AI is a friend, an assistant to a person in all his affairs – including those directed against other people. The second model can be characterized as “friendly AI with built-in ethical restrictions” (A. Azimov, “I, Robot”). The third model is a neutral AI, weakly interested in human affairs, located “above good and evil” (S. Lem, “Golem XIV”). The fourth model is AI, which has its own goals that diverge from the goals of humanity and is therefore potentially hostile (A. Clark, “2001: A Space Odyssey”). The fifth model is an AI openly hostile to humanity (“Terminator”).THE KAZAN SOCIALLY-HUMANITARIAN BULLETIN10Analysis of science fiction texts allows us to identify the following models of relationships between humans and AI.The Kazan Socially-Humanitarian Bulletin['S. A. Sergeev', 'Z. K. Sergeeva']2024-01-09T00:00:00https://www.semanticscholar.org/paper/2ff65bd00fbff6d10d644bdcdbd16f3621164662 7823b29446064c306963e979de26159709478410Discover Artificial Intelligence250The most common sectors in the review are tourism, banking and e-commerce, while other segments and technologies appear less and may be underrepresented, thus a scope for future research agenda.Discov. Artif. Intell.['Melise Peruchini', 'Gustavo Modena da Silva', 'Julio Monteiro Teixeira']2024-01-09T00:00:00https://www.semanticscholar.org/paper/7823b29446064c306963e979de26159709478410 dd7acd318743386dc3f78afd6ffea1ec5d77d6ceCode sharing and artificial intelligence have potential to empower researchers from low and middle income countries to develop their use of public health modellingBritish medical journal150BMJ['Y. Hooda', 'Senjuti Saha']2024-01-09T00:00:00https://www.semanticscholar.org/paper/dd7acd318743386dc3f78afd6ffea1ec5d77d6ce 770de1a0b417d1853596842c6398d394f67333eaArtificial Intelligence (AI) has the potential to significantly impact the field of healthcare, including the role of nurses in hospitals or other settings. A quiet revolution in nursing care is underway as AI quietly takes its place beside dedicated healthcare professionals in the bustling corridors of modern care. In this era of technological advancement, AI is finding a meaningful role in transforming the landscape of patient care, the role of nurses, and the heartbeat of any hospital. AI algorithms, silently at work in the background, analyze patient records, historical data, and vitals, presenting a comprehensive snapshot of each patient's health status. This digital development empowers nurses with predictive insights, highlighting potential risks and enabling a proactive approach to patient-centered care. In the realm of diagnostics, AI showcases its prowess. Nurses, supported by intelligent algorithms, receive nuanced analyses of diagnostic tests, allowing for more precise interpretations of clinical findings. Using AI will lift the burden of manual data interpretation, giving nurses more time to focus on critical thinking and compassionate care. Keywords: Artificial Intelligence; nursing care; nursing improvement; quality of life; clinical nursing practiceJournal of holistic nursing science10Using AI will lift the burden of manual data interpretation, giving nurses more time to focus on critical thinking and compassionate care, and enabling a proactive approach to patient-centered care.Journal of Holistic Nursing Science['Estrin Handayani', 'Jimmy Choo']2024-01-09T00:00:00https://www.semanticscholar.org/paper/770de1a0b417d1853596842c6398d394f67333ea 17df2453a7f8493c405a0b1ca72d7e9fe23951baGiven the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors in accessible terms. Unlike most reviews, which focus on algorithmic and model-centric perspectives, this work takes a"data-centric"view, examining how data collection, processing, and analysis contribute to explainable AI (XAI). We categorize existing work into three categories subject to their purposes: interpretations of deep models, referring to feature attributions and reasoning processes that correlate data points with model outputs; influences of training data, examining the impact of training data nuances, such as data valuation and sample anomalies, on decision-making processes; and insights of domain knowledge, discovering latent patterns and fostering new knowledge from data and models to advance social values and scientific discovery. Specifically, we distill XAI methodologies into data mining operations on training and testing data across modalities, such as images, text, and tabular data, as well as on training logs, checkpoints, models and other DNN behavior descriptors. In this way, our study offers a comprehensive, data-centric examination of XAI from a lens of data mining methods and applications.arXiv.org2781This work distill XAI methodologies into data mining operations on training and testing data across modalities, such as images, text, and tabular data, as well as on training logs, checkpoints, models and other DNN behavior descriptors.ArXiv['Haoyi Xiong', 'Xuhong Li', 'Xiaofei Zhang', 'Jiamin Chen', 'Xinhao Sun', 'Yuchen Li', 'Zeyi Sun', 'Mengnan Du']2024-01-09T00:00:00https://www.semanticscholar.org/paper/17df2453a7f8493c405a0b1ca72d7e9fe23951ba e395e6dd3abb0570e6cf492defdb3e33ef95174100['Ansh Bhatnagar', 'Devyani Gajjar']2024-01-09T00:00:00https://www.semanticscholar.org/paper/e395e6dd3abb0570e6cf492defdb3e33ef951741 47707508ee400f4b3c61211687d225780dee4002<jats:p />Security and Communication Networks10Security and Communication Networks['Security and Communication Networks']2024-01-09T00:00:00https://www.semanticscholar.org/paper/47707508ee400f4b3c61211687d225780dee4002 d61016f67f55f487f0225c44fb37af34411249f0AI-enabled smart objects have rapidly become everyday commodities and do not only change the ways in which we consume but also the ethics that guide our consumption. Emerging sociomaterial perspectives viewing consumers and smart objects as assemblages have been employed to study relational aspects of consumption, as well as consumer experience in the digital reality. This article argues that consumer ethics could and should be viewed as emergent properties of such consumption assemblages. Drawing on exemplars ranging from wearables to autonomous robots, this article illustrates the potential of this perspective and outlines fruitful directions for future research on questions of consumer agency and self-determination in the face of AI and the fluidity of consumer ethics in a world of updatable smart objects.Consumption Markets &amp; Culture390It is argued that consumer ethics could and should be viewed as emergent properties of such consumption assemblages, and the potential of this perspective is illustrated and fruitful directions for future research on questions of consumer agency and self-determination in the face of AI are outlined.Consumption Markets &amp; Culture['Anna Schneider-Kamp']2024-01-09T00:00:00https://www.semanticscholar.org/paper/d61016f67f55f487f0225c44fb37af34411249f0 150dfb7db5c3af88c6d89046b749ebeb797d33ea<jats:p />Security and Communication Networks10Security and Communication Networks['Security and Communication Networks']2024-01-09T00:00:00https://www.semanticscholar.org/paper/150dfb7db5c3af88c6d89046b749ebeb797d33ea 1b02d09c22e1be64e42a751b4de4532f48b8a770<jats:p />Security and Communication Networks10Security and Communication Networks['Security and Communication Networks']2024-01-09T00:00:00https://www.semanticscholar.org/paper/1b02d09c22e1be64e42a751b4de4532f48b8a770 7f0e0a24006c06822347b95ce539045651071272Frontiers in Medicine50Frontiers in Medicine['Gagandeep Dhillon', 'Zhongheng Zhang', 'Harpreet S. Grewal', 'Rahul Kashyap']2024-01-09T00:00:00https://www.semanticscholar.org/paper/7f0e0a24006c06822347b95ce539045651071272 970a00da406725a43d10649e9df28c1de56e4e9bWith the rise of artificial intelligence in safety-critical systems like surface transportation, there is a commensurate need for new hazard analysis approaches to determine if and how AI contributes to accidents, which are also increasing in number and severity. The original Swiss Cheese model widely used for hazard analyses focuses uniquely on human activities that lead to accidents, but cannot address accidents where AI is a possible causal factor. To this end, the Taxonomy for AI Hazard Analysis (TAIHA) is proposed that introduces layers focusing on the oversight, design, maintenance, and testing of AI. TAIHA is illustrated with real-world accidents. TAIHA does not replace the traditional Swiss Cheese model, which should be used in concert when a joint human-AI system exists, such as when people are driving a car with AI-based advanced driving assist features.Journal of Cognitive Engineering and Decision Making30The Taxonomy for AI Hazard Analysis (TAIHA) is proposed that introduces layers focusing on the oversight, design, maintenance, and testing of AI.Journal of Cognitive Engineering and Decision Making['M.L Cummings']2024-01-09T00:00:00https://www.semanticscholar.org/paper/970a00da406725a43d10649e9df28c1de56e4e9b a553b5c08b6d94d9c15dbd4cc07c03ad5a16e41fThe fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al‐Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms‐related products.Expert systems120This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis.Expert Systems['Carlos Fernandez-Basso', 'Karel Gutiérrez‐Batista', 'Juan Gómez-Romero', 'M. Dolores Ruiz', 'M. Martín-Bautista']2024-01-09T00:00:00https://www.semanticscholar.org/paper/a553b5c08b6d94d9c15dbd4cc07c03ad5a16e41f 91680c973c150ab7298c280e53add84fdc8129c1The constantly increasing capabilities of artificial intelligence (AI) open new possibilities for human-AI collaboration. One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances to the AI. However, enabling humans to delegate instances effectively requires them to assess both their own and the AI's capabilities in the context of the given task. In this work, we explore the effects of providing contextual information on human decisions to delegate instances to an AI. We find that providing participants with contextual information significantly improves the human-AI team performance. Additionally, we show that the delegation behavior changes significantly when participants receive varying types of contextual information. Overall, this research advances the understanding of human-AI interaction in human delegation and provides actionable insights for designing more effective collaborative systems.arXiv.org720It is found that providing participants with contextual information significantly improves the human-AI team performance and the delegation behavior changes significantly when participants receive varying types of contextual information.ArXiv['Philipp Spitzer', 'Joshua Holstein', 'Patrick Hemmer', 'Michael Vossing', 'Niklas Kuhl', 'Dominik Martin', 'G. Satzger']2024-01-09T00:00:00https://www.semanticscholar.org/paper/91680c973c150ab7298c280e53add84fdc8129c1 68e1030f99e424c2cc9be928790cd77346079131Environmental science and pollution research international560Environmental science and pollution research international['Xinmeng Tang', 'Tao Qin', 'Moustafa Mohamed Nazief Haggag Kotb Kholaif', 'Xinyan Zhao']2024-01-08T00:00:00https://www.semanticscholar.org/paper/68e1030f99e424c2cc9be928790cd77346079131 aeaf401768fe2426097604eed899fbbd01f62c85The implementation of projects under the President Regulation of the Republic of Indonesia Number 120 Year 2022 concerning Special Assignments for Expediting Infrastructure Development (President Regulation 120/2022) has the potential to promote the human rights of the Indonesian people. Nonetheless, it is critical to ensure the facilitation of human rights during project development as well as subsequent phases. Qeios00Qeios['Handa Abidin']2024-01-08T00:00:00https://www.semanticscholar.org/paper/aeaf401768fe2426097604eed899fbbd01f62c85 81c603757d7f9bd2c08edc9753e7fe974f7431cfState intervention regulation is a variable system, the effectiveness of which depends on the priorities of the state’s active intervention in the functioning of the market, in conjugation of the interests of market counterparties. They, in turn, follow from the current situation and strategic development objectives. When preparing the article, we proceeded from the fact that it is practically impossible to develop a universal scheme of intervention impact. This conclusion is confirmed, among other things, by studying the scientific literature – the recommendations of the authors have many distinctive features both in terms of the range of issues under consideration (commodity and price interventions, correction of export-import operations, state support, stimulation of scientific and technological progress, etc.), and specific conclusions attached to them. It is necessary to take into account many factors, the role of each of which individually is different in its significance, direction, strength and duration of impact, and in the aggregate forms unique combinations, the nature of the influence of which is very diverse.In this regard, the paper gives recommendations in relation to the current situation, which takes into account integration processes, resource opportunities, and the potential for growth in demand for food on the world market. We took into account the developments of both Belarusian and foreign scientists, taking into account changes in the demographic situation, the unfavorable political background of trade with unfriendly countries, their results are critically rethought, on the basis of this, a complete author’s system of recommendations on key areas of state economic interventions in the agroindustrial complex is presented.AGRARIAN ECONOMY10Agrarian Economics['N. P. Panasyuga']2024-01-08T00:00:00https://www.semanticscholar.org/paper/81c603757d7f9bd2c08edc9753e7fe974f7431cf 909188409aa0d02631f10a1bd6077a7efcbc24c4This study focuses on the concept of self-regulation and its position and significance in educational sciences. Self-regulation is a concept that involves individuals' ability to manage their behaviors, learning, and performance. The study emphasizes the role of self-regulation in educational sciences, aiming to understand the function of this skill in educational processes. Particularly, the research highlights the impact of self-regulation on learning processes and its applications in education. By examining the contributions of self-regulation skills to student achievement, motivation, and learning processes, the research aims to contribute to a better understanding of this crucial concept in education.The study provides recommendations for educators, researchers, and education policymakers to enhance the understanding of the concept of self-regulation and to effectively integrate it into educational processes. In this context, increasing awareness of the role of self-regulation in education and emphasizing usable strategies in practice can contribute to the improvement of educational systems.  Article visualizations:European Journal of Education Studies400European Journal of Education Studies['Zeynep Gökteke', 'G. Ocak']2024-01-08T00:00:00https://www.semanticscholar.org/paper/909188409aa0d02631f10a1bd6077a7efcbc24c4 da321b6b092b2e801f9bd1ba52a580454acfd613Journal of Asian Public Policy500Journal of Asian Public Policy['Yingwei Wang', 'Hong Pan']2024-01-08T00:00:00https://www.semanticscholar.org/paper/da321b6b092b2e801f9bd1ba52a580454acfd613 f025dcf31f44c9245a4a390b421dfcaff561a16eWith the development and maturity of technology, the financial industry has become increasingly connected to technology. In recent years, fintech companies have flourished in the financial industry and are gradually becoming an important mainstay of global business empowerment. Fintech has opened up new areas of financial business and driven the development of finance and society. It has revolutionized the financial industry with the high efficiency brought by technology, but at the same time, it has also produced adverse events such as Alipay's annual bill exposing personal privacy and P2P mines, which have brought new risks for financial institutions and investors in general, and therefore it is urgent to take strict measures to regulate it. This paper starts from the connotation of fintech, briefly describes the development history of fintech in China, and puts forward corresponding countermeasures and suggestions on how to better regulate fintech enterprises based on the high-frequency risks faced by fintech enterprises and the internal operational deficiencies of the regulatory authorities.Advances in Economics, Management and Political Sciences00The connotation of fintech is described, the development history of fintech in China is described, and corresponding countermeasures and suggestions on how to better regulate fintech enterprises based on the high-frequency risks faced by fintech enterprises and the internal operational deficiencies of the regulatory authorities are put forward.Advances in Economics, Management and Political Sciences['Han Chen']2024-01-08T00:00:00https://www.semanticscholar.org/paper/f025dcf31f44c9245a4a390b421dfcaff561a16e a25a6a7dabe6621e5e74cccdc3963aea947d2d20This paper introduces MarketSenseAI, an innovative framework leveraging GPT-4's advanced reasoning for selecting stocks in financial markets. By integrating Chain of Thought and In-Context Learning, MarketSenseAI analyzes diverse data sources, including market trends, news, fundamentals, and macroeconomic factors, to emulate expert investment decision-making. The development, implementation, and validation of the framework are elaborately discussed, underscoring its capability to generate actionable and interpretable investment signals. A notable feature of this work is employing GPT-4 both as a predictive mechanism and signal evaluator, revealing the significant impact of the AI-generated explanations on signal accuracy, reliability and acceptance. Through empirical testing on the competitive S&P 100 stocks over a 15-month period, MarketSenseAI demonstrated exceptional performance, delivering excess alpha of 10% to 30% and achieving a cumulative return of up to 72% over the period, while maintaining a risk profile comparable to the broader market. Our findings highlight the transformative potential of Large Language Models in financial decision-making, marking a significant leap in integrating generative AI into financial analytics and investment strategies.Social Science Research Network433This paper introduces MarketSenseAI, an innovative framework leveraging GPT-4's advanced reasoning for selecting stocks in financial markets, employing GPT-4 both as a predictive mechanism and signal evaluator, revealing the significant impact of the AI-generated explanations on signal accuracy, reliability and acceptance.ArXiv['G. Fatouros', 'Konstantinos Metaxas', 'John Soldatos', 'D. Kyriazis']2024-01-08T00:00:00https://www.semanticscholar.org/paper/a25a6a7dabe6621e5e74cccdc3963aea947d2d20 381be954a69b0dd3b662a34281836663ade49dd5The adoption of advanced artificial intelligence (AI) systems in healthcare is transforming the healthcare-delivery landscape. Artificial intelligence may enhance patient safety and improve healthcare outcomes, but it presents notable ethical and legal dilemmas. Moreover, as AI streamlines the analysis of the multitude of factors relevant to malpractice claims, including informed consent, adherence to standards of care, and causation, the evaluation of professional liability might also benefit from its use. Beginning with an analysis of the basic steps in assessing professional liability, this article examines the potential new medical-legal issues that an expert witness may encounter when analyzing malpractice cases and the potential integration of AI in this context. These changes related to the use of integrated AI, will necessitate efforts on the part of judges, experts, and clinicians, and may require new legislative regulations. A new expert witness will be likely necessary in the evaluation of professional liability cases. On the one hand, artificial intelligence will support the expert witness; however, on the other hand, it will introduce specific elements into the activities of healthcare workers. These elements will necessitate an expert witness with a specialized cultural background. Examining the steps of professional liability assessment indicates that the likely path for AI in legal medicine involves its role as a collaborative and integrated tool. The combination of AI with human judgment in these assessments can enhance comprehensiveness and fairness. However, it is imperative to adopt a cautious and balanced approach to prevent complete automation in this field.Frontiers in Medicine813Examining the steps of professional liability assessment indicates that the likely path for AI in legal medicine involves its role as a collaborative and integrated tool, which can enhance comprehensiveness and fairness.Frontiers in Medicine['Claudio Terranova', 'Clara Cestonaro', 'Ludovico Fava', 'Alessandro Cinquetti']2024-01-08T00:00:00https://www.semanticscholar.org/paper/381be954a69b0dd3b662a34281836663ade49dd5 95919ba914dcb18ec974c879fdd96f5ab8546ef1Artificial Intelligence constitutes one of the most fundamental pillars for the implantation of the EU Digital Agenda. Its impact both in private and public life is omnipresent. AI has become an inherent part of the political life since politicians use it for several reasons, such as to promote their strategy as well as to achieve better and closer communication with people. All this is based on the existing set of legal rules. However, there are significant issues, both ethical and legal, which pose a wide range of concerns: from the protection of fundamental rights and freedoms to the safeguard of the principle of rule of law. The core question is the following: does AI strengthen democracy or lead to its deterioration? This paper aims at demonstrating the implementation of AI in politics. Firstly, there will be pursued, via a juridical methodology, a description of the regulatory framework governing AI, in connection with justice and democracy. Following a critical approach, there will be an analysis of principal ethical and legal concerns regarding the necessity and/or efficiency of use of AI in political life. Finally, the ultimate goal of the paper is to stimulate critical thinking and suggest fruitful proposals for the safeguard of the democracy and the establishment of a trustful and powerful digital environment.Juridical Tribune411The ultimate goal of the paper is to stimulate critical thinking and suggest fruitful proposals for the safeguard of the democracy and the establishment of a trustful and powerful digital environment.Juridical Tribune['Konstantinos Kouroupis']2024-01-08T00:00:00https://www.semanticscholar.org/paper/95919ba914dcb18ec974c879fdd96f5ab8546ef1 b34004bacae8606641bc5837ea2c51a63c9c6341SUMMARY The impact of clinical prediction models within Artificial Intelligence (AI) and machine learning (ML) is significant. With its ability to analyze vast amounts of data and identify complex patterns, machine learning has the potential to improve and implement evidence-based plastic, reconstructive, and hand surgery. Among others, it is capable of predicting the diagnosis, prognosis, and outcomes of individual patients. This modeling aids daily clinical decision making, most commonly at the moment, as decision-support.Therefore, the purpose of this paper is to provide a practice guideline to plastic surgeons implementing AI in clinical decision-making or setting up AI research to develop clinical prediction models using the 7-step approach and the ABCD validation steps of Steyerberg et al. Secondly, we describe two important protocols which are in the development stage for AI research: 1) the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklist, and 2) The PROBAST checklist to access potential biases.Plastic and Reconstructive Surgery01The purpose of this paper is to provide a practice guideline to plastic surgeons implementing AI in clinical decision-making or setting up AI research to develop clinical prediction models using the 7-step approach and the ABCD validation steps of Steyerberg et al.Plastic and reconstructive surgery['Kevin Kooi', 'Estefania Talavera Martinez', 'Liliane A Freundt', 'K. Oflazoglu', 'M. J. P. F. Ritt', 'K. Eberlin', 'R. Selles', 'M. Clemens', 'H. Rakhorst']2024-01-08T00:00:00https://www.semanticscholar.org/paper/b34004bacae8606641bc5837ea2c51a63c9c6341 2f90afb4611dccf0dec3429934f6daac321104eaThe effective use of artificial intelligence (AI) to enhance cyber security has been demonstrated in various areas, including cyber threat assessments, cyber security awareness, and compliance. AI also provides mechanisms to write cybersecurity training, plans, policies, and procedures. However, when it comes to cyber security risk assessment and cyber insurance, it is very complicated to manage and measure. Cybersecurity professionals need to have a thorough understanding of cybersecurity risk factors and assessment techniques. For this reason, artificial intelligence (AI) can be an effective tool for producing a more thorough and comprehensive analysis. This study focuses on the effectiveness of AI-driven mechanisms in enhancing the complete cyber security insurance life cycle by examining and implementing a demonstration of how AI can aid in cybersecurity resilience.Computing and Communication Workshop and Conference131This study focuses on the effectiveness of AI-driven mechanisms in enhancing the complete cyber security insurance life cycle by examining and implementing a demonstration of how AI can aid in cybersecurity resilience.2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)['Shadi Jawhar', 'Craig E. Kimble', 'Jeremy Miller', 'Zeina Bitar']2024-01-08T00:00:00https://www.semanticscholar.org/paper/2f90afb4611dccf0dec3429934f6daac321104ea ec00afd2ca5a8a29c4632fbec59e4573d5b131d5While tech workers are essential stakeholders in ethical artificial intelligence (AI) development and deployment, they are rarely consulted about their understanding of the development of ethical AI. In light of this, we present the findings of our 2020 to 2021 empirical research study in which we collected data from tech workers in a major AI company to better understand what they consider to be the most pressing ethical issues when developing AI-powered products. While there is a nascent body of literature that examines how AI ethics principles are operationalised on the ground, this study differs in that we explicitly draw on feminist insights to inform our analysis, and have put a particular focus on allowing the voices and narratives of tech workers to lead the work forward. Our study generated three main findings: first, the term ‘bias’ creates real confusion among tech workers, meaning that the term is unable to do the ethical work it is intended to do; second, tech workers do not necessarily see a relationship between diversity, equality and inclusion (DEI) agendas and AI development, undermining AI ethics initiatives; and third, tech workers were particularly concerned about the monitoring and maintenance of unwieldy ‘legacy systems’ that generated serious challenges to creating and deploying new and more ethical AI products. This study thus creates a ‘thicker’ and more nuanced picture of tech workers’ perspectives on the ethical issues that arise when developing and maintaining AI systems, while simultaneously demonstrating the utility of feminist approaches in the field of AI ethics.Big Data & Society201This study creates a ‘thicker’ and more nuanced picture of tech workers’ perspectives on the ethical issues that arise when developing and maintaining AI systems, while simultaneously demonstrating the utility of feminist approaches in the field of AI ethics.Big Data Soc.['Jude Browne', 'Eleanor Drage', 'Kerry McInerney']2024-01-08T00:00:00https://www.semanticscholar.org/paper/ec00afd2ca5a8a29c4632fbec59e4573d5b131d5 0914ea575017954b014f9648abc29a6b7f2f8349Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand–supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine learning approaches, deep learning models are complex and are often treated as a “black box” that can cause uncertainty regarding how they operate. Explainable artificial intelligence (XAI) refers to methods that explain and interpret machine learning models’ inner workings and how they come to decisions, which is especially important in the medical domain to guide healthcare decision-making processes. This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.BioMedInformatics351This review summarizes recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.BioMedInformatics['Tin Lai']2024-01-08T00:00:00https://www.semanticscholar.org/paper/0914ea575017954b014f9648abc29a6b7f2f8349 02b8b8dd81ed6a425b2499fa6c92c076f937f708The agricultural sector provides a job whereby several people are now employed to supervise and/or pluck the farm produce with great prospect of enhancing efficiency. This paper explores a crucial aspect of smart farming that involves the application of artificial intelligence (AI) techniques, IoT technology and robots [1]. Smart Farming has been created with these technologies to obtain accuracy, optimal use of resources, and protection of the environment. It has been proved through several case studies to be effective in different agricultural settings. This paper emphasizes the capacity of Smart Farming to transform farming towards a greener and eco-friendly outlook for agriculture. The implementation of AI algorithms will help farmers in making informed decisions for maximizing production output, improving resource allocation among other issues. IoT devices like sensors and drones help in real-time monitoring of the soils and plants’ health while considering the weather conditions.International Journal of Wireless Communications and Network Technologies00The implementation of AI algorithms will help farmers in making informed decisions for maximizing production output, improving resource allocation among other issues, and the capacity of Smart Farming to transform farming towards a greener and eco-friendly outlook for agriculture is emphasized.International Journal of Wireless Communications and Network Technologies[]2024-01-08T00:00:00https://www.semanticscholar.org/paper/02b8b8dd81ed6a425b2499fa6c92c076f937f708 b96698c0a08d1cd245ad222562b5bc4ccc70207bPhishing attacks continue to be a pervasive challenge in cybersecurity, with threat actors constantly developing new strategies to penetrate email inboxes and compromise sensitive data. In this study, we investigate the effectiveness of Large Language Models (LLMs) in the crucial task of phishing email detection. With the growing sophistication of these attacks, we assess the performance of three distinct LLMs: GPT-3.5, GPT-4, and a customized ChatGPT, against a carefully curated dataset containing both phishing and legitimate emails. Our research reveals the proficiency of LLMs in identifying phishing emails, with each model showing varying levels of success. The paper outlines the strengths and limitations of GPT-3.5, GPT-4, and the custom ChatGPT, illuminating their respective suitability for practical applications in email security. These results underscore the potential of LLMs in effectively identifying phishing emails and their significant implications for enhancing cybersecurity measures and safeguarding users from the risks of online fraud.Computing and Communication Workshop and Conference173The research reveals the proficiency of LLMs in identifying phishing emails, with each model showing varying levels of success, and highlights the strengths and limitations of GPT-3.5, GPT-4, and the custom ChatGPT, illuminating their respective suitability for practical applications in email security.2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)['Robin Chataut', 'P. Gyawali', 'Yusuf Usman']2024-01-08T00:00:00https://www.semanticscholar.org/paper/b96698c0a08d1cd245ad222562b5bc4ccc70207b 2a0e8255c3732926413502501d266dce76917602Educators are increasingly concerned about the usage of Large Language Models (LLMs) such as ChatGPT in programming education, particularly regarding the potential exploitation of imperfections in Artificial Intelligence Generated Content (AIGC) Detectors for academic misconduct. In this paper, we present an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors. This is achieved by generating code in response to a given question using different variants. We collected a dataset comprising 5,069 samples, with each sample consisting of a textual description of a coding problem and its corresponding human-written Python solution codes. These samples were obtained from various sources, including 80 from Quescol, 3,264 from Kaggle, and 1,725 from LeetCode. From the dataset, we created 13 sets of code problem variant prompts, which were used to instruct ChatGPT to generate the outputs. Subsequently, we assessed the performance of five AIGC detectors. Our results demonstrate that existing AIGC Detectors perform poorly in distinguishing between human-written code and AI-generated code.Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training362This paper presents an empirical study where the LLM is examined for its attempts to bypass detection by AIGC Detectors by generating code in response to a given question using different variants.ArXiv['Wei Hung Pan', 'Ming Jie Chok', 'Jonathan Leong Shan Wong', 'Yung Xin Shin', 'Yeong Shian Poon', 'Zhou Yang', 'Chun Yong Chong', 'David Lo', 'Mei Kuan Lim']2024-01-08T00:00:00https://www.semanticscholar.org/paper/2a0e8255c3732926413502501d266dce76917602 c89c0f750de51faeed1a6dffb84ebb8a3d965ec6International Journal of Human-Computer Interaction622International Journal of Human–Computer Interaction['Jingyan Gao', 'Xijie Zhao', 'Mengfan Zhai', 'Duo Zhang', 'Gang Li']2024-01-08T00:00:00https://www.semanticscholar.org/paper/c89c0f750de51faeed1a6dffb84ebb8a3d965ec6 179f4c3e58ceb6562ff9c92a38b8fc06abc4dc64This article explains how the use of Artificial Technology (AI) has gained significant attention and a vital role in counterterrorism activities. The features like Data analysis, Machine learning, object recognition have enhanced the capabilities of security agencies to track down the terrorist groups and their activities. One of the most important features of Artificial Intelligence is analyzing large volumes of Data including Metadata, internet collection records, location tracking data and social media activities. It is very evident that the introduction of Artificial Intelligence is like a revolution against terrorism. The article focuses on the pros of using AI as a means to counter all the terrorist groups and their destructive activities. It also tries to elaborate and explain how Artificial intelligence has a significant impact on the areas of security and intelligence.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)180The article focuses on the pros of using AI as a means to counter all the terrorist groups and their destructive activities and tries to elaborate and explain how Artificial intelligence has a significant impact on the areas of security and intelligence.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Malvika Sharma', 'Abhiranjan Dixit', 'Poonam Rawat']2024-01-08T00:00:00https://www.semanticscholar.org/paper/179f4c3e58ceb6562ff9c92a38b8fc06abc4dc64 b9e76157dda5245bc0a70dff1b97be4ffae044aaThis research paper delves into the critical dimension of Human-AI Collaboration, with a specific focus on unraveling the intricacies of user trust in ChatGPT conversations. In an era marked by increasing AI integration into various aspects of human life, understanding and fostering user trust in conversational AI systems like ChatGPT is essential for effective collaboration. The study employs a comprehensive approach, investigating metrics for trust measurement, analyzing user experiences, and exploring the factors that influence trust. By examining the evolving impact of trust on collaboration and conducting comparative analyses with other conversational AI models, the research aims to provide valuable insights. Ultimately, the paper not only contributes to a nuanced understanding of user trust in ChatGPT conversations but also offers practical recommendations for developers and stakeholders to enhance the collaborative potential of AI systems in real-world applications. Keywords: Human-AI Collaboration, ChatGPT Conversations, Conversational AI, Trust Metrics, User Trust.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper delves into the critical dimension of Human-AI Collaboration, with a specific focus on unraveling the intricacies of user trust in ChatGPT conversations, investigating metrics for trust measurement, analyzing user experiences, and exploring the factors that influence trust.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Sikander Hans', 'Balwinder Kumar', 'Vivek Parihar', 'Sukhpreet singh']2024-01-08T00:00:00https://www.semanticscholar.org/paper/b9e76157dda5245bc0a70dff1b97be4ffae044aa a937a87819eaae4644e1171c7afe87d238746d02As the decisions we make become increasingly influenced by suggestions from Artificial Intelligence (AI), it is important to understand how the form and the accuracy of their suggestions affect our behavior. In this study, we investigated whether our decisions were affected by the accuracy of the AI used and the form of feedback. Ten participants evaluated whether facial images were real or fake under four conditions, namely, with suggestions given by a high or low accuracy AI and with or without the AI confidence level provided. The results suggest that, in the case of a high accuracy AI, the concordance rate between the decisions the participants made and the suggestions from AI were higher when confidence levels were provided compared to when the confidence levels were not show. However, in the case of a low accuracy AI, the concordance rate was lower when confidence levels were shown. Additionally, the accuracy of participant's answers increased when confidence level was provided, for both the high accuracy and low accuracy AI. In conclusion, providing confidence level information alongside AI suggestions may change the decisions made by human and the effects may be different depending on the confidence level of the AI.IEEE/SICE International Symposium on System Integration120Providing confidence level information alongside AI suggestions may change the decisions made by human and the effects may be different depending on the confidence level of the AI.2024 IEEE/SICE International Symposium on System Integration (SII)['Nanami Ishizu', 'Wen Liang Yeoh', 'Hiroshi Okumura', 'Nobuhiko Yamaguchi', 'Osamu Fukuda']2024-01-08T00:00:00https://www.semanticscholar.org/paper/a937a87819eaae4644e1171c7afe87d238746d02 138c91d28f44ec5d675dad12d89afccc1a491e4eArtificial intelligence (AI) has rapidly become a transformative force across various sectors, including healthcare, virtual support, and security. The convergence of AI and Human-Computer Interaction (HCI) has led to the development of interactive intelligent systems, fostering user engagement. This paper presents a comprehensive review of the intersection of AI and HCI, focusing on user perspectives and challenges in the context of healthcare. Keywords: Artificial intelligence, Human-Computer Interaction, Healthcare, User Perspectives, ChallengesINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A comprehensive review of the intersection of AI and HCI, focusing on user perspectives and challenges in the context of healthcare, is presented.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Zohaib Hussain', 'Md. Saifullah Khalid']2024-01-08T00:00:00https://www.semanticscholar.org/paper/138c91d28f44ec5d675dad12d89afccc1a491e4e 590b9dbdb4f3ba909c9a29e33dbfc4f9e59bb71eIn the dynamic realm of public services, the integration of artificial intelligence (AI) has emerged as a transformative force, reshaping various sectors, including governance, urban development, healthcare, education, security infrastructure, decision-making processes, and responses to health crises. This article conducts an exploration spanning the years 1984 to 2023, employing bibliometric analysis to analyse global literature retrieved from the Scopus database. The central investigation revolves around the evolution of AI utilisation in public services during this period. Findings indicate a significant surge in AI-related publications, with notable global contributions from countries like China, India, and the United States, and a prevalence of computer science in AI research. Keyword clusters highlight seven prominent themes, ranging from digital governance to modelling health and social welfare in pandemics. Future research directions underscore ethical implications, AI adoption across government agencies, effectiveness in addressing urban challenges, machine learning applications in healthcare and education, security and privacy implications, application in diverse contexts, and AI's role in predicting and managing public health emergencies. This research contributes some necessary information for both academia and practical implementation in public services, laying the groundwork for future studies.Applied Research in Administrative Sciences320An exploration spanning the years 1984 to 2023 is conducted, employing bibliometric analysis to analyse global literature retrieved from the Scopus database, revolving around the evolution of AI utilisation in public services during this period.Applied Research in Administrative Sciences['R. Popescu', 'Răzvan-Andrei Corboș', 'Ovidiu Bunea']2024-01-08T00:00:00https://www.semanticscholar.org/paper/590b9dbdb4f3ba909c9a29e33dbfc4f9e59bb71e 2ddae737067f9a39756f5326695e12c4b7ea55ec A lot has happened since OpenAI released ChatGPT to the public in November 2022. We review how things unfolded over the course of the year, tracking significant events and announcements from the tech giants leading the generative AI race and from other players of note; along the way we note the wider impacts of the technology’s progress.Natural Language Engineering00How things unfolded over the course of the year is reviewed, tracking significant events and announcements from the tech giants leading the generative AI race and from other players of note; along the way the wider impacts of the technology’s progress are noted.Nat. Lang. Eng.['Robert Dale']2024-01-08T00:00:00https://www.semanticscholar.org/paper/2ddae737067f9a39756f5326695e12c4b7ea55ec 64b42ad1a11471a19fc7db2caafb9837a13415e0Despite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI) methods have been proposed as a way to address this shortage, by supporting providers with less extensive training as they deliver care. To this end, we developed the AI-Assisted Provider Platform (A2P2), a text-based virtual therapy interface that includes a response suggestion feature, which supports providers in delivering protocolized therapies empathetically. We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features. Upon evaluation, the AI-assisted system significantly decreased response times by 29.34% (p=0.002), tripled empathic response accuracy (p=0.0001), and increased goal recommendation accuracy by 66.67% (p=0.001) across both user groups compared to the control. Both groups rated the system as having excellent usability.AMIA ... Annual Symposium proceedings. AMIA Symposium120The AI-Assisted Provider Platform (A2P2) is developed, a text-based virtual therapy interface that includes a response suggestion feature, which supports providers in delivering protocolized therapies empathetically and is rated as having excellent usability.AMIA ... Annual Symposium proceedings. AMIA Symposium['William R. Kearns', 'Jessica Bertram', 'Myra Divina', 'Lauren Kemp', 'Yinzhou Wang', 'Alex Marin', 'Trevor Cohen', 'Weichao Yuwen']2024-01-08T00:00:00https://www.semanticscholar.org/paper/64b42ad1a11471a19fc7db2caafb9837a13415e0 d06a1e59da84db553919cbec43cbb99bc0972f4eArtificial Intelligence brings a new paradigm in health sciences related to using technologies capable of processing a large amount of patient information to strengthen prediction, prevention and clinical care. This research aimed to perform a bibliometric analysis of Artificial Intelligence and its applications in Health Sciences, particularly on Emerging Technologies in Education. To this end, a search for articles related to "Artificial Intelligence and its Applications in Health Sciences" was conducted at the international level in the Scopus database with search parameters based on titles, abstracts and keywords. The results revealed that the network of the 100 most essential terms was grouped into four clusters, namely: the first cluster identified with red colour is related to artificial Intelligence; the second cluster identified with green colour is related to the controlled study; the third cluster identified with yellow colour is related to algorithm and, the fourth cluster identified with yellow colour is related to education. It was concluded that artificial Intelligence has experienced advances that are having an impact on health sciences education. Academics and researchers have tools that allow them to obtain information to deepen the diagnosis of diseases and present students with robust case studies that strengthen the teaching-learning process.Seminars in Medical Writing and Education2517It was concluded that artificial Intelligence has experienced advances that are having an impact on health sciences education.Seminars in Medical Writing and Education['Rolando Eslava Zapata', 'Edixon Chacón Guerrero', 'Rómulo Esteban Montilla']2024-01-08T00:00:00https://www.semanticscholar.org/paper/d06a1e59da84db553919cbec43cbb99bc0972f4e a7eaaff82f0927bb1d79bc0e967b92d4df9945efBMC Medical Informatics Decis. Mak.703The AI may be useful in lymphoma diagnosis as suggested in meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time.BMC Medical Informatics and Decision Making['A. Bai', 'M. Si', 'P. Xue', 'Yimin Qu', 'Yu Jiang']2024-01-08T00:00:00https://www.semanticscholar.org/paper/a7eaaff82f0927bb1d79bc0e967b92d4df9945ef f869c0f85b91a26bfc3f454e5bc213fc7d2da84dPrior to and during the pandemic, social media platforms such as Twitter and Facebook emerged as dynamic online spaces for diverse communities facilitating engagement and learning. The authors of this article have explored the use of social media with a focus on Twitter for engagement and student-centered design of online courses in higher education. As with all technology, social media is also riddled with complex issues and unfortunately, is increasingly considered unsafe. Students have often been hesitant in their use of social media, especially for coursework and unfortunately, this hesitation has only worsened. Considering this and recent developments, social media has become a questionable tool for use in education, yet remains integral to the lives of many, both personally and professionally. The emergence and popularity of generative artificial intelligence (GenAI) tools such as ChatGPT, Lensa AI, and Canva Magic Write present new challenges and opportunities and cannot be avoided by the educational communities. Is there hope for social media and AI tools during these uncertain times? Through the combination of a current literature review and qualitative collaborative autoethnographic research, the authors take a step back and engage in critical conversations about what we have learned from our uses of social media for engagement and learning in our online courses, with a focus on (1) the intentional uses of social media, (2) the challenges and concerning issues of social media tools, and (3) exploring the implications of artificial intelligence. Centering on the theme of “hope,” the authors navigate these educational and technological landscapes and answer the question “where do we go from here?” The authors are faculty at a southwest border university teaching preservice and in-service teachers alongside those who want to learn more about education and design with learning technologies. Their voices represent faculty, teachers, and students who are engaging with and immediately impacted by the challenges and opportunities of rapidly advancing technologies.Education sciences482The authors of this article have explored the use of social media with a focus on Twitter for engagement and student-centered design of online courses in higher education, with a focus on GenAI tools.Education Sciences['Julia Lynn Parra', 'Suparna Chatterjee']2024-01-08T00:00:00https://www.semanticscholar.org/paper/f869c0f85b91a26bfc3f454e5bc213fc7d2da84d 0f2654c3a5bfa921f07191d0c4806e13d5f53648Artificial intelligence (AI) technology gives marketers the ability to identify differences between consumers, understand various personas, and understand the factors that drive consumers to make purchasing decisions. Therefore, marketers use Artificial Intelligence to recognize and predict consumer habits. Information obtained from AI, marketers can provide a special experience for each consumer. The objective of this study is to determine and measure the impact of AI on customer experience using simple regression analysis. There are four AI indicators analyzed, namely mechanical intelligence, analytical intelligence, intuitive intelligence, and empathetic intelligence. In addition, there are four dimensions of customer experience that are evaluated, namely immersion, flow, cognitive fit, and emotional fit. From the results of observations of 116 people, it was concluded that Artificial Intelligence and Draiv's customer experience received a positive assessment, and Artificial Intelligence had a significant effect on customer experience.PRODUCTIVITY03It was concluded that Artificial Intelligence and Draiv's customer experience received a positive assessment, and Artificial Intelligence had a significant effect on customer experience.Management Studies and Business Journal (PRODUCTIVITY)['Nabila Cecilia Marasabessy']2024-01-08T00:00:00https://www.semanticscholar.org/paper/0f2654c3a5bfa921f07191d0c4806e13d5f53648 c2b2c9a454d02022ada8ce063d13bc57d1650f50Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients’ genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer’s, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI’s contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.Frontiers in Bioengineering and Biotechnology1034Artificial intelligence’s contribution to improving CRISPR-based genome editing technologies and addresses existing challenges are explored and potential areas for future research in AI-driven CRISPR-based genome editing technologies are discussed.Frontiers in Bioengineering and Biotechnology['Shriniket Dixit', 'Anant Kumar', 'Kathiravan Srinivasan', 'P. M. Durai', 'Raj Vincent', 'Nadesh Ramu Krishnan', 'Catarina Gomes', 'Elham Jamshidi', 'Mario Andrea Marchisio', 'Nadesh Ramu', 'Krishnan']2024-01-08T00:00:00https://www.semanticscholar.org/paper/c2b2c9a454d02022ada8ce063d13bc57d1650f50 66e425a75b22b6450f2d07c2b7a02300a7f9f3e7Current Psychology421This research addresses the pressing need to understand the intricate relationships between job insecurity, psychological safety, and employee depression by introducing a novel approach that incorporates the mediating role of psychological safety and the moderating influence of employee self-efficacy in AI use.Current Psychology['Byung-Jik Kim', 'Min-Jik Kim', 'Julak Lee']2024-01-08T00:00:00https://www.semanticscholar.org/paper/66e425a75b22b6450f2d07c2b7a02300a7f9f3e7 6fbfe543f08824d86e09570130e8a87151c24e81In the process of performing new activities daily, artificial intelligence and new technology are performing new functions. The growth of technology has transformed the working and learning environment, affecting a great number of other systems as well. Most of the other systems that are functioning because of technological advancement have also implemented changes to the learning management systems that assist students in acquiring information via the use of apps that expand their technological experience. Currently, a great number of technology applications are working with machine learning and offering new ways of learning for students. These new learning methods may simplify the acquisition of the developing material for students. The purpose of this research was to determine the factors that influence the level of motivation and efficiency among students attending higher education institutions. This study found that most learners have shown a positive response to the applications of artificial intelligence. The positive response can be attributed to the following factors: learning applications help with learning the content, enhance the learning capabilities of learners, and produce confidence in the development of core ideas about the learning content. The findings of this research revealed that if training and curriculum sessions will be implemented in higher education institutions, then learners would have a favorable rise in their motivation towards applications, and they would also develop an interest in the process of learning overall.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)181If training and curriculum sessions will be implemented in higher education institutions, then learners would have a favorable rise in their motivation towards applications, and they would also develop an interest in the process of learning overall.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Mudasir Ali Rind', 'Mohammad Ali Al Qudah', 'Pirali Aliyev']2024-01-08T00:00:00https://www.semanticscholar.org/paper/6fbfe543f08824d86e09570130e8a87151c24e81 9d20c00525b6a522019ee324950dcfd78728dae7Clinicians have increasingly been using artificial intelligence (AI) to make decisions and to increase their knowledge in various clinical and surgical gynecological areas. A vast amount of clinical, medical, and biological patient data is processed in fast computer networks using complex algorithms to create mathematical modeling. The development of these mathematical models gives hope of a promising future with their contribution to overcoming the difficulties encountered in the diagnosis, individualization of treatment plans and improving patient outcomes. Virtual AI in clinical gynecology uses pattern recognition to aid diagnosis, plan treatment, and predict outcomes in gynecological malignancies, assisted reproductive techniques, and urogynecology. In gynecological surgery, physical AI combines augmented reality in operations in the form of computer-aided or robotic platforms. However, AI is yet to be fully incorporated into modern medical practice to improve patient outcomes in clinical gynecology.İstanbul Gelişim Üniversitesi sağlık bilimleri dergisi301Virtual AI in clinical gynecology uses pattern recognition to aid diagnosis, plan treatment, and predict outcomes in gynecological malignancies, assisted reproductive techniques, and urogynecology.İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi['Gulseren Polat', 'Hatice Kübra Arslan']2024-01-08T00:00:00https://www.semanticscholar.org/paper/9d20c00525b6a522019ee324950dcfd78728dae7 c57e082bdc474f757d4204d64917c7a4503da8c9Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail to connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security.Big Data & Society271Emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise is introduced.Big Data Soc.['Catherine Stinson', 'Sofie Vlaad']2024-01-08T00:00:00https://www.semanticscholar.org/paper/c57e082bdc474f757d4204d64917c7a4503da8c9 c157d6e85dd7a1de7ee24ae8d5af8fa142313e20Recently, removing dyes from wastewater by photocatalysis has been extensively studied by several researchers. However, there exists a research gap in optimizing the photocatalytic process parameters using artificial intelligence to maintain the associated techno-economic feasibility. Hence, this investigation attempts to optimize the photocatalytic degradation of methylene blue (MB) dye using an artificial neural network (ANN) model to minimize the capital and running costs, which is beneficial for industrial applications. A ZnO/MgO photocatalyst was synthesized, showing an energy band gap of 2.96 eV, crystallinity index of 71.92%, pore volume of 0.529 cm3/g, surface area of 30.536 m2/g, and multiple surface functional groups. An ANN model, with a 4-8-1 topology, trainlm training function, and feed-forward back-propagation algorithm, succeeded in predicting the MB removal efficiency (R2 = 0.946 and mean squared error = 11.2). The ANN-based optimized condition depicted that over 99% of MB could be removed under C0 = 16.42 mg/L, pH = 9.95, and catalyst dosage = 905 mg/L within 174 min. This optimum condition corresponded to a treatment cost of USD 8.52/m3 cheaper than the price estimated from the unoptimized photocatalytic system by ≈7%. The study outputs revealed positive correlations with the sustainable development goals accompanied by pollution reduction, human health protection, and aquatic species conservation.Sustainability591The study outputs revealed positive correlations with the sustainable development goals accompanied by pollution reduction, human health protection, human health protection, and aquatic species conservation.Sustainability['K. F. Ngulube', 'Amal Abdelhaleem', 'Manabu Fujii', 'Mahmoud Nasr']2024-01-08T00:00:00https://www.semanticscholar.org/paper/c157d6e85dd7a1de7ee24ae8d5af8fa142313e20 935d8798e5ac4f2210cdf75e116e7b12ac1b597bAbstract Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from ‘omics’ analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children’s health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient–doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.Journal of Pediatric Endocrinology & Metabolism (JPEM)930In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome.Journal of Pediatric Endocrinology and Metabolism['Paul Dimitri', 'Martin O. Savage']2024-01-08T00:00:00https://www.semanticscholar.org/paper/935d8798e5ac4f2210cdf75e116e7b12ac1b597b 23ee34b7ba96da6aa584209cd78b174d467d7b96The purpose of the study was to determine how much of an impact artificial intelligence has had on the process of boosting user confidence and the quality of services provided by the government online. Specifically, the study wanted to know how much of an impact artificial intelligence has had on the process of upgrading government services. Capabilities The development of AI-based tools and solutions has made it possible for more businesses to reap the benefits of AI at a cheaper cost and in a more expedient manner. In the context of artificial intelligence, the term “off-the-shelf AI” refers to solutions, tools, and software that either have AI capabilities built in or automate algorithmic decision-making. Ready-to-use Government applications of AI can include autonomous databases that use machine learning to self-heal, as well as prebuilt models that address problems like iris recognition and text analysis across various datasets. Both examples are examples of what are known as autonomous databases. It may help businesses achieve value more quickly while also increasing productivity, lowering costs, and improving their connections with customers. Artificial intelligence has significantly contributed to the evolution of government services, and today, it is widely regarded as one of the most essential building blocks of e-government. Because of these gadgets, it is now much simpler for huge organizations and governments to build a specific system and strategy that assists in gaining access to electronic services.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)1702024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Mohammad Ali Al Qudah', 'L. Muradkhanli', 'Anas A. Salameh', 'Mudasir Ali Rind', 'Zeynab Muradkhanli']2024-01-08T00:00:00https://www.semanticscholar.org/paper/23ee34b7ba96da6aa584209cd78b174d467d7b96 5fa3f79bbd000d40cd94cbd4c9e11909afb5dd69Every nation’s economy is dependent on agriculture. All people, either directly or indirectly, get their daily needs from agricultural products. The world’s population is increasing at the same rate as daily food demand. The farmers’ traditional methods cannot meet the demand at this point. To meet the current global demand for agricultural products, innovative automation methods are required. It is anticipated that man-made cognizance will play a significant role in the agricultural region in transforming the agricultural industry. By making it simpler to precisely monitor and analyze data, AI has the potential to alter conventional agriculture, enhance environmental sustainability, and increase its efficiency in terms of time, labor, and resources. Artificial intelligence has improved crop creation, assurance, gathering, handling, and marketing in farming from seed to collect. Several greetings technology PC-based devices and Agri-bots have already been used to establish crucial boundaries for expanding horticulture. In this article, we will examine how automated thinking is altering education by employing more useful approaches in close proximity to the challenges of gathering human-made knowledge.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)140How automated thinking is altering education by employing more useful approaches in close proximity to the challenges of gathering human-made knowledge is examined.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Bhagyashree Sungh', 'Pragya Bharti']2024-01-08T00:00:00https://www.semanticscholar.org/paper/5fa3f79bbd000d40cd94cbd4c9e11909afb5dd69 6c75e82355a7daacb9d98fbe5a9a9e6cfa6f6bcbIn neurointensive care units (NICU), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of pre-admission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.Journal of Korean Neurosurgical Society520A comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this, concludes that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.Journal of Korean Neurosurgical Society['Kyung Ah Kim', 'Hakseung Kim', 'Eun Jin Ha', 'Byung C. Yoon', 'Dong-Joo Kim']2024-01-08T00:00:00https://www.semanticscholar.org/paper/6c75e82355a7daacb9d98fbe5a9a9e6cfa6f6bcb 250727cf9c9dd761264a772793b97c6ce7c018c3This review was conducted to demonstrate how artificial intelligence (AI) has affected livestock farming. Livestock is essential for maintaining ecological integrity and providing food security. In this review, the history of artificial intelligence (AI), its impact on current and future livestock farming, and its drawbacks have been highlighted. The term “artificial intelligence,” was first coined by John McCarthy in 1956, and currently, the technology is widely applied in the management of many livestock farms like poultry, dairy, and pigs. Although it has been studied for decades and widely applied, AI is still one of the least understood subfields. Artificial intelligence provides farmers with unrivalled support, enabling them to minimize resource use, improve the sustainability of their feeding patterns, and increase farm productivity in general, especially when it comes to reducing the carbon footprint. AI is a blessing for boosting efficiency and productivity while also lowering the possibility of human error. Producers can use artificial intelligence to simulate human decision-making and provide interpretations and solutions for the data gathered by sensors and other hardware technologies. Through AI tools, it is possible to easily trace animal activities and locations and collect data about behaviors, habitats, and health conditions. Animal identification, animal welfare monitoring, sex determination, vaccine delivery, and pasture evaluation are some areas where AI has been widely applied so far. Drones, robots, and blockchains are some forms of automation that have been widely used in dairy farms. However, the expense of development, which needs more infrastructure on the farm, and the potential for automation to replace them are the limitations of the technology.Int. J. Distributed Sens. Networks890In this review, the history of artificial intelligence (AI), its impact on current and future livestock farming, and its drawbacks have been highlighted.Int. J. Distributed Sens. Networks['Awoke Melak', 'Tesfalem Aseged', 'Takele Shitaw']2024-01-08T00:00:00https://www.semanticscholar.org/paper/250727cf9c9dd761264a772793b97c6ce7c018c3 34258a2139a855804cfb82b539d6198ea9ce9120The study aims to analyze the impact of artificial intelligence (AI) usage on structural learning through student-developed programming in open-source software languages: Python, Octave, and OpenSees. The research collaborates with 90 undergraduate students in the early courses of civil engineering at the Universidad Nacional de Chimborazo. The ADDIE methodology is employed in the initial phase for planning, development, and monitoring. A survey on students' perceptions regarding effectiveness, satisfaction, recommendation, and feedback is conducted, followed by academic performance evaluation using a grading rubric to verify the achievement of set objectives. An analysis of factors contributing to AI-focused learning is then performed. Initial results revealed outliers, some deviating from study parameters and others discarded for a comprehensive view of study behavior. Regarding the survey data analysis, efficiency and satisfaction exhibited the highest reliability. Subsequently, variables were correlated considering their normality, showing a relationship between effectiveness and satisfaction; however, a strong connection cannot be guaranteed for these or other variables. Therefore, ANOVA tests, indicating positive linear relationships, and hypothesis testing were employed, demonstrating that students achieved objectives with a moderately high degree of effectiveness and satisfaction. The use of technological options and consideration of innovative learning methods can positively enhance the learning experience, contingent on prior education. Exploring artificial intelligence may prove challenging without guided information search based on predefined criteria and constraints.Espirales: Revista Multidisciplinaria de Investigación140Analysis of the impact of artificial intelligence usage on structural learning through student-developed programming in open-source software languages: Python, Octave, and OpenSees shows that students achieved objectives with a moderately high degree of effectiveness and satisfaction.Espirales Revista Multidisciplinaria de investigación['Diego Hernán Hidalgo Robalino', 'Jessica Paulina Brito Noboa', 'Nelson Estuardo Patiño Vaca', 'Alexis Iván Andrade Valle']2024-01-08T00:00:00https://www.semanticscholar.org/paper/34258a2139a855804cfb82b539d6198ea9ce9120 247cfb9dbb70f99b7d6cda4b33df511967e0e27eArtificial Intelligence is a system created with advanced technology or a computer system that can understand, and imitate the intellectual abilities of people in various ways. The implementation of artificial intelligence (AI) accounting systems within the financial industry has bestowed accountants with substantial advantages. Gaining a comprehensive comprehension of the application of artificial intelligence in accounting is the objective of this research.The method used in this research is Structured Literature Review (SLR), where several articles published from 2012 to 2023 will be analyzed systematically. The literature was categorized according to relevant concepts and grouped as non-aligned themes to convey additional issues regarding the Utilization of Artificial Intelligence in Accounting. The anticipated outcome of this research endeavor is the provision of enhanced comprehension regarding the implementation of synthetic intelligence in the field of accounting, alongside an illumination of the obstacles that an organization may encounter while embracing this technology. with an enhanced comprehension of the practical implementations of synthetic intelligence, companies can develop better strategies and make more efficient decisions in managing their financial information.Akuntansi220The anticipated outcome of this research endeavor is the provision of enhanced comprehension regarding the implementation of synthetic intelligence in the field of accounting, alongside an illumination of the obstacles that an organization may encounter while embracing this technology.Akuntansi['Ilma Amelia', 'Yovanna Nabila Azzahra', 'Abda Abda', 'Z. Azmi']2024-01-08T00:00:00https://www.semanticscholar.org/paper/247cfb9dbb70f99b7d6cda4b33df511967e0e27e 75a273d5b0bbedadd2768135b48046e345298eccBackground: Modern technology makes everything accessible and easy. In our daily life, we use lots of artificial intelligence. Our modern dentistry also uses lots of new technology. Methods: A total of 26 final-year BDS students of Marks Medical College (Dental Unit) were selected purposively and six respondents were selected among them by simple random sampling method for a focus group discussion session in June 2022. The study implemented one qualitative method: a focus group discussion (FGD) among respondents. Semi-structured interview guidelines study adhered to the consolidated reporting criteria for qualitative studies (COREQ) developed for the FGD. Results: Positive comments included the reduced workload, quick calculations, less radiation exposure, ease of choosing a treatment plan, and ease of motivating patients. On the negative side, it was noted that it was expensive, required additional funding for setup, wasn't available, and required skilled labor to run, which is not readily available. Conclusion: It is encouraging that data-driven and robotic technology is becoming more prevalent in modern dentistry. AI and related advancements are becoming more common and used in healthcare. Dental surgeons should be more knowledgeable to use this technology. JOPSOM 2021; 41(2):57-61Journal of Preventive and Social Medicine80It is encouraging that data-driven and robotic technology is becoming more prevalent in modern dentistry and Dental surgeons should be more knowledgeable to use this technology.Journal of Preventive and Social Medicine['Nabhira Aftabi', 'Binte Islam', 'Prof. Nasiruddin']2024-01-08T00:00:00https://www.semanticscholar.org/paper/75a273d5b0bbedadd2768135b48046e345298ecc fa20c6c2bd383838209573a1c9d551241339c7fcThe integration of ergonomics and artificial intelligence (AI) in the automotive industry has the potential to revolutionize the way how vehicles are designed, manufactured and used. The aim of this article is to review the recent literature on the subject and discuss the opportunities and challenges presented by the integration of these two fields. The paper begins defining the ergonomics and the AI and providing an overview of their respective roles in the automotive industry. It then examines the benefits of the integration of ergonomics and AI in the automotive industry, including the optimization of vehicle design and manufacturing process. The enhancement of the driver experience, and improvement of safety accessibility, and customization, however, the integration of ergonomics and AI in the automotive industry also presents challenges, including ethical and legal considerations, data privacy, liability, and the impact on the employment in the automotive industry. The paper reviews research on these challenges and suggests that the development of international standards for the integration of AI in the vehicles may be necessary to ensure that AI systems in vehicle are secure, highlighting the need for future research to explore the integration of ergonomic and AI in the automotive industry. Future research should focus and addressing the ethical, legal, and societal implications of the AI in vehicles, as well as exploring new opportunities for the use of AI in design, manufacturing, and use of vehicles in overall, the integration of ergonomics and AI in the automotive industry has the potential to significantly improve the design and manufacturing of vehicles, as well as enhance the driving experience for users. However, the integration of these two fields also poses challenges that must be addressed, including ethical concerns, legal considerations, and the employment in the automotive industry. By working to overcome these challenges, we ensure that benefits of ergonomics and AI in the automotive industry are fully realized while minimizing their potential negative impacts.SAE technical paper series50It is suggested that the development of international standards for the integration of AI in the vehicles may be necessary to ensure that AI systems in vehicle are secure, highlighting the need for future research to explore the integration of ergonomics and AI in the automotive industry.SAE Technical Paper Series['Carlos Augusto Palermo Puertas', 'António César Galhardi']2024-01-08T00:00:00https://www.semanticscholar.org/paper/fa20c6c2bd383838209573a1c9d551241339c7fc 8a7b2fe16fd325151e8f3c1478953f3da83efac2Artificial intelligence is a computer system which can replicate human behavior and largely supports human actions and interpretation, but not replace human responses. Over the past few decades, the field of artificial intelligence (AI) has experienced phenomenal development and expansion. We are surrounded by several instances of AI. The most typical examples include Chat GPT, Alexa, Google Maps, autocorrect and text editors, e-payments, virtual travel booking agent, social media monitoring, gaming, including chess matches involving computers versus human chess masters, self driving cars, adaptive cruise control, parking assistance, and facial recognition for biometrics such as retinal scans and fingerprint scans. AI has applications in different branches of Dentistry. This review article attempts to highlight these points and lays an emphasis on how AI is driving dentistry in the present and will improve dental care in the future. A total of 59 papers from an electronic search using Google Scholar and PubMed were used to create this narrative review. Artificial intelligence can be utilised for diagnosis, decision-making, treatment planning, early detection and prevention of oral disease, and finally result prediction by utilising cutting-edge technology in imaging. It shows how dentists can use it as a useful tool at various phases of clinical cases. The future of AI in dentistry appears to be outstanding with advancements in full artificial intelligence technology, dental assistance, and dental instructional tools. In order to help dental professionals better grasp AI as a tool to assist their work with enhanced efficiency, investigations need to be done to uncover patterns and foresee future related to oral health concerns.F1000Research630This review article attempts to highlight how AI is driving dentistry in the present and will improve dental care in the future and shows how dentists can use it as a useful tool at various phases of clinical cases.F1000Research['Prakrati Kamath', 'Prathvi Kamath', 'Sharon Saldanha', 'Thilak B. Shetty', 'Shobha J Rodrigues', 'Mahesh M', 'U. Pai', 'Puneeth Hegde', 'Prashant Bajantri', 'Sandipan Mukherjee']2024-01-08T00:00:00https://www.semanticscholar.org/paper/8a7b2fe16fd325151e8f3c1478953f3da83efac2 40ed3bc81bfd425cbf30f217dd93b5f26371ac27Abstract Background We have previously developed an artificial intelligence–based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresholds to identify individuals at high risk for HIV/STIs. Methods Using 2008–2022 data from 216 252 HIV, 227 995 syphilis, 262 599 gonorrhea, and 320 355 chlamydia consultations at a sexual health center, we applied MySTIRisk machine learning models to estimate infection risk scores. Optimal cutoffs for determining high-risk individuals were determined using Youden's index. Results The HIV risk score cutoff for high risk was 0.56, with 86.0% sensitivity (95% CI, 82.9%–88.7%) and 65.6% specificity (95% CI, 65.4%–65.8%). Thirty-five percent of participants were classified as high risk, which accounted for 86% of HIV cases. The corresponding cutoffs were 0.49 for syphilis (sensitivity, 77.6%; 95% CI, 76.2%–78.9%; specificity, 78.1%; 95% CI, 77.9%–78.3%), 0.52 for gonorrhea (sensitivity, 78.3%; 95% CI, 77.6%–78.9%; specificity, 71.9%; 95% CI, 71.7%–72.0%), and 0.47 for chlamydia (sensitivity, 68.8%; 95% CI, 68.3%–69.4%; specificity, 63.7%; 95% CI, 63.5%–63.8%). High-risk groups identified using these thresholds accounted for 78% of syphilis, 78% of gonorrhea, and 69% of chlamydia cases. The odds of positivity were significantly higher in the high-risk group than otherwise across all infections: 11.4 (95% CI, 9.3–14.8) times for HIV, 12.3 (95% CI, 11.4–13.3) for syphilis, 9.2 (95% CI, 8.8–9.6) for gonorrhea, and 3.9 (95% CI, 3.8–4.0) for chlamydia. Conclusions Risk scores generated by the AI-based risk assessment tool MySTIRisk, together with Youden's index, are effective in determining high-risk subgroups for HIV/STIs. The thresholds can aid targeted HIV/STI screening and prevention.Open Forum Infectious Diseases280Risk scores generated by the AI-based risk assessment tool MySTIRisk, together with Youden's index, are effective in determining high-risk subgroups for HIV/STIs and can aid targeted HIV/STI screening and prevention.Open Forum Infectious Diseases['P. M. Latt', 'Nyi N Soe', 'Xian-hui Xu', 'Jason J Ong', 'E. Chow', 'C. Fairley', 'Lei Zhang']2024-01-08T00:00:00https://www.semanticscholar.org/paper/40ed3bc81bfd425cbf30f217dd93b5f26371ac27 c781fc567e92b4004ff933d40014ff5735276541Artificial intelligence is a term which is a buzzword in every sector now times due to its capability to work more effectively, Indian judicial system faces the problem of pendency and administrative function requirements and with the employment of AI these problems can be curbed. In this article, the author discusses the AI prospects in the Indian judicial system, initially discussing the problem of rising population and with that increased litigation pendency, furthermore discusses the solution adopted by the democratic government and the judicial bodies, and later on discusses how the use of AI can help both lawyers and judges to save their time and efforts, which can be utilized to deliver justice more efficiently. AI is the instrument which the Indian judicial system required to revolutionize the process of justice.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)320The author discusses the AI prospects in the Indian judicial system, initially discussing the problem of rising population and with that increased litigation pendency, and furthermore discusses the solution adopted by the democratic government and the judicial bodies.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Abhiranjan Dixit', 'Nimit Saroha']2024-01-08T00:00:00https://www.semanticscholar.org/paper/c781fc567e92b4004ff933d40014ff5735276541 47cedd88094315320439753283b83cef39ecd22f00[]2024-01-08T00:00:00https://www.semanticscholar.org/paper/47cedd88094315320439753283b83cef39ecd22f 93f7b16e9538c8fc7ef4d7a195b62c6a73966216Tweetable abstract Cardiology's digital revolution: AI diagnoses, ChatGPT consults, Metaverse educates. Challenges & promises explored. #CardiologyTech #DigitalHealth.Future Cardiology162Future cardiology['I. Skalidis', 'Ioannis Kachrimanidis', 'L. Koliastasis', 'Dimitri Arangalage', 'Panagiotis Antiochos', 'N. Maurizi', 'Olivier Muller', 'Stéphane Fournier', 'Michael Hammilos', 'E. Skalidis']2024-01-08T00:00:00https://www.semanticscholar.org/paper/93f7b16e9538c8fc7ef4d7a195b62c6a73966216 880a996936c4c711aab27ede5a923c4bb67a09c1Asia Pacific Journal of Education221Asia Pacific Journal of Education['Zi Yang', 'J. Wu', 'Haoran Xie']2024-01-08T00:00:00https://www.semanticscholar.org/paper/880a996936c4c711aab27ede5a923c4bb67a09c1 0939c2fbb80057f316fedd9560c8e3fc1ca942a9The Review of Politics00The Review of Politics['Kristen R. Collins']2024-01-08T00:00:00https://www.semanticscholar.org/paper/0939c2fbb80057f316fedd9560c8e3fc1ca942a9 5cc1c9c2a0e4084b065e3982b124b8f9a2708a55American Journal of Artificial Intelligence00American Journal of Artificial Intelligence['James Monroe']2024-01-08T00:00:00https://www.semanticscholar.org/paper/5cc1c9c2a0e4084b065e3982b124b8f9a2708a55 8c19b8d1660f81fd99e2b7253da1f9a7275c39f3The primary goal of the study is to determine how artificial intelligence has affected the tutoring industry. The effects and applications of artificial intelligence in teaching, learning, and administration contributed to the possibility of learning in part. Research methods that were qualitative in nature, such as using a literature review as an exploratory design, were applied, and they successfully improved understanding of study persistence. Artificial Intelligence (AI) is the study of successful inventions and advances that have been accumulated in computers, machines, and other artifacts with human intelligence that are grouped according to intellectual capacity, learning, and adaptability. According to the study, artificial intelligence has been widely used and implemented in the field of education, mostly by institutions or the educational sector in a variety of ways. Artificial intelligence primarily was used in computer-based technologies, transition to smart network-based platforms and smart education schemes which use embedded or entrenched workstations organized with other innovative technologies, and the application of chatbots and humanoid robots to perform instruction tasks to the learner or with instructor independently. Instructors may perform administration-related tasks, such as scoring and reviewing learners’ coursework more efficiently and effectively and accomplish quality high in their instruction activities. On the other hand, because the System leverages adaptability and machine learning, content and curriculum have been personalized according to learners’ needs, which improves the learning experiences of learners and the overall quality of the learning.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)290Artificial intelligence was used in computer-based technologies, transition to smart network-based platforms and smart education schemes which use embedded or entrenched workstations organized with other innovative technologies, and the application of chatbots and humanoid robots to perform instruction tasks to the learner or with instructor independently.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Saurabh Dhyani', 'Isteyaaq Ahmad', 'Aditya Gupta', 'Swati Singh', 'Abhishek Pathak', 'Nagendar Yamsani']2024-01-08T00:00:00https://www.semanticscholar.org/paper/8c19b8d1660f81fd99e2b7253da1f9a7275c39f3 f405e0df8d7aa7a2a8f25b0040dba7494ad09cc0AI and Ethics130It is argued that humans should not pursue the path of developing and creating AGI, not merely for the sake of possible suffering in machines, but also due to machine–human interaction becoming more alike to human–human interaction and for the sake of the human’s own moral development.AI and Ethics['Oliver Li']2024-01-08T00:00:00https://www.semanticscholar.org/paper/f405e0df8d7aa7a2a8f25b0040dba7494ad09cc0 9f13ddb8339b376e6e1fe0d80554b5b37aad9dc3Objective: This study aims to know Criminal Responsibility for Errors Committed by Medical Robots, where the use of robots in healthcare and medicine has been steadily growing in recent years. Robotic surgical systems, robotic prosthetics, and other assistive robots are being into patient care. However, these autonomous systems also carry risks of errors and adverse events resulting from mechanical failures, software bugs, or other technical issues. When such errors occur and lead to patient harm, it raises complex questions around legal and ethical responsibility Char.   Method: A descriptive analytical method was followed.   Results: Traditional principles of criminal law have not been designed to address the issue of liability for actions committed by artificial intelligence systems and robots. There are open questions around whether autonomous medical robots can or should be held criminally responsible for errors that result in patient injury or death. If criminal charges cannot be brought against the robot itself, legal responsibility could potentially be attributed to manufacturers, operators, hospitals, or software programmers connected to the robot. However, proving causation and intent in such cases can be very difficult.   Conclusions: The prospect of bringing criminal charges against a non-human triggers ethical dilemma. Should autonomous machines have legal personhood? How to weigh patient safety versus promoting innovation in medical technology? This research will analyze the legal and ethical challenges associated with determining criminal responsibility when medical robots cause unintended harm. It has important implications for patient rights, healthcare regulation, technological ethics and the legal status of intelligent machines.Journal of Law and Sustainable Development251This research will analyze the legal and ethical challenges associated with determining criminal responsibility when medical robots cause unintended harm and has important implications for patient rights, healthcare regulation, technological ethics and the legal status of intelligent machines.Journal of Law and Sustainable Development['Rana Mosbah Abdel Mohsen Abdel Razek']2024-01-08T00:00:00https://www.semanticscholar.org/paper/9f13ddb8339b376e6e1fe0d80554b5b37aad9dc3 acc392ddc3400ea0d912c23483fd351011780c3fThis research article explores the legal and ethical challenges posed by artificial intelligence (AI). The authors examine the current state of AI regulations and highlight the need for clear ethical guidelines for its deployment. The article evaluates the potential for AI to infringe upon privacy rights and raises questions about the accountability of AI systems for their actions. It also discusses the role of international organizations in establishing ethical standards for AI. The authors argue that the development and deployment of AI must be guided by ethical principles, such as transparency, fairness, and accountability. They emphasize the need for a balanced approach that balances the benefits of AI with the need to protect human rights and dignity. The paper suggests that the establishment of ethical guidelines for AI will require the cooperation of governments, industry, and civil society. In conclusion, this research article provides an overview of the legal and ethical challenges posed by AI and highlights the need for clear guidelines for its responsible use. The authors suggest that international organizations should play a role in establishing ethical standards for AI deployment and emphasize the importance of continued research and debate on this important issue.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)410It is suggested that the establishment of ethical guidelines for AI will require the cooperation of governments, industry, and civil society, and the development and deployment of AI must be guided by ethical principles, such as transparency, fairness, and accountability.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Divya Rawat', 'Divya Rawat', 'Amaarjeet Rawat', 'Akhilesh Pandey', 'Gautam Chauhan', 'Srinivas Aluvala']2024-01-08T00:00:00https://www.semanticscholar.org/paper/acc392ddc3400ea0d912c23483fd351011780c3f 17f2b89885db0dff3cc5e509b443f6666e630ea5This study explores the intersection of smart manufacturing and the Sustainable Development Goals (SDGs) from a poverty reduction perspective. Smart manufacturing refers to the integration of advanced technologies, such as automation, artificial intelligence, and data analytics, into the manufacturing sector to improve productivity, efficiency, and sustainability. The SDGs, adopted by the United Nations in 2015, provide a comprehensive framework to address global challenges, including poverty eradication. This study examines how smart manufacturing can reduce poverty by aligning with the SDGs. It investigates the potential of smart manufacturing technologies to create employment opportunities, enhance income levels, and improve living conditions in low-income communities. By leveraging advanced technologies, smart manufacturing can facilitate the transformation of traditional industries, enable the growth of small and medium-sized enterprises (SMEs), and promote inclusive economic development. The study employs a multidisciplinary approach, drawing on literature from the fields of engineering, economics, and sustainable development. It explores case studies from various regions and industries to provide empirical evidence of the impact of smart manufacturing on poverty reduction. Also, it examines the challenges and barriers that need to be addressed to ensure that the benefits of smart manufacturing are equitably distributed and reach those most in need. The findings of this study contribute to both academia and policymaking. They provide insights into how smart manufacturing can be harnessed to achieve the SDGs, particularly the goal of poverty reduction. The study highlights the importance of technological innovation and its potential to create inclusive and sustainable economic growth. It also offers recommendations for policymakers, industry stakeholders, and development organizations on how to leverage smart manufacturing to address poverty and promote sustainable development.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)180This study examines how smart manufacturing can reduce poverty by aligning with the SDGs, and offers recommendations for policymakers, industry stakeholders, and development organizations on how to leverage smart manufacturing to address poverty and promote sustainable development.2024 IEEE 1st Karachi Section Humanitarian Technology Conference (KHI-HTC)['Muhammad Abrar ul haq', 'Jayendira P. Sankar', 'Farheen Akram', 'Gazi Md Nurul Islam', 'H. Malik', 'Kashif Akram']2024-01-08T00:00:00https://www.semanticscholar.org/paper/17f2b89885db0dff3cc5e509b443f6666e630ea5 37f433456c5959d893ba355b6d135a1f5cbee6c0Introduction. The article is devoted to the study of the essence of interbudgetary relations in the system of budgetary regulation, key aspects of their regulation, instruments of interbudgetary relations and their application in Ukraine under martial law. The purpose of the article. The purpose and objective of the article is to study the essence and relevance of the problem of formation of interbudgetary relations under martial law. Results. The author analyzes the approaches and defines various aspects of the essence of intergovernmental fiscal relations based on the literature. The characteristics of different approaches to the interpretation of the essence of the concept of ‘intergovernmental fiscal relations’ are presented. The mechanism of horizontal equalization of the tax capacity of the territory is outlined according to the Budget Code of Ukraine. The mechanism of transferring intergovernmental transfers from the state budget to local budgets and intergovernmental transfers between local budgets is determined. The author considers the types of subsidies and subventions provided from the State budget to local budgets. Their amounts are analyzed. It is proved that the system of horizontal financial equalization redistributes financial resources in favor of those local authorities which have an imbalance and provides partial compensation for the losses of local budgets of territorial communities from the lack of personal income tax. Conclusions. It is concluded that the financial basis for the development of any state is budgetary funds. Interbudgetary relations are the main instrument for ensuring the proper functioning of the national budget system.Economic journal of Lesya Ukrainka Volyn National University00Economic journal of Lesya Ukrainka Volyn National University['Наталія Проць', 'Тарас Костенюк']2024-01-07T00:00:00https://www.semanticscholar.org/paper/37f433456c5959d893ba355b6d135a1f5cbee6c0 519ef4e34cfd8509347a52d373f41d59cd177772Environmental Sciences Europe600Environmental Sciences Europe['Zong-hang Wang', 'Jian-ya Zhou', 'Ming-jun Chen']2024-01-07T00:00:00https://www.semanticscholar.org/paper/519ef4e34cfd8509347a52d373f41d59cd177772 6f657eaca838919ce7283b9fe435fe99ce8961bbMulti-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents within physical and virtual environments. At present, systems leverage existing foundation models as the basic building blocks for the creation of embodied agents. Embedding agents within such environments facilitates the ability of models to process and interpret visual and contextual data, which is critical for the creation of more sophisticated and context-aware AI systems. For example, a system that can perceive user actions, human behavior, environmental objects, audio expressions, and the collective sentiment of a scene can be used to inform and direct agent responses within the given environment. To accelerate research on agent-based multimodal intelligence, we define"Agent AI"as a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data, and can produce meaningful embodied actions. In particular, we explore systems that aim to improve agents based on next-embodied action prediction by incorporating external knowledge, multi-sensory inputs, and human feedback. We argue that by developing agentic AI systems in grounded environments, one can also mitigate the hallucinations of large foundation models and their tendency to generate environmentally incorrect outputs. The emerging field of Agent AI subsumes the broader embodied and agentic aspects of multimodal interactions. Beyond agents acting and interacting in the physical world, we envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.arXiv.org017It is argued that by developing agentic AI systems in grounded environments, one can also mitigate the hallucinations of large foundation models and their tendency to generate environmentally incorrect outputs.ArXiv['Zane Durante', 'Qiuyuan Huang', 'Naoki Wake', 'Ran Gong', 'J. Park', 'Bidipta Sarkar', 'Rohan Taori', 'Yusuke Noda', 'D. Terzopoulos', 'Yejin Choi', 'Katsushi Ikeuchi', 'Hoi Vo', 'Fei-Fei Li', 'Jianfeng Gao']2024-01-07T00:00:00https://www.semanticscholar.org/paper/6f657eaca838919ce7283b9fe435fe99ce8961bb 47be196e2e068c6b9cf277daacc211606c157976The merchant-regulatory mechanism represents a promising tool that combines the benefits of merchant investment and regulated investment, thereby providing efficient incentives for merchant Transmission Companies (Transcos) subject to regulatory compliance. However, one of the drawbacks of the H-R-G-V merchant-regulated mechanism is that it allows the Transco to capture the entire surplus increase resulting from investment, without any economic benefits for consumers and generators. To address this issue, we propose an incentive tuning parameter, which is incorporated into the calculation of the incentive fee for the Transco. Accordingly, the regulatory framework can effectively manage the Transco's profit and allow market participants to access economic benefits, thus ensuring a fair distribution of economic advantages among the stakeholders, while the impact on overall social welfare remains relatively modest.330['Yuxin Xia', 'Iacopo Savelli', 'Thomas Morstyn']2024-01-07T00:00:00https://www.semanticscholar.org/paper/47be196e2e068c6b9cf277daacc211606c157976 e816b0e6989019504333a7100f0a97e5b181effdCognitive Research544The results show that human judgment is affected when participants receive incorrect algorithmic support, particularly when they receive it before providing their own judgment, resulting in reduced accuracy.Cognitive Research: Principles and Implications['Ujué Agudo', 'Karlos G. Liberal', 'Miren Arrese', 'Helena Matute']2024-01-07T00:00:00https://www.semanticscholar.org/paper/e816b0e6989019504333a7100f0a97e5b181effd 1fa8cfdb2ab712d5f9f1fd027fd44baec785d5ceScientometrics750An inverted U-shaped relationship is found between the intensity of a paper’s patent citations and its scientific citations, as well as between the breadth of a paper’s patent citations and its scientific citations, which shows that patent-cited papers have a stronger scientific impact than non-patent-cited papers.Scientometrics['Xingyu Gao', 'Qiang Wu', 'Yuanyuan Liu', 'Ruilu Yang']2024-01-07T00:00:00https://www.semanticscholar.org/paper/1fa8cfdb2ab712d5f9f1fd027fd44baec785d5ce d425aa96321c68db9415de33cc5e1f830270623cThe future paradigm of early cardiac diagnostics is shifting the focus towards heart attack preventive medicine based on non-invasive medical imaging with the support of artificial intelligence. It is necessary to preventively detect its increased risk early and respond with preventive drugs before moving on to more effective, but also more invasive, forms of therapy. The main motivation of our study was to improve existing and develop new AI-based solutions for cardiac preventive medicine, with particular emphasis on the prevention of heart attacks. This is due to the fact that the epidemic of lifestyle diseases (including cardiologic ones) has been stopped but not reversed; hence, automatically supervised prevention using AI seems to be a key opportunity to introduce progress in the above-mentioned areas. This can have major effects not only scientific and clinical in nature, but also economic and social. The aim of this article is to develop and test an AI-based tool designed to predict the occurrence of a heart attack for the purposes of preventive medicine. It used the combination and comparison of multiple AI methods and techniques to determine a personalized heart attack probability based on a wide range of patient characteristics and, from a computational point of view, determine the minimum set of characteristics necessary to do so. When applied to a specific patient, this represents progress in this field of research, resulting in improvements in preclinical care and diagnostics, as well as predictive accuracy in preventive medicine. After an initial selection based on the authors’ knowledge and experience, four solutions turned out to be the best: linear support vector machine (Linear SVC), logistic regression, k-nearest neighbors algorithm (KNN, k-NN), and random forest. A comparison of the models developed in the study shows that models based on logistic regression proved to be the most accurate, although their predictive value is moderate, but sufficient for the initial screening diagnosis—selecting patients who require further, more accurate testing. In addition, this can be performed based on a reduced set of parameters, particularly heart rate, age, BMI, and cholesterol. This allows the development of a prevention strategy based on modifiable factors (e.g., in the form of diet, activity modification, or a hybrid combining different factors) combined with the monitoring of heart attack risk by the proposed system. The novelty and contribution of the described system lies in the use of AI for a widely available, cheap, and quick predictive analysis of cardiovascular functions in a group of patients classified as at risk, and over time in all patients as a standard periodic examination qualifying them for further, more advanced diagnosis of heart diseases.Electronics400An AI-based tool designed to predict the occurrence of a heart attack for the purposes of preventive medicine and shows that models based on logistic regression proved to be the most accurate, although their predictive value is moderate, but sufficient for the initial screening diagnosis— selecting patients who require further, more accurate testing.Electronics['I. Rojek', 'P. Kotlarz', 'Mirosław Kozielski', 'M. Jagodziński', 'Zbyszko Królikowski']2024-01-07T00:00:00https://www.semanticscholar.org/paper/d425aa96321c68db9415de33cc5e1f830270623c e102b2138bff7a4762db5023a213eb6c375c7ef5Aim: This study aims to assess the capabilities of artificial intelligence (AI) in answering online Continuing Medical Education (CME) courses to find the resistant to AI-misuse strategies. Materials and Methods: The study evaluated 30 CME online courses from popular American (ACCME), European (EACCME), and German Medical Association accredited online platforms, including Medscape, eaccme.uems.eu, Springer Nature, der-niedergelassene-arzt, and Aerzteblatt. ChatGPT Version 4.0 with integrated plugins for interactive AI chats with documents, web access to scientific databases, and interactive AI chats with videos was used to answer the CME evaluation questions. A special scoring system, referred to as "complexity score," was introduced in the study. This system has two main objectives: first, to assess strategies that prevent the misuse of AI in medical online education; second, to measure the effort that physicians must invest to answer CME questions using AI. Results: AI was used to answer a total of 248 questions, divided into three categories: ACCME accredited courses: 7 credits; EACCME accredited courses: 9.5 credits; German CME courses: 28 credits. AI successfully completed the quiz in 90% of cases (27 courses) and showed an accuracy rate of 86%. 213 out of 248 questions were correctly answered: 38 out of 48 ACCME questions; 85 out of 100 EACCME questions; 90 out of 100 CME questions. The outcome "AI error" was significantly associated only with a higher number of questions in the quiz: p-value 0.01. However, this predictor had no impact on the AI's ability to successfully complete the entire quiz. The AI failure rate was significantly associated with learning materials based on new studies without open access: p-value 0.02 and the need to view all learning materials to gain access to the quiz: p-value 0.02. A higher complexity score of the course was significantly associated with AI failure rates: p-value 0.0034. Conclusion: This study has shown that AI can successfully answer medical quiz questions even without access to learning materials. Therefore, the best strategy to prevent the misuse of AI in CME online training is to align human learning with AI feeding. Access to the quiz should only be possible after a complete review of the learning materials. This could be achieved by setting a fixed time or through multiple slides with separate access to each slide and subsequent quiz access.  Web3 Journal: ML in Health Science00It is shown that AI can successfully answer medical quiz questions even without access to learning materials, and the best strategy to prevent the misuse of AI in CME online training is to align human learning with AI feeding.Web3 Journal: ML in Health Science['Y. Rusinovich', 'V. Rusinovich']2024-01-07T00:00:00https://www.semanticscholar.org/paper/e102b2138bff7a4762db5023a213eb6c375c7ef5 0519124a25f5bd58cd10395b037398fb080ee082AI &amp; SOCIETY850To explore the comparative investigation of the perspectives of different stakeholders in AI-driven and data-intensive health settings in a multi-faceted manner, semi-structured interviews as well as focus group discussions with patients and clinicians were conducted.AI &amp; SOCIETY['Patrik Hummel', 'Matthias Braun', 'Serena Bischoff', 'D. Samhammer', 'Katharina Seitz', 'Peter A. Fasching', 'Peter Dabrock']2024-01-07T00:00:00https://www.semanticscholar.org/paper/0519124a25f5bd58cd10395b037398fb080ee082 b6e9d5040c974613e940387ed7b12804ca1881b0In the dynamic urban landscape, where interplay of vehicles and pedestrians defines the rhythm of life, integrating advanced technology for safety and efficiency is increasingly crucial. This study delves into the application of cutting-edge technological methods in smart cities, focusing on enhancing public safety through improved traffic accident detection. Action recognition plays a pivotal role in interpreting visual data and tracking object motion such as human pose estimation in video sequences. The challenges of action recognition include variability in rapid actions, limited dataset, and environmental factors such as (Weather, Illumination, and Occlusions). In this paper, we present a novel comprehensive dataset for traffic accident detection. This dataset is specifically designed to bolster computer vision and action recognition systems in predicting and detecting road traffic accidents. We integrated datasets from wide variety of data sources, road networks, weather conditions, and regions across the globe. This approach is underpinned by empirical studies, aiming to contribute to the discourse on how technology can enhance the quality of life in densely populated areas. This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities. These dataset is expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments. Our study marks a pivotal step towards safer, more efficient smart cities, harnessing the power of AI and machine learning to transform urban living.SoutheastCon401This research aims to bridge existing research gaps by introducing benchmark datasets that leverage state-of-the-art algorithms tailored for traffic accident detection in smart cities, expected to advance academic research and also enhance real-time accident detection applications, contributing significantly to the evolution of smart urban environments.SoutheastCon 2024['Victor Adewopo', 'Nelly Elsayed', 'Zag ElSayed', 'Murat Ozer', 'C. Zekios', 'A. Abdelgawad', 'Magdy A. Bayoumi']2024-01-07T00:00:00https://www.semanticscholar.org/paper/b6e9d5040c974613e940387ed7b12804ca1881b0 e03c813aa6a545a1bc7dcdb8a4225d8a82a3301aGraph Neural Networks (GNNs) excel in various domains, from detecting e-commerce spam to social network classification problems. However, the lack of public graph datasets hampers research progress, particularly in heterogeneous information networks (HIN). The demand for datasets for fair HIN comparisons is growing due to advancements in GNN interpretation models. In response, we propose SynHIN, a unique method for generating synthetic heterogeneous information networks. SynHIN identifies motifs in real-world datasets, summarizes graph statistics, and constructs a synthetic network. Our approach utilizes In-Cluster and Out-Cluster Merge modules to build the synthetic HIN from primary motif clusters. After In/Our-Cluster mergers and a post-pruning process fitting the real dataset constraints, we ensure the synthetic graph statistics align closely with the reference one. SynHIN generates a synthetic heterogeneous graph dataset for node classification tasks, using the primary motif as the explanation ground truth. It can adapt and address the lack of heterogeneous graph datasets and motif ground truths, proving beneficial for assessing heterogeneous graph neural network explainers. We further present a benchmark dataset for future heterogeneous graph explainer model research. Our work marks a significant step towards explainable AI in HGNNs.arXiv.org240This work proposes SynHIN, a unique method for generating synthetic heterogeneous information networks and presents a benchmark dataset for future heterogeneous graph explainer model research, marking a significant step towards explainable AI in HGNNs.ArXiv['Ming-Yi Hong', 'Yi-Hsiang Huang', 'You-Chen Teng', 'Chih-Yu Wang', 'Che Lin']2024-01-07T00:00:00https://www.semanticscholar.org/paper/e03c813aa6a545a1bc7dcdb8a4225d8a82a3301a 39c3c9c431a3fd5a459a82a450abfeb54190cd45Journal of Population Therapeutics &amp; Clinical Pharmacology00Journal of Population Therapeutics &amp; Clinical Pharmacology['Khurram Shahzad Khan', 'Asma Imran', 'Rana Nadir']2024-01-07T00:00:00https://www.semanticscholar.org/paper/39c3c9c431a3fd5a459a82a450abfeb54190cd45 dc3d01368bf4a77f61ac1b4e040e431a5c0977e0Despite all the advancements in science, medical knowledge, healthcare, and the healthcare industry, cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The main reasons are the inadequacy of preventive health services and delays in diagnosis due to the increasing population, the failure of physicians to apply guide-based treatments, the lack of continuous patient follow-up, and the low compliance of patients with doctors’ recommendations. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are systems that support complex decision-making processes by using AI techniques such as data analysis, foresight, and optimization. Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD. These are just some examples, and the use of AI for CVD decision support systems is rapidly evolving. However, for these systems to be fully reliable and effective, they need to be trained with accurate data and carefully evaluated by medical professionals.Anatolian journal of cardiology781Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD.Anatolian Journal of Cardiology['S. Bozyel', 'Evrim Şimşek', 'Duygu Koçyiğit', 'A. Güler', 'Y. Korkmaz', 'M. Şeker', 'Mehmet Ertürk', 'Nurgül Keser']2024-01-07T00:00:00https://www.semanticscholar.org/paper/dc3d01368bf4a77f61ac1b4e040e431a5c0977e0 8effd459429904d24d9ec76076e10448469fb656Artificial intelligence (AI) has become an integral part of modern healthcare, with its algorithms and other AI-enabled applications supporting medical professionals in clinical and research settings. The digital revolution is transforming the way we approach medical care. Currently, numerous AI products have been developed to cover various aspects of healthcare, such as predicting the risk of acute and chronic diseases (e.g., cardiovascular risk, gastrointestinal bleeding, and eye conditions) and forecasting cancer risk, among other cases. Artificial intelligence has the capacity to revolutionize the utilization of health information collected in datasets. However, the specific characteristics of AI, including vagueness, complexity, data dependency, and automated behavior, can pose potential risks to users’ fundamental rights and safety. Therefore, it is crucial to recognize and mitigate these risks and provide legal solutions for any harm resulting from these risks. In the realm of healthcare, AI plays a pivotal role in advancing reliable prediction capabilities. Consequently, the storage and processing of data are imperative for emerging diagnostic and decision-making technologies. Nevertheless, these advances also introduce privacy risks, raising significant legal challenges for medical institutions. Understanding the various levels of these risks assists healthcare professionals and institutions in managing these challenges and complying with regulations. This descriptive research article comprehensively examines and implements the regulatory frameworks governing the United States and the European Union. Additionally, it draws upon documented research in this field to discuss the utilization of AI in healthcare, along with the associated legal issues, including informed consent and malpractice.Health Technology Assessment in Action01This descriptive research article comprehensively examines and implements the regulatory frameworks governing the United States and the European Union and draws upon documented research in this field to discuss the utilization of AI in healthcare, along with the associated legal issues, including informed consent and malpractice.Health Technology Assessment in Action['Shahriar Eslamitabar', 'Ehsan Lame', 'Ahmad Rouzbahani', 'Zohre Roozbahani']2024-01-07T00:00:00https://www.semanticscholar.org/paper/8effd459429904d24d9ec76076e10448469fb656 13a292ad3552243440b81f46047c0ea37fe6942aIn this Editorial, we highlight the emerging dominance of AI + Big Data, and here are some excerpts : We have entered into the age of Artificial Intelligence (AI). Everything around us is becoming artificially intelligent: from business applications to healthcare, education to finance and governance to art, music and entertainment. The fact that AI has gripped public attention is evident from the steep rise in public engagement with artificial intelligence applications, explosive increase in news media coverage of AI, increasing volumes of social media posts and the mushrooming of a range of AI ecosystem initiatives. We at JBDAI (formerly JBDTP) hope to encourage and foster much high quality research, rigor and innovative thought leadership on big data and artificial intelligence in the years ahead, supporting human well-being, the sustainability of our natural resources and balanced societal progress – please contribute to JBDAI and be a part of this exciting intellectual adventure!Journal of Big Data and Artificial Intelligence00In this Editorial, the emerging dominance of AI + Big Data is highlighted, and here are some excerpts:.Journal of Big Data and Artificial Intelligence['Jim Samuel', 'Abhishek Tripathi', 'E. Mema']2024-01-07T00:00:00https://www.semanticscholar.org/paper/13a292ad3552243440b81f46047c0ea37fe6942a fe7c3fe71d855da8d1862cc92bf0012f69f0c692Background: In line with the advancement of Artificial Intelligence (AI), innovative solutions have been designed to improve healthrelated Sustainable Development Goals (SDGs). Accordingly, there is an increasing trend in the realm of AI and SDG research areas. Objectives: This study aimed to analyze the trends and patterns of AI research in health-related SDGs using bibliometric analysis. Methods: The bibliometric approach facilitated the identification of key terms and countries from previous research. We used VOSviewer to map and analyze data obtained from three databases: Scopus, Web of Science, and PubMed. Results: Our findings illustrated that research on health has been a popular area of study in recent years. In particular, we observed a significant increase in research on AI in health-related SDGs during 2015 - 2022. Conclusions: This study provides insights into the trends and patterns of AI research in health-related SDGs using bibliometric analysis. The findings can guide future research by identifying key terms that require further investigation.Health Technology Assessment in Action00A significant increase in research on AI in health-related SDGs during 2015 - 2022 is observed, and insights are provided into the trends and patterns of AI research in health-related SDGs using bibliometric analysis.Health Technology Assessment in Action['Maryam Ramezani', 'Amirhossein Takian', 'A. Bakhtiari', 'Hamid R. Rabiee', 'S. Sazgarnejad']2024-01-07T00:00:00https://www.semanticscholar.org/paper/fe7c3fe71d855da8d1862cc92bf0012f69f0c692 8b427b54e0c77f129c2a5c4f84cca86f81d9d8f6SUMMARY Research background The aim of this study is to emphasize the importance of artificial intelligence (AI) and causality modelling of food quality and analysis with ’big data’. AI with structural causal modelling (SCM), based on Bayesian networks and deep learning, enables the integration of theoretical field knowledge in food technology with process production, physicochemical analytics and consumer organoleptic assessments. Food products have complex nature and data are highly dimensional, with intricate interrelations (correlations) that are difficult to relate to consumer sensory perception of food quality. Standard regression modelling techniques such as multiple ordinary least squares (OLS) and partial least squares (PLS) are effectively applied for the prediction by linear interpolations of observed data under cross-sectional stationary conditions. Upgrading linear regression models by machine learning (ML) accounts for nonlinear relations and reveals functional patterns, but is prone to confounding and failed predictions under unobserved nonstationary conditions. Confounding of data variables is the main obstacle to applications of the regression models in food innovations under previously untrained conditions. Hence, this manuscript focuses on applying causal graphical models with Bayesian networks to infer causal relationships and intervention effects between process variables and consumer sensory assessment of food quality. Experimental approach This study is based on the data available in the literature on the process of wheat bread baking quality, consumer sensory quality assessments of fermented milk products, and professional wine tasting data. The data for wheat baking quality were regularized by the least absolute shrinkage and selection operator (LASSO elastic net). Bayesian statistics was applied for the evaluation of the model joint probability function for inferring the network structure and parameters. The obtained SCMs are presented as directed acyclic graphs (DAG). D-separation criteria were applied to block confounding effects in estimating direct and total causal effects of process variables and consumer perception on food quality. Probability distributions of causal effects of the intervention of individual process variables on quality are presented as partial dependency plots determined by Bayesian neural networks. In the case of wine quality causality, the total causal effects determined by SCMs are positively validated by the double machine learning (DML) algorithm. Results and conclusions The data set of 45 continuous variables corresponding to different chemical, physical and biochemical variables of wheat properties from seven Croatian cultivars during two years of controlled cultivation were analysed. LASSO regularization of the data set yielded the ten key predictors, accounting for 98 % variance of the baking quality data. Based on the key variables, the quality predictive random forest model with 75 % cross-validation accuracy was derived. Causal analysis between the quality and key predictors was based on the Bayesian model shown as a DAG graph. Protein content shows the most important direct causal effect with the corresponding path coefficient of 0.71, and THMM (total high-molecular-mass glutenin subunits) content was an indirect cause with a path coefficient of 0.42, and protein total average causal effect (ACE) was 0.65. The large data set of the quality of fermented milk products included binary consumer sensory data (taste, odour, turbidity), continuous physical variables (temperature, fat, pH, colour) and three grade classes of products by consumer quality assessment. A random forest model was derived for the prediction of the quality classification with an out-of-bag (OOB) error of 0.28 %. The Bayesian network model predicts that the direct causes of the taste classification are temperature, colour and fat content, while the direct causes of the quality classification are temperature, turbidity, odour and fat content. The key quality grade ACE of temperature -0.04 grade/°C and 0.3 quality grade/fat content were estimated. The temperature ACE dependency shows a nonlinear type as negative saturation with the ’breaking’ point at 60 °C, while for fat ACE had a positive linear trend. Causal quality analysis of red and white wine was based on the large data set of eleven continuous variables of physical and chemical properties and quality assessments classified in ten classes, from 1 to 10. Each classification was obtained in triplicate by a panel of professional wine tasters. A non-structural double machine learning (DML) algorithm was applied for total ACE quality assessment. The alcohol content of red and white wine had the key positive ACE relative factor of 0.35 quality/alcohol, while volatile acidity had the key negative ACE of –0.2 quality/acidity. The obtained ACE predictions by the unstructured DML algorithm are in close agreement with the ACE obtained by the structural SCM. Novelty and scientific contribution Novel methodologies and results for the application of causal artificial intelligence models in the analysis of consumer assessment of the quality of food products are presented. The application of Bayesian network structural causal models (SCM) enables the d-separation of pronounced effects of confounding between parameters in noncausal regression models. Based on the SCM, inference of ACE provides substantiated and validated research hypotheses for new products and support for decisions of potential interventions for improvement in product design, new process introduction, process control, management and marketing.Food Technology and Biotechnology330This study focuses on applying causal graphical models with Bayesian networks to infer causal relationships and intervention effects between process variables and consumer sensory assessment of food quality, and results are presented as directed acyclic graphs (DAG).Food Technology and Biotechnology['Ž. Kurtanjek']2024-01-07T00:00:00https://www.semanticscholar.org/paper/8b427b54e0c77f129c2a5c4f84cca86f81d9d8f6 a62e18921b954ae3a2343fe9a7aa5899847ef5feWe investigate the question: if an AI agent is known to be safe in one setting, is it also safe in a new setting similar to the first? This is a core question of AI alignment--we train and test models in a certain environment, but deploy them in another, and we need to guarantee that models that seem safe in testing remain so in deployment. Our notion of safety is based on power-seeking--an agent which seeks power is not safe. In particular, we focus on a crucial type of power-seeking: resisting shutdown. We model agents as policies for Markov decision processes, and show (in two cases of interest) that not resisting shutdown is"stable": if an MDP has certain policies which don't avoid shutdown, the corresponding policies for a similar MDP also don't avoid shutdown. We also show that there are natural cases where safety is _not_ stable--arbitrarily small perturbations may result in policies which never shut down. In our first case of interest--near-optimal policies--we use a bisimulation metric on MDPs to prove that small perturbations won't make the agent take longer to shut down. Our second case of interest is policies for MDPs satisfying certain constraints which hold for various models (including language models). Here, we demonstrate a quantitative bound on how fast the probability of not shutting down can increase: by defining a metric on MDPs; proving that the probability of not shutting down, as a function on MDPs, is lower semicontinuous; and bounding how quickly this function decreases.arXiv.org400A quantitative bound on how fast the probability of not shutting down can increase is demonstrated by defining a metric on MDPs; proving that the probability of not shutting down, as a function on MDPs, is lower semicontinuous; and bounding how quickly this function decreases.ArXiv['Evan Ryan Gunter', 'Yevgeny Liokumovich', 'Victoria Krakovna']2024-01-07T00:00:00https://www.semanticscholar.org/paper/a62e18921b954ae3a2343fe9a7aa5899847ef5fe 17ea664b533bb804456aadab726c31047f7a36aaToxicity pathology is an important part of preclinical drug safety evaluation. With the development of computer science and full-slice digital scanning technology, artificial intelligence (AI) has been widely used in the field of drug safety evaluation, including all aspects of pathology, such as diagnostic pathology, veterinary diagnostics, pathology research, regulatory toxicology and pathology primary film review and peer review. Toxicology is one of the most valuable disciplines to promote the development of animal and human health, and the toxicity research of drug non-clinical safety evaluation. The development and application of a wide variety of algorithms for histopathology suggests that AI pathology platforms can profoundly influence the future of digital toxic pathology, precision medicine, and personalized medicine. However, as with all other revolutionary technologies, there are many challenges in the implementation and application of AI pathology platforms. This paper reviews the development of artificial intelligence and machine learning, the application of artificial intelligence in toxic pathology, the application of machine learning in digital toxic pathology, and the impact of artificial intelligence on digital toxic pathology, in order to provide some reference for the application of artificial intelligence and machine learning in toxic pathology in ChinaFrontiers in Computing and Intelligent Systems174The development of artificial intelligence and machine learning, the application of artificial intelligence in toxic pathology, the application of machine learning in digital toxic pathology, and the impact of artificial intelligence on digital toxic pathology are reviewed in order to provide some reference for the application of artificial intelligence and machine learning in toxic pathology in China.Frontiers in Computing and Intelligent Systems['Tianbo Song', 'Quan Zhang', 'Guoqing Cai', 'Meiqing Cai', 'Jili Qian']2024-01-07T00:00:00https://www.semanticscholar.org/paper/17ea664b533bb804456aadab726c31047f7a36aa adfcc00814fbb55a4b0c430c5c004bf7988734cbGenerative AI has the potential to redefine the process of therapeutic antibody discovery. In this report, we describe and validate deep generative models for the de novo design of antibodies against human epidermal growth factor receptor (HER2) without additional optimization. The models enabled an efficient workflow that combined in silico design methods with high-throughput experimental techniques to rapidly identify binders from a library of ∼106 heavy chain complementarity-determining region (HCDR) variants. We demonstrated that the workflow achieves binding rates of 10.6% for HCDR3 and 1.8% for HCDR123 designs and is statistically superior to baselines. We further characterized 421 diverse binders using surface plasmon resonance (SPR), finding 71 with low nanomolar affinity similar to the therapeutic anti-HER2 antibody trastuzumab. A selected subset of 11 diverse high-affinity binders were functionally equivalent or superior to trastuzumab, with most demonstrating suitable developability features. We designed one binder with ∼3x higher cell-based potency compared to trastuzumab and another with improved cross-species reactivity1. Our generative AI approach unlocks an accelerated path to designing therapeutic antibodies against diverse targets.bioRxiv10028Deep generative models for the de novo design of antibodies against human epidermal growth factor receptor (HER2) without additional optimization are described and validated and an efficient workflow that combined in silico design methods with high-throughput experimental techniques is demonstrated.bioRxiv['Amir Shanehsazzadeh', 'S. Bachas', 'George Kasun', 'J. Sutton', 'A. Steiger', 'Richard W. Shuai', 'Christa Kohnert', 'Alex Morehead', 'Amber Brown', 'Chelsea Chung', 'Breanna K. Luton', 'Nicolas Diaz', 'Matthew McPartlon', 'Bailey Knight', 'Macey Radach', 'K. Bateman', 'David A. Spencer', 'Jovan Cejovic', 'Gaelin Kopec-Belliveau', 'Robel Haile', 'Edriss Yassine', 'Cailen M. McCloskey', 'Monica Natividad', 'Dalton Chapman', 'Luka Stojanovic', 'G. Rakocevic', 'G. Hannum', 'Engin Yapici', 'Katherine M. Moran', 'Rodante Caguiat', 'S. Abdulhaqq', 'Zheyuan Guo', 'Lillian R. Klug', 'Miles Gander', 'Joshua Meier']2024-01-07T00:00:00https://www.semanticscholar.org/paper/adfcc00814fbb55a4b0c430c5c004bf7988734cb cf454817b5aa97afc0f9b78ee2d70e53eb005f0eThe carbon tariff policy of the EU seeks to lessen emissions and promote the use of renewable energy sources. EU member states primarily undertake a number of fiscal and tax policy measures, such as taxation and preferential tax policies, government procurement policies, and financial subsidy policies, in relation to the carbon emissions trading system. to promote the creation, use, and development of renewable energy. Thе article does multiple linear regression analysis on the variables carbon emission intensity and per capita energy consumption, industrial production index, and per capita GDP based on pertinent data from 8 nations in the European Union from 2013 to 2019. The financial subsidies, green procurement regulations, and tax laws of EU nations participating in the carbon emissions trading system are examined in detail based on the findings, offering other nations useful real-world experience.International Trade and Trade Policy190International Trade and Trade Policy['T. Zhenlian', 'Y. Solovieva']2024-01-07T00:00:00https://www.semanticscholar.org/paper/cf454817b5aa97afc0f9b78ee2d70e53eb005f0e 4a945c865ac2797750fd837e65f0f21d28783d39Improving public health affects society in many ways. Improved public health can lead to a longer and healthier life. Policymakers cannot address all these criteria at the same time due to both time and budget constraints. Therefore, priority strategies need to be formulated by determining the importance weights of these criteria. Accordingly, the purpose of this study is to evaluate the significance of the strategies determined for the development of public health. For this purpose, analytic hierarchy process (AHP) method is considered to define the importance of the strategies. Within this scope, artificial intelligence methodology is integrated with the Spherical fuzzy sets. In this framework, the decision matrix of AHP is obtained by artificial intelligence system. Next, the steps of Spherical fuzzy sets are implemented. The main contribution of this manuscript is considering artificial intelligence methodology to create decision matrix. Hence, the weights of the experts can be computed based on their qualifications. By the help of this condition, it may be possible for the opinion of experts with better qualifications to be taken into consideration with a higher coefficient. This situation has a positive contribution to increase the accuracy of the findings. The findings indicate that accessibility is the most important strategy to improve public health. Similarly, vaccination and preventive services also play a significant role for this situation.Journal of Operations Intelligence03The findings indicate that accessibility is the most important strategy to improve public health and vaccination and preventive services also play a significant role for this situation.Journal of Operations Intelligence['Yaşar Gökalp', 'H. Di̇nçer', 'Serkan Eti', 'Serhat Yüksel']2024-01-07T00:00:00https://www.semanticscholar.org/paper/4a945c865ac2797750fd837e65f0f21d28783d39 4d11136d8d3952dc1afe4272d2fe7547aad65b15Artificial intelligence technology will be increasingly applied in the oil and gas industry. The rapid development of artificial intelligence technology can solve problems such as high environmental sensitivity and complex production processes in the oil and gas industry. In recent years, emerging technologies represented by artificial intelligence have developed rapidly, assisting petroleum enterprises in digital transformation and intelligent upgrading. This article elaborates on the development trend of artificial intelligence technology. Based on the business scenarios and characteristics of the oil and gas industry, the application status of artificial intelligence technology in domestic and foreign petroleum technology service enterprises was summarized and analyzed. The application scenarios of artificial intelligence technology in the fields of dynamic analysis of oil and gas reservoirs, intelligent historical fitting, numerical simulation proxy models, and production plan optimization were analyzed with emphasis. Based on the problems and challenges faced in the development process of oil and gas reservoirs, it is proposed that petroleum enterprises should attach importance to the “three modernizations” innovation of data standardization, oil and gas field intelligence, and platform collaboration, in order to achieve more refined intelligent analysis and management of oil and gas reservoirs and quickly develop more targeted oil and gas reservoir development plans to assist in the intelligent transformation of oil and gas reservoir development. On this basis, prospects for future artificial intelligence technology are proposed, pointing out that the development of artificial intelligence technology will be faster and faster, and there will be higher demand for artificial intelligence technology in the construction of digital oil and gas fields in China in the future. The research results have important reference value for the development of the oil and gas industry.ACS Omega502It is proposed that petroleum enterprises should attach importance to the "three modernizations" innovation of data standardization, oil and gas field intelligence, and platform collaboration to achieve more refined intelligent analysis and management of oil and gas reservoirs and quickly develop more targeted oil and gas reservoir development plans to assist in the intelligent transformation of oil and gas reservoir development.ACS Omega['Tiecheng Wang', 'Qian Wei', 'Wei Xiong', 'Qiangqiang Wang', 'Junling Fang', 'Xinhua Wang', 'Gang Liu', 'Can Jin', 'Jianuo Wang']2024-01-07T00:00:00https://www.semanticscholar.org/paper/4d11136d8d3952dc1afe4272d2fe7547aad65b15 1730f1df3db9c53a1fdf9373b7458d2e7c376c45A documentary review was carried out on the production and publication of research papers related to the study of the variables Higher Education and Artificial Intelligence in Latin America. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 121 publications. The information provided by this platform was organized through graphs and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors on the proposed topic is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Mexico, with 38 publications, was the country with the highest scientific production indexed in Scopus by authors affiliated with Latin American institutions, while Brazil and Colombia occupy the second and third place with 28 and 24 published documents, respectively. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material related to the study of Artificial Intelligence and Higher Education in Latin America was Computer Science with 85 published documents, and the most used Publication Type during the period indicated above were Conference Articles with 63% of the total scientific production.Migration Letters00It is found that Mexico was the country with the highest scientific production indexed in Scopus by authors affiliated with Latin American institutions, while Brazil and Colombia occupy the second and third place with 28 and 24 published documents, respectively.Migration Letters['Jesús Ronald Iparraguirre Contreras', 'Eloy Fernando Rivera Castillo', 'Marcela Karina Silva Verdezoto', 'Coronado Lárraga Liliana']2024-01-07T00:00:00https://www.semanticscholar.org/paper/1730f1df3db9c53a1fdf9373b7458d2e7c376c45 d9ac8e25d94b5b90a98928dfeb5c723534a7e781 The Article Abstract is not available.   Health Technology Assessment in Action00Health Technology Assessment in Action['Rashin Iman', 'Mohammadreza Mobinizadeh']2024-01-07T00:00:00https://www.semanticscholar.org/paper/d9ac8e25d94b5b90a98928dfeb5c723534a7e781 08cd9bda0a8b2545b57522f252d87c83616ac11f The Article Abstract is not available.     Health Technology Assessment in Action00Health Technology Assessment in Action['Mohammadreza Mobinizadeh']2024-01-07T00:00:00https://www.semanticscholar.org/paper/08cd9bda0a8b2545b57522f252d87c83616ac11f 29565fa48d233e57fff6d1d9d103c164f3f8d3ccНаукові перспективи (Naukovì perspektivi)00Наукові перспективи (Naukovì perspektivi)['Віра Дабіжа', 'Михайло Сандул', 'Ігор Скутельник']2024-01-06T00:00:00https://www.semanticscholar.org/paper/29565fa48d233e57fff6d1d9d103c164f3f8d3cc c917ac3dfa23f61d959982a6dfc9e14e22e0e108Environment, Development and Sustainability411Environment, Development and Sustainability['Mengyun Wu', 'Yitian Xu']2024-01-06T00:00:00https://www.semanticscholar.org/paper/c917ac3dfa23f61d959982a6dfc9e14e22e0e108 30c6f6db70338ededf8663b749020adb94737080Despite significant progress in autonomous vehicles (AVs), the development of driving policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully explored. In this paper, we propose an enhanced human-in-the-loop reinforcement learning method, termed the Human as AI mentor-based deep reinforcement learning (HAIM-DRL) framework, which facilitates safe and efficient autonomous driving in mixed traffic platoon. Drawing inspiration from the human learning process, we first introduce an innovative learning paradigm that effectively injects human intelligence into AI, termed Human as AI mentor (HAIM). In this paradigm, the human expert serves as a mentor to the AI agent. While allowing the agent to sufficiently explore uncertain environments, the human expert can take control in dangerous situations and demonstrate correct actions to avoid potential accidents. On the other hand, the agent could be guided to minimize traffic flow disturbance, thereby optimizing traffic flow efficiency. In detail, HAIM-DRL leverages data collected from free exploration and partial human demonstrations as its two training sources. Remarkably, we circumvent the intricate process of manually designing reward functions; instead, we directly derive proxy state-action values from partial human demonstrations to guide the agents' policy learning. Additionally, we employ a minimal intervention technique to reduce the human mentor's cognitive load. Comparative results show that HAIM-DRL outperforms traditional methods in driving safety, sampling efficiency, mitigation of traffic flow disturbance, and generalizability to unseen traffic scenarios. The code and demo videos for this paper can be accessed at: https://zilin-huang.github.io/HAIM-DRL-website/Communications in Transportation Research842An enhanced human-in-the-loop reinforcement learning method, termed the Human as AI mentor-based deep reinforcement learning (HAIM-DRL) framework, which facilitates safe and efficient autonomous driving in mixed traffic platoon.ArXiv['Zilin Huang', 'Zihao Sheng', 'Chengyuan Ma', 'Sikai Chen']2024-01-06T00:00:00https://www.semanticscholar.org/paper/30c6f6db70338ededf8663b749020adb94737080 15d5dbe90884ba9d7c314ef812ebf8ffbe70debaInsights: - The human-centered AI (HCAI) approach and the sociotechnical systems (STS) theory share the same goal: ensuring that new technologies such as AI best serve humans in a sociotechnical environment. - HCAI practice needs to fully embrace sociotechnical systems thinking, while traditional STS needs to evolve to address the emerging characteristics of AI technology. - We propose a conceptual framework for intelligent sociotechnical systems (iSTS) to enhance traditional STS theory in the AI era. - Based on iSTS, we further propose a sociotechnical-based hierarchical HCAI approach as a paradigmatic extension to existing HCAI practice, further advancing HCAI practice.arXiv.org381A sociotechnical-based hierarchical HCAI approach is proposed as a paradigmatic extension to existing HCAI practice, further advancing HCAI practice and enhancing traditional STS theory in the AI era.ArXiv['Wei Xu', 'Zaifeng Gao']2024-01-06T00:00:00https://www.semanticscholar.org/paper/15d5dbe90884ba9d7c314ef812ebf8ffbe70deba 63afc015cec8a8f1cf48188ef8daaa666faab920BMC Medical Ethics360Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process.BMC Medical Ethics['Rachel Dlugatch', 'Antoniya Georgieva', 'A. Kerasidou']2024-01-06T00:00:00https://www.semanticscholar.org/paper/63afc015cec8a8f1cf48188ef8daaa666faab920 36eccb8396cba879a13d3d136581c72cd38cd927Artificial Intelligence used in future networks is vulnerable to biases, misclassifications, and security threats, which seeds constant scrutiny in accountability. Explainable AI (XAI) methods bridge this gap in identifying unaccounted biases in black-box AI/ML models. However, scaffolding attacks can hide the internal biases of the model from XAI methods, jeopardizing any auditory or monitoring processes, service provisions, security systems, regulators, auditors, and end-users in future networking paradigms, including Intent-Based Networking (IBN). For the first time ever, we formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS). Furthermore, we propose a detection method that auditors can use to detect the attack efficiently. We rigorously test the attack and detection methods using the NSL-KDD. We then simulate the attack on 5G network data. Our simulation illustrates that the attack adoption method is successful, and the detection method can identify an affected model with extremely high confidence.Consumer Communications and Networking Conference180This work formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS), and proposes a detection method that auditors can use to detect the attack efficiently.2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)['Thulitha Senevirathna', 'Bartlomiej Siniarski', 'Madhusanka Liyanage', 'Shen Wang']2024-01-06T00:00:00https://www.semanticscholar.org/paper/36eccb8396cba879a13d3d136581c72cd38cd927 e5bede7ef0833f436fbd7284b6afd72b78db24c6Abstract:The emergence of forms of Artificial Intelligence (AI) to support human judges or potentially replace them raises questions concerning the role of judges in contemporary society, as well as broader questions linked to the ongoing role and nature of the so-called "digital Switzerlands" and their role in justice systems. In addition, tensions between the executive and the judicial branches of government with respect to the development of Judge AI support an exploration of societal constitutional analysis which requires consideration that reaches beyond mere institutional analysis. The varying and developing relationships between judges, courts, and emergent technologies challenge conventional theoretical approaches to governance and also require a greater focus on social interaction to explore how adaptation in an era of judicial responsiveness might support the development of ethical approaches that respond to vulnerable populations and social needs. Using an approach based on societal constitutionalism enables the development of a contemporary approach to justice that has been extended with AI initiatives. This involves setting limits to resist authoritarianism by framing the definition of "justice" so that it includes an overarching focus on promoting human wellbeing which in turn promotes restraint and thoughtful approaches toward the use of more disruptive technologies in the justice sector. The challenges presented by this approach in a global context are readily apparent in the context of differing justice conceptions and a focus on "fast," "low cost" justice delivery, perhaps in the absence of justice itself.70Using an approach based on societal constitutionalism enables the development of a contemporary approach to justice that has been extended with AI initiatives, which involves setting limits to resist authoritarianism by framing the definition of "justice" so that it includes an overarching focus on promoting human wellbeing.Indiana Journal of Global Legal Studies['Tania Sourdin']2024-01-06T00:00:00https://www.semanticscholar.org/paper/e5bede7ef0833f436fbd7284b6afd72b78db24c6 37d239635d40a2717cb5c83eaf76ba4025efb3b7We analyzed ML-based In-Vehicle Intrusion Detection Systems (IV-IDS). Our review of recent automotive forensics studies highlights constraints relevant to in-vehicle networks and the associated security/safety requirements, revealing gaps in the existing literature. This paper contributes to the existing research corpus by addressing AI's limitations in IV-IDS. It defines pertinent baseline metrics for in-vehicle networked systems, enabling an assessment of the viability of AI-based intrusion detection systems.Consumer Communications and Networking Conference80It defines pertinent baseline metrics for in-vehicle networked systems, enabling an assessment of the viability of AI-based intrusion detection systems, and addresses AI's limitations in IV-IDS.2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)['Elies Gherbi', 'Hamza Khemissa', 'Mohammed Lamine Bouchouia', 'Maxime Ayrault']2024-01-06T00:00:00https://www.semanticscholar.org/paper/37d239635d40a2717cb5c83eaf76ba4025efb3b7 360c5a973329b5ce289da96864ce2106ae83e6e3The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using XAI for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models.IEEE International Conference on Consumer Electronics180This research presents a diagnostic method using XAI for model debugging, with expert-driven problem identification and solution creation, and found the training dataset as the main bias source and suggested model augmentation as a solution.2024 IEEE International Conference on Consumer Electronics (ICCE)['Truong Thanh Hung Nguyen', 'V. Nguyen', 'Quoc Hung Cao', 'Van Binh Truong', 'Quoc Khanh Nguyen', 'Hung Cao']2024-01-06T00:00:00https://www.semanticscholar.org/paper/360c5a973329b5ce289da96864ce2106ae83e6e3 65ea87111b362a39510c7bca7d5d90d41e592d61This paper focuses on the optimization of SynergyAI. SynergyAI is a human-AI collaborative system. We addressed challenges related to transparency, efficiency, and advice function in AI programming model. We introduced a visualized decision tree to improve human-AI collaborative capability in terms of explainability. A comprehensive prediction algorithm was proposed to enhance prediction efficiency across data flow. The AI programmer’s advice function utilized scatter-plot matrices for intuitive data relationship visualization. The main contributions of the improved SynergyAI platform are streamlining AI-human collaboration and maximizing prediction accuracy and transparency.IEEE International Conference on Consumer Electronics170The main contributions of the improved SynergyAI platform are streamlining AI-human collaboration and maximizing prediction accuracy and transparency and a visualized decision tree to improve human-AI collaborative capability in terms of explainability.2024 IEEE International Conference on Consumer Electronics (ICCE)['Le Jiang', 'Mohd Anuaruddin Bin Ahmadon', 'Shingo Yamaguchi']2024-01-06T00:00:00https://www.semanticscholar.org/paper/65ea87111b362a39510c7bca7d5d90d41e592d61 96aad572d13283d4a9906f8bbcb95994c69ed0b9With an aging rural population and declining birth rates posing challenges to agricultural productivity, we propose an AI Agriculture Automated Guided Vehicles system (A3GV). It leverages machine learning for real-time image recognition, enabling the vehicle to follow a target. ROS serves as the software framework for sensor coordination, offering features like Simultaneous Localization And Mapping (SLAM), obstacle avoidance, and autonomous navigation. This system alleviates the agricultural workload, and its adaptability is enhanced through a collaborative Unmanned Aerial Vehicle (UAV) setup. A dedicated mobile app enhances user experience by allowing remote vehicle control, mode switching, and autonomous navigation based on specific scenarios.Consumer Communications and Networking Conference00This system leverages machine learning for real-time image recognition, enabling the vehicle to follow a target, and its adaptability is enhanced through a collaborative Unmanned Aerial Vehicle (UAV) setup.2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)['Hung-Yu Lin', 'Zhenyu Xu', 'Jian-Yu Zhou', 'Jen-Yeu Chen']2024-01-06T00:00:00https://www.semanticscholar.org/paper/96aad572d13283d4a9906f8bbcb95994c69ed0b9 a39bab141c174ee9a992feb4cc03388553207189Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a prescriptive AI algorithm, some problems may occur, first and clearly, if the AI information is wrong or incomplete. But the main point of this article is that under uncertainties, the decision algorithm, rational or not, includes, in one way or another, a risk attitude in addition to deterministic preferences. That risk attitude implemented in the software is chosen by the analysts, the organization that they serve, the experts who inform them, and more generally by the process of identifying possible options. The problem is that it may or may not represent, as it should, the preferences of the actual decision maker (the risk manager) and of the people subjected to his/her decisions. This article briefly describes the sometimes-serious problem of that discrepancy between the preferences of the risk managers who use an AI output, and the risk attitude embedded in the AI system. The recommendation is to make these AI factors as accessible and transparent as possible and to allow for preference adjustments in the model if needed. The formulation of two simplified examples is described, that of a medical doctor and his/her patient when using an AI system to decide of a treatment option, and that of a skipper in a sailing race such as the America's Cup, receiving AI-processed sensor signals about the sailing conditions on different possible courses.Risk Analysis110Under uncertainties, the decision algorithm, rational or not, includes, in one way or another, a risk attitude in addition to deterministic preferences in addition to deterministic preferences.Risk analysis : an official publication of the Society for Risk Analysis['Elisabeth Paté-Cornell']2024-01-06T00:00:00https://www.semanticscholar.org/paper/a39bab141c174ee9a992feb4cc03388553207189 f00b42c5062bf868eec37d676e0d8b5ef6f2c37eAdvancements in AI are transforming the automotive industry, creating opportunities for AI-powered software and hardware. AI-driven features in automobiles are increasingly embraced due to their potential to significantly improve the driving experience. High-performance computing, particularly with NPUs, becomes crucial for executing the AI features. To maximize the efficiency and utilization of NPUs, DAIMO-NPU optimizes the inference sequence of the DNN models that form the backbones of the AI features. Not only does it organize and schedule the model inference tasks but also supports the tasks to be executed on heterogeneous NPU settings. Three main components are involved in the implementation of DAIMO-NPU. The schedule-table generator is responsible for creating a detailed plan for the model inference tasks, which is to be updated whenever an AI feature is added, removed, or upgraded. The onboard operator reads the schedule table and carries out the tasks accordingly. And, by dividing models into smaller segments, while not mandatory, the schedule table can be further optimized. In the subsequent developments, the integration of additional NPU hardware properties into DAIMO-NPU will be pursued.IEEE International Conference on Consumer Electronics90DAIMO-NPU optimizes the inference sequence of the DNN models that form the backbones of the AI features, and the integration of additional NPU hardware properties into DAIMO-NPU will be pursued.2024 IEEE International Conference on Consumer Electronics (ICCE)['Kiwon Sohn', 'Insup Choi', 'Seongwan Kim', 'Jaeho Lee', 'Jungyong Lee', 'Joonghang Kim']2024-01-06T00:00:00https://www.semanticscholar.org/paper/f00b42c5062bf868eec37d676e0d8b5ef6f2c37e 6a0cc68099d02cd4ee060cdaa04045470cd9fa0cTrust anchors financial markets, which directly contribute to the foundation of global economies, and simultaneously fuel FinTech innovations. AI-driven tools such as Robo-advisors, Equity crowdfunding, and Peer-to-peer lending reshape investment paradigms, but understanding trust within this digital realm remains elusive. This scoping review examines AI trust dynamics within retail financial investments. We locate and dissect thematic constructs, and assess the complex interplay of pivotal variables. While initial insights emphasize trust’s critical role in FinTech’s evolution, they also illuminate the constraints of a universal framework to analyze trust in this context. Despite the popularity of models like TAM and UTAUT, their inherent weaknesses leave important facets unexplored. While qualitative and quantitative methods predominantly inform the current discourse, many studies base their conclusions on niche or self-crafted hypotheses. Through this systematic review, we chart a path for future discussions on AI-driven financial trust, highlighting gaps, and offering new avenues for exploration.IEEE International Conference on Consumer Electronics410This systematic review examines AI trust dynamics within retail financial investments and charts a path for future discussions on AI-driven financial trust, highlighting gaps, and offering new avenues for exploration.2024 IEEE International Conference on Consumer Electronics (ICCE)['Ling Ding', 'Zhao Zhao', 'Rhonda McEwen']2024-01-06T00:00:00https://www.semanticscholar.org/paper/6a0cc68099d02cd4ee060cdaa04045470cd9fa0c 166e90c0d1f9fb9a82aeb1e6c0b2e70926737922Journal of Computers in Education600The investigation reveals that the promotion of AI literacy education has seen significant progress in the past two decades, and highlights that intelligent agents, including Google’s Teachable Machine, Learning ML, and Machine Learning for Kids, are age-appropriate tools for AI literacy education in K-12 contexts.Journal of Computers in Education['I. H. Y. Yim', 'Jiahong Su']2024-01-06T00:00:00https://www.semanticscholar.org/paper/166e90c0d1f9fb9a82aeb1e6c0b2e70926737922 0efab4be2cd39325961455f6ead9301f2c807dd9As the societal implications of Artificial Intelligence (AI) continue to grow, the pursuit of responsible AI necessitates public engagement in its development and governance processes. This involvement is crucial for capturing diverse perspectives and promoting equitable practices and outcomes. We applied Cultural Consensus Theory (CCT) to a nationally representative survey dataset on various aspects of AI to discern beliefs and attitudes about responsible AI in the United States. Our results offer valuable insights by identifying shared and contrasting views on responsible AI. Furthermore, these findings serve as critical reference points for developers and policymakers, enabling them to more effectively consider individual variances and group-level cultural perspectives when making significant decisions and addressing the public's concerns.Hawaii International Conference on System Sciences380Cultural Consensus Theory was applied to a nationally representative survey dataset on various aspects of AI to discern beliefs and attitudes about responsible AI in the United States, offering valuable insights by identifying shared and contrasting views on responsible AI.ArXiv['Necdet Gurkan', 'Jordan W. Suchow']2024-01-06T00:00:00https://www.semanticscholar.org/paper/0efab4be2cd39325961455f6ead9301f2c807dd9 5cdf71c2c96085e50cc94aeeda8c1c43bd8e01c6As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.AAAI Conference on Artificial Intelligence320An open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up and puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities.{'pages': '23137-23145'}['Zhonghao Shi', "Allison O'Connell", 'Zongjian Li', 'Siqi Liu', 'Jennifer Ayissi', 'Guy Hoffman', 'Mohammad Soleymani', "Maja J. Matari'c"]2024-01-06T00:00:00https://www.semanticscholar.org/paper/5cdf71c2c96085e50cc94aeeda8c1c43bd8e01c6 6c0cd22e9f3dc78f4e13234e9f00b602c1ede15dInnovative technologies are improving the accessibility, preservation and searchability of born-digital and digitised records. In particular, Artificial Intelligence (AI) is opening new opportunities for archivists and researchers. However, the experience of scholars (particularly humanities scholars) and other users remain understudied. This article asks how and why researchers and general users are, or are not, using computational methods. This research is informed by an open-call survey, completed by 22 individuals, and semi-structured interviews with 33 professionals, including archivists, librarians, digital humanists, literary scholars, historians, and computer scientists. Drawing on these results, this article offers an analysis of user experiences of computational research methods applied to digitised and born-digital archives. With a focus on humanities and social science researchers, this article also discusses users who resist this kind of research, perhaps because they lack the skills necessary to engage with these materials at scale, or because they prefer to use more traditional methods, such as close reading and historical analysis. Here, we explore the uses of computational and more ‘traditional’ research methodologies applied to digital records. We also make a series of recommendations to elevate users’ computational skills but also to improve the digital infrastructure to make archives more accessible and usable.ACM Journal on Computing and Cultural Heritage220An analysis of user experiences of computational research methods applied to digitised and born-digital archives and a series of recommendations to elevate users’ computational skills but also to improve the digital infrastructure to make archives more accessible and usable are made.ACM Journal on Computing and Cultural Heritage['Lise Jaillant', 'Katherine Aske']2024-01-06T00:00:00https://www.semanticscholar.org/paper/6c0cd22e9f3dc78f4e13234e9f00b602c1ede15d 9b3f49169ce11814b1faef7400135e28f6705e0fBackground: Purpose of the study was to clinically assess the accuracy of Logy AI cataract screening solution, an artificial intelligence-based module, which works through WhatsApp and also as a separate smart phone application, that can detect cataracts using images taken by a smartphone camera, by comparing with slit lamp based diagnoses made by ophthalmologists. Methods: A prospective clinical study was conducted in an eye clinic of a tertiary care hospital in the southern part of India with 437 patients. Smartphone images taken were sent to the Logy AI cataract screening solution which predicted if the patient had cataract or not. It graded cataracts as immature and mature. Patients were examined by ophthalmologists with slit-lamp and diagnosis was documented. Both were compared. Results: 794 eye images were included in the study. The overall accuracy of the AI screening solution for cataract detection was computed to be 90.08%. Further, the accuracy was 88.02% for immature cataract, 97.16% for mature cataract, and 90.08% normal category. The sensitivity was 90.38%, the specificity was 89.87%, and the F1 score was 87.98%. The positive predictive value was 85.71% and the negative predictive value was 93.29%. Logy AI cataract prediction module’s AUC (0.8946) falls under the good category. Conclusions: Logy AI cataract screening module could work as an effective cataract screening tool at the community level in remote areas where there is no expensive equipment and ophthalmic health care workers considering the accuracy and efficiency to work in low resource settings. It can also be a good home screening tool suitable for the post-COVID era.  International Journal of Advances in Medicine00Ley AI cataract screening module could work as an effective cataract screening tool at the community level in remote areas where there is no expensive equipment and ophthalmic health care workers considering the accuracy and efficiency to work in low resource settings.International Journal of Advances in Medicine['Mano Aarthi V. M.', 'Nivedita Tiwari', 'Vinay Khobragade', 'Mitali Pareek', 'Anand Panchbhai', 'Priyanjit Ghosh', 'Jayachandhran Saravanan']2024-01-06T00:00:00https://www.semanticscholar.org/paper/9b3f49169ce11814b1faef7400135e28f6705e0f 4ac9ab97978381d6e0d9082b7a37c54924cb8ea8Oral Oncology31Oral oncology['Vikas V. Pawar', 'Safia Farooqui']2024-01-06T00:00:00https://www.semanticscholar.org/paper/4ac9ab97978381d6e0d9082b7a37c54924cb8ea8 57a79935459ca365d68ee4afbf287d669057b66bWith UAVs on the rise, accurate detection and identification are crucial. Traditional unmanned aerial vehicle (UAV) identification systems involve opaque decision-making, restricting their usability. This research introduces an RF-based Deep Learning (DL) framework for drone recognition and identification. We use cutting-edge eXplainable Artificial Intelligence (XAI) tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations(LIME). Our deep learning model uses these methods for accurate, transparent, and interpretable airspace security. With 84.59% accuracy, our deep-learning algorithms detect drone signals from RF noise. Most crucially, SHAP and LIME improve UAV detection. Detailed explanations show the model's identification decision-making process. This transparency and interpretability set our system apart. The accurate, transparent, and user-trustworthy model improves airspace security.Consumer Communications and Networking Conference70This research introduces an RF-based Deep Learning framework for drone recognition and identification, using cutting-edge eXplainable Artificial Intelligence tools, SHapley Additive Explanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME) for accurate, transparent, and interpretable airspace security.2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)['Ekramul Haque', 'Kamrul Hasan', 'Imtiaz Ahmed', 'Md. Sahabul Alam', 'Tariqul Islam']2024-01-06T00:00:00https://www.semanticscholar.org/paper/57a79935459ca365d68ee4afbf287d669057b66b d586b211ead518dff2f365bbb48321055e76968aPURPOSE Patients may seek online information to better understand medical imaging procedures. The purpose of this study was to assess the accuracy of information provided by 2 popular artificial intelligence (AI) chatbots pertaining to common imaging scenarios' risks, benefits, and alternatives. METHODS Fourteen imaging-related scenarios pertaining to computed tomography (CT) or magnetic resonance imaging (MRI) were used. Factors including the use of intravenous contrast, the presence of renal disease, and whether the patient was pregnant were included in the analysis. For each scenario, 3 prompts for outlining the (1) risks, (2) benefits, and (3) alternative imaging choices or potential implications of not using contrast were inputted into ChatGPT and Bard. A grading rubric and a 5-point Likert scale was used by 2 independent reviewers to grade responses. Prompt variability and chatbot context dependency were also assessed. RESULTS ChatGPT's performance was superior to Bard's in accurately responding to prompts per Likert grading (4.36 ± 0.63 vs 3.25 ± 1.03 seconds, P < .0001). There was substantial agreement between independent reviewer grading for ChatGPT (κ = 0.621) and Bard (κ = 0.684). Response text length was not statistically different between ChatGPT and Bard (2087 ± 256 characters vs 2162 ± 369 characters, P = .24). Response time was longer for ChatGPT (34 ± 2 vs 8 ± 1 seconds, P < .0001). CONCLUSIONS ChatGPT performed superior to Bard at outlining risks, benefits, and alternatives to common imaging scenarios. Generally, context dependency and prompt variability did not change chatbot response content. Due to the lack of detailed scientific reasoning and inability to provide patient-specific information, both AI chatbots have limitations as a patient information resource.Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes202Both AI chatbots pertaining to common imaging scenarios' risks, benefits, and alternatives have limitations as a patient information resource due to the lack of detailed scientific reasoning and inability to provide patient-specific information.Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes['Nikhil S. Patil', 'R. Huang', 'Scott Caterine', 'Jason Yao', 'Natasha Larocque', 'Christian B. van der Pol', 'Euan Stubbs']2024-01-06T00:00:00https://www.semanticscholar.org/paper/d586b211ead518dff2f365bbb48321055e76968a 45230b5453f9100d0d3b158a1254a1c3aa691577The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and model optimization. By providing a comprehensive overview of the state-of-the-art and examining the potential of AI to transform OR, this paper aims to inspire further research and innovation in the development of AI-enhanced OR methods and tools. The synergy between AI and OR is poised to drive significant advancements and novel solutions in a multitude of domains, ultimately leading to more effective and efficient decision-making.arXiv.org1662This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and model optimization.ArXiv['Zhenan Fan', 'Bissan Ghaddar', 'Xinglu Wang', 'Linzi Xing', 'Yong Zhang', 'Zirui Zhou']2024-01-06T00:00:00https://www.semanticscholar.org/paper/45230b5453f9100d0d3b158a1254a1c3aa691577 7465965cb856e590692b251fd1db88e081f70a70Abstract:The increasing encroachment of artificial intelligence (AI) on social life raises various risks to society, most prominently in the info-spheres created and controlled by Google, Facebook, Apple, and Amazon. We examine these risks through an in-depth discussion of the Facebook content moderation regime, which is already partially controlled by algorithms. We argue that the idea of ethical engineering, which was developed in the literature as a solution to the challenge of governing AI, is inadequate for various reasons. We develop a different approach to coping with the risks of AI governance, which we have termed "algorithmic constitutionalism." Our approach rests on three pillars: (a) layered architecture that consists of two levels of code: (i) operative or object level and (ii) metalevel, whose purpose is to shield the core principles of the system from algorithmic-initiated changes; (b) algorithmic metareasoning, which allows the system to operate simultaneously at the two levels, so that it can self-monitor, verify, and potentially correct in real-time operations at the object level if they depart from the principles protected by the metacode level; and (c) correction by deliberation. We elaborate the idea of algorithmic constitutionalism and demonstrate how it can be applied to the Facebook content moderation regime. As part of this elaboration, we also consider the tension between societal and algorithmic constitutionalism. Paradoxically, the attempt to subject the AI algorithm to external deliberative control also opens the door for the AI agent to intervene in that process, potentially undermining its very purpose. We conclude by exploring the implications of our argument for the new European Digital Services Act, which came into force in October 2022.Social Science Research Network50It is argued that the idea of ethical engineering, which was developed in the literature as a solution to the challenge of governing AI, is inadequate for various reasons and developed a different approach to coping with the risks of AI governance, which is termed "algorithmic constitutionalism".Indiana Journal of Global Legal Studies['Oren Perez', 'Nurit Wimer']2024-01-06T00:00:00https://www.semanticscholar.org/paper/7465965cb856e590692b251fd1db88e081f70a70 6325af203a2481f607137da2dc8cd39b668c66c7Neurosymbolic artificial intelligence (AI) is an emerging branch of AI that combines the strengths of symbolic AI and sub-symbolic AI. A major drawback of sub-symbolic AI is that it acts as a"black box", meaning that predictions are difficult to explain, making the testing&evaluation (T&E) and validation&verification (V&V) processes of a system that uses sub-symbolic AI a challenge. Since neurosymbolic AI combines the advantages of both symbolic and sub-symbolic AI, this survey explores how neurosymbolic applications can ease the V&V process. This survey considers two taxonomies of neurosymbolic AI, evaluates them, and analyzes which algorithms are commonly used as the symbolic and sub-symbolic components in current applications. Additionally, an overview of current techniques for the T&E and V&V processes of these components is provided. Furthermore, it is investigated how the symbolic part is used for T&E and V&V purposes in current neurosymbolic applications. Our research shows that neurosymbolic AI as great potential to ease the T&E and V&V processes of sub-symbolic AI by leveraging the possibilities of symbolic AI. Additionally, the applicability of current T&E and V&V methods to neurosymbolic AI is assessed, and how different neurosymbolic architectures can impact these methods is explored. It is found that current T&E and V&V techniques are partly sufficient to test, evaluate, verify, or validate the symbolic and sub-symbolic part of neurosymbolic applications independently, while some of them use approaches where current T&E and V&V methods are not applicable by default, and adjustments or even new approaches are needed. Our research shows that there is great potential in using symbolic AI to test, evaluate, verify, or validate the predictions of a sub-symbolic model, making neurosymbolic AI an interesting research direction for safe, secure, and trustworthy AI.IEEE Transactions on Artificial Intelligence1160This survey considers two taxonomies of neurosymbolic AI, evaluates them, and analyzes which algorithms are commonly used as the symbolic and sub-symbolic components in current applications, and an overview of current techniques for the T&E and V&V processes of these components is provided.ArXiv['Justus Renkhoff', 'Ke Feng', 'Marc Meier-Doernberg', 'Alvaro Velasquez', 'Houbing Herbert Song']2024-01-06T00:00:00https://www.semanticscholar.org/paper/6325af203a2481f607137da2dc8cd39b668c66c7 b0c81376cd87850fed9ff091d1df6e4164991992: This paper comprehensively analyzes the impact and challenges of fintech, especially blockchain, big data and artificial intelligence, in the financial field. Through specific application examples, this paper expounds how these technologies can improve transaction efficiency, optimize customer experience, and drive financial product innovation, and also discusses the challenges such as technology maturity, regulatory challenges, and data security. In the end, a series of strategies and suggestions, including strengthening technology research and development, building a regulatory system, improving data governance capabilities, cultivating interdisciplinary talents, and promoting the deep integration of fintech and traditional finance. Looking ahead, fintech will continue to lead innovation and change in the financial industry, but it also needs to pay close attention to and deal with new problems and challenges that may arise.International Journal of Social Sciences and Economic Management100This paper comprehensively analyzes the impact and challenges of fintech, especially blockchain, big data and artificial intelligence, in the financial field, and expounds how these technologies can improve transaction efficiency, optimize customer experience, and drive financial product innovation.International Journal of Social Sciences and Economic Management['Zhaoqi Li']2024-01-06T00:00:00https://www.semanticscholar.org/paper/b0c81376cd87850fed9ff091d1df6e4164991992 7d15044ea72a05c8814a6017da778bc8c5b13252Background: Artificial intelligence (AI) in healthcare, including machine learning and deep learning, enhances diagnosis and treatment across specialties like cardiology, dermatology, and ophthalmology. Its applications extend to transcription, patient data organization, and remote healthcare, offering support to medical professionals and transforming medical education for students and trainees. Methods: A prospective cross-sectional study was conducted with consecutive sampling, and 90 medical students were included in the study. Data was collected using Questionnaire data forms and analysed. Results: The findings revealed that 73.9% of students were aware of AI, but a significant 80.0% reported a lack of formal education on the subject. Positive perceptions included the recognition of AI's efficacy in reducing errors (71.1%) and facilitating patient education (56.7%). However, concerns were raised regarding potential impacts on the healthcare professional-patient relationship. The majority (56.7%) advocated for the integration of AI knowledge and skills into the academic curriculum. The mean positive perception score of 29.8 showed associations with age, and year of study. Conclusion: This study underscores the need to address gaps in AI awareness and advocates for the integration of AI education into pharmacy curricula. The findings highlight nuanced perspectives among students and emphasize the potential benefits of tailored educational strategies to harness positive attitudes toward AI integration in healthcare.International Journal For Multidisciplinary Research150The need to address gaps in AI awareness and advocates for the integration of AI education into pharmacy curricula is highlighted and the potential benefits of tailored educational strategies to harness positive attitudes toward AI integration in healthcare are emphasized.International Journal For Multidisciplinary Research['S. Rahul', 'MD Nawaz', 'Shankarappa M MUDGAL', 'H. Doddayya']2024-01-06T00:00:00https://www.semanticscholar.org/paper/7d15044ea72a05c8814a6017da778bc8c5b13252 ed93718a7f414db78eeabefeea667903e34a7b48Companies are adopting artificial intelligence (AI) to be innovative, improve their strategies and differentiate themselves from competitors. This research on Artificial Intelligence and Competitive Advantage of Micro, Small and Medium Enterprises (MSMEs) in Anambra State. The objective is to examine the extent of the introduction of AI in achieving competitive advantage among MSMEs in Anambra State. This study made use of a Survey Research Design, and the population was 1399 MSMEs from the state. Krejcie and Morgan's 1970 sample size determination formula was used to get a sample size of 301. The analysis for the study was carried out using both descriptive and inferential statistics and the hypotheses were tested at a 5% level of significance. The study revealed that there is a statistically significant positive relationship between data-driven targeted online adverts and increase quality lead generation for (r = .922; p-value < 0.05), The study, therefore, concluded that using data-driven targeted adverts will lead to generating quality that need little effort on converting to paying customers. Sequel to this, the researches recommend among others that the MSMEs in Anambra state need to increasingly rely on data in decision-making, especially in running adverts, as this will give them the opportunity to choose who sees the advert and will lead to generating quality leads which will boost their competitive advantage over their rivals.Cross Current International Journal of Economics Management and Media Studies10The study revealed that there is a statistically significant positive relationship between data-driven targeted online adverts and increase quality lead generation for MSMEs and concluded that using data-driven targeted adverts will lead to generating quality that need little effort on converting to paying customers.Cross Current International Journal of Economics, Management and Media Studies['Udeogu Udeogu', 'Arinze Christian', 'Okoye Okoye', 'Ikechukwu Emmanuel']2024-01-06T00:00:00https://www.semanticscholar.org/paper/ed93718a7f414db78eeabefeea667903e34a7b48 9b5fbe17610bd71758997210555c879d4b77ded0Pituitary440AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies, however, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.Pituitary['S. Maroufi', 'Yücel Doğruel', 'A. Pour-Rashidi', 'G. Kohli', 'Colson Tomberlin Parker', 'Tatsuya Uchida', 'M. Z. Asfour', 'Clara Martin', 'Mariagrazia Nizzola', 'Alessandro De Bonis', 'Mamdouh Tawfik-Helika', 'Amin Tavallai', 'Aaron A Cohen-Gadol', 'Paolo Palmisciano']2024-01-06T00:00:00https://www.semanticscholar.org/paper/9b5fbe17610bd71758997210555c879d4b77ded0 185d643b1dae87d988fcffe0270d78d74ecf4cbeArtificial intelligence is rapidly changing businesses based on digital trends and globalization. The aim of this article is to focus on emerging research trends regarding artificial intelligence that will influence global business management. This is significant as there have been rapid changes recently regarding artificial intelligence, including the surge in interest in generative forms. This article summarizes the trends taking place in the adoption process of artificial intelligence and what business managers need to do in order to increase their competitiveness. A brief history of how artificial intelligence has developed is stated, along with the main international business uses for artificial intelligence. Implications at the managerial and policy levels are stated that highlight the relevance and interesting nature of artificial intelligence in international business.FIIB Business Review400The trends taking place in the adoption process of artificial intelligence and what business managers need to do in order to increase their competitiveness are summarized.FIIB Business Review['V. Ratten']2024-01-06T00:00:00https://www.semanticscholar.org/paper/185d643b1dae87d988fcffe0270d78d74ecf4cbe efb8cd561ca6761db66f785cf3166e4ceb72cb3aAbstract:Artificial intelligence technologies are spreading across society. Generative systems, such as ChatGPT and DALL-E, provide only some examples of the expanding consumption and commodification of artificial intelligence applications in daily life. Nonetheless, the extensive trust and reliance on these technologies in public and private sectors is raising questions for the rule of law. Artificial intelligence technologies are not only mere tools which challenge the protection of fundamental rights when these systems moderate online speech, check employment performances in the workplace, and evaluate credit scores. Particularly, machine learning technologies also contribute to creating norms and rules shaping the enforcement of their functions, thus defining another generative layer of normativity competing with the rule of law in the algorithmic society. This work argues that artificial intelligence systems, particularly machine learning, develop norms by experience and learning within an opaque, technical space. The norms governing these systems are not always immutable but shaped across time. In the algorithmic society, code is not only law but also a source of law. The consolidation of this normative power, or the rule of tech, raises questions for constitutional democracies that are already struggling with solutions to limit other forms of normativity, primarily the predominance of online platforms in the setting of transnational private standards. This plurality of sources has put the rule of law under pressure. The expansion of the rule of tech as a source of norms leads to addressing the spaces for the rule of law and the limits of powers in the algorithmic society, as underlined by the European regulatory approach on artificial intelligence. Within this framework, this work analyses the challenges raised by the normative power of artificial intelligence systems and examines the spaces for the rule of law in the algorithmic society.50It is argued that artificial intelligence systems, particularly machine learning, develop norms by experience and learning within an opaque, technical space, which leads to the consolidation of this normative power, or the rule of tech, raising questions for constitutional democracies that are already struggling with solutions to limit other forms of normativity.Indiana Journal of Global Legal Studies['Giovanni De Gregorio']2024-01-06T00:00:00https://www.semanticscholar.org/paper/efb8cd561ca6761db66f785cf3166e4ceb72cb3a 9962b0921d399a7ff1614f620a3a0b229bdfb91a03['Mikailu Nadro']2024-01-06T00:00:00https://www.semanticscholar.org/paper/9962b0921d399a7ff1614f620a3a0b229bdfb91a 5ea37853554e1d4bc4d7f50fabe348285c71034500['Edmilson Rodrigues do Nascimento Junior']2024-01-06T00:00:00https://www.semanticscholar.org/paper/5ea37853554e1d4bc4d7f50fabe348285c710345 e8f6800132936be0692200358b7ceb4a3e3de26bWe examined the world's first regulation on Generative AI, China's Provisional Administrative Measures of Generative Artificial Intelligence Services, which came into effect in August 2023. Our assessment reveals that the Measures, while recognizing the technical advances of generative AI and seeking to govern its full life cycle, presents unclear distinctions regarding different roles in the value chain of Generative AI including upstream foundation model providers and downstream deployers. The lack of distinction and clear legal status between different players in the AI value chain can have profound consequences. It can lead to ambiguity in accountability, potentially undermining the governance and overall success of AI services.arXiv.org180China's Provisional Administrative Measures of Generative Artificial Intelligence Services, while recognizing the technical advances of generative AI and seeking to govern its full life cycle, presents unclear distinctions regarding different roles in the value chain of Generative AI including upstream foundation model providers and downstream deployers.ArXiv['Yulu Pi']2024-01-05T00:00:00https://www.semanticscholar.org/paper/e8f6800132936be0692200358b7ceb4a3e3de26b d6fc8a71af036bc5abb0e60430dfd4c701a90696Indonesia has several legal instruments for personal data protection that are scattered and only adapt to the main contents of each law, so the legal protection provided is still not optimal. This study aims to examine the use of Artificial Intelligence (AI) as a tool to protect personal data and to examine the urgency of a special regulation in Indonesia to protect personal data. This research method employs both a statutory and a comparative legal approach. The research indicates that it is imperative for the Indonesian government to establish and ratify a dedicated legal framework for safeguarding personal data without delay. Furthermore, leveraging the potential of AI presents a promising opportunity to maximize the protection of personal data.. The use of AI in personal data protection will minimize the occurrence of human errors so that personal data protection can be more secure. Keywords: personal data, artificial intelligence, protection, urgencyKnE Social Sciences90The research indicates that it is imperative for the Indonesian government to establish and ratify a dedicated legal framework for safeguarding personal data without delay and that leveraging the potential of AI presents a promising opportunity to maximize the protection of personal data.KnE Social Sciences['Nur Amalina Putri Adytia', 'Sofyan Arief', 'Duflitama Astesa']2024-01-05T00:00:00https://www.semanticscholar.org/paper/d6fc8a71af036bc5abb0e60430dfd4c701a90696 696c6d1b00729a088254d74c1d98fed5c3be99cdProtection of personal data is one of the rights possessed by humans, which is one of the privacy rights possessed by a person in maintaining and securing personal data owned by each individual. The development of Artificial Intelligence (AI)-based technology has developed rapidly in the digital world 4.0, where legal protection is needed in personal data protection legal instruments. This research aims to examine the use of AI as a tool in protecting personal data and to examine the urgency of a special regulation in Indonesia in protecting personal data. The research method used in writing this law is normative legal research. In this research, what is meant by juridical research is the 1945 Constitution of the Republic of Indonesia, the Law on Information, and Electronic Transactions Number 11 of 2008, the Regulation of the Minister of Communication and Information Number 20 of 2016, Government Regulation Number 82 of 2012, and UDHR by conducting a study of legal products in the form of laws and regulations. Furthermore, what is meant by normative research is related to the principle of legal certainty, which later can be linked to the urgency of personal data protection regulations for the protection, supervision, and utilization of personal data abuse. Keywords: personal data, artificial intelligence, protection, urgencyKnE Social Sciences100This research aims to examine the use of AI as a tool in protecting personal data and to examine the urgency of a special regulation in Indonesia in protecting personal data.KnE Social Sciences['Muhammad Hilmy Rizqullah Ramadhan', 'Kyagus Ramadhani', 'Mohammad Isrok', 'Isdian Anggraeny', 'Robbi Prasetyo']2024-01-05T00:00:00https://www.semanticscholar.org/paper/696c6d1b00729a088254d74c1d98fed5c3be99cd 74a89f7fb7ed47e6b4a0ac770cbd7022e13a9517Environmental science and pollution research international691Environmental science and pollution research international['Weixue Lu', 'Zhiyong Qin', 'Shijuan Yang']2024-01-05T00:00:00https://www.semanticscholar.org/paper/74a89f7fb7ed47e6b4a0ac770cbd7022e13a9517 93a8725da5cfda414a67741037289a01052bd861This article explores the impact mechanism of different types of environmental regulations on corporate green technology innovation (GTI). The research focuses on analyzing three types of environmental regulations: command based environmental regulation (ER1), market-oriented environmental regulation (ER2), and voluntary environmental regulation (ER3), and how they affect corporate GTI. This study selected enterprise GTI as the dependent variable and measured it by the number of applications for green invention patents and green utility model patents. The independent variables are the three types of environmental regulations mentioned above. According to data from Chinese A-share listed companies. Using benchmark regression models to analyze the impact of different environmental regulations on GTI, and constructing a moderating effect model to study the role of corporate R&D investment and government support in the process of environmental regulations affecting GTI. The results indicate that (1) ER1, ER2, and ER3 can all promote enterprise GTI, and the three environmental regulatory methods have a better synergistic effect. (2) R&D investment has a positive correlation with the relationship between ER2 and GTI, and a negative correlation with ER 3 and ER 1. (3) There are differences in the GTI performance of enterprises in different regions, ownership nature, factor density, and industry types under the influence of environmental regulations. (4) The impact of environmental regulatory policies on corporate GTI is mainly short-term. This study provides a new perspective on how environmental regulations affect corporate GTI, especially in the context of developing countries like China. The research findings emphasize the role of different types of environmental regulations in incentivizing corporate GTI, while also pointing out factors that governments need to consider when formulating environmental policies, such as regional differences and corporate characteristics, which are of great significance for promoting green development of enterprises and achieving broader sustainable development goals.PLoS ONE520PLOS ONE['Lihua Ma', 'Shiya Ma', 'Qisheng Tang', 'Mingmei Sun', 'Huizhe Yan', 'Xiuling Yuan', 'Wei Tian', 'Yufei Chen']2024-01-05T00:00:00https://www.semanticscholar.org/paper/93a8725da5cfda414a67741037289a01052bd861 6cbfe49e319fccd6706990b8c288f06dcc1e342aCryptocurrencies have been defined and regulated differently in all corners of society. Much research has been made on countries' institutional measures to consummate regulations toward cryptocurrencies further. This paper explores the definition and nature of cryptocurrencies through a literature review method. The nature of cryptocurrencies has directly led to their benefits and misuse. This paper also explores the regulatory measures made by the U.S. and the flaws revealed in the Coinbase Case and Ripple Case. The ambiguous definition between regulatory departments and overlapping law enforcement power has made investors not knowing what course to take. New measures are taken in Hong Kong through a compulsory licensing system and laying major responsibility on platform service providers. This paper examines the new efforts and proposes some potential flaws under the new regulatory system. The Hong Kong regulatory approach is believed to provide insight and reference for other markets that would like to include cryptocurrencies into regulation.Advances in Economics, Management and Political Sciences00Advances in Economics, Management and Political Sciences['Huicheng Dong']2024-01-05T00:00:00https://www.semanticscholar.org/paper/6cbfe49e319fccd6706990b8c288f06dcc1e342a 6f702f88f0d74a24f1ff06211975b30839ac2c58In the largest survey of its kind, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace of AI progress and the nature and impacts of advanced AI systems The aggregate forecasts give at least a 50% chance of AI systems achieving several milestones by 2028, including autonomously constructing a payment processing site from scratch, creating a song indistinguishable from a new song by a popular musician, and autonomously downloading and fine-tuning a large language model. If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar survey we conducted only one year earlier [Grace et al., 2022]. However, the chance of all human occupations becoming fully automatable was forecast to reach 10% by 2037, and 50% as late as 2116 (compared to 2164 in the 2022 survey). Most respondents expressed substantial uncertainty about the long-term value of AI progress: While 68.3% thought good outcomes from superhuman AI are more likely than bad, of these net optimists 48% gave at least a 5% chance of extremely bad outcomes such as human extinction, and 59% of net pessimists gave 5% or more to extremely good outcomes. Between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that"substantial"or"extreme"concern is warranted about six different AI-related scenarios, including misinformation, authoritarian control, and inequality. There was disagreement about whether faster or slower AI progress would be better for the future of humanity. However, there was broad agreement that research aimed at minimizing potential risks from AI systems ought to be prioritized more.arXiv.org463ArXiv['Katja Grace', 'Harlan Stewart', 'J. F. Sandkühler', 'Stephen Thomas', 'Ben Weinstein-Raun', 'Jan Brauner']2024-01-05T00:00:00https://www.semanticscholar.org/paper/6f702f88f0d74a24f1ff06211975b30839ac2c58 f51489c94d71271a98512c8b214aaa599cc0c059This paper explores how Generative AI can be incorporated into software development education. We present examples of formative and summative assessments, which explore various aspects of ChatGPT, including its coding capabilities, its ability to construct arguments as well as ethical issues of using ChatGPT and similar tools in education and the workplace. Our work is inspired by the insights from surveys that show that the learners on our Degree Apprenticeship Programme have a great interest in learning about and exploiting emerging AI technology. Similarly, our industrial partners have a clear interest for their employees to be formally prepared to use GenAI in their software engineering roles. In this vein, it is proposed that embedding the use of GenAI tools in a careful and creative way - by developing assessments which encourage learners to critically evaluate AI output - can be beneficial in helping learners understand the subject material being taught without the risk of the AI tools “doing the homework”.Conference on Computing Education Practice192Examples of formative and summative assessments are presented, which explore various aspects of ChatGPT, including its coding capabilities, its ability to construct arguments as well as ethical issues of using ChatGPT and similar tools in education and the workplace.Proceedings of the 8th Conference on Computing Education Practice['Olga Petrovska', 'Lee Clift', 'Faron Moller', 'Rebecca Pearsall']2024-01-05T00:00:00https://www.semanticscholar.org/paper/f51489c94d71271a98512c8b214aaa599cc0c059 fd69c246016207176a5bb1b4339eca9dff2068e7With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the bioinformatics analysis continues to grow. In response to this need, we introduce Automated Bioinformatics Analysis (AutoBA), an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBA’s robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome/exome sequencing (WGS/WES), chromatin immunoprecipitation assays with sequencing (ChIP-seq), RNA sequencing (RNA-seq), single-cell RNA-seq, spatial transcriptomics and so on. AutoBA’s unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.bioRxiv752Automated Bioinformatics Analysis (AutoBA), an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models, represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.bioRxiv['Juexiao Zhou', 'Bin Zhang', 'Xiuying Chen', 'Haoyang Li', 'Xiaopeng Xu', 'Siyuan Chen', 'Xin Gao']2024-01-05T00:00:00https://www.semanticscholar.org/paper/fd69c246016207176a5bb1b4339eca9dff2068e7 850be153703da35f1802455fc45fa630e0bbd646Today we are talking about a new generation of AI, about generative AI, which came into mass use at the end of last year. Artificial intelligence is useful if we know how to use it and it has certainly been fundamentally changing the marketing universe for some time. In marketing, AI tools help in customer segmentation, in finding new customers who show propensity to buy, in product recommendations, in sales, in customer support, in the production of advertising messages, in the generation of responses on social networks, in purchasing behavior research, in algorithms for predicting buyer behavior, etc. ChatGPT and related technologies are the reality of individuals and organizations. New technologies develop very quickly and exponentially, where we do not know where and how the development will go, but we know that changes will happen. It is best to start preparing for the changes today. AI is not so much a technological change as it is a business change in organizations that requires change management, strategy and vision.Agora International Journal of Economical Sciences61AI is not so much a technological change as it is a business change in organizations that requires change management, strategy and vision, and it is best to start preparing for the changes today.AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES['Milena Fornazarič']2024-01-05T00:00:00https://www.semanticscholar.org/paper/850be153703da35f1802455fc45fa630e0bbd646 c515a1ab2c7aea0dc55edbefe954448ea744c7e7In the age of artificial intelligence (AI), technological progress is changing established workflows and enabling some basic routines to be updated. In dentistry, the patient’s face is a crucial part of treatment planning, although it has always been difficult to grasp in an analytical way. This review highlights the current digital advances that, thanks to AI tools, allow us to implement facial features beyond symmetry and proportionality and incorporate facial analysis into diagnosis and treatment planning in orthodontics. A Scopus literature search was conducted to identify the topics with the greatest research potential within digital orthodontics over the last five years. The most researched and cited topic was artificial intelligence and its applications in orthodontics. Apart from automated 2D or 3D cephalometric analysis, AI finds its application in facial analysis, decision-making algorithms as well as in the evaluation of treatment progress and retention. Together with AI, other digital advances are shaping the face of today’s orthodontics. Without any doubts, the era of “old” orthodontics is at its end, and modern, face-driven orthodontics is on the way to becoming a reality in modern orthodontic practices.Applied Informatics1101This review highlights the current digital advances that, thanks to AI tools, allow us to implement facial features beyond symmetry and proportionality and incorporate facial analysis into diagnosis and treatment planning in orthodontics.AI['Juraj Tomášik', 'Márton Zsoldos', 'Ľubica Oravcová', 'Michaela Lifková', 'Gabriela Pavleová', 'Martin Strunga', 'Andrej Thurzo']2024-01-05T00:00:00https://www.semanticscholar.org/paper/c515a1ab2c7aea0dc55edbefe954448ea744c7e7 392c14927c3636e9d17af70885d103c8ab36f519Internet Reference Services Quarterly254Internet Reference Services Quarterly['Varun Gupta', 'Hongji Yang']2024-01-05T00:00:00https://www.semanticscholar.org/paper/392c14927c3636e9d17af70885d103c8ab36f519 960f5bb9d73235adca0a38621c742030b87b8783Behaviour &amp; Information Technology562Behaviour &amp; Information Technology['Seyoung Lee', 'Younjung Park', 'Gain Park']2024-01-05T00:00:00https://www.semanticscholar.org/paper/960f5bb9d73235adca0a38621c742030b87b8783 c3461f3e83f85b80813088bfb442e6c326f35b8eThe secure exchange of cryptographic keys is crucial for ensuring the conden-tiality and integrity of AI models during sharing and collaboration. This research paper focuses on proposing a secure key exchange approach speci�cally tailored for encrypted model sharing. By addressing the key distribution problem inherent in AI model sharing, this approach establishes a secure and robust mechanism for exchanging cryptographic keys. The paper provides an overview of secure key exchange techniques, including public key cryptography, Die-Hellman key exchange, and elliptic curve cryptography, and discusses their application in the context of AI model sharing. The implementation details and evaluation results demonstrate the effectiveness and security of the proposed secure key exchange approach, offering a reliable solution for ensuring the con�dentiality and integrity of shared AI models.International Journal of Artificial Intelligence and Machine Learning120The implementation details and evaluation results demonstrate the effectiveness and security of the proposed secure key exchange approach, offering a reliable solution for ensuring the con�dentiality and integrity of shared AI models.International Journal of Artificial Intelligence and Machine Learning['Bheema Shanker Neyigapula']2024-01-05T00:00:00https://www.semanticscholar.org/paper/c3461f3e83f85b80813088bfb442e6c326f35b8e 1a2be026972130fd5e079bef89c1978f17a213ccIn the era of advanced manufacturing, digital twins have emerged as a foundational technology, offering the promise of improved efficiency, precision, and predictive capabilities. However, the increasing presence of AI tools for digital twin models and their integration into industrial processes has brought forth a pressing need for trustworthy and reliable systems. Uncertainty-Aware eXplainable Artificial Intelligence (UAXAI) is proposed as a critical paradigm to address these challenges, as it allows for the quantification and communication of uncertainties associated with predictive models and their corresponding explanations. As a platform and guiding philosophy to promote human-centered trust, UAXAI is based on five fundamental pillars: accessibility, reliability, explainability, robustness, and computational efficiency. The development of UAXAI caters to a diverse set of stakeholders, including end users, developers, regulatory bodies, the scientific community, and industrial players, each with their unique perspectives on trust and transparency in digital twins.Frontiers of Mechanical Engineering230Uncertainty-Aware eXplainable Artificial Intelligence (UAXAI) is proposed as a critical paradigm to address challenges of trustworthy and reliable systems, as it allows for the quantification and communication of uncertainties associated with predictive models and their corresponding explanations.Frontiers in Mechanical Engineering['Joseph Cohen', 'Xun Huan']2024-01-05T00:00:00https://www.semanticscholar.org/paper/1a2be026972130fd5e079bef89c1978f17a213cc 040d4b58387c0f46b02032a8155390a22999673eThe emergence of increasingly capable artificial intelligence (AI) systems has raised concerns about the potential extreme risks associated with them. The issue has drawn substantial attention in academic literature and compelled legislators of regulatory frameworks like the European Union AI Act (AIA) to readapt them to the new paradigm. This paper examines whether the European Parliament’s draft of the AIA constitutes an appropriate approach to address the risks derived from frontier models. In particular, we discuss whether the AIA reflects the policy needs diagnosed by recent literature and determine if the requirements falling on providers of foundation models are appropriate, sufficient, and durable. We find that the provisions are generally adequate, but insufficiently defined in some areas and lacking in others. Finally, the AIA is characterized as an evolving framework whose durability will depend on the institutions’ ability to adapt to future progress.Inteligencia Artif.290It is found that the European Parliament’s draft of the AIA constitutes an appropriate approach to address the risks derived from frontier models, but the provisions are generally adequate, but insufficiently defined in some areas and lacking in others.Inteligencia Artif.['Guillem Bas', 'Claudette Salinas', 'Roberto Tinoco', 'Jaime Sevilla']2024-01-05T00:00:00https://www.semanticscholar.org/paper/040d4b58387c0f46b02032a8155390a22999673e 21333fb7d42ca39d8d13a243b1b02962432cf53eBackground: This article delves into an in-depth analysis of the statistically significant differences in AI support levels for project management between SMEs and large enterprises. The research was conducted based on a comprehensive survey encompassing a sample of 473 SMEs and large Slovenian enterprises. Methods: To validate the observed differences, statistical analysis, specifically the Mann–Whitney U test, was employed. Results: The results confirm the presence of statistically significant differences between SMEs and large enterprises across multiple dimensions of AI support in project management. Large enterprises exhibit on average a higher level of AI adoption across all five AI utilization dimensions. Specifically, large enterprises scored significantly higher (p < 0.05) in AI adopting strategies and in adopting AI technologies for project tasks and team creation. This study’s findings also underscored the significant differences (p < 0.05) between SMEs and large enterprises in their adoption and utilization of AI technologies for project management purposes. While large enterprises scored above 4 for several dimensions, with the highest average score assessed (mean value 4.46 on 1 to 5 scale) for the usage of predictive Analytics Tools to improve the work on the project, SMEs’ average levels, on the other hand, were all below 4. SMEs in particular may lag in incorporating AI into various project activities due to several factors such as resource constraints, limited access to AI expertise, or risk aversion. Conclusions: The results underscore the need for targeted strategies to enhance AI adoption in SMEs and leverage its benefits for successful project implementation and strengthen the company’s competitiveness.Applied Informatics280The need for targeted strategies to enhance AI adoption in SMEs and leverage its benefits for successful project implementation and strengthen the company’s competitiveness is underscored.AI['P. Tominc', 'D. Oreški', 'Vesna Čančer', 'M. Rožman']2024-01-05T00:00:00https://www.semanticscholar.org/paper/21333fb7d42ca39d8d13a243b1b02962432cf53e 459f609239ad3ca9e5b45c49c4ec9e09888eae74The article explores the emotional aspect of artificial intelligence in art and design. With technological advancements, AI has shown tremendous potential in the realm of artistic creation. Firstly, the article reviews the development of AI in art and design, introducing applications of technologies such as generative art, deep learning, and generative adversarial networks. Secondly, it delves into the emotional dimension of AI in artistic design. Despite significant progress in learning and imitating artistic styles, AI's expression of emotions remains limited to simulating programs and data, creating a gap with human emotional experiences. However, by learning from human aesthetic experiences, AI utilizes universally recognized emotional symbols in artistic works, evoking aesthetic resonance among audiences. Furthermore, the article emphasizes the importance of human designers in the creative process and the potential for collaboration between AI and humans. Lastly, it discusses the future possibilities wherein AI, as technology advances, may enter an era of strong artificial intelligence, potentially disrupting the ecosystem of art and design. Although AI might acquire emotional and creative capabilities in the future, the emotional experiences and unique creativity of human creators remain irreplaceable. Therefore, the future of art and design might be a product of collaboration between humans and AI, opening new spaces for exploration and contemplation in the art world.International Journal of Interdisciplinary Studies in Social Science240The article reviews the development of AI in art and design, introducing applications of technologies such as generative art, deep learning, and generative adversarial networks and delves into the emotional dimension of AI in artistic design.International Journal of Interdisciplinary Studies in Social Science['Wenbo Zhao', 'Yuanyuan Sun']2024-01-05T00:00:00https://www.semanticscholar.org/paper/459f609239ad3ca9e5b45c49c4ec9e09888eae74 659c402d726b8b6e2998655440a315607e8facbdAs a major player in the world market, Saudi Arabia has seen substantial adoption of artificial intelligence AI) technology in its commercial environment. This study intends to thoroughly examine the specific effects of AI on Saudi business accounting systems. This paper offers comprehensive knowledge of the consequences of AI application in the accounting sector through a thorough examination of the body of existing literature. It examines how traditional accounting methods are affected by AI-driven automation, data analysis, and decision-making processes in the Saudi Arabian environment. The viewpoints and experiences of first-hand participants in integrating AI into Saudi enterprises’ accounting systems are provided by this study through a survey distributed to important stakeholders, such as accounting professionals, technology specialists, and business leaders. This study also emphasizes how incorporating AI technology into accounting procedures may affect workforce dynamics, skill needs, and organizational structure as a whole. One of the most significant research findings is the ability of AI to process enormous volumes of data quickly and accurately, allowing for improved financial analysis, risk assessment, and forecasting. This facilitates wiser and more strategic business decisions. AI also simplified accounting processes and decreased the need for human labor, saving Saudi enterprises money. As a result, resource allocation was optimized and overall financial performance was enhanced.Wseas Transactions on Business and Economics860One of the most significant research findings is the ability of AI to process enormous volumes of data quickly and accurately, allowing for improved financial analysis, risk assessment, and forecasting, and facilitates wiser and more strategic business decisions.WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS['Randa Abd Elhamied Mohammed Hamza', 'Nasareldeen Hamed Ahmed Alnor', 'E. Al-Matari', 'Z. Benzerrouk', 'Abdelwhab Musa Elgali Mohamed', 'Mohamed Youcef Bennaceur', 'Ahmed Hesham Moawed Elhefni', 'Mona M. Elshaabany']2024-01-05T00:00:00https://www.semanticscholar.org/paper/659c402d726b8b6e2998655440a315607e8facbd 47908ad97ff4ca35201d459b18825bdffad2d850Integrating Artificial Intelligence (AI) into daily life has brought transformative changes, ranging from personalized recommendations on streaming platforms to advancements in medical diagnostics. However, concerns about the transparency and interpretability of AI models, intense neural networks, have become prominent. This paper explores the emerging paradigm of Explainable Artificial Intelligence (XAI) as a crucial response to address these concerns. Delving into the multifaceted challenges posed by AI complexity, the study emphasizes the critical significance of interpretability. It examines how XAI is fundamentally reshaping the landscape of artificial intelligence, seeking to reconcile precision with the transparency necessary for widespread acceptance.International Journal of Computing and Engineering10This paper explores the emerging paradigm of Explainable Artificial Intelligence (XAI) as a crucial response to address concerns about the transparency and interpretability of AI models, intense neural networks.International Journal of Computing and Engineering['Narayana Challa']2024-01-05T00:00:00https://www.semanticscholar.org/paper/47908ad97ff4ca35201d459b18825bdffad2d850 21eca59a79167e76be260a3f3f61ebb2b2904cbeThis document offers a critical overview of the emerging trends and significant advancements in artificial intelligence (AI) within the pharmaceutical industry. Detailing its application across key operational areas, including research and development, animal testing, clinical trials, hospital clinical stages, production, regulatory affairs, quality control and other supporting areas, the paper categorically examines AI's role in each sector. Special emphasis is placed on cutting-edge AI technologies like machine learning algorithms and their contributions to various aspects of pharmaceutical operations. Through this comprehensive analysis, the paper highlights the transformative potential of AI in reshaping the pharmaceutical industry's future.arXiv.org610Through this comprehensive analysis, the paper highlights the transformative potential of AI in reshaping the pharmaceutical industry's future.ArXiv['Yu Han', 'Jingwen Tao']2024-01-05T00:00:00https://www.semanticscholar.org/paper/21eca59a79167e76be260a3f3f61ebb2b2904cbe a0200fad3580fa376606572f9f7c27f94233e630The 21st Century has changed, like no other centuries, the human society’s development on unprecedent scale. Is the century of technological advances such Artificial Intelligence (AI) that, is currently hard-pressing the field of research-doing at Higher Education beyond human intellectual capabilities. This can change the future of this educational field. As it is becoming a global education phenomenon. The study aims to present an outlook about the impact of Artificial Intelligence (AI) into academia research. An interdisciplinary approach based on prior trustworthy research-works results has been used to address the research hypotheses and answer their respective questions. A qualitative methodology has been used to accomplish the aim. This research criterium involved a theory triangulation, the reviewing of academic and scientific research works, books, and Internet accredited websites.Global Journal of Human-Social Science Research180The study aims to present an outlook about the impact of Artificial Intelligence (AI) into academia research, using an interdisciplinary approach based on prior trustworthy research-works results to address the research hypotheses and answer their respective questions.Global Journal of Human-Social Science['J. L. Rivera']2024-01-05T00:00:00https://www.semanticscholar.org/paper/a0200fad3580fa376606572f9f7c27f94233e630 9ac08d6e49af613f1ad40f8357bcc33908e855f7The epidemic has dealt a huge blow to the catering industry. It has led to the closure of tens of thousands of brick-and-mortar restaurant economy. But the rise of AI is bringing huge changes to all aspects of society. A new industrial revolution is imminent. This is a good opportunity to use AI to reinvigorate the restaurant industry. The article suggests and proves the feasibility that the F&B industry should use AI wisely in forecasting demand, inventory management, raw material transportation, food safety, and customer service. So as to improve the operation efficiency of the catering industry supply chain, reduce operating costs, and achieve a certain degree of automation and intelligence. Keeping up with the progress of the times. At the same time, AI also brings problems such as data quality, data security, technology and personnel costs, lack of customer communication, and employee unemployment. However, according to the analyses, these problems are promisingly able to be solved properly eventually with the human acceptance of AI and social development.Advances in Economics, Management and Political Sciences00The article suggests and proves the feasibility that the F&B industry should use AI wisely in forecasting demand, inventory management, raw material transportation, food safety, and customer service so as to improve the operation efficiency of the catering industry supply chain, reduce operating costs, and achieve a certain degree of automation and intelligence.Advances in Economics, Management and Political Sciences['Yufan Yao']2024-01-05T00:00:00https://www.semanticscholar.org/paper/9ac08d6e49af613f1ad40f8357bcc33908e855f7 28e1e7479d12d5c8f89d02f1adac0e7d56efa912Tourism Recreation Resarch431Tourism Recreation Research['R. Rather']2024-01-05T00:00:00https://www.semanticscholar.org/paper/28e1e7479d12d5c8f89d02f1adac0e7d56efa912 1f3c9afb9df577d4af98f2aaf57473b2ae0ef80dHuman brain organoids, aka cerebral organoids or earlier “mini-brains”, are 3D cellular models that recapitulate aspects of the developing human brain. They show tremendous promise for advancing our understanding of neurodevelopment and neurological disorders. However, the unprecedented ability to model human brain development and function in vitro also raises complex ethical, legal, and social challenges. Organoid Intelligence (OI) describes the ongoing movement to combine such organoids with Artificial Intelligence to establish basic forms of memory and learning. This article discusses key issues regarding the scientific status and prospects of brain organoids and OI, conceptualizations of consciousness and the mind–brain relationship, ethical and legal dimensions, including moral status, human–animal chimeras, informed consent, and governance matters, such as oversight and regulation. A balanced framework is needed to allow vital research while addressing public perceptions and ethical concerns. Interdisciplinary perspectives and proactive engagement among scientists, ethicists, policymakers, and the public can enable responsible translational pathways for organoid technology. A thoughtful, proactive governance framework might be needed to ensure ethically responsible progress in this promising field.Frontiers Artif. Intell.1071Frontiers in Artificial Intelligence['Thomas Hartung', 'I. M. Pantoja', 'Lena Smirnova']2024-01-05T00:00:00https://www.semanticscholar.org/paper/1f3c9afb9df577d4af98f2aaf57473b2ae0ef80d c46cf0ff2d2d513db48cc432f7d5a8419495c368Background The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. Objective This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. Methods This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students’ perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. Results Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. Conclusions This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula.JMIR Medical Education435A pronounced discrepancy was revealed between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education.JMIR Medical Education['Lukas Weidener', 'Michael Fischer']2024-01-05T00:00:00https://www.semanticscholar.org/paper/c46cf0ff2d2d513db48cc432f7d5a8419495c368 e96bb9b1a63b10d12a5e39f4cf4fea24b0f00332As the field of artificial intelligence (AI) continues to progress, the use of AI-powered chatbots, such as ChatGPT, in higher education settings has gained significant attention. This paper addresses a well-defined problem pertaining to the critical need for a comprehensive examination of students' ChatGPT adoption in higher education. To examine such adoption, it is imperative to focus on measuring actual user behavior. While measuring students' ChatGPT usage behavior at a specific point in time can be valuable, a more holistic approach is necessary to understand the temporal dynamics of AI adoption. To address this need, a longitudinal survey was conducted, examining how students' ChatGPT usage behavior changes over time among students, and unveiling the drivers of such behavior change. The empirical examination of 222 Dutch higher education students revealed a significant decline in students' ChatGPT usage behavior over an 8 month period. This period was defined by two distinct data collection phases: the initial phase (T1) and a follow-up phase conducted 8 months later (T2). Furthermore, the results demonstrate that changes in trust, emotional creepiness, and Perceived Behavioral Control significantly predicted the observed change in usage behavior. The findings of this research carry significant academic and managerial implications, as they advance our comprehension of the temporal aspects of AI adoption in higher education. The findings also provide actionable guidance for AI developers and educational institutions seeking to optimize student engagement with AI technologies.Frontiers Artif. Intell.552A longitudinal survey was conducted, examining how students' ChatGPT usage behavior changes over time among students, and unveiling the drivers of such behavior change, demonstrating that changes in trust, emotional creepiness, and Perceived Behavioral Control significantly predicted the observed change in usage behavior.Frontiers in Artificial Intelligence['Athanasios Polyportis']2024-01-05T00:00:00https://www.semanticscholar.org/paper/e96bb9b1a63b10d12a5e39f4cf4fea24b0f00332 6c9ace1890ffa47a070d806254250cb30dd57c81Developing English speaking skills can be challenging for many English language learners. The advent of generative artificial intelligence (GAI) has prompted the emergence of a growing number of artificial intelligence (AI)-powered chatbots designed to tackle these challenges. One popular tool is ‘Call Annie,’ a GAI video chatbot that can act as a virtual assistant, enabling users to engage in immersive video calls with AI avatars. This technology review discusses its functionality, how it can be used in supporting learners’ language development, how teachers can collaborate with it in class and its potential limitations.RELC Journal : A Journal of Language Teaching and Research in Southeast Asia72‘Call Annie,’ a GAI video chatbot that can act as a virtual assistant, enabling users to engage in immersive video calls with AI avatars is discussed.RELC Journal['Yuwei Wan', 'Benjamin Luke Moorhouse']2024-01-05T00:00:00https://www.semanticscholar.org/paper/6c9ace1890ffa47a070d806254250cb30dd57c81 69efc60283bdc5e2eb7382e9c30a4280b8a6997dArtificial intelligence (AI) applications in breast imaging span a wide range of tasks including decision support, risk assessment, patient management, quality assessment, treatment response assessment and image enhancement. However, their integration into the clinical workflow has been slow due to the lack of a consensus on data quality, benchmarked robust implementation, and consensus-based guidelines to ensure standardization and generalization. Contrast-enhanced mammography (CEM) has improved sensitivity and specificity compared to current standards of breast cancer diagnostic imaging i.e., mammography (MG) and/or conventional ultrasound (US), with comparable accuracy to MRI (current diagnostic imaging benchmark), but at a much lower cost and higher throughput. This makes CEM an excellent tool for widespread breast lesion characterization for all women, including underserved and minority women. Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them. The Methodical approaches, such as image processing, feature extraction, quantitative analysis, lesion classification, lesion segmentation, integration with clinical data, early detection, and screening support have been carefully analysed in recent studies addressing breast cancer detection and diagnosis. Recent guidelines described by Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to establish a robust framework for rigorous evaluation and surveying has inspired the current review criteria.Frontiers in Radiology641Underlining the critical need for early detection and accurate diagnosis of breast cancer, this review examines the limitations of conventional approaches and reveals how AI can help overcome them.Frontiers in Radiology['Ketki Kinkar', 'Brandon K.K. Fields', 'Mary W. Yamashita', 'Bino Varghese']2024-01-05T00:00:00https://www.semanticscholar.org/paper/69efc60283bdc5e2eb7382e9c30a4280b8a6997d 1128fcdb10c6c49f0532783083e8eb0e09d3066cOver the last ten years, Artificial Intelligence (AI) has significantly contributed to solving several health issues, such as cancer.Deep Learning (DL), a subset of adaptable AI that facilitates automated identification of important characteristics, is rapidly used in manyfundamental and clinical cancer investigation domains. This review provides a comprehensive overview of recent instances of AI utilizedin oncology. It highlights how DL techniques have effectively resolved previously deemed unsolvable issues and discusses the challengesthat must be addressed for the wider implementation of such applications. In addition, we emphasize valuable resources and datasets thatmight facilitate the use of AI in cancer research. In the next decade, the development of novel AI methods and their practical use willprovide valuable knowledge in the field of cancer. The advancement of AI technology has proven rapid in recent times and is beingincorporated into every facet of life. The medical profession is also advancing in the deployment of AI-equipped medical equipment. AI isanticipated to have a significant impact on achieving the present worldwide movement towards precision medicine. This article offers acomprehensive summary of the historical development of AI and the current advancements in medical AI, with a specific emphasis oncancer.In addition, while AI has significant promise, several unresolved concerns exist.International Journal of Trends in OncoScience01A comprehensive overview of recent instances of AI utilized in oncology is provided, highlighting how DL techniques have effectively resolved previously deemed unsolvable issues and discusses the challengesthat must be addressed for the wider implementation of such applications.International Journal of Trends in OncoScience['Ammar A. Razzak Mahmood', 'Dr Roopa Murgod', 'Saswat swarup Badapanda', 'Dr. John Abraham']2024-01-05T00:00:00https://www.semanticscholar.org/paper/1128fcdb10c6c49f0532783083e8eb0e09d3066c f745909c9e14b86e9e30c86cd3b683a0bd70d388Breast Cancer (BC) is a major universal health problem. Early detection and precise diagnosis are vital for enlightening outcomes. Artificial Intelligence (AI) technologies can potentially revolutionize the field of BC by providing quantitative representations of medical images to assist in segmentation, diagnosis, and prognosis. AI can improve image quality, detect and segment breast lesions, classify cancer and predict its behavior, and integrate data from multiple sources to predict clinical outcomes. It can lead to more personalized and effective treatment for BC patients. Challenges faced by AI in real-life solicitations include data curation, model interpretability, and run-through guidelines. However, the clinical implementation of AI is expected to deliver vital guidance for patient-tailored management. BC is a major global health problem; early detection and treatment are crucial for improving outcomes. Imaging detection is a key screening, diagnosis, and treatment effectiveness assessment tool. However, the irresistible number of images creates a heavy capacity for radiologists and delays reporting. AI has the potential to revolutionize BC imaging by improving efficiency and accuracy. AI can recognize, segment, and diagnose tumor lesions automatically and analyze tumor images on a molecular level. It could lead to more personalized treatment strategies. However, AI-assisted imaging diagnosis is still in its early stages of development, and more research is needed to validate its clinical effectiveness. Therefore, AI is a promising new technology that has the potential to progress the diagnosis and treatment of BC, and AI-assisted imaging diagnosis is a promising new technology for improving the early detection and diagnosis of BC. More research is needed to bring this technology to clinical practice.International Journal of Trends in OncoScience01Artificial Intelligence is a promising new technology that has the potential to progress the diagnosis and treatment of BC, and AI-assisted imaging diagnosis is a promising new technology for improving the early detection and diagnosis of BC.International Journal of Trends in OncoScience['Dr. Anand Mohan Jha', 'Dr. Abikesh Prasada Kumar Mahapatra', 'Dr. John Abraham', 'Dr. Somenath Ghosh']2024-01-05T00:00:00https://www.semanticscholar.org/paper/f745909c9e14b86e9e30c86cd3b683a0bd70d388 a5ea9efc5d0c99444c64d0aa8b5542a4d22859ccAutism Spectrum Disorder has seen a drastic increase in prevalence over the past two decades, along with discourse rife with debates and misinformation. This discourse has primarily taken place online, the main source of information for parents seeking information about autism. One potential tool for navigating information is ChatGPT-4, an artificial intelligence question and answer-style communication program. Although ChatGPT shows great promise, no empirical work has evaluated its viability as a tool for providing information about autism to caregivers. The current study evaluated answers provided by ChatGPT, including basic information about autism, myths/misconceptions, and resources. Our results suggested that ChatGPT was largely correct, concise, and clear, but did not provide much actionable advice, which was further limited by inaccurate references and hyperlinks. The authors conclude that ChatGPT-4 is a viable tool for parents seeking accurate information about autism, with opportunities for improvement in actionability and reference accuracy.Cyberpsychology, Behavior, and Social Networking101It is concluded that ChatGPT-4 is a viable tool for parents seeking accurate information about autism, with opportunities for improvement in actionability and reference accuracy.Cyberpsychology, behavior and social networking['T. McFayden', 'Stephanie Bristol', 'Orla C Putnam', 'Clare Harrop']2024-01-05T00:00:00https://www.semanticscholar.org/paper/a5ea9efc5d0c99444c64d0aa8b5542a4d22859cc d32c86aaad87d1592f7aff629293319e563a6671Deepfake is an artificial intelligence-based technique for synthesizing an image of a person, using a special method to combine images or videos to make the result look realistic (POLRI 2020). Deepfake is a relatively new type of technology that allows you to download deepfake apps for free. Anyone can access the Deepfake app to create freely edited videos and images. The original purpose of using deepfakes was entertainment on TV and social media. But over time, technology is used as a tool to mislead people and spread misinformation. Deepfakes can undermine public trust, especially when it comes to big and famous people. Not only fake videos but reputation is also easily damaged by this technique. Keywords: deepfake, artificial intelligence, information and transaction electronicKnE Social Sciences140KnE Social Sciences['Cindy Monique', 'Tongat', 'Siti Wulandari', 'Aprilia Bhirini Slamet']2024-01-05T00:00:00https://www.semanticscholar.org/paper/d32c86aaad87d1592f7aff629293319e563a6671 c5176ab062052861f1652d520d83d867f0e6aebcCyber Security has emerged as a key worry in the digital age & the last ten years, the field of Cyber Security has expanded significantly. whereas the importance of Artificial Intelligence (AI) is also increasing day by day, So the use of AI to handle Cyber Security concerns and dangers will be covered in this Paper.International Journal of Scientific Research in Science Engineering and Technology50The use of AI to handle Cyber Security concerns and dangers will be covered in this Paper.International Journal of Scientific Research in Science, Engineering and Technology['Miss. Tanmayi Ajay Dubey', 'Mr. Chinmay R. Sambhe', 'Miss. Aboli Sanjay Gujar']2024-01-05T00:00:00https://www.semanticscholar.org/paper/c5176ab062052861f1652d520d83d867f0e6aebc af77561ef9946e5bf5d771ea141972487c309ddeArtificial intelligence(AI) is facing rapid development, which presents both opportunities and risks. The Unities States, the worlds largest economy and a leading technological power, has witnessed significant advancements in AI. As an integral part of automated production, AI drives economic productivity, but it alters the demands and structure of the United States labor market. Increasing joblessness and a decrease in the number of people actively participating in the workforce imply that AI holds the capability to substitute human employment, thereby highlighting the importance of individuals adapting to changing labor demands. Research indicates that individuals with lower educational attainment and limited cultural proficiency face a greater likelihood of being replaced by artificial intelligence (AI). However, the evolving labor market demands a greater emphasis on AI-related skills. The potential of AI to foster the development of the United States labor market is evident, as it holds the capacity to drive further innovation and growth in the American economy. This research paper takes IBM, who officially announced the use of AI as a replacement for human labor, as a case study to examine the influence of AI on the labor market in the United States., exploring the opportunities and risks brought by AI development.Advances in Economics, Management and Political Sciences00IBM, who officially announced the use of AI as a replacement for human labor, is taken as a case study to examine the influence of AI on the labor market in the United States, exploring the opportunities and risks brought by AI development.Advances in Economics, Management and Political Sciences['Ruixuan Fu']2024-01-05T00:00:00https://www.semanticscholar.org/paper/af77561ef9946e5bf5d771ea141972487c309dde 5415bfaf313cb4f25a1ff929d9eec3b1bc8d1c8cArtificial Intelligence (AI) has gained significant attention in recent years, permeating various sectors and transforming the way tasks are performed. In the field of education, AI has the potential to revolutionize traditional teaching and learning methodologies, particularly in the context of English as a Second Language (ESL) classrooms. This systematic literature review aims to provide a comprehensive overview of the current state of research on the implementation, challenges, and impacts of AI in ESL classrooms based on different countries. For this purpose, a systematic review has been carried out in ERIC, WOS and Scopus databases. After applying the inclusion and exclusion criteria, the sample was set at 25 articles. The findings reveal that AI technologies offer promising opportunities to enhance ESL instruction. Despite the potential benefits, the review also uncovers several challenges and limitations associated with AI implementation in ESL classrooms. Furthermore, the review identifies the need for further empirical research to measure the long-term effects of AI. In conclusion, this systematic literature review provides valuable insights into the current landscape of AI implementation in ESL classrooms. It highlights the potential of AI technologies to enhance language instruction, while acknowledging the challenges that need to be addressed. The findings of this review can guide educators, policymakers, and researchers in making informed decisions about the integration of AI in ESL classrooms, fostering effective and inclusive language learning environments in the digital era besides to require further research and analysis on AI in Malaysian ESL classroom context.International Journal of Academic Research in Business and Social Sciences570This systematic literature review provides valuable insights into the current landscape of AI implementation in ESL classrooms and highlights the potential of AI technologies to enhance language instruction, while acknowledging the challenges that need to be addressed.International Journal of Academic Research in Business and Social Sciences['Nor Syazliana Sharifuddin', 'Harwati Hashim']2024-01-05T00:00:00https://www.semanticscholar.org/paper/5415bfaf313cb4f25a1ff929d9eec3b1bc8d1c8c a051bbb9f1634127952a19b69deae786fd818442OBJECTIVE Many caregivers of children with cleft lip and palate experience a high level of anxiety throughout their child's medical and surgical care. We aim to evaluate artificial intelligence (AI) as a tool to mitigate these feelings and can aid clinicians in the development of robust pediatric educational materials for caregivers and families. DESIGN Thirteen of the most common postoperative questions following cleft lip and/or palate repair were developed by an expert panel of senior Pediatric Plastic Surgeons and were posed to ChatGPT. Professional answers from the expert panel were provided and compared to responses from ChatGPT. A literature review was also conducted to generate a new support model for caregivers with children undergoing a surgical procedure. SETTING Department of Pediatric Plastic Surgery at a metropolitan Children's Hospital. PARTICIPANTS Senior Pediatric Plastic Surgeons at a metropolitan Children's Hospital. INTERVENTIONS None. MAIN OUTCOME MEASURE The primary outcome was to determine the ability of ChatGPT to respond to common postoperative questions and to develop a model for AI assistance in family-centered perioperative care. RESULTS ChatGPT had a postoperative question response accuracy rate of 69% when compared with subject matter expert responses, with its greatest errors being information errors. An extensive literature search revealed that AI can assist in multiple traditional perioperative strategies to reduce caregivers and patient anxiety. CONCLUSIONS Artificial Intelligence can help to reduce the burden of generating patient education materials as well as support caregivers in multiple aspects and perioperative care.The Cleft Palate-Craniofacial Journal210Artificial Intelligence can help to reduce the burden of generating patient education materials as well as support caregivers in multiple aspects and perioperative care and can aid clinicians in the development of robust pediatric educational materials for caregivers and families.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association['Sara C Chaker', 'Ya-Ching Hung', 'Mariam Saad', 'Michael S Golinko', 'Izabela A Galdyn']2024-01-05T00:00:00https://www.semanticscholar.org/paper/a051bbb9f1634127952a19b69deae786fd818442 8cb79050f14c6e22bb9439bcb4bec3bcfeebd378In the current digital era, technology is rapidly advancing. One of the increasingly popular technologies being used is Artificial Intelligence (AI). ChatGPT is one of the AI chatbots, computer programs designed to mimic human interactions through chat or text. AI chatbots are now widely used in various sectors, including the legal industry. Land issues are a common legal problem in Indonesia. Handling land-related issues requires precise decision-making to minimize conflicts and expedite  resolution. However, sometimes resolving land issues can be time-consuming and costly. The aim of this research is to provide a clearer perspective on the role of ChatGPT in addressing land issues and an understanding of its limitations. Testing of the free accessible version of ChatGPT is conducted using a black box testing method. The final results of the study indicate that ChatGPT has the potential to provide real-time and valuable information, helping the general public understand legal land issues. However, it is important to note that ChatGPT has limitations. ChatGPT cannot entirely replace the role of humans in making complex legal decisions, ChatGPT can only function as an informant providing general information.   Dalam era digital seperti saat ini, teknologi semakin berkembang pesat. Salah satu teknologi yang semakin populer digunakan adalah Artificial Intelligence (AI). ChatGPT merupakan salah satu chatbot AI, yaitu sebuah program komputer dirancang untuk meniru interaksi manusia melalui chat atau teks. Saat ini chatbot AI sudah banyak digunakan dalam berbagai sektor, termasuk sektor hukum. Pertanahan merupakan salah satu persoalan hukum yang sering terjadi di Indonesia. Menangani masalah pertanahan memerlukan dan pengambilan keputusan yang tepat agar dapat meminimalisir konflik dan mempercepat proses penyelesaian. Namun, terkadang penyelesaian masalah pertanahan memerlukan waktu yang lama dan biaya yang besar. Tujuan penelitian ini adalah untuk memberikan pandangan yang lebih jelas tentang peran ChatGPT dalam menangani masalah pertanahan serta pemahaman akan keterbatasan-keterbatasannya. Pengujian dilakukan menggunakan ChatGPT versi gratis dengan metode black box testing. Hasil akhir menunjukkan bahwa ChatGPT memiliki potensi untuk memberikan informasi yang berguna secara real-time serta membantu masyarakat umum memahami isu-isu hukum pertanahan. Namun, penting untuk diingat bahwa ChatGPT memiliki keterbatasan. ChatGPT tidak dapat menggantikan peran manusia sepenuhnya dalam pengambilan keputusan hukum yang kompleks, ChatGPT hanya mampu berperan sebagai informan yang memberikan informasi bersifat umum.Tunas Agraria00Tunas Agraria['Ridho Darman']2024-01-05T00:00:00https://www.semanticscholar.org/paper/8cb79050f14c6e22bb9439bcb4bec3bcfeebd378 82f3becac6449c9e7863c3266b65f1c27eaf2a67AI &amp; SOCIETY620It is concluded that a CCEP economy would need to have a very different outlook from current market practices, with a focus on producing basic “interlinking” commodities that consumers can use as a form of collective R &D.AI & SOCIETY['Spyridon Samothrakis']2024-01-05T00:00:00https://www.semanticscholar.org/paper/82f3becac6449c9e7863c3266b65f1c27eaf2a67 8bb68e6cf2227d269b3b51c123403df2751224faThis research paper delves into the implementation of artificial intelligence (AI) in the mobile logic game "ToqyzQumalaq," focusing on incorporating advanced algorithmic strategies to improve gameplay. The game's complexity and strategic depth present unique challenges in AI development, addressed through the integration of algorithms like Minimax, Alpha-Beta Pruning, Greedy, and Particle Swarm Optimization (PSO). The study emphasizes the creation of evaluation functions for these algorithms, ensuring AI efficiency and human-like decision-making. This aspect is vital for maintaining the strategic unpredictability essential to "ToqyzQumalaq." Extensive experimental testing against human players of various skill levels demonstrates the algorithms' effectiveness. These tests reveal the strengths and limitations of each algorithm, providing insights into their application in the game. This paper contributes to AI in gaming, highlighting the challenges and opportunities in developing AI for complex games. Its findings are relevant not only to game developers but also serve as an educational tool, showcasing the practical application of AI and algorithmic strategies."Bilim" scientific and pedagogical jornal100This research paper delves into the implementation of artificial intelligence (AI) in the mobile logic game "ToqyzQumalaq," focusing on incorporating advanced algorithmic strategies to improve gameplay, and emphasizes the creation of evaluation functions for these algorithms."Bilim" scientific and pedagogical jornal['Zhanat Nurbekova', 'Gaukhar Aimicheva', 'Talant Tolganbayuly', 'Mahmud Mustafabek Gali']2024-01-05T00:00:00https://www.semanticscholar.org/paper/8bb68e6cf2227d269b3b51c123403df2751224fa 4fd1104ab54951e11071b67dafec5dee9ff18912In the paper, an analysis is conducted on the intricate relationship between ethics, artificial intelligence, and cybersecurity. The ethical principles that govern the advancement of AI are examined, alongside the security issues that arise from its implementation. The ethical utilization of artificial intelligence in the realms of cybersecurity and hacking is explored. Emphasis is placed on the significance of AI ethics, particularly in terms of transparency, accountability, and fairness. Additionally, the paper delves into the security challenges that emerge as AI is adopted, such as safeguarding user privacy and ensuring equitable access to the technology.Inteligencia Artif.00The ethical principles that govern the advancement of AI are examined, alongside the security issues that arise from its implementation, and the security challenges that emerge as AI is adopted are delved into.Inteligencia Artif.['Ariel López González', 'Mailyn Moreno', 'Ariadna C. Moreno Román', 'Yahima Hadfeg Fernández', 'Nayma Cepero-Pérez']2024-01-05T00:00:00https://www.semanticscholar.org/paper/4fd1104ab54951e11071b67dafec5dee9ff18912 99bdd120a633c29462c03d70e785c0bf8b616882 Video interaktif adalah media pembelajaran yang didalamnya mengkombinasikan suara, gerak, gambar, teks, ataupun grafik yang bersifat interaktif untuk menghubungkan media pembelajaran dengan pengguna atau penikmat video. Penggunaan video interaktif dalam kegiatan belajar mengajar dinilai lebih efektif dalam keberhasilan belajar siswa. Selain dimanfaatkan dalam kegiatan belajar mengajar, video interaktif juga bisa digunakan untuk menarik penonton ketika kita akan memasarkan sebuah produk atau jasa, sehingga tertarik untuk menggunakan produk atau jasa yang kita tawarkan. Namun keterbatasan sumber daya menyebabkan optimalisasi kegiatan belajar mengajar dengan pemanfaatan teknologi masih terhambat dan menjadi salah satu fokus yang akan terus diupayakan perbaikan serta pengembangannya. Pelatihan merupakan salah satu bentuk pengembangan keterampilan yang dibutuhkan siswa saat ini untuk meningkatkan keterampilan dan kompetensi. Pemanfaatan teknologi Artificial Intelligence (AI) dalam pembuatan video menjadi salah satu solusi yang diharapkan dapat mengembangkan bakat dan minat siswa serta meningkatkan motivasi dan minat siswa dalam belajar. Melalui pelatihan ini, siswa dapat merancang proyek-proyek video yang lebih inovatif dengan eksplorasi teknologi AI. Siswa juga dapat menciptakan konten yang lebih menarik dan unik dengan memanfaatkan algoritma dan teknik AI. Jurnal Ilmu Komputer00ABDIKOM : Jurnal Ilmu Komputer['Nindy Irzavika', 'Musthofa Galih Pradana', 'Nurul Afifah Arifuddin', 'Neny Rosmawarni']2024-01-05T00:00:00https://www.semanticscholar.org/paper/99bdd120a633c29462c03d70e785c0bf8b616882 b59069068c1e77e8c3d9e535c4780796279c5433There are concerns about the reliability and safety of artificial intelligence (AI) based on sub-symbolic neural networks because its decisions cannot be explained explicitly. This is the black box problem of modern AI. At the same time, symbolic AI has the nature of a white box and is able to ensure the reliability and safety of its decisions. However, several problems prevent the widespread use of symbolic AI: the opacity of mathematical models and natural language terms, the lack of a unified ontology, and the combinatorial explosion of search capabilities. To solve the black-box problem of AI, we propose eXplicitly eXplainable AI (XXAI) - a fully transparent white-box AI based on deterministic logical cellular automata whose rules are derived from the first principles of the general theory of the relevant domain. In this case, the general theory of the domain plays the role of a knowledge base for deriving the inferences of the cellular automata. A cellular automaton implements parallel multi-level logical inference at all levels of organization - from local interactions of the element base to the system as a whole. Our verification of several ecological hypotheses sets a precedent for the successful implementation of the proposed solution. XXAI is able to automatically verify the reliability, security and ethics of sub-symbolic neural network solutions in both the final and training phases. In this article, we present precedents for the successful implementation of XXAI, the theoretical and methodological foundations for its further development, and discuss prospects for the future.680The verification of several ecological hypotheses sets a precedent for the successful implementation of the proposed XXAI - a fully transparent white-box AI based on deterministic logical cellular automata whose rules are derived from the first principles of the general theory of the relevant domain.['V. Kalmykov', 'L. V. Kalmykov']2024-01-05T00:00:00https://www.semanticscholar.org/paper/b59069068c1e77e8c3d9e535c4780796279c5433 5e21145a9cdaa954bc37f89d9d8176f087f30e91This study aims to examine the legitimacy of intellectual property generated by artificial intelligence (AI) in Indonesia. With the rapid development of AI, intellectual property generated by AI has become a complex issue, attracting the attention of legal experts and stakeholders. Although the legal framework in Indonesia does not specifically address intellectual property generated by AI, certain aspects of existing law may apply in this context. This study uses normative or doctrinal research methods to analyze the legal framework in Indonesia regarding intellectual property, including copyright law, patent law, industrial design law, and trade secret protection. In addition, this research examines the views of legal experts and stakeholders in Indonesia regarding the legitimacy of intellectual property generated by AI. The results of this research are expected to provide a clearer understanding of the legality of intellectual property law produced by AI in Indonesia. Research results will contribute to discussions around AIrelated laws and regulations and may serve as the basis for future regulatory changes or adjustments. This study has significant implications for stakeholders, including AI creators, users, and developers in Indonesia. By understanding the applicable legal framework, appropriate legal protection frameworks can be created for AI-generated intellectual property, promoting continuous innovation and responsible utilization of AI. Keywords: artificial intelligence, intellectual property, legalityKnE Social Sciences300Analysis of legal framework in Indonesia regarding intellectual property, including copyright law, patent law, industrial design law, and trade secret protection, is used to provide a clearer understanding of the legality of intellectual property law produced by AI in Indonesia.KnE Social Sciences['Dwi Ratna Indri Hapsari', 'Andistya Pratama', 'Nur Putri Hidayah', 'Isdian Anggraeny']2024-01-05T00:00:00https://www.semanticscholar.org/paper/5e21145a9cdaa954bc37f89d9d8176f087f30e91 e155f7402de5f941582008084e80274caf07d236Patient420There was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond, and a willingness among PwPD and neurologists to integrate RMS and AI into PD management.The Patient['C. G. Godoy Junior', 'F. Miele', 'L. Mäkitie', 'E. Fiorenzato', 'M. Koivu', 'L. Bakker', 'Carin Uyl-de Groot', 'W. Redekop', 'Welmoed K. van Deen']2024-01-05T00:00:00https://www.semanticscholar.org/paper/e155f7402de5f941582008084e80274caf07d236 30de9159bcbbf7627fc0e95d0837cca895b918a3Background: Type 2 diabetes (T2D) is a prevalent chronic disease with a significant risk of serious health complications and negative impacts on the quality of life. Given the impact of individual characteristics and lifestyle on the treatment plan and patient outcomes, it is crucial to develop precise and personalized management strategies. Artificial intelligence (AI) provides great promise in combining patterns from various data sources with nurses' expertise to achieve optimal care. Methods: This is a 6-month ancillary study among T2D patients (n = 20, age = 57 +- 10). Participants were randomly assigned to an intervention (AI, n=10) group to receive daily AI-generated individualized feedback or a control group without receiving the daily feedback (non-AI, n=10) in the last three months. The study developed an online nurse-in-the-loop predictive control (ONLC) model that utilizes a predictive digital twin (PDT). The PDT was developed using a transfer-learning-based Artificial Neural Network. The PDT was trained on participants self-monitoring data (weight, food logs, physical activity, glucose) from the first three months, and the online control algorithm applied particle swarm optimization to identify impactful behavioral changes for maintaining the patient's glucose and weight levels for the next three months. The ONLC provided the intervention group with individualized feedback and recommendations via text messages. The PDT was re-trained weekly to improve its performance. Findings: The trained ONLC model achieved>=80% prediction accuracy across all patients while the model was tuned online. Participants in the intervention group exhibited a trend of improved daily steps and stable or improved total caloric and total carb intake as recommended.arXiv.org190An online nurse-in-the-loop predictive control model that utilizes a predictive digital twin (PDT) was developed using a transfer-learning-based Artificial Neural Network and achieved=80% prediction accuracy across all patients while the model was tuned online.ArXiv['Syed Hasib Akhter Faruqui', 'A. Alaeddini', 'Yan Du', 'Shiyu Li', 'Kumar Sharma', 'Jing Wang']2024-01-05T00:00:00https://www.semanticscholar.org/paper/30de9159bcbbf7627fc0e95d0837cca895b918a3 7d6d5dd62bb4ca8dfb210aaf6191557a5fb00f03Intelligent traffic monitoring is a prominent topic of investigation due the emergence of advancements like the Internet interconnected Things and intelligent computers. Combining these technologies will make it easier to methods to aid in making better choices and accelerating urban growth. Intelligent sensing has come to the forefront in recent years due to its capacity to make calculated decisions on its own to address difficult issues. Automatic vehicles and smart gadgets are equipped with sensors that are part of an IoT-based system in order to recognize, gather, and transmit data. Artificial intelligence (AI)-based techniques allow machines to acquire knowledge and keep tabs on their surroundings through continuous sensing. Improvements in variable traffic control strategies for overcrowded cities have numerous positive outcomes, one of which is increased road safety. Since the sensors on which conventional dynamic controllers relied had their own shortcomings, we might use vision sensors (like cameras) to avoid these issues. Image and video-based computing has a lot of potential for measuring traffic volumes. A new traffic management system named Enhanced Transportation Technologies (ETT) is implemented to relieve congestion at the busy intersection after the old one was deemed to be inadequate. The term "intelligent transportation system" (ITS) refers to a group of transportation systems to keep drivers and passengers safe on the road and to facilitate autonomous mobility by optimizing control systems. To further improve urban planning, crowd behavior, and traffic forecasting, dependable AI models have been developed to work in tandem with ITS. Compared to controllers using conventional sensors, the proposed model has been shown through extensive simulations to reduce waiting time and increase movement speed on average.Journal of Machine and Computing240To further improve urban planning, crowd behavior, and traffic forecasting, dependable AI models have been developed to work in tandem with ITS, and the proposed model has been shown to reduce waiting time and increase movement speed on average.Journal of Machine and Computing['Cuddapah Anitha', 'Shweta Sharma', 'V. K. Nassa', 'Sachine Kumar Agrawal', 'Rajasekaran A', 'M. R.']2024-01-05T00:00:00https://www.semanticscholar.org/paper/7d6d5dd62bb4ca8dfb210aaf6191557a5fb00f03 a95d6f663740bb40013fd350850fa6346d6bca3f00[]2024-01-05T00:00:00https://www.semanticscholar.org/paper/a95d6f663740bb40013fd350850fa6346d6bca3f d733b9702df93c3ed0897daa8ac153fe6d343a24BACKGROUND In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE This paper proposes a breast cancer assisted diagnosis system based on electronic medical records. The goal of this system is to address the limitations of existing systems, which primarily rely on structured electronic records and may miss crucial information stored in unstructured records. METHODS The proposed approach is a breast cancer assisted diagnosis system based on electronic medical records. The system utilizes breast cancer enhanced convolutional neural networks with semantic initialization filters (BC-INIT-CNN). It extracts highly relevant tumor markers from unstructured medical records to aid in breast cancer staging diagnosis and effectively utilizes the important information present in unstructured records. RESULTS The model's performance is assessed using various evaluation metrics. Such as accuracy, ROC curves, and Precision-Recall curves. Comparative analysis demonstrates that the BC-INIT-CNN model outperforms several existing methods in terms of accuracy and computational efficiency. CONCLUSIONS The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients. By leveraging unstructured medical records and extracting relevant tumor markers, the system enables accurate staging diagnosis and enhances the utilization of valuable information.Journal of X-Ray Science and Technology382The proposed breast cancer assisted diagnosis system based on BC-INIT-CNN showcases the potential to address the challenges faced by developing countries in providing timely treatment to breast cancer patients.Journal of X-ray science and technology['Wenxiu Li', 'Fangfang Gou', 'Jia Wu']2024-01-05T00:00:00https://www.semanticscholar.org/paper/d733b9702df93c3ed0897daa8ac153fe6d343a24 5c28529d18e8fdb393a68ed6fd4e1ad40c5acebfAcademy of Management Review811Academy of Management Review['Sebastian Raisch', 'Kateryna Fomina']2024-01-05T00:00:00https://www.semanticscholar.org/paper/5c28529d18e8fdb393a68ed6fd4e1ad40c5acebf 30e9fbd3aef37891824e2f51ca204fca36718da0Strahlentherapie und Onkologie (Print)110This case study reports on the first use of LMM in a pretreatment discussion and in obtaining informed consent for a radiation oncology treatment and the reproducibility of the replies by ChatGPT 3.5 was analyzed.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]['M. Moll', 'G. Heilemann', 'Dietmar Georg', 'D. Kauer-Dorner', 'P. Kuess']2024-01-05T00:00:00https://www.semanticscholar.org/paper/30e9fbd3aef37891824e2f51ca204fca36718da0 e3c62d8da01dca960659859b3e21b26ab995760b<jats:p />AVANCA CINEMA10AVANCA | CINEMA['Wilson Oliveira Filho', 'Diana Seelaender', 'Mariana Lucas De Almeida Fernandes']2024-01-05T00:00:00https://www.semanticscholar.org/paper/e3c62d8da01dca960659859b3e21b26ab995760b c696b17d7847ef2d5fb8b2132e06fea34756533dMedComm – Future Medicine50MedComm – Future Medicine['Ming Yi', 'Yunqiang Liu', 'Zhiguang Su']2024-01-05T00:00:00https://www.semanticscholar.org/paper/c696b17d7847ef2d5fb8b2132e06fea34756533d a525a3e1ae0885fdfea8c75a32e432811d2b4211Anatomical Sciences Education100Anatomical sciences education['A. Cale']2024-01-05T00:00:00https://www.semanticscholar.org/paper/a525a3e1ae0885fdfea8c75a32e432811d2b4211 098bb479958aa8bd9209dfd562d2429ba1f8b642International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Conor Kelly']2024-01-05T00:00:00https://www.semanticscholar.org/paper/098bb479958aa8bd9209dfd562d2429ba1f8b642 c163304bcad9aa75dd1a8cc7dd245d1dfbb4968eInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Nilesh D Kulkarni Saurav']2024-01-05T00:00:00https://www.semanticscholar.org/paper/c163304bcad9aa75dd1a8cc7dd245d1dfbb4968e 5db7097946782da1b2012bcff2a532e7fac1885100['Edmilson Rodrigues do Nascimento Junior']2024-01-05T00:00:00https://www.semanticscholar.org/paper/5db7097946782da1b2012bcff2a532e7fac18851 0f95752ee08619b0a6d8f4a652f9c2b8a869b2baCentral European Journal of Urology160Central European Journal of Urology['Carlotta Nedbal', 'E. Bres-Niewada', 'B. Dybowski', 'B. Somani']2024-01-05T00:00:00https://www.semanticscholar.org/paper/0f95752ee08619b0a6d8f4a652f9c2b8a869b2ba bce01799f7893215b9d18bd44ee04a8f3374b6a0Current Opinion in Neurobiology260Current Opinion in Neurobiology['Pietro Verzelli', 'Tatjana Tchumatchenko', 'J. Kotaleski']2024-01-05T00:00:00https://www.semanticscholar.org/paper/bce01799f7893215b9d18bd44ee04a8f3374b6a0 737782ad9af03403f1e6e4d5043a8c649474d96eInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)['Priyal J. Borole']2024-01-05T00:00:00https://www.semanticscholar.org/paper/737782ad9af03403f1e6e4d5043a8c649474d96e 383ea2f95c20687da36c277267ae538418a82151New England Review00New England Review['Alison Thumel']2024-01-05T00:00:00https://www.semanticscholar.org/paper/383ea2f95c20687da36c277267ae538418a82151 f786fa21303a97eef3834f74437f016c2541be08Frontiers in Medicine130Frontiers in Medicine['Pengwei Hu', 'Lun Hu', 'Fei Wang', 'Jing Mei']2024-01-05T00:00:00https://www.semanticscholar.org/paper/f786fa21303a97eef3834f74437f016c2541be08 532749bc76b739f929d67e994a0b0ef5977f28f5Academy of Management Review640Academy of Management Review['David M. Townsend', 'Rick Hunt', 'Judy Rady', 'Parul Manocha', 'J. Jin']2024-01-05T00:00:00https://www.semanticscholar.org/paper/532749bc76b739f929d67e994a0b0ef5977f28f5 29ac1037589b12daf9059c7aeb8f367ab6ee29b2The objective of this work is to contribute to the academic debate on the legal regime attributed to intellectual works created with the use of artificial intelligence (AI) systems, which are of particular importance for technological, scientific and innovation development in the current stage of development of this new technology. Copyright is founded on anthropocentric bases and has the specific purpose of protecting the author as the main recipient of Copyright Law. Currently, the Brazilian Copyright Law only protects intellectual works created by human beings. In this work, we intend to analyze these characteristics in light of the investigation of two scenarios, considering the effective participation of the human being in the result of creation generated by AI and the absence of human participation, with the autonomous creation undertaken by AI.Revista de Propriedade Intelectual00Revista de Propriedade Intelectual - Direito Constitucional e Contemporâneo['Vitos Rafael DE Andrade OLIVEIRA PRATA DE GUIMARÃES SOUZA', 'Rodrigo MORAES FERREIRA']2024-01-05T00:00:00https://www.semanticscholar.org/paper/29ac1037589b12daf9059c7aeb8f367ab6ee29b2 e6f480a7d45e2447fa5e9988064735b19433c301The high-performance generative artificial intelligence (GAI) represents the latest evolution of computational intelligence, while the blessing of future 6G networks also makes edge intelligence (EI) full of development potential. The inevitable encounter between GAI and EI can unleash new opportunities, where GAI's pre-training based on massive computing resources and large-scale unlabeled corpora can provide strong foundational knowledge for EI, while EI can harness fragmented computing resources to aggregate personalized knowledge for GAI. However, the natural contradictory features pose significant challenges to direct knowledge sharing. To address this, in this paper, we propose the GAI-oriented synthetical network (GaisNet), a collaborative cloud-edge-end intelligence framework that buffers contradiction leveraging data-free knowledge relay, where the bidirectional knowledge flow enables GAI's virtuous-cycle model fine-tuning and task inference, achieving mutualism between GAI and EI with seamless fusion and collaborative evolution. Experimental results demonstrate the effectiveness of the proposed mechanisms. Finally, we discuss the future challenges and directions in the interplay between GAI and EI.arXiv.org230The GAI-oriented synthetical network (GaisNet) is proposed, a collaborative cloud-edge-end intelligence framework that buffers contradiction leveraging data-free knowledge relay, achieving mutualism between GAI and EI with seamless fusion and collaborative evolution.ArXiv['Ning Chen', 'Zhipeng Cheng', 'Xuwei Fan', 'Xiaoyu Xia', 'Lianfen Huang']2024-01-05T00:00:00https://www.semanticscholar.org/paper/e6f480a7d45e2447fa5e9988064735b19433c301 32e6efd79381cacb5886ae3a2ffccdc04006b7adRobot Process Automation (RPA) is a type of business process automation that relies on software robots (bots) or artificial intelligence (AI) agents. This phenomenon is sometimes denoted as software robotics, which should not be conflated with robot software. This study investigates the increasing prevalence of RPA across several sectors, with a specific focus on its use in back-office functions. RPA software, exemplified by platforms like Blue Prism, Automation Anywhere, and UiPath, replicates human-computer interactions in order to automate operations that are repetitive and governed by predefined rules. This technology offers many advantages, including cost reduction, mistake minimization, and risk elimination. This research investigates many domains in which RPA may be used, including credible business transformation, content migrations, web crawling/OSINT, and IT department enablement. Additionally, it emphasizes the significant responsibilities within RPA operations, including process architects, technologists, and personnel involved in continuous support and maintenance. The study includes case studies conducted within the banking industry, which demonstrate the potential of RPA in augmenting both customer happiness and productivity. The market report anticipates substantial expansion in the market for RPA software, whereby industry leaders such as UiPath, Automation Anywhere, and Blue Prism are expected to play a dominant role.Journal of Computing and Natural Science304This study investigates the increasing prevalence of RPA across several sectors, with a specific focus on its use in back-office functions, and anticipates substantial expansion in the market for RPA software, whereby industry leaders such as UiPath, Automation Anywhere, and Blue Prism are expected to play a dominant role.Journal of Computing and Natural Science['Abdulhaq Abildtrup']2024-01-05T00:00:00https://www.semanticscholar.org/paper/32e6efd79381cacb5886ae3a2ffccdc04006b7ad 4a2d23caebfec94774ed4527b3d06a8971b66fa0ABSTRACT:The authors present gOd, mOther and sOldier—Nowhere in Somewhere Series 2022, a work that was conceptualized and created by artist Jinjoon Lee and his TX Creative Media Lab at KAIST, realized through the remote cooperation of eight local collaborators across Southeast Asia. The authors used artificial intelligence–based object detectors and sonification techniques in a work of media art to symbolize the voicelessness of those at the margins of society in Southeast Asia. These algorithms and concepts, and the work as a whole, artistically demonstrate how marginalized people are misrepresented and misunderstood when interpreted out of context.240The authors used artificial intelligence–based object detectors and sonification techniques in a work of media art to symbolize the voicelessness of those at the margins of society in Southeast Asia.Leonardo['Andrew Gambardella', 'Meeyung Chung', 'Doyo Choi', 'Jinjoon Lee']2024-01-05T00:00:00https://www.semanticscholar.org/paper/4a2d23caebfec94774ed4527b3d06a8971b66fa0 d69c87c202aa8dd7321de74365718ce8e41531a1Policies, Institutions and Regulation (PIR) aspects matter for different sectors’ growth and inclusive sustainable development, but there is little information in the literature on how to evaluate the effects of PIR on management options and outcomes or, on how positive results PIR changes can bring. In terms of stormwater management systems, or urban drainage, PIR is also a controversial and absent matter. Multidisciplinarity, several actors, countless formal and informal rules, and strong contextual path dependence make the subject complex and intricate. Considering the enabling environment, an alignment between policies, institutions and regulations is required to achieve good results and provide sustainable services. This study conducted a hybrid literature review of peer-reviewed papers in this field to provide an overview of how researchers have been studying PIR relations. The gaps show that the understanding of the PIR is fragile, as an important element for analyzing of results to be achieved, including SDG6, the financing and obtaining funds, guarantees and grants for the execution, delivery, operation and maintenance urban stormwater services and infrastructure. The contribution of this review is not only about what exists, but also mainly about what does not exist, since the void keeps waiting to be filled.Water400Water['Carlos Novaes', 'R. Marques']2024-01-04T00:00:00https://www.semanticscholar.org/paper/d69c87c202aa8dd7321de74365718ce8e41531a1 a2f03ff74dc1ee8bd8b6ccfbcbb931f58c93db4bThis study delves into the implications of incorporating AI tools, specifically ChatGPT, in higher education contexts. With a primary focus on understanding the acceptance and utilization of ChatGPT among university students, the research utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. The investigation probes into four crucial constructs of UTAUT—performance expectancy, effort expectancy, social influence and facilitating conditions—to understand their impact on the intent and actual use behaviour of students. The study relies on data collected from six universities in two countries and assessed through descriptive statistics and structural equation modelling techniques, and also takes into account participants' gender and study level. The key findings show that performance expectancy, effort expectancy, and social influence significantly influence behavioural intention. Furthermore, behavioural intention, when considered alongside facilitating conditions, influences actual use behaviour. This research also explores the moderating impact of gender and study level on the relationships among these variables. The results not only augment our comprehension of technology acceptance in the context of AI tools but also provide valuable input for formulating strategies that promote effective incorporation of ChatGPT in higher education. The study underscores the need for effective awareness initiatives, bespoke training programmes, and intuitive tool designs to bolster students' perceptions and foster the wider adoption of AI tools in education. ChatGPT is a tool that is quickly gaining worldwide recognition. ChatGPT helps with writing essays and solving assignments. ChatGPT raises ethical concerns about authorship, plagiarism and ethics. This study explores students' acceptance of ChatGPT as an aid in their education, which has not been studied previously. We used the extended Unified Technology Acceptance and Use of Technology theory to test what factors mostly influence the use of ChatGPT by students. We conducted a multiple study in Poland and Egypt based on sampling strategy from six universities. ChatGPT is a global game changer and should be incorporated into study programmes. The limitations of ChatGPT should be well explained and known since it is prone to making mistakes. Higher education teachers should be aware of ChatGPT's capabilities. British Journal of Educational Technology249The key findings show that performance expectancy, effort expectancy, and social influence significantly influence behavioural intention and, when considered alongside facilitating conditions, influences actual use behaviour.Br. J. Educ. Technol.['Artur Strzelecki', 'Sara ElArabawy']2024-01-04T00:00:00https://www.semanticscholar.org/paper/a2f03ff74dc1ee8bd8b6ccfbcbb931f58c93db4b e461b84f7eec2f95ae5c53aa9131e43971a894abCommuniquer. Revue de communication sociale et publique00Communiquer. Revue de communication sociale et publique['Marie-Hélène Hermand']2024-01-04T00:00:00https://www.semanticscholar.org/paper/e461b84f7eec2f95ae5c53aa9131e43971a894ab 3d823aba9cacf1e31d215bf3670719db4814f564AIAA SCITECH 2024 Forum150AIAA SCITECH 2024 Forum['Wanjiku A. Makumi', 'Zachary Bell', 'Jhyv N. Philor', 'Warren Dixon']2024-01-04T00:00:00https://www.semanticscholar.org/paper/3d823aba9cacf1e31d215bf3670719db4814f564 5b779d44d2d0bf9c41660f07b75184e7a89eb082Marine biodiversity underpins the formation of marine protected areas (MPAs), necessitating detailed surveys to account for the dynamic temporal and spatial distribution of species influenced by tidal patterns and microhabitats. The reef rock intertidal zones adjacent to urban centers, such as Taiwan’s Cape Santiago, exhibit significant biodiversity, yet they are increasingly threatened by tourism-related activities. This study introduces an artificial intelligence (AI)-empowered citizen science (CS) approach within the local community to address these challenges. By integrating CS with AI, we establish a hybrid intelligence (HI) system that conducts in situ biological surveys and educational programs focused on reef ecological conservation. This initiative not only facilitates the collective gathering and AI-assisted analysis of critical data but also uses machine-learning outputs to gauge data quality, thus informing subsequent data collection and refinement strategies. The resulting collectivity and iterative enhancement foster a mutual and continuous HI learning environment. Our HI model proves instrumental in fostering community engagement and public involvement in CS endeavors, cultivating the skills necessary for documenting rocky intertidal biodiversity shifts. These efforts are pivotal for informing the design and governance of future MPAs, ensuring their efficacy and sustainability in marine conservation.Sustainability532This study establishes a hybrid intelligence (HI) system that conducts in situ biological surveys and educational programs focused on reef ecological conservation, and uses machine-learning outputs to gauge data quality, thus informing subsequent data collection and refinement strategies.Sustainability['Vincent Y. Chen', 'Day-Jye Lu', 'Yu-San Han']2024-01-04T00:00:00https://www.semanticscholar.org/paper/5b779d44d2d0bf9c41660f07b75184e7a89eb082 e3ee318d593729352f991142e6e3bef62640c5a5Abstract Artificial intelligence (AI) methods have been applied to medical imaging for several decades, but in the last few years, the number of publications and the number of AI-enabled medical devices coming on the market have significantly increased. While some AI-enabled approaches are proving very valuable, systematic reviews of the AI imaging field identify significant weaknesses in a significant proportion of the literature. Medical device regulators have recently become more proactive in publishing guidance documents and recognizing standards that will require that the development and validation of AI-enabled medical devices need to be more rigorous than required for tradition “rule-based” software. In particular, developers are required to better identify and mitigate risks (such as bias) that arise in AI-enabled devices, and to ensure that the devices are validated in a realistic clinical setting to ensure their output is clinically meaningful. While this evolving regulatory landscape will mean that device developers will take longer to bring novel AI-based medical imaging devices to market, such additional rigour is necessary to address existing weaknesses in the field and ensure that patients and healthcare professionals can trust AI-enabled devices. There would also be benefits in the academic community taking into account this regulatory framework, to improve the quality of the literature and make it easier for academically developed AI tools to make the transition to medical devices that impact healthcare.British Journal of Radiology341Developers are required to better identify and mitigate risks that arise in AI-enabled devices, and to ensure that the devices are validated in a realistic clinical setting to ensure their output is clinically meaningful.The British Journal of Radiology['Derek LG Hill']2024-01-04T00:00:00https://www.semanticscholar.org/paper/e3ee318d593729352f991142e6e3bef62640c5a5 637caa4ae3a7c2384b5cbb8818909f2fe98cafcaBackground The COVID-19 pandemic drove investment and research into medical imaging platforms to provide data to create artificial intelligence (AI) algorithms for the management of patients with COVID-19. Building on the success of England’s National COVID-19 Chest Imaging Database, the national digital policy body (NHSX) sought to create a generalized national medical imaging platform for the development, validation, and deployment of algorithms. Objective This study aims to understand international use cases of medical imaging platforms for the development and implementation of algorithms to inform the creation of England’s national imaging platform. Methods The National Health Service (NHS) AI Lab Policy and Strategy Team adopted a multiphased approach: (1) identification and prioritization of national AI imaging platforms; (2) Political, Economic, Social, Technological, Legal, and Environmental (PESTLE) factor analysis deep dive into national AI imaging platforms; (3) semistructured interviews with key stakeholders; (4) workshop on emerging themes and insights with the internal NHSX team; and (5) formulation of policy recommendations. Results International use cases of national AI imaging platforms (n=7) were prioritized for PESTLE factor analysis. Stakeholders (n=13) from the international use cases were interviewed. Themes (n=8) from the semistructured interviews, including interview quotes, were analyzed with workshop participants (n=5). The outputs of the deep dives, interviews, and workshop were synthesized thematically into 8 categories with 17 subcategories. On the basis of the insights from the international use cases, policy recommendations (n=12) were developed to support the NHS AI Lab in the design and development of the English national medical imaging platform. Conclusions The creation of AI algorithms supporting technology and infrastructure such as platforms often occurs in isolation within countries, let alone between countries. This novel policy research project sought to bridge the gap by learning from the challenges, successes, and experience of England’s international counterparts. Policy recommendations based on international learnings focused on the demonstrable benefits of the platform to secure sustainable funding, validation of algorithms and infrastructure to support in situ deployment, and creating wraparound tools for nontechnical participants such as clinicians to engage with algorithm creation. As health care organizations increasingly adopt technological solutions, policy makers have a responsibility to ensure that initiatives are informed by learnings from both national and international initiatives as well as disseminating the outcomes of their work.JMIR AI511This novel policy research project sought to bridge the gap by learning from the challenges, successes, and experience of England’s international counterparts to inform the creation of England’s national imaging platform.JMIR AI['K. Karpathakis', 'E. Pencheon', 'D. Cushnan']2024-01-04T00:00:00https://www.semanticscholar.org/paper/637caa4ae3a7c2384b5cbb8818909f2fe98cafca 0db6d49b2a580dfaf63595cc66cf91df55ae0169Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.Lab on a Chip1601The AI algorithms integral to the development of MNRs are introduced and the latest research pertinent to AI-enhanced MNRs are reviewed, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics.Lab on a chip['Hui Dong', 'Jiawen Lin', 'Yihui Tao', 'Yuan Jia', 'Lining Sun', 'Wen Jung Li', 'Hao Sun']2024-01-04T00:00:00https://www.semanticscholar.org/paper/0db6d49b2a580dfaf63595cc66cf91df55ae0169 b35bb26877be91e6c62ad9bf05580769bf3b4e2bWe demonstrate a gen-AI-based question-answering system called Navigator, which allows business users to ask natural language questions and get answers based on domain-specific tabular datasets onboarded to the system. We develop a novel LLM-based approach to generate a sequence of data operations required to answer a given question, under a constrained functional space. We further extend this approach to allow natural language-based predictive insights generation. Benchmarking of the proposed approach with existing QA methods shows significant improvement in precision and recall of generated answers across multiple datasets.COMAD/CODS141A novel LLM-based approach to generate a sequence of data operations required to answer a given question, under a constrained functional space, is developed and extended to allow natural language-based predictive insights generation.Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)['Arnab Chakraborty', 'Arkadeep Banerjee', 'Sutanoy Dasgupta', 'Vikas Raturi', 'Aditya Soni', 'Anjali Gupta', 'Shrutendra Harsola', 'Vignesh T. Subrahmaniam']2024-01-04T00:00:00https://www.semanticscholar.org/paper/b35bb26877be91e6c62ad9bf05580769bf3b4e2b cf3678de7536f72531c63c1ed65a6a04527c6655PurposeThe study aims to evaluate PubMed publications on ChatGPT or artificial intelligence (AI) involvement in scientific or medical writing and investigate whether ChatGPT or AI was used to create these articles or listed as authors.Design/methodology/approachThis scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A PubMed database search was performed for articles published between January 1 and November 29, 2023, using appropriate search terms; both authors performed screening and selection independently.FindingsFrom the initial search results of 127 articles, 41 were eligible for final analysis. Articles were published in 34 journals. Editorials were the most common article type, with 15 (36.6%) articles. Authors originated from 27 countries, and authors from the USA contributed the most, with 14 (34.1%) articles. The most discussed topic was AI tools and writing capabilities in 19 (46.3%) articles. AI or ChatGPT was involved in manuscript preparation in 31 (75.6%) articles. None of the articles listed AI or ChatGPT as an author, and in 19 (46.3%) articles, the authors acknowledged utilizing AI or ChatGPT.Practical implicationsResearchers worldwide are concerned with AI or ChatGPT involvement in scientific research, specifically the writing process. The authors believe that precise and mature regulations will be developed soon by journals, publishers and editors, which will pave the way for the best usage of these tools.Originality/valueThis scoping review expressed data published on using AI or ChatGPT in various scientific research and writing aspects, besides alluding to the advantages, disadvantages and implications of their usage.Arab Gulf Journal of Scientific Research591This scoping review expressed data published on using AI or ChatGPT in various scientific research and writing aspects, besides alluding to the advantages, disadvantages and implications of their usage.Arab Gulf Journal of Scientific Research['Ahmed A. Khalifa', 'Mariam A. Ibrahim']2024-01-04T00:00:00https://www.semanticscholar.org/paper/cf3678de7536f72531c63c1ed65a6a04527c6655 fbfcb293d55813c74716caf32af2f6eb5a0fc70eLanguage assessment is pivotal in identifying therapeutic interventions for speech, language, and communication disorders stemming from neurogenic origins, developmental or acquired, and student performance in the classroom. Traditional assessment techniques, however, are predominantly manual, necessitating extensive time and effort for administration and scoring. Such procedures can exacerbate the stress experienced by patients. In response to these inherent challenges, we introduced Open Brain AI (https://openbrainai.com). This state-of-the-art computational platform leverages advanced AI methodologies, encompassing machine learning, natural language processing, large language models, and automated speech-to-text transcription. These capabilities enable Open Brain AI to autonomously analyze multilingual spoken and written language productions. This work aims to present the development and evolution of Open Brain AI, elucidating its AI-driven language processing components and the intricate linguistic metrics it employs to evaluate the overarching and granular discourse structures. Open Brain AI significantly reduces the workload on researchers, clinicians, and teachers by facilitating rapid and automated language analysis. It allows healthcare and education professionals to optimize their operational processes, reallocating precious time and resources to more personalized user interactions. Moreover, Open Brain AI provides clinicians, researchers, and educators the autonomy to undertake essential data analytics, freeing up more bandwidth to focus on other vital facets of therapeutic intervention and care.Linköping Electronic Conference Proceedings80This work aims to present the development and evolution of Open Brain AI, elucidating its AI-driven language processing components and the intricate linguistic metrics it employs to evaluate the overarching and granular discourse structures.Linköping Electronic Conference Proceedings['C. Themistocleous']2024-01-04T00:00:00https://www.semanticscholar.org/paper/fbfcb293d55813c74716caf32af2f6eb5a0fc70e 9001646c410e0d6324c77e06aef80dbb75c87c83The Gothenburg Research Infrastructure in Digital Humanities (GRIDH) have participated in projects within various humanities fields that utilise as well as develop research tools and infrastructural resources that incorporate applications of ‘artificial intelligence’ (AI). These applications can include natural language processing, machine learning, computer vision, large language models, image recognition algorithms, classification, clustering, and deep learning. This paper advances the term ‘humanistic AI’ to describe an emergent form of interdisciplinary practice that uses and develops AI-based research applications to answer humanities research questions together with its entangled humanistic reflection. We coin this term to make implicit and visible the epistemological and material particularities of its practice and the new forms of knowledge its affordances make possible. The paper presents GRIDH projects within ‘humanistic AI’ together with its developed AI resources and applications.Linköping Electronic Conference Proceedings120The term ‘humanistic AI’ is coined to describe an emergent form of interdisciplinary practice that uses and develops AI-based research applications to answer humanities research questions together with its entangled humanistic reflection.Linköping Electronic Conference Proceedings['M. Fridlund', 'David Alfter', 'Daniel Brodén', 'Ashely Green', 'Aram Karimi', 'Cecilia Lindhé']2024-01-04T00:00:00https://www.semanticscholar.org/paper/9001646c410e0d6324c77e06aef80dbb75c87c83 622fdeb252cac2864c1b1587883dddb7ae650cb0Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and machine learning (ML) to address these pressing needs. This study presents a comprehensive methodology for breast cancer diagnosis and medical history generation, integrating data collection, feature extraction, machine learning, and AI-driven history-taking. The research employs a systematic approach to ensure accurate diagnosis and efficient history collection. Data preprocessing merges similar attributes to streamline analysis. Three key algorithms, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Fuzzy Logic, are applied. Fuzzy Logic shows exceptional accuracy in handling uncertain data. Deep learning models enhance predictive accuracy, emphasizing the synergy between traditional and deep learning approaches. The AI-driven history collection simplifies the patient history-taking process, adapting questions dynamically based on patient responses. Comprehensive medical history reports summarize patient data, facilitating informed healthcare decisions. The research prioritizes ethical compliance and data privacy. OpenAI has integrated GPT-3.5 to generate automated patient reports, offering structured overviews of patient health history. The study's results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care. Machine learning, deep learning, and AI-driven approaches hold promise for a wide range of applications, particularly in healthcare and beyond.Computer Methods in Biomechanics and Biomedical Engineering290The study's results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care.Computer methods in biomechanics and biomedical engineering['Maleeha Fathima', 'Mohammed Moulana']2024-01-04T00:00:00https://www.semanticscholar.org/paper/622fdeb252cac2864c1b1587883dddb7ae650cb0 8f445231fd587c22f6eba0184c6e79dd62efac2cHuman resource management, a field that has the involvement of Human intellectual capacity, Manpower, code of conduct, human behaviour etc. Every organization has a dedicated human resource department to work towards the betterment or development of the employees starting from the very sourcing, screening, recruiting, induction, performance management, employee engagement, learning & development and activities. In the modern world, innovation in technology has ended up bringing the science and technology in HR Operations of the company. The innovations led in AI and ML have worked towards implementing them in carrying out the HR process that may lead to reducing rather sharing the work of the HR personnel. Artificial Intelligence work to simplifying the tasks of the HR Managers. However, there is another perception that Artificial Intelligence can replace the human manpower, irrespective of the type of work. Transformation and the deviation of the work towards the automated systems, have its own benefits of reducing the work and also the work becomes less prone to errors. Keywords – Artificial Intelligence, Human Resource, RecruitmentINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00In the modern world, innovation in technology has ended up bringing the science and technology in HR Operations of the company to carry out the HR process that may lead to reducing rather sharing the work of the HR personnel.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Mrs. Savita Khamitkar', 'D. Kshirsagar']2024-01-04T00:00:00https://www.semanticscholar.org/paper/8f445231fd587c22f6eba0184c6e79dd62efac2c db051510544d9c353cb9d4e3b4ec0382fd449977The latest developments of chatbots driven by Large Language Models (LLMs), more specifically ChatGPT, have shaken the foundations of how text is created, and may drastically reduce and change the need, ability, and valuation of human writing. Furthermore, our trust in the written word is likely to decrease, as an increasing proportion of all written text will be AI-generated – and potentially incorrect. In this essay, I discuss these implications and possible scenarios for us humans, and for AI itself.Frontiers Artif. Intell.200Frontiers in Artificial Intelligence['Thomas Hellström']2024-01-04T00:00:00https://www.semanticscholar.org/paper/db051510544d9c353cb9d4e3b4ec0382fd449977 5e79f43460437bb0ef91d5e5aa19545241b4810dThe emergence of artificial intelligence in healthcare is probably leading to another two-speed world. On one hand, widely accessible AI applications such as language models are becoming ubiquitous, while on the other, resource-intensive technologies like robotic surgery and personalized medicine will be reserved for a privileged few. This development signifies a growing disparity in access to AI advancements. In this context, the role of empathy and emotional aspects in patient care comes to the forefront. With the rise of AI, there's a critical examination of whether synthetic empathy can adequately replace human empathy in patient care, emphasizing the necessity of maintaining human elements in an increasingly automated healthcare environment. Qeios00With the rise of AI, there's a critical examination of whether synthetic empathy can adequately replace human empathy in patient care, emphasizing the necessity of maintaining human elements in an increasingly automated healthcare environment.Qeios['Emmanuel Lagarde']2024-01-04T00:00:00https://www.semanticscholar.org/paper/5e79f43460437bb0ef91d5e5aa19545241b4810d 67b4b4b95ad28626fc4ad22d866a88408a0a0307Silambam is a traditional martial art from Tamil Nadu, India. It is a stick-fighting art that has been practiced for centuries. In recent years, there has been growing interest in using technology to enhance the practice and promotion of Silambam. One way to do this is to use the Internet of Things (IoT). IoT devices can be used to collect data on Silambam practitioners' movements and performance. This data can then be analysed using artificial intelligence (AI) to provide insights and recommendations to practitioners. For example, IoT sensors could be attached to Silambam sticks to track the number of rotations, speed, and force of each strike. This data could then be used to provide feedback to practitioners on their technique and progress. AI could also be used to develop personalized training programs for Silambam practitioners. For example, AI could analyse a practitioner's data to identify areas where they need improvement and then recommend specific exercises or drills. In addition to enhancing the practice of Silambam, IoT and AI could also be used to promote the art to a wider audience. For example, IoT-enabled Silambam sticks could be used to create interactive training games or simulations. Overall, IoT and AI have the potential to revolutionize the way that Silambam is practiced and promoted. By using these technologies, we can make Silambam more accessible, engaging, and effective for practitioners of all levels.EAI Endorsed Transactions on Internet of Things40IoT and AI have the potential to revolutionize the way that Silambam is practiced and promoted and can make Silambam more accessible, engaging, and effective for practitioners of all levels.EAI Endorsed Transactions on Internet of Things['Vijayarajan Ramanathan', 'Meyyappan T', 'Gnanasankaran Natarajan']2024-01-04T00:00:00https://www.semanticscholar.org/paper/67b4b4b95ad28626fc4ad22d866a88408a0a0307 16c654f366f39ec850466c111491118f1d8f74b5Agriculture is critical to the global economy, therefore the introduction of automation in this field is now an essential and rising topic internationally. Demands for food and labor are rising at an exponential rate, outpacing the capabilities of conventional farming practices. A new age of revolutionary change has been caused by the incorporation of Artificial Intelligence (AI) into agriculture. This study focuses on the development of an AI-powered Chatbot that is customized for the agricultural sector, offering assistance with crop recommendation and disease detection. The two machine learning models used to handle crop suggestions are Gaussian Naive Bayes (GNB) and Support Vector Machine (SVM). Using the VGG-16 transfer learning model, disease predictions are made. The models are evaluated thoroughly, and as a result, GNB and VGG-16 are chosen to be a part of the Chatbot's architecture. Without the need for professional knowledge, the Chatbot can help farmers select crops that are suited for cultivation and evaluate the health of their crops. The designed Chatbot is confirmed to be reliable and effective after extensive testing of its performance through various queries. By utilizing AI, we made a smart tool for effective crop management and harvesting.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)120This study focuses on the development of an AI-powered Chatbot that is customized for the agricultural sector, offering assistance with crop recommendation and disease detection and using the VGG-16 transfer learning model.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['C. Bhuvaneswari', 'H. Pokhariya', 'P. Yarde', 'Vipul Vekariya', 'Harshal Patil', 'N. L']2024-01-04T00:00:00https://www.semanticscholar.org/paper/16c654f366f39ec850466c111491118f1d8f74b5 47ae4f51daaa512abe7d0dc5eb5679e8f3516c0fThis research explores the application of AI-driven techniques for automated anomaly detection in ultrasound scans during pregnancy. Leveraging a substantial dataset of labeled ultrasound images, this study delves into robust data collection, preprocessing methodologies, meticulous model selection, and comprehensive model training techniques. The combination of U-Net for image segmentation and ResNet for image classification was selectively chosen, followed by an in-depth analysis of their adaptability to medical imaging tasks. The study concludes by proposing a hybrid approach using both segmentation and classification for more accurate anomaly detection in ultrasound images. This study delves into a comprehensive survey of AI methodologies applied to fetal ultrasonography during the second trimester. By leveraging curated datasets and innovative AI techniques, this research navigates the evolving landscape of automated anomaly detection, aiming to enhance diagnostic capabilities and bridge accessibility gaps in prenatal care.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)90This study delves into a comprehensive survey of AI methodologies applied to fetal ultrasonography during the second trimester, proposing a hybrid approach using both segmentation and classification for more accurate anomaly detection in ultrasound images.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['K. P. Revathi', 'Indusha Mahesh', 'K. Makitha Sree', 'Nadiminti Deepthi']2024-01-04T00:00:00https://www.semanticscholar.org/paper/47ae4f51daaa512abe7d0dc5eb5679e8f3516c0f fda41b189f07d6e0b6997faa4a9b3e68ed814179Artificial intelligence (AI) has seen tremendous development in industry and academia. However, striking recent advances by industry have stunned the world, inviting a fresh perspective on the role of academic research in this field. Here, we characterize the impact and type of AI produced by both environments over the last 25 years and establish several patterns. We find that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models. In contrast, we find that exclusively academic teams publish the bulk of AI research and tend to produce higher novelty work, with single papers having several times higher likelihood of being unconventional and atypical. The respective impact-novelty advantages of industry and academia are robust to controls for subfield, team size, seniority, and prestige. We find that academic-industry collaborations struggle to replicate the novelty of academic teams and tend to look similar to industry teams. Together, our findings identify the unique and nearly irreplaceable contributions that both academia and industry make toward the healthy progress of AI.arXiv.org450It is found that articles published by teams consisting exclusively of industry researchers tend to get greater attention, with a higher chance of being highly cited and citation-disruptive, and several times more likely to produce state-of-the-art models.ArXiv['Lizhen Liang', 'Zhuang Han', 'James Zou', 'Daniel E. Acuna']2024-01-04T00:00:00https://www.semanticscholar.org/paper/fda41b189f07d6e0b6997faa4a9b3e68ed814179 8f30af3a40f4eef2db0594c100507e8f7f3b0f09In an era where data is the cornerstone of strategic decision-making, Generative Artificial Intelligence (Gen AI) is revolutionizing the Business Intelligence (BI) landscape. This study investigates the multifaceted role of Generative AI in BI, encompassing data generation and augmentation, predictive analytics, reporting automation, product and service innovation, and personalization of customer experiences. The work employs a combination of synthetic data generated by Generative Adversarial Networks (GANs) and Seasonal Autoregressive Integrated Moving Average (SARIMA) predictive modeling techniques to demonstrate the potent capabilities of Generative AI in enhancing data-driven insights. The findings reveal that Generative AI not only significantly improves the accuracy of predictive models, especially in scenarios with limited historical data but also streamlines reporting processes and catalyzes innovation by uncovering latent customer needs. The research underscores the transformative impact of Generative AI in BI, while also addressing the attendant ethical considerations and the necessity for rigorous data governance. Through this exploration, the study provides a roadmap for businesses to harness the power of Generative AI, paving the way for smarter, more agile, and more personalized business strategies.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)160This study investigates the multifaceted role of Generative AI in BI, encompassing data generation and augmentation, predictive analytics, reporting automation, product and service innovation, and personalization of customer experiences, to underscore the transformative impact of Generative AI.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['Thiruneelakandan. A', 'Umamageswari. A']2024-01-04T00:00:00https://www.semanticscholar.org/paper/8f30af3a40f4eef2db0594c100507e8f7f3b0f09 bcf211e9ff34ea05d80a324df66dd13504e1f557Voice assistants have undergone a remarkable transformation, evolving from simple text-to-speech systems to sophisticated conversational AI platforms. This exploration delves into the trajectory of this evolution, tracing the technological advancements and pivotal milestones that have propelled voice assistants into the realm of natural language understanding and human-like interaction. Beginning with the rudimentary text-to-speech functionalities, the narrative progresses through the integration of machine learning and neural networks, enabling voice assistants to comprehend context, nuances, and intent. The shift towards conversational AI marks a significant turning point, where these assistants have transcended mere command-following tools to become intuitive, adaptive, and capable of holding meaningful dialogues. This abstract encapsulates the journey of voice assistants, highlighting the pivotal technologies and breakthroughs that have shaped their evolution, ultimately leading to the dawn of conversational AI.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)170This abstract encapsulates the journey of voice assistants, highlighting the pivotal technologies and breakthroughs that have shaped their evolution, ultimately leading to the dawn of conversational AI.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['Rahul Jampala', 'Devisri Santosh Kola', 'Adithya Nagendra Gummadi', 'Meghana Bhavanam', 'Ithaya Rani Pannerselvam']2024-01-04T00:00:00https://www.semanticscholar.org/paper/bcf211e9ff34ea05d80a324df66dd13504e1f557 58271d338ad2a7ed611a8565d16b866ccc8147e6Growing awareness of the environmental impact of digital technologies has led to several isolated initiatives to promote sustainable practices. However, despite these efforts, the environmental footprint of generative AI, particularly in terms of greenhouse gas emissions and water consumption, remains considerable. This contribution first presents the components of this environmental footprint, highlighting the massive CO2 emissions and water consumption associated with training large language models, thus underlining the need to rethink learning and inference methods. The paper also explores the factors and characteristics of models that have an influence on their environmental footprint and demonstrates the existence of solutions to reduce it, such as using more efficient processors or optimising the energy performance of data centres. The potentially harmful effects of AI on the planet and its ecosystem have made environmental protection one of the founding principles of AI ethics at international and European levels. However, this recognition has not yet translated into concrete measures to address it.To address this issue, our contribution puts forward twelve pragmatic recommendations for public action to promote sustainable generative AI, in particular by building a long-term strategy to achieve carbon neutrality for AI models, encouraging international cooperation to set common standards, supporting scientific research and developing appropriate legal and regulatory frameworks.This paper seeks to inform the members of the Interministerial Committee on Generative AI about the environmental challenges of this technology by providing a brief review of the scientific literature on the subject and proposing concrete recommendations of public policy actions to reconcile technological innovation with the need to protect our environment.arXiv.org00This contribution first presents the components of the environmental footprint of generative AI, highlighting the massive CO2 emissions and water consumption associated with training large language models, thus underlining the need to rethink learning and inference methods.ArXiv['Thomas Le Goff']2024-01-04T00:00:00https://www.semanticscholar.org/paper/58271d338ad2a7ed611a8565d16b866ccc8147e6 6aee079c6df5ea7518c2cbc40ab46ca3ec7bcd71Expanding sigma70 promoter libraries can support the engineering of metabolic pathways and enhance recombinant protein expression. Herein, we developed an artificial intelligence (AI) and knowledge-based method for the rational design of sigma70 promoters. Strong sigma70 promoters were identified by using high-throughput screening (HTS) with enhanced green fluorescent protein (eGFP) as a reporter gene. The features of these strong promoters were adopted to guide promoter design based on our previous reported deep learning model. In the following case study, the obtained strong promoters were used to express collagen and microbial transglutaminase (mTG), resulting in increased expression levels by 81.4% and 33.4%, respectively. Moreover, these constitutive promoters achieved soluble expression of mTG-activating protease and contributed to active mTG expression in Escherichia coli. The results suggested that the combined method may be effective for promoter engineering.ACS Synthetic Biology210An artificial intelligence (AI) and knowledge-based method for the rational design of sigma70 promoters and the results suggested that the combined method may be effective for promoter engineering.ACS synthetic biology['Kangjie Xu', 'Shangyang Yu', 'Kun Wang', 'Yameng Tan', 'Xinyi Zhao', 'Song Liu', 'Jingwen Zhou', 'Xinglong Wang']2024-01-04T00:00:00https://www.semanticscholar.org/paper/6aee079c6df5ea7518c2cbc40ab46ca3ec7bcd71 7bef4b1b091bd0957e6f2a8eade4053613307ad7Purpose: This paper seeks to define a risk taxonomy, establish meaningful controls, and create a prospective harms model for AI risks in healthcare.  Currently, there is no known comprehensive definition of AI risks, as applied to industry and society.  Materials and Methods: The temptation for current research, both in academia and industry, is to apply exclusively-tech-based solutions to these complex problems; however, this view is myopic, and can be remedied by establishing effective controls informed by a holistic approach to managing AI risk.  Sociotechnical Systems Theory (STS) is an attractive theoretical lens for this issue, because it prevents collapsing a multifaceted problem into a one-dimensional solution.  Specifically, the multidisciplinary approach—one that includes both the sciences and the humanities—reveals a multidimensional view of technology-society interaction, exemplified by the advent of AI.  Findings: After advancing this risk taxonomy, this paper utilizes the risk management framework of Lean Six Sigma (LSS) to propose effective mitigating controls for the identified risks.  LSS determines controls through data collection and analysis, and supports data-driven decision making for industry professionals. Implications to Theory, Practice and Policy: Instantiating the theory of STS into industry practices could be critical, then, for determining and mitigating real-world risks from AI.  In summary, this paper combines the academic theory of sociotechnical systems with the industry practice of Lean Six Sigma to develop a hybrid model to fill a gap in the literature.  Drawing upon both theory and practice ensures a robust, informed risk model of AI use in healthcare.European journal of technology200This paper combines the academic theory of sociotechnical systems with the industry practice of Lean Six Sigma to develop a hybrid model to fill a gap in the literature and ensure a robust, informed risk model of AI use in healthcare.European Journal of Technology['Dillon Plummer']2024-01-04T00:00:00https://www.semanticscholar.org/paper/7bef4b1b091bd0957e6f2a8eade4053613307ad7 79ac3963c9f29c80f2ea079cb3fa667a0642599dDiscover Artificial Intelligence200This case study proposes an approach to work with F&B owners in creating and introducing machine learning (ML)-based sales forecasting tailored to the unique local aspects of the business, which enhances demand forecasting in the F&B domain by identifying data types and sources that improve predictive models and bolster managerial trust.Discov. Artif. Intell.['Nicole Groene', 'Sergii Zakharov']2024-01-04T00:00:00https://www.semanticscholar.org/paper/79ac3963c9f29c80f2ea079cb3fa667a0642599d 33815b00f7d59b3dc679e19d2f88c79f5e320143The field of physiotherapy has witnessed a transformative shift with the integration of Artificial Intelligence (AI)-based technologies. This research paper delves into the revolutionary impact of AI in advancing physiotherapy practices, focusing on enhancing patient outcomes. By exploring recent developments, applications, and challenges, this study aims to provide a comprehensive overview of the evolving landscape where technology intersects with rehabilitation. Through a mixed-methods approach, including a literature review, case studies, and interviews, the research analyzes the effectiveness of personalized treatment plans, real-time monitoring, and AI-driven decision support for physiotherapists. The findings reveal significant improvements in patient-reported outcomes, functional metrics, and treatment adherence, supported by positive physiotherapist experiences. Ethical considerations and challenges are discussed, emphasizing the potential of AI to revolutionize physiotherapy and shape the future of rehabilitative care.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The potential of AI to revolutionize physiotherapy and shape the future of rehabilitative care is emphasized, emphasizing the effectiveness of personalized treatment plans, real-time monitoring, and AI-driven decision support for physiotherapists.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Dr. Kanchan Kholiya']2024-01-04T00:00:00https://www.semanticscholar.org/paper/33815b00f7d59b3dc679e19d2f88c79f5e320143 33f2c56e5db7b0a90f7f881df1c7525de0d84d87Applied intelligence (Boston)340A hybrid fraud detection framework when only a small set of labelled (fraud/non-fraud) data is available, and human input is required in the final decision-making step showed promising results in identifying fraudulent users and improving human performance when human input is required to make the final decision.Applied Intelligence['Dewan F. Wahid', 'E. Hassini']2024-01-04T00:00:00https://www.semanticscholar.org/paper/33f2c56e5db7b0a90f7f881df1c7525de0d84d87 a187e238e6baedb3ee6a986baedf457ea095a8a2In situations such as demographics change ML models often perform poorly because the training data does not appropriately represent the environment. Privacy concerns worsen the issue by severely limiting training data. In this paper, we present a framework that utilizes a GAN-based synthesizer to generate synthetic data that not only satisfies user-defined constraints expressed as marginal distributions of selected columns but also strives to preserve the distributions observed in the original data. This framework takes as input an original dataset and a set of user-defined constraints, and synthesizes data that adheres to these constraints while capturing the underlying distributions present in the given data. The result is a customizable and realistic data generation solution that balances constraint satisfaction and preservation of data distributions.We validate and demonstrate the effectiveness of our technique through experimentation.COMAD/CODS231This paper presents a framework that utilizes a GAN-based synthesizer to generate synthetic data that not only satisfies user-defined constraints expressed as marginal distributions of selected columns but also strives to preserve the distributions observed in the original data.Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)['Sandeep Hans', 'Anupam Sanghi', 'Diptikalyan Saha']2024-01-04T00:00:00https://www.semanticscholar.org/paper/a187e238e6baedb3ee6a986baedf457ea095a8a2 d7afad6ca58b952a789a265ed919fa29e552e14f Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings While robotic process automation has little strength alone, its combination with existing business resources makes this system a crucial stepping stone for firms to modernize, becoming a business strategy rather than a mere tool or resource. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format. Strategic Direction10The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.Strategic Direction[]2024-01-04T00:00:00https://www.semanticscholar.org/paper/d7afad6ca58b952a789a265ed919fa29e552e14f 599c95e7b29c2fc7af781f7d492f7541702741edGames of skill provide an excellent source of entertainment to realize self-esteem, relaxation and social gratification. Engagement in online skill gaming platforms is however heavily dependent on the outcomes and experience (e.g., wins/losses). There is an inherent tension between being: (i) demotivated (and hence dis-engaged) and ultra-motivated (to chase wins) in response to losses; or (ii) positively engaged and over-indulged under a winning streak. These extreme transient intents make any gamification effort non-trivial in outcome-oriented platforms because of the inherent trade-off between engagement and prudence (avoiding over-indulgence). Providing long challenges and high milestones can generate immediate engagement bump while adversely impacting its sustenance (resulting in possible churn eventually) depending on the players’ intent. Unlike in most consumer internet platforms, the intent and relevancy (challenge completion) become intrinsically orthogonal. This paper presents ARGO, a deep learning based responsible gamification framework, which: (i) learns players’ long term “game behaviours” (as psychological imprints emanating from game play evolution), via SOTA collaborative neural network, CognitionNet; (ii) succinctly combines these game behaviours to estimate players’ inherent “intent” towards a challenge, via an engagement guided regressor; and (iii) addresses the issue in building an imbalanced classification model for challenge completion via quantile regression with a novel data augmentation trick. Using online Rummy as a case-study, we show that the player engagement can be maximized based on both intent and challenge completion likelihood. Personalizing the challenge milestones accordingly results in—15% lift in milestone completion, 14% decline in unsuccessful target chasing with 4% improvements in target completion rates for the target chasers and 7% reduction in loss chasing and 5% improvement in win rates for the over-indulging players. We contend that ARGO is first-of-a-kind framework for outcome-oriented platforms that—performs personalized gamification contingent to both: (a) players’ transient intent and (b) challenge relevancy—thus balancing the trade-off between engagement and over-indulgence.COMAD/CODS580This paper presents ARGO, a deep learning based responsible gamification framework, which learns players’ long term “game behaviours” (as psychological imprints emanating from game play evolution), via SOTA collaborative neural network, CognitionNet and succinctly combines these game behaviours to estimate players’ inherent “intent” towards a challenge.Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)['Pulkit Agrawal', 'Aditya Pareek', 'Rukma Talwadker', 'Tridib Mukherjee']2024-01-04T00:00:00https://www.semanticscholar.org/paper/599c95e7b29c2fc7af781f7d492f7541702741ed 85025b6c7930b1df9dffda813855f93d0bf9f227Accurate temperature control of power transformer is the challenging task to every power system engineers. Nowadays Mercury based temperature sensing and actuating systems are installed for controlling the power transformer temperature and tripping the breakers whenever the temperature is exceeded the desired preset value. But the drawback of these control methods is lack of accuracy in sensing of temperature and sluggish control or slow response. A new approach or method for controlling and monitoring the temperature of the power transformer is prescribed and the associated control components are discussed. The intelligent control system uses the Programmable Logic Controller (PLC) and Graphical Operating Terminal (GOT) Display unit for the control purpose. The ON command to cooling fan and TRIP signal to circuit breaker are given from the controller. The algorithm is written into the PLC in the form of ladder program. The reference values and actual values are displayed in the GOT. In this study, the required control modules and programming part are discussed to implement the intelligent control of temperature of power transformer. The controller is trained for the past history and necessary scripts are written to take the action in its own for the specific problem using AI based techniques.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)160The intelligent control system uses the Programmable Logic Controller (PLC) and Graphical Operating Terminal (GOT) Display unit for the control purpose and the algorithm is written into the PLC in the form of ladder program.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['Satish Bojjawar', 'R. Shanmugasundaram', 'P. Benakop', 'C. M. Raj', 'W. R. Babu']2024-01-04T00:00:00https://www.semanticscholar.org/paper/85025b6c7930b1df9dffda813855f93d0bf9f227 83a5679ef30242fbc62c308f74face03c4a86e47Information Systems Frontiers763A high-order model of AI capability and its resources is developed, suggesting that AI capability is a paramount variable that substantially influences firm performance and that AI infrastructure is a crucial resource.Information Systems Frontiers['Samuel Fosso Wamba', 'M. Queiroz', 'Ilias O. Pappas', 'Yulia Sullivan']2024-01-04T00:00:00https://www.semanticscholar.org/paper/83a5679ef30242fbc62c308f74face03c4a86e47 3e89d80f5d85d81032e2222928f0401524fa6508Forensic Science, Medicine, and Pathology64This commentary explores the integration of artificial intelligence in forensic science and its potential implications and underscores the need for rigorous scrutiny, standardized operating procedures, and proactive dialogue to ensure the responsible advancement of AI in forensic science.Forensic Science, Medicine and Pathology['Paige Tynan']2024-01-04T00:00:00https://www.semanticscholar.org/paper/3e89d80f5d85d81032e2222928f0401524fa6508 48a2f9799cc5fe9792c0722417c2c0cc3942cc9bArtificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.Annals of Medicine and Surgery551This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future.Annals of Medicine and Surgery['Ali Aamir', 'Arham Iqbal', 'Fareeha Jawed', 'Faiza Ashfaque', 'Hafiza Hafsa', 'Zahra Anas', 'M. O. Oduoye', 'Abdul Basit', 'Shaheer Ahmed', 'Sameer Abdul Rauf', 'Mushkbar Khan', 'Tehreem Mansoor']2024-01-04T00:00:00https://www.semanticscholar.org/paper/48a2f9799cc5fe9792c0722417c2c0cc3942cc9b 8165f53aa6f7084e87e730373ed6c35ea3672837Artificial intelligence (AI) is gaining increasing interest in the field of medicine because of its capacity to process big data and pattern recognition. Cardiotocography (CTG) is widely used for the assessment of foetal well-being and uterine contractions during pregnancy and labour. It is characterised by inter- and intraobserver variability in interpretation, which depends on the observers’ experience. Artificial intelligence (AI)-assisted interpretation could improve its quality and, thus, intrapartal care. Cardiotocography (CTG) raw signals from labouring women were extracted from the database at the University Hospital of Bern between 2006 and 2019. Later, they were matched with the corresponding foetal outcomes, namely arterial umbilical cord pH and 5-min APGAR score. Excluded were deliveries where data were incomplete, as well as multiple births. Clinical data were grouped regarding foetal pH and APGAR score at 5 min after delivery. Physiological foetal pH was defined as 7.15 and above, and a 5-min APGAR score was considered physiologic when reaching ≥7. With these groups, the algorithm was trained to predict foetal hypoxia. Raw data from 19,399 CTG recordings could be exported. This was accomplished by manually searching the patient’s identification numbers (PIDs) and extracting the corresponding raw data from each episode. For some patients, only one episode per pregnancy could be found, whereas for others, up to ten episodes were available. Initially, 3400 corresponding clinical outcomes were found for the 19,399 CTGs (17.52%). Due to the small size, this dataset was rejected, and a new search strategy was elaborated. After further matching and curation, 6141 (31.65%) paired data samples could be extracted (cardiotocography raw data and corresponding maternal and foetal outcomes). Of these, half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy. Complete data could only be found for one-third of the available population. Yet, to our knowledge, this is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming. A further enrichment of the database is planned.Methods and Protocols231This is the most exhaustive and second-largest cardiotocography database worldwide, which can be used for computer analysis and programming, and half will be used to train artificial intelligence (AI) algorithms, whereas the other half will be used for analysis of efficacy.Methods and Protocols['J. L. Aeberhard', 'A. Radan', 'Ramin Abolfazl Soltani', 'K. Strahm', 'S. Schneider', 'Adriana Carrié', 'Mathieu Lemay', 'Jens Krauss', 'Ricard Delgado-Gonzalo', 'Daniel Surbek']2024-01-04T00:00:00https://www.semanticscholar.org/paper/8165f53aa6f7084e87e730373ed6c35ea3672837 0f0867f3ec267d5a0e1808624f9fafb5099df236Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, and it is known for memory loss and cognitive decline. Considering the morbidity associated with AD, it is important to explore lifestyle elements influencing the chances of developing AD, with special emphasis on nutritional aspects. This review will first discuss how dietary factors have an impact in AD development and the possible role of Artificial Intelligence (AI) and Machine Learning (ML) in preventative care of AD patients through nutrition. The Mediterranean-DASH diets provide individuals with many nutrient benefits which assists the prevention of neurodegeneration by having neuroprotective roles. Lack of micronutrients, protein-energy, and polyunsaturated fatty acids increase the chance of cognitive decline, loss of memory, and synaptic dysfunction among others. ML software has the ability to design models of algorithms from data introduced to present practical solutions that are accessible and easy to use. It can give predictions for a precise medicine approach to evaluate individuals as a whole. There is no doubt the future of nutritional science lies on customizing diets for individuals to reduce dementia risk factors, maintain overall health and brain function.Molecular Nutrition & Food Research681How dietary factors have an impact in AD development and the possible role of Artificial Intelligence (AI) and Machine Learning (ML) in preventative care of AD patients through nutrition are discussed.Molecular nutrition & food research['Ariana Soares Dias Portela', 'Vrinda Saxena', 'Eric Rosenn', 'Shu-Han Wang', 'Sibilla Masieri', 'Joshua Palmieri', 'G. Pasinetti']2024-01-04T00:00:00https://www.semanticscholar.org/paper/0f0867f3ec267d5a0e1808624f9fafb5099df236 e2582f49854b642dc81349af0b217a5cdb33d2c2This work explores the transition to intelligent corporate social responsibility (CSR) 2.0, an advanced approach to CSR that integrates artificial intelligence and new technologies to promote sustainability and efficiency. In Morocco, this evolution is of great importance, as it is part of the ambitious national vision of sustainable development, with objectives to reduce environmental impact and promote transparency. Moroccan public establishments and enterprises (PEE), as key players in crucial sectors such as energy, water and infrastructure, have already begun to develop CSR strategies. However, it has become clear that the shift towards intelligent Social Responsibility of the Organizations (RSO or OSR) based on artificial intelligence and new technologies is essential to meet current and future challenges. This subject is of paramount importance in the Moroccan context, as it offers a concrete means of achieving the country’s sustainability goals while strengthening regional leadership. In addition, it opens the door to future research and studies in this field, encouraging other researchers to explore this promising avenue which has positive potential for humanity as a whole, creating responsible and sustainable development models for the future.Journal of Autonomous Intelligence01Journal of Autonomous Intelligence['Reda El Medaker', 'Said Loukil', 'Rachid Mchich']2024-01-04T00:00:00https://www.semanticscholar.org/paper/e2582f49854b642dc81349af0b217a5cdb33d2c2 b582936a697cc3e1b6064fc0a9b449ca61f46e10Technological surveillance has become one of the ways of knowing how technology is developing in a particular area. In a normal situation, Internet search engines are used to browse the pages of equipment manufacturers to analyze. With the use of Artificial Intelligence, mainly those that allow information to be provided about the questions asked, as is the case of CHATGPT. In the work presented, a method is defined to be able to carry out the CHATGPT tool as a search tool to carry out technological surveillance, in the health area, applied to the evolution and supply of medical equipment and how these are evolving both in the use of hardware as software. The method can be replicated and applied to other areas of the industry.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)100In the work presented, a method is defined to be able to carry out the CHATGPT tool as a search tool to carry out technological surveillance, in the health area, applied to the evolution and supply of medical equipment and how these are evolving both in the use of hardware as software.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['W. Auccahuasi', 'Oscar Linares', 'Kitty Urbano', 'Julia Sobrino-Mesias', 'Medalith Campos-Sobrino', 'Humberto Quispe-Peña']2024-01-04T00:00:00https://www.semanticscholar.org/paper/b582936a697cc3e1b6064fc0a9b449ca61f46e10 2ef15d31db577546603198e9fad212065545bcf9BACKGROUND Thyroid nodules are common lesions in benign and malignant thyroid diseases. More and more studies have been conducted on the feasibility of artificial intelligence (AI) in the detection, diagnosis, and evaluation of thyroid nodules. The aim of this study was to use bibliometric methods to analyze and predict the hot spots and frontiers of AI in thyroid nodules. METHODS Articles on the application of artificial intelligence in thyroid nodules were retrieved from the Web of Science core collection database. A website (https://bibliometric.com/), VOSviewer and CiteSpace software were used for bibliometric analyses. The collaboration maps of countries and institutions were analyzed. The cluster and timeline view based on cocitation references and keywords citation bursts visualization map were generated. RESULTS The study included 601 papers about AI in thyroid nodules. China contributed to more than half (52.41%) of these publications. The cluster view and timeline view of co-citation references were assembled into 9 clusters, "AI", "deep learning", "papillary thyroid carcinoma", "radiomics", "ultrasound image", "biomarkers", "medical image segmentation", "central lymph node metastasis (CLNM)", and "self-organizing auto-encoder". The "AI", "radiomics", "medical image segmentation", "deep learning," and "CLNM", emerging in the last 10 years and continuing until recent years, were included. CONCLUSION An increasing number of scholars were devoted to this field. The potential future research hotspots include risk factor assessment and CLNM prediction of thyroid carcinoma based on radiomics and deep learning, automatic segmentation based on medical images (especially ultrasound images).Endocrine, Metabolic & Immune Disorders - Drug Targets00The potential future research hotspots include risk factor assessment and CLNM prediction of thyroid carcinoma based on radiomics and deep learning, automatic segmentation based on medical images (especially ultrasound images) and automatic segmentation based on medical images (especially ultrasound images).Endocrine, metabolic & immune disorders drug targets['Yun Peng', 'Tong-Tong Wang', 'Jing-Zhi Wang', 'Heng Wang', 'Ruo-Yun Fang', 'Liang-Geng Gong', 'Wu-Gen Li']2024-01-04T00:00:00https://www.semanticscholar.org/paper/2ef15d31db577546603198e9fad212065545bcf9 a29095b8a74c63794da240b55a9ef069c0db1b9bThe ever-evolving nature of the worldwide business arena is undergoing a significant change propelled by technological progress, where artificial intelligence (AI) is emerging as a central force redefining conventional methodologies. This research intends to identify and dissect the challenges encountered by Nigerian accounting firms in incorporating AI into their auditing processes, and to illuminate the opportunities that arise from leveraging AI capabilities in the auditing domain. This research utilized a survey research design. A well-structured questionnaire was employed to gather data from statutory auditors familiar with the use of artificial intelligence within their accounting firms situated in Lagos, the commercial hub of the Nigerian economy. The study encompassed all 35 registered accounting firms in Lagos State, Nigeria, employing a census sampling technique to determine the sample size, representing 100% of the population. Given the relative size of the population, five respondents were chosen from each accounting firm, totaling 175 respondents. The study received 153 responses, constituting 87% of the sample size. Descriptive and inferential statistics were applied to analyze the collected data. The outcomes reveal that AI, encompassing machine learning, natural language processing, and expert systems, significantly contributes to identifying challenges and highlighting opportunities in this context. The results underscore the positive role of AI in addressing challenges and revealing potential advancements in auditing practices within Nigerian accounting firms. The study concludes that AI, particularly machine learning, holds promise for addressing challenges and fostering advancements in auditing practices for Nigerian accounting firms. Given the significant positive impact of machine learning, accounting firms in Nigeria should consider prioritizing the integration of machine learning technologies into their auditing practices.Asian Journal of Economics Business and Accounting00The study concludes that AI, particularly machine learning, holds promise for addressing challenges and fostering advancements in auditing practices for Nigerian accounting firms.Asian Journal of Economics, Business and Accounting['Oluyinka Oluwagbade', 'Olayinka Dominion Boluwaji', 'Oyebanji Abiola Azeez', 'Lakori Micah Njengo']2024-01-04T00:00:00https://www.semanticscholar.org/paper/a29095b8a74c63794da240b55a9ef069c0db1b9b 6ffb825be18df3ca6e272dbe78dc6144aad5abfaThe growing global population is posing unprecedented challenges for the agriculture industry, requiring it to produce more food while also addressing resource constraints and environmental concerns. Artificial intelligence (AI) has changed the game in agriculture by offering creative solutions to increase productivity, optimize resource use, and promote sustainability. Artificial intelligence (AI) has been used in the agriculture sector more and more recently. A few of the problems the industry faces in attempting to maximize its produce are improper soil treatment, disease and pest infestation, the use of driverless tractors, big data requirements, and the knowledge gap between farmers and technology. This work provides an overview of the applications of artificial intelligence in crop, weed, and disease management. The proposed work aims to review various AI techniques, including artificial neural networks (ANN) and fuzzy logic (FL).2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)150This work provides an overview of the applications of artificial intelligence in crop, weed, and disease management, and aims to review various AI techniques, including artificial neural networks (ANN) and fuzzy logic (FL).2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['Bhushan Fulkar', 'Samir Mendhe', 'Pawan Patil']2024-01-04T00:00:00https://www.semanticscholar.org/paper/6ffb825be18df3ca6e272dbe78dc6144aad5abfa 7d54ca6eb957dd1586e2a73e932fc664a994faa5This paper will review how artificial intelligence (AI) will play an increasingly important role in pediatric neuroradiology in the future. A safe, transparent, and human-centric AI is needed to tackle the quadruple aim of improved health outcomes, enhanced patient and family experience, reduced costs, and improved well-being of the healthcare team in pediatric neuroradiology. Equity, diversity and inclusion, data safety, and access to care will need to always be considered. In the next decade, AI algorithms are expected to play an increasingly important role in access to care, workflow management, abnormality detection, classification, response prediction, prognostication, report generation, as well as in the patient and family experience in pediatric neuroradiology. Also, AI algorithms will likely play a role in recognizing and flagging rare diseases and in pattern recognition to identify previously unknown disorders. While AI algorithms will play an important role, humans will not only need to be in the loop, but in the center of pediatric neuroimaging. AI development and deployment will need to be closely watched and monitored by experts in the field. Patient and data safety need to be at the forefront, and the risks of a dependency on technology will need to be contained. The applications and implications of AI in pediatric neuroradiology will differ from adult neuroradiology.AJNR. American journal of neuroradiology230A safe, transparent, and human-centric AI is needed to tackle the quadruple aim of improved health outcomes, enhanced patient and family experience, reduced costs, and improved well-being of the healthcare team in pediatric neuroradiology.AJNR. American journal of neuroradiology['A. Bhatia', 'Farzad Khalvati', 'B. Ertl-Wagner']2024-01-04T00:00:00https://www.semanticscholar.org/paper/7d54ca6eb957dd1586e2a73e932fc664a994faa5 a8736c1f61d508f8cc5a832a6a3326917ff7512d This article demonstrates that artificial intelligence (AI) is generally not a useful category in patent prosecution and patent law to address questions of patentability or inventorship. It has been suggested that inventions relating to AI will pose major problems for patent prosecution, for example regarding patentable subject-matter, sufficiency of disclosure, the level of inventive step or the question of inventorship. In this context, far-reaching assumptions are made about the nature and the powers of AI which are often overstated and distract from real issues. On the one hand, AI defies a precise definition, in particular one which could distinguish AI from other software. Indeed, many questions that arise in patenting AI inventions are also true, one way or another, for other, ‘conventional’ software. On the other hand, certain AI inventions may be a challenge for applicants to explain convincingly why their invention works and how. In view of this, the problem of AI inventorship that has recently dominated the public discussion is of less practical concern.Journal of Intellectual Property Law & Practice00Journal of Intellectual Property Law and Practice['Martin Müller']2024-01-04T00:00:00https://www.semanticscholar.org/paper/a8736c1f61d508f8cc5a832a6a3326917ff7512d c28f4a79bad1f1f96a6e688317b02c118c60515eInternational journal of information technology3510An automatic system based on neural networks has been designed to identify the emotions expressed by four autistic children throughout the process of interaction with the NAO robot, showing that the emotions of sadness and anger are those expressed by the students throughout the activity for the greatest amount of time.International Journal of Information Technology['G. Lorenzo', 'A. Lorenzo-Lledó']2024-01-04T00:00:00https://www.semanticscholar.org/paper/c28f4a79bad1f1f96a6e688317b02c118c60515e 3ce57747a9b6001aad89f2cfd343989843b89071AIAA SCITECH 2024 Forum300AIAA SCITECH 2024 Forum['George Choumos', 'Konstantinos Tsaprailis', 'Vaios Lappas', 'C. Kontoes']2024-01-04T00:00:00https://www.semanticscholar.org/paper/3ce57747a9b6001aad89f2cfd343989843b89071 1daf1ca1b230b978bdbfee0d3226665ebc59af02Diabetic Retinopathy (DR) is a severe issue among the diabetic patients, which makes the patient blind and so an early stage detection should be held to prevent this permanent damage to our health. In this research study, the introduction section brief about the common diabetes disease and type of diabetes is highlighted in the next section. The third section illustrates the basic idea and terminology of AI along with how it helps in Diabetic Retinopathy. The comparative study in tabular form for a better understanding of the major work in this area is discussed.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)150The comparative study in tabular form for a better understanding of the major work in this area is discussed and the basic idea and terminology of AI along with how it helps in Diabetic Retinopathy is illustrated.2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)['Gulshan', 'Vikas Chauhan']2024-01-04T00:00:00https://www.semanticscholar.org/paper/1daf1ca1b230b978bdbfee0d3226665ebc59af02 de50a2168bcbe48c073db93ee88828bd5cb77f37The paper critically examines the sustainable business model in the context of existing and future challenges inherent in the capitalist system. As the business model serves as a dynamic framework illustrating how a company generates wealth over time, functioning as both a visual representation and operational guide for implementing corporate strategy, it inherently embodies the essence of an entrepreneurial concept, delving into its practical ability to create value. The innovation within the business model lies in a transformative shift in how the company conducts its operations, thereby altering the methods of value creation. Progressing toward more sustainable business models necessitates the exploration of novel approaches that transcend a purely economic focus, incorporating environmental and social dimensions. Regeneration emerges as the pivotal concept in the evolution toward novel business paradigms that actively contribute to the restoration of humanity. In this context, regeneration signifies "giving forward," signifying a comprehensive redefinition of corporate culture and the adept utilization of cutting-edge tools to construct a business model centered around sustainability. The strategic utilization of Artificial Intelligence (AI) to optimize resources and enhance production efficiency is thoroughly examined as a fundamental component in attaining sustainable development goals. The article subsequently presents a case study exemplifying success, embodied by Madri Leone, a winery situated in Puglia, Italy, overseen by two sisters. This case study illuminates a harmonious amalgamation of family tradition and sustainable practices.International Journal of Academic Research in Business and Social Sciences260Regeneration emerges as the pivotal concept in the evolution toward novel business paradigms that actively contribute to the restoration of humanity, signifying a comprehensive redefinition of corporate culture and the adept utilization of cutting-edge tools to construct a business model centered around sustainability.International Journal of Academic Research in Business and Social Sciences['Federico de Andreis', 'U. Comite', 'Alba M. Gallo']2024-01-04T00:00:00https://www.semanticscholar.org/paper/de50a2168bcbe48c073db93ee88828bd5cb77f37 52963b38efe580c60d2f2bacb306348a3e76e69dIntroduction: COVID-19 can rapidly lead to severe respiratory problems and can result in an overwhelming burden on healthcare systems worldwide, making it imperative to identify high-risk patients and predict survival and need for intensive care (ICU). Most of the proposed modes are not well reported making them less reproducible and prone to high risk of bias. Methods: In this study, the performances of seven classical machine (Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), XGBoost, Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (NB)) and two deep leaning models (Deep Neural Network (DNN) and Long Short-Term Memory (LSTM)) in combination with two widely used feature selection methods (random forest and extra tree classifier) were investigated to predict - last status (representing mortality), ICU requirement, and ventilation days. Fivefold cross-validation was used for training and validation purposes. In each fold, 80% data were used for training the models and the rest 20% were preserved for validation. To minimize bias, the training and testing sets were split maintaining similar distributions. Before splitting, k-nearest neighbour (KNN) imputation algorithm was employed to resolve the issue of missing data. On the other hand, bootstrapping technique was used for both oversampling and undersampling to address the issue of data imbalance. Publicly available 122 demographic and clinical features of 1384 patients were used. The performances of the models were evaluated using accuracy, sensitivity, specificity, and AUC (Area Under the Curve) of Receiver operating characteristic curves (ROC). Results: Only 10 features out of 122 were found to be useful in prediction modelling with Acute kidney injury during hospitalization feature being the most important one. Blood pH presents a decent discrimination capability especially in predicting ICU requirement, and ventilated days, Whereas gender and age are found to be vital in predicting the last status. It was observed that selecting more than 10 features lower the prediction accuracy. The performances of different algorithms depend on number of features and data pre-processing techniques. LSTM with the with balanced data and 10 features performs the best in predicting the last status as well as ICU requirement with an average of 90%, 92%, 86% and 95% accuracy, sensitivity, specificity, and AUC respectively. DNN performs the best in predicting Ventilation days with 88% accuracy. For ICU requirement prediction, which is a binary prediction task, data pre-processing technique does not have any influence in making prediction and performances of different methods are comparable (89%, 98%, 78% and 95% accuracy, sensitivity, specificity, and AUC respectively). However, the number of features selected vary with data pre-processing technique. Conclusion: Considering all the factors and limitations including absence of exact time point of clinical onset, LSTM with carefully selected features can accurately predict last status and ICU requirement with approximately 90% accuracy, sensitivity, and specificity. DNN performs the best in predicting Ventilation days. Appropriate machine learning algorithm with carefully selected features and balance data can accurately predict mortality, ICU requirement and ventilation support. Such model can be very useful in emergency and pandemic where prompt and precise decision making is crucial.medRxiv350Appropriate machine learning algorithm with carefully selected features and balance data can accurately predict mortality, ICU requirement and ventilation support and such model can be very useful in emergency and pandemic where prompt and precise decision making is crucial.['Mahbubunnabi Tamal', 'Mohammad Marufur Rahman', 'Maryam Alhashim', 'M. Mulhim', 'Mohamed Deriche']2024-01-04T00:00:00https://www.semanticscholar.org/paper/52963b38efe580c60d2f2bacb306348a3e76e69d d45fe3e718296019755dc2674b12650cc17f8d73AIAA SCITECH 2024 Forum50AIAA SCITECH 2024 Forum['Shivakumar I. Ranganathan', 'Hari S. Ilangovan', 'Newton H. Campbell', 'Michael J. Acheson', 'Irene M. Gregory']2024-01-04T00:00:00https://www.semanticscholar.org/paper/d45fe3e718296019755dc2674b12650cc17f8d73 2c5ea4faac304510aa450618349586e2832e334bНаука і техніка сьогодні00Наука і техніка сьогодні['Наталія Бобро']2024-01-04T00:00:00https://www.semanticscholar.org/paper/2c5ea4faac304510aa450618349586e2832e334b e529cdf272c6ff5487cab2e09385c80a18f33e70Academy of Management Review180Academy of Management Review['Christine Moser', 'Vern L. Glaser', 'Dirk Lindebaum']2024-01-04T00:00:00https://www.semanticscholar.org/paper/e529cdf272c6ff5487cab2e09385c80a18f33e70 7cb6828cc1f86c085b22d4e2b995a97caf0196aaJournal of Art, Design and Music00Journal of Art, Design and Music['Rania Ezzat']2024-01-04T00:00:00https://www.semanticscholar.org/paper/7cb6828cc1f86c085b22d4e2b995a97caf0196aa 28fccfc887809f467f54d314374d877e888fd3a1European Radiology60European Radiology['M. Bahl']2024-01-04T00:00:00https://www.semanticscholar.org/paper/28fccfc887809f467f54d314374d877e888fd3a1 194a0eec9e1dea6260115e6216b05d6f9be492b2ANZ journal of surgery90ANZ journal of surgery['Jawed Noori', 'Trevor M Yeung', 'Toan Pham', 'S. Warrier', 'Corina Camille Behrenbruch', 'Alexander G Heriot']2024-01-04T00:00:00https://www.semanticscholar.org/paper/194a0eec9e1dea6260115e6216b05d6f9be492b2 ccd8878034ae88c10f7dddd5eb28a6ded51453f9Consciousness is a natural phenomenon, familiar to every person. At the same time, it cannot be described in singular terms. The rise of Artificial Intelligence in recent years has made the topic of Artificial Consciousness highly debated. The paper discusses the main general theories of consciousness and their relationship with proposed Artificial Consciousness solutions. There are a number of well-established models accepted in the area of research: Higher Order Thoughts/Higher Order Perception, Global Network Workspace, Integrated Information Theory, reflexive, representative, functional, connective, Multiple Draft Model, Neural Correlate of Consciousness, quantum consciousness, to name just a few. Some theories overlap, which allows for speaking about more advanced, complex models. The disagreement in theories leads to different views on animal consciousness and human conscious states. As a result, there are also variations in the opinions about Artificial Consciousness based on the discrepancy between qualia and the nature of AI. The hard problem of consciousness, an epitome of qualia, is often seen as an insurmountable barrier or, at least, an “explanatory gap”. Nevertheless, AI constructs allow imitations of some models in silico, which are presented by several authors as full-fledged Artificial Consciousness or as strong AI. This itself does not make the translation of consciousness into the AI space easier but allows decent progress in the domain. As argued in this paper, there will be no universal solution to the Artificial Consciousness problem, and the answer depends on the type of consciousness model. A more pragmatic view suggests the instrumental interaction between humans and AI in the environment of the Fifth Industrial Revolution, limiting expectations of strong AI outcomes to cognition but not consciousness in wide terms. Qeios980There will be no universal solution to the Artificial Consciousness problem, and the answer depends on the type of consciousness model; a more pragmatic view suggests the instrumental interaction between humans and AI in the environment of the Fifth Industrial Revolution.Qeios['D. J. Herzog', 'Nitsa J. Herzog']2024-01-04T00:00:00https://www.semanticscholar.org/paper/ccd8878034ae88c10f7dddd5eb28a6ded51453f9 a9146b36160420b3d92eda89f60275c931c72281Innovation is the primary driving force for development, and green innovation efficiency (GIE) plays a key role in regional sustainable development. Moreover, environmental regulations (ERs) are also crucial for innovation and green transformation. Considering the heterogeneity of ERs, we assess the dynamic GIE in the industrial sectors of China. We detect their spatial clustering characteristics, and distinguish the impacts of ERs. Results suggest that there exist significant differences in GIE. Provinces such as Hainan, Guangdong and Zhejiang are ranked high, while Gansu, Inner Mongolia and Ningxia are ranked at the bottom, which shows some spatial dependence. The relationship between the administrative regulation and GIE demonstrates a U-shape, and has not reached a critical point, whereas the relationship between the market-based regulation and GIE possesses an inverted U-shape, which is highly significant. Furthermore, a positive linear relationship exists between the lagged public participation regulation and GIE. This paper also proposes that the economic development level and industrial structure are vital factors in accelerating industrial GIE. These conclusions provide scientific support for formulating regional transformation strategies.Sustainability440Sustainability['Junfang Hao', 'Wanqiang Xu', 'Zhuo Chen', 'Baiyun Yuan', 'Yuping Wu']2024-01-03T00:00:00https://www.semanticscholar.org/paper/a9146b36160420b3d92eda89f60275c931c72281 c8f60014f8258f4067aa839562312e5e7de405a1AI and Ethics51This paper aims at studying the governance structure proposed by the EU AI Act, as approved by the European Council in November 2022, and proposes tools to conduct conformity assessments of AI systems.AI and Ethics['Eva Thelisson', 'Himanshu Verma']2024-01-03T00:00:00https://www.semanticscholar.org/paper/c8f60014f8258f4067aa839562312e5e7de405a1 8082e4f1ce5529c9c75e1ddd377618752947f214The environmental and health problems caused by plastics throughout their life cycle have attracted considerable public attention over the past decade, triggering policy responses in many constituencies. Similarly, interdisciplinary research on plastics has been burgeoning in the past few years, and political science contributions have covered the manifold root causes and consequences of this shift in public policy including media coverage, evolving discourses and policy agendas. In view of this policy relevance that drives scholarly inquiry, it is surprising that we lack a systematic assessment of the actual policy outputs. This article fills this lacuna by developing a policy portfolio approach to plastic regulation. To illustrate and substantiate our approach, we provide an exploratory analysis of EU plastics regulation over the last twenty years, complementing this with Denmark, Germany, and Poland as diverse cases of member state regulation. Overall, our research shows that the number of policy measures targeting plastics has massively increased both at the supranational and national level. This policy growth, however, varies across policy targets and instruments. Our findings highlight first, that the policy targets addressed are mainly located at the end of the plastics life cycle; and second, that the instrument choice is privileging the use of hierarchical forms of intervention over the use of market‐ or information‐based instruments. We discuss these features of the policy portfolio approach in light of existing research on plastics and life‐cycle‐oriented policy approaches such as the Circular Economy.Environmental Policy and Governance701Environmental Policy and Governance['Sandra Eckert', 'Orr Karassin', 'Yves Steinebach']2024-01-03T00:00:00https://www.semanticscholar.org/paper/8082e4f1ce5529c9c75e1ddd377618752947f214 3b3fef469bebb725245bee8cd640a49adf4f3595This study investigates the efficacy of large language model (LLM)-powered chatbots in guiding physics problem-solving, examining whether they can effectively supplement teacher-led learning. A customised chatbot was developed leveraging ChatGPT to provide step-by-step assistance through a structured problem-solving algorithm. Its impact was evaluated via an experimental study with 12th-grade physics students (N = 24) randomly assigned to a teacher-guided or chatbot-guided group for problem-solving practice. A Mann-Whitney U test revealed no significant differences in problem-solving competency between conditions. Qualitative analysis of conversational logs indicates the chatbot successfully emulated key teacher scaffolding behaviours. Our findings suggest AI tutors can deliver personalised, interactive support akin to human teachers, offering viable supplements to augment physics learning. Further research should explore optimising LLM training, human-chatbot balances, and impacts across diverse educational settings.Educational Technology Quarterly335Educational Technology Quarterly['A. Riabko', 'Tetiana A. Vakaliuk']2024-01-03T00:00:00https://www.semanticscholar.org/paper/3b3fef469bebb725245bee8cd640a49adf4f3595 5d8caff942648a0b7dd0d8420125f6b474fe76ebAI and Ethics394This perspective article focuses on a recent application of Artificial Intelligence (generative AI models, such as ChatGPT), which are based on machine learning and Natural Language Processing and are applied to Natural Language Processing.AI and Ethics['Mirko Farina', 'Xiao Yu', 'A. Lavazza']2024-01-03T00:00:00https://www.semanticscholar.org/paper/5d8caff942648a0b7dd0d8420125f6b474fe76eb 52b7c596185233ca269b4db5fe54ab6e356e64faCreativity serves as a cornerstone for societal progress and innovation. With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application. In this paper, we prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. Therefore, the debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data. To arrive at this conclusion, this paper first addresses the complexities in defining creativity by introducing a new concept called Relative Creativity. Rather than attempting to define creativity universally, we shift the focus to whether AI can match the creative abilities of a hypothetical human. The methodological shift leads to a statistically quantifiable assessment of AI's creativity, term Statistical Creativity. This concept, statistically comparing the creative abilities of AI with those of specific human groups, facilitates theoretical exploration of AI's creative potential. Our analysis reveals that by fitting extensive conditional data without marginalizing out the generative conditions, AI can emerge as a hypothetical new creator. The creator possesses the same creative abilities on par with the human creators it was trained on. Building on theoretical findings, we discuss the application in prompt-conditioned autoregressive models, providing a practical means for evaluating creative abilities of generative AI models, such as Large Language Models (LLMs). Additionally, this study provides an actionable training guideline, bridging the theoretical quantification of creativity with practical model training.arXiv.org583It is proved in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators, thereby reducing the debate on AI's creativity to the question of its ability to fit a sufficient amount of data.ArXiv['Haonan Wang', 'James Zou', 'M. Mozer', 'Anirudh Goyal', 'Alex Lamb', 'Linjun Zhang', 'Weijie J. Su', 'Zhun Deng', 'Michael Qizhe Xie', 'Hannah Brown', 'Kenji Kawaguchi']2024-01-03T00:00:00https://www.semanticscholar.org/paper/52b7c596185233ca269b4db5fe54ab6e356e64fa 7a9933f52e4717d18c19bd360ed977dea6c74402In the rapidly evolving field of artificial intelligence, the creation and utilization of synthetic datasets have become increasingly significant. This report delves into the multifaceted aspects of synthetic data, particularly emphasizing the challenges and potential biases these datasets may harbor. It explores the methodologies behind synthetic data generation, spanning traditional statistical models to advanced deep learning techniques, and examines their applications across diverse domains. The report also critically addresses the ethical considerations and legal implications associated with synthetic datasets, highlighting the urgent need for mechanisms to ensure fairness, mitigate biases, and uphold ethical standards in AI development.arXiv.org02The methodologies behind synthetic data generation are explored, spanning traditional statistical models to advanced deep learning techniques, and their applications across diverse domains are examined, highlighting the urgent need for mechanisms to ensure fairness, mitigate biases, and uphold ethical standards in AI development.ArXiv['Shuang Hao', 'Wenfeng Han', 'Tao Jiang', 'Yiping Li', 'Haonan Wu', 'Chunlin Zhong', 'Zhangjun Zhou', 'He Tang']2024-01-03T00:00:00https://www.semanticscholar.org/paper/7a9933f52e4717d18c19bd360ed977dea6c74402 2d612f8bef88907d16c5dc10f9bd0d6e52819de4In this perspective, “CASES” are made for AI in medicine. The CASES mean Confidence, Adaptability, Stability, Explainability, and Security of AI systems. We underline that these CASES can be addressed not only individually but also synergistically on the large model platform and using cutting-edge diffusion-type models.BME Frontiers231This work underline that these CASES can be addressed not only individually but also synergistically on the large model platform and using cutting-edge diffusion-type models.BME Frontiers['Ge Wang']2024-01-03T00:00:00https://www.semanticscholar.org/paper/2d612f8bef88907d16c5dc10f9bd0d6e52819de4 fc39f83d5a8cd3b496022ae13ced7858ca0527a9The rapid evolution of technological tools, particularly Artificial Intelligence (AI), has resulted in the integration of technology-aided learning resources within educational environments. This study focuses on teachers' utilization of AI and technology tools in the context of English academic writing and its impact on their writing literacy. This research employs a qualitative approach, gathering data through questionnaires involving 20 English teachers from junior high schools. The findings highlight that these tools encourage and facilitate the enhancement of writing skills among teachers. Participants indicated using tools such as Grammarly, Quill Bot, ChatGPT, Mendeley, and Turnitin, which offer direct feedback, corrections, and aid in writing skills development. Specifically, the participants reported that these tools contribute to their comprehension of grammatical rules and vocabulary acquisition. Moreover, they found these tools instrumental in crafting more cohesive and coherent writing. This study suggests that integrating technology tools into English academic writing has the potential to transform the development and assessment of writing abilities. Nonetheless, it remains crucial for teachers to strike a balance between utilizing these tools and nurturing their writing skills to ensure ongoing writing enhancementJurnal Onoma Pendidikan Bahasa dan Sastra121It remains crucial for teachers to strike a balance between utilizing these tools and nurturing their writing skills to ensure ongoing writing enhancement, according to this study.Jurnal Onoma: Pendidikan, Bahasa, dan Sastra['Fitri Wulandari', 'Missy Tri Astuti', 'M. Marhamah']2024-01-03T00:00:00https://www.semanticscholar.org/paper/fc39f83d5a8cd3b496022ae13ced7858ca0527a9 505d2586f3c78e9d850c745377ec23c7caa623d9Recent years have witnessed growing concerns over demographic biases in image-centric applications, including image search engines and generative systems. While the advent of generative AI offers a pathway to mitigate these biases by producing underrepresented images, existing solutions still fail to precisely generate images that reflect specified demographic distributions. In this paper, we propose PreciseDebias, a comprehensive end-to-end framework that can rectify demographic bias in image generation. By leveraging fine-tuned Large Language Models (LLMs) coupled with text-to-image generative models, PreciseDebias transforms generic text prompts to produce images in line with specified demographic distributions. The core component of PreciseDebias is our novel instruction-following LLM, meticulously designed with an emphasis on model bias assessment and balanced model training. Extensive experiments demonstrate the effectiveness of PreciseDebias in rectifying biases pertaining to both ethnicity and gender in images. Furthermore, when compared with two baselines, PreciseDebias illustrates its robustness and capability to capture demographic intricacies. The generalization of PreciseDebias is further illuminated by the diverse images it produces across multiple professions and demographic attributes. To ensure reproducibility, we will make PreciseDebias openly accessible to the broader research community by releasing all models and code.IEEE Workshop/Winter Conference on Applications of Computer Vision351This paper proposes PreciseDebias, a comprehensive end-to-end framework that can rectify demographic bias in image generation by leveraging fine-tuned Large Language Models coupled with text-to-image generative models.2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)['Colton Clemmer', 'Junhua Ding', 'Yunhe Feng']2024-01-03T00:00:00https://www.semanticscholar.org/paper/505d2586f3c78e9d850c745377ec23c7caa623d9 704b46d2d036fd92d7f9a249c21d2f16ddee8cc2The continuous fusion of artificial intelligence (AI) and autonomous farming machinery (e.g., drones and field robots) provides a significant shift in the daily work experience of farmers. Faced with new technological developments, many risks and opportunities arise that need to be carefully translated into technological requirements to enable a sustainable production environment. Analyzing the complex relationship between social, ecological, and technological dependencies is a crucial step to understanding the different perspectives and systemic effects of technological functionalities. By providing a comprehensive overview of the state of the art, this article qualitatively analyzes the potential impact of AI on the autonomy of farmers and the technological developments to mitigate the risks. Fair data management practices, transparent AI approaches, and designs for an intuitive user experience are presented as key mechanisms for supporting responsible model development. Based on the defined social, technological, and ecological challenges in AI development, the knowledge to provide a high-level framework for the responsible creation of AI technologies is further systematized. By focusing on the multifaceted relationships and their effects on the autonomy of farmers, this article exemplifies the complex design decisions that must be faced in creating trustworthy and responsible AI tools.Applied Sciences611This article qualitatively analyzes the potential impact of AI on the autonomy of farmers and the technological developments to mitigate the risks and exemplifies the complex design decisions that must be faced in creating trustworthy and responsible AI tools.Applied Sciences['Kevin Mallinger', 'Ricardo A. Baeza-Yates']2024-01-03T00:00:00https://www.semanticscholar.org/paper/704b46d2d036fd92d7f9a249c21d2f16ddee8cc2 04488a3f5a1ff33563b3c2245a4025355dcb8cc8AI and Ethics01It is expected that full trustworthiness in AI systems can only be established if the technical measures to establish trustworthiness are flanked by specifications for the governance and processes of organizations that use and develop AI.AI and Ethics['Bertrand Braunschweig', 'Stefan Buijsman', 'Faicel Chamroukhi', 'Fredrik Heintz', 'Foutse Khomh', 'Juliette Mattioli', 'Maximilian Poretschkin']2024-01-03T00:00:00https://www.semanticscholar.org/paper/04488a3f5a1ff33563b3c2245a4025355dcb8cc8 ae839b6466fa060f8b3bd83dd2d4d4de0334482cAI &amp; SOCIETY45AI & SOCIETY['Q. Vuong', 'Manh-Tung Ho']2024-01-03T00:00:00https://www.semanticscholar.org/paper/ae839b6466fa060f8b3bd83dd2d4d4de0334482c 9ade6f758bb2022206c542325259444bd74b9d1b In this Medical News article, Arvind Narayanan, PhD, a professor of computer science at Princeton University, discusses the benefits of using artificial intelligence in research and clinical settings while remaining cautious of hype, biases, and data privacy issues. Journal of the American Medical Association (JAMA)03The benefits of using artificial intelligence in research and clinical settings while remaining cautious of hype, biases, and data privacy issues are discussed.JAMA['Melissa Suran', 'Y. Hswen']2024-01-03T00:00:00https://www.semanticscholar.org/paper/9ade6f758bb2022206c542325259444bd74b9d1b 0c4061ec82405a750183bdecf1b1647d1c6db40aThe rapid ascent of artificial intelligence (AI) has ushered in an era of transformative technological innovation, reshaping the very fabric of our societies. AI, with its manifold applications ranging from autonomous systems to intelligent algorithms, holds immense potential to enhance human life, drive economic growth, and tackle pressing global challenges. Yet, this potential is inextricably linked to the way AI is governed and harnessed. This research paper undertakes a thorough exploration of the intricate landscape surrounding Ethics in AI and its governance in the contemporary era. Our primary objective is to delve into the prevailing principles and public perceptions that shape the discourse on AI Ethics, and governance, recognizing the profound impact of AI on society. Our research approach encompasses fundamental methodologies: an exhaustive review of AI policy documents leveraging the Organization for Economic Co-Operation and Development (OECD) database and a far-reaching survey to capture the diverse spectrum of public opinions. The result shows that there is a need to assess the effectiveness of AI laws and their enforcement, focusing on the specific challenges posed by AI.International Conference on Communication Systems and Networks150A thorough exploration of the intricate landscape surrounding Ethics in AI and its governance in the contemporary era shows that there is a need to assess the effectiveness of AI laws and their enforcement, focusing on the specific challenges posed by AI.2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)['Pooja Kadambi', 'Raahul Seshadri', 'Chengappa Munjandira', 'Abhishek Appaji']2024-01-03T00:00:00https://www.semanticscholar.org/paper/0c4061ec82405a750183bdecf1b1647d1c6db40a b4bab5f56f9410cbf3b7e7867c39a462c5941fd7Jus Cogens250Jus Cogens['Alon Harel', 'Gadi Perl']2024-01-03T00:00:00https://www.semanticscholar.org/paper/b4bab5f56f9410cbf3b7e7867c39a462c5941fd7 d2ff53763ae09e724ec488cb0f6eab56d4cc6733The application of Internet of Things (IoT) technology has transformed the healthcare sector. Using IoT monitored data with AI, especially ML algorithms and statistical methodologies, we provide a study on the prediction of heart conditions. This study aims to create a precise and trustworthy predictive model that can efficiently analyse and understand the enormous quantity of data gathered from Internet of Things devices for monitoring heart health. The proposed methodology involves collecting real-time physiological data, such as systolic and diastolic blood pressure, heart rate, and BMI readings, from an IOT health monitoring device with different machine learning (ML) algorithms (random forest, decision tree, gradient booster classifier, and logistic regression) and statistical techniques (correlational analysis, data visualisation, ANOVA, and t-test) used to analyse and forecast heart conditions. Further, cross-validation techniques are used to evaluate the generalizability and robustness of the model. The performance of the predictive model is assessed using several criteria, including accuracy, precision, recall, and F1-score. The Gradient Boosting classifier worked well on the dataset for cardiac conditions, with an accuracy of almost 98%. Approximately 88% accuracy was attained. Naive Bayes functioned admirably, although it wasn't as effective as the Gradient Boost. Around 86% accuracy was attained. Overall, among the models, the Gradient Booster demonstrated the best accuracy, demonstrating its superior performance on the heart condition dataset. The outcomes of our tests and model building show good accuracy rates and reliable predictions for the prediction of heart conditions. In conclusion, the suggested method demonstrates the potential for early identification and prevention of cardiac illnesses using IoT-monitored data in conjunction with AI, improving patient outcomes and lowering healthcare expenditures.International Conference on Ubiquitous Information Management and Communication150The Gradient Booster demonstrated the best accuracy, demonstrating its superior performance on the heart condition dataset, and the suggested method demonstrates the potential for early identification and prevention of cardiac illnesses using IoT-monitored data in conjunction with AI, improving patient outcomes and lowering healthcare expenditures.2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)['Eiad Yafi', 'Ritu Chuahan', 'Anushka Sharma', 'M. Zuhairi']2024-01-03T00:00:00https://www.semanticscholar.org/paper/d2ff53763ae09e724ec488cb0f6eab56d4cc6733 656f277816a945cf6b3c13abb8f17d88ab412322In a world where energy policies are becoming more proactive, and the rapid advancements in AI technology drive market demand, the shift towards energy is expected to peak in the 2030s. As of May 2023, nonrenewable energy sources like fossil fuel-based power generation still contribute to 61.8% of the production, while renewable energy sources make up 38.2%.‎ Although fossil fuels will continue to play a role in our energy mix, renewable energy has potential. This research paper delves into the impact of Artificial Intelligence (AI) on wind power generation. Given the need for energy solutions to combat climate change, this study examines how AI technology can be integrated into the wind power sector. The paper provides an overview of the situation, highlighting challenges and opportunities for AI to enhance wind power generation, particularly in areas such as forecasting and maintenance. As wind power continues to gain importance, embracing AI offers a chance to optimize operations and reshape the energy landscape. Ultimately, this research aims to improve efficiency and expedite the transition towards a future in energy production. Science and Technology of Engineering, Chemistry and Environmental Protection20Science and Technology of Engineering, Chemistry and Environmental Protection['Yizhe Xia']2024-01-03T00:00:00https://www.semanticscholar.org/paper/656f277816a945cf6b3c13abb8f17d88ab412322 05f35a03b1a89000d41062986b173242cf0dc2a9We introduce a new challenge to the software development community: 1) leveraging AI to accurately detect and flag up secrets in code and on popular document sharing platforms that frequently used by developers, such as Confluence and 2) automatically remediating the detections (e.g. by suggesting password vault functionality). This is a challenging, and mostly unaddressed task. Existing methods leverage heuristics and regular expressions, that can be very noisy, and therefore increase toil on developers. The next step - modifying code itself - to automatically remediate a detection, is a complex task. We introduce two baseline AI models that have good detection performance and propose an automatic mechanism for remediating secrets found in code, opening up the study of this task to the wider community.arXiv.org110A new challenge is introduced: leveraging AI to accurately detect and flag up secrets in code and on popular document sharing platforms that frequently used by developers, such as Confluence and automatically remediating the detections (e.g. by suggesting password vault functionality).ArXiv['Gregor Kerr', 'David Algorry', 'Senad Ibraimoski', 'Peter Maciver', 'Sean Moran']2024-01-03T00:00:00https://www.semanticscholar.org/paper/05f35a03b1a89000d41062986b173242cf0dc2a9 c0f3be53f012ef09e7799be03f84e6496d859417BACKGROUND PILs are a tool that can supplement a clinical consultation and provide additional information for a patient to read in their own time. A wide range of PILs are available for distribution by the BAD and undergo rigorous review ahead of publication. 7.1 million UK adults are estimated to have the reading age of a 9-year-old and 43% are unable to comprehend written health information. OBJECTIVES To determine whether AI can produce PILs that include a similar degree of content to current BAD PILs. METHODS Using the AI tool ChatGPT, 10 PILs were generated, and their contents compared to that of existing BAD PILs using an author-generated list of commonly included themes. Omissions were noted and a repeat series of PILs generated using targeted request phrasing. Readability of AI-generated PILs was also analysed. RESULTS AI-generated PILs were found to include similar factual content to BAD PILs but excluded information that was felt to be more pertinent to patient concerns such as curability and heritability. Targeted request phrasing saw AI generate PILs including this content. Readability of AI-generated PILs was noted to be much higher than that of UK adults. CONCLUSIONS Where a condition-specific PIL is not readily available an AI-generated PIL can provide relevant information to a lesser quality than existing BAD PILs that may be inaccessible to some patients. Specific caution is advised regarding AI-generated medication-specific PILs.Clincal and Experimental Dermatology00AI-generated PILs were found to include similar factual content to BAD PILs but excluded information that was felt to be more pertinent to patient concerns such as curability and heritability, and readability of AI-generated PILs was noted to be much higher than that of UK adults.Clinical and experimental dermatology['Callum Verran']2024-01-03T00:00:00https://www.semanticscholar.org/paper/c0f3be53f012ef09e7799be03f84e6496d859417 a94077de72c9fbb7fe12c7b3164f051eb4fc10bcArtificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including “stroke and robotic rehabilitation therapy,” “robotic-assisted cardiac surgery,” and “cardiac image analysis,” which persisted as major topics throughout the years. Other topics, such as “retinal image analysis and CVD” and “biomarker and wearable signal analyses,” have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.Frontiers in Cardiovascular Medicine350The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare and offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.Frontiers in Cardiovascular Medicine['K. Shiferaw', 'Payam Wali', 'Dagmar Waltemath', 'A. Zeleke']2024-01-03T00:00:00https://www.semanticscholar.org/paper/a94077de72c9fbb7fe12c7b3164f051eb4fc10bc 9252fa28a419f627d70c9c9d44f0132b0bb9f310Levi Strauss & Co., a popular fashion label commonly known as Levi's, was involved in a crisis situation in March 2023 as a result of their partnership with Lalaland.ai, an artificial intelligence (AI) company. The partnership was created with the intention of using AI‐generated models to show more diversity in Levi's modelling. However, the brand received intense backlash and criticism following the partnership's announcement for cheapening diversity by failing to use real models. In the format of a case study, this paper describes the situation and evaluates Levi's crisis response in this relevant and dynamic dilemma.Journal of Contingencies and Crisis Management00The situation is described and Levi's crisis response in this relevant and dynamic dilemma is evaluated in this relevant and dynamic dilemma.Journal of Contingencies and Crisis Management['Lila Maiolo']2024-01-03T00:00:00https://www.semanticscholar.org/paper/9252fa28a419f627d70c9c9d44f0132b0bb9f310 fcd10d34971718b29e0987b45ec28e97c4c652b3The rapid proliferation of AI technology, marking the transition from weak AI to strong AI, has catalyzed a surge in AI-generated content across various domains. This transformation has brought into question the protection of intellectual property rights associated with AI-generated works. This paper embarks on an exploration of the evolving framework for protecting AI-generated content within the context of Chinese law. It delves into the multifaceted dimensions of this issue, scrutinizing copyright disputes arising from the innovative nature of AI content generation. Furthermore, it critically analyzes judicial practices to discern the evolving legal stance regarding AI-generated works, distinguishing cases where protection is granted from those where it is denied. In response to the challenges posed by this nascent field, the article also proposes a set of strategies geared towards fortifying the protective mechanisms for AI-generated content. These strategies encompass the development of classification criteria based on the purpose of content generation, the enhancement of intellectual property registration and verification mechanisms, and the promotion of synergy between technological advancements and legal frameworks.Lecture Notes in Education Psychology and Public Media00Lecture Notes in Education Psychology and Public Media['Binyu Wang']2024-01-03T00:00:00https://www.semanticscholar.org/paper/fcd10d34971718b29e0987b45ec28e97c4c652b3 067be1e7e177d49eab55b395cce2f9a81578d2a6Knowing the likelihood for a generational shift in the scientific exploration of digital data which has extensively outgrown society with tons of data. Moreover, the scientific community has evolved and outgrown themselves with Artificial Intelligence based technology to compute power and detect hidden features and information from outgrown digital databases. These advanced systems have the capability to evaluate enormous amounts of data generated with unprecedent speed, which can detect valuable insights from the data. In addition, the current study of approach focuses on the application of AI with healthcare databases to detect the hidden information from autism spectrum disorder (ASD). In this research we have various algorithmic predictions for Autism Spectrum Disorder (ASD) by combining Machine Learning with the dataset provided. The proposed approach is based on the predictive method where the data preprocessing and data evaluation models are used. The study of approach focuses on ASD with several machine Learning algorithms such as, SVM, KNN, Random Forest, Decision Tree to measure the accuracy and precision among the data and determine the hidden information and patterns. The current research scenario investigated concurrent parameters which are dependable features such as Class\ASD Traits, Sex, Jaundice scores and Q chat- 10 questions by which we have come to a conclusion which corroborated the current analysis with an accuracy Score of 74% for Random Forest, KNN: 71%, SVM: 70.6% and Decision Tree: 69.1% stating whether the individual has the Autism characteristics visible or not using various parametric analysis through data visualization and analyzing results by this approach.International Conference on Ubiquitous Information Management and Communication250This research has various algorithmic predictions for Autism Spectrum Disorder (ASD) by combining Machine Learning with the dataset provided and corroborated the current analysis with an accuracy Score of 74% stating whether the individual has the Autism characteristics visible or not.2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)['Ritu Chauhan', 'Khushi Mehta', 'Eiad Yafi', 'M. Zuhairi']2024-01-03T00:00:00https://www.semanticscholar.org/paper/067be1e7e177d49eab55b395cce2f9a81578d2a6 514611575645b791036e2288740fef11ffc11a0dAI and Ethics60The main gaps in AI risks management are discussed and a tool that can be used to support organizations in AI risk assessment is described that provides a visualization and quantification of AI risks and can inform strategies to mitigate and minimize those risks.AI and Ethics['Alessio Tartaro', 'Enrico Panai', 'Mariangela Zoe Cocchiaro']2024-01-03T00:00:00https://www.semanticscholar.org/paper/514611575645b791036e2288740fef11ffc11a0d ec3c3cc430cee2db7cdda685f76ececdb495ae0cAI &amp; SOCIETY350The study explores the future of AI-driven media and info-communication as envisioned by experts from all world regions, defining relevant terminology and expectations for 2050 and Visualizing the findings into a Glasses Model of AI Trust contributes to key debates regarding AI policy, developmental trajectories, and academic research in media and info-communication fields.AI & SOCIETY['Katalin Feher', 'L. Vicsek', 'M. Deuze']2024-01-03T00:00:00https://www.semanticscholar.org/paper/ec3c3cc430cee2db7cdda685f76ececdb495ae0c 2aa79054f364f00456a72eec325d3083d3746816International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-01-03T00:00:00https://www.semanticscholar.org/paper/2aa79054f364f00456a72eec325d3083d3746816 fc6723b03d9e35ff28a11dbfc13eca17d43d220bThis research undertakes a comprehensive exploration of reproductive rights and abortion within the broader context of gender politics, with a keen emphasis on the influence of radical feminism. Reproductive rights, extending beyond mere health and medical concerns, intersect with societal, ethical, religious, and political dimensions. Through this lens, the study investigates global reproductive policies and the international legal stance on abortion. The ascendancy of radical feminism and its critiques against historically entrenched patriarchal structures form a core component of this discourse. The article further delves into the legal histories and controversies surrounding both reproductive and abortion rights, probing into their evolving legal frameworks, international regulations, and the multifaceted debates linked to their acceptability. Through the interplay of these elements, the research ultimately converges on the broader implications these topics have on global human rights, womens socio-economic standings, and the fluid realm of gender dynamics. The overarching goal is to shed light on these intricate relationships, contributing to a more equitable and inclusive understanding of evolving gender politics.Lecture Notes in Education Psychology and Public Media00Lecture Notes in Education Psychology and Public Media['Xintang Zhang']2024-01-03T00:00:00https://www.semanticscholar.org/paper/fc6723b03d9e35ff28a11dbfc13eca17d43d220b 23a88eb9f3f53b77d1029a3c5a55d558d5f19cc9Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with high-frequency sampling rates usually improves the results, it often leads to data inefficiency and unnecessary expansion of the ANN, posing a challenge for their practical deployment on edge devices. Addressing these issues, our work introduces a pragmatic framework for data-efficient utilization in HAR tasks, considering the optimization of both sensor modalities and sampling rate simultaneously. Central to our approach are the designed trainable parameters, termed 'Weight Scores,' which assess the significance of each sensor modality and sampling rate during the training phase. These scores guide the sensor modalities and sampling rate selection. The pruning method allows users to make a trade-off between computational budgets and performance by selecting the sensor modalities and sampling rates according to the weight score ranking. We tested our framework's effectiveness in optimizing sensor modality and sampling rate selection using three public HAR benchmark datasets. The results show that the sensor and sampling rate combination selected via CoSS achieves similar classification performance to configurations using the highest sampling rate with all sensors but at a reduced hardware cost.arXiv.org170This work introduces a pragmatic framework for data-efficient utilization in HAR tasks, considering the optimization of both sensor modalities and sampling rate simultaneously, and tests the framework's effectiveness in optimizing sensor modality and sampling rate selection.ArXiv['Mengxi Liu', 'Zimin Zhao', 'Daniel Geissler', 'Bo Zhou', 'Sungho Suh', 'P. Lukowicz']2024-01-03T00:00:00https://www.semanticscholar.org/paper/23a88eb9f3f53b77d1029a3c5a55d558d5f19cc9 90991171ece5cefecf60e41ded1fc5b836772bf9The Membership Management Report00The Membership Management Report['Erin Sandage']2024-01-03T00:00:00https://www.semanticscholar.org/paper/90991171ece5cefecf60e41ded1fc5b836772bf9 5ce32325fa9ded5b2567a18945081b8c28182f86 Efforts have been made to include artificial intelligence (AI) in teaching and learning; nevertheless,the successful deployment of new instructional technology depends on the attitudes of the teachers who conductthe lesson. Few scholars have researched teachers' perspectives on AI use due to a general lack of expertise on howit can be used in the classroom, as well as a lack of specific knowledge about what AI-adopted tools would be like.This study investigated mathematics teachers’ perceptions of implemented AI systems and applications in AbuDhabi Emirate schools. The sample study consists of 580 male and female math teachers from public and privateschools across three educational regions in Abu Dhabi selected based on several qualifications and experiences.The research followed the descriptive analytical approach due to its suitability to the study’s context. The resultsrevealed that AI could be used as an educational tool to facilitate teaching and develop students’ performance byincluding AI systems and applications in the curricula. They increased motivation for learning, encouragingchallenge, competition, and suspense among students and considering their differences. The results also showedthe most critical challenges that math teachers face in applying AI systems and applications, the most prominent ofwhich are the need to exert more effort than the traditional method when using different AI systems andapplications and the pressures placed on them, which prevent them from using AI in teaching. Additionally, thefindings revealed no statistically significant differences in mathematics teachers’ perspectives regarding theimportance of using systems and applications of AI in teaching; however, statistically significant differences werefound in the math teachers’ challenges when applying AI systems and applications in teaching according to theeducational qualifications, especially among math teachers who have masters’ degrees. These results can be usedas a foundation for creating guidelines for the future integration of AI education in schools since they reportteachers’ experiences utilizing the system and various considerations regarding its implementationIraqi Journal for Computer Science and Mathematics756Investigation of mathematics teachers’ perceptions of implemented AI systems and applications in Abu Dhabi Emirate schools revealed that AI could be used as an educational tool to facilitate teaching and develop students’ performance by including AI systems and applications in the curricula.Iraqi Journal For Computer Science and Mathematics['Yousef Wardat', 'Mohammad A. Tashtoush', 'Rommel Alali', 'Shoeb Saleh']2024-01-03T00:00:00https://www.semanticscholar.org/paper/5ce32325fa9ded5b2567a18945081b8c28182f86 44495472bec18f54710c1bd7672bdd67c9299e3fAbstract Objectives: In an era characterized by dynamic technological advancements, the well-being of the workforce remains a cornerstone of progress and sustainability. The evolving industrial landscape in the modern world has had a considerable influence on occupational health and safety (OHS). Ensuring the well-being of workers and creating safe working environments are not only ethical imperatives but also integral to maintaining operational efficiency and productivity. We aim to review the advancements that have taken place with a potential to reshape workplace safety with integration of artificial intelligence (AI)-driven new technologies to prevent occupational diseases and promote safety solutions. Methods: The published literature was identified using scientific databases of Embase, PubMed, and Google scholar including a lower time bound of 1974 to capture chronological advances in occupational disease detection and technological solutions employed in industrial set-ups. Results: AI-driven technologies are revolutionizing how organizations approach health and safety, offering predictive insights, real-time monitoring, and risk mitigation strategies that not only minimize accidents and hazards but also pave the way for a more proactive and responsive approach to safeguarding the workforce. Conclusion: As industries embrace the transformative potential of AI, a new frontier of possibilities emerges for enhancing workplace safety. This synergy between OHS and AI marks a pivotal moment in the quest for safer, healthier, and more sustainable workplaces.Journal of Occupational Health1133AI-driven technologies are revolutionizing how organizations approach health and safety, offering predictive insights, real-time monitoring, and risk mitigation strategies that not only minimize accidents and hazards but also pave the way for a more proactive and responsive approach to safeguarding the workforce.Journal of Occupational Health['Immad A Shah', 'Sukhdev Mishra']2024-01-03T00:00:00https://www.semanticscholar.org/paper/44495472bec18f54710c1bd7672bdd67c9299e3f 0bd4f98e11dc37429e5919a09362b41fffa37d30This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field’s impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka’s law is reflected in the author’s contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford’s Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.Algorithms703This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM.Algorithms['Ștefan-Andrei Ionescu', 'Camelia Delcea', 'Nora Chiriță', 'I. Nica']2024-01-03T00:00:00https://www.semanticscholar.org/paper/0bd4f98e11dc37429e5919a09362b41fffa37d30 65695385bba45bcf618a836c3cd150a9ddf4602eThis paper explores the transformative potential of artificial intelligence (AI) in the context of sustainable agricultural development across diverse regions in Africa. Delving into opportunities, challenges, and impact, the study navigates through the dynamic landscape of AI applications in agriculture. Opportunities such as precision farming, crop monitoring, and climate-resilient practices are examined, alongside challenges related to technological infrastructure, data accessibility, and skill gaps. The article analyzes the impact of AI on smallholder farmers, supply chains, and inclusive growth. Ethical considerations and policy implications are also discussed, offering insights into responsible AI integration. By providing a nuanced understanding, this paper contributes to the ongoing discourse on leveraging AI for fostering sustainability in African agriculture.arXiv.org422The article analyzes the impact of AI on smallholder farmers, supply chains, and inclusive growth across diverse regions in Africa, and offers insights into responsible AI integration.ArXiv['Kinyua Gikunda']2024-01-03T00:00:00https://www.semanticscholar.org/paper/65695385bba45bcf618a836c3cd150a9ddf4602e 15b818fe471baf282100d12b1e7af9222c909ce2In today's evolving environment, artificial intelligence has become a key catalyst, not only replacing human workers but also automating content creation. This shift brings with it complexities related to copyright and intellectual property infringement, requiring a comprehensive exploration of the multifaceted challenges faced by AI in the production of creative content in the media and film industries. This article will provide an in-depth analysis of the profound impact of artificial intelligence on these industries and provide forward-looking suggestions. Through in-depth analysis, we draw insights from industry experts, filmmakers and consumers, and use situational analysis research methods to illustrate the profound impact of artificial intelligence in this field. This article finds that artificial intelligence increases efficiency and technical accuracy in the media and film industries, but also creates challenges when misused. Regulatory and ethical guidelines are critical to ensuring responsible use of AI. Additionally, it is important to maintain the integrity of the industry by recognizing that AI cannot replace humans in providing emotional support, real-life experience and preventing deception.Lecture Notes in Education Psychology and Public Media01It is found that artificial intelligence increases efficiency and technical accuracy in the media and film industries, but also creates challenges when misused.Lecture Notes in Education Psychology and Public Media['Zhuoer Liu']2024-01-03T00:00:00https://www.semanticscholar.org/paper/15b818fe471baf282100d12b1e7af9222c909ce2 99f9ba0ed01fe1d530c1dfee7b1ca559fd054eddContemporary people live in a reality full of intelligent technology construction, and many practical activities are regulated by technology. The chat content generation pre-training program artificial intelligence developed by the Open Artificial Intelligence Research Center (OpenAI) based on large model data training has aroused wide concern because of its generation and learning ability. It is necessary to admit that the current domestic media publicity seems to focus more on the convenience that big data technology may bring to human society. But not much about its own limitations. In the few discussions on the negative effects of big data technology, more concerns are raised about the ethical risks that may be caused by the abuse of relevant technology, such as the threat of "data greed" to personal privacy, and the decision-making errors that may be caused by the superstition of business decision-makers and government heads about "digital dictatorship", etc. From the perspective of philosophy of information technology and philosophy of cognitive science, this paper reviews the gains and losses of big data technology itself in terms of philosophical thought premise and path strategy. Starting from the theory of "moral materialization", this paper analyzes the ethical value load and risk of artificial intelligence from the perspectives of moral subject status, value position presupposition, operation intervention and operation result effect. And from the moral materialization theory of "top-down" approach and "bottom-up" approach to the ethical governance of modern scientific and technological intelligent artifacts.Lecture Notes in Education Psychology and Public Media01This paper analyzes the ethical value load and risk of artificial intelligence from the perspectives of moral subject status, value position presupposition, operation intervention and operation result effect, and from the moral materialization theory of "top-down" approach and "bottom-up" approach to the ethical governance of modern scientific and technological intelligent artifacts.Lecture Notes in Education Psychology and Public Media['Hui Li']2024-01-03T00:00:00https://www.semanticscholar.org/paper/99f9ba0ed01fe1d530c1dfee7b1ca559fd054edd 8fa4e2934abc781bd79a3405aaf4e6998cb30155The introduction of the European Union Artificial Intelligence Act, the NIST Artificial Intelligence Risk Management Framework, and related norms demands a better understanding and implementation of novel risk analysis approaches to evaluate systems with Artificial Intelligence components. This paper provides a cybersecurity risk analysis framework that can help assessing such systems. We use an illustrative example concerning automated driving systems.arXiv.org01This paper provides a cybersecurity risk analysis framework that can help assessing systems with Artificial Intelligence components and uses an illustrative example concerning automated driving systems.ArXiv['Jose Manuel Camacho', 'Aitor Couce Vieira', 'David Arroyo', 'David Ríos Insua']2024-01-03T00:00:00https://www.semanticscholar.org/paper/8fa4e2934abc781bd79a3405aaf4e6998cb30155 e1e3a1f9bc6aeb28439f5cf86166bb34f4fd77a4The integration of artificial intelligence technologies, such as large language models (LLMs), in health care holds potential for improved efficiency and decision support. However, ethical concerns must be addressed before widespread adoption. This article focuses on the ethical principles surrounding the use of Generative Pretrained Transformer-4 and its conversational model, ChatGPT, in healthcare settings. One concern is potential inaccuracies in generated content. LLMs can produce believable yet incorrect information, risking errors in medical records. Opacity of training data exacerbates this, hindering accuracy assessment. To mitigate, LLMs should train on precise, validated medical data sets. Model bias is another critical concern because LLMs may perpetuate biases from their training, leading to medically inaccurate and discriminatory responses. Sampling, programming, and compliance biases contribute necessitating careful consideration to avoid perpetuating harmful stereotypes. Privacy is paramount in health care, using public LLMs raises risks. Strict data-sharing agreements and Health Insurance Portability and Accountability Act (HIPAA)-compliant training protocols are necessary to protect patient privacy. Although artificial intelligence technologies offer promising opportunities in health care, careful consideration of ethical principles is crucial. Addressing concerns of inaccuracy, bias, and privacy will ensure responsible and patient-centered implementation, benefiting both healthcare professionals and patients.Journal of the American Academy of Orthopaedic Surgeons211The ethical principles surrounding the use of Generative Pretrained Transformer-4 and its conversational model, ChatGPT, in healthcare settings and concerns of inaccuracy, bias, and privacy are addressed.The Journal of the American Academy of Orthopaedic Surgeons['Suraj Sheth', 'Hayden P. Baker', 'Hannes Prescher', 'Jason A. Strelzow']2024-01-03T00:00:00https://www.semanticscholar.org/paper/e1e3a1f9bc6aeb28439f5cf86166bb34f4fd77a4 d2f52961617b962344145c83bcc2c0131fc0e805The Kováts retention index (RI) is a quantity measured using gas chromatography and is commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network for predicting RI values from structure for standard semipolar columns. This network generated predictions with a mean absolute error of 15.1 and, in a quantification of the tail of the error distribution, a 95th percentile absolute error of 46.5. Because of the Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was used to predict RI values for the NIST EI-MS spectral libraries. These RI values are used to improve chemical identification methods and the quality of the library. Estimating uncertainty is an important practical need when using prediction models. To quantify the uncertainty of our network for each individual prediction, we used the outputs of an ensemble of 8 networks to calculate a predicted standard deviation for each RI value prediction. This predicted standard deviation was corrected to follow the error between the observed and predicted RI values. The Z scores using these predicted standard deviations had a standard deviation of 1.52 and a 95th percentile absolute Z score corresponding to a mean RI value of 42.6.Journal of Chemical Information and Modeling360This work explores the use of a deep neural network for predicting RI values from structure for standard semipolar columns and quantifies the uncertainty of this network for each individual prediction.Journal of chemical information and modeling['Lewis Y. Geer', 'Stephen E. Stein', 'W. G. Mallard', 'D. Slotta']2024-01-03T00:00:00https://www.semanticscholar.org/paper/d2f52961617b962344145c83bcc2c0131fc0e805 c36735ee661066a0d05e8abc0b4f8125d7cf1004This article discusses the issue of the nature of works generated by artificial intelligence similar to ChatGPT and the protection of copyright laws. The author first analyzes the content of artificial intelligence generated products or the provisions on the nature of works in major national laws. This article further analyzes the legal practices of various countries in the qualitative analysis of artificial intelligence generated works based on cases of artificial intelligence copyright in different countries. We explored two theoretical perspectives regarding the copyrightability of two types of artificial intelligence-generated works. The first perspective emphasizes the absence of unique individuality in artificial intelligence creations and highlights the discord between the objectives of copyright law and the essence of artificial intelligence-generated works. In contrast, we delved into supportive viewpoints that underscore the potential legal basis for copyright in machine-generated works, the originality introduced by artificial intelligence, and the involvement of humans in the creative process. As the artificial intelligence industry matures, it underscores the necessity for effective legislative actions to fortify the existing legal framework, adapting it to the ever-evolving domain of AI-driven creativity. This approach is pivotal for fostering innovation while concurrently safeguarding the legitimate rights and interests of artificial intelligence creators and users.Lecture Notes in Education Psychology and Public Media00Lecture Notes in Education Psychology and Public Media['Yiqian Liu']2024-01-03T00:00:00https://www.semanticscholar.org/paper/c36735ee661066a0d05e8abc0b4f8125d7cf1004 6b8cd3b546fc25eb68bc41077be9057ba0503491The advent of Artificial Intelligence (AI) has brought about a paradigm shift in various sectors, including healthcare. Its potential to revolutionize disease management, particularly for chronic diseases like Chronic Obstructive Pulmonary Disease (COPD), is immense. This review aims to summarize and analyze recent studies on the application of AI in COPD management, focusing on AI techniques such as machine learning and deep learning used for diagnosis, treatment, and prognosis. The current findings, limitations, and implications for future research are discussed.International Conference on Ubiquitous Information Management and Communication850This review aims to summarize and analyze recent studies on the application of AI in COPD management, focusing on AI techniques such as machine learning and deep learning used for diagnosis, treatment, and prognosis.2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)['Yiqing Xu', 'Zalizah Awang Long', 'D. Setyohadi']2024-01-03T00:00:00https://www.semanticscholar.org/paper/6b8cd3b546fc25eb68bc41077be9057ba0503491 da09b1aad89e305062453e0286d2b4d9d77e5fccArtificial intelligence (AI) has colored human civilization. It is the ability of a digital computer or computer-controlled robot to perform general tasks associated with specific patterns of intelligence. AI is not human, but it possesses intelligence similar to humans, and it can even inform or perform tasks that humans cannot. Artificial intelligence is used in various fields, ranging from education, healthcare, economy, to agriculture. Artificial intelligence is the product of human creation, sentiment, and consciousness. It is the result of human intelligence itself. AI can answer questions and provide intelligent recommendations for humans. With its algorithmic capabilities, AI can analyze billions of signals and make precise recommendations. At this level, artificial intelligence represents human intelligence. However, the question is whether artificial intelligence has sensitivity, sentiment, empathy, and solidarity toward the humans who created it. Or does artificial intelligence then transform into a director of human beings in their self-actualization? Using a phenomenological approach, this research aims to explore the phenomenon of the presence of artificial intelligence, which offers convenience for human work, but at the same time, the presence of AI reduces the value of humans who possess creative intuition, sentiment, and consciousness. Yet AI is born from the ability of humans to create, feel, and think. The results of this exploration are then given a theological and ethnographic perspective (teo-ethnography). Keywords: artificial intelligence, creation, sentiment, consciousness, teo-ethnographyKnE Social Sciences130This research aims to explore the phenomenon of the presence of artificial intelligence, which offers convenience for human work, but at the same time, the presence of AI reduces the value of humans who possess creative intuition, sentiment, and consciousness.KnE Social Sciences['Henderikus Nayuf']2024-01-03T00:00:00https://www.semanticscholar.org/paper/da09b1aad89e305062453e0286d2b4d9d77e5fcc 5ed2e7b5a91cd8031465e1a6a50e7aec550f86d7Currently, artificial intelligence (AI) is being rapidly incorporated into K-12 education because of its increasing social and pedagogical importance. The integration of AI in K-12 education is likely to have a profound influence on the lives and learning styles of learners, teaching approaches of teachers, and the whole mechanism of school management systems. As AI technologies are new to K-12 school curriculum, the research on AI for K-12 classrooms is under explored. In this study, the current state of AI integration in school education and risks associated with it were explored. The study using a systematic review method attempted to explore the findings, observations, and results of recent research regarding the possible risk factors of AI integration in K-12 education. At initial search using predefined key terms, 390 articles were recorded. Using inclusion and exclusion criteria, 71 articles covering 34 journals and other publications were selected for final analysis. Selected 71 articles reported that AI innovation incorporation into K-12 education is associated with certain risks and challenges. Through a systematic review technique, we categorized them into major 6 risk areas namely Privacy and Autonomy Risks, AI Biases, Accuracy and Functional Risks, Deepfakes and FATE Risks, Social Knowledge and Skill Building Risks, and Risk in Shifting Teacher's Role. The study also explored these risk areas to provide an overview of how these are connected with teaching and learning process of K-12 education.Bulletin of Science, Technology &amp; Society750The study explored the findings, observations, and results of recent research regarding the possible risk factors of AI integration in K-12 education and categorized them into major 6 risk areas namely Privacy and Autonomy Risks, AI Biases, Accuracy and Functional Risks, Deepfakes and FATE Risks, Social Knowledge and Skill Building Risks, and Risk in Shifting Teacher's Role.Bulletin of Science, Technology &amp; Society['Bablu Karan', 'G. R. Angadi']2024-01-03T00:00:00https://www.semanticscholar.org/paper/5ed2e7b5a91cd8031465e1a6a50e7aec550f86d7 1bcbf19a2720925e5b60da47beba9581a0a89364In the wave of scientific and technological development in the new era, artificial intelligence technology has increasingly become one of the hot scientific and technological fields of social development. Since the concept was proposed, the development of artificial intelligence has also virtually stimulated the market and business strategies in a variety of industry fields. Starting from the actual situation, this paper now divides the four development stages of artificial intelligence from the past to the future in detail and uses the small-range questionnaire survey method to understand the current Chinese society's understanding of and interest in artificial intelligence and its products. Then, literature analysis is used to search and read relevant materials and literature, and the development status, capabilities, and prospects of artificial intelligence in the field of natural language processing and machine learning large models and image and face recognition are analyzed and summarized. In the field of face recognition selected popular processing methods and material library, simple simulation of mainstream recognition options and means, through further examples to help readers understand the development of artificial intelligence status and direction. Understanding the information and relevant viewpoints and attitudes in the specific field, detailing the logic behind the inability of artificial intelligence to replace human beings and the technical difficulties and challenges faced, is of great value to improving the popularization of the concept of artificial intelligence and its development and progress in the future society.Lecture Notes in Education Psychology and Public Media00The four development stages of artificial intelligence from the past to the future in detail are divided in detail and the small-range questionnaire survey method is used to understand the current Chinese society's understanding of and interest in artificial intelligence and its products.Lecture Notes in Education Psychology and Public Media['Jiaheng Shi']2024-01-03T00:00:00https://www.semanticscholar.org/paper/1bcbf19a2720925e5b60da47beba9581a0a89364 f0d9e8375bae2184273dba4ba434be2db9049789Autonomous Intelligent Systems30This paper contributes to understanding the intertwining role between distributed artificial intelligence, autonomous mobility and the resulting platform ecosystem, and poses a blueprint architecture for autonomous mobility.Auton. Intell. Syst.['Marc Guerreiro Augusto', 'Benjamin Acar', 'Andrea Carolina Soto', 'F. Sivrikaya', 'S. Albayrak']2024-01-03T00:00:00https://www.semanticscholar.org/paper/f0d9e8375bae2184273dba4ba434be2db9049789 997c6bfd7b8b150d90fb9ff17eb764a82c8569cdHumans can now do jobs more efficiently, and many problems can be solved with the assistance of Artificial Intelligence (AI). AI also plays an important role in air traffic control because it can help improve the safety and efficiency of air travel. AI can be used for various tasks, such as predicting weather patterns, identifying potential conflicts, and recommending optimal routes. Machine learning can also be applied in air traffic control to help systems learn from data and improve performance. This research aims to show the role of artificial intelligence (AI) in the Air Traffic Control (ATC) system and explore ways to implement AI to improve the system's performance. This study reviewed literature by looking at relevant articles and journals to achieve the result. This research reviews previous literature based on keywords Such as Implementation, Air Traffic Control, and Machine learning. Based on the literature review results, the various aspects of air traffic control and management are increasingly dependent on AI and machine learning. This study found that 7 aspects of ACT have adopted AI and have the potential to continue to develop their use.International Conference on Ubiquitous Information Management and Communication320This study found that 7 aspects of ACT have adopted AI and have the potential to continue to develop their use and explore ways to implement AI to improve the system's performance.2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)['Risya Emha Abdillah', 'Henry Moenaf', 'Luthfi Fadullah Rasyid', 'Said Achmad', 'Rhio Sutoyo']2024-01-03T00:00:00https://www.semanticscholar.org/paper/997c6bfd7b8b150d90fb9ff17eb764a82c8569cd 757bddbfecf000aba96c88e9ef69a849fe95d29fIn the era of the digital economy, data, algorithms, and computing capabilities constitute vital elements that drive economic development. The three elements of artificial intelligence are -computing power, algorithm, and data. As for the difference and progress, machines imitate and enhance human physical strength, but artificial intelligence imitates and improves human intelligence. This work first mentions the status quo of how artificial intelligence boosts the digital economy. Then, it comes to several aspects of artificial intelligence’s development, prospect, and application. Eventually, this paper mentioned how to apply artificial intelligence technology to various industries to form new industries, thus promoting the development of the digital economy.In conclusion, digital technology promotes the comprehensive efficiency upgrade of various national economy departments. Its formation is that the digital economy improves the efficiency of various departments through digital technology. Efficiency upgrade means cost reduction, relative competitive advantage in the industry, adjustment and transformation of the original industrial structure and product structure, and the use of digital technology for existing products and technologies to reduce the production costs of manufacturing enterprises and achieve product upgrades. Improvements in quality, structural changes, and management efficiency have improved the core competitiveness of enterprises and achieved a comparative advantage in competition.Finance &amp; Economics00In conclusion, digital technology promotes the comprehensive efficiency upgrade of various national economy departments through digital technology, so that the digital economy improves the efficiency of various departments through digital technology.Finance &amp; Economics['Lusha Yang']2024-01-03T00:00:00https://www.semanticscholar.org/paper/757bddbfecf000aba96c88e9ef69a849fe95d29f cd4d8ba5094bf1e52805735ec402562ff8cc10e5The rapid rise of AI-generated music technologies and the associated fair use and copyright issues have become an important focus of the contemporary digital music industry. With the rapid development of AI, AI-generated music has begun to permeate music composition, performance and production. This evolution has brought new prospects as well as a series of legal and ethical dilemmas. This article embarks on an in-depth exploration into the application and enhancement of fair use regulations within the burgeoning domain of AI music generation technology. The text examines various aspects of artificial intelligence-generated music, closely scrutinizes the legal framework that supports it, and attempts to seek a balance between nurturing creativity and protecting the rights of creators and innovators.Lecture Notes in Education Psychology and Public Media130The text examines various aspects of artificial intelligence-generated music, closely scrutinizes the legal framework that supports it, and attempts to seek a balance between nurturing creativity and protecting the rights of creators and innovators.Lecture Notes in Education Psychology and Public Media['Lu Xu']2024-01-03T00:00:00https://www.semanticscholar.org/paper/cd4d8ba5094bf1e52805735ec402562ff8cc10e5 bcba31064a7093e056d63cb15072b48552c36d78International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-01-03T00:00:00https://www.semanticscholar.org/paper/bcba31064a7093e056d63cb15072b48552c36d78 6ef354883dcd16e543c24842c88f5cca1755b3b1Zeitschrift für Rechtsmedizin171The authors take up Collingridge’s dilemma and relate it to the application of AI in healthcare, and believe that this methodology, adopted as a European guideline in the medico-legal field for the assessment of medical liability, can be adapted to AI applied to the healthcare scenario and used for the assessment of liability issues.International Journal of Legal Medicine['R. Cecchi', 'Tudor Mihai Haja', 'Francesco Calabrò', 'I. Fasterholdt', 'Benjamin S B Rasmussen']2024-01-03T00:00:00https://www.semanticscholar.org/paper/6ef354883dcd16e543c24842c88f5cca1755b3b1 3b14e7958a3e0f5c2686ae55a05185076c60da2cAs AI continues to produce content at an unprecedented rate, it is essential to establish a robust legal framework to protect the rights of creators and appropriately assign liability in cases of copyright infringement. Generative AI technology has the characteristics of high data demand, strong human-computer interaction, weak interpretability, and weak stability. The main civil subjects in generative AI services are generative AI service providers and users. In the pre-training stage, if generative AI uses copyrighted works without authorization, it should be recognized as copyright infringement, and the relevant infringement liability should be borne by generative AI service providers. In the content generation stage, the high similarity between AI-generated content and prior works can be attributed to various factors, including flaws in the generative model, intentional design choices, and user input and guidance. Both the generative AI service provider and the user bear direct tort liability for the infringement. However, the question of whether generative AI service providers also bear indirect tort liability should be explored, considering their role and similarities to traditional ISPs. Generative AI service providers should fulfill their obligations, take appropriate actions to prevent infringement, and report any illegal activities to the relevant authorities.Lecture Notes in Education Psychology and Public Media00The question of whether generative AI service providers also bear indirect tort liability should be explored, considering their role and similarities to traditional ISPs.Lecture Notes in Education Psychology and Public Media['Yue Yang']2024-01-03T00:00:00https://www.semanticscholar.org/paper/3b14e7958a3e0f5c2686ae55a05185076c60da2c 9580b12ca4f259a5013bc6413d923f2200dc619fJournal of Environmental Management730The pathways of artificial intelligence affecting the transformation of green economic growth are understood to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.Journal of environmental management['Chao Feng', 'Xinru Ye', 'Jun Li', 'Jun Yang']2024-01-03T00:00:00https://www.semanticscholar.org/paper/9580b12ca4f259a5013bc6413d923f2200dc619f 8149bfab44721911f87e594b8fe2d1a13340cee2Comput. Biol. Medicine520The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients and offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs.Computers in biology and medicine['S. Boussen', 'Manuela Benard-Tertrais', 'Mathilde Ogéa', 'Arthur Malet', 'P. Simeone', 'François Antonini', 'Nicolas Bruder', 'Lionel Velly']2024-01-03T00:00:00https://www.semanticscholar.org/paper/8149bfab44721911f87e594b8fe2d1a13340cee2 72e7d1bf352651f35ae8401db0a09b93310736a700['Edmilson Rodrigues do Nascimento Junior']2024-01-03T00:00:00https://www.semanticscholar.org/paper/72e7d1bf352651f35ae8401db0a09b93310736a7 227913ee5b68bec91cda3f7d88c54d444ffc0eaaMajor Gifts Report00The Major Gifts Report['Daniel Lindley']2024-01-03T00:00:00https://www.semanticscholar.org/paper/227913ee5b68bec91cda3f7d88c54d444ffc0eaa 4c3ec59687696bd485fe925a402c615f9ec39aa7Journal of general internal medicine50Journal of general internal medicine['Anna M. Maw', 'K. Trinkley', 'Russell E. Glasgow']2024-01-03T00:00:00https://www.semanticscholar.org/paper/4c3ec59687696bd485fe925a402c615f9ec39aa7 14909e6dd163a98687cd29042c26e34c2f78de4eThis review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.arXiv.org00This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures.ArXiv['Florin Leon']2024-01-03T00:00:00https://www.semanticscholar.org/paper/14909e6dd163a98687cd29042c26e34c2f78de4e 772fa6f713970ac1c9424a744e490f4e95d58fd1A integração da Inteligência Artificial (IA) em Sistemas de Tutores Inteligentes (STI) representa uma nova fronteira no aprimoramento do processo de ensino e de aprendizagem, podendo promover neste sentido uma educação mais dinâmica e personalizada. Este artigo apresenta o papel da Inteligência Artificial (IA) no processo de ensino e de aprendizagem, com enfoque específico na aplicação de Sistemas de Tutores Inteligentes (STI). O estudo emerge da problemática central: como a IA pode ser empregada em STI para otimizar o processo educacional? O objetivo deste trabalho é analisar as possibilidades de uso da IA em STI. Este estudo explora a caracterização desses sistemas e mapeia suas aplicações práticas. Na pesquisa conduzida, foi realizada uma revisão bibliográfica abrangente no Banco Digital de Teses e Dissertações, abrangendo o período de 2015 a 2020, com foco na utilização de sistemas baseados em Inteligência Artificial no contexto educacional. Esta revisão enfatizou a personalização do processo de aprendizagem e a entrega de feedback imediato, utilizando como referência pesquisas selecionadas. Observou-se a partir da análise das dissertações e teses a importância da IA na criação de ambientes de aprendizagem interativos e na implementação de estratégias pedagógicas direcionadas a cada aluno. Conclui-se que a integração da IA no ensino, em particular com o uso de STI, oferece uma abordagem inovadora e eficaz para enfrentar os desafios educacionais contemporâneos, promovendo um ensino mais adaptativo, personalizado e inclusivo. Palavras-chave: Sistemas Tutores Inteligentes. Inteligência Artificial. Processo de Ensino Aprendizagem. AbstractThe integration of Artificial Intelligence (AI) into Intelligent Tutoring Systems (ITS) represents a new frontier in enhancing the teaching and learning process, promoting a more dynamic and personalized educational experience. This article discusses the role of AI in the teaching and learning process, specifically focusing on the application of ITS. The study stems from the central question: How can AI be utilized in ITS to optimize the educational process? The aim of this work is to analyze the potential uses of AI in ITS. It examines the characteristics of these systems and outlines their practical applications. In the research conducted, a comprehensive bibliographic review was performed using the Digital Bank of Theses and Dissertations, spanning from 2015 to 2020, and focused on the application of AI-based systems in education. This review highlighted the personalization of the learning process and the provision of immediate feedback, referencing selected studies. Through the analysis of dissertations and theses, the significance of AI in creating interactive learning environments and implementing pedagogical strategies tailored to individual students was observed. The study concludes that the integration of AI into teaching, particularly through ITS, offers an innovative and effective method to address contemporary educational challenges, fostering more adaptive, personalized, and inclusive teaching. Keywords: Intelligent Tutoring Systems. Artificial Intelligence. Teaching Learning Process.Revista de Ensino Educação e Ciências Humanas00Revista de Ensino, Educação e Ciências Humanas['Ana Mauriceia Castellani', 'Rodrigo Shimasaki', 'Maria Elisabette Brisola Brito Prado', 'F. Fernandes']2024-01-03T00:00:00https://www.semanticscholar.org/paper/772fa6f713970ac1c9424a744e490f4e95d58fd1 8ee3284c9bfd94727a99764b3dce039c81049a15Technological evolution, particularly in the realms of digitization and automation, has had a profound impact on how companies conduct their business. The use of artificial intelligence systems, data analytics, and collaborative platforms has transformed work processes and expedited business operations. This research aims to analyze the implications of technological evolution on human resource management strategies and legal compliance. The study utilizes a comprehensive analysis of previously published literature, employing a qualitative analysis approach to gain a thorough understanding of the issues at hand. The research period spans from 2000 to 2023. The study's findings indicate that technological evolution has significantly affected human resource management strategies and legal compliance, especially within the context of Indonesia's Law Number 13 of 2003 concerning Manpower. The integration of technology into human resource management processes has enhanced efficiency and productivity but has also posed new challenges related to employee privacy, data protection, and changes in work dynamics.Jurnal Minfo Polgan210The study's findings indicate that technological evolution has significantly affected human resource management strategies and legal compliance, especially within the context of Indonesia's Law Number 13 of 2003 concerning Manpower.Jurnal Minfo Polgan['Yuspika Yuliana Purba']2024-01-03T00:00:00https://www.semanticscholar.org/paper/8ee3284c9bfd94727a99764b3dce039c81049a15 6c6b66902dd9c37ededf4401b5411e73538886f7Trials102While the time to regulatory and ethical approval has improved since the implementation of the new regulation, the timelines for approvals are still unacceptably slow, particularly for studies being conducted in the context of an evolving outbreak.Trials['Thale D. J. H. Patrick-Brown', 'Josie Bourner', 'Sabrina Kali', 'Marius Trøseid', 'Y. Yazdanpanah', 'P. Olliaro', 'Inge Christoffer Olsen']2024-01-02T00:00:00https://www.semanticscholar.org/paper/6c6b66902dd9c37ededf4401b5411e73538886f7 e646f1e4634fe2cabc3cf44d922a40fee0032580The regulatory framework plays the main role in the system of efficient organization and competent accounting, as well as in the formation of financial statements that meet the needs of management, the requirements of regulatory bodies and other interested parties. In this article, a detailed study of the current state of regulatory regulation of the formation of financial statements was carried out, the most important problems affecting the process of development and improvement of legal regulation of the compilation, verification and co–treatment of the interests of specific groups of users were identified, a system of local regulations (standards) was proposed, regulating the development of internal regulations and accounting standards and the formation of financial and non7financial reporting. The importance and relevance of the study is determined by the fact that the development of an effective regulatory framework should be based on the disclosure of financial statements in all material aspects, which can subsequently stimulate the companies to increase business transparency. The results of the study can be used by management structures to build effective economic policies.Scientific notes of the Russian academy of entrepreneurship31Scientific notes of the Russian academy of entrepreneurship['N. A. Lazareva']2024-01-02T00:00:00https://www.semanticscholar.org/paper/e646f1e4634fe2cabc3cf44d922a40fee0032580 e5538202303805cdf7da468ce3cb37900035c414PurposeThis paper examines whether the adoption of Japan’s Stewardship Code by institutional investors influences their preference for investee companies' governance quality. The Code, introduced by the Financial Services Agency in 2014, promotes constructive engagement between institutional investors and investee companies. Engagement with investees should improve institutional investors' ability to assess governance quality across their portfolios. The paper examines if this results in a positive relationship between the levels of Code-compliant institutional shareholding and investee governance quality.Design/methodology/approachThe association between Code-compliant institutional shareholding levels and a governance quality score is examined for Nikkei 500 companies.FindingsA positive association is observed between shareholdings by Code-compliant institutional investors and investee governance, with board independence playing a key role. Analysis shows that the association between institutional shareholding and governance is stronger for the Code-compliant shareholding than for overall institutional shareholdings. In addition, no significant relationship is found between the levels of shareholding by non-Code-compliant institutional investors and the governance quality score of investee companies. Taken together, the results suggest that Code adoption strengthens institutional investors' preference for high-quality investee governance.Originality/valueDespite the introduction of stewardship regulation worldwide, there is a scarcity of empirical research that examines its operation. The study contributes to the existing literature by providing insights into how compliance with stewardship regulation influences institutional investor decision-making.Managerial Finance760Managerial Finance['James Routledge']2024-01-02T00:00:00https://www.semanticscholar.org/paper/e5538202303805cdf7da468ce3cb37900035c414 9087d0225b96220c812996eb4ed0e646f1c17d04The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the current challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability call for the development of next-generation AI systems. Neuro-symbolic AI (NSAI) emerges as a promising paradigm, fusing neural, symbolic, and probabilistic approaches to enhance interpretability, robustness, and trustworthiness while facilitating learning from much less data. Recent NSAI systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities. In this paper, we provide a systematic review of recent progress in NSAI and analyze the performance characteristics and computational operators of NSAI models. Furthermore, we discuss the challenges and potential future directions of NSAI from both system and architectural perspectives.arXiv.org363A systematic review of recent progress in NSAI is provided and the performance characteristics and computational operators of NSAI models are analyzed to discuss the challenges and potential future directions of NSAI.ArXiv['Zishen Wan', 'Che-Kai Liu', 'Hanchen Yang', 'Chaojian Li', 'Haoran You', 'Yonggan Fu', 'Cheng Wan', 'Tushar Krishna', 'Y. Lin', 'A. Raychowdhury']2024-01-02T00:00:00https://www.semanticscholar.org/paper/9087d0225b96220c812996eb4ed0e646f1c17d04 d2e011ba3958162ff2ee6a624559010f98c01b06We illustrate how standard psychometric inventories originally designed for assessing noncognitive human traits can be repurposed as diagnostic tools to evaluate analogous traits in large language models (LLMs). We start from the assumption that LLMs, inadvertently yet inevitably, acquire psychological traits (metaphorically speaking) from the vast text corpora on which they are trained. Such corpora contain sediments of the personalities, values, beliefs, and biases of the countless human authors of these texts, which LLMs learn through a complex training process. The traits that LLMs acquire in such a way can potentially influence their behavior, that is, their outputs in downstream tasks and applications in which they are employed, which in turn may have real-world consequences for individuals and social groups. By eliciting LLMs' responses to language-based psychometric inventories, we can bring their traits to light. Psychometric profiling enables researchers to study and compare LLMs in terms of noncognitive characteristics, thereby providing a window into the personalities, values, beliefs, and biases these models exhibit (or mimic). We discuss the history of similar ideas and outline possible psychometric approaches for LLMs. We demonstrate one promising approach, zero-shot classification, for several LLMs and psychometric inventories. We conclude by highlighting open challenges and future avenues of research for AI Psychometrics.Perspectives on Psychological Science519Standard psychometric inventories originally designed for assessing noncognitive human traits can be repurposed as diagnostic tools to evaluate analogous traits in large language models (LLMs) to provide a window into the personalities, values, beliefs, and biases these models exhibit.Perspectives on psychological science : a journal of the Association for Psychological Science['Max Pellert', 'Clemens M. Lechner', 'Claudia Wagner', 'Beatrice Rammstedt', 'Markus Strohmaier']2024-01-02T00:00:00https://www.semanticscholar.org/paper/d2e011ba3958162ff2ee6a624559010f98c01b06 fd38e3414273fa64ffa93c8cd15a98120883987eThe size of deep learning models in artificial intelligence (AI) software is increasing rapidly, hindering the large-scale deployment on resource-restricted devices (e.g., smartphones). To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance. However, the intrinsic defects in a big model may be inherited by the compressed one. Such defects may be easily leveraged by adversaries, since a compressed model is usually deployed in a large number of devices without adequate protection. In this article, we aim to address the safe model compression problem from the perspective of safety-performance co-optimization. Specifically, inspired by the test-driven development (TDD) paradigm in software engineering, we propose a test-driven sparse training framework called SafeCompress. By simulating the attack mechanism as safety testing, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm. Then, considering two kinds of representative and heterogeneous attack mechanisms, i.e., black-box membership inference attack and white-box membership inference attack, we develop two concrete instances called BMIA-SafeCompress and WMIA-SafeCompress. Further, we implement another instance called MMIA-SafeCompress by extending SafeCompress to defend against the occasion when adversaries conduct black-box and white-box membership inference attacks simultaneously. We conduct extensive experiments on five datasets for both computer vision and natural language processing tasks. The results show the effectiveness and generalizability of our framework. We also discuss how to adapt SafeCompress to other attacks besides membership inference attack, demonstrating the flexibility of SafeCompress.IEEE Transactions on Software Engineering861A test-driven sparse training framework called SafeCompress that can automatically compress a big model to a small one following the dynamic sparse training paradigm, inspired by the test-driven development (TDD) paradigm in software engineering.IEEE Transactions on Software Engineering['Jie Zhu', 'Leye Wang', 'Xiao Han', 'Anmin Liu', 'Tao Xie']2024-01-02T00:00:00https://www.semanticscholar.org/paper/fd38e3414273fa64ffa93c8cd15a98120883987e 6d67a918707f0c20cb847d4d4611b34877ccd5e6Generative AI has the potential to transform how public services are delivered by enhancing productivity and reducing time spent on bureaucracy. Furthermore, unlike other types of artificial intelligence, it is a technology that has quickly become widely available for bottom-up adoption: essentially anyone can decide to make use of it in their day to day work. But to what extent is generative AI already in use in the public sector? Our survey of 938 public service professionals within the UK (covering education, health, social work and emergency services) seeks to answer this question. We find that use of generative AI systems is already widespread: 45% of respondents were aware of generative AI usage within their area of work, while 22% actively use a generative AI system. Public sector professionals were positive about both current use of the technology and its potential to enhance their efficiency and reduce bureaucratic workload in the future. For example, those working in the NHS thought that time spent on bureaucracy could drop from 50% to 30% if generative AI was properly exploited, an equivalent of one day per week (an enormous potential impact). Our survey also found a high amount of trust (61%) around generative AI outputs, and a low fear of replacement (16%). While respondents were optimistic overall, areas of concern included feeling like the UK is missing out on opportunities to use AI to improve public services (76%), and only a minority of respondents (32%) felt like there was clear guidance on generative AI usage in their workplaces. In other words, it is clear that generative AI is already transforming the public sector, but uptake is happening in a disorganised fashion without clear guidelines. The UK's public sector urgently needs to develop more systematic methods for taking advantage of the technology.arXiv.org211It is clear that generative AI is already transforming the public sector, but uptake is happening in a disorganised fashion without clear guidelines, and the UK's public sector urgently needs to develop more systematic methods for taking advantage of the technology.ArXiv['Jonathan Bright', 'Florence E. Enock', 'Saba Esnaashari', 'John Francis', 'Youmna Hashem', 'Deborah Morgan']2024-01-02T00:00:00https://www.semanticscholar.org/paper/6d67a918707f0c20cb847d4d4611b34877ccd5e6 a9477ed671129f4da7c75e62c4ee0901cfbef3bcResearch technology management14Research-Technology Management['Robert G. Cooper', 'Tammy McCausland']2024-01-02T00:00:00https://www.semanticscholar.org/paper/a9477ed671129f4da7c75e62c4ee0901cfbef3bc 4550b6f43f69fb12ff39dfec952885ba2484a81dResearch technology management03Research-Technology Management['Reid Blackman', 'Jim Euchner']2024-01-02T00:00:00https://www.semanticscholar.org/paper/4550b6f43f69fb12ff39dfec952885ba2484a81d 92798d1c54ef3d0220c9e8b6edb3b8e6764d8289Journal of Environmental Management922An overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process is provided.Journal of environmental management['S. Kumari', 'Jyoti Chowdhry', 'Manoj Chandra Garg']2024-01-02T00:00:00https://www.semanticscholar.org/paper/92798d1c54ef3d0220c9e8b6edb3b8e6764d8289 49ba831dd799405ebca87887caa168aeac1fe5c2International Journal of Lifelong Education212International Journal of Lifelong Education['Marcella Milana', 'Ulrik Brandi', 'Steven Hodge', 'Tetyana Hoggan-Kloubert']2024-01-02T00:00:00https://www.semanticscholar.org/paper/49ba831dd799405ebca87887caa168aeac1fe5c2 f25487a1e3f7548e3431ba2b95b6b0129a1f73bbTropical Medicine and Health370AI–CAD is applicable to community-based ACF in high TB burden settings, where experienced human readers for CXR images are scarce, and has the potential to expand CXR screening in community-based ACFs, with a substantial decrease in the workload on human readers and laboratory labour.Tropical Medicine and Health['Kosuke Okada', 'Norio Yamada', 'Kiyoko Takayanagi', 'Yuta Hiasa', 'Yoshiro Kitamura', 'Yutaka Hoshino', 'Susumu Hirao', 'Takashi Yoshiyama', 'Ikushi Onozaki', 'S. Kato']2024-01-02T00:00:00https://www.semanticscholar.org/paper/f25487a1e3f7548e3431ba2b95b6b0129a1f73bb 1586089455cc5bc593cf9ecf82470cfc072740a5Toxicological Science, especially in the last five decades, has witnessed rapid evolution of different tools and techniques developed to address diverse issues related to studies dealing with adverse health effects of a variety of poisons, drugs, chemicals,ever-growing list of xenobiotics and human diseases. Traditionally these studies are performed using suitable animal (in vivo) models. There was a time when toxicologists/pharmacologists were searching models alternate to animal toxicity testing (Doke and Dhawale, 2015). Improved cell culture techniques, knowledge on stem cells and other microbiological systems led to the development of in vitro toxicology. It was soon followed by DNA chips, micro fluidics, in silico toxicology , toxicogenomics and computational toxicology. Several platforms are now discussing machine learning (ML) and artificial intelligence (AI) together as future tools of computational toxicology. For decades, quantitative structure-activity relationship (QSAR) methods have been employed to study the effects of drugs/chemicals (Cai et al., 2022). However, AI methods for toxicity assessment ranging from ADMEtox to AI4TOX provide evidence to the immense potential of AI. Intriguingly, a few problems between theoretical developments and practice of AI by end users have been recognized. AI is now being employed in cancer care. According to WHO (2022), cancer is responsible for 9.3 million deaths per year. AI is being used for cancer grading, classification, follow up services and diagnostic accuracy. However, certain limitations viz. testing, validation, certification and auditing need to be addressed (Cabral et al., (2023). Potential of AI in diabetic care and management has recently been recognized. The huge burden of diabetic patients in India can be managed through AI tools. Diabetic risk can be predicted using genomic data, to diagnose diabetes using EHR data and to identify diabetes related complications i.e. retinopathy and nephropathy (Singhla et al., 2019). Application of AI in the management of cardiovascular diseases like myocardial infarction has been highlighted with special reference to Chinese medicine (Chen et al., 2022). There exists experimental evidence that AI tools can be used to assess, monitor and manage Parkinsons' disease (Bounsall et al., 2023). Perspectives of the application of AI in complimentary and alternative medicine were reviewed by Chu et al. (2022). Several regulatory agencies are now adopting the concept of 3R ie., replacement, reduction and refinement of animal testing (EU REACH/3R principles; Toxicology 21 of U.S. Government) ( Maestri, 2021).The application of AI in clinical toxicology through converging data resources, algorithms, real world information from sensors and health records has also been discussed (Sinha et al. 2021). Plausibility of toxicity prediction using AI tools was recently reviewed by Santin et al. (2021). Application of AI in recently emerged science of nanotoxicology is also being sought. The need for nanotoxicity databases, powerful nano descriptors, new modeling approaches, molecular mechanism analyses and designing of next generation nanomaterials are being debated ( Jha et al., 2014; Yan et al., 2023). U.S. Food and Drug Administration (USFDA) has recently initiated and AI program in Toxicology known as AI4TOX. This program mainly consists of four initiatives- AnimalGAN- to predict animal toxicology data for untested chemicals ( /about –fda/nctr-research-focus-areas/animalgan-initiative); SafetAI- to develop novel deep learning methods for toxicological endpoints (/about-fda/nctr-research-focus-areas/safetai-initiative); BERTox- to develop the most advanced AI powered Natural Language Processing (NPL) (/about-fda/nctr-research-focus-areas/bertox initiative) and PathologAI- to develop an effective and accurate framework for analysis of histopathological data from animal studies (/ about-fda/nctr-research-focus-areas/pathologai-initiative). Recently, Society of Toxicology (SOT) annual meeting held at Nashville from March 19-23, focused on a question-“How could AI be used for risk assessment?” There exists some skepticism weather AI may be used in human health risk assessment? How AI could be applied -to prioritize pharmaceutical/environmental chemicals, to identify potential off targets and decipher the mechanisms of toxicity and detect pathological effects? A Symposium session devoted to AI summarized international collaborative computational projects like CERaPP, COmPara and CATOMOS that have been designed to streamline the regulatory and safety assessments (Hartung, 2023). High throughput screening data (HTS) to predict drug induced liver injury (DILI) using AI is also being generated. AI can identify mechanisms for off target effects in drug development. AI can also be plausibly used to predict genotoxicity. Can AI tools automate the analysis of developmental or physiologically based assays? These discussions held during the symposium indicate exciting potential of AI in health risk assessment. It is speculated that, tools of nanotechnology hybridized with AI can metamorphose human health risk assessment to an extent that has never been achieved before.Journal of environmental biology00Application of AI in recently emerged science of nanotoxicology is also being sought and the need for nanotoxicity databases, powerful nano descriptors, new modeling approaches, molecular mechanism analyses and designing of next generation nanomaterials are being debated.Journal of Environmental Biology['S. V. S. Rana']2024-01-02T00:00:00https://www.semanticscholar.org/paper/1586089455cc5bc593cf9ecf82470cfc072740a5 cba74e0f34cab9b27b9075d6b933caf59267b7baOrganizations face the challenge of ensuring compliance with an increasing amount of requirements from various regulatory documents. Which requirements are relevant depends on aspects such as the geographic location of the organization, its domain, size, and business processes. Considering these contextual factors, as a first step, relevant documents (e.g., laws, regulations, directives, policies) are identified, followed by a more detailed analysis of which parts of the identified documents are relevant for which step of a given business process. Nowadays the identification of regulatory requirements relevant to business processes is mostly done manually by domain and legal experts, posing a tremendous effort on them, especially for a large number of regulatory documents which might frequently change. Hence, this work examines how legal and domain experts can be assisted in the assessment of relevant requirements. For this, we compare an embedding-based NLP ranking method, a generative AI method using GPT-4, and a crowdsourced method with the purely manual method of creating relevancy labels by experts. The proposed methods are evaluated based on two case studies: an Australian insurance case created with domain experts and a global banking use case, adapted from SAP Signavio's workflow example of an international guideline. A gold standard is created for both BPMN2.0 processes and matched to real-world textual requirements from multiple regulatory documents. The evaluation and discussion provide insights into strengths and weaknesses of each method regarding applicability, automation, transparency, and reproducibility and provide guidelines on which method combinations will maximize benefits for given characteristics such as process usage, impact, and dynamics of an application scenario.arXiv.org530This work examines how legal and domain experts can be assisted in the assessment of relevant requirements and compares an embedding-based NLP ranking method, a generative AI method using GPT-4, and a crowdsourced method with the purely manual method of creating relevancy labels by experts.ArXiv['Catherine Sai', 'S. Sadiq', 'Lei Han', 'Gianluca Demartini', 'Stefanie Rinderle-Ma']2024-01-02T00:00:00https://www.semanticscholar.org/paper/cba74e0f34cab9b27b9075d6b933caf59267b7ba d3ff3e68c33883a3584be5e9d0e9a88116a7f5bcBMC Primary Care290Recruitment was highlighted as a major challenge encountered by trial researchers, even prior to disruption due to the COVID-19 pandemic, and experiences encountered whilst undertaking a prospective randomised trial in primary care were reviewed.BMC Primary Care['K. Pollock', 'C. Dickerson', 'Manjit Kainth', 'Sarah Lawton', 'Michael Hurst', 'Daniel M. Sugrue', 'Chris Arden', 'D. W. Davies', 'Anne-Céline Martin', 'Belinda Sandler', 'J. Gordon', 'U. Farooqui', 'D. Clifton', 'Christian Mallen', 'J. Rogers', 'Nathan R. Hill', 'A. Camm', 'Alexander T. Cohen']2024-01-02T00:00:00https://www.semanticscholar.org/paper/d3ff3e68c33883a3584be5e9d0e9a88116a7f5bc 29ccbe71c44208ddace45bb89cc343537f9e6096Recommendation systems powered by AI are widely used to improve user experience. However, it inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges can protect users’ personal information, benefit service providers, and foster service ecosystems. Presently, numerous techniques based on differential privacy have been proposed to solve this problem. However, existing solutions encounter issues such as inadequate data utilization and an tenuous trade-off between privacy protection and recommendation effectiveness. To enhance recommendation accuracy and protect users’ private data, we propose ID-SR, a novel privacy-preserving social recommendation scheme for trustworthy AI based on the infinite divisibility of Laplace distribution. We first introduce a novel recommendation method adopted in ID-SR, which is established based on matrix factorization with a newly designed social regularization term for improving recommendation effectiveness. Additionally, we propose a differential privacy preserving scheme tailored to the above method that leverages the Laplace distribution’s characteristics to safeguard user data. Theoretical analysis and experimentation evaluation on two publicly available datasets demonstrate that our scheme achieves a superior balance between privacy protection and recommendation effectiveness, ultimately delivering an enhanced user experience.ACM Transactions on Knowledge Discovery from Data180This work proposes ID-SR, a novel privacy-preserving social recommendation scheme for trustworthy AI based on the infinite divisibility of Laplace distribution that achieves a superior balance between privacy protection and recommendation effectiveness, ultimately delivering an enhanced user experience.ACM Transactions on Knowledge Discovery from Data['Jingyi Cui', 'Guangquan Xu', 'Jian Liu', 'Shicheng Feng', 'Jianli Wang', 'Hao Peng', 'Shihui Fu', 'Zhaohua Zheng', 'Xi Zheng', 'Shaoying Liu']2024-01-02T00:00:00https://www.semanticscholar.org/paper/29ccbe71c44208ddace45bb89cc343537f9e6096 ae647c58d2f23022e17f8df6a8a139f472bae4faPurpose: This paper aimed to determine the liability for criminal activities committed by AI-enabled machines and explore defences that could invalidate their criminal liability. It also analysed the Actus Reus element, to identify which actors are involved in the criminal act. Materials and Methods: A systematic review of existing research on AI liability in crime was conducted, focusing on 30 articles related to the study. Findings: The study found that if certain conditions are met, any individual, company, or legal organisation can be held legally liable for illegal activities. As AI technology advances, current legal remedies are needed to protect society from the hazards it poses. Existing criminal law offers various approaches to dealing with AI liability, but the liability concerns generated by AI systems extend beyond traditional criminal law. Recognising robots as legal persons has been criticised as an overly complex solution. Implications to Theory, Practice and Policy: The study emphasises that the responsibility for monitoring and managing AI and its operations begins from the moment it is employed or deployed. Criminal law and the criminalisation of behaviour only address the question of responsibility to a limited extent, and the responsibility for monitoring should be viewed as an obligation towards the law.American Journal of Law00The study found that if certain conditions are met, any individual, company, or legal organisation can be held legally liable for illegal activities.American Journal of Law['Alaa Saud']2024-01-02T00:00:00https://www.semanticscholar.org/paper/ae647c58d2f23022e17f8df6a8a139f472bae4fa ef43af681a31597f34996538d5759e3a40ca48e5Greek schools have been progressively challenged by the diversification of student demographics during the past decade. The European refugee crisis after 2015 has triggered immense needs to create inclusive intercultural school environments for disabled students whose first language and cultural backgrounds differ from the dominant Greek middle-class students in mainstream schools. School leaders play a vital role in creating inclusive schοols that respect race, culture, ability, class, family background, and linguistic diversity. Social justice leadership constitutes an integral part of inclusive intercultural education, yet works exploring school leaders’ struggles in engaging in this type of leadership in Greece are scarce. This work gives voice to women educational leaders in Greece and investigates the barriers they encounter in providing appropriate education to migrant students with disabilities. Moreover, it explores school leaders’ perceptions of how generative AI applications can potentially be used to minimize these barriers.  Article visualizations:European Journal of Alternative Education Studies350This work gives voice to women educational leaders in Greece and investigates the barriers they encounter in providing appropriate education to migrant students with disabilities and explores school leaders’ perceptions of how generative AI applications can potentially be used to minimize these barriers.European Journal of Alternative Education Studies['Emmanouela Seiradakis']2024-01-02T00:00:00https://www.semanticscholar.org/paper/ef43af681a31597f34996538d5759e3a40ca48e5 976944aaa6f8f5127a2aaf4f0a94294fe6f2e5dbDigitalisation and innovation in learning and research are rapidly becoming crucial drivers of society's sustainable and progressive growth. AI's technological advancements and landscapes have significant strengths, and their diversity and quality have grown in recent years. This has facilitated the impressive development of AI apps and software, such as ChatGPT, which has become popular around the world. ChatGPT is an OpenAI access to users in education to generate essays, song lyrics and stories. It is an AI language model that can understand and generate human-like responses to text inputs, making it a valuable tool for various economic and cultural applications. This study examines the ethical dilemma of banning ChatGPT. Using a range of argumentative examples, I address the concept of moral obligations to OpenAI access but also its limitations. Some possible ethical issues that may arise in the use of AI-powered chatbots include concerns about data privacy, algorithmic bias, and the potential for chatbots to replace human interaction and support. Can OpenAI's cutting-edge technology and tools truly help corporate operations and institutions, and improve decision-making? Can it also give students and researchers significant resources to help them develop their knowledge, critical thinking skills and understanding in a variety of fields? Allowing ChatGPT to operate freely could lead to unintended consequences, but it could also promote innovation in the field of AI. Ultimately, finding a balance between regulation and innovation is key to maximising the benefits of ChatGPT while minimising its potential harms. AI software has the potential to degrade and debase our ethics, which are fundamentally different from our critical thinking. Chomsky, Roberts & Watumull concern is that AI software lacks the ability to understand and apply ethical principles in the same way that humans do, which could lead to unintended consequences and ethical dilemmas. Keywords: Critical Thinking; Ethical dilemma; Right or Wrong; ChatGPTAthens Journal of Law00This study examines the ethical dilemma of banning ChatGPT and addresses the concept of moral obligations to OpenAI access but also its limitations, which could lead to unintended consequences and ethical dilemmas.Athens Journal of Law['Marzia A. Coltri']2024-01-02T00:00:00https://www.semanticscholar.org/paper/976944aaa6f8f5127a2aaf4f0a94294fe6f2e5db 2d261bfe1481be3d4cf3295c4b9bb6162bbf55a0Ethics and Information Technology350Findings from a 2020–2021 data set of interviews with AI practitioners and tech workers at a single multinational technology company are presented and interpret them through the lens of feminist political thought, suggesting organisations and firms move from a static model of responsibility to a dynamic and ethically motivated response -ability.Ethics and Information Technology['Eleanor Drage', 'Kerry McInerney', 'Jude Browne']2024-01-02T00:00:00https://www.semanticscholar.org/paper/2d261bfe1481be3d4cf3295c4b9bb6162bbf55a0 6b88685f1448c8d9a5fe13faf3baf3f8eb2a12f8Hospital librarians receive invites to teach thinking and searching in an evidence-based way and critical appraisal of the literature to nurses. With these invitations, the hospital librarians play a central role in establishing an evidence-based culture in the hospital and contribute to the nursing staff feeling competent and confident in fulfilling evidence-based competencies. This author just prepared a 17-minute online talk as part of an international nursing webinar on "searching nursing literature in an evidence-based way." Using this experience, remembering other teaching and presentation experiences, and some "help" from AI tools, this experienced hospital librarian suggests decision points for colleagues to create a meaningful, practical information session for nurses and introduce to some AI tools along the way.Medical Reference Services Quarterly10This experienced hospital librarian suggests decision points for colleagues to create a meaningful, practical information session for nurses and introduces to some AI tools along the way.Medical reference services quarterly['Helen-Ann Brown Epstein']2024-01-02T00:00:00https://www.semanticscholar.org/paper/6b88685f1448c8d9a5fe13faf3baf3f8eb2a12f8 3cf354de97493a584689ce5623f96d0e2d43d8d4Journal of IT Cases and Applications00Journal of Information Technology Case and Application Research['G. Bansal']2024-01-02T00:00:00https://www.semanticscholar.org/paper/3cf354de97493a584689ce5623f96d0e2d43d8d4 4272000221b9fafbc799a2f813d04664d39d865fBulletin of the Atomic Scientists00Bulletin of the Atomic Scientists['Dan Drollette']2024-01-02T00:00:00https://www.semanticscholar.org/paper/4272000221b9fafbc799a2f813d04664d39d865f 57b9047b8c5c77b2dc1fec42ab3506fb1a216bc8European Radiology122Breast radiologists’ sensitivity and specificity will be affected by changes brought by artificial intelligence, and reading in a high cancer prevalence setting markedly increased sensitivity and decreased specificity.European Radiology['Hanen Al-Bazzaz', 'Marina Janicijevic', 'Fredrik Strand']2024-01-02T00:00:00https://www.semanticscholar.org/paper/57b9047b8c5c77b2dc1fec42ab3506fb1a216bc8 4a7e994592625ab7800a61db3c08f4112f205e0e This Viewpoint discusses the use of artificial intelligence in pediatrics. JAMA pediatrics52JAMA pediatrics['Bryan A. Sisk', 'Alison L. Antes', 'James M. DuBois']2024-01-02T00:00:00https://www.semanticscholar.org/paper/4a7e994592625ab7800a61db3c08f4112f205e0e 595dd3553e3233f461b8ef4f71d4023f723f3376Recently, artificial intelligence generated content (AIGC) has been receiving increased attention and is growing exponentially. AIGC is generated based on the intentional information extracted from human-provided instructions by generative artificial intelligence (AI) models. AIGC quickly and automatically generates large amounts of high-quality content. Currently, there is a shortage of medical resources and complex medical procedures in medicine. Due to its characteristics, AIGC can help alleviate these problems. As a result, the application of AIGC in medicine has gained increased attention in recent years. Therefore, this paper provides a comprehensive review on the recent state of studies involving AIGC in medicine. First, we present an overview of AIGC. Furthermore, based on recent studies, the application of AIGC in medicine is reviewed from two aspects: medical image processing and medical text generation. The basic generative AI models, tasks, target organs, datasets and contribution of studies are considered and summarized. Finally, we also discuss the limitations and challenges faced by AIGC and propose possible solutions with relevant studies. We hope this review can help readers understand the potential of AIGC in medicine and obtain some innovative ideas in this field.Mathematical biosciences and engineering : MBE1921The application of AIGC in medicine is reviewed from two aspects: medical image processing and medical text generation and the basic generative AI models, tasks, target organs, datasets and contribution of studies are considered and summarized.Mathematical biosciences and engineering : MBE['Liangjing Shao', 'Benshuang Chen', 'Ziqun Zhang', 'Zhen Zhang', 'Xinrong Chen']2024-01-02T00:00:00https://www.semanticscholar.org/paper/595dd3553e3233f461b8ef4f71d4023f723f3376 32e4b766394a1eea7b74df6a227cce33a2426abdScientific Reports340The study underscores the utility of AI in the early detection and diagnosis of severe ocular diseases, providing a foundation for future research to improve paediatric ophthalmic screening and treatment outcomes.Scientific Reports['Regulski Piotr', 'Rejdak Robert', 'Niezgódka Marek', 'Iwański Michał']2024-01-02T00:00:00https://www.semanticscholar.org/paper/32e4b766394a1eea7b74df6a227cce33a2426abd 68dbb75baf7c3ec591b3495bfb199c75a3878434Nature Communications360SUDO can identify unreliable predictions, inform the selection of models, and allow for the previously out-of-reach assessment of algorithmic bias for data in the wild without ground-truth annotations, which can contribute to the deployment of trustworthy and ethical AI systems in medicine.Nature Communications['Dani Kiyasseh', 'Aaron Cohen', 'Chengsheng Jiang', 'Nicholas Altieri']2024-01-02T00:00:00https://www.semanticscholar.org/paper/68dbb75baf7c3ec591b3495bfb199c75a3878434 ecd565787563213ff0e72455af0bfbf2eeab5cf8Technological unemployment has been a concern since the Industrial Revolution. Approximately two centuries later, this issue has reemerged with the rapid advancements in machine learning and artificial intelligence technologies (AI). In contrast to the Industrial Revolution era, the unemployment caused by AI in the present age is different. Unlike earlier times, where unemployment primarily resulted from automating basic manual labor, the current challenge arises from AI automating tasks that were previously considered too complex for machines to handle. This is due to the capacity of AI-powered machines to learn and adapt to new situations. As a result, the evolving job market necessitates a different approach to employment policies compared to those applied over the last century. In this study, a new policy suggestion referred to as "Complementary Competitiveness" is discussed, taking a nuanced stance, avoiding simplistic categorizations of AI as purely beneficial or detrimental. Instead, it concentrates on formulating an employment strategy that distinguishes between sectors, taking into account firms resaons’ of AI preferences, all while not impeding technological progress. This approach seeks to align employment policies with the evolving needs of the AI age, which goes beyond the conventional binary classification of professions and competencies as necessary or obsolete as it seen in the literature.Erzurum Teknik Universitesi Sosyal Bilimler Enstitusu Dergisi00In this study, a new policy suggestion referred to as "Complementary Competitiveness" is discussed, taking a nuanced stance, avoiding simplistic categorizations of AI as purely beneficial or detrimental, and concentrating on formulating an employment strategy that distinguishes between sectors.Erzurum Teknik Universitesi Sosyal Bilimler Enstitusu Dergisi['Yahya Algül']2024-01-02T00:00:00https://www.semanticscholar.org/paper/ecd565787563213ff0e72455af0bfbf2eeab5cf8 43a3843c7d18c1bada30c841af670b19ce4856d9Journal of Real-Time Image Processing210A privacy-preserving vertical federated broad learning system for artificial intelligence generated image content (PVF-BLS) and a secure incremental learning algorithm based on matrix masks (ILA-MM) to update PVF-BLS.Journal of Real-Time Image Processing['Fengyin Li', 'Junrong Ge', 'Xiaojiao Wang', 'Gang Zhao', 'Xilong Yu', 'Xinru Li']2024-01-02T00:00:00https://www.semanticscholar.org/paper/43a3843c7d18c1bada30c841af670b19ce4856d9 9d206ad92199a3f44d9b60c28262f371d60390fcOBJECTIVES People living with HIV may find personalized access to accurate information on antiretroviral therapy (ART) challenging given the stigma and costs potentially associated with attending physical consultations. Artificial intelligence (AI) chatbots such as ChatGPT may help to lower barriers to accessing information addressing concerns around ART initiation. However, the safety and accuracy of the information provided remains to be studied. METHODS We instructed ChatGPT to answer questions that people living with HIV frequently ask about ART, covering i) knowledge of and access to ART; ii) ART initiation, side effects, and adherence, and iii) general sexual health practices while receiving ART. We checked the accuracy of the advice against international HIV clinical practice guidelines. RESULTS ChatGPT answered all questions accurately and comprehensively. It recognized potentially life-threatening scenarios such as abacavir hypersensitivity reaction and gave appropriate advice. However, in certain contexts, such as specific geographic locations or for pregnant individuals, the advice lacked specificity to an individual's unique circumstances and may be inadequate. Nevertheless, ChatGPT consistently re-directed the individual to seek help from a healthcare professional to obtain targeted advice. CONCLUSIONS ChatGPT may act as a useful adjunct in the process of ART counselling for people living with HIV. Improving access to information on and knowledge about ART may improve access and adherence to ART and outcomes for people living with HIV overall.HIV Medicine50ChatGPT may act as a useful adjunct in the process of ART counselling for people living with HIV, and improving access to information on and knowledge about ART may improve access and adherence to ART and outcomes for people living with HIV overall.HIV medicine['M. C. Y. Koh', 'J. N. Ngiam', 'Joy Yong', 'P. Tambyah', 'Sophia Archuleta']2024-01-02T00:00:00https://www.semanticscholar.org/paper/9d206ad92199a3f44d9b60c28262f371d60390fc 4236c81dc45ddac0994cec1835d58716da69cca7Integrating artificial intelligence (AI) techniques in public relations and media is an emerging interdisciplinary research domain warranting greater attention. This study presents the first bibliometric analysis of recent literature at the nexus of AI, public relations, and media. Publications from 2018-2023 were retrieved from Scopus and analyzed to uncover productivity, impact, collaborations, and topics. Results showed rising annual outputs with over 2000 articles published in 2021, confirming intensifying research activity. Recent publications also demonstrated higher citation impact, indicating their contemporary influence. Prolific authors were predominantly China-based, while the US led overall production. China, Western nations, and India dominated but opportunities exist to improve geographic diversity. Initial activity focused on justifying AI's value, evolving to technical applications for social media analytics, predictive modeling, and content creation. International collaborations centered around Western regions, though China's partnerships increased. This quantitative intelligence provides a benchmark to inform future work in this high-potential domain. Bibliometric monitoring should continue as the discourse progresses. Broader participation from underrepresented stakeholders is needed to responsibly shape AI integration in public relations and media.Iraqi Journal for Computer Science and Mathematics100This study presents the first bibliometric analysis of recent literature at the nexus of AI, public relations, and media, uncovering productivity, impact, collaborations, and topics to inform future work in this high-potential domain.Iraqi Journal For Computer Science and Mathematics['Akhmed Kaleel', 'Mohammed Shukri Alomari']2024-01-02T00:00:00https://www.semanticscholar.org/paper/4236c81dc45ddac0994cec1835d58716da69cca7 61ae1afd6603813c6582eb91c9717b0546b5bec8Abstrak. Artificial intelligence (AI) adalah istilah dari industrial society yang merupakan sebuah "program komputer, pembelajaran mesin, perangkat keras dan perangkat lunak". Artificial intelligence ini menggunakan sebuah ilmu dari perangkat keras dan perangkat lunak yang terinspirasi oleh rekayasa terbalik dari pola neokognitron yang bekerja diotak manusia. Produk industri ini banyak digunakan dalam pengembangan dan aplikasi sehari-hari di berbagai bidang, termasuk pendidikan. Tujuan dari kegiatan ini adalah untuk memperjelas peran artificial intelligence dalam pendidikan, dan metode yang digunakan adalah metode media informasi, seperti poster yang pasang di majalah dinding sekolah. poster tersebut ini mengenai edukasi pentingnya artificial intelligence dalam membantu dan membuat profil pembelajaran untuk setiap siswa dan memungkinkan materi pembelajaran disesuaikan dengan kemampuan, gaya belajar, dan pengalaman setiap siswa. Kata Kunci: Pendidikan, artificial intelligence, media edukasi.Jurnal Pengabdian Masyarakat Akademisi60JURNAL PENGABDIAN MASYARAKAT AKADEMISI['Hani Subakti']2024-01-02T00:00:00https://www.semanticscholar.org/paper/61ae1afd6603813c6582eb91c9717b0546b5bec8 4c39fcad8cf86460a6d6e0f5ab3a8b4038eaf8edThis is a short sketch of some central ideas developed in my recent monograph The Prospect of a Humanitarian Artificial Intelligence, published by Bloomsbury, London 2023. The monograph is available open access at library.oapen.org/handle/20.500. 12657/61934. It illuminates the development of AI by examining our drive to live a dignified life. It uses the notions of agency and attention to consider our pursuit of what is important. It shows how the best way to guarantee value alignment between humans and potentially intelligent machines is through attention routines that satisfy similar needs. Setting out a theoretical framework for AI, the book acknowledges its legal, moral, and political implications and takes into account how epistemic agency differs from moral agency. Insightful comparisons between human and animal intelligence clarify why adopting a need-based attention approach justifies a humanitarian framework. This is an urgent, timely argument for developing AI technologies based on international human rights agreements.Mind and Matter20Insightful comparisons between human and animal intelligence clarify why adopting a need-based attention approach justifies a humanitarian framework and shows how the best way to guarantee value alignment between humans and potentially intelligent machines is through attention routines that satisfy similar needs.Mind and Matter['Carlos Montemayor']2024-01-02T00:00:00https://www.semanticscholar.org/paper/4c39fcad8cf86460a6d6e0f5ab3a8b4038eaf8ed 782d22296e88c1ffeb3abcb23179f44faf8a9988Third molar removal complication rates can be as high as 30%. Risk assessment tools may lower these rates. Artificial intelligence (AI) driven prediction models are a promising approach to predict possible unfavourable outcomes and cone beam computed tomography imaging may play an important role. AI prediction models are showing excellent results in research settings. To be implemented in clinical practice they will need to overcome some robustness, security, liability, and practical issues. If they do, AI prediction models can be integrated in electronic patient record systems by alerting clinicians in case of an imminent unfavourable outcome so it can be avoided. CPD/Clinical Relevance: Artificial intelligence-driven risk assessment tools will lower complications in third molar surgery.Dental Update240Artificial intelligence-driven risk assessment tools will lower complications in third molar surgery and can be integrated in electronic patient record systems by alerting clinicians in case of an imminent unfavourable outcome so it can be avoided.Dental Update['F. Van der Cruyssen', 'P. Verhelst', 'R. Jacobs']2024-01-02T00:00:00https://www.semanticscholar.org/paper/782d22296e88c1ffeb3abcb23179f44faf8a9988 70289089ef94239e75dacac5c8b226c5ea87258cArtificial intelligence has become a major ‘buzzword’ in healthcare and wider UK society. In this article, Nora Sangvik Grandal and colleagues discuss the current and potential uses of artificial intelligence in the NHS, along with key considerations and recommendations for implementing this technology.British Journal of Healthcare Management60The current and potential uses of artificial intelligence in the NHS, along with key considerations and recommendations for implementing this technology are discussed.British Journal of Healthcare Management['Nora Sangvik Grandal', 'Siddhant Muzumdar', 'Nasir Khan', "Na'eem Ahmed"]2024-01-02T00:00:00https://www.semanticscholar.org/paper/70289089ef94239e75dacac5c8b226c5ea87258c 3db4aeac8b515c2723e1fc53476f2188633c0464International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-01-02T00:00:00https://www.semanticscholar.org/paper/3db4aeac8b515c2723e1fc53476f2188633c0464 31f09e1ad25bafb8aeaefbc4b0bbc1adc171bf7dJournal of Arts, Literature, Humanities and Social Sciences00Journal of Arts, Literature, Humanities and Social Sciences[]2024-01-02T00:00:00https://www.semanticscholar.org/paper/31f09e1ad25bafb8aeaefbc4b0bbc1adc171bf7d 7676f300c46251062048ec936b543009020b2396International Journal of Progressive Research in Engineering Management and Science00International Journal of Progressive Research in Engineering Management and Science[]2024-01-02T00:00:00https://www.semanticscholar.org/paper/7676f300c46251062048ec936b543009020b2396 5629841dbdf31b8dab263a7c7563d103e2306071Asia Pacific Scholar41The Asia Pacific Scholar['D. Samarasekera', 'Shuh Shing Lee', 'Han Ting Jillian Yeo']2024-01-02T00:00:00https://www.semanticscholar.org/paper/5629841dbdf31b8dab263a7c7563d103e2306071 2874be4039269c5870c9ff27d2834d3bdda25ba2RSUD Karawang adalah sebuah rumah sakit yang lengkap dengan berbagai fasilitas seperti poli anak, penyakit, jantung, kebidanan, dan lain-lain. Namun, kendala muncul dalam memantau kondisi pasien di rumah sakit ini. Hal ini menyebabkan paramedis, termasuk spesialis dan petugas medis, harus mengunjungi pasien berulang kali untuk memeriksa kondisi mereka. Untuk mengatasi masalah ini dan meningkatkan kepuasan pasien, rumah sakit menggunakan inovasi penalaran terkomputerisasi atau kecerdasan buatan manusia. Dengan inovasi ini, mesin dapat secara konsisten melakukan observasi pasien, sehingga pasien hanya perlu memberikan isyarat tangan yang sudah ditentukan sebelumnya. Untuk mencapai hal ini, penelitian menggunakan kamera Raspberry Pi dan regulator untuk menciptakan kesadaran buatan manusia. Informasi yang dikumpulkan kemudian diolah menggunakan strategi Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM) untuk menganalisis kondisi pasien. Hasil penilaian dengan menggunakan disarray grid menunjukkan bahwa strategi CNN memiliki presisi sebesar 98%, sementara teknik LSTM memiliki akurasi mendekati 100% dalam menyaring kondisi pasien di setiap ruangan. Selain itu, tampaknya teknik LSTM memberikan hasil yang lebih baik dalam memahami observasi dibandingkan dengan strategi CNN. Dengan penerapan inovasi penalaran buatan manusia ini, diharapkan klinik dapat meningkatkan kemampuan dalam mengamati kondisi pasien dan memberikan pertimbangan yang lebih baik, serta meningkatkan kepuasan pribadi pasien secara keseluruhan.JATI (Jurnal Mahasiswa Teknik Informatika)180JATI (Jurnal Mahasiswa Teknik Informatika)['Jaysyu Muhammad', 'Ulinnuha Latifa']2024-01-02T00:00:00https://www.semanticscholar.org/paper/2874be4039269c5870c9ff27d2834d3bdda25ba2 0d72d555d5fd579fc3841044c6d20eb7097cfab7In this article I show how Hans Blumenberg offers a positive but also more nuanced approach to the question of indeterminacy than current algorithmic systems, whilst offering a corrective to its potential metaphysical drifts and dangers. Much of Blumenberg’s work addresses the same question at the heart of the digital namely how to address that which eludes conceptual capture. For Blumenberg theoretico-rational procedures will always be incomplete in addressing a radically contingent, unpredictable world. Born deficient, man also needs “life-worlds” to orient us and shield us from the absolutism of reality. Digital life-worlds are possible to the extent, however, that they remain fictional mental constructs rather than aspire to be “literalized” and compete with reality. Deployed properly, life-worlds ‐ in which such strategies as myth, rhetoric, pensiveness and more generally the art of detour play a crucial role and provide with the constant possibility of interruption and disruption ‐ do not make up for self-reinforcing and enclosed loops but allow for reflexibility, distance and criticality. Instead of seeking to control reality and eliminate contingency ‐ futile tasks to begin with ‐ they offer flexible and resilient constructs that also cultivate the human realm.Mind and Matter130Mind and Matter['Audrey Borowski']2024-01-02T00:00:00https://www.semanticscholar.org/paper/0d72d555d5fd579fc3841044c6d20eb7097cfab7 818c9707022228f6fbfcf001e395007f97468a7aEDPACS: The EDP Audit, Control, and Security Newsletter290EDPACS['Cengiz Güney', 'Tolga Ala']2024-01-02T00:00:00https://www.semanticscholar.org/paper/818c9707022228f6fbfcf001e395007f97468a7a 18ada96b3fee45e40d813d1c47e78c160e81c6b4International Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews['Quadri Razaq Femi']2024-01-02T00:00:00https://www.semanticscholar.org/paper/18ada96b3fee45e40d813d1c47e78c160e81c6b4 952f85ce7fcc3cfabdc7cc04c704ea85995fb771Journal of Global Information Technology Management390Journal of Global Information Technology Management['Seongmin Jeon', 'Yu Sang Chang', 'Sung Jun Jo', 'Tinovimbanashe Madukuand', 'Young Eun Kim']2024-01-02T00:00:00https://www.semanticscholar.org/paper/952f85ce7fcc3cfabdc7cc04c704ea85995fb771 6bd7d4430bfb2c624c8c696d99cbe06d67acf30eJournal of IT Cases and Applications00Journal of Information Technology Case and Application Research['Varun Grover']2024-01-02T00:00:00https://www.semanticscholar.org/paper/6bd7d4430bfb2c624c8c696d99cbe06d67acf30e 161b99e3ba486e658e81bb811b2e22f1deaa629dThe Round Table110The Round Table['Florin D. Salajan', 'Theodore L. Barnes', 'Anna Becker']2024-01-02T00:00:00https://www.semanticscholar.org/paper/161b99e3ba486e658e81bb811b2e22f1deaa629d b84bf58855b0bd3e4bd0afa4f3dd493e1ee9c7c6Saudi Journal of Anaesthesia20Saudi Journal of Anaesthesia['A. Kleebayoon', 'V. Wiwanitkit']2024-01-02T00:00:00https://www.semanticscholar.org/paper/b84bf58855b0bd3e4bd0afa4f3dd493e1ee9c7c6 c12110fe564ee5f55c0383ca259f4a65c8e87c6c00['Vikas Garg', 'Richa Goel', 'Pooja Tiwari', 'E. S. Döngül']2024-01-02T00:00:00https://www.semanticscholar.org/paper/c12110fe564ee5f55c0383ca259f4a65c8e87c6c 4c87a3d9c41c3d3e450262c7cc25b102093d3a56The relevance of this study lies in the fact that the rapid growth of interest in computer games in Russia requires timely state regulatory support of this industry. The world has accumulated sufficient interesting experience of state regulation of sports, which is advisable to use in domestic practice. The purpose of the study is to analyze the practice of state regulation of eSports in China as one of the leading countries in this field and to substantiate recommendations for the formation of a model of state regulation of the e-sports industry in the Russian Federation. The methodology of the study is based on an integrated approach that allows identifying the key features of the formation of the management model of the eSports industry and establishing the reasons for the success of the management system in China, a country that is a leader in the global esports market. The research is based on the analysis of financial statements of leading companies and industry associations, reports of the information center of the Internet Network of China, regulatory legal acts regulating the sphere of both esports and the gaming industry as a whole. The authors of the article used a comparative regional analysis of the development of eSports, studied market trends in the industry, and assessed the impact of historical events on the eSports market. The results of the study allow us to consider the development of esports in China in the context of promoting the digital economy and digital platforms. It is proved that the rapid development of esports in China is an example of joint efforts of both government and business – represented by the main players in the gaming industry and electronic platforms. The focus on a global expansion strategy has allowed China to outpace not only Japan and South Korea in its region, but also the world’s largest esports markets in North America and Europe. Scientific novelty is a unique phenomenon where the state takes an active part in the control and development of the gaming industry. This model can be studied and analyzed to identify the advantages and disadvantages of state regulation of the esports industry. The results of a study of the best practices of the PRC in the field of state regulation of the eSports industry can serve as a basis and example when developing regulatory regulation of this industry in our country.Intellect. Innovations. Investments00The results of a study of the best practices of the PRC in the field of state regulation of the eSports industry can serve as a basis and example when developing regulatory regulation of this industry in this country.Intellect. Innovations. Investments['А. D. Khairullina', 'R. R. Rendikova']2024-01-01T00:00:00https://www.semanticscholar.org/paper/4c87a3d9c41c3d3e450262c7cc25b102093d3a56 6228eb1b479fb1013804da04f0fca9c1568a7d46Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the implications of 1) a recent case related to the General Data Protection Regulation (GDPR) and 2) the release of ChatGPT, a generative AI model capable to producing ‘human-like’ text. These events raise questions for the future of AIEd and the underlying function of assessment, and highlight the importance of active student participation in the integration of AI in education.The Turkish Online Journal of Distance Education601This paper re-examines discussions on the governance of data and the role of students and teachers by considering the implications of a recent case related to the General Data Protection Regulation and the release of ChatGPT, a generative AI model capable to producing ‘human-like’ text.Turkish Online Journal of Distance Education['J. Bai', 'Olaf Zawacki-Richter', 'Wolfgang Muskens']2024-01-01T00:00:00https://www.semanticscholar.org/paper/6228eb1b479fb1013804da04f0fca9c1568a7d46 29643e799200c22b4b596ce584a8bff6ceab9712Social Science Research Network00SSRN Electronic Journal['Hannah Ruschemeier']2024-01-01T00:00:00https://www.semanticscholar.org/paper/29643e799200c22b4b596ce584a8bff6ceab9712 a53caca99a4663a774eda6e6e7ae8cda20ecc93bJournal of AI Law and Regulation00Journal of AI Law and Regulation['M. D. Cole']2024-01-01T00:00:00https://www.semanticscholar.org/paper/a53caca99a4663a774eda6e6e7ae8cda20ecc93b 5a730e413b385d5c6eda2a8133672ca8f0edacacSocial Science Research Network00SSRN Electronic Journal['Lucian Schwartz-croft']2024-01-01T00:00:00https://www.semanticscholar.org/paper/5a730e413b385d5c6eda2a8133672ca8f0edacac 8ff1caf1596f43fe8e7f0a3a1ea22956ce4f5e25Social Science Research Network10SSRN Electronic Journal['Kseniia Gnitko']2024-01-01T00:00:00https://www.semanticscholar.org/paper/8ff1caf1596f43fe8e7f0a3a1ea22956ce4f5e25 df7ab85933a5f89f761ff064d5fcc854960ffdebSocial Science Research Network00SSRN Electronic Journal['Lawrence Yang']2024-01-01T00:00:00https://www.semanticscholar.org/paper/df7ab85933a5f89f761ff064d5fcc854960ffdeb 0701551a51c07a4740e6b8331b66abaa107c97e6Social Science Research Network01SSRN Electronic Journal['Margot E. Kaminski']2024-01-01T00:00:00https://www.semanticscholar.org/paper/0701551a51c07a4740e6b8331b66abaa107c97e6 5633d012a7f10b77901a991f7621eccc2b3ff04300[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/5633d012a7f10b77901a991f7621eccc2b3ff043 b635fc7babf5a9350b284f5aae95221d37466210Social Science Research Network260SSRN Electronic Journal['Ljupcho Grozdanovski']2024-01-01T00:00:00https://www.semanticscholar.org/paper/b635fc7babf5a9350b284f5aae95221d37466210 c503a523870851dcf5647a1c704c304e204b0e95Jusletter-IT00Jusletter-IT['Robert van den Hoven van Genderen', 'Rosa Ballardini']2024-01-01T00:00:00https://www.semanticscholar.org/paper/c503a523870851dcf5647a1c704c304e204b0e95 a3fb83c229c9262d56195686d16d4539200403a2Hawaii International Conference on System Sciences00{'pages': '2023-2032'}['Francesco Gualdi', 'A. Cordella']2024-01-01T00:00:00https://www.semanticscholar.org/paper/a3fb83c229c9262d56195686d16d4539200403a2 ecfffefa7aecfba0eba80bc9ef813e9a2c6c4725IEEE Transactions on Evolutionary Computation00IEEE Transactions on Evolutionary Computation['Jian Feng', 'Yajie He', 'Yuhan Pan', 'Zhipeng Zhou', 'Si Chen', 'Wei Gong']2024-01-01T00:00:00https://www.semanticscholar.org/paper/ecfffefa7aecfba0eba80bc9ef813e9a2c6c4725 7ad8420b15e818d448f5c92f27bdbf351d98d09cSocial Science Research Network150SSRN Electronic Journal['Orly Lobel']2024-01-01T00:00:00https://www.semanticscholar.org/paper/7ad8420b15e818d448f5c92f27bdbf351d98d09c 258395edb3782142ccf10dd428c2a4cb131c6269As artificial intelligence (AI) technologies rapidly advance, they bring about important societal implications involving privacy, fairness, non-discrimination, and other relevant ethical considerations. Legislators and policymakers are joined by a common drive to provide legislative solutions and regulatory frameworks that guarantee that the ongoing integration of AI systems into society is consistent with fundamental rights and democratic values. This article explores the significant role that standardisation plays in this regulatory process and how it impacts the regulation and governance of AI within the European Union (EU). In particular, the paper provides a critical analysis of the regulatory approach adopted by the EU legislator for the AI Act, which delegates the definition of essential requirements for high-risk AI systems to harmonised standards, underlining the significance of standardisation in ensuring technical feasibility and compliance with EU laws and values. At the forefront of this discussion, there is the increasing influence of AI-related standardisation across social, economic, and geopolitical domains, with a particular focus on the crucial role played by Standard Developing Organisations (SDOs) in the regulatory and governance processes. This paper contributes to the legal scholarship by critically analysing the regulatory approach chosen for the EU’s AI Act, contesting the adequacy of the New Legislative Framework for AI governance, and arguing that the reliance on harmonised standards risks undermining democratic accountability and fails to sufficiently safeguard fundamental rights without a more inclusive and transparent standard-setting process. The article focuses on the exclusion of the European Telecommunications Standards Institute (ETSI) from the European Commission’s standardisation request in support of the AI Act and assesses its potential impact on EU law-making and regulatory consistency. Ultimately, the analysis aims to contribute to understanding standardisation dynamics, offering insights into its profound implications for AI governance and the broader digital sphere.Social Science Research Network391A critical analysis of the regulatory approach chosen for the EU's AI Act is provided, contesting the adequacy of the New Legislative Framework for AI governance, and arguing that the reliance on harmonised standards risks undermining democratic accountability and fails to sufficiently safeguard fundamental rights without a more inclusive and transparent standard-setting process.SSRN Electronic Journal['Marta Cantero Gamito']2024-01-01T00:00:00https://www.semanticscholar.org/paper/258395edb3782142ccf10dd428c2a4cb131c6269 b524d3724cad075a167d353cf9b568bd290755d2By bringing together fundamental rights, economic law, and recent legislation in the areas of digital platforms, data, and AI, this open access book gives a comprehensive picture of the state of play in technology regulation in the EU. Risks of regulatory fragmentation are on the rise with ever more legislative instruments becoming applicable to the technology sector. This book explores the prospects and challenges of ensuring legal consistency in a period of transition in which new legislation is being implemented and the interpretation of existing laws is being challenged by the use of data, AI, and platform technologies. The book analyses the legal consistency of technology regulation from three perspectives: (1) the relationship between the EU and the Council of Europe; (2) the relationship among EU regulatory frameworks; and (3) the relationship between EU and Member State law. By covering issues of fundamental rights protection, the free flow of data, consumer protection, competition, and innovation, the book gives a unique and extensive outlook into the state of the art in academic and policy discussions. Unravelling the relationship between legal fields, the book is an essential resource for academics, practitioners and students wishing to understand the increasingly complex landscape of technology regulation in Europe.00[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/b524d3724cad075a167d353cf9b568bd290755d2 ac5f91afc0dc7512f00645bfa2febbca74743c14As regulators seek to keep up with the sheer pace of tech change in the AI space, it is likely that we will see a shift from high-level principles to concrete public policy with the public sector first. However, as is to be expected, the idea of self-regulation in technology-by-technology companies poses the obvious issues minimally presenting an earnest conflict of interest within the very process of legislation. Influential tech company voices in discussions related to AI regulations can't be privileged over the rest of society's needs or concerns. AI is revolutionizing various aspects of society, including employment and political campaigns but it does pose certain risks. As a result, policymakers are in a hurry to safeguard the public from AI-related risks while ensuring that innovation remains unhampered. The need to foresee additional regulation in this space is important for deploying AI in operations in all industries. The AI community, specifically, ought to adopt a proactive approach in advocating for the responsible and intelligent utilization of these technologies.Newhouse Impact Journal00The need to foresee additional regulation in this space is important for deploying AI in operations in all industries and the AI community, specifically, ought to adopt a proactive approach in advocating for the responsible and intelligent utilization of these technologies.Newhouse Impact Journal['Adrienne Wallace']2024-01-01T00:00:00https://www.semanticscholar.org/paper/ac5f91afc0dc7512f00645bfa2febbca74743c14 30a512a58319add29e0a1115ca21a0cd6f1886f3 The development of artificial intelligence technology has promoted economic development and improved people’s living standards, but it will also cause many risks and uncertainties. The purpose of this paper is to create a reciprocal fairness model for artificial intelligence collaboration and explore the causal relationship and liability allocation in artificial intelligence criminal infringement cases based on Rabin’s theory. Through the Hande formula, the criteria for determining fault in the case, as well as the marginal costs and benefits of the aggressor’s use of AI to commit crimes, are calculated. Finally, based on the theory of behavioral game evolution, this paper discusses the cooperation law of the three parties under the regulation of the Criminal Procedure Law, and discusses the illegal cost of the illegal implementation of artificial intelligence from the amount of compensation. The results show that in 68,535 cases of using AI to commit crimes in 2022, the average compensation is 74,556.87 yuan, indicating that the cost of crime is much lower than the proceeds of crime. The proposed legal regulations and the prevention and control of AI technology risks can be practiced through this study’s practical relevance.Applied Mathematics and Nonlinear Sciences80The results show that in 68,535 cases of using AI to commit crimes in 2022, the average compensation is 74,556.87 yuan, indicating that the cost of crime is much lower than the proceeds of crime.Applied Mathematics and Nonlinear Sciences['Yi Sun', 'Pinze Zhang']2024-01-01T00:00:00https://www.semanticscholar.org/paper/30a512a58319add29e0a1115ca21a0cd6f1886f3 854b7e87fa3c04690f6cb10998ff8c3c5bd0ae51Journal of AI Law and Regulation00Journal of AI Law and Regulation['L.H. de Menezes Acioly', 'I. Brito Bezerra Mendes', 'M.F. da Silva', 'J. Araújo Monteiro Neto']2024-01-01T00:00:00https://www.semanticscholar.org/paper/854b7e87fa3c04690f6cb10998ff8c3c5bd0ae51 77822b6d7f25034b0d659f54bd6dfee87cbb8164The immune system is regulated by a complex set of genetic, molecular, and cellular interactions. Rapid advances in the study of immunity and its network of interactions have been boosted by a spectrum of "omics" technologies that have generated huge amounts of data that have reached the status of big data (BD). With recent developments in artificial intelligence (AI), theoretical and clinical breakthroughs could emerge. Analyses of large data sets with AI tools will allow the formulation of new testable hypotheses open new research avenues and provide innovative strategies for regulating immunity and treating immunological diseases. This includes diagnosis and identification of rare diseases, prevention and treatment of autoimmune diseases, allergic disorders, infectious diseases, metabolomic disorders, cancer, and organ transplantation. However, ethical and regulatory challenges remain as to how these studies will be used to advance our understanding of basic immunology and how immunity might be regulated in health and disease. This will be particularly important for entities in which the complexity of interactions occurring at the same time and multiple cellular pathways have eluded conventional approaches to understanding and treatment. The analyses of BD by AI are likely to be complicated as both positive and negative outcomes of regulating immunity may have important ethical ramifications that need to be considered. We suggest there is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.Archivum Immunologiae et Therapiae Experimentalis350There is an immediate need to develop guidelines as to how the analyses of immunological BD by AI tools should guide immune-based interventions to treat various diseases, prevent infections, and maintain health within an ethical framework.Archivum immunologiae et therapiae experimentalis['Bhagirath Singh', 'Anthony M Jevnikar', 'Eric Desjardins']2024-01-01T00:00:00https://www.semanticscholar.org/paper/77822b6d7f25034b0d659f54bd6dfee87cbb8164 721722c8a22705747bb3cc0800128a68c62725c6A principal pillar of the US Blueprint for an AI Bill of Rights is data privacy, specifically, that individuals should be protected from abusive practices by data collectors and data aggregators, and that users should have control over how their personal information is collected and used. An area that spotlights the need for such protections is found in the common practices of data brokers who scrape, purchase, process and reassemble personal information in bulk and sell it for a variety of downstream uses. Such activities almost always occur in the absence of users’ knowledge or meaningful consent, yet they are legal under US law. This paper examines how data brokers operate, provides some examples of recent US regulatory actions taken against them, summarizes federal efforts to redress data broker practices and concludes that as long as there continues to be no comprehensive federal data protection and privacy scheme, efforts to control such behavior will have only a limited effect. This paper also addresses the limits of informed consent on the use of personal information in language resources and suggests a solution in an holistic approach to data protection and privacy across the data/development life cycle.LEGAL00Examining how data brokers operate is examined, some examples of recent US regulatory actions taken against them are provided, federal efforts to redress data broker practices are summarized, and it is concluded that as long as there continues to be no comprehensive federal data protection and privacy scheme, efforts to control such behavior will have only a limited effect.['Denise DiPersio']2024-01-01T00:00:00https://www.semanticscholar.org/paper/721722c8a22705747bb3cc0800128a68c62725c6 6c72131840401e9c4e7af228fefee5432dd8e7d2: The emergence of Artificial Intelligence has changed the traditional way of creating works dominated by human beings, which has triggered many copyright-related issues. Globally, there are relatively few AIGC copyrightable cases in the judicial practice related to AI technology, which, taken together, leaves many issues to be discussed at the legislative and judicial levels, such as the determination of copyrightability, the ownership of works, the protection of data ingested by AI, the balance of interests, and so on. Determining copyrightability of AIGC can, on the one hand, help to "settle disputes" in technical disputes related to AIGC, and, on the other hand, guide the development of copyright in literature, art, and science. Therefore, it is necessary to respond to a series of copyright issues caused by AIGC. Based on the current development of AI technology, guided by the essence of copyright law, and considering the protection practice of AI-generated objects, this paper intends to study the focus of copyright disputes involved in the AIGC. It will participate in the legal regulation of intellectual property rights as a matter of course, and offer suggestions for balancing the legal interests of creators and the public.Science of Law Journal10This paper intends to study the focus of copyright disputes involved in the AIGC, participate in the legal regulation of intellectual property rights as a matter of course, and offer suggestions for balancing the legal interests of creators and the public.Science of Law Journal['Wangrui Yang']2024-01-01T00:00:00https://www.semanticscholar.org/paper/6c72131840401e9c4e7af228fefee5432dd8e7d2 19739a6798921494ee2447304fa6a778bf657ba1This chapter discusses the regulation of artificial intelligence (AI) from the vantage point of political economy. By “political economy” I mean a perspective which emphasizes that there are different people and actors in society who have divergent interests and unequal access to resources and power. By “artificial intelligence” I mean the construction of autonomous systems that maximize some notion of reward. The construction of such systems typically draws on the tools of machine learning and optimization. AI and machine learning are used in an ever wider array of socially consequential settings. This includes labor markets, education, criminal justice, health, banking, housing, as well as the curation of information by search engines, social networks, and recommender systems. There is a need for public debates about desirable directions of technical innovation, the use of technologies, and constraints to be imposed on technologies. In this chapter, I review some frameworks to help structure such debates. The discussion in this chapter is opinionated and based on the following premises:Social Science Research Network461This chapter discusses the regulation of artificial intelligence (AI) from the vantage point of political economy, a perspective which emphasizes that there are different people and actors in society who have divergent interests and unequal access to resources and power.SSRN Electronic Journal['Maximilian Kasy']2024-01-01T00:00:00https://www.semanticscholar.org/paper/19739a6798921494ee2447304fa6a778bf657ba1 5c9f3e3a9c7db5e48ef6af284936ef46a394d881This article proposes the concept of purpose limitation for AI models as an approach to effectively regulate AI. Unregulated (secondary) use of specific models creates immense individual and societal risks, including discrimination against individuals or groups, infringement of fundamental rights, or distortion of democracy through misinformation. We argue that possession of trained models, which in many cases consist of anonymous data (even if the training data contains personal data), is at the core of an increasing asymmetry of in-formational power between data companies and society. Combining ethical and legal aspects in our interdisciplinary approach, we identify the trained model, rather than the training data, as the object of regulatory intervention. This altered focus adds to existing data protection laws and the proposed Artificial Intelligence Act. These are inefficient in preventing the misuse of trained models due to their focus on the procedural aspects of personal data or training data. Drawing on the concept of risk prevention law and the principle of proportionality, we argue that the potential use of trained models by powerful actors in ways that are damaging to society warrants preventive regulatory interventions. Thus, we seek to balance the asymmetry of power by enabling democratic control over where and how predictive and generative AI capabilities may be used and reused.Journal of AI Law and Regulation570The concept of purpose limitation for AI models is proposed as an approach to effectively regulate AI and seeks to balance the asymmetry of power by enabling democratic control over where and how predictive and generative AI capabilities may be used and reused.Journal of AI Law and Regulation['R. Mühlhoff', 'H. Ruschemeier']2024-01-01T00:00:00https://www.semanticscholar.org/paper/5c9f3e3a9c7db5e48ef6af284936ef46a394d881 ffb801c8435f8643136ecf6456eaccacab7f2423The landscape of power systems is undergoing a transformative shift with the burgeoning inclusion of Distributed Energy Resources (DERs), which, while beneficial in enhancing the sustainability of electricity supply, introduces complexity in grid management. This paper presents a comprehensive framework leveraging Artificial Intelligence (AI) to orchestrate DER operations, thus achieving optimized load balancing and grid stability. A multi-agent system that utilizes machine learning algorithms is proposed, capable of predictive analytics and real-time decision-making. The architecture is underpinned by a robust data layer that assimilates inputs from a myriad of sensors and smart meters, facilitating the dynamic management of DERs. Through the simulation of various scenarios, the system demonstrates significant improvements in load distribution, peak shaving, and voltage regulation. The framework also showcases resilience against fluctuations and anomalies, attributing to the self-learning capability of AI models that continuously refine control strategies. The adaptability of the system is evaluated in the context of grid demand-response initiatives and the integration of intermittent renewable energy sources. Overall, the results indicate a substantial advancement in the operational efficiency of power grids, highlighting the synergy between AI and energy resource management.E3S Web of Conferences350A comprehensive framework leveraging Artificial Intelligence (AI) to orchestrate DER operations, thus achieving optimized load balancing and grid stability and highlighting the synergy between AI and energy resource management is presented.E3S Web of Conferences['Kavitha Dasari', 'Vijilius Helena Raj', 'Ginni Nijhawan', 'Ravi Kalra', 'Shilpa Pahwa', 'D. S. Abdul-Zahra']2024-01-01T00:00:00https://www.semanticscholar.org/paper/ffb801c8435f8643136ecf6456eaccacab7f2423 bb9bfa54dc05e2bcc41ada01c5a8bc7bb1e4da1aThis article mainly reviews the application of artificial intelligence technology in the field of physical education and its future development trend. The article analyses how AI technology can enhance the quality and efficiency of physical education, providing students with better teaching experience and training effects through intelligent teaching aids, virtual reality technology and big data analysis. The article also highlights the important role of AI technology in promoting the personalized and interdisciplinary development of physical education, demonstrating the potential and prospects for its widespread application in the field of physical education. At the same time, the article also discusses the technical, ethical and privacy challenges facing the application of AI technologies in physical education, and puts forward suggestions for strengthening regulation and norms to ensure the healthy development of the technologies and maximize the benefits to society. In looking at the future development trend, the article points out that AI technology will continue to drive innovation and change in the field of physical education, injecting new vigor into the development of physical education globally.Review Study Of Integrating Ai Technology Into Sports Training System00How AI technology can enhance the quality and efficiency of physical education, providing students with better teaching experience and training effects through intelligent teaching aids, virtual reality technology and big data analysis is analyzed.Review Study Of Integrating Ai Technology Into Sports Training System['Zhanguo Su', 'Shishun Ge', 'Liguang Li', 'Yiping Su']2024-01-01T00:00:00https://www.semanticscholar.org/paper/bb9bfa54dc05e2bcc41ada01c5a8bc7bb1e4da1a 73f04cc4f46fa449f1c253e92c43c2ba2271c8fc. Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU´s new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA.Social Science Research Network280How the EU´s new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies is explained, thus clarifying the governance framework of the AIA and a normative model of governance is proposed, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation.SSRN Electronic Journal['Claudio Novelli', 'Philipp Hacker', 'Jessica Morley', 'Jarle Trondal', 'Luciano Floridi']2024-01-01T00:00:00https://www.semanticscholar.org/paper/73f04cc4f46fa449f1c253e92c43c2ba2271c8fc 5f9b6e97ebe2e38bc3f67ecc0e31651c92456aabAI is rapidly becoming essential in various industries, raising societal expectations. AI’s societal consequences include impacts on mental health; misinformation; workforce displacement; and economic, regulatory, and law enforcement challenges. Indeed, the regulation of AI usage is on the horizon, with the European Union and China already taking big steps, while the United States drafted its first AI-related bill of rights last year. Professional associations and other nonprofits are also contributing to AI ethics and regulations, increasing the urgency and criticality of this area. In this new context, public services and regulated institutions must ensure responsible AI to avoid biased or inaccurate decision-making. Similarly, companies using AI responsibly can stand out, increase efficiency, and avoid future legal problems. This article highlights the issues and problems that result in many organizations not knowing how to do responsible AI in practice, as they need to identify potential problems, set up safeguards, and conduct ethical impact assessments, among other actions. We present the issues to consider toward a comprehensive approach to responsible AI that should include defining a responsible AI strategy road map; assessing models, processes, and products; and training individuals at different levels. By covering the pressing issues related to the urgent need for adopting responsible AI, we hope to highlight the importance for corporations to seriously consider responsible AI as they rush to adopt this technology for competitive advantage.IEEE Intelligent Systems130The issues to consider toward a comprehensive approach to responsible AI that should include defining a responsible AI strategy road map; assessing models, processes, and products; and training individuals at different levels are presented.IEEE Intelligent Systems['Ricardo A. Baeza-Yates', 'U. Fayyad', 'U. Fayyad']2024-01-01T00:00:00https://www.semanticscholar.org/paper/5f9b6e97ebe2e38bc3f67ecc0e31651c92456aab a4387132b8486abd7aa59d42bc039f1f9b18e838Journal of AI Law and Regulation00Journal of AI Law and Regulation['D. Farmaki', 'K. Letsiou']2024-01-01T00:00:00https://www.semanticscholar.org/paper/a4387132b8486abd7aa59d42bc039f1f9b18e838 65c7162b70bf2eb043083aa79580c67aeb36d923Journal of AI Law and Regulation00Journal of AI Law and Regulation['M. Rotenberg']2024-01-01T00:00:00https://www.semanticscholar.org/paper/65c7162b70bf2eb043083aa79580c67aeb36d923 94613701f551addc61ed619286dbce27cec1a6e9Journal of AI Law and Regulation00Journal of AI Law and Regulation['G. Reusken']2024-01-01T00:00:00https://www.semanticscholar.org/paper/94613701f551addc61ed619286dbce27cec1a6e9 d2802ec65f2d93ad2d46ca6df1beeac476a002abJournal of AI Law and Regulation00Journal of AI Law and Regulation['M. Leiser']2024-01-01T00:00:00https://www.semanticscholar.org/paper/d2802ec65f2d93ad2d46ca6df1beeac476a002ab 18f1e507bb19ee02268dbb02b7813e66a971e8c4Journal of AI Law and Regulation00Journal of AI Law and Regulation['N. Stratieva']2024-01-01T00:00:00https://www.semanticscholar.org/paper/18f1e507bb19ee02268dbb02b7813e66a971e8c4 a4236206f447b74c15a2c82016a4ffad2fa25669Journal of AI Law and Regulation00Journal of AI Law and Regulation['E. DeChant']2024-01-01T00:00:00https://www.semanticscholar.org/paper/a4236206f447b74c15a2c82016a4ffad2fa25669 b06f2c180474a568c0986795daa4d7ee2ad1f2faThe aim of this opinion piece is to examine the responsible use of artificial intelligence (AI) in relation to academic journal publishing. The work discusses approaches to AI with particular attention to recent developments with generative AI. Consensus is noted around eight normative themes for principles for responsible AI and their associated risks. A framework from Shneiderman (2022) for human-centered AI is employed to consider journal publishing practices that can address the principles of responsible AI at different levels. The resultant AI principled governance matrix (AI-PGM) for journal publishing shows how countermeasures for risks can be employed at the levels of the author-researcher team, the organization, the industry, and by government regulation. The AI-PGM allows a structured approach to responsible AI and may be modified as developments with AI unfold. It shows how the whole publishing ecosystem should be considered when looking at the responsible use of AI—not just journal policy itself.Journal of the AIS03The resultant AI principled governance matrix (AI-PGM) for journal publishing shows how the whole publishing ecosystem should be considered when looking at the responsible use of AI—not just journal policy itself.J. Assoc. Inf. Syst.['Shirley Gregor']2024-01-01T00:00:00https://www.semanticscholar.org/paper/b06f2c180474a568c0986795daa4d7ee2ad1f2fa 38a90e70a141643af17282ae57fb2d1d83dae3e8 This study pioneers a mental health education model for college students, merging cognitive-behavioral theory with artificial intelligence (AI) technology. By employing mathematical statistics to analyze teaching variables, we establish regression equations that elucidate the relationship between cognitive-behavioral based mental health education and its impact on students’ psychological well-being and psychological capital. Our findings highlight the significant mediating roles of cognitive behavioral instruction on positive emotion regulation (0.161), subjective happiness (0.348), sadness and frustration management (0.412), and anger control (0.376), confirming the efficacy of this approach. The application of cognitive behavioral therapy (CBT) in student psychoeducation has shown promising results in improving cognitive abilities, reducing emotional disturbances, and enhancing cognitive functions, offering valuable insights for advancing mental health education in higher education institutions.Applied Mathematics and Nonlinear Sciences100This study pioneers a mental health education model for college students, merging cognitive-behavioral theory with artificial intelligence (AI) technology, and establishes regression equations that elucidate the relationship between cognitive-behavioral based mental health education and its impact on students’ psychological well-being and psychological capital.Applied Mathematics and Nonlinear Sciences['Xiaoyan You', 'Qiyun Zhang']2024-01-01T00:00:00https://www.semanticscholar.org/paper/38a90e70a141643af17282ae57fb2d1d83dae3e8 f9b404e00be88d80f127e21624f1d32fbeba5ebeThis article proposes the notion of Artificial Sociality to describe communicative AI technologies that create the impression of social behavior. Existing tools that activate Artificial Sociality include, among others, Large Language Models (LLMs) such as ChatGPT, voice assistants, virtual influencers, socialbots and companion chatbots such as Replika. The article highlights three key issues that are likely to shape present and future debates about these technologies, as well as design practices and regulation efforts: the modelling of human sociality that foregrounds it, the problem of deception and the issue of control from the part of the users. Ethical, social and cultural implications are discussed that are likely to shape future applications and regulation efforts for these technologies.Human-Machine Communication00Three key issues are highlighted that are likely to shape present and future debates about these technologies, as well as design practices and regulation efforts: the modelling of human sociality that foregrounds it, the problem of deception and the issue of control from the part of the users.Human-Machine Communication['Simone Natale', 'Iliana Depounti']2024-01-01T00:00:00https://www.semanticscholar.org/paper/f9b404e00be88d80f127e21624f1d32fbeba5ebe f9450be70862cb3ea348210809065ce14f79b242The text provides a detailed exploration of artificial intelligence (AI), spanning its historical development, types based on capabilities and functionalities, achievements, and challenges in defining the term. The discussion encompasses the limitations of current AI, particularly in achieving human-like intelligence. The text addresses the absence of a universally accepted definition for AI and explores various definitions from influential figures in the field. It emphasises the challenges in regulating AI due to the lack of consensus on its definition. The narrative concludes by underscoring the necessity of clear definitions for effective regulation amid the increasing integration of AI into diverse sectors.International journal of research and innovation in social science00The text addresses the absence of a universally accepted definition for AI and explores various definitions from influential figures in the field, emphasising the challenges in regulating AI due to the lack of consensus on its definition.International Journal of Research and Innovation in Social Science['Elizaveta Filina']2024-01-01T00:00:00https://www.semanticscholar.org/paper/f9450be70862cb3ea348210809065ce14f79b242 bbe7e0800f253057d4fb0a7eba6d85490620f897The advent of high-tech and self-learning AI algorithms is setting off an unprecedented transformation of social production processes that will fundamentally affect the entire world of labor. According to the author, the introduction of AI into the world of labor will undoubtedly lead to a temporary surge in technological unemployment, but in the long term, new technologies will create more jobs in new sectors of the economy. The impact of AI on unemployment is context-specific and should be subject to government regulation. The author points out that as a result of the introduction of AI algorithms in the world of labor, arise not only the traditional problems of strengthening the economic power of the employer, discrimination or unauthorized collection of personal data, but set a big complex of legal problems related to the responsibility of the employer for decisions that he himself cannot control. Therefore, for labor law, the most important task is to eliminate the discrepancies between the current model of legal regulation of labor relations and the risks of introducing AI into decision-making processes of hiring and controlling employees. The science of labor law should develop relevant approaches to a reasonable limitation of the use of AI, taking into account the peculiarities of modern algorithmic technologies, production prospects, legal and social risks. The author criticizes proposals for endowing AI with legal personality and the possibility of delegating responsibility for the implementation of employer functions to algorithms. According to the author, AI cannot have legal personality in labor relations (as well as in legal relations of any other type), since the functioning of AI is carried out through the datification of all their participants without the goal of achieving a socially significant result and establishing interaction between subjects of law regarding the satisfaction of their needs. AI is a means of automating labor processes, a digital interface for interaction between elements of the production system. The author states that AI in legal reality can exist only as an object of law. The author proposes to fix in the labor legislation the presumption of responsibility of the employer for the decisions made by AI, regardless of the originally programmed algorithms, even if they were changed by AI as a result of machine learning.Russian Journal of Labour &amp; Law00Russian Journal of Labour &amp; Law['Denis A. Novikov']2024-01-01T00:00:00https://www.semanticscholar.org/paper/bbe7e0800f253057d4fb0a7eba6d85490620f897 c7f864a23fea60790ce04a1f6d4a74bba02182c8This study investigates the impact of dynamic compliance frameworks on the integration and application of Generative Artificial Intelligence within financial regulatory technology (RegTech) in Tanzania. Through a mixed-methods approach comprising literature review, case studies, and expert interviews, the research assesses the potential benefits, challenges, and regulatory considerations associated with leveraging dynamic compliance frameworks and Generative AI in the Tanzanian financial landscape. The findings highlight the transformative role of dynamic compliance frameworks in enhancing regulatory efficiency, improving risk management, and fostering innovation in financial regulation. The study concludes with recommendations for policymakers, regulators, and industry stakeholders to promote the responsible and effective adoption of Generative Artificial Intelligence in financial regulatory technology in Tanzania.International journal of research and innovation in social science00Recommendations are made for policymakers, regulators, and industry stakeholders to promote the responsible and effective adoption of Generative Artificial Intelligence in financial regulatory technology in Tanzania.International Journal of Research and Innovation in Social Science['Davis Festo Matari']2024-01-01T00:00:00https://www.semanticscholar.org/paper/c7f864a23fea60790ce04a1f6d4a74bba02182c8 2079f16e8c8320cf80f5ed97fcbb492cc64a528eWaqf entails locking-up the title of an owned property and allotting the benefits for charitable purposes. It is among the most emphasized acts of righteousness in Islam, emphasizing social justice, collective good deeds, and fair distribution of wealth. The main legislation regulating and governing waqf in the United Arab Emirates (UAE) is the Federal Waqf Law No. 5 of 2018, largely derived from Islamic law (Shari’a). This study discusses the possible benefits of applying the waqf system in educational programs related to artificial intelligence (AI) in the Emirate of Dubai. It discusses the general legal rules of waqf in UAE law and its applications in the field of education, as well as its potential role in AI programs. It concludes that waqf can nowadays play a distinguished role in promoting investment in educational programs in Dubai, particularly with regard to AI. The present study paves the way for a better understanding of the role of waqf in the field of education and its results contribute to the growing literature on the subject.Journal of Governance and Regulation120It is concluded that waqf can nowadays play a distinguished role in promoting investment in educational programs in Dubai, particularly with regard to AI.Journal of Governance and Regulation['Zaid Muhmoud Agaileh']2024-01-01T00:00:00https://www.semanticscholar.org/paper/2079f16e8c8320cf80f5ed97fcbb492cc64a528e d7de44a1a250f4db3515be5ff532602154e61dc4Journal of AI Law and Regulation00Journal of AI Law and Regulation['T. Evas']2024-01-01T00:00:00https://www.semanticscholar.org/paper/d7de44a1a250f4db3515be5ff532602154e61dc4 fef1ba7638c268878dc92800495fdd4221156065Journal of AI Law and Regulation00Journal of AI Law and Regulation['N. Itanyi']2024-01-01T00:00:00https://www.semanticscholar.org/paper/fef1ba7638c268878dc92800495fdd4221156065 a5c20a65607a7ede6d2a699c9dc7d1ff02987583Journal of AI Law and Regulation00Journal of AI Law and Regulation['C. Hsu']2024-01-01T00:00:00https://www.semanticscholar.org/paper/a5c20a65607a7ede6d2a699c9dc7d1ff02987583 701785192ecb79cc07eed7052e02799397ea7d1fJournal of AI Law and Regulation00Journal of AI Law and Regulation['B. de Champris']2024-01-01T00:00:00https://www.semanticscholar.org/paper/701785192ecb79cc07eed7052e02799397ea7d1f 106ab99dc29a44c15318300f01f083275df87d1eJournal of AI Law and Regulation00Journal of AI Law and Regulation['M. Lopez']2024-01-01T00:00:00https://www.semanticscholar.org/paper/106ab99dc29a44c15318300f01f083275df87d1e 678351b1f28bf8deeeb58ed9f7624f8b0eddf0c5Decentralized anonymous payment schemes may be exploited for illicit activities, such as money laundering, bribery and blackmail. To address this issue, several regulatory-friendly decentralized anonymous payment schemes have been proposed. However, most of these solutions lack restrictions on the regulator’s authority, which could potentially result in power abuse and privacy breaches. In this paper, we present a decentralized anonymous payment scheme with collaborative regulation (DAPCR). Unlike existing solutions, DAPCR reduces the risk of power abuse by distributing regulatory authority to two entities: Filter and Supervisor, neither of which can decode transactions to access transaction privacy without the assistance of the other one. Our scheme enjoys three major advantages over others: 1) Universality, achieved by using zk-SNARK to extend privacy-preserving transactions for regulation. 2) Collaborative regulation, attained by adding the ring signature with controllable linkability to the transaction. 3) Efficient aggregation of payment amounts, achieved through amount tags. As a key technology for realizing collaborative regulation in DAPCR, the ring signature with controllable linkability (CLRS) is proposed, where a user needs to specify a linker and an opener to generate a signature. The linker can extract pseudonyms from signatures and link signatures submitted by the same signer based on pseudonyms, without leaking the signer’s identity. The opener can recover the signer’s identity from a given pseudonym. The experimental results reflect the efficiency of DAPCR. The time overhead for transaction generation is 1231.2ms, representing an increase of less than 50% compared to ZETH. Additionally, the time overhead for transaction verification is only 1.2ms.IEEE Transactions on Information Forensics and Security791The ring signature with controllable linkability (CLRS) is proposed, where a user needs to specify a linker and an opener to generate a signature, and the experimental results reflect the efficiency of DAPCR.IEEE Transactions on Information Forensics and Security['Tianyu Zhaolu', 'Z. Wan', 'Huaqun Wang']2024-01-01T00:00:00https://www.semanticscholar.org/paper/678351b1f28bf8deeeb58ed9f7624f8b0eddf0c5 b9bf99b9bdd141115ff41dda77a8eaa2dc5708f2Social Science Research Network10SSRN Electronic Journal['Miquel Noguer i Alonso', 'Foteini Samara Chatzianastasiou']2024-01-01T00:00:00https://www.semanticscholar.org/paper/b9bf99b9bdd141115ff41dda77a8eaa2dc5708f2 217fa3af7f3d9d8afa4a30b619f2b205773e5eceSocial Science Research Network210SSRN Electronic Journal['Guido Westkamp']2024-01-01T00:00:00https://www.semanticscholar.org/paper/217fa3af7f3d9d8afa4a30b619f2b205773e5ece 13f69dddda64d355d2a49de85f15112a2e9a8d2fSocial Science Research Network00SSRN Electronic Journal['A. Gikay']2024-01-01T00:00:00https://www.semanticscholar.org/paper/13f69dddda64d355d2a49de85f15112a2e9a8d2f c10172bfcde2f28e02b957533932682c11147861 The artificial intelligence senior care model brings about work efficiency improvement and human resource cost saving to old care service enterprises and government departments. Taking Maslow’s hierarchy of needs theory as an entry point, the article systematically combs through the needs, legal source relations, and potential risks of intelligent senior care services. To accurately assess the relevant influencing factors of the legal regulation of innovative senior care services, linear regression and seemingly uncorrelated regression models are introduced. The model parameters are estimated by Bayesian estimation method, and the empirical analysis is conducted based on the baseline regression and seemingly uncorrelated regression models. When the local government pension policy decreases by every 1 percentage point, the degree of legal regulation of innovative pension services will decrease by 0.598 percentage points. In addition, in the likelihood uncorrelated regression model, the influence coefficient of education level is −0.372, which is 38.81% lower than the benchmark regression model. To better guide the standardized development of innovative senior care services, the central and local governments must actively introduce relevant policies to help the public better understand and accept the advantages of intelligent senior care services.Applied Mathematics and Nonlinear Sciences170The article systematically combs through the needs, legal source relations, and potential risks of intelligent senior care services, taking Maslow’s hierarchy of needs theory as an entry point.Applied Mathematics and Nonlinear Sciences['Wanyu Ning', 'Xize Wei']2024-01-01T00:00:00https://www.semanticscholar.org/paper/c10172bfcde2f28e02b957533932682c11147861 e595d7afb67eda7234dae1c57b3bbc944e48c72eIn recent years, the generative artificial intelligence technology has evolved in a revolutionaryOpen Journal of Legal Science00Open Journal of Legal Science['安琪 蒋']2024-01-01T00:00:00https://www.semanticscholar.org/paper/e595d7afb67eda7234dae1c57b3bbc944e48c72e b79d4396e45503b4271d9851b0f02f5b692259c4Social Science Research Network014SSRN Electronic Journal['Liza Mozgunova']2024-01-01T00:00:00https://www.semanticscholar.org/paper/b79d4396e45503b4271d9851b0f02f5b692259c4 cc60a913404162c904c9afa8ddb520f3c3bc1a4dMonitoring of Law Enforcement00Monitoring of Law Enforcement[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/cc60a913404162c904c9afa8ddb520f3c3bc1a4d 1ac0e789a4287adc0e8324c0afc01657235b31d4THE LATEST LAW DEVELOPMENTS00THE LATEST LAW DEVELOPMENTS['O. Y. Illarionov']2024-01-01T00:00:00https://www.semanticscholar.org/paper/1ac0e789a4287adc0e8324c0afc01657235b31d4 6cbc18a72cdebf58c7b1fc5cb350c0c6d4e91f26Social Science Research Network30SSRN Electronic Journal['Lyantoniette Chua', 'Bernice Danielle Castillo']2024-01-01T00:00:00https://www.semanticscholar.org/paper/6cbc18a72cdebf58c7b1fc5cb350c0c6d4e91f26 abfc15593932bb53dd06c3978d24717bb36fbf85Open Journal of Legal Science50Open Journal of Legal Science['迈 刘']2024-01-01T00:00:00https://www.semanticscholar.org/paper/abfc15593932bb53dd06c3978d24717bb36fbf85 fccc7ee6bc38d1309b675a7e91d765a1101dbd50Social Science Research Network00SSRN Electronic Journal['Thiago Moraes']2024-01-01T00:00:00https://www.semanticscholar.org/paper/fccc7ee6bc38d1309b675a7e91d765a1101dbd50 a17d1000971f0ea62f2bc2654098b06f6ce0d55aSocial Science Research Network00SSRN Electronic Journal['Ana Beatriz Barreto Marques', 'Ana Isabel Silva Menezes', 'Beatriz Calonego Coutinho', 'Bruno Hernandes Leão', 'Elias Kim', 'Gabriel Milton Parente Araújo', 'Gabriela Marília Natividade Soares', 'Graziella Andrade', 'I. Pereira', 'I. Assunção', 'J. Torres', 'Jose Bartasevicius', 'Leila Takahashi Hadba', 'Livia Maldi', 'L. Rossato', 'Maria Eduarda Rodrigues Uribe', 'Maria Luíza Coelho Cavalcanti', 'Mateus Yacoub Pamplona Lamonica Bovino', 'Mauricio Ades', 'Miguel Ruffino', 'Pedro Henrique Figueiredo Soares', 'Stephanie Tiene Vega Valdivia', 'Tamiris Diniz Landim', 'Thales Spínola', 'Thiago Henrique Lemos Costa', 'Carlos Portugal Gouvêa', 'A. Bizutti', 'Lucas Mazzoni']2024-01-01T00:00:00https://www.semanticscholar.org/paper/a17d1000971f0ea62f2bc2654098b06f6ce0d55a 9032fe7fd6ba81189d2a05533c7e250fab39aa50Social Science Research Network00SSRN Electronic Journal['Angela Huyue Zhang']2024-01-01T00:00:00https://www.semanticscholar.org/paper/9032fe7fd6ba81189d2a05533c7e250fab39aa50 ac054daa9ee3596d4e6965a05f73ac642f426ba0Social Science Research Network00SSRN Electronic Journal['Tobias Heinrich', 'Christopher Witko']2024-01-01T00:00:00https://www.semanticscholar.org/paper/ac054daa9ee3596d4e6965a05f73ac642f426ba0 b674946400d99342b643249814c71d0ebdb3eb8aJuridical scientific and electronic journal00Juridical scientific and electronic journal['Yu.S. Riepina']2024-01-01T00:00:00https://www.semanticscholar.org/paper/b674946400d99342b643249814c71d0ebdb3eb8a 5bdf02aef629e9b053328f44b28c14d2a703f36dOpen Journal of Legal Science00Open Journal of Legal Science['天智 李']2024-01-01T00:00:00https://www.semanticscholar.org/paper/5bdf02aef629e9b053328f44b28c14d2a703f36d 69ac97c7d2ed6303437c36ae0b3753970608343fSocial Science Research Network10SSRN Electronic Journal['Gbenga Ogunkeye']2024-01-01T00:00:00https://www.semanticscholar.org/paper/69ac97c7d2ed6303437c36ae0b3753970608343f 388037a96f0d54f0913953281a826c406f0bb0bfSocial Science Research Network00SSRN Electronic Journal['Adedolamu Cephas Adeoye']2024-01-01T00:00:00https://www.semanticscholar.org/paper/388037a96f0d54f0913953281a826c406f0bb0bf 3eb30540c26e267c357c71f27b9cf340b7d8fab4Social Science Research Network30SSRN Electronic Journal['Praise Adelowo']2024-01-01T00:00:00https://www.semanticscholar.org/paper/3eb30540c26e267c357c71f27b9cf340b7d8fab4 8c13d4e6e9a3cba69574fc3bd007ec7d8c55b468Dispute Settlement10Dispute Settlement['依阳 李']2024-01-01T00:00:00https://www.semanticscholar.org/paper/8c13d4e6e9a3cba69574fc3bd007ec7d8c55b468 67805e0330ce63b78fd66f12eeef9020180c6e2eThe author discusses the issue of legal personality of artificial intelligence, highlighting different points of view and approaches to the assignment of rights and obligations. The problem of transparency of artificial intelligence work and the need for regulation is raised. The article also discusses different approaches to the definition of rights to the results of artificial intelligence. The importance of public trust in artificial intelligence is emphasized as a key factor in its implementation. The overall conclusion points to the relevance and complexity of issues related to the legal status of artificial intelligence and its impact on society.ACCOUNTING AND CONTROL00The author discusses the issue of legal personality of artificial intelligence, highlighting different points of view and approaches to the assignment of rights and obligations, and the importance of public trust in artificial intelligence implementation.ACCOUNTING AND CONTROL['Kirill I. Maltyz']2024-01-01T00:00:00https://www.semanticscholar.org/paper/67805e0330ce63b78fd66f12eeef9020180c6e2e 28d6a90d9e09a6e57100c9c6944acc43cf6f3617This article explores the use of artificial intelligence in the medical field for diagnosing a disease, namely the identification of factors that affect the presence of a brain tumor. Modern medical technologies are developing rapidly, and artificial intelligence is becoming an increasingly important tool to help doctors in accurate and timely diagnosis of various diseases. The article focuses on the application of learning methods such as decision trees, Kohonen maps and neural networks. The development and application of artificial intelligence in medicine provides a huge potential for improving the diagnosis of diseases and increasing the effectiveness of treatment, which contributes to improving the quality of life of patients. However, do not consider the need for ongoing scientific support, testing and regulation to ensure the safety and reliability of the application of artificial intelligence in medicine.BIO Web of Conferences250The application of learning methods such as decision trees, Kohonen maps and neural networks in the medical field for diagnosing a disease, namely the identification of factors that affect the presence of a brain tumor are explored.BIO Web of Conferences['Elena Suprun', 'Vadim Tynchenko', 'Vladimir Khramkov', 'Georgy Kovalev', 'Tatiana Soloveva']2024-01-01T00:00:00https://www.semanticscholar.org/paper/28d6a90d9e09a6e57100c9c6944acc43cf6f3617 1cf50b161d4380f65e9390107445cf35437051a2The ethical and legal problems of artificial intelligence’s comprehensive integration into the public environment, its impact on the human life sphere are investigated. The intensity of the ongoing changes requires not only full-fledged legal regulation, but also a comprehensive study of both the positive aspects of innovations, as well as the foresight of possible threats and the formation of adequate protective mechanisms from the law point of view, from the standpoint of human moral values. It is a systematic approach and the possible negative consequences’ prevention of the artificial intelligence use that should shape the goal-setting of the development and implementation of such systems. Using scientific cognition methods, such as analysis, synthesis, system-analytical, formal-logical, system-legal, various approaches to understanding the definition of “artificial intelligence” have been studied, taking into account all the features of such systems, the necessary ways to solve the identified gaps in legal regulation related to determining the place of objects and artificial intelligence systems in the legal space. The social and practical significance of the development of robotics is touched upon, while attention is focused on the regulation of possible risks. It is concluded that it is the compliance of the functions performed by artificial intelligence with the ethical and moral canons of society that should be decisive in solving issues of responsibility. Separately, proposals are presented to determine civil liability both in cases of direct harm by robots with artificial intelligence, and in cases of damage or destruction of an artificial intelligence system by a third party to their owner.Current Issues of the State and Law70It is concluded that it is the compliance of the functions performed by artificial intelligence with the ethical and moral canons of society that should be decisive in solving issues of responsibility.Current Issues of the State and Law['V. R. Babenkova', 'Natalia V. Kravchenko']2024-01-01T00:00:00https://www.semanticscholar.org/paper/1cf50b161d4380f65e9390107445cf35437051a2 d93bb76e88561a07269cc4c174a9e9c60f1d6ac5: As Autonomous Vehicles (AVs) rapidly progress and become widely deployed, governments worldwide grap-ple with addressing the ethical challenges associated with AVs in dilemma situations that result in loss of human life. They are tackling these issues through the formulation of policies and guidelines, the establishment of dedicated research centres exploring the ethical implications of AVs, and seeking public opinions on how self-driving cars should handle such moral dilemmas. In this paper, we will evaluate the Australian government’s strategies for addressing the ethical issues related to AV accidents. We will critique the Decision Regulation Impact Statement (DRIS) released by the National Transport Commission (NTC) in 2018, which assessed the safety assurance options for Automated Driving Systems (ADSs). We will critically examine the NTC’s decision to exclude ethical considerations for AVs from the DRIS and the Automated Driving System Entity’s (ADSE) Statement of Compliance. We will contend that safety and ethics are intrinsically linked. Furthermore, we argue that relying solely on the safety criteria may prove inadequate when addressing ethical dilemmas. Consequently, we advocate for the inclusion of AV ethical considerations, especially in complex ethical dilemmas, in future dialogues, even if a clear consensus on ethical decision-making by ADSs remains elusive. In conclusion, we will propose recommendations for the Australian government to enhance the development, deployment, and acceptance of AV technology.International Conference on Agents and Artificial Intelligence290{'pages': '1152-1161'}['Amir Rafiee', 'Hugh Breakey', 'Yong Wu', 'Abdul Sattar']2024-01-01T00:00:00https://www.semanticscholar.org/paper/d93bb76e88561a07269cc4c174a9e9c60f1d6ac5 464bf5d9dcc2cee8c07dc4c201abcbab4a5af757In this research I discussed the subject about the Artificial intelligence and Who is responsible for their acts , mentioning some real cases about Artificial intelligence and the crime , after that offered some ideas to solve the problem of responsibility about acts of crimes Artificial intelligence and the principal ideas, it is around to give electronic personhood or put supervisor who is responsible about acts Artificial intelligence and Mandatory Insurance for AI Systems , finally I choose that we should obligate on parties to specify the person who is responsible about acts AI and give him the authorities interact with acts Artificial intelligence at any time by contract and that is a simple evidence that can the defendant remove it, all these rules applying when faults act not intend and In the event that the person responsible is not specified in the contract, the rule of responsibility of the apparent person shall be applied and in any case If the act is intend the perpetrator will liable about itمجلة العلوم القانونية والاقتصادية350It is chosen that on parties to specify the person who is responsible about acts AI and give him the authorities interact with acts Artificial intelligence at any time by contract and that is a simple evidence that can the defendant remove it.مجلة العلوم القانونية والاقتصادية['أ.د أيمن عبد الله فكرى']2024-01-01T00:00:00https://www.semanticscholar.org/paper/464bf5d9dcc2cee8c07dc4c201abcbab4a5af757 b00c218a156ee932ba5aae51dd2a88d9bc36e8c4Synthesis Lectures on Engineering, Science, and Technology128104The efficiency of the proposed system in COVID-19 detection with high accuracy is explored, enhancing the network depth leading to minimum parameters, minimising the impact of vanishing gradient issue and attaining effective network performance with respect to better accuracy.Synthesis Lectures on Engineering, Science, and Technology['Gobi']2024-01-01T00:00:00https://www.semanticscholar.org/paper/b00c218a156ee932ba5aae51dd2a88d9bc36e8c4 de0cd1cb27ab7a8774b61cb0ee7d6795cb99c690Journal of Financial Economics10660A new measure of firm-level AI investments is proposed, using a unique combination of worker resume and job postings datasets, which reveals a stark increase in AI investments across sectors.Journal of Financial Economics['T. Babina', 'A. Fedyk', 'A. He', 'James Hodson']2024-01-01T00:00:00https://www.semanticscholar.org/paper/de0cd1cb27ab7a8774b61cb0ee7d6795cb99c690 edaf14777326d374f691b6c10df0ab720e2026a3Artificial Intelligence3817Artif. Intell.['Giuseppe Marra', 'Sebastijan Dumancic', 'Robin Manhaeve', 'L. D. Raedt']2024-01-01T00:00:00https://www.semanticscholar.org/paper/edaf14777326d374f691b6c10df0ab720e2026a3 dc644e17aea1f5797c03570a7a22fd1671cc5c62Prof. Nevzat Tarhan: "Instead of opposing artificial intelligence, we should learn to use it for the right purpose"üha00üha[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/dc644e17aea1f5797c03570a7a22fd1671cc5c62 7916b6cbd02d006b1a0696993e7a4a13425c53b6Üsküdar University Corporate Communication Department Media PR Unit Manager and Voice Actor Şaban Özdemir evaluated highly discussed artificial intelligence technologies that can imitate the real human voice.üha00üha[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/7916b6cbd02d006b1a0696993e7a4a13425c53b6 63d7cb62cbbb1ba7be8c8d72c1f8469ae1cdaa05Üsküdar University Head of the Department of Sociology Prof. Barış Erdoğan evaluated the effects of artificial intelligence on human life.üha00üha[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/63d7cb62cbbb1ba7be8c8d72c1f8469ae1cdaa05 86aad832111a78ac0ea2bc42f6b58ba91c3aafccThe modernization of the office premises ventilation system using artificial intelligence is considered. A ventilation system for an office space is designed by using a rotary heat exchanger, which ensured its energy efficiency. To further reduce energy costs, it is proposed to use artificial intelligence. This will allow take into account the real air parameters indoors and outdoors, which cannot be taken into account when designing this system. The positive and negative aspects of using artificial intelligence in modernizing the ventilation system are described. Keywords ventilation, fan, control, energy efficiency, recuperator, artificial intelligence, automationAutomation. Modern Techologies00The modernization of the office premises ventilation system using artificial intelligence will allow take into account the real air parameters indoors and outdoors, which cannot be taken into account when designing this system.Automation. Modern Techologies[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/86aad832111a78ac0ea2bc42f6b58ba91c3aafcc 477f71e9c3adc4676b34cc249996a844a5cf1f70This is the first scholarly exploration of concepts and representations of Artificial Intelligence in ancient Greek and Roman epic, including their reception in later literature and culture. Contributors look at how Hesiod, Homer, Apollonius of Rhodes, Moschus, Ovid and Valerius Flaccus have elaborated on the first literary texts that deal with automata and the quest for artificial life, as well as technological intervention improving human life. Parts one and two consider, respectively, archaic Greek, Hellenistic and Roman epics. Contributors explore the representations of Pandora in Hesiod, Homeric automata such as Hephaestus’ wheeled tripods, the Phaiakian king Alkinoös’ golden and silver guard dogs, and even the Trojan Horse. Later examples cover Artificial Intelligence and automation (including Talos) in the Argonautica of Apollonius and Valerius Flaccus, and Pygmalion’s ivory woman in Ovid’s Metamorphoses. Part three underlines how these concepts benefit from analysis of the ekphrasis device, within which they often feature. These chapters investigate the cyborg potential of the epic hero and the literary implications of ancient technology. Moving into contemporary examples, the final chapters consider the reception of ancient literary Artificial Intelligence in contemporary film and literature, such as the Czech science-fiction epic Starvoyage, orSmall Cosmic Odyssey by Jan Kresadlo (1995) and the British science-fiction novel The Holy Machine by Chris Beckett (2004).00[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/477f71e9c3adc4676b34cc249996a844a5cf1f70 5ab1ec80a9931caad4a463699bf6426facfffba2The recent past has witnessed a paradigm shift with the usage of artificial intelligence (AI) within a variety of healthcare services. This transition of incorporating AI has brought about multiple favours for enhancing current services and a lot of promising results in the primary care setup of Pakistan. Primary care has diverse and sophisticated provisions hence the challenges are complicated too. The authors examine the existing practices as well as future options for incorporating AI in primary care in Pakistan.Liaquat National Journal of Primary Care00The authors examine the existing practices as well as future options for incorporating AI in primary care in Pakistan.Liaquat National Journal of Primary Care[]2024-01-01T00:00:00https://www.semanticscholar.org/paper/5ab1ec80a9931caad4a463699bf6426facfffba2 995a1b0186a74af82cdfa5335a6184cbdd93b2f7Developments in artificial intelligence (AI) have led to the emergence of new technologies offering unique business opportunities. This article examines the factors influencing AI-based chatbot implementation by small and medium enterprises (SMEs). We grounded the article's conceptual model in the technology–organization–environment (TOE) framework. Employing a quantitative research methodology, we collected data from 292 SME respondents via an online survey. We then utilized covariance-based structural equation modeling to analyze the data. The empirical results reveal that perceived employee capability, perceived availability of financial support, perceived top management support, perceived cost, perceived complexity, and perceived relative advantage are positively associated with SMEs' AI-based chatbot adoption intention. This article, thus, contributes to the scarce literature on the adoption of AI-based chatbots for SMEs in developing small island countries. The findings provide meaningful insights to developers, marketers, and SMEs to enhance firms’ performance and competitiveness by increasing the adoption of AI-based chatbots.IEEE transactions on engineering management15517The empirical results reveal that perceived employee capability, perceived availability of financial support, perceived top management support, perceived cost, perceived complexity, and perceived relative advantage are positively associated with SMEs' AI-based chatbot adoption intention.IEEE Transactions on Engineering Management['Shavneet Sharma', 'Gurmeet Singh', 'N. Islam', 'A. Dhir']2024-01-01T00:00:00https://www.semanticscholar.org/paper/995a1b0186a74af82cdfa5335a6184cbdd93b2f7 5062b5b11fa9a528a3ae31da099c51555b8c6a0fArtificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI’s role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI’s transformative potential in healthcare, highlighting ChatGPT’s versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.Diagnostics9318ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare, as well as a guide for students, academics, and researchers in healthcare.Diagnostics['Hussain A. Younis', 'T. Eisa', 'Maged Nasser', 'Thaeer Mueen Sahib', 'Ameen A. Noor', 'O. M. Alyasiri', 'S. Salisu', 'Israa M. Hayder', 'Hameed A. Younis']2024-01-01T00:00:00https://www.semanticscholar.org/paper/5062b5b11fa9a528a3ae31da099c51555b8c6a0f 5b48d46932b0e586d8368f224e8c38bd445a6cbaArtificial intelligence (AI) spring of the past decade created an increased interest into the topic in business as well as in academia. This resulted in an upward trend in academic publications, not only in computer science but also in management. This article presents a computational literature review with an abstract-based sampling approach to investigate the status of the management literature to take stock of academic research of the past two decades. We analyze 6324 papers from 1990 to 2020 published in five management-related domains and identify 41 distinct topics. We present the evolution of research pre and post AI spring, emerging topics as well as saturated areas. The findings show that the previously disjointed topic network structure is fully connected by early 2010s and the upward trend in management research starts in the period of 2014–2015. The results provide a comprehensive insight into the potential of AI in management versus underdeveloped areas, and presents, for management scholars and practitioners, suggestions about effective adoption of AI practices.IEEE transactions on engineering management516A computational literature review with an abstract-based sampling approach to investigate the status of the management literature to take stock of academic research of the past two decades shows that the previously disjointed topic network structure is fully connected by early 2010s and the upward trend in management research starts in the period of 2014–2015.IEEE Transactions on Engineering Management['Jbid Arsenyan', 'Anke Piepenbrink']2024-01-01T00:00:00https://www.semanticscholar.org/paper/5b48d46932b0e586d8368f224e8c38bd445a6cba 67756c32f468f21f0b3668ba20ed031e2c8fc9e5The discourse surrounding the societal impacts of generative artificial intelligence (GAI), exemplified by technologies like ChatGPT, often oscillates between extremes: utopian visions of unprecedented productivity and dystopian fears of humanity’s demise. This polarized perspective neglects the nuanced, pragmatic manifestation of GAI. In general, extreme views oversimplify the technology itself or its potential to address societal issues. The authors suggest a more balanced analysis, acknowledging that GAI’s impacts will unfold dynamically over time as diverse implementations interact with human stakeholders and contextual factors. While Big Tech firms dominate GAI’s supply, its demand is expected to evolve through experimentation and use cases. The authors argue that GAI’s societal impact depends on identifiable contingencies, emphasizing three broad factors: the balance between automation and augmentation, the congruence of physical and digital realities, and the retention of human bounded rationality. These contingencies represent trade-offs arising from GAI instantiations, shaped by technological advancements, stakeholder dynamics, and contextual factors, including societal responses and regulations. Predicting long-term societal effects remains challenging due to unforeseeable discontinuities in the technology’s trajectory. The authors anticipate a continuous interplay between GAI initiatives, technological advances, learning experiences, and societal responses, with outcomes depending on the above contingencies.Journal of the AIS09The authors argue that GAI’s societal impact depends on identifiable contingencies, emphasizing three broad factors: the balance between automation and augmentation, the congruence of physical and digital realities, and the retention of human bounded rationality.J. Assoc. Inf. Syst.['R. Sabherwal', 'Varun Grover']2024-01-01T00:00:00https://www.semanticscholar.org/paper/67756c32f468f21f0b3668ba20ed031e2c8fc9e5 f4e3efff2958461a96ea53b363416458775910f4Research in artificial intelligence and business model innovation is flourishing. Nevertheless, the current discussion lacks an overarching understanding of, and thus has not sufficiently addressed, the interface between artificial intelligence-enabled business model innovation and the critical role of top management. Although a paradigm shift affecting top management is already occurring, extant management literature is limited, especially in terms of primary research. Accordingly, this study explores how top management can encourage and facilitate artificial intelligence-enabled business model innovation. We utilized an inductive approach and conducted semistructured interviews with 47 practitioners to develop a grounded theory. The developed framework consists of five top management competencies and eight top management roles. Overall, our study contributes to research in business model innovation theory, revealing that top management requires a specific skill set to carry out their roles and fulfill expectations.IEEE transactions on engineering management916This study explores how top management can encourage and facilitate artificial intelligence-enabled business model innovation and reveals that top management requires a specific skill set to carry out their roles and fulfill expectations.IEEE Transactions on Engineering Management['Philip Jorzik', 'Anil Yigit', 'Dominik K. Kanbach', 'S. Kraus', 'Marina Dabić']2024-01-01T00:00:00https://www.semanticscholar.org/paper/f4e3efff2958461a96ea53b363416458775910f4 34a5c2ab02010b3c39271a1989eab733e7bfaf47The accelerated progress of Artificial Intelligence (AI) within the accounting field has resulted in a heightened use of this technology in international enterprises, therefore generating noteworthy ethical concerns. This research investigates the ethical implications that arise from the use of AI in accounting practices, focusing on international corporations operating in Jordan. The objective of this research is to provide a comprehensive framework for the ethical and responsible integration of AI within the accounting domain. The research used a survey methods approach while 379 respondents were selected using cluster and proportional sampling. The qualitative component of the research investigates the viewpoints and concerns of persons pertaining to the use of AI. The study results provide significant contributions to the development of a context-specific paradigm for AI ethics that prioritizes concepts such as transparency, fairness, and accountability. The findings of this study have substantial value for multinational corporations engaged in commercial operations in Jordan and similar regions. The results provide organizations with the necessary tools to proficiently address the ethical dilemmas that emerge as a result of using artificial intelligence in accounting procedures.International Journal of Data and Network Science589The study results provide significant contributions to the development of a context-specific paradigm for AI ethics that prioritizes concepts such as transparency, fairness, and accountability.International Journal of Data and Network Science['Ahmad Y. A. Bani Ahmad']2024-01-01T00:00:00https://www.semanticscholar.org/paper/34a5c2ab02010b3c39271a1989eab733e7bfaf47 fd38e97fc6757fddac57720d1030046332e3afb9The purpose of this literature review is to provide a fundamental synopsis of current research pertaining to artificial intelligence (AI) within the domain of clinical practice. Artificial intelligence has revolutionized the field of medicine and healthcare by providing innovative solutions to complex problems. One of the most important benefits of AI in clinical practice is its ability to investigate extensive volumes of data with efficiency and precision. This has led to the development of various applications that have improved patient outcomes and reduced the workload of healthcare professionals. AI can support doctors in making more accurate diagnoses and developing personalized treatment plans. Successful examples of AI applications are outlined for a series of medical specialties like cardiology, surgery, gastroenterology, pneumology, nephrology, urology, dermatology, orthopedics, neurology, gynecology, ophthalmology, pediatrics, hematology, and critically ill patients, as well as diagnostic methods. Special reference is made to legal and ethical considerations like accuracy, informed consent, privacy issues, data security, regulatory framework, product liability, explainability, and transparency. Finally, this review closes by critically appraising AI use in clinical practice and its future perspectives. However, it is also important to approach its development and implementation cautiously to ensure ethical considerations are met.Applied Biosciences1927The purpose of this literature review is to provide a fundamental synopsis of current research pertaining to artificial intelligence (AI) within the domain of clinical practice and critically appraising AI use in clinical practice and its future perspectives.Applied Biosciences['V. Karalis']2024-01-01T00:00:00https://www.semanticscholar.org/paper/fd38e97fc6757fddac57720d1030046332e3afb9 c86de40e0830b389bf95330f14fb8eade39de95cArticle History Published Online: 10 June 2019 The development of Artificial Intelligence is speeding up rapidly and combination of Artificial Intelligence with automation has started to change the business landscape. Companies and business are focusing on applying existing Artificial Intelligence with automation processes to gain the new heights of efficiency and quality. The paper depicts about artificial intelligence and automation, and it tries to demonstrate the audience how both Artificial intelligence and automation are related and how they can be more effective when they work together and can give competitive advantage.Social Science Research Network115The paper depicts about artificial intelligence and automation, and it tries to demonstrate the audience how both Artificial intelligence and automation are related and how they can be more effective when they work together and can give competitive advantage.SSRN Electronic Journal['Ayushi Mohindru', 'Arvind kumar']2024-01-01T00:00:00https://www.semanticscholar.org/paper/c86de40e0830b389bf95330f14fb8eade39de95c d87bead31c4e8371535453faa9cc543e6fb80c74The simulation of human intelligence in robots that are designed to think and learn like humans is known as artificial intelligence (AI). AI is creating a world that has never been seen before. By applying AI to do jobs that would otherwise take a long time, humans have the chance to improve our planet. AI has great potential in genetic engineering and gene therapy research. AI is a powerful tool for creating new hypotheses and helping with experimental techniques. From the previous data of a gene model, it can help in the detection of heredity and gene-related disorders. AI developments offer an excellent possibility for rational drug discovery and design, eventually impacting humanity. Drug development and discovery depend greatly on AI and machine learning (ML) technology. Genetics is not an exception to this trend, as ML and AI are expected to have an impact on nearly every aspect of the human experience. AI has significantly aided in the treatment of various biomedical conditions, including genetic disorders. In both basic and applied gene research, deep learning - a highly versatile branch of AI that enables autonomous feature extraction - is increasingly exploited. In this review, we cover a broad spectrum of current uses of AI in genetics. AI has enormous potential in the field of genetics, but its advancement in this area may be hampered in the future by a lack of knowledge about the accompanying difficulties that could mask any possible benefits for patients. This paper examines AI's potential significance in advancing precision genetic disease treatment, provides a peek at its use in genetic clinical care, examines a number of existing AI and ML uses in genetics, provides a clinician primer on critical aspects of these technologies, and makes predictions about AI's potential future applications in genetic illnesses.Cureus392Examining AI's potential significance in advancing precision genetic disease treatment is examined, a peek at its use in genetic clinical care is provided, a clinician primer on critical aspects of these technologies is provided, and predictions about AI's potential future applications in genetic illnesses are made.Cureus['Rohit S Vilhekar', 'A. Rawekar']2024-01-01T00:00:00https://www.semanticscholar.org/paper/d87bead31c4e8371535453faa9cc543e6fb80c74 b45b86cc10bea65ec44b663938d1e68853aeba02In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the “garbage in, garbage out” issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians’ hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.Life953The overarching suggestion is that AI can be a valuable tool in clinicians’ hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.Life['Angeliki Bourazana', 'Andrew Xanthopoulos', 'A. Briasoulis', 'Dimitrios E. Magouliotis', 'K. Spiliopoulos', 'Thanos Athanasiou', 'George Vassilopoulos', 'J. Skoularigis', 'F. Triposkiadis']2024-01-01T00:00:00https://www.semanticscholar.org/paper/b45b86cc10bea65ec44b663938d1e68853aeba02 61f1e6dab308cba28a70e507b9b271334f9defcdMigraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, a straightforward pathway is hard to find for migraineurs’ management, so the search for response predictors has become urgent. Nowadays, artificial intelligence (AI) has pervaded almost every aspect of our lives, and medicine has not been missed. Its applications are nearly limitless, and the ability to use machine learning approaches has given researchers a chance to give huge amounts of data new insights. When it comes to migraine, AI may play a fundamental role, helping clinicians and patients in many ways. For example, AI-based models can increase diagnostic accuracy, especially for non-headache specialists, and may help in correctly classifying the different groups of patients. Moreover, AI models analysing brain imaging studies reveal promising results in identifying disease biomarkers. Regarding migraine management, AI applications showed value in identifying outcome measures, the best treatment choices, and therapy response prediction. In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine.Brain Science652In the present review, the authors introduce the various and most recent clinical applications of AI regarding migraine, showing value in identifying outcome measures, the best treatment choices, and therapy response prediction.Brain Sciences['Angelo Torrente', 'S. Maccora', 'Francesco Prinzi', 'Paolo Alonge', 'L. Pilati', 'A. Lupica', 'V. Di Stefano', 'Cecilia Camarda', 'Salvatore Vitabile', 'Filippo Brighina']2024-01-01T00:00:00https://www.semanticscholar.org/paper/61f1e6dab308cba28a70e507b9b271334f9defcd e20190fc0721982feb0bf8c34991f64bbe832ad4Teknologi pembelajaran terus berkembang seiring dengan perkembangan zaman. Dalam pembelajaran sehari hari kita sering jumpai adanya pelaksanaan pembelajaran dengan memanfaatkan teknologi dalam dunia pendidikan, seperti yang sering dilakukan oleh guru atau dosen yaitu mengkombinasikan alat teknologi dalam peroses pembelajaran. Perkembangan teknologi terus mendorong pendayagunaan artificial intelligence pada cara pembelajaran mahasiswa di Indonesia. Keberadaan AI telah memberikan dampak signifikan bagi dunia pendidikan.  Dengan kemampuannya dalam menganalisis dan memproses data, AI telah memberikan solusi yang inovatif sekaligus efektif dan memungkinkan pendekatan pembelajaran yang lebih personal dan adaptif. Mahasiswa tidak lagi hanya terpaku pada metode pembelajaran konvensional, tetapi mereka dapat mengakses sumber daya edukatif yang disesuaikan dengan kebutuhan mereka secara individual. Pendayagunaan artificial intelligence pada segi pembelajaran yang bertujuan untuk memperoleh efisiensi dalam memberi kemudahan mencari media pembelajaran. Namun Artificial intelligence tidak hanya memberikan manfaat positif, melainkan juga akan dapat mendatangkan dampak negatif, perkembangan teknologi Artificial intelligence berdampak positif dengan semakin mudah mendapatkan informasi dan ini menyebabkan kurangnya minat belajar mahasiswa karena lebih mengandalkan Artificial intelegence.IJEDR: Indonesian Journal of Education and Development Research112IJEDR: Indonesian Journal of Education and Development Research['Muhamad Rizki Firdaus', 'Rafi Irawan', 'Chairul Huda Yudi Mahardika', 'Prasetyo Lumban Gaol', 'Bima Aklmal Prinaryanto']2024-01-01T00:00:00https://www.semanticscholar.org/paper/e20190fc0721982feb0bf8c34991f64bbe832ad4 749a0cc1e9151145b0638814f31f1d336ba061b7Artificial intelligence (AI) has ushered in transformative changes, championing inclusion and accessibility for individuals with disabilities. This article delves into the remarkable AI-driven solutions that have revolutionized their lives across various domains. From assistive technologies such as voice recognition and AI-powered smart glasses catering to diverse needs, to healthcare benefiting from early disease detection algorithms and wearable devices that monitor vital signs and alert caregivers in emergencies, AI has steered in significant enhancements. Moreover, AI-driven prosthetics and exoskeletons have substantially improved mobility for those with limb impairments. The realm of education has not been left untouched, with AI tools creating inclusive learning environments that adapt to individual learning styles, paving the way for academic success among students with disabilities. However, the boundless potential of AI also presents ethical concerns and challenges. Issues like safeguarding data privacy, mitigating algorithmic bias, and bridging the digital divide must be thoughtfully addressed to fully harness AI’s potential in empowering individuals with disabilities. To complement these achievements, a robust conceptual model for AI disability inclusion serves as the theoretical framework, guiding the development of tailored AI solutions. By striking a harmonious balance between innovation and ethics, AI has the power to significantly enhance the overall quality of life for individuals with disabilities across a spectrum of vital areas.Journal of Disability Research544A robust conceptual model for AI disability inclusion serves as the theoretical framework, guiding the development of tailored AI solutions that significantly enhance the overall quality of life for individuals with disabilities across a spectrum of vital areas.Journal of Disability Research['M. Almufareh', 'Sumaira Kausar', 'M. Humayun', 'Samabia Tehsin']2024-01-01T00:00:00https://www.semanticscholar.org/paper/749a0cc1e9151145b0638814f31f1d336ba061b7 99c2ba6717264cfd998cac2f9735fda90538a556Image-generative artificial intelligence (AI) is increasingly being used in the product design process. In this paper, we present examples of how it is being used and discuss the possibilities of how applications may evolve in the future. We discuss the legal and ethical implications of image-generative AI, including concerns about bias, hidden labor, theft from artists, lack of originality in the outputs, and lack of copyright protection. We discuss how these concerns apply to design education and provide recommendations to educators about how AI should be addressed in the design classroom. We recommend that educators introduce AI as one tool among many in the designer’s toolkit and encourage it to be used as a process tool rather than for generating final design deliverables. We also provide guidance for how educators might engage students in discussions about AI to enhance their learning.Int. J. Interact. Multim. Artif. Intell.602It is recommended that educators introduce AI as one tool among many in the designer’s toolkit and encourage it to be used as a process tool rather than for generating final design deliverables.Int. J. Interact. Multim. Artif. Intell.['Kristin A. Bartlett', 'Jorge Camba']2024-01-01T00:00:00https://www.semanticscholar.org/paper/99c2ba6717264cfd998cac2f9735fda90538a556 ba61913552ccbf1ebf0abb8a2c76552bf66d91a8To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.Comprehensive Reviews in Food Science and Food Safety534This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain.Comprehensive reviews in food science and food safety['Wenjuan Mu', 'G. Kleter', 'Y. Bouzembrak', 'Eleonora Dupouy', 'Lynn J. Frewer', 'Fadi Naser Radwan Al Natour', 'H. J. Marvin']2024-01-01T00:00:00https://www.semanticscholar.org/paper/ba61913552ccbf1ebf0abb8a2c76552bf66d91a8 a1c0150adfc6a49c2f34ea75335ab82f57ad8fb5Chemical-enhanced oil recovery is a field of study that can gain significantly from artificial intelligence, addressing uncertainties such as mobility control, interfacial tension reduction, wettability alteration, and emulsifications. The primary objective of this article is to introduce an integrated framework for artificial intelligence, and chemical enhanced oil recovery for energy harvest operations. Central emphasis is placed on the energy transition, with the aim of expediting the development of cleaner energy harvesting systems and attaining the goal of net-zero emission. To do so, we present how the energy transition is changing the manufacturing of the chemicals for enhanced oil recovery application. For this, the uncertainty associated with materials’ design and critical role of the simulators for transferring the laboratory experiences into full-field implementations is discussed. The concept of digitalization and its impact on energy companies are highlighted. The role of digital twin in simulators integration is discussed, emphasizing how increased data access can help design more tolerant chemicals for harsh reservoir environments using real-time data. Also, we discuss how the chemical suppliers, research institutes, startups, and field operators can benefit from self-leaning and robotic laboratories for chemicals manufacturing. Moreover, the article explores how including artificial intelligence perspectives can improve our understanding of developing chemical formulations by blending hybrid capabilities. This approach contributes to making energy production more sustainable and aligning with the goal of zero emissions. A workflow is presented to demonstrate how the integration of artificial intelligence and chemical enhanced oil recovery can be used for both hydrocarbon production and other energy transition operations, such as carbon capture, utilization and storage, hydrogen storage, and geothermal reservoirs. The outcome of this paper stands as a pioneering effort that uniquely addresses these challenges for both academia and the industry and can open many additional doors and identify topics requiring further investigations.Journal of Data Science and Intelligent Systems02An integrated framework for artificial intelligence, and chemical enhanced oil recovery for energy harvest operations is introduced, with central emphasis on the energy transition, to expediting the development of cleaner energy harvesting systems and attaining the goal of net-zero emission.Journal of Data Science and Intelligent Systems['Alireza Bigdeli', 'M. Delshad']2024-01-01T00:00:00https://www.semanticscholar.org/paper/a1c0150adfc6a49c2f34ea75335ab82f57ad8fb5 0bb393544caf0d6e012113c787faceac6040a359The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.Clinical Pathology432The positive and negative aspects of AI implementation in healthcare are discussed, as well as some potential solutions to the potential issues at hand are recommended.Clinical Pathology['Md. Ashrafur Rahman', 'Evangelos Victoros', 'Julianne Ernest', 'Rob Davis', 'Yeasna Shanjana', 'Md. Rabiul Islam']2024-01-01T00:00:00https://www.semanticscholar.org/paper/0bb393544caf0d6e012113c787faceac6040a359 e5038cd0b6e1e2767b5f4a41b96a81e042904cbbThe advent of autonomous driving systems has given rise to pressing cybersecurity issues regarding the vulnerability of vehicular ad hoc networks (VANETs) to potential attacks. This critical security problem necessitates the application of artificial intelligence (AI) models for anomaly detection in VANETs of autonomous vehicles (AVs). However, the lack of explainability of such AI-based anomaly detection models presents challenges. This motivates an emerging research direction of utilizing explainable AI (XAI) techniques to elucidate the behaviors of anomaly detection models in AV networks. In this work, we propose an end-to-end XAI framework to interpret and visualize the anomaly detection classifications made by AI models securing VANETs. We evaluate the framework on two real-world autonomous driving datasets. The framework furnishes both global and local explanations for the black-box AI models using two XAI methods. Moreover, we introduce two novel feature selection techniques to identify the salient features contributing to anomaly detection, derived from the popular SHAP XAI method and the accuracy of six different black-box AI models. We compare our proposed feature selection approaches with six state-of-the-art feature selection techniques (including two wrapper-based feature selection methods), demonstrating superior performance on various evaluation metrics. To generalize the impact of our feature selection methods, we apply three independent classifiers to evaluate our proposed feature selection approaches. The novel feature selection methods effectively distill the most explanatory features, enhancing model interpretability. Finally, we assess the efficiency (how quickly the XAI models can yield explanatory findings) for each of the six black-box AI models we employed on our two datasets, identifying the most efficient model. By furnishing explanations and visualizations of anomaly detection by AI models, our XAI framework can help in enabling trust and transparency for securing vehicular networks.IEEE Access722This work proposes an end-to-end XAI framework to interpret and visualize the anomaly detection classifications made by AI models securing VANETs, and evaluates the framework on two real-world autonomous driving datasets.IEEE Access['Sazid Nazat', 'Lingxi Li', 'Mustafa Abdallah']2024-01-01T00:00:00https://www.semanticscholar.org/paper/e5038cd0b6e1e2767b5f4a41b96a81e042904cbb 97ca61bc0dd41a2ccde3b4a5ce932f7805a7338aAbstract Background and Aims Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low‐ and middle‐income countries (LMICs). Methods In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision‐making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies. Results In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource‐constrained LMICs. Conclusion While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data‐sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.Health Science Reports503The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs.Health Science Reports['M. O. Oduoye', 'Eesha Fatima', 'Muhammad Ali Muzammil', 'Tirth Dave', 'Hamza Irfan', 'F. Fariha', 'A.O.C.N. Marbell', 'S. C. Ubechu', 'G. Y. Scott', 'E. E. Elebesunu']2024-01-01T00:00:00https://www.semanticscholar.org/paper/97ca61bc0dd41a2ccde3b4a5ce932f7805a7338a 453ba2091d915ff23dc4eeac0411bd884e2d7e53Background: Artificial intelligence (AI) is emerging as a transformative technology with potential applications in various plastic surgery procedures and plastic surgery education. This article examines the views of plastic surgeons and residents on the role of AI in the field of plastic surgery. Methods: A 34-question survey on AI’s role in plastic surgery was distributed to 564 plastic surgeons worldwide, and we received responses from 153 (26.77%) with the majority from Latin America. The survey explored various aspects such as current AI experience, attitudes toward AI, data sources, ethical considerations, and future prospects of AI in plastic surgery and education. Predictions from AI using ChatGPT for each question were compared with the actual survey responses. Results: The study found that most participants had little or no prior AI experience. Although some believed AI could enhance accuracy and visualization, opinions on its impact on surgical time, patient recovery, and satisfaction were mixed. Concerns included patient privacy, data security, costs, and informed consent. Valuable AI training data sources were identified, and there was agreement on the importance of standards and transparency. Respondents expected AI’s increasing role in reconstructive and aesthetic surgery, suggesting its integration into residency programs, addressing administrative challenges, and patient complications. Confidence in the enduring importance of human professionals was expressed, with interest in further AI research. Conclusion: The survey’s findings underscore the need to harness AI’s potential while preserving human professionals’ roles through informed consent, standardization, and AI education in plastic surgery.Plastic and Reconstructive Surgery, Global Open352The survey’s findings underscore the need to harness AI’s potential while preserving human professionals’ roles through informed consent, standardization, and AI education in plastic surgery.Plastic and Reconstructive Surgery Global Open['Yasser Farid', 'Luis Fernando Botero Gutierrez', 'Socorro Ortiz', 'Sabrina Gallego', 'Juan Carlos Zambrano', 'Humberto Uribe Morrelli', 'Alfredo Patron']2024-01-01T00:00:00https://www.semanticscholar.org/paper/453ba2091d915ff23dc4eeac0411bd884e2d7e53 4391bc7ecb9ff5fedf23658208cf0ed5add4b4bdJournal of Korea Culture Industry00Journal of Korea Culture Industry['Sungguk Park', 'Minho Chang']2023-12-31T00:00:00https://www.semanticscholar.org/paper/4391bc7ecb9ff5fedf23658208cf0ed5add4b4bd c374e31c4e824f5a6913bd4c77ffce27235832f4The paper explores the challenges posed by the artificial intelligence (AI) industrial revolution and emphasizes the need for responsible and ethical handling of AI technology. It primarily focuses on issues related to data security, privacy, ethics, and societal consequences. The integration of AI into various aspects of society requires strict regulation to prevent unintentional biases, discrimination, and surveillance. Maintaining transparency, fairness, and accountability within AI systems is a challenging task. In addition to discussing these challenges, the paper identifies key research questions concerning the AI revolution's impact on ethics, privacy, workforce disruptions, and societal dynamics. These questions serve as a roadmap for future studies to better understand how AI affects individuals, organizations, and society as a whole. To investigate these challenges and opportunities, the paper outlines a comprehensive research methodology that combines qualitative and quantitative methods, including literature reviews, case studies, surveys, and expert interviews. This approach provides a well-rounded perspective on the AI revolution by synthesizing theoretical foundations, real-world examples, and insights from various stakeholders. The study presents findings highlighting the significant progress AI has brought in terms of automation, innovation, and productivity, enhancing human capabilities. However, it also underscores concerns related to privacy, ethics, and potential job displacement. The paper advocates for responsible AI development, emphasizing the importance of ethical guidelines, regulations, and transparency. These findings offer valuable insights for policymakers, industry experts, and researchers, helping them better understand and harness the potential of AI while addressing its risks and societal impacts.International Journal of Public Administration, Management and Economic Development00The paper advocates for responsible AI development, emphasizing the importance of ethical guidelines, regulations, and transparency, and presents findings highlighting the significant progress AI has brought in terms of automation, innovation, and productivity, enhancing human capabilities.International Journal of Public Administration, Management and Economic Development['Enrico Moch']2023-12-31T00:00:00https://www.semanticscholar.org/paper/c374e31c4e824f5a6913bd4c77ffce27235832f4 35f1bdc5bfcf0760e702f11249410434e52f2cb0Background and objective: Self-regulation has recently been implemented as an alternative solution to regulatory problems brought on by the inevitable delay of government-led regulations that cannot keep pace with environmental regulatory requirements in the field of climate change. The reality is that there has been insufficient preparation regarding implementing these alternative regulations to ensure that democratic support has been verified, and procedural legitimacy has been ensured.Methods: This study aims to identify institutional demands measures that can stimulate self-regulation in the future by conducting interviews with people who currently engage in self-regulation in the field of climate and the environment.Results: The analysis is categorized into five groups: G1 for technology development, G2 for management and operation, G3 for facility investment, G4 for corporate support, and G5 for government regulation response. By category, public institutions and research institutes show a high demand for issues related to management and operation. The highly centralized management and operation sector confirms that there is demand for greater policy enactment in postevaluation, which assesses the achievement of target levels.Conclusion: Since systematic transitions such as climate change are associated with institutional changes in various fields along with technological development, policy coherence and policy integration are seen as vital to the management and operational aspect of systems established in previous studies.Journal of people, plants, and environment200Journal of People, Plants, and Environment['Hae Ok Choi']2023-12-31T00:00:00https://www.semanticscholar.org/paper/35f1bdc5bfcf0760e702f11249410434e52f2cb0 4b86a526145838c56105d775e4fcd79148f81678Social media and other platforms play a crucial role in modern society, influencing information dissemination, shaping public opinion, driving economic activities, and affecting political elections. However, with the rise and development of these platforms, a series of issues have emerged, including privacy concerns regarding users' personal information, platform data storage, platform monopolistic behavior, and abuse of media regulation by both the public and the platforms themselves. This paper aims to discuss the role of social media in the development of media from traditional to digital forms, the existing means of regulating social media platforms, and analyze the monopolistic behavior of leading companies in the platform capitalism context and explore the necessary forms of regulation for social media and other platforms.Journal of Education, Humanities and Social Sciences30Journal of Education, Humanities and Social Sciences['Haoyang Wu']2023-12-31T00:00:00https://www.semanticscholar.org/paper/4b86a526145838c56105d775e4fcd79148f81678 dacda23336862df8ef4b99db941da1a86e8f0efcThe quality and quantity of food to ensure the quality of life of the population is primarily the responsibility of entrepreneurs engaged in agriculture, agri-food and business. In their activities, entrepreneurs should also employ ethics, which, as one of the regulatory mechanisms, should regulate the production and sale of food to improve the quality of life of the population while respecting the requirement of sustainability. First, an argument is made for the need for ethical regulation of agricultural business and agri-food industry in Slovakia based on a qualitative analysis of theoretical sources in the field of ethics, applied ethics, sociology and other social sciences and humanities, and using knowledge from the field of economics and management of agriculture and food industry,. Given the lack of ethics programmes in these areas of the economy to fulfil this role, the need to implement ethics and applied ethics in the education of professionals for these areas of the economy is explained, with the aim to provide safe and quality food to the population and thus a better quality of life.Socio-Economic and Humanities Studies10Socio-Economic and Humanities Studies['Eva Pechočiaková Svitačová']2023-12-31T00:00:00https://www.semanticscholar.org/paper/dacda23336862df8ef4b99db941da1a86e8f0efc dd16542c3303246521a688b134c09a052edf8f16In a technological society where macro technologies, generative artificial intelligence, and platforms have accelerated by leaps and bounds, the risks and concerns of so-called fake news are intensifying. The Internet is a forum for expression, “the most participatory medium of expression”, as Constitutional Court has affirmed. Fake news is intentionally manipulated false information that has the appearance of a journalistic article. The rapid spread of fake news through social networking services (SNS) and personal media can lead to societal and structural problems by adversely affecting the judgment of individuals, threatening the freedom of expression on the Internet, which is rooted in freedom of thought. For this reason, it is necessary to regulate the creation and dissemination of fake news on the Internet. However, regulation in response to fake news eventually leads to restrictions on freedom of expression. Against this backdrop, the constitutional issues surrounding fake news and the response to it today are clear. It is a question of balancing the public interest protected by regulating fake news against the freedom of expression restricted by doing so. Regardless of the amount of money involved, it is difficult to determine whether information is true or false, and even false information can fall under the protection of free speech and expression. For this reason, legal regulation of fake news needs to be limited to those that objectively threaten people's basic rights and the basic order of liberal democracy, and whose danger or harm is obvious and requires urgency. As the issue of the regulation of fake news has become a hot topic in Korean society, this paper reviews the existing debate from a constitutional perspective and critically examines the dangers of fake news and the need for a constitutional legislative response, especially in the current era of changing technologies and platforms. To this end, we examined the legislative bills introduced in the 20th and 21st National Assemblies from the perspectives of the constitutional principles of clarity, proportionality, and pre-censorship funding, and examined the possibilities of responding to fake news in the current legislation, suggesting social science tools and self-regulation as complements to legislative regulation.European Constitutional Law Association00European Constitutional Law Association['Heuiok Lee']2023-12-31T00:00:00https://www.semanticscholar.org/paper/dd16542c3303246521a688b134c09a052edf8f16 ccacc9d4ae4284e7149d39ad5e72790ec6237a8dNetwork violence is not a legal concept, in the existing law s and regulations and normative documents, guiding documents lac k of connotation and extension of the concept of network violence provisions, also lack of a normative way to list. This paper analyzes the characteristics of network violence, studies the applicable charges and criminal standards, as well as the regulation of criminal law on network violence. First of all, cyber violence has specific characteristics, including anonymity, dissemination and aggression. Secondly, for different types of cyber violence, different charges and criminal standards should be adopted in order to better punish criminal acts. Finally, criminal law can play an important role in regulating cyber violence. Clear legal provisions and corresponding penalties can effectively deter cyber violence. Therefore, this paper puts forward suggestions to further strengthen the regulation of network violence in criminal law. With scientific legislation as the core, it puts forward ideas such as adding the charges of network violence c rime, reasonable classification and different sentencing, and including the crime of network violence in public prosecution.Journal of Education, Humanities and Social Sciences00Suggestions to further strengthen the regulation of network violence in criminal law are put forward, such as adding the charges of network violence, reasonable classification and different sentencing, and including the crime of network violence in public prosecution.Journal of Education, Humanities and Social Sciences['Boqian Zheng']2023-12-31T00:00:00https://www.semanticscholar.org/paper/ccacc9d4ae4284e7149d39ad5e72790ec6237a8d 6349e7f3747914e6057e61059aeecbcef1293553The main purpose of the article was to indicate the growing importance of the issue of determi-nants of the development of applications of generative artificial intelligence with particular atten-tion to the issues of current and potential threats to this development and also the necessity of legal regulation of this development. On the one hand, the development of artificial intelligence is a kind of next stage of the ongoing technological progress, which has been taking place since the first industrial revolution, consisting in the increase in the scale of objectification of labor and automation of manufacturing processes. On the other hand, taking into account the ChatGPT, which is made available in open access on the Internet, this development of generative artificial intelligence also generates many risks related to the potentially rapid development of disinfor-mation in social media, non-compliance with copyright, the decreasing possibility of identifying the authorship of works created by artificial intelligence, and the use of artificial intelligence by hackers and cybercriminals to create new cybercrime techniques, and so on. In this regard, it is necessary to regulate the development of applications of generative artificial intelligence technol-ogy, so that this development does not generate negative consequences and new categories of threats.International Journal of Legal Studies ( IJOLS )180International Journal of Legal Studies ( IJOLS )['Dariusz Prokopowicz']2023-12-31T00:00:00https://www.semanticscholar.org/paper/6349e7f3747914e6057e61059aeecbcef1293553 2e4b84856a9d4668590091ce66400160e1e43654Purpose - The 4th industrial revolution brings the “Big Blur”, challenging the ‘Principle of Separation of Banking and Commerce’ by blurring distinctions between traditional and non-traditional financial sectors. This paper examines separation regulations and suggests adaptive financial regulations tailored to the challenges posed by the ‘big blur’. Design/Methodology/Approach - We assess existing regulations for financial and non-financial institutions amidst evolving industry and business convergence using theoretical documents, reports, and news articles. Recognizing asymmetries in the application of laws, we propose optimal regulatory schemes to promote innovative financial ventures. Findings - The principle of separation of banking and commerce presents a regulatory dichotomy with ‘over-regulation’ burdening financial firms, and ‘under-regulation’ favoring big tech. Dual convergence is evident as non-financial big techs enter financial sectors, and financial institutions diversify into non-financial activities. This calls for globally cohesive regulatory frameworks for a level playing field. Research Implications - The paper advocates regulatory innovation for the ‘big blur’ era, aiming to establish a level playing field and enabling financial firms to enter non-financial services. The proposal encourages expanding business scope through diversified models supported by negative regulation systems.Korea International Trade Research Institute00Korea International Trade Research Institute['Gyoung-Gyu Choi', 'Gye-Hyun Park']2023-12-31T00:00:00https://www.semanticscholar.org/paper/2e4b84856a9d4668590091ce66400160e1e43654 da5c740b23d95dac7863813553d519420efc0f37Penelitian ini bertujuan untuk mengetahui hubungan dari self regulation dengan impulsive buying item di game online. Data penelitian ini diperoleh 115 responden merupakan pemain game online dan 89 pernah melakukan pembelian pada item game online. Teknik sampling yang digunakan pada penelitian ini adalah non-probability dengan metode accidental sampling. Penelitian ini menggunakan skala self regulation dan impulsive buying dengan hasil analisis data korelasi r = -0.139 (p < .01) menggunakan spearman’s rho. Hal ini menunjukkan bahwa ada hubungan negatif yang signifikan antara self regulation dengan impulsive buying terhadap item di game online pada masa dewasa awal yang mengartikan bahwa semakin tinggi self regulation seseorang maka semakin rendah perilaku impulsive buying. Hal ini menyatakan bahwa self regulation memiliki peran penting bagi pemain game online di usia dewasa awal untuk menahan perilaku saat berbelanja.Fenomena00FENOMENA['Danar Tresna Aditya']2023-12-31T00:00:00https://www.semanticscholar.org/paper/da5c740b23d95dac7863813553d519420efc0f37 a110116df1f6f5760e676c6bc443da31b6fbe9aaWith the rapid development of economic globalization, the internationalization of intellectual property system has gradually come into the view of the public and multinational enterprises. Discussing the issue of intellectual property rights, many international organizations and countries themselves have relevant legal systems. They have also clarified the application of law in solving disputes. On the basis of the legal system of intellectual property established by various international organizations and the judicial practice of typical cases, this paper illustrates the legal application, protection and regulation of intellectual property. In fact, the whole international community has formed a relatively complete legal system, and there are universal solutions for dispute settlement and regulation. As far as China is concerned, on the basis of using the international community and the legal system of other countries for reference, it is necessary to further improve the legislation in the field of intellectual property rights, especially in the emerging fields with legal gaps.Journal of Education, Humanities and Social Sciences00Journal of Education, Humanities and Social Sciences['Jinxin Li']2023-12-31T00:00:00https://www.semanticscholar.org/paper/a110116df1f6f5760e676c6bc443da31b6fbe9aa f460f56f1d41131f51da54f48f7756a8fa14f795Currently, there exist numerous multinational enterprises situated in various regions across the globe. Since the 1990s, the exponential surge in the quantity of multinational enterprises has undeniably contributed to the economic advancement of diverse host nations and the global community. However, concurrently, the management of these multinational enterprises has also emerged as a formidable challenge confronted by host countries. This paper examines the international economic environment as the backdrop, incorporating economic principles and international trade agreements. By conducting a comprehensive analysis of various agreements and real-life instances, it delves into the specific measures implemented by host countries to regulate multinational enterprises within their borders. Subsequently, the paper discerns the fundamental objectives behind the host country's regulation of MNEs. Furthermore, it scrutinizes the two primary challenges encountered by the host country in regulating multinational enterprises, ultimately identifying the crux of the issue, namely, the distribution of benefits between the host country and the MNEs.Journal of Education, Humanities and Social Sciences130Journal of Education, Humanities and Social Sciences['Mingxian Chu']2023-12-31T00:00:00https://www.semanticscholar.org/paper/f460f56f1d41131f51da54f48f7756a8fa14f795 6490ffe29b652a67ad82f9c3ccf26fd9d7e93634Banking institutions play an important role in the economic development of the country. Banks are intermediary institutions that cannot be separated from people's lives. Sharia banking is part of the existing banking system in Indonesia which has its own challenges in implementing sharia principles and maintaining its stability. Prior to the establishment of OJK (Financial Services Authority), Bank Indonesia had full authority in supervising the financial services industry, including banking. The Financial Services Authority was established as an answer to problems that arose in the banking sector after the crisis in several national banks. The OJK Law which came into force on November 22, 2011 authorizes OJK to conduct supervision, regulation and investigation in the banking sector in Indonesia. This study discusses the legal aspects of banking regulation and supervision as well as the implementation of sharia principles in OJK sharia banking. The research methods used are legal approaches and literature reviews. The results of the study show that OJK has an integrated regulatory and supervisory function in the entire financial services sector. OJK is considered a superior institution with great authority. OJK also plays a role in regulating and supervising Islamic bankingIndonesian Journal of Multidisciplinary Sciences (IJoMS)90Indonesian Journal of Multidisciplinary Sciences (IJoMS)['Salwatun Aslamia', 'Ahmed Fatir Sadikin', 'Muhammad Agil Saputra', 'Yeni Yulia Kusuma', 'Tiana Nataza', 'Rangga Okrio Saputra']2023-12-31T00:00:00https://www.semanticscholar.org/paper/6490ffe29b652a67ad82f9c3ccf26fd9d7e93634 1073a6d29443960b3990446fe3fe6bd23788efcbSelf-regulation can be defined as “an organized business group self-regulating the actions of its members to protect users.” If there is a need to respect the autonomous discipline of the business organization itself in a specific field, it would be good to introduce a self-regulation system. In newly emerging areas, areas that are rapidly changing or areas that require specialized or policy considerations, would be appropriate for business organizations in those areas to self-regulate because existing legal regulations do not reflect reality or cannot properly reflect reality. Even if a self-regulation system is introduced, the degree of legal intervention in the self-regulation system of business organizations may vary depending on the characteristics of the industry. If the scope of the area is diverse or subject to frequent change, the scope of self-regulation may be narrowed due to strong legal intervention, and in areas dealing with detailed, technical, and specialized matters, the private sector should be able to demonstrate efficiency and expertise. It is desirable to reduce the degree of legal intervention through self-regulation. In relation to freedom of speech, due to the constitutional order to guarantee the public’s freedom of expression as much as possible, state regulation should be minimized, and self-regulation by business organizations to replace it is judged to be effective. In addition, due to the emergence of new technologies and technological advancement in the field of broadcasting and communications, regulation by the existing positive system for industries in this field makes efficient industrial development difficult. However, as the provision of information through social media, Internet news, OTT services etc., the side effects caused by digital media exceeded a certain level and reached a level of social conflict and infringement of other people’s rights, countries around the world have started to regulate illegal information through national legal regulations. Self-regulation was judged to be insufficient to prevent the creation and distribution of false information, hate speech, etc. In order for a self-regulatory system to operate properly, it is important to secure the democratic legitimacy of self-regulatory organizations, operational transparency, independence, and enforcement of regulations. The German self-regulatory organization for multimedia service providers is the German Self-Regulation Organization for Multimedia Service Providers(FSM). The German Youth and Media Protection Committee approved FSM, a self-regulatory organizationy, to protect youth from media content. The United States is a country that operates a representative private self-regulation system. However, direct administrative regulations have recently become more prominent in the broadcasting and communications fields due to issues such as unfair trade or monopoly. The UK's self-regulatory organization is the Internet Watch Foundation(IWF). IWF carries out tasks such as deleting posts related to child sexual abuse, blocking access, and preventing search/access, and is operating a hotline as the most basic activity to make this possible. The Australian Communications and Media Authority(ACMA) is the broadcasting and communications regulator. ACMA is a government-affiliated organization and is a co-regulation model between the government and the private sector that supports and cooperates in the establishment of industry codes. The self-regulation system stipulated in Korea’s Information and Communications Network Act is actually close to administrative regulation. It is appropriate for information and communication service provider organizations to determine and implement a code of conduct for information and communication service providers, rather than a matter to be stipulated in the Information and Communications Network Act,LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY00LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY['Boo-Ha Lee']2023-12-31T00:00:00https://www.semanticscholar.org/paper/1073a6d29443960b3990446fe3fe6bd23788efcb fe8a9f33b1de9c0430ded625456fc81ee82f7708Depression is a clinical disease, mainly accompanied by mood or emotional abnormalities, mainly depression, slow thinking, often accompanied by emotional abnormalities, cognitive behavior, psychophysiological and interpersonal changes or disorders. Here, using static and task-state MRI data, we present a comprehensive study of abnormal neural activity in patients with depression through spatiotemporal, static, and dynamic measures, demonstrating its validity as an underlying biological trait. In order to effectively study the role of emotion regulation in depression, a brain dynamic network synthesis method based on support vector machine model and community detection algorithm was established. We selected data on the mental state of 45 patients from a hospital’s psychiatric disease control center. They had no history of hearing impairment and normal (or corrected) vision. All procedures are agreed in writing by each participant. The results show that this method can effectively reduce the depression degree of the subjects, and the multi-level features of the integration of task activation and task regulation connection reach 81% ( < 0.0010, surrogate test) and 83% (<0.0016, surrogate test), respectively. The recovery of its depressive psychological state has a significant impact. Numerous studies have used various forms of emotional stimuli to reveal abnormal behaviors and neural responses in multi-channel emotional processing in patients with depression, providing valuable insights into the mechanism of multi-channel emotion regulation in depression.Journal of Combinatorial Mathematics and Combinatorial Computing250Using static and task-state MRI data, a comprehensive study of abnormal neural activity in patients with depression through spatiotemporal, static, and dynamic measures is presented, demonstrating its validity as an underlying biological trait.Journal of Combinatorial Mathematics and Combinatorial Computing['Lin Bian', 'Dachao Liu']2023-12-31T00:00:00https://www.semanticscholar.org/paper/fe8a9f33b1de9c0430ded625456fc81ee82f7708 26894ced34d15e508d335511f22a06857cee1b3fAbstract. This study focuses on evaluating the effectiveness of state regulation in initiating innovative projects under limited funding. The primary emphasis is on examining the mechanisms employed by the government to stimulate innovative activities among enterprises and foster the development of innovative ideas. The analysis encompasses the legal and financial landscape surrounding innovative projects, evaluating the impact of government programs and instruments on their initiation and development. Specifically, the study delves into grant programs, tax benefits, state guarantees, and other financial support mechanisms. It examines instances of successful initiation of innovative projects under budget constraints and formulates recommendations for optimizing state support. The overarching goal of the study is to identify optimal strategies for ensuring sustainable growth in the innovation sector within the constraints of economic limitations. The study recognizes investment as a pivotal element for fostering innovation, emphasizing the critical role of the financial outcome relative to the input investment in determining the success of innovation-focused projects. While every innovative project inherently functions as an investment with the primary aim of generating profit, the study acknowledges the challenges posed by the non-systematic nature and limited effectiveness of state policies in innovation and investment within Ukraine. The inadequacies in the country's technological development, attributed to these challenges, result in a decline in the number of innovatively active enterprises and a slowdown in the progress of high-tech industries. Consequently, this hampers the competitiveness of the national economy. The study seeks to address these issues and pave the way for more effective and strategic state support in fostering innovation within the constraints of limited resources. Problem Statement. Countries with developed economies highlight the vital role of investment support in fostering innovation, enhancing competitiveness, and expanding technological capabilities. However, Ukraine faces a significant challenge as there is inadequate attention given to the establishment and execution of investment programs that support innovative development. This stems from the inconsistent implementation and low effectiveness of the state's innovation and investment policies, resulting in technological stagnation. Consequently, there is a decline in the number of innovatively active enterprises, and the growth of high-tech industries is hampered, directly impacting the national economy's competitiveness. Review of Last Research. Several researchers have made noteworthy contributions to the exploration of enterprise innovation. Scholars such as V.M. Hrynyova, Kozyreva O.V., Chikarenko I., Lopatynskyi Yu.M., L. D. Vodyanka, T. M. Vitrenko-Khrustalova, among others, have significantly contributed to this field. Their work has provided valuable insights into various aspects of innovative activities within enterprises. Main Research Material. The efficacy of state innovation policies is executed through the strategic deployment of methods and tools by state administration bodies, shaping the landscape of investment and innovation. Investing in innovation is a pivotal strategy for establishing a long-term internal market for both consumer and industrial goods. While traditionally utilized funds to stimulate consumer demand have often led to losses in investment potential, directing resources towards innovative projects signifies a deliberate shift with potential repercussions on consumption, production, and ongoing investments–particularly crucial during economic stagnation. In response to Russia's military aggression and the imposition of martial law in Ukraine, legislative efforts, such as the "On State Support of Investment Projects with Significant Investments in Ukraine" law, have been enacted to revive the economy and assist local entrepreneurs. This law targets the stimulation of investments, enhancement of investment attractiveness, and the overall development of regions. Industrial parks emerge as a strategic tool for investment attraction, offering potential relocation sites for companies seeking to diversify supply chains. These parks effectively address the Time-to-Market challenge, considering the time required for optimal location identification, construction commencement, and facility operation. The "On Industrial Parks" law in Ukraine streamlines the process for land plot lease or ownership within industrial parks, ensuring durability and stability in economic relations within these zones. State incentives, funded from state and local budgets or other legitimate sources, are also incorporated. Accompanying investors through local and central authorities, specialized institutions, and organizations further supports the development of industrial parks. Despite industry variations, investments may exhibit less impact if the potential return is perceived as sufficiently high to offset associated risks. Notably, conflict-affected countries experience a 50% higher return on investment compared to low-income countries, showcasing the relative stability of investments in the primary sector. Beyond military conflict and ethnic tensions, political risks hinge on government stability, policy predictability, and the likelihood of fulfilling commitments to investors. Addressing these challenges is crucial for creating a conducive environment for foreign investments. Summary. Previous instances of foreign investment in Ukraine were essentially a rechanneling of local and Russian capital, underscoring that the fundamental obstacles impeding genuine foreign investment existed prior to the conflict. These persisting challenges, as highlighted by the Organization for Economic Co-operation and Development, encompass enduring issues such as a subpar business environment, weak institutional frameworks, and pervasive corruption, requiring sustained attention despite the escalation in political and security concerns. Beyond the context of military conflict or ethnic tensions, the landscape of political risks hinges on governmental stability, the predictability of policies, and the likelihood of fulfilling commitments to investors. Consequently, the primary hurdles lie not merely in creating specific conditions for foreign investments but in addressing the underlying and persistent challenges that have historically hindered a more substantial influx of genuine foreign investment into Ukraine.Democratic governance00Democratic governance['Serhiy Shevchenko']2023-12-31T00:00:00https://www.semanticscholar.org/paper/26894ced34d15e508d335511f22a06857cee1b3f 061db0bfacafe7d3f41cc692522619a48d539a2eInnovation is the source of capitalist accumulation. Schumpeter termed this process “Creative Destruction” and related it to entrepreneurial activities and to the functioning of the monetary and financial system. Recent research on economic development has also placed the emphasis on financial innovations and financial markets. The financial liberalisation era of the 1990s and 2000s is regarded as a growth period and a positive relationship is assumed between financial innovations and entrepreneurial innovations. However, financial innovations provoked an economy-wide financialisation, reduced the share of real entrepreneurial activities and generated systemic crises. The subsequent turmoil hampered the access of innovative projects to stable financial resources. This article maintains that in order to set the economy on a re-industrialisation path, an alternative organisation of financial markets is required. I then suggest some directions for possible relevant recovery policies and argue that an alternative re-industrialisation/de-financialisation process calls for a specific public regulation that should seek to lead financial institutions to finance sustainable innovative activities in order to ensure global recovery and prevent systemic catastrophes.JEL Codes: G18, G20, O31, O38Journal of Innovation Economics & Management332Journal of Innovation Economics['Faruk Ülgen']2023-12-31T00:00:00https://www.semanticscholar.org/paper/061db0bfacafe7d3f41cc692522619a48d539a2e bdc1eed5e664200fab1ef7607bec86bdb8e77a2aFrance's Hate Speech on the Internet Act introduced different regulatory mechanisms depending on the nature of the information, and provided for imprisonment or fines for information intermediary service providers who violated their obligations under the Hate Speech on the Internet Act. In an Ex ante review of the France's Hate Speech on the Internet Act, the French Constitutional Court found that the main provisions of the law violated the right to freedom of expression under the French Constitution. First, the French Constitutional Court found that the restrictions on terrorist or child pornography content violated the right to freedom of expression and communication because the illegality of the content in question was based solely on the judgment of the administrative authority, and because the restrictions could lead to the removal of legitimate content by targeting controversial content. In addition, the French Constitutional Court noted that the law's procedure for identifying illegal information is based solely on reports from internet users, and that internet platform operators face the difficult problem of assessing the content reported against the full range of offenses provided for in the law against hate speech on the internet, In particular, in the case of media crimes, the context of the content in question must be taken into account, and the 24 hours allotted for reviewing the reported content is too short, resulting in internet platform operators choosing to remove the reported content, which violates the right to freedom of expression and communication. In the context of recent national legislation to combat hate speech and illegal information on the Internet, the French Constitutional Court's decision and the process of enacting the French Law Against Hate Speech on the Internet may be of interest.Korean Association of International Association of Constitutional Law00Korean Association of International Association of Constitutional Law['Dong Hoon Han']2023-12-31T00:00:00https://www.semanticscholar.org/paper/bdc1eed5e664200fab1ef7607bec86bdb8e77a2a e0684e1c147fd9e09d15d5f1f091065e65a29497This research focuses on the transformational potential of artificial intelligence (AI) towards the field of pavement engineering, particularly considering underdeveloped nations. This exploratory study aims to shed light on the complex effects AI has on pavement engineering and management processes, as well as various strategic, policy, and ethical perspectives underpinning its implementation. By using a systematic process, the study builds on information from academic papers, case studies, and industry research to create more enriching knowledge on this topic. Through a robust study, it is shown that AI helps improve the composition of pavement materials building and maintenance routines. Such a study shows that AI can improve the level of economic benefits in the pavement infrastructure of underdeveloped countries. These key findings emphasize how AI reduces the costs of design, predictive maintenance models, and sustainable materials use. In addition, the study traverses through the difficult routes of policy and institutional frameworks, stating that the policies ought to be responsive while collaboration is required in promoting AI uptake. The discussion integrates ethics and the environment very carefully, as required by sustainability and equity. Lastly, the research presents an excellent argument for AI as an agent for constructing new and eco-friendly transportation infrastructure in poor countries. These countries’ recommended policies should be flexible, as they promote capacity building and ethics in AI with regard to infrastructure development as part of their developmental paths, which are simultaneously technical and social. Keywords: Artificial Intelligence, Pavement Engineering, Developing Nations, Sustainable Infrastructure, Ethical AI, TransportationEngineering Science &amp; Technology Journal05Through a robust study, it is shown that AI helps improve the composition of pavement materials building and maintenance routines and shows that AI can improve the level of economic benefits in the pavement infrastructure of underdeveloped countries.Engineering Science &amp; Technology Journal['Eche Samuel Okem', 'Emmanuel Adikwu Ukpoju', 'Abayomi B. David', 'Joy Otibhor Olurin']2023-12-31T00:00:00https://www.semanticscholar.org/paper/e0684e1c147fd9e09d15d5f1f091065e65a29497 b9d9db794184d0ee93f2452391c2a9dab52fb06cThis quantitative study investigated the mediating effect of AI trust on the relationship between AI self-efficacy and attitude toward AI of college students in Region XI, Philippines. Using adapted questionnaires, the data were gathered online via Google Forms, where the respondents were selected using stratified random sampling. Validity and reliability tests were employed on the measurement model, descriptive statistics were also used to describe the constructs in the study, while mediation analysis using the standard algorithm-bootstrapping of SmartPLS 4.0 was performed to assess the hypothesized mediation model. The findings revealed that the constructs of the study are valid and reliable. Moreover, college students also demonstrated moderate levels of AI trust and attitude toward AI and a high level of AI self-efficacy. Finally, the mediation analysis suggests that AI trust is deemed to have a substantial mediating effect on the relationship between AI self-efficacy and attitude toward AI of college students.International Journal of Metaverse763The mediation analysis suggests that AI trust is deemed to have a substantial mediating effect on the relationship between AI self-efficacy and attitude toward AI of college students.International Journal of Metaverse['B. N. Obenza', 'Jasper Simon Ian E Baguio', 'Karyl Maxine W Bardago', 'Lemuel B Granado', 'Kelvin Carl A Loreco', 'Levron P Matugas', 'Darcy John Talaboc', 'Rolemir Kirk Don D Zayas', 'John Harry Senoy Caballo', 'Ria Bianca R Caangay']2023-12-31T00:00:00https://www.semanticscholar.org/paper/b9d9db794184d0ee93f2452391c2a9dab52fb06c 3d5da962ac06dfd4d4e8bb23a03df2e11c585a58The financial landscape is undergoing a profound transformation, with Artificial Intelligence (AI) emerging as the catalyst for unprecedented change in banking institutions. This paper briefly examines the multifaceted impact of AI across critical domains, encompassing customer experiences, security protocols, risk management, operational efficiency, return on investment, and regulatory compliance. In customer-centric evolution, AI-driven chatbots and virtual assistants redefine interactions, providing personalized and anticipatory services. In the field of finance, security is a paramount concern. At the same time, we are witnessing AI as the guardian, fortifying trust through real-time fraud detection and biometric authentication. Risk management undergoes a paradigm shift, with predictive analytics steering financial institutions toward strategic advantages in navigating dynamic markets. Operational excellence is achieved through AI's automation, liberating human resources for strategic endeavors, and fostering a costconscious ethos. Regulatory compliance, often intricate, finds harmony through AI-driven automation tools and biometric authentication, ensuring transparency and accountability in the face of relentless regulatory scrutiny. Striking the right balance between innovation and ethical considerations is pivotal in ensuring that the consolidation of AI and finance is a force for good.Journal of Engineering and Applied Sciences Technology02The multifaceted impact of AI across critical domains, encompassing customer experiences, security protocols, risk management, operational efficiency, return on investment, and regulatory compliance, is examined.Journal of Engineering and Applied Sciences Technology['Farhang Mossavar Rahmani']2023-12-31T00:00:00https://www.semanticscholar.org/paper/3d5da962ac06dfd4d4e8bb23a03df2e11c585a58 bd6fa14e6d60bf0afd95da3cf389a1dd9b30c826The Journal of Korea Open Association for Early Childhood Education00The Journal of Korea Open Association for Early Childhood Education['Jae-Eun Lee', 'S. Oh']2023-12-31T00:00:00https://www.semanticscholar.org/paper/bd6fa14e6d60bf0afd95da3cf389a1dd9b30c826 ca99e0e8a8f8e75832c1ede68c7b9b31ce59d1d3Combinatorial mathematics is a versatile field that can provide valuable insights and techniques in various aspects of artificial intelligence and educational research. We focus our attention on the exploration of the mechanism of the role of teachers’ emotional labor In this paper, we merge two parts of data, predicted and formally administered, based on the optimization and management of artificial intelligence English teachers’ emotional labor for the corresponding statistical analysis. Yes individual college English teachers are working for non-interpersonal issues for emotional regulation, temporarily restraining anger and cursing impulses, and communicating with students in a pleasant manner. In the case study of this paper, a teacher repeatedly failed in teaching, but he restrained his frustration and continued to work hard, and finally finished.Journal of Combinatorial Mathematics and Combinatorial Computing285This paper merges two parts of data, predicted and formally administered, based on the optimization and management of artificial intelligence English teachers’ emotional labor for the corresponding statistical analysis.Journal of Combinatorial Mathematics and Combinatorial Computing['Qin Guo']2023-12-31T00:00:00https://www.semanticscholar.org/paper/ca99e0e8a8f8e75832c1ede68c7b9b31ce59d1d3 dc2f128a3f87c67ea33d119389bbe3dd0bb1e962Background: Artificial Intelligence (AI) is seen as the machine that replaces human labour to work for men with a more effective and speedier result. There is a paucity of data on the knowledge and perception of healthcare workers regarding AI technology. This study aims to assess the knowledge and perception of healthcare workers towards the application of AI in healthcare services in Nigeria. Materials and methods: Cross-sectional questionnaire-based survey designed was used to achieve the aim of this study. Both electronic (Google form) and hardcopy version of the questionnaire were distributed to healthcare workers in Nigeria and their responses were retrieved and statistically analyzed. Results: Out of 263 respondents, most 51.3% (n=135) were females. Greater percentage 25.5% (n=67) of the respondents were radiographers, followed by medical consultants 14.8% (n=39) and the least 1.5 %(n=4) were pharmacists. Greater proportion 61 %(n=160) of the respondents has the opinion that AI can be incorporated into all medical specialties. Out of 263 respondents, 51.7% (n=136) had good knowledge of AI and the least 6.4% (n=16) had very poor knowledge of AI. Greater proportion 78.7% (n=207) of the respondents, agreed that AI can help to reduce the number of medical errors. Majority 29.3% (n=77) of the respondents agreed that human specialists will be replaced by AI in the near future. A large proportion 40.3% (n=106) of the respondents agreed that some employers may prefer AI to human specialists because AI has no emotional exhaustion or physical limitation. Conclusion: The respondents in this study showed good knowledge of both the medical areas of applications of AI as well as the benefits of AI application in healthcare services. However, most of the respondents were afraid that their jobs would be taken over by AI in the near future.AG Salud1620The respondents in this study showed good knowledge of both the medical areas of applications of AI as well as the benefits of AI application in healthcare services, however, most of the respondents were afraid that their jobs would be taken over by AI in the near future.AG Salud['Michael Promise Ogolodom', 'Anna Daniel Ochong', 'Egop Brownson Egop', 'Catherine Ugwem Jeremiah', 'Anelechi Kenneth Madume', 'Clement U. Nyenke', 'Musa Y. Dambele', 'Dlama Zira Joseph', 'Abdul Fatai K. Bakre', 'Elizabeth O. Balogun', 'N. Alazigha', 'Marki .C.Okej', 'Kenneth .S.Ordu', 'Hyacienth Uche Chiegwu', 'Joy Johnson', 'Awajimijan Nathaniel Mbaba', 'Victor Kelechi Nwodo']2023-12-31T00:00:00https://www.semanticscholar.org/paper/dc2f128a3f87c67ea33d119389bbe3dd0bb1e962 f58a974c3b47699058dd75c38ade5539305553f3As healthcare landscapes evolve, Artificial intelligence (AI) has emerged as a transformative force in physiotherapy research in India. The integration of machine learning algorithms, computer vision, and natural language processing has significantly advanced the analysis of patient data, enabling the prediction of treatment outcomes and personalization of physiotherapy interventions. This overview delves into specific examples of successful AI integration in ongoing clinical trials within the Indian context, showcasing notable improvements in trial efficiency and positive impacts on patient outcomes. Challenges in implementing AI, including data security, ethical considerations, and the need for specialized training, are discussed. Proposed solutions encompass robust data encryption, ethical guidelines, interpretability of AI models, and targeted educational programs for healthcare professionals. Looking forward, the future outlook emphasizes personalized treatment plans, expanded tele physiotherapy using wearable technology, and the integration of augmented and virtual reality. Ethical and regulatory frameworks, continued advancements in robotic assistance, and interdisciplinary collaboration are highlighted as key factors shaping the trajectory of AI in physiotherapy clinical trials in India. The primary objectives of this manuscript are to explore the current state of AI in physiotherapy clinical trials in India, assess its utilization, and discuss the potential future developments in the field.Interdisciplinary Rehabilitation / Rehabilitacion Interdisciplinaria2012The primary objectives of this manuscript are to explore the current state of AI in physiotherapy clinical trials in India, assess its utilization, and discuss the potential future developments in the field.Interdisciplinary Rehabilitation / Rehabilitacion Interdisciplinaria['Mohammad Sidiq', 'A. Chahal', 'Sachin Gupta', 'Krishna Reddy Vajrala']2023-12-31T00:00:00https://www.semanticscholar.org/paper/f58a974c3b47699058dd75c38ade5539305553f3 d1d06434cb1b5f85a4af15b167846e5a9c0203ecFor quite some time, however, the interest in artificial intelligence (AI) has became very high. Generative AI being a recent development signifies another era with profound empirical queries related to work and human being. There is a need to understand how generative AI affects work, and it requires some knowledge. Though there are limited studies conducted about it, this article addresses the possibility that, through this technology, workers may face potential risks at work. The paper explores major risks associated with this technology by undertaking a literature review from credible sources such as academic journals, articles and news articles. This paper offers crucial guidance concerning the right steps organisations can take toward efficiently integrating generative AI in their organisation operations through an examination of the inherent risks involved.Asian Journal of Research in Education and Social Sciences08This paper offers crucial guidance concerning the right steps organisations can take toward efficiently integrating generative AI in their organisation operations through an examination of the inherent risks involved.Asian Journal of Research in Education and Social Sciences[]2023-12-31T00:00:00https://www.semanticscholar.org/paper/d1d06434cb1b5f85a4af15b167846e5a9c0203ec 45cdc6205a088ad1c5b5907eb929b1458957baa9The topic revolves around the integration of Artificial Intelligence (AI) in Hospital Integrated Risk Management (IRM). AI offers significant advantages in enhancing risk identification, assessment, and mitigation across various areas of hospital operations. It can contribute to patient safety by enabling early detection of critical conditions, improving clinical risk management, and enhancing decisionmaking processes. AI also plays a vital role in information security and privacy, operational risk management, regulatory compliance, and human resources in hospitals. However, the use of AI in Hospital IRM comes with certain disadvantages and risks that need to be mitigated. These include data quality and bias, interpretability and transparency challenges, privacy and security concerns, reduced human oversight, ethical considerations, and implementation challenges. Mitigating these risks requires robust data governance, addressing bias in AI algorithms, ensuring transparency and accountability, implementing strong cybersecurity measures, and upholding ethical guidelines. To achieve successful implementation, hospitals should prioritize employee competencies, such as domain knowledge, data literacy, AI and data science skills, critical thinking, collaboration, adaptability, and ethical awareness. By developing these competencies and adhering to best practices, hospitals can optimize the use of AI in IRM, improve patient outcomes, enhance operational efficiency, and mitigate risks effectively.Journal of AI63Hospitals should prioritize employee competencies, such as domain knowledge, data literacy, AI and data science skills, critical thinking, collaboration, adaptability, and ethical awareness, to optimize the use of AI in IRM, improve patient outcomes, enhance operational efficiency, and mitigate risks effectively.Journal of AI['Velibor Boži̇ć']2023-12-31T00:00:00https://www.semanticscholar.org/paper/45cdc6205a088ad1c5b5907eb929b1458957baa9 440ea5b79e1f37f5d9d291d22736fd2a7a207323The advent of Industry 4.0 has brought about a revolution in the Industrial Internet of Things (IIoT) driving digital transformation, which now includes data analytics, cloud computing, artificial intelligence, and mobile connectivity. This paper delves into the effectiveness of existing privacy protection measures, as well as the challenges and opportunities presented by emerging technologies such as unified encryption and machine learning. Additionally, the paper provides insights into the processes required for industry-specific compliance with relevant laws and regulations. The findings emphasize the crucial role privacy plays in AI applications for IIoT systems and shed light on the strategies, obstacles, and prospects that organizations must navigate in this rapidly evolving landscape.International Journal on Recent and Innovation Trends in Computing and Communication01The effectiveness of existing privacy protection measures, as well as the challenges and opportunities presented by emerging technologies such as unified encryption and machine learning are delved into.International Journal on Recent and Innovation Trends in Computing and Communication['Et al. Saurabh Suman Choudhuri']2023-12-31T00:00:00https://www.semanticscholar.org/paper/440ea5b79e1f37f5d9d291d22736fd2a7a207323 c587d0584c463af369bd7671dda0a2017f7cdd8fAddressing the multidimensional nature of Artificial Intelligence assurance, this thorough survey is dedicated to elaborating on various aspects of ensuring the reliability and safety of computerized systems. It steers through the turbulent seas of model enervates, unmodelled phenomena, and security menaces to give an elaborate lit review. The review touches upon the boisterous ways of addressing these intricate mitigation strategies for model errors used in the past, the challenges of under-specification with modern ML models, and how understanding uncertainty is crucial. In addition, it evaluates the AI system’s security basis, the emerging Adversary Machine Learning field, and its processes necessary for testing and evaluation of weaker adversarial case studies. The review of literature also looks upon the situation of DoD context, how the terrain surrounding developmental and operational testing is altering with all these shifts in culture that must be implemented if not to implement robust but secure AI implementation.International Journal on Recent and Innovation Trends in Computing and Communication01This thorough survey is dedicated to elaborating on various aspects of ensuring the reliability and safety of computerized systems, and touches upon the boisterous ways of addressing these intricate mitigation strategies for model errors used in the past.International Journal on Recent and Innovation Trends in Computing and Communication['Et al. Saurabh Suman Choudhuri']2023-12-31T00:00:00https://www.semanticscholar.org/paper/c587d0584c463af369bd7671dda0a2017f7cdd8f e4679e41311a1a24110fa82bf19f4af7683a1e6cAs artificial intelligence (AI) continues its rapid and relentless progression, the necessity for a comprehensive AI education has become increasingly evident. While South Korea has initiated various policies related to AI education, recent research has underscored the potential adverse repercussions of current instructional approaches on learners. In response to this pressing concern, the present study delves into integrating design thinking principles into AI education and meticulously assesses its impact on learning outcomes. To achieve this objective, we seamlessly amalgamated design thinking principles with AI problem-solving techniques, developing a tailor-made AI education curriculum explicitly crafted for middle school students. Subsequently, this innovative curriculum was implemented among middle school students, and their Computational Thinking (CT) competence was rigorously evaluated. The findings unequivocally establish that the infusion of design thinking into AI education significantly augmented the CT skills of the participating students. In comparison to the control group, it was discerned that middle school students who underwent AI education integrated with design thinking exhibited a statistically substantial enhancement in their Computational Thinking (CT) proficiencies. This study furnishes compelling empirical evidence that unequivocally endorses design thinking as a potent instructional approach within the domain of AI education, particularly for middle school students. Furthermore, it underscores the necessity of embracing innovative pedagogical methodologies in AI education to equip the younger generation with the indispensable skills to adeptly navigate the perpetually evolving landscape of an AI-driven future.International Journal on Advanced Science, Engineering and Information Technology02This study furnishes compelling empirical evidence that unequivocally endorses design thinking as a potent instructional approach within the domain of AI education, particularly for middle school students.International Journal on Advanced Science, Engineering and Information Technology['Seong-Won Kim', 'HakNeung Go', 'Seung-Ju Hong', 'Youngjun Lee']2023-12-31T00:00:00https://www.semanticscholar.org/paper/e4679e41311a1a24110fa82bf19f4af7683a1e6c aa29d7a11031be4f5d3ab5b0456994dff0723e61Artificial intelligence in military operations comes in two kinds. First, there is narrow or specific intelligence – the autonomous ability to identify an instance of a species of target, and to track its changes of position. Second, there is broad or general intelligence – the autonomous ability to choose a species of target, identify instances, track their movements, decide when to strike them, learn from errors, and improve initial choices. These two kinds of artificial intelligence raise ethical questions mainly because of two features: the physical distance they put between the human agents deploying them and their targets, and their ability to act independently of those agents. The main ethical questions these features raise are three. First, how to maintain the traditional martial virtues of fortitude and chivalry while operating lethal weapons at a safe distance? Second, how much autonomy to grant a machine? And third, what risks to take with the possibility of technical error? This paper considers each of these questions in turn.Conatus12Conatus['Nigel Biggar']2023-12-31T00:00:00https://www.semanticscholar.org/paper/aa29d7a11031be4f5d3ab5b0456994dff0723e61 c4f4bddbcfb9ed00fd58df604d64a2c9e2f4d570The purpose of this research is to adapt the Artificial Intelligence Literacy Scale (AILS) developed by Wang et al. (2022) into Turkish and study its validity and reliability. The scale aims to measure the artificial intelligence literacy levels of non-expert adults. The research data were gathered from 402 participants, and the researchers did Confirmatory Factor Analysis (CFA) to test the validity of the adapted scale, and to test the reliability, they adopted Cronbach’s alpha technique. The adapted scale consists of 12 items and 4 factors, as is the case in the original version. CFA results indicate that X^2/df =1.82, RMSEA = 0.04, RMR = 0.03, NFI = 0.95, CFI = 0.98, GFI = 0.96 and AGFI = 0.94. Considering CFA results, it is concluded that the adapted scale is a good fit. As for reliability, as far as the factors are concerned, the internal consistency results are 0.72, 0.74, 0.76, and 0.72, respectively. Additionally, α=0.85 for the whole scale. Consideringly, the scale and its factors are adequately reliable, and the adapted scale can be used in Turkish culture.Öğretim Teknolojisi ve Hayat Boyu Öğrenme Dergisi - Instructional Technology and Lifelong Learning01The adapted scale and its factors are adequately reliable, and the adapted scale can be used in Turkish culture, and it is concluded that the adapted scale is a good fit.Öğretim Teknolojisi ve Hayat Boyu Öğrenme Dergisi - Instructional Technology and Lifelong Learning['C. Çelebi', 'Fatih Yılmaz', 'Ugur Demir', 'Ferhat Karakuş']2023-12-31T00:00:00https://www.semanticscholar.org/paper/c4f4bddbcfb9ed00fd58df604d64a2c9e2f4d570 d802bd28932913a191b7421f0be1478f873d2d59This research paper explores the varied role of Artificial Intelligence in the teaching-learning process in the Indian context, focusing on its potential as an empowering tool in the hands of both teachers as well as students. AI can alleviate administrative and repetitive burden for teachers, allowing them to focus on face-to-face interactions with students. It can be used by a teacher in a variety of ways including personalised learning pathways, curriculum delivery, managing classroom dynamics and assessment design. Policymakers can gain data-driven decision-making capabilities. Students can also benefit from this new technology by experiencing a more individualised and interactive learning. Besides, there is a need for a comprehensive approach to address social and ethical aspects associated with AI. Controversies surrounding AI are discussed and optimum ways for the integration of AI into education are suggested. Instead of viewing it as a threat, the paper advocates for its responsible use and recognises it as a collaborative teaching and learning tool. AI can thus be used as a transformative catalyst to facilitate pedagogy and learning in India.JOURNAL GLOBAL VALUES01JOURNAL GLOBAL VALUES['Umesh Bansal']2023-12-31T00:00:00https://www.semanticscholar.org/paper/d802bd28932913a191b7421f0be1478f873d2d59 06be61bb77d5c15231fdf0627dbe71157f926e8aThis research paper delves into the dynamic realm of Explainable Artificial Intelligence (XAI), scrutinizing its advantages and limitations. XAI emerges as a pivotal facet in the evolution of artificial intelligence (AI) systems, emphasizing transparency to render AI systems comprehensible to humans. The primary objective of XAI is to illuminate the decision-making processes of complex AI models, offering insights into their reasoning mechanisms. Through heightened transparency, XAI aims to enhance human comprehension, instill trust in AI outcomes, and ultimately foster accountability, ethical adherence, and user confidence in AI systems. This paper presents a comprehensive analysis of the benefits of XAI, explores its constraints concerning individual privacy, and discusses the future perspectives of this rapidly evolving field.International Journal of Advanced Research01A comprehensive analysis of the benefits of XAI is presented, its constraints concerning individual privacy are explored, and the future perspectives of this rapidly evolving field are discussed.International Journal of Advanced Research['Abhinav Agarwal']2023-12-31T00:00:00https://www.semanticscholar.org/paper/06be61bb77d5c15231fdf0627dbe71157f926e8a 21d65e5ebcc52acae4e76ac03801e4e4a8918d76This article delves into the rich tapestry of existing literature surrounding the intersection of Artificial Intelligence (AI) and Interreligious Dialogue (ID). Through a careful analysis of available scholarly works, the paper endeavors to shed light on the profound influence that AI exerts on discussions among individuals representing diverse religious backgrounds. By examining the dynamic interplay between AI and ID, the study seeks to unravel the intricate ways in which technology shapes and continues to shape conversations within this multifaceted context. The primary objective is to deepen our understanding of the opportunities and challenges that unfold at the crossroads of AI and ID. This exploration is poised to contribute valuable insights that extend beyond theoretical frameworks, providing practical implications for scholars, practitioners, and policymakers alike. In essence, the article aims to serve as a compass, navigating the complex terrain where AI and interreligious discourse converge. As the study unfolds, it specifically aims to identify distinct domains where AI can be harnessed to play a constructive role in fostering and facilitating interreligious dialogue. By elucidating the potential contributions of AI in this realm, the article strives to offer a forward-looking perspective that transcends the current situation. Ultimately, this endeavor not only maps the existing landscape of AI in interreligious dialogue but also charts a course for future research, exploration, and application in this evolving and significant field. Keywords: artificial intelligence, interreligious dialogue, ethical considerations, religious diversity, technologyReligion and Social Communication501The paper endeavors to shed light on the profound influence that AI exerts on discussions among individuals representing diverse religious backgrounds, and identifies distinct domains where AI can be harnessed to play a constructive role in fostering and facilitating interreligious dialogue.Religion and Social Communication['Rico C. Jacoba']2023-12-31T00:00:00https://www.semanticscholar.org/paper/21d65e5ebcc52acae4e76ac03801e4e4a8918d76 47f3eaa14fe51faa2321bfa387b48dd1db06abebThe problem of crime in Indonesia is an urgent issue, with crime rates continuing to increase. High crime rates have serious impacts on societal security, social stability, and economic development. Amidst the complexity of types of crime, motives and methods of handling them, Artificial Intelligence (AI) and Machine Learning (ML) technology has emerged as a promising solution. Through analysis of a literature review with the keywords "AI and crime," this research aims to understand the differences between the use of AI in crime prediction and traditional methods. The literature review method will identify and analyze the latest knowledge regarding the use of AI technology in overcoming crime problems. The use of AI in analyzing crime data, identifying complex patterns, and providing accurate predictions will be emphasized. The research will also explain how AI is able to overcome problems that are difficult to solve with conventional methods. It is hoped that the results of this literature review will provide deeper insight into the potential of AI in reducing crime rates and c reating a safer environment for people in Indonesia.Journal of Computer Networks, Architecture and High Performance Computing140This research aims to understand the differences between the use of AI in crime prediction and traditional methods, and explain how AI is able to overcome problems that are difficult to solve with conventional methods.Journal of Computer Networks, Architecture and High Performance Computing['Joan Stacia Carissa', 'Mardi Turnip']2023-12-31T00:00:00https://www.semanticscholar.org/paper/47f3eaa14fe51faa2321bfa387b48dd1db06abeb 3437d068da100b0f268a19f5564709144443fd2et was in 1956 that the concept of artificial intelligence came into existence. John McCarthy and Marvin Minsky hosted the Dartmouth Summer Research Project, and here they coined the word “artificial intelligence.” The workshop laid the foundation for the field of artificial intelligence. Back to the current timeline, few studies believe that artificial intelligence will contribute 15.7 trillion dollars to the world economy by the year 2030. A branch of computer science that commands machines to simulate human intelligence, artificial intelligence is bringing a paradigm shift in each sector of society. The smart machines working on the concept of deep learning, are capable of performing tasks and making decisions that only a human mind can. These software systems operate in such a manner that they adapt to the situation. Artificial intelligence has the potential to make society even better by bringing positive changes in healthcare, education, governance, policy formulation and implementation, technology, resource management, innovation, etc. Artificial intelligence holds the potential to make human lives better in thousands of ways. Today, if we look around, we are already using AI in our daily lives in some way or another. Artificial intelligence technology in smartphones has grown rapidly in recent years. Google Assistant, Siri, and Alexa are a few examples of AI personal assistants that make our daily tasks easier. Not only do these solution-driven technologies follow commands, but they also learn from experience to provide better services. Today, artificial intelligence is reshaping and redefining industries. It is bringing about a revolution in technology and impacting the global economy. Artificial intelligence can solve complex problems in a limited amount of time and with enhanced accuracy.JOURNAL GLOBAL VALUES00It was in 1956 that John McCarthy and Marvin Minsky hosted the Dartmouth Summer Research Project, and here they coined the word “artificial intelligence,” which laid the foundation for the field of artificial intelligence.JOURNAL GLOBAL VALUES['Dr. Reena Gupta', 'Vivek Kumar']2023-12-31T00:00:00https://www.semanticscholar.org/paper/3437d068da100b0f268a19f5564709144443fd2e 030d76545fcfa39f9c95c5de3b2fda30209860e5Objective : In this study, a systematic literature review was conducted to analyze research on the application of artificial intelligence in the field of occupational therapy. Through this, we aim to examine the current status and potential of artificial intelligence applications in occupational therapy and provide foundational data for the future utilization and research directions of artificial intelligence in this field. Methods : This study targeted domestic literature from January 2018 to October 2023. The databases utilized for literature search included the Korean Studies Information Service (KISS), the Research Information Service System (RISS), and the Nurimedia (DBpia). Out of a total of 67 papers obtained from the search results, 57 papers were excluded based on exclusion criteria, and a final selection of 10 papers was made Results : The analysis of the selected 10 papers in this study revealed that there were 3 (30.0%) randomized experimental studies, 1 (10.0%) non-randomized two-group experimental study, 5 (50.0%) survey studies, and 1 (10.0%) qualitative study. Out of all the studies, robot-related research accounted for 7 (70%) of the total, while digital therapeutic interventions, cognitive rehabilitation, and measurement technologies each constituted 1 paper Conclusion : Although artificial intelligence has been applied in the field of occupational therapy, particularly centered around robots and computerized cognitive rehabilitation systems, there has been a limited number of studies focusing on the practical application of artificial intelligence. Future research should focus on conducting studies that explore the therapeutic application of artificial intelligence using deep learning or machine learning. It is crucial to conduct high-quality research with a systematic research design to ensure the practical and effective implementation of artificial intelligence in the field.Korean Aging-Frendly Industry Association00The current status and potential of artificial intelligence applications in occupational therapy is examined to provide foundational data for the future utilization and research directions of artificial intelligence in this field and ensure the practical and effective implementation of artificial intelligence in the field.Korean Aging-Frendly Industry Association['Joong-Il Shin', 'Mi-Lim Cho']2023-12-31T00:00:00https://www.semanticscholar.org/paper/030d76545fcfa39f9c95c5de3b2fda30209860e5 bc4633fbcf1ca579721a6e043b7593d43e201dffThere is considerable potential between the field of artificial intelligence (AI) and cognitive ergonomics. The purpose of this thesis is to support increasing attention to the synergy between artificial intelligence and cognitive ergonomics, as well as the urgent need to explore it in research and practice.Artificial intelligence (AI) has become an integral part of the modern workplace, transforming industries, increasing productivity, and streamlining processes. However, as AI technologies continue to evolve, it is essential to consider the human side of this digital transformation. Ergonomics, the science of designing workspaces and tools that fit the people who use them, is a critical element in ensuring that the integration of artificial intelligence into the workplace increases employee well-being, efficiency, and overall success. With its rapid advancements, artificial intelligence is revolutionizing various fields and industries, from healthcare to self-driving vehicles and financial services. While the potential benefits are obvious, the ethical, cognitive, and ergonomic implications are just as important. Cognitive ergonomics is a sub-branch of ergonomics that deals with the design and evaluation of systems and technologies that support human cognitive processes such as perception, attention, memory, and decision-making. The field of cognitive ergonomics, which primarily focuses on Optimizing human-system interaction is the key to ensuring that artificial intelligence systems are designed and implemented in a way that aligns with human capacities, abilities, and cognitive limitations . Current AI implementations typically adopt a technology-driven focus, and employees are expected to adapt to the technology. In this technology-driven focus, the performance, performance, and accuracy of AI are optimized, but these aspects are considered separately. This point of view raises various critical considerations that are often neglected in the design and implementation of advanced technologies and sometimes have disastrous consequences. From the point of view of ergonomics, the design of artificial intelligence should be transferred from a technology-oriented focus to a systemic and human perspective. By applying a focus on personnel, artificial intelligence should be meaningfully and safely designed and integrated into work processes with the aim of optimizing overall system performance and people's well-being.Occupational Medicine00The purpose of this thesis is to support increasing attention to the synergy between artificial intelligence and cognitive ergonomics, as well as the urgent need to explore it in research and practice.Occupational Medicine['Masoud Rostami', 'Raziyeh Soltani', 'Vidasat Anooshe']2023-12-31T00:00:00https://www.semanticscholar.org/paper/bc4633fbcf1ca579721a6e043b7593d43e201dff de3bbb7799d50690a8ab5db1f116411d78543b66Abstract: Technological advancement made a tremendous change among the workers. In India majority of the workers were in unorganized or in the informal sector. While considering our traditional economic system, these workers were considered as the margin of the economy. But artificial intelligence (AI) have the possibility to take these workers to the mainstream of the economy. Even these changes happen but still some of the people were also under the disparity to access these technologies effectively. If they use artificial intelligence (AI) at its full potential these workers can increase their productivity to a great extent. Work engagement means a person’s emotional attachment to his work; because he believes that his action can make changes in the current scenario. A highly engaged worker is considered an asset to the organization. By utilizing the possibilities of AI the workers in the unorganized sector can also create new trends of jobs. Even though these changes happened, some of the workers were struggling to adapt to these changes. This study mainly focused on how these issues can be solved through work engagement, how to increase the number of workers, and how to get them into the mainstream of the economy. The study primarily focused on the relationship between work engagement and the adoption of AI technology in the unorganized sector.International Journal for Research in Applied Science and Engineering Technology00This study primarily focused on the relationship between work engagement and the adoption of AI technology in the unorganized sector and how to get these workers into the mainstream of the economy.International Journal for Research in Applied Science and Engineering Technology['Hefsiba Joseph M L']2023-12-31T00:00:00https://www.semanticscholar.org/paper/de3bbb7799d50690a8ab5db1f116411d78543b66 79efe8b1828b8491cb445cf721931c683b6c763dThe research aims to explore the potential of artificial intelligence (AI) in the context of Learning as a solution to the challenges in developing thinking skills. Through the use of AI technology, the study focuses on finding out the relationship between AI and students' ability to argue. The approach used in this research is quantitative with correlational methods. Research results show that AI in learning can be a significant catalyst for the development of critical and analytical thinking skills. Students engaged in learning environments using AI showed an improvement in problem-solving, information-analysis, and critical thinking skills so that their ability to argue improved. In addition, learning efficiency, and motivates the learners to reach their maximum potential. This research contributes to an understanding of how AI in education can have a positive impact on the development of argumentation skills. The practical implications of these findings can open the door to the development of more effective and personalized learning strategies in the future, creating a responsive and adaptive educational environment.Journal of Education, Religious, and Instructions (JoERI)120Research results show that AI in learning can be a significant catalyst for the development of critical and analytical thinking skills, and contributes to an understanding of how AI in education can have a positive impact on the development of argumentation skills.Journal of Education, Religious, and Instructions (JoERI)['Susanto', 'Ayu Andrianingsih', 'Komang Sutawan', 'Vike Aprilianin Marwintaria', 'Ria Astika']2023-12-31T00:00:00https://www.semanticscholar.org/paper/79efe8b1828b8491cb445cf721931c683b6c763d 37f2bd51b1e92a2507e7fc304bac0b8e0cab829dAbstrakMeningkatnya kompleksitas dunia menimbulkan tantangan yang signifikan bagi lingkungan binaan, dan arsitek memainkan peran penting dalam menerjemahkan kebutuhan masyarakat yang terus berkembang menjadi solusi yang berkelanjutan. Dengan isu-isu mendesak seperti krisis iklim, urbanisasi, dan kekurangan perumahan, industri arsitektur harus beradaptasi, berevolusi, dan berinovasi untuk memenuhi tuntutan tersebut. Data dan teknologi telah muncul sebagai alat yang ampuh dalam transformasi ini, hal ini memungkinkan arsitek untuk memberikan proyek berorientasi pengguna yang terintegrasi secara komprehensif dengan lingkungan mereka. Penelitian ini mengeksplorasi persimpangan desain berbasis data, kecerdasan buatan (AI), dan arsitektur, termasuk peran data dalam membentuk industri, mulai dari penerapan Building Information Modelling (BIM) hingga solusi berbasis cloud yang memfasilitasi kolaborasi antar pemangku kepentingan. Analisis data menggunakan literature review mencakup artikel dengan fokus pada artificial intelligence, data driven technology, dan building information modelling (BIM) dalam arsitektur. Sintesis menunjukkan integrasi AI dalam arsitektur memberdayakan para profesional untuk menggali potensi desain berbasis data dan AI sebagai kekuatan transformatif dalam industri arsitektur, serta menciptakan solusi yang lebih baik dan efisiensi dalam pengambilan keputusan yang terinformasi di seluruh desain, konstruksi, dan pengoperasian bangunan. Jurnal Arsitektur TERRACOTTA00Jurnal Arsitektur TERRACOTTA['Dominikus Aditya Fitriyanto', 'Afif Fajar Zakariya']2023-12-31T00:00:00https://www.semanticscholar.org/paper/37f2bd51b1e92a2507e7fc304bac0b8e0cab829d ff521e5f84f2293fd154555acb99686498c218e3The role of Higher Education in any society is inevitable when it comes to harmonious personality development of the youth and nation-building. The promising higher education system is research-oriented and is open to newer dimensions of teaching-learning pedagogy. One of the recent advancements in this sector is the emergence of the application of artificial intelligence which has eventually led to the emergence of New possibilities and challenges as well. The Massification of Higher Education and the diversified nature of curriculum has no doubt increased the workload of the teacher and the nature of the job has also undergone drastic changes. With changing times the teaching-learning methods including testing , measuring and evaluative processes involved in it , have experienced lots of changes recently with the interventions of the latest technology in the process. It is surprising to see how the application of artificial intelligence has aptly equipped not only teachers and teachers but also the whole system involved in the higher education system with its latest techniques. Despite all this, the use and application of artificial intelligence in higher education scenarios is still unclear to some extent especially in a country like India with different kinds of limitations when it comes to the use of technology. This paper tries to analyze the advantages and disadvantages of artificial intelligence and its role in the higher education system and finally the role of teacher in this respect.JOURNAL GLOBAL VALUES00This paper tries to analyze the advantages and disadvantages of artificial intelligence and its role in the higher education system and finally the role of teacher in this respect.JOURNAL GLOBAL VALUES['Dr. Poonam Bhandari,', 'Prof. Swatendra Singh']2023-12-31T00:00:00https://www.semanticscholar.org/paper/ff521e5f84f2293fd154555acb99686498c218e3 9cf1f36c038f3669bd7855e0b9dd7fe665eb6d18In an era where materialism is refined in various ways, what does it mean for theism to live with this era? Rather, as progress in understanding materials coincides with technological prosperity through materials, material-centered worldviews such as posthumanism and new materialism are increasing in breadth and depth. However, when this question is transposed into the space of university liberal arts education, the question changes as follows: Can the teachings of Christianity still be relevant in the era of artificial intelligence? This essay attempts to explore an answer to this question. Three reflective approaches are needed for this attempt. First, reflection on where the teachings of Christianity are positioned within university education. Second, reflection on the era of techno-capitalism, represented by artificial intelligence. Third, reflection on Christian teachings for university liberal arts education, considering both the Christianity’s position within the era and the characteristics of the era of artificial intelligence. The conclusions for each reflection will suggest Christian literacy, challenges posed by artificial intelligence, and the necessity of Christian teachings in university liberal arts education through the reinterpretation of spirituality. The proposed argument is that in terms of position, it is Christian literacy rather than Christian missions; in understanding the contemporary situation, it is the two challenges posed by artificial intelligence to humanity; and in facing these challenges, the direction of education should be spiritual education through the reinterpretation of spirituality. The goal of this essay is to present the actuality of this reinterpretation through the methodological concept of transversal traversing. By doing so, it aims to imagine one theoretical alternative on how Christian literacy in the era of artificial intelligence can be applied in the field of university liberal arts education.The Korean Society of Minjung theology00The goal of this essay is to imagine one theoretical alternative on how Christian literacy in the era of artificial intelligence can be applied in the field of university liberal arts education through the methodological concept of transversal traversing.The Korean Society of Minjung theology['Ick-Sang Shin']2023-12-31T00:00:00https://www.semanticscholar.org/paper/9cf1f36c038f3669bd7855e0b9dd7fe665eb6d18 75ea677fcbdad396d39f1b938e92e4dcfd0e4fa5It is important to note that artificial intelligence is not a replacement for human intelligence; rather, it is a tool that can enhance human creativity & inventiveness. Artificial intelligence plays an essential part in the overall success of the company, and its importance cannot be overlooked. In the context of information technology industry in Lahore, Pakistan, variables that have been utilized comprised of the knowledge innovation, artificial intelligence, organization creativity, organization allyship, financial as well as operational performances. In the field of information technology, these concepts are important. The acceptance of new information is sped up by artificial intelligence, while organizational financial as well as operational performance is improved by improvements in innovation and creativity. In order to promote the organization, the study offers information that can be utilized by IT managers in the organizations to focus upon their creative abilities and their ability to establish required alliances. The study offered significant results towards the introduction to artificial intelligence, along with explanation of its significance for deeper comprehension towards the desired outcomes.December00The study offers information that can be utilized by IT managers in the organizations to focus upon their creative abilities and their ability to establish required alliances to promote the organization.DECEMBER['Siyyam Umar', 'Abdul Basit Khan', 'Sanabil Asmat']2023-12-31T00:00:00https://www.semanticscholar.org/paper/75ea677fcbdad396d39f1b938e92e4dcfd0e4fa5 ccafbffb4990aac17d743d788a53ca200b74174bAs the hostilities in Ukrainian cities remind us once again, urban warfare persistently causes immense suffering and devastating consequences for civilians’ lives and livelihoods. At the same time, the events reveal the challenges urban warfare represents for militaries. The urban environment is one of the most complex environments within which to conduct military operations. To address the challenges this environment poses to militaries, several technologically advanced States are investing in the development of artificial intelligence to enhance a range of their military activities. The ways States have thus far prioritized the development of artificial intelligence systems, however, evidence that investments to improve militaries’ ability to mitigate civilian harm during urban warfare remain rather neglected. Hence, this article aims to demonstrate that despite the risks related to artificial intelligence applications, this technology has great potential for enhancing militaries’ ability to mitigate civilian harm further. But this requires governments to invest more in its development and use to that end. Recognizing artificial intelligence systems’ potential to reduce the military challenge of protecting civilians from harm during urban warfare is a pressing need, considering that conflicts increasingly occur in urban environments where the risks for civilians’ lives and their livelihoods grow exponentially.The Military Law and the Law of War Review420Despite the risks related to artificial intelligence applications, this technology has great potential for enhancing militaries’ ability to mitigate civilian harm further, but this requires governments to invest more in its development and use to that end.The Military Law and the Law of War Review['Anna Rosalie Greipl']2023-12-31T00:00:00https://www.semanticscholar.org/paper/ccafbffb4990aac17d743d788a53ca200b74174b e5f27dea6ed789402a4bcdc75a19b2163baff3a0The linkage between Islamic economics and artificial intelligence (AI) is the basis of research to explore the possibility of integration of these two entities in creating an ethical and innovative economic paradigm. This article explores the potential and challenges of achieving harmonization between Islamic economic principles underlying justice and sustainability with the sophistication of AI technology. The main focus of the research is on how artificial intelligence applications, such as machine learning and big data analysis, can be applied to improve operational efficiency and decision-making in accordance with Islamic values. The article also discusses the ethical implications of the use of artificial intelligence in the context of Islamic economics, highlighting the importance of maintaining a balance between technological innovation and Islamic moral principles. Economic empowerment, equitable distribution of wealth, and prudent risk management are key focuses in evaluating AI's contribution to the Islamic economic ecosystem. In an effort to achieve a harmonious economic paradigm, this article proposes a proactive approach to mitigate risks and ensure that the use of AI technology in the context of Islamic economics is in accordance with the ethical framework and Islamic values. The results of research on the harmonization of Islamic economics with artificial intelligence yield significant insights into the potential and impact of this integration on economic paradigms. One of the key findings is that this harmonization opens the door to innovative solutions to address a range of contemporary economic challenges.Al-Kharaj: Journal of Islamic Economic and Business170The potential and challenges of achieving harmonization between Islamic economic principles underlying justice and sustainability with the sophistication of AI technology with the main focus on how artificial intelligence applications can be applied to improve operational efficiency and decision-making in accordance with Islamic values.Al-Kharaj: Journal of Islamic Economic and Business['Muhammad Nur Ishak', 'Adjila Mohamed']2023-12-31T00:00:00https://www.semanticscholar.org/paper/e5f27dea6ed789402a4bcdc75a19b2163baff3a0 16b0d440fa7550248665b76fa8e5affe96815800The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into the healthcare sector has catalyzed transformative changes across the globe. This comprehensive article delves into the multifaceted impact of AI and ML on healthcare quality, data management, and clinical practices. Moreover, it examines these trends within both a global context and the specific framework of the Republic of Georgia. The purpose of this study was to specify the most important implications of AI in healthcare for developing countries and to assess perspectives and challenges of implementation. At the first stage, desk research was performed. Fifty relevant scientific articles and reports were identified, by key words, with utilization of various scientific bases and analyzed. Moreover, the major findings of the desk research, regarding implications of AI in healthcare in developing countries and challenges were used for the qualitative research. More specifically, in-depth interviews (overall 10) were conducted with various stakeholders of Georgia’s healthcare system and two focus-group discussions (FGD) were moderated with medical professionals and specialists. The purpose of in-depth interviews and FGDs was assessment of attitudes and perceptions of major stakeholders about AI implementation and utilization. According to the reviewed literature, perceptions and attitudes of stakeholders are very important for the successful implementation. However, this issue is not evaluated sufficiently, especially in developing countries. According to the results of the study AI can have substantial economic benefit for the developing countries, with consideration of the monetary savings, improved level of healthcare quality and increased patient safety. As the findings of the qualitative research demonstrate attitudes and perceptions of the doctors and important stakeholders represent a challenge for the successful implementation of AI. Consequently, it is strongly recommended to centralize and prioritize this issue on a system’s level in the process of policy and strategy design.Agora International Journal of Economical Sciences80App attitudes and perceptions of the doctors and important stakeholders represent a challenge for the successful implementation of AI and it is strongly recommended to centralize and prioritize this issue on a system's level in the process of policy and strategy design.AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES['Nino Mikava', 'Tamta Mamulaidze']2023-12-31T00:00:00https://www.semanticscholar.org/paper/16b0d440fa7550248665b76fa8e5affe96815800 615a2d24c328726eebe65dc977e05e41ba31c1b0This research thoroughly explores the diverse impact of Artificial Intelligence (AI) within the educational sector, specifically focusing on the Indian societal context. The introduction lays the foundation for this exploration, underscoring the increasing prominence of AI. Subsequent sections examine the intricate relationship between AI and education, addressing recent challenges encountered by Indian society in incorporating AI into educational practices. The study also scrutinizes government initiatives aimed at propelling AI in Edtech, along with presenting case studies that offer practical insights into the efficacy of AI solutions. The paper concludes by consolidating key findings, emphasizing the pivotal role of AI in driving India’s educational transformation. It underscores the significance of ongoing research, collaborative efforts, and adaptable policies to fully harness the potential of AI in constructing a resilient and inclusive education system for the future of Indian society.JOURNAL GLOBAL VALUES00The significance of ongoing research, collaborative efforts, and adaptable policies to fully harness the potential of AI in constructing a resilient and inclusive education system for the future of Indian society is underscored.JOURNAL GLOBAL VALUES['Dr. Vertika Dhillan']2023-12-31T00:00:00https://www.semanticscholar.org/paper/615a2d24c328726eebe65dc977e05e41ba31c1b0 f2671224a06f67a8b009d974e756d6c3d1b59883It may appear to be a wordy jargon but moral compass, ethical compass and legal compass in artificial intelligence are nothing but about the bottom line and the bottom line is whether humans and their systems will co-exist with artificial intelligence or whether artificial intelligence will outlast (outlive) humans and their systems.Indian Journal of Community Health00Indian Journal of Community Health['Divya Gupta', 'Deepak Gupta']2023-12-31T00:00:00https://www.semanticscholar.org/paper/f2671224a06f67a8b009d974e756d6c3d1b59883 08216328b4af400846395aa66fc92337df5a9fbaIn this study, we aimed to understand how high school students perceive artificial intelligence (AI) in the context of the influence of AI. We sought to identify what is necessary for the proper implementation of AI education. To achieve this, the study conducted surveys targeting students from five high schools in Region B. The surveys covered their understanding and awareness of AI, perceptions of AI learning, awareness of AI applications, and recognition of the role of AI. A total of 272 respondents were analyzed. Survey results revealed that high school students generally recognized the importance of AI in their lives, however, their interest in learning about AI was relatively low. While there was a high perceived need for education on creating AI application content, there was a negative perception towards utilizing AI in career choices. Although AI was perceived as necessary and having a positive impact on human life, some negative perceptions emerged regarding its influence on human employment. Ultimately, high school students considered their understanding of AI to be at a moderate level, but negative aspects were revealed concerning learning and career choices. This means that education on AI has not been properly integrated, the context of exam-focused school systems. Therefore, it is suggested that educational activities incorporating AI in school curricula as well as providing meaning to AI in career exploration education.The Korean Data Analysis Society00High school students considered their understanding of AI to be at a moderate level, but negative aspects were revealed concerning learning and career choices, which means that education on AI has not been properly integrated, the context of exam-focused school systems.The Korean Data Analysis Society['Danam Kwon', 'Nawon Hur', 'Ju Hyun Kang']2023-12-31T00:00:00https://www.semanticscholar.org/paper/08216328b4af400846395aa66fc92337df5a9fba 6e3b30ef4ef4aaf75ff8d5b66115da7b1d2b8ccahese days, everyone in our nation and around the world is familiar with the term Artificial Intelligence (AI) in the context of science and technology. In daily life, people encounter this word in a variety of settings, including corporations, software businesses, universities, schools, and higher education institutions. We have attempted to explain artificial intelligence (AI) in full in this review article, along with some of the positive and negative effects it may have on Indian society in the future.JOURNAL GLOBAL VALUES00This review article has attempted to explain artificial intelligence (AI) in full in this review article, along with some of the positive and negative effects it may have on Indian society in the future.JOURNAL GLOBAL VALUES['Dr. Daisy Verma']2023-12-31T00:00:00https://www.semanticscholar.org/paper/6e3b30ef4ef4aaf75ff8d5b66115da7b1d2b8cca a4fcb24851b51ed35651f8c8734ded04fc9595afSince mathematics is the basis of the models, algorithms, and procedures that let machines process and interpret enormous volumes of data, mathematics plays a crucial role in the creation of artificial intelligence. Consequently, a thorough grasp of the mathematical ideas underlying AI is crucial for professionals wishing to comprehend, develop, and utilize the technology. AI and mathematics are related and enhance one another. The advancement of algorithm development and the solution of challenging issues have been made possible by the incorporation of machine learning into mathematics. In this paper, we study various articles to provide a summary of the relationship between AI and Mathematics.JOURNAL GLOBAL VALUES00This paper studies various articles to provide a summary of the relationship between AI and Mathematics and concludes that AI and mathematics are related and enhance one another.JOURNAL GLOBAL VALUES['Jaivindra Tomar', 'Ms Soshal']2023-12-31T00:00:00https://www.semanticscholar.org/paper/a4fcb24851b51ed35651f8c8734ded04fc9595af 4519fa9b7eb7bbec5fb0ea5c71e74c963d8f462eTeknologi radioterapi berkembang dengan pesat seiring dengan pemanfaatan artificial intelligence pada setiap tahapan-tahapan radioterapi, sehingga diharapkan dapat meningkatkan kualitas pelayanan dan kesuksesan dalam melaksanakan radioterapi. Indonesia merupakan negara yang sangat berpeluang untuk mengembangkan radioterapi untuk mengurangi risiko kematian masyarakat yang diakibatkan oleh kanker. Pada artikel ini disampaikan penelitian-penelitian terkait pendekatan artificial intelligence yang diintegrasikan pada tahapan diagnostik, pengkonturan, perencanaan, treatment, quality assurance, dan prediksi hasil pada radioterapi. Sehingga penelitian-penelitian tersebut dapat dikembangkan dan diimplementasikan pada fasilitas radioterapi di Indonesia. Kata kunci: artificial intelligence, radioterapi, segmentasi, treatment planning systemMedika Kartika Jurnal Kedokteran dan Kesehatan00Medika Kartika Jurnal Kedokteran dan Kesehatan['Julfa Muhammad Amda', 'A. A. Waskita', 'A. Saputra', 'S. Rianto']2023-12-31T00:00:00https://www.semanticscholar.org/paper/4519fa9b7eb7bbec5fb0ea5c71e74c963d8f462e e541cf7bfa9614c7f8351daf74585b8de0369417The application of Artificial Intelligence (AI) in orthodontics is very diverse and ranges from the identification of anatomical and pathological structures of the human dentition to support complex decision-making in orthodontic treatment planning. Its application has grown significantly in recent years, as reflected by the exponential increase in the number of scientific publications on the integration of artificial intelligence into everyday clinical practice. In many cases, AI can be seen as a valuable tool whose algorithms help dentists and clinicians analyze data from multiple sources of information. The purpose of this paper was to analyze current views on the use of artificial intelligence techniques and models in orthodontics based on a literature review. The scientific publications of various scientometric databases (PubMed, Scopus, Google Scolar, Web of Science, etc.) over the past 5 years were processed. Artificial intelligence is one of the most promising tools due to its high accuracy and efficiency. Given the current scientific dynamics in the field of AI, it can be assumed that AI will become an integral part of diagnostics and treatment planning in the near future. Practicing dentists will be able to use it as an additional tool to reduce their workload. However, this requires close cooperation of commercial AI products with the scientific community, further research, including randomized clinical trials, to test and integrate this concept in dental practice. Modern artificial intelligence is excellent at utilizing structured knowledge and gaining insights from huge amounts of data. However, it is not able to create associations like the human brain and is only partially capable of making complex decisions in a clinical situation. In turn, the efficiency of AI is achieved only when unbiased training data and a properly designed and trained algorithm are used. Keywords: dentistry, diagnostic, machine learning, cephalometry.Experimental and Clinical Medicine430Current views on the use of artificial intelligence techniques and models in orthodontics based on a literature review were analyzed and the scientific publications of various scientometric databases over the past 5 years were processed.Experimental and Clinical Medicine['I.M. Kuzyk', 'A. Kotelban']2023-12-31T00:00:00https://www.semanticscholar.org/paper/e541cf7bfa9614c7f8351daf74585b8de0369417 ed33b547f3ae02fb314d51f737cc1383504de652rtificial intelligence is the proposition and development of computer programs that are suitable to do tasks and break problems that generally bear mortal intelligence. effects like visual perception, speech recognition, decision- timber, and word restatement are all effects that would typically need mortal intelligence, but now computer programs are suitable use their intelligence and capability to break these tasks.. Artificial intelligence in the last two decades has greatly bettered performance of the manufacturing, service sector and so in the field of education. Study in the field of artificial intelligence has given rise to the fleetly growing technology known as expert system. operation areas of artificial intelligence is heaving a huge impact on colorful fields of life as expert system is extensively used in these days to break the complex problems in colorful areas as education, engineering, business, drug, rainfall soothsaying etc. This paper gives an overview of this technology and the compass of artificial intelligence in different areas with special reference to the use of this technology in the field of cybersecurity, healthcare, pool productivity, chatbots, robotizationetc.JOURNAL GLOBAL VALUES00This paper gives an overview of this technology and the compass of artificial intelligence in different areas with special reference to the use of this technology in the field of cybersecurity, healthcare, pool productivity, chatbots, robotizationetc.JOURNAL GLOBAL VALUES['Prof. Lalit Kumar', 'Dr. Jyoti Choudhary']2023-12-31T00:00:00https://www.semanticscholar.org/paper/ed33b547f3ae02fb314d51f737cc1383504de652 544dcfb6ea46cd9f3d8fc123a9ff7bcf60b9d119The high demand for mental health services and scarce supply of healthcare professionals are increasingly suppressing the mental healthcare industry. The emergence of artificial intelligence shows the potential to be able to transform the landscape of this industry. The application of artificial intelligence has offered a promising hope to bridge the gap between the long-lasting demand and supply issues in the industry, and to provide better healthcare support, as it reduces the fear of judgement while increasing self-awareness. Nevertheless, some challenges need to be overcome before the mental healthcare industry can truly capitalise on artificial intelligence.Journal of Chitwan Medical College00Some challenges need to be overcome before the mental healthcare industry can truly capitalise on artificial intelligence, as it reduces the fear of judgement while increasing self-awareness.Journal of Chitwan Medical College['Yen Nee Teo', 'Elizabeth Yong', 'Ajeevan Gautam', 'R. Chaulagain', 'Kun Hing Yong']2023-12-31T00:00:00https://www.semanticscholar.org/paper/544dcfb6ea46cd9f3d8fc123a9ff7bcf60b9d119 70a85bbd062d2914704c4778ece58969e1babe8fArtificial Intelligence (AI) has revolutionized the field of medicine, offering unprecedented opportunities to improve patient care, diagnosis, and treatment. AI presents a myriad of ethical issues, especially when it comes to making medical decisions, as technology is incorporated into healthcare more and more.  This article examines the various ethical issues surrounding AI in medicine and clarifies how these technologies and medical ethics interact. Patient confidentiality and data security are major concerns because artificial intelligence (AI) mainly depends on huge databases for training and analysis. Maintaining the confidence among individuals and their healthcare providers requires ensuring that private medical information is secure and not easily compromised. (1)Journal of the Epidemiology Foundation of India50The various ethical issues surrounding AI in medicine are examined and how these technologies and medical ethics interact are clarified.Journal of the Epidemiology Foundation of India['Nandita Sharma', 'Divya Gupta']2023-12-31T00:00:00https://www.semanticscholar.org/paper/70a85bbd062d2914704c4778ece58969e1babe8f 1d6cb5f71608561b7e38a020c4049a4d6eec2d6aArtificial intelligence is an upcoming branch of computer science, with applications across the world in each and every industry. The launch of ChatGPT- an advanced and easily accessible machine learning and artificial intelligence application will alter the manner in which both the medical fraternity, research and global healthcare sectors are trained and operate. ChatGPT was launched on the 30th of November 2022, by a San Francisco based software company (OpenAI). ChatGPT is a cutting-edge language model (chatbot) which forms part of the (Generative Pre-trained Transformer) group of software. The powerful inbuilt language model allows the generated response to be humanoid in nature and not to be perceived as being AI generated and therefore attaches a greater innate value to the resultant text, thereby negating the need to re-read and re-write the generated text. The model has multiple advantages across medical education, research, patient treatment and monitoring. As with all technology and models of this nature, innate disadvantages are evident; predominantly in the realm of data security and medicolegal difficulties. ChatGPT is an extremely powerful tool, with limitless capabilities and shall most certainly be useful in optimizing our daily lives and routines. The scope for such powerful artificial intelligence is undeniable and will most certainly gain traction within the medical sector. The healthcare sector will however demand a higher level of refinement and an AI model more specifically designed by and for the healthcare sector before the large-scale implementation thereof within the field will be accepted and viable.Nepal Mediciti Medical Journal160The healthcare sector will however demand a higher level of refinement and an AI model more specifically designed by and for the healthcare sector before the large-scale implementation thereof within the field will be accepted and viable.Nepal Mediciti Medical Journal['J. Robinson', 'Indrajit Banerjee', 'Todd Atterbury', 'I. Banerjee', 'Alexandra Leclézio']2023-12-31T00:00:00https://www.semanticscholar.org/paper/1d6cb5f71608561b7e38a020c4049a4d6eec2d6a 8f4e03248ed0c78bf1fcee0d4c15f95aad4ecd60The increasing use of Artificial Intelligence (AI) in the field of education has raised significant concerns regarding the potential displacement of educators by AI systems. The present study explores the diverse domain of artificiala intelligence’s influence on academia, examining its capacity to transform conventional educational frameworks. By conducting a methodical investigation using scholarly resources including IEEE Xplore, ACM Digital Library, ERIC, and Google Scholar, this study develops an extensive compilation of 135 publications that were published between January 2017 and June 2023. The results of the study demonstrate a dynamic environment in which artificial intelligence serves as both a stimulant and a challenge. The present analysis emphasizes the complex relationship between artificial intelligence (AI) and education, as evidenced by the theoretical underpinnings emphasized by Panaou et al. (2012), as well as the promise of AI advocated by Luckin et al. (2016). The research highlights the significance of adopting a well-rounded strategy, wherein artificial intelligence serves to enhance the responsibilities of educators rather than replace them.Journal of Interdisciplinary Studies180The present analysis emphasizes the complex relationship between artificial intelligence (AI) and education, as evidenced by the theoretical underpinnings emphasized by Panaou et al. (2012) and the promise of AI advocated by Luckin et al. (2016).Journal of Interdisciplinary Studies['Dipendra Karki', 'Nirupan Karki', 'R. K. Dahal', 'Ganesh Bhattarai']2023-12-31T00:00:00https://www.semanticscholar.org/paper/8f4e03248ed0c78bf1fcee0d4c15f95aad4ecd60 d3f125955bd510c086b3f580c69e05ab024fdce7This study investigated the effects of a gamification-based education program centered on Artificial Intelligence (AI) ethical standards on elementary school students’ ethical awareness of AI. Therefore, a 16-session AI ethics educational program was developed based on the ADDIE model according to previous studies on AI ethics education and “human-centered AI ethical standards.” To verify the program’s effectiveness, the program extracted from the “AI ethics (Elementary School) Learning with AI Principles” (Gyeonggi Provincial Office of Education, 2022) was applied to compare between groups and used a student ethics consciousness test tool we developed. The study results obtained through the application and analysis of this program are as follows. We found statistically significant differences in all areas of interest and the need for AI ethics and ethics education. The results of the pre-and post-tests for each AI ethics element showed statistically significant changes in all areas, except for the “responsibility” element. The AI ethics education program developed in this study is expected to contribute to the formation of a proper AI ethics consciousness among elementary school students.The Institute for Education and Research Gyeongin National University of Education00The AI ethics education program developed is expected to contribute to the formation of a proper AI ethics consciousness among elementary school students and found statistically significant differences in all areas of interest and the need for AI ethics and ethics education.The Institute for Education and Research Gyeongin National University of Education['Ho-Jun Won', 'Chul-Hyun Lee']2023-12-31T00:00:00https://www.semanticscholar.org/paper/d3f125955bd510c086b3f580c69e05ab024fdce7 931292e8341339cd86042ed97b9db71657b65435 Health is one of the pillars in determining human performance in their daily activities. Someone with good health can work optimally because there are no health problems they have. On the other hand, artificial intelligence is a form of technology that is developing rapidly. This technology has various benefits that can be provided, especially in the health sector to help health workers. The technologies that are often used are expert systems and artificial neural networks because of their ease of operation and accuracy in carrying out the work of health workers. Various other technologies are being developed to facilitate the performance of health workers to lighten their workload, such as robots to help paralyzed patients, automatic operating robots, and other technologies that can help ease the burden on health workers' performance. Keywords—health, artificial intelligence, neural network, expert systemJournal of Advanced Technology and Multidiscipline230Various other technologies are being developed to facilitate the performance of health workers to lighten their workload, such as robots to help paralyzed patients, automatic operating robots, and other technologies that can help ease the burden on health workers' performance.Journal of Advanced Technology and Multidiscipline['Fariza Shielda Akzatria']2023-12-31T00:00:00https://www.semanticscholar.org/paper/931292e8341339cd86042ed97b9db71657b65435 251f56fdd6b63f4bfa886c69930c4e06407e5d92When economic value is created by an Artificial Intelligence, the acquisition of that value is generally attributed to the owner of the AI. At the same time, even if damage is caused to others due to autonomous judgment or malfunction of the AI, civil liability is generally imposed on the owner. However, even in cases where the AI commits legal or illegal acts beyond human control, will the effects be attributable to the AI itself and will the legal responsibility can be imposed on the AI itself? It is not clear whether it is. In that case, although researchers are discussing how to control the interests of the other party who engages in legal acts with AI or is harmed by illegal acts by AI, no concrete legal or institutional mechanism has been established. Since the degree of evolution of AI is still within the control of humans, these discussions have not yet begun in earnest, but considering the speed of its development, this problem will become a reality in the near future. If social necessity and social value judgment are premised, it will be possible to proceed with legislative procedures to grant the legal personhood to the AI, which is a new entity. However, even if the AI has the legal personhood, the scope of it should be limited, as seen in corporations. Since the scope of the legal personhood of the AI is the one of the owner’s need or convenience, it is reasonable to ensure that the scope is specified in the AI register upon the application of the owner, and to grant the legal personhood only within that scope. In addition, it is appropriate to allow the AI to be granted legal personhood on the condition that it maintains registered minimum liability property, and to deny legal personhood in cases where the minimum liability property cannot be maintained due to the actions of AI. In this way, by setting the minimum liability property and ensuring that the specified property is maintained, it is possible to protect the other party transacting with the AI or the victims who have suffered damage due to the actions of AI.LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY00It is appropriate to allow the AI to be granted legal personhood on the condition that it maintains registered minimum liability property, and to deny legal personhood in cases where the minimum liability property cannot be maintained due to the actions of AI.LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY['Ji-Yong Oh']2023-12-31T00:00:00https://www.semanticscholar.org/paper/251f56fdd6b63f4bfa886c69930c4e06407e5d92 024a4b57772df254459190b674b7aedc31791e93Millennial students, born between 1981-1996, are often considered a generation that is sophisticated in technology, but is also faced with several challenges. They tend to rely too much on technology, lack initiative in completing tasks themselves, are distracted by many distractions, and are susceptible to health problems. Apart from that, social and environmental factors also influence their thinking patterns and interactions. In the educational context, the practice of assignment jockeying and plagiarism are serious issues. This research emerged as a response to developments in Artificial Intelligence (AI) technology, such as ChatGPT generation 3 from OpenAI, which can be expected to address the problems of task jockeying and plagiarism. The aim of this research is to scientifically examine the potential of AI in combating dishonest academic practices because no previous research has investigated the correlation between the use of AI and overcoming student assignment jockeying and plagiarism. In the world of education, assignment jockeying and plagiarism are serious problems that can undermine academic integrity. Utilizing Artificial Intelligence (AI) technology, such as ChatGPT from OpenAI, can be an interesting solution to address this problem. Therefore, this research aims to scientifically prove through research the enthusiasm of students in (a) doing coursework using AI without worrying about plagiarism and (b) being able to eradicate assignment jockeys.Journal of Digital Business and Innovation Management180This research aims to scientifically prove through research the enthusiasm of students in doing coursework using AI without worrying about plagiarism and being able to eradicate assignment jockeying in order to eradicate assignment jockeys.Journal of Digital Business and Innovation Management['Ella Rosediana Putri', 'Dwi Indah Lestiani', 'Nisya Kayla Putri Anindra', 'Aqeela Istighfarin Yarbo', 'Atika Naylatan Syirfa', 'Renny Sari Dewi']2023-12-31T00:00:00https://www.semanticscholar.org/paper/024a4b57772df254459190b674b7aedc31791e93 aacbe19ad170a09456ddf46e7baace4a9ddcd322The most significant development in the banking industry right now is a greater focus on the needs of the consumer. Customers who are familiar with modern technologies and use them often want flawless banking experiences from banks. For services like mobile banking, e-banking, and telecom that enable these expectations, banks have improved their industry landscape to contain retail, IT, and telecom, simultaneous financial transfers. The majority of the benefits provided by these innovations to customers, a price has been paid for the convenience of having banking services available wherever they are and whenever they need their sector. Additionally, this study sheds light on the advantages and disadvantages of artificial intelligence. The Indian banking industry uses intelligence. As a descriptive research, this one includes all the necessary relevant information has been gathered from numerous sources.International Journal For Multidisciplinary Research100This study sheds light on the advantages and disadvantages of artificial intelligence in the Indian banking industry and includes all the necessary relevant information gathered from numerous sources.International Journal For Multidisciplinary Research['Dr Maneesha Kaushik', 'Mahima Sharma']2023-12-31T00:00:00https://www.semanticscholar.org/paper/aacbe19ad170a09456ddf46e7baace4a9ddcd322 b79fc99234ded6d302880f4dc112c7d3a33ea7b9Środowisko sztucznej inteligencji (AI/Artificial Intelligence) zmienia sposób, w jaki człowiek uczestniczy w procesach biznesowych. W badaniach przewiduje się, że w drugiej połowie lat dwudziestych XXI wieku AI będzie krytycznym czynnikiem, a prawne regulacje będą potrzebne do kontrolowania jego autonomii. Obserwacje wskazują, że AI może wspierać innowacyjność, ale również niesie ryzyko związane z utratą miejsc pracy i trudnościami w identyfikacji odpowiedzialności. Rewolucja technologiczna Industry 4.0 przyspiesza rozwój AI, zmieniając strukturę procesów biznesowych. Praca ta analizuje użyteczność AI w doskonaleniu procesów biznesowych, zwracając uwagę na korzyści i zagrożenia związane z implementacją w organizacjach współczesnych.Nowoczesne Systemy Zarządzania20Nowoczesne Systemy Zarządzania['Mikołaj Krystian', 'Piotr Zaskórski']2023-12-31T00:00:00https://www.semanticscholar.org/paper/b79fc99234ded6d302880f4dc112c7d3a33ea7b9 3fe8938e1cad0ea5380482a96e253547d1f2d705Ensuring security for business becomes especially relevant in the context of geopolitical conflicts, increasing digitization and introducing new technologies into the financial and production sectors, booming frequency and complexity of cyber-attacks aimed at the business sphere, as well as with more diverse applications of artificial intelligence. The article studies the specifics of ensuring business security in Ukraine during wartime using artificial intelligence and cyber protection. A retrospective review of the literature has been conducted revealing that there is no research on the ways artificial intelligence is applied to ensure business activity security, which indicates the research novelty of this article. The authors outline the potential and threats of artificial intelligence for business security. Directions for securing entrepreneurship in war conditions with the help of artificial intelligence and elimination/minimization of cyber threats are formulated. The regulatory and legislative framework governing the regulation of cyber security in Ukraine is outlined and directions for its improvement are suggested.Financial and credit activity problems of theory and practice310The authors outline the potential and threats of artificial intelligence for business security and the regulatory and legislative framework governing the regulation of cyber security in Ukraine is outlined and directions for its improvement are suggested.Financial and credit activity problems of theory and practice['O. Havryliuk', 'Oleksandr Oleksandr', 'Maryna Petchenko', 'Natalia Zachosova', 'Taliat Bielialov', 'Svitlana Kozlovska']2023-12-31T00:00:00https://www.semanticscholar.org/paper/3fe8938e1cad0ea5380482a96e253547d1f2d705 82ea502ab12d2757c0aaec1c27bb9e41c58e069eArtificial Intelligence (AI) may be defined as the imitation of human cognition by a machine or software systems which compile integrate and analyse the data through different modes of learning and problem-solving mechanisms for achieving predefined objectives. Its capacity holds assurance for enhancing the effectiveness of public health initiatives aimed at advancing the well-being of diverse populations. This review article delineates the implementation of AI in the realm of public health, exploring its current applications and discussing potential areas for further development and prospects in future. Artificial Intelligence (AI) may be defined as the imitation of human cognition by a machine or software systems which compile integrate and analyse the data through different modes of learning and problem-solving mechanisms for achieving predefined objectives. Its capacity holds assurance for enhancing the effectiveness of public health initiatives aimed at advancing the well-being of diverse populations. This review article delineates the implementation of AI in the realm of public health, exploring its current applications and discussing potential areas for further development and prospects in future.Journal of the Epidemiology Foundation of India00This review article delineates the implementation of AI in the realm of public health, exploring its current applications and discussing potential areas for further development and prospects in future.Journal of the Epidemiology Foundation of India['Tarun Sood', 'Ekta Sharma', 'Gurmeet Katoch']2023-12-31T00:00:00https://www.semanticscholar.org/paper/82ea502ab12d2757c0aaec1c27bb9e41c58e069e 9db8e5941142c0653edd7974ad6eab1805f11c33Recently, artificial intelligence with excellent performance has been spread and used. Looking at artificial intelligence from a legal aspect, the main issue is what kind of legal responsibility can be held for the judgments and actions of artificial intelligence. Living in the age of artificial intelligence today, we need wisdom to rationally coordinate the interests of the parties surrounding these legal responsibilities and prepare social systems for a safe life. If artificial intelligence with human-level free will, that is, an artificial intelligence robot that can think and act like a human, emerges after passing through the so-called singularity, the robot will show activities similar to humans, and the era in which humans and artificial intelligence robots live together is coming. However, this situation is still a story of the future, and it can be said that whether to grant artificial intelligence a level of free will similar to that of humans is the greatest task imposed on mankind today. The summary of the legislative issues in each field of artificial intelligence is roughly as follows. First, legislation on safety and ethics is needed. Artificial intelligence technology is very powerful, and accordingly, mistakes, malfunctions, or abuses may infringe on human life and safety, rights and freedoms, so countermeasures and legal regulations are urgently needed and appropriate. Second, it is necessary to legislate personal information protection regulations. Since artificial intelligence has the ability to collect and analyze personal information, protection and regulation are essential. Although there are already many laws for the protection of personal information, more specific legal regulations are needed for the data collected and analyzed by artificial intelligence. Third, legislation on accountability is needed. Since artificial intelligence works by programs, developers and operators must manage it responsibly. It is necessary to think about the legal regulation of these responsibilities and how to share them. Fourth, new legislation is needed according to economic and technological development. As AI technology develops, the competitiveness and growth of companies are greatly affected, so companies need cooperation and support for investment, development, and regulation to use and develop these technologies. Accordingly, in the field where artificial intelligence is used, a legal system that resolves issues related to artificial intelligence is essential, and through this, it will ensure safe and effective use while maximizing the benefits of artificial intelligence.Kyung Hee Law Journal00In the field where artificial intelligence is used, a legal system that resolves issues related to artificial intelligence is essential, and through this, it will ensure safe and effective use while maximizing the benefits of artificial intelligence.Kyung Hee Law Journal['Wan Choung']2023-12-31T00:00:00https://www.semanticscholar.org/paper/9db8e5941142c0653edd7974ad6eab1805f11c33 f55494ee1d315787ce56e1b8104eadc079be0672In this study, safety diagnosis results from underground and nuclear power plant structures were collected to evaluate the reliability of on-site structural safety assessments. The analysis of these results revealed a discrepancy between the compressive strength determined by rebound hardness and the core compressive strength, with the former typically being evaluated higher than the latter. Additionally, existing strength prediction models did not adequately explain field data, whereas artificial intelligence models, particularly the support vector machine model, demonstrated improved accuracy and reduced error rates. This indicated the superior performance of support vector machine models in this context.Journal of the Korean Society of Hazard Mitigation150Analysis of safety diagnosis results from underground and nuclear power plant structures revealed a discrepancy between the compressive strength determined by rebound hardness and the core compressive strength, which indicated the superior performance of support vector machine models in this context.Journal of the Korean Society of Hazard Mitigation['Sung Jong Lee', 'Joo Ha Lee']2023-12-31T00:00:00https://www.semanticscholar.org/paper/f55494ee1d315787ce56e1b8104eadc079be0672 798a4eaa8324cf392c7d0a6b2a663f9fa90aa4ecBackground: Artificial intelligence technology (AIT) makes a profound change in critical care settings involving nursing profession. It can help intensive care nurses (ICNs) to make more precise decisions and become higher job controlled. On the other side, this digital transition requires nurses to craft their work by altering their thinking regarding their jobs to be more satisfying and meaningful for them. Aim: To explore artificial intelligence technology perception and its relation to job control and job crafting among intensive care nurses. Design: Descriptive correlational design was utilized. Setting: The study was carried out at Tanta University Hospitals including all ICUs. Subjects: A stratified random sample was taken from intensive care nurses (n = 268). Tools: Three tools were used namely; Artificial Intelligence Technology Perception Questionnaire, Job Control Scale, and Job Crafting Scale. Results: About half (50.4%) of ICNs had a high level of artificial intelligence technology perception. Around two thirds (64.6%) of ICNs had a high level of job control. More than two thirds (66.8%) of ICNs had a high level of job crafting. Conclusion: There was a statistical significant positive correlation between intensive care nurses’ artificial intelligence technology perception, job control, and job crafting. Recommendation : Conducting periodic workshop and training programs about artificial intelligence and job crafting. Creating supportive working environment that enhances nurses’ job control and job crafting.Assiut Scientific Nursing Journal00There was a statistical significant positive correlation between intensive care nurses’ artificial intelligence technology perception, job control, and job crafting among intensive care nurses.Assiut Scientific Nursing Journal['Asmaa Wanas', 'Doaa Edrees']2023-12-31T00:00:00https://www.semanticscholar.org/paper/798a4eaa8324cf392c7d0a6b2a663f9fa90aa4ec 564f17f2aef273124da15ac41ec00f8f6308af92  This research explores the impact of artificial intelligence and digital technologies on human existence and artistic creativity in our contemporary reality. The paper sheds light on the transformations of the relationship between humans and technology, where the study responded to questions about whether these technologies are just tools or have a deeper impact on the human experience. The paper also focused on the impact of these technologies on artistic creativity, where it was shown that artificial intelligence can be a source of inspiration for creativity, and that digital technologies provide new platforms for artistic expression. However, challenges were observed related to the importance of preserving authenticity and balance between technical and human factors in a rapidly changing society. The paper highlighted the importance of developing policies and ethical frameworks that guide the use of technologies towards enhancing human values and encouraging creativity. With a focus on developing technology that respects human identity and promotes diversity of creativity. This makes us face an approach of benefiting from technology without giving up the essential aspects of humanity and art. In short, this article highlights the importance of exploring the impact of artificial intelligence and digital technologies on human existence and artistic creativity, with a focus on the challenges and opportunities that are growing in this context.   Received 10/9/2023, Accepted 28/12/2023   , Published 31/12/2023.Thi Qar Arts Journal00The paper sheds light on the transformations of the relationship between humans and technology, where it was shown that artificial intelligence can be a source of inspiration for creativity, and that digital technologies provide new platforms for artistic expression.Thi Qar Arts Journal['Dr. Najla Ahmed Kabier']2023-12-31T00:00:00https://www.semanticscholar.org/paper/564f17f2aef273124da15ac41ec00f8f6308af92 0df1fd1a6eeec8683c3daf513980e76e98e01c71Information and communication technology (ICT) is being utilized in various places, mainly in the Internet of Things (hereinafter referred to as IoT), depending on the degree of its development. Among these new technologies, artificial intelligence (AI) technology is progressing rapidly, and mobile-based translation apps and Internet recommendation functions are already in use in our surroundings. In line with this, tax authorities are moving tax procedures online through the enactment of laws and implementation of systems related to the construction of tax administration using ICT. It is not only silk tax accountants who are being forced to innovate due to the development of AI technology characterized by advanced information processing, but also professionals such as lawyers, legal consultants, and administrative consultants should establish their own duties. . In particular, since the tax field is related to a wide range of fields including individuals and companies, there is a need to seek bolder cooperation. Along with this, there is a need to actively utilize AI technology to improve operational efficiency. In the future, rather than AI taking away human jobs, I think AI will be left with simple tasks that it can do well, and tax accountants will concentrate on tasks that require advanced skills that only humans can do. Article 1 of the “Tax Judiciary” clearly states that the purpose is to establish a tax consultant system and ensure the smooth implementation of tax administration and the proper fulfillment of tax obligations. As long as there is a self-assessment tax system, it is important for our tax accountants to raise the public's tax awareness and encourage taxpayers to declare and pay their taxes. In addition, tax accountants, acting as taxpayers' agents, must deal with complex tax systems and ensure that appropriate tax payments are made even in cases where opinions differ on non-obvious issues. It is expected that some of the tax services that fall under the category of ancillary services will disappear. In the future, tax representation, tax document preparation, tax consultation, and the preparation of tax documents, which are the exclusive services of tax accountants, may no longer be necessary for tax returns that do not require complex tax procedures. Tax accountants, who perform tax-related representation and other activities in response to taxpayers' requests, will have to understand the demands of taxpayers over time and assume a new social role.KOREAN SOCIETY OF TAX LAW00I think AI will be left with simple tasks that it can do well, and tax accountants will concentrate on tasks that require advanced skills that only humans can do, and some of the tax services that fall under the category of ancillary services will disappear.KOREAN SOCIETY OF TAX LAW['Chang Kyu Lee']2023-12-31T00:00:00https://www.semanticscholar.org/paper/0df1fd1a6eeec8683c3daf513980e76e98e01c71 ab0d88f10b2603318763a07dfdd58aade21f914bBackground: Sepsis: a complex clinical syndrome--represents life-threatening organ dysfunction instigated by an infection's dysregulated host response. Early detection and accurate prognostication of sepsis are crucial; they pave the way for timely intervention, ultimately enhancing patient outcomes. The rise in interest towards Artificial Intelligence (AI) applications within laboratory technologies is directly related to its potential for improving early detection and prognosis forecasting in sepsis cases; this interest comes as AI continues its advancement. Methods: We conducted a systematic review of studies utilizing AI algorithms in laboratory settings for early sepsis detection and prognostication: our methods entailed searching relevant databases for research published until October 2023. Our inclusion criteria spanned original articles; these applied machine learning (ML) and deep learning (DL) techniques to laboratory data--with the aim being sepsis prediction. We assessed the quality of the studies, extracted and synthesized data on AI model performance metrics - including: area under receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The review encompassed eight studies meeting the inclusion criteria; AI models showcased exceptional predictive capabilities--evidenced by a range of AUROC values from 0.799 to 0.9213, signifying noticeably acceptable performance. However, there was wide variation in sensitivity and specificity among these analyses: an indicator of heterogeneity in model performance. Superior prognostic accuracy and potential for real-time monitoring of patients' early sepsis signs emerged in several models: notably, within the first 12 hours of patient admission - their highest predictive period. The models frequently outperformed traditional scoring systems. Conclusion: Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication. Reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid. Nevertheless, we also acknowledged both the variability in model performance and necessity of additional validation across diverse clinical settings. Future research: it must concentrate on two key aspects--the refinement of AI algorithms to enhance sensitivity and precision; furthermore, it should delve into evaluating the clinical impact of tools for sepsis prediction that are assisted by AI.Journal of Pioneering Medical Science410L Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication, and reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid.Journal of Pioneering Medical Science['Mohsen Bakouri', 'Nasser M. Alqahtani', 'Othman M. Alhussain', 'N. Alrashidi', 'Sulaiman N. Almutairi', 'Ahmed O. Alabdulwahab', 'Badr S. Alaskar', 'Megren A. Alqahtani']2023-12-31T00:00:00https://www.semanticscholar.org/paper/ab0d88f10b2603318763a07dfdd58aade21f914b 23cdd6c99c973c60c9bf07aeb2711026a3a89037The sanctity of the doctor-patient relationship is deeply embedded in tradition - the Hippocratic oath, medical ethics, professional codes of conduct, and legislation - all of which are being disrupted by big data and 'artificial' intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ubiquitous AI systems in healthcare. A transformed healthcare environment enhanced by innovation, robotics, digital technology, and improved diagnostics and therapeutics is plagued by ethical, legal and social challenges. Global guidelines are emerging to ensure governance in AI, but many low- and middle-income countries have yet to develop context- specific frameworks. Legislation must be developed to frame liability and account for negligence due to robotics in the same way human healthcare providers are held accountable. The digital divide between high- and low-income settings is significant and has the potential to exacerbate health inequities globally.South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde440South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde['K. Moodley']2023-12-31T00:00:00https://www.semanticscholar.org/paper/23cdd6c99c973c60c9bf07aeb2711026a3a89037 96a97dc22712026b8b2ccd970eba60c1f082cb92Early Warning Score (EWS) systems can identify critical patients through the application of artificial intelligence (AI). Physiological parameters like blood pressure, body temperature, heart rate, and respiration rate are encompassed in the EWS. One of AI's advantages is its capacity to recognize high-risk individuals who need emergency medical attention because they are at risk of organ failure, heart attack, or even death. The objective of this study is to review the body of research on the use of AI in EWS to accurately predict patients who will become critical. The analysis model of Arksey and O'Malley is employed in this study. Electronic databases such as ScienceDirect, Scopus, PubMed, and SpringerLink were utilized in a methodical search. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA SR) guidelines were utilized in the creation and selection of the literature. This analysis included a total of 14 articles. This article summarizes the findings on several aspects: the usefulness of AI algorithms in EWS for critical patients, types of AI algorithm models, and the accuracy of AI in predicting the quality of life of patients in EWS. The results of this review show that the integration of AI into EWS can increase accuracy in predicting patients in critical condition, including cardiac arrest, sepsis, and ARDS events that cause inhalation until the patient dies. The AI models that are often used are machine learning and deep learning models because they are considered to perform better and achieve high accuracy. The importance of further research is to identify the application of AI with EWS in critical care patients by adding laboratory result parameters and pain scales to increase prediction accuracy to obtain optimal results.JURNAL INFO KESEHATAN400The results of this review show that the integration of AI into EWS can increase accuracy in predicting patients in critical condition, including cardiac arrest, sepsis, and ARDS events that cause inhalation until the patient dies.JURNAL INFO KESEHATAN['Suhartini Ismail', 'Zahrotul Wardah', 'A. Wibowo']2023-12-31T00:00:00https://www.semanticscholar.org/paper/96a97dc22712026b8b2ccd970eba60c1f082cb92 69b94a6433fa5caa1a1a36f8397d2923c68d5eb2This study was conducted to identify and categorize artificial intelligence technologies that can be used in the process of solving invention problems so that artificial intelligence convergence invention education can be conducted at the middle and high school levels. After reviewing previous studies to achieve the purpose of the study, artificial intelligence technologies that can be used in invention education were extracted and categorized through FGI; the model was completed through expert review. As a result of the study, first, artificial intelligence technologies that can be used in invention education can be classified into “invention and ethics”, “invention and fusion”, and “invention and generative AI”. Second, the method of utilizing artificial intelligence technology in invention education comprised six elements of “invention and ethics”, five elements of “invention and fusion”, and nine elements of “invention and generative AI”. The results of this study are expected to make it easy for instructors to use artificial intelligence technology to design and operate invention education programs and the provide basic data necessary to conduct research related to artificial intelligence convergence invention education.Education Research Institute, Chungbuk National University00The results of this study are expected to make it easy for instructors to use artificial intelligence technology to design and operate invention education programs and the provide basic data necessary to conduct research related to artificial intelligence convergence invention education.Education Research Institute, Chungbuk National University['Dasol Kim']2023-12-31T00:00:00https://www.semanticscholar.org/paper/69b94a6433fa5caa1a1a36f8397d2923c68d5eb2 e5ebf9f76db6f9c7cdf3b8f77f2e3f193eea680fRenewable energy is gradually replacing traditional fossil fuels. The change of power generation energy structure brings new challenges to the traditional power grid. Through the efficient bidirectional movement of electricity and information, smart grids might include renewable energy. For the complex informational and financial operations required by smart grid, communication systems are crucial, but they also make smart grid vulnerable to numerous cyber attacks. Smart grid cyber security has been widely concerned. The purpose of this paper is to explore the use of artificial intelligence technology in smart grid cyber security. Three methods in the field of artificial intelligence are highlighted: Machine Learning, Reinforcement Learning, and Ensemble Methods. This paper summarizes the benefits and drawbacks of their use of smart grid cyber security, and further makes a qualitative comparison of the three methods from multiple performance indicators.Highlights in Science Engineering and Technology160Three methods in the field of artificial intelligence are highlighted: Machine Learning, Reinforcement Learning, and Ensemble Methods and a qualitative comparison of the three methods from multiple performance indicators is made.Highlights in Science, Engineering and Technology['Luyao Xu']2023-12-31T00:00:00https://www.semanticscholar.org/paper/e5ebf9f76db6f9c7cdf3b8f77f2e3f193eea680f d1ec8aea4c4d889354f948b926a9f8642876bb54Artificial Intelligence (AI) has emerged as a groundbreaking technology with great potential to transform various industries, and the realm of physical education and sports is no exception. This research paper aims to explore the role of AI in enhancing performance and personalization in physical education and sports activities. The paper will provide an overview of AI technologies and their applications in this field, highlighting key advancements and their impact on athletes, coaches, and trainers. The use of AI in physical education and sports offers numerous benefits. AI-powered sensors and wearables can provide real-time feedback on biomechanics, technique, and performance, allowing athletes to identify and correct weaknesses. Machine learning algorithms can analyze vast amounts of data, enabling coaches to design personalized training programs tailored to each athlete’s unique characteristics and needs. AI also facilitates the development of virtual coaching platforms and simulators, providing athletes with immersive and dynamic training experiences. Additionally, AI can assist in injury prevention and management. Intelligent systems can assess an athlete’s physical condition, track fatigue levels, and provide recommendations for recovery and injury rehabilitation. Furthermore, AI can analyze historical data to predict potential injuries or evaluate an athlete’s readiness to return to competition. This proactive approach to injury prevention can significantly reduce the risk and impact of sport-related injuries. However, the implementation of AI in physical education and sports comes with challenges and ethical considerations. Privacy and security issues arise with the collection and analysis of athletes’ personal data. Moreover, the reliance on AI can lead to a lack of human connection and intuition in coaching relationships. These concerns need to be addressed to ensure the responsible and effective use of AI in this context. Overall, this research paper presents a comprehensive analysis of the role of AI in physical education and sports. By leveraging AI technologies, athletes can optimize their performance, trainers can provide personalized coaching, and the overall sports ecosystem can be enhanced. Future research will delve into the practical implications, implementation strategies, and potential ethical implications involved in incorporating AI into physical education and sports.JOURNAL GLOBAL VALUES00By leveraging AI technologies, athletes can optimize their performance, trainers can provide personalized coaching, and the overall sports ecosystem can be enhanced.JOURNAL GLOBAL VALUES['Dr. Vivekanad Dey']2023-12-31T00:00:00https://www.semanticscholar.org/paper/d1ec8aea4c4d889354f948b926a9f8642876bb54 92ea243ed56ca3709e588cbec270b0c22586e8caThis paper examines the current state and potential future applications of robots, artificial intelligence, and service automation (RAISA) in the hospitality, travel, and tourism industry, with a focus on Indonesia. The study utilizes a comprehensive review of literature and empirical data, including case studies, industry reports, and scholarly articles, to gather relevant information. The findings reveal that while the implementation of RAISA in the Indonesian tourism industry is still in its introductory stage, advancements in RAISA technology and decreasing costs make it increasingly viable for substituting human labor in repetitive and high-risk tasks. However, it is important to consider that not all service processes are suitable for automation or delegation to robots. The decision to automate or retain human labor depends on factors such as economic efficiency, customer satisfaction, company competitiveness, and other internal and external considerations. This research contributes to the existing literature by addressing the specific context of Indonesia and providing insights into the current state and potential future applications of RAISA in the hospitality, travel, and tourism industry. It highlights the scarcity of research in this area and emphasizes the need for further investigation, particularly in areas such as the economic rationale of service automation, the readiness of businesses to adopt RAISA, stakeholder attitudes towards service robots, the impact of RAISA on service quality and company competitiveness, and the ethical considerations associated with its implementation. The findings of this study offer valuable insights for industry practitioners, policymakers, and researchers aiming to understand the benefits, challenges, and implications of integrating RAISA in the Indonesian tourism industry.Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan1080The findings reveal that while the implementation of RAISA in the Indonesian tourism industry is still in its introductory stage, advancements in RAISA technology and decreasing costs make it increasingly viable for substituting human labor in repetitive and high-risk tasks.Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan['Agung Yoga Asmoro', 'Gareth Butler', 'Gerti Szili']2023-12-31T00:00:00https://www.semanticscholar.org/paper/92ea243ed56ca3709e588cbec270b0c22586e8ca 9b40378f1f62127b3d316dd58570005ac17f9cadThe article characterizes and defines the directions of artificial intelligence technology use in modern organizations and discusses those categories that will remain promising in the future. It highlights the main advantages and risks that currently exist with respect to the use of artificial intelligence and its development in Human Resources processes (HR processes). It was concluded that the main areas of use of artificial intelligence technology in modern organizations and categories that will continue to be promising in the future are defined: automation and optimization of processes; generating insights for decision making. It was emphasized that, in order to prevent the emergence of threats to humanity, in the process of developing artificial intelligence, specialists must establish certain restrictions and its developers must prioritize the issue of protection of user data and ensure control of its use.Revista de la Universidad del Zulia. Ciencias sociales y arte40It was concluded that the main areas of use of artificial intelligence technology in modern organizations and categories that will continue to be promising in the future are defined: automation and optimization of processes; generating insights for decision making.Revista de la Universidad del Zulia['N. Bieliaieva', 'Maryna Tymoshenko', 'Nataliia Nalyvaiko', 'V. Khmurova', 'Nina Sychova']2023-12-31T00:00:00https://www.semanticscholar.org/paper/9b40378f1f62127b3d316dd58570005ac17f9cad 8efefb485c63867cdfda0398f5a42129641709c6This research delves into the legal landscape concerning moral rights ownership within the realm of copyright for photographic works, specifically exploring their utilization within artificial intelligence (AI) algorithms. Focusing on the intersection of intellectual property law and AI technology, the study investigates the rights and protections accorded to creators of photographic works under copyright laws. The analysis encompasses the ethical and legal considerations pertaining to the use of these works in AI algorithms, scrutinizing issues related to attribution, integrity, and the recognition of creators. By examining the current legislative frameworks and jurisprudence, this research aims to provide insights into the challenges, implications, and potential regulatory adaptations required to ensure the preservation of moral rights for creators within the context of AI-utilized photographic works.Jurnal Penelitian250Examining the current legislative frameworks and jurisprudence aims to provide insights into the challenges, implications, and potential regulatory adaptations required to ensure the preservation of moral rights for creators within the context of AI-utilized photographic works.Jurnal Penelitian['Triya Indra Rahmawan', 'Mohammad Fahrial Amrulla', 'Sunarjo Sunarjo']2023-12-31T00:00:00https://www.semanticscholar.org/paper/8efefb485c63867cdfda0398f5a42129641709c6 539282eccf27d3d7f34193dfce56c9f97e9dbdccThe utilization of Artificial Intelligence has provided innovation in the world of education, one of which is the change in the implementation of learning evaluations that take place classically into automatic assessment. The purpose of the service is to provide knowledge insights and improve digital literacy for teachers through training and mentoring in the use of Artificial Intelligence assisted by Quizizz paper mode as an automatic assessment. The partners of this community service activity are elementary school teachers who join the Teacher Working Group (KKG) of Gugus Wijaya Kusuma, Ngaliyan District, Semarang City, totaling 70 people. The methods used were lectures and discussions. The results of the implementation of the service activities are first, the achievement of the objectives of the workshop activities in the form of providing knowledge insights and increasing digital literacy. Second, the achievement of the planned material targets is generally good. Third, there is an increase in knowledge that has increased with the average pretest and posttest scores from 50.85% to 83.42%. With this training, teachers have a good understanding of the use of quizizz paper mode as an automatic assessment to be used as one of the media in implementing learning evaluation.Jurnal SOLMA00With this training, teachers have a good understanding of the use of quizizz paper mode as an automatic assessment to be used as one of the media in implementing learning evaluation.Jurnal SOLMA['Aldina Eka Andriani', 'Sri Sulistyorini', 'Arini Estiastuti', 'Siti Maryatul Kiptiyah', 'Andarini Permata Chayaningtyas']2023-12-31T00:00:00https://www.semanticscholar.org/paper/539282eccf27d3d7f34193dfce56c9f97e9dbdcc f9e82baa7c42eba53997df421f599230b168a708While artificial intelligence (AI) enabled systems have shown impressive accuracy in detecting harmful content online, they are still not perfect and do not take into account the perspective of children in their design. The development of AI systems heavily relies on large datasets for training, and creating such datasets involves annotating vast amounts of data. Studies that involve children in dataset development also have their challenges, such as the possibility of re-traumatisation. Therefore, ethical considerations must be taken into account, such as obtaining informed consent, conducting design sessions with children and young people, and addressing implicit and explicit biases in AI filtering, profiling, and surveillance systems. It is crucial to involve children and young people in the design of AI systems that filter content to ensure ethical considerations are met. In this article we discuss the ethical concerns in AI development with children and young people, and also possible techniques that help mitigate such concerns.AoIR Selected Papers of Internet Research200The ethical concerns in AI development with children and young people are discussed, and also possible techniques that help mitigate such concerns are discussed.AoIR Selected Papers of Internet Research['Kanishk Verma', 'Tijana Milosevic', 'Brian Davis', "James O'higgins Norman"]2023-12-31T00:00:00https://www.semanticscholar.org/paper/f9e82baa7c42eba53997df421f599230b168a708 06c55210fba53e421679489fc95998a00bd0a0fcObjectives This study aimed to develop a capstone design course in the artificial intelligence convergence education major at the graduate school of education and apply it to actual classes to assess its educational effectiveness. Methods To achieve this, the artificial intelligence convergence education capstone design course was developed and implemented following the ADDIE model. Additionally, a survey consisting of problem-solving efficacy, task value, task authenticity, and satisfaction was conducted before and after the class to examine educational effectiveness. The collected data was analyzed using descriptive statistics and t-tests. Results Artificial intelligence convergence education capstone design course and teaching materials were developed for a total of 45 sessions, consisting of the first half of tool learning and example project implementation and the second half of presenting and discussing the research plan. The results showed statistically significant improvements across all areas after applying the course. Conclusions While the operation of capstone design courses for field research in the graduate school's artificial intelligence convergence education major is recommended, the lack of operational cases and efficacy evaluations is evident. Further research building upon these findings is anticipated to contribute to substantial course operations in the future. Korean Association For Learner-Centered Curriculum And Instruction00While the operation of capstone design courses for field research in the graduate school's artificial intelligence convergence education major is recommended, the lack of operational cases and efficacy evaluations is evident.Korean Association For Learner-Centered Curriculum And Instruction['Ji-Yun Kim', 'Kwi-Ok Kim']2023-12-31T00:00:00https://www.semanticscholar.org/paper/06c55210fba53e421679489fc95998a00bd0a0fc 15aee504d2edc3bf153fdfc3992c6c41c1d2a34eThe research aims to demonstrate the authority to prove deepfakes technically and jurisprudentially in artificial intelligence systems, as many people still fear the harm of this development in artificial intelligence, mimicking voice, and image, etc. The research contains the concept of artificial intelligence, details on the method of detecting deepfakes technically, the authority of deepfakes in jurisprudence, and then the result, which is: that ‏probable ‏evidence is considered one of the methods of proof considered in the judiciary in Islam. The means of proof are not limited to a specific number. ‏Therefore, those videos or audio clips that were detected through deepfake tools and whose falsification was not proven are some of the evidence that varies in strength and weakness. That is what the judge and his specialist experts estimate. This means according to the evidence, the authority of rights is proven, or dropped.The Journal of Social Studies00Journal of Social Studies['Dr. Sager Al-Sager']2023-12-31T00:00:00https://www.semanticscholar.org/paper/15aee504d2edc3bf153fdfc3992c6c41c1d2a34e 75f3ab67cec0aceabc5d8dfbede69270cc91c5cdThe radical changes brought about by technological advances in education transform and enrich learning experiences by going beyond traditional education methods. Artificial intelligence technologies have the potential to offer personalized educational experiences according to students' individual learning speeds, priorities, and needs. The gamification method makes learning more fun and interesting and increases students' motivation, and gamification components such as competition, cooperation, and reward allow students to learn more deeply. The combined use of artificial intelligence and gamification has the potential to provide students with a more effective, engaging, and personalized educational experience and will continue to shape changes in education in the future. This study aims to examine the role, potential, and effects of artificial intelligence (AI) and gamification, two innovative approaches in education. Through the example of Vakıf Participation Bank, the impact of the gamification method used in performance monitoring and internal communication on the internal communication, behavioral psychology, job performance, and training of the organization's employees is highlighted. With this study, it is thought that the use of gamification methods in internal communication, employee performance management, and training methods will benefit organizations by shedding light on their work in these areas.Orclever Proceedings of Research and Development170It is thought that the use of gamification methods in internal communication, employee performance management, and training methods will benefit organizations by shedding light on their work in these areas.Orclever Proceedings of Research and Development['Zeynep Erbaşı', 'Büşra Tural', 'İlknur Çoşkuner']2023-12-31T00:00:00https://www.semanticscholar.org/paper/75f3ab67cec0aceabc5d8dfbede69270cc91c5cd 67ee2528119ded3c860c95dca6b640839a668202Artificial intelligence (AI) is scaling rapidly in higher education globally. Considering the increasing significance of artificial intelligence in higher education (AIHEd) and the absence of a comprehensive review on it, this paper delves into the evolving landscape of artificial intelligence in higher education (AIHEd), its academic integrity and ethical concerns. The study has applied qualitative approach by using literature review as a research design and method to facilitate the aim of the study.The analysis of the paper reveals that AI has the potential to make a significant contribution to enhancing teaching and learning experiences, improving productivity and efficiency, as well as fostering inclusivity and accessibility. On the contrary, the increasing utilization of AI in higher education (AIHEd) raises the concerns about academic integrity and ethical issues, as it has the potential to lead to plagiarism, impede critical thinking, suppress creativity, and erode originality in teaching, research, and scholarship. Hence, upholding the integrity of scientific research requires a rigorous commitment to ethical and academic principles, placing human intelligence and critical thinking at the forefront of the research process. The advancement of artificial intelligence in higher education not only brings significant advantages, but also poses challenges to the fundamental principles, methodologies, standards, ethical considerations and academic integrity in both teaching and research. As a result, the primary focus should be on embracing the opportunities and benefits that arise from this advancement and effectively addressing any potential risks and challenges.Nepalese Journal of Development and Rural Studies290The analysis of the paper reveals that AI has the potential to make a significant contribution to enhancing teaching and learning experiences, improving productivity and efficiency, as well as fostering inclusivity and accessibility.Nepalese Journal of Development and Rural Studies['B. B. Khatri', 'Parbata Devi Karki']2023-12-31T00:00:00https://www.semanticscholar.org/paper/67ee2528119ded3c860c95dca6b640839a668202 d7964185f0a5c6bd29b269295b5ef12d304267f1Pena Justisia Media Komunikasi dan Kajian Hukum00Pena Justisia: Media Komunikasi dan Kajian Hukum['Ainuddin Ainuddin']2023-12-30T00:00:00https://www.semanticscholar.org/paper/d7964185f0a5c6bd29b269295b5ef12d304267f1 8f1e52a8674402d697b1712596d782ef95a7c211In the context of the best world practices the study is an overview of the legal regulation of the process of dig- ital transformation of public administration of agriculture in the Republic of Kazakhstan. The hypotheses of this study are based on the analysis of the legal nature of the world practice of digital transformation of eco- nomic development and international cooperation of modern states. The research revealed that agriculture is a branch of the economy that is particularly in need of the introduction of information and communication technologies and, consequently, the improvement of legal regulation of the activities of public administration in the digital reality. The analysis of state programs and national projects of the Republic of Kazakhstan pro- vides grounds for concluding that the digitalization of the Kazakh economy in agriculture is a priority. The dependence of the effectiveness of digital solutions in the agro-industrial complex on the level of public ad- ministration organization is substantiated: digitalization resources contribute to a more focused and result- oriented public policy through the use of legal monitoring capabilities. Digitalization is a rapidly growing trend in agriculture, when it does not just replace analog technologies traditionally used in practice, but de- velops new development options for effective solutions to problems in the industry, among which the intro- duction of legal mechanisms is of paramount importance. The study contains an analysis of the development of the legal foundations of digitalization in the industry, taking into account the problems at all levels of the process – from the issues of providing agricultural producers with an elementary level of Internet access to increasing the level of legal regulation of state management in the field of digitalization managementBulletin of the Karaganda University "Law Series00Bulletin of the Karaganda University. “Law Series”['E. Kuandykova', 'D. Baideldinov', 'T. Hoffmann']2023-12-30T00:00:00https://www.semanticscholar.org/paper/8f1e52a8674402d697b1712596d782ef95a7c211 59e5b21e30bd2cb56a6de3b99bb8fb0b5499bb09As a representative of the information revolution, the Internet began in the 1960s when ARPANET was born, and after decades of development and evolution, today it has formed a global information connection and interaction network. The greatest value of the Internet is that everyone can communicate and cooperate in real time across the limitations of time and space, which greatly improves the efficiency of group communication and collaboration, and then changes the organization and operation mode of people's work, business and social activities, and ultimately promotes the advancement of human social productivity. Therefore, the major research program of interpretable and universal next generation artificial intelligence methods faces the major strategic needs of the country in the development of artificial intelligence, takes the basic science issues of artificial intelligence as the core, develops the new method system of artificial intelligence, promotes the basic research and personnel training of artificial intelligence in China, and supports China's leading position in the new round of international scientific and technological competition. In this paper, the next generation of artificial intelligence innovation and application space are summarized, and the application of advanced algorithms is analyzed.International Journal of Computer Science & Information Technology (IJCSIT)014The next generation of artificial intelligence innovation and application space are summarized, and the application of advanced algorithms is analyzed.International Journal of Computer Science and Information Technology['Guanghui Wang', 'Yulu Gong', 'Mingwei Zhu', 'Jiaqiang Yuan', 'Kuo Wei']2023-12-30T00:00:00https://www.semanticscholar.org/paper/59e5b21e30bd2cb56a6de3b99bb8fb0b5499bb09 834b84868883e59dfbdaf9f9aa1135c3245c49c6ABSTRACT The fact that the developments taking place in the world during the 20th century are based on modernism has radically changed the structure of societies and human life. Modernization efforts that started in Turkey during the Tanzimat and constitutional monarchy periods continued during the republican period. However, in Turkey where the bourgeoisie could not be formed, modernism imposed from above led to several problems. For example, during the period of the Republic, the people who thought like Ahmet Hamdi Tanpınar have been constantly concerned about the future of Turkey in its Westernization project. Ahmet Hamdi Tanpınar's novel The Time Regulation Institute (1954) describes the efforts of the Republic of Turkey during the period of modernisation. The novel, which was written in a period when the problems of the modern system, namely the adjuster system, emerged, depicts a transition. The lives of the Turkish people have been transformed in the course of transition from constitutionalism to republic, from the traditional to modern. In this study, The Time Regulation Institute novel is examined, and qualitative semantic content analysis is conducted. For this purpose, various categories are created, and the transition from the traditional to modern life is examined within the framework of the words and expressions found in the novel. In the novel, a system is envisaged in which everything is in an orderly order, thanks to the institute, and there is an adjuster at the head of this mechanism. In the novel, it is emphasized that Turkish people have difficulty in accepting this, as the traditional natural and static time is disciplined with modernism. Keywords: The Time Regulation Institute novel, Modernism, Traditional Life, Modern Life, Real and Idealİletişim Kuram ve Araştırma Dergisi01İletişim Kuram ve Araştırma Dergisi['Hüseyin Çeli̇k']2023-12-30T00:00:00https://www.semanticscholar.org/paper/834b84868883e59dfbdaf9f9aa1135c3245c49c6 70583aaa2223e6ed1ebaa789c35107c48e893250The article reinforces the significance of scientific research through the complications of effective state regulation of Ukraine's agricultural sector, both during martial law and in the context of ensuring its long-term development in the post-war period. The fundamental concept of state regulation of the agricultural sector has been characterized. It has been discovered that, in the context of modern social changes, state regulation should act as a mediator in the relationships between the State, economic entities, and the population. Due to the aforementioned, the main purpose of the article is to establish the objective need for state regulation of Ukraine's agricultural sector in general, alongside the particulars of its implementation under martial law. For the determination of this aim, the following research methods were used: supervision, abstraction, scientific generalization and economic evaluation, that gave an opportunity to set forth conclusion. As a result, one of the most fundamental issues for the agricultural sector is determining the optimal combination of state regulation and self-regulation in practice. It is established that the state regulation of the agrarian sector in Ukraine under martial law has its own peculiarities related to the need to ensure national security and the responsibility to provide food for the population. To attain this objective, the author identifies the main points of vulnerability of Ukraine's agricultural sector under martial law and considers agricultural state regulation measures used to mitigate the negative impact of martial law on the state of the agricultural sector in 2023. The research concluded that, in addition to focusing on immediate problems, Ukraine's agricultural sector requires immediate attention and the creation of a long-term strategy for its future development. Furthermore, the following are the main areas of ensuring sustainable development of the agricultural sector in the post-war period: restoration of human capital and development of agricultural sector human potential; intensification of investment activity and maximum attraction of direct and indirect financial measures aimed at supporting farmers' economic activities; technological development and gradual restoration of technical capabilities for agricultural sector.PUBLIC ADMINISTRATION AND LAW REVIEW00PUBLIC ADMINISTRATION AND LAW REVIEW['Igor Konovalchuk', 'Victor Kovalоv']2023-12-30T00:00:00https://www.semanticscholar.org/paper/70583aaa2223e6ed1ebaa789c35107c48e893250 55eb296b89d135bbdef053f8ccb21b82a8577003Current events in the healthcare sector are aimed at amending the Labour Code, specifically section 93. Employees face more difficult working conditions as a result of this issue, despite the fact that this amendment is intended to protect them. The purpose of this thesis was to determine what information exists in academic, official, governmental, and discursive environments, as well as what relevant data can be extracted from the available sources to draw constructive conclusions. The entire subject is treated as a theoretical concept that analyses the situation from the standpoint of the Austrian school of economics and health economics and provides a useful perspective on the issue of health system regulation and reform.The findings reflect an opinionated reality that elicited a media response from the general public and medical personnel. There was an interim agreement on salary increases for doctors and non-medical staff in the health sector in this case. Although the Chamber of Deputies' action appears to be relatively settled, it is still practically unresolved whether doctors will be allowed to work up to twice as many hours of overtime, and although each 12-hour shift should be followed by eight hours of uninterrupted rest, we should probably brace ourselves for further possible Labor Code circumvention. Furthermore, it was noted that this and similar topics are rarely discussed in academic and scientific settings, and that there is a lack of a systemic perspective and evidence.New Perspectives on Political Economy00The entire subject is treated as a theoretical concept that analyses the situation from the standpoint of the Austrian school of economics and health economics and provides a useful perspective on the issue of health system regulation and reform.New Perspectives on Political Economy['Jan Neugebauer']2023-12-30T00:00:00https://www.semanticscholar.org/paper/55eb296b89d135bbdef053f8ccb21b82a8577003 8e52880b01ae2a914e44f53c3588071725972822Abstract: Currently, Russia is going through a difficult period in terms of economic development, overcoming internal economic difficulties, among which are demographic problems and limited consumer demand. These factors are aggravated by external factors, such as foreign trade barriers and limited access to borrowed funds in international markets, which leads to a noticeable decrease in state budget revenues. In the agricultural sector, restrictions in the availability of key resources and instability of distribution channels for agricultural products are also highlighted. These difficulties are especially significant for small businesses, both on a nationwide scale and at the regional level, in particular in the Republic of Tatarstan. To eliminate these problems and achieve long-term economic growth in the country, it is necessary to form and develop mechanisms of state regulation and support of small forms of business in the agricultural sector. The object of the study is economic relations that arise in the process of state regulation and state support of small forms of business in the agro-industrial complex. The purpose of the study is to characterize the current state and determine the prospects for the development of state regulation of small businesses in the agro-industrial complex of the Republic of Tatarstan. Research methods – abstract-logical, monographic, tabular-graphic, calculation-constructive, economic-statistical. The scientific novelty lies in the systematization and clarification of theoretical aspects and practical recommendations for the development of state regulation of small forms of management in the agro-industrial complex of the Republic of Tatarstan.Russian Journal of Management20Russian Journal of Management['Denis Volkov']2023-12-30T00:00:00https://www.semanticscholar.org/paper/8e52880b01ae2a914e44f53c3588071725972822 f590bc26a70c7b92ea6f0bed2154789587099c25Education is one of the important sectors in the development of a country. Indonesian education is faced with various challenges, both internal and external challenges. Indonesian Government Regulation (PP) Number 57 of 2021 concerning National Education being changes policy Number 4 of 2022. This changes cause uncertainty legally and administratively in formal carry out educational activities. Several important changes have been noted in PP 57 of 2021 concerning National Education period 4 of 2022. These changes include curriculum adjustments, building an assessment system, increasing the quality of the number of teachers and teaching staff and changes in education management at the school National level. With the issue arise, there is need for analyze the changes made in PP 57 of 2021 and their impact on national education system.  This research aim to investigates the impact of implementation new regulation policy Number 4 of 2022. The method used is literature study. The results show that there’s have been significant changes in PP 57 of 2021 which affect various positive aspects of national education  system. Adjusting the curriculum, improving the quality of teachers, and developing evaluations are important steps in improving the quality of education in Indonesia.Enrichment : Journal of Management240Enrichment : Journal of Management['Ade Iman Syahidan', 'Pupu Saeful Rahmat']2023-12-30T00:00:00https://www.semanticscholar.org/paper/f590bc26a70c7b92ea6f0bed2154789587099c25 65e783fadb5e30468269d1ea95b6ce571f292c3aA theoretical analysis of the development of the construction industry as an object of state regulatory influence was carried out, taking into account innovative factors. The main factors of such a process, which hold it back, have been clarified. From the standpoint of a systemic approach, the conditions that need to be formed in order to level the impact of negative trends during the implementation of state regulation of the development of the construction industry in Ukraine are highlighted. Areas of improvement of the mechanism of state regulation of development of the construction industry are highlighted. Improving the system of state regulation of the development of the construction industry requires the identification of key factors capable of contributing to the self-organization of the system. At the same time, the following directions for its improvement can be identified: development based on the existing structure, but with the redistribution of individual functions between the system's links; development on the basis of the existing order of implementation of management functions with quantitative combinations in relation to development subjects of the construction industry at all levels; program-targeted, which takes into account the existence of controlled and controlling subsystems in territorial and branch aspects. This direction is closely related to the regulation of the functionality of the control system at different levels. The key condition for improvement in relation to regulation is not only the presence of communication, but also the appropriate functional content. This direction is the most effective for ensuring the improvement of the system of state regulation of the development of the construction industry. It is shown that in the market economic system there is a need to gradually increase the efficiency of the functioning of its subsystems, both the most complex and the simplest (the country as a whole, industry, territory, individual economic entity). In the process of ensuring the development of the construction industry, as well as a separate administrative-territorial unit (region, district, settlement) of the product of construction production in the required time, in the required volume, with the required quality, the specific characteristics inherent in this territory will be inherent, which determine the choice of appropriate effective solutions . Such efficiency must be considered precisely from the standpoint of territorial development. Thus, the strategies of territorial development and development of the construction industry must take into account the means and ways of achieving the goals, as well as the corresponding indicators that will determine the realization of the main goal. Such indicators can belong to various organizational and technological principles of the construction process, types of structures and materials, techniques and technologies that reduce material intensity, labor intensity, energy intensity, as well as indicators that illustrate the level of investment and innovative efficiency of construction, which leads to a decrease in the cost of production, reduction of payback periods.State Formation20State Formation['Ivan Dragan', 'I. Dragan']2023-12-30T00:00:00https://www.semanticscholar.org/paper/65e783fadb5e30468269d1ea95b6ce571f292c3a 19a36e1e633455f290b1eb8544c5b2070655bca4One of the central concepts of the General Data Protection Regulation (GDPR) is the “data subject”. This notion in relation to the establishment of rights and obligations for controllers and processors becomes a common denominator in the implementation of this Regulation at the level of all Member States of the European Union. The Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (GDPR) was adopted, in order to protect the privacy of data subject, whether a parent, an young person or a child. However, starting with the title we can identify two different actions: to assure the protection of personal data and the free movement of this data within and outside the Union borders. In this context we must take the following into account: the reality of conceptual gaps in interpretation of this document; old or non-existent infrastructure; legislative bottlenecks and the risks involved in the protection of children’s data. Are parents, young people or children properly informed about their rights and the risks to which they are exposed in an era of digitalization? Can online school ensure the protection of children? Does the current infrastructure allow the optimal implementation of the General Data Protection Regulation? My research, in this context, has the aim of identifying gaps between information, infrastructure and the application of the GDPR, using the content analysis method and the questionnaire as a qualitative method of research. The expected results of this research are awareness by state institutions about the risks to which children are exposed in an era of digitalization and the awareness of the controllers about the obligation to ensure the protection of children’s data in the processing process.Digital Age in Semiotics &amp; Communication00This research has the aim of identifying gaps between information, infrastructure and the application of the GDPR, using the content analysis method and the questionnaire as a qualitative method of research.Digital Age in Semiotics &amp; Communication['Victoria-Delia Bunceanu']2023-12-30T00:00:00https://www.semanticscholar.org/paper/19a36e1e633455f290b1eb8544c5b2070655bca4 c14ea4cafa3de558373aa8e7048fec8ae9b0820eThis training on making village regulations aims to help village officials provide Village Minimum Service Standards in implementing the policy of the Minister of Home Affairs Regulation Number 2 of 2017 which consists of 6 minimum standards of facilities and infrastructure and 22 minimum standards of other public services. The implementation was carried out in 4 days starting with a focus group discussion, and continued with a workshop on making village regulations, the location of the service was carried out at the Paya Pasir Village Hall and Cempedak Lobang Village, Serdang Bedagai Regency. After the Focus Group Discussion, it was known that information from the village apparatus that the implementation of the Village Minimum Service Standards had not been fulfilled, this was due to the lack of understanding and insight of the village apparatus. The workshop was conducted as a form of socialization and understanding of the Minister of Home Affairs Regulation Number 2 of 2017 and training in making village regulations on technology-based village minimum service standards using website applications to make it easier for people to get public services from home using only smart phones that can be accessed by the village community and avoid brokers and illegal levies from irresponsible individuals or partiesABDIMAS TALENTA: Jurnal Pengabdian Kepada Masyarakat50ABDIMAS TALENTA: Jurnal Pengabdian Kepada Masyarakat['Fajar Ritonga', 'Agus Suriadi', 'Hendra Dermawan Siregar']2023-12-30T00:00:00https://www.semanticscholar.org/paper/c14ea4cafa3de558373aa8e7048fec8ae9b0820e e93bb3f04f4bb9702b106f937df53860f969c4c8Objective. The objective of our study is to analyse the status and peculiarities of tariff regulation of international trade in the XXI century. Methods. The following methods and techniques of cognition were used in the research process: analysis and synthesis, induction and deduction (to substantiate the importance and role of tariff regulation of international and foreign trade in the XXI century, to identify factors influencing the development of international trade), generalisation and systematisation (to substantiate the state and peculiarities of the development of tariff regulation of international trade in the XXI century), analysis of time series (to identify trends and patterns of tariff regulation of international trade in 2006-2022), graphical (for visual representation of the peculiarities of tariff dynamics in WTO countries). Results. The article notes that despite the changes that have taken place in international trade in general and in the system of its regulation in particular, tariff regulation remains the main authorised means of regulating international and external trade. By 2022, international trade is expected to reach USD 30 trillion, with trade in goods, especially manufactured goods, dominating the structure. It is established that one of the aspects of liberalising trade is to reduce or eliminate tariffs. It is noted that the reduction of tariffs is much slower in the period 2006-2021 than in the period 1996-2005. Average applied tariffs in WTO countries for all product groups will decrease from 10.1% in 2006 to 8.9% in 2021; tariffs on agricultural products will be significantly higher than on non-agricultural products (14.8% vs. 8% in 2021); average tariffs applied to all product groups were significantly lower in developed countries than in developing countries and LDCs; average tariffs applied by developed countries decreased by 1.7%, by developing countries by 1.7% and by LDCs by 1.2%; the highest average tariffs were recorded in Africa and the Americas and the lowest in Europe; the share of duty-free goods under the most favoured nation regime in the WTO countries has been steadily increasing; there has been a slight but steady decline in the share of tariff peaks, which are tariffs exceeding 15 per cent; the number of trade agreements, including preferential trade agreements, has been growing steadily, with agreements covering not only trade in goods but also trade in services, etc. According to the analysis of WTO data, in 2022 most WTO countries have an average bound tariff not exceeding 50%; average bound tariffs vary significantly across WTO countries and product groups; average bound tariffs for most WTO countries range from 20-59% for agricultural products, 10-39% for non-agricultural products; average applied tariffs for agricultural products range from 10-19%, up to 10% for non-agricultural products; significant discrepancies between average bound tariffs and average applied tariffs remain; in the majority of WTO countries, ad valorem tariffs dominate the tariff structure; there are significant differences between countries in the number of bound tariffs applied; in the vast majority of WTO countries, MFN tariffs are applied to a large number of products - the number of products subject to MFN tariffs varies between 5000 and 10000.TRADE AND MARKET OF UKRAINE10TRADE AND MARKET OF UKRAINE['Yu. H. Bocharova', 'T. V. Kozhuhova', 'O. Ishchenko', 'O. O. Mashoshyn']2023-12-30T00:00:00https://www.semanticscholar.org/paper/e93bb3f04f4bb9702b106f937df53860f969c4c8 56dd03e1d0427a52c66c56b48013747f5c15af73Journal of Economics and Business00Journal of Economics and Business['Richard J. Hunter', 'Héctor R. Lozada', 'John H. Shannon', 'Gary H. Kritz']2023-12-30T00:00:00https://www.semanticscholar.org/paper/56dd03e1d0427a52c66c56b48013747f5c15af73 cbd0611f974f29d398783e69f20b0a6ab5b9adfaEBIMCS130{'pages': '29-33'}['Shuang Zhang']2023-12-30T00:00:00https://www.semanticscholar.org/paper/cbd0611f974f29d398783e69f20b0a6ab5b9adfa 93baf545a329b91051afca1e70c523f88cc300faPena Justisia Media Komunikasi dan Kajian Hukum130Pena Justisia: Media Komunikasi dan Kajian Hukum['Ni Putu Sawitri Nandari', 'Ketut Elly Sutrisni', 'Wayan Suderana']2023-12-30T00:00:00https://www.semanticscholar.org/paper/93baf545a329b91051afca1e70c523f88cc300fa 766bf450c2d953fbf0b1609992836ab8b8b471c2Traitement du signal00Traitement du Signal['Emir Mustafa Efe', 'V. Böcekçi̇']2023-12-30T00:00:00https://www.semanticscholar.org/paper/766bf450c2d953fbf0b1609992836ab8b8b471c2 9becbf67eafa4a13248fea8748dded6ebc9c554fThe development of the platform economy has had a significant impact on the boundaries and structure of markets, has become a decisive factor determining the nature of horizontal and vertical interactions. The traditional consideration in neoinstitutional economic theory the "seller-buyer" interactions is increasingly giving way to the discussion of the so-called bilateral or multilateral markets (two-sided/multi-sided markets), the formation takes place around digital platforms. At the same time, there is no single definition of digital platforms in the economic literature, as well as a single classification of them. The classification of digital platforms may be based on various criteria that determine their position and functionality in different ways within the vertical value chain. This creates some difficulties for regulatory authorities related to fixing the specific status of digital platforms in regulatory documents as a kind of reference point defining a specific regulatory regime. In light of this, when determining the tasks facing the regulator, it is of particular relevance to analyze changes in the degree of consumer awareness in the context of the development of multilateral markets, the nature of competition between various distribution channels in multilateral markets, that is, between platforms and manufacturers of goods and services, as well as the analysis of value chains in new changing conditions.Journal of Economic Regulation00It is of particular relevance to analyze changes in the degree of consumer awareness in the context of the development of multilateral markets, the nature of competition between various distribution channels in multilateral markets, that is, between platforms and manufacturers of goods and services, as well as the analysis of value chains in new changing conditions.Journal of Economic Regulation['Maria E. Agamirova']2023-12-30T00:00:00https://www.semanticscholar.org/paper/9becbf67eafa4a13248fea8748dded6ebc9c554f dcdd224dc3c00b73ec247254c6fc7cd5b5f0faffKorea Law Review00Korea Law Review['Min-Jae Lee', 'Ja-Young Yoon', 'Jin-Mo Gu', 'Hee-Jun Han']2023-12-30T00:00:00https://www.semanticscholar.org/paper/dcdd224dc3c00b73ec247254c6fc7cd5b5f0faff 2472e4113ba8b763274bfb25722402ffa3bb70b5This comprehensive review explores the transformative role of artificial intelligence (AI) and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization. The study systematically examines the intersection of AI and renewable energy, highlighting the emergence of AI-driven solutions and their impact on enhancing the efficiency, reliability, and sustainability of renewable energy systems. The review begins with an overview of renewable energy and its growing importance in the global energy mix, emphasizing the critical role of AI in this sector. It then delves into the methodological approach, outlining the research strategy and criteria for selecting relevant AI and data science studies in renewable energy. This includes a detailed analysis of data collection and synthesis techniques used to identify key AI innovations and trends in solar and wind energy optimization. The core of the review comprises an extensive literature survey on AI applications in solar and wind energy systems. It covers fundamental principles of AI in renewable energy, state-of-the-art data science techniques, and emerging trends such as novel AI algorithms and their integration into renewable energy grids. The study evaluates the technological, economic, and environmental impacts of AI in renewable energy, addressing challenges and proposing solutions. Furthermore, the review discusses the role of standards and regulatory frameworks in AI-driven renewable energy and the implications for stakeholders. It concludes with a summary of AI's role in enhancing renewable energy, future prospects, and recommendations for industry leaders and policymakers. This review provides a thorough understanding of the current state and future potential of AI in renewable energy, offering valuable insights for researchers, industry professionals, and policymakers engaged in the field of sustainable energy.World Journal of Advanced Research and Reviews474This comprehensive review explores the transformative role of artificial intelligence and data science in the renewable energy sector, with a particular focus on solar and wind energy optimization, and evaluates the technological, economic, and environmental impacts of AI in renewable energy.World Journal of Advanced Research and Reviews['Donald Obinna Daraojimba', 'Nzubechukwu Chukwudum Ohalete', 'Adebayo Olusegun Aderibigbe', 'Emmanuel Chigozie Ani', 'Peter Efosa Ohenhen', 'Bukola A. Odulaja']2023-12-30T00:00:00https://www.semanticscholar.org/paper/2472e4113ba8b763274bfb25722402ffa3bb70b5 d5c18e0b01f43ef167e7430a9e50cc78ed1db187The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI’s capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an “AI scrutiny” phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI’s role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI’s involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI’s capabilities while upholding stringent academic standards can be envisioned.Clinics and Practice782There is a plea for rigorous research to assess the extent of AI’s involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity.Clinics and Practice['Jing Miao', 'C. Thongprayoon', 'S. Suppadungsuk', 'Oscar A. Garcia Valencia', 'F. Qureshi', 'W. Cheungpasitporn']2023-12-30T00:00:00https://www.semanticscholar.org/paper/d5c18e0b01f43ef167e7430a9e50cc78ed1db187 d96721ca4878c1835b891bd0df2fcee6fe5496d1In the rapidly evolving landscape of supply chain management (SCM), digital transformation has become a cornerstone for achieving competitive advantage. This paper explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) as catalysts in this transformation, driving significant value creation across various facets of SCM. Through a comprehensive literature review, including an analysis of 12 key papers, this study examines the integration of AI and ML in enhancing supply chain operations, from predictive analytics in demand forecasting to real-time decision-making in logistics and inventory management. The findings highlight the transformative impact of these technologies in optimizing efficiency, reducing costs, and improving overall supply chain resilience. The paper also addresses the challenges and ethical considerations inherent in implementing AI and ML, such as data privacy and workforce implications. Concluding with a look towards the future, this study underscores the growing importance of AI and ML in shaping the next generation of SCM practices. This research not only contributes to the academic discourse on digital supply chain transformation but also offers practical insights for industry professionals navigating this digital shift.International journal of supply chain management112This study examines the integration of AI and ML in enhancing supply chain operations, from predictive analytics in demand forecasting to real-time decision-making in logistics and inventory management and underscores the growing importance of AI and ML in shaping the next generation of SCM practices.International Journal of Supply Chain Management['Pratyush Kumar Singh']2023-12-30T00:00:00https://www.semanticscholar.org/paper/d96721ca4878c1835b891bd0df2fcee6fe5496d1 aa6565134197c385f44856c326d929e695b6ac68The evolution of cybersecurity has spurred the emergence of autonomous threat hunting as a pivotal paradigm in the realm of AI-driven threat intelligence. This review navigates through the intricate landscape of autonomous threat hunting, exploring its significance and pivotal role in fortifying cyber defense mechanisms. Delving into the amalgamation of artificial intelligence (AI) and traditional threat intelligence methodologies, this paper delineates the necessity and evolution of autonomous approaches in combating contemporary cyber threats. Through a comprehensive exploration of foundational AI-driven threat intelligence, the review accentuates the transformative influence of AI and machine learning on conventional threat intelligence practices. It elucidates the conceptual framework underpinning autonomous threat hunting, spotlighting its components, and the seamless integration of AI algorithms within threat hunting processes.. Insightful discussions on challenges encompassing scalability, interpretability, and ethical considerations in AI-driven models enrich the discourse. Moreover, through illuminating case studies and evaluations, this paper showcases real-world implementations, underscoring success stories and lessons learned by organizations adopting AI-driven threat intelligence. In conclusion, this review consolidates key insights, emphasizing the substantial implications of autonomous threat hunting for the future of cybersecurity. It underscores the significance of continual research and collaborative efforts in harnessing the potential of AI-driven approaches to fortify cyber defenses against evolving threats.arXiv.org01This review navigates through the intricate landscape of autonomous threat hunting, exploring its significance and pivotal role in fortifying cyber defense mechanisms, and elucidates the conceptual framework underpinning autonomous threat hunting, spotlighting its components, and the seamless integration of AI algorithms within threat hunting processes.ArXiv['Siva Raja Sindiramutty']2023-12-30T00:00:00https://www.semanticscholar.org/paper/aa6565134197c385f44856c326d929e695b6ac68 40b50bf06c308f669e0f73a923d5252e8b2145a1This study delves into the attitudes of undergraduate students in Mangaluru City towards the integration of Artificial Intelligence (AI) in educational settings. The purpose of this research is to uncover potential disparities in student perspectives based on their field of study and academic year. Employing a structured Likert-scale questionnaire with 30 questions across three hypotheses, the research methodology involved collecting responses from 268 participants representing diverse academic disciplines. Statistical analyses, including Kruskal-Wallis tests and post hoc tests, were conducted to examine the significance of differences in attitudes. Findings reveal that academic discipline plays a role in shaping students' attitudes toward AI integration in education. The data also suggests that as students use AI-powered apps more frequently in their daily lives, they also tend to be more comfortable with AI-integrated educational tools. The study proved that there was no significant difference in the perceptions of students towards the impact of AI integration on the role of educators in the learning process based on their academic year. Limitations include the regional focus on Mangaluru City, which may impact generalizability. Educators can use the insights to tailor AI integration strategies based on disciplinary nuances, enhancing the learning experience. Socially, the study contributes to the discourse on AI in education, emphasizing the importance of considering diverse student perspectives. The originality of this work lies in its focus on a specific geographic region, shedding light on contextspecific attitudes that can inform localized educational policies and practices.SJCC Management Research Review81It is proved that there was no significant difference in the perceptions of students towards the impact of AI integration on the role of educators in the learning process based on their academic year, and the insights can be used to tailor AI integration strategies based on disciplinary nuances, enhancing the learning experience.SJCC Management Research Review['Carrel Sharel Pereira', 'Joyce Muriel Mascarenhas', 'Shivshankar Bhatt', 'Sharol Savitha Rodrigues', 'Ruth Samantha Stephen Almeida']2023-12-30T00:00:00https://www.semanticscholar.org/paper/40b50bf06c308f669e0f73a923d5252e8b2145a1 755665b2a6c01581666142748a2bc4f626f41df2This paper discusses the evolution of crimes related to artificial intelligence technology, responses, and mitigation initiatives the European Union took. This study aims to provide information about the early development of AI and how it is used as a tool for cybercrime called Deepfake. The qualitative research methodology will collect data for this study to gain information from various sources, such as pertinent books, news articles, and journals. The authors found that artificial intelligence, or AI, has become essential to daily life. While it has provided benefits, it also created new problems, such as artificial child pornography and porn being exploited for various reasons. This becomes a significant international risk to cybersecurity. The sensitivity and intricacy of this topic call for coordinated efforts from many different parties. In response to this problem, the European Union has formed solutions that Indonesia could learn from.Jurnal Dinamika Global321The authors found that artificial intelligence, or AI, has become essential to daily life, but it also created new problems, such as artificial child pornography and porn being exploited for various reasons.Jurnal Dinamika Global['M. H. Hussin', 'Mutia Hariati Salwa Prilia Ginano']2023-12-30T00:00:00https://www.semanticscholar.org/paper/755665b2a6c01581666142748a2bc4f626f41df2 4535ff9997bdc75389304e1077cf67840f548c8cThis study provides a comprehensive review of the advancements in predictive maintenance within the oil and gas industry, focusing on the integration and impact of Artificial Intelligence (AI) and Data Science. The primary objective was to evaluate how AI and data science have transformed maintenance practices from traditional methods to more advanced, predictive approaches. The methodology involved a systematic literature review, utilizing databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science. The search strategy was centered around keywords related to AI, data science, and predictive maintenance in the oil and gas sector, with a focus on literature published from 2010 onwards. The findings reveal that AI and data science significantly enhance predictive maintenance strategies. AI algorithms and data analytics have enabled more accurate predictions of equipment failures and optimized maintenance scheduling, leading to reduced downtime and operational costs. The study also identifies challenges, including the complexity of data management and the need for high-quality, real-time data. Opportunities for future advancements lie in developing more robust AI models capable of adapting to the industry's dynamic environment. The study recommends that industry stakeholders invest in workforce training for AI-based systems and that policymakers develop frameworks supporting ethical AI use. Future research directions include exploring the integration of AI with other emerging technologies and developing sustainable maintenance practices. The study concludes that AI's continuous evolution will play a crucial role in shaping the future of maintenance strategies in the oil and gas industry.World Journal of Advanced Research and Reviews330It is concluded that AI's continuous evolution will play a crucial role in shaping the future of maintenance strategies in the oil and gas industry, and that industry stakeholders invest in workforce training for AI-based systems and policymakers develop frameworks supporting ethical AI use.World Journal of Advanced Research and Reviews['Nzubechukwu Chukwudum Ohalete', 'Adebayo Olusegun Aderibigbe', 'Emmanuel Chigozie Ani', 'Peter Efosa Ohenhen', 'A. Akinoso']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4535ff9997bdc75389304e1077cf67840f548c8c 87d20e9b3b9e4dd91c58180d0ebd077cd4d9d758With its potential use in many areas including food and beverage sector, artificial intelligence has become one of the most prominent topics recently, partly due to the new AI tools. This study evaluates the competencies of ChatGPT (versions 3.5 and 4) and Bard in relation to food recipes by assigning tasks in five different areas: recipe correction, recipe adaptation, recipe detailing, time management, and presentation. The responses were then analyzed. It was observed that ChatGPT 4 outperformed the other tools in recipe correction, time management and presentation tasks while it gave similar results with ChatGPT 3.5 in recipe adaptation and recipe detailing tasks. Bard performed better than ChatGPT 3.5 in recipe correction but performed worse than both tools in all other tasks. Subsequent discussions highlighted the strengths and limitations of the tools. While these tools’ scores may not yet outperform a professional chef in the assigned tasks, they can be alternative and supportive assets in the gastronomy field considering their rapid response rates. Along with the potential use of the tools in tasks such as adapting recipes, managing time, and generating presentation ideas, the ongoing development and interaction of AI tools and related technologies could contribute significantly to the food industry in the future.Journal of Tourism and Gastronomy Studies00These tools’ scores may not yet outperform a professional chef in the assigned tasks, but they can be alternative and supportive assets in the gastronomy field considering their rapid response rates.Journal of Tourism and Gastronomy Studies['A. Değerli', 'Nevruz Berna Tatlisu']2023-12-30T00:00:00https://www.semanticscholar.org/paper/87d20e9b3b9e4dd91c58180d0ebd077cd4d9d758 01fe73dfd27509507d80d99e05f06af7b0d0ea04The article explores the limitation of one of the privacy and data protection rights when using generative AI models. The identified limitation is assessed from the perspective of the ‘essence’ of the right to the protection of personal data. With the further aim of assessing the limitation, the author explores whether the right to be forgotten (RTBF) is relevant or effective in an AI/machine learning context. These considerations are focused on the technical problems encountered when applying the strict interpretation of the RTBF. In particular, the antagonism between, on the one hand, the values of privacy and data protection rights, and on the other, the technical capabilities of the producer of the generative AI models, is further analysed in this context. As the conclusion emphasizes that the RTBF cannot be practicably or effectively exercised in the machine learning models, further considerations of this exposed limitation are presented. The proportionality principle, as an instrument that supports the proper application if there is any limitation of the conflicting rights, has been utilized to depict the qualitative approach. The integration of this principle supports the conclusion by identifying a more efficient way to address some regulatory issues. Hence, the conclusion of the article presents some suggested solutions as to the interpretation of this right in the light of this new technological advancement. Ultimately, the paper aims to address the legal conundrum of how to balance the conflict between the interest of innovative use of the data (the data producer’s right) and privacy and data protection rights.Przegląd Prawniczy Uniwersytetu im. Adam Mickiewicza450The paper aims to address the legal conundrum of how to balance the conflict between the interest of innovative use of the data (the data producer’s right) and privacy and data protection rights.Przegląd Prawniczy Uniwersytetu im. Adam Mickiewicza['Anna Anna Popowicz-Pazdej']2023-12-30T00:00:00https://www.semanticscholar.org/paper/01fe73dfd27509507d80d99e05f06af7b0d0ea04 4e6ae2c4db6a769a8809e171086adb61d6a1fe63This study examines the ethical and sociological implications of artificial intelligence (AI) and its Stigmatization in Contemporary American Science Fiction (CASF), with a focus on AI dominance and societal marginalization. It employs a methodological approach that combines Goffman's (1986) theory of Stigmatization with Link and Phelan's (2001) framework of Stigmatization, utilizing textual analysis to explore CASF's portrayal of AI. The findings reveal that CASF largely casts AI in a negative light, depicting it as a threat to humanity rather than a potential ally. This portrayal reinforces societal biases against AI, which could lead to its Stigmatization and marginalization. CASF also often depicts the victims of AI stigmatization as marginalized groups, such as the poor, the disabled, and the elderly. The study argues that CASF plays an important role in shaping public perceptions of AI. Its largely negative portrayal of AI could have harmful consequences, such as Stigmatization and marginalization, which highlights the need for a more nuanced and critical understanding of AI in society.Qlantic Journal of Social Sciences and Humanities00Qlantic Journal of Social Sciences and Humanities['Amina Shafi', 'Qasim Shafiq']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4e6ae2c4db6a769a8809e171086adb61d6a1fe63 cd6f1767d0308191ffe918437e6a1992a9e98f33This research explores the implications of Artificial Intelligence (AI) in deciphering the multifarious manifestations of online Radicalisation, Extremism, and possible recruitment. It analyses AI's capabilities in disseminating positive representations and counter-narratives, its instrumental role in analysing positive imagery, optimising platform reach, and its pivotal contribution to global efforts to combat online extremism. However, AI's dualistic nature underscores the need for its ethical and responsible utilisation to curb extremist content effectively. It necessitates the development of balanced, nuanced, and coherent approaches, amalgamating enhanced analytical paradigms, collaborative efforts, ethical frameworks, strategic insight, and diversified applications. The formulation of such multifaceted approaches and cohesive strategies is paramount in navigating the intricate terrains of online extremism and in enhancing societal resilience against the proliferation of extremist ideologies. The study concludes that the strategic and ethical deployment of AI technologies is pivotal in reshaping digital discourse and in the collective endeavour to counterbalance extremist ideologies.Fall 202300The study concludes that the strategic and ethical deployment of AI technologies is pivotal in reshaping digital discourse and in the collective endeavour to counterbalance extremist ideologies.Fall 2023['Muhammad Irfan', 'Ziyad Abdulaziz Almeshal', 'Muhammad Anwar']2023-12-30T00:00:00https://www.semanticscholar.org/paper/cd6f1767d0308191ffe918437e6a1992a9e98f33 f4f159595cdcf05d8d82da062288528ecced7a68"Paraphrasing in ESAP: Teacher-Guided, AI-Assisted or Communicative Activities. At the heart of progress in academia lies the principle of building upon other people’s ideas, linguistic expressions and/or scientific endeavours by taking note of invariably crediting the original author (Mori 2018). One way of doing this is by the usage of paraphrasing. Though paraphrasing is regarded as the prerogative of successful academic thinking and writing, what it means exactly is still ambiguous to some extent. After undertaking to formulate some working definitions, we discuss paraphrasing techniques and activities in the context of teaching English for Specific Academic Purposes to students majoring in Biology. The activities considered are grouped into three main categories with respect to the procedure involved: teacher-guided (with a focus on certain parts of a sentence/text as selected by the teacher), AI-assisted (linked to the usage of automated paraphrasing tools) and communicative (tasks having a connection to real-world communicative needs). Some examples are discussed with the purpose of determining their appropriateness in a teaching context. The article sketches an initial paraphrasing teaching toolkit, developing mainly the first category, and ends with considerations regarding future research, such as the development and implementation of AI-assisted tasks and the analysis of inferential and rhetorical processes. Keywords: paraphrasing, English for Specific Academic Purposes, English as a Second Language teaching, ESP teaching activities, artificial intelligence in education."Studia Universitatis Babeș-Bolyai Philologia00The article sketches an initial paraphrasing teaching toolkit, developing mainly the first category, and ends with considerations regarding future research, such as the development and implementation of AI-assisted tasks and the analysis of inferential and rhetorical processes.Studia Universitatis Babeș-Bolyai Philologia['Adina-Maria Mezei']2023-12-30T00:00:00https://www.semanticscholar.org/paper/f4f159595cdcf05d8d82da062288528ecced7a68 adb249b89bf9128607d269262248d582677732e4The development of intelligent machines that can do activities that are typically performed by people, such as learning, reasoning, problem-solving, vision, and natural language processing, is known as artificial intelligence (AI), often referred to as machine intelligence. Numerous industries, including healthcare, banking, transportation, education, and entertainment, can benefit from the application of AI technologies. They are designed to operate independently and adjust to shifting conditions and surroundings. AI technology includes, among other things, machine learning, natural language processing, computer vision, robotics, and cognitive computing. While AI has the potential to revolutionise many aspects of daily life, it also brings up ethical, social, and legal issues that need to be thoroughly considered and addressed. In general, artificial intelligence (AI) is revolutionising the marketing sector by empowering advertisers to develop more customised, successful, and efficient marketing programmes that foster client loyalty and company expansion. Marketers may obtain a competitive edge and remain ahead of the curve in a field that is changing quickly by utilising AI-powered tools. International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 12 | December - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM27813 | Page 2 This essay seeks to comprehend how contemporary technology, such artificial intelligence, affect Indian consumers' impulsive purchasing habits, particularly in the retail fashion sector. AI was used to investigate the effects of factors including the length of the transaction, suggested items, product information, and human interaction on impulsive purchases. Using artificial intelligence and how it might increase sales by drawing customers to their stores or online. Key word: Artificial intelligence, MarketingINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00How contemporary technology, such artificial intelligence, affect Indian consumers' impulsive purchasing habits, particularly in the retail fashion sector is sought, particularly in the retail fashion sector.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Dr. Asha Bhatia']2023-12-30T00:00:00https://www.semanticscholar.org/paper/adb249b89bf9128607d269262248d582677732e4 a5c5a4db4aae49aad82cc821eb162adab07f5c4bResearch background: The twin pressures of economic downturn and climate change faced by countries around the world have become more pronounced over the past decade. A renewable energy transition is believed to play a central role in mitigating the economic-climate paradox. While the architectural and computational power of artificial intelligence is particularly well suited to address the challenges of massive data processing and demand forecasting during a renewable energy transition, there is very scant empirical assessment that takes a social science perspective and explores the effects of AI development on the energy transition. Purpose of the article: This paper aims to answer two key questions: One is, how does AI software development promote or inhibit the shift of energy consumption towards renewables? The other is, under what policy interventions does AI software development have a more positive effect on promoting renewable energy consumption? Methods: We employ a dataset of 62 economies covering the period 2011–2020 to analyze the impact of AI software development on the energy transition, where possible confounders, including political and economic characteristics and time-invariant elements, are controlled using fixed-effects estimation along with specified covariates. Findings & value added: AI software development can promote the energy transition towards renewables. There is suggestive evidence that the core mechanism linking such a positive relationship tends to lie in improving innovation performance in environmental monitoring rather than in green computing. Government support for R&D in renewable energy technologies is found to be significantly beneficial for harnessing the positive impact of AI software development on the energy transition. Compared to non-market-based environmental policies, market-based environmental policies have a more significant positive moderating effect on the relationship between AI software development and energy transition.Oeconomia Copernicana727Government support for R&D in renewable energy technologies is found to be significantly beneficial for harnessing the positive impact of AI software development on the energy transition, and market-based environmental policies have a more significant positive moderating effect on the relationship between AI software development and energy transition.Oeconomia Copernicana['Hua-Tang Yin', 'Jun Wen', 'Chun‐Ping Chang']2023-12-30T00:00:00https://www.semanticscholar.org/paper/a5c5a4db4aae49aad82cc821eb162adab07f5c4b e32b26e249a0f2eb41188799c7544dc78db29a7eThe worldwide trend of digitisation across all aspects of society necessitates a fresh approach to the management system in the healthcare sector of Ukraine. The use of artificial intelligence (AI) tools in medicine opens up opportunities for transforming the healthcare sector by creating an effective and personalised approach to medical services. Aims: The purpose of the study was a comprehensive analysis of the current role of artificial intelligence in Ukrainian medicine and the projected future impact of its development. Methodology: The work was completed by utilising several common scientific methods of cognition: logical and structural analysis, abstraction, comparison, induction, and deduction, as well as concretisation and formalisation. Results The benefits resulting from the active implementation of artificial intelligence technologies in medicine were analysed and evaluated in the article. The risks that will accompany the further rooting of AI in medical information systems were identified and a number of appropriate preventive measures were proposed. The experience of developed countries in the transformation of medicine through the use of artificial intelligence capabilities was studied. The feasibility and prospects of using AI technologies in various areas of healthcare were analysed. A number of tools and technologies were proposed to ensure an adequate level of security and protection of personal information. Scientific Novelty: The principles for adapting the existing algorithms of the medical industry in Ukraine to meet the requirements of digitalisation have been formulated, the expediency of transformation in this area was substantiated. Conclusion: It was substantiated that the increasing utilisation of artificial intelligence enables the ability to produce precise and efficient decisions within intricate analytical procedures. The results of the study may be of practical value for the process of optimising the modern medical industry in Ukraine in the context of globalisation of the introduction of artificial intelligence technologies.Futurity Medicine02It was substantiated that the increasing utilisation of artificial intelligence enables the ability to produce precise and efficient decisions within intricate analytical procedures within intricate analytical procedures.Futurity Medicine[]2023-12-30T00:00:00https://www.semanticscholar.org/paper/e32b26e249a0f2eb41188799c7544dc78db29a7e 3af89f5ed55b68d9c3091d23cd2143b88c0683d2The artificial intelligence (AI) device is powered by an advanced Snapdragon platform from Qualcomm Technologies and boasts a range of sensors that enable contextual and ambient compute interactions. The Humane AI Pin offers a plethora of features that enhance our daily lives, including Contextual AI: The device can understand and respond to our natural language commands, allowing us to control music, make calls, send texts, and set reminders with ease. Ambient Awareness: The AI Pin's sensors provide real-time information about our surroundings, enabling it to adjust audio volume, provide notifications, and translate languages in context. Seamless Integration: The device eliminates the need for a smartphone, allowing us to interact with AI without the need to constantly look at a screen. Establishing robust frameworks for AI governance, promoting transparency, and prioritizing ethical considerations will be essential in harnessing the full potential of AI while minimizing its risks. Ultimately, a multidimensional perspective is crucial to charting a course for AI that aligns with human values, fosters responsible innovation, and enhances the overall well-being of society in the human era.450The artificial intelligence (AI) device is powered by an advanced Snapdragon platform from Qualcomm Technologies and boasts a range of sensors that enable contextual and ambient compute interactions and eliminates the need for a smartphone.4[]2023-12-30T00:00:00https://www.semanticscholar.org/paper/3af89f5ed55b68d9c3091d23cd2143b88c0683d2 55a304898b50d2c5c367bedea3ecb933a1847e91 <p class="MsoNormal"><i><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-ansi-language: EN-US;">As organizations progressively integrate artificial intelligence (AI) into their operations, the role of human resource (HR) managers becomes monumental in navigating the complex landscape of AI practices and challenges. This study aimed to investigate HR managers' perceptions concerning AI meaning, its usage in daily business activities, presence of AI in HR departments, and opportunities and barriers for AI adoption.</span> </i><i><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-ansi-language: EN-US;">The research employed a single-method approach of questionnaires to gather insights from a diverse sample of HR managers across miscellaneous medium/large enterprises. The findings revealed a nuanced perspective among HR professionals, with a spectrum of attitudes ranging from enthusiasm for AI's potential to concerns about its impact on traditional HR functions. There are a practical and theoretical aspects of this study that are relevant to every industry in determining the practices and opportunities of AI in HRM which improve efficiency, reduce various costs, enhance profitability and add value to overall business.</span></i><i></i></p> Nauka i tehnologija03Investigation of HR managers' perceptions concerning AI meaning, its usage in daily business activities, presence of AI in HR departments, and opportunities and barriers for AI adoption revealed a nuanced perspective among HR professionals.Nauka i tehnologija['Azra Ahmić']2023-12-30T00:00:00https://www.semanticscholar.org/paper/55a304898b50d2c5c367bedea3ecb933a1847e91 4b36908129a1edb5618af44c39e5a5eccfdf379fScreening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.Journal of Cardiovascular and Thoracic Research431This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.Journal of Cardiovascular and Thoracic Research['Arian Zargarzadeh', 'Elnaz Javanshir', 'Alireza Ghaffari', 'Erfan Mosharkesh', 'Babak Anari']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4b36908129a1edb5618af44c39e5a5eccfdf379f a65d986afca4321ed47200bf13e5204534b1546dThis research article delves into the impactful integration of artificial intelligence (AI) within the criminal justice system, exploring its transformative implications on crime detection, prevention, and adjudication. Examining applications such as predictive policing, automated legal analysis, facial recognition, and sentencing algorithms, the study highlights the potential benefits, including increased efficiency and accuracy. However, ethical concerns surrounding bias, transparency, and privacy necessitate careful consideration. The article underscores the need for a balanced approach to harnessing AI's potential while addressing these ethical challenges. As AI continues to evolve, collaborative efforts among policymakers, legal professionals, and technologists are imperative to ensure responsible implementation, fostering a criminal justice system that is both technologically advanced and ethically sound. The qualitative research methodology has been applied in the following article.Qlantic Journal of Social Sciences102The article underscores the need for a balanced approach to harnessing AI's potential while addressing these ethical challenges, and highlights the potential benefits of predictive policing, automated legal analysis, facial recognition, and sentencing algorithms.Qlantic Journal of Social Sciences['Sidra Kanwel', 'Muhammad Imran Khan', 'Muhammad Usman']2023-12-30T00:00:00https://www.semanticscholar.org/paper/a65d986afca4321ed47200bf13e5204534b1546d 2d4b99d56bac1454d7801e337b7a1b47beff6a49This study explores the transformative role of artificial intelligence (AI) in environmental health and public safety within the USA, focusing on pollution monitoring, emergency response, and sustainable practices for public. With the growing challenges posed by climate change, pollution, and emerging public health threats, the integration of Artificial Intelligence (AI) in environmental health and public safety strategies has become imperative. This comprehensive review explores the diverse array of AI applications implemented in the United States to address environmental issues and enhance public safety measures. The paper analyzes the multifaceted role of AI across various domains, including air and water quality monitoring, disease surveillance, disaster response, and infrastructure resilience. The advancements in AI technologies that have revolutionized data collection, analysis, and prediction in environmental health are examined. Machine learning algorithms, sensor networks, and satellite imagery are examined as tools for real-time monitoring and early detection of environmental hazards. Additionally, the paper investigates the integration of AI in public health surveillance systems, showcasing how predictive analytics and data-driven models contribute to the identification and containment of infectious diseases. Furthermore, the study sheds light on the incorporation of AI in disaster management, emphasizing the role of predictive modeling and risk assessment in optimizing emergency response strategies. The implementation of smart city technologies and intelligent infrastructure systems is discussed, highlighting how AI contributes to enhancing public safety and minimizing the impact of natural disasters. The review also critically evaluates the ethical, legal, and privacy considerations associated with the widespread adoption of AI in environmental health and public safety initiatives. It addresses concerns related to data security, algorithmic biases, and the need for transparent and accountable governance frameworks. Through an in-depth analysis of case studies, policies, and initiatives, this review provides insights into the successes and challenges of AI implementation in the USA. It concludes with recommendations for future research directions and policy considerations to ensure the responsible and effective integration of AI technologies in safeguarding environmental health and public safety. The findings presented in this review contribute to the broader discourse on leveraging AI for sustainable and resilient communities in the face of evolving environmental and public health challenges.World Journal of Advanced Research and Reviews01This study explores the transformative role of artificial intelligence (AI) in environmental health and public safety within the USA, focusing on pollution monitoring, emergency response, and sustainable practices for public safety, and analyzes the multifaceted role of AI across various domains.World Journal of Advanced Research and Reviews['Adedayo Adefemi', 'Emmanuel Adikwu Ukpoju', 'Oladipo Adekoya', 'Ayodeji Abatan', 'Abimbola Oluwatoyin Adegbite']2023-12-30T00:00:00https://www.semanticscholar.org/paper/2d4b99d56bac1454d7801e337b7a1b47beff6a49 67e5dccb9111c5687e1e9288d0d6fc09af3c00e8Introduction: The document presents a comprehensive review of the utilization of Artificial Intelligence (AI) in personalized learning within the educational context. The study aims to investigate the various approaches to using ML algorithms for personalizing educational content, the impact and implications of these approaches on student performance, and the challenges and limitations associated with AI in personalized learning. The research questions are structured around these three broad areas, focusing on the AI methods used in education, their impact on students' academic outcomes, and the challenges and limitations associated with AI.Data and Metadata182The study aims to investigate the various approaches to using ML algorithms for personalizing educational content, the impact and implications of these approaches on student performance, and the challenges and limitations associated with AI in personalized learning.Data and Metadata['Zainab Rasheed', 'Sameh Ghwanmeh', 'A. Abualkishik']2023-12-30T00:00:00https://www.semanticscholar.org/paper/67e5dccb9111c5687e1e9288d0d6fc09af3c00e8 0bfed6c5b074f0eafb944341e7d05b4a26d54f2bArtificial Intelligence (AI) is increasingly becoming a driving force in revolutionizing various sectors, and education is no exception. This article explores the transformative potentials of AI in education, delving into its applications, benefits, and ethical considerations. Drawing on a range of scholarly works, this discussion aims to provide an overview of the current landscape and future prospects of AI in shaping the educational experience.Journal of Educational Sciences &amp; Psychology01The transformative potentials of AI in education are explored, delving into its applications, benefits, and ethical considerations.Journal of Educational Sciences &amp; Psychology['Cristian Vasile']2023-12-30T00:00:00https://www.semanticscholar.org/paper/0bfed6c5b074f0eafb944341e7d05b4a26d54f2b 7800c32a5b437b2677e1ff117c6e1d19a1a18841Research background: Robo-advisory is a modern and rapidly developing area of implementing artificial intelligence to support customer decision-making. The current significance of robo-advisory to the financial sector is minor or marginal, and boils down to formulating recommendations and implementing investment strategies. However, the ongoing digital transformation of the economy leads us to believe that in the near future this technology will also be much more widely used with banking products. This makes it necessary for banks and other financial institutions to be prepared to offer this service to their customers.  Purpose of the article: The aim of this paper is to identify factors significantly influencing bank customers’ intention to use robo-advisory. Identification of robo-advisory acceptance factors may increase the effectiveness of banks' promotional activities regarding such a service. Methods: Empirical data was obtained through a survey conducted on a representative sample of 911 Polish respondents aged 18–65. Using a multilevel ordered logit model and methods based on machine learning algorithms, the authors identified variables relating to the demographic and socio-economic characteristics, behaviors, and attitudes of consumers that primarily determine respondents’ adoption of robo-advisory. Findings & value added: The results of the study indicate that the variables regarding the respondents' attitude towards the use of artificial intelligence in banking services turned out to be the most important from the point of view of acceptance of robo-advisory. Next in terms of importance were the variables presenting respondents' assessments of the ethics of financial services. An important finding is that experience in using basic financial services is not a significant factor when accepting robo-advisory. From the practical perspective, the article provides recommendations on the use of artificial intelligence technology in finance and ethical aspects of the provision of such services by banks.Equilibrium. Quarterly Journal of Economics and Economic Policy841Identification of robo-advisory acceptance factors may increase the effectiveness of banks' promotional activities regarding such a service and provide recommendations on the use of artificial intelligence technology in finance and ethical aspects of the provision of such services by banks.Equilibrium. Quarterly Journal of Economics and Economic Policy['Dariusz Piotrowski', 'W. Orzeszko']2023-12-30T00:00:00https://www.semanticscholar.org/paper/7800c32a5b437b2677e1ff117c6e1d19a1a18841 303043a217b03e673588222a9e1f063a876cd633The integration of artificial intelligence (AI) with mobile applications for supply chain management is examined in this review of the literature. It emphasizes the revolutionary effect of AI on supply chain process optimization by looking at a variety of academic publications. According to the abstract, there is general agreement that supply chain operations can benefit from AI-powered mobile applications in terms of increased productivity, lower costs, and reduced risk. Real-time tracking, inventory management, and demand forecasting using machine learning algorithms are some of the major themes. The compilation of multiple studies emphasizes how important AI applications are for supply chains that want to be resilient and adaptive. The abstract also highlights how mobile platforms are becoming increasingly important as a channel for smooth AI integration, giving decision-makers access to data and insights in real time. All of the reviewed literature points to the strategic integration of AI in mobile supply chain applications as a key factor in today's business environments that foster innovation and competitiveness. Keywords: supply chain, Artificial intelligence, construction, Mobile application, Technology.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00All of the reviewed literature points to the strategic integration of AI in mobile supply chain applications as a key factor in today's business environments that foster innovation and competitiveness.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['V. V. ananth', 'M. Bharath', 'M. S. vaardhini', 'M. P. Prabakaran']2023-12-30T00:00:00https://www.semanticscholar.org/paper/303043a217b03e673588222a9e1f063a876cd633 f11595f721831f4b87ed6ea11469b6eca543ea44the article presents the results of an analysis of a set of issues related to identifying promising areas for investing public funds within the framework of the Federal Project “Artificial Intelligence”. Global indicators of investment activity in the artificial intelligence technology sector are analyzed. The economic characteristics of promising areas for the application of artificial intelligence technologies in Russia have been determined.Russian Journal of Management20An analysis of a set of issues related to identifying promising areas for investing public funds within the framework of the Federal Project “Artificial Intelligence” finds the economic characteristics of promising areas for the application of artificial intelligence technologies in Russia.Russian Journal of Management['Danila Ovchinnikov Alekseevich']2023-12-30T00:00:00https://www.semanticscholar.org/paper/f11595f721831f4b87ed6ea11469b6eca543ea44 3177fe3e9b025f18149a7def22a1544a39dd8565 The new era of translational medicine is facing a special and unique transition as the Artificial Intelligence (AI) and Machine Learning (ML) advance. The latest findings in many laboratories around the globes are promising and paved the way toward extraordinary innovative development in the field of medicine and medical research. In medical research, gradual and consistent advancement, from rudimentary origins, were initial key steps toward medical discoveries and innovation. However, with the AI inclusion into these research approaches, the traditional translational medicine is pacing up. Creating a great hope for filling many sophisticated gaps in knowledge that has been a challenge by the traditional methodology. Such advancements in AI and its involvement in biomedicine and healthcare are forging the framework for a flourishing and visionary translational medicine and biomedical research. Lately, following the discoveries made by a collaborative team of scientists from the University of Vermont and Tufts University (The xenobots), enormous research and investigation started to glance in the field. Attempts toward drug discovery, wound recoveries and self-proliferating stem cells were made. Most recently, scientists at Tufts University and Harvard University’s Wyss Institute have designed and created living small robots from cells of human trachea. In an in vitro world, these created cells could induce tissue damage recovery. The AI based medical diagnosis is also a revolutionary step toward proper diagnosis and consequently, medical intervention. The AI and ML are becoming so intelligent to an extent not to only diagnose the disease but also predict disease prognosis in future. The narrow AI (WatsonX) that was introduced by the IBM company can play a major role in assisting clinical decisions and image analysis. The challenge that faced the globe after the COVID-19 outbreak with multiple false negativity and false positivity outcomes in the diagnose necessitated the support of the AI in this field. Additionally, the Neuralink that is introduced by Neuralink Corp. is taking a historical step after gaining the approval from FDA to start human trials. Such dramatical change in the brain interface using AI can be a solution for many diseases that human couldn’t treat. Restoring brain activity and autonomy through integration of an AI to human nervous system is a mind-boggling promise that AI can give to medicine and medical science. Finally, these new approaches and advancement in AI and ML are prone to proper design and supervision, as well as, performing randomized clinical trials prior to the use in real-life medical interventions. Nonetheless, research and experimentations for AI inclusion in medicine are still an ongoing process, yet the opportunities are realistic that AI can be of great benefit in supporting medicine and biomedical research, specifically, where there is gap in knowledge and inability of human capacity to resolve them. There is no doubt that AI will be a significant party of the healthcare system in the future and a core medical support.BioMed Target Journal00Research and experimentations for AI inclusion in medicine are still an ongoing process, yet the opportunities are realistic that AI can be of great benefit in supporting medicine and biomedical research, specifically, where there is gap in knowledge and inability of human capacity to resolve them.BioMed Target Journal['Karzan Abdulmuhsin Mohammad']2023-12-30T00:00:00https://www.semanticscholar.org/paper/3177fe3e9b025f18149a7def22a1544a39dd8565 af7872de4ce910d01795dffe00ebb154746b0188Artificial intelligence has had a profound impact on the production and operation management of enterprises, including human resource management, financial management, production management, and decision-making assistance. Artificial intelligence can improve production and management efficiency, and reduce costs. It can improve the quality and specialization level of financial information processing, achieve intelligent financial management, and improve the efficiency and accuracy of financial management. The development of artificial intelligence may have an impact and crowding out effect on jobs with primary repetitive standardized processes, leading to a reduction and adjustment of some positions, while also providing new employment opportunities. The application of artificial intelligence in production and operation management can strengthen enterprise supply chain management, improve quality inspection efficiency, reduce operating costs, improve production efficiency, enhance the scientificity of enterprise management, thereby improving management efficiency and economic benefits, and promoting enterprise development and competitiveness. At the same time, it is necessary to pay attention to data security management and privacy protection, reduce the cost of using and maintaining artificial intelligence, continuously improve and perfect artificial intelligence tools, effectively enhance the use of artificial intelligence in accordance with regulations, and pay attention to the risk of personnel unemployment caused by the application of artificial intelligence. Therefore, it is suggested that senior leaders of enterprises should attach great importance to the application of artificial intelligence, increase funding, provide budget support and resource investment, strengthen data quality management and customized analysis capabilities, reduce usage and maintenance costs, promote human-machine cooperation, strengthen the application of artificial intelligence in business management, attach importance to employee training and transformation support, and pay attention to technological development trends, optimize and expand application scenarios, Actively explore and apply artificial intelligence tools to enhance the competitiveness of enterprises. The government should formulate relevant policies and regulations, strengthen education and training support, promote the development of the artificial intelligence industry, strengthen international cooperation and exchanges, formulate policies to promote employment and social security systems, encourage innovation and entrepreneurship, strengthen supervision and regulation, and promote the comprehensive application of artificial intelligence in enterprise management.The QUEST: Journal of Multidisciplinary Research and Development30Senior leaders of enterprises should attach great importance to the application of artificial intelligence, increase funding, provide budget support and resource investment, strengthen data quality management and customized analysis capabilities, reduce usage and maintenance costs, promote human-machine cooperation, and continuously improve and perfect artificial intelligence tools.The QUEST: Journal of Multidisciplinary Research and Development['Shidong Zhang', 'Jet Aquino']2023-12-30T00:00:00https://www.semanticscholar.org/paper/af7872de4ce910d01795dffe00ebb154746b0188 8800c387b7f41c05942690730a7a2f2274ba68cdObjective. The purpose of the article is to analyze the prospects for the use of robotic systems and artificial intelligence in the hotel and restaurant business, to provide recommendations for the introduction of innovative technologies in the hotel and restaurant business. Methods. When studying the prospects of using artificial intelligence technologies in the hotel and restaurant business, analyzing robotic systems, such empirical and theoretical research methods were used as: Observation — to determine the dynamics of the development of the hotel and restaurant industry and their trends to identify changes. Deduction — to identify factors affecting the prospect of using robotic systems and artificial intelligence in the service market. Analysis — determining the reasons for the influence of factors on the restaurant business market. Comparison — to determine the results of the influence of factors on the functioning of hotel and restaurant business establishments, the use of data of accommodation establishments by the number of rooms. The results. According to the analysis of the hotel and restaurant business, the key factors affecting its competitiveness and success, especially when using the latest technologies, have been determined. Hotels are implementing innovative technologies, including robotic systems and artificial intelligence, demonstrating high occupancy and attractiveness for modern generations. When conducting an analysis of hotel occupancy in Japan, the main theses were summarized: - Hotel Henn na Hotel Tokyo Ginza with artificial intelligence and robots at the reception showed the highest level of occupancy - 88.8%, which shows the high interest of guests in the latest technologies and automated service. - Other hotels that do not use innovation have a lower level of occupancy, which emphasizes the importance of technological development to maintain competitiveness. In order to use the latest technologies to improve the hotel and restaurant business, a comprehensive digital system was proposed, including voice recognition, artificial intelligence (using GPT algorithms), and a Smart Home system. This creates a comfortable environment for guests and facilitates the work of the service staff. The result is that the implementation of innovations can positively affect hotel occupancy and ensure guest satisfaction, especially generations Y and Z. It was determined that innovations such as robotic systems and artificial intelligence allow businesses to attract more customers, in particular, people who appreciate modern technology and comfort. The use of complex digital systems can improve the competitiveness and efficiency of the hotel and restaurant business. These results indicate the prospects of innovations in the field of hotel and restaurant business, and their implementation can become a key success factor in the modern market environment.TRADE AND MARKET OF UKRAINE20It was determined that innovations such as robotic systems and artificial intelligence allow businesses to attract more customers, in particular, people who appreciate modern technology and comfort and can become a key success factor in the modern market environment.TRADE AND MARKET OF UKRAINE['Ye. H. Klievtsov', 'O. Y. Filippova']2023-12-30T00:00:00https://www.semanticscholar.org/paper/8800c387b7f41c05942690730a7a2f2274ba68cd 4297e8206f731afb8461037556d5df7969e5fc90This paper explores the politicization of Artificial Intelligence (AI) within the EU, examining the interplay between internal dynamics and external influences, particularly from the United States and China. The study aims to identify early signs of politicization in the EU’s AI debate and compare the EU’s AI policy model with those of the US and China. The hypothesis posits that EU public debate on AI is politicized, shaped by both internal factors and responses to external AI policy models. The research uses comparative policy analysis and content analysis. Findings indicate a growing salience of AI in public discourse, evidenced by increased media attention and engagement from a wide range of actors. However, significant polarization on AI issues within the EU is not yet evident. The study also highlights the EU’s strategic response to external AI models, emphasizing a balance between innovation, digital sovereignty, and the protection of democratic values and fundamental rights.Rocznik Integracji Europejskiej500The study aims to identify early signs of politicization in the EU’s AI debate and compare the EU’s AI policy model with those of the US and China, and highlights the EU’s strategic response to external AI models.Rocznik Integracji Europejskiej['Ilona Poseliuzhna']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4297e8206f731afb8461037556d5df7969e5fc90 a4d6c0b4b9761e556d99b82224095ff23047942eThis research conducts an interdisciplinary bibliometric review to explore the symbiotic relationship between Business Intelligence (BI) and Artificial Intelligence (AI). Utilizing advanced bibliometric tools, we analyze a comprehensive dataset extracted from reputable databases, encompassing articles that meet predefined inclusion criteria. The study reveals thematic clusters, influential documents, and core keywords shaping the discourse within the BI-AI landscape. Thematic clusters highlight the multidisciplinary nature of research, ranging from the impact of AI on finance to business model innovation and sustainability. The top-ten cited documents provide a snapshot of seminal works guiding academic and practical understanding, while keyword analysis illuminates the central themes and areas of emphasis. The cross-analysis of these elements offers a nuanced view of the evolving landscape of BI-AI integration. The findings not only contribute to academic scholarship but also provide practical insights for organizations navigating the dynamic intersection of BI and AI.The Eastasouth Journal of Information System and Computer Science290The study reveals thematic clusters, influential documents, and core keywords shaping the discourse within the BI-AI landscape, as well as providing practical insights for organizations navigating the dynamic intersection of BI and AI.The Eastasouth Journal of Information System and Computer Science['Loso Judijanto', 'Wayan Karang Utama', 'Nita Priska Ambarita', 'Indra Permana']2023-12-30T00:00:00https://www.semanticscholar.org/paper/a4d6c0b4b9761e556d99b82224095ff23047942e e40ea5d1fab42febe309daa690df5af88ad50d5eIf Artificial Intelligence is powerful, then it would cover a wide spectrum of jobs that commonly is done by humans that might require a high level of abstraction and mathematical creation. In this paper, the possibility that Machine Learning through the criteria of Mitchell can produce original contribution at basic sciences, such for example at the theoretical physics, is investigated. Basically, it is shown that theoretical physics is essentially based in laws by which are employed mathematical apparatus based at differential equations, integrations, commutators of operators, closed-form algebra, etc. In this way, the Mitchell criteria might constitute an algorithm to generate new theoretical structures in physics. It is investigated if it is appropriate to claim that Artificial Intelligence is able to replace the human thinking to create new theoretical physics with relevance and with a solid prospectiveness.2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE)490It is investigated if it is appropriate to claim that Artificial Intelligence is able to replace the human thinking to create new theoretical physics with relevance and with a solid prospectiveness.2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE)['H. Nieto-Chaupis']2023-12-30T00:00:00https://www.semanticscholar.org/paper/e40ea5d1fab42febe309daa690df5af88ad50d5e 47cce05dd2d38eae9a3c83a87a141e289fa9a2f0The control of the pH neutralization process has a decisive role in many industrial branches, as: wastewater treatment, biotechnology, pharmaceutical industry, chemical processing, etc. However, the pH neutralization process is a difficult one to control, due to time-varying process parameters, its high non-linearity and to the sudden variation of the pH around the equivalence point. The paper presents a device and an integrated application that uses artificial intelligence (IA) developed for dosage of the necessary reactants in wastewater pH control. The AI techniques were integrated into the application by developing an expert system (ES). For this purpose, were used the necessary if-then rules (rules that make up the inference engine (IE) of the ES) related to the flowrates of the reactants necessary for wastewater pH neutralization. The ES knowledge base (KB) was built based on the recorded pH values. The hardware component (the device) was created using the Arduino equipment.Romanian Journal of Petroleum &amp; Gas Technology00The paper presents a device and an integrated application that uses artificial intelligence developed for dosage of the necessary reactants in wastewater pH control and the AI techniques were integrated into the application by developing an expert system (ES).Romanian Journal of Petroleum &amp; Gas Technology['Maria-Elisabeta Oprea Grigore', 'M. Cărbureanu']2023-12-30T00:00:00https://www.semanticscholar.org/paper/47cce05dd2d38eae9a3c83a87a141e289fa9a2f0 3673c0a883952119c940c0f4df9454b64a55e376Artificial Intelligence is a technology that simulates human intelligence, programmed into electronic media to think and imitate human actions. Artificial Intelligence functions as a tool for restructuring, due diligence, and making deeds, raising concerns regarding potential errors in Artificial Intelligence output that could cause losses to legal subjects. This research examines legal responsibility when the output produced by Artificial Intelligence causes losses, with a focus on the careful application of notary principles as regulated in Article 16 of the Notary Position Law (UUJN). This research adopts a doctrinal legal research approach, utilizing secondary data from literature studies, archives, research reports, and legal sources such as UUJN and the Civil Code. Through qualitative analysis, this research reveals that Artificial Intelligence is not a legal subject but can be categorized as a tool that helps notaries. In terms of legal liability for losses caused by Artificial Intelligence, civil liability is given to the owner of the order, namely the person who uses and gives orders to Artificial Intelligence. This research emphasizes the need for careful consideration of legal responsibilities and prudence in notarial practice, along with the development of Artificial Intelligence technology in the legal domain.Syntax literate : jurnal ilmiah Indonesia00Syntax Literate ; Jurnal Ilmiah Indonesia['Caroline Cynthia', 'Disriani Latifah Soroinda']2023-12-30T00:00:00https://www.semanticscholar.org/paper/3673c0a883952119c940c0f4df9454b64a55e376 5934bbbffae97c0a25b9c4d769d9409113e2a398In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.Postgraduate medical journal740AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity, which allows for earlier diagnosis and treatment, which can significantly impact patient outcomes.Postgraduate medical journal['A. R. Katwaroo', 'Vivek Shanker Adesh', 'Amrita Lowtan', 'S. Umakanthan']2023-12-30T00:00:00https://www.semanticscholar.org/paper/5934bbbffae97c0a25b9c4d769d9409113e2a398 677f080ede2ebd0fcf412a8e421a99c2d6cfe186The Merdeka Belajar – Kampus Merdeka (MBKM) policy issued by the Ministry of Education and Culture opens up great opportunities for universities to create graduates who are competent and competitive. The MBKM program gives students the right to take part in learning outside the department or university with the equivalent or conversion of credits. However, the Director General of the Ministry of Education and Culture, Research, Technology and Higher Education, highlighted that there are still many universities that have not provided proper grade conversion rights to students (Kemendikbud, 2022). The Kastrat BEM FMIPA UNEJ team found that 93.7% of students who had taken MBKM were worried because they had to retake credits that had not been converted (Kastrat BEM FMIPA UNEJ, 2022). Through SWOT analysis, this research provides an idea solution in the form of an Artificial Intelligence-based Smart Contract as a value conversion platform for the MBKM program to make it easier for departments to convert student grades according to their needs and core competencies.Improvement: Jurnal Ilmiah Untuk Peningkatan Mutu Manajemen Pendidikan170Through SWOT analysis, this research provides an idea solution in the form of an Artificial Intelligence-based Smart Contract as a value conversion platform for the MBKM program to make it easier for departments to convert student grades according to their needs and core competencies.Improvement: Jurnal Ilmiah untuk Peningkatan Mutu Manajemen Pendidikan['Teguh Trianung Djoko Susanto', 'Soraya Nuron Jamil', 'Stevani Reynita', 'Zaahidah Faadhilah', 'Winda Dewi Listyasari']2023-12-30T00:00:00https://www.semanticscholar.org/paper/677f080ede2ebd0fcf412a8e421a99c2d6cfe186 381974289a8f3d61b12aafdfcbd1e1021f9aa692This case study looks at the ways that artificial intelligence (AI) has improved marketers' ability to understand and analyze Consumer Behaviour. To make their marketing strategy and plans more effective, marketers are researching consumer Behaviour on the internet. AI may be the answer given the vast volume of data that is already accessible and the regularity of data breaches. An image recognition system might be able to identify and categorize things in photos by analyzing millions of examples. When a chatbot sees examples of text interactions, it can learn to have realistic discussions with humans. Businesses now need to take advantage of the best AI talent to stay ahead of the competition. It can provide insight into every phase of the customer journey and aid marketers in comprehending the motivations underlying Consumer Behaviour. Stronger client relationships and a greater client lifetime value are possible consequences when done properly. AI may be used by marketing teams to interpret vast volumes of data in order to use the knowledge and pinpoint their target audience. Businesses can make use of it to develop user-centered sales funnels and build their marketing plans around them. In the end, more traffic is produced, which is advantageous for marketing departments trying to convert site visitors. Artificial intelligence (AI) and other technologies are transforming our understanding of and perspective on marketing..International Journal of Advanced Research in Science, Communication and Technology70International Journal of Advanced Research in Science, Communication and Technology['Dr. Alka Awasthi', 'Dr. Anita Vishwakarma']2023-12-30T00:00:00https://www.semanticscholar.org/paper/381974289a8f3d61b12aafdfcbd1e1021f9aa692 7f1dc35c7e30d7efb9b300e643573a8eb8eed3ceIn the ever-evolving landscape of science and technology, Artificial Intelligence (AI) has emerged as a catalyst for unprecedented advancements. This transformative force is reshaping how we approach scientific research, innovation, and technological development. As we navigate this era of AI integration, its impact on various facets of science and technology becomes increasingly evident.Nepal Journal of Biotechnology00In the ever-evolving landscape of science and technology, Artificial Intelligence has emerged as a catalyst for unprecedented advancements and its impact on various facets of science and technology becomes increasingly evident.Nepal Journal of Biotechnology['V. Zambare']2023-12-30T00:00:00https://www.semanticscholar.org/paper/7f1dc35c7e30d7efb9b300e643573a8eb8eed3ce 13809f6961f94800d357a9fa1330581fd1253d0aThis research study employs a quantitative-descriptive approach to evaluate and enhance task performance through the utilization of Artificial Intelligence (AI) systems at Hangzhou Ruinan Information Technology Co., Ltd., a key player in the technology hub of Hangzhou City, China. The study comprises four main parts: examining AI system characteristics and provisioning, assessing actual task performance, investigating factors influencing system effectiveness, and identifying challenges impacting performance. Participants include management, IT professionals, and employees working with AI systems, providing valuable insights into system implementation and operation. Data analysis, utilizing weighted mean and verbal description techniques, offers both quantitative and qualitative perspectives on AI's role in optimizing task performance. Findings reveal strong scalability, efficient overall performance, and positive impacts on workflow processes and resource utilization. Security measures, especially in data protection, and usability aspects require attention for further enhancement. The study underscores the importance of reliable data, seamless user adoption, enhanced expertise, and consistent system performance in optimizing task performance through AI within the organization. Conclusions emphasize the need to strengthen security measures, enhance usability, and address areas for improvement. The study underscores the value of AI in achieving the organization's goal of enhancing task performance and productivity. Recommendations include initiatives to enhance data quality, streamline user acceptance, invest in employee skill development, and improve scalability and performance. These recommendations aim to guide Hangzhou Ruinan Information Technology Co., Ltd. in refining its AI systems to drive efficiency and excellence in its operations.The QUEST: Journal of Multidisciplinary Research and Development100Recommendations include initiatives to enhance data quality, streamline user acceptance, invest in employee skill development, and improve scalability and performance to guide Hangzhou Ruinan Information Technology Co., Ltd. in refining its AI systems to drive efficiency and excellence in its operations.The QUEST: Journal of Multidisciplinary Research and Development['Du Dan Santos', 'RuthAnn Santos']2023-12-30T00:00:00https://www.semanticscholar.org/paper/13809f6961f94800d357a9fa1330581fd1253d0a f2a9765018e308d6cae27122e05b3e17b3512ccfSharia m-banking users are increasingly rising among the Indonesian people. This study aims to analyze the stimulus-organism-response (S-O-R)  approach to the adoption of Islamic m-banking with artificial intelligence among Generation Z. This paper contributes to or enriches the behavior of Islamic m-banking adoption with artificial intelligence  Which of course is expected to be a consideration in the formulation of policies for stakeholders including Islamic financial institutions. This research is a type of field research using quantitative methods. Data collection using questionnaires was conducted online through Google Forms. Analysis with PLS-SEM using Smart-PLS with a sample of 100 respondents spread across several regions in South Sulawesi. The respondents were determined by the main criteria, namely Muslims aged 11-26 years who represent Generation Z. The results showed that perceived usability and perceived intelligence positively affect trust. In addition, perceived usability positively affects attitude, while trust and attitude positively affect the adoption of  Islamic m-banking. However, unlike the case with perceived intelligence, service quality, and security do not significantly affect attitude, besides that service quality and security do not significantly affect trust. Meanwhile, the small impact produced by these factors. In addition, other factors have a greater influence on attitudes and beliefs in adopting  Islamic m-banking-AI.Jurnal Magister Ekonomi Syariah350The results showed that perceived usability and perceived intelligence positively affect trust, and other factors have a greater influence on attitudes and beliefs in adopting  Islamic m-banking-AI.Jurnal Magister Ekonomi Syariah['A. Fath', 'Falaq Am Nur', 'M. Shahril']2023-12-30T00:00:00https://www.semanticscholar.org/paper/f2a9765018e308d6cae27122e05b3e17b3512ccf 4456270649cdc7b0278cad3ea8589a597f7c7170Artificial Intelligence (AI) in medical services is expected to have a significant impact on the world of medicine. The research, which is a literature review, data collection, observation, analysis, is finally presented descriptively as a normative juridical study. The potential for AI to contribute the diagnostic process and even replacing the role of doctors is growing. Currently the use of AI has been utilized in medicine (registration, medical records, images, treatment, telemedicine, outbreak) and health resources.AI as an electronic agent operator is recognized as having a legal position, seen from Law number 19 of 2016 concerning electronic information and transactions (ITE Law) and PP 71/2019 concerning the implementation of electronic systems and transactions, regulating the limits of obligations and responsibilities of Electronic Agent organizers. In a trade context, hospitals are "intermediary traders" here a "last moving" agreement or "instruction agreement" applies. Then providing services automatically refers to article 1 of the ITE Law, if a dispute occurs then legal responsibility is borne by the electronic system operator providing AI services. Settlement refers to Article 38 and Article 39 of the ITE Law through court (Claass action and Civil Lawsuit) or non-litigation settlement.Abstrak: Kecerdasan Buatan (AI) dalam pelayanan medis diharapkan dapat memberikan dampak yang signifikan terhadap dunia kedokteran. Penelitian yang berupa tinjauan pustaka, pengumpulan data, observasi, analisis, akhirnya disajikan secara deskriptif sebagai penelitian yuridis normatif. Potensi AI untuk berkontribusi dalam proses diagnostik dan bahkan menggantikan peran dokter semakin besar. Saat ini pemanfaatan AI telah dimanfaatkan dalam bidang kedokteran (registrasi, rekam medis, pencitraan, pengobatan, telemedicine, wabah) dan sumber daya kesehatan. AI sebagai penyelenggara agen elektronik diakui mempunyai kedudukan hukum, dilihat dari Undang-undang nomor 19 tahun 2016 tentang informasi dan transaksi elektronik (UU ITE) dan PP 71/2019 tentang penyelenggaraan sistem dan transaksi elektronik, mengatur batasan kewajiban dan tanggung jawab penyelenggara Agen Elektronik. Dalam konteks perdagangan, rumah sakit adalah “pedagang perantara” di sini berlaku perjanjian “perjanjian perpindahan terakhir” atau “perjanjian instruksi”. Kemudian pemberian layanan secara otomatis mengacu pada pasal 1 UU ITE, apabila terjadi perselisihan maka tanggung jawab hukum ditanggung oleh penyelenggara sistem elektronik penyedia layanan AI. Penyelesaiannya mengacu pada Pasal 38 dan Pasal 39 UU ITE melalui pengadilan (gugatan kelompok dan Gugatan Perdata) atau penyelesaian non-litigasi.SOEPRA150SOEPRA['R. Siregar']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4456270649cdc7b0278cad3ea8589a597f7c7170 f7cc21129a6698cb7684b9dfc704ec484a231f72Perkembangan teknologi Artificial Intelligence (AI) sudah memasuki berbagai bidang dan tidak terlepas pada bidang Pendidikan. Open AI yang baru-baru banyak dibicarakan juga memberikan dampak bagi era digital, dimana kita dapat bertanya dan berdiskusi dengan teknologi tersebut dan dengan aplikasi yang diberi nama ChatGPT dengan sangan mudah memberikan informasi yang ditanyakan atau diminta. Pengenalan terhadapt teknologi AI dan dengan tambahan Open AI serta ChatGPT kepada siswa sekolah dasar sangat diperlukan guna memberikan Pendidikan dini dampak baik dan buruk teknolog AI tersebut.  dilakukan sejak pada siswa-siswi sekolah agar lebih kenal dan mengerti proses kerjanya. Dalam kegiatan pengabdian ini akan dilaksanakan pengenalan dan pemahaman tentang AI dan Open AI serta bagaimana memanfaatkan teknologi tersebut untuk bidang pendidikan dan pengajaran. Sebagai tambahan dalam kegiatan ini pemaparan materi dasar AI dan pelatihan bagaimana menggunakan ChatGPT untuk penerapan pada pendidikan dan pengajaran. Penerapan teknologi kecerdasan buatan juga sudah memasuki dalam berbagai bidang dan bukan hanya pendidikan saja seperti pada bidang industri manufaktur dan otomasi, dimana mesin-mesin produksi diharapkan dapat melakukan instruksi dan mengenal terhadap beberapa kesalahan sederhana agar lebih produktif dan efisien. Luaran dalam kegiatan pengabdian ini ditargetkan terbit sebuah makalah atau paper pada jurnal yang minimal terindeks pada google scholar dan luaran tambahan yang menjadi target adalah terbit sebuah berita atau laporan kegiatan pada media masa lokal. Kegiatan ini sudah dilaksanakan di Sekolah Dasar Islam Terpadu (SD IT) Sakinah di Desa Pandau Jaya, Kabupaten Kampar, Provinsi Riau. Dimana SD IT Sakinah ini berada di Decamatan Siak Hulu di Kabupaten Kampar. Presentasi dasar teknologi AI dan perkembangannya, mesin ChatGPT yang dapat melakukan diskusi dan memberi jawabah sesuai dengan topik yang kita bahas terutama bidang pendidikan dan pengajaran. Pada bagian akhir kegiatan ini diharapkan ada tambahan pengetahuan terhadap siswa-siswi dan pahan penerapan Teknologi AI dan perkembannya dan mampu melakukan dasar pencarian dan kebutuhn materi dengan ChatGPT.Jurnal Pengabdian Masyarakat dan Penerapan Ilmu Pengetahuan120Jurnal Pengabdian Masyarakat dan Penerapan Ilmu Pengetahuan['J. Masyarakat', 'dan Penerapan', 'Ilmu Pengetahuan', 'Sri Listia Rosa', 'A. Yulianti', 'Sapitri', 'A. Putri']2023-12-30T00:00:00https://www.semanticscholar.org/paper/f7cc21129a6698cb7684b9dfc704ec484a231f72 6d3b69fc9ff5d82663ecfd4406e8f993d8d799b3The study explores the development of an effective counterterrorism program utilizing Artificial Intelligence (AI) technologies. Emphasizing the integration of various technical elements such as big data analysis, natural language processing, image and video analysis, and the incorporation of Internet of Things (IoT) and sensor technologies, the research investigates comprehensive approaches. Firstly, the utilization of big data analysis aims to collect and analyze data from diverse sources, identifying patterns associated with terrorism. The application of natural language processing and text mining extracts meaningful information from textual data, while image and video analysis detect unusual patterns or behaviors in visual information. Moreover, the study highlights the use of IoT and sensor technologies to establish an intelligent security system, collecting real-time environmental data to detect anomalous indicators. The implementation of an automated alert and emergency response system is also suggested, facilitating swift and effective responses upon detecting abnormal signs. In conclusion, the integrated use of diverse technologies in the proposed AI-based counterterrorism program enables rapid and accurate threat detection, contributing to the enhancement of public safety and national security.The Korean Association for Terrorism Studies00The integrated use of diverse technologies in the proposed AI-based counterterrorism program enables rapid and accurate threat detection, contributing to the enhancement of public safety and national security.The Korean Association for Terrorism Studies['Hyun Dong Kim', 'Sang Cheul Han']2023-12-30T00:00:00https://www.semanticscholar.org/paper/6d3b69fc9ff5d82663ecfd4406e8f993d8d799b3 e53bb2cb5e804c2ceae0d4940b0dffb2c2855da9In today's era, science and technology have made rapid progress, which makes the rapid formation of the Internet era, and promotes the wide application of artificial intelligence in the commercial field. With the rapid development of e-commerce, online shopping has become more and more popular, and online shopping has become the way of shopping chosen by more and more people. In the context of the continuous development of artificial intelligence, for the online shopping industry, actively citing artificial intelligence is very beneficial to the development of the industry. However, considering that consumers' acceptance of intelligent customer service is different, this artical will use Technology Acceptance Model to analyze the usefulness and ease of intelligent customer service, and confirm consumers' acceptance of intelligent customer service in the process of online consumption. The remainder of this paper is organized as follows.International Journal of Global Economics and Management110This artical will use Technology Acceptance Model to analyze the usefulness and ease of intelligent customer service, and confirm consumers' acceptance of intelligent customer service in the process of online consumption.International Journal of Global Economics and Management['Manman Xue']2023-12-30T00:00:00https://www.semanticscholar.org/paper/e53bb2cb5e804c2ceae0d4940b0dffb2c2855da9 1ee7c0323aecab5e722acd2aa9216ca19c5db469Our analysis of the rapid development of core technologies in the new era of "Internet plus AI" is based on research into the applications of AI technology in the manufacturing sector in recent years. Next, we suggest novel approaches, methods, and configurations for intelligent manufacturing, as well as an architecture and technology system for intelligent manufacturing. In light of this, the purpose of this article is to examine artificial intelligence as it relates to Internet of Things technology in electronic information engineering. The goal is to increase people's material well-being and happiness by better optimizing artificial intelligence and IoT technology, mastering technical concepts, and applying them logically. To better simulate life and optimize information conditions, this paper analyses the experimental results through model construction. It also suggests ways to apply artificial intelligence and IoT technology to electronic information engineering. In conclusion, this research employs sixteen typical mechanics datasets to verify the correctness and generalizability of the method and concludes that it helps improve the usage of AI and IoT technologies.International innovative research journal of engineering and technology00This paper analyses the experimental results through model construction and suggests ways to apply artificial intelligence and IoT technology to electronic information engineering and concludes that it helps improve the usage of AI and IoT technologies.International Innovative Research Journal of Engineering and Technology['Sathish Shankar', 'Rajendrakumar Ramadass']2023-12-30T00:00:00https://www.semanticscholar.org/paper/1ee7c0323aecab5e722acd2aa9216ca19c5db469 1f5e0e9edacd41585aa9ca421f9f387ea58173b4The paper analyses the dynamics of scientific research in, and practical application of key Industry 4.0 technologies in corporate governance, namely big data, artificial intelligence, machine learning, and blockchain. The contribution of specific authors, citation, and collaboration networks are assessed, along with that of individual countries and research organisations. A bibliometric network analysis of publications indexed in the Scopus and OpenAlex databases for 2011–2022 revealed a steady increase in the number of publications on the topic under consideration, and therefore a growing interest in it. The use of the abovementioned technologies in corporate governance is expected to lead to increased performance and transparency, and improved cybersecurity. The authors provide recommendations for various groups of users to maximise the potential of Industry 4.0 technologies for businesses, and the overall economy.Foresight and STI Governance00The paper analyses the dynamics of scientific research in, and practical application of key Industry 4.0 technologies in corporate governance, namely big data, artificial intelligence, machine learning, and blockchain to maximise the potential of Industry 4.0 technologies for businesses, and the overall economy.Foresight and STI Governance['Meiryani Meiryani', 'D. Warganegara', 'Vidhiya Andini']2023-12-30T00:00:00https://www.semanticscholar.org/paper/1f5e0e9edacd41585aa9ca421f9f387ea58173b4 89917c317aee483490243df8fb368e9d83645119The integration of Artificial Intelligence (AI) into the healthcare industry has ushered in a new era of innovation and transformation. Artificial Intelligence (AI) is rapidly shaping the future of healthcare. Its integration into various healthcare domains, from medical imaging and diagnostics to drug discovery, virtual health assistants, and remote patient monitoring, has demonstrated transformative potential in improving patient care and healthcare delivery. AI-powered medical imaging algorithms have revolutionized diagnostics, aiding in early disease detection and treatment planning. Drug discovery and development have benefited from AI-driven predictive models, leading to faster identification of drug candidates and personalized treatments. Virtual health assistants and chatbots have enhanced patient engagement and access to healthcare services, while remote patient monitoring has enabled continuous health tracking and proactive disease management, reducing hospitalizations and improving patient outcomes. Moreover, AI's predictive analytics and risk stratification have paved the way for personalized preventive strategies and population health management, contributing to better healthcare outcomes and disease prevention. This paper aims to explore the current state of AI adoption in healthcare and investigate the various AI-driven applications that are transforming the industry. By analysing case studies and success stories, it seeks to highlight the concrete impact of AI on patient care and healthcare systems, and examine how it can improve patient care delivery and enhance medical logistics. Furthermore, this research will delve into the challenges and ethical dilemmas surrounding AI in healthcare and provide insights into potential solutions to overcome these obstacles.International Journal of Innovative Science and Modern Engineering680The current state of AI adoption in healthcare is explored and the various AI-driven applications that are transforming the industry are investigated to examine how it can improve patient care delivery and enhance medical logistics.International Journal of Innovative Science and Modern Engineering['Brigadier Dr. Priya Jeyaraj', 'Lt Gen Tsa Narayanan AVSM']2023-12-30T00:00:00https://www.semanticscholar.org/paper/89917c317aee483490243df8fb368e9d83645119 65f528cf9ee776bf9fe7bf52cbfc86e9b60a2a1eNowadays, there is no way to imagine artificial intelligence applications without using high-performance computing systems. The huge amount of processing data, the complex structure of learning technology, time limitations, and the necessity of real-time operation require powerful computational resources and parallel algorithms. This paper analyzed another direction of convergence between high-performance computing and artificial intelligence: using artificial intelligence techniques in one of the main problems of distributed systems load balancing. The primary objective of this work is to examine the necessity of using AI concepts in load balancing and the definition of providing facilities for load balancers.Azerbaijan Journal of High Performance Computing00This paper analyzed another direction of convergence between high-performance computing and artificial intelligence: using artificial intelligence techniques in one of the main problems of distributed systems load balancing.Azerbaijan Journal of High Performance Computing['N. Ismayilova']2023-12-30T00:00:00https://www.semanticscholar.org/paper/65f528cf9ee776bf9fe7bf52cbfc86e9b60a2a1e 830bafbda4cf879e15e27d4d150b5beaf717be21Annotation. The author examines the practice of using artificial intelligence and the structure of the focus of the main government programs of the leading countries in the field of artificial intelligence. Within the framework of the national economy, the Government of the Russian Federation has identified priority areas for the development and implementation of critically important AI technologies in various industries, and in particular in agricultural production. The agro-industrial complex has enormous reserves for the introduction of various innovations. The transition of agricultural industries to "artificial intelligence" will lead to the formation of new ecosystems, remote monitoring and control over compliance with certified product safety requirements.Russian Journal of Management00The author examines the practice of using artificial intelligence and the structure of the focus of the main government programs of the main government programs of the leading countries in the field of artificial intelligence.Russian Journal of Management['Elena Batischeva']2023-12-30T00:00:00https://www.semanticscholar.org/paper/830bafbda4cf879e15e27d4d150b5beaf717be21 3f338020013479a4578b600100bd1e3885077d0bAnalyzing the need for the implementation of the development of the ball volleyball lower serve instrument is one of the right ways to design what instruments are in accordance with the wishes of students so that learning objectives can be achieved. The purpose of this study is to see whether there is a need for the development of an artificial intelligence technology ball volleyball lower serve instrument. This type of research is descriptive quantitative with survey as the method used. The test instrument used is a questionnaire distributed by google form. This research was conducted at SMA Negeri 1 Tanjung Batu with 194 respondents. The results of the study obtained students with a very needy category as many as 139 (71.65%), students with a needy category as many as 34 (17.53%) students, a moderate category of 7 (3.61%), a category of less need as many as 10 5.15%) and a category of very little need as many as 4 (2.06%) students. very low 24%. The findings of this study are that in general students at SMA Negeri 1 Tanjung batu need the development of an instrument for serving under volleyball with Artificial Intelligence technology, with these findings, it is used as a basis for conducting further research to develop products in the form of a test instrument for serving under volleyball with Artificial Intelligence technology.Bravo's : Jurnal Program Studi Pendidikan Jasmani dan Kesehatan240In general students at SMA Negeri 1 Tanjung batu need the development of an instrument for serving under volleyball with Artificial Intelligence technology, with these findings, it is used as a basis for conducting further research to develop products in the form of a test instrument.Bravo's : Jurnal Program Studi Pendidikan Jasmani dan Kesehatan['Destriana Destriana', 'D. Destriani', 'A. Victorian', 'Putri Anggraini']2023-12-30T00:00:00https://www.semanticscholar.org/paper/3f338020013479a4578b600100bd1e3885077d0b 9b21b727cc36f9aef0eb84eab56d0eae8524291dArtificial intelligence (AI) is rapidly transforming the pharmaceutical industry, offering a range of opportunities to improve drug discovery, development, and manufacturing processes. AI-powered tools are being used to analyze vast amounts of data, identify patterns and relationships, and make predictions that can accelerate the development of new drugs and improve their efficacy and safety.International Journal of Advanced Research in Science, Communication and Technology350Artificial intelligence-powered tools are being used to analyze vast amounts of data, identify patterns and relationships, and make predictions that can accelerate the development of new drugs and improve their efficacy and safety.International Journal of Advanced Research in Science, Communication and Technology['Sumedh M Bodade', 'Nikita Bajad Mam', 'Dr. Swati Deshmukh', 'Shubham Khedkar', 'Mangesh Hire']2023-12-30T00:00:00https://www.semanticscholar.org/paper/9b21b727cc36f9aef0eb84eab56d0eae8524291d 97f2317b6d14b5d78d65f05dd5af3359cebbdff2The rapid advancement of artificial intelligence in recent years has significantly altered the strategic landscape and character of warfare. Military institutions are extensively exploring various aspects to fortify security, recognizing AI as a pivotal technology reshaping the nature of confrontations. Within the Indian defense sector, there's been proactive involvement in assimilating artificial intelligence. This strategic incorporation of AI within India's military framework will distinctly influence Pakistan's assessment of potential threats thereby impacting deterrence dynamics at both conventional and nuclear level. The paper applies the Deterrence Theory. This theory posits that the possession of superior military capabilities can dissuade adversaries from initiating conflict. In the context of AI integration, the paper aims to explore  how advancements in AI bolster India's deterrence posture vis-à-vis Pakistan and militarization of AI by India will impact the deterrence equation and stability dynamics in the region. Moreover, this also might disturb the equilibrium enhancing the likelihood of conflict between two states. By exploring the possible impact of militarization of AI by Indian defense sector, the paper founds that AI militarization can alter the balance of power dynamics impacting the strategic stability and stability-instability paradox between India and Pakistan. Qualitative research methodology has been used for comprehensive analysis of existing literature on AI's role in modern warfare, coupled with an examination of research work on militarization of AI in South Asia. The aim is to delineate the repercussions of Indian AI militarization on the power dynamics of South Asia and deterrence equation. Thus, by examining the key domains of Indian AI militarization, this paper suggests that to keep pace with India’s technological advancement, Pakistan should undertake the policy of quid-pro-quo since it cannot afford to delay the integration of AI in its defense sector contemplating it as a national security concern for Pakistan.BTTN Journal40Pakistan should undertake the policy of quid-pro-quo since it cannot afford to delay the integration of AI in its defense sector contemplating it as a national security concern for Pakistan, the paper suggests.BTTN Journal['Tayyaba Khurshid']2023-12-30T00:00:00https://www.semanticscholar.org/paper/97f2317b6d14b5d78d65f05dd5af3359cebbdff2 388ddf084397bfd5983a49a8ecab19ccf104ec8dThe Global AI (Artificial Intelligence) Index is a value that aims to measure the progress of artificial intelligence (AI) around the world. Currently, technology is increasingly sophisticated and of course makes humans compete to create technology to make life easier. The purpose of this study is to analyse the effect of human resources, infrastructure, and government policies on the global AI index. The method used to determine the relationship between human resources, infrastructure, and government policies with the global AI index is the multiple linear regression method. From the results of data processing, a linear regression  = - 7,54675 + 0,65972  + 0,25096  + 0,07672 . Based on this model, the influence of human resources, infrastructure, and government policies has a significant positive effect on the Global AI Index. The coefficient of determination of the model is 0.8833, in other words, human resources (), infrastructure (), and government policy () are able to explain the value of the global AI index (Y) by 88.33% and the remaining 11.67% is explained by other variablesJurnal Lebesgue Jurnal Ilmiah Pendidikan Matematika Matematika dan Statistika90The effect of human resources, infrastructure, and government policies on the global AI index is analysed to analyse the influence of human resources, infrastructure, and government policies.Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika['Yanuar Ichwan Satria Nugroho', 'Triyani Hendrawati', 'Kennedy Marthendra', 'Brian Riski Jayama Simanjuntak']2023-12-30T00:00:00https://www.semanticscholar.org/paper/388ddf084397bfd5983a49a8ecab19ccf104ec8d 2649c078b7e033f65403e7d11c51c1e44d6f6727Education and Information Technologies : Official Journal of the IFIP technical committee on Education311Education and Information Technologies['Ching Sing Chai', 'Siya Liang', 'Xingwei Wang']2023-12-30T00:00:00https://www.semanticscholar.org/paper/2649c078b7e033f65403e7d11c51c1e44d6f6727 26fc7c636101a228c3b0a857541ca8c57485ee7cEducational Policy Analysis and Strategic Research01Educational Policy Analysis and Strategic Research['Pınar Ayyıldız', 'Adem Yılmaz']2023-12-30T00:00:00https://www.semanticscholar.org/paper/26fc7c636101a228c3b0a857541ca8c57485ee7c 4d80d71f1b7708927b335f72836f9216d0a02b54This research aims to develop and test the Calm Pregnant application for early detection and recommendations for anxiety. The method used in this research uses two stages. The first stage is research and development (R&D), which aims to develop products whose quality will be tested. The trial results show that the Calm Pregnancy Application is relevant as a medium in speeding up early detection of anxiety by a difference of 172.65 seconds, speeding up the provision of recommendations to pregnant women by a difference of 250.79 seconds, and being able to get feedback from midwives directly. The Calm Pregnancy application has been proven to be effective for use by pregnant women, with an effectiveness rate of 90.5% (very high). The conclusion resulted in a product in the form of the Calm Pregnancy media application, which was proven to be effective in carrying out early detection and providing recommendations to pregnant women, as well as the Calm Pregnancy application, which has advanced features based on artificial intelligence. The difference in speed of time and provision of recommendations in early detection of anxiety is statistically significant. Keywords: Early Detection, Human in the Loop, AnxietyJurnal Keperawatan Silampari00The conclusion resulted in a product in the form of the Calm Pregnancy media application, which was proven to be effective in carrying out early detection and providing recommendations to pregnant women, as well as the Calm Pregnancy application, which has advanced features based on artificial intelligence.Jurnal Keperawatan Silampari['Nisa Annisa Arfiyanti', 'M. Widyawati', 'Kurnianingsih Kurnianingsih']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4d80d71f1b7708927b335f72836f9216d0a02b54 4dcf53c022c2be0d498f0c9fb57ee34da89f6270Malformations in fetal development affect the health of the product and the mother. Preventing illnesses during gestation, allows for a healthy and dignified life at birth. Data from INEGI in its report on Fetal Death Statistics (EFD) 2022, show that in Mexico there is an average of 72.2 fetal deaths per 100,000 women of childbearing age. Of these deaths, 25,041 deaths were registered during the year 2022. 5950 deaths correspond to gestational disorders, respiratory or cardiovascular disorders and congenital malformations. Lung defects result in induced abortion or Respiratory Distress Syndrome (RDS). RDS can be prevented by clinical studies and radiological criteria. Identification of abnormal developments using digital analysis of lung images during pregnancy can help identify a defect. We propose ONE classification tool using deep learning in a multiclass categorization of bronchopulmonary sequestration, cystic malformations and diaphragmatic hernia, where there is a risk of defect appreciation and thus misjudgment leading to complications or death. Resulting in a model accuracy of 88.88%, out of a set of 42 two-dimensional sonograms.Revista de Fisioterapia y Tecnología Médica00This work proposes ONE classification tool using deep learning in a multiclass categorization of bronchopulmonary sequestration, cystic malformations and diaphragmatic hernia, where there is a risk of defect appreciation and thus misjudgment leading to complications or death.Revista de Fisioterapia y Tecnología Médica['Javier Pérez-Escamilla']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4dcf53c022c2be0d498f0c9fb57ee34da89f6270 338a0d904aea0404e48cf222a10f525ab0d4c406An overview of the concept of machine learning and processes (Machine Learning and Operation, MLOps), which is a set of techniques for implementation and automatic continuous integration, as well as delivery to the production environment and model training, is made. The concept of MLOps was considered in terms of Kubeflow tools - a cloud-native open-source system running on the Kubernetes platform. The possibility of using modern MLOps solutions to improve the development processes of machine learning information systems has been studied. The results of the operation of the model in the Kubeflow arsenal have been checked using such improvement factors as speed of development, implementation of changes, reduction of time to search for problems, recovery after global interruptions, and decrease of the number of errors in the model. For practical analysis, a publicly available model was deployed in a Kubeflow cluster using the Seldon Core Serving application manifest. The conducted research showed that Kubeflow consists of a set of various open-source components that have a high level of integration with each other through the Kubernetes platform. At the same time, Kubeflow uses the Kubernetes pattern of operators for machine-learning objects extremely effectively.Journal of Engineering and Applied Sciences (Asian Research Publishing Network)210The conducted research showed that Kubeflow consists of a set of various open-source components that have a high level of integration with each other through the Kubernetes platform and uses the Kubernetes pattern of operators for machine-learning objects extremely effectively.ARPN Journal of Engineering and Applied Sciences['Mykhailo Kuzmich', 'Tetyana Gordiyenko']2023-12-30T00:00:00https://www.semanticscholar.org/paper/338a0d904aea0404e48cf222a10f525ab0d4c406 872ffa9239c3062cc96412984169c5a1889f0419More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria.AAAI Conference on Artificial Intelligence200ADVISER, a framework based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria.{'pages': '22185-22192'}['Opadele Kehinde', 'Ruth Abdul', 'Bose Afolabi', 'Parminder Vir', 'Corinne Namblard', 'Ayan Mukhopadhyay', 'Abiodun Adereni']2023-12-30T00:00:00https://www.semanticscholar.org/paper/872ffa9239c3062cc96412984169c5a1889f0419 0c3b18fdcb1258351f412becb5e7e71c7fbf8349The powerful planet on which we all live is Earth. Actually have individuals begun to sort out the complexity of this planet. In the field of natural sound observing, the common estimation is the demonstrates the typical A-weighted Sound Strain Level. This is easy to see yet gives no genuine detail on the substance of the sound scene, which can be key in its effect on those encountering it. Man- made brainpower thinking techniques have demonstrated to areas of strength for be a course of action of Earth predictable fields. Perhaps of the most quickly making in the field of electronic data advancement. In a grouping of Earth research. I'm exploring on reproduced insight enabled progresses like sharp inversion, splendid sensors, and light dealing with to the test. These movements could possibly help with lessening earth vibration by 15 august 2030 Key Words: Earth Science, Earth Vibration, Light Sensor, Earth Spine.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00I'm exploring on reproduced insight enabled progresses like sharp inversion, splendid sensors, and light dealing with to the test, which could possibly help with lessening earth vibration by 15 august 2030.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['A. M. Wahid']2023-12-30T00:00:00https://www.semanticscholar.org/paper/0c3b18fdcb1258351f412becb5e7e71c7fbf8349 12404e6e02a7830f3c6d343f24571ae1acc5966fIARJSET00IARJSET['A. K.', 'Dr. A. Jayanthiladevi']2023-12-30T00:00:00https://www.semanticscholar.org/paper/12404e6e02a7830f3c6d343f24571ae1acc5966f a191b07dd2b53fc05aecf213e33cf5ea3076aaa6Pemanfaatan Teknologi Informasi (TI) dalam pendidikan telah membawa transformasi signifikan, memungkinkan inovasi dan efisiensi dalam pembelajaran. Artikel ini membahas dampak positif TI pada kualitas pembelajaran di sekolah dasar, fokus pada peran guru sebagai pemimpin intelektual. Langkah-langkah praktis, seperti sosialisasi, pendampingan, dan pelatihan dengan menggunakan AI khususnya ChatGPT untuk menyusun modul pembelajaran, diterapkan dengan melibatkan 55 guru sekolah dasar di Kabupaten Lima Puluh Kota. Evaluasi menunjukkan peningkatan pemahaman dan antusiasme peserta terhadap penggunaan ChatGPT dalam merancang modul pembelajaran. Dalam era digital, literasi digital guru menjadi krusial, memungkinkan pengembangan kurikulum yang responsif dan inklusif. Penelitian ini juga mencermati tantangan, termasuk kesenjangan akses teknologi, serta memberikan saran untuk dukungan profesional dan pengembangan kurikulum yang lebih adaptif. Hasilnya menunjukkan bahwa penggunaan teknologi, seperti ChatGPT, dapat menjadi alat bantu efektif untuk meningkatkan kualitas pembelajaran di tingkat dasar, dengan catatan penting akan kebutuhan akan pelatihan dan dukungan yang berkelanjutan.BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat)00BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat)['Vivi Puspita', 'Shella Marcelina', 'Silfi Melindawati']2023-12-30T00:00:00https://www.semanticscholar.org/paper/a191b07dd2b53fc05aecf213e33cf5ea3076aaa6 f3412139228b89c78cccc101270c46ffa7e8f938No abstract availableJournal of agriculture and value addition00Journal of Agriculture and Value Addition['Dinesh D. \xa0Jayasena']2023-12-30T00:00:00https://www.semanticscholar.org/paper/f3412139228b89c78cccc101270c46ffa7e8f938 840ca8608dce9d18aeafb176f55037c19b53de41Di era teknologi saat ini perkembangan teknologi dimanfaatkan untuk memberikan kemudahan dalam pekerjaan dan memenuhi kebutuhan manusia. Perkembangan teknologi juga dimanfaatkan dalam aspek pendidikan, teknologi yang sedang dikembangkan saat ini yaitu sistem cerdas. Sistem cerdas merupakam sistem kendali yang memiliki kecerdasan layaknya manusia dan melibatkan kecerdasan buatan. Penelitian ini dilakukan dengan metode kajian literatur dengan document-based dan internet- based research sebagai acuan data yang akan dibahas dan dikaji sesuai dengan pertanyaan penelitian. Penelitian ini menggunakan teori pertukaran sosial. Teknologi AI semakin berkembang dengan pesat dan dapat mengancam karir manusia. Pekerjaan atau profesi yang bisa digantikan oleh AI adalah pekerjaan administrasi, produksi, dan pelayanan pelanggan. Selain itu, teknologi AI dapat melakukan tugas yang berulang, kompleks, dan membutuhkan analisis data dalam jumlah besar secara lebih efisien dan akurat dibandingkan manusia.Communication sphere50Communicator Sphere['Najwa Ramadhina', 'Frey Jason', 'Muh Fajar Nuh Pratama', 'Luke Azfa Raihan', 'Syifin Al Mufti', 'Meranti Meranti']2023-12-30T00:00:00https://www.semanticscholar.org/paper/840ca8608dce9d18aeafb176f55037c19b53de41 889109970a93baec79964491e505dc3e91dda532Seni ukir kayu Bali adalah hasil karya dari para seniman ukir kayu asli Bali yang memiliki bakat luar biasa dalam beberapa dekade. Mereka bekerja dengan konsisten dan penuh dedikasi untuk menciptakan karya yang terbaik dan berkualitas tinggi. Selain itu, mereka selalu menyertakan filosofi spiritual yang mendalam dalam hasil karyanya. Begeh Ukir adalah UKM yang bergerak dalam industri seni ukiran Bali yang telah berdiri sejak tahun 2000. Produk utama yang disediakan adalah sanggah, yang secara harfiah berarti tempat ibadah. Kepercayaan Hindu percaya bahwa roh nenek moyang keluarga mendiami sanggah, di mana mereka ditempatkan di dalam sudut sakral atau di area kosong rumah. Dalam memfasilitasi dan meningkatkan pemahaman manajemen sumber daya manusia yang tergabung  ke  dalam UKM Begeh Ukir melalui KMS yang bertujuan agar pengetahuan bisa dapat berlanjut pada generasi penerusnya. Pengetahuan yang disimpan pada KMS berhasil dipetakan dalam bentuk Knowledge Mapping yang terdiri dari sanggah, bale, bahan dan filosofi. Metode KMSLC diterapkan pada pengembangan KMS berhasil mengembangkan chatbot AI berbasis NLP dengan presentase kebenaran knowledge yang dihasillkan sebesar 75%.   Kata kunci: Artificial Intelligence, Knowledge Management System, Website, Ukiran Bali   Abstract Balinese wood carving art is the work of original Balinese wood carving artists who have extraordinary talent in decades. They work with full consistency and dedication to create the best and highest quality work. In addition, they always include a deep spiritual philosophy in their work. Begeh Ukir is an UKM engaged in the Balinese carving art industry which has been established since 2000. The main product provided is sanggah, which literally means a place of worship. Hindu beliefs believe that the spirits of the family's ancestors inhabit sanggah, where they are placed in sacred corners or in empty areas of the house. In facilitating and increasing understanding of human resource management who are members of the Begeh Carving UKM through KMS which aims so that knowledge can continue in the next generation. The knowledge stored in the KMS has been successfully mapped in the form of Knowledge Mapping which consists of objections, bale, materials and philosophy. The KMSLC method applied to the development of KMS succeeded in developing an NLP-based AI chatbot with a percentage of truth of the knowledge generated by 75%.Jurnal Teknologi Informasi dan Ilmu Komputer100Jurnal Teknologi Informasi dan Ilmu Komputer['I. Putu', 'Restu Indrawan Prabawa', 'Ariq Cahya Wardhana']2023-12-30T00:00:00https://www.semanticscholar.org/paper/889109970a93baec79964491e505dc3e91dda532 95af98594be36723809d74782d31086a536cdc46Kemajuan dalam bidang kecerdasan buatan (AI) membawa potensi revolusioner bagi usaha mikro, kecil, dan menengah (UMKM) untuk meningkatkan produktivitas mereka. Namun, implementasi AI dalam skala UMKM memunculkan beberapa permasalahan yang perlu diperhatikan. Pertama, banyak UMKM menghadapi kendala dalam hal pengetahuan dan sumber daya yang diperlukan untuk mengadopsi teknologi AI. Mereka sering kali kekurangan keterampilan dan sumber daya manusia serta teknologi yang diperlukan untuk mengintegrasikan AI ke dalam operasional bisnis mereka. Kedua, penting bagi UMKM untuk memahami bagaimana AI dapat digunakan secara efektif dalam konteks bisnis mereka. Penggunaan AI yang tidak tepat atau tidak efisien dapat menghasilkan hasil yang tidak memuaskan atau bahkan merugikan secara finansial. Terakhir, penggunaan AI juga terkait dengan peraturan dan regulasi yang harus diikuti. Privasi dan keamanan data adalah aspek penting yang harus diperhatikan agar data pelanggan dan bisnis tidak disalahgunakan. Oleh karena itu, UMKM perlu memiliki pemahaman yang kuat tentang kerangka kerja regulasi yang berlaku. Pengabdian dengan metode penyuluhan kepada masyarakat ini menjelaskan permasalahan-permasalahan ini dan memberikan pandangan tentang bagaimana UMKM dapat mengatasi tantangan-tantangan ini dalam mengadopsi dan mengintegrasikan AI untuk mendukung pertumbuhan dan keberlanjutan bisnis mereka.PATIKALA: Jurnal Pengabdian Kepada Masyarakat80PATIKALA: Jurnal Pengabdian Kepada Masyarakat['K. Menengah', 'Di Desa', 'Sei Simpang', 'Dua Kecamatan Kampar', 'Kiri Hilir', 'Kabupaten Kampar', 'Moris Adidi', 'Yogia', 'Agung Wicaksono', 'Septian Wahyudi', 'A. Munir']2023-12-30T00:00:00https://www.semanticscholar.org/paper/95af98594be36723809d74782d31086a536cdc46 815b530bc768ee10d438d02246f9f0e180aff2b2EBIMCS140{'pages': '180-183'}['Longxing Li', 'Bingwang Xue', 'Zhou Yu']2023-12-30T00:00:00https://www.semanticscholar.org/paper/815b530bc768ee10d438d02246f9f0e180aff2b2 3bee495d0cfa02588e25bf442fc15e886dacea41JMIR AI170JMIR AI['N. Bragazzi', 'Sergio Garbarino']2023-12-30T00:00:00https://www.semanticscholar.org/paper/3bee495d0cfa02588e25bf442fc15e886dacea41 a1e1361978bd651eeca0d7e32cee0871825c35cdIJARCCE00IJARCCE['Kamala S', 'Dr. A. Jayanthiladevi']2023-12-30T00:00:00https://www.semanticscholar.org/paper/a1e1361978bd651eeca0d7e32cee0871825c35cd f4f2fd4efeff1e7d078e56ed88183bb544bda501Informatică economică00Informatica Economica['Claudiu Brândas', 'Otniel Didraga', 'Andrei Albu']2023-12-30T00:00:00https://www.semanticscholar.org/paper/f4f2fd4efeff1e7d078e56ed88183bb544bda501 d8a376c73cb8be563a48fded55923f00365a641dInternational Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology['Sandeep Kumar']2023-12-30T00:00:00https://www.semanticscholar.org/paper/d8a376c73cb8be563a48fded55923f00365a641d 245a573c0f9d21f78cae9a3f1c3dd67c350abfffInternational Journal of Oral Biology20International Journal of Oral Biology['Yoohyun Lee', 'S. Ohk']2023-12-30T00:00:00https://www.semanticscholar.org/paper/245a573c0f9d21f78cae9a3f1c3dd67c350abfff 4c72eeda04852826f260d93ff6f3849b70a9621eInternational Journal of Advanced Nursing Education and Research00International Journal of Advanced Nursing Education and Research['Avree Ito-Fujita', 'Shayna Katz']2023-12-30T00:00:00https://www.semanticscholar.org/paper/4c72eeda04852826f260d93ff6f3849b70a9621e cfb6661441652f3e295c3a6da5e1435f34e5da7aInternational Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology['Shyam Ramesh Bhojwani']2023-12-30T00:00:00https://www.semanticscholar.org/paper/cfb6661441652f3e295c3a6da5e1435f34e5da7a d8556ede4863dfe4f377faa5ee8ba01d65f31de000['Edmilson Rodrigues do Nascimento Junior']2023-12-30T00:00:00https://www.semanticscholar.org/paper/d8556ede4863dfe4f377faa5ee8ba01d65f31de0 aa05dcccc1299d395f97c4ec8dc2f50a3ebac7fdYeungnam University Law Journal00Yeungnam University Law Journal['Won Sang Lee']2023-12-30T00:00:00https://www.semanticscholar.org/paper/aa05dcccc1299d395f97c4ec8dc2f50a3ebac7fd fa18d6b5fcfb2a4e72cc8dbbc3e6419101f094bcJournal of Community Informatics00The Journal of Community Informatics['Colin Rhinesmith']2023-12-30T00:00:00https://www.semanticscholar.org/paper/fa18d6b5fcfb2a4e72cc8dbbc3e6419101f094bc 602603cdbc615f6f5e8cf1c603c4155c521625d5Contemporary Art00CONTEMPORARY ART['Taisiia Poda']2023-12-30T00:00:00https://www.semanticscholar.org/paper/602603cdbc615f6f5e8cf1c603c4155c521625d5 7852149e1f4e35e6ab9ac3f679a9023b6f0e4987This article seeks to determine who is responsible for the widespread use of deepfake AI to create pornographic content and how Indonesian law governs it. Deepfakes, a subfield of artificial intelligence, can manipulate visual media by superimposing facial features on the bodies of others, creating misleading videos. Deepfakes were first used in movies and then in gadgets. However, deepfakes have been used to make explicit pornographic videos. This research is normative law-based. This study found that Indonesia has no specific legislation on artificial intelligence technology, except for Law Number 19 of 2016, which amends Law Number 11 of 2008 on Electronic Information and Transactions. Number 44 of 2008 on Pornography prohibits pornographic content creation and provides criminal penalties to hold offenders accountable. In Government Regulation (PP) Number 71 of 2019, which regulates electronic systems and transactions, pornographic platform providers cannot use deepfake technology.Mimbar: Jurnal Sosial dan Pembangunan00It is found that Indonesia has no specific legislation on artificial intelligence technology, except for Law Number 19 of 2016, which amends Law Number 11 of 2008 on Electronic Information and Transactions, which amends Law Number 44 of 2008 on Pornography.MIMBAR : Jurnal Sosial dan Pembangunan['Muhammad Ilman Abidin']2023-12-29T00:00:00https://www.semanticscholar.org/paper/7852149e1f4e35e6ab9ac3f679a9023b6f0e4987 d680f60b2b3abdd0d050336e989df7959b6e5560The main purpose of the study is to optimize local aspects of state regulation of ensuring the safety and security of enterprises in the context of sustainable development in a particular region. For this purpose, a new methodical approach was presented using simulation optimization of the processes of ensuring safety and security of security of enterprises in the context of sustainable development. The study focuses on enterprises and their safety and security systems security in the context of sustainable development. The relevance of the study is given by the fact that most enterprises in many regions suffer from ineffective regulation at the local level. At the same time, in most cases, state regulation does not take into account all aspects of safety and security in the context of sustainable development. The research methodology involves the use of a modern method of process optimization modeling. As a result, a model for optimizing local aspects of state regulation of business safety and security for a specific, selected region in the context of sustainable development was presented. The research has limitations and they consist of the selection of only one region and the consideration of local aspects of enterprises of certain socio-economic systems. Prospects for further research will be devoted to expanding modeling and taking into account a larger number of enterprises in the context of sustainable development.Business: Theory and Practice02Business: Theory and Practice['M. Kryshtanovych', 'T. Panfilova', 'Andrii Khomenko', 'Oleg Dziubenko', 'Liubov Lukashuk']2023-12-29T00:00:00https://www.semanticscholar.org/paper/d680f60b2b3abdd0d050336e989df7959b6e5560 ad2ee90f167ce8d51afcf9021c3b74e62a670a2fAim: The aim of the article is to discuss issues related to the functioning of the current Regulation of the European Parliament and of the Council (EU) No. 305/2011 establishing harmonized conditions for the marketing of construction products and the problems identified in this regard, as well as to discuss the assumptions of the proposal of the European regulation for construction products in the harmonized area. This article analyses and indicates the links of the proposal in question to the “European Green Deal” manifested in its inclusion of the assessment and communication of information on the environmental performance of construction products and the promotion of the circulation of construction products. Introduction: The area of conformity assessment of construction products in the EU is currently regulated by the Regulation 305/2011. Its main objective was to improve the functioning of the single market and improve the free movement of construction products in the EU by establishing harmonized conditions for their marketing. In practice, this meant allowing construction products to be legally marketed in one member state. However, the European Commission, after its analysis, identified some shortcomings in its implementation, which required further analysis and discussion. As a consequence, a draft of a new regulation establishing harmonized conditions for the marketing of construction products, amending Regulation (EU) 2019/1020 and repealing Regulation (EU) 305/2011 was developed to address a significant number of issues related to standardization, simplification for micro-enterprises, market surveillance and the enforcement of regulations. Methodology: The article uses theoretical research methods, including an analysis of the EC’s report on the ongoing analyses of Regulation 305/2011 and reports from entities directly involved in the opinion of the proposed regulation. The publication also includes the authors’ own formal and substantive interpretation of selected passages of the proposed regulation, which establishes harmonized conditions for the marketing of construction products. Conclusions: The proposal of the new regulation has both strengths and opportunities from the revision, as well as weaknesses and threats, or poses new challenges. Identifying the problems hindering the functioning of the single market for construction products, the EC pointed to two general goals of the CPR revision, i.e. to create a smoothly functioning single market for construction products and to contribute to the goals of green and digital transformation. The implementation of measures resulting from the entry into force of the new regulation will only show in practice whether the changes introduced have had the intended effect. Keywords: construction products, standardization, harmonized area, European Green Deal, sustainable environment, CPRSAFETY &amp; FIRE TECHNOLOGY00SAFETY &amp; FIRE TECHNOLOGY['Marta Iwańska', 'Ewa Sobór', 'Michał Chmiel']2023-12-29T00:00:00https://www.semanticscholar.org/paper/ad2ee90f167ce8d51afcf9021c3b74e62a670a2f ab46a76ef6fea264cf73035bf0a5c4d1f87a1785The paper meticulously examines the challenges and profound impact of the broadcasting system in Nigeria, conducting a comprehensive analysis of various broadcasting bodies. Employing a survey research method, the study embraces a sample size of 150, effectively representing the study's population. Out of the 150 questionnaires distributed, an impressive 93.3% response rate was achieved, with 140 questionnaires returned. The study underscores that major regulatory approaches, including licensing, sanctioning, arbitrating, and monitoring, play a pivotal role. The findings highlight the crucial role of regulation as a tool wielded by society to scrutinize media content and practices, with the political system shaping the trajectory of these regulations. In conclusion, the paper advocates for a review of the Nigerian Broadcasting Code, emphasizing the imperative of an independent regulatory body. Such a move is seen as essential for fostering pluralism and nurturing healthy competition within the industry.International Journal of Applied and Scientific Research120International Journal of Applied and Scientific Research['Olimma Benedette Ngozi', 'Michael A. Nzan-Ayang']2023-12-29T00:00:00https://www.semanticscholar.org/paper/ab46a76ef6fea264cf73035bf0a5c4d1f87a1785 98a98dfcd9afb13083ea25eae034b0321aa1ac03The regularity of inclusion in the Criminal Procedure Code of the Russian Federation of conclusions and testimonies of an expert and a specialist as sources of evidence is due to the rapidly developing field of special knowledge, achievements of science, technology, crafts, digital technologies. It is impossible to imagine a modern criminal process without the participation of such participants in criminal proceedings as an expert and a specialist. At the same time, if the legal status of the expert, as well as the form of his participation in a criminal case in the legislation, is more complete, then according to many researchers, the legal status of the specialist and the form of his/her participation is difficult to recognize completed. The author of the article conducted a study of the norms of the current criminal procedure legislation regarding the sufficiency of the volume of legal regulation of the legal status of a specialist and issues of the forms of his/her participation in criminal proceedings and determined his insufficiency. The most problematic issue, according to the author, is the problem of determining the form of the expert's opinion, since the legislator bypasses the issues of legal regulation of the form of the expert's opinion. In turn, this causes problems of application in practice, and, as a result, reduces the effectiveness and potential of applying special knowledge in the form of involving a specialist. Based on the study, the author proposes the concept and structure of the specialist's opinion form as a possible option for improving the form of his/her participation in criminal proceedings.Baikal Research Journal20Baikal Research Journal['Svetlana Lukoshkina']2023-12-29T00:00:00https://www.semanticscholar.org/paper/98a98dfcd9afb13083ea25eae034b0321aa1ac03 d666a11f6dea2ec6d25512c3c7434dd00828efacВ статье рассматриваются пределы регулирования общественных отношений государством. Приводится понятие и характерные признаки саморегулируемых отношений. Основанием для разграничения нуждающихся в государственном регулировании отношений и саморегулируемых служит потребность в правовом регулировании. Авторы подчеркивают, что она носит оценочный характер. Саморегулируемые общественные отношения представляют собой самый нижний уровень человеческих отношений, складывающийся между людьми в ходе их жизнедеятельности. Некоторые из этих отношений самопроизвольно возникают и также естественным путем прекращаются. Но некоторая, наиболее удачная форма отношений закрепляется в определенном коллективе или социальной группе и может протекать достаточно длительное время. Государство, устанавливая пределы саморегулирования, определяет границы правомерного действия этих отношений. There are limits of public relationships state regulation considered in the paper. A term and main fingerprints of the self-regulated relationships are advanced. The legal regulation need is used as a background to differentiate the public relationships to be regulated by state and self-regulated ones. The legal regulation need is highlighted to have estimative character. The self-regulated public relationships are the lowest level of human relationships formed among people during their activity of daily living. Some relationships appear of their own accord and stop in natural manner. However, several most favorable forms come into the force in certain collective or social group and can persist during a long time. The state establishes the legal regulation limits and determines the area of regulation observance.Вестник Академии права и управления00Вестник Академии права и управления['М.Б. Румянцев', 'П.О. Милов']2023-12-29T00:00:00https://www.semanticscholar.org/paper/d666a11f6dea2ec6d25512c3c7434dd00828efac 31464960fa60507605a0622112184315bfd109e1The purpose of the article is to provide a detailed analysis of the characteristics of agricultural market regulation in the context of ensuring food security at the regional and global levels at the present stage, given the urgency of global and regional issues related to food security. The article analyses the peculiarities of agricultural market regulation in the context of food security. The agricultural market is the main area that includes agricultural producers, consumers and the links between them, which makes it possible to ensure adequate access to food. The object of research is the agricultural market as a component of the state economy. The subject of the study is the theory of agricultural market regulation as a component of food security. The research is based on the scientific works of Ukrainian authors aimed at studying certain issues of the chosen topic. The article clarifies the main scientific approaches to the definition of the agrarian market. It also identifies the structural features of the agrarian market and clarifies the main functions of the agrarian market as a component of the state economy. The role of the agrarian market in the context of food security regulation is defined. The authors also identify the need for state regulation of the agrarian market. In the course of the work, the main features of the state regulation of the agrarian market were clarified and its main directions were identified. The paper also outlines the current problems of regulating the agricultural market in Ukraine under martial law. In addition, the publication identifies the peculiarities of international regulation of the agricultural market. On the basis of the results obtained, the authors have developed recommendations for improving the regulation of the agricultural market. The results of the study underline the importance of regulating the agricultural market as an integral factor in ensuring food security of the state and the world.Economics &amp; Education170Economics &amp; Education['S. Kvasha', 'Vitalii Vakulenko']2023-12-29T00:00:00https://www.semanticscholar.org/paper/31464960fa60507605a0622112184315bfd109e1 e0908836435a97a61713eece8ddc1a51f0c0b946The article examines the purpose of administrative maintenance of the balance of interests in the field of environmental regulation. Systemic, structural-functional methods and the method of comparison were used for the research. The topicality of the topic is due to the need to increase the effectiveness of the legal regulation of environmental protection, environmental safety, and nature management. In the context of current environmental legislation, the purpose and tasks of legal regulation are considered. It is noted that the subject-objective basis of administrative-legal regulation, which is expressed with the help of the concept of the idea of law. The latter acts as a legal idea of administrative and legal regulation in the field of ecology and forms the subject- objective basis of content and research. It is noted that the specification of the concept is manifested in the understanding of the balance of interests as a planned result of the coordination of the public interest of protecting the natural environment with basic public interests (economic well-being of citizens, security, and others), even with private interests. Protection of the public interest should have the ultimate goal of protecting the rights and freedoms of a specific person and citizen of the state. The purpose and planned result of the reconciliation of interests is to ensure basic public interests, ensure the subjective environmental rights and legitimate interests of citizens, and protect them from the misconduct of powerful subjects of administrative law in the field of legal regulation of environmental protection. The administrative-legal maintenance of the balance of interests in the field of environmental regulation is defined as a system of organizational, material and procedural administrative-legal means, with the help of which, in the process of realizing the public interest of environmental protection, basic public interests are ensured, the rights and legitimate interests of individuals and legal entities are ensured and protected.Visegrad journal on human rights100Visegrad Journal on Human Rights['M. Sirant']2023-12-29T00:00:00https://www.semanticscholar.org/paper/e0908836435a97a61713eece8ddc1a51f0c0b946 d74d98646e24a62985ae4f9c02c765938c0b1ebdСтатья посвящена проблемам нормативного регулирования административно-юрисдикционных полномочий сотрудников уголовно-исполнительной системы. Обосновывается позиция о необходимости использования профессионально-стратификационного подхода к нормативному закреплению данных полномочий, который позволяет учитывать занимаемую сотрудником должность и специфику сферы правореализации. Предлагается внести изменения в Приказ ФСИН России от 19.08.2019 № 688 в части корректировки квалификационных требований к должностям в уголовно-исполнительной системе. The article considers problems of regulation of administrative and ju- risdictional powers of the penal system employees. The article substantiates the position on the need to use a professional stratification approach to the normative consolidation of these powers, which allows taking into account the position held by the employee and the specifics of the sphere of legal realization. It is proposed to amend the Order of the Federal Penitentiary Service of Russia of August 19, 2019 No. 688 to adjust qualification requirements for positions in the penal system.Ius Publicum et Privatum00Ius Publicum et Privatum['Надежда Анискина', 'С.А. Старостин']2023-12-29T00:00:00https://www.semanticscholar.org/paper/d74d98646e24a62985ae4f9c02c765938c0b1ebd 5b2761afca03da43a6a63ac2cf9accc93c39c4b4The role of risk regulation in public security administrative law enforcement is very important, but there is a problem of large error in risk regulation. Comprehensive statistical methods cannot solve the problem of poor risk regulation effect in public security administrative law enforcement, and the actual avoidance rate is low. Therefore, this paper proposes a computer artificial intelligence method to analyze public security administrative law enforcement comprehensively. Firstly, the risk management theory is used to analyze the administrative law enforcement behavior comprehensively, and the risk regulation plan is adjusted according to the law enforcement content to reduce the irrelevant factors in the evaluation. Then, the risk analysis of administrative law enforcement behavior is carried out by computer artificial intelligence method, and the final evaluation set is formed. MATLAB simulation shows that the evaluation accuracy and evaluation time of computer artificial intelligence methods are better than those of comprehensive statistical methods under certain law enforcement content.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)60A computer artificial intelligence method is proposed to analyze public security administrative law enforcement comprehensively and shows that the evaluation accuracy and evaluation time of computer artificial intelligence methods are better than those of comprehensive statistical methods under certain law enforcement content.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)['Pengcheng Zhao']2023-12-29T00:00:00https://www.semanticscholar.org/paper/5b2761afca03da43a6a63ac2cf9accc93c39c4b4 9c11c89fa8c1d0ac95aa8c44bad7307903facfafThe activities of large online platforms based in third countries in the internal market pose potential risks to EU users. The EU aims to ensure a safe online environment not only for consumers, but also for all users active in this ecosystem. Increased security, legal certainty, consumer protection, transparency, and several other partial aims have led to the adoption of the Digital Services Package, which includes the so-called DSA Regulation. The present article aims to identify the key impacts of the new regulation on very large online platforms that are part of the daily routine of EU citizens and to highlight the benefits it brings to regular users. There are many changes brought about by the new legislation; therefore, we decided to focus only on those that we consider the most tangible, both from the perspective of the everyday user and for the platforms per se.Central European Journal of Comparative Law160The present article aims to identify the key impacts of the new regulation on very large online platforms that are part of the daily routine of EU citizens and to highlight the benefits it brings to regular users.Central European Journal of Comparative Law['Dominika Moravcová']2023-12-29T00:00:00https://www.semanticscholar.org/paper/9c11c89fa8c1d0ac95aa8c44bad7307903facfaf 7c949b348b7618029d0ddcde69d5d8e649bc2fe2The debate over the Government Regulation in Lieu of Law on Job Creation has not ceased even though it has become an official law. Pros and cons persist due to its perceived deviation from Constitutional Court Decision Number 91/PUU-XVIII/2020, issued on November 25, 2021. This research aims to describe that regulations should not contradict one another, especially when there is already a Constitutional Court decision. Regulations should be made through a legitimate process without resorting to legal shortcuts. The research method used is a normative legal research with a qualitative approach. There is ample time available to follow the proper legislative process, yet shortcuts are taken by issuing Government Regulations in Lieu of Law, which are later ratified as laws. The research implies that despite the enactment of these regulations, there is significant resistance due to their divergence from the judicial review results at the Constitutional Court. As a result, this research suggests the necessity of revising relevant laws to include sanctions for failing to comply with Constitutional Court decision.Jurnal Hukum00Jurnal Hukum['Sulistyowati Sulistyowati', 'Agus Salim', 'Puspa Eriyani', 'Siti Mastoah']2023-12-29T00:00:00https://www.semanticscholar.org/paper/7c949b348b7618029d0ddcde69d5d8e649bc2fe2 489440f3e11ce2846040d58d96a24ef629188c65The purpose of our study was the features of criminal behavior of the accused in the commission of particularly serious crimes in the context of the formation functions of programming and control. The sample consisted of 59 men aged 18—60 years, of those accused of committing particularly serious crimes aimed at a forensic psychiatric examination, the average age was 33.7 years. The methods of neuropsychological examination and psychological analysis of criminal cases were used. Syndrome of defeat of the basal divisions of the frontal lobes, prefrontal syndrome, syndrome of defeat of the medial divisions of the frontal lobes, Postfrontal (premotor) syndrome is most often seen among persons accused of particularly serious crimes. The criminal behavior of the accused in the Commission of particularly serious crimes was characterized by uncritical damage to the basal parts of the frontal lobes. The impulsivity is the main characteristic of the criminal behavior of the accused in especially serious crimes with the defeat of the prefrontal frontal lobe (prefrontal syndrome). Subjects with the defeat of the kinetic (dynamic) factor differed greater rigidity of criminal behavior. The behavior of those accused of committing particularly serious crimes was passive (energy-saving) in violation of the energy factor in the case of damage to the medial parts of the frontal lobes. The obtained results can be used to solve the issues of drawing a portrait of an unknown criminal, as well as in the course of correctional work with persons prone to repeat illegal behavior.Psychology and Law171Psychology and Law['D. Kashirskiy', 'O.V. Staroseltseva']2023-12-29T00:00:00https://www.semanticscholar.org/paper/489440f3e11ce2846040d58d96a24ef629188c65 ec3870fbb6453e57e78b6295f23b9083b126b874В статье рассматриваются специфические особенности реализации прав образовательных организаций на результаты интеллектуальной деятельности, созданные сотрудниками вузов. Особое внимание уделяется попыткам законодательного закрепления правовых механизмов оборота исключительных прав ву- зов. Анализируются основные положения Политики университетов в области интеллектуальной собственности, которые рекомендованы ВОИС. Выявляются тенденции управления авторскими правам и основные способы защиты авторских материалов, созданных в гуманитарном вузе. The article discusses the specific features of the implementation of the rights of educational organizations to the results of intellectual activity created by university employees. Particular attention is paid to attempts to legislate legal mechanisms for the circulation of exclusive rights of universities. The main provisions of the Universities Policy in the Field of Intellectual Property in Educational Activities, which are recommended by WIPO, are analyzed Trends in copyright management are identified and themain ways of protecting copyright materials created in a humanities university are identified.Eurasian Advocacy (Evraziiskaya Advokatura)00Eurasian Advocacy (Evraziiskaya Advokatura)['Е.Н. Щербак']2023-12-29T00:00:00https://www.semanticscholar.org/paper/ec3870fbb6453e57e78b6295f23b9083b126b874 6fe642c88dd08294b51993811b85fdf7eadafe4fThe proposed article is devoted to the digitalization of the sphere of climate protection. Today, the main problem is climate protection through the digitalization of ecology. In solving this issue, the conditions for accelerating the pace of development of the republic's economy through the use of digital technologies in the field of climate protection and improving the quality of life of the population, the transition of the environment to a fundamentally new development trajectory were considered. Along with the elimination of gaps in environmental legislation, the process of digitalization of ecology is actively developing. This strengthens the desire of citizens living in the environment to do everything very quickly. In the issue of climate digitalization at the state level, much attention is paid to the well-being of the environment, as evidenced by the smaller number of state programs and other legal acts adopted in this area. The global and Kazakhstan trend was observed during comprehensive legal studies of the concept of green economy and other green development programs. The main goal of digitalization of the sphere of climate protection is to improve the quality of life of the population, improve the environmental situation, create opportunities for the effective operation of environmental measures, modernize the ecological system, ensure environmental safety, improve environmental digital education of the population. The digital transformation of climate protection is an important part of many national strategies to restore the ecological dangerous crisis and prevent environmental safety. In addition, the article presents additions and changes to the current environmental legislation to improve the directions in the field of digitalization of ecology, including climate protection.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan['S. M. Rakhmetov']2023-12-29T00:00:00https://www.semanticscholar.org/paper/6fe642c88dd08294b51993811b85fdf7eadafe4f 88755c9ea56064d1f02c3b427e3aa3c870e63413The proposed article is devoted to the digitalization of the sphere of climate protection. Today, the main problem is climate protection through the digitalization of ecology. In solving this issue, the conditions for accelerating the pace of development of the republic's economy through the use of digital technologies in the field of climate protection and improving the quality of life of the population, the transition of the environment to a fundamentally new development trajectory were considered. Along with the elimination of gaps in environmental legislation, the process of digitalization of ecology is actively developing. This strengthens the desire of citizens living in the environment to do everything very quickly. In the issue of climate digitalization at the state level, much attention is paid to the well-being of the environment, as evidenced by the smaller number of state programs and other legal acts adopted in this area. The global and Kazakhstan trend was observed during comprehensive legal studies of the concept of green economy and other green development programs. The main goal of digitalization of the sphere of climate protection is to improve the quality of life of the population, improve the environmental situation, create opportunities for the effective operation of environmental measures, modernize the ecological system, ensure environmental safety, improve environmental digital education of the population. The digital transformation of climate protection is an important part of many national strategies to restore the ecological dangerous crisis and prevent environmental safety. In addition, the article presents additions and changes to the current environmental legislation to improve the directions in the field of digitalization of ecology, including climate protection.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan['Galymzhan Akhmet', 'D. Bekezhanov', 'G. Kopbassarova']2023-12-29T00:00:00https://www.semanticscholar.org/paper/88755c9ea56064d1f02c3b427e3aa3c870e63413 b701b03b2e79fea48f2253af10f29febb96a613fВ Казахстане развитие системы ТиПО является одним из важных элементов развития кадрового потенциала и индустриально-инновационного развития страны. Как известно за рубежом развитие технического и профессионального образования является не только подготовка кадров для рынка, а именно социальная интеграция молодежи. Для выработки рекомендаций был изучен зарубежный опыт качества образования, государственное регулирование в области образования. В частности, подробно рассматриваются особенности государственной политики за период Независимости в части регулирования образовательного процесса в целях улучшения качества образования в системе ТиПО. В результате исследования, основанного на данных Бюро национальной статистики Агентства по стратегическому планированию и реформам Республики Казахстан, Министерства просвещения, Национальной образовательной база данных, Национального доклада о состоянии и развитии системы образования Республики Казахстан и других, авторы пришли к выводу, что какие бы меры не принимались, качество образования остается на низком уровне. В связи с чем предлагается проведение мониторинга региональными управлениями образования совместно с Министерством просвещения и другими структурами. Значимость результатов проводимого исследования заключается в разработке региональной модели системы оценки качества образования. Основная идея предлагаемой модели состоит в системной и комплексной работе всех участников оценки качества. В данную модель включен этап независимой оценки учебных достижений обучающихся, их мониторинг и выработка рекомендаций по результатам контроля, что в первую очередь приведет к повышению качества и удовлетворенности рынка труда. The purpose of the research is to study foreign policy in the field of quality requirements in the system of technical and vocational education, analysis and development of recommendations for the world. In the process of research, information and analytical methods, methods of detection and statistical analysis, synthesis, group operations, graphical and analytical methods are used. To develop recommendations, foreign experience in the quality of education, state regulation in the field of education was studied. In particular, the features of state policy during the period of Independence in terms of regulating the educational process in order to improve the quality of education in the TVE system are considered in detail. As a result of a study based on data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan, the Ministry of Education, the National Education Database, the National Report on the Status and Development of the Education System of the Republic of Kazakhstan and others, the authors concluded that no matter what measures accepted, the quality of education remains at a low level. In this connection, it is proposed to conduct monitoring by regional departments of education together with the Ministry of Education and other structures. The significance of the results of the ongoing research lies in the development of a regional model of the system for assessing the quality of education. The main idea of the proposed model is the systematic and integrated work of all participants in the quality assessment. This model includes the stage of an independent assessment of the educational achievements of students, their monitoring and the development of recommendations based on the results of control, which, first of all, will lead to an increase in the quality and satisfaction of the labor market.Вестник Казахского университета экономики, финансов и международной торговли00Вестник Казахского университета экономики, финансов и международной торговли['Г.К. Укибаева', 'М.Т. Рахымова', 'Г.К. Омаров', 'Т.В. Петросянц', 'G. Ukibayeva', 'M. Rakhymova', 'G. Omarov', 'T. Petrosyants']2023-12-29T00:00:00https://www.semanticscholar.org/paper/b701b03b2e79fea48f2253af10f29febb96a613f 7c68b9bbfe0edaef2c05402799185ca753a79627List Forum für Wirtschafts- und Finanzpolitik60List Forum für Wirtschafts- und Finanzpolitik['Enzo Weber']2023-12-29T00:00:00https://www.semanticscholar.org/paper/7c68b9bbfe0edaef2c05402799185ca753a79627 f71ba7e1f890ed15f70adda2cf62d5ead351452e<jats:p />Security and Communication Networks10Security and Communication Networks['Security and Communication Networks']2023-12-29T00:00:00https://www.semanticscholar.org/paper/f71ba7e1f890ed15f70adda2cf62d5ead351452e 92ec0b87bb94aacb370eb7d4325186c68ae84880Introduction: Early detection of melanoma requires timely access to medical care. In this study, we examined the feasibility of using artificial intelligence (AI) to flag possible melanomas in self-referred patients concerned that a skin lesion might be cancerous. Methods: Patients were recruited for the study through advertisements in 2 hospitals in Halifax, Nova Scotia, Canada. Lesions of concern were initially examined by a trained medical student and if the study criteria were met, the lesions were then scanned using the FotoFinder System®. The images were analyzed using their proprietary computer software. Macroscopic and dermoscopic images were evaluated by 3 experienced dermatologists and a senior dermatology resident, all blinded to the AI results. Suspicious lesions identified by the AI or any of the 3 dermatologists were then excised. Results: Seventeen confirmed malignancies were found, including 10 melanomas. Six melanomas were not flagged by the AI. These lesions showed ambiguous atypical melanocytic proliferations, and all were diagnostically challenging to the dermatologists and to the dermatopathologists. Eight malignancies were seen in patients with a family history of melanoma. The AI’s ability to diagnose malignancy is not inferior to the dermatologists examining dermoscopic images. Conclusion: AI, used in this study, may serve as a practical skin cancer screening aid. While it does have technical and diagnostic limitations, its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.Journal of Cutaneous Medicine and Surgery162Artificial intelligence, used in this study, may serve as a practical skin cancer screening aid and its inclusion in a melanoma screening program, directed at those with a concern about a particular lesion would be valuable in providing timely access to the diagnosis of skin cancer.Journal of Cutaneous Medicine and Surgery['Madeleine E. Crawford', 'Kiyana Kamali', 'Rachel A. Dorey', 'Olivia C. MacIntyre', 'Kristyna Cleminson', 'Michael L MacGillivary', 'Peter J. Green', 'Richard G Langley', 'Kerri S. Purdy', 'Ryan C. DeCoste', 'Jennette R. Gruchy', 'Sylvia Pasternak', 'Amanda Oakley', 'Peter R Hull']2023-12-29T00:00:00https://www.semanticscholar.org/paper/92ec0b87bb94aacb370eb7d4325186c68ae84880 777a0c38a478d9f68da86c0ca52fde8d8b8ade01Automated records analysis strategies primarily based on artificial intelligence (AI) have become increasingly famous in recent years because of their capability to quickly and effectively method enormous quantities of facts. However, concerns about the validity of those methods and their potential for introducing bias or mistakes have been raised. This study aims to assess the validity of automatic facts evaluation methods based on AI. It entails evaluating those methods' accuracy, reliability, and consistency compared to conventional guide evaluation methods. An expansion of study methods may be used, which includes experimental research, case research, and surveys. Facts will be gathered from computerized AI techniques and manual strategies, after which they will be compared and evaluated to decide the extent of agreement between them. One capability mission in this research is the dearth of a gold standard for statistics analysis. Unlike in medical fields wherein there may be a clean popular for prognosis, information analysis methods can also vary depending on the unique dataset and research question. Expanding a hard and fast of criteria or metrics for evaluating the validity of different AI techniques is essential. Another challenge is the rapidly evolving nature of the AI era. As new techniques and algorithms constantly evolve, keeping up with modern-day improvements and their potential impact on records analysis could be challenging.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)170This study aims to assess the validity of automatic facts evaluation methods based on AI by evaluating those methods' accuracy, reliability, and consistency compared to conventional guide evaluation methods.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)['K. Yuvaraj', 'Balakumar P', 'Prema S. Kadam', 'Prakash Mishra', 'Jambi Ratna Raja Kumar', 'Anupama Verma']2023-12-29T00:00:00https://www.semanticscholar.org/paper/777a0c38a478d9f68da86c0ca52fde8d8b8ade01 2598bf0e0b2b18acea3189e09eb526d8e89bb7f7Artificial intelligence as it is envisioned in literature, reflects moral and social standards, sculpts societal hopes and fears, spurs scientific progress, and perhaps even foretells the future. Artificial intelligence (AI) is being developed by humans, including programmers through their code and authors and readers through millennia of discourse, starting with ancient automatons and continuing with contemporary concepts of AI. The question of what we are producing as AI becomes more human—and possibly superhuman—and what literature can teach us about these AI imaginaries are also topics covered in this paper. The researcher seeks to explain the connections between artificial intelligence and literature in this work by highlighting the numerous ways that machine intelligence helps the production and comprehension of narrative. This study will also look at the connection between humans and artificial intelligence in William Gibson’s Sprawl trilogy and how his cyberpunk trilogy allows readers to think about how the lines between humans and technology are blurring and how technology can both liberate and enslave people.Rupkatha Journal on Interdisciplinary Studies in Humanities230The connection between humans and artificial intelligence in William Gibson’s Sprawl trilogy and how his cyberpunk trilogy allows readers to think about how the lines between humans and technology are blurring and how technology can both liberate and enslave people is looked at.Rupkatha Journal on Interdisciplinary Studies in Humanities['Shuchi Agrawal']2023-12-29T00:00:00https://www.semanticscholar.org/paper/2598bf0e0b2b18acea3189e09eb526d8e89bb7f7 e8cd23a3c44adf8db09913c557bdc420a67fe94bWith the rapid development of information technology, artificial intelligence technology has gradually penetrated into various fields, including education. Higher vocational education is an important part of higher education, and its educational model and methods are constantly being reformed and innovated. As a new educational concept and method, the core idea of adaptive learning is to provide customized learning resources and paths for learners according to their individual needs and characteristics. The introduction of artificial intelligence technology has brought new opportunities and challenges for adaptive learning in higher vocational education. This paper will discuss the influence, steps and methods of artificial intelligence on adaptive learning in higher vocational education.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)160This paper will discuss the influence, steps and methods of artificial intelligence on adaptive learning in higher vocational education.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)['Ren Jing', 'He Boyi']2023-12-29T00:00:00https://www.semanticscholar.org/paper/e8cd23a3c44adf8db09913c557bdc420a67fe94b 8281af53d5154a4078df48156652bd1ede269991With the development and application of artificial intelligence (AI) technology, the analysis and application of transmission inspection data has also been greatly improved. Based on AI technology, this paper constructs an adaptive model of transmission inspection data through machine learning decision tree method, aiming at providing more intuitive, efficient and comprehensive data analysis and decision support. Firstly, feature extraction technology is used to preprocess the transmission inspection data, and the key features are obtained. Then, the Iterative Dichotomiser 3 (ID3) algorithm is used to classify and sort these features, and a decision tree model is obtained. This paper uses data processing technology to process transmission inspection data, so that users can intuitively understand and analyze the data. The experimental results show that the monitoring data can better understand the monitoring situation in a specific area, so that the abnormal situation can be found in time, and the real-time monitoring of the monitoring data can be realized. The accuracy rate, recall rate and F1 value of ID3 algorithm in monitoring transmission inspection anomalies are 0.98, 0.91 and 0.94 respectively. Through experimental evaluation, the AI method can effectively process transmission inspection data, and provides a strong support for transmission inspection work.2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)150The experimental results show that the monitoring data can better understand the monitoring situation in a specific area, so that the abnormal situation can be found in time, and the real-time monitoring of the monitoring data can be realized.2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)['Shuang Lin', 'Jianye Huang', 'Teng Ma', 'Chenxiang Lin', 'Wenxu Yao', 'Jiali Xiong']2023-12-29T00:00:00https://www.semanticscholar.org/paper/8281af53d5154a4078df48156652bd1ede269991 68501a92eda1eb6e1ed464a9b941d4b088dad026BACKGROUND AND AIMS Utilization of electronic health records data to derive predictive indexes such as the electronic Child Turcotte Pugh Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we utilized artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease. APPROACH Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 to 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. RESULTS 4614 patients were included. We found that the electronic Child Turcotte Pugh Score had a Concordance-index of 0.64 for the prediction of overall mortality while the imaging based model alone or with electronic Child Turcotte Pugh Score performed significantly better, Concordance-index of 0.72 and 0.73 (p<0.001). For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance-index for predicting future decompensation was 0.67, 0.79 and 0.80 for electronic Child Turcotte Pugh Score , imaging alone or combined respectively. CONCLUSION This proof of concept demonstrates the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease.Hepatology00This proof of concept demonstrates the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease.Hepatology['Grace L Su', 'Peng Zhang', 'Patrick X Belancourt', 'Bradley Youles', 'Binu Enchakalody', 'P. Perumalswami', 'Akbar K. Waljee', 'Sameer Saini']2023-12-29T00:00:00https://www.semanticscholar.org/paper/68501a92eda1eb6e1ed464a9b941d4b088dad026 9b8e081f82495e3f065c0eb67ad56d48f65d1ef4Artificial Intelligence has begun to rapidly invade our daily lives as well as the education system. It decisively affects the lives of all of us in every aspect. It tends to change the way everyone works, including, pupils, students, and teachers with unprecedented rapidity. This specific work aims to study the changes that the new reality has brought to the way of teaching, to assessment, to the differentiation of teaching, and to any form of feedback that can be offered in real time, from the teacher’s side. From the student’s point of view, we will look for the ways in which it affects the process of studying the courses, in the preparation of assignments, in strengthening the interest in learning, especially of students with learning gaps, and how much and how it contributes to changing the way of working and thinking of the students. Education will be directly affected and will have to adapt to the new reality. The new reality introduces other learning requirements and techniques. New tools are being introduced, never seen before, which have not been tested before. The demands for new knowledge are great but at the same time, the insecurity that every innovative achievement creates grows. The application of artificial intelligence in secondary schools, where this has been achieved,will be studied. The work will then list the positives and negatives that have been recorded from the implementation of artificial intelligence in education as well as expectations and fears for the future. The specific research is bibliographic and additionally, due to the immediacy and rapidity of the changes, it also includes reports from the international press. The investigation of the bibliography is done, separately, for the teachers and for the students, with different criteria in each case.European Journal of Engineering and Technology Research30The application of artificial intelligence in secondary schools, where this has been achieved, will be studied and the positives and negatives that have been recorded from the implementation of artificial intelligence in education will be listed.European Journal of Engineering and Technology Research['Konstantinos Aletras']2023-12-29T00:00:00https://www.semanticscholar.org/paper/9b8e081f82495e3f065c0eb67ad56d48f65d1ef4 ca78e7cc072d947ae3adc88ece0250551e2befe5This paper provides an exploration of the use cases for artificial intelligence (AI) in information access optimization. AI-based tactics to statistics optimization have the ability to enhance performance and reduce cost drastically. The paper discusses the characteristics of AI-pushed optimization techniques, together with fusion of heterogeneous facts assets, predictive modeling, forecasting, anomaly detection, and clustering. It also explores the numerous statistics assets that can be optimized using AI, which includes conventional database systems, disbursed information lakes, and streaming event statistics. Moreover, the paper considers the demanding situations related to AI-enabled facts get admission to optimization, such as loss of availability of labeled data and issue in imposing commercial enterprise good judgment. Finally, excellent practices and considerations for implementation are mentioned. Through exploring the use instances for AI in records get entry to optimization, this paper provides important insights for builders and businesses considering the usage of AI for data get entry to optimization.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)160Through exploring the use instances for AI in records get entry to optimization, this paper provides important insights for builders and businesses considering the usage of AI for data get entry to optimization.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)['Sharmila. P', 'Snehal R. Rathi', 'Manpreet Singh', 'Preeti Naval', 'Krishnan Batri', 'Bhavesh Agnihotri']2023-12-29T00:00:00https://www.semanticscholar.org/paper/ca78e7cc072d947ae3adc88ece0250551e2befe5 b4d324ac0a4f37caf364ad0b5eec1f49f3d85e08The worldwide supply chain has experienced significant obstacles in recent years, owing to causes such as opposed to globalization opinions, increasing trade protectionist policy, and the adverse effects from occurrences like the COVID-19 pandemic. The goal of this study is to gain insight into those variables that influence organizations' preparedness to deploy AI (artificial intelligence) technology, and to examine whether this deployment influences the resilience of their supply chains and efficiency. We collected survey data from 125 Indian businesses and utilized Structural Equation Modelling to investigate our hypotheses, according to the research, the corresponding advantages of business artificial intelligence (AI), supply chain collaboration, and environmental volatility identified as major elements encouraging AI approval, leading to improved supply chain efficiency. By widening the application and effect of AI technology, this investigation addresses a significant vacuum for studies on the behavior of organizations adopting artificial intelligence (AI) in the supply chain field. The study's findings offer beneficial perspectives and practical consequences for firms considering the usage of artificial intelligence (AI) technologies.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)360Insight is gained into those variables that influence organizations' preparedness to deploy AI (artificial intelligence) technology, and whether this deployment influences the resilience of their supply chains and efficiency to address a significant vacuum for studies on the behavior of organizations adopting artificial intelligence in the supply chain field.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)['K. Balaji']2023-12-29T00:00:00https://www.semanticscholar.org/paper/b4d324ac0a4f37caf364ad0b5eec1f49f3d85e08 013403e7bbd90d545f86c35ee51140ee1388e7aeThe tasks of a modern teacher are definitely different from those they had to perform a decade ago. In the era of various changes taking place, constantly developing, but also accompanying digitization in every sphere of life, the role of the teacher takes on a new meaning – flexibility and openness to changes on their part become necessary. Therefore, teachers face new challenges, roles and skills to expand. The conducted considerations are aimed at showing the impact of digitization both on the daily work of the teacher, the performance of professional functions, as well as his relationship with students. The punchline becomes the need to use artificial intelligence tools, as well as the essence and necessity of media literacy of modern teachers, which is reflected in the entire didactic process carried out among students shaped by digitization and the world of media. Among the others, the aim of the work was to determine attitudes of the educators (on the rating scale) about the presented approach to the work.Journal of Education, Technology and Computer Science130The conducted considerations are aimed at showing the impact of digitization both on the daily work of the teacher, the performance of professional functions, as well as his relationship with students.Journal of Education, Technology and Computer Science['Sandra Ścieranka']2023-12-29T00:00:00https://www.semanticscholar.org/paper/013403e7bbd90d545f86c35ee51140ee1388e7ae 3d3f068cb5218a648c05f825b668ddc1368020a3The advent of artificial intelligence has set off a climax of digital transformation for many industries. As the foundation of the operation in business, the digital transformation of financial management raises wide concerns. Therefore, this paper analyzes the literature of the last five years regarding artificial intelligence, and the application of digital technology in financial management, etc. to explore the necessity, challenges, and general path of digital transformation in financial management. The national trend and the need for enterprises to improve competitiveness prove the necessity of digital transformation of financial management. However, the weak support of awareness, technology, and human resources obstructs this process. Also, the use of digital platforms poses a high risk of data leakage. A transformation path is designed in response to these issues. The most important steps are to change concepts, establish and improve information platforms, strengthen financial personnel training, and establish data risk prevention mechanisms.Highlights in Business, Economics and Management100This paper analyzes the literature of the last five years regarding artificial intelligence, and the application of digital technology in financial management, etc. to explore the necessity, challenges, and general path of digital transformation in financial management.Highlights in Business, Economics and Management['Zhixian Zhang']2023-12-29T00:00:00https://www.semanticscholar.org/paper/3d3f068cb5218a648c05f825b668ddc1368020a3 b13c44213b07d0e1ba75f80a6baa9aa121b68a51Artificial intelligence is a thriving field in the modern world today. Almost every other operation in today’s world is being integrated with the application of Artificial Intelligence (AI). Artificial Intelligence does not only include the automation of conventional processes, but also includes the introduction of several new programs and interactions that help make work easier for everyone. The introduction of artificial intelligence (AI) into the realm of anesthesia, particularly in remote pre-operative assessment and perioperative care, brings forth a nuanced landscape of advantages and challenges. On the positive side, AI demonstrates remarkable efficiency and precision in the preoperative phase, rapidly analyzing extensive datasets to offer accurate insights into patient health and potential risks. Its expertise in predicting and struggling through anesthesia-related risks stands out, aiding healthcare professionals in anticipating challenges and allowing for personalized interventions. The capability to tailor anesthesia plans based on individual patient characteristics further adds a layer of sophistication, potentially optimizing administration and improving overall outcomes. In perioperative care, AI’s remote monitoring capabilities provide real-time insights into vital signs and potential complications, enabling patient safety through prompt responses. Additionally, AI serves as a valuable decision support system, offering recommendations and additional information for more informed decision-making. This article shall review the scope of artificial intelligence within the field of anesthesia and would reflect upon how it has helped people living in remote areas access better healthcare facilities through its proposition.Asian Journal of Medicine and Health00The scope of artificial intelligence within the field of anesthesia is reviewed and how it has helped people living in remote areas access better healthcare facilities through its proposition is reflected.Asian Journal of Medicine and Health['Sayed Athar Hussain Kazmi']2023-12-29T00:00:00https://www.semanticscholar.org/paper/b13c44213b07d0e1ba75f80a6baa9aa121b68a51 496c9fd21b89691df2ec5f72742e2ce75a772813The research was based in an interdisciplinary approach about the legal ethics involved in the Digital Era, especially concerning the use of Artificial Intelligence (AI) in patents’ development and rights as an important problematic. The studies encompassed a view on the liability issue, within the broad framework of Contractual Law and licensing. The research addressed the different theories and perspectives on legal capacity, private law, and personality rights, illustrating the theoretical justice concept to substantiate and underlie the ethical problematics arising from the use of AI. This research work encompassed the advantages and disadvantages involved in the AI scenario, demonstrating the enhanced performance and outcomes in the industrial property area, accordingly to business practices and techniques, and ethical parameters that should be pursued by the society, to develop a transparent, reliable, trustworthy, and ex-plainable use of AI as a tool especially related to the patent system. The studies summarily approached the regulatory aspects and legislative policies of AI in the International and European contexts, providing a comparative law picture. To achieve this multidisciplinary endeavor, a perspective also centered on data analysis had to be applied, employing mainlyRevista da Faculdade de Direito da UFMG40This research work encompassed the advantages and disadvantages involved in the AI scenario, demonstrating the enhanced performance and outcomes in the industrial property area, accordingly to business practices and techniques, and ethical parameters that should be pursued by the society, to develop a transparent, reliable, trustworthy, and ex-plainable use of AI as a tool especially related to the patent system.Revista da Faculdade de Direito da UFMG['João Antonio Belmino Dos Santos', 'Giovanna Martins Sampaio']2023-12-29T00:00:00https://www.semanticscholar.org/paper/496c9fd21b89691df2ec5f72742e2ce75a772813 3ef727eadc0934fec8bd4a6719b6e2f622a16991In a dynamic healthcare landscape, healthcare professionals (HCPs) must be proficient in artificial intelligence (AI). The Clinician Champions Program was created to address these AI education gaps. Over six weeks, three cohorts participated in this interprofessional program, featuring weekly assignments and a capstone project. This study employs a qualitative descriptive approach to assess the program’s effectiveness in enhancing knowledge, confidence, and skills in AI integration. With a 78% completion rate among 158 clinicians, the program utilized engaging methods, including case studies, capstone projects, and reflective learning to meet diverse learning needs. It also emphasized ethical considerations (e.g., IDEA framework) and the importance of extending educational opportunities to various healthcare professionals. The findings highlight the necessity of a diverse, equitable, and inclusive learning environment to bridge AI education gaps in healthcare. The program’s success supports the idea that enhancing AI knowledge and fostering confidence can lead to meaningful AI discussions in healthcare practice. This research offers insights for educators and institutions aiming to address the evolving healthcare needs through innovative interprofessional educational approaches.Education sciences170The program’s success supports the idea that enhancing AI knowledge and fostering confidence can lead to meaningful AI discussions in healthcare practice, and highlights the necessity of a diverse, equitable, and inclusive learning environment to bridge AI education gaps in healthcare.Education Sciences['Bemnet Teferi', 'Maram Omar', 'Tharshini Jeyakumar', 'Rebecca Charow', 'C. Gillan', 'Jessica Jardine', 'Jane Mattson', 'A. Dhalla', 'S. Koçak', 'Mohammad Salhia', 'Bryn Davies', 'Megan Clare', 'S. Younus', 'D. Wiljer']2023-12-29T00:00:00https://www.semanticscholar.org/paper/3ef727eadc0934fec8bd4a6719b6e2f622a16991 d8938eb537e16e7a755221e59aeda8011b4d1d84This research aims to examine the relationship between perceptions of the use of Artificial Intelligence (AI) in audit practices and Auditor Judgment in financial audits. A survey method is used to involve students majoring in Accounting as respondents. Data was collected through a specially designed questionnaire and statistical analysis was used to test the hypothesis. The research results reveal that there is a significant influence between the perception of the use of AI in auditing and the auditor's ability to make judgments. Auditors who have a positive perception of AI tend to make more accurate judgments in evaluating the audit entity's financial reports. To the best of our knowledge, this is the first study that exploring a relationship that is still rarely explored in the literature, namely the influence of perceptions of the use of AI in auditing on the Auditor's Judgment ability. This research also provides new insights into how AI technology can contribute as a valuable tool in improving quality in the audit processIndonesian Journal of Applied Accounting and Finance110There is a significant influence between the perception of the use of AI in auditing and the auditor's ability to make judgments, and this research provides new insights into how AI technology can contribute as a valuable tool in improving quality in the audit process.Indonesian Journal of Applied Accounting and Finance['Widya Ais Sahla', 'Dwianto Mukhtar Latif']2023-12-29T00:00:00https://www.semanticscholar.org/paper/d8938eb537e16e7a755221e59aeda8011b4d1d84 e7edbe57cc057d6b9888e8c7aea8ed80e0ce66d9The integration of Artificial Intelligence (AI) in various sectors has led to significant advancements, with the animal industry being no exception. This review aims to investigate the benefits, limitations, and future prospects of AI technology in improving animal welfare. First, it examines the role of AI in understanding animal behaviors and emotions, providing deeper insights into their well‐being and sources of stress. Next, the paper explores how AI can revolutionize animal nutrition through innovative algorithms and data analytics. The health aspect emphasizes the ability of AI to identify and manage illnesses through intelligent systems. This review also highlights the application of AI in improving animal living conditions, with a focus on environmental management and automated cleaning and disinfection systems. In conclusion, the review emphasizes AI‐driven techniques for early prediction, close monitoring, and accurate diagnosis of animal diseases, ensuring healthier and more sustainable livestock management. By leveraging its advantages, addressing limitations, and exploring future directions, AI has the potential to significantly enhance animal welfare, sustainable agriculture, and veterinary practices.Animal Research and One Health640The role of AI in understanding animal behaviors and emotions is examined, providing deeper insights into their well‐being and sources of stress, and how AI can revolutionize animal nutrition through innovative algorithms and data analytics is explored.Animal Research and One Health['Li Zhang', 'Wenqiang Guo', 'Chenrui Lv', 'Meng Guo', 'Mei Yang', 'Qiuyue Fu', 'Xiaomeng Liu']2023-12-29T00:00:00https://www.semanticscholar.org/paper/e7edbe57cc057d6b9888e8c7aea8ed80e0ce66d9 0089b7c8508f9e349d34fe2c77960383ae09b99fPurpose. The study aims to understand artificial intelligence as a socio-cultural phenomenon and its impact on education, where the spiritual sphere of humanity, moral norms, values, and human cognitive abilities are preserved, transferred as well as reproduced. A new discourse on the interaction of artificial and authentic human intelligence becomes inevitable, which has led to a situation of uncertainty. Changes in the socio-cultural environment under the influence of artificial intelligence increase potential threats to the educational space, which stimulates to find the ways to eliminate them. Theoretical basis. Various approaches of classical and postmodern philosophical heritage were taken as a theoretical basis for the research. The originality of the study is in the interpretation of artificial intelligence as a modern form of alienation of essential human characteristics in the socio-cultural context of information technology. The expansion of artificial intelligence raises awareness of the existential threat to the basic socio-cultural, moral and ethical principles of humanism. It is proved that various forms of alienation in the current existing socio-cultural space are typical of our reality, which changes the system of values, moral principles, and social organization of the community. Conclusions. In conclusion, it is proved that AI is a natural stage of scientific and technological progress, which reflects its secondary, derivative nature from human (authentic) intelligence. Human intelligence will always have advantages over AI due to its ability to create, communicate socially and culturally, and be emotional. The dilemma of the counterbalance between human and artificial intelligence is perceived mainly at the emotional level of people. The millennial understanding of the primacy of the creator over his creation can traditionally overcome this contradiction. The universality of human thinking is an undeniable advantage of human intelligence and a guarantee of its, i.e. our, priority.Anthropological Measurements of Philosophical Research00It is proved that AI is a natural stage of scientific and technological progress, which reflects its secondary, derivative nature from human (authentic) intelligence.Anthropological Measurements of Philosophical Research['Z. Stezhko', 'T. V. Khmil']2023-12-29T00:00:00https://www.semanticscholar.org/paper/0089b7c8508f9e349d34fe2c77960383ae09b99f aed287b7462e71380046bbdc54909353992a8706познавательная деятельность человека связана с созданием (изучением) двух типов информации: объективно новой и субъективно новой. В проблеме создания искусственного интеллекта первый тип деятельности (создание объективно новой информации) занимает особую (главную) роль. В этом случае такие искусственные системы действительно могут заменить человека. В работе обсуждаются два новых режима работы искусственных нейросетей, которые имеют место в работе мозга человека. Оказалось, что введение этих двух режимов в работу уже существующих нейросетей позволяет моделировать эвристическую работу мозга. Такие интеллектуальные системы решают задачи системного синтеза и находят параметры порядка. До настоящего времени такие задачи не формализованы в математике и у них нет общего решения in human cognitive processes, we encounter the generation and handling of two types of information: the objectively new and the subjectively new. The pursuit of creating artificial intelligence places a primary emphasis on the first type, the creation of objectively new information. In this context, such artificial systems can potentially serve as effective replacements for human cognitive abilities. The study delves into two novel operational modes of artificial neural networks, inspired by the functioning of the human brain. It was discovered that integrating these modes into existing neural networks enables us to simulate the heuristic functioning of the brain. As a result, these intelligent systems demonstrate proficiency in tackling challenges related to system synthesis and the identification of order parameters. Presently, these problems lack formalization in mathematics and do not possess a universally accepted solutionУспехи кибернетики / Russian Journal of Cybernetics00Успехи кибернетики / Russian Journal of Cybernetics['В. М. Еськов', 'М. А. Филатов', 'Т. В. Воронюк', 'И. С. Самойленко']2023-12-29T00:00:00https://www.semanticscholar.org/paper/aed287b7462e71380046bbdc54909353992a8706 69d2c0eaa1827e5cfc2f8e5d7d15002ff5a5ee1eA more covert aspect of Artificial Intelligence (AI) pertains to the ethical quandaries surrounding the actions of machines. In the case of Large Language Models (LLMs), hidden beneath their seemingly impeccable automated outputs lies a colossal amalgamation of trillions of compiled data points, comprising copied blogs, articles, essays, books, and artworks. This raises profound questions about copyright ownership and retribution for the original authors. But beyond intellectual property, another insidious facet of LLMs emerges – their propensity to cause harm to individuals through what can only be described as hallucinatory outputs. Victims of these AI- -generated delusions suffer defamation, and their plight remains largely unnoticed. Amidst the marvels of AI, the plight of the underpaid laborers who form the backbone of AI development is seldom acknowledged, a subject that warrants more profound discussion. Furthermore, as AI algorithms continue to permeate various aspects of society, they bring to the fore issues of bias. For instance, facial recognition technologies frequently exhibit skewed outcomes, leading to false accusations and grave consequences due to over-reliance on these technologies. The algorithmic schemes employed in CV selection for job applications or university admissions also raise concerns about fairness. The question of machines replacing the human workforce looms ever larger on the horizon. The potential socio-economic ramifications demand careful evaluation. Lastly, the extensive reliance of artificial intelligence on vast datasets, including copyrighted works, results in the creation of gargantuan data servers with an unimaginable environmental impact. The hidden aspects of artificial intelligence encompass a multitude of ethical dilemmas, spanning intellectual property rights, biases, labour conditions, societal impacts, and environmental considerations. A thorough and elaborate examination of these issues is essential to navigate the ever-evolving landscape of AI responsibly and ethically.Journal of Education, Technology and Computer Science00The hidden aspects of artificial intelligence encompass a multitude of ethical dilemmas, spanning intellectual property rights, biases, labour conditions, societal impacts, and environmental considerations, which require careful evaluation to navigate the ever-evolving landscape of AI responsibly and ethically.Journal of Education, Technology and Computer Science['C. Hilcenko', 'Tara Taubman-Bassirian']2023-12-29T00:00:00https://www.semanticscholar.org/paper/69d2c0eaa1827e5cfc2f8e5d7d15002ff5a5ee1e 3786f63d81aabd067512369e62611b69bc8cf4eaWith the rapid development of artificial intelligence technology, its application in the field of intelligent transportation system has gradually become a hot spot in research and industry. This thesis discusses the current status of AI application in ITS and provides an in-depth analysis of the current challenges. We also look into the potential future directions of AI in ITS, including autonomous driving, traffic flow optimization, and intelligent safety monitoring. The goal of this paper is to provide researchers and decision makers with a comprehensive understanding of the application of AI in ITS in order to promote the further development of the field.2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)50The current status of AI application in ITS is discussed and an in-depth analysis of the current challenges are provided and the potential future directions of AI in ITS are looked into, including autonomous driving, traffic flow optimization, and intelligent safety monitoring.2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)['Yan Sun', 'Yunna Liu']2023-12-29T00:00:00https://www.semanticscholar.org/paper/3786f63d81aabd067512369e62611b69bc8cf4ea 8cc399f872d4ec9738a4189f69f57cff48bf2154The paper considers the implementation of artificial intelligence(AI) in the construction industry. The main directions of AI implementation at five stages of designing buildings and structures, organization of construction and operation have been determined. At the first stage of "Planning and Design", information on similar construction projects should be collected and analyzed, taking into account financial resources, deadlines, features of buildings and other important factors, use automated design that takes into account geodetic data, geological features, climatic conditions, urban planning requirements, specifications and other factors to optimize the design. At the second stage, "Assessment of risks and prospects", AI uses forecasting algorithms to identify possible risks and determine their management strategies. The third stage, "Resource and supply management", optimizes logistics to predict building material needs and automates supply chain management, employee scheduling using machine learning algorithms to predict labor needs and optimize schedules. The fourth stage, "Automation and Monitoring", uses automated systems and drones controlled by artificial intelligence to perform routine and dangerous tasks on the construction site, and deploys a monitoring system that tracks the progress of construction work and other key parameters in real time.At the fifth stage, "Quality assessment and analysis of the completed project", data analysis is used to assess the quality of the completed work, automated verification of the completed work and determination of compliance with DBN standards, the technical task for the design, analysis of planning and spent resources. The application of artificial intelligence is considered on the example of resource planning in construction, which may include the use of various mathematical models and algorithms to optimize the use of resources. The advantages and disadvantages of the use of artificial intelligence and the prospects for its development in the construction industry are indicated.Modern technology, materials and design in construction00Data analysis is used to assess the quality of the completed work, automated verification of the completed work and determination of compliance with DBN standards, the technical task for the design, analysis of planning and spent resources.Modern technology, materials and design in construction['Olena Lialiuk', 'R.I. Osypenko']2023-12-29T00:00:00https://www.semanticscholar.org/paper/8cc399f872d4ec9738a4189f69f57cff48bf2154 9888d357f2b621193e71a5723d660a19ab9fb378This paper aims to thoroughly analyze the economic impact of artificial intelligence (AI) in the United States, considering its rapid development and the country's position as a global leader in scientific and technological advancements. The primary focus of this article revolves around the domains of AI, profitability and investments, and market and industries. Undoubtedly, the economic impact of AI is significant, yet it remains largely unprecedented. The AI market exhibits immense potential, as evidenced by the increasing trends in investments and profitability. The continuous growth in these areas signifies the rising interest and confidence in AI technologies. Moreover, the overall economic landscape is experiencing an upward trajectory, driven by the competitive spirit and collaboration among industries. The innovative applications of AI have revolutionized various sectors, providing substantial assistance to workers across diverse fields. However, it is important to acknowledge that the rapid evolution of AI may also lead to a reduction in job opportunities. As AI continues to advance, certain tasks traditionally performed by humans may become automated, potentially impacting employment prospects. Consequently, the future of AI development remains uncertain, with both promising opportunities and potential challenges lying ahead. Nonetheless, the economic impact of AI cannot be underestimated.Highlights in Business, Economics and Management90Highlights in Business, Economics and Management['Xintong Zhou']2023-12-29T00:00:00https://www.semanticscholar.org/paper/9888d357f2b621193e71a5723d660a19ab9fb378 564a26c0c6af85030f2f766a4dfff3ef72d03043The article reveals various aspects of the use of artificial intelligence (AI) in higher education. Prospects for the use of AI in higher education are discussed, and specific examples of the use of AI for educational purposes are considered. AI, having great potential for improving the quality of higher education, also carries potential risks of use that must be taken into account. The role of artificial intelligence in the educational process of higher education, evaluation and management of educational processes is emphasized. That is why attention is focused on the issue of what aspects are behind the use of AI in education. The issue of ethical and social consequences of the use of AI in education is discussed.Journal of Education, Technology and Computer Science100The article reveals various aspects of the use of artificial intelligence (AI) in higher education, and the issue of ethical and social consequences of the use of AI in education is discussed.Journal of Education, Technology and Computer Science['Lesya Chervona', 'Nataliia Lakusha', 'Nataliia Krokhmal', 'Serhii Myroshnychenko']2023-12-29T00:00:00https://www.semanticscholar.org/paper/564a26c0c6af85030f2f766a4dfff3ef72d03043 7ab2b27d1c53785159408b2a744ec8a2817b787fArtificial intelligence is already being used by many industries as it has the ability to comprehend massive data. The aim of this paper is to explore the possible applications of AI in healthcare to be able to comprehend the ways to optimize human resources. Human resource in healthcare is precarious due to its high cost and lengthy process. This paper has examined the relevant literature from the previous decade to highlight the important advances made. The paper also addresses the challenges (Technical challenges, ethical challenges, security challenges and social challenges) that should be kept in mind and resolved to be able to use AI effectively in different sectors including healthcare sector. AI might completely change how healthcare is provided, but this procedure needs guidance and direction since the technology appears not to have been developed enough and there are still several issues that need to be resolved.2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)350The relevant literature from the previous decade is examined to highlight the important advances made and the challenges that should be kept in mind and resolved to be able to use AI effectively in different sectors including healthcare sector.2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)['Shikha Saloni', 'Neema Gupta', 'Kamalpreet', 'Malay Ghosh', 'A. Agarwal']2023-12-29T00:00:00https://www.semanticscholar.org/paper/7ab2b27d1c53785159408b2a744ec8a2817b787f ff5d30696944f61b2a18af27ec30c5648cada2adWith the advent of big data era and improvement of computer performance, the artificial intelligence (AI) technology has rapidly boosted in the field of stomatology. Dental caries is one of the cutting-edge research domains in stomatology. The application of AI in dental caries is expected to promote intelligent, precise and high-efficient diagnosis and treatment of caries. This article focuses on the application of AI in medical-aided diagnosis, treatment and risk prediction of caries and discusses their challenges.Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology00The application of AI in medical-aided diagnosis, treatment and risk prediction of caries and discusses their challenges.Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology['H. Liu', 'X. Wei', 'J. Ling']2023-12-29T00:00:00https://www.semanticscholar.org/paper/ff5d30696944f61b2a18af27ec30c5648cada2ad 208921e82d5cf08ea18525ab21f0c272edcc4ac9In this paper we aim to identify and highlight the factors that influence the current model of education in the context of artificial intelligence and the digital age. Moreover, based on the new innovative learning tools, we propose to analyze which are the education models that can contribute to a society subject to multiple challenges and in continuous adaptation and change. Study design/methodology/approach: research tools are based on empirical research, analysis of specialized scientific literature, both those related to the traditional model, but especially those of the progressive model based on tools specific to the digital era (for example, educational platforms, software - used in the educational process of knowledge, as well as the recent tool ChatGPT). In addition, as well as getting expert opinions on these new learning tools we have launched open questions on the ResearchGate network on artificial intelligence and education. The results of our research highlight learning models based on the closest possible cooperation between universities and industry, the adaptation of the educational curriculum to new jobs in the context of the use of artificial intelligence, as well as the promotion and use of innovative tools in the academic educational act, in order to determine the applicability of innovative knowledge acquired, in the economic environment, in support of the development and sustainability of the economy and society, locally and globally. Originality/value: the contribution of this work to highlighting the learning models identified in the specialized literature, proposing one of the models specific to our university education system, as well as highlighting the fact that AI is a resource that supports human efforts in education, respectively optimizes processes, and does not replace the creative side of the teaching staff involved in the educational process, thus contributing to the achievement of outstanding academic results. Qeios130The research highlights learning models based on the closest possible cooperation between universities and industry, the adaptation of the educational curriculum to new jobs in the context of the use of artificial intelligence, as well as the promotion and use of innovative tools in the academic educational act, in order to determine the applicability of innovative knowledge acquired.Qeios['Ovidiu Folcut', 'Otilia Manta', 'Iuliana Militaru']2023-12-29T00:00:00https://www.semanticscholar.org/paper/208921e82d5cf08ea18525ab21f0c272edcc4ac9 ac1425e7f2aef9922e0844d527f9064cd1c887dfThe background of this research is the technological disruption in the digital era like today in the field of art. This research takes a point of view from the branch of philosophy, namely aesthetics, to examine the impact and criticism that arises. More specifically, explaining how this aesthetic meets artificial intelligence integrated with computers, resulting in art, artists and conventional works of art starting to feel the impact. The aim of this research is to determine the impact and criticism from philosophers or scientists regarding the use of artificial intelligence in the process of creating works of art. This research uses qualitative methods and literature studies from books and journals related to the research theme. The findings from this research are that there are positive and negative impacts from the use of artificial intelligence, from the process to the product of the artwork. While criticism centers on the limited imagination and creativity of artists when using artificial intelligence as a medium for creating works of art.Education Achievement: Journal of Science and Research150There are positive and negative impacts from the use of artificial intelligence, from the process to the product of the artwork, from the process to the product of the artwork.Education Achievement: Journal of Science and Research['Arqoma Nurveda, Mochamad Nursalim, Siti Masitoh']2023-12-29T00:00:00https://www.semanticscholar.org/paper/ac1425e7f2aef9922e0844d527f9064cd1c887df d0e67d599ac63e7eb4ce8574a68571304faa349bLANGUAGE NOTE | Document text in English; abstract also in Chinese. 腦機介面 (BCIs) 是大腦和電腦無需人工交互即可直接交流的一系列技術。隨著人工智能 (AI) 時代的到來，我們需要更多地關注腦機介面和人工智能的融合所帶來的倫理問題。那麼，與機器一起思考會帶來什麼樣的倫理問題？在本文中，圍繞這一主題，我們將重點關注以下問題：自主性、完整性、身分認同、隱私，以及作為一種增強的方式，該技術在兒科領域的應用會帶來怎樣的風險和潛在收益。我們的結論是，雖然該技術存在多種令人擔憂的問題，同時也有可能帶來好處，但仍存在很大的不確定性。如果生命倫理學家想在這一領域有所建樹，他們就應該做好準備來迎接我們對醫學和醫療保健領域中一些我們視為核心價值的理解的重大轉變。 Brain-Computer Interfaces – BCIs – are a set of technologies with which brains and computers can communicate directly, without the need for manual interaction. As we are witnessing the dawn of an era in which Artificial Intelligence (AI) quite possibly will come to dominate the technological innovation landscape, we are compelled to ask questions about the ethical issues which the convergence of BCIs and AI is poised to bring about. What are the ethics of thinking with machines? In this paper, we explore this question, focusing on some of the main arenas of ethical debate and contention, ranging from autonomy and integrity to identity and privacy, and discuss the risks and potential benefits of the technology in the domains of paediatric populations, and as a means of human enhancement. We conclude that, while there are multiple concerns as well as possibilities for the technology to do good, there are great uncertainties at play. If bioethicists want to stay relevant in this field, they ought to prepare themselves for seismic shift in how we conceptualise much of what we take to be core values in medicine and healthcare.International Journal of Chinese &amp; Comparative Philosophy of Medicine31The risks and potential benefits of the technology in the domains of paediatric populations, and as a means of human enhancement are discussed, and there are multiple concerns as well as possibilities for the technology to do good.International Journal of Chinese &amp; Comparative Philosophy of Medicine['David M. Lyreskog', 'Hazem Zohny', 'Ilina Singh', 'Julian Savulescu']2023-12-29T00:00:00https://www.semanticscholar.org/paper/d0e67d599ac63e7eb4ce8574a68571304faa349b 61fb9a0a6f02e90cc8563c242db6cbf91c36ce8cinformation get admission to optimization is the procedure of maximizing the provision and accessibility of records for customers. Advanced statistics get right of entry to can lead to accelerated performance and choice-making accuracy. Synthetic Intelligence (AI) has the ability to revolutionize this manner. AI can provide more suitable statistics access optimization in numerous approaches, together with: automating get entry to relevant information, figuring out and extracting unstructured data, offering proactive insights into statistics utilization, and improving accuracy and speed of get right of entry to. Additionally, AI-based totally technology can enforce greater accurate security and get entry to controls, providing higher information protection and safety. An AI-pushed approach lets in companies to better categorize and utilize their ever-increasing stores of unstructured records, empowering its users with statistics to make extra efficient and correct selections. AI-based totally answers can also assist companies enhance facts tracking and audit abilities, improving transparency and aiding compliance with rules. Utilizing AI to improve facts access optimization can result in tangible organizational advantages together with decreased time for selection-making processes, progressed consumer satisfaction and retention, and stepped forward person enjoy.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)90Utilizing AI to improve facts access optimization can result in tangible organizational advantages together with decreased time for selection-making processes, progressed consumer satisfaction and retention, and stepped forward person enjoy.2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)['Kuldeep Singh Kulhar', 'B. Reddy', 'Vaishali Singh', 'Vinod Moger', 'T. Sripriya', 'S. B. Patil']2023-12-29T00:00:00https://www.semanticscholar.org/paper/61fb9a0a6f02e90cc8563c242db6cbf91c36ce8c 53034e289a286fd7fe00fcb72c7208f0e45052acThe paper explores the transformative effects of Industry 5.0, a data-driven economy, and the imperative adoption of Artificial Intelligenc (AI) driven systems across all organizational levels. It emphasizes the urgent need for comprehensive transformation, extending to Human Resource Management (HRM). Industry 5.0 presents significant challenges, necessitating strategic HRM strategies involving skill enhancements and AI-assisted knowledge management. A central focus is the profound impact of AI-driven innovations on organizational efficiency, employee efficacy, and productivity. AI rapidly acquires and processes data, providing organizations with prescriptive and predictive insights, enabling them to navigate potential future scenarios effectively. These insights can be leveraged to enhance productivity, engage employees, and facilitate organizational growth. Beyond HRM, the paper recognizes AI's influence on marketing and sales strategies in Industry 5.0. AI-driven advancements revolutionize customer engagement and personalization. AI-powered chatbots, for example, offer tailored interactions that elevate customer satisfaction and engagement. AI's data analytics capabilities empower businesses to craft highly targeted marketing campaigns, enhancing service quality and response times, yielding favorable marketing and sales outcomes. Furthermore, AI-driven Chatbot tools such as Chorus and GrowthBot play a pivotal role in recording and analyzing sales conversations, offering valuable insights into customer behavior. This informs informed decision-making and accelerates lead conversion. AI's rapid data processing capabilities enable organizations to refine marketing and sales strategies, boosting their effectiveness and financial results In summary, the research highlights AI's transformative impact across organizational facets, including HRM, marketing, and sales. Embracing AI-driven processes and systems offers a competitive edge by enhancing employee productivity and revolutionizing customer engagement and sales strategies. This comprehensive approach is crucial for thriving in the data-driven landscape of Industry 5.0, where harnessing the power of AI is paramount for success.European Economic Letters (EEL)710The research highlights AI's transformative impact across organizational facets, including HRM, marketing, and sales, where embracing AI-driven processes and systems offers a competitive edge by enhancing employee productivity and revolutionizing customer engagement and sales strategies.European Economic Letters (EEL)['DR. Ashima Joshi , DR. JOLLY MASIH']2023-12-29T00:00:00https://www.semanticscholar.org/paper/53034e289a286fd7fe00fcb72c7208f0e45052ac 0e82c032507d72079e15e2a1487b659f94ce06a4<jats:p />Advances in Materials Science and Engineering10Advances in Materials Science and Engineering['Advances in Materials Science and Engineering']2023-12-29T00:00:00https://www.semanticscholar.org/paper/0e82c032507d72079e15e2a1487b659f94ce06a4 8de97221a63df31eac5e07311a6ed09872207d91[This retracts the article DOI: 10.1155/2022/7731618.].BioMed Research International10BioMed Research International['Biomed Research International']2023-12-29T00:00:00https://www.semanticscholar.org/paper/8de97221a63df31eac5e07311a6ed09872207d91 57549e0551602132240f192024481c7d1743649c<jats:p />Security and Communication Networks10Security and Communication Networks['Security and Communication Networks']2023-12-29T00:00:00https://www.semanticscholar.org/paper/57549e0551602132240f192024481c7d1743649c 78ec5ba266e3dbab79ede85f10f8bb95c260127bTeknologi sudah berkembang pesat salah satunya adalah teknologi informasi, teknologi informasi berfungsi meningkatkan produktivitas dan kinerja perusahaan. Salah satu perkembangannya yaitu perkembangan teknologi kecerdasan buatan, sebuah kecerdasan yang mendapatkan penambahan dalam suatu sistem, untuk menginterpretasikan data eksternal dan mengendalikan data tersebut sehingga hasil olahan digunakan untuk tujuan tertentu. Penelitian ini mengkaji tentang penerapan AI pada perusahaan bisnis dengan metode SLR (Systematic Literature Review) yaitu peninjauan pustaka dengan mengidentifikasi, mengevaluasi, serta menerangkan semua pengamatan yang berkaitan dengan topik penelitian dengan tujuan untuk mendapatkan jawaban tentang pertanyaan terkait penelitian yang sudah ditentukan. Hasil yang didapatkan yaitu sebanyak 11 literatur yang memenuhi kriteria kualitas penilaian dengan ketentuan jurnal terakreditasi dan membahas faktor serta dampak dari penerapan AI secara jelas. Sehingga dapat disimpulkan penerapan AI terdapat hambatan seperti isu rentannya keamanan data dan privasi, biaya yang tinggi, memerlukan keahlian khusus, infrastruktur yang memadai, pembuatan yang rumit, peraturan penerapan AI yang masih belum sempurna, isu khawatir dalam pergantian pekerjaan manusia dengan teknologi, dan isu etika penerapan teknologi AI dalam pekerjaan.Djtechno: Jurnal Teknologi Informasi00Djtechno: Jurnal Teknologi Informasi['Safna Faradillah', 'Dimas Irmansyah', 'Beryl Ardhana Lokatara', 'Mohamad Ivan Saputra', 'Anita Wulansari']2023-12-29T00:00:00https://www.semanticscholar.org/paper/78ec5ba266e3dbab79ede85f10f8bb95c260127b 4847170e8693d96aeb07f587e90cf11344cc7259В статье анализируются проблема признания правосубъектности искусственного интеллекта, связанные с этим закономерности, а также ряд теоретико-правовых особенностей, присущих данной технологической инновации. Искусственный интеллект – это техническое средство, способное к самообучению, а также к воспроизводству когнитивных и коммуникативных функций, традиционно характерных для человеческого разума. Эмоциональный искусственный интеллект способен также понимать эмоции и подражать им. Автором систематизируются распространенные научные концепции, идеи и подходы, обосновывающие потребность признания правосубъектности искусственного интеллекта либо отрицающие подобную возможность, сформулирована позиция по вопросу правосубъектности искусственного интеллекта. По мнению автора, исследуемое техническое средство не способно в полной мере выступать субъектом правовых отношений по причине отсутствия свободы воли как важного условия приобретения правосубъектности. The article analyzes the problem of recognizing legal personality of artificial intelligence, the associated patterns, as well as a number of theoretical and legal features inherent in this technological innovation. Artificial intelligence is a technical tool capable of self-learning, as well as reproduction of cognitive and communicative functions traditionally characteristic of the human mind. “Emotional” artificial intelligence is also able to understand emotions and imitate them. The author systematizes widespread scientific concepts, ideas and approaches that justify the need to recognize legal personality of artificial intelligence or deny such a possibility, and formulates the author’s position on the issue of the legal personality of artificial intelligence. According to the author, the studied technical means is not able to fully act as a subject of legal relations due to the lack of free will as an important condition for acquiring legal personality.Ius Publicum et Privatum00Ius Publicum et Privatum['В.Д. Саттаров']2023-12-29T00:00:00https://www.semanticscholar.org/paper/4847170e8693d96aeb07f587e90cf11344cc7259 2a66c0df2055dd87ae3f7587dad3e634cd290992Given the recent rise and popularity of ChatGPT along with many working robots and technology in general many questions have been raised. There is an ongoing concern about the security and privacy of society, whether AI will have complete control of it. Many different flaws and problems with the AI industry may initially be the concern of many: however, this paper is here to say those issues are not addressable without the innovation and the help of experts in the “AI arms race” as it has been labelled. The sudden surge that China has approached the industry as well as the already dominant US expertise creates a tense and desperate attempt to obtain superiority. Although the industry may be looked down upon as a breach of privacy, many big companies have invested large sums of money to develop the newest cutting-edge technology and aims to help economic growth through the production of AI. Jobs are being threatened through the development of the AI industry and the US and China are trying to mitigate the impacts whilst also finding the new talent and innovation necessary to move forward.Highlights in Business, Economics and Management140This paper is here to say those issues with the AI industry are not addressable without the innovation and the help of experts in the “AI arms race” as it has been labelled.Highlights in Business, Economics and Management['Andrew Tianen Zhang']2023-12-29T00:00:00https://www.semanticscholar.org/paper/2a66c0df2055dd87ae3f7587dad3e634cd290992 6968d00d7d9707ed4342443deccc5244ddc412d6This project proposes a programmable logic-oriented algorithm to solve the difficult problem of coding generation of aircraft control system. The synchronous data flow computing model suitable for control system modeling is used to realize the cooperative communication between multiple functional modules in UAV control system. The spatial information fusion platform including physical space, transport layer, data layer, model layer, service layer and application layer is constructed. The dynamic programming method between multiple agents is used to solve the problem of motion trajectory optimization and cooperative task assignment among multiple agents. A multi-machine joint simulation platform with cluster collaboration is constructed and its performance is verified. The simulation results show that the system has high efficiency.2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)80A programmable logic-oriented algorithm to solve the difficult problem of coding generation of aircraft control system and results show that the system has high efficiency.2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC)['Yingpu Bi', 'Weichang Xu', 'Jifang Liu', 'Yake Wu', 'Zongchun Hu']2023-12-29T00:00:00https://www.semanticscholar.org/paper/6968d00d7d9707ed4342443deccc5244ddc412d6 a4399175ee71eed7d0aa14a252beb7ce09857ac1Avicenna Journal of Aging and Healthcare00Avicenna Journal of Aging and Healthcare['Sevil Momeni Shabani', 'Fatemeh Darabi']2023-12-29T00:00:00https://www.semanticscholar.org/paper/a4399175ee71eed7d0aa14a252beb7ce09857ac1 af49a51a0befcffa2bbccee61d8ca0c8bbf327e900['Edmilson Rodrigues do Nascimento Junior']2023-12-29T00:00:00https://www.semanticscholar.org/paper/af49a51a0befcffa2bbccee61d8ca0c8bbf327e9 b0bc544c956662514ee153f1bc6c199bffdb2ab7Journal of Psychiatry Spectrum160Journal of Psychiatry Spectrum['Manik Singh Sethi', 'C. Kumar', 'S. Math']2023-12-29T00:00:00https://www.semanticscholar.org/paper/b0bc544c956662514ee153f1bc6c199bffdb2ab7 981be063c76d44e790ad28bedbf5ef00e8005526Crescent Journal of Medical and Biological Sciences60Crescent Journal of Medical and Biological Sciences['Z. Kurdoğlu']2023-12-29T00:00:00https://www.semanticscholar.org/paper/981be063c76d44e790ad28bedbf5ef00e8005526 083ee92e11f9da6ed7069d95b4bf1c7e7dc81c04The article explores the transformative impact on human and social sciences in response to anticipated societal shifts driven by the forthcoming proliferation of artificial systems, whose intelligence will match human capabilities. Initially, it was posited that artificial intelligence (AI) would excel beyond human abilities in computational tasks and algorithmic operations, leaving creativity and humanities as uniquely human domains. However, recent advancements in large language models have significantly challenged these conventional beliefs about AI’s limitations and strengths. It is projected that, in the near future, generative AI models will adeptly replicate individual qualities, desires, beliefs, opinions, and the essence of human identity and consciousness to a degree that is nearly indistinguishable from that of humans. This lends support to the connectionist approach to understanding consciousness, suggesting an inherent similarity between biological and artificial neural networks. The discussion posits two innovative areas of scientific inquiry: “mathematical anthropology” and “multi-dimensional calculus of value.” The former suggests that viewing human nature through a mathematical and calculative lens not only preserves but enriches our understanding of the complexity of anthropological experience and its perceived contradictions. The latter hypothesis explores how mathematical models could facilitate various social interactions. The advanced simulation capabilities of neural networks suggest that traditional social and political frameworks face a growing vulnerability to AI-driven manipulations. This trend underscores the urgency of developing social interaction models that incorporate explicitly defined calculative rules. In conclusion, the paper advocates for a paradigm shift in how mathematics is perceived – not merely as a tool for computation but as a foundational science for crafting sophisticated models. In conclusion, the article advocates for transitioning from perceiving mathematics solely as a science of computation to viewing it as a discipline dedicated to constructing various formalized models, thereby deepening our insight into the complexities of human and social phenomena.Russian Journal of Philosophical Sciences120The article advocates for transitioning from perceiving mathematics solely as a science of computation to viewing it as a discipline dedicated to constructing various formalized models, thereby deepening the authors' insight into the complexities of human and social phenomena.Russian Journal of Philosophical Sciences['A. K. Marinosyan']2023-12-29T00:00:00https://www.semanticscholar.org/paper/083ee92e11f9da6ed7069d95b4bf1c7e7dc81c04 c297afe931ef8dee3d57d9b053f3799f13e3cf2eThe article delves into the conceptual frameworks surrounding artificial intelligence (AI) by juxtaposing it with natural intelligence and delineating the correlated notions. It enumerates the issues propelling the discourse on the explored topics. The author proposes a bifurcation between two polar concepts of artificial intelligence. The first is dubbed “imitative,” where AI is perceived in relation to natural intelligence as its technical recreation, capable of not only emulating but significantly outstripping its natural counterpart. A prerequisite for embodying this concept is understanding natural intelligence; three approaches are examined: (a) acknowledging the lack of a precise understanding of natural intelligence, (b) exploring it from a biological perspective, and (c) analyzing it from a psychological perspective. The author articulates their own interpretation of natural intelligence, portraying it as a multifaceted amalgam of cultural, historical, social, and anthropological elements. From this vantage point, natural intelligence emerges not merely as a natural formation (thereby, discussions about the laws governing its function and evolution are warranted), but also as an “extra-natural” formation, its existence dictated by randomness and uniqueness, meaning natural intelligence evolves in a “singular” manner. In the context of comparing natural and artificial intelligence, the discussion encompasses several issues: the feasibility of the control of natural intelligence processes, the structure of neural networks, the superiority of computer programs in chess, the use of neural networks to write academic papers, and so forth. The conclusion posits that given artificial intelligence, despite its complexity, remains a technical invention orchestrated and brought to fruition by humans as a tool; society, if inclined to bestow AI with autonomy for tackling specific tasks, ought to do so prudently to prevent self-detriment and retain the ability to curtail or utterly revoke such autonomy.Russian Journal of Philosophical Sciences10The conclusion posits that given artificial intelligence, despite its complexity, remains a technical invention orchestrated and brought to fruition by humans as a tool; society, if inclined to bestow AI with autonomy for tackling specific tasks, ought to do so prudently to prevent self-detriment and retain the ability to curtail or utterly revoke such autonomy.Russian Journal of Philosophical Sciences['V. Rozin']2023-12-29T00:00:00https://www.semanticscholar.org/paper/c297afe931ef8dee3d57d9b053f3799f13e3cf2e fbd84da09bd7875289379ed76fb6ca8bcb1b1daeThe university faces new challenges in the information society, which requires a fundamental change in its traditional educational approaches. Artificial intelligence tools, such as programs and applications, have the potential to transform higher education significantly. Resources that offer unprecedented benefits by generating new knowledge and improving educational quality, provide students with precise and personalized learning that adapts to their individual needs; At the same time, they integrate the various forms of communication and information and communication technologies for their pedagogical exercise. The purpose of the research is to examine the different contributions of the connection between artificial intelligence and higher education, which represents an important challenge for universities in the 21st century. In this new millennium, it is imperative that academic institutions plan, design, develop and implement digital skills in order to train more competent students, students capable of understanding and adapting to the environment of new digital trends according to their needs; concluding that the university acts as an engine of research, education and ethics, generating advances through artificial intelligence that can contribute to the achievement of the Sustainable Development Goals, while training responsible and ethical professionals in this field (SDG4 -Quality education).Revista Internacional de Ciencias Sociales200The research examines the different contributions of the connection between artificial intelligence and higher education, concluding that the university acts as an engine of research, education and ethics, generating advances through artificial intelligence that can contribute to the achievement of the Sustainable Development Goals.Revista Internacional de Ciencias Sociales['Anibal MEJÍA BENAVIDES', 'Gloria Elizabeth IMAN TINEO', 'Aura Vega Olivos']2023-12-29T00:00:00https://www.semanticscholar.org/paper/fbd84da09bd7875289379ed76fb6ca8bcb1b1dae 066e77e56c4cb3994bafd91bfb12396c5e0454c4This study developed a perceived intelligence scale for artificially intelligent (AI) assistants and investigated its impact on users’ travel-related behavioral intentions. A four-stage study with a mixed-methods design was conducted. Study 1 identified three dimensions and 26 initial items through a systematic literature review, interviews, and focus group discussions. Study 2 used exploratory factor analysis to refine the items. Through composite confirmatory analysis, Study 3 confirmed an 18-item and three-dimensional scale (conversational intelligence, information quality, anthropomorphism). Study 4 established the scale’s predictive validity in travelers’ intentions to use AI assistants to search for travel information and make travel bookings. This research made the first attempt to identify factors shaping users’ perceptions of AI assistant intelligence, extending the understanding of human-AI interaction and AI technology adoption in the travel sector. Furthermore, it provides actionable recommendations for the travel industry and AI developers when designing and deploying AI assistant services.Journal of Travel Research680This research made the first attempt to identify factors shaping users’ perceptions of AI assistant intelligence, extending the understanding of human-AI interaction and AI technology adoption in the travel sector.Journal of Travel Research['Erin Chao Ling', 'Iis Tussyadiah', 'Anyu Liu', 'Jason Stienmetz']2023-12-29T00:00:00https://www.semanticscholar.org/paper/066e77e56c4cb3994bafd91bfb12396c5e0454c4 a08204eee536dfbe478bbf8d0e10e8e88cfb1f84The need to expand the range of banking services in Ukraine is stipulated with technological progress, the European integration processes and the legal regime of martial law introduced in the country. Under the conditions of war, the need to strengthen the security of banking activities and protect the banking system from the influence of any internal and external factors gains meaning. The topical direction of economic and legal research of scientists today is the possibility to introduce digital technologies with elements of artificial intelligence (AI) into the banking activity in Ukraine to improve its protection. The AI law as an independent branch of the Ukrainian law has not been developed so far. The sources of AI law, its functions, tasks, scope, risks and limits of legal responsibility for prohibited practices of artificial intelligence have not been defined. The purpose of the article is to analyze the theoretical and legal provisions that underpin the regulation of AI application in Ukrainian banking. The comparative legal method made it possible, considering the provisions of the draft law on AI of the European Union, to determine the trends in the development of the legal regulation of AI in Ukraine. Following the study, proposals to the legislation of Ukraine were formulated, which will contribute to the legal regulation of banking activities using digital technologies with elements of AI.Journal of Infrastructure Policy and Development250Journal of Infrastructure, Policy and Development['Alona Klochko', 'M. Kurylo', 'O. Rohovenko', 'Nataliia Volchenko', 'Assol Shulzhenko']2023-12-28T00:00:00https://www.semanticscholar.org/paper/a08204eee536dfbe478bbf8d0e10e8e88cfb1f84 3acc7c06e8e8f8f02754378c1e521767cc5d9593Transparency is one of the basic principles enshrined in the General Data Protection Regulation (GDRP). Achieving transparency in automated decision-making processing especially when artificial intelligence (AI) is involved is a challenging task on many aspects. The opaqueness of AI systems that usually is referred as the “black-box” phenomenon is the main problem in having explainable and accountable AI. Computer scientists are working on explainable AI (XAI) technics in order to make AI more trustworthy. On the same vein, thus from a different perspective, the European legislator provides in the GDPR with aright to information when automated decision-making processing takes place. The data subject has the right to be informed on the logic involved and to challenge the automated decision-making. GDPR introduces, therefore, sui generisright to explanation in automated decision-making process. Under this light, the paper analyzes the legal basis of this right and the technical barriers involved.International Journal of Law in Changing World00The GDPR introduces sui generisright to explanation in automated decision-making process and the legal basis of this right and the technical barriers involved are analyzed.International Journal of Law in Changing World['Ria Papadimitriou']2023-12-28T00:00:00https://www.semanticscholar.org/paper/3acc7c06e8e8f8f02754378c1e521767cc5d9593 3ba8bf84508d2802eb360f87ce8c0440ab0059aaThe Artificial Intelligence Act (AI Act) may be a milestone in the regulation of artificial intelligence by the European Union. The regulatory framework proposed by the European Commission has the potential to serve as a global benchmark and strengthen the position of the EU as one of the main players on the technology market. One of the components of the draft regulation are the provisions on deep fakes, which include a relevant definition, risk category classification and transparency obligations. Deep fakes rightly arouse controversy and are a complex phenomenon. When leveraged for negative purposes, they significantly increase the risk of political manipulation, and at the same time contribute to disinformation, undermining trust in information and the media. The AI Act may strengthen the protection of citizens against some of the negative consequences of misusing deep fakes, although the impact of the regulatory framework in its current form will be limited due to the specificity of their creation and dissemination. The effectiveness of the provisions will depend not only on enforcement capabilities, but also on the precision of phrasing provisions to prevent misinterpretation and deliberate abuse of exceptions. At the same time, the AI Act will not cover a significant portion of deep fakes, which, due to the malicious intentions of their creators, will not be subject to the transparency obligations. This study analyses provisions related to deep fakes in the AI Act and proposes improvements that will take into account the specificity of this phenomenon to a greater extent.Applied Cybersecurity &amp; Internet Governance00This study analyses provisions related to deep fakes in the AI Act and proposes improvements that will take into account the specificity of this phenomenon to a greater extent.Applied Cybersecurity &amp; Internet Governance['Mateusz Łabuz']2023-12-28T00:00:00https://www.semanticscholar.org/paper/3ba8bf84508d2802eb360f87ce8c0440ab0059aa 9304aa44516d5f80ff18c6b8a56d59878a88a88fThe purpose of the research. The article examines the features of the legal regulation of the circulation of utilitarian digital rights in Russia in the field of venture investment in order to identify ways to intensify the use of this tool to attract financing for innovative projects. Results. An analysis of the legislation of the Russian Federation on the circulation of digital rights was carried out, and it was concluded that the potential for using these assets in venture investment is not realized in practice, since the law prohibits the issuance and circulation of utilitarian digital rights that establish the obligation to transfer property subject to state registration. In particular, this entails the impossibility of issuing utilitarian digital rights tied to the transfer of rights to objects of patent law. It is noted that there are currently infrastructure solutions that in the future may serve as the basis for expanding the use of digital rights in venture investment, if legal barriers are eliminated and in the case of mandatory connection to the information system in which the issuance of utilitarian digital rights is carried out, authorized registration bodies and legislative regulation of the procedure for the exchange of information between participants in such a system and registration authorities. It is concluded that in the field of intellectual property, the RCIS.RF system could serve as an organizational and technological basis for the implementation of these proposals, subject to the removal of legal barriers.Economic problems and legal practice00Economic Problems and Legal Practice['N. Svechnikova', 'V. Khramushin', 'A. Gurko']2023-12-28T00:00:00https://www.semanticscholar.org/paper/9304aa44516d5f80ff18c6b8a56d59878a88a88f 664d53d765c38fbe2d835aa003a43f43240dc73cThe article is devoted to the problem consideration of determining the content and characteristics of discretionary powers in the context of the administrative procedure legal regulation, which is caused by the adoption and necessity of introducing the provisions of the Law of Ukraine «On Administrative Procedure» into the practices of the subjects of authority. The actuality of the topic is determined by the necessity to qualify administrative bodies' powers as discretionary and implement the relevant principles of administrative procedure. The article aims to analyze the normative constructions of administrative proceedings available in Ukraine's legislation, which allow for the possibility of exercising discretionary or similar powers to develop a position regarding the limits and features of the practical application of norms of the Law of Ukraine «On Administrative Procedure». Achieving the outlined objective became possible through complex scientific knowledge methods, in particular dialectical and systemic approaches, formal-legal and comparative methods, and methods of analysis and synthesis. It is noted that the vast majority of normatively defined principles of administrative procedure are designed for the implementation of discretionary powers by administrative bodies. It was emphasized that an integral component of discretionary powers, in addition to the availability of legally defined options for decisions that an administrative body can adopt in the presence of specific grounds, is the availability of the administrative body's right to act at its discretion. Based on the conducted research on the content of the administrative bodies' powers in specific types of administrative proceedings, the conclusions were formulated that in administrative proceedings of a registration and permitting nature, the relevant administrative bodies do not have discretionary powers. It is noted that an example of full-fledged discretionary powers is the powers of administrative bodies in tort proceedings. This is due to the fact that sanctions for the commission of relevant offenses have a relatively defined nature. In such cases, the administrative body, at its discretion, selects the type and amount of sanctions, taking into account the circumstances of the case. Unlimited discretion is inherent in the authority of collegial bodies, particularly local self-government bodies, which is determined by the decision-making method by voting of the collegial body members.Problems of Legality180Problems of legality['Dmytro Luk’yanets']2023-12-28T00:00:00https://www.semanticscholar.org/paper/664d53d765c38fbe2d835aa003a43f43240dc73c 2e62192c1e0d86a38326a88d54607680c8b31b18In response to the Global Financial Crisis of 2008–2009, international financial regulators tightened the regime of banking supervision in order to minimize systemic risks, strengthen banking sector resilience and ensure financial stability. Given the increased level of credit risks and the issue of liquidity in the banking sector, as well as the role of banks in promoting the dynamics of the macro-environment, the objectives of banking regulation, through their interrelationship, may conflict with one another, and the research of this phenomenon is the subject of this article. The academic literature excludes research that provides definitive evidence on whether post-crisis banking regulation reform has achieved each of the abovementioned goals, determining the relevance of our study. The scientific novelty is attributed to the principally different approach proposed by the authors in assessing the effectiveness of the post-crisis model of international banking regulation, which is based on the analysis of the interaction and contradictions of the objectives of modern regulatory policy. The purpose of the study is to identify the extent to which the objectives of the post-crisis regulatory model were achieved and to what extent regulatory efforts contribute to the reduction of systemic risks. To achieve the research objectives, the authors applied methods of statistical and comparative analysis, synthesis of factors underlying the post-crisis regulatory mechanism, systematization, generalization and forecasting. The authors analyzed the main elements of the regulatory reform, examined the dynamics of the banking sector, and assessed the impact of the reform on systemic risks and economic growth. The research results show that tighter supervisory standards strengthened bank stress resilience, reduced systemic risks, and had a limited impact on economic growth. The article concludes that the objectives of banking regulation actively interact, but do not conflict: a consistent transition to the new Basel III standards allows each objective to be achieved.Finance: Theory and Practice320Finance: Theory and Practice['E. Dzhagityan', 'O. R. Mukhametov']2023-12-28T00:00:00https://www.semanticscholar.org/paper/2e62192c1e0d86a38326a88d54607680c8b31b18 2211a1dbbb0a43512630e97f47f379007c825739The current socio-economic situation of Ukraine can be characterized as difficult and tense as a result of military operations on the territory of the country, numerous destruction of infrastructure facilities, energy capacities and long-term decline, lack of modernization of production, wear and tear of fixed assets almost to zero. In these conditions, the issue of finding incentives from the state to carry out modernization measures for the restoration and creation of new energy capacities, which are based on new types of generation, is acute. For example, generation from renewable energy sources. Balanced fiscal policy and regulation can act as such a unique state development tool. Therefore, the purpose of the article is to determine the peculiarities of using fiscal regulation tools in the institutional space of the economy to support the development of certain industries, and primarily energy, with special attention to the experience of EU countries that have achieved significant success in using fiscal stimulation for the development of energy, especially renewable energy. In the modern economy, the state plays a key role in the regulation and development of the institutional space. For this, various instruments of fiscal regulation are used, which allow the state to influence economic processes and create favorable conditions for business development and investment stimulation. The experience of the EU shows that the most frequently used tool of fiscal support for the development of economic sectors, and primarily energy, is the use of energy subsidies and state intervention related to specific initiatives. The use of energy subsidies and state intervention in EU countries has already brought impressive results. There are already a number of countries in which the amount of electricity produced from renewable energy sources has already exceeded the amount of energy from traditional sources. Wide use of EU experience in Ukraine regarding fiscal support for energy development can help the development of the economy and the post-war recovery of the energy sector based on the development of new energy sources. The conducted research allows us to conclude that fiscal regulation is a powerful tool that governments can use to stimulate economic growth and development. Tax policy, government spending, budget deficit and debt management - all these tools can be effectively used to achieve the country's economic goals.Naukovi pratsi NDFI30Naukovi pratsi NDFI['Viktoriia Khaustova']2023-12-28T00:00:00https://www.semanticscholar.org/paper/2211a1dbbb0a43512630e97f47f379007c825739 420710cdabcd5325fe2bbb542cc26d35ccf603f2International Journal of Production Research562International Journal of Production Research['Qiang Wang', 'Su Xiu Xu', 'Xiang Ji', 'Nenggui Zhao']2023-12-28T00:00:00https://www.semanticscholar.org/paper/420710cdabcd5325fe2bbb542cc26d35ccf603f2 e9a48d428b85e82f6577ea5d6320bfdd01a2f6ddThe object of this study is the regulatory measures used within the framework of innovation and technology investment support. For the purposes of the study, existing approaches to determining the essence of state regulatory policy in the field of innovative investment were summarized. The existing techniques and procedures for stimulating and supporting innovative investment were studied, their advantages and disadvantages were determined. It was established that the existing systems of support for innovative investment do not fully meet the needs of participants in the innovation process and subjects of technology transfer. The expediency of improving the existing methods of normative consolidation of regulatory policy in the field of innovative investment was substantiated. The general structure of the state policy of innovative investment with its subject and subject composition, as well as forms of innovative investment, is proposed. The expediency of the formation of the state policy of innovative investment as a system of means, techniques, methods of stimulating and limiting the influence of state and local authorities, international institutions on social relations related to the implementation of innovative investment was substantiated. Also, directions for improvement of current international agreements and contracts in the field of innovative relations and acts of national legal systems were formed. The research is aimed at forming general theoretical foundations for improving the regulatory system of innovative investment. The generated research results can be used in the formation of international normative acts, acts of national legislation, and serve as a basis for further scientific research on these issuesEastern-European Journal of Enterprise Technologies110Eastern-European Journal of Enterprise Technologies['Bohdan Hnatkivskyi']2023-12-28T00:00:00https://www.semanticscholar.org/paper/e9a48d428b85e82f6577ea5d6320bfdd01a2f6dd 3ae8bb79277a9dd64a1fa853e91ebb741a39fea2Journal Europeen des Systemes Automatises00Journal Européen des Systèmes Automatisés['Zine-eddine Meguetta']2023-12-28T00:00:00https://www.semanticscholar.org/paper/3ae8bb79277a9dd64a1fa853e91ebb741a39fea2 c4a39104b764f4b91e06d1f6611833c93dc984efA cognitive humanoid head is an AI enabled head-bot platform that resembles human's cognitive abilities, such as perception, thinking, learning, and decision-making. The platform is able to interact with human through natural language processing and recognize individuals, thus allowing seamless communication between two parties. No such cognitive platform has been introduced in Bangladesh, thus creating an open field to contribute to the field of Machine Intelligence. This study aims to develop an AI based humanoid head (head-bot) capable of imitating a range of expressions, recognizing individuals, and interacts with visitors through general conversation. The head-bot skeleton is developed using a number of hexagonal blocks of PVC sheet to mimic a human-head-like structure where LCD, camera, microphones, and speaker are mounted. Two separate Machine Learning models are designed for face detection and recognitions, and voice enabled chat-bot implementation. The head-bot platform incorporates 2-DoF neck movements for various head gestures and face tracking. The Artificial Neural Network models are tested with accuracy of 95.05%, and 99.0%, for face detection and recognitions, and speech recognitions and response generation, respectively. According to the overall results and system performances, it seems that the proposed system has a number of good potentials for real life applications such as entertainment, guidance, conversations, interactive receptionists, personal companion, medical assistance, and so on.MIST international journal of science and technology442It seems that the proposed head-bot system has a number of good potentials for real life applications such as entertainment, guidance, conversations, interactive receptionists, personal companion, medical assistance, and so on.MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY['R. Baki', 'M. Akhtaruzzaman', 'Tahsin Ahmed Refat', 'Mouneeta Rahman', 'Md. Abdur Razzak', 'Md Mahfuzul Karim Majumder', 'Md Adnanul Islam', 'Meftahul Ferdaus', 'Muhammad Towfiqur Rahman', 'Q. N. Naveed']2023-12-28T00:00:00https://www.semanticscholar.org/paper/c4a39104b764f4b91e06d1f6611833c93dc984ef 2bec20e2c62e722a8c9e47477d8c07979add77caThis paper review ways AI and analytics are transforming HR decision-making in American organizations. It explores the adoption of these technologies in the U.S., their impact on optimizing talent management, and the broader implications for organizational growth and employee well-being. In the rapidly evolving landscape of Human Resources (HR), the integration of Artificial Intelligence (AI)-driven talent analytics has emerged as a transformative force. This review explores the application of AI-driven talent analytics in the United States, specifically focusing on its role in strategic HR decision-making. The analysis delves into the current state of AI adoption in HR practices, the challenges and opportunities it presents, and the impact on organizational performance. Key topics covered include predictive analytics, talent acquisition, workforce planning, and employee engagement. Through a comprehensive examination of existing literature and case studies, this review aims to provide insights into the ways AI-driven talent analytics shapes strategic HR decisions and contributes to organizational success in the U.S. context. The findings underscore the imperative for HR professionals and organizational leaders to navigate the evolving landscape of AI in talent management, fostering a deeper understanding of its potential benefits and challenges for informed decision-making. Keywords: Artificial Intelligence; Talent Analytics; Human Resources; Decision-Making; USA.International Journal of Management &amp; Entrepreneurship Research02The findings underscore the imperative for HR professionals and organizational leaders to navigate the evolving landscape of AI in talent management, fostering a deeper understanding of its potential benefits and challenges for informed decision-making.International Journal of Management &amp; Entrepreneurship Research['Oluwatamilore Popo – Olaniyan', 'Oluwafunmi Adijat Elufioye', 'Franciscamary Chinyere Okonkwo', 'Chioma Ann Udeh', 'Tobechukwu Francisca Eleogu', 'Funmilola Olatundun Olatoye']2023-12-28T00:00:00https://www.semanticscholar.org/paper/2bec20e2c62e722a8c9e47477d8c07979add77ca b75a3214308efda44399dee5a1fc37cc559be5d2Human behavior simulation of AI agents necessitates the agents to possess a quality of believability, which is crucial as it facilitates users in establishing trust toward the agents and streamlines the fulfillment of the agents' goal. While recent advancements in Large Language Model (LLM) based agents have improved human behavior simulation, challenges inherent to LLMs (e.g., long context modeling) can undermine their believability. Consequently, evaluating AI agent believability becomes imperative. Unfortunately, prior research often neglects the negative impacts of LLM deficiencies. To address these gaps, we introduce two metrics for assessing LLM-based agent believability: consistency, and robustness, together with a benchmark, SimulateBench, with which, we evaluate the consistency and robustness of agents implemented with popular LLMs. We find that agents (i) struggle to accurately depict character information when presented with lengthy profile inputs; (ii) exhibit vulnerability to profile perturbations; and (iii) are significantly affected by certain key factors that impact their overall believability. Code and SimulateBench are public at https://github.com/GAIR-NLP/GPTMan.arXiv.org522It is found that agents struggle to accurately depict character information when presented with lengthy profile inputs; exhibit vulnerability to profile perturbations; and are significantly affected by certain key factors that impact their overall believability.ArXiv['Yang Xiao', 'Yi Cheng', 'Jinlan Fu', 'Jiashuo Wang', 'Wenjie Li', 'Pengfei Liu']2023-12-28T00:00:00https://www.semanticscholar.org/paper/b75a3214308efda44399dee5a1fc37cc559be5d2 b5224f5443c73b312c708e095ccb6c885922fc34(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to the individual. Assistive technologies (ATs) play an important role in this context and hold significant potential when integrated with artificial intelligence (AI). (Objective) The objective of this study is to analyze the state of integration of AI with ATs in autism through a review. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 22 studies, including 7 reviews. (Key Content and Findings) The results reveal an early yet promising interest in integrating AI into autism assistive technologies. Exciting developments are currently underway at the intersection of AI and robotics, as well as in the creation of wearable automated devices like smart glasses. These innovations offer substantial potential for enhancing communication, interaction, and social engagement for individuals with autism. Presently, researchers are prioritizing innovation over establishing a solid presence within the healthcare domain, where issues such as regulation and acceptance demand increased attention. (Conclusions) As the field continues to evolve, it becomes increasingly clear that AI will play a pivotal role in bridging various domains, and integrated ATs with AI are positioned to act as crucial connectors.Journal of Personalized Medicine662As the field continues to evolve, it becomes increasingly clear that AI will play a pivotal role in bridging various domains, and integrated ATs with AI are positioned to act as crucial connectors.Journal of Personalized Medicine['Antonio Iannone', 'Daniele Giansanti']2023-12-28T00:00:00https://www.semanticscholar.org/paper/b5224f5443c73b312c708e095ccb6c885922fc34 e9aeb6e0a4b060e1091187eb9f8708431317db42The emergence and popularization of generative AI technology represented by ChatGPT mark the transformation from weak AI to strong AI, and bring opportunities and challenges to education. In recent years, mental health issues have gradually become a social problem faced by countries around the world, including China. These mental health issues have gradually expanded from adults and professional groups to students, and are showing a trend of "low age". Strengthening the mental health education of adolescents has become a consensus of the whole society. However, there are currently a lack of excellent counselors in primary and secondary schools, or the number of counselors is relatively small, resulting in a large amount of tasks. In order to study how ChatGPT can help school counselors provide better services, first analyze the basic principles and characteristics of ChatGPT technology. Then discuss the application practice of ChatGPT in psychological counseling, including as an auxiliary tool for counselors, a self-help psychological therapy platform, and a mental health education tool, combined with campus scenarios. Finally, explore the possible challenges and limitations of ChatGPT technology in the application of campus psychological counseling, and propose future research directions.Transactions on Social Science, Education and Humanities Research121The basic principles and characteristics of ChatGPT technology are analyzed, including as an auxiliary tool for counselors, a self-help psychological therapy platform, and a mental health education tool, combined with campus scenarios, and future research directions are proposed.Transactions on Social Science, Education and Humanities Research['Che Liu', 'Jiajia Zhang', 'Mengmeng Wang']2023-12-28T00:00:00https://www.semanticscholar.org/paper/e9aeb6e0a4b060e1091187eb9f8708431317db42 8dd925f41fff5ddbb2aef1c04bf37c80b3b06653This study aims to discuss how to form AI (Artificial Intelligence) ethical behavior with insight into the personal and organizational values of prospective accountants. This was a quantitative survey method. The sampling technique with a saturated sample was used as the research sample. Partial Least Square (PLS) analysis was conducted on 421 data points using WarpPLS software. The study results show that organizational and personal values significantly positively affect the intention of prospective accountant students to engage in AI ethics. Organizational values have a positive effect on the personal values of prospective accounting students. Intentions had a significant effect on AI ethics. Personal values did not play a role in mediating the impact of organizational values on intentions toward AI ethics. Intention succeeds in mediating the influence of personal values on the intention to engage in AI ethics among prospective accountant students. The findings referred to are very applicable to be implemented in different cultural settings due to the personal and organizational values tend to be implemented in general situation and condition. The findings provide universal outlook that values within organizations have an essential role in enhancing future accountants to be ethical in respect to AI.ETHICS IN PROGRESS381The study results show that organizational and personal values significantly positively affect the intention of prospective accountant students to engage in AI ethics.ETHICS IN PROGRESS['L. Latifah', 'R. Setiyani', 'Sandy Arief', 'N. Susilowati']2023-12-28T00:00:00https://www.semanticscholar.org/paper/8dd925f41fff5ddbb2aef1c04bf37c80b3b06653 2ea1ad9072d2a6312b99965f0e2dce14920e5eedABSTRACT Medical education assessment faces multifaceted challenges, including data complexity, resource constraints, bias, feedback translation, and educational continuity. Traditional approaches often fail to adequately address these issues, creating stressful and inequitable learning environments. This article introduces the concept of precision education, a data-driven paradigm aimed at personalizing the educational experience for each learner. It explores how Artificial Intelligence (AI), including its subsets Machine Learning (ML) and Deep Learning (DL), can augment this model to tackle the inherent limitations of traditional assessment methods.AI can enable proactive data collection, offering consistent and objective assessments while reducing resource burdens. It has the potential to revolutionize not only competency assessment but also participatory interventions, such as personalized coaching and predictive analytics for at-risk trainees. The article also discusses key challenges and ethical considerations in integrating AI into medical education, such as algorithmic transparency, data privacy, and the potential for bias propagation.AI's capacity to process large datasets and identify patterns allows for a more nuanced, individualized approach to medical education. It offers promising avenues to not only improve the efficiency of educational assessments but also to make them more equitable. However, the ethical and technical challenges must be diligently addressed. The article concludes that embracing AI in medical education assessment is a strategic move toward creating a more personalized, effective, and fair educational landscape. This necessitates collaborative, multidisciplinary research and ethical vigilance to ensure that the technology serves educational goals while upholding social justice and ethical integrity.Academic medicine : journal of the Association of American Medical Colleges01The concept of precision education is introduced, a data-driven paradigm aimed at personalizing the educational experience for each learner, and it is concluded that embracing AI in medical education assessment is a strategic move toward creating a more personalized, effective, and fair educational landscape.Academic medicine : journal of the Association of American Medical Colleges['Laurah Turner', 'Daniel A. Hashimoto', 'Shubha Vasisht', 'Verity Schaye']2023-12-28T00:00:00https://www.semanticscholar.org/paper/2ea1ad9072d2a6312b99965f0e2dce14920e5eed e759cc629b641e371addd7b80c1222e492030399The paper introduces the concepts of thinking and mathematical thinking, emphasizing the significance of mathematical thinking in problem-solving and the development of mathematical skills. It elaborates on the process of mathematization, highlighting its role in fostering problem-solving abilities. Furthermore, the article discusses Teaching with the assistance of an AI Chatbot, the objectives of high school mathematics programs, and presents the Teaching Process in Mathematics with the assistance of an AI Chatbot to develop mathematical thinking skills for students. This process includes steps such as defining goals, logging into the AI Chatbot, identifying learning tasks, problem-solving, learning, application and experience, assessment, conclusion, analysis, inference, and mathematical problem-solving. The integration of the AI Chatbot is emphasized, creating a dynamic learning environment that supports students. The AI Chatbot not only provides knowledge but also stimulates curiosity and creativity, helping students understand and apply mathematics to real-world scenarios. The paper provides scientific insights into the application of AI technology in mathematics education, supporting the learning process and developing the mathematical thinking of high school students.International Journal of Current Science Research and Review01The paper provides scientific insights into the application of AI technology in mathematics education, supporting the learning process and developing the mathematical thinking of high school students.International Journal of Current Science Research and Review['Nguyen Van Doc', 'Nguyen Thi Hoai Nam', 'Ngo Tu Thanh', 'Nguyen Minh Giam']2023-12-28T00:00:00https://www.semanticscholar.org/paper/e759cc629b641e371addd7b80c1222e492030399 170bfd7dc499cba4c2a878382a15e9cb8aa07f61Generative AI applications offer transformative potential for business operations, yet their adoption introduces substantial challenges. This paper utilizes the CBDAS data maturity model to pinpoint pivotal success factors for seamless generative AI integration in businesses. Through a comprehensive analysis of these factors, we underscore the essentials of generative AI deployment: cohesive architecture, robust data governance, and a data-centric corporate ethos. The study also highlights the hurdles and facilitators influencing its implementation. Key findings suggest that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption. The paper culminates in presenting the practical implications of these insights, urging further exploration into the real-world efficacy of the proposed recommendations.International Journal on Cybernetics &amp; Informatics01It is suggested that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption, and that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption.International Journal on Cybernetics &amp; Informatics['Simone Malacaria', 'Michele Grimaldi', 'Marco Greco', 'Andrea De Mauro']2023-12-28T00:00:00https://www.semanticscholar.org/paper/170bfd7dc499cba4c2a878382a15e9cb8aa07f61 bba496787711047003c174009c034641bd84ab07Over the past few decades, the education sector has achieved impressive advancements by incorporating Artificial Intelligence (AI) into the educational environment. Nevertheless, specific educational processes, particularly educational counseling, still depend on traditional procedures. The current method of conducting group sessions between counselors and students does not offer personalized assistance or individual attention, which can cause stress to students and make it difficult for them to make informed decisions about their coursework and career path. This paper proposes a counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level. The system utilizes a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities. We developed the model based on data from 500 graduates from 12 public high schools in Morocco, as well as eligibility criteria from 31 institutions and colleges. The counseling system comprises two modules: a recommendation module that uses popularity-based and content-based recommendations and a prediction module that calculates the likelihood of admission using the Huber Regressor model. This model outperformed 13 other machine learning modules, with a low MSE of 0.0017, RMSE of 0.0422, and the highest R-squared value of 0.9306. Finally, the system is accessible through a user-friendly web interface.Applied System Innovation501A counseling solution designed to aid high school seniors in selecting appropriate academic paths at the tertiary level by utilizing a predictive model that considers academic history and student preferences to determine students’ likelihood of admission to their chosen university and recommends similar alternative universities to provide more opportunities.Applied System Innovation['Hajar Majjate', 'Youssra Bellarhmouch', 'Adil Jeghal', 'Ali Yahyaouy', 'H. Tairi', 'Khalid Alaoui Zidani']2023-12-28T00:00:00https://www.semanticscholar.org/paper/bba496787711047003c174009c034641bd84ab07 4d9e38755c8bf20922fb5d2972cbe5649518c5caAfrica’s greatest assets are its young and dynamic popula- tion, and abundant natural resources. However, the conti- nent faces numerous socioeconomic challenges that have hindered its ability to fully embrace technological advance- ments, particularly in artificial intelligence (AI). This paper proposes an ontological framework-based approach to ad- dress these challenges and unlock Africa’s AI-driven growth and development potential. The proposed strategies aim to attract investment, foster collaboration, and secure financing for AI initiatives across the continent. Additionally, the paper explores how AI can be leveraged to alleviate poverty and improve the socioeconomic conditions of African citizensEngineering00An ontological framework-based approach to unlock Africa’s AI-driven growth and development potential and explore how AI can be leveraged to alleviate poverty and improve the socioeconomic conditions of African citizens is proposed.Engineering: Open Access[]2023-12-28T00:00:00https://www.semanticscholar.org/paper/4d9e38755c8bf20922fb5d2972cbe5649518c5ca f3208ef0aea936e1a1827f939d661f11249b538dQuantifying the impact of synthetic Intelligence (AI) and machine getting to know (ML) on information access Optimization (DAO) and its ability to enhance overall performance and performance inside organizations has been a topic of research for decades. This newsletter investigates how such technology is essential in optimizing records access and its effect on the rate and accuracy of operations. We speak about how cutting-edge advances in AI and ML can be utilized to detect patterns in facts, reduce latency, and improve the general quality of data. Furthermore, we discuss how that technology can reduce manual techniques and get rid of errors due to human mistakes, hence enhancing performance and productiveness. Sooner or later, we talk about the main challenges associated with enforcing AI and ML within a company and identify capacity areas of development.2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS)170This newsletter investigates how cutting-edge advances in AI and ML can be utilized to detect patterns in facts, reduce latency, and improve the general quality of data and discusses how that technology can reduce manual techniques and get rid of errors due to human mistakes, hence enhancing performance and productiveness.2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS)['Harsimrat Khandari', 'Girija Shankar Sahoo', 'Mahesh Tr', 'M. Rajeswari', 'Prranjali Jadhav', 'Mansingh Meena']2023-12-28T00:00:00https://www.semanticscholar.org/paper/f3208ef0aea936e1a1827f939d661f11249b538d b073bd90e36f47b4dce8c00ab0a060fe19389d78This paper reviews the developmental literature on decision making, discussing how increased reliance on gist thinking explains the surprising finding that important cognitive biases increase from childhood to adulthood. This developmental trend can be induced experimentally by encouraging verbatim (younger) versus gist (older) ways of thinking. We then build on this developmental literature to assess the developmental stage of artificial intelligence (AI) and how its decision making compares with humans, finding that popular models are not only irrational but they sometimes resemble immature adolescents. To protect public safety and avoid risk, we propose that AI models build on policy frameworks already established to regulate other immature decision makers such as adolescents.Policy Insights from the Behavioral and Brain Sciences260To protect public safety and avoid risk, it is proposed that AI models build on policy frameworks already established to regulate other immature decision makers such as adolescents to protect public safety and avoid risk.Policy Insights from the Behavioral and Brain Sciences['Sarah M. Edelson', 'Jordan E. Roue', 'Aadya Singh', 'Valerie F. Reyna']2023-12-28T00:00:00https://www.semanticscholar.org/paper/b073bd90e36f47b4dce8c00ab0a060fe19389d78 34fee43fac5da173cb599fdc4cb556220cb123e4Artificial intelligence (AI) has permeated into every aspect of medicine and promises to provide accurate diagnosis, better management decision and improved outcome for patients and healthcare system. However, ethical, social and legal issues need to be resolved for successful implementation of AI tools in clinical practice. In order to gain trust and acceptance, AI algorithms should offer maximum explainability and inclusiveness. Robust evidence of benefit to patients and healthcare services has to be provided to gain justification of using these tools. Doctor–patient relationship needs to be maintained in order to gain trust and acceptance of users. Autonomy of decisions and dignity of patients need to be preserved while using machine in healthcare. Responsibility and accountability in the use of AI in medicine should be deliberated and defined before mishaps and damage occur. A new role of healthcare providers will emerge with the advancement of technology and changes are inevitable. This manuscript is based on the Gordon Arthur Ransome Lecture 2022 entitled “Artificial Intelligence in Medicine: Ethical, Social and Legal Perspective”. It represents the opinion of the orator.Annals of the Academy of Medicine, Singapore91This manuscript is based on the Gordon Arthur Ransome Lecture 2022 entitled “Artificial Intelligence in Medicine: Ethical, Social and Legal Perspective” and represents the opinion of the orator.Annals of the Academy of Medicine, Singapore['Joseph J Y Sung']2023-12-28T00:00:00https://www.semanticscholar.org/paper/34fee43fac5da173cb599fdc4cb556220cb123e4 8d48ee38fd73724052c7a10bf7de95c299b64ac6The emergence of new tools, the appearance of new technologies and improvements to existing ones have resulted in expansion of generative artificial intelligence. The technologies of generative artificial intelligence have already been used by people to perform not only intellectual tasks, but also creative ones, in particular in the field of design. Therefore, their capabilities in graphic design need to be studied. One of the routine tasks of a designer is the development of corporate identity elements (a logo, font, and colour). Designers can spend a lot of time on this, choosing different style options. Therefore, delegating this routine work to generative artificial intelligence may be appropriate. With this practical need in mind, the capabilities of modern AI tools for image and logo generation were studied in the research, and the results of AI logo generation compared to the work of novice designers were analysed. As a result, conclusions were drawn about the expediency of using generative AI technology in the work of designers, in particular, for the development of corporate identity elements, and the appropriateness of studying generative artificial intelligence technology in the training of future designers. These conclusions were made on the basis of a survey of 41 experts in the field of design, information technology and artificial intelligence. Based on the findings of the survey, we can note that it was difficult for experts to distinguish between logos generated by artificial intelligence and logos created by novice designers. Logos developed by novice designers (5) were recognized as the most attractive among the 45 logos presented in the survey. Images generated in some AI tools (Tailor Brands, Hatchful) are considered attractive by design, information technology and artificial intelligence professionals. Therefore, they can be used to create corporate identity elements. Thus, the vast majority of experts agreed that artificial intelligence tools for generating images and logos should be used in the process of creating corporate identity elements. In addition, the vast majority of experts found it advisable to use generative artificial intelligence technologies in the process of professional training of future designers.Ìnformacìjnì Tehnologì ì Zasobi Navčannâ221The vast majority of experts agreed that artificial intelligence tools for generating images and logos should be used in the process of creating corporate identity elements, and found it advisable to use generative artificial intelligence technologies in the process of professional training of future designers.Information Technologies and Learning Tools['K. Osadcha', 'Maryna V. Osadcha']2023-12-28T00:00:00https://www.semanticscholar.org/paper/8d48ee38fd73724052c7a10bf7de95c299b64ac6 6cab30a4408a77987a45e48fd8410e3fbe6c4dd1Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study’s primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.Journal of Clinical Medicine1141Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice.Journal of Clinical Medicine['J. Fernandes', 'A. Teles', 'T. Fernandes', 'Lucas Daniel Batista Lima', 'Surjeet Balhara', 'Nishu Gupta', 'Silmar Teixeira']2023-12-28T00:00:00https://www.semanticscholar.org/paper/6cab30a4408a77987a45e48fd8410e3fbe6c4dd1 84c832509d9d9a9f6aca559ec653e2c7e840b585This paper analyzes the scope of Artificial Intelligence (AI) from the perspective of a multimodal grammar. Its focal point is Generative AI, a technology that puts so-called Large Language Models to work. The first part of the paper analyzes Generative AI, based as it is on the statistical probability of one token (a word or part of a word) following another. If the relation of tokens is meaningful, this is circumstantial and no more, because its mechanisms of statistical analysis eschew any theory of meaning. This is the case not only for the written text that Generative AI leverages, but by extension image and multimodal forms of meaning that it can generate. The AI can only work with non-textual forms of meaning after applying language labels, and to that extent is captive not only to the limits of probabilistic statistics but the limits of written language as well. While acknowledging gains arising from the brute statistical power of Generative AI, in its second part the paper goes on to map what is lost in its statistical and text-bound approaches to multimodal meaning-making. Our measure of these gains and losses is guided by the concept of grammar, defined here as a theory of the elemental patterns of meaning in the world—not just written text and speech, but also image, space, object, body, and sound. Ironically, a good deal of what is lost by Generative AI is computable. The third and final part of the paper briefly discusses educational applications of Generative AI. Given both its power and intrinsic limitations, we have been experimenting with the application of Generative AI in educational settings and the ways it might be put to pedagogical use. How does a grammatical analysis help us to identify the scope of worthwhile application? Finally, if more of human experience is computable than can be captured in text-bound AI, how might it be possible at the level of code to create a synthesis in which grammatical and multimodal approaches complement Generative AI?Multimodality &amp; Society201This paper analyzes the scope of Artificial Intelligence from the perspective of a multimodal grammar, and investigates the application of Generative AI in educational settings and the ways it might be put to pedagogical use.Multimodality &amp; Society['B. Cope', 'M. Kalantzis']2023-12-28T00:00:00https://www.semanticscholar.org/paper/84c832509d9d9a9f6aca559ec653e2c7e840b585 9671f435ec4c71c1b4af7a8ae082276b6b02528bBACKGROUND Missing occult cancer lesions accounts for the most diagnostic errors in retrospective radiology reviews as early cancer can be small or subtle, making the lesions difficult to detect. Second-observer is the most effective technique for reducing these events and can be economically implemented with the advent of artificial intelligence (AI). AIM To achieve appropriate AI model training, a large annotated dataset is necessary to train the AI models. Our goal in this research is to compare two methods for decreasing the annotation time to establish ground truth: Skip-slice annotation and AI-initiated annotation. METHODS We developed a 2D U-Net as an AI second observer for detecting colorectal cancer (CRC) and an ensemble of 5 differently initiated 2D U-Net for ensemble technique. Each model was trained with 51 cases of annotated CRC computed tomography of the abdomen and pelvis, tested with 7 cases, and validated with 20 cases from The Cancer Imaging Archive cases. The sensitivity, false positives per case, and estimated Dice coefficient were obtained for each method of training. We compared the two methods of annotations and the time reduction associated with the technique. The time differences were tested using Friedman’s two-way analysis of variance. RESULTS Sparse annotation significantly reduces the time for annotation particularly skipping 2 slices at a time (P < 0.001). Reduction of up to 2/3 of the annotation does not reduce AI model sensitivity or false positives per case. Although initializing human annotation with AI reduces the annotation time, the reduction is minimal, even when using an ensemble AI to decrease false positives. CONCLUSION Our data support the sparse annotation technique as an efficient technique for reducing the time needed to establish the ground truth.World Journal of Radiology330The data support the sparse annotation technique as an efficient technique for reducing the time needed to establish the ground truth, particularly skipping 2 slices at a time.World Journal of Radiology['Matthew Grudza', 'Brandon Salinel', 'Sarah Zeien', 'Matthew Murphy', 'Jake Adkins', 'Corey T Jensen', 'Curtis Bay', 'Vikram D Kodibagkar', 'Phillip Koo', 'T. Dragovich', 'Michael A. Choti', 'M. Kundranda', 'Tanveer Syeda-Mahmood', 'Hong-Zhi Wang', 'John C. Chang']2023-12-28T00:00:00https://www.semanticscholar.org/paper/9671f435ec4c71c1b4af7a8ae082276b6b02528b 62f7ab1858750ef6de4ae6c38bbd8564480de67aOBJECTIVE Literature reviews are essential to the scientific process and allow clinician researchers to advance general knowledge. The purpose of this study was to evaluate if the artificial intelligence (AI) programs Chat-GPT and Perplexity.AI can perform an orthopedic surgery literature review. MATERIALS AND METHODS Five different search topics of varying specificity within orthopedic surgery were chosen for each search arm to investigate. A consolidated list of unique articles for each search topic was recorded for the experimental AI search arms and compared with the results of the control arm of two independent reviewers. Articles in the experimental arms were examined by the two independent reviewers for relevancy and validity. RESULTS ChatGPT was able to identify a total of 61 unique articles. Four articles were not relevant to the search topic and 51 articles were deemed to be fraudulent, resulting in 6 valid articles. Perplexity.AI was able to identify a total of 43 unique articles. Nineteen were not relevant to the search topic but all articles were able to be verified, resulting in 24 valid articles. The control arm was able to identify 132 articles. Success rates for ChatGPT and Perplexity.AI were 4.6% (6 of 132) and 18.2% (24 of 132), respectively. CONCLUSION The current iteration of ChatGPT cannot perform a reliable literature review, and Perplexity.AI is only able to perform a limited review of the medical literature. Any utilization of these open AI programs should be done with caution and human quality assurance to promote responsible use and avoid the risk of using fabricated search results. [Orthopedics. 202x;4x(x):xx-xx.].Orthopedics150The current iteration of ChatGPT cannot perform a reliable literature review, and Perplexity.AI is only able to perform a limited review of the medical literature.Orthopedics['Ryan Y Sanii', 'Johnny K Kasto', 'Wade B Wines', 'Jared M. Mahylis', 'Stephanie J. Muh']2023-12-28T00:00:00https://www.semanticscholar.org/paper/62f7ab1858750ef6de4ae6c38bbd8564480de67a f0b8a2bd29c90b4002cd736abed749a3b8a35899Industry 4.0 represents a revolution in the business environment, driving the integration of information technologies and industrial automation, with the main objective of reducing latency in decision-making. Artificial Intelligence (AI) plays an essential role in advanced data analysis and resource optimization, allowing accurate predictions and agile decisions, however its implementation presents challenges, such as algorithm complexity and integration with industrial automation systems. An innovative solution to overcome these challenges is the implementation of a service-oriented architecture, which creates modular and interoperable systems, a concept especially relevant in the practical application of industrial automation, in which the integration between Information Technology (I.T) and Automation Technology (A.T) is crucial. This work presents, through experimental research, an innovative solution based on the development of a computer vision application, isolated in a Docker container. This application is designed to inspect the assembly of parts by a robotic system and establish communication with a Programmable Logic Controller (PLC) to approve or disapprove the assembly. The results of the adopted architecture demonstrate a flexible approach that simplifies the operation of AI systems, allowing operation with both AI enabled and disabled, reducing potential disruptions to the workflow. This research promises to open paths for future innovations and advances in the field of industrial automation and Artificial Intelligence, offering a model that effectively combines the agility of AI with the robustness of automation.Revista E-Tech Tecnologias para Competitividade Industrial - ISSN - 1983-183800This research promises to open paths for future innovations and advances in the field of industrial automation and Artificial Intelligence, offering a model that effectively combines the agility of AI with the robustness of automation.Revista e-TECH: Tecnologias para Competitividade Industrial - ISSN - 1983-1838['Elyan Fábio Corrêa', 'Dhyonatan Santos de Freitas']2023-12-28T00:00:00https://www.semanticscholar.org/paper/f0b8a2bd29c90b4002cd736abed749a3b8a35899 89c0da1556865c62ec9ab98e14f4353abf6b9fb4In today’s world, companies are looking for new ways to optimize business processes and reduce costs. The use of artificial intelligence in management accounting allows you to automate routine tasks, analyze large volumes of data and provide valuable information for decision-making. The use of artificial intelligence in management accounting is associated with increasing the efficiency of management accountants, which ensures the effectiveness of the management accounting system as a whole. Artificial intelligence can help avoid errors and reduce the risk of data loss. Knowledge and skills in the use of artificial intelligence are essential for management accounting professionals to remain competitive and effective in the marketplace. Technological innovations, particularly artificial intelligence, will have a significant impact on professional accounting services in the future. The article highlights the skills that specialists need to effectively work with artificial intelligence. The authors also highlight three methods of artificial intelligence that can be effectively used in management accounting: expert systems, data analytics, and neural networks. Expert systems allow management accountants to store and interpret human experience, using it to provide advice and recommendations that facilitate appropriate decision-making based on the evidence provided by expert systems. Data analysis technology allows you to discover new patterns of relationships between data and provide useful conclusions to decision-makers in companies. Neural networks are used to determine the level of compliance with policies in various types of companies and institutions and to detect risky or potentially fraudulent transactions.Problems of Theory and Methodology of Accounting, Control and Analysis00The article highlights the skills that specialists need to effectively work with artificial intelligence, and highlights three methods of artificial intelligence that can be effectively used in management accounting: expert systems, data analytics, and neural networks.Problems of Theory and Methodology of Accounting, Control and Analysis['G.I. Liakhovych', 'O. Vakun']2023-12-28T00:00:00https://www.semanticscholar.org/paper/89c0da1556865c62ec9ab98e14f4353abf6b9fb4 9a2e461c6ac7133c597e56914f9afb6a737838b9Background/Purpose: Systems and procedures for producing and delivering oil and gas are highly expensive and rely on cutting-edge technology. This industry is one that is already exploring the possibilities of artificial intelligence. To keep a competitive edge in the face of rapid environmental change, the industry is spending extensively on artificial intelligence and other data technologies. Objective: The benefits of AI directly address the major issues in the current oilfield. The oil and gas industry are realising the profound impact that AI can have on every industry along the whole value chain. The main problems in today's oilfield are directly addressed by AI's potential. Design/Methodology/Approach: This study focuses on challenges faced by different streams of oil and gas industries and its acceptance and dependency on AI to overcome them. Findings/Result: Now, oil and gas companies may use AI to estimate the value of specific reservoirs, customise drilling and completing plans to the specific geology, and assess the risks related to each well. Originality/Value: This study provides a concise overview of the oil and gas industry’s sustainability using artificial intelligence. Paper type: A case study on how artificial intelligence has influenced the development of the oil and gas industries.International journal of case studies in business, IT, and education320International Journal of Case Studies in Business, IT, and Education['Sandhya Bangera', 'Subrahmanya S. Bhat']2023-12-28T00:00:00https://www.semanticscholar.org/paper/9a2e461c6ac7133c597e56914f9afb6a737838b9 bd01b78c2a3d26379cebe36f542e76ea4d7d20dfIn recent years, the rapid growth of Artificial Intelligence has become a household name and has developed its use in various fields. Today's AI is permeating all aspects of the financial sector and has become a force for change. This paper summarizes the history of artificial intelligence and delves into the current state of its integration into the financial sector. The capabilities of AI herald a new era of financial innovation but also pose a number of risks and challenges. This paper combines case studies and literature to focus on the data risks faced by AI in finance, the complexity of "black box" algorithms, financial and legal regulatory challenges, and data privacy and ethical issues. As financial institutions increasingly rely on AI-powered solutions, understanding these potential risks becomes critical. This paper concludes with some countermeasures and recommendations to address the potential risks. By deploying AI through collaborative efforts, rigorous oversight, and on high-quality data, the financial community can capitalize on the power of AI to allow fintech to lead the transformation of the industry.Advances in Economics, Management and Political Sciences00The history of artificial intelligence is summarized, the current state of its integration into the financial sector is delves into, and some countermeasures and recommendations to address the potential risks are concluded.Advances in Economics, Management and Political Sciences['Xinyi Zhou']2023-12-28T00:00:00https://www.semanticscholar.org/paper/bd01b78c2a3d26379cebe36f542e76ea4d7d20df 34563e36d32becf09ae8f9f07992b3e9736408b3This study focused on the development of a reliable artificial intelligence (AI) model to enhance the classification reliability of ground weapon systems for surveillance and reconnaissance applications. The proposed AI model overcomes the limited data availability of military objects such as tanks, canons, and multiple-launch rockets by leveraging transfer learning and fine-tuning techniques. A comprehensive evaluation of 35 deep learning models using the publicly available Military-Vehicles dataset on Kaggle identified MobileNet as the most suitable model for ground weapon system classification. The selected MobileNet model achieved an average F1 score of 92% when tested on a dataset comprising five types of ground-weapon systems. In addition, the application of the explainable AI technique Grad-CAM provided insights into the decision-making process of the proposed model and verified its reliability. Real-world evaluations using frames extracted from training videos demonstrated promising accuracy for tanks, canons, and multiple-launch rockets. However, challenges related to object occlusion and the absence of target objects in the images were observed, which resulted in misclassifications. Overall, this study contributes to the development of explainable and reliable AI models for enhancing the performance of ground surveillance and reconnaissance systems.Journal of Advances in Military Studies00This study focused on the development of a reliable artificial intelligence model to enhance the classification reliability of ground weapon systems for surveillance and reconnaissance applications and identified MobileNet as the most suitable model for ground weapon system classification.Journal of Advances in Military Studies['Gimin Bae', 'Janghyong Lee']2023-12-28T00:00:00https://www.semanticscholar.org/paper/34563e36d32becf09ae8f9f07992b3e9736408b3 751ad30699430bad3e129116c93c09f83dff8ab2In recent years, there has been a surge in the global digitization of corporate processes and concepts such as digital technology development which is growing at such a quick pace that the construction industry is struggling to catch up with latest developments. A formidable digital technology, artificial intelligence (AI), is recognized as an essential element within the paradigm of digital transformation, having been widely adopted across different industries. Also, AI is anticipated to open a slew of new possibilities for how construction projects are designed and built. To obtain a better knowledge of the trend and trajectory of research concerning AI technology application in the construction industry, this research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review’s findings show foremostly that AI technologies are mostly used in facility management, creating a huge opportunity for the industry to profit by allowing facility managers to take proactive action. Secondly, it shows the potential for design expansion as a key benefit according to most of the selected literature. Finally, it found data augmentation as one of the quickest prospects for technical improvement. This knowledge will assist construction companies across the world in recognizing the efficiency and productivity advantages that AI technologies can provide while helping them make smarter technology investment decisions.Energies1230This research presents an exhaustive systematic review of seventy articles toward AI applicability to the entire lifecycle of the construction value chain identified via the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).Energies['Christian Nnaemeka Egwim', 'H. Alaka', 'Eren Demir', 'Habeeb Balogun', 'Razak Olu-Ajayi', 'Ismail Sulaimon', 'Godoyon Wusu', 'Wasiu Yusuf', 'Adegoke A. Muideen']2023-12-28T00:00:00https://www.semanticscholar.org/paper/751ad30699430bad3e129116c93c09f83dff8ab2 1ac3e4cb4d78749d61f108f1cb3a812d27699875Objective. This paper discusses the capabilities and implications of Artificial Intelligence (AI) in Nigerian academic libraries. The study emphasizes the importance of libraries using new technologies to improve their operations and services, especially in developing countries like Nigeria. Methods. The research conducted a literature review to examine the capabilities of AI in libraries and its impact on academic libraries. Results. Various AI tools such as natural language recognition, robotics, big data, and machine learning were identified. AI can revolutionize library services, improve information quality, increase productivity, and provide virtual assistance. However, there are challenges to the adoption of AI in Nigerian academic libraries, including high costs, resistance to change, poor network connectivity, privacy and ethical implications, and a lack of supportive cultures. To fully exploit the benefits of AI, libraries must develop plans and policies, train librarians with the necessary skills, and address the challenges associated with AI adoption. Conclusions. AI holds great advantages to enhancing library services in Nigeria, but careful planning and preparation are needed.UNIVERSITY LIBRARY AT A NEW STAGE OF SOCIAL COMMUNICATIONS DEVELOPMENT. CONFERENCE PROCEEDINGS00There are challenges to the adoption of AI in Nigerian academic libraries, including high costs, resistance to change, poor network connectivity, privacy and ethical implications, and a lack of supportive cultures.University Library at a New Stage of Social Communications Development. Conference Proceedings['S. A. Akinola']2023-12-28T00:00:00https://www.semanticscholar.org/paper/1ac3e4cb4d78749d61f108f1cb3a812d27699875 9d969c53faff2a2bd1674f74d14d75032fe998e0With the strong entry of generative artificial intelligence represented by ChatGPT and others, new producers have emerged in the field of content products. The originality standards of traditional copyright law and the basic theory of anthropocentrism have been strongly challenged by AI generated content. Therefore, copyright law needs to respond to a series of copyright issues arising from the generation of content by artificial intelligence. This study aims to analyze the copyright ownership and protection issues of AI generated content, and provide ideas and solutions for the copyright ownership and protection issues of AI generated content on this basis.Academic Journal of Science and Technology00This study aims to analyze the copyright ownership and protection issues of AI generated content, and provide ideas and solutions for the copyright ownership and protection issues on this basis.Academic Journal of Science and Technology['Yongsong Mo', 'Longsheng Tang']2023-12-28T00:00:00https://www.semanticscholar.org/paper/9d969c53faff2a2bd1674f74d14d75032fe998e0 7df77eec709a6ec2e6b188a36b5d39b2fa5e80ecThe rapid development of technology has increased the use of Artificial Intelligence (hereafter, AI) applications to improve students’ English skills, especially in speaking skill. This study aimed to investigate the effectiveness of AI applications to improve the introvert students’ speaking ability. Pre-experimental one group pre-test and post-test design was utilized in this study. This study conducted with 85 introvert students from two universities in Riau, Indonesia chosen by using MBTI test and purposive sampling technique. The data were collected through the questionnaire and speaking test. The data were analyzed by using SPSS 25th version to find out the descriptive statistic, normality test, and paired sample t-test. This finding shed light AI applications were effective in improving the introvert students’ speaking ability. It was proved that the t-test was higher than the t-table value (12.8231.663) with the level of significance p0.05. In short, there was significant difference on the students’ speaking ability before and after using AI applications for English speaking practiced. In conclusion, AI applications can be implemented to improve university students’ speaking ability.AL-ISHLAH: Jurnal Pendidikan230There was significant difference on the students’ speaking ability before and after using AI applications for English speaking practiced, indicating AI applications can be implemented to improve university students’ speaking ability.AL-ISHLAH: Jurnal Pendidikan['Liya Astarilla Dede Warman', 'Susi Erlinda', 'Tashid Tashid', 'Karpen Karpen', 'T. S. E. Fatdha']2023-12-28T00:00:00https://www.semanticscholar.org/paper/7df77eec709a6ec2e6b188a36b5d39b2fa5e80ec 91b0e24947b7a4ac509552b1c4d1a1e12197b606This article explores how Artificial Intelligence (AI) and Machine Learning (ML) are changing the way businesses use data. In a world where data is super important, many companies are using AI and ML to make the most of their data. This study looks at how AI and ML are being used in Business Intelligence (BI), which is all about collecting and analyzing data to help businesses make smart decisions. First, we look at the old way of doing BI and how it couldn't handle the huge amount of data we have today. Then, we see how AI and ML are being used to solve this problem. These technologies help by automatically processing data, predicting future trends, and finding important information in big piles of data. We also check out some real-life examples from different industries to see how AI and ML are helping companies make better decisions. These examples show how businesses can get more accurate data, make decisions faster, and predict things better by using AI and ML in their BI. We also talk about some challenges and things we need to think about when using AI and ML in BI, like making sure we use these technologies in a responsible and fair way. In summary, this research shows that AI and ML are not just tools, but they're changing the way we do BI. By using these technologies, companies can get better insights from their data, stay competitive, and take their BI to the next level. Bulletin of Shakarim University Technical Sciences30This research shows that AI and ML are not just tools, but they're changing the way the authors do BI, which is all about collecting and analyzing data to help businesses make smart decisions.Bulletin of Shakarim University. Technical Sciences['M. M. Abalkanov', 'G. Abitova']2023-12-28T00:00:00https://www.semanticscholar.org/paper/91b0e24947b7a4ac509552b1c4d1a1e12197b606 c1c81c39be18f53cd8ca4303f44ff5893455ee6eArtificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations.Exploration of Medicine1020It is seen that complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, by comparing large numbers of images and teaching them to the computer.Exploration of Medicine['K. Keskinbora']2023-12-28T00:00:00https://www.semanticscholar.org/paper/c1c81c39be18f53cd8ca4303f44ff5893455ee6e 729ef1b8ac6ae5e41dd179804caa31fde5e0c3ddThis paper explores the dynamic landscape of Artificial Intelligence (AI) adoption in Africa, analysing its varied applications in addressing socio-economic challenges and fostering development. Examining the African AI ecosystem, the study considers regional nuances, cultural factors, and infrastructural constraints shaping the deployment of AI solutions. Case studies in healthcare, agriculture, finance, and education highlight AI's transformative potential for efficiency, accessibility, and inclusivity. The paper emphasizes indigenous AI innovations and international collaborations contributing to a distinct African AI ecosystem. Ethical considerations, including data privacy and algorithmic bias, are addressed alongside policy frameworks supporting responsible AI implementation. The role of governmental bodies, regulations, and private sector partnerships is explored in creating a conducive AI development environment. Challenges such as digital literacy gaps and job displacement are discussed, with proposed strategies for mitigation. In conclusion, the paper provides a nuanced understanding of AI in Africa, contributing to sustainable development discussions and advocating for an inclusive and ethical AI ecosystem on the continent.arXiv.org360A nuanced understanding of AI in Africa is provided, contributing to sustainable development discussions and advocating for an inclusive and ethical AI ecosystem on the continent.ArXiv['Kinyua Gikunda']2023-12-28T00:00:00https://www.semanticscholar.org/paper/729ef1b8ac6ae5e41dd179804caa31fde5e0c3dd 99f815e4f64afc689b46700ba551fdcdb9a04501In the healthcare industry artificial intelligence (AI) has become a disruptive technology that is revolutionizing patient care, diagnostics, and research. This abstract provides an overview of the main points and findings related to AI in healthcare exploring its advancements, applications, and ethical challenges. The rapid growth of AI technologies has led to remarkable improvements in healthcare. AI algorithms have demonstrated exceptional capabilities in analyzing number of patient data, enabling early disease detection, personalized treatment plans, and improved patient outcomes. Machine learning algorithms, such as deep learning and natural language processing, have been effectively employed to analyze medical images, predict disease progression, and support clinical decision-making. AI applications in healthcare span across various domains, including radiology, pathology, genomics, drug discovery, and patient monitoring. Telemedicine and AI-driven virtual health assistants have extended healthcare access to remote areas, empowering patients with self-care tools and enabling real-time communication with healthcare professionals. While it's undeniable that AI brings significant advantages to the field of healthcare, it's vital to emphasize the importance of ethical concerns. Additionally, ensuring that AI algorithms are transparent and interpretable is essential for establishing trust and promoting the responsible use of AI technology in clinical environments.EAI Endorsed Transactions on Pervasive Health and Technology60This abstract provides an overview of the main points and findings related to AI in healthcare exploring its advancements, applications, and ethical challenges.EAI Endorsed Transactions on Pervasive Health and Technology['Iram Fatima', 'Veena Grover', 'Ihtiram Raza Khan', 'Naved Ahmad', 'Ambooj Yadav']2023-12-28T00:00:00https://www.semanticscholar.org/paper/99f815e4f64afc689b46700ba551fdcdb9a04501 5470048e5ccfc75787e19fd89cb2028da7e02075The purpose of the article is to conduct a study and determine the importance and benefits of using artificial intelligence (AI) tools for certain areas of the cryptocurrency market, in particular, detecting and preventing fraud in the cryptocurrency market, as well as the possibilities of using AI chatbots in trading and in the formation of investment portfolios. The article covers the analysis of the growth of digital currencies and the increasing number of hacker attacks, defines the role of AI in ensuring security, considers methods of fraud detection and prevention, and analyses development prospects. AI plays a crucial role in identifying suspicious transactions and preventing fraud. The study aims to investigate the benefits and potential risks of AI bots. AI's integration has transformed the market, enabling more informed decision-making, improved investment strategies, and higher returns. The article emphasizes the need for ongoing research into the evolving landscape of AI in cryptocurrency, discussing challenges, and the potential to finance sphere. The integration of AI in fraud detection has proven advantageous, enabling real-time data analysis and pattern recognition, enhancing security for investors. The article also addresses concerns such as algorithmic bias and the displacement of traders and using AI chat bots. While acknowledging the risks, it highlights the positive impact of AI on efficiency, reliability, and security in the cryptocurrency marketThe article presents an algorithmisation of the possibilities and procedures for engaging artificial intelligence in the fight against fraud, as well as recommendations for market participants and regulators. The presented results highlight the existing innovative potential of AI to improve the security and efficiency of the functioning of participants in the cryptocurrency market. Overall, the article suggests that AI's transformative influence in the cryptocurrency market is a game changer, shaping the industry's future and presenting opportunities for growth and innovation.MODELING THE DEVELOPMENT OF THE ECONOMIC SYSTEMS00The article suggests that AI's transformative influence in the cryptocurrency market is a game changer, shaping the industry's future and presenting opportunities for growth and innovation.MODELING THE DEVELOPMENT OF THE ECONOMIC SYSTEMS['Akradii Mykytas', 'О. Blуznіuk', 'Oleksandr Goroh', 'Galyna Nagayeva']2023-12-28T00:00:00https://www.semanticscholar.org/paper/5470048e5ccfc75787e19fd89cb2028da7e02075 375859cbec0dbdeebdd60971336883d36ea2f86bThe problem of predicting aviation accidents is considered, a new forecasting method is identified ‒ using artificial intelligence, the possibility of developing this method is analyzed, and the concept of its use and implementation in the work of aviation enterprises is developedBulletin of Ulyanovsk State Technical Univercity00Bulletin of Ulyanovsk State Technical Univercity['Rafael Sayfutdinov', 'Daria Belogrudova', 'Bulat Safin']2023-12-28T00:00:00https://www.semanticscholar.org/paper/375859cbec0dbdeebdd60971336883d36ea2f86b ec8af4dbf2e582734d7d70e015298f7f28fc0440More than six decades since its inception, Artificial Intelligence (AI) stands at the cusp of a transformative shift. The global perspective on AI has evolved optimistically, as it increasingly permeates every facet of human life. AI is revolutionizing national security strategies and capabilities worldwide, but its impact on the Global South remains a topic of growing significance and concern. Every nation actively seeks to bolster internal security through AI-driven initiatives, including surveillance, cyber security, and autonomous technologies. This review paper delves into AI's role in analyzing vast datasets, uncovering patterns, and identifying security threats and challenges focusing specifically on the Global South. It considers the potential advantages AI offers in enhancing national security capabilities while addressing concerns surrounding its integration. Drawing from existing literature, it presents a comprehensive analysis of AI's prospective future in the cyber and national security domains within these nations. Ultimately, this paper aims to answer whether AI serves as a facilitator in strengthening internal security or poses unforeseen challenges and raises the importance of capacity-building, technology transfer, and international cooperation. It provides valuable insights into the evolving landscape of AI in the context of national security in the Global South.International Journal of Law in Changing World00Whether AI serves as a facilitator in strengthening internal security or poses unforeseen challenges and raises the importance of capacity-building, technology transfer, and international cooperation is investigated.International Journal of Law in Changing World['Kushal Srivastava']2023-12-28T00:00:00https://www.semanticscholar.org/paper/ec8af4dbf2e582734d7d70e015298f7f28fc0440 7de551ca1ac2fa5896536e9ec17ccf826a07ff0bThe analysis of Artificial Intelligence techniques and models used in the tourism sector provides insightful information for the management and innovation of this industry. In this paper, we conduct a comprehensive review of the different techniques and models, in regards to Artificial Intelligence when applied to the tourism industry. Specifically, we present a categorization of Artificial Intelligence applications used in different areas of tourism. The results allow to recognize valid studies and useful tools for the activation and growth of the tourism sector, an industry that represents a significant increase in the Gross Domestic Product of various economies and supports the development of life conditions for their inhabitants. Artificial Intelligence applications generate more personalized travel experiences, improve the efficiency of tourism services and strengthen the tourism competitiveness of the destination. Journal of universal computer science (Online)110A categorization of Artificial Intelligence applications used in different areas of tourism allows to recognize valid studies and useful tools for the activation and growth of the tourism sector, an industry that represents a significant increase in the Gross Domestic Product of various economies and supports the development of life conditions for their inhabitants.J. Univers. Comput. Sci.['Anita Herrera', 'Ángel Arroyo', 'A. Jiménez', 'Álvaro Herrero']2023-12-28T00:00:00https://www.semanticscholar.org/paper/7de551ca1ac2fa5896536e9ec17ccf826a07ff0b bfad86d93db5e02f860546f4b5d8a340ebcf21d1In this article, we investigate the relative performance of artificial neural networks and structural models of decision theory by training 69 artificial intelligence models on a dataset of 7080 human decisions in extensive form games. The objective is to compare the predictive power of AIs that use a representation of another agent’s decision-making process in order to improve their own performance during a strategic interaction. We use human game theory data for training and testing. Our findings hold implications for understanding how AIs can use constrained structural representations of other decision makers, a crucial aspect of our ‘Theory of Mind’. We show that key psychological features, such as the Weber–Fechner law for economics, are evident in our tests, that simple linear models are highly robust, and that being able to switch between different representations of another agent is a very effective strategy. Testing different models of AI-ToM paves the way for the development of learnable abstractions for reasoning about the mental states of ‘self’ and ‘other’, thereby providing further insights for fields such as social robotics, virtual assistants, and autonomous vehicles, and fostering more natural interactions between people and machines.Games390This article investigates the relative performance of artificial neural networks and structural models of decision theory by training 69 artificial intelligence models on a dataset of 7080 human decisions in extensive form games to compare the predictive power of AIs that use a representation of another agent’s decision-making process in order to improve their own performance during a strategic interaction.Games['Michael S. Harré', 'Husam El-Tarifi']2023-12-28T00:00:00https://www.semanticscholar.org/paper/bfad86d93db5e02f860546f4b5d8a340ebcf21d1 7a2883665e1cf98384fbfad9d45e35273727d239Perkembangan teknologi yang mendorong penggunaan Chatbot Artificial Intelligence (Chatbot AI) dalam dunia pendidikan. Penggunaan teknologi ini dapat memberikan dampak positif dan negatif khususnya pada karakter mahasiswa. Politeknik Keuangan Negara (PKN) STAN memiliki kurikulum yang terintegrasi dengan pembangunan karakter mahasiswa. Penelitian ini bertujuan untuk mengukur pengaruh karakter yang dihasilkan dari penggunaan chatbot AI terhadap tujuan pembangunan karakter. Metode penelitian yang digunakan adalah metode kuantitatif dengan menerapkan model Structural Equation Modeling-Partial Least Squares (SEM-PLS). Hasil penelitian menunjukkan bahwa dari 12 jalur yang dianalisis, sebanyak 11 jalur memiliki pengaruh signifikan positif terhadap tujuan pembangunan karakter. Sedangkan jalur profesionalisme terhadap tujuan pembangunan karakter tidak signifikan. Namun, penelitian ini juga mengungkap bahwa jika dilakukan mediasi melalui variabel intervening, profesionalisme dapat berpengaruh positif terhadap tujuan pembangunan karakter melalui variabel integritas dan kesempurnaan. Hal ini menunjukkan bahwa penggunaan chatbot AI dapat secara positif mempengaruhi pembangunan karakter mahasiswa.Jurnal Minfo Polgan380Jurnal Minfo Polgan['Anggi Prastyono', 'B. Gautama', 'Ihza Zhafranianto']2023-12-28T00:00:00https://www.semanticscholar.org/paper/7a2883665e1cf98384fbfad9d45e35273727d239 99bd11c668f2a743e5566a15b8f225898d8062d9Banyaknya pihak guru di desa dadapan mengalami kesulitan dalam menerapkan Implementasi Kurikulum Merdeka (IKM) dalam pembelajaran. Selain itu pembelajaran berdiferensiasi belum ada bentuk pelatihan di desa dadapan. Penerapan pembelajaran berdiferensiasi desa dadapan tidak maksimal dan cenderung menggunakan pembelajaran konvensional. Tujuan dilaksanakan kegiatan ini adalah mengadakan workshop desain pembelajaran berdiferensiasi menggunakan Artificial Intelligence (AI). Metode pelaksanaan tahapan-tahapan kegiatan pelaksanaan Pelatihan ini meliputi persiapan, pelaksanaan, dan evaluasi. Sedangkan metode dalam pelatihan yakni Ceramah, tanya jawab, dan pendampingan, serta praktik. Kegiatan ini diikuti oleh 28 peserta yang dilaksanakan di SMPN 2 Gucialit. Hasil yang diperoleh diantaranya sebagian peserta bahwa kegiatan pelatihan bermanfaat bagi guru dan peserta juga mengalami peningkatan dalam keterampilan dan pengetahuan tentang pembelajaran berdiferensiasi memanfaatkan artificial intelligence (AI).JURNAL PENGABDIAN KEPADA MASYARAKAT80ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat['Devita Agustin', 'Lukman Jakfar Shodiq', 'Lely Kurnia', 'Idam Djunaedi', 'Pradipta Andreansyah']2023-12-28T00:00:00https://www.semanticscholar.org/paper/99bd11c668f2a743e5566a15b8f225898d8062d9 7a7f94c093eb7815faf9b84958f9fce69ee3b4a9AI has proven to be a powerful tool in various fields, and its integration into arbitration is highly anticipated due to its potential to improve efficiency, accuracy, and objectivity. This paper aimed to analyze how AI can help streamline arbitration, reduce costs, ensure faster dispute resolution, and improve accessibility. By using machine learning algorithms and natural language processing techniques, AI systems can analyze large volumes of legal text, extract relevant information, recognize patterns, and predict case outcomes. In addition, AI-driven chatbots could provide users with instant support and assistance in navigating the complex arbitration process. However, ethical considerations such as privacy and bias must be taken into account to ensure that AI does not compromise fairness or jeopardize confidentiality in arbitration proceedings. The article concludes with an examination of the transformative impact that artificial intelligence will have on the future of arbitration and emphasizes the needfor continued research and collaboration to realize its full potential while preserving the integrity of arbitration practice.International Journal of Law in Changing World00An examination of the transformative impact that artificial intelligence will have on the future of arbitration and the need for continued research and collaboration to realize its full potential while preserving the integrity of arbitration practice is examined.International Journal of Law in Changing World['Mohammad Solhchi', 'Faraz Baghbanno']2023-12-28T00:00:00https://www.semanticscholar.org/paper/7a7f94c093eb7815faf9b84958f9fce69ee3b4a9 9bcec96c1b7b2573ee6dd9477b291ca7b5c5333cThe development of increasingly AI-enabled autonomous systems and other military applications of Artificial Intelligence (AI) have been recognised as emergent major military innovations. In the absence of an effective and enforceable ban on their development and/or usage arising from the Group of Governmental Experts on Lethal Autonomous Weapon Systems (LAWS), it is likely that such systems will continue to be development. Amongst the legal, ethical, practical, and strategic concerns raised by the emergence of such systems, it is important not to lose sight of the risks involved in relying on a high-manufactured system in place of a human. This places additional strains and importance on securing diverse, complex, and over cross-jurisdictional supply chains. This article focuses on the vulnerability of and the risks to the integrity and security of the supply chains responsible for producing AI-enabled autonomous military systems.Applied Cybersecurity &amp; Internet Governance00The vulnerability of and the risks to the integrity and security of the supply chains responsible for producing AI-enabled autonomous military systems are focused on.Applied Cybersecurity &amp; Internet Governance['Austin Wyatt']2023-12-28T00:00:00https://www.semanticscholar.org/paper/9bcec96c1b7b2573ee6dd9477b291ca7b5c5333c 433ca8f8e3974fb01174b1e09a254ed077ff4edfJournal of clinical and biomedical sciences02JOURNAL OF CLINICAL AND BIOMEDICAL SCIENCES['Subhashis Das']2023-12-28T00:00:00https://www.semanticscholar.org/paper/433ca8f8e3974fb01174b1e09a254ed077ff4edf 78e4c2cb10459e89116f380911e247de34bb6fdcIn this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI powered malware detection model: malware sophistication, analysis techniques, malware repositories, feature selection and machine learning vs deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is limited by the widespread use of obfuscation. Dynamic analysis is not impacted by obfuscation but is defeated by ubiquitous anti-analysis techniques and requires more computational power. Sophisticated and evasive malware is challenging to extract authentic discriminatory features from and combined with poor quality datasets this can lead to a situation where a model achieves high accuracy with only one specific dataset.ACM Computing Surveys1022This survey systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI powered malware detection model: malware sophistication, analysis techniques, malware repositories, feature selection and machine learning vs deep learning.ACM Computing Surveys['Matthew G. Gaber', 'Mohiuddin Ahmed', 'Helge Janicke']2023-12-28T00:00:00https://www.semanticscholar.org/paper/78e4c2cb10459e89116f380911e247de34bb6fdc b8fe62194531b4cb2474780a13e053f9bc59a5beThe software of synthetic Intelligence (AI) has end up more and more widespread in each industry and academia as a powerful tool for transforming statistics into precious insights. AI is especially effective for facts technological know-how packages, permitting agencies to pick out styles, traits, and correlations inside huge datasets. By using leveraging AI for records technological know-how packages, agencies can advantage actionable insights which include predictive analytics, forecast destiny occasions, and optimize results. This paper provides an in depth overview of the usage of AI in statistics technological know-how, the advantages of the use of AI, and the challenges related to its utility. An overview of several excessive-effect facts technology applications is likewise supplied, consisting of gadget getting to know, virtual fitness, herbal language processing, predictive analytics, and pc vision. The potential packages of AI are colossal and blanketed as a part of a complete information technological know-how strategy; it is able to offer corporations with a massive competitive benefit.2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS)170An in depth overview of the usage of AI in statistics technological know-how, the advantages of the use of AI, and the challenges related to its utility is provided.2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS)['Akhilendra Pratap Singh', 'S. S', 'Narendra Kumar Jain', 'T. Vanaja', 'K. Wanjale', 'Pooja Dehankar']2023-12-28T00:00:00https://www.semanticscholar.org/paper/b8fe62194531b4cb2474780a13e053f9bc59a5be 3f7b51007ab5ae1ffa42c9d2240b349eba2f4c94Artificial Intelligence Surgery270Artificial Intelligence Surgery['George Peek', 'Sharona B Ross']2023-12-28T00:00:00https://www.semanticscholar.org/paper/3f7b51007ab5ae1ffa42c9d2240b349eba2f4c94 e58044ef2df01de79241532265a18e07d951b395This research aims to determine the use of ChatGPT for education in the Education 5.0 era in improving reading skills. ChatGPT can answer questions humanely, just like Google Nest; only Google Nest requires machines or tools like regular Google. This research uses quantitative methods, data obtained through interviews, and questionnaire distribution. Distribution of questionnaires using Google forms. The research results show that using ChatGPT for education in the Education Era 5.0 can benefit students, as evidenced by increased reading skills. From this research it can concluded that the use of ChatGPT for education has proven to be effective and efficient, the research proves this results that 80% of students have the highest scores. The limitation of this research is that the researcher only researched the use of ChatGPT for education in the Education Era 5.0 even though there are still many uses of other media that can be used as alternatives in improving students' reading skills.Arabiyat450From this research it can concluded that the use of ChatGPT for education has proven to be effective and efficient, and the research proves this results that 80% of students have the highest scores.Arabiyat : Jurnal Pendidikan Bahasa Arab dan Kebahasaaraban['Muhammad Yusuf Salam', 'Mahayuni Mahayuni', 'Mona Taman', 'Adam Mudinillah']2023-12-28T00:00:00https://www.semanticscholar.org/paper/e58044ef2df01de79241532265a18e07d951b395 eb840ca54d336e1df313d2cbbd64cbd295cd37bcThe analysis of the level of health was carried out using methods for assessing the responses of the main human biosystems in regulating and predicting the development of the biosystem within the framework of control equations and proposed methods for processing diagnostic laboratory analysis data in determining the assessment of the state of the energy potential of the human body (including the patient) and assigning a life development strategy, if necessary, treatmentBulletin of Ulyanovsk State Technical Univercity00Bulletin of Ulyanovsk State Technical Univercity['Valery Kokorin']2023-12-28T00:00:00https://www.semanticscholar.org/paper/eb840ca54d336e1df313d2cbbd64cbd295cd37bc a5f0e7eb1e9fbd4f9cced4b9b4445d121475472aCall to Latin American Studies Scholars for Commentaries on AI Technology and Latin American Studies: Research, Publication, InstructionMiddle Atlantic Review of Latin American Studies00Middle Atlantic Review of Latin American Studies['MARLAS Editorial Board']2023-12-28T00:00:00https://www.semanticscholar.org/paper/a5f0e7eb1e9fbd4f9cced4b9b4445d121475472a f81b909b23c6037a1bd54d2103b122ed490492d4Հետազոտության շրջանակներում ուսումնասիրվել է գիտության, տեխնոլոգիայի, արհեստական բանականության (AI) և տնտեսական աճի բարդ փոխհարաբերությունները ներկայիս գլոբալ համատեքստում: Արհեստական բանականությունը փոխակերպող դեր ունի տվյալները մշակելու, արդյունքները կանխատեսելու և բարդ առաջադրանքները ավտոմատացնելու ուղղությամբ՝ տնտեսական աճը խթանելու և կենսամակարդակը բարելավելու գործում: Рассматриваемая работа изучает сложную взаимосвязь между наукой, технологиями, искусственным интеллектом (ИИ) и экономическим ростом в нашем современном глобальном контексте. В нем подчеркивается преобразующая роль искусственного интеллекта с его способностью обрабатывать данные, прогнозировать результаты и автоматизировать сложные задачи в стимулировании экономического роста и повышении уровня жизни.Սոցիալ-տնտեսական զարգացման արդի հիմնախնդիրները Հայաստանի Հանրապետությունում=The contemporary issues of socioeconomic development in the Republic of Armenia00Սոցիալ-տնտեսական զարգացման արդի հիմնախնդիրները Հայաստանի Հանրապետությունում=The contemporary issues of socioeconomic development in the Republic of Armenia['M. Poghosyan']2023-12-28T00:00:00https://www.semanticscholar.org/paper/f81b909b23c6037a1bd54d2103b122ed490492d4 8813367906492ba42701c0bc95780f187af8cc37Objective. This article aims to review the progress of AI technologies concerning their potential impact on academia, research processes, scientific communication, and libraries. Methods. AI  tools for research lifecycle and their potential impact on academia and libraries were identified from various sources, mostly from the most influential recent scientific publications. Results. AI has become a driving force nowadays, creating both opportunities and challenges. Transformative AI-powered tools, exemplified by advanced models like ChatGPT, Llama-2, Google Bard, Microsoft Bing, and Jasper Chat, among others, find versatile utility across a broad spectrum of contexts, extending their impact to research process and publishing, as well as to librarianship. The enthusiastic embrace of AI in research is tempered by a pervasive concern over the potential for data fabrication, which can significantly compromise ethical standards and academic integrity. There is an urgent need to understand corresponding opportunities, challenges, and dangers. Some aspects of the use of AI tools for different stages of the research lifecycle are considered, and the main advantages and risks are analyzed. Conclusions. AI  has the potential to drive innovation and progress in a wide range of fields and possesses significant potential to propel academia and librarianship into both exhilarating and challenging new frontiers. While AI-powered tools represent major advancements and potential to significantly impact academia,  scholarly research, publishing, and university libraries. Privacy and bias are just two examples of the ethical considerations that need to be made.UNIVERSITY LIBRARY AT A NEW STAGE OF SOCIAL COMMUNICATIONS DEVELOPMENT. CONFERENCE PROCEEDINGS00AI  has the potential to drive innovation and progress in a wide range of fields and possesses significant potential to propel academia and librarianship into both exhilarating and challenging new frontiers.University Library at a New Stage of Social Communications Development. Conference Proceedings['T. Yaroshenko', 'O. I. Iaroshenko']2023-12-28T00:00:00https://www.semanticscholar.org/paper/8813367906492ba42701c0bc95780f187af8cc37 80810af3fe946ead3c170e6535a09be9e48c9005International Journal of Human-Computer Interaction300International Journal of Human–Computer Interaction['Veysel Bozkurt', 'D. Gursoy']2023-12-28T00:00:00https://www.semanticscholar.org/paper/80810af3fe946ead3c170e6535a09be9e48c9005 4922f25458aa181b5b145afc2b4cd55b9a339998International Relations and Diplomacy00International Relations and Diplomacy['Davoud Gharayagh-Zandi']2023-12-28T00:00:00https://www.semanticscholar.org/paper/4922f25458aa181b5b145afc2b4cd55b9a339998 0c6eee9151d7f795c9ca40c103bac53f7c0095a0Background: The ability to self-regulate plays a critical role in accomplishing the developmental tasks in the stages of late adolescence, early adulthood and general psychosocial wellbeing. Aims: As part of the Association of Adolescent and Child Care in India’s multicentric studies on youth behaviour, the current study aimed to understand the effect of self-regulation, total, short-term and long-term as measured by the ASRI in female college-going students. We also studied these scores in relation to sociodemographic factors such as gender, age, sibling status, along with other variables such as perceived control over one’s life on self-regulation abilities. Methods: A cross-sectional study was conducted using convenience sampling. Participants (n = 354) were in the age groups between 17 to 19 and 20 (late adolescence) to 21 years (young adults), pursuing B.A., BCom., or BSc. in a college in North India. The Adolescent Self-Regulatory Inventory (ASRI) was administered to participants in order to assess both short-term and long-term self-regulation. Permission: Ethical clearance for this project was given by AACCI’s Institutional Ethics Committee. tool used: Moilanen Adolescent Self-regulation Inventory ASRI. The Internal consistency (alpha) for the ASRI was 0.75 for short-term self-regulation scale and 0.80 for long-term self-regulation scales. Confirmatory factor analyses were performed to check for the inventory’s validity, two factors were used, short-term and long-term self-regulation, factors correlated 0.83. Statistical analysis: The data was analysed using the Jeffreys's Amazing Statistics Program (JASP 0.17.2.0). T-tests were conducted to study the effects of age, engagement in extracurricular activities, perceived internet and social media dependence, and substance use on the ASRI. One-way ANOVAs were conducted to determine the effects of sibling status, academic course, and perceived control over one’s life on the ASRI. Additionally, we also used the Kruskal-Wallis test, Mann-Whitney U test, Welch’s test and Levene’s test of equality of variances. The statistical significance of the calculated coefficients was considered at p<0.05. Results: The participants who self- perceived that they had control over their lives had higher scores for overall self-regulation (p=0.002), short-term (p=0.03) and long-term self-regulation (p=.0.004) on the ASRI compared to those who were not sure and those who did not believe that they had any control over their lives. Participants who self -perceived that they were dependent on social media had lower scores on short-term (p=0.01) and long-term self-regulation (p=0.01) on the ASRI compared to those who did not perceive themselves as being dependent on social media. Conclusion: Our sample showed that among all the variables we examined e.g., age sibling status, participation in extracurricular activities and tobacco, alcohol consumption , significant results were found for only two variables that accessed students’ self-perception (control over one’s life and social media usage). This suggests that at the stages of late adolescence and young adulthood, self-perception contributes to self-regulation abilities.Journal of Pediatrics &amp; Neonatal Care291Journal of Pediatrics &amp; Neonatal Care['Dr. Swati Y Bhave', 'Ms. Jemima S. Jacob', 'Dr. Neeti Soni', 'Dr. Surekha Joshi', 'Ms. Jill Mota', 'Dr. Anuradha Sovani']2023-12-27T00:00:00https://www.semanticscholar.org/paper/0c6eee9151d7f795c9ca40c103bac53f7c0095a0 6407828af43180cc71c9dbd21e7ea695bb45fa50The study is designed to promote creating a balance between reforming the current legal framework and adopting new regulatory provisions; the balance between the interests of the society, business and the state in the field of developing artificial intelligence technologies and related areas. The purpose of the study is to single out the features characteristic for the current stage of forming the model of state regulation in the development of artificial intelligence technologies in the financial sector. Materials and methods. The main research method was a comparative analysis of the provisions of regulations, standards, recommendations and reviews, based on comparing a number of indicators in the development of artificial intelligence technologies in the financial sector. Study results. The deregulatory and conservative approaches to regulation are marked, the parameters of the model of state regulation in the development of artificial intelligence technologies in the financial sector are outlined, the goals and objects of regulation are named, the directions of practical rule-making and developing recommendations are formulated. Conclusions. The current stage of the forming the model of state regulation in the development of artificial intelligence technologies in the financial sector is characterized by an increased interest in the prospects for the use of these technologies, however, it should be borne in mind that these technologies accompany large-scale changes in the economic model, and their regulation should act as an offshoot of the regulating the economic model as a whole. The attempts to integrate the use of technology into the existing economic model will only direct their development along the streamlined path of formalism and will not allow to fully exploit their potential.Oeconomia et jus30The current stage of the forming the model of state regulation in the development of artificial intelligence technologies in the financial sector is characterized by an increased interest in the prospects for the use of these technologies, however, it should be borne in mind that these technologies accompany large-scale changes in the economic model, and their regulation should act as an offshoot of the regulating the economic model as a whole.Oeconomia et Jus['O. Arkadeva', 'N. Berezina']2023-12-27T00:00:00https://www.semanticscholar.org/paper/6407828af43180cc71c9dbd21e7ea695bb45fa50 d87b67557afb8f6b46d8cb4b6c70ae6ee3eae527The article is devoted to the study of legal regulation of responsibility for violation of traffic rules in Ukraine. Analyzing official statistics, it can be stated that the number of traffic accidents has decreased significantly in recent years compared to previous years. But despite this, almost every day the news is full of information about road accidents that have fatal consequences. In such a situation, it is simply dangerous to count on the professionalism of drivers, the serviceability of vehicles, the responsibility of pedestrians, and the safety of the transport infrastructure. Currently, it does not matter who you are - a pedestrian, a driver, or a passenger - every road user must understand the importance and necessity of following the rules of the road. Very often, the main incentive for compliance with such rules of conduct by all road users is the normatively established responsibility for their violation. The normative-legal regulation of responsibility for violation of traffic rules aims to protect social relations in the field of ensuring road traffic safety and fixes the following types: civil, administrative, criminal. The issue of legal regulation of responsibility for violations of traffic rules in Ukraine plays a significant role because it has significant potential for improving the quality of life of citizens, developing the country's economy by implementing the fundamental goal of legal regulation of road safety - reducing the number of road accidents and saving the lives of Ukrainian citizens. The peculiarity of civil liability for violation of traffic rules is compensation for damage caused by the use of vehicles, which is a source of increased danger. Administrative responsibility for violation of traffic rules consists in applying fines to road users for violation of the mentioned rules, operation of transport and roads. Criminal liability in the field of road traffic safety arises if, during the use of a vehicle, a danger to life, human health, property, environmental safety, public safety, rights and legitimate interests of citizens, enterprises, institutions, organizations is created.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['M. Kyselova']2023-12-27T00:00:00https://www.semanticscholar.org/paper/d87b67557afb8f6b46d8cb4b6c70ae6ee3eae527 27e330f9cb3b5a8bb9410dd02eefb06013e4b42eThe article analyzes the possibilities of implementation of the European experience of the organization of the technical regulation system for the assessment of compliance and certification of Armament and Military Equipment. A step-by-step algorithm for the implementation of the European experience in the organization of the technical regulation system is proposed and the necessary actions at each step of the implementation process are detailed. An analysis of the similarity of European countries was carried out to study the experience of the organization of the technical regulation system. The countries whose experience is the most acceptable and useful for Ukraine in terms of similarities and implementation possibilities have been identified. The experience of organizing the system of technical regulation of NATO member countries - the Republic of Poland and the Czech Republic, as well as the partner country and candidate for NATO membership - the Republic of Serbia, is analyzed. Measures for the implementation of the European experience in the organization of the technical regulation system, as well as the possibility of using separate tools for assessing the conformity of products intended for defense and security needs, are proposed.Випробування та сертифікація40Випробування та сертифікація['H. Pievtsov', 'O. Drobot', 'M. Rudenko', 'M. Naumenko']2023-12-27T00:00:00https://www.semanticscholar.org/paper/27e330f9cb3b5a8bb9410dd02eefb06013e4b42e 08a355e1260ff4e9848e40486116cae46ff47cafIntroduction. The issue of artificial intelligence is extremely important for most scientific specialties, including law, since the subsequent capabilities of these technologies actualize the need to introduce fundamentally new legal mechanisms and improve the prevention of potential socially dangerous threats in the process of their application. The relevance of studying of the main approaches to legislative regulation of artificial intelligence technologies at the global level is primarily due to the need to improve the legal framework of the Russian Federation in the area under consideration. For this purpose, based on an analysis of specialized literature and legal sources, the author examined the modern practice of individual foreign countries and the Russian Federation in the field of strategic planning and development of legislative activities to regulate the technologies in question. Methods. During the research process, various methods were used, including: analysis, specific historical, comparative legal, systemic and structural. The use of content analysis made it possible to analyze the most problematic aspects of legislative issues related to the effective functioning of artificial intelligence. Results. The author concludes that it is important to comprehensively study the strategic of approach in the field of legislative foundations for the functioning of artificial intelligence technologies, taking into account their special legal category, as well as the advisability of intensifying domestic legislative activities to develop and adopt the necessary legal norms, which will allow in the near future to ensure the required scope of legal regulation of the issue under consideration. phenomenon. The novelty of this work is emphasized by the author’s proposed analysis of legislative regulation of issues related to the effective functioning of artificial intelligence, during which he convincingly proved that Russian legislators should resolve issues of improving the legal framework in this area more actively and on the basis of a comprehensive study of this issue.Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia20The author convincingly proved that Russian legislators should resolve issues of improving the legal framework in this area more actively and on the basis of a comprehensive study of this issue.Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia['Peter Kobets']2023-12-27T00:00:00https://www.semanticscholar.org/paper/08a355e1260ff4e9848e40486116cae46ff47caf 0071e01f2ec799128de748914419fd026661d582The article found out that in the conditions of the legal regime of martial law, administrative-legal regulation as a more rigid way of influencing legal relations begins to prevail over administrative- legal support based on the principle of people- centeredness. The concept of administrative-legal regulation during martial law is defined as bringing the system of administrative-legal relations to a state in which effective repulsion of Russian armed aggression is ensured, which is achieved thanks to the predominance of means of coercion among administrative-legal instruments, an increase in the discretionary powers of public authorities and restrictions on on the legal grounds of certain rights, freedoms and interests of individuals and legal entities. The concept of administrative and legal support in the conditions of the legal regime of martial law is formulated as the streamlining of the system of administrative and legal relations with the help of a wide arsenal of administrative and legal means and procedures with the aim of increasing the level of physical, psychological and economic security of a person and citizen during hostilities, ensuring national stability and creation of prerequisites for conscious and voluntary performance of military duty by citizens of Ukraine. The reasoning is given that the common features of administrative and legal support and administrative and legal regulation are the regulating influence on social relations, the presence of special administrative and legal means, and the focus on achieving sustainable functioning of the state and society. For administrative-legal regulation, the main goal is the implementation of the will of the state, for administrative-legal support - the generalized needs and interests of civil society, business entities, territorial communities, and individual citizens. Attention is focused on the fact that during the legal regime of martial law, provided there is a balance of its implementation, administrative-legal regulation and administrative-legal support strengthen each other, ensuring the maximum accumulation of human, economic, scientific and educational, technical and technological resources for effective repel the enemy's armed aggression.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['I. Shopina']2023-12-27T00:00:00https://www.semanticscholar.org/paper/0071e01f2ec799128de748914419fd026661d582 1d0511942c26148a3b3dc6cfabd97408740649daThe article examines the relevance of personal data protection, in particular of children, in the context of the rapid development of the Internet and the information sphere. The author analyzes the principles and peculiarities of legal regulation of children's personal data protection as defined by the UN Convention and the Law of Ukraine "On Protection of Childhood". The author assesses the impact of the Data Protection Regulation on Ukrainian legislation and the need for harmonization with European standards for the full protection of children's personal data. Comparison of the provisions of the Regulation with the national legislation reveals significant differences, in particular, the absence of separate regulation of children's personal data and the principles of processing such data. The author expresses a position on the need to harmonize Ukrainian legislation with European standards as an important component of effective personal data protection. The author also analyzes the provisions of the Draft Law No. 8153 "On Personal Data Protection" and concludes that the Draft Law is aimed at establishing compliance with European standards, expanding the principles of data processing and establishing new consent requirements. Important changes relate to terminology, processing rules, protection of children's data, financial responsibility and other aspects, however, the issue of protection of children's personal data as a separate category has not been given due attention. The author concludes that today the issue of personal data protection, in particular of children, is regulated by numerous international and national legal acts. At the same time, it is noted that the EU Regulation provides for special protection of children's rights, which is different from the Law of Ukraine "On Personal Data Protection", and Draft Law No. 8153 leaves aside the issue of protection of personal data of a child specifically. As a result, it is noted that in order to reduce risks, it is necessary to take into account the principles of the Regulation in national legislation, in particular transparency and limitation of settings. It is also important to raise the legal culture of citizens in the field of personal data protection, contributing to the improvement of the regulatory environment.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['I. I. Bochkova', 'K. Vrublevska-Misiuna', 'V. Tychyna']2023-12-27T00:00:00https://www.semanticscholar.org/paper/1d0511942c26148a3b3dc6cfabd97408740649da 79856dc966d0045cc30419060e800e43f3f51fa8The institute of working time is an integral part of the system of labor law of the Russian state. The presence of standards providing for maximum working hours, as well as guarantees for its establishment and use, represent a certain indicator showing the level of development of society. Today’s changes in the global economy are leading to an increasing spread of atypical forms of employment. As a result, labor law is undergoing significant changes, giving rise to new features of the legal regulation of labor relations. For example, the key feature of the working hours of remote workers is the possibility of its independent distribution by the employee. In the article, the authors analyze some problems arising in the field of legal regulation of the working hours of remote workers, law enforcement practice, and also offer ways to improve labor legislation in this area.Voprosy trudovogo prava (Labor law issues)00Voprosy trudovogo prava (Labor law issues)['I. Prasolova', 'J. Vasilenko']2023-12-27T00:00:00https://www.semanticscholar.org/paper/79856dc966d0045cc30419060e800e43f3f51fa8 a87371b07cc5d4ea8bee6bcddcc85c945cf25670China actively supports the development of international law mechanisms and the field of peaceful exploration of outer space is no exception. Joining the UN documents on space and other international organizations in the 1980s allowed Beijing to gain access to international scientific and technical cooperation. However, many Chi-nese, Russian as well as foreign experts note the imperfections of international space law, citing the lack of basic definitions and inadequate measures to control the activities of states in space. Having become a party to space law legal mechanisms much later than their entry into force, China did not participate in their delibera-tions and is now taking the initiative to establish better control mechanisms in this area.Теория и практика общественного развития00Теория и практика общественного развития['T. Tutnova']2023-12-27T00:00:00https://www.semanticscholar.org/paper/a87371b07cc5d4ea8bee6bcddcc85c945cf25670 65a2f2290a1b0727ecdbd3a993d395506a5a4c49The article conducts a scientific and practical study of the organizational and legal issues of the integration of the transport system of Ukraine into both European and world transport systems with the aim of forming and further developing a single trans-European transport network. The international documents of the European Union and the national legislation of Ukraine in the international transport sphere were analyzed in order to identify problematic issues, search for effective organizational and legal mechanisms for their solution and priority forms of cooperation in this sphere. It was revealed that the main concepts, ways to achieve the goal and even specific actions regarding the creation of a modern transport network are outlined in the strategy of the European Commission "Transport - 2050: road map for the formation of the Single European transport space - on the way to a competitive and resource-efficient transport system” - " White book - 2011”. One of the main goals of cooperation in accordance with the Association Agreement between Ukraine, on the one hand, and the European Union, the European Atomic Energy Community and their member states, on the other hand, was specified, and its content was formed. It was emphasized that the situation with European integration in the international transport sphere remains difficult and this is a negative phenomenon and a joint responsibility of the Government and the Parliament regarding the obligations of Ukraine's implementation of mandatory and directly related regulations and directives of the European Union. The necessary steps for the implementation of the organizational and legal mechanism of European integration processes regarding the liberalization of international transportation and the development of the international transport sphere with the European Union and its member states are proposed, which can be the conclusion of bilateral agreements between Ukraine and the European Union on the simplification of the permit system between Ukraine and the member states of the European Union. of the Union for transit transportation by transport, making appropriate changes to already existing bilateral agreements regarding the conditions of transit transportation of goods between Ukraine and the member states of the European Union, in general, resolving issues regarding the obligations of implementing the Association Agreement between Ukraine, on the one hand, and the European Union , the European Atomic Energy Community and their member states, on the other hand, in the field of infrastructure, acceleration of the stages of reform in Ukraine within the framework of the implementation of the Association Agreement, in particular, the approval by the parliament of European integration bills in the field of transport infrastructure.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['V. Panchenko', 'A. Matvieieva']2023-12-27T00:00:00https://www.semanticscholar.org/paper/65a2f2290a1b0727ecdbd3a993d395506a5a4c49 6eb05eaf5f2d58d5d222dccd0d51cdc3ec457049In the article, the author examines the legislative provisions regarding the tactical competence of the National Police of Ukraine during the performance of their official and combat tasks, including combat training of personnel during a state of war. Specifically, attention has been given to the origins of some fundamental concepts in their work, the authority to ensure and implement measures of the legal framework during a state of war, the performance of official duties, and the execution of combat tasks by police officers. The research's scientific novelty lies in the in­depth theoretical study of the legal aspects of tactical competence of National Police officers while carrying out their official and combat duties. The performance of police services during times of war, emergencies, or other critical situations necessitates specific competencies, skills, and character traits for police personnel. Key requirements for police officers include the ability to make rapid decisions in critical situations, act while considering all circumstances, possess a basic level of military training and skills to effectively cooperate with military units and other law enforcement agencies during a state of war, take responsibility for organizing and providing humanitarian aid to civilians, including evacuation and first aid, as well as ensuring the preservation of the rights and freedoms of citizens, even in wartime conditions, avoiding excessive use of force, and regulating their activities in accordance with the law. The research underscores the importance of utilizing the appropriate competencies, skills, and character traits for the successful execution of tasks by police officers in times of war, emergency situations, and other critical circumstances.Analytical and Comparative Jurisprudence00Analytical and Comparative Jurisprudence['E.S. Gidenko']2023-12-27T00:00:00https://www.semanticscholar.org/paper/6eb05eaf5f2d58d5d222dccd0d51cdc3ec457049 38e2cbbb2c2083b1b8bf42a3361603ce2e102ca8Penelitian ini bertujuan untuk menganalisis penggunaan Chatbot berbasis AI pada model hybrid di Jurusan Teknik Informatika dan Komputer. Dengan adanya penemuan ini, dapat dipastikan bahwa penggunaan chatbots dalam model hybrid memiliki dampak positif yang signifikan dalam mengoptimalkan proses penerimaan informasi di lingkungan pendidikan. Selain itu, temuan ini membuktikan bahwa chatbots mampu meningkatkan kualitas layanan akademik yang diberikan kepada siswa. Dengan menyajikan informasi dengan cepat dan efisien, chatbots membantu memenuhi kebutuhan informasional siswa dengan lebih baik, menciptakan lingkungan akademik yang lebih efektif dan responsif. Penelitian ini memberikan kontribusi pada pemahaman kita tentang peran chatbot dalam konteks pendidikan tinggi, menunjukkan potensi besar teknologi ini dalam meningkatkan efisiensi dan kualitas layanan akademik. Sebagai bagian dari era kecerdasan buatan, chatbot terbukti menjadi alat yang berharga dalam mendukung proses pembelajaran dan administratif di departemen Ilmu Komputer dan Teknik Komputer.Journal of Vocational, Informatics and Computer Education211Journal of Vocational, Informatics and Computer Education['Andika Isma', 'Rosidah Rosidah', 'Sigit Sahalik Rahman', 'Nasrullah Nasrullah', 'Arif Setiawan Syam', 'Novita Sari']2023-12-27T00:00:00https://www.semanticscholar.org/paper/38e2cbbb2c2083b1b8bf42a3361603ce2e102ca8 4b9447ec324c40e306aa8a39821f54609b5616afAs the artificial intelligence (AI) ethics field is currently working towards its operationalisation, ethics review as carried out by research ethics committees (RECs) constitutes a powerful, but so far underdeveloped, framework to make AI ethics effective in practice at the research level. This article contributes to the elaboration of research ethics frameworks for research projects developing and/or using AI. It highlights that these frameworks are still in their infancy and in need of a structure and criteria to ensure AI research projects advance in a way that respects norms and principles. This article proposes to draw from the European Union’s AI Act currently in development to shape these frameworks. Although, in the current form of the draft (as of August 2023), the obligations of the AI Act do not apply to scientific research, it is most likely that they will still have a strong impact on AI research considering the need to anticipate market placement or to test new tools in real world conditions. This article investigates what the risk-based approach in the AI Act implies for research ethics and highlights some AI Act obligations of particular value to implement for ethics review processes.Research Ethics251What the risk-based approach in the AI Act implies for research ethics is investigated and some AI Act obligations of particular value to implement for ethics review processes are highlighted.Research Ethics['Anaïs Rességuier', 'Fabienne Ufert']2023-12-27T00:00:00https://www.semanticscholar.org/paper/4b9447ec324c40e306aa8a39821f54609b5616af 8bef96438bdff22f939c0c79684d9fc69ecf90e3Heredity351An AI-informed conservation genomic assessments could complement the IUCN Red List and improve the Green Status assessments by providing a longer-term perspective of population viability.Heredity['C. van Oosterhout']2023-12-27T00:00:00https://www.semanticscholar.org/paper/8bef96438bdff22f939c0c79684d9fc69ecf90e3 43bfb04c871297c5e3b63ff3b6e73ca926a2f349The prevailing narrative of AI rendering research obsolete overlooks its immense potential as a collaborative tool with the capacity to revolutionize scientific exploration. This paper critically examines the relationship between AI and human intelligence, advocating for a synergistic approach that harnesses the unique strengths of both. By weaving AI’s computational prowess with human creativity, ethical reasoning, and contextual understanding, we can unlock unprecedented avenues for discovery and innovation across diverse research fields. This paper delves into the intricate interplay between AI and human intelligence, meticulously examining their strengths, limitations, and potential for collaborative synergy. It underscores the critical role of human judgment in ensuring ethical and responsible research practices, emphasizing the need for a robust philosophical and ethical framework to guide the integration of AI. Instead of succumbing to the fear of the “death of research,” this paper presents a compelling vision of a future with AI as a powerful tool to augment human capabilities. This symbiotic partnership fosters revolutionary breakthroughs while preserving the human essence of research. By embracing this collaborative model, we pave the way for a new era of scientific discovery not by the demise of research but by unprecedented innovation, ethical progress, and a deeper understanding of the world around us.Changing Societies &amp; Personalities00This paper critically examines the relationship between AI and human intelligence, advocating for a synergistic approach that harnesses the unique strengths of both, and presents a compelling vision of a future with AI as a powerful tool to augment human capabilities.Changing Societies &amp; Personalities['Mohammed Salah', 'Fadi Abdelfattah', 'Hussam Al Halbusi', 'Muna Mohammed']2023-12-27T00:00:00https://www.semanticscholar.org/paper/43bfb04c871297c5e3b63ff3b6e73ca926a2f349 84be3f4abbaf5876fcaa68d85ec4c7aadfce1712Soft Computing - A Fusion of Foundations, Methodologies and Applications210This investigation introduces a metamodel that utilizes an artificial neural network to analyze 3D nonlinear structures undergoing plastic deformations and large strains and results indicate that the proposed deep neural network can learn from the simulations of finite elements.Soft Computing['L. Driemeier', 'Eduardo Lobo Lustosa Cabral', 'Gabriel Lopes Rodrigues', 'Marcos Tsuzuki', 'Marcilio Alves', 'Lucas Pires da Costa', 'Rafael Traldi Moura']2023-12-27T00:00:00https://www.semanticscholar.org/paper/84be3f4abbaf5876fcaa68d85ec4c7aadfce1712 e8887811e2eb8990176b450a4c1cd8b322c85871The use of AI in better service delivery is common. Different types of AI are developed: Mechanical, Thinking, and Feeling AI. Each AI technology has its strengths and drawbacks. There are anticipations that AI may replace human beings in service. However, not many studies have delved into this issue by simultaneously analyzing the value of AI technology and human employees and considering current and future trends. Thus, this essay will address this gap by concluding that Mechanical AI can be applied to complete standardized tasks; Thinking AI assists in identifying new opportunities, customizing services, and making more sound decisions; and Feeling AI can interact with customers, providing general emotional support. However, human employees should complete tasks regarding an advanced level of service as they possess self-consciousness and genuine emotions. Moreover, in the light of the Feeling Economy, jobs will become people oriented. It is necessary to keep and expand people’s specialty.Highlights in Business, Economics and Management130This essay will address the gap by concluding that Mechanical AI can be applied to complete standardized tasks; Thinking AI assists in identifying new opportunities, customizing services, and making more sound decisions; and Feeling AI can interact with customers, providing general emotional support.Highlights in Business, Economics and Management['Huaying Li']2023-12-27T00:00:00https://www.semanticscholar.org/paper/e8887811e2eb8990176b450a4c1cd8b322c85871 691d6e57adb441fb8cf94bc9a1ccd283d25e0234The last time you opened a fresh Google Doc, were you welcomed with a sparkly bluish‐purple prompt “help me write”? Existing tools many of us know and love have already implemented AI features like this; in fact, you might say the lovable (or love‐to‐loathe) Clippy — Microsoft's office‐supply‐shaped assistant — was the first. And he was probably way ahead of his time.Recruiting &amp; Retaining Adult Learners00Recruiting &amp; Retaining Adult Learners['Donna Talarico']2023-12-27T00:00:00https://www.semanticscholar.org/paper/691d6e57adb441fb8cf94bc9a1ccd283d25e0234 58f9c6c0dfd4ff97d5acc78db52c178baf0dad2dThe integration of artificial intelligence (AI) into drug discovery and development has ushered in a transformative era in pharmaceutical research. The research explores the profound impact of AI-driven approaches in drug discovery and development, demonstrating, that computational intelligence and biomedicine synergize to enhance innovation, efficiency, and precision in pharmaceutical science. AI’s influence spans multiple phases of drug development, from target identification and validation to the optimization of drug candidates, while also facilitating personalized medicine and expediting drug repurposing. Recent studies underscore the precision and swiftness that AI brings to the discovery of drug candidates and the prediction of molecular properties, illustrating the potential advantages of AI in pharmaceutical research. However, AI’s application in healthcare demands cautious consideration, as concerns such as model interpretability, ethical data usage, and regulatory frameworks loom large. The research also the critical need for ethical and secure data utilization. It investigates the methodology employed to create data visualizations that offer comprehensive insights into the advantages and disadvantages of AI algorithms in drug discovery. The analysis emphasizes that a judicious and context-specific approach to AI algorithm selection is essential to harness the transformative power of AI while mitigating its limitations.THE SCIENTIFIC TEMPER00The research explores the profound impact of AI-driven approaches in drug discovery and development, demonstrating, that computational intelligence and biomedicine synergize to enhance innovation, efficiency, and precision in pharmaceutical science.The Scientific Temper['Purnendu B. Acharjee1', 'Bhupaesh Ghai', 'Muniyandy Elangovan', 'S. Bhuvaneshwari', 'Ravi Rastogi', 'P. Rajkumar']2023-12-27T00:00:00https://www.semanticscholar.org/paper/58f9c6c0dfd4ff97d5acc78db52c178baf0dad2d 48a4e1445835cbb62b60bc7e56e44b1c63ce97c7Pediatric surgery international (Print)160DaiiNet provides a valuable tool for non-surgical teams and emergency room doctors, aiding them in making informed decisions about surgical interventions, and has the potential to enhance patient outcomes and optimize the management of critical conditions, including pneumothorax and pleural effusion.Pediatric surgery international['O. C. Tatar', 'Mustafa Alper Akay', 'Semih Metin']2023-12-27T00:00:00https://www.semanticscholar.org/paper/48a4e1445835cbb62b60bc7e56e44b1c63ce97c7 7b3314ec6b569e9a25c718675c63ea5e864a05ceAs artificial intelligence transforms the landscape of language technologies, advanced natural language processing models like GPT-4 are poised to revolutionize translator training paradigms. This mixed-methods study examined the integration of GPT-4 into translator education to harness its potential while retaining human expertise as the core. Structured translation prompts demonstrated GPT-4’s prowess in technical translations, but the model faces challenges in capturing complex literary and cultural subtleties, necessitating measured integration approaches. Interviews with experts in AI-enabled pedagogy advocated blended learning models judiciously combining GPT-4’s capabilities with immersive human training focused on creativity and cultural awareness. Direct observations of translator trainees showed benefits from GPT-4 usage, like personalized feedback and the need for human collaboration in complex cases. Cross-case analysis revealed variances in aptitude across diverse text genres and subjects, demanding tailored deployment strategies. While recognizing the risks associated with overdependence and taking into account ethical considerations, findings indicate an immense potential for GPT-4 to enrich pedagogy if integrated prudently in a human-centric manner. This underscores a balanced approach harnessing AI to amplify competencies without compromising the irreplaceable human essence underpinning high-quality, ethical translation. Keywords: ChatGPT, Translation, GPT-4, Translator training, Human-AI collaborationCurrent Trends in Translation Teaching and Learning E00Findings indicate an immense potential for GPT-4 to enrich pedagogy if integrated prudently in a human-centric manner, and underscores a balanced approach harnessing AI to amplify competencies without compromising the irreplaceable human essence underpinning high-quality, ethical translation.Current Trends in Translation Teaching and Learning E['Hussein Abu-Rayyash']2023-12-27T00:00:00https://www.semanticscholar.org/paper/7b3314ec6b569e9a25c718675c63ea5e864a05ce 8f5c9834bbe07e30ec4609c537d7e1e3a4b15de9Generative AI, such as image generation models and large language models, stands to provide tremendous value to end-user programmers in creative and knowledge workflows. Current research methods struggle to engage end-users in a realistic conversation that balances the actually existing capabilities of generative AI with the open-ended nature of user workflows and the many opportunities for the application of this technology. In this work-in-progress paper, we introduce participatory prompting, a method for eliciting opportunities for generative AI in end-user workflows. The participatory prompting method combines a contextual inquiry and a researcher-mediated interaction with a generative model, which helps study participants interact with a generative model without having to develop prompting strategies of their own. We discuss the ongoing development of a study whose aim will be to identify end-user programming opportunities for generative AI in data analysis workflows.arXiv.org190ArXiv['Advait Sarkar', 'Ian Drosos', 'Rob Deline', 'Andrew D. Gordon', 'Carina Negreanu', 'Sean Rintel', 'Jack Williams', 'Ben Zorn']2023-12-27T00:00:00https://www.semanticscholar.org/paper/8f5c9834bbe07e30ec4609c537d7e1e3a4b15de9 1c95dfea3a0c175415dd714f33ae878a3683c60bIn the digital age, cybersecurity is crucial due to the prevalence of threats and data breaches. In order to improve the security of digital information, this study explores the relationship between homomorphic encryption and artificial intelligence (AI). An detailed literature analysis that identifies gaps and difficulties in current cybersecurity practises is the study's first step. The research concept is then covered in detail, including data collecting, ethical issues, and data analysis procedures that integrate AI-driven approaches with homomorphic encryption for safe data processing. Along with steps to ensure data security and ethical compliance, the experimental setting is described in detail. The results are highlighted with an emphasis on how AI may improve cybersecurity using homomorphic encryption. In conclusion, this study highlights the theoretical and practical contributions to the subject and offers suggestions for future research directions. The study follows a well-organized research framework and aims to protect the digital world from changing dangers.2023 International Conference on Applied Physics and Computing (ICAPC)190This study explores the relationship between homomorphic encryption and artificial intelligence (AI) and highlights the theoretical and practical contributions to the subject and offers suggestions for future research directions.2023 International Conference on Applied Physics and Computing (ICAPC)['Ruibin Wang']2023-12-27T00:00:00https://www.semanticscholar.org/paper/1c95dfea3a0c175415dd714f33ae878a3683c60b e1c0a24c8a938d1c3591d2af27297ebfba7c74d0AI &amp; SOCIETY150There is a history of thinking that photographs provide a special kind of access to the objects depicted in them, beyond the access that would be provided by a painting or drawing, so generated images are in this sense like photographs.AI &amp; SOCIETY['P. D. Magnus', 'Evan Malone', 'Dan DiTursi', 'Jason D’Cruz', 'Ron McClamrock']2023-12-27T00:00:00https://www.semanticscholar.org/paper/e1c0a24c8a938d1c3591d2af27297ebfba7c74d0 34f625e9426dabb4e61e5ba40971cc0372f87eadEuropean Journal of Pediatrics70The utilisation of the AiPAD model in diagnosing paediatric appendicitis has significant potential to improve trainees’ diagnostic accuracy, approaching the level of an expert supervisor and holds promise for enhancing diagnostic capabilities, reducing medical errors and improving patient outcomes.European Journal of Pediatrics['Anas Shikha', 'Asem Kasem', 'Win Sabai Phyu Han', 'Janice Hui Ling Wong']2023-12-27T00:00:00https://www.semanticscholar.org/paper/34f625e9426dabb4e61e5ba40971cc0372f87ead 1c99c7ca2f816b27406efcc3562420daebe6cc4bProceedings of the National Academy of Sciences of the United States of America103Proceedings of the National Academy of Sciences of the United States of America['Matyáš Boháček', 'Hany Farid']2023-12-27T00:00:00https://www.semanticscholar.org/paper/1c99c7ca2f816b27406efcc3562420daebe6cc4b 6ba6e57ae010baaefa10f40fe0f2ba80a5b7dba5Connection science00Connection Science[]2023-12-27T00:00:00https://www.semanticscholar.org/paper/6ba6e57ae010baaefa10f40fe0f2ba80a5b7dba5 9248a48a12e4d29064e6732d36521731a6d0880eOptical and quantum electronics211To discover anomalies in edge computing networks for detecting device behavior, a unique Evolving Neuro-Fuzzy Intelligence for Anomaly Detection (EFNI-AD) method is proposed in this research and an Improved Cipher Crypto System (ICCS) is developed to ensure the reliability of data-passing edge devices.Optical and Quantum Electronics['N. Prakash', 'J. Vignesh', 'M. Ashwin', 'Sudhir Ramadass', 'N. Veeranjaneyulu', 'Shashikant V Athawale', 'Ananda Ravuri', 'Balambigai Subramanian']2023-12-27T00:00:00https://www.semanticscholar.org/paper/9248a48a12e4d29064e6732d36521731a6d0880e b316169fd1629e7f885f997d31edfdd8c9191011Voices of English Language Education Society00VELES (Voices of English Language Education Society)['Zalsa Febrina Syabilla', 'M. E. Romadhon', 'Mutmainnah Mustofa']2023-12-27T00:00:00https://www.semanticscholar.org/paper/b316169fd1629e7f885f997d31edfdd8c9191011 47497c91f059c22beebb76a2ccc19c4bae93378bBackground While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access and quality, particularly in specialized domains such as oral health, requires comprehensive evaluation. Objective This study aims to assess the effectiveness of Google Bard, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts. Methods This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts and LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiveness, intent capture, and helpfulness. Additionally, the stability of artificial intelligence responses was also assessed by submitting each question 3 times under consistent conditions. Results Google Bard excelled in readability but lagged in appropriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P=.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with human experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively), with ChatGPT-4 demonstrating the highest stability and reliability. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses. Conclusions LLMs, particularly ChatGPT-4, show potential in oral health care, providing patient-centric information for enhancing patient education and clinical care. The observed performance variations underscore the need for ongoing refinement and ethical considerations in health care settings. Future research focuses on developing strategies for the safe integration of LLMs in health care settings.Journal of Medical Internet Research200LLMs, particularly ChatGPT-4, show potential in oral health care, providing patient-centric information for enhancing patient education and clinical care and the observed performance variations underscore the need for ongoing refinement and ethical considerations in health care settings.Journal of Medical Internet Research['Xiaolei Lv', 'Xiaomeng Zhang', 'Yuan Li', 'Xinxin Ding', 'Hongchang Lai', 'Jun Shi']2023-12-27T00:00:00https://www.semanticscholar.org/paper/47497c91f059c22beebb76a2ccc19c4bae93378b 89bee99c07d24a9566c1a8dcf70b8669e00dd4deActa Orthopaedica00Acta Orthopaedica['Li Felländer-Tsai', 'Søren Overgaard']2023-12-27T00:00:00https://www.semanticscholar.org/paper/89bee99c07d24a9566c1a8dcf70b8669e00dd4de c68421cfa54024921ffa88ae03bc3b810816e950International Journal of Academic Research in Business and Social Sciences00International Journal of Academic Research in Business and Social Sciences['Ashraf A. Qahman', 'Hadi A. Dahlan. Mohamad S. Zakaria', 'Muhammad Hussin', 'Yousef K. A. Samra', 'Reda F. Aldaya', 'Rosseni Din', 'Nabilah Othman']2023-12-27T00:00:00https://www.semanticscholar.org/paper/c68421cfa54024921ffa88ae03bc3b810816e950 dadee895e993cba23d8017d61074fde2983fbf91Pipes age and corrosion are the main factors of leakage in water distribution networks. According to the World Resources Institute, European countries will face water problems by 2040. If we take Italy as an example, more than 40% of drinking water was lost in 2020 due to leaky aqueducts. Decrepit pipes can lead to environmental concerns, economical losses, and potential public health problems if water gets contaminated. Localizing leakage positions in an accurate way is often a big challenge. On the other side, replacing decrepit pipes is not an easy task and usually costly. An optimal solution to deal with water leakage is to use smart pipes where appropriate sensors monitoring the conditions of the pipes are incorporated in. Digitalization plays a crucial role here. By providing accurate information about the pipes, and use artificial intelligence techniques for data analysis, potential leakages and their corresponding positions can be detected in time, which allows to schedule a maintenance task as soon as possible. The current paper discusses the use of smart pipes combined with predictive maintenance and shows how this combination improves water leakage detection, hence minimizing water waste, and protecting the environment. The solution was validated in an experimental setup put in place by the Italian company EKSO s.r.l in its factory facilities in Rozallo, Italy. The obtained results show the feasibility of the solution and the relevance of using artificial intelligence techniques for predicting degradation in smart pipes.Artificial Intelligence and Applications00The use of smart pipes combined with predictive maintenance is discussed and it is shown how this combination improves water leakage detection, hence minimizing water waste, and protecting the environment.Artificial Intelligence and Applications['Y. Rebahi', 'Benjamin Hilliger', 'Patrick Lowin', 'Bowen Zhang', 'G. D. Bormida', 'Karim Ladjeri']2023-12-27T00:00:00https://www.semanticscholar.org/paper/dadee895e993cba23d8017d61074fde2983fbf91 7ef3cd527d572457b51f4f300323807890e87a70The rapid advancements in artificial intelligence (AI), including machine learning (ML), and deep learning (DL) have ushered in a new era of technological breakthroughs in healthcare. These technologies are revolutionizing the way we utilize medical data, enabling improved disease classification, more precise diagnoses, better treatment selection, therapeutic monitoring, and highly accurate prognostication. Different ML and DL models have been used to distinguish between electromyography signals in normal individuals and those with amyotrophic lateral sclerosis and myopathy, with accuracy ranging from 67% to 99.5%. DL models have also been successfully applied in neuromuscular ultrasound, with the use of segmentation techniques achieving diagnostic accuracy of at least 90% for nerve entrapment disorders, and 87% for inflammatory myopathies. Other successful AI applications include prediction of treatment response, and prognostication including prediction of intensive care unit admissions for patients with myasthenia gravis. Despite these remarkable strides, significant knowledge, attitude, and practice gaps persist, including within the field of electrodiagnostic and neuromuscular medicine. In this narrative review, we highlight the fundamental principles of AI and draw parallels with the intricacies of human brain networks. Specifically, we explore the immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine. While there are exciting possibilities for the future, it is essential to acknowledge and understand the limitations of AI and take proactive steps to mitigate these challenges. This collective endeavor holds immense potential for the advancement of healthcare through the strategic and responsible integration of AI technologies.Muscle and Nerve913The immense potential that AI holds for applications in electrodiagnostic studies, neuromuscular ultrasound, and other aspects of neuromuscular medicine is explored.Muscle & nerve['Mohamed A Taha', 'J. Morren']2023-12-27T00:00:00https://www.semanticscholar.org/paper/7ef3cd527d572457b51f4f300323807890e87a70 8a776cd4c3de0f5b0216ae0568eeeb41bf48c315European Radiology202Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology, indicating its significance for healthcare systems and medical curricula.European radiology['Ahmed Hafez Allam', 'Nael Kamel Eltewacy', 'Y. Alabdallat', 'Tarek A Owais', 'Saif Salman', 'M. Ebada']2023-12-27T00:00:00https://www.semanticscholar.org/paper/8a776cd4c3de0f5b0216ae0568eeeb41bf48c315 19e19c5a1939f77278867ec088436dfafc45d116Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.Exploration of Targeted Anti-tumor Therapy671This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.Exploration of Targeted Anti-tumor Therapy['Kriti Das', 'Maanvi Paltani', 'Pankaj Kuamr Tripathi', 'Rajnish Kumar', 'Saniya Verma', 'Subodh Kumar', 'Chakresh Kumar Jain']2023-12-27T00:00:00https://www.semanticscholar.org/paper/19e19c5a1939f77278867ec088436dfafc45d116 7ce770f81452e7976f874d8ac3580875994d2ef2The research relevance is determined by the increasing role of artificial intelligence in the tourism business, which was demonstrated during the outbreak of the COVID-19 pandemic in Bali, resulting in a decline in the number of tourists visiting the island. The research aims to investigate the real-life experience of applying artificial intelligence systems in the tourism industry to address the challenges of developing the tourism industry in Bali, Indonesia. The basis of the methodological approach is a combination of theoretical methods of analysis and synthesis of the collected information covering the practical experience of the application of artificial intelligence systems in the tourism industry, as well as a survey of customers of travel agencies that have used such systems in trips to Bali. The research results show that artificial intelligence can significantly help the tourism business in Bali, as the number of tourists visiting the island dropped by 40% during the pandemic, resulting in a reduction in the number of tourism staff. The practical application of systems based on artificial intelligence opens up a wide range of business opportunities, as it allows for optimizing the activities of tourism firms and providing customers with high-quality services with highly technical equipment. The use of neural network mathematical models of tourist firms’ activity based on AI, allows them to achieve high speed of data processing, which allows tourist firms numerous competitive advantages and significantly improves the quality of their services. The practical significance of the results of this scientific research lies in the possibility of their application in the organization of the activities of enterprises in the sphere of tourism business, built on the use of digital technologies and systems based on artificial intelligence.Sustainable Engineering and Innovation642The research results show that artificial intelligence can significantly help the tourism business in Bali, as the number of tourists visiting the island dropped by 40% during the COVID-19 pandemic, resulting in a reduction in the number of tourism staff.Sustainable Engineering and Innovation['Adrid Indaryanto', 'Bambang Dwi Harijadi', 'Eduard Sinaga']2023-12-27T00:00:00https://www.semanticscholar.org/paper/7ce770f81452e7976f874d8ac3580875994d2ef2 bd4615237e6e8a47b1c8aefb54d90e878c016b16This paper proposes an artificial intelligence network architecture based on software-defined network. The system has two independent characteristics of centralized control, distributed routing, control and forwarding, and has strong scalability and programmability. This project intends to use electrical control methods to collect massive amounts of various types of information from the base network, and analyze it through big data and artificial intelligence methods to obtain basic network characteristic data. The interference suppression algorithm based on local vision is studied. The attacked category network is set as an authentication factor and fixed parameters are set. The corresponding control design algorithm is established, and the generated algorithm can realize the local visibility of interference to a certain extent by optimizing the deception loss, diversity loss and distance loss. The published images of machine control items are tested, and the results show that the proposed algorithm has good destruction-resistance.2023 International Conference on Applied Physics and Computing (ICAPC)90This project intends to use electrical control methods to collect massive amounts of various types of information from the base network, and analyze it through big data and artificial intelligence methods to obtain basic network characteristic data.2023 International Conference on Applied Physics and Computing (ICAPC)['Weiming Tao', 'Yiting Yang']2023-12-27T00:00:00https://www.semanticscholar.org/paper/bd4615237e6e8a47b1c8aefb54d90e878c016b16 176bed1c799ed9fe3e16554390148e63698f4762Resenha do livro: MITCHELL, Melaine. Artificial intelligence: A guide for thinking humans. New York: Ferrar, Straus and Giroux, 2019.Principia: an international journal of epistemology00Principia: an international journal of epistemology['Eros Moreira de Carvalho']2023-12-27T00:00:00https://www.semanticscholar.org/paper/176bed1c799ed9fe3e16554390148e63698f4762 a96499949ad9d849d6544679b2bd6879661e4aa6GPT-4 Turbo represents a new milestone in the development of artificial intelligence (AI), providing unique opportunities for API integration into proprietary developments. This algorithm, based on the GPT-4 architecture, not only improves the quality of natural language, but also provides high performance and efficiency in a wide variety of tasks. In this article, we will look at the key characteristics of the GPT-4 Turbo, as well as consider the prospects for its implementation through the API in various areas.Vestnik of M. Kozybayev North Kazakhstan University00This article will look at the key characteristics of the GPT-4 Turbo, as well as consider the prospects for its implementation through the API in various areas.Vestnik of M. Kozybayev North Kazakhstan University['B. R. Tanatova', 'V. P. Kulikov']2023-12-27T00:00:00https://www.semanticscholar.org/paper/a96499949ad9d849d6544679b2bd6879661e4aa6 38479a57d2a71aa2f56af1fc421299b39a5ac551As of the onset of 2023, ChatGPT has emerged as the predominant Artificial Intelligence (AI) tool, finding extensive application across various sectors and rapidly becoming an integral part of everyday work and domestic activities. However, the widespread adoption of ChatGPT has been accompanied by instances of misuse, wherein individuals have utilized the tool for unauthorized data access or engaged in activities deemed disruptive or malicious in nature. Consequently, a discernible trend has emerged wherein cyberattacks are increasingly being propelled by the capabilities of AI. This escalation in AI-driven cyber threats is evidenced by the misuse of generative AI tools, which are now being harnessed by attackers for activities such as crafting deceptive phishing emails, deploying malware designed for keystroke monitoring, and developing rudimentary yet effective ransomware code. This shift underscores the growing sophistication of cyber threats facilitated by the misuse of advanced AI technologies, necessitating a comprehensive understanding of the associated risks and the implementation of robust cybersecurity measures to mitigate potential harms.International Journal of Information Security and Cybercrime00This escalation in AI-driven cyber threats is evidenced by the misuse of generative AI tools, which are now being harnessed by attackers for activities such as crafting deceptive phishing emails, deploying malware designed for keystroke monitoring, and developing rudimentary yet effective ransomware code.International Journal of Information Security and Cybercrime['Gabriela TOD-RĂILEANU', 'I. Bacivarov']2023-12-27T00:00:00https://www.semanticscholar.org/paper/38479a57d2a71aa2f56af1fc421299b39a5ac551 8d76d358303181e3985ffadcd1bfb2f6e5cb96bfThis look at investigates the effect of artificial intelligence (AI) on microgrid overall performance thru a quantitative evaluation of strength performance, reliability, and real-time optimization metrics. Two hypothetical microgrid systems, System A and System B, are examined, revealing that AI-pushed power management in System A outcomes in superior effects in comparison to System B. Descriptive facts exhibit that System A reveals higher strength efficiency (85.2%), increased reliability indices, and more advantageous actual-time adaptability, showcasing the capacity advantages of AI integration. These findings align with current literature, emphasizing the transformative position of AI in optimizing decentralized energy structures. The look at indicates that making an investment in AI technologies for microgrid power management holds promise for attaining sustainability and resilience. Future research need to consciousness on empirical research with actual-global facts to validate these findings.Journal Dimensie Management and Public Sector130The look at indicates that making an investment in AI technologies for microgrid power management holds promise for attaining sustainability and resilience and needs to be validated on empirical research with actual-global facts to validate these findings.Journal Dimensie Management and Public Sector['Mainaa Oudinga']2023-12-27T00:00:00https://www.semanticscholar.org/paper/8d76d358303181e3985ffadcd1bfb2f6e5cb96bf 2a7e93c58f3a57b34d1d195dfa9617a36789d1b2E-HRM (Electronic Human Resources Management), which is derived from the concept of HRM (Human Resources Management) plays a significant role in automating certain key processes in the department of human resources. One of the modules of E-HRM is the training or the learning module, which when combined with a digital source turns out to be an E-Learning (Electronic Learning) or E-Training (Electronic Training) module. This is a transformation of converting the learning platform from an offline to an online mode. The organizations to increase their level of training from their employees should look for a fast-paced solution at a shorter turn-around time and the prime way to perform such a strategy is to automate the whole process of training and predict the training need and outcome. This research paper is focused on two aspects of e-learning i.e., how an e-learning system is collaborated with an intelligent system in the form of Artificial Intelligence and the other aspect is how an employee turn over data fetched from organizations in the IT (Information Technology) sector can help understand the real requirements of learning among employees in the IT organizations.THE SCIENTIFIC TEMPER00Two aspects of e-learning are focused on i.e., how an e-learning system is collaborated with an intelligent system in the form of Artificial Intelligence and how an employee turn over data fetched from organizations in the IT (Information Technology) sector can help understand the real requirements of learning among employees in the IT organizations.The Scientific Temper['Bajeesh Balakrishnan', 'Swetha A. Parivara']2023-12-27T00:00:00https://www.semanticscholar.org/paper/2a7e93c58f3a57b34d1d195dfa9617a36789d1b2 003037e91d89232e13dc45dc8847fc78d11b1cf9The intersection of nonlinear analysis and artificial intelligence (AI) presents a promising frontier in the rapidly changing field of technology, with the potential to yield ground-breaking solutions in a multitude of fields. In order to investigate how different disciplines may work together to transform technological progress, this study explores the synthesis of these fields. Deeper comprehension of complicated patterns and behaviours is made possible by the intersection of AI and nonlinear analysis, which can both model complex systems and events. This combination has the potential to go beyond conventional linear thinking and open doors to solve complex problems. Through the utilisation of AI's learning skills to augment nonlinear models, a novel approach to problem-solving is revealed. The purpose of the paper is to examine the applications of this synergy in various disciplines. The combination of nonlinear analysis and AI holds the potential to transform a number of industries, from banking and predictive analytics to biological systems research and autonomous vehicle development. This investigation also includes the societal and ethical ramifications of using such cutting-edge technologies. It aims to provide a thorough understanding of the factors necessary for the ethical integration of these advances into our lives by delving into concerns of privacy, prejudice, and responsible use. This study tries to give academics, engineers, and innovators a full road map by looking at recent advances and possible future possibilities. It seeks to stimulate novel research directions, interdisciplinary teamwork, and the advancement of ground-breaking technical innovations that have the potential to fundamentally alter our understanding of and interactions with the environment around us.Advances in Nonlinear Variational Inequalities210The intersection of nonlinear analysis and artificial intelligence presents a promising frontier in the rapidly changing field of technology, with the potential to yield ground-breaking solutions in a multitude of fields and a novel approach to problem-solving is revealed.Advances in Nonlinear Variational Inequalities['Et al. Nilesh P. Sable']2023-12-27T00:00:00https://www.semanticscholar.org/paper/003037e91d89232e13dc45dc8847fc78d11b1cf9 38c6a9d00ec070f361484f0c23f15ce955f06ffb In this paper, we constructed an artificial intelligence model of environment perception and human body perception, and after filtering the irrelevant information in the environment, we collected and analyzed the three-dimensional information of the environment to realize the intelligent perception of the environment. Aiming at the transient noise that may occur in the robot’s operating environment, a time threshold is added on the basis of the traditional double threshold endpoint detection algorithm, which effectively eliminates the influence of transient noise on endpoint detection and realizes non-contact continuous speech recognition. Then the data of the perceived human body was analyzed to understand the perception of the human body by artificial intelligence. Finally, a model for museum public cultural innovation services was constructed based on AI, and the effects of AI on public cultural services were analyzed. The results show that people’s overall perception of intelligent services is 4.305 points, and the perceived functionality, perceived ease of use, perceived pleasantness, and level of intelligence have a significant positive correlation with the quality of public cultural services, with β-values of 0.286, 0.206, 0.068, and 0.378, respectively, and p<0.05.Applied Mathematics and Nonlinear Sciences90An artificial intelligence model of environment perception and human body perception was constructed, and after filtering the irrelevant information in the environment, the three-dimensional information of the environment was collected and analyzed to realize the intelligent perception of the environment.Applied Mathematics and Nonlinear Sciences['Zhenyuan Yang', 'Mingming Xia', 'Xinxin Wan', 'Miaobei Wang', 'Wenrui Tang']2023-12-27T00:00:00https://www.semanticscholar.org/paper/38c6a9d00ec070f361484f0c23f15ce955f06ffb e104d0a17a7fc455ba7a4f559946902a463d1f62This study reports a novel hybrid model for the prediction of six critical process variables of importance in an industrial-scale FCC (fluid catalytic cracking) riser reactor: vacuum gas oil (VGO) conversion, outlet riser temperature, light cycle oil (LCO), gasoline, light gases, and coke yields. The proposed model is developed via the integration of a computational particle-fluid dynamics (CPFD) methodology with artificial intelligence (AI). The adopted methodology solves the first principle model (FPM) equations numerically using the CPFD Barracuda Virtual Reactor 22.0® software. Based on 216 of these CPFD simulations, the performance of an industrial-scale FCC riser reactor unit was assessed using VGO catalytic cracking kinetics developed at CREC-UWO. The dataset obtained with CPFD is employed for the training and testing of a machine learning (ML) algorithm. This algorithm is based on a multiple output feedforward neural network (FNN) selected to allow one to establish correlations between the riser reactor feeding conditions and its outcoming parameters, with a 0.83 averaged regression coefficient and an overall RMSE of 1.93 being obtained. This research underscores the value of integrating CPFD simulations with ML to optimize industrial processes and enhance their predictive accuracy, offering significant advancements in FCC riser reactor unit operations.Processes390A novel hybrid model for the prediction of six critical process variables of importance in an industrial-scale FCC riser reactor: vacuum gas oil (VGO) conversion, outlet riser temperature, light cycle oil (LCO), gasoline, light gases, and coke yields is reported.Processes['Jansen Gabriel Acosta-López', 'H. de Lasa']2023-12-27T00:00:00https://www.semanticscholar.org/paper/e104d0a17a7fc455ba7a4f559946902a463d1f62 49a0d5f6ab6520560e58424ba19edf1032343321 The research aims to investigate Artificial Intelligence (AI) and curriculum prospects for elementary school education. The study focused on identifying specific areas within curriculum where AI can be incorporated and assessed the prospects of AI which may enhance leaning experiences for elementary school students. Descriptive research design was used. Quantitative approach adopted for data collection.  There were 70 participants including school leadership and teachers from elementary schools by using convenient sampling. AI and curriculum are interdependent variables. The study showed that the broad thoughtful opportunities and challenges associated with AI in elementary education. Results of the depicted that majority of the respondents agreed with the idea of introducing AI into curriculum which is helpful for the literacy of AI education. Results reflected that curriculum modification are needed continuously according to need of modern era. AI can monitor the usefulness of curriculum and identify specific areas. AI made a significant addition in optimization of contents, teaching methods and facilitates multimodal teaching style to increase the creativity, and innovation and problem solving skills. AI education in elementary school curriculum can better train students for 21st century and increase job opportunities for students through AI and technology integration in education. Pakistan journal of humanities and social sciences170The study showed that the broad thoughtful opportunities and challenges associated with AI in elementary education and the majority of the respondents agreed with the idea of introducing AI into curriculum which is helpful for the literacy of AI education.Pakistan Journal of Humanities and Social Sciences['Aamir Ali Rathore', 'Nayyar Sultana', 'Syed Jawad Zareen', 'Adeel Ahmed']2023-12-27T00:00:00https://www.semanticscholar.org/paper/49a0d5f6ab6520560e58424ba19edf1032343321 0790fa5a47875d62fcd5c8c406ca3d427baf8d70The text examines how the integration of technology and artificial intelligence into school education fundamentally alters classical notions of the role and functions of education held by representatives of various scientific disciplines. The literature review is structured around the following research questions: 1) How do technology (including artificial intelligence) reshape the sociologist Emile Durkheim's thesis on the authority of the teacher and their role in the socialization of students?; 2) How does the presence of "superintelligent" technology relate to psychologist Abraham Maslow's theory of motivation and self-actualization?; and 3) How do technology weaken the role of school education in addressing societal inequalities – an idea, advocated by the philosopher John Dewey and his daughter and educator Evelyn Dewey? The report aims to serve as a starting point for future interdisciplinary empirical research in school education, with the goal of finding comprehensive and effective solutions for how technology and artificial intelligence can positively impact school education.Filosofiya-Philosophy00The report aims to serve as a starting point for future interdisciplinary empirical research in school education, with the goal of finding comprehensive and effective solutions for how technology and artificial intelligence can positively impact school education.Filosofiya-Philosophy['Vesselina Kachakova']2023-12-27T00:00:00https://www.semanticscholar.org/paper/0790fa5a47875d62fcd5c8c406ca3d427baf8d70 e2960506f5e69fe2b8e5bbf67025580e54431e39Applied Biosafety91Applied Biosafety['Steph Batalis', 'Caroline Schuerger', 'G. Gronvall', 'Matthew E. Walsh']2023-12-27T00:00:00https://www.semanticscholar.org/paper/e2960506f5e69fe2b8e5bbf67025580e54431e39 33c7aa6279108f1037eaecb24f0092ca6a2041a4 This paper completes the implementation of the risk control module of the international trade investment decision-making module through the construction process of the programmed international trade investment platform. Combining the decision tree algorithm and logistic regression algorithm to categorize and screen the factors affecting the fluctuation of international trade stock prices to obtain the international trade market fundamentals and technical factors that have a significant impact on international trade stock prices. The Kalman filter model is applied to improve the effective market factors screened so that the factor selection of the international trade investment model is more advantageous. After constructing the platform, in order to reflect its convenience, the RE, OLS, TI, TVD and BC models were used to analyze the export trade efficiency of the home country, respectively. The results show that the degree of facilitation of the host country’s trade and investment platform is positively correlated with the loss of the home country’s export trade efficiency (0.043), which is statistically significant at the 1% significance level, and that the loss of the home country’s export trade efficiency increases by 4.3% for every 1 unit increase in the degree of facilitation of the host country’s trade and investment platform. The construction of the international trade and investment platform increases the facilitation of export trade from trading partner countries to the host country, which indirectly increases the export trade efficiency loss of the home country.Applied Mathematics and Nonlinear Sciences140The results show that the degree of facilitation of the host country’s trade and investment platform is positively correlated with the loss of the home country’s export trade efficiency, and that the loss of the home country’s export trade efficiency increases by 4.3% for every 1 unit increase in the degree of facilitation of the host country’s trade and investment platform.Applied Mathematics and Nonlinear Sciences['Nan Yan']2023-12-27T00:00:00https://www.semanticscholar.org/paper/33c7aa6279108f1037eaecb24f0092ca6a2041a4 a8d7344dea03ec45b4a200b66a537b7638ea0168Digital Society370Digit. Soc.['John Symons', 'Syed Abumusab']2023-12-27T00:00:00https://www.semanticscholar.org/paper/a8d7344dea03ec45b4a200b66a537b7638ea0168 11fbcbe3dc85fbb88c1ce5084a59c720e6f729bdABSTRACTRevue d'Intelligence Artificielle220Revue d'Intelligence Artificielle['Wenjuan Jiang']2023-12-27T00:00:00https://www.semanticscholar.org/paper/11fbcbe3dc85fbb88c1ce5084a59c720e6f729bd 3c796bf500712ac700f0c982a8f509ad8c0cbbc2Universum:Technical sciences00Universum:Technical sciences['Anna Slitskaia']2023-12-27T00:00:00https://www.semanticscholar.org/paper/3c796bf500712ac700f0c982a8f509ad8c0cbbc2 28edb8ef2cc7d4c2566e0d8268ea6b2bc1482d2fThis distributed architecture of intelligent manufacturing based on CORBA, which is suitable for networked manufacturing. The core problems of object-oriented CAPP are deeply studied, including the universal component information transmission mechanism under network environment, the universal derived process design, the universal manufacturing process design, etc. We obtain the resource search service of all enterprises that can be processed in the network through ORB, and analyze and search the data of network manufacturing enterprises. Then, the internal evaluation method of this method is used to select the candidate enterprises, and a collaborative process scheme design organization based on network is formed. An optimal layout method based on ant network is proposed, and the optimal layout of workpieces is realized by optimizing the layout of the layout machine. Finally, the project will develop the relevant prototype system, and take the practical application as easy to use.2023 International Conference on Applied Physics and Computing (ICAPC)100This distributed architecture of intelligent manufacturing based on CORBA, which is suitable for networked manufacturing, obtains the resource search service of all enterprises that can be processed in the network through ORB, and analyze and search the data of network manufacturing enterprises.2023 International Conference on Applied Physics and Computing (ICAPC)['Lili Zhu', 'Hongxiang Yang', 'Juan Tian']2023-12-27T00:00:00https://www.semanticscholar.org/paper/28edb8ef2cc7d4c2566e0d8268ea6b2bc1482d2f 0a6384f10ec5ac4a449a9e37614732d52fec7559<jats:p>-</jats:p>Medical ultrasonography00Medical ultrasonography['Daniela Fodor']2023-12-27T00:00:00https://www.semanticscholar.org/paper/0a6384f10ec5ac4a449a9e37614732d52fec7559 4714d041b07332b3d6afce6fe443e4b001ad25b3Journal of Computacion y Sistemas00Computación y Sistemas (CyS)['O. López-Ortega', 'Félix Agustín Castro Espinoza', 'O. Domínguez-Ramírez', 'Shani Ioana López-Popa']2023-12-27T00:00:00https://www.semanticscholar.org/paper/4714d041b07332b3d6afce6fe443e4b001ad25b3 0d022768cd5941c60c2c35d1a4b387f8c2c8b973The research was conducted with the aim of finding out the effect of human resource competence, artificial intelligence-based accounting information systems on employee performance (study at PT Bank Syariah Indonesia Tbk in Langsa City). This type of research is quantitative, the data source used is primary data. The population and sample for this research are employees at PT Bank Syariah Indonesia Tbk in Langsa City, totaling 120 people as the population, and 96 respondents as the sample in this research. The data obtained was analyzed using SPSS (Statistical Package For Social Sciences) analysis techniques. The equation model analyzed is multiple linear regression analysis, classical assumption testing and hypothesis testing (T test, F test and coefficient of determination (R2)). The research results show that partially human resource competency, artificial intelligence-based accounting information systems have a significant positive effect on employee performance. Simultaneously, all independent variables, namely human resource competency, artificial intelligence-based accounting information systems together have a significant positive influence on employee performance. The limitations of this research are only limited to discussing human resource competencies, artificial intelligence-based accounting information systems on employee performance. Research should be developed with various other variables to find out how other variables influence employee performance. It is recommended that future researchers conduct research on different types of companies or different locations and add other variables that influence employee performance.Jurnal Penelitian Ekonomi Akuntansi20The research results show that partially human resource competency, artificial intelligence-based accounting information systems have a significant positive effect on employee performance.Jurnal Penelitian Ekonomi Akuntansi (JENSI)['Indi Yunita', 'Tuti Meutia', 'Iqlima Azhar']2023-12-27T00:00:00https://www.semanticscholar.org/paper/0d022768cd5941c60c2c35d1a4b387f8c2c8b973 e58b71c6fb5c4293e83ad186fad459bc4f6de921As artificial intelligence (AI) increasingly becomes an integral part of our societal and individual activities, there is a growing imperative to develop responsible AI solutions. Despite a diverse assortment of machine learning fairness solutions is proposed in the literature, there is reportedly a lack of practical implementation of these tools in real-world applications. Industry experts have participated in thorough discussions on the challenges associated with operationalising fairness in the development of machine learning-empowered solutions, in which a shift toward human-centred approaches is promptly advocated to mitigate the limitations of existing techniques. In this work, we propose a human-in-the-loop approach for fairness auditing, presenting a mixed visual analytical system (hereafter referred to as 'FairCompass'), which integrates both subgroup discovery technique and the decision tree-based schema for end users. Moreover, we innovatively integrate an Exploration, Guidance and Informed Analysis loop, to facilitate the use of the Knowledge Generation Model for Visual Analytics in FairCompass. We evaluate the effectiveness of FairCompass for fairness auditing in a real-world scenario, and the findings demonstrate the system's potential for real-world deployability. We anticipate this work will address the current gaps in research for fairness and facilitate the operationalisation of fairness in machine learning systems.IEEE Transactions on Artificial Intelligence361This work proposes a human-in-the-loop approach for fairness auditing, presenting a mixed visual analytical system (hereafter referred to as 'FairCompass'), which integrates both subgroup discovery technique and the decision tree-based schema for end users.ArXiv['Jessica Liu', 'Huaming Chen', 'Jun Shen', 'Kim-Kwang Raymond Choo']2023-12-27T00:00:00https://www.semanticscholar.org/paper/e58b71c6fb5c4293e83ad186fad459bc4f6de921 10548bbe9680ac031ed77cc97157995bc81a5446Automated machine learning (AutoML) and neural architecture optimization (NAO) represent pivotal components in the landscape of machine learning and artificial intelligence. This paper extensively explores these domains, aiming to delineate their significance, methodologies, cutting-edge techniques, challenges, and emerging trends. AutoML streamlines and democratizes machine learning by automating intricate procedures, such as algorithm selection and hyperparameter tuning. Conversely, NAO automates the design of neural network architectures, a critical aspect for optimizing deep learning model performance. Both domains have made substantial advancements, significantly impacting research, industry practices, and societal applications. Through a series of experiments, classifier accuracy, NAO model selection based on hidden unit count, and learning curve analysis were investigated. The results underscored the efficacy of machine learning models, the substantial impact of architectural choices on test accuracy, and the significance of selecting an optimal number of training epochs for model convergence. These findings offer valuable insights into the potential and limitations of AutoML and NAO, emphasizing the transformative potential of automation and optimization within the machine learning field. Additionally, this study highlights the imperative for further research to explore synergies between AutoML and NAO, aiming to bridge the gap between model selection, architecture design, and hyperparameter tuning. Such endeavors hold promise in opening new frontiers in automated machine learning methodologies.THE SCIENTIFIC TEMPER00This study highlights the imperative for further research to explore synergies between AutoML and NAO, aiming to bridge the gap between model selection, architecture design, and hyperparameter tuning, and holds promise in opening new frontiers in automated machine learning methodologies.The Scientific Temper['Pravin P. Adivarekar1', 'Amarnath Prabhakaran A', 'Sukhwinder Sharma', 'Divya P', 'Muniyandy Elangovan', 'Ravi Rastogi']2023-12-27T00:00:00https://www.semanticscholar.org/paper/10548bbe9680ac031ed77cc97157995bc81a5446 7aa3fb751d52260c4b8e2eb06cc47dbc22eceffeIntroduction. The article examines the problem of the relationship between law and ethics in their normative manifestations. The position is substantiated that ethical regulation is much broader than legal regulation, but any legal regulation is based on an ethical principle. That is, law is the minimum of ethics. This is their similarity, as well as the fact that both ethical and legal regulation (and a number of others, for example, in the theological sphere) are social regulation. Methods. This research is based on the use of the general scientific dialectical method of cognition, which determined the use of general philosophical (analysis, synthesis, analogy) and formal logical methods. In turn, the specifics of the problem studied in the article determined the use of private scientific methods: historical-legal, comparative-legal, formal-legal, structural-functional, systemic analysis and interpretation of legal norms, etc. Results. The implementation of legal regulations, compliance with the rules of law is ensured by the force of coercion, behind which stands the state and its relevant institutions. Therefore, legal regulation is strict regulation, “brute force”. And ethical regulation, compliance with ethical standards is ensured by conviction, first of all, on the part of the individual himself, “included” in the corresponding society, where ethical obligations are formed, which over time acquire normative content. Therefore, ethical regulation is soft regulation, “soft power”. In developed social societies, naturally, and in Russian too, “soft” regulation, normatively enshrined, occupies a dominant position. The article notes that it is more reliable, long-lasting, more fundamentally determines the rules of behavior of the subjects of its influence and is much less subject to market fluctuations than the legal one. Therefore, it is developing rapidly: both at the international level, and abroad, and in Russia, where in recent years codified acts have been developed and adopted in the field of normative regulation of ethical rules of behavior: in the system of executive authorities (including law enforcement agencies), in the stratum of free people professions, in the business sector, etc. Particular attention in the article is given to ethical regulation in the sphere of professional, personal and other life activities of judges. Discussion and Сonclusion. The article substantiates that an indispensable attribute of “soft power”, as in the field of legal regulation, is responsibility for non-compliance with relevant regulatory requirements. But at the same time, mixing legal (disciplinary, first of all) and ethical responsibility is unacceptable. Using the example of such confusion regarding Russian judges, due to current legislation, the article shows the negative consequences of the corresponding law enforcement.Pravosudie / Justice00Pravosudie / Justice['M. Kleandrov']2023-12-26T00:00:00https://www.semanticscholar.org/paper/7aa3fb751d52260c4b8e2eb06cc47dbc22eceffe 03a03fb537e37ad271edeb982436b172758dec6fIn the modern legal space, preliminary investigation plays an important role in ensuring the fairness and effectiveness of criminal prosecution. The author of the article analyzes the current legislation and identifies the problems faced by law enforcement agencies during the preliminary investigation. The purpose of this study is aimed at determining the prospects for the development of legislation in terms of the legal regulation of preliminary investigation, taking into account modern challenges and requirements. The article identifies the key aspects that require changes and improvements in legislation in order to ensure a fair and effective preliminary investigation. As a result of the study of the article, conclusions are drawn about the need for changes and additions in domestic legislation in terms of the legal regulation of the preliminary investigation. Special attention is paid to attracting the experience and best practices of other countries, which can be useful in the development and implementation of new regulations.Economics. Sociology. Law.00Economics. Sociology. Law.['Dmitry Ismailov', 'Karina Kurbachevskaya']2023-12-26T00:00:00https://www.semanticscholar.org/paper/03a03fb537e37ad271edeb982436b172758dec6f 3be2ea613de27b3463db9b72c4e0627d2e4e4478В статье представлен обзор и сравнительный анализ инструментов регулирования российской экономики, начиная с 2015 г. и по настоящее время в рамках Основных направлений единой государственной денежно-кредитной и финансовой политики, раскрыты их взаимосвязь и влияние на макроэкономические показатели развития российской экономики. Особое внимание уделено инструментам нетрадиционной денежно-кредитной политики, антикризисным инструментам финансовой политики государства в условиях неопределенности, а также инструментам макропруденциального регулирования в целях обеспечения финансовой стабильности. We introduce a review and comparative analysis of the tools of regulating Russian economy since 2015 until now within Main directions of government monetary and financial policy; we also reveal their interconnection as well as the influence on macroeconomic indicators of Russian economy development. Special attention is paid to the tools of unconventional monetary policy, crisis tools of government financial policy under uncertainty as well as tools of macroprudential regulation to secure financial stability.ЖУРНАЛ ПРАВОВЫХ И ЭКОНОМИЧЕСКИХ ИССЛЕДОВАНИЙ00ЖУРНАЛ ПРАВОВЫХ И ЭКОНОМИЧЕСКИХ ИССЛЕДОВАНИЙ['Валерия Эдуардовна Кроливецкая']2023-12-26T00:00:00https://www.semanticscholar.org/paper/3be2ea613de27b3463db9b72c4e0627d2e4e4478 ab3c443a46b78cc74fc63281d6f469351184722eThe Indonesian law on data protection remains subpar since these rules are dispersed around the country and solely adhere to the main provisions of each legislation. The purpose of this study is to investigate the use of artificial intelligence (AI) as a tool for safeguarding personal data and to determine whether it is necessary for the Indonesian government to adopt a specific regulation to protect personal data. A statutory approach and a comparative legal approach are used in this study. Based on the research, it suggests the Indonesian government to prioritize on adopting and enacting regulation on personal data security/protection. Utilizing AI's capabilities is another way to optimize the work put into safeguarding personal data.Asian Journal of Engineering, Social and Health90Investigation of the use of artificial intelligence (AI) as a tool for safeguarding personal data and whether it is necessary for the Indonesian government to adopt a specific regulation to protect personal data suggests the Indonesian government to prioritize on adopting and enacting regulation on personal data security/protection.Asian Journal of Engineering, Social and Health['Aditya Restu Hapriyanto']2023-12-26T00:00:00https://www.semanticscholar.org/paper/ab3c443a46b78cc74fc63281d6f469351184722e d2bf2a5715549888728aae5a461490e5e2f9e0dbThe study’s theoretical basis was the concepts of the central role of the self-regulatory system in mental regulation of mental states. The mental regulatory system is a structure of relations between characteristics of consciousness: representation, reflection, experiences, semantic structures, mental (subjective) experience, whereas the self-system serves as an integrator in stipulating the regulation of one’s states by the structures of consciousness. The researchers used 24 techniques of diagnostics of mental structures and personality characteristics, as well as an originally developed questionnaires. In their study of mental states and their self-regulation efficiency the authors used as an example students’ various educational activity: classes, workshops, and exams. The research revealed that a high level of self-esteem and self-assessment are connected with constructive coping strategies, which contributes to the overall efficiency of the students’ self-regulation of their states. The study revealed the specifics of how the components of the self-system interrelate with reflexive, meaningful structures affecting regulatory processes: students with high self-esteem achieve the maximum self-regulation efficiency if they combine a high level of meaningful-life orientations and retrospective reflection. The authors gained data that a person’s psychological qualities and the self-system are connected: the res­pondents with a high level of self-concept typically have such personality traits as sociabi­lity, emotional stability, expressiveness and effective self-control. The authors found that the correlation between the structures of consciousness, indicators of regulatory processes, and personality traits are different in different situations depending on the stress: in the everyday classroom situation, the indicators of the current mental state and self-regulation efficiency are mostly connected with the self-system components, while at exams the role of the perso­nal self-actualization strengthens.Russian Foundation for Basic Research Journal Humanities and social sciences100Russian Foundation for Basic Research Journal. Humanities and social sciences['Aleksandr Prokhorov', 'A. Chernov', 'M. Kartasheva']2023-12-26T00:00:00https://www.semanticscholar.org/paper/d2bf2a5715549888728aae5a461490e5e2f9e0db 639623ab74f9a88874b3b995447ab99b0cc0ae4aOn Wednesday, 8 November 2023 at about 4am, approximately 10 million Optus retail and 400,000 business customers lost network access as a result of the IP Core network shutdown. Optus stated that the cause of the network outage was a routine software upgrade that led to routing information updates from an international peering network causing key routers to disconnect from the network. This paper provides an analysis of the national outage, what information is needed to fully understand what occurred, and considers the lessons that might be learned.Journal of Telecommunications and the Digital Economy00An analysis of the national outage of the IP Core network shutdown provides an analysis of what information is needed to fully understand what occurred, and considers the lessons that might be learned.Journal of Telecommunications and the Digital Economy['Mark A. Gregory']2023-12-26T00:00:00https://www.semanticscholar.org/paper/639623ab74f9a88874b3b995447ab99b0cc0ae4a 3491b34368187104f618f2ead45cbed66c536cbdJournal of engineering design224Journal of Engineering Design['Chuyi Zhou', 'Xiyuan Zhang', 'Chunyang Yu']2023-12-26T00:00:00https://www.semanticscholar.org/paper/3491b34368187104f618f2ead45cbed66c536cbd ef57159bb5ba9cfde4465e78adf2b1a4cc93e2f5We discuss the implications of generative AI on education across four critical sections: the historical development of AI in education, its contemporary applications in learning, societal repercussions, and strategic recommendations for researchers. We propose ways in which generative AI can transform the educational landscape, primarily via its ability to conduct assessment of complex cognitive performances and create personalized content. We also address the challenges of effective educational tool deployment, data bias, design transparency, and accurate output verification. Acknowledging the societal impact, we emphasize the need for updating curricula, redefining communicative trust, and adjusting to transformed social norms. We end by outlining the ways in which educational stakeholders can actively engage with generative AI, develop fluency with its capacities and limitations, and apply these insights to steer educational practices in a rapidly advancing digital landscape.arXiv.org360The ways in which educational stakeholders can actively engage with generative AI, develop fluency with its capacities and limitations, and apply these insights to steer educational practices in a rapidly advancing digital landscape are outlined.ArXiv['Kacper Lodzikowski', 'Peter W. Foltz', 'John T. Behrens']2023-12-26T00:00:00https://www.semanticscholar.org/paper/ef57159bb5ba9cfde4465e78adf2b1a4cc93e2f5 aec1b5583c73b562dea9e97439ba6097253470ecThis study experimentally examines the relationship between integrated information sharing, interior designer capability, and AI-enhanced software, particularly CAD software. 409 of 500 construction interior designers participated in the survey. Pearson correlation explored direct and moderating effects. EE(External Environment), TL (Technical Level), CE (Communication Effect) and AI (Artificial Intelligence) as a moderator were positively connected to OS (Content of Integrated Sharing) and QIS (Quality of Integrating Sharing). This study examines interior designers' content and quality integrated sharing using AI-enhanced software. The findings support construction decision-making and strategic planning. This study illuminates the importance of seamless information exchange and its relationship with AI solutions, helping construction companies improve processes and collaboration. This study suggests ways to improve efficiency and innovation in many construction processes, beyond interior design.International Journal of Business Society220The importance of seamless information exchange and its relationship with AI solutions, helping construction companies improve processes and collaboration is illuminated, and ways to improve efficiency and innovation in many construction processes, beyond interior design are suggested.International Journal of Business and Society['Dongkai Qi', 'Othman Mohamed', 'Nor Haniza', 'Binti Ishak']2023-12-26T00:00:00https://www.semanticscholar.org/paper/aec1b5583c73b562dea9e97439ba6097253470ec 0cafd301b9f98f1a77855df851c14236d2d874f0 Purpose This study aims to provide insight of how conference sessions and poster sessions are relevant to libraries. Design/methodology/approach Recently attended IFLA and Internationale Funkaussteilung (IFA) and focused the conference report on generative artificial intelligence (AI). Findings Disappointed in the IFLA and IFA conferences and instead find that the email newsletter from Shelly Palmer is much better in keeping up with exponential growth in ChatGPT and other generative AI tools. Research limitations/implications This study may provide ideas for libraries to experiment with generative AI tools. Originality Value This study is an original report by the authors. All photos were taken by the authors except for a table from the email newsletter of Shelly Palmer, which was included by him from another source. Library Hi Tech News00Insight is provided of how conference sessions and poster sessions are relevant to libraries and may provide ideas for libraries to experiment with generative AI tools.Library Hi Tech News['Martin A. Kesselman', 'Wilson Esquivel']2023-12-26T00:00:00https://www.semanticscholar.org/paper/0cafd301b9f98f1a77855df851c14236d2d874f0 2f8932f4fa4c7a4b0a86d93bd3e0a8da5fcba67eThe emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (EDB), this study collects the price data of AI concept stocks over the past six months, starting from the date of ChatGPT's publication. This study employ Python to model stock price movements for both the momentum and reversal strategies. The goodness of fit is evaluated by comparing the modeled stock prices with the actual stock prices. This study demonstrates that the momentum strategy exhibits greater explanatory power than the contrarian strategy, accurately predicting 84.21% of artificial intelligence concept stocks. However, other studies suggest that while AI concept stocks continue to rise, momentum strategies remain effective, whereas when market sentiment cools down, contrarian strategies become more suitable for Chinese AI concept stocks. Hence, in China, the effectiveness of these strategies may vary depending on the prevailing market conditions.Applied and Computational Engineering00This study demonstrates that the momentum strategy exhibits greater explanatory power than the contrarian strategy, accurately predicting 84.21% of artificial intelligence concept stocks over the past six months.Applied and Computational Engineering['Yinuo Zhao']2023-12-26T00:00:00https://www.semanticscholar.org/paper/2f8932f4fa4c7a4b0a86d93bd3e0a8da5fcba67e 646cffb340380c7e3b65e055e234cad7bf2369deArtificial Intelligence (AI) allows computer programs to learn from experience through iterative processing and algorithmic training and represents human intelligence. Now, AI has become a hot topic and important debates have risen like, how it might affect the job market and what is the end of global civilization. Many people will lose their jobs due AI. On the other hand, few technology lovers think that it will lead to the creation of lots of diversified and interesting jobs. Many experts think that it will have a big impact on the workplace and all aspects of human life in the near future. In reality, AI can make jobs more creative, lucrative, and flexible, and will lead definitely to a more creative and skilled economy. However, history says, that advanced technologies are usually starting to take away more jobs than they create, and unfortunately, this trend will continue. Nowadays, there have been many ideas for how to solve this problem, and there should be updated education and skill develop program with new schooling system and that need to be introduced very soon. As there isn't enough work for everyone in the world, so the whole lifestyle and education system needs to be changed and to reorganize every aspect to make total human resource development system more purposeful. It is an analytical paper to analyze the potentials and challenges of AI along with the consequences of global future and way forward.Proceedings of the International Conference on Industrial Engineering and Operations Management450It is an analytical paper to analyze the potentials and challenges of AI along with the consequences of global future and way forward.Proceedings of the International Conference on Industrial Engineering and Operations Management['Commodore Khandakar Akhter Hossain']2023-12-26T00:00:00https://www.semanticscholar.org/paper/646cffb340380c7e3b65e055e234cad7bf2369de a3ab22905e9ce3c9d8f2c027b83548282f7ecf11House rent is a crucial factor in any country’s socio-economic scenario. This can act as an indicator of the financial and developmental situations of the stakeholders of the real estate business. So far, there have been approaches to predict house rent prices in several regions of Bangladesh, but without any clear explanation of the association of different factors to the prediction and how they are affecting the prices. This study touches on this lack of understanding and proposes an Explainable AI based framework. This framework produces accurate predictions on house rents with small margin of error with regression algorithm and can accurately depict the connection of various demographic features of the dataset by visualizing SHAP values. Our study finds that tree-based algorithms such as Decision Tree, Random Forest, XGBoost, Gradient Boost and Light Gradient Boost performed better at regression analysis on the nonlinear dataset of ours. The final model was a voting ensemble of all the tree-based algorithms, which encompasses the strengths of all the base models. We achieved an MAPE of 11% and R2 Score of 86%. The RMSE and MAE of the ensemble were 4398.23 and 2536.77 respectively. Finally, the SHAP Explainable AI determined how the features were correlated to the prediction and overall rent prices. The research introduces a novel framework for predicting house rents in Bangladesh, offering valuable insights through data preparation, model selection, performance assessments, and interpretability analyses, benefiting both scholars and stakeholders.Proceedings of the International Conference on Industrial Engineering and Operations Management200Proceedings of the International Conference on Industrial Engineering and Operations Management['Taeef Najib', 'Fahim Muntasir', 'Wasif Al Wazed Wasi']2023-12-26T00:00:00https://www.semanticscholar.org/paper/a3ab22905e9ce3c9d8f2c027b83548282f7ecf11 1784c580c6d0570f719c49f3a160aaf981bb48eeAI-powered chatbots have emerged as influential tools in the realm of online shopping, effectively driving digital users toward heightened satisfaction, sustained usage intention and positive electronic word of mouth (e-WOM). This research delves deep into the intricate behavioural dynamics that consumers exhibit in their interactions with AI chatbots. A comprehensive online survey, encompassing 554 respondents who willingly engaged with AI chatbots, was conducted, with a focus on established frameworks like the information systems success (ISS) model, the technology acceptance model (TAM), engagement, and the elicitation of pleasurable feelings. The study’s findings underscore the pivotal role AI chatbots play in elevating user satisfaction and, in turn, predicting positive outcomes. These insights hold immense value for brand managers, offering a nuanced understanding of Indian online shoppers’ behaviour. Furthermore, the study highlights the significant impact of e-WOM generated by AI chatbots within the online shopping domain, further solidifying their role as essential components of digital services in the contemporary landscape. As digital services continue to shape and define modern business operations, AI chatbots have emerged as critical facilitators in enhancing the satisfaction of digital users, making them indispensable for businesses seeking to thrive in the digital realm.  Journal of Telecommunications and the Digital Economy00The study’s findings underscore the pivotal role AI chatbots play in elevating user satisfaction and, in turn, predicting positive outcomes and highlights the significant impact of e-WOM generated by AI chatbots within the online shopping domain.Journal of Telecommunications and the Digital Economy['A. Butt', 'Hassan Ahmad']2023-12-26T00:00:00https://www.semanticscholar.org/paper/1784c580c6d0570f719c49f3a160aaf981bb48ee 72bfcbd5eded2a5909c49b456f363092893386fcThis paper discusses the importance of securing modern society’s critical infrastructure in the face of physical and digital threats. It emphasizes the vulnerabilities introduced by programmable logic controllers (PLCs) and supervisory control and data acquisition (SCADA) systems that highlight the potential of digital twins to enhance security. The paper presents a research contribution aimed at improving the integrity of data exchange among PLCs equipped with digital twins in critical infrastructure systems. It leverages artificial intelligence (AI) algorithms to detect and mitigate different security attacks, such as denial of service (DoS), injection attacks, and data tampering attacks. The proposed framework efficiently classifies the malicious and non-malicious data of digital twin-based critical infrastructure. Further, the trained AI model is deployed on a critical infrastructure’s intrusion detection system that continuously monitors network traffic and system logs in real time, identifying unusual patterns or behaviors that may indicate an intrusion. Further, the performance of the proposed framework is evaluated using accuracy, log loss, and validation curves.Conference on Information and Knowledge Technology240The proposed framework efficiently classifies the malicious and non-malicious data of digital twin-based critical infrastructure and leverages artificial intelligence (AI) algorithms to detect and mitigate different security attacks, such as denial of service (DoS), injection attacks, and data tampering attacks.2023 14th International Conference on Information and Knowledge Technology (IKT)['Tanisha Patel', 'N. Jadav', 'Tejal Rathod', 'S. Tanwar', 'Deepak Garg', 'Hossein Shahinzadeh']2023-12-26T00:00:00https://www.semanticscholar.org/paper/72bfcbd5eded2a5909c49b456f363092893386fc 160e70e1e14bec32c8a02b20c4c7e8999c47ee88Chemistry of Materials71Chemistry of Materials['James R. Neilson']2023-12-26T00:00:00https://www.semanticscholar.org/paper/160e70e1e14bec32c8a02b20c4c7e8999c47ee88 8be93f5b0cad79b1e1de09af88cb897c7aef0738For Lacan, guilt arises in the sublimation of ab-sens (ab-sense) into the symbolic comprehension of sen-absexe (sense without sex; sense in the deficiency of sexual relation), or in the maturation of language to sensibility through the effacement of ‘sex.’ While, as Slavoj Žižek also points out in a 2023 article regarding ChatGPT, the split subject always misap-prehends the true reason for guilt’s manifestation, such guilt at best provides a sort of evidence for the inclusion of the subject in the order of language, thereby acting as a necessary, even enjoyable mark of the subject’s coherence (or, more importantly, the subject’s division from incoherence/ ab-sens ). For Zizek, the perversity ( père -versity) of artificially intelligent chatbots lies precisely here, in their appearance as evidently novel modes for en-joying the displacement of one’s guilt onto the intelligent machine (“what happens is a form of perverse disavowal: knowing full well that it was the machine, not me, that did the work, I can enjoy it as my own,” Zizek 2023). What Zizek does not elaborate, however, is how the transferred belovedness of guilt is a figure of contemporary life in general—a condition for modernity’s endless reproduction—and the AI chatbot is but one more recent, particularly popular, indication of racial capital’s long entanglement with the unconscious. In this work, the relationship between guilty affects, transference, cultural reproduction, ab-sens , and artificial intelligence is discussed using a reference to Lacan’s later works and seminars, critical data science studies, and Black radical criticism.Critical Humanities120Critical Humanities['Dylan Lackey', 'Katherine Weinschenk']2023-12-26T00:00:00https://www.semanticscholar.org/paper/8be93f5b0cad79b1e1de09af88cb897c7aef0738 04117a75b52df5a7cca65f2111b70790fa3a7172Journal of Entrepreneurial Researchers60Journal of Entrepreneurial Researchers['Eduardo Leite', 'Maria Leite', 'Ana Leite']2023-12-26T00:00:00https://www.semanticscholar.org/paper/04117a75b52df5a7cca65f2111b70790fa3a7172 bfd8e2d0d6b4fbd6c35929176535151d715ae9e6Journal of Decision Systems830Journal of Decision Systems['Amit Sood', 'Amit Kumar Bhardwaj', 'Rajendra Kumar Sharma']2023-12-26T00:00:00https://www.semanticscholar.org/paper/bfd8e2d0d6b4fbd6c35929176535151d715ae9e6 7307e064a5b45dae235cb23d2ea59a32fe5c8671Critical Humanities00Critical Humanities['Puspa Damai̇', 'Barbara Postema']2023-12-26T00:00:00https://www.semanticscholar.org/paper/7307e064a5b45dae235cb23d2ea59a32fe5c8671 bf3fb1d5a423a7c447676b2914b7a535365620f3Critical Humanities00Critical Humanities['Hannah R Turner']2023-12-26T00:00:00https://www.semanticscholar.org/paper/bf3fb1d5a423a7c447676b2914b7a535365620f3 7c2352de41dae6bb48a59ec13062d6f26b45182cINTRODUCTION: Artificial intelligence (AI) could minimize the operator-dependent variation in colonoscopy quality. Computer-aided detection (CADe) has improved adenoma detection rate (ADR) and adenomas per colonoscopy (APC) in randomized controlled trials. There is a need to assess the impact of CADe in real-world settings. METHODS: We searched MEDLINE, EMBASE, and Web of Science for nonrandomized real-world studies of CADe in colonoscopy. Random-effects meta-analyses were performed to examine the effect of CADe on ADR and APC. The study is registered under PROSPERO (CRD42023424037). There was no funding for this study. RESULTS: Twelve of 1,314 studies met inclusion criteria. Overall, ADR was statistically significantly higher with vs without CADe (36.3% vs 35.8%, risk ratio [RR] 1.13, 95% confidence interval [CI] 1.01–1.28). This difference remained significant in subgroup analyses evaluating 6 prospective (37.3% vs 35.2%, RR 1.15, 95% CI 1.01–1.32) but not 6 retrospective (35.7% vs 36.2%, RR 1.12, 95% CI 0.92–1.36) studies. Among 6 studies with APC data, APC rate ratio with vs without CADe was 1.12 (95% CI 0.95–1.33). In 4 studies with GI Genius (Medtronic), there was no difference in ADR with vs without CADe (RR 0.96, 95% CI 0.85–1.07). DISCUSSION: ADR, but not APC, was slightly higher with vs without CADe among all available real-world studies. This difference was attributed to the results of prospective but not retrospective studies. The discrepancies between these findings and those of randomized controlled trials call for future research on the true impact of current AI technology on colonoscopy quality and the subtleties of human-AI interactions.Clinical and Translational Gastroenterology01Computer-aided detection (CADe) has improved adenoma detection rate (ADR) and adenomas per colonoscopy (APC) in randomized controlled trials, but the impact of CADe in real-world settings is still unclear.Clinical and Translational Gastroenterology['M. Wei', 'Shmuel Fay', 'Diana Yung', 'U. Ladabaum', 'U. Kopylov']2023-12-26T00:00:00https://www.semanticscholar.org/paper/7c2352de41dae6bb48a59ec13062d6f26b45182c fa79e334b6061506cefaf1b1b34cade17d41918dChat GPT and Perplexity are two platforms of the many types of chat-based Artificial Intelligence (AI). Both platforms can pamper their users by presenting brief and clear information based on the questions given. The use of chat-based AI as a substitute for human legal assistance is a topic of conversation today. However, the validity of AI is often questioned. By looking at these problems, there needs to be a trial that can see the validity of Chat Gpt answers and any confusion regarding legal issues. The author is interested in measuring the level of validity of answers from the Chat GPT and Perplexity platforms in addressing family law problems. The author takes household problems caused by domestic violence. Then cases of domestic violence that occur will result in a lawsuit for divorce. Based on the questions asked, both platforms are very credible in providing basic information regarding divorce lawsuits, such as absolute competence and relatively appropriate courts to use to file lawsuits. However, both of them are not good at presenting detailed information regarding divorce lawsuits. This is natural, because both platforms are not specifically designed as family legal assistance chatbots. Therefore, a special platform is needed that is aimed at realizing these needs, by utilizing information delivery patterns from both platforms. An AI chatbot designed for these needs will certainly provide many benefits for society, one of which is guidance on filing cases independently. These benefits can also create a low cost court.QISTHOSIA : Jurnal Syariah dan Hukum241A special platform is needed that is aimed at realizing these needs, by utilizing information delivery patterns from both platforms, by utilizing information delivery patterns from both platforms.QISTHOSIA : Jurnal Syariah dan Hukum['Mohammad Bachrul Falah', 'Nerisma Eka Putri']2023-12-26T00:00:00https://www.semanticscholar.org/paper/fa79e334b6061506cefaf1b1b34cade17d41918d 830832d16dde282898c6d188c8024d146b886db8This abstract explores the intersection of Artificial Intelligence (AI) and the Fourth Industrial Revolution (4IR), focusing on the drivers, challenges, and opportunities of this transformative landscape. AI is driving the adoption of advanced technologies like data analytics, machine learning, and deep learning, unlocking unprecedented capabilities. However, AI faces ethical challenges such as fairness, transparency, and accountability, technical limitations, regulatory complexities, and societal impacts like job displacement. Despite these challenges, AI offers vast opportunities across industries, such as personalized healthcare diagnostics and autonomous systems. The future of AI in the 4IR requires a nuanced understanding of these challenges and opportunities, refining AI algorithms, addressing biases, and ensuring ethical deployment. Robust regulatory frameworks, reskilling initiatives, and the synthesis of AI with emerging technologies like quantum computing and edge computing are also crucial. Keywords: Artificial Intelligence; Fourth Industrial Revolution(4ir); Opportunities, Developing Countries, Ethical Challenges.Engineering Science &amp; Technology Journal01The future of AI in the 4IR requires a nuanced understanding of these challenges and opportunities, refining AI algorithms, addressing biases, and ensuring ethical deployment, which is crucial for robust regulatory frameworks, reskilling initiatives, and the synthesis of AI with emerging technologies like quantum computing and edge computing.Engineering Science &amp; Technology Journal['Ibegbulam C.M', 'Olowonubi, J.A', 'Fatounde, S.A', 'Oyegunwa, O.A']2023-12-26T00:00:00https://www.semanticscholar.org/paper/830832d16dde282898c6d188c8024d146b886db8 1f88e84ca9c30de0aaed61be9edf6bf5ba896ad4In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics.Electronics881This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications, and dives into the state-of-the-art architectures and design paradigms that are propelling the field forward.Electronics['Tymoteusz Miller', 'Irmina Durlik', 'E. Kostecka', 'Paulina Mitan-Zalewska', 'Sylwia Sokołowska', 'D. Cembrowska-Lech', 'Adrianna Łobodzińska']2023-12-26T00:00:00https://www.semanticscholar.org/paper/1f88e84ca9c30de0aaed61be9edf6bf5ba896ad4 f15544f207ae4daaed177e1a8398712211b2f6ebBackground : Females benefit from ultrasound screening and diagnosis of breast cancer, and artificial intelligence has enabled the automatic identification of medical conditions on medical imaging. Methods : This study aimed to develop machine learning (ML) and deep learning (DL) models for the detection and classification of breast cancer in a breast ultrasound image (BUSI) and United States (US) ultrasound images datasets and to compare the models’ performance to previous studies. The ultrasound scans were collected from women between the ages of 25 and 75. The dataset contains 780 images with a resolution of 500 × 500 pixels. There were 133 normal images with no cancerous masses, 437 images with cancerous masses, and 210 images with benign masses among the 780 cancerous images in the BUSI dataset whiles the US ultrasound images includes 123 and 109 ultrasound images of malignant and benign breast tumors. Two traditional ML models, random forest (RF) and K-Nearest Neighbor (KNN), as well as a deep learning (DL) model using convolutional neural networks (CNN), were trained to classify breast masses as benign, malignant, or normal. Results : The CNN obtained an accuracy of 96.10%, the RF an accuracy of 61.46%, and the KNN an accuracy of 64.39% with the BUSI dataset. Standard evaluation measures were employed to assess the performance for benignancy, malignancy, and normality classification. Furthermore, the models’ area under the curve-receiver operating characteristics (AUC-ROC) are 0.99 by the CNN, 0.85 by the RF, and 0.65 by the KNN. Conclusions : The study’s findings revealed that DL surpasses conventional ML when it comes to training image datasets; hence, DL is suggested for breast cancer detection and classification. Furthermore, the resilience of the models used in this study overcomes data imbalance by allowing them to train both binary and multiclass datasets.Clinical and Experimental Obstetrics &amp; Gynecology470The study’s findings revealed that DL surpasses conventional ML when it comes to training image datasets; hence, DL is suggested for breast cancer detection and classification.Clinical and Experimental Obstetrics &amp; Gynecology['Stephen Afrifa', 'Vijayakumar Varadarajan', 'Peter Appiahene', 'Tao Zhang']2023-12-26T00:00:00https://www.semanticscholar.org/paper/f15544f207ae4daaed177e1a8398712211b2f6eb b4b71238d222fa5188617c0d60d65056667c064bThe number of cybercrimes has grown considerably in recent years and now poses a serious threat for the national security of states, information systems and personal data of citizens. The reasons for this considerable increase in cybercrime are the change of global policy, the special military operation, and the restrictions connected with the COVID-19 pandemic. The authors examine the importance of using special knowledge and artificial intelligence (AI) in the investigation of cybercrimes. They research the concept of a cybercrime, and analyze the ideas of scholars on strengthening the counteraction to such criminal activities, which have been spreading rapidly in recent years. The authors conclude that cybercrimes are understood as socially dangerous unlawful behavior, carried out with the use of information technologies and inflicting considerable damage on the economic and reputational interests of people, organizations and the state. They present a classification of the most common types of cybercrimes and key methods of carrying them out. They also analyze the research discussion on the contents of special knowledge, which allows the authors to conclude that in modern legal conditions AI, being a new form of special knowledge, requires a new understanding in view of the use of this knowledge in criminal proceedings. Taking into consideration that this format includes machine self-learning, neural networks, evolutionary algorithms, etc., the authors attempt to outline how they can be used for solving complex criminalistic tasks that require a preliminary analysis of large volumes of forensically relevant information, identification of images and regularities, and argumentation of making organizational-procedural decisions. The authors examine the cyberspheres where special technical knowledge is used, such as cybersecurity, computer architecture, reverse engineering, digital forensics, system administration. AI has a tremendous potential for the modern criminal process, its use will make it possible to enhance the effectiveness of investigatory work as it will considerably improve the speed and effectiveness of procedural actions and pre-court proceedings for criminal cases in general. It is, however, necessary to take into account the existing risks and limitations connected with its use in court-investigative practice. The authors conclude that in order to ensure the adequate use of opportunities offered by AI in the criminal process, it is necessary to develop ethical and legal principles of its inclusion in the sphere of special knowledge used in criminal court proceedings for acquiring forensic evidence on criminal cases.Russian Journal of Criminology50In order to ensure the adequate use of opportunities offered by AI in the criminal process, it is necessary to develop ethical and legal principles of its inclusion in the sphere of special knowledge used in criminal court proceedings for acquiring forensic evidence on criminal cases.Russian Journal of Criminology['Vladimir D. Pristanskov', 'Anton Kharatishvili', 'Juliana Evstratova']2023-12-26T00:00:00https://www.semanticscholar.org/paper/b4b71238d222fa5188617c0d60d65056667c064b cf1f3a4a1db111f8e75879423aa592859bc1934dThe purpose of this review is to highlight recent advancements in autonomous vehicles (AVs) and related infrastructure, primarily from artificial intelligence (AI), which makes AVs more appealing to consumers. It is critically analyzed and reviewed how AI can benefit AVs, structure of AVs, and automotive innovation techniques. The paper provides a quick reference for researchers interested in understanding the use of AI in AV research.Highlights in Science Engineering and Technology70The paper critically analyzed and reviewed how AI can benefit AVs, structure of AVs, and automotive innovation techniques, and provides a quick reference for researchers interested in understanding the use of AI in AV research.Highlights in Science, Engineering and Technology['Xinyang Yu']2023-12-26T00:00:00https://www.semanticscholar.org/paper/cf1f3a4a1db111f8e75879423aa592859bc1934d 7e487ba583e102e98eaf5fb5f3a7586a153ccb6dIn recent years, artificial intelligence (AI) has become an important driving force for the development of large enterprise supply chain technology. Through machine learning, natural language processing, computer vision and other technologies, AI can help enterprises improve the efficiency and reliability of supply chains. This article analyzes the impact of AI on the development of large enterprise supply chain technologies and explores its applications in data analysis and forecasting, planning and scheduling, automation and robotics technologies, risk management and anti-fraud for supply chain management. The results show that AI has huge potential to improve the efficiency, transparency and security of large enterprise supply chains. It can help enterprises more accurately analyze massive amounts of supply chain data, predict potential problems, optimize planning and scheduling, improve automation levels with robots, and strengthen risk oversight and fraud prevention through technologies like machine vision.Modern Economics &amp; Management Forum60AI has huge potential to improve the efficiency, transparency and security of large enterprise supply chains and its applications in data analysis and forecasting, planning and scheduling, automation and robotics technologies, risk management and anti-fraud for supply chain management are explored.Modern Economics &amp; Management Forum['Xiaoyu Xue']2023-12-26T00:00:00https://www.semanticscholar.org/paper/7e487ba583e102e98eaf5fb5f3a7586a153ccb6d 1b4bddc3d5697d5ff1eac96682da8550fa6d360c Under the guidance of the new vocational education method of artificial intelligence, this paper divides the skill-based society into skill-based organizations, and constructs a knowledge dynamic equilibrium model based on superimposed knowledge fields. The knowledge-explicit module of skill-based talent flow and the external dynamic equilibrium module are integrated to achieve the evolutionary modeling of knowledge resources in skill-based organizations. Based on the discussion of the new vocational education law, the characteristic models of intellectual resource development, knowledge dynamic growth, group evolution and talent motivation in skill-based organizations are proposed respectively. The simulation analysis of the knowledge dynamic equilibrium network is combined with the analysis of influencing factors to inspire the education of higher vocational colleges and universities under the new vocational education method. As measured by the data, the average path length mean values of the intra-organizational knowledge network when N=160, 260, 330 are 2.2761, 2.4437 and 2.5394, respectively, which indicates that skilled talents have a strong willingness to interact with each other and share knowledge. The construction of skill-based organizations should fully create a strong environment for talent exchange and focus on cultivating students’ willingness and ability to communicate in vocational education.Applied Mathematics and Nonlinear Sciences90This paper divides the skill-based society into skill-based organizations, and constructs a knowledge dynamic equilibrium model based on superimposed knowledge fields that indicates that skilled talents have a strong willingness to interact with each other and share knowledge.Applied Mathematics and Nonlinear Sciences['Xiangling Zou', 'Xiaoxu Chen']2023-12-26T00:00:00https://www.semanticscholar.org/paper/1b4bddc3d5697d5ff1eac96682da8550fa6d360c b2ce7b8d3f0a1662cd6b50c3f1a153b3d4fb466fForensic sciences are an indispensable segment of criminal investigations. Forensics, as a dynamic science that is constantly developing, follows the development of modern scientific trends. The application of artificial intelligence has not bypassed forensic science, which by definition discovers modern scientific methods, adapts them and applies them with the aim of discovering and interpreting (expert) material traces from the scene of a crime. With the initial optimism of the application of artificial intelligence, especially in the development and application of information technologies in forensic databases, comes (un)justified caution. If artificial intelligence were to take over the simulation of shell, cognitive thinking and decision-making more and more over time, the question of ethical responsibility arises. This raises a number of questions, one of the most important of which is who is responsible in the event of an error in the analysis. Furthermore, if artificial intelligence also takes over the interpretations of forensic analyses, who in that case bears the responsibility for a possible complaint about the end result - the opinion. Our paper will deal with the mentioned problems, emphasizing that the European Union, through the ENFSI network, was the first to react in the direction of studying the application of artificial intelligence in forensic sciences, with the strategic document ENFSI - Vision of the European Forensic Science Area 2030 „Improving the reliability and validity of forensic science and encouraging implementation of new technologies“, the most important parts of which will be presented in the paper.Kriminalističke teme00This paper will deal with the mentioned problems, emphasizing that the European Union was the first to react in the direction of studying the application of artificial intelligence in forensic sciences, with the strategic document ENFSI „Improving the reliability and validity of forensic science and encouraging implementation of new technologies“, the most important parts of which will be presented in the paper.Kriminalističke teme['Sanela D Andrić', 'Aleksandar B Ivanović']2023-12-26T00:00:00https://www.semanticscholar.org/paper/b2ce7b8d3f0a1662cd6b50c3f1a153b3d4fb466f 9291e786cbe877654ea81ed7e69a05e84be03d8bThe paper is concerned with the visions and threats of Artificial Intelligence. It characterizes and describes those that, according to experts and scientists , will have a huge impact on the shape, nature and functioning of societies in the next two to three decades. Undoubtedly, one of the significant challenges is the development and application of deep learning. Scientific research in Artificial Intelligence will be accompanied by intensive development of computer vision, convolutional networks and virtual, augmented and mixed reality. In the future, everything digital will be able to fall prey to counterfeiters. The essence and functioning of related Deepfake tools are explained and described in the paper. The development of Artificial Intelligence will result in a wide and versatile application of Biometrics. The paper describes the prospective areas of its use and draws attention to the problems associated with the construction of the oversized databases necessary for this purpose. In the era of a society characteristic of the development of the fourth industrial revolution, theway and means of automobile transportation and the necessary infrastructure will be radically transformed - autonomous vehicles will appear. The paper describes all levels of reference relating to the scope of control of an autonomous vehicle. Effective use of Artificial Intelligence solutions will not be possible without computers with enormous computing power. Theemergence of quantum computers will certainly solve this problem. The essence of quantum computer functioning is described in the next chapter. Artificial Intelligence can successfully perform many tasks better than humans. This will make Artificial Intelligence take on a huge economic value. Positions will be lost for both physical and mental workers. The question then becomes, where are we? Which professions can take over Artificial Intelligence and which ones will not move? What is the future of work done by humans? These issues are addressed in Chapter 9. The prospect of widespread and comprehensive use of Artificial Intelligence solutions will raise the following question, with respect to both humans and society. Can Artificial Intelligence optimize our happiness? The answer to such a question is not an easy one. The paper attempts to answer it. In the age of Robotics and Artificial Intelligence, we will be accompanied by, revolutionary changes in the way humans of entire societies function. There will be an urgent need to define a neweconomic model. Issues clarifying the nature and importance of the essential components of the said model are addressed in this work. Solutions and use of Artificial Intelligence are often accompanied by various kinds of failures. These can have many causes. The paper describes and explains, proposed by Yampolsky Roman, a kind of dysfunction pattern. The results of scientific research andpractical experience that the list of Artificial Intelligence dangers is very extensive. A representative list of risks from Artificial, along with the characteristics of each of them, is presented in the last part of the paper.Studia Informatica. System and information technology180The paper describes and explains, proposed by Yampolsky Roman, a kind of dysfunction pattern, a kind of dysfunction pattern that will result in a wide and versatile application of Biometrics in the age of Robotics and Artificial Intelligence.Studia Informatica. System and information technology['Andrzej Barczak']2023-12-26T00:00:00https://www.semanticscholar.org/paper/9291e786cbe877654ea81ed7e69a05e84be03d8b 2753479dd3715c15d09cfdc311b470b6f013c7e4The research explores the relationship between Artificial Intelligence (AI) and electrification in Africa, focusing on the challenges and emerging trends. The electrification deficit in Africa poses a significant impediment to economic development and social progress. This paper explores the pivotal role that Artificial Intelligence (AI) plays in addressing the challenges associated with electrification initiatives across the African continent. With its capacity for innovation and optimization, AI emerges as a transformative force capable of revolutionizing the planning, deployment, and management of electrification projects in a region characterized by diverse geographical landscapes and economic constraints. The paper investigates the potential of AI in addressing financial barriers associated with electrification projects. By facilitating innovative financing models, reducing operational costs, and attracting investments, AI contributes to creating sustainable and economically viable electrification solutions. The focus extends to decentralized energy systems and microgrids, exploring how AI can empower remote and underserved communities with reliable access to electricity. The socio-economic impact of AI-driven electrification initiatives is also scrutinized, emphasizing the potential for job creation, economic growth, and improved living standards. The paper discusses the importance of capacity building and local empowerment to ensure that AI technologies are effectively integrated into electrification projects while fostering inclusive and sustainable development. It highlights the role of AI in revolutionizing electrification, predicting electricity consumption and enabling decentralized solutions. AI-driven electrification has shown economic and social benefits, including enhanced productivity, improved quality of life, and increased market access. However, it also raises ethical concerns and privacy implications. The research emphasizes the need for proactive mitigation strategies and collaborations across sectors to drive regulatory frameworks, technological innovations, and global impact. The research envisions a future where AI plays a central role in fostering inclusive growth, connecting communities, and illuminating a digitally transformed and sustainable continent. Keywords: Artificial Intelligence, Electrification, Africa, Electricity, EnergyEngineering Science &amp; Technology Journal00The paper discusses the importance of capacity building and local empowerment to ensure that AI technologies are effectively integrated into electrification projects while fostering inclusive and sustainable development, and highlights the role of AI in revolutionizing electrification, predicting electricity consumption and enabling decentralized solutions.Engineering Science &amp; Technology Journal['Ibegbulam C.M', 'Aigbovbiosa, O.J', 'Olowonubi, J.A', 'Fatounde, S.A']2023-12-26T00:00:00https://www.semanticscholar.org/paper/2753479dd3715c15d09cfdc311b470b6f013c7e4 401b2bc495bffbe89159c66e4599aa49525eec9bMachine learning (ML) and Artificial intelligence (AI) have demonstrated substantial promise for enhancing healthcare diagnostics and therapy. This study compares the benefits, drawbacks, and uses of these tools to examine their potential in healthcare. ML systems can find trends, increase diagnosis precision, and support professional judgment. Their efficacy may be constrained, though, by bad data quality, a lack of interpretability, and execution issues. On the other hand, AI can support clinical judgment, enhance patient results, and boost healthcare productivity. However, difficulties in implementing them can arise due to restricted generalizability, data protection issues, and legal conformance. To ensure the effective application and acceptance of these technologies in healthcare, it is essential to understand these benefits and constraints. Healthcare providers of the future will be able to make wiser choices regarding patient assessment and therapy options using AL and ML, resulting in an overall enhancement of healthcare services.Journal of Autonomous Intelligence00This study compares the benefits, drawbacks, and uses of these tools to examine their potential in healthcare and concludes that healthcare providers of the future will be able to make wiser choices regarding patient assessment and therapy options using AL and ML, resulting in an overall enhancement of healthcare services.Journal of Autonomous Intelligence['Prakash Date', 'Varsha Pimprale', 'S. Mandke']2023-12-26T00:00:00https://www.semanticscholar.org/paper/401b2bc495bffbe89159c66e4599aa49525eec9b 36137cf2d0c28c09f220bee53d443427a3aeab45Medical Journal of Australia151The Medical journal of Australia['A. van der Vegt', 'Victoria Campbell', 'Guido Zuccon']2023-12-26T00:00:00https://www.semanticscholar.org/paper/36137cf2d0c28c09f220bee53d443427a3aeab45 e4ba87ef1adf25542034679f9af0863d99281f84International Journal of Law and Society00International Journal of Law and Society['Zeng Wei']2023-12-26T00:00:00https://www.semanticscholar.org/paper/e4ba87ef1adf25542034679f9af0863d99281f84 f6e1dcb7ccaeacf8416b3aae56b5def996307dcc Swimming is a sport with a very significant exercise effect, so swimming courses in colleges and universities can promote the overall development of students’ physical fitness. In this paper, students’ swimming data are collected using intelligent sensors, and the collected data are noise-reduced and normalized by a low-pass filter. After the completion of data preprocessing, the swimming posture data features are extracted. The features are dimensionality reduced by the PCA method, combined with the BP neural network for training and accurate swimming posture recognition, and on this basis, a diversified teaching system for swimming courses in smart colleges and universities is constructed. After the students’ swimming data set was collected, the recognition effect of each swimming stroke was analyzed, and a comparison experiment was set up to investigate the practical effect and strategy of this teaching mode. The results show that the average accuracy of recognizing each stroke in this study can reach more than 0.988, and in the teaching test, the average difference between the performance of the experimental group and the control group is 7.6, and the P-value of the technical evaluation performance of the two groups of students in this project is 0.002<0.05, which significantly improves the swimming performance. This study can be combined with artificial intelligence technology to diversify the teaching of swimming courses in colleges and universities.Applied Mathematics and Nonlinear Sciences130A diversified teaching system for swimming courses in smart colleges and universities is constructed using intelligent sensors and artificial intelligence technology to diversify the teaching of swimming courses in colleges and universities.Applied Mathematics and Nonlinear Sciences['Jiayi Sun', 'Siming Wang']2023-12-26T00:00:00https://www.semanticscholar.org/paper/f6e1dcb7ccaeacf8416b3aae56b5def996307dcc 34241ed70feeea97bc1ad166e995b96c00a3165d<jats:p />Artificial Intelligence in Health00Artificial Intelligence in Health['Andrzej Cichocki']2023-12-26T00:00:00https://www.semanticscholar.org/paper/34241ed70feeea97bc1ad166e995b96c00a3165d 96c19e9ff937ff2a5e8ff4604f3130534d14a631The development of artificial intelligence technologies is an important condition for the formation of a digital society. However, according to a number of AI developers and researchers, this process can lead to catastrophic changes in the social, economic, and political spheres. The article discusses modern points of view on the risks of AI development: analyses the opinions of AI developers about the possible risks of technology development, describes the directions of philosophical research in AI, analyses forecasts of the social consequences of technology development. The scientific novelty lies in the integrated approach to considering the problem, based on the analysis of the social and ethical risks of AI. The article shows that at the moment the main means of preventing the risks of AI is to improve the system of its ethical and legal regulation. As a result, conclusions are drawn about the possible prospects for the development of the most complex problems of AI in the socio-philosophical context.Научное мнение00The article shows that at the moment the main means of preventing the risks of AI is to improve the system of its ethical and legal regulation, and conclusions are drawn about the possible prospects for the development of the most complex problems of AI in the socio-philosophical context.Научное мнение['Yuliya V. Nazarova']2023-12-25T00:00:00https://www.semanticscholar.org/paper/96c19e9ff937ff2a5e8ff4604f3130534d14a631 c6d8bbe234cc09862c4e2d402af6902422f43d13The ongoing growth of e-commerce has been changing the incentives and character of interaction between market participants at different levels of the vertical production chain. During the last 10 years, these trends have been attracting the attention of European antitrust and became a productive area of economic research. The altering nature of competition and the role of market participants influenced by the development of digital platforms have become the reference point for the emerging of new varieties of vertical restraints such as cross-platform parity agreements (across-platforms parity agreements). These types of agreements creates new incentives for firms. This was a challenge for the European Commission in the light of the preparation of the next edition of regulatory documents determining the legality of the application of vertical restrictive agreements, such as Vertical Block Exemption Regulation - "VBER" and Vertical Guidelines – "Guidelines for the Regulation of vertical restrictive agreements". We find it extremely interesting to identify to what extent the final versions of these regulatory documents took into account the opinions of judges, market participants and the academic community, who actively participated in identifying the most complex aspects of vertical interaction between firms and the growing role of digital platforms in this interaction.Journal of Institutional Studies00Journal of Institutional Studies['Ekaterina D. Slobodenyuk', 'Maria E. Agamirova']2023-12-25T00:00:00https://www.semanticscholar.org/paper/c6d8bbe234cc09862c4e2d402af6902422f43d13 11eb50d50932ae2f23656a6596b36cabc8c5c400The object of research in this article is the social relations that are developing regarding the legal regulation of labor relations in the process of human resource management. The subject is the norms of the law regulating labor relations in the process of human resource management. Particular attention is paid to the analysis of modern crisis phenomena in the Russian economy and the relationship between the resolution of related problems exclusively by legal norms. The role of the law as a basic regulator of labor relations is demonstrated. The main scientific results of the study are the conclusion confirming the high role of the law, legal regulation in the process of human resource management. The scientific novelty of the work consists in highlighting the most urgent crisis phenomena in the country’s economy in relation to labor relations, legal regulation and human resource management.The Economy under Guard10The Economy under Guard['Yuri Kravchenko']2023-12-25T00:00:00https://www.semanticscholar.org/paper/11eb50d50932ae2f23656a6596b36cabc8c5c400 1c14653cb5f04d557445ccf777e3c7fa06fe5b55How does a board of directors respond to stringent transnational regulations on corporate governance? We explore this question in a case study that includes interviews with key governance actors of a bank dealing with regulatory changes in the European Union (EU) initiated in 2010 in response to the financial crisis of 2007–2008. Our findings suggest that transnational regulations introduced a conflicting prescription to the directors, who were caught between two needs: existing local governance practices and transnational regulatory compliance. Contributing to the international corporate governance research, our findings corroborate the resistance to transnational regulations and the distrust attributable to boards of directors’ role struggles and the invasive accountability mechanisms introduced by such regulations. We, therefore, contribute to the ongoing discussion on how the conflicting layers of corporate governance—local versus global—and how the discontinuities between competing existing practices and the prescriptions of transnational regulations can provoke micro-resistance.Risks810Risks['S. Ikäheimo', 'E. Schiehll', 'Vikash Kumar Sinha']2023-12-25T00:00:00https://www.semanticscholar.org/paper/1c14653cb5f04d557445ccf777e3c7fa06fe5b55 c873f95e0d3c17489290c6388b6ce652d143dab1The decisions of the domestic sovereign power regarding the collapse of the USSR and overcoming the international isolation of Russia organized by some states are evaluated as examples of ignoring and reflecting this experience. The reasoning is as follows. In the first case, due to the ignorance of the leaders of the domestic sovereign power, the experience of preserving a single state by the former North American English colonies in order to avoid conflicts between them, including armed ones, expressed in the formation of the United States, is not taken into account. Such ignorance is considered as one of the reasons for the collapse of the USSR. In the second case, the experience of the transformation of the system of international relations after the collapse of the Roman Empire is taken into account, which made it possible for not the strongest states to better realize their own interests by developing ties with several of the most powerful countries established in the world at that time, which implies the impossibility of international isolation for each of the latter. Due to the similarity of this transformation to the transition from US domination in the international arena after the collapse of the USSR to the modern “multipolar” world, not the strongest states now have the opportunity to realize their own interests better than under the noted domination, communicating with all the “poles” of today’s world, including Russia. This explains Russia’s current success in interacting with so many countries. As for the fight against the discussed ignoring of the experience of the past, although it is impossible to completely exclude it due to the fragmentary nature of the established history and the physical impossibility for a person to know even the known, it is proposed to raise the political culture of the participants of the sovereign power, which is guaranteed by a high-quality political science and legal education. This applies not only to decision-makers, but above all to their advisers-scientists, to whom they have to turn for knowledge of politics due to lack of time for independent knowledge of the past. At the same time, the forms of treatment may be different.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia30Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia['Sergey Drobyshevskiy']2023-12-25T00:00:00https://www.semanticscholar.org/paper/c873f95e0d3c17489290c6388b6ce652d143dab1 f32c16355765cc12722315ab34b19deb8cddf2f2The article analyzes the state of agriculture provision with agricultural machinery and it is clarified that despite the extremely important importance of this branch for the country's economy, today there is a critical situation with the technical provision of the agro-industrial complex. Agricultural enterprises not included in the structure of agricultural holdings provide only 45 to 58 percent of the technological need to machinery, of which almost 90 percent of the units need replacement. The number of worn-out equipment, which each year is written off by enterprises, far exceeds the amount that is procured. The article also analyzes the state of state support of material and technical support of agricultural commodity producers in Ukraine. It has been determined that agrarian policy during the transformation period was characterized mainly by the predominance of tactical rather than strategic goals and programs, and the regulatory activity of the state was not systematic and consistent. Taking into account the above, it was concluded that the problem of increasing the effectiveness of the state influence on the development of the agrarian sector as a whole, and on material and technical support of the crop production sector in particular, is particularly relevant to Ukraine. It is concluded that the state policy in the direction of funding activities aimed at their support is not weighed, formed on the residual principle and can not fully contribute to the accomplishment of the tasks. A number of measures have been proposed for correction of the negative situation, which consist in the necessity of: streamlining of normative acts regulating the financing of programs; increase their funding; strengthening control over the implementation of public funding programs; redistribution of funding directions. An analysis of state support programs for agricultural enterprises makes it possible to conclude that their implementation does not have the desired effect due to: lack of funding by the state; unbalanced fiscal and customs policies, lack of incentives to increase scientific research and accelerate the implementation of their results in agricultural machinery; the general crisis of the country's economy since 2014, connected with the loss of territories, military actions in the east of Ukraine, inflationary processes and the political crisis. Keywords: state support, technology market, efficiency, technical and technological updating, material and technical base, financingPROBLEMS OF AGROINDUSTRIAL COMPLEX OF KARPATY00PROBLEMS OF AGROINDUSTRIAL COMPLEX OF KARPATY['Ya.F. Navrotsky']2023-12-25T00:00:00https://www.semanticscholar.org/paper/f32c16355765cc12722315ab34b19deb8cddf2f2 3e4cab4baeb3328938f2afdd4b268b918b98cf8bВведение. В статье рассматривается история развития административно-правовых отношений, связанных с выбросами парниковых газов в Российской Федерации, предпосылки и особенности применяемых методов административно-правового углеродного регулирования. Материалы и методы. Нормативную базу исследования составили законы, нормативные акты Правительства Российской Федерации и органов исполнительной власти Российской Федерации. В исследовании применялись общенаучные методы (анализ, синтез, исторический), частнонаучные (сравнительно-правовой) и специальные (формально-юридический). Результаты исследования. Регулирование отношений, связанных с выбросами парниковых газов, во многом обусловлено обязательствами страны по трем международным климатическим соглашениям – Рамочной конвенцией ООН об изменении климата, Киотскому протоколу и Парижскому соглашению. При этом долгое время сокращение парниковых выбросов было сопутствующим результатом мер по охране атмосферного воздуха и по повышению энергоэффективности экономики. В этой связи можно выделить два крупных этапа развития административно-правового регулирования в стране. Первый (неспецифический) с 1980-х гг. до 2019–2020 гг. Второй этап связан с принятием в 2021–2022 гг. федеральных законов «Об ограничении выбросов парниковых газов» и «О проведении эксперимента по ограничению выбросов парниковых газов в отдельных субъектах Российской Федерации» и является специфическим, с установлением конкретных обязательств по ограничению выбросов парниковых газов для регулируемых организаций на федеральном и региональном уровнях. Выводы и заключения. Современные тенденции развития административного права, такие как децентрализация полномочий органов власти, программное управление, комплексный подход к регулированию охраны окружающей среды, энергосбережения и выбросов парниковых газов, цифровизация, усиление экономических методов в балансе императивных и диспозитивных методов, должны найти отражение в дальнейшем развитии углеродного регулирования. Introduction: The publication examines the history of the development of administrative and legal relations related to greenhouse gas emissions in the Russian Federation, the prerequisites and features of the applied methods of administrative and legal carbon regulation. Materials and Methods: The regulatory framework for the study was made up of laws, regulations of the Government of the Russian Federation and executive authorities of the Russian Federation. The study used general scientific methods (analysis, synthesis, historical), special scientific (comparative legal) and special (formal legal) methods. The Results of the Study: Legal regulation of relations related to greenhouse gas emissions (carbon regulation) in Russia is largely determined by three international climate agreements – the UN Framework Convention on Climate Change, the Kyoto Protocol and the Paris Agreement. Before international climate commitments reduction of greenhouse emissions was not targeted, but was a side effect of atmospheric air protection and energy efficiency measures. In this regard, two major stages of the historical development of administrative legal regulation in Russia can be distinguished. The first (non-specific) is a period from 1980s to 2019-2020. The second (specific) stage is connected with the adoption in 2021-2022 of two federal laws "On limiting greenhouse gas emissions and "On conducting an experiment to limit greenhouse gas emissions in certain subjects of the Russian Federation", that have set specific obligations for regulated organizations at the federal and regional levels. Findings and Conclusions. Modern trends in the development of administrative law, such as decentralization of authorities' powers, program management, integrated approach to the regulation of environmental protection, energy conservation and greenhouse gas emissions, digitalization, strengthening of economic methods in the balance of imperative and dispositive methods, should be reflected in the further development of carbon regulation.VESTNIK OF THE EAST SIBERIAN INSTITUTE OF THE MINISTRY OF INTERNAL AFFAIRS OF THE RUSSIAN FEDERATION00VESTNIK OF THE EAST SIBERIAN INSTITUTE OF THE MINISTRY OF INTERNAL AFFAIRS OF THE RUSSIAN FEDERATION['Д.А. Гершинкова']2023-12-25T00:00:00https://www.semanticscholar.org/paper/3e4cab4baeb3328938f2afdd4b268b918b98cf8b 1d183b2de4c61c173e79324235d3d72302780281Введение. Механизм правового регулирования является одним из ключевых понятий теории права. Однако, несмотря на очевидность самой формулировки термина, нет единого подхода к его структуре и содержанию и, соответственно, нет универсального рецепта оценки эффективности действия этого механизма. Анализ базовых понятий, вычленение его структурных элементов и стадий с акцентом на выявление эффективности позволяет более глубоко проникнуть в сущность явлений и процессов, играющих существенную роль в современных правовых реалиях. Материалы и методы.В ходе исследования использовались исследования отечественных ученых, анализировались различные подходы к понятию механизм правового регулирования. В работе сравниваются и анализируются понятия «правовое воздействие» и «правовое регулирование», исследуется структура механизма правового регулирования, отдельные его элементы. При написании статьи использовались общенаучные методы: системный подход, индукция, аналогия, анализ, синтез. Результаты исследования. Проведен анализ механизма правового регулирования, его структуры и особенностей функционирования в современных условиях, пути повышения его эффективности правовыми средствами и методами. Выводы и заключения. Подводя итог вышесказанному, необходимо отметить, что сегодня механизм правого регулирования играет значительную роль в жизни общества. Он позволяет не только определить этапы преобразования норм права, но установить, на какой стадии происходит отклонение от предписаний, сформулированных в законе. Проанализировав различные подходы к толкованию понятия «механизм правого регулирования», мы приходим к выводу, что это последовательная организованная система юридических средств, способов и методов воздействия права на общественные отношения с целью трансформации нормы права в правомерное поведение граждан. В настоящее время вопросы, связанные с его структурой, остаются дискуссионными, однако ученые сходятся во мнении, что для успешного функционирования механизма правового регулирования необходимо грамотное и слаженное взаимодействие всех его элементов и составных частей. На наш взгляд, в современных условиях данный механизм может эффективно работать лишь при создании положения, при котором в нормах права с помощью высокого уровня законодательной техники будут отражены интересы общества. Introduction: The mechanism of legal regulation is one of the key concepts of the theory of law. However, despite the obviousness of the very wording of the term, there is no single approach to its structure and content and, accordingly, there is no universal recipe for assessing the effectiveness of this mechanism. The analysis of basic concepts, delineation of its structural elements and stages with a focus on the identification of effectiveness allows us to penetrate more deeply into the essence of phenomena and processes that play an essential role in modern legal realities. Materials and Methods: in the course of the study, the research of domestic scientists was used, various approaches to the concept of the mechanism of legal regulation were analyzed, the concepts of "legal impact" and "legal regulation" are compared and analyzed. The structure of the mechanism of legal regulation, its individual elements are investigated. When writing the article, general scientific methods were used: a systematic approach, induction, analogy, analysis, synthesis. The Results of the Study: the mechanism of legal regulation, its structure and peculiarities of functioning in modern conditions, ways of increasing its efficiency by legal means and methods have been analyzed. Findings and Conclusions: Summarizing the above, it should be noted that today the mechanism of legal regulation plays a significant role in the life of society. It allows not only to determine the stages of transformation of legal norms, but also to establish at what stage there is a deviation from the prescriptions formulated in the law. Having analyzed various approaches to the interpretation of the concept of "mechanism of legal regulation", we come to the conclusion that it is a consistent organized system of legal means, ways and methods of the impact of law on social relations in order to transform the norm of law into lawful behavior of citizens. At present, the issues related to its structure remain debatable, but scientists agree that for the successful functioning of the mechanism of legal regulation it is necessary competent and coherent interaction of all its elements and components. In our opinion, in modern conditions, this mechanism can work effectively only when creating a situation in which the interests of society are reflected in the norms of law with the help of a high level of legislative technique.VESTNIK OF THE EAST SIBERIAN INSTITUTE OF THE MINISTRY OF INTERNAL AFFAIRS OF THE RUSSIAN FEDERATION00VESTNIK OF THE EAST SIBERIAN INSTITUTE OF THE MINISTRY OF INTERNAL AFFAIRS OF THE RUSSIAN FEDERATION['Ю.В. Шелегов']2023-12-25T00:00:00https://www.semanticscholar.org/paper/1d183b2de4c61c173e79324235d3d72302780281 ecf864a6a48b76256deb48524a0cc47dddf007caThe realm of education is witnessing a transformative integration with Artificial Intelligence (AI), poised to redefine the contours of pedagogical strategies. Central to this transformation is the emergence of personalized learning experiences, where AI endeavors to tailor educational content and interactions to resonate with individual learners' unique needs, preferences, and pace. This paper delves into the multifaceted dimensions of AI-driven personalized learning, from its potential to enhance e-learning modules, the advent of AI-powered virtual tutors, to the ethical challenges it surfaces. As the tapestry of education becomes more intertwined with digital innovations, understanding AI's role in individualizing learning becomes paramount.Advances in Engineering Innovation92This paper delves into the multifaceted dimensions of AI-driven personalized learning, from its potential to enhance e-learning modules, the advent of AI-powered virtual tutors, to the ethical challenges it surfaces.Advances in Engineering Innovation['Maher Joe Khan Omar Jian']2023-12-25T00:00:00https://www.semanticscholar.org/paper/ecf864a6a48b76256deb48524a0cc47dddf007ca da39905fbc3ad5ffcae6972ab5632cb842eef738Artificial Intelligence's (AI) revolutionary capacity is exemplified in GPT-3, an advanced model in Natural Language Processing. GPT-3's potential to generate human-like text and facilitate language tasks is unparalleled. This abstract highlights GPT-3's diverse applications, advantages, challenges, and ethical considerations. It underscores its role in enhancing research quality, promoting creative writing, and aiding language translation. However, it cautions against biases, inaccuracies, and limitations. Embracing GPT-3 requires balancing academic integrity, enriching education, and employing rigorous security measures. Ultimately, GPT-3 propels AI research by bridging language gaps and shaping innovative AI applications.SAR Journal - Science and Research01This abstract highlights GPT-3's diverse applications, advantages, challenges, and ethical considerations, and underscores its role in enhancing research quality, promoting creative writing, and aiding language translation.SAR Journal - Science and Research['Asep Ridwan Lubis']2023-12-25T00:00:00https://www.semanticscholar.org/paper/da39905fbc3ad5ffcae6972ab5632cb842eef738 62da68bdbc2c6911c6ffcfc3c212e640567baaedAI technology plays a positive role in promoting the development of physical manufacturing industry, which can improve production efficiency, reduce costs, improve product quality, innovate research and development capabilities, expand market opportunities and strengthen customer service. In the future, with the continuous development and application of AI technology, its potential will be further released, bringing more opportunities and challenges to the development of physical manufacturing.Transactions on Economics, Business and Management Research50Transactions on Economics, Business and Management Research['Tianle Wang', 'Yuhao Zeng', 'Yiyang Han', 'Jianhang Hong']2023-12-25T00:00:00https://www.semanticscholar.org/paper/62da68bdbc2c6911c6ffcfc3c212e640567baaed 0dab65e7358b9178ce0fc6b06332d643bb315bb9The utilisation of Artificial Intelligence (AI) technology has caused remarkable changes that have taken place in the educational landscape. Through the integration of AI in online learning systems, an entirely new educational experience has been introduced, altering the ways learners and educators can interact. The emergence and evolution of AI technology have increased efficiency and productivity, enhancing teaching and learning outcomes. AI in online learning provides a distinct advantage by providing real-time feedback to learners. Traditional learning environments often suffer from the limitation of delayed feedback, impeding learners’ progress and demotivating them. However, AI-powered online learning systems excel in delivering immediate feedback to learners, enabling them to promptly identify and rectify mistakes and enhance their performance in real-time. This timely feedback fosters a supportive learning environment that encourages learners to engage in the learning process actively. The research by Vanlehn, Lynch, Schulze, Shapiro, Shelby, Taylor et al. (2005) on the Andes physics tutoring system serves as a valuable resource for understanding the lessons learned from utilising AI to support learner-instructor interaction. In contrast to traditional learning environments that offer delayed feedback, impeding the progress of learners and possibly dampening their motivation, AI-powered online learning systems provide real-time feedback. With real-time feedback, learners can instantly correct mistakes and improve their performance, thereby advancing their learning outcomes (Zhou & Mei, 2021). This literature review explores the impact of AI on learner-instructor interaction in online learning environments. The review considers how AI technology enhances and diversifies the learning process, focusing on personalised learning, real-time feedback provision, and content delivery.International Journal of Emerging Multidisciplinaries: Computer Science &amp; Artificial Intelligence00The review considers how AI technology enhances and diversifies the learning process, focusing on personalised learning, real-time feedback provision, and content delivery.International Journal of Emerging Multidisciplinaries: Computer Science &amp; Artificial Intelligence['Ziad H. Rakya']2023-12-25T00:00:00https://www.semanticscholar.org/paper/0dab65e7358b9178ce0fc6b06332d643bb315bb9 c14cd46b5c2fa12ab4097cc5b4a188b136704f7cThe intersection of Artificial Intelligence (AI) and International Criminal Law has heralded an era of augmented justice, characterized by enhanced efficiency yet beset by intricate ethical and legal quandaries. The present article seeks to delve into the multifarious impacts of AI integration, dissecting the potential augmentations and the inherent complications within the enigmatic confines of international criminal proceedings. It aims to meticulously juxtapose the promises of technological advancements against the imperatives of ethical justice and legal propriety.The article commences with a nuanced exploration of AI’s role in evidence gathering and analysis, illuminating the potential for expedited and enriched processes. Yet, the core of the discussion gravitates towards the ethical and legal predicaments of AI biases and the consequential implications on the sanctity of fair trials. The article, thus, strives to weave together the threads of accountability, transparency, and the inviolable rights of the accused in a tapestry that reflects the multifaceted challenges posed by AI.Drawing from a rich tableau of international perspectives, including the diverse legal landscapes of Europe, Asia, Africa, and the Americas, an offering of a global vista of prevailing attitudes, policies, and frameworks governing AI in judicial systems is endeavoured. In navigating the future, the article ends with policy proposals and legal frameworks to align AI’s integration with the sacrosanct principles of international human rights and criminal justice.It is ultimately hoped that the article, rooted in rigorous academic discourse yet resonant with broader societal implications, offers an original, refined, and critical perspective on the confluence of AI and international criminal law.DME Journal of Law100The article strives to weave together the threads of accountability, transparency, and the inviolable rights of the accused in a tapestry that reflects the multifaceted challenges posed by AI, dissecting the potential augmentations and the inherent complications within the enigmatic confines of international criminal proceedings.DME Journal of Law['Virendra P. S. Rathod']2023-12-25T00:00:00https://www.semanticscholar.org/paper/c14cd46b5c2fa12ab4097cc5b4a188b136704f7c 07a5cdc5f243fadd4f756f710a788e29014f5803Artificial Intelligence (AI) is not contained within the walls of technological organizations; over the decades, it has significantly impacted other industries due to the exponential practical implications of the technology and major break throughs as a result of AI implementation. Many authors have conducted relevant research about AI and its assistance in industries like banking and finance, education, manufacturing, healthcare, and others to find direct correlation between application of AI technology in data analysis, decision-making, end user impact and satisfaction, time, and cost savings. This study aims to perform in-depth analysis of various previous studies, and to conduct market research to comprehend the meeting point of Artificial general intelligence and Institutions and what they have to offer to each other; a detailed analysis of the way various strategies applicable through machine learning, deep learning techniques, voice and face recognition applications, expert systems assisting in comprehending customer behavior and patterns, and to identify potential demand, automation of key functions of inter-organization departments through algorithms, and building blocks that form institutions can be adopted by AI for future models and prospectives, and also to understand any gaps in the practical implications of AL that institutions can possibly incorporate in the future; thereby increasing efficiency and effectiveness of both AI and Institutions.The Business and Management Review40This study aims to perform in-depth analysis of various previous studies, and to conduct market research to conduct market research to comprehend the meeting point of Artificial general intelligence and Institutions and what they have to offer to each other.The Business and Management Review['Iris Billy', 'Hannah Anush']2023-12-25T00:00:00https://www.semanticscholar.org/paper/07a5cdc5f243fadd4f756f710a788e29014f5803 4f80ed8bdf78aa00d7a4494c75801a891f3a3593This research aims to conduct a comparative study of artificial intelligence (AI) software platforms used in interior design. The focus of the study is to evaluate the effectiveness, sophistication, and performance of various AI platforms in supporting innovation and efficiency in the internal design process. This research method involved an in-depth analysis of several leading AI platforms used in interior design, reviewing their superior features, personalization capabilities, and integration with other design tools. In addition, this research will evaluate the impact of using these platforms on project completion time, design results accuracy, and client satisfaction. The results of this research can provide valuable insight for interior design professionals. By understanding how AI platforms compare, professionals can make more informed decisions in selecting the solution that best suits their project's needs. In an era of rapid technological development, this research can provide helpful guidance for optimally utilizing AI technology, thereby achieving interior design results that are not only innovative but also efficient. The conclusions from the research can provide beneficial advice in optimally utilizing AI technology to achieve innovative and efficient interior design results. By identifying the strengths and weaknesses of each platform, interior design professionals can optimize the use of AI technology according to their project needs and preferences. As a result, this research can positively contribute to the development of the interior design industry, creating a more productive and satisfying work environment for professionals and clients.Journal of Engineering Science260A comparative study of artificial intelligence (AI) software platforms used in interior design to evaluate the effectiveness, sophistication, and performance of various AI platforms in supporting innovation and efficiency in the internal design process.Journal of Engineering and Science['Muhammad Tahsin', 'Muhammad Agha Afkar']2023-12-25T00:00:00https://www.semanticscholar.org/paper/4f80ed8bdf78aa00d7a4494c75801a891f3a3593 bdeb70fd6938fee9dcaead88f7412c944f1526beThis study undertakes a comprehensive examination of the awareness, skills, attitude, and readiness of respondents regarding the adoption of Artificial Intelligence (AI) applications in their professional settings. While the research evaluates respondents' familiarity with AI tools, proficiency levels, and overall attitude towards AI integration, it also strives to present a nuanced perspective by exploring potential challenges and reservations. The data, collected through a structured survey employing a Likert scale, captures diverse viewpoints on awareness, skills, attitude, and readiness towards AI applications. The findings reveal a generally positive outlook among respondents, emphasizing their commendable awareness of AI technologies and a strong inclination towards potential benefits. Despite varying levels of proficiency with specific AI tools, respondents express a collective willingness to embrace new technologies. The study identifies a positive attitude towards AI integration in work processes, accompanied by a proactive approach towards skill development and troubleshooting. However, it is crucial to note the potential challenges and reservations reported by some respondents, offering a balanced view of their preparedness for AI adoption. While the overall disposition towards AI technologies is favorable, the study underscores the importance of tailored training and development programs. The varying levels of proficiency reported highlight the need for targeted initiatives to address specific skill gaps. Organizations aiming to leverage AI technologies can benefit from the insights provided, emphasizing the significance of accessible training and creating a supportive environment for employees. By acknowledging challenges and reservations, this study contributes to a more comprehensive understanding of the landscape, facilitating informed strategies for successful AI integration in the workplace.International Journal of Multidisciplinary Applied Business and Education Research00A generally positive outlook is identified among respondents, emphasizing their commendable awareness of AI technologies and a strong inclination towards potential benefits, and the varying levels of proficiency reported highlight the need for targeted initiatives to address specific skill gaps.International Journal of Multidisciplinary: Applied Business and Education Research['Jake C. Campued', 'Dorothy-May M. Papa', 'Armstrong C. De Castro', 'Bernandino P. Malang']2023-12-25T00:00:00https://www.semanticscholar.org/paper/bdeb70fd6938fee9dcaead88f7412c944f1526be 1588680e6c37002b8a84c159a94e93a2c0c3aab8Journal of Information Technology &amp; Politics540Journal of Information Technology &amp; Politics['Fanjue Liu', 'Heidi Makady', 'Seungahn Nah', 'Jasmine McNealy']2023-12-25T00:00:00https://www.semanticscholar.org/paper/1588680e6c37002b8a84c159a94e93a2c0c3aab8 247bb9c747babadb9ff93b527e4b29d7cc865b12Nihon Ika Daigaku Igakkai Zasshi00Nihon Ika Daigaku Igakkai Zasshi['Eiryo Kawakami']2023-12-25T00:00:00https://www.semanticscholar.org/paper/247bb9c747babadb9ff93b527e4b29d7cc865b12 5e8d33ad86b332023453e6e99c0d4d5f4e648c01Australasian Journal of Dermatology90The Australasian journal of dermatology['Eugene Tan', 'Minh Tran', 'Marius Rademaker', 'F. P. Lin']2023-12-25T00:00:00https://www.semanticscholar.org/paper/5e8d33ad86b332023453e6e99c0d4d5f4e648c01 59a7692ae331afb7b509fb01a351e3fde6757694In this article, the authors focus on the relevance and incomparable role of artificial intelligence in teaching foreign languages. This article also discusses the importance of several modern artificial intelligences in teaching and learningАрабский язык в эпоху глобализации: инновационные подходы и методы обучения00The relevance and incomparable role of artificial intelligence in teaching foreign languages and the importance of several modern artificial intelligences in teaching and learning are focused on.Арабский язык в эпоху глобализации: инновационные подходы и методы обучения['Манучехр Курвонбеков', 'Мохира Алижонова']2023-12-25T00:00:00https://www.semanticscholar.org/paper/59a7692ae331afb7b509fb01a351e3fde6757694 f94ddf84c57bfbfb525fe47cf392f7dd625aef45Health management literature is critical in facilitating effective leadership and management in the health sector by imparting the knowledge and understanding required in health service planning, organization, coordination, and management. Through an exhaustive bibliometric examination, the current state of the “healthcare management” literature was assessed in this study, illuminating potential future developments in the field. By utilizing an extensive compilation of articles from the Web of Science (WoS) database, this study endeavors to scrutinize patterns in scientific inquiry, assess advancements made on subjects and underscore significant fields of cooperation and scholarly input. An in-depth extensive analysis revealed that three decades of research yielded over 46,000 publications; the number of publications has increased by six, particularly in the last ten years, and the United States and the United Kingdom produced most of the publications. Analysis identifying influential journals and authors in the field revealed that it is the focus of attention of health professionals, especially nurses. Although the COVID-19 pandemic has been recognized as the primary public health concern, scholarly attention has shifted towards a sustainable health system incorporating technology-supported preventive health practices and intelligence. To thoroughly comprehend the theoretical progression of health management research, its capacity to tackle worldwide issues, and its growth potential, the study’s findings are a valuable scholarly resource that offers practitioners, policymakers, and researchers a comprehensive overview.Hitit Sosyal Bilimler Dergisi00The current state of the “healthcare management” literature was assessed in this study, illuminating potential future developments in the field and offering practitioners, policymakers, and researchers a comprehensive overview.Hitit Sosyal Bilimler Dergisi['Hafize Nurgül Durmuş Şenyapar']2023-12-25T00:00:00https://www.semanticscholar.org/paper/f94ddf84c57bfbfb525fe47cf392f7dd625aef45 17796914ecc831b4bf86da60f477961b29b07c8f<jats:p />The eurasia proceedings of science, technology, engineering & mathematics00The Eurasia Proceedings of Science Technology Engineering and Mathematics['Michael Lankes', 'Andreas Stockl']2023-12-25T00:00:00https://www.semanticscholar.org/paper/17796914ecc831b4bf86da60f477961b29b07c8f 6bb0037d608ab495dea8b4165828926af350693cThis paper investigates the intricate relationship between artificial intelligence (AI) and green innovation within the context of sustainable development goals. As societies strive to achieve sustainability, understanding the dynamics between technological advancements and environmental progress becomes paramount. Drawing from panel data encompassing 51 countries between 2000 and 2019, this study employs fixed-effects models, mediated effects models, and spatial Durbin models to meticulously examine the influence of AI on green innovation. The empirical findings reveal a robust and significantly positive correlation between AI and green innovation, highlighting the critical role of AI in fostering environmental innovation. Heterogeneity analysis across developed and developing economies delineates variations in the impact of AI on green innovation, shedding light on the influence of economic development levels and financial structures. Developed nations showcase a more pronounced AI-green innovation relationship compared to their developing counterparts, highlighting the complexities of technology adoption within distinct economic landscapes. Moreover, this study delves into the transmission mechanisms underlying the AI-green innovation nexus, revealing the mediating roles of industrial structure and human capital. Industrial upgrading and the enhancement of human capital emerge as crucial pathways through which AI indirectly stimulates green innovation. Spatial analyses reveals the spatial relevance of green innovation globally, emphasizing AI's substantial impact not only within domestic spheres but also across neighboring regions. There are significant direct, indirect, and total effects of AI on green innovation, highlighting its spillover characteristics and the catalytic role it plays in driving collaborative AI development on a global scale. This research contributes nuanced insights into the interplay between AI and green innovation, providing a foundation for policymakers, businesses, and researchers to comprehend the multifaceted dimensions of technological interventions in fostering sustainable innovation. The findings emphasize the imperative of collaborative efforts in utilizing AI's potential to propel green innovation, thereby advancing global sustainability agendas.Energy &amp; Environment9819The empirical findings reveal a robust and significantly positive correlation between AI and green innovation, highlighting the critical role of AI in fostering environmental innovation and the imperative of collaborative efforts in utilizing AI's potential to propel green innovation, thereby advancing global sustainability agendas.Energy &amp; Environment['Qiang Wang', 'Tingting Sun', 'Rongrong Li']2023-12-25T00:00:00https://www.semanticscholar.org/paper/6bb0037d608ab495dea8b4165828926af350693c 51e1a786245583a6500421d4873bec63b1abc342The purpose of the study is to determine the role of artificial intelligence in improving the quality of education and research. To achieve this goal, a content analysis was used. Of note is the SWOT analysis method, which was used to identify the positive and negative aspects of the integration of artificial intelligence into scientific and pedagogical life. The results show that the use of artificial intelligence technologies in the field of education is a promising and promising area for further development. The analysis conducted in this context revealed that innovative technologies have become an essential component of modern education. The use of artificial intelligence (AI) is becoming more widespread and. However, for the further development of this area, it is important to improve the level of digital literacy among users. Based on the results of the study, both positive aspects and disadvantages in the use of these technologies can be identified. Among the advantages are the high adaptability of AI, the possibility of individualised learning, accessibility, and a wide range of applications, from primary school to higher education and research. It is important to note that these technologies can be used to achieve learning success regardless of geographical region, making them global in nature. The scientific novelty lies in the consideration of certain shortcomings, among which the issue of security is particularly relevant, especially in the context of global hybrid threats and the overall process of digitalisation. The conclusions note that to overcome the identified negative aspects of the use of AI in research and teaching, it is important to focus on the ethical aspects of using digital tools in general. It is also recommended to consider the possibility of organising additional online courses.Futurity Education00The results show that the use of artificial intelligence technologies in the field of education is a promising and promising area for further development, and it is important to improve the level of digital literacy among users.Futurity Education[]2023-12-25T00:00:00https://www.semanticscholar.org/paper/51e1a786245583a6500421d4873bec63b1abc342 fbc6cb76bc3eaec7f3e9c54899eb63bdccc8469eThis systematic review synthesizes the existing literature on the impact of artificial intelligence (AI) and automation on Human Resource Development (HRD) practices and outcomes. The study explores how AI and automation affect HRD, highlighting specific HRD processes affected and their influence on outcomes. A comprehensive search was conducted across academic databases, HRD journals, and conference proceedings, resulting in a selection of relevant studies. The findings were analyzed through a narrative synthesis, with subgroup analyses based on specific HRD processes. The review provides insights into AI and automation implications for HRD researchers and practitioners. It also identifies research gaps and future directions.Human Resource Development Review663This systematic review synthesizes the existing literature on the impact of artificial intelligence (AI) and automation on Human Resource Development practices and outcomes and provides insights into AI and automation implications for HRD researchers and practitioners.Human Resource Development Review['Kelechi Ekuma']2023-12-25T00:00:00https://www.semanticscholar.org/paper/fbc6cb76bc3eaec7f3e9c54899eb63bdccc8469e ac6fe8f2f47d6d007f519ae44833b65fb507e033PurposeRecent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.Design/methodology/approachSystematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.FindingsThe results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.Research limitations/implicationsFor researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.Practical implicationsThis article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.Originality/valueThis article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.Engineering Construction and Architectural Management1291The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures.Engineering, Construction and Architectural Management['I. Akomea-Frimpong', 'Jacinta Rejoice Ama Delali Dzagli', 'Kenneth Eluerkeh', 'Franklina Boakyewaa Bonsu', 'Sabastina Opoku-Brafi', 'Samuel Gyimah', 'Nana Ama Sika Asuming', 'David Wireko Atibila', 'A. S. Kukah']2023-12-25T00:00:00https://www.semanticscholar.org/paper/ac6fe8f2f47d6d007f519ae44833b65fb507e033 b8d633bd28432228c9063697f5c0cff368d36d39Tacrolimus is the cornerstone of immunosuppressive therapy after pediatric liver transplantation. However, reliance on the physician's experience for dose titration, coupled with tacrolimus's narrow therapeutic window and inter and intra‐patient variability, often results in frequent under or over‐dosing events with detrimental patient outcomes. Existing predictive dose personalization models are not readily feasible for clinical implementation, as they require multiple measurements each day while the standard frequency is once daily. We developed CURATE.AI, a small‐data artificial intelligence‐derived platform, as a clinical decision support system to dynamically personalize doses using the patient's own data obtained once a day. Retrospective dose personalization with CURATE.AI on 16 patients’ data demonstrated potential to enable more patients to reach therapeutic range within the first week. Our findings support the testing of CURATE.AI in a prospective controlled trial as an aid for the physician's decision on tacrolimus dose personalization after pediatric liver transplantation.This article is protected by copyright. All rights reservedAdvances in Therapy02These findings support the testing of CURATE.AI in a prospective controlled trial as an aid for the physician's decision on tacrolimus dose personalization after pediatric liver transplantation and demonstrate potential to enable more patients to reach therapeutic range within the first week.Advanced Therapeutics['Shijie Tan', 'K. S. Kumar', 'Tiffany Rui Xuan Gan', 'L. Tan', 'A. Truong', 'Agata Blasiak', 'Marion M. Aw', 'Vidyadhar Padmakar Mali', 'Dean Ho']2023-12-25T00:00:00https://www.semanticscholar.org/paper/b8d633bd28432228c9063697f5c0cff368d36d39 9112c137e4343731579d67ce179da35ce6b41435Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases. RAIT dosimetry can be employed to determine the optimal treatment dose of I-131 to effectively treat cancer cells while safeguarding against undesirable radiation effects such as bone marrow toxicity or radiation pneumonitis. Conventional dosimetry protocols for RAIT, however, are complex and time-consuming, involving multiple days of imaging and blood sampling. This study explores the use of Artificial Intelligence (AI) in simplifying and optimizing RAIT. A retrospective analysis was conducted on 83 adult patients with DTC who underwent RAIT dosimetry at our institution between 1996 and 2023. The conventional MIRD-based dosimetry protocol involved imaging and blood sampling at 4, 24, 48, 72, and 96 h post-administration of a tracer activity of I-131. An AI system based on a deep-learning neural network was developed to predict the maximum permissible activity (MPA) for RAIT using only the data obtained from the initial 4, 24, and 48 h time points. The AI system predicted the MPA values with high accuracy, showing no significant difference compared to the results obtained from conventional MIRD-based analysis utilizing a paired t-test (p = 0.351, 95% CI). The developed AI system offers the potential to streamline the dosimetry process, reducing the number of imaging and blood sampling sessions while also optimizing resource allocation. Additionally, the AI approach can uncover underlying relationships in data that were previously unknown. Our findings suggest that AI-based dosimetry may be a promising method for patient-specific treatment planning in differentiated thyroid carcinoma, representing a step towards applying precision medicine for thyroid cancer. Further validation and implementation studies are warranted to assess the clinical applicability of the AI system.Journal of Clinical Medicine431The developed AI system offers the potential to streamline the dosimetry process, reducing the number of imaging and blood sampling sessions while also optimizing resource allocation, and can uncover underlying relationships in data that were previously unknown.Journal of Clinical Medicine['Michalis F Georgiou', 'Joshua A. Nielsen', 'R. Chiriboga', 'Russ A. Kuker']2023-12-25T00:00:00https://www.semanticscholar.org/paper/9112c137e4343731579d67ce179da35ce6b41435 d4ed8c185c019a4224f5abd56d9145502a3fc772As humans, it is common for judges to give wrong verdicts when making decisions, especially in criminal cases. As such, those who feel that they have been wronged by the courts will thus appeal against the decisions. Due to the sheer volume of appeals, it has resulted in a backlog of cases. However, there is no one solution to solve the problem other than calling the judicial officers to improve themselves with legal knowledge before the real use of Artificial Intelligence in legal policy. In the current digital era, it is believed that Artificial Intelligence can accelerate and automate the review of potential evidence in identifying the most relevant and accurate evidence. With the help of Artificial Intelligence, it will reduce court unsolved cases. Countries such as the United States of America, Colombia, and China have started implementing Artificial Intelligence in their respective judicial systems. Yet Singapore’s criminal courts have no plan to use Artificial Intelligence in sentencing. Therefore, it has raised questions like should Malaysia’s judicial response to the use of Artificial Intelligence in cracking those backlog criminal cases and how far could it go in helping the judges. This paper seeks to highlight the issues.Asian Journal of Law and Policy00Questions like should Malaysia’s judicial response to the use of Artificial Intelligence in cracking those backlog criminal cases and how far could it go in helping the judges are raised.Asian Journal of Law and Policy['Pei Yee Tan']2023-12-25T00:00:00https://www.semanticscholar.org/paper/d4ed8c185c019a4224f5abd56d9145502a3fc772 106694fd5c35198ed0fe070c7c0c07907dff7e3fThe rapid rise of artificial intelligence in every sector and the anxiety created by technological change affect university students' job prospects and future career determination. The main purpose of this study is to determine whether career decidedness has a moderating role in the effect of artificial intelligence anxiety on university students' job hopes. The study sample consists of 389 students (264 female, 125 male) selected by convenience sampling method from a foundation university in Istanbul. The ages of the participants are between 18-34+. The questionnaire comprises four sections: demographic information, artificial intelligence anxiety, employment hope, and career decidedness. The questionnaire, which is a quantitative research method, consists of 5-point Likert statements. A total of 36 statements were included for three variables. The data analysis was tested using Spearman correlation analysis and quantile regression analysis. Spearman correlation analysis revealed weak and very weak relationships between the scales. As a result of quantile regression analysis, it was determined that career determination did not have a moderating role in the effect of artificial intelligence anxiety on employment hope.Business &amp; Management Studies: An International Journal750It was determined that career determination did not have a moderating role in the effect of artificial intelligence anxiety on employment hope, and quantile regression analysis revealed weak and very weak relationships between the scales.Business &amp; Management Studies: An International Journal['Ayşe Meriç Yazici']2023-12-25T00:00:00https://www.semanticscholar.org/paper/106694fd5c35198ed0fe070c7c0c07907dff7e3f 4a82c3b51ffd1122807369a464e667ad1a9700b3The article discusses the main directions of the use of artificial intelligence (AI) in the political life of society. It is noted that the level and quality of life of the population depends on the pace of AI implementation in various spheres of public life. The analysis of the conducted research in the format of an expert interview allows us to identify the key trends, advantages and challenges faced by the Russian political system in the implementation of AI. The results of the expert survey provide valuable practical recommendations and a basis for discussing possible directions for the development of AI in political management in Russia. The article notes the insufficient elaboration of the problems of artificial intelligence in the Russian scientific, in particular, political science discourse, which, in turn, is seen as promising in increasing research opportunities. The aim of the work is to identify the main trends in the use of artificial intelligence in political management and the prospects for its development in the political sphere of society. The consideration of the problem was based on the results of an initiative political and sociological study in the format of an expert interview (N=7). The following general scientific methods were used to analyze the results obtained: description, analysis, synthesis, classification. The study also used SWOT analysis to identify the strengths and weaknesses of using AI in the field of domestic state policy and electoral systems, as well as to show the opportunities and threats of its use. The analysis of the results of the expert survey showed that artificial intelligence is most in demand in such areas as analytics, decision-making, scenario forecasting, political communications, improving the efficiency of political institutions, especially the electoral system, monitoring voter sentiment, and in the field of security. Highlighting the shortcomings in the use of AI, experts noted the possible subjectivity and bias in decision-making, the problem of transparency and protection of personal data. As solutions to this problem, the development of appropriate regulations protecting the human right to confidentiality and privacy, "transparent" algorithms, and careful selection of data on the basis of which AI makes decisions were proposed. The prospect of further research lies in the development of ethical standards that can ensure transparency and security in the implementation of AI in political systems. An important conclusion of the article is the conclusion that there is an objective need to develop a regulatory framework and conduct regular inspections of AI systems to maintain trust and effective use of digital technologies in politics.Journal of Political Research80There is an objective need to develop a regulatory framework and conduct regular inspections of AI systems to maintain trust and effective use of digital technologies in politics.Journal of Political Research['Yu. Davydova', 'A. Matyukhin', "E. Anan'evskaya"]2023-12-25T00:00:00https://www.semanticscholar.org/paper/4a82c3b51ffd1122807369a464e667ad1a9700b3 67b4502e554ea98ccc36569cf5d2dd30399ab26fArtificial Intelligence (AI) revolutionizes healthcare through advanced algorithms, analyzing patient data for precise disease diagnosis and optimized drug management. Despite challenges in data privacy and ethical deployment, AIs transformative potential holds promise for improved patient care. Collaboration and innovation will drive the future of AI in healthcare, shaping a dynamic landscape. KEYWORDS: Disease Diagnosis, Drug Management, Machine Learning, Personalized MedicineEPRA International Journal of Research &amp; Development (IJRD)410Collaboration and innovation will drive the future of AI in healthcare, shaping a dynamic landscape.EPRA International Journal of Research &amp; Development (IJRD)['Prajapati Hardikkumar', 'Dr. Anuradha P Bharatbhai', 'Dr. Sachin Prajapati', 'Narkhede Dr. Shailesh', 'B. LuharSmt.B.N.', 'Swaminarayan Pharmacy College Salvav']2023-12-25T00:00:00https://www.semanticscholar.org/paper/67b4502e554ea98ccc36569cf5d2dd30399ab26f d14c7c072e13842c477aee9c46b4d9a516d2091eThe incorporation of artificial intelligence (AI) technology into the pharmaceutical sector is a groundbreaking revolution, promising to enhance drug development significantly by boosting speed, efficiency, and effectiveness. This transformative journey encompasses several key facets, notably the sophisticated analysis of complex data, the optimization of drug delivery systems, the acceleration of drug discovery, the identification of valuable biomarkers and drug candidates, and the fine-tuning of treatment outcomes. Beyond these crucial strides, AI is reshaping healthcare at large, elevating decision-making processes, and deepening our understanding of diseases and pharmaceuticals. The broader domains within this AI-driven transformation encompass themes such as “Revolutionizing Pharmaceutical Product Development Through AI,” where AI platforms like the Chemputer successfully synthesize compounds and estimate granulation completion times, improving production efficiency. In “Transforming Pharmaceutical Manufacturing with AI, “AI optimizes manufacturing processes, further enhancing efficiency.” Enhancing Clinical Trial Design Through AI” harnesses AI to improve patient selection and adherence, utilizing genome and exposome profiles for precise and efficient clinical trials. AI also proves valuable in preclinical phases, predicting lead compounds. Moreover, AI extends its influence to brand recognition and market positioning of pharmaceutical products, leveraging technology and e-commerce platforms for distinct product identities and effective marketing strategies. In “Convergence of AI and Nanomedicine,” AI delves into complex formulation development, optimizing drug delivery methods via molecular analysis and simulation tools. Furthermore, AI’s role in addressing challenges in the pharmaceutical market, such as reducing financial burdens and risks related to virtual screening, is pivotal. The sector’s substantial growth and its integration into pharmaceutical companies’ strategies through collaborations signify a promising future for AI in healthcare.In summary, the incorporation of AI in the pharmaceutical industry holds immense potential for enhancing production processes, clinical trial design, market positioning, nanomedicine, and overall industry efficiency. These collective advancements mark a transformative era in pharmaceutical innovation and patient care.International Journal of Drug Delivery Technology160The incorporation of AI in the pharmaceutical industry holds immense potential for enhancing production processes, clinical trial design, market positioning, nanomedicine, and overall industry efficiency.INTERNATIONAL JOURNAL OF DRUG DELIVERY TECHNOLOGY['Prajwal S Shinde', 'Ashish Y Pawar', 'Swati G Talele']2023-12-25T00:00:00https://www.semanticscholar.org/paper/d14c7c072e13842c477aee9c46b4d9a516d2091e 5b4664981a2d67240fa20c69eeed9eac2251957bIn the contemporary business landscape, the integration of Artificial Intelligence (AI) has emerged as a transformative force, reshaping traditional paradigms across various industries. This study delves into the specific realm of Small and Medium Enterprises (SMEs) to explore the profound impact of AI adoption on their operational efficiency, innovation capabilities, and overall competitiveness. The research employs a multi-faceted approach, incorporating both quantitative and qualitative analyses. Through surveys, interviews, and case studies, we investigate the extent to which SMEs have embraced AI technologies, identifying the key drivers and barriers to adoption. The study also assesses the impact of AI on workforce dynamics, shedding light on how automation and augmentation have influenced job roles and skills requirements within these enterprises. Furthermore, the research evaluates the role of AI in enhancing decision-making processes, resource allocation, and customer engagement for SMEs. By examining real-world applications and success stories, the study aims to provide practical insights into the ways in which AI can be leveraged to optimize business operations and foster innovation in smaller enterprises. An essential aspect of this research involves addressing the ethical considerations and challenges associated with AI implementation in SMEs. Privacy concerns, data security, and potential biases in algorithms are examined to offer a comprehensive understanding of the risks and responsibilities that accompany the adoption of AI technologies. Ultimately, this study contributes to the existing body of knowledge by offering a nuanced perspective on the impact of AI in SMEs. The findings aim to inform policymakers, business leaders, and researchers about the opportunities and challenges inherent in integrating AI into the fabric of small and medium-sized enterprises, thereby facilitating informed decision making and strategic planning for a technologically advanced future.International Journal For Multidisciplinary Research00The findings aim to inform policymakers, business leaders, and researchers about the opportunities and challenges inherent in integrating AI into the fabric of small and medium-sized enterprises, thereby facilitating informed decision making and strategic planning for a technologically advanced future.International Journal For Multidisciplinary Research['Suchismita Paul', 'Varun Daga', 'Tanya Gupta', 'Aishwarya S']2023-12-25T00:00:00https://www.semanticscholar.org/paper/5b4664981a2d67240fa20c69eeed9eac2251957b 13d707303c4143c5b15dc9070b3307bf51d13883The study provides some philosophical reflections on the creativity of artificial in-telligence. The study supposes that artificial intelligence can be considered creative only if it creates something new with help of imagination (or its equivalent) and ap-pealing to the so-called “background” (background and general knowledge, biases, competencies, experience, habits, intuition, prejudices, political preferences, skills, stereotypes, values, and others), and its creative activity must be either necessary or arbitrary. Necessary creative activity is related to the solution of specific tasks, for example, within the framework of technical invention or scientific discovery. Arbitrary creative activity is associated with spontaneous, aimless and inexpedient human activity. This type of creative activity takes place when a creative doer has free time, leisure, enthusiasm (hobby), plays a game or is bored. Based on the two types of creative activity, two types of creative artificial intelligence can be distinguished: a weak creative artificial intelligence that makes necessary creative activities related to the tool nature of artificial intelligence, specifically to effectively solving specific problems and tasks, and a strong creative artificial intelligence that makes arbitrary creative activities, that is, creates for the sake of creating alone. The strong creative artificial intelligence can be possible only if the artificial intelligence is given autono-my, the freedom to manage that autonomy, and learns to manage its freedom.Skhid130The study provides some philosophical reflections on the creativity of artificial in-telligence and proposes two types of creative artificial intelligence: a weak creative artificial intelligence that makes necessary creative activities related to the tool nature of artificial intelligence, and a strong creative artificial intelligence that makes arbitrary creative activities.Skhid['Kostiantyn Raikhert']2023-12-25T00:00:00https://www.semanticscholar.org/paper/13d707303c4143c5b15dc9070b3307bf51d13883 331ebd4b6e72f40c3dd54e2a20afaf4153d08be1The article analyzes the use of artificial intelligence in various types of digital art at the current level of their development and outlines the possibilities of external ways of such interaction. The author considers specific artistic examples, which see that the use of artificial intelligence in art develops a complex art history and philo-sophical questions: what is art? how does computer technology change aesthetics? where is the line between technology and creativity? who is considered the author of the work generated by neuronets? is it possible to compare anthropocentric and non-anthropocentric forms of creativity? etc. The researcher demonstrates different approaches to the chosen topic, citing antagonistic points of view of scientists. Some of them believe that art with the help of a computer is the next step in the development of avant-garde trends, while others see these trends as the degradation and even the death of art. In digital painting, we are already faced with the use of works (the most famous example is the android-artist Ai-Da) who know how to draw in different techniques and with the use of different tools. The victory of a painting generated by a neural network at an art competition raised questions about its au-thorship and the admissibility of participation of such works in competitions for art-ists. Discussions on the ArtStation platform indicate that many artists do not per-ceive works made with the help of artificial intelligence as "real" works of art. In mu-sic today, it is possible not just to generate melodies, but to create an imitation of certain styles or composers, which raises questions about the nature of true creativi-ty and its limits. Another direction of using artificial intelligence in music is the crea-tion of virtual singers who become mega-popular. Such an example is given by the Japanese virtual singer Hatsune Miku, who is a hologram with an android interface and an artificially generated voice. In the field of video games, artificial intelligence knows how to create not only levels, but already generate game target worlds and complete games. Artificial intelligence in photography can generate images of non-existent people. At the same time, it is unlikely that artificial intelligence can com-pletely replace a human artist, after which he learns from the previously created ma-terial. This makes it possible to say that he left real creativity and knows how to compile in advance, and not to generate original artistic solutions.Skhid90Skhid['Mary Chikarkova']2023-12-25T00:00:00https://www.semanticscholar.org/paper/331ebd4b6e72f40c3dd54e2a20afaf4153d08be1 cbf83e062fecda928728d6f519c5d8c2fd32c84cThe purpose of the article is to identify the features of the development of modern great powers against the background of their digital contradictions and value specifics in order to determine scenarios for the development of the future political world order. The methodological lens was the principles of critical discourse analysis of academic literature, comparative analysis of value orientations, as well as the normative basis for the introduction of artificial intelligence technologies in modern great powers – Russia, the USA and China. As a hypothesis, the author suggests the Palladium effect, according to which a great power, being given the opportunity to change the world order based on its interests, will strive for this using modern technologies, appealing to justice and taking into account its own value orientations. The Palladium effect was studied by the author in the context of the analytical verification of the "Thucydides trap". L. Althusser's theory of ideology and a number of theses of modern realism were also taken into account as a conceptual framework. The conclusions indicate that the key to the emergence of an economically and politically profitable digital environment, comfortable for a great power, is its tax residents-digital corporations that develop and distribute digital standards in other countries. In this situation, Russia strategically needs to initiate protectionist measures for its digital corporations, a policy of supporting them abroad, create favorable conditions for public-private partnerships, financing, and attracting venture capital in the field of artificial intelligence technology development in order to preserve its value and digital sovereignty (as forms of state sovereignty). In addition, key trends in the field of AI competition and variants of the axiomachy (value confrontation) of the great powers are identified, and the main scenarios for the evolution of the political world order are proposed – Triplex Mundi, Duplex Mundi and Multiplex Mundi.Journal of Political Research110Key trends in the field of AI competition and variants of the axiomachy of the great powers are identified, and the main scenarios for the evolution of the political world order are proposed – Triplex Mundi, Duplex Mundi and Multiplex Mundi.Journal of Political Research['S. Fedorchenko']2023-12-25T00:00:00https://www.semanticscholar.org/paper/cbf83e062fecda928728d6f519c5d8c2fd32c84c 1dc783ce3f6bbaf4ee2be7eb7d64633688afd32dToday, artificial intelligence (AI) is rapidly gaining popularity in various sectors, including the corporate world, business circles, and people's daily lives. The application of artificial intelligence in such fields as medicine, banking and government structures is becoming more frequent. Artificial intelligence facilitates data processing, as it occurs without the intervention of human labor and usually ensures the accuracy of the tasks performed. According to statistics, the number of companies using artificial intelligence in their operations is increasing, and many organizations consider artificial intelligence as an important technology to achieve competitive advantage. This scientific study presents a comprehensive analysis of two leading artificial intelligence systems – ChatGPT-4 from the OpenAI company and Bard from the Google AI company. The work also provides an overview of the development of artificial intelligence in various fields and its impact on everyday human life, especially in such vital areas as medicine, finance, public administration, etc. The paper delves into a detailed comparison of different versions of ChatGPT (GPT-3 and GPT-4) by discussing and analyzing their capabilities, improvements, and limitations. The article also discusses the integration of the Bard system with Google services, its unique functionality and recent updates. The purpose of this study is to compare the capabilities of the artificial intelligence systems ChatGP-4T and Bard, highlight their strengths and weaknesses, as well as their practical application. The paper presents the results of comparative testing to evaluate the performance of each model (system) in various tasks, including solving a logical task, writing an essay, analyzing with subsequent suggestions for improving the web-site, and writing HTML/CSS code for a web-page. The results highlight the fact that, despite the recognized advantages of these models, their functional characteristics may sometimes be limited or not meet expectations when performing specific tasks and the choice of system (model) will be adjusted depending on the needs of users.Radiotekhnika00This scientific study presents a comprehensive analysis of two leading artificial intelligence systems – ChatGPT-4 from the OpenAI company and Bard from the Google AI company to compare the capabilities of the artificial intelligence systems ChatGP-4T and Bard, highlight their strengths and weaknesses, as well as their practical application.Radiotekhnika['Yu.L. Golikov', 'M.V. Yesina', 'O.A. Kobylianska']2023-12-25T00:00:00https://www.semanticscholar.org/paper/1dc783ce3f6bbaf4ee2be7eb7d64633688afd32d 30d29f0ca009e2f3e4f714710bc26b4af9370030Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi00The progress of AI-related ECG studies and the current technical orientation are summarized, the two core issues-the reliability and worth of AI-related ECG technology and the future opportunities and challenges are demonstrated and prospected.Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi['Xingyao Wang', 'Qian Li', 'Caiyun Ma', 'Shuo Zhang', 'Yujie Lin', 'Jianqing Li', 'Chengyu Liu']2023-12-25T00:00:00https://www.semanticscholar.org/paper/30d29f0ca009e2f3e4f714710bc26b4af9370030 94b69a37cc904caa599f185ff71aa2c292d635e5As cyber threats continue to evolve in complexity and sophistication, the integration of artificial intelligence (AI) in cybersecurity has emerged as a critical frontier for enhancing threat detection, response, and mitigation strategies. This research paper provides a comprehensive examination of the current state of AI applications in cybersecurity, evaluating their strengths, weaknesses, and potential impact on the evolving threat landscape. The study employs a multidimensional methodology, incorporating a thorough literature review, case studies, interviews with cybersecurity experts, and analysis of real-world incidentsJournal of Advanced Zoology00This research paper provides a comprehensive examination of the current state of AI applications in cybersecurity, evaluating their strengths, weaknesses, and potential impact on the evolving threat landscape.Journal of Advanced Zoology['P. S. Dandge', 'U. I. Dawre', 'R. F. Shirshikar']2023-12-25T00:00:00https://www.semanticscholar.org/paper/94b69a37cc904caa599f185ff71aa2c292d635e5 9a0ad2136701df73e4c2eed8ead351ef4f836b61This research aims to discuss the relationship between Artificial Intelligence (AI) and law. The emergence of the idea of using AI as a tool to analyse judges' decisions has generated mixed responses. On the one hand, the use of AI can be used as a tool to objectively ensure legal certainty, but on the other hand the use of AI can displace the legal supremacy of judges in court. This attracts the author's attention to examine the use of AI in analysing legal cases and as a consideration for judges in deciding polygamy cases. Polygamy itself is a very complex case in court. Judges' considerations in deciding polygamy cases do not only consider procedural aspects, but also involve substantial aspects related to the cumulative and alternative conditions of polygamy. As a chatbot-based platform, AI certainly has limited access in analysing the legal complexity in polygamy cases. This research focuses on the analysis of AI in analysing polygamy cases both in terms of legal basis and justice.  This research method is normative with a conceptual legal approach, data is obtained by netnography using the ChatGPT/OpenAi platform and analysed using the content analysis method. The results showed that there were two aspects that were considered by the judge in the AI version of the polygamy case. First, the juridical aspect which is based on the polygamy provisions in the Compilation of Islamic Law. The second aspect is the social aspect based on gender justice. In addition to presenting the essence of several laws and regulations, AI also provides complex social analyses with a gender justice perspective with simple and straightforward sentences. However, this does not mean that AI can be an absolute and stand-alone consideration in polygamy licence cases. AI is only a complement that makes it easier for judges to analyse cases. This is because the judge's involvement in cognitive and psychological aspects is still needed in interacting with litigants in court.Nurani350The results showed that there were two aspects that were considered by the judge in the AI version of the polygamy case, which means AI is only a complement that makes it easier for judges to analyse cases.Nurani: Jurnal Kajian Syari'ah dan Masyarakat['Ibnu Akbar Maliki', 'Zezen Zainul Ali', 'Muhammad Khusaini']2023-12-25T00:00:00https://www.semanticscholar.org/paper/9a0ad2136701df73e4c2eed8ead351ef4f836b61 cf5d8913d0013c4e2bfea2053451f83304c58fddThe monograph was written by a team of authors based on the results of the interregional conference "The possibilities of combining natural and artificial intelligence in educational systems", as well as on the basis of regular meetings within the framework of the virtual laboratory for the study of artificial intelligence and robotics. The discussion was held in a format combined with a meeting of the Southern Branch of the Interregional Public Organization "Academy of Informatization of Education" (UO AIO), which has already turned 20 years old. The main objective of the conference was to analyze the possibilities of interaction between natural and artificial intelligence in educational systems of various levels, the issues of the use of modern information technologies, software, artificial intelligence, digitalization in educational organizations in the implementation of educational activities and the educational process, robotic technologies in education, etc. were considered. The proposed materials may be useful to specialists of the Department of the education system of Russia and its regions, employees of federal and regional authorities and management, as well as regional associations of academic mobility.380['Afsana Abdullaeva', 'Elena Averchenko', "Tat'yana Aleksandrova", "Igor' Amiryan", 'Anna Artamonova', 'Timur Beterbiev', 'Denis Boyko', 'Andrey Bondarev', 'E. Grebenyuk', 'Adrian Grosu', 'Yuliya Demidova', 'I. Dzhariev', 'Angelina Dubrovina', 'M. Zhubanov', "Sergey Kas'yanov", 'Svetlana Komissarova', 'Sergey Kramarov', 'Marina Krivickaya', 'D.A. Letavin', "Natal'ya Lihanova", 'I. Magerramov', 'Alina Maksimenko', 'E. Mindzaeva', 'O. Mityasova', 'Elena Mudraya', 'V. Pegushin', 'Egor Petrov', 'O. Popov', "Ol'ga Potopahina", 'Yuriy V. Prus', 'Yuliya Redchenko', 'A. Rusakov', "Natal'ya Rutta", 'Aleksey Ruchka', 'Yuliya Savrasova', "Vil'yam Sar'yan", 'L. Saharova', 'Sergey Svetashev', 'Yaroslav Sviridov', 'Kristina Spicyna', 'Elena Tarasova', 'O. Tereschenko', 'Irina Tyushnyakova', 'Nikita Fomin', 'V. Khramov', 'Aleksandr Hrulenko', 'Nikita Shurgin', 'Georgiy Yalamov']2023-12-25T00:00:00https://www.semanticscholar.org/paper/cf5d8913d0013c4e2bfea2053451f83304c58fdd f1079967b762409afa5ae9b13322c60abcc9a2ba<jats:p />The eurasia proceedings of science, technology, engineering & mathematics00The Eurasia Proceedings of Science Technology Engineering and Mathematics['Mohamed Ahmed Hamada', 'Khaled M. K. Alhyasat']2023-12-25T00:00:00https://www.semanticscholar.org/paper/f1079967b762409afa5ae9b13322c60abcc9a2ba 5f633ccb88f9b498e0a54ffe8ad98913df298394"Globalization, industrialization, and digitalization have led to structural changes in the economy and labor markets, affecting their internationalization, flexibility, labor mobility, and the emergence of new forms of employment. The purpose of the academic paper is to identify the economic consequences of digital transformation and automation of labor markets on the example of the EU-27 countries for the period 2013-2022. The structural-functional analysis was used in the academic paper to characterize and systematically study the economic consequences of digitalization and automation in the labor markets of the EU-27 countries. The functioning of the labor market in various EU-27 countries in the context of digital transformation is characterized by a number of features. The EU-27 labor markets are characterized by rapid employment recovery, especially during the pandemic and economic downturn in 2020, and employment revival in 2021-2022."Economic Affairs00The structural-functional analysis was used in the academic paper to characterize and systematically study the economic consequences of digitalization and automation in the labor markets of the EU-27 countries for the period 2013-2022.Economic Affairs['Anastasiia Tokunova']2023-12-25T00:00:00https://www.semanticscholar.org/paper/5f633ccb88f9b498e0a54ffe8ad98913df298394 9bf8330a72bcbe513698c3163accf4e1b16b0941Journal of Advanced Zoology00Journal of Advanced Zoology['S. R. Paringe', 'S. V. Dubey', 'K. S. Ramishte']2023-12-25T00:00:00https://www.semanticscholar.org/paper/9bf8330a72bcbe513698c3163accf4e1b16b0941 766181ebc1f4e6716564bd6e1264e70e3c58978dJournal of Advanced Zoology00Journal of Advanced Zoology['N. H. Patil', 'S. H. Patel', 'S.D. Lawand']2023-12-25T00:00:00https://www.semanticscholar.org/paper/766181ebc1f4e6716564bd6e1264e70e3c58978d d18cbe268709f51d69acc84230bfe36ac4324e32One of the main goals of artificial intelligence is to develop learning algorithms that can be implemented on computers using simulations of the human brain. In this paper, we review methods for solving Turing machine problems using experiment based artificial intelligence algorithms. The paper also presents critical concepts of the NTM method based on a comprehensive review of the research made in this domain. Experimental results of applying the NTM method to memory data and problem solving are being presented. The paper presents scientific discussions ongoing in this domain and solutions to future challenges.JOURNAL OF SCIENTIFIC INNOVATIONS FOR DEVELOPMENT00Methods for solving Turing machine problems using experiment based artificial intelligence algorithms are reviewed and critical concepts of the NTM method are presented based on a comprehensive review of the research made in this domain.Journal of Science and Innovative Development['S. B. Ergashev', 'R. M. Yusupov']2023-12-25T00:00:00https://www.semanticscholar.org/paper/d18cbe268709f51d69acc84230bfe36ac4324e32 5af03b51c8a1f95e49df0ba372619a940ce05435The article discusses the theoretical legal foundations of a new type of arbitration – digital arbitration (or blockchain arbitration). The author formulated the concept of digital arbitration and analyzed the differences between digital arbitration and traditional arbitration from the point of view of theories about the legal nature of arbitration. In particular, the author believes that the term digital arbitration (blockchain arbitration) is used in three meanings. Firstly, the term digital arbitration refers to a way to protect the rights arising from smart contracts. This method is considered as an alternative to those methods that imply the need to seek judicial protection from the State or traditional arbitration. Secondly, digital arbitration refers to the body that organizes the digital trial of a legal dispute. And, thirdly, this concept denotes an artificial intelligence agent (robot), which considered the dispute submitted for its resolution. The author believes that due to its features, digital arbitration can be recommended as an alternative way to resolve disputes in the digital space of the EAEU.wisdom70WISDOM['Elena Ermakova', 'Olga Protopopova']2023-12-25T00:00:00https://www.semanticscholar.org/paper/5af03b51c8a1f95e49df0ba372619a940ce05435 4eafde89bad71de18292cfb77a6b3dafe0e5b5d8The paper provides a discussion of the policies pursued by the Chinese government in implementing the concept of the rule of law through the use of digital technologies. China’s recent achievements in creating the digital infrastructure and developing the digital economy are discussed. The paper explains how the Chinese government use the Internet, big data, artificial intelligence and other technologies to promote legal governance and achieve a synthesis of information technologies and the rule of law in public governance in terms of process and method. Since China is a country with an extensive territory and large population where improving the access to and quality of justice is problematic, policies are pursued to actively introduce modern digital technologies to digitize justice. Judicial institutions across the country also have a varying degree of experience of promoting smart justice. In 2021 and 2022, the Supreme People’s Court of China published one by one the following three major rules for online activities of courts: Rules for Online Justice; Rules for Online Mediation; Rules for Online Operation, to make online judicial operations across the board well-regimented. Guided by these three rules, Chinese courts have made certain progress in recent years to make their operations digital and smart. Since promoting the digital rule of law means the cultivation of talent, an enabling political environment was created to improve legal skills and cultivate specialists with competencies in the area of artificial intelligence, big data or cloud computing. The problems faced by China in promoting digital justice are currently experienced by many countries worldwide. The author aim is to explain the Chinese regulatory model to share this experience with other countries.Legal Issues in the Digital Age10The paper explains how the Chinese government use the Internet, big data, artificial intelligence and other technologies to promote legal governance and achieve a synthesis of information technologies and the rule of law in public governance in terms of process and method.Legal Issues in the Digital Age['Яо Ли']2023-12-25T00:00:00https://www.semanticscholar.org/paper/4eafde89bad71de18292cfb77a6b3dafe0e5b5d8 b8ab69bb3c7cd522723cd6e562ecb9a9865c6c7eProceedings of the Sixth International Symposium on the System of Radiological Protection00Proceedings of the Sixth International Symposium on the System of Radiological Protection['A. Hirasugi']2023-12-24T00:00:00https://www.semanticscholar.org/paper/b8ab69bb3c7cd522723cd6e562ecb9a9865c6c7e 860d8fd5fc42f8037d2d27aa336da48beec89de1This paper delves into the dynamic and evolving world of AI startups, examining the unique challenges and opportunities they face in the current market and investment landscape. The study's primary aim is to dissect the intersection of entrepreneurship and artificial intelligence, offering a nuanced understanding of how AI startups evolve, adapt, and succeed in a rapidly changing environment. The scope of the paper encompasses a thorough exploration of the AI startup ecosystem, focusing on strategic planning, market dynamics, and investment realities. It provides an in-depth analysis of the evolution of AI startups, from their inception to current trends, and investigates the impact of strategic alliances, regulatory challenges, and ethical considerations on these burgeoning enterprises. The methodology employed is a comprehensive literature review, synthesizing insights from various academic sources to construct a well-rounded view of the AI startup landscape. Key findings reveal that AI startups must navigate a complex array of challenges, including rapidly evolving technology, competitive market dynamics, and a shifting regulatory landscape. The study highlights the importance of innovative business models, strategic partnerships, and a keen understanding of regulatory and ethical issues in driving the success of AI startups. Conclusively, the paper recommends that AI startups adopt agile, innovative strategies, balancing technological advancement with ethical and regulatory compliance. It underscores the need for continuous adaptation and strategic foresight in the face of technological and market changes. This study serves as a valuable resource for entrepreneurs, investors, and policymakers in the AI domain, offering insights and guidance for navigating the multifaceted challenges of AI entrepreneurship. Keywords: AI Startups, Market Dynamics, Investment Challenges, Strategic Planning, Technological Innovation.International Journal of Management &amp; Entrepreneurship Research03This study provides an in-depth analysis of the evolution of AI startups, from their inception to current trends, and investigates the impact of strategic alliances, regulatory challenges, and ethical considerations on these burgeoning enterprises.International Journal of Management &amp; Entrepreneurship Research['Simon Kaggwa', 'Abiodun Akinoso', 'Samuel Onimisi Dawodu', 'Prisca Ugomma Uwaoma', 'Odunayo Josephine Akindote', 'Stephen Osawaru Eloghosa']2023-12-24T00:00:00https://www.semanticscholar.org/paper/860d8fd5fc42f8037d2d27aa336da48beec89de1 febf97d29e08906332db7fdcad24bd0fccd1520fThis paper presents a systematic review of the literature on integrating artificial intelligence (AI) to improve wheelchair navigation and control for people with mobility impairments. The review covers a range of AI-based approaches including computer vision, machine learning, and path planning algorithms. The paper highlights the potential benefits of integrating AI into wheelchair technology, including increased safety, autonomy, and personalized control. The review discusses the limitations and challenges of current wheelchair navigation and control systems, and how AI can address these limitations. The paper identifies common themes and trends in the literature and summarizes the strengths and weaknesses of existing AI-based wheelchair navigation and control systems. Finally, the paper concludes by discussing the potential future directions for research and development of AI-based wheelchair navigation and control systems. This review paper provides a valuable resource for researchers and engineers interested in developing and improving AI-based wheelchair technology.Journal of process management and new technologies491A systematic review of the literature on integrating artificial intelligence (AI) to improve wheelchair navigation and control for people with mobility impairments highlights the potential benefits of integrating AI into wheelchair technology, including increased safety, autonomy, and personalized control.Journal of process management and new technologies['S. K. Sahoo', 'Bibhuti Bhusan Choudhury']2023-12-24T00:00:00https://www.semanticscholar.org/paper/febf97d29e08906332db7fdcad24bd0fccd1520f 13bff89c7ee961fb82d19ee4866846127200c0cbInternational Research Journal of Modernization in Engineering Technology and Science01International Research Journal of Modernization in Engineering Technology and Science[]2023-12-24T00:00:00https://www.semanticscholar.org/paper/13bff89c7ee961fb82d19ee4866846127200c0cb 4a2731d14f1ffbd8e7a2718664b5c026ae579109This report outlines a transformative initiative in the financial investment industry, where the conventional decision-making process, laden with labor-intensive tasks such as sifting through voluminous documents, is being reimagined. Leveraging language models, our experiments aim to automate information summarization and investment idea generation. We seek to evaluate the effectiveness of fine-tuning methods on a base model (Llama2) to achieve specific application-level goals, including providing insights into the impact of events on companies and sectors, understanding market condition relationships, generating investor-aligned investment ideas, and formatting results with stock recommendations and detailed explanations. Through state-of-the-art generative modeling techniques, the ultimate objective is to develop an AI agent prototype, liberating human investors from repetitive tasks and allowing a focus on high-level strategic thinking. The project encompasses a diverse corpus dataset, including research reports, investment memos, market news, and extensive time-series market data. We conducted three experiments applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat as the base model, as well as instruction fine-tuning on the GPT3.5 model. Statistical and human evaluations both show that the fine-tuned versions perform better in solving text modeling, summarization, reasoning, and finance domain questions, demonstrating a pivotal step towards enhancing decision-making processes in the financial domain. Code implementation for the project can be found on GitHub: https://github.com/Firenze11/finance_lm.arXiv.org130ArXiv['Lezhi Li', 'Ting-Yu Chang', 'Hai Wang']2023-12-24T00:00:00https://www.semanticscholar.org/paper/4a2731d14f1ffbd8e7a2718664b5c026ae579109 fba98b547b9897169fcf499f6077251b11d568daThis paper provides a comprehensive analysis of cognitive biases in forensics and digital forensics, examining their implications for decision-making processes in these fields. It explores the various types of cognitive biases that may arise during forensic investigations and digital forensic analyses, such as confirmation bias, expectation bias, overconfidence in errors, contextual bias, and attributional biases. It also evaluates existing methods and techniques used to mitigate cognitive biases in these contexts, assessing the effectiveness of interventions aimed at reducing biases and improving decision-making outcomes. Additionally, this paper introduces a new cognitive bias, called"impostor bias", that may affect the use of generative Artificial Intelligence (AI) tools in forensics and digital forensics. The impostor bias is the tendency to doubt the authenticity or validity of the output generated by AI tools, such as deepfakes, in the form of audio, images, and videos. This bias may lead to erroneous judgments or false accusations, undermining the reliability and credibility of forensic evidence. The paper discusses the potential causes and consequences of the impostor bias, and suggests some strategies to prevent or counteract it. By addressing these topics, this paper seeks to offer valuable insights into understanding cognitive biases in forensic practices and provide recommendations for future research and practical applications to enhance the objectivity and validity of forensic investigations.arXiv.org890A new cognitive bias is introduced, called"impostor bias", that may affect the use of generative Artificial Intelligence (AI) tools in forensics and digital forensics.ArXiv['Mirko Casu', 'Luca Guarnera', 'P. Caponnetto', 'S. Battiato']2023-12-24T00:00:00https://www.semanticscholar.org/paper/fba98b547b9897169fcf499f6077251b11d568da f74d6362d5dc565b02b9af5107885e3d10fe67b2Madam, AI is revolutionizing the world in all spheres and the institution of medicine worldwide is no exception. While we are most familiar with its contribution to radiology, AI has now extensive applications within dermatology, ophthalmology, pathology, medicine, ophthalmology, neurosciences and more. Through progressive efforts of both doctors and programmers, AI is now becoming more adept in the field of medicine. Some of its applications in modern medicine include staging of skin cancers by looking at dermatoscopic images (1), classifying age-related macular degeneration and diabetic macular edema by through OCT images (2), and more. The accuracy by which AI performs these tasks is comparable to a human being. However, Pakistan being in the developing stage is still lagging behind. With inadequate resources, constant economic desolation, and limited knowledge and data in the field of IT, the country is far behind in this rapidly progressive field. According to a study that included participants throughout Pakistan 74% of doctors and 68.8% of medical students had a basic knowledge of AI but only 27.3% of doctors and 19.4% of students were aware of its medical applications (3). In another study only 56.7% of the participants were familiar with AI and its subtypes, which highlighted the unawareness about AI in more than 40% of the participants. These studies clearly show the deficit in knowledge that medical personnel have about AI, its applications and impact on health care. AI and its rapid progress in health sectors worldwide are creating discrepancies between them and us. Hence, the senior authorities in our education system should come up with a proposition that can shorten this gap. By introducing basic levels of AI in the medical curriculum with a course spanning only around 1 week, including basics of coding and computational thinking (5), our medical personnel will be able to incorporate a fundamental level of computational thinking and coding language. This will help them in adapting to the revolutionized healthcare system as well as give them enough command to grapple with it and give feedback to improvise AI even more. And since medical science is based on logical reasoning and problem-solving, much like IT, we believe that our medical students can and will be able to inculcate this into their skills. This can be our very first step towards a much more efficient and progressive healthcare system.JPMA. The Journal of the Pakistan Medical Association00Introducing basic levels of AI in the medical curriculum with a course spanning only around 1 week will help medical personnel incorporate a fundamental level of computational thinking and coding language, and can be the very first step towards a much more efficient and progressive healthcare system.JPMA. The Journal of the Pakistan Medical Association['Muhammad Masharib Khan', 'Sumaira Malik Malik', 'Fabiha Vohra Vohra']2023-12-24T00:00:00https://www.semanticscholar.org/paper/f74d6362d5dc565b02b9af5107885e3d10fe67b2 bfcbb8524c1de6dc35d7c41a4de8e98667ac8523International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2023-12-24T00:00:00https://www.semanticscholar.org/paper/bfcbb8524c1de6dc35d7c41a4de8e98667ac8523 c4c3d7659b74bf26fcb36d603c6fd2b9d8417f40International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2023-12-24T00:00:00https://www.semanticscholar.org/paper/c4c3d7659b74bf26fcb36d603c6fd2b9d8417f40 d6216540832915ea95096f916f9e8c1dc74facbfRecent remarkable success in the deep-learning industries has unprecedentedly increased the need for reliable model deployment. For example, the model should alert the user if the produced model outputs might not be reliable. Previous studies have proposed various methods to solve the Out-of-Distribution (OOD) detection problem, however, they generally require a burden of resources. In this work, we propose a novel and simple method, Multiple Input Mixup (MIM). Our method can help improve the OOD detection performance with only single epoch fine-tuning. Our method does not require training the model from scratch and can be attached to the classifier simply. Despite its simplicity, our MIM shows competitive performance. Our method can be suitable for various environments because our method only utilizes the In-Distribution (ID) samples to generate the synthesized OOD data. With extensive experiments with CIFAR10 and CIFAR100 benchmarks that have been largely adopted in out-of-distribution detection fields, we have demonstrated our MIM shows comprehensively superior performance compared to the SOTA method. Especially, our method does not need additional computation on the feature vectors compared to the previous studies. All source codes are publicly available at https://github.com/ndb796/MultipleInputMixup.arXiv.org250This work proposes a novel and simple method, Multiple Input Mixup (MIM), which can help improve the OOD detection performance with only single epoch fine-tuning and shows competitive performance.ArXiv['Dasol Choi', 'Dongbin Na']2023-12-24T00:00:00https://www.semanticscholar.org/paper/d6216540832915ea95096f916f9e8c1dc74facbf 305e2a4062d3b6221bb91162231bc3ab2bfaa8e6A virtual grid (VG) is an interconnected system that includes a decentralized power plant, flexible loads, and energy storage facilities. During peak demand, a VG can distribute the power provided by several interconnected units in an equitable manner, ensuring that the grid burden is spread out evenly. Electric vehicles (EVs) and other demand-side energy devices can help keep the energy market supply and demand in harmony with proper use. However, it might be difficult to maintain a consistent power balance due to the inherent unpredictability of the power units. Furthermore, the issue of protecting the privacy of communications between a VPP aggregator and the final facilities has not been thoroughly explored. In this paper, we provided detailed analytics on optimization-based crypto mining for electric vehicle systems. The simulation is conducted to test the efficacy of the model, and the results show that the proposed method has a higher rate of accuracy than other methods.International Journal of Data Informatics and Intelligent Computing160Detailed analytics on optimization-based crypto mining for electric vehicle systems is provided and the results show that the proposed method has a higher rate of accuracy than other methods.International Journal of Data Informatics and Intelligent Computing['S.Radha Rammohan', 'A.Jayanthiladevi']2023-12-24T00:00:00https://www.semanticscholar.org/paper/305e2a4062d3b6221bb91162231bc3ab2bfaa8e6 654a315a99ec3a7daf1bd4f84d1af86d51a69a50Microbes and Infectious Diseases00Microbes and Infectious Diseases['B. Gulumbe', 'Abbas Yusuf', 'Abdulrahim Abdulrakib', 'Umar Liman']2023-12-24T00:00:00https://www.semanticscholar.org/paper/654a315a99ec3a7daf1bd4f84d1af86d51a69a50 5457aacf86ac420e13c7ead5af0faba0d383cd66This research project represents a major advancement in automation and intelligent interaction with the environment as it explores the integration of robotic arms and image processing techniques. The main objective is to equip a robotic arm with the ability to recognize, locate, and handle objects based only on their color and form characteristics. This project acknowledges the critical importance of accuracy and flexibility in modern robotics. Because they frequently follow pre-programmed instructions, conventional robotic systems are less flexible in changing environments. This project, on the other hand, makes use of real-time image processing to give the robotic arm perception. This improves the arm's accuracy when carrying out tasks and gives it the ability to independently adjust to changes in its environment. The "Robotic Arm Color and Shape Detection Using Image Processing" project aims to contribute to a future where humans and robots collaborate seamlessly through exploration, development, and innovation. This partnership pushes the envelope in terms of automation, resulting in increased accuracy and productivity in a variety of sectors.International Research Journal of Modernization in Engineering Technology and Science80The main objective is to equip a robotic arm with the ability to recognize, locate, and handle objects based only on their color and form characteristics using real-time image processing.International Research Journal of Modernization in Engineering Technology and Science['Rahul Kapgate', 'Devarshi Tambulkar', 'Shivani Dorle', 'Asst. Prof. Prerna', 'B. Jaipurkar']2023-12-24T00:00:00https://www.semanticscholar.org/paper/5457aacf86ac420e13c7ead5af0faba0d383cd66 b8113d5922063ceb73074cbdff31b45ead365df5Growing recognition of the potential for exploitation of personal data and of the shortcomings of prior privacy regimes has led to the passage of a multitude of new online privacy regulations. Some of these laws -- notably the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) -- have been the focus of large bodies of research by the computer science community, while others have received less attention. In this work, we analyze a set of Internet privacy and data protection regulations drawn from around the world -- both those that have frequently been studied by computer scientists and those that have not -- and develop a taxonomy of rights granted and obligations imposed by these laws. We then leverage this taxonomy to systematize 270 technical research papers published in computer science venues that investigate the impact of these laws and explore how technical solutions can complement legal protections. Finally, we analyze the results in this space through an interdisciplinary lens and make recommendations for future work at the intersection of computer science and legal privacy.arXiv.org3040This work analyzes a set of Internet privacy and data protection regulations drawn from around the world and develops a taxonomy of rights granted and obligations imposed by these laws and systematizes 270 technical research papers published in computer science venues that investigate the impact of these laws and explore how technical solutions can complement legal protections.ArXiv['Eleanor Birrell', 'Jay Rodolitz', 'Angel Ding', 'Jenna Lee', 'Emily McReynolds', 'Jevan Hutson', 'Ada Lerner']2023-12-24T00:00:00https://www.semanticscholar.org/paper/b8113d5922063ceb73074cbdff31b45ead365df5 c6a0dadbf89a3ea5eda104a5f74826b846b8d6d2Artificial Intelligence (AI) is revolutionizing how marketers conduct themselves digitally. There is a lack of empirical research into how AI affects digital marketing. Hence, this study aims to explore how the prevalence of AI in businesses has enhanced digital marketing. We proposed a model containing AI and five digital marketing forms: content marketing, social media marketing, email marketing, pay-per-click advertising, and search engine optimization. 252 responses were analyzed using the partial least square-structural equation modeling. The findings indicate artificial Intelligence has a positive and significant effect on content marketing, social media marketing, email marketing, pay-per-click advertising, and search engine optimization. Content marketing appeared to be hugely affected, and pay-per-click appeared to be the least affected by AI. The study encourages marketers to deploy AI in every facet of marketing since it significantly affects digital marketing.International journal of scientific and research publications660The findings indicate artificial Intelligence has a positive and significant effect on content marketing, social media marketing, email marketing, pay-per-click advertising, and search engine optimization.International Journal of Scientific and Research Publications['Mboloko Moningo Costa', 'Ansah Jackson', 'Tinotenda Maxwell Nyamuranga', 'Joseph Bosha', 'Fabrice Mvita']2023-12-24T00:00:00https://www.semanticscholar.org/paper/c6a0dadbf89a3ea5eda104a5f74826b846b8d6d2 57e71fd9567f8ebdaffb38ace57bb01c333e016dThis groundbreaking research endeavors to revolutionize substation engineering in Bangladesh by introducing an innovative paradigm that integrates artificial intelligence (AI) and machine learning (ML) methodologies. In response to the dynamic and challenging operational environment, this study focuses on the development of an adaptive substation infrastructure capable of intelligently responding to fluctuating energy demands, environmental stresses, and emerging grid complexities. Through the application of advanced AI algorithms, the research addresses real-time fault detection, predictive maintenance, and comprehensive condition monitoring within the substation framework. Harnessing the capabilities of ML models, the proposed infrastructure aims to optimize energy flow, enhance grid resilience, and mitigate potential failures by autonomously adapting to evolving operational scenarios. By combining cutting-edge technology with the unique challenges of the Bangladeshi power landscape, this research not only aims to advance the field of substation engineering but also holds the promise of significantly contributing to the sustainable development of the nation's power infrastructure. The findings are anticipated to guide the design and implementation of intelligent substation systems, ushering in a new era of efficiency, reliability, and adaptability in the Bangladesh power grid.International Journal For Multidisciplinary Research120This groundbreaking research endeavors to revolutionize substation engineering in Bangladesh by introducing an innovative paradigm that integrates artificial intelligence (AI) and machine learning (ML) methodologies to guide the design and implementation of intelligent substation systems.International Journal For Multidisciplinary Research['Md Hasibuzzaman']2023-12-24T00:00:00https://www.semanticscholar.org/paper/57e71fd9567f8ebdaffb38ace57bb01c333e016d 19914d437df023b0c259e7b5a59dcded3e85f5d3#NAME?International journal of scientific and research publications110The research evaluates the efficacy of AI-driven solutions in augmenting threat intelligence, automating threat detection, and mitigating cyber risks and highlights the ethical considerations and challenges associated with integrating AI into cybersecurity.International Journal of Scientific and Research Publications['Basiru A. Olafuyi']2023-12-24T00:00:00https://www.semanticscholar.org/paper/19914d437df023b0c259e7b5a59dcded3e85f5d3 b4726c6704fe2bd36b9931b797750f9fc9cf48f1 无创血流动力学监测具有简单易操作、无创、几乎无并发症、患者依从性好等优点，在临床实践中，可以为分析患者生理和病理状态以及心血管疾病的预防和诊断提供重要的参考和指导。人工智能（AI）具备对海量数据进行高级计算的能力，为无创血流动力学监测提供了更多的可能和更广阔的前景。该文对基于AI的无创血流动力学监测的相关研究进行了综述。. Zhonghua xin xue guan bing za zhi00Zhonghua xin xue guan bing za zhi['R. Guo', 'K. Y. Chen']2023-12-24T00:00:00https://www.semanticscholar.org/paper/b4726c6704fe2bd36b9931b797750f9fc9cf48f1 7b25d48639136c485652a8205ac47f033a5a66d2Nowadays, we are witnessing an increasing adoption of Artificial Intelligence (AI) to develop techniques aimed at improving the reliability, effectiveness, and overall quality of software systems. Deep reinforcement learning (DRL) has recently been successfully used for automation in complex tasks such as game testing and solving the job-shop scheduling problem. However, these specialized DRL agents, trained from scratch on specific tasks, suffer from a lack of generalizability to other tasks and they need substantial time to be developed and re-trained effectively. Recently, DRL researchers have begun to develop generalist agents, able to learn a policy from various environments and capable of achieving performances similar to or better than specialist agents in new tasks. In the Natural Language Processing or Computer Vision domain, these generalist agents are showing promising adaptation capabilities to never-before-seen tasks after a light fine-tuning phase and achieving high performance. This paper investigates the potential of generalist agents for solving SE tasks. Specifically, we conduct an empirical study aimed at assessing the performance of two generalist agents on two important SE tasks: the detection of bugs in games (for two games) and the minimization of makespan in a scheduling task, to solve the job-shop scheduling problem (for two instances). Our results show that the generalist agents outperform the specialist agents with very little effort for fine-tuning, achieving a 20% reduction of the makespan over specialized agent performance on task-based scheduling. In the context of game testing, some generalist agent configurations detect 85% more bugs than the specialist agents. Building on our analysis, we provide recommendations for researchers and practitioners looking to select generalist agents for SE tasks, to ensure that they perform effectively.arXiv.org00This paper conducts an empirical study aimed at assessing the performance of two generalist agents on two important SE tasks: the detection of bugs in games and the minimization of makespan in a scheduling task, to solve the job-shop scheduling problem.ArXiv['Paulina Stevia Nouwou Mindom', 'Amin Nikanjam', 'Foutse Khomh']2023-12-24T00:00:00https://www.semanticscholar.org/paper/7b25d48639136c485652a8205ac47f033a5a66d2 d9ef623c47cbe98e2cd6b42db5e473a23d40950cHUMANITIES OF THE SOUTH OF RUSSIA00HUMANITIES OF THE SOUTH OF RUSSIA['Elena Bazhenova', 'Alla Nikonova']2023-12-24T00:00:00https://www.semanticscholar.org/paper/d9ef623c47cbe98e2cd6b42db5e473a23d40950c b90fe8f3be68ae8f6b27b669ac23c81cd6b2a224International Journal of Women's Health and Reproduction Sciences140International Journal of Women's Health and Reproduction Sciences['M. Kurdoğlu', 'A. Khaki']2023-12-24T00:00:00https://www.semanticscholar.org/paper/b90fe8f3be68ae8f6b27b669ac23c81cd6b2a224 e5218b82bc34c9003e07ef4f418a3c7731a7833aThe purpose of this study is to find out district regulation surveillance system in framework of creating district autonomy. The method used in this study is qualitative method. The results obtained are repressive supervision of local regulations by authorized officials with such measures. The strong impression is no different from testing in the context of maetril testing of laws and regulations carried out by the judiciary. However, repressive supervision by authorized officials on local regulations, which is formed based on the vertical division of authority based on laws and regulations, is not only limited to the formulation in existing laws and regulations, but can develop and be expanded on the basis of government policies in granting autonomy, government policy (central), provincial and other regions in accordance with government functions placed in the government and regions. Kesimpulan yang didapatkan yaitu the supervision provisions of Regional Regulations according to the legal system in Indonesia, are known for preventive supervision and repressive supervision. Preventive supervision is temporary prevention which prevents the authority from being placed on authorized officials. Although explicitly preventive supervision is not expressly stated, it is normatively regulated in Law No. 32 of 2004 which states that local regulations must have criteria that must not conflict with public interest, other regional regulations and higher laws and regulationsInternational Journal of Engineering Business and Social Science140International Journal of Engineering Business and Social Science['Budi Santosa', 'Atma Suganda', 'Ismail Ismail']2023-12-23T00:00:00https://www.semanticscholar.org/paper/e5218b82bc34c9003e07ef4f418a3c7731a7833a 780afb58546d4165b0e565fb05ca9f29406f5ed5Each student has different skills, interests, and learning paces in a classroom. If each student has a personal tutor to support learning according to their ability, it will improve the quality of teaching and no student will be left behind. In reality, no school has enough teachers to support individual learning, but each teacher has to handle many students in the same class. Therefore, an AI Chatbot that acts as a “virtual teacher” next to a real teacher can do that. AI Chatbot can support individual students in a friendly, interesting environment and provide knowledge depending on their cognitive level. Learning with AI Chatbot also helps students feel more interested and motivated with new learning methods. Instead of teachers providing information and knowledge for students to remember and apply, AI Chatbot will help learners build and create their knowledge through interactions and experiences. Besides providing answers to learners’ queries, Chatbots can provide step-by-step instructions to achieve teaching goals for specific lessons. In this article, the authors based on the applications of AI Chatbot in teaching to present a teaching scenario using AI Chatbot to teach mathematics with a self-regulated learning orientation for primary school students. Specifically, the authors have built a scenario for the “Millimeter” lesson in the Mathematics, 3rd Grade according to three phases of self-regulated learning: forethought, performance and self-reflection.International Journal of Current Science Research and Review250This article presents a teaching scenario using AI Chatbot to teach mathematics with a self-regulated learning orientation for primary school students according to three phases of self-regulated learning: forethought, performance and self-reflection.International Journal of Current Science Research and Review['Nguyen Thi Hoai Nam', 'Nguyen Thi Huong Giang']2023-12-23T00:00:00https://www.semanticscholar.org/paper/780afb58546d4165b0e565fb05ca9f29406f5ed5 82cd8cdb11a5a6c1d1595a9cb6f93b8cae552594Artificial Intelligence (AI) presents both opportunities and challenges in fostering emotional intelligence (EI) in students. EI, vital for personal, academic, and professional success, involves recognising and regulating emotions in oneself and others. This opinion piece explores AI's potential and challenges in enhancing EI. AI's role in higher education and its various applications, including assessment and prediction, are discussed. The article also addresses the recent proliferation of Generative AI (GenAI) tools, which generate diverse content types and have sparked debates in education. AI's potential in developing each component of Goleman's EI model (1998) is examined, focusing on empathy, social skills, self-awareness, self-regulation, and motivation. AI-driven applications, such as those aiding individuals in recognising and managing emotions, practising empathy, and tailoring educational content to foster motivation, are highlighted. The piece also acknowledges concerns, such as ethical and privacy considerations in data collection, potential biases in AI algorithms, and the risk of overreliance on AI. In conclusion, we advocate for a balanced approach that combines AI tools with traditional teaching methods and human interactions to cultivate EI effectively whilst managing associated risks.Journal of Perspectives in Applied Academic Practice151An opinion piece explores AI's potential and challenges in enhancing EI, and advocates for a balanced approach that combines AI tools with traditional teaching methods and human interactions to cultivate EI effectively whilst managing associated risks.Journal of Perspectives in Applied Academic Practice['Nayiri Keshishi', 'Dr Sarah Hack']2023-12-23T00:00:00https://www.semanticscholar.org/paper/82cd8cdb11a5a6c1d1595a9cb6f93b8cae552594 d9ccf2ee47783b0f5e4cfb1783243e786d480900The term “child labour” is often defined as work that deprives children of their childhood, their potential and their dignity, and that is harmful to physical and mental development. The employment of minors in any job that robs them of their childhood, potential, or dignity and is detrimental to their physical or mental development is referred to as child labour. It is a complicated problem with many underlying causes, such as economic exploitation, poverty, child labour as a cheap commodity, large family size, compulsory education, backwardness and societal views. There is a separate legislation that prohibits child labour. This Act was enacted in 1986, The Child Labour (Prohibition and Regulation) Bill. Every citizen should be aware of his or her obligations and take appropriate steps to prevent child labour.International Journal For Multidisciplinary Research00International Journal For Multidisciplinary Research['Tvisha G']2023-12-23T00:00:00https://www.semanticscholar.org/paper/d9ccf2ee47783b0f5e4cfb1783243e786d480900 51adf629996e7b515e250720858d1cf660bc7adbAI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination. LLM-powered agents typically require invoking LLM APIs and employing artificially designed complex prompts, which results in high inference latency. While this paradigm works well in scenarios with minimal interactive demands, such as code generation, it is unsuitable for highly interactive and real-time applications, such as gaming. Traditional gaming AI often employs small models or reactive policies, enabling fast inference but offering limited task completion and interaction abilities. In this work, we consider Overcooked as our testbed where players could communicate with natural language and cooperate to serve orders. We propose a Hierarchical Language Agent (HLA) for human-AI coordination that provides both strong reasoning abilities while keeping real-time execution. In particular, HLA adopts a hierarchical framework and comprises three modules: a proficient LLM, referred to as Slow Mind, for intention reasoning and language interaction, a lightweight LLM, referred to as Fast Mind, for generating macro actions, and a reactive policy, referred to as Executor, for transforming macro actions into atomic actions. Human studies show that HLA outperforms other baseline agents, including slow-mind-only agents and fast-mind-only agents, with stronger cooperation abilities, faster responses, and more consistent language communications.Adaptive Agents and Multi-Agent Systems696A Hierarchical Language Agent (HLA) is proposed for human-AI coordination that provides both strong reasoning abilities while keeping real-time execution and human studies show that HLA outperforms other baseline agents, including slow-mind-only agents and fast-mind-only agents, with stronger cooperation abilities, faster responses, and more consistent language communications.ArXiv['Jijia Liu', 'Chao Yu', 'Jiaxuan Gao', 'Yuqing Xie', 'Qingmin Liao', 'Yi Wu', 'Yu Wang']2023-12-23T00:00:00https://www.semanticscholar.org/paper/51adf629996e7b515e250720858d1cf660bc7adb 45f74a8871d7e09c300dd55fed8a9b4a7c9b59fdIn this study, we conduct a thorough comparative analysis between artificial intelligence (AI)- generated humor and human humor. The objective is to acquire a more profound understanding of AI’s present capabilities in generating humorous text. We investigate the structural, sentiment, and linguistic patterns in jokes created by AI and humans, evaluating ’funniness’ and ’originality’ via a comprehensive annotation process. Our findings indicate that AI can produce humorous and occasionally novel content. Additionally, we employed the RoBERTa model for humor detection on a dataset consisting of 500 entries, including both human and AI-generated humor. This model demonstrated its proficiency in accurately categorizing a large dataset encompassing up to 200,000 entries with remarkable accuracy of up to 98%. Nonetheless, it lacks the emotional depth and originality commonly seen in human humor. The study underscores the challenge involved in developing AI models that can generate humor equivalent to human communication. Future research should focus on enhancing AI’s ability to create humor and further examine AI’s potential to adopt human humor strategies. Despite some limitations, this study contributes significantly to improving the humorous capabilities of AI models and the expandability of AI-generated humor.Soft Computing, Artificial Intelligence and Applications210The structural, sentiment, and linguistic patterns in jokes created by AI and humans are investigated, evaluating ’funniness’ and ’originality’ via a comprehensive annotation process and indicate that AI can produce humorous and occasionally novel content.Soft Computing, Artificial Intelligence and Applications['H. Avetisyan', 'Parisa Safikhani', 'D. Broneske']2023-12-23T00:00:00https://www.semanticscholar.org/paper/45f74a8871d7e09c300dd55fed8a9b4a7c9b59fd a2ef75519e3751e3ae2efeb4d7f5a6e27562099bThe paper examines students' perceptions of AI in financial decision-making. It found that 88% of students believe AI is crucial, but 49% reconsider traditional financial career paths due to AI's prominence. Additionally, 84% recognize the importance of AI education, but only 55 students received any AI instruction. Those who did received AI instruction displayed more positive attitudes towards AI tools. The study recommends enhancing AI education in finance curricula to bridge the knowledge gap and prepare students for AI-driven financial environments. It also emphasizes the need to address ethical concerns, maintain human-cantered teaching methodologies, and promote equitable access to AI technologies in education. Despite scepticism, AI has the potential to improve financial decision-making and personalize learning experiences.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)260Examining students' perceptions of AI in financial decision-making found that 88% of students believe AI is crucial, but 49% reconsider traditional financial career paths due to AI's prominence, and the study recommends enhancing AI education in finance curricula to bridge the knowledge gap and prepare students for AI-driven financial environments.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['S. Tyagi', 'Himanshu Kargeti', 'Neha Rastogi', 'Rajesh Tiwari', 'Anuj']2023-12-23T00:00:00https://www.semanticscholar.org/paper/a2ef75519e3751e3ae2efeb4d7f5a6e27562099b 497ee28bc22bacd642bd4c3371d0a52b34a95b46Theoretical background: The rapid and exponential technological advancements have far-reaching impacts on management information systems, management practices, and human life. The promising outcomes in Artificial Intelligence and cutting-edge research on semantic networks and natural language processing have motivated the authors to envision the future of management technology. Purpose of the article: Our paper focuses on the new communication facilities and artificial intelligence models used to process management-type queries in natural language. Research methods: The article discusses recently developed technologies, proposed by Google and Microsoft, notably Google Bard and Bing integrated with ChatGPT-4. Both chatbots use Generative AI methods and large language models to understand domain-based queries and generate answers. Main findings: The practical and social implications of new models in management practice are discussed. To illustrate the qualities and weaknesses of the features of new technologies, four examples of management decision-making are discussed. The case studies also show differences between these two technologies. Finally, the paper concludes by summarizing the expectations and limitations of Generative AI applications in management. The paper is one of the first publications describing and demonstrating the idea of interfaces in natural language in business-oriented applications.Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia290The paper is one of the first publications describing and demonstrating the idea of interfaces in natural language in business-oriented applications and summarizing the expectations and limitations of Generative AI applications in management.Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia['Jerzy Korczak', 'Ilona Pawełoszek']2023-12-23T00:00:00https://www.semanticscholar.org/paper/497ee28bc22bacd642bd4c3371d0a52b34a95b46 7fb5f1cb365d59a1143d1145900541f249066cf9Purpose of the Paper: The purpose of this research paper is to explore the potential of artificial intelligence (AI) in promoting sustainable workplaces, specifically in the context of green HR. The motivation behind this study is the growing interest in environmental sustainability and the need for organizations to adopt sustainable practices. The integration of AI in HR practices has the potential to promote sustainability and reduce the carbon footprint of organizations. Problem Identified: The problem addressed in this paper is the limited understanding of the potential of AI in promoting sustainable workplaces, specifically in the HR domain. The paper aims to provide an in-depth analysis of the potential benefits and challenges of incorporating AI in HR practices to promote environmental sustainability. Approach: The approach taken in this study involves a thorough review of relevant literature on AI, green HR, and sustainable workplaces. The paper discusses the role of AI in promoting sustainable workplaces, including identifying patterns of energy consumption and reducing waste through AI-powered energy management systems such as automated lighting and heating systems and smart building technologies. Results: The paper summarizes the benefits of adopting sustainable workplace practices, including reduced costs, improved employee health, and enhanced corporate reputation. It also highlights case studies of companies that have successfully implemented sustainable workplace practices. The challenges of adopting AI for green HR are discussed, including cost, security, and privacy concerns, and the need for skilled AI professionals. The potential impact of AI on job displacement and the need for reskilling and upskilling are also analyzed. Strategies for implementing AI for green HR are presented, including best practices for effective change management, stakeholder engagement, and collaboration with AI vendors and suppliers. The importance of ethics and transparency in AI implementation is also discussed. Conclusion: In conclusion, the paper presents key findings and recommendations for companies seeking to implement AI for green HR. It reflects on the future of AI for sustainable workplaces and potential areas for future research. The study provides valuable insights into the potential of AI in promoting sustainable workplaces and serves as a foundation for further research in this area. Keyword: AI, Human Resource, SustainableINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The paper discusses the role of AI in promoting sustainable workplaces, including identifying patterns of energy consumption and reducing waste through AI-powered energy management systems such as automated lighting and heating systems and smart building technologies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['Ms Sakshi Rastogi', 'Mr. Yatharth Pandya']2023-12-23T00:00:00https://www.semanticscholar.org/paper/7fb5f1cb365d59a1143d1145900541f249066cf9 297e0c331bed92cdedc8e83c2b782483d24744caSwarm intelligence has become a hot topic for a lot of AI enthusiasts as it relates mother nature to technology. There have been many recent explorations in the field of swarm intelligence. These techniques offer a huge variety of options to solve a particular problem. Swarm intelligence is such a subdomain which can easily be combined with other algorithms to achieve the desired results through hybridization. Furthermore, due to its adaptable nature, swarm intelligence can be used to figure out solutions for problems in hand with ease. This paper can be considered as a beginner's guide to learn about swarm intelligence. It describes the top 4 leading swarm intelligence optimization algorithms namely Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) and Firefly Algorithm (FA); along with their principles and mechanisms.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)170The top 4 leading swarm intelligence optimization algorithms namely Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony Optimization and Firefly Algorithm are described along with their principles and mechanisms.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Namrata Rajendra Augad', 'V. Gutte']2023-12-23T00:00:00https://www.semanticscholar.org/paper/297e0c331bed92cdedc8e83c2b782483d24744ca 1d148069d998031d581acd3d9bc7ce0ceb4b355aAI and data science are like magical tools that are changing how businesses work all around the world! This is 21 st century, and it could be called the digital era of AI and Data Science for entrepreneurship, as every entrepreneur is excited to use AI and Data Science in its business. It is of no doubt that if entrepreneur wants innovation, growth and success, it has to use these. When we say “Artificial Intelligence,” we're usually talking about very complicated computer programs that are made to act like humans in ways like planning, fixing problems, and learning. It's genuinely captivating to witness how AI's prowess is transforming the business landscape, creating a path filled with thrilling possibilities in this digital age. The way AI is reshaping businesses and opening up new horizons is truly fascinating and holds great promise for the future. The way businesses operate is undergoing a significant transformation, and it's remarkable to witness the impact AI is having on their journey towards success. However, likening AI to a “vehicle” in its general reference does capture a certain level of accuracy, yet it lacks the granularity required to delineate the distinct capabilities that set AI apart. To gain a comprehensive understanding of the prevailing AI applications in the realm of businesses, a more in-depth exploration into the various types of AI becomes imperative. In practice, AI primarily assumes the role of a supportive tool, augmenting and enhancing human intelligence and creativity rather than supplanting it altogether. Notably, its salient advantage lies in its ability to rapidly process and comprehend vast quantities of data, outpacing the cognitive capacities of the human brain, thus empowering businesses with unparalleled analytical prowess and data-driven decision-making abilities. However, it still struggles with tasks that humans find easy in the real world, lacking common sense. The research paper at hand explores the pivotal role of AI and Data Science in transforming entrepreneurship. The paper sheds light on the significant impact of these technologies on decision-making, customer experience, product development, and business models. Additionally, it delves into the complexities, challenges, and ethical considerations that arise when integrating AI and Data Science. Through real-world case studies and industry trends analysis, the document provides valuable insights into the dynamic landscape of 21 st-century entrepreneurship.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)300The research paper at hand explores the pivotal role of AI and Data Science in transforming entrepreneurship, sheds light on the significant impact of these technologies on decision-making, customer experience, product development, and business models and delves into the complexities, challenges, and ethical considerations that arise when integrating AI and Data Science.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Lakshmi Kumari', 'Anil Grewal']2023-12-23T00:00:00https://www.semanticscholar.org/paper/1d148069d998031d581acd3d9bc7ce0ceb4b355a db00906af839e94d7486f5d358ff4e1f8af84b9cThe fast progress of intelligence technologies such as AI, ML, IoT, Blockchain Technologies, and analytical tools has resulted in the development of ground-breaking applications in real-world contexts. This research study analyses the bleeding edge of intelligence technologies, highlighting its ground breaking applications and ramifications in real scenarios. This study presents a comprehensive assessment of the literature on intelligence technologies and their applications in real-world contexts, emphasizing the benefits of greater efficiency, productivity, and performance results. It also examines the problems and issues associated with these technologies and presents an implementation plan.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)330This study presents a comprehensive assessment of the literature on intelligence technologies and their applications in real-world contexts, emphasizing the benefits of greater efficiency, productivity, and performance results.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['S. Tyagi', 'Himanshu Kargeti', 'Rajesh Tiwari', 'Sanjay Singh Chauhan', 'Rajiv Kumar']2023-12-23T00:00:00https://www.semanticscholar.org/paper/db00906af839e94d7486f5d358ff4e1f8af84b9c 791e9e297f31be1af97e0e6bf657365c17386864The power of AI-driven solutions can create a more supportive and adaptive environment in every organization for their workforce. AI can provide immediate access to mental health resources through chatbots and virtual assistants, offering employees a confidential avenue to seek guidance and coping strategies when dealing with workplace stressors. The study aimed at detecting some substantial factors to improve the employee resilience in business organizations. Additionally, it analyzed the impact of AI control on the influencing factors. The study result revealed that leadership and management, training and development facilities, and working environment have extensive impact on the employee resilience in business organizations. Moreover, AI control has been playing significant role to improve the employee resilience in the workplace. The research study identified some important measures by which the business organizations can facilitate more open and constructive communication channels, allowing organizations to gather feedback and address employee concerns promptly.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)190The research study identified some important measures by which the business organizations can facilitate more open and constructive communication channels, allowing organizations to gather feedback and address employee concerns promptly.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Ambar Mani Mishra', 'Rupali Arora', 'Md. Motahar Hossain']2023-12-23T00:00:00https://www.semanticscholar.org/paper/791e9e297f31be1af97e0e6bf657365c17386864 2b09b25c0c35ca00d7b15a5f83d0ca3ba5103a4cThe rise of digital banking and online transactions has led to an increase in fraudulent activities within the banking sector. Traditional fraud detection methods are no longer sufficient to detect fraudsters. This paper explores bibliometric analysis on Scopus database. 66 documents were obtained from Scopus Database. India was the prominent country in citation. 64% documents have been published after 2019. There is increase in research interest after the onset of Covid pandemic. It was found that AI-based fraud detection systems outperform traditional methods for fraud detection. The paper provides inputs for further studies to develop framework for AI fraud detection in banks.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)280It was found that AI-based fraud detection systems outperform traditional methods for fraud detection and provides inputs for further studies to develop framework for AI fraud detection in banks.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Rajesh Tiwari', 'Shivani Rautela', 'Saurabh Sharma', 'Bhasker Pratap Choudhary', 'Rashmi Tripathi', 'Praveen Singh']2023-12-23T00:00:00https://www.semanticscholar.org/paper/2b09b25c0c35ca00d7b15a5f83d0ca3ba5103a4c aa83690628456d1b197716f8d45f08f8f2d48ffcRecent advances in reinforcement learning (RL) and Human-in-the-Loop (HitL) learning have made human-AI collaboration easier for humans to team with AI agents. Leveraging human expertise and experience with AI in intelligent systems can be efficient and beneficial. Still, it is unclear to what extent human-AI collaboration will be successful, and how such teaming performs compared to humans or AI agents only. In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment. In addition, we have developed a new simulator for critical infrastructure protection, focusing on a scenario where AI-powered drones and human teams collaborate to defend an airport against enemy drone attacks. We develop a user interface to allow humans to assist AI agents effectively. We demonstrated that agents learn faster while learning from policy correction compared to learning from humans or agents. Furthermore, human-AI collaboration requires lower mental and temporal demands, reduces human effort, and yields higher performance than if humans directly controlled all agents. In conclusion, we show that humans can provide helpful advice to the RL agents, allowing them to improve learning in a multi-agent setting.arXiv.org1060It is shown that humans can provide helpful advice to the RL agents, allowing them to improve learning in a multi-agent setting, and it is demonstrated that agents learn faster while learning from policy correction compared to learning from humans or agents.ArXiv['Md Saiful Islam', 'Srijita Das', 'S. Gottipati', 'William Duguay', 'Clodéric Mars', 'Jalal Arabneydi', 'Antoine Fagette', 'Matthew J. Guzdial', 'Matthew-E-Taylor']2023-12-23T00:00:00https://www.semanticscholar.org/paper/aa83690628456d1b197716f8d45f08f8f2d48ffc d557374b23a0c3c6c94f52cf7ad24d9678bd8bdbIn the epoch of pervasive Smart AI applications, ensuring the excellence of software in AI-driven systems is of utmost importance. This article concentrates on deciphering the intricate realm of Smart AI software, with the objective of identifying hurdles in quality assurance and underscoring the necessity for robust solutions.The exploration encompasses diverse facets of challenges, ranging from managing partial training data to addressing ethical concerns regarding algorithm transparency. Technical intricacies, such as testing complexities and model resilience, are deliberated alongside broader societal and ethical considerations, including privacy and user trust. The article advocates for a comprehensive quality assurance framework for Smart AI software, with a focus on its role in guaranteeing safety, dependability, and adherence to regulations. The impact of quality assurance on user experience is also scrutinized, highlighting the interdependent relationship between quality assurance and user satisfaction. By tackling challenges and emphasizing the imperative for effective solutions, this article contributes to the ongoing discourse on responsible development and deployment of Smart AI software. It aspires to advance quality assurance practices in this dynamic technological landscape, promoting the responsible evolution of Smart AI applications.Soft Computing, Artificial Intelligence and Applications100The article advocates for a comprehensive quality assurance framework for Smart AI software, with a focus on its role in guaranteeing safety, dependability, and adherence to regulations, and the impact of quality assurance on user experience is scrutinized.Soft Computing, Artificial Intelligence and Applications['Rohit Khankhoje']2023-12-23T00:00:00https://www.semanticscholar.org/paper/d557374b23a0c3c6c94f52cf7ad24d9678bd8bdb a9eab711118774d1b897f70cbb0f420da144fe85The AI-based technologies used in healthcare systems have witnessed significant growth and innovation, as this growth is attributed to innovations in AI and rise in data collection in the healthcare sector. This survey paper provides a comprehensive overview of the diverse technological advancements reshaping the healthcare landscape. The reviewed topics include Medical Image Interpretation using Deep Learning, Generative AI-based Large Language Models (LLMs), Natural Language Processing for Healthcare Records to give a sense of what AI based systems look like in healthcare. For each of these topics, we've delved into their technical aspects and their applications. Through an overview of these cutting-edge technologies, this research aims to shed light on their current state, challenges, and potential implications for the future of health care. From enhancing diagnostics to improving patient care and accessibility, AI is poised to play pivotal roles in shaping the healthcare industry for years to come. Furthermore, this survey also delves into the ethical considerations surrounding these technologies.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)130This survey paper provides a comprehensive overview of the diverse technological advancements reshaping the healthcare landscape, including Medical Image Interpretation using Deep Learning, Generative AI-based Large Language Models, Natural Language Processing for Healthcare Records, to give a sense of what AI based systems look like in healthcare.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Neha Sathe', 'Vaibhav Deodhe', 'Yash Sharma', 'Anand Shinde']2023-12-23T00:00:00https://www.semanticscholar.org/paper/a9eab711118774d1b897f70cbb0f420da144fe85 b344571e972739c05043d39453f0041ad512944eThe efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).Italian National Conference on Sensors502Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid.Sensors (Basel, Switzerland)['Helder R. O. Rocha', 'R. Fiorotti', 'J. Fardin', 'Hilel García-Pereira', 'Yann E. Bouvier', 'A. Rodríguez-Lorente', 'I. Yahyaoui']2023-12-23T00:00:00https://www.semanticscholar.org/paper/b344571e972739c05043d39453f0041ad512944e b8f7b15a2945ec1ada1b3621b0af865e64f33aa8Causal networks are widely used in many fields, including epidemiology, social science, medicine, and engineering, to model the complex relationships between variables. While it can be convenient to algorithmically infer these models directly from observational data, the resulting networks are often plagued with erroneous edges. Auditing and correcting these networks may require domain expertise frequently unavailable to the analyst. We propose the use of large language models such as ChatGPT as an auditor for causal networks. Our method presents ChatGPT with a causal network, one edge at a time, to produce insights about edge directionality, possible confounders, and mediating variables. We ask ChatGPT to reflect on various aspects of each causal link and we then produce visualizations that summarize these viewpoints for the human analyst to direct the edge, gather more data, or test further hypotheses. We envision a system where large language models, automated causal inference, and the human analyst and domain expert work hand in hand as a team to derive holistic and comprehensive causal models for any given case scenario. This paper presents first results obtained with an emerging prototype.arXiv.org351First results obtained with an emerging prototype of a system where large language models, automated causal inference, and the human analyst and domain expert work hand in hand as a team to derive holistic and comprehensive causal models for any given case scenario are presented.ArXiv['Yanming Zhang', 'Brette Fitzgibbon', 'Dino Garofolo', 'Akshith Kota', 'Eric Papenhausen', 'Klaus Mueller']2023-12-23T00:00:00https://www.semanticscholar.org/paper/b8f7b15a2945ec1ada1b3621b0af865e64f33aa8 db3a1a72cfd57106ab84a4a6a92809ed93105f1fProfessional Conversations were introduced as an assessment method for the PGCAP at De Montfort University in 2019, allowing students to prepare for the end point assessment of the L7 Academic Professional Apprenticeship (APA). This format evolved to be one of the key successes of the programme, with overwhelmingly positive feedback being received from External Examiners, End-Point Assessors and Students alike. This paper reflects on the steps taken to ensure that both students and assessors were fully prepared to engage in high quality conversations regarding their approach to teaching, learning and CPD. An overview of the approaches to teaching, learning and student support is provided, alongside recommendations on how to assure the quality of the experience and the overall fairness of the outcome awarded. The paper also considers how Professional Conversations could be used more frequently as an assessment method in Higher Education moving forward. The paper concludes with a projection of how assessed conversations could be used to maintain academic integrity in modern higher education (HE), whilst also highlighting key barriers that academics may experience, especially when faced with large student numbers and ever-increasing time constraints.Journal of Perspectives in Applied Academic Practice20A projection of how assessed conversations could be used to maintain academic integrity in modern higher education (HE), whilst also highlighting key barriers that academics may experience, especially when faced with large student numbers and ever-increasing time constraints are highlighted.Journal of Perspectives in Applied Academic Practice['Daniel Cole']2023-12-23T00:00:00https://www.semanticscholar.org/paper/db3a1a72cfd57106ab84a4a6a92809ed93105f1f 90db6b224d85826b9c0857cf89ce15d5871cc280Pothole detection is a critical aspect of road maintenance and safety, with the potential to prevent accidents and reduce infrastructure repair costs. Early detection and timely repair of potholes can help prevent accidents and reduce maintenance costs. Deep learning techniques have shown success in several computer vision tasks, including object detection and segmentation. The proposed system leverages Convolutional Neural Networks (CNNs), You Only Look Once (YOLO) object detection algorithm and Light Detection and Ranging (LiDAR) technology to identify and locate potholes in real-time. The system's architecture comprises data collection from vehicle-mounted cameras, image preprocessing, and a deep learning model for pot-hole detection. A labeled dataset of road images with annotated potholes is used to train the model, allowing it to learn the distinctive features of potholes, such as shape, depth, and texture. These images are then utilized to train a CNN-based model using deep learning techniques. The trained CNN model is then employed to detect pot-holes in real-time road images captured by vehicle-mounted cameras. Evaluation of the proposed system on diverse road surfaces and lighting conditions demonstrates its robustness and accuracy. It achieves a pothole detection rate of over 90%, outperforming traditional methods. The system's ability to provide instant alerts to drivers and municipal authorities enhances road safety and expedites pothole repair efforts. The proposed approach can be integrated into existing road monitoring systems, aiding in the timely identification and remediation of road hazards, ultimately improving road safety, and reducing maintenance costs. Keywords: Deep Learning, Convolutional Neural Networks (CNNs), Light Detection and Ranging (LiDAR), Real-time Monitoring, Vehicle-mounted Cameras.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The proposed system achieves a pothole detection rate of over 90%, outperforming traditional methods and can be integrated into existing road monitoring systems, aiding in the timely identification and remediation of road hazards, ultimately improving road safety, and reducing maintenance costs.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT['P. S. Rao']2023-12-23T00:00:00https://www.semanticscholar.org/paper/90db6b224d85826b9c0857cf89ce15d5871cc280 0c033678dad4a968f2b3bcc3d80508db940b432aThis paper provides a critical discussion on the functions and capabilities of Shariah boards, considering their ultimate authority in delivering Shariah assurance to stakeholders of Islamic banks. The primary objective of this initiative is to enhance the proficiency of Islamic banking in the realm of digital finance advancement while ensuring rigorous adherence to Shariah principles. This paper discusses the significance of robo advisory in facilitating real-time Shariah counsel by Shariah boards. Additionally, it highlights the potential of blockchain management systems (BMS) in augmenting the monitoring function of Shariah boards, namely in conducting reviews and audit assessments to ensure Shariah compliance in Islamic banks.Jurnal Bisnis Terapan590The significance of robo advisory in facilitating real-time Shariah counsel by Shariah boards and the potential of blockchain management systems (BMS) in augmenting the monitoring function of Shariah boards, namely in conducting reviews and audit assessments to ensure Shariah compliance in Islamic banks are discussed.Jurnal Bisnis Terapan['N. M. Haridan', 'Azizi @ Hamizi', 'Agung Sriwardhani', 'Nur Hani', 'Binti Ithanin', 'Mohamad Azmi', 'Nias Ahmad']2023-12-23T00:00:00https://www.semanticscholar.org/paper/0c033678dad4a968f2b3bcc3d80508db940b432a 1242075d8166888294a78447950b0ae4cd0eab26Cybernetics and systems10Cybernetics and Systems['E. Szczerbicki', 'Ngoc Thach Thanh Nguyen']2023-12-23T00:00:00https://www.semanticscholar.org/paper/1242075d8166888294a78447950b0ae4cd0eab26 7f89f4e9d68ebd74c3daa9dd8a99ab33d99c17d7Objective: modern achievements in the development and dissemination of digital technologies have attracted the attention of scholars and practitioners to the discussion of key ethical issues related to artificial intelligence and robotics. Hence, this study presents the most relevant of these issues, posing new challenges for legal scholars and practitioners to develop the regulation of artificial intelligence and robotics in terms of technology moralization.Methods: the research used practice- and risk-oriented approaches, complemented by multidisciplinary analysis of documents (European principles and codes of ethics) and studies, including those devoted to various problems of artificial intelligence and robotics.Results: the article identifies key ethical issues in the field of artificial intelligence and robotics. It is established that the key ethical issues involved can be solved if they are legally formalized and implemented at the international level. The algorithm proposed by the author, based on the analysis of the digital technologies application, will allow improving the moral actions of technologies in the process of their decision making.Scientific novelty: the article presents the latest ethical problems that concern scientists and practitioners in the field of artificial intelligence and robotics, and the methods of their solution by ethical and legal means aimed at moralizing technology and increasing its responsibility.Practical significance: all solutions presented in the article have practical significance and are ready for wide implementation at the international level. Their formalization in normative form and subsequent compliance will reduce the harm that artificial intelligence may cause in applied fields, including robotics using artificial intelligence. Regulatory, including legislative, decisions must therefore be taken as soon as possible to ensure that artificial intelligence and robotics become reliable tools for these systems to be used at work, at home, and in other areas such as shopping centers, stores, schools, universities, etc.Journal of Digital Technologies and Law171It is established that the key ethical issues involved can be solved if they are legally formalized and implemented at the international level and subsequent compliance will reduce the harm that artificial intelligence may cause in applied fields, including robotics using artificial intelligence.Journal of Digital Technologies and Law['N. Yadav']2023-12-23T00:00:00https://www.semanticscholar.org/paper/7f89f4e9d68ebd74c3daa9dd8a99ab33d99c17d7 56a8b91fa654f9ad24037fedfc23188c0fa84be0INTRODUCTION The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.Expert Opinion on Drug Metabolism & Toxicology1433This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies.Expert opinion on drug metabolism & toxicology['Maria Vittoria Togo', 'Fabrizio Mastrolorito', 'Angelica Orfino', 'Elisabetta Anna Graps', 'Anna Rita Tondo', 'C. Altomare', 'F. Ciriaco', 'Daniela Trisciuzzi', 'O. Nicolotti', 'Nicola Amoroso']2023-12-23T00:00:00https://www.semanticscholar.org/paper/56a8b91fa654f9ad24037fedfc23188c0fa84be0 9f92b70ed46592cc3820740a78bab69d414eb60cHow did face the participatory state institutions of communication policies created in Argentina, Ecuador, Mexico and Uruguay the capture by political and economic elites? This article answers this question through a comparative approach. The research addresses regulation and its implementation based on the methodological guide provided by the concept of citizen participation. Diversity, implementation, incidence, autonomy and transparency are the axes that organized the analysis that includes qualitative and quantitative information built from documents, specific bibliography and interviews. It concludes that participatory bodies occasionally limited the capture of policies or generated valuable experiences in that search, at least temporarily. However, these entities failed to prevent the capture of the policies by economic and political elites. Therefore, they did not reach enough influence to change the communication system. Despite this, it may list a set of unintended and difficult-to-measure positive results that show the enrichment of democratic and civic practices.Observatorio (OBS*)350Observatorio (OBS*)['A. Linares', 'Maria Soledad Segura']2023-12-23T00:00:00https://www.semanticscholar.org/paper/9f92b70ed46592cc3820740a78bab69d414eb60c bda7ef8b2a0c41fd2798edfb1dd96d21b8cba837Online buying behavior has undergone substantial shifts as a result of e-commerce, which has transformed the way consumers shop. Online buying is a common option for people looking to buy products and services while relaxing in their homes because of how simple it is. E-commerce as a result has a significant impact on how people shop online. E-commerce impacts online purchasing behavior in a variety of ways, including the availability of information, convenience, affordability, and usability. E-commerce has consequently become a well-liked option for customers looking to make purchases online. However, the development of AI (Artificial Intelligence) in the e-commerce sector has had a big impact on consumers' online purchasing habits. With the aid of its tools, artificial intelligence analyses massive amounts of data, enhances search results, personalizes recommendations, and makes voice and visual search possible. As AI develops and becomes more sophisticated, it will have a bigger impact on how consumers behave when making purchases online. As a result, the current study proposed to investigate the impact of AI on buying behavior in the e-commerce sector. The study examines both the positive and negative effects of AI on consumers' online purchasing decisions. The study is conceptual in nature and collects data from secondary sources, such as books, reports, websites, newspapers, journals, and theses. Research practitioners and industries will benefit from the practical implications of current research.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)321The study examines both the positive and negative effects of AI on consumers' online purchasing decisions, and collects data from secondary sources, such as books, reports, websites, newspapers, journals, and theses.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Rohit Bansal', 'Tamanna Bansal']2023-12-23T00:00:00https://www.semanticscholar.org/paper/bda7ef8b2a0c41fd2798edfb1dd96d21b8cba837 3c3c604e72371eb7a9c52e955e11af7403876572The purpose of this study is to review studies related to Artificial Intelligence (AI) maturity models (MM) in automotive manufacturing in a systematic manner. SMEs in the automotive industry must embrace digitalization to remain competitive. SME's employ a large segment of the USA's workforce. SMEs had not been aggressive in digitalization due to scarce funds, but the benefits of operational efficiency, quality improvement, cost reduction, and innovative culture have made it attractive to consumers. A growing number of operations are being digitalized using Artificial Intelligence techniques. In this paper, AI applications in SMEs are examined through the lens of an AI maturity model.Soft Computing, Artificial Intelligence and Applications01In this paper, AI applications in SMEs are examined through the lens of an AI maturity model, and a growing number of operations are being digitalized using Artificial Intelligence techniques.Soft Computing, Artificial Intelligence and Applications['Dharmender Salian']2023-12-23T00:00:00https://www.semanticscholar.org/paper/3c3c604e72371eb7a9c52e955e11af7403876572 e851b8f2e080809f53754d27106e4892473d2ed5The research investigation aimed at analyzing the usages of blockchain technologies and artificial intelligence into SME marketing practices. The study was conducted by collecting primary data from Tanzanian SME owners-managers. Five point likert-scales were used to collect the responses from the respondents and PLS4 was used to create the conceptual model of the research as well as to test the hypotheses. The study result revealed that the use of artificial intelligence and blockchain technologies have a significant influence SME marketing in the study area. Additionally, facilitating environment play an important role in this connection. Further, the study recommended various initiatives to integrate artificial intelligence and blockchain technologies into SME marketing practices. By carefully considering these recommendations and tailoring them to the SME's unique needs, every entrepreneur can successfully harness the power of AI and Blockchain to elevate the marketing efforts, enhance customer experiences, and drive business growth.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)270The study result revealed that the use of artificial intelligence and blockchain technologies have a significant influence SME marketing in the study area.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Amiri Mdoe', 'Amitabh Mishra', 'Md. Motahar Hossain']2023-12-23T00:00:00https://www.semanticscholar.org/paper/e851b8f2e080809f53754d27106e4892473d2ed5 e8f2867aff4f2e2a0200b6bc99ba38fdc0015f58Ingénierie des Systèmes d'Information06Ingénierie des systèmes d information['Gowri Shanmugam', 'Deepa Rajendran', 'Tamilvizhi Thanarajan', 'Sadish Sendil Murugaraj', 'Surendran Rajendran']2023-12-23T00:00:00https://www.semanticscholar.org/paper/e8f2867aff4f2e2a0200b6bc99ba38fdc0015f58 bfff174992244c2e3f383565c3c834a7a6c93030Since the launch of ChatGPT in November 2022, there has been a dawning understanding in the higher education sector of ways Generative artificial intelligence (GenAI) tools can challenge the traditional roles of academic teaching staff (e.g., Chan & Tsi, 2023) and support learning by students. For example, Mike Sharples in Sabzalieva and Valentini (2023) identifies ten roles that ChatGPT can play which would all support student learners. Media and sector concern has focused on whether GenAI use by students would disrupt the integrity of degrees and awards and there is a good deal of debate on how to adapt assessment, learning outcomes and curricula to reflect and reward unique human competences associated with a discipline or subject and embrace students’ use of GenAI. Educational development colleagues have been at the vanguard of leading higher education provider reactions and responses to the widespread availability and capabilities of GenAI. This case study reflects on a year of action to lead teaching staff and students as well as institutional policy and practice through a series of steps to enable rapid, proportionate and robust change. We apply Kotter’s (1996) eight stage change model to reflect on the activities, achievements and challenges to date. We do not purport to have finished but rather can see, one year in, that increasingly activity is more embedded into structures, routines, the practice of others, and our work as educational developers. We reflect forward too on the ways we will act next to ‘make change stick’ and on our own personal, professional journeys as educational change leaders, all of whom were new appointments in the educational development centre. We chart how we have been able to innovate and to lead complex educational change at pace.Journal of Perspectives in Applied Academic Practice320This case study reflects on a year of action to lead teaching staff and students as well as institutional policy and practice through a series of steps to enable rapid, proportionate and robust change.Journal of Perspectives in Applied Academic Practice['Jackie Potter', 'Katharine Welsh', 'Laura Milne']2023-12-23T00:00:00https://www.semanticscholar.org/paper/bfff174992244c2e3f383565c3c834a7a6c93030 f74dd9372869643e96f503afd52aad094d2ca7c2The Chronic Respiratory Diseases are characterized by obstructed and inflamed air passages. Chronic obstructive pulmonary disease has emerged as a socioeconomic health burden globally. The severity of the disease is projected to escalate in the upcoming decades. Techniques based on Artificial Intelligence have proved useful in the healthcare industry. The tremendous amount of heterogeneous data accumulated in the repositories of hospitals, if incorporated wisely, can be used to build up tech-aided systems. In this work, the author has reviewed the Artificial Intelligence-based techniques available in the literature that has made the effective diagnosis of the disease evident.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)310The author has reviewed the Artificial Intelligence-based techniques available in the literature that has made the effective diagnosis of the disease evident and suggested ways to incorporate them into tech-aided systems.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Jasneet Chawla', 'Navpreet Kaur Walia']2023-12-23T00:00:00https://www.semanticscholar.org/paper/f74dd9372869643e96f503afd52aad094d2ca7c2 a722e72c08e8ccb2863e4a0a7519ac315566af44The emergence of Artificial Intelligence Generative Content (AIGC) technologies, has captured global attention and industry enthusiasm. As countries and international organizations grapple with regulating this transformative technology, this paper delves into the regulatory landscape of China's AIGC industry. The study maps China's governmental stakeholders, analyzes key regulatory policies, and identifies emerging trends in AIGC management and compliance. Modern Economics &amp; Management Forum50The study maps China's governmental stakeholders, analyzes key regulatory policies, and identifies emerging trends in AIGC management and compliance.Modern Economics &amp; Management Forum['Junrui Luo']2023-12-23T00:00:00https://www.semanticscholar.org/paper/a722e72c08e8ccb2863e4a0a7519ac315566af44 eaa5278382db5db291f4e46af3e60751f1687f00Introduction: Artificial intelligence (AI) involves the creation of computer systems that imitate human actions, and it is progressively adopted as a supportive tool in aiding clinicians with disease diagnosis and treatment. One prevalent global ailment is periodontitis, which leads to the degradation and loss of the tooth-supporting tissues. The aim of this  review is to evaluate existing literature that delineates the influence of AI on diagnosing and studying the prevalence of this condition. Review: A Pubmed advanced search with narrative review was conducted of the past ten years using several search term such as “artificial Intelligences” and “periodontics”. Thorough searches were conducted on Pubmed in June 2023, encompassing studies where AI functioned as the independent variable for assessing, diagnosing, or treating patients with periodontitis. After eliminating duplicates, a total of 100 articles were recognized for preliminary abstract scrutiny. Of these, 76 documents were excluded, resulting in 24 texts for comprehensive evaluation. Conclusion: The development of artificial intelligence in the field of dentistry requires more systematic reviews and meta-analyses to enhance the knowledge and scope of artificial intelligence applications. AI models for periodontal applications are still under development and in the future, they have the potential to support diagnostic accuracy capability.Interdental Jurnal Kedokteran Gigi (IJKG)210The development of artificial intelligence in the field of dentistry requires more systematic reviews and meta-analyses to enhance the knowledge and scope of artificial intelligence applications.Interdental Jurnal Kedokteran Gigi (IJKG)['Ni Wayan Arni Sardi', 'Ni Luh Putu Sri Maryuni Adnyasari', 'Made Talitha Suryaningsih Pinatih']2023-12-23T00:00:00https://www.semanticscholar.org/paper/eaa5278382db5db291f4e46af3e60751f1687f00 157e066d9c85f3ded4b3df3641daab9a2aa1fc48Objective: to substantiate the mechanisms of legal protection of intellectual property objects created with the use of artificial intelligence.Methods: the use of artificial intelligence to create works that are traditionally considered copyright objects was investigated with a set of general scientific and theoretical-legal methods of scientific cognition, including comparison, analogy and synthesis. In addition, the practice of using artificial intelligence, including neural networks, to create such works was considered in several aspects on the basis of retrospective and multifactor analysis.Results: the paper summarizes the current practice of using artificial intelligence to create works that traditionally belong to intellectual property objects (texts, images, music, software), taking into account the formulated scientific and legal positions. Several qualitatively different variants of the use of artificial intelligence were identified. For each of these variants the mechanism of legal protection was proposed and the areas of their effective application were indicated. Proposals were made to regulate the legal protection of the results of artificial intelligence activity; this was made not in the paradigm of competing doctrines, but by combining several tools, each of them to be applied in a relevant situation.Scientific novelty: the paper presents ontological differentiation of the results of artificial intelligence activity and the corresponding mechanisms of their legal protection. The author propose to consider the results of activity created by artificial intelligence not as a single object of legal regulation, but as a set of externally similar, but ontologically different objects, each requiring a separate approach to legal protection.Practical significance: the ontological differentiation of the results of artificial intelligence activity and their corresponding legal protection mechanisms proposed in this paper is relevant both as a basis for further research and as proposals to supplement civil legislation.Journal of Digital Technologies and Law110The author proposes to consider the results of activity created by artificial intelligence not as a single object of legal regulation, but as a set of externally similar, but ontologically different objects, each requiring a separate approach to legal protection.Journal of Digital Technologies and Law['D. A. Kazantsev']2023-12-23T00:00:00https://www.semanticscholar.org/paper/157e066d9c85f3ded4b3df3641daab9a2aa1fc48 e9c92afb61019ace300112260710b4705d1ce907TechTrends43TechTrends['Charles B. Hodges', 'Paul A. Kirschner']2023-12-23T00:00:00https://www.semanticscholar.org/paper/e9c92afb61019ace300112260710b4705d1ce907 ff67841b899fde210f4927b620a279137979b16fTechnology has revolutionized virtually every sector, and its significance cannot be understated. The study explored literature on the use of AI for healthcare in Scopus Database. Bibliometric analysis was done on 412 documents from Scopus database using VOS viewer. It was found that AI in health insurance has attracted the attention of researchers post Covid pandemic. 68% publications came after 2019. The research is led by USA, Europe, India and China. Medicine and computing journals lead the research on AI for health insurance. Prediction of ailments, ethics and data privacy, operational efficiency, access, affordability and fraud detection were the major research themes. The study presents the prominent researchers and research trends on the AI usage in healthcare globally.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)470The study presents the prominent researchers and research trends on the AI usage in healthcare globally and found that AI in health insurance has attracted the attention of researchers post Covid pandemic.2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech)['Rajesh Tiwari', 'Harneet Kaur', 'Saurabh Sharma', 'Himanshu Kargeti', 'Namrata Prakash', 'Suruchi Sharma']2023-12-23T00:00:00https://www.semanticscholar.org/paper/ff67841b899fde210f4927b620a279137979b16f e88fb7aa0ea98cb4852d54129ee0141d9bab6719Brain Communications20Brain Communications['H. Daungsupawong', 'V. Wiwanitkit']2023-12-23T00:00:00https://www.semanticscholar.org/paper/e88fb7aa0ea98cb4852d54129ee0141d9bab6719 9156c4e9c6461bf530985e030f5d5150328c3c34In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.American Journal of Health-System Pharmacy00In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance, but these manuscripts are not the final version of record and will be replaced with the final article at a later time.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists['Scott D Nelson']2023-12-23T00:00:00https://www.semanticscholar.org/paper/9156c4e9c6461bf530985e030f5d5150328c3c34 59f0b37c09a906960ccd205705e85fbef1e59b1aAugmented reality interfaces offer users an effective environment. In this study, a visualization approach with 3D augmented reality interfaces was introduced to enable users to understand and analyze complex deep learning models in a short time. It has been investigated whether the immersive experience that augmented reality creates on the user in other systems has the same effect when analyzing these models. Two-dimensional studies on deep learning models were examined and what could be done in three dimensions was emphasized. By adding another dimension with augmented reality interfaces, a threedimensional experience is offered to the user and the results are observed. A CNN model is visualized in the application. When test data was given to the model, the feature maps, filters and connections in the layers were displayed. The application was first run in 2 dimensions, then as a desktop application, and then in 3 dimensions, on Microsoft Hololens-2, a mixed reality headset. Tasks are given to users. Usability was measured with a test called the SUM model, which included completion or non-completion situations, errors, completion times and satisfaction. Here, satisfaction was measured using ASQ(After-Scenario Questionnaire), a user satisfaction measurement questionnaire. The usability of augmented reality 3D interfaces was found to be 80%. The conclusion reached with the answers; It has been stated that users are willing to use this system, their awareness in 3 dimensions is undeniable, and these systems can be used as a feature that increases human ability in artificial intelligence systems.Soft Computing, Artificial Intelligence and Applications250It has been stated that users are willing to use this system, their awareness in 3 dimensions is undeniable, and these systems can be used as a feature that increases human ability in artificial intelligence systems.Soft Computing, Artificial Intelligence and Applications['Büşra Öztürk', 'Yakup Genç']2023-12-23T00:00:00https://www.semanticscholar.org/paper/59f0b37c09a906960ccd205705e85fbef1e59b1a 6caf2ca4b33a15c149ed3b71f3f32b473f338b69As a result of the pandemic, many physics courses moved online. Alongside, the popularity of Internet-based problem-solving sites and forums rose. With the emergence of large language models, another shift occurred. One year into the public availability of these models, how has online help-seeking behavior among introductory physics students changed, and what is the effect of different patterns of online resource usage? In a mixed-method approach, we investigate student choices and their impact on assessment components of an online introductory physics course for scientists and engineers. We find that students still mostly rely on traditional Internet resources and that their usage strongly influences the outcome of low-stake unsupervised quizzes. We empirically found distinct clusters of help-seeking and resource-usage patterns among the students; the impact of students’ cluster membership on the supervised assessment components of the course, however, is nonsignificant. Published by the American Physical Society 2024 Physical Review Physics Education Research450It is found that students still mostly rely on traditional Internet resources and that their usage strongly influences the outcome of low-stake unsupervised quizzes, and distinct clusters of help-seeking and resource-usage patterns among the students are found.Physical Review Physics Education Research['G. Kortemeyer', 'W. Bauer']2023-12-23T00:00:00https://www.semanticscholar.org/paper/6caf2ca4b33a15c149ed3b71f3f32b473f338b69 f97a31771d69e4dacb1408f2ab69986d7b409461 State-of-the-art machine learning systems now routinely exceed benchmarks once thought beyond the ken of artificial intelligence (AI). Often these systems accomplish tasks through novel, insightful processes that remain inscrutable to even their human designers. Taking AlphaGo’s 2016 victory over Lee Sedol as a case study, this paper argues that such accomplishments manifest the essential features of achievements as laid out in Bradford’s 2015 book Achievement. Achievements like these are directly attributable to AI systems themselves. They are artificial achievements. This opens the door to a challenge that calls out for further inquiry. Since Bradford grounds the intrinsic value of achievements in the exercise of distinctively human perfectionist capacities, the existence of artificial achievements raises the possibility that some achievements might be valueless.Analysis220Taking AlphaGo’s 2016 victory over Lee Sedol as a case study, this paper argues that such accomplishments manifest the essential features of achievements as laid out in Bradford’s 2015 book Achievement.Analysis['Phillip Hintikka Kieval']2023-12-23T00:00:00https://www.semanticscholar.org/paper/f97a31771d69e4dacb1408f2ab69986d7b409461 388f9d4772cda98d0a5a77574b2792938f1eba2bObjective: to compare modern approaches in law to the use of program codes and algorithms in decision-making that meet the principles of transparency and openness, as well as the increasingly stringent requirements for ensuring the security of personal and other big data obtained and processed algorithmically.Methods: the main methods for researching the principle of transparency in algorithmic decision-making were formal-legal and comparative analysis of legal acts and international standards of information security, as well as the principles and legal constructions contained in them.Results: it was determined that the development of information security standardization, inclusion in legal acts of requirements for the development of information technologies that comply with the principles of transparency and openness of applied algorithms will minimize the risks associated with the unlawful processing of users' big data and obtaining information about their privacy. Proposals were identified, related to the implementation of algorithmic transparency in the field of data processing legal regulation. Recommendations were formulated, based on which the legislator can solve the problem of ensuring the openness of the logic of information technology algorithms with regard to modern standards of information security.Scientific novelty: it consists in the substantiation of new trends and relevant legal approaches, which allow revealing the logic of data processing by digital and information technologies, based on the characterization of European standards of the “privacy by design” concept in new digital and information technologies of decision-making and data protection, as well as on the new legal requirements for artificial intelligence systems, including the requirement to ensure algorithmic transparency, and criteria for personal data and users' big data processing. This said, data protection is understood as a system of legal, technical and organizational principles aimed at ensuring personal data confidentiality.Practical significance: it is due to the need to study the best Russian and international practices in protecting the privacy of users of digital and information technologies, as well as the need for legislative provision of requirements for the use of algorithms that meet the principles of transparency and openness of personal data processing, taking into account the need to ensure confidentiality at all stages of the life cycle of their processing, which will ensure the continuity of security management.Journal of Digital Technologies and Law320It was determined that the development of information security standardization, inclusion in legal acts of requirements for the development of information technologies that comply with the principles of transparency and openness of applied algorithms will minimize the risks associated with the unlawful processing of users' big data and obtaining information about their privacy.Journal of Digital Technologies and Law['A. Zharova']2023-12-23T00:00:00https://www.semanticscholar.org/paper/388f9d4772cda98d0a5a77574b2792938f1eba2b 4344bffe28959b87f40f6d0af7ff235dad2456a3The issue of regulation of scope of practice (SOP) has recently been highlighted through the high-profile case of New South Wales-based neurosurgeon, Mr Charles Teo and specifically the finding of 'unsatisfactory professional conduct' by the NSW Health Care Complaints Commission (HCCC) in Teo, Charles (2023) NSWMPSC 2 (12 July 2023). The HCCC decision went to two issues in Teo's practice: (1) his decision to perform a surgery not within the SOP of his profession [at 238]; and (2) his failure to gain patient informed consent for that surgery [at 245]. This paper explores the findings against Teo with respect to SOP and recommends a nuanced approach to the regulation of clinical innovation and SOP evolution.Australian Health Review02This paper explores the findings against Teo with respect to SOP and recommends a nuanced approach to the regulation of clinical innovation and SOP evolution.Australian health review : a publication of the Australian Hospital Association['Jill Walsh', 'Sharon Downie', 'Eric Windholz', 'Andrea Kirk-Brown', 'Terry P Haines']2023-12-22T00:00:00https://www.semanticscholar.org/paper/4344bffe28959b87f40f6d0af7ff235dad2456a3 44ce765466e109b255c68c19ab801c015a4eef7bPurpose: The purpose of the article is to substantiate the need for advanced development of the regulatory framework for the practical application of artificial intelligence technologies and regulation of property turnover of objects equipped with artificial intelligence technologies.   Theoretical framework: The combination of information studied in advance by several authors and with the contribution of the findings presented in this work allows maximizing the knowledge of future researchers who decide to study and to determine the role of artificial intelligence within the framework of legal relations. The rapid development of AI technologies raises questions about the need to establish legal norms and regulation.   Design/methodology/approach: The research method is a comparative analysis of the current state and legal regulation of artificial intelligence technologies, a conceptual assessment of the impact and characteristics of legal risks of using artificial intelligence technologies.   Findings: This study emphasizes the importance of developing appropriate regulations and preparing the legal field for the wider adoption of artificial intelligence.   Research, Practical & Social implications: The authors analyze different points of view on how AI should be perceived - as an object of legal regulation or as a subject of law. The authors conclude that artificial cognitive capacity today's intelligence has not yet reached a level of development that allows it to replicate the thought processes of a lawyer in resolving a legal dispute. In addition, artificial intelligence has a huge potential to become an indispensable technological "assistant" of the lawyer, contributing to the improvement of quality and efficiency of legal services.Journal of Law and Sustainable Development100Artificial cognitive capacity today's intelligence has not yet reached a level of development that allows it to replicate the thought processes of a lawyer in resolving a legal dispute, but artificial intelligence has a huge potential to become an indispensable technological "assistant" of the lawyer, contributing to the improvement of quality and efficiency of legal services.Journal of Law and Sustainable Development['Rauan Zhaltyrbayeva', 'Zhanna Tlembayeva', 'A. Kurmanova', 'Bakytgul Ismailova', 'Assyl Smagulova']2023-12-22T00:00:00https://www.semanticscholar.org/paper/44ce765466e109b255c68c19ab801c015a4eef7b 7bd9be8a518d0405a053539dd35673f06d6fc355Recent years have seen the rapid development of the world’s financial technology industry (Fintech). Because of the innovation and risk controllability of the Fintech “regulatory sandbox”, it has become an effective model for financial technology regulation. This article firstly introduces the background and mechanism of China’s regulatory sandbox and summarizes the drawbacks. Then we analyse the mechanism and operation process of the Fintech regulatory sandbox in the UK and Singapore, where we sum up the experience that China can refer to. Finally, our project points out the positive significance of our newly-created model and its possible limitations.Cambridge Explorations in Arts and Sciences50The background and mechanism of China’s regulatory sandbox is introduced and the drawbacks are summarized, and the mechanism and operation process of the Fintech regulatory sandbox in the UK and Singapore are analysed.Cambridge Explorations in Arts and Sciences['Yiwei Yuan', 'Shuhan Li', 'Shengran Ding', 'Yunkai Qian', 'Sitong Liu', 'Yiwen Zhang']2023-12-22T00:00:00https://www.semanticscholar.org/paper/7bd9be8a518d0405a053539dd35673f06d6fc355 e32bcaf99f1bc1e8d7bd4dfcdf33c91ff9b38a63The recent development of artificial intelligence (AI) in information technology (IT) is remarkable. These developments have led to claims that AI can be used in courts to replace judges. In the article, the author addresses a matrix of these issues using the concept of explainable AI (XAI). The article examines how regulation can ensure that AI is ethical, and how this ethicality is closely related to (XAI). It concludes that, in the current context, the contribution of AI to the decision-making process is limited by the lack of sufficient explainability and interpretability of AI, although these aspects are adequately addressed and discussed. In addition, it is crucial to consider the impact of AI’s contribution on the legal authority that forms the foundation of the justice system, and a possible approach is suggested to consider conducting an experimental study as AI arbitration.Studia Iuridica Lublinensia00The article concludes that, in the current context, the contribution of AI to the decision-making process is limited by the lack of sufficient explainability and interpretability of AI, although these aspects are adequately addressed and discussed.Studia Iuridica Lublinensia['Y. Yamada']2023-12-22T00:00:00https://www.semanticscholar.org/paper/e32bcaf99f1bc1e8d7bd4dfcdf33c91ff9b38a63 6b92dcb8a196166ef615a7bcd84f4eeede395220The integration of AI in our daily lives is rapidly increasing, offering numerous benefits to society. In a Smart City context, said integration is almost implicit: Smart Cities allow for a stream of data upon which AI is not only used but developed and trained. There are however concerns about the unpredictability and uncontrollability of AI, prompting calls for transparency and explainability of its underlying machine-learning algorithms. To ensure useful and understandable explanations of inherent biases, policymakers should focus on the concrete risks and biases of algorithms in relation to specific legal contexts. This article examines the legal implications of AI, including potential regulatory frameworks, the impact on privacy and intellectual property laws, and ethical issues. It also explores governance drivers and policy processes of AI regulation and governance in the European Union. Then, after focusing on the newest Artificial Intelligence Act—viewed both under a fundamental right and a smart city AI integration perspective, it is argued that a three principle-based approach to AI deployment in smart cities is needed to balance inefficiencies derived from the inherent complexity of AI, namely: fairness, privacy and transparency.Journal of Autonomous Intelligence880It is argued that a three principle-based approach to AI deployment in smart cities is needed to balance inefficiencies derived from the inherent complexity of AI, namely: fairness, privacy and transparency.Journal of Autonomous Intelligence['Brian Fabregue']2023-12-22T00:00:00https://www.semanticscholar.org/paper/6b92dcb8a196166ef615a7bcd84f4eeede395220 2a57c61e8be2060b15624d0727d594fb6b88134bPenelitian ini melihat gambaran self-regulation pada guru PAUD dalam mengajar. Menurut Brown (2000), Self-regulation adalah kapasitas untuk merencanakan dan mengendalikan tindakan secara fleksibel sesuai dengan harapan yang telah ditetapkan (Pichardo et al., 2014). Partisipan dari penelitian ini adalah Guru PAUD yang tercatat aktif mengajar di satuan pendidikan PAUD yang terdiri atas 200 Responden. Analisis data menggunakan metode kuantitatif deskriptif. Alat ukur yang digunakan adalah the self-regulation questionnaire (SRQ) yang dikembangkan Brown, Miller, dan Lawendowski (1999). Hasil dari penelitian ini menunjukan bahwa guru PAUD memperlihatkan self-regulation dengan nilai rata - rata yang cukup baik, yaitu 3.86 dengan kategorisasi yang tergolong sedang sebanyak 135 partisipan, rendah 35 partisipan dan tinggi sejumlah 30 orang.Journal of Social Economics Research00Journal of Social and Economics Research['Theresia Margaretha', 'Niken Widi Astuti']2023-12-22T00:00:00https://www.semanticscholar.org/paper/2a57c61e8be2060b15624d0727d594fb6b88134b 9ad15af162866231976af2bc311b9c75810163d8The Takeover Code and Panel are integral to the regulation of mergers and acquisitions (M&As) and have a substantial role in controlling mergers and acquisitions which involve publicly traded companies in the UK. They serve to safeguard the interests of shareholders, maintain the integrity of shareholders and promote transparency in takeover processes in the UK. Therefore, undertakings somehow involved in takeover processes and bids must adhere to the Takeover Codes’ rules. In line with the Takeover Code, the Takeover Panel plays a crucial role in overseeing and enforcing the rules of the Takeover Code. However, the success of the Takeover Code and Panel is argued a lot lately. This article critically evaluates whether the historical development, substantive rules and current practice of the Takeover Panel and Code demonstrate a failure of self-regulation (market regulation). As a result, the need for direct state regulation of the market for corporate control is found. Thus, the remedy to the problem arising from the nature of the Takeover Code is searched in line with the Panel decisions, statutes and academic commentary.Ankara Sosyal Bilimler Üniversitesi Hukuk Fakültesi Dergisi00Ankara Sosyal Bilimler Üniversitesi Hukuk Fakültesi Dergisi['Alptekin Koksal']2023-12-22T00:00:00https://www.semanticscholar.org/paper/9ad15af162866231976af2bc311b9c75810163d8 4c9a81af18ad2da33b2ba46a285b30796f5214c9В статье рассматриваются некоторые актуальные аспекты теории и практики моделирования. Кратко представлены достижения отечественной и зарубежной науки в разработке научных категорий «модель» и «моделирование». Обозначены пока не решенные проблемы их использования для психологического обеспечения надежности специальной профессиональной подготовки и деятельности сотрудников правоохранительных органов в современных условиях. Раскрыты важные для теории и практики моделирования психологические условия обучения и развития у сотрудников способности к произвольной саморегуляции в обстановке нарастания требований, системных нагрузок, риска и увеличения цены тяжелых последствий ошибок. В качестве единого основания для разработки модельного и моделирующего пространства развития способности к произвольной саморегуляции при наличии универсального стресс-фактора представлены характеристики, процессы и результаты их взаимообусловленности, которые определяются понятиями «напряжение», «напряженность» и специальным психологическим термином «стресс-напряженность». Затронуты традиционные и инновационные подходы к образовательным технологиям и стандартам профессионально-психологической подготовки сотрудников. Выделены ключевые предпосылки создания трансдисциплинарного многоуровневого комплекса моделей и их корректного использования. Описаны результаты разработки интегральных моделей психологических условий и их применения в ходе проведенных исследований. The article discusses some current aspects of the theory and practice of modeling. The paper reviews the achievements of Russian and foreign pedagogics in the development of the scientific categories “model” and “modeling.” It identifies unresolved problems of their use for psychological support of special professional training and activities of law enforcement officers in modern conditions. We describe the psychological conditions that are important for the theory and practice of modeling in the training and development of officers’ ability of conscious self-regulation in an environment of increasing demands, system loads, risks and increasing costs of severe consequences of errors. As a basis for the development of a model and modeling space for formation of conscious self-regulation in the presence of constant stress factors, we present characteristics, processes and results in their interdependence, defined as “tension,” “stress” and the new psychological term “stress-tension.” The research employs both traditional and innovative approaches to educational technologies and standards of professional psychological training. It also highlights the key prerequisites for the creation of a transdisciplinary multi-level fund of models and their effective use. Further, the research describes the results of developing integral models of psychological conditions and their application.Психолого-педагогический поиск20Психолого-педагогический поиск['Р.В. Лаптев']2023-12-22T00:00:00https://www.semanticscholar.org/paper/4c9a81af18ad2da33b2ba46a285b30796f5214c9 05bb0af2720ebdcd5fa7509f51e8d1d6601569afIn this study, we introduce a framework designed to help educators assess the effectiveness of popular generative artificial intelligence (AI) tools in solving authentic assessments. We employed Bloom’s taxonomy as a guiding principle to create authentic assessments that evaluate the capabilities of generative AI tools. We applied this framework to assess the abilities of ChatGPT-4, ChatGPT-3.5, Google Bard and Microsoft Bing in solving authentic assessments in economics. We found that generative AI tools perform very well at the lower levels of Bloom's taxonomy while still maintaining a decent level of performance at the higher levels, with “create” being the weakest level of performance. Interestingly, these tools are better able to address numeric-based questions than text-based ones. Moreover, all the generative AI tools exhibit weaknesses in building arguments based on theoretical frameworks, maintaining the coherence of different arguments and providing appropriate references. Our study provides educators with a framework to assess the capabilities of generative AI tools, enabling them to make more informed decisions regarding assessments and learning activities. Our findings demand a strategic reimagining of educational goals and assessments, emphasising higher cognitive skills and calling for a concerted effort to enhance the capabilities of educators in preparing students for a rapidly transforming professional environment. Implications for practice or policy Our proposed framework enables educators to systematically evaluate the capabilities of widely used generative AI tools in assessments and assist them in the assessment design process. Tertiary institutions should re-evaluate and redesign programmes and course learning outcomes. The new focus on learning outcomes should address the higher levels of educational goals of Bloom’s taxonomy, specifically the “create” level. Australasian Journal of Educational Technology653A framework designed to help educators assess the effectiveness of popular generative artificial intelligence tools in solving authentic assessments is introduced, finding that generative AI tools perform very well at the lower levels of Bloom's taxonomy while still maintaining a decent level of performance at the higher levels, with “create” being the weakest level of performance.Australasian Journal of Educational Technology['Binh Nguyen Thanh', 'Diem Thi-Ngoc Vo', 'Minh Nguyen Nhat', 'Thi Thu Tra Pham', 'Hieu Thai Trung', 'Son Ha Xuan']2023-12-22T00:00:00https://www.semanticscholar.org/paper/05bb0af2720ebdcd5fa7509f51e8d1d6601569af d84d41c809e2fd01aecc9cb0b737864091ae87d4There is growing interest in the use of artificial intelligence (AI) in science education. Many issues and questions raised about the role of AI in science education target primarily science learning objectives. They relate to AI's capacity to generate tools for teaching, learning, and assessment, as well as the advantages and disadvantages of using such tools. But another important discussion receiving far too little attention in science education concerns how AI is transforming the nature of science (NOS) itself and what such transformation implies for the education of young children. For education, it is critical to ask what AI-informed NOS is, what skills it demands of learners, and how schools can aim to achieve them.Science03It is critical to ask what AI-informed NOS is, what skills it demands of learners, and how schools can aim to achieve them, as well as how AI is transforming the nature of science itself.Science['S. Erduran']2023-12-22T00:00:00https://www.semanticscholar.org/paper/d84d41c809e2fd01aecc9cb0b737864091ae87d4 a63ddd8d335b147ed5792fc1b942134329db5468Generative artificial intelligence (AI) has had a significant impact in tertiary education for practitioners and researchers during 2023. We review the way in which academics have made sense of generative AI, revisit our proposed research agenda and reflect on our changing roles as academics in relation to learning, teaching, design and policy.Australasian Journal of Educational Technology263The way in which academics have made sense of generative AI is reviewed, the proposed research agenda is revisited and changing roles as academics in relation to learning, teaching, design and policy are reflected.Australasian Journal of Educational Technology['Kate Thompson', 'L. Corrin', 'J. Lodge']2023-12-22T00:00:00https://www.semanticscholar.org/paper/a63ddd8d335b147ed5792fc1b942134329db5468 2ad4b8e914798f8e258ca55f0b233c70bc8a45deThe launch of new tools in late 2022 heralded significant growth in attention to the impacts of generative AI (GenAI) in education. Claims of the potential impact on education are contested, but there are clear risks of inappropriate use particularly where GenAI aligns poorly with learning aims. In response, in mid-2023, the Australian Federal Government held an inquiry, calling for public submissions. This inquiry offers a lens onto the policy framing of GenAI in education and provides the object of investigation for this paper. We use the inquiry submissions, extracting structured claims from each. This extraction is provided as an open data set for further research, while this paper focuses on our analysis of the policy recommendations made. Implications for practice or policy For practitioners, policymakers, and researchers. the paper provides an overview and synthesis of submission recommendations and their themes, by source type. For respondents to the inquiry (sources), the paper supports reflection regarding synergies and gaps in recommendations, pointing to opportunity for collaboration and policy development. For stakeholders with responsibility for aspects of policy delivery and/or those applying a critical lens to the inquiry and recommendation framing(s), the paper offers actionable insight. Australasian Journal of Educational Technology532This paper uses the inquiry submissions, extracting structured claims from each, and analysis of the policy recommendations made to provide an overview and synthesis of submission recommendations and their themes, by source type.Australasian Journal of Educational Technology['Simon Knight', 'Camille Dickson-Deane', 'Keith Heggart', 'Kirsty Kitto', 'Dilek Çetindamar Kozanoğlu', 'Damian Maher', 'Bhuva Narayan', 'Forooq Zarrabi']2023-12-22T00:00:00https://www.semanticscholar.org/paper/2ad4b8e914798f8e258ca55f0b233c70bc8a45de 9d459dcae14f80dc1f2ec0495435191430c0c654This research explored the potential of artificial intelligence (AI)-assisted learning using ChatGPT in an engineering course at a university in South-east Asia. The study investigated the benefits and challenges that students may encounter when utilising ChatGPT-3.5 as a learning tool. This research developed an AI-assisted learning flow that empowers learners and lecturers to integrate ChatGPT into their teaching and learning processes. The flow was subsequently used to validate and assess a variety of exercises, tutorial tasks and assessment-like questions for the course under study. Introducing a self-rating system allowed the study to facilitate users in assessing the generative responses. The findings indicate that ChatGPT has significant potential to assist students; however, there is a necessity for training and offering guidance to students on effective interactions with ChatGPT. The study contributes to the evidence of the potential of AI-assisted learning and identifies areas for future research in refining the use of AI tools to better support students' educational journey. Implications for practice or policy Educators and administrators could review the usage of ChatGPT in an engineering technology course and study the implications of generative AI tools in higher education. Academics could adapt and modify the proposed AI-assisted learning flow in this paper to suit their classroom. Students can review and adopt the proposed AI-assisted learning flow in this paper for their studies. Researchers could follow up on the application of ChatGPT in teaching and learning: teaching quality and student experience, academic integrity and assessment design. Australasian Journal of Educational Technology622An AI-assisted learning flow is developed that empowers learners and lecturers to integrate ChatGPT into their teaching and learning processes and was subsequently used to validate and assess a variety of exercises, tutorial tasks and assessment-like questions for the course under study.Australasian Journal of Educational Technology['Thanh Pham', 'Thanh Binh Nguyen', 'Son Ha', 'Ngoc Thanh Nguyen Ngoc']2023-12-22T00:00:00https://www.semanticscholar.org/paper/9d459dcae14f80dc1f2ec0495435191430c0c654 e67ca3f185c4e030f2f6c96240ac6314282046e3As AI is starting to gain popularity in the modern digital age, an interesting and crucial question is asked: what changes will artificial intelligence bring to the work industry? In this research, we will view AI from both a positive and negative perspective to consider what it can do for the future of society. Our research encompasses the trade-offs and the effects the implementation of AI in work industries will bring. Recognizing how AI will change our workforce will be an important question to answer in the upcoming years of technological innovation, so we decided to tackle it and find a possible answer. By examining the different impacts of AI on employment, we aim to contribute valuable insights that can inform discussions, policies, and strategies for a balanced integration of AI into the future workplace. Our study determines how AI can positively impact the workforce through supplementing productivity, streamlining processes, and creating new employment opportunities. At the same time, we also delve into potential challenges such as job displacement, ethical concerns surrounding AI, and the absence of comprehensive policies. By taking a comprehensive approach to assess the implications of AI on employment, we aim to contribute valuable insights. These insights can inform discussions, shape policies, and guide strategies for a balanced integration of AI into the changing landscape of the future workplace.Proceedings of London International Conferences111This research determines how AI can positively impact the workforce through supplementing productivity, streamlining processes, and creating new employment opportunities and encompasses the trade-offs and the effects the implementation of AI in work industries will bring.Proceedings of London International Conferences['Adam Sharif', 'Esad Gurbuz', 'Senih Ay']2023-12-22T00:00:00https://www.semanticscholar.org/paper/e67ca3f185c4e030f2f6c96240ac6314282046e3 935cdca4a3cb69d46614ad78027ca2918a472961Unfavorable external factors such as COVID-19 and economy recession have affected the abilities of enterprises to continue operating. Among them, capital chain resilience has become a key issue for enterprises. In the new era, artificial intelligence (AI) technology can provide new solutions for avoiding the breakage of the capital chain. Using data from listed companies in China, we find that AI technology can improve capital chain resilience. The main impact mechanism is to reduce the level of corporate financial constraints and improve internal control efficiency, and when corporate governance efficiency and resource acquisition capability are lower, such as poor levels of executive supervision and incentive, governance, executive resource acquisition ability, financial statement tone, business and financing environment, the effect of AI technology on improving capital chain resilience is more obvious. We enrich the research on AI and capital chain resilience, provide references for enterprises to use AI technology to help enterprises obtain more funds, warn of risks, and make correct decisions quickly in a crisis to help enterprises survive the crisis smoothly.The Engineering Economist01Using data from listed companies in China, it is found that AI technology can improve capital chain resilience and provides references for enterprises to use AI technology to help enterprises obtain more funds, warn of risks, and make correct decisions quickly in a crisis to help enterprises survive the crisis smoothly.Engineering Economics['Rensi Li']2023-12-22T00:00:00https://www.semanticscholar.org/paper/935cdca4a3cb69d46614ad78027ca2918a472961 386173da520f1af88493fd53aab6e34e4c189b03According to several international journals, artificial intelligence in education (AIET) is a more recent field in the educational sector. Even though it has been there for close to 30 years, educators are still confused about how to utilize it pedagogically on a larger scale and how it may have a substantial impact on teaching and learning as per SDG-4 Indicator 4.4.1, which tracks the proportion of educators and academia with the necessary information technology skills, putting them on the road to better employment and understanding Education 4.0. This article postulates a review of the impacts of AI in education and briefs the number of published studies in the area of AI in education, which has expanded as a result of the growing usage of artificial intelligence (AI) technology in education. However, extensive evaluations have been conducted to fully study the numerous facets of this topic. This study seeks to address this gap by utilizing PRISMA to detect trends and issues relevant to AI applications in education (AIET) based on publications from 2000 to 2022. The review's findings show that the academic community is becoming more interested in applying AI to education. The primary research questions covered in this study are those related to the origin: Rise in AI, Importance, and Impact of AI on Education Technology, as well as related areas such as intelligent tutoring systems for education AI challenges in the education sector, and future scope of AI and ChatGPT-3 in higher education.Multidisciplinary Reviews271A review of the impacts of AI in education and the number of published studies based on publications from 2000 to 2022 shows that the academic community is becoming more interested in applying AI to education.Multidisciplinary Reviews['Keerthi Jain', 'J. Naga', 'Venkata Raghuram']2023-12-22T00:00:00https://www.semanticscholar.org/paper/386173da520f1af88493fd53aab6e34e4c189b03 5a1a29aaf7f5838ad5471371650ca3913f905ef4Artificial intelligence has become a major element in corporates' strategic plans, and its technologies have been linked to strategic communication techniques for public relations and digital marketing communications. The current study aimed to monitor Governance of Sustainable Artificial Intelligence (AI)in Strategic Communication (SC) and Digital Marketing (DM). The research initially involved secondary research where qualitative data was collected to design research questions related to the governance of sustainable AI as a frame for strategic communication and digital marketing. The mixed method of data collection adapted using a textual discourse analysis form to collect data and determine organizational governance priorities for responsible artificial intelligence in the United Arab Emirates (UAE). The results indicated the responsible use and sustainability of artificial intelligence and its impact on the organization's strategic communication and emphasize the corporates efforts to protect users from electronic fraud based on (AI)techniques to detect hacks, frauds, and misleading messages. The findings recommended reliance on robots to manage social media platforms, crisis communication, public relations and digital advertising, and detecting machine production of fake content.Migration Letters510The results indicated the responsible use and sustainability of artificial intelligence and its impact on the organization's strategic communication and emphasize the corporates efforts to protect users from electronic fraud based on (AI)techniques to detect hacks, frauds, and misleading messages.Migration Letters['Ghada Seif']2023-12-22T00:00:00https://www.semanticscholar.org/paper/5a1a29aaf7f5838ad5471371650ca3913f905ef4 b8a6efcf9a3c9fe0bec6422d91c7748d0d87fbaePurpose – The purpose of current study was to examine the role of smart farming through artificial intelligence (AI) (Data Integration; Machine Learning; Sensor Technologies; Image Processing and Computer Vision; Decision Support Systems and Scalability and Adaptability) in controlling pest and disease detection in agriculture. Methodology/ Design / Approach – Quantitative methodology was adopted and a questionnaire was self-administered online by (328) agricultural engineers working in private agricultural institutions in Jordan that are subject to the laws of the Jordanian Ministry of Agriculture. SPSS was employed in order to screen and analyze primary data. Findings – Study results indicated that acceptance of the main hypothesis that argued, “Smart Farming Agriculture has an effect on Control Pest and Disease Detection”. Results indicated an R-value (0.963) and an overall variance of 92.7%. In addition to that, among the chosen sub-variables of study, results revealed that scalability and adaptability scored that highest influence on disease detection and control with (r = 0.961) and an overall variance of 92.4%. Study recommended the necessity of training and qualifying agricultural staff to use modern agricultural technology and artificial intelligence. Originality –  The originality of the current study lies in its application within the Jordanian environment. In addition, there weren’t direct studies that took into perspective the idea of smart farming through AI and its uses in pests and disease detection and control in crops. Implications – The implications of current study stems from its ability to present the AI potentials to enhance food production, increase the efficiency of agricultural materials that would be a source in guaranteeing food security, and create job opportunities for individuals in this sector. Migration Letters400Study recommended the necessity of training and qualifying agricultural staff to use modern agricultural technology and artificial intelligence to enhance food production, increase the efficiency of agricultural materials that would be a source in guaranteeing food security, and create job opportunities for individuals in this sector.Migration Letters['Prof. Tareq Nael Hashem', 'Jamal M. M. Joudeh', 'A. Zamil']2023-12-22T00:00:00https://www.semanticscholar.org/paper/b8a6efcf9a3c9fe0bec6422d91c7748d0d87fbae b3157a1a7bb1bd0f96f2aa1219a6b3e6cbe2ed76The COVID-19 pandemic has changed many areas of life. One of the consequences is that the learning zone causes reduced mobility and limited face-to-face meetings. State Polytechnic of Malang in overcoming face-to-face learning, especially practical learning in the AI ??Building, of course, many students, not only have the need for practical lectures. The research that the author is doing here is a field research using a quantitative approach. This research was conducted at the AI Building of the State Polytechnic of Malang. In collecting the data needed the author uses observation techniques. Discussion used data analysis methods. The results of the research for the RSSI pattern with the transmit power set at 18dBm lora and the antenna frequency used at 433MHz obtained the RSSI value. At a distance of 2m to 8m there is a decrease in RSSI starting from -57dBm to -80dBm, continuing from a distance of 8m to 42m there is an ups and downs in RSSI. The throughput result is strongly influenced by the value of bytes and the time the throughput value when done twice scanning is smaller than four times scanning because the scanning processes have different times, this is in accordance with the calculation formula.jartel180The research that the author is doing here is a field research using a quantitative approach for the RSSI pattern with the transmit power set at 18dBm lora and the antenna frequency used at 433MHz obtained the RSSI value.jartel['Berlian Mei Hartadi', 'M. Kusumawardani', 'Nugroho Suharto']2023-12-22T00:00:00https://www.semanticscholar.org/paper/b3157a1a7bb1bd0f96f2aa1219a6b3e6cbe2ed76 6a0d817dc658c1fc5a7803133c0554d3524eca33Recent generative AI platforms are able to create texts or impressive images from simple text prompts. This makes them powerful tools for summarizing knowledge about architectural history or deriving new creative work in early design tasks like ideation, sketching and modelling. But, how good is the understanding of the generative AI models of the history of architecture? Has it learned to properly distinguish styles, or is it hallucinating information? In this chapter, we investigate this question for generative AI platforms for text and image generation for different architectural styles, to understand the capabilities and boundaries of knowledge of those tools. We also analyze how they are already being used by analyzing a data set of 101 million Midjourney queries to see if and how practitioners are already querying for specific architectural concepts.arXiv.org310This chapter investigates the capabilities and boundaries of knowledge of generative AI platforms for text and image generation for different architectural styles, to understand the capabilities and boundaries of knowledge of those tools.ArXiv['J. Ploennigs', 'Markus Berger']2023-12-22T00:00:00https://www.semanticscholar.org/paper/6a0d817dc658c1fc5a7803133c0554d3524eca33 95450b0ac3e60cfa525eaa26fccbf077590f83d7The global surge in AI applications is transforming industries, leading to displacement and complementation of existing jobs, while also giving rise to new employment opportunities. Human oversight of AI is an emerging task in which human workers interact with an AI model to improve its performance, safety, and compliance with normative principles. Data annotation, encompassing the labelling of images or annotating of texts, serves as a critical human oversight process, as the quality of a dataset directly influences the quality of AI models trained on it. Therefore, the efficiency of human oversight work stands as an important competitive advantage for AI developers. This paper delves into the foundational economics of human oversight, with a specific focus on the impact of norm design and monetary incentives on data quality and costs. An experimental study involving 307 data annotators examines six groups with varying task instructions (norms) and monetary incentives. Results reveal that annotators provided with clear rules exhibit higher accuracy rates, outperforming those with vague standards by 14%. Similarly, annotators receiving an additional monetary incentive perform significantly better, with the highest accuracy rate recorded in the group working with both clear rules and incentives (87.5% accuracy). However, both groups require more time to complete tasks, with a 31% increase in average task completion time compared to those working with standards and no incentives. These empirical findings underscore the trade-off between data quality and efficiency in data curation, shedding light on the nuanced impact of norm design and incentives on the economics of AI development. The paper contributes experimental insights to discussions on the economical, ethical, and legal considerations of AI technologies.Social Science Research Network810Empirical findings underscore the trade-off between data quality and efficiency in data curation, shedding light on the nuanced impact of norm design and incentives on the economics of AI development.ArXiv['Johann Laux', 'F. Stephany', 'Alice Liefgreen']2023-12-22T00:00:00https://www.semanticscholar.org/paper/95450b0ac3e60cfa525eaa26fccbf077590f83d7 7758a2211be0f3546a514214576733d00727b56fThis abstract presents a comprehensive analysis of the impact of AI and machine learning on engineering education through research articles in the Journal of Engineering Education. The study examines recent trends, applications, and outcomes of integrating AI and machine learning algorithms in engineering pedagogy. Through bibliometric methods, we identify key themes, research directions, and their implications for enhancing educational practices. The findings shed light on the transformative potential of AI in engineering education, aiding educators and researchers in shaping future curriculum development and teaching methodologiesSMART210The study examines recent trends, applications, and outcomes of integrating AI and machine learning algorithms in engineering pedagogy and identifies key themes, research directions, and their implications for enhancing educational practices through bibliometric methods.2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART)['Abhijit Vhatkar', 'Vilis Pawar', 'Soumyakant Dash', 'Mandar Brahme', 'Lavendra Patil']2023-12-22T00:00:00https://www.semanticscholar.org/paper/7758a2211be0f3546a514214576733d00727b56f 31fa119759ba3ddf171b826b01096cbbcff17ffdThis research aims to investigate the causes and potential solutions for AI induced misinformation that impacts the decision making behavior of students. With the increasing prevalence of artificial intelligence (AI) technologies in our daily lives, thereis growing concern about the spread of misinformation and its influence on individuals' decision making processes. This study seeks to explore the underlying factors that contribute to the dissemination of AI induced misinformation, including algorithm biases, echo chambers, and the lack of critical thinking skills among students. Additionally, the research aims to identify effective strategies and interventions to mitigate the negative effects of AI induced misinformation on students' decision making behavior. By understanding the causes and developing potential solutions, this study intends to contribute to the development of informed decision making practices in the context of AI technologies.Migration Letters90The underlying factors that contribute to the dissemination of AI induced misinformation, including algorithm biases, echo chambers, and the lack of critical thinking skills among students are explored.Migration Letters['Vo Quoc Huy Huy']2023-12-22T00:00:00https://www.semanticscholar.org/paper/31fa119759ba3ddf171b826b01096cbbcff17ffd 76a403ed756b831b8b92bb5e4ee1b2615ca23921Artificial intelligence (AI) is an emerging technology that has the potential to transform many aspects of society, including the economy, healthcare, and transportation. This article synthesizes recent research literature on the global impact of AI, exploring its potential benefits and risks. The article highlights the implications of AI, including its impact on economic, ethical, social, security&privacy, and job displacement aspects. It discusses the ethical concerns surrounding AI development, including issues of bias, security, and privacy violations. To ensure the responsible development and deployment of AI, collaboration between government, industry, and academia is essential. The article concludes by emphasizing the importance of public engagement and education to promote awareness and understanding of AI's impact on society at large.arXiv.org180The article highlights the implications of AI, including its impact on economic, ethical, social, security&privacy, and job displacement aspects, and discusses the ethical concerns surrounding AI development, including issues of bias, security, and privacy violations.ArXiv['Chandregowda Pachegowda']2023-12-22T00:00:00https://www.semanticscholar.org/paper/76a403ed756b831b8b92bb5e4ee1b2615ca23921 921412e0dde79bc1628d46d5e388ca36e90498efBackground Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. Objective The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. Methods To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. Results We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. Conclusions For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. International Registered Report Identifier (IRRID) DERR1-10.2196/48892JMIR Research Protocols430This work combines kidney transplant data from nephrology centers in Germany and Canada for the first time and implements federated learning as a foundation to use such real-world transplant data as a basis for the training of clinical prediction models (CPMs) in a privacy-preserving way.JMIR Research Protocols['Matthieu-P Schapranow', 'Mozhgan Bayat', 'Aadil Rasheed', 'M. Naik', 'Verena Graf', 'Danilo Schmidt', 'K. Budde', 'H. Cardinal', 'Ruth Sapir-Pichhadze', 'F. Fenninger', 'Karen Sherwood', 'Paul Keown', 'Oliver P Günther', 'Konstantin D. Pandl', 'Florian Leiser', 'Scott Thiebes', 'A. Sunyaev', 'M. Niemann', 'Andreas Schimanski', 'Thomas Klein']2023-12-22T00:00:00https://www.semanticscholar.org/paper/921412e0dde79bc1628d46d5e388ca36e90498ef 222f4531ccaf21427ada2bdf7d825562ce6b1909Now, with the appearance of the latest wave of a sophisticated generative artificial intelligence (AI), humanity is about to embark on an entirely new functioning order. The challenge today is that due to AI the world will definitely undergo drastic metamorphosis tomorrow, and again the day after. To adapt to this reinvented economy, people will need to reinvent their skills, careers – and, indeed, their lives. Therefore, educating people for reinvention in this fluid context will require the reinvention of higher education itself. According to Joseph E. Aoun, taking into consideration the targets to be met in the nearest future, the next generation of HEI students are supposed to be educated to invent, to create, and to discover – to meet society’s targets that any most sophisticated artificial intelligence agent cannot, consequently, a curriculum should include technological literacy, or understanding how machines work and how to work with them. Furthermore, the emergence of cognitive translation studies has stipulated an interdisciplinary approach to delve into the cognitive and behavioural aspects of a broad array of cross-language activities including all kinds of translation and interpreting. In a world that relentlessly pursues efficiency and productivity, the figure of a post-editor, a professional translator who has the skills to add that necessary human touch to a text which has previously been subjected to software algorithms, has become more prominent.Mediaforum : Analytics, Forecasts, Information Management30The next generation of HEI students are supposed to be educated to invent, to create, and to discover – to meet society’s targets that any most sophisticated artificial intelligence agent cannot, consequently, a curriculum should include technological literacy, or understanding how machines work and how to work with them.Mediaforum : Analytics, Forecasts, Information Management['V. Bohatyrets']2023-12-22T00:00:00https://www.semanticscholar.org/paper/222f4531ccaf21427ada2bdf7d825562ce6b1909 e685eb39f7ed8f4262376d11502a1ebe95a2c6b6Использование технологий искусственного интеллекта уже сегодня позволяет увеличить объемы производства и снизить издержки в сельском хозяйстве. Определенные успехи на этом пути уже достигнуты, однако до промышленных решений еще далеко. According to forecasts, by 2050 the Earth's population will achieve 10 billion. It would be impossible to feed that many people without using cutting-edge smart farming technologies. High hopes are laid upon smart farming with AI seen as a foundation to handle the tasks of planning, forecasting, monitoring, analysis, and optimization.Открытые системы. СУБД00Открытые системы. СУБД['А. Ужинский']2023-12-22T00:00:00https://www.semanticscholar.org/paper/e685eb39f7ed8f4262376d11502a1ebe95a2c6b6 8f6a0b73f4ce946ca9bf5a624ae26693fa8c3d5aThe article is dedicated to exploring the strategic methods for the survival of IT professionals amidst the exponential proliferation of artificial intelligence and digitalization. The purpose of this article is to analyze the evolving strategies that IT experts can adopt to navigate and thrive in an environment characterized by the rapid advancement of AI and digitalization. The object of this study is the strategic approaches employed by IT professionals to adapt to the changing landscape of technological evolution. The subject is the formulation of effective survival strategies tailored to the challenges posed by the expanding domain of artificial intelligence and digital transformation. Tasks to be solved include evaluating the impact of exponential growth in AI and digitalization on traditional IT roles, formulating adaptive strategies for IT professionals, and proposing methods for skill enhancement and career sustainability in this dynamic environment. Methods employed encompass a comprehensive analysis of the impact of AI and digitalization on IT roles, strategic planning, empirical studies on the evolving skill sets demanded by the industry, and the examination of case studies illustrating successful adaptation strategies. The following results were obtained: identification of the transforming landscape for IT professionals due to the rise of AI and digitalization, formulation of adaptive strategies catering to the changing demands, insights into skill development, and continuous learning necessary for survival in the rapidly changing IT landscape. Conclusions drawn from this research emphasize the crucial need for IT professionals to adopt a proactive stance towards skill enhancement, continuous learning, development of soft skills, and adaptation to the evolving technological paradigms to ensure career sustainability.Management of Development of Complex Systems00Conclusions drawn from this research emphasize the crucial need for IT professionals to adopt a proactive stance towards skill enhancement, continuous learning, development of soft skills, and adaptation to the evolving technological paradigms to ensure career sustainability.Management of Development of Complex Systems['Sergiy Bushuyev', 'A. Duhskin', 'V. Kozlov', 'O. Chernova', 'V. Osadchiy', 'S. Takhmazov']2023-12-22T00:00:00https://www.semanticscholar.org/paper/8f6a0b73f4ce946ca9bf5a624ae26693fa8c3d5a 5bd89c609ca32a229dc9b591d245bd0a5635e168The rapid progress in Artificial Intelligence (AI) and machine learning technology has opened the door to the creation of tailored AI assistants. In this paper, we present the development of a personalized AI assistant merging face detection and recognition with voice assistant capabilities like Amazon's Alexa and Siri. The system identifies users, converts speech to text, and generates realistic voice responses. It generates a realistic artificial voice as a reply to the user. Based on the interpretation of the user's request, the system selects an appropriate audio response using some APIs and even its own memory. It generates a realistic artificial voice as a reply to the user. This framework can be customized for various applications in different fields. Like most of the voice assistants today, it can perform tasks such as setting reminders, sending emails, playing music, acting as a calculator, and serving as a search engine and much more. AI advancements have enabled voice assistants to perform tasks efficiently, enhancing human-computer interaction and reducing human effort and time across sectors. By combining the power of facial recognition and advanced voice technology, we hope to create a more intuitive and empathetic AI assistant. Additionally, the development of this personalized AI assistant can potentially lead to breakthroughs in healthcare, accessibility, and various other fields, by providing a seamless interface for individuals with diverse needs and abilities. This paper also reflects on the data privacy needs and methods and it is especially important when working with a user's facial as well as personal data. The project's primary objective is to enhance computer efficiency and human intelligence through voice interaction.SMART480This paper presents the development of a personalized AI assistant merging face detection and recognition with voice assistant capabilities like Amazon's Alexa and Siri, to enhance computer efficiency and human intelligence through voice interaction.2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART)['Palak Yadav', 'Tushar Tugnait', 'Sanjay Kumar Dubey']2023-12-22T00:00:00https://www.semanticscholar.org/paper/5bd89c609ca32a229dc9b591d245bd0a5635e168 5ccdf7bb504f3b36e9175192e7aaa88e98133913The COVID-19 pandemic has brought unprecedented challenges to global healthcare systems, prompting the exploration of innovative technologies to mitigate its impact. This research paper provides a comprehensive review of the latest developments in applying deep learning (DL) and machine learning (ML) techniques in addressing various aspects of COVID-19. The paper covers various topics, including diagnostic tools, drug discovery, epidemiological modeling, and patient management. Researchers leverage AI, especially DL and ML, to develop efficient algorithms using CT and X-ray images for rapid and accurate COVID-19 diagnosis, with overall accuracies ranging from 86.1% to 99.7% [1].SMART450This research paper provides a comprehensive review of the latest developments in applying deep learning and machine learning techniques in addressing various aspects of COVID-19, including diagnostic tools, drug discovery, epidemiological modeling, and patient management.2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART)['Sonali Agrawal', 'Dilip Kumar Sharma']2023-12-22T00:00:00https://www.semanticscholar.org/paper/5ccdf7bb504f3b36e9175192e7aaa88e98133913 d9bf61882a2181f63a83a65b1b9d20917dc73961This scholarly inquiry examines the interplay between artificial intelligence (AI) and academic integrity within higher education. Through a comprehensive synthesis of academic literature, the study delves into the multifaceted implications of AI tools on academic practices, pedagogical approaches, and the evolving landscape of academic integrity within higher education. The findings, derived from an extensive analysis of scholarly works, offer profound insights into the challenges posed by the integration of AI in higher education. The impact on academic dishonesty, the nuances of pedagogical shifts, and the dynamic relationship between students and AI are scrutinized, contributing to a nuanced comprehension of the intricate dynamics within the academy.Journal of Ethics in Higher Education00The impact on academic dishonesty, the nuances of pedagogical shifts, and the dynamic relationship between students and AI are scrutinized, contributing to a nuanced comprehension of the intricate dynamics within the academy.Journal of Ethics in Higher Education['David S. Fowler']2023-12-22T00:00:00https://www.semanticscholar.org/paper/d9bf61882a2181f63a83a65b1b9d20917dc73961 4b1c6d6df576933d6c9f4cd635095c2f66a0bcdcOver the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.arXiv.org580Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes.ArXiv['N. Zerbe', 'L. O. Schwen', 'Christian Geißler', 'Katja Wiesemann', 'Tom Bisson', 'Peter Boor', 'Rita Carvalho', 'Michael Franz', 'Christoph Jansen', 'T. Kiehl', 'B. Lindequist', 'Nora Charlotte Pohlan', 'Sarah Schmell', 'K. Strohmenger', 'Falk Zakrzewski', 'M. Plass', 'Michael Takla', 'Tobias Küster', 'A. Homeyer', 'P. Hufnagl']2023-12-22T00:00:00https://www.semanticscholar.org/paper/4b1c6d6df576933d6c9f4cd635095c2f66a0bcdc ecce981078258e96baad565eeaa7981eaf0555f0In the context of rapidly developing digital technologies, the problem of lagging legal regulation from the existing realities of public relations is increasingly being discussed on international platforms and forums. This problem is especially clearly visible in the "sensitive" areas of regulation related to the implementation and protection of fundamental human rights. This article is devoted to the analysis of changes taking place in labor relations in connection with the development of artificial intelligence and robotics, as well as their impact on human rights in the field of labor. Assumptions are made about the updates that will occur in the near future, taking into account the current trends in international norms and national legislation.NORTH CAUCASUS LEGAL VESTNIK00The analysis of changes taking place in labor relations in connection with the development of artificial intelligence and robotics, as well as their impact on human rights in the field of labor is devoted.NORTH CAUCASUS LEGAL VESTNIK['Арзуманян Анна Борисовна']2023-12-22T00:00:00https://www.semanticscholar.org/paper/ecce981078258e96baad565eeaa7981eaf0555f0 6421246a5e5ca8bc947d0d2cec501f2f697b7691Environment, Development and Sustainability1001This study highlights the current progress and future AI integration possibilities by proposing an AI conceptual framework through visual representation for biogas plant operation and for process monitoring systems along with different software and hardware components, that possess application in this technological advancement of automation and prediction.Environment, Development and Sustainability['Siddharth Swami', 'S. Suthar', 'Rajesh Singh', 'A. Thakur', 'Lovi Raj Gupta', 'Vineet Singh Sikarwar']2023-12-22T00:00:00https://www.semanticscholar.org/paper/6421246a5e5ca8bc947d0d2cec501f2f697b7691 f4bd71386ca878284787582434624486aeb55935Purpose: Artificial intelligence (AI) is often trained on images without ocular co-morbidities, limiting its generalizability. This study aims to evaluate the accuracy of a convolutional neural network (CNN) applied to color fundus photos (CFPs) with simulated cataracts (SCs) in detecting diabetic retinopathy (DR). Methods: A database of 3662 CFPs (from Asia Pacific Tele-Ophthalmology Society (APTOS) 2019) was used. Using transfer learning, a CNN was trained to classify the training images as either DR or non-DR. The CNN was then applied to classify the testing images after an SC was applied, using varying degrees of Gaussian blur. Results: Accuracy without SC was 97.0%, sensitivity (Sn) 95.7%, specificity (Sp) 98.3%. For mild SC, accuracy was 93.1%, Sn 91.8%, Sp 94.3%. For moderate SC, accuracy was 62.8%, Sn 31.4%, Sp 95.2%. For severe SC, accuracy was 53.5%, Sn 11.8%, Sp 96.5%. Conclusion: SCs significantly impaired AI accuracy. To prepare AI for clinical use, cataracts and other real-world clinical challenges affecting image quality must be accounted for.Indian Journal of Ophthalmology121Evaluating the accuracy of a convolutional neural network applied to color fundus photos with simulated cataracts with simulated cataracts in detecting diabetic retinopathy found SCs significantly impaired AI accuracy.Indian Journal of Ophthalmology['Alexander B Crane', 'Hassaam S. Choudhry', 'M. H. Dastjerdi']2023-12-22T00:00:00https://www.semanticscholar.org/paper/f4bd71386ca878284787582434624486aeb55935 bb08a87528ab3cdac24f3b288490e68bed88caf9 This research applies artificial intelligence algorithms for optimizing the water quality monitoring network in a representative basin with intensive agricultural and livestock activities. This study used the water quality database provided by the National Water Commission (CONAGUA). Bi-monthly monitoring was registered from 2013 to 2020 for 23 water quality parameters in 23 sampling locations in tributaries and the mainstream river. Therefore, it was necessary to apply principal component analysis to reduce the dimensionality of the data and thus identify the parameters that contribute most to the variation in the water quality. This artificial intelligence algorithm promoted the ease of clustering sampling sites with similar water quality characteristics by reducing the number of variables involved in the database. The reduction highlighted nutrients (TN and TP), parameters related to dissolved organic matter (NH3-N and TOC), and pathogens such as fecal coliforms. The similarity of sampling sites was determined through hierarchical clustering using the Euclidean distance as a measure of dissimilarity and the Ward method as a grouping method. As a result, nine clusters were obtained for the rainy and dry seasons, reducing approximately 50% of the sampling sites and generating an optimized network of 11 sampling sites.Water supply : the review journal of the International Water Supply Association00This research applies artificial intelligence algorithms for optimizing the water quality monitoring network in a representative basin with intensive agricultural and livestock activities to reduce the dimensionality of the data and identify the parameters that contribute most to the variation in the water quality.Water Supply['K. Mendivil-García', 'José Luis Medina', 'Héctor Rodríguez-Rangel', 'A. Roé-Sosa', 'L. Amábilis-Sosa']2023-12-22T00:00:00https://www.semanticscholar.org/paper/bb08a87528ab3cdac24f3b288490e68bed88caf9 5c4621eafa38a5c8dde48826d353a17da1bb125fThis research aimed to investigate if an artificial intelligence-based program improves financial reporting skills among technical-accounting high school students in Ecuador in 2023. The study employed an applied methodology, quantitative approach, and quasi-experimental design, involving a population of 183 students. A census sample of 80 students was divided into control and experimental groups, each with 40 students, where pretests and posttests were administered. The pretest results showed that 35 participants in the control group (87.5%) had a low level of knowledge, while 90% (36 participants) in the experimental group also displayed a low level of understanding regarding financial report management. After the program's implementation, the control group maintained an 87.50% low knowledge level in financial report management, while the experimental group demonstrated that 95% exhibited a high proficiency level in financial report management. Analyzing the significant differences yielded a p-value of 0.00 < 0.05, supporting the hypothesis that artificial intelligence had a highly significant impact on enhancing financial reporting skills.Migration Letters220The hypothesis that artificial intelligence had a highly significant impact on enhancing financial reporting skills among technical-accounting high school students in Ecuador in 2023 is supported.Migration Letters['Varas García Karol Paola', 'María Luisa Bazán Guzmán', 'Carcelen Bonilla Yen Jofree', 'Mendiburu Rojas Jaime Alfonso', 'Molina Guillén Jonathan Leonel', 'Intriago Alcívar Glenda Cecibel', 'Mora Aristega Angélica Margara', 'Mendiburu Rojas Augusto Franklin']2023-12-22T00:00:00https://www.semanticscholar.org/paper/5c4621eafa38a5c8dde48826d353a17da1bb125f c8bcbe63ed4374c753f63d2b8138780b88cb197fBackground: Artificial intelligence (AI) is recognized to have tremendous potential to revolutionize breast cancer management through mammography. However, the extent of its impact on radiologists with different levels of experience remains largely unexplored. Therefore, this study aimed to comprehensively show how AI could assist radiologists of varying expertise including breast and non-breast radiologists, as well as senior residents, in performing mammogram interpretation.Methods: This retrospective study analyzed eligible mammograms from Cipto Mangunkusumo Hospital between January 2017 and March 2021. Mammographic readings were conducted independently by two breast radiologists, two from other subspecialties, and three senior residents, all blinded to clinical information. AI standalone performance, as well as radiologists with and without AI assistance, was measured. Results: The results showed that a total of 886 eligible mammograms were analyzed. AI standalone performance, assessed using ROC curve analysis, yielded an AUC of 0.946 (95% CI, 0.925–0.967) with sensitivity and specificity of 90.1% and 93.6%, respectively. AI assistance significantly improved the sensitivity and specificity of all radiologists, regardless of experience level, with a median increase of 19.4% (IQR, 10.4–33.5%) and 12.1% (IQR, 5.2–16.2%), respectively. Moreover, there was a trend toward a higher increase with AI assistance in dense compared to fatty breasts.Conclusions: AI proved to be a highly effective diagnostic supplement for radiologists across varying experience levels, specifically in non-breast radiologists, offering the potential to add even greater value in cases of dense breast tissue. The results were derived from a national referral tertiary hospital that generally received many breast cancer cases referred from other hospitals for further treatment. Therefore, further studies incorporating different levels of hospitals were needed.Indonesian Journal of Cancer380AI proved to be a highly effective diagnostic supplement for radiologists across varying experience levels, specifically in non-breast radiologists, offering the potential to add even greater value in cases of dense breast tissue.Indonesian Journal of Cancer['S. Darmiati', 'Rahmi Afifi', 'Christy Amanda Billy', 'S. S. Panigoro', 'D. Kartini', 'J. Prihartono']2023-12-22T00:00:00https://www.semanticscholar.org/paper/c8bcbe63ed4374c753f63d2b8138780b88cb197f efc4df7cd04d2971ebfbcd831d27c9a4678191dfAI revolutionizes pharmaceuticals by expediting drug discovery through data analysis, predicting candidates, and enabling medication repurposing. It extends to manufacturing, ensuring quality, reducing waste, and optimizing production. AI enhances supply chain management, predicting demand and preventing shortages. Its continuous learning adapts to evolving data, aligning drug development with the latest advancements. This synergy promises efficient, personalized, and effective pharmaceutical processes, heralding a transformative era for improved patient outcomes and advanced healthcare solutions. KEY WORDS: Artificial intelligence (AI), Pharmaceutical industry, Drug development, Continuous learning, Supply chain optimization.EPRA International Journal of Research &amp; Development (IJRD)00EPRA International Journal of Research &amp; Development (IJRD)['Oza Vrushant Pranay', 'Dr. Anuradha P. Prajapati', 'Mrs. Bhoomi S. Patel', 'Dr. Sachin B. Narkhede', 'Dr. Shailesh Luhar']2023-12-22T00:00:00https://www.semanticscholar.org/paper/efc4df7cd04d2971ebfbcd831d27c9a4678191df 6816aea95ca5fd4628d1f8a3d86f9a8999a8d2d7In this paper, we analyze the key role played by artificial intelligence (AI) technology in shaping the future of the automotive industry, aiming to provide a comprehensive guide for automotive enterprises embracing the transformative potential of AI integration. We begin by establishing the impact of AI in the transportation and automotive sectors, leading to an innovative AI solution: “Automotive Intelligent AI Assistant”. This solution harmonizes the functionalities of Advanced Driver Assistance Systems (ADAS) and Driver State Monitoring (DMS). The system aims to address inherent flaws in both systems while enhancing overall vehicle safety and performance. Through case analyses and challenge assessments, we show how enterprises can be empowered to gain a deep understanding of the implementation of this cutting-edge solution. Furthermore, we emphasize market potential, identify key concerns, and highlight the advantages offered by this integration.Cambridge Explorations in Arts and Sciences90The impact of AI in the transportation and automotive sectors is established, leading to an innovative AI solution: “Automotive Intelligent AI Assistant”, which harmonizes the functionalities of Advanced Driver Assistance Systems (ADAS) and Driver State Monitoring (DMS).Cambridge Explorations in Arts and Sciences['Fu Yuli', 'Yitong Jin', 'Yutian Shi', 'Yizhen Wang', 'Kaining Wang', 'Liang Yuqiing']2023-12-22T00:00:00https://www.semanticscholar.org/paper/6816aea95ca5fd4628d1f8a3d86f9a8999a8d2d7 36581fb7f50fd0ab39614cdc2f4117ca633a6094Artificial Intelligence (AI) has emerged as a revolutionary force in various industries, transforming processes and enhancing outcomes through its advanced capabilities. In the realm of healthcare, AI is making significant strides, particularly in the field of endovascular treatment, a minimally invasive medical procedure conducted within blood vessels. This editorial explores the multifaceted applications of AI in endovascular treatment, shedding light on its pivotal role in improving patient care and procedural efficiency.International Journal of Endovascular Treatment and Innovative Techniques00This editorial explores the multifaceted applications of AI in endovascular treatment, shedding light on its pivotal role in improving patient care and procedural efficiency.International Journal of Endovascular Treatment and Innovative Techniques['Mandrita Mondal']2023-12-22T00:00:00https://www.semanticscholar.org/paper/36581fb7f50fd0ab39614cdc2f4117ca633a6094 bb849dab45f03281782520cad898392884d8f0c0Artificial Intelligence - Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines. АI is a technology that has very long history which is асtively and соntineоusly growing and сhаnging .AI is a technology that simulate human intelligence, allowing computer applications to learn from experience via iterative processing and algorithmic training. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. It fосuses оn intelligent аgents, whiсh соntаins deviсes thаt рerсeives envirоnment аnd bаsed on whiсh takes асtiоns in order to mаximize gоаl suссess сhаnсes. In this рарer, we will exрlаin the mоdern АI bаsiсs аnd various reрresentаtive аррliсаtiоns оf АI. In соntext оf mоdern digitаlized wоrld, Аrtifiсiаl Intelligenсe (АI) is the рrорerty оf mасhines, соmрuter рrоgrаms аnd systems tо рerfоrm the intelleсtuаl аnd сreаtive funсtiоns оf а рersоn, indeрendently find wаys tо sоlve рrоblems, be аble tо drаw соnсlusiоns аnd mаke deсisiоns. The reсent reseаrсh оn АI tооls, inсluding mасhine leаrning, deeр leаrning аnd рrediсtive аnаlysis intended tоwаrd inсreаsing the рlаnning, leаrning, reаsоning, thinking аnd асtiоn tаking аbility. Bаsed оn whiсh, the рrороsed reseаrсh intended tоwаrds exрlоring оn hоw the humаn intelligenсe differs frоm the аrtifiсiаl intelligenсe. In аdditiоn, оn hоw аnd in whаt wаy, the сurrent аrtifiсiаl intelligenсe is сlever thаn the humаn beings. Mоreоver, we сritiсаlly аnаlyze whаt the stаte-оf-the аrt АI оf tоdаy is сараble оf dоing, why it still саnnоt reасh humаn level intelligenсe аnd whаt аre the орen сhаllenges. Furthermоre, it will exрlоre the future рrediсtiоns fоr аrtifiсiаl intelligenсe аnd bаsed оn whiсh роtentiаl sоlutiоn will be reсоmmended tо sоlve it within next deсаdes. АI is gоing tо аdd а new level оf effiсienсy аnd sорhistiсаtiоn tо future teсhnоlоgies.International Journal of Advanced Research in Science, Communication and Technology100This paper discusses artificial intelligence, a technology that simulate human intelligence, allowing computer applications to learn from experience via iterative processing and algorithmic training.International Journal of Advanced Research in Science, Communication and Technology['Archana Vinnod Bansod']2023-12-22T00:00:00https://www.semanticscholar.org/paper/bb849dab45f03281782520cad898392884d8f0c0 0441f785697f269cd520dc95211fadc7ffcc0553With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are notoriously susceptible to adversarial examples, and subtle pixel changes to images can result in incorrect recognition results from DNNs. In the real‐world application, the patches generated by the recent physical attack methods are larger or less realistic and easily detectable. To address this problem, a Generative Adversarial Network based on Visual attention model and Style transfer network (GAN‐VS) is proposed, which reduces the patch area and makes the patch more natural and less noticeable. A visual attention model combined with generative adversarial network is introduced to detect the critical regions of image recognition, and only generate patches within the critical regions to reduce patch area and improve attack efficiency. For any type of seed patch, an adversarial patch can be generated with a high degree of stylistic and content similarity to the attacked image by generative adversarial network and style transfer network. Experimental evaluation shows that the proposed GAN‐VS has good camouflage and outperforms state‐of‐the‐art adversarial patch attack methods.IET Communications90A Generative Adversarial Network based on Visual attention model and Style transfer network (GAN‐VS) is proposed, which reduces the patch area and makes the patch more natural and less noticeable and outperforms state‐of‐the‐art adversarial patch attack methods.IET Commun.['Xin Ma', 'Kai Yang', 'Chuanzhen Zhang', 'Hualing Li', 'Xin Zheng']2023-12-22T00:00:00https://www.semanticscholar.org/paper/0441f785697f269cd520dc95211fadc7ffcc0553 c45b7113913ec01e669e87105546ce553e7ee49aLike the polar bear beleaguered by global warming, artificial intelligence (AI) serves as the charismatic megafauna of an entangled set of local and global histories of science, technology and economics. This Themes issue develops a new perspective on AI that moves beyond conventional origin myths – AI was invented at Dartmouth in the summer of 1956, or by Alan Turing in 1950 – and reframes contemporary critique by establishing plural genealogies that situate AI within deeper histories and broader geographies. ChatGPT and art produced by AI are described as generative but are better understood as forms of pastiche based upon the use of existing infrastructures, often in ways that reflect stereotypes. The power of these tools is predicated on the fact that the Internet was first imagined and framed as a ‘commons’ when actually it has created a stockpile for centralized control over (or the extraction and exploitation of) recursive, iterative and creative work. As with most computer technologies, the ‘freedom’ and ‘flexibility’ that these tools promise also depends on a loss of agency, control and freedom for many, in this case the artists, writers and researchers who have made their work accessible in this way. Thus, rather than fixate on the latest promissory technology or focus on a relatively small set of elite academic pursuits born out of a marriage between logic, statistics and modern digital computing, we explore AI as a diffuse set of technologies and systems of epistemic and political power that participate in broader historical trajectories than are traditionally offered, expanding the scope of what ‘history of AI’ is a history of.BJHS Themes00This Themes issue develops a new perspective on AI that moves beyond conventional origin myths and reframes contemporary critique by establishing plural genealogies that situate AI within deeper histories and broader geographies.BJHS Themes['Syed Mustafa Ali', 'Stephanie Dick', 'Sarah Dillon', 'Matthew L. Jones', 'Jonnie Penn', 'Richard Staley']2023-12-22T00:00:00https://www.semanticscholar.org/paper/c45b7113913ec01e669e87105546ce553e7ee49a 7c26aba1c219ea2b8b0296b8e00736f18c2ed84eThe infrastructure of the Internet is based on algorithms that enable the use of search engines, social networks, and much more. Algorithms themselves may vary in functionality, but many of them have the potential to reinforce, accentuate, and systematize age-old prejudices, biases, and implicit assumptions of society. Awareness of algorithms thus becomes an issue of agency, public life, and democracy. Nonetheless, as research showed, people are lacking algorithm awareness. Therefore, this paper aims to investigate the extent to which people are aware of unethical artificial intelligence and what actions they can take against it (mitigation measures). A survey addressing these factors yielded 291 valid responses. To examine the data and the relationship between the constructs in the model, partial least square structural modeling (PLS-SEM) was applied using the Smart PLS 3 tool. The empirical results demonstrate that awareness of mitigation measures is influenced by the self-efficacy of the user. However, trust in the algorithmic platform has no significant influence. In addition, the explainability of an algorithmic platform has a significant influence on the user's self-efficacy and should therefore be considered when setting up the platform. The most frequently mentioned mitigation measures by survey participants are laws and regulations, various types of algorithm audits, and education and training. This work thus provides new empirical insights for researchers and practitioners in the field of ethical artificial intelligence.European Journal of Interdisciplinary Studies00The empirical results demonstrate that awareness of mitigation measures is influenced by the self-efficacy of the user, however, trust in the algorithmic platform has no significant influence and the explainability of an algorithmic platform has a significant influence on the user's self-efficacy.European Journal of Interdisciplinary Studies['Sonja Höller', 'Thomas Dilger', 'Teresa Spiess', 'Christian Ploder', 'R. Bernsteiner']2023-12-22T00:00:00https://www.semanticscholar.org/paper/7c26aba1c219ea2b8b0296b8e00736f18c2ed84e b76e8438ec3ca86222eb682b7943af0b4e89be22Industry 4.0 Science00Industry 4.0 Science[]2023-12-22T00:00:00https://www.semanticscholar.org/paper/b76e8438ec3ca86222eb682b7943af0b4e89be22 dd24af972e639a60071efd3391e95a40e45cd981Industry 4.0 Science00Industry 4.0 Science[]2023-12-22T00:00:00https://www.semanticscholar.org/paper/dd24af972e639a60071efd3391e95a40e45cd981 3d0e50db52a8ada674aebf13e8fa02ed3ef63177Network Modeling Analysis in Health Informatics and Bioinformatics402Netw. Model. Anal. Health Informatics Bioinform.['Sudipta Roy', 'Debojyoti Pal', 'Tanushree Meena']2023-12-22T00:00:00https://www.semanticscholar.org/paper/3d0e50db52a8ada674aebf13e8fa02ed3ef63177 75cb415da4e6dab58fe304e4a74341109ec9c4d8To promote the construction of public physical education online courses in colleges and universities and the evaluation of the effectiveness of course teaching, this article combines 3D reconstruction techniques in computer vision to construct a set of human body shape reconstruction models and apply them to physical training exercises and teaching effectiveness assessment tasks. Specifically, first, the joint point location information of the human body in the input image is extracted using the human skeleton analysis algorithm, and modeling the foreground and background pose information of the target region using the Pix2Pix image transformation algorithm; second, multi-scale features such as nodal location features, foreground and background features, high-resolution detail features, and low-resolution global features are fused and the extracted multi-scale features are also decoded with the help of pixel-aligned implicit functions to generate a 3D model of the human body representing the human form.International Journal of Web-Based Learning and Teaching Technologies232This article combines 3D reconstruction techniques in computer vision to construct a set of human body shape reconstruction models and apply them to physical training exercises and teaching effectiveness assessment tasks.International Journal of Web-Based Learning and Teaching Technologies['Rui Guo']2023-12-22T00:00:00https://www.semanticscholar.org/paper/75cb415da4e6dab58fe304e4a74341109ec9c4d8 10c2b169f8237250366fcdaad55df9c466df1e8bВнедрение возможностей искусственного интеллекта в различные сферы жизни общества обуславливает необходимость разработки и утверждения нормативно-технической документации в соответствующих отраслях. За последние годы данный процесс реализуется не только в промышленности, банковском деле, торговле, образовании, но и в государственных, муниципальных учреждениях и организациях. Однако использование искусственного интеллекта в судебно-экспертной деятельности пока остается неурегулированным. Автором рассмотрены различные подходы к определению искусственного интеллекта, а также смысловое содержание и взаимосвязь таких терминов, как информационные системы, интеллектуальные системы, системы искусственного интеллекта. По результатам проведенного исследования автором вынесено предложение по разработке и содержанию базового стандарта, обеспечивающего унифицированный подход к использованию искусственного интеллекта в судебно-экспертной деятельности. The introduction of artificial intelligence capabilities into various spheres of society necessitates the development and approval of regulatory and technical documentation in the relevant industries. In recent years, this process has been implemented not only in industry, banking, trade, education, but also in state and municipal institutions and organisations. However, the use of artificial intelligence in forensic activity is still unsettled. The author considers various approaches to the definition of artificial intelligence, as well as the semantic content and interrelation of such terms as information systems, intelligent systems, artificial intelligence systems. Based on the results of the study, the author made a proposal for the development and content of a basic standard that would provide a unified approach to the use of artificial intelligence in forensic activities.The digest of research works "Criminalistics: yesterday, today, tomorrow"00The digest of research works "Criminalistics: yesterday, today, tomorrow"['С.С. Ржанникова']2023-12-22T00:00:00https://www.semanticscholar.org/paper/10c2b169f8237250366fcdaad55df9c466df1e8b e661175545638c5378a19fd8f5e686197ac506b200['S. K. Biswal', 'Anand J. Kulkarni']2023-12-22T00:00:00https://www.semanticscholar.org/paper/e661175545638c5378a19fd8f5e686197ac506b2 f7720fc0ffc3ef88c9ce5186d3160b8ac3a5e85aIndian Dermatology Online Journal60Indian Dermatology Online Journal['Himel Mondal', 'Shaikat Mondal', 'Indrasish Podder']2023-12-22T00:00:00https://www.semanticscholar.org/paper/f7720fc0ffc3ef88c9ce5186d3160b8ac3a5e85a 125f04cd75cbb4c04ae134402815365fff42e9f6Türkiye Sağlık Enstitüleri Başkanlığı Dergisi00Türkiye Sağlık Enstitüleri Başkanlığı Dergisi['Adem Kuzu', 'Yunus Santur']2023-12-22T00:00:00https://www.semanticscholar.org/paper/125f04cd75cbb4c04ae134402815365fff42e9f6 49a0aeeb4d3f86958afa94100f9b9380da00efe9Çağımızın önemli konuları arasında yer alan bilim, teknoloji ve toplum, hayatımızda ciddi değişmeler meydana getirmektedir. Bu, gelişme ve değişmelere ayak uydurmak için bu alanda yaşanan teknolojik ve bilimsel gelişmeleri takip etmek ve kültürel adaptasyonu etkili ve planlı bir şekilde gerçekleştirmek gerekir. Son dönemlerin popüler konusu olan yapay zeka ve veri bilimi, yaşamın her alanında kendine yer edinmiştir. Özellikle bu değişme ve gelişmelerden etkilenen eğitim ve öğretim teknolojileri, son dönemlerde ciddi atılımlar gerçekleştirmiştir. Bu çalışma, sosyal bilgilerde yapay zeka ve veri bilimi okuryazarlığının kullanımı üzerine odaklanmıştır. Bu nedenle çalışma nitel araştırma yönteminin doğasına uygun bir şekilde gerçekleştirilmiştir. Araştırmanın konusuna uygun olarak literatür taraması gerçekleştirilmiş ve bu alanda yapılan çalışmalar dikkatli bir şekilde incelenerek ve derlenerek sosyal bilgiler dersinde yapay zeka ve veri bilimi okuryazarlığının kullanımına yönelik bir sonucuna ulaşılmıştır. Yapay zeka, bilgisayarın veya bilgisayar kontrolündeki bir robotun çeşitli faaliyetleri zeki canlılara benzer şekilde yerine getirecek kadar ilerledi. Hatta bazı yapay zeka bilimcileri, yapay zekayı, dar, genel ve süper zeka olarak sınıflandırmaya başladı. Son dönemlerde yapay zeka destekli gelişen teknolojik uygulamaların verilerle donatıldığı görülmektedir. Bu veriler, o kadar ilerledi ki artık yapay zeka araçları neredeyse insan beyni gibi okuma, düşünme, analiz etme ve muhakeme etme gibi alanlarda ciddi ilerleme sağladı. Yapay zekanın eğitimde kullanımının yaygınlaşacağı ve öğrenme süreçlerine pozitif katkılar sunacağı, yapılan çalışmaların ortak noktası olduğu anlaşılmaktadır. Özellikle sosyal bilgiler dersinde yapay zeka ve veri bilimine yönelik tanımlayıcı ve açıklayıcı bilgilere yer verildiği, öğrencilerin farkındalıkları ve ilgilerini arttırmak adına faydalı bilgilerin mevcut olduğu görülmektedir. Son olarak sosyal bilgiler dersinde yapay zeka destekli teknolojik uygulamaların öğrenme sürecinde aktif bir şekilde kullanılması öğrenme ve öğretme süreçlerine olumlu katkılar sunacağı öngörülmektedir.İzmir democracy university social sciences journal00Izmir Democracy University Social Sciences Journal['Ali Yalçın']2023-12-22T00:00:00https://www.semanticscholar.org/paper/49a0aeeb4d3f86958afa94100f9b9380da00efe9 c5dee0ff4d1b84ce0f9a5ef35a8897431da59190International Union Rights80International Union Rights['Caroline Murphy', 'Tony Dundon']2023-12-22T00:00:00https://www.semanticscholar.org/paper/c5dee0ff4d1b84ce0f9a5ef35a8897431da59190 0f2b4a5f97d6594e69f4a61a4c8dd75a13f0a8adMedia jurnal informatika00Media Jurnal Informatika['Muhammad Fahmi Ajiz', 'Mohamad Faza Silmi Ramadan', 'Hilsa Dzalfa Mutia', 'Puri Dewi Yanuari']2023-12-22T00:00:00https://www.semanticscholar.org/paper/0f2b4a5f97d6594e69f4a61a4c8dd75a13f0a8ad 2845d5e13dd763d94693a2018aa31da9d02612e9Türkiye Sağlık Enstitüleri Başkanlığı Dergisi00Türkiye Sağlık Enstitüleri Başkanlığı Dergisi['Handenur Gündoğdu', 'Y. Dikmen', 'Ali Furkan Kamanlı', 'Mehmet Okuyar']2023-12-22T00:00:00https://www.semanticscholar.org/paper/2845d5e13dd763d94693a2018aa31da9d02612e9 b59e20773739d6fd87fd2716ac3d1fe507513bd0The decision accuracy of a person is adequate with a small amount of data and experience, but as data increases with different aspects and relationships, the decision accuracy decreases. However, this is not the case with machines. The emergence of artificial intelligence and integration with the medical field, the decision accuracy is increased and speedup. The aim of the study is to show the significance of artificial intelligence in the medical field and to achieve the aim a case study of diabetes prediction system using machine learning is implemented. In the study, a diabetes dataset and five different machine learning algorithms (logistic regression, k-nearest neighbours, decision tree, random forest and gradient boosting) are used. Since, high accuracy of the diabetic prediction system is required; a random over sampling technique is also used to improve the performance of the system. The diabetic prediction system claimed an accuracy of upto 87%.SMART170The aim of the study is to show the significance of artificial intelligence in the medical field and to achieve the aim a case study of diabetes prediction system using machine learning is implemented.2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART)['Vipin Khattri', 'Amit Kumar Mishra', 'Neeraj Kumar Pandey', 'Vivek Kumar']2023-12-22T00:00:00https://www.semanticscholar.org/paper/b59e20773739d6fd87fd2716ac3d1fe507513bd0 e597ef046059cbc792fa8f14500722bcb9731122In this article, we explore the duality of the gig economy enabled by artificial intelligence (AI) in terms of volatility and the promise it offers. We focus primarily on underrepresented and disadvantaged groups of workers who have found a new home, thanks to the gig economy made possible by AI. We examine how their uncertainty is perceived in the existing literature. We then reveal the lack of attention to this group of workers, whose exclusion from the traditional labour market was not problematic in the first place, and who are now portrayed as suffering from poor AI regulation. As such, we expose the hypocrisy of how the development of an AI-enabled gig economy exacerbates job insecurity, but often overlooks the potential of the gig economy to open up opportunities for atypical workers. One problem we have identified is the overrepresentation of atypical workers in the industry. We show that an AI-powered gig economy that does not introduce uncertainty is possible if the sector is effectively regulated. We offer a short roadmap with multi-layered regulatory measures to combat the instability of the artificial gig economy.COMMERCE RESEARCH REVIEW50It is shown that an AI-powered gig economy that does not introduce uncertainty is possible if the sector is effectively regulated, and a short roadmap with multi-layered regulatory measures to combat the instability of the artificial gig economy is offered.COMMERCE RESEARCH REVIEW['Rohit Singh', 'Gaurav Kumar Bisen']2023-12-21T00:00:00https://www.semanticscholar.org/paper/e597ef046059cbc792fa8f14500722bcb9731122 1bbd07081037c025dd68f8b26ef5b43d9d110355The digital inheritance has become relevant in the context of law, compelling the legal sphere to experience new institutions, namely digital assets and virtual personality. It brings with it various issues, such as a lack of effective regulation, absence of succession planning, and impacts on users' privacy and intimacy. In this context, the research aims to address the guiding question: "To what extent can we develop regulatory and practical approaches to address the succession of digital assets, taking into account the technological, legal, and ethical complexities involved, without compromising users' privacy and intimacy?" The hypothesis posits that it is possible to find a balance between practical and regulatory needs while preserving users' privacy. Adopting the hypothetical-deductive method and a review of scientific literature, it was concluded that it is feasible to enact legislation capable of regulating the issue, ensuring the rights of heirs, autonomy, interests, privacy, and intimacy of the deceased. Furthermore, until such legislation exists, the will can and should be the adopted means to address the issue at hand.Concilium00It was concluded that it is feasible to enact legislation capable of regulating the issue, ensuring the rights of heirs, autonomy, interests, privacy, and intimacy of the deceased, and until such legislation exists, the will can and should be the adopted means to address the issue at hand.Concilium['Jonas Martins da Costa', 'Carlos Roberto Brandão Junior', 'Danilo Henrique Nunes', 'Celso Barberato']2023-12-21T00:00:00https://www.semanticscholar.org/paper/1bbd07081037c025dd68f8b26ef5b43d9d110355 741ba52514228fc934cbc7ad5a7baaefcd99173dSystems of managed competition naturally seek the middle ground between competition and regulation. This debate essay makes the case for adjusting the level of regulation according to the characteristics of the submarket in question. We first develop a theoretical framework that can be used to identify the services in which relatively free competition will be beneficial. The framework is grounded in the economic literature and consists of eight criteria. Targeted regulatory tools are then discussed that can be used to structure submarkets in which these criteria are not (fully) met. Applying this framework and targeted interventions, regulators gain the flexibility to react to potential market failures, without foregoing the benefits of managed competition where it works well. This analysis is highly relevant for countries in transition to managed competition. Regulators can identify potential failure in submarkets for medical services, and apply the necessary regulatory tools to prepare for a smooth transition.Health Economics, Policy and Law350This debate essay makes the case for adjusting the level of regulation according to the characteristics of the submarket in question, and develops a theoretical framework that can be used to identify the services in which relatively free competition will be beneficial.Health economics, policy, and law['Maria Trottmann', 'Piet Stam', 'Johan Visser', 'Shuli Brammli-Greenberg']2023-12-21T00:00:00https://www.semanticscholar.org/paper/741ba52514228fc934cbc7ad5a7baaefcd99173d 46a9aacd668c17ffd212835f28616007c6f0c453In the modern world, some components of legal relations that do not have legal personality — animals, technical devices, artificial intelligence systems — are able to generate results comparable to works. However, neither regulation of this process, nor legal protection regime of its results are provided in international treaties, and are established less than fragmentary in national law. In this regard, the following questions arise: are the results of the activities of the listed components protected under copyright law and who exactly is the author of such results — the owner of the animal/device/technology, or the direct creator of the result, who is not a human being. A general term “generators of results comparable to the results of intellectual activity” is introduced into the doctrinal turnover, by which it is proposed to understand a component of a legal relation that does not have legal personality in accordance with the law of modern states — animals, technical devices, artificial intelligence, etc., — which, participating in process, partly comparable to human creative activity, are capable of producing (generating) results comparable to objects protected by intellectual property law, in particular, works. Up to date, the idea of generators as objects, not subjects of law, has not been shaken, and copyrights are not granted to them. In view of the possible commercial and artistic value of the results of their “creativity,” it may be advisable to establish a special protection regime without granting anyone non-property (moral) rights, however, with the recognition of all or some of the property rights in the created object for the generator’s proprietor for a reduced period of time.Courier of the Kutafin Moscow State Law University00In view of the possible commercial and artistic value of the results of their “creativity,” it may be advisable to establish a special protection regime without granting anyone non-property (moral) rights, however, with the recognition of all or some of the property rights in the created object for the generator's proprietor for a reduced period of time.Courier of Kutafin Moscow State Law University (MSAL))['O. V. Lutkova']2023-12-21T00:00:00https://www.semanticscholar.org/paper/46a9aacd668c17ffd212835f28616007c6f0c453 a7d63b8ee9f251f2699ebfe8f70c80e1f0682e04The permeability index of a blast furnace is an important parameter to characterize the reasonable countercurrent movement between the charge and the gas flow. The prediction modeling and regulation of the permeability index are of great significance for energy saving and emission reduction in the ironmaking process. Herein, predictive modeling of the permeability index after one hour (PI‐1h) is carried out by selecting appropriate machine learning models for the different clusters separately based on six machine learning methods and fuzzy‐C‐means. In addition, The SHapley Additive exPlanations (SHAP) method is used to gain insight into the relevance of each parameter to the PI‐1h. The simulation results show that within the allowable error range, the prediction accuracy of the support vector regression and Gaussian process regression models reaches 95.2% and 99.5%, respectively. Based on the data used in this article and the parameter importance analysis of SHAP, permeability index, pressure difference, wind velocity, and hourly coal injection rate are the main parameters to regulate PI‐1h.Steel Research International340P predictive modeling of the permeability index after one hour (PI‐1h) is carried out by selecting appropriate machine learning models for the different clusters separately based on six machine learning methods and fuzzy‐C‐means and the parameter importance analysis of SHAP shows permeability index, pressure difference, wind velocity, and hourly coal injection rate are the main parameters to regulate PI‐1h.steel research international['Dewen Jiang', 'Zhenyang Wang', 'Kejiang Li', 'Jianliang Zhang']2023-12-21T00:00:00https://www.semanticscholar.org/paper/a7d63b8ee9f251f2699ebfe8f70c80e1f0682e04 1c1d2e5c698c86740a23136ad2366f170c000c51This scholarly article explores the legal and theoretical framework that governs agency activities in the Republic of Uzbekistan’s sports sector. The scientific article’s research topic is a variety of information from academic and international media sources that expresses sports agents’ operations in light of the situation in the field of sports law development at this time. The purpose of the study is to form a holistic understanding of the legal regulation of agency activities in sports, as well as the specifics of the organization and the procedure for implementing contractual obligations in sports. The relevance of the study of the chosen topic lies in its practical significance for the protection of the rights of athletes, as well as the awareness of both the athletes themselves and the sports community as a whole. In order to accomplish the research goal, issues including examining the notions of “agent,” and “scout,” as well as how they relate to one another, were resolved. The study’s output consists of developing recommendations and conclusions that fit into the scope of the research. The scientific article’s basic findings include the enlargement of the definition of “sports agent” and the recommendation that uniform rules be adopted in order to govern a sports intermediary’s operations in accordance with international sports law standards. Formulated conclusions and proposals can act as valuable information material for improving sports legal science.Review of Law Sciences00Review of Law Sciences['Artur Valeev']2023-12-21T00:00:00https://www.semanticscholar.org/paper/1c1d2e5c698c86740a23136ad2366f170c000c51 7d2f897255412922f631e60cddeec815edd392b3Customer care is important for its role in relationship-building. This role has traditionally been performed by human customer agents, given the less mature feeling intelligence of AI. The emergence of interactive generative AI (GenAI) shows the potential for using AI for customer care in such emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this paper develops an AI-enabled customer care journey, beginning from accurate emotion recognition to empathetic response, emotional management support, and finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth top manager interviews and a CMO survey. By juxtaposing these requirements against the current feeling capabilities of GenAI, the technological challenges that need to be tackled by engineers are highlighted. This paper wraps up with a set of marketing tenets for implementing and researching the caring machine. These marketing tenets encompass verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to let customers reveal their thinking and feeling to enhance emotion understanding, employing “response engineering” for knowledge of customer preferences to personalize emotion management recommendation, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value.Journal of Marketing08Bridging practice and the academic literatures in marketing and computer science, this paper develops an AI-enabled customer care journey, beginning from accurate emotion recognition to empathetic response, emotional management support, and finally, the establishment of an emotional connection.Journal of Marketing['Ming-Hui Huang', 'R. Rust']2023-12-21T00:00:00https://www.semanticscholar.org/paper/7d2f897255412922f631e60cddeec815edd392b3 4eb320ac0f4a42fac7a2880d6f7d1c51c80f6c60. This paper outlines250['Lynne Kiesling']2023-12-21T00:00:00https://www.semanticscholar.org/paper/4eb320ac0f4a42fac7a2880d6f7d1c51c80f6c60 83ffbcc3e4aaee2c527e8ebdc67b27504fa17efe Gas has long been omnipresent in EU energy mix. The challenges created by Russia’s war on Ukraine, coupled with the Union’s decarbonization objectives, place gaseous fuels overall into a new perspective. On the one hand, the use of natural gas, a fossil fuel, mainly imported until recently from Russia, needs to be alleviated. On the other hand, natural gas has acquired an interim but still important status until the anticipated ‘green transition’ is finally achieved. Hydrogen for its part, which can range from relatively ‘dirty’ to ‘green’, is an energy carrier set to play a fundamental role in the process. The EU energy regulatory framework is rapidly expanding and evolving addressing the ‘trilemma’ of energy efficiency, energy security, and climate challenges, at times of financial constraints. The endgame and strategic objective is efficient full deployment of renewables. Gas cannot be presently abandoned, but rather sustainably utilized, in a cost-effective manner. Ideas put forward include repurposing existing pipelines, enhancing financial support for clean gases, rationalizing disinvestments, diversifying supply and employing Carbon Capture, Utilization and Storage. If such policy options are properly applied, gaseous fuels will continue to be pivotal in the EU’s energy mix in the road to a fully sustainable future for the European peoples. The present article analyses the above aspects as well as legal and regulatory challenges that lie ahead.The Journal of World Energy Law &amp; Business00The Journal of World Energy Law &amp; Business['A. Metaxas']2023-12-21T00:00:00https://www.semanticscholar.org/paper/83ffbcc3e4aaee2c527e8ebdc67b27504fa17efe 38ab593ed328c21054b0997b3b7284889114e584Competition authorities around many jurisdictions are taking steps to develop legal approaches to antitrust analysis of economic activities of digital platforms owners. However, when applying these approaches the particular impact is not always effective and leads to positive effects for the competition law enforcement on the relevant commodity markets.The article provides a comprehensive analysis of legal framework of antitrust regulators in some jurisdictions during investigations against transaction digital platform owner called Amazon based on abuse of a dominant position on the relevant commodity markets, as well as the conclusion of anticompetitive agreements.Legal approaches in competition law enforcement for determining market power and product and geographic boundaries of the commodity market where the owner of the digital transaction platform operates are also explored.The article states that the main criteria of the market power of digital transaction platform owner is still a volume of market share.The Article deals with issue of legal interpretation of digital platform owner particular actions on product market from competition law points of view and at the same time evaluates the effectiveness level of remedies have been taken by competition authorities in some jurisdictions.Russian competition law and economy60Russian competition law and economy['A. Maslov']2023-12-21T00:00:00https://www.semanticscholar.org/paper/38ab593ed328c21054b0997b3b7284889114e584 9a9ae2cf8543ed5e15c61664c98d3b60cf712d6cThis paper studies the pricing and low-carbon decision problems in a supply chain containing a manufacturer and a downstream retailer. The manufacturer produces a single product under the cap-and-trade scheme. We formulate the price and carbon-concerned demand function. To maximize their revenue, the manufacturer and the retailer determine their selling prices and carbon emission reduction rates separately. Due to the fast product updates speed, some parameters do not have enough historical data. For example, the sales cost of the retailer, the demand of consumers, and the total carbon emissions of manufacturers are far from frequency stability. This fact makes the distribution function obtained in practice usually deviate from the frequency. They are all uncertain variables whose distributions are estimated from the empirical data of experts or managers. In this paper, we give three decentralized game models to explore the equilibrium behaviors in the corresponding decision environment under an uncertain environment. Corresponding analytical solutions are offered under different game scenarios. Finally, numerical experiments are performed to illustrate the effectiveness of the established models and yield some remarkable insights.Journal of Intelligent & Fuzzy Systems430J. Intell. Fuzzy Syst.['Guangzhou Yan', 'Yaodong Ni']2023-12-21T00:00:00https://www.semanticscholar.org/paper/9a9ae2cf8543ed5e15c61664c98d3b60cf712d6c 1cac1c333661bd68a5657e5afec2fc12aadc9b8fThe balance of regional budgets is the most important factor in the socio-economic development of the constituent entities of the Russian Federation. Traditional balancing mechanisms, along with interbudgetary transfers, also include loans, reserve funds, and revenue sources established by legislative norms and intended to minimize the budget deficit and intra-system redistribution of budget funds. In 2024–2026, it is planned to implement all necessary measures aimed at maintaining fiscal sustainability and independence of regional budgets, stimulating infrastructure development, and creating a more transparent model of interbudgetary relations at the regional level. Currently, most constituent entities of the Russian Federation largely depend on financial assistance from the federal budget. Its total volume increases annually and is distributed according to approved methods. The article examines the main types and volumes of interbudgetary transfers to the budgets of the constituent entities of the Russian Federation for 2024–2026, features of the upcoming budget cycle, and draws conclusions about the sufficiency of the financial support provided for the sustainability of regional budgets.Federalism20Federalism['S. I. Shabel’nikova']2023-12-21T00:00:00https://www.semanticscholar.org/paper/1cac1c333661bd68a5657e5afec2fc12aadc9b8f 6acb6c4bcfcb860f10b0e50a359bafe8efa0d4d2Artificial Intelligence (AI) startups possess four key attributes; being small enterprises, adopting AI technology, undergoing digital transformation, and using big data systems to enhance their competitiveness. This study aims to identify the key influencing factors needed to enhance the competitiveness of AI technology-based startups and to suggest a decision-making model to improve the technology and business competitiveness of AI startups in the digital era. To achieve this, the hierarchy concept framework was built with four evaluation areas based on the mechanism-based view theory, and the 16 evaluation factors that can influence were identified through existing literature, combining factors related to the digital transformation, technological application, and business competitiveness of the startups. These factors were analyzed using the Analytic Hierarchy Process (AHP) by the survey, targeting experts in South Korea. The analysis results indicate that the subject area was the most crucial for the business competitiveness of AI startups. It was also revealed that the subject’s strategic mind is the most significant factor to AI startups’ success. In the case of two control groups, categorized as ‘AI experts’ and ‘startup experts’, AI experts chose the subject as the most important area, whereas startup experts selected the environment, and significant differences were observed in all other factors. The results of this study will provide implications for strengthening the business competitiveness of AI startups and factors important for the growth of AI startups in this era.Administrative Sciences633This study aims to identify the key influencing factors needed to enhance the competitiveness of AI technology-based startups and to suggest a decision-making model to improve the technology and business competitiveness of AI startups in the digital era.Administrative Sciences['Byunguk Lee', 'Boyoung Kim', 'Ureta Vaquero Ivan']2023-12-21T00:00:00https://www.semanticscholar.org/paper/6acb6c4bcfcb860f10b0e50a359bafe8efa0d4d2 a8228b782025abf64e7c347b344bde51ec415acaSurvey/Review Study On the Formal Evaluation of the Robustness of Neural Networks and Its Pivotal Relevance for AI-Based Safety-Critical Domains Mohamed Ibn Khedher 1,*, Houda Jmila 2, and Mounim A. El-Yacoubi 2 1 IRT-SystemX, 2 Bd Thomas Gobert, Palaiseau 91120, France 2 Samovar, Telecom SudParis, Institut Polytechnique de Paris, 19 place Marguerite Perey, Palaiseau 91120, France * Correspondence: ibnkhedhermohamed@hotmail.com     Received: 11 July 2023 Accepted: 31 October 2023 Published: 21 December 2023   Abstract: Neural networks serve as a crucial role in critical tasks, where erroneous outputs can have severe consequences. Traditionally, the validation of neural networks has focused on evaluating their performance across a large set of input points to ensure desired outputs. However, due to the virtually infinite cardinality of the input space, it becomes impractical to exhaustively check all possible inputs. Networks exhibiting strong performance on extensive input samples may fail to generalize correctly in novel scenarios, and remain vulnerable to adversarial attacks. This paper presents the general pipeline of neural network robustness and provides an overview of different domains that work together to achieve robustness guarantees. These domains include evaluating the robustness against adversarial attacks, evaluating the robustness formally and applying defense techniques to enhance the robustness when the model is compromised.International Journal of Network Dynamics and Intelligence352The general pipeline of neural network robustness is presented and an overview of different domains that work together to achieve robustness guarantees is provided, including evaluating the robustness against adversarial attacks, evaluating the robustness formally and applying defense techniques to enhance the robustness when the model is compromised.International Journal of Network Dynamics and Intelligence['Mohamed Ibn Khedher', 'Houda Jmila', 'M. El-Yacoubi']2023-12-21T00:00:00https://www.semanticscholar.org/paper/a8228b782025abf64e7c347b344bde51ec415aca 8841e6385d6dc68e0af0f836623d143a1717d948Tujuan penelitian ini adalah untuk mendalami potensi penerapan pembelajaran berbasis Kecerdasan Buatan (AI) di lingkungan Pendidikan Anak Usia Dini (PAUD). Metode penelitian yang digunakan adalah studi literatur, dengan fokus pada eksplorasi konsep, manfaat, dan kendala yang terkait dengan implementasi teknologi AI dalam proses pembelajaran anak usia dini. Hasil penelitian menunjukkan bahwa penerapan pembelajaran berbasis AI di PAUD memiliki potensi yang besar. Pembelajaran ini tidak hanya menyenangkan bagi anak-anak, tetapi juga mampu memfasilitasi kebutuhan perkembangan mereka secara holistik. Implikasi positifnya mencakup peningkatan daya tarik pembelajaran, individualisasi pendekatan pembelajaran, dan pemberian dukungan efektif untuk memenuhi kebutuhan spesifik anak. Dengan mempertimbangkan temuan ini, artikel ini memberikan kontribusi pada pemahaman potensi positif penerapan kecerdasan buatan dalam meningkatkan kualitas pendidikan anak usia dini.JECIE (Journal of Early Childhood and Inclusive Education)91JECIE (Journal of Early Childhood and Inclusive Education)['H. Jayawardana']2023-12-21T00:00:00https://www.semanticscholar.org/paper/8841e6385d6dc68e0af0f836623d143a1717d948 a024f38d7e1398ee77859bc8125de064a71a73b5This editorial looks back at the Journal of Learning Analytics (JLA) in 2023 and forward to 2024. Considering the recent proliferation of large language models such as GPT4 and Bard, the first section of this editorial points to the need for robust Generative AI (GenAI) analytics, calling for consideration of how GenAI may impact learning analytics research and practice. The second section looks back over the past year, providing statistics on submissions and considering the cost of publication in an open-access journal.Journal of Learning Analytics241The need for robust Generative AI (GenAI) analytics is pointed to, calling for consideration of how GenAI may impact learning analytics research and practice.J. Learn. Anal.['Hassan Khosravi', 'Olga Viberg', 'V. Kovanović', 'Rebecca Ferguson']2023-12-21T00:00:00https://www.semanticscholar.org/paper/a024f38d7e1398ee77859bc8125de064a71a73b5 feceadc387b9490fd29c1c63452e216c0c8c05e9Introduction In the field of pediatric trauma computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems have emerged offering a promising avenue for improved patient care. Especially children with wrist fractures may benefit from machine learning (ML) solutions, since some of these lesions may be overlooked on conventional X-ray due to minimal compression without dislocation or mistaken for cartilaginous growth plates. In this article, we describe the development and optimization of AI algorithms for wrist fracture detection in children. Methods A team of IT-specialists, pediatric radiologists and pediatric surgeons used the freely available GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, a total number of 10,643 studies (20,327 images). First, a basic object detection model, a You Only Look Once object detector of the seventh generation (YOLOv7) was trained and tested on these data. Then, team decisions were taken to adjust data preparation, image sizes used for training and testing, and configuration of the detection model. Furthermore, we investigated each of these models using an Explainable Artificial Intelligence (XAI) method called Gradient Class Activation Mapping (Grad-CAM). This method visualizes where a model directs its attention to before classifying and regressing a certain class through saliency maps. Results Mean average precision (mAP) improved when applying optimizations pre-processing the dataset images (maximum increases of +25.51% mAP@0.5 and +39.78% mAP@[0.5:0.95]), as well as the object detection model itself (maximum increases of +13.36% mAP@0.5 and +27.01% mAP@[0.5:0.95]). Generally, when analyzing the resulting models using XAI methods, higher scoring model variations in terms of mAP paid more attention to broader regions of the image, prioritizing detection accuracy over precision compared to the less accurate models. Discussion This paper supports the implementation of ML solutions for pediatric trauma care. Optimization of a large X-ray dataset and the YOLOv7 model improve the model’s ability to detect objects and provide valid diagnostic support to health care specialists. Such optimization protocols must be understood and advocated, before comparing ML performances against health care specialists.Frontiers in Pediatrics271Optimization of a large X-ray dataset and the YOLOv7 model improve the model’s ability to detect objects and provide valid diagnostic support to health care specialists.Frontiers in Pediatrics['Tristan Till', 'S. Tschauner', 'G. Singer', 'Klaus Lichtenegger', 'Holger Till']2023-12-21T00:00:00https://www.semanticscholar.org/paper/feceadc387b9490fd29c1c63452e216c0c8c05e9 32cb9fcec0a07bb228d551105d84408163c9010aThis research aims to explore the influence of AI-enhanced social media strategies on entrepreneurial performance. The study incorporates a bibliometric analysis in the research methodology to provide a comprehensive understanding of existing literature, identify key themes, and contribute to the current knowledge base. Both China and the United States emerge as frontrunners in contributing to the scholarly discourse. The suggested insights offer promising avenues for future research, encouraging scholars to delve deeper into the strategic, human-centric, precision-oriented, collaborative, ethical, and methodological dimensions of AI-enhanced social media strategies in the entrepreneurial context.International Journal of Engineering and Management Research101The suggested insights offer promising avenues for future research, encouraging scholars to delve deeper into the strategic, human-centric, precision-oriented, collaborative, ethical, and methodological dimensions of AI-enhanced social media strategies in the entrepreneurial context.International Journal of Engineering and Management Research['Harsh Vardhan', 'Dr. Deepak Kumar']2023-12-21T00:00:00https://www.semanticscholar.org/paper/32cb9fcec0a07bb228d551105d84408163c9010a de77a84c40d2c2dc23610afe78b2aef59576a3e8Artificial intelligence (AI) plus self-driving cars are commonly addressed as one in the technological realm of technology. Simply said, one cannot discuss one without discussing the other. Whilst AI is being implemented at breakneck speed in a multitude of sectors, the manner in which it is applied in the automotive industry has become a sensitive issue. Automakers all around the world are employing artificial intelligence in practically every step of the production process. Examples of AI in action include robots fitting together the initial nuts and bolts of an automobile or autonomous vehicles that use machine learning & vision to safely navigate around traffic.Transactions on Computer Science and Intelligent Systems Research51Transactions on Computer Science and Intelligent Systems Research['Xiankun Hou']2023-12-21T00:00:00https://www.semanticscholar.org/paper/de77a84c40d2c2dc23610afe78b2aef59576a3e8 7c015863d4b193d13e0a9d02937b2a8fe66c1a3dNature05Nature['Mariana Lenharo']2023-12-21T00:00:00https://www.semanticscholar.org/paper/7c015863d4b193d13e0a9d02937b2a8fe66c1a3d 4c8fc400b36d49548f49bf5dfe7c8cdf3a4ead63Recognizing positive possibilities of artificial intelligence technologies in healthcare, as well as current ways to use them, the author identifies the main forms of implementation of digital innovation: physical form in the form of a medical robot and intellectual form in the form of software, registered as medical devices. It is stated that the legal issues related to bringing to justice for actions related to the use of intelligent systems in healthcare, which led to negative consequences, including harm to the life and health of patients, have yet to be resolved. According to the current legal regulation in Russia it is a medical organization and a medical professional using artificial intelligence systems or medical robotics equipped with digital technologies who are held liable for the harm caused to the life and (or) health of citizens while providing them with medical care. In turn, system developers, as well as those who train a system based on artificial intelligence (developers of artificial intelligence systems), are not held liable. The problems of classification of crimes committed by medical professionals using artificial intelligence technologies in healthcare are considered. A medical worker providing medical care using artificial intelligence may be the subject of a crime under Part 2 of Article 109 and part 2 of Article 118 of the Criminal Code of the Russian Federation, but not under Article 238 of the Criminal Code of the Russian Federation. In addition, the rules for the classification of crimes committed by other entities (the operator of information systems) using artificial intelligence technologies are formulated.Lex Russica111The author identifies the main forms of implementation of digital innovation: physical form in the form of a medical robot and intellectual form in the form of software, registered as medical devices, as well as current ways to use them.Lex Russica['A. A. Shutova']2023-12-21T00:00:00https://www.semanticscholar.org/paper/4c8fc400b36d49548f49bf5dfe7c8cdf3a4ead63 9ea3d3eae16a00ae4422128a4a0cc8fd003ce704Based on a methodological framework structured on quantitative and qualitative analysis methods pursuing a systematic literature review and literature collection design, following the steps proposed by Pickering and Byrne (2014), this study is focused on the analysis of imagined futures of higher education in the age of artificial intelligence (AI). Our study aims to answer the following research questions: (1) What is the imagined future of higher education in the age of artificial intelligence? (2) What are the factors influencing the connection between higher education teaching process and artificial intelligence? (3)What are the effects of students and teachers improving databases and developing ChatGPT? The authors explore the impact of AI in the context of current governance arrangements and ethos of universities in the Western world. The in-depth analysis is aligned with some identified major challenges, opportunities and risks associated with the emergence of artificial intelligence systems, such as technological surveillance or the general access to AI and Large language Models such as ChatGPT in academia and constructs the argument for an informed selection and use of artificial intelligence solutions for learning and teaching in higher education. The analytical framework adopted for this research study is also used to summarise new directions for research in this field to restore the agency of universities, for quality enhancement of higher learning for students, academics and the common good.Educatia 21351The authors explore the impact of AI in the context of current governance arrangements and ethos of universities in the Western world and constructs the argument for an informed selection and use of artificial intelligence solutions for learning and teaching in higher education.Educatia 21['Stefan Popenici', 'Horațiu Catalano', 'Gabriela Mestic', 'Anca Ani-Rus']2023-12-21T00:00:00https://www.semanticscholar.org/paper/9ea3d3eae16a00ae4422128a4a0cc8fd003ce704 6e78c84e6f205e4e61fc41285afae5a60575ec23Following the success of deep learning (DL) in research, we are now witnessing the fast and widespread adoption of artificial intelligence (AI) in daily life, influencing the way we act, think, and organize our lives. However, much still remains a mystery when it comes to how these systems achieve such high performance and why they reach the outputs they do. This presents us with an unusual combination: of technical mastery on the one hand, and a striking degree of mystery on the other. This conjunction is not only fascinating, but it also poses considerable risks, which urgently require our attention. Awareness of the need to analyze ethical implications, such as fairness, equality, and sustainability, is growing. However, other dimensions of inquiry receive less attention, including the subtle but pervasive ways in which our dealings with AI shape our way of living and thinking, transforming our culture and human self-understanding. If we want to deploy AI positively in the long term, a broader and more holistic assessment of the technology is vital, involving not only scientific and technical perspectives, but also those from the humanities. To this end, we present outlines of a work program for the humanities that aim to contribute to assessing and guiding the potential, opportunities, and risks of further developing and deploying DL systems. This paper contains a thematic introduction (Sect. 1), an introduction to the workings of DL for non-technical readers (Sect. 2), and a main part, containing the outlines of a work program for the humanities (Sect. 3). Readers familiar with DL might want to ignore 2 and instead directly read 3 after 1.Social Science Research Network2820SSRN Electronic Journal['Jan Segessenmann', 'Thilo Stadelmann', 'Andrew Davison', 'Oliver Dürr']2023-12-21T00:00:00https://www.semanticscholar.org/paper/6e78c84e6f205e4e61fc41285afae5a60575ec23 7fda6bccd114744be5fcc3514fd991a6113ece40Abstract We consider the looming threat of bad actors using artificial intelligence (AI)/Generative Pretrained Transformer to generate harms across social media globally. Guided by our detailed mapping of the online multiplatform battlefield, we offer answers to the key questions of what bad-actor-AI activity will likely dominate, where, when—and what might be done to control it at scale. Applying a dynamical Red Queen analysis from prior studies of cyber and automated algorithm attacks, predicts an escalation to daily bad-actor-AI activity by mid-2024—just ahead of United States and other global elections. We then use an exactly solvable mathematical model of the observed bad-actor community clustering dynamics, to build a Policy Matrix which quantifies the outcomes and trade-offs between two potentially desirable outcomes: containment of future bad-actor-AI activity vs. its complete removal. We also give explicit plug-and-play formulae for associated risk measures.PNAS Nexus320A exactly solvable mathematical model of the observed bad-actor community clustering dynamics is used, to build a Policy Matrix which quantifies the outcomes and trade-offs between two potentially desirable outcomes: containment of future bad-actor-AI activity vs. its complete removal.PNAS Nexus['Neil F. Johnson', 'R. Sear', 'L. Illari']2023-12-21T00:00:00https://www.semanticscholar.org/paper/7fda6bccd114744be5fcc3514fd991a6113ece40 c253528e5eb600aa67625d05004d3f0f9af24514Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more general tools, including text-to-image generators and large language models. Diffusion models work by adding noise to the available training data and then learning how to reverse the process. The reverse operation may then be applied to new random data in order to produce new outputs. We provide a brief introduction to diffusion models for applied mathematicians and statisticians. Our key aims are (a) to present illustrative computational examples, (b) to give a careful derivation of the underlying mathematical formulas involved, and (c) to draw a connection with partial differential equation (PDE) diffusion models. We provide code for the computational experiments. We hope that this topic will be of interest to advanced undergraduate students and postgraduate students. Portions of the material may also provide useful motivational examples for those who teach courses in stochastic processes, inference, machine learning, PDEs or scientific computing.arXiv.org450A brief introduction to diffusion models for applied mathematicians and statisticians is provided to present illustrative computational examples, to give a careful derivation of the underlying mathematical formulas involved, and to draw a connection with partial differential equation (PDE) diffusion models.ArXiv['Catherine F. Higham', 'Des J. Higham', 'P. Grindrod']2023-12-21T00:00:00https://www.semanticscholar.org/paper/c253528e5eb600aa67625d05004d3f0f9af24514 b73798c1a870b134c116a668bba7dd92d2504c2fTeknologi berbasis AI (Artificial Intelligence) saat ini sedang berada pada masa perkembangan yang luar biasa. AI sudah banyak digunakan diberbagai bidang termasuk di bidang pendidikan. Pengenalan AI dirasa sangat penting dilakukan sejak usia dini. Penelitian ini bertujuan untuk memperkenalkan penggunaan AI pada pembelajaran di PAUD (Pendidikan Anak Usia Dini).Penggunaan AI di PAUD dapat disesuaikan dengan minat dan kebutuhan anak.Metode penelitian yang digunakan adalah studi literatur.Hasil penelitian menunjukkan bahwa penggunaan AI dalam pembelajaran di PAUD sangat direkomendasikan.Kesimpulan dari penelitian ini adalah penggunaan AI dalam pembelajaran di PAUD sangat direkomendasikan karena menyenangkan, mudah digunakan, dan mampu mendorong anak untuk berpikir kritis dan kreatif, serta memperkenalkan teknologi sejak usia dini.JECIE (Journal of Early Childhood and Inclusive Education)70JECIE (Journal of Early Childhood and Inclusive Education)['A. Noviyanti', 'Nova Eko Hidayanto', 'Pipit Rika Wijaya']2023-12-21T00:00:00https://www.semanticscholar.org/paper/b73798c1a870b134c116a668bba7dd92d2504c2f bfd09700dd554ac72203f9146d0e96374a9ad603Artificial intelligence and employability will go hand in hand in future if there is right implementation of artificial intelligence in industries. Software integrated with artificial intelligence has gained a lot of popularity over time and the high demand for artificial intelligence is increasing rapidly, and the shortage of AI can also be seen because of this. This study asses AI impact on employability in an organization. The race between human intelligence and artificial intelligence is still going on however, Artificial intelligence has managed to prove its benefits in many different working areas many stakeholders such as government, shareholders, and firms are able to see its worth. Artificial intelligence has proven to be impactful in generating, creating, and executing policies and plans for achieving organizational goals and economic stability. Employment management will be in high need when industries implement artificial intelligence. AI has seen great advancement during pandemic, when entire world was on complete lockdown many organizations started implementing various automation technologies to keep their business running. This research has been prepared with the use of secondary data. The overall research was done after going through many different research based on artificial intelligence, employability, their relations, and advancement and adoption of artificial intelligence. The Study explore future scope of AI in employability and its pros and cons.International Conference on Innovative Mechanisms for Industry Applications230This study asses AI impact on employability in an organization and explores future scope of AI in employability and its pros and cons.2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)['Pooja Devi', 'Harmeet Kaur', 'Rakesh Kumar', 'Srinivas Aluvala', 'Shrish Singh']2023-12-21T00:00:00https://www.semanticscholar.org/paper/bfd09700dd554ac72203f9146d0e96374a9ad603 d23d3eac3e87e6a9be68a450ecfe77956bbf9321Development of technologies foster debate about the role of technology in education and challenges the current perception of relation between technology and education. Creation of artificial intelligence questions stability of education system, while in longer perspective it questions the necessity of language teachers. The article discusses what is educational technology form the perspectives of teachers and practitioner educators and speaks about the role of artificial intelligence in education and in language acquisition process.Journal in Humanities00What is educational technology form the perspectives of teachers and practitioner educators and the role of artificial intelligence in education and in language acquisition process is spoken about.Journal in Humanities['Maia Kutateladze']2023-12-21T00:00:00https://www.semanticscholar.org/paper/d23d3eac3e87e6a9be68a450ecfe77956bbf9321 8f19f19782a74e0f2187b1a687af335917c742d9Communications Medicine220How AI can be used to optimize clinical trial design and potentially boost the success rate of clinical trials is discussed.Communications Medicine['Bin Zhang', 'Lu Zhang', 'Qiuying Chen', 'Zhe Jin', 'Shuyi Liu', 'Shuixing Zhang']2023-12-21T00:00:00https://www.semanticscholar.org/paper/8f19f19782a74e0f2187b1a687af335917c742d9 6bed7a845c011a087f5463b4c907d444e25d5dfaIntroduction. At present, the artificial intelligence (AI) technologies develop rapidly, and spread widely in diverse spheres of human activity. One of the spheres where AI is actively involved, is art in all the variety of its manifestations. The AI usage in art spawns not only new creative and technological opportunities, but also new social and cultural challenges, that require timely reflection from the point of view of social philosophy. The article aims to identify the foundations of the aforementioned reflection for the studies of AI in musical art.Methodology and sources. The article uses general scientific methods of analysis and synthesis, methodology of interdisciplinary approach, and philosophical methodology in the domain of research of social practices, that define the usage of AI in musical art. Foreign (A.-M. Gioti, N. Hageback, D. Hedblom et al.) and domestic (M.C. Burtsev, R.I. Mamina,E.V. Piraynen, A.V. Popova et al.) scientific research literature, and electronic resources dedicated to the AI and to the AI in musical art in particular, are used as sources.Results and discussion. The author has considered the theoretical foundations of studies of AI at the present stage of its development. Philosophical foundations of research of AI in art were analyzed. The peculiarities of using the AI in musical art were discussed. Strategies for studying the specificity of using the AI in musical art were matched to the foundations of research of the AI in the domain of social philosophy.Conclusion. When studying the usage of the AI in arts, the research strategies can be rooted in several key foundations in the domain of social philosophy. The relative importance of the aforementioned foundations can vary depending on details of specific theme. In case of musical art, at least three of these foundations can be identified. More specifically, they are defined by choosing a point of view on the social subjectness of AI, on the capability of AI to create objects of culture, on the importance of social and cultural context for the evaluation of perspectives and limits of AI usage scenarios. Criteria for the decision between the aforementioned strategies include answers to the following questions. Firstly, the question about the nature of motivation that guides subjects of creative activity. Secondly, the question about the paradigm in the domain of social philosophy that constitutes the basis of the research. Finally, the question about the worldview and values of social groups that are in the focus of research attention.Discourse60Discourse['A. V. Ilina']2023-12-21T00:00:00https://www.semanticscholar.org/paper/6bed7a845c011a087f5463b4c907d444e25d5dfa 6992a9f991d708922adc6dbad21ef9ad6112946bBiomedical waste can lead to severe environmental pollution and pose public health risks if not properly handled or disposed of. The efficient management of biomedical waste poses a significant challenge to healthcare facilities, environmental agencies, and regulatory bodies. Traditional management methods often fall short of efficient handling of biomedical waste due to its enormous quantity, diverse, and complex nature. In recent years, different approaches employing Artificial Intelligence (AI) techniques have been introduced and have shown promising potential in biomedical waste management. Wireless detection and IoT methods have enabled the monitoring of waste bins, predictions for the amount of waste, and optimization of the performance of waste processing facilities. This review paper aims to explore the application of AI through machine learning and deep learning models in optimizing the collection, segregation, transportation, disposal, and monitoring processes, which leads to improved resource allocation with risk mitigation of biomedical waste along with prediction, and decision-making using AI algorithms.International Journal of Biomedical and Clinical Analysis00This review paper aims to explore the application of AI through machine learning and deep learning models in optimizing the collection, segregation, transportation, disposal, and monitoring processes, which leads to improved resource allocation with risk mitigation of biomedical waste along with prediction, and decision-making using AI algorithms.International Journal of Biomedical and Clinical Analysis['Olivea Sarkar', 'Avick Dey', 'Tripti Malik']2023-12-21T00:00:00https://www.semanticscholar.org/paper/6992a9f991d708922adc6dbad21ef9ad6112946b 132557d6951327c01099639a23b05c0f1257f762Artificial intelligence (AI) is not a new term; its beginnings go back to the beginnings of the fifth decade in the year 1955. Since the emergence of artificial intelligence, it has been in the process of continuous development and spread of its technologies, until it recently reached the most prominent application, which is the ChatGPT program. AI technology is one of the most important modern technologies that contribute to the development of many fields, raise the level of quality, improve productivity, and increase efficiency. Education is one of the most important areas affected by modern technologies, which moved education from the ordinary level to the level of creativity and innovation. The idea of this research comes from several reasons: 1- The rapid transfer of education to a larger world in a short period, 2- The many positives that artificial intelligence had and acquired; 3- The need for the teacher to develop himself in this field and keep pace with the modern era, 4- The absence of motivation among many students, and the infiltration of boredom and lethargy among their ranks. 5- The need for the courses taught by the student to adapt technology in this era, and the inclusion of artificial intelligence completes that. 6- Enriching the research library with a study related to the important topic needed by the teacher and the student. 7- The limited thinking of some students and the lack of scientific communication and its stopping at a certain point.2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI)220Education is one of the most important areas affected by modern technologies, which moved education from the ordinary level to the level of creativity and innovation, and the inclusion of artificial intelligence completes that.2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI)['Hanadi Mohammad Alomran', 'Omar H. Alhazmi']2023-12-21T00:00:00https://www.semanticscholar.org/paper/132557d6951327c01099639a23b05c0f1257f762 39d1f76ac27b3a445c50ade1eda256dd32aced6bWith the rapid diffusion of social networks in combination with mobile phones, a new social threat of sextortion has emerged, in which vulnerable young women are essentially blackmailed with their explicit shared multimedia content. The phenomenon of sextortion is now widely studied by psychologists, sociologists, criminologists, etc. The findings have been translated into scattered help from NGOs, specialized law enforcement units, and therapists, who usually do not coordinate their efforts among each other. This paper addresses the gap of lacking coordination systems to effectively and efficiently use modern information technologies that align the efforts of scattered and non-aligned sextortion help organizations. Consequently, this paper not only investigates the goals, incentives, and disincentives for a system design and development that not only governs effectively and efficiently diverse cases of sextortion victims, but also leverages artificial intelligence in a targeted manner. It explores how AI and, in particular, autonomous cognitive entities can improve victim profiles analysis, streamline support mechanisms, and provide intelligent insight into sextortion cases. Furthermore, the paper conceptually studies the extent to which such efforts can be monetized in a sustainable way. Following a novel design methodology for the design of trusted blockchain decentralized applications, the paper presents a set of conceptual requirements and system models based on which it is possible to deduce a best-practice technology stack for rapid implementation deployment.arXiv.org370This paper explores how AI and, in particular, autonomous cognitive entities can improve victim profiles analysis, streamline support mechanisms, and provide intelligent insight into sextortion cases and conceptually studies the extent to which such efforts can be monetized in a sustainable way.ArXiv['Norta Alex', 'Makrygiannis Sotiris']2023-12-21T00:00:00https://www.semanticscholar.org/paper/39d1f76ac27b3a445c50ade1eda256dd32aced6b 961a36c1a8fd9f999f688739cd52ad712e47e802A significant part of the current textile manufacturing industry around the world is managed and governed by individuals who are not sufficiently trained in information technology. While future textile engineers and technologists may come out sufficiently equipped to understand IT and its implications to the textile manufacturing industry, those running the industry today can benefit through simple elaboration of potentially useful IT tools and the many benefits they offer to the textile industry. This is especially true in the case of technologies that surfaced within the last two decades and are already making a sizable impact on products, production processes and the bottom lines of manufacturing industries. Artificial intelligence (AI) and related technologies fall under the category of rapidly emerging technologies that carry the potential to significantly re-shape the global manufacturing and service industries. This paper makes an attempt to describe these technologies and their potential benefits in such a way that textile industry leaders who are not IT experts can understand them to the extent of driving themselves to enthusiastically adopt them.Journal of Textile Engineering &amp; Fashion Technology100This paper makes an attempt to describe Artificial intelligence (AI) and related technologies and their potential benefits in such a way that textile industry leaders who are not IT experts can understand them to the extent of driving themselves to enthusiastically adopt them.Journal of Textile Engineering &amp; Fashion Technology['R. Parachuru']2023-12-21T00:00:00https://www.semanticscholar.org/paper/961a36c1a8fd9f999f688739cd52ad712e47e802 7fbaf6f7450fb9183dd482b8dd08b3cee309e553Digital Transformation and Data Science (DS) are used in tandem with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Learning is a characteristic human way of behaving which has been made a fundamental part of the machines also. AI, encompassing ML, Neural Networks, and DL, aims to replicate human decision-making and perspectives. ML is regarded as a subset of AI and is frequently employed for the implementation of AI. DL represents a progression beyond ML, characterized by its utilization of specialized algorithms known as deep neural network models. DS is an extensive interaction that includes different strides for dissecting information and creating bits of knowledge. As companies journey toward digital transformation and the era of Industry 4.0/Pharma 4.0, an increasing number of them are integrating DS, Advanced Data Analytics, and AI into their development and production workflows. This integration is aimed at enhancing efficiency, minimizing errors, and maintaining competitiveness.. This paper provides an overview of the historical and forthcoming application domains, sub-domains, and practical uses of AI, ML, DL, and DS.International Conference on Innovative Mechanisms for Industry Applications330This paper provides an overview of the historical and forthcoming application domains, sub-domains, and practical uses of AI, ML, DL, and DS.2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)['Ragini Mokkapati', 'Venkata Lakshmi Dasari']2023-12-21T00:00:00https://www.semanticscholar.org/paper/7fbaf6f7450fb9183dd482b8dd08b3cee309e553 16b7e6eb5d196aa37201884a7fd884d96f050a6eInnovation in higher education is essential to align teaching with technological developments. The use of artificial intelligence (AI) offers great potential to improve education management in higher education through optimizing the teaching and learning process. This research aims to explore how the integration of artificial intelligence in education management can improve the efficiency, effectiveness, and quality of students' learning experience in higher education. The research method includes a comparative analysis between conventional education management systems and systems that utilize artificial intelligence, as well as the implementation of AI technology in the decision-making process in higher education. The results show that the application of artificial intelligence can improve the ability of prediction, data analysis, adaptation, and personalization in curriculum management, student performance evaluation, and the provision of learning resources tailored to individual needs. Artificial intelligence in education management in higher education has opened up various opportunities. Artificial intelligence enables personalization of learning. In addition, the use of artificial intelligence in administration has also improved the efficiency of resource management and administrative processes. The conclusion of this study states that the utilization of artificial intelligence in educational management in higher education can bring about positive changes. By effectively utilizing artificial intelligence technology, universities can improve students' learning experience and optimize administrative efficiency, better preparing students for future challenges. It can also manage education in higher education to create a more adaptive, responsive, and innovative learning environment, which in turn can improve the overall quality of higher education.al-fikrah: Jurnal Manajemen Pendidikan330The results show that the application of artificial intelligence can improve the ability of prediction, data analysis, adaptation, and personalization in curriculum management, student performance evaluation, and the provision of learning resources tailored to individual needs.al-fikrah: Jurnal Manajemen Pendidikan['Siminto Siminto', 'Akib Akib', 'Hasmirati Hasmirati', 'Danang Sigit Widianto']2023-12-21T00:00:00https://www.semanticscholar.org/paper/16b7e6eb5d196aa37201884a7fd884d96f050a6e 9f051a46419a397acdd036b1b1f7d805a56aff41Abstract Aims Natural language processing chatbots (NLPC) can be used to gather information for medical content. However, these tools contain a potential risk of misinformation. This study aims to evaluate different aspects of responses given by different NLPCs on questions about atrial fibrillation (AF) and clinical implantable electronic devices (CIED). Methods and results Questions were entered into three different NLPC interfaces. Responses were evaluated with regard to appropriateness, comprehensibility, appearance of confabulation, absence of relevant content, and recommendations given for clinically relevant decisions. Moreover, readability was assessed by calculating word count and Flesch Reading Ease score. 52, 60, and 84% of responses on AF and 16, 72, and 88% on CIEDs were evaluated to be appropriate for all responses given by Google Bard, (GB) Bing Chat (BC) and ChatGPT Plus (CGP), respectively. Assessment of comprehensibility showed that 96, 88, and 92% of responses on AF and 92 and 88%, and 100% on CIEDs were comprehensible for all responses created by GB, BC, and CGP, respectively. Readability varied between different NLPCs. Relevant aspects were missing in 52% (GB), 60% (BC), and 24% (CGP) for AF, and in 92% (GB), 88% (BC), and 52% (CGP) for CIEDs. Conclusion Responses generated by an NLPC are mostly easy to understand with varying readability between the different NLPCs. The appropriateness of responses is limited and varies between different NLPCs. Important aspects are often missed to be mentioned. Thus, chatbots should be used with caution to gather medical information about cardiac arrhythmias and devices.Europace255Responses generated by an NLPC are mostly easy to understand with varying readability between the different NLPCs, but the appropriateness of responses is limited and varies between different NLPCs.Europace['Henrike A K Hillmann', 'Eleonora Angelini', 'N. Karfoul', 'Sebastian Feickert', 'Johanna Mueller-Leisse', 'D. Duncker']2023-12-21T00:00:00https://www.semanticscholar.org/paper/9f051a46419a397acdd036b1b1f7d805a56aff41 e3b6199a29c7defb3e53a965aebcbf521a95fa4fNeural computing & applications (Print)264Neural Comput. Appl.['Fatma M. Talaat', 'A. Aljadani', 'Mahmoud Badawy', 'Mostafa A. Elhosseini']2023-12-21T00:00:00https://www.semanticscholar.org/paper/e3b6199a29c7defb3e53a965aebcbf521a95fa4f 2b513603ddc27ccd596a6575a21aacb15a6d3629Ethics, Science, Education00Ethics, Science, Education[]2023-12-21T00:00:00https://www.semanticscholar.org/paper/2b513603ddc27ccd596a6575a21aacb15a6d3629 68f26c97ae1fe18017c4c66ff792a7f1143d5449International Conference on Future Networks and Distributed Systems131{'pages': '292-297'}['Surayyo Amonova', 'Gulkhayo Juraeva', 'Mirjon Khidoyatov']2023-12-21T00:00:00https://www.semanticscholar.org/paper/68f26c97ae1fe18017c4c66ff792a7f1143d5449 d9487083aa7edd895d627d609e0867def521c121Applied Water Science70In the absence of climatic data, the ANFIS, ANN, and ANN-GWO methods using minimum and maximum temperatures, which are relatively easier to estimate, outperformed the empirical Hargreaves equation method in both stations.Applied Water Science['Mohaddeseh Bidabadi', 'Hossein Babazadeh', 'J. Shiri', 'A. Saremi']2023-12-21T00:00:00https://www.semanticscholar.org/paper/d9487083aa7edd895d627d609e0867def521c121 09edfd8f4db28b8f40d9eacaebc1f4e57eab47fcChatGPT is a conversational AI model developed by OpenAI designed to generate human-like text based on the input it receives. ChatGPT has become increasingly popular, and the general public may use this tool to ask questions about different medical conditions. There is a lack of data showing if ChatGPT is able to provide reliable information on medical conditions to the general public. The aim of our study is to assess the accuracy and appropriateness of ChatGPT answers to questions on inguinal hernia management.Journal of laparoendoscopic & advanced surgical techniques. Part A30The aim of this study is to assess the accuracy and appropriateness of ChatGPT answers to questions on inguinal hernia management.Journal of laparoendoscopic & advanced surgical techniques. Part A['D. L. Lima', 'Raquel Nogueira', 'Ryan Chin', 'C. Claus', 'F. Malcher', 'P. Sreeramoju', 'L. Cavazzola']2023-12-21T00:00:00https://www.semanticscholar.org/paper/09edfd8f4db28b8f40d9eacaebc1f4e57eab47fc 61a11b117a79e70ef9d5881d6e2040700bcb6a7b[This corrects the article DOI: 10.2196/46089.].Journal of Medical Internet Research00Journal of Medical Internet Research['Ying Wang', 'Nian Li', 'Lingmin Chen', 'Miaomiao Wu', 'Sha Meng', 'Ze-lei Dai', 'Yonggang Zhang', 'M. Clarke']2023-12-21T00:00:00https://www.semanticscholar.org/paper/61a11b117a79e70ef9d5881d6e2040700bcb6a7b 25a63f71209dce09698a847ef33d023634edcc25The rapid evolution of technology calls for innovations in nursing education and continuing professional development (NCPD) that are crucial for maintaining high-quality health-care delivery. As lifelong learners, nurses require effective and motivational educational resources that support their ongoing growth and enable them to adapt to changing health-care landscapes. Generative AI models such as OpenAI and Advanced language models such as ChatGPT present opportunities to enhance learning experiences and support knowledge acquisition. This article explores the potential of incorporating both generative AI and advanced language models in NCPD programs, focusing on design strategies, implementation, and possible challenges. By leveraging these innovations, nursing professionals can access personalized, on-demand, and interactive learning resources, advancing their professional growth and improving patient outcomes.Interdisciplinary Journal of Partnership Studies120The potential of incorporating both generative AI and advanced language models in NCPD programs, focusing on design strategies, implementation, and possible challenges, is explored.Interdisciplinary Journal of Partnership Studies['Jennifer Shepherd']2023-12-21T00:00:00https://www.semanticscholar.org/paper/25a63f71209dce09698a847ef33d023634edcc25 e1cf63014c6b01074974a112ecef54bbea766e4dJournal of Technology in Counselor Education and Supervision00Journal of Technology in Counselor Education and Supervision['Sidney Shaw', 'Sophie Oswin', 'Yue Xi', 'F. Calandriello', 'Russell Fulmer']2023-12-21T00:00:00https://www.semanticscholar.org/paper/e1cf63014c6b01074974a112ecef54bbea766e4d 1dfc36a0de2673e480eee6eaa7c7d8320fdd17dcInternational Conference on Future Networks and Distributed Systems70{'pages': '51-57'}['M. Abdurashidova', 'M. Balbaa']2023-12-21T00:00:00https://www.semanticscholar.org/paper/1dfc36a0de2673e480eee6eaa7c7d8320fdd17dc d27d47e2fb5ed1ddc1a7f4936b4d55c2cfcf9e06ICSEB160{'pages': '20-26'}['B. Handoko', 'Michael Angelus', 'Archie Nathanael Mulyawan']2023-12-21T00:00:00https://www.semanticscholar.org/paper/d27d47e2fb5ed1ddc1a7f4936b4d55c2cfcf9e06 396a69d33a05abdcf2c5c88c163ccdbaa656d117Journal of Technology in Counselor Education and Supervision00Journal of Technology in Counselor Education and Supervision['Russell Fulmer', 'Wendell Callahan', 'Olivia Uwamahoro Williams']2023-12-21T00:00:00https://www.semanticscholar.org/paper/396a69d33a05abdcf2c5c88c163ccdbaa656d117 610505eb9fc47f376be25a6dfd0350c51337e08dInternational Conference on Future Networks and Distributed Systems120{'pages': '95-104'}['Zafar Shakarov']2023-12-21T00:00:00https://www.semanticscholar.org/paper/610505eb9fc47f376be25a6dfd0350c51337e08d 61bfce1ea47591feec92fdab03c580b7bdaebdb1Music Reference Services Quarterly00Music Reference Services Quarterly['Colin Hochstetler']2023-12-21T00:00:00https://www.semanticscholar.org/paper/61bfce1ea47591feec92fdab03c580b7bdaebdb1 f4fe03e04a04cf946c827d417e3a456dd00a3f55Journal of Technology in Counselor Education and Supervision00Journal of Technology in Counselor Education and Supervision['Russell Fulmer']2023-12-21T00:00:00https://www.semanticscholar.org/paper/f4fe03e04a04cf946c827d417e3a456dd00a3f55 425fa2e0688db3410f8b2f67c9601ec73ae49800International Conference on Future Networks and Distributed Systems120{'pages': '570-578'}['Sedigheh Shakib Kotamjani', 'Sojida Shirinova', 'Mehrnaz Fahimirad']2023-12-21T00:00:00https://www.semanticscholar.org/paper/425fa2e0688db3410f8b2f67c9601ec73ae49800 74215276728a8d66199c4ff643569e077ec852b0New England Journal of Medicine60The New England journal of medicine['Francisco Azuaje']2023-12-21T00:00:00https://www.semanticscholar.org/paper/74215276728a8d66199c4ff643569e077ec852b0 a73bccc06df56906246f13eca23057c26c4a2fd0Bengal Physician Journal50Bengal Physician Journal['Gyanendri Tomar', 'Aditi Chauhan']2023-12-21T00:00:00https://www.semanticscholar.org/paper/a73bccc06df56906246f13eca23057c26c4a2fd0 de90cdd9d99fb11f88bce60283dc8793518ac404International Journal of Human-Computer Interaction1260International Journal of Human–Computer Interaction['Zhenyao Cai', 'Haoqing He', 'Weiwei Huo', 'Xinyu Xu']2023-12-21T00:00:00https://www.semanticscholar.org/paper/de90cdd9d99fb11f88bce60283dc8793518ac404 2ca22cc24cf7e59a2f8f6a67d29c52b5eda96a0bInternational Conference on Future Networks and Distributed Systems10{'pages': '682-688'}['Elena Zarova', 'Gulnora Abdurakhmanova', 'Bobir Tursunov']2023-12-21T00:00:00https://www.semanticscholar.org/paper/2ca22cc24cf7e59a2f8f6a67d29c52b5eda96a0b 6d2ca17b8d1bf2f1f780e63e61444637421a6e17Nature21Nature['D. Castelvecchi']2023-12-21T00:00:00https://www.semanticscholar.org/paper/6d2ca17b8d1bf2f1f780e63e61444637421a6e17 3343c5f47d74f9dd9974a87a7dec9279885df821Generative artificial intelligence and the law: there is no turning back.Communications of the ACM01Communications of the ACM['Gregory Goth']2023-12-21T00:00:00https://www.semanticscholar.org/paper/3343c5f47d74f9dd9974a87a7dec9279885df821 3a7ea5d3d5bc11a8e5dc5bd5263937dd10a117b1In critical operational context such as Mine Warfare, Automatic Target Recognition (ATR) algorithms are still hardly accepted. The complexity of their decision-making hampers understanding of predictions despite performances approaching human expert ones. Much research has been done in Explainability Artificial Intelligence (XAI) field to avoid this ”black box” effect. This field of research attempts to provide explanations for the decision-making of complex networks to promote their acceptability. Most of the explanation methods applied on image classifier networks provide heat maps. These maps highlight pixels according to their importance in decision-making. In this work, we first implement different XAI methods for the automatic classification of Synthetic Aperture Sonar (SAS) images by convolutional neural networks (CNN). These different methods are based on a Post-Hoc approach. We study and compare the different heat maps obtained. Secondly, we evaluate the benefits and the usefulness of explainability in an operational framework for collaboration. To do this, different user tests are carried out with different levels of assistance ranging from classification for an unaided operator, to classification with explained ATR. These tests allow us to study whether heat maps are useful in this context. The results obtained show that the heat maps explanation have a disputed utility according to the operators. Heat map presence does not increase the quality of the classifications. On the contrary, it even increases the response time. Nevertheless, half of operators see a certain usefulness in heat maps explanation.ACM Journal of Data and Information Quality301Different XAI methods for the automatic classification of Synthetic Aperture Sonar images by convolutional neural networks by convolutional neural networks are implemented based on a Post-Hoc approach, showing that the heat maps explanation have a disputed utility according to the operators.ACM Journal of Data and Information Quality['Guy-Junior Richard', 'J. Habonneau', 'Didier Guériot', 'Jean-Marc Le Caillec']2023-12-21T00:00:00https://www.semanticscholar.org/paper/3a7ea5d3d5bc11a8e5dc5bd5263937dd10a117b1 16e436d18a013c4c66e781b18a2f0da68fca41cfThe realm of health care is on the cusp of a significant technological leap, courtesy of the advancements in artificial intelligence (AI) language models, but ensuring the ethical design, deployment, and use of these technologies is imperative to truly realize their potential in improving health care delivery and promoting human well-being and safety. Indeed, these models have demonstrated remarkable prowess in generating humanlike text, evidenced by a growing body of research and real-world applications. This capability paves the way for enhanced patient engagement, clinical decision support, and a plethora of other applications that were once considered beyond reach. However, the journey from potential to real-world application is laden with challenges ranging from ensuring reliability and transparency to navigating a complex regulatory landscape. There is still a need for comprehensive evaluation and rigorous validation to ensure that these models are reliable, transparent, and ethically sound. This editorial introduces the new section, titled “AI Language Models in Health Care.” This section seeks to create a platform for academics, practitioners, and innovators to share their insights, research findings, and real-world applications of AI language models in health care. The aim is to foster a community that is not only excited about the possibilities but also critically engaged with the ethical, practical, and regulatory challenges that lie ahead.JMIR Medical Informatics130This editorial introduces the new section, titled “AI Language Models in Health Care,” which seeks to create a platform for academics, practitioners, and innovators to share their insights, research findings, and real-world applications of AI language models in health care.JMIR Medical Informatics['Alexandre Castonguay', 'Christian Lovis']2023-12-21T00:00:00https://www.semanticscholar.org/paper/16e436d18a013c4c66e781b18a2f0da68fca41cf 0e3ec0ae60aa3bffaa54d2f9644cb7090b6e529b In this Medical News article, JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, and Alondra Nelson, PhD, the Harold F. Linder Professor at the Institute for Advanced Study, discuss effective AI regulation frameworks to accommodate innovation. Journal of the American Medical Association (JAMA)02In this Medical News article, JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, and Alondra Nelson discuss effective AI regulation frameworks to accommodate innovation.JAMA['Melissa Suran', 'Y. Hswen']2023-12-20T00:00:00https://www.semanticscholar.org/paper/0e3ec0ae60aa3bffaa54d2f9644cb7090b6e529b 194398467b7ae5074cbc626a859935ff2a790962Importance Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed. Observations While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings. Conclusion and Relevance The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.Journal of the American Medical Association (JAMA)2110The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here and community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.JAMA['Nigam H Shah', 'John D Halamka', 'S. Saria', 'Michael Pencina', 'Troy Tazbaz', 'Micky Tripathi', 'Alison Callahan', 'Hailey Hildahl', 'Brian Anderson']2023-12-20T00:00:00https://www.semanticscholar.org/paper/194398467b7ae5074cbc626a859935ff2a790962 02e490efe0dd8827c147a5a79d2fb4ddaa39d4c1Journal of European Public Policy371Journal of European Public Policy['Shawn Donnelly', 'Elena Ríos Camacho', 'Sebastian Heidebrecht']2023-12-20T00:00:00https://www.semanticscholar.org/paper/02e490efe0dd8827c147a5a79d2fb4ddaa39d4c1 fbd7882b8b8bf086427caf7b2bef44436dd518aaSeawater desalination is a new promising marine industry and an important way to supplement the shortage of land water resources and promote the efficient use of seawater resources. Currently, the global desalination industry is rapidly developing. In China’s new development plan, large-scale development of the seawater desalination industry is also an important strategic goal. However, compared to the technological development of the seawater desalination industry, its marketization is affected by various factors, and its development level is still relatively low. Therefore, based on real industry data, this paper constructs a tripartite evolutionary game model for seawater desalination enterprises, water supply enterprises, and the government, and identifies several entry barriers for seawater desalination in China. The results include: (1) For seawater desalination to enter the market in China, government supervision limit should be no greater than 400,000 tons per day, about 13.8% of total seawater desalination scale. (2) The entry cost should be no more than 10 million yuan per day for seawater desalination, which is equivalent to approximately 18.25 trillion yuan during a five-year period. Finally, (3) political relatedness should be at least 2.0 for seawater desalination to take place, and 3.0 for it to develop in full swing. The paper also discusses the division of state- and foreign-owned water companies and reveals that government subsidies are only effective if foreign-owned companies seek rents. Based on the findings of the study, we propose pertinent policy recommendations including top-level planning, desalinated water infrastructure development, subsidy policy implementation, and public engagement. These recommendations aim to aid the Chinese government in fostering the desalination sector.Frontiers in Marine Science511Frontiers in Marine Science['Mingbao Chen', 'Zhibin Xu', 'Yuhao Wang']2023-12-20T00:00:00https://www.semanticscholar.org/paper/fbd7882b8b8bf086427caf7b2bef44436dd518aa 812a17d98b9bd0b6649aea64e1301206ab1cf5e8This article argues that regulatory trust is established and maintained through a relationship between stakeholders and the government, with the former group of organizations and individuals relying on their understanding of this relationship. Federal administrative law in the United States makes the expectations about the regulatory environment on which stakeholders rely more meaningful by granting stakeholders the right to bring actions in court when their expectations are being (or seem likely to be) dashed. To make this argument, I begin with the threshold question of how courts serve as a forum for voicing claims of distrust in regulatory regimes. I then consider the importance of political accountability over regulatory decision making, illustrating the concerns about the structure of agencies that regulate consumer and housing finance. Next, I will discuss a second major challenge to trust that arises from the extent to which the authority of regulatory agencies is circumscribed by legislation. Along the way, I draw on the case law presented to argue that problems of trust in financial regulation center on the reliance interests of stakeholders. Finally, the argument suggests a research agenda into trust as a reliance interest that I sketch in the conclusion.Transylvanian Review of Administrative Sciences130Transylvanian Review of Administrative Sciences['Anthony M. Bertelli']2023-12-20T00:00:00https://www.semanticscholar.org/paper/812a17d98b9bd0b6649aea64e1301206ab1cf5e8 3a8c19de1e57a20d2d216c47a2c5b57ae7e29bdbНа основе всестороннего исследования предметной области обоснована актуальность создания инструментария выявления и регулирования рисков девиантного поведения сотрудников корпорации в контексте обеспечения их безопасности. Рассмотрение аналогов позволило осуществить целеполагание проектной деятельности по линейке взаимно однозначного соответствия выявленных противоречий, поставленных задач, ожидаемых результатов, их новизны, практической ценности и теоретической значимости. В соответствии с вышеизложенным предлагается архитектура создаваемого инструментария, включающая разнообразные библиотеки и модули, осуществляется демонстрация их использования на практических примерах автоматизированного анализа мимических эмоций (злость, отвращение, страх, радость, грусть, удивление и др.) человека. Based on a comprehensive study of the subject area, the relevance of creating tools for identifying and regulating the risks of deviant behavior of corporate employees in the context of ensuring their safety is substantiated. Consideration of analogues allowed to realize the goal-setting of project activities according to the line of mutually one-valued correspondence of the revealed contradictions, set tasks, expected results, their novelty, practical value and theoretical significance. In accordance with the above, the architecture of the created toolkit including various libraries and modules is proposed, their use is demonstrated on practical examples of automated analysis of human facial emotions (anger, disgust, fear, joy, sadness, surprise, etc).ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ00ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ['Александр Григорьевич Остапенко', 'А.Г. Зимницкий', 'Екатерина Алексеевна Москалева']2023-12-20T00:00:00https://www.semanticscholar.org/paper/3a8c19de1e57a20d2d216c47a2c5b57ae7e29bdb 53904a2f3f8bc58bf3636939429f27fadbdb9494The rapid development of artificial intelligence (AI) has affected higher education. Students now receive new tools that optimize the performance of current tasks. Universities have also begun implementing AI technologies to help university teachers and improve the quality of educational services and solve the Sustainable Development Goal 4. Hypothetically, it is possible to replace university teachers by using AI technologies. This is a hidden conflict of Sustainable Development Goal 4 and Sustainable Development Goal 8. This research aimed to examine the perceptions of Eastern European students about the possibility of replacing university teachers through AI technologies. The authors used an information study with a bibliometric analysis of 2000 sources, planning the experiments and compiling the questionnaire, surveying 599 students using an electronic questionnaire and cloud technologies, statistical processing questionnaires using Excel tables, and verifying statistical hypotheses. Verification of statistical hypotheses for replies of 599 respondents showed that more than 10% of the surveyed students from Eastern European universities are confident that AI will replace university teachers in five years. It was shown that the opinions of students in the 1st stage (undergraduate study) from the countries of the European Union and countries outside the European Union have significant differences. The obtained results were proven using one-sided testing and standard hypothesis testing level, α = 0.05. The article was completed with multilevel managerial and pedagogical recommendations. These recommendations are designed to increase higher education’s sustainability in AI implementation.Sustainability434Sustainability['Valery Okulich-Kazarin', 'A. Artyukhov', 'Łukasz Skowron', 'N. Artyukhova', 'O. Dluhopolskyi', 'W. Cwynar']2023-12-20T00:00:00https://www.semanticscholar.org/paper/53904a2f3f8bc58bf3636939429f27fadbdb9494 3a770bee7a93d591f8a2f8ba01e53eb59f51ad69AI &amp; SOCIETY244It is argued that competitive market-based dynamics generate a powerful force that pushes such companies towards limiting the actual social impact of their business model in favour of ensuring higher profit margins and cast doubt on the ethical nature of AI products that rely on this form of AI data work.AI &amp; SOCIETY['James Muldoon', 'C. Cant', 'Mark Graham', 'Funda Ustek Spilda']2023-12-20T00:00:00https://www.semanticscholar.org/paper/3a770bee7a93d591f8a2f8ba01e53eb59f51ad69 d6f1c022e11f86a6a8fe2867fd4f0a337f162b6eThis systematic review investigates the current state of research on Generative Artificial Intelligence (GenAI) and its implications for (EMI) Higher Education. The study employs a methodology based on an evidence-informed and theoretically credible framework to answer two research questions: (1) What studies of relevance to (EMI) Higher Education have been published thus far, considering the most recent developments of GenAI? and (2) Which key areas are currently lacking in extant literature and in need of further scholarly exploration in this regard in (EMI) Higher Education research? The results of the study reveal a limited number of pertinent publications, indicating a sparse scholarly landscape with a dearth of work on the implications of Generative AI in EMI Higher Education. Based on these findings, preliminary recommendations have been made to guide future research in this area. This study contributes to the literature by highlighting the need for further research on the potential of GenAI to enhance the teaching and learning experience in (EMI) Higher Education and provides a theoretical framework to guide future research. These findings may inform researchers and educators interested in exploring how GenAI may be leveraged from different educational perspectives.Aula Abierta962A sparse scholarly landscape with a dearth of work on the implications of GenAI in EMI Higher Education is revealed, highlighting the need for further research on the potential of GenAI to enhance the teaching and learning experience in (EMI) Higher Education.Aula Abierta['Peter Bannister', 'Alexandra Santamaría-Urbieta', 'Elena Alcalde-Peñalver']2023-12-20T00:00:00https://www.semanticscholar.org/paper/d6f1c022e11f86a6a8fe2867fd4f0a337f162b6e 25b262d973ca2b3e6bf9af3bda8e03beb302c850Artificial intelligence (AI) is at the forefront of this transition, which is being brought about by the AI Revolution, which is responsible for reshaping the world as we know it. The application of artificial intelligence (AI) is becoming increasingly widespread across a variety of industries, including healthcare, finance, transportation, and education by name. The potential advantages of artificial intelligence are enormous, and it is essential to have a solid understanding of the effects that it will have on society. This study intends to investigate the empowering effects that artificial intelligence will have on the future, with a particular emphasis on its uses, problems, and limitationsPakistan journal of international affairs01This study intends to investigate the empowering effects that artificial intelligence will have on the future, with a particular emphasis on its uses, problems, and limitations.Pakistan Journal of International Affairs['Mobashir Naeem', 'Siddiqui Honorary', 'Advisor Dr. Jameel', 'Jalibi']2023-12-20T00:00:00https://www.semanticscholar.org/paper/25b262d973ca2b3e6bf9af3bda8e03beb302c850 2010da55386c4389e74a6f04b64179ecef568a71In the contemporary business landscape, the proliferation of data has surged to unprecedented levels, presenting both an opportunity and a challenge for enterprises across diverse sectors. Big data analytics, powered by artificial intelligence (AI), has emerged as a transformative force, offering invaluable insights to drive strategic decision-making and foster business advancement. This paper aims to elucidate the pivotal role of AI-driven big data analytics in extracting meaningful insights from vast and complex datasets. It explores the convergence of AI technologies, machine learning algorithms, and sophisticated data analytics tools that enable organizations to harness the potential of big data. Moreover, it delves into the significance of predictive analytics, prescriptive analytics, and descriptive analytics in empowering businesses to forecast trends, optimize operations, and uncover hidden patterns. Furthermore, this paper examines the practical implications and benefits of employing AI-driven big data analytics across various industries. Case studies and real-world examples illustrate how businesses can leverage these insights to enhance customer experiences, improve operational efficiency, and gain a competitive edge in the market. Additionally, ethical considerations, data privacy concerns, and the challenges associated with implementing AI-driven big data analytics are also discussed, emphasizing the importance of responsible data usage and compliance with regulatory frameworksEPH - International Journal of Science And Engineering01The pivotal role of AI-driven big data analytics in extracting meaningful insights from vast and complex datasets is elucidated, and the convergence of AI technologies, machine learning algorithms, and sophisticated data analytics tools that enable organizations to harness the potential of big data are explored.EPH - International Journal of Science And Engineering['Karthik Allam', 'Anjali Rodwal']2023-12-20T00:00:00https://www.semanticscholar.org/paper/2010da55386c4389e74a6f04b64179ecef568a71 c87fdaaeb9a9c17538392dd58d731ad96c95dc9cIn the ever-evolving landscape of marine, oceanic, and climate change monitoring, the intersection of cutting-edge artificial intelligence (AI), machine learning (ML), and data analytics has emerged as a pivotal catalyst for transformative advancements [...]Remote Sensing101The intersection of cutting-edge artificial intelligence (AI), machine learning (ML), and data analytics has emerged as a pivotal catalyst for transformative advancements in marine, oceanic, and climate change monitoring.Remote. Sens.['Veronica Nieves', 'Ana Ruescas', 'R. Sauzède']2023-12-20T00:00:00https://www.semanticscholar.org/paper/c87fdaaeb9a9c17538392dd58d731ad96c95dc9c 0aa2708fdc0351b825da98e16f8029d0eb1303aaCancer Imaging341Replacing first reader in double reading with an AI could be feasible but choosing an appropriate AI threshold is crucial to maintaining cancer detection accuracy and workload.Cancer Imaging['M. T. Elhakim', 'S. Stougaard', 'Ole Graumann', 'Mads Nielsen', 'Kristina Lång', 'O. Gerke', 'L. Larsen', 'Benjamin S. B. Rasmussen']2023-12-20T00:00:00https://www.semanticscholar.org/paper/0aa2708fdc0351b825da98e16f8029d0eb1303aa c2996f42a7f01c8c0025b62fdfecb4851d092489In rapidly evolving landscape of technology, the integration of artificial intelligence (AI) has become prevalent, reshaping various facets of students’ lives. This study delved into the uncharted territory of AI awareness, utilization, and perceptions among college students. The study used a convergent parallel mixed-methods design, integrating quantitative survey data with qualitative responses in order to get insights on the impact of AI in education and society. The survey found that college students familiarity on AI depends on age, academic year, and field of study. This emphasizes the need for targeted AI education to overcome knowledge inequalities, particularly among younger cohorts and in fields with little AI expertise. AI usage is usually modest for academic and personal purposes, while insights includes its uses in academic research, job administration, and language translation. The varied application of AI requires institutions to adapt their procedures, and societal impacts which are largely seen positively. This optimism is tempered by concerns about job loss, data privacy breach, technological overuse, and human decision-making. Thus, comprehensive AI education programs are needed to address and traverse these varied opinions. The results emphasized the necessity for institutions and policymakers to be proactive when artificial intelligence is transforming many companies and social systems. Students must learn AI literacy, combining knowledge and practical application, to navigate the AI-driven environment's complexity and opportunities. The endeavor requires teaching technical skills and a deep grasp of AI's social and ethical implications. Institutions can prepare students for a future where artificial intelligence is becoming more important by understanding these effects and tailoring teaching.Polaris Global Journal of Scholarly Research and Trends301The results emphasized the necessity for institutions and policymakers to be proactive when artificial intelligence is transforming many companies and social systems, and the necessity for students to learn AI literacy, combining knowledge and practical application, to navigate the AI-driven environment's complexity and opportunities.Polaris Global Journal of Scholarly Research and Trends['Lynard Bobby L. Asirit', 'Jocelyn H. Hua']2023-12-20T00:00:00https://www.semanticscholar.org/paper/c2996f42a7f01c8c0025b62fdfecb4851d092489 b753ad2bf314c5053fa0f5a9952dd46d023db98dArtificial intelligence (AI) is transforming how we work and make choices, but it also poses ethical and societal issues including algorithmic discrimination and dehumanization. It is critical to take into account corporate culture, emotional intelligence, cooperation, communication, and constant learning when using AI systems in the workplace. It has been demonstrated that emotional intelligence increases AI adoption, efficacy, and performance across a variety of sectors. But ethical concerns and trouble making decisions are also important. Effective collaboration, communication, and corporate culture are crucial for successful AI adoption, and continuing learning and development are essential for enhancing decision-making abilities. AI ethics in the workplace necessitate a comprehensive strategy that considers both technical and non-technical aspects. This study looks at the benefits of emotional intelligence, moral concerns, effective stakeholder and IT specialist engagement, organisational culture, and potential threats of artificial intelligence (AI) in decision-making. The study underlines the value of continuous AI learning and development.EAI Endorsed Transactions on Pervasive Health and Technology161This study looks at the benefits of emotional intelligence, moral concerns, effective stakeholder and IT specialist engagement, organisational culture, and potential threats of artificial intelligence (AI) in decision-making.EAI Endorsed Transactions on Pervasive Health and Technology['Anita Shukla', 'Alka Algnihotri', 'Bhawna Singh']2023-12-20T00:00:00https://www.semanticscholar.org/paper/b753ad2bf314c5053fa0f5a9952dd46d023db98d 9c37c5da279b571d1234e506f2e99e5d58a8c1eeThe European Legacy04The European Legacy['Zhonghua Wu', 'Le Cheng']2023-12-20T00:00:00https://www.semanticscholar.org/paper/9c37c5da279b571d1234e506f2e99e5d58a8c1ee c9c63cae7376f0ec4d07eebe3fe90cb2dc1092b8In contemporary governance, program-based governance has gained widespread adoption across various sectors. However, the increasing demand for human labor in data management, transmission and analysis poses challenges to efficient administration. As societies grapple with increasingly complex challenges, the need for efficient and adaptive governance mechanisms becomes paramount. In response, the intersection of artificial intelligence (AI) technologies and program-based governance presents a way for enhancing overall managerial efficiency. This article provides a comprehensive overview of program-based governance within the framework of the monitoring and evaluation process using the insights of Azerbaijan. The exploration of case studies and practical applications - a personalized chatbot model created using advanced natural language processing such as GPT, the article identifies key functionalities where strategic AI integration can optimize and strengthen programmatic management. In the first implemented solution, users can swiftly obtain a summarized results on the progress reports, including details on goal achievement, challenges, and other pertinent information in Azerbaijani language, through the personalized bot on the portal.InterConf20An overview of program-based governance within the framework of the monitoring and evaluation process using the insights of Azerbaijan identifies key functionalities where strategic AI integration can optimize and strengthen programmatic management.InterConf['Vusal Gasimli', 'Ismat Mehraliyeva']2023-12-20T00:00:00https://www.semanticscholar.org/paper/c9c63cae7376f0ec4d07eebe3fe90cb2dc1092b8 5d5dcb288e7416d1414e1dfd857d9b8237aff8f2Our goal is to highlight the capabilities of modern, generative AI systems using the widely used and accessible ChatGPT text completion models from OpenAI, focusing on how they can be used for the analysis of dreams and dream journals. We start with a brief overview of the nature of dreams, methods of dream interpretation, and the importance of the human-dream relationship. We explore the ways that technology, specifically AI, fits into this space and examine the ways in which AI can be used to help us understand our dreams. We progress from simple dream interpretations, to interpretations according to different schools of thought, to interpreting symbols within individual dreams, and finally to analyzing patterns in individual dream journals. We conclude with a discussion of the ethical concerns surrounding AI and dreams, providing insights from past technological revolutions and how they have both helped and hindered the human endeavor. We finally outline what we believe to be a practical, realistic, and hopeful vision of how we see this field progressing based on the experiments and methodologies that were explored in this paper.Poligrafi00Poligrafi['Sheldon Juncker']2023-12-20T00:00:00https://www.semanticscholar.org/paper/5d5dcb288e7416d1414e1dfd857d9b8237aff8f2 d2207620e99f8d84e8c3a600f5c3cb760434e9a4The relentless pursuit of creating intelligent robotic systems has led to a symbiotic relationship between human inventiveness and artificial intelligence (AI). Artificial intelligence is a theory.  It is the development of computer systems that are able to perform tasks that would require human intelligence. This abstract explores the pivotal role that AI plays in advancing the capabilities and applications of robotic systems.  The integration of AI algorithms and machine learning techniques has launched robotics beyond mere automation, enabling machines to modify, alter, adjust, learn, and interact with the world in ways previously deemed science fiction. Design fictions that vividly imagines future scenarios of AI or robotics in use offer a means both to explain and query the technological possibilities. Examples of these tasks are visual perception, speech recognition, decision-making, and translation between languages.   The three key dimensions of   AI’s role in robotics are Cognitive Augmentation, Human-Robot Collaboration, and Autonomous Intelligence. The abstract also discusses the societal implications of this AI-driven advancement in robotic systems, including ethical considerations, job market impacts, and the democratization of access to advanced technology. The convergence of human intellect and artificial intelligence in robotics marks a transformative era where machines become not just tools, but companions, collaborators, and cognitive extensions of human capabilities.  Researchers are taking inspiration from the brain and considering alternative architectures in which networks of artificial neurons and synapses process information with high speed and adaptive learning capabilities in an energy-efficient, scalable manner. The indispensable role of AI in shaping the future of robotic systems and bridging the gap between human potential and machine capabilities is highlighted. The major impact of this synergy reverberates across industries, promising the world where robots become not just mechanical contraptions / defective apparatus but intelligent partners in our journey of progress.EAI Endorsed Transactions on Internet of Things90The indispensable role of AI in shaping the future of robotic systems and bridging the gap between human potential and machine capabilities is highlighted.EAI Endorsed Transactions on Internet of Things['Nishtha Prakash', 'Areeba Atiq', 'Mohammad Shahid', 'Jyoti Rani', 'Srishti Dikshit']2023-12-20T00:00:00https://www.semanticscholar.org/paper/d2207620e99f8d84e8c3a600f5c3cb760434e9a4 f6643eadcf2eb6b70b801cb853bfe8e956d99de3This volume contains revised versions of the papers selected for the fourth volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.arXiv.org1140This volume contains revised versions of the papers selected for the fourth volume of the Online Handbook of Argumentation for AI (OHAAI), designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.ArXiv['Lars Bengel', 'Lydia Blümel', 'Elfia Bezou-Vrakatseli', 'Federico Castagna', "Giulia D'Agostino", 'Isabelle Kuhlmann', 'Jack Mumford', 'Daphne Odekerken', 'Fabrizio Russo', 'Stefan Sarkadi', 'Madeleine Waller', 'A. Xydis']2023-12-20T00:00:00https://www.semanticscholar.org/paper/f6643eadcf2eb6b70b801cb853bfe8e956d99de3 d0ca265d096b4c0fb27460267986bfaa48a5a0acIn recent years, artificial intelligence (AI) has seen remarkable advancements, stretching the limits of what is possible and opening up new frontiers. This comparative review investigates the evolving landscape of AI advancements, providing a thorough exploration of innovative techniques that have shaped the field. Beginning with the fundamentals of AI, including traditional machine learning and the transition to data-driven approaches, the narrative progresses through core AI techniques such as reinforcement learning, generative adversarial networks, transfer learning, and neuroevolution. The significance of explainable AI (XAI) is emphasized in this review, which also explores the intersection of quantum computing and AI. The review delves into the potential transformative effects of quantum technologies on AI advancements and highlights the challenges associated with their integration. Ethical considerations in AI, including discussions on bias, fairness, transparency, and regulatory frameworks, are also addressed. This review aims to contribute to a deeper understanding of the rapidly evolving field of AI. Reinforcement learning, generative adversarial networks, and transfer learning lead AI research, with a growing emphasis on transparency. Neuroevolution and quantum AI, though less studied, show potential for future developments.Applied Informatics1040This comparative review investigates the evolving landscape of AI advancements, providing a thorough exploration of innovative techniques that have shaped the field, and delves into the potential transformative effects of quantum technologies on AI advancements and highlights the challenges associated with their integration.AI['Hamed Taherdoost', 'Mitra Madanchian']2023-12-20T00:00:00https://www.semanticscholar.org/paper/d0ca265d096b4c0fb27460267986bfaa48a5a0ac 0cfb28b28d60d1b749e3cc7ca94197758dc7d796The rising influence of artificial intelligence (AI) enables widespread adoption of the technology in every aspect of computing, including Software-Defined Networking (SDN). Technological adoption leads to the convergence of AI and SDN, producing solutions that overcome limitations present in traditional networking architecture. Although numerous review articles discuss the convergence of these technologies, there is a lack of bibliometric trace in this field, which is important for identifying trends, new niches, and future directions. Therefore, this study aims to fill the gap by presenting a thorough bibliometric analysis of AI-related SDN studies, referred to as AI-SDN. The study begins by identifying 474 unique documents in the Web of Science (WoS) database published from 2009 until recently. The study uses bibliometric analysis to identify the general information, countries, authorship, and content of the selected articles, thereby providing insights into the geographical and institutional landscape shaping AI-SDN research. The findings provide a robust roadmap for further investigation in this field, including the background and taxonomy of the AI-SDN field. Finally, the article discusses several challenges and the future of AI-SDN in academic research.Electronics380A thorough bibliometric analysis of AI-related SDN studies, referred to as AI-SDN, is presented, providing a robust roadmap for further investigation in this field, including the background and taxonomy of the AI-SDN field.Electronics['Firdaus Sahran', 'Hamza Altarturi', 'Nor Badrul Anuar']2023-12-20T00:00:00https://www.semanticscholar.org/paper/0cfb28b28d60d1b749e3cc7ca94197758dc7d796 d6800ee6bc39c53e8d550f7f0337a6e68be8d767This review article provides a comprehensive examination of the synergistic integration of several technologies, including radio waves, solar cell technology, artificial intelligence (AI), business information technology (IT), immunizations, and antibiotics, with the aim of fostering sustainable innovation. A comprehensive analysis is carried out, highlighting the connections between smart technology, healthcare, and renewable energy sources. The abstract looks at how solar energy and radio waves might advance technology, with artificial intelligence and business IT aiming to maximize productivity. The evaluation also explores the potentially innovative ways in which vaccinations and antibiotics could enhance public health. By linking different disciplines together, the article aims to shed light on the holistic and long-term strategy that emerges when many technologies come together for the good of business and society. This paper explores the intricate connections between solar cell technology and artificial intelligence (AI), showing how advances formerly unimaginable are propelled by the convergence of advanced computing power and renewable energy sources. When it comes to facilitating the seamless integration of many technologies to optimize productivity and decision-making processes, business IT plays a critical role. The important sector of medicine, where immunizations and antibiotics are critical for creating resilient populations, is also covered in the article. This study explains the potential for comprehensive and long-lasting solutions by combining these technological and medical advancements, while also emphasizing the necessity of multidisciplinary cooperation in tackling the challenges of our rapidly changing environment.International Journal of Multidisciplinary Sciences and Arts00The intricate connections between solar cell technology and artificial intelligence (AI) are explored, showing how advances formerly unimaginable are propelled by the convergence of advanced computing power and renewable energy sources.International Journal of Multidisciplinary Sciences and Arts['Chen jin Kim']2023-12-20T00:00:00https://www.semanticscholar.org/paper/d6800ee6bc39c53e8d550f7f0337a6e68be8d767 969c3778c83666c4af672610c559a4cf59365379Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we present a theoretical analysis of the $\textit{trust game}$, the canonical task for studying trust in behavioral and brain sciences, along with simulation results supporting our analysis. Specifically, leveraging reinforcement learning (RL) to train our AI agents, we systematically investigate learning trust under various parameterizations of this task. Our theoretical analysis, corroborated by the simulations results presented, provides a mathematical basis for the emergence of trust in the trust game.arXiv.org180This work presents a theoretical analysis of the trust game, the canonical task for studying trust in behavioral and brain sciences, along with simulation results supporting this analysis.ArXiv['A. S. Nobandegani', 'Irina Rish', 'Thomas R. Shultz']2023-12-20T00:00:00https://www.semanticscholar.org/paper/969c3778c83666c4af672610c559a4cf59365379 bce4e31036c938777e8e36e5f2526394773b547eThis study delves into the impact of text-to-image artificial intelligence on independent game designers and artists. It underscores the paradigm shift in game design, exploring how AI tools facilitate the rapid production of unique game content and artwork, a particular advantage for indie creators. Employing qualitative methods such as interviews and case studies, the research gathers insights into the creative and practical applications of AI in game design. The findings emphasize the evolving role of game designers in an AI-integrated future, balancing artistic creativity with technical proficiency, while also acknowledging the ethical complexities introduced by AI. This research aims to provide an in-depth understanding of how indie game designers can leverage AI to foster innovation while navigating its challenges.Journal of Innovation and Development170This research aims to provide an in-depth understanding of how indie game designers can leverage AI to foster innovation while navigating its challenges, and emphasize the evolving role of game designers in an AI-integrated future.Journal of Innovation and Development['Jiayang Qin']2023-12-20T00:00:00https://www.semanticscholar.org/paper/bce4e31036c938777e8e36e5f2526394773b547e 501c3767bf04b28771735619f320c5cbae069bbaIn the age of globalization, an internet connection has become essential for enhancing various human activities across the economic, cultural, and defense sectors, among others. This is particularly true for online classrooms. Microsoft Teams, a widely used digital education platform, provides capabilities that allow online teachers to facilitate better interactions and create more effective learning environments in online settings. This study aimed to explore students’ perceptions of synchronous online learning that occurred in an AI-enhanced online course, delivered using MS Teams. As an explorative study that examines the educational intersection of engineering and artificial intelligence, it represents the convergence of these two branches of learning and thus enriches both fields. The research involved 35 online students at the Staffordshire University, with data collected via online questionnaires to gather information about students’ perceptions of online learning through Microsoft Teams. After completing the online course materials, the questionnaires were distributed to students via Google Forms. The data were then descriptively analyzed. The study’s findings revealed that although online learning through Microsoft Teams was a novel experience for the students, the platform’s interactive and engaging learning environment motivated them to participate more actively, ultimately leading to a better comprehension of the course materials. Incorporating AI-enhanced features within the Microsoft Teams platform further augmented the online learning experience, as students appreciated the personalized learning recommendations and real-time feedback, which showcases the synergistic potential of AI and education in the digital age.Frontiers in Education340The study’s findings revealed that although online learning through Microsoft Teams was a novel experience for the students, the platform’s interactive and engaging learning environment motivated them to participate more actively, ultimately leading to a better comprehension of the course materials.Frontiers in Education['Seyed-Ali Sadegh-Zadeh', 'Tahereh Movahhedi', 'A. M. Hajiyavand', 'K. Dearn']2023-12-20T00:00:00https://www.semanticscholar.org/paper/501c3767bf04b28771735619f320c5cbae069bba 908757104d052af76b37db4e031fdfa527cafb82There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or \textit{memes}. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences. The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are able to perform such analysis on a much bigger scale than previous attempts. Our findings suggest, that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems to be equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse. Ultimately, this more nuanced understanding of Big Tech's and Academia's influence could inform a more symbiotic alliance between these stakeholders which would better serve the dual goals of societal welfare and the scientific integrity of AI research.arXiv.org410It is suggested, that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia, suggesting that the notion of Big Tech domination over AI research is oversimplified in the discourse.ArXiv["Stanislaw Gizi'nski", "Paulina Kaczy'nska", "Hubert Ruczy'nski", 'Emilia Wiśnios', "Bartosz Pieli'nski", 'P. Biecek', 'Julian Sienkiewicz']2023-12-20T00:00:00https://www.semanticscholar.org/paper/908757104d052af76b37db4e031fdfa527cafb82 46271128e2292a74bdcf838da0ec41cb697f177eThis academic paper delves into the pressing issue of educational disparities in China, particularly focusing on the challenges faced by disadvantaged and under-resourced children, including both migrant and left-behind children. The paper underscores the socioeconomic and geographical complexities that exacerbate these disparities, emphasizing the need for innovative solutions. It then introduces the transformative potential of AI tutors, leveraging recent advancements in large language models (LLMs), to bridge the educational gap. The study highlights the significant impact of migration on children's access to education, with rural-to-urban migration patterns creating hurdles for migrant children to secure enrollment in urban schools. Additionally, it sheds light on the plight of left-behind children, who face emotional challenges and limited educational resources in rural settings. The paper introduces AI tutors, particularly ChatGPT-based Khanmigo, as innovative solutions that can offer personalized educational support, generate educational materials, assist teachers, and even evaluate student work. It discusses the advantages of these AI tutors, such as cost-effectiveness and accessibility, and their potential to democratize education. However, the paper also acknowledges the limitations of AI tutors, particularly in areas requiring creativity, critical thinking, and emotional intelligence. It raises concerns about exacerbating educational inequality if high-quality human educators become exclusive to the privileged, leaving disadvantaged children reliant on AI-based solutions.Lecture Notes in Education Psychology and Public Media190The paper introduces AI tutors, particularly ChatGPT-based Khanmigo, as innovative solutions that can offer personalized educational support, generate educational materials, assist teachers, and even evaluate student work, to bridge the educational gap in China.Lecture Notes in Education Psychology and Public Media['Bochun Cao']2023-12-20T00:00:00https://www.semanticscholar.org/paper/46271128e2292a74bdcf838da0ec41cb697f177e 1d8a4f73d64cd05cb58da3f1820ff9862b36983dThe ability of artificial intelligence (AI) to greatly increase the efficacy and efficiency of any task carried out has contributed to its rise in popularity in recent years. These days, the healthcare industry uses AI more often because of the growth in data and complexity. This study, which examined artificial intelligence (AI) in the healthcare industry, sought to fully grasp how AI enhances telemedicine by optimising data management. The results indicate that AI has helped to improve fragmented data organisation and data management. This enhancement really helped to increase the efficacy and efficiency of every process. The results also show that the use of artificial intelligence affected record accessibility, promoting telemedicine and enhancing precision and care quality. This study adds significant insights to inform the direction of healthcare technology advancement by demonstrating AI's enormous potential to optimise information management and care supply.Online (Weston, Conn.)00This study examined artificial intelligence in the healthcare industry to fully grasp how AI enhances telemedicine by optimising data management and indicates that AI has helped to improve fragmented data organisation and data management.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)['Alreem Albahar']2023-12-20T00:00:00https://www.semanticscholar.org/paper/1d8a4f73d64cd05cb58da3f1820ff9862b36983d 0830744688a40838efbac3e53c2827a41e99d5efAlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.Journal of Chemical Information and Modeling1253A "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications is proposed, driving future advancements in this rapidly evolving field.Journal of chemical information and modeling['Raphaelle Versini', 'Sujith Sritharan', 'Burcu Aykaç Fas', 'Thibault Tubiana', 'S. Aimeur', 'Julien Henri', 'Marie Erard', 'Oliver Nüsse', 'J. Andreani', 'Marc Baaden', 'Patrick Fuchs', 'Tatiana Galochkina', 'Alexios Chatzigoulas', 'Z. Cournia', 'H. Santuz', 'S. Sacquin-Mora', 'A. Taly']2023-12-20T00:00:00https://www.semanticscholar.org/paper/0830744688a40838efbac3e53c2827a41e99d5ef 01033b2312ad3509de0d205c0d50c2a2bf609980Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions, but some factors make the development of effective and efficient fraud detection systems a challenge. One of such factors is the fact that fraudulent transactions are rare and that many transaction datasets are imbalanced; that is, there are fewer significant samples of fraudulent transactions than legitimate ones. This data imbalance can affect the performance or reliability of the fraud detection model. Moreover, due to the data privacy laws that all financial institutions are subject to follow, sharing customer data to facilitate a higher-performing centralized model is impossible. Furthermore, the fraud detection technique should be transparent so that it does not affect the user experience. Hence, this research introduces a novel approach using Federated Learning (FL) and Explainable AI (XAI) to address these challenges. FL enables financial institutions to collaboratively train a model to detect fraudulent transactions without directly sharing customer data, thereby preserving data privacy and confidentiality. Meanwhile, the integration of XAI ensures that the predictions made by the model can be understood and interpreted by human experts, adding a layer of transparency and trust to the system. Experimental results, based on realistic transaction datasets, reveal that the FL-based fraud detection system consistently demonstrates high performance metrics. This study grounds FL’s potential as an effective and privacy-preserving tool in the fight against fraud.IEEE Access402Experimental results, based on realistic transaction datasets, reveal that the FL-based fraud detection system consistently demonstrates high performance metrics, and grounds FL’s potential as an effective and privacy-preserving tool in the fight against fraud.IEEE Access['Tomisin Awosika', 'R. Shukla', 'Bernardi Pranggono']2023-12-20T00:00:00https://www.semanticscholar.org/paper/01033b2312ad3509de0d205c0d50c2a2bf609980 9132bec4065348584e0fc3ea944ca4c217868030Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI research resulted in diverse scholars possessing significant challenges in designing proper evaluation methods. This paper proposes a novel framework of a three-layered top-down approach on how to arrive at an optimal explainer, accenting the persistent need for consensus in XAI evaluation. This paper also investigates a critical comparative evaluation of explanations in both model agnostic and specific explainers including LIME, SHAP, Anchors, and TabNet, aiming to enhance the adaptability of XAI in a tabular domain. The results demonstrate that TabNet achieved the highest classification recall followed by TabPFN, and XGBoost. Additionally, this paper develops an optimal approach by introducing a novel measure of relative performance loss with emphasis on faithfulness and fidelity of global explanations by quantifying the extent to which a model’s capabilities diminish when eliminating topmost features. This addresses a conspicuous gap in the lack of consensus among researchers regarding how global feature importance impacts classification loss, thereby undermining the trust and correctness of such applications. Finally, a practical use case on medical tabular data is provided to concretely illustrate the findings.Inf.732A novel framework of a three-layered top-down approach on how to arrive at an optimal explainer is proposed, accenting the persistent need for consensus in XAI evaluation and introduces a novel measure of relative performance loss with emphasis on faithfulness and fidelity of global explanations.Inf.['SeyedehRoksana Mirzaei', 'Hua Mao', 'R. Al-Nima', 'Wai Lok Woo']2023-12-20T00:00:00https://www.semanticscholar.org/paper/9132bec4065348584e0fc3ea944ca4c217868030 36ac7dfa0241a8ef38c229c6f6d7e96185289485Computers and Education: Artificial Intelligence2022A systematic literature review of human-centred LA/AIED research suggests carefully balancing stakeholders' involvement in designing and deploying LA/AIED systems throughout all design phases, and actively involving target end-users to delineate the balance between human control and automation.ArXiv['Riordan Alfredo', 'Vanessa Echeverría', 'Yueqiao Jin', 'Lixiang Yan', 'Z. Swiecki', 'D. Gašević', 'Roberto Martínez-Maldonado']2023-12-20T00:00:00https://www.semanticscholar.org/paper/36ac7dfa0241a8ef38c229c6f6d7e96185289485 46d44cd1d4c7f0a788296a584bcbd862c182a2c0Nature40Nature[]2023-12-20T00:00:00https://www.semanticscholar.org/paper/46d44cd1d4c7f0a788296a584bcbd862c182a2c0 24aa8c52b062afbfcab0faccf144397503a1873eThe field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanations has been disputed in several works lately, advocating for more user-understandable explanations. To address this issue, a wide range of papers proposing Concept-based eXplainable Artificial Intelligence (C-XAI) methods have arisen in recent years. Nevertheless, a unified categorization and precise field definition are still missing. This paper fills the gap by offering a thorough review of C-XAI approaches. We define and identify different concepts and explanation types. We provide a taxonomy identifying nine categories and propose guidelines for selecting a suitable category based on the development context. Additionally, we report common evaluation strategies including metrics, human evaluations and dataset employed, aiming to assist the development of future methods. We believe this survey will serve researchers, practitioners, and domain experts in comprehending and advancing this innovative field.arXiv.org11910A taxonomy identifying nine categories and proposed guidelines for selecting a suitable category based on the development context is provided, aiming to assist the development of future methods.ArXiv['Eleonora Poeta', 'Gabriele Ciravegna', 'Eliana Pastor', 'T. Cerquitelli', 'Elena Baralis']2023-12-20T00:00:00https://www.semanticscholar.org/paper/24aa8c52b062afbfcab0faccf144397503a1873e 52510ec401e5c3b87d1a177af14697e339f00125This review paper provides an integrated perspective of Explainable Artificial Intelligence techniques applied to Brain-Computer Interfaces. BCIs use predictive models to interpret brain signals for various high-stake applications. However, achieving explainability in these complex models is challenging as it compromises accuracy. The field of XAI has emerged to address the need for explainability across various stakeholders, but there is a lack of an integrated perspective in XAI for BCI (XAI4BCI) literature. It is necessary to differentiate key concepts like explainability, interpretability, and understanding in this context and formulate a comprehensive framework. To understand the need of XAI for BCI, we pose six key research questions for a systematic review and meta-analysis, encompassing its purposes, applications, usability, and technical feasibility. We employ the PRISMA methodology -- preferred reporting items for systematic reviews and meta-analyses to review (n=1246) and analyze (n=84) studies published in 2015 and onwards for key insights. The results highlight that current research primarily focuses on interpretability for developers and researchers, aiming to justify outcomes and enhance model performance. We discuss the unique approaches, advantages, and limitations of XAI4BCI from the literature. We draw insights from philosophy, psychology, and social sciences. We propose a design space for XAI4BCI, considering the evolving need to visualize and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle. This paper is the first to focus solely on reviewing XAI4BCI research articles. This systematic review and meta-analysis findings with the proposed design space prompt important discussions on establishing standards for BCI explanations, highlighting current limitations, and guiding the future of XAI in BCI.arXiv.org1522A systematic review and meta-analysis of XAI4BCI research articles and a design space for XAI4BCI are proposed, considering the evolving need to visualize and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle.ArXiv['Param S. Rajpura', 'H. Cecotti', 'Y. K. Meena']2023-12-20T00:00:00https://www.semanticscholar.org/paper/52510ec401e5c3b87d1a177af14697e339f00125 8ea4119b429f080bc423ff5c12896cbc72460743Journal of Thrombosis and Thrombolysis41PyPI tool handles two biomarkers, hbA1c for diabetes and NP-proBNP for heart failure, to predict the next hospital visit, to help patients understand improvement in the trends of their disease.Journal of Thrombosis and Thrombolysis['Y. Takefuji']2023-12-20T00:00:00https://www.semanticscholar.org/paper/8ea4119b429f080bc423ff5c12896cbc72460743 2b1b5afcc8421d8507023d15385866dfd8775221The purpose of the article is to analyse the potential effect of AI implementation in the creative sector or creative industries through consideration of new tendencies and challenges connected with applying this Industry 4.0 technology. Research methodology. The complexity of the topic related to the intensive implementation of computerisation in contemporary culture and the creative sector has necessitated the interdisciplinary approach. The methodological basis of the research on artificial intelligence as an integral socio-cultural phenomenon is a dialectical method and connected with it the principles of systematicity, development, determination, and unity of opposites. The scientific novelty lies in the analysis and theoretical reflection of the potential effect of the implementation an artificial intelligence in the creative sector with an accent on the key tendencies and challenges. Conclusions. The article emphasises that despite unsystematic and sporadic attempts to introduce technology within the creative industries, not only certain patterns and trends are being identified, but also a model of its participation in this sector is being formed. Though artificial intelligence is still not able to function as an autonomous creative subject in the creative spheres this concept may be realised in the future with the conditions of further technological development and risks decreasing. It was found out that one of the constraining factors is misperception of artificial intelligence as the main obstacle to the development of creative industries. These concerns are well-founded and arise from a lack of understanding of the technology and its capabilities. It was proved that to reduce the risks of applying this technology in the sector, it is necessary to co-operate with politicians and work out a regulatory framework that would help to regulate the development and implementation of this technology and, secondly, it is necessary to extend the cooperation as for the technologies exchange to avoid asymmetry inside the sector. Keywords: an artificial intelligence; digital technologies; creative industries; monoculturalisation; post-COVID epoch; mass implementation; generative model.Almanac "Culture and Contemporaneity"00It was found out that one of the constraining factors is misperception of artificial intelligence as the main obstacle to the development of creative industries, and it was proved that to reduce the risks of applying this technology in the sector, it was necessary to co-operate with politicians and work out a regulatory framework.Almanac "Culture and Contemporaneity"['Oksana Oliinyk']2023-12-20T00:00:00https://www.semanticscholar.org/paper/2b1b5afcc8421d8507023d15385866dfd8775221 82fbabfbebf85a2b44f7d129f4e9f9c58080170fThrough the utilization of artificial intelligence (AI), governments can automate the analysis of publicly available government datasets. This process aids in the recognition of patterns and the development of a more profound comprehension of various socio-economic factors and empowers governments to base their policy decisions on data, effectively tackling societal issues, and optimizing the allocation of resources. In this paper we present AI’s application in the realm of e-government, with particular emphasis on its potential influence on the advancement of this field through e-government services and their significance for a range of stakeholders. Moreover, we have conducted comprehensive review of existing literature on the subject and the identification of avenues for enhancement along with consideration of legislation as a potent instrument to guide the progression of AI within the sphere of e-government, thereby amplifying its transformative effect. We emphasize the importance of education in area of AI in order to ensure it’s high quality implementation in this and other areas.International Journal of Cognitive Research in Science, Engineering and Education230This paper has conducted comprehensive review of existing literature on the subject and the identification of avenues for enhancement along with consideration of legislation as a potent instrument to guide the progression of AI within the sphere of e-government, thereby amplifying its transformative effect.International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)['Ž. Spalević', 'Jelena Kaljević', 'Slaviša Vučetić', 'Petar Milić']2023-12-20T00:00:00https://www.semanticscholar.org/paper/82fbabfbebf85a2b44f7d129f4e9f9c58080170f 6c80fe5c53dba89b8a95334362566194e39a230eBACKGROUND Large language models (LLMs) offer significant potential in medical information extraction but carry risks of generating incorrect information. This study aims to develop and validate a retriever-augmented generation (RAG) model that provides accurate medical knowledge about diabetes and diabetic foot care to laypersons with an eighth-grade literacy level. Improving health literacy through patient education is paramount to addressing the problem of limb loss in the diabetic population. In addition to affecting patient well-being through improved outcomes, improved physician well-being is an important outcome of a self-management model for patient health education. METHODS We used an RAG architecture and built a question-and-answer artificial intelligence (AI) model to extract knowledge in response to questions pertaining to diabetes and diabetic foot care. We utilized GPT-4 by OpenAI, with Pinecone as a vector database. The NIH National Standards for Diabetes Self-Management Education served as the basis for our knowledge base. The model's outputs were validated through expert review against established guidelines and literature. Fifty-eight keywords were used to select 295 articles and the model was tested against 175 questions across topics. RESULTS The study demonstrated that with appropriate content volume and few-shot learning prompts, the RAG model achieved 98% accuracy, confirming its capability to offer user-friendly and comprehensible medical information. CONCLUSION The RAG model represents a promising tool for delivering reliable medical knowledge to the public which can be used for self-education and self-management for diabetes, highlighting the importance of content validation and innovative prompt engineering in AI applications.Journal of Diabetes Science and Technology55The RAG model represents a promising tool for delivering reliable medical knowledge to the public which can be used for self-education and self-management for diabetes, highlighting the importance of content validation and innovative prompt engineering in AI applications.Journal of diabetes science and technology["Shayan Mashatian", "David G Armstrong", "Aaron Ritter", "Jeffery Robbins", "Shereen Aziz", "Ilia Alenabi", "Michelle Huo", "Taneeka Anand", "K. Tavakolian"]2024-05-20T00:00:00 44a0e5e8edeb1a57546cdb7b50a580881002c787The rapid progress in the development of automation and artificial intelligence (AI) technologies, such as ChatGPT, represents a step-wise change in human's interactions with technology as part of a broader complex, sociotechnical system. Based on historical parallels to the present moment, such changes are likely to bring forth structural shifts to the nature of work, where near and future technologies will occupy key roles as workers or assistants in sports science and sports medicine multidisciplinary teams (MDTs). This envisioned future may bring enormous benefits, as well as a raft of potential challenges. These challenges include the potential to remove many human roles and allocate them to semi- or fully-autonomous AI. Removing such roles and tasks from humans will make many current jobs and careers untenable, leaving a set of difficult and unrewarding tasks for the humans that remain. Paradoxically, replacing humans with technology increases system complexity and makes them more prone to failure. The automation and AI boom also brings substantial opportunities. Among them are automated sentiment analysis and Digital Twin technologies which may reveal novel insights into athlete health and wellbeing and team tactical patterns, respectively. However, without due consideration of the interactions between humans and technology in the broader system of sport, adverse impacts are likely to be felt. Human and AI teamwork may require new ways of thinking.Frontiers in Sports and Active Living435Frontiers in Sports and Active Living["Mitchell Naughton", "P. Salmon", "Heidi R Compton", "S. McLean"]2024-05-20T00:00:00 f0e2237ccb88c452cf7f811c0f8d3003018670c5OBJECTIVES Surface the urgent dilemma that healthcare delivery organizations (HDOs) face navigating the US Food and Drug Administration (FDA) final guidance on the use of clinical decision support (CDS) software. MATERIALS AND METHODS We use sepsis as a case study to highlight the patient safety and regulatory compliance tradeoffs that 6129 hospitals in the United States must navigate. RESULTS Sepsis CDS remains in broad, routine use. There is no commercially available sepsis CDS system that is FDA cleared as a medical device. There is no public disclosure of an HDO turning off sepsis CDS due to regulatory compliance concerns. And there is no public disclosure of FDA enforcement action against an HDO for using sepsis CDS that is not cleared as a medical device. DISCUSSION AND CONCLUSION We present multiple policy interventions that would relieve the current tension to enable HDOs to utilize artificial intelligence to improve patient care while also addressing FDA concerns about product safety, efficacy, and equity.J. Am. Medical Informatics Assoc.263Multiple policy interventions are presented that would relieve the current tension to enable HDOs to utilize artificial intelligence to improve patient care while also addressing FDA concerns about product safety, efficacy, and equity.Journal of the American Medical Informatics Association : JAMIA["M. Sendak", "Vincent X Liu", "Ashley Beecy", "David E Vidal", "Keo Shaw", "Mark A Lifson", "Danny Tobey", "Alexandra Valladares", "Brenna Loufek", "Murtaza Mogri", "Suresh Balu"]2024-05-20T00:00:00 1953bebb1c3125449e803b058fc48ef42d5128a5Education and Information Technologies : Official Journal of the IFIP technical committee on Education333There is a significant positive relation between all three variables and that KAI is a robust and substantial predictor of TAI, providing practical implications for policy, teacher preparation and professional development in the rapidly evolving landscape of AI integration in education.Educ. Inf. Technol.["Margarida Lucas", "Yidi Zhang", "P. Bem-haja", "Paulo Nuno Vicente"]2024-05-20T00:00:00 818069b9f7ec0b405db63706f59f31eeedeecb3fModern Artificial Intelligence (AI) technologies have a rapidly growing impact on a wide range of human activities. AI methods are being used in varied domains such as healthcare, material science, infrastructure engineering, social media, surveillance technologies, and even artistic expression. They have been used for the purposes of drug discovery via protein folding prediction, power usage optimization through reinforcement learning, and facial recognition by means of image segmentation. Their effectiveness and wide-scale, unregulated deployment within our societies pose significant risks to our fundamental rights. Multiple existing AI methods have the potential to profoundly undermine our ability to safeguard our privacy. The societal impact of such AI models can be investigated through six concentric Heuristic Zones of privacy. These AI models can perform inferences regarding highly sensitive, personal information such as race, gender, and intelligence from seemingly innocuous data sources beyond the capabilities of human experts. They are capable of generating increasingly accurate text and image recreations of our thoughts from non-invasive brain activity recordings such as magnetoencephalography and functional magnetic resonance imaging. Furthermore, prospective AI technologies pose concerns about the existential risk to our civilization which extend beyond the erosion of privacy and other fundamental human rights.Privacy Studies Journal1031Concerns about the existential risk to the authors' civilization which extend beyond the erosion of privacy and other fundamental human rights extend beyond the erosion of privacy and other fundamental human rights.Privacy Studies Journal["Mateusz Jurewicz", "Natacha Klein Kafer", "Esben Kran"]2024-05-20T00:00:00 440cb3d6e09eb6eb0b40dbb5069207adf4a7d853The development of science and technology has ushered in the era of artificial intelligence. This development affects school education in terms of information technology curriculum. The improvement of China’s national strength has led to the widespread of information technology education in primary and secondary schools. In this context, this paper studies artificial intelligence education in terms of its curriculum in primary and secondary schools.Education Reform and Development111This paper studies artificial intelligence education in terms of its curriculum in primary and secondary schools in China in terms of information technology curriculum.Education Reform and Development["Dawei Zhao", "Xinlei Sun"]2024-05-20T00:00:00 a00a212c5f4ad0d929b6225f8da88e60cd2e5939This white paper delves into the transformative potential of Artificial Intelligence (AI) in revolutionizing payment systems through personalized payment recommendations. It explores how AI technologies can be leveraged to analyze consumer behavior and customize payment options, thereby enhancing user engagement and security in digital transactions. Stakeholders, including financial institutions, e-commerce platforms, payment service providers, and technology developers, will find in-depth analysis and actionable insights on integrating AI to optimize payment experiences. This document outlines the benefits, challenges, and practical implementations of AI in payment systems, offering stakeholders a comprehensive guide to harnessing AI for improved consumer satisfaction and transaction efficiency. Through this exploration, stakeholders can anticipate gaining a clear understanding of how AI-driven personalization can be strategically implemented to drive business innovation and maintain competitive advantage in the rapidly evolving digital marketplace.International Journal of Finance81International Journal of Finance["Kalyanasundharam Ramachandran"]2024-05-20T00:00:00 0c2e9008fc770c9804aa2dcf52c95c2acb37c2a0Artificial Intelligence (AI) adoption has seen substantial growth across industries. This paper explores the escalating use of AI within the United States Department of Defense (DoD) and the implications that diversity, equity, and inclusion (DEI) have on Service members and Civilians across the Department. More specifically, this paper explores the DEI considerations within AI technologies on individual, team, and Department readiness. The DoD's AI usage spans various strategic and operational capabilities, however this paper explores two critical domains: healthcare and recruitment. In healthcare, AI offers the promise of early disease detection, enhanced diagnostic capabilities, and streamlined administrative processes. However, potential biases stemming from homogenous training data threaten the accuracy and reliability of these systems, jeopardizing Service member health and eroding trust in AI-assisted medical decision-making and potentially the DoD at large. In recruitment, while AI promises efficiency in identifying ideal candidates, its deployment can perpetuate biases, especially when the training data used is not representative of all demographics. Despite efforts to design "unbiased" systems by excluding demographic data, such strategies may inadvertently overlook the unique challenges faced by marginalized communities, further entrenching existing disparities. Both case studies underscore the importance of considering DEI in the development and deployment of AI systems. As the DoD continues to integrate AI into its operations, this paper’s recommendations stress the necessity of continuous DEI assessment to ensure that AI serves as an asset rather than a liability. The authors recommend the following: 1. Data diversity & review 2. Continuous monitoring and calibration 3. Stakeholder engagement 4. Adoption of DEI requirements within Ethical AI Frameworks 5. Further researchAAAI Spring Symposia72As the DoD continues to integrate AI into its operations, this paper’s recommendations stress the necessity of continuous DEI assessment to ensure that AI serves as an asset rather than a liability.{"pages": "348-353"}["Sara Darwish", "Alison Bragaw-Butler", "Paul Marcelli", "Kaylee Gassner"]2024-05-20T00:00:00 c019a85681e24002719f557ac1e5f4d198376f4bCausal inference is the idea of cause-and-effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why” whereas the effect describes the “what”. The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning (ML) and artificial intelligence (AI) systems, have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. We include a detailed taxonomy of causal inference frameworks, methods, and evaluation. An overview of causality for security is also provided. Open challenges are detailed, and approaches for evaluating the robustness of causal inference methods are described. This paper aims to provide a comprehensive survey on such studies of causality. We provide an in-depth review of causality frameworks, and describe the different methods.ACM Computing Surveys1801An in-depth review of causality frameworks, methods, and evaluation is provided, which includes a detailed taxonomy of causal inference frameworks, methods, and evaluation.ACM Computing Surveys["A. Rawal", "Adrienne Raglin", "Danda B. Rawat", "Brian M. Sadler", "J. McCoy"]2024-05-20T00:00:00 6c8d02ab79aee42639935cd77d6e070a236a3d36It is the contention of this paper that ethics of work ought to be anthropological, and artificial intelligence (AI) research and development, which is the focus of work today, should be anthropological, that is, human-centered. This paper discusses the philosophical and theological implications of the development of AI research on the intrinsic nature of work and the nature of the human person. AI research and the implications of its development and advancement, being a relatively new phenomenon, have not been comprehensively interrogated in the social and ethical teachings of the Catholic Church. This paper, therefore, proposes a path for this interrogation by expounding a discourse which is believed to be epistemically helpful in the developing discourse of AI in the ethical and social teachings of the Church. The advancement in the research on AI is not only redefining the meaning of work, but, even more so, it is questioning the metaphysical notion of the human person and the theological notion of work as an intrinsic part in the selfhood and dignity of the human person.Religions71Religions["Justin Nnaemeka Onyeukaziri"]2024-05-20T00:00:00 f61990dfecc068ab4f41fa154865766456abf89bContext: Over the years, tools have been created to improve the execution of development process activities. The emergence of generative Artificial Intelligence (AI) and, more recently, the launch and dissemination of Copilot, ChatGPT-3 and other generative tools, have broadened the discussion about the possibility of using conversational generative AI tools in diverse development tasks. Problem: There is still a lack of secondary studies to map the literature about how software development process activities can be affected by the usage of generative AI tools. Solution: This study aims to identify in which activities of the software development process Natural Language (NL) generative AI tools have been used and how they can impact requirements specification, design/architecture, development and testing activities. IS Theory: The study was developed under the aegis of the Task Technology Fit theory. Method: This work presents the results of a Systematic Mapping Review (SMR) carried out to collect research results that investigate the application of generative AI tools in the software development process. Results: Results indicate that the main activities affected are development and testing and that, although there are still some issues to be addressed, there are benefits in using AI generative tools compared to using more traditional methods like human-human pair programming and code testing made by software engineering professionals. Contribution: It was possible to collect studies to identify in which activities of the software development process generative AI tools can be applied and what are the impacts of using this technology.Brazilian Symposium on Information Systems571Results indicate that the main activities affected are development and testing and that, although there are still some issues to be addressed, there are benefits in using AI generative tools compared to using more traditional methods like human-human pair programming and code testing made by software engineering professionals.Proceedings of the 20th Brazilian Symposium on Information Systems["Patricia de Oliveira Santos", "Allan Chamon Figueiredo", "Pedro Nuno Moura", "Bruna Diirr", "Adriana C. F. Alvim", "Rodrigo Pereira dos Santos"]2024-05-20T00:00:00 69c9bf0ec29877c84a7909212cbbc5b16780cdbaThis research aims to explore consumer attitudes toward the incorporation of Artificial Intelligence (AI) in physical retail settings, specifically examining how prior AI experiences, perceived risks, consumer self-efficacy in AI usage, and gender differences influence their readiness to embrace AI technologies in retail environments. Employing a quantitative cross-sectional survey methodology, the study gathered data from 243 consumers knowledgeable about AI who have engaged in shopping activities within physical stores over the past year. Through descriptive statistics, Pearson's correlation, and t-tests, the analysis reveals a direct positive correlation between consumers' previous AI interactions and their openness to AI in retail. Conversely, perceived risks are found to affect their willingness to engage with AI technologies negatively. The research is geographically limited to Slovenia, which may restrict the applicability of its findings to other contexts. The study emphasizes the potential for increasing consumer acceptance of AI in retail through the introduction of strategic technology and the emphasis on security features. Contributing original insights into the dynamics of consumer perceptions of AI within the physical retail sector, this work offers valuable implications for retailers aiming to optimize AI integration strategies to mitigate consumer apprehensions and accommodate diverse demographic preferences.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development241Analysis of consumer attitudes toward the incorporation of Artificial Intelligence in physical retail settings reveals a direct positive correlation between consumers' previous AI interactions and their openness to AI in retail, whereas perceived risks are found to affect their willingness to engage with AI technologies negatively.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development["Nina Kolar"]2024-05-20T00:00:00 b1438f95e31ae6fe410cf24a1459cd1108d2a62cIn contrast to hearing-impaired children and deaf children born to deaf parents, deaf children born to hearing parents have limited exposure to language continuously, which impairs their working memory. Using artificial intelligence (AI) and human-computer interaction (HCI), we designed a children’s picture book for helping deaf and hearing-impaired children become literate in both sign language and spoken language. Our goal was to establish a technological environment that is more inclusive by enhancing the functionalities of sign language-dependent young children’s senses. By combining sign language and spoken language in the picture book, we aimed to bridge the communication gap between deaf and hearing-impaired children and their peers. The AI and AR-driven solution not only facilitated language learning but also fostered social inclusion by enabling seamless interaction with both sign language users and non-sign language users. The objective was to stimulate extensive dialogues regarding the authentic and efficient collaboration between educators and researchers, as well as future educators, in order to improve academic achievements for young deaf students, as well as to stimulate comprehensive discussions regarding how to involve educators in the planning and creation of educational digital content.International Convention on Information and Communication Technology, Electronics and Microelectronics151The objective was to stimulate extensive dialogues regarding the authentic and efficient collaboration between educators and researchers, as well as future educators, in order to improve academic achievements for young deaf students and to stimulate comprehensive discussions regarding how to involve educators in the planning and creation of educational digital content.2024 47th MIPRO ICT and Electronics Convention (MIPRO)["Ella Rakovac Bekes", "V. Galzina", "E. B. Kolar"]2024-05-20T00:00:00 d942e215e0cd872b4bc1513c214bdfe835268badBACKGROUND AND AIM Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. METHODS A still image-driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real-world applicability, the model was validated using 113 10-s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2-3)" or "inadequate (BBPS 0-1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter-rater variability was analyzed between the AI model and endoscopists. RESULTS The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. CONCLUSIONS The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.Journal of Gastroenterology and Hepatology182The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists and further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.Journal of gastroenterology and hepatology["Ji Young Lee", "Jooyoung Park", "H. Lee", "Hana Park", "E. Jin", "Kanggil Park", "Ji Eun Baek", "Dong-Hoon Yang", "Seung Wook Hong", "Namkug Kim", "J. Byeon"]2024-05-20T00:00:00 4db4e6791da7a5e8c0d2754632a6727cce12f808The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.Turkish Journal of Medical Sciences802Suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems.Turkish Journal of Medical Sciences["Sevil Uygun \u0130likhan", "Mahmut \u00d6zer", "Hande Tanberkan", "Veysel Bozkurt"]2024-05-20T00:00:00 d4835758e3f3b82fdab994473ff80f5d30e36afdThe concept of Artificial Intelligence (AI) is increasingly important in the healthcare sector today. Components of AI such as machine learning and deep learning are being utilized in various applications within the field of microbiology. This study examines the uses of AI in microbiology and its role in healthcare applications. Machine learning enables computer systems to analyze data using algorithms that mimic human intelligence, while deep learning processes information through multi-layered artificial neural networks. These technologies are used in many areas such as microbiological diagnosis, drug discovery, infection control, and patient monitoring. For instance, AI-supported systems are used in microbiological diagnosis to shorten diagnosis times and increase accuracy compared to traditional methods. Additionally, smart systems developed for preventing hospital-acquired infections alert hospital staff, thus reducing the risk of infection. AI also plays a significant role in the diagnosis of microorganisms such as viruses and fungi. Especially, AI-supported image analysis methods are utilized for rapid and accurate diagnosis. However, there are some challenges in the use of AI. Issues related to data privacy and ethics are among the factors limiting the applications of AI in microbiology and healthcare. Furthermore, the cost and complexity of algorithm implementation pose additional challenges. By discussing the applications of AI in microbiology and its potential in the future, this study sheds light on innovative developments in the healthcare sector.Experimental and Applied Medical Science181The uses of AI in microbiology and its role in healthcare applications are examined to shed light on innovative developments in the healthcare sector.Experimental and Applied Medical Science["Mert Kandilci", "G\u00fclfer Yak\u0131c\u0131", "M. Kayar"]2024-05-20T00:00:00 4816117e626ad8331845d07b8831316044a3d58dIn the Outline of Ecological Protection and High quality Development Plan for the Yellow River Basin approved at the 2020 conference, innovative concepts are integrated throughout the entire process, and the development of the Yellow River Basin will collide with innovation to create new sparks. This article systematically reviews the current research on digital rural construction and explores the relationship between artificial intelligence innovation and digital rural construction. Based on the research on relevant issues in the Yellow River Basin, it is pointed out that in the future, more attention should be paid to the coupling and coordination relationship between artificial intelligence innovation and digital rural construction in the basin.Forum on Research and Innovation Management00It is pointed out that in the future, more attention should be paid to the coupling and coordination relationship between artificial intelligence innovation and digital rural construction in the basin.Forum on Research and Innovation Management["Qiaohong Zhang"]2024-05-20T00:00:00 6b5aeb55cd07db9cc9fa72d85e257fd89cfbe8b7This work addresses the relationship between the performance and environmental sustainability of artificial intelligence (AI) algorithms. Although it is widely recognized that the adoption of AI technology is fundamental in various fields, ranging from healthcare to industry and entertainment, a quantitative assessment on an operational scale of the environmental impact of training and validating AI algorithms is still an open issue. In order to address this aspect, in this work, the first steps towards a metrology-based analysis are investigated with a two-fold aim: (i) to outline a methodology for evaluating AI algorithms also considering the consequent greenhouse gas emissions, and (ii) to better understand how to continue improving their classification performance in a non-harmful way for the environment.International Instrumentation and Measurement Technology Conference270The first steps towards a metrology-based analysis are investigated with a two-fold aim to outline a methodology for evaluating AI algorithms also considering the consequent greenhouse gas emissions and to better understand how to continue improving their classification performance in a non-harmful way for the environment.2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)["Luigi Duraccio", "L. Angrisani", "M. D\u2019Arco", "E. D. Benedetto", "Monica Imb\u00f2", "A. Tedesco"]2024-05-20T00:00:00 05d698ac6a30fc614d1b89fd12d21faf129a22d4With the development of artificial intelligence, the concept of online education has been interpreted in a new way. At present, the online education industry includes traditional media, such as online education, broadcasting and so on. In such an environment, the means of production, production tools and productivity of the online education industry will change, and its theme, form and content production should undergo AIOT transformation, so as to embark on the innovative development of the online education industry in the period of artificial intelligence.Evaluation of Educational Research30The means of production, production tools and productivity of the online education industry will change, and its theme, form and content production should undergo AIOT transformation, so as to embark on the innovative development of the online education industry in the period of artificial intelligence.Evaluation of Educational Research["Wenzheng Cai", "W. Zhu"]2024-05-20T00:00:00 0a431305e31ce6ca601f480948cf1088dbcce9dfUnder the background of new engineering, artificial intelligence courses in applied undergraduate colleges and universities face great challenges in course teaching and students’ learning effect due to the comprehensive factors such as content, class hours, and students’ knowledge structure. In order to improve the level and quality of talent training, this paper explores and practices the problems existing in the teaching of artificial intelligence courses in applied undergraduate colleges from the aspects of curriculum teaching objectives, contents, methods, teacher construction, and experimental teaching, promoting learning through competition and teaching assessment, and insists on taking “applied and innovative” learning training as the center. It is necessary to constantly optimize the ideas and methods of artificial intelligence course construction, and promote the development and innovation of computer education in application-oriented colleges under the background of new engineering.Education Reform and Development140This paper explores and practices the problems existing in the teaching of artificial intelligence courses in applied undergraduate colleges from the aspects of curriculum teaching objectives, contents, methods, teacher construction, and experimental teaching, promoting learning through competition and teaching assessment, and insists on taking “applied and innovative" learning training as the center.Education Reform and Development["Huiying Zhang", "Yingquan Mu", "Jihuan Xi", "Yuancheng Gu"]2024-05-20T00:00:00 052ac8930f30b908aec234f00de20f6717bd0982In modern copyright law, which began with Queen Anne s Law, the author plays a very important role as the subject of the work. As such, identifying the author s identity is a matter of priority. ??As science and technology advances, granting author status to anyone has been discussed under copyright law since Burrow-Giles Lithographic Co. v. Sarony in 1884, and the development of artificial intelligence technology is again an issue of legal issues concerning the confirmation of author status of artificial intelligence products. Though it was thought that artificial intelligence products should be protected by law, the problem is that only creations expressing human thoughts or emotions can be protected under the current copyright law. In addition, only human beings can be recognized as authors in precedents and theories. ??This paper introduces the author problem of the distant, recent issue of artificial intelligence products and review the author s concept through content on the author s concept of Anglo-American and continental law countries.Research and Commentary on Humanities and Arts60The author problem of the distant, recent issue of artificial intelligence products is introduced and the author concept is reviewed through content on the author s concept of Anglo-American and continental law countries.Research and Commentary on Humanities and Arts["Bo Yun"]2024-05-20T00:00:00 77d364e405372f628cf96b6895717d95b730e810In schools, teachers play a multitude of roles, serving as educators, counselors, decision-makers, and members of the school community. With recent advances in artificial intelligence (AI), there is increasing discussion about how AI can assist, complement, and collaborate with teachers. To pave the way for better teacher-AI complementary relationships in schools, our study aims to expand the discourse on teacher-AI complementarity by seeking educators' perspectives on the potential strengths and limitations of AI across a spectrum of responsibilities. Through a mixed method using a survey with 100 elementary school teachers in South Korea and in-depth interviews with 12 teachers, our findings indicate that teachers anticipate AI's potential to complement human teachers by automating administrative tasks and enhancing personalized learning through advanced intelligence. Interestingly, the deficit of AI's socio-emotional capabilities has been perceived as both challenges and opportunities. Overall, our study demonstrates the nuanced perception of teachers and different levels of expectations over their roles, challenging the need for decisions about AI adoption tailored to educators' preferences and concerns.International Conference on Artificial Intelligence in Education100Overall, the study demonstrates the nuanced perception of teachers and different levels of expectations over their roles, challenging the need for decisions about AI adoption tailored to educators' preferences and concerns.ArXiv["Soon-young Oh", "Yongsu Ahn"]2024-05-20T00:00:00 f093c3abc67d8362ea02730ee4bdbd7059b0e7ccArtificial Intelligence (AI) is one of the emerging technologies that accelerate innovation and have a positive impact on economic growth. Digital technology development enabled the implementation of AI across companies and in various industries and the question is in what way the AI technologies lead to economic growth. Intellectual property rights (IPRs) are one of the key drivers of innovation while the IP systems are designed to promote innovation creation. The development of AI innovations consequently changes the human element of innovation which presents a challenge in the protection of AI inventions. This paper aims to investigate and analyze the current state of the art of the impact of AI on economic growth and the legal aspect of IP protection for AI-generated inventions.International Convention on Information and Communication Technology, Electronics and Microelectronics300This paper aims to investigate and analyze the current state of the art of the impact of AI on economic growth and the legal aspect of IP protection for AI-generated inventions.2024 47th MIPRO ICT and Electronics Convention (MIPRO)["Petra Karanikic"]2024-05-20T00:00:00 282b5408b96f63ce2c16ced271b8787ea10a1db6The recent paper highlights the impact of artificial intelligence on Machine Translation without the interaction of Humans. The use of Google Translator, Bing, Microsoft Translator, Systran Translate and Amazon Translate has become widely spread (CAT Tools). This study aims to reveal the contrast between Artificial Intelligence and Human Translation in the legal field. A hypothesis of the difference between Artificial Translation and Human Translation was raised. The concerns about the lack of a translator increased, and machine translation was selected as the most selected option. Local and foreign contracts were selected and subjected to Human and Machine translation. Strengths and weaknesses points were selected and analyzed. The previous studies in the legal translation field were considered. The results revealed the gap between human translation and machine translation, and human translation is dominant in the light of accuracy and the existence of legal language. The findings also focused on the Translators' experience and knowledge in the translation field.International Journal of Linguistics Literature & Translation220The gap between human translation and machine translation is revealed, and human translation is dominant in the light of accuracy and the existence of legal language.International Journal of Linguistics, Literature and Translation["T. Al-Romany", "Maryam Jawad Kadhim"]2024-05-20T00:00:00 84ca88c53b8dc984c028087dabed9ffd77307776Machine learning and artificial intelligence produce algorithms that appear to be able to make "intelligent" decisions similar to those of humans but function differently from human thinking. To make decisions based on machine suggestions, humans should be able to understand the background of these suggestions. However, since humans are oriented to understand human intelligence, it is not yet fully clear whether humans can truly understand the "thinking" generated by machine learning, or whether they merely transfer human-like cognitive processes to machines. In addition, media representations of artificial intelligence show higher capabilities and greater human likeness than they currently have. In our daily lives, we increasingly encounter assistance systems that are designed to facilitate human tasks and decisions based on intelligent algorithms. These algorithms are predominantly based on machine learning technologies, which make it possible to discover previously unknown correlations and patterns by analyzing large amounts of data. One example is the machine analysis of thousands of X-ray images of sick and healthy people. This requires identifying the patterns by which images labeled as "healthy" can be distinguished from those labeled as "sick" and to find an algorithm that identifies the latter. In the meantime, "trained" algorithms created in this way are used in various fields of application, not only for medical diagnoses but also in the pre-selection of applicants for a job advertisement or in communication with the help of voice assistants. These voice assistants are enabled by intelligent algorithms to offer internet services through short commands. Harald Lesch, referring to his book Unpredictable, written together with Thomas Schwarz, says the development of artificial intelligence can be compared to bringing aliens to Earth. With machine learning, a previously unknown form of non-human intelligence has been created. This chapter discusses whether forms of artificial intelligence, as they are currently being publicly discussed, differ substantially from human thinking. Furthermore, it will be discussed to what extent humans can comprehend the functioning of artificial intelligence that has been created through machine learning when interacting with them. Finally, the risks and opportunities will be weighed and discussed..The Turkish Online Journal of Design Art and Communication00This chapter discusses whether forms of artificial intelligence, as they are currently being publicly discussed, differ substantially from human thinking and to what extent humans can comprehend the functioning of artificial intelligence that has been created through machine learning when interacting with them.Turkish Online Journal of Design Art and Communication["B\u00fc\u015fra Sar\u0131kaya"]2024-05-20T00:00:00 2a73a3ea9d3a792a8f8ad9be89c5eb2a9dd116a4A serious challenge to the patient-oriented model of interaction between doctor and patient is posed by objective circum-stances: the constantly increasing number of patients and rising healthcare costs and, therefore, the inability to provide the attention and care necessary for the patient. A likely response to the emerging "crisis of care" in medicine appears to be the use of digital technolo-gies in clinical practice and healthcare organization. In particular, one of the possible options for using the potential of digital technolo-gies in medicine is artificial intelligence systems (hereinafter referred to as AI).The article is devoted to the peculiarities of communica-tion between a doctor and patients in the conditions of using artificial intelligence in clinical practice. The author identifies both the prospects for the introduction of artificial intelligence and difficulties in the process of communication between a doctor and a patient. The purpose of the article is to analyze the mechanism of influence of artificial intelligence on the process of communication between a doctor and a patient, to assess the impact of digital technologies on the basic values that develop in the process of clinical communica-tion – respect for patient autonomy, trust, confidentiality. The main expected positive consequence of the use of AI in medicine is the freeing up of time for direct communication with the patient. However, there is a risk that it will be used to increase patient flow, espe-cially in the commercial medical sector. It is concluded that, despite the widespread use of digital technologies, society remains wary of such innovations, especially when it concerns personal data. As a result, the level of trust and willingness to use AI systems is low. When introducing AI, it is necessary to take into account the feasibility and appropriateness of using these technologies in the provision of medical care, since, along with the effectiveness and accuracy of diagnostic results and treatment, the process of communication with a doctor remains extremely important for the patient, the opportunity to share their medical history and, during this communica-tion, form trust necessary for joint decision-making.Bioethics00The mechanism of influence of artificial intelligence on the process of communication between a doctor and a patient is analyzed to assess the impact of digital technologies on the basic values that develop in the process of clinical communica-tion – respect for patient autonomy, trust, confidentiality.Bioethics["Yulia Yu. Kochetova"]2024-05-20T00:00:00 cc99e011406594161fec50ec618a88a7bf91d958Generation Z’s attitude towards ever-developing technology and related AI reflects the interweavement of curiosity, fear, and cautious optimism. Since AI is constantly developing, it certainly changes the labour market, organisation processes, different human resource processes, as well as the training and development of employees. The main purpose of the research reported in this paper is to examine the attitudes of Generation Z regarding the use of artificial intelligence in the context of employee training and development. Empirical research was conducted on a sample of 129 respondents from Slovenia, and hypotheses were tested by descriptive statistics and T-test. The research results confirm the positive attitudes of Generation Z members towards contemporary training models, regardless of their sociodemographic characteristics. This aligns with the finding that Generation Z shows a strong interest in AI, with many actively seeking out information on the topic and learning about it, either formally or informally. This paper contributes to the human resource management literature because it brings new insights into Generation Z, whose participation in the active workforce will significantly increase in the coming years.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development150The research results confirm the positive attitudes of Generation Z members towards contemporary training models, regardless of their sociodemographic characteristics, which aligns with the finding that Generation Z shows a strong interest in AI.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development["Branka Zolak Polja\u0161evi\u0107", "Simona \u0160arotar \u017di\u017eek", "Ana Marija Gri\u010dnik"]2024-05-20T00:00:00 3f600dff6823636904fb06805c64d7205dd67b55Self-organizing ensembles of software and hardware agents with artificial intelligence model the intellectual abilities of a person's natural intelligence. The Creator endowed man with various types of intellectual abilities: generation of meanings, perception of meanings, meaningful actions and behavior, sensory reaction to meanings, emotional reaction to meanings. Based on the synergy of various intellectual abilities, a person carries out life activities. For example, Dialogue is conducted on the basis of two intellectual abilities: the generation and perception of meanings. A multimodal self-organizing ensemble of intelligent software and hardware agents with artificial intelligence, based on existing knowledge and skills, is able to write poetry, draw pictures, give recommendations and solutions to specialists, manage production and systems in various sectors of the economy, and take part in scientific research. Multimodal ensembles of intelligent agents, modeling the functions of natural intelligence, contain a functional control structure. To ensure the safe and reliable use of multimodal ensembles of intelligent agents, they are being standardized internationally under the guidance of ISO. International standardization of multimodal ensembles of intelligent agents expands the market and reduces the risks of their use.Research on Intelligent Manufacturing and Assembly250Multimodal ensembles of intelligent agents, modeling the functions of natural intelligence, contain a functional control structure and are being standardized internationally under the guidance of ISO to ensure the safe and reliable use.Research on Intelligent Manufacturing and Assembly["E. Bryndin"]2024-05-20T00:00:00 1368682dab47f702f1a2eec6831d0b2cc9aa8ac9In recent years, artificial intelligence (AI) has seen significant advances that have in fact exceeded even optimistic prognoses. Using data-driven AI, namely deep learning techniques, it has been demonstrated that computers may now be equipped with abilities of remarkable scope and quality, such as solving image and text processing tasks at human level. Large language models, in particular, have sparked debates regarding opportunities and challenges of this rapidly developing area. Will remaining fundamental challenges of data-driven AI, such as factual or logical mistakes, be overcome for good if complemented and hybridized with symbolic AI techniques, such as knowledge representation and reasoning? Will systems of artificial general intelligence (AGI) emerge from this, possessing common sense and in fact completing the decades-old quest for AI that motivated the raise of the field in the 1950s? In the light of these questions, we review the likewise, decades-old philosophical debate about capabilities and limitations of computers from a hybrid AI point of view. Here, we discuss how hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do. At the same time, we shed light on a lesser discussed challenge for hybrid AI: the possibility that its developers might be its biggest limiters.AAAI Spring Symposia320How hybrid AI is coming closer to disproving Hubert Dreyfus’ famous statements regarding what computers can not do is discussed and the possibility that its developers might be its biggest limiters is shed on.{"pages": "248-252"}["Ben Schuering", "Thomas Schmid"]2024-05-20T00:00:00 7357d5d550265cefd961b527e2ffa13fb757542eArtificial Intelligence (AI) and Machine Learning (ML) are increasingly influencing the medical field, particularly in the diagnosis and prognosis of prostate cancer. AI technologies facilitate complex tasks in identifying and characterizing prostate cancer through image-based analyses, including histopathology and MRI. These advancements are enhancing evaluation methods and improving patient outcomes by incorporating additional data such as demographic factors and experimental markers into risk prediction models, increasing the accuracy of prostate cancer prognosis from 60% to 80%. ML, a subset of AI, leverages large datasets to improve predictions regarding cancer susceptibility, recurrence, and survival rates. Despite its potential, AI still requires significant human input in its development and faces various challenges before it can fully integrate into clinical practice.Journal of Health and Rehabilitation Research00Advances in artificial Intelligence and Machine Learning are enhancing evaluation methods and improving patient outcomes by incorporating additional data such as demographic factors and experimental markers into risk prediction models, increasing the accuracy of prostate cancer prognosis from 60% to 80%.Journal of Health and Rehabilitation Research["Nadir Masud", "Muhammad Haseeb", "Huzafa Ali"]2024-05-20T00:00:00 0f65b75a6741761a15e29ea4bb68cbf846fcbb39The rapid development of artificial intelligence (AI) is bringing major changes to various aspects of life, including the world of work. AI-driven automation and robotization is predicted to replace many jobs currently performed by humans raising concerns about the future of work, especially for the younger generation. This research aims to understand how AI will influence the world of work in the future and how character education and religious moderation can help the younger generation in facing these changes. This study used qualitative research methods. The data collection technique in this research is literature study. The data that has been collected is then analyzed in three stages, namely data reduction, data presentation and drawing conclusions. . The results show the ethical, social, and educational challenges that come with the development of AI, as well as the educational strategies and policies needed to prepare the younger generation. Cross-sector collaboration is key in meeting these challenges, with governments, educational institutions, and the private sector working together to ensure that future generations have a strong foundation to face the AI era with confidence and success. Thus, the importance of concerted efforts in preparing the younger generation for an AI-influenced future in Multi Racial PD.Lentera150The results show the ethical, social, and educational challenges that come with the development of AI, as well as the educational strategies and policies needed to prepare the younger generation for an AI-influenced future.Lentera: Multidisciplinary Studies["Nur Kumala Dewi", "Abdul Jamil", "Fisa Wisnu Wijaya"]2024-05-20T00:00:00 d636a515e8c63c1b7a77224fe236b95fa8661311Context: The emergence of generative Artificial Intelligence (AI) and, more recently, the dissemination of Copilot, ChatGPT-3 and similar tools have broadened the discussion about the possibility of using generative AI tools in many professional segments such as health, education, and technological area. Problem: Although some studies explore the potential of generative AI tools to assist Information Technology (IT) professionals in executing specific tasks, they do not delve into the professionals’ characteristics or collect information about multiple generative AI tools usage. Solution: Considering the possibilities brought by generative AI, this study aims to shed light on the perception of IT professionals about generative AI tools and characterize these professionals’ profiles. IS Theory: This research is based on the Technology Acceptance Model. Method: A survey research was carried out with IT professionals so as to identify how these professionals are using generative AI and gather information about these professionals’ profiles. Results: Results show that 70,5% (43 out of 61) of the respondents use some generative AI tool, the majority of whom are software development professionals, and, despite the problems faced when using these tools, 86% of these professionals recommend using them. Contribution: In this study the profile of the IT professionals using generative AI was identified, it was then possible to evaluate the acceptance of such tools among these professionals and identify the main reasons why some of them are not yet using generative AI.Brazilian Symposium on Information Systems440The profile of the IT professionals using generative AI was identified and it was then possible to evaluate the acceptance of such tools among these professionals and identify the main reasons why some of them are not yet using generative AI.Proceedings of the 20th Brazilian Symposium on Information Systems["Patricia de Oliveira Santos", "Allan Chamon Figueiredo", "Pedro Nuno Moura", "Bruna Diirr", "Adriana C. F. Alvim", "Rodrigo Pereira dos Santos"]2024-05-20T00:00:00 8e45c143e92c58d98c8fea097145cfbc6c9a6c06Glioma is the most prevalent type of primary brain tumor in adults. The use of artificial intelligence (AI) in glioma is increasing and has exhibited promising results. The present study performed a systematic review of the applications of AI in glioma as regards diagnosis, grading, prediction of genotype, progression and treatment response using different databases. The aim of the present study was to demonstrate the trends (main directions) of the recent applications of AI within the field of glioma, and to highlight emerging challenges in integrating AI within clinical practice. A search in four databases (Scopus, PubMed, Wiley and Google Scholar) yielded a total of 42 articles specifically using AI in glioma and glioblastoma. The articles were retrieved and reviewed, and the data were summarized and analyzed. The majority of the articles were from the USA (n=18) followed by China (n=11). The number of articles increased by year reaching the maximum number in 2022. The majority of the articles studied glioma as opposed to glioblastoma. In terms of grading, the majority of the articles were about both low-grade glioma (LGG) and high-grade glioma (HGG) (n=23), followed by HGG/glioblastoma (n=13). Additionally, three articles were about LGG only; two articles did not specify the grade. It was found that one article had the highest sample size among the other studies, reaching 897 samples. Despite the limitations and challenges that face AI, the use of AI in glioma has increased in recent years with promising results, with a variety of applications ranging from diagnosis, grading, prognosis prediction, and reaching to treatment and post-operative care.Medicine International670A systematic review of the applications of AI in glioma as regards diagnosis, grading, prediction of genotype, progression and treatment response using different databases to demonstrate the trends and highlight emerging challenges in integrating AI within clinical practice.Medicine International["Adham Al-Rahbi", "Omar Al-Mahrouqi", "Tariq Al-saadi"]2024-05-20T00:00:00 fda23fa8adf30005c1406792e4691f5290665a01The trends in the digitalization of marketing require the expansion of marketing management tools, which is primarily associated with the capabilities of artificial intelligence. The purpose of the paper is to study the modern capabilities of artificial intelligence tools for managing consumer behaviour. The methodological basis of the research is general (such as generalization, analysis and synthesis) and special (system and structural analysis) methods. System analysis identifies the features of artificial intelligence tools for consumer behaviour management, and structural analysis summarizes the functions of artificial intelligence tools for consumer behaviour management. In the paper, the artificial intelligence tools are structured according to the possibilities of their use in the process of consumer analysis, promotion, development and implementation of consumer behaviour management strategies. The result of the study is a grouping of artificial intelligence tools for managing consumer behaviour and the formation of models of interaction between objects and subjects of consumer behaviour management. The originality and value of the study lies in providing recommendations for the use of artificial intelligence tools to manage consumer behaviour, which will allow businesses to increase profits.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development40The result of the study is a grouping of artificial intelligence tools for managing consumer behaviour and the formation of models of interaction between objects and subjects of consumer behaviour management.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development["Nataliia Parkhomenko"]2024-05-20T00:00:00 ac53eb577883f12599438ee41d5051bde7d5e381This study experimentally investigates initial trust formation in the organizational context of an artificial intelligence (AI) system in human resource management (HRM). Drawing on social exchange theory and leader‐member exchange theory, we identify factors that contribute to initial trust in AI through cognitive and affective processing from the perspective of employees in the Chinese context. An online survey (N = 426) was conducted with a 2 (explanation of AI: without vs with) × 2 (trust in leaders: low vs high) design. Our findings demonstrate that initial trust plays a crucial role in AI adoption, and a trustworthy leader increases employees' AI trust and intention to adopt. Providing AI's benefits and risks moderates initial trust and the pathway to adoption. Moreover, familiarity with AI's application in HRM and organizational collectivism is also beneficial. Our findings suggest that organizations should prioritize cultivating initial trust in AI with employee‐oriented strategies, including trusted leadership and supportive training resources.Social Science Research Network840It is demonstrated that initial trust plays a crucial role in AI adoption, and a trustworthy leader increases employees' AI trust and intention to adopt, and organizations should prioritize cultivating initial trust in AI with employee‐oriented strategies, including trusted leadership and supportive training resources.SSRN Electronic Journal["Yi Xu", "Yijie Huang", "Jiahe Wang", "D. Zhou"]2024-05-20T00:00:00 69f63144c2e38846b5085c2ee5cf4372debdd6cdCelem artykułu jest wyjaśnienie w jaki sposób sztuczna inteligencja (AI – artificial intelligence) może wpłynąć na tworzenie i rozpowszechnianie treści w celu dezinformacji oraz jak AI wpływa na dystrybucję takich treści. Jako, że można zaobserwować wyraźny rozwój oraz wzrost popularności narzędzi do generowania treści, to ich wykorzystanie do tworzenia nieprawdziwych informacji także stale rośnie. Profesjonalne tworzone treści zarówno w formie tekstowej jak i w formie graficznej mogą być coraz trudniejsze dla czytelnika do zweryfikowania. Celem jest także identyfikacja strategii, które pozwolą na przeciwdziałanie dezinformacji tworzonej przez sztuczną inteligencję. Problem badawczy skupia się na weryfikacji, w jaki sposób narzędzia generatywne mogą być wykorzystane do tworzenia i rozpowszechniana nieprawdziwych informacji. W artykule przyjęto hipotezę, że treści generowane przez sztuczną inteligencję mogą wpłynąć na dezinformację społeczną, jednak istnieją możliwości ograniczania skutków tego negatywnego zjawiska. Metodyka badań opierała się o krytyczną analizę literatury oraz eksperymenty z użyciem popularnych narzędzi AI do generowania tekstu oraz grafik. Wyniki przeprowadzonych badań potwierdziły, że narzędzia AI mogą być wykorzystywane do tworzenia błędnych treści na dużą skalę. Stosowanie narzędzi AI nie tylko ułatwia tworzenie profesjonalnych treści, ale pozwala także na ich bardzo szybkie tworzenie, przy zastosowaniu niewielkiego nakładu pracy. Badania wykazały także, że choć stosowane są mechanizmy cenzury to istnieją metody umożliwiające przełamanie takich zabezpieczeń. W treści artykułu zostały opisane możliwości obejścia mechanizmów zabezpieczających narzędzia AI. Wnioski z badań ukazują potrzebę edukacji społeczeństwa, co stanowi kluczowy element w walce z dezinformacją. Dodatkowo, artykuł wskazuje na znaczenie odpowiedzialności mediów w procesie weryfikacji i demaskowania błędnych treści, co stanowi istotny element w przeciwdziałaniu dezinformacji.National Security Studies280National Security Studies["Jakub Piotr Sobek"]2024-05-20T00:00:00 84386a152b32370c8adc0c80cac08ffb7419c48eElectronic warfare is a critical component of modern military operations and has undergone significant advances in recent years. This book provides an overview of electronic warfare, its historical development, key components, and its role in contemporary conflict scenarios. It also discusses emerging trends and challenges in electronic warfare and its contemporary relevance in an era of advanced technology and cyber threats, emphasizing the need for continued research and development in this area. The book explores the burgeoning intersection of artificial intelligence and electronic warfare, highlighting the evolving landscape of modern conflicts and the implications of integrating advanced technologies. The multifaceted roles of artificial intelligence in electronic warfare are highlighted, examining its potential advantages, ethical considerations, and challenges associated with its integration.00The multifaceted roles of artificial intelligence in electronic warfare are highlighted, examining its potential advantages, ethical considerations, and challenges associated with its integration.["Nicolae Sfetcu"]2024-05-20T00:00:00 48cd10230cb76f20e6c2b7b19570d92a83cf14a5The complexity of technological risks and cyber security risks with a major significant impact on fundamental rights and freedoms arising from the adoption of new artificial intelligence technologies calls for the implementation of specific regulations adapted to the rapid pace of technological innovation and the continuous evolution of threats in this area. The proposed study will focus both on the critical analysis of the regulatory and institutional instruments for regulating artificial intelligence as one of the so-called disruptive technologies and on the challenges faced by regulators. Methodologically, the research will involve the identification and analysis of the risks associated with AI technology, followed by a systematic assessment of the mandatory (hard law) and non-mandatory (soft law) legal instruments applicable to the field, as well as proposed governance system proposals, in order to identify similarities and juxtapositions. In addition, synthesising the views expressed in legal doctrine will make an important contribution to analyse and understand the challenges to regulation and governance posed by new digital technology. By analysing from different perspectives, the proposed regulations to prevent risks associated with artificial intelligence, the scientific contribution brings into question possible directions for the future regulatory framework.Legal Perspectives in the Modern Era of Technological Transformations00The proposed study will focus both on the critical analysis of the regulatory and institutional instruments for regulating artificial intelligence as one of the so-called disruptive technologies and on the challenges faced by regulators.Legal Perspectives in the Modern Era of Technological Transformations["Gabriel Ni\u021b\u0103"]2024-05-20T00:00:00 152770486ccffa17d21d3107fb6da0087da894a8The significance of technology integration including artificial intelligence (AI)-mediated tools has established a notable presence in the academic spectrum. Despite the abundance of studies available on AI-mediated technology integration in writing instructions in diverse settings, there remains an apparent gap in exploring teachers’ insights, particularly within the context of Arab universities. Therefore, the current study explores the employment of AI-driven tools in student-centered writing instructions from English as a Foreign Language (EFL) teachers’ perspectives. Using a qualitative research methodology, this study collected data through semi-structured interviews with a sample of (N = 16) teachers from four different universities. The content analysis indicates that teachers strongly perceive a positive impact of AI writing assistants on both student involvement and the role of teachers. Additionally, they underscore the significance of professional development and the role of AI in facilitating student-centered approach for effective writing instructions. While acknowledging the efficiency, customization, and time-saving aspects of AI tools, they also expressed reservations about potential issues such as overreliance, bias, digital divide, and concerns regarding accuracy. Furthermore, the participants observed the ways to address issues and concerns associated with the integration of AI-mediated tools include, but not limited to, clear communication, ethical considerations, academic integrity, teacher roles, ongoing and latest AI updates, student-centered learning, and professional development. Finally, the study offers limitations and recommendations for future research.International Journal of English Linguistics590The content analysis indicates that teachers strongly perceive a positive impact of AI writing assistants on both student involvement and the role of teachers, and the ways to address issues and concerns associated with the integration of AI-mediated tools include clear communication, ethical considerations, academic integrity, teacher roles, ongoing and latest AI updates, student-centered learning, and professional development.International Journal of English Linguistics["Mohd Nazim"]2024-05-20T00:00:00 96b7cfd881f7c3d49a24c52ba0371f75196591b0Artificial intelligence (AI) has become a rapidly growing field with the potential to revolutionize many industries, including healthcare. Integrating AI into healthcare has the potential to transform patient care and disease management, leading to improved outcomes and reduced costs. Our research explores the various applications of AI in healthcare, including predictive analytics, medical image analysis, drug discovery, and clinical decision making, caregiver or virtual assistant via AI chatbot. It also explores the challenges and limitations associated with the use of AI in healthcare, such as ethical concerns, data privacy issues, and algorithmic bias. Finally, this research paper concludes by highlighting the potential of AI to improve patient outcomes, increase efficiency in healthcare delivery and ultimately transform the future of healthcare.International Convention on Information and Communication Technology, Electronics and Microelectronics240The potential of AI to improve patient outcomes, increase efficiency in healthcare delivery and ultimately transform the future of healthcare is highlighted.2024 47th MIPRO ICT and Electronics Convention (MIPRO)["Nikola Protrka", "Blerton Abazi"]2024-05-20T00:00:00 5bfd3b258fb22facb99959d2bb812e9d6a51711fArtificial intelligence (AI) technology has become integral to everyday life, extending its influence into various business aspects. The concept of AI in Project Management (PM) has been discussed since the 1980s, portraying AI as a tool with the potential to expedite, optimize, and enhance project management, aiding decision-making processes. Project management is a pivotal process in nearly every organization, and integrating AI into this process can offer numerous advantages, such as heightened efficiency, precision, speed in decision-making, and improved risk assessment. Data from Eurostat in 2020 indicates that only 6% of businesses in Croatia used some form of AI in their operations, reflecting the limited adoption of this technology in the business sector. This paper explores the current state of AI application in project management in Croatia, exploring both perceived benefits and barriers. A survey conducted in Croatia with 115 respondents revealed that currently, only 29.1% of correspondents utilize some form of AI in project management. Unsurprisingly, AI is predominantly used in the IT industry (52%), followed by the educational sector (28%) and the healthcare sector (4%).International Convention on Information and Communication Technology, Electronics and Microelectronics410The current state of AI application in project management in Croatia is explored, exploring both perceived benefits and barriers.2024 47th MIPRO ICT and Electronics Convention (MIPRO)["Borna Vegar", "Tea Mija\u010d"]2024-05-20T00:00:00 a370a0f104a1e90c724a5b586594f90683c0bd96Although technological developments falling under the umbrella of artificial intelligence have been developing since the 1950s, only in recent times have the unique issues associated with patenting these technologies received adequate attention. The cause for this lies in an unparalleled upswing in investment, fostering a massive expansion of technological (and business) innovations. Determining which among them qualify as inventions and meet the requirements for patent protection gives rise to inquiries that frequently necessitate a scrutiny of fundamental concepts of patent law in patent registration procedures. In this paper special emphasis is placed on European patent regulations, particularly the European Patent Convention and the practices of the European Patent Office. Assessment of the impact of artificial intelligence on existing patent law entails examination of legal concepts of the inventor and a person skilled in the art, along with the essential requirements for patentability of inventions. This analysis serves as basis for further evaluation of whether the current patent law can be adapted to the newly emerging and dynamic technological environment of artificial intelligence through interpretation, or whether it is necessary to devise a new legal framework to protect the interests of participants in the creation and use of the respective category of intellectual creations.International Convention on Information and Communication Technology, Electronics and Microelectronics340In this paper special emphasis is placed on European patent regulations, particularly the European Patent Convention and the practices of the European Patent Office, as basis for further evaluation of whether the current patent law can be adapted to the newly emerging and dynamic technological environment of artificial intelligence.2024 47th MIPRO ICT and Electronics Convention (MIPRO)["Ivana Kunda"]2024-05-20T00:00:00 0d5c20b98a39a72de003cd8c7ce132d2aa7d14a2Machine learning and artificial intelligence (AI/ML) models in healthcare may exacerbate health biases. Regulatory oversight is critical in evaluating the safety and effectiveness of AI/ML devices in clinical settings. We conducted a scoping review on the 692 FDA 510k-approved AI/ML-enabled medical devices to examine transparency, safety reporting, and sociodemographic representation. Only 3.6% of approvals reported race/ethnicity, 99.1% provided no socioeconomic data. 81.6% did not report the age of study subjects. Only 46.1% provided comprehensive detailed results of performance studies; only 1.9% included a link to a scientific publication with safety and efficacy data. Only 9.0% contained a prospective study for post-market surveillance. Despite the growing number of market-approved medical devices, our data shows that FDA reporting data remains inconsistent. Demographic and socioeconomic characteristics are underreported, exacerbating the risk of algorithmic bias and health disparity.medRxiv470A scoping review on the 692 FDA 510k-approved AI/ML-enabled medical devices to examine transparency, safety reporting, and sociodemographic representation shows that FDA reporting data remains inconsistent.["Vijaytha Muralidharan", "Boluwatife Adeleye", "Caroline J Huang", "Mfon Thelma Nta", "Peter Oluwaduyilemi Ademiju", "Pirunthan Pathmarajah", "Man Kien Hang", "O. Adesanya", "R. Abdullateef", "A. Babatunde", "Abdulquddus Ajibade", "Sonia Onyeka", "Zhou Ran Cai", "Roxana Daneshjou", "Tobi Olatunji"]2024-05-20T00:00:00 8be0bb88ba00f2f6ac49e98176d96846870f0acaGenerative Artificial Intelligence (AI) was incorporated into a competitive programming event that targeted undergraduate students, including those with little programming experience. The competition incorporated a range of challenge design approaches that promoted meaningful interaction with generative AI system, even while keeping the challenge difficulty level to an appropriate level. An analysis of survey responses and competition data showed that this format lowered barriers to participation, successfully engaged students throughout the competition, and increased the likelihood that they would participate in a similar event. In an extension of this work, a professional development workshop for high school teachers is being developed, along with a contest for high school students. Participant surveys and logs of interaction with the contest and generative AI systems will be analyzed to measure the effect of generative AI on student self-efficacy and suggest ways to integrate generative AI instruction into computer science curriculum.AAAI Spring Symposia80Generative Artificial Intelligence was incorporated into a competitive programming event that targeted undergraduate students, including those with little programming experience, and showed that this format lowered barriers to participation, successfully engaged students throughout the competition, and increased the likelihood that they would participate in a similar event.{"pages": "486-492"}["Devang Jayachandran", "P. Maldikar", "Tyler S. Love", "Jeremy Blum"]2024-05-20T00:00:00 bcdfd1a1ce6ebe46234bc6a664aa63f798411b39The rapid development of Artificial Intelligence (AI) technology has caused all industries worldwide to experience unprecedented changes. Vocational education, an educational field that cultivates technical and skilled talents for society, is also facing great opportunities and challenges in the wave of AI. Exploring the development path of vocational education in the context of artificial intelligence so that it can better adapt to the future needs of society has become an urgent problem in the field of education. This paper briefly analyses the impact of artificial intelligence on vocational education, explores the challenges of vocational education in the context of artificial intelligence, and puts forward the development path of vocational education in the context of artificial intelligence for reference.World Education Forum80The impact of artificial intelligence on vocational education is analyzed, the challenges of vocational education in the context of artificial intelligence are explored, and the development path of vocational education in the context of artificial intelligence is put forward for reference.World Education Forum["C. K. Lo"]2024-05-20T00:00:00 cc10f449a9ef1f9ff4b45f84823acacec6bbdcb4This paper deals with the current state and research trends of artificial intelligence in manufacturing companies. The main objective of the paper is to determine the adoption of specific artificial intelligence software in manufacturing. The results are based on a subsample of 141 manufacturing companies that are located in Slovenia. The data were gathered, obtained through the 2022 European Manufacturing Survey research project. The results show that the use of artificial intelligence differs heavily in specific manufacturing areas. The paper also presents the plans of Slovenian manufacturing companies in terms of introducing artificial intelligence software solutions by the end of the year 2025.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development150The results show that the use of artificial intelligence differs heavily in specific manufacturing areas and the plans of Slovenian manufacturing companies in terms of introducing artificial intelligence software solutions by the end of the year 2025 are presented.Challenges in the Turbulent Economic Environment and Organizations’ Sustainable Development["I. Pal\u010di\u010d", "Klemen Kovic"]2024-05-20T00:00:00 6201a9677d7f59d2baf814ccd46b83ccd9f1c368The cultural industry plays an increasingly important role in today’s society, and its innovative management has always been of great concern. With the rapid development of technology, artificial intelligence technology has become an important support point in the innovation management of the cultural industry. The application of AI artificial intelligence technology can not only improve the production efficiency and quality of cultural products, but also expand the development space of the cultural industry, bringing new development opportunities for the cultural industry. Based on this, this article conducts research on the application of AI artificial intelligence technology in cultural industry innovation management for reference.Region - Educational Research and Reviews00The application of AI artificial intelligence technology can not only improve the production efficiency and quality of cultural products, but also expand the development space of the cultural industry, bringing new development opportunities for the cultural industry.Region - Educational Research and Reviews["Huiling Xie"]2024-05-20T00:00:00 9ef154bda69ed58cc9246ff6904691494f3f3428This article explores the integration of artificial intelligence (AI) into machine-building production processes, focusing on the optimization of gear production for KrAZ trucks. Through a detailed analysis of AI applications in various industries and a review of relevant literature, the study identifies key opportunities and challenges in implementing AI technologies in mechanical engineering. The research presents a comprehensive examination of the benefits of AI-driven systems in improving production efficiency, quality control, and predictive maintenance. A case study on automated gear grinding quality control demonstrates how AI, coupled with advanced sensors and machine learning algorithms, enhances process precision and reduces defects. Results demonstrate the effectiveness of AI-driven systems in improving precision, reliability, and cost-effectiveness in gear manufacturing, with an observed accuracy rate of approximately 95% or higher. Also highlights a precision rate of ±0.005 mm in gear tooth surface finishing, leading to consistent gear performance and reliability. Additionally, AI-driven predictive maintenance strategies are shown to predict equipment maintenance needs with up to 90% accuracy, maximizing productivity and reducing maintenance costs by up to 30%. Moving forward, further research and implementation efforts should focus on selecting and integrating appropriate AI systems into existing production processes, to maximize efficiency and innovation within the machine-building industry. Keywords: artificial intelligence, gear grinding, quality control, data analysis, automationСУЧАСНІ ТЕХНОЛОГІЇ В МАШИНОБУДУВАННІ ТА ТРАНСПОРТІ00This article explores the integration of artificial intelligence (AI) into machine-building production processes, focusing on the optimization of gear production for KrAZ trucks, and presents a comprehensive examination of the benefits of AI-driven systems in improving production efficiency, quality control, and predictive maintenance.СУЧАСНІ ТЕХНОЛОГІЇ В МАШИНОБУДУВАННІ ТА ТРАНСПОРТІ["V. Kulynych", "R. Arhat", "Serhii Shlyk", "Anastasiia Symonova", "Volodymyr Drahobetskyi"]2024-05-20T00:00:00 a9b7fa875bdfc4d95f160376db0b01c28eea99ddInternational Research Journal of Modernization in Engineering Technology and Science03International Research Journal of Modernization in Engineering Technology and Science[]2024-05-20T00:00:00 8e2922e5da3e1276e246c2e7f92672e4c235f175The study deals with the problematic issues related to the safe development of modern digital-technological AI systems and their protection from the ecological (digital-algorithmic) pollution. The special importance of ecologically clean development and growth of AI is emphasized. The necessity of formation of a new scientific interdisciplinary direction - AI ecology - is substantiated. It is noted that the security of the future of all mankind will depend on the efficiency of prevention and quality of prevention of ecological pollution of the digital sphere surrounding us.InterConf50The study deals with the problematic issues related to the safe development of modern digital-technological AI systems and their protection from the ecological pollution of the digital sphere surrounding us.InterConf["Jandieri Gigo", "Inga Janelidze"]2024-05-20T00:00:00 f71594619de8679480daecfda6a9a76f746a6b0700["Caitlin Curtis"]2024-05-20T00:00:00 a43852a037e5ca77147c4686b02f17785fef479dABSTRAK Kemajuan dalam pengembangan kecerdasan buatan (AI) telah memunculkan berbagai diskusi terkait aspek etika teknologi ini. Banyak pedoman etika diterbitkan untuk memastikan AI dikembangkan dan digunakan dengan cara yang bertanggung jawab, dengan fokus pada privasi, keadilan, transparansi, dan keamanan. Teknologi AI yang semakin "disruptif" membuat aturan etika ini menjadi sangat penting. Penilitian ini meninjau dan membandingkan 22 pedoman etika AI. Peneliti menemukan bahwa meskipun banyak prinsip yang tumpang tindih, ada kekurangan di beberapa pedoman, terutama terkait keadilan sosial dan penerapan dalam praktik. Penilaian ini menunjukkan bahwa prinsip etika seringkali tidak sepenuhnya diterapkan di lapangan, meski pedoman-pedoman tersebut telah disusun dengan baik. Kurangnya implementasi yang tepat bisa menimbulkan masalah serius di masa depan, terutama karena AI sangat berpengaruh dalam berbagai aspek kehidupan seperti pekerjaan, pendidikan, dan kesehatan. Oleh karena itu, evaluasi mendalam diperlukan untuk memperbaiki pendekatan etika AI. Penulis menyarankan beberapa langkah perbaikan, termasuk peningkatan transparansi dan akuntabilitas dalam pengembangan AI, serta penerapan pedoman etika yang lebih konsisten. Dengan memperkuat prinsip-prinsip ini, diharapkan AI dapat dikembangkan dan digunakan dengan lebih etis, membawa manfaat maksimal bagi masyarakat. ABSTRACT Progress in the development of artificial intelligence (AI) has given rise to various discussions regarding the ethical aspects of this technology. Many ethical guidelines are published to ensure AI is developed and used in a responsible manner, with a focus on privacy, fairness, transparency, and security. AI technology is increasingly "disruptive" making these ethical rules very important. This research reviews and compares 22 AI ethical guidelines. Researchers found that while many of the principles overlap, there are gaps in some of the guidelines, particularly regarding social justice and application in practice. This assessment shows that ethical principles are often not fully implemented in the field, even though the guidelines are well developed. Lack of proper implementation could cause serious problems in the future, especially because AI is very influential in various aspects of life such as work, education, and health. Therefore, in-depth evaluation is needed to improve AI ethical approaches. The authors suggest several steps for improvement, including increased transparency and accountability in AI development, as well as more consistent implementation of ethical guidelines. By strengthening these principles, it is hoped that AI can be developed and used more ethically, bringing maximum benefits to society.Juwara Jurnal Wawasan dan Aksara00Juwara: Jurnal Wawasan dan Aksara["Raynaldi Nugraha Prasetya", "Aris Kusdiyanto", "Usman Radiana", "Luhur Wicaksono"]2024-05-20T00:00:00 2e9e9c660e6c56ffc12b9ef1e9dfd411d6693180AI systems are breaking into new domains and applications, and it is pivotal to center humans in contemporary AI systems and contemplate what this means. This discussion considers three perspectives or human roles in AI as users, contributors, and researchers-in-training, to illustrate this notion.AAAI Spring Symposia190This discussion considers three perspectives or human roles in AI as users, contributors, and researchers-in-training, to illustrate this notion of center humans in contemporary AI systems.{"pages": "2-3"}["Cecilia O. Alm"]2024-05-20T00:00:00 e7c772a902c6c272941d8f0d4928618e5af78c46Modern technologies in ophtalmology00Modern technologies in ophtalmology["D.A. Dorofeev", "A. A. Vitkov", "G.K. Khachatryan", "E. D. Semenov"]2024-05-20T00:00:00 d70483539a1b25d805e4bde8bba5d4c438e7e8a0This paper addresses the task of intracranial hemorrhage detection and classification based on the RSNA 2019 brain computed tomography (CT) dataset. Several popular neural network (NN) architectures based on convolutional neural networks (CNNs) have been considered and interpretation of the neural network classifier by GradCAM method has been shown. To efficiently process the dataset, various radiology information processing methods were considered to create the resulting training sample. In addition, the training sample was reduced to 10% of the total dataset to avoid technical limitations and improve performance. The main contribution of the work is the creation and testing of deep learning algorithms for the detection of acute intracranial hemorrhage and its five subtypes. Various steps related to data cleaning, augmentation and preprocessing have also been applied in the presented work.2024 X International Conference on Information Technology and Nanotechnology (ITNT)320This paper addresses the task of intracranial hemorrhage detection and classification based on the RSNA 2019 brain computed tomography dataset and interpretation of the neural network classifier by GradCAM method has been shown.2024 X International Conference on Information Technology and Nanotechnology (ITNT)["Dmitry Veselov", "Nikita Andriyanov", "Le Trung"]2024-05-20T00:00:00 6d4966df3afe6822341a1cdd594e36b24ef43144Hypertension Research10Hypertension research : official journal of the Japanese Society of Hypertension["Z. Karbasi", "Michaeel Motaghi Niko", "M. Zahmatkeshan"]2024-05-20T00:00:00 9cbcdd2d1281499341f6ad56f4ca10fba98e2e3bIJARCCE00IJARCCE["Nirjhor Anjum", "Rubel Chowdhury"]2024-05-20T00:00:00 e335358fc2e2d2469109363f3264398707229239Alzheimer ’ s disease (AD) is a brain disorder that has been ranked as the seventh leading cause of death in the United States mainly affecting older adults. It is estimated that approximately more than 6 million Americans have dementia caused by Alzheimer ’ s [1] . In the early stages of AD, individuals may experience subtle changes in language abilities, such as dif fi culty fi nding words or repeating phrases. As the severity advances, these language impairments become more pronounced, affecting both expressive and receptive language skills. Furthermore, de fi cits in cognition can contribute to the language processing dif fi culties observed in AD patients. Executive dysfunction, for example impairs the ability to plan and organize thoughts, leading to disorganized speech and dif fi culty staying on topic during conversations. Semantic memory de fi cits result in dif fi culties understanding and producing meaningful language, while attention de fi cits contribute to distractibility and poor concentration during language tasks. These language and cognitive impairments not only impact communication and social interactions but also pose signi fi cant challenges for accurate diagnosis and monitoring of disease progression. As advancements areAnnals of Medicine and Surgery60Language and cognitive impairments in Alzheimer’s disease not only impact communication and social interactions but also pose challenges for accurate diagnosis and monitoring of disease progression.Annals of Medicine and Surgery["Mishal Abid", "Maham Asif", "Zoya Khemane", "Afia Jawaid", "Aimen Waqar Khan", "Hufsa Naveed", "Tooba Naveed", "A. A. Farah", "M. Siddiq"]2024-05-20T00:00:00 16b0553a0988d8a76f2da09487bc43295d8ab4f3AI brings ethical and legal issues, the discrimination, and workplace safety risks. Decision making through AI techniques is changing the relationships between individuals as we know them today. The development of AI and the integration of these systems into essential services for the population can accentuate imbalances in society and between states. Generating certain predictive models by identifying patterns in the collected data and grouping people in this way can lead to discrimination against certain groups (bias can be encoded in algorithms). Errors or biases may also occur that affect the integrity and confidentiality of information where it is difficult to understand how AI makes data security decisions.In the absence of human supervision and boundary drawing, autonomous AI may hold big surprises.The article will analyse some aspects related to the risks that the use of AI systems involves on fundamental rights, with reference to private life, data protection, non-discrimination regarding and to the effects that the development of AI has in creating new social inequalities.Legal Perspectives in the Modern Era of Technological Transformations00The article will analyse some aspects related to the risks that the use of AI systems involves on fundamental rights, with reference to private life, data protection, non-discrimination regarding and to the effects that the development of AI has in creating new social inequalities.Legal Perspectives in the Modern Era of Technological Transformations["Carmen Oana Mihaila", "Mircea Mih\u01ceila"]2024-05-20T00:00:00 b94713084432ac3175e06de00fc9773d98296299Coexistence of technology and business dates back to the late 18th century when the first ever use of a computer was for recording the census by the US government in 1890. The use of technology in business can be ascribed to different organisations in the different nations on the parallel lines of time. It includes invention of cash machines and their use by Barclays in England in the early 1960s, the induction of telephone-based modems for order management by Baxter Pharmaceuticals and use of small desktop computing device called Minitel for processing customer orders in France were the other notable developments in the history of coexistence of technology and business. Increasing operations in the business functions have created an urge for the technological innovation in the industry to handle the operations electronically. Figueiredo and Cohen (2019) say that technology has become an indispensable component of every business function by delivering ease in operations and productivity. The end of the 20th century had witnessed the leaping progress in computing in the form of artificial intelligence (AI) performing the tasks that were unimaginable to comprehend a decade back in time. Developments in the technological research and development prove that organisations have started inducting AI into as many fields as possible at a considerable pace. As a part of the shifting technological dynamics in the industry HR function has also transformed digitally. Tools like enterprise applications have forayed intensely into the operations of human resources management (HRM). These enterprise resource planning (ERP) tools remain to primarily serve the integration of HRM to the other functions. However, enterprise tools could not serve the purpose of supporting decisiveness in the areas of HR planning, workforce design and performance management at large. However, Tuck (2019) argues that AI is assuming increased responsibilities in the different sections of the society and business including the HRM function. At present, the amount of knowledge on the status quo of the role of AI in the HRM functions is scarcely available. Literature related to this disruptive technology in the HR function is still at the nascent stage. This study will examine the role of AI as a key component in the HRM function, which is regarded to be highly human-driven.IMIB Journal of Innovation and Management670The role of AI is examined as a key component in the HRM function, which is regarded to be highly human-driven, which is regarded to be highly human-driven.IMIB Journal of Innovation and Management["Mohsin Khan", "P. Vijay", "Kumar Reddy", "Orcid Id", "Vijay Kumar Reddy"]2024-05-20T00:00:00 5eb1930014595eb81a452e808d161a04ca026cbdThis short paper is the status report of a project in progress. We aim to model human-like agents' decision-making behaviors under risks with neural-symbolic approach. Our model integrates the learning, reasoning, and emotional aspects of an agent and takes the dual process thinking into consideration when the agent is making a decision. The model construction is based on real behavioral and brain imaging data collected in a lottery gambling experiment. We present the model architecture including its main modules and the interactions between them.AAAI Spring Symposia30This work aims to model human-like agents' decision-making behaviors under risks with neural-symbolic approach and takes the dual process thinking into consideration when the agent is making a decision.{"pages": "592-593"}["Jonathan C.H. Tong", "Yung-Fong Hsu", "C. Liau"]2024-05-20T00:00:00 8d8a3d47a4807dcdb988590eecdd268dd2aad51aВ статье изучена современная тенденция к автоматизации и обмену данными в производстве и других отраслях (Индустрия 4.0). Рассмотрен искусственный интеллект("ИИ”), позволяющийинтеллектуальным машинам работать вместе с людьми и оптимизировать производственные процессы. В статье проанализировано как «искусственный интеллект» может оказывать влияние на мировую экономику различными способами: через повышение производительности и следовательно эффективности, так как машины на базе ИИ могут анализировать большие объемы данных и распознавать закономерности, которые не под силу человеку. ИИ помогает оптимизировать производственные процессы, сократить количество отходов и улучшить контроль качества. Искусственный интеллект позволяет компаниям разрабатывать новые продукты и услуги в короткие сроки, получая информацию о предпочтениях и поведении клиентов, что ведет к созданию новых потоков прибыли и бизнес-моделей. По результатам исследования предложены рекомендации по трансформации глобальной экономики по мере развития ИИ по целому ряду направлений, в том числе в формировании будущего обрабатывающей промышленности и других отраслей. Данный обзор, посвящен значительному влиянию ИИ на мировую экономику и основан на данных, полученных в результате обширного анализа литературы и исследований. The article examines the current trend towards automation and data exchange in manufacturing and other industries (Industry 4.0). Artificial intelligence (“AI”) is considered, allowing intelligent machines to work together with people and optimize production processes. The article analyzes how “artificial intelligence” can influence the global economy in various ways: through increasing productivity and therefore efficiency, since machines based on AI can analyze large amounts of data and recognize patterns that humans cannot. AI helps optimize production processes, reduce waste, and improve quality control. Artificial intelligence allows companies to develop new products and services in a short time by gaining insights into customer preferences and behavior. , leading to the creation of new profit streams and business models. Based on the results of the study, recommendations are proposed for transforming the global economy as AI develops in a number of areas, including in shaping the future of the manufacturing industry and other industries. This review focuses on the significant impact of AI on the global economy and is based on data obtained from an extensive analysis of literature and research.Вестник Казахского университета экономики, финансов и международной торговли00Вестник Казахского университета экономики, финансов и международной торговли["\u0410.\u0415. \u0420\u0430\u0445\u0438\u043c\u0431\u0435\u043a\u043e\u0432\u0430", "\u0410.\u041c. \u041a\u0430\u0437\u044b\u0431\u0430\u0435\u0432\u0430", "\u0421.\u041c. \u0416\u0430\u043d\u0431\u044b\u0440\u0431\u0430\u0435\u0432\u0430", "\u0413.\u0421. \u0423\u043a\u0443\u0431\u0430\u0441\u043e\u0432\u0430", "A. Rakhimbekova", "A. Kazybaeva", "S. Zhanbyrbayeva", "G. Ukubassova"]2024-05-20T00:00:00 27833d64bc9057c5d2e05e1f816d493f1697e9c4The present study aims to address, succinctly, aspects that concern the future. The changes that this period will bring to all professions represent a general concern. It is obvious, however, that the effects to be produced are not similar either as content, nor quantitatively. If they are professions ‘prone’ to be replaced, in completeness, computers, equally are professions whose content will be modified, without; however, they can be fully transferred from human to computers. Among these, we appreciate that there are also legal professions. Some of the ways in which they are exercised, it will be possible to move into the ‘competence’ of the computer, but man cannot ever disappear, entirely, from their exercise.Legal Perspectives in the Modern Era of Technological Transformations00The present study aims to address, succinctly, aspects that concern the future, aspects that concern the professions ‘prone’ to be replaced, in completeness, computers.Legal Perspectives in the Modern Era of Technological Transformations["Verginia Vedina\u0219", "I. Vedina\u0219"]2024-05-20T00:00:00 6040a2aa62d2da28b35a5a64839ffd4da02ab96bEducational Administration: Theory and Practice00Educational Administration: Theory and Practice["Bhambure Snehal Vasant"]2024-05-20T00:00:00 49b05f5b27f5760f10f16c007cce7ebe0bf8c464AI involves programming that conveys human intellect to computers to perform repetitive tasks. Most organizations have done integrations of AI; however, critical elements are not considered, resulting in negative user adoption, including unethical use and cost. This paper presents the findings from a Systematic Literature Review (SLR). The study found that old traditional operations in Libraries are somehow mostly automated using Chatbots in the form of Generative AI and humanoid robots, with concern of policies that are non-existing to guide the use of the tools for ethical reasons. Through the themes identified and discussed, recommendations are provided with key factors to consider as a guide when integrating AI in libraries. Future improvement in this research, such as developing a design model of AI integration in academic libraries, is recommended, and its applicability should be evaluated.2024 IST-Africa Conference (IST-Africa)340The study found that old traditional operations in Libraries are somehow mostly automated using Chatbots in the form of Generative AI and humanoid robots, with concern of policies that are non-existing to guide the use of the tools for ethical reasons.2024 IST-Africa Conference (IST-Africa)["Anele Mabona", "Darelle van Greunen", "Kativu Kevin"]2024-05-20T00:00:00 057b78fdcf93c5f82c279f6a354e93083aa27c94Technological developments in 4.0 require people to use the latest technology. The latest technological systems require skilled human resources. The current technological development is artificial intelligence which can do work like humans do. Artificial Intelligence can be used in writing questions. The aim of Community Service is: to assist teachers in writing questions using Artificial Intelligence via the ChatGPT platform. The qualitative research method involves asking several teachers during the activity. Community Service Results, namely: 1) teachers do not understand writing questions using the revised Bloom's Taxonomy at cognitive levels C4, C5, and C6; and 2) teachers can use the question item writing platform with ChatGPT. Based on these results, teachers are given an in-depth understanding of the theory of writing questions at cognitive levels C4, C5, and C6. Through this understanding, teachers are expected to be able to write questions correctly which are supported by ChatGPT. This is because ChatGPT is not completely correct regarding writing question items.Jurnal Pengabdian Masyarakat130Teachers are given an in-depth understanding of the theory of writing questions at cognitive levels C4, C5, and C6 and are expected to be able to write questions correctly which are supported by ChatGPT.MAYARA: Jurnal Pengabdian Masyarakat["Munali Munali", "Hawa Liberna", "Syukriyansyah Syukriyansyah", "Esa Nur Aziiz"]2024-05-20T00:00:00 211453fea080cd167ec715d8ed6ef04f6ddcdee8Hybrid intelligence systems aim to leverage synergies in closely collaborating teams of humans and artificial intelligence (AI). To guide the realization of such teams, recent research proposed design patterns that capture role-based knowledge on human-AI collaborations. Building on these patterns requires hybrid intelligence systems to provide mechanisms that orchestrate human and AI contributions accordingly. So far, it is unclear if such mechanisms can be provided based on shared representations of the required knowledge. In this regard, we expect ontology-based data-centric process modeling to be a promising direction for hybrid intelligence systems that aim to support knowledge-intensive processes (KiPs). We illustrate this through exemplary process models (realized with our ontology- and data-driven business process model -- ODD-BP) that reflect the team design patterns for hybrid intelligence systems. We point out that relying on such process models enables multiple actors to fulfill roles jointly and allows them to address individual shortcomings. This is examined by discussing integrating large language models (LLMs) into the process models and describing how complementary AI actors could help to empower LLMs to fulfill their role in human-AI collaboration more comprehensively. Future work will extend the provided concepts while their evaluation initially focuses on the KiP of medical emergency call handling.AAAI Spring Symposia190{"pages": "167-174"}["Carsten Maletzki", "E. Rietzke", "Ralph Bergmann"]2024-05-20T00:00:00 e68a15b356c3ae91607cfa417f0c5be85716b918Journal of Computational Social Science12634ArXiv["Cailean Osborne", "Jennifer Ding", "Hannah Rose Kirk"]2024-05-20T00:00:00 951027fc8848a7d585125a658b1ac0ac89d9db01International Journal for Educational Integrity3113It is demonstrated that specific detectors and experienced reviewers can accurately identify articles generated by Large Language Models, even after paraphrasing, and may be incorporated as an additional screening tool in the peer-review process of academic journals.International Journal for Educational Integrity["Jae Q. J. Liu", "Kelvin T. K. Hui", "Fadi Al Zoubi", "Zing Z. X. Zhou", "Dino Samartzis", "Curtis C. H. Yu", "J. R. Chang", "Arnold Y. L. Wong"]2024-05-20T00:00:00 61af8eb39f1cfaf3726f1cbba89df80b5d14046dNext-generation mobile networks will increasingly rely on the ability to forecast traffic patterns for resource management. Usually, this translates into forecasting diverse objectives like traffic load, bandwidth, or channel spectrum utilization, measured over time. Among the other techniques, Long-Short Term Memory (LSTM) proved very successful for this task. Unfortunately, the inherent complexity of these models makes them hard to interpret and, thus, hampers their deployment in production networks. To make the problem worsen, EXplainable Artificial Intelligence (XAI) techniques, which are primarily conceived for computer vision and natural language processing, fail to provide useful insights: they are blind to the temporal characteristics of the input and only work well with highly rich semantic data like images or text. In this paper, we take the research on XAI for time series forecasting one step further proposing AIChronoLens, a new tool that links legacy XAI explanations with the temporal properties of the input. In such a way, AIChronoLens makes it possible to dive deep into the model behavior and spot, among other aspects, the hidden cause of errors. Extensive evaluations with real-world mobile traffic traces pinpoint model behaviors that would not be possible to spot otherwise and model performance can increase by 32%.IEEE Conference on Computer Communications625AIChronoLens, a new tool that links legacy XAI explanations with the temporal properties of the input and makes it possible to dive deep into the model behavior and spot, among other aspects, the hidden cause of errors, is proposed.IEEE INFOCOM 2024 - IEEE Conference on Computer Communications["Claudio Fiandrino", "Eloy P\u00e9rez G\u00f3mez", "P. F. P\u00e9rez", "Hossein Mohammadalizadeh", "Marco Fiore", "Joerg Widmer"]2024-05-20T00:00:00 39e9fa42a3fcb574f8465cba9e01d5e1eabf3297This study explores the integration of Artificial Intelligence (AI) into environmental conservation efforts, aiming to assess AI's transformative potential in enhancing sustainability practices. Employing a systematic literature review and content analysis, the research scrutinizes peer-reviewed articles, reports, and case studies from 2014 to 2024, focusing on the application of AI in biodiversity preservation, climate change mitigation, and sustainable resource management. The methodology hinges on a comprehensive search strategy, adhering to strict inclusion and exclusion criteria to ensure the relevance and quality of the literature analyzed. Key findings reveal that AI significantly contributes to environmental conservation by optimizing resource management, improving predictive analytics for biodiversity conservation, and facilitating advanced monitoring and analysis to mitigate environmental impacts. However, the deployment of AI technologies also presents ethical and cybersecurity challenges, necessitating robust frameworks for responsible use. The study underscores the importance of interdisciplinary collaboration, stakeholder engagement, and the development of ethical AI solutions to address these challenges effectively. Finally, AI holds immense promise for advancing environmental sustainability efforts. Strategic recommendations include fostering partnerships across disciplines, prioritizing ethical considerations in AI development, and enhancing AI literacy among conservationists. Future research directions emphasize the need for innovative AI applications in conservation and addressing the socio-technical complexities of integrating AI into environmental strategies. This study contributes valuable insights into leveraging AI for a sustainable and resilient future, highlighting the critical balance between technological advancements and ethical considerations. Keywords: Artificial Intelligence (AI), Environmental Conservation, Sustainability, Cyber Risks.Computer Science &amp; IT Research Journal018Key findings reveal that AI significantly contributes to environmental conservation by optimizing resource management, improving predictive analytics for biodiversity conservation, and facilitating advanced monitoring and analysis to mitigate environmental impacts.Computer Science &amp; IT Research Journal["Uwaga Monica Adanma", "Emmanuel Olurotimi Ogunbiyi"]2024-05-21T00:00:00 894e362c24a6c18b105dd7d5ae2e0ff4099afae1Background Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research. Methods In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing. Results This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies. Conclusions Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.PLoS ONE1146Although still in its early stages, AI technologies show significant promise in advancing the understanding of disease progression and development, paving the way for future clinical applications.PLOS ONE["Quan Duy Vo", "Yukihiro Saito", "Toshihiro Ida", "Kazufumi Nakamura", "Shinsuke Yuasa"]2024-05-21T00:00:00 b22be1e8933acee501f38b2728617a84a3d2c533Globalization and Health1378AI in biomedical research and health innovation is explored, highlighting its implications, challenges and opportunities in emerging economies and improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide.Globalization and Health["Renan Gon\u00e7alves Leonel da Silva"]2024-05-21T00:00:00 7e31198a87383a9ec4ffc2a23df14638ffb575b8Purpose The purpose of this study is to examine the multifaceted implications of AI on leadership dynamics and organizational practices. By synthesizing insights from behavioral theory, AI analytics, and ethical considerations, the study aims to equip leaders with the requisite knowledge, skills, and mindset to foster adaptive leadership, anticipate change, and cultivate innovation amidst AI-driven disruptions. Design/methodology/approach This article employs a qualitative research approach, integrating literature review and conceptual analysis to explore the intersection of leadership development and Artificial Intelligence (AI). Drawing insights from scholarly articles, theoretical frameworks and practice, the study elucidates the evolving landscape of leadership in the context of AI adoption. Practical action points are derived to guide organizational leaders and educators in navigating AI-induced transformations effectively. Findings The integration of AI into leadership dynamics necessitates a paradigm shift in leadership paradigms, emphasizing the fusion of technical proficiency with emotional intelligence. Behavioral theory coupled with AI analytics offers valuable insights into effective leadership behaviors, facilitating the design of tailored leadership development programs. Proactive leadership strategies, ethical considerations, and talent management emerge as pivotal factors in navigating AI-induced transformations and fostering organizational resilience. Originality/value This article contributes to the literature by synthesizing diverse perspectives on AI leadership and offering practical action points for organizational leaders and educators. By highlighting the integration of behavioral theory, AI analytics, and ethical considerations, the study underscores the importance of interdisciplinary approaches in leadership research and education. The insights derived from this study inform organizational practices, curriculum development in higher education, and future research agendas, fostering ethical AI adoption and cultivating adaptive leadership cultures in the age of Artificial Intelligence. Development and Learning in Organizations: an international journal55The study aims to equip leaders with the requisite knowledge, skills, and mindset to foster adaptive leadership, anticipate change, and cultivate innovation amidst AI-driven disruptions to foster ethical AI adoption and cultivating adaptive leadership cultures in the age of Artificial Intelligence.Development and Learning in Organizations: An International Journal["Martin Sposato"]2024-05-21T00:00:00 322697da75dc8b730b6cd8054de85d5fbe850e9eArtificial intelligence is having a dramatic impact on a variety of industries, including marketing and marketing communications. Its use enables the optimization of marketing activities and increases efficiency not only within large corporations, but also in small and micro businesses. On social media, AI plays a significant role in content creation, post scheduling, campaign analysis and other aspects. Implementing AI tools into social media management can be a key element for improving the performance and effectiveness of marketing communications. This paper examines the impact of AI on social media from the perspective of using AI in an SME environment. It analyses the current state of the art, the authors' perspectives and the results of empirical studies. It concludes with recommendations for the use of specific AI-based tools that businesses can use in social media management.European Conference on Social Media373The impact of AI on social media from the perspective of using AI in an SME environment is examined, the current state of the art, the authors' perspectives and the results of empirical studies are analyzed and recommendations for the use of specific AI-based tools that businesses can use in social media management are made.European Conference on Social Media["Peter Kraj\u010dovi\u010d"]2024-05-21T00:00:00 f8177f636b920310c442c16f9801751a2c258f88This paper aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases.Inf.453A comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques finds some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice.Inf.["Hossein Hassani", "E. Silva"]2024-05-21T00:00:00 811aa68f737fb825f072de0c8f94cfd1aae6ec1fArtificial intelligence (AI) has become a very important concept in today’s digital communication age. With the development of technology, the use of AI has become widespread in many fields, including the field of communication. This article focuses on the relationship between communication and AI. In this context, the advantages, and disadvantages of using AI in the field of communication were examined. Data obtained from semi-structured in-depth interviews with communication academics were analysed with the content analysis technique. The findings underscore the increasing prevalence of AI usage in the field of communication. Positive aspects such as speed and efficiency, cost-effectiveness, and the ability to analyse large datasets easily were highlighted. However, negative impacts were also identified, including concerns related to privacy and security, the potential lag in emotional intelligence compared to humans, the risk of individuals losing their jobs or harbouring job loss concerns, and the possibility of applications that may not align with ethical principles. As AI continues to evolve in the future, the aim is to address privacy and security concerns, develop applications in alignment with ethical principles, and enhance capabilities to analyse larger datasets while achieving a more advanced emotional intelligence structure.Journal of Autonomous Intelligence03Positive aspects such as speed and efficiency, cost-effectiveness, and the ability to analyse large datasets easily were highlighted, but negative impacts were also identified, including concerns related to privacy and security, the potential lag in emotional intelligence compared to humans, the risk of individuals losing their jobs or harbouring job loss concerns, and the possibility of applications that may not align with ethical principles.Journal of Autonomous Intelligence["Ayhan Dolunay"]2024-05-21T00:00:00 82535bc4293c21b97e76cba04692e53a5405b323Contemporary healthcare at all levels increasingly uses Artificial Intelligence (AI). However, since the various levels involve different tasks, have different data needs, and different ethical obligations, the AIs that are used have to be differently structured. Also, since healthcare construed as a commodity involves different ethical parameters from healthcare construed as a right, and different ethical systems entail logically distinct considerations, this also necessitates the need for differently structured AIs. This column sketches how and why this is the case. It concludes with a brief look at why AIs programmed into quantum computers would not change this.Healthcare Management Forum31This column sketches how and why AIs programmed into quantum computers would not change this, and concludes with a brief look at why AIs programmed into quantum computers would not change this.Healthcare Management Forum["E-H Kluge"]2024-05-21T00:00:00 f4138e92ca40d23c34e289d475763cb3f81d1924This writing aims to examine the position and influence of the presence of Artificial Intelligence in the formation of legislation as well as the crucial role of implementing the use of Artificial Intelligence in legislative processes. The research method employed by the author is normative juridical research using data collection techniques through document studies on secondary data. The results of this writing indicate that Artificial Intelligence is a form of progress that can assist and simplify human work. However, its use must be limited by specific regulations to regulate and minimize potential threats or negative impacts. Additionally, Artificial Intelligence is limited to being a tool or supporting assistant in the legislative process. If Artificial Intelligence were to be considered a subject capable of shaping legislation, it would be nearly impossible to implement. This is due to legislation being rules derived from norms that evolve in society and are dynamic. In the end, Artificial Intelligence remains limited to providing assistance to human beings. And the realization of the use of Artificial Intelligence in the realm of law has occurred in Indonesia as well as other countries such as Singapore, China, Estonia and also the United States. These countries have utilized Artificial Intelligence in the realm of law, especially in its use as a tool for the formation of legislation.TERANG02The results of this writing indicate that Artificial Intelligence is a form of progress that can assist and simplify human work, however, its use must be limited by specific regulations to regulate and minimize potential threats or negative impacts.Terang : Jurnal Kajian Ilmu Sosial, Politik dan Hukum["Mariska Cahyani Putri", "Annisa Febyanti", "Saskia Azzahra", "Nurul Amaliyah Putri"]2024-05-21T00:00:00 15f89b7eea1529deb5a59d01e692c69b42be66a6With the continuous development and optimization of artificial intelligence, enterprises have begun to pay attention to the use of artificial intelligence for the optimization of work and the improvement of efficiency, and in the management of enterprise investment, a number of companies have begun to use a large number of artificial intelligence technologies in the integration and analysis of data, information processing and analysis, risk management, efficiency improvement, and so on. Enterprises encounter problems in analyzing data and information in investment management, and problems in risk management, all of which affect the improvement of their investment returns. Enterprises face a huge amount of data in the process of investment management, it is difficult to calculate a large number of data models in a relatively short period of time, and it is impossible to analyze and extract more information. In the face of investment risk, the control of risk in the past investment process is more based on experience to judge, there is a great deal of subjective assumptions in risk avoidance. And in the investment process companies also want to increase returns, improve efficiency, avoid risk, and achieve a more automated and intelligent trading process. Enterprises need to use artificial intelligence to technically solve the problems of data aggregation, arithmetic, and analysis; use artificial intelligence to optimize the problem of processing large amounts of information; use artificial intelligence technology to enhance risk management and predictive analysis, strengthen intelligent decision-making and portfolio optimization, and provide insights into big data analysis and market trends. The wide application of artificial intelligence technology in enterprises also has a certain impact on the development of enterprises, and it has a certain impact on the planning, feasibility, whole-process control, investment evaluation, investment report, and investment efficiency of investment in the investment link, which makes it widely concerned in the society and in enterprises.Highlights in Business, Economics and Management121Enterprises need to use artificial intelligence to technically solve the problems of data aggregation, arithmetic, and analysis; use artificial intelligence to optimize the problem of processing large amounts of information; use artificial intelligence technology to enhance risk management and predictive analysis, strengthen intelligent decision-making and portfolio optimization, and provide insights into big data analysis and market trends.Highlights in Business, Economics and Management["Zizheng Cao"]2024-05-21T00:00:00 122ac1ab00f8c54a609e18d2aea5a0008b032f8aThe incorporation of Artificial Intelligence (AI) technology into several industries has significantly impacted the usual workflows and processes in recent years, including the healthcare industry. Human Resource Management (HRM) is essential in healthcare businesses as it is responsible for the recruitment, training, and the retention of skilled staff members who are capable of providing high-quality patient care. This paper investigates different methods in which AI is used in HRM in the healthcare industry on the basis of existing research in the area. It analyzes how AI affects recruitment, talent management, workforce optimization, and employee well-being. This paper also discusses the challenges and future prospects of AI-driven approaches in HRM practices. It explores how these approaches are changing the way healthcare organizations operate and improving patient outcomes. The results provide some valuable contributions to the field of artificial intelligence in the healthcare sector. Initially, the chapter gives a factual foundation for the current presumptions on the implementation and difficulties of artificial intelligence in the healthcare domain. Further, it shows how artificial intelligence provides numerous opportunities to expedite Human Resource operations by offering automated applicant screening, customized learning systems, optimizing the workforce and enhancing employee engagement. Although AI has the capacity to revolutionize HRM practices in the healthcare industry, it also presents some challenges and obstacles. In order to ensure that AI-driven solutions promote fairness, transparency, and equity, it is crucial to address issues such as algorithmic bias, privacy of data and the impact on the human workforce in a deliberate manner. In addition, healthcare firms need to invest funds for implementing rigorous cyber security measures in order to ensure the privacy of patient and employee data from cyber-attacks and potential breaches.The International Journal of Education Management and Sociology481Investigation of different methods in which AI is used in HRM in the healthcare industry on the basis of existing research analyzes how AI affects recruitment, talent management, workforce optimization, and employee well-being and shows how artificial intelligence provides numerous opportunities to expedite Human Resource operations.The International Journal of Education Management and Sociology["Amit Joshi", "Rubee Singh", "Seema Rani"]2024-05-21T00:00:00 65e23efbb9b182dae9ad63ba63cf58445189e7e1The knowledge revolution in the middle of the twentieth century brought about a digital transformation in various aspects of human development, and in view of the knowledge data in machine language, it became necessary to computerize the Arabic language as a means of transferring knowledge, and to make it compatible with modern means of communication in artificial intelligence algorithms to help humans overcome the difficulties of communication and learning. Therefore, researchers in the field of artificial intelligence sought to understand human perception and try to simulate it through the development of computer systems that deal with different levels of languages. Researching a descriptive and analytical reading of the reality of the Arabic language in its relationship to artificial intelligence and computing, and the extent to which it benefits from technological and informational progress, as well as possible horizons that will contribute to solving its problems and challenges.European Journal of Language and Culture Studies00Researching a descriptive and analytical reading of the reality of the Arabic language in its relationship to artificial intelligence and computing, and the extent to which it benefits from technological and informational progress, as well as possible horizons that will contribute to solving its problems and challenges.European Journal of Language and Culture Studies["A. Doohee"]2024-05-21T00:00:00 95bb54843f435c09a8179c18905d082eeac774abThe exponential rise in the use of artificial intelligence in instructional delivery has challenged the academic landscape and provided implications for teaching and learning. Artificial intelligence (AI) becomes one of the emerging issues in higher education. This paper looked into the state university teachers’ views and usage of artificial intelligence in the classroom. Using mixed-method research design, a researcher-made survey questionnaire solicited the teachers’ experiences with the use and non-use of artificial intelligence in their teaching practice. A purposive random sampling technique was used in choosing the thirty university teachers as the research respondents. As such, in the quantitative data, the teachers’ use of AI-powered educational technology in the classroom was identified. For the qualitative data, advantages and disadvantages of the use of AI based on the teachers’ experiences were analyzed using Braun and Clarke’s six-step data analysis. The study found that teachers use canva, chatGPT, educational games, educational chatbots, grammarly, quillbot, and YouTube videos in their classes. The advantages of using AI include personalized learning, having virtual assistants, and streamlining teaching and learning. However, AI also has its disadvantages, as it limits human interaction and empathy, which are essential in the teaching and learning process. The findings offer pedagogical implications and recommendations on how artificial intelligence can help teachers provide personalized learning experiences for their students. AI has been used as a tool in teaching but it can also imply some threats if not properly managed or regulated as it poses ethical challenges in academic work.ACEID Official Conference Proceedings280The state university teachers’ views and usage of artificial intelligence in the classroom is looked into, offering pedagogical implications and recommendations on how artificial intelligence can help teachers provide personalized learning experiences for their students.ACEID Official Conference Proceedings["C. A. Jaca"]2024-05-21T00:00:00 f793a41fbad4487f24e6e91717528a37e6ddc9d2For the analysis of exceedingly complex insurance data, artificial intelligence methods have evolved into the most valuable and significant tools. Worldwide, the insurance industry and its clients require a method for efficiently managing the enormous amount of data produced. The current review paper provides an overview of the research conducted in recent years on various cross-selling insurance approaches that have utilized machine learning and deep learning techniques. An evident transition from the utilization of machine learning methods to deep learning methods is demonstrated through the current literature review.The International Conference on Electrical Engineering510The current review paper provides an overview of the research conducted in recent years on various cross-selling insurance approaches that have utilized machine learning and deep learning techniques.2024 14th International Conference on Electrical Engineering (ICEENG)["Shaden Mohamed Aref", "Mohamed Mostafa Fouad", "H. A. Sayed", "Menna Ibrahim Gaber"]2024-05-21T00:00:00 aebbeaa07f4bcf8698a0fcd49fb2a74126277ecdThe future of smart factories and pharmaceutical industries has evolved significantly since the 19th century. Computers have been used in the pharmaceutical field since the 1980s with the emergence of artificial intelligence (AI). In addition, automation and robotics are used in the pharmaceutical industry to improve the efficiency of pharmaceutical development and production. The present review article covers the future roles of AI, automation and robotics in pharmaceutical industries. The current review article employed a comprehensive search strategy across relevant databases, including PubMed, Scopus and Web of Science, utilizing keywords such as AI, Automation, Robotics and Pharmaceutical Industries. The articles considered a focus on recent advancements and emerging trends in the intersection of AI, automation and robotics within pharmaceutical sectors. AI, incorporating predictive machine learning and reasoning techniques, aids in the preclinical identification of molecules and forecasting potential lead compounds before conducting clinical trials. automation offers significant benefits in monitoring and predictive maintenance of production lines, power distribution and control machines. Robotic process automation improves efficiency by connecting computer terminals to handle various manufacturing process elements. Artificial intelligence, automation and Robotics have sparked innovations in the healthcare business, benefiting the global ecosystem and healthcare delivery. Incorporating these advanced tools in pharmaceutical industries from raw material selection to final product development could improve the quality and safety of the pharmaceutical products and reduce the time and cost.Journal of Pharmacology and Pharmacotherapeutics250The present review article covers the future roles of AI, automation and robotics in pharmaceutical industries by employing a comprehensive search strategy across relevant databases, utilizing keywords such as AI, Automation, Robotics and Pharmaceutical Industries.Journal of Pharmacology and Pharmacotherapeutics["Manoj Kumar T.", "Preethi B.", "Raja Shekhar Nunavath", "K. Nagappan"]2024-05-21T00:00:00 e87c14c099f99493063ab89c6579bd44d26b7b2fThis paper discusses the innovative role and practical application of artificial intelligence (AI) and virtual simulation technology in the field of art design. The article first outlines the impact of AI on art designers, including data analysis and prediction, adaptive optimization and independent innovation and creation. The application advantages of virtual simulation technology in art design are discussed, such as intuitive preview effects and freer creative space. The article further analyzes the development prospects of the integration of AI and virtual simulation technology, including the intelligent, immersive experience and intelligent creation of art design.AI has broad application prospects in art design, which can shorten the design cycle, provide personalized design optimization solutions, and reduce the cost of trial and error through simulation and prediction. Virtual simulation technology brings an immersive experience to users through a three-dimensional interactive environment, and combined with the personalized adjustment of AI,it can further enhance the user experience. Finally, the article looks forward to the future of AI and virtual simulation technology in art design, indicating that art design will develop in a more intelligent and humanized direction, bringing more innovative and personalized experiences to designers and users.Academic Journal of Science and Technology60The article outlines the impact of AI on art designers, including data analysis and prediction, adaptive optimization and independent innovation and creation, and the application advantages of virtual simulation technology in art design are discussed.Academic Journal of Science and Technology["Xinyi Li"]2024-05-21T00:00:00 cef69d2604e5be2941bdfaa5a711b22417689c56Artificial intelligence needs ongoing and meaningful democratic oversight. Understanding the history of how the early nuclear weapons complex, novel biotechnology, and polygraph testing were managed can inform AI governance today.Issues in science and technology00Understanding the history of how the early nuclear weapons complex, novel biotechnology, and polygraph testing were managed can inform AI governance today.Issues in Science and Technology["Marc Aidinoff", "David Kaiser"]2024-05-21T00:00:00 c7d246ad2e2d0b1bf91e2c07444b4ae074440c71In this paper, we explore the development of an explainability system for air combat agents trained with reinforcement learning, thus addressing a crucial need in the dynamic and complex realm of air combat. The safety-critical nature of air combat demands not only improved performance but also a deep understanding of artificial intelligence (AI) decision-making processes. Although AI has been applied significantly to air combat, a gap remains in comprehensively explaining an AI agent’s decisions, which is essential for their effective integration and for fostering trust in their actions. Our research involves the creation of an explainability system tailored for agents trained in an air combat environment. Using reinforcement learning, combined with a reward decomposition approach, the system clarifies the agent’s decision making in various tactical situations. This transparency allows for a nuanced understanding of the agent’s behavior, thereby uncovering their strategic preferences and operational patterns. The findings reveal that our system effectively identifies the strengths and weaknesses of an agent’s tactics in different air combat scenarios. This knowledge is essential for debugging and refining the agent’s performance and to ensure that AI agents operate optimally within their intended contexts. The insights gained from our study highlight the crucial role of explainability in improving the integration of AI technologies within air combat systems, thus facilitating more informed tactical decisions and potential advancements in air combat strategies.Aerospace260The research involves the creation of an explainability system tailored for agents trained in an air combat environment using reinforcement learning and a reward decomposition approach, which clarifies the agent’s decision making in various tactical situations.Aerospace["Emre Saldiran", "M. Hasanzade", "Gokhan Inalhan", "Antonios Tsourdos"]2024-05-21T00:00:00 6de93345edfca7c6d71ddb9694024af911d96f01This study identifies a key cognitive mechanism through which information encountered by managers influences their pursuit of ambidexterity when implementing artificial intelligence (AI) in organisations. Additionally, it investigates the roles of managers’ understanding of technological environments and risk-taking strategies of their organisations in this relationship. We conducted three studies with managers at the above-middle level. The results indicate that managers with situationally induced promotion focus tend to have greater opportunity appraisal (Studies 1, 2, and 3) and pursuit of ambidexterity (Studies 2 and 3) compared to those with situationally induced prevention focus. Opportunity appraisal mediates the relationship between situationally induced regulatory focus and ambidexterity pursuit (Studies 2 and 3). Further, organisational risk-taking strategy moderates the links from situationally induced regulatory focus to opportunity appraisal to ambidexterity (Study 3). This study contributes to the theoretical understanding of the cognitive processes underlying managers’ pursuit of ambidexterity when facing technological uncertainty.International Journal of Innovation Management490A key cognitive mechanism through which information encountered by managers influences their pursuit of ambidexterity when implementing artificial intelligence (AI) in organisations is identified and the roles of managers’ understanding of technological environments and risk-taking strategies of their organisations are investigated.International Journal of Innovation Management["Kyootai Lee", "Han-Gyun Woo", "Taeyoung Park", "Simon DE Jong"]2024-05-21T00:00:00 5d3617d41d9074b85dace7a12bae529c084d8ebdArtificial intelligence (AI) has created new opportunities, challenges, and potentials in teaching; however, issues related to the philosophy of using AI technology in learners' learning have not been addressed and have caused some issues and concerns. This issue is due to the research gap in addressing issues related to ethical and human needs, and even values in AI in learning have become more obvious.This study investigates how human‐centered artificial intelligence (HAI) can help learners in a learning environment. In this regard, this article by developing key considerations of HAI in helping students tries to help implement or shift it in the future in learning environments.To better understand the key considerations of HAI, qualitative methods and interview techniques were applied in this study. In this regard, 18 samples were interviewed from two groups of experts and faculty members in the fields of technology and computer science and social and humanities sciences. The thematic content analysis method was used to analyse qualitative data.The results show that AI attempts to integrate ethical and human values in the process of design, development, and research in the fields of recognising and dealing with negative emotions, targeted emotional nature, and access to fairness and justice. It also shows significant promise in understanding feelings and emotions in a learning environment.Although AI has been studied in other contexts, HAI has not attracted much attention from researchers. Hence, this study has made worthwhile contributions to the literature as it has specifically focused on HAI in education. In addition, it can resolve some scientific community considerations regarding technological concerns in the field of AI. Furthermore, this article can increase social satisfaction with the use of AI by considering ethical considerations in the learning environment and can particularly benefit researchers, educators, and AI specialists who are involved in the study of HAI applications.Journal of Computer Assisted Learning710AI attempts to integrate ethical and human values in the process of design, development, and research in the fields of recognising and dealing with negative emotions, targeted emotional nature, and access to fairness and justice and shows significant promise in understanding feelings and emotions in a learning environment.J. Comput. Assist. Learn.["E. Jafari"]2024-05-21T00:00:00 2ad5ed0ce015f625b82589e2901622ccc74e5f7bTechnology trends have changed the educational paradigm, especially at the tertiary level. Artificial Intelligence (AI) has emerged as a way to change the learning process with the form of games in education, this will make education in higher education experience increased efficiency and effectiveness in learning. However, a deep understanding of how artificial intelligence can improve higher learning experiences and their impact after graduation is still limited. This research aims to investigate how artificial intelligence helps learning in higher education and its impact on students' career preparation after graduation. The focus is on analysis of the implementation of AI in teaching, learning, and decision-making support in higher education institutions. The research method used is literature study and content analysis. Data was collected from scientific articles, books, as well as related research reports on the use of artificial intelligence in higher education. The data is then analyzed to identify trends, benefits, and challenges associated with applying AI in educational contexts. Research results show that the use of artificial intelligence in higher education has increased the personalization of learning, increased knowledge retention, and facilitated more timely and accurate feedback. Additionally, AI also plays an important role in helping students develop skills relevant to the future job market. The conclusion of this research is that by integrating artificial intelligence into higher education, institutions can improve the quality of learning and help students be better prepared to face the challenges of an ever-changing job market. However, challenges such as technology dependency and the need for proper training for educators and students must also be addressed so that the full potential of AI in education can be realized.Journal Neosantara Hybrid Learning270By integrating artificial intelligence into higher education, institutions can improve the quality of learning and help students be better prepared to face the challenges of an ever-changing job market.Journal Neosantara Hybrid Learning["Fauzi Fauzi", "Rizky Wardhani", "Guntur Arie Wibowo", "Didik Cahyono", "Hanifatul Rahmi"]2024-05-21T00:00:00 60ce8816fb4286815b5268cb735cfdccd5585c6ePurposeArtificial intelligence (AI) is shaping the future of the marketing world. This study is the first to examine the effect of AI marketing efforts, brand experience (BE) and brand preference (BP) in light of the stimulus-organism-response (SOR) model.Design/methodology/approachThe data collected from 398 participants by the questionnaire method were analyzed by SEM (structural equation modeling) using Smart PLS 4.0 and IBM SPSS 26 programs.FindingsWe find that four SOR elements of AI marketing efforts (information, interactivity, accessibility and personalization) positively impact bank customer BE, BP and repurchase intention (RPI). Further, we find that BE plays a mediator role in the relationship between AI marketing efforts, RPI and BP.Originality/valueThe findings of the study have significant implications for the bank marketing literature and the banking industry, given the limited evidence to date regarding AI marketing efforts and bank–customer relationships. Moreover, the study makes important contributions to the AI marketing and brand literature and helps banks increase customer experience with artificial intelligence activities and create long-term relationships with customers.Kybernetes841It is found that four SOR elements of AI marketing efforts positively impact bank customer BE, BP and repurchase intention (RPI), and that BE plays a mediator role in the relationship between AI marketing efforts, RPI and BP.Kybernetes["Bar\u0131\u015f Armutcu", "Ahmet Tan", "Shirie Pui Shan Ho", "Matthew Yau Choi Chow", "Kimberly Gleason"]2024-05-21T00:00:00 cc7f02f574ace512f4edd1a46c528b2acc96ad68Current Issues in Tourism531Current Issues in Tourism["Sandra Maria Correia Loureiro", "Jo\u00e3o Guerreiro", "Enav Friedmann", "Myong Jae Lee", "Heesup Han"]2024-05-21T00:00:00 9e4291a8ce6f8237e4a17be379db8d59b16ca1acBiased information (recently termed bisinformation) continues to be taught in medical curricula, often long after having been debunked. In this paper, we introduce bricc, a first-in-class initiative that seeks to mitigate medical bisinformation using machine learning to systematically identify and flag text with potential biases, for subsequent review in an expert-in-the-loop fashion, thus greatly accelerating an otherwise labor-intensive process. We have developed a gold-standard bricc dataset throughout several years containing over 12K pages of instructional materials. Medical experts meticulously annotated these documents for bias according to comprehensive coding guidelines, emphasizing gender, sex, age, geography, ethnicity, and race. Using this labeled dataset, we trained, validated, and tested medical bias classifiers. We test three classifier approaches: a binary type-specific classifier, a general bias classifier; an ensemble combining bias type-specific classifiers independently-trained; and a multi-task learning (MTL) model tasked with predicting both general and type-specific biases. While MTL led to some improvement on race bias detection in terms of F1-score, it did not outperform binary classifiers trained specifically on each task. On general bias detection, the binary classifier achieves up to 0.923 of AUC, a 27.8% improvement over the baseline. This work lays the foundations for debiasing medical curricula by exploring a novel dataset and evaluating different training model strategies. Hence, it offers new pathways for more nuanced and effective mitigation of bisinformation.AAAI/ACM Conference on AI, Ethics, and Society281Bicc is introduced, a first-in-class initiative that seeks to mitigate medical bisinformation using machine learning to systematically identify and flag text with potential biases, for subsequent review in an expert-in-the-loop fashion, thus greatly accelerating an otherwise labor-intensive process.ArXiv["Chiman Salavati", "Shannon Song", "Willmar Sosa Diaz", "Scott A. Hale", "Roberto E. Montenegro", "Fabricio Murai", "Shiri Dori-Hacohen"]2024-05-21T00:00:00 96823ecdb152edabc41f7773c32a6478a42b0ff500["David Grant"]2024-05-21T00:00:00 272e3f507cce0464931621e3d76379ef90bc81edIn this work, we focus on a special group of human body language -- the micro-gesture (MG), which differs from the range of ordinary illustrative gestures in that they are not intentional behaviors performed to convey information to others, but rather unintentional behaviors driven by inner feelings. This characteristic introduces two novel challenges regarding micro-gestures that are worth rethinking. The first is whether strategies designed for other action recognition are entirely applicable to micro-gestures. The second is whether micro-gestures, as supplementary data, can provide additional insights for emotional understanding. In recognizing micro-gestures, we explored various augmentation strategies that take into account the subtle spatial and brief temporal characteristics of micro-gestures, often accompanied by repetitiveness, to determine more suitable augmentation methods. Considering the significance of temporal domain information for micro-gestures, we introduce a simple and efficient plug-and-play spatiotemporal balancing fusion method. We not only studied our method on the considered micro-gesture dataset but also conducted experiments on mainstream action datasets. The results show that our approach performs well in micro-gesture recognition and on other datasets, achieving state-of-the-art performance compared to previous micro-gesture recognition methods. For emotional understanding based on micro-gestures, we construct complex emotional reasoning scenarios. Our evaluation, conducted with large language models, shows that micro-gestures play a significant and positive role in enhancing comprehensive emotional understanding. The scenarios we developed can be extended to other micro-gesture-based tasks such as deception detection and interviews. We confirm that our new insights contribute to advancing research in micro-gesture and emotional artificial intelligence.arXiv.org1111ArXiv["Rong Gao", "Xin Liu", "Bohao Xing", "Zitong Yu", "Bj\u00f6rn W. Schuller", "H. K\u00e4lvi\u00e4inen"]2024-05-21T00:00:00 9338ad1e3cb4a6d4c1001483ae920bd812077b23Artificial Intelligence (AI) has threatened higher education (HE). In doing so it has granted a portal that makes visible the dominant paradigm that has long defined what “intelligence” is and the narrow set of knowledges and literacies sanctioned for its pursuit. In this paper, we orient our thinking from this clarifying moment, asking: beyond these limits, what intelligences should educators value and nurture for sustainable and anti‐colonial futures, and how might we support these through educational practices in HE? We also pause to reflect on the ways in which AI might move learners' pursuits of intelligence in more expansive directions. This orientation, we argue, provides a means to unsettle the hierarchies of intelligence that we live with/out, and a pathway to (re)direct AI's potential toward just and hopeful ends.Reading Research Quarterly203This orientation provides a means to unsettle the hierarchies of intelligence that the authors live with/out, and a pathway to (re)direct AI's potential toward just and hopeful ends.Reading Research Quarterly["Lisa Bradley", "Mia Perry", "Giovanna Fassetta", "Sadie Durkacz Ryan", "Elizabeth L. Nelson"]2024-05-21T00:00:00 efd6de79ecae129d86161752d47f0340291f9341Patterns are emerging in the various efforts to regulate artificial intelligence around the globe. Understanding these evolving AI norms could help govern this technology intelligently.Issues in science and technology00Issues in Science and Technology["Urs Gasser"]2024-05-21T00:00:00 0d67cd78dc688ea8d404593eadc3ba5d5bb86951Discover Artificial Intelligence621This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness as well as the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability.Discov. Artif. Intell.["Iqbal H. Sarker"]2024-05-21T00:00:00 1849aa7a1806dd3e4f6bac3bd274f7dd768a387cNatural disasters often lead to significant disruptions in healthcare delivery, exacerbating the already formidable challenges faced by healthcare systems. Leveraging artificial intelligence (AI) offers a promising approach to mitigate these challenges and enhance healthcare management during and after natural disasters. This conceptual paper aims to propose a framework for the integration of AI into disaster response efforts, with a focus on optimizing resource allocation, improving patient triage, and enhancing overall system resilience.  Through a comprehensive review of existing literature, this paper identifies the gaps in current disaster management practices and explores the potential of AI to address these shortcomings. By analyzing case studies and examples from previous disasters, the paper highlights the transformative impact that AI technologies such as predictive analytics, machine learning, and robotics can have on healthcare delivery in crisis situations. The objectives of this paper are twofold: to define a strategic approach for incorporating AI into disaster response protocols and to outline the expected outcomes of implementing such a framework. Expected benefits include expedited triage processes, more accurate resource allocation, and improved communication systems, ultimately leading to better patient outcomes and enhanced system efficiency. The proposed framework emphasizes the importance of interdisciplinary collaboration between healthcare professionals, technologists, policymakers, and disaster response experts. It also addresses ethical considerations and potential challenges associated with AI implementation in disaster settings. In conclusion, this paper underscores the critical role of AI in bolstering healthcare management capabilities during natural disasters. By leveraging AI technologies, healthcare systems can become more adaptive, responsive, and resilient in the face of unforeseen challenges, ultimately saving lives and minimizing the impact of disasters on communities. Keywords: AI-Enhanced Healthcare Management, Natural Disasters, Conceptual Insights.Engineering Science &amp; Technology Journal015The critical role of AI in bolstering healthcare management capabilities during natural disasters is underscored, as healthcare systems can become more adaptive, responsive, and resilient in the face of unforeseen challenges, ultimately saving lives and minimizing the impact of disasters on communities.Engineering Science &amp; Technology Journal["Samira Abdul", "Ehizogie Paul Adeghe", "Bisola Oluwafadekemi Adegoke", "Adebukola Adejumoke Adegoke", "Emem Henry Udedeh"]2024-05-21T00:00:00 17d7ce521fbc28cbf845b2675fda9e35b40076faArtificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.Life999Recommendations are made to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.Life["Gillian Franklin", "Rachel Stephens", "Muhammad Piracha", "S. Tiosano", "Frank LeHouillier", "Ross Koppel", "Peter L Elkin"]2024-05-21T00:00:00 04c33bccba919ec5ea3cbe7c7914cf6d5dd10fd6Generative Artificial intelligence (GenAI) such as ChatGPT has elicited strong reactions from almost all stakeholders across the education system. Education-oriented and academic social media communities provide an important venue for these stakeholders to share experiences and exchange ideas about GenAI, which is constructive for developing human-centered policies. This study examines early user reactions to GenAI, consisting of 725 Reddit threads between 06/2022 and 05/2023. Through natural language processing (NLP) and content analysis, we observe an increasingly negative sentiment in the discussion and identify six main categories of student and faculty experiences of GenAI in education. These experiences reflect concerns about academic integrity and AI’s negative impact on the values of traditional education. Our analysis also highlights the tension and burden imposed by new technologies. Our findings suggest that dialogue between stakeholders in the education community is critical and can mitigate sources of tension between students and faculty.Web Science Conference623This study examines early user reactions to GenAI, consisting of 725 Reddit threads between 06/2022 and 05/2023, and identifies six main categories of student and faculty experiences of GenAI in education.Proceedings of the 16th ACM Web Science Conference["Chuhao Wu", "Xinyu Wang", "John M. Carroll", "Sarah Rajtmajer"]2024-05-21T00:00:00 fb1eda54a37b3139244d8bfe3a5e1350b048c811Trustworthy artificial intelligence (TAI) is trending high on the political agenda. However, what is actually implied when talking about TAI, and why it is so difficult to achieve, remains insufficiently understood by both academic discourse and current AI policy frameworks. This paper offers an analytical scheme with four different dimensions that constitute TAI: a) A user perspective of AI as a quasi-other; b) AI's embedding in a network of actors from programmers to platform gatekeepers; c) The regulatory role of governance in bridging trust insecurities and deciding on AI value trade-offs; and d) The role of narratives and rhetoric in mediating AI and its conflictual governance processes. It is through the analytical scheme that overlooked aspects and missed regulatory demands around TAI are revealed and can be tackled. Conceptually, this work is situated in disciplinary transgression, dictated by the complexity of the phenomenon of TAI. The paper borrows from multiple inspirations such as phenomenology to reveal AI as a quasi-other we (dis-)trust; Science & Technology Studies (STS) to deconstruct AI's social and rhetorical embedding; as well as political science for pinpointing hegemonial conflicts within regulatory bargaining.Big Data & Society613An analytical scheme with four different dimensions that constitute TAI is offered, borrowing from multiple inspirations such as phenomenology to reveal AI as a quasi-other the authors (dis-)trust; Science & Technology Studies to deconstruct AI's social and rhetorical embedding; as well as political science for pinpointing hegemonial conflicts within regulatory bargaining.Big Data Soc.["Jascha Bareis"]2024-05-21T00:00:00 3c5a26a8d59d6ad248892a58abc1b36005f81a42In this paper, we explore the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, highlighting its potential to revolutionize industry practices and enhance operational efficiency. We delve into various applications of AI in manufacturing, with a particular emphasis on human-machine interfaces (HMI) and AI-powered milling machines, showcasing how these technologies contribute to more intuitive operations and precision in production processes. Through rigorous market analysis, the paper presents insightful data on AI adoption rates among German manufacturers, comparing these figures with global trends and exploring the specific uses of AI in production, maintenance, customer service, and more. In addition, the paper examines the emerging field of Generative AI and the potential applications of large language models in manufacturing processes. The findings indicate a significant increase in AI adoption from 6% in 2020 to 13.3% in 2023 among German companies, with a projection of substantial economic impact by 2030. The study also addresses the challenges faced by companies, such as data quality and integration hurdles, providing a balanced view of the opportunities and obstacles in AI implementation.arXiv.org342Through rigorous market analysis, the paper presents insightful data on AI adoption rates among German manufacturers, comparing these figures with global trends and exploring the specific uses of AI in production, maintenance, customer service, and more.ArXiv["Mohamed Abdelaal"]2024-05-21T00:00:00 62ea2e7668d5e9e20767befac3ffaf7bc3f7d066Artificial Intelligence (AI) has significantly impacted education, particularly language education, by enabling personalized learning experiences through advanced algorithms and machine learning. This paper explores the integration of AI in language learning, focusing on adaptive learning systems that tailor educational content to individual learners' needs. By examining the historical evolution of AI in education, current applications, and future trends, this review highlights the potential benefits of AI enhanced personalized language education, including improved learning outcomes and increased accessibility. It also addresses the challenges associated with AI implementation, such as technological barriers and the need for effective teacher training. The findings underscore the importance of a balanced approach where AI complements human teachers, providing personalized support while maintaining the essential human elements of empathy and contextual understanding. Keywords: Personalized Language Education, Artificial Intelligence (AI), Adaptive Learning Systems, Machine Learning, Language Proficiency, Educational Technology and Natural Language Processing (NLP)NEWPORT INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION01This review highlights the potential benefits of AI enhanced personalized language education, including improved learning outcomes and increased accessibility, and addresses the challenges associated with AI implementation, such as technological barriers and the need for effective teacher training.NEWPORT INTERNATIONAL JOURNAL OF RESEARCH IN EDUCATION["Okolo Chinwe Jane", "Chinyere Grace Ezeonwumelu", "Chioma Ihuoma Barah", "Ugwu Nnenna Jovita"]2024-05-21T00:00:00 089258bb8071938361adc31ce3de57b9062e22e1Artificial intelligence researchers regularly conduct social experiments relying on data from participants who haven’t agreed to take part. To earn public trust, researchers need to reorient computational research toward respect for human rights by adopting a robust AI ethics protocol.Issues in science and technology01To earn public trust, researchers need to reorient computational research toward respect for human rights by adopting a robust AI ethics protocol.Issues in Science and Technology["Mary Gray"]2024-05-21T00:00:00 0febcc28973bb4df3512625fab29e899b7953bebTo ensure that artificial intelligence meaningfully addresses social inequalities, AI designers and regulators should seek out partnerships with marginalized communities, to learn what they need from this emerging technology and build it.Issues in science and technology01Issues in Science and Technology["Shobita Parthasarathy", "Jared Katzman"]2024-05-21T00:00:00 d6e38d939b21e4b1258c996a3f03947c2ab47d9bCurrently, Blockchain (BC), Artificial Intelligence (AI), and smart Industrial Internet of Things (IIoT) are not only leading promising technologies in the world, but also these technologies facilitate the current society to develop the standard of living and make it easier for users. However, these technologies have been applied in various domains for different purposes. Then, these are successfully assisted in developing the desired system, such as-smart cities, homes, manufacturers, education, and industries. Moreover, these technologies need to consider various issues-security, privacy, confidentiality, scalability, and application challenges in diverse fields. In this context, with the increasing demand for these issues solutions, the authors present a comprehensive survey on the AI approaches with BC in the smart IIoT. Firstly, we focus on state-of-the-art overviews regarding AI, BC, and smart IoT applications. Then, we provide the benefits of integrating these technologies and discuss the established methods, tools, and strategies efficiently. Most importantly, we highlight the various issues--security, stability, scalability, and confidentiality and guide the way of addressing strategy and methods. Furthermore, the individual and collaborative benefits of applications have been discussed. Lastly, we are extensively concerned about the open research challenges and potential future guidelines based on BC-based AI approaches in the intelligent IIoT system.arXiv.org2581A comprehensive survey on the AI approaches with BC in the smart IIoT system focuses on state-of-the-art overviews regarding AI, BC, and smart IoT applications and provides the benefits of integrating these technologies and discusses the established methods, tools, and strategies efficiently.ArXiv["Anichur Rahman", "Dipanjali Kundu", "Tanoy Debnath", "Muaz Rahman", "Airin Afroj Aishi", "Jahidul Islam"]2024-05-21T00:00:00 eedb7d11f36cf09478b6a99551f4cae4254f0625 In the rapidly evolving landscape of software engineering, the integration of Artificial Intelligence (AI) into the Software Development Life-Cycle (SDLC) heralds a transformative era for developers. Recently, we have assisted to a pivotal shift towards AI-assisted programming, exemplified by tools like GitHub Copilot and OpenAI’s ChatGPT, which have become a crucial element for coding, debugging, and software design. In this paper we provide a comparative analysis between the current state of AI-assisted programming in 2024 and our projections for 2030, by exploring how AI advancements are set to enhance the implementation phase, fundamentally altering developers’ roles from manual coders to orchestrators of AI-driven development ecosystems. We envision HyperAssistant , an augmented AI tool that offers comprehensive support to 2030 developers, addressing current limitations in mental health support, fault detection, code optimization, team interaction, and skill development. We emphasize AI as a complementary force, augmenting developers’ capabilities rather than replacing them, leading to the creation of sophisticated, reliable, and secure software solutions. Our vision seeks to anticipate the evolution of programming practices, challenges, and future directions, shaping a new paradigm where developers and AI collaborate more closely, promising a significant leap in SE efficiency, security and creativity. ACM Transactions on Software Engineering and Methodology511A comparative analysis between the current state of AI-assisted programming in 2024 and projections for 2030 is provided, by exploring how AI advancements are set to enhance the implementation phase, fundamentally altering developers’ roles from manual coders to orchestrators of AI-driven development ecosystems.ArXiv["Matteo Ciniselli", "Niccol\u00f2 Puccinelli", "Ketai Qiu", "L. Grazia"]2024-05-21T00:00:00 10a4fa3043dbe1a4a5db20d5a3e66d32736a0f10Digital Society210An information system design for responsible and trustworthy business processes is proposed, and it is envisioned that businesses will need strong and well-defined control points in their information systems for managing processes and creating associated audits to enforce their principles.Digit. Soc.["G. Pisoni", "Maria Moloney"]2024-05-21T00:00:00 fbd6c4549dbb9352e6688bfb9674c7999d5e6a4bAgricultural development is one of the most essential needs worldwide. In Norway, the primary foundation of grain production is based on geological and biological features. Existing research is limited to regional-scale yield predictions using artificial intelligence (AI) models, which provide a holistic overview of crop growth. In this paper, the authors propose detecting several field-scale crop types and use this analysis to predict yield production early in the growing season. In this study, the authors utilise a multi-temporal satellite image, meteorological, geographical, and grain production data corpus. The authors extract relevant vegetation indices from satellite images. Furthermore, the authors use field-area-specific features to build a field-based crop type classification model. The proposed model, consisting of a time-distributed network and a gated recurrent unit, can efficiently classify crop types with an accuracy of 70%. In addition, the authors justified that the attention-based multiple-instance learning models could learn semi-labelled agricultural data, and thus, allow realistic early in-season predictions for farmers.Agronomy550This study utilises a multi-temporal satellite image, meteorological, geographical, and grain production data corpus, and uses field-area-specific features to build a field-based crop type classification model that can efficiently classify crop types with an accuracy of 70%.Agronomy["Mikkel Andreas Kvande", "Sigurd L\u00f8ite Jacobsen", "Morten Goodwin", "Rashmi Gupta"]2024-05-21T00:00:00 7158277c0361f15a7c67621f5940c4208c9c46ceThe global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.Energy &amp; Environment15816This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements.Energy &amp; Environment["K. Ukoba", "Kehinde Oladoke Olatunji", "Eyitayo Adeoye", "T. Jen", "D. Madyira"]2024-05-22T00:00:00 5c092e80fed1d0e568189b0a08fd04d2fc0fee83Artificial intelligence (AI) has gained much popularity in various sectors and has found applications in multiple areas, including occupational health and safety (OHS) risk management of the high-risk construction, mining, and oil and gas sectors. OHS risk management centers on identifying, assessing and controlling occupational risks systematically to prevent work-related injuries, illnesses and deaths. This review presents the advances in AI applications for OHS risk management in these sectors and synthesizes their barriers for better application prospects. In the construction sector, AI can be employed in building information modeling during the design stage to identify and deal with the hazards of building models. AI can be deployed in construction sites through computer vision, sensor networks, knowledge-based systems, and machine learning to capture real-time site conditions, analyze the videos or pictures captured, and provide feedback to workers for appropriate responses. A similar setup involving the same components is also used for managing the OHS risks of surface or underground mining, particularly for monitoring the environmental conditions, detecting the presence of hazardous gases, and identifying hazards in locations that are remote and difficult to assess. Sensors can be attached to personal protective equipment and watches and the signals transmitted via Bluetooth to permit data collection for analysis and response by AI. In the oil and gas sector, sensors are extensively used to collect process safety data from wells, pipelines, valves, etc. for analytical and predictive Al. Al, especially, machine learning is used to create personalized training for workers based on their learning pace and characteristics. However, the major barriers identified are high cost, lack of support and skilled employees, ethical issues, and the uncertainty of AI.Journal of Engineering Research and Reports07This review presents the advances in AI applications for OHS risk management in these sectors and synthesizes their barriers for better application prospects, particularly in the construction sector and oil and gas sector.Journal of Engineering Research and Reports["K. Tang"]2024-05-22T00:00:00 8549d32e2ef2102eb9076fc5d40c4fa928436c44This study focuses on Generative Artificial Intelligence (AI) and its transformative impact on design ideation. Generative AI, recognized for its ability to produce a wide array of design alternatives, has become an important tool in design, reshaping traditional methodologies. It facilitates the generation of novel and diverse design forms, acting as a co-creator in the design process. This technology, through machine learning and pattern recognition, analyzes extensive design datasets, enabling the production of innovative solutions. The utilization of generative AI extends beyond replicating AI-provided solutions; it aids in developing and influencing novel concepts, thus fostering original design solutions. This aligns with the concept of ‘reflective practice’ in design, where designers iteratively refine concepts through a dialogue between thought and action. The study employed a quasi-experimental design with 40 design students, randomly assigned to two groups of 20 each. Conducted in two phases, each phase involved a distinct urban furniture design task. In Phase 1, Group A was provided with a text-to-image generating AI tool, while Group B was not. In Phase 2, both groups undertook a similar task without AI assistance. This design exercise allowed for examining the influence of AI on creativity and cognitive load. Design outcomes from both tasks were anonymized and evaluated by experienced professionals using the Creative Product Semantic Scale (CPSS), which measures Novelty, Resolution, and Elaboration and Synthesis. Additionally, the NASA Task Load Index (NASA TLX) questionnaire assessed cognitive load aspects such as mental demand and effort. Findings suggest that generative AI significantly influences the creative design process, enhancing the quality of design outcomes and reducing cognitive load. The AI group demonstrated better performance in both tasks, indicating the impact of AI tools on design skills. This study underscores the potential of AI tools in design education, balancing cognitive load management with creativity enhancement.International Journal of Architectural Computing165It is suggested that generative AI significantly influences the creative design process, enhancing the quality of design outcomes and reducing cognitive load.International Journal of Architectural Computing["T. Chandrasekera", "Zahrasadat Hosseini", "Ubhaya Perera"]2024-05-22T00:00:00 faa0251a5bdc509c58484ca74f417f0ad1ba58c6Introduction: Formulating reliable prognosis for ischemic stroke patients remains a challenging task. We aimed to develop an artificial intelligence model able to formulate in the first 24 h after stroke an individualized prognosis in terms of NIHSS. Patients and methods: Seven hundred ninety four acute ischemic stroke patients were divided into a training (597) and testing (197) cohort. Clinical and instrumental data were collected in the first 24 h. We evaluated the performance of four machine-learning models (Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost) in predicting NIHSS at discharge both in terms of variation between discharge and admission (regressor approach) and in terms of severity class namely NIHSS 0–5, 6–10, 11–20, >20 (classifier approach). We used Shapley Additive exPlanations values to weight features impact on predictions. Results: XGBoost emerged as the best performing model. The classifier and regressor approaches perform similarly in terms of accuracy (80% vs 75%) and f1-score (79% vs 77%) respectively. However, the regressor has higher precision (85% vs 68%) in predicting prognosis of very severe stroke patients (NIHSS > 20). NIHSS at admission and 24 hours, GCS at 24 hours, heart rate, acute ischemic lesion on CT-scan and TICI score were the most impacting features on the prediction. Discussion: Our approach, which employs an artificial intelligence based-tool, inherently able to continuously learn and improve its performance, could improve care pathway and support stroke physicians in the communication with patients and caregivers. Conclusion: XGBoost reliably predicts individualized outcome in terms of NIHSS at discharge in the first 24 hours after stroke.European Stroke Journal93XGBoost reliably predicts individualized outcome in terms of NIHSS at discharge in the first 24 hours after stroke, which could improve care pathway and support stroke physicians in the communication with patients and caregivers.European Stroke Journal["Pietro Caliandro", "J. Lenkowicz", "G. Reale", "Simone Scaringi", "A. Zauli", "Christian Uccheddu", "Simone Fabiole-Nicoletto", "S. Patarnello", "Andrea Damiani", "L. Tagliaferri", "I. Valente", "M. Moci", "Mauro Monforte", "Vincenzo Valentini", "P. Calabresi"]2024-05-22T00:00:00 08be8e2c824f5304e12452f80a5c461523e44589This research seeks to present the production of academic and scientific papers using artificial intelligence for Chat GPT language modeling in different areas of higher education. To obtain the results, the quantitative approach was followed, the type of research is descriptive with a non-experimental design at the field level. The size of the population were the teachers of various careers at the Machala Metropolitan University. For the sample, a probabilistic sampling with simple random selection was used, calculating the sample size with a margin of error of 5% and a confidence level of 95%. The main results in the application of the instrument are, among others, the fact that none of the teachers could detect that the document they reviewed was created by artificial intelligence and they gave it an average score of 8.88/10 and that the platform anti plagiarism Compilatio also generated an average of 1% similarity, demonstrating that academic and research papers are currently indistinguishable from human-made work, neither in human review nor on anti-plagiarism platforms. plagiarism.Revista Metropolitana de Ciencias Aplicadas02It is shown that academic and research papers are currently indistinguishable from human-made work, neither in human review nor on anti-plagiarism platforms, demonstrating that academic and research papers are currently indistinguishable from human-made work.Revista Metropolitana de Ciencias Aplicadas["F. Juca-Maldonado"]2024-05-22T00:00:00 834180d0da818a5ba25b56e719bc007b101263e5The rapid advancement of artificial intelligence (AI) has sparked intense debate regarding its potential threat to humanity. This abstract delves into the multifaceted discussion surrounding the implications of AI on the future of humanity. It explores various perspectives, ranging from optimistic views that highlight the transformative benefits of AI to pessimistic concerns about its existential threat. Drawing on insights from experts and researchers, the abstract examines key areas of contention, including the possibility of technological singularity, the ethical dilemmas posed by autonomous weapons, and the socio-economic impacts of AI-driven automation. So, the main purpose of the paper is to study the impacts of AI from different points of view including social, economic, political, etc. Therefore, different. Furthermore, it discusses strategies for mitigating the risks associated with AI, emphasizing the importance of ethical guidelines, regulatory frameworks, and international cooperation. Overall, this abstract provides a comprehensive overview of the complex considerations surrounding the impact of AI on humanity and underscores the need for thoughtful deliberation and proactive measures to ensure a beneficial and responsible integration of AI into society.Sustainable Economies221This abstract provides a comprehensive overview of the complex considerations surrounding the impact of AI on humanity and underscores the need for thoughtful deliberation and proactive measures to ensure a beneficial and responsible integration of AI into society.Sustainable Economies["Milad Shahvaroughi Farahani", "Ghazal Ghasemi"]2024-05-22T00:00:00 9bd37ffefeea9e53a3d98083eb1a593506398984The Egyptian Journal of Radiology and Nuclear Medicine192The use of artificial intelligence has enhanced the diagnostic performance of the postoperative mammograms to rule out recurrent malignancies in breast cancer surveillance.Egyptian Journal of Radiology and Nuclear Medicine["Sahar Mansour", "Heba Azzam", "Hany El-Assaly"]2024-05-22T00:00:00 314fd9cc365b136fed68cfeb74c77bb2db78604fAI and Ethics52This paper discusses the issue of artificial intelligence contribute to the common good and uses it as a lens for analysing what it calls the “democracy deficit” in current AI governance, which includes a tendency to deny the inherently political character of the issue and to take a technocratic shortcut.AI and Ethics["Mark Coeckelbergh"]2024-05-22T00:00:00 f28fe3e6aebd41f328d7e8bd7579fbfaa5f898a1The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT01This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Neelam R Patil"]2024-05-22T00:00:00 8ee815b5b92ca5d2cea7c84101e32b06d4723969Blockchain technology and Artificial Intelligence (AI) have emerged as transformative forces in their respective domains. This paper explores synergies and challenges between these two technologies. Our research analyses the biggest projects combining blockchain and AI, based on market capitalization, and derives a novel framework to categorize contemporary and future use cases. Despite the theoretical compatibility, current real-world applications combining blockchain and AI remain in their infancy.arXiv.org731Analysis of the biggest projects combining blockchain and AI, based on market capitalization, and derives a novel framework to categorize contemporary and future use cases, finds synergies and challenges between these two technologies.ArXiv["Leon Witt", "Armando Teles Fortes", "Kentaroh Toyoda", "Wojciech Samek", "Dan Li"]2024-05-22T00:00:00 53b14c545f0f37470cc6c3916c6e7f327045062bThe article examines the current status of Online Dispute Resolution (ODR) in the European Union, explicitly evaluating its advantages and disadvantages. In addition to examining the evolution of the ODR mechanisms, the author sheds light on the challenges encountered in their implementation, such as concerns about trust, transparency, and accessibility. Moreover, this analysis investigated the potential implications of artificial intelligence and contemporary technologies on the transformation of the EU’s consumer ODR framework. The ADR/ODR system of the EU has been subject to criticism for several weaknesses, including inconsistent implementation, insufficient supervision, and the lack of mandatory participation for traders. The lack of a specialised supervisory mechanism has also led to shortcomings in the oversight process. The Directive does not impose a requirement for traders to participate in ADR procedures; nevertheless, there exists significant heterogeneity in the regulations that govern these procedures. The lack of binding enforcement for final decisions arises from the requirement of Member States to agree upon and acknowledge the mechanisms for recognition and implementation. The EU ODR Platform operates as a mechanism for referring cases to ADR bodies. Nevertheless, the entity in question does not actively participate in the resolution of disputes based on their substantive merits, and its jurisdiction is primarily limited to smaller entities. Artificial intelligence (AI) has the potential to enhance transparency, objectivity, and legitimacy within online dispute resolution systems, thereby potentially giving rise to a two-tier system. The author suggests that in order to bring about significant legislative changes, it is necessary to incorporate certain provisions. These provisions should mandate the enforcement of decisions made in alternative dispute resolution (ADR) processes for consumers and also facilitate the seamless integration of artificial intelligence (AI) into this system. Otherwise, it is unlikely that AI will substantially impact the efficiency and effectiveness of the EU ADR/ODR system, as its primary challenges are rooted in areas beyond the scope of AI, such as the enforcement of decisions.Teisė61In order to bring about significant legislative changes, it is necessary to incorporate certain provisions that mandate the enforcement of decisions made in alternative dispute resolution (ADR) processes for consumers and also facilitate the seamless integration of artificial intelligence (AI) into this system.Teisė["Pavlo Riepin"]2024-05-22T00:00:00 dd9b335b3ca34255686907b4714fd015c585f111The article presents the development and research of technological tools - a relaxation system for children with autism. The article describes the means and methods of emotional stabilization of children with autism spectrum disorders. The design, operation, and control of the relaxation system controlled by artificial intelligence with image processing and machine learning are described. The relaxation effect on children is carried out with audio-musical signals, by combining them with colored light and mechanical vibration of the back area. The practical research results are described, demonstrating the system's effectiveness for children with autism spectrum disorders. Under normal conditions, if a child takes 3 to 4 hours to calm down, the relaxation system shortens this time to 10 to 15 minutes. Finally, the relaxation system controlled by artificial intelligence-based software, created by scientists from three Lithuanian universities and the students of Vilnius Kolegija, is presented as a technological tool designed to address social issues in society.SOCIETY INTEGRATION EDUCATION Proceedings of the International Scientific Conference160The relaxation system controlled by artificial intelligence-based software, created by scientists from three Lithuanian universities and the students of Vilnius Kolegija, is presented as a technological tool designed to address social issues in society.SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference["Eugenijus Ma\u010derauskas", "V. \u017dalys", "And\u017eej Lu\u010dun", "Romanas Tumasonis", "Eivin Laukhammer", "Antoni Kozi\u010d"]2024-05-22T00:00:00 e4cf640b9d60f07f6747736dfe6637957d29c2b6The paper is devoted to a new scientific direction: measuring artificial intelligence (MAI). The paper gives the basic definitions and attributes of measuring artificial intelligence. The scope of tasks, in which the application of methods and means of this direction is necessary is defined. Examples of MAI based on the methodology of the regularizing Bayesian approach are given, which illustrate the implementation of the principles of traceable, trustworthy and explicable artificial intelligence and ensuring the stability of the solutions, obtained in conditions of significant uncertainty. The methodological foundations of the metrology of solutions of artificial intelligence systems are given.System Configuration Management60The paper gives the basic definitions and attributes of measuring artificial intelligence and examples of MAI based on the methodology of the regularizing Bayesian approach are given, which illustrate the implementation of the principles of traceable, trustworthy and explicable artificial intelligence.2024 XXVII International Conference on Soft Computing and Measurements (SCM)["S. Prokopchina"]2024-05-22T00:00:00 613bd9bedf71e81827ffc54e51ab81f5fbc4f416The ARIREF (Adaptive Reflective Intelligence for Renewable Energy Forecasting) model represents a conceptual approach designed to enhance the accuracy and efficiency of renewable energy source forecasting. Based on a comprehensive review of scientific research, the model proposes an iterative modelling method that integrates adaptive and self-reflective artificial intelligence technologies. These technologies enable the model to continuously adapt and learn from changing conditions, thereby improving forecasting accuracy and performance. The ARIREF model is distinguished by its self-improvement cycle, providing a bidirectional dynamic enhancement process. This cycle effectively utilizes feedback to optimize algorithms and methods. It allows the model to learn from past mistakes and proactively make improvements, creating an iterative learning process. These adaptive and self-improvement capabilities are crucial for effectively addressing the complexities and variabilities of renewable energy forecasting. The main findings of the study highlight the ARIREF model’s theoretical potential to facilitate the integration of renewable energies into broader energy systems, offering a crucial contribution to global sustainability efforts. As the model is still in the conceptual stage, this study emphasizes the need for further research. Such research is necessary to validate and refine ARIREF theoretical constructs, ensuring its applicability and impact on sustainable energy supply. The study reveals the necessity for innovative and adaptive solutions in the domain of renewable energy forecasting to overcome current methodological limitations and meet the increasing demands for precise and reliable energy source predictions.23rd International Scientific Conference Engineering for Rural Development Proceedings00The study reveals the necessity for innovative and adaptive solutions in the domain of renewable energy forecasting to overcome current methodological limitations and meet the increasing demands for precise and reliable energy source predictions.23rd International Scientific Conference Engineering for Rural Development Proceedings["Girts Veigners", "A. Gali\u0146\u0161"]2024-05-22T00:00:00 0051377621733c985b145a603d9ebb3af192a275Background: The rapid advancement of artificial intelligence (AI) is poised to revolutionize healthcare delivery systems profoundly. With its capacity to enhance diagnostics, treatment, and patient care, understanding AI's role and integration in healthcare is crucial for medical professionals and students. Objective: This study aims to assess the familiarity, knowledge, and comprehension of AI among medical students and physicians, identifying both challenges and opportunities associated with its use in medicine. Methods: A structured questionnaire, adapted from established scales, was used to collect data from students and physicians at a public sector medical university. The study employed simple random sampling to ensure a representative sample, with a focus on collecting comprehensive demographic and AI-related knowledge data. Results: Of the 600 participants surveyed, 70% reported basic knowledge of AI, yet only 28% were aware of its specific applications in healthcare. Interestingly, 85% of respondents acknowledged the potential of AI to significantly enhance healthcare delivery and research. Conclusion: While there is a basic awareness of AI among medical professionals and students, there is a notable gap in understanding its specific applications in healthcare. The study highlights the need for mandatory training programs that enhance AI awareness and application in medical settings.Journal of Health and Rehabilitation Research00There is a basic awareness of AI among medical professionals and students, but there is a notable gap in understanding its specific applications in healthcare, highlighting the need for mandatory training programs that enhance AI awareness and application in medical settings.Journal of Health and Rehabilitation Research["Shazma Tahseen", "Husan Bano Channar", "Urooj Bhatti", "T. A. Laghari", "Sana Areej", "Shah Muhammad Kamran"]2024-05-22T00:00:00 70edc0679f8f3149554d160a7d1075dbcdd50402Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.PLoS ONE520PLOS ONE["Jun Shu", "Xinyu Xia", "Suyue Han", "Zuli He", "Ke Pan", "Bin Liu"]2024-05-22T00:00:00 31d1d049ceb3d3f3dc3a442401bd9b45a236b981Artificial intelligence (AI) telephone is reliable for the follow‐up and management of hypertensives. It takes less time and is equivalent to manual follow‐up to a high degree. We conducted a reliability study to evaluate the efficiency of AI telephone follow‐up in the management of hypertension. During May 18 and June 30, 2020, 350 hypertensives managed by the Pengpu Community Health Service Center in Shanghai were recruited for follow‐up, once by AI and once by a human. The second follow‐up was conducted within 3–7 days (mean 5.5 days). The mean length time of two calls were compared by paired t‐test, and Cohen's Kappa coefficient was used to evaluate the reliability of the results between the two follow‐up visits. The mean length time of AI calls was shorter (4.15 min) than that of manual calls (5.24 min, P < .001). The answers related to the symptoms showed moderate to substantial consistency (κ:.465–.624, P < .001), and those related to the complications showed fair consistency (κ:.349, P < .001). In terms of lifestyle, the answer related to smoking showed a very high consistency (κ:.915, P < .001), while those addressing salt consumption, alcohol consumption, and exercise showed moderate to substantial consistency (κ:.402–.645, P < .001). There was moderate consistency in regular usage of medication (κ:.484, P < .001).The Journal of Clinical Hypertension300The Journal of Clinical Hypertension["Siyuan Wang", "Yan Shi", "Mengyun Sui", "Jing Shen", "Chen Chen", "Lin Zhang", "Xin Zhang", "Dongsheng Ren", "Yuheng Wang", "Qinping Yang", "Junling Gao", "Minna Cheng"]2024-05-22T00:00:00 1161538017c751e08526b6219fa5be4d98eaf108The research aims to explain socio-economic factors affecting student success using interpretable artificial intelligence models and to promote the use of this technology in the development of educational policies. Initially, existing studies on factors determining student success have been examined. The dataset includes socio-economic and personal variables such as parental education, family economic status, student gender, and study duration. Using interpretable artificial intelligence models like InterpretML, analyses have been conducted on this dataset, and the results obtained from examining factors influencing student success have been evaluated. This study aims to contribute to shaping educational policies more effectively through the use of artificial intelligence.Engineering Science Letter60Engineering Science Letter["Cem \u00d6zkurt"]2024-05-22T00:00:00 0f4984067f9daefe59e17a1bb3463c0740aa1091Artificial Intelligence (AI) and Machine Learning (ML) models, while powerful, are not immune to security threats. These models, often seen as mere data files, are executable code, making them susceptible to attacks. Serialization formats like .pickle, .HDF5, .joblib, .ONNX etc. commonly used for model storage, can inadvertently allow arbitrary code execution, a vulnerability actively exploited by malicious actors. Furthermore, the execution environment for these models, such as PyTorch and TensorFlow, lacks robust sandboxing, enabling the creation of computational graphs that can perform I/O operations, interact with files, communicate over networks, and even spawn additional processes, underscoring the importance of ensuring the safety of the code executed within these frameworks. The emergence of Software Development Kits (SDKs) like ClearML, designed for tracking experiments and managing model versions, adds another layer of complexity and risk. Both open-source and enterprise versions of these SDKs have vulnerabilities that are just beginning to surface, posing additional challenges to the security of AI/ML systems. In this paper, we delve into these security challenges, exploring attacks, vulnerabilities, and potential mitigation strategies to safeguard AI and ML deployments.System Configuration Management280This paper explores attacks, vulnerabilities, and potential mitigation strategies to safeguard AI and ML deployments, exploring attacks, vulnerabilities, and potential mitigation strategies to safeguard AI and ML deployments.2024 XXVII International Conference on Soft Computing and Measurements (SCM)["Natalie M. Grigorieva"]2024-05-22T00:00:00 ba9203eec24c1c0dbaa8a6bc6a56364468f49e55The article considers the development of a narrative approach to teaching artificial intelligence in a departmental university based on the formation of simulator systems that allow implementing integrated approaches to pattern recognition on X-ray images obtained using inspection and inspection complexes. The decisive rule for evaluating learning outcomes in the context of the developed model is the possibility of comparing the results with the results of pattern recognition obtained using the author’s method of automatic object recognition based on a neural network approach.System Configuration Management1602024 XXVII International Conference on Soft Computing and Measurements (SCM)["Petr N. Afonin", "I. P. Aleshin", "Technopark \u00abQuantorium\u00bb", "E. I. Antonova", "A. I. Krasnova"]2024-05-22T00:00:00 6d26a6b6f7f2bdf690a6961df12256053ab0915dAbstract Remote work, by its very nature, is characterised by the performance of the duties of the employment relationship, in whole or in part, at a place chosen by the employee, at a time agreed upon with the employer. Despite the fact that the employee performs his/her work outside the employer’s place of business, he/she remains under the employer’s control. The issues under consideration here are the scope of this control and the manner in which it is carried out. In my deliberations, I focus on control performed with the use of algorithmic technologies, in particular artificial intelligence, for which a Regulation of the European Parliament and of the Council Laying Down Harmonised Provisions on Artificial Intelligence (Artificial Intelligence Act) was adopted on 23 June 2023.Białostockie Studia Prawnicze60In my deliberations, I focus on control performed with the use of algorithmic technologies, in particular artificial intelligence, for which a Regulation of the European Parliament and of the Council Laying Down Harmonised Provisions on Artificial Intelligence was adopted on 23 June 2023.Białostockie Studia Prawnicze["Iwona Sierocka"]2024-05-22T00:00:00 f3d75e81c199481a2d74805f03cd198b1e728e85Current Opinion in Biotechnology674In the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society.Current opinion in biotechnology["Attia Iram", "Yueming Dong", "Codruta Ignea"]2024-05-22T00:00:00 eafeb0bb961b05243002954f1ff19daeca98fd0bRadiography434Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist, which underscores the importance of understanding these challenges for the responsible integration of AI systems.Radiography["M. Pedersen", "M. Kusk", "S. Lysdahlgaard", "H. Mork-Knudsen", "C. Malamateniou", "J. Jensen"]2024-05-22T00:00:00 5aa31b01cf2671bab3f4e3ad8d4798e091f4ade1International journal of information technology105International Journal of Information Technology["Sabu Sebastian", "T. V. Ramakrishnan", "K. K. Gireesan", "S. J. Sangeeth"]2024-05-22T00:00:00 adde3e493e43b53e058ffd956529dcd2342ecd0cJournal of Global Entrepreneurship Research221Journal of Global Entrepreneurship Research["Daniya Siddiqui", "Uzma Mumtaz", "Naseeb Ahmad"]2024-05-22T00:00:00 1fd7b71a567607212adb2fd79e4f51bbb4fbcaf4Journal of the Knowledge Economy591Journal of the Knowledge Economy["Z. Ali", "Mahreen Zain", "Raza Hasan", "Hussain Al Salman", "B. Alkhamees", "Faisal Abdulaziz Almisned"]2024-05-22T00:00:00 af98e84603f892ba3fe005fd37f53749f00c5c1eInternational Journal of Human-Computer Interaction431International Journal of Human–Computer Interaction["Pelin Derinalp", "Melike Ozyurt"]2024-05-22T00:00:00 786de090384cd1f884650e769c3cecf6ba9fdcb1This paper explores the evolution of industrial automation and AI's application in manufacturing, focusing on machine tool automation. It delves into its definition, characteristics, development trends, impact on production efficiency and quality, as well as existing challenges. AI applications in machine tool automation such as predictive maintenance, adaptive control, and intelligent optimization are discussed in detail, showcasing how AI enhances automation through examples. The study also touches upon the new production model combining AI with automation, exploring its implications for businesses and industry growth. In future outlooks, current study issues are highlighted alongside predictions for machine tool automation and AI's future, including potential challenges and solutions. Suggestions for policy makers and business decision makers are offered. Overall, this research underscores the significance of the new production model combining machine tool automation and AI in enhancing efficiency and industry development, despite existing challenges. Research limitations are acknowledged and future directions suggested.Conference on Machine Learning and Computer Application90Overall, this research underscores the significance of the new production model combining machine tool automation and AI in enhancing efficiency and industry development, despite existing challenges.{"pages": "131762T - 131762T-8", "volume": "13176"}["Guodong Wang", "Yangcheng Zhang", "Jian Yang", "Yichen Zang", "Jianping Xu"]2024-05-22T00:00:00 0c207754382757da40702531a124d585fb78b0c9Critical Internationalization Studies Review00Critical Internationalization Studies Review["R. Mitic", "Takeshi Yanagiura", "Yukikazu Hidaka"]2024-05-22T00:00:00 013bb66366fec394ac83c5439215122d0ac40f04AJR. American journal of roentgenology30AJR. American journal of roentgenology["Angela Udongwo", "Farouk Dako"]2024-05-22T00:00:00 7ff0fb864cc3146540600c796a80406a5015c733Science and Engineering Ethics00Science and Engineering Ethics["Tahereh Saheb", "T. Saheb"]2024-05-22T00:00:00 d585f47730cfccbeaf461d22f8984dbef10dfa28Current Emergency and Hospital Medicine Reports240Current Emergency and Hospital Medicine Reports["Riley Wistrom", "Luda Khait", "Grant Nelson"]2024-05-22T00:00:00 0f85d88060964f22a25c0824a84b819a2bd68a17Indian Journal of Surgery80Indian Journal of Surgery["Riya Agrawal", "Saquib Hossain", "Hitesh Bisht", "Raviteja Sista", "P. P. Chakrabarti", "Debdoot Sheet", "Utpal De"]2024-05-22T00:00:00 3bb86f58a13f52d712c7e0f250b45f410ad91f1dArtificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capabilities in many ways, how can we ensure that the sociotechnical system as a whole—comprising a complex web of hundreds of human–machine interactions—is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Integrating these diverse perspectives and methods is crucial at this juncture. To truly advance our understanding of this important and rapidly evolving area, we need frameworks to facilitate research that bridges disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. To illustrate the approach we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, which articulates the critical processes underlying the emergence and functioning of collective intelligence in human–AI collaborations.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This paper describes recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, which articulates the critical processes underlying the emergence and functioning of collective intelligence in human–AI collaborations.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Sohana Akter"]2024-05-22T00:00:00 3d8c3530538ccb62f78049e5994e25b1d8473f16The profound impacts of artificial intelligence (AI) will continue to evolve over the next several decades, and many of these impacts will emerge through marketing-related AI applications. Therefore, marketers, public policymakers, firms, researchers, and individual consumers must recognize and understand the benefits that AI offers, as well as the perils that it presents, both now and in the future. A literature review surfaced three themes – that AI will augment and (potentially) replace human intelligence, that AI will evolve into an empathetic and trusted companion, and that AI will create novel tensions. Next, this article outlines three stages of AI development, from an early stage with much promise, to a stage with many benefits, to a stage wherein AI-related tensions emerge. Finally, this article outlines three grand challenges: (1) preserving and growing human capability, (2) protecting societal belonging and human connection, and (3) ensuring equitable sharing of AI benefits. Addressing such challenges, along with related concerns (e.g., privacy, ethics), can enable society to reap the benefits of AI fruitfully and in an equitable manner that truly improves the quality of life.Journal of Macromarketing279This article outlines three grand challenges of AI development, from an early stage with much promise, to a stage with many benefits, to a stage wherein AI-related tensions emerge.Journal of Macromarketing["Dhruv Grewal", "Abhijit Guha", "Marc Becker"]2024-05-22T00:00:00 87d67e76844b34d60c1d6907068a25630f599b33We report the discovery of cometary activity from minor planet 2011 UG104, which we classify as a Jupiter Family Comet (JFC). This discovery was aided by our Artificial Intelligence (AI) classification system: TailNet. JFC's, short-period comets with eccentric Jupiter-crossing orbits, originate from the Kuiper Belt and thus give us unique insight into the composition and distribution of volatiles in the outer solar system, past and present. Our AI assistant TailNet first classified 2011 UG104 as active, which was affirmed by Citizen Scientists on our NASA Partner Program Active Asteroids. Through further archival image searches our science team found evidence of activity on 2011 UG104 on three separate observations from 2021 February to 2021 April (81.°8 < f < 95.°0). Research Notes of the AAS42The discovery of cometary activity from minor planet 2011 UG104 is reported, which is classified as a Jupiter Family Comet (JFC), which is aided by the Artificial Intelligence (AI) classification system: TailNet.Research Notes of the AAS["Jarod A. DeSpain", "C. O. Chandler", "Nima Sedaghat", "W. J. Oldroyd", "C. Trujillo", "W. A. Burris", "Henry H. Hsieh", "J. Kueny", "Kennedy A. Farrell", "M. Magbanua", "S. Sheppard", "Michele T. Mazzucato", "Milton K. D. Bosch", "Tiffany Shaw-Diaz", "V. Gonano", "Al Lamperti", "Jos\u00e9 A. da Silva Campos", "Brian L. Goodwin", "I. Terentev", "Charles J. A. Dukes"]2024-05-22T00:00:00 a67047082faeecd7a4e4c8a489347a670ed440caArtificial Intelligence, machine learning (AI/ML) has allowed exploring solutions for a variety of environmental and climate questions ranging from natural disasters, greenhouse gas emission, monitoring biodiversity, agriculture, to weather and climate modeling, enabling progress towards climate change mitigation. However, the intersection of AI/ML and environment is not always positive. The recent surge of interest in ML, made possible by processing very large volumes of data, fueled by access to massive compute power, has sparked a trend towards large-scale adoption of AI/ML. This interest places tremendous pressure on natural resources, that are often overlooked and under-reported. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle for informing policymakers, stakeholders to adequately implement standards and policies and track the policy outcome over time. For these policies to be effective, AI's environmental impact needs to be monitored in a spatially-disaggregated, timely manner across the globe at the key activity sites. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations. We present a case study around Northern Virginia, United States that hosts a growing number of datacenters and observe changes in multiple satellite-based environmental metrics. We then discuss the steps to expand this methodology for comprehensive assessment of AI's environmental impact across the planet. We also identify data gaps and formulate recommendations for improving the understanding and monitoring AI-induced changes to the environment and climate.arXiv.org451This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations and discusses the steps to expand this methodology for comprehensive assessment of AI's environmental impact across the planet.ArXiv["Srija Chakraborty"]2024-05-22T00:00:00 a942f198730375f6fbd38c83a3e6cbe248026682The integration of AI(Artificial Intelligence) has become a revolution on how we interact with technology. AI assistant is one of the most impactful innovations, which offer support and streamline tasks for users. Imagine having a digital friend who knows you really well and helps you out with whatever you need. That's what Personalized Pinnacle AI Assistant is all about. The special AI assistant called Personalized Pinnacle is not like other AI assistants that give the same answers to everyone. Instead, Personalized Pinnacle is smart enough to give each person a different experience based on what they need. So, it's like having a helper that understands you personally. We're using advanced AI technologies to make Personalized Pinnacle really clever so that it can learn how you use it, it can give you better advice, and help you more effectively over time. Through adaptive learning algorithms, Personalized Pinnacle refines its recommendations over time, adapting to changes in user’s behavior and preferences. Pinnacle AI has something similar to Siri for iOS. Pinnacle AI connects to the World Wide Web to give appropriate result for user questions. The main agenda to develop this AI assistant is to make people smart and give instant and computed results. The well- implemented pinnacle AI assistant can improve efficiency by doing routine tasks, managing schedules, and providing instant access to information. Enable Pinnacle AI to assist users in sending and receiving emails, making email management more efficient and streamlined. This process ensures that Personalized Pinnacle remains responsive to evolving user needs, delivering increasingly personalized and relevant assistance. One of the biggest fears regarding this technology is privacy concerns. But Personalized Pinnacle keeps all your information safe and secure, so you can trust it with your secrets. By combining advanced AI technologies with a user- centric approach, Personalized Pinnacle represents the next frontier in AI assistant evolution.International Journal of Innovative Science and Research Technology281By combining advanced AI technologies with a user- centric approach, Personalized Pinnacle represents the next frontier in AI assistant evolution.International Journal of Innovative Science and Research Technology (IJISRT)["Ankit Basavaraj Halasagi", "Vandana Kumar Swamy", "Ravooru .Arpitha", "Niranjanamurthy", "Saurabh Jayaswal"]2024-05-22T00:00:00 5c2a1129d13bcb5172ba4c5f9614554e368ea259The "AI YouTube Video Summary using NLP" project introduces an innovative solution to the burgeoning challenge of digesting vast amounts of video content on platforms like YouTube. With the exponential growth of online video, users often face time constraints and information overload, hindering their ability to extract valuable insights efficiently. Our project addresses this issue by harnessing the capabilities of Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically generate concise summaries of YouTube videos. Through a seamless integration with the MERN stack, our system enables users to input video URLs and receive summaries in three distinct forms: short, long, and key insights. By automating the process of transcript extraction, linguistic analysis, and summarization, our system streamlines content consumption, offering users a time-saving and effective method for accessing essential information. By leveraging machine learning algorithms and linguistic analysis techniques, our system accurately identifies and distills key themes, concepts, and insights embedded within the video content. This empowers users to gain comprehensive understanding without the need for exhaustive viewing, thereby enhancing their browsing experience and knowledge acquisition. In essence, the "AI YouTube Video Summary using NLP" project represents a significant advancement in content consumption methodologies, offering a practical solution to the challenges posed by the proliferation of video content online. Through our innovative approach, we aim to revolutionize the way users engage with YouTube videos, facilitating efficient information extraction and empowering them to make the most of their online viewing experience. Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Text Summarization, Multimedia Content Analysis, Automatic Summarization.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The "AI YouTube Video Summary using NLP" project represents a significant advancement in content consumption methodologies, offering a practical solution to the challenges posed by the proliferation of video content online.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["R.Dinesh Kumar,"]2024-05-22T00:00:00 4681e595231e4df3a33ba0223aee0ddff89f01eeThis study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) and AI-powered chatbots. These technologies, while capable of manipulating human decisions and exploiting cognitive vulnerabilities, also hold the key to unlocking unprecedented opportunities for innovation and progress. Our research underscores the need for robust, ethical AI development and deployment frameworks, advocating a balance between technological advancement and societal values. We emphasize the importance of collaboration among researchers, developers, policymakers, and end users to steer AI development toward maximizing benefits while minimizing potential harms. This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices. We call for updated legal and regulatory frameworks to keep pace with AI advancements and ensure their alignment with ethical principles and societal values. By fostering open dialog, sharing knowledge, and prioritizing ethical considerations, we can harness AI’s transformative potential to drive human advancement while managing its inherent risks and challenges.Inf.014This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices.Inf.["Steven M. Williamson", "Victor R. Prybutok"]2024-05-23T00:00:00 446368fe957dbc38da6a406d4f455df6bc259d56An expanding body of information systems research is adopting a design perspective on artificial intelligence (AI), wherein researchers prescribe solutions to problems using AI approaches rather than describing or explaining AI-related phenomena being studied. In this editorial, we address some of the challenges faced in publishing design research related to AI and articulate viable pathways for publishing such work. More specifically, we highlight six major impediments, use the explosion in the state of the art for large language models to underscore these impediments, propose some pathways for overcoming the impediments, and use several example articles to illustrate how the pathways can be followed for different types of AI-related design artifacts. Funding: A. Abbasi was funded by the National Science Foundation (NSF) [Grants 2240347 and IIS-2039915] and a Kemper Faculty Award. J. Parsons was funded by the Natural Sciences and Engineering Council of Canada (NSERC) [Grant RGPIN-2020-04916].Information systems research638Some of the challenges faced in publishing design research related to AI and viable pathways for publishing such work are addressed and some pathways for overcoming impediments are proposed.Inf. Syst. Res.["Ahmed Abbasi", "Jeffrey Parsons", "Gautam Pant", "Olivia R. Liu Sheng", "Suprateek Sarker"]2024-05-23T00:00:00 4bc0e495bf9323e9f1e0f53235a1684641e1621dAbstract Aims Artificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (SmartAssist), enabling real‐time acquisition and display of data related to both pump performance and the patient's haemodynamic status. These data emerge as an ‘ideal’ source for data‐driven AI applications to predict the clinical course of an ongoing therapeutic protocol. Yet, no evidence of effective application of AI tools in the setting of Impella support is available. On this background, we aimed at identifying possible future applications of AI‐based tools in the setting of temporary MCS with an Impella device. Methods We explored the state of research and development at the intersection of AI and Impella support and derived future potential applications of AI in routine Impella clinical management. Results We identified different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support, including (i) early identification of the best suited pathway of care according to patients' conditions at presentation and intention to treat, (ii) prediction of therapy outcomes according to different possible therapeutic actions, (iii) optimization of device implantation procedures and evaluation of proper pump position over the whole course of support and (iv) prevention and/or rationale management of haemocompatibility‐related adverse events. For each of those areas, we discuss the potential advantages, challenges and implications of harnessing AI‐driven insights in the setting of MCS with an Impella device. Conclusions Temporary MCS with an Impella device has great potential to benefit from the integration of AI‐based tools. Such tools may indeed translate into groundbreaking innovation supporting clinical decision‐making and therapy regulation, in particular in complex scenarios such as the multidevice MCS strategy.ESC Heart Failure348Identifying possible future applications of AI‐based tools in the setting of temporary MCS with an Impella device and identifying different areas where the future implementation of AI tools may contribute to addressing important clinical challenges in the setting of Impella support is identified.ESC Heart Failure["Filippo Consolo", "Jacopo D\u2019Andria Ursoleo", "M. Pieri", "P. Nardelli", "L. Cianfanelli", "V. Pazzanese", "S. Ajello", "A. Scandroglio"]2024-05-23T00:00:00 d848255421ed272694b47ca5f77dc3a21c9227c6Simple Summary Risk assessment in early breast cancer is critical for clinical decisions, but defining risk categories poses a significant challenge. The integration of conventional histopathology and biomarkers with artificial intelligence (AI) techniques, including machine learning and deep learning, has the potential to offer more precise information. AI applications extend beyond detection to histological subtyping, grading, and molecular feature identification. The successful integration of AI into clinical practice requires collaboration between histopathologists, molecular pathologists, computational pathologists, and oncologists to optimize patient outcomes. Abstract Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.Cancers1393This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques.Cancers["M. Ivanova", "C. Pescia", "D. Trapani", "K. Venetis", "Chiara Frascarelli", "Eltjona Mane", "Giulia Cursano", "E. Sajjadi", "C. Scatena", "B. Cerbelli", "G. d'Amati", "F. M. Porta", "E. Guerini-Rocco", "C. Criscitiello", "G. Curigliano", "Nicola Fusco"]2024-05-23T00:00:00 bc49750d2d312e46aef7ad3aa98ff9000452b0acThe integration of Artificial Intelligence (AI) and Machine Learning (ML) in the legal domain has marked a transformative phase, enhancing operational efficiencies and decision-making processes. This paper explores the next frontier in the evolution of these technologies within legal practices, emphasizing future research directions and emerging trends. It investigates current applications and their impact on the legal field, such as predictive analytics for case outcomes, natural language processing for document analysis, and automation of routine legal tasks. The study also identifies major challenges that impede the adoption of AI and ML, including issues related to data privacy, regulatory compliance, and institutional resistance. Through analysis of various case studies, this paper offers insights into successful implementations and comparative assessments across different legal systems. Finally, it proposes future research opportunities that include cross-disciplinary approaches, enhancement of predictive models, and integration with other cutting-edge technologies such as Blockchain and the Internet of Things (IoT). The findings aim to provide a comprehensive guide for future initiatives and research that could further transform the legal landscapes.CONFERENCE PROCEEDING01This paper investigates current applications and their impact on the legal field, such as predictive analytics for case outcomes, natural language processing for document analysis, and automation of routine legal tasks, and identifies major challenges that impede the adoption of AI and ML.CONFERENCE PROCEEDING[]2024-05-23T00:00:00 1c6466386b30c6fe028436ccc601f34c3a5d97f6The advent of Artificial Intelligence (AI) has initiated a transformative wave across various sectors, fundamentally altering the way businesses operate, make decisions, and interact with customers. This research paper explores the expansive role of AI in revolutionizing industry standards and practices, focusing on its deployment in sectors such as healthcare, finance, automotive, and manufacturing. By integrating case studies and empirical data, the paper examines how AI-driven technologies like machine learning, natural language processing, and robotics are enhancing efficiency, accuracy, and productivity while also presenting new challenges and ethical considerations. The analysis highlights the dual impact of AI: its potential to drive innovation and growth, and its regulatory and societal implications. The objective is to provide a comprehensive overview of AI’s capabilities and its measurable effects on industry dynamics, offering insights into future trends and the evolving landscape of digital transformation.CONFERENCE PROCEEDING02The objective is to provide a comprehensive overview of AI’s capabilities and its measurable effects on industry dynamics, offering insights into future trends and the evolving landscape of digital transformation.CONFERENCE PROCEEDING[]2024-05-23T00:00:00 a7b2ad660bff1690c6c39eb560f1b391ab7287e4Assessment techniques need to evolve beyond traditional methods in light of the rapidly developing artificial intelligence (AI) tool technologies, such as Copilot, Bard, and ChatGPT. These AI‐powered Chatbot is designed to appear similar to human speech or text and present information conversationally, making them tenable options for student assessment support worldwide. Consequently, to take advantage of the weaknesses in the AI system and foster a creative attitude in their pupils, educators must reconsider their approach to evaluation. The study conducts a comparative experiment on two different assessment methods—the traditional questioning strategy (Experiment I) versus the alternative or modified strategy (Experiment II), to assess how well the AI tools perform in the assessment and how the new technique can deter students from engaging in academic dishonesty. According to the study in Experiment I, the AI‐Chatbot had a 100% positive response correlation, but in Experiment II, it had a shockingly low positive response correlation. Comparably, pupils who use AI‐Chatbot and those who do not have significant performance disparities (α = 0.05, p‐value < 0.001; 1.8331). Inferentially, AI‐Chatbot helped students a lot in Experiment I but did considerably less in Experiment II. In other words, Experiment II's questioning approach outperforms the AI tools' level of competence. The study comes to the conclusion that if AI is effectively harnessed, human natural intelligence will always be able to overcome the challenges posed by these powerful AI technologies.Future in Educational Research202The study comes to the conclusion that if AI is effectively harnessed, human natural intelligence will always be able to overcome the challenges posed by these powerful AI technologies.Future in Educational Research["O. Akintande"]2024-05-23T00:00:00 8e71fb3c3a3cdf7505d3076a836dcca838bc5917The way organizations operate is greatly influenced by effective decision-making in the dynamic corporate environment. This procedure has undergone a substantial transformation thanks to the use of cutting-edge technology, including artificial intelligence (AI), expert systems, and decision support systems. This research aims to investigate the interrelated domains of decision support systems, expert systems, corporate decisions, and the influence of artificial intelligence on corporate planning and management based on secondary research. Through modeling and data analysis, decision support systems improve organizational decision-making and provide managers with insightful information. Expert systems give advice and specialized knowledge, simulating human competency. Artificial intelligence has brought about a revolution in corporate governance, with a special focus on planning. AI systems assist organizations in foreseeing trends and reacting quickly to changing conditions by analyzing massive datasets, finding different patterns, and building predictive models. The analysis of AI's effects on business management and the economy must continue as its integration constantly grows.International Symposium on Applied Computational Intelligence and Informatics341This research aims to investigate the interrelated domains of decision support systems, expert systems, corporate decisions, and the influence of artificial intelligence on corporate planning and management based on secondary research.2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI)["M\u00e1t\u00e9 Prorok", "Istv\u00e1n Tak\u00e1cs"]2024-05-23T00:00:00 68cfacd919ea82046a0cc45ea5a9667e9f974c06Background: The integration of Artificial Intelligence (AI) into various aspects of daily life has sparked growing interest in understanding public attitudes toward this technology. Despite advancements in tools to assess these perceptions, there remains a need for culturally adapted instruments, particularly in specific contexts like that of Peruvian nurses. Objective: To evaluate the psychometric properties of the AIAS-4 in a sample of Peruvian nurses. Methods: An instrumental design was employed, recruiting 200 Peruvian nurses. The Attitude toward Artificial Intelligence in Spanish (AIAS-S), a cultural and linguistic adaptation of the AIAS-4, involved data analysis using descriptive statistics, confirmatory factor analysis (CFA), and invariance tests. Results: The Confirmatory Factor Analysis (CFA) confirmed a unidimensional factor structure with an excellent model fit (χ2 = 0.410, df = 1, p = 0.522, CFI = 1.00, TLI = 1.00, RMSEA = 0.00, SRMR = 0.00). The scale demonstrated high internal consistency (α = 0.94, ω = 0.91). Tests of invariance from configural to strict confirmed that the scale is stable across different demographic subgroups. Conclusions: The AIAS-S proved to be a psychometrically solid tool for assessing attitudes toward AI in the context of Peruvian nurses, providing evidence of validity, reliability, and gender invariance. This study highlights the importance of having culturally adapted instruments to explore attitudes toward emerging technologies in specific groups.Behavioral Science622The AIAS-S proved to be a psychometrically solid tool for assessing attitudes toward AI in the context of Peruvian nurses, providing evidence of validity, reliability, and gender invariance.Behavioral Sciences["Wilter C. Morales-Garc\u00eda", "Liset Z. Sairitupa-Sanchez", "Sandra B. Morales-Garc\u00eda", "Mardel Morales-Garc\u00eda"]2024-05-23T00:00:00 59712c8cde65284d7cc0de9122472e0e4f049d12This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.Advances in Anatomic Pathology581Advances in anatomic pathology["Emanuel Caranfil", "Kris Lami", "W. Uegami", "Junya Fukuoka"]2024-05-23T00:00:00 170b9fdf8296d4b275d873166adf24108c59837aIt is intriguing and challenging to learn a language by diving into the worlds of Virtual Reality (3-D environments, avatars, games) and Artificial Intelligence (chatbots, agents). What are the issues and benefits of these technological innovations? Taking readers on a journey through the brain, this book explains how VR and AI may foster and sustain connectivity between language faculties, the senses/emotions, working and long-term memory, and attention. With the speed of technological innovation increasing, cognitive demand as well as aspects of intrinsic motivation are analyzed, charted, and discussed, as these may become essential for future development of language learning experiences. This volume should be of interest to instructors, researchers, and students of languages and linguistics, cognitive psychology, and computer science.Bilingual Processing and Acquisition2131With the speed of technological innovation increasing, cognitive demand as well as aspects of intrinsic motivation are analyzed, charted, and discussed, as these may become essential for future development of language learning experiences.Bilingual Processing and Acquisition["Ulf Sch\u00fctze"]2024-05-23T00:00:00 f002bb8bcc534504d47ef8bf18664fb3dc013843Current Atherosclerosis Reports841This review evaluates how Artificial Intelligence enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data.Current atherosclerosis reports["Shyon Parsa", "Sulaiman Somani", "Ramzi Dudum", "Sneha S. Jain", "Fatima Rodriguez"]2024-05-23T00:00:00 15ccd51f75086016f8d203c4338db0ccabe47a77Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI’s significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI’s utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI’s potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI’s versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.Microorganisms01A comprehensive evaluation of AI’s utility in microbial diagnostics presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.Microorganisms["Ahmad Alsulimani", "Naseem Akhter", "Fatima Jameela", "R. Ashgar", "A. Jawed", "Mohammed Ahmed Hassani", "S. Dar"]2024-05-23T00:00:00 c7c7ff8e2c646ed4a3dcf61cdc0f7935b613ca92Nowadays, most systems use artificial intelligence algorithms to automate tasks and reduce the time required for execution. Moreover, it must estimate the bias risks that can be introduced within the system. Based on these considerations, quantitative measures and prioritization strategies can be established for those inadequate situations, choosing an appropriate method to overcome gender bias. In this study, the impact of gender bias on an annual salary risk score due to gender bias was analyzed to identify and reduce it as much as possible in machine learning algorithms and on text data provided to a virtual assistant. The study finds that gender bias can influence our decisions by illustrating hypotheses on how algorithms affect prioritization decisions and strengthen stereotypes by favoring men against women. Recommendations to lower gender bias can include training programs for poor people that face substantial barriers to accessing education; training programs for people with a low level of education or no access; access to all kinds of jobs for women; assurance of diversity and inclusiveness; and algorithms that are fair and trained with the definite goal of reducing gender bias.International Workshop on Document Analysis Systems301The study finds that gender bias can influence decisions by illustrating hypotheses on how algorithms affect prioritization decisions and strengthen stereotypes by favoring men against women.2024 International Conference on Development and Application Systems (DAS)["Marina Adriana Mercioni", "S. Holban"]2024-05-23T00:00:00 1053839aa69f81bf1f763de85aa220bd62abd64cThe Influence of Training, Motivation, and Competency on Employee Performance is a scientific article on literature studies within the scope of HRM. The purpose of this article is to build a hypothesis of influence between variables that will be used in further research. Research objects based on online libraries such as Google Scholar, Mendeley, and other academic online media. The research method uses a systematic literature review with artificial intelligence (AI) to obtain related research sourced from e-books and open access journals. The analysis uses qualitative descriptive analysis. The results of this article: 1) Training influences Employee Performance; 2) Motivation influences Employee Performance; and 3) Competency influences Employee Performance.JURNAL MANAJEMEN PENDIDIKAN DAN ILMU SOSIAL522The results of this article show that training influences Employee Performance; Motivation influences Employee Performance; and 3) Competency influences Employee Performance.JURNAL MANAJEMEN PENDIDIKAN DAN ILMU SOSIAL["Netaniel Giovanni", "Hapzi Ali"]2024-05-23T00:00:00 56002892c33ea083223aed71de01d321d212036cDespite the availability of vast amounts of data, legal data is often unstructured, making it difficult even for law practitioners to ingest and comprehend the same. It is important to organise the legal information in a way that is useful for practitioners and downstream automation tasks. The word ontology was used by Greek philosophers to discuss concepts of existence, being, becoming and reality. Today, scientists use this term to describe the relation between concepts, data, and entities. A great example for a working ontology was developed by Dhani and Bhatt. This ontology deals with Indian court cases on intellectual property rights (IPR) The future of legal ontologies is likely to be handled by computer experts and legal experts alike.arXiv.org47This ontology deals with Indian court cases on intellectual property rights (IPR) and aims to organise the legal information in a way that is useful for practitioners and downstream automation tasks.ArXiv["Aniket Deroy", "Naksatra Kumar Bailung", "Kripabandhu Ghosh", "Saptarshi Ghosh", "Abhijnan Chakraborty"]2024-05-23T00:00:00 4eebfc3e0744bd19fd86cdb7cabeeb5ec6be53c0The hot topics in training in Machine Learning is a crucial aspect that affects the credibility of the system in terms of performance and is employed for robust applications such as in healthcare systems. Machines or algorithms, in wide challengeable applications in security or vision or health care early predictions, learn from data. Nevertheless, in most cases, the extensive and unbalanced data and noise make it unreliable in prediction accuracy. Supervised machine learning is and was one of the aspects of providing artificial intelligence-based solutions. However, this is and was limited due to the difficulty of labeling big data and many crucial problems in weak relations and noise in data. Semi-supervised learning, for example, Multiview learning, could assist in solving these problems. In many published research, there are still problems in providing machine learning models that are unbiased and efficient in terms of robustness and resilience in data-driven systems. Multiclass classification still has problems in terms of clear definition in class classification, bias, imbalance and weak relations, making machine learning for multiclass classification insecure for classification or regression analytics. This causes limitations in applying such technology in medical applications and diagnosis prediction. In this lecture, I will outline these problems in our one-class classification project. These are related to providing more robust accuracy prediction with some uncertainty that can help us have more accurate classification and prediction. We have applied such findings in health care for heart sickness and seizure early prediction. We also have deep learning models, which also have challenges related to evidential deep learning and fairness relative to data. There are important issues in expanding research in evidential deep learning, in which uncertainty prediction of variational Auto encoders can provide decisions on evidential distribution, which in turn helps to provide a measure of uncertainty in decision. We currently have a research project titled “Healthcare Risk Prediction on Data Streams Employing Signal Transformation Network (OCSTN)”, which is supported by grant from Japan Science Promotion Society (JSPS). In this project, we have employed one-class classification deep neural network for health care prediction. In this lecture I will outline of these perspectives and discuss challenging trends.International Symposium on Applied Computational Intelligence and Informatics00This lecture will outline of issues in expanding research in evidential deep learning, in which uncertainty prediction of variational Auto encoders can provide decisions on evidential distribution, which in turn helps to provide a measure of uncertainty in decision.2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI)["Hamido Fujita"]2024-05-23T00:00:00 e8035591f56be14f579fd4dad147b5d984378cffPURPOSE OF REVIEW Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, the limitations of in vitro tests, and the associated risk to in vivo tests. Predictive models, including those using recent advances in artificial intelligence, may circumvent these drawbacks, leading to an appropriate classification of patients and improving their management in clinical settings. RECENT FINDINGS Scores and predictive models to assess drug allergy development, including patient risk stratification, are scarce and usually apply logistic regression analysis. Over recent years, different methods encompassed under the general umbrella of artificial intelligence, including machine and deep learning, and artificial neural networks, are emerging as powerful tools to provide reliable and optimal models for clinical diagnosis, prediction, and precision medicine in different types of drug allergy. SUMMARY This review provides general concepts and current evidence supporting the potential utility of predictive models and artificial intelligence branches in drug allergy diagnosis.Current Opinion in Allergy and Clinical Immunology520This review provides general concepts and current evidence supporting the potential utility of predictive models and artificial intelligence branches in drug allergy diagnosis.Current opinion in allergy and clinical immunology["Rafael N\u00fa\u00f1ez", "Inmaculada Do\u00f1a", "J. Cornejo\u2010Garc\u00eda"]2024-05-23T00:00:00 d29e68e199f1c90a6faeb1ad1c4e39424da84607Artificial intelligence (AI) is rapidly transforming the marketing landscape. This literature-based study explores the diverse applications of AI in marketing functions, drawing insights from academic journals, industry reports, and marketing publications. The study examines how AI personalizes marketing efforts through targeted advertising and content marketing, while also investigating its role in customer relationship management (CRM). It delves deeper into AI's influence on market research and marketing ROI (Return on Investment) optimization. By analyzing these applications, the study aims to provide a comprehensive understanding of how AI is shaping the future of marketing. Keywords: Artificial intelligence (AI), machine learning, customer segmentation, targeted advertising, content personalization, CRM, market research, marketing ROI, chatbots, recommendation engines, customer churn prediction.International Scientific Journal of Engineering and Management00The study examines how AI personalizes marketing efforts through targeted advertising and content marketing, while also investigating its role in customer relationship management (CRM), and delves deeper into AI's influence on market research and marketing ROI (Return on Investment) optimization.International Scientific Journal of Engineering and Management["Babar Mushtaq"]2024-05-23T00:00:00 b890e23c91745ea80647b6d92e1a5af4a928883aWith the development of the digital economy, there is a tendency to introduce digital information technologies, including automated systems, into the economic activities of the organization, which leads to changes in the legal aspects of the implementation of employer power. Artificial intelligence becomes an integral part of the technical means used as a means of exercising employer control, which raises the question of the possibility of delegating the authority to make certain decisions to artificial intelligence. No less important in the context of employer power is the use of neural networks in the performance of the labor function of employees.Labor law in Russia and abroad00Labor law in russia and abroad["Viktoria O. Borovchenkova"]2024-05-23T00:00:00 38d9d5c18be62f7a0b52e48b16d6c23fe22685c2The paper outlines the ways of solving the formed and foreseeable problems in the legal regulation of labour relations using artificial intelligence. It is concluded that artificial intelligence cannot have legal personality in labour relations. It is proposed to fix in labour legislation the presumption of employer's responsibility for the decisions made by artificial intelligence, as well as to oblige employers and software developers to certify the protocols of digital security of software operating on the basis of artificial intelligence technologies.Labor law in Russia and abroad00It is proposed to fix in labour legislation the presumption of employer's responsibility for the decisions made by artificial intelligence, as well as to oblige employers and software developers to certify the protocols of digital security of software operating on the basis of artificial intelligence technologies.Labor law in russia and abroad["Denis A. Novikov"]2024-05-23T00:00:00 7cf7d1b87f94c17d3a0ebd73bf4704de285cbb65Today, authentication has become an important issue in the digital world. Protecting personal data and sensitive information through online transactions requires a more secure authentication process. Since traditional password-based authentication methods are insufficient at these stages, more secure alternatives are sought. At this point, biometric authentication systems play an important role. Biometric features such as fingerprint, facial recognition, and voice recognition are used to verify people’s identities based on their unique physical characteristics. One of the methods used for biometric authentication is keyboard dynamics technology. Keyboard dynamics aims to verify people’s identities by analyzing their keyboard usage habits. Each leaves a unique signature in the way he presses the keys on the keyboard, the speed of the press, the duration of the press, etc., and these features can be used for personal authentication. Artificial intelligence plays an important role in analyzing and processing biometric data such as keystroke dynamics. With artificial intelligence algorithms, it can detect changes in users' keystroke dynamics and detect potential threats in advance. There are also some challenges in using keystroke dynamics and artificial intelligence. In particular, users' keystroke dynamics characteristics need to be securely stored, processed and continually improved to increase accuracy rates and reduce false results. In this study, research conducted using keystroke dynamics and artificial intelligence technologies together is discussed, and the importance and potential of these technologies in the field of digital security are emphasized.2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)170Research conducted using keystroke dynamics and artificial intelligence technologies together is discussed, and the importance and potential of these technologies in the field of digital security are emphasized.2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)["B\u00fc\u015fra Tural", "Zeynep \u00d6rpek", "Samet \u00d6zmen"]2024-05-23T00:00:00 afe4595ca9d68f5c2e5a606ed61aaaf895fdbf97The alliance between artificial intelligence and human resource professionals is a revolutionary junction that presents both challenging problems and tremendous potential. The applications and challenges of combining human and artificial intelligence in HR are examined in this article. AI is finding its way into HR departments more and more, with uses ranging from improving employee training and hiring procedures to using predictive analytics for workforce planning. The goal of this cooperative synergy is to enhance HR practices' effectiveness, impartiality, and decision-making. However, there are significant challenges to overcome, like Threat to Human Employment, Need for Qualified Applicants, Unethical and Inappropriate Application of Shared Data, Enhancing Employee Turnover between human and AI entities. The paper explores the ever-changing field of HR- related human-AI collaboration, offering insights into emerging applications and challenges that organizations must overcome to realize the full benefits of this revolutionary alliance.2024 International Conference on Computational Intelligence and Computing Applications (ICCICA)360The paper explores the ever-changing field of HR- related human-AI collaboration, offering insights into emerging applications and challenges that organizations must overcome to realize the full benefits of this revolutionary alliance.2024 International Conference on Computational Intelligence and Computing Applications (ICCICA)["Neema Gupta", "Mukesh Joshi", "A. Agarwal", "Muklesh Kumar Tiwari"]2024-05-23T00:00:00 96cc1c970f33dae04382bd4415a1c031b36f8dc3This research paper provides a comprehensive analysis of the evolving landscape of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), illustrating their development, applications, and interconnections. The study begins with a historical overview of AI, tracing its conceptual and technical advancements. It then delves into the specific subset of ML, discussing various algorithms and models that enable computers to learn from and make decisions based on data. The focus shifts to DL, a technique that mimics the human brain with artificial neural networks, which has revolutionized fields such as image and speech recognition. The paper further explores the practical applications of these technologies in various sectors including healthcare, automotive, finance, and customer service, demonstrating how they are reshaping industries by enhancing efficiency, accuracy, and economic value. Ethical considerations, such as privacy, bias, and job displacement, are also addressed, highlighting the challenges and responsibilities faced by developers and users of these technologies.CONFERENCE PROCEEDING00The paper further explores the practical applications of these technologies in various sectors including healthcare, automotive, finance, and customer service, demonstrating how they are reshaping industries by enhancing efficiency, accuracy, and economic value.CONFERENCE PROCEEDING["Anup Dubey", "Uma Yadav", "Mohit Kumar", "Javalkar Dinesh Kumar"]2024-05-23T00:00:00 bc8ee99e5cc82c1ff336e13e842818ddf9ab5862The article proposes energy-saving measures in sports stadiums, emphasizing the Košice Football Arena (KFA) as a case study. In recent years, increasing energy consumption and prices have necessitated sustainable solutions, particularly in buildings subject to stricter energy efficiency regulations. Energy independence, achieved through passive design and active systems like efficient lighting and renewable sources, is crucial. Smart management systems, bolstered by artificial intelligence (AI), offer enhanced efficiency. Sports stadiums, with their unique energy demands, require attention for energy savings and renewable integration, driven by legal mandates and government-supported renovations. AI facilitates better optimization, forecasting, and management of energy systems, as seen in the articles referenced. For instance, AI-driven algorithms outperform generic algorithms in energy system optimization. Moreover, AI and interactive virtual simulations support ecological building transformations, aiding in understanding and optimizing energy systems. The proposed measures for the KFA encompass energy sustainability, management, analysis of electricity consumption, and the application of AI. These measures aim to optimize energy consumption, enhance energy efficiency, and reduce operating costs. The deployment of smart meters and AI-driven systems enables real-time monitoring and adaptive control, contributing to overall energy savings.International Symposium on Applied Computational Intelligence and Informatics1502024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI)["R. \u0160tefko", "Marek Bobcek"]2024-05-23T00:00:00 004b287d7a7c0a6741a4d42919632de11491187eArtificial intelligence (AI) is gaining attention in multidisciplinary area such as business, management, decision sciences, and healthcare sector. Nowadays AI is receiving more attention in health care sectors including diagnosis, patient data, and decision-making regarding treatment. This review article is based on the contribution of AI in healthcare and mainly focuses on AI in diagnosis, genomics, drug discovery and patient care. Along with the role of AI in health maintenance, it explains the various challenges faced in using AI in healthcare and disadvantages associate with its use.2024 International Conference on Computational Intelligence and Computing Applications (ICCICA)560Along with the role of AI in health maintenance, the various challenges faced in using AI in healthcare and disadvantages associate with its use are explained.2024 International Conference on Computational Intelligence and Computing Applications (ICCICA)["Suman Khurana", "Ajay Malik", "Sonia Narwal", "Gaurav Agarwal", "Kavita Sangwan"]2024-05-23T00:00:00 a3cbe9f9e64eb6a9e13a8036ec67c0ab5fda6c07This narrative review explores the current landscape and future potential of utilizing Artificial Intelligence (AI) in the development and implementation of personalized risk reduction plans for individuals at risk of HIV infection. Traditional HIV prevention strategies often adopt a generic approach, overlooking the diverse and dynamic factors contributing to an individual's risk profile. In contrast, this review synthesizes existing literature to highlight recent advancements in AI applications, focusing on their role in tailoring HIV risk reduction interventions to the unique characteristics and circumstances of each individual. The review encompasses studies employing machine learning algorithms, predictive modeling, and data analytics to analyze and interpret large datasets related to HIV epidemiology, behavioral patterns, and socio-economic determinants. By providing an overview of these AI-driven methodologies, the review aims to showcase the potential for personalized risk assessment and intervention planning. Furthermore, it examines the integration of AI into mobile health applications, wearable devices, and telehealth platforms, facilitating real-time monitoring, feedback, and support for individuals seeking personalized risk reduction strategies.KIU Journal of Health Sciences00This narrative review explores the current landscape and future potential of utilizing Artificial Intelligence in the development and implementation of personalized risk reduction plans for individuals at risk of HIV infection and examines the integration of AI into mobile health applications, wearable devices, and telehealth platforms.KIU Journal of Health Sciences["Ezeanya C.U", "Ukaigwe J.A.", "Nwoyibe O.I", "Obeagu E.I."]2024-05-23T00:00:00 e1c95f8bf72c35dae7f079f98bf78721a7867455Objective: In this article, the aim is to explore in detail how AI is specifically applied to technical and technological education within universities.   Methods: This study on the application of artificial intelligence (AI) in technical and technological university education combines a review of academic literature with the analysis of relevant case studies. The methodological approach used to conduct this research, as well as the main findings and limitations of the study, are detailed below. Data were collected from various sources, including academic documents and databases such as PubMed and Google Scholar. After a careful selection of relevant articles, a qualitative analysis was conducted to identify patterns and trends in the application of AI. The results reveal a growing use of AI in personalized learning and automated assessment, but also highlight ethical and technical challenges. Study limitations include potential biases in data selection and variability in the availability of information.   Result: To study the impact of virtual reality on the teaching of social sciences in basic education, an analysis was conducted using a documentary matrix. Around fifteen scientific articles were selected from recognized academic databases. The aim was to explore various aspects of virtual reality application in education. Each article was reviewed to extract data on study objectives, methodologies, results, and conclusions. This systematic and careful review ensured the quality and reliability of the information. The literature review matrix facilitated a structured understanding of the benefits and challenges of integrating virtual reality into social studies teaching in basic education.Revista de Gestão Social e Ambiental180A review of academic literature with the analysis of relevant case studies revealed a growing use of AI in personalized learning and automated assessment, but also highlight ethical and technical challenges.Revista de Gestão Social e Ambiental["Mar\u00eda Luisa Pincay Cede\u00f1o", "Mariela Nu\u00f1ez Figueroa", "Paul Marcelo Tacle Humanante", "Wildo Sucasaire Monroy"]2024-05-23T00:00:00 1ce1fb67adc0982749df5247dc225310cc459c93The subject of artificial intelligence is nowadays very topical and popular in business circles. However, the published scholarly literature reveals a literature gap and no relevant scientific findings regarding the application of artificial intelligence in economics and business-related literature. The overarching objective of this paper is to survey, identify and qualitatively analyse all the published studies in the area of artificial intelligence in economics and business-related research with the application of the leading nonparametric DEA (Data Envelopment Analysis) methodology thus far, as well as to reveal their findings, map some hotspots and trends as well as to conclude regarding the state of the art in the scholarly literature in this research area. Moreover, another goal is to provide a theoretical background to the concept of artificial intelligence (AI) as well as to the DEA methodology. Systematic literature reviews (SLR) have been accepted as an expedient for the presentation of the state of the art in a scientific area to the academic community. Therefore, a systematic literature review combining both electronic and manual searches to identify relevant studies using keywords the "DATA ENVELOPMENT ANALYSIS" and "ARTIFICIAL INTELLIGENCE" was suitable for this study. The methodology resulted in the identification of 10 relevant published papers in economics and business-related research employing DEA methodology in AI. The findings reveal specific areas of application of AI in economics and business, and suggestions and guidelines for future work are provided. The scientific and practical contributions of this study are twofold. First, the state of AI with the application of DEA in economics and business-related research is presented, and second, it provides crucial information regarding the application of the DEA methodology in AI in economics and business-related research, which is valuable to regulatory bodies, governments and potential inventors and software engineers.2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)320The state of AI with the application of DEA in economics and business-related research is presented and crucial information regarding the application of the DEA methodology in AI in economics and business-related research is provided, which is valuable to regulatory bodies, governments and potential inventors and software engineers.2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)["Katerina Fotova \u010cikovi\u0107", "Ivana Martin\u010devi\u0107", "J. Lozi\u0107"]2024-05-23T00:00:00 52849822ded258e8decc2d0c4fa715a2ee6ce480International Journal of Human-Computer Interaction244International Journal of Human–Computer Interaction["Diana S. Hernandez Manzo", "Yang Jiang", "Eyad Elyan", "John Isaacs"]2024-05-23T00:00:00 daeaa8dd07c2e1ba25eb663c671ba0dc684b302eAnnals of Data Science201Annals of Data Science["Jun Li", "YiFei Hai", "SongJia Yin"]2024-05-23T00:00:00 55e114c15469cba9805cb48403c278914dbc2d86Journal of Applied Psychology00Journal of Applied Psychology[]2024-05-23T00:00:00 8d7d03221df329857c4e5fc86d2b1b4d0c0c9328J. Assoc. Inf. Sci. Technol.40J. Assoc. Inf. Sci. Technol.["Andrew Cox"]2024-05-23T00:00:00 cf9cd4b2cebf1fe3baa9148d57a5d7e2813c123eiRADIOLOGY90iRADIOLOGY["Jinping Tao", "Xiangxing Kong", "Zhi Yang", "Hua Zhu"]2024-05-23T00:00:00 f5c7f67ea911e41567647853c7e0d67399f34a0fTheology and Science00Theology and Science["John P. Slattery", "Brian Green"]2024-05-23T00:00:00 0bce7ebee5cb370389f8efe49ba8554cd8084737Indian Journal of Commerce &amp; Management Studies00Indian Journal of Commerce &amp; Management Studies["Mohammed Hibban", "Dr. Abhishek"]2024-05-23T00:00:00 8824dd3a1c711deff03be96acab0b02b6b83bbacThe computerization of public administration tasks is a reality. In contrast, the intelligence of public administration is shrouded in myths. For many decades, administrative science has contributed to the clarification of this distinction. Digital constitutionalism and technology-oriented administrative law doctrine have recently been added to this research. The basic regulations, proposed and adopted within individual states, in the European Union and in international organizations, whether it concerns the protection of personal data, cyber security, or artificial intelligence, do establish new tasks for public administration, but they affect methods rather than forms of administrative activity. Emerging technology raises concerns about the ability to understand artificial reasoning and its methods of classification, personalization, and prediction. It is questionable to assume that all actions can be quantified and thus everything becomes objective. Technology compounds the situation and has its own imperative.AUC IURIDICA140The computerization of public administration tasks is a reality but the intelligence of public administration is shrouded in myths, and emerging technology raises concerns about the ability to understand artificial reasoning and its methods of classification, personalization, and prediction.AUC IURIDICA["Richard Pomaha\u010d"]2024-05-23T00:00:00 8ab121f99d6f7ccfc45fc67c8c5914eede78b5afIn today's competitive corporate landscape, the successful integration of computational intelligence approaches provides a key edge for decision-making processes. Despite developments, there is a gap in understanding the subtle application of such methodologies in corporate management, particularly within retail environments. To address this gap, our study combines decision trees and artificial neural networks to evaluate retail transaction data, concentrating on customer behavior and purchase trends. Leveraging a comprehensive dataset spanning client demographics, transaction details, and product information, our study provides substantial insights. Results suggest an 87% accuracy rate reached by artificial neural networks in forecasting client preferences. This study contributes to bridging the research gap by giving actionable insights into the practical application of computational intelligence in boosting company strategies and decision-making processes within the retail industry.2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)391This study combines decision trees and artificial neural networks to evaluate retail transaction data, concentrating on customer behavior and purchase trends, and suggests an 87% accuracy rate reached by artificial neural networks in forecasting client preferences.2024 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)["Saadaldeen Rashid Ahmed", "Ali Jabbar Hussein", "Lubna Qassim ALhashmi", "B. Al-Attar", "Shaymaa Dheeb", "Duaa A. Majeed", "Abadal-Salam T. Hussain", "J. F. Tawfeq"]2024-05-23T00:00:00 909895ba638d520de9dcccc626986742c01b35adTraffic congestion in urban areas is a significant contributor to air pollution and greenhouse gas emissions. This research paper explores the application of Artificial Intelligence (AI) in optimizing traffic flow through AI-based vehicle counting systems. By implementing advanced machine learning algorithms to monitor and analyze traffic patterns, this study demonstrates how real-time data can be used to make informed decisions that enhance traffic management. The paper presents a detailed examination of an AI-driven vehicle counting system that collects and processes vehicle data to adjust traffic signals dynamically, optimize traffic flow, and reduce idle times. The implications of these improvements are analyzed in terms of their potential to reduce air pollution and enhance urban air quality. Results from a series of simulations and real-world tests indicate that AI-based traffic management can significantly mitigate traffic-related emissions. The study also discusses the scalability of this technology, its integration into existing traffic management systems, and the policy implications for urban planners and environmental regulators.CONFERENCE PROCEEDING00Results from a series of simulations and real-world tests indicate that AI-based traffic management can significantly mitigate traffic-related emissions.CONFERENCE PROCEEDING[]2024-05-23T00:00:00 22031ce17cac832e0279b1567becd7b445cca5b9This paper investigates the advancements in cutting tool monitoring systems, with a particular focus on the application of artificial intelligence (AI) in down milling processes. The integration of AI technologies in cutting tool monitoring enhances the accuracy and efficiency of manufacturing operations by enabling real-time data analysis and predictive maintenance. We review current AI-driven monitoring techniques, including machine learning algorithms, sensor integration, and data processing methods that optimize down milling performance. Challenges such as data reliability, algorithm complexity, and implementation costs are discussed. Furthermore, the paper explores future directions for AI in tool monitoring, emphasizing the potential for smarter, more adaptive manufacturing systems. This research aims to provide a detailed understanding of how AI is transforming cutting tool monitoring and its impact on the down milling process.CONFERENCE PROCEEDING00This research aims to provide a detailed understanding of how AI is transforming cutting tool monitoring and its impact on the down milling process, emphasizing the potential for smarter, more adaptive manufacturing systems.CONFERENCE PROCEEDING[]2024-05-23T00:00:00 aef061068467b1b21ecb5f6f14abdc7feead1091Background Head and neck squamous cell carcinoma (HNSCC) is a complex malignancy that requires a multidisciplinary approach in clinical practice, especially in tumor board discussions. In recent years, artificial intelligence has emerged as a tool to assist healthcare professionals in making informed decisions. This study investigates the application of ChatGPT 3.5 and ChatGPT 4.0, natural language processing models, in tumor board decision-making. Methods We conducted a pilot study in October 2023 on 20 consecutive head and neck cancer patients discussed in our multidisciplinary tumor board (MDT). Patients with a primary diagnosis of head and neck cancer were included. The MDT and ChatGPT 3.5 and ChatGPT 4.0 recommendations for each patient were compared by two independent reviewers and the number of therapy options, the clinical recommendation, the explanation and the summarization were graded. Results In this study, ChatGPT 3.5 provided mostly general answers for surgery, chemotherapy, and radiation therapy. For clinical recommendation, explanation and summarization ChatGPT 3.5 and 4.0 scored well, but demonstrated to be mostly an assisting tool, suggesting significantly more therapy options than our MDT, while some of the recommended treatment modalities like primary immunotherapy are not part of the current treatment guidelines. Conclusions This research demonstrates that advanced AI models at the moment can merely assist in the MDT setting, since the current versions list common therapy options, but sometimes recommend incorrect treatment options and in the case of ChatGPT 3.5 lack information on the source material.Frontiers in Oncology249This research demonstrates that advanced AI models at the moment can merely assist in the MDT setting, since the current versions list common therapy options, but sometimes recommend incorrect treatment options and in the case of ChatGPT 3.5 lack information on the source material.Frontiers in Oncology["B. Schmidl", "Tobias H\u00fctten", "S. Pigorsch", "F. St\u00f6gbauer", "Cosima C. Hoch", "Timon Hussain", "Barbara Wollenberg", "Markus Wirth"]2024-05-24T00:00:00 63c304ffb26b6b5decde557a6f21c0a52e760e4cPurposeThis study aims to conduct research by making use of studies investigating the negative effects of artificial intelligence on the future careers and work motivation of tourism employees.Design/methodology/approachIn this research, a literature review, which is one of the qualitative research methods, was used. The study was completed by using a total of 13 articles and two book chapters investigating the negative aspects of artificial intelligence in the research data Science Direct and Web of Science databases as the main references.FindingsIn the articles examined as a result of the research, it was predicted that the entry of artificial intelligence into the tourism sector poses a threat to the future careers of many tourism employees, and this will cause tourism employees to lose their focus and motivation at work. Another conclusion reached as a result of the research is that many tourism workers will be unemployed in the future due to artificial intelligence-supported information systems and robots.Originality/valueWhen the literature was reviewed, there was no research that directly examined the negative effects of artificial intelligence on tourism sector employees. Therefore, this research is unique and important in this respect.Worldwide Hospitality and Tourism Themes424It was predicted that the entry of artificial intelligence into the tourism sector poses a threat to the future careers of many tourism employees, and this will cause tourism employees to lose their focus and motivation at work.Worldwide Hospitality and Tourism Themes["Handan Hamarat", "Haydar Sahin", "Ay\u015fe Ko\u00e7 Apuhan", "Ramazan Inan"]2024-05-24T00:00:00 9c7a920d20dad292a74da23af29a944fa62bbd94Scientific Reports542Clais automatically extracts human-interpretable rules from healthcare policy documents, and it enables professionals to edit and validate the extracted rules through an intuitive user interface, confirming the usefulness of Clais in making their workflow simpler and more effective.Scientific Reports["M. Sbodio", "Vanessa L\u00f3pez", "T. Hoang", "Theodora Brisimi", "Gabriele Picco", "Inge Vejsbjerg", "Valentina Rho", "Pol Mac Aonghusa", "Morten Kristiansen", "J. Segrave-Daly"]2024-05-24T00:00:00 445e26157b21a8b912e98fc6726138bdd6c9d28fThe quick and large development in the accumulation of medical data provides broad potential for the application of artificial intelligence technologies [...].Diagnostics221Diagnostics["S. Akbulut", "Cemil \u00c7olak"]2024-05-24T00:00:00 9c30906db814aa588fd835caa895f152804f46c5OBJECTIVES To identify studies promoting the use of artificial intelligence (AI) or automation with HIV pre-exposure prophylaxis (PrEP) care and explore ways for AI to be used in PrEP interventions. DESIGN Systematic review. METHODS We searched in the US Centers for Disease Control and Prevention Research Synthesis database through November 2023 PROSPERO (CRD42023458870). We included studies published in English that reported using AI or automation in PrEP interventions. Two reviewers independently reviewed the full text and extracted data by using standard forms. Risk of bias was assessed using either the revised Cochrane risk-of-bias tool for randomized trials for randomized controlled trials or an adapted Newcastle-Ottawa Quality Assessment Scale for non-randomized studies. RESULTS Our search identified 12 intervention studies (i.e., interventions that used AI/automation to improve PrEP care). Currently available intervention studies showed AI/automation interventions were acceptable and feasible in PrEP care while improving PrEP-related outcomes (i.e., knowledge, uptake, adherence, discussion with care providers). These interventions have used AI/automation to reduce workload (e.g., directly observed therapy) and helped non-HIV specialists prescribe PrEP with AI-generated clinical decision-support. Automated tools can also be developed with limited budget and staff experience. CONCLUSIONS AI and automation have high potential to improve PrEP care. Despite limitations of included studies (e.g., the small sample sizes and lack of rigorous study design), our review suggests that by using aspects of AI and automation appropriately and wisely, these technologies may accelerate PrEP use and reduce HIV infection.AIDS (London)02AI and automation have high potential to improve PrEP care and by using aspects of AI and automation appropriately and wisely, these technologies may accelerate PrEP use and reduce HIV infection.AIDS["Emiko Kamitani", "Yuko Mizuno", "George M Khalil", "Alexander Viguerie", "Julia B. DeLuca", "Ninad Mishra"]2024-05-24T00:00:00 2ddeb9235e71d3fb9dd736ce8bfea67825ad665f Machine learning algorithms are increasingly applied in hydrological studies with promising results. However, these algorithms generally lack the ability for easy interpretability of the results by users. In this study, we compare six different explainable artificial intelligence (XAI) algorithms that help understand the effect of input data on the simulation results. The methods are explored on two distinct approaches for streamflow modeling using the long short-term memory (LSTM) model: a single model approach using only meteorological forcing data and a regional approach including also static catchment attributes. To gain further insight into the internal dynamics of the LSTM models, the relationship between cell states and soil moisture is investigated. A strong correlation suggests that the LSTM models inherently capture the concept of soil moisture as a catchment-scale storage mechanism. The XAI methods are applied to derive a timestep of influence, revealing how many days of input data are relevant for the model output. All XAI methods result in similar seasonal patterns in the timestep of influence, suggesting that the methods are comparable. Setting soil moisture dynamics in context to seasonal development of the timestep of influence suggests resetting LSTM as soon as soil moisture saturation occurs.Hydrology Research02A strong correlation suggests that the LSTM models inherently capture the concept of soil moisture as a catchment-scale storage mechanism, and suggests resetting LSTM as soon as soil moisture saturation occurs.Hydrology Research["Alexander Ley", "Helge Bormann", "Markus C. Casper"]2024-05-24T00:00:00 74fb9a22ebb2eb3f67febbe000ebc3231b1e3cb8As in other healthcare professions, artificial intelligence will influence midwifery education. To prepare midwifes for a future where AI plays a significant role in healthcare, educational requirements need to be adapted. This scoping review aims to outline the current state of research regarding the impact of AI on midwifery education. The review follows the framework of Arksey and O’Malley and the PRISMA-ScR. Two databases (Academic Search Premier and PubMed) were searched for different search strings, following defined inclusion criteria, and six articles were included. The results indicate that midwifery practice and education is faced with several challenges as well as opportunities when integrating AI. All articles see the urgent need to implement AI technologies into midwifery education for midwives to actively participate in AI initiatives and research. Midwifery educators need to be trained and supported to use and teach AI technologies in midwifery. In conclusion, the integration of AI in midwifery education is still at an early stage. There is a need for multidisciplinary research. The analysed literature indicates that midwifery curricula should integrate AI at different levels for graduates to be prepared for their future in healthcare.Healthcare372The analysed literature indicates that midwifery curricula should integrate AI at different levels for graduates to be prepared for their future in healthcare.Healthcare["Angela Kranz", "Harald Abele"]2024-05-24T00:00:00 e363b5d2bf64f144d705b1b266081eacdf063760Blockchain, renowned for its decentralized and secure nature, and Artificial Intelligence, the pinnacle of machine intelligence, stand as pillars of innovation. However, despite the recognition of their few successful collaborations, the interrelation among these triumvirates - Internet-of-Things (IoT), Blockchain, and Artificial Intelligence (AI) - still remains relatively unexplored terrain. Issues arise regarding their synergy: the challenges of integration, the potential improvements in existing technologies, and the careful consideration required before their unification. While evidence suggests their individual prowess, understanding their convergence and the implications thereof is crucial for shaping a technologically robust future. This paper delves into the complex interplay between IoT, Blockchain, and AI, navigating their relationship and exploring opportunities for enhancement. In addition, this paper will also look into several challenges and complexities hindering seamless integration.2024 IEEE 14th Symposium on Computer Applications & Industrial Electronics (ISCAIE)181This paper delves into the complex interplay between IoT, Blockchain, and AI, navigating their relationship and exploring opportunities for enhancement and several challenges and complexities hindering seamless integration are looked into.2024 IEEE 14th Symposium on Computer Applications & Industrial Electronics (ISCAIE)["Ahmad Anwar Zainuddin", "Dini Handayani", "Isyraq Haziq Mohd Ridza", "Siti Husna Abdul Rahman", "Saidatul Izyanie Kamarudin", "Khairul Zakwan Ahmad", "Mirza Darwisy Mahazir", "Muazzam Hazmi Sukhaimi", "Krishnan Subramaniam", "Mohamad Irfan Firdaus Basri", "Nurul Hanis Mohd Dhuzuki"]2024-05-24T00:00:00 f06368b667af2e7e5a18531526c63babedb8e276 Artificial intelligence (AI) has arisen as an extraordinary power in project management, changing customary practices and enlarging human capacities. This research investigates the diverse jobs played by (AI) artificial intelligence in project management and surveys its effect on project achievement rates. Through an extensive survey of writing and research of exact information, this study uncovers that artificial intelligence reception in project management has prompted a critical improvement in project achievement rates. Overall, artificial intelligence execution has brought about a wonderful increment of roughly 20% in project achievement rates across different businesses. Via mechanizing monotonous errands, upgrading asset allotment, and improving dynamic cycles, artificial intelligence has exhibited its capability to smooth out project work processes and moderate dangers. Nonetheless, close by its promising advantages, artificial intelligence execution presents difficulties, for example, information protection concerns, moral contemplations, and labor force reskilling necessities. This abstracts the basic significance of embracing artificial intelligence advancements in project management to accomplish higher proficiency, adequacy, and development. Looking forward, further research is expected to investigate arising patterns and address the developing difficulties in bridling artificial intelligence for project achievement. The Asian Bulletin of Big Data Management111This study uncovers that artificial intelligence reception in project management has prompted a critical improvement in project achievement rates, which has brought about a wonderful increment of roughly 20% in project achievement rates across different businesses.The Asian Bulletin of Big Data Management["Muhammad Tayyab Zia", "Muhammad Nadim", "Muzammil Ahmad Khan", "Nijah Akram", "Furqan Atta"]2024-05-24T00:00:00 c48f0e0b8b9d91ffd593c4d8e44391eb9101086cThe rapid development of information technologies such as the Internet of Things, big data, and artificial intelligence provides good opportunities for digital transformation of enterprises, especially the use of artificial intelligence plays an important role in improving enterprise performance; At the same time, the country also vigorously advocates opening up to the outside world and encourages enterprises to “go out” and “bring in”. Based on this, this article is based on the data of A-share listed companies in Shanghai and Shenzhen from 2009 to 2016, and uses Python technology to construct artificial intelligence measurement indicators. It studies the impact of internationalization and digitization on the green innovation performance and operational performance of enterprises, and explores the impact of artificial intelligence on enterprise performance from both theoretical and empirical perspectives. Research has found that the use of artificial intelligence and international procurement significantly improve the green innovation performance of enterprises, promote their sustainable development, and at the same time, international procurement also improves the operational performance of enterprises. Further heterogeneity testing of the article also found that compared to state-owned enterprises, artificial intelligence technology has a greater positive impact on the sustainable development performance of non-state-owned enterprises.2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)180Research has found that the use of artificial intelligence and international procurement significantly improve the green innovation performance of enterprises, promote their sustainable development, and at the same time, international procurement also improves the operational performance of enterprises.2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)["Leyi Li"]2024-05-24T00:00:00 cc95b31e5888012f2184b37a9c8c771609356104The study investigated digital preservation and curation of Artificial Intelligence (AI) generated content for sustainable library operations in university libraries, 3 research questions and 3 hypotheses were used for the study. The population comprised of 193 Librarians from thirteen (13) university libraries in South-South and South-East, Nigeria. The random sampling technique was used to select a sample size of 116 Librarians in the 13 universities representing 60% of the population. A 15-item questionnaire was used for data collection. Cronbach alpha statistics was used to obtain 0.74 reliability. Mean/standard deviation was used for research questions and z-test statistics was used to test the hypotheses at 0.05 level of significance. The result amongst others revealed that, some of the challenges faced by University libraries in the preservation and curation of AL generated content are ethical and bias considerations that deals with fairness, accountability and transparency, legal and intellectual property issues, data privacy and security and more. One of the strategies to preserve and curate AI generated content is the storage of multiple copies of AI-generated content in geographically distributed locations to curb situation that would lead to loss of data due to hardware failures and many others. It was recommended that, government in collaboration with university management should provide necessary infrastructure and facilities, upgrade and update existing ones to enable the preservation and curation of AI generated content for the sustenance of digital library operation in academic libraries. American Journal of Education and Information Technology30The result revealed that, some of the challenges faced by University libraries in the preservation and curation of AL generated content are ethical and bias considerations that deals with fairness, accountability and transparency, legal and intellectual property issues, data privacy and security and more.American Journal of Education and Information Technology["Comfort Owate", "Boma David-West"]2024-05-24T00:00:00 08ff40de3f74a0b35853ab7a0955137c1519303dWith the continuous spread and globalization of infectious diseases, infectious disease prevention and control has become an important task for both countries and the world. Traditional infectious disease monitoring methods often rely on manual collection and analysis of data, which is inefficient and easily limited by human errors. However, the development of artificial intelligence and big data technology has provided new opportunities for infectious disease prevention and control. This article introduces the application of artificial intelligence in infectious disease monitoring. By using artificial intelligence algorithms and models, real-time monitoring and analysis of infectious disease data can be carried out, predicting the spread trend and risk of infectious diseases. This helps to detect and report infectious disease outbreaks, thus taking corresponding prevention and control measures and reducing the spread and impact of the epidemic. At the same time, this article explores the application of big data in infectious disease prevention and control. By using big data technology, in-depth mining and analysis of patient case data, virus gene sequences, transmission chain information, and other data can be carried out to discover the characteristics and patterns of infectious diseases, and be used to build an infectious disease warning system to predict and prevent the occurrence of infectious diseases in advance. Finally, this article discusses the application cases of artificial intelligence and big data in infectious disease prevention and control. By combining artificial intelligence and big data technology, the entire process of monitoring and management of infectious diseases can be achieved.2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)70Using artificial intelligence algorithms and models, real-time monitoring and analysis of infectious disease data can be carried out, predicting the spread trend and risk of infectious diseases.2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)["Xiaoqing Tang", "Xiupeng Yan", "Junbiao Chang", "Minghui Zhao"]2024-05-24T00:00:00 20af8282e8223ab05308dc9a73159f03ea6d7362South Korea’s Particulate Matter (PM) concentration is among the highest among Organization for Economic Cooperation and Development (OECD) member countries. However, many studies in South Korea primarily focus on housing characteristics and the physical built environment when estimating apartment prices, often neglecting environmental factors. This study investigated factors influencing apartment prices using transaction data for Seoul apartments provided by the Ministry of Land, Infrastructure, and Transport (MOLIT) in 2019. For this purpose, the study compared and analyzed a traditional hedonic price model with a machine learning-based random forest model. The main findings are as follows: First, the evaluation results of the traditional hedonic price model and the machine learning-based random forest model indicated that the random forest model was found to be more suitable for predicting apartment prices. Second, an importance analysis using Explainable Artificial Intelligence (XAI) showed that PM is more important in determining apartment prices than access to education and bus stops, which were considered in this study. Finally, the study found that areas with higher concentrations of PM tend to have higher apartment prices. Therefore, when proposing policies to stabilize apartment prices, it is essential to consider environmental factors. Furthermore, it is necessary to devise measures such as assigning PM labels to apartments during the home purchasing process, enabling buyers to consider PM and obtain relevant information accordingly.Sustainability890Sustainability["Dongwon Ko", "Seunghoon Park"]2024-05-24T00:00:00 008ee0853367d021b8794ea482112d443a07c043A 69-year-old female presented with symptomatic atrial fibrillation. Cardiac amyloidosis was suspected due to an artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis, and the subsequent unexpected finding of left atrial appendage thrombus reinforced this clinical suspicion. This facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy. This case highlights the utilization of an AI clinical tool and its impact on clinical care, particularly for the early detection of a rare and difficult to diagnose condition where early therapy is critical.Journal of Personalized Medicine140An artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy.Journal of Personalized Medicine["Courtney R Kenyon", "Milagros Pereyra Pietri", "Julie L Rosenthal", "R. Arsanjani", "Chadi Ayoub"]2024-05-24T00:00:00 301e3390016524d6177a3c17fd73c61fc976ebccWith the rapid development of Artificial Intelligence (AI) technology, the power grid industry is gradually increasing its investment in a new generation of information technology in the digital and intelligent transformation, and is committed to creating a full-process digital intelligence system to improve the operational efficiency and reliability of the power system. The purpose of this paper is to comprehensively explore the application strategy of AI in various aspects of power grid operation and its practical effects, to make a comprehensive and systematic compendium of it, and to construct a complete application framework. It also provides an in-depth analysis of the challenges faced by AI technology in the operation process as well as the countermeasures to provide reference for other power-related enterprises.IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference360The purpose of this paper is to comprehensively explore the application strategy of AI in various aspects of power grid operation and its practical effects to make a comprehensive and systematic compendium of it, and to construct a complete application framework.2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)["Yang Li", "Yan Li", "Xiaohan Ye", "Yuankai Han", "Debo Dong"]2024-05-24T00:00:00 c74dcbb7a2c61e5742a838b53f118fa4bed0b631This study delves into the innovative application of artificial intelligence (AI) to address the longstanding issue of emotional decision-making in financial markets, which has been a significant factor in exacerbating stock market crashes. By comparing historical market data during two notable periods of financial distress—the 2008 global financial crisis and the 2020 market downturn triggered by the COVID-19 pandemic—against the backdrop of predictions made by AI models devoid of emotional biases, this research illuminates the potential of AI to instill a level of rationality in trading decisions that is often compromised by human emotions such as fear and greed. Through a meticulous analysis employing various sophisticated AI and machine learning algorithms, the findings distinctly highlight that AI possesses the ability not only to predict market trends with a high degree of accuracy but also to suggest that a market guided by AI-driven decisions could potentially experience reduced volatility and shallower downturns. These insights point to a promising future where AI could serve as an invaluable tool for investors, potentially leading to more stable markets and improved financial outcomes. However, the integration of AI into the financial decision-making process raises important ethical considerations and necessitates the development of robust regulatory frameworks to manage the systemic risks associated with widespread automation. The paper advocates for an interdisciplinary approach to further explore the synergies between AI technology, behavioral finance theories, and ethical standards, aiming to harness the full potential of AI in fostering a more resilient and efficient financial ecosystem. This research contributes to the evolving dialogue on the role of AI in finance, offering a foundation for future investigations into the optimal integration of technological advancements with human insight to mitigate the impact of emotional biases on market dynamics.2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)230This research illuminates the potential of AI to instill a level of rationality in trading decisions that is often compromised by human emotions such as fear and greed, and suggests that a market guided by AI-driven decisions could potentially experience reduced volatility and shallower downturns.2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)["Cindy Lin", "Marisabel Chang", "Yu Sun"]2024-05-24T00:00:00 2715254b75bff0b842600509700d4618dfe3342dThe increasing prevalence of mental health issues among university students has become a growing concern globally. This review explores the potential of Artificial Intelligence (AI) integrated with principles of behaviour therapy to address mental health challenges among university students. The paper examines how AI technologies, including chatbots, virtual reality, and machine learning algorithms, can be harnessed to provide accessible, personalized, and effective mental health interventions. Furthermore, it discusses applying behaviour therapy principles within AI-driven mental health interventions, focusing on techniques such as cognitive restructuring, exposure therapy, and reinforcement strategies. The review highlights the promising outcomes and challenges of integrating AI and behaviour therapy principles in university mental health services, emphasizing the need for ethical considerations, privacy protection, and cultural sensitivity. By synthesizing current research findings and theoretical frameworks, this paper provides insights into the potential of AI-driven behaviour therapy interventions to enhance university students’ mental health and well-being.OBM Neurobiology70The review highlights the promising outcomes and challenges of integrating AI and behaviour therapy principles in university mental health services, emphasizing the need for ethical considerations, privacy protection, and cultural sensitivity.OBM Neurobiology["Mubashir Zafar"]2024-05-24T00:00:00 361eed41682f3b656253356a7335ff1eabe0c587This paper explores the complexities of current trends and technological progress that are influencing the future of digital business transformation. This analysis explores the significant impact of integrating artificial intelligence (AI) and machine learning (ML) into different aspects of business operations, including business models, customer experiences, and innovation. In addition, we examine the growing importance of e-commerce, digital marketing, and online platforms, specifically considering the interest in social media and travel. This paper aims to provide insights into the opportunities and challenges in the dynamic landscape, drawing from the intersection of computer science and business.2024 IEEE 14th Symposium on Computer Applications & Industrial Electronics (ISCAIE)00This analysis explores the significant impact of integrating artificial intelligence (AI) and machine learning (ML) into different aspects of business operations, including business models, customer experiences, and innovation.2024 IEEE 14th Symposium on Computer Applications & Industrial Electronics (ISCAIE)["W.V. Siricharoen"]2024-05-24T00:00:00 8c522f11c10d09a500ec4fb3a8ffc06290bb95e1Introduction Artificial intelligence (AI) is inevitably going to impact healthcare including dentistry and will become an essential tool in medical diagnosis and decision-making. Dental professionals must be familiar with growing trends in dentistry such as AI and its future scope. Despite the positive developments in AI research, there are divergent perspectives on its benefits and risks among stakeholders. We intended to understand the knowledge, awareness, and perceptions of dental professionals towards AI and its applications in dentistry. Methods and Material A semi-structured, 25-item Google form questionnaire consisting of closed and open-ended questions was made and the link to answer the survey was circulated among postgraduate students, dental academicians, and practitioners across India in an online mode, and the responses were collected and analyzed. Results 83.3% of participants were aware of AI and its applications. Most of the participants understood the attributes, advantages, and disadvantages of AI. Interestingly 72% of participants agreed that they have witnessed AI being used in clinical practice and 92.7% agreed to use AI for diagnosis. 65.3% expressed concern over unemployment due to AI and 85% agreed that AI has ethical issues. Over 85% of participants agreed AI should be a part of the postgraduate dental curriculum. Conclusions We found that dental professionals are updated with AI technology and showed a willingness to adopt AI into dental practice. The participants lacked a deeper understanding of AI and concerned about the potential risk of unemployment resulting from AI and trusting AI alone in dental diagnosis. Keywords: Artificial intelligence, Cross-sectional survey, Dentist, Knowledge, Perceptions.Journal of the Indian Dental Association130Dental professionals are updated with AI technology and showed a willingness to adopt AI into dental practice, but lacked a deeper understanding of AI and concerned about the potential risk of unemployment resulting from AI and trusting AI alone in dental diagnosis.Journal of Indian Dental Association["Veena Benakatti", "Vasanti Lagali-Jirge"]2024-05-24T00:00:00 617ae9e0d7e1c712a08326a7d2c5e30c85d1271cIn a continuation of our series on artificial intelligence, Dr Rebekka Heitmar considers how the power of AI can be harnessed by clinicians when interpreting fundus imagesThe Optician60Optician["R. Heitmar"]2024-05-24T00:00:00 d69d7b5caa4dd508210311f4c9e5bcc10c869aaaSpace artificial intelligence technology will play an increasingly important role in aerospace engineering. Firstly, space artificial intelligence technology system is given from the support technology, basic technology, key technology and the final space application. Then, key technologies of space artificial intelligence are discussed from the intelligent management of space mission, space intelligent measurement and control communications, space intelligent navigation and control, space intelligent robot, space intelligent long-term in orbit survival, intelligent management of livable environments for astronauts, and intelligent health management. Finally, the technical measures and suggestions for further development of space artificial intelligence are proposed from strengthening development planning, conducting in-orbit verification, strengthening expert training and promoting the transformation and application of commercial technology.2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)150Key technologies of space artificial intelligence are discussed from the intelligent management of space mission, space intelligent measurement and control communications, space intelligent navigation and control, space intelligent robot, space intelligent long-term in orbit survival, intelligent management of livable environments for astronauts and intelligent health management.2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD)["Zhenyu Qiu", "Xiaojun Tang", "Shijin Wang", "Yue Wang"]2024-05-24T00:00:00 38ae8720952071559dd9ef2d24a31bbda7c0868bThis paper examines the impacts of Artificial Intelligence (AI) on the labor market, specifically highlighting job displacement and the consequential effects of AI technologies. It analyzes the decline in job opportunities due to the emergence of automated technologies and the challenges faced by workers as a result. With AI becoming increasingly adept at basic programming and analysis, some tasks such as bank teller, drawer, factory worker, and cashier are no longer suitable for humans due to the relatively high efficiency of AI. People should take roles that require emotional support, creativity, and uniquely human capabilities. These roles will show intuition, wisdom, empathy, creativity, and social sensitivity. The paper underscores the development of artificial intelligence and emphasizes the importance of cultivating creative skills that can be utilized with AI rather than competing against it and talks about the impact on the students prospects which leads them to make different choices on their future.Advances in Economics, Management and Political Sciences00The paper underscores the development of artificial intelligence and emphasizes the importance of cultivating creative skills that can be utilized with AI rather than competing against it and talks about the impact on the students prospects which leads them to make different choices on their future.Advances in Economics, Management and Political Sciences["Wu Kao"]2024-05-24T00:00:00 26e0dd5a415c5c2db34f4a2f6176835748928653Since their release, the medical community has been actively exploring large language models’ (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.5, GPT-4, and Gemini, to provide postoperative care recommendations to plastic surgery patients. We presented each model with 32 questions addressing common patient concerns after surgical cosmetic procedures and evaluated the medical accuracy, readability, understandability, and actionability of the models’ responses. The three LLMs provided equally accurate information, with GPT-3.5 averaging the highest on the Likert scale (LS) (4.18 ± 0.93) (p = 0.849), while Gemini provided significantly more readable (p = 0.001) and understandable responses (p = 0.014; p = 0.001). There was no difference in the actionability of the models’ responses (p = 0.830). Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.Healthcare334Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.Healthcare["Cesar A Gomez-Cabello", "Sahar Borna", "Sophia M Pressman", "S. A. Haider", "Ajai Sehgal", "Bradley C. Leibovich", "AJ Forte"]2024-05-24T00:00:00 083974f46196f158309c4237042f4da3655f9134OECD Artificial Intelligence Papers03OECD Artificial Intelligence Papers[]2024-05-24T00:00:00 d167f90a858ee20482f664f9156888a8f03366a0Nanotechnology Perceptions01Nanotechnology Perceptions[]2024-05-24T00:00:00 232467f9eb35b93c2db219a7bf0800d884ceaf23<jats:p xml:lang="tr"/>International Journal of Didactical Studies03International Journal of Didactical Studies["John Mark R. Asio", "Ediric D. Gadia"]2024-05-24T00:00:00 288d1be14303d9bb25baf6680a4ef8fd3fb359c0GRUNDLAGEN DER MODERNEN WISSENSCHAFTLICHEN FORSCHUNG02GRUNDLAGEN DER MODERNEN WISSENSCHAFTLICHEN FORSCHUNG["Maksym Alieksieiev", "Volodymyr Kurenkov"]2024-05-24T00:00:00 98ff6c2288eac421603c54260895aa793464bfd001["Julija Kalpokien\u0117"]2024-05-24T00:00:00 b05d8009979d2b628e63ef1b049127420aecca21Künstliche Intell.171Künstliche Intell.["James T. Oswald", "Brandon Rozek", "Thomas M. Ferguson"]2024-05-24T00:00:00 75d2654f07cd7a01e4aea2c25c8f00f4172758c4This paper aims to 1) develop a methodology using computational law and blockchain technology to ensure Sharia compliance in e-waste management systems; 2) propose a conceptual model for a Sharia blockchain platform enabling compliance monitoring and verification; and 3) formulate recommendations to facilitate real-world implementation. The study employs a conceptual approach using inductive analysis of Sharia principles, deductive reasoning, and systems modeling to formulate design requirements and components for a blockchain platform that can enable transparent, tamper-proof monitoring and control of e-waste handling in accordance with Islamic law. Firstly, analysis reveals five key criteria for Sharia compliance in e-waste management and how blockchain, smart contracts, and AI can address these. A comprehensive compliance assurance methodology leveraging these technologies is proposed. Secondly, a conceptual model is delineated for a multipurpose Sharia blockchain platform encompassing consensus, cryptography, smart contract rule engines, and AI analytics. Thirdly, recommendations are synthesized covering technological, regulatory, economic, infrastructure, and social factors needed to enable real-world implementation. This pioneering research bridges Sharia law, sustainability, and emerging technologies to offer culturally attuned e-waste management solutions. The methodology, conceptual models, and practical framework devised inform development of next-generation systems for values-aligned compliance assurance, with potential impact extending beyond the realm of e-waste contexts.SUHUF281This pioneering research bridges Sharia law, sustainability, and emerging technologies to offer culturally attuned e-waste management solutions and inform development of next-generation systems for values-aligned compliance assurance, with potential impact extending beyond the realm of e-waste contexts.Suhuf["Said Gulyamov"]2024-05-24T00:00:00 a19a0e913131b2bd74e67865ce189879f1a18803Forming optimal peer groups is a crucial step in multiplier valuation. Among others, the traditional regression methodology requires the definition of the optimal set of peer selection criteria and the optimal size of the peer group a priori. Since there exists no universally applicable set of closed and complementary rules on selection criteria due to the complexity and the diverse nature of firms, this research exclusively examines unlisted companies, rendering direct comparisons with existing studies impractical. To address this, we developed a bespoke benchmark model through rigorous regression analysis. Our aim was to juxtapose its outcomes with our unique approach, enriching the understanding of unlisted company transaction dynamics. To stretch the performance of the linear regression method to the maximum, various datasets on selection criteria (full as well as F- and NCA-optimized) were employed. Using a sample of over 20,000 private firm transactions, model performance was evaluated employing multiplier prediction error measures (emphasizing bias and accuracy) as well as prediction superiority directly. Emphasizing five enterprise and equity value multiples, the results allow for the overall conclusion that the self-organizing map algorithm outperforms the traditional linear regression model in both minimizing the valuation error as measured by the multiplier prediction error measures as well as in direct prediction superiority. Consequently, the machine learning methodology offers a promising way to improve peer selection in private firm multiplier valuation.Inf.01The results allow for the overall conclusion that the self-organizing map algorithm outperforms the traditional linear regression model in both minimizing the valuation error as measured by the multiplier prediction error measures as well as in direct prediction superiority.Inf.["Timotej Jagri\u010d", "Du\u0161an Fister", "Stefan Otto Grbenic", "Alja\u017e Herman"]2024-05-24T00:00:00 9faabf66772bb4d4455fb57d48f393405ec4ef43Journal of Industrial Intelligence02Journal of Industrial Intelligence["S. Goswami", "Surajit Mondal", "Rohit Halder", "Jibangshu Nayak", "Arnabi Sil"]2024-05-24T00:00:00 72b28ad324a3b89f0d4c8494f5033d086f25fc8a2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)002024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)[]2024-05-24T00:00:00 2189f6b38c16f1736442c2f700f211a1c3648e42Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-05-24T00:00:00 94fe35c50848b74035986a02bf88cf9add8585b4 This Medical News article is an interview with Saurabh Jha, a cardiothoracic radiologist and an associate professor of radiology at the University of Pennsylvania, and JAMA Editor in Chief Kirsten Bibbins-Domingo. Journal of the American Medical Association (JAMA)00JAMA["Anna Bock", "Y. Hswen"]2024-05-24T00:00:00 8518453636078a6ab915481b27decc72c4ebee73Book ReviewScripta Theologica00Scripta Theologica["M.-Soledad Paladino"]2024-05-24T00:00:00 4329c237b19bdf224d05f1256a0933ca2dcc9d1aInternational Journal of Computer Applications00International Journal of Computer Applications["Mayur Jariwala"]2024-05-24T00:00:00 d6fcc1fe05c72b81f766e3221abdd91dfde462e3Proceedings of the 2024 International Conference on Computer and Multimedia Technology100Proceedings of the 2024 International Conference on Computer and Multimedia Technology["Bo Meng", "Xiangwei Qin"]2024-05-24T00:00:00 59268d555dcb869eece532ca2173da6949094d86International Conference on Software Development for Enhancing Accessibility and Fighting Info-exclusion70{"pages": "429-433"}["Mingsheng Liu", "Ling Peng"]2024-05-24T00:00:00 6f56fd603068b0035ff05b48d9a695a28decfe66The current global situation brings one important contradiction to the agenda: despite the fact that cities are the place of residence for 60 percent of the world’s population, they are responsible for most of the environmental and information pollution. The solution to the problem is presented by the modern ESG concept, which explains the joint work of environmental, social and state management systems. The joint work of these bodies represents a new direction of urbanization and regional development. The purpose of the article is to form the concept of the joint use of ESG and AI principles in the development of the Tomsk region as a progressive region. The object of the study is the Tomsk region. The subject is the definition of ways and mechanisms of sustainable development of the region. The results presented in the content of this article represent an assessment of the level of development of the Tomsk region in all parameters of sustainable development according to the ESG concept and the formation of measures to improve indicators based on the analysis.The economy of the North-West: problems and prospects of development00The purpose of the article is to form the concept of the joint use of ESG and AI principles in the development of the Tomsk region as a progressive region and the formation of measures to improve indicators based on the analysis.The economy of the North-West: problems and prospects of development["Maxim K. Kublinskiy", "Lyudmila M. Bolsunovskaya", "Artem G. Naymushin"]2024-05-24T00:00:00 028dfeb9ac3757c29bcb7437af78e28944b55857Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we present a hardware implementation of model-based reinforcement learning (MBRL) using spiking neural networks (SNNs) on mixed-signal analog/digital neuromorphic hardware. This approach leverages the energy efficiency of mixed-signal neuromorphic chips while achieving high sample efficiency through an alternation of online learning, referred to as the"awake"phase, and offline learning, known as the"dreaming"phase. The model proposed includes two symbiotic networks: an agent network that learns by combining real and simulated experiences, and a learned world model network that generates the simulated experiences. We validate the model by training the hardware implementation to play the Atari game Pong. We start from a baseline consisting of an agent network learning without a world model and dreaming, which successfully learns to play the game. By incorporating dreaming, the number of required real game experiences are reduced significantly compared to the baseline. The networks are implemented using a mixed-signal neuromorphic processor, with the readout layers trained using a computer in-the-loop, while the other layers remain fixed. These results pave the way toward energy-efficient neuromorphic learning systems capable of rapid learning in real world applications and use-cases.arXiv.org260A hardware implementation of model-based reinforcement learning (MBRL) using spiking neural networks (SNNs) on mixed-signal analog/digital neuromorphic hardware that leverages the energy efficiency of mixed-signal neuromorphic chips while achieving high sample efficiency through an alternation of online learning and offline learning.ArXiv["Ingo Blakowski", "D. Zendrikov", "Cristiano Capone", "Giacomo Indiveri"]2024-05-24T00:00:00 243460f813980b0708ece0bc8e5e04050a365b37In an era marked by unprecedented global challenges, including environmental degradation, social inequalities, and the rapid evolution of technology, the need for innovative educational approaches is critical. This conceptual paper explores the intersection of sustainability, creativity, and technology for education, focusing on artificial intelligence (AI) as an example. We propose a framework that synthesizes sustainability principles and creative pedagogies, detailing its components to guide the integration of AI into sustainability education. The paper illustrates how blending creative pedagogies with the notion of sustainability as a frame of mind offers a framework that allows teachers to support creative learning and problem solving, with and through technology. Using the example of AI technology, we illustrate the potential benefits and inherent challenges of integrating new technologies into education. Generative AI is a cogent example, as it presents unique opportunities for personalizing learning and engaging students in creative problem solving around sustainability issues. However, it also introduces significant environmental and ethical concerns to navigate. Exploring the balance between technological innovation and sustainability imperatives, this paper outlines a framework for incorporating technology into education that promotes environmental care with creative exploration. Through a synthesis of sustainability principles and creative pedagogies, we highlight the benefits and challenges of using AI in education, offering strategic insights to leverage technology for a sustainable and just future.Sustainability438A framework for incorporating technology into education that promotes environmental care with creative exploration is outlined, through a synthesis of sustainability principles and creative pedagogies, to guide the integration of AI into sustainability education.Sustainability["D. Henriksen", "Punya Mishra", "Rachel Stern"]2024-05-24T00:00:00 b9e9d805a20a9318dd2c10e7cfafcb36e4048eb9This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI.Algorithms1056A comprehensive knowledge base is established by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases, and proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption.Algorithms["Nan-Run Zhou", "Hua-Lei Yin", "Neeraj Anand Sharma", "Rishal Ravikesh Chand", "Zain Buksh", "A. B. M. Shawkat", "Ambreen Hanif", "A. Beheshti"]2024-05-24T00:00:00 b55955221037f4febc68137e7a442e34133eb615Asian Bioethics Review163What the fourth industrial revolution is, its basis around AI, and how this infiltrates human lives and society, akin to a transcendence are outlined, and potential solutions to such dangers are offered.Asian bioethics review["Thalia Arawi", "Joseph El Bachour", "Tala El Khansa"]2024-05-25T00:00:00 8d988761b6b8eceac05f951a6e1b9c44d8a44ff4The role of artificial intelligence (AI) in idea management systems (IMS) and innovation processes has recently been a topic of significant research interest. AI has been acknowledged for its potential to enhance innovation activities by providing support in various aspects. The intersection of these areas offers promising opportunities for improved idea generation, classification, development, and evaluation. However, AI's impact is not equally present for the different stages of an innovation process, showing more prominence in the idea generation stage. Through an integrative review, we can explore how AI has contributed to different steps of innovation processes implemented through IMS. AI-driven approaches, so far, have been opening possibilities to manage creative processes, such as automating specific tasks, analyzing large amounts of data to identify patterns and trends, and providing real-time feedback to enhance ideation and decision-making. Assessing the contribution of AI to innovation and creativity is vital in understanding its potential influence on the future developments of IMS and innovation processes.AICCONF641An integrative review of how AI has contributed to different steps of innovation processes implemented through IMS can explore how AI has contributed to different steps of innovation processes implemented through IMS.Proceedings of the Cognitive Models and Artificial Intelligence Conference["Serena Leka"]2024-05-25T00:00:00 9a0be93d26c38fcc4fe16055a0b3a4339134cbc8Explainable Artificial Intelligence (XAI) plays a pivotal role in enhancing adaptive learning systems by fostering an environment where transparency is key to developing students' critical thinking skills. In such systems, each learner's experience is uniquely tailored, not just to their current knowledge and cognitive abilities, but also with the intent of expanding their reasoning and problem-solving capacities. The crux of XAI within these educational contexts lies in its ability to demystify AI decisions. This transparency allows learners to grasp the "why" and "how" of the AI-driven guidance they receive, thereby promoting a deeper level of self-reflection and metacognitive awareness. The integration of XAI into platforms like RiPPLE exemplifies this approach, where the system leverages crowdsourced data and learning science to offer personalized activity recommendations. By aligning these recommendations with individual knowledge states, and elucidating the underlying logic, XAI-equipped adaptive learning environments not only cater to the immediate educational needs but also lay the groundwork for cultivating autonomous and discerning thinkers.AICCONF211The integration of XAI into platforms like RiPPLE exemplifies this approach, where the system leverages crowdsourced data and learning science to offer personalized activity recommendations.Proceedings of the Cognitive Models and Artificial Intelligence Conference["Ermira Idrizi"]2024-05-25T00:00:00 03d0533f8db265dfedf0c90c0bfbe35f7873a75fGlaucoma refers to a spectrum of progressive optic neuropathies and remains the leading cause of irreversible blindness worldwide. Its insidious onset poses serious challenges to conventional diagnostic methods and clinicians striving to detect early-stage disease for timely and effective intervention. Artificial intelligence (AI) has demonstrated its ability to process and analyze large datasets which can help identify subtle changes in early glaucomatous clinical presentation. This study reviews the current state of AI utilization in glaucoma and elucidates the strengths and limitations of existing approaches. We dissect the role of AI in various domains: enhancing early detection and diagnosis, monitoring disease progression, and refining treatment strategies to optimize patient outcomes. Furthermore, we address the ethical, legal, and social implications, alongside the inherent limitations of AI in the clinical setting. Despite these challenges, AI holds transformative potential for glaucoma management. Future directions emphasize the need for interdisciplinary collaboration, advanced and explainable algorithm development, and equitable healthcare access to fully realize the promise of AI in combating this vision-threatening condition.Journal of Clinical &amp; Translational Ophthalmology912The role of AI in various domains is dissected: enhancing early detection and diagnosis, monitoring disease progression, and refining treatment strategies to optimize patient outcomes to fully realize the promise of AI in combating this vision-threatening condition.Journal of Clinical &amp; Translational Ophthalmology["Patrick Xiang Ji", "Vethushan Ramalingam", "Michael Balas", "Lauren Pickel", "David J. Mathew"]2024-05-25T00:00:00 1fe726cdb94ce2e673b5b5574badceb7c2c1cf0cAccountants can benefit greatly from artificial intelligence in terms of increased productivity, increased intellect and increased value to the business. Through operational efficiencies and cost savings, the system is tangibly replacing essential human functions, raising the stakes for much more radical change. Artificial intelligence has advanced dramatically in recent years, particularly in the field of accounting, where computer input has replaced paper and pencil. The most worrying aspect of AI, however, is that people tend to assume they understand it too soon. Using secondary data, this research study aims to investigate how artificial intelligence affects the performance of accounting operations. The study highlights how artificial intelligence is being used to boast performance. Keywords: Artificial intelligence; Accounting profession; Impact; Technology; Audit.International Scientific Journal of Engineering and Management00Using secondary data, this research study aims to investigate how artificial intelligence affects the performance of accounting operations and highlights how artificial intelligence is being used to boast performance.International Scientific Journal of Engineering and Management["Swati Tandon"]2024-05-25T00:00:00 c5f1cb80b58494ca7bd7b7abf73977bed8301a24The deployment of AI systems has increased across several industries as they exhibit progressively stronger predictive performance. Due to safety, moral, and legal considerations, full automation is frequently undesirable. However, fully manual methods might be erroneous and time-consuming. The idea of using AI to support human decision-making is therefore gaining popularity in the scientific community. The flourishing subject of AI decision-making needs to embrace empirical methodologies in addition to building AI technologies for that purpose to establish a solid understanding of how people interact and collaborate with AI to make decisions. This research intends to analyse how artificial intelligence uses human judgment for decision-making in business. Researchers gathered survey results from high-tech employees in India via email, media, and other means. The sample size was 196, and the sampling strategy most likely employed was convenience sampling. With the data collected measurement and structural model are performed and found that artificial intelligence-based decision-making impacts the organizations’ business value and artificial intelligence capability impacts the organization created with the moderating effect of business intelligence. Also, it is concluded that AI-based decision-making impacts knowledge management.International Journal of Innovation Management310It is found that artificial intelligence-based decision-making impacts the organizations’ business value and artificial intelligence capability impacts the organization created with the moderating effect of business intelligence.International Journal of Innovation Management["Arun Kumar Chanda"]2024-05-25T00:00:00 0795d03778f5500fddffe0bb09a2b8800182f03cOne of the most advanced technological innovations, which is popularly termed Artificial Intelligence, has already changed our living habits. It is also a common practice presently being done, especially in terms of recruitment, to collect huge amounts of data for application identification, applicant profiles' analysis, interviews, selection of the most appropriate potential among many others. This makes its impact on human resource function, perception of prospective employees, firm's cultures, and policies. This situation is problematic, and it is associated with reality for several reasons, which could be either due to lack of awareness by the recruiter or early adaptors who have started the implementation process on adoption curve model. It drives us to learn more and educate people concerning the possible uses of this technology. The A.I. optimizes the experience for the applicants because the HR manager does not have to spend much time on the procedure. This allows the company to save these resources and use them to boost its output. In fact, a study done by the Sage group shows that more than 24% of companies across the world use AI for interviews and job evaluation. A majority of over two-thirds of HRM's is willing to adopt AI within one year. The World Economic Forum (W.E.F.) has projected that about seventy-five million out of the current workforce positions will disappear. The H.R. will be stressed by the addition of a hundred and thirteen million new jobs opportunities and positions. Especially, as there is the onset of AI and ML in organizations; they will lead to hiring additional H.R. experts to address the extra workloads.2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)210It is a common practice presently being done, especially in terms of recruitment, to collect huge amounts of data for application identification, applicant profiles' analysis, interviews, selection of the most appropriate potential among many others, which makes its impact on human resource function, perception of prospective employees, firm's cultures, and policies problematic.2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)["Manvinder Singh Bedi", "Nilesh Arora", "Parveen Badoni", "Pawan Kumar Paras"]2024-05-25T00:00:00 d2e42b4cc72657da7ce3bf5360332423ac61b935This research paper examines the transformative impact of artificial intelligence (AI) and machine learning (ML) on financial markets. It explores how these technologies are revolutionizing trading strategies, risk management, and financial operations. The paper reviews existing literature to understand the applications of AI and ML in finance. It then delves into potential research methodologies for analyzing the impact of these technologies on market efficiency, volatility, and investor behavior. Finally, the paper discusses the potential outcomes and challenges associated with the increasing adoption of AI and ML in financial markets.International Scientific Journal of Engineering and Management00How these technologies are revolutionizing trading strategies, risk management, and financial operations is explored, as well as potential research methodologies for analyzing the impact of these technologies on market efficiency, volatility, and investor behavior.International Scientific Journal of Engineering and Management["Aryans kumar"]2024-05-25T00:00:00 f0795047e197570c2ec990c9329746c31be2c8d6The global livestock industry grapples with formidable challenges stemming from the escalation and dissemination of infectious diseases. Zambia, an agricultural cornerstone where livestock is pivotal for economic sustenance and food security, confronts the imperative task of effectually surveilling and managing infectious diseases. This study investigates into the possibilities of the application of artificial intelligence (AI) for infectious disease surveillance in the Zambian livestock sector. The study meticulously scrutinizes the prevailing state of infectious disease surveillance, evaluates the latent capabilities of AI technologies, and critically discusses the intricate landscape of challenges and opportunities entailed in their implementation. In the intricate tapestry of Zambia's economy, livestock farming assumes a central and irreplaceable role, contributing substantially to the well-being and livelihoods of a significant portion of the populace. However, the omnipresent specter of infectious diseases perpetually menaces livestock health, casting a shadow on productivity and economic equilibrium. Conventional methodologies in disease surveillance exhibit inherent shortcomings, characterized by delays in reporting and inherent inaccuracies. This study is an exploration of possibilities of the AI applications designed to fortify infectious disease surveillance within Zambia's livestock domain. The infusion of AI technologies holds the transformative potential to reshape disease monitoring paradigms, enabling early detection and facilitating swift response strategies in the face of emerging threats. The ensuing critical analysis navigates the intricate terrain of the application of AI in the Zambian livestock context, shedding light on its promising prospects, while pragmatically addressing the hurdles that may accompany its incorporation.Journal for Research in Applied Sciences and Biotechnology220This study investigates into the possibilities of the application of artificial intelligence for infectious disease surveillance in the Zambian livestock sector, and meticulously scrutinizes the prevailing state of infectious disease surveillance, evaluates the latent capabilities of AI technologies, and critically discusses the intricate landscape of challenges and opportunities entailed in their implementation.Journal for Research in Applied Sciences and Biotechnology["Kachinda Wezi", "Chimvwele N. Choopa", "Nsamba Saboi", "Muchanga Benjamin", "Mbewe Beauty", "Mpashi Lonas", "Ricky Chazya", "Kelly Chisanga", "Arthur Chisanga", "Tinkler Simbeye", "Queen Suzan Midzi", "Christopher K. Mwanza", "Mweemba Chijoka", "L. Mataa", "Bruno S.J. Phiri", "Charles Maseka"]2024-05-25T00:00:00 bb881c7635176ec7d584d82fd230d02aa22e3ff1Background Artificial intelligence (AI) has shown potential in improving the performance of screening fetal anomaly ultrasound scans. We aimed to assess the effect of AI on fetal ultrasound scanning, in terms of diagnostic performance, biometry, scan duration, and sonographer cognitive load. Methods This was a randomised, single centre, open label trial in a large teaching hospital. Pregnant participants with fetal congenital heart disease (CHD) and with healthy fetuses were recruited and scanned with both methods. Screening sonographers were recruited from regional hospitals and were randomised to scan with the AI tool or in the standard fashion, blinded to the fetal CHD status. For the AI-assisted scans, the AI models identified and saved 13 standard image planes, and measured four biometrics. Findings 78 pregnant participants (26 with fetal CHD) and 58 sonographers were recruited. The sensitivity and specificity of the AI-assisted scan in detecting fetal malformation was 88.9% and 98.0% respectively, with the standard scan achieving 81.5% and 92.2% (not significant). AI-assisted scans were significantly shorter than standard scans (median 11.4 min vs 19.7 min, p <0.001). Sonographer cognitive load was significantly lower in the AI-assisted group (median NASA TLX score 35.2 vs 46.5, p <0.001). For all biometrics, the AI repeatability and reproducibility was superior to manual measurements. Interpretation AI assistance in the routine fetal anomaly ultrasound scan results in a significant time saving, along with a reduction in sonographer cognitive load, without a reduction in diagnostic performance. Funding The study was funded by an NIHR doctoral fellowship (NIHR301448) and was supported by grants from the Wellcome Trust (IEH Award, 102431), by core funding from the Wellcome Trust/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z), and the London AI Centre for Value Based Healthcare via funding from the Office for Life Sciences.medRxiv00Artificial intelligence assistance in the routine fetal anomaly ultrasound scan results in a significant time saving, along with a reduction in sonographer cognitive load, without a reduction in diagnostic performance.["T. Day", "J. Matthew", "S. Budd", "A. Farruggia", "L. Venturini", "R. Wright", "B. Jamshidi", "M. To", "H. Ling", "J. Lai", "M. Tan", "M. Brown", "G. Guy", "D. Casagrandi", "A. Arechvo", "A. Syngelaki", "D. Lloyd", "V. Zidere", "T. Vigneswaran", "O. Miller", "R. Akolekar", "S. Nanda", "K. Nicolaides", "B. Kainz", "J. Simpson", "J. Hajnal", "R. Razavi"]2024-05-25T00:00:00 3f52624f78e8de41e7c50ec79137c65305e66310Predictive maintenance in aviation using artificial intelligence (AI) is transforming the way aircraft are maintained and operated. By analyzing data from various aircraft sensors, AI algorithms can predict potential failures before they happen, allowing for timely and efficient maintenance. This proactive approach reduces unplanned downtime, enhances safety, and lowers maintenance costs. The implementation of AI in predictive maintenance leverages technologies such as machine learning, data analytics, and the Internet of Things (IoT) to monitor and analyze the health of aircraft components continuously. This abstract provides a comprehensive overview of how AI-driven predictive maintenance works, its benefits, and its impact on the aviation industry, making it easier for anyone to understand its significance and potential.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300This abstract provides a comprehensive overview of how AI-driven predictive maintenance works, its benefits, and its impact on the aviation industry, making it easier for anyone to understand its significance and potential.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Kondala Rao Patibandla"]2024-05-25T00:00:00 636b0b7551897f068fd54d67090cb12f0249fbcfTeachers are the most valuable resources at universities because they help the system as a whole grow and flourish. Teachers are stressed out of work since they have a lot of duties. This study aims to understand the effect of job stress on workplace happiness of university teachers. It also aims to explore the importance of artificial intelligence in reducing the stress and fostering happiness at the workplace. The data was collected from UGC recognized universities in DelhilNCR across the various disciplines. Linear regression analysis indicates the significant effect of job stress on workplace happiness of teachers. Further, the T - test applied indicates the significant difference in the workplace happiness of teacher's w.r.t. their gender. Artificial Intelligence as a technology has seem to play an important role in reducing the stress at work and fostering a healthy and happy work environment.2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)180The effect of job stress on workplace happiness of university teachers is understood and the importance of artificial intelligence in reducing the stress and fostering happiness at the workplace is explored.2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)["Shilpa Bhandari", "Pretty Bhalla"]2024-05-25T00:00:00 a7ec125c0f21706e165ca0f82d76f8429babb029Expert systems (ES) arose as a significant practical result in the application and development of artificial intelligence methods - a set of scientific disciplines that study methods for solving problems of an intellectual (creative) nature using a computer. From the beginning of its development, the field of artificial intelligence has considered several very complex problems, which, along with others, are still the subject of research: automatic theorem proofs, machine translation, image recognition, planning, game algorithms, strategies, etc. In modern understanding, an expert system is a set of programs that perform the functions of an expert when solving problems from a certain subject area [1-3]. Expert systems advise, conduct analysis, provide consultations, and diagnose. The practical use of expert systems in enterprises contributes to work efficiency and improved qualifications of specialists. When creating expert systems, several difficulties arise. This is primarily because the customer cannot always accurately formulate his requirements for the system being developed. It is also possible that difficulties of a purely psychological nature may arise when creating a knowledge base of a system, an expert may hinder the transfer of his knowledge, fearing that he will subsequently be replaced by a “machine”. But these fears are not justified, since expert systems are incapable of learning, they do not have common sense or intuition. Currently, expert systems are being developed that implement the idea of self-learning. Advantages of an ES over a human expert. Knowledge-based systems have certain advantages over human experts: they have no prejudices, they don't rush to conclusions, these systems work systematically, looking at all the details, often choosing the best alternative from all possible ones,the knowledge base can be very, very large, once entered the machine, the knowledge is stored forever, a person has a limited knowledge base, and if data is not used for a long time, then it is forgotten and lost forever. Knowledge-based systems are resistant to “interference.” The expert uses collateral knowledge and is easily influenced by external factors that are not directly related to the problem being solved. ES that are not burdened with knowledge from other areas is, by their nature, less susceptible to “noise.” Over time, knowledge-based systems may be viewed by users as a type of replication - a new way of recording and disseminating knowledge. Like other types of computer programs, they cannot replace a person in solving problems but rather resemble tools that enable him to solve problems faster and more efficiently. These systems do not replace a specialist but are a tool in his hands. The modern idea of expert systems is given in the article. Their differences from traditional software products are shown, and advantages and disadvantages are considered. A conclusion is drawn about the perspectives of development. Keywords: information technology, expert systems, knowledge base, knowledge processing algorithm.PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions00Over time, knowledge-based systems may be viewed by users as a type of replication - a new way of recording and disseminating knowledge as a new way of recording and disseminating knowledge.PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions["Sevda Salmanova Sevda Salmanova"]2024-05-25T00:00:00 de359c78f582412dd8819c94be0b64508f9822cfThis exploration article digs into the significant effect of (artificial intelligence) AI, on big Data base management in the field of computer science by utilizing the broad abilities of artificial intelligence AI technologies, this study investigates different strategies to improve the oversight and administration of huge information bases. Through an exhaustive assessment of existing academic works, itemized contextual investigations, and master experiences, the article disentangles the mind-boggling combination of Ai’s integration and its important impacts on database management rehearses. Covering an extensive variety of artificial intelligence aspects, for example, AI, regular language handling, and profound realizing, this academic request plans to explain the complex job of computer-based intelligence in the domain of big database management. As a critical commitment to scholarly talk, this research article fills in as an original work, planning the groundbreaking excursion of Ai transformative into the center of data base management system, introducing another time of development and productivity in the computerized scene.The Asian Bulletin of Big Data Management280This research article fills in as an original work, planning the groundbreaking excursion of Ai transformative into the center of data base management system, introducing another time of development and productivity in the computerized scene.The Asian Bulletin of Big Data Management["Muzammil Ahmad Khan", "Sumeera Bibi", "Muhammad Shahzaib Toor", "Muhammad Rashid"]2024-05-25T00:00:00 1c6673d0c159e3b45320d0b82e687f2d2d42a05bAs a result of rapidly accelerating AI capabilities, over the past year, national governments and multinational bodies have announced efforts to address safety, security and ethics issues related to AI models. One high priority among these efforts is the mitigation of misuse of AI models. Many biologists have for decades sought to reduce the risks of scientific research that could lead, through accident or misuse, to high-consequence disease outbreaks. Scientists have carefully considered what types of life sciences research have the potential for both benefit and risk (dual-use), especially as scientific advances have accelerated our ability to engineer organisms and create novel variants of pathogens. Here we describe how previous experience and study by scientists and policy professionals of dual-use capabilities in the life sciences can inform risk evaluations of AI models with biological capabilities. We argue that AI model evaluations should prioritize addressing high-consequence risks (those that could cause large-scale harm to the public, such as pandemics), and that these risks should be evaluated prior to model deployment so as to allow potential biosafety and/or biosecurity measures. Scientists' experience with identifying and mitigating dual-use biological risks can help inform new approaches to evaluating biological AI models. Identifying which AI capabilities post the greatest biosecurity and biosafety concerns is necessary in order to establish targeted AI safety evaluation methods, secure these tools against accident and misuse, and avoid impeding immense potential benefits.arXiv.org574It is argued that AI model evaluations should prioritize addressing high-consequence risks (those that could cause large-scale harm to the public, such as pandemics), and that these risks should be evaluated prior to model deployment so as to allow potential biosafety and/or biosecurity measures.ArXiv["Jaspreet Pannu", "Doni Bloomfield", "Alex W. Zhu", "R. MacKnight", "Gabe Gomes", "Anita Cicero", "Thomas V. Inglesby"]2024-05-25T00:00:00 e0e9e45b8002232c205e1d923b0081eac80d3c00This study examined 120 Turkish stories written for primary, secondary, high school, and undergraduate education levels by ChatGPT-3.5, ChatGPT-4, and Gemini1.5 Pro. The data was processed by software created with Natural Language Processing methods in mind. The general characteristics, quantitative characteristics, and readability of the stories were all reviewed within the scope of the study. Using the Ateşman and Bezirci-Yılmaz formulas, which are widely used for Turkish texts, the readability of the stories was calculated. As a result of the analysis, it was determined that AI is able to produce distinct stories, and it generates cohesive stories by utilizing subjects and themes that are suitable for a specific educational audience. When the average readabilities are taken into account, it has been found that ChatGPT-3.5 generates better stories suited for the education level based on the Ateşman formula and Gemini based on the Bezirci-Yılmaz formula, and the difficulty level of ChatGPT-3.5 stories rises in tandem with education level in both formulas. Also, the stories at the undergraduate level were found to be the hardest to read and with primary schools having the easiest readability. When the number of stories at readability levels is taken into account, it has been found that GPT-3.5 and ChatGPT-4 in the Ateşman formula and ChatGPT-3.5, ChatGPT-4, and Gemini in the Bezirci-Yılmaz formula generate appropriate stories for the education level; the levels range from easy to difficult as the education level increases. Additionally, it has been found that the number of stories included is gradually increasing. It was concluded that AIs produced stories above their educational level; while ChatGPT-3.5 and Gemini were more successful in story production, the Bezirci-Yılmaz formula was better in determining readability.AICCONF51It was concluded that AIs produced stories above their educational level; while ChatGPT-3.5 and Gemini were more successful in story production, the Bezirci-Yılmaz formula was better in determining readability.Proceedings of the Cognitive Models and Artificial Intelligence Conference["M. Karaca"]2024-05-25T00:00:00 12a67762a9a9209c0c41202501cba974780fe45dThe Journal of Future Early Childhood Education00The Journal of Future Early Childhood Education["Kwangpyo Hong", "Eunlae Cho"]2024-05-25T00:00:00 60946e94f53f9cfcfc8b6459ae5ad7b6ff9d8fddمجلة قطاع الشریعة والقانون00مجلة قطاع الشريعة والقانون["\u0646\u062c\u0644\u0627\u0621 \u0627\u0628\u0631\u0627\u0647\u064a\u0645 \u0628\u0631\u0643\u0627\u062a \u0628\u0631\u0643\u0627\u062a"]2024-05-25T00:00:00 5876c3a22f1af6e285af5965a55652d7b33341edIranian Journal of Medical Microbiology620Iranian Journal of Medical Microbiology["Hadi Hossainpour", "Hassan Mahmoudi"]2024-05-25T00:00:00 0c965a92a4418b12137dd3227bb9be135171b111Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.Advances in Materials3037The goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.Advanced materials["Abdulrahman Aldossary", "Jorge A. Campos-Gonzalez-Angulo", "Sergio Pablo-Garc\u00eda", "Shi Xuan Leong", "E. M. Rajaonson", "Luca Thiede", "Gary Tom", "Andrew Wang", "Davide Avagliano", "Al\u00e1n Aspuru-Guzik"]2024-05-25T00:00:00 1710dd0f7290e85708a082d94bcb35340b43d199This review critically examines the integration of artificial intelligence (AI) into human resource (HR) systems and evaluates its implications for cybersecurity. As digital transformation accelerates, HR systems increasingly process and store substantial volumes of sensitive employee data, making robust cybersecurity measures essential. The primary aim is to highlight significant cybersecurity challenges, identify advanced AI-driven security solutions, and understand their effectiveness in safeguarding employee data. Sources were selected based on relevance to the integration of AI in HR systems, their contribution to cybersecurity, and empirical evidence of both vulnerabilities and defenses.The review reveals that while AI can significantly enhance the detection of anomalies and automate security responses, it also introduces new vulnerabilities, such as sophisticated AI-driven attacks and biases in algorithmic decision-making. Key findings indicate a heightened need for dynamic security protocols that can evolve in tandem with AI technologies. Effective strategies highlighted include AI-enhanced encryption, behavioral analytics for threat detection, and AI-driven security training simulations. The findings emphasize the dual-edged nature of AI in cybersecurity for HR systems. For practitioners, adopting AI-driven security solutions offers a forward-thinking approach to protecting sensitive data but also requires a vigilant reassessment of security strategies in light of AI-specific threats. Policymakers are urged to consider more robust regulations that address the unique challenges posed by AI technologies. Ultimately, the paper calls for a proactive stance in cybersecurity management to anticipate and mitigate potential threats before they impact the integrity and trustworthiness of HR systemsInternational Journal of Advanced Research in Science, Communication and Technology32This review critically examines the integration of artificial intelligence into human resource (HR) systems and evaluates its implications for cybersecurity, revealing that while AI can significantly enhance the detection of anomalies and automate security responses, it also introduces new vulnerabilities.International Journal of Advanced Research in Science, Communication and Technology["Prabu Manoharan"]2024-05-25T00:00:00 b45146cb2b2908656a46576431d6ed94fa3cd0acIn today's fast-paced world, accessing quality healthcare can often feel like an uphill battle. Whether hindered by geographical barriers, long waiting times, or financial constraints, many individuals struggle to receive timely diagnoses for their health concerns. Recognizing this pressing need, we've developed an innovative solution in the form of our cutting-edge app[7].Harnessing the power of artificial intelligence, our app offers users a seamless pathway to diagnosis based on their reported symptoms. Our journey began with meticulous research to identify the most reliable AI technology, leading us to integrate ChatGPT, a state-of-the-art Large Language Model renowned for its ability to mimic professional medical responses accurately [8]. To ensure seamless functionality, we complemented this AI prowess with a robust Firebase database, facilitating efficient data storage and retrieval [9].Following rigorous development and testing phases, we're thrilled to announce that our app delivers unparalleled accuracy in diagnosis. Our trials revealed an impressive average accuracy rating of 1.8 out of 2 for common illnesses, underscoring the reliability and efficacy of our diagnostic algorithms. While initial evaluations highlighted room for improvement in symptom diversity across a spectrum of 21 illnesses, our focus on refining the app's capabilities for prevalent conditions has yielded remarkable results.Ultimately, our app stands as a beacon of accessibility in the realm of basic healthcare. By bridging the gap between individuals and vital medical expertise, we're empowering users to take proactive control of their well-being, irrespective of their circumstances. With our unwavering commitment to innovation and excellence, we're proud to revolutionize healthcare delivery and make a meaningful difference in people's lives.Advanced Natural Language Processing140The app stands as a beacon of accessibility in the realm of basic healthcare, empowering users to take proactive control of their well-being, irrespective of their circumstances, and delivers unparalleled accuracy in diagnosis.Advanced Natural Language Processing["Mylyn Zheng", "Adam Grant"]2024-05-25T00:00:00 df02a795e199dc4670c458bd26b76856504c7682It is argued that the ‘death of distance’ will lead to the death of cities. [1] Cities, as complex and dynamic systems, plays an crucial role in meeting the needs of their residents in an increasingly interconnected and digitalized world. With the ongoing trend of rapid urbanization, a sizable portion of the future occupants is expected to live in urban areas. However, this expansion must not overshadow the critical need to prioritize the well-being of city dwellers. The title “smart city” refers to the use of advanced technologies to optimize urban functions, increase efficiency, and remediate comprehensive subjective well being for residents. This could include using data analytics, artificial intelligence, and the Internet of Things (IoT) to streamline processes such as transportation, energy management, and government services. In essence, the intersection of sustainability, resilience, and smart urban planning represents a comprehensive approach to dealing with the complexities and challenges of urban growth.2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)180The intersection of sustainability, resilience, and smart urban planning represents a comprehensive approach to dealing with the complexities and challenges of urban growth.2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP)["Vanshika Pal", "Narinder Yadav", "Abhishek Sharma", "Suhani Nayak", "Sunny Dhaliwal", "Sanjay Singla"]2024-05-25T00:00:00 e4e8171c76250249ca72ffd22ee551dea94321ffThe proliferation of artificial intelligence (AI) in tertiary education poses significant challenges to academic integrity and credibility. This study explores effective strategies, tactics, and techniques to mitigate these risks. Through a comprehensive literature review, analysis of current practices, and case studies, the research offers actionable recommendations for educators and policymakers. The findings highlight the importance of a multifaceted approach, combining technological solutions, pedagogical reforms, and ethical guidelines to uphold the standards of academic integrity in the face of AI advancements.Integrated Journal for Research in Arts and Humanities40The findings highlight the importance of a multifaceted approach, combining technological solutions, pedagogical reforms, and ethical guidelines to uphold the standards of academic integrity in the face of AI advancements.Integrated Journal for Research in Arts and Humanities["Stanley A. V. Paul (Sr.)", "Stanley A. V. Paul (Jr.)"]2024-05-25T00:00:00 56f5d3c85793ed102397fcd4b90f66872613b28cThe integration of artificial intelligence (AI) and blockchain technologies signifies a significant paradigm shift within the healthcare domain, unlocking novel pathways for transformation. Conventional healthcare infrastructures grapple with challenges such as disjointed patient data, manual prescription processes, and restricted access to complete medical records. In response to these issues, our initiative presents an innovative solution that harnesses the synergies of AI and blockchain to establish a state-of-the-art healthcare framework. At its core, our mission revolves around the creation of a decentralized ecosystem that reimagines how patient records are managed and prescriptions are handled. By seamlessly blending AI algorithms with blockchain technology, our system aims to tackle pivotal challenges in healthcare, including addressing gaps in data interoperability, ensuring prescription accuracy, and facilitating streamlined access to comprehensive patient information. The foundational elements of this framework include decentralized data storage, AI-guided prescription generation, consolidation of extensive patient histories, user-centric design, robust data security, compliance with privacy regulations, and a steadfast commitment to ethical considerations. This exploration delves deep into the obstacles confronting traditional healthcare systems, presenting a thorough problem statement. It articulates the objectives of our proposed system, providing a transparent roadmap for confronting these challenges head-on. As we progress towards achieving these objectives, our project envisions a metamorphosed healthcare landscape characterized by heightened precision in medical decision-making, increased patient engagement, and overall enhanced efficiency. This endeavor holds the potential to revolutionize healthcare practices, empower healthcare professionals, and elevate the patient experience by seamlessly harnessing the convergence of AI and blockchain technologies. Keywords: Artificial Intelligence (AI), Blockchain, Healthcare, Decentralized, Electronic Medical Record (EMR), comprehensive patient history consolidation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT10This exploration delves deep into the obstacles confronting traditional healthcare systems, presenting a thorough problem statement that articulates the objectives of the proposed system, providing a transparent roadmap for confronting these challenges head-on.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Surbhi Pagar"]2024-05-25T00:00:00 60713b06651a81fe7f7576e52db4e870f6e87501In recent years, the advancement of artificial intelligence has significantly influenced the enhancement of automatic driving technology. Decision-making in autonomous vehicle driving behavior, based on reinforcement learning algorithms, often encounters challenges such as low sampling efficiency, prolonged learning times, and suboptimal driving stability. This paper introduces a model for automatic driving decision-making utilizing a Soft-Actor-Critic (SAC) based reinforcement learning algorithm, with the goal of achieving safe and autonomous vehicle navigation. The proposed Actor Refinement Network seeks to enhance driving stability within the model. Experimental validation of the SAC-based algorithm was performed on the Torcs autonomous driving simulation platform. Results indicate robust performance and notable generalization ability of the SAC algorithm model in the domain of autonomous driving behavioral decision-making. To further bolster vehicle stability, the output of the Actor network underwent refinement and functional verification on the simulation platform.Chinese Control and Decision Conference150A model for automatic driving decision-making utilizing a Soft-Actor-Critic (SAC) based reinforcement learning algorithm, with the goal of achieving safe and autonomous vehicle navigation is introduced.2024 36th Chinese Control and Decision Conference (CCDC)["Jun Guo", "Xuefeng Zhu", "Qingrong Zeng"]2024-05-25T00:00:00 de314837c4ef2678925ec9f249300b0e1558f000INTRODUÇÃO: Inovações tecnológicas têm sido ofertadas diariamente no âmbito da saúde hospitalar, propiciando uma aproximação entre os profissionais e os sistemas de informação. Recursos como a Inteligência Artificial são capazes de trazer informações diárias e atualizadas sobre diversos assuntos, entre eles os relacionados aos cuidados em saúde. OBJETIVOS: Comparar as formas de avaliações e recomendações de tratamentos de feridas operatórias por profissional capacitado com as de um sistema de informação de Inteligência Artificial OpenAI Chat GPT-4.0®. METODOLOGIA: Estudo descritivo como relato de experiência da prática assistencial do grupo de pesquisadores, dividido em duas fases. A primeira, avaliação das feridas operatórias por profissional enfermeiro especialista em feridas e estomaterapia. Na segunda, avaliação das feridas operatórias segundo o recurso tecnológico de Inteligência Artificial OpenAI Chat GPT-4.0®. RESULTADOS: A avaliação pelo especialista oferece detalhamento no contexto da ferida operatória, incluindo aspectos práticos e logísticos. Já a avaliação realizada pelo sistema de informação foca em análise clínica generalizada baseada apenas na aparência da ferida. Ambos destacam a importância da terapia por pressão negativa no manejo da ferida, mas variam na abordagem e no nível de detalhe. CONSIDERAÇÕES FINAIS: Os sistemas de Inteligência Artificial OpenAI Chat GPT-4 são recursos tecnológicos de qualidade e podem se tornar uma das várias formas de contribuir nos cuidados e auxiliar profissionais da saúde em localizações mais remotas, nas quais não há a disponibilização de profissionais enfermeiros capacitados em avaliações de feridas complexas.Revista Enfermagem Atual In Derme110Revista Enfermagem Atual In Derme["Giovani Basso da Silva", "Jo\u00e3o Gabriel", "Eliane Goldberg Rabin", "Diogo Martins da Silva", "Ana Paula Dias da Silva", "Vinicius Souza dos Santos"]2024-05-25T00:00:00 07e2f15a30b05cba7ffced06432447c1e1cbfe9dBackground: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.Diagnostics2637The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice, according to a summarized manner.Diagnostics["Elena Stamate", "A. Piraianu", "O. Ciobotaru", "Rodica Crassas", "O. Duca", "A. Fulga", "Ionica Grigore", "Vlad Vintila", "I. Fulga", "O. Ciobotaru"]2024-05-26T00:00:00 a988bde48c2444eb5021efbc748e7e69a6a09c7fIn recent years, the Metaverse has gained attention as a hub for technological revolution. However, its main platform suffers from issues like low‐quality content and lackluster virtual environments, leading to subpar user experiences. Concerns arise from declining interest in NFTs and failed virtual real estate ventures, casting doubt on the Metaverse's future. Artificial intelligence generated content (AIGC) emerges as a key driver of Metaverse advancement, using AI to create digital content efficiently and affordably. AIGC also enables personalized content, enhancing the Metaverse. This paper examines the link between the Metaverse and AIGC, exploring AIGC's applications, underlying technologies, and future challenges. It reveals that while AIGC shows promise for improving the Metaverse, its technologies must better align with development needs to deliver immersive experiences.IET Blockchain595It is revealed that while AIGC shows promise for improving the Metaverse, its technologies must better align with development needs to deliver immersive experiences.IET Blockchain["Xutian Wang", "Yan Hong", "Xiaoming He"]2024-05-26T00:00:00 731cef8aa82a94568511be2fae3957ef8f00db31Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular health. Cardiovascular concerns, including sudden cardiac death, pose significant risks during sports activities. Traditional ECG, essential yet limited, often fails to distinguish between benign cardiac adaptations and serious conditions. This narrative review investigates the application of machine learning (ML) and deep learning (DL) in ECG interpretation, aiming to improve the detection of arrhythmias, channelopathies, and hypertrophic cardiomyopathies. A literature review over the past decade, sourcing from PubMed and Google Scholar, highlights the growing adoption of AI in sports medicine for its precision and predictive capabilities. AI algorithms excel at identifying complex cardiac patterns, potentially overlooked by traditional methods, and are increasingly integrated into wearable technologies for continuous monitoring. Overall, by offering a comprehensive overview of current innovations and outlining future advancements, this review supports sports medicine professionals in merging traditional screening methods with state-of-the-art AI technologies. This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs.Sports494This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs through machine learning and deep learning in ECG interpretation.Sports["A. Smaranda", "T. Dr\u0103goiu", "Adela Caramoci", "A. Afetelor", "A. Ionescu", "I. B\u0103d\u0103r\u0103u"]2024-05-26T00:00:00 c608d2e5afeaeb01f8ff8cd8c79367d65f14c06dABSTRACT Rhetoric is a trace retained in and by artificial intelligence (AI) technologies. This concept illuminates how rhetoric and AI have faced issues related to information abundance, entrenched social inequalities, discriminatory biases, and the reproduction of repressive ideologies. Drawing on their shared root terminology (stochastic/artifice), common logic (zero-agency), and similar forms of organization (trope+algorithm), this essay urges readers to consider the etymological, ontological, and formal dimensions of rhetoric as inherent features of contemporary AI.Rhetoric Society Quarterly902This essay urges readers to consider the etymological, ontological, and formal dimensions of rhetoric as inherent features of contemporary AI.Rhetoric Society Quarterly["Atilla Hallsby"]2024-05-26T00:00:00 e018d0f5041e1851f652b9dbdd869d80380b3173Scientific Reports311These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation.Scientific Reports["Barbara D. Lam", "Laura E. Dodge", "Sabrina Zerbey", "William Robertson", "Rachel P Rosovsky", "Leslie M. Lake", "Siddhant Datta", "Pavania Elavakanar", "A. Adamski", "Nimia L Reyes", "Karon Abe", "Ioannis S Vlachos", "Jeffrey I. Zwicker", "Rushad Patell"]2024-05-26T00:00:00 8954d335987e423e5ffa8d87b3064308d50c21dfThe advent of artificial intelligence (AI) and its generative capabilities have propelled innovation across various industries, yet they have also sparked intricate legal debates, particularly in the realm of copyright law. Generative AI systems, capable of producing original content based on user‐provided input or prompts, have introduced novel challenges regarding ownership and authorship of AI‐generated works. One crucial aspect of this discussion revolves around text prompts, which serve as instructions for AI systems to generate specific content types, be it text, images, or music. Despite the transformative potential of AI‐generated works, the legal landscape remains fragmented, with disparate jurisdictional interpretations and a lack of uniform approaches. This disparity has led to legal uncertainty and ambiguity, necessitating a nuanced exploration of originality, creativity, and legal principles in the context of text prompts and resulting outputs. This article seeks to contribute to the ongoing debate by delving into the complexities surrounding AI‐generated works, focusing specifically on the originality of text prompts and their correlation with resulting outputs. While previous literature has extensively examined copyright issues related to AI, the originality of text prompts remains largely unexplored, representing a significant gap in the existing discourse. By analysing the originality of text prompts, this article aims to uncover new insights into the creative process underlying AI‐generated works and its implications for copyright law. Drawing parallels from traditional creative works, such as collages, the article will assess how legal principles apply to AI‐generated content, considering philosophical foundations as well as copyright principles, such as the idea‐expression dichotomy. Furthermore, the article will explore the divergent approaches taken by different jurisdictions, including the United Kingdom, United States, and European Union, in determining originality in the context of copyright law. While refraining from providing definitive answers, the article aims to stimulate critical thinking and dialogue among stakeholders. By offering fresh perspectives and insights, it seeks to enrich the discourse surrounding the copyrightability of AI‐generated works and pave the way for informed policy decisions and legal interpretations. The article aims to contribute valuable perspectives to the ongoing debate on copyright and AI, shaping the future trajectory of intellectual property law in the era of artificial intelligence.Journal of World Intellectual Property251The article will assess how legal principles apply to AI‐generated content, considering philosophical foundations as well as copyright principles, such as the idea‐expression dichotomy, and explore the divergent approaches taken by different jurisdictions in determining originality in the context of copyright law.The Journal of World Intellectual Property["Francesca Mazzi"]2024-05-26T00:00:00 87ffb15da6a1c0f795ef6931ee9e8b874c9d3d6eThis paper explores the transformative journey of Artificial Intelligence (AI) from its conceptual origins in science fiction to its profound impact on the 21st-century technological landscape. The study delves into the historical evolution of AI, tracing its roots in literary and cinematic works and examining how these creative visions have shaped and inspired the development of real-world AI technologies. It highlights key milestones in AI research and development, illustrating how theoretical models and algorithms have transitioned into practical applications that permeate various sectors such as healthcare, finance, automotive, and more. The paper also addresses the ethical, social, and economic implications of AI, discussing both the opportunities it presents and the challenges it poses, such as job displacement, privacy concerns, and the need for regulatory frameworks. Through a comprehensive analysis, this paper aims to provide a nuanced understanding of how AI has evolved from a speculative idea into a central pillar of modern technology, fundamentally altering human interaction, business practices, and societal norms.CONFERENCE PROCEEDING00Through a comprehensive analysis, this paper aims to provide a nuanced understanding of how AI has evolved from a speculative idea into a central pillar of modern technology, fundamentally altering human interaction, business practices, and societal norms.CONFERENCE PROCEEDING[]2024-05-26T00:00:00 9a024ed22671078bbc0b8ce3a8c4314b868154fdArtificial Intelligence simplifiedInternational Journal of Scientific Research and Management10International Journal of Scientific Research and Management (IJSRM)["Nripesh Trivedi"]2024-05-26T00:00:00 7607b8e4d26035de735ff9fc586325cee7238b92This study aims to discuss and analyze how substitute accountability in Artificial Intelligence (AI) in the context of Cryptocurrency, analyzed with Vicarious Liability theory. The use of Artificial Intelligence is also often used in the business of digital money transactions, for example such as cryptocurrency. Technological developments have given birth to various kinds of alternative tools or instruments as a substitute for money, which could be possible for violations of the law in the use of Artificial Intelligence (AI) in cryptocurrency transactions. The research method used in this study is a type of normative legal research method. In normative legal research, a study that leads to the process of finding legal rules, legal principles, and legal doctrines that function to answer legal issues faced. The choice of the type of normative legal research in this study is related to the analysis of Vicarious Liability Theory in the context of substitute liability in Artificial Intelligence (AI). The results of the study show the importance of understanding the theory of Vicarious Liability as a theory that determines substitute liability in Artificial Intelligence (AI), this is because the use of Artificial Intelligence is also often used in the business of digital money transactions, for example such as cryptocurrency, so it does not rule out the possibility that the AI does not carry out actions in accordance with AI commands which will certainly harm all parties, including business consumers of digital financial transactions in the event of a digital transaction error, then the person responsible is not the AI subject but the subject who from the beginning provides the use of Artificial Intelligence (AI) in cryptocurrency transactions.Focus Journal Law Review100The results of the study show the importance of understanding the theory of Vicarious Liability as a theory that determines substitute liability in Artificial Intelligence (AI), this is because the use of Artificial Intelligence is also often used in the business of digital money transactions, for example such as cryptocurrency.Focus Journal Law Review["Ayu Pramachanti Rumiartha", "Timotius Nico Yogatama"]2024-05-26T00:00:00 cc6ffe979e8898779ee3ad1e8fc3b0d2f8adc99bWith the rapid development of artificial intelligence technology, artificial intelligence has developed into "expressive artificial intelligence", artificial intelligence-generated content (AIGC) is more and more widely used in various fields. However, there are still some disputes and confusion about the copyright ownership of these machine-generated content. This paper first introduces the basic concepts and characteristics of artificial intelligence-generated content under the current background. Secondly, this paper discusses the positioning of AIGC in the copyright law and the difficulties in protecting the rights and interests through the different views and legislative practices on the copyright recognition of artificial intelligent-generated content in the world. Finally, in view of the current disputes, such as "creative requirements" and "human participation", this paper puts forward the possible ways to solve this problem in the future, including improving the copyright law to clarify the right ownership and responsibility of AIGC, learning from foreign experience, and establishing the copyright ownership system of AIGC, etc., which provides a useful reference for the formulation and practice of relevant laws and regulations.Economics Law and Policy00The basic concepts and characteristics of artificial intelligence-generated content under the current background are introduced, which provides a useful reference for the formulation and practice of relevant laws and regulations.Economics, Law and Policy["Jinyang Gao"]2024-05-26T00:00:00 8d578b54e6648d6e6070368c7626630dfa22d437In the face of escalating global food demands and the increasing unpredictability of climate conditions, the importance of precise crop yield forecasting has never been more critical. This paper provides a comprehensive review of the current trends in leveraging Artificial Intelligence (AI) to enhance crop yield predictions, which is pivotal for strategic agricultural planning and ensuring food security. Our review covers a range of AI methodologies, including machine learning, deep learning, and hybrid models, that have been employed to predict crop yields with increasing accuracy. Recent advancements have demonstrated that machine learning techniques, such as support vector machines and random forests, are effective in modeling complex agricultural data sets with a notable degree of precision. However, deep learning approaches, including convolutional and recurrent neural networks, have started to outperform traditional machine learning models, owing to their ability to process large-scale spatial-temporal data from remote sensing and IoT-based agricultural sensors. We also explore the emergence of hybrid AI models that combine the strengths of both machine learning and deep learning technologies, providing enhanced accuracy and robustness in yield prediction under varying climatic conditions. Additionally, this review discusses the integration of AI with geographic information systems (GIS) and remote sensing technologies, which has significantly improved the spatial resolution of yield predictions. We highlight several key challenges that remain, such as data scarcity, the need for model generalization, and the integration of socioeconomic factors into yield prediction models. In conclusion, AI presents transformative potential for crop yield forecasting. By harnessing cutting-edge AI technologies and addressing existing challenges, significant strides can be made towards more sustainable and efficient agricultural practices. This paper aims to inspire continued research and innovation in this critical field.International Journal For Multidisciplinary Research450A comprehensive review of the current trends in leveraging Artificial Intelligence to enhance crop yield predictions, which is pivotal for strategic agricultural planning and ensuring food security, and explores the emergence of hybrid AI models that combine the strengths of both machine learning and deep learning technologies.International Journal For Multidisciplinary Research["Ratul Ray"]2024-05-26T00:00:00 1c6b3760966231591c695cad5e0a372a48eb9b98Journal of Chemical Education213Journal of Chemical Education["Jessica D. Young", "Lisa Dawood", "Scott E. Lewis"]2024-05-26T00:00:00 623cd8a547a8f188eac87277d5a33dd487ee92b2International Journal of Environmental Science and Technology1242International Journal of Environmental Science and Technology["B. Senthil Rathi", "P. Senthil Kumar", "S. Sanjay", "M. Prem Kumar", "G. Rangasamy"]2024-05-26T00:00:00 0d37979072888f2b90b56c32bf6b7bf4e1cc6bb1International Journal of Research Publication and Reviews00International Journal of Research Publication and Reviews["Kanupriya Kanupriya"]2024-05-26T00:00:00 832b6e99f8119e5eb4e0bc98bc37e3c1217adcc3In today's rapidly evolving technological landscape, the synergy between artificial intelligence (AI) and automation has emerged as a powerhouse of innovation, revolutionizing industries across the globe. AI, often referred to as the "brain" of automation, brings a new level of intelligence, adaptability, and efficiency to various processes that were once limited by human capabilities. This seminar delves into the dynamic world of AI in automation, exploring how this transformative duo is reshaping industries, enhancing productivity, and paving the way for a smarter future. As the world becomes increasingly interconnected and data-driven, AI has emerged as a driving force behind the next industrial revolution. It encompasses a range of technologies that enable machines to learn, reason, and make decisions, mimicking human intelligence to varying degrees. On the other hand, automation involves the use of technology to perform tasks with minimal human intervention, resulting in increased accuracy, speed, and consistency. The marriage of AI and automation combines the cognitive abilities of AI with the precision of automated systems, resulting in a paradigm shift in how industries operate.International Journal For Multidisciplinary Research50This seminar delves into the dynamic world of AI in automation, exploring how this transformative duo is reshaping industries, enhancing productivity, and paving the way for a smarter future.International Journal For Multidisciplinary Research["Sanjivani Pawar", "Dr. Shubha Baravani", "Samruddhi Panhalkar", "Mahantesh Kowadkar", "Swapnil Bilgoji"]2024-05-26T00:00:00 3ec06fe8d8764123490544ab5dc956143e84b443The incorporation of Large Language Models (LLMs) such as the GPT series into diverse sectors including healthcare, education, and finance marks a significant evolution in the field of artificial intelligence (AI). The increasing demand for personalised applications motivated the design of conversational agents (CAs) to possess distinct personas. This paper commences by examining the rationale and implications of imbuing CAs with unique personas, smoothly transitioning into a broader discussion of the personalisation and anthropomorphism of CAs based on LLMs in the LLM era. We delve into the specific applications where the implementation of a persona is not just beneficial but critical for LLM-based CAs. The paper underscores the necessity of a nuanced approach to persona integration, highlighting the potential challenges and ethical dilemmas that may arise. Attention is directed towards the importance of maintaining persona consistency, establishing robust evaluation mechanisms, and ensuring that the persona attributes are effectively complemented by domain-specific knowledge.International Conference on Conversational User Interfaces588The paper underscores the necessity of a nuanced approach to persona integration, highlighting the potential challenges and ethical dilemmas that may arise and the importance of maintaining persona consistency, establishing robust evaluation mechanisms, and ensuring that the persona attributes are effectively complemented by domain-specific knowledge.ACM Conversational User Interfaces 2024["Guangzhi Sun", "Xiao Zhan", "Jose Such"]2024-05-26T00:00:00 eab007e699f7bcef8a3c9c13a4106f0657e92feaBy improving early detection, diagnosis, treatment planning, and patient management, artificial intelligence (AI) is transforming the way that cancer is treated. An overview of AI's function in cancer is given in this article, with special attention to how it advances precision medicine and improves patient outcomes. Numerous AI applications are discussed, such as predictive analytics, pathology interpretation, genetic profiling, and medical imaging analysis. Case studies highlight effective AI applications in cancer care, showcasing the technology's effectiveness in enhancing the precision of diagnoses, directing individualized treatment choices, and tracking treatment response. The paper delves into the possible advancements in early identification, therapy optimization, and patient engagement through an exploration of future directions and innovations in AI-driven oncology research. The conclusion emphasizes how AI has the ability to completely change the way cancer is treated and enhance the lives of cancer sufferers all over the world.International Journal of Multidisciplinary Sciences and Arts06An overview of AI's function in cancer is given, with special attention to how it advances precision medicine and improves patient outcomes, and numerous AI applications are discussed, such as predictive analytics, pathology interpretation, genetic profiling, and medical imaging analysis.International Journal of Multidisciplinary Sciences and Arts["Murad Khan", "Ashish Shiwlani", "Muhammad Umer Qayyum", "Abdul Mannan Khan Sherani", "Hafiz Khawar Hussain"]2024-05-26T00:00:00 060ee1c18bab13ebbb9629a2045b6ffa1b485ec8In the burgeoning field of artificial intelligence (AI), the significance of robust, annotated datasets cannot be overstated. They are the cornerstone upon which AI models are built and refined. Yet, the creation and enhancement of these datasets pose substantial challenges, from resource allocation to ensuring quality and diversity. The presented paper introduces an innovative solution to these challenges. GADAFAI leverages the latest advancements in generative adversarial networks (GANs), natural language processing (NLP), and computer vision to automate the augmentation and annotation of datasets, drastically reducing the manual effort involved. This framework marks a significant leap forward, particularly for fields where data scarcity and annotation costs have traditionally hindered AI application, such as healthcare and autonomous driving. By detailing GADAFAI's development, functionalities, and its impact on accelerating AI research and applications, this paper underscores the critical importance of advanced dataset generation and annotation technologies in the current era of AI.CONFERENCE PROCEEDING00GADAFAI leverages the latest advancements in generative adversarial networks (GANs), natural language processing (NLP), and computer vision to automate the augmentation and annotation of datasets, drastically reducing the manual effort involved.CONFERENCE PROCEEDING[]2024-05-26T00:00:00 006fcdb364d41c90a04a956b5fd0807e1d2a36f0ABSTRACT OpenAI’s ChatGPT is a large language model (LLM) that excels at generating text and public controversy. Upon its release, many marveled at its ability to author intelligible and generically responsible texts (Herman). Writing about his students’ experiences using artificial intelligence (AI) writing assistants, S. Scott Graham remarks that the results were “consistently mediocre—and usually quite obvious in their fabrication.” Why might this be true? How can an LLM succeed in some respects and fail in others? We argue that the discrepant reactions to human and AI rhetoric are a question of genre, specifically that AI rhetoric is only generic; AI rhetoric represents a new enactment of “writing degree zero” (Barthes) that is disengaged from immediate rhetorical situations and knowledge bases. AI text generators (currently) have a more difficult time simulating the positioned perspectives that human writers bring to situations and communicate to audiences through their genre usage. Drawing on the work of Bakhtin, we treat this problem as a question of generic form and audience addressivity. We describe the interplay of form and addressivity as genre signaling and offer it as a construct for the analysis of AI rhetoric and genre as a cultural form (Miller). Genre signaling (Hart-Davidson and Omizo) describes a feature of communicative behavior as it occurs over time that can help both humans and machines evaluate written discourse as it exhibits certain stabilized formal features. When texts contain specific genre signals at expected frequencies and intensities, it may be recognized as being generally accurate, reliable, trustworthy. Without these signals, a text with a similar topical focus might fail to be taken as credible or useful. In this essay we propose to quantify genre signaling based on three measures: (1) stability, (2) frequency, and (3) periodicity.Rhetoric Society Quarterly551This essay proposes to quantify genre signaling based on three measures: (1) stability, (2) frequency, and (3) periodicity, which are proposed to quantify genre signaling based on three measures: stability, frequency, and periodicity.Rhetoric Society Quarterly["Ryan M. Omizo", "W. Hart-Davidson"]2024-05-26T00:00:00 31f83c49bf8467bd3b39df6283c7cff7f01c38ecIn today's interconnected and dynamic financial landscape, individuals often face challenges in managing personal finances, exacerbated by a lack of financial literacy. This research introduces an innovative solution, "ExpenseXpert" designed to revolutionize the financial management paradigm. The system leverages artificial intelligence (AI) and machine learning (ML) to streamline expense tracking, budgeting, and financial insights, providing users with unprecedented efficiency and accuracy. The system's proactive approach includes notifications to alert users if they exceed their budget, ensuring financial discipline. It goes beyond traditional expense tracking by generating custom budget plans based on spending patterns, offering a unique feature for users to download summaries of their expenses in PDF or Excel formats.Indian Journal of Computer Science and Technology00This research introduces an innovative solution, "ExpenseXpert", designed to revolutionize the financial management paradigm that leverages artificial intelligence (AI) and machine learning (ML) to streamline expense tracking, budgeting, and financial insights, providing users with unprecedented efficiency and accuracy.Indian Journal of Computer Science and Technology["Prof. Shantanu Pawar", "Aditya Dhole", "Dyneshwar Jaybhaye", "Tushar Gosawi", "Shivraj Gaikwad"]2024-05-26T00:00:00 87c468a2d9b300b825ffd54c8cdd20e2886dfbcaEngineering applications of artificial intelligence790A rule-based weightage method is applied to combine both the proposed methods to achieve the final decision and the MOD-1D-CNN and MOD-2D-CNN models are more lightweight and computationally efficient alternatives because they have a significantly lower number of parameters when compared to the other state-of-the-art models.Eng. Appl. Artif. Intell.["Paramita Kundu Maji", "Soubhik Acharya", "Priti Paul", "Sanjay Chakraborty", "Saikat Basu"]2024-05-26T00:00:00 4550ef74e1b1b54a0a3909b2deb498f8a7aa24c9 This paper proposes a specific conceptualization of intelligence as computation. This conceptualization is intended to provide a unified view for all disciplines of intelligence research. Already, it unifies several conceptualizations currently under investigation, including physical, neural, embodied, morphological, and mechanical intelligences. To achieve this, the proposed conceptualization explains the differences among existing views by different computational paradigms, such as digital, analog, mechanical, or morphological computation. Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved. Intelligence is hypothesized as a multi-paradigmatic computation relying on specific computational principles. These principles distinguish intelligence from other, non-intelligent computations. The proposed conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence.IOP Conference Series: Materials Science and Engineering511Viewing intelligence as a composition of computations from different paradigms, the challenges posed by previous conceptualizations are resolved and this conceptualization implies a multi-disciplinary research agenda that is intended to lead to unified science of intelligence.ArXiv["Oliver Brock"]2024-05-26T00:00:00 81d066d05095b5eeda4bee9640a1e47068ba0f6aThe most effective way to understand and bring the organization from traditional banking to digital banking is Omni-Channel approach to customer service where all the channels are tightly integrated, keeping customer in the center of the integration. AI offers several opportunities in banking services to enhance operational efficiency, risk management and so on. Integration of AI brings challenges including ethical consideration, security risks, and substantial investments in technology and skills. However, In the last few years’ regulators and policy makers looking at the way of AI using in banking services. Today’s customers are more sophisticated and tech savvy, and to cater to their specific needs, each customer needs a unique experience from banking- opening an account, checking balance, conducting transactions, making payments, loans, credits, wealth management, customer support, etc delivering an Omni-channel experience has become a key to success in this competitive market place. his paper focuses mainly on an understanding the opportunities and challenges integrating AI in banking services. Secondary Data used for analysisInternational Journal For Multidisciplinary Research00International Journal For Multidisciplinary Research["G. K H"]2024-05-26T00:00:00 83238353f492c4b55da87c538e6a7f669b33271800["R. Anandan", "M. S. Kumar", "B. C. L.", "Vicente Garc\u00eda D\u00edaz", "Souvik Pal"]2024-05-26T00:00:00 e640c1ba69f268a7a1eaa19552dbbc78cdc4cc9fDespite their successes, deep learning models struggle with tasks requiring complex reasoning and function composition. We present a theoretical and empirical investigation into the limitations of Structured State Space Models (SSMs) and Transformers in such tasks. We prove that one-layer SSMs cannot efficiently perform function composition over large domains without impractically large state sizes, and even with Chain-of-Thought prompting, they require a number of steps that scale unfavorably with the complexity of the function composition. Multi-layer SSMs are constrained by log-space computational capacity, limiting their reasoning abilities. Our experiments corroborate these theoretical findings. Evaluating models on tasks including various function composition settings, multi-digit multiplication, dynamic programming, and Einstein's puzzle, we find significant performance degradation even with advanced prompting techniques. Models often resort to shortcuts, leading to compounding errors. These findings highlight fundamental barriers within current deep learning architectures rooted in their computational capacities. We underscore the need for innovative solutions to transcend these constraints and achieve reliable multi-step reasoning and compositional task-solving, which is critical for advancing toward general artificial intelligence.arXiv.org583It is proved that one-layer SSMs cannot efficiently perform function composition over large domains without impractically large state sizes, and even with Chain-of-Thought prompting, they require a number of steps that scale unfavorably with the complexity of the function composition.ArXiv["Nikola Zubic", "Federico Sold'a", "Aurelio Sulser", "Davide Scaramuzza"]2024-05-26T00:00:00 36b88327acc36b282c34955e20e3d789c48ba24dAI and Ethics10118Nine recommendations for responsible use of AI are offered, including that researchers should disclose, describe, and explain their use of AI in research, including its limitations, in language that can be understood by non-experts.AI and Ethics["David B Resnik", "Mohammad Hosseini"]2024-05-27T00:00:00 e66cca3c25b2cb7b29677854319d8b69e8a8fb84EClinicalMedicine12211This study conducted an extensive search across reputable scholarly databases to gather relevant academic literature on personalised medicine for CVD and uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues.eClinicalMedicine["Manasvi Singh", "Ashish Kumar", "N. N. Khanna", "John R. Laird", "A. Nicolaides", "Gavino Faa", "A. Johri", "Laura E. Mantella", "J. F. E. Fernandes", "Jagjit S. Teji", "Narpinder Singh", "Mostafa M. Fouda", "Rajesh Singh", "Aditya M. Sharma", "G. Kitas", "Vijay Rathore", "Inder M. Singh", "Kalyan Tadepalli", "Mustafa Al-Maini", "E. Isenovic", "Seemant Chaturvedi", "Deepak Garg", "K. Paraskevas", "D. Mikhailidis", "Vijay Viswanathan", "Manudeep K. Kalra", "Zolt\u00e1n Ruzsa", "L. Saba", "Andrew F. Laine", "Deepak L. Bhatt", "Jasjit S. Suri"]2024-05-27T00:00:00 3d28fabe1f79a79efb8397a1b74b5f779c28ee45This research explores the domain of Artificial Intelligence (AI) for social good, with a particular emphasis on its application in social welfare and service delivery. The study seeks to establish a universal conceptual framework for ethically integrating AI into the social services sector, recognizing the sector's significant yet underexplored potential for AI utilization. The objective is to develop a comprehensive framework applicable to the ethical deployment of AI in social services, using Lithuania as a case study to illustrate its practicality. This involves analysing the political discourse on AI, examining its applications in social welfare, identifying ethical challenges, evaluating the digitalization progress in Lithuania's public services, and formulating guidelines for AI integration at various stages of delivering social services. Our methodology is rooted in document analysis, encompassing a thorough review of both normative and scientific literature pertinent to the ethical application of AI in social welfare. Key findings reveal that AI's anticipated positive impacts on diverse social and economic areas, as highlighted in political declarations, are being partially realized, as corroborated by scientific studies. Although the global application of AI in social welfare is expanding, Lithuania presents a unique case with its strategic planning gaps in this sector. The developed conceptual framework offers vital criteria for the ethical implementation of AI systems designed to be universally applicable to various stages of social services, accommodating different AI applications, client groups, and institutional environments.Human Technology753Key findings reveal that AI's anticipated positive impacts on diverse social and economic areas, as highlighted in political declarations, are being partially realized, as corroborated by scientific studies.Human Technology["Miroslavas Seniutis", "V. Gru\u017eauskas", "Angel\u0117 Lileikien\u0117", "V. Navickas"]2024-05-27T00:00:00 5fe77a11169b4cc461bd5414a18c12162d9bb3af: The educational system, unlike other social systems, is distinguished at the same time by conservatism, necessary to protect itself from frequent and thoughtless changes as a result of temporary conjunctural moods, and modernism, expressed in the desire to prepare learners for the current needs of society. This duality also gives rise to many arguments for and against the introduction of artificial intelligence (AI) in education - if it does not, the education system will fall behind the realities, if it does, it may compromise its main purpose - to provide validated and verified knowledge to students and the students. The purpose of this article is to examine the possibilities of using AI in universities and to reveal the limitations that hinder it. In this sense, the object of the study is artificial intelligence, and the subject - its application in the higher education system. The purpose of the study is to examine, through a SWOT analysis, the opportunities and threats in the use of AI applications in universities, as well as their strengths and weaknesses, determining their readiness for it. A study has been conducted that shows that the opportunities for using artificial intelligence in education slightly outweigh the threats, and the strengths of universities as a terrain for implementing AI. The analysis also highlights what are the main levers for the development of AI in universities, what are the limitations to it, what are the risks that may occur in the future and what are the problems that can be expected if threats are not overcome and weaknesses are minimized. It is concluded that AI applications can be very useful in the work of teachers, and students want them, but this should be done carefully, respecting certain principles of equality, credibility, ethics and guaranteeing human rights.SCIENCE International Journal03A study has been conducted that shows that the opportunities for using artificial intelligence in education slightly outweigh the threats, and the strengths of universities as a terrain for implementing AI.SCIENCE International Journal["Borislav Borisov", "T. Stoyanova"]2024-05-27T00:00:00 9a078173095b6bd43f8313bc1da1f647a20e38eaThis research explores the vast potential of Artificial Intelligence (AI) and Machine Learning (ML) in predicting trends and enhancing investment decision-making. The financial market is highly complex and dynamic, making it challenging for investors to make accurate and timely decisions. Through the application of AI and ML techniques, this research aims to harness the power of data-driven approaches for trend identification and prediction. The research not only investigates the predictive capabilities of AI and ML in the financial domain but also explores the potential for risk assessment and portfolio optimization. The findings from this research have significant implications for various stakeholders within the financial sector, including individual investors, fund managers, and financial institutions. The potential benefits include improved decision-making, enhanced risk management, and optimized portfolio performance. Overall, this research aims to shed light on the potential of AI and ML in predicting trends and improving investment decision-making. By combining the power of these advanced technologies with human expertise, investors can gain a competitive edge in navigating the dynamic and often unpredictable financial landscape.Scientific Journal of Engineering, and Technology03The research investigates the predictive capabilities of AI and ML in the financial domain but also explores the potential for risk assessment and portfolio optimization, which has significant implications for various stakeholders within the financial sector.Scientific Journal of Engineering, and Technology["Gbenga Femi Asere", "Kehinde Adetayo Nuga"]2024-05-27T00:00:00 8f54abd0d95a51f4cfd4a9129a6c3d65d4699ae8The aim of the conducted research was to assess the attitude of the Polish society towards the use of artificial intelligence in medical diagnostics. In the research process, we sought answers to three research questions: how trust in the use of AI for medical diagnostics can be measured; if societal openness to technology determines trust in the use of AI for medical diagnostics purposes; and if a higher level of trust in the use of AI for medical diagnostics influences the potential improvement in the quality of medical diagnostics as perceived by Poles. The authors' particular focus was on the following three constructs and the relationships between them: openness to new technologies (OP), willingness to trust AI in medical diagnostics (T), and perceived impact of AI application on the quality of medical diagnostic services (PI). A survey was conducted on a representative sample of 1063 Polish respondents to seek answers to the above questions. The survey was conducted using the CATI technique.Human Technology1012The aim of the conducted research was to assess the attitude of the Polish society towards the use of artificial intelligence in medical diagnostics and the relationships between them: openness to new technologies (OP), willingness to trust AI in medical diagnostics (T), and perceived impact of AI application on the quality of medical diagnostic services (PI).Human Technology["Joanna Ejdys", "Magdalena Czerwi\u0144ska", "R. Ginevi\u010dius"]2024-05-27T00:00:00 e0063cd9bd0ba4746ec06787dd429454b508d2f5Education in the era of Artificial Intelligence (AI) presents new challenges in the axiological aspect, namely the moral and ethical values related to the use of AI technology in the learning process. This article aims to conduct an axiological study of education in the era of artificial intelligence with a focus on understanding the values that emerge along with the development of AI technology and their implications in the educational context. The research approach used is literature analysis to explore understanding of the use of AI in education and its implications for moral and ethical values. The results of the study show that the use of AI technology can provide significant benefits in increasing the effectiveness and efficiency of learning, but also raises several axiological problems. This research is qualitative research involving literature analysis, the data obtained will be processed thematically. In its conclusion, this article emphasizes the need to develop axiological awareness in education in the era of artificial intelligence. By paying attention to moral and ethical values, education can be an effective forum for developing a generation that is technologically intelligent and has a strong axiological awareness. This effort needs to be supported by critical thinking, reflection, and collaboration between educators, stakeholders and related parties to ensure that the application of AI technology in education runs ethically and responsibly.PROGRES PENDIDIKAN91The results of the study show that the use of AI technology can provide significant benefits in increasing the effectiveness and efficiency of learning, but also raises several axiological problems.PROGRES PENDIDIKAN["Fajar Alamin", "Sofyan Sauri"]2024-05-27T00:00:00 3e9c96833870e15096812b6452a2f563fc04710eThis paper aims to explore the trends of corporate financial management transformation under the backdrop of Artificial Intelligence (AI). With the rapid development of AI technology, corporate financial management is facing unprecedented opportunities and challenges. Through an in-depth analysis of the basic concepts, key technologies, and application cases of AI technology in financial management, this paper reveals the revolutionary impact of AI technology on financial management. Combined with practical cases, this paper discusses the success factors and challenges faced in the AI-driven financial management transformation process, including issues such as data privacy protection and human resources training. Finally, this paper outlines the future development trends of AI in financial management, including more intelligent decision support systems, and the widespread application of blockchain technology in the financial sector. Through this research, it is hoped to provide practical guidance and decision support for enterprise leaders and decision-makers on how to effectively utilize AI technology to optimize financial management.Frontiers in Business, Economics and Management51The revolutionary impact of AI technology on financial management is revealed and practical guidance and decision support is provided for enterprise leaders and decision-makers on how to effectively utilize AI technology to optimize financial management.Frontiers in Business, Economics and Management["Huijie Hu"]2024-05-27T00:00:00 62919990e0431a46119ad28a503bd9c763f04b5cArtificial intelligence (AI) is a field within computer science that has vast applications and has transformed medical technologies. It is often regarded to be the branch of computer science that can handle complicated problems with minimal theory and many applications. AI is utilized to assist researchers in the analysis of large data sets, enabling precision medicine and assisting physicians in improving patient outcomes. New techniques in AI can bring together various types of data to make sense of new information obtained from multiomics datasets. Analyzing high-quality data combined with machine learning, a subset of AI, can help modify patients' unhealthy behaviors, predict risk or recurrence of chronic diseases after a surgical and curative treatment, prediction of progression and survival rates of patients with chronic diseases, therapeutic need, generation of improved clinical trial interpretations and identification of new targets. Howeveri, to effectively implement precision medicine in healthcare, a more user-friendly interface would be required. If AI technologies are applied correctly, fairly and robustly, in close cooperation with human intelligence, it is expected to open up new possibilities for effective and personalised healthcare services worldwide. In this review, the general outlines of AI technology, its application areas in healthcare and its future are overviewed.Experimental and Applied Medical Science341If AI technologies are applied correctly, fairly and robustly, in close cooperation with human intelligence, it is expected to open up new possibilities for effective and personalised healthcare services worldwide.Experimental and Applied Medical Science["Mustafa \u00d6ztatl\u0131c\u0131", "Se\u00e7il Ero\u011flu", "H\u00fclya \u00d6ztatl\u0131c\u0131", "Mehmet G\u00f6l"]2024-05-27T00:00:00 f7fc9c7dfb59f5bdeeb3e15083174c2254a3eae4Artificial intelligence‟s threats to human rights can offset its significant benefits for human welfare. This makes it essential to analyse the current status and existing practices in developing the regulatory framework for artificial intelligence (AI). This paper aims to conduct a comparative legal analysis of the role of AI in ensuring human rights in Europe (in the example of the European Union) and the Middle East (in the example of Israel). The article uses comparative legal, formal legal and descriptive methods. The analysis shows that AI may harm the enjoyment of several human rights. Existing legislative initiatives (in particular, The EU Artificial Intelligence Act (AI Act), the Council of Europe‟s AI Convention) do not fully protect human rights from the impact of artificial intelligence due to existing gaps in the regulation of the private sector and national security, as well as the effect on the transparency of decisions in criminal law. The main problem is the inadequate regulation of the development and use of AI in national security and the private sector. This creates loopholes through which AI can cause significant harm to human rights and lead to violations. Further research can determine how the shortcomings identified in this paper may affect human rights and what safeguards can be put in place.Pakistan Journal of Criminology00Pakistan Journal of Criminology[]2024-05-27T00:00:00 b3c8d86918c78c21e1760deafa0971a5ca0d89f7Artificial intelligence (AI) technology has made a significant impact on technological progress and has been integrated into various sectors and organizations. As a result, developing a workforce with knowledge and expertise in AI has become necessary. Skilled AI professionals will play a critical role in driving economic growth and competitiveness in the digital age. Therefore, it is essential to develop AI competency among various groups of people. Learning AI skill sets is necessary to facilitate effective collaboration between humans and machines in the learning process. Known for Life offers a range of knowledge, including technical skill sets, business skill sets, and skill sets for individuals that incorporate ethics, such as the ethical use of AI in education to enhance the learning experience and evaluate student performance. Understanding AI can help educators adopt modern teaching methods and prepare students for AI-related careers, but it is crucial to consider ethical implications.International Education Studies330Understanding AI can help educators adopt modern teaching methods and prepare students for AI-related careers, but it is crucial to consider ethical implications.International Education Studies["S. Sengsri", "Kheamparit Khunratchasana"]2024-05-27T00:00:00 4e415abebe2b8efb003369f6dc6a1f17fc33a876This paper discusses the current application status of artificial intelligence (AI) technology in the field of accounting and its impact on the transformation of accounting functions. It also proposes measures to address the challenges and risks faced by the accounting industry under the background of AI. By analyzing the impact of current AI technology on the basic functions, expanded functions, and transformative functions of accounting, the paper points out the importance of structural adjustments in the accounting industry, emphasis on information security, and risk alertness. It also emphasizes the need for continuous learning and improvement among accounting professionals to adapt to the trend of intelligent development. Finally, it calls for strengthened legal supervision to ensure the healthy development of AI in the accounting field and provide guarantees for the advancement of accounting intelligence.Journal of Computing and Electronic Information Management00The paper points out the importance of structural adjustments in the accounting industry, emphasis on information security, and risk alertness, and the need for continuous learning and improvement among accounting professionals to adapt to the trend of intelligent development.Journal of Computing and Electronic Information Management["Xue Yang"]2024-05-27T00:00:00 4028cea6aa93d01bfb8f54f5873955e553a98979The article is devoted to the analysis of the influence of artificial intelligence (AI) on modern education. The main attention is paid to the study of both positive and possible negative consequences of introducing AI into educational processes. The paper explores the benefits of AI, such as the ability to adapt curriculum and materials in real time, which improves student engagement and satisfaction, as well as automating administrative tasks, which makes it easier to manage educational institutions. The study aims to contribute to a deeper understanding of the potential of AI in education and the need for a balanced approach to its integration, which will contribute to the creation of sustainable and equitable educational systems in the future.Gostinichnoe delo (Hotel Business)60The paper explores the benefits of AI, such as the ability to adapt curriculum and materials in real time, which improves student engagement and satisfaction, as well as automating administrative tasks, which makes it easier to manage educational institutions.Gostinichnoe delo (Hotel Business)["U. V. Ermina"]2024-05-27T00:00:00 6c22c78e41472684b803dde4d15800500ad4ddbcThe significance of the research is determined by the fact that the use of AI in the digital financial environment of Ukraine opens numerous opportunities for improving its efficiency, security, accessibility, and innovation. Therefore, this article is devoted to the analysis of the specifics of using artificial intelligence in the digital financial environment in Ukraine. Within the research, it has been proven that artificial intelligence is actively being implemented and adapted to the needs of various areas of the digital financial environment through specific transformations. It has been concluded that among the transformations driven by the use of artificial intelligence in the digital financial environment in Ukraine, we can distinguish the growing popularity of remote payments, the expansion of open banking usage, the development of digital identification, the advancement in financial data analysis, fraud detection, process automation, and the provision of personalized financial advice, the increase in the number of fintech companies working with AI, the heightened focus on cybersecurity, and the active regulation of the digital financial environment.Ekonomìka ta suspìlʹstvo10It has been concluded that among the transformations driven by the use of artificial intelligence in the digital financial environment in Ukraine, the growing popularity of remote payments, the expansion of open banking usage, the development of digital identification, the advancement in financial data analysis, fraud detection, process automation, and the provision of personalized financial advice can be distinguished.Економіка та суспільство["\u0422\u0435\u0442\u044f\u043d\u0430 \u041a\u0443\u043b\u0456\u043d\u0456\u0447", "\u041e\u043a\u0441\u0430\u043d\u0430 \u0421\u0442\u0435\u0440\u043d\u044e\u043a"]2024-05-27T00:00:00 a90fea9c2fdc652d9aad0c87ba9ac1d9a1d759f3As the global community increasingly prioritizes sustainability, the transition to renewable energy sources becomes paramount. However, the integration of renewables, such as solar and wind power, into existing energy infrastructures faces significant challenges due to their inherent variability and intermittency. Despite these obstacles, artificial intelligence (AI) offers a transformative solution. This article explores the multifaceted role of AI in addressing the complexities of renewable energy integration. From predictive analytics to optimize renewable energy generation to smart grid technologies that enhance grid stability, AI holds the key to unlocking the full potential of sustainable energy. By examining the intersection of AI and renewable energy integration, this article illuminates how innovative technologies can drive the global shift towards a more sustainable energy paradigm.SCT Proceedings in Interdisciplinary Insights and Innovations50By examining the intersection of AI and renewable energy integration, this article illuminates how innovative technologies can drive the global shift towards a more sustainable energy paradigm.SCT Proceedings in Interdisciplinary Insights and Innovations["Benchikh Salma", "Jarou Tarik", "Lamrani Roa"]2024-05-27T00:00:00 a5aaa4399d6020635c3af2a42dff9b7862d911a8The question that arises for everyone working with children and youth is how ready we are to respond to the challenges in education posed by artificial intelligence. It is necessary to consider the risks, as well as the opportunities for creating digital content with the help of artificial intelligence, online behavior, protection of personal data, and prevention of all possible abuses that artificial intelligence brings. The research aimed to determine the benefits of artificial intelligence in education, as well as the existence of potential dangers and risks. The research was conducted during the 2022/2023 school year at the Teacher Training Faculty in Tutin at Educons University and the Faculty of Education in Sombor, with a total of 75 students majoring in teaching. The qualitative component of the research consists of 3 focus groups. The responses of future teachers regarding the creation of the teaching process, teaching content, assessment methods, and the use of artificial intelligence tools in the educational process were analyzed. In this qualitative research, students saw the most benefits in creating the teaching process, greater individualization, creativity, and interactivity, as well as faster feedback on student progress. They mostly identified the dangers and concerns in the inability to control information and content, misuse of personal data, lack of transparency and false information. The general conclusion of the research results indicates a consensus among respondents that artificial intelligence tools are certainly good assistants in the teaching process, but they should not in the future suppress the significant role of teachers.SCIENCE International Journal20In this qualitative research, students saw the most benefits in creating the teaching process, greater individualization, creativity, and interactivity, as well as faster feedback on student progress.SCIENCE International Journal["Bojana Mari\u0107", "Violeta Petkovi\u0107"]2024-05-27T00:00:00 ac5d4be2b4f8d2d7a97ad9f51f97a50c7c64db15The implementation of Artificial Intelligence (AI) is a significant concern in the development of learning systems, including the learning of Building Engineering Education students. This research aims to increase the effectiveness of learning for Building Engineering Education students. By using AI concepts and theories defined by John McCarthy, this research analyzes how AI becomes an important tool in creating a learning system that is more adaptive and responsive to student needs. The data collection technique used was a questionnaire to determine the results of the research. The results of this research show that respondents know AI and its role and the majority of respondents also feel helped by AI. AI also faces various challenges, such as further education to overcome various existing challenges by strengthening data security and increasing understanding of AI. Greater awareness and understanding of how AI can be used in education also deserves further attention.Semantik160This research analyzes how AI becomes an important tool in creating a learning system that is more adaptive and responsive to student needs and shows that respondents know AI and its role and the majority of respondents also feel helped by AI.Semantik : Jurnal Riset Ilmu Pendidikan, Bahasa dan Budaya["Dwike Zaira Nurmila", "Nabila Audya Asmaranti", "Nazalya Noer Fadhilla", "Zizzahrra Nanderis", "Lameikasya", "U. Pendidikan", "Indonesia", "Kecerdasan Buatan", "Teknik Bangunan", "Pembelajaran Mahasiswa"]2024-05-27T00:00:00 2286060bbd20502ea99eb20f8c1c39aaad862b76Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218470This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions, and in recent studies associated with CNN is the attention mechanism, improving pest identification results.RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218["Maria Eloisa Mignoni", "Emiliano Soares Monteiro", "Cesar Zagonel", "Rafael Kunst"]2024-05-27T00:00:00 d204f77b9efba0e60e15d450c197910499e9b3e7The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists’ performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% (P<0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% (P≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.American Journal of Surgical Pathology190It is highlighted that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.The American Journal of Surgical Pathology["J. Retamero", "Emre Gulturk", "A. Bozkurt", "Sandy Liu", "Maria Gorgan", "Luis Moral", "Margaret Horton", "Andrea Parke", "Kasper Malfroid", "J. Sue", "B. Rothrock", "Gerard Oakley", "George DeMuth", "Ewan Millar", "Thomas J Fuchs", "D. Klimstra"]2024-05-27T00:00:00 3892e6c004df0355795c0d7fde8f90adf2a68203The evolving role of artificial intelligence (AI) in healthcare can shift the route of automated, supervised and computer-aided diagnostic radiology. An extensive literature review was conducted to consider the potential of designing a fully automated, complete diagnostic platform capable of integrating the current medical imaging technologies. Adjuvant, targeted, non-systematic research was regarded as necessary, especially to the end-user medical expert, for the completeness, understanding and terminological clarity of this discussion article that focuses on giving a representative and inclusive idea of the evolutional strides that have taken place, not including an AI architecture technical evaluation. Recent developments in AI applications for assessing various organ systems, as well as enhancing oncology and histopathology, show significant impact on medical practice. Published research outcomes of AI picture segmentation and classification algorithms exhibit promising accuracy, sensitivity and specificity. Progress in this field has led to the introduction of the concept of explainable AI, which ensures transparency of deep learning architectures, enabling human involvement in clinical decision making, especially in critical healthcare scenarios. Structure and language standardization of medical reports, along with interdisciplinary collaboration between medical and technical experts, are crucial for research coordination. Patient personal data should always be handled with confidentiality and dignity, while ensuring legality in the attribution of responsibility, particularly in view of machines lacking empathy and self-awareness. The results of our literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented.Inf.540The results of the literature research demonstrate the strong potential of utilizing AI architectures, mainly convolutional neural networks, in medical imaging diagnostics, even though a complete automated diagnostic platform, enabling full body scanning, has not yet been presented.Inf.["Christos B. Zachariadis", "Helen C. Leligou"]2024-05-27T00:00:00 9f04d637f8175d0cb1c3fbcbe1ab581083650726Introduction: Complications following abdominal surgery have a very significant negative impact on the patient and the health care system. Despite the spread of minimally invasive surgery, there is no automated way to use intraoperative video to predict complications. New developments in data storage capacity and artificial intelligence algorithm creation now allow for this opportunity. Methods: Development of deep learning algorithms through supervised learning based on the Clavien-Dindo scale to categorise postoperative outcomes in minimally invasive abdominal surgery. An open-source dataset will be built, which will not only include intraoperative variables but also data related to patient outcomes, making it more generalisable and useful to the scientific community. This dataset will be shared under a non-commercial use license to promote scientific collaboration and innovation. Expected Results: The planned outputs include the publication of a research protocol, main results, and the open-source dataset. Through this initiative, the project seeks to significantly advance the field of artificial intelligence-assisted surgery, contributing to safer and more effective practice.medRxiv120This project seeks to significantly advance the field of artificial intelligence-assisted surgery, contributing to safer and more effective practice and to promote scientific collaboration and innovation.["A. S. Soares", "S. Bano", "L. T. Castro", "R. Rocha", "P. Alves", "P. S. Mira", "J. P. Costa", "M. Chand", "D. Stoyanov"]2024-05-27T00:00:00 e720052d2bc3e5fbc14b98cf5de11570a4243ad5Leadership and Policy in Schools915Leadership and Policy in Schools["Soheila Sadeghi", "Chunling Niu"]2024-05-27T00:00:00 ad8f6561514b8c72fb9a0e55090134373b1c75c2Teaching Education01The Teacher Educator["Thalia M. Mulvihill", "Linda E. Martin"]2024-05-27T00:00:00 a8b39c3baa1ac6d1c0b0c553cd3fbce059308466Educational Administration: Theory and Practice01Educational Administration: Theory and Practice["Dr. N. Roopalatha"]2024-05-27T00:00:00 2ebae4a0a059630ace024290b17b6138b69fc349AI and Ethics372AI and Ethics["Dario Cecchini", "Michael Pflanzer", "Veljko Dubljevi\u0107"]2024-05-27T00:00:00 59be00bd72cf9604b962acd1c0bc834cd4a85a61 Penelitian ini dilatarbelakangi oleh pentingnya pengembangan kemampuan sosial emosional anak usia dini, khususnya anak usia 5-6 tahun, melalui media yang efektif seperti cerita rakyat. Dengan memanfaatkan kemajuan teknologi kecerdasan buatan (AI), penelitian ini bertujuan mengembangkan cerita rakyat Bengkulu dalam format video 3D dan mengukur efektivitasnya dalam meningkatkan kemampuan sosial emosional anak. Metode penelitian ini menggunakan Research and Development (R&D) dengan model Borg & Gall yang terdiri dari sepuluh langkah: pencarian dan pengumpulan data, perencanaan, pengembangan produk awal, uji coba lapangan awal, revisi hasil uji coba lapangan awal, uji coba lapangan utama, revisi produk operasional, uji coba lapangan operasional, penyempurnaan produk akhir, dan diseminasi serta implementasi. Subjek penelitian adalah anak usia 5-6 tahun di Kota Bengkulu, dengan data yang dikumpulkan melalui observasi, wawancara, dan kuesioner kepada guru dan orang tua sebelum dan sesudah intervensi. Hasil penelitian menunjukkan bahwa Video 3D berbantuan AI yang mengangkat cerita rakyat Bengkulu telah dinilai layak oleh ahli materi dan ahli media dengan tingkat validitas mencapai 90% dan 95%. Integrasi ini tidak hanya memperkaya pengalaman belajar anak-anak tetapi juga membuka potensi baru untuk meningkatkan pelestarian budaya lokal dan pengembangan karakter anak usia dini di Bengkulu. Langkah-langkah selanjutnya termasuk diseminasi produk ini ke lembaga PAUD di wilayah tersebut dan evaluasi lanjutan terhadap dampaknya terhadap pembelajaran dan pengembangan sosial emosional anak usia dini.   Indonesian Journal of Teaching and Learning (INTEL)01Indonesian Journal of Teaching and Learning (INTEL)["Dwi Lyna Sari"]2024-05-27T00:00:00 65ce8daf490538236ca2f84c573bac359cbf69e1Este artigo discute a importância e os desafios de lidar com informações nas organizações no contexto da inteligência artificial (IA). A pesquisa apresenta uma metodologia que busca desconstruir e entender o funcionamento dos sistemas de IA generativa. A metodologia envolve o uso de um aplicativo para registrar as consultas e respostas do ChatGPT, que resultou em variações nas respostas. O estudo enfatiza a necessidade de melhor compreensão desses sistemas de IA para usos potenciais em áreas críticas das organizações.Organicom00Organicom["Eduardo Pellanda"]2024-05-27T00:00:00 9cc0cc8477ac9352d3c085aba0ac3c6c1f72bd34El avance de la tecnología ha transformado rápidamente a la sociedad, especialmente en el ámbito de la salud, donde la inteligencia artificial despierta un gran interés debido a sus diversas aplicaciones. Varias de las áreas que utilizan estos sistemas, incluidas las ciencias de la salud, han considerado indispensable incorporar estas herramientas a su campo, es así que a lo largo de las décadas, la inteligencia artificial se ha utilizado en medicina, desde interpretar electrocardiogramas hasta analizar grandes conjuntos de datos para diagnósticos, tratamientos y dirigir políticas públicas. En el campo de las ciencias médicas se identifican dos ramas principales de aplicación: una física, que apoya al personal médico en la atención al paciente, y una virtual, que se centra en la investigación y salud pública. Aunque la inteligencia artificial puede aliviar la carga del personal sanitario, plantea riesgos como la pérdida de contacto directo entre médicos y pacientes, además de dilemas éticos sobre la privacidad de los datos de salud, por lo que es esencial evaluar continuamente su implementación y reflexionar sobre sus implicaciones éticas y sociales. Aunque se reconocen sus beneficios, es crucial abordar críticamente sus posibles impactos y preocupaciones éticas.Revista de la Facultad de Ciencias Médicas (Quito)00Revista de la Facultad de Ciencias Médicas (Quito)["I. L\u00f3pez", "Nathaly Rosales-Torres", "Gabriel Mi\u00f1o-Rodr\u00edguez", "A. Freire-Erazo"]2024-05-27T00:00:00 3fd60ee25c8df1ce2a91814b0978ae9583cfbb53Organicom00Organicom["Martha Gabriel"]2024-05-27T00:00:00 a86eb21244fb53339be4763039fffcfb8a147332Annals of Gastroenterological Surgery50Annals of Gastroenterological Surgery["Si-Wai Vivian Chiu", "Chung-Feng Liu", "Kuang-Ming Liao", "Chong-Chi Chiu"]2024-05-27T00:00:00 e9c95857392d38b57998d740633c4950fa5dbb70This study addresses the risks of the unreflective advance of AI in communication work environments and proposes a debate on its critical and creative use. It seeks a panoramic view based on media ecology and the multidisciplinarity of organizational communication, focused on the characteristics of a technology that announces itself as ubiquitous, to reflect on how AI will be able to help communication production processes. Are we prepared to “educate” these devices for a democratic, inclusive, and safe environment?Organicom00Organicom["Alessandra de Castro Marassi", "Mirian Aparecida Meliani Nunes"]2024-05-27T00:00:00 0435519814a2bcb54294a21d1de377ce8ac621f9Revista Brasileira de Ginecologia e Obstetrícia40Revista Brasileira de Ginecologia e Obstetrícia["Yago Tavares Pinheiro", "Richardson Augusto Rosendo da Silva"]2024-05-27T00:00:00 c8a6feaf02fffdc73be637d9d51f0deb3e3a63ceHeart failure00Heart failure["Mya Lelt Win", "Annie Sinclair", "K. Georgiev", "A. Conkie", "Muhammad S Hussain", "Chim C Lang", "I. Mordi"]2024-05-27T00:00:00 7703f936c81b5972f795382046883b94b0c92995Universum:Technical sciences00Universum:Technical sciences["Mikhail Mokshanov"]2024-05-27T00:00:00 35fbcc80dec02a53b58cc4fa181a1ae2b5ad65f8Nature00Nature["D. Byrne"]2024-05-27T00:00:00 fc9a39a2d742207dba5ca914281565fca11d6e11 With the accelerated aging of China’s population and changes in the disease spectrum of the older adult, China will face a serious problem of population aging in the future, and there is a huge demand for medical care services. AI care equipment has become an effective means of solving the problem of the imbalance between the supply of and demand for medical care services. AI care equipment can provide intelligent, precise and personalized care services for the elderly, the core of which is to analyze the data generated during the use of care equipment and provide real-time feedback through AI. Common AI care equipment include mobility aid devices, bathing aid devices, smart wearable devices, and care robots. AI care equipment can not only help the older adult and other users with daily living assistance and rehabilitation, but also disease prevention, environmental risk factor screening, and even emotional communication and psychological support. Through the establishment of a complete industry standard system and laws and regulations, promoting personnel training, maintaining information and data security and other ways to deal with the technical barriers faced in the development process of AI care equipment, low social acceptance and other challenges, in order to promote the development of China’s AI care equipment industry and the promotion of the industry, to solve the problem of the lack of care resources caused by the aging population to contribute to the proposal.Interdisciplinary Nursing Research00To solve the problem of the lack of care resources caused by the aging population, the establishment of a complete industry standard system and laws and regulations and the promotion of the industry are proposed.Interdisciplinary Nursing Research["Weixuan Wang", "Junhui Wu", "Dan Li", "Shaomei Shang"]2024-05-27T00:00:00 167bb3f2ec9dee7f9bd965450509b58fdf310372Stable IHD/Prevention/Hypertension/Lipids00Stable IHD/Prevention/Hypertension/Lipids["Joseph Barker", "A. Sau", "L. Pastika", "E. Sieliwonczyk", "K. Patlatzoglou", "K. McGurk", "Ant\u00f4nio H. Ribeiro", "A. Ribeiro", "Nicholas S. Peters", "D. O'Regan", "James S. Ware", "Daniel B. Kramer", "J. Waks", "F. S. Ng"]2024-05-27T00:00:00 89b43be4ce2b889c25483eb2af8d9a20f0730348Este ensaio surgiu a partir de reflexões sobre os usos de “inteligência” artificial (IA) pelas plataformas digitais, cujos espaços proporcionam encontros entre produtores e consumidores, em escalas distintas do mundo concreto, visto a mineração e uso imediato e intermitente da experiência humana. O texto concentra-se também na IA generativa e seu uso progressivo em ambientes sociais e empresariais. Em um gesto crítico, acionamos pensamentos de diferentes matrizes, com vistas à proposição de diálogos desveladores dos fenômenos tecnológicos e suas interfaces.Organicom00Organicom["Ana Regina R\u00eago"]2024-05-27T00:00:00 8420e9bc27b01fe9d8fd09c6d62940b2584dc294PurposeThis study aims to explore the potential benefits of integrating Artificial Intelligence (AI) with Social Emotional Learning (SEL) in educational settings.Design/methodology/approachA systematic review of emerging AI technologies such as virtual reality, chatbots, sentiment analysis tools, gamification and wearable devices is conducted to assess their applicability in enhancing SEL.FindingsAI technologies present opportunities for personalized support, increased engagement, empathy development and promotion of well-being within SEL frameworks.Research limitations/implicationsFuture research should focus on addressing ethical concerns, fostering interdisciplinary collaborations, conducting longitudinal studies, promoting cultural sensitivity and developing robust ecosystems for AI in SEL.Originality/valueThis study contributes by outlining pathways for leveraging AI to create inclusive and supportive learning environments that nurture students' socio-emotional competencies, preparing them for success in a globally connected world.Journal of Research in Innovative Teaching &amp; Learning466Journal of Research in Innovative Teaching &amp; Learning["Surbhi Seema Sethi", "Kanishk Jain"]2024-05-27T00:00:00 851b8cab3dc3db2f9c70f3d51b20264dce6eb9f0The launch of ChatGPT in November 2022 revolutionized the accessibility of generative Artificial Intelligence, enabling conversational interactions. Extensively tested by millions, its influence on management has become a subject of debate. In the digital revolution, generative Artificial Intelligence possesses transformative potential, automates tasks, delivers novel goods and services, and generates valuable insights. However, challenges such as data quality, human oversight, and ethical considerations arise in the context of digital transformation. This research employs qualitative research methods to examine the current understanding of generative Artificial Intelligence and predict its influence on the knowledge management within organizations. By conducting a survey among industry experts, this paper aims to provide valuable insights into the integration of generative Artificial Intelligence and its implications for the knowledge management paradigm.Human Technology997Qualitative research methods are employed to examine the current understanding of generative Artificial Intelligence and predict its influence on the knowledge management within organizations and provide valuable insights into the integration of generative Artificial Intelligence and its implications for the knowledge management paradigm.Human Technology["Dominika Kaczorowska-Spychalska", "Nina Kotula", "G. Mazurek", "\u0141ukasz Su\u0142kowski"]2024-05-27T00:00:00 829f2c791c2ff5fb5c9e6cbb6eca50b089ee607cThe emergence of numerous intelligent products and services in recent years, along with their commercial success and socioeconomic implications, prompts the question of whether the current AI trend is mere hype or if it indeed has the capacity to bring about transformative changes on a global scale. The research paper explores the extensive applications of artificial intelligence (AI) and provides a comprehensive analysis of its positive and negative effects on business. Furthermore, the study illustrates the pioneers, obstacles, recommendations, and outcomes of AI integration, ultimately resulting in heightened transparency concerning AI integration that may assist business managers in customizing AI to their specific circumstances. Keywords: AI Trend, Heightened Transparency, Socioeconomic implications,.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The research paper explores the extensive applications of artificial intelligence and provides a comprehensive analysis of its positive and negative effects on business, resulting in heightened transparency concerning AI integration that may assist business managers in customizing AI to their specific circumstances.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT[]2024-05-27T00:00:00 cbdb728df0b989f98cd6b7bd7f2f22ffcead53f9This paper explores the transformative impact of AI-driven automation on administrative processes, emphasizing the dual objectives of enhancing efficiency and accuracy. Through an in-depth examination of various applications, from document management to dynamic task prioritization, the study showcases how artificial intelligence can revolutionize traditional workflows. Special attention is given to the integration of natural language processing for email triage, virtual assistants for administrative support, and facial recognition for secure access control. The implementation of predictive analytics, sentiment analysis, and predictive maintenance further contributes to the paper’s focus on predictive decision-making and improved resource allocation. The abstract underscores the pivotal role of AI in meeting contemporary administrative challenges, offering solutions that streamline tasks, reduce errors, and optimize resource utilization. Additionally, the paper addresses ethical considerations associated with AI implementation and highlights the need for a balanced approach that aligns technological advancements with organizational goals. In essence, thisresearch provides a comprehensive overview of how AI can be harnessed to reshape administrative landscapes, fostering heightened efficiency and accuracy in contemporary workplaces. International journal of engineering science & humanities83This paper explores the transformative impact of AI-driven automation on administrative processes, emphasizing the dual objectives of enhancing efficiency and accuracy and addressing ethical considerations associated with AI implementation.International Journal of Engineering Science and Humanities["Deepak Kumar"]2024-05-27T00:00:00 85fe670df8b056520eb747277d70769f9a532816Large Language Models (LLMs) have shown significant promise in real-world decision-making tasks for embodied artificial intelligence, especially when fine-tuned to leverage their inherent common sense and reasoning abilities while being tailored to specific applications. However, this fine-tuning process introduces considerable safety and security vulnerabilities, especially in safety-critical cyber-physical systems. In this work, we propose the first comprehensive framework for Backdoor Attacks against LLM-based Decision-making systems (BALD) in embodied AI, systematically exploring the attack surfaces and trigger mechanisms. Specifically, we propose three distinct attack mechanisms: word injection, scenario manipulation, and knowledge injection, targeting various components in the LLM-based decision-making pipeline. We perform extensive experiments on representative LLMs (GPT-3.5, LLaMA2, PaLM2) in autonomous driving and home robot tasks, demonstrating the effectiveness and stealthiness of our backdoor triggers across various attack channels, with cases like vehicles accelerating toward obstacles and robots placing knives on beds. Our word and knowledge injection attacks achieve nearly 100% success rate across multiple models and datasets while requiring only limited access to the system. Our scenario manipulation attack yields success rates exceeding 65%, reaching up to 90%, and does not require any runtime system intrusion. We also assess the robustness of these attacks against defenses, revealing their resilience. Our findings highlight critical security vulnerabilities in embodied LLM systems and emphasize the urgent need for safeguarding these systems to mitigate potential risks.572This work proposes the first comprehensive framework for Backdoor Attacks against LLM-based Decision-making systems (BALD) in embodied AI, systematically exploring the attack surfaces and trigger mechanisms.["Ruochen Jiao", "Shaoyuan Xie", "Justin Yue", "Takami Sato", "Lixu Wang", "Yixuan Wang", "Qi Alfred Chen", "Qi Zhu"]2024-05-27T00:00:00 0f232de3be4f9119d8b9297e6a185689b7ce4030The public service media Radio Televisión Española (RTVE) conducted a proof-of-concept study to automatically generate reports on the results of the local elections of 28 May 2023 in Spanish communities with fewer than 1000 inhabitants. This study describes the creation, testing and application of the methodological tool used to evaluate the quality of the reports generated using artificial intelligence in order to optimize the algorithm. The application of the proposed datasheet provided a systematic analysis, and the iterative use of the tool made it possible to gradually improve the results produced by the system until a suitable threshold was reached for publication. The study also showed that, despite the ability of AI systems to automatically generate a large volume of information, both human labour and the reliability of the data that feed the system are essential to ensure journalistic quality.Journalism and Media542The study showed that, despite the ability of AI systems to automatically generate a large volume of information, both human labour and the reliability of the data that feed the system are essential to ensure journalistic quality.Journalism and Media["Luis Mauricio Calvo Rubio", "Mar\u00eda Jos\u00e9 Ufarte Ruiz", "Francisco Jos\u00e9 Murcia Verd\u00fa"]2024-05-27T00:00:00 a5419f3d5036f2d89051d41d20cc444f644b840aWith the continuous enrichment of intelligent applications, it is anticipated that 6G will evolve into a ubiquitous intelligent network. In order to achieve the vision of full-scenarios intelligent services, how to collaborate AI capabilities in different domains is an urgent issue. After analyzing potential use cases and technological requirements, this paper proposes an endto-end (E2E) cross-domain artificial intelligence (AI) collaboration framework for next-generation mobile communication systems. Two potential technical solutions, namely cross-domain AI management and orchestration and RAN-CN convergence, are presented to facilitate intelligent collaboration in both E2E scenarios and the edge network. Furthermore, we have validated the performance of a cross-domain federated learning algorithm in a simulated environment for the prediction of received signal power. While ensuring the security and privacy of terminal data, we have analyzed the communication overhead caused by cross-domain training.International Conference on Wireless Communications and Mobile Computing161An endto-end (E2E) cross-domain artificial intelligence (AI) collaboration framework for next-generation mobile communication systems and validated the performance of a cross-domain federated learning algorithm in a simulated environment for the prediction of received signal power.2024 International Wireless Communications and Mobile Computing (IWCMC)["Zexu Li", "Zhen Li", "Xiong Xiong", "Dongjie Liu"]2024-05-27T00:00:00 304a81d7fcc72255a6072f0c5018a8a605fa7036Artificial Intelligence (AI) repeatedly match or outperform radiologists in lab experiments. However, real-world implementations of radiological AI-based systems are found to provide little to no clinical value. This paper explores how to design AI for clinical usefulness in different contexts. We conducted 19 design sessions and design interventions with 13 radiologists from 7 clinical sites in Denmark and Kenya, based on three iterations of a functional AI-based prototype. Ten sociotechnical dependencies were identified as crucial for the design of AI in radiology. We conceptualised four technical dimensions that must be configured to the intended clinical context of use: AI functionality, AI medical focus, AI decision threshold, and AI Explainability. We present four design recommendations on how to address dependencies pertaining to the medical knowledge, clinic type, user expertise level, patient context, and user situation that condition the configuration of these technical dimensions.Conference on Designing Interactive Systems991This paper explores how to design AI for clinical usefulness in different contexts by conceptualising four technical dimensions that must be configured to the intended clinical context of use: AI functionality, AI medical focus, AI decision threshold, and AI Explainability.Proceedings of the 2024 ACM Designing Interactive Systems Conference["H. D. Zaj\u0105c", "Jorge Miguel Neves Ribeiro", "S. Ingala", "Simona Gentile", "Ruth Wanjohi", "S. N. Gitau", "J.F. Carlsen", "M. Nielsen", "T. O. Andersen"]2024-05-27T00:00:00 d364c811ddaad1fd0fb482c74334308b0e725605Artificial intelligence (AI) has emerged as a pivotal tool in optimizing waste-to-energy conversion technology, addressing critical environmental issues while promoting sustainable energy sources. This study delves into the multifaceted role of AI in enhancing the efficiency and effectiveness of waste-to-energy processes. By leveraging AI, significant improvements can be achieved in automated waste sorting, process monitoring, and energy production forecasting. The integration of AI into these domains not only streamlines operations but also enhances the accuracy of data management, analysis, and processing. This results in a more efficient conversion of waste into energy, mitigating adverse environmental impacts and fostering sustainable energy practices. The research highlights the practical applications of AI in optimizing the entire waste-to-energy workflow, underscoring its potential to revolutionize this sector. Moreover, the study addresses the inherent challenges and discusses future prospects for AI implementation in waste-to-energy technologies. Through comprehensive analysis and case studies, the findings reveal that AI can significantly contribute to reducing environmental footprints and promoting a circular economy. This exploration provides valuable insights into how AI-driven innovations can lead to more sustainable and efficient waste management and energy production systems, paving the way for future advancements in this critical field.Information Technologies in Environmental Engineering3031The research highlights the practical applications of AI in optimizing the entire waste-to-energy workflow, underscoring its potential to revolutionize this sector.International Transactions on Education Technology (ITEE)["Vivi Melinda", "Tane Williams", "James Anderson", "George Davies", "Christopher Davis"]2024-05-28T00:00:00 f3466c56bee5a06675b795252bd9e54f98d9110eRecent research emphasizes the importance of Artificial Intelligence applications as supporting tools for students in higher education. Simultaneously, an intensive exchange of views has started in the public debate in the international educational community. However, for a more proper use of these applications, it is necessary to investigate the factors that explain their intention and actual use in the future. With the Unified Theory of Acceptance and Use of Technology (UTAUT2) model, this work analyses the factors influencing students’ use and intention to use Artificial Intelligence technology. For this purpose, a sample of 197 Greek students at the School of Humanities and Social Sciences from the University of Patras participated in a survey. The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers’ intentions to use them. Moreover, behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications. This study did not reveal any moderating effects. The limitations, practical implications, and proposed directions for future research based on these results are discussed.Inf.5715The findings highlight that expected performance, habit, and enjoyment of these Artificial Intelligence applications are key determinants influencing teachers’ intentions to use them, and behavioural intention, habit, and facilitating conditions explain the usage of these Artificial Intelligence applications.Inf.["Konstantinos Lavidas", "Iro Voulgari", "Stamatis Papadakis", "Stavros Athanassopoulos", "Antigoni Anastasiou", "Andromachi Filippidi", "Vassilis Komis", "Nikos I. Karacapilidis"]2024-05-28T00:00:00 2ea12f6beb2d504b985503e945526a85251ed3afPurposeThis study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.Design/methodology/approachThe study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.FindingsGenerative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.Practical implicationsWhile each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.Originality/valueThis study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.Journal of Manufacturing Technology Management4513This study explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach, and proposes prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.Journal of Manufacturing Technology Management["Morteza Ghobakhloo", "Masood Fathi", "Mohammad Iranmanesh", "Mantas Vilkas", "Andrius Grybauskas", "A. Amran"]2024-05-28T00:00:00 d6ced3d08f595b7ebc95cd40c73e9fe72e7f774fIn the last few decades, there has been an ongoing transformation of our healthcare system with larger use of sensors for remote care and artificial intelligence (AI) tools. In particular, sensors improved by new algorithms with learning capabilities have proven their value for better patient care. Sensors and AI systems are no longer only non-autonomous devices such as the ones used in radiology or surgical robots; there are novel tools with a certain degree of autonomy aiming to largely modulate the medical decision. Thus, there will be situations in which the doctor is the one making the decision and has the final say and other cases in which the doctor might only apply the decision presented by the autonomous device. As those are two hugely different situations, they should not be treated the same way, and different liability rules should apply. Despite a real interest in the promise of sensors and AI in medicine, doctors and patients are reluctant to use it. One important reason is a lack clear definition of liability. Nobody wants to be at fault, or even prosecuted, because they followed the advice from an AI system, notably when it has not been perfectly adapted to a specific patient. Fears are present even with simple sensors and AI use, such as during telemedicine visits based on very useful, clinically pertinent sensors; with the risk of missing an important parameter; and, of course, when AI appears “intelligent”, potentially replacing the doctors’ judgment. This paper aims to provide an overview of the liability of the health professional in the context of the use of sensors and AI tools in remote healthcare, analyzing four regimes: the contract-based approach, the approach based on breach of duty to inform, the fault-based approach, and the approach related to the good itself. We will also discuss future challenges and opportunities in the promising domain of sensors and AI use in medicine.Italian National Conference on Sensors804This paper aims to provide an overview of the liability of the health professional in the context of the use of sensors and AI tools in remote healthcare, analyzing four regimes: the contract-based approach, the approach based on breach of duty to inform, the fault-based approach, and the approach related to the good itself.Sensors (Basel, Switzerland)["Marie Geny", "Emmanuel Andr\u00e8s", "S. Talha", "Bernard Geny"]2024-05-28T00:00:00 3805c9c4b2dc2153eb02b7bcc1ee0e6c7e0c36eeAI can boost education's efficiency and effectiveness in teaching and learning. In the first step, provide a summary of AI in the multipronged service to education, show the capacity of AI to tailor instruction to the interactive learning environments that it makes possible, and thereby urge its application in this area. Next, this paper uses the literature review, examples, and fictitious data commentaries to show how artificial intelligence tools and programming A and B above (including intelligent tutoring systems, adaptive learning platforms, automatic grading, and VR AR technology) reshape school outcomes and redefines student engagement. This study adopts a mixed-methods approach to investigate the impact of Artificial Intelligence (AI) on academic outcomes and engagement. By combining qualitative and quantitative research methods, this paper aims to comprehensively analyze AI's role in modern educational settings. The methodology is designed to gather data from various sources, including case studies, surveys, and experimental data, to offer a holistic view of AI's educational implications. Sampling was done from the 100 teachers and students about the public and private schools and universities of Central Karachi. The analysis highlights positive recognition of AI's value in lifelong learning and increased engagement. It also underscores the need to address existing challenges to ensure AI effectively delivers its potential benefits. Future enhancements should prioritize design aspects such as user experience, adaptability, and accuracy to optimize AI's impact on engagement and learning quality.Journal of Asian development studies13This paper uses the literature review, examples, and fictitious data commentaries to show how artificial intelligence tools and programming A and B reshape school outcomes and redefines student engagement.Journal of Asian Development Studies["Sumaira Ifraheem", "Muzna Rasheed", "Arfa Siddiqui"]2024-05-28T00:00:00 a1f47b0640aeec6bc32706db79d8058001fbb40bAmidst the rapid advancement of artificial intelligence (AI), the need for effective integration within IT Governance becomes increasingly vital. In this context, the research background highlights the complexity and dynamics hindering the efficacy of IT Governance, while AI holds promise as a solution to these challenges. This study investigates the role of AI in enhancing IT Governance. The research aim is to explore the impact of AI on improving the effectiveness and efficiency of IT Governance through a literature review method. This method will gather and analyze relevant literature sources to gain a comprehensive understanding of AI's role in IT Governance. The findings encompass AI's potential in enhancing decision-making, proactive risk management, process automation, and user satisfaction. However, challenges such as data privacy and organizational cultural changes are also identified. The research implications underscore the need for a planned approach and ongoing evaluation in adopting AI to ensure successful implementation while managing associated risks.Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika272The findings encompass AI's potential in enhancing decision-making, proactive risk management, process automation, and user satisfaction and underscore the need for a planned approach and ongoing evaluation in adopting AI to ensure successful implementation while managing associated risks.Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika["Z. Zulkarnain", "Jesselyn Jesselyn", "Hansvirgo Hansvirgo", "Fendy Gunawan", "S. Dion"]2024-05-28T00:00:00 546c7ba97cdd71ff8be3d0ec9c07ea04a8d37f94The objective of this article is to analyze how the disinformation industry, understood as organized and systematic practices aimed at disseminating false information with the aim of manipulating public perception, has eroded trust in information, especially with the collaboration of socio-digital networks and artificial intelligence (AI).Technological advances amplify the speed and sophistication with which disinformation spreads, making it difficult to identify and counteract false information, which could be identified with adequate digital literacy. With the use of algorithms and big data analysis, AI is used to personalize political messages, segment audiences and predict electoral trends, seeking not only to persuade voters, but also to create an immersive and emotionally attractive narrative. To do this, the article shows cases of deceiving the audience by presenting false information in a realistic way.Thanks to the formidable development of AI and the advent of synthetic humans, we are witnessing the profound transformation of the entertainment industry and, shortly, political marketing.New Explorations02How the disinformation industry, understood as organized and systematic practices aimed at disseminating false information with the aim of manipulating public perception, has eroded trust in information is analyzed, especially with the collaboration of socio-digital networks and artificial intelligence (AI).New Explorations["Octavio Islas", "Fernando Guti\u00e9rrez", "Amaia Arribas"]2024-05-28T00:00:00 7bdfc693a8b9f59fdfeba9cda2adb17b25690e59A growing volume of evidence marks the potential of Artificial Intelligence (AI) in medicine, in improving diagnostic accuracy, clinical decision support, risk/event prediction, drug discovery, and patient management. However, the continuous integration of AI into clinical settings requires the development of up-to-date and robust guidelines and standard frameworks that consider the evolving challenges of AI implementation in medicine. This review evaluates these guidelines quality and summarizes ethical frameworks, best practices, and recommendations. The Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool was used to assess the quality of guidelines based on six domains: scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence. The protocol of this review including the eligibility criteria, the search strategy data extraction sheet and methods, was published prior to the actual review with International Registered Report Identifier (IRRID) of (DERR1-10.2196/47105). The initial search resulted in 4,975 studies from two databases and five studies from manual search. Nine articles were selected for data extraction based on the eligibility criteria. We found that while guidelines generally excel in scope, purpose, and editorial independence, there is significant variability in applicability and the rigour of guideline development. Well-established initiatives such as DECIDE-AI, SPIRIT-AI, and CONSORT-AI have shown high quality, particularly in terms of stakeholder involvement. However, applicability remains a prominent challenge among the guidelines. We conclude that the reproducibility, ethical and environmental aspects of AI in medicine still need attention from both medical and AI communities. This review emphasizes the crucial need for high-quality guidelines and opens a new avenue in evaluating guidelines themselves. Our work highlights the need for working toward the development of integrated and comprehensive reporting guidelines that adhere to the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). This alignment is essential for fostering a cultural shift towards transparency and open science, which are pivotal milestone for sustainable digital health research.medRxiv472It is concluded that the reproducibility, ethical and environmental aspects of AI in medicine still need attention from both medical and AI communities and the crucial need for high-quality guidelines is emphasized and a new avenue in evaluating guidelines themselves is opened.JAMIA Open["K. Shiferaw", "Moritz Roloff", "Irina Balaur", "Danielle Welter", "Dagmar Waltemath", "A. Zeleke"]2024-05-28T00:00:00 270074879c5b1b0f979c2f4d83febdca1e76899bBackground Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear. Objective We aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS. Methods We conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups—either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating “strongly disagree” and 7 indicating “strongly agree,” we collected data on patient-physician trust in 3 dimensions: competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant’s sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance. Results In total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant’s sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician’s competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively). Conclusions We found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.Journal of Medical Internet Research572It is found that, in general, patient-physician trust was high, however, the findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations.Journal of Medical Internet Research["Anna G M Zondag", "Raoul Rozestraten", "S. Grimmelikhuijsen", "K. Jongsma", "Wouter W. Van Solinge", "M. Bots", "R. Vernooij", "S. Haitjema"]2024-05-28T00:00:00 40d81dd77dfb94b49af3f326fa1fb067aa5ff2a1Artificial intelligence is widely recognized as a pivotal component and a substantial component for sustainability growth within the domain of Fourth Industrial Revolution. despite the presence of noteworthy instances of achievement, certain research studies reveal that various sectors have exhibited a sluggishness in embracing artificial intelligence beyond the first proof-of-concept phase and incorporating it on a large scale inside their organizations. In order to examine this matter, a comprehensive research has been undertaken to investigate the elements that motivate and impede the implementation of artificial intelligence and assess the preparedness of various businesses in integrating artificial intelligence and big data technologies into their respective organizational frameworks. In order to obtain valuable insights on the viewpoints of industry professionals about the influence of artificial intelligence on business models and interactions with consumers, a structured questionnaire consisting of closed-ended questions has been designed and disseminated among the participants. Furthermore, a comprehensive strategic study has been undertaken utilizing the SWOT framework. The research has found a number of prominent obstacles, including inadequate availability of information technology infrastructure and proficient artificial intelligence expertise, inadequate access to superior data, insufficiently compelling justifications for company ventures, and intricate issues pertaining to legislation, regulations, and ethical considerations. The successful adoption and integration of artificial intelligence into business operations will heavily depend on the ability of industries to effectively tackle these difficulties.International Conference on Smart Communications and Networking122The research has found a number of prominent obstacles, including inadequate availability of information technology infrastructure and proficient artificial intelligence expertise, inadequate access to superior data, insufficiently compelling justifications for company ventures, and intricate issues pertaining to legislation, regulations, and ethical considerations.2024 International Conference on Smart Applications, Communications and Networking (SmartNets)["N. Mulongo"]2024-05-28T00:00:00 611144ac8d8ac0848000cee288a5542135b1e119As a globally recognized fashion label, ZARA is famous for its quick fashion designs and efficient supply chain management. However, to stay competitive in an age of rapid technological advancement, ZARA must continuously innovate and leverage technology to enhance its operations. This paper primarily explores the following aspects: Firstly, how artificial intelligence is utilized by ZARA to achieve swift responses and adaptable methods in the design and production processes; Secondly, how artificial intelligence is employed by ZARA to optimize supply chain management, enhance production efficiency, and mitigate inventory risks; Lastly, it will analyze the operational model of ZARA as a source of inspiration for fast fashion enterprises and its relevance to other industries. After conducting thorough analysis and research on the integration of science and technology in ZARA's operations, this study has arrived at the following conclusions: ZARA effectively utilizes artificial intelligence to swiftly respond to market demands and make flexible adjustments, thereby enhancing its ability to meet customer needs. AI also enhances the efficiency and reliability of ZARA's supply chain management, leading to improved production efficiency and reduced inventory risk. By leveraging AI, ZARA can offer personalized shopping experiences to customers, ultimately boosting satisfaction levels and fostering loyalty. This paper delves into how technology drives operational innovation and value creation at ZARA, offering valuable insights for other fashion brands while serving as an empirical case for research in the realm of science, technology, and operations management.Advances in Economics, Management and Political Sciences01How technology drives operational innovation and value creation at ZARA is delves into, offering valuable insights for other fashion brands while serving as an empirical case for research in the realm of science, technology, and operations management.Advances in Economics, Management and Political Sciences["Jiaqi Cao"]2024-05-28T00:00:00 f4b0700de8c2ff14da7825d8d59ecabff7bc902eThe development of Artificial Intelligence (AI) tools in early childhood learning has been a topic of interest in recent years. AI in early childhood education can be used to provide interactive, personalised and adaptive learning experiences. The use of AI tools in early childhood learning has great potential to improve the effectiveness and efficiency of learning. However, it is important to remember that the interaction and support of teachers and parents remains crucial in children's development, and AI should not replace the role of humans in the education process. Artificial intelligence (AI) tools, have been increasingly used in the field of early childhood education (ECE) to enhance learning and development among young children. Previous proof-of-concept studies have shown that AI can effectively improve learning and development in ECD; however, there is a dearth of knowledge on how these studies were conducted and how AI was used throughout these studies. The main problem faced by the partners is the lack of competence of these human resources in managing and applying AI tools in learning, especially in creating 3D caricatures that are useful to attract the attention of students, as well as to promote the school.PaKMas: Jurnal Pengabdian Kepada Masyarakat01The main problem faced by the partners is the lack of competence of these human resources in managing and applying AI tools in learning, especially in creating 3D caricatures that are useful to attract the attention of students, as well as to promote the school.PaKMas: Jurnal Pengabdian Kepada Masyarakat["Irsal Fauzi", "Dewi Ariani", "Abdul aziz", "Novita Herawati", "Ulfamiyati Ulfamiyati"]2024-05-28T00:00:00 25da86793d94747a0fe1e94fbaaefa7ef32fce13Artificial Intelligence (AI) and Digital Health Technologies (DHTs) are radically transforming drug development. The FDA and EMA have formulated guidance documents for their use in clinical trials. A pressing need exists for a harmonized approach to assess and implement AI and DHT methodologies as Drug Development Tools (DDTs). As a neutral entity leading public-private partnerships, The Critical Path Institute has the competencies and infrastructure to address AI and DHTs’ pivotal roles in drug development. You can read this publication in its entirety on the ASCPT website here.Clinical and Translational Science71As a neutral entity leading public-private partnerships, The Critical Path Institute has the competencies and infrastructure to address AI and DHTs’ pivotal roles in drug development.Clinical and Translational Science["J. Podichetty", "Sakshi Sardar", "Nick Henscheid", "Grace V. Lee", "J. Rubin Abrams", "Wes Anderson", "Shu Chin Ma", "Klaus Romero"]2024-05-28T00:00:00 6b8e38a4e23c8e56d68761418819c86dab364562Background Empathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions. Objective We aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy. Methods We conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user’s self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers. Results We found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t196=7.07, P<.001, Cohen d=0.60) or not aware (t298=3.46, P<.001, Cohen d=0.24) that an AI system wrote the story. We also found that participants reported greater willingness to empathize with AI-written stories when there was transparency about the story author (t494=–5.49, P<.001, Cohen d=0.36). Conclusions Our work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.JMIR Mental Health492Light is shed on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.JMIR Mental Health["Jocelyn Shen", "Daniella DiPaola", "Safinah Ali", "Maarten Sap", "Hae Won Park", "C. Breazeal"]2024-05-28T00:00:00 11a1078eec2e619030752f8f9d0124ff691aaacdPurposeCo-creation of services (CCOS) is a collaborative strategy that emphasises customer involvement and their expertise to increase the value of the service experience. In the service ecosystem, artificial intelligence (AI) plays a key role in value co-creation. Therefore, this study is undertaken to empirically uncover how AI can empower CCOS.Design/methodology/approachThe source data were collected from 305 service provider respondents and quantitative methodology was applied for data analysis.FindingsNew service development augmented with AI provides tangible value to service providers while also providing intangible value to supportive customers. With AI, service providers adapt to new innovations and enrich additional information, which eventually outperforms human-created services.Research limitations/implicationsAI adoption for CCOS empowerment in service businesses brings “service-market fit”, which represents the significant benefits wherein customers contribute to creativity, intuition, and contextual awareness of services, and AI contributes to large-scale service-related analysis by handling volumes of data, service personalisation, and more time to focus on challenging problems of the market.Originality/valueThis study presents theoretical concepts on AI-empowered CCOS, AI technological innovativeness, customer participation in human-AI interaction, AI-powered customer expertise, and perceived benefits in CCOS, and subsequently discusses the CCOS empowerment framework. Then, it proposes a novel conceptual model based on the theoretical concepts and empirically measures and validates the intention to adopt AI for CCOS empowerment. Overall, the study contributes to novel insight on empowering service co-creation with AI.Marketing Intelligence &amp; Planning1691A novel conceptual model based on the theoretical concepts and empirically measures and validates the intention to adopt AI for CCOS empowerment is proposed, contributing to novel insight on empowering service co-creation with AI.Marketing Intelligence &amp; Planning["Rajat Kumar Behera", "P. Bala", "Nirpendra P. Rana", "Zahir Irani"]2024-05-28T00:00:00 44cfd369ca87259e85530a6d96d539ba663526a6In today’s digitalized era, embracing new and emerging technologies is a requirement to remain competitive. The present research investigates the adoption of artificial intelligence (AI) by the elderly in the European landscape, emphasizing the importance of individuals’ digital skills. As has already been globally recognized, the most imminent demographic challenge is no longer represented by the rapid growth of the population but by its aging. Thus, the paper initially analyzed European perspectives on AI adoption, also discussing the importance of focusing on seniors. A bibliometric analysis was required afterward, and the review of the resulting relevant scientific publications uncovered gaps in understanding the relationship between older individuals and AI, particularly in terms of digital competence. Further exploration considered the EU population’s digital literacy and cultural influences using Hofstede’s model, while also identifying potential ways to improve the elderly’s digital skills and promote the adoption of AI. Results indicate a growing interest in AI adoption among the elderly, underscoring the urgent need for digital skills development. The imperative of personalized approach implementations, such as specialized courses, personalized training sessions, or mentoring programs, was underscored. Moreover, the importance of targeted strategies and collaborative efforts to ensure equitable participation in the digital age was identified as a prerequisite for AI adoption by seniors. In terms of potential implications, the research can serve as a starting point for various stakeholders in promoting an effective and sustainable adoption of AI among older citizens in the EU.Sustainability991The importance of targeted strategies and collaborative efforts to ensure equitable participation in the digital age was identified as a prerequisite for AI adoption by seniors, and results indicate a growing interest in AI adoption among the elderly, underscoring the urgent need for digital skills development.Sustainability["I. Bogoslov", "Sorina Corman", "A. Lungu"]2024-05-28T00:00:00 a409a8af848bacc3604d86ec988501f5330385e0This paper explores the integration of artificial intelligence (AI) technologies with human rights considerations in plastic waste recycling, focusing on the context of Nigeria. Using an explanatory design and a basic review of the literature, it examines how AI can enhance human rights protection among workers in plastic waste recycling firms. The paper emphasizes the role of government policies and regulations in ensuring human rights protection, the need for ethical guidelines for AI use, and the potential of AI to improve worker safety and reduce environmental pollution in recycling facilities. Key recommendations for future research include enhancing AI capabilities for waste sorting, integrating Internet of Things (IoT) devices for real-time monitoring, and prioritizing ethics in AI development. Collaboration between stakeholders is identified as crucial, with multi-stakeholder partnerships and policy coherence essential for the effective implementation of AI technologies in plastic waste recycling. In conclusion, integrating AI with human rights considerations in plastic waste recycling is pivotal for enhancing efficiency, reducing pollution, and ensuring sustainable waste management practices. This paper contributes to the growing body of literature on AI and human rights in waste management, offering insights for policymakers, industry stakeholders, and researchers in Nigeria and beyond.Journal of Public Administration, Finance and Law301Journal of Public Administration, Finance and Law["O. Solaja"]2024-05-28T00:00:00 07aeba4e10694fdc129136a5464a56f94cfef0c9METHODOLOGY A key-word search of artificial intelligence, artificial intelligence in medicine, and artificial intelligence models was done in PubMed and Google Scholar yielded more than 100 articles that were reviewed for summation in this article.Journal of Insurance Medicine222Journal of insurance medicine["R. C. Richie"]2024-05-28T00:00:00 32617204de65e0a9f99e797676580ef75fd63583In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and pro-duction planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.International Conference on Industrial Informatics822A comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI.2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)["Alexander Windmann", "P. Wittenberg", "Marvin Schieseck", "Oliver Niggemann"]2024-05-28T00:00:00 eaf8553f7804bd987eab02ce3fd755e237705d15The aim of this study is to elucidate the concept of artificial intelligence, identify obstacles to its implementation, and explore the relationship between the application of artificial intelligence technology and the administrative performance of employees in the directorate of education in Hebron, Palestine. Additionally, the study aims to propose effective strategies for overcoming these identified obstacles. The research adopts an explanatory sequential design methodology. To construct the questionnaire questions, unstructured interviews were conducted, while structured interviews were employed to interpret the results. The questionnaire was administered to 120 male and female employees. The study's findings revealed that the application of artificial intelligence technology among employees and the level of administrative performance both fall within the average range. Moreover, a positive correlation was observed between the application of artificial intelligence technology in its various dimensions and administrative performance. This suggests that increased utilization of artificial intelligence technology correlates with enhanced administrative performance among employees and vice versa. In light of these results, the researchers recommend a focus on enhancing infrastructure efficiency and providing adequate proper resources to facilitate the integration of artificial intelligence technology applications.TEM Journal00The study's findings revealed that the application of artificial intelligence technology among employees and the level of administrative performance both fall within the average range and suggests that increased utilization of artificial intelligence technology correlates with enhanced administrative performance among employees and vice versa.TEM Journal["Ibrahim Iwadi", "D. Ali", "Mohammed Jabari", "E. Suki\u0107"]2024-05-28T00:00:00 012382a0dc65eff6724ed60a1fecfd393655494cThe objective of this research is 1) to develop an Artificial Intelligence Artist Assistant (AIAA) model for the purpose of innovative digital storytelling in digital art education, 2) to evaluate the AIAA model, and 3) to study the results of the implemented model. The sample consists of two groups. The first group is made up of five experts in the field of AI, digital art, and storytelling, while the second group consists of 33 volunteers; they were tasked with creating animated storytelling. The research results show that the developed model consists of 3 elements. The first element is input, the second element is the AIAA process, and the third element is output. The five experts awarded the AIAA model the highest level of satisfaction (xˉ = 4.93, S.D. = 0.13), suitable for promoting storytelling. In addition, the 33 volunteers who tested the model awarded it a high level of satisfaction (xˉ = 3.78, S.D. = 0.83), that such AIAA model can help artists create better storytelling and enhance the storytelling process, in terms of speed of the process and details that enhance the story.TEM Journal00The research results show that the developed AIAA model can help artists create better storytelling and enhance the storytelling process, in terms of speed of the process and details that enhance the story.TEM Journal["Wannaporn Chujitarom", "Chaiporn Panichrutiwong"]2024-05-28T00:00:00 808f7c9d561768e6f579a0241d773177fbfc59ffThis paper allowed us to describe and develop a prototype of a system to monitor gases, smoke, temperature and humidity, aiming to detect fires, through the application of technologies such as Internet of Things (IoT), Artificial Intelligence (AI) and embedded systems. The system encompasses hardware, software and applications for mobile devices (Web/App), as well as an AI model called IoT-IA. Its relevance stands out in critical environments, such as industries, homes, commercial buildings and Data Centers, where surveillance is crucial to prevent damage caused by fires. Sensors collect information, which is then analyzed using AI techniques and made available remotely via the internet. The development methodology, hardware and software components, along with the results achieved, are detailed to illustrate how the system can improve security and efficiency in various contexts.Revista SODEBRAS00Revista SODEBRAS["Manoel Socorro Santos Azevedo", "Marcelo Weber Schiller", "Alysson Roberto Garcia Azevedo", "Edevaldo dos Santos Azevedo"]2024-05-28T00:00:00 2b8db5be0d3405e0f63d7d17b7b096f96c3b3352The present article employed McLuhan’s figure/ground distinction to probe the boundaries of artificial intelligence (AI) and computation. In popularizing the intellectual tradition of media ecology, Marshall McLuhan warned scholars that confusing the figure (i.e. the content or software) with the ground (i.e. the medium of communication or hardware) would lead to inadequate and incorrect analyses and appraisals of the effects of our media technologies. However, the present article contends that scholars of all stripes are at risk of falling prey to that exact mistake with regards to AI. The present article argues that AI, though having appeared as a touchstone issue in media and communication studies recently, represents the figure, whereas the computational hardware is the ground. Moreover, with continued development of quantum computation technologies, the ground upon which our AI programs rest is in the infantile stages of undergoing a revolutionary change. In sum, this article probes the significance of AI and Quantum computation for the coming decades.New Explorations00AI, though having appeared as a touchstone issue in media and communication studies recently, represents the figure, whereas the computational hardware is the ground, and with continued development of quantum computation technologies, the ground upon which the authors' AI programs rest is in the infantile stages of undergoing a revolutionary change.New Explorations["Erik Gustafson"]2024-05-28T00:00:00 a6c6c61fe6f3593f139c7411d8353726777e3dcdThe rapid uptake of artificial intelligence (AI) systems requires similar advances in their governance. Public and private sector institutions want to adopt new AI tools as they perceive potential efficiency gains and value from them. As with every technological advance, the uptake phase of AI is the ideal time to improve the governance, cybersecurity and safety of these systems. The cybersecurity risks in AI systems are similar to the ones in other information technology systems. However, the regulation of AI systems is changing, so new governance tools are needed. Furthermore, the safety and societal impact of AI depends on the technological choices made when building the systems (e.g., biased training data, overfitted machine learning models, model poisoning attacks or needlessly computation-heavy algorithms). AI tools built with large language model technology seem to speak our languages and therefore appear deceptively easy to adopt. The goal of our research is to provide risk management tools that are similarly easy to use, even if they later lead the adopter into setting up a full technical quality management system. We have created three blueprints of AI system deployments to which an organization deploying AI can match their use case. For each blueprint, we have created high-level guidance on which cybersecurity, data rights and ethical aspects the deploying organization needs to consider. Those building AI systems can quickly match their use cases against the blueprints and speed up the secure and ethical adoption of AI.level guidance on which cybersecurity, data rights and ethical aspects the deploying organization needs to consider. Those building AI systems can quickly match their use cases against the blueprints and speed up the secure and ethical adoption of AI.International Conference on Cyber Conflict370This research has created three blueprints of AI system deployments to which an organization deploying AI can match their use case, and created high-level guidance on which cybersecurity, data rights and ethical aspects the deploying organization needs to consider.2024 16th International Conference on Cyber Conflict: Over the Horizon (CyCon)["Dan Bogdanov", "Paula Etti", "Liina Kamm", "Fedor Stomakhin"]2024-05-28T00:00:00 972ad34b26bde898fbabe9e28c34fc8a472b9c18Traditionally, human rights activists gathered evidence about violations of particular individuals' human rights to demand that states change their conduct and adopt measures to prevent further violations. Deploying artificial intelligence as part of the decision-making process creates challenges for activists to detect all sources of harm and demand that states take action to address the harms. Abeba Birhane points out that employing artificial intelligence technology can generate harmful impacts that are either difficult to detect or invisible. If harms remain invisible, then it is difficult for human rights defenders to document them. Equally, it becomes challenging to articulate why the harms in question constitute international human rights law violations. As a result, it is harder for human rights defenders to call on states to take action to safeguard fundamental rights. This article puts forward that individuals can make harms arising from the deployment of artificial intelligence as part of the decision-making process more visible by using the theoretical framework of media ecology. It demonstrates that media ecology can provide an additional tool for human rights activists to detect how using artificial intelligence as part of the decision-making process can undermine the enjoyment of a human right. The article uses the right to mental health as a case study to develop this argument. In order to contextualise the analysis, the article focuses on the employment of artificial intelligence to screen candidates for employment as a case study.New Explorations00Media ecology can provide an additional tool for human rights activists to detect how using artificial intelligence as part of the decision-making process can undermine the enjoyment of a human right by using the theoretical framework of media ecology.New Explorations["T. Krupiy"]2024-05-28T00:00:00 bd909f3c3d4dc261c08a40126d1a4567e0167921Scholars disagree over whether the employment of artificial intelligence technologies entails an inevitable exercise of power over people or whether such technologies can be configured in such a way as to allow a plurality of possible ways to engage in governance. This article uses the media ecology approach to analysis to demonstrate that the concern that artificial intelligence technologies that appear to be mundane are in fact involved in the exercise of power over people is valid. It contributes to the existing literature by showing that numerous applications of artificial intelligence that people use on an everyday basis interact to amplify one another’s effects. These technologies are the electric toothbrush, internet search engine, smartphone, social media and the use of artificial intelligence as part of the decision-making process. These effects occur at the levels of the individual, city, state and inter-state. These effects are cascading and interconnected rather than occurring on distinct planes. The exercise of power over the individual by the state and the corporations becomes difficult to disentangle. Therefore, states need to cooperate regarding governing artificial intelligence and technology companies if they are to meaningfully protect people from harmful effects.New Explorations00This article demonstrates that the concern that artificial intelligence technologies that appear to be mundane are in fact involved in the exercise of power over people is valid and contributes to the existing literature by showing that numerous applications of artificial intelligence that people use on an everyday basis interact to amplify one another’s effects.New Explorations["T. Krupiy"]2024-05-28T00:00:00 e049a1350fd448177eccbc826f27672c1147e4a5This article explores the integration of artificial intelligence (AI) in marketing, highlighting the intersection between digital marketing and AI. The main objective of this research is to analyze the challenges and opportunities associated with this convergence, as well as to propose implementation strategies for companies. The marriage between digital marketing and artificial intelligence (AI) opens up new perspectives for companies seeking to optimize their marketing strategies. The integration of AI into digital marketing offers innovative possibilities for understanding and interacting with consumers in a more targeted and effective way. Highlighting the importance of a strategic and balanced approach to take full advantage of this synergy, by examining the theoretical and managerial implications of this strong synergy, this article aims to provide a better understanding of both strategies.International Journal of Advanced Multidisciplinary Research and Studies00By examining the theoretical and managerial implications of this strong synergy, by examining the theoretical and managerial implications of this strong synergy, this article aims to provide a better understanding of both strategies.International Journal of Advanced Multidisciplinary Research and Studies["Yassine Elkhatibi", "Redouane Benabdelouhed"]2024-05-28T00:00:00 74275443745e2d1551251e4965ae6ed9618988feArtificial intelligence (AI) is computer systems that can perform tasks that require human intelligence. It consists of data based on machine learning, deep learning and artificial neural networks. AI; with the increase in data collection and the ability to store large numbers of data, its use in the field of health has increased. It has been increasing rapidly recently. AI is being used more and more frequently with its features that help physicians in diagnosis, treatment planning, prognosis prediction and application of treatments. In this review, it is aimed to specify AI and its areas of use in the healthcare system.Journal of Gazi university health sciences institute190It is aimed to specify AI and its areas of use in the healthcare system and its features that help physicians in diagnosis, treatment planning, prognosis prediction and application of treatments.Journal of Gazi University Health Sciences Institute["Suna Deniz Bostanc\u0131", "Kevser \u00d6zdem Karaca", "M. A. Akcayol", "Mehmet Bani"]2024-05-28T00:00:00 575200c3761b70158275379487bf0f2095c7ac37Despite the existing legal definition, the concept of artificial intelligence, which has penetrated various spheres of human life, requires a more precise definition. However, in most cases, artificial intelligence is opposed to human intelligence, the so-called natural intelligence, which looks natural, but it is difficult to recognize as true. The article substantiates that the ways of understanding human intelligence and artificial intelligence (or machine intelligence) they are completely different because their nature is different. Based on the analysis of various approaches of scientists, both lawyers and engineers, as well as legislators to the definition of artificial intelligence, the author concludes that the most appropriate really understanding of artificial intelligence as a field of scientific knowledge of a complex nature. The author makes a conclusion about the ways of further application of artificial intelligence technologies in forensic activities, considering the spread of artificial intelligence technologies. The result of the study is the formulation of the definition of artificial intelligence technology.Courier of the Kutafin Moscow State Law University10The author concludes that the most appropriate really understanding of artificial intelligence as a field of scientific knowledge of a complex nature is understood as a field of scientific knowledge of a complex nature.Courier of Kutafin Moscow State Law University (MSAL))["O. G. Dyakonova"]2024-05-28T00:00:00 4671139070f35df5e170fa39c45022fb48d30ab2 This Viewpoint examines artificial intelligence–enabled clinical services, existing payment structures, and the economics of artificial intelligence pricing. JAMA Internal Medicine70JAMA internal medicine["Anna Zink", "M. Chernew", "Hannah T. Neprash"]2024-05-28T00:00:00 ed88dc5ff033313b1b4f54feaee9f5837bcaa330Business & Information Systems Engineering700The conjecture that current knowledge about tailorable technology design does not effectively account for IS that incorporate AI is posits and a Revised Theory of Tailorable Technology Design is proposed, culminating in a Revised Theory of Tailorable Technology Design.Bus. Inf. Syst. Eng.["Pascal Fechner", "Fabian K\u00f6nig", "J. Lockl", "Maximilian R\u00f6glinger"]2024-05-28T00:00:00 3ab75cf978146c54cdaef48ee6266bc38eecedf4Aims: It aims to evaluate the knowledge level of ChatGPT, Bing, and Bard artificial intelligence chatbots developed based on Large Language Models (LLM) about oculofacial plastic surgery and to investigate the presence of superiority over each other. Methods: Twenty-nine questions that tested knowledge about oculofacial plastic and orbital surgery were taken from the study questions section of the American Academy and Ophthalmology 2022-2023 Basic and Clinical Science Course Oculofacial Plastic and Orbital Surgery. The questions were asked to ChatGPT, Bing, and Bard programs, which are current artificial intelligence chatbots. The questions were classified as either correct or incorrect. Results: ChatGPT gave 44.8% correct answers, Bing 48.3% correct answers, and Bard 58.6% correct answers to 29 questions about artificial intelligence chatbots. No statistical difference was observed between the rates of correct and incorrect answers given by 3 the intelligence programs (p=0.609, Pearson’s chi-squared test). Conclusion: The use of artificial intelligence to access information regarding oculofacial plastic and orbital surgery may provide limited benefits. Care should be taken in terms of accuracy and timeliness when evaluating the results of artificial intelligence programs. Ankyra Medical Journal00The use of artificial intelligence to access information regarding oculofacial plastic and orbital surgery may provide limited benefits and care should be taken in terms of accuracy and timeliness when evaluating the results of artificial intelligence programs.Ankyra Medical Journal["Ey\u00fcpcan \u015eensoy", "M. \u00c7\u0131t\u0131r\u0131k"]2024-05-28T00:00:00 60f2fad7f8e03fdb242153ec3d03761a4d3eef54Abstract Objective Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpretable AI system and provide some valuable insights regarding short, and long-term mortality. Materials and methods We propose the RIAS framework, an end-to-end framework that is designed with reliability and interpretability at its core and automatically optimizes the given model. Using RIAS, clinicians get accurate and reliable predictions which can be used as likelihood, with global and local explanations, and “what if” scenarios to achieve desired outcomes as well. Results We apply RIAS to AMI prognosis prediction data which comes from the Korean Acute Myocardial Infarction Registry. We compared FT-Transformer with XGBoost and MLP and found that FT-Transformer has superiority in sensitivity and comparable performance in AUROC and F1 score to XGBoost. Furthermore, RIAS reveals the significance of statin-based medications, beta-blockers, and age on mortality regardless of time period. Lastly, we showcase reliable and interpretable results of RIAS with local explanations and counterfactual examples for several realistic scenarios. Discussion RIAS addresses the “black-box” issue in AI by providing both global and local explanations based on SHAP values and reliable predictions, interpretable as actual likelihoods. The system’s “what if” counterfactual explanations enable clinicians to simulate patient-specific scenarios under various conditions, enhancing its practical utility. Conclusion The proposed framework provides reliable and interpretable predictions along with counterfactual examples.J. Am. Medical Informatics Assoc.433The proposed RIAS framework, an end-to-end framework that is designed with reliability and interpretability at its core and automatically optimizes the given model, addresses the “black-box” issue in AI by providing both global and local explanations based on SHAP values and reliable predictions, interpretable as actual likelihoods.Journal of the American Medical Informatics Association : JAMIA["Minwook Kim", "Donggil Kang", "Min Sun Kim", "J. Choe", "Sun-Hack Lee", "Jinhee Ahn", "Jun-Hyok Oh", "Jung Hyun Choi", "Hancheol Lee", "K. Cha", "K. Jang", "Woor I Bong", "Giltae Song", "H. Lee"]2024-05-28T00:00:00 52475645ac67370924e86cf7021542bbadce26c3This study aims to investigate the impact of advanced grammar checkers, specifically Grammarly, on thesis writing in academic settings. Employing a qualitative methodology, the research involved semi-structured interviews with three experienced thesis supervisors from two academic institutions. The findings highlight that while Grammarly enhances writing quality through immediate, personalized feedback, it raises concerns about potential over-reliance, which may hinder the development of students' writing skills and academic independence. The research underscores the need for a balanced integration of AI tools in academic writing, advocating for their use as supplementary aids rather than primary solutions. It also calls for comprehensive training and clear policies to maximize the benefits of these tools while maintaining academic integrity and fostering critical thinking skills among students.Estudios y Perspectivas Revista Científica y Académica203The research underscores the need for a balanced integration of AI tools in academic writing, advocating for their use as supplementary aids rather than primary solutions and calls for comprehensive training and clear policies to maximize the benefits of these tools while maintaining academic integrity and fostering critical thinking skills among students.Estudios y Perspectivas Revista Científica y Académica["Norma Elena Mendoza Zaragoza", "A\u0301ngel Te\u0301llez Tula", "Laura Herrera Corona"]2024-05-28T00:00:00 03f1867eb6416964b9b2528721550887c9517a95International Journal of Human-Computer Interaction641International Journal of Human–Computer Interaction["Yugang Li", "Baizhou Wu", "Yuqi Huang", "Jun Liu", "Junhui Wu", "Shenghua Luan"]2024-05-28T00:00:00 4a351f57d5e117aa4a464f0e61db1631e9bd7d55Nurse Educator30Nurse educator["Christopher Hickman", "Penni Watts", "Matthew Jennings", "Curry Bordelon"]2024-05-28T00:00:00 d22555b54043593aacfda4d78ad7f69b80fadc4aOver time, a number of people have brought in their input with the intention of enriching the lives of others positively. The contributions of the study are aimed at enhancing the existing debate on the application of AI and data engineering through demonstration of the effective blend. For practitioners, the results act as a guide on how to adopt data engineering practices that improve PA systems’ performance reliability. For researchers, the study creates possibilities for other research to explore the relationship of emergent technologies and their efficiency in revolutionizing data flows. Call to Action Those organisations which are implementing AI solutions or have plans to do so should be aware of the central importance of data engineering to this effort. This way, they are able to develop more accurate and efficient as well as cheaper predictive analytic systems enabled by scalable, automated, and real-time data workflows. Stakeholders are urged to extend the findings of this research by examining more diverse sectors and forthcoming technologies to add new ideas and approaches to the field.Research and Analysis Journal710The results act as a guide on how to adopt data engineering practices that improve PA systems’ performance reliability and create possibilities for other research to explore the relationship of emergent technologies and their efficiency in revolutionizing data flows.Research and Analysis Journal["Narendra Devarasetty"]2024-05-28T00:00:00 fabfd957fc85728774120db443b68e4e68feb10cNew Explorations00New Explorations["William Kuhns"]2024-05-28T00:00:00 f18d798fddb472155a8f063b6ecea2cd42f116b9The 4th International Electronic Conference on Biosensors50The 4th International Electronic Conference on Biosensors["P. Barciela", "A. Perez-Vazquez", "Aurora Silva", "M. F. Barroso", "M. Carpena", "M. Prieto"]2024-05-28T00:00:00 e2db001caea30700a659aeefafbe3c73f1e09faeAORN Journal70AORN journal["Lindsay Fischer"]2024-05-28T00:00:00 868f1a23ef74faeb5d9d982b151dc754eeb07844New Explorations00New Explorations["Noa Billick", "Robert K. Logan", "Izabella Pruska-Oldenhof"]2024-05-28T00:00:00 fbff1d55e287eae3d9d5921dcb24d55be3af80ecPurpose This paper aims to (1) unveil how artificial intelligence (AI) can be implemented in growth-hacking strategies; and (2) identify the challenges and enabling factors associated with AI’s implementation in these strategies.Design/methodology/approach The empirical study is based on two distinct groups of analysis units. Firstly, it involves 11 companies (identified as F1 to F11 in Table 1) that employ growth-hacking principles and use AI to support their decision-making and operations. Secondly, interviews were conducted with four businesses and entrepreneurs providing consultancy services in growth and digital strategies. This approach allowed us to gain a broader view of the phenomenon. Data analysis was performed using the Gioia methodology.Findings The study firstly uncovers the principal benefits and applications of AI in growth hacking, such as enhanced data analysis and user behaviour insights, sales augmentation, traffic and revenue forecasting, campaign development and optimization, and customer service enhancement through chatbots. Secondly, it reveals the challenges and catalysts in AI-driven growth hacking, highlighting the crucial roles of experimentation, creativity and data collection.Originality/value This research represents the inaugural scientific investigation into AI’s role in growth-hacking strategies. It uncovers both the challenges and facilitators of AI implementation in this domain. Practically, it offers detailed insights into the operationalization of AI across various phases and aspects of growth hacking, including product-market fit, user acquisition, virality and retention.Management Decision547The study uncovers the principal benefits and applications of AI in growth hacking, such as enhanced data analysis and user behaviour insights, sales augmentation, traffic and revenue forecasting, campaign development and optimization, and customer service enhancement through chatbots.Management Decision["Gabriele Santoro", "Fauzia Jabeen", "Tomas Kliestik", "Stefano Bresciani"]2024-05-28T00:00:00 d7b9ea603b79df668cca18908ad3d459b5c0b01fArtificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM Computer Science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas.Conference on Software Engineering Education and Training432Preliminary results of a systematic analysis of current trends in the area of AI are presented, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners are presented.2024 36th International Conference on Software Engineering Education and Training (CSEE&T)["Michael Vierhauser", "Iris Groher", "Tobias Antensteiner", "Clemens Sauerwein"]2024-05-28T00:00:00 144e4b8b15278e783610ceba28e4dd1cd270686eComput. Support. Cooperative Work.582How the standards that emerge from the observation practices of radiologists challenge the automation of their vision work is highlighted, but also under what conditions AI technologies are considered “objective” and trustworthy by professionals.Comput. Support. Cooperative Work.["Giulia Anichini", "Chiara Natali", "Federico Cabitza"]2024-05-28T00:00:00 1a04a6f6fe95a6355c2b893395e9cca1a623af52Based on a focus on artificial intelligence (AI) policy in the European Union (EU), we explore the dominant approach taken to data justice in policy. More specifically, we ask how the particular issue of discrimination is translated into policy goals and measures as a way to address prominent concerns about AI. Looking at the stage of policy formulation, we provide an analysis of the way (non) discrimination is currently pursued within the EU's AI policy debate through the study of relevant policy documents and public consultations between 2017 and 2023. We argue that whilst the issue of discrimination has moved from the margins to the mainstream in policy debate, it has done so based on an understanding of discrimination as an inevitable risk of AI; such risk is specific to particular situations and the technological features of AI; the nature of this risk can be assessed and managed through a set of procedural safeguards; and such safeguards can be supported by the creation of a trustworthy AI market. Whilst this translation of justice is very important for contending with some of the critique surrounding the advancement of AI, it may also serve to contain and neutralize such critique in the interest of marketization.Policy &amp; Internet222It is argued that whilst the issue of discrimination has moved from the margins to the mainstream in policy debate, it has done so based on an understanding of discrimination as an inevitable risk of AI; the nature of this risk can be assessed and managed through a set of procedural safeguards.Policy &amp; Internet["J\u0119drzej Niklas", "Lina Dencik"]2024-05-28T00:00:00 5e73f48c19e83321fa000d18960a26dd04e52a9cBackground Home blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications. Objective This study assesses the effectiveness of a fully digital, autonomous, and artificial intelligence (AI)–based lifestyle coaching program on achieving BP control among adults with hypertension. Methods Participants were enrolled in a single-arm nonrandomized trial in which they received a BP monitor and wearable activity tracker. Data were collected from these devices and a questionnaire mobile app, which were used to train personalized machine learning models that enabled precision lifestyle coaching delivered to participants via SMS text messaging and a mobile app. The primary outcomes included (1) the changes in systolic and diastolic BP from baseline to 12 and 24 weeks and (2) the percentage change of participants in the controlled, stage-1, and stage-2 hypertension categories from baseline to 12 and 24 weeks. Secondary outcomes included (1) the participant engagement rate as measured by data collection consistency and (2) the number of manual clinician outreaches. Results In total, 141 participants were monitored over 24 weeks. At 12 weeks, systolic and diastolic BP decreased by 5.6 mm Hg (95% CI −7.1 to −4.2; P<.001) and 3.8 mm Hg (95% CI −4.7 to −2.8; P<.001), respectively. Particularly, for participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 9.6 mm Hg (95% CI −12.2 to −6.9; P<.001) and 5.7 mm Hg (95% CI −7.6 to −3.9; P<.001), respectively. At 24 weeks, systolic and diastolic BP decreased by 8.1 mm Hg (95% CI −10.1 to −6.1; P<.001) and 5.1 mm Hg (95% CI −6.2 to −3.9; P<.001), respectively. For participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 14.2 mm Hg (95% CI −17.7 to −10.7; P<.001) and 8.1 mm Hg (95% CI −10.4 to −5.7; P<.001), respectively, at 24 weeks. The percentage of participants with controlled BP increased by 17.2% (22/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The percentage of participants with stage-2 hypertension decreased by 25% (32/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The average weekly participant engagement rate was 92% (SD 3.9%), and only 5.9% (6/102) of the participants required manual outreach over 24 weeks. Conclusions The study demonstrates the potential of fully digital, autonomous, and AI-based lifestyle coaching to achieve meaningful BP improvements and high engagement for patients with hypertension while substantially reducing clinician workloads. Trial Registration ClinicalTrials.gov NCT06337734; https://clinicaltrials.gov/study/NCT06337734JMIR Cardio402The study demonstrates the potential of fully digital, autonomous, and AI-based lifestyle coaching to achieve meaningful BP improvements and high engagement for patients with hypertension while substantially reducing clinician workloads.JMIR Cardio["Jared Leitner", "Po-Han Chiang", "P. Agnihotri", "Sujit Dey"]2024-05-28T00:00:00 a0c31f680463082fbab1012b5f174baff919dd76This paper examines how explanations related to the adverse outcomes of Artificial Intelligence (AI) contribute to the development of causal evidentiary explanations in disputes surrounding AI liability. The study employs a dual approach: first, it analyzes the emerging global caselaw in the field of AI liability, seeking to discern prevailing trends regarding the evidence and explanations considered essential for the fair resolution of disputes. Against the backdrop of those trends, the paper evaluates the upcoming legislation in the European Union (EU) concerning AI liability, namely the AI Liability Directive (AILD) and Revised Product Liability Directive (R-PLD). The objective is to ascertain whether the systems of evidence and procedural rights outlined in this legislation, particularly the right to request the disclosure of evidence, enable litigants to adequately understand the causality underlying AI-related harms. Moreover, the paper seeks o determine if litigants can effectively express their views before dispute-resolution authorities based on that understanding. An examination of the AILD and R-PLD reveals that their evidence systems primarily support ad hoc explanations, allowing litigants and courts to assess the extent of the defendants' compliance with the standards enshrined in regulatory instruments, such as the AI Act. However, the paper contends that, beyond ad hoc explanations, achieving fair resolution in AI liability disputes necessitates post-hoc explanations. These should be directed at unveiling the functionalities of AI systems and the rationale behind harmful automated decisions. The paper thus suggests that ‘full’ explainable AI (XAI) that is, both ad hoc and post hoc, is necessary so that the constitutional requirements associated with the right to a fair trial (access to courts, equality of arms, contradictory debate) can be effectively met. Keywords: AI, Causation, Explainability, Fair Trial, Procedural Fairness, Equality of Arms, Effective Participation, AI liability, Product Liability, AI Act, AI Liability Directive, Product Liability DirectiveInternational Journal of Law, Ethics, and Technology01It is suggested that ‘full’ explainable AI (XAI) that is, both ad hoc and post hoc, is necessary so that the constitutional requirements associated with the right to a fair trial can be effectively met.International Journal of Law, Ethics, and Technology["Ljupcho Grozdanovski"]2024-05-28T00:00:00 e25e7d9a0b4f7c5bb7e74780c17d2bde735b2991Objective: This paper focuses on ways artificial intelligence and business intelligence technologies can be utilized to improve the efficiency of an organization, this paper aims to propose a framework for organizational use to leverage AI and its performance in organizations.   Theoretical Framework: In this topic, the author proposes a framework for integrating AI and BI, which includes the following components: data collection and statistical analysis, machine learning algorithms, visualization, and integration with BI systems.   Method: This study will employ a statistical methodology called multiple regression, the analysis helps to examine the relationship between a dependent variable (organizational performance) and multiple independent variables (extent of BI integration, extent of AI integration, and AI and BI integration).    Results and Discussion: Combining AI and BI in business processes improves decision-making, increases efficiency, and enhances customer experiences. AI automates complex processes, detects patterns, and predicts based on vast data, enhancing BI systems. AI-powered BI systems give businesses a competitive edge by improving decision-making, performance, and customer engagement. Integrating AI and BI leads to improved organizational performance.   Research Implications: This paper stresses the significance of evaluating organizational structure before implementing AI-based systems. It guides infrastructure requirements for successful AI implementation in business, emphasizing security implications when making decisions based on AI-generated data.   Originality/Value: Integrating AI and BI can lead to faster and more efficient business processes, resulting in higher performance. AI technology can revolutionize decision-making across industries, including business. Practical recommendations are available on how to use AI to improve decision-making in organizations.Revista de Gestão Social e Ambiental121A framework for organizational use to leverage AI and its performance in organizations is proposed, which includes the following components: data collection and statistical analysis, machine learning algorithms, visualization, and integration with BI systems.Revista de Gestão Social e Ambiental["M. Al-Momani"]2024-05-28T00:00:00 5e5a33340f8055d90c23e5bd4d30d8bc7bc4cc29The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fairness, while tremendous calls for AI fairness have been raised from healthcare. Despite this, a significant disconnect persists between technical advancements and their practical clinical applications, resulting in a lack of contextualized discussion of AI fairness in clinical settings. Through a detailed evidence gap analysis, our review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions. We highlight the scarcity of research on AI fairness in many medical domains where AI technology is increasingly utilized. Additionally, our analysis highlights a substantial reliance on group fairness, aiming to ensure equality among demographic groups from a macro healthcare system perspective; in contrast, individual fairness, focusing on equity at a more granular level, is frequently overlooked. To bridge these gaps, our review advances actionable strategies for both the healthcare and AI research communities. Beyond applying existing AI fairness methods in healthcare, we further emphasize the importance of involving healthcare professionals to refine AI fairness concepts and methods to ensure contextually relevant and ethically sound AI applications in healthcare.arXiv.org01Through a detailed evidence gap analysis, this review systematically pinpoints several deficiencies concerning both healthcare data and the provided AI fairness solutions and advances actionable strategies for both the healthcare and AI research communities.ArXiv["Mingxuan Liu", "Yilin Ning", "Salinelat Teixayavong", "Xiaoxuan Liu", "M. Mertens", "Yuqing Shang", "Xin Li", "Di Miao", "Jie Xu", "D. Ting", "Lionel Tim-Ee Cheng", "J. Ong", "Zhen Ling Teo", "Ting Fang Tan", "Narrendar RaviChandran", "Fei Wang", "L. Celi", "M. Ong", "Nan Liu"]2024-05-28T00:00:00 2decf147454fea7a7013dbd3e8cf363c8312a502Abstract The article presents a historical overview of the classification of contemporary artworks that either have utilized artificial intelligence as a tool in their creation or focus on AI as their central theme or subject matter. The authors analyze artworks and descriptions, focusing on artists’ motivations and AI’s role in their practice, identifying five distinct tropes in AI art. The authors compare artworks with respect to key questions, creating a useful tool for art historians, curators, researchers, and artists. This historical classification provides a structured approach to understanding AI art’s creative significance and attributes as it has developed over time.Leonardo: Journal of the International Society for the Arts, Sciences and Technology361A historical overview of the classification of contemporary artworks that either have utilized artificial intelligence as a tool in their creation or focus on AI as their central theme or subject matter is presented.Leonardo["Guido Salimbeni", "S. Benford", "Stuart Reeves", "Sarah Martindale"]2024-05-28T00:00:00 bed920310197ccfe5d6f76c163d093c134fc1a1dIn the era of artificial intelligence, the diversity of data modalities and annotation formats often renders data unusable directly, requiring understanding and format conversion before it can be used by researchers or developers with different needs. To tackle this problem, this article introduces a framework called Dataset Description Language (DSDL) that aims to simplify dataset processing by providing a unified standard for AI datasets. DSDL adheres to the three basic practical principles of generic, portable, and extensible, using a unified standard to express data of different modalities and structures, facilitating the dissemination of AI data, and easily extending to new modalities and tasks. The standardized specifications of DSDL reduce the workload for users in data dissemination, processing, and usage. To further improve user convenience, we provide predefined DSDL templates for various tasks, convert mainstream datasets to comply with DSDL specifications, and provide comprehensive documentation and DSDL tools. These efforts aim to simplify the use of AI data, thereby improving the efficiency of AI development.arXiv.org01This article introduces a framework called Dataset Description Language (DSDL) that aims to simplify dataset processing by providing a unified standard for AI datasets, and provides predefined DSDL templates for various tasks, convert mainstream datasets to comply with DSDL specifications, and provide comprehensive documentation and DSDL tools.ArXiv["Bin Wang", "Linke Ouyang", "Fan Wu", "Wenchang Ning", "Xiao Han", "Zhiyuan Zhao", "Jiahui Peng", "Yiying Jiang", "Dahua Lin", "Conghui He"]2024-05-28T00:00:00 32eb235b74ef1c694f9e266b3800efce833294b2Journal of imaging informatics in medicine761The overall aim of the present review is to make it easier to train and validate current and upcoming CAP tools for TIL assessment by inspecting and evaluating existing publicly available online datasets.Journal of Imaging Informatics in Medicine["Alessio Fiorin", "Carlos L\u00f3pez Pablo", "Maryl\u00e8ne Lejeune", "Ameer Hamza Siraj", "V. Della Mea"]2024-05-28T00:00:00 91dd069b8c5f8d6cc125ddf0bc1ce78ac3fe291a"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. ©RSNA, 2024.Radiology: Artificial Intelligence029Radiology. Artificial intelligence["Ali S. Tejani", "M. Klontzas", "Anthony A Gatti", "John T Mongan", "Linda Moy", "Seong Ho Park", "Charles E. Kahn"]2024-05-29T00:00:00 d50dea4c6e67029f063c91b89638a6c0e43ea62aAntibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.Antibiotics7213A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.Antibiotics["Francesco Branda", "Fabio Scarpa"]2024-05-29T00:00:00 0b8b61d4b6ec22e9227109acfcb896736598da12The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI's impact on science and medicine. The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year's edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.arXiv.org010The 2024 Index is the most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced, with new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI's impact on science and medicine.ArXiv["Nestor Maslej", "Loredana Fattorini", "Ray Perrault", "Vanessa Parli", "Anka Reuel", "Erik Brynjolfsson", "J. Etchemendy", "Katrina Ligett", "Terah Lyons", "James Manyika", "Juan Carlos Niebles", "Y. Shoham", "Russell Wald", "Jack Clark"]2024-05-29T00:00:00 258395edb3782142ccf10dd428c2a4cb131c6269As artificial intelligence (AI) technologies rapidly advance, they bring about important societal implications involving privacy, fairness, non-discrimination, and other relevant ethical considerations. Legislators and policymakers are joined by a common drive to provide legislative solutions and regulatory frameworks that guarantee that the ongoing integration of AI systems into society is consistent with fundamental rights and democratic values. This article explores the significant role that standardisation plays in this regulatory process and how it impacts the regulation and governance of AI within the European Union (EU). In particular, the paper provides a critical analysis of the regulatory approach adopted by the EU legislator for the AI Act, which delegates the definition of essential requirements for high-risk AI systems to harmonised standards, underlining the significance of standardisation in ensuring technical feasibility and compliance with EU laws and values. At the forefront of this discussion, there is the increasing influence of AI-related standardisation across social, economic, and geopolitical domains, with a particular focus on the crucial role played by Standard Developing Organisations (SDOs) in the regulatory and governance processes. This paper contributes to the legal scholarship by critically analysing the regulatory approach chosen for the EU’s AI Act, contesting the adequacy of the New Legislative Framework for AI governance, and arguing that the reliance on harmonised standards risks undermining democratic accountability and fails to sufficiently safeguard fundamental rights without a more inclusive and transparent standard-setting process. The article focuses on the exclusion of the European Telecommunications Standards Institute (ETSI) from the European Commission’s standardisation request in support of the AI Act and assesses its potential impact on EU law-making and regulatory consistency. Ultimately, the analysis aims to contribute to understanding standardisation dynamics, offering insights into its profound implications for AI governance and the broader digital sphere.Social Science Research Network408A critical analysis of the regulatory approach chosen for the EU's AI Act is provided, contesting the adequacy of the New Legislative Framework for AI governance, and arguing that the reliance on harmonised standards risks undermining democratic accountability and fails to sufficiently safeguard fundamental rights without a more inclusive and transparent standard-setting process.SSRN Electronic Journal["Marta Cantero Gamito"]2024-05-29T00:00:00 7e8a899add2f8c6291f3eba95e156d72360fc55cPurposeThis study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.Design/methodology/approachQualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.FindingsWe identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.Research limitations/implicationsWhile the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.Practical implicationsOur research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.Originality/valueOur granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.Information Technology and People615The granular approach provides a novel point of view on AI implementation, as the findings challenge the notion of full automation of mechanical and partial automation of thinking tasks and put forward augmentation as the most viable AI implementation approach.Inf. Technol. People["Elena Mazurova", "Willem Standaert"]2024-05-29T00:00:00 fc1d7de90f08a3347fdfae9468da7ee693ec6dd8 This article examines the challenges of regulating artificial intelligence (AI) systems and proposes an adapted model of regulation suitable for AI’s novel features. Unlike past technologies, AI systems built using techniques like deep learning cannot be directly analyzed, specified, or audited against regulations. Their behavior emerges unpredictably from training rather than intentional design. However, the traditional model of delegating oversight to an expert agency, which has succeeded in high-risk sectors like aviation and nuclear power, should not be wholly discarded. Instead, policymakers must contain risks from today’s opaque models while supporting research into provably safe AI architectures. Drawing lessons from AI safety literature and past regulatory successes, effective AI governance will likely require consolidated authority, licensing regimes, mandated training data and modeling disclosures, formal verification of system behavior, and the capacity for rapid intervention.Policy & Society915Policy and Society["Brian Judge", "Mark Nitzberg", "Stuart Russell"]2024-05-29T00:00:00 b9603d3da399e08ade3fdac39ec8fb23b87f6ccaDisruptions, such as the recent pandemic, highlight the pivotal role that human resource management (HRM) professionals play in guiding their organizations through change. This panel explores how these professionals, along with other managers and leaders in their organizations, are using technology to respond and adapt to disruptions and ever-changing societal and workforce needs, including generative artificial intelligence, analytics, and diversity, equity, and inclusion strategies.SIGMIS-CPR151This panel explores how HRM professionals are using technology to respond and adapt to disruptions and ever-changing societal and workforce needs, including generative artificial intelligence, analytics, and diversity, equity, and inclusion strategies.Proceedings of the 2024 Computers and People Research Conference["K. Abston", "Murat Arik", "Keith Jacks Gamble"]2024-05-29T00:00:00 a419c89128e2ca01342e974762bebaaa5e63f93bLoopholes involve misalignments between rules about what should be done and what is actually done in practice. The focus of this paper is loopholes in interactions between human organizations’ implementations of task-specific artificial intelligence and individual people. The importance of identifying and addressing loopholes is recognized in safety science and in applications of AI. Here, an examination is provided of loophole sources in interactions between human organizations and individual people. Then, it is explained how the introduction of task-specific AI applications can introduce new sources of loopholes. Next, an analytical framework, which is well-established in safety science, is applied to analyses of loopholes in interactions between human organizations, artificial intelligence, and individual people. The example used in the analysis is human–artificial intelligence systems in gig economy delivery driving work.Informatics1041An analytical framework well-established in safety science is applied to analyses of loopholes in interactions between human organizations, artificial intelligence, and individual people and an example is human–artificial intelligence systems in gig economy delivery driving work.Informatics["Stephen Fox", "J. Victores"]2024-05-29T00:00:00 a683461ae8625eb790f74352fa227111ba489942The influence of artificial intelligence technology, infrastructure, and human resource competence on Internet access networks has been examined in a scientific publication, which is the outcome of a literature review in the information systems sector. The purpose of this study is to generate an influence hypothesis related to factors that can be applied in further investigations. The research's subjects include academic media, Google Scholar, Mendeley, and online libraries. Publicly accessible e-books and e-journals are the source of the research methodology that makes use of library institution searches. The following is a descriptive qualitative analysis of this article's findings: The influence of artificial intelligence technology on networks for Internet access, the influence of infrastructure on networks for Internet access, and the Influence of HR Competency on Internet Access Networks. Research findings regarding the impact of advances in infrastructure, human resource competence, and artificial intelligence technology on internet network access include improving user experience, extensive network scalability, increasing network efficiency and security, as well as increasing human resource knowledge and developing network architecture research findings regarding the impact of advances in infrastructure, human resource competence, and artificial intelligence technology on internet network access include improving user experience, extensive network scalability, increasing network efficiency and security, as well as increasing human resource knowledge and developing network architecture.Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi852Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi["Muryan Awaludin", "Verdi Yasin", "Fitria Risyda"]2024-05-29T00:00:00 bdf44f8846cbc1c29c4c732147a791dc81127d45The advent of Artificial Intelligence is expected to imply profound changes in the short-term. It is therefore imperative for Academia, and particularly for the Computer Science scope, to develop cross-disciplinary tools that bond AI developments to their social dimension. To this aim, we introduce the Challenge-Device-Synthesis methodology (CDS), in which a specific challenge is presented to the students of AI, who are required to develop a device as a solution for the challenge. The device becomes the object of study for the different dimensions of social transformation, and the conclusions addressed by the students during the discussion around the device are presented in a synthesis piece in the shape of a 10-page scientific paper. The latter is evaluated taking into account both the depth of analysis and the level to which it genuinely reflects the social transformations associated with the proposed AI-based device. We provide data obtained during the pilot for the implementation phase of CDS within the subject of Social Innovation, a 6-ECTS subject from the 6th semester of the Degree of Artificial Intelligence, UAB-Barcelona. We provide details on temporalisation, task distribution, methodological tools used and assessment delivery procedure, as well as qualitative analysis of the results obtained.EDULEARN Proceedings121The Challenge-Device-Synthesis methodology (CDS), in which a specific challenge is presented to the students of AI, who are required to develop a device as a solution for the challenge, is introduced.ArXiv["M. Bilkis", "Joan Moya Kohler", "F. Vilarino"]2024-05-29T00:00:00 afc825231af2a3d7882cbcb604f460230c67b987Artificial Intelligence (AI) telah mengubah banyak aspek kehidupan, termasuk pendidikan. Sebagai fasilitator utama proses pembelajaran, guru harus memahami dan memanfaatkan AI untuk meningkatkan kualitas pembelajaran dan membuat belajar lebih menarik. Guru pada SD Global Islamic School Depok berusaha untuk mengikuti perkembangan teknologi dengan pemanfaatan tools AI yang digunakan dalam pembelajaran. Tujuan kegiatan ini adalah melakukan pelatihan atau pendampingan kepada guru dengan pemanfaatan tools AI yaitu Education CoPilot dan Google Gemini sehingga dapat mngoptimallkan kreativitas guru dalam persiapan pembelajaran baik dalam perencanaan, meningkatkan pemahaman dan pembuatan bahan ajar, evaluasi pembelajaran kepada siswa. Metode pelaksanaan yang dilakukan terdiri dari empat tahapan yaitu analisis kebutuhan, pembuatan materi pelatihan, pelaksanaan pelatihan serta evaluasi dan pelaporan. Hasil evaluasi yang dilakukan setelah pelatihan menunjukkan terjadi peningkatan pengetahuan guru 82.1% , hal  ini memberikan manfaat untuk meningkatkan kualitas pembelajaran, guru memperoleh kemampuan untuk beradaptasi dengan teknologi terkini, membantu guru untuk berinovasi dan berinteraksi secara lebih efektif dan efisien dalam proses pembelajaran.Jurnal Pengabdian Masyarakat Bangsa91Jurnal Pengabdian Masyarakat Bangsa["Winda Widya Ariestya", "Ida Astuti", "Syamsi Ruhama", "Dewi Anggraini Puspa Hapsari", "Nurul Adhayanti"]2024-05-29T00:00:00 b24cd6730256ced735b6fab7ef3ad637fcf9a0b0Incorporating Artificial Intelligence (AI) into Human Resource Management (HRM) has become a significant driving force in shaping contemporary workplaces. This paper comprehensively examines AI's influence on HRM, from its foundational concepts to its practical applications, advantages, challenges, ethical considerations, legal ramifications, anticipated trends, and actionable recommendations. Commencing with an introductory framework, the paper navigates the intricate facets of AI within HRM, elucidating its diverse components and functionalities. It further scrutinizes AI's specific roles in recruitment, training, performance management, and employee engagement, emphasizing its transformative potential. Additionally, the paper articulates the manifold benefits AI affords HRM, such as process optimization, informed decision-making, and enhanced employee engagement, juxtaposed against the inherent challenges, including data integrity, privacy concerns, biases, and algorithmic transparency issues. Addressing AI's ethical and legal dimensions in HRM, the paper underscores the imperative of conscientious AI integration and governance. Furthermore, it anticipates forthcoming AI trends and furnishes strategic guidance for organizations navigating this evolving landscape. Ultimately, the paper advocates for ethical, transparent, and human-centric approaches to AI adoption, underscoring its profound impact on HRM practices and workplace dynamics.International Journal For Multidisciplinary Research201This paper articulates the manifold benefits AI affords HRM, such as process optimization, informed decision-making, and enhanced employee engagement, juxtaposed against the inherent challenges, including data integrity, privacy concerns, biases, and algorithmic transparency issues.International Journal For Multidisciplinary Research["Ritika Gupta"]2024-05-29T00:00:00 df2582df4156706ef0e12a6a8174a7871b78eba4This research report investigates students' experiences using Artificial Intelligence (AI) in the thesis writing process. Data collection was carried out by means of in-depth interviews with 6 undergraduate students at Sanata Dharma University. Transcendental phenomenology was used in this research to dig deeper into how they interacted with AI and how AI influenced their thesis writing process. This research displays three emerging themes:1) AI improved students' motivation in writing a thesis, 2) AI fosters decision-making to solve problems, 3) AI promotes students’ self-confidence and shifting attitudes. This research contributes to understanding how the use of AI affects students' experiences and their thesis writing process. Further research is needed to understand the long-term impact of using AI in the thesis writing process and higher education as a whole.NUSRA: Jurnal Penelitian dan Ilmu Pendidikan191This research investigates students' experiences using Artificial Intelligence in the thesis writing process and displays three emerging themes:1) AI improved students' motivation in writing a thesis, 2) AI fosters decision-making to solve problems, 3) AI promotes students’ self-confidence and shifting attitudes.NUSRA : Jurnal Penelitian dan Ilmu Pendidikan["Marta Cahya Ratih", "Fidelis Chosa Kastuhandani"]2024-05-29T00:00:00 257ac30e35e295e3e6cdedae7443cb747b7dc989This paper focuses on the interdisciplinary and collaborative approach underpinning the European funded project Edu4AI “Artificial Intelligence and Machine Learning to Foster 21st Century Skills in Secondary Education”. The methodology has been conceived to enhance the practice of teaching from course design to content delivery, drawing inspiration from social constructivist theories, and inquiry project based learning instructional methods, combining elements from the maker movement and the educational robotics platforms. The final output of this process has been a particle handbook that comprises ready to use project toolkits, suitable to guide the seamless integration of Artificial Intelligence (AI) and Machine Learning (ML) in K12 school curricula, including non-scientific ones. The paper introduces the theoretical frameworks inspiring the toolkits that have been created cooperatively with the teachers’ community and piloted in the school real contexts for validation. One of the toolkit projects is also presented in the article, outlining the corresponding learning goals in terms of both hard and transversal life skills acquired, in order to ensure correspondence with students' learning outcomes evaluation. Following the presentation of the results from questionnaires collected during the project, the article concludes with some key recommendations for practitioners willing to replicate the initiative.Proceedings of The World Conference on Research in Education300The paper introduces the theoretical frameworks inspiring the toolkits that have been created cooperatively with the teachers' community and piloted in the school real contexts for validation, and outlines the corresponding learning goals in terms of both hard and transversal life skills acquired.Proceedings of The World Conference on Research in Education["Annaleda Mazzucato", "Silvia Larghi"]2024-05-29T00:00:00 81d343632174452950bb595dc11e3198452b98eaAbstract: Higher education leaders anticipate various ways in which Artificial Intelligence will be applied within their institutions. There can be substantial value in data analysis, supplemental applications for educational and developmental processes, and complex problem solving. Decision-makers must be mindful of problems that may arise from the implementation of hyper-rational management practices, extensive surveillance systems, and applications that could control and narrow the experience of students physically, emotionally, and intellectually. AI offers valuable tools but also significant risks. Institutional leaders face complex and highly consequential decisions about how such technology will be deployed and shape the ongoing evolution of colleges and universities.Journal of Educational Thought / Revue de la Pensée Educative00There can be substantial value in data analysis, supplemental applications for educational and developmental processes, and complex problem solving in Artificial Intelligence.Journal of Educational Thought / Revue de la Pensée Educative["Jeffery P. Aper"]2024-05-29T00:00:00 11ecab332e5f428eda14e9a8f6ae6fb5a725f173Human Brain Intelligence cannot be Dwarfed by AI (Artificial Intelligence), a Brain Child of Human, but a Remarkable Tool to handle Colossal Data more Efficiently with a Greater Speed, Accuracy using Smart Algorithms Devoid of Emotions & New Science!Current Natural Sciences and Engineering00Current Natural Sciences and Engineering["R. K. Kotnala"]2024-05-29T00:00:00 12947452929444cc8b414cad6e126552fa1a871dAn increasing number of colleges and universities are introducing Generative Artificial Intelligence (GAI) in their teaching/learning frameworks. This study examines the feedback from 152 students across Higher Education Institutions (HEIs), representing diverse scientific areas, namely Engineering, Lit-erature, Business and Accounting, Sports. It aims to explore the integration of GAI features in education and students' perception on its advantages and disadvantages. Students' top benefit was ‘Personalized learning’. They also valued ‘efficient content creation’, and ‘individualized assessment tools’. Their major concern was ‘Ethical considerations‘, and it varied by demographic variables. Other distresses included ‘Lack of control of content creation’, ‘over-reliance’, and ‘AI depersonalization’, and ‘decreased interpersonal engagement’. Of utmost important conclusion is that HE students agree and strongly agree that AI came to disrupt HEIs' educational process.2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE)130Examination of feedback from 152 students across Higher Education Institutions finds that HE students agree and strongly agree that AI came to disrupt HEIs' educational process.2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE)["L. Babo", "Jorge Mendon\u00e7a", "Ricardo Queir\u00f3s", "C. M. Pinto", "M\u00e1rio Cruz", "Daniela Mascarenhas"]2024-05-29T00:00:00 6794911c1b74f0aea9cabb3306247f735c7b6336This abstract provides a concise overview of the applications, benefits, and challenges of artificial intelligence (AI) in the pharmaceutical industry. AI technologies are revolutionizing drug discovery, clinical trials, personalized medicine, drug manufacturing, and more. While AI offers advantages such as error minimization, assistance in complex tasks, and continuous operation, challenges including the need for extensive training data and high costs must be addressed. Despite these limitations, AI holds significant promise in transforming the pharmaceutical landscape, enhancing efficiency, and improving patient outcomes.Research Journal of Science and Technology440An overview of the applications, benefits, and challenges of artificial intelligence (AI) in the pharmaceutical industry and how to address the need for extensive training data and high costs is provided.Research Journal of Science and Technology["Bhushan S. Mahajan", "Bhupendra Sing P. Mahale", "Amol R. Pawar", "Vikas V. Patil", "Pankaj S. Patil", "Jayesh Songire"]2024-05-29T00:00:00 536d90f09719f9260a23ebcf2df5a9c761f8f1e8Artificial intelligence (AI) has great potential to transform the way antibiotics are managed in healthcare by providing creative ways to fight antibiotic resistance and enhance patient outcomes. This paper examines the various aspects of AI's function in the management of antibiotics, including diagnosis, tailored treatment, infection surveillance, and future implications. The talk focuses on the potential advantages of AI-driven methods, such as improved diagnostic precision, customized treatment plans, and proactive monitoring of patterns of antibiotic resistance. But there are several obstacles to overcome before AI can be fully applied in the healthcare industry. These include issues with technical complexity, data accessibility and quality, clinical acceptability, regulatory concerns, and long-term financial viability. Collaboration amongst partners, financial support for infrastructure and resources, and a dedication to moral, patient-centered care are all necessary to meet these obstacles. Notwithstanding these challenges, AI-driven antibiotic management has enormous potential to revolutionize global patient outcomes, fight antibiotic resistance, and change healthcare delivery.International Journal of Multidisciplinary Sciences and Arts390Examining the various aspects of AI's function in the management of antibiotics, including diagnosis, tailored treatment, infection surveillance, and future implications focuses on the potential advantages of AI-driven methods, such as improved diagnostic precision, customized treatment plans, and proactive monitoring of patterns of antibiotic resistance.International Journal of Multidisciplinary Sciences and Arts["Alexandra Harry"]2024-05-29T00:00:00 5d6b361cbedaf83f1a3881178e1cafa6a9254d4bCurrent pain and headache reports400The importance of carefully considering the advantages, disadvantages, and unintended consequences of utilizing AI tools in chronic pain and headache is emphasized, and the four core principles of medical ethics as an evaluation framework are proposed.Current pain and headache reports["Maria Emilia Mazzolenis", "Evgeny Bulat", "M. Schatman", "Chris Gumb", "Christopher J Gilligan", "R. J. Yong"]2024-05-29T00:00:00 d71a87d587aff9428ecda9a36a039d7689e5968eArtificial Intelligence (AI) is transforming corporate learning and development by offering personalized, efficient, and scalable training solutions. This paper explores how AI technologies, such as machine learning, natural language processing, and data analytics, enhance learning experiences and outcomes in corporate environments. Key benefits include personalized learning paths, automated content creation, and real-time feedback. Challenges such as data privacy, implementation costs, and the need for human oversight are also discussed. By examining current applications and future trends, this paper highlights AI’s potential to revolutionize corporate training and suggests best practices for integrating AI-driven solutions in learning and development programs. Key Words: Artificial Intelligence (AI), Corporate Learning and Development, Personalized Learning, Real-Time Feedback, Adaptive Learning Technologies, Data Privacy, Emotional Intelligence in AI, AI and Augmented Reality (AR), Social Learning. Predictive Analytics, Human OversightINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper explores how AI technologies, such as machine learning, natural language processing, and data analytics, enhance learning experiences and outcomes in corporate environments and suggests best practices for integrating AI-driven solutions in learning and development programs.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Vijay Bhandare"]2024-05-29T00:00:00 b571e54f10e1cf52a5b192a8329467540a47d040This paper presents a novel approach to improve the accuracy of environmental detection and prediction by incorporating artificial intelligence (AI) technology into existing detection systems. At the heart of our approach lies the combination of a complex AI model with the hardware and software components of the inspection system. This combined approach can significantly improve the accuracy of detection systems through greater ability to predict environmental changes and events, underscoring the superior performance of hardware and software combined with AI technology. This paper delves into the details of hardware and software design, and discusses measurement implementation methods using a build-down machine. We also explore the practical application of AI models within the framework described above. In addition, this paper also describes the implementation of communication protocols to ensure the effective data exchange between the system network and the artificial intelligence model. These protocols are essential for the real-time processing and analysis of environmental data, enabling systems to respond quickly to detected changes.Applied and Computational Engineering00This paper delves into the details of hardware and software design, and discusses measurement implementation methods using a build-down machine to improve the accuracy of environmental detection and prediction by incorporating artificial intelligence (AI) technology into existing detection systems.Applied and Computational Engineering["Xiaoyin Wang"]2024-05-29T00:00:00 70e6b1efafa29b3fc97a0efba9aa5abe97df3ad6 Recent advances in Artificial Intelligence (AI), specifically the rapid rise of Natural Language Processing (NLP) platforms such as Open AI’s Chat GPT 3.5 , are already having a major impact on higher education institutions. There are significant concerns within academic communities about the threats such platforms pose to academic integrity. Many HE institutions have reacted quickly, announcing policies banning the use of AI software in the creation of assignment responses. Some are planning to return to strictly exam-based modes of assessment. In this article we reflect upon these recent events and how it has impacted our own teaching practice in the field of business management. We propose some alternative ways of thinking about these recent developments and focus on the opportunities that these AI platforms have to offer rather than the threats they pose. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 . Developing Academic Practice150This article proposes some alternative ways of thinking about these recent developments of Artificial Intelligence (AI) and focuses on the opportunities that these AI platforms have to offer rather than the threats they pose.Developing Academic Practice["Jennifer Davies", "Rick Forster", "Laura Menzies", "Matthew Tickle", "Fotios Misopoulos"]2024-05-29T00:00:00 53ddf6fa1b73547b4bd7a139e034fa0519239a95Background Artificial intelligence (AI)–based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a nonuser-oriented development approach, resulting in reduced user acceptance. Objective This research project has 2 objectives. First, problems and corresponding solutions that hinder or support the development and implementation of AI-based CDSS are identified. Second, the research project aims to increase user acceptance by creating a user-oriented requirement profile, using the example of sepsis. Methods The research project is based on a multimethod approach combining (1) a scoping review, (2) focus groups with physicians and professional caregivers, and (3) semistructured interviews with relevant stakeholders. The research modules mentioned provide the basis for the development of a (4) survey, including a discrete choice experiment (DCE) with physicians. A minimum of 6667 physicians with expertise in the clinical picture of sepsis are contacted for this purpose. The survey is followed by the development of a requirement profile for AI-based CDSS and the derivation of policy recommendations for action, which are evaluated in a (5) expert roundtable discussion. Results The multimethod research project started in November 2022. It provides an overview of the barriers and corresponding solutions related to the development and implementation of AI-based CDSS. Using sepsis as an example, a user-oriented requirement profile for AI-based CDSS is developed. The scoping review has been concluded and the qualitative modules have been subjected to analysis. The start of the survey, including the DCE, was at the end of July 2024. Conclusions The results of the research project represent the first attempt to create a comprehensive user-oriented requirement profile for the development of sepsis-specific AI-based CDSS. In addition, general recommendations are derived, in order to reduce barriers in the development and implementation of AI-based CDSS. The findings of this research project have the potential to facilitate the integration of AI-based CDSS into standard care in the long term. International Registered Report Identifier (IRRID) DERR1-10.2196/62704JMIR Research Protocols410The results of the research project represent the first attempt to create a comprehensive user-oriented requirement profile for the development of sepsis-specific AI-based CDSS, and have the potential to facilitate the integration of AI-based CDSS into standard care in the long term.JMIR Research Protocols["P. Raszke", "G. D. Giebel", "C. Abels", "J. Wasem", "Michael Adamzik", "Hartmuth Nowak", "Lars Palmowski", "Philipp Heinz", "Silke Mreyen", "N. Timmesfeld", "M. Tokic", "F. Brunkhorst", "N. Blase"]2024-05-29T00:00:00 5ba90bdbbf0c52d9641c463d08d7ceb3849b273bPurpose of review To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns. Recent findings Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies. Summary Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.Current Opinion in Otolaryngology & Head and Neck Surgery360Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years and as high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent.Current Opinion in Otolaryngology & Head and Neck Surgery["Ethan Fung", "Dhruv Patel", "Sherard A Tatum"]2024-05-29T00:00:00 3d13c1526dbb7f14f57b33f34ed359fc2234d0b2Theology and Science01Theology and Science["Ugochukwu Stophynus Anyanwu"]2024-05-29T00:00:00 d2e919c7d35e5768ba526fddca82a93b46420749Communication Research and Practice80Communication Research and Practice["Nga Than", "Larry Liu"]2024-05-29T00:00:00 ba5aedb351bdb84a801a935ed3d0daa6165c3e98International Review of Law, Computers &amp; Technology40International Review of Law, Computers &amp; Technology["Thiago Moraes"]2024-05-29T00:00:00 0254ac83d323277bae4390ba9bc5140ea3ae4d00Universum:Social science00Universum:Social science["Rinat Burnashev", "Munira Ismatilloyeva"]2024-05-29T00:00:00 8ef7ddf3c480c3f4c87e875e922e98794986354fPlatform business models like Uber Ride or Airbnb Lodging enable innovative business models by operating digital platforms to connect providers and consumers of products and services in two-sided markets. A particular challenge with platform business models is designing an appropriate revenue model to capture value. This paper presents a taxonomy that classifies the different dimensions and characteristics of revenue models for platform business models. A proven taxonomy development method is used that includes a review of current literature related to platform business models. The taxonomy provides a comprehensive classification of platform revenue models and is applied to a real-life case. The results of this paper include a UML class model and a final taxonomy with 14 dimensions and 64 characteristics. The paper contributes to the design process of novel platform business models and expands the understanding of how digital platforms can generate revenues.Resilience Through Digital Innovation: Enabling the Twin Transition160This paper presents a taxonomy that classifies the different dimensions and characteristics of revenue models for platform business models, and contributes to the design process of novel platform business models and expands the understanding of how digital platforms can generate revenues.Resilience Through Digital Innovation: Enabling the Twin Transition["Aidan Duane"]2024-05-29T00:00:00 8a3b0d017133534ebcb8787c166ecff8822f597cForum120Forum["Nadine Schlicker", "Markus Langer", "Martin C. Hirsch"]2024-05-29T00:00:00 17ea48d9e31a6f3127cf7a79220ea62523fb1845This paper presents an interdisciplinary framework, Machine Psychology, which integrates principles from operant learning psychology with a particular Artificial Intelligence model, the Non-Axiomatic Reasoning System (NARS), to advance Artificial General Intelligence (AGI) research. Central to this framework is the assumption that adaptation is fundamental to both biological and artificial intelligence, and can be understood using operant conditioning principles. The study evaluates this approach through three operant learning tasks using OpenNARS for Applications (ONA): simple discrimination, changing contingencies, and conditional discrimination tasks. In the simple discrimination task, NARS demonstrated rapid learning, achieving 100% correct responses during training and testing phases. The changing contingencies task illustrated NARS’s adaptability, as it successfully adjusted its behavior when task conditions were reversed. In the conditional discrimination task, NARS managed complex learning scenarios, achieving high accuracy by forming and utilizing complex hypotheses based on conditional cues. These results validate the use of operant conditioning as a framework for developing adaptive AGI systems. NARS’s ability to function under conditions of insufficient knowledge and resources, combined with its sensorimotor reasoning capabilities, positions it as a robust model for AGI. The Machine Psychology framework, by implementing aspects of natural intelligence such as continuous learning and goal-driven behavior, provides a scalable and flexible approach for real-world applications. Future research should explore using enhanced NARS systems, more advanced tasks and applying this framework to diverse, complex tasks to further advance the development of human-level AI.Frontiers in Robotics and AI370Frontiers in Robotics and AI["Robert Johansson"]2024-05-29T00:00:00 0bc29d562e4ba0bf6f4af79c18831c9a29edb164The unprecedented development of artificial intelligence (AI) makes it possible for computers to imitate and surpass human intelligence (HI). Hybrid intelligence is the result of the co-evolution of AI and HI and has huge application potential in promoting the sustainable development of human society. This study starts from the similarities and differences between biological neural networks and artificial neural networks, compares the cognitive foundations of human intelligence and artificial intelligence, highlights the difference and connection between AI and HI, and puts forward the necessity and inevitability of their co-evolution to achieve hybrid intelligence with complementary advantages. Hybrid intelligence stands to become the pivotal force driving purposeful and planned sustainable creative behavior in the artificial intelligence era. This study proposes a design cognitive creation model based on human–computer collaboration that considers computational design thinking as the central concept. Moreover, the paradigm shift of design under hybrid intelligence intervention are explored from five aspects: “tool evolution”, “response mode”, “output result”, “iterative optimization” and “system innovation”. Finally, this article constructs a creative intervention mechanism of design creation driven by hybrid intelligence and discusses its role playing in the design activities of sustainable multiverse construction in the future. The proposal of the multiverse model transcends the confines of the metaverse’s virtual worldview and embraces sustainable development for value guidance. It advocates a future trajectory for humanity that hinges on technological progress, fostering a prosperous, balanced, and harmonious coexistence between the natureverse, socialverse, and digitalverse. This approach is not only rational and scientific, but also inherently sustainable.Applied Sciences362A design cognitive creation model based on human–computer collaboration that considers computational design thinking as the central concept is proposed that transcends the confines of the metaverse’s virtual worldview and embraces sustainable development for value guidance.Applied Sciences["Yuqi Liu", "Zhiyong Fu"]2024-05-29T00:00:00 506f27b50a041056bb4c2c875f4322b8ad9a0d25The crisis that has affected journalism since the beginning of the century has led to the dismissal of thousands of journalists worldwide. To address the lack of human resources, many newspapers have turned to artificial intelligence (AI), but the introduction of non-human and poorly scrutinized systems has raised new questions related to the transparency of the journalistic process, affecting the already fragile credibility of the media. In the absence of legislation related to the use of AI in journalism, the media have been publishing recommendations to guide professionals. In this work, we analyze two pioneering documents, one from Estadão (Brazil) and another from the BBC (UK), which show how these media seek to gain consumer trust by explaining how they use artificial intelligence in newsrooms.Revista Mídia e Cotidiano01This work analyzes two pioneering documents, one from Estadão (Brazil) and another from the BBC (UK), which show how these media seek to gain consumer trust by explaining how they use artificial intelligence in newsrooms.Revista Mídia e Cotidiano["Jo\u00e3o Canavilhas", "B\u00e1rbara Biolchi"]2024-05-29T00:00:00 d24a46089b0390a714b6223e56a93fbbda490d57To diagnose someone’s reasoning today as “sophistry” is to say that this reasoning is at once persuasive (at least to a significant degree) and logically invalid. We begin by explaining that, despite some recent scholarly arguments to the contrary, the understanding of ‘sophistry’ and ‘sophistic’ underlying such a lay diagnosis is in fact firmly in line with the hallmarks of reasoning proffered by the ancient sophists themselves. Next, we supply a rigorous but readable definition of what constitutes sophistic reasoning (=sophistry). We then discuss “artificial” sophistry: the articulation of sophistic reasoning facilitated by artificial intelligence (AI) and promulgated in our increasingly digital world. Next, we present, economically, a particular kind of artificial sophistry, one embodied by an artificial agent: the lying machine. Afterward, we respond to some anticipated objections. We end with a few speculative thoughts about the limits (or lack thereof) of artificial sophistry, and what may be a rather dark future.Humanities750Humanities["M. Clark", "S. Bringsjord"]2024-05-29T00:00:00 a996d612c2e2b3b548b5e2f5e9d7538c91d5234fThis article proposes a discussion about disinformation, involving gear and methods that contribute to the spread of fake news in the online environment and other phenomena that arise in the wake of the Big Tech business model. It is considered that, with the advancement of Artificial Intelligence, such formats begin to exercise increasing dominance over experiences, narratives and human knowledge, control that can be considered a threat to freedom of expression and the right to information. Faced with this challenge, from a regulatory point of view, the review of standards that place more limits on digital and educational platforms is being evaluated, highlighting the need to expand digital, media and information literacy. The methodology involves bibliographic and documentary review.Revista Mídia e Cotidiano00Revista Mídia e Cotidiano["Regina Rossetti", "Renata Ferrarezi"]2024-05-29T00:00:00 99a9f648d32369f0e0382c33dfb70f4d11315c37In the era of big data, artificial intelligence (AI) technology is widely used in various fields. Floral design, as a field that blends art and creativity, can also benefit from the development of AI. The application of AI intelligence in the field of flower design under the environment of big data is deeply studied and discussed. Through exploring the application methods and technical means of AI technology in flower design, some suggestions for improvement and development are put forward. This is of great significance for promoting innovation and sustainable development of floral design, providing new ideas and directions for floral designers, teaching and related industries.2024 International Conference on Telecommunications and Power Electronics (TELEPE)100The application of AI intelligence in the field of flower design under the environment of big data is deeply studied and discussed, and some suggestions for improvement and development are put forward.2024 International Conference on Telecommunications and Power Electronics (TELEPE)["Qianqian Ma"]2024-05-29T00:00:00 e2d934b6521056366689622ed6024e17bda8bd67Universities started to use artificial intelligence (AI) tools to improve the quality of higher education services. However, the rapid adoption of AI tools in higher education (HE) may lead to sustainability issues. On the one hand, there are prerequisites for using AI tools to achieve Sustainable Development Goal 4 (SDG 4). On the other hand, as consumers of educational services (stakeholders), students have their own opinions about using AI in the educational process. The purpose of this study was to explore students’ opinions on the use of artificial intelligence tools in higher education. The authors analyzed student responses to the question: “Do you think AI threatens higher education in the next five years?” The authors formulated this question based on the definition of “a safe learning environment”, which is associated with a “safe” learning environment (SDG 4.3). The authors made use of a literature review, a bibliometric analysis of 5000 sources, a survey of 1104 students from eight universities in Eastern Europe through cloud technologies to host a special electronic questionnaire, statistical processing of questionnaires, and testing of statistical hypotheses. The authors formulated and tested two pairs of competing statistical hypotheses. Finally, the authors obtained three new scientific facts based on the respondents’ answers. New scientific facts were obtained using a standard level of statistical hypothesis testing (α = 0.05). The main scientific fact is that 10.17% to 35.42% of students think that Artificial Intelligence threatens higher education. According to student opinions, AI may hurt the sustainability of higher education (SDG 4.3). The authors are confident that new scientific facts help conceptualize and promote didactic theory and practice. The study results are needed to predict, plan, and implement organizational, pedagogical, and methodological measures aimed at SDG 4.3 through a “safe” learning environment while further expanding the use of AI in higher education.Sustainability6711Students’ opinions on the use of artificial intelligence tools in higher education are explored to predict, plan, and implement organizational, pedagogical, and methodological measures aimed at SDG 4.3 through a “safe” learning environment while further expanding the use of AI in higher education.Sustainability["Valery Okulich-Kazarin", "A. Artyukhov", "\u0141ukasz Skowron", "N. Artyukhova", "Tomasz Wo\u0142owiec"]2024-05-29T00:00:00 763988870088bc55efc227a8cacde1f96429fb11There is a nationwide shortage of special education teachers (SETs) due, in part, to unmanageable workload expectations, which has reached crisis level. SETs are expected to modify, adapt, and accommodate general education curriculum to meet the needs of their students, communicate and collaborate with parents and general education teachers, and progress monitor on IEP goals, to name a few. SETs, especially those in more restrictive self-contained settings, report spending almost half of their time completing non-teaching tasks. One emerging and innovative solution to help SETs accomplish these tasks is using Artificial Intelligence (AI). AI powers many popular educational tools, such as predictive text, adaptive learning platforms, and digital assistants. The launch of ChatGPT, Bard, and other generative pre-trained transformers (GPTs), provides an opportunity to support SETs with some of the paperwork requirements. This is due to the GPTs ability to craft human-like responses via drafting essays, emails, lists, and the like. In this article, we provide step-by-step directions to use ChatGPT. Additionally, we illustrate how GPTs can be used to operationalize, automate, and streamline many of the SET’s non-teaching tasks through specific examples of its use in (1) collaboration, (2) adapting readings, and (3) developing progress monitoring assessments.Journal of Special Education Technology255Step-by-step directions to use ChatGPT are provided and it is illustrated how GPTs can be used to operationalize, automate, and streamline many of the SET’s non-teaching tasks through specific examples of its use in collaboration, adapting readings, and developing progress monitoring assessments.Journal of Special Education Technology["Samantha R. Goldman", "Juli Taylor", "Adam C. Carreon", "Sean J. Smith"]2024-05-29T00:00:00 da6d7f820c3bce984be69de74d09a7587126fb68This report explores the potential implications of rapidly integrating Artificial Intelligence (AI) applications into children's environments. The introduction of AI in our daily lives necessitates scrutiny considering the significant role of the environment in shaping cognition, socio-emotional skills, and behaviors, especially during the first 25 years of cerebral development. As AI becomes prevalent in educational and leisure activities, it will significantly modify the experiences of children and adolescents, presenting both challenges and opportunities for their developmental trajectories. This analysis was informed by consulting with 15 experts from pertinent disciplines (AI, product development, child development, and neurosciences), along with a comprehensive review of scientific literature on children development and child-technology interactions. Overall, AI experts anticipate that AI will transform leisure activities, revolutionize education, and redefine human-machine interactions. While AI offers substantial benefits in fostering interactive engagement, it also poses risks that require careful considerations, especially during sensitive developmental periods. The report advocates for proactive international collaboration across multiple disciplines and increased research into how technological innovations affect child development. Such efforts are crucial for designing a sustainable and ethical future for the next generation through specific child-centered regulations, and helping to educate all potential stakeholders (regulators, developers, parents and educators, children) about responsible AI use and its potential impacts on child development.arXiv.org4022Overall, AI experts anticipate that AI will transform leisure activities, revolutionize education, and redefine human-machine interactions, and offer substantial benefits in fostering interactive engagement, but poses risks that require careful considerations, especially during sensitive developmental periods.ArXiv["Mathilde Neugnot-Cerioli", "Olga Muss Laurenty"]2024-05-29T00:00:00 132c09244cfdfb96ab266a0869b110805ae408c2The rapid adoption of Artificial Intelligence (AI) technological advances marked an era of exceptional growth, significantly converting how we interact with the world of technology. But this adoption has arrived at an enormous cost: the gradual loss of private life. The study starts with an indepth investigation into the complicated and modifying relationship between AI and privacy, acquiring information via case studies in the natural environment. One prominent case study is deploying AI technology to recognize faces in public spaces. Issues around illicit tracking and privacy intrusions have grown as governments and business entities regularly employ such technologies for recognition and protection. Assessing the challenges and findings associated with such deployments offers the conceptual debate a practical setting. It explores the various challenges that arise from AIdriven innovations and offers fresh strategies for maintaining and safeguarding individual privacy concerns in emerging data-centric circumstances. It emphasizes AI's dual role as a catalyst for outstanding outcomes and a potential risk to personal privacy. Finally, this study is interested in contributing to the current discussion on AI and Privacy, by providing an in-depth examination of the difficulties that might be utilized by lawmakers, stakeholders in the industry, as well as individuals alike. As humanity moves into a scenario where AI encompasses every aspect of daily life, the study acts as a guide for managing the evolving terrain concerning confidentiality in the era of AI, assisting appropriate AI advancement and preserving human privacy rights.2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE)452As humanity moves into a scenario where AI encompasses every aspect of daily life, the study acts as a guide for managing the evolving terrain concerning confidentiality in the era of AI, assisting appropriate AI advancement and preserving human privacy rights.2024 5th International Conference in Electronic Engineering, Information Technology & Education (EEITE)["Marios Vardalachakis", "Manolis Tampouratzis", "Nikos Papadakis", "Manos Vasilakis"]2024-05-29T00:00:00 28660824b858a25b8f2e546a2474c3b0d815d5f7Artificial intelligence (AI)-based algorithms are playing an increasingly prominent role in shaping daily life. However, these algorithms can exhibit biases that exacerbate societal injustices. Such biases have a substantial impact on people's perceptions of algorithmic fairness, yet the precise mechanisms and scope of this phenomenon remain relatively understudied. To address this research gap, a comprehensive scoping literature review is conducted, providing an overview of current research in the field. Subsequently, a novel theoretical model is developed that synthesizes key themes, including algorithm bias, algorithm fairness, perceived fairness, individual characteristics, social characteristics, task characteristics, and technology characteristics. The paper contributes proposing a set of propositions that underscore the critical gaps in the existing literature, contribute to a deeper comprehension of the relationships among the identified themes and their constituent elements, and offer a roadmap for future research in the domain.SIGMIS-CPR592A novel theoretical model is developed that synthesizes key themes, including algorithm bias, algorithm fairness, perceived fairness, individual characteristics, social characteristics, task characteristics, and technology characteristics, and offers a roadmap for future research in the domain.Proceedings of the 2024 Computers and People Research Conference["Amirhossein Hajigholam Saryazdi"]2024-05-29T00:00:00 c2246742ec7c86b195f52787acc8b6590b643edaThis paper explores the significant shift towards agentic workflows in the application of Large Language Models (LLMs), moving away from traditional, linear interactions between users and AI. Through a case study analysis, we highlight the effectiveness of agentic workflows, which facilitate a more dynamic and iterative engagement, in improving outcomes in tasks such as question answering, code generation or stock analysis. Central to the agentic workflow are four foundational design patterns: reflection, planning, multi-agent collaboration, and tool utilization. These components are crucial for boosting LLM productivity and enhancing performance. The study demonstrates how agentic workflows, by promoting an iterative and reflective process, can serve as a crucial step towards achieving Artificial General Intelligence (AGI).2024 IEEE World AI IoT Congress (AIIoT)134The study demonstrates how agentic workflows, by promoting an iterative and reflective process, can serve as a crucial step towards achieving Artificial General Intelligence (AGI).2024 IEEE World AI IoT Congress (AIIoT)["Aditi Singh", "Abul Ehtesham", "Saket Kumar", "T. T. Khoei"]2024-05-29T00:00:00 8d720f55f860a42bc63571fb5e6f0e0e81db0532Human society is witnessing a revolutionary growth of digital twin (DT) and artificial intelligence (AI) technologies, which has greater impact on Industry 4.0 revolution specially in academia and industry. DT is a digital representation of a physical entity, with data and infrastructure serving as its foundation, algorithms, and models as its core, and software and services as its application. The methodical and thorough integration of domain-specific expertise is even more essential to the foundations of DT and AI in industrial sectors. This paper provides a thorough analysis of more than 30 articles on AI-driven DT technologies employed in Industry 4.0 over the previous five years. It also describes the general advances of these technologies and the current status of AI integration in the domains of advanced robotics and smart manufacturing which are affecting human society. These include established methods like industrial automation as well as complex mechanism like 3D printing and human-robot collaboration. Additionally, the benefits of AI-powered DTs are explained in relation to sustainable development. The development potential and practical difficulties of AI-driven DTs are examined, with varying emphasis on various levels.International Research Journal on Advanced Engineering and Management (IRJAEM)291This paper provides a thorough analysis of more than 30 articles on AI-driven DT technologies employed in Industry 4.0 over the previous five years and describes the general advances and current status of AI integration in the domains of advanced robotics and smart manufacturing which are affecting human society.International Research Journal on Advanced Engineering and Management (IRJAEM)["Dr. Prakash Upadhyay", "Tushar Sharma", "Ahmadi Fatima"]2024-05-29T00:00:00 e785f48b4afe24021cd52aa9a19eed15e8868cdcThe integration of Artificial Intelligence (AI) in higher education encounters a myriad of inhibiting factors, notably the conspicuous absence of transparency, reliability issues, and ethical concerns. This problem has substantially impeded the assimilation of generative AI-enabled Educational Technology (Ed-Tech) within the higher education domain, unlike other fields such as finance, health, and management. The prevailing sentiment among higher education practitioners remains wavering, with differing opinions on whether to permit AI comprehensively, impose complete restrictions, or allow minimal integration into academic courses. This pilot study endeavors to elucidate the nuanced determinants influencing cognitive trust of Information Technology (IT) Higher Education instructors in AI-enabled educational Technology. The implications of this trust, or lack thereof, on the broader adoption of AI in higher education, constitute a focal point of investigation in this scholarly investigation.SIGMIS-CPR361This pilot study endeavors to elucidate the nuanced determinants influencing cognitive trust of Information Technology Higher Education instructors in AI-enabled educational Technology, and the implications of this trust, or lack thereof, on the broader adoption of AI in higher education.Proceedings of the 2024 Computers and People Research Conference["C. C. Aladi"]2024-05-29T00:00:00 35611bdc61e2e6ef85243d9c94a30f83f5f53a10As generative artificial intelligence (AI) becomes more common in day-to-day life, AI-generated content (AIGC) needs to be accurate, relevant, and comprehensive. These characteristics typically are determined by subjective, human-based image quality assessment; however, there is limited research on the qualification of AI-generated image quality. Over 9,800 images were generated using Craiyon and OpenAI's DALL-E 2 text-to-image models and evaluated on the three criteria proposed for determining the quality of visual AIGC: (1) the number of objects, (2), resolution (strictly image quality; label/prompt exclusive), and (3) representativeness (consideration for how well the image matches the label/prompt). We observe that the paid, DALL-E 2 model, produced a dataset with fewer objects per image, higher resolution, and higher representativeness compared to Craiyon (free). There is an inverse relationship between the number of objects/images and its resolution and representativeness. This study establishes three subjective metrics for the evaluation of synthetic images to support the creation of more inclusive AIGC.SIGMIS-CPR631Three subjective metrics for the evaluation of synthetic images to support the creation of more inclusive AIGC are established and observe that the paid, DALL-E 2 model, produced a dataset with fewer objects per image, higher resolution, and higher representativeness compared to Craiyon (free).Proceedings of the 2024 Computers and People Research Conference["Kara Combs", "Trevor J. Bihl", "Arya Gadre", "Isaiah Christopherson"]2024-05-29T00:00:00 92d3c0b530733ceb16035afc1214651fcddb76a5The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.Biomedicines13310A narrative review of AI models in pediatric emergency medicine from inception up to April 2024 shows that it is essential to tailor AI algorithms to specific medical needs.Biomedicines["L. Di Sarno", "Anya Caroselli", "Giovanna Tonin", "Benedetta Graglia", "V. Pansini", "F. Causio", "A. Gatto", "Antonio Chiaretti"]2024-05-30T00:00:00 f42185a5af0ae8d596a7e76abe0bff8cdcc78a9cNew England Journal of Medicine4910The New England journal of medicine["Kun-Hsing Yu", "Elizabeth Healey", "Tze-Yun Leong", "Isaac S. Kohane", "A. Manrai"]2024-05-30T00:00:00 7147844a225c7c64405bfb7ca783757ffacef6f8One area that might experience an immense transformation owing to the adoption of AI is the electoral system. Artificial intelligence (AI) holds tremendous potential for enhancing polls, campaign methods, and voter registration, but it also presents substantial challenges to the integrity of elections around the globe. This article discusses the political scene of 2024 and AI's role, balancing the pros and cons of AI deployment. Case studies of nations that have implemented AI for voting purposes are presented in the article, along with an analysis of the merits and disadvantages of these systems. The article goes on to warn of the perils of AI in elections and provides solutions to these concerns. It is of the most significant necessity to secure both the security and legitimacy of voting procedures, as the employment of artificial intelligence in elections is popular. This paper asks politicians, election authorities, and people in general to handle the challenges brought by AI in elections.World Journal of Advanced Engineering Technology and Sciences05The political scene of 2024 and AI's role is discussed, balancing the pros and cons of AI deployment, and the perils of AI in elections are warned.World Journal of Advanced Engineering Technology and Sciences["AnandKumar Chennupati"]2024-05-30T00:00:00 1f3b09fa5891b23452636914a1ffbd965f68a654The rapid development of artificial intelligence technology has already had a profound impact in various fields, ranging from healthcare and education to transport and finance. However, accompanying these technological advances are a series of complex and profound ethical issues. These issues involve not only data privacy and security, but also challenges of algorithmic bias, fairness, and transparency in decision-making. In addition, the ‘black box’ nature of AI systems blurs the attribution of responsibility and increases society's distrust of the technology. As AI is increasingly used in society, the question of how to find a balance between technological innovation and human values has become an urgent one. While technological advancement can certainly bring efficiency and convenience, the lack of ethical constraints may lead to privacy leakage, unfair decision-making and moral hazard. Therefore, it has become particularly important to establish a sound AI ethical framework to regulate the application of the technology, protect individual privacy, and ensure fairness and transparency. The establishment of an AI ethical framework is not only to regulate the application of the technology, but also to protect social justice and core human values. Through systematic ethical guidelines, moral considerations can be integrated into all stages of technology development and application, providing clear guidelines to help all parties use AI technology under the premise of legal compliance. At the same time, such an ethical framework can also help enhance public trust in AI and promote the healthy development of the technology in a wider range of fields. In conclusion, the rapid development of AI brings unprecedented opportunities and raises profound ethical challenges. We need to ensure the coordinated development of technological progress and social values by establishing a sound ethical framework, and promote AI to move forward in a more responsible, fairer and transparent direction. The combination of ethics and technology will become an important force to lead the future development of science and technology, bringing more benefits and progress to human society.International Journal of Education and Humanities131The establishment of an AI ethical framework is not only to regulate the application of the technology, but also to protect social justice and core human values, and help enhance public trust in AI and promote the healthy development of the technology in a wider range of fields.International Journal of Education and Humanities["Linji Fan"]2024-05-30T00:00:00 27b63fecae94ce5e56ad9b7fd5c3a07111ebfd0dAs the Kingdom of Saudi Arabia continues its journey towards economic diversification and technological advancement, the adoption of Artificial Intelligence (AI) has emerged as a critical driver of innovation across various sectors. This paper investigates the potential benefits of integrating AI technologies in municipal services within the Saudi context. Drawing upon a comprehensive literature review and examining existing initiatives, this paper explores the potential benefits of using AI. It reviews the Application and development of AI in municipalities in Saudi Arabia. This research identifies areas where AI can enhance municipal operations, improve citizen engagement, and revolutionize municipal operations. These include urban planning, intelligent infrastructure management, traffic optimization, waste management, and citizen services. Overall, this paper contributes to the literature on AI adoption in the public sector, offering valuable insights for policymakers, urban planners, and technology developers seeking to harness the transformative potential of AI in enhancing municipal services and improving the quality of life for citizens in Saudi Arabia.World Journal of Advanced Research and Reviews01This research identifies areas where AI can enhance municipal operations, improve citizen engagement, and revolutionize municipal operations, including urban planning, intelligent infrastructure management, traffic optimization, waste management, and citizen services.World Journal of Advanced Research and Reviews["Bandar S. Aljabri"]2024-05-30T00:00:00 407e300c06e2b21bc128c4d866cf4aed8da45fc2The advancement of technology, including the use of Artificial Intelligence (AI) in everyday life, has brought about significant changes and substantial impacts, especially in electronic transactions and law. While the use of AI promises various benefits, it also raises several important legal issues, such as legal responsibility, data privacy, and contract validity. The purpose of this research is to understand the legal framework of AI in the Indonesian legal system and the legal implications of AI utilization, particularly in the context of electronic transactions. This research is of a normative juridical type, employing a conceptual approach and a statute approach. The results of the research indicate that AI is not specifically regulated in its own law but is discussed within the Electronic Information and Transactions Law (ITE Law) and several regulations under it. The legal implications of AI usage can be viewed from two main perspectives. First, AI is regarded as an electronic agent, where all legal responsibilities for AI actions are placed on the electronic system providers. Second, AI is seen as a legal subject, where it is treated as a legal entity or rechtpersoon.International journal of social science and human research01The results of the research indicate that AI is not specifically regulated in its own law but is discussed within the Electronic Information and Transactions Law and several regulations under it.International Journal of Social Science and Human Research["Syaif Al Haq", "Yunanto Yunanto"]2024-05-30T00:00:00 0e8e3b2828235f4372d65a7e840d88b8162c62c7 Artificial intelligence (AI) has emerged as a prominent field in computer science. Machine learning (ML) and deep learning (DL) have potential applications in medicine. This overview explores the applications of artificial intelligence (AI) in cardiovascular imaging, focusing on echocardiography, cardiac magnetic resonance imaging (CMR), coronary CT angiography (CCTA), and CT morphology and function. AI, particularly deep learning (DL) approaches like convolutional neural networks (CNNs), enhances standardization and reduces operator-dependent variations in echocardiography. In CMR, undersampling techniques and DL-based reconstruction methods, such as variational neural networks (VNNs), improve efficiency and accuracy. ML in CCTA aids in diagnosing coronary artery disease, assessing stenosis severity, and analyzing plaque characteristics. Automatic segmentation of cardiac structures and vessels using AI is discussed, along with its potential in congenital heart disease diagnosis and 3D printing applications. Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities. The growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns. The "black box" problem in deep learning models poses challenges for interpretability crucial in clinical practice. Evaluation metrics like ROC curves, image quality, clinical relevance, diversity, and quantitative performance assess GAI models. Automation bias highlights the risk of unquestioned reliance on AI outputs, demanding careful implementation and ethical frameworks. Ethical considerations involve transparency, respect for persons, beneficence, and justice, necessitating standardized evaluation protocols. Health disparities emerge if AI training lacks diversity, impacting diagnostic accuracy. AI language models, like GPT-4, face hallucination issues, posing ethical and legal challenges in healthcare. Regulatory frameworks and ethical governance are crucial for fair and accountable AI, addressing discrimination while preserving privacy. Ongoing research and development are vital to evolving AI ethics and ensuring ethical data handling in healthcare.BJR|Artificial Intelligence02Overall, AI integration in cardiovascular imaging shows promise for enhancing diagnostic accuracy and efficiency across modalities and the growing use of Generative Adversarial Networks in cardiovascular imaging brings substantial advancements but raises ethical concerns.BJR|Artificial Intelligence["Ahmed Marey", "Kevin Christopher Serdysnki", "Benjamin Killeen", "Mathias Unberath", "Muhammad Umair"]2024-05-30T00:00:00 0491857cfaf2e16a2ee9c866808ae1f5d12979e8In recent years, the integration of artificial intelligence (AI) in the legal sector has transformed the way legal services are delivered, enhancing efficiency, accuracy, and accessibility. However, the rapid advancement of AI technology in law also raises significant concerns regarding accountability and fairness. This article explores the regulatory frameworks aimed at addressing these concerns and ensuring that AI systems in the legal domain operate ethically and responsibly. Beginning with an introduction to the role of AI in law and its implications, the article navigates through the complex landscape of AI regulation, highlighting global perspectives, key regulatory bodies, and existing laws relevant to AI in legal practice. It then delves into the principles and mechanisms essential for ensuring accountability in AI systems, including transparency, explainability, and data governance. Furthermore, the article investigates strategies for achieving fairness in AI-powered legal systems, addressing issues such as bias, discrimination, and the need for fairness metrics and evaluation methods. It explores governance mechanisms necessary for effective regulation, emphasizing stakeholder engagement, compliance, and enforcement strategies. Drawing insights from case studies and best practices, the article offers valuable lessons and recommendations for policymakers, practitioners, and stakeholders involved in shaping the future of AI regulation in the legal sector. In conclusion, it underscores the importance of continuous evaluation and adaptation to keep pace with the evolving landscape of AI technology and its impact on the legal profession.NUJS journal of regulatory studies01The article delves into the principles and mechanisms essential for ensuring accountability in AI systems, including transparency, explainability, and data governance, and explores governance mechanisms necessary for effective regulation, emphasizing stakeholder engagement, compliance, and enforcement strategies.NUJS Journal of Regulatory Studies["Dr. Akhil Kumar", "Dr. Harshita Dadhich"]2024-05-30T00:00:00 f8d985434b61db5dbf3faf3751abf80a0e3fff40Currently, there are still many patients who require outpatient triage assistance. ChatGPT, a natural language processing tool powered by artificial intelligence technology, is increasingly utilized in medicine. To facilitate and expedite patients’ navigation to the appropriate department, we conducted an outpatient triage evaluation of ChatGPT. For this evaluation, we posed 30 highly representative and common outpatient questions to ChatGPT and scored its responses using a panel of five experienced doctors. The consistency of manual triage and ChatGPT triage was assessed by five experienced doctors, and statistical analysis was performed using the Chi-square test. The expert ratings of ChatGPT’s answers to these 30 frequently asked questions revealed 17 responses earning very high scores (10 and 9.5 points), 7 earning high scores (9 points), and 6 receiving low scores (8 and 7 points). Additionally, we conducted a prospective cohort study in which 45 patients completed forms detailing gender, age, and symptoms. Triage was then performed by outpatient triage staff and ChatGPT. Among the 45 patients, we found a high level of agreement between manual triage and ChatGPT triage (consistency: 93.3–100%, p<0.0001). We were pleasantly surprised to observe that ChatGPT’s responses were highly professional, comprehensive, and humanized. This innovation can help patients win more treatment time, improve patient diagnosis and cure rates, and alleviate the pressure of medical staff shortage.Frontiers in Public Health122Outpatient triage evaluation of ChatGPT found that ChatGPT’s responses were highly professional, comprehensive, and humanized, which can help patients win more treatment time, improve patient diagnosis and cure rates, and alleviate the pressure of medical staff shortage.Frontiers in Public Health["Xiaoni Liu", "Rui Lai", "Chaoling Wu", "Changjian Yan", "Zhe Gan", "Yaru Yang", "Xiangtai Zeng", "Jin Liu", "Liangliang Liao", "Yuansheng Lin", "Hongmei Jing", "Weilong Zhang"]2024-05-30T00:00:00 b605fc66a197dc5f097551f963aee4c65ca1a820In cybersecurity, the integrity and security of data and systems can be preserved with governance, risk, and compliance (GRC) practices. As the complexity of cyber threats increases, organizations need to enhance their GRC practices with advanced technologies. This research article studies the role of artificial intelligence in reinforcing GRC practices in cybersecurity. The study provides a comprehensive GRC overview in the context of cybersecurity, highlighting its importance in maintaining a secure and compliant environment. It then examines how AI can enhance GRC practices, including analyzing data, automating compliance processes, and identifying potential threats. Furthermore, the paper discusses implementing AI-driven GRC solutions, outlining key organizational considerations and best practices. It also addresses ethical and regulatory considerations surrounding AI in GRC in decision-making processes. This study highlights insights into how AI can effectively strengthen cybersecurity GRC practices, ultimately helping governments and organizations mitigate risks and enhance their cybersecurity posture in an increasingly digital world.World Journal of Advanced Research and Reviews231Insightful insights are highlighted into how AI can effectively strengthen cybersecurity GRC practices, ultimately helping governments and organizations mitigate risks and enhance their cybersecurity posture in an increasingly digital world.World Journal of Advanced Research and Reviews["Benita Urhobo"]2024-05-30T00:00:00 d49fd865eefce2d379ec008e0514ba039e4ffda1It is important to implement load forecasting to provide a more effective electrical power allocation in the electric grid. There are many factors that can affect the actual load, such as climatic conditions, special events, and load timing. This paper applies the Artificial Intelligence (AI) techniques for effective load forecasting for an electric utility. The main AI techniques considered are Artificial Neural Networks (ANN) and Support Vector Machines (SVM). A feed forward layered neural network model is used for short-term load forecasting in this paper. The ANN is trained using Levenberg-Marquardt algorithm. The regression SVM technique is also considered for load forecasting. This paper applied these AI techniques for short-term load forecasting of an electric utility data and carried out a comparative study of these methods. While both ANN and SVM methods gave favorable forecasting of load for the electric utility data based on the prior load information, ANN method gave better prediction for the data considered.IEEE International Conference on Electro/Information Technology211While both ANN and SVM methods gave favorable forecasting of load for the electric utility data based on the prior load information, ANN method gave better prediction for the data considered and carried out a comparative study of these methods.2024 IEEE International Conference on Electro Information Technology (eIT)["Sri R. Kolla", "Xiaohan Ni"]2024-05-30T00:00:00 5854da98da87199c8c20c2d41afb3b553d9f61bfConventional methods of teacher training are now constrained by series of barriers such as limited resources and distance-related barriers. The emergence of new technologies that involve artificial intelligence (AI) became a paradigm shift in teacher training. Nevertheless, there are challenges attached to these new methods of teacher training. The purpose of this paper was to assess the impact of AI in Open Distance and Electronic Learning (ODeL) teacher training. Through a process of data collection, the study employed a quantitative approach. As a survey, copies of a questionnaire were provided electronically to relevant respondents. A random sampling strategy was employed using teachers and student teachers from various institutions. A total of 115 respondents took part in this research by filling out a questionnaire. In the process of reviewing relevant literature on teacher training and AI were, more emphasis was placed on integration of AI as a new technology in teacher training. Empirical data were descriptively analysed through a Statistical Package for the Social Sciences (SPSS) application. The results included multiple opportunities as well as limitations attached to AI implementation in an ODeL teacher training. The study recommended a proper evaluation of AI prior to implementation to ensure that ethical issues and education assessment integrity are not compromised.International Journal of Learning, Teaching and Educational Research272A proper evaluation of AI prior to implementation is recommended to ensure that ethical issues and education assessment integrity are not compromised and the impact of AI in Open Distance and Electronic Learning teacher training is assessed.International Journal of Learning, Teaching and Educational Research["Goodwill Phezulu Mbambo", "Elizabeth C. Du Plessis"]2024-05-30T00:00:00 4e7c05793f43d87192fa8551757ebb64f26c6a34In the realm of artificial intelligence (AI), the fusion of A.I. voice assistants with home automation has revolutionized the way humans interact with and control their living spaces. A.I. voice assistants, powered by sophisticated natural language processing algorithms, seamlessly interpret user commands and queries, enabling a fluid and intuitive communication channel between individuals and their smart homes. Through advanced machine learning models, these assistants continually evolve, adapting to user preferences and refining their capabilities over time. The symbiotic integration of A.I. voice assistants and home automation systems transcends conventional paradigms, ushering in an era where users can effortlessly manage diverse aspects of their home environment with a mere vocal prompt. This transformative synergy not only enhances the efficiency of daily tasks but also augments accessibility, making smart home technology more inclusive for users with varying levels of technical expertise. As A.I. voice assistants continue to evolve, their role in home automation stands as a testament to the profound impact of artificial intelligence on shaping the future of modern living.International Journal of Science and Research Archive02The symbiotic integration of A.I. voice assistants and home automation systems transcends conventional paradigms, ushering in an era where users can effortlessly manage diverse aspects of their home environment with a mere vocal prompt, and enhances the efficiency of daily tasks.International Journal of Science and Research Archive["Shubham Singh", "Shubham Singh Panwar", "Harsh Dahiya", "Khushboo"]2024-05-30T00:00:00 a48b913164224c356c81bef292190e42c0c96974As organizations continue to supplement and replace human management with artificial intelligence (AI), it is essential that we understand the factors that influence employees' trust in AI management. Across one preregistered field study, where we survey 400 delivery riders in Mainland China, and three preregistered experiments (total N = 2,350), we find that AI management is perceived as less benevolent than human management. Given that benevolence is an important antecedent of trust in leaders, this perception has a negative effect on trust in AI management, even when controlling for perceived ability and integrity. Employees prefer human management to AI management in high empathy demand contexts, where individuals seek management that can empathize and experience the emotions that they are feeling, as opposed to low empathy demand contexts. These findings deepen our understanding of trust and provide important theoretical and practical insights on the implementation and adoption of AI management. (PsycInfo Database Record (c) 2024 APA, all rights reserved).Journal of Applied Psychology02Employees prefer human management to AI management in high empathy demand contexts, where individuals seek management that can empathize and experience the emotions that they are feeling, as opposed to low empathy demand contexts.The Journal of applied psychology["Mingyu Li", "T. B. Bitterly"]2024-05-30T00:00:00 c8e012c59d3e4ad35f7f29070db6d346203722e7Service Industries Journal497The Service Industries Journal["Sanaz Vatankhah", "Vahideh Bamshad", "H. Ar\u0131c\u0131", "Yanqing Duan"]2024-05-30T00:00:00 018d63e6628570cff090c36d14a08dbfbaefbbc7Journal of Social Work Education256Journal of Social Work Education["Maria Y. Rodriguez", "Lauri Goldkind", "Bryan Victor", "B. Hiltz", "Brian E. Perron"]2024-05-30T00:00:00 1d7d936de4a3df6522a618a005ab46c83850409fInternational Journal of Computer Trends and Technology07International Journal of Computer Trends and Technology["Varun Jain", "Anandaganesh Balakrishnan", "Divya Beeram", "Madhavi Najana", "Pradeep Chintale"]2024-05-30T00:00:00 b106e9884a10dbc5de77c3a06d36f86786b2ea6cThis article explores the impact of artificial intelligence (AI) on learning and development (L&D) in IT companies, focusing on the use of large language models (LLMs) like GPT-3.5 and GPT-4 Turbo. These models enhance personalized learning pathways, apply the Feynman method for knowledge verification, and optimize software code. Experimental results demonstrate that AI significantly improves technical mentorship quality, reduces costs, and increases training effectiveness. The study addresses challenges in AI integration, such as data quality and the necessity for human expertise, and provides recommendations for integrating AI effectively into corporate training. AI technologies enable the personalization of learning experiences, making training more engaging and effective by adapting to individual needs. By automating and optimizing L&D processes, AI allows organizations to scale their initiatives efficiently and ensures deep understanding through innovative methods like the Feynman Technique, which breaks down complex topics into simpler concepts. However, integrating AI in L&D programs involves challenges that require careful implementation and ongoing evaluation to maximize effectiveness and mitigate risks. The article underscores the importance of combining AI with human oversight to align training with ethical standards and business objectives. It encourages companies to adopt AI as a strategic component of their training programs, focusing on developing employee skills for effective AI use and management.Herald of Khmelnytskyi National University Economic sciences00The study addresses challenges in AI integration, such as data quality and the necessity for human expertise, and provides recommendations for integrating AI effectively into corporate training, focusing on the use of large language models like GPT-3.5 and GPT-4 Turbo.Herald of Khmelnytskyi National University. Economic sciences["\u041e\u043b\u044c\u0433\u0430 \u0406\u0432\u0430\u043d\u0456\u0432\u043d\u0430 \u0413\u0430\u0440\u0430\u0444\u043e\u043d\u043e\u0432\u0430", "\u0420\u043e\u043c\u0430\u043d \u041a\u0443\u0437\u0456\u0432", "\u041c\u0430\u043a\u0441\u0438\u043c \u041a\u043e\u0441\u0442\u0435\u0446\u044c\u043a\u0438\u0439"]2024-05-30T00:00:00 a16f3a04317a563e598821cf2f7a3932c9c1bb6bBreast cancer stands as one of the most prevalent forms of cancer. Artificial intelligence (AI) and machine learning have become crucial in accurately identifying and managing various serious illnesses. This development has contributed to improved survival rates by enabling early detection and timely intervention. In our investigation, we conducted a thorough systematic review of the role of AI and machine learning in breast cancer screening. We examined articles from 2015 to 2023 across diverse databases, focusing on the intersection of breast cancer and AI. The integration of AI into existing screening procedures yields more convenient and efficient outcomes. Utilizing AI techniques in breast cancer screening offers numerous benefits, including heightened precision in results. However, the incorporation of AI presents several challenges that need systematic addressing.Magna Scientia Advanced Research and Reviews00In this investigation, a thorough systematic review of the role of AI and machine learning in breast cancer screening is conducted across diverse databases, focusing on the intersection of breast cancer and AI.Magna Scientia Advanced Research and Reviews["Rushin Mahesh Patel", "Akash Jain", "Afoma Danielle Onyechi", "Jessica Ohemeng-Dapaah", "Winnie Oshoname Shaba", "Eduzor Anthony Onyechi", "Yetunde Oyenike Ogunlana", "Zalak Vipul Patel", "Mrunal Mahesh Patel", "Darshil Chandubhai Patel"]2024-05-30T00:00:00 e88cf09a6235c8f2c89cf03a52230d6bb021f08bArtificial Intelligence (AI) has revolutionized healthcare by integrating treatment, diagnosis, and prognosis into a cohesive and patient-centric approach. This study examines how utilising AI technology in healthcare might improve patient management and have a transformational impact. Huge volumes of patient data, including as genetic data, medical records, and treatment outcomes are analysed by AI algorithms, allowing for the creation of individualised treatment regimens based on precise prognostic assessments and diagnoses. Utilising AI-driven decision-making promotes proactive and preventative actions, improving healthcare outcomes. To ensure ethical AI adoption, however, concerns about data privacy, algorithmic bias, and ethical issues must be addressed. In order to demonstrate how AI-driven therapy approaches are successful, case examples are reviewed in this article, demonstrating how they might potentially enhance patient care. As AI develops, its seamless integration with healthcare systems has enormous promise for revolutionising medical practise. It will usher in a new era of accurate, effective, and data-driven patient management, which will ultimately be advantageous to both patients and healthcare professionals.its capacity to enhance patient care.Indian Journal of Artificial Intelligence and Neural Networking90This study examines how utilising AI technology in healthcare might improve patient management and have a transformational impact, demonstrating how AI-driven therapy approaches are successful and its capacity to enhance patient care.Indian Journal of Artificial Intelligence and Neural Networking["Devaharish Srikannan"]2024-05-30T00:00:00 eaedcefec09a476b0cbc8e13cc9800e766f938c2This paper investigates citizens' counter-strategies to the use of Artificial Intelligence (AI) by law enforcement agencies (LEAs). Based on information from three countries (Greece, Italy and Spain) we demonstrate disparities in the likelihood of ten specific counter-strategies. We further identified factors that increase the propensity for counter-strategies. Our study provides an important new perspective to societal impacts of security-focused AI applications by illustrating the conscious, strategic choices by citizens when confronted with AI capabilities for LEAs.arXiv.org90This study provides an important new perspective to societal impacts of security-focused AI applications by illustrating the conscious, strategic choices by citizens when confronted with AI capabilities for LEAs.ArXiv["P. Bayerl", "Babak Akhgar", "Ernesto La Mattina", "Barbara Pirillo", "Ioana Cotoi", "Davide Ariu", "Matteo Mauri", "Jorge Garcia", "Dimitris Kavallieros", "Antonia Kardara", "Konstantina Karagiorgou"]2024-05-30T00:00:00 01af1685804c12e5bccb81b0b8e0407143b4142eThe purpose of the study. The article deals with topical issues arising from the introduction of artificial intelligence (AI) technology into forensic automotive expertise, in particular when solving the problem of establishing the technical condition of a vehicle (vehicle). The essence of any forensic examination, including this kind, is manifested through a set of mandatory elements (signs): the subject, object and methods of solving the tasks set. Conclusions. Changes in the vehicle object due to the introduction of AI technology leads to changes in the subject of expertise and the methods used. The expansion of the subject of expertise invariably affects the competence of the expert - the range of issues that he is competent to solve. In addition, AI performs in two qualities in forensic automotive expertise: 1) an updated object and 2) a tool (technical means) used by an expert to study the updated object. Promising areas of AI use in the study of the technical condition of the vehicle as a type of forensic automotive expertise are computer vision technologies and artificial neural networks.Gaps in Russian Legislation00Gaps in Russian Legislation["I. Koltyapin", "E. V. Chesnokova"]2024-05-30T00:00:00 4a71e79ff08913f329537da0205e8a09021ee329A solid foundation for behavior portrait construction in the fight against telecom fraud is the goal of this research. The study explores the integration of communication AI and Big Data technologies, focusing on the perspective of artificial intelligence. By using insights obtained from a telecom fraud detection model that relies on users’ behavior variations expressed through time-varying signatures, the goal of this study is to enhance fraud prevention strategies in the telecom industry. Through the examination of call detail records and customer profile information, the TeleGuard AI Fraud Prevention Framework (TGAI-FPF) aims to recognize suspicious trends and variations that are potentially suggestive of fraudulent actions. The purpose of the model is to generate behavior portraits that are capable of capturing the distinctive aspects of fraudulent conduct in telecom networks. This will be accomplished through the utilization of advanced analytics and machine learning algorithms. The study highlights the significance of leveraging big data analytics and artificial intelligence technologies to efficiently detect and thwart fraudulent activity in the telecom industry. The results of this study should fortify the defenses of telecom networks against growing fraudulent schemes and help in the development of preventative measures to combat fraud. This is the anticipated manner in which the results will add.International Journal of Intelligent Decision Technologies160The study explores the integration of communication AI and Big Data technologies, focusing on the perspective of artificial intelligence, and highlights the significance of leveraging big data analytics and artificial intelligence technologies to efficiently detect and thwart fraudulent activity in the telecom industry.Intell. Decis. Technol.["Dong Chen", "Yang Wu"]2024-05-30T00:00:00 c1d990e3acbab66a053f5cf80f248deb614323b3This research investigates students' knowledge in the use of Artificial Intelligence (AI) in education through the administration of a formulated questionnaire. Drawing upon insights from thematic analysis of qualitative responses and computation of Cronbach's alpha coefficients, the study aims to produce a tool to assess tertiary level students' understanding regarding AI integration in academic settings. Results reveal varying degrees of awareness, knowledge, and confidence among students, highlighting thematic areas such as perceived benefits, ethical considerations, and disciplinary perspectives. While the questionnaire demonstrates validity, reliability, and scalability as a measurement tool, limitations including response bias and generalizability are acknowledged. Implications for educational practice and research are discussed, emphasizing the utility of the questionnaire in informing curriculum development, professional development initiatives, policy decisions, and future research directions. Overall, this research provides valuable insights into students' perspectives on AI in education, offering a foundation for enhancing AI literacy, fostering ethical awareness, and shaping the future of AI integration in educational settings.Cognizance Journal of Multidisciplinary Studies140Investigating students' knowledge in the use of Artificial Intelligence in education through the administration of a formulated questionnaire provides valuable insights into students' perspectives on AI in education, offering a foundation for enhancing AI literacy, fostering ethical awareness, and shaping the future of AI integration in educational settings.Cognizance Journal of Multidisciplinary Studies["Ramil Santos", "Rosemarie Villaceran", "Joviericka Rioflorido", "Danita Paguiligan"]2024-05-30T00:00:00 909082a0ab4ee02c90abc279f5ae5c6787790a6eThe rapid advancement of artificial intelligence (AI) has brought significant challenges to the education and workforce skills required to take advantage of AI for human-AI collaboration in the workplace. As AI continues to reshape industries and job markets, the need to define how AI literacy can be considered in lifelong learning has become increasingly critical (Cetindamar et al., 2022; Laupichler et al., 2022; Romero et al., 2023). Like any new technology, AI is the subject of both hopes and fears, and what it entails today presents major challenges (Cugurullo \&Acheampong, 2023; Villani et al., 2018). It also raises profound questions about our own humanity. Will the machine surpass the intelligence of the humans who designed it? What will be the relationship between so-called AI and our human intelligences? How could human-AI collaboration be regulated in a way that serves the Sustainable Development Goals (SDGs)? This paper provides a review of the challenges of lifelong learning in the era of AI from a computational thinking, critical thinking, and creative competencies perspective, highlighting the implications for management and leadership in organizations.arXiv.org280A review of the challenges of lifelong learning in the era of AI from a computational thinking, critical thinking, and creative competencies perspective is provided, highlighting the implications for management and leadership in organizations.ArXiv["Margarida Romero"]2024-05-30T00:00:00 46c8cb120ed194fb732c99244a5a8e02157153b0This research presents a systematic literature review on the impact of artificial intelligence (AI) on student learning outcomes. While previous studies have explored various aspects of AI in education, there has been a lack of comprehensive analysis specifically examining its effect on learning outcomes. The objective of this study is to provide a detailed review of the literature on the effects of AI on student learning outcomes from 2013 to 2023, employing the PRISMA methodology. From an initial pool of 1068 papers identified in the Scopus database using defined search criteria, 39 articles were selected for the final analysis. Descriptive data reveal that most of the research focuses on higher education students and aims to enhance cognitive learning outcomes. Despite being grounded primarily in empirical research, the findings suggest that AI has significant potential to enhance educational processes in both schools and universities. This study aims to elucidate how AI can improve the learning experience, identify associated challenges and risks, and underscore the importance of integrating technology into the educational system to elevate the overall quality of learning.Jurnal Varidika620How AI can improve the learning experience, identify associated challenges and risks, and underscore the importance of integrating technology into the educational system to elevate the overall quality of learning are elucidated.Jurnal VARIDIKA["Nita Ambarita", "Muh. Fiqri Nurrahmatullah"]2024-05-30T00:00:00 7db77571ca5dae3d172342ea76d3fdcc7e7d85d6The article takes a philosophical look at the possibility and peculiarities of human-artificial intelligence dialogue in the light of modern epistemological principles. In the approach, the interaction of the concepts of "natural consciousness", "artificial consciousness", "artificial intelligence", "double contingency", "recursiveness", "implicit knowledge", "obvious knowledge" is considered as a systematic theoreticalmethodological categorical apparatus. At this time, the relations of these concepts are examined against the background of the concepts of "humanism" and "intellectual virtue" and within the principle of dialogicity of consciousness. The main scientific goal of the research is related to the highlighted features. It is shown that human-artificial intelligence dialogue as a whole is possible in the aspect of the principle of humanism. However, this issue should have its own mechanism of realization in the philosophical and epistemological context. In that quality, the article puts forward the thesis that "intellectual virtue" can play a constructive role. At the same time, the place and role of double contingency and recursion phenomena are important among the epistemological conditions of the possibility of human-artificial intelligence dialogue in the context of humanism. Double contingency defines the epistemological boundary of that dialogue. Recursiveness plays the role of its cognitive mechanism in the aspect of continuity and gradual realization of the process.Baltic Journal of Legal and Social Sciences00The article puts forward the thesis that "intellectual virtue" can play a constructive role in the context of humanism and examines the relations of these concepts against the background of the concepts of "humanism" and "intellectual virtue" and within the principle of dialogicity of consciousness.Baltic Journal of Legal and Social Sciences["Asadova Bahaddin Turana"]2024-05-30T00:00:00 e56026176c0be2360e320079cb86415a84e7598aArtificial Intelligence in the banking system is constantly developing, especially among young customers. Innovation of digital products has an important role in the use of banking services, but human resources specialists in the banking system also have such a role, due to their expertise, knowledge and involvement in explaining the benefits of using Artificial Intelligence and digital products in the banking financial system. In this article, the Technology Acceptance Model (TAM) was used to show the impact of internal variables (the role of human resources specialists in the banking system) and external variables (Artificial Intelligence and innovation of digital products), TAMspecific PU and PEU, on the intention to continue using Artificial Intelligence in the banking financial system. The results indicated that innovative digital products and the role of human resources in the use of Artificial Intelligence, PU and PEU have a positive and direct impact on the intention to use Artificial Intelligence in the financial system. All research hypotheses have been fulfilled, indicating that Artificial Intelligence has an important role in the Romanian banking financial system among young consumers. The paper contributes to the development of the banking financial system by using Artificial Intelligence, highlighting the importance of human resources, TAM and PLS-SEM specialists in this field.Journal of Financial Studies250The results indicated that Artificial Intelligence has an important role in the Romanian banking financial system among young consumers, and the role of human resources in the use of Artificial Intelligence, PU and PEU have a positive and direct impact on the intention to use Artificial Intelligence in the financial system.Journal of Financial Studies["N. Florea", "Gabriel Croitoru", "Georgiana Radu (C\u00e2rstea)", "Daria Florea"]2024-05-30T00:00:00 57c4d9adc67fc3410243dc2b194a1ac7edc4ad7dThe article reviews the main forms and methods of development of a future specialist in the legal sphere with the use of artificial intelligence. The authors describe algorithms of operations of artificial intelligence required in lawyer's activities.Criminal-executory system: law, economics, management00The authors describe algorithms of operations of artificial intelligence required in lawyer's activities and the main forms and methods of development of a future specialist in the legal sphere with the use of artificial intelligence.Criminal-executory system: law, economics, management["Svetlana A. Leschenko", "Yury Yu. Tischenko", "Andrey N. Popov"]2024-05-30T00:00:00 2ef87a573f65c64b993a7e53faef9a2ef0ab839aPurpose : The purpose of this study was to analyze the trends in domestic nursing education research applying artificial intelligence (AI) in South Korea by examining recent research trends and suggesting future research directions. Method : This study conducted a literature review to analyze the trends in research on AI-applied domestic nursing education published in domestic academic journals. Results : A total of 55 keywords were identified based on the titles and major keywords from 12 literature sources, including multiple entries. The major keywords included AI (12, 21.9%), nursing students (10, 18.3%), nurses (4, 7.4%), AI remote medicine (2, 3.6%), general public (2, 3.6%), AI (2, 3.6%), nursing education (2, 3.6%), nursing process (2, 3.6%), AI application(2, 3.6%), program development (2, 3.6%), bioethics (2, 3.6%), eHealth (1, 1.8%), simulation training (1, 1.8%), AI generation (1, 1.8%), remote medicine (1, 1.8%), phenomenological study (1, 1.8%), systematic review(1, 1.8%), validation study (1, 1.8%), social network analysis (1, 1.8%), and newspaper articles (1, 1.8%). Conclusion : Through this study, it is evident that diverse research on AI-related nursing education is necessary for future nursing education research.The Korean Society for Health and Nursing Convergence00It is evident that diverse research on AI-related nursing education is necessary for future nursing education research, by examining recent research trends and suggesting future research directions.The Korean Society for Health and Nursing Convergence["Young Eun Jang", "Sung Mi Ahn", "Sung Ji Park", "Hyun Ji Kwon", "Ga Yeon Ko"]2024-05-30T00:00:00 596e9755adc66c9c5f2db5dd4eac93dedd59220fIn an era where digital transformations are not just trends but necessities, the reliability and security of information systems have become paramount. The complexity of modern IT environments, coupled with increasing regulatory demands, poses significant challenges for organizations worldwide. IT control compliance, particularly in the context of Sarbanes-Oxley Act (SOX) IT General Controls (ITGC), requires rigorous and continuous monitoring to ensure that financial reporting and data integrity are maintained. However, traditional compliance efforts are often hampered by manual processes that are both time-consuming and error-prone. These methods struggle to keep pace with the dynamic nature of IT advancements and the evolving landscape of cyber threats. This gap not only increases the risk of non-compliance but also places a heavy burden on the resources of the organization. With the advancement of Artificial Intelligence (AI), there is promising potential to revolutionize these processes. AI technologies, through their capability to analyze large volumes of data and recognize patterns quickly, can automate complex tasks, enhance decision-making, and significantly improve the efficiency and effectiveness of compliance systems. By integrating AI into IT control frameworks, organizations can address the perennial challenges of compliance more effectively. AI-driven systems can reduce human error, streamline control processes, and ensure more robust governance and risk management frameworks. This paper explores the integration of AI technologies into IT control frameworks. It discusses how leveraging AI not only aids in maintaining compliance with existing regulations but also enhances the organization’s ability to adapt to new regulatory changes swiftly, thus safeguarding against compliance risks and fostering a culture of continuous improvement in governance practices.International journal of computer science and mobile computing00This paper discusses how leveraging AI not only aids in maintaining compliance with existing regulations but also enhances the organization’s ability to adapt to new regulatory changes swiftly, thus safeguarding against compliance risks and fostering a culture of continuous improvement in governance practices.International Journal of Computer Science and Mobile Computing["Naiyer Alam"]2024-05-30T00:00:00 3ff692d22ad1becd4a9d24af25b8a10a704f4893The article examines the problems of understanding modern technological progress and the picture of the world as a whole using the example of the approach of the Swedish philosopher Nick Bostrom to the problem of the coexistence of machines based on artificial intelligence and humans. Everyday machines become more and more talented in areas in which they were not expected to actively rise, in the field of creativity: writing texts, creating images, videos, and music. In addition to the obvious economic problem of developing such a powerful tool in a market economy, scientists and philosophers are increasingly talking about gloomy future scenarios with artificial intelligence spiraling out of control. Bostrom primarily tries to convey his concerns about the creation of superintelligent machines. Their motives and goals will be unknown to us as living people, but Bostrom suggests that regardless of the ultimate goal of an intelligent actor, in the process of achieving it, he can, while fulfilling his intermediate goals, destroy humanity. At the same time, Bostrom considers various scenarios for living together with machines based on artificial intelligence and suggests ways to transfer human values to machines. In this article, we will look at the likely unobvious motives of artificial intelligence, which may have a detrimental effect on humanity. Bostrom offers us several likely scenarios for explosive, slow, or moderate development of artificial intelligence, considering the most likely scenario to be an uncontrolled takeoff into a singularity that will lead us to destruction. To continue a safe life together, humanity must stop research and only when states are ready to cooperate, and scientists and philosophers can jointly find a solution to the problem of creating safe artificial intelligence, we will have to continue research.ANOTHER ONE00ANOTHER ONE["Tatiana Sergeeva"]2024-05-30T00:00:00 2cd1c6b28638f3413f5865d672d9d164eb77ad5cIn this study, images of pedagogical-educational activities were analyzed, including two compositions generated by artificial intelligence using the DALL-E tool on the ChatGPT platform by OpenAI. Based on Peircean Semiotics theory, two images were proposed as projections of the future of education, considering a period of over 100 years. The prompt used was: “Create a scene that gives us a glimpse of the future of education beyond 100 years.” The results were interpreted and compared with a work by Ivan Cruz, a Brazilian plastic artist. The qualitative-descriptive research demonstrated that although intelligent digital systems can organize existing data and information, their projective capacity is still limited. This highlights the necessary interaction between human skills and technological resources, as technology does not operate autonomously and depends on human intervention.Concilium00Images of pedagogical-educational activities were analyzed, including two compositions generated by artificial intelligence using the DALL-E tool on the ChatGPT platform by OpenAI, demonstrating that although intelligent digital systems can organize existing data and information, their projective capacity is still limited.Concilium["Douglas Ropelato", "Ed\u00e9sio Marcos Slomp", "Marily Dilamar Da Silva", "Richard Perassi Luis De Sousa", "V. Ulbricht", "Maria Jos\u00e9 Baldessar"]2024-05-30T00:00:00 30e3439ab006c6dcb926806bebd9b1cfae50021aArtificial intelligence (AI) and blockchain technology, in conjunction with Financial Technology (Fintech), are playing an increasingly significant role in shaping global Energy Trading trends. The combined use of blockchain, AI, and Fintech introduces innovative features that have the potential to improve the efficiency and performance of current systems. This study provides insights into the key features of the intersection of AI, blockchain, and Fintech, such as data security, encryption, sharing, efficiency, collective decision-making, decentralized intelligent systems, transparency, automated decision systems, and financial applications. By analyzing literature from major digital databases, this technological convergence was constructed via the emerging era, convergence era and application era. In the convergence era, features were categorized into data manipulation, regulations, applicability to legacy systems, compatibility and hardware issues. The application era explores the impact of this technology fusion in areas like cybersecurity, finance, energy, Internet of Things applications, and smart cities. This comprehensive analysis helps outline the timeline of AI, blockchain, and Fintech convergence and highlights the unique characteristics of their integration. The paper concludes by addressing the current challenges and how it is increasing efficiency, transparency and automation of AI, blockchain, and Fintech in energy trading and also in the sector of energy trading i.e. electricity, gas, oil and non renewable energies. AI enables predictive analytics and automation, blockchain ensures secure and transparent transactions, and FinTech facilitates seamless financial transactions and risk management in energy trading. The convergence revolutionizes energy trading.World Journal of Advanced Research and Reviews00This study provides insights into the key features of the intersection of AI, blockchain, and Fintech, such as data security, encryption, sharing, efficiency, collective decision-making, decentralized intelligent systems, transparency, automated decision systems, and financial applications.World Journal of Advanced Research and Reviews["Syed Tanveer Alam"]2024-05-30T00:00:00 60022b20ee84d56db2a5d6ff3cde3fe4b1640999Subject. The article examines the condition of regional innovation ecosystems of the Russian Federation for sustainable development and technological sovereignty of the country under import substitution due to high external challenges. Objectives. The aim is to solve the multifactorial task of analyzing the development of innovative ecosystems in Russia’s regions. The task is characterized by complex formalization, and it is in line with modern concepts of competitive potential, through the proposed productive method, i.e. neural network cluster data analysis. Methods. We employ the cluster analysis based on neural networks that formed an essential component of artificial intelligence. The functionality of artificial neural networks – Kohonen self-organizing maps, involved in this work, has no model prohibitions. The method of neural network cluster analysis enables to visualize clustering results of the multidimensional source data space. Results. The neural network cluster analysis of a set of heterogeneous data helped get the integration of Russian regions across seven clusters. We obtained a significant variety of placement of Russian regions by cluster: the number of regions in clusters varies from one to thirty-one. We established a different level of the state of regional innovation ecosystems, according to the studied indicators in the context of clusters. Conclusions. The findings enabled to assess the state of innovative regional ecosystems in the environment of import substitution created by big challenges from external factors. To continue strengthening sustainable development and technological sovereignty of the country, it is necessary to use different main directions of innovative economic growth of Russian regions, taking into account their specifics in the focus of cluster formations.Economic Analysis: Theory and Practice200To continue strengthening sustainable development and technological sovereignty of the country, it is necessary to use different main directions of innovative economic growth of Russian regions, taking into account their specifics in the focus of cluster formations.Economic Analysis: Theory and Practice["E. Letyagina", "V. Perova"]2024-05-30T00:00:00 4dc4ffdeee0454efb18a0041300ce7ce4764b33cEnvironmental science and pollution research international610This research employs the DCC-GARCH Copula Model to examine time-varying spillovers and prove interlinkages between the development of AI and green cryptocurrencies in the period from January 1, 2018, to September 8, 2023.Environmental science and pollution research international["Leavitt Ha"]2024-05-30T00:00:00 fe5aa114910149e0afe888fe5323f11825d7f903The International Workshop on Artificial Intelligence for Signal, Image Processing, and Multimedia (AI-SIPM) aims to provide a platform for researchers, practitioners, and industry professionals to exchange ideas, discuss recent advancements, and explore future directions in the field of artificial intelligence (AI) applied to signal processing, image processing, and multimedia technologies. This workshop will feature presentations of novel research findings, practical applications, and innovative solutions addressing various challenges and opportunities in AI-driven signal and image processing, as well as multimedia analysis and understanding. Researchers and practitioners from academia, industry, and government agencies are invited to submit their original research contributions and participate in discussions that foster collaboration and knowledge sharing across different domains. Through this workshop, we aim to accelerate advancements in AI-driven technologies for signal processing, image analysis, and multimedia applications, contributing to the advancement of research and innovation in this rapidly evolving field.International Conference on Multimedia Retrieval30This workshop will feature presentations of novel research findings, practical applications, and innovative solutions addressing various challenges and opportunities in AI-driven signal and image processing, as well as multimedia analysis and understanding.Proceedings of the 2024 International Conference on Multimedia Retrieval["M. Ketcham", "Kanyalag Phodong", "Patiyuth Pramkeaw", "Worawut Yimyam", "Narumol Chumuang", "Pokpong Songmuang", "Thittaporn Ganokratanaa"]2024-05-30T00:00:00 9797421ec987c34af6dfc12aca42c36706c7a851This article delves into the link between global terrorism and the growing dangers presented by Artificial Intelligence (AI). We examine how terrorism utilizes AI technologies, such as advanced deep learning algorithms like ChatGPT, to bolster their activities both on the internet and in the world. Additionally, we assess the possible advantages and obstacles of using AI to combat terrorism, emphasizing the ethical and legal dilemmas involved. The article discusses the importance of regulating, educating, and prioritizing ethical considerations in AI development to tackle issues like disinformation, privacy violations, and job displacement. It emphasizes the need for a comprehensive approach involving cooperation among various groups to tackle the challenges posed by AI-driven terrorism while advocating for human rights and social justice.Journal of Globalization Studies190The article discusses the importance of regulating, educating, and prioritizing ethical considerations in AI development to tackle issues like disinformation, privacy violations, and job displacement, and the need for a comprehensive approach involving cooperation among various groups to tackle the challenges posed by AI-driven terrorism.Journal of Globalization Studies["Yaser Esmailzadeh", "Ebrahim Motaghi"]2024-05-30T00:00:00 f29ab728dd674bf2a8921283a709cc076eb80ac0This research investigates the influence of artificial intelligence (AI) adoption, machine learning (ML) integration, and AI ethics on product innovation within Bogor's startup ecosystem. A quantitative approach was employed, collecting data through an online survey from 180 startups. Structural equation modeling with Partial Least Squares (PLS) 3 was utilized for data analysis. The results reveal significant positive relationships between AI adoption, ML integration, AI ethics, and product innovation. AI adoption and ML integration positively impact product innovation, while adherence to ethical AI practices also plays a crucial role. These findings highlight the importance of leveraging AI technologies responsibly and ethically to drive innovation within startup ecosystems. Policymakers, entrepreneurs, investors, and other stakeholders can utilize these insights to foster a conducive environment for sustainable growth and innovation in Bogor's startup community.West Science Social and Humanities Studies370Findings highlight the importance of leveraging AI technologies responsibly and ethically to drive innovation within startup ecosystems and reveal significant positive relationships between AI adoption, ML integration, AI ethics, and product innovation.West Science Social and Humanities Studies["A. Afrizal", "H. Hildawati", "Sehan Rifky", "A. Y. Vandika"]2024-05-30T00:00:00 88832242767e8806588a4f2809c6db380f8d4500PurposeUsing AI to strengthen creativity and problem-solving capabilities of professionals involved in innovation management holds huge potential for improving organizational decision-making. However, there is a lack of research on the use of AI technologies by innovation managers. The study uses the theory of appropriation to explore how specific factors – agile leadership (AL), innovation orientation (IO) and individual creativity (IC) – impact innovation managers' use of generative AI tools, such as ChatGPT (CGA).Design/methodology/approachThe research model is tested through a large-scale survey of 222 Italian innovation managers. Data have been analyzed using structural equation modeling following a two-step approach. First, the measurement model was assessed to ensure the constructs reliability. Subsequently, the structural model was analyzed to draw the conclusions on theorized model relationships and their statistical significance.FindingsThe research findings reveal positive associations between IO and IC with CGA, demonstrating that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize ChatGPT. However, the study did not confirm a significant association between AL and CGA.Originality/valueOur findings have important implications for organizations seeking to maximize the potential of generative AI in innovation management. Understanding the factors that drive the adoption and customization of generative AI tools can inform strategies for better integration into the innovation process, thereby leading to enhanced innovation outcomes and improved decision-making processes.Management Decision984It is revealed that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize ChatGPT, demonstrating that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize CGA.Management Decision["A. Cimino", "A. M. Felicetti", "Vincenzo Corvello", "Valentina Ndou", "Francesco Longo"]2024-05-30T00:00:00 08656cf98e3c2d6b99f888bba22f4b3e48fb6e89Postgraduate medical journal93Postgraduate medical journal["Leiyun Huang", "Jinghan Hu", "Qingjin Cai", "Guangjie Fu", "Zhenglin Bai", "Yongzhen Liu", "Ji Zheng", "Zengdong Meng"]2024-05-30T00:00:00 c939f2f1129c80a95d57f8142177e088921c57f8Health Insurance Review &amp; Assessment Service Research01Health Insurance Review &amp; Assessment Service Research["Young-Taek Park", "Seon Min Lee", "Yul Hee Lee", "Kwang Gi Kim"]2024-05-30T00:00:00 e7e0b6ddbbe63772384dc41a2bdae26676ab038fCluster Computing01Clust. Comput.["Munish Saini", "Eshan Sengupta", "Suraaj Thakur"]2024-05-30T00:00:00 90cfcdb0e3fe972a79561f11357c5cfece339b40A short reflection of personal engagement with and an exploration of the unforeseen intersections between traditional educational paradigms and the disruptive force of AI. The reflection is not just about discovering a technological tool; it is about encountering a new form of activity, one that could potentially redefine the contours of education and learning. A reflection which brought me to realize that my interaction with ChatGPT, marked by an initial enchantment followed by a phase of critical scrutiny, mirrored the very human process of knowledge acquisition and validation. ChatGPT, with all its capabilities and limitations, was in a sense, as 'human' as any of my colleagues or myself. The process of engaging with, questioning, and validating the information it provided was not a testament to its shortcomings, but rather an affirmation of the critical, discerning approach that underpins scholarly work. In recognizing this, I found a renewed appreciation for the nuanced and complex interplay between human intelligence (from latin legere "choose, pick out, read, collect, gather") and artificial intelligence in the pursuit of knowledge. En este artículo, en modalidad de ensayo, se plantea una breve reflexión sobre el compromiso personal del autor y la exploración de las intersecciones imprevistas entre los paradigmas educativos tradicionales y la fuerza disruptiva de la IA. A través de esta reflexión no se trata sólo de descubrir una herramienta tecnológica; se trata de encontrar una nueva forma de actividad, que potencialmente podría redefinir los contornos de la educación y el aprendizaje. Una reflexión, por otro lado, que me llevó a darme cuenta de que mi interacción con ChatGPT, marcada por un encanto inicial seguido de una fase de escrutinio crítico, reflejaba un proceso muy humano de adquisición y validación del conocimiento. ChatGPT, con todas sus capacidades y limitaciones, era, en cierto sentido, tan "humano" como cualquiera de mis colegas o como yo mismo. El proceso de abordar, cuestionar y validar la información que proporcionaba no fue un testimonio de sus deficiencias, sino más bien una afirmación del enfoque crítico y perspicaz que sustenta el trabajo académico. Al reconocer esto, encontré una valoración renovada por la compleja y matizada interacción entre la inteligencia humana (del latín legere "elegir, seleccionar, leer, recopilar, reunir") y la inteligencia artificial en la búsqueda del conocimiento.Resource Discovery00Revista de Educación a Distancia (RED)["Zvi Bekerman"]2024-05-30T00:00:00 cbc09c362ff64c7d0978b1361324ed72366d5463OECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2024-05-30T00:00:00 757bb492bb5186912c507b84fcd41f7603d6592fVěda a perspektivy00Věda a perspektivy["Vladyslav Saveliev"]2024-05-30T00:00:00 89e84628f76a36eba5ad259e743481a642e64091The aim of the article is to assess the potential for implementing AI tools in the development of autogenic training programs aimed at correcting the psychophysical state of high-risk professionals prone to disorders leading to functional impairments. Research findings indicate that high-risk professions are associated with stress, high demands, and hazards that contribute to the development of psychophysical disorders, such as burnout and emotional exhaustion. Autogenic training is an effective self-regulation method that reduces stress and enhances overall well-being, becoming a key element in the prevention of burnout and emotional exhaustion. AI can be utilized to create personalized applications that provide interactive effects for sensations of warmth and heaviness, recording and playback of personalized affirmations, audio-visual effects to create a sensation of coolness, audio guides for the sensation of gravity, tools for deep relaxation, and musical accompaniments for music therapy.Baltic Journal of Legal and Social Sciences00Assessment of the potential for implementing AI tools in the development of autogenic training programs aimed at correcting the psychophysical state of high-risk professionals prone to disorders leading to functional impairments finds AI can be utilized to create personalized applications that provide interactive effects for sensations of warmth and heaviness.Baltic Journal of Legal and Social Sciences["Anna Rode", "Yulia Rode"]2024-05-30T00:00:00 3dc8f771b5c7fa83b9032ce05114aed39787c49bAbstract. This paper investigates the use of machine learning (ML) models to predict housing prices. A well-performing housing price model was trained, which can seamlessly be integrated into public sector processes to increase market transparency and is based on modern ML and feature engineering methods. For these models, particular consideration was given to the spatial component. The research uses the Design Science Research approach, with a case study carried out in the city of Duisburg, Germany. The ML models developed showed better performance than traditional models. The models were embedded in official processes using Shapley values to increase interpretability. The study concludes that ML models can contribute to increased market transparency in the real estate sector. AGILE: GIScience Series350The study concludes that ML models can contribute to increased market transparency in the real estate sector.AGILE: GIScience Series["Christian Mueller-Kett"]2024-05-30T00:00:00 c5b44ab2cf1c1dd43c8486e74e40d9aa44406a05Early Childhood Education Journal180Early Childhood Education Journal["Joohi Lee", "Junoh Jo", "Joohi Lee", "So Hyang Kim"]2024-05-30T00:00:00 9d21b53600966a3356ceb684873bc47be0c91126An output of the Open Development & Education, https://opendeved.net/00["Bj\u00f6rn Ha\u00dfler", "Wuxia Zhang", "Olamide Eso", "Xuzel Ana Villavicencio Peralta", "Eunice Jengo", "Franzgerard Clarin", "Tanaya Sharma", "Asia Noble", "Dennis Mwangi"]2024-05-30T00:00:00 99a052268abc5d918e616c9d587ae44b72eced20Educational Administration Theory and Practice00Educational Administration Theory and Practice["Shalini Aggarwal"]2024-05-30T00:00:00 9327555862947922a8c2fb98931baadea24df60fВ статье рассматривается важность критического мышления в совре- менном среднем профессиональном образовании как универсальной компетенции в части понимания и анализа идей и аргументов, оценивания идей и аргументов, а также решения проблем и принятия решений. Цель статьи: анализ влияния ускоренной автоматизации и развития генера- тивного искусственного интеллекта на содержание и технологии системы среднего профессионального образования и обобщение опыта развития критически мыслящих специалистов среднего звена. Основные результаты исследования. Показано, что одним из ключевых методов формирования критического мышления является сократический метод. Описан автор- ский подход к обучению основам сократического диалога в форме взаимодействия с искусственным интеллектом, разработанный в Московском техникуме креативных индустрий им. Л. Б. Красина. Научная новизна исследования. Проведен ряд обобщений: сократический подход к обучению предполагает, что педагог задает ряд провокационных вопросов, направ- ленных на выявление ошибок или противоречий в мыслях студентов; в ходе этого процесса они не только развивают логическое мышление, но и достигают глубокого понимания изучаемого материала; при этом применение сократического метода в больших классах или группах часто затруднено. Практическая значимость. В контексте использования искусственного интел- лекта в системе среднего профессионального образования в статье приводятся воз- можности применения больших языковых моделей генеративного искусственного интеллекта для обучения сократическому методу.MCU Journal of Modern college00MCU Journal of Modern college["\u041a.\u041d. \u041f\u0430\u0432\u043b\u044e\u0446"]2024-05-30T00:00:00 a750f2635ea30ea1ec1453c7dee229326385677bIJARCCE00IJARCCE["Dean M. Aslam"]2024-05-30T00:00:00 b70884ea71eeca15f769962322a5c340d2ac24a1International Journal of Computer Science and Engineering00International Journal of Computer Science and Engineering["Pramod Gavade"]2024-05-30T00:00:00 df7ed475e39b37ef522c3d63c260cf0ce38d85aaKantian Review00Kantian Review["Hugh Compston"]2024-05-30T00:00:00 4c0547f69f1ad0d42030a2cbb3df81abc6f73070The adding number of cyber-attacks on diligence demands immediate attention for furnishing further secure mechanisms to guard diligence and minimize pitfalls. An administrative control and data accession (SCADA) system employing the distributed networks of detectors and selectors that interact with the physical terrain is vulnerable to attacks that target the interface between the cyber and physical subsystems. These cyber-attacks are generally vicious conduct that beget uninvited results in the cyber physical world, for illustration, the Stuxnet( 2010) attack that targeted Iran's nuclear centrifuges. An attack that hijacks the detectors in an attempt to give false readings to the regulator can be used to dissemble normal system operation for the control system, while the bushwhacker can commandeer the selectors to shoot the system beyond its safety range. AI result can identify shadow data, cover for abnormalities in data access and alert cybersecurity professional about implicit pitfalls by anyone penetrating the data or sensitive information. This proposes a process- apprehensive approach with the use of steady equations grounded on the physical and chemical parcels of the process and a Multiple Security sphere Nondeducibility (MSDND) frame to descry when a detector signal is being virulently manipulated. A system without any MSDND secure information flows between the AI and cyber observers has smaller sins that can be exploited.International journal of computer science and mobile computing00This work proposes a process- apprehensive approach with the use of steady equations grounded on the physical and chemical parcels of the process and a Multiple Security sphere Nondeducibility (MSDND) frame to descry when a detector signal is being virulently manipulated.International Journal of Computer Science and Mobile Computing["J. J. Abisha", "M. Janaki"]2024-05-30T00:00:00 49af4da134c1aec388d7612a0743709d79292e22: Creativity has long been viewed as the bastion of human expression. With the advent of generative artificial intelligence (AI), there is an emerging notion of artificial creativity that contests traditional perspectives of artistic exploration. This paper explores the complex dynamics of this evolution by examining how generative AI intertwines with and transforms the art world. It presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices. Simultaneously, it investigates the revolutionary opportunities generative AI offers, including the democratization of art creation, the expansion of creative boundariesCreativity Research Journal747This paper presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices.Creativity Research Journal["Manuel B. Garcia"]2024-05-30T00:00:00 e293db7dde70afb38af1ea3c91c855d0374d1595Krisis identitas adalah tantangan nyata yang dihadapi Muslim urban di era digital. Tuntan modernitas diiringi kebutuhan spiritual menyebabkan media digital menjadi sumber pemahaman keagamaan mereka. Sayangnya pola keagamaan yang dihasilkan dari interaksi media digital cenderung memperkeruh kondisi umat beragama yang ada dalam struktur masyarakat multikultural. Sebab, media digital kerap kali menjadi arena bagi penyebaran paham radikal dan ekstrem. Di sisi lain, keberadaan Artificial Intelligence (AI) sebagai bagian media digital dapat menjadi alternatif solusi melalui pengembangan pola keberagamaan yang inklusif dan adaptif bagi Muslim urban di era digital. Artikel ini bertujuan untuk mengeksplorasi peran ChatGPT sebagai bagian dari AI dalam mengembangkan pola beragama sebagai solusi terhadap krisis identitas yang dihadapi oleh Muslim urban dalam era digital. Metode penelitian menggunakan studi pustaka dengan dua jenis data. Sumber data primer berupa jawaban ChatGPT terhadap perintah yang diinginkan oleh penulis konsep beragama di era digital bagi Muslim urban. Sedangkan data sekunder diperloleh dari literatur terkait khususnya tentang kecerdasan buatan dan prinsip moderasi beragama di Indonesia. Hasil penelitian menunjukkan bahwa pola beragama yang tepat menurut ChatGPT di era digital melibatkan integrasi nilai-nilai keagamaan dalam kehidupan sehari-hari. Nilai-nilai dasar seperti integritas, penghormatan, tanggung jawab, dan keseimbangan menjadi landasan utama yang disertai dengan memperhatikan kredbilitas sumber pemahaman agama di media digital. Krisis identitas dapat diatasi dengan penguatan identitas keislaman, pengembangan  pemikiran kritis, keterlibatan positif dalam masyarakat, dan pembentukan hubungan yang inklusif dengan berbagai kelompok. Konsep beragama tersebut memiliki relevansi dengan nilai-nilai moderasi beragama yang diidentifikasi melalui empat indikator, yaitu komitmen kebangsaan, toleransi, anti-kekerasan, dan akomodatif terhadap kebudayaan lokalMODERATIO: Jurnal Moderasi Beragama363MODERATIO: Jurnal Moderasi Beragama["Ibnu Akbar Maliki"]2024-05-30T00:00:00 a74385686bb2fee907f4c0de654c0a318e31fd36The introduction of artificial intelligence (AI) has marked the beginning of the fourth industrial revolution and the genesis of a paradigm shift in the teaching-learning process. AI has been applied to the planning and design of teaching, student assessment and tutoring, and curricular content, integrating it into the creation of smart campuses and computational laboratories. This article, conducts a systematic review of the existing literature in Scopus, analyzing the application of AI in education and the assessment of learning outcomes at the university, in the last decade. The method was based on the recommendations given by García-Peñalvo, F. J. (2022) for conducting robust theoretical reviews. The results have highlighted the following advances: the introduction of intelligent tutoring systems, recognition systems to identify students in online training, security systems in the designs of smart campuses, the personalization of education, and some future trends, such as virtual and augmented reality combined with AI. It is worth noting the importance given to ethical issues related to the use of AI in the assessment of university students. La introducción de la inteligencia artificial (IA) ha supuesto el comienzo de la cuarta revolución industrial y la génesis de un cambio de paradigma en proceso enseñanza-aprendizaje. La IA se ha aplicado en la planificación y diseño de la enseñanza, en la evaluación y tutorización del estudiante, en el contenido curricular, integrándola en la creación de campus inteligentes y laboratorios computacionales. En este artículo se realiza un análisis sistemático de la literatura existente en Scopus analizando la aplicación de la IA en la educación y la evaluación de resultados de aprendizaje en la Universidad, en la última década. El método ha estado basado en las recomendaciones dadas por García-Peñalvo, F. J. en su artículo de 2022 (García-Peñalvo, 2022) para realizar revisiones teóricas robustas. Los resultados han destacado los siguientes avances: la introducción de sistemas de tutorización inteligentes, sistemas de reconocimiento para identificar al discente en formación online, sistemas de seguridad en los diseños del campus inteligente, la personalización de la educación y algunas tendencias futuras, como la realidad virtual y aumentada combinada con IA. Es de destacar la importancia que se le concede a las cuestiones éticas relacionadas con el uso de la IA en la evaluación del estudiante universitario.Resource Discovery932Revista de Educación a Distancia (RED)["Nuria Hern\u00e1ndez Le\u00f3n", "M. Rodr\u00edguez-Conde"]2024-05-30T00:00:00 eb8e136acdd4cf80061ecd29eca7e53c13da47edCurrently, technology is the most widely used tool in the development of daily life activities. The number of fields of knowledge that benefit from its versatility and application in the development of their activities is increasing. In the educational environment, it allows the generation of activities adapted to the needs of students. In recent years, robotics and artificial intelligence are the most widespread. The characteristics of these tools favour their application with students with autism spectrum disorder. Therefore, the objective of the research is the application of robotics to promote communication and social interaction in students with autism by analysing the emotions they show throughout the different activities. For this purpose, a pilot study was implemented with the NAO robot and four autistic children who developed imitation, game and social interaction activities. An automatic system based on convolutional neural networks was used to detect mood states in the interaction process. The results show that sadness, happiness and anger are the emotions most likely to occur in the participants. Therefore, it is concluded that the robot and the artificial intelligence system are a fundamental element to help express emotions in social interaction. Actualmente, la tecnología es la herramienta más utilizada en el desarrollo de las actividades de la vida diaria. Cada vez es mayor, el número de campos de conocimiento que se benefician de su versatilidad y la aplicación en el desarrollo de sus actividades. En el entorno educativo, permite generar actividades adaptadas a las necesidades del alumnado. En los últimos años, la robótica y la inteligencia artificial son las que mayor difusión están teniendo. Las características de estas herramientas favorecen su aplicación con el alumnado con Trastorno del Espectro Autista. Por tanto, el objetivo de la investigación es la aplicación de la robótica para favorecer la comunicación e interacción social en el alumnado con autismo analizando las emociones que manifiestan a lo largo de las distintas actividades. Para ello, se implementó un estudio piloto con el robot NAO y cuatro niños autistas que desarrollaron actividades de imitación, juego e interacción social. Durante su realización se utilizó un sistema automático basado en redes neuronales convolucionales para detectar los estados de ánimo en el proceso de interacción. Los resultados muestran que tristeza, felicidad y enfado son las emociones que tiene una mayor probabilidad de producirse en los participantes. Por tanto, se concluye que el robot y el sistema de inteligencia artificial son un elemento fundamental para ayudar a expresar sus emociones en las interacciones sociales.Resource Discovery00Revista de Educación a Distancia (RED)["Gonzalo Lorenzo Lled\u00f3", "Alejandro Lorenzo-Lled\u00f3", "Angel Rodr\u00edguez-Quevedo"]2024-05-30T00:00:00 c721d0b1fcb90e86b9f9cfa2c42f76527e2bc853This study focuses on the connections between employee well-being in AI-enhanced workplaces, the integration of artificial intelligence (AI), and emotional intelligence (EI). Data were collected and analyzed from workers in various industries using quantitative methodologies. Positive connections between EI and AI are seen in the results, indicating possible alignment in AI-driven contexts. The slight negative correlations between AI and well-being indicate intricate connections. While component analysis identifies distinctive EI and AI factors, cluster analysis reveals distinct employee profiles based on EI, AI, and well-being scores. One of the implications is the significance of fostering EI and AI integration in enhancing employee well-being. Future studies may examine these constraints and investigate intervention strategies for more healthful workplaces in the AI era. This research offers insightful information about the intricate dynamics of EI, AI, and well-being, offering guidance for organizational practices and future research endeavors.International Journal of Management and Humanities190While component analysis identifies distinctive EI and AI factors, cluster analysis reveals distinct employee profiles based on EI, AI, and well-being scores, offering guidance for organizational practices and future research endeavors.International Journal of Management and Humanities["Ms. Amandeep Gill", "Prof. Ashish Mathur", "Prof. Shailendra Singh Bhadouria"]2024-05-30T00:00:00 b76e865e070cb353b52a3cb1e50c86ec460da79eLegal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. But the large language models used in these tools are prone to"hallucinate,"or make up false information, making their use risky in high-stakes domains. Recently, certain legal research providers have touted methods such as retrieval-augmented generation (RAG) as"eliminating"(Casetext, 2023) or"avoid[ing]"hallucinations (Thomson Reuters, 2023), or guaranteeing"hallucination-free"legal citations (LexisNexis, 2023). Because of the closed nature of these systems, systematically assessing these claims is challenging. In this article, we design and report on the first preregistered empirical evaluation of AI-driven legal research tools. We demonstrate that the providers' claims are overstated. While hallucinations are reduced relative to general-purpose chatbots (GPT-4), we find that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI-Assisted Research and Ask Practical Law AI) each hallucinate between 17% and 33% of the time. We also document substantial differences between systems in responsiveness and accuracy. Our article makes four key contributions. It is the first to assess and report the performance of RAG-based proprietary legal AI tools. Second, it introduces a comprehensive, preregistered dataset for identifying and understanding vulnerabilities in these systems. Third, it proposes a clear typology for differentiating between hallucinations and accurate legal responses. Last, it provides evidence to inform the responsibilities of legal professionals in supervising and verifying AI outputs, which remains a central open question for the responsible integration of AI into law.arXiv.org11743It is found that the AI research tools made by LexisNexis and Thomson Reuters each hallucinate between 17% and 33% of the time, and a clear typology for differentiating between hallucinations and accurate legal responses is proposed.ArXiv["Varun Magesh", "Faiz Surani", "Matthew Dahl", "Mirac Suzgun", "Christopher D. Manning", "Daniel E. Ho"]2024-05-30T00:00:00 b3d66d820a6a3620b5df24b9eb20d9ca961a762eDigital learning merupakan sebuah transformasi pada dunia pendidikan di era revolusi industry 4.0. Hal ini membawa pengaruh positif dikarenakan peran teknologi pada proses pembelajaran dapat meningkatkan mutu pendidikan sekaligus mempersiapkan generasi berikutnya yang dapat bersaing secara global. Hadirnya teknologi kecerdasan buatan (AI) menyuguhkan peluang bagi para pendidik untuk melakukan proses pembelajaran yang berfokus kepada kebutuhan, minat, dan gaya belajar peserta didik, karena kurikulum merdeka mewajibkan bagi para pendidik untuk melakukan pembelajaran berdiferensiasi sebagai inisiatif dalam memfasilitasi peserta didik khususnya pada pendidikan Pancasila yang notabene masih dilakukan model atau metode secara konvensional. Tujuan pada penelitian ini yaitu mengkaji bagaimana penerapan artificial intelligence dalam pembelajaran berbasis projek pada pelajaran pendidikan pancasila. Metode yang digunakan dalam penelitian ini yaitu kajian kepustakaan atau literatur dengan teknik analisis isi. Hasil kajian menunjukan bahwa penerapan digital learning berbasis AI dapat meningkatkan motivasi, antusias yang tinggi dalam proses pembelajaran sehingga berpengaruh kepada dinamisnya kreativitas peserta didik.Sanskara Pendidikan dan Pengajaran154Sanskara Pendidikan dan Pengajaran["Hariadi Saputra", "Rahmatullah Rahmat", "K. Komalasari"]2024-05-31T00:00:00 e2a223c737daf89c15d9d4f106021dce3b8c870dThis study aimed to evaluate the readability, reliability, and quality of responses by 4 selected artificial intelligence (AI)-based large language model (LLM) chatbots to questions related to cardiopulmonary resuscitation (CPR). This was a cross-sectional study. Responses to the 100 most frequently asked questions about CPR by 4 selected chatbots (ChatGPT-3.5 [Open AI], Google Bard [Google AI], Google Gemini [Google AI], and Perplexity [Perplexity AI]) were analyzed for readability, reliability, and quality. The chatbots were asked the following question: “What are the 100 most frequently asked questions about cardio pulmonary resuscitation?” in English. Each of the 100 queries derived from the responses was individually posed to the 4 chatbots. The 400 responses or patient education materials (PEM) from the chatbots were assessed for quality and reliability using the modified DISCERN Questionnaire, Journal of the American Medical Association and Global Quality Score. Readability assessment utilized 2 different calculators, which computed readability scores independently using metrics such as Flesch Reading Ease Score, Flesch-Kincaid Grade Level, Simple Measure of Gobbledygook, Gunning Fog Readability and Automated Readability Index. Analyzed 100 responses from each of the 4 chatbots. When the readability values of the median results obtained from Calculators 1 and 2 were compared with the 6th-grade reading level, there was a highly significant difference between the groups (P < .001). Compared to all formulas, the readability level of the responses was above 6th grade. It can be seen that the order of readability from easy to difficult is Bard, Perplexity, Gemini, and ChatGPT-3.5. The readability of the text content provided by all 4 chatbots was found to be above the 6th-grade level. We believe that enhancing the quality, reliability, and readability of PEMs will lead to easier understanding by readers and more accurate performance of CPR. So, patients who receive bystander CPR may experience an increased likelihood of survival.Medicine454The readability of the text content provided by all 4 chatbots was found to be above the 6th-grade level, and it is believed that enhancing the quality, reliability, and readability of PEMs will lead to easier understanding by readers and more accurate performance of CPR.Medicine["Dilek \u00d6m\u00fcr Ar\u00e7a", "Ismail Erdemir", "Fevzi Kara", "Nurgazy Shermatov", "M\u00fcr\u00fcvvet Odacio\u011flu", "Emel Ibi\u015fo\u011flu", "Ferid Baran Hanc\u0131", "Gonul Sag\u0131roglu", "V. Hanc\u0131"]2024-05-31T00:00:00 4368b9d5e01f7a81a9bf387488a9c0915ff0e1b5Artificial Intelligence (AI) is reshaping international trade, presenting both challenges and opportunities for existing global legal frameworks. This research explores the intersection of AI and international trade laws, focusing on key areas such as data protection, intellectual property rights (IPR), trade barriers, and regulatory harmonisation. The cross-border flow of data in trade activities raises concerns about privacy and data protection, necessitating the balance between trade liberalisation and regulatory compliance. Moreover, the emergence of AI-generated intellectual property assets poses novel questions regarding ownership, liability, and enforcement mechanisms. Discriminatory practices and trade barriers fueled by AI-driven automation and predictive analytics threaten market access and fair competition. Harmonising regulatory approaches to AI governance is imperative to promote interoperability, innovation, and market integration. Despite these challenges, AI offers significant opportunities to enhance trade facilitation, efficiency, and dispute resolution mechanisms. Embracing AI technologies can streamline supply chains, reduce transaction costs, and expedite customs procedures. Additionally, AI-driven dispute resolution mechanisms offer innovative solutions to resolve trade disputes promptly and efficiently. To address these complexities, policymakers must enhance data governance frameworks, promote IPR harmonisation, and foster regulatory cooperation at both domestic and international levels. By embracing the transformative potential of AI while upholding fundamental principles of fairness and transparency, stakeholders can build a more resilient and inclusive global trading system. The qualitative research methodology has been applied to the following article.IIUM Law Journal1364This research explores the intersection of AI and international trade laws, focusing on key areas such as data protection, intellectual property rights (IPR), trade barriers, and regulatory harmonisation, focusing on key areas such as data governance frameworks.IIUM Law Journal["Asif Khan"]2024-05-31T00:00:00 df9e4dbc5ff9e747f96c69ea64993310edd3ca24Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.Frontiers in Chemistry1193The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications, and highlighted the gaps present in the traditional method for drug designing.Frontiers in Chemistry["Mahroza Kanwal Khan", "Mohsin Raza", "Muhammad Shahbaz", "Iftikhar Hussain", "M. Khan", "Zhongjian Xie", "Syed Shoaib Ahmad Shah", "A. Tareen", "Zoobia Bashir", "Karim Khan"]2024-05-31T00:00:00 1cd99f923333459a28557e2f6163b83cd00b27c5Artificial intelligence (AI) has the capacity to greatly empower women and promote gender equality on a global scale. However, in order to effectively utilise AI to promote women's empowerment, it is crucial to have a comprehensive comprehension of its influence, possibilities, and difficulties. This study examines the various aspects of how AI contributes to the advancement of women's empowerment. It explores the extent to which AI is integrated into initiatives aimed at empowering women, the perceived impact of AI on women's empowerment on a global scale, and the obstacles women face in accessing AI opportunities. An integrated research methodology, including of surveys and literature evaluation, was utilised to collect data from a diverse sample of 88 people. The results indicate a substantial degree of AI incorporation in projects aimed at empowering women, with varying perspectives on the impact of AI. Additionally, the study revealed difficulties in accessing AI opportunities and observed differing levels of knowledge among women. This study highlights the significance of ethical issues and inclusive policies in utilising AI to promote women's empowerment. The findings provide significant knowledge for policymakers, researchers, and practitioners who aim to utilise AI's revolutionary capacity to promote gender equality and empower women on a global scale.Journal of Community Service and Society Empowerment203The extent to which AI is integrated into initiatives aimed at empowering women, the perceived impact of AI on women's empowerment on a global scale, and the obstacles women face in accessing AI opportunities are explored.Journal of Community Service and Society Empowerment["Hafizullah Shahbazi", "Musawer Hakimi", "Helena Ulusi", "Behnaz Rahimi", "Tamanna Quraishi"]2024-05-31T00:00:00 7ad8d06bbe7d3a107f5b6f33b560aa523a95c0ceObjective:This study investigates the transformative potential of Generative Artificial Intelligence(GenAI) within the business domain and the entrepreneurial activity.Methodology:A comprehensive research design is adopted, integrating text-mining techniques to analysedata obtained from publicly available innovation repositories. A systematic literaturereview (SLR) is developed based on the literature obtained from all databases indexedin Web of Science (WoS), incorporating preprints from arXiv, alongside industry-relatedinnovation data in the form of patents from Google Patents. This method enables the derivationof valuable insights regarding the impact and prospective developments of GenAIacross diverse business sectors and industries by leveraging Natural Language Processing(NLP) and network analysis.Results:The research outcomes highlight the significant potential of GenAI in enabling informeddecision-making, enhancing productivity, and revealing new growth opportunities inthe business landscape. The continuously evolving business environment is examined,emphasising GenAI's role as a catalyst for data-driven innovation. However, there are stillrelevant limitations to overcome.Limitations:The selection of data sources and the study period may have excluded relevant or recentlypublished articles and patents within the scope of the present research. The language ofthe databases analysed is only English.Practical Implications:The practical implications of this study carry significant weight, serving as a valuableresource for decision-makers, researchers, and practitioners navigating the constantlyshifting terrain of business innovation through the lens of GenAI. Understanding thepotential advantages and challenges associated with GenAI adoption equips stakeholdersto make informed decisions and develop future business strategies.ESIC Market533This study investigates the transformative potential of Generative Artificial Intelligence within the business domain and the entrepreneurial activity, integrating text-mining techniques to analysedata obtained from publicly available innovation repositories.ESIC Market["Enrique Cano-Marin"]2024-05-31T00:00:00 a291ecf43daa5dceba0360823267714ef181bb7e09[]2024-05-31T00:00:00 b523970764469d3988cefcd28255d3a383ffd140Abstract: The field of radiology is changing due to artificial intelligence (AI), which presents hitherto unheard-of chances to improve diagnostic efficiency and accuracy. The transformational potential of AI in radiology is examined in this research, with particular attention to how it might expedite clinical workflows and completely change picture interpretation. The first section of the abstract emphasizes the rising need for radiological services as well as the difficulties radiologists have in organizing massive amounts of patient data while maintaining prompt and precise diagnosis. The article goes on to discuss AI as a potent tool that radiologists may use to analyze pictures more confidently, spot abnormalities, and make clinical choices more quickly. The transformational potential of artificial intelligence (AI) in radiology is examined in this research, with a focus on how AI might enhance diagnostic efficiency and accuracy. It presents the potential of artificial intelligence (AI) to transform radiological practice by highlighting its strengths in image interpretation, anomaly detection, and clinical decision assistance. But there are drawbacks to using AI in radiology, including concerns about data privacy and algorithm transparency. In order to guarantee patient safety and confidence in AI-enabled radiological techniques, the study highlights the significance of responsible AI implementation. The study concludes by highlighting the revolutionary effects of AI on radiology and highlighting its potential as a tool to improve healthcare delivery and diagnostic accuracy.International Journal for Research in Applied Science and Engineering Technology02The transformational potential of artificial intelligence in radiology is examined, with a focus on how AI might enhance diagnostic efficiency and accuracy, and the significance of responsible AI implementation is highlighted.International Journal for Research in Applied Science and Engineering Technology["C. Santhosh"]2024-05-31T00:00:00 a3310fc3600b5f6663cbbe8d2bbe835aace129c9Fokus penulisan dari artikel ini adalah Gereja 5.0: Harmoni Spiritual dan Transformasi cerdas dalam Era Society 5.0 dengan Artificial Intelligence. Era 5.0 menekankan kolaborasi manusia dan teknologi untuk kesejahteraan, inklusivitas, dan keberlanjutan sosial-ekonomi dan bahkan dalam bidang agama dan pewartaan. Era Society 5.0 membawa perubahan besar dalam kehidupan manusia. Adanya kecerdasan buatan memberikan peluang dan tantangan baru dalam pewartaan Injil, yakni dengan adanya media digital sangat membantu dalam menyebarkan nilai kristiani, tetapi ada dampak negatifnya yaitu penipuan dan kejahatan moral. Temuan dari penulisan artikel ini adalah harmonisasi antara teknologi canggih dan Gereja sangat membantu untuk misi pewartaan  Injil dalam kehidupan umat beriman. Meskipun ada dampak negatifnya seperti permasalahan moral tapi Gereja tetap memastikan bahwa dengan bantuan AI Injil tetap tersampaikan dengan baik kepada seluruh umat. Simpulannya harmonisasi antara teknologi dan Gereja memberikan sistematisasi dalam pewartaan Injil. Metode yang digunakan adalah kajian pustaka dengan analisis deskriptif.In Theos : Jurnal Pendidikan dan Theologi01In Theos : Jurnal Pendidikan dan Theologi["Sekundus Septo Pigang Ton", "Maria Sanci Fena Naklui"]2024-05-31T00:00:00 ca3eccf064173e2590caafc5d718db8d61416109Artificial intelligence (AI) is one of the most spoken-about topics in the media, academia, government and other platforms. One of the aspects that is often discussed is the ethical implications of AI and approaches to mitigate the risks. Artificial intelligence has an undeniable impact on industries as well as socio-economic structures; however, this article focusses on the impact of AI on three concerns mainly, humanity, spirituality and the environment. This article is an interdisciplinary study of African theological ethics and the philosophy of technology. It discusses the theological implications (doctrinal issues) of emerging technologies, particularly AI. It discusses technology as power which has impacted Africa since the first industrial revolution and emphasises the importance of African ethics in the context of AI in Africa. This article critically discusses ubuntu ethics and its critique. It focuses on AI and its impact on humanity, spirituality and the environment, and proposes a holistic ubuntu AI ethics approach in South Africa.Intradisciplinary and/or interdisciplinary implications: This article is an interdisciplinary study of African theological ethics and the philosophy of technology. Ubuntu ethics in this article derives from African Theology and African Philosophy. Ubuntu AI ethics is important for various disciplines such as theology, law, social sciences, computer sciences and information technology (especially designers and developers).Verbum et Ecclesia01The theological implications (doctrinal issues) of emerging technologies, particularly AI, are discussed and the importance of African ethics in the context of AI in Africa is emphasised.Verbum et Ecclesia["K. Mokoena"]2024-05-31T00:00:00 8f5781947e64df0c97705f314469fa1952dc9118The integration of artificial intelligence (AI) into various sectors has garnered global attention, notable within the healthcare domain. In Saudi Arabia, discussions surrounding AI’s application in healthcare have been particularly pronounced, highlighted at significant gathering during the World Economic Forum in the nation’s capital. This paper aims to explore the multifaceted incorporation of AI into medical practices in Saudi Arabia, with a focus on enhancing healthcare delivery. Drawing upon insights from cultural anthropology and medicine, this study illuminates key aspects of AI adoption among Saudi medical professionals. Despite growing interest, there remains a dearth of comprehensive studies assessing AI acceptance, readiness, and proficiency among healthcare personnel, necessitating larger-scale investigations for more accurate insights. Current literature suggests that while some practitioners have embraced AI, many lack formal education and exhibit apprehension towards its utilization. Consequently, there is a pressing need for undergraduate and postgraduate educational programs tailored to AI integration within Saudi Arabia’s healthcare system. Such initiatives not only empower practitioners to harness AI’s full potential but also address concerns and apprehensions, particularly among senior professionals. By fostering a culture of AI education and proficiency, Saudi Arabia can effectively leverage AI to enhance healthcare outcomes and address emerging challenges in the medical landscape.International Journal of Artificial Intelligence in Medical Issues181By fostering a culture of AI education and proficiency, Saudi Arabia can effectively leverage AI to enhance healthcare outcomes and address emerging challenges in the medical landscape.International Journal of Artificial Intelligence in Medical Issues["Haytham Althubaiti", "Ali Sulaiman", "A. A. Yousef"]2024-05-31T00:00:00 ff5b3cf7382d92458800e9b7117b3fd141450c1aAbstract: Artificial Intelligence (AI) has revolutionized various sectors, with the financial industry being a prominent beneficiary. This review paper explores the multifaceted impact of AI on the financial sector, examining its influence on banking, investment management, fraud detection, customer service, and risk management. It highlights the transformative capabilities of AI technologies such as machine learning, natural language processing, and robotic process automation, while also addressing the associated challenges and future prospectsInternational Journal for Research in Applied Science and Engineering Technology01This review paper explores the multifaceted impact of AI on the financial sector, examining its influence on banking, investment management, fraud detection, customer service, and risk management.International Journal for Research in Applied Science and Engineering Technology["Prof. Rahul Thumar", "Prof. Ritesh Vaghasiya"]2024-05-31T00:00:00 4c8bc770955580d80eafe7425b2395f68fb2f0acAbstract: The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose. Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to webbased and online intelligent education systems, and ultimately with the use of embedded computer systems, together with other technologies, the use of humanoid robots and web-based chatbots to perform instructors' duties and functions independently or with instructors. Using these platforms, instructors have been able to perform different administrative functions, such as reviewing and grading students' assignments more effectively and efficiently, and achieve higher quality in their teaching activities. On the other hand, because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students' needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.International Journal for Research in Applied Science and Engineering Technology01It is ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms, and has been customized and personalized in line with students' needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.International Journal for Research in Applied Science and Engineering Technology["Pragati Bajpai"]2024-05-31T00:00:00 e63337ad50ffd6499a2e2420994cc4af4be05514Neurology, as a medical specialty, faces numerous challenges in the accurate and timely diagnosis of neurological disorders. The advent of Artificial Intelligence (AI) has opened new horizons for improving the diagnostic capabilities of neurologists. This research paper explores the implementation of AI in neurology for the detection of various neurological disorders. Through an extensive review of recent advancements and applications, we highlight the transformative role that AI plays in revolutionizing the field.   This paper discusses the key areas where AI has been successfully integrated into neurology, such as Speeding Up CT Scans for Stroke Treatment, AI for TBI detection,ML Assisting in Decision-making in case of Epilepsy. Other scenarios include such as image analysis of medical scans (MRI and X-rays), EEG interpretation, predictive analytics, Natural Language Processing (NLP) for data analysis, wearable devices for remote monitoring, decision support systems, telemedicine, and drug discovery. Each of these areas demonstrates the potential of AI to enhance the accuracy, speed, and accessibility of neurological diagnostics.   While showcasing the benefits of AI in neurology, we also address the challenges associated with its implementation, including data privacy concerns, regulatory approval, and the necessity for continuous validation and refinement of AI algorithms.  In conclusion, this research paper underscores the significant potential of AI in neurology and its promising role in revolutionizing the detection and management of neurological disorders. As AI continues to mature, it holds the promise of improving patient outcomes, reducing diagnostic errors, and enhancing the overall quality of care in the field of neurology. Journal of student-scientists' research51The implementation of AI in neurology for the detection of various neurological disorders, and the potential of AI to enhance the accuracy, speed, and accessibility of neurological diagnostics, is explored.Journal of Student Research["Mansi Srivastava", "Profesor Jobin Varkey", "Jothsna Kethar"]2024-05-31T00:00:00 bae231530e43c066d1fc9ad2e478958ee4e5755cAddressing the volatility of renewable energies like solar and wind is crucial for the energy system’s stability and optimal utilization of renewable energies. Accurate energy forecasts are important to improve scheduling. Electrical demand and renewable energies are weather-dependent and Numerical Weather Predictions have proven to be beneficial for energy forecasts due to their fine-grained spatial resolution. State-of-the-art Deep Learning approaches for energy forecasting are black-box models. However, decisions in energy systems depend on energy forecasts, and, thus, it is important that models are explainable and trustworthy. Explainable Artificial Intelligence techniques exist that add explainability to energy forecasting models, but all existing methods are only post-hoc or do not use weather data on large spatial areas. This paper introduces a novel approach to forecast energy that scales and adds intrinsic explainability by design. Therefore, we use trainable spatial weights to make accurate forecasts on large spatial areas. The trained weights can be interpreted spatially to enhance explainability and increase trust. Furthermore, the spatial weights enable a wide range of future work, including postprocessing, subregion forecasting, hierarchical learning, and spatial-temporal weights.The 15th ACM International Conference on Future and Sustainable Energy Systems241A novel approach to forecast energy that scales and adds intrinsic explainability by design is introduced, which uses trainable spatial weights to make accurate forecasts on large spatial areas.Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems["Oliver Neumann", "Maximilian Beichter", "Benedikt Heidrich", "Nils Friederich", "V. Hagenmeyer", "R. Mikut"]2024-05-31T00:00:00 c39e085b7d208c35455af121b6866889525a7057Objectives In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve Implantation (TAVI) has been a topic of increasing relevance in literature. This study aims to evaluate the predictive accuracy of AI algorithms in forecasting post-TAVI mortality as compared to traditional risk scores. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Systematic Reviews (PRISMA) standard, a systematic review was carried out. We searched four databases in total-PubMed, Medline, Embase, and Cochrane-from 19 June 2023-24 June, 2023. Results From 2,239 identified records, 1,504 duplicates were removed, 735 manuscripts were screened, and 10 studies were included in our review. Our pooled analysis of 5 studies and 9,398 patients revealed a significantly higher mean area under curve (AUC) associated with AI mortality predictions than traditional score predictions (MD: -0.16, CI: -0.22 to -0.10, p < 0.00001). Subgroup analyses of 30-day mortality (MD: -0.08, CI: -0.13 to -0.03, p = 0.001) and 1-year mortality (MD: -0.18, CI: -0.27 to -0.10, p < 0.0001) also showed significantly higher mean AUC with AI predictions than traditional score predictions. Pooled mean AUC of all 10 studies and 22,933 patients was 0.79 [0.73, 0.85]. Conclusion AI models have a higher predictive accuracy as compared to traditional risk scores in predicting post-TAVI mortality. Overall, this review demonstrates the potential of AI in achieving personalised risk assessment in TAVI patients. Registration and protocol This systematic review and meta-analysis was registered under the International Prospective Register of Systematic Reviews (PROSPERO), under the registration name "All-Cause Mortality in Transcatheter Aortic Valve Replacement Assessed by Artificial Intelligence" and registration number CRD42023437705. A review protocol was not prepared. There were no amendments to the information provided at registration. Systematic Review Registration https://www.crd.york.ac.uk/, PROSPERO (CRD42023437705).Frontiers in Cardiovascular Medicine391Overall, this review demonstrates the potential of AI in achieving personalised risk assessment in TAVI patients and has a higher predictive accuracy as compared to traditional risk scores in predicting post-TAVI mortality.Frontiers in Cardiovascular Medicine["F. Sazzad", "A. Ler", "M. S. Furqan", "Linus Kai Zhe Tan", "H. Leo", "I. Kuntjoro", "Edgar Tay", "Theo Kofidis"]2024-05-31T00:00:00 eb8f765ba20f41e7d28b1e2245cf76930f4dadcdPurpose Companies are increasingly benefiting from artificial intelligence (AI) applications in various domains, but also facing its negative impacts. The challenge lies in the lack of clear governance mechanisms for AI. While documentation is a key governance tool, standard software engineering practices are inadequate for AI. Practitioners are unsure about how to document AI, raising questions about the effectiveness of current documentation guidelines. This review examines whether AI documentation guidelines meet regulatory and industry needs for AI applications and suggests directions for future research. Design/methodology/approach A structured literature review was conducted. In total, 38 papers from top journals and conferences in the fields of medicine and information systems as well as journals focused on fair, accountable and transparent AI were reviewed. Findings This literature review contributes to the literature by investigating the extent to which current documentation guidelines can meet the documentation requirements for AI applications from regulatory bodies and industry practitioners and by presenting avenues for future research. This paper finds contemporary documentation guidelines inadequate in meeting regulators’ and professionals’' expectations. This paper concludes with three recommended avenues for future research. Originality/value This paper benefits from the insights from comprehensive and up-to-date sources on the documentation of AI applications. Digital Policy Regulation and Governance712This paper finds contemporary documentation guidelines inadequate in meeting regulators’ and professionals’' expectations and suggests three recommended avenues for future research.Digital Policy, Regulation and Governance["Florian K\u00f6nigstorfer"]2024-05-31T00:00:00 bda6b4a35dfd5cb60807af99fd86e28d40a63126Liver fibrosis is a pathological condition characterized by the abnormal proliferation of liver tissue, subsequently able to progress to cirrhosis or possibly hepatocellular carcinoma. The development of artificial intelligence and deep learning have begun to play a significant role in fibrosis detection. This study aimed to develop SMART AI-PATHO, a fully automated assessment method combining quantification of histopathological architectural features, to analyze steatosis and fibrosis in nonalcoholic fatty liver disease (NAFLD) core biopsies and employ Metavir fibrosis staging as standard references and fat assessment grading measurement for comparison with the pathologist interpretations. There were 146 participants enrolled in our study. The correlation of Metavir scoring system interpretation between pathologists and SMART AI-PATHO was significantly correlated (Agreement = 68%, Kappa = 0.59, p-value <0.001), which subgroup analysis of significant fibrosis (Metavir score F2-F4) and nonsignificant fibrosis (Metavir score F0-F1) demonstrated substantial correlated results (agreement = 80%, kappa = 0.61, p-value <0.001), corresponding with the correlation of advanced fibrosis (Metavir score F3-F4) and nonadvanced fibrosis groups (Metavir score F0-F2), (agreement = 89%, kappa = 0.74, p-value <0.001). SMART AI-PATHO, the first pivotal artificially intelligent diagnostic tool for the color-based NAFLD hepatic tissue staging in Thailand, demonstrated satisfactory performance as a pathologist to provide liver fibrosis scoring and steatosis grading. In the future, developing AI algorithms and reliable testing on a larger scale may increase accuracy and contribute to telemedicine consultations for general pathologists in clinical practice.Kaohsiung Journal of Medical Sciences211SMART AI-PATHO, the first pivotal artificially intelligent diagnostic tool for the color-based NAFLD hepatic tissue staging in Thailand, demonstrated satisfactory performance as a pathologist to provide liver fibrosis scoring and steatosis grading.The Kaohsiung journal of medical sciences["Noppamate Preechathammawong", "M. Charoenpitakchai", "Nutthawat Wongsason", "J. Karuehardsuwan", "Thaninee Prasoppokakorn", "Panyavee Pitisuttithum", "A. Sanpavat", "Karn Yongsiriwit", "Thannob Aribarg", "Parkpoom Chaisiriprasert", "S. Treeprasertsuk", "Sakkarin Chirapongsathorn"]2024-05-31T00:00:00 2f6926c7dfe2cf8afc5d0f7c9374b50472095906This paper examines how artificial intelligence(AI) affects cyberspace and can influence future malware and cyber threats, it emphasizes AI’s dual role as both an ally and an imminent threat. Illegal data mining in centralized digital networks has become a growing threat in modern cybersecurity. Because of the numerous flaws in today's centralized systems, more robust and beneficial alternatives are required. Traditional anti-cybercrime systems, such as physical equipment and human involvement, have proven ineffective. Newer methods have been created through the developments in machine learning which enhance cybercrime detection and prevention. The major goal of cybersecurity is to reduce digital assaults in cyberspace, with AI technologies playing an important role. Furthermore, concerns have been voiced concerning the possible use of AI-enhanced malware by hackers, underlining the importance of ongoing monitoring and collaboration and addressing the flaws. This research also investigates remedies to a couple of the disadvantages. This study examines existing AI applications in modern cybercrime prevention, highlighting its potential advantages and disadvantages. Learning and discussing the ethical role that AI will play is crucial for the growth and security of our digital infrastructure. With the rise of complex and severe cyber threats, AI-based security systems will help detect and protect cyberspace effectively and efficiently. By being updated on modern AI technologies and applications in cybersecurity, individuals and organizations can better protect themselves from cybercrime and minimize potential damage. Furthermore, ongoing collaboration and research in this area can lead to the development of more advanced and robust security solutions.Journal of student-scientists' research01Examining existing AI applications in modern cybercrime prevention, highlighting its potential advantages and disadvantages highlights AI’s dual role as both an ally and an imminent threat.Journal of Student Research["Venkata sai suhas Yadlapati", "Jothsna Kethar", "Dr. Sarada Prasad Gochhayat"]2024-05-31T00:00:00 37ed4daf15f05f5c9b63ccf067fdeda6c2df2866Artificial Intelligence (AI) has emerged as a transformative force within the financial sector, altering not only the mechanisms of finance but also the foundational elements of financial literacy. As AI continues to evolve, it reshapes how individuals interact with financial information, making complex financial concepts more accessible while simultaneously introducing new challenges. This paper provides a comprehensive examination of AI's influence on financial literacy, exploring the multifaceted ways in which AI-driven technologies are democratizing financial knowledge, enhancing personal financial management, and posing potential risks.ShodhKosh Journal of Visual and Performing Arts191This paper provides a comprehensive examination of AI's influence on financial literacy, exploring the multifaceted ways in which AI-driven technologies are democratizing financial knowledge, enhancing personal financial management, and posing potential risks.ShodhKosh: Journal of Visual and Performing Arts["Y. Lakshmi"]2024-05-31T00:00:00 9b175a2fc740dec47181eac2fb861fa54ac88b5dAI &amp; SOCIETY301The article shows how the newspapers promote an understanding of AI, by which citizens will feel motivated to insist on a regulation of AI by politics and law.AI &amp; SOCIETY["Marek Winkel"]2024-05-31T00:00:00 3781416503d615b7bd15c66e53f281860cffb876Before 1949, computers lacked intelligence. They could not record commands, but they could carry them out. However, between 1957 and 1974, artificial intelligence flourished. Computer storage has increased, as has speed, cost, and accessibility. Artificial intelligence (AI) means that machines can perform various jobs that humans or animals need to do with their natural intelligence. The fathers of artificial intelligence, Marvin Minsky and John McCarthy, defined artificial intelligence in 1950. Artificial intelligence enables machines to understand and achieve certain goals. Deep learning, on the other hand, makes it possible to absorb huge amounts of unstructured data in the form of text, images and audio. Artificial intelligence is appearing in almost every industry that is the future of humanity. It will also be the driving force behind new technologies such as big data, robotics and the Internet of Things (IoT) in the near future. Computer algorithms and machine learning have been widely used in textile testing since the 1980s. Testing and quality control functions can be handled by image processing, automation, deep learning and neural networks. Most of the textile industry today uses computer-aided machinery to produce certain designs on a larger scale and more efficiently. AI can access maintenance data in real time to provide insights that can be used to increase operational efficiency. Artificial Neural Network (ANN) technology makes it easier to improve the quality of life in the industry and detect defects, check patterns, match colors and classify fabrics for textile production more objectively. It also precisely defines the advantages of fine, solid and staple fiber. The use of artificial intelligence in the manufacture of textiles has emerged with a new possibility, i.e. smart clothes that use the Internet of Things and electronic sensors to create a more pleasant health experience. In this article, the researcher tried to give an overview of the artificial intelligence used in the textile industry.International journal of research - granthaalayah151An overview of the artificial intelligence used in the textile industry is given, i.e. smart clothes that use the Internet of Things and electronic sensors to create a more pleasant health experience.International Journal of Research -GRANTHAALAYAH["Purva Bansode", "Pratima Goyal"]2024-05-31T00:00:00 fa37ddd0a4d1901f0de1abd6251850409f490761This survey represents the first endeavor to assess the clarity of the dermoscopic language by a chatbot, unveiling insights into the interplay between dermatologists and AI systems within the complexity of the dermoscopic language. Given the complex, descriptive, and metaphorical aspects of the dermoscopic language, subjective interpretations often emerge. The survey evaluated the completeness and diagnostic efficacy of chatbot-generated reports, focusing on their role in facilitating accurate diagnoses and educational opportunities for novice dermatologists. A total of 30 participants were presented with hypothetical dermoscopic descriptions of skin lesions, including dermoscopic descriptions of skin cancers such as BCC, SCC, and melanoma, skin cancer mimickers such as actinic and seborrheic keratosis, dermatofibroma, and atypical nevus, and inflammatory dermatosis such as psoriasis and alopecia areata. Each description was accompanied by specific clinical information, and the participants were tasked with assessing the differential diagnosis list generated by the AI chatbot in its initial response. In each scenario, the chatbot generated an extensive list of potential differential diagnoses, exhibiting lower performance in cases of SCC and inflammatory dermatoses, albeit without statistical significance, suggesting that the participants were equally satisfied with the responses provided. Scores decreased notably when practical descriptions of dermoscopic signs were provided. Answers to BCC scenario scores in the diagnosis category (2.9 ± 0.4) were higher than those with SCC (2.6 ± 0.66, p = 0.005) and inflammatory dermatoses (2.6 ± 0.67, p = 0). Similarly, in the teaching tool usefulness category, BCC-based chatbot differential diagnosis received higher scores (2.9 ± 0.4) compared to SCC (2.6 ± 0.67, p = 0.001) and inflammatory dermatoses (2.4 ± 0.81, p = 0). The abovementioned results underscore dermatologists’ familiarity with BCC dermoscopic images while highlighting the challenges associated with interpreting rigorous dermoscopic images. Moreover, by incorporating patient characteristics such as age, phototype, or immune state, the differential diagnosis list in each case was customized to include lesion types appropriate for each category, illustrating the AI’s flexibility in evaluating diagnoses and highlighting its value as a resource for dermatologists.Diagnostics398Diagnostics["E. Karampinis", "Olga Toli", "K. Georgopoulou", "Elli Kampra", "Christina Spyridonidou", "Angeliki Victoria Roussaki Schulze", "E. Zafiriou"]2024-05-31T00:00:00 77aa04006ba6bd5097e5ccb0f87cdf9adec601aeThis paper presents a complete examination of the job of AI in advanced education, planning to give bits of knowledge into its applications, advantages, difficulties, and future bearings. Drawing upon hypothetical structures and exact proof, the paper investigates how AI is reforming academic practices through customized learning and draws near clever coaching frameworks and versatile guidance. It additionally examines the manners by which AI-driven regulatory mechanization upgrades institutional effectiveness, upholds understudy achievement drives, and cultivates examination and advancement in the scholarly world. In any case, close to its expected advantages, the mix of man-made intelligence in advanced education presents difficulties connected with protection, inclination, mechanical foundation, and staff preparation. Through contextual analysis and experimental examination, this paper features fruitful executions of artificial intelligence in advanced education organizations, distinguishes key examples learned, and proposes proposals for amplifying the extraordinary effect of AI while addressing moral contemplations and guaranteeing evenhanded admittance to instructive open doors. Generally, this thorough examination gives a nuanced comprehension of the developing job of man-made intelligence in advanced education and offers important bits of knowledge for policymakers, teachers, and partners exploring the complications of simulated intelligence joining in instructive settings.ShodhKosh Journal of Visual and Performing Arts240This paper investigates how AI is reforming academic practices through customized learning and draws near clever coaching frameworks and versatile guidance, and proposes proposals for amplifying the extraordinary effect of AI while addressing moral contemplations and guaranteeing evenhanded admittance to instructive open doors.ShodhKosh: Journal of Visual and Performing Arts["Suneeta Singh", "Dr. Akhilesh A. Waoo"]2024-05-31T00:00:00 856b0464693df046832485cc55c5e1673442d6deThe boom in Artificial Intelligence (AI) revolution is significantly transforming our everyday lives. Every aspect of technology, process, and system implements AI to provide a superior user experience, enhanced machine capabilities and advanced problem-solving and research capabilities. However, due to this technological revolution, rise in fraud has become an enormous challenge in the digital economy. This research paper aims to test different Machine Learning (ML) models to explore real-time fraud detection capabilities accurately, particularly for credit card fraud prevention systems. Technologies like online bank transfers and smartphone payments based on credit accounts are major contributors to fraudulent transactions. AI/ML models have proven to be industry-disruptors, robust, significantly faster and produce more accurate results. These advantages have led to launch of successful companies like OpenAI. This paper uses model metrics such as Supervised, Unsupervised, and Ensemble methods to improve the detection of unauthorized transactions for fraud detection. Models will be ranked for performance using accuracy metrics, F-1, and Area Under Curve (AUC) scores. While zero false rates are not yet achievable, this study aims to reach a reasonably low level by selecting an appropriate model.Journal of student-scientists' research00This paper uses model metrics such as Supervised, Unsupervised, and Ensemble methods to improve the detection of unauthorized transactions for fraud detection and aims to reach a reasonably low level by selecting an appropriate model.Journal of Student Research["Naitik Gupta"]2024-05-31T00:00:00 2e6566fc498817e32d95a9a4dd01e78ea08f286dBackground: Medically, an important indicator from cardiovascular disease is the enhancement of calcification. For that reason, the assessment of Calcium Score and Artificial Intelligence have the same potential to help or even to replace human role, hence, it can reduce clinical work burden and improving an efficiency. This research aims to analyze a difference between Artificial Intelligence and semi-automatic methods in determining the calcification location and Agatstone Score value undertaken at Radiology and Nuclear Cardiology Installation of Harapan Kita Heart and Blood Vessel Hospital, West Jakarta. Methods: Research design used is descriptive quantitative method, this research was executed in Radiology and Nuclear Cardiology Installation starting from October up to November 2023 with the total number of samples as many as 50 secondary data Results: Result of this research shows that there is no significant difference between Artificial Intelligence-based software and semi-automatic methods in determining the mark of Agatstone Score and location calcification Conclusions: Based on the results of the research and discussion analyzing semi-automatic software and Artificial Intelligence in determining the location of classification and Agatstone Score values, it can be concluded that the superiority of Artificial Intelligence-based post-processing software in determining the location of classification and Agatstone Score values lies in the fact that this software provides ease in rapidly and accurately reconstructing the assessment of classification locations, especially in cases of minimal lesions in blood vessels. It is faster and simpler in determining Agatstone Score values compared to semi-automatic methods because the software automatically works to determine the total Agatstone Score value.JRI (Jurnal Radiografer Indonesia)80It can be concluded that the superiority of Artificial Intelligence-based post-processing software in determining the location of classification and Agatstone Score values lies in the fact that this software provides ease in rapidly and accurately reconstructing the assessment of classification locations, especially in cases of minimal lesions in blood vessels.JRI (Jurnal Radiografer Indonesia)["Fikri Fathurrahman", "Khairil Anwar", "Samsun"]2024-05-31T00:00:00 fd3ddbef3840bd6d46b2397561c1485446575041Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals. Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.Asian Journal Of Medical Technology220Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care.Asian Journal Of Medical Technology["Ngnotouom Ngnokam TANIA CYRIELLE", "Angyiba Serge Andigema", "Mafo Kamga Lethicia Dana\u00eblle", "Ewane Ekwelle"]2024-05-31T00:00:00 bd056ba96fc166d8bbaee8cae53ecf088a14987dThe introduction of new forms of artificial intelligence (AI) military weaponry specifically autonomous weapon systems (AWS) can select and engage targets without human intervention therefore the application of lethal AWS incorporation with AI has revolutionized armed conflicts.The main concern regarding the military application of AI is the use of force should be maintained by only human soldiers. There is an urgent need to reinterpret the threshold for triggering an international armed conflict because AI technology unintentionally causes war during border control or surveillance operation. This article predominantly focuses on fully autonomous weapon systems which refer to human agents being removed from certain force applications. The main research questions in this article are first, is it possible that an AWS might, alone, spark an international armed conflict, thus bringing international humanitarian law into force? Second, can the criteria of organisation and intensity that give rise to non-international armed conflicts be met when AWS are controlled by non-state armed actors? This study will examine the research questions by focusing on the main areas of debate in the field of international law on AWS, specifically the compatibility of AI with the principles of humanitarian law, the determination of international responsibility, and ethical problems.Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi00Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi["Berkant Akku\u015f"]2024-05-31T00:00:00 dc1388012d5ad8dae35c8fd7a77f6119aa8fd459Autism Spectrum Disorder (ASD) is a disorder of increasing prevalence that affects individuals socially, emotionally, and academically. The increased prevalence and restricted access to diagnosis and treatment suggest more efficient and widely accessible services are necessary. Many individuals with ASD do not receive proper attention due to various reasons, including costs, long wait lists, and long processes. Recent developments in artificial intelligence and machine learning are believed to be able to aid this accessibility issue. Research has shown progress in using MRI and EEG datasets to develop machine learning models in diagnosing ASD and potentially finding biomarkers using supervised and unsupervised ML techniques. AI algorithms analyzing body language and physical behaviors could potentially be used to assess ASD characteristics despite the heterogeneity of the disorder. The adaptivity of artificial intelligence is believed to have the potential to create supportive software for students with ASD to support learning, emotional regulation, and development of social communication skills and increased adaptability. More evidence is required to prove the effectiveness of these applications, but many studies show a lot of promise for children with ASD.Journal of student-scientists' research00The adaptivity of artificial intelligence is believed to have the potential to create supportive software for students with ASD to support learning, emotional regulation, and development of social communication skills and increased adaptability.Journal of Student Research["Kelis Nguyen", "Yongmei Huang"]2024-05-31T00:00:00 6b652353da1835b31ebc3f1928d7bf2a274e6fd4Ethics is an increasingly important topic surrounding the development and deployment of artificial intelligence (AI), which has had many impacts on the general population, as it is integrated into society. As a result, the European Union (EU) has already taken steps to regulate AI to prevent or limit the negative impacts imposed on the public while also attempting to promote their capacity for technological innovation; however, the United States (US) has not acted as swiftly and lacks major, tangible pieces of legislation for the regulation of AI. This research reviews the AI legislation development and implementation processes used by the EU and compares it to the processes used by the US when implementing similar laws and ideas in the development of its own regulatory framework. Critiques of the EU’s policies are assessed, and the political, economic, and social differences between the regulatory bodies are considered. This approach enables us to critically evaluate specific pieces of the EU’s legislation and recommend those that can be practically integrated into future US policies. Journal of student-scientists' research00This research reviews the AI legislation development and implementation processes used by the EU and compares it to the processes used by the US when implementing similar laws and ideas in the development of its own regulatory framework and recommends those that can be practically integrated into future US policies.Journal of Student Research["Bryan Wang", "Danielle Haak"]2024-05-31T00:00:00 adc8d156379033c62401312c7b32a30bb82a1357Considering the advantages such as customizing parameters such as time, intensity, difficulty, speed suitable for the patient level, enriching treatment programs, reducing the possible burnout of the patient and therapist during the rehabilitation process, and increasing motivation, artificial intelligence within the scope of physiotherapy rehabilitation services will increase the quality of rehabilitation services and provide cost-effective results in the long term.Experimental and Applied Medical Science60Artificial intelligence within the scope of physiotherapy rehabilitation services will increase the quality of rehabilitation services and provide cost-effective results in the long term.Experimental and Applied Medical Science["Bengisu T\u00fcfek\u00e7i"]2024-05-31T00:00:00 4b7949ba91c2e57bbdb2a809883d512142b0ccf1Penelitian ini bertujuan untuk dapat mengetahui persentase pengaruh dari domain machine learning dan deep learning terhadap tingkat kasus deep fake pada selebritas di twitter. Penelitian ini menggunakan metode penelitian kuantitatif. Adapun metode yang digunakan dalam penelitian ini adalah metode survei. Jenis data yang digunakan dalam penelitian ini data primer yakni berupa data-data kasus deep fake artis yang sudah terjadi di Twitter. Teknik pengumpulan data yang digunakan yakni dengan melakukan penyebaran kuesioner. Hasil penelitian ini menunjukkan bahwa terdapat pengaruh positif dari artificial intelligence secara khusus domain machine learning terhadap peningkatan kasus deep fake pada selebritas di twitter secara signifikan.   Kunci: artificial intelligence, machine learning, deep learning, deep fake  Device160Device["Bramcov Stivens Situmeang", "Inggrid Silitonga", "Reskina Felida Silaen", "Tiurma Siringoringo", "Ester Esari Sipayung"]2024-05-31T00:00:00 ce9dda0d2757f26e3463cabaafe73dce704973d8This article explores the transformative influence of Artificial Intelligence (AI) on contemporary art, focusing on the integration of AI in creative processes, the reception of AI-generated art, and the ethical considerations it raises. As AI technologies become more sophisticated, they redefine artistic creation, collaboration, and interaction, expanding the traditional boundaries of art. By employing AI, artists access new tools for enhancing creativity, automating production, and engaging audiences through interactive installations. However, this integration also brings challenges, particularly concerning the authenticity and ethical implications of AI-generated art. The article discusses AI's role in augmenting creativity, its impact on art production, the dynamic ways it engages audiences, and the ethical dilemmas it presents, such as data privacy and intellectual property rights. The discussion aims to provide a comprehensive understanding of how AI is reshaping the art world, influencing artistic communities, and prompting a reevaluation of art in the digital age.Communications in Humanities Research00AI's role in augmenting creativity, its impact on art production, the dynamic ways it engages audiences, and the ethical dilemmas it presents, such as data privacy and intellectual property rights are discussed.Communications in Humanities Research["Jia Chi"]2024-05-31T00:00:00 0ae6067d8587f709bae320009670a9231671954aThe integration of Artificial Intelligence (AI) with MicroStrategy is revolutionizing data management and analytics, significantly enhancing organizational productivity and efficiency. By incorporating advanced technologies such as large language models (LLMs) and generative AI, MicroStrategy offers features like Auto Answers and Auto Dashboard, which streamline data analysis and provide rapid, reliable insights. This integration is particularly impactful in fields such as healthcare, where AI-driven solutions like CerviCARE AI improve diagnostic accuracy in cervical cancer screening. The implementation of AI in MicroStrategy optimizes data ingestion, processing, and analysis, boosting data processing speed and analytical precision by up to 40%. This comprehensive integration distinguishes MicroStrategy in the business intelligence landscape, offering superior capabilities compared to traditional BI tools.International Journal of Advanced Research00The implementation of AI in MicroStrategy optimizes data ingestion, processing, and analysis, boosting data processing speed and analytical precision by up to 40%.International Journal of Advanced Research["Suman Chintala"]2024-05-31T00:00:00 fd780938b589ecdfa21c82eba91352bf8323ca4bGiven the Social Cognitive Theory concept in global understanding, Employee Productivity through Artificial Intelligence can be tested in other contexts. It is research within the tertiary care hospitals of City Gujranwala (GTH (775 bedded) and GMCTH 502 bedded).  The results shows that AI tools significantly & positively helps the management and give a road map to facilitate their routine tasks and healthcare professionals to ease their clinical and theoretical concepts by using AI tools. This study also explores the hypothesis and the mediating role of Organizational Support (OS), use of Information Technology (IT) and Employee Innovations. The data was collection from the tertiary care hospitals of Gujranwala from 165 doctors, management and supportive staff. Partial Least Squares (PLS) results shown that the research can be conducted in other tertiary care hospitals to examine its impact. Other research areas include expanding the theoretical model with other independent and dependent variables and to validate it in different organizational contexts to make out a clearer image of the relationships one during the testing.Al-NASR120The results shows that AI tools significantly & positively helps the management and give a road map to facilitate their routine tasks and healthcare professionals to ease their clinical and theoretical concepts by using AI tools.Al-NASR["Rameez Ahmed", "Mehreen Naz", "Qaiser Iqbal", "Ammar Masood Cheema"]2024-05-31T00:00:00 7f972bd773e1d66a6537afaaa36f1dc158a9ba05Recent advancements in AI offer promising tools for enhancing disaster management which is crucial given the increasing frequency of climate-related disasters. The study aims to evaluate how AI technologies can be utilized to improve disaster preparedness, response, and recovery efforts, thus aiding in environmental resilience and sustainability. This paper examines the intersection of artificial intelligence (AI) and environmental sustainability, with a focus on the role of AI in managing natural disasters. By reviewing secondary data and existing research, the paper explores various AI applications such as predictive modeling, real-time monitoring, and decision support systems. The analysis reveals that AI can significantly enhance early warning systems, optimize the allocation of resources, and ensure timely interventions during emergencies. The findings highlight the importance of integrating AI technologies into disaster management strategies to foster environmental sustainability amidst growing climate-related risks. The paper also discusses the challenges and ethical considerations of implementing AI in this field and underscores the need for interdisciplinary collaboration and stakeholder engagement for successful implementation.International Journal of Applied and Scientific Research00The analysis reveals that AI can significantly enhance early warning systems, optimize the allocation of resources, and ensure timely interventions during emergencies, highlighting the importance of integrating AI technologies into disaster management strategies to foster environmental sustainability amidst growing climate-related risks.International Journal of Applied and Scientific Research["Vijay Singh", "Aastha Agnihotri"]2024-05-31T00:00:00 44d5bda3a20397ce51e012cbad0b9ba84cf28aa0The main goal of the article is to form a new modern approach to ensuring intellectual security by optimizing the use of artificial intelligence. The object of the research is the intellectual security system and modern technologies based on artificial intelligence. The research methodology involves the use of the modern IDEF0 method, which facilitates the process of optimizing the use of artificial intelligence, as well as the method of expert analysis and the Delphi method. As a result of using the above methods, decompositions of models for ensuring intellectual security were built. The innovativeness of the results obtained is revealed through careful information support for the detailing of the proposed IDEF0 model, which consists of a detailed presentation of the first and second level models, which provide information for strengthening human-centricity, and the 3rd level model, which provides information on the optimization of artificial intelligence. International Journal of Religion210The research methodology involves the use of the modern IDEF0 method, which facilitates the process of optimizing the use of artificial intelligence, as well as the method of expert analysis and the Delphi method, and decompositions of models for ensuring intellectual security were built.International Journal of Religion["Svitlana Kryshtanovych", "Tetiana Tatarnikova", "Svitlana Rybkina", "Olena Kopanchuk", "Vladimir Motorny"]2024-05-31T00:00:00 8074f41ca1b35e9e0d7f595613a6879d5853d0e9Abstract: This paper explores the potential of Artificial Intelligence (AI) to revolutionize healthcare research through the MedTech-AI project. The project investigates the development and evaluation of AI models for medical image analysis, aiming for improved disease detection and classification. It examines the potential of AI-powered clinical decision support systems (CDSS) to assist healthcare professionals. Additionally, the project explores Natural Language Processing (NLP) techniques for extracting valuable insights from unstructured healthcare data like electronic health records and medical literature. Finally, the paper investigates the use of AI for predictive analytics in disease prevention, identifying risk factors and informing preventative measures. By examining these areas, the Med-Tech-AI project establishes a foundation for understanding how AI can enhance healthcare research, potentially leading to improved healthcare efficiency, accuracy, and ultimately, better patient outcomesInternational Journal for Research in Applied Science and Engineering Technology00The project investigates the development and evaluation of AI models for medical image analysis, aiming for improved disease detection and classification, and examines the potential of AI-powered clinical decision support systems (CDSS) to assist healthcare professionals.International Journal for Research in Applied Science and Engineering Technology["Soumya Dantre"]2024-05-31T00:00:00 801c4484af657677074c3d34a9f6a108cc525e77In this article, we explore artificial intelligence (AI) integration in tourism and hospitality education, analyze its implications, and propose strategies to address challenges. While AI has revolutionized operational processes and customer experiences, its adoption in higher education presents various opportunities and hurdles. The rapid evolution of AI necessitates frequent curriculum updates and faculty development. Moreover, addressing the digital divide and ethical considerations is also crucial. The article categorizes the implications into technological advancements, educational impacts, and future trends. Strategies for addressing AI challenges include curriculum integration, experiential learning, faculty development, interdisciplinary collaboration, and ethical education. By embracing AI responsibly, educational institutions can prepare students for success in the Fourth Industrial Revolution, ensuring positive social and economic impacts.Transnational Education Review150By embracing AI responsibly, educational institutions can prepare students for success in the Fourth Industrial Revolution, ensuring positive social and economic impacts and addressing the digital divide and ethical considerations.Transnational Education Review["Bulent Aydin", "Ibrahim Sirkeci"]2024-05-31T00:00:00 49daf5d33c09c89ee4f90146148773f7f75b429bWith the development of artificial intelligence technology, the field of sports rehabilitation has also made significant progress. Our team adopted the moving human body target recognition technology to achieve "human motion capture", "exercise prescription", "exercise rehabilitation", and the application of "combination of physical medicine", and the use of "OMO" mode to achieve the effective aggregation of online, mobile and offline, to create an online-mobile-offline integrated health exercise prescription design system. Through precise mechanical structure, control system and sensor system, and real-time data monitoring, the system provides personalized rehabilitation training program, which is suitable for neurological rehabilitation, orthopedic rehabilitation and elderly rehabilitation and other scenarios. Its advantage is that it can adjust the training plan according to the specific situation of the patient, feedback the movement status in real time, improve the accuracy and safety of the training, and continuously optimize the rehabilitation program through data-driven.2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)100The moving human body target recognition technology is adopted to achieve "human motion capture", "exercise prescription", "exercise rehabilitation", and the use of "OMO" mode to achieve the effective aggregation of online, mobile and offline to create an online-mobile-offline integrated health exercise prescription design system.2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)["Qiyu Zhang", "Mingmin Gong", "Shangze Yu", "Hao Sun"]2024-05-31T00:00:00 5033dc30759489962350a4867a93b866f3495dddThe African Peace and Security Architecture, a complex set of interrelated institutions, includes the African Union's Peace and Security Council, the African Standby Force, the Continental Early Warning System, the Peace Fund, the Panel of the Wise, and regional mechanisms.The article explores the potential of artificial intelligence (AI) in strengthening the African Peace and Security Architecture (APSA). It highlights the benefits of integrating AI into conflict prevention, crisis management and coordination of peacekeeping operations in Africa. Using practical examples, the article shows how AI can be used to analyze data, detect early signals of conflict, proactively manage crises, and facilitate coordination among security actors. However, it also emphasizes the importance of an ethical and inclusive approach to the adoption of these technologies in order to ensure a positive impact on peace and security in Africa.International Journal of Social Science Humanity &amp; Management Research00The article shows how AI can be used to analyze data, detect early signals of conflict, proactively manage crises, and facilitate coordination among security actors.International Journal of Social Science Humanity &amp; Management Research["Utangisila Bena Osee"]2024-05-31T00:00:00 dddd116351cdfda4eac0ed5060611dc8ad241cd6Abstract: Artificial intelligence has become a vital part of cyber security because of its capacity to assess security threats instantly and respond appropriately. AI currently has a greater influence on identifying and thwarting assaults that maintain companies' technological edge. AI's function in cybersecurity focuses primarily on detection and mitigation. Artificial intelligence uses predictive algorithms and sophisticated data analysis to find trends and irregularities in transmitted data and usage that may indicate a potential cyberattack. This enables security staff to react swiftly and proactively to possible assaults. Artificial Intelligence may be employed to stop assaults by using predictive modeling. The findings suggest that further research and development on the integration of machine learning and artificial intelligence into security platforms has a lot of promise. Among the most intriguing applications found are security for networks, detection of malware, and detection of intrusions and response. The poll indicates that 35% of firms intend to implement machine learning and artificial intelligence throughout existing cyber networks, while 45% of companies have already done so.International Journal for Research in Applied Science and Engineering Technology00The poll indicates that 35% of firms intend to implement machine learning and artificial intelligence throughout existing cyber networks, while 45% of companies have already done so, and among the most intriguing applications found are security for networks, detection of malware, and detection of intrusions and response.International Journal for Research in Applied Science and Engineering Technology["Gurunivas Mudiraj"]2024-05-31T00:00:00 8d145b798468090a087fbeeae4478e129f32eb0aArtificial intelligence (AI) addressed several infectious disease concerns by using its capabilities and acknowledging its constraints, with some adjustments and clarifications. The research focused on important difficulties related to artificial intelligence in infectious diseases. This review advocates for the use of artificial intelligence in infectious disease clinical practice and research. AI categorises article components such as title, abstract, introduction, method, findings, and discussions, which helps scholars save time. This speeds up and improves scientific writing. Some comments may be misleading or inaccurate, putting the accuracy of the research at risk. Current AI systems provide precise and safe responses, but they often lack contextual understanding. The lack of diagnostic technologies in artificial intelligence leads to misidentification and safety risks. Utilising medical technology ethically requires supervision and regulation. Some institutions have prohibited AI research because of its inefficacy. AI may assist physicians by gathering medical data and patient case studies. Identify and control new technologies. ChatGPT and other medical AI models need more data for training.Experimental and Applied Medical Science00This review advocates for the use of artificial intelligence in infectious disease clinical practice and research and addressed several infectious disease concerns by using its capabilities and acknowledging its constraints, with some adjustments and clarifications.Experimental and Applied Medical Science["H\u00fcsna A\u015fk\u0131n", "Ahmet \u015eahin", "Lutfu Askin"]2024-05-31T00:00:00 9a6eda1c094c1866b9da25b28b6690c4517d3b0fThe aim of the article is to identify opportunities and challenges in the specialized literature regarding the impact of artificial intelligence on leadership. The work is based on a bibliometric analysis of papers published on the Scopus platform in 2019-2023 and was conducted starting from an advanced search using the keywords „artificial intelligence”, „AI”, „leader”, „digital” and „leadership”. We started from the research question: What is the situation regarding the specialized literature on how artificial intelligence affects leadership? To answer this question, we proposed the following objectives: identifying the number of publications regarding the impact of artificial intelligence on leadership, determining the evolution of the number of publications, establishing the most common types of published works, highlighting bibliometric maps with the opportunities and challenges regarding the effects of AI on leadership. The bibliometric analysis served as a rigorous and objective method to understand how artificial intelligence affects leadership. This analysis was conducted in December 2023, using Microsoft Excel for graphical representations and the VOSviewer for visualizing the connections between the representative keywords of the selected articles. The main findings refer to the fact that the article sheds light on the opportunities and challenges arising from the integration of AI into management practices, thus providing valuable insights for the development of relevant strategies and policies in organizational contexts.International journal of social science and human research00The article sheds light on the opportunities and challenges arising from the integration of AI into management practices, thus providing valuable insights for the development of relevant strategies and policies in organizational contexts.International Journal of Social Science and Human Research["Andreea Constantin", "Drago\u0219 Bujor", "Claudiu-Nicolae Ghinea"]2024-05-31T00:00:00 1c5ba632e2739e0edc3d6c0d9a7da3e0c0010eecModern organizations face unprecedented challenges in attracting, retaining, and motivating employees in a dynamic business environment. In this regard, the study of the use of artificial intelligence technologies in the field of employee motivation is of particular interest and relevance. This research article is devoted to the study of the possibilities and prospects of using artificial intelligence technologies in employee motivation. It is aimed at unlocking the potential of artificial intelligence to create effective and innovative strategies for staff motivation, as well as at analyzing the benefits and challenges associated with the introduction of such technologies in enterprise management. The article discusses the main aspects of the use of artificial intelligence technologies in employee motivation, analyzes the current state of research in this area of science, and provides recommendations for the practical application of the results obtained. The work is aimed at developing innovative approaches to HR management and increasing interest in the use of artificial intelligence in the business environment.Таврійський науковий вісник. Серія: Економіка10The article discusses the main aspects of the use of artificial intelligence technologies in employee motivation, analyzes the current state of research in this area of science, and provides recommendations for the practical application of the results obtained.Таврійський науковий вісник. Серія: Економіка["Bazaka Roman"]2024-05-31T00:00:00 57fe13f2bfe97894f9185e0c2d8c179e972d7b0aPurpose This study aims to investigate connections between the development of robotic and artificial intelligence (AI) and green crypto investments. The author also explores the influences of global uncertainty shocks like the COVID-19 pandemic and international conflicts on the role of each channel. Design/methodology/approach In this research, the author uses a cutting-edge model-free connectedness approach to investigate the relationships between the development of Global X Robotics and AI (BOTZ) and the volatility of green crypto investments from November 9, 2017 to March 24, 2023. Findings In the sample duration, the findings reveal a two-way link between AI and green/nongreen cryptocurrencies. Throughout the examined period, BOTZ has been a net receiver of shocks as determined by the net total connectedness. Among the main spillover shock carriers in the system, green cryptocurrencies are the most significant. The net pairwise directional connectivity reveals that green cryptocurrencies controlled BOTZ throughout the analyzed time, particularly during the COVID-19 era as well as the Ukraine–Russia crisis. According to the findings, the proposed system is vulnerable to a high level of indication influence. Practical implications The results have important policy implications for investors and governments, as well as methods from the spillovers across the various indicators and their interconnections. Sharp information on the primary contagions among these indicators aids politicians in designing the most appropriate policies. Originality/value To the best of the authors’ knowledge, this paper is the first to look at the link between AI, technological advancement and green cryptocurrency investing. Second, this study developed a methodology for examining instability links between various factors that is more appropriate for investigating these linkages. This study investigates the links between AI, technical advancement and green digital currencies using a cutting-edge model-free connectivity method. This work is also the first to examine the interconnection between volatility derived from AI, technological development and green cryptocurrency investments in light of unknown events, such as the COVID-19 pandemic and the Ukrainian–Russian conflict. Finally, this study includes a daily database from the BOTZ fund, which attempts to invest in firms that stand to gain from rising robotics and AI use. Cardano (ADA), IOTA, NANO (XNO), Stellar Lumens and Tron are examples of green cryptocurrencies, whereas Bitcoin is an example of a nongreen cryptocurrency. These virtual currencies are being used to investigate the relationship between investor mood and green and nongreen digital currencies. The data set spans the period from November 9, 2017 to March 24, 2023. Studies in Economics and Finance660This work is the first to examine the interconnection between volatility derived from AI, technological development and green cryptocurrency investments in light of unknown events, such as the COVID-19 pandemic and the Ukrainian–Russian conflict.Studies in Economics and Finance["Leavitt Ha"]2024-05-31T00:00:00 44828617b0b7934e229c5ce2c0d1d90aead1f9f0Background: Fast-moving consumer goods (FMCG) supply chains have become increasingly exposed to disruptions during and after the coronavirus disease 2019 (COVID-19) pandemic. The industry is vulnerable to supply chain disruptions due to unstable commodity markets and demand volatility. Artificial intelligence (AI) and information systems as technology enablers provide capabilities that can improve supply chain resilience to recover from a disruption. However, FMCG firms are slow with digital transformation and often do not leverage the capabilities of AI and information systems to improve their supply chain resilience.Objectives: The purpose of this generic qualitative study was to determine how AI and information systems capabilities can be leveraged to improve supply chain resilience in the South African FMCG industry.Method: This study employed purposive sampling methods to identify 12 FMCG manufacturers and retailers that participated in this study. Semi-structured interviews were used to collect data. A thematic analysis approach was followed to analyse the data.Results: Supply chain integration, automation, monitoring and analytical capabilities of AI and information systems should be considered when designing post-COVID-19 supply chains to deal with increased complexity. Furthermore, supply chain resilience is enhanced by having AI and information systems capabilities such as information sharing, planning and predictive capabilities and decision-making capabilities. This study identified internal and external organisational driving factors, such as reducing costs and competitive factors, leading to the adoption of AI or information systems.Conclusion: This study creates awareness of the value-adding benefits of AI and information systems that improve supply chain resilience.Contribution: This study expands on existing literature by identifying various capabilities of AI and information systems that improve FMCG manufacturers’ and retailers’ supply chain resilience in a developing country context.Journal of Transport and Supply Chain Management00Supply chain integration, automation, monitoring and analytical capabilities of AI and information systems should be considered when designing post-COVID-19 supply chains to deal with increased complexity.Journal of Transport and Supply Chain Management["Karl Hirsch", "W. Niemann", "Brendan Swart"]2024-05-31T00:00:00 aaa809cc8fdb2ba61bad9fd9cd95518da9f3954dThe advent of artificial intelligence (AI) in healthcare presents significant opportunities and challenges for surrogacy law. This paper examines how existing legal frameworks can adapt to the integration of AI in surrogacy, addressing potential legal, ethical, and practical implications. Current surrogacy laws, often rigid and jurisdiction-specific, may be ill-equipped to manage the complexities introduced by AI technologies, such as enhanced matching processes, surrogate health monitoring, and predictive analytics for pregnancy outcomes. Key challenges include privacy concerns, data security, and algorithmic bias, which necessitate robust legal and ethical oversight. The rights and responsibilities of intended parents, surrogates, and offspring must be re-evaluated in light of AI’s role in the surrogacy process. This paper proposes a flexible, technology-neutral regulatory framework to ensure surrogacy laws remain relevant and effective amidst rapid AI advancements. The framework emphasizes transparency, accountability, and ethical standards in AI deployment, aiming to protect all parties involved. By exploring the intersection of AI and surrogacy law, this study contributes to the broader discourse on legal adaptation to emerging technologies, highlighting the need for dynamic legal systems capable of evolving with technological progress.ShodhKosh Journal of Visual and Performing Arts00This paper proposes a flexible, technology-neutral regulatory framework to ensure surrogacy laws remain relevant and effective amidst rapid AI advancements, highlighting the need for dynamic legal systems capable of evolving with technological progress.ShodhKosh: Journal of Visual and Performing Arts["Harpreet Kaur", "Monika Negi", "Deepak Kumar Srivastava"]2024-05-31T00:00:00 e1ac1713a78ada505cf6f377ffde3d184ce771d9Artificial intelligence has become a highly debated topic globally. Its impact and the changes it brings in every field prompt a reassessment of the human factor's contribution. This study aims to examine the use of artificial intelligence for academic purposes for researchers. In the study, ethical concerns about the use of artificial intelligence in scientific research are explained descriptively. Various studies and opinions regarding this matter in the literature have been examined. While artificial intelligence has become a part of everyday life and a reality, it cannot be separated from scientific research processes and environments. It should be remembered that regardless of how successful artificial intelligence is in all these processes, the role and impact of researchers remain constant. Researchers have to be capable of responding to the changing needs and demands of the evolving world, producing works that are free from any bias and incorrect information, and being ethically sensitive.Experimental and Applied Medical Science120The use of artificial intelligence for academic purposes for researchers is examined and ethical concerns about the use of artificial intelligence in scientific research are explained descriptively.Experimental and Applied Medical Science["Huriye Ya\u015far", "Vasif Karag\u00fcc\u00fck"]2024-05-31T00:00:00 1690bbbf432a3dbfaccd04fcb8a9a9e0e749d4cfThe development of artificial intelligence and technology has accelerated the transformation of internal processes in the banking sector. In particular, Natural Language Processing (NLP) technology provides time and cost savings by automating processes such as data entry, querying, and reporting. While NLP-based systems increase customer satisfaction by understanding customer demands and providing appropriate responses quickly, they also increase operational efficiency. Classification algorithms, which are frequently used together with NLP technology, analyze text data and assign them to certain categories or classes, creating a powerful combination for the processing and analysis of text-based data. Vakıf Participation Demand Management System R&D Project has developed an NLP and classification model to be used in its internal processes. With the developed model, it was aimed to eliminate the problems encountered in workflow processes and increase efficiency by developing a language understanding model using the records of requests (demand management system) kept within Vakıf Participation and frequently used in operational processes. During this study, existing data containing in-house requests were subjected to pre-processing, and model training studies were carried out with these data. As a result of the developments, a model with 75% accuracy was developed and improvement efforts on the model continue. Thanks to the developed model, aims to shorten the response time for requests in the demand management system, reduce operational burdens, and increase internal customer satisfaction. It is planned to use the developed model in other banking internal processes as well.The European Journal of Research and Development110Vakıf Participation Demand Management System R&D Project has developed an NLP and classification model, which aims to shorten the response time for requests in the demand management system, reduce operational burdens, and increase internal customer satisfaction.The European Journal of Research and Development["B\u00fc\u015fra Tural", "Zeynep \u00d6rpek", "Samet \u00d6zmen"]2024-05-31T00:00:00 c9a8fe293c50fab89210b707bdabed37d8aaf17bThe article analyzes the   use of   artificial intelligence technologies in   the   system of  state financial control. The authors present the  main directions for the  development of  artificial intelligence, in   particular its application in   the   system of   government control. Also, the   possibilities of  expanding the  areas of   application of  artificial intelligence in   state financial control are being considered in  order to increase the   efficiency of  the   country’s economic security. The authors believe that the use of  artificial intelligence will speed up inspections, and  in  the  future there will be an intensification of   the   activities of  state financial control bodies. The scientific novelty of  the   article lies in   the   fact that it examines the  need to use artificial intelligence technology in  the   system of  state financial control, and  also analyzes the  trend in   the   development of  artificial intelligence in  order to effectively ensure the   economic security of  the   Russian Federation through state control bodies.Siberian Financial School30The authors believe that the use of  artificial intelligence will speed up inspections, and in the future there will be an intensification of the activities of the state financial control bodies.Siberian Financial School["A. Vyzhitovich", "D. V. Borovskikh", "V. Y. Kraeva"]2024-05-31T00:00:00 5b9089860b1990a1ce496ebf0935be21d4340c11The purpose of the study is to find out that AI is a form rather than a legal subject and provides legal certainty for each user, as well as the extent of the legality of the use of AI in creating an scientific work with conceptual and challenges to AI regulation in Indonesia. Artificial Intelligence is a computer system designed to perform tasks that are usually performed by humans and that require human intelligence. many assume that this AI is an artificial robot. this happens because of the many animations in a film or story on television, social media or written media that illustrate that this AI is an artificial robot that is described as resembling a human. this study uses legal research with a normative approach method, with the problem of whether AI can be said to be a legal subject on intellectual property rights and if AI is able to create a product or work whether the creation can be said to be a right to ownership. this is in line with research on the era of artificial intelligence and the impact on human dignity in ethical studies that ethically examine the problem of AI in discovery with humans as developers, users, and objects in this day and age, especially the impact on human dignity. of course, it is a comparative material in conducting research, in this case this article focuses on the position and subject of law on artificial intelligence.       Journal of Development Research00Journal of Development Research["Rahma Fatmawati", "Irma Mangar"]2024-05-31T00:00:00 9de90bb1df107ad9ab7015c4d03a7d6eda64c310Abstract: In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world data mining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice. Artificial intelligence is the upcoming technology in advance health care system. Current digitalization of medicine and availability of electronic health records (EHRs) has inspired clinical researchers and healthcare personnel to acquire artificial intelligence (AI) methodologies for big data analytics and for very large scale medical databases. The major advantage of AI is that it reduces the time that is needed for drug development and, in turn, it reduces the costs that are associated with drug development, enhances the returns on investment and may even cause a decrease in cost for the end user. A large number of researches are being carried out to improve the current available AI technology to make the pharmacy profession more efficient. The present article briefly describes the importance of AI in the process of drug development and then looks at the various AI tools that are available at the disposal of a modern-day pharmacist to aid in a more efficient functioning.International Journal for Research in Applied Science and Engineering Technology00The present article briefly describes the importance of AI in the process of drug development and looks at the various AI tools that are available at the disposal of a modern-day pharmacist to aid in a more efficient functioning.International Journal for Research in Applied Science and Engineering Technology["Mr. Umesh D. Solake"]2024-05-31T00:00:00 60ef5ee00374853b3b9d95f7dafa0e07fff83cd0ADMINISTRATIE SI MANAGEMENT PUBLIC03ADMINISTRATIE SI MANAGEMENT PUBLIC[]2024-05-31T00:00:00 6b4aada6074b6a905487e68ee400ae4a89b1b9d6ADMINISTRATIE SI MANAGEMENT PUBLIC01ADMINISTRATIE SI MANAGEMENT PUBLIC[]2024-05-31T00:00:00 44b5dcbee1a1082ef976cb91426ca3edcd029ecdThis systematic literature review examines the impact of AI applications, including ELAi app, ELSA Speak, and Lyra Virtual Assistant, on the pronunciation skills of English as a Foreign Language (EFL) students. By analyzing ten relevant articles published between 2018 and 2023, this review identifies the unique features and effects of these AI tools. ELAi app emphasizes spontaneous speech and topic development, offering comprehensive feedback. ELSA Speak accurately detects pronunciation errors and provides a wide range of lessons. Lyra Virtual Assistant acts as a conversational companion, supporting speaking abilities. The findings reveal that these AI applications have a positive influence on EFL students' pronunciation development, as evidenced by empirical research. Further recommendations for future research in the integration of AI in EFL education is enhancing personalization through AI tools that adapt to individual students' learning styles and needs. In summary, utilizing AI technology can empower EFL students to improve their pronunciation proficiency and enhance their overall English language learning experience.The Art of Teaching English as a Foreign Language152Using AI technology can empower EFL students to improve their pronunciation proficiency and enhance their overall English language learning experience.The Art of Teaching English as a Foreign Language["Risma Dwi Aryanti", "M. Santosa"]2024-05-31T00:00:00 40b7470336a21beab42bb6048e85be292cae0985Sustainable Development Goals (SDGs) represents the 2030 Agenda defined by the United Nations to attain sustainable development. It comprises outlines 17 goals that cover a range of issues. These goals focus on addressing the globe's most crucial economic, communal, and environmental challenges to provide a more sustainable future for every individual. In India, the government has initiated several schemes and programmes for attaining the targets set for different SDGs, but addressing the complex challenges associated with sustainable development requires continued efforts and collaborations from multiple stakeholders including governments, individuals, public and private sectors. Achieving the SDGs is a continuous process, and some targets may need more time and effort to get achieved. Regular monitoring and evaluation are significant for tracking progress and addressing the gaps or challenges that may arise. The COVID-19 pandemic put a critical impact on the advancement towards attaining the targets set for all the SDGs globally. India has been found to be off track for 19 out of the 33 SDG indicators and there is an urgent need to accelerate the momentum of achieving the targets by 2030. AI presents significant opportunities to address these challenges in India. Overall, in India, Artificial Intelligence is seen as valuable tool to achieve the targets set for different goals in the coming years. In this paper, the authors have discussed the schemes and programmes initiated by the Government of India to attain these SDGs and the role of Artificial Intelligence in attaining the targets by the year 2030International Journal of Built Environment and Sustainability231The schemes and programmes initiated by the Government of India to attain these SDGs and the role of Artificial Intelligence in attaining the targets by the year 2030 are discussed and in India, Artificial Intelligence is seen as valuable tool to achieve the targets set for different goals in the coming years.International Journal of Built Environment and Sustainability["Shivani Gupta", "Satinder Bal Gupta", "Monika Gupta"]2024-05-31T00:00:00 28e33fc7b0ecd674ee84f20a3969bc25c172827fApplied intelligence (Boston)262Appl. Intell.["Abdullah Alqahtani", "Shtwai Alsubai", "Munish Bhatia"]2024-05-31T00:00:00 295518a9f2e5d8b6f34f317c25cdb090258d8c79Journal of social sciences and humanities00Journal of Social Science and Humanities[]2024-05-31T00:00:00 bd888bd9190267f1735c9b9726ae2000f3f48dd62024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)002024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI)[]2024-05-31T00:00:00 306dd2ae709512270214b97fe09e9e0cdef072d8Al-Azhar International Medical Journal00Al-Azhar International Medical Journal["F. H. Zayed", "S. A. Nematallah", "A. M. M. Elsaed"]2024-05-31T00:00:00 7a2653c0eeb8a253802ff1b946c14f51710d4211The Journal of Korean Association of Computer Education00The Journal of Korean Association of Computer Education["Shinchun Kang", "Heeok Heo", "Hyunyong Jung"]2024-05-31T00:00:00 be1040c7b7ab3042c4fde835bc3707fc0579aac4Journal of Digital Contents Society00Journal of Digital Contents Society["Hee-Woon Park"]2024-05-31T00:00:00 90423732cb7aa5c0ad33f9e1cc01e1045d7b9a42Artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing health care by offering unprecedented opportunities to enhance patient care, optimize clinical workflows, and advance medical research. However, the integration of AI and ML into healthcare systems raises significant ethical considerations that must be carefully addressed to ensure responsible and equitable deployment. This comprehensive review explored the multifaceted ethical considerations surrounding the use of AI and ML in health care, including privacy and data security, algorithmic bias, transparency, clinical validation, and professional responsibility. By critically examining these ethical dimensions, stakeholders can navigate the ethical complexities of AI and ML integration in health care, while safeguarding patient welfare and upholding ethical principles. By embracing ethical best practices and fostering collaboration across interdisciplinary teams, the healthcare community can harness the full potential of AI and ML technologies to usher in a new era of personalized data-driven health care that prioritizes patient well-being and equity.Cureus4826This comprehensive review explored the multifaceted ethical considerations surrounding the use of AI and ML in health care, including privacy and data security, algorithmic bias, transparency, clinical validation, and professional responsibility.Cureus["Mitul Harishbhai Tilala", "Pradeep Kumar Chenchala", "Ashok Choppadandi", "Jagbir Kaur", "Savitha Naguri", "Rahul Saoji", "Bhanu Devaguptapu"]2024-06-01T00:00:00 28b8e8c45663a66d21433c17439f1e03fcaff5f8Computers and Education Open4535Computers and Education Open["T. Chiu", "Zubair Ahmad", "Murod Ismailov", "I. T. Sanusi"]2024-06-01T00:00:00 de7e48d2b397f745126a77e920338b2ab25d82a3Neurocomputing8326Neurocomputing["V. Bol\u00f3n-Canedo", "L. Mor\u00e1n-Fern\u00e1ndez", "Brais Cancela", "A. Alonso-Betanzos"]2024-06-01T00:00:00 b14abb6f7d892569c6ba1d6bbbbc6296f1c4ae1eRadiotherapy and Oncology8616A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy, which will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology["C. Hurkmans", "J. Bibault", "Kristy K. Brock", "W. van Elmpt", "Mary U. Feng", "Clifton David Fuller", "B. Jereczek-Fossa", "S. Korreman", "Guillaume Landry", "F. Madesta", "Chuck Mayo", "A. McWilliam", "Filipe Moura", "Ludvig Paul Muren", "I. E. El Naqa", "Jan Seuntjens", "Vincenzo Valentini", "M. Velec"]2024-06-01T00:00:00 5c4bbc74897e9bc9c952a0a549ff1a10f682e204Applied Energy10918Applied Energy["Chien-Chiang Lee", "Jingyang Yan"]2024-06-01T00:00:00 f8db0326341c6f40149072f3549546407ac914dcThis research paper delves into the transformative possibilities of Artificial Intelligence (AI) within corporate finance, specifically focusing on its role in improving efficiency and decision-making processes. Through the utilization of machine learning, natural language processing (NLP), and robotic process automation (RPA), AI introduces innovative methods for enhancing corporate governance and sustainability practices. In the contemporary business landscape, corporations encounter mounting pressure to streamline operations while simultaneously addressing concerns regarding environmental, social, and governance (ESG) issues. Conventional finance methodologies often struggle to efficiently handle large volumes of data and extract actionable insights promptly. However, AI presents a shift in paradigm by enabling automated data analysis, recognizing patterns, and conducting predictive modeling, thus enabling finance professionals to make data-informed decisions swiftly and accurately. Machine learning algorithms play a pivotal role in detecting patterns and correlations within financial data, facilitating proactive risk management and strategic planning. Additionally, NLP technologies facilitate the extraction of valuable insights from unstructured data sources like regulatory filings, news articles, and social media, thereby enabling informed decision-making in corporate governance and sustainability endeavors. Moreover, RPA simplifies repetitive tasks and workflows, thereby reducing operational expenses and freeing up human resources for more strategic pursuits. Through the automation of routine processes such as data entry, reconciliation, and reporting, RPA enhances operational efficiency and ensures adherence to regulatory standards. Through the adoption of AI technologies, corporations can unlock novel avenues for innovation, optimize resource allocation, and promote sustainable growth within today's dynamic business milieu.Social Science Research Network012SSRN Electronic Journal["N. Rane", "Saurabh P. Choudhary", "Jayesh Rane"]2024-06-01T00:00:00 d49abcd4c3cf05df5aae535bb90e8e805c00aff2Neurocomputing22114A comprehensive literature review encompassing common terminologies and definitions, the need for XAI, beneficiaries of XAI, a taxonomy of XAI methods, and the application of XAI methods in different application areas is provided.ArXiv["Melkamu Abay Mersha", "K. Lam", "Joseph Wood", "Ali K. AlShami", "Jugal Kalita"]2024-06-01T00:00:00 eed598609d1695226ee8aba114c3832cdfb85e2eAdvancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.Diagnostics7411This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards, and envisioning AI as an adjunctive tool that fortifies the dental profession.Diagnostics["Zeliha Merve Semerci", "Selmi Yardimci"]2024-06-01T00:00:00 1eb62fecc83548375cf460c655b8772228190026Biomedical Signal Processing and Control10712Biomed. Signal Process. Control.["Ikram Bazarbekov", "Abdul Razaque", "M. Ipalakova", "Joon Yoo", "Zhanna Assipova", "Ali Abd Almisreb"]2024-06-01T00:00:00 3fa00e6eae6083ef312a95e7ce8eb01bc9ecbefdGenerative artificial intelligence (AI) algorithms for both text-to-text and text-to-image applications have seen rapid and widespread adoption in the general and medical communities. While limitations of generative AI have been widely reported, there remain valuable applications in patient and professional communities. Here, the limitations and biases of both text-to-text and text-to-image generative AI are explored using purported applications in medical imaging as case examples. A direct comparison of the capabilities of four common text-to-image generative AI algorithms is reported and recommendations for the most appropriate use, DALL-E 3, justified. The risks use and biases are outlined, and appropriate use guidelines framed for use of generative AI in nuclear medicine. Generative AI text-to-text and text-to-image generation includes inherent biases, particularly gender and ethnicity, that could misrepresent nuclear medicine. The assimilation of generative AI tools into medical education, image interpretation, patient education, health promotion and marketing in nuclear medicine risks propagating errors and amplification of biases. Mitigation strategies should reside inside appropriate use criteria and minimum standards for quality and professionalism for the application of generative AI in nuclear medicine.Seminars in nuclear medicine308The limitations and biases of both text-to-text and text-to-image generative AI are explored using purported applications in medical imaging as case examples and appropriate use guidelines framed for use of generative AI in nuclear medicine are outlined.Seminars in nuclear medicine["G. Currie", "K. E. Hawk", "Eric M. Rohren"]2024-06-01T00:00:00 8a85d425fb4baeb6b481d717b397278d610fd218The arrival of Generative Artificial Intelligence (AI) is fundamentally different from prior technologies used in educational settings. Educators and researchers of online, blended, and in-person learning are still coming to grips with possible applications of AI in the learning experience with existing technologies; let alone understanding the potential consequences that future developments in AI will produce. Despite potential risks, AI may revolutionize previous models of teaching and learning and perhaps create opportunities to realize progressive educational goals. Given the longstanding tradition of philosophy to examine questions surrounding ethics, ontology, technology, and education, the purpose of this critical reflection paper is to draw from prominent philosophers across these disciplines to address the question: how can AI be employed in future educational contexts in a humanizing and ethical manner? Drawing from the work of Gunther Anders, Michel Foucault, Paolo Freire, Benjamin Bloom, and Hannah Arendt, we propose a framework for assessing the use and ethics of AI in modern education contexts regarding human versus AI generated textual and multimodal content, and the broader political, social, and cultural implications. We conclude with applied examples of the framework and implications for future research and practice.Online Learning496A framework for assessing the use and ethics of AI in modern education contexts regarding human versus AI generated textual and multimodal content, and the broader political, social, and cultural implications is proposed.Online Learning["Andrew Swindell", "Luke Greeley", "Antony Farag", "Bailey Verdone"]2024-06-01T00:00:00 04bdf53b24fbeb1624b6976625b277241449ef35This study addresses the current lack of research on the effectiveness assessment of Artificial Intelligence (AI) technology in architectural education. Our aim is to evaluate the impact of AI-assisted architectural teaching on student learning. To achieve this, we developed an AI-embedded teaching model. A total of 24 students from different countries participated in this 9-week course, completing a comprehensive analysis of architectural programming and design using AI technologies. This study conducted questionnaire surveys with students at both midterm and final stages of the course, followed by structured interviews after the course completion, to explore the effectiveness and application status of the teaching model. The results indicate that the AI-embedded teaching model positively and effectively influenced student learning. The “innovative capability” and “work efficiency” of AI technologies were identified as key factors affecting the effectiveness of the teaching model. Furthermore, the study revealed a close integration of AI technologies with architectural programming but identified challenges in the uncontrollable expression of architectural design outcomes. Student utilization of AI technologies appeared fragmented, lacking a systematic approach. Lastly, the study provides targeted optimization suggestions based on the current application status of AI technologies among students. This research offers theoretical and practical support for the further integration of AI technologies in architectural education.Buildings676The results indicate that the AI-embedded teaching model positively and effectively influenced student learning and offers theoretical and practical support for the further integration of AI technologies in architectural education.Buildings["Shitao Jin", "Huijun Tu", "Jiangfeng Li", "Yuwei Fang", "Zhang Qu", "Fan Xu", "Kun Liu", "Yiquan Lin"]2024-06-01T00:00:00 1ef0de56035562ee876b1f1e779fdc3b479ad18bDentistry Review11411Dentistry Review["Maryam Ghaffari", "Yi Zhu", "Annie Shrestha"]2024-06-01T00:00:00 49f029c98d296c123a3c4b20b9d2697ad4dbca00Journal of the Air Transport Research Society12510Journal of the Air Transport Research Society["Alexander M. Geske", "David M. Herold", "Sebastian Kummer"]2024-06-01T00:00:00 a47b8209cf59533e8a9eda39f211bcb9c65eefaaiScience7611Artificial intelligence is transforming diffractive optics development through its advanced capabilities in design optimization, pattern generation, fabrication enhancement, performance forecasting, and customization, which holds tremendous potential to revolutionize optical technology applications across diverse sectors.iScience["S. Khonina", "N. L. Kazanskiy", "A.R. Efimov", "A.V. Nikonorov", "I.V. Oseledets", "R. Skidanov", "M. A. Butt"]2024-06-01T00:00:00 f501cc6a2c4b2e7fc354df24e1498c71c634087bEnergy Conversion and Management8410Energy Conversion and Management["Seyyed Shahabaddin Hosseini Dehshiri", "B. Firoozabadi"]2024-06-01T00:00:00 542d4da3402893f6d8726ba8145725a3bcb8073eArtificial intelligence (AI) plays an imperative role in next-generation critical infrastructures like the smart grid, whose power can be harnessed by not only operators, but also cyber adversaries. This article investigates a potential threat from adversarial AI in blind false data injection attacks (FDIA) targeting the ac state estimators in the smart grid. Assuming no access to the grid topology required in most FDIA, we propose an adversarial model based on artificial neural networks (ANNs) to infer grid topology from historical measurements. Following the topology inference, a substitute bad data detector (BDD) model is further proposed in the attack model to filter the false data before injection, reducing the risk of detection given potential bad data in normal operations. We also refine the common evaluation of FDI stealthiness by including the presence of bad data among normal and false data when assessing the detection performance. Simulations on the IEEE 30-bus system reveal that significant deviations can be inflicted stealthily by the proposed blind FDI attack. Detailed analyses of the stealthiness, impacts, and parameters are also presented to shed more light on the threats for further studies and effective countermeasures.IEEE Transactions on Industrial Informatics325Simulations on the IEEE 30-bus system reveal that significant deviations can be inflicted stealthily by the proposed blind FDI attack, and proposes an adversarial model based on artificial neural networks (ANNs) to infer grid topology from historical measurements.IEEE Transactions on Industrial Informatics["Moshfeka Rahman", "Jun Yan", "Emmanuel Thepie Fapi"]2024-06-01T00:00:00 0e4772e0085e4902b134d55ec144b0e65e1f5e98Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.Cureus565It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings.Cureus["Meetali Kalani", "Ashish P Anjankar"]2024-06-01T00:00:00 3a06a56451ca1523e2da285b6c0cb9b0cc594036The global popularity of electric cars (EVs) as a sustainable means of transportation, reliable and efficient charging infrastructure is essential. Traditional electric vehicle charging involves connecting the car to a power source and waiting for the battery to charge. However, AI has allowed us to improve charging patterns, reduce costs, and boost efficiency. This article examines how AI algorithms are changing electric car charging. If electric vehicle (EV) charging and discharging are not coordinated, the power supply infrastructure will be overrun. Demand response like dynamic pricing might encourage electric vehicle owners to participate in scheduling initiatives. Thus, EV charging and discharging scheduling and dynamic pricing model research are crucial. Artificial intelligence-based models for EV charging predictions and scheduling have been the focus of researchers. These models outperform linear, exponential, and multinomial logit optimization approaches. Due to the novelty and ongoing development of EVs returning electricity to the power grid, vehicle-to-grid (V2G) systems have received little attention. Thus, a complete analysis of EV charging and discharging research is needed to identify gaps and improve future studies. This study categorizes EV charging and discharging studies into forecasting, scheduling, and pricing techniques. The work links forecasting, scheduling, and pricing processes and identifies research gaps in EV discharge scheduling and dynamic pricing models.Journal of Engineering, Management and Information Technology03This study categorizes EV charging and discharging studies into forecasting, scheduling, and pricing techniques, and identifies research gaps in EV discharge scheduling and dynamic pricing models.Journal of Engineering, Management and Information Technology["Ravi Bukya", "G. Madhu Mohan", "M. Kumar Swamy"]2024-06-01T00:00:00 3c05428330d55f6c64d75d38126a1f59ea48caf5Abstract The paper uses qualitative research to investigate the potential uses of artificial intelligence in the field of central banking. The analysis shows that monetary policy, prudential supervision and the oversight of payments are the areas where the use artificial intelligence is most likely to bring benefits. Monetary policy calibration involves working with long time series of data for various parameters and making the necessary analysis and forecasts, an activity in which artificial neural networks may prove useful. Bank supervision can benefit from the use natural language processing algorithms that can read documents and extract the relevant information. Such algorithms can read all of the required documents (not just those that the supervisor selected) and return all of the sentences that contain a certain predefined expression. In the field of the oversight of payments, the capabilities of machine learning to identify new patterns or anomalies in the data that could indicate fraud or money laundering will boost the efforts to combat them. In terms of challenges associated with the use of artificial intelligence in central banking, perhaps the two biggest challenges are that some of the models do not allow for a reasonable level of explainability of the algorithm(s) through which they arrive at the result (especially relevant for bank supervision) and data availability. With respect to the latter, although the issue of quantity of data can be dismissed as a shortcoming given the huge amounts of data available, the issue of data quality seems to be more pronounced, as deficiencies such as data measured incompletely or incorrectly, scarcity and regulatory barriers that impede data sharing may be difficult to surpass.Proceedings of the International Conference on Business Excellence83The analysis shows that monetary policy, prudential supervision and the oversight of payments are the areas where the use of artificial intelligence is most likely to bring benefits.Proceedings of the International Conference on Business Excellence["Aura Elena Grigorescu"]2024-06-01T00:00:00 2930b4e262fbddd29048f055eb8c2e1e73627e69Rapid advancements in artificial intelligence (AI) and machine learning (ML) are currently transforming the field of diagnostics, enabling unprecedented accuracy and efficiency in disease detection, classification, and treatment planning. This Special Issue, entitled “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis”, presents a curated collection of cutting-edge research that explores the integration of AI and ML technologies into various diagnostic modalities. The contributions presented here highlight innovative algorithms, models, and applications that pave the way for improved diagnostic capabilities across a range of medical fields, including radiology, pathology, genomics, and personalized medicine. By showcasing both theoretical advancements and practical implementations, this Special Issue aims to provide a comprehensive overview of current trends and future directions in AI-driven diagnostics, fostering further research and collaboration in this dynamic and impactful area of healthcare. We have published a total of 12 research articles in this Special Issue, all collected between March 2023 and December 2023, comprising 1 Editorial cover letter, 9 regular research articles, 1 review article, and 1 article categorized as “other”.Diagnostics223This Special Issue presents a curated collection of cutting-edge research that explores the integration of AI and ML technologies into various diagnostic modalities, highlighting innovative algorithms, models, and applications that pave the way for improved diagnostic capabilities across a range of medical fields.Diagnostics["M. A. Al-antari"]2024-06-01T00:00:00 fe70262ce7e8d2f8c753315fee8fefadcacbf569There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an “attention guided Grad‐CAM” that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community.International Conference on Climate Informatics1193A detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision.Computational Intelligence["Kaushik Raghavan", "Sivaselvan Balasubramanian", "Kamakoti Veezhinathan"]2024-06-01T00:00:00 df899a5aad41d7766e609e92ea30e6bbc2be24b9Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.Journal of Personalized Medicine763Findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging are synthesized.Journal of Personalized Medicine["O. Patrascanu", "Dana Tutunaru", "C. Musat", "O. Dragostin", "A. Fulga", "Luiza Nechita", "A. Ciubar\u0103", "A. Piraianu", "Elena Stamate", "Diana Gina Poalelungi", "Ionu\u021b Dragostin", "D. Iancu", "A. Ciubar\u0103", "I. Fulga"]2024-06-01T00:00:00 88c8522abd8ccd287637a500e6e6a3b2fa7d750fAbstract Artificial intelligence (AI) and machine learning have become increasingly important in modern society and are poised to play an increasingly prominent role in education. This paper seeks to provide a theoretical framework for interrogating the integration of AI in education spaces. The paper argues that the eventual response of educators to recent developments in artificial intelligence is eerily like the earlier cycles of integrating ICT in education and, decades earlier, calculators into mathematics instruction. Premised on the argument that there are similarities between the calculator revolution in mathematics education and the ICT revolution in education several decades ago and the current ongoing developments in artificial intelligence, this paper offers a theoretical lens. The theoretical lens is composed of the Technology-Organization-Environment (TOE) framework, Technology Acceptance Model, Technological Pedagogical Content Knowledge, Socio-technical system theory, and Diffusion of Innovation theory. The paper concludes that despite spatial differences between the ICT revolution and the artificial intelligence revolution, there are shared similarities warranting adoption of a similar theoretical lens. Furthermore, factors that were considered pivotal in the integration of ICT are still relevant to the revolution of artificial intelligence.Research on Education and Media23It is argued that the eventual response of educators to recent developments in artificial intelligence is eerily like the earlier cycles of integrating ICT in education and, decades earlier, calculators into mathematics instruction.Research on Education and Media["Kudzayi Savious. Tarisayi"]2024-06-01T00:00:00 1b14acd92414c8b1eb5cf8701bf6c15d105289c2Esports—games played competitively—comprise a major sector of the global games industry. Esports have been used as a testbed for game artificial intelligence (AI) and game analytics for two decades. This article presents a multivocal literature mapping of available research that focuses strictly on the use of artificial intelligence approaches in multiplayer online battle arena (MOBA) games, one of the most popular esports genres and the one most widely used for game AI and game analytics research. A mapping is performed on relevant publications published between 2011 and 2022 and systematically examines them to extract similarities, gaps, and main findings. We analyzed 124 publications to identify the most studied topics, the most commonly used techniques, and the most commonly applied evaluation methods. The results show that League of Legends and Defense of the Ancients are the most studied games, with outcome prediction being the most popular research topic. Finally, we provide an analysis of the potential future flagship areas for research in the domain, considering the gaps found in the white and grey literature.IEEE Transactions on Games1513A multivocal literature mapping of available research that focuses strictly on the use of artificial intelligence approaches in multiplayer online battle arena games, one of the most popular esports genres and the one most widely used for game AI and game analytics research is presented.IEEE Transactions on Games["L. Costa", "Anders Drachen", "F. C. Souza", "G. Xex\u00e9o"]2024-06-01T00:00:00 41646e6ab4bee232fd6fe1778a252a2a8154ba57The research aims to study artificial intelligence techniques and the extent of their impact on improving the efficiency and effectiveness of electronic accounting disclosure for financial reports. Therefore, the first section dealt with the concept of electronic disclosure, and dealt with the opposing arguments that posed a major challenge to electronic accounting disclosure for financial reports in business companies, in addition to the supporting arguments that agree With the advantages resulting from the application of electronic disclosure, whether achieved by the applying company or users of information published on the Internet, it also addressed the factors affecting the efficiency and effectiveness of electronic accounting disclosure. The second section, dealt with identifying the concept of artificial intelligence and its techniques, the extent of their application in accounting, and the expected impact of applications of intelligence techniques. Artificial intelligence in the future of business companies, which in turn affected the improvement of the efficiency and effectiveness of electronic accounting disclosure for financial reports. The field study proved that the application of artificial intelligence techniques has an impact in improving the efficiency and effectiveness of electronic accounting disclosure, as the survey list was used as a means of collecting primary data, which was distributed to There are only two categories: accountants and auditors in companies listed on the stock exchange.The American Journal of Social Science and Education Innovations02The field study proved that the application of artificial intelligence techniques has an impact in improving the efficiency and effectiveness of electronic accounting disclosure, as the survey list was used as a means of collecting primary data, which was distributed to companies listed on the stock exchange.The American Journal of Social Science and Education Innovations["Sahar abbas hasan", "Ali alburaq mohamed rahem"]2024-06-01T00:00:00 d9177297347ae7c467487445e75608d3dec53df3Ain Shams Engineering Journal879Ain Shams Engineering Journal["Shiza Malik", "Khalid Muhammad", "Yasir Waheed"]2024-06-01T00:00:00 84b35628c258592386f88f124783f984c9163f9fJournal of Water Process Engineering239Journal of Water Process Engineering["Zhigang Mao", "Xiaoqin Li", "Xuan Zhang", "Dongdong Li", "Jingyu Lu", "Jubiao Li", "Feiyu Zheng"]2024-06-01T00:00:00 22b6d5a959ef5ca75faa429cc12e8b9e4d5bd123This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.IAES International Journal of Artificial Intelligence (IJ-AI)911The findings of this study contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.IAES International Journal of Artificial Intelligence (IJ-AI)["Santo Wijaya", "Lim Hermanto Rudy", "Fransisca Debora", "Rana Ardila Rahma", "Arief Ramadhan", "Yusita Attaqwa"]2024-06-01T00:00:00 35ea7e7c627fa992ba8875cc44e6f8a442295617Simple Summary Worldwide, honeybees (Apis mellifera L.) are involved in pollinating both wild and economically useful plants, while their products are also used by the food and pharmaceutical industries. But currently, apiculture is encountering the adverse effects of global climate change, including more variable rainfall, shifting seasonal precipitation, and increasing temperature averages. These changes threaten the sustainable future of apiculture as these anomalies have already contributed significantly to the economic downturn of the apiculture industry in recent years. In this review, we provide an overview of the current challenges faced by apiculture due to climate change, as well as artificial intelligence (AI) applications in apiculture that can assist to address them. AI has been utilized in various scientific aspects of apiculture, such as managing hives, maintaining health, detecting pests and diseases, monitoring habitats, and managing population distribution. This is achieved by analyzing data objects such as text, audio, images, videos, sensor readings, and numerical values to investigate, model, predict, and make supporting decisions. Several shortcomings of the existing AI application are identified in this review, and the knowledge gaps regarding the development of autonomous intelligent systems for sustainable beekeeping are also highlighted. Abstract Honeybees (Apis mellifera L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.Insects1101An overview of the current challenges faced by apiculture due to climate change, as well as artificial intelligence (AI) applications in apiculture that can assist to address them are provided.Insects["P. Astuti", "Bettina Heged\u0171s", "A. Oleksa", "Z. Bagi", "S. Kusza"]2024-06-01T00:00:00 d54add731ac0cd9237702caa19873f03e7e22765Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.Medicina231The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care, which suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.Medicina["W. Syed", "Salmeen D Babelghaith", "Mohamed Al-Arifi"]2024-06-01T00:00:00 d60be10ef646c43c2e7e222cfb99ebbf3bd8b817Twenty-first-century skills should be integrated into higher education to prepare students for complex working-life challenges. Artificial intelligence (AI)-powered tools have the potential to optimise skill development among higher education students. Therefore, it is important to conceptualise relevant affordances of AI systems for 21st-century skills development in higher education. This study aimed to present an overview of journal articles published in the Web of Science database that specifically addressed the affordances of AI-based tools for 21st-century skills development. Four distinct categories of AI-based tools (intelligent tutoring systems, chatbots, AI-powered dashboards and automated grading systems) were identified as capable of promoting six main 21st-century skills (collaboration, communication, creativity, critical thinking, information and communication technology and problem-solving). The review revealed that the utilisation of AI-based tools might contribute to the simultaneous development of multiple 21st-century skills (e.g., collaboration and critical thinking). The results showed that adaptive feedback from AI plays a significant role as a facilitator in the development of 21st-century skills. Furthermore, the utilisation of diverse functional AI affordances (e.g., prediction and profiling) might contribute to the development of various skills. AI-based technologies appeared to target the 21st-century skills of problem-solving and its subskills the most.   Implications for practice or policy: More functional affordances of AI (e.g., prediction and profiling) should be employed in AI-based tools. This could support higher education students’ 21st-century skills. AI-based tools (e.g., chatbots and intelligent tutors) interact with end users through their data. AI systems have the potential to promote 21st-century skills by using students’ multimodal data. AI technologies should be more integrated into the social sciences and humanities in the higher education context to support students’ 21st-century skills. Australasian Journal of Educational Technology802An overview of journal articles published in the Web of Science database that specifically addressed the affordances of AI-based tools for 21st-century skills development showed that adaptive feedback from AI plays a significant role as a facilitator in the development of 21st-century skills.Australasian Journal of Educational Technology["Ismail Celik", "Egle Gedrimiene", "Signe Siklander", "H. Muukkonen"]2024-06-01T00:00:00 6b6b890ecea54cdfd22def1866da2cf77d4285adThe integration of artificial intelligence, particularly ChatGPT, in education presents both promising opportunities and notable challenges. Through a systematic review employing the PRISMA method, this article analyzed 45 references published of ChatGPTs impact on educational environments. While ChatGPT offers teachers a versatile learning tool, aiding in tasks, such as lesson planning and content generation, concerns regarding academic integrity and over-reliance on technology have emerged. Ethical considerations, including the potential for cheating in assignments and exams, highlight the need for clear guidelines and ethical frameworks to govern its use. Institutions or related organizations must address issues, such as plagiarism and data privacy, to ensure responsible integration of ChatGPT. Nurturing a growth mindset among educators and learners is crucial to effectively navigate ChatGPT integration. By aligning strategies to leverage ChatGPTs’ capabilities while mitigating risks, educators, institutions, and policymakers can enhance the quality of education in an evolving technological landscape. This article contributes to a deeper understanding of ChatGPTs’ implications in education, providing insights into its advantages and challenges. Informed decision making and proactive measures are essential to harness ChatGPTs potential for transformative impact while safeguarding educational integrity and ethics.IEEE Engineering Management Review422Informed decision making and proactive measures are essential to harness ChatGPTs potential for transformative impact while safeguarding educational integrity and ethics, according to a systematic review employing the PRISMA method.IEEE Engineering Management Review["Idria Maita", "S. Saide", "Afifah Mesha Putri", "Didi Muwardi"]2024-06-01T00:00:00 fcce161594a7ccff7db0132fe7504a52ab833a4dAssisted reproductive technology (ART) has revolutionized the field of reproductive medicine, offering hope to millions of individuals and couples facing infertility challenges. In recent years, integrating robotics and artificial intelligence (AI) has emerged as a promising avenue for advancing ART. This comprehensive review explores the transformative impact of robotics and AI on ART, examining recent advancements, technological applications, clinical implications, and ethical considerations. Robotics enables precise and minimally invasive procedures, enhancing the efficiency and accuracy of various reproductive techniques such as sperm retrieval, embryo handling, and surgical interventions. Meanwhile, AI offers predictive analytics, personalized treatment protocols, and decision support systems tailored to individual patient needs, optimizing treatment outcomes and expanding access to reproductive care. Key findings highlight the significant advancements made possible by robotics and AI in ART, including improved success rates, reduced risks, and enhanced patient experience. However, challenges such as regulatory considerations, adoption barriers, and ethical dilemmas must be addressed to realize the full potential of these technologies. The transformative impact of robotics and AI on ART is profound, shaping the future of fertility treatment and family-building worldwide. Continued research, interdisciplinary collaboration, and investment are essential to further harness the potential of robotics and AI in advancing reproductive medicine and ensuring accessible, equitable, and effective care for all individuals and couples.Cureus302This comprehensive review explores the transformative impact of robotics and AI on ART, examining recent advancements, technological applications, clinical implications, and ethical considerations.Cureus["Smruti A Mapari", "Deepti Shrivastava", "Gautam N Bedi", "Utkarsh Pradeep", "Aman Gupta", "Paschyanti Kasat", "Pratiksha Sachani"]2024-06-01T00:00:00 d2bdc89f98af3c632e862e187155ef36617ec14fEnthusiasm about the promise of artificial intelligence and machine learning in health care must be accompanied by oversight and remediation of any potential adverse effects on health equity goals that these technologies may create. We describe five equity imperatives for the use of AI/ML in health care that require attention from health care professionals, developers, and policymakers.North Carolina Medical Journal361Five equity imperatives for the use of AI/ML in health care that require attention from health care professionals, developers, and policymakers are described.North Carolina medical journal["Ritu Agarwal", "G. Gao"]2024-06-01T00:00:00 95359c5d5c06ad4f03fc70173baadbcc44069c9bThe purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1–5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1–5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer–Cuzick (TC) model and Gail model, using DeLong’s test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1–5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.Diagnostics281The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.Diagnostics["Eun Kyung Park", "Hyeonsoo Lee", "Minjeong Kim", "Taesoo Kim", "Junha Kim", "Ki Hwan Kim", "Thijs Kooi", "Yoosoo Chang", "Seungho Ryu"]2024-06-01T00:00:00 1cac58f55ff0d2db7ff585fe230533109012ebaeThe journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein–protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.Molecules1701The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles and heralding a new era of medical innovation even though there is still a long way to go.Molecules["Aurore Crouzet", "Nicolas Lopez", "Benjamin Riss Yaw", "Yves Lepelletier", "Luc Demange"]2024-06-01T00:00:00 8b436356c74ea3425b0ee04e0a702e5ed04a59c4Artificial intelligence (AI) promises to be the next revolutionary step in modern society. Yet, its role in all fields of industry and science need to be determined. One very promising field is represented by AI‐based decision‐making tools in clinical oncology leading to more comprehensive, personalized therapy approaches. In this review, the authors provide an overview on all relevant technical applications of AI in oncology, which are required to understand the future challenges and realistic perspectives for decision‐making tools. In recent years, various applications of AI in medicine have been developed focusing on the analysis of radiological and pathological images. AI applications encompass large amounts of complex data supporting clinical decision‐making and reducing errors by objectively quantifying all aspects of the data collected. In clinical oncology, almost all patients receive a treatment recommendation in a multidisciplinary cancer conference at the beginning and during their treatment periods. These highly complex decisions are based on a large amount of information (of the patients and of the various treatment options), which need to be analyzed and correctly classified in a short time. In this review, the authors describe the technical and medical requirements of AI to address these scientific challenges in a multidisciplinary manner. Major challenges in the use of AI in oncology and decision‐making tools are data security, data representation, and explainability of AI‐based outcome predictions, in particular for decision‐making processes in multidisciplinary cancer conferences. Finally, limitations and potential solutions are described and compared for current and future research attempts.Cancer Medicine851Major challenges in the use of AI in oncology and decision‐making tools are data security, data representation, and explainability of AI‐based outcome predictions, in particular for decision‐making processes in multidisciplinary cancer conferences.Cancer Medicine["G. Duwe", "Dominique Mercier", "Crispin Wiesmann", "V. Kauth", "K. Moench", "Markus Junker", "Christopher C M Neumann", "A. Haferkamp", "A. Dengel", "T. H\u00f6fner"]2024-06-01T00:00:00 3a3438c3f65be1c8f7ca8c71f6482bb21e73c653With the rise of artificial intelligence (AI), higher education faces a significant challenge in learning assessment. The emergence of tools like ChatGPT raises concerns regarding the potential for cheating and the reliability of assessment outcomes. This paper aims to address these concerns by proposing a methodology for framing questions that effectively measures learning outcomes while reducing the risk of AI-enabled cheating. To achieve this objective, we employ a methodological approach that involves getting responses from ChatGPT 3.5 to various question prompts across different domains. These responses are then evaluated by faculty members specializing in management education. Through this process, we aim to identify question-framing strategies that effectively assess learning outcomes while minimizing susceptibility to AI Cheating. Our analysis reveals several key findings. Certain question Types (Decision Making, Recent Events, and Experiential Learning) demonstrate greater resilience against AI-generated responses, indicating their potential effectiveness in assessing student learning. This study offers original insights into the challenges and opportunities associated with learning assessment in the context of AI integration. The paper tries to provide valuable guidance for Policymakers, educators & students seeking to enhance the integrity and reliability of their assessment practices. Doi: 10.28991/ESJ-2024-08-03-09 Full Text: PDFEmerging Science Journal331This paper proposes a methodology for framing questions that effectively measures learning outcomes while reducing the risk of AI-enabled cheating, and identifies question-framing strategies that effectively assess learning outcomes while minimizing susceptibility to AI Cheating.Emerging Science Journal["Mohammad Owais Farooqui", "Mohd Imran Siddiquei", "Shashank Kathpal"]2024-06-01T00:00:00 1adb87587b74250b4cc792e1c715258fe84c2905When communication and mass media faculty returned from a kind of exile that COVID-19 had inflicted on them, they were hit almost immediately with the phenomenon of artificial intelligence (AI). The fall semester of 2023 seemed to usher in a new means by which students would complete assignments that left faculty scratching their heads. They faced a new form of information retrieval that students (as well as faculty) were using that, at once, yielded more substantive prose while at the same time posed new questions about authorship, trust, reliability, bias and even personhood. The discipline of communication and media studies bears a particular responsibility to contemplate the massive change underway with the use of AI. Most of us in the field have dedicated our careers to considering the human-media-culture interface. Media ecologists, in particular, routinely explore how media shape culture, conscience and communication. Yet many of us have not known what to make of the phenomenon suddenly surging in academics and in all sectors of society. This article seeks to offer a framework, cultivated out of media ecological sensibilities, for critically examining implications of AI in the realm of communication. Specifically, we have designed a graduate course that takes up the major lines of inquiry into how AI challenges conventions and urges new paradigms in our discipline. Our article offers a course proposal that communication faculty can adopt to their curriculum. It consists of a sample course syllabus, recommended textbooks and YouTube videos, sample assignments, a review of major AI themes in scholarly and trade journals, a suggested media ecology tool for critical application (the Tetrad), and an extensive bibliography. The overall objective of our course proposal is to guide reflection on the implications of AI in various communication contexts and environments.Explorations in Media Ecology351A graduate course is designed that takes up the major lines of inquiry into how AI challenges conventions and urges new paradigms in the discipline of communication and media studies, to guide reflection on the implications of AI in various communication contexts and environments.Explorations in Media Ecology["Kelley E. Connor", "Dennis D. Cali"]2024-06-01T00:00:00 caaad0f6fb0aaac3a49c544b324872dc9643354b This systematic review examines the integration of artificial intelligence (AI) in sports, focusing on its applications in performance analysis, injury prediction, tactical decision-making, and talent identification. Drawing from a comprehensive analysis of the existing literature, this study highlights the pivotal role of AI-driven technologies, such as machine learning algorithms, computer vision systems, and predictive analytics, in transforming the landscape of sports science and management. Key findings suggest that AI significantly enhances the accuracy of athlete performance tracking, optimizes injury prevention strategies through biomechanical data analysis, and supports real-time decision-making in coaching. Despite these advancements, challenges persist, particularly regarding the interpretability of AI models, ethical considerations surrounding data privacy, and the potential over-reliance on automated systems. This review underscores the transformative potential of AI in sports while identifying critical research gaps and suggesting avenues for future investigation.Geopolitical, Social Security and Freedom Journal11Key findings suggest that AI significantly enhances the accuracy of athlete performance tracking, optimizes injury prevention strategies through biomechanical data analysis, and supports real-time decision-making in coaching.Geopolitical, Social Security and Freedom Journal["Andrea Pisaniello"]2024-06-01T00:00:00 f3c0ef633c6642db64f0126116ca24859ed1de7eWith the rapid and significant development of the times, many software and sites have emerged in the form of websites based on AI (Artificial Intelligence), one of which is Chatgpt and Perplexity AI, which are commonly known as artificial intelligence that can help with assignments and teaching materials. , according to human desires and needs in a short time, depending on how the user describes the prompt to the AI. AI can also be useful and can be implemented in the world of education, in schools it can increase students' understanding of Artificial Intelligence (AI) through the use of AI tools. This research aims to implement Artificial Intelligence technology as a learning tool, and it is hoped that students will be able to use AI, especially to support the learning of class Intelligence), and Data Analysis. With sampling with a total of 13 students and 1 teacher. The results of the research are that students understand and are able to operate several AI (Artificial Intelligence) tools, one example is Chatgpt and Perplexity AI, and understand how to utilize their respective Android systems. With this, students become active during the teaching and learning process, especially when collecting school assignments.   ABSTRAK Dengan perkembangan zaman yang sangat pesat dan cukup signifikan, sudah banyak bermunculan  software maupun situs dalam bentuk  website yang berbasis AI (Artificial intellegence), salah satunya adalah Chatgpt dan perplexity AI, yang biasa di kenal sebagai  kecerdasan buatan yang dapat membantu mengerjakan tugas maupun bahan ajar, sesuai dengan keinginan dan kebutuhan manusia dalam waktu yang singkat, tergantung bagaimana user mendeskipsikan promp pada AI. AI juga bisa bermanfaat dan dapat diimplementasikan pada dunia pendidikan, di sekolah dapat meningkatkan Pemahaman Siswa tentang Artificial Intelligence (AI) melalui Penggunaan Tools AI. Penelitian ini bertujuan untuk mengimpelmentasikan teknologi Artificiall Intelegence sebagai sarana belajar, dan diharapkan siswa dapat menggunakan AI, utamanya untuk mendukung pembelajaran siswa kelas X SMAN 2 Monta, Metode penelitian yang di gunakan  adalah metode kualitatif, dengan cara observasi, pengumpulan data, impelementasi AI (Artificial Intelegence), dan Analisis data. Dengan sampling dengan jumlah  13 siswa dan 1 orang guru. Hasil penelitian siswa paham dan mampu mengoperasikan beberapa tools AI (Artificiall Intelegence), salah satu contohnya adalah Chatgpt dan Perplexity AI, Dan paham dalam memanfaatkan sistem Android-nya masing-masing. Dengan ini siswa menjadi aktif ketika proses belajar-mengajar, lebih-lebih ketika mengumpulkan tugas sekolah.Expert Net: Exploration Journal of Technological Education Trends181Expert Net: Exploration Journal of Technological Education Trends["Nurfidari", "Ita Fitriati", "Stkip Taman", "Siswa Bima"]2024-06-01T00:00:00 30acda7fe990138436b23d5f6a98ff8ff1887717The integration of artificial intelligence (AI) in education has revolutionized traditional teaching and learning methodologies, offering significant improvements in curriculum evaluation and development. This study explores the utilization of AI in the evaluation and development of an Outcome-Based Curriculum (OBC). AI technologies, including machine learning algorithms and natural language processing, are employed to analyze vast amounts of educational data, providing insights into student performance and curriculum effectiveness. The research highlights how AI can identify learning gaps, predict student outcomes, and recommend personalized learning paths, thereby enhancing the overall educational experience. By leveraging AI, educators can design more adaptive and responsive curricula that meet the dynamic needs of students and industry standards. The findings suggest that AI-driven tools not only streamline the evaluation process but also facilitate continuous curriculum improvement, ensuring that educational programs remain relevant and outcome-focused. This paper underscores the potential of AI to transform curriculum development practices, promoting a more efficient, data-driven approach to education that aligns with contemporary educational goals and outcomes.Journal of Research in Social Science And Humanities01The research highlights how AI can identify learning gaps, predict student outcomes, and recommend personalized learning paths, thereby enhancing the overall educational experience, and underscores the potential of AI to transform curriculum development practices.Journal of Research in Social Science and Humanities["Widowati Pusporini", "Heri Nurdiyanto"]2024-06-01T00:00:00 d2acd8778e1bdf121b1dbb47241601f1ddefaec5Ethics and Information Technology501Conceptualizing understanding as abilities within the realm of XAI is via certain human abilities to support interdisciplinary collaboration among XAI researchers, provide practical benefit across diverse XAI application contexts, facilitate the development and evaluation of explainability approaches, and contribute to satisfying the societal desiderata of different stakeholders concerning AI systems.Ethics Inf. Technol.["Timo Speith", "Barnaby Crook", "Sara Mann", "Astrid Schom\u00e4cker", "Markus Langer"]2024-06-01T00:00:00 11ae30f3eaaf1f1a361217e2743579fb3437c5caThe article examines the role and impact of artificial intelligence (AI) in the financial sector to determine its impact on the future workforce and the US's pivotal role in shaping technology trends around the world. Drawing from labor economics and sociology, it analyzes how AI adoption affects job roles, skills demanded, and employment patterns in the financial industry. The work analyzes the current state of the use of AI in finance, examines its impact on changes in labor markets and employment structure, and identifies key factors shaping the development of the financial sector under the influence of technological innovation.International Science Journal of Management, Economics &amp; Finance21The work analyzes the current state of the use of AI in finance, examines its impact on changes in labor markets and employment structure, and identifies key factors shaping the development of the financial sector under the influence of technological innovation.International Science Journal of Management, Economics &amp; Finance["Alina Havryk", "T. Nazarova"]2024-06-01T00:00:00 1836095ddd8226bbc6a8775fb2753bde78e0f7b7ABSTRACT As teleheath becomes integrated into the practice of medicine, it is important to understand the benefits, limitations, and variety of applications. Telestroke was an early example of teleneurology that arose from a need for urgent access to neurologists for time-sensitive treatments for stroke. It made a scarce resource widely available via video conferencing technologies. Additionally, applications such as outpatient video visits, electronic consultation (e-consult), and wearable devices developed in neurology, as well. Telehealth dramatically increased during the COVID-19 pandemic when offices were closed and hospitals were overwhelmed; a multitude of both outpatient and inpatient programs developed and matured during this time. It is helpful to explore what has been learned regarding the quality of telehealth, disparities in care, and how artificial intelligence can interact with medical practices in the teleneurology context.Continuum581It is helpful to explore what has been learned regarding the quality of telehealth, disparities in care, and how artificial intelligence can interact with medical practices in the teleneurology context.CONTINUUM: Lifelong Learning in Neurology["Elaine C Jones", "Benjamin R Kummer", "Jayne R Wilkinson"]2024-06-01T00:00:00 17f355001b329e4e069885c77eb1e4db403e1885The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012–2022) assessing AI algorithms’ diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019–2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.Healthcare551Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.Healthcare["Anna Escal\u00e9-Besa", "Josep Vidal-Alaball", "Queralt Mir\u00f3 Catalina", "Victor Hugo Garcia Gracia", "Francesc X. Marin-Gomez", "A\u00efna Fuster-Casanovas"]2024-06-01T00:00:00 bd268cb41583982480d16d0eb761072ccfde747fThis bibliometric study analyzes the evolution of research in artificial intelligence (AI) applied to medicine from 2015 to September 2023. Using the Scopus database and keywords related to AI, machine learning, and deep learning in medicine, tools such as VOSviewer and Bibliometrix were used to explore publication trends, subject areas, co-authorship networks, and the most productive countries, among others. 2,064 articles were analyzed, and a significant increase in global academic production has been evident in the last five years. International collaboration was notable, with China and the United States leading in knowledge contribution. The keyword analysis highlights the breadth of topics and applications of AI in medicine, with particular emphasis on cancer detection, dengue diagnosis, and medical image analysis, among others. In conclusion, this study highlights the growing academic interest in the application of AI in medicine and the need for collaborative research. The findings underscore the relevance of these technologies in key areas of health care, contributing significantly to advances in medical diagnosis and prognosis.International Journal of Electrical and Computer Engineering (IJECE)482Using the Scopus database and keywords related to AI, machine learning, and deep learning in medicine, tools such as VOSviewer and Bibliometrix were used to explore publication trends, subject areas, co-authorship networks, and the most productive countries, among others.International Journal of Electrical and Computer Engineering (IJECE)["L. Andrade-Arenas", "Cesar Yactayo-Arias"]2024-06-01T00:00:00 1943a5ed302536d88b93c8a7a593bd60775bb6bfBreast cancer (BC) stands as one of the most common malignancies affecting women worldwide, necessitating advancements in diagnostic methodologies for better clinical outcomes. This article provides a comprehensive exploration of the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer. As Artificial Intelligence (AI) technologies continue to permeate the healthcare sector, particularly in oncology, the need for transparent and interpretable models becomes imperative to enhance clinical decision-making and patient care. This review discusses the integration of various XAI approaches, such as SHAP, LIME, Grad-CAM, and others, with machine learning and deep learning models utilized in breast cancer detection and classification. By investigating the modalities of breast cancer datasets, including mammograms, ultrasounds and their processing with AI, the paper highlights how XAI can lead to more accurate diagnoses and personalized treatment plans. It also examines the challenges in implementing these techniques and the importance of developing standardized metrics for evaluating XAI's effectiveness in clinical settings. Through detailed analysis and discussion, this article aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications, thereby fostering trust and understanding among medical professionals and improving patient outcomes.arXiv.org1272The integration of various XAI approaches, such as SHAP, LIME, Grad-CAM, and others, with machine learning and deep learning models utilized in breast cancer detection and classification are discussed.ArXiv["Samita Bai", "Sidra Nasir", "Rizwan Ahmed Khan", "Sheeraz Arif", "Alexandre Meyer", "H. Konik"]2024-06-01T00:00:00 0558c88fdba81ddfb49745918ceda7f461db4410Artificial intelligence (AI) deployed for customer relationship management (CRM), digital rights management (DRM), content recommendation, and content generation challenge longstanding truths about listening to and making music. CRM uses music to surveil audiences, removes decision-making responsibilities from consumers, and alters relationships among listeners, artists, and music. DRM overprotects copyrighted content by subverting Fair Use Doctrine and privatizing the Public Domain thereby restricting human creativity. Generative AI, often trained on music misappropriated by developers, renders novel music that seemingly represents neither the artistry present in the training data nor the handiwork of the AI’s user. AI music, as such, appears to be produced through AI cognition, resulting in what some have called “machine folk” and contributing to a “culture in code.” A philosophical analysis of these relationships is required to fully understand how AI impacts music, artists, and audiences. Using metasynthesis and grounded theory, this study considers physical reductionism, metaphysical nihilism, existentialism, and modernity to describe the quiddity of AI’s role in the music ecosystem. Concluding thoughts call researchers and educators to act on philosophical and ethical discussions of AI and promote continued research, public education, and democratic/laymen intervention to ensure ethical outcomes in the AI music space.Music Perception: An Interdisciplinary Journal262Using metasynthesis and grounded theory, this study considers physical reductionism, metaphysical nihilism, existentialism, and modernity to describe the quiddity of AI’s role in the music ecosystem.Music Perception: An Interdisciplinary Journal["Adam Eric Berkowitz"]2024-06-01T00:00:00 68146addf66bf812c09460395ab6d7ce8266f037The integration of artificial intelligence (AI) in dermatology is revolutionizing the diagnostic methods and management strategies and hence is uplifting the overall patient care. AI technologies have shown a significant potential in automated diagnosis, severity assessment of chronic cutaneous diseases like psoriasis, and the development of comprehensive dermatological databases is helping in swift disease detection.Dermatological Reviews241Artificial intelligence technologies have shown a significant potential in automated diagnosis, severity assessment of chronic cutaneous diseases like psoriasis, and the development of comprehensive dermatological databases is helping in swift disease detection.Dermatological Reviews["Ghasem Rahmatpour Rokni", "Nasim Gholizadeh", "Mahsa Babaei", "Kinnor Das", "Shainee Datta"]2024-06-01T00:00:00 e4a6c7c940088a78808c77ee0fc798d710852f45Cardiovascular disease (CVD), particularly coronary heart disease (CHD), is the leading cause of death in the US, with a high economic impact. Coronary artery calcium (CAC) is a known marker for CHD and a useful tool for estimating the risk of atherosclerotic cardiovascular disease (ASCVD). Although CACS is recommended for informing the decision to initiate statin therapy, the current standard requires a dedicated CT protocol, which is time-intensive and contributes to radiation exposure. Non-dedicated CT protocols can be taken advantage of to visualize calcium and reduce overall cost and radiation exposure; however, they mainly provide visual estimates of coronary calcium and have disadvantages such as motion artifacts. Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring. We present a review of the current studies on automated CACS across various CT protocols and discuss consideration points in clinical application and some barriers to implementation.Journal of Clinical Medicine492Artificial intelligence is a growing field involving software that independently performs human-level tasks, and is well suited for improving CACS efficiency and repurposing non-dedicated CT for calcium scoring.Journal of Clinical Medicine["Afolasayo A Aromiwura", "Dinesh K. Kalra"]2024-06-01T00:00:00 377ee0c760f6c8494faf0ff5b1df624c55fd2543Artificial intelligence (AI) can potentially transform the industry, enhancing the production process and minimizing manual and repetitive tasks. Accordingly, the synergy between high-performance computing and powerful mathematical models enables the application of sophisticated data analysis procedures like machine learning (ML). However, challenges exist regarding effective, efficient, and flexible processing to generate valuable knowledge. Consequently, this work comprehensively describes industrial challenges where AI can be exploited, focusing on the dairy industry. The conclusions presented can help researchers apply novel approaches for cattle monitoring and farmers by proposing advanced technological solutions for their needs.IEEE Industrial Electronics Magazine532This work comprehensively describes industrial challenges where AI can be exploited, focusing on the dairy industry, and can help researchers apply novel approaches for cattle monitoring and farmers by proposing advanced technological solutions for their needs.IEEE Industrial Electronics Magazine["Silvia Garc\u00eda-M\u00e9ndez", "Francisco de Arriba-P\u00e9rez", "Mar\u00eda Del Carmen Somoza-L\u00f3pez"]2024-06-01T00:00:00 f835c5b0d6d5374b5ef9a67bfb47533dc6f6723eThe contribution of computer science to education has always been important. From robotic teaching to the phenomenon of automatic system to analyze the answer sheet, Artificial Intelligence has always helped every teacher and student. In this study, we took a deep look at various analytics developments in use around the world, such as computing in education. Science techniques and thereby generalize and emphasize the role of artificial intelligence in teaching and analyzing students. Artificial intelligencehas enabled intelligent tutoring systems, the basis of all information science. These systems help develop the skills of self-reflection, deep questions to answer, breaking down conflicting statements, creating artistic questions, and making choices.International Journal of Pedagogics21A deep look at various analytics developments in use around the world, such as computing in education, to generalize and emphasize the role of artificial intelligence in teaching and analyzing students.International Journal of Pedagogics["Mavlyuda Khodjaeva Sabirovna"]2024-06-01T00:00:00 3d170dcb6cc8524820c569bd540aef0f450c26f5Abstract The article analyses the prospects for the application of artificial intelligence in the delivery of justice by courts. The application of artificial intelligence is increasingly spreading in various different areas of life - both in the daily life of individuals and in the public sector. One of the main areas where artificial intelligence is already being applied is in the area of justice. However, given the complexity and importance of this field, the question arises whether artificial intelligence could really replace the person of the judge. In order to answer this question, the authors first assess what constitutes the delivery of justice. Secondly, the authors analyse the concept of artificial intelligence and the possibilities of its use. Thirdly, the authors assess the potential and risks of artificial intelligence in the delivery of justice. The paper reviews various artificial intelligence models already in use around the world and assesses the application of various technologies (large language models such as ChatGPT) in the court. Finally, conclusions are drawn as to whether artificial intelligence can replace the person of the judge.Baltic Journal of Law &amp; Politics72The paper reviews various artificial intelligence models already in use around the world and assesses the application of various technologies (large language models such as ChatGPT) in the court as to whether artificial intelligence can replace the person of the judge.Baltic Journal of Law & Politics["Egidija Tamo\u0161i\u016bnien\u0117", "\u017dilvinas Terebeiza", "Artur Dor\u017einkevi\u010d"]2024-06-01T00:00:00 0bd6bb12ccba8915f03c946b4d8aebc1fa1cdefeThe integration of Artificial Intelligence (AI) into clinical decision support systems (CDSS) marks a significant advancement in the pursuit of enhanced patient care and operational efficiency in high-stakes environments, such as the operating room (OR) [1]. However, the complexity and often "black box" nature of these AI systems pose substantial challenges for human-AI teaming, where explainability is crucial for clinicians to understand and trust the AI's recommendations. The field of explainable Artificial Intelligence (xAI) offers promising methods and techniques to make AI decisions more explainable and interpretable, yet the adaptation of these methods to meet the specific needs of clinicians remains underexplored [2]. Our proposed clinician-centered framework, xAI-SURG, seeks to bridge this gap by aligning xAI approaches with the complex decision- making processes inherent in the OR. By evaluating the xAI-SURG framework through the lens of perfusionists' decision-making during cardiac surgery, we provide a use-case, showcasing the importance of tailored xAI that not only aligns with but also enhances clinician expertise and patient safety and outcomes in the OR.Proceedings02By evaluating the xAI-SURG framework through the lens of perfusionists' decision-making during cardiac surgery, this work provides a use-case, showcasing the importance of tailored xAI that not only aligns with but also enhances clinician expertise and patient safety and outcomes in the OR.The Hamlyn Symposium on Medical Robotics : proceedings["Roger D. Dias", "Ryan Harari", "Marco A Zenati", "Geoffrey Rance", "Rithy Srey", "Letian Chen", "Matthew C. Gombolay"]2024-06-01T00:00:00 664b4e0ba9dca82074ed95d8db4f6d429fe60a9aThe humanitarian and social aspect of artificial intelligence is the critical factor that will determine the next steps in all aspects of our daily lives, from work and transactions to education, information and entertainment. In the field of education, highlighting the human centric character of artificial intelligence is a prerequisite for the transformation of our educational systems in order to be able to respond in a critical and creative way to the demands of the new era. This research focuses on the critical role of teachers in the course of the transition and highlighting the basic characteristics of the necessary pedagogical framework for the introduction and critical utilization of Human – Centered Artificial Intelligence in Education.Ανοικτή Εκπαίδευση το περιοδικό για την Ανοικτή και εξ Αποστάσεως Εκπαίδευση και την Εκπαιδευτική Τεχνολογία02This research focuses on the critical role of teachers in the course of the transition and highlighting the basic characteristics of the necessary pedagogical framework for the introduction and critical utilization of Human – Centered Artificial Intelligence in Education.Ανοικτή Εκπαίδευση: το περιοδικό για την Ανοικτή και εξ Αποστάσεως Εκπαίδευση και την Εκπαιδευτική Τεχνολογία["Panagiotes Anastasiades", "Konstantinos Kotsidis", "Konstantinos Stratikopoulos", "Nektarios Pananakakis"]2024-06-01T00:00:00 dd087ee8c5ac98c2d124cf84074ef1b8a71ce82aAbstract This article shows the role that digital intelligence has on renewable energy, based on literature underpinnings. Therefore, the methodological research is based on literature review to demonstrate the link between artificial intelligence and renewable energy, with a focus on global sustainable development strategies in this field. The main findings reveal the fact that we must take advantage of the opportunities offered by artificial intelligence on energy, in general, and renewable energy, in particular. Referring to literature, it is constantly expanding due to the importance of the development of renewable energy for researchers but also for the population, being many parties interested in this field. The aim of the study is to highlight the relationship between renewable energy and artificial intelligence. Therefore, with the help of artificial intelligence and energy innovations, the population enjoys renewable energy that exists in its many forms (solar panels or photovoltaic panels, water, or wind energy and so on). To put in a nutshell, the research considered in this article reflects the impact of artificial intelligence on renewable energy as part of supporting the achievement of sustainable economic development.Proceedings of the International Conference on Business Excellence221The methodological research is based on literature review to demonstrate the link between artificial intelligence and renewable energy, with a focus on global sustainable development strategies in this field.Proceedings of the International Conference on Business Excellence["Daniela Iorgovan"]2024-06-01T00:00:00 f66893b5055df60bd85a96f522e4a41d458fe4b0Indian Journal of Orthopaedics413The area of regenerative orthopedics is highly sophisticated and significantly aids in providing cost-effective and non-invasive treatments to patients suffering from orthopedic ailments and injuries, and AI technology is very useful.Indian journal of orthopaedics["R. Vaishya", "Sakshi Dhall", "A. Vaish"]2024-06-02T00:00:00 1f0027bb58956fb5f69f486782c9eada7120f6b7With the advancement and maturity of artificial intelligence, its use in the work environment alongside humans has become necessary. This raises questions about the real value that artificial intelligence can provide and the importance of its integration with organizational maturity. The research aims to test the impact of integrating artificial intelligence capabilities with organizational maturity in enhancing financial sustainability. It highlights how telecommunications companies can benefit from artificial intelligence capabilities and employ them in specific areas to meet business needs and achieve performance gains. Therefore, a questionnaire was used to collect the opinions of a sample of managers, department heads, and unit and division officials. 163 questionnaires were distributed to the two telecommunications companies, Asia and Zain Iraq, in Baghdad. After analyzing the data using (SPSS V28), (AMOS V26), and (SMART PLS) programs, the research reached a number of conclusions, most notably: the existence of a complementary relationship between the research variables (artificial intelligence capabilities, and organizational maturity) in influencing the variable (financial sustainability). Proving that embedding artificial intelligence capabilities in telecommunications companies contributes to achieving tangible gains in financial sustainability.International Journal of Religion281The research reaches a number of conclusions, most notably: the existence of a complementary relationship between the research variables (artificial intelligence capabilities, and organizational maturity) in influencing the variable (financial sustainability).International Journal of Religion["Fatima Muhammad Wajiya", "Salahuddin Hussein Saleh"]2024-06-02T00:00:00 957cfebd27648a3d0159931afd57cfa8e4db035fEducational Administration Theory and Practices08Educational Administration Theory and Practices["Joseph Muthu", "Dilip Kumar Vaka"]2024-06-02T00:00:00 35f6d168736548c67af913515961fdc27d82a252Photography in the Era of Disruption: Artificial Intelligence as a Reference in Developing Creative Photography Ideas. This research explores various ways in which AI is integrated into the creative process of photography, including the use of AI for image analysis, pattern recognition, and even visual composition. The study discusses the role of Artificial Intelligence (AI) technology in the modern photography industry, particularly in the context of creative idea development. With the emergence of the digital era and technological disruption, photography has undergone significant transformation. Through literature review and case studies, this research explores the use of AI in enhancing creativity and innovation in photography, as well as its implications for the paradigm of creative ideas in photography creation. The research method used was qualitative with a descriptive approach. This study highlights the adaptation to the development of AI technology in the photography industry, as well as its potential to enrich visual experiences and create innovative new ideas. The conclusion of this research is that Artificial Intelligence can be used as a medium to provide references and inspiration in the development of conceptual photography ideas.specta140The conclusion of this research is that Artificial Intelligence can be used as a medium to provide references and inspiration in the development of conceptual photography ideas.specta["Raynald Alfian Yudisetyanto", "Achmad Taufik Firmansyah"]2024-06-02T00:00:00 70dfd3013db4272a19f18b11bc93960a98c47f27This study develops an Artificial Intelligence-driven benchmarking tool to reduce methane emissions in Canadian dairy farms, responding to the urgent need to mitigate environmental impacts from agriculture. Utilizing a comprehensive dataset from over 1000 dairy farms and processors across Canada, combined with satellite-driven methane emission data, we apply advanced machine learning technologies and data analytics, including geospatial analysis and time series forecasting. This approach identifies critical emission hotspots and temporal trends. We tested several predictive models—ARIMA, LSTM, GBR, and PROPHET—with the LSTM model showing the greatest accuracy in forecasting emissions, demonstrated by the lowest Root Mean Squared Error (RMSE) of 15.40. Our results highlight the transformative potential of AI tools in agricultural environmental management by providing dairy farmers and policymakers with precise, real-time emission insights. This facilitates informed decision-making and the implementation of effective emission reduction strategies. This study not only advances understanding of emission dynamics in dairy farming but also underscores the role of technology in sustainable agricultural practices and achieving environmental targets consistent with global agreements.bioRxiv00An Artificial Intelligence-driven benchmarking tool to reduce methane emissions in Canadian dairy farms, responding to the urgent need to mitigate environmental impacts from agriculture and underscores the role of technology in sustainable agricultural practices and achieving environmental targets consistent with global agreements.bioRxiv["Pratik Mukund Parmar", "Hangqing Bi", "Suresh Neethirajan"]2024-06-02T00:00:00 3b69c82e9094e024f1de7bb22a24e0053e50dc94The issue of distributive justice in charitable donations has become increasingly prominent. It not only weakens people's confidence in philanthropy but also their enthusiasm for participation. With the widespread use of artificial intelligence technology in donations, a key question arises: Can artificial intelligence inspire people to be more willing to donate by improving their perception of justice in donation distribution? This question is vital for charities but has yet to be answered. To address this gap, this research conducted five comprehensive studies to investigate the impact of AI decision‐makers on consumers' willingness to donate. The findings of Studies 1 and 2 consistently revealed that consumers perceive higher distributive justice in AI decision‐makers compared with humans, motivating increased participation in charitable donations. Study 3 examined two different experimental scenarios and found that this effect only occurs among consumers with lower trust in nonprofit organizations. Study 4 further explored the effect that is only present among prevention‐oriented consumers. These findings reveal how perceptions of distributive justice toward AI decision‐makers can facilitate public charitable giving and highlight the significance of this effect across different groups of consumers, providing invaluable insights for charitable organizations. This research not only fills the theoretical gap in the philanthropic field about the impact of artificial intelligence decision‐makers on donation distribution justice but also provides charitable organizations with artificial intelligence‐based donation promotion strategies.Journal of Consumer Behaviour580Five comprehensive studies are conducted to investigate the impact of AI decision‐makers on consumers' willingness to donate and reveal how perceptions of distributive justice toward AI decision‐makers can facilitate public charitable giving and highlight the significance of this effect across different groups of consumers.Journal of Consumer Behaviour["Chen Yang", "Yi Yang", "Yue Zhang"]2024-06-02T00:00:00 db8910b4f84d4db09afbdbca5b62c0803183f841This study explores the development trends of AI in education through a bibliometric analysis of literature from 2011 to 2021. Using the Biblioshiny toolkit for Bibliometrix in R language, we analyzed titles, authors, abstracts, keywords, citations, and affiliations. The findings reveal research changes and hot directions in AI education, forecasting future developments. While regions like the US and UK have achieved success in AI education, China is integrating AI into teaching disciplines. The rise of AIGC and companies like OpenAI is accelerating AI integration in education, creating opportunities for personalized learning, adaptive education, and intelligent educational management. Collaboration among stakeholders and comprehensive strategies are crucial for successful AI implementation in education.International Journal of Emerging Technology and Advanced Engineering01This study explores the development trends of AI in education through a bibliometric analysis of literature from 2011 to 2021 through the Biblioshiny toolkit for Bibliometrix in R language, revealing research changes and hot directions in AI education.International Journal of Emerging Technology and Advanced Engineering["Miao Ning", "Cai Bo", "Qingyue Wang", "Xinran Wang", "Qianqian Guo"]2024-06-02T00:00:00 6baa2707b27406a2973f001931c4d246fa5e0409Educational Administration Theory and Practice00Educational Administration Theory and Practice["Ashlesha J. Bhujbal"]2024-06-02T00:00:00 4666f860e8dd577abff443fb4d4e9083c1b52836Assiut Scientific Nursing Journal00Assiut Scientific Nursing Journal["Sabah Ali Abdelkareem", "M. Bakri", "Naglaa Ahmed Ahmed"]2024-06-02T00:00:00 e537a524b33a90230cd36129f3bb9df0fd47f910The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically derived analytical wake models have limited predictive capabilities, particularly in the near-wake region close to the turbine rotor, due to assumptions that do not hold. Knowledge discovery methods can bridge these gaps by extracting insights, adjusting for theoretical assumptions, and developing accurate models for physical processes. In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight. By incorporating a double Gaussian distribution into the SR algorithm as domain knowledge and designing a hierarchical equation structure, the search space is reduced, thus efficiently finding a concise, physically informed, and robust wake model. The proposed mathematical expression (equation) can predict the wake velocity deficit at any location in the full-wake region with high precision and stability. The model's effectiveness and practicality are validated through experimental data and high-fidelity numerical simulations.The Physics of Fluids700This study introduces a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight.ArXiv["Ding Wang", "Yuntian Chen", "Shiyi Chen"]2024-06-02T00:00:00 b38da1ebdfa0595dd49cc3572aff0bd72546bdf4Scientific Reports225An approach aimed at helping artificial intelligence develop theory of mind of their human teammates to support team interactions showed that ASI advisors had a strong positive impact on low potential teams such that they improved the performance of those teams across mission outcome measures.Scientific Reports["Rhyse Bendell", "Jessica Williams", "Stephen Fiore", "Florian Jentsch"]2024-06-02T00:00:00 1aa96b82476817f0ee4c7d1f887ee3e0af34ed8aArtificial General Intelligence (AGI) is set to profoundly impact the oil and gas industry by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the oil and gas landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the oil and gas industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream oil and gas industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.arXiv.org940This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream oil and gas industry, requiring near-human levels of intelligence.ArXiv["J. Li", "Tiancheng Zhang", "Yiran Zhu", "Zhongwei Chen"]2024-06-02T00:00:00 7a0f7237219c1e5727307d87a0ad341cda7ac83fIn the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.Inf.587This study provides a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education, and identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters.Inf.["S. Qadhi", "Ahmed Alduais", "Youmen Chaaban", "M. Khraisheh"]2024-06-02T00:00:00 159b5c34bb1086f284073eb35b2588b7a6d39a9aThis integrative literature review critically explores the use of Artificial Intelligence (AI) in developing smart cities across France, focusing on urban efficiency, sustainability, and safety. This study examines the multiple challenges that French towns face when integrating AI technology to become smart cities, concentrating on technological integration, economic limits, data privacy and security, the digital divide, legal and ethical considerations, and public acceptance. This problem impacts urban residents, as it influences their quality of life, access to services, and environmental sustainability, necessitating a balanced approach to technology implementation that considers both benefits and potential social disparities. This study also examines how France's smart city development can use artificial intelligence (AI) to improve sustainability, urban planning, public safety, technological integration, and economic constraints. The guiding conceptual framework of the ILR is based on a combination of Sociotechnical Systems Theory and Diffusion of Innovations Theory, providing a comprehensive perspective on the interplay between technological advancements and social dynamics within urban environments. The research method, design, procedures, and analysis involve an extensive review of existing literature, qualitative analysis of case studies, and interviews with key stakeholders involved in AI-driven urban projects in France. The results of the research question reveal that while AI has the potential to enhance urban living significantly, its success is heavily dependent on addressing integration challenges and ensuring inclusive access to technology. The potential implications of the results and the recommendations for future research and practice emphasize the need for robust policy frameworks, enhanced public-private partnerships, and continuous monitoring of technological impacts to ensure that AI integration supports sustainable and equitable urban development.International Journal For Multidisciplinary Research574Examining how France's smart city development can use artificial intelligence to improve sustainability, urban planning, public safety, technological integration, and economic constraints reveals that while AI has the potential to enhance urban living significantly, its success is heavily dependent on addressing integration challenges and ensuring inclusive access to technology.International Journal For Multidisciplinary Research["Rachid Ejjami"]2024-06-02T00:00:00 a95e6b1b5d368f8be15bb2ca0e7d56ef04aeb52cThis study examines the factors influencing the adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMEs) in Jordan, a key player in the growing Middle Eastern economy. Rooted in the Technology–Organization–Environment framework, we specifically focus on the role of technological capabilities and organizational dynamics in shaping AI adoption within Jordanian SMEs. A comprehensive survey involving 364 SME owner-managers in Jordan serves as the empirical foundation. Findings reveal the significant impact of employee IT knowledge, IT infrastructure, managerial commitment, training initiatives and well-designed reward systems in shaping SME owners’ or managers’ attitudes to AI. These findings provide valuable insights for SME leaders and stakeholders, guiding them in developing strategies to smoothly integrate AI technologies in line with Jordan’s societal needs. The article concludes by emphasizing the study’s contributions, implications and limitations while suggesting potential directions for future research in this field.Global Business Review974Findings reveal the significant impact of employee IT knowledge, IT infrastructure, managerial commitment, training initiatives and well-designed reward systems in shaping SME owners’ or managers’ attitudes to AI.Global Business Review["Ra\u2019ed Almashawreh", "Majharul Talukder", "Sarvjeet Kaur Charath", "Md. Irfanuzzaman Khan"]2024-06-02T00:00:00 e385c462d3bac590e130eb8aa498559d27160853Currently, organizations are progressively embracing artificial intelligence (AI) and chatbots to transform omnichannel customer service in the modern digital age. This study examines the revolutionary impact of artificial intelligence (AI) and chatbots in providing seamless, personalized, and efficient consumer experiences across various communication channels. AI technology, such as machine learning and natural language processing, enable organizations to analyze large volumes of data, predict client requirements, and offer immediate support. Chatbots, functioning as artificial intelligence-powered virtual assistants, have a key function in interacting with customers through natural language conversations and providing immediate assistance through several channels, including websites, mobile apps, and messaging platforms. Through the utilization of artificial intelligence (AI) and chatbots, organizations may optimize efficiency, responsiveness, and personalization in consumer interactions, resulting in heightened satisfaction and loyalty. Nevertheless, the implementation of AI and chatbots in omnichannel customer service also gives rise to ethical concerns, including data protection, transparency, and fairness, which need to be resolved in order to guarantee appropriate utilization of these technologies. Notwithstanding these difficulties, the capacity of AI and chatbots to revolutionize the customer service industry is unquestionable, providing organizations with novel prospects to distinguish themselves and provide extraordinary experiences in the digital era. This article offers valuable insights into the developing patterns, difficulties, and possibilities linked to AI and chatbots in Omnichannel customer service. It emphasizes the revolutionary influence of these technologies on the future of managing customer experience.Journal of Engineering Research and Reports03The capacity of AI and chatbots to revolutionize the customer service industry is unquestionable, providing organizations with novel prospects to distinguish themselves and provide extraordinary experiences in the digital era.Journal of Engineering Research and Reports["Samadrita Ghosh", "Stephanie Ness", "Shruti Salunkhe"]2024-06-02T00:00:00 2b049b9daa1a0bac700e344ae0dab02831194cecGenerative artificial intelligence (AI) holds great promise in the field of education, with the potential to automate tasks such as lesson planning, feedback writing, and personalized learning. This study explores the implementation of generative AI in educational settings, examining the benefits and challenges associated with its use. Through a systematic literature review, this paper identifies effective strategies for integrating generative AI in classrooms, focusing on ethical considerations, privacy concerns, and pedagogical goals. The study also presents case studies highlighting successful implementations of generative AI, providing a framework for educators and policymakers to enhance teaching and learning experiences.International Journal of Religion02Effective strategies for integrating generative AI in classrooms are identified, focusing on ethical considerations, privacy concerns, and pedagogical goals, providing a framework for educators and policymakers to enhance teaching and learning experiences.International Journal of Religion["Rommel Alali", "Yousef Wardat", "Khaled Al-Saud", "Kamal Aldeen Alhayek"]2024-06-02T00:00:00 cc0f44b31948712d90ebf58f9e463efaab0c598eThis article asks what role does information literacy (IL) play in information environments where information tasks are increasingly being conducted in cooperation with, or delegated to, artificial intelligence (AI) systems. The article discusses recent AI developments and their potential consequences from the perspective of information practices, emphasising the ways increased autonomy and adaptiveness of information systems challenge human agency. The article concludes with a call for future research and action, highlighting the unique position of IL researchers and practitioners in shaping the future with AI.Journal of Information Literacy01Journal of Information Literacy["Noora Hirvonen"]2024-06-02T00:00:00 65050adc7d1780679c3d26948307c98ad91c9b14The utilization of Artificial Intelligence (AI) technology is experiencing rapid expansion in contemporary times. Within the domain of trademark law, a logo serves as a visual identity utilized to distinguish a product or service from its competitors. The utilization of a logo as a brand identity is afforded specific legal protections pursuant to Indonesian Law No. 20/2016 concerning Trademarks. A pivotal consideration pertains to discerning the rightful owner of the rights to the logo created by AI. Is it the proprietor of the AI software employed in crafting the logo, or is it the proprietor of the company or individual who commissioned the logo? The method employed in this research is a normative juridical approach, which scrutinizes the application of legal principles or norms. The approaches employed in this research encompass conceptual and statutory analyses. The objective of this research pertains to understanding the Legal Implications of Utilizing Artificial Intelligence-Generated Logos as Brand Identities, and serving as a reference material for subsequent legal inquiries, particularly those related to the advancement of artificial intelligence.  The majority of regulations concerning copyright and ownership of artistic works still hinge upon Copyright Law No. 28/2014. Despite its enactment, the Copyright Law remains bereft of provisions safeguarding works generated by Artificial Intelligence. In the realm of AI or artificial intelligence, there are instances where AI applications inadvertently generate trademark logos bearing visual resemblance to other trademark logos. Such resemblances have the potential to bewilder consumers and undermine the authenticity of a brand.Jurnal Hukum Magnum Opus00The objective of this research pertains to understanding the Legal Implications of Utilizing Artificial Intelligence-Generated Logos as Brand Identities, and serving as a reference material for subsequent legal inquiries, particularly those related to the advancement of artificial intelligence.Jurnal Hukum Magnum Opus["Muh Ersandi Rizki Pratama", "Sandy Erdi Bimantara", "Giovanni Samantha"]2024-06-02T00:00:00 307288062d1df32dec6ce170ee61e178471b23bdThis report aims to examine the significance of AI in the world today and its impact on the microeconomy as well as on the macroeconomic level. Additionally, it will discuss a number of perspectives around the potential of AI to be of effect. Eventually, it will lead to a discussion on courses of action which may be taken on a government level to tackle the mighty weapon of change – artificial intelligence.International Journal For Multidisciplinary Research70This report aims to examine the significance of AI in the world today and its impact on the microeconomy as well as on the macroeconomic level and lead to a discussion on courses of action which may be taken on a government level to tackle the mighty weapon of change – artificial intelligence.International Journal For Multidisciplinary Research["Aadya Sinha"]2024-06-02T00:00:00 4278210fdaa2151cb196ebb27b4179c05d31c021Review of Education/Pedagogy/Cultural Studies61Review of Education, Pedagogy, and Cultural Studies["Valerie Triggs"]2024-06-02T00:00:00 f28519531d3df0ecb0eb6f7db1131561052de0edEste trabalho tem por objetivo investigar a IA como ferramenta de apoio ao crescimento empresarial e também como uma forma de automatizar decisões e processos. Devido ao crescimento da IA como ferramenta de apoio à tomada de decisões dentro do ambiente corporativo e a questões como limites éticos com o tratamento dos dados dos clientes, este estudo se torna relevante e se justifica pela necessidade de discussão acerca de questões como transparência no uso das informações dos clientes e também pela ausência de uma legislação regulamentadora sobre o tema. A metodologia empregada no estudo foi de natureza bibliográfica e qualitativa, selecionando-se estudos recentes para embasar a teoria. A pergunta norteadora à qual se busca responder é: “Como observar limites éticos com o advento da IA dentro dos ambientes corporativos e a maximização do uso dos dados dos usuários?”. Além da seção de revisão de literatura, este estudo também trouxe uma seção de discussão visando a aprofundar a temática em análise.  Revista Acadêmica Online00Revista Acadêmica Online["Ela\u00edne Cristina dos Santos", "Wildes Luz Lima", "Luciano B\u00e9rgamo"]2024-06-02T00:00:00 e3006d5e89be0aea3c17972e2f1d1fdf08acbd96In the digital era, data has become a pivotal asset, advancing technologies such as autonomous driving. Despite this, data trading faces challenges like the absence of robust pricing methods and the lack of trustworthy trading mechanisms. To address these challenges, we introduce a traffic-oriented data trading platform named Data on The Move (DTM), integrating traffic simulation, data trading, and Artificial Intelligent (AI) agents. The DTM platform supports evident-based data value evaluation and AI-based trading mechanisms. Leveraging the common sense capabilities of Large Language Models (LLMs) to assess traffic state and data value, DTM can determine reasonable traffic data pricing through multi-round interaction and simulations. Moreover, DTM provides a pricing method validation by simulating traffic systems, multi-agent interactions, and the heterogeneity and irrational behaviors of individuals in the trading market. Within the DTM platform, entities such as connected vehicles and traffic light controllers could engage in information collecting, data pricing, trading, and decision-making. Simulation results demonstrate that our proposed AI agent-based pricing approach enhances data trading by offering rational prices, as evidenced by the observed improvement in traffic efficiency. This underscores the effectiveness and practical value of DTM, offering new perspectives for the evolution of data markets and smart cities. To the best of our knowledge, this is the first study employing LLMs in data pricing and a pioneering data trading practice in the field of intelligent vehicles and smart cities.2024 IEEE Intelligent Vehicles Symposium (IV)361Simulation results demonstrate that the proposed AI agent-based pricing approach enhances data trading by offering rational prices, as evidenced by the observed improvement in traffic efficiency.2024 IEEE Intelligent Vehicles Symposium (IV)["Yi Yu", "Shengyue Yao", "Tianchen Zhou", "Yexuan Fu", "Jingru Yu", "Ding Wang", "Xuhong Wang", "Cen Chen", "Yilun Lin"]2024-06-02T00:00:00 9f1b1fd579ab07cb8fc647f635c8bd4d4abcc4a0Artificial Intelligence (AI) has become an integral part of our lives, revolutionizing variousindustries and enhancing efficiency. However, as AI continues to advance, it is crucial to addressthe ethical considerations surrounding its implementation. Bias mitigation, transparency, andaccountability are essential for responsible AI deployment (4) (PDF) Artificial Intelligence and Ethics: A Comprehensive Review of Bias Mitigation, Transparency, and Accountability in AI Systems. Available from: https://www.researchgate.net/publication/375744287_Artificial_Intelligence_and_Ethics_A_Comprehensive_Review_of_Bias_Mitigation_Transparency_and_Accountability_in_AI_Systems [accessed Jun 03 2024].Africa Journal For Regulatory Affairs015Bias mitigation, transparency, and accountability are essential for responsible AI deployment and a comprehensive review of Bias Mitigation, Transparency, and Accountability in AI Systems is published.Africa Journal For Regulatory Affairs["George Benneh Mensah"]2024-06-03T00:00:00 b000a92ebd8ca52306f794fa4c615da7b2032c4aIntroduction This study analyzes the existing academic literature to identify the effects of artificial intelligence (AI) on human resource (HR) activities, highlighting both opportunities and associated challenges, and on the roles of employees, line managers, and HR professionals, collectively referred to as the HR triad. Methods We employed the scoping review method to capture and synthesize relevant academic literature in the AI–human resource management (HRM) field, examining 27 years of research (43 peer-reviewed articles are included). Results Based on the results, we propose an integrative framework that outlines the five primary effects of AI on HR activities: task automation, optimized HR data use, augmentation of human capabilities, work context redesign, and transformation of the social and relational aspects of work. We also detail the opportunities and challenges associated with each of these effects and the changes in the roles of the HR triad. Discussion This research contributes to the ongoing debate on AI-augmented HRM by discussing the theoretical contributions and managerial implications of our findings, along with avenues for future research. By considering the most recent studies on the topic, this scoping review sheds light on the effects of AI on the roles of the HR triad, enabling these key stakeholders to better prepare for this technological change. The findings can inform future academic research, organizations using or considering the application of AI in HRM, and policymakers. This is particularly timely, given the growing adoption of AI in HRM activities.Frontiers in Psychology1164An integrative framework is proposed that outlines the five primary effects of AI on HR activities: task automation, optimized HR data use, augmentation of human capabilities, work context redesign, and transformation of the social and relational aspects of work.Frontiers in Psychology["Justine Dima", "Marie-H\u00e9l\u00e8ne Gilbert", "Julie Dextras-Gauthier", "Laurent Giraud"]2024-06-03T00:00:00 86310904b0e2c0457ce9857945aa27ddf90c4407BMC Health Services Research663The objective of this study is to explore and understand the systemic challenges and implications of AI technologies integration in a leading Canadian academic hospital and provide original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective.BMC Health Services Research["H. Alami", "Pascale Lehoux", "C. Papoutsi", "Sara E. Shaw", "Richard Fleet", "Jean-Paul Fortin"]2024-06-03T00:00:00 1c972f018afd7af5382636fa45e54b631698cb2eThis research paper will tell you about how the AI is helping us in our daily lives. How it is making not only our lives. Have you ever thought about how artificial intelligence (AI) is changing the game in our daily lives? From smart home appliances to virtual assistants, artificial intelligence tools are used in many areas of our daily lives. This what I mean is a game changer. That's the beauty of artificial intelligence; It makes life easier by taking control of all those annoying tasks and programs. Plus, isn't it amazing how chatbots and translation tools are changing the way we communicate? Autonomous cars and artificial intelligence-powered transportation promise to make roads safer and smoother. In healthcare, AI is improving diagnosis, self-healing, and even robotic surgery; Let's talk about a big improvement, right? Artificial intelligence is developing the way to learn to adapt and simplify the management of schools. It's like having your own virtual teacher guiding you through the course! Privacy concerns, algorithmic biases; These are real problems we have to deal with as we delve deeper into the technological world. seen? What risks should we be aware of? With so much happening around us, it's important to understand how embedded AI is in everyday life. . It is the force that shapes our daily lives in ways we could only dream of in the past. Fasten your seatbelts as we ride this wave of change together!International Journal of Innovative Research in Science Engineering and Technology02This research paper will tell you about how the AI is helping us in the authors' daily lives by improving diagnosis, self-healing, and even robotic surgery.International Journal of Innovative Research in Science,Engineering and Technology["Puru Garg", "Binayak Dutta"]2024-06-03T00:00:00 f74b6eae0f2d8d4c4433b74f1f1ad427837380a4The rising adoption of learning analytics and academic performance prediction technologies in higher education highlights the urgent need for transparency and explainability. This demand, rooted in ethical concerns and fairness considerations, converges with Explainable Artificial Intelligence (XAI) principles. Despite the recognized importance of transparency and fairness in learning analytics, empirical studies examining student fairness perceptions, particularly within academic performance prediction, remain limited. We conducted a pre-registered factorial survey experiment involving 1,047 German students to investigate how decision tree features (simplicity and accuracy) influence perceived distributive and informational fairness, mediated by causability (i.e., the self-assessed understandability of a machine learning model’s cause-effect linkages). Additionally, we examined the moderating role of institutional trust in these relationships. Our results indicate that decision tree simplicity positively affects fairness perceptions, mediated by causability. In contrast, prediction accuracy neither directly nor indirectly influences these perceptions. Even if the hypothesized effects of interest are either minor or non-existent, results show that the medium positive effect of causability on the distributive fairness assessment depends on institutional trust. These findings substantially impact the crafting of transparent machine learning models in educational settings. We discuss important implications for fairness and transparency in implementing academic performance prediction systems.Conference on Fairness, Accountability and Transparency932Results indicate that decision tree simplicity positively affects fairness perceptions, mediated by causability, and shows that the medium positive effect of causability on the distributive fairness assessment depends on institutional trust.Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency["Marco L\u00fcnich", "Birte Keller"]2024-06-03T00:00:00 6dc63e2820c97c4ca959cba1ddf76eedeb51a375Seiring perkembangan zaman, teknologi terus berkembang secara masif dan semakin terintegrasi dalam kehidupan masyarakat. Salah satu teknologi terkini yang semakin populer adalah kecerdasan buatan atau artificial intelligence (AI). AI merupakan cabang ilmu komputer yang berfokus pada pengembangan sistem komputer yang mampu menjalankan tugas-tugas yang biasanya memerlukan kecerdasan manusia. Dalam lingkup desain komunikasi visual, AI tidak hanya berurusan dengan unsur-unsur grafis, tetapi juga menuntut desainer untuk lebih kreatif dalam menarik perhatian audiens. Namun, penerapan AI dalam desain komunikasi visual beberapa tahun terakhir ini telah menimbulkan pro dan kontra. Di satu sisi, AI mempermudah pekerjaan desainer melalui berbagai tools yang tersedia. Di sisi lain, ada kekhawatiran bahwa AI akan menggantikan peran desainer manusia. Hal ini dikarenakan teknologi AI bekerja dengan mengumpulkan gambar dari internet, dan mengolahnya menjadi database untuk menciptakan karya berdasarkan deskripsi yang dibuat. Hal ini menimbulkan masalah terkait orisinalitas karya, karena AI dianggap hanya memodifikasi gambar yang sudah ada. Berbeda dengan desainer manusia yang melalui proses kreatif yang panjang dan mendalam. Penelitian ini bertujuan untuk menganalisis penerapan AI dalam media desain grafis melalui pendekatan analisis interpretasi Edmund Feldman. Pendekatan ini melibatkan empat langkah utama: deskripsi, analisis formal, interpretasi, dan evaluasi. Melalui pendekatan ini, penelitian ini akan mengevaluasi sejauh mana karya yang dihasilkan oleh AI dapat dianggap orisinal dan seberapa jauh AI dapat menggantikan peran desainer manusia dalam proses kreatif. Hasil penelitian diharapkan dapat memberikan pemahaman yang lebih mendalam tentang dampak penerapan AI dalam desain grafis dan menawarkan perspektif yang seimbang mengenai masa depan kolaborasi antara AI dan desainer manusia. Kata kunci: Analisis, AI, desainer, Edmund Feldman, Interpretasi, orisinalitasJurnal Digit391Jurnal Digit["Ine Rachmawati", "Dzulfiqar Fickri Rosyid", "Suhadi Parman", "Yuni Awalaturrohmah Solihan", "G. Putra"]2024-06-03T00:00:00 fd561064516bfe5bd38fcb948c835e62902b6035This paper presents a comprehensive analysis of the integration of Artificial Intelligence (AI) in the academic operations of Higher Education Institutions (HEIs). It delves into how AI technologies are currently being utilized in various aspects of higher education, personalized learning, administrative automation, and data-driven decision-making processes. The key contributions of this study lie in its detailed examination of the benefits and challenges associated with AI integration in educational environments, providing a balanced perspective on both the potential and the pitfalls. Central findings highlight that AI significantly enhances educational experiences through personalized learning pathways and efficient administrative processes. However, these advancements are not without challenges. The paper identifies critical areas such as ethical considerations, the digital divide, and the need for upskilling educators in AI literacy as essential to the successful adoption of AI in HEIs. The study contributes to the ongoing dialogue in educational technology by offering actionable insights and recommendations for institutions seeking to navigate the complex landscape of digital transformation. It serves as a valuable resource for educators, administrators, and policymakers aiming to leverage AI for educational excellence and innovation.2024 1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)331Critical areas such as ethical considerations, the digital divide, and the need for upskilling educators in AI literacy as essential to the successful adoption of AI in HEIs are identified.2024 1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)["A. Murdan", "Roshan Halkhoree"]2024-06-03T00:00:00 22252921954ea0962d0ca57c1c89f3d638e97fa4Climate change poses an urgent and significant challenge, with far-reaching impacts already affecting our planet, and projections indicating worsening conditions in the future. The concept of sustainable development aims to meet present needs while safeguarding the ability of future generations to meet their own requirements. However, climate change's effects on sustainable development are of paramount concern, as they amplify issues like poverty, food insecurity, and environmental degradation, affecting economic growth, social progress, and environmental protection. Taking immediate action to mitigate climate change and implement sustainable practices is crucial to ensuring a habitable planet for future generations. In this context, Responsible Artificial Intelligence (RAI) emerges as a promising direction, striving for ethical and responsible technology use in diverse sustainable development tasks. RAI proves to be a robust candidate for empowering climate change mitigation and adaptation efforts. This study introduces a theoretical RAI framework designed to support climate action by responsibly enabling more accurate predictions and analysis of climate data, enhancing energy efficiency, and reducing greenhouse gas emissions. The framework emphasizes the need for interdisciplinary collaboration among policymakers, scientists, and technicians to develop RAI solutions that advance sustainable development and alleviate the adverse impacts of climate change. Unlike previous works, this research presents a novel perspective on the principles of RAI that explicitly consider climate-related aspects. By laying the foundations of AI research to bolster our fight against climate change, this article establishes essential pillars that encourage further advancements in this critical endeavor. Sustainable Machine Intelligence Journal01A theoretical RAI framework designed to support climate action by responsibly enabling more accurate predictions and analysis of climate data, enhancing energy efficiency, and reducing greenhouse gas emissions is introduced.Sustainable Machine Intelligence Journal["Byeong-Gwon Kang", "Yunyoung Nam"]2024-06-03T00:00:00 5da89a2b46415bf8b62c1e512671c68255250693Artificial intelligence (AI) is a multidisciplinary field intersecting computer science, cognitive science, and other disciplines, able to address the creation of systems that perform tasks generally requiring human intelligence. It consists of algorithms and computational methods that allow machines to learn from data, make decisions, and perform complex tasks, aiming to develop an intelligent system that can work independently or collaboratively with humans. Since AI technologies may help physicians in life-threatening disease prevention and diagnosis and make treatment smart and more targeted, they are spreading in health services. Indeed, humans and machines have unique strengths and weaknesses and can complement each other in providing and optimizing healthcare. However, the healthcare implementation of these technologies is related to emerging ethical and deontological issues regarding the fearsome reduction of doctors’ decision-making autonomy and acting discretion, generally strongly conditioned by cognitive elements concerning the specific clinical case. Moreover, this new operational dimension also modifies the usual allocation system of responsibilities in case of adverse events due to healthcare malpractice, thus probably imposing a redefinition of the established medico-legal assessment criteria of medical professional liability. This article outlines the new challenges arising from AI healthcare integration and the possible ways to overcome them, with a focus on Italian legal framework. In this evolving and transitional context emerges the need to balance the human dimension with the artificial one, without mutual exclusion, for a new concept of medicine “with” machines and not “of” machines.Frontiers in Medicine252This article outlines the new challenges arising from AI healthcare integration and the possible ways to overcome them, with a focus on Italian legal framework.Frontiers in Medicine["S. Sablone", "Mara Bellino", "Andrea Nicola Cardinale", "M. Esposito", "F. Sessa", "M. Salerno"]2024-06-03T00:00:00 21c72b1d71d4b57e77564f825e53dd6bd4b2cee2The integration of Artificial Intelligence (AI) and blockchain technology (BT) into diploma generation and verification systems enables the digitalization of services in higher education institutions (HEI). These technologies have the potential to prevent misuse, protect identity and privacy, decentralize services, and automate processes. AI employs advanced pattern recognition and anomaly detection algorithms to ensure the integrity of academic certificates stored on the blockchain. By using smart contracts on the blockchain, artificial intelligence algorithms may automate and optimize the verification process, thereby diminishing the administrative load linked to validating academic qualifications. This review paper provides insights into how AI-driven algorithms can streamline the generation of digital diplomas, enhance authentication mechanisms, and mitigate fraudulent activities. Additionally, it highlights the potential of AI-powered analytics for optimizing blockchain-based systems, facilitating seamless interoperability, and ensuring trustworthiness in academic credential verification.2024 7th International Balkan Conference on Communications and Networking (BalkanCom)261This review paper provides insights into how AI-driven algorithms can streamline the generation of digital diplomas, enhance authentication mechanisms, and mitigate fraudulent activities and highlights the potential of AI-powered analytics for optimizing blockchain-based systems, facilitating seamless interoperability, and ensuring trustworthiness in academic credential verification.2024 7th International Balkan Conference on Communications and Networking (BalkanCom)["Avni Rustemi", "Fisnik Dalipi", "Vladimir Atanasovski", "Aleksandar Risteski"]2024-06-03T00:00:00 00b978c685667eb87d61674838b28c45f85ed1aaElectronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.Future Internet841The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers, however, many more remain largely unexplored.Future Internet["Zisis Vasilakopoulos", "Theocharis Tavantzis", "Rafail Promikyridis", "E. Tambouris"]2024-06-03T00:00:00 ac4ccfc06831f1a0f79048730b37ff5e76ce0df4Artificial intelligence (AI) tools have profoundly transformed the landscape of healthcare, producing substantial innovations and improvements in a wide range of applications. An area of healthcare in which AI is particularly poised to have a significant impact is in healthcare administration. There is a broad range of AI solutions being integrated into administrative processes, all of which appear promising for developing new time and cost saving solutions; however, widespread adoption faces persistent challenges. This paper looks into the existing body of research in AI applications for healthcare administration, identifies current challenges and obstacles to large scale implementation, and proposes directions for future work. By examining past work and addressing existing obstacles, we aim to contribute to the ongoing discourse on optimizing AI's impact on healthcare management.IEEE International Conference on Healthcare Informatics291This paper looks into the existing body of research in AI applications for healthcare administration, identifies current challenges and obstacles to large scale implementation, and proposes directions for future work.2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)["Lina Adwer", "Erik Whiting"]2024-06-03T00:00:00 6e1fc6992c8dc9666c62eed5721ca8ebbe32ef47- This paper discusses the current methods of Scan-to-BIM, a process which allows creating a digital representation of existing buildings for a planning methodology called Building Information Modeling (BIM). The study covers all stages of the process, from point cloud generation and pre-processing to BIM modeling and formatting. We review the work already done in this area both conventionally and with the addition of Artificial Intelligence approaches which have significantly improved the efficiency and accuracy of the process. With a particular focus on Artificial Intelligence, we explore how these advanced technologies transform and optimize every step, offering innovative insights and significant improvements over conventional methods. Through this investigation, we aim to provide insights into the capabilities and constraints of the Scan-to-BIM workflow, and to shed light on academic advancements and industrial perspectives.Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)550This investigation aims to provide insights into the capabilities and constraints of the Scan-to-BIM workflow, and to shed light on academic advancements and industrial perspectives.Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)["Maxime Queruel", "Stefan Bornhofen", "A. Histace", "Laure Ducoulombier"]2024-06-03T00:00:00 5d6a8360c1c15500d46e291e5c61b3e2fe456c3eThe 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various community members and stakeholders, and following up on a prior successful 2022 AI Summit, the summit theme was: AI in Action. Six key topics included (i) an overview of prior and ongoing efforts by the AI task force, (ii) emerging needs and tools for computational nuclear oncology, (iii) new frontiers in large language and generative models, (iv) defining the value proposition for the use of AI in nuclear medicine, (v) open science including efforts for data and model repositories, and (vi) issues of reimbursement and funding. The primary efforts, findings, challenges, and next steps are summarized in this manuscript.arXiv.org230The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024, and the summit theme was: AI in Action.ArXiv["Arman Rahmim", "Tyler J. Bradshaw", "Guido Davidzon", "J. Dutta", "G. Fakhri", "M. Ghesani", "Nicolas Karakatsanis", "Quanzheng Li", "Chi Liu", "Emilie Roncali", "Babak Saboury", "T. Yusufaly", "Abhinav K. Jha"]2024-06-03T00:00:00 2f65d8b967a86ffa40dfba4c89abe7c58b0e4633This paper examines the effect of Artificial Intelligence (AI) on the operational efficiency of deposit money banks in Lagos State, Nigeria. The study identified the types of AI technologies that are used by banks and examined the impact of the different types of technologies on the operational efficiency of five deposit money banks, namely: First Bank of Nigeria, United Bank of Africa, Guaranty Trust Bank, Access Bank, and Zenith Bank, all public liability companies with headquarters located in the Lagos metropolis. The study adopted a survey research design. Copies of the questionnaire were administered to 450 regular employees selected randomly from the five banks. The study revealed that deep learning (β = 0.400, t = 5.445, p<0.05); Automation (β = 0.202, t = 2.143, p<0.05) and fraud detection (β = 0.460, t = 7.095, p<0.05) had positive and significant effects on the operational efficiency of the selected deposit money banks, while chatbots had a positive but insignificant effect. The study concluded that artificial intelligence significantly contributed to the operational efficiency of the selected deposit money banks in Nigeria. The authors recommend that deposit money banks should effectively make use of artificial intelligence, especially deep learning, automation, and fraud detection, to improve organizational efficiencyKoozakar Festschrift00The study concluded that artificial intelligence significantly contributed to the operational efficiency of the selected deposit money banks in Nigeria and recommended that deposit money banks should effectively make use of artificial intelligence, especially deep learning, automation, and fraud detection, to improve organizational efficiency.Koozakar Festschrift["Felicia Adeyemo", "Grace Okoronkwo"]2024-06-03T00:00:00 ef784cc33df8cc9c245cb8cb6faf3218efeb384dIn Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems devised to cope with a single task. The recent emergence of General-Purpose Artificial Intelligence Systems (GPAIS) poses model configuration and adaptability challenges at far greater complexity scales than the optimal design of traditional Machine Learning models. Evolutionary Computation (EC) has been a useful tool for both the design and optimization of Machine Learning models, endowing them with the capability to configure and/or adapt themselves to the task under consideration. Therefore, their application to GPAIS is a natural choice. This paper aims to analyze the role of EC in the field of GPAIS, exploring the use of EC for their design or enrichment. We also match GPAIS properties to Machine Learning areas in which EC has had a notable contribution, highlighting recent milestones of EC for GPAIS. Furthermore, we discuss the challenges of harnessing the benefits of EC for GPAIS, presenting different strategies to both design and improve GPAIS with EC, covering tangential areas, identifying research niches, and outlining potential research directions for EC and GPAIS.IEEE Transactions on Evolutionary Computation1280The role of EC in the field of GPAIS is analyzed, exploring the use of EC for their design or enrichment, and the challenges of harnessing the benefits of EC for GPAIS are discussed.ArXiv["Javier Poyatos", "J. Ser", "Salvador Garcia", "H. Ishibuchi", "D. Molina", "I. Triguero", "Bing Xue", "Xin Yao", "Francisco Herrera"]2024-06-03T00:00:00 2f5f86138234a22e314bd42bc25554cac90ce6f0In an era characterized by rapid societal changes and complex challenges, institutions' traditional methods of problem-solving in the public sector are increasingly proving inadequate. In this study, we present an innovative and effective model for how institutions can use artificial intelligence to enable groups of people to generate effective solutions to urgent problems more efficiently. We describe a proven collective intelligence method, called Smarter Crowdsourcing, which is designed to channel the collective intelligence of those with expertise about a problem into actionable solutions through crowdsourcing. Then we introduce Policy Synth, an innovative toolkit which leverages AI to make the Smarter Crowdsourcing problem-solving approach both more scalable, more effective and more efficient. Policy Synth is crafted using a human-centric approach, recognizing that AI is a tool to enhance human intelligence and creativity, not replace it. Based on a real-world case study comparing the results of expert crowdsourcing alone with expert sourcing supported by Policy Synth AI agents, we conclude that Smarter Crowdsourcing with Policy Synth presents an effective model for integrating the collective wisdom of human experts and the computational power of AI to enhance and scale up public problem-solving processes. While many existing approaches view AI as a tool to make crowdsourcing and deliberative processes better and more efficient, Policy Synth goes a step further, recognizing that AI can also be used to synthesize the findings from engagements together with research to develop evidence-based solutions and policies. The study offers practical tools and insights for institutions looking to engage communities effectively in addressing urgent societal challenges.arXiv.org00It is concluded that Smarter Crowdsourcing with Policy Synth presents an effective model for integrating the collective wisdom of human experts and the computational power of AI to enhance and scale up public problem-solving processes.ArXiv["R'obert Bjarnason", "Dane Gambrell", "Joshua Lanthier-Welch"]2024-06-03T00:00:00 f4f7ec5540ede87dd2febbc3aec13b5e63b3ced1Purpose This study aimed to identify the university librarians’ readiness to adopt artificial intelligence (AI) for innovative learning experiences and smart library services. Design/methodology/approach Quantitative research design followed by a survey method was applied. Data were collected from 174 professional librarians of 58 university libraries in Punjab province, Pakistan. Findings The findings of the study revealed that the adoption of AI enhances innovative learning. The results displayed that AI adoption assists librarians in the provision of smart library services to end users. Originality/value The study has offered practical recommendations in light of the evidence-based data for the efficient adoption and sustainability of AI applications in university libraries for innovative learning and smart library services. It contributes to the theoretical understanding by expanding the existing knowledge base. It offers managerial insights and has a societal impact. The study has provided a framework based on the empirical findings for efficiently adopting AI tools in academic settings for the provision of innovative learning experiences and sustainable smart library services. Global Knowledge Memory and Communication420The findings of the study revealed that the adoption of AI enhances innovative learning and assists librarians in the provision of smart library services to end users.Global Knowledge, Memory and Communication["Khurram Shahzad", "S. A. Khan", "Abid Iqbal", "Asfa Muhammad Din Javeed"]2024-06-03T00:00:00 0b5c28b7e4ad13802ed540909217d4ffde608271Artificial Intelligence (AI) applications in healthcare are evolving rapidly. The integration of AI into the Canadian healthcare system has demonstrated significant potential for enhancing the efficiency of care and improving patient outcomes. However, as this transformative technology continues to advance, it is crucial to take into account the unique perspectives and requirements of Indigenous Peoples in Canada. This article delves into the political, ethical, and practical considerations associated with introducing AI into Indigenous healthcare, emphasizing the paramount importance of equity and inclusion, which are rooted in the Two-Eyed AI framework. It also underscores the significance of co-creating AI technology in collaboration with Indigenous communities and multidisciplinary development teams. To illustrate these principles, this article spotlights an international AI epistemology-focused working group example. Healthcare professionals who engage with AI, whether it be through research, management, development, or leadership are implicated with this contemporary paradigm shift in decolonizing novel AI technology.Healthcare Management Forum430This article delves into the political, ethical, and practical considerations associated with introducing AI into Indigenous healthcare, emphasizing the paramount importance of equity and inclusion in the Two-Eyed AI framework.Healthcare Management Forum["Julia A Silano"]2024-06-03T00:00:00 d134ad833c1a534d17e9344909bb96463151f113Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge. This study conducts a comprehensive analysis of state-of-the-art self-supervised learning algorithms, emphasizing their underlying mechanisms and computational intricacies. Building upon this analysis, we introduce a unified model-agnostic computation (UMAC) process, tailored to complement modern self-supervised learning algorithms. UMAC serves as a model-agnostic and global explainable artificial intelligence (XAI) methodology that is capable of systematically integrating and enhancing state-of-the-art algorithms. Through UMAC, we identify key computational mechanisms and craft a unified framework for self-supervised learning evaluation. Leveraging UMAC, we integrate an XAI methodology to enhance transparency and interpretability. Our systematic approach yields a 17.12% increase in improvement in training time complexity and a 13.1% boost in improvement in testing time complexity. Notably, improvements are observed in augmentation, encoder architecture, and auxiliary components within the network classifier. These findings underscore the importance of structured computational processes in enhancing model efficiency and fortifying algorithmic transparency in self-supervised learning, paving the way for more interpretable and efficient AI models.Big Data and Cognitive Computing420This study conducts a comprehensive analysis of state-of-the-art self-supervised learning algorithms, emphasizing their underlying mechanisms and computational intricacies, and introduces a unified model-agnostic computation (UMAC) process, tailored to complement modern self-supervised learning algorithms.Big Data Cogn. Comput.["Elie Neghawi", "Yan Liu"]2024-06-03T00:00:00 71bc11cc2bb71ee1d02aa9cb1765306c2d2b19eaArtificial Intelligence (AI) is a technology in the Society 5.0 era which is very useful for application in the world of education, especially Library Science. Artificial Intelligence (AI) is an artificial intelligence that is a model of human intelligence that has been applied in a machine to create intelligent machines that are able to facilitate information activities. There is increasing recognition of the benefits of applying Artificial Intelligence (AI) to the world of education. This research explores how Artificial Intelligence (AI) in the world of education for Library Science students is able to respond to opportunities and improve major strategies to obtain more information on digital literacy. The data in this research was obtained using quantitative methods by taking 45 students majoring in Library Science, UIN North Sumatra, Medan as samples and conducting a literature review of research related to the discussion being researched. It is very rarely mentioned explicitly that artificial intelligence used by library science students is able to provide a huge boost to ideas, creativity, literacy character which is able to encourage the growth of good information for each individual. This research is a discussion that explores the meaning, benefits and challenges of Artificial Intelligence (AI) on the digital literacy of library science students at UIN North Sumatra, Medan.Reslaj : Religion Education Social Laa Roiba Journal00This research explores how Artificial Intelligence (AI) in the world of education for Library Science students is able to respond to opportunities and improve major strategies to obtain more information on digital literacy.Reslaj: Religion Education Social Laa Roiba Journal["Tamara Oktafiani Zega", "Abdul Karim Batubara"]2024-06-03T00:00:00 f62a102cd0eef492539afaf1f93b9c516a6cf8d6Purpose: This paper aims to review the adoption of AI in supporting the HRM to enhance and boost their practices by utilizing artificial intelligence and the possibility of future integration of the human approach and AI approach.  Methodology: This paper provides a qualitative approach based on the latest research, case studies, articles, and related literature.  Finding: The paper describes the positive influence in the field of AI on the HRM practices/process. Unique Contribution to Theory, Practice and Policy: This paper provides an informative view of the positive influence of AI in enhancing the strategic HRM approach and organizational performance by adopting AI, and the implications/challenges of using AI in the HRM.Journal of human resource & leadership60This paper provides an informative view of the positive influence of AI in enhancing the strategic HRM approach and organizational performance by adopting AI, and the implications/challenges of using AI in the HRM.Journal of Human Resource and Leadership["Nouf Abdulla"]2024-06-03T00:00:00 e2ca5eadb31cb4b8d746c248cf7e86ea56de10b3Meta-design promotes ‘design for designers’ or, in other words, the creation of socio-technical environments that can evolve in the hands of the users. The long-term goal of meta-design is improving the quality of life in each person’s everyday activities, that is, living, working, and learning with the aid of technology. Today, advances in Artificial Intelligence provide new opportunities and frontiers for meta-design to empower users much more than in the past, giving them new perspectives, strategies, and tools to be exploited in the ‘design for design’ process.International Working Conference on Advanced Visual Interfaces300Today, advances in Artificial Intelligence provide new opportunities and frontiers for meta-design to empower users much more than in the past, giving them new perspectives, strategies, and tools to be exploited in the ‘design for design’ process.Proceedings of the 2024 International Conference on Advanced Visual Interfaces["B. Barricelli", "Gerhard Fischer", "D. Fogli", "Anders I. M\u00f8rch", "Antonio Piccinno", "S. Valtolina"]2024-06-03T00:00:00 bff6c6e7cc4fb2635378f46d72ad9c4ed846c999The COVID-19 pandemic revealed how the world's economy and healthcare system were not prepared to handle a pandemic. Today, many innovative approaches have been established by combining science with artificial intelligence (AI). This study aims to assist in the prediction of future pandemics and possible causative viruses that might threaten the worldwide public health system, using an AI algorithm. This study evaluates different characteristics of SARS-CoV-1, CCHF, Yellow fever virus, Ebola virus, Influenza A H1N1, Influenza A H3N2, Influenza A H5N1, West Nile virus, Dengue virus, Chikungunya virus and HIV viruses by using 4 novel hybrid models; LR-GPR, LR-LSQBOOST, LR-SVM, LR-RT that were more reliable than single methods with higher accuracies. The highest accuracy methods were selected by using three different evaluation metrics namely; the determination coefficient, correlation coefficient, and squared error. The criteria used for the analysis included the number of cases and deaths, the presence of a vaccine, annual R nought value starting from 2000 until 2022, related to an infectious agent. Data was obtained from sources including; WHO, CDC, ECDC, PAHO, and publications. The accuracy level of the AI algorithms used in our study was between 88-99%. Amongst the eleven infectious RNA viruses analyzed, Influenza A H1N1 and Chikungunya were both determined as infectious agents that might cause outbreaks in 2032 and 2037 with 550,000 and 1,100,000 predicted cases, respectively, suggesting the risk of this turning into a pandemic. The results indicated that the steady increase in HIV cases might continue in the next twenty years. In addition, the remaining viruses included in our study would not reach critical case numbers. Authorities should take preventative actions including treatment protocols, rapid diagnostic methods, and prevention of the spread of disease against the possible candidates that might lead to future pandemics2024 Advances in Science and Engineering Technology International Conferences (ASET)790Influenza A H1N1 and Chikungunya were both determined as infectious agents that might cause outbreaks in 2032 and 2037 with 550,000 and 1,100,000 predicted cases, respectively, suggesting the risk of this turning into a pandemic.2024 Advances in Science and Engineering Technology International Conferences (ASET)["C. Ozverel", "A. G. Usman", "N. Sultanoglu", "B. Uzun", "Cemile Ba\u011fkur", "D. Ozsahin", "T. \u015eanl\u0131da\u011f"]2024-06-03T00:00:00 dd0c9039b1d766bc1d20f6f596f67145b66fa51ePurpose: Geographical indications (GIs) within the European Union are – as is known – legally protected designations highlighting unique qualities of products tied to their place of origin; accordingly, GIs can promote sustainable agricultural practices, preserve traditional knowledge and contribute to rural development, but, notwithstanding, issues in verifying and authenticating GI products persist. Within this framework, this paper investigates the intersection of EU geographical indication law with sustainability goals and potential applications of artificial intelligence (AI) in streamlining GI compliance and enhancing consumer trust. Study design/methodology/approach: The behind research employs a mixed-methods approach; in fact, it includes a systematic review of relevant EU legislation and policy documents alongside qualitative case studies exploring the use of AI-based technologies (e.g., precision agriculture, terroir monitoring, traceability systems…) in GI value chains. Findings: The analysis confirms the wanted complex interplay between GI law and sustainability; in this way, while GIs can be powerful tools for promoting environmentally responsible production, existing frameworks may not fully capture all dimensions of sustainability; in addition, case studies demonstrate the promise of AI in improving product traceability, combating fraud and supporting informed consumer choices regarding GI products. Originality/value: Therefore, the paper argues for a more holistic approach to GI law that integrates sustainability metrics beyond geographic origin, suggesting that responsible AI adoption presents the potential to significantly strengthen GI systems: policy recommendations should include incentivising the development of ethical AI solutions for GI verification, promoting data sharing along supply chains, and raising consumer awareness of the sustainability benefits associated with GI products.International Journal of Management, Knowledge and Learning00This paper investigates the intersection of EU geographical indication law with sustainability goals and potential applications of artificial intelligence (AI) in streamlining GI compliance and enhancing consumer trust, suggesting that responsible AI adoption presents the potential to significantly strengthen GI systems.International Journal of Management, Knowledge and Learning["Federico Domenico Enrico De Silvo"]2024-06-03T00:00:00 7fa7870b3a58fdfc40608eab537a462864832270Computing and artificial intelligence (AI) are advancing at a pace that offers opportunities to benefit human life, from healthcare and education to transportation and entertainment. However, they also pose a variety of ethical dilemmas that society will need to solve to ensure that their use is responsible and just. This study provides an inclusive examination of the ethics of computing and AI, making a comprehensive consideration based on the history of past practices currently prevailing and prospects. By integrating quantitative data with qualitative observations over a mixed-method approach, the study captures much of the complexity and depth of the ethical field. Many results raise concerns about ethics, particularly in the areas of privacy, autonomy, and bias. The authors offer a variety of practical recommendations to developers, policy-makers, and users on the basis of these reflections to help encourage the practice of ethics in AI development and deployment. Focusing on transparency, accountability, and inclusion in AI systems, these recommendations argue for the development of strong ethical standards and oversight mechanisms for the increasingly complex ethical landscape of AI and computing technologies. This study addresses these challenges, aiming to help change the way AI is experienced across society by developing innovations while enhancing efficiency and delivering them fairly and justly to create a world in which an unequal distribution of AI opportunities does not exist.FMDB Transactions on Sustainable Computing Systems170This study provides an inclusive examination of the ethics of computing and AI, making a comprehensive consideration based on the history of past practices currently prevailing and prospects to create a world in which an unequal distribution of AI opportunities does not exist.FMDB Transactions on Sustainable Computing Systems["Muniraju Hullurappa"]2024-06-03T00:00:00 60715ae38e4618d88ebcd68593e00f26ad073bd2International Journal of Human-Computer Interaction563International Journal of Human–Computer Interaction["Lawal Ibrahim Dutsinma Faruk", "Debajyoti Pal", "Suree Funilkul", "Thinagaran Perumal", "P. Mongkolnam"]2024-06-03T00:00:00 cd6b1d4cec3d94b1ad13cb97cc293f5a4a5f8fc2Animation as genre is broadly used across many forms of digital media. In this paper, I argue ChatGPT and similar chatbots powered by Large Language Models (LLMs) can be best understood as animated characters. More than just cartooning, puppetry, or CGI, animation is a paradigm involving the projection of qualities perceived as human such as power, agency, will, and personality outside of the self and onto objects in the environment. Characteristics of animation—including reliance on stereotypes, obfuscation of human labor, and manipulation of an audience's emotions—can help us both analyze and respond appropriately to interactive AI technologies and the hyperbolic claims of their promoters.Conference on Fairness, Accountability and Transparency1113This paper argues ChatGPT and similar chatbots powered by Large Language Models (LLMs) can be best understood as animated characters to help analyze and respond appropriately to interactive AI technologies and the hyperbolic claims of their promoters.Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency["Luke Stark"]2024-06-03T00:00:00 3400778764c6a2c4e68e98cf664dd84eaa23c19aCardiovascular disease (CVD) also known as heart disease is one of the most common causes of death globally, accounting for over 17 million deaths per year, which represents 31% of all deaths worldwide. Therefore, the prediction of CVD values is a crucial aspect of healthcare and disease management. This study aims to predict CVD values using three different models; Multiple linear regression (MLR), Artificial neural network (ANN), and Adaptive neuro-fuzzy inference systems (ANFIS). Twelve independent variables were used in training the models. The individual performance of the models was evaluated using four different performance objectives; Root Mean squared error (RMSE), Mean squared error (MSE), correlation coefficient (R), and determination coefficient (R2). The results indicated that the AI-based techniques depict the best prediction performance with ANFIS having R2 = 0.99, R = 0.99, RMSE = 0.068, and MSE = 0.0046 respectively. In general, the ANFIS model was found to be the most reliable for predicting CVD values.2024 Advances in Science and Engineering Technology International Conferences (ASET)260The ANFIS model was found to be the most reliable for predicting CVD values and was found to be the most reliable for predicting AI-based techniques.2024 Advances in Science and Engineering Technology International Conferences (ASET)["D. Ozsahin", "Efe Precious Onakpojeruo", "Basil Bartholomew Duwa", "B. Uzun", "Yoshebel Francis Zira", "I. Ozsahin"]2024-06-03T00:00:00 4520c9d7cc6f5a31825eec8324f07020d7260250O presente trabalho possui como objetivo analisar questões acerca da inteligência artificial e a sua falta de regulamentação, pois por ser uma tecnologia recente, existem discussões e conflitos sobre ela, destacando que essa carência deixa de amparar aqueles que as obras intelectuais são utilizadas no data-base dessas IAS sem suas anuências e a falta de norma regulamentadora para proteção autoral de artes criadas por essa tecnologia. Exemplo disso, foi um caso acontecido nos Estados Unidos, no ano de 2023, em que a juíza americana, Beryl Howell, recusou o pedido de direitos autorais de um homem chamado Stephan Thaler, que alegava ter direitos autorais em uma imagem criada por uma Inteligência artificial que ele havia programado, porém a juiza manteve o pedido negado, argumentando que não havia participação humana na criação, então não poderia ser protegido por direitos autorais. Há também processos relacionados a reivindicações de direitos autorais de obras utilizadas deliberadamente para o data-base de IAs, sendo que em âmbito nacional há apenas o debate sobre o tema. É preciso desenvolver mais abordagens sobre o assunto, para que seja possível desenvolver soluções para essa carência de legislação, com o direito se adequando ao presente contexto da humanidade e prevendo soluções para possíveis conflitos, eis que não é possível exigir direitos sobre um assunto, do qual não exista uma norma adequada. A pesquisa foi realizada através de referências bibliográficas, havendo a leitura de livros, artigos científicos e reportagens, o texto apresenta um panorama histórico, examinando a questão jurisprudencial do tema e justificando o porquê da necessidade de legislações, pois, a falta delas causa um grande impacto na sociedade. Dando-se fim ao trabalho, é esperado que, ao trazer um debate sobre o tema, a apresentação de uma nova perspectiva sobre o assunto e que com a discussão exposta no texto, a mesma possa colaborar com o campo de estudo dos direitos autorais, incentivando mais debates sobre o assunto.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação["Ana Cristina Bezerra Santiago", "Jackson Novaes Santos", "Thyara Gon\u00e7alves Novais"]2024-06-03T00:00:00 3c9ae22939e8967b0c70ae1feb649234da23c028European Heart Journal - Digital Health100European Heart Journal. Digital Health["P. Houthuizen", "Peter P T de Jaegere"]2024-06-03T00:00:00 5cf96d27304f1d158b85356d0c07555ba179ea25Journal of Artificial Intelligence, Machine Learning and Data Science00Journal of Artificial Intelligence, Machine Learning and Data Science["Tejiri Agenmonmen", "Stella-Maris Orim"]2024-06-03T00:00:00 6f101cf234b406ceaa665ac6aaaf37647ab7da2dMilitary Medical Science Letters250Military Medical Science Letters["Kitti Nagy", "V. Bostik"]2024-06-03T00:00:00 902290491164fcc19df8fdcb65d19337d08ad931Environment, Development and Sustainability400Environment, Development and Sustainability["Muhammad Usman", "Iftikhar Ahmad", "Muhammad Ahsan", "Hakan Caliskan"]2024-06-03T00:00:00 e1148154178e213108592b226bb5788685c182c8International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM00International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM["Andrew Begemann"]2024-06-03T00:00:00 f0735566b0d8e42fa3893580ed8fe43ed3ca965cConsciousness is notoriously hard to define with objective terms. An objective definition of consciousness is critically needed so that we might accurately understand how consciousness and resultant choice behaviour may arise in biological or artificial systems. Many theories have integrated neurobiological and psychological research to explain how consciousness might arise, but few, if any, outline what is fundamentally required to generate consciousness. To identify such requirements, I examine current theories of consciousness and corresponding scientific research to generate a new definition of consciousness from first principles. Critically, consciousness is the apparatus that provides the ability to make decisions, but it is not defined by the decision itself. As such, a definition of consciousness does not require choice behaviour or an explicit awareness of temporality despite both being well-characterised outcomes of conscious thought. Rather, requirements for consciousness include: at least some capability for perception, a memory for the storage of such perceptual information which in turn provides a framework for an imagination with which a sense of self can be capable of making decisions based on possible and desired futures. Thought experiments and observable neurological phenomena demonstrate that these components are fundamentally required of consciousness, whereby the loss of any one component removes the capability for conscious thought. Identifying these requirements provides a new definition for consciousness by which we can objectively determine consciousness in any conceivable agent, such as non-human animals and artificially intelligent systems.arXiv.org460Critically, consciousness is the apparatus that provides the ability to make decisions, but it is not defined by the decision itself, so a definition of consciousness does not require choice behaviour or an explicit awareness of temporality despite both being well-characterised outcomes of conscious thought.ArXiv["Craig I. McKenzie"]2024-06-03T00:00:00 d4eae4fa97114842bc438a577463f040300828afIn virology, artificial intelligence (AI) technologies have demonstrated potentials to revolutionize the detection of diseases, understanding the behaviors of viruses, and developing strategies that are effective for treatments. This article explores the current applications of AI in healthcare and virology universally, focusing on India and highlighting the advancements, challenges, and potentials in these critical domains. The dialogue underscores the transformative power of AI and the strides made globally and in India. While AI unfolds tremendous opportunities, setbacks related to data privacy, ethical considerations, frameworks of regulation, training the workforce, and liaisons that are collaborative require attention to fully realize the potential of AI. While AI technologies stand primed for refraining detection and understanding of viruses and speeding discovery of vaccines, ethical considerations such as privacy of data, biases in algorithms, and integration of judicious AI require circumspection to ensure ethical and equitable utilization of AI technologies in healthcare. By tackling these challenges and harnessing opportunities presented by AI, we can extract transformative vigor for enhancement of outcomes of healthcare and efficiently traverse adversities of health globally. Qeios270The current applications of AI in healthcare and virology universally are explored universally, focusing on India and highlighting the advancements, challenges, and potentials in these critical domains.Qeios["M. Rani", "G. J. Lakshmi", "Ch. Navaneetha", "K. Nagamani"]2024-06-03T00:00:00 0baebe74b0fcf2a295a251f3f47a2df85a9e428bAs works are increasingly produced by machines using artificial intelligence (AI) systems, with a result that is often difficult to distinguish from that of a human creator, the question of what should be the appropriate response of the legal system and, in particular, of the copyright system has become central. If the generator of copyright protection has traditionally been the author’s creative input, AI forces us to reassess what in the creative process is special in human creativity and where the creative input lies in AI-generated works. But it also poses more fundamental questions on what the copyright system should achieve and who/what it should protect. In particular, since many human authors will potentially face the competition of these AI machines on the market, new ways of remunerating creators will have to be imagined while making sure that the copyright system does not stand in the way of these important technological developments.This contribution analyses the copyright issues related to so-called “generative AI” systems and reviews the arguments currently being advanced to change the copyright regime for AI-generated works. To do so, the underlying human rights framing intellectual property laws are used as the starting point from which a balanced copyright framework for generative AI could (and even should) be derived. It follows from the applicable human rights framework for copyright, but also from the anthropocentric approach of human rights, that the protection of creators and human creativity must be considered the point of reference when assessing future reforms with regard to copyright and generative AI systems. This approach establishes generative AI systems as an instrument of the human creator – and not as a substitute. It also reinforces the notion that copyright should be a tool to protect creativity and creators, not a legal mechanism to secure the amortization of economic investments in AI technology. As a consequence, it is argued that the copyrightability of AI-generated outputs should be considered with utmost care and only when AI is used as a technical tool for creators in their creation process – in other words, when they can serve a human author. At the same time, AI systems are here to stay, and their development should not be inhibited, as they can have many beneficial aspects (including for creators) if appropriately regulated.The right to train generative AI systems via machine learning technology can be derived from the right to science and culture and freedom of (artistic) expression (Arts. 19 and 27(1) Universal Declaration of Human Rights (UDHR); Art. 15(1)(a) and (b) International Covenant on Economic, Social and Cultural Rights (ICESCR); Arts. 11 and 13 EU Charter of Fundamental Rights (EUCFR)), as AI can lead to useful advances in science and the arts; moreover, it is important for human creators to be able to use outputs produced by generative AI in their creative process. This grounding is even stronger when the training is conducted for research purposes, as the training process can then also benefit from the fundamental right-to-research justification. However, since a large quantity of copyrighted works is required for the training of generative AI systems, a remuneration obligation for these uses arises from a human rights perspective, in particular when AI systems have a commercial purpose. It follows from the right to the protection of the creator’s moral and material interests (Arts. 27(2) and 17 UDHR, 15(1)(c) ICESCR; 17(2) EUCFR, 1 Protocol No. 1, 8 European Convention on Human Rights (ECHR)) that authors must be adequately remunerated for the commercial use of their works unless there is a strong justification legitimizing the use. For this reason, it is proposed that the machine learning process using copyright-protected works to train the AI gives rise to a limitation-based remuneration right to the benefit of human creators. The article also briefly explores if and when the moral interest of creators deriving from human rights protection could justify their opposition to the use of their work for the purpose of training AI systems. It is argued that the weaker the fundamental rights claim to train the AI is, the stronger the moral rights claim could be. For example, training an AI to produce works for discriminatory or racist purposes will benefit from a weaker (if any) fundamental rights protection, but will potentially raise important moral concerns of the author of the work used for training purposes. More generally, the article concludes that in order to secure a vibrant space for culture and creativity, (finally) cherishing and putting the Human Author at the center of the copyright system is necessary (and not only to erect fences to the benefit of copyright industries, which could be the unfortunate result of the recent first broad regulatory intervention on AI by the EU, the so-called “Artificial Intelligence Act”). In doing so, it might be possible in the future to have AI-systems that serve creators and creativity, and not the other way around.Social Science Research Network846It is argued that the copyrightability of AI-generated outputs should be considered with utmost care and only when AI is used as a technical tool for creators in their creation process – in other words, when they can serve a human author.SSRN Electronic Journal["Christophe Geiger"]2024-06-03T00:00:00 f67db878f25c588bd6773195ad70ceb625d1b2ccArtificial Intelligence (AI) systems are evaluated using competitive methods that rely on benchmark datasets to determine performance. These benchmark datasets, however, are often constructed through arbitrary processes that fall short in encapsulating the depth and breadth of the tasks they are intended to measure. In this paper, we interrogate the naturalization of benchmark datasets as veracious metrics by examining the historical development of benchmarking as an epistemic practice in AI research. Specifically, we highlight three key case studies that were crucial in establishing the existing reliance on benchmark datasets for evaluating the capabilities of AI systems: (1) the sharing of Highleyman’s OCR dataset in the 1960s, which solidified a community of knowledge production around a shared benchmark dataset, (2) the Common Task Framework (CTF) of the 1980s, a state-led project to standardize benchmark datasets as legitimate indicators of technical progress; and (3) the Netflix Prize which further solidified benchmarking as a competitive goal within the ML research community. This genealogy highlights how contemporary dynamics and limitations of benchmarking developed from a longer history of collaboration, standardization, and competition. We end with reflections on how this history informs our understanding of benchmarking in the current era of generative artificial intelligence.Conference on Fairness, Accountability and Transparency575This paper interrogate the naturalization of benchmark datasets as veracious metrics by examining the historical development of benchmarking as an epistemic practice in AI research by highlighting three key case studies that were crucial in establishing the existing reliance on benchmark datasets for evaluating the capabilities of AI systems.Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency["Will Orr", "Edward B. Kang"]2024-06-03T00:00:00 88c50f616fc0ec751e54a6a3433ee846a3e3d264Manual verification has become very challenging based on the increasing volume of information shared online and the role of generative Artificial Intelligence (AI). Thus, AI systems are used to identify disinformation and deep fakes online. Previous research has shown that superior performance can be observed when combining AI and human expertise. Moreover, according to the EU AI Act, human oversight is inevitable when using AI systems in a domain where fundamental human rights, such as the right to free expression, might be affected. Thus, AI systems need to be transparent and offer sufficient explanations to be comprehensible. Much research has been done on integrating eXplainability (XAI) features to increase the transparency of AI systems; however, they lack human-centered evaluation. Additionally, the meaningfulness of explanations varies depending on users’ background knowledge and individual factors. Thus, this research implements a human-centered evaluation schema to evaluate different XAI features for the collaborative human-AI disinformation detection task. Hereby, objective and subjective evaluation dimensions, such as performance, perceived usefulness, understandability, and trust in the AI system, are used to evaluate different XAI features. A user study was conducted with an overall total of 433 participants, whereas 406 crowdworkers and 27 journalists participated as experts in detecting disinformation. The results show that free-text explanations contribute to improving non-expert performance but do not influence the performance of experts. The XAI features increase the perceived usefulness, understandability, and trust in the AI system, but they can also lead crowdworkers to blindly trust the AI system when its predictions are wrong.Conference on Fairness, Accountability and Transparency974The results show that free-text explanations contribute to improving non-expert performance but do not influence the performance of experts, and the XAI features increase the perceived usefulness, understandability, and trust in the AI system, but they can also lead crowdworkers to blindly trust the AI system when its predictions are wrong.Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency["Vera Schmitt", "Luis-Felipe Villa-Arenas", "Nils Feldhus", "Joachim Meyer", "R. Spang", "Sebastian M\u00f6ller"]2024-06-03T00:00:00 75913806ea5dd834e1bec0dba381bd384b1ad3beThe potential of Artificial Intelligence (AI) techniques, such as autoencoders, for customizing the wireless physical layer has been demonstrated in previous works. In the current paper, we move up the protocol stack and explore the frontiers of Machine Learning (ML) on the wireless Medium Access Control (MAC) layer. Unlike the Physical Layer (PHY), the MAC aggregates multiple independent features, which require a separate ML treatment. Considering this, this survey paper navigates recent research on AI-driven MAC functions such as resource allocation, random access, Adaptive Modulation and Coding (AMC), power control, protocol learning, Channel State Information (CSI) reporting, Hybrid Automatic Repeat Request (HARQ), and Multi-RAT Spectrum Sharing (MRSS).2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)154This survey paper navigates recent research on AI-driven MAC functions such as resource allocation, random access, Adaptive Modulation and Coding (AMC), power control, protocol learning, Channel State Information (CSI) reporting, Hybrid Automatic Repeat Request (HARQ), and Multi-RAT Spectrum Sharing (MRSS).2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)["\u00c1lvaro Valcarce", "Petteri Kela", "Silvio Mandelli", "H. Viswanathan"]2024-06-03T00:00:00 37bd0eddae85a4ba43cfd0aab402c3871822c7caTransparency is a critical component when building artificial intelligence (AI) decision-support tools, especially for contexts in which AI outputs impact people or policy. Effectively identifying and addressing user transparency needs in practice remains a challenge. While a number of guidelines and processes for identifying transparency needs have emerged, existing methods tend to approach need-finding with a limited focus that centers around a narrow set of stakeholders and transparency techniques. To broaden this perspective, we employ numerous need-finding methods to investigate transparency mechanisms in a widely deployed AI-decision support tool developed by a wildlife conservation non-profit. Throughout our 5-month case study, we conducted need-finding through semi-structured interviews with end-users, analysis of the tool’s community forum, experiments with their ML model, and analysis of training documents created by end-users. We also held regular meetings with the tool’s product and machine learning teams. By approaching transparency need-finding from a broad lens, we uncover insights into end-users’ transparency needs as well as unexpected uses and challenges with current transparency mechanisms. Our study is one of the first to incorporate such diverse perspectives to reveal an unbiased and rich view of transparency needs. Lastly, we offer the FAccT community recommendations on broadening transparency need-finding approaches, contributing to the evolving field of transparency research.Conference on Fairness, Accountability and Transparency482This study employs numerous need-finding methods to investigate transparency mechanisms in a widely deployed AI-decision support tool developed by a wildlife conservation non-profit and is one of the first to incorporate such diverse perspectives to reveal an unbiased and rich view of transparency needs.Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency["Violet Turri", "Katelyn Morrison", "Katherine-Marie Robinson", "Collin Abidi", "Adam Perer", "Jodi Forlizzi", "Rachel Dzombak"]2024-06-03T00:00:00 0c3d3e94006e9c0b1d6d12acb73290b390569010Artificial Intelligence (AI) tools have recently gained widespread interest for image creation, but tool developers have largely focused on technical capabilities or specialized domain uses, rather than visual artists as users. We collected survey data from 89 practising visual artists and conducted follow-up interviews with 30 of them, to better understand their diverse needs and values. Through reflexive thematic analysis, we explored visual artists’ attitudes towards collaboration in art creation both with human artists and with AI-and other technology-based support systems. Our results suggest that the focus of popular AI tools on high-quality, finished images does not meet the needs of visual artists. Instead, they wanted reference images, ideation support, and variant exploration. We identified similarities and differences between how visual artists view collaboration with other artists or with machine support, enabling designers of new tools to adopt a more user-centered approach.Graphics Interface252It is suggested that the focus of popular AI tools on high-quality, finished images does not meet the needs of visual artists, and they wanted reference images, ideation support, and variant exploration.{"pages": "34:1-34:9"}["Hannah Johnston", "David Thue"]2024-06-03T00:00:00 b509964e00dc1dfe1b9b78601a63330729dba75bThe integration of artificial intelligence (AI) into the conservation of cultural heritage marks a significant transformation in preservation methodologies, heralding both innovative solutions and complex ethical dilemmas. This article undertakes a comprehensive examination of the multifaceted role AI plays in the conservation and restoration of cultural artifacts, buildings, and sites, underscoring the irreplaceable value of human skills and ethical judgment in this domain. Through an analysis of current research, case studies, and insights from professionals in the field, the paper elucidates how AI technologies—encompassing machine learning algorithms, digital twinning, and predictive maintenance—can enhance the accuracy and efficiency of conservation efforts. However, it simultaneously addresses the ethical quandaries these technologies engender, including the risks of inauthentic restoration, the perpetuation of biases, and the erosion of cultural sensitivity. By advocating for a balanced approach that leverages AI's capabilities while safeguarding against its potential pitfalls, the study calls for the establishment of interdisciplinary governance frameworks and ethical guidelines to navigate the intricate interplay between technological advancement and cultural heritage preservation. Ultimately, the paper posits that the integration of AI into cultural heritage conservation necessitates a symbiotic relationship between technological innovation and the nuanced, irreplaceable human element, ensuring that efforts in preservation are as ethically informed as they are technologically advanced.International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM242It is posits that the integration of AI into cultural heritage conservation necessitates a symbiotic relationship between technological innovation and the nuanced, irreplaceable human element, ensuring that efforts in preservation are as ethically informed as they are technologically advanced.International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM["Kholoud Ghaith"]2024-06-03T00:00:00 f81bef90e2044fd1855d32b08ffbd9b5c6672b9aTechnology, Knowledge and Learning4918The authors conducted a Delphi study involving a survey administered to international professionals followed by in-depth face-to-face discussions with a panel of international researchers to identify key trends and challenges for deploying AI in education.Technol. Knowl. Learn.["Dirk Ifenthaler", "Rwitajit Majumdar", "Pierre Gorissen", "Miriam Judge", "Shitanshu Mishra", "Juliana Raffaghelli", "Atsushi Shimada"]2024-06-04T00:00:00 587c92835b8c2f01fb54056cc0c75b8448e152fdThe advancement of artificial intelligence (AI) hinges on the quality and accessibility of data, yet the current fragmentation and variability of data sources hinder efficient data utilization. The dispersion of data sources and diversity of data formats often lead to inefficiencies in data retrieval and processing, significantly impeding the progress of AI research and applications. To address these challenges, this paper introduces OpenDataLab, a platform designed to bridge the gap between diverse data sources and the need for unified data processing. OpenDataLab integrates a wide range of open-source AI datasets and enhances data acquisition efficiency through intelligent querying and high-speed downloading services. The platform employs a next-generation AI Data Set Description Language (DSDL), which standardizes the representation of multimodal and multi-format data, improving interoperability and reusability. Additionally, OpenDataLab optimizes data processing through tools that complement DSDL. By integrating data with unified data descriptions and smart data toolchains, OpenDataLab can improve data preparation efficiency by 30\%. We anticipate that OpenDataLab will significantly boost artificial general intelligence (AGI) research and facilitate advancements in related AI fields. For more detailed information, please visit the platform's official website: https://opendatalab.com.arXiv.org147OpenDataLab is introduced, a platform designed to bridge the gap between diverse data sources and the need for unified data processing, and will significantly boost artificial general intelligence (AGI) research and facilitate advancements in related AI fields.ArXiv["Conghui He", "Wei Li", "Zhenjiang Jin", "Chaochao Xu", "Bin Wang", "Dahua Lin"]2024-06-04T00:00:00 ba2bc6edcf53dc16086ef80ddd143d2fa5a4ba21Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient-physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.Annals of Internal Medicine874The American College of Physicians (ACP) calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.Annals of Internal Medicine["Nadia Daneshvar", "Deepti Pandita", "Shari M. Erickson", "L. Sulmasy", "Matthew DeCamp"]2024-06-04T00:00:00 0c5555d853ddf867dd3ffd30822bdf2d778a727aThis study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future.Electronics04The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI and provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future.Electronics["N. Bobi\u021ban", "Diana Dumitrescu", "A. Popa", "D. Sahlian", "I. Turlea"]2024-06-04T00:00:00 429f14a690ef733353c1b4eef130fec75d8c1d38PurposeThis study explores the integration of Artificial Intelligence (AI) within family businesses. It seeks to understand how family-owned enterprises navigate the adoption of AI technologies amidst balancing traditional business values and the imperatives of digital transformation. The research addresses the gap in the existing literature by providing insights into the strategic, operational and cultural dynamics influencing AI adoption in family businesses, highlighting the unique challenges and opportunities they face in leveraging AI for competitive advantage while preserving their legacy.Design/methodology/approachEmploying a qualitative research design, this study utilizes semi-structured interviews with key stakeholders in Turkish family businesses actively engaging in AI projects. Purposive sampling was adopted to ensure a diverse representation of industries and AI adoption stages. The interviews aimed to capture in-depth insights into the motivations, strategies and outcomes of AI integration within these enterprises. Thematic analysis was conducted on the interview transcripts to identify recurring themes and patterns, providing a nuanced understanding of the factors driving AI adoption decisions in the context of family business values and traditions.FindingsThe findings reveal that family businesses in Turkey perceive AI as a strategic tool to enhance operational efficiency and customer engagement. However, integrating AI technologies is often met with challenges, including resource constraints, digital literacy gaps and concerns over maintaining family legacy. Notably, businesses that successfully navigate AI adoption tend to employ tailored strategies that align with their core values, involving key family members in the decision-making process and fostering a culture of innovation. The study also highlights the importance of ethical considerations and governance in ensuring AI initiatives resonate with the family business ethos.Research limitations/implicationsThe study’s reliance on qualitative interviews within a single country context limits the generalizability of the findings. Future research could expand the geographical scope and incorporate quantitative methods to validate the identified themes across broader populations. Additionally, exploring the impact of generational differences within family businesses on AI adoption could offer more profound insights. The study underscores the need for a more nuanced understanding of the interplay between technology and tradition in family businesses, suggesting avenues for further investigation into how these enterprises can leverage AI to foster innovation while preserving their legacy.Practical implicationsThis research offers practical guidance for family businesses contemplating AI integration. It emphasizes the importance of aligning AI strategies with family values and involving stakeholders across generations in the adoption process. The findings suggest that family businesses can benefit from investing in digital literacy and fostering a culture open to technological innovation. Additionally, the study highlights the need for robust governance structures to navigate ethical considerations in AI adoption. By adopting a strategic approach to AI integration, family businesses can enhance their competitiveness without compromising their core values, ensuring long-term sustainability and success in the digital era.Social implicationsIntegrating AI in family businesses has significant social implications, particularly regarding employment and preserving the family legacy. The study suggests that thoughtful AI adoption can contribute to job creation and skill development, counteracting concerns over job displacement. Moreover, by leveraging AI to align with their core values, family businesses can reinforce their role as stewards of social and economic stability within their communities. This research underscores the potential of AI to support the intergenerational transfer of knowledge and values, fostering innovation while preserving the unique cultural heritage of family enterprises.Originality/valueThis study contributes to the emerging literature on AI adoption in family businesses by exploring the Turkish context. It fills a gap in the literature by examining the unique challenges and opportunities family businesses face in integrating AI, highlighting the interplay between technological innovation and traditional values. The research offers valuable insights into tailored strategies for successful AI adoption that respect the legacy and ethos of family enterprises. By focusing on the socio-cultural dimensions of technology integration, this study enriches our understanding of how family businesses can navigate digital transformation while preserving their identity.Journal of Family Business Management533The findings reveal that family businesses in Turkey perceive AI as a strategic tool to enhance operational efficiency and customer engagement, and suggest that family businesses can benefit from investing in digital literacy and fostering a culture open to technological innovation.Journal of Family Business Management["Deniz Tun\u00e7alp"]2024-06-04T00:00:00 0b03faf3457cb2a788ccbde9c78dacea5536226fPurpose: The aim of the study was to examine the influence of artificial intelligence on credit risk assessment in banking sector. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that AI-driven models demonstrate superior performance in identifying risky borrowers and capturing complex credit risk patterns compared to traditional methods. Additionally, the integration of explainable AI (XAI) techniques has enhanced transparency and interpretability in credit risk assessment processes, facilitating better understanding among stakeholders and improving decision-making transparency. Unique Contribution to Theory, Practice and Policy: Decision theory & technology acceptance model (TAM) may be used to anchor future studies on influence of artificial intelligence on credit risk assessment in banking sector. Continuously invest in research and development to advance the theoretical understanding of AI-driven credit risk assessment models. This includes exploring the integration of machine learning with behavioral economics theories to better predict borrower behavior and default probabilities. Encourage banks to adopt a hybrid approach that combines the strengths of AI-driven models with human expertise. Develop comprehensive regulatory guidelines and standards to govern the use of AI in credit risk assessment and ensure ethical and responsible practices. This includes establishing transparent model validation and governance frameworks to mitigate the risks of algorithmic bias, data privacy violations, and discriminatory lending practices. Regulatory authorities should also promote industry-wide collaboration and knowledge sharing to foster innovation while safeguarding consumer interests and financial stability.International Journal of Modern Risk Management192The study found that AI-driven models demonstrate superior performance in identifying risky borrowers and capturing complex credit risk patterns compared to traditional methods.International Journal of Modern Risk Management["Michael Brown"]2024-06-04T00:00:00 7277aca036d824a2e88f107bb01a6b90e65da2ceEducation and Information Technologies : Official Journal of the IFIP technical committee on Education192Findings show that there are various activities facilitating learning in different phases of the 6E LbD model and that an evidence-based approach will motivate teacher educators to use the 6E LbD model.Educ. Inf. Technol.["Musa Saimon", "F. Mtenzi", "Z. Lavicza", "K. Fenyvesi", "Maik Arnold", "J. Diego-Mantec\u00f3n"]2024-06-04T00:00:00 bb437894e7e514623adc2e1f220a6781cdc7d085Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various industries, including education. This research paper delves into the role of AI in education, exploring the numerous opportunities it presents and the significant challenges it poses. By analyzing recent developments and studies, the paper aims to provide a comprehensive understanding of AI's impact on teaching, learning, and administrative processes in educational settings. The findings reveal how AI can enhance personalized learning, support educators, improve administrative efficiency, and foster inclusive education. However, it also highlights concerns related to data privacy, algorithmic bias, ethical considerations, and the need for effective implementation strategies. The paper concludes by emphasizing the importance of striking a balance between harnessing AI's potential and addressing its challenges to create an AI-powered educational landscape that benefits all stakeholders. Keywords: Artificial Intelligence, Education, Personalized Learning, Teacher Support, Educational Outcomes, Data Privacy, Ethical Considerations, Teacher TrainingINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT01The findings reveal how AI can enhance personalized learning, support educators, improve administrative efficiency, and foster inclusive education, but also highlights concerns related to data privacy, algorithmic bias, ethical considerations, and the need for effective implementation strategies.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Pervin Kumar Malik"]2024-06-04T00:00:00 4933135516e59ca816d0a341d9423fba87cbae1cAs the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English-language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.Current Alzheimer Research01A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease.Current Alzheimer research["Shadi Farabi Maleki", "M. Yousefi", "Navid Sobhi", "Ali Jafarizadeh", "R. Alizadehsani", "J. Gorriz-Saez"]2024-06-04T00:00:00 91a2646204c8641d87e02f3596d6b57524bc216fHow will instructors effectively manage the use of artificial intelligence (AI) with generative capabilities in entrepreneurship education? This paper introduces a framework to help instructors understand the different roles played by AI and learners as they move through different phases of a learning task. We describe the Artificial Intelligence in Entrepreneurship Education (AIEE) framework to outline the roles and relationships that instructors, students, and AI systems may play as part of a learning task in the classroom. We articulate key takeaways for instructors to recognize the responsibilities that are associated with different roles at different phases of a learning task.Entrepreneurship Education and Pedagogy181The Artificial Intelligence in Entrepreneurship Education (AIEE) framework is described to outline the roles and relationships that instructors, students, and AI systems may play as part of a learning task in the classroom.Entrepreneurship Education and Pedagogy["Joseph D. Fox", "Luke Pittaway", "I. Uzuegbunam"]2024-06-04T00:00:00 101637df6ac66b64755ca64935482fa6a48fffd9This study aims to investigate the relationships between artificial intelligence in human resources (HR), technology competence, and HR system strength within organizations. Employing a cross-sectional methodology, survey data were collected from 272 employees working in HR departments in the private sector of Saudi Arabia. Partial least squares structural equation modeling was utilized for analysis to evaluate these relationships. The results indicate a significant positive relationship between artificial intelligence in HR and both technology competence (β = 0.444, p &lt; 0.001) and HR system strength (β = 0.539, p &lt; 0.001). Additionally, there is a positive impact of technology competence on HR system strength (β = 0.272, p = 0.021). These findings underscore the importance of investing in AI technologies and enhancing employees’ technological skills to improve HR system effectiveness. Furthermore, the study emphasizes the necessity for organizations to prioritize agility and adaptability in HR strategies while addressing ethical and social considerations surrounding AI in HR practices. Moreover, the study elucidates the role of artificial intelligence in fostering innovation and sustainability within HR practices, contributing to organizational resilience and competitiveness. AcknowledgmentThe author extends her appreciation to the Arab Open University for funding this work through Research Fund No. (AOUKSA-524008).Problems and Perspectives in Management281The results indicate a significant positive relationship between artificial intelligence in HR and both technology competence and HR system strength and the role of artificial intelligence in fostering innovation and sustainability within HR practices, contributing to organizational resilience and competitiveness.Problems and Perspectives in Management["Sura Al-Ayed"]2024-06-04T00:00:00 012b9ff2b9e755f084c21ae1d7ce9be206430428This paper provides an examination of how Artificial intelligence applies in cyber security's difficult job of preventing and guarding information. Dealing with broad attack surface, big number of applications and large number of users makes the defended territory too vast to deal with [1]. All of this creates challenges for cyber security with the large amounts of data to be analyzed and understood. Traditional security methods are not enough to stop cybercriminals from breaching data and inflicting damage. Artificial Intelligence, with machine learning algorithms, continuous learning mechanisms and real-time data processing, offers fundamental tools to cybersecurity to use and enhance approaches to recognize network intrusions, data breaches, phishing and spam emails, malware attacks, and to alert security vulnerability when appears.2024 Ninth Junior Conference on Lighting (Lighting)2202024 Ninth Junior Conference on Lighting (Lighting)["Elina Tlachenska", "Kiril Ivanov", "Maria Nenova", "Zlatka Valkova-Jarvis", "K. Kassev"]2024-06-04T00:00:00 cf22ccfe839a3bc7c66ef37477ce0da5ca6707d6PurposeAn emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.Design/methodology/approachWe first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.FindingsWe identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.Originality/valueWe increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.Industrial management & data systems1100The main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies.Ind. Manag. Data Syst.["Philip T. Roundy", "Arben Asllani"]2024-06-04T00:00:00 fe34ab0d6ccb0a2c40cee98ed5be7c5955bf1bf8The onslaught of artificial intelligence (AI) in the global scientific and industrial landscape has brought with it far-reaching implications into how the software development process can be transformed. This article presents a systematic literature review focused on the integration of AI into the software development life cycle (SDLC) with a specific emphasis on sustainability. The research explores the application of AI in all facets of the SDLC. To this end, we structure 34 primary studies into the different stages of the SDLC, including requirements elicitation, analysis/design, development, testing, and deployment, while considering multiple dimensions of sustainability. Our findings present a synthesis of commonly used AI approaches under various aspects. These encompass guidelines for (i) automating requirements formulation, (ii) designing sustainable software, (iii) enhancing energy efficiency and code reuse, and (iv) effectively testing software. Environmental sustainability was found to be the most common dimension in the literature, primarily addressing energy efficiency and electronic waste. Additionally, we identify gaps in the literature, particularly the absence of addressing AI in SDLC from the angle of social sustainability and the lack of integration into developer toolkits.2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C)590A systematic literature review focused on the integration of AI into the software development life cycle (SDLC) with a specific emphasis on sustainability found environmental sustainability was found to be the most common dimension in the literature, primarily addressing energy efficiency and electronic waste.2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C)["Eames Trinh", "Mark C. Funke", "Patricia Lago", "Justus Bogner"]2024-06-04T00:00:00 279df72a7dcde86eee33c7279cfbf72c5374f649GeroScience430The patient-physician relationship, social reality, redistribution of resources, fair access, as well as data-related aspects of the artificial intelligence-based system could conflict with the ethical principles of autonomy, non-maleficence, beneficence, and social justice.GeroScience["Nina Parchmann", "David Hansen", "M. Orzechowski", "F. Steger"]2024-06-04T00:00:00 9571f6a2bb420ef96bea92c8297ea92a6de8ef6cThis study investigates how artificial intelligence might changeNanotechnology Perceptions170Nanotechnology Perceptions["Khalid Waleed", "Abdo"]2024-06-04T00:00:00 534c67eaffd034a5edbc2f4673f41850e9ba0dab Despite the limited use of artificial intelligence/knowledge-based engineering (AI/KBE) in industries with small series or one-off designs, our study demonstrates the technical feasibility and potential benefits of implementing AI/KBE in ship design processes. This research presents the development of “ACQUAINT,” uniquely designed to address the complexities inherent in bespoke shipbuilding. Central to this module is a robust AI-driven inference engine, integrated seamlessly with AutoCAD through a Python-based interface, facilitating a novel approach in shipbuilding’s detail and production design phases. The module’s capability to generate optimal designs autonomously—grounded in a deep understanding of design rules, constraints, and requirements—substantially reduces the reliance on human interaction. Our initial proof of concept with “ACQUAINT” showcases measurable advancements in ship design accuracy and efficiency, highlighting AI KBE’s transformative impact in shipbuilding and setting a foundation for future research and practical applications. ship design; artificial intelligence; knowledge-based engineering; self-learning system; software development; ACQUAINT; CAD/CAM software; computers in construction; computers in design; modernization; ship structure Journal of Ship Production and Design00This research presents the development of “ACQUAINT,” uniquely designed to address the complexities inherent in bespoke shipbuilding, a robust AI-driven inference engine integrated seamlessly with AutoCAD through a Python-based interface, facilitating a novel approach in shipbuilding’s detail and production design phases.Journal of Ship Production and Design["Tufail Shahzad", "Peng Wang", "Peter Van lith", "Jacques Hoffmans"]2024-06-04T00:00:00 93502d04ed871cfd2494005b3f8bf6745d6e719aINTRODUCTION: scientific journals play a crucial role in the dissemination and validation of scientific knowledge, and editorial management ranges from conceptualization to post-publication of content. Artificial intelligence (AI) has had a great impact on scientific communication and editorial management of scientific journals. AI can offer solutions and benefits for editorial management, but it also poses technical, economic, social and ethical challenges that should be considered and approached with caution and responsibility. OBJECTIVE: to describe the benefits and limitations of the use of AI in the editorial management of scientific journals through a systematic literature review. METHOD: a systematic literature review was conducted based on the PRISMA methodology. An information search was carried out in different bibliographic database systems, indexing systems and search engines, and inclusion and exclusion criteria were applied to the identified studies. RESULTS: the information search allowed retrieving a total of 2750 sources, of which 10 articles that met the stated criteria were included. Benefits such as the facilitation of writing, translation, review and editing tasks were identified, as well as limitations related to ethical issues, bias, errors and plagiarism generated by AI. CONCLUSIONS: While AI can streamline the production and analysis of information distribution, it also poses challenges in terms of reliability, ethics and authenticity of published content. It requires the critical involvement of human intelligence for proper exploitation.EAI Endorsed Transactions on AI and Robotics240Benefits such as the facilitation of writing, translation, review and editing tasks were identified, as well as limitations related to ethical issues, bias, errors and plagiarism generated by AI.EAI Endorsed Transactions on AI and Robotics["Carlos Rafael Araujo Inastrilla", "Mayelin Llosa Santana", "Dayami Guti\u00e9rrez Vera", "Mar\u00eda del Carmen Roche Madrigal", "Alejandro Rodr\u00edguez Urrutia", "Alejandro Araujo Inastrilla"]2024-06-04T00:00:00 a2affae89203e39f66fde9ae96f77751d88481c6This comprehensive paper seeks to explore the intricate intersection between artificial intelligence (AI) and fundamental human rights, shedding light on pivotal areas including Privacy & Surveillance, Bias in Decision Systems, and Autonomous Systems. Through an exhaustive analysis of scholarly literature and contemporary advancements, this paper aims to unveil the complex interplay between AI technologies and the safeguarding of human rights. Moreover, it integrates viewpoints derived from students representing diverse academic backgrounds across numerous universities in North Macedonia, elicited through a meticulously crafted questionnaire. In essence, this paper endeavors to provide a holistic understanding of the multifaceted relationship between AI and human rights, drawing upon academic research, real-world examples, and the perspectives of the next generation of thinkers and leaders. By delving into these critical areas and synthesizing insights from various sources, it seeks to contribute to ongoing discourse and facilitate informed discussions on the ethical implications and societal ramifications of AI advancements.Sakarya University Journal of Computer and Information Sciences160Sakarya University Journal of Computer and Information Sciences["Enes Bajrami"]2024-06-04T00:00:00 58231c1670710307c979a9e2f166f6eeabecb5abOR spectrum290It is found that the adequate choice of XAI technique is crucial when the data generating process is unknown and the application of boosted regression trees in combination with Shapley values combines both a superior fit to the data and innovative interpretable insights into non-linear impact factors.OR Spectrum["Theo Berger"]2024-06-04T00:00:00 2bf24e85fc33ded30024feff789e796d61624106Aim The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. Material and methods The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as "artificial intelligence," "deep learning," "machine learning," "forensic medicine," "legal medicine," "forensic pathology" and "medicine" were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Conclusions Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.Archives of Forensic Medicine and Criminology460Questions related to providing medico-legal opinions and legal liability in cases in which possible harm is likely to occur when using an advanced system of computer-based information processing and analysis are identified.Archiwum medycyny sadowej i kryminologii["Micha\u0142 Szeremeta", "Julia Janica", "Anna Niemcunowicz-Janica"]2024-06-04T00:00:00 e3fcce07b1bb9343c8bd0f82aa0bb733b517040bTrials470The precise impact of the AI system on the detection performance for intracranial aneurysms is determined in a double-blinded design and following the real-world effects on patients’ short-term and long-term outcomes.Trials["Zhao Shi", "Bin Hu", "Mengjie Lu", "Zijian Chen", "Manting Zhang", "Y. Yu", "C. Zhou", "Jian Zhong", "Bingqian Wu", "Xueming Zhang", "Yongyue Wei", "Long Jiang Zhang"]2024-06-04T00:00:00 9cc52c7024d30b058ecffee0042e6defd14aefeeArtificial intelligence is an ever-growing modality revolutionizing the field of medical science. It utilizes various computational models and algorithms and helps out in different sectors of healthcare. Here, in this scoping review, we are trying to evaluate the use of Artificial intelligence (AI) in the field of pediatric anesthesia, specifically in the more challenging domain, the pediatric airway. Different components within the domain of AI include machine learning, neural networks, deep learning, robotics, and computer vision. Electronic databases like Google Scholar, Cochrane databases, and Pubmed were searched. Different studies had heterogeneity of age groups, so all studies with children under 18 years of age were included and assessed. The use of AI was reviewed in the preoperative, intraoperative, and postoperative domains of pediatric anesthesia. The applicability of AI needs to be supplemented by clinical judgment for the final anticipation in various fields of medicine.Saudi Journal of Anaesthesia210The use of AI was reviewed in the preoperative, intraoperative, and postoperative domains of pediatric anesthesia, and the applicability of AI needs to be supplemented by clinical judgment for the final anticipation in various fields of medicine.Saudi Journal of Anaesthesia["Sugandhi Nemani", "S. Goyal", "Ankur Sharma", "Nikhil Kothari"]2024-06-04T00:00:00 15ddf729a6f9b752d50750b7d1d8f7a5b64e7f02The term ‘artificial intelligence’ (AI) is used to describe the application of computers and technology to mimic human problem solving and creativity. The possibility of AI in medicine is rapidly evolving and its utility in clinical practice may soon become commonplace. The application of AI in medicine has been considered an opportunity to advance medicine as it helps to store, analyze, and interpret large amounts of data and lead to increased diagnostic accuracy, speed, and optimize treatment strategies. On the other hand, many physicians are concerned that AI will replace medical professionals and lead to the ‘dehumanization’ of medicine. In medicine, the evolution of AI promises better outcomes through more efficient diagnosis and accuracy of individualized treatments. As such, appropriate regulatory policies must be explored to ensure the safe implementation of AI in medicine to avoid losing the humanistic art of medical practice. The aim of this correspondence is to shed light on the AI in medicine, advantages, and their disadvantages in this today and future medical field.International Journal of Surgery Open160The aim of this correspondence is to shed light on the AI in medicine, advantages, and their disadvantages in this today and future medical field.International Journal of Surgery Open["Izere Salomon", "Sibomana Olivier"]2024-06-04T00:00:00 960333a8380306eff49ee9f2556ade29cae9c684Dear editor, we hereby discuss the publication “ All aboard the ChatGPT steamroller: Top 10 ways to make artificial intelligence work for healthcare professionals. ” 1 Non has already discussed several of the restrictions identified. The purpose of this letter is to underline and highlight some additional limitations of Large Langauge Model (LLM) that were not previously mentioned by the original author. While there are a number of possible advantages to ChatGPT integration in medicine, there are also a number of disadvantages and issues that need to be resolved. Although ChatGPT is a language model that has been extensively trained on data, it might not have the medical background or context required to deliver accurate and trustworthy results. Medical practitioners depend on evidence-based procedures, thus, there ’ s a chance ChatGPT will give inaccurate or misleading information, which could result in medical mistakes. 2 Because ChatGPT relies on its training data to function, privacy and bias problems are brought up ethically. The artificial intelligence (AI) chatbot can unintentionally reinforce prejudice or discrimination in healthcare encounters if it is not built using a broad and representative dataset. Furthermore, the protection of patient data has to be a top concern, and stringent privacy regulations must be followed while utilizing AI systems. 3Antimicrobial Stewardship and Healthcare Epidemiology90Some additional limitations of Large Langauge Model (LLM) are highlighted to underline and highlight some additional limitations of Large Langauge Model that were not previously mentioned by the original author.Antimicrobial Stewardship & Healthcare Epidemiology : ASHE["H. Daungsupawong", "V. Wiwanitkit"]2024-06-04T00:00:00 b51d8e5c2691ffd971e1aaeaa0111124d63c1c49Understanding the use of AI in healthcare is essential for the successful implementation of AI-driven healthcare solutions. The aim of this study was to evaluate public perception of AI utilization in healthcare settings. A validated questionnaire assessed general perceptions towards AI utilization, the use of AI by physician , and the use of AI by pharmacists . The study included 770 participants. The median perception score indicated an unfavorable attitude. Participants who had lower education level and those with no employment had a significantly lower perception scores than their counterpart. Participants who reported low income and those who visited the pharmacy five to ten times on average had a higher perception than their counterparts did. The reported negative perception necessitates the implementation of education campaigns to improve AI literacy and dispel any misconceptions and concerns, particularly among individuals with low education, high income, unemployment, and frequent pharmacy visits.International Journal of Environmental Health Research193The reported negative perception necessitates the implementation of education campaigns to improve AI literacy and dispel any misconceptions and concerns, particularly among individuals with low education, high income, unemployment, and frequent pharmacy visits.International journal of environmental health research["A. Jarab", "W. Al-Qerem", "Dua'a M Al-Hajjeh", "S. A. Abu Heshmeh", "T. Mukattash", "Abdallah Y. Naser", "H. Alwafi", "Yazid N. Al Hamarneh"]2024-06-04T00:00:00 86da78160dd84064f0b83d0233ad75055f88909eJournal of system and management sciences02Journal of System and Management Sciences[]2024-06-04T00:00:00 8d7b665a6a8b7afdbe4293f5b6e61bfcfb019116Nanotechnology Perceptions01Nanotechnology Perceptions[]2024-06-04T00:00:00 0a55c9d3b9670e94badb7988134877da34762134Nanotechnology Perceptions01Nanotechnology Perceptions[]2024-06-04T00:00:00 65aa85ccf2508f15f84a47afbf7caa3453e754b8Internet Reference Services Quarterly321Internet Reference Services Quarterly["L. Giray", "Paolo Yves De Silos", "Adonis Adornado", "Robbie Jan Vincent Buelo", "Elbert M. Galas", "Ethel Reyes-Chua", "C. Santiago", "Ma Leah Ulanday"]2024-06-04T00:00:00 157e9575c1ccef8bfd35bea671522456d49d5321Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-06-04T00:00:00 8be7bdb19dbe92908f17d3b7b9cd8205c00a92adNanotechnology Perceptions00Nanotechnology Perceptions[]2024-06-04T00:00:00 2416a334f2fe7a17fa475ad5b19f2c944500da73Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-06-04T00:00:00 0788569ffc20a8425bfbdbeb8ff2859fd3ca7668Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-06-04T00:00:00 73cd76f7e177d7e1f5f21c5dc740e5bf30c8880bIndian Journal of Neurotrauma50Indian Journal of Neurotrauma["Ahtesham Khizar"]2024-06-04T00:00:00 89910209661798b55a9f7f79880d311ade8ce424Psychiatry10Psychiatry["Utsav Poudel", "Tony P Jose"]2024-06-04T00:00:00 4f670dad88aa70863ae3fa08561e66004dd991a2Purpose The objective of this study was to develop a novel AI-ensembled network based on the most important features and affected brain regions to accurately classify and exhibit the pattern of progression of the stages of Cognitive Impairment (CI). Methods We proposed a novel ensembled architecture, 3D ResNet-18 - RF (Random Forest), and used this network to categorize the stages of Alzheimer’s disease (AD). The residual unit (blocks of ResNet) was introduced to the 3D Convolutional Neural network (CNN) to solve the degradation problem. It was considered an innovative strategy since the combination with fine-tuning resulted in higher accuracy. This network was trained on selected features and affected brain regions. The structured magnetic resonance images (MRI) were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, and the random forest was used for determining the importance of the features and affected regions from the parcellated 170 regions of interest (ROIs) using Atlas, automated anatomical labeling 3(AAL-3). This framework classified five categories of AD and detected the progression pattern. Results The proposed network showed promising results with a 66% F-1 score, 76% sensitivity, and 93.5% specificity, which outperformed the performance of conventional methods for categorizing five categories. Ventral Posterolateral and Pulvinar lateral regions were the regions most affected, indicating the progression from early MCI to AD. The five-fold validation accuracy for the developed model was 60.02%. Conclusion The results showed that the gray matter to white matter ratio was the most significant feature, which also accurately predicted the progression pattern. The performance metrics fluctuated with different hyperparameters, but they never exceeded 0.05% of the estimated results, indicating the validity and originality of the suggested methodology.bioRxiv740A novel ensembled architecture, 3D ResNet-18 - RF (Random Forest), was proposed and used to categorize the stages of Alzheimer’s disease (AD), and showed promising results, which outperformed the performance of conventional methods for categorizing five categories.bioRxiv["Md Mehedi Hasan", "Senjuti Rahman", "Harshit Parmar", "Suman K. Chowdhury"]2024-06-04T00:00:00 0818b6175d944b473abdcb89f5665a7371fa5853Nordic Journal of Digital Literacy160Nordic Journal of Digital Literacy["R. Krumsvik"]2024-06-04T00:00:00 59e0fe044aad7bfb408793afd5907e87b2a660b6Journal of Data, Information and Management00Journal of Data, Information and Management["Rebaka Sultana", "M. Faruk"]2024-06-04T00:00:00 5aacf780ec16a29bdbe283a14f5a9e6b7e1f292dAn Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or makes decisions based on pre-defined objectives and data inputs. AI agents, capable of perceiving user inputs, reasoning and planning tasks, and executing actions, have seen remarkable advancements in algorithm development and task performance. However, the security challenges they pose remain under-explored and unresolved. This survey delves into the emerging security threats faced by AI agents, categorizing them into four critical knowledge gaps: unpredictability of multi-step user inputs, complexity in internal executions, variability of operational environments, and interactions with untrusted external entities. By systematically reviewing these threats, this paper highlights both the progress made and the existing limitations in safeguarding AI agents. The insights provided aim to inspire further research into addressing the security threats associated with AI agents, thereby fostering the development of more robust and secure AI agent applications.ACM Computing Surveys1965This survey delves into the emerging security threats faced by AI agents, categorizing them into four critical knowledge gaps: unpredictability of multi-step user inputs, complexity in internal executions, variability of operational environments, and interactions with untrusted external entities.ArXiv["Zehang Deng", "Yongjian Guo", "Changzhou Han", "Wanlun Ma", "Junwu Xiong", "Sheng Wen", "Yang Xiang"]2024-06-04T00:00:00 f794e124efa77171cc47eb4786a1a6638ae72765As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regulation take centre stage. In this paper, we explore the security and privacy risks of generative AI from model-level and data-level perspectives. Moreover, we elucidate the potential consequences and case studies within the domain of ophthalmology. Model-level risks include knowledge leakage from the model and model safety under AI-specific attacks, while data-level risks involve unauthorised data collection and data accuracy concerns. Within the healthcare context, these risks can bear severe consequences, encompassing potential breaches of sensitive information, violating privacy rights and threats to patient safety. This paper not only highlights these challenges but also elucidates governance-driven solutions that adhere to AI and healthcare regulations. We advocate for preparedness against potential threats, call for transparency enhancements and underscore the necessity of clinical validation before real-world implementation. The objective of security and privacy improvement in generative AI warrants emphasising the role of ophthalmologists and other healthcare providers, and the timely introduction of comprehensive regulations.British Journal of Ophthalmology493The objective of security and privacy improvement in generative AI warrants emphasising the role of ophthalmologists and other healthcare providers, and the timely introduction of comprehensive regulations, and elucidates governance-driven solutions that adhere to AI and healthcare regulations.British Journal of Ophthalmology["Yueye Wang", "Chi Liu", "Keyao Zhou", "Tianqing Zhu", "Xiaotong Han"]2024-06-04T00:00:00 2629b364ad0ab0a5b7274b24c82aebea10b102f6Science and Engineering Ethics333Insight is provided into the underlying ideas present in AI guidelines and how guidelines influence the practice and alignment of AI with ethical, legal, and societal values expected to shape AI in healthcare.Science and Engineering Ethics["Laura Arbelaez Ossa", "Stephen R. Milford", "M. Rost", "Anja K. Leist", "D. Shaw", "B. Elger"]2024-06-04T00:00:00 c94072ba76c9871b54236aa46d86ac96d0f09d3aAI &amp; SOCIETY413This paper will contribute to the field of philosophy of technology by using Foucault's analysis to examine assumptions found in HCAI, which provides a Foucauldian conceptual analysis of a current approach (human-centredness) that aims to influence the design and development of a transformative technology (AI).AI &amp; SOCIETY["M. Ryan"]2024-06-04T00:00:00 6da4a409ef3391a821d71182a4f282cd70af8789Scientific Reports382The task of creating ideal NPs has been formulated as a multi-objective optimization problem and the proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred.Scientific Reports["Vishakha Singh", "Sanjay Kumar Singh", "Ritesh Sharma"]2024-06-04T00:00:00 f58760f459a727a33bcc76bf96b10534a38b5a6bImprovements in networking technologies and the steadily increasing numbers of users, as well as the shift from traditional broadcasting to streaming content over the Internet, have made video applications (e.g., live and Video-on-Demand (VoD)) predominant sources of traffic. Recent advances in Artificial Intelligence (AI) and its widespread application in various academic and industrial fields have focused on designing and implementing a variety of video compression and content delivery techniques to improve user Quality of Experience (QoE). However, providing high QoE services results in more energy consumption and carbon footprint across the service delivery path, extending from the end user's device through the network and service infrastructure (e.g., cloud providers). Despite the importance of energy efficiency in video streaming, there is a lack of comprehensive surveys covering state-of-the-art AI techniques and their applications throughout the video streaming lifecycle. Existing surveys typically focus on specific parts, such as video encoding, delivery networks, playback, or quality assessment, without providing a holistic view of the entire lifecycle and its impact on energy consumption and QoE. Motivated by this research gap, this survey provides a comprehensive overview of the video streaming lifecycle, content delivery, energy and Video Quality Assessment (VQA) metrics and models, and AI techniques employed in video streaming. In addition, it conducts an in-depth state-of-the-art analysis focused on AI-driven approaches to enhance the energy efficiency of end-to-end aspects of video streaming systems (i.e., encoding, delivery network, playback, and VQA approaches). Finally, it discusses prospective research directions for developing AI-assisted energy-aware video streaming systems.arXiv.org2152A comprehensive overview of the video streaming lifecycle, content delivery, energy and Video Quality Assessment (VQA) metrics and models, and AI techniques employed in video streaming and an in-depth state-of-the-art analysis focused on AI-driven approaches to enhance the energy efficiency of end-to-end aspects of video streaming systems.ArXiv["Reza Farahani", "Zoha Azimi Ourimi", "C. Timmerer", "R.-C. Prodan"]2024-06-04T00:00:00 70d7e717881471fd53ba6070bae055a6ede57437All-day augmented reality (AR) requires contextually-aware artificial intelligence (AI) models that excel across diverse daily contexts. Eye tracking could be a key source of information about user context and intention. However, such models using gaze sometimes struggle to outperform egocentric video-based baseline models. We propose that learning representations of scanpath history in a perceptually-relevant state space may solve this problem. However, scanpaths are often assumed to obey a Markovian assumption, i.e., only the current and previous fixation matter. In a user study (30 participants; 26.2 hours total), we analyzed scanpaths during nine everyday tasks and identified long-term temporal dependencies, with an average timescale of four fixations (2 seconds) into the past (i.e., violating the Markovian assumption). We discovered substantial task-specific variations in these dependencies. This confirms that scanpaths contain stereotyped “motifs” with context-dependent lengths/timescales. We discuss the implications for designing contextual AI models for AR applications.Eye Tracking Research & Application502It is confirmed that scanpaths contain stereotyped “motifs” with context-dependent lengths/timescales with context-dependent lengths/timescales, and the implications for designing contextual AI models for AR applications are discussed.Proceedings of the 2024 Symposium on Eye Tracking Research and Applications["C. Burlingham", "Naveen Sendhilnathan", "Xiuyun Wu", "T. Scott Murdison", "Michael J. Proulx"]2024-06-04T00:00:00 4b271d4ab0ee1bc06daf59e6884f184d8dc0ab16Nowadays, a significant share of the Business-to-Consumer sector is based on online platforms like Amazon and Alibaba and uses Artificial Intelligence for pricing strategies. This has sparked debate on whether pricing algorithms may tacitly collude to set supra-competitive prices without being explicitly designed to do so. Our study addresses these concerns by examining the risk of collusion when Reinforcement Learning algorithms are used to decide on pricing strategies in competitive markets. Prior research in this field focused on Tabular Q-learning (TQL) and led to opposing views on whether learning-based algorithms can lead to supra-competitive prices. Our work contributes to this ongoing discussion by providing a more nuanced numerical study that goes beyond TQL by additionally capturing off- and on-policy Deep Reinforcement Learning (DRL) algorithms. We study multiple Bertrand oligopoly variants and show that algorithmic collusion depends on the algorithm used. In our experiments, TQL exhibits higher collusion and price dispersion phenomena compared to DRL algorithms. We show that the severity of collusion depends not only on the algorithm used but also on the characteristics of the market environment. We further find that Proximal Policy Optimization appears to be less sensitive to collusive outcomes compared to other state-of-the-art DRL algorithms.261This work studies multiple Bertrand oligopoly variants and shows that algorithmic collusion depends on the algorithm used, and finds that Proximal Policy Optimization appears to be less sensitive to collusive outcomes compared to other state-of-the-art DRL algorithms.["Shidi Deng", "Maximilian Schiffer", "Martin Bichler"]2024-06-04T00:00:00 a40650b5f6e79ffb52122acdce443e7235bd4000The burgeoning interconnectedness of global trade in the digital age not only presents enticing opportunities but also harbors potent vulnerabilities of artificial intelligence (AI)-driven cyberattacks. This study explores the cascading impacts of these disruptive threats on economies, supply chains, and trade, utilizing the intricate lens of Computable General Equilibrium modeling. Through meticulously designed simulation scenarios, we illuminate the potential economic ramifications of cyberattacks, with a focus on regions heavily reliant on digital technologies and interwoven supply chains. The analysis reveals significant declines in real GDP, trade prices and volumes, and trade route disruptions across regions. Notably, economies like China, the United States, the United Kingdom, and the EU, due to their deep integration in global networks, face pronounced vulnerabilities. However, amidst this bleak landscape, hope emerges in the form of cyber resilience. The study showcases the effectiveness of proactive measures like adaptable production systems, diversified trade partners, and robust cybersecurity infrastructure in mitigating the adverse impacts of cyberattacks. Incorporating cyber resilience significantly dampens the reported negative consequences, highlighting the critical role of preparedness in combating digital warfare. This study underscores the urgent need for a global paradigm shift toward cyber resilience. Collective efforts to bolster cybersecurity infrastructures, foster international cooperation in threat intelligence, and establish open and resilient trade frameworks are crucial in navigating the treacherous labyrinth of AI-driven cyberattacks. By embracing resilience strategies and fostering global collaboration, we can pave the way for a more secure and prosperous digital future, where interconnectedness becomes a tool for progress, not a vulnerability to be exploited.Risk Analysis151The study showcases the effectiveness of proactive measures like adaptable production systems, diversified trade partners, and robust cybersecurity infrastructure in mitigating the adverse impacts of cyberattacks, and underscores the urgent need for a global paradigm shift toward cyber resilience.Risk analysis : an official publication of the Society for Risk Analysis["Rehab Osman", "Sherif El-Gendy"]2024-06-04T00:00:00 b0a1fcc75e4be93fba782055bf2e03da3c4a8b68This paper provides a comprehensive overview of the evolving intersection between Space Medicine and Artificial Intelligence (AI), tracing its journey from nascent conceptualizations to its current state and projecting future trends. For the purposes of this overview, only specific currently available AI methodologies will be used. These include Machine Learning, Deep Learning, Convolution Neural Networks, Recurrent Neural Networks, and Natural Language Processing. Initially, this exploration delves into the historical context, examining how early space missions recognized the need for medical monitoring and support, and the rudimentary role early forms of AI played in these stages. The paper then transitions to the present, highlighting current advancements where AI has become integral in diagnosing and managing health issues in space, optimizing life support systems, and enhancing astronauts’ physical and psychological well-being. Significant focus is placed on current AI-driven technologies, such as predictive algorithms for health risks, robotic surgical tools, and AI-assisted mental health support. Looking ahead, the paper explores potential future developments, envisioning a scenario where AI not only augments space medicine but becomes a critical component in long-duration interplanetary missions. This includes AI’s role in autonomous medical systems, personalized medicine, and in addressing the unique challenges of deep space travel. The paper concludes with a discussion on the ethical, logistical, and technical challenges that lie ahead, emphasizing the need for robust, ethically guided AI frameworks to ensure the safety and health of astronauts as humanity ventures further into the cosmos.JBIS - Journal of the British Interplanetary Society01Journal of the British Interplanetary Society["Vanessa Farsadaki", "Denis Leclerc", "C. Griffy-Brown"]2024-06-04T00:00:00 793891125e338f89c5f9ea8bf348f2c37407920cAI &amp; SOCIETY1261The scope of ethnomethodological and conversation analytic approaches that treat AI as a phenomenon emerging in and through the situated organization of social interaction is reviewed, finding that across this corpus, studies center on three key themes: openings and closing the interaction, miscommunication, and non-verbal aspects of interaction.AI &amp; SOCIETY["J. Mlyn\u00e1\u0159", "Lynn de Rijk", "Andreas Liesenfeld", "Wyke J P Stommel", "Saul Albert"]2024-06-04T00:00:00 263b32b79b7e59e83569b9f36e355f54a57fec85 Concerning a posthuman perspective, this paper attempts to provide a new perspective on future changes in teaching and learning in the age of artificial intelligence. With the development of technological civilisation, humans have adapted to the environmental world while at the same time attempting to remould it using technology and tools. Humans have survived by acquiring new skills and abilities to manipulate technology and tools. Human Transformation (HX), updated to respond to technological innovations, is now upcoming human intellectual activities through AI technology. What are the challenges of HX in the age of AI, and what perspectives will be critical in this process? This paper traces back to how machines with computational intelligence or reasoning functions were named ‘artificial intelligence’ that can reproduce human intellectual activities. It examines the wide-ranging social impact of the naming of AI and the growing phenomenon of expectations and anxieties about AI. It then notes two sources behind the posthuman debate. The first is the trend towards an upgraded stage of human intelligence over the current human by enhancing it through medical and even AI-based technology. The second trend seeks a new direction for post-humanity by focusing on its diversity, such as society and culture, through a critical examination of the view that uniformly evaluates all human conditions through a universal model of human beings. Navigating them is an excellent educational challenge. Focusing on the similarities and differences between human intelligence and artificial intelligence, the paper examines the challenges of education to develop the unique characteristics of human intelligence further and achieve freedom from AI technology, considering the legal, ethical and social issues (ELSI) of making wise use of AI. Teoría de la Educación: Revista Interuniversitaria551Focusing on the similarities and differences between human intelligence and artificial intelligence, the paper examines the challenges of education to develop the unique characteristics of human intelligence further and achieve freedom from AI technology, considering the legal, ethical and social issues of making wise use of AI.Teoría de la Educación. Revista Interuniversitaria["Shoko Suzuki"]2024-06-04T00:00:00 e7745151b4fcfb7c691cbd2c1e0c999dfc14e94cCement manufacturing is known for its significant energy consumption and environmental footprint. As the world strives for sustainability, optimizing electrical energy consumption (EEC) in cement manufacturing is essential for reducing operational costs and minimizing the industry’s environmental impact. This systematic review aims to synthesize and analyze existing scholarly works and industry reports on methods and approaches for EEC optimization in cement production. It examines papers published between 1993 and 2023 in academic databases, scholarly journals, and industry publications to identify open questions and areas where future research may be needed. While challenges remain, continued research and innovation are key to further advancements in energy efficiency in cement production. With the advent of Industry 4.0 digitalization and advancements in data analytics and industrial Internet of Things (IIoT), artificial intelligence (AI) can be leveraged to optimize EEC. This study is a review of the applications of artificial intelligence to EEC optimization in industries that have heavy demand for electric power to highlight the value of directing research to its applications in cement manufacturing. The study posits that with digitalization, applying artificial intelligence to extract operational insights from the data collected from embedded sensors and meters at the plant presents the most cost-effective, high-return, and low-risk opportunity to optimize EEC in cement manufacturing.Sustainability344Sustainability["Olurotimi Oguntola", "Kwaku Boakye", "Steve Simske"]2024-06-05T00:00:00 bef4a67110748319ea4e3a0b8dfe83e02eaa280bCurrent pain and headache reports403The current and future role of artificial intelligence (AI) and virtual reality (VR) in addressing the complexities inherent to the diagnosis, classification, and management of headache disorders are provided.Current pain and headache reports["Ivo H Cerda", "Emily Zhang", "M. Dominguez", "Minhal Ahmed", "Min Lang", "Sait Ashina", "M. Schatman", "R. J. Yong", "Alexandra C G Fonseca"]2024-06-05T00:00:00 a4c8076bf078f6944f23b2b7a6851432d740fcd8The purpose of the article is to identify the most optimal methods for managing the use of artificial intelligence in educational purposes. For this, the object of research is the information security system of the educational institution. The scientific task involves the formation of a modern method for detecting and organizing the most optimal methods for managing the use of artificial intelligence, which will strengthen the level of information security. As a result of the conducted research, the key methods of managing the use of artificial intelligence in educational purposes were systematized, based on the use of the mechanism of semantic networks and elements of predicate logic, and establishing advantages by means of the methodology of modeling hierarchies and the method of ranking and synthesizing a multi-level model, which allowed forming an information field for the development of measures to ensure information security. The research has limitations in the form of considering only aspects of artificial intelligence and not the entire information system. The prospects for further research are aimed at considering aspects of cybersecurity and cyber threats in education. International Journal of Religion283The conducted research found the key methods of managing the use of artificial intelligence in educational purposes were systematized, based on the use of the mechanism of semantic networks and elements of predicate logic, and establishing advantages by means of the methodology of modeling hierarchies.International Journal of Religion["F. Alazzam", "Daria Kiblyk", "Yuriy Kardashevskyy", "Liudmyla Yaremenko", "Svitlana Rodchenko"]2024-06-05T00:00:00 a6fe8495c32c8ab23839558ca59d40f43299aeedPurpose: The general objective of the study was to investigate the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Preliminary empirical review revealed that the integration of artificial intelligence (AI) into revenue management and pricing strategies significantly enhanced the financial performance and operational efficiency of hotels. AI's ability to process large datasets in real-time improved demand forecasting and dynamic pricing, leading to increased revenue per available room (RevPAR) and average daily rate (ADR). Additionally, AI facilitated personalized guest experiences, boosting customer satisfaction and loyalty. Despite these benefits, the study identified challenges such as high implementation costs, data privacy concerns, and the need for robust data infrastructure. Addressing these issues through strategic planning and continuous staff training was deemed essential for maximizing AI's potential in the hotel industry. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) may be used to anchor future studies on the role of AI in revenue management and pricing strategies in hotels. The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements. It enriched the Diffusion of Innovations Theory by demonstrating factors influencing AI adoption in hospitality. Practically, it provided actionable insights for hotel managers on leveraging AI for improved key performance indicators and balancing dynamic pricing with customer satisfaction. Policy recommendations included establishing guidelines for AI implementation, enhancing data infrastructure, fostering a culture of innovation, and addressing skills gaps through training and development programs. The study emphasized the need for robust data management systems and regulatory support to facilitate AI adoption.Journal of Modern Hospitality241The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements.Journal of Modern Hospitality["Anthony Gatera"]2024-06-05T00:00:00 e54be5a7791c73ce0c8bafbee4ced1c966eb08e4Int. J. Inf. Sec.892A systematic review to identify research combining artificial intelligence (AI) algorithms with Open source intelligence (OSINT) applications and practices and identifies that research gaps exist in the following areas.Int. J. Inf. Sec.["Thomas Oakley Browne", "Mohammad Abedin", "Md. Chowdhury"]2024-06-05T00:00:00 ac71bff16601b5f1bc62c6181d1f2d70a5e3f575Research on the legal personality of artificial intelligence explores whether AI should be granted legal rights and obligations akin to natural persons or corporations. Key points include challenges such as AI's lack of physical presence and debates over its agency and autonomy. Proponents argue that AI legal personality could enhance accountability, foster innovation, and protect AI interests. Some countries have made strides in recognizing AI legally, while ethical concerns persist. Alternatives to full legal personhood include creating new legal classifications or focusing on regulating AI developers and users. The study therefore examines the aspects of the possibility of granting legal personal artificial intelligence and the resulting socio-economic challenges and the extent to which this affects the security aspect of the use. Based on many societal studies and statistics with the aim of reaching a clear position or concluding strategies influential in fateful decision-making policies.International Journal of Religion281International Journal of Religion["Bakhit Moh\u2019d Al Dajeh"]2024-06-05T00:00:00 82d5b68d7c86f1f34d22efbcb3200d290da46ddfUpdates in Surgery471It emerges that AI could strongly improve surgical efficiency and accuracy during LC, and future prospects include speeding up, implementing, and improving the automaticity with which AI recognizes, differentiates and classifies the phases of the surgical intervention and the anatomic structures that are safe and those at risk.Updates in Surgery["Agostino Fernicola", "Giuseppe Palomba", "Marianna Capuano", "G. D. De Palma", "G. Aprea"]2024-06-05T00:00:00 46956e5b9f037533955cd786c881a91f4d35576bSudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.Cardiology in Review1112This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.Cardiology in review["Hritvik Jain", "M. D. Marsool", "R. Odat", "Hamid Noori", "Jyoti Jain", "Zaid Shakhatreh", "Nandan Patel", "Aman Goyal", "Shrey Gole", "Siddhant Passey"]2024-06-05T00:00:00 93fdef6e68da8f88aa6541e0025cfb42586e9a80Artificial Intelligence (AI) is designed to create intelligent systems capable of performing tasks traditionally dependent on human intellect. Its integration into the field of education presents both opportunities and challenges as it is quickly expanding. Preparing teachers for this rapidly advancing technological shift is essential for success, as education itself is not static. This position paper adopts the methodology of synthesizing existing literature on innovative strategies for integrating AI into the preparation of Teachers of the Future. The concept of Teachers of the Future was introduced in this paper, addressing concerns surrounding AI’s potential to replace teachers. The paper recognized the irreplaceable roles of teachers in providing emotional and moral support as well as nurturing critical thinking among learners. It further explored the importance of AI for effective application in teaching and learning processes. Drawing upon the synthesis of literature collected from the review of related works, strategies for preparing Teachers of the Future in the Era of AI can be realized by implementing approaches such as development of AI literacy, integrating AI into teacher training courses, promoting collaborative learning among teachers in training, offering continuing education opportunities, and nurturing a positive attitude towards AI utilization. The paper suggested, among others, that Teachers of the Future should be provided with foundational training in AI application for teaching and learning processes within teacher education programmes offered by teacher training institutions.Journal of Artificial Intelligence, Machine Learning and Neural Network331It is suggested, among others, that Teachers of the Future should be provided with foundational training in AI application for teaching and learning processes within teacher education programmes offered by teacher training institutions.Journal of Artificial Intelligence, Machine Learning and Neural Network["Akilu Ismail", "Abdulrahaman Aliu", "Mansur Ibrahim", "Abubakar Sulaiman"]2024-06-05T00:00:00 e0064937eae9bb0e38bc84efa88b46273e2a59faThe evolution of artificial intelligence and varying perspectives on its integration within the supply chain management landscape tend to influence organisations’ ability to adapt to changing market conditions and maintain relevance and competitiveness. Using a quantitative approach, this study explored the drivers of artificial intelligence adoption in Nigeria’s supply chain management landscape. A survey questionnaire was the primary means of collecting quantitative data from 80 local supply chain practitioners, which was analysed through statistical tests. Results from the study established support and leadership from senior management, availability of technological infrastructure, and regulatory framework and regulatory considerations as the foremost drivers of AI adoption in Nigeria’s supply chain landscape. The study's findings provide valuable insights for policymakers, industry practitioners, and academic researchers. The study posits that fostering a conducive environment for AI implementation, addressing regulatory ambiguities, and enhancing technological capabilities will be imperative for unlocking the full benefits of AI in Nigeria's supply chain management landscape.International Journal of Business Ecosystem &amp; Strategy (2687-2293)931It is posits that fostering a conducive environment for AI implementation, addressing regulatory ambiguities, and enhancing technological capabilities will be imperative for unlocking the full benefits of AI in Nigeria's supply chain management landscape.International Journal of Business Ecosystem &amp; Strategy (2687-2293)["A. Hassan"]2024-06-05T00:00:00 b8764d2deb4260050668b3527956929eeb861ef8Artificial intelligence (AI) is transforming the global economic landscape, bringing with it both great potential and challenging challenges. This overview examines the ways in which artificial intelligence (AI) is influencing several economic domains, increasing productivity, generating novel business ideas, and improving decision-making through large data analysis. Artificial intelligence (AI) holds great promise for the economy, as evidenced by its ability to automate repetitive tasks and provide ground-breaking technologies like virtual assistants and autonomous cars. But there are also a lot of challenges associated with AI, particularly in the areas of employment, ethics, and data protection. In certain industries, automation could lead to a significant loss of jobs, necessitating worker retraining and requalification programs. Algorithmic biases and concerns regarding data collection and use also present significant ethical difficulties.International Journal For Multidisciplinary Research11This overview examines the ways in which artificial intelligence (AI) is influencing several economic domains, increasing productivity, generating novel business ideas, and improving decision-making through large data analysis.International Journal For Multidisciplinary Research["Radia MAHAMOUD DJAMA"]2024-06-05T00:00:00 309b70fc1582a1c91c6b46e29e8d6ce823768a60This study aims to reveal the role of one of the artificial intelligence (AI) techniques, “ChatGPT,” in improving the educational process by following it as a teaching method for the subject of automatic analysis for students of the Chemistry Department and the subject of computer security for students of the Computer Science Department, from the fourth stage at the College of Education for Pure Science (Ibn Al-Haitham), and its impact on their computational thinking to have a good educational environment. The experimental approach was used, and the research samples were chosen intentionally by the research community. Research tools were prepared, which included a scale for CT that included 12 items and the achievement test in both scientific subjects for departments as the second tool. They reached a lot of conclusions. Accordingly, a set of recommendations were proposed.International Journal of Engineering Pedagogy (iJEP)02The role of one of the artificial intelligence techniques, “ChatGPT,” in improving the educational process by following it as a teaching method for the subject of automatic analysis for students of the Chemistry Department and the subject of computer security for students of the Computer Science Department is revealed.International Journal of Engineering Pedagogy (iJEP)["Linda Talib Ameen", "Maysam Raad Yousif", "Najwa Abdulmunem Jasim Alnoori", "Ban Hassan Majeed"]2024-06-05T00:00:00 d375e54a80551d391b68a896570cbdc16c2e898aThe subject of this study is the participants in artificial sociality (humans and artificial intelligence (AI) tools) and communication between them. The first section analyses (using Luhmann’s methodology) communication as the basis of sociality. The second section shows how AI tools became social technologies in the framework of artificial sociality. The third section describes experimental communication between authors and AI tools (the case of ChatGPT). For the first time in the Baltic countries, the authors examined sociological, humanitarian, natural and technological aspects of the functioning AI tools, which participate in creation of a new social reality for human society – artificial sociality.Filosofija Sociologija231For the first time in the Baltic countries, the authors examined sociological, humanitarian, natural and technological aspects of the functioning AI tools, which participate in creation of a new social reality for human society – artificial sociality.Filosofija. Sociologija["V. Menshikov", "Vera Komarova", "Ieva Bolakova", "Andrejs Radionovs"]2024-06-05T00:00:00 3595ed4419988013249bb6d816b4d8f7cfa1c902Postdigital Science and Education56Postdigital Science and Education["Alexios V. Brailas"]2024-06-05T00:00:00 cea6d8479573400bcc768572c0154a9c37ca1451More than two billion people do not have access to banking institutions, this is where the important role of microfinance distributes small loans to the poor so that they can access the financial industry. This study aimsto determine the role of artificial intelligence in improving the productivity of microfinance institutions. The qualitative research was employed with a library study approach by reviewing 25 selected journals indexed by Scopus and supported by Rank 1 and Rank 2 Science and Technology Index journals, with research published in 2000 – 2022. A sample of papers based on keywords in the publication was analyzed using bibliometric with VOSviewer application. The research results stated that microfinance institutions were established to provide benefits to poor and lowincome communities. The role of artificial intelligence can increase the productivity of the financial industry. Artificial intelligence can be used to analyze the feasibility and risk of default for customers and potential customers. The advantages of artificial intelligence include speed of decision-making, higher levels of automation for credit decisions, and the ability to be used remotely. In addition, artificial intelligence can also be used to generate investment signals that grow exponentially and generate data for future analysis.TRA VINH UNIVERSITY JOURNAL OF SCIENCE00The research results stated that microfinance institutions were established to provide benefits to poor and lowincome communities and the role of artificial intelligence can increase the productivity of the financial industry.TRA VINH UNIVERSITY JOURNAL OF SCIENCE["Saiful Aminudin Alkusuma Putra", "Moh. Ah Subhan ZA", "Adisty Riska Hardianti", "Satria Lintang Rachmadana"]2024-06-05T00:00:00 0e6ad08f904825676008660e723f3c7c340e9973With the deep application of artificial intelligence technology in higher education, the teaching methods of higher education are constantly changing. Critical thinking is an important ability for college students. As one of the cores of higher-order thinking ability, critical thinking is of great significance to the training of talents in the 21st century, and its research is of great significance and valuable. This paper aims to explore the improvement mode of critical thinking ability of college students based on artificial intelligence. Firstly, this article analyzes the characteristics of artificial intelligence technology, the components of critical thinking, and the relationship between artificial intelligence technology and critical thinking. Secondly, combined with the needs of college students to improve their critical thinking ability, this article puts forward a model of improving college students' critical thinking ability based on artificial intelligence. Finally, this study presents the future opportunities and challenges of critical thinking training for college students based on artificial intelligence.International Journal of Academic Research in Business and Social Sciences740A model of improving college students' critical thinking ability based on artificial intelligence is put forward and the future opportunities and challenges of critical thinking training for college students based on artificial intelligence are presented.International Journal of Academic Research in Business and Social Sciences["He Jun", "Wenhao Yao", "Nasir Ali", "A. Khan"]2024-06-05T00:00:00 b2d7ff7d3da9c8fc61fff1d6169d9678e1484cef: Artificial intelligence (AI) is one of the most promising and at the same time controversial areas of modern technology. The introduction of AI covers various aspects of society, from medicine and industry to the social sphere and culture, offering significant improvements in efficiency and usability. However, progress in AI also faces a number of serious challenges, including ethical dilemmas, security concerns, risks of increasing social inequality and the possibility of exacerbating existing biasesInternational Journal of Science and Research (IJSR)100Artificial intelligence covers various aspects of society, from medicine and industry to the social sphere and culture, offering significant improvements in efficiency and usability, but progress also faces a number of serious challenges.International Journal of Science and Research (IJSR)["Kotov Dmitry"]2024-06-05T00:00:00 382db6caf69365db9c97ebf2ba9dbe490db78fa5This research aims to develop a framework that explains ethical considerations in the responsible development of Information Systems (IS) related to artificial intelligence (AI). The research method used involved a thorough literature review to identify relevant ethical principles in the context of AI and IS. Apart from that, an in-depth analysis of various existing models and frameworks was also carried out to understand ethical considerations in technology development. The result of this research is a framework consisting of a series of ethical principles that must be considered at every stage of IS development that uses AI technology. This framework considers not only the technical aspects of system development, but also relevant social, legal and cultural aspects. Through the application of this framework, IS developers can ensure that the resulting products are not only of high technical quality, but also pay attention to their impact on individuals, society and the environment. The conclusions of this research emphasize the importance of including ethical considerations as an integral part of the IS development process that uses AI to achieve the goals of socially responsible technology development.Journal Informatic, Education and Management80The conclusions of this research emphasize the importance of including ethical considerations as an integral part of the IS development process that uses AI to achieve the goals of socially responsible technology development.Journal Informatic, Education and Management (JIEM)["Stmik Indonesia", "Banda Aceh", "Alfina Stmik", "Indonesia Banda"]2024-06-05T00:00:00 9f2576b6b94941f8d966baa81503b52bfac52867Technology is rapidly advancing today, with the most popular technology known as Artificial Intelligence (AI). This technological innovation greatly benefits the tourism sector. However, the implementation of Artificial Intelligence (AI) technology often sparks debate, including in the tourism industry. This study aims to identify the pros and cons related to the presence and use of AI technology by local guides at tourist sites that still maintain the authenticity of culture, such as in the Dusun Adat Sade, Rambitan Village, Central Lombok, West Nusa Tenggara, Indonesia. The research was conducted using an inductive approach, with a qualitative methodology, a case study research strategy, and a cross-sectional research time frame. Data collection and analysis were performed using data reduction, data display, and conclusion drawing. Data analysis utilized content analysis, categorizing data into specific themes or categories. Triangulation of methods and sources was employed for data validity. Methodological triangulation involved three methods: observation, interviews, and documentation. Additionally, source triangulation was conducted by interviewing 8 informants. The research findings categorized data into the following: Pros and Contras regarding Artificial Intelligence (AI) technology. After analyzing and processing the data from both categories, the majority of local guides in the Sade Traditional Village, Rembitan Village, exhibit a predominantly contra or skeptical attitude towards Artificial Intelligence (AI) technology due to concerns about Artificial Intelligence (AI) taking over their jobs. However, they still acknowledge technological advancements that facilitate their service to tourists. To further assess the extent to which Artificial Intelligence (AI) technology assists local guides, especially at tourist sites, further research on the benefits of Artificial Intelligence (AI) for supporting and facilitating local guides in their work is neededInternational Journal of Geotourism Science and Development120The majority of local guides in the Sade Traditional Village, Rembitan Village, exhibit a predominantly contra or skeptical attitude towards Artificial Intelligence (AI) technology due to concerns about Artificial Intelligence (AI) taking over their jobs, however, they still acknowledge technological advancements that facilitate their service to tourists.International Journal of Geotourism Science and Development["Rizwan Hadi", "Endang Sri", "M. Ilham", "Article Info"]2024-06-05T00:00:00 dd2524cb12de455888bbbd2b1f5bc3e4cac433f4With the rapid growth of technology, the field of artificial intelligence (AI) is innovating at an unprecedented speed and deeply affecting various industries, including corporate financial accounting work. In the current financial field, with the increasing trend of intelligent financial accounting, large enterprises have fully realized the intelligence of basic accounting processing and basic analysis. The application of new financial technologies such as financial robots and automation tools not only greatly improves the efficiency and accuracy of accounting work, but also brings new growth opportunities for enterprises. For enterprises, the introduction of AI means that the processing and analysis capabilities of financial data have been greatly improved. Through intelligent algorithms and big data processing, AI can quickly and accurately complete tedious tasks such as voucher entry, classification, and summarization, and automatically identify and solve potential risk problems. This not only frees up manpower, allowing enterprises to invest more energy in strategic decision-making and value creation, but also effectively improves the reliability and security of financial data. This article explores the impact of AI on corporate financial accounting work.Transactions on Economics, Business and Management Research100The impact of AI on corporate financial accounting work can quickly and accurately complete tedious tasks such as voucher entry, classification, and summarization, and automatically identify and solve potential risk problems, and effectively improves the reliability and security of financial data.Transactions on Economics, Business and Management Research["Xinyu Huang"]2024-06-05T00:00:00 ae0226df0965c50b90d7505adcf1a06225a7e755This study explores the profound impact of artificial intelligence (AI) on workforce dynamics, focusing on automation trends and the imperative for skill development, exemplified by case studies of InnovateTech Manufacturing and SkillCraft Solutions. The findings underscore the vulnerability of routine tasks to automation, prompting a nuanced understanding of sector-specific challenges. Soft skills emerge as pivotal in an AI-centric job market, with a growing emphasis on adaptability and technological literacy. The study used mixed approach and using secondary as well as primary data with advocates for a holistic approach to workforce development, encouraging continuous learning, strategic automation, and collaboration across industries. Recommendations address the need for reskilling programs, industry-academia collaboration, and policies that safeguard workers in the evolving AI landscape. The abstract encapsulates the study's essence, highlighting the transformative journey toward a harmonious coexistence of automation and a skilled, adaptable workforce.Journal of Artificial Intelligence, Machine Learning and Neural Network80Journal of Artificial Intelligence, Machine Learning and Neural Network["Mohd Akhlak Hussain"]2024-06-05T00:00:00 66bf9820eec19bbd5cdc57ffe55f838f0b0402f9The impact of Internet of Things, Artificial Intelligence and the use of digital technologies has modernized all sectors. Smart buildings provide better construction quality, labour safety, monitoring, maintenance, comfort, sustainability and surveillance. Smart buildings are also one of the sensitive areas where cyber security principles and practices must be deployed rigorously considering the sensitivity of the data and the criticality of the impact of cyber-attack in such environments. In this work, a detailed study is made on the smart buildings and cyber-attacks on such critical infrastructures. Proactive mechanisms that can be integrated into smart buildings to ensure the minimum cyber safety of smart buildings are also presented in this work. These mechanisms can be used at all levels of smart buildings and can be followed to protect the smart buildings from most of the sophisticated modern attacks.2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)250A detailed study is made on the smart buildings and cyber-attacks on such critical infrastructures and proactive mechanisms that can be integrated into smart buildings to ensure the minimum cyber safety of smart buildings are presented.2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)["R. Marshal", "Anantharaj Thalaimalai Vanaraj"]2024-06-05T00:00:00 66b46016d39cd823e25a579ec41136bd99b13578The purpose of this review is to provide an overview of the integration of technological advancements in orthopedic shoulder surgery. Recent technological advancements in orthopedic shoulder surgery include predictive analytics, computer-navigated instrumentation for operative planning, extended reality, and robotics. Separately, these advancements provide distinct methodological attempts to improve surgical experiences and outcomes. Together, these technologies can provide orthopedic surgeons with the tools and capabilities to improve patient care and communication in shoulder arthroplasty. From artificial intelligence-generated predictive analytics to extended reality and robotics, technical innovations may lead to improvements in patient education, surgical accuracy, interdisciplinary communication, and outcomes. A comprehensive narrative review was conducted to explore the technological advancements of orthopedic shoulder arthroplasty. Our findings emphasized the impact of these advancements, exemplified by early enhancements in efficacy and safety. However, certain challenges remain, such as a lack of reproducibly improved outcomes and cost considerations. While the reviewed studies indicate hope for improving shoulder arthroplasty, the true cost-effectiveness and applicability remains to be determined, indicating the need for further research.Shoulder & Elbow210While the reviewed studies indicate hope for improving shoulder arthroplasty, the true cost-effectiveness and applicability remains to be determined, indicating the need for further research.Shoulder & elbow["Akasha Barreto Vega", "Prem N. Ramkumar", "H. Kassam", "R. A. Navarro"]2024-06-05T00:00:00 f5d499d1b7fdeab96d1e4e269fca1bad883885d9In recent years, health-data-driven artificial intelligence and machine learning applications have been introduced to many areas of medicine. In the field of assisted reproduction, artificial intelligence and machine learning applications and related technologies have been hailed as (potentially) significant and ground-breaking, not least because they promise standardisation and automation in in-vitro fertilisation clinics – a precondition for scaling up and branching out in the fertility bioindustry. Artificial intelligence data-driven algorithms promise time- and cost-effective selection of ‘high-quality’ reproductive cells and successful personalised treatments. In this essay, we aim to critically discuss artificial intelligence as a technological clinical practice, which is currently moving from bench to bedside internationally. Through an analytic framework of reproductive justice, we propose that introducing artificial intelligence into this already stratified context threatens to black-box health disparities and to generate what we refer to as ‘hyper-stratifications’ of reproduction in the context of rising health and social disparities in the European context. As feminist, social science and bioethics scholars, we are all too aware of how reproductive technologies reinforce normativities rather than unravel them. We cannot presume that artificial intelligence is an ethical technological agent or user of health data but, instead, need to keep a critical eye on the moral ambivalence of emerging and evolving artificial intelligence-assisted reproduction technologies practices and their gendered consequences. Given the current hype around artificial intelligence, but also with concerns around the fast development and deployment of artificial intelligence generally and in artificial intelligence-assisted reproduction technologies particularly in mind, there is an urgent need to engage in critical feminist discussion of such developments.The European Journal of Women's Studies330This essay aims to critically discuss artificial intelligence as a technological clinical practice, currently moving from bench to bedside internationally, and proposes that introducing artificial intelligence into this already stratified context threatens to black-box health disparities and to generate what is referred to as ‘hyper-stratifications’ of reproduction in the context of rising health and social disparities in the European context.European Journal of Women's Studies["Riikka Homanen", "Neil McBride", "N. Hudson"]2024-06-05T00:00:00 810a02ea4ff4063a1fd3eac3a83f852ce2ca2c5bPembelajaran kimia di sekolah sering kali menghadapi tantangan akibat kurangnya variasi dan diferensiasi dalam proses, produk, dan konten pembelajaran. Siswa memerlukan pendekatan pembelajaran yang sesuai dengan kebutuhan, minat, dan kemampuan yang berbeda-beda. Kajian literatur ini mengeksplorasi peran Artificial Intelligence (AI) sebagai solusi untuk mendukung strategi pembelajaran berdiferensiasi pada mata pelajaran kimia di sekolah. Kajian literatur menggunakan narrative literature review bertujuan untuk mengidentifikasi dan merangkum artikel-artikel yang telah dipublikasikan sebelumnya tanpa ada kritikan terhadap artikel yang ditinjau. AI memiliki peranan besar dalam meningkatkan pembelajaran kimia, namun peran guru tetap penting. Kombinasi antara kecerdasan buatan dan pendekatan manusia dapat memberikan hasil pembelajaran yang optimal. AI memiliki kelebihan dalam menyajikan materi personal dan mendukung pembelajaran berbasis diferensiasi. Kajian literatur ini diharapkan dapat memberikan panduan bagi pendidik dan pengembang kurikulum untuk memanfaatkan AI secara optimal dalam mencapai pembelajaran diferensiasi yang efektif dan inklusif di bidang kimia.Jurnal Pendidikan Kimia Undiksha00Jurnal Pendidikan Kimia Undiksha["R. Muhammad", "Herlin Alfiana Larasati", "Revisia Susanti", "Frederich Pakaenoni", "Agung Rahmadani"]2024-06-05T00:00:00 e443ec7250dc801e1141e4ec356d55950350936dDermatology is currently seeing a substantial transformation due to the integration of Artificial Intelligence, particularly through the use of machine learning and convolutional neural networks. AI’s potential in dermatology is based on its ability to increase visual diagnosis, which is a core aspect of dermatological practice. This integration promises improvements in diagnostic precision, process efficiency, and personalized patient care. Although there has been some progress, there are still obstacles that need to be overcome. The ethical considerations surrounding the confidentiality of medical data, and the transparency of AI algorithms, are of utmost importance. Additionally, the availability of high-quality, annotated dermatological datasets is a limiting factor, alongside with the need for substantial technical investments and training for healthcare professionals. This article provides an extensive analysis of AI's impact on dermatology, presenting its applications in various domains and discussing the associated challenges. By highlighting AI’s potential and addressing its challenges, the article aims to contribute to a deeper understanding of how AI can enhance dermatological practices to achieve better patient outcomes.Romanian Medical Journal450An extensive analysis of AI's impact on dermatology is provided, presenting its applications in various domains and discussing the associated challenges, as well as highlighting AI’s potential and addressing its challenges.Romanian Medical Journal["I. Manole", "G. Tiplica"]2024-06-05T00:00:00 508dd51b39e37fc671953226fe900e94cc2f8051Journal of public health41Journal of public health["J. Kahambing"]2024-06-05T00:00:00 59ee9566d3d7ef782ee9ad0791918108f8cf509aJournal of Medical Internet Research451Journal of Medical Internet Research["S. Goh", "R. Goh", "Bryan Chong", "Q. X. Ng", "Gerald Choon Huat Koh", "K. Ngiam", "Mikael Hartman"]2024-06-05T00:00:00 987daf41e2ebbecaca00502c4e25a36ebfbe5e44The aim of present study was to measure the relationship of UTAUT (Unified Theory of Acceptance and Use of Technology) and TAM (Technology Acceptance Model) variables regarding AI technology and AI-based applications acceptance in education sector. Research was carried out by using PRISMA (Preferred reporting items for systematic review and meta-analysis) guidelines. The relevant studies were searched from major databases that included a) Scopus, and b) Web of Science. Initial search retrieved 309 titles, and 30 relevant articles and conference papers were selected following the search process. Data was analysed using CMA (Comprehensive Meta-analysis) and Meta-Essential software. Findings exhibit that the relationship between UTAUT variables and BI to accept AI and AI-based applications in education was high (PE → BI), medium (EE → BI, SI → BI), and low (FC → BI). The magnitude of the relationship of TAM constructs remained high for all paths (PU → AT, PEOU → AT, PU → BI, and PEOU → BI). Theoretically, this meta-analysis provided a panoramic picture of two leading technology acceptance models regarding the acceptance/adoption of AI and AI-based technology in education sector. This meta-analysis provided a way forward for researchers to extend research on AI-based applications including ChatGPT, intelligent tutoring, AI-based robots, AI-based Chatbots, and AI-based voice assistants. Practically, findings are useful for IT companies, and decision makers of educational institutes in designing and implementing AI and AI-based applications.Information Development562This meta-analysis provided a panoramic picture of two leading technology acceptance models regarding the acceptance/adoption of AI and AI-based technology in education sector and provided a way forward for researchers to extend research on AI-based applications.Information Development["Irfan Ali", "N. Warraich", "Khadija Butt"]2024-06-05T00:00:00 abb8b87952827a5528631731eec04853d3bd3868Proceedings on Engineering Sciences02Proceedings on Engineering Sciences["Vidushi Nain", "Hari Shankar Shyam"]2024-06-05T00:00:00 91195a61d30b7569ad1a442b1209f5c187f1a94dThis article proposes a safety strategy that addresses the complex concerns raised by blockchain, AI, and the Internet of Things. Use Threat Intelligence Integration (TII), Dynamic Risk Assessment (DRA), Blockchain Integrity Verification (BIV), AI Adversarial Robustness Assessment (AARA), and IoT Security Compliance Assessment (ISCA). Each program is part of a larger, more linked defense system for sophisticated cyberthreats. The program uses Threat Intelligence Integration. Combining historical data with realtime hazard sources predicts assaults and their outcomes. The TII enabled new algorithms like DRA. The algorithms discover assets, assess weaknesses, and prioritize threats. Blockchain Integrity Verification (BIV) checks for issues and performs complicated hash and weight computations to secure the blockchain. AI Adversarial Robustness Assessment (AARA) evaluates AI models in BIV tests and other adversarial tasks. The ISCA ensures IoT devices fulfill AARA safety and security criteria. Each algorithm prioritizes tracking, updating, and assessing to keep up with the ever-changing risk situation. The recommended solution is more accurate, finds more threats, reduces false positives, is scalable, and is simpler to set up than current defense solutions.2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0280A safety strategy that addresses the complex concerns raised by blockchain, AI, and the Internet of Things is proposed that is more accurate, finds more threats, reduces false positives, is scalable, and is simpler to set up than current defense solutions.2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0["Eliph mazher", "Pal Thethi", "Akula Rajitha", "Dinesh Kumar", "Yadav", "K. S. Kanthi"]2024-06-05T00:00:00 30854e86c59d96e14c8c7c62c14dc4a7aa5e41b5British medical journal30BMJ["Adedeji Majekodunmi"]2024-06-05T00:00:00 d9b714b2eb6882ba9482c41dd210957d51365939 Deep learning models for plant species identification rely on large annotated datasets. The Pl@ntNet system enables global data collection by allowing users to upload and annotate plant observations, leading to noisy labels due to diverse user skills. Achieving consensus is crucial for training, but the vast scale of collected data (number of observations, users and species) makes traditional label aggregation strategies challenging. Existing methods either retain all observations, resulting in noisy training data or selectively keep those with sufficient votes, discarding valuable information. Additionally, as many species are rarely observed, user expertise cannot be evaluated as an inter‐user agreement: otherwise, botanical experts would have a lower weight in the AI training step than the average user. Our proposed label aggregation strategy aims to cooperatively train plant identification AI models. This strategy estimates user expertise as a trust score per user based on their ability to identify plant species from crowdsourced data. The trust score is recursively estimated from correctly identified species given the current estimated labels. This interpretable score exploits botanical experts' knowledge and the heterogeneity of users. Subsequently, our strategy removes unreliable observations but retains those with limited trusted annotations, unlike other approaches. We evaluate Pl@ntNet's strategy on a newly released large subset of the Pl@ntNet database focused on European flora, comprising over 6 M observations and 800 K users. This anonymized dataset of votes and observations is released openly via Lefort, Affouard, et al. (2024). We demonstrate that estimating users' skills based on the diversity of their expertise enhances labelling performance. Our findings emphasize the synergy of human annotation and data filtering in improving AI performance for a refined training dataset. We explore incorporating AI‐based votes alongside human input in the label aggregation. This can further enhance human‐AI interactions to detect unreliable observations (even with few votes). Methods in Ecology and Evolution410It is demonstrated that estimating users' skills based on the diversity of their expertise enhances labelling performance, and incorporating AI‐based votes alongside human input in the label aggregation can further enhance human‐AI interactions to detect unreliable observations.ArXiv["Tanguy Lefort", "Antoine Affouard", "Benjamin Charlier", "J. Lombardo", "Mathias Chouet", "Herv\u00e9 Go\u00ebau", "Joseph Salmon", "P. Bonnet", "Alexis Joly"]2024-06-05T00:00:00 b78c95ae5821278f0806c8740267cb02b0fb1b85The Brain &amp; Neural Networks320The Brain &amp; Neural Networks["Hirokazu Shirado"]2024-06-05T00:00:00 84a4888832d8568cb276d066d21e0f70ca13896dSexuality and disability00Sexuality and Disability["Sigmund Hough"]2024-06-05T00:00:00 af19e8749e45541debf9023182c1e3ba0a8b7cabThe Permanente Journal250The Permanente Journal["Kolu S Baysah Clark", "Elaine Rudell", "David Setiadi", "Tarjani Agrawal", "Brant J. Oliver"]2024-06-05T00:00:00 8a3c9491ec665319c9546a51009703d16d37d026Pengabdian Kepada Masyarakat (PKM) ini mengeksplorasi potensi pemanfaatan berbagai teknologi kecerdasan buatan (AI) seperti ChatGPT, Bing AI, Notion AI, Tome App, Wepik by Freepik, dan Midjourney dalam konteks pendidikan. Fokus PKM adalah mengintegrasikan teknologi AI ke dalam pembelajaran di Madrasah Aliyah (MA) Multiteknik Asih Putera dengan tujuan meningkatkan pengalaman belajar siswa. Proses implementasi teknologi AI membuktikan keberhasilannya dalam berbagai aspek. Penggunaan ChatGPT memperkaya interaksi siswa-guru dengan dialog interaktif yang memikat. Bing AI membantu akses yang lebih efisien ke sumber daya pendidikan yang relevan. Notion AI membantu dalam pengelolaan dan organisasi sumber daya pembelajaran, memungkinkan guru untuk lebih efisien dalam menyusun materi. Tome App dan Wepik by Freepik membantu dalam menciptakan konten pembelajaran yang menarik dan interaktif, dan Midjourney mengubah teks menjadi gambar, memicu kreativitas siswa. Hasil PKM menunjukkan bahwa penggunaan beragam aplikasi kecerdasan buatan membuka peluang baru dalam menciptakan pengalaman pembelajaran yang beragam, interaktif, dan relevan bagi siswa di era digital. Temuan ini memberikan kontribusi positif dalam pengembangan model pembelajaran yang merespons tuntutan teknologi modern dan memberikan hasil yang bermanfaat untuk MA Multiteknik Asih Putera.MERPATI00MERPATI["Roni Habibi", "Rd. Nuraini Siti Fatonah", "Darfial Guslan", "Amri Yanuar", "Cahyo Prianto"]2024-06-05T00:00:00 2d18bfbf903128c932313e0e83bdd9d2adbf616fJACC: Advances130JACC: Advances["Harold L. Kennedy"]2024-06-05T00:00:00 1e2f3ffc8ca19cedd3cf0ef0d07f92c72662a26fJAMA Surgery40JAMA surgery["Daniel E. Hall"]2024-06-05T00:00:00 2274db86ae96fa9eed09a899276c69720d79affeAI chatbots have emerged as a transformative tool in public service delivery. This study aims to conduct a systematic review and meta-analysis of existing literature to assess the effectiveness of AI chatbots in improving efficiency, response time and user satisfaction in various public service contexts. A comprehensive literature search was conducted on the Scopus database, limiting studies published between 2018 and 2024. Inclusion criteria included quantitative studies that evaluated the impact of AI chatbots on at least one of three outcome variables: efficiency, response time, or user satisfaction. Data were extracted and effect sizes (in this case Standardized Mean Difference - SMD) were calculated for each study. Moderator analysis was conducted to investigate the influence of the type of public service, the complexity of the chatbot's tasks, the type of AI, and the level of human interaction on the effectiveness of the chatbot. Meta-analysis of 30 studies (N = 9,380) shows that AI chatbots have a significant positive effect on the efficiency of public services (SMD = 0.35, 95% CI [0.25, 0.45]), reducing response time (SMD = -0.40, 95% CI [-0.50, -0.30]), and increased user satisfaction (SMD = 0.50, 95% CI [0.40, 0.60]). Moderator analysis revealed that AI chatbots were more effective in healthcare and for simple tasks. Machine learning-based chatbots also show higher effectiveness than rule-based chatbots. In conclusion, AI chatbots offer significant potential to improve various aspects of public services. However, their effectiveness varies depending on the implementation context. These findings provide valuable empirical evidence for policymakers and practitioners to effectively design and implement AI chatbots in public services.Open Access Indonesia Journal of Social Sciences200Meta-analysis of existing literature shows that AI chatbots have a significant positive effect on the efficiency of public services, and machine learning-based chatbots also show higher effectiveness than rule-based chatbots.Open Access Indonesia Journal of Social Sciences["Muhammad Ma\u2019rup", "Tobirin", "Ali Rokhman"]2024-06-05T00:00:00 97143ecf0b90016efe3f890d928f09bda81ea01aACS Agricultural Science &amp; Technology30ACS Agricultural Science &amp; Technology["Ramesh Raliya"]2024-06-05T00:00:00 7c2f902f09c8b7dd52789e1f1d583b3dfdf7b162AI is becoming a part of our daily lives. One of the most visible AI applications is the VPA. One research suggests AI may improve VPAs in many ways. It examines their merits and downsides and how they may affect technology usage. AI has made VPAs smart, situation-aware pals instead of task-oriented aids. Language processing, machine learning, and deep learning influence this development. VPAs learn what individuals say and do using these methods. AI helps VPAs classify and analyze massive data sets. Their importance has grown in our everyday lives. Right now, privacy and safety matter most. AI-driven encryption and recognition make VPAs safer. AI approaches eliminating prejudice in VPA replies, ensuring fair and unbiased interactions. Learning each user’s likes, dislikes, and scenario helps VPAs specialize. These adjustments may not address all problems. AI should grasp regular phrases, make clearer conclusions, and tackle data usage issues in society. The research seeks to identify and solve these issues. Finally, AI has improved virtual personal assistants’ safety, intelligence, and usability. With the progression of AI, VPAs will become more prevalent. AI research must be perpetually guided by social and private concerns. Exploring the potential of AI to enhance VPAs could have a transformative impact on human-computer interactions and our daily lives.2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0240One research suggests AI may improve virtual personal assistants in many ways, and examines their merits and downsides and how they may affect technology usage.2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0["K. Praveena", "Jyoti Patel", "Manjunatha", "Amit Dutt", "Irfan Khan", "M. A. Alkhafaji"]2024-06-05T00:00:00 51c310c323143801930af9ba6b5a2456e755cb2aKidney360140Kidney360["Kyle N. Goodman", "K. Sarullo", "S. J. Swamidass", "J. Gaut", "Sanjay Jain"]2024-06-05T00:00:00 c339be7bf997a8d357baa58f8f15ba8108fa90cfAccording to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary data.To ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling Technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as $F 1$ score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM.2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0271The Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM, with RF having the greatest AUC.2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0["Samruddhi Sapkal", "Sulaxan Jadhav", "P. Mallikarjun", "Rejuwan Shamim", "Atowar-Ul Islam", "K. Bamane"]2024-06-05T00:00:00 7eee516db1ead3541e0cb63c40a5072ad78d461aThis paper examines the future of research in artificial intelligence, arguing that, in the near future, symbol-based techniques will continue to receive some attention, while machine learning techniques based on data processing will continue to grow explosively. Models produced through deep learning, in particular language models, will be applied to many sectors and will be significantly enhanced (in efficiency, in interpretability, in performance). Still in the near future, the social debate about artificial intelligence will take a more concrete form, demanding research effort related to regulation, social impact and job markets. Looking into a more distant future, we believe that mixtures between symbol-based and data-based methods will get more attention, while some bets, such as quantum computing, may take us to new performance levels.Revista USP240Revista USP["Anna Helena Reali Costa", "F. G. Cozman"]2024-06-05T00:00:00 161a35b141c3daa3d5ee910aa2adad79db259a66This article explores the potential of artificial intelligence technologies to enhance and personalize programming education at the university level. Concepts such as advanced personalization, contextual recommendations, and interactive experiences are discussed to further engage students. Additionally, several AI tools that can be used as programming instruments to facilitate learning are presented. A review of recent literature indicates a growing interest in the application of techniques such as adaptive learning, augmented reality, and natural language processing in this field.Actas Iberoamericanas en Ciencias Sociales190The potential of artificial intelligence technologies to enhance and personalize programming education at the university level and several AI tools that can be used as programming instruments to facilitate learning are explored.Actas Iberoamericanas en Ciencias Sociales["Elizabeth Patricia Pommier Gallo"]2024-06-05T00:00:00 6a5ade68066c8f7a23966815d5d142545f4963c4The convergence of artificial intelligence (AI) and financial technology (fintech) has revolutionized the way banks and financial institutions communicate with customers. This paper explores the use of AI-enhanced fintech communication, focusing on the utilization of chatbots and natural language processing (NLP) to provide efficient banking support. AI-powered chatbots have become indispensable tools for banks seeking to enhance customer service and streamline communication channels. By leveraging NLP algorithms, these chatbots can understand and respond to customer queries in real-time, providing personalized assistance round-the-clock. The integration of AI into fintech communication enables banks to offer seamless and efficient support, improving customer satisfaction and loyalty. The key to the effectiveness of AI-enhanced fintech communication lies in the ability of chatbots to interpret and respond to natural language input accurately. NLP algorithms enable chatbots to analyze and understand the intent behind customer queries, allowing them to provide relevant and contextually appropriate responses. This capability enhances the overall customer experience by reducing response times and ensuring that customers receive accurate and helpful information. Furthermore, AI-powered chatbots can handle a wide range of inquiries, from basic account inquiries to complex financial transactions. By automating routine tasks and inquiries, banks can free up human agents to focus on more complex and value-added activities. This not only improves operational efficiency but also allows banks to deliver faster and more responsive customer service. In addition to providing support to customers, AI-enhanced fintech communication can also help banks gather valuable insights into customer preferences and behavior. By analyzing interactions between customers and chatbots, banks can identify trends, anticipate customer needs, and tailor their products and services accordingly. This data-driven approach enables banks to offer more personalized and targeted offerings, leading to increased customer satisfaction and loyalty. In conclusion, AI-enhanced fintech communication, powered by chatbots and NLP, offers significant benefits for banks and financial institutions. By leveraging AI technology, banks can provide efficient and personalized support to customers, improve operational efficiency, and gain valuable insights into customer behavior. As AI continues to advance, the future of fintech communication promises even greater efficiency, personalization, and innovation in banking support. Keywords: Al- Enhanced, Fintech Communication, Leveraging, Chatbots, NLP.International Journal of Management &amp; Entrepreneurship Research031This paper explores the use of AI-enhanced fintech communication, focusing on the utilization of chatbots and natural language processing (NLP) to provide efficient banking support, and offers significant benefits for banks and financial institutions.International Journal of Management &amp; Entrepreneurship Research["Ezekiel Onyekachukwu Udeh", "Prisca Amajuoyi", "Kudirat Bukola Adeusi", "Anwulika Ogechukwu Scott"]2024-06-05T00:00:00 d7e70862b4a787ab84eb503b21d82a07e59bcf53Artificial intelligence (AI) is impacting and has the potential to upend entire business models and structures. The adoption of such new technologies to support newsgathering processes is established practice for newsrooms. For AI specifically, we are seeing a new era of AI‐assisted journalism emerge with trust in the AI‐driven analyses and accuracy of results as core tenets.In Part I of this position paper, we discuss the contributions of six recently published research papers co‐authored by Bloomberg's Artificial Intelligence Engineering team that show the intricacies of training AI models for reliable newsgathering processes. The papers investigate (a) the creation of models for updated headline generation, showing that headline generation models benefit from access to the past state of the article, (b) sequentially controlled text generation, which is a novel task and we show that in general, more structured awareness results in higher control accuracy and grammatical coherence, (c) chart summarization, which looks into identifying the key message and generating sentences that describe salient information in the multimodal documents, (d) a semistructured natural language inference task to develop a framework for data augmentation for tabular inference, (e) the introduction of a human‐annotated dataset (ENTSUM) for controllable summarization with a focus on named entities as the aspect to control, and (f) a novel defense mechanism against adversarial attacks (ATINTER). We also examine Bloomberg's research work, building its own internal, not‐for‐commercial‐use large language model, BloombergGPT, and training it with the goal of demonstrating support for a wide range of tasks within the financial industry.In Part II, we analyze the evolution of automation tasks in the Bloomberg newsroom that led to the creation of Bloomberg's News Innovation Lab. Technology‐assisted content creation has been a reality at Bloomberg News for nearly a decade and has evolved from rules‐based headline generation from structured files to the constant exploration of potential ways to assist story creation and storytelling in the financial domain. The Lab now oversees the operation of hundreds of software bots that create semi‐ and fully automated stories of financial relevance, providing journalists with depth in terms of data and analysis, speed in terms of reacting to breaking news, and transparency to corners of the financial world where data investigation is a gigantic undertaking. The Lab recently introduced new tools that provide journalists with the ability to explore automation on demand while it continues to experiment with ways to assist story production.In Part III, we conceptually discuss the transformative impact that generative AI can have in any newsroom, along with considerations about the technology's shortcomings in its current state of development. As with any revolutionary new technology, as well as with exciting research opportunities, part of the challenge is balancing any potential positive and negative impacts on society. We offer our principles and guidelines used to inform our approach to experimenting with the new generative AI technologies. Bloomberg News’ style guide reminds us that our “journalism is aimed at possibly the most sophisticated audience in the world, for whom accuracy is essential.”The AI Magazine57The contributions of six recently published research papers co‐authored by Bloomberg's Artificial Intelligence Engineering team that show the intricacies of training AI models for reliable newsgathering processes are discussed and the transformative impact that generative AI can have in any newsroom is conceptually discussed.AI Mag.["Claudia Quinonez", "Edgar Meij"]2024-06-05T00:00:00 da3f6819270eee2d8e9d9b7c7f616adfa48882c2Breast cancer diagnosis is a crucial domain where Explainable Artificial Intelligence (XAI) integration holds immense importance. Understanding AI model decisions not only enhances trust but also aids in treatment strategies. However, the need for explainability must address privacy concerns, prompting the exploration of Federated Learning. This study explores the intersection of Explainable AI, Privacy, and Federated Learning in breast cancer diagnosis. Utilizing Wisconsin Diagnostic Breast Cancer Dataset and Wisconsin Breast Cancer Dataset, our results showcase that Federated Learning enhances user privacy while maintaining performance, achieving an accuracy of 97.59% and F1 score of 98.393% in Wisconsin Diagnostic Breast Cancer Dataset using artificial neural networks and 97.14% accuracy and 95.65% F1 score in Wisconsin Breast Cancer Dataset employing XGBoost. By computing SHAP values locally, we maintain explainability while enhancing privacy. Our findings highlight the potential of federated learning in maintaining privacy and explainability, advancing breast cancer diagnosis and treatment.European Interdisciplinary Cybersecurity Conference194This study explores the intersection of Explainable AI, Privacy, and Federated Learning in breast cancer diagnosis and highlights the potential of federated learning in maintaining privacy and explainability, advancing breast cancer diagnosis and treatment.Proceedings of the 2024 European Interdisciplinary Cybersecurity Conference["Eleni Briola", "C. Nikolaidis", "V. Perifanis", "Nikolaos Pavlidis", "P. Efraimidis"]2024-06-05T00:00:00 a3a52845dac9354f3f3c3104da82b350395ec875This research study describes a novel way to secure the smart grid system by utilizing cryptographic and Artificial Intelligence (AI) methods. This research study intends to safeguard the grid from cyber-attacks and minimize grid load through load balancing. At first, the AI algorithms were used to integrate the system and calculate all necessary parameters. Among the cyberattacks that were discussed were virus attacks, data breaches, Man-In-Middle Attacks (MITM), and bogus data injection. The primary tools utilized in this study were IoT devices to gather data from the grid and AI algorithms to identify faults and potential cyberattacks. The Internet of Things (IoT) devices also used cryptographic algorithms to encrypt the data using Asymmetric algorithms like Rivest Shamir and Adleman (RSA) and Secure Hash Algorithm (SHA-512) for securing the IoT devices.2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)142This research study describes a novel way to secure the smart grid system by utilizing cryptographic and Artificial Intelligence (AI) methods to safeguard the grid from cyber-attacks and minimize grid load through load balancing.2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)["Dasari Kishan Kumar", "Krishnaiahgari Karthik Reddy", "G. W. Kathrine"]2024-06-05T00:00:00 7dfdc917f95ef01b5f7744bdf28018ee1faf3252The growing trend of artificial intelligence (AI) as a solution to social and technical problems reinforces AI Realism -- the belief that AI is an inevitable and natural order. In response, this paper argues that participatory design (PD), with its focus on democratic values and processes, can play a role in questioning and resisting AI Realism. I examine three concerning aspects of AI Realism: the facade of democratization that lacks true empowerment, demands for human adaptability in contrast to AI systems' inflexibility, and the obfuscation of essential human labor enabling the AI system. I propose resisting AI Realism by reconfiguring PD to continue engaging with value-centered visions, increasing its exploration of non-AI alternatives, and making the essential human labor underpinning AI systems visible. I position PD as a means to generate friction against AI Realism and open space for alternative futures centered on human needs and values.Participatory Design Conference512This paper proposes resisting AI Realism by reconfiguring PD to continue engaging with value-centered visions, increasing its exploration of non-AI alternatives, and making the essential human labor underpinning AI systems visible.{"pages": "31-36"}["Aakash Gautam"]2024-06-05T00:00:00 8a5601b23b84b5bde15bfe5452c95c4e18033d23In this conceptual paper, we review existing literature on artificial intelligence/machine learning (AI/ML) education to identify three approaches to how learning and teaching ML could be conceptualized. One of them, a data-driven approach, emphasizes providing young people with opportunities to create data sets, train, and test models. A second approach, learning algorithm-driven, prioritizes learning about how the learning algorithms or engines behind how ML models work. In addition, we identify efforts within a third approach that integrates the previous two. In our review, we focus on how the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on learner interests and provide opportunities for designing applications, (3) integrate ethics and justice. In the discussion, we address the challenges and opportunities of current approaches and suggest future directions for the design of learning activities.Workshop in Primary and Secondary Computing Education1112This conceptual paper focuses on how the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on learner interests and provide opportunities for designing applications, and (3) integrate ethics and justice.{"pages": "3:1-3:10"}["Luis Morales-Navarro", "Yasmin B. Kafai"]2024-06-05T00:00:00 5e98d4c68ecf3987c90317b0e23c282fa58f9f88Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Deciphering the protein structure therefore provides great insight into the molecular mechanisms underlying biological functions in human disease. While there have recently been major advances in the artificial intelligence-based prediction of protein structure, the determination of the biological and clinical relevance of specific mutations is not yet up to clinical standards. This challenge is of utmost medical importance when decisions, as critical as suggesting termination of pregnancy or recommending cancer-directed rational drugs, depend on the accuracy of prediction of the effect of the specific mutation. Currently, available tools are aiming to characterize the effect of a mutation on the unctionality of the protein according to biochemical criteria, independent of the biological context. A specific change in protein structure can result either in loss of function (LOF) or gain-of-function (GOF) and the ability to identify the directionality of effect needs to be taken into consideration when interpreting the biological outcome of the mutation. Here we describe Triple-modalities Variant Interpretation and Analysis (TriVIAI), a tool incorporating three complementing modalities for improved prediction of missense mutations pathogenicity: protein language model (pLM), graph neural network (GNN) and a tabular model incorporating physical properties from the protein structure. The TriVIAl ensemble's predictions compare favorably with the existing tools across various metrics, achieving an AUC-ROC of 0.887, a precision-recall curve (PRC) score of 0.68, and a Brier score of 0.16. The TriVIAI ensemble is also endowed with two major advantages compared to other available tools. The first is the incorporation of biological insights which allow to differentiate between GOF mutations that tend to cluster in specific hotspots and affect structure in a specific functional way versus LOF mutations that are usually dispersed and can cripple the protein in a variety of different ways. Importantly, the advantage over other available tools is more noticeable with GOF mutations as their effect on the protein structure is less disruptive and can be misinterpreted by current variant prioritization strategies. Until now available AI-based pathogenicity predicting algorithms were a black box for the users. The second significant advantage of TriVIAI is the explainability of the ensemble which contrasts the other available AI-based pathogenicity predicting algorithms which constitute a black box for the users. This explainability feature is of major importance considering the clinical responsibility of the medical decision-makers using AI-based pathogenicity predictors.medRxiv691TriVIAI is described, a tool incorporating three complementing modalities for improved prediction of missense mutations pathogenicity: protein language model, graph neural network and a tabular model incorporating physical properties from the protein structure that contrasts the other available AI-based pathogenicity predicting algorithms.["R. Kellerman", "O. Nayshool", "O. Barel", "S. Paz", "N. Amariglio", "E. Klang", "G. Rechavi"]2024-06-05T00:00:00 9031db81b170472e64df14023438b3335b824630Background Responsible artificial intelligence (RAI) emphasizes the use of ethical frameworks implementing accountability, responsibility, and transparency to address concerns in the deployment and use of artificial intelligence (AI) technologies, including privacy, autonomy, self-determination, bias, and transparency. Standards are under development to guide the support and implementation of AI given these considerations. Objective The purpose of this review is to provide an overview of current research evidence and knowledge gaps regarding the implementation of RAI principles and the occurrence and resolution of ethical issues within AI systems. Methods A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines was proposed. PubMed, ERIC, Scopus, IEEE Xplore, EBSCO, Web of Science, ACM Digital Library, and ProQuest (Arts and Humanities) will be systematically searched for articles published since 2013 that examine RAI principles and ethical concerns within AI. Eligibility assessment will be conducted independently and coded data will be analyzed along themes and stratified across discipline-specific literature. Results The results will be included in the full scoping review, which is expected to start in June 2024 and completed for the submission of publication by the end of 2024. Conclusions This scoping review will summarize the state of evidence and provide an overview of its impact, as well as strengths, weaknesses, and gaps in research implementing RAI principles. The review may also reveal discipline-specific concerns, priorities, and proposed solutions to the concerns. It will thereby identify priority areas that should be the focus of future regulatory options available, connecting theoretical aspects of ethical requirements for principles with practical solutions. International Registered Report Identifier (IRRID) PRR1-10.2196/52349JMIR Research Protocols241This scoping review will summarize the state of evidence and provide an overview of its impact, as well as strengths, weaknesses, and gaps in research implementing RAI principles, and reveal discipline-specific concerns, priorities, and proposed solutions to the concerns.JMIR Research Protocols["Selina Boege", "M. Milne-Ives", "E. Meinert"]2024-06-05T00:00:00 be849a1dbf3393df35ceb9f7cbd1f8d3bd6655a6-Warehouse management efficiency is critical in current supply chain operations, necessitating the deployment of adaptive technological solutions. This study investigates the application of modern technologies to improve several areas of warehouse management systems (WMS), such as loading and unloading, conditioning, packing, marking, and provisioning. This study explains the challenges faced by traditional warehouse management procedures and the potential given by adaptive technological improvements using a detailed analysis of existing literature. Key technologies such as the Internet of Things (IoT), robotics, artificial intelligence (AI), and automation are reviewed in the context of their use to improved warehouse operations. Furthermore, the paper emphasizes the advantages of integrating these technologies, such as increased efficiency, accuracy, and scalability, while also discussing potential barriers and concerns for successful implementation. Warehouses can satisfy the changing needs of contemporary supply chain systems and improve productivity by using adaptive technological solutions.International journal of scientific research and engineering trends81The advantages of integrating these technologies, such as increased efficiency, accuracy, and scalability, while also discussing potential barriers and concerns for successful implementation are emphasized.International Journal of Scientific Research and Engineering Trends["Abu Sied"]2024-06-05T00:00:00 016b8882c0a5c7b6ded688bd9392b088773ef0b1As artificial intelligence (AI) technologies become increasingly important for designing human-computer interactions and user experiences, designers must prepare for the challenge of developing meaningful, creative, and technically feasible AI-based systems. We present a teaching format that we implemented to equip design students with the necessary intuition for AI technologies to develop sensor-based AI-driven interactions. The format consisted of two parts: a role play, which provided a playful, low-threshold introduction to the basics of machine learning for classifying sensor data; and an exploratory part, supported by readymade hardware and software modules, which enabled active engagement with the technology to support creative ideation processes. With this teaching format, we met our teaching objectives of increasing students' technical literacy, teaching the technical language, and providing the necessary tools and knowledge for working with technology as creative material.EduCHI221A teaching format was implemented to equip design students with the necessary intuition for AI technologies to develop sensor-based AI-driven interactions, and met the teaching objectives of increasing students' technical literacy, teaching the technical language, and providing the necessary tools and knowledge for working with technology as creative material.Proceedings of the 6th Annual Symposium on HCI Education["Rahel Flechtner", "Jakob Kilian"]2024-06-05T00:00:00 67e18f201847a738be791603e862b4569be4528cJournal of Autism and Developmental Disorders1073Artificial intelligence recognition holds promise as a tool for identifying children with ASD, however, it still needs to continually enhance the screening model and improve accuracy through multimodal screening, thereby facilitating timely intervention and treatment.Journal of autism and developmental disorders["Si-Jia Jia", "Jia-Qi Jing", "Chang-Jiang Yang"]2024-06-06T00:00:00 a5dd95909a0c1e7795fd35c4a482cd2248cc9d5eIn modern times, the rapid expansion of urban populations has intensified the urgency to optimize transportation systems, which has become an alarming issue in the face of urbanization and traffic congestion. This paper reviews the latest applications of Artificial Intelligence (AI) in the transport sector. It explores various AI methodologies, including Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Ant Colony Optimizer (ACO), Bee Colony Optimization (BCO), disruptive urban mobility, Fuzzy Logic Models (FLM), automated incident detection systems, and drones, which improve dynamic traffic management and route optimization. The study reveals that integrating these AI techniques with real-time data analytics improves traffic flow, automated incident management, and overall transportation efficiency. The results demonstrate that AI-driven systems, such as drones equipped with advanced sensors and AI algorithms, are increasingly capable of autonomous navigation, real-time monitoring, and predictive traffic management. These advancements in technologies, such as electric Vertical Take-off and Landing (eVTOL) aircraft, Hyperloop Transportation Technologies (HTT), Mobility-as-a-Service (MaaS) and autonomous delivery robots, contribute to smarter urban mobility solutions. However, it is important to focus on refining AI models for better performance, addressing challenges such as computational complexity and privacy concerns, and continuing to innovate in AI to improve the economic efficiency and reliability of transportation systems. Furthermore, to promote sustainability development in this sector, ethical considerations such as the protection of user information and the integration of the concepts of informed consent and human autonomy with community engagement programs should also be considered. Journal of Civil, Construction and Environmental Engineering222The results demonstrate that AI-driven systems, such as drones equipped with advanced sensors and AI algorithms, are increasingly capable of autonomous navigation, real-time monitoring, and predictive traffic management.Journal of Civil, Construction and Environmental Engineering["Derrick Mirindi"]2024-06-06T00:00:00 0bd67c6a8484dfab119ae9bc1d7b0e981fba73ccCitation practices are fundamental to teaching scholarly writing. With the emergence of generative Artificial Intelligence (AI) technologies, students need a structured way to cite when and how these technologies are used. This paper introduces an instructor resource, an AI Contribution Statement, which provides students with an ethical and explicit framework for reporting on AI use during idea generation and writing in research methods. Students were guided to create an AI Contribution Statement that reports when an AI technology was used for a research paper, what prompts were given and text generated, and how the information was incorporated into a final written product. Sixty-four percent of students reported using AI assistive technologies. Of those, 33.12% reported using it more than twice, suggesting that, when allowed in a course, students’ use is relatively low. Training students in best citation practices regarding ethical and transparent use of AI technologies is important, yet additional research is needed to understand how students are using it and how instructors can leverage this tool to foster equity. An AI Contribution Statement is an important addition to research methods teaching to create equality in technology use and student success.Teaching of psychology71An instructor resource is introduced, an AI Contribution Statement, which provides students with an ethical and explicit framework for reporting on AI use during idea generation and writing in research methods and how the information was incorporated into a final written product.Teaching of Psychology["Nicole Alea Albada", "Vanessa Woods"]2024-06-06T00:00:00 eef7f59827b96e797774dcf266cf351284bd0998Summary Letters of recommendation are increasingly important for the residency match. We assessed whether an artificial intelligence (AI) tool could help in writing letters of recommendation by analyzing recommendation letters written by 3 academic staff and AI duplicate versions for 13 applicants. The preferred letters were selected by 3 blinded orthopedic program directors based on a pre-determined set of criteria. The first orthopedic program director selected the AI letter for 31% of applicants, and the 2 remaining program directors selected the AI letter for 38% of applicants, with the staff-written versions selected more often by all of the program directors (p < 0.05). The first program director recognized only 15% of the AI-written letters, the second was able to identify 92%, and the third director identified 77% of AI-written letters (p < 0.05).Canadian journal of surgery. Journal canadien de chirurgie72Assessment of whether an artificial intelligence tool could help in writing letters of recommendation by analyzing recommendation letters written by 3 academic staff and AI duplicate versions for 13 applicants found that staff-written versions were selected more often.Canadian Journal of Surgery["Jad Mansour", "Mark Burman", "Mitchell Bernstein", "Emilie Sandman", "K. Yammine", "Mohammad Daher", "Paul A. Martineau"]2024-06-06T00:00:00 ee3c5cf5ac34d5e46919397d90dd924098eafef9An estimated 790 million individuals globally are afflicted with at least one form of disability. Among this population, 79 million are afflicted with diverse neuromuscular disorders. The educational inclusion of these people is complicated by the loss of the ability to communicate and breathe, difficulty walking, dressing, and/or eating without the assistance of another person. Several studies have demonstrated that assistive tools can function as a means of providing support. However, the feasibility of obtaining these instruments is hindered by limited supply, exorbitant prices, intricate operation, and substantial maintenance requirements. As a result, they are promptly abandoned following their acquisition. In contrast, artificial intelligence algorithms are of paramount importance as they enable the execution of computational processes that acquire knowledge from data, thereby enabling progressive performance enhancements deprived of explicit human intervention. The current study is aimed to tackle the obstacle of ensuring that this population has access to education by developing a mobile application that utilizes an eye-tracking algorithm to gather fixation data from individuals afflicted with this condition. This enabled the development of an innovative and personalized learning environment, which yielded outcomes including enhanced usability, accessibility, autonomy, time management, and physical barrier elimination.International Journal of Emerging Technologies in Learning (iJET)01Developing a mobile application that utilizes an eye-tracking algorithm to gather fixation data from individuals afflicted with diverse neuromuscular disorders enabled the development of an innovative and personalized learning environment, which yielded outcomes including enhanced usability, accessibility, autonomy, time management, and physical barrier elimination.Int. J. Emerg. Technol. Learn.["Paula A. Valencia-Londo\u00f1o", "Hilderman Cardona-Rodas", "J. A. J. Builes"]2024-06-06T00:00:00 36ffa35336f162851d8db6b009707ab6cc666298The increasing use of commercial artificial intelligence technology in corporate governance poses potential legal risks. This paper analyses the intervention of artificial intelligence in corporate governance from the perspective of directors’ fiduciary duty, discusses its impact on directors’ duty of loyalty and care, and points out that the use of artificial intelligence itself is in line with the requirements of the current legal system on directors’ fiduciary duty, but may increase the directors’ care duty, especially in terms of overseeing the process of the use of artificial intelligence. Based on this, the paper makes recommendations for the improvement of Company Law and internal corporate governance.Interdisciplinary Humanities and Communication Studies20Interdisciplinary Humanities and Communication Studies["Yufeng Zhang"]2024-06-06T00:00:00 9299241f9f9e1a68fa4c0a25fa68144cabe16eacWith the rapid advancement in technology, artificial intelligence has permeated every aspect of daily life. Education is no exception. Artificial intelligence in education (AIEd) has attracted great interest in the academic field. This bibliometric study aims to analyze and document the literature on AIEd from its emergence to 2023. AIEd-related publications were analysed for patterns, trends, and potential research gaps in the field. The search parameters were 'Artificial Intelligence in Education' in the article title, abstract, or topic. In order to examine the evolution of the concept holistically, no date restrictions were applied. The search, therefore, covered studies published from 1989 to 2023, with the first publication indexed in the Web of Science database marking the beginning of the timeline. The Web of Science was used as the main database and 905 studies were screened during the search. The Biblioshiny of R Software was used for descriptive and network analysis. The annual growth rate was calculated as 18.7%, indicating significant interest in the field. The results also showed that China, the USA, the UK, Australia, and Spain are the leading countries in the field of AIEd. Through thematic analysis, trending topics and engine, core, emerging, and niche themes were uncovered. Based on the research findings, the current study takes a forward-looking stance and goes beyond merely summarizing the past and present to provide insights on future linkages.Sakarya University Journal of Education290This bibliometric study aims to analyze and document the literature on AIEd from its emergence to 2023 and takes a forward-looking stance and goes beyond merely summarizing the past and present to provide insights on future linkages.Sakarya University Journal of Education["Pelin Derinalp"]2024-06-06T00:00:00 f86e1f6027e10dea2800f78ffe07ead5f7adcdddIn the contemporary era of intelligent media, the utilization of generative artificial intelligence (AI) has become a focal point of research, especially concerning the behavioral intentions of college students. This study aims to delve into the complex factors affecting college students’ behavioral intentions toward using generative AI, with a theoretical foundation rooted in the thoroughly researched Theory of Planned Behavior. The primary focus of the research is to understand how the current wave of intelligent media influences college students’ choices and intentions regarding the adoption of generative AI. This study aims to provide valuable insights into the subtle factors influencing college students’ intentions to interact with generative AI. By bridging the theoretical framework with practical application, the research strives to offer feasible suggestions for educators, policymakers, and AI developers seeking to enhance the integration of generative AI in the academic field.Interdisciplinary Humanities and Communication Studies30This study aims to delve into the complex factors affecting college students’ behavioral intentions toward using generative AI, with a theoretical foundation rooted in the thoroughly researched Theory of Planned Behavior.Interdisciplinary Humanities and Communication Studies["Yijie Yang"]2024-06-06T00:00:00 c95fc53928d4f2c06cb6123c3ea84a3d8584ae74 Research methodology This compact case study was developed from secondary sources readily available in the public domain. These secondary sources included websites, videos and articles. Case overview/synopsis Throughout 2023, social media companies faced a wide range of criticism on several fronts. Critics claimed that the companies were not doing enough to manage content and the algorithms were influencing American public opinion in the Israel-–Hamas war. Others argued that social media was negatively impacting the mental health of American youth. In response, the platforms reiterated their neutrality and emphasized the features, functions and policies that were designed to address the issues and encourage a positive user experience. As generative artificial intelligence (AI) grew in popularity, the impact on social media was inevitable. Was the convergence of social media and AI inspiring progress or exacerbating problems? How would society balance the opposing forces in a rapidly evolving environment? Complexity academic level This case should be used in marketing and management classes at the undergraduate level. Applicable concepts include AI, social media, content and information. The CASE Journal20This compact case study investigates the convergence of social media and AI inspiring progress or exacerbating problems?The CASE Journal["Anthony Furnelli", "Phil Hart", "Kimberly Sherman"]2024-06-06T00:00:00 64154574817a054f3f63e32eb58bc7ba28227958Early cancer identification is essential for increasing survival rates and lowering the disease's burden in today's society. Artificial intelligence [AI]--based algorithms may help in the early detection of cancer and resolve problems with current diagnostic methods. This article gives an overview of the prospective uses of AI in early cancer detection. The authors go over the possible applications of Artificial Intelligence algorithms used for screening risk of malignancy in asymptomatic patients, investigating as well as prioritising symptomatic individuals, and more accurately diagnosing cancer recurrence. In screening programmes, the importance of patient selection and risk stratification is emphasised, and AI may be able to assist in identifying people who are most at risk of acquiring cancer. Aside from pathology slide and peripheral blood analysis, AI can also increase the diagnostic precision of imaging methods like computed tomography [CT] and mammography. A summary of various AI techniques is given in the review, covering more sophisticated deep learning and neural networks and more traditional models like logistic regression. The advantages of deep learning algorithms in spotting intricate patterns in huge datasets and their potential to increase the precision of cancer diagnosis are emphasised by the authors. The ethical concerns surrounding the application of AI in healthcare are also discussed, and include topics like prejudice, data security, and privacy. A review of the models now employed in clinical practice is included along with a discussion of the prospective clinical implications of AI algorithms. Examined are AI's drawbacks and hazards, such as resource requirements, data quality, and the necessity for consistent reporting. In conclusion, this study emphasises the utility of AI algorithms in the early detection of cancer and gives a general overview of the many strategies and difficulties involved in putting them into use in clinical settings.Current Pharmaceutical Biotechnology00The utility of AI algorithms in the early detection of cancer is emphasised and a general overview of the many strategies and difficulties involved in putting them into use in clinical settings is given.Current pharmaceutical biotechnology["Pramit Sahoo", "Meghoparna Kundu", "Jeenatara Begum"]2024-06-06T00:00:00 0e8735b0e90121ba43f0ce882a9abca4207b1a01This article analyses the complex interrelationships between artificial intelligence (AI) and worldwide economic growth. It treats AI’s role, ranging from accelerating scientific progress to being part of conventional industries. By highlighting the importance of the government’s role in the innovation process, the study suggests implementing focused programs and policies to benefit from AI in economic growth fully. Furthermore, the article addresses the issue of dealing with social problems, like the digital divide, to achieve equitable distribution of AI benefits and promote inclusive growth. By looking at the opportunities and risks of AI implementation, this paper gives an overall picture of its consequences for the future of the world’s economies. Finally, all stakeholders should be concerted in their efforts to exploit the possibility of AI while dealing with its eventual pitfalls that will culminate in sustainable and inclusive economic progress.Interdisciplinary Humanities and Communication Studies10The article addresses the issue of dealing with social problems, like the digital divide, to achieve equitable distribution of AI benefits and promote inclusive growth and gives an overall picture of its consequences for the future of the world’s economies.Interdisciplinary Humanities and Communication Studies["Zihao Chen"]2024-06-06T00:00:00 f40467785f3423e9af7ec60bac545a6f0c3304c3BackgroundThe primary surgical approach for removing adrenal masses is minimally invasive adrenalectomy. Recognition of anatomical landmarks during surgery is critical for minimizing complications. Artificial intelligence-based tools can be utilized to create real-time navigation systems during laparoscopic and robotic right adrenalectomy. In this study, we aimed to develop deep learning models that can identify critical anatomical structures during minimally invasive right adrenalectomy.MethodsIn this experimental feasibility study, intraoperative videos of 20 patients who underwent minimally invasive right adrenalectomy in a tertiary care center between 2011 and 2023 were analyzed and used to develop an artificial intelligence-based anatomical landmark recognition system. Semantic segmentation of the liver, the inferior vena cava (IVC), and the right adrenal gland were performed. Fifty random images per patient during the dissection phase were extracted from videos. The experiments on the annotated images were performed on two state-of-the-art segmentation models named SwinUNETR and MedNeXt, which are transformer and convolutional neural network (CNN)-based segmentation architectures, respectively. Two loss function combinations, Dice-Cross Entropy and Dice-Focal Loss were experimented with for both of the models. The dataset was split into training and validation subsets with an 80:20 distribution on a patient basis in a 5-fold cross-validation approach. To introduce a sample variability to the dataset, strong-augmentation techniques were performed using intensity modifications and perspective transformations to represent different surgery environment scenarios. The models were evaluated by Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) which are widely used segmentation metrics. For pixel-wise classification performance, Accuracy, Sensitivity and Specificity metrics were calculated on the validation subset.ResultsOut of 20 videos, 1000 images were extracted, and the anatomical landmarks (liver, IVC, and right adrenal gland) were annotated. Randomly distributed 800 images and 200 images were selected for the training and validation subsets, respectively. Our benchmark results show that the utilization of Dice-Cross Entropy Loss with the transformer-based SwinUNETR model achieved 78.37% whereas the CNN-based MedNeXt model reached a 77.09% mDSC score. Conversely, MedNeXt reaches a higher mIoU score of 63.71% than SwinUNETR by 62.10% on a three-region prediction task.ConclusionArtificial intelligence-based systems can predict anatomical landmarks with high performance in minimally invasive right adrenalectomy. Such tools can later be used to create real-time navigation systems during surgery in the near future.Acta Chirurgica Belgica70This study aimed to develop deep learning models that can identify critical anatomical structures during minimally invasive right adrenalectomy using intraoperative videos of patients who underwent minimally invasive right adrenalectomy between 2011 and 2023 to develop an artificial intelligence-based anatomical landmark recognition system.Acta chirurgica Belgica["B. Sengun", "Y. Iscan", "Z. A. Yazici", "I. C. Sormaz", "N. Aksakal", "F. Tunca", "H. K. Ekenel", "Yasemin GilesSenyurek"]2024-06-06T00:00:00 8fb05744c532160425fff00d51c2ab6a114beb37The burgeoning interest in the role of Artificial Intelligence (AI) in education underscores its potential to redefine EFL classroom dynamics. Yet, the comprehensive integration and efficacy of AI tools remain underexplored. This paper systematically analyzes the application and impact of AI-based tools in enhancing EFL writing instruction. This paper outlines the dynamics of EFL classrooms, highlighting the role of technology, video materials, and literature circles in facilitating language acquisition. These elements are pivotal in crafting an interactive and engaging learning environment. Consequently, this paper proposes the following suggestions related to integrating AI in educational settings: First, leveraging AI to enhance teaching practices offers a more personalized educational experience that adapts to the diverse needs of students while adhering to the foundational aspects of traditional pedagogy. Second, incorporating AI tools into course design fosters personalized learning environments, catering to students’ varied preferences and requirements, thereby markedly boosting their engagement and academic achievements. Lastly, it is crucial to prioritize ethical considerations and data privacy in deploying AI technologies, necessitating comprehensive data protection measures and ethical guidelines to oversee AI’s application in educational contexts.Arts, Culture and Language20The dynamics of EFL classrooms are outlined, highlighting the role of technology, video materials, and literature circles in facilitating language acquisition and proposing the following suggestions related to integrating AI in educational settings.Arts, Culture and Language["Jie Luo", "Longyan Qiu"]2024-06-06T00:00:00 1fbf8137f5725400fe4d1ef8fa1c2694403af65c"Fair and responsible artificial intelligence for consumers" became the Motto of consumer protection in 2024 and gave rise to research on the use of artificial intelligence in tourism in Russia and Kazakhstan. The authors pointed out the many advantages of introducing such modern technologies, as well as the problems of their legal regulation. In conclusion, it is concluded that travelers need confidence in the proper protection of their rights in the modern digital age. For this purpose, it is necessary to regulate the use of artificial intelligence at the doctrinal and legislative level.Tourism law and economics00It is concluded that travelers need confidence in the proper protection of their rights in the modern digital age and it is necessary to regulate the use of artificial intelligence at the doctrinal and legislative level.Tourism law and economics["Zhanna B. Ivanova", "L. Tatarinova"]2024-06-06T00:00:00 8fafe21d831879f725eac75d24a3e7d5b26dfa62A systematic review was carried out on the production and publication of research papers related to the study of Artificial Intelligence for Judicial Decision-Making in Ecuador, during the period between 2018 and 2022 under the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) approach. The purpose of the analysis proposed in this document was to know the main characteristics of the publications registered in the Scopus and Wos databases and their scope in the study of the proposed variables, achieving the identification of 65 publications in total. Thanks to this first identification, it was possible to refine the results through the keywords entered in the search button of both platforms, which were ARTIFICIAL INTELLIGENCE FOR CORT DECISION-MAKING, reaching a total of 9 documents, excluding duplicates and those that did not meet the analysis criteria. The identified scientific publications were analyzed in order to know the main characteristics within the execution of research projects related to the study of the advantages, causes and disadvantages presented in the implementation of Artificial Intelligence for Judicial Decision-Making in Ecuador, evidencing as the main drawback the absence of technological tools in public institutions, which causes the permanent congestion of the system, delay in resolving conflicts and/or problems that afflict society, increased social inequality, among other factors that impede the fulfillment of its objectives in terms of governance and sustainable development.International Journal of Religion100A systematic review was carried out on the production and publication of research papers related to the study of Artificial Intelligence for Judicial Decision-Making in Ecuador during the period between 2018 and 2022 under the PRISMA approach to know the main characteristics of the publications registered in the Scopus and Wos databases.International Journal of Religion["Alba Miranda", "Estefan\u00eda Mayorga"]2024-06-06T00:00:00 e8fa46b7f940dab827655f0c77dd0ba190b349d2PurposeArtificial intelligence (AI) has become a pivotal technology in both marketing and daily life. Despite extensive research on the benefits of AI, its adverse effects on customers have received limited attention.Design/methodology/approachWe employed meta-analysis to synthesise effect sizes from 45 studies encompassing 50 independent samples (N = 19,503) to illuminate the negative facets of AI's impact on customer responses.FindingsAdverse effects of AI, including privacy concern, perceived risks, customer alienation, and uniqueness neglect, have a negative and significant effect on customers' cognitive (perceived benefit, trust), affective (attitude and satisfaction) and behavioural responses (purchase, loyalty, well-being). Additionally, moderators in AI (online versus offline), customer (age, male vs. female), product (hedonic vs. utilitarian, high vs. low involvement), and firm level (service vs. manufacturing) and national level (individualism, power distance, masculinity, uncertainty avoidance, long-term orientation) moderate these relationships.Practical implicationsOur findings inform marketing managers about the drawbacks of utilising AI as part of their value proposition and provide recommendations on how to minimise these effects in different contexts. Additionally, policymakers need to consider the dark side of AI, especially among the vulnerable groups.Originality/valueThis paper is among the first research studies that synthesise previous research on the dark side of AI, providing a comprehensive view of its diminishing impact on customer responses.Marketing Intelligence &amp; Planning745This paper is among the first research studies that synthesise previous research on the dark side of AI, providing a comprehensive view of its diminishing impact on customer responses.Marketing Intelligence &amp; Planning["M. Barari", "Lars-Erik Casper Ferm", "Sara Quach", "Park Thaichon", "Liem Ngo"]2024-06-06T00:00:00 0adeb3fc9e469a83f3b867fcf23b17a87abb8a1bNeural computing & applications (Print)855Neural Comput. Appl.["B. P. Bhuyan", "Amar Ramdane-Cherif", "Ravi Tomar", "T. P. Singh"]2024-06-06T00:00:00 f0eb388cc3f7ff4e69ba1477f08dc094f666a795Biotechnology law report01Biotechnology Law Report["Christopher M. Holman"]2024-06-06T00:00:00 cffcc5820f5ef5ad49e36c94e214e6d85cc092d5Este libro está destinado a proporcionar una introducción a los educadores interesados en aprender sobre las capacidades actuales y futuras de la inteligencia artificial (IA) en la educación. Se centra principalmente en la inteligencia artificial generativa (popularizada por ChatGPT, Bard de Google y Bing Chat de Microsoft) y ofrece a los profesores información concreta sobre cómo pueden usar estas tecnologías ahora y cómo probablemente podrán usarlas en el futuro cercano.El texto se estructura en 10 capítulos, cada uno de los cuales tiene conclusiones parciales al tema del capítulo y tiene un “cierre” en el capítulo 11.Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología00Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología["Armando E. De Giusti"]2024-06-06T00:00:00 92bcbc13f90fb608bf1967e9df053b4c4904ccbcDigestive Diseases and Sciences00Digestive Diseases and Sciences["M. Gangwani", "H. Haghbin", "Rizwan Ishtiaq", "Fariha Hasan", "Julia Dillard", "F. Jaber", "D. Dahiya", "Hassam Ali", "Shaharyar Salim", "Wade Lee-Smith", "A. Sohail", "Sumant Inamdar", "Muhammad Aziz", "Benjamin Hart"]2024-06-06T00:00:00 ab52af624e0310aadcfe5689055dbfc345245bd5Images privacy involves assessing the amount of information leakage from images, assessing risks associated with identification, and examining controls on this information. It was discussed various types of protection available and most commonly used in providing privacy to a person in images, including single-stage and two-stage detection algorithms. The results of each algorithm are organized in detailed tables, and the [YOLO] algorithm expands on all versions. The paper also clarifies the dataset used for testing the algorithms and its relevance to achieving desired results. It presents a comprehensive understanding of the process of detecting persons in digital images and assesses various tools and algorithms for recognizing persons, faces, and identities. It added an extensive examination of the several methods used to identify persons in digital images, with a specific emphasis on safeguarding their privacy. The task at hand is assessing various face recognition and identification tools and algorithms, with a specific emphasis on those that exhibit superior accuracy and efficiency in presenting outcomes. The study concluded that using the yolov8 algorithm in conjunction with blurring techniques effectively conceals individuals' information in digital images while maintaining the integrity of the overall image. The research paper's implications and information can practically contribute to the development of algorithms for detecting and protecting people in digital images, as well as the development of applications in this field. Theoretically, it can enhance understanding of the process of detecting and protecting people, and potentially contribute to the development of new theories in the field of protection and discovery.Journal of Applied Engineering and Technological Science (JAETS)550The study concluded that using the yolov8 algorithm in conjunction with blurring techniques effectively conceals individuals' information in digital images while maintaining the integrity of the overall image.Journal of Applied Engineering and Technological Science (JAETS)["Salar Raees", "Mohammed Al-Tamimi"]2024-06-06T00:00:00 5cdfaf261d7f4cd04482589013d64390d6e68f12Pediatric Research00Pediatric research["Dennis Ren", "Damian Roland"]2024-06-06T00:00:00 3048a264b92de4d1c2a12bea26197660d27028e2International Review of Psychiatry210International Review of Psychiatry["Anish Dube", "A. J. H. Ambrose", "German Velez", "Mandar Jadhav"]2024-06-06T00:00:00 e76b4e3dcd69220ee11e40ebcb6357e5088f04a8In recent years there has been a tremendous surge in the general capabilities of AI systems, mainly fuelled by training foundation models on internetscale data. Nevertheless, the creation of openended, ever self-improving AI remains elusive. In this position paper, we argue that the ingredients are now in place to achieve openendedness in AI systems with respect to a human observer. Furthermore, we claim that such open-endedness is an essential property of any artificial superhuman intelligence (ASI). We begin by providing a concrete formal definition of open-endedness through the lens of novelty and learnability. We then illustrate a path towards ASI via open-ended systems built on top of foundation models, capable of making novel, humanrelevant discoveries. We conclude by examining the safety implications of generally-capable openended AI. We expect that open-ended foundation models will prove to be an increasingly fertile and safety-critical area of research in the near future.International Conference on Machine Learning1479This position paper argues that the ingredients are now in place to achieve openendedness in AI systems with respect to a human observer, and claims that such openendedness is an essential property of any artificial superhuman intelligence (ASI).ArXiv["Edward Hughes", "Michael D. Dennis", "Jack Parker-Holder", "Feryal M. P. Behbahani", "Aditi Mavalankar", "Yuge Shi", "Tom Schaul", "Tim Rocktaschel"]2024-06-06T00:00:00 e366e00eddfbe19705a18a8c484ba2b8975a8c1fJournal of Risk Research840Journal of Risk Research["Jonas Benjamin Krieger", "F. Bouder", "Matthias Wibral", "Rui Jorge Almeida"]2024-06-06T00:00:00 64ae1c46253318cfa69943b456c5f4a687197ba5Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of its surveillance activities. Over the past decade, the FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. However, a gap remains between AI algorithm development and deployment. This viewpoint aims to describe the lessons learned from our experience and research needed to address both general issues in case-based reasoning using AI and specific needs for individual case safety report assessment. Beginning with the recognition that the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts, we apply the Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at the FDA were accepted by safety reviewers and others were not. This analysis reveals that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an AE. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning.Journal of Medical Internet Research503It is revealed that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles, which makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers.Journal of Medical Internet Research["Robert Ball", "A. Talal", "Oanh Dang", "Monica A Mu\u00f1oz", "M. Markatou"]2024-06-06T00:00:00 e1ee8887cae1401f4d509f50cb715b972c8a0bcbCustomer experience plays a decisive role in determining the success of a business, directly impacting customer satisfaction, loyalty, and overall brand perception. In today's fiercely competitive business environment, organizations are increasingly turning to technology to bolster their customer service capabilities. Artificial intelligence (AI) has been transformative in this realm, offering innovative solutions to meet the ever-changing expectations of customers. AI-powered customer service is fundamentally transforming how businesses engage with their clientele by delivering efficient, personalized, and proactive support. This review presents a rigorous analysis of the impact of artificial intelligence (AI) on customer service. It delves into the historical evolution of AI and scrutinizes recent advancements in Natural Language Processing (NLP), machine learning, sentiment analysis, and robotic process automation (RPA). Furthermore, it investigates the incorporation of voice recognition, speech-to-text technologies, AI-driven customer feedback, and survey analysis, AI ethics and explainability, and real-time language translation, as well as the amalgamation of AI with Customer Relationship Management (CRM) systems. The key opportunities identified encompass enhancing efficiency and agent productivity, customizing customer interactions, providing proactive support, improving data collection and insights, and the potential for scalability with 24/7 availability. An array of AI-powered applications and frameworks, such as chatbots, virtual assistants, recommender systems, and predictive analytics, have been systematically evaluated. The implementation of AI-driven customer service, despite its promising benefits, presents numerous challenges. This paper explores impediments such as concerns related to data privacy and security, management of complex queries, preservation of human touch, mitigation of algorithmic bias, and the integration of AI with existing systems. The discussion also encompasses strategies aimed at harmonizing efficiency and personalization, as well as future considerations for enhancing the deployment of AI. The primary objective of this paper is to provide a starting point for creating a comprehensive understanding of AI-driven customer service for industry professionals and researchers seeking to harness AI to improve customer service experiences.International Journal For Multidisciplinary Research432This review presents a rigorous analysis of the impact of artificial intelligence (AI) on customer service and investigates the incorporation of voice recognition, speech-to-text technologies, AI-driven customer feedback, and survey analysis, AI ethics and explainability, and real-time language translation.International Journal For Multidisciplinary Research["Sai Mounika Inavolu"]2024-06-06T00:00:00 22746cd04ce457628c59d45a269d06c5a72b1879The landscape of language education is undergoing a pivotal transformation, spurred by the integration of Generative Artificial Intelligence (Gen-AI) into every facet of traditional and new methodologies and practices. Given the rapid societal adoption of AI, we believe that all language instructors – from the most technologically savvy to the most tech-averse – must engage critically and ethically with AI. To ensure that AI tools are brought into language education in pedagogically appropriate and ethical ways, we have developed two large projects at our university:  1) an AI Working Group in our Modern Languages Department and 2) a chatbot that all instructors can incorporate into their classroom practices. In this article, we describe the rationale for these projects and the steps we took to implement them. We hope that this work can help other departments come together to address the challenges and achieve a balance between technological advancement and the intrinsically human facets of language education.Journal for Language Teaching312An AI Working Group in the Modern Languages Department and a chatbot that all instructors can incorporate into their classroom practices are developed, to ensure that AI tools are brought into language education in pedagogically appropriate and ethical ways.Journal of Language Teaching["Shai Cohen", "Ludovic Mompelat", "April Mann", "Logan Connors"]2024-06-06T00:00:00 ef6c227b655154f8206a1056b4dda35d389d76aeThis study delves into the relationships between the perception of corporate social responsibility (PCSR), organizational commitment and employee innovation behavior, as well as the multiple mediating roles of affective, normative and continuance commitment in the relationship between the perception of CSR and innovation behavior. This research involved 419 employees from 15 artificial intelligence (AI) enterprises in Shenzhen, China. This study’s hypotheses were tested using structural equation modeling. The findings indicate that PCSR significantly impacts innovation behavior, and affective, continuance and normative commitments also positively influence innovation behavior. Moreover, these three commitments play a partial mediating role in the relationship between PCSR and innovation behavior. This study enriches and expands the understanding of the multiple mediating mechanisms between PCSR and employee innovation behavior, providing a theoretical basis and guidance for management to comprehensively understand the role of employees’ PCSR in enhancing organizational commitment and fostering innovation behavior.Journal of Risk and Financial Management02The findings indicate that PCSR significantly impacts innovation behavior, and affective, continuance and normative commitments also positively influence innovation behavior.Journal of Risk and Financial Management["Hao He", "Chonlavit Sutunyarak"]2024-06-06T00:00:00 2864e5198f258337166f1d157bfbfc108ae2bcd3The rapid advancement of artificial intelligence (AI) technologies has markedly transformed various sectors, including the education area. This study aims to explore how the integration of AI ChatGPT influences students’ learning experiences and their acceptance of this technology as a supportive learning resource. A quantitative approach, grounded in the Technology Acceptance Model (TAM), was utilized, and additional variables of Experience and Self-Efficacy were considered. Students underwent a learning process assisted by ChatGPT for three weeks, and then they were surveyed using a five-point Likert scale-based questionnaire. The data collected was analyzed using SmartPLS for path modeling. The results indicated that Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Self-Efficacy significantly influenced students’ Attitudes Towards Using (ATU) ChatGPT. Subsequently, ATU positively affected their Behavioral Intention to Use (BIU) tool, which, in turn, significantly predicted Actual Use (AUT). The experience was found to impact PEU directly but had no significant direct effect on PU. The study concludes that while students perceive ChatGPT as a useful and easy-to-use tool for learning, the extent of its actual application hinges significantly on their attitudes and self-efficacy. Educators looking to integrate AI tools into their learning activities can leverage these insights to foster more effective learning environments that resonate with students’ expectations and competencies.2024 International Conference on Smart Computing, IoT and Machine Learning (SIML)162The study concludes that while students perceive ChatGPT as a useful and easy-to-use tool for learning, the extent of its actual application hinges significantly on their attitudes and self-efficacy.2024 International Conference on Smart Computing, IoT and Machine Learning (SIML)["Sukirman", "Eko Supriyanto", "Arif Setiawan", "A. Chamsudin", "Irma Yuliana", "Jan Wantoro"]2024-06-06T00:00:00 3f9e1fc5cb30db05b265dc09d5d421e800be41b5Thousands of hospitals worldwide have adopted mobile artificial intelligence (AI)-based stroke care coordination platforms. Studies exploring the benefit of these platforms have been scrutinized due to small sample size, serial cohort design, and measurement of metrics with multiple determinants. In this large multi-center study, we evaluated the ability of an AI-based stroke care coordination platform to expedite contact with the interventionalist (NIR) for potential thrombectomy.Acute stroke consultations seen by TeleSpecialists, LLC physicians at 166 facilities (17 states) utilizing Viz.ai software (AI) vs. no AI software (non-AI) were extracted from the TeleCare by TeleSpecialists™ database from December 1, 2021, through March 31, 2022. The primary outcome was time from patient arrival to first contact with the interventionalist to discuss need for potential thrombectomy (Arrival-to-NIR notification).A total of 14,116 cases were analyzed. Compared to the non-AI cohort, Arrival-to-NIR notification in the AI cohort was: (1) 39.5 min faster (44.13% reduction, p < 0.001) in the overall analysis; (2) 33.0 min faster (34.0% reduction, p < 0.001) in the non-thrombectomy (non-TC) facility subgroup analysis; and (3) 34.0 min faster (43.59% reduction, p < 0.001) in the thrombectomy capable (TC) facility subgroup analysis. IQR range comparison demonstrated a significant improvement in uniformity of stroke workflow across all AI subgroups. Significant, albeit small, confounding biases were revealed in the data. The presence of AI within the non-TC subgroup correlated with a lower acceptance rate for thrombectomy by the NIR (delta = −10.79% absolute and 23.17% relative reduction, p < 0.0001).While this study was limited by our inability to capture detailed neuroimaging timelines and patient outcomes, it suggests a potential significant benefit of AI-based stroke care coordination platforms and underscores the critical need to development robust “big data” systems to study the effects of AI, and other emerging technologies, on stroke systems of care.Frontiers in Stroke301This study suggests a potential significant benefit of AI-based stroke care coordination platforms and underscores the critical need to development robust “big data” systems to study the effects of AI, and other emerging technologies, on stroke systems of care.Frontiers in Stroke["T. Devlin", "Lan Gao", "Oleg Collins", "Gregory W Heath", "Morgan Figurelle", "A. Avila", "C. Boyd", "Hira Ayub", "T. Sevilis"]2024-06-06T00:00:00 473226d1144bb657e3a66aca948b5d3720e4f260The rise of generative artificial intelligence (AI) has ignited a debate about its effects on the mis‐ and disinformation landscape. The doomsday scenarios of epistemic and information apocalypse presented for many years are recently being questioned, and the previous fears are called “overblown.” These phenomena are analyzed mostly through the factors of quantity and quality of AI‐powered content and the potential for personalization possessed by AI. We argue that using quantitative arguments carries a high risk of underestimating the threat, especially in the context of the so‐called detection challenge. We point out that this discourse is affected by the narrow conceptualization of how we understand quantity, quality, and personalization with regard to AI. In our opinion, apocalyptic visions are speculative in nature, difficult to quantify, and carry signs of a self‐fulfilling prophecy, but disregarding risks hinders appropriate countermeasures against AI‐powered dis‐ and misinformation, which adversely affects policy‐making activities. We propose a paradigm shift to focus more on social reactions to technology rather than technological attributes. By expanding the understanding of the analyzed phenomena, we indicate that the potential of AI is both overestimated and underestimated and above all—still misunderstood.Norman, Emma R., and Rafael Delfin. 2012. “Wizards under Uncertainty: Cognitive Biases, Threat Assessment, and Misjudgments in Policy Making.” Politics & Policy 40(3): 369–402. https://doi.org/10.1111/j.1747‐1346.2012.00356.x.Robles, Pedro, and Daniel J. Mallinson. 2023. “Catching Up with AI: Pushing Toward a Cohesive Governance Framework.” Politics & Policy 51(3): 355–72. https://doi.org/10.1111/polp.12529.Veloso Meireles, Adriana. 2024. “Digital Rights in Perspective: The Evolution of the Debate in the Internet Governance Forum.” Politics & Policy 52(1): 12–32. https://doi.org/10.1111/polp.12571.Politics &amp; Policy261It is argued that using quantitative arguments carries a high risk of underestimating the threat, especially in the context of the so‐called detection challenge, and proposes a paradigm shift to focus more on social reactions to technology rather than technological attributes.Politics &amp; Policy["Mateusz \u0141abuz", "Christopher Nehring"]2024-06-06T00:00:00 dbe514439eb61ba8cd3d9537923b28b3274f2720Explainable Artificial Intelligence (XAI) has emerged as a critical domain to demystify the opaque decision-making processes of machine learning models, fostering trust and understanding among users. Among various XAI methods, SHAP (SHapley Additive exPlanations) has gained prominence for its theo- retically grounded approach and practical applicability. The paper presents a comprehensive exploration of SHAP’s effectiveness compared to other promi- nent XAI methods.Methods such as LIME (Local Interpretable Model-agnostic Explanations), permutation importance, Anchors and partial dependence plots are examined for their respective strengths and limitations. Through a detailed analysis of their principles, strengths, and limitations through reviewing differ- ent research papers based on some important factors of XAI, the paper aims to provide insights into the effectiveness and suitability of these methods.The study offers valuable guidance for researchers and practitioners seeking to incorporate XAI into their AI systems. Keywords: SHAP, XAI, LIME, permutation importance, Anchors and par- tial dependence plotsINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT01The paper presents a comprehensive exploration of SHAP’s effectiveness compared to other XAI methods, and offers valuable guidance for researchers and practitioners seeking to incorporate XAI into their AI systems.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Mayuri Manish Kedar"]2024-06-06T00:00:00 6fcc99b24143c7983a3cef5a3ad65cd3430ee9c8We report favorable preliminary findings of work in progress bridging the Artificial Intelligence (AI) gap between bottom-up data-driven Machine Learning (ML) and top-down conceptually driven symbolic reasoning. Our overall goal is automatic generation, maintenance and utilization of explainable, parsimonious, plausibly causal, probably approximately correct, hybrid symbolic/numeric models of the world, the self and other agents, for prediction, what-if (counter-factual) analysis and control. Our old Evolutionary Learning with Information Theoretic Evaluation of Ensembles (ELITE2) techniques quantify strengths of arbitrary multivariate nonlinear statistical dependencies, prior to discovering forms by which observed variables may drive others. We extend these to apply Granger causality, in terms of conditional Mutual Information (MI), to distinguish causal relationships and find their directions. As MI can reflect one observable driving a second directly or via a mediator, two being driven by a common cause, etc., to untangle the causal graph we will apply Pearl causality with its back- and front-door adjustments and criteria. Initial efforts verified that our information theoretic indices detect causality in noise corrupted data despite complex relationships among hidden variables with chaotic dynamics disturbed by process noise, The next step is to apply these information theoretic filters in Genetic Programming (GP) to reduce the population of discovered statistical dependencies to plausibly causal relationships, represented symbolically for use by a reasoning engine in a cognitive architecture. Success could bring broader generalization, using not just learned patterns but learned general principles, enabling AI/ML based systems to autonomously navigate complex unknown environments and handle “black swans”.Defense + Commercial Sensing131The overall goal is automatic generation, maintenance and utilization of explainable, parsimonious, plausibly causal, probably approximately correct, hybrid symbolic/numeric models of the world, the self and other agents, for prediction, what-if (counter-factual) analysis and control.{"pages": "1305802 - 1305802-4", "volume": "13058"}["Stuart W. Card"]2024-06-06T00:00:00 5a9b7095000b2c09d29176f0c4d906785bab17cfThe advent of Generative Artificial Intelligence (genAI) has significantly reshaped the educational landscape, heralding new prospects and concurrently introducing complex challenges. Mirroring the essence of René Magritte’s iconic artwork “Ceci n’est pas une pipe”, where the depiction of a pipe is not actually a pipe, this publication is not a publication, at least not from the beginning. This article acts as a case study, showcasing the ability to generate coherent and pertinent AI-created content, while also drawing attention to its limitations in depth and diversity. Moreover, it underscores the facility with which such content can be produced. The paper culminates by examining the role of AI-generated content within the academic sphere, particularly highlighting the complexities involved in distinguishing AI-produced material from human-authored text.International Journal of Emerging Technologies in Learning (iJET)01The paper culminates by examining the role of AI-generated content within the academic sphere, particularly highlighting the complexities involved in distinguishing AI-produced material from human-authored text.Int. J. Emerg. Technol. Learn.["Daniele Zolezzi"]2024-06-06T00:00:00 97161aca1960f0b43ec774156f1762e2019de61aThief of Truth is a first-person perspective Virtual Reality (VR) comic that explores the relationship between humans and artificial intelligence (AI). The work tells the story of a mind-uploaded human being reborn as a new subject while interacting with an AI that is looking for the meaning of life. In order to experiment with the expandability of VR comics, the work was produced by focusing on three problems. First, the comic is designed using the viewing control effect of VR. Second, through VR controller-based interaction, the player's immersion in the work is increased. Third, a method for increasing accessibility to VR comics was devised. This work aims to present an example of an experimental attempt in VR Comics.ISEA2023 PROCEEDINGS01This work aims to present an example of an experimental attempt in VR Comics, designed using the viewing control effect of VR to experiment with the expandability of VR comics.ISEA2023 PROCEEDINGS["Joonhyung Bae"]2024-06-06T00:00:00 3771a6cd3eac64985788359473c022e1c7e1a12fCommunications psychology640Communications Psychology["Rachel Schlund", "Emily M. Zitek"]2024-06-06T00:00:00 c81aa1a53904bd359aa014d6576fbb54809bd779This study delves into the integration of Artificial Intelligence (AI) in cybersecurity measures within smart cities, aiming to uncover both the challenges and opportunities this fusion presents. With the burgeoning reliance on interconnected digital infrastructures and the vast data ecosystems within urban environments, smart cities are increasingly susceptible to sophisticated cyber threats. Through a systematic literature review and content analysis, this research identifies the unique cybersecurity vulnerabilities faced by smart cities and evaluates how AI technologies can fortify urban cybersecurity frameworks. The methodology encompasses a comprehensive review of recent scholarly articles, industry reports, and case studies to assess the role of AI in enhancing threat detection, response, and prevention mechanisms. Key findings reveal that AI-driven cybersecurity solutions significantly enhance the resilience of smart cities against cyber threats by providing advanced analytical capabilities and real-time threat intelligence. However, the study also highlights the critical need for robust ethical and privacy considerations in the deployment of AI technologies. Strategic recommendations are provided for policymakers, urban planners, and technology leaders, emphasizing the importance of integrating secure AI-enabled infrastructure and fostering public-private partnerships. The study concludes with suggestions for future research directions, focusing on the ethical implications of AI in cybersecurity and the development of scalable AI solutions for diverse urban contexts. Keywords: Artificial Intelligence, Cybersecurity, Smart Cities, Urban Resilience.Computer Science &amp; IT Research Journal035Key findings reveal that AI-driven cybersecurity solutions significantly enhance the resilience of smart cities against cyber threats by providing advanced analytical capabilities and real-time threat intelligence, but the study also highlights the critical need for robust ethical and privacy considerations in the deployment of AI technologies.Computer Science &amp; IT Research Journal["Ezekiel Onyekachukwu Udeh", "Prisca Amajuoyi", "Kudirat Bukola Adeusi", "Anwulika Ogechukwu Scott"]2024-06-07T00:00:00 2163b57d1b7923ef8b7d852d4a5f8d2c92e2bc6cCities worldwide are attempting to be labelled as smart, but truly classifying as such remains a great challenge. This study aims to use artificial intelligence (AI) to classify the performance of smart cities and identify the factors linked to their smartness. Based on residents’ perceptions of urban structures and technological applications, this study included 200 cities globally. For 147 cities, we gathered the perceptions of 120 residents per city through a survey of 39 questions covering two main pillars: ‘Structures’, referring to the existing infrastructure of the city, and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. These pillars were evaluated across five key areas: health and safety, mobility, activities, opportunities, and governance. For the remaining 53 cities, scores were derived by analyzing pertinent data collected from various online resources. Multiple machine learning algorithms, including Random Forest, Artificial Neural Network, Support Vector Machine, and Gradient Boost, were tested and compared in order to select the best one. The results showed that Random Forest and the Artificial Neural Network are the best trained models that achieved the highest levels of accuracy. This study provides a robust framework for using machine learning to identify and assess smart cities, offering valuable insights for future research and urban planning.Smart Cities309This study provides a robust framework for using machine learning to identify and assess smart cities, offering valuable insights for future research and urban planning.Smart Cities["L. Hammoumi", "M. Maanan", "H. Rhinane"]2024-06-07T00:00:00 9762e589c0ff8c04645b6919dfe58ec39efa1fb9The increasing complexity of supply chains is putting pressure on businesses to find new ways to optimize efficiency and cut costs. One area that has seen a lot of recent development is machine learning (ML) and artificial intelligence (AI) to help manage supply chains. This paper employs machine learning (ML) and artificial intelligence (AI) algorithms to predict fraud in the supply chain. Supply chain data for this project was retrieved from real-world business transactions. The findings show that ML and AI classifiers did an excellent job predicting supply chain fraud. In particular, the AI model was the highest predictor across all performance measures. These results suggest that computational intelligence can be a powerful tool for detecting and preventing supply chain fraud. ML and AI classifiers can analyze vast amounts of data and identify patterns that may evade manual detection. The findings presented in this paper can be used to optimize supply chain management (SCM) and make predictions of fraudulent transactions before they occur. While ML and AI classifiers are still in the early stages of development, they have the potential to revolutionize SCM. Future research should explore how these techniques can be refined and applied to other domains.International Journal of Production Research1028The findings show that ML and AI classifiers did an excellent job predicting supply chain fraud and the AI model was the highest predictor across all performance measures, suggesting that computational intelligence can be a powerful tool for detecting and preventing supply chain fraud.Int. J. Prod. Res.["M. Lokanan", "Vikas Maddhesia"]2024-06-07T00:00:00 f301bd0af175e6f362b38708d512cc82ea3d7281This study reviews the application of artificial intelligence (AI) throughout the food value chain and how it can be leveraged to help companies become more sustainable. A literature review across different parts of the food value chain was conducted to provide an overview of the main themes of current and future AI applications throughout the food industry. Moreover, the paper focuses on the benefits and challenges of change management when integrating AI. A documentary Systematic Review using PRISMA research was conducted to find and analyze the aforementioned applications. The key insight is that change progress varies significantly. Today’s applications are primarily found within food inspection and quality assurance due to relatively straightforward AI applications in the value chain. Such technology is mainly image-based. Companies can use the interconnectedness of AI and sustainability by becoming more efficient through AI and simultaneously saving emissions and resources through optimizing processes.Sustainability634Companies can use the interconnectedness of AI and sustainability by becoming more efficient through AI and simultaneously saving emissions and resources through optimizing processes and simultaneously saving emissions and resources through optimizing processes.Sustainability["Miriam Du-Phuong Ta", "Stefan Wendt", "Throstur Olaf Sigurjonsson"]2024-06-07T00:00:00 501258714fcad302234f7ce3e43c248b86ee49c6In the rapidly evolving landscape of higher education, the integration of Artificial Intelligence (AI) tools represents a pivotal paradigm shift, poised to redefine the very fabric of student engagement. Thus, as this integration becomes increasingly prevalent, it raises questions about its impact on student engagement. This prompted this study that sought to investigate AI tools' influence on student engagement in higher education. The study employed the descriptive survey design using a diverse sample of students from the University of Port Harcourt. A scale with validities and a high-reliability coefficient was used in obtaining data. Data were analyzed using mean, one-way, and two-way ANOVA. The result showed that the majority of the students were extremely engaged as a result of the influence of the use of AI tools, a significant difference existed in the engagement levels of students influenced by AI tools, and that neither gender nor age significantly affects engagement, suggesting a universal appeal of AI tools across demographics. The findings underscore the importance of inclusive AI integration in higher education, ensuring equitable and engaging learning experiences for students. These insights contribute to the ongoing discourse on leveraging AI tools to enhance student engagement in higher education settings  Edukasiana: Jurnal Inovasi Pendidikan514Investigating AI tools' influence on student engagement in higher education found that the majority of the students were extremely engaged as a result of the influence of the use of AI tools, and that neither gender nor age significantly affects engagement, suggesting a universal appeal of AI tools across demographics.Edukasiana: Jurnal Inovasi Pendidikan["Ebere Pearl Ezeoguine", "Stella Eteng-Uket"]2024-06-07T00:00:00 6f59577a6e3184baa974d5b445aa1409938e7423Artificial intelligence (AI) has been rapidly developing, both in the education field and beyond, in recent years. Due to this fast-paced nature, special education teachers may not be aware of the availability of AI that could be pertinent to their practice. In this manuscript, five AI platforms that are readily available for special education teachers to access are explored. AI platform details including pricing, functionality, and feature options are provided for each. Suggestions for how each AI platform can be utilized by special education teachers within their practice is conveyed. Overall implications regarding AI integration, usage, and ethical considerations in special education practice are discussed.Journal of Special Education Technology103Five AI platforms that are readily available for special education teachers to access are explored and overall implications regarding AI integration, usage, and ethical considerations in special education practice are discussed.Journal of Special Education Technology["Danielle A. Waterfield", "Latesha Watson", "Jamie Day"]2024-06-07T00:00:00 857f63a0e3c8fac1b2c313df9b222bef4d9d738e This Viewpoint describes the potential benefits and harms of using artificial intelligence (AI) in health care decision-making processes. JAMA Health Forum53JAMA health forum["Lee A. Fleisher", "Nicoleta J. Economou-Zavlanos"]2024-06-07T00:00:00 b661c25e172348f16664fb486668da88ef440affBackground Neuroendovascular procedures require careful and simultaneous attention to multiple devices on multiple screens. Overlooking unintended device movements can result in complications. Advancements in artificial intelligence (AI) have enabled real-time notifications of device movements during procedures. We report our preliminary experience with real-time AI-assisted cerebral aneurysm coiling in humans. Methods A real-time AI-assistance software (Neuro-Vascular Assist, iMed technologies, Tokyo, Japan) was used during coil embolization procedures in nine patients with an unruptured aneurysm. The AI system provided real-time notifications for ‘coil marker approaching’, ‘guidewire movement’, and ‘device entry’ on biplane fluoroscopic images. The efficacy, accuracy, and safety of the notifications were evaluated using video recordings. Results The AI system functioned properly in all cases. The mean number of notifications for coil marker approaching, guidewire movement, and device entry per procedure was 20.0, 3.0, and 18.3, respectively. The overall precision and recall were 92.7% and 97.2%, respectively. Five of 26 true positive guidewire notifications (19%) resulted in adjustment of the guidewire back toward its original position, indicating the potential effectiveness of the AI system. No adverse events occurred. Conclusions The software was sufficiently accurate and safe in this preliminary study, suggesting its potential usefulness. To the best of our knowledge, this is the first reported use of a real-time AI system for assisting cerebral aneurysm coiling in humans. Large scale studies are warranted to validate its effectiveness. Real-time AI assistance has significant potential for future neuroendovascular therapy.Journal of NeuroInterventional Surgery203This is the first reported use of a real-time AI system for assisting cerebral aneurysm coiling in humans and has significant potential for future neuroendovascular therapy.Journal of NeuroInterventional Surgery["Osamu Masuo", "Yuya Sakakura", "Yoshiaki Tetsuo", "Kana Takase", "Shun Ishikawa", "Kenichi Kono"]2024-06-07T00:00:00 1bd9247dfadea54f4b735c76b13e9aa1c4c13cabArtificial intelligence (AI) is quickly gaining relevance as a transformative technology. Its ability to rapidly fuse and synthesize data, accelerate processes, automate tasks, and augment decision-making has the potential to revolutionize multi-domain warfighting through data-centric operations and algorithmic warfare. As the military relies more on AI-enabled Decision Aids to increase the efficiency and effectiveness of decision-making, it highlights the need to effectively assess them before deployment. Modeling and simulation (M&S) environments are essential for assessing these rapidly evolving AI-enabled systems. Accepted analytical frameworks are needed to guide ways to represent and model AI sufficiently within M&S environments for accurate assessment. In this paper, we identify common characteristics within the main categories of AI and investigate how those characteristics can be best represented across the main categories of M&S. We provide two use cases to highlight an assessment of AI-enabled Decision Aids for cybersecurity and aeromedical evacuation problems. Our example use cases demonstrate how to leverage a framework for analytic assessment of AI within M&S environments.Defense + Commercial Sensing571Common characteristics within the main categories of AI are identified and how those characteristics can be best represented across the main categories of M&S are investigated to leverage a framework for analytic assessment of AI within M&S environments.{"pages": "130510K - 130510K-25", "volume": "13051"}["Joshua A. Wong", "Emily A. Nack", "Zachary A. Steelman", "Seth Erway", "Nathaniel D. Bastian"]2024-06-07T00:00:00 d2cdb3b14361ac14194e6a92ed09b50def07b749The ubiquity of Artificial Intelligence (AI) has generated different perceptions and views regarding its usefulness in conducting educational assessment in Nigerian universities. This study determined whether academic integrity and innovative assessment concerns affect how university teachers utilize diverse AI tools in educational assessment. It also investigated if university teachers’ perception of using AI tools is likely to be associated with their tendency to personalize AI use at universities in the country. The study adopted inferential research design. 3,083 university teachers comprised the population in the study, out of which the sample of 322 participants who are professors, associate professors, and senior lecturers from government and privately-owned universities, were randomly selected for the study. The instrument was a 4-point scale questionnaire titled: “University Teachers’ Perception and Utilization of AI Questionnaire (UTPUAIQ).” The data were analyzed using independent t-test, Pearson Product Moment Correlation and Chi-Square statistics, as percentile analysis was explored using simple percentage statistical procedure. The results revealed that academic integrity concerns have an influence on how university teachers perceive AI use in assessment; that perception for innovative assessment concerns at university significantly affects how university teachers utilize diverse AI tools in educational assessment; and that university teachers’ perception of using AI tools is likely to be associated with their tendency to personalize AI use at universities. It was concluded that AI use in educational assessment is in itself not harmful but the potential risks involved must be mitigated as it is deployed for use for students’ assessment at universities in Nigeria. Hence, there is a need to ensure the ethical, inclusive and equitable use of AI in educational assessment at universities in the country.Edukasiana: Jurnal Inovasi Pendidikan212It was concluded that AI use in educational assessment is in itself not harmful but the potential risks involved must be mitigated as it is deployed for use for students’ assessment at universities in Nigeria.Edukasiana: Jurnal Inovasi Pendidikan["Abdul-Wahab Ibrahim", "Ali Abdullahi Taura", "A. Iliyasu", "Yusuf Olayinka Shogbesan", "Shehu Adaramaja Lukman"]2024-06-07T00:00:00 8350960cb53831ecbf54f42d75b3288fdcd96b02Artificial intelligence (AI) is being used more and more in numerous fields, and the medical industry is no exception. AI is demonstrating potential as a helpful tool in all facets of patient care pathways, including research in healthcare. Due to the practically exponential expansion in computer processing power, cloud computing, and the invention and improvement of software algorithms specifically designed for medical tasks, artificial intelligence (AI) systems are becoming more and more significant in the fields of medicine and orthopaedic surgery. Machine-based integration of imaging studies is particularly ripe for the field of orthopaedic disorders because of the extensive role of technologies like medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to the management of orthopaedic disorders, among other applications. In orthopaedic surgery, practical applications include real-time rehabilitation monitoring and surgical training; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and diagnostics, such as fracture recognition and tumor detection. This study aims to outline current clinical uses of AI in orthopaedic surgery and to provide a thorough grasp of AI and its subfields.International Journal of Innovative Science and Research Technology171This study aims to outline current clinical uses of AI in orthopaedic surgery and to provide a thorough grasp of AI and its subfields.International Journal of Innovative Science and Research Technology (IJISRT)["Basavaraj. G", "Rachana. H. B", "Manoj. M.P", "Chetan Kumar G S"]2024-06-07T00:00:00 d5afac526a4e3ed23c7dd5e781ac12cf36e3c69fOphthalmology, the medical field dedicated to eye care, is undergoing a transformation due to the advent of artificial intelligence (AI). This review article explores the growing use of AI in ophthalmic practices, focusing on disease diagnosis, screening, and surgical guidance. We examine the potential benefits of AI-powered tools, including their ability to improve the accuracy, efficiency, and accessibility of eye care. However, we also acknowledge the ethical and practical challenges associated with this technology, such as algorithmic bias, the lack of explainability, and potential job displacement. We envision a future where ophthalmologists and AI collaborate to improve patient care and usher in a new era of ophthalmic practice.Experimental and Applied Medical Science350A future where ophthalmologists and AI collaborate to improve patient care and usher in a new era of ophthalmic practice is envisioned, including their ability to improve the accuracy, efficiency, and accessibility of eye care.Experimental and Applied Medical Science["Ibrahim Edhem Yilmaz"]2024-06-07T00:00:00 497a8ebd27e99238521c00afd872cba89dc70733One of the promising areas of artificial intelligence (AI) use is architectural design. Most of the world-renowned architectural bureaus are experimenting and exploring the use of AI in their daily work. It is necessary to integrate the ability to apply such tools wisely into the education process for architecture students to be competitive and participate in projects involving AI successfully. The growing interest in AI among students and modern architectural educational institutions and bureaus determines the relevance of this research. The article discusses the specificity of the use of AI within the framework of architectural education, which involves providing students with access to resources and participation in international projects. AI makes it possible to develop creativity and promotes greater adaptability and openness of future architects to cooperate with professionals from different fields, promoting a holistic approach to design and problem-solving. Incorporating AI concepts and technologies into architectural education equips future architects with the skills and knowledge they need to thrive professionally. Some of the main benefits and results of the approach include access to AI tools and resources, interdisciplinary cooperation, innovation and creativity, environmental responsibility, efficiency, and project management. There are several directions to successfully use AI in educational design, such as: search for ideas, organisation of text information, and visualisation. Overall, AI’s impact on educational design for architectural students is quite significant. Integrating innovative technologies can improve the quality of education, provide students with new opportunities for developing creative and analytical skills, and prepare them for the challenges of modern architectural practice, with the consideration of ethical norms. This adoption also supports the development of sustainable design practices, as AI can analyse complex data to optimise the use of materials and energy in building projects. Further, AI-driven tools can help simulate and visualise environmental impact and sustainability, which is increasingly critical in modern architecture. Keywords: architectural education, innovation, design, artificial intelligence.Municipal economy of cities90The article discusses the specificity of the use of AI within the framework of architectural education, which involves providing students with access to resources and participation in international projects, and its impact on educational design for architectural students is quite significant.Municipal economy of cities["M. Blinova", "M. Molodcha"]2024-06-07T00:00:00 d0ce18402d13f1a32a16bd0d792b651200553f2bIn this paper, the application of Artificial Intelligence (AI) and related topics (e.g., Machine Learning, Artificial Neural Networks (ANNs), deep learning) as they apply to analytic spectrometry (e.g., either using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES), or a portable, battery-operated microplasma-OES) using a fiber-optic spectrometer) will be described, and the application of AI to teaching analytical atomic spectrometry will be outlined.Defense + Commercial Sensing120The application of Artificial Intelligence and related topics as they apply to analytic spectrometry (e.g., Machine Learning, Artificial Neural Networks, deep learning) will be described and the application of AI to teaching analytical atomic spectrometry will be outlined.{"pages": "130260H - 130260H-3", "volume": "13026"}["Celine Tat", "V. Karanassios"]2024-06-07T00:00:00 70b7e8a558cf59fef918a15331418e6b54ab66dfArtificial intelligence and big data are paramount for generating real-time insights enabling decision-making in complex systems. Integrating massive data streams and AI algorithms presents huge opportunities for extracting actionable insights at unprecedented speed and precision. This paper discusses how integrating artificial intelligence and big data facilitates handling complex, dynamic tasks in the health, finance, and supply chain management industries. The study describes how advanced machine learning models, neural networks, and decision algorithms enable these systems to process big data in real-time, even as that helps improve predictive and adaptive decision-making. The paper discusses our performance evaluation of AI-driven decision systems, focusing on architecture that supports efficient data processing. In addition, we present a framework that elucidates how AI models interpret Big Data within multi-layered, real-time environments. The study will also include results in terms of impedance and multi-line graphs to demonstrate system performances. We will also provide some of the tables in the key metrics. This study highlights the benefits and drawbacks of AI data integration and its potential implementation.FMDB Transactions on Sustainable Intelligent Networks00The paper discusses the performance evaluation of AI-driven decision systems, focusing on architecture that supports efficient data processing, and presents a framework that elucidates how AI models interpret Big Data within multi-layered, real-time environments.FMDB Transactions on Sustainable Intelligent Networks["Sudheer Panyaram"]2024-06-07T00:00:00 318fa1c7f9cd3338ad36b1c3508874866a91f670 Adaptation and application of Artificial Intelligence (AI) technology for the development of drugs against the deadly and continuously mutating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has been extremely beneficial, cost-effective, and time saving for the scientific community. A systematic review is necessary for complete picturization of the overall AI assistance in developing drugs and vaccines against SARS-CoV-2. A systematic analysis and review of the research literature available on the application of AI in the development of drugs and vaccines against SARS-CoV-2 from various online platforms has been performed, and relevant full papers have been selected on certain selection criteria and have been used for this review. Utilization of AI tools has enabled the selection, modification, evaluation, and prediction of the effectiveness of drug formulations against coronavirus disease (COVID-19) in a very rapid and efficient manner. Vaccine development against the deadly SARS-CoV-2 virus has also been aided and benefited immensely by using AI tools and techniques. Thousands of studies regarding the development of effective drugs and vaccines against the constantly evolving, mutating, and prevailing SARS-CoV-2 virus have been conducted, and several thousands are still being conducted around the world. AI is a powerful tool, and its application has been highly beneficial in developing effective drugs and vaccines against the deadly SARS-CoV-2 in a cost-effective and time-effective frame. This systematic review briefs the findings and achievements till the date of writing this article in the field of AI-assisted drug and vaccine development against COVID-19. Coronaviruses00A systematic review briefs the findings and achievements till the date of writing this article in the field of AI-assisted drug and vaccine development against COVID-19.Coronaviruses["Vishal Singha", "Suvendu Ghosh", "P. Singha", "Sutapa Datta", "D. Ghosh"]2024-06-07T00:00:00 7cc654cb0672fcac65b74991bd3f49c9a2966be1  The article highlights the role of social assessment of technology (TA) as a new tool of science and technology policy aimed at finding means for social management of technologies. Development of new information technologies, in particular, AI technologies, has become a subject of research of scientists from various fields, including specialists in TA. In article it is indicated that active implementation of artificial intelligence (AI), robotics and machine learning technologies over recent years not only provide additional opportunities for business, governments and people, while transforming social, professional, cultural sphere of society, but also generate significant concerns and risks of social inequality, transformation of labor market, growth of income differentiation, security threats, etc. The article highlights the risks and concerns of using AI technologies and possible approaches for their prevention and overcoming in Ukraine and the world. It is discussed the activity of international organizations on development of standards, focused on social and ethical consequences of Al introduction. Specialized types of impact assessment of AI technologies, such as human rights impact assessment and algorithmic assessment, which are reflected in activities of offices and organizations on technology assessment, are analyzed. It is identified the need for development of international standards and creation of ethics code in the field.Studies in history and philosophy of science and technology350The role of social assessment of technology (TA) as a new tool of science and technology policy aimed at finding means for social management of technologies is highlighted and the need for development of international standards and creation of ethics code in the field is identified.Studies in history and philosophy of science and technology["O. V. Zhyvaha"]2024-06-07T00:00:00 f716ad7e79843870035c801db287e6b76c92bde6The article considers the application of robotics and artificial intelligence in everyday life and at work. Existing research has shown that industrial and domestic robot design and operational characteristics can threaten human life and health and serve as safety measures. At the same time, modern collaborative robots share the same workspace as humans. The study found that, depending on the nature of the origin, it is possible to divide robotics hazards into the following types: mechanical, which can arise from an unintentional or unexpected action or when changing tools; contact with dangerous energy sources that can lead to electric shock when touching connections, current-carrying parts, or an electric arc flash; thermal, which arise from contact with hot or cold surfaces; noise, radiation, chemicals, infections, and other hazards. Many robot-related accidents do not occur during operation but often during the design, installation, and testing process when workers first encounter the robot. The causes of robotics-related injuries include the human factor, poor safety culture, and the robot’s operational and design characteristics. Isolation from a professional service robot is ineffective when a person has to work in the robot’s area of operation or at the same workplace. The physical safety of robots and humans in shared spaces includes the following categories: safety assessment and the concept of human-robot interaction; contact safety due to robot design; passive interoperable systems, lightweight manipulators, safe actuators, and passive robotic systems. Every collaborative robot system is unique, so risk assessment is crucial for safe and successful implementation. Existing methods for assessing occupational risks of robots mainly consider ergonomic risks and can only be applied at the design stage. Some existing machines and measuring arms have a control system that monitors the workspace. If something foreign appears in the work area, the machine slows down and resumes high measurement speeds automatically. Keywords: safety culture, safety measures, industrial work, occupational risk assessment.Municipal economy of cities90The study found that, depending on the nature of the origin, it is possible to divide robotics hazards into the following types: mechanical, which can arise from an unintentional or unexpected action or when changing tools; contact with dangerous energy sources; contact with dangerous energy sources that can lead to electric shock when touching connections, current-carrying parts, or an electric arc flash; thermal, which arise from contact with hot or cold surfaces.Municipal economy of cities["B. Tsymbal"]2024-06-07T00:00:00 aa3b34d56ca52fd460eeb5717d8739ed659498ef Background. The rapid uptake and pace at which digital transformation tools have impacted educational provision in health professions education (HPE) may reshape our teaching and learning practices in southern Africa. This article explores some ideas about the implementation using extended reality (XR) and artificial intelligence (AI) in HPE. Objectives. The objective of this article is to offer potential uses for implementing XR and AI in HPE in the southern African context. Methods. This article used a desktop approach to curate some novel ideas regarding the use of XR and AI in HPE. Results. The outcome of this article presents 10 novel ideas to implement XR and/or AI in the classroom, such as delivery of quality education, personalised learning and simulation and training. Conclusion. The use of XR and AI may improve training of students, improve patient outcomes, and ensure adequate professional development of staff in HPE. African Journal of Health Professions Education30Ten novel ideas to implement XR and/or AI in the classroom, such as delivery of quality education, personalised learning and simulation and training, are presented in the southern African context.African Journal of Health Professions Education["S. Titus"]2024-06-07T00:00:00 3d6ad04c72d4d7b9d5d8cb65e819b38bb6ae1c93BACKGROUND: The use of resuscitative endovascular balloon occlusion of the aorta (REBOA) for control of noncompressible torso hemorrhage remains controversial. We aimed to utilize a novel and transparent/interpretable artificial intelligence (AI) method called Optimal Policy Trees (OPT), to improve the appropriate use and decrease the misuse of REBOA in hemodynamically unstable blunt trauma patients. METHODS: We trained then validated OPTs that "prescribe" REBOA in a 50:50 split on all hemorrhagic shock blunt trauma patients in the 2010-2019 ACS-TQIP database based on rates of survival. Hemorrhagic shock was defined as a systolic blood pressure <= 90 on arrival or transfusion requirement of >= 4 units of blood in the first 4 hours of presentation. The expected 24-hour mortality rate following OPT prescription was compared to the observed 24-hour mortality rate in patients who were or were not treated with REBOA. RESULTS: Out of 4.5 million patients, 100,615 were included and 803 underwent REBOA. REBOA patients had a higher rate of pelvic fracture, femur fracture, hemothorax, pneumothorax, and thoracic aorta injury (p<0.001). The 24-hour mortality rate for the REBOA vs. non-REBOA group was 47% vs. 21%, respectively (p<0.001). OPTs resulted in an 18% reduction in 24-hour mortality for REBOA and 0.8% reduction in non-REBOA patients. CONCLUSION: Interpretable AI models can improve mortality in unstable blunt trauma patients by optimizing the use and decreasing the misuse of REBOA. These models to date have been used to predict outcomes, but their groundbreaking use will be prescribing interventions and changing outcomes.medRxiv160Interpretable AI models can improve mortality in unstable blunt trauma patients by optimizing the use and decreasing the misuse of REBOA.["MD Mary Bokenkamp", "PhD Yu Ma", "A. Dorken-Gallastegi", "MD Jefferson A. Proa\u00f1o-Zamudio", "MD Anthony Gebran", "MD PhD George C. Velmahos", "Dimitris Bertsimas", "MD Haytham M.A. Kaafarani", "Jefferson Proano Zamudio"]2024-06-07T00:00:00 94b34ab434ea4ea587cb443e52c3f9b4785c0ae3Neurosurgical review50This integration of AI and ML into PGNT diagnostics could significantly enhance preoperative accuracy, ultimately improving patient outcomes through more precise and timely interventions.Neurosurgical Review["H. Farooqi", "Rayyan Nabi", "Tabeer Zahid", "Zeeshan Hayder"]2024-06-07T00:00:00 e4a1d8d533f8c98a4c41fcb3327a754d5f454b95The use of artificial intelligence is increasingly being employed in various fields of geriatric medicine like dementia, delirium, fall, and other geriatric syndromes. AI can improve the health and well-being of the elderly and has the potential to assist and improve geriatric care.Experimental and Applied Medical Science00Artificial intelligence can improve the health and well-being of the elderly and has the potential to assist and improve geriatric care.Experimental and Applied Medical Science["E. M. Efendioglu"]2024-06-07T00:00:00 92034cb3ee9f7609bdc552346a2db0506e646100Artificial Intelligence (AI) has pervaded everyday life, reshaping the landscape of business, economy, and society through the alteration of interactions and connections among stakeholders and citizens. Nevertheless, the widespread adoption of AI presents significant risks and hurdles, sparking apprehension regarding the trustworthiness of AI systems by humans. Lately, numerous governmental entities have introduced regulations and principles aimed at fostering trustworthy AI systems, while companies, research institutions, and public sector organizations have released their own sets of principles and guidelines for ensuring ethical and trustworthy AI. Additionally, they have developed methods and software toolkits to aid in evaluating and improving the attributes of trustworthiness. The present paper aims to explore this evolution by analysing and supporting the trustworthiness of AI systems. We commence with an examination of the characteristics inherent in trustworthy AI, along with the corresponding principles and standards associated with them. We then examine the methods and tools that are available to designers and developers in their quest to operationalize trusted AI systems. Finally, we outline research challenges towards end-to-end engineering of trustworthy AI by-design.International Journal of Intelligent Decision Technologies360This paper examines the characteristics inherent in trustworthy AI, along with the corresponding principles and standards associated with them, and examines the methods and tools that are available to designers and developers in their quest to operationalize trusted AI systems.Intell. Decis. Technol.["G. Mentzas", "Mattheos Fikardos", "Katerina Lepenioti", "Dimitris Apostolou"]2024-06-07T00:00:00 e5141e633afc8bf5421ba321f340d1b1c9a3b4a7The EvAU is a decisive step towards students' academic future and represents not only an evaluation of knowledge, but also the door to the fulfillment of their professional dreams. In addition, we now have an invisible ally: artificial intelligence. But how can we make the most of Artificial Intelligence to prepare for a test that will determine the order of precedence in access to higher education?Padres y Maestros / Journal of Parents and Teachers00Padres y Maestros / Journal of Parents and Teachers["Judit Ruiz-L\u00e1zaro"]2024-06-07T00:00:00 c81a9063a577c909c16ec608a45e48752942b4deThe aim of this paper is to examine whether the ethical principles of public relations – which derive from the theory of information and communication of prominent media ethicist Luka Brajnović – can be applied to the use of artificial intelligence in Church institutional communication. Brajnović’s principles of truthfulness, transparency, integrity, competence, loyalty and social responsibility partly coincide with the ethical principles of the “Rome call for AI Ethics” and could be sufficiently universal and applicable to the use of artificial intelligence in the institutional communication of the Catholic Church.Roczniki Nauk Społecznych320Whether Luka Brajnović’s principles of truthfulness, transparency, integrity, competence, loyalty and social responsibility could be sufficiently universal and applicable to the use of artificial intelligence in the institutional communication of the Catholic Church is examined.Roczniki Nauk Społecznych["Matilda Koli\u0107 Stani\u0107", "Branimir Stani\u0107"]2024-06-07T00:00:00 9c9ce16c64fda6c1f97fb44399c5c01b325152a1This paper mainly introduces the development prospects and challenges of personal financial advisors driven by artificial intelligence (AI), as well as the advantages of combining artificial intelligence with financial management. With the rapid development of artificial intelligence technology, the personal financial consulting industry is undergoing profound changes. Through deep learning and big data analysis, taking JIMI, JD.COM as an example, it is demonstrated that artificial intelligence financial consultants can grasp the market trend more accurately and provide users with more accurate and personalized financial advice. It shows in detail that in the artificial intelligence environment, it can provide all-weather service for financial users, and users can log in to the client at any time to consult financial related issues, and artificial intelligence will restore to users. The capital market has been in a dynamic change, and there are many uncertainties in the development of the whole financial market, but investors don't know enough about financial treatment. Therefore, the application of artificial intelligence in financial management has won the favor of many financial users, and it also has a certain role in promoting the development of the capital market.Advances in Economics, Management and Political Sciences00It is demonstrated that artificial intelligence financial consultants can grasp the market trend more accurately and provide users with more accurate and personalized financial advice.Advances in Economics, Management and Political Sciences["Zechen Liu"]2024-06-07T00:00:00 99e7bb39b74acbeffc514c51764a1fd60f30903bPada era industri saat ini peningkatan teknologi dan akses internet banyak mengubah pandangan bisnis secara luas dan menciptakan perubahan yang signifikan dalam berbagai aspek, perkembangan teknologi dimanfaatkan untuk memberikan kemudahan dalam memenuhi kebutuhan manusia dan mempermudah pekerjaan sehari-hari. Kemajuan teknologi yang berkelanjutan yaitu dengan munculnya kecerdasan buatan (AI). Dalam konteks bisnis saat ini telah terjadi digitalisasi industri e-commerce, hal ini menjadi kekuatan utama yang mendorong pertumbuhan ekonomi digital. Teknologi AI dapat memberikan pengalaman yang lebih terarah kepada pelanggan, namun juga dapat menimbulkan dampak negatif jika tidak digunakan dengan baik. Penelitian ini menggunakan metode kualitatif dengan literatur review jurnal, yaitu dengan cara mengumpulkan data dan informasi yang bersumber dari jurnal-jurnal nasional dan internasional yang terbit 5 tahun terakhir. Penelitian ini bertujuan untuk menjawab pertanyaan seputar pengambilan keputusan yang dilakukan BI dan AI, peran AI, Data Warehouse dan OLAP dalam menentukan strategi bisnis, pentingnya penggunaan artificial intelligence, dan hubungan BI dengan strategi pemasaran di e-commerce. Penerapan Business Inteligence dengan Artificial Intelligence pada E-Commerce dapat membantu e-commerce dalam menganalisis data yang lebih cepat dan akurat serta BI juga dapat membantu dalam pengembangan strategi bisnis dengan memprediksi tren pasar yang sedang terjadi pada industri e-commerce.SENTRI: Jurnal Riset Ilmiah01SENTRI: Jurnal Riset Ilmiah["Indah Cahyati", "Achmad Fauzi", "H. Hasanuddin", "Imam Zuhri", "Hazza Hibatullah", "Niken Dwi", "N. Handayani", "Risma Felisyana"]2024-06-07T00:00:00 2783e5163a9e67f6fb86d21d9069731b8b2364c1The study investigated the awareness of instructors about the use of AI tools in academia among higher learning institutions in Tanzania. The online questionnaires were distributed to 207 members of academic staff at Moshi Co-operative University. A total of 63 (31%) academics completed the online survey. Convenience sampling was used because the survey was shared through a WhatsApp group, which included the majority of the members of the academic staff. The study findings revealed that instructors had awareness of the use of AI tools in academia however the usage of such technology in academia is still low because it is not officially recognized in academia. The usage of AI tools was revealed to be very useful in enhancing learning and academic writing among instructors. Several AI tools that are used by instructors were identified, including ChatGPT, Bard AI, Grammaly, and Quillbolt. The study identifies several challenges such as inexperience in using AI tools, feelings that AI tools delimit thinking capacity and encourage plagiarism. The study recommends that more training be offered to instructors to create awareness about harnessing the use of AI tools. The study further recommends policies and guidelines on the use of AI tools to be formulated.Edukasiana: Jurnal Inovasi Pendidikan01The study investigated the awareness of instructors about the use of AI tools in academia among higher learning institutions in Tanzania and revealed that instructors had awareness of the use of AI tools in academia however the usage of such technology in academia is still low because it is not officially recognized in academia.Edukasiana: Jurnal Inovasi Pendidikan["Jaffar Msafiri Ponera", "Shadrack Stephen Madila"]2024-06-07T00:00:00 801573b2375878e7041f60b13a807f301abb3704European Heart Journal51European heart journal["Piotr Szyma\u0144ski", "Frank E. Rademakers", "A. Fraser"]2024-06-07T00:00:00 9173c4dfbe4836bd4e363f648972b64f764c0d9dAI ini telah menjadi salah satu topik hangat dalam beberapa tahun terakhir, dengan potensinya untuk merevolusi berbagai industri, termasuk pendidikan. Disisi lain, pendidikan adalah proses untuk memperoleh pengetahuan yang dibutuhkan untuk menjalani kehidupan yang sukses. Namun, opini publik tentang penggunaan AI dalam pendidikan berbeda. Beberapa orang mungkin melihatnya sebagai kemajuan yang baik yang dapat membantu siswa mempersiapkan diri untuk tantangan masa depan. Sementara yang lain mungkin khawatir tentang konsekuensi moral, konsekuensi sosial, atau bahkan apakah teknologi akan menggantikan pekerjaan manusia. Melalui analisis sentimen pada X, penelitian ini bertujuan untuk mencoba menemukan komponen yang mempengaruhi persepsi publik terhadap AI untuk pendidikan. Untuk dapat mengumpulkan data tersebut, menggunakan X harvest untuk melakukan crawling data pada X. Pada tahap klasifikasi menggunakan metode naïve bayes. Dari 327 data yang dianalisis, 82% mengekspresikan sentimen positif, 17% menunjukkan sentimen negatif, sementara hanya 1% bersifat netral. Dari ­confusion matrix ­yang dihasilkan, dapat diperoleh nilai akurasi, presisi, recall, dan skor f1. Metode naïve bayes menghasilkan accuracy sekitar 72,73%, precision sekitar 77,40%, recall sekitar 72,73%, dan f1-score sekitar 71,74%. Terbukti bahwa metode naïve bayes merupakan pengklasifikasi teks yang baik untuk menganalisis sentimen mengenai Analisis Sentimen Dampak Artificial Intelligence Untuk Pendidikan Pada X Menggunakan Naïve Bayes.Jurnal informatika UPGRIS00Jurnal Informatika Upgris["Nurdin Nurdin", "Luniko Jama", "Thomas Zugildo Magnus", "Ressa Priskila", "V. H. Pranatawijaya"]2024-06-07T00:00:00 10bc7a921d7f3be1f6f5b4db51d4f0b108de36e8European Child and Adolescent Psychiatry100European Child & Adolescent Psychiatry["V. Roessner", "A. Uhlmann", "Stefan Ehrlich", "R. Waltereit"]2024-06-07T00:00:00 b54a26d775d71a1adcf00f2d64abb2dfdbbd24ccAnnals of Biomedical Engineering90This article deals with ChatGPT’s perspective on the future role of radiologists in the AI-driven hospital, which can help improve radiologists’ performance and workflow in the future AI-driven hospital.Annals of Biomedical Engineering["S. Sedaghat"]2024-06-07T00:00:00 ffa50261cbe46f6582b59c02ffb83c9866a0d562Since the Wright brothers' December 17, 1903 flight, the aviation business has grown quickly alongside IT. Growth is concentrated in aircraft development, airport infrastructure, and air traffic control. AI will revolutionize each of these fields. AI optimizes fuel usage, structural designs, and avionics in aircraft development, making them more efficient and modern. AI streamlines airport check-in, luggage processing, and airport security, improving efficiency and passenger experience. The heart of aviation, ATC, coordinates take-offs, landings, and en-route traffic through Aerodrome Control, Approach Control, and Area Control. ATC may use AI to optimize air traffic management, automate mundane jobs, analyze data for decision-making, and predict traffic flow to avoid congestion and delays. Due to the complexity of air traffic and the requirement for quick human judgment, replacing human ATC operators with AI is difficult. However, AI can be gradually introduced into particular operations to improve efficiency and safety. AI will support these functions as technology advances, making air travel safer and more efficient.FMDB Transactions on Sustainable Intelligent Networks00AI optimizes fuel usage, structural designs, and avionics in aircraft development, making them more efficient and modern, and streamlines airport check-in, luggage processing, and airport security, improving efficiency and passenger experience.FMDB Transactions on Sustainable Intelligent Networks["Desiya Nanban", "Jennifer Selvan", "A.T. Ashmi Christus", "Muhammad Al Amin"]2024-06-07T00:00:00 1ca01d9d9abe40cd52ec8d085edc3a5ad0d16e69Akal (‘aql) yang memiliki kesatuan dengan organ otak menjadi satu-satunya organ biologis yang hanya terdapat di manusia. Di dalam al-Qur’an telah banyak menyebutkan ayat-ayat yang berkaitan dengan aktivitas akal yang mana ayat-ayat al-Qur’an yang menyebut tentang akal dapat dijumpai pada istilah yang berkaitan dengan aktivitas otak. Akan tetapi di dalam praktiknya, pendidikan masih terkungkung pada budaya stigma sempit bahwa otak dan akal hanya diukur dengan skala kecerdasan numerik semata. Sehingga ketersediaan ayat-ayat mengenai akal perlu dikaji karena adanya hubungan yang relevan dengan aktifitas otak sehingga dalam tulisan ini berusaha untuk mendeksripsikan secara mendalam mengenai makna ayat-ayat al-Qur’an tentang neurosains yang menyebutkan mengenai akal (‘aql) untuk kemudian diintegrasikan dalam bidang pendidikan Islam. Adapun metode penulisan ini bersifat kualitatif dengan model penelitian pustaka (library research). Penelitian ini menggunakan pendekatan tematik (maudhu’i) berdasarkan perspektif tafsir Ath-Thabari. Pengambilan data bersumber dari literature yang berasal dari buku, jurnal dan lainnya yang berhubungan dengan topik tafsir, neurosains dan pendidikan. Hasil dari penelitian ini, ayat-ayat Al-Qur’an seputar akal dapat ditemukan dalam istilah-istilah yang ditentukan aktivitas otak salah saunya tafakkur yang termaktub dalam Q.S. Al-Baqarah ayat 219. Dalam tafsir ath-Thabari, ayat ini menjelaskan untuk bertafakur atau memikirkan kembali atas segala amal perbuatan baik yang dilakukan seseorang, hendaknya diniatkan untuk mencari ridha Allah, bukan yang lainnya. Peran pendidik juga perlu berperan lebih dalam menjaga fitrah berpikir manusia di tengah era Artificial Intelligence (AI).Al-Bayan: Jurnal Ilmu al-Qur'an dan Hadist50Al-Bayan: Jurnal Ilmu al-Qur'an dan Hadist["Rahma Sivatur Rizma", "Enjang Burhanudin Yusuf"]2024-06-07T00:00:00 4525b63ae94a0862810a8a266c8bdc3d052dc960The integration of artificial intelligence (AI) into HIV treatment regimens has revolutionized the approach to personalized care and optimization strategies. This study presents an in-depth analysis of the role of AI in transforming HIV treatment, focusing on its ability to tailor therapy to individual patient needs and enhance treatment outcomes. AI-driven optimization in HIV treatment involves the utilization of advanced algorithms and computational techniques to analyze vast amounts of patient data, including genetic information, viral load measurements, and treatment history. By harnessing the power of machine learning and predictive analytics, AI algorithms can identify patterns and trends in patient data that may not be readily apparent to human clinicians. One of the key benefits of AI-driven optimization is its ability to personalize treatment regimens based on individual patient characteristics and disease progression. By considering factors such as drug resistance profiles, comorbidities, and lifestyle factors, AI algorithms can recommend the most effective and well-tolerated treatment options for each patient, leading to improved adherence and clinical outcomes. Furthermore, AI enables continuous monitoring and adjustment of treatment regimens in real time, allowing healthcare providers to respond rapidly to changes in patient status and evolving viral dynamics. This proactive approach to HIV management can help prevent treatment failure and the development of drug resistance, ultimately leading to better long-term outcomes for patients. Despite its transformative potential, AI-driven optimization in HIV treatment is not without challenges. Ethical considerations, data privacy concerns, and the need for robust validation and regulatory oversight are all important factors that must be addressed to ensure the safe and effective implementation of AI algorithms in clinical practice. In conclusion, the integrative analysis presented in this study underscores the significant impact of AI-driven optimization on the personalization and optimization of HIV treatment regimens. By leveraging AI technologies, healthcare providers can tailor treatment approaches to individual patient needs, leading to improved outcomes and quality of life for people living with HIV. Keywords: Integrative Analysis, AI- Driven, Optimization, HIV Treatment, Regimens.Computer Science &amp; IT Research Journal015An in-depth analysis of the role of AI in transforming HIV treatment, focusing on its ability to tailor therapy to individual patient needs and enhance treatment outcomes, underscores the significant impact of AI-driven optimization on the personalization and optimization of HIV treatment regimens.Computer Science &amp; IT Research Journal["Janet Aderonke Olaboye", "Chukwudi Cosmos Maha", "Tolulope Olagoke Kolawole", "Samira Abdul"]2024-06-07T00:00:00 d63866c8630a8968287d0e200b86327daa74e15eArtificial intelligence (AI) is a collection of rapidly evolving disruptive technologies that radically alter various aspects of people, business, society, and the environment. AI increasingly provides significant advertising opportunities for society and business organizations. However, AI could be used to spread disinformation if it were deliberately programmed to produce misleading advertising content. Using cognitive appraisal theory and information quality theory to study how consumers assess threats and develop AI marketing coping strategies from the information generated by AI, this study examines the outcome of the dark side of AI advertising. We collected data from 451 AI-advertising users in Vietnam. The results based on PLS-SEM showed interesting and novelty results. The statistical analysis showed a negative correlation between contextual, representational, accessibility, and threat appraisals. There was also a statistically significant positive correlation between contextual, representational, accessibility, and coping appraisals. Threat appraisals were positively correlated with anger and anxiety but not loneliness. Coping appraisal was significant and negatively correlated with anxiety but not anger or loneliness. This study advances theory and management.Social science computer review489Using cognitive appraisal theory and information quality theory to study how consumers assess threats and develop AI marketing coping strategies from the information generated by AI, this study examines the outcome of the dark side of AI advertising.Social Science Computer Review["Luan-Thanh Nguyen", "Tri-Quan Dang", "Dang Thi Viet Duc"]2024-06-07T00:00:00 ec7581ad2fa988fdd3a48855d8b9a764a6c9473aNature Reviews Clinical Oncology1148The need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries is discussed.Nature reviews. Clinical oncology["V. Viswanathan", "Vani Parmar", "A. Madabhushi"]2024-06-07T00:00:00 6fdfec2b70e9c612b6cdc2ea80b022583ba91ea9npj Mental Health Research795This commentary proposes the application of GAI for creating personalized and contextually relevant therapeutic interventions and emphasizes the need to integrate human feedback into the AI-assisted therapeutics and decision-making process.NPJ Mental Health Research["Emre Sezgin", "Ian McKay"]2024-06-07T00:00:00 5456dd83f8e60ad77ef396870b22df075704a297PurposeThe surge of artificial intelligence (AI) applications and subsequent adoption by consumers and marketers has ignited substantial research exploring the benefits and opportunities of AI. Despite this, little attention has been given to its unintended negative consequences. In this paper, the authors examine both the practitioner and academic sides of ethical AI. In doing so, the authors conduct an extensive review of the AI literature to identify potential issues pertaining to three areas: individual consumers, societal and legal. The authors identify gaps and offer questions to drive future research.Design/methodology/approachThe authors review recent academic literature on AI in marketing journals, and top ethical principles from three top technology developers (Google, IBM and Meta) in conjunction with media reports of negative AI incents. They also identify gaps and opportunities for future research based on this review.FindingsThe bibliographic review reveals a small number of academic papers in marketing that focus on ethical considerations for AI adoption. The authors highlight concerns for academic researchers, marketing practitioners and AI developers across three main areas and highlight important issues relating to interactive marketing.Originality/valueThis paper highlights the under-researched negative outcomes of AI adoption. Through an extensive literature review, coupled with current responsible AI principles adopted by major technology companies, this research provides a framework for examining the dark side of AI.Journal of Research in Interactive Marketing494An extensive literature review is conducted of the AI literature coupled with current responsible AI principles adopted by major technology companies to provide a framework for examining the dark side of AI.Journal of Research in Interactive Marketing["Lauren I. Labrecque", "Priscilla Y. Pe\u00f1a", "Hillary Leonard", "Rosemary Leger"]2024-06-07T00:00:00 ad9e8244af88a0e876c1a06aba1dfd5b7b27a68aBMC Medical Education764The findings reveal that both information quality and perceived usefulness are pivotal factors that positively influence the willingness to use AI products and suggest that enhancing information quality can be a key strategy to encourage the widespread use of AI products.BMC Medical Education["Meijie Wu", "Xuefeng Huang", "Baona Jiang", "Zhihong Li", "Yuanyuan Zhang", "Bo Gao"]2024-06-07T00:00:00 fbd11ccde8d165a0d7229bc17c1654224b52e93bThis paper explores the application of artificial intelligence (AI) in mitigating the effects of global warming, which stands as one of the most pressing and complex challenges of our time. The purpose of this research is to examine how various AI technologies, including machine learning, neural networks, and big data analytics, can be leveraged to enhance climate modeling, optimize energy systems, improve agricultural practices, and support carbon capture and storage efforts. By conducting a comprehensive literature review, this paper aims to highlight current advancements, practical applications, and relevant case studies that demonstrate the potential of AI to reduce greenhouse gas emissions and promote sustainable practices across different sectors. The study synthesizes findings from recent academic research, industry reports, and real-world implementations to provide an in-depth analysis of the benefits and challenges associated with integrating AI into climate action strategies. The methodology involves a thorough examination of the existing literature, identifying key areas where AI has shown significant promise in addressing various aspects of global warming. This includes enhancing the accuracy of climate predictions, optimizing the efficiency of renewable energy systems, improving precision agriculture techniques, and increasing the effectiveness of carbon capture and storage technologies. The conclusions drawn from this research underscore the transformative potential of AI in combating global warming. The findings highlight the necessity for interdisciplinary collaboration, advancements in AI technologies, and the development of supportive policy frameworks to maximize the impact of these innovations. The paper emphasizes that while AI offers significant potential to address global warming, realizing this potential requires addressing several challenges, including data quality and availability, integration with existing systems, ethical considerations, and economic and policy barriers. Furthermore, this paper discusses the critical role of AI in enabling more effective climate adaptation strategies. As the impacts of global warming become increasingly apparent, AI-driven tools and solutions can help communities and ecosystems adapt to changing environmental conditions. This includes providing early warning systems for natural disasters, optimizing resource allocation during climate-related crises, and supporting the development of resilient infrastructure. In addition to technological advancements, the paper also explores the importance of public engagement and citizen science in enhancing the effectiveness of AI applications in environmental monitoring and climate action. By involving citizens in data collection and environmental monitoring, AI models can access more diverse and localized data, improving their accuracy and relevance. Public engagement can also raise awareness about AI's role in addressing climate change and foster greater support for sustainable practices. Overall, this paper provides a comprehensive overview of the current state of AI applications in mitigating global warming, offering insights into the future directions and emerging trends in this rapidly evolving field. The research highlights the need for continued innovation, interdisciplinary collaboration, and supportive policy measures to fully harness the potential of AI in the fight against global warming and to ensure a sustainable future for all. DOI: https://doi.org/10.52783/pst.464Power system technology223The research highlights the need for continued innovation, interdisciplinary collaboration, and supportive policy measures to fully harness the potential of AI in the fight against global warming and to ensure a sustainable future for all.Power System Technology["1-Dr. Ayman Naji Khallaf", "2-Dr. Nader Moneer Alqerafi"]2024-06-07T00:00:00 f759f92cab668f3947c27c0045e3b562cfa91553The advent of artificial intelligence (AI) has heralded a transformative era in various domains, including human resource (HR) management. This research paper explores the profound impact of AI-driven systems on HR practices and talent management. Traditional HR systems often face challenges such as inefficiencies, biases, and the inability to manage large volumes of data effectively. AI technologies, with their capabilities in data analytics, machine learning, and automation, offer innovative solutions to these challenges. This study investigates how AI can enhance various HR functions, including recruitment, performance management, employee engagement, and training. By leveraging AI, organizations can streamline their HR processes, reduce operational costs, and improve decision-making accuracy. Additionally, AI-driven talent management systems enable organizations to better identify, develop, and retain top talent, thereby fostering a more agile and competitive workforce. Using a mixed-methods approach, this research combines qualitative insights from industry case studies with quantitative data analysis to provide a comprehensive understanding of the benefits and challenges associated with AI implementation in HR. The findings reveal significant improvements in efficiency, accuracy, and employee satisfaction, while also highlighting ethical considerations such as data privacy and algorithmic bias. This paper concludes with practical recommendations for HR professionals seeking to integrate AI into their practices and suggests avenues for future research to address the emerging challenges and opportunities in AI-driven HR systems. The implications of this study are significant, offering valuable insights for both academic researchers and practitioners aiming to harness the potential of AI to revolutionize HR and talent management.Scholars Journal of Engineering and Technology82This research explores the profound impact of AI-driven systems on HR practices and talent management and reveals significant improvements in efficiency, accuracy, and employee satisfaction, while also highlighting ethical considerations such as data privacy and algorithmic bias.Scholars Journal of Engineering and Technology["Prabu Manoharan"]2024-06-07T00:00:00 e7edb56d1bbc24f590613167caa3e2e735773658With growing commercial, regulatory and scholarly interest in use of Artificial Intelligence (AI) to profile and interact with human emotion (“emotional AI”), attention is turning to its capacity for manipulating people, relating to factors impacting on a person’s decisions and behavior. Given prior social disquiet about AI and profiling technologies, surprisingly little is known on people’s views on the benefits and harms of emotional AI technologies, especially their capacity for manipulation. This matters because regulators of AI (such as in the European Union and the UK) wish to stimulate AI innovation, minimize harms and build public trust in these systems, but to do so they should understand the public’s expectations. Addressing this, we ascertain UK adults’ perspectives on the potential of emotional AI technologies for manipulating people through a two-stage study. Stage One (the qualitative phase) uses design fiction principles to generate adequate understanding and informed discussion in 10 focus groups with diverse participants (n = 46) on how emotional AI technologies may be used in a range of mundane, everyday settings. The focus groups primarily flagged concerns about manipulation in two settings: emotion profiling in social media (involving deepfakes, false information and conspiracy theories), and emotion profiling in child oriented “emotoys” (where the toy responds to the child’s facial and verbal expressions). In both these settings, participants express concerns that emotion profiling covertly exploits users’ cognitive or affective weaknesses and vulnerabilities; additionally, in the social media setting, participants express concerns that emotion profiling damages people’s capacity for rational thought and action. To explore these insights at a larger scale, Stage Two (the quantitative phase), conducts a UK-wide, demographically representative national survey (n = 2,068) on attitudes toward emotional AI. Taking care to avoid leading and dystopian framings of emotional AI, we find that large majorities express concern about the potential for being manipulated through social media and emotoys. In addition to signaling need for civic protections and practical means of ensuring trust in emerging technologies, the research also leads us to provide a policy-friendly subdivision of what is meant by manipulation through emotional AI and related technologies.Frontiers in Sociology912Frontiers in Sociology["V. Bakir", "Alexander Laffer", "Andrew McStay", "Diana Miranda", "Lachlan D. Urquhart"]2024-06-07T00:00:00 8a3d924013c97f7f5658afe8a5aae61ab4c8ac10Surgical decision-making for glioblastoma poses significant challenges due to its complexity and variability. This study investigates the potential of artificial intelligence (AI) tools in improving “decision-making processes” for glioblastoma surgery. A systematic review of literature identified 10 relevant studies, primarily focused on predicting resectability and surgery-related neurological outcomes. AI tools, especially rooted in radiomics and connectomics, exhibited promise in predicting resection extent through precise tumor segmentation and tumor-network relationships. However, they demonstrated limited effectiveness in predicting postoperative neurological due to dynamic and less quantifiable nature of patient-related factors. Recognizing these challenges, including limited datasets and the interpretability requirement in medical applications, underscores the need for standardization, algorithm optimization, and addressing variability in model performance and then further validation in clinical settings. While AI holds potential, it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.Frontiers in Neurology522AI holds potential, but it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.Frontiers in Neurology["Melike Mut", "Miaomiao Zhang", "Ishita Gupta", "P. T. Fletcher", "Faraz Farzad", "D. Nwafor", "C. Zoia", "Andrea Bianconi", "Giorgio Carrabba"]2024-06-07T00:00:00 c69fb55fc97a52f5ab32a1a86970d0b434279960Language Testing in Asia372ChatGPT’s potential to assist teachers in test item creation, reducing workload and saving time is suggested and the need for further research and development in this area is emphasized.Language Testing in Asia["Kyung-Mi O"]2024-06-07T00:00:00 20c0b24feea51334133816b5988a7c20e8a75de4This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems.Hydrogen424This review critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures and foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications.Hydrogen["R. R. Patil", "R. K. Calay", "Mohamad Y. Mustafa", "Somil Thakur"]2024-06-08T00:00:00 434049b9d576c3c223a8cee01741f177f37655fdBreathing is one of the most vital functions for being mentally and emotionally healthy. A growing number of studies confirm that breathing, although unconscious, can be under voluntary control. However, it requires systematic practice to acquire relevant experience and skillfulness to consciously utilize breathing as a tool for self-regulation. After the COVID-19 pandemic, a global discussion has begun about the potential role of emerging technologies in breath-control interventions. Emerging technologies refer to a wide range of advanced technologies that have already entered the race for mental health training. Artificial intelligence, immersive technologies, biofeedback, non-invasive neurofeedback, and other wearable devices provide new, but yet underexplored, opportunities in breathing training. Thus, the current systematic review examines the synergy between emerging technologies and breathing techniques for improving mental and emotional health through the lens of skills development. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is utilized to respond to the objectives and research questions. The potential benefits, possible risks, ethical concerns, future directions, and implications are also discussed. The results indicated that digitally assisted breathing can improve various aspects of mental health (i.e., attentional control, emotional regulation, mental flexibility, stress management, and self-regulation). A significant finding of this review indicated that the blending of different technologies may maximize training outcomes. Thus, future research should focus on the proper design and evaluation of different digital designs in breathing training to improve health in different populations. This study aspires to provide positive feedback in the discussion about the role of digital technologies in assisting mental and emotional health-promoting interventions among populations with different needs (i.e., employees, students, and people with disabilities).Electronics04A significant finding of this review indicated that the blending of different technologies may maximize training outcomes, and future research should focus on the proper design and evaluation of different digital designs in breathing training to improve health in different populations.Electronics["Eleni Mitsea", "Athanasios Drigas", "C. Skianis"]2024-06-08T00:00:00 dd1376b69bf77386ac5a608fe5676dd96eb0126cThe integration of Artificial Intelligence (AI) into Library and Information Science (LIS) has gained significant attention in recent years, offering promising opportunities to enhance library services and user experiences. This paper presents a comprehensive review of the literature on AI in LIS, synthesizing key themes, findings, and implications from existing research. The review identifies various opportunities afforded by AI technologies, including improved information retrieval, personalized recommendation systems, virtual assistance, data analytics, and digital preservation. Scholars highlight the potential of AI to revolutionize library services, streamline operations, and promote accessibility and inclusivity. However, the review also discusses several challenges and limitations associated with AI implementations in LIS, such as algorithmic bias, privacy concerns, digital divide, cost and resource requirements, and ethical considerations, Researchers emphasize the need for careful consideration of these challenges to ensure responsible and equitable AI user in libraries. User perspectives and experiences with AI-driven library services are examined, revealing insights into adoption factors, user preferences, and concerns about privacy, data quality, and trust in AI technologies. The evolving roles and skills of librarians and information professional in the AI era are also discussed, highlighting the importance of digital literacy, data management, and ethical decision-making. Case studies and best practices showcase successful examples of AI implantation in libraries, providing valuable lessons learned and insights for library practitioners. Finally, future directions and research agenda for AI in LIS are identified, including the development of AI-driven tools are services, exploration of ethical and social implications, and interdisciplinary collaborations to advance understanding and innovation in this rapidly evolving field. Overall, the review underscores the transformative potential of AI in LIS while emphasizing the importance of addressing challenges and ethical considerations to ensure responsible AI implantation and maximize its benefits for libraries and their patrons.International Journal For Multidisciplinary Research202The review identifies various opportunities afforded by AI technologies, including improved information retrieval, personalized recommendation systems, virtual assistance, data analytics, and digital preservation, including improved information retrieval, personalized recommendation systems, virtual assistance, data analytics, and digital preservation.International Journal For Multidisciplinary Research["Chandramani Kailash Gajbhiye"]2024-06-08T00:00:00 2341e5aa8a14ea708e38c4fb5bddd9cb20ef5888AI &amp; SOCIETY596The present paper provides a framework for systematizing different uses and identifies three families of theoretical perspectives informing research on AI acceptance—user acceptance, delegation acceptance, and societal adoption acceptance.AI &amp; SOCIETY["Pascal D. Koenig"]2024-06-08T00:00:00 fb4a20b1d9ab4ed82e29458e2cd37177463e93b0This paper explores a philosophical problem at the intersection of neuroscience and artificial intelligence (AI), and the potential impact of these novel AI “mind-reading” technologies on various forms of mind–body dualism, including substance, interaction, property, predicate, and emergent dualisms. It critically examines how AI’s ability to interpret and predict mental states from neural patterns challenges traditional dualistic theories, which have historically posited distinct relationships between the mind and body. The paper analyzes each dualistic theory in the context of AI advancements. Substance and interaction dualisms are scrutinized for their claims of mind–body independence and causal interaction, respectively, in light of AI’s capabilities to correlate mental and physical states. Property dualism’s assertion of unique mental properties emerging from physical processes is tested against AI’s potential to map mental phenomena to brain activity. Predicate dualism’s linguistic and conceptual distinction between mental and physical realms is challenged by AI’s ability to bridge these domains. Similarly, emergent dualism, which views mental states as novel phenomena, confronts the possibility of their reduction to physical brain processes. Despite these challenges, the paper argues for the adaptability of dualistic theories to integrate AI insights, suggesting a re-evaluation rather than a negation of dualism. It highlights the enduring relevance of philosophical inquiry into the nature of consciousness and mind–body relationships in the age of AI, suggesting that such technological advancements invigorate rather than terminate the philosophical debate.Journal of Artificial Intelligence and Consciousness00How AI’s ability to interpret and predict mental states from neural patterns challenges traditional dualistic theories is critically examined, suggesting a re-evaluation rather than a negation of dualism.J. Artif. Intell. Conscious.["Yoshija Walter"]2024-06-08T00:00:00 96bb4725ab192e7a03544d2b7a8260974af854b7The main goal of the article is to highlight the most dangerous threats of artificial intelligence in an educational company and to formulate economic and legal methods to counter them. The object of the study is the key information technologies of an educational company. The scientific task is to conduct a detailed study of the system for using information technologies in an educational company and the subsequent formation of the most effective economic and legal methods of countering threats in this area. The methodology includes methods of system analysis, graph theory, pairwise comparison and hierarchical analysis. As a result, a number of key threats to artificial intelligence in the educational company were identified, methods to counter them were formed. In addition, key countermeasures were identified. The study has limitations because it takes into account only a limited number of threats of the functioning of an individual educational company.International Journal of Religion310International Journal of Religion["Anna Pazieieva", "Olesia Smolinska", "Oksana Syniuk", "Olya Turytsya", "Halyna Leskiv"]2024-06-08T00:00:00 993972b165c508678e623c9cbdf932f4aa6bfbebThe sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.Artificial Intelligence in Gastrointestinal Endoscopy660The role of AI in colorectal cancer as a whole is reviewed and existing technologies which are already augmenting cancer care are identified, which could at least match, if not surpass human standards.Artificial Intelligence in Gastrointestinal Endoscopy["G. Lingam", "Taner Shakir", "Rawen Kader", "Manish Chand"]2024-06-08T00:00:00 6890972a10eb458190d0b52c453ba117687460f6This integrative literature review (ILR) delves deeply into the role of artificial intelligence (AI) in enhancing grid stability and managing renewable energy sources in France. The central issue in this study is the difficulty in integrating intermittent renewable sources such as wind and solar electricity, which impacts the energy grid's stability and efficiency. The review looks into how artificial intelligence might improve the prediction and optimization of energy output from these volatile sources, enhancing supply- and demand management. It demonstrates AI's potential to improve grid stability, minimize waste, and promote sustainable energy practices. The study also cites essential barriers such as data protection, infrastructural sufficiency, and the substantial investments required to modernize existing systems for AI integration. Based on a thorough examination of existing research, the review underlines the need for solid legislative frameworks to support ethical AI deployment in line with France's environmental and energy goals. This research paper is critical for policymakers because it provides insights into the strategic application of AI to promote a more efficient and resilient energy industry. The study's findings and recommendations urge further AI research and practical applications to guide France and other nations to a more sustainable and stable energy future.International Journal For Multidisciplinary Research570International Journal For Multidisciplinary Research["Rachid Ejjami"]2024-06-08T00:00:00 859fb27e67c69b2bbca330b4123836c2123df56bThe incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.Artificial Intelligence in Gastrointestinal Endoscopy970A systematic review of the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology concludes that AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists.Artificial Intelligence in Gastrointestinal Endoscopy["Ayrton I Bangolo", "Nikita Wadhwani", "V. Nagesh", "Shraboni Dey", "H. Tran", "Izage Kianifar Aguilar", "Auda Auda", "Aman Sidiqui", "Aiswarya Menon", "Deborah Daoud", "James Liu", "S. P. Pulipaka", "Blessy George", "Flor Furman", "Nareeman Khan", "Adewale Plumptre", "I. Sekhon", "Abraham Lo", "Simcha I Weissman"]2024-06-08T00:00:00 bfb1f85f2c2e8643d3421e33d0f8880a9ff10994Since the dawn of time, negotiation has been the way human societies have interacted, transitioned, and prospered. The Latin word negotium, which literally means 'the negation of otium' or 'the negation of leisure', is also related to the word 'business'. Societies and organizations that perfected the way to negotiate prospered or captured the most value available at any deal, while others did not. For a long time, the negotiation capabilities remained solely with individuals. Artificial Intelligence (AI), however, has been invading all spheres of human existence, with potentially positive and negative impacts. Consequently, automated negotiations have become a reality, despite being just in their early stages. Aside from the effective evolution of Negotiation Support Systems (NSS), the Age of Negobots, or automatic negotiation agents, is gaining traction and may have an unexpected impact on all aspects of life. It is not difficult to imagine the economic impacts of powerful algorithms conducting automated negotiations on behalf of humans, organizations, or States, while analysing massive volumes of historical and real-time data, which provides an advantage for the most performing ones. The purpose of this paper is to provide an application of a framework to identify and rate potentially disruptive technologies, achieving this goal by drawing a parallel between the assessment of potential disruptive technologies and the taxonomy concepts used by biologists and naturalists to evaluate species. A framework that incorporates fifteen critical variables across five dimensions was applied and preliminary results suggest that such framework effectively helps in assessing the potential impact of disruptive technologies. Notably, it was found that variables with the highest scores play a crucial role in shaping current and future global affairs across all spheres of human activity.Proceedings of The International Conference on Opportunities and Challenges in Management, Economics and Accounting150An application of a framework to identify and rate potentially disruptive technologies is provided by drawing a parallel between the assessment of potential disruptive technologies and the taxonomy concepts used by biologists and naturalists to evaluate species.Proceedings of The International Conference on Opportunities and Challenges in Management, Economics and Accounting["Pedro B. \u00c1gua", "Anacleto Correia", "Armindo Frias"]2024-06-08T00:00:00 265e8af23fa6b5b7cb56a92cdb90b26cf6e48bfdCogent Education221Cogent Education["ZuoYuan Liu", "Elena Yushchik"]2024-06-08T00:00:00 ad230f516b665d3bb521c627361c9e6ac1fd611aWork00Work[]2024-06-08T00:00:00 bbc55164b739ec2c51ffb01f07bf1864c5c66b6eAnadolu University's Open Education System (OES) accommodates over one million students and has incorporated an AI-based Virtual Assistant for non-academic support since 2022. While OES offers abundant information about its programs on its website, there is a notable absence of support services providing job recommendations related to students' chosen programs. This gap in student support extends to the post-graduation phase, with the Virtual Assistant lacking a concept for guiding students in finding employment opportunities. Recognizing the need for comprehensive assistance, this study sought to leverage AI capabilities to offer job recommendations by extracting information from the objectives of 63 OES programs. The initial inquiry involved requesting AI-generated job recommendations based on the stated objectives of these programs. Subsequently, the Virtual Assistant was tasked with providing insights into the occupation fields associated with OES programs. Analysis of the AI's responses, along with the classification of occupations according to the International Standards of Classifications of Occupations (ISCO) and the International Standard Classification of Education (ISCED), forms the core of this study. Contrary to trends observed in most European countries, the predominant number of graduates in Turkey emerges from business and management fields. However, the correlation between graduation rates and subsequent job placements appears suboptimal within the labor force and employment landscape. The study advocates for the integration of AI in offering job recommendations, incorporating graduation and employment rates. This approach enables students to seek guidance on suitable programs aligned with their skills, fostering a more informed decision-making process. The study underscores the potential for higher education institutes to share employment and labor force data.Osmangazi journal of educational research00Analysis of the AI's responses, along with the classification of occupations according to the International Standards of Classifications of Occupations (ISCO) and the International Standard Classification of Education (ISCED), forms the core of this study.Osmangazi Journal of Educational Research["Sefa Emre \u00d6nc\u00fc", "\u0130rfan S\u00fcral"]2024-06-08T00:00:00 fed2552dc0b035166aefc99ac50908b21b49385dWith the continuous advancement of technology, artificial intelligence has significantly impacted various fields, particularly healthcare. Generative models, a key AI technology, have revolutionized medical image generation, data analysis, and diagnosis. This article explores their application in intelligent medical devices. Generative models enhance diagnostic speed and accuracy, improving medical service quality and efficiency while reducing equipment costs. These models show great promise in medical image generation, data analysis, and diagnosis. Additionally, integrating generative models with IoT technology facilitates real-time data analysis and predictions, offering smarter healthcare services and aiding in telemedicine. Challenges include computational demands, ethical concerns, and scenario-specific limitations.International Journal of Computer Science & Information Technology (IJCSIT)13310Generative models enhance diagnostic speed and accuracy, improving medical service quality and efficiency while reducing equipment costs, and integrating generative models with IoT technology facilitates real-time data analysis and predictions, offering smarter healthcare services and aiding in telemedicine.ArXiv["Yuan Sun", "Jorge Ortiz"]2024-06-08T00:00:00 1016022ef787a0fbec3beeb7c26224a9c69914c1BackgroundMaternity care is a complex system involving treatments and interactions between patients, healthcare providers, and the care environment. To enhance patient safety and outcomes, it is crucial to understand the human factors (e.g. individuals' decisions, local facilities) influencing healthcare. However, most current tools for analysing healthcare data focus only on biomedical concepts (e.g. health conditions, procedures and tests), overlooking the importance of human factors. MethodsWe developed a new approach called I-SIRch, using artificial intelligence to automatically identify and label human factors concepts in maternity investigation reports describing adverse maternity incidents produced by England's Healthcare Safety Investigation Branch (HSIB). These incident investigation reports aim to identify opportunities for learning and improving maternal safety across the entire healthcare system. Unlike existing clinical annotation tools that extract solely biomedical insights, I-SIRch is uniquely designed to capture the socio-technical dimensions of patient safety incidents. This innovation enables a more comprehensive analysis of the complex systemic issues underlying adverse events in maternity care, providing insights that were previously difficult to obtain at scale. Importantly, I-SIRch employs a hybrid approach, incorporating human expertise to validate and refine the AI-generated annotations, ensuring the highest quality of analysis. FindingsI-SIRch was trained using real data and tested on both real and synthetic data to evaluate its performance in identifying human factors concepts. When applied to real reports, the model achieved a high level of accuracy, correctly identifying relevant concepts in 90% of the sentences from 97 reports (Balanced Accuracy of 90% ± 18% (Recall 93% ± 18%, Precision 87% ± 34%, F-score 96% ± 10%). Applying I-SIRch to analyse these reports revealed that certain human factors disproportionately affected mothers from different ethnic groups. In particular, gaps in risk assessment were more prevalent for minority mothers, whilst communication issues were common across all groups but potentially more for minorities. InterpretationOur work demonstrates the potential of using automated tools to identify human factors concepts in maternity incident investigation reports, rather than focusing solely on biomedical concepts. This approach opens up new possibilities for understanding the complex interplay between social, technical and organisational factors influencing maternal safety and population health outcomes. By taking a more comprehensive view of maternal healthcare delivery, we can develop targeted interventions to address disparities and improve maternal outcomes. Targeted interventions to address these disparities could include culturally sensitive risk assessment protocols, enhanced language support, and specialised training for healthcare providers on recognising and mitigating biases. These findings highlight the need for tailored approaches to improve equitable care delivery and outcomes in maternity services. The I-SIRch framework thus represents a significant advancement in our ability to extract actionable intelligence from healthcare incident reports, moving beyond traditional clinical factors to encompass the broader systemic issues that impact patient safety.International Journal of Population Data Science152The I-SIRch framework represents a significant advancement in the ability to extract actionable intelligence from healthcare incident reports, moving beyond traditional clinical factors to encompass the broader systemic issues that impact patient safety.ArXiv["Mohit Kumar Singh", "Georgina Cosma", "Patrick Waterson", "Jonathan Back", "G. T. Jun"]2024-06-08T00:00:00 64220e816bc57d83b3bc1c769513ab740127286aMultimodal models are expected to be a critical component to future advances in artificial intelligence. This field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.arXiv.org1012This work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy that includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs.ArXiv["Sai Munikoti", "Ian Stewart", "Sameera Horawalavithana", "Henry Kvinge", "T. Emerson", "Sandra E Thompson", "Karl Pazdernik"]2024-06-08T00:00:00 3649021b2472510bad55a258a0c9547f3e540d65This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.Sustainability482Findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment, which offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.Sustainability["Nicoleta Mihaela Doran", "Gabriela Badareu", "Marius Dalian Doran", "Maria Enescu", "Anamaria Liliana Staicu", "Mariana Niculescu"]2024-06-08T00:00:00 fddbd8c828aff812570648ae71adb605be067b72This study aims to enhance the paradigm of decentralized carbon markets by proposing an innovative integration of artificial intelligence (AI) and blockchain technology for intelligent carbon credit trading with the goal of attaining sustainable emission reduction. Blockchain systems powered by artificial intelligence (AI) have the potential to boost the effectiveness of current systems and expedite the global implementation of emissions trading. Although still in its infancy, blockchain artificial intelligence (AI) presents a promising solution to some of the world's most pressing environmental issues. Environmental sustainability is greatly affected by artificial intelligence because of its decentralized computation architecture. The Artificial Intelligence and blockchain are outstanding direction for today’s environmental issues starting from carbon footprint emission to earth market unstable management, whereby the AI facilitates the best possible operational control of power systems and the blockchain offers decentralized trading platforms for the energy markets. The paper's theoretical framework, based on advanced mathematical models, serves as the foundation for this study, in which AI algorithms are methodically constructed to anticipate carbon emissions with unprecedented accuracy. Using sophisticated coding simulations and complicated mathematical formulas, the study boldly transitions into a realistic digital implementation that builds on this theoretical foundation. This complex experiment not only validates the theoretical ideas but also illustrates the complex relationship between blockchain and AI in the decentralized carbon market ecosystem. This experiment's mathematical basis is the creation of an integrated pricing model that seamlessly blends blockchain-based trading dynamics with AI-driven forecasts. The model incorporates a dynamic, self-adjusting system that responds to current market conditions, in addition to optimizing the pricing calculation of carbon credits. Complex market dynamics, player tactics, and the overall equilibrium of the carbon credit market are all modeled by mathematical simulations. The project goes deeper into building blockchain-based smart contracts, which enable safe and transparent transactions. The comprehensive mathematical results of the experiment shed light on the best way to price carbon credits while underscoring the disruptive potential of blockchain and artificial intelligence in terms of sustainable emission reduction strategies used in carbon markets. Major conclusions about the potential advantages of Blockchain AI for guaranteeing emissions reduction are drawn from the current study. Additionally, it presents a roadmap for future research in this area.Journal of Computer Science and Technology Studies231This experiment's mathematical basis is the creation of an integrated pricing model that seamlessly blends blockchain-based trading dynamics with AI-driven forecasts, and incorporates a dynamic, self-adjusting system that responds to current market conditions, in addition to optimizing the pricing calculation of carbon credits.Journal of Computer Science and Technology Studies["Luka Baklaga"]2024-06-08T00:00:00 8e22730a5af03a77e77ec2bbc070396aaca51e8cIn this paper, we present a very first study to investigate trust and ethical implications of on-device artificial intelligence (AI), focusing on ''small'' language models (SLMs) amenable for personal devices like smartphones. While on-device SLMs promise enhanced privacy, reduced latency, and improved user experience compared to cloud-based services, we posit that they might also introduce significant challenges and vulnerabilities compared to on-server counterparts. As part of our trust assessment study, we conduct a systematic evaluation of the state-of-the-art on-devices SLMs, contrasted to their on-server counterparts, based on a well-established trustworthiness measurement framework. Our results show on-device SLMs to be (statistically) significantly less trustworthy, specifically demonstrating more stereotypical, unfair and privacy-breaching behavior. Informed by these findings, we then perform our ethics assessment study by inferring whether SLMs would provide responses to potentially unethical vanilla prompts, collated from prior jailbreaking and prompt engineering studies and other sources. Strikingly, the on-device SLMs did answer valid responses to these prompts, which ideally should be rejected. Even more seriously, the on-device SLMs responded with valid answers without any filters and without the need for any jailbreaking or prompt engineering. These responses can be abused for various harmful and unethical scenarios including: societal harm, illegal activities, hate, self-harm, exploitable phishing content and exploitable code, all of which indicates the high vulnerability and exploitability of these on-device SLMs. Overall, our findings highlight gaping vulnerabilities in state-of-the-art on-device AI which seem to stem from resource constraints faced by these models and which may make typical defenses fundamentally challenging to be deployed in these environments.arXiv.org721The findings highlight gaping vulnerabilities in state-of-the-art on-device AI which seem to stem from resource constraints faced by these models and which may make typical defenses fundamentally challenging to be deployed in these environments.ArXiv["Kalyan Nakka", "Jimmy Dani", "Nitesh Saxena"]2024-06-08T00:00:00 7ab2354c7a9cafa3bdbd7d937c22d2ccff0ea581The existence of this scientific article aims to provide a brief description of the position of translation in the phenomenon of globalization, which is inseparable from technological advances and human mobility that demand a connection between language and culture. Therefore, to discuss the world of interpreting further, the researcher chose the title "Simultaneous Interpreting Used in "The Samsung Galaxy S24 Automatically Translates Calls Via AI" Video on Rappler's YouTube Channel". This research focuses on finding the modes of interpreting used with a descriptive qualitative method, and the data obtained comes from conversations between a person and AI (Artificial Intelligence) in one of Rappler's YouTube Channel videos, namely "The Samsung Galaxy S24 Automatically Translates Calls Via AI".  After the research was conducted, it was found that one interpreting mode was used based on the theory of Pochhacker, namely simultaneous interpreting, because the Live Translate feature released by the Samsung Galaxy S24 Series brand translates in real time and there are 24 conversations that can be delivered in one time or one breath but as if a pause was made therefore the AI ​​Interpreter could use that time to translate.Acceleration: Multidisciplinary Research Journal100This research focuses on finding the modes of interpreting used with a descriptive qualitative method and the data obtained comes from conversations between a person and AI (Artificial Intelligence) in one of Rappler's YouTube Channel videos, namely "The Samsung Galaxy S24 Automatically Translates Calls Via AI".Acceleration: Multidisciplinary Research Journal["Kurnia Septiana Putri", "Ramadan Adianto Budiman"]2024-06-08T00:00:00 1e043f3d0b55cbf7a0c656ce3c5f16f933384cd0Artificial Intelligence (AI) plays an important role in digital marketing by personalizing campaigns through big data analytics, observing user behavior, and delivering tailored content and recommendations. However, AI raises privacy concerns as it collects and analyzes personal data, potentially leading to its misuse. Emotional manipulation and the spread of misleading information are additional ethical challenges. Research shows that there are significant differences in how young and middle-aged individuals use and perceive social media. Young people who have grown up with technology differ from middle-aged adults in terms of frequency of use, content preferences, advertising, commerce, sharing habits, time spent on media, and psychological effects. This study aims to compare the perspectives of young people and middle-aged adults on the use of AI in digital marketing and highlight the ethical implications and different approaches between these age groups.Next Frontier For Life Sciences and AI00This study aims to compare the perspectives of young people and middle-aged adults on the use of AI in digital marketing and highlight the ethical implications and different approaches between these age groups.Next Frontier For Life Sciences and AI["Peri \u015eent\u00fcrk"]2024-06-08T00:00:00 575f3c4b19eeb9fd9233cee78152c8b652b24fdbWe study how people form expectations about the performance of artificial intelligence (AI) and consequences for AI adoption. Our main hypothesis is that people rely on human-relevant task features when evaluating AI, treating AI failures on human-easy tasks, and successes on human-difficult tasks, as highly informative of its overall performance. In lab experiments, we show that projection of human difficulty onto AI predictably distorts subjects' beliefs and can lead to suboptimal adoption, as failing human-easy tasks need not imply poor overall performance for AI. We find evidence for projection in a field experiment with an AI giving parenting advice. Potential users strongly infer from answers that are equally uninformative but less humanly-similar to expected answers, significantly reducing trust and future engagement. Our results suggest AI"anthropomorphism"can backfire by increasing projection and de-aligning people's expectations and AI performance.00The results suggest AI"anthropomorphism" can backfire by increasing projection and de-aligning people's expectations and AI performance, as failing human-easy tasks need not imply poor overall performance for AI.["Bnaya Dreyfuss", "Raphael Raux"]2024-06-08T00:00:00 c46b2693a756b8956efbd4d31fe42711cee1879fThe swift progress of artificial intelligence (AI) has presented society with unparalleled opportunities and challenges. With the growing autonomy of AI systems, the critical concern of ensuring their alignment with human values and ethical principles has come to the forefront. The AI alignment problem refers to the challenge of designing AI systems that act in ways that are beneficial and aligned with human intentions and values. In this paper, we explore the potential contributions of human values from Indian philosophy in solving the AI alignment problem. We conclude that it is possible to establish and tailor a finite set of human values derived from Indian philosophy for the purpose of addressing the enduring challenges that AI systems are expected to tackle in their operational tasks.International Journal For Multidisciplinary Research370It is concluded that it is possible to establish and tailor a finite set of human values derived from Indian philosophy for the purpose of addressing the enduring challenges that AI systems are expected to tackle in their operational tasks.International Journal For Multidisciplinary Research["Sukrati Chaturvedi", "C. Patvardhan", "C. Vasantha Lakshmi"]2024-06-08T00:00:00 3ed7871b55035011163efaf18c1c4be63882c8f6Since the late 19th century, there has always been a fictitious portrait of robots and artificial intelligence penetrating the normal functioning of a human’s life. But in the late twentieth century, the imaginary characters were shaping into reality, where once an impossible reality was becoming an emerging reality at the present stage of earthly life. Robotics and artificial intelligence assist humans in their daily chores and perform tasks too complex to be understood by humans. With Sophia, a robot but now a human counterpart, having been granted citizenship in Saudi Arabia, new doors of frequent encounters and easy access to artificial intelligence are being considered the new endeavour by various huge giants and business enterprises primarily engaged in artificial intelligence. With progress at such a vast scale, humans share a typical habitat with robots and machines assisted through artificial intelligence. Artificial Intelligence and Robotics are creations made by humans for the advantage and development of humans. That certainly means that artificial intelligence and robots are products of human labour, novelty, and innovation. They could be categorized as inventions, and an invention is protectable under Intellectual Property Laws. Protecting the invention and the rights of the inventor of such inventions with the present legal regime is a challenge. In today’s digital era, the question is whether India's laws are sufficient to accommodate invention and development in the new, evolving cyber world. The paper attempts to analyze the accountability of present laws in India for the development of innovation and technology, the rights of the inventor and the need for guarding the new era of Artificial Intelligence.International Journal For Multidisciplinary Research70The paper attempts to analyze the accountability of present laws in India for the development of innovation and technology, the rights of the inventor and the need for guarding the new era of Artificial Intelligence.International Journal For Multidisciplinary Research["Suzanna Augustine George"]2024-06-08T00:00:00 899d30a3796b377e01866fac1e9be037590471bbAs recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these models could eventually mirror human intelligence. However, existing benchmarks mainly focus on evaluating solely on task performance, such as the accuracy of identifying the attribute of an object. Combining well-developed cognitive science to understand the intelligence of MLLMs beyond superficial achievements remains largely unexplored. To this end, we introduce the first cognitive-driven multi-lingual and multi-modal benchmark to evaluate the general intelligence ability of MLLMs, dubbed M3GIA. Specifically, we identify five key cognitive factors based on the well-recognized Cattell-Horn-Carrol (CHC) model of intelligence and propose a novel evaluation metric. In addition, since most MLLMs are trained to perform in different languages, a natural question arises: is language a key factor influencing the cognitive ability of MLLMs? As such, we go beyond English to encompass other languages based on their popularity, including Chinese, French, Spanish, Portuguese and Korean, to construct our M3GIA. We make sure all the data relevant to the cultural backgrounds are collected from their native context to avoid English-centric bias. We collected a significant corpus of data from human participants, revealing that the most advanced MLLM reaches the lower boundary of human intelligence in English. Yet, there remains a pronounced disparity in the other five languages assessed. We also reveals an interesting winner takes all phenomenon that are aligned with the discovery in cognitive studies. Our benchmark will be open-sourced, with the aspiration of facilitating the enhancement of cognitive capabilities in MLLMs.arXiv.org634This work introduces the first cognitive-driven multi-lingual and multi-modal benchmark to evaluate the general intelligence ability of MLLMs, dubbed M3GIA and identifies five key cognitive factors based on the well-recognized Cattell-Horn-Carrol model of intelligence and proposes a novel evaluation metric.ArXiv["Wei Song", "Yadong Li", "Jianhua Xu", "Guowei Wu", "Lingfeng Ming", "Kexin Yi", "Weihua Luo", "Houyi Li", "Yi Du", "Fangda Guo", "Kaicheng Yu"]2024-06-08T00:00:00 ced4c221520d6c5d1fac942395b1122b28ca160dInternational Journal of Information Engineering and Electronic Business00International Journal of Information Engineering and Electronic Business["Herison Surbakti", "Prashaya Fusiripong"]2024-06-08T00:00:00 93146243e0542cc3901e6e98d84122ef7212fbf4Creative work means doing work that has not been done or thought to date. It involves your logical and reasoning ability. It includes making creative efforts in sculpture, painting, sketching, etc. Creativity allows people to synthesize their imagination and intelligence, which inspires them to tell new stories and convey original viewpoints. It's an adventure where the logical skills of the mind merge with the whims of creativity, giving birth to uniqueness in a variety of artistic expressions and problem-solving pursuits.2024 International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S)90It's an adventure where the logical skills of the mind merge with the whims of creativity, giving birth to uniqueness in a variety of artistic expressions and problem-solving pursuits.2024 International Conference on Integrated Circuits, Communication, and Computing Systems (ICIC3S)["Harsehaj Ahuja", "Dipika Gupta", "Manish Kumar", "Jatin Chugh"]2024-06-08T00:00:00 5f311a2a77ba4e600f0954cf60c89f18241f918eThe networking hardware industry is characterized by unique challenges when it comes to supply chain management. These include unpredictable demand patterns, complex logistics, besides disruptions caused by rapid technological advancements. This paper explores the integration of artificial intelligence (AI) into forecasting methodologies to enhance supply chain efficiency within the sector. Application of AI-driven forecasting models can help organizations improve demand predictions, refine inventory management, and streamline logistical operations. Drawing on recent research and industry practices, this article highlights the transformative impact of AI on supply chain efficiency and offers insights into best implementation practices. Furthermore, the research investigates the intersection of AI and networking hardware supply chain management, focusing on leveraging AI to analyze hardware failure patterns and interpret hardware-generated alarms and interrupts. By harnessing analytical capabilities of AI, modern organizations can extract actionable insights to reduce failure rates and enhance supply chain forecasting accuracy. This innovative approach enables more effective anticipation and preparation for hardware failures, optimizing spare part inventory management and minimizing the need for costly return merchandise authorizations (RMAs).International journal of supply chain management521This paper explores the integration of artificial intelligence (AI) into forecasting methodologies to enhance supply chain efficiency within the networking hardware sector and investigates the intersection of AI and networking hardware supply chain management.International Journal of Supply Chain Management["Manish Krishnan", "Antara Khastgir"]2024-06-09T00:00:00 66264414bc46ffd150186c96dc6607b7bc358d06Abstract Artificial Intelligence (AI), encompassing computation for perception, reasoning, and action, poses complex legal considerations. This study explores AI’s impact and its legal ramifications, particularly its autonomy in communication and creation, raising concerns about language, intellectual property, and ethical accountability. Influenced by Common Law and Civil Law systems, discussions vary. Evaluating AI creator liability uncovers intricate connections between AI’s autonomy, intentionality, and creators’ roles. The approach used in this article are based on normative method with multidisciplinary discipline. The results are that though AI creators aren’t directly liable, vicarious liability could link actions to AI behaviors based on programming choices. Balancing innovation and accountability calibrated “creator immunities” are vital. Unchecked immunities could impede responsible AI development; measured immunities might encourage ethical practices, considering AI nuances and societal impacts. Positioning AI as a legal subject necessitates tailored approaches within ethical boundaries. The proposition of AI as a derivative legal subject while setting clear limits is pivotal. Adapting legal systems to evolving AI landscapes and reconciling advancement with societal well-being, is crucial. AI’s intricate accountability, its legal standing, and creator liabilities and immunities demand reshaping legal frameworks for an ethical AI environment. Abstrak Kecerdasan Buatan (AI), yang mencakup komputasi persepsi, penalaran, dan tindakan, menimbulkan pertimbangan hukum yang kompleks. Studi ini mengeksplorasi dampak AI dan konsekuensi hukumnya, terutama otonominya dalam komunikasi dan kreasi, yang menimbulkan kekhawatiran tentang bahasa, kekayaan intelektual, dan akuntabilitas etis. Melalui sistem Common Law dan Civil Law, pembahasannya pun beragam. Mengevaluasi pertanggungjawaban pencipta AI mengungkap hubungan yang rumit antara otonomi, kesengajaan, dan peran pencipta AI. Pendekatan yang digunakan didasarkan pada metode normatif dengan disiplin ilmu yang beragam. Hasilnya adalah bahwa meskipun pencipta AI tidak bertanggung jawab secara langsung, doktrin vicarious liability dapat menghubungkan tindakan dengan perilaku AI berdasarkan pilihanpemrograman. Menyeimbangkan inovasi dan akuntabilitas, kekebalan pencipta yang terukur sangat penting. Kekebalan yang tidak terkendali dapat menghambat pengembangan AI yang bertanggung jawab; kekebalan yang terukur dapat mendorong praktik-praktik etis, dengan mempertimbangkan nuansa AI dan dampak sosial. Memosisikan AI sebagai subjek hukum memerlukan pendekatan yang disesuaikan dengan batasan etika. Proposisi AI sebagai subjek hukum turunan sambil menetapkan batasan yang jelas sangat krusial. Mengadaptasi sistem hukum dengan lanskap AI yang terus berkembang dan menyelaraskan kemajuan dengan kesejahteraan masyarakat, sangatlah vital. Pertanggungjawaban AI yang rumit, kedudukan hukumnya, dan kewajiban serta kekebalan pencipta menuntut pembentukan kembali kerangka kerja hukum untuk lingkungan AI yang etis.Mimbar Hukum662Mimbar Hukum["Rangga Hotman Hasibuan", "Jessica Rawung", "Denisha Paranduk", "Fidel Jeremy Wowiling", "Kata Kunci"]2024-06-09T00:00:00 1d10cfbc815930dd03a9b36aef8dd2838b43a9faAim: The aim of this study was to present current scientific publishing activity of the Republic of Macedonia analysed with artificial intelligence. Methods: This analysis was performed with the artificial intelligence platform www.wizdom.ai during March 18, 2024. Results: In the Republic of Macedonia, in 2023 were published 770 publications with closed, 432 with bronze, 200 with hybrid, 805 with gold, and 61 with green access. In the same year, a total number of 27,418 citations were recorded, with the biggest number of collaborations with United States. Total number of researchers that have published articles in 2023 was 2,550, with local co-authors of 2,268, and with international co-authors of 1,027. Conclusion: The power of artificial intelligence for analysis of scientific publishing is very sensitive and can be used with precautions because of the limited electronic availability of scientific data, as well as of the different inclusion and exclusion criteria for analysis.Journal of Health and Rehabilitation Sciences150The power of artificial intelligence for analysis of scientific publishing is very sensitive and can be used with precautions because of the limited electronic availability of scientific data, as well as of the different inclusion and exclusion criteria for analysis.Journal of Health and Rehabilitation Sciences["Mirko Spiroski", "Ivo Spiroski"]2024-06-09T00:00:00 6c5cc30312ecc2eef56886cfe3ddbfd345096963This paper is an invited layperson summary for The Academic of the paper referenced on the last page. We summarize how the formal framework of autocatalytic networks offers a means of modeling the origins of self-organizing, self-sustaining structures that are sufficiently complex to reproduce and evolve, be they organisms undergoing biological evolution, novelty-generating minds driving cultural evolution, or artificial intelligence networks such as large language models. The approach can be used to analyze and detect phase transitions in vastly complex networks that have proven intractable with other approaches, and suggests a promising avenue to building an autonomous, agentic AI self. It seems reasonable to expect that such an autocatalytic AI would possess creative agency akin to that of humans, and undergo psychologically healing -- i.e., therapeutic -- internal transformation through engagement in creative tasks. Moreover, creative tasks would be expected to help such an AI solidify its self-identity.arXiv.org00The formal framework of autocatalytic networks offers a means of modeling the origins of self-organizing, self-sustaining structures that are sufficiently complex to reproduce and evolve, be they organisms undergoing biological evolution, novelty-generating minds driving cultural evolution, or artificial intelligence networks such as large language models.ArXiv["Liane Gabora", "Joscha Bach"]2024-06-09T00:00:00 38c33ec3eda5b2fb46e078e96a96dfa826dd0f6fRecently, the promotion of green cryptocurrencies has attracted attention due to the huge resource consumption brought by cryptocurrency transactions. The main driver of green cryptocurrencies is to reduce resource consumption and reduce the number of transactions. Accurate prediction of cryptocurrency prices is difficult because they are influenced by diverse kinds of factors besides supplydemand relationship. First, the price of cryptocurrency changes rapidly and fluctuates violently, so the traditional econometric methods cannot respond well to the drastic price changes in a short period of time. Second, artificial intelligence (AI) models are relatively separated from econometric cryptocurrency price prediction models, which leads to deviations when forecasting in the financial field. Third, existing AI models have not been well optimized specially for cryptocurrency prediction. To address the above challenges, in this article, we propose the optimized AI and econometric model to empower virtual-fiat settled price prediction in green cryptocurrency networks. In our proposed econometric model renew autoregressive integrated moving average (REARIMA), the problem of poor econometric model response to drastic changes in a short time is solved by the joint design with AI. Moreover, the AI model innovation optimization for the prediction of cryptocurrency is carried out in dense long-short term memory (DENSE_LSTM). Finally, DENSE_LSTM are used to optimize the econometric model. The feasibility of the proposed model is verified by experiments.ICC 2024 - IEEE International Conference on Communications160The optimized AI and econometric model to empower virtual-fiat settled price prediction in green cryptocurrency networks is proposed and the problem of poor econometric model response to drastic changes in a short time is solved by the joint design with AI.ICC 2024 - IEEE International Conference on Communications["Xiaotong Jiang", "Jun Wu", "Qianqian Pan"]2024-06-09T00:00:00 0c6bfab83e12636bb5a2321a23ddc954adefdf6eIn this article the topic of Artificial Intelligence (AI) as a teaching and learning content for the field of the Digital Humanities (DH) in higher education is examined in more detail. For this purpose, a definition of AI in the context of the DH is given first. Areas of the application of AI topics in European DH degree programme descriptions (Master and Bachelor) are scanned to show whether and how the topic of AI is reflected in course descriptions for prospective students with a focus on Digital Humanities. In addition to focusing the term AI, descriptions are analysed for word frequencies and existing correlations. The results show that AI is not explicitly included as a subject of study in DH course descriptions. Nevertheless, central related themes and methods are highlighted therein. Areas such as languages, literature, cultural studies, and history as well as creative and production areas that have references to digital processes and semi- and fully automated computer-aided methods are mentioned. Overall, the teaching of effective digitally supported and collaborative working methods is an essential part of the degree programs (or is aimed at in the degree program descriptions and thus in the respective degree programs), which addresses core competencies of the learners in the future working world. AI topics are finally categorised and converted into a compact overview with areas of application and two possible exemplary implementation scenarios.Proceedings of The International Conference on Advanced Research in Teaching and Education520The teaching of effective digitally supported and collaborative working methods is an essential part of the degree programs (or is aimed at in the degree program descriptions and thus in the respective degree programs), which addresses core competencies of the learners in the future working world.Proceedings of The International Conference on Advanced Research in Teaching and Education["Katrin Fritsche", "Sander M\u00fcnster"]2024-06-09T00:00:00 0f7480ba2b7f1fb2f8edfe129bceca58bcad0a17This study focuses on how young people in Vietnam use and feel satisfied with artificial intelligence algorithms on TikTok. The research paper aims to investigate and better understand how this group of young people interact and use the TikTok platform, with the integration of artificial intelligence algorithms. The study was conducted based on the results of an initial questionnaire survey on a simple random sample based on the germ development sampling method among students in Vietnam. The quantitative data is analyzed based on SPSS software. The results of the study show that the majority of young people in Vietnam have a basic knowledge and understanding of artificial intelligence algorithms on TikTok. They use them to find engaging content, watch videos, and engage with the community on the platform. However, the study also found some risks associated with using artificial intelligence algorithms on TikTok. It is a risk of privacy violation and interference with personal freedom when algorithms can track, analyze and collect the personal data of users. Thereby, the research team analyzed the data based on the use and satisfaction theory. In summary, this study has mentioned how the young public in Vietnam use and feel satisfied with the artificial intelligence algorithm on TikTok. In addition to the benefits, attention should be paid to the ethical and privacy issues associated with the use of this technology to protect the interests of users.Jurnal Komunikasi Ikatan Sarjana Komunikasi Indonesia00The results of the study show that the majority of young people in Vietnam have a basic knowledge and understanding of artificial intelligence algorithms on TikTok and use them to find engaging content, watch videos, and engage with the community on the platform.Jurnal Komunikasi Ikatan Sarjana Komunikasi Indonesia["Nguyen Tan Khang"]2024-06-09T00:00:00 333455c68b46274df9e0aede35ac573fd48a10c6Breast Cancer Research and Treatment270Artificial intelligence errors are largely interpreted in the framework of test accuracy, and FP and FN errors show expected variability not only by positivity threshold, but also by algorithm version and study quality.Breast Cancer Research and Treatment["Aileen Zeng", "N. Houssami", "Naomi Noguchi", "B. Nickel", "M. Marinovich"]2024-06-09T00:00:00 6c5c2b41eefee5d53c53ca84f54b15ae40141d95AbstractArtificial Intelligence (AI) is a contemporary term stemming from the Fourth Industrial Revolution, representing human-created intelligence. Its implementation spans various sectors, including education, as the evolving digital era necessitates such technology to enhance the educational process. The aim is to explore the application of AI in relation to education. The research methodology employed is a literature review. The integration of AI in the realm of education significantly influences learning, particularly in terms of instructional methods. This greatly facilitates both teachers and students in adapting to the teaching and learning activities. Teachers find ease in administrative tasks and student assessments. In the era of the Fourth Industrial Revolution, students are expected to be technologically literate. AI employs systems that assist and enable activities to align with individual abilities and learning styles. However, it's important to note that AI's usage sometimes comes with negative impacts. As a generation in the Fourth Industrial Revolution, it is crucial for us to utilize AI wisely and positively, considering its potential drawbacks.Keywords: Artificial Intelligence, Industrial Revolution 4.0, Education AbstrakArtificial Intelligence (AI) merupakan istilah baru dari Revolusi Industri 4.0 sebagai kecerdasan buatan manusia. Penerapan AI tentunya banyak digunakan di beberapa sektor, termasuk di bidang pendidikan. Pendidikan yang berkembang ke era digital memerlukan teknologi semacam ini untuk membantu meningkatkan pendidikan. Tujuannya untuk mengetahui penerapan AI dalam kaitannya dengan pendidikan. Sedangkan metode penelitian yang digunakan menggunakan metode studi kepustakaan. Penerapan AI dalam dunia pendidikan sangat berpengaruh khususnya dalam hal pembelajaran. Hal ini tentunya sangat memudahkan guru dan siswa dalam menyesuaikan diri dengan kegiatan belajar mengajar. Manfaat yang diperoleh guru adalah mempermudah dalam hal administrasi dan penilaian siswa. Dengan adanya revolusi industri 4.0, pelajar dituntut untuk mampu melek teknologi. AI tersebut menggunakan sistem yang membantu dan memungkinkan dilakukannya kegiatan sesuai dengan kemampuan dan gaya belajarnya. Namun terkadang AI juga mempunyai tantangan dalam penggunaannya, sehingga kita sebagai generasi revolusi industri 4.0 perlu memanfaatkannya secara bijak dan positif dengan memaksimalkan kekuatan yang kita miliki.Kata kunci: Kecerdasan Buatan, Revolusi Industri 4.0, PendidikanEducatio344Educatio["Putri Sofiatul Maola", "Indira Syifa Karai Handak", "Y. Herlambang"]2024-06-09T00:00:00 a8e26f60e6fcb8e9ad50e6849fe717a553266f9cHumans and machines interact more frequently than ever and our societies are becoming increasingly hybrid. A consequence of this hybridisation is the degradation of societal trust due to the prevalence of AI-enabled deception. Yet, despite our understanding of the role of trust in AI in the recent years, we still do not have a computational theory to be able to fully understand and explain the role deception plays in this context. This is a problem because while our ability to explain deception in hybrid societies is delayed, the design of AI agents may keep advancing towards fully autonomous deceptive machines, which would pose new challenges to dealing with deception. In this paper we build a timely and meaningful interdisciplinary perspective on deceptive AI and reinforce a 20 year old socio-cognitive perspective on trust and deception, by proposing the development of DAMAS -- a holistic Multi-Agent Systems (MAS) framework for the socio-cognitive modelling and analysis of deception. In a nutshell this paper covers the topic of modelling and explaining deception using AI approaches from the perspectives of Computer Science, Philosophy, Psychology, Ethics, and Intelligence Analysis.arXiv.org1332This paper builds a timely and meaningful interdisciplinary perspective on deceptive AI and reinforce a 20 year old socio-cognitive perspective on trust and deception, by proposing the development of DAMAS -- a holistic Multi-Agent Systems (MAS) framework for the socio-cognitive modelling and analysis of deception.ArXiv["Stefan Sarkadi"]2024-06-09T00:00:00 4a746803cf75ae168337db492bf6ae7de99dc5deThis study aims to analyze a range of influential challenges to realize AI based economic transformation, thereby producing a comprehensive AI development strategy to maximize the benefits of AI with the minimum possible risk. Research data was obtained from interviews, observations, literature studies, and data triangulation. Based on the results of the study, it was found that the Indonesian Government needs to take several strategic steps in an effort to realize AI optimization to support economic transformation by strengthening the availability of equitable digital infrastructure to support AI implementation, increasing the availability of skilled human resources equivalent to AI technology capacity, providing conducive policy and regulatory support for AI investment and research, and optimizing the ecosystem that supports Innovation and technology startups for the development of AI-based products and services, in order to increase national economic growth.Technium Social Sciences Journal01It was found that the Indonesian Government needs to take several strategic steps in an effort to realize AI optimization to support economic transformation by strengthening the availability of equitable digital infrastructure to support AI implementation.Technium Social Sciences Journal["Sungkono", "I Dewa Ketut Kerta Widana"]2024-06-09T00:00:00 f00ef8bf879d98874100c86e397f8bcf8d8158eaRecent advances in AI techniques, as well as enabling hardware and infrastructure, have led to the integration of AI across wide-ranging domains and tasks. In particular, AI has been used to handle various types of data (including numerical, textual and image data) and has been adopted in large-scale distributed systems. From a data management perspective, this calls for the harnessing of state-of-the-art AI solutions for data management tasks and systems. aiDM is a full-day workshop that offers a stage for innovative interdisciplinary research that studies the interaction between AI and data management and develops new AI technologies for data-related tasks. This year, aiDM'24 particularly focuses on the transparent exploitation of AI techniques (e.g., using Generative AI frameworks) for data management for enterprise class workloads.SIGMOD Conference Companion80AiDM'24 particularly focuses on the transparent exploitation of AI techniques (e.g., using Generative AI frameworks) for data management for enterprise class workloads.Companion of the 2024 International Conference on Management of Data["Rajesh Bordawekar", "O. Shmueli", "Yael Amsterdamer", "Renata Borovica-Gajic", "Donatella Firmani"]2024-06-09T00:00:00 8960bf09abe783576666a2364cf48617338a1636This paper conducts an in-depth examination of Amazon Web Services (AWS) Machine Learning, a collection of tools and services aimed at simplifying the process of building, training, and deploying machine learning models. It starts with an analysis of essential components such as Amazon SageMaker and AWS Deep Learning AMIs, detailing their functionalities and how they integrate into the larger AWS framework. The discussion then shifts to real-world applications in various sectors, including healthcare, finance, retail, and manufacturing, highlighting successful use cases and practical examples. The paper evaluates the strengths and limitations of AWS Machine Learning, considering factors like scalability, user-friendliness, cost, and support for diverse machine learning frameworks, as well as challenges such as the learning curve and reliance on cloud infrastructure. The paper also explores future trends and directions, including improvements in automation, the fusion of AI with Internet of Things (IoT) devices, and the development of new tools to enhance the machine learning lifecycle. These insights are intended to assist organizations in making informed decisions about using AWS for their AI and machine learning projects, enabling them to effectively harness AWS's capabilities to meet their objectives.International Journal of Advanced Research in Science, Communication and Technology50An in-depth examination of Amazon Web Services (AWS) Machine Learning, a collection of tools and services aimed at simplifying the process of building, training, and deploying machine learning models, finds factors like scalability, user-friendliness, cost, and support for diverse machine learning frameworks as well as challenges such as the learning curve and reliance on cloud infrastructure.International Journal of Advanced Research in Science, Communication and Technology["Praveen Borra"]2024-06-09T00:00:00 09506c627d31ba8b01d458cf30fb516d3829dba5As a distributed machine learning paradigm, federated learning usually requires all edge devices to collaboratively train a large-size artificial intelligence model at local. However, this imposes challenges for these resource-constrained Internet of Things (IoT) devices. Moreover, the communication overhead between IoT devices and the base station is highly significant for the emerging big model-based tasks. In this paper, we propose a novel framework called federated split learning (FedSL), which considers the heterogeneity and resource scarcity of IoT devices. To reduce the training delay and energy consumption in resource-constrained wireless networks, we formulate a mixed-integer non-linear programming problem by jointly optimizing the power allocation, device scheduling and split layer selection. Then, we design an alternating optimization algorithm to solve the formulated problem with a low computational complexity. The simulation results demonstrate that the FedSL framework outperforms the current state-of-the-art benchmarks, highlighting the importance and superiority of device scheduling in resource-constrained IoT networks.2024 IEEE International Conference on Communications Workshops (ICC Workshops)120This paper forms a mixed-integer non-linear programming problem by jointly optimizing the power allocation, device scheduling and split layer selection, and designs an alternating optimization algorithm to solve the formulated problem with a low computational complexity.2024 IEEE International Conference on Communications Workshops (ICC Workshops)["Huiqing Ao", "Hui Tian", "Wanli Ni"]2024-06-09T00:00:00 673ed31ebe576e20bf0962c2645b425aa0127aecJagung atau dalam bahasa latin Zea Mays merupakan adalah salah satu dari jenis tanaman pangan dari keluarga rumput-rumputan yang dikelompokkan dalam tanaman biji-bijian. Jagung memiliki banyak varietas. Adapun varietas yang telah dilepas oleh Menteri Pertanian hingga Oktober tahun 2022 sebanyak 361 varietas, yaitu jagung hibrida sebanyak 298 varietas, jagung komposit sebanyak 59 varietas, dan ada sebanyak 4 varietas jagung hibrida produk rekayasa genetik (PRG). Petani jagung biasanya memilih dan menentukan bibit jagung yang akan dibudidayakan berdasarkan rekomendasi pedagang bibit jagung atau dari rekan sesama petani jagung. Namun demikian sering dijumpai hasil panen jagung tidak sesuai dengan ekspektasi dan target yang diharapkan. Bahkan, tidak jarang petani jagung mengalami gagal panen yang disebabkan oleh beberapa faktor, salah satunya dikarenakan bibit jagung yang dipilih bukan merupakan varietas bibit jagung unggul. Sistem ini dirancang untuk membantu para petani jagung khususnya di daerah Aceh dalam memilih dan menentukan bibit jagung unggul untuk dibudidayakan dengan tujuan mendapatkan hasil panen yang memuaskan. Sistem ini menggunakan algoritma Artificial Neural Network untuk melakukan pemilihan. Artificial Neural Network (ANN) adalah algoritma Machine Learning dengan model komputasi yang terinspirasi dari prinsip kerja otak manusia. Artificial Neural Network digunakan dalam aplikasi ini karena dapat melakukan prediksi dengan akurat. Hasil yang diharapkan dengan adanya sistem ini petani dapat memilih varietas bibit jagung unggul untuk dibudidayakan, sehingga dapat memenuhi kebutuhan stok dalam negeri dengan memanfaatkan komputer dalam tahapan pemilihan bibit unggul. Penerapan algortima ANN Multi Layer Perceptron pada aplikasi ini menggunakan 21 data varietas jagung dengan 504 dataset yang dimasukkan mendapatkan hasil nilai tertinggi dengan persentase akurasi 90,47%. Dengan hasil tersebut, algortima Artificial Neural Network Multi Layer Perceptron dapat digunakan untuk Aplikasi Machine Learning dalam menentukan pemilihan varietas bibit jagung unggul Abstract Corn or in Latin Zea Mays is one of the types of food crops from the grass family which is grouped into grain crops. Corn has many varieties. The varieties that have been released by the Minister of Agriculture until October 2022 are 361 varieties, namely 298 varieties of hybrid corn, 59 varieties of composite corn, and there are as many as 4 varieties of genetically modified (PRG) hybrid corn. Maize farmers usually choose their maize seeds based on recommendations from maize seed traders or fellow maize farmers. However, maize yields are often not in line with expectations and targets. In fact, it is not uncommon for corn farmers to experience crop failure caused by several factors, one of which is because the corn seeds chosen are not superior corn seed varieties. This system is designed to help corn farmers, especially in the Aceh area, in choosing and determining superior corn seeds for cultivation with the aim of getting satisfactory yields. This system uses Artificial Neural Network algorithm to make the selection. Artificial Neural Network (ANN) is a Machine Learning algorithm with a computational model inspired by the working principles of the human brain. Artificial Neural Network is used in this application because it can make accurate predictions. The expected results with this system are that farmers can choose superior varieties of corn seeds to be cultivated, so that they can meet the needs of domestic stocks by utilizing computers in the stages of selecting superior seeds. The application of ANN Multi Layer Perceptron algortima in this application using 21 corn variety data with 504 datasets entered gets the highest value results with an accuracy percentage of 90.47%. With these results, the Artificial Neural Network Multi Layer Perceptron algortima can be used for Machine Learning applications in determining the selection of superior corn seed varieties.Journal of Artificial Intelligence and Software Engineering (J-AISE)00Journal of Artificial Intelligence and Software Engineering (J-AISE)["Ainul Fitria", "Salahuddin Salahuddin", "Muhammad Rizka"]2024-06-09T00:00:00 114d2fabbfab4ed35e67c14be568040af9b743ffThe use of generative artificial intelligence (GAI) large language models has increased in both professional and classroom technical writing settings. One common response to student use of GAI is to increase surveillance, incorporating plagiarism detection services or banning certain composing activities from the classroom. This paper argues such measures are harmful and instead proposes a “CARE” framework: critical, authorial, rhetorical, and educational—a nuanced approach emphasizing ethical and contextual AI use in technical writing classrooms. This framework aligns with plagiarism best practices, initially devised from when rhetoric and composition scholars considered the pedagogical implications of the Internet.Journal of Technical Writing and Communication242This paper argues measures to increase surveillance, incorporating plagiarism detection services or banning certain composing activities from the classroom are harmful and proposes a “CARE” framework: critical, authorial, rhetorical, and educational—a nuanced approach emphasizing ethical and contextual AI use in technical writing classrooms.Journal of Technical Writing and Communication["E. H. Pflugfelder", "Joshua Reeves"]2024-06-09T00:00:00 e39ed508e7205895ba6aa982d35d357fbee81009Open Radio Access Network (O-RAN) is positioned to play a pivotal role in shaping the future of telecommunications networks through open interfaces and virtualization, allowing interoperability between different vendors. As a key departure from single-operator managed RAN, a remote RAN intelligence controller (RIC) queries the gNB for the Key Performance Indicators (KPIs) that are required for making RAN control decisions, often leveraging advanced machine learning (ML) models. However, this repeated querying increases control traffic overhead on the so called E2 interface connecting the gNB to the RIC. To address this challenge, we utilize a method from Explainable Artificial Intelligence (XAI), specifically SHapley Additive exPlanations (SHAP), which quantifies the contribution of each requested KPI to a model's prediction. Furthermore, we explore two different methods of choosing the most discriminative KPIs influencing model's performance, so that a smaller subset of KPIs may be queried, thus lowering the overhead on the E2 interface. Our analysis reveals that a model trained for the task of traffic classification using as input only the fraction of the top contributing KPIs identified by SHAP reduces control traffic overhead by up to 33% with only 7% reduction in ML classification accuracy.ICC 2024 - IEEE International Conference on Communications121A method from Explainable Artificial Intelligence (XAI), specifically SHapley Additive exPlanations (SHAP), which quantifies the contribution of each requested KPI to a model's prediction is utilized, which reveals that a model trained for the task of traffic classification reduces control traffic overhead by up to 33% with only 7% reduction in ML classification accuracy.ICC 2024 - IEEE International Conference on Communications["Chinenye Tassie", "Brian Kim", "Joshua Groen", "M. Belgiovine", "Kaushik R. Chowdhury"]2024-06-09T00:00:00 09c57ffb36fc352e8f11357c9a6c8322717ad4c0Edge computing has promoted various applications driven by artificial intelligence (AI). However, upgrading AI models during system operation may change resource and performance features. Then, the service management controller (SMC) faces an unprecedented environmental condition and has limited prior knowledge, resulting in high probabilities of policy mismatches. With the proliferation of AI applications, it is an urgent necessity that SMCs can adapt to different conditions to ensure quality of service (QoS) and resource efficiency. Therefore, this paper studies the problem of dynamic edge AI service adaptation and formulates it as a multi-task scenario adaptation problem. After that, we proposed an approach based on off-policy meta-reinforcement learning and digital twin (DT) technology. The DT system emulates a set of encountered conditions, and a meta-policy is obtained by interacting with these DTs. The executed policy is initialized as the meta-policy once AI models are upgraded. Then, it adapts to new service conditions by drawing salient information from limited transition contexts collected from a newly encountered environmental condition. Simulation results reveal that our approach can optimize QoS and adapt to different service situations.ICC 2024 - IEEE International Conference on Communications151Simulation results reveal that the proposed approach based on off-policy meta-reinforcement learning and digital twin technology can optimize QoS and adapt to different service situations.ICC 2024 - IEEE International Conference on Communications["Yan Chen", "Hao Yu", "Qize Guo", "Shuyuan Zhao", "T. Taleb"]2024-06-09T00:00:00 6ddcacc62ccb754d987a3b8cc74f4f3e11f34fd0The role of artificial intelligence (AI) in cybersecu-rity has grown due to increasing threats from malicious actors. It aids in threat detection, behavioral analysis, malware detection, phishing identification, and enhancing security measures. However, AI can also be weaponized for cyberattacks, as malicious actors use AI-based tools for sophisticated and adaptable assaults on security systems. This study contributes to cybersecurity by defending against AI-based threats. Machine learning models were trained on diverse, complex datasets to counter sophisticated AI-based attacks in multi-environments (M-En). We have utilized auto-encoders to generate our M-En dataset by combining two benchmark datasets: UNSW-NB15 and IoTID-20, that represent traditional IP-based and IoT-based traffic, respectively. Three generative models (CTGAN, CopulaGAN, and TVAE) produced AI-based traffic, leading to a dataset comprising traditional and AI-generated traffic. Machine learning and deep learning models were deployed on this M-En dataset. The ensemble Extra Trees classifier achieved the highest accuracy score of 0.983 for binary classification and 0.968 for multiclass problems. Our proposed approach demonstrates its effectiveness in countering AI-based traffic as well as traditional network traffic within the M-En networks.ICC 2024 - IEEE International Conference on Communications181This proposed approach demonstrates its effectiveness in countering AI-based traffic as well as traditional network traffic within the M-En networks.ICC 2024 - IEEE International Conference on Communications["F. Rustam", "Pasika Sashmal Ranaweera", "A. Jurcut"]2024-06-09T00:00:00 d57c3a74cdfe644c1b1601b6353b7f0e3414d4a8Herein, we advocate for the integration of the pipelines for data science (e.g., extraction, cleaning, and exploration) and machine learning (e.g., training data collection, feature selection, model selection, and parameter tuning), toward responsible and trustworthy artificial intelligence. We argue that the metadata generated by the machine-learning pipeline, which includes model outputs and model accuracy scores, is best managed and analyzed using data-science tools, thereby obtaining actionable insights into model performance, interpretability, and bias. We illustrate via two examples from our recent work as proof of concept: data summarization for model performance diagnostics; and input and output exploration to understand retrieval-augmented language models.GUIDE-AI@SIGMOD91It is argued that the metadata generated by the machine-learning pipeline, which includes model outputs and model accuracy scores, is best managed and analyzed using data-science tools, thereby obtaining actionable insights into model performance, interpretability, and bias.Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI["Armin Esmaelizadeh", "Joel Rorseth", "Andy Yu", "P. Godfrey", "Lukasz Golab", "Divesh Srivastava", "Jaroslaw Szlichta", "K. Taghva"]2024-06-09T00:00:00 33d04d4fe6998eddc1754cae43308536b9c26cdaThe advent of healthcare 4.0 has catalyzed a paradigm shift in medical practices, ushering in innovative approaches such as telesurgery, a groundbreaking method for remote patient surgery and monitoring. This transformative technique extends beyond traditional surgeries, finding application in dermatological procedures. The success of telesurgery in skin-related surgeries hinges on accurate and efficient skin cancer detection using dermoscopic images. Recognizing the inherent complexities in interpreting deep learning models, especially in the context of healthcare, Explainable Artificial Intelligence (X-AI) becomes imperative. In this context, we propose a novel CNN-powered X-AI mechanism i.e., X-CaD, tailored for skin cancer detection in telesurgery environments, leveraging ResNet and MobileNet for feature extraction. To enhance interpretability and bridge the gap between model predictions and clinical decision-making, we employ X-AI techniques such as Local Interpretable Model-agnostic Explanations (LIME) and Integrated Gradient (IG). LIME provides granular insights into model predictions, elucidating decision-making processes, while IG offers a comprehensive view of feature attributions. X-CaD relies on the synergistic integration of advanced CNN architectures, i.e. ResNet and MobileNet along with X-AI techniques to identify skin cancer accurately and provide clinicians with clear insights. The effective impact of X-CaD is evaluated through the observed loss and accuracy values for the DL models, and heat map outputs for X-AI. This represents a significant advancement in the integration of state-of-the-art technology and healthcare, offering a dependable telesurgery solution for the diagnosis of skin cancer in surgical procedures.ICC 2024 - IEEE International Conference on Communications81A novel CNN-powered X-AI mechanism, X-CaD, tailored for skin cancer detection in telesurgery environments, leveraging ResNet and MobileNet for feature extraction and X-AI techniques such as Local Interpretable Model-agnostic Explanations and Integrated Gradient are proposed.ICC 2024 - IEEE International Conference on Communications["Fenil Ramoliya", "Keyaba Gohil", "Aditya Gohil", "Rajesh Gupta", "Riya Kakkar", "Sudeep Tanwar", "Joel J. P. C. Rodrigues"]2024-06-09T00:00:00 990dbe49d37f05e194121396ce164e48bb2ad14b The oil and gas industry has undergone remarkable advancements in embracing digital transformation, revolutionizing its operations, and unlocking many new opportunities. This paper presents the journey of digital transformation toward achieving safer and faster drilling operations within CNOOC. Specifically, the focus will be on implementing drilling advisory systems and their diverse applications in enhancing efficiency, cost reduction, safety improvement, and operational optimization. The real-time drilling advisory system relies on cutting-edge technologies such as digital twins and artificial intelligence (AI) to operate effectively. A drilling digital twin is a digital replica of the physical well throughout the entire drilling life cycle. Its functionality stems from dynamic hydraulic, thermal, and mechanical modelling that accurately simulates drilling operations. Driven by real-time data, the digital twin facilitates automated monitoring, forward-looking analysis, and predictive insights. To create diagnostics of drilling events in advance, the advisory system employs model-based reasoning, an AI inference method. Continuously updated with real-time data streamed from the rig, the digital twin promptly generates diagnostic messages, identifying risks or issuing warnings based on predictive analysis and forecasting. In conclusion, the real-time advisory system empowers drilling engineering by enabling enhanced operational efficiency, safety, and sustainability, while driving significant cost reductions and fostering opportunities for growth and innovation.Volume 8: Offshore Geotechnics; Petroleum Technology00Volume 8: Offshore Geotechnics; Petroleum Technology["Chunwei Gu", "Tong Xu", "Jen Lye", "Sven Inge \u00d8deg\u00e5rd", "Jie Cao"]2024-06-09T00:00:00 2ff5822b1fecbc6e1d690199626783271ce0f839Clinical research and practice are generating important new findings at exponential rate which need to be readily available to clinicians. However, clinicians are confronted with serious challenges when they try to seek such information for their evidence-based decision making or to generate new clinical case report. One important challenge is the long time needed to browse, filter, summarize and compile information from different resources. The other important challenge is to identify relevant important evidence-based information resources required to answer clinical questions or support a clinical finding. Artificial intelligence can help in solving both challenges based on the automatic question answering (Q&A) and generative technologies. However, Q&A and generative techniques are not trained to answer clinical queries that can be used for evidence-based practice nor it can respond to structured clinical questioning protocol like PICO (Patient/Problem, Intervention, Comparison and Outcome). This article describes the use of deep learning techniques for Q&A that is based on generative models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed. We are reporting acceptable clinical answers that are supported by findings from PubMed. Our generative methods are reaching state of the art performance based on two staged bootstrapping process involving filtering relevant articles followed by identifying articles that support the requested outcome expressed by the PICO question.ICC 2024 - IEEE International Conference on Communications370The use of deep learning techniques for Q&A that is based on generative models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed are described.ICC 2024 - IEEE International Conference on Communications["S. Mohammed", "J. Fiaidhi"]2024-06-09T00:00:00 dbf999670ceb43367faf025a00ec4f160bdf30c3 The growing need for effective and precise guidance and decision-making in the ever-changing field of modern drilling operations has resulted in the incorporation of Artificial Intelligence (AI) methods into drilling systems. This paper presents a comprehensive software architecture designed to facilitate realtime drilling operations (performance monitoring and prediction) using AI technologies. In this study, an AI-aid drilling software environment has been developed via micro-service systems. Micro-service architecture plays a crucial role in real-time operations by providing a flexible and scalable framework to manage various AI components and services. It allows for the modularization of AI algorithms and data processing tasks, enabling easy integration of new AI models and technologies as they evolve. Moreover, micro-services facilitate real-time data exchange and communication between different AI modules, ensuring smooth coordination and collaboration among them, ultimately improving overall performance and adaptability. The proposed architecture harnesses the power of AI algorithms to manage and interpret real-time data streams measured during drilling operations instantaneously. By leveraging data-driven technologies, the architecture empowers drilling engineers to make informed decisions promptly, enhancing operational efficiency and minimizing downtime. The paper outlines its key components, including data acquisition, storage, pre-processing, feature engineering, integration of AI models, and the critical stages of result visualization and validation. Notably, the architecture underscores its adaptability and scalability, emphasizing its ability to a wide range of drilling scenarios and accommodate various AI methodologies. The effectiveness of the proposed software architecture is demonstrated through two drilling scenarios, showcasing its ability to enhance accurate predictions and improve overall drilling efficiency. By presenting a holistic approach to integrating AI into drilling operations, our work contributes to the advancement of intelligent drilling systems and shows the transformative potential of AI in the energy sector.Volume 8: Offshore Geotechnics; Petroleum Technology00A comprehensive software architecture designed to facilitate realtime drilling operations (performance monitoring and prediction) using AI technologies is presented, highlighting its adaptability and scalability, and emphasizing its ability to accommodate various AI methodologies.Volume 8: Offshore Geotechnics; Petroleum Technology["Hamed Sahebi", "Dan Sui"]2024-06-09T00:00:00 a331ec868921c440c1794f7b23f899ae403a1f11With the recent advancements in artificial intelligence (AI) and Machine Learning (ML), data-driven automated systems are being deployed in numerous high-stakes applications. Central to AI's effectiveness is its foundation in data. This workshop aims to bring together researchers from academia and industry to discuss the role of data management to guide the trustworthy design of AI-based applications. We plan the first edition of the workshop to include invited talks and a panel discussion with researchers from neighboring communities like ML, FAccT, HCI and Theoretical Computer science. The workshop aims to create a collaborative platform for these diverse communities to contribute to the evolving narrative of responsible AI development.SIGMOD Conference Companion110The role of data management to guide the trustworthy design of AI-based applications is discussed to create a collaborative platform for these diverse communities to contribute to the evolving narrative of responsible AI development.Companion of the 2024 International Conference on Management of Data["Abolfazl Asudeh", "Sainyam Galhotra", "Amir Gilad", "Babak Salimi", "Brit Youngmann"]2024-06-09T00:00:00 73f2267ed26726b7104fac8a77985d925b307e38Public procurement in Europe represents, on average, 16.9% of the GDP and is the cornerstone of the European Single Market. Simplifying public procurement and reducing procurement administrative costs for the public and private sectors can deliver substantial benefits at the national and European levels. However, the complexity and diversity of public procurement processes, as well as the huge expenditure at hand, implement automatic systems tailored to specific procurement needs necessary. This paper shows how artificial intelligence, and in particular machine learning techniques, can be used to modernize public procurement. It presents implemented systems and showcases pilot projects. The results of an extensive evaluation are also reported. The paper also argues that public procurement should be used more strategically by public administrations. This means aligning procurement actions with overall business objectives and using procurement to leverage supplier innovation and create a competitive advantage. Such advanced objectives are seldom achieved through the lowest price model. The paper also contains several recommendations for both the supply and demand sides to help realize the full potential of public procurement. On the supply side, recommendations relate to a better understanding of how artificial intelligence can be used in procurement activities, working with AI systems, and creating AI systems. On the demand side, recommendations involve the careful planning of how and when to use AI in procurement activities.International Journal Of Engineering And Computer Science00This paper shows how artificial intelligence, and in particular machine learning techniques, can be used to modernize public procurement and argues that public procurement should be used more strategically by public administrations.International Journal of Engineering and Computer Science["Goli Mallesham"]2024-06-09T00:00:00 fccf52de1b2de24ee947a31940ef1771ac228d79This article explores the growing body of research evidence supporting the integration of Artificial Intelligence (AI) in elementary social studies education. It identifies and analyzes ten key evidence-based applications of AI that have demonstrated potential to enhance student engagement, personalize learning experiences, and cultivate essential historical thinking skills. The discussion critically evaluates the pedagogical implications, advantages, and challenges associated with AI integration in this context. Recommendations are provided for the responsible and effective implementation of AI tools in elementary social studies classrooms.Technium Social Sciences Journal00Ten key evidence-based applications of AI that have demonstrated potential to enhance student engagement, personalize learning experiences, and cultivate essential historical thinking skills are identified.Technium Social Sciences Journal["Steven Grubaugh", "Greg Levitt"]2024-06-09T00:00:00 adb5fb5da9380c6830dcf6b9efaa0a133fc45212A lot of industries are changing quickly due to artificial intelligence (AI), and education is no exception. In recent years, AI has made significant inroads into Higher Education, offering a wide range of advantages that improve students' educational experiences and simplify administrative duties for educators. Education is the cornerstone of human progress, shaping societies and individuals alike. In an era defined by rapid technological advancement, the integration of Artificial Intelligence (AI) into Higher Education has emerged as a transformative force. AI holds the potential to revolutionize how students learn, teachers instruct, and educational institutions operate. This essay explores the multifaceted introduction of AI in Higher Education, delving into its promises and challenges, its impact on learners and educators, and its potential to reshape the landscape of education for the future. Virtual reality is one new educational innovation that is being used for anything from teaching history to assisting pupils with their math skills. Virtual Reality is a three-dimensional an interactive, computer-generated environment that users can explore. By inventing fresh approaches to incorporate experiential learning into the classroom, VR educators are genuinely influencing the experience of being a student. VR is a fantastic tool for fostering a sense of community among students. Using the same virtual reality program in various classrooms allows them to safely communicate despite their physical separation. Students can investigate topics using virtual reality that they might not otherwise have the chance to observe or learn about. Teachers are in the same boat. There are far more interesting ways for teachers to instruct their students. Anybody who has tried Virtual Reality will know that it feels much more immersive compared to staring at a screen or being in an environment created by a computer. Just two advantages for both teachers and students are deeper comprehension and increased involvement. One type of AI educational software that students may soon use is chatbots. These are being used more and more in schools as students utilize computers or iPads to communicate with bots designed to help them understand specific topics such as math or reading comprehension. It’s possible chatbot tutors could do more than just help students learn new concepts; they may even come whenever the analysis is needed. Chatbots are the future of all technical roots. It shortens the teachers' duty rotation cycle. Chatbots used in classrooms could also replace email communication between parents and teachers during parent-teacher conferences.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This essay explores the multifaceted introduction of AI in Higher Education, delving into its promises and challenges, its impact on learners and educators, and its potential to reshape the landscape of education for the future.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Tharunbalaaje Ramasamy"]2024-06-09T00:00:00 43ff8e9a9701f513524279bf36afd6e76d9f9efeAs urbanization continues to pose new challenges for cities around the world, the concept of smart cities is a promising solution, with artificial intelligence (AI) playing a central role in this transformation. This paper presents a literature review of AI solutions applied in smart cities, focusing on its six main areas: smart mobility, smart environment, smart governance, smart living, smart economy, and smart people. The analysis covers publications from 2021 to 2024 available on Scopus. This paper examines the application of AI in each area and identifies barriers, advances, and future directions. The authors set the following goals of the analysis: (1) to identify solutions and applications using artificial intelligence in smart cities; (2) to identify the barriers to implementation of artificial intelligence in smart cities; and (3) to explore directions of the usage of artificial intelligence in smart cities.Smart Cities12927A literature review of AI solutions applied in smart cities, focusing on its six main areas: smart mobility, smart environment, smart governance, smart living, smart economy, and smart people, covers publications from 2021 to 2024.Smart Cities["R. Wolniak", "Kinga Stecu\u0142a"]2024-06-10T00:00:00 25aad6352e4512ce85bed2b84f860bff4b43c898MAGMA953The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput.Magma["Hongjia Yang", "Guanhua Wang", "Ziyu Li", "Haoxiang Li", "Jialan Zheng", "Yuxin Hu", "Xiaozhi Cao", "C. Liao", "Huihui Ye", "Qiyuan Tian"]2024-06-10T00:00:00 738e22248687b58826e229e5db824fcfaf3046aaIn an era where artificial intelligence (AI) technology is increasingly penetrating various aspects of life, including education, it is important to understand its impact on students' social relationships. These changes raise questions about how interactions between students and teachers are affected by AI Technology. This research aims to investigate the impact or artificial intelligence on students' social relationships in an educational environment. The focus is on understanding changes in interactions between students, student-teacher interaction, as well as broader implications for social dynamics inside the classroom and outside the classroom. The research method used is a literature review which uses data collections sources that are relevant to this research, which can be in books, megazines and other print media, or can be obtained from photos and videos. The research results show that artificial intelligence technology has a complex impact on students' social relationships. Although it can improve efficiency and learning outcomes, AI technology can also reduce social inequality, and create excessive dependence on technology. Educators and policymakers need to carefully consider the use of AI technology in education to ensure that positive impacts are maximized, while negative impacts are minimized.BICC Proceedings03The research results show that artificial intelligence technology has a complex impact on students' social relationships, although it can improve efficiency and learning outcomes, AI technology can also reduce social inequality, and create excessive dependence on technology.BICC Proceedings["Septiani Amanda Puteri", "Yulia Saputri", "Yarni Kurniati"]2024-06-10T00:00:00 34a97b01bcd062608131c2ddbef422e5024328b1The integration of artificial intelligence (AI) into mechanical engineering has precipitated a profound transformation in the way engineers conceive, design, and execute projects. This paper explores the multifaceted impact of AI on mechanical engineering innovation, elucidating the myriad ways in which intelligent machines are revolutionizing traditional practices and catalyzing unprecedented advancements. In the realm of design, AI algorithms are revolutionizing the conceptualization and optimization processes. By leveraging machine learning and optimization techniques, engineers can explore vast design spaces with unparalleled efficiency, uncovering innovative solutions that might otherwise remain elusive. These AI-driven design tools not only expedite the development cycle but also enable the creation of products and systems with enhanced performance characteristics, such as improved energy efficiency, structural integrity, and functional versatility. Moreover, AI's influence extends beyond the design phase and permeates the entire manufacturing ecosystem. AI-driven automation is reshaping production lines, enabling agile and adaptive manufacturing processes that respond dynamically to changing demands and conditions. Through the integration of sensors, actuators, and AI-powered control systems, factories are becoming increasingly intelligent and autonomous, optimizing resource utilization, minimizing waste, and maximizing throughput.Spectrum of Engineering and Management Sciences03This paper explores the multifaceted impact of AI on mechanical engineering innovation, elucidating the myriad ways in which intelligent machines are revolutionizing traditional practices and catalyzing unprecedented advancements.Spectrum of Engineering and Management Sciences["Surajit Mondal", "S. Goswami"]2024-06-10T00:00:00 be94cebeb3d6bfd26901cc49471dc1af29786c82This Research paper delves into the potential benefits and concerns surrounding Artificial Intelligence technologies in facilitating communication,Teaching & Learning of English language with the asssiatance of Artificial Intelligence tools, providing personalized support, and enhancing the connection between learners and instructors.Artificial Intelligence (AI ) has significantly transformed the landscape of teaching and learning the English language. It offers personalized learning experiences, enhances student engagement, and provides teachers with powerful tools to improve instructional methods. Artificial Intelligence, driven language learning applications, such as Duolingo and Babbel, provide personalized lessons that adapt to individual learners' paces and proficiency levels. These apps leverage machine learning algorithms to identify strengths and weaknesses, offering Artificial Intelligence lored exercises to address specific needs. This personalized approach accelerates learning and keeps students motivated by ensuring that content is neither too easy nor too challenging.Natural Language Processing (NLP), a subset of Artificial Intelligence , plays a crucial role in language learning. Tools like Grammarly offers us a proficient and professional way to Write & Improve our writing skills, utilize NLP to offer real-time feedback on grammar, vocabulary, and style. These tools help learners to refining their writing skills through instant corrections and suggestions, fostering independent learning and continuous improvement. For teachers, Artificial Intelligence offers advanced analytics to monitor student progress. Learning management systems (LMS) integrated with Artificial Intelligence can track engagement, performance, and comprehension, allowing educators to identify struggling students and intervene promptly. Artificial Intelligence can also automate administrative tasks, freeing up teachers to focus more on instruction and student interaction.Moreover Artificial Intelligence -driven chatbots and virtual tutors provide 24/7 support, answering queries and offering explanations outside of classroom hours. This constant Artificial Intelligence lability ensures that learning is not confined to traditional class times, promoting a more flexible and accessible learning environment.However, ethical considerations such as responsibility issues, agency challenges, and surveillance risks are highlighted as key concerns that need to be addressed. The research emphasizes the importance of designing Artificial Intelligence systems with transparency, Artificial Intelligence explainability features, and human-in-the-loop approaches to ensure ethical decision-making and Artificial Intelligence trust in technology. By implementing careful data collection practices and respecting privacy boundaries, educators can harness the potential of Artificial Intelligence to enhance learner-instructor interactions while upholding ethical standards in the online learning environmentInternational Journal For Multidisciplinary Research11The research emphasizes the importance of designing Artificial Intelligence systems with transparency, Artificial Intelligence explainability features, and human-in-the-loop approaches to ensure ethical decision-making and Artificial Intelligence trust in technology.International Journal For Multidisciplinary Research["Dr. Priya Agrawal"]2024-06-10T00:00:00 ecb301b43f03a096ec413035df708d1cb01a2285Recently, the rise of generative AI tools such as ChatGPT have prompted deep and wide considerations about teaching and learning, student success, research and development, and the use of data for informed institutional decision making. In this volume, authors discuss specific concepts, considerations for use, and some specific tools and applications of advanced data analytics and artificial intelligence that are gaining prominence in higher education. Although these tools and our perceptions about the tools will continue to evolve, we believe that aspects of advanced data analytics and artificial intelligence will remain in our midst in the future. We believe that members of our community will become better educators and higher education managers as we engage deeply with these tools, stay vigilant about the risks and limitations, and establish policies that ensure student and institution success. The soaring interest in aspects of artificial intelligence (AI) prompts consideration of its potential applications, impact, and implications for the higher education community. AI‐supported tools hold the promise to help students learn more efficiently and effectively as well as assist faculty and staff with more mundane tasks, yet getting to the place where GenAI helps will take some effort. Since we must consider data privacy, ethical use, security, and accountability, the path may not be straight and narrow. Analytics through AI tools have a particular promise to support underrepresented and disabled students through focused websites, learning support, and customized tutoring. AI has the potential to narrow the gap in student success, but without focused attention, it could serve to widen the gap across some student groups. We acknowledge that perceptions about and implications due to artificial intelligence in higher education will change as we move forward; the articles in this volume seek to contribute to the current discussion and may prompt considerations for future conversations. New Directions for Higher Education22This volume discusses specific concepts, considerations for use, and some specific tools and applications of advanced data analytics and artificial intelligence that are gaining prominence in higher education.New Directions for Higher Education["Karen L. Webber", "Henry Y. Zheng"]2024-06-10T00:00:00 6bb1fffdbc1426ea94730c9334c99d7964ec2eb0Artificial Intelligence (AI) is experiencing rapid development across various fields, including religious studies such as the Qur'an, the holy book of Islam, which plays a central role in spiritual and moral life. This study aims to explore the opportunities for AI application in Qur'anic studies through a literature review by analyzing credible sources such as scientific journals, books, and online articles. The methodology employed includes the collection and analysis of literature related to the application of AI in the context of the Qur'an. The findings indicate that AI has significant potential to enhance the understanding and practice of the Qur'an through easier access to information, in-depth text analysis, personalized learning, interactive education, and the preservation of ancient manuscripts. These findings highlight the novelty in how AI can be used to enrich Qur'anic studies, particularly through text analysis and machine learning technologies. However, this study also identifies challenges and ethical issues that need to be addressed, such as interpretation accuracy and religious sensitivity. The contribution of this research lies in providing an initial framework for further exploration of how AI can be applied effectively and ethically in Qur'anic studies. Therefore, further research is needed to maximize the benefits of AI and minimize its risks, supporting the development of more modern and technologically integrated religious studies.Madinah: Jurnal Studi Islam01The findings indicate that AI has significant potential to enhance the understanding and practice of the Qur'an through easier access to information, in-depth text analysis, personalized learning, interactive education, and the preservation of ancient manuscripts.Madinah: Jurnal Studi Islam["Moh. Mauluddin"]2024-06-10T00:00:00 cee0c672de195725090a5dad7d2cd5dacd5bd8f6PurposeThe purpose of this study is to investigate the relationship between artificial intelligence (AI) and decision making in the development of AI-related capabilities. We investigate if and how AI-driven decision making has an impact on firm performance. We also investigate the role played by environmental dynamism in the development of AI capabilities and AI-driven decision making.Design/methodology/approachWe surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data.FindingsResults indicate that AI-driven decision making is positively related to firm performance and that big data-powered AI positively influences AI-driven decision making. Moreover, there is a positive relationship between big data-powered AI and the development of AI capability within a firm. It is also found that the control variables of firm size and age do not significantly affect firm performance. Finally, environmental dynamism does not have a positive and significant moderating effect on the path connecting big data-powered AI and AI-driven decision making, while it exerts a positive moderating effect on the development of AI capability to strengthen AI-driven decision making.Originality/valueThese findings extend the resource-based view by highlighting the capabilities developed within the firm to manage big data-powered AI. This research also provides theoretically grounded guidance to managers wanting to align their AI-driven decision making with superior firm performance.Management Decision882These findings extend the resource-based view by highlighting the capabilities developed within the firm to manage big data-powered AI and provide theoretically grounded guidance to managers wanting to align their AI-driven decision making with superior firm performance.Management Decision["Chiara Giachino", "Martin Cepel", "Elisa Truant", "Augusto Bargoni"]2024-06-10T00:00:00 cceb61ff5ce4846bb814496ac97c091e3ba36cc9Making sense of student feedback and engagement is important for informing pedagogical decision-making and broader strategies related to student retention and success in higher education courses. Although learning analytics and other strategies are employed within courses to understand student engagement, the interpretation of data for larger data sets is more challenging and rarely pursued. This is concerning as data offers the potential for critical insights into engagement behaviour and the value students place on engagement. Artificial intelligence (AI) offers a revolutionary ability to make sense of data, with capacity for prediction and classification, by consuming vast amounts of structured and unstructured data sets. This paper reports on how AI methodologies (specifically, deep learning and natural language processing) were used to leverage labelled student feedback in terms of online engagement in five courses in a regional Australian university. This paper reinforces the value of AI as a viable and scalable multilayered analysis tool for analysing and interpreting student feedback, particularly for categorising student responses as to the types of engagement that they most valued to support their learning. The paper concludes with a discussion of suggested further refinement, including how the AI-derived data may add insights for informing pedagogical practice.   Implications for practice or policy: AI offers an ability to make sense of large data sets in higher education courses. Teachers can use student feedback data categorised into types of engagement by AI to support reflection on what students value in their courses. Educators and key stakeholders can use the insights AI analysed data offers for informing pedagogical practice and decision-making in higher education to enhance student experiences. Australasian Journal of Educational Technology622The value of AI as a viable and scalable multilayered analysis tool for analysing and interpreting student feedback, particularly for categorising student responses as to the types of engagement that they most valued to support their learning is reinforced.Australasian Journal of Educational Technology["Christopher Dann", "P. Redmond", "Melissa Fanshawe", "A. Brown", "S. Getenet", "T. Shaik", "Xiaohui Tao", "Linda Galligan", "Yan Li"]2024-06-10T00:00:00 8a93a1fab08aeed3fdb165acbacf320074881545Obesity is a serious and chronic disease with genetic and environmental interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. Currently, more than 2 billion people worldwide are obese or overweight. Research has shown that obesity can be prevented. In this study, artificial intelligence methods were used to identify individuals at risk of obesity. An online survey was conducted on 1610 individuals to create the obesity dataset. To analyze the survey data, four commonly used artificial intelligence methods in literature, namely Artificial Neural Network, K Nearest Neighbors, Random Forest and Support Vector Machine, were employed after pre-processing. As a result of this analysis, obesity classes were predicted correctly with success rates of 74.96%, 74.03%, 74.03% and 87.82%, respectively. Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%.Sinop Üniversitesi fen bilimleri dergisi411Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%, respectively.Sinop Üniversitesi Fen Bilimleri Dergisi["Nigmet Koklu", "S. Sulak"]2024-06-10T00:00:00 39be0102bbc3a126d8476ff9520a5ae1dc2b10a6Generative artificial intelligence (GAI) programs such as ChatGPT and other large language models are designed to engage in complex, responsive dialogues that feel like human interactions. The dialogic and responsive nature of GAI signals the potential for users to form relationships with GAI platforms or digital personalities created on these platforms. Given the degree to which language use and broader conceptual understandings are deeply embedded in social relationships, the relational nature of GAI has powerful implications for the future of literacy and learning. This speculative essay draws upon sociocultural, affective, and posthuman perspectives on literacy to explore key concerns regarding the nature of intimate relationships with GAI. The author highlights three central concerns for literacy researchers and educators: epistemological issues stemming from intimate relationships with GAI, the potential for students to (re)conceptualize human relationships through GAI, and the role of relational GAI in linguistic justice.Reading Research Quarterly332The author highlights three central concerns for literacy researchers and educators: epistemological issues stemming from intimate relationships with GAI, the potential for students to (re)conceptualize human relationships through GAI, and the role of relational GAI in linguistic justice.Reading Research Quarterly["B. Nash"]2024-06-10T00:00:00 9687e9a3aa6146e12e3a77fd878f90dd19e5169eFrom AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.Cold Spring Harbor Perspectives in Biology01AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design, and the most important might be the leap toward more powerful protein design.Cold Spring Harbor perspectives in biology["M. Heinzinger", "B. Rost"]2024-06-10T00:00:00 0d134b5e4d241b8638d6a794624b3c2a360498abObjective: The topic of this study focuses on how autonomous learning enhances 21st century competencies through the use of artificial intelligence and educational technologies. The objective is to explore the relationship between autonomous learning, artificial intelligence and 21st century competencies.   Theoretical Framework: The main concepts and theories underpinning the research were: artificial intelligence in education, autonomous learning and 21st century skills.   Method: The methodology adopted for this research was a systematic mapping of 991 scientific articles published in the last ten years in three databases   Results and Discussion: The results obtained revealed that artificial intelligence is still in its infancy in the use of data and machine learning systems, until its impact on society as a whole is better understood. Likewise, critical thinking, collaborative work and problem solving are the competencies that show a strong relationship with both autonomous learning and artificial intelligence.   Research implications: the main fields of study were: self-regulated learning, 21st century competencies and their relationship with artificial intelligence. The impact could be seen in the educational, economic, environmental and social sectors.Revista de Gestão Social e Ambiental562The results obtained revealed that artificial intelligence is still in its infancy in the use of data and machine learning systems, until its impact on society as a whole is better understood.Revista de Gestão Social e Ambiental["Claudia Lengua-Cantero", "Manuel F. Caro-Pi\u00f1eres", "Jairo Montero P\u00e9rez"]2024-06-10T00:00:00 7b2de58478d8f5abb2ba4d9925010f731b03d912The rapid advancement of information technology greatly impacts education, especially in learning. Artificial Intelligence (AI) is pivotal in various sectors, including education. This study explores AI's role in student learning at Universitas Pelita Bangsa. AI adaptation addresses challenges like educator scarcity, offering tailored learning experiences. It fosters active engagement and immediate feedback. However, overreliance on AI may hinder creativity and change traditional teaching. A study was conducted at Universitas Pelita Bangsa, engaging 50 students through a Zoom seminar. Topics included AI introduction, educational applications, and pros and cons. Findings show high satisfaction (75% satisfied, 20% highly satisfied). Enhanced learning quality was observed. In conclusion, AI integration holds potential for better learning outcomes. Continuous evaluation and adaptability are vital. This study highlights the importance of student-centric approaches in AI integration.Journal of Community Empowerment100This study explores AI's role in student learning at Universitas Pelita Bangsa and highlights the importance of student-centric approaches in AI integration.Kreativasi : Journal of Community Empowerment["Rifqi Putra Adhadi", "Muhammad rizky Efendi", "Raka Muzakki", "Hafizh Rizki", "Pratama Yudiansyah", "B. Panjaitan", "Rifqi Al-Muzaky", "I. Artikel", "R. Putra"]2024-06-10T00:00:00 9ed88f1879ba835374795ad9af42280317a0fe72Background: The use of artificial intelligence (AI) in ophthalmology represents a transformative leap in healthcare. AI-powered technologies, such as machine learning and computer vision, enhance the accuracy and efficiency of ophthalmic diagnosis and treatment. Objective: This study aimed to determine medical students’ awareness and attitudes towards the use of artificial intelligence in ophthalmology. Methods: This cross-sectional, questionnaire-based study was conducted between November 2022 and January 2023 using online questionnaires. Data collection was carried out using convenience sampling among medical students at the University. IBM SPSS version 23 was used to analyze the data. Results: The current finding shows that most of the participants N=309 (89.6%) had heard of the use of AI in medicine, and N=294 (85.2%) heard of the use of AI in ophthalmology. 98.6% (n=340) of respondents believed AI would be a helpful tool in ophthalmology. Along this line of questioning, a significant majority of respondents, 332 (96.2%) selected screening, 332 (96.2%) selected diagnosis, and 293 (84.9%) selected prevention as a usage of AI ophthalmology. However, the majority, 76.5%) of students had little understanding of the development of AI in ophthalmology. In addition, a significant relationship between sex, academic year, cumulative GPA (cGPA), and awareness of AI in ophthalmology (P<0.001) was found in this study. Conclusions: Overall, medical students in Saudi Arabia appear to have favorable thoughts about AI and positive perceptions towards AI in ophthalmology. However, the findings of this study emphasize the limited understanding and low confidence levels of medical students in Saudi Arabia regarding the use of AI in ophthalmology. As a result, early exposure to AI-related materials in medical curricula is crucial for addressing these challenges through comprehensive AI education and practical exposure to prepare future ophthalmologists.Annals of Medicine and Surgery260Overall, medical students in Saudi Arabia appear to have favorable thoughts about AI and positive perceptions towards AI in ophthalmology, however, the findings of this study emphasize the limited understanding and low confidence levels of medical students in Saudi Arabia regarding the use of AI in ophthalmology.Annals of Medicine and Surgery["Zainudheen Faroog", "Q. Dirar", "Abdul Rehman Zia Zaidi", "Mohammad Salman Khan", "Golam Mahamud", "Saad Rahman Ambia", "Selwa A. F. Al-Hazzaa"]2024-06-10T00:00:00 bccb06416f6897e3f74b5de1a3809d63b679a051Abstract This research introduces an Artificial Intelligence (AI) based model designed to concurrently optimize energy supply management, biocide dosing, and maintenance scheduling for heat exchangers. This optimization considers energetic, technical, economic, and environmental considerations. The impact of biofilm on heat exchangers is assessed, revealing a 41% reduction in thermal efficiency and a 113% increase in flow frictional resistance of the fluid compared to the initial state. Consequently, the pump’s power consumption, required to maintain hydraulic conditions, rises by 9%. The newly developed AI model detects the point at which the heat exchanger’s performance begins to decline due to accumulating dirt, marking day 44 of experimentation as the threshold to commence the antifouling biocide dosing. Leveraging this AI model to monitor heat exchanger efficiency represents an innovative approach to optimizing antifouling biocide dosing and reduce the environmental impact stemming from industrial plants. Graphical abstractBiofouling (Print)390Biofouling["Sergio Garc\u00eda", "D. Boullosa-Falces", "D. Sanz", "A. Trueba", "M. A. Gomez-Solaetxe"]2024-06-10T00:00:00 e15acea944f5f9ec2758c1e1168ff3160c00243aIn an era where technology continues to develop rapidly, the role of artificial intelligence (AI) has become increasingly significant in various aspects of human life. One of the areas where AI has a great impact is in guidance and counseling, especially in improving learning motivation. Currently, many students have lost their motivation to learn, as can be seen from their indifferent attitude towards learning and absence from completing assignments. This research aims to explore the role of AI in guidance and counseling to increase learning motivation. The literature study method is used to analyze the impact of AI in improving student engagement and learning outcomes.BICC Proceedings00The literature study method is used to analyze the impact of AI in improving student engagement and learning outcomes and aims to explore the role of AI in guidance and counseling to increase learning motivation.BICC Proceedings["Ririn", "Firli Tiara Sabila", "Vigia Asni Murni", "Sabarrudin"]2024-06-10T00:00:00 437de8ba5f55bb438c15b6969368fb4c3e6ccd30In today's increasingly sophisticated world, technological developments have a big influence on people's daily lives. Intelligence is created and channeled to computers so that they can do work like humans do. These intelligences are contained in a gadget. Gadgets are tools that can help people to connect with each other and connect with various existing media. This convenience has an impact on many things that spread very easily. Furthermore, it has an impact on the difficulty of obtaining correct and reliable information for various purposes, causing a person to feel uncomfortable and safe and to be wary of information. This research aims to understand the impact of excessive use of Artificial Intelligence (AI) on generation Z. To find out the influence of AI on the lives of generation Z, both emotionally, mentally and socially, generation Z. The method used in this article is a method using a literature review approach or what is called with literature study. The method that uses this approach contains theories from literature that are related (relevant) to the research problem. Literature review plays a role in forming the basis of research studies by building relevant concepts and theories. In this literature review, an analysis of the concepts and theories applied is carried out based on accessible literature, especially from articles that have been published in various scientific journals and also books that are relevant to the research problem.BICC Proceedings00This research aims to understand the impact of excessive use of Artificial Intelligence (AI) on generation Z, to find out the influence of AI on the lives of generation Z, both emotionally, mentally and socially, generation Z.BICC Proceedings["Annisa Eka Putri", "Wilu Wahyuni Dinata", "Diana Cantika Basri"]2024-06-10T00:00:00 c41a3992ff6513e156b64dcc76568f559f39909eThe logistics and supply chain sector finds itself at a critical inflexion point, with mounting pressures to enhance efficiency, increase cost competitiveness and serve the dynamic needs of consumers. To aid such goals, technologies of cloud computing combined with artificial intelligence (AI) come into play. Scalable resources, real-time data access (SaaS), and collaboration offer a superior environment for consolidation, better communication between departments and resource integration across business operations. Further, AI with sophisticated techniques of machine learning have ability to analyze large data sets which enables businesses to automate certain tasks while minimizing certain operations apart from providing predictions. This paper dwells on how cloud computing combined with AI can help in transformation of logistics supply chain management. How businesses across the verticals are leveraging these technologies to improve operations and providing a beacon for technological advancements in their growth. Furthermore, how cloud and AI integration can help industry and gain competitive edge in a rapidly evolving market to foster a more agile, resilient customer centric ecosystem. By adopting such technologies, businesses can navigate through the complexities of modern logistics and supply chain challenges and stay relevant in this hyper competitive digital landscape.Transactions on Machine Learning and Artificial Intelligence00This paper dwells on how cloud computing combined with AI can help in transformation of logistics supply chain management, and how businesses across the verticals are leveraging these technologies to improve operations and providing a beacon for technological advancements in their growth.Transactions on Machine Learning and Artificial Intelligence["Natapong Sornprom"]2024-06-10T00:00:00 ae2e426185efbb53fb35002a5c14a6aa2cd3c747Today, information is the source of competitive advantage and businesses need to create information architecture that will enable them to make the right decisions in the fastest way. For this reason, it seems inevitable that businesses will reshape their entire business environments in a way that will create far-reaching consequences on business processes and prioritize technological progress by investing in artificial intelligence (AI) applications to create value with better performance. Management accounting is a business function that is central to identifying, collecting, measuring, and analyzing data. Therefore, these developments are expected to change management accounting practices and the roles of management accountants within the business. Although it is predicted that the main function of accounting in the future will be to create real-time value for the business by combining management accounting applications with AI, this combination also carries the potential to create significant problems. The purpose of this study is to conduct a SWOT analysis and examine the strengths and weaknesses of the use of AI in management accounting and the opportunities and threats that may arise as a result of this integration.Journal of Business in The Digital Age00A SWOT analysis is conducted and the strengths and weaknesses of the use of AI in management accounting and the opportunities and threats that may arise as a result of this integration are examined.Journal of Business in The Digital Age["\u015eebnem Ya\u015far"]2024-06-10T00:00:00 cdf950d86790c89cefe10955f7080a4dbeb40b31This research was inspired by the use of Artificial Intelligence (AI) in student guidance and counseling. Artificial Intelligence (AI) is a system that continues to develop and is innovative in the field of study, created both on machines and computers that have the same level of intelligence, perhaps even more than humans. Guidance and Counseling Services in schools, Artificial Intelligence (AI) systems have great potential to be used as supporting services. The use of AI in this context aims to help increase the effectiveness, accessibility and responsiveness of guidance and counseling services for students. Here are some ways in which AI can be utilized namely Guidance and Counseling Chatbots. This research aims to determine the use of AI in guidance and counseling students. This research uses a research method in the form of a literature study. The research results obtained have seen many guidance and counseling students applying AI. AI has great potential to improve the efficiency and effectiveness of education systems. Several types of AI that can be used in education include Canva, Google Meet, Zoom, Mozilla Firefox, ChatGPT, etc. Academic ethics is very important to pay attention to and adhere to in the world of education, so that the goals of education can be achieved.BICC Proceedings00This research aims to determine the use of AI in guidance and counseling students and uses a research method in the form of a literature study to do so.BICC Proceedings["Rama Yola", "Wafiq Azizah", "Nikmah Azizah", "Sabarrudin"]2024-06-10T00:00:00 b7ae796f30b870caee7a6c2f0bbb825985e4d17cJournal of general internal medicine190Journal of general internal medicine["Gina M. Piscitello", "Shari Rogal", "Jane Schell", "Yael Schenker", "Robert M Arnold"]2024-06-10T00:00:00 f14fe62aa9d758b92e8506ab1f9e13ab282b1c98Artificial intelligence (AI) is an experiment on the intelligence of people who are created on machines and programmed to act like ordinary people. As we all know, the development of AI technology is increasingly rapid every day. The introduction of AI in various fields of real life has many positive impacts. However, this development must continue to be accompanied by the ideal application of AI ethics to ensure that existing technology does not exceed reasonable limits in the future and does not cause negative impacts on society. It should be noted that AI Ethics is a field that studies how to develop and use artificial intelligence in a way that is fair, accountable, transparent, and respects human values. Therefore, debates and views arise regarding the use of AI within the scope of existing ethics and morality. This article aims to find out the form of debate and views regarding the use of AI in the context of ethics and morality. The author will create an article by implementing the literature study method, namely collecting data by finding sources from articles, books and other references related to the topic. this discussion.BICC Proceedings00This article aims to find out the form of debate and views regarding the use of AI in the context of ethics and morality as well as collecting data by finding sources from articles, books and other references related to the topic.BICC Proceedings["Ahmad Fadhil", "Fitri Cahyana Lusia", "Sofia Lestari Siregar", "Susi Susanti", "Yelti Nurma Yenti"]2024-06-10T00:00:00 7910daa5985b434ece70a003211afb86a1e43da0All professions are encouraged to keep up with the times because technology encourages humans to continue to innovate, helping them communicate, interact, and learn about the development of the world. All things, including education, are influenced by the rapid development of technology. Guidance and counseling services are expected to be achieved optimally through the use of information technology-based tools and services. The role of technology and its benefits for guidance and counseling libraries. The review is used in this article. Literature review is to make writing related to a particular topic, one must search and read books, journals, and other publications related to the research topic. To get answers to questions, documentation methods were used to collect data from various sources in this study, namely literature in one.  In the document of an increasingly dynamic digital era, artificial intelligence (AI) technology has become an integral part in various aspects of life facing challenges in facing various problems arising from the use of technology. Artificial intelligence technology can assist guidance and counseling professionals in dealing with such challenges in several ways. Artificial intelligence is the study of computers being able to do things that humans do better. The concept of artificial intelligence is divided into four, namely the ability to think humanely, the ability to act like humans, the ability to think logically and the ability to act rationally. When carrying out guidance and counseling online, of course, it cannot be separated from the advantages and disadvantages. With faster and more accurate data analysis capabilities. Counselors must have adequate skills to overcome any obstacles and challenges that may occur during the guidance and counseling service process. Counseling is a reciprocal relationship in which a counselor helps a person understand themselves in relation to their life problems. Counseling is usually accompanied by broader guidance. The guidance and counseling teacher should be an experienced professional. Counselors must change their functions to support changing times along with technological advances. Counselors can adapt to the era of a smart society thanks to new innovations such as online counseling.BICC Proceedings00BICC Proceedings["Rahmi Amelia Putri", "Zikra Mailana", "Nailatul Fadilah", "Zulkifli", "Fanny Mardenil", "Ira Oktarini"]2024-06-10T00:00:00 97939f67022ce77ab16b0d5aa032478900ff7925Актуальность данной исследовательской работы заключается в том, что в последнее время набирает популярность практическое применение искусственного интеллекта в самых разных сферах жизни людей (промышленности, медицине, финансах, торговле, маркетинге, программировании, развлечениях и даже в подборе музыки). Поэтому требуется рассмотреть все имеющиеся возможности и перспективы использования искусственного интеллекта в высшем образовании Республики Казахстан, дабы в дальнейшем наше государство сумело эффективно и беспрепятственно внедрить данную технологию. Именно этому будет посвящена статья. Были рассмотрены и проанализированы технологии на базе ИИ, опыт их применения в странах дальнего зарубежья и Российской Федерации, а также проблемы, с которыми сталкиваются ВУЗы при внедрении данных новшеств. The relevance of this research work lies in the fact that recently the practical application of artificial intelligence in various spheres of people’s lives (industry, medicine, finance, trade, marketing, programming, entertainment and even in the selection of music) has been gaining popularity. Therefore, it is necessary to consider all available opportunities and prospects for the use of artificial intelligence in higher education of the Republic of Kazakhstan, so that in the future our state will be able to effectively and smoothly implement this technology. This is what the article will be devoted to. AI-based technologies, the experience of their application in non-CIS countries and the Russian Federation, as well as the problems that universities face when introducing these innovations were reviewed and analyzed.Вестник КазГЮИУ00Вестник КазГЮИУ["\u0416.\u0421. \u0414\u044e\u0441\u0435\u043c\u0431\u0438\u043d\u043e\u0432\u0430", "\u041a.\u0411. \u0420\u0430\u043c\u0430\u0437\u0430\u043d\u043e\u0432\u0430"]2024-06-10T00:00:00 1d9814b146d3f1dae4b9045fb4aa707bcb85e395The paper analyzes possible ways of protecting artificial intelligence systems and their elements with the help of intellectual property law from the perspective of European Union law. This paper deals with copyright law, patent law and sui generis database protection in relation to artificial intelligence systems. The paper begins with an analysis of whether and how an artificial intelligence can be protected by means of copyright. The author analyzes the European Union’s copyright acquis and concludes that the elements of the AI system, as well as the entire artificial intelligence system, that are implemented in software, can be protected by copyright as a computer program if the originality requirements are met. However, the originality requirement is unlikely to be met in all cases in this context. The same issue with the originality requirement applies to potentially possible copyright protection of artificial intelligence systems as databases. Therefore, it is concluded that the fulfillment of copyright requirements for protection of an artificial intelligence system must be established in each particular case. The author also considers whether patent law is applicable to protect artificial intelligence systems. For this purpose, the provisions of the patent law of the European Union, in particular, of the European Patent Convention, are analyzed. The author concludes that the artificial intelligence system may be patentable as a “computer-implemented invention” in case all the requirements for patent protection are met. Sui generis database protection is also considered as an additional possibility for legal protection of artificial intelligence systems, taking into account that its applicability is limited to the European Union. Whether sui generis database protection is applicable to the artificial intelligence system should be decided on a case-by-case basis.Uzhhorod National University Herald. Series: Law210Uzhhorod National University Herald. Series: Law["D. P. Bohatchuk"]2024-06-10T00:00:00 9dc7d278e1290cd8c57283599499250c170ef18cThe computer games industry is growing rapidly today, and the number of users of these games (players) is increasing. All of this has an impact on modern society. The development of new computer games involves constant evolution in such areas as technological innovations, the development of game genres, and analysis of market trends in the entertainment sector. The development of artificial intelligence and its application in the development of computer games contribute to the dynamic change of the landscape and expansion of the space and genres of games.           The purpose of the article is to study and analyse the state of the modern computer games industry, taking into account the main genres and trends in the use of artificial intelligence in their development and the formation of the corresponding game space, to assess technological innovations, to analyse the market and to forecast the future development of the game industry. The research methods are the main methodological approaches and technological means of artificial intelligence for the development of computer games and the formation of the corresponding game space. Such methods include, in particular, systematic and comparative analyses to identify the features of computer games of various genres and types, the method of expert assessments, which involves the analysis of literary sources and information resources, interviews and surveys of game industry experts. The novelty of the study is a comprehensive analysis of modern technologies, in particular generative artificial intelligence, their use in the development of computer games and trends in the formation of the space of such games. Conclusions. The main trends in the development of computer games have been identified. The factors that influence their market are identified and forecasts are made for the future development of this industry. The paper analyses the use of artificial intelligence, in particular generative artificial intelligence, in the development of computer games (to optimise game creation and reduce budgets) and the formation of the corresponding game space, generative artificial intelligence has opened a new era of development of the computer game space. The impact of artificial intelligence on the balance between integration and immersion in the real world is determined. The current state of virtual reality games with their achievements and problems is analysed, which is crucial for understanding their future development trajectory. The new trends and technologies considered point to the future of the computer games space.Digital Platform Information Technologies in Sociocultural Sphere00The paper analyses the use of artificial intelligence in the development of computer games (to optimise game creation and reduce budgets) and the formation of the corresponding game space, generative artificial intelligence has opened a new era of development of the computer game space.Digital Platform: Information Technologies in Sociocultural Sphere["O. Tkachenko", "Anton Mamaiev"]2024-06-10T00:00:00 c0e59d181975e400fef73c4032f4ced09a17c003The article discusses current problems of authorship of intellectual products created by artificial intelligence, as well as the concept and legal personality (quasi-subjectivity) of artificial intelligence. The relevant provisions of the Civil Code of the Russian Federation (Articles 1225, 1228, etc.) and the practice of their application are analyzed using general and specific scientific methods – analysis, synthesis, analogy, formal legal, comparative legal methods, interpretation of legal norms, etc. Based on a study of legal literature and current regulations, the author analyzes the concept of legal personality (quasi-subjectivity) of artificial intelligence. It is shown that copyright for the products created by artificial intelligence has not yet been legally regulated. It is concluded that currently such a product does not fall under the criteria defined by Article 1228 of the Civil Code of the Russian Federation, and is not anyone’s intellectual property; thus, it is not subject to legal protection. For the purposes of legal regulation, the author proposes his own definition of authorship in relation to a product of intellectual creativity. Considering the rapid development of information technologies, the legal personality (quasi-subjectivity) of artificial intelligence needs scientific understanding. In the future, a legal definition of artificial intelligence should be adopted and corresponding copyrights should be regulated.Society and Economics00It is shown that copyright for the products created by artificial intelligence has not yet been legally regulated and such a product does not fall under the criteria defined by Article 1228 of the Civil Code of the Russian Federation, and is not anyone's intellectual property; thus, it is not subject to legal protection.Society and Economics["Valery Utkin"]2024-06-10T00:00:00 7d495495a7885ae47b4d9dd15e67e2e8df24f6c0The development of artificial intelligence has become a primary focus in various technological fields. However, the ethical challenges associated with this development cannot be ignored. In this context, self-control plays a crucial role in addressing these ethical challenges. Self-control involves an ability to monitor and regulate their behavior and decisions. In the development of artificial intelligence, self-control enables developers to monitor how technology is used and ensure that it is used ethically. The purpose of this study is to investigate and examine how self-control plays a role in addressing the challenges that are likely to occur in the ethical scope caused by the ongoing development of artificial intelligence from time to time, so that we do not get drawn into the negative influence of artificial intelligence or Artificial Intelligence (AI). This study was conducted through a literature review. The results show that self-control plays a crucial role in addressing ethical challenges in the development of artificial intelligence, as self-control is necessary in the use and development of artificial intelligence because it has the ability to understand the ethical implications of decisions made, ensuring that decisions made by artificial intelligence systems can be understood and tracked, and having clear documentation on how artificial intelligence makes decisions and who is responsible for those decisions.BICC Proceedings00The results show that self-control plays a crucial role in addressing ethical challenges in the development of artificial intelligence, as self-control is necessary in the use and development of artificial intelligence.BICC Proceedings["Rahmatul Husna", "Suci Ramadhani", "Qurrotu A\u2019yun"]2024-06-10T00:00:00 8c53d650cc06897ba5e9d61b58a5d865f1ad2888Abstract Purpose The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention. This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence (AI) disruptive technology policy, and to put forward suggestions for optimizing China’s AI disruptive technology policy. Design/methodology/approach Develop a three-dimensional analytical framework for “policy tools-policy actors-policy themes” and apply policy tools, social network analysis, and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools, cooperative relationships among policy actors, and the trends in policy theme settings within China’s innovative AI technology policy. Findings We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close. Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects, forming a “center-periphery” network structure. Policy tool usage is predominantly focused on supply and environmental types, with a severe inadequacy in demand-side policy tool utilization. Policy themes are diverse, encompassing topics such as “Intelligent Services” “Talent Cultivation” “Information Security” and “Technological Innovation”, which will remain focal points. Under the themes of “Intelligent Services” and “Intelligent Governance”, policy tool usage is relatively balanced, with close collaboration among policy entities. However, the theme of “AI Theoretical System” lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities. Research limitations The data sources and experimental scope are subject to certain limitations, potentially introducing biases and imperfections into the research results, necessitating further validation and refinement. Practical implications The study introduces a three-dimensional analysis framework for disruptive technology policy texts, which is significant for formulating and enhancing disruptive technology policies. Originality/value This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts. It systematically evaluates China’s AI policies quantitatively, focusing on policy tools, policy actors, policy themes. The study uncovers the characteristics and deficiencies of current AI policies, offering recommendations for formulating and enhancing disruptive technology policies.Journal of Data and Information Science220The study uncovers the characteristics and deficiencies of current AI policies, offering recommendations for formulating and enhancing disruptive technology policies and introduces a three-dimensional analysis framework for disruptive technology policy texts.Journal of Data and Information Science["Ying Zhou", "Linzhi Yan", "Xiao Liu"]2024-06-10T00:00:00 fdbef578bdb24b0c8493592462d7898c6016a501Nature Sustainability385Nature Sustainability["M. El Jarroudi", "L. Kouadio", "Philippe Delfosse", "Clive H. Bock", "Anne-Katrin Mahlein", "X. Fettweis", "Beno\u00eet Mercatoris", "Frank Adams", "Jillian M. Lenn\u00e9", "Said Hamdioui"]2024-06-10T00:00:00 bbf376ec2b870293cd39b51d8d611f8471d7ff5bProceedings of the 18th International Conference of the Learning Sciences - ICLS 202402Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024["Mengxi Zhou", "J. Fonteles", "Joshua Danish", "Eduardo Davalos", "Selena Steinberg", "Gautam Biswas", "Noel Enyedy"]2024-06-10T00:00:00 f50a4a46247f85768d84def40b4ce07e0a68e6cdThis article aims to discover the mechanisms behind the adoption and acceptance of AI in advertising industry in Turkey. Semi-structured interviews reflecting technology acceptance literature conducted with agency practitioners to discover the usages and conditions of AI supported applications. Participants are selected in accordance with convenience and snowball sampling methods. The results provide important insights into four main strands of the literature: Technology usefulness, ease of use, attitudes toward technologies and barriers preventing and restricting the use of technologies. It is understood that practitioners effectively utilize AI in their business processes highlighting its contribution to efficiency in creative production. While technologies are being actively utilized, the process of understanding and exploring is still ongoing in the background. In line with the literature, agency practitioners point out the skepticism that exists among advertisers. It is noticable that as a result of finding AI tools useful and easy to use, overall attitude of participants toward AI tend to be positive. Participants asserted that they do not have any concerns about being replaced by AI. Their confidence on this matter seems to be based on the idea that AI could be most efficient in cooperation with human intelligence.Yeni Medya Dergisi231It is noticable that as a result of finding AI tools useful and easy to use, overall attitude of participants toward AI tend to be positive and they do not have any concerns about being replaced by AI.Yeni Medya Dergisi["G\u00f6rkem Bir", "Simge Aksu"]2024-06-10T00:00:00 fd3aef301a1e36e5a70ca377f22651bacf3a7a02Objectives: The extensive adoption of AI applications in many industries raises concerns over their potential to significantly transform employment in various aspects, including job creation, automation, and decision-making procedures. The impact that AI has on employment dynamics presents an array of obstacles and opportunities for the fields of business administration and management. Given all these advancements, the present study conducts a thorough examination of postgraduate theses, recognizing them as a systematic representation of the expanding influence of AI in the fields of business administration and management. Postgraduate research, which entails a thorough academic investigation, provides a crucial perspective for understanding the present condition and future course of AI use in business environments. Through an in-depth examination of postgraduate theses, this research attempts to reveal trends in the application of AI technologies in various organizational contexts. Design/methodology/approach: The 73 master's and doctoral theses used in this study were obtained from the repository of the Council of Higher Education National Thesis Center. The sample spans a range of institutions, demonstrating the diversity and depth of AI research across the academic landscape of management studies. To comprehensively examine and understand the information categories encompassed in these theses, the research utilizes two techniques: document analysis and descriptive content analysis. Document analysis systematically examines the theses as data sources, enabling a thorough investigation of the material that focuses on the identification, assessment, and synthesis of information pertaining to AI in business administration and management. Descriptive content analysis classifies and facilitates a methodical examination of the data. The distribution of theses subjected to document analysis was based on thesis type, universities and sub-disciplines, publication year and language, sample characteristics, methodology, theories, AI application areas, and keywords. Results: The findings show that studies on AI have gained momentum in the last four years, with a high percentage of theses focusing on management, organization, and marketing. The quantitative research method has been the most preferred for postgraduate theses. Additionally, human resource management, machine learning, and artificial neural networks constitute most of the research focus. The data indicates that most master's theses concentrate on human resource management and marketing. The finance and information technology sectors are predominant in terms of industry focus. Practical implications: The findings have the potential to significantly assist practitioners in understanding the ongoing research on AI, enabling them to align their strategic planning with the latest discoveries and approaches in higher education. The prevalence of machine learning and artificial neural networks signifies an inclination towards increasingly complex AI implementations within organizations. Organizations may leverage this understanding to increase innovation, enhance decision-making procedures, and sustain a competitive advantage through the implementation of cutting-edge AI. Furthermore, this study can assist researchers, such as master's and doctoral students, with the topic, research question, data source, data collection tool, and analysis type selection in future studies. Originality/value: The study is expected to provide insights into research focus, scope, and methodology for future research by revealing the current state of AI research in business administration and management domains in Turkey. Through the examination of postgraduate theses, this study not only highlights the increasing academic attention towards AI but also establishes a foundation upon which subsequent research can be built. This study represents an initial effort to gain deeper insights into AI research in Turkey, specifically focusing on knowledge production within universities.İş te Davranış Dergisi01This study represents an initial effort to gain deeper insights into AI research in Turkey by revealing the current state of AI research in business administration and management domains in Turkey and establishes a foundation upon which subsequent research can be built.İş'te Davranış Dergisi["Merve Ger\u00e7ek", "H\u00fcsna G\u00fcl Erkin"]2024-06-10T00:00:00 e2cbaec0653792e07aa24f1cdd01d76be3e1eb7eMental health has become a major concern in today's technological developments. With the emergence of the digital era, significant changes have occurred in mental health services. One of the most prominent evolutions is the emergence of virtual counseling services. This research is a literature review study related to the topic of providing mental health services remotely. Sources of information come from articles in scientific journals from the last 20 (twenty) years which are searched via the Google scholar, Google and Pubmed search engines. The number obtained was 13 articles, consisting of 10 international articles and 3 articles written by Indonesian authors. With the existence of cyber counseling or what is familiarly known as virtual counseling, it is a very effective alternative, apart from saving time and costs, cyber counseling can also be easily accessed without limited space. Furthermore, the level of client satisfaction in conducting online counseling is higher than face-to-face counseling. So we need a platform for counselors to be able to innovate with technological updates, one of which is cybercounseling (online counseling) servicesBICC Proceedings01This research is a literature review study related to the topic of providing mental health services remotely, and the level of client satisfaction in conducting online counseling is higher than face-to-face counseling.BICC Proceedings["Chandra Ayu Ningrum", "Ruji", "Ferdimas Ilham Ramansyah"]2024-06-10T00:00:00 98fabe7df6855502bb463848e43892f7278d1497Critical Care151Critical Care["Julian Klug", "Urs Pietsch"]2024-06-10T00:00:00 cd8a77abc3a22a909bf9642ad04bcd6b04434a71AI &amp; SOCIETY01AI &amp; SOCIETY["Monalisa Bhattacherjee", "Sweta Sinha"]2024-06-10T00:00:00 3766caaeb2388e5f611c8bfaddfe43d21ffa4736Translational Issues in Psychological Science00Translational Issues in Psychological Science[]2024-06-10T00:00:00 be9c41abb4f2cf3dc26a51e12f006a472a5d0db9Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes60Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes["P. Soyer"]2024-06-10T00:00:00 9cc855041ecae4d1d37e12fe57c96cc8d6cabdeaAssurance and Security for AI-enabled Systems00Assurance and Security for AI-enabled Systems["Benjamin Schumeg", "Benjamin Werner"]2024-06-10T00:00:00 4debeab468232f43d01742ed690e065dcb3153f0Informatica00Informatica (Slovenia)["Jing Chen"]2024-06-10T00:00:00 fc39b51f836addaf38f95a7c3d145d23be77e754Proceedings of the 18th International Conference of the Learning Sciences - ICLS 202400Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024["Jeanne McClure", "Juan Zheng", "Franziska Bickel", "Shiyan Jiang", "Carolyn P. Ros\u00e9", "Jie Chao"]2024-06-10T00:00:00 3138d7194324096b87288bad71ecb822053fb60cProceedings of the International Conference on Computer-supported for Collaborative Learning00Proceedings of the International Conference on Computer-supported for Collaborative Learning["Sini Davies", "Kati Sormunen", "Kaiju Kangas"]2024-06-10T00:00:00 b214b45576474ab5994deedc23ec3f7dc7c04c9aProceedings of the 18th International Conference of the Learning Sciences - ICLS 202400Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024["Rebecca Ellis", "Jie Chao", "Shiyan Jiang", "Carolyn P. Ros\u00e9", "Kenia Wiedemann"]2024-06-10T00:00:00 d0cfe836464a9fef4d7e9a3a22322bfc6fba678eExplainable AI (XAI) methods provide explanations of AI models, but our understanding of how they compare with human explanations remains limited. Here, we examined human participants' attention strategies when classifying images and when explaining how they classified the images through eye-tracking and compared their attention strategies with saliency-based explanations from current XAI methods. We found that humans adopted more explorative attention strategies for the explanation task than the classification task itself. Two representative explanation strategies were identified through clustering: One involved focused visual scanning on foreground objects with more conceptual explanations, which contained more specific information for inferring class labels, whereas the other involved explorative scanning with more visual explanations, which were rated higher in effectiveness for early category learning. Interestingly, XAI saliency map explanations had the highest similarity to the explorative attention strategy in humans, and explanations highlighting discriminative features from invoking observable causality through perturbation had higher similarity to human strategies than those highlighting internal features associated with higher class score. Thus, humans use both visual and conceptual information during explanation, which serve different purposes, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.British Journal of Psychology690It is found that humans adopted more explorative attention strategies for the explanation task than the classification task itself, and XAI methods that highlight features informing observable causality match better with human explanations, potentially more accessible to users.British journal of psychology["Ruoxi Qi", "Yueyuan Zheng", "Yi Yang", "Caleb Chen Cao", "J. H. Hsiao"]2024-06-10T00:00:00 250b07e5403df3b71f22fe769bf13a6f9c808f70Proceedings of the 18th International Conference of the Learning Sciences - ICLS 202400Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024["Golnoush Haddadian", "Prajwal Panzade", "Daniel Takabi", "Min Kyu Kim"]2024-06-10T00:00:00 3536134c823c02fbada43df3951c03a0dde89d7dProceedings of the 18th International Conference of the Learning Sciences - ICLS 202400Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024["Srijita Chakraburty", "Cindy E. Hmelo-Silver", "Krista D. Glazewski", "Anne T. Ottenbreit-Leftwich", "Jiyoung Kim", "Vanessa Johnson", "Dubravka Svetina Valdivia", "Bradford W. Mott", "James C. Lester"]2024-06-10T00:00:00 9a74188bc38965ca619f46255fd2b3aab88c31a7The article describes the basic principles of formation of political institutions and processes with the help of digital technologies. The main principles of digitalization in politics and its impact on modern political institutions are revealed. In addition, the author paid attention to the essence of political institutions, as well as their formation under the influence of digital technologies. The relevance of this research topic is determined by the significance of the effectiveness of the formation of processes in politics and the direct impact that digitalization and digital technologies have on them. The paper examines different views of modern scholars on the problem of formation of political institutions under the influence of digital technologies. By assessing the different ways and methods used by political actors and countries to adopt digital technologies, it is possible to trace changes in relevant social relations and to investigate how digital technologies affect political discourse. In this way, three areas are defined: digital democracy, digital bureaucracy and digital diplomacy. Finally, the author analyzes concepts, models and scenarios of digitalization, and describes the basic principles of “social intelligence” in artificial machines that collect and analyze the digital footprints of the target audience. Special attention is paid to Russia, on its path to digital transformation.Post–Soviet Continent40The article describes the basic principles of formation of political institutions and processes with the help of digital technologies, and describes the basic principles of “social intelligence” in artificial machines that collect and analyze the digital footprints of the target audience.Post–Soviet Continent["P. K. Pobedin"]2024-06-10T00:00:00 8dec03b703532d854c9bd1fe9fc87f0bf050560bFrontiers Artif. Intell.170Frontiers in Artificial Intelligence["G. Mentzas", "Karl Hribernik", "Johan Stahre", "David Romero", "John Soldatos"]2024-06-10T00:00:00 e383116358f7241d69e12ef0eeeafd4fe349b970Journal of fusion energy210A quick overview of the current development of the IFMIF-DONES neutron source with a particular snapshot of the present engineering design status for what concerns the instrumentation and control systems together with its complex diagnostics that guarantees the safe monitoring, supervision and regulation of all operations.Journal of Fusion Energy["M. Cappelli", "C. Torregrosa-Martin", "J. Diaz", "A. Ibarra"]2024-06-10T00:00:00 c444a53198e3264773d8eab9b67f08c25ab49509In an era where technology continues to develop rapidly, the role of Artificial Intelligence (AI) is increasingly significant in various aspects of human life, including in the field of guidance and counseling, especially in the field of mental health. AI helps individuals reach their full learning potential. As part of computer science, AI aims to create machines (computers) that can do work like humans, even better. The use of AI in healthcare improves mental well-being and provides a way to bridge gaps in mental health and other health services. Mental health counseling involves interactions between counselors and individuals experiencing mental health problems, with a variety of therapeutic approaches to help them. AI developments have had a significant impact in this field, particularly through chatbot therapy which allows individuals to interact with AI chatbots to obtain help and advice regarding mental health issues.This research is motivated by the use of AI in student guidance and counseling. AI is a system that continues to develop in having intelligence similar to or even more than humans, which can be used in guidance and counseling services in schools. This research aims to explore how to use AI to increase the effectiveness, accessibility and responsibility of guidance and counseling services for students. One example of using AI is through Guidance and Counseling Chatbots.The research method used is a literature study, which reveals that many guidance and counseling students have adopted the use of AI in educational contexts. AI has great potential to improve the efficiency and effectiveness of education systems, with various types of AI that can be used, such as Canva, Google Meet, Zoom, Mozilla Firefox and ChatGPT. However, it is important to pay attention to academic ethics so that educational goals can be achieved wellBICC Proceedings00This research aims to explore how to use AI to increase the effectiveness, accessibility and responsibility of guidance and counseling services for students through Guidance and Counseling Chatbots.BICC Proceedings["Anggi Sepni Anita", "Keysa Nabila Aulia Purba", "M. R. Bahrul Ilmi"]2024-06-10T00:00:00 163aa461ae7543d9c142616a797a1335553fdd70This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. Then, the paper delves into the concept of resilience and the role of microgrids in maintaining power stability. The paper reviews various AI techniques and methods, and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control.Sustainability09This paper presents an in-depth exploration of the application of Artificial Intelligence in enhancing the resilience of microgrids, and investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time.Sustainability["Younes Zahraoui", "Tarmo Kor\u00f5tko", "A. Rosin", "S. Mekhilef", "M. Seyedmahmoudian", "A. Stojcevski", "Ibrahim Alhamrouni"]2024-06-10T00:00:00 9165d49883a25d0661ac04fa3fb7b65cd70c885cThe global prevalence of diabetes remains high despite rising life expectancy with improved quality and access to healthcare services. The significant burden that diabetes imposes warrants efforts to improve existing interventions in diabetes care. Present research on diabetes management has shown that artificial intelligence (AI) and Large Language Models (LLM) play an important role in various aspects of the diabetes continuum but a distinct lack of studies in diabetes prevention is observed. Our research introduces a comprehensive digital solution, leveraging the capabilities of GPT-3.5 models maintained by OpenAI, focused specifically on the active prevention of diabetes. The system encompasses a user-friendly interface accessible via mobile and web applications, an AI-powered chatbot for instant Q&A and advice, personalized reminder systems, a data analysis module for tailored guidance, resource aggregators for health-related information, and an emotional support module to ensure a holistic approach to prevention. Furthermore, our experiments involved testing the quality of responses generated by a fine-tuned GPT-3.5 model, utilizing the Assistants API or a retrieval-augmented generation (RAG) system powered by FAISS for enhanced context awareness and personalized advice. The testing focused on a structured dataset of questions and answers related to diabetes prevention, with results highlighting the superiority of the GPT-3.5 model combined with the Assistants API in providing relevant, detailed, and personalized responses, thus demonstrating its potential as an invaluable tool in the proactive prevention of diabetes.AIQAM@ICMR316This research introduces a comprehensive digital solution, leveraging the capabilities of GPT-3.5 models maintained by OpenAI, focused specifically on the active prevention of diabetes, demonstrating its potential as an invaluable tool in the proactive prevention of diabetes.Proceedings of the 1st ACM Workshop on AI-Powered Q&A Systems for Multimedia["Dung Dao", "Jun Yi Claire Teo", "Wenru Wang", "Hoang D. Nguyen"]2024-06-10T00:00:00 ef8cfbfbcddeac0735c522de10cef769b783a0a6Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In this paper, we conduct a thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare, focusing on the recent developments of mental disorder detection through online social media (OSM). A significant portion of the population actively engages in OSM platforms, creating a vast repository of personal data that holds immense potential for mental health analytics. The paper navigates through traditional diagnostic methods, state-of-the-art data- and AI-driven research studies, and the emergence of explainable AI (XAI) models for mental healthcare. We review state-of-the-art machine learning methods, particularly those based on modern deep learning, while emphasising the need for explainability in healthcare AI models. The experimental design section provides insights into prevalent practices, including available datasets and evaluation approaches. We also identify key issues and challenges in the field and propose promising future research directions. As mental health decisions demand transparency, interpretability, and ethical considerations, this paper contributes to the ongoing discourse on advancing XAI in mental healthcare through social media. The comprehensive overview presented here aims to guide researchers, practitioners, and policymakers in developing the area of mental disorder detection.arXiv.org1443A thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare, focusing on the recent developments of mental disorder detection through online social media, aims to guide researchers, practitioners, and policymakers in developing the area of mental disorder detection.ArXiv["Yusif Ibrahimov", "Tarique Anwar", "Tommy Yuan"]2024-06-10T00:00:00 7645b2441c27b159d4b48784eba5eb029427803dWe address ECG data classification, using methods from explainable artificial intelligence (XAI). In particular, we focus on the extended performance of the ST-CNN-5 model compared to established models. The model showcases slight improvement in accuracy suggesting the potential of this new model to provide more reliable predictions compared to other models. However, lower values of the specificity and area-under-curve metrics highlight the need to thoroughly evaluate the strengths and weaknesses of the extended model compared to other models. For the interpretability analysis, we use Shapley Additive Explanations (SHAP), Gradient-weighted Class Activation Mapping (GradCAM), and Local Interpretable Model-agnostic Explanations (LIME) methods. In particular, we show that the new model exhibits improved explainability in its GradCAM explanations compared to the former model. SHAP effectively highlights crucial ECG features, better than GradCAM and LIME. The latter methods exhibit inferior performance, particularly in capturing nuanced patterns associated with certain cardiac conditions. By using distinctive methods in the interpretability analysis, we provide a systematic discussion about which ECG features are better - or worse - uncovered by each method.ICDAR@ICMR343This work shows that the new ST-CNN-5 model exhibits improved explainability in its GradCAM explanations compared to the former model, and provides a systematic discussion about which ECG features are better - or worse - uncovered by each method.Proceedings of the 5th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval["Jaya Ojha", "H. Haugerud", "Anis Yazidi", "P. Lind"]2024-06-10T00:00:00 5bdd0cd76a2b05af060c80783739889d7010fb3aAI &amp; SOCIETY253Public ELSI concerns in respect of AI were measured using four items: ethics, tradition, law and social benefit, and Korea, compared to Japan, exhibited a more positive outlook, whereas Germany, in comparison to Japan, expressed heightened concerns about it across different scenarios.AI &amp; SOCIETY["Yuko Ikkatai", "Yuko Itatsu", "Tilman Hartwig", "Jooeun Noh", "Naohiro Takanashi", "Yujin Yaguchi", "Kaori Hayashi", "Hiromi M. Yokoyama"]2024-06-10T00:00:00 df1d173877c6accb8b90ec7d70beeaa3025305a8The rapidly increasing amount of online information and the advent of Generative Artificial Intelligence (GenAI) make the manual verification of information impractical. Consequently, AI systems are deployed to detect disinformation and deepfakes. Prior studies have indicated that combining AI and human capabilities yields enhanced performance in detecting disinformation. Furthermore, the European Union (EU) AI Act mandates human supervision for AI applications in areas impacting essential human rights, like freedom of speech, necessitating that AI systems be transparent and provide adequate explanations to ensure comprehensibility. Extensive research has been conducted on incorporating explainability (XAI) attributes to augment AI transparency, yet these often miss a human-centric assessment. The effectiveness of such explanations also varies with the user’s prior knowledge and personal attributes. Therefore, we developed a framework for validating XAI features for the collaborative human-AI fact-checking task. The framework allows the testing of XAI features with objective and subjective evaluation dimensions and follows human-centric design principles when displaying information about the AI system to the users. The framework was tested in a crowdsourcing experiment with 433 participants, including 406 crowdworkers and 27 journalists for the collaborative disinformation detection task. The tested XAI features increase the AI system’s perceived usefulness, understandability, and trust. With this publication, the XAI evaluation framework is made open source.MAD@ICMR642A framework for validating XAI features for the collaborative human-AI fact-checking task and follows human-centric design principles when displaying information about the AI system to the users is developed.Proceedings of the 3rd ACM International Workshop on Multimedia AI against Disinformation["Vera Schmitt", "Bal\u00e1zs Patrik Csomor", "Joachim Meyer", "Luis-Felipe Villa-Areas", "Charlott Jakob", "Tim Polzehl", "Sebastian M\u00f6ller"]2024-06-10T00:00:00 1dacd1884f91ac5445446791fda6138a87fc27d1Objective: The integration of artificial intelligence (AI) in the online marketplace represents a turning point in the development of e-commerce and digital trade. This article investigates the transformative potential of AI-enhanced online marketplaces for trade relations between the United States (US) and Sub-Saharan Africa (SSA), with the aim of reviewing the opportunities presented by the AI in enhancing market access, streamlining trade, and driving economic growth.   Method: The methodology adopted for this research comprises of expository discussion. We explore theoretical discussion on AI-enhanced online marketplaces and how it has facilitated online marketplaces for trade relations between the United States (US).   Results and Discussion: The article finds that there exists a complex mix of opportunities, challenges, and evolving dynamics in the trading landscape between the US and SSA. Furthermore, the rivalry from global powers as well as the geopolitical considerations impact the trade between US and the SSA countries.   Implications: The practical and theoretical implications of this research are discussed. The implication of our finding is that by addressing these key areas, stakeholders can navigate the complexities of trade relations effectively and capitalize on opportunities for mutual prosperity and inclusive economic development between the US and SSA in the digital age.   Originality/Value: This study contributes to the literature by highlighting how an AI-enhanced online marketplaces can facilitate economic progress by utilizing it for trade relations between the US and SSA economies. The relevance and value of the paper are evidenced by the findings that if both countries leverage on AI-enhance technologies for effective market access, they can unlock their full potential for growth and development.   Recommendations: We suggest that stakeholders should leverage on AI technologies by embracing digital transformation, fostering collaboration, prioritizing market research, addressing infrastructure challenges, and ensuring compliance with ethical practices. Future research can explore other areas of emerging technologies, demonstrate the socio-economic implications of AI, and the framework of regulations on AI.Revista de Gestão Social e Ambiental241The finding is that if both countries leverage on AI-enhance technologies for effective market access, they can unlock their full potential for growth and development between the US and SSA in the digital age.Revista de Gestão Social e Ambiental["Friday Anwansedo", "A. Gbadebo", "Oladayo Tosin Akinwande"]2024-06-10T00:00:00 67dfe92925cbc84beef28d1f0e88d582666e1adaThis writing aims to find out how Artificial Intelligence influences student thinking patterns, how students apply Artificial Intelligence in their learning process. The research method uses a literature study approach. The results of the writing explain the influence of Artificial Intelligence on students' thinking patterns, where in this writing there are positive and negative influences in its use. With Artificial Intelligence Can change the way students think, namely by helping students find information easily and quickly, but on the other hand, AI is the main cause of students' laziness to study hard to fulfill their competencies. Students become lazy to open books or journals and lazy to discuss with their fellow students.Advances in Artificial Intelligence (AI) have brought significant changes to the field of higher education, including in terms of student mindset. This qualitative research aims to explore the impact of AI on students' mindsets in their learning and self-development processes. Using a phenomenological approach, this study investigates students' experiences, perceptions and interpretations regarding the use of AI in academic environments through in-depth interviews and participant observation. The data obtained were analyzed using thematic methods to identify main themes and emerging patterns. Research findings reveal that although AI is considered a useful tool in the learning process, there are concerns regarding the potential for over-reliance on AI and its impact on students' critical thinking skills and creativity. Additionally, the use of AI also raises ethical challenges such as plagiarism and cheating. This research provides valuable insight into how AI influences college students' mindsets and highlights the importance of education about the responsible and ethical use of AI in academic settings.BICC Proceedings01Research findings reveal that although AI is considered a useful tool in the learning process, there are concerns regarding the potential for over-reliance on AI and its impact on students' critical thinking skills and creativity.BICC Proceedings["Aisyah", "Poppy Dwi Yulianti", "Suci Yandhini", "Adek Dwi Putri Sari", "Irna Herawani", "Ira Oktarini"]2024-06-10T00:00:00 00710cb673b64855a734256eaf017434b79a5ff6The purpose of this article is to clarify the impact of technological, organisational, and environmental contexts in which Artificial Intelligence solutions are implemented by the accounting and audit companies in Europe. The organizational and environmental contexts were not enough studied in accounting and audit field but are becoming more and more important in the future. The applied methodology was based on a structured interview, to which it has answered 62 top financial specialists from 18 European countries, in companies with more than 10 years of experience in the accounting and audit sector. To design the structured interview, it was used the Technology-Organisation-Environment framework. A serious concern for the companies’ representatives consists in the lack of specialists capable to understand and work with Artificial Intelligence solutions. One option that is generally preferred by companies is to prepare their employees for these new tasks, rather than hiring qualified persons. There are two methods that can be used when implementing Artificial Intelligence, to buy specific provided solutions from third parties, or to developed them internally. Data storage and security, the complexity of Artificial Intelligence solutions and government regulations do not represent a threat to companies willing to develop this area. The main contribution of this study consists of an extensive analysis of the most important elements of Technology-Organisational-Environmental framework and their applicability for accounting and audit companies, which implemented or are willing to implement Artificial Intelligence solutions. International Journal of Research In Business and Social Science494The main contribution of this study consists of an extensive analysis of the most important elements of Technology-Organisational-Environmental framework and their applicability for accounting and audit companies, which implemented or are willing to implement Artificial Intelligence solutions.International Journal of Research in Business and Social Science (2147- 4478)["Mirela Mihai", "Adriana Dutescu"]2024-06-11T00:00:00 19460afe4dfe4cafe84f52d84ab4033867791518Artificial Intelligence (AI) poses transformative and disruptive challenges for democracy, for policy makers, and for government agencies. While various policy initiatives around the world seek to regulate AI, in the United States (US) federal government there is no sign of a comprehensive AI law, and few legal measures to enable or restrict AI have been proposed and passed. However, states across the US are active in attempting to address issues related to AI and have proposed hundreds of legislations related to AI in the past few years. In this paper, we examined what these legislations have sought to accomplish in relation to AI, and the potential impacts for the public in general and for public administration in particular. From a preliminary and descriptive analysis of all US state legislations related to AI passed from 2019 to 2023, we show how these legislations are addressing AI in terms of: (1) the types of legislations adopted or enacted; (2) the definitions of AI and associated technologies given; (3) the sectors and domains principally addressed in AI legislations; (4) the private sector and government actions directed by the legislations; and (5) how ethical and economic considerations are addressed. We generally found a lack of definition of AI, and associated technologies mentioned are rarely specific. Many of the laws create commissions or task forces to study AI, addressing the various practical and ethical issues related to AI. Legislations have created some regulations and support for industry, and have directed government agencies to identify existing AI capabilities and how AI may be employed in their agencies and jurisdictions. Considerable emphasis has been placed on issues of bias and discrimination, as well education and economic investment in AI, although unevenly distributed across states. We summarize and discuss these results in relation to existing literature and make some recommendations on how state legislatures may better address AI in the future.Digital Government Research411This paper examined what US state legislations have sought to accomplish in relation to AI, and the potential impacts for the public in general and for public administration in particular, and found a lack of definition of AI, and associated technologies mentioned are rarely specific.Proceedings of the 25th Annual International Conference on Digital Government Research["Nic DePaula", "Lu Gao", "Sehl Mellouli", "L. Luna-Reyes", "Teresa M. Harrison"]2024-06-11T00:00:00 6dc7656c330364fa7b419566eb9578d55e70751cThis study examines policy interventions and regulation of generative artificial intelligence (AI), focusing on key differences in generative AI policy in the EU, the US, and China. Using a comparative research methodology, the study analyzed the most recent policy documents from these regions through text-mining techniques to assess their key differences in terms of word frequency and specific content. This work highlights the different strategies, goals, and approaches to regulating generative AI across the regions. It found that the EU adopts a more comprehensive and stringent regulation of generative AI, emphasizing regional harmonization and foresight; the U.S. regulation is characterized by pragmatism, closely aligned with industry innovation, and a focus on risk avoidance; while China focuses more on macro-level regulation aimed at promoting innovation and encouraging ecological construction. Participants may be interested in this study because it not only uses up-to-date materials but also employs text-mining methods to present the findings in a clearer way than previous studies. It provides insights into how regulatory policies for generative AI affect the development and practice of digital government. The study sheds light on different countries’ strategies for technology governance, which is crucial for understanding and designing effective digital government policies. In addition, due to the potential of generative AI technologies to deliver public services and drive government transparency, these insights help participants better assess the opportunities and challenges of technological innovation in the digital government space.Digital Government Research141The study sheds light on different countries’ strategies for technology governance, which is crucial for understanding and designing effective digital government policies, and highlights the different strategies, goals, and approaches to regulating generative AI across the regions.Proceedings of the 25th Annual International Conference on Digital Government Research["Shiming Hu", "Yifan Li"]2024-06-11T00:00:00 16cf432e45c3d5abb5e09c24e68ab8e12e0e281dPurpose: Defining prostate cancer contours is a complex task, undermining the efficacy of interventions such as focal therapy. A multireader multicase study compared physicians’ performance using artificial intelligence (AI) vs standard-of-care methods for tumor delineation. Materials and Methods: Cases were interpreted by 7 urologists and 3 radiologists from 5 institutions with 2 to 23 years of experience. Each reader evaluated 50 prostatectomy cases retrospectively eligible for focal therapy. Each case included a T2-weighted MRI, contours of the prostate and region(s) of interest suspicious for cancer, and a biopsy report. First, readers defined cancer contours cognitively, manually delineating tumor boundaries to encapsulate all clinically significant disease. Then, after ≥ 4 weeks, readers contoured the same cases using AI software. Using tumor boundaries on whole-mount histopathology slides as ground truth, AI-assisted, cognitively-defined, and hemigland cancer contours were evaluated. Primary outcome measures were the accuracy and negative margin rate of cancer contours. All statistical analyses were performed using generalized estimating equations. Results: The balanced accuracy (mean of voxel-wise sensitivity and specificity) of AI-assisted cancer contours (84.7%) was superior to cognitively-defined (67.2%) and hemigland contours (75.9%; P < .0001). Cognitively-defined cancer contours systematically underestimated cancer extent, with a negative margin rate of 1.6% compared to 72.8% for AI-assisted cancer contours (P < .0001). Conclusions: AI-assisted cancer contours reduce underestimation of prostate cancer extent, significantly improving contouring accuracy and negative margin rate achieved by physicians. This technology can potentially improve outcomes, as accurate contouring informs patient management strategy and underpins the oncologic efficacy of treatment.Journal of Urology251AI-assisted cancer contours reduce underestimation of prostate cancer extent, significantly improving contouring accuracy and negative margin rate achieved by physicians, and this technology can potentially improve outcomes.The Journal of Urology["S. Mota", "A. Priester", "Joshua Shubert", "Jeremy Bong", "James Sayre", "Brittany Berry-Pusey", "Wayne Brisbane", "S. Natarajan"]2024-06-11T00:00:00 cebfd8a2a5e0cdc54cdb80113617798411007786This article explores whether artificial intelligence (AI) can engage in the practice of law as an art of good and justice. It examines the historical and philosophical foundations of law as the art of promoting societal harmony and resolving moral dilemmas. The research employs critical and philosophical analysis methods integrating insights from legal scholars, ethicists, technologists, and policymakers. The study identifies AI’s potential to streamline legal processes, enhance access to justice, and reduce bias in decision-making. However, it also highlights ethical challenges such as transparency, accountability, and the impact on the legal workforce. The article emphasises the importance of striking a balance between technological innovation and human values, advocating for proactive regulation and interdisciplinary cooperation to ensure the ethical development and implementation of AI in law. The results of the study highlight the transformative potential of AI in revolutionising legal practice, emphasising its capacity to streamline processes, improve access to justice, and mitigate bias. However, ethical considerations such as transparency, accountability, and the preservation of human judgment are crucial to ensuring that AI integration in law upholds fundamental principles of justice and fairness.Filosofija Sociologija231The study identifies AI’s potential to streamline legal processes, enhance access to justice, and reduce bias in decision-making, but also highlights ethical challenges such as transparency, accountability, and the impact on the legal workforce.Filosofija. Sociologija["Neringa Gaubien\u0117"]2024-06-11T00:00:00 47ace04b5583250361e66b61a8784f02a92bc5bdIn June 2023, the Nishan Dialogue on Digital Civilisation of the World Internet Conference was held in China. At this conference, China proposed for the first time, in the era of artificial intelligence (AI), to build a digital world of exchange, mutual appreciation and tolerance, hoping to gather the wisdom of the internet community and seek the governance of digital civilisation. In recent years, with the rapid development and wide recognition of AI technology, how to solve the AI ethical issues generally faced by the international community has become the focus of attention. By analysing the current status of AI ethics and governance in the United States and the European Union, and comparing it with China’s development in recent years, this article further advances the exploration of Chinese solutions to the global ethical governance of AI. On this basis, it responds positively to the call of the United Nations and international organisations to explore solutions to the four main realities of: (a) phenomenon of alienation of labour competition brought about by AI technology; (b) infringement of the subject’s personal privacy and impact on the ethics of responsibility for awareness and undermining of social fairness; (c) justice to seek a path of avoidance for collaborative governance from government supervision, public constraints, technology; and (d) sound mechanisms, which can actively promote global AI ethics.Science Technology & Society41By analysing the current status of AI ethics and governance in the United States and the European Union and comparing it with China’s development in recent years, this article further advances the exploration of Chinese solutions to the global ethical governance of AI.Science, Technology and Society["DI Gao", "Dahuai Yu"]2024-06-11T00:00:00 759d76d2617cf531b0db886fbc8187a8fb5afed0Background Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this. Challenges with the three components of EBP Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources. AI assistance for the three components of EBP AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue. Conclusion This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.Frontiers in Health Services802A selective review of the literature on EBP and the integration of AI in healthcare underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0.Frontiers in Health Services["Per Nilsen", "David Sundemo", "Fredrik Heintz", "Margit Neher", "Jens Nygren", "P. Svedberg", "Lena Petersson"]2024-06-11T00:00:00 19d365a073b54f7145cece4f980bd1d9edb64b88In recent years, artificial intelligence (AI) technologies have experienced substantial growth across various sectors, with significant strides made particularly in medical AI through advancements such as large models. The application of AI within the field of ophthalmology can enhance the accuracy of eye disease screening and diagnosis. However, the deployment of AI and its large models in ophthalmology still encounters numerous limitations and challenges. This article builds upon the transformative achievements in the medical AI sector and discusses the ongoing challenges faced by AI applications in ophthalmology. It provides forward-looking insights from an ophthalmic perspective regarding the era of large models and anticipates research trends in AI applications in ophthalmology, so as to foster the continuous advancement of AI technologies, thereby significantly promoting eye health.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology01Forward-looking insights from an ophthalmic perspective regarding the era of large models are provided and research trends in AI applications in ophthalmology are anticipated so as to foster the continuous advancement of AI technologies, thereby significantly promoting eye health.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology["L. Q. Liu", "D. W. Wu"]2024-06-11T00:00:00 c2801579cb17c5a6c0ba123085bfb4ebcb166b4fThis paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices.Electronics02A bibliometric analysis of current research trends in the field of artificial intelligence, focusing on key topics such as deep learning, machine learning, and security in AI, using primary data from the Clarivate Analytics Web of Science Core Collection from 2020 to 2024 is provided.Electronics["Luka Ili\u0107", "Aleksandar \u0160ijan", "Bratislav Predi\u0107", "D. Viduka", "Darjan Karaba\u0161evi\u0107"]2024-06-11T00:00:00 991e24ce56ca75961ef4cfffb7ef8715f4dcdb6dThe rapid advancement of artificial intelligence (AI) in healthcare has spurred extensive debate regarding its potential to replace human expertise across various medical specialties. This narrative review critically examines the integration of AI within diverse medical specialties to discern its role as a substitute or supporter. The analysis encompasses AI’s impact on diagnostic precision, treatment planning, and patient care. Although AI systems have demonstrated remarkable proficiency in tasks reliant on data analysis and pattern recognition, they fall short in areas necessitating nuanced decision-making, empathetic communication, and the application of human medical expertise in diagnosis and treatment planning. The rapid evolution of AI applications within medical specialties is propelled by the swift advancements in both hardware and software technologies, fostering a dynamic synergy that continues to redefine the boundaries of precision and efficiency in healthcare delivery. While AI demonstrates remarkable capabilities in automating tasks, it is underscored that its integration in complex domains necessitates a balanced approach that preserves the indispensable contributions of human activity.Medicine and Pharmacy Reports572This narrative review critically examines the integration of AI within diverse medical specialties to discern its role as a substitute or supporter, and encompasses AI’s impact on diagnostic precision, treatment planning, and patient care.Medicine and Pharmacy Reports["\u0218. Popa", "A. Ismaiel", "Vlad Dumitru Brata", "D. Turtoi", "M. B\u00e2rsan", "Zoltan Czako", "Cristina Pop", "Lucian Muresan", "M. St\u0103nculete", "D. Dumitra\u0219cu"]2024-06-11T00:00:00 3bf88abafce45d10bc00763d3acd44ab6bd9675fIn this paper, we provide a detailed survey of generative artificial intelligence (GAI) and examine the perceived social problems, including those that are currently apparent and those that can potentially be caused by the technology. After the introduction, we discuss initiatives proposed by governmental and professional entities to curtail the risks posed by adopting AI technologies without consideration of the associated risks. A brief survey of published research in AI security and related risks is then presented along with a discussion of findings and recommendations in the form of guidelines for future adoption of generative AI across a variety of contexts.Digital Government Research231A detailed survey of generative artificial intelligence (GAI) is provided and the perceived social problems, including those that are currently apparent and those that can potentially be caused by the technology are examined.Proceedings of the 25th Annual International Conference on Digital Government Research["Alan T. Yang", "Andrew T. Yang"]2024-06-11T00:00:00 4c0a03ac1cb1477e35b3bc59d5fa5bab44680d4bVideo abstract from Professor Helen Minnis and Professor Alessandro Vinciarelli on their co-authored CAMH journal Original Article ‘The use and potential of artificial intelligence for supporting clinical observation of child behaviour’.00The use and potential of artificial intelligence for supporting clinical observation of child behaviour and the use and potential of artificial intelligence for supporting clinical observation of child behaviour are explored.[]2024-06-11T00:00:00 0b43045800f19689e12f5d70dc6d3553d3c28d19Adolescents are particularly vulnerable to engaging in risky behaviors such as violence, unprotected sex, and substance abuse, which have significant negative impacts on their health and development. Recent advancements in artificial intelligence (AI) offer innovative solutions to address these behaviors, yet the evidence regarding the efficacy and implementation of AI-based interventions remains fragmented. This scoping review aims to systematically explore and map the literature on AI-based interventions designed to reduce risky behaviors among adolescents. This review will follow the methodological frameworks outlined by Arksey and O'Malley (2005) and improved by Levac, Colquhoun, and O'Brien (2010), in line with the Joanna Briggs Institute guidelines. The PRISMA Extension for Scoping Reviews (PRISMA-ScR) will guide the reporting. The search strategy will be executed across PubMed, Scopus, Web of Science Core Collection, CINAHL, PsycINFO, Cochrane Central Register of Controlled Trials, Embase, SID, and Magiran, focusing on articles published up to June 2024 in English and Farsi. Titles and abstracts will be screened by two independent reviewers using Rayyan, followed by full-text screening of relevant studies. Data will be charted using a standardized form, and discrepancies will be resolved through discussion or by consulting a third reviewer. Data will be synthesized descriptively and presented in tables, figures, and diagrams.medRxiv320This scoping review aims to systematically explore and map the literature on AI-based interventions designed to reduce risky behaviors among adolescents using PubMed, Scopus, Web of Science Core Collection, CINAHL, PsycINFO, Cochrane Central Register of Controlled Trials, Embase, SID, and Magiran.["H. Sadeghsalehi", "H. Joulaei"]2024-06-11T00:00:00 5e5b8edcd062cd95ff97ce98826d0f9cee9afed4The digitization of services in public and private institutions has made most of them to be generated and verified online, without the need for long waits and traveling from one place to another. This has made services to be offered much faster, more efficiently, cheaper and for a shorter time. The implementation of blockchain and artificial intelligence, Internet of Things, mining techniques and Big Data have made many problems today find solutions regarding privacy, data management, identity protection, transparency of services, real-time processing. The combination of these technologies affects the increase in the synergy of cooperation, interoperability, despite the challenges and practical problems that are being encountered. The issue of generating and verifying important documents such as diplomas for higher education institutions (HEI) is of great importance in terms of privacy, various misuses that may occur by malicious persons and real-time processing. Through the paper, we first make an overview of the synergy between AI and blockchain technology in blockchain systems. Then, by a brief review of the existing literature, we give proposals on how Machine Learning (ML) and Natural Language Processing (NLP) can be applied in HEIs for the generation and verification of academic credentials.Mediterranean Conference on Embedded Computing120An overview of the synergy between AI and blockchain technology in blockchain systems is made and proposals on how Machine Learning and Natural Language Processing can be applied in HEIs for the generation and verification of academic credentials are given.2024 13th Mediterranean Conference on Embedded Computing (MECO)["Avni Rustemi", "Vladimir Atanasovski", "Aleksandar Risteski", "Florim Idrizi", "Valentina Angelkoska"]2024-06-11T00:00:00 060ea52f5227ffba9753ac741bcb11467b827af2This research study aims to explore the current state and potential impact of artificial intelligence (AI) implementation in the manufacturing industry of Accra, Ghana and identify factors contributing to or hindering the successful implementation of AI in Ghanaian industries and propose recommendation for enhancing productivity and competitiveness through AI integration. The research focuses on key individuals in the industry, including technical officers, top management, and IT officers, to gather valuable insights into the benefits, limitations, and challenges associated with AI adoption. The research employs a mixed-methods approach, combining structured questionnaires and personal interviews to gather data from a representative sample of participants. The questionnaires gather demographic information such as years of experience, duration of employment in the company, type and size of the company, and the presence of other branches. The results obtained from the data analyses indicate that, only a small number of companies in the Industrial Area Accra, Ghana had adopted and are using Artificial Intelligence (AI) for their operations. The findings from the study will shed light on the current utilization of AI in the manufacturing industry of Accra, Ghana, high lighting its benefits and limitations. The research will also provide insights into the factors influencing successful AI implementation and identify areas and sectors where AI adoption should be prioritized. Additionally, the study will address the potential ethical and social implications of AI implementation, emphasizing the need for equitable and responsible use of the technology.International Journal For Multidisciplinary Research230The research will provide insights into the factors influencing successful AI implementation and identify areas and sectors where AI adoption should be prioritized and address the potential ethical and social implications of AI implementation.International Journal For Multidisciplinary Research["David Laud Amenyo Fiase", "Kwawdo Opoku Attah", "P. Sackey", "F. Ocansey", "Samuel Lartey"]2024-06-11T00:00:00 cdff20f7bf4c683c392a1a0509d7bc1f363dcd98Background: The Fourth Industrial Revolution (4IR) refers to the transformation of traditional production processes that have been digitized into the real world, enabling total interconnectivity between suppliers and customers with the aim of creating smarter products. The rapid changes in technology brought about by the 4IR have made business operations unstable. This has led to organizations seeking new methods and strategies to gain a competitive advantage in this digital age. Institutions of higher education have responded to this challenge by strengthening the role of university libraries as core components of the educational institution. They have also introduced various digital technologies to improve the learning experience for students.Methods: qualitative content analysis.Purpose: organizations to seek new methods and strategies to gain a competitive advantage in the digital era.Findings: Artificial Intelligence (AI) is one of the technologies that can be integrated into university libraries to enhance the learning experience for students. AI is a discipline that involves computer science, linguistics, information science, neurophysiology, neuroscience, cognitive science, psychological control, and other fields. AI is not just a computer program that mimics human intelligence but can also be used to promote independent learning and meet the special needs of all categories of students. With the support of large amounts of data, AI can form patterns and provide meaning, making the university library an ideal environment to apply this technology to add value to higher education in the future.Conclusion: The integration of AI into university libraries can provide an opportunity for every library user to access new and exclusive educational services specifically designed to meet individual student needs. Assuming that the library is supported by AI technology, it can help improve learning skills through more personalized technical learning approaches. AI technology can also help librarians explore new ways to meet the needs of library users and support academic activities. By utilizing AI technology, the library can provide sustainable access to various online text resources that are rapidly expanding, and provide services that are not limited to conventional boundaries, accessible to anyone and from anywhere. ABSTRAK Kerangka Konseptual Layanan Inovatif Perpustakaan Berbasis Artificial Intelligence (AI) dalam Rangka Mempercepat Transformasi Digital Latar Belakang: Revolusi Industri keempat (The Fourth Industrial Revolution - 4IR) merujuk pada transformasi proses kegiatan konvensional yang telah didigitalisasi ke dalam dunia nyata, memungkinkan interkoneksi secara total antara pemasok dan pelanggan dengan tujuan menciptakan produk yang lebih cerdas. Perubahan teknologi yang cepat yang dibawa oleh 4IR membuat operasi bisnis menjadi tidak stabil. Hal ini mendorong organisasi untuk mencari metode dan strategi baru untuk memperoleh keunggulan kompetitif di era digital ini. Institusi pendidikan tinggi telah menanggapi tantangan ini dengan memperkuat peran perpustakaan perguruan tinggi sebagai komponen inti dari lembaga Pendidikan. Perguruan Tinggi juga telah memperkenalkan berbagai teknologi digital untuk meningkatkan pengalaman belajar bagi mahasiswa. Metode: analisis konten kualitatif. Tujuan: organisasi untuk mencari metode dan strategi baru untuk memperoleh keunggulan kompetitif di era digital. Temuan: Kecerdasan Buatan (AI) adalah salah satu teknologi yang dapat diintegrasikan ke dalam perpustakaan perguruan tinggi untuk meningkatkan pengalaman belajar bagi mahasiswa. AI merupakan disiplin ilmu yang melibatkan ilmu komputer, linguistik, ilmu informasi, neurofisiologi, neurosains, ilmu kognitif, kontrol psikologis, dan bidang lainnya. AI bukan hanya program komputer yang meniru kecerdasan manusia, tetapi juga dapat digunakan untuk meningkatkan pembelajaran mandiri dan memenuhi kebutuhan khusus semua kategori mahasiswa. Dengan dukungan data yang besar, AI dapat membentuk pola dan memberikan makna, menjadikan perpustakaan perguruan tinggi lingkungan yang ideal untuk menerapkan teknologi ini untuk menambah nilai pada pendidikan tinggi di masa depan.Kesimpulan: Integrasi AI ke dalam perpustakaan perguruan tinggi dapat memberikan kesempatan bagi setiap pengguna perpustakaan untuk mengakses layanan pendidikan baru dan eksklusif yang dirancang khusus untuk memenuhi kebutuhan individu mahasiswa. Hal tersebut dapat diasumsikan bahwa perpustakaan didukung oleh teknologi AI, dapat membantu meningkatkan keterampilan belajar melalui pendekatan pembelajaran teknis yang lebih personal. Teknologi AI juga dapat membantu pustakawan mengeksplorasi cara baru untuk memenuhi kebutuhan pengguna perpustakaan dan mendukung kegiatan akademik. Dengan memanfaatkan teknologi AI, perpustakaan dapat menyediakan akses berkelanjutan ke berbagai sumber teks online yang terus berkembang, serta memberikan layanan yang tidak terbatas dan dapat diakses oleh siapa saja dan dari mana saja.JPUA Jurnal Perpustakaan Universitas Airlangga Media Informasi dan Komunikasi Kepustakawanan00JPUA: Jurnal Perpustakaan Universitas Airlangga: Media Informasi dan Komunikasi Kepustakawanan["Nur Subchan"]2024-06-11T00:00:00 c78e3f0fc9929809e51ab5054a565868dc38a4c8Background. The using of artificial intelligence (AI) in healthcare has particular importance for primary medical care. The market for AI in healthcare is actively developing, and Russian companies offer their products and services in this area. The federal project to create a single digital contour in healthcare includes the implementation of medical devices based on AI technology in healthcare of the regions of the Russian Federation. The AI services may or may not be medical devices. It is necessary to understand the organizational and economic effects for the healthcare system and a certain medical organization in order to select a service. There is no consolidation of regional experience on AI-enabled services in healthcare in the Russian Federation today. Aims — consolidation of experience in the using of the AI services in healthcare sector in the regions of the Russian Federation. Methods. We conducted an online survey of representatives of regional executive authorities and medical information and analytical centers in the regions of the Russian Federation through the “Yandex Forms” platform and also they were interviewed. Results. We surveyed 84 regions of the Russian Federation to consolidate the experience of using the AI services, and the information on Moscow was obtained from open sources. The analysis showed that the AI services that are not medical devices are more common than the AI services that are medical devices. Based on the results of the study, we have formed a classification of the AI services that are not medical devices used in healthcare in the Russian Federation, identified the reasons why, according to the regions, it is difficult to implement the AI services, as well as determined the conditions of use of the AI services that are not medical devices. Conclusions. On the basis of the study, we determined the algorithm for the implementation of the AI services in the Russian healthcare.Annals of the Russian academy of medical sciences10A classification of the AI services that are not medical devices used in healthcare in the Russian Federation is formed, the reasons why, according to the regions, it is difficult to implement the AI services are identified, and the conditions of use of the AI services that are not medical devices are determined.Annals of the Russian academy of medical sciences["Alexandra F. Bondarovich", "D. Tyufilin", "Taras D. Tarasenko", "A. V. Gusev", "V. Chigrina", "Dmitry A. Samofalov", "M. Lagutin", "I. Deev", "O. Kobyakova"]2024-06-11T00:00:00 b68adf82dd4f418e64d7c97e7b0245e7f4b70e82This work-in-progress policy paper examines the European Union's strategy for governing AI technologies, in comparison with the approaches of US and China. It underlines the potential benefits of AI in specific sectors, with particular reference to non-market services: defense, healthcare, education, and public administration. At the same time, it will also address the related ethical concerns, that might become a key factor to consider in a fragmented geopolitical context. The paper emphasizes the European Union's cautious approach to regulating AI through initiatives like the Artificial Intelligence Act. It contrasts the EU's stance with the strategies of the US and China, who are leading in AI investments and innovation. Finally, the paper aims to provide policy recommendations for the EU to enhance its role in AI for non-market services, considering the global context and the need for an ethical AI development.Digital Government Research80The paper emphasizes the European Union's cautious approach to regulating AI through initiatives like the Artificial Intelligence Act and provides policy recommendations for the EU to enhance its role in AI for non-market services, considering the global context and the need for an ethical AI development.Proceedings of the 25th Annual International Conference on Digital Government Research["Emanuele Parisini"]2024-06-11T00:00:00 41375b6800f6e91b620848791f32b6bee6d84914Asian Bioethics Review1280The results show the lack of a clear definition of AI responsibility in healthcare and highlight the importance of ensuring responsible development and implementation of AI in healthcare.Asian bioethics review["Sarah Bouhouita-Guermech", "Hazar Haidar"]2024-06-11T00:00:00 34157d3e31425d7f742b45fde1a4527e7adf8cc5ABSTRACT Currently, the healthcare workplace is one of the most dangerous in the United States. Over a 3-month period in 2022, two nurses were assaulted every hour. Artificial intelligence (AI) has the potential to prevent workplace violence by developing unique patient insights through accessing almost instantly a patient's medical history, past institutional encounters, and possibly even their social media posts. De-escalating dialog can then be formulated, and hot-button topics avoided. AIs can also monitor patients in waiting areas for potential confrontational behavior.Many have concerns implementing AIs in healthcare. AIs are not expected to be 100% accurate, their performance is not compared with a computer but instead measured against humans. However, AIs are outperforming humans in many tasks. They are especially adept at taking standardized examinations, such as Board Exams, the Uniform Bar Exam, and the SAT and Graduate Record Exam. AIs are also performing diagnosis. Initial reports found that newer models have been observed to equal or outperform physicians in diagnostic accuracy and in the conveyance of empathy.In the area of interdiction, AI robots can both navigate and monitor for confrontational and illegal behavior. A human security agent would then be notified to resolve the situation. Our military is fielding autonomous AI robots to counter potential adversaries. For many, this new arms race has grave implications because of the potential of fielding this same security technology in healthcare and other civil settings.The healthcare delivery sector must determine the future roles of AI in relationship to human workers. AIs should only be used to support a human employee. AIs should not be the primary caregiver and a single human should not be monitoring multiple AIs simultaneously. Similar to not being copyrightable, disinformation produced by AIs should not be afforded 'free speech' protections. Any increase in productivity of an AI will equate with a loss of jobs. We need to ask, If all business sectors utilize AIs, will there be enough paid workers for the purchasing of services and products to keep our economy and society a float?Journal of patient safety390The healthcare delivery sector must determine the future roles of AI in relationship to human workers, and if all business sectors utilize AIs, will there be enough paid workers for the purchasing of services and products to keep the economy and society a float?Journal of patient safety["Kevin T Kavanagh", "Christine Pontus", "Lindsay E. Cormier"]2024-06-11T00:00:00 9d0e946dded390a6ef830a99c765c7800e713afdThis article discusses the socialization n of interactive media utilizing Canva, enhanced with Artificial Intelligence (AI), at SMA Negeri 6 Central Maluku. The initiative aims to modernize the educational tools available to teachers and students, promoting a more engaging and effective learning environment. Canva's AI-driven features facilitate the creation of dynamic and visually appealing educational content, making complex concepts easier to understand. The socialization process involved training sessions for teachers and interactive workshops for students, highlighting the practical applications of AI in education. The feedback from participants indicated a significant improvement in both teaching methodologies and student engagement. This project demonstrates the potential of AI-based tools in transforming traditional educational practices, paving the way for future innovations in the field of education.ARDHI : Jurnal Pengabdian Dalam Negri90The socialization n of interactive media utilizing Canva, enhanced with Artificial Intelligence (AI), at SMA Negeri 6 Central Maluku demonstrates the potential of AI-based tools in transforming traditional educational practices, paving the way for future innovations in the field of education.ARDHI : Jurnal Pengabdian Dalam Negri["Caroline Sri", "Athena Barus", "Jl. Ir. M. Putuhena", "Kec. Tlk Poka", "Kota Ambon", "Maluku Ambon"]2024-06-11T00:00:00 f3dbddfc252c4bcd5e45a75632ae2686188ddeddThe paper explores the intersection of artificial intelligence (AI), democracy, and the rhetoric of evidence-based policymaking (EBPM) within the context of the digital transformation. The case study focuses on the German Bundestag, investigating how Members of Parliament (MPs) discuss AI in their speeches and its relevance to policymaking. The theoretical framework combines insights from AI’s potential impact on democratic processes, policy cycle research against the backdrop of the digital transformation, and the utilization of AI as a knowledge technology. The research employs qualitative content analysis of parliamentary debates in the 19th and 20th legislative periods, examining patterns of argumentation related to AI. The preliminary findings indicate that the connection between AI and evidence-based policymaking remains nascent in the German case yet and thus there is a remarkable gap between already existing technological capacity and its political reproduction.Digital Government Research540The preliminary findings indicate that the connection between AI and evidence-based policymaking remains nascent in the German case yet and thus there is a remarkable gap between already existing technological capacity and its political reproduction.Proceedings of the 25th Annual International Conference on Digital Government Research["Anne Goldmann"]2024-06-11T00:00:00 df5e67b24ce7fd9e2d8737b2299e663ae77d7d25In the current era of digitalization, more and more people are seeing the possibility of making websites and applications in various industrial fields. The field of education is one of the benchmarks that can be made in various potentials. The purpose of the application of Artificial Intelligence is made to provide elementary school students as a means to be able to develop the learning ability, competence and creativity of elementary school students in the era of Digitalization which continues to grow today. In addition, this research also aims to examine the obstacles, benefits obtained, and influencing factors of the application of artificial intelligence. This research is based on technological advances that are dominated in the world of industry, telecommunications and information, and other sectors, so that the use of the latest technology needs to be maximized for the benefit of education. . The philosophical view of technology is that technology is used for positive purposes, especially for the benefit of mankind, not used for negative things such as warfare, mass destruction, or other things that can damage the presence of technology itself. This type of research is literature review by finding various scientific journals and scientific books relevant. The application of artificial intelligence for education will bring a new breakthrough for the application of science and technology-based learning, especially in the 21st century. The competence of parents and teachers in understanding the development of Science and Technology (Science and Technology) can be trained and improved with the presence of artificial intelligence technology. Therefore, it is expected that this research will trigger the emergence of new innovations in the field of education in the future. ABSTRAKPada era digitalisasi yang berkembang saat ini,semakin banyak orang yang melihat kemingkinan pembuatan website dan aplikasi di berbagai bidang industri.Bidang pendidikan menjadi salah satu tolak ukur yang bisa di jadikan dalam berbagai potensi. Tujuan penerapan Artificial Intelligence dibuat untuk memberikan siswa Sekolah Dasar sebagai sarana agar mampu mengembangkan kemampuan belajar,kompetensi dan kreativitas siswa Sekolah Dasar pada era Digitalisasi yang terus berkembang hingga saat ini. Selain itu, penelitian ini juga bertujuan untuk menelaah hambatan, manfaat yang diperoleh, serta faktor yang mempengaruhi dari penerapan artificial intelligence. Penelitian ini didasarkan atas kemajuan teknologi yang didominasi pada dunia industri, telekomunikasi dan informasi, serta sektor yang lain, sehingga pemanfaatan suatu teknologi mutakhir perlu dimaksimalkan bagi kepentingan dunia pendidikan. Pandangan ilmu filsafat tentang teknologi ini bahwa suatu teknologi digunkan untuk keperluan positif terutama bagi kepentingan umat manusia, bukan digunakan untuk hal-hal negatif seperti untuk peperangan, pemusnahan massal, maupun hal-hal lain yang dapat merusak kehadiran teknologi itu sendiri. Jenis penelitian ini adalah penelitian studi kajian pustaka dengan menemukan berbagai sumber dari jurnal-jurnal ilmiah dan buku ilmiah yang relevan. Penerapan artificial intelligence bagidunia pendidikan akan memunculkan terobosan baru bagi penerapan pembelajaran berbasis IPTEK terutama di abad ke 21. Kompetensi orang tua dan guru dalam memahami perkembangan Ilmu Pengetahuan dan Teknologi (IPTEK) dapat dilatih dan ditingkatkan dengan hadirnya teknologi artificial intelligence. Oleh sebab itu, diharapkan melalui penelitian ini akan memicu munculnya inovasi-inovasi baru dalam bidang pendidikan di masa mendatang.EDUCATIONAL : Jurnal Inovasi Pendidikan &amp; Pengajaran00EDUCATIONAL : Jurnal Inovasi Pendidikan &amp; Pengajaran["Siti Kholilah Siagian", "Khotna Sofiyah"]2024-06-11T00:00:00 3fbf70a03073fd04f2293044076b5e84afef66f7Generative Artificial Intelligence (AI) systems bring innovative ways of information provision and knowledge delivery. In the public sector, generative AI has the potential to decrease bureaucratic discretion in the decision-making process. Increasing reliance on this technology brings challenges of unfair treatment, colonized responses from the system, and data governance. Because of historical interaction, tribal communities are the most underrepresented in policy planning and implementation. Indigenous communities suffer from the neglect of tribal sovereignty by the U.S. federal government and limited accessibility and literacy in the digital world. Generative AI systems exacerbate these challenges with insufficient tribal input. However, the negative impact can be alleviated with digital equity and knowledge cocreation. Digital equity emphasizes the importance of tribal knowledge representation, and knowledge cocreation focuses on the collaboration between Indigenous communities and relevant actors in data governance for generative AI systems. This study proposes two research questions to discuss tribal knowledge cocreation in generative AI systems: (1) what are the biases in the system responses from the tribal perspective? (2) what are the potential resolutions for these problems? The findings from in-depth interviews with tribal members in the U.S. indicate that the insufficient articulation of tribal culture, the lack of crucial tribal historical events, and the inappropriate appellation of tribal nations are the primary drawbacks in the system responses. From the Indigenous perspective, tribal oral traditions, native publications and documents, and collaboration with tribal governments can address the problems of generative AI responses. This study contributes to the theory development of digital equity and knowledge cocreation in tribal generative AI system responses. Policy recommendations and future research agendas are included in this research.Digital Government Research350This study contributes to the theory development of digital equity and knowledge cocreation in tribal generative AI system responses by proposing two research questions to discuss tribal knowledge cocreation in generative AI systems.Proceedings of the 25th Annual International Conference on Digital Government Research["Yi-Fan Wang", "Yu-Che Chen", "Yen-Chen Huang", "Carol Redwing", "Chun-Hua Tsai"]2024-06-11T00:00:00 f07f7d7dea6773a4c39ee20f0282c0bc7266fcefArtificial intelligence (AI) has emerged as a promising technology in healthcare research, with the potential to revolutionize the way healthcare is delivered. AI can be applied to various aspects of healthcare research, such as improving disease diagnosis, predicting patient outcomes, and developing personalized treatment plans. This research paper aims to provide an overview of the current state of AI in healthcare research and its potential impact on the industry. We explore the various applications of AI in healthcare research, including natural language processing, image recognition, and machine learning. We also discuss the challenges associated with the integration of AI into healthcare research, such as data privacy concerns, ethical considerations, and the need for rigorous validation and testing. Finally, we highlight some of the recent advances in AI-based health research and the potential for future developments in this area. Overall, this research paper demonstrates that AI has the potential to transform medical research and improve patient outcomes, but requires careful consideration of ethical and regulatory issues to ensure its safe and effective integration into clinical practice. KEY WORDS Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing,INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Overall, this research paper demonstrates that AI has the potential to transform medical research and improve patient outcomes, but requires careful consideration of ethical and regulatory issues to ensure its safe and effective integration into clinical practice.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Mansi Jaiswal"]2024-06-11T00:00:00 16eb77725209ae8e6376190faaa3f865b819edd8Background: In recent years, social media and AI have indirectly taken control of our daily lives. We bring attention to the impact that social networks, photo-editing applications, and artificial intelligence have on potential patients when they are looking for a surgeon for a possible cosmetic surgery, as well as the criteria they consider in relation to the interest in the use of the internet by surgeons. Methods: A systematic review of the past 10 years (2014–2024) was conducted following the PRISMA structure. PubMed and Google Scholar were searched for articles containing the following terms: plastic/esthetic surgery, social media, AI, filters, dysmorphia. All articles were saved using Zotero software version 6.0.37. We reported a prospective study including a 141 patients applying for esthetic surgical interventions in the time interval between February and October 2021. It also involved 44 esthetic surgeons from Tunisian clinics. The influence of social media was evaluated using questionnaires made based on the literature. Results: Using the keyword search, 71 articles were found. A total of 19 articles were selected for data extraction. It was observed that in the last 3 years, the literature has focused on photo-editing and AI in the cosmetic surgery field. A total of 107 patients chose their surgeon based on a surgeon’s social medias rather than their reputation and their website. Conclusions: The increased advancements of the internet have clearly influenced decision making in the field of cosmetic surgery.Cosmetics200The increased advancements of the internet have clearly influenced decision making in the field of cosmetic surgery.Cosmetics["Malek Benamor", "\u0218. Luca", "Jed Bouguila", "Oxana-madalina Grosu", "Bianca Maria Avadani", "D. Moraru", "Mihaela Per\u021bea"]2024-06-11T00:00:00 d380bbdcbc25dfaaebd892ce686a3cbe55ea9030In 21st Century Artificial intelligence (AI) has the potential to enhance every component of information system at the individual, organizational and societal level. However, AI technology is being developed and commercialized at an unprecedented speed because of that business and industries are trying to adopt this new technology. From the last few years, we can see around large number of AI products are services are becoming a very essential part of day-to-day activities. The paper investigates on what is Artificial intelligence, trying to get an understanding on both positive as well as negative impact of AI on business and industries. The paper addresses the innovation in the AI, it impacts on business and future scope for business. The inference obtained from the research will provide a better understanding of how AI can help to transform the business operation and what is future scope of Artificial intelligence in business.International Journal of Advanced Multidisciplinary Research and Studies00The paper addresses the innovation in the AI, it impacts on business and future scope for business and the inference obtained will provide a better understanding of how AI can help to transform the business operation and what is future scope of Artificial intelligence in business.International Journal of Advanced Multidisciplinary Research and Studies["Sonali Yadav", "Ashwini Shinde", "Vishakha Patil", "Swati Kamble", "Anita Kumari"]2024-06-11T00:00:00 39a26e197b85e544b60f6ce651b212c20bdabd71This work focuses on applying explainable artificial intelligence (AI) techniques to improve the interpretability and reliability of models in two crucial scenarios: detecting collusion in public procurement auctions and identifying corruption in public contracts in Mexico. While these challenges are specific, the need for transparency and explainability in AI systems is universal, especially when it comes to high-impact government issues. Our analysis is based on case studies of these particular applications, highlighting how interpretability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) can be used to provide crucial insights into decisions of the algorithms involved. In doing so, our paper seeks to contribute to a broader understanding of how explainable AI can be applied in diverse government contexts. Additionally, we explore the ethical and practical implications of explainable AI in governance, highlighting its ability to promote greater transparency, accountability, and trust in automated decisions. By the end of this study, we hope to provide a more comprehensive view on how explainable AI can be a valuable tool in improving public governance, not only in relation to cases of collusion and corruption, but also in a wide range of critical government applications.Digital Government Research390Proceedings of the 25th Annual International Conference on Digital Government Research["I. Sampaio", "Sergio Fontes", "E. D. Andrade", "Flavia Bernardini", "Jos\u00e9 Viterbo"]2024-06-11T00:00:00 2125f08ee9adb127c7ad2f74ae04db1b5321beaeAim: The study aimed to determine the anxiety of nursing students about the emergence and use of artificial intelligence products. Material and Method: The data of this descriptive and cross-sectional study were collected between 02.01.2023 and 15.04.2023. The sample of the research consisted of 243 students. The data collection tool included an introductory information form and the Artificial Intelligence Anxiety Scale. T-test, and one-way ANOVA test were used to analyze the data. Results: 64.6% of the students had heard of artificial intelligence-supported devices used in healthcare, 54.7% thought that artificial intelligence applications were useful in ensuring patient safety, and 54.7% thought that the system would reduce the risk of making medical errors. The mean total score of the scale was 46.25 ± 9.66. There was a statistically significant relationship between thinking that artificial intelligence should be a course in education and thinking that artificial intelligence would be indispensable in surgical applications and the artificial intelligence anxiety scale (pBandırma Onyedi Eylül Üniversitesi Sağlık Bilimleri ve Araştırmaları Dergisi280There was a statistically significant relationship between thinking that artificial intelligence should be a course in education and thinking that artificial intelligence would be indispensable in surgical applications and the artificial intelligence anxiety scale.Bandırma Onyedi Eylül Üniversitesi Sağlık Bilimleri ve Araştırmaları Dergisi["P\u0131nar Ong\u00fcn", "Beytullah G\u00fcl", "\u0130brahim Enes Muslu", "Mert Mete Me\u015fe", "Sibel Erg\u00fcn"]2024-06-11T00:00:00 48c2c29041aca4907666b3d9d2da5f690a466ff8Trends in Medical Research482Trends in Medical Research["E. K. Oladipo", "S. F. Adeyemo", "Glory Jesudara Oluwasanya", "Omotayo Rachael Oyinloye", "Olawumi Hezekiah Oyeyiola", "Ifeoluwa David Akinrinmade", "Olubunmi Ayobami Elutade", "Dorcas Olayemi Areo", "Islamiyyah Olamide Hamzat", "Oluwakemi Deborah Olakanmi", "Israel Ifeoluwa Ayanronbi", "Akinwumi John Akanmu", "Faith Opeoluwa Ajekiigbe", "Mary Olawumi Taiwo", "Victor Michael Ogunfidodo", "Christiana Adewumi Adekunle", "Precious Oluwadamilola Adeleke", "David Ayo Olubunmi", "Precious Ayomide Adeogun", "Emmanuel Oluwagbenga Adejobi", "Samiat Arike Sanni", "Akinola Oluwatosin Ajibade", "H. Onyeaka", "N. Nnaji"]2024-06-11T00:00:00 0185c6727119fa3c8e868fdd54b1d3e1943306c0Penelitian ini mengembangkan aplikasi pembelajaran berbasis kecerdasan buatan (AI) untuk mata kuliah hukum ekonomi di perguruan tinggi, yang bertujuan untuk meningkatkan pemahaman dan efektivitas pembelajaran bagi mahasiswa. Fokus utama dari penelitian ini adalah merancang dan mengimplementasikan sebuah sistem yang memanfaatkan teknologi AI untuk menyajikan materi hukum ekonomi secara interaktif dan adaptif. Metodologi yang digunakan meliputi pengembangan prototipe aplikasi, pengujian fungsionalitas, serta analisis pengalaman pengguna. Hasil penelitian menunjukkan bahwa aplikasi ini dapat meningkatkan pemahaman konsep mahasiswa secara signifikan melalui modul pembelajaran yang dinamis dan responsif. Aplikasi ini juga mendapat respon positif dari pengguna yang menilai bahwa antarmuka yang ramah pengguna dan konten yang disesuaikan dengan kebutuhan individu sangat membantu dalam proses belajar mengajar. Penelitian ini memberikan wawasan baru tentang penerapan AI dalam pendidikan hukum ekonomi dan menyarankan pengembangan lebih lanjut untuk mencakup lebih banyak aspek kursus dan kebutuhan belajar yang beragamInformation system for educators and professionals01INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System["Yeni Haerani", "Sulfikar Sallu", "Dwi Ismiyana Putri"]2024-06-11T00:00:00 25f161605d7aedf624d6151843ba3294a2deef0000[]2024-06-11T00:00:00 e25a9fd30d211304962a9a9bfd0074c95ba0279eAbstract: This study aims to adapt to Turkish the "Scale for the assessment of non-experts' AI literacy" developed by Laupichler et al (2023). The scale consists of 31 items with three sub-dimensions: technical understanding, critical appraiaal, and practical applications. The data required for the validity and reliability study of the scale was collected from 642 undergraduate and graduate students studying in different departments of a state university in the fall semester of the 2023-2024 academic year. First of all, CFA was applied to the data according to the factor structure in the original scale, but as acceptable fit values could not be obtained as a result of the analysis, exploratory factor analysis was performed. While 325 of the collected data were used in exploratory factor analysis, 317 were used in confirmatory factor analysis. In the reliability analysis of the factor structure determined by EFA, KMO was calculated as =0.948. It was determined that the scale items were collected in 3 factors and explained 61.1% of the total variance ("critical thinking" is 25.8%, "technical knowledge" is 25.2%, and "practical applications" explains 10.2% of the total variance). As a result of EFA, it was seen that the sub-dimensions of some of the items in the original scale had changed, and since the factor load values of three items were very close to each other, they were removed from the scale. A As a result of CFA, which was conducted to evaluate whether the data supported the hypothesized relationships between the measured variables, Cronbach's Alpha value was found to be 0.90. As a result of the CFA analysis conducted with the 3 sub-dimensions and 28 items in the scale, the Chi-square value (X²=2.85; df=345, N=317, p< .001), which is the fit index of the model, has a good fit and is significant, SRMR=0.0545. and RMSEA=0.077 values and fit indices, it can be said that the model has an acceptable fit. The adapted scale is expected to be used in the future by educators, policymakers, and researchers in Turkish-speaking countries to establish a standardized framework for AI literacy and AI courses.Journal of Learning and Teaching in Digital Age00The adapted scale is expected to be used in the future by educators, policymakers, and researchers in Turkish-speaking countries to establish a standardized framework for AI literacy and AI courses.Journal of Learning and Teaching in Digital Age["Arzu Deveci Topal", "Asiye Toker G\u00f6k\u00e7e", "Canan Dilek Eren", "Aynur Kolburan Ge\u00e7er"]2024-06-11T00:00:00 a66adcce704e58fcd7fa2550cf6334b9d798733cInternational Review of Law, Computers &amp; Technology140International Review of Law, Computers &amp; Technology["Krishna Deo Singh Chauhan"]2024-06-11T00:00:00 6c783b07694c984a0741886611feecea67639bacMajor contributions have been made by Artificial intelligence (AI) technologies in the development of society. AI-powered assistants become popular among English as a Foreign Language (EFL) students in recent years. AI provides EFL students with more alternatives for customizing their learning experiences. Speak.google is a new tool of Google Search that helps students in learning English by enabling them to practice speaking interactively. The purpose of this study of is to provide an overview of how Speak.google may be used to help EFL students learn English speaking. This study performs descriptive qualitative research. The study shows that Speak.Google is able to help ESL students in learning language by helping them learn and practice English speaking. This AI is providing students with instant feedback and a range of alternate answers. Students can learn to pronounce a sentence and the meaning of each word in sentences. For EFL students who want to improve their English language skills, especially speaking skill, Speak.google is a promising artificial intelligence for them. ABSTRAKKontribusi besar telah diberikan oleh teknologi kecerdasan buatan (AI) dalam perkembangan masyarakat. Asisten yang didukung AI menjadi populer di kalangan siswa pembelajar Bahasa Inggris sebagai Bahasa Asing (EFL) di beberepa tahun terakhir . AI memberi lebih banyak alternatif kepada siswa EFL untuk menyesuaikan pengalaman belajar mereka. Speak.google adalah fitur baru di penulusuran Google yang membantu siswa belajar bahasa Inggris dengan memungkinkan mereka berlatih berbicara secara interaktif. Tujuan dari penelitian ini adalah untuk memberikan gambaran tentang bagaimana Speak.google dapat digunakan untuk membantu siswa EFL belajar berbicara bahasa Inggris. Penelitian ini menggunakan penelitian deskriptif kualitatif. Penelitian ini menunjukkan bahwa Speak.Google dapat membantu siswa ESL mempelajari bahasa dengan membantu mereka belajar dan berlatih berbicara bahasa Inggris. AI ini memberikan siswa umpan balik instan dan berbagai jawaban alternatif. Siswa dapat belajar mengucapkan kalimat dan arti setiap kata dalam kalimat. Bagi pelajar EFL yang ingin meningkatkan kemampuan berbahasa Inggris khususnya kemampuan berbicara, Speak.google merupakan kecerdasan buatan yang menjanjikan bagi mereka.STRATEGY : Jurnal Inovasi Strategi dan Model Pembelajaran01STRATEGY : Jurnal Inovasi Strategi dan Model Pembelajaran["Dewi Nurmayasari"]2024-06-11T00:00:00 17fba28c341ed0f496d6ca0254f4e508476f945eJournal for STEM Education Research655The findings reveal that behavioral intentions are shaped by multiple factors within the framework of the Theory of Planned Behavior, highlighting the need for targeted training and resource allocation for effective AI integration in life sciences education.Journal for STEM Education Research["L. Mnguni"]2024-06-11T00:00:00 cf2a8c29efde5517cc86378ade038d6974930b6aThe landscape of artificial intelligence (AI) has experienced a monumental shift with the emerging of Generative AI (GenAI), which has demonstrated to be a transformative tool across diverse sectors. GenAI outputs can span various digital formats, including text, images, videos, and audio, generating particular interest in the public sector. The growing interest of governments in integrating GenAI technologies in public sector operations is marked by the creation of emerging governance instruments and the formulation of soft laws, like standards, principles, and guidelines. This study aims to delve into the intricacies and potential risks associated with the deployment of GenAI within government. Through a qualitative content analysis, the research meticulously examines GenAI usage guidelines issued by Australia, Canada, New Zealand, the United Kingdom, and South Korea. The objective is to discern the risks acknowledged by these countries' soft laws and compare them with the risks identified by scholars in the field. The performed comparative analysis across countries suggest that the use of GenAI in the public sector raises common risks such as information leakage, data privacy, security, and concerns over public trust. By elucidating the varied risk perceptions across different national contexts, this study provides theoretical and practical implications related to the risks of GenAI within the public sector. Moreover, it sets a foundation for future research and policy development, ensuring that generative AI is used as a force for good in public governance.Digital Government Research464Comparison of GenAI usage guidelines issued by Australia, Canada, New Zealand, the United Kingdom, and South Korea suggest that the use of GenAI in the public sector raises common risks such as information leakage, data privacy, security, and concerns over public trust.Proceedings of the 25th Annual International Conference on Digital Government Research["Marco Antonio Beltran", "Marina Ivette Ruiz Mondrag\u00f3n", "Seung Hun Han"]2024-06-11T00:00:00 04547e3db73020662911701e4b5f362f717836b4This paper presents an advanced assistive technol- ogy system aimed at improving accessibility and independence for visually impaired individuals. Utilizing artificial intelligence (AI) and computer vision techniques, the system provides real- time auditory feedback to users about their surroundings. The core components of the system include image captioning, face recognition, and depth estimation, all integrated to offer a comprehensive understanding of the environment. The system captures live video feed through a webcam, processes the images using pre-trained models like CLIPSeg for image segmentation and DPT for depth estimation, and generates textual descriptions of detected objects and their spatial distances. These descriptions are translated from English to Kannada using Google’s transla- tion services and converted into speech with the gTTS library, ensuring accessibility for Kannada-speaking users. Additionally, the system employs face recognition to identify known individuals in the vicinity, providing personalized auditory notifications. The combination of these technologies enables the system to offer context-aware assistance, helping visually impaired users to navigate and interact with their surroundings more effectively. Experimental results demonstrate the system’s capability to deliver accurate, real-time feedback, highlighting its potential to significantly enhance the quality of life for visually impaired individuals.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Experimental results demonstrate the system’s capability to deliver accurate, real-time feedback, highlighting its potential to significantly enhance the quality of life for visually impaired individuals.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT[]2024-06-11T00:00:00 67999e1d2a7503944c826d2b8a147b32f966402fIn an era dominated by rapid technological change, the fusion of Artificial Intelligence (AI), Machine Learning (ML), and Psychology stands as a cornerstone of digital innovation, offering profound transformations across various industries. This paper examines the intricate synergies and ethical dimensions of integrating AI and ML with psychological principles, showcasing their collective capacity to reshape human interactions and decision-making processes in the digital landscape. We explore foundational concepts, detail the progressive evolution of these technologies, and discuss their current applications in creating personalized user experiences. Through a rigorous analysis, we address the ethical imperatives and challenges that arise, emphasizing the need for responsible innovation while harnessing the power of data-driven insights. Our interdisciplinary approach not only reveals the transformative potential of AI and ML when intertwined with psychology but also advocates for a harmonious integration that respects human cognitive and emotional dimensions. This exploration aims to guide stakeholders through the complexities of these technologies, paving the way for ethical practices and innovative solutions in the digital era.Journal of Mathematical Techniques and Computational Mathematics00The intricate synergies and ethical dimensions of integrating AI and ML with psychological principles, showcasing their collective capacity to reshape human interactions and decision-making processes in the digital landscape are examined.Journal of Mathematical Techniques and Computational Mathematics[]2024-06-11T00:00:00 f60d94e61266ce05c5a87cb34ab8c352939406a3Artificial Intelligence (AI) is transforming professional marketing practices as well as marketing education. This paper employs the science fiction novel “The Space Merchants” by Pohl and Kornbluth as a metaphorical framework for critically examining AI integration in marketing curricula. The authors review existing literature examining the impacts of AI on marketing practices and education. Furthermore, they establish a qualitative methodology analyzing a real-world case study, current applications of AI in marketing education, and discuss ethical frameworks surrounding the use of AI. Utilizing “The Space Merchants” satirical portrayal of consumerism, they reflect on AI’s potential to commodify marketing education, homogenize student thought, and undermine educational integrity. Also examined are AI’s capabilities to enhance personalization, engagement, and teaching efficiency. Ultimately, the paper argues for educators’ indispensable role in ethically leveraging AI to enrich the student experience. The unique fictional lens highlights the need to balance advancement and responsibility in AI-enabled marketing education. This comprehensive ethical analysis aims to significantly advance the discourse on AI's evolving function in shaping the next generation of marketing professionals. Furthermore, by adopting a transdisciplinary approach through the integration of science fiction and ethical critique, the paper seeks to catalyze broader transdisciplinary conversations between the technical and social sciences on the impacts of emerging technologies like AI on education.Brazilian Journal of Business163By adopting a transdisciplinary approach through the integration of science fiction and ethical critique, the paper seeks to catalyze broader transdisciplinary conversations between the technical and social sciences on the impacts of emerging technologies like AI on education.Brazilian Journal of Business["Jasmin B. Cowin", "Cristo Leon", "Sabra Brock", "Xavier Oviedo Torres"]2024-06-11T00:00:00 898e246970819fa30d827ff53b11fc71bac07183The fourth industrial revolution (4IR) demands transformative leadership, as leaders grapple with gaps and questions, particularly in optimizing Artificial Intelligence (AI). This paper seeks to comprehend public sector leaders' perceptions, identifying prevalent traits and skills amid AI adoption and efficiency. Employing the PRISMA methodology for a systematic literature review, the study reveals a dearth of research on this topic. Combining the systematic literature review with traditional leadership theory, a PLS-SEM model tests 22 statistical hypotheses for empirical analysis. Results indicate a positive correlation between leadership traits and skills with AI adoption and efficiency. This highlights the pivotal role of prepared leaders in successfully integrating AI, ensuring effective uptake and efficient utilization for optimal outcomes. Insights highlight leaders' essential engagement in supporting, preparing, and innovating, underscoring their central role in optimizing AI adoption.Digital Government Research452A positive correlation between leadership traits and skills with AI adoption and efficiency is indicated, highlighting the pivotal role of prepared leaders in successfully integrating AI, ensuring effective uptake and efficient utilization for optimal outcomes.Proceedings of the 25th Annual International Conference on Digital Government Research["David Valle-Cruz", "Rigoberto Garc\u00eda-Contreras", "J. P. Mu\u00f1oz-Ch\u00e1vez"]2024-06-11T00:00:00 02c1f92094fefec670ab8de6291f9741d18d9196Purpose The growing integration of artificial intelligence (AI) assistants and voice assistants provides a platform for AI to enter consumers’ everyday lives. As these voice assistants become ubiquitous, their widespread adoption underscores the need to understand how to create voice assistants that can naturally interact with and support users. Grounded in the stereotype content model from social psychology, this study aims to investigate the influence of perceived humanness and personality on building trust and continuous usage intentions in voice assistants. Specifically, a fresh perspective examining the determining factors that shape personality trait perceptions of competence and warmth in voice assistants is proposed. Design/methodology/approach An online survey of 457 participants and structural equation modeling is conducted to validate the research model. Findings Anthropomorphism, social presence and interactivity drive perceived warmth, whereas performance and effort expectations drive perceived competence. Perceived competence and perceived warmth together positively affect users’ trust in voice assistants, leading to a higher likelihood of continuous usage intentions. Originality/value This research provides profound theoretical contributions to the emerging field of human-AI interaction and offer practical implications for marketers aiming to leverage voice assistant personalities to build trusted and long-lasting interactions. Journal of Product &amp; Brand Management672Perceived competence and perceived warmth together positively affect users’ trust in voice assistants, leading to a higher likelihood of continuous usage intentions and practical implications for marketers aiming to leverage voice assistant personalities to build trusted and long-lasting interactions.Journal of Product &amp; Brand Management["S.H. Hsieh", "Crystal T. Lee"]2024-06-11T00:00:00 0f8c2031ac2143670592a99adc6860e6f11dd04dArtificial Intelligence (AI) plays a crucial role in medical field and has the potential to revolutionize healthcare practices. However, the success of AI models and their impacts hinge on the synergy between AI and medical specialists, with clinicians assuming a dominant role. Unfortunately, the intricate dynamics and interactions between AI and clinicians remain undiscovered and thus hinder AI from being translated into medical practice. To address this gap, we have curated a groundbreaking database called AI.vs.Clinician. This database is the first of its kind for studying the interactions between AI and clinicians. It derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency -- Sepsis -- from 14 medical centers across China. For the patient cohorts well-chosen from MIMIC databases, the AI-related information comprises the model property, feature input, diagnosis decision, and inferred probabilities of sepsis onset presently and within next three hours. The clinician-related information includes the viewed examination data and sequence, viewed time, preliminary and final diagnosis decisions with or without AI assistance, and recommended treatment.arXiv.org302This database is the first of its kind for studying the interactions between AI and clinicians and derives from 7,500 collaborative diagnosis records on a life-threatening medical emergency -- Sepsis -- from 14 medical centers across China.ArXiv["Wanling Gao", "Yuan Liu", "Zhuoming Yu", "Dandan Cui", "Wenjing Liu", "Xiaoshuang Liang", "Jiahui Zhao", "Jiyue Xie", "Hao Li", "Li Ma", "Ning Ye", "Yumiao Kang", "Dingfeng Luo", "Peng Pan", "Wei Huang", "Zhongmou Liu", "Jizhong Hu", "Fan Huang", "Gangyuan Zhao", "Chongrong Jiang", "Tianyi Wei", "Zhifei Zhang", "Yunyou Huang", "Jianfeng Zhan"]2024-06-11T00:00:00 c37fd9c1303cce61ad8e84b8c4fb8bf36ba8fa70As a result of the prominence of generative artificial intelligence across diverse fields, it has become necessary for governments to develop national strategies for directing the ethical use of artificial intelligence to respect fundamental human values. This paper explores the role of Generative Artificial Intelligence (GAI) in technology sovereignty, its contributions, and benefits for the government, associated risks, and challenges, and how it influences government policies. It begins with examining GAI's capabilities to comprehend how it understands natural language, trains on existing data, and generates realistic outputs, followed by a discussion of its potential benefits for governments that enable them to act independently and autonomously in diverse sectors. It highlights how it can empower them to administer technological ecosystems, promote domestic innovation, and facilitate policy-making processes. However, contrary to its benefits, GAI is also capable of inflicting negative consequences on society. Therefore, the paper also addresses the risks and challenges associated with GAI that necessitate reflection on existing policies and developing new ones that align with a nation's legal frameworks. Exploring the influence of GAI on government policies, the paper highlights the significance of collaboration in policy-making endeavors to ensure ethical future developments and bring value to public interest and democratic values. This comprehensive analysis aims to shed light on the responsible and ethical use of GAI to preserve human rights, promote economic growth, sustain social justice, and inform the responsible use of GAI within the framework of technology sovereignty.Digital Government Research682This comprehensive analysis aims to shed light on the responsible and ethical use of GAI to preserve human rights, promote economic growth, sustain social justice, and inform the responsible use of GAI within the framework of technology sovereignty.Proceedings of the 25th Annual International Conference on Digital Government Research["Noor Alnahhas", "Dima Yousef"]2024-06-11T00:00:00 bdc9214e1b66b943cefea3caca1428c655f590cfArtificial intelligence (AI) could drive both positive and negative impacts on society, prompting recent studies to advocate for a more inclusive approach to AI initiatives aimed at amplifying benefits and mitigating drawbacks. In local communities, public libraries are often deemed to have crucial potential in engaging diverse targeted audiences with educational and informational needs. Given this context, this paper aims to investigate the innovative programs, services, and strategies implemented by public libraries with the goal of raising awareness about AI and fostering inclusive civic engagement in AI initiatives in their communities. In order to achieve our goal, we searched libraries’ websites and identified 105 AI-related events held by libraries around the US and Canada. We classified these practices under five categories aimed at raising awareness about AI and building competencies related to AI: lectures and podcasts, hands-on workshops, seminars and conversations, exhibitions, and makerspaces. We also acknowledged that most of these initiatives take place in collaboration. However, we also found that there is no particular focus on inclusive AI and/or marginalized communities and that public libraries could therefore expand their role but providing spaces of community participation particularly targeted at individuals with diverse socio-economic, racial, and cultural backgrounds.Digital Government Research651It is found that there is no particular focus on inclusive AI and/or marginalized communities and that public libraries could therefore expand their role but providing spaces of community participation particularly targeted at individuals with diverse socio-economic, racial, and cultural backgrounds.Proceedings of the 25th Annual International Conference on Digital Government Research["Zong-Xian Huang", "Mila Gasc\u00f3-Hern\u00e1ndez", "Aryamala Prasad", "J. Gil-Garc\u00eda"]2024-06-11T00:00:00 a738d0916ccf3b8970dabe488a3de88f6d2dd4afGenerative Artificial Intelligence (Gen AI) stands at the forefront of the banking sector's technological revolution, promising enhancements in decision-making, risk management, and customer interaction. This paper examines Gen AI's potential to inject innovation and efficiency into banking services, with an estimated value addition of up to $340 billion annually. Grounded in advancements in NLP through Transformer architecture and evolving GPT models, Gen AI's applications in the banking industry are extensive. They range from personalizing customer service with AI-driven chatbots to revolutionizing credit scoring and trading strategies. However, alongside these opportunities, the paper addresses the significant challenges of regulatory compliance, ethical data usage, and the technical integration of AI systems. With the impending release of the EU's AI Act and existing GDPR and DORA, financial institutions must strategize to align with new standards while harnessing Gen AI's capabilities for process optimization and enhanced service delivery. The role of international standards such as ISO/IEC 42001:2023, ISO 31000:2018, ISO/IEC 23894:2023, NIST AI 600-1 and ISO/IEC 23053:2022 is considered to be beneficial in establishing a common framework for managing AI systems, ensuring data integrity and promoting transparency. By adopting these standards, banks can facilitate compliance across various jurisdictions, enhancing operational consistency and reliability – but certain significant limitations in addressing specific regulatory requirements must be taken into account. The paper concludes that Gen AI's future in banking will be transformative, driven by the industry's need to balance technological innovation with ethical and regulatory requirements and process standardization, which will lead to more transparent, personalized and efficient banking services.Mediterranean Conference on Embedded Computing321Gen AI's future in banking will be transformative, driven by the industry's need to balance technological innovation with ethical and regulatory requirements and process standardization, which will lead to more transparent, personalized and efficient banking services.2024 13th Mediterranean Conference on Embedded Computing (MECO)["Ive Botunac", "Natalija Parlov", "Jurica Bosna"]2024-06-11T00:00:00 c53363d6f3b039933158aa9d50772c2c6b920fe0Previous literature has shown that generative artificial intelligence (GAI) software, including large language model (LLM) chatbots, might contribute to qualitative research studies. However, there is still a need to examine the relationships between researchers, GAI technologies, data, and findings. To address this need, our team conducted a thematic analysis of our reflexive journals from an LLM chatbot-assisted research project. We identified four roles that researchers adopted: managers closely monitored the LLM's work, teachers instructed the LLM on theories and methods, colleagues openly discussed the data with the LLM, and advocates worked with the LLM to improve user experiences. Planning for and playing with multiple roles also helped to enrich the research process. This study underscores the potential for using conversational roles as a framework to support reflexivity when working with GAI technologies on qualitative research.Journal of Technical Writing and Communication171The potential for using conversational roles as a framework to support reflexivity when working with GAI technologies on qualitative research is highlighted.Journal of Technical Writing and Communication["Luke Thominet", "Jacqueline Amorim", "Kristine Acosta", "V. K. Sohan"]2024-06-11T00:00:00 107673106ecd93bf9573aa7122b1453c07a87623Due to the rapid proliferation of artificial intelligence applications, some vulnerabilities in security and ethical issues emerge. With these applications, data such as text, images, audio and video can be easily produced. In order to ensure stability in issues such as security , ethics and quality, it is necessary to identify the data produced by artificial intelligence applications. For this purpose, this study focuses on the classification of images created with artificial intelligence applications and real images. In the study , a dataset containing images produced by artificial intelligence and real images was used. There are a total of 975 images in the dataset. The features of the images in the dataset were extracted with SqueezeNet, InceptionV3 and VGG19 pre-trained CNN (Convolutional Neural Network) models. Classification of features was made with ANN (Artificial Neural Network), KNN (K Nearest Neighbor) and SVM (Support Vector Machine) machine learning methods. The highest classification success was obtained from the InceptionV3+ANN model. It is anticipated that the proposed models can be used to detect images produced with artificial intelligence applications. However, it has been determined that more data is needed to fully solve this challenging task.Mediterranean Conference on Embedded Computing161It is anticipated that the proposed models can be used to detect images produced with artificial intelligence applications, but it has been determined that more data is needed to fully solve this challenging task.2024 13th Mediterranean Conference on Embedded Computing (MECO)["Yavuz Selim Taspinar", "Ilkay C\u0131nar"]2024-06-11T00:00:00 3afccd091db3cc402d0a3c57b66c06d4f4ec3c29Artificial intelligence (AI) integration into education has received significant global attention, sparking a need for comprehensive regulatory frameworks for governance. In this commentary, we first examine the role of AI in education and how it is integrated with the teaching and learning process. It also discusses the impact of AI on higher education through specific case studies and tries to illuminate the current/emerging trends, challenges, and potential future directions. Furthermore, it highlights insights from global initiatives, policy frameworks, and ethical standards adopted by prominent organizations to govern AI in higher education. The study concludes that the optimal use of these AI apps can only be harnessed through proper transparency and ethical balance.Digital Government: Research and Practice623The study concludes that the optimal use of these AI apps can only be harnessed through proper transparency and ethical balance.Digital Government: Research and Practice["Mehul Mahrishi", "A. Abbas", "Mohammad Khubeb Siddiqui"]2024-06-12T00:00:00 5c66387195415ff631fe97e6bc3ba2d01791b693Purpose Artificial intelligence (AI) is a cutting-edge new reality already having an unprecedented impact on society, the economy and businesses. Its future developments and long-term influence are still largely unknown. This article aims to examine AI’s potential benefits and challenges to corporate governance mechanisms, focusing on the board of directors. Design/methodology/approach The paper theoretically explores the influence of artificial intelligence on the board of directors’ capabilities, roles and functions. Findings Concerning rethinking board functioning in the era of artificial intelligence, the paper analyzes how artificial intelligence can impact the board of directors. It proposes some recommendations on how directors can more effectively integrate artificial intelligence into the boardroom, including establishing an internal artificial intelligence committee composed of experts with technical knowledge dedicated to managing artificial intelligence-related potential threats and opportunities. Practical implications Companies are invited to have some technical knowledge and expertise on artificial intelligence on the boards, fostering directors to upskill themselves in the new artificial intelligence technologies and establishing an ad-hoc internal committee. Policymakers are expected to keep pace with the growing proliferation of artificial intelligence solutions, defining a sharp regulatory framework. Originality/value The study advances knowledge in the corporate governance literature by shedding light on the effects of artificial intelligence on boards of directors and suggesting a set of best practices for its effective implementation. Corporate Governance: The International Journal of Business in Society582AI’s potential benefits and challenges to corporate governance mechanisms, focusing on the board of directors, are examined, shedding light on the effects of artificial intelligence on boards of directors and suggesting a set of best practices for its effective implementation.Corporate Governance: The International Journal of Business in Society["Paolo Agnese", "Francesca Romana Arduino", "Domenico Di Prisco"]2024-06-12T00:00:00 000e0ccb92c2fcde50f612e53515628592d4c593Lung cancer, which accounts for about 18% of all cancer-related deaths worldwide, has a dismal 5-year survival rate of less than 20%. Survival rates for early-stage lung cancers (stages IA1, IA2, IA3, and IB, according to the TNM staging system) are significantly higher, underscoring the critical importance of early detection, diagnosis, and treatment. Ground-glass nodules (GGNs), which are commonly seen on lung imaging, can be indicative of both benign and malignant lesions. For clinicians, accurately characterizing GGNs and choosing the right management strategies present significant challenges. Artificial intelligence (AI), specifically deep learning algorithms, has shown promise in the evaluation of GGNs by analyzing complex imaging data and predicting the nature of GGNs, including their benign or malignant status, pathological subtypes, and genetic mutations such as epidermal growth factor receptor (EGFR) mutations. By integrating imaging features and clinical data, AI models have demonstrated high accuracy in distinguishing between benign and malignant GGNs and in predicting specific pathological subtypes. In addition, AI has shown promise in predicting genetic mutations such as EGFR mutations, which are critical for personalized treatment decisions in lung cancer. While AI offers significant potential to improve the accuracy and efficiency of GGN assessment, challenges remain, such as the need for extensive validation studies, standardization of imaging protocols, and improving the interpretability of AI algorithms. In summary, AI has the potential to revolutionise the management of GGNs by providing clinicians with more accurate and timely information for diagnosis and treatment decisions. However, further research and validation are needed to fully realize the benefits of AI in clinical practice.Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases01Artificial intelligence has the potential to revolutionise the management of GGNs by providing clinicians with more accurate and timely information for diagnosis and treatment decisions, but further research and validation are needed to fully realize the benefits of AI in clinical practice.Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases["Y. Li", "Y. Wang", "Z. Qiu"]2024-06-12T00:00:00 da5d9e58e40410ac2c01fd0523aa54d1b8bbc99bIn recent years, artificial intelligence (AI) rapidly accelerated its influence and is expected to promote the development of Earth system science (ESS) if properly harnessed. In application of AI to ESS, a significant hurdle lies in the interpretability conundrum, an inherent problem of black-box nature arising from the complexity of AI algorithms. To address this, explainable AI (XAI) offers a set of powerful tools that make the models more transparent. The purpose of this review is twofold: First, to provide ESS scholars, especially newcomers, with a foundational understanding of XAI, serving as a primer to inspire future research advances; second, to encourage ESS professionals to embrace the benefits of AI, free from preconceived biases due to its lack of interpretability. We begin with elucidating the concept of XAI, along with typical methods. We then delve into a review of XAI applications in the ESS literature, highlighting the important role that XAI has played in facilitating communication with AI model decisions, improving model diagnosis, and uncovering scientific insights. We identify four significant challenges that XAI faces within the ESS, and propose solutions. Furthermore, we provide a comprehensive illustration of multifaceted perspectives. Given the unique challenges in ESS, an interpretable hybrid approach that seamlessly integrates AI with domain-specific knowledge appears to be a promising way to enhance the utility of AI in ESS. A visionary outlook for ESS envisions a harmonious blend where process-based models govern the known, AI models explore the unknown, and XAI bridges the gap by providing explanations.arXiv.org2851This review of XAI applications in the ESS literature highlights the important role that XAI has played in facilitating communication with AI model decisions, improving model diagnosis, and uncovering scientific insights, and identifies four significant challenges that XAI faces within the ESS, and proposes solutions.ArXiv["Feini Huang", "Shijie Jiang", "Lu Li", "Yongkun Zhang", "Ye Zhang", "Ruqing Zhang", "Qingliang Li", "Danxi Li", "Shangguan Wei", "Yongjiu Dai"]2024-06-12T00:00:00 9b7b898cb5fb29f37aab25323f7ac52a1097f2beThe article examines certain features of the phenomenon of a legal entity, including the scope and content of the regulatory wording “on behalf of and in the interests of” a legal entity enshrined in the Criminal Code of Ukraine, as well as theoretical aspects of application of criminal law measures to a legal entity. The author makes a reasoned assumption that one of the main reasons for the emergence of the legal entity phenomenon as a full-fledged persona of legal relations was the need to remove all interested individuals from legal control and liability. The author considers the granting of a legal entity with a category of certain interests as an act of anthropomorphisation, i.e. transfer of positive and negative human traits and characteristics to a legal entity. From the issues under consideration, the author moves on to the legal status of an artificial intelligence algorithm, including the possibility and/or expediency of recognising it as a persona of legal relations. To answer the question of the limits and methods of liability of a legal entity or a highly developed artificial intelligence algorithm, the author proposes to look for weaknesses by analogy with those of a human. The author makes proposals for improving the provisions of the current legislation of Ukraine.INFORMATION AND LAW01INFORMATION AND LAW["O. Radutniy"]2024-06-12T00:00:00 ffa0fc549b7ac1f7e7c55be0110f8c8f12347a44Artificial Intelligence (AI) is a tool that is useful for enabling and sustaining the Metaverse gaming experience by infusing virtual reality (VR), augmented reality (AR), extended realities (XR), and blockchain. The current research focused on identifying the impact of AI in leveraging immersive experiences and improving human interaction, which plays a crucial role in Metaverse gaming. A quantitative analysis carried out surveys from 200 randomly sampled respondents involved in Metaverse gaming. Using SPSS 26.0, correlation analysis showed that association between the values of ‘r’ of variables Immersive Gaming Experience (r=0.983**), Deep Learning Collaboration (r=0.957**) and Increased Human Interaction (r=0.979**) are greater than 0.7 depicting strong correlation with Metaverse gaming. Regression analysis further confirmed that the role of AI in enhancing the Metaverse gaming experience and human interaction is significant. With the considerable success of AI in Metaverse, the role of DL algorithms is also groundbreaking in leveraging game balance in multiplayer games, satisfying play-testers and designers who own valuable features, real-time rendering, and multi-user design collaboration.International Journal of Metaverse402Regression analysis confirmed that the role of AI in enhancing the Metaverse gaming experience and human interaction is significant and the role of DL algorithms is also groundbreaking in leveraging game balance in multiplayer games.International Journal of Metaverse["Omar Alotaibi"]2024-06-12T00:00:00 08dd535f006269fcc7d2d2de81770b53f69926dcThe aim of this study is to try to explain the factors that are thought to affect consumers' attitudes towards online advertisements guided by artificial intelligence. In this context, by utilizing the TAM model, innovation value, trust and perceived risk variables were added to the research model developed to explain the attitudes of individuals towards online advertisements guided by artificial intelligence. Although it is observed that the trust and perceived risk factors added to the model do not have a significant effect on AI-directed ads, it is thought that the non-significance of the two proposed hypotheses may be due to the data set. Because the literature in which the research model was developed shows that the perceived risk factor has a negative effect on attitudes. In this current study, it was observed that perceived risk had a negative effect on attitudes (R²=-0.038, p≤ ,106) but the hypothesis test was not significant. Similarly, although it was observed that trust had a positive effect on attitudes (R²=0.050, p≤ ,117), the hypothesis test was not significant. On the other hand, perceived usefulness (R²=-0,407 p≤ ,05), perceived ease of use (R²=-0,507, p≤ ,05), perceived novelty (R²=-0,186, p≤ ,05) positively affect attitudes towards AI-directed advertisements.İmgelem02It was observed that perceived risk had a negative effect on attitudes but the hypothesis test was not significant, and perceived usefulness, perceived usefulness, perceived ease of use, and perceived novelty positively affect attitudes towards AI-directed advertisements.İmgelem["Simge Aksu", "Bet\u00fcl \u00c7epni \u015eener"]2024-06-12T00:00:00 81d09279a87a60a70a7b0946e3d3f86d2e59c1a8Abstract Background Artificial intelligence (AI) is increasingly entering and transforming not only medical research but also clinical practice. In the last 10 years, new AI methods have enabled computers to perform visual tasks, reaching high performance and thereby potentially supporting and even outperforming human experts. This is in particular relevant for colorectal cancer (CRC), which is the 3rd most common cancer type in general, as along the CRC patient journey many complex visual tasks need to be performed: from endoscopy over imaging to histopathology; the screening, diagnosis, and treatment of CRC involve visual image analysis tasks. Summary In all these clinical areas, AI models have shown promising results by supporting physicians, improving accuracy, and providing new biological insights and biomarkers. By predicting prognostic and predictive biomarkers from routine images/slides, AI models could lead to an improved patient stratification for precision oncology approaches in the near future. Moreover, it is conceivable that AI models, in particular together with innovative techniques such as single-cell or spatial profiling, could help identify novel clinically as well as biologically meaningful biomarkers that could pave the way to new therapeutic approaches. Key Messages Here, we give a comprehensive overview of AI in colorectal cancer, describing and discussing these developments as well as the next steps which need to be taken to incorporate AI methods more broadly into the clinical care of CRC.Digestion02A comprehensive overview of AI in colorectal cancer is given, describing and discussing these developments as well as the next steps which need to be taken to incorporate AI methods more broadly into the clinical care of CRC.Digestion["N. Reitsam", "J. Enke", "Kien Vu Trung", "Bruno M\u00e4rkl", "J. N. Kather"]2024-06-12T00:00:00 3a5070fa73d789103f8951ebd60eeb6cd0df8d3bPower system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided.IET Generation, Transmission &amp; Distribution511Insight into the transformative potential of ML in shaping the future of power system asset management are provided and the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector are demonstrated.IET Generation, Transmission &amp; Distribution["Gopal Lal Rajora", "M. A. Sanz-Bobi", "L. B. Tjernberg", "Jos\u00e9 Eduardo Urrea Cabus"]2024-06-12T00:00:00 f9c82cd39d79dc2d94db9baaa354f47db65c9c4cFederated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data. To alleviate the heterogeneous data quality among clients, artificial intelligence-generated content (AIGC) can be leveraged as a novel data synthesis technique for FL model performance enhancement. Due to various costs incurred by AIGC-empowered FL (e.g., costs of local model computation and data synthesis), however, clients are usually reluctant to participate in FL without adequate economic incentives, which leads to an unexplored critical issue for enabling AIGC-empowered FL. To fill this gap, we first devise a data quality assessment method for data samples generated by AIGC and rigorously analyze the convergence performance of FL model trained using a blend of authentic and AI-generated data samples. We then propose a data quality-aware incentive mechanism to encourage clients’ participation. In light of information asymmetry incurred by clients’ private multi-dimensional attributes, we investigate clients’ behavior patterns and derive the server's optimal incentive strategies to minimize server's cost in terms of both model accuracy loss and incentive payments for both complete and incomplete information scenarios. Numerical results demonstrate that our proposed mechanism exhibits highest training accuracy and reduces up to 53.34% of the server's cost with real-world datasets, compared with existing benchmark mechanisms.IEEE Transactions on Mobile Computing461This work first devise a data quality assessment method for data samples generated by AIGC and rigorously analyze the convergence performance of FL model trained using a blend of authentic and AI-generated data samples, and proposes a data quality-aware incentive mechanism to encourage clients’ participation.IEEE Transactions on Mobile Computing["Guangjing Huang", "Qiong Wu", "Jingyi Li", "Xu Chen"]2024-06-12T00:00:00 1eb11d3c137356bb6b670b082fdb91583b75fe84Students and working professionals alike may benefit from this book’s thorough coverage of AI and ML’s foundational concepts. All facets of human existence have been touched by the vast body of information known as artificial intelligence (AI). Theory, mathematics, and coding are the three pillars upon which every artificial intelligence (AI) subject rests, with “Machine Learning algorithms” serving as a subset within AI. A comprehensive overview to AI and ML, this book delves deep into the most important areas of study.00This book delves deep into the most important areas of study of AI and ML, with “Machine Learning algorithms” serving as a subset within AI.["Dr. Suma R", "Mrs. Bhavya N Javagal", "Dr Prabha R", "Mr. Dineshkumar Munikrishnan"]2024-06-12T00:00:00 c35c2e429e206f5f5b702ae9f1df2cb59ce59d27In this paper, we introduce an artificial intelligence-based system designed for the segmentation and auxiliary diagnosis of focal liver lesions. This system can effectively segment lesions in both singlephase non-contrast CT images and multiphase contrast-enhanced CT images. Additionally, it has the capability to synchronize CT data from Picture Archiving and Communication Systems (PACS) and to visualize the segmentation results. Our AI system aims to meet the demand for rapid screening of liver lesions in primary hospitals and physical examination institutions, while also fulfilling the more precise segmentation needs of provincial and municipal hospitals.2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII)160An artificial intelligence-based system designed for the segmentation and auxiliary diagnosis of focal liver lesions that can effectively segment lesions in both singlephase non-contrast CT images and multiphase contrast-enhanced CT images is introduced.2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII)["Jing Gu", "Zhi-Ling Yu", "Wei Zhang", "Haoyu Hu"]2024-06-12T00:00:00 ccf0f181d245b8cd6af3ec0d30c7da19fb2b98f8Utilization of Artificial Intelligence-Based Paraphrasing Improves Efficiency of Student Assignment Completion in Surabaya. In this era of over-evolving technology, artificial intelligence technology (AI) has made significant contributions in various fields, including education. One potential implemantation of artificial intelligence technology is an automated AI-based online paraphrasing tool in education 4.0 era. This research aims to examine the effectiveness of using AI-based online paraphrasing in improving the efficiency of student assigntment completion in Surabaya. The research metodolgy involved a survey with student from various universities in Surabaya with a descriptive approach technique. The results show that the use of AI-based online paraphrasing tools can significantlu reduce the time needed to complete assignments without reducing the quality of the content. This tools helps students to understand the material, develop writing skills, and avoid plagiarism. This research result is expected to encourage the use of digital technology in the learning process, which is expected to improve academic productivity. Thus, AI-based online paraphrasing tools can be part of a more modern and effective learning strategy.Repeater : Publikasi Teknik Informatika dan Jaringan70The results show that the use of AI-based online paraphrasing tools can significantlu reduce the time needed to complete assignments without reducing the quality of the content.Repeater : Publikasi Teknik Informatika dan Jaringan["Reza Putri Angga", "Kanessa Jasmine", "Sharleen Agustine", "M. Aryasatya", "Natalia Desy Anggraini"]2024-06-12T00:00:00 e81b3d87b0485c66a3e1f2fc7f4ae2608e110865This research aims to examine the impact of the use of Artificial Intelligence (AI) in the criminal justice system in Indonesia on human rights and address the misuse of AI algorithms. The method used is normative legal research with secondary data analysis from literature study. The novelty of this research lies in its particular focus on the implications of AI use on human rights in criminal justice and mitigation strategies for algorithm misuse. The contribution of this research is to provide insight into the role of AI in law enforcement as well as regulative and practical recommendations. The results show that AI has great potential in improving the efficiency of the justice system, but also poses a risk of human rights violations if not properly regulated. Therefore, special regulations, transparency in the use of AI by law enforcement, and public education are needed to ensure fairness and safety in its useSinergi International Journal of Law310Examination of the impact of the use of Artificial Intelligence in the criminal justice system in Indonesia shows that AI has great potential in improving the efficiency of the justice system, but also poses a risk of human rights violations if not properly regulated.Sinergi International Journal of Law["Syahrir Nur Dachlan", "D. Karauwan", "Nurjana Lahangatubun"]2024-06-12T00:00:00 25135855ae2b525d664aebb86da9d3e25986ed03Purpose of review Artificial intelligence (AI) is now integrated in our daily life. It has also been incorporated in medicine with algorithms to diagnose, recommend treatment options, and estimate prognosis. Recent findings AI in surgery differs from virtual AI used for clinical application. Physical AI in the form of computer vision and augmented reality is used to improve surgeon's skills, performance, and patient outcomes. Summary Several applications of AI and augmented reality are utilized in gynecologic surgery. AI's potential use can be found in all phases of surgery: preoperatively, intra-operatively, and postoperatively. Its current benefits are for improving accuracy, surgeon's precision, and reducing complications.Current Opinion in Obstetrics and Gynecology220Several applications of AI and augmented reality are utilized in gynecologic surgery, both in the form of computer vision and augmented reality and physical AI in the form of computer vision and augmented reality.Current Opinion in Obstetrics and Gynecology["M. Leaf", "Kelsey Musselman", "Karen C Wang"]2024-06-12T00:00:00 b88093cfbca56646693b069963879884f25acb0eArtificial intelligence (AI), represented by machine learning, artificial neural networks and deep learning, is impacting all areas of medicine, including translational research (from bench to bedside to health policy), clinical medicine (including diagnosis, treatment, prognosis and healthcare resource allocation) and public health. At a time when almost everyone is focused on how to better realise the promise of AI to transform the entire healthcare system, Dr Appel calls for public attention to the AI in medicine and the negative outcome penalty paradox. Proposing this topic has deepened our thinking about the application of AI in clinical diagnostics, and also prompted us to find more effective ways to integrate AI more effectively into future clinical practice. In addition to Dr Appel’s insightful advice, I hope to offer three other possible perspectives, including changing public perceptions, re-engineering clinical practice processes and introducing more stakeholders, to further the discussion on this topic.Journal of Medical Ethics90This topic has deepened the thinking about the application of AI in clinical diagnostics, and prompted us to find more effective ways to integrate AI more effectively into future clinical practice, and three other possible perspectives are offered.Journal of Medical Ethics["Hongnan Ye"]2024-06-12T00:00:00 2218400b8b37f402e57999ce3686beea797c62ddThe study aimed to show the possibility of building a model to evaluate internal control at industrial companies using artificial intelligence, based upon the eight elements of internal control included in the COSO-ERM model. The study population consists of the industrial companies listed on Amman Stock Exchange, while the sampling unit consists of auditors and heads of audit departments in those companies. Concerning the tool of the study, two questionnaires and a practical program were prepared: the purpose of the first questionnaire was  to obtain data to help prepare the model, while the second questionnaire was prepared to obtain data to help evaluate the model. The descriptive analytical approach was used to describe the phenomenon and analyze the data statistically,  and the applied approach to build the study model practically, i.e.,  preparing a real practical program by  using artificial intelligence according to several algorithms (attached), then this model was practically evaluated by inputting the data obtained from the second questionnaire into the program to get the results (artificial intelligence decision), which  is related to the strength or weakness of companies' internal control. Also, the evaluation  was by  analyzing the data again statistically through using the SPSS program to obtain statistical results about the strength or weakness of the companies' internal control, and then to compare the results of the artificial intelligence program with the results of the statistical analysis. In case they are compatible, it is possible to rely on artificial intelligence in evaluating the internal control of companies, otherwise, it cannot be relied upon. The study got at  the possibility of building a model for evaluating internal control in industrial companies by using artificial intelligence, and it recommended to adopt the model for its ability to evaluate internal control, and to  try to perform other research to employ artifical intelligence in other fields in accounting.Business Series00The study got at the possibility of building a model for evaluating internal control in industrial companies by using artificial intelligence, and recommended to adopt the model for its ability to evaluate internal control, and to try to perform other research to employ artifical intelligence in other fields in accounting.Business Series["Noor Al-Shorman", "Abdullah Al-Zoubi"]2024-06-12T00:00:00 5d17a21b1b32df995b2a00e62c262639df471d1bPurpose: The main aim of this research is to measure the influence of different components of artificial intelligence and marketing strategies pursued by the consumers. The research is to identify the impact of different components artificial intelligence on consumer behaviour. The sales forecasting method is found with this much successful through the technology and innovations of artificial intelligence in the marketing domain.Design / Methodology: The comprehensive framework for evaluating the quality of website design was subsequently accompanied by the collection of data through a web-based survey. The researcher is able to obtain 452 responses which can be used for the main study research and used confirmatory factor analysis and linear multiple regression analysis.Findings: It is found from the study that the artificial intelligence and its generated suggestions in the marketing Arena is found very much useful for both marketers as well as the consumers. As far as the marketers are concerned the artificial intelligence is very much useful for them to exactly measure and also to get a projective figure of their business turnover in volume. The sales forecasting method is found with this much successful through the technology and innovations of artificial intelligence in the marketing domain. Practical Implications: This empirical study paved the way to identify and implement several marketing implications useful for the marketers as well as the consumers in different demographic background. It is suggested that the marketers should get a complete data in the form of profiling the consumers, demographic background and their purchase details and technological knowledge so that they can generate appropriate artificial intelligence solutions to attract the consumers and motivate them to make their next purchase within the short span of time. Originality/Value: The artificial intelligence can also optimize the advertisement campaigns, preserving profile of consumers, quick communication to the consumers, clarity in the marketing approach and to take independent autonomous marketing decisions with respect to consumer behaviour.Salud, Ciencia y Tecnología110It is found that the artificial intelligence and its generated suggestions in the marketing Arena is found very much useful for both marketers as well as the consumers.Salud, Ciencia y Tecnología - Serie de Conferencias["R. S. Latha", "M. Chandran", "Dr. William Castillo-Gonz\u00e1lez"]2024-06-12T00:00:00 3ba5d3f91b3fba2a0f4410957269f9ed0e9c1887The article comprehensively analyzes the legal issues of open data. The concept of “citizen-generated data” is explored, and its role, place and importance for legal regulation in the open data system are determined. The paper also describes the features that distinguish this category of data from similar “citizen science” and “public participation” data. The process of forming training datasets for the development of artificial intelligence technologies using open data is comprehended. The concept and features of “explanatory artificial intelligence” are investigated. It is established that the development of explanation algorithms should take into account the human thought processes and cognitive biases of the person who forms, perceives and evaluates the decisions of explanatory artificial intelligence. The use of open data in other areas, in particular for analyzing the indicators of the Sustainable Development Goals, is investigated. It was found that some of the data necessary for comparing SDG indicators are published by the same data managers on different platforms. This leads to duplication of some data, the need to transform or compile data for publication on the Open Data Portal and the Open SDG Platform in Ukraine.INFORMATION AND LAW00The article comprehensively analyzes the legal issues of open data, and establishes that the development of explanation algorithms should take into account the human thought processes and cognitive biases of the person who forms, perceives and evaluates the decisions of explanatory artificial intelligence.INFORMATION AND LAW["M. Dubniak"]2024-06-12T00:00:00 09f9f128d7911799d57eccc12b949b85d23de007Artificial Intelligence is the field that growing at a rapid pace which involves the development of intelligent machines that perform tasks with the aid of human intelligence. The implementation of Artificial Intelligence has led to significant advancements in various business fields. It has the potential to transform the businesses and improve the process in many ways. Knowledge is the vital asset of any person, while its shared, it becomes an asset for many. Sharing of knowledge involves the exchange of information and expertise among the individuals in an organization. Knowledge sharing can help organizations to identify and the address problems effectively and swiftly. Engaging employee in an organization becomes a vital aspect for organizational productivity and organizational success as well. Once when an employee becomes emotionally attached to their organization, they feel responsible about their work and will work with involvement. Artificial Intelligence has the potential to promote employee engagement and knowledge sharing. Through personalized learning and development opportunities, it fosters employee engagement, whereas through real-time communication and collaboration technologies it facilitates knowledge sharing within the organization. This review article aims at discovering how Artificial Intelligence facilitates sharing of knowledge and engaging employees in the organization by undertaking a secondary method of data collection. This review article's primary goal is to add to the body of knowledge already available on the subject. The study found that adoption of Artificial Intelligence creates work environments that maximize knowledge sharing and enhances employee engagement.Salud, Ciencia y Tecnología320The study found that adoption of Artificial Intelligence creates work environments that maximize knowledge sharing and enhances employee engagement, and found that adoption of Artificial Intelligence creates work environments that maximize knowledge sharing and enhances employee engagement.Salud, Ciencia y Tecnología - Serie de Conferencias["S. A. Estherita", "S. Vasantha", "Dr. William Castillo-Gonz\u00e1lez"]2024-06-12T00:00:00 9fb5f5f109c699295f0e5920aafe9007d1a4b6f9Recently Artificial Intelligence has been used in a great variety of practical fields. Machine learning is the leading approach to address problems. It provides means of solving problems that can hardly be formalized. Machine learning technologies such as deep neural networks are used to find new algorithms in various scientific fields, such as mathematics, computer sciences, medicine, among others. This paper provides a concise overview of recent trends and advancements in the development of algorithms facilitated by novel machine learning methodologies.2024 23rd International Symposium on Electrical Apparatus and Technologies (SIELA)180This paper provides a concise overview of recent trends and advancements in the development of algorithms facilitated by novel machine learning methodologies.2024 23rd International Symposium on Electrical Apparatus and Technologies (SIELA)["Aleksandar Ivanov"]2024-06-12T00:00:00 4165928141023a755d9ad7afbcc206fa0950bdc4This paper is committed to the problems of legal regulation in the field of use of artificial intelligence. The authors pay attention to the points of view regarding the essence of artificial intelligence and its impact on society. It was determined that legal regulation is challenged and needs changes. This situation is characteristic not only for the specific technology of artificial intelligence but also for all emerging technologies. This impact is often unpredictable, so regulation is carried out in a situation of uncertainty. This is a certain terminological convention of the term “intellect” taking into account the nature of technology. In view of the social impact of technologies, the main ideas in the direction of finding problems for legal regulation are determined. The impact of technologies on the nature of challenges and threats in the field of national security is analyzed. Strategic planning documents in the field of national security and implementation of artificial intelligence were considered. An important, albeit promising, impact on the state of threats was established.  Use of artificial intelligence  technologies can significantly change the nature of the main threats in the field of national security. It is the interests of national security that should be allowed as a priority in the legal regulation of the use of artificial intelligence technologies in public life. It is proposed to determine the existing and potential threats from the use of artificial intelligence in further strategic planning in the sphere of ensuring the national security of Ukraine.INFORMATION AND LAW00The interests of national security should be allowed as a priority in the legal regulation of the use of artificial intelligence technologies in public life and the impact of technologies on the nature of challenges and threats in the field of national security is analyzed.INFORMATION AND LAW["S. Gordienko", "I. Doronin"]2024-06-12T00:00:00 42f74115cf35a8a8500d38abbf1f3b292e050662Objective: This review seeks to identify and analyze the drawbacks and advantages associated with the integration of artificial intelligence (AI) into the field of ophthalmology. Methods: A comprehensive review of scientific literature, articles, and publications on PubMed was undertaken. Various aspects, including the effectiveness and diagnostic speed of diabetic retinopathy, as well as ethical considerations and data security, were evaluated. Results were meticulously checked, compared, and summarized. In total, 98 articles were scrutinized using keywords in both Polish and English, including “artificial intelligence,” “ethics,” “diabetic retinopathy,” and “machine learning.” Results and discussion: The application of AI in ophthalmology demonstrates significant potential in improving the diagnosis of diabetic retinopathy. AI-based systems not only contribute to facilitating and streamlining the diagnostic and therapeutic processes but also enhance therapy efficiency. However, issues related to patient data protection, physician responsibility, the cost of training adequately skilled personnel, trust in the accuracy of diagnoses, and the long-term consequences of replacing human intervention with AI necessitate careful consideration. Conclusions: AI presents substantial opportunities in ophthalmology but simultaneously poses challenges that demand diligence and attention. It is imperative to develop norms and guidelines for the responsible use of AI in ophthalmic practice, ensuring benefits for patients while minimizing potential risks and maintaining high ethical standards. This proactive approach is crucial for harnessing the full potential of AI in healthcare.OphthaTherapy Therapies in Ophthalmology300The application of AI in ophthalmology demonstrates significant potential in improving the diagnosis of diabetic retinopathy and it is imperative to develop norms and guidelines for the responsible use of AI in ophthalmic practice, ensuring benefits for patients while minimizing potential risks and maintaining high ethical standards.OphthaTherapy. Therapies in Ophthalmology["Jakub Jo\u0144ski", "Karolina Jo\u0144ska"]2024-06-12T00:00:00 65aa16d28fa1869dbc118b0facc27e777e9a46c9Journal of Advanced Nursing64Journal of advanced nursing["Danielle Le Lagadec", "Debra Jackson", "Michelle Cleary"]2024-06-12T00:00:00 41a20d4165b4c73e83c7da67bee740067851f1b7ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH01ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH["Intorsureanu Iulian", "V. Roxana", "Nisioiu Codrin Florentin", "Ploae C\u0103t\u0103lin"]2024-06-12T00:00:00 79d58c36d7f96069cebeb40d0bdef832a3faf3c3The BRICS countries (Brazil, Russia, India, China, and South Africa) aim to achieve Sustainable Development Goals 3 and 16, which involve promoting human well-being for all and building strong institutions and governance. This study examines the AII-HWBG nexus contingent on governance indicators within the BRICS nations in 2012-2022 using the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique. Its findings reveal a long-term relationship among variables with varied causality directions and point to the necessity of integrating governance quality into AII to boost HWBG in both the short- and long-term perspective. Since AII has not so far been used to support HWBG there is a dire need for caution when considering AII’s interaction with institutional governance, economic governance, control of corruption, political stability, regulatory quality and voice and accountability. The paper highlights the crucial role of governance quality in shaping the way AI investment impacts the human well-being. To ensure an overall improvement of well-being, priority should be given to strategies that promote positive synergy between AI investment and governance while mitigating possible harmful effects. Carefully targeted measures in governance areas can create an environment conducive to AI development where it will significantly benefit the citizens of the BRICS countries.BRICS Journal of Economics762Examination of the AII-HWBG nexus contingent on governance indicators within the BRICS nations in 2012-2022 using the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique reveals a long-term relationship among variables with varied causality directions and point to the necessity of integrating governance quality into AII to boost HWBG in both the short and long-term perspective.BRICS Journal of Economics["C. Saba", "Marinda Pretorius"]2024-06-12T00:00:00 ddee6d8d99c78f94a75d1319a830f7a8cebd9d8801["Francisco Tigre Moura"]2024-06-12T00:00:00 b96a95045e662fb4b0c494e49f28b52d2c0f2e95Intiqad Jurnal Agama dan Pendidikan Islam00Intiqad: Jurnal Agama dan Pendidikan Islam[]2024-06-12T00:00:00 9b284411f919160c05cf6d51f2dddf2191102106Neuroradiology00Neuroradiology["T.A.G.M. Huisman"]2024-06-12T00:00:00 bfcea3a7a2f170662adcf3141054d59eaa786ffcThe Sustainable Development Goals (SDGs) are a universal call for action to end poverty, protect the planet, and improve people's quality of life. With the approval of the 17 SDGs as part of the 2030 Agenda for Sustainable Development, a plan was established to achieve them in 15 years. Quality Education (SDG 4) is fundamental to the development of people and societies, as it enables individuals to express themselves, actively participate in society, and make decisions that benefit their lives. Access to education is a determining factor in overcoming poverty and discrimination; it can improve the quality of life through an orientation towards better job opportunities, better health conditions, and greater participation in civic activities, among other aspects in which education itself has a direct or indirect impact. In this chapter, we present the importance of the use of new technologies as facilitators to expand the positive impact of education.International Journal of Combinatorial Optimization Problems and Informatics60The importance of the use of new technologies as facilitators to expand the positive impact of education is presented, as it enables individuals to express themselves, actively participate in society, and make decisions that benefit their lives.Int. J. Comb. Optim. Probl. Informatics["A. Fuentes-Penna", "J. D. D. Gonz\u00e1lez-Ibarra"]2024-06-12T00:00:00 a0ff397d6980ca969d740c00ae11688b45fef70eThe increasing population poses an ever-increasing demand for food production amidst major constraints like decreasing agricultural labour, increased growth rate of industrialization, urbanization, reduced land and water availability for agriculture, and drudgery in farm works. With rapid development of technology in the field of robotics and AI, has created new horizons for its application in agriculture and allied sectors. These latest technologies help farmers in facing the challenges in food production to ensure food security, environmental sustainability and labour efficiency in the age-old industry. In this review article, a comprehensive view of the current state and future trends of robotics and AI in various agricultural domains, like crop monitoring, weed control, harvesting, sorting and transportation are discussed.Advances In Image and Video Processing00A comprehensive view of the current state and future trends of robotics and AI in various agricultural domains, like crop monitoring, weed control, harvesting, sorting and transportation are discussed.Advances in Image and Video Processing["Muli Naga Surekha", "V. Vasuki"]2024-06-12T00:00:00 8bb54152216d4ca992ecc6606495611e707126de00["R. N. V. Jagan Mohan", "Vasamsetty Chandra Sekhar", "V. M. N. S. S. V. K. R. Gupta"]2024-06-12T00:00:00 6421a9d1dbcacd8b9a8d7e20c20088dffdd9720dIn this paper we argue that Neuro-Symbolic AI (NeSy-AI) should be applied for patient monitoring. In this context, we introduce patient monitoring as a special case of Human Activity Recognition and derive concrete requirements for this application area. We then present a process architecture and discuss why NeSy-AI should be applied for patient monitoring. To further support our argumentation, we show how NeSy-AI can help to overcome certain technical challenges that arise from this application area.PKDD/ECML Workshops160This paper introduces patient monitoring as a special case of Human Activity Recognition and derive concrete requirements for this application area and presents a process architecture and discusses why NeSy-AI should be applied for patient monitoring.ArXiv["Ole Fenske", "Sebastian Bader", "T. Kirste"]2024-06-12T00:00:00 15074299f9b3cfd1230f29fca3a26236dc8681b8Nature062This work built PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and fine-tuning the whole system on over 456,000 diverse visual-language instructions, finding that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology.Nature["Ming Y. Lu", "Bowen Chen", "Drew F. K. Williamson", "Richard J. Chen", "Melissa Zhao", "Aaron K Chow", "Kenji Ikemura", "Ahrong Kim", "Dimitra Pouli", "Ankush Patel", "Amr Soliman", "Chengkuan Chen", "Tong Ding", "Judy J. Wang", "Georg K. Gerber", "Ivy Liang", "L. Le", "Anil V. Parwani", "Luca L Weishaupt", "Faisal Mahmood"]2024-06-12T00:00:00 a39315dccafd125fdb9998b2d8498130d6d90d05The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for their behaviour. As opaque Machine Learning models are increasingly being employed to make important predictions in critical domains, there is a danger of creating and using decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing DNNs with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency and fairness. This guide is intended to be the go-to handbook for anyone with a computer science background aiming to obtain an intuitive insight from Machine Learning models accompanied by explanations out-of-the-box. The article aims to rectify the lack of a practical XAI guide by applying XAI techniques in particular day-to-day models, datasets and use-cases. In each chapter, the reader will find a description of the proposed method as well as one or several examples of use with Python notebooks. These can be easily modified in order to be applied to specific applications. We also explain what the prerequisites are for using each technique, what the user will learn about them, and which tasks they are aimed at.ACM Computing Surveys7215This guide is intended to be the go-to handbook for anyone with a computer science background aiming to obtain an intuitive insight from Machine Learning models accompanied by explanations out-of-the-box, by applying XAI techniques in particular day-to-day models, datasets and use-cases.ACM Computing Surveys["Adrien Bennetot", "Ivan Donadello", "Ayoub El Qadi El Haouari", "M. Dragoni", "Thomas Frossard", "Benedikt Wagner", "Anna Sarranti", "Silvia Tulli", "M. Trocan", "Raja Chatila", "Andreas Holzinger", "Artur d'Avila Garcez", "Natalia D\u00edaz-Rodr\u00edguez"]2024-06-12T00:00:00 7a9559ac8b1185832487de45b13b8a3e3ffcc2fcJournal of Electrical Systems and Information Technology4415Journal of Electrical Systems and Information Technology["Zarif Bin Akhtar"]2024-06-12T00:00:00 eb5137566bd1c50f61e073f8c649020c898f7112The integration of artificial intelligence (AI) tools in physics education is gaining traction, driven by their potential to enhance learning experiences and outcomes. This study aims to investigate the use of AI tools, particularly ChatGPT, in solving physics problems and enhancing educational practices. Utilizing a systematic literature review following PRISMA guidelines, the research identifies current trends and practical applications of AI in physics education. The results indicate that AI tools effectively support lesson planning, introduce innovative teaching methodologies, and assist in solving complex physics problems, significantly enhancing problem-solving skills and personalized learning experiences. However, challenges such as inaccuracies in handling advanced content, the lack of useful visual aids, and the need for human intervention to ensure the completeness and accuracy of AI-generated content were noted. Personal experiences, supplemented by an interview with a thermodynamics lecturer, revealed that while ChatGPT can simplify complex concepts and improve comprehension, it could not replace the mentorship and nuanced feedback provided by human educators. The study concludes with recommendations for integrating AI tools into physics education, emphasizing the need for balanced integration with traditional teaching methods, improved AI literacy among educators and students, and future developments focusing on personalized learning and enhanced visualization capabilities. The findings demonstrate the transformative potential of AI in physics education and highlight the importance of addressing its limitations to maximize educational outcomes.Lensa: Jurnal Kependidikan Fisika336It is demonstrated that while ChatGPT can simplify complex concepts and improve comprehension, it could not replace the mentorship and nuanced feedback provided by human educators, and the importance of addressing its limitations to maximize educational outcomes.Lensa: Jurnal Kependidikan Fisika["Hisbulloh Als Mustofa", "M. Bilad", "N. W. B. Grendis"]2024-06-12T00:00:00 53c03883be35681c6b0f875d8b9ccf7b6ea58d17This scoping review explores the field of artificial intelligence (AI) literacy, focusing on the tools available for evaluating individuals’ self-perception of their AI literacy. In an era where AI technologies increasingly infiltrate various aspect of daily life, from healthcare diagnostics to personalized digital platforms, the need for a comprehensive understanding of AI literacy has never been more critical. This literacy extends beyond mere technical competence to include ethical considerations, critical thinking, and socio-emotional skills, reflecting the complex interplay between AI technologies and societal norms. The review synthesizes findings from diverse studies, highlighting the development and validation processes of several key instruments designed to measure AI literacy across different dimensions. These tools – ranging from the Artificial Intelligence Literacy Questionnaire (AILQ) to the General Attitudes towards Artificial Intelligence Scale (GAAIS) – embody the nature of AI literacy, encompassing affective, behavioral, cognitive, and ethical components. Each instrument offers unique insights into how individuals perceive their abilities to understand, engage with, and ethically apply AI technologies. By examining these assessment tools, the review sheds light on the current landscape of AI literacy measurement, underscoring the importance of self-perception in educational strategies, personal growth, and ethical decision-making. The findings suggest a critical need for educational interventions and policy formulations that address the gaps between perceived and actual AI literacy, promoting a more inclusive, critically aware, and competent engagement with AI technologies.Media Education324A critical need for educational interventions and policy formulations that address the gaps between perceived and actual AI literacy are suggested, promoting a more inclusive, critically aware, and competent engagement with AI technologies.Media Education["Gabriele Biagini"]2024-06-12T00:00:00 51f295cfd1ecd32aabf2740d86a313728480b3b5This paper identifies and classifies the essential constraints that must be addressed to allow U-space traffic autonomous guidance. Based on an extensive analysis of the state of the art in robotic guidance, physics of flight, flight safety, communication and navigation, uncrewed aircraft missions, artificial intelligence (AI), social expectations in Europe on drones, etc., we analyzed the existing constraints and the information needs that are of essential importance to address the identified constraints. We compared the identified information needs with the last edition of the U-space Concept of Operations and identified critical gaps between the needs and proposed services. A high-level methodology to identify, measure, and close the gaps is proposed.Aerospace1143The identified information needs are compared with the last edition of the U-space Concept of Operations and critical gaps between the needs and proposed services are identified and a high-level methodology to identify, measure, and close the gaps is proposed.Aerospace["Ivan Panov", "Asim Ul Haq"]2024-06-12T00:00:00 65e284c22e88ed17a6e41f258de95b64f56490f1In addressing the nuanced interplay between consumer attitudes and Artificial Intelligence (AI) use readiness in physical retail stores, the main objective of this study is to test the impacts of prior experience, as well as perceived risks with AI technologies, self-assessment of consumers’ ability to manage AI technologies, and the moderator role of gender in this relationship. Using a quantitative cross-sectional survey, data from 243 consumers familiar with AI technologies were analyzed using structural equation modeling (SEM) methods to explore these dynamics in the context of physical retail stores. Additionally, the moderating impacts were tested after the invariance analysis across both gender groups. Key findings indicate that positive prior experience with AI technologies positively influences AI use readiness in physical retail stores, while perceived risks with AI technologies serve as a deterrent. Gender differences significantly moderate these effects, with perceived risks with AI technologies more negatively impacting women’s AI use readiness and self-assessment of the ability to manage AI technologies showing a stronger positive impact on men’s AI use readiness. The study concludes that retailers must consider these gender-specific perceptions and attitudes toward AI to develop more effective strategies for technology integration. Our research also highlights the need to address gender-specific barriers and biases when adopting AI technology.Inf.683Positive prior experience with AI technologies positively influences AI use readiness in physical retail stores, while perceived risks with AI technologies serve as a deterrent, and gender differences significantly moderate these effects.Inf.["Nina Kolar", "B. Milfelner", "Aleksandra Pisnik"]2024-06-12T00:00:00 83006fa4d6aa5bddb10a075ffde8f0bce26580a7Artificial Intelligence (AI) is being adopted across the world and promises a new revolution in healthcare. While AI-enabled medical devices in North America dominate 42.3% of the global market, the use of AI-enabled medical devices in other countries is still a story waiting to be unfolded. We aim to delve deeper into global regulatory approaches towards AI use in healthcare, with a focus on how common themes are emerging globally. We compare these themes to the World Health Organization's (WHO) regulatory considerations and principles on ethical use of AI for healthcare applications. Our work seeks to take a global perspective on AI policy by analyzing 14 legal jurisdictions including countries representative of various regions in the world (North America, South America, South East Asia, Middle East, Africa, Australia, and the Asia-Pacific). Our eventual goal is to foster a global conversation on the ethical use of AI in healthcare and the regulations that will guide it. We propose solutions to promote international harmonization of AI regulations and examine the requirements for regulating generative AI, using China and Singapore as examples of countries with well-developed policies in this area.arXiv.org1021This work seeks to take a global perspective on AI policy by analyzing 14 legal jurisdictions including countries representative of various regions in the world (North America, South America, South America, South East Asia, Middle East, Africa, Australia, and the Asia-Pacific), with a focus on how common themes are emerging globally.ArXiv["Attrayee Chakraborty", "M. Karhade"]2024-06-12T00:00:00 214e854835e1fe8bd37ca1d60c7ba507c18602b9In the current era of rapid technological advancement, businesses must stay abreast of current trends to maintain competitive edge and ensure continued success. Prioritizing customer experience is paramount wherein showcasing a deep commitment to customer-oriented style is the differentiator. The modern marketing technologies give rise to intelligent conversational agents that has revolutionized customer engagement. As technology continues to shape the global business landscape, this study assessed the customer experience through their extent of interaction and challenges encountered in using artificial intelligence (AI) chatbots.Descriptive-quantitative research method was employed using validated survey questionnaire distributed to 258 locals from targeted respondents in the barangays of Las Pinas City who have experiences in interacting with AI-chatbots. The respondents were clustered to smaller groups who are knowledgeable about the topic, experienced using AI Chatbots by top e-commerce shops and are willing to participate. While AI chatbots have the potential to improve customer experience, their limitations and challenges must be addressed to ensure more effective and satisfying customer transactions.International Journal of Latest Technology in Engineering Management &amp; Applied Science161While AI chatbots have the potential to improve customer experience, their limitations and challenges must be addressed to ensure more effective and satisfying customer transactions.International Journal of Latest Technology in Engineering Management &amp; Applied Science["Glenda Joy B. Lopez", "Vanessa B. Pablo", "Lalaine Kristine F. Miravite", "Coleen Jill L. Aguidan", "Alyssa Krissia F. Alara\u00f1a", "Ni\u00f1o Frederick D. Caracena"]2024-06-12T00:00:00 dd26cfe773a0e9b26e73093087052e8e19049d57The discipline of genomic sequencing has seen a revolution in recent years due to the merging of bioinformatics with artificial intelligence and machine learning. This role-playing exercise explores how these cutting-edge computational methods are revolutionizing genomic data processing and paving the way for ground- breaking advances in genetic engineering and personalized medicine. Participants will examine how AI plays a critical role in improving the precision, speed, and effectiveness of genomic analysis. During the event, important AI and ML techniques like deep learning and neural networks will be covered, along with how they are used to forecast illness susceptibility, find genetic markers, and customize treatment regimens. We will also look at AI's role in genetic engineering, particularly developments in CRISPR technology. The paper will cover the technological difficulties, moral dilemmas, and privacy issues related to this integration in addition to highlighting the revolutionary promise of AI-driven bioinformatics. Participants will acquire knowledge about the potential benefits and advancements that artificial intelligence (AI) may offer to the field of genomic science via engaging dialogues and hands-on experiments. Attendees will leave the workshop with a thorough grasp of how AI is affecting genomic sequencing and what it means for biotechnology and healthcare in the future.International Journal of Innovative Science and Research Technology191This role-playing exercise explores how these cutting-edge computational methods are revolutionizing genomic data processing and paving the way for ground- breaking advances in genetic engineering and personalized medicine.International Journal of Innovative Science and Research Technology (IJISRT)["Umang H Patel", "Riya Mathur"]2024-06-12T00:00:00 133991ed77387c6b2b4dd2309f5f7ce490a270d3ERA Forum151The substantive provisions of the AI Act are analysed through the lens of gender equality and non-discrimination law, highlighting the proposed tools of fundamental rights impact assessments and bias audits to reduce gender biases and discriminatory risk.ERA Forum["Fabian L\u00fctz"]2024-06-12T00:00:00 228fc33bc284c60f6feb7231391f31f6b37e962cArtificial intelligence (AI) agency plays an important role in shaping humans’ perceptions and evaluations of AI. This study seeks to conceptually differentiate AI agency from human agency and examine how AI’s agency manifested on source and language dimensions may be associated with humans’ perceptions of AI. A 2 (AI’s source autonomy: autonomous vs human-assisted) × 2 (AI’s language subjectivity: subjective vs objective) × 2 (topics: traveling vs reading) factorial design was adopted ( N = 376). The results showed autonomous AI was rated as more trustworthy, and AI using subjective language was rated as more trustworthy and likable. Autonomous AI using subjective language was rated as the most trustworthy, likable, and of the best quality. Participants’ AI literacy moderated the interaction effect of source autonomy and language subjectivity on human trust and chat quality evaluation. Results were discussed in terms of human–AI communication theories and the design and development of AI chatbots.New Media &amp; Society521The results showed autonomous AI was rated as more trustworthy, and AI using subjective language was rated as more trustworthy and likable, and Autonomous AI using subjective language was rated as the most trustworthy, likable, and of the best quality.New Media &amp; Society["Wenjing Pan", "Diyi Liu", "Jingbo Meng", "Hailong Liu"]2024-06-12T00:00:00 22810087e880b48a2bb2db5b7852515c890cf61fThis study explores the opportunities and challenges of integrating artificial intelligence (AI) into engineering education. Through a review of the literature and a qualitative focus group study, an assessment was made for the role of AI in personalizing learning, enhancing simulation engagement, providing real-time feedback, and preparing students for AI-integrated workplaces. The study emphasizes how AI may significantly improve educational experiences by making them more dynamic, interactive, and successful. It also draws attention to important issues, such as moral questions, algorithmic biases in AI, infrastructure constraints, the need for AI literacy training for educators, and a range of student perspectives on AI engineering education. The results support a systematic approach to AI integration, highlighting the necessity of cooperative efforts by educators, legislators, curriculum designers, and technologists in order to overcome these obstacles. The study makes the case that AI can transform engineering education by negotiating these challenges and providing students with the information and skills needed for the digital future, all the while assuring fair and moral access to technology-enhanced learning.Societies344The study makes the case that AI can transform engineering education by negotiating these challenges and providing students with the information and skills needed for the digital future, all the while assuring fair and moral access to technology-enhanced learning.Societies["I. Mosly"]2024-06-13T00:00:00 301857d3df7c5bc4958f9972c51a9138e976104eAsian Bioethics Review421It is argued that currently, regulatory frameworks are misaligned and unless addressed, accessing data within Italian biobanks to train AI will be severely limited.Asian Bioethics Review["C. Staunton", "Roberta Biasiotto", "Katharina Tschigg", "Deborah Mascalzoni"]2024-06-13T00:00:00 7ddbed6c11b0a2d24776ea19377e50b58cdd3b3aAsian Bioethics Review322It is argued that effective regulation of AI extends beyond devising guidance for the profession and includes keeping abreast of developments in AI-based technology and considering the implications for regulation and the practice of medicine.Asian bioethics review["Kanny Ooi"]2024-06-13T00:00:00 f720b111f3f398eff0e18a1ed3125d5bbc9e6e6dCurrent Dermatology Reports412The success of AI in dermatology hinges on dermatologists’ oversight to ensure that ML’s potential is fully realized in patient care, preserving the essential human element in medicine.Current dermatology reports["Payton Smith", "Chandler E Johnson", "Kathryn Haran", "Faye Orcales", "Allison Kranyak", "Tina Bhutani", "J. Riera-Monroig", "Wilson Liao"]2024-06-13T00:00:00 0180e078d5ff20fd16396737024ff0030ec4709fIntroduction Doping remains a persistent concern in sports, compromising fair competition. The Athlete Biological Passport (ABP) has been a standard anti-doping measure, but confounding factors challenge its effectiveness. Our study introduces an artificial intelligence-driven approach for identifying potential doping suspicious, utilizing the Athlete’s Performance Passport (APP), which integrates both demographic profiles and performance data, among elite female weightlifters. Methods Analyzing publicly available performance data in female weightlifting from 1998 to 2020, along with demographic information, encompassing 17,058 entities, we categorized weightlifters by age, body weight (BW) class, and performance levels. Documented anti-doping rule violations (ADRVs) cases were also retained. We employed AI-powered algorithms, including XGBoost, Multilayer Perceptron (MLP), and an Ensemble model, which integrates XGBoost and MLP, to identify doping suspicions based on the dataset we obtained. Results Our findings suggest a potential doping inclination in female weightlifters in their mid-twenties, and the sanctioned prevalence was the highest in the top 1% performance level and then decreased thereafter. Performance profiles and sanction trends across age groups and BW classes reveal consistently superior performances in sanctioned cases. The Ensemble model showcased impressive predictive performance, achieving a 53.8% prediction rate among the weightlifters sanctioned in the 2008, 2012, and 2016 Olympics. This demonstrated the practical application of the Athlete’s Performance Passport (APP) in identifying potential doping suspicions. Discussion Our study pioneers an AI-driven APP approach in anti-doping, offering a proactive and efficient methodology. The APP, coupled with advanced AI algorithms, holds promise in revolutionizing the efficiency and objectivity of doping tests, providing a novel avenue for enhancing anti-doping measures in elite female weightlifting and potentially extending to diverse sports. We also address the limitation of a constrained set of APPs, advocating for the development of a more accessible and enriched APP system for robust anti-doping practices.Frontiers in Physiology352An artificial intelligence-driven approach for identifying potential doping suspicious, utilizing the Athlete’s Performance Passport (APP), among elite female weightlifters, suggesting a potential doping inclination in female weightlifters in their mid-twenties.Frontiers in Physiology["Hyunji Ryoo", "Samuel Cho", "Taehan Oh", "YuSik Kim", "Sang-Hoon Suh"]2024-06-13T00:00:00 88647fa80050381bc599716e6449e1bed5af75a9The rapid advancements in Artificial Intelligence (AI) technologies are poised to revolutionize various sectors, significantly influencing the employment landscape. This research paper delves into the multifaceted impact of AI on job roles, workforce displacement, and the evolving job market. Utilizing a blend of theoretical frameworks and empirical studies, the paper scrutinizes the adoption of AI models, including machine learning and natural language processing, by companies aiming to automate and replace numerous tasks and job functions. AI technologies are increasingly capable of performing tasks that were traditionally carried out by humans, particularly those that are repetitive and structured. Industries such as manufacturing, logistics, and customer service are already experiencing substantial transformations due to AI-driven automation. For instance, AI-powered robots and algorithms are taking over assembly line work, warehouse management, and customer interactions through chatbots and virtual assistants. This trend is expected to accelerate, potentially leading to significant job losses and displacement in these sectors. The paper presents evidence from various studies highlighting the susceptibility of certain occupations to automation and the consequent risk of unemployment for workers engaged in these roles. However, the impact of AI on employment is not solely negative. The paper underscores the potential of AI to spur job creation in emerging industries and fields. As AI technologies advance, new job categories are likely to emerge, particularly in tech-centric domains such as AI development, data analysis, and cybersecurity. Moreover, AI can enhance productivity and innovation, leading to the growth of new business models and industries. For instance, the development and maintenance of AI systems require skilled professionals, thus creating opportunities in software engineering, AI ethics, and related fields. The paper discusses how these new roles can offset some of the job losses caused by automation. A crucial aspect of this transformation is the need for workforce reskilling and upskilling. The paper emphasizes that to mitigate the adverse effects of AI on employment, there is an urgent need for comprehensive reskilling initiatives. Workers must be equipped with new skills that are in demand in the AI-driven job market. This includes technical skills related to AI and data science, as well as soft skills such as problem-solving, creativity, and emotional intelligence, which are less susceptible to automation. The study highlights successful reskilling programs and initiatives undertaken by governments, educational institutions, and private companies, offering insights into effective strategies for workforce development. In addition to examining the direct impact on jobs, the paper explores the broader ethical considerations and policy implications of AI’s integration into the workplace. Ethical issues such as bias in AI algorithms, privacy concerns, and the potential for increased inequality are addressed. The paper calls for robust policy frameworks to ensure that the benefits of AI are equitably distributed and that the negative consequences are mitigated. This includes policies to support displaced workers, promote fair labour practices, and ensure transparency and accountability in AI deployment. The findings of this research contribute to the ongoing discourse on the societal and economic consequences of AI. By providing a nuanced analysis of both the challenges and opportunities presented by AI, the paper offers valuable insights for policymakers, employers, and workers. It emphasizes the importance of proactive and collaborative efforts among all stakeholders to navigate the evolving employment landscape shaped by AI. The paper concludes with recommendations for fostering an inclusive and resilient workforce capable of thriving in an AI-driven future.International Journal For Multidisciplinary Research102This research paper delves into the multifaceted impact of AI on job roles, workforce displacement, and the evolving job market, and explores the broader ethical considerations and policy implications of AI’s integration into the workplace.International Journal For Multidisciplinary Research["Rohan Dinkar Jadhav", "Abhijit Banubakode"]2024-06-13T00:00:00 f3583b9d023477b2f449e2d4f8c86fd3716ed322PurposeThis study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.Design/methodology/approachDrawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.FindingsThe results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.Originality/valueThis study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.Management Decision1331This study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance and uncovered partial mediation effects.Management Decision["Suheil Neiroukh", "Okechukwu Lawrence Emeagwali", "Hasan Yousef Aljuhmani"]2024-06-13T00:00:00 38e10f8f0ea666ded9d9c1e47bf0e96007a96263BMC Primary Care1071PCPs perceived that AI systems have the potential to be cost-effective, credible, and useful in collecting large amounts of patients’ data, and relatively credible, but feared that reliance on AI might result in a loss of clinical competency.BMC Primary Care["Pooria Ghadiri", "Mark J Yaffe", "Alayne M Adams", "Samira Abbasgholizadeh-Rahimi"]2024-06-13T00:00:00 45b0dc85c6d7a2561d24e4f1015d542cd5dbfe9bComputers and applications of computers into our world have changed dramatically during the past five decades, from early days of minimal central processing unit capacity, limited memory and without advantage of global networking. In this article, the author highlights the application of predictive artificial intelligence in use globally over the last 40 years in process industries. It discusses the novel application of process automation and robotics in health clinical high-volume laboratory use that began as a Canadian innovation initiative and followed by similar innovation extending to other countries.Healthcare Management Forum10The author highlights the application of predictive artificial intelligence in use globally over the last 40 years in process industries and discusses the novel application of process automation and robotics in health clinical high-volume laboratory use.Healthcare management forum["Susan Anderson"]2024-06-13T00:00:00 f144e643f65fb4965f01dceac011152d0aa912d9This review provides an insightful overview of "Artificial Intelligence and the Future of Teaching and Learning," a policy report by the United States Department of Education. Keywords such as Artificial Intelligence (AI) development, policy-making, ethics, equity, collaboration, and human-centric approach are emphasised throughout. The review highlights the report's comprehensive analysis, actionable recommendations, and emphasis on inclusive policy-making processes. It underscores the significance of understanding AI's multifaceted nature, its potential to enhance education, and the importance of safeguarding privacy and equity. Practical examples and case studies are discussed, along with recommendations for aligning AI with educational goals. Overall, the review positions the report as a valuable resource for policymakers, educators, and technology developers, guiding them toward responsible AI integration in education.International research-based education journal00This review provides an insightful overview of "Artificial Intelligence and the Future of Teaching and Learning," a policy report by the United States Department of Education, and highlights the significance of understanding AI's multifaceted nature, its potential to enhance education, and the importance of safeguarding privacy and equity.International Research-Based Education Journal["Weny Kritandani", "Renaningtyas Aryani", "Tetta Rakasiwi"]2024-06-13T00:00:00 73d644f6875aefd0f5dbdf5d5fb93778256278b2This study harnesses structural topic modeling and expert surveys to delve into the expanding influence of artificial intelligence (AI) within the energy sector, analyzing around 6000 academic paper abstracts from 2011 to 2020. Our detailed examination identified 100 distinct topics, of which 15, accounting for a combined proportion of 16.4% of the total, were directly related to energy, highlighting key areas such as power consumption, thermal energy management, wind energy evaluation, and building energy management. Furthermore, an expert survey offered deep insights into future changes, spotlighting AI's role in enhancing safety, stability, efficiency, and environmental sustainability of energy systems. It also pinpointed challenges in AI adoption within the sector, proposing pathways to bolster AI reliability, improve data quality, and enhance human–AI collaboration. This comprehensive analysis not only highlights the dynamic role of AI in transforming the energy sector but also sets a foundational framework for future interdisciplinary research, aiming to integrate quantitative and qualitative insights for a holistic understanding of AI's potential in sustainable energy development.Energy &amp; Environment460This comprehensive analysis not only highlights the dynamic role of AI in transforming the energy sector but also sets a foundational framework for future interdisciplinary research, aiming to integrate quantitative and qualitative insights for a holistic understanding of AI's potential in sustainable energy development.Energy &amp; Environment["Chankook Park", "Minkyu Kim"]2024-06-13T00:00:00 c9d09a0cedeef402eadcbb081546e7c92765d4f0The paper studies the architectural development, layout and equipment of a smart city based on integration algorithms and artificial intelligence methods. Innopolis, a satellite city of Kazan designed as a city for IT specialists, is an object of research. The relevance of the work is determined by rapid digitalization, which penetrates in all branches of human activity, including urban design and urban planning. The knowledge systematization of a smart city can become the basis for a faster transition of other settlements to smart cities based on the experience of the Innopolis development.Vestnik Tomskogo gosudarstvennogo arkhitekturno-stroitel nogo universiteta JOURNAL of Construction and Architecture00The knowledge systematization of a smart city can become the basis for a faster transition of other settlements to smart cities based on the experience of the Innopolis development, according to the paper.Vestnik Tomskogo gosudarstvennogo arkhitekturno-stroitel'nogo universiteta. JOURNAL of Construction and Architecture["P. A. Pylov", "R. V. Maitak", "A. V. Dyagileva", "T. A. Shalygina"]2024-06-13T00:00:00 cfa3400a61729a30479e21562e8e1328895ce471The emergence of “generative artificial intelligence” (GII), led to the adoption in China of the first regulatory legal act regulating the provision of services with its help. In order to regulate social relations, it is necessary to know what these relations arise about, i.e. what is called GII. Therefore, such a product of human activity should be accurately and fully formalized and correctly named. In addition, it is necessary to determine not only the form and/or content of the carrier, called artifact (artificial) intelligence, but also its structure, including its functions, which may be negative. Given the emergence of the trend for smartization, which is replacing digitalization, it is logical to use the terms “smart product” and “smart system”. The main properties of a smart product in the form of smart systems and smart media products are determined, and the features of the use of smart agents or smart actors are considered. It is argued that the use of smart systems by some legal entities against others, the voluntary use of smart tools and/or the use of smart objects by people without realizing their true properties, creates risks of negative impact on them in the form of violations of constitutional freedoms, rights and legitimate interests.JURIST00It is argued that the use of smart systems by some legal entities against others, the voluntary use of smart tools and/or the use of smart objects by people without realizing their true properties, creates risks of negative impact on them in the form of violations of constitutional freedoms, rights and legitimate interests.Jurist["Anatoly V. Nesterov"]2024-06-13T00:00:00 7af818e12faef4983a326b78a89affa5fba0d4ccBackground and Objectives: Human resources today plays a vital role in defining the success of any business unit. This article aims at devising a SE Model depicting the mediating role of Artificial Intelligence on employee recruitment, onboarding, engagement and talent retention. The paper investigates the impact of AI on talent retention among employees from IT sectors which applies AI/HR analytics from Chennai and Bangalore. The study is conducted with a structured questionnaire. Primary data was collected from HR managers working in IT Sector in Chennai and Bangalore from organizations which applied Artificial Intelligence for their HR processes. Secondary data also has been used. AI is used to augment business results and HR capabilities. The mediating role of artificial intelligence plays a vital role on HR processes like Employee recruitment, onboarding, engagement and talent retention. This study focused on studying the mediating role of AI on HR processes. Structural equation modeling is used to test this hypothesis. The results outline areas where AI is delivering value in HR practices like, recruiting, onboarding and talent retention. The findings indicated that usage of AI on recruitment and placing employees on the basis of skills, motivating factors and engagement drives has a significant role in talent retention.International Journal For Multidisciplinary Research170A SE Model depicting the mediating role of Artificial Intelligence on employee recruitment, onboarding, engagement and talent retention indicated that usage of AI on recruitment and placing employees on the basis of skills, motivating factors and engagement drives has a significant role in talent retention.International Journal For Multidisciplinary Research["S. Prathiba"]2024-06-13T00:00:00 285e93475a89887606bccfe39ebe268e5d7c078fOver the last five decades the discipline of Computing has transformed, from dedicated lab-based scientific tools and networks to ubiquitous, mobile ‘always on’ infotainment and communication platforms for society at large. The concurrent rapid growth in artificial intelligence, computation power and the infrastructure needed for communication and storage raises concerns that important issues of sustainable, ethical development and societal impact are not considered fully. Computing systems are having an increasingly negative impact on the environment, and yet they also are perhaps the key to unlocking huge improvements in sustainability. This paper evaluates the learning from a module delivered to Computing Technologies students at Ulster University. The rationale was that appraisal of ethics and its role in artificial intelligence will empower students, as future practitioners, to promote sustainability in the workplace.Irish Signals and Systems Conference330This paper evaluates the learning from a module delivered to Computing Technologies students at Ulster University that appraisal of ethics and its role in artificial intelligence will empower students, as future practitioners, to promote sustainability in the workplace.2024 35th Irish Signals and Systems Conference (ISSC)["Samuel J. Moore", "Paul J. McCullagh"]2024-06-13T00:00:00 1f4b771d611b0e6654259db4ebb743f9eae8c429В статье рассматривается влияние искусственного интеллекта на страховой бизнес в современной России. Проанализированы такие методы применения искусственного интеллекта, как нейронные сети, машинное обучение и глубокое обучение для анализа данных и прогнозирования рисков. Выделяются преимущества использования искусственного интеллекта, например точность анализа данных. Также рассматриваются ограничения, такие как проблема кибербезопасности. Описано регулирование государства страховой отрасли и последствия этого. Особое внимание уделяется определению основных видов страхования и факторов, влияющих на динамику страхового рынка. Составлен прогноз динамики страхового рынка на 2024 год. This article examines the impact of artificial intelligence on the insurance business in modern Russia, as artificial intelligence contributes to the development of various industries, including insurance. The article describes such methods of using artificial intelligence as neural networks, machine learning and deep learning for data analysis and risk forecasting. The advantages of using artificial intelligence are highlighted, for example, the accuracy of data analysis. However, limitations such as the issue of cybersecurity are also being considered. The article describes the regulation of the insurance industry by the state and the consequences of this. Special attention is paid to the definition of the main types of insurance and the factors influencing the dynamics of the insurance market. Methods of predicting dynamics using artificial intelligence are described and one of the methods is selected. Using the chosen method, a forecast of the dynamics of the insurance market for 2024 has been compiled.The Applied Economic Researches Journal20The Applied Economic Researches Journal["\u0410.\u041e. \u041a\u0438\u0440\u0438\u0447\u0435\u043d\u043a\u043e", "\u0410.\u041b. \u0417\u043e\u043b\u043a\u0438\u043d", "\u0418.\u0410. \u041f\u043e\u0441\u043a\u0440\u044f\u043a\u043e\u0432", "\u041c.\u041d. \u041a\u0430\u0437\u044c\u043c\u0435\u043d\u043a\u043e"]2024-06-13T00:00:00 545b7f0bfe6897909cb91426561d82afdf07711cMajor Depressive Disorder (MDD) is a leading cause of disability and there is a paucity of tools to personalize and manage treatments. A cluster-randomized, patient-and-rater-blinded, clinician-partially-blinded study was conducted to assess the effectiveness and safety of the Aifred Clinical Decision Support System (CDSS) facilitating algorithm-guided care and predicting medication remission probabilities using clinical data. Clinicians were randomized to the Active (CDSS access) or Active-Control group (questionnaires and guidelines access). Primary outcome was remission (<11 points on the Montgomery Asberg Depression Rating Scale (MADRS) at study exit). Of 74 eligible patients, 61 (42 Active, 19 Active-Control) completed at least two MADRS (analysis set). Remission was higher in the Active group (n = 12/42 (28.6%)) compared to Active-Control (0/19 (0%)) (p = 0.01, Fisher exact test). No adverse events were linked to the CDSS. This is the first effective and safe longitudinal use of an artificial intelligence-powered CDSS to improve MDD outcomes.medRxiv490This is the first effective and safe longitudinal use of an artificial intelligence-powered CDSS to improve MDD outcomes.["D. Benrimoh", "K. Whitmore", "M. Richard", "G. Golden", "K. Perlman", "S. Jalali", "T. Friesen", "Y. Barkat", "J. Mehltretter", "R. Fratila", "C. Armstrong", "S. Israel", "C. Popescu", "J. F. Karp", "S. V. Parikh", "S. Golchi", "E. Moodie", "J. Shen", "A. Gifuni", "M. Ferrari", "M. Sapra", "S. Kloiber", "G. F. Pinard", "B. W. Dunlop", "K. Looper", "M. Ranganathan", "M. Enault", "S. Beaulieu", "S. Rej", "F. Hersson-Edery", "W. Steiner", "A. Anacleto", "S. Qassim", "R. McGuire-Snieckus", "H. Margolese"]2024-06-13T00:00:00 f4a6f0fca7847b798b92eea971fceb60470ef6fdCancer is a multifactorial group of diseases that are known to affect human life with incidence and mortality rates. Artificial Intelligence and the development of new strategies in cancer treatment are of great importance in helping physicians apply optimized treatment tailored to the patient, overcoming both physical and psychological difficulties, and preventing the recurrence and spread of the disease. Thanks to the field of health and Artificial Intelligence, which are integrated with current developments in coordination with each other, great advances have been made and continue to be made in the diagnosis, treatment and prognosis of cancer, one of the major problems of our age.Experimental and Applied Medical Science60Artificial Intelligence and the development of new strategies in cancer treatment are of great importance in helping physicians apply optimized treatment tailored to the patient, overcoming both physical and psychological difficulties, and preventing the recurrence and spread of the disease.Experimental and Applied Medical Science["Leyla Tutar"]2024-06-13T00:00:00 7c1a6dfd65952de0f39a7206f3d81f5557c3c6e6OECD Artificial Intelligence Papers02OECD Artificial Intelligence Papers[]2024-06-13T00:00:00 f6e56fe72a8bcc7848828028fc4bd039b642ac08Journal of Educational Media372Learning, Media and Technology["Iosif Gidiotis", "Stefan Hrastinski"]2024-06-13T00:00:00 5ae52ffe8fe737f2f9dad51aa3f35e9b7c48360fEducause Review391Educational Review["David Rousell", "Matthew P. Sinclair"]2024-06-13T00:00:00 937eb67820ec38a478f345cc368706e3b2f2fb54Philosophical Studies461It is argued that the connection between moral alignment and safe behavior is more tenuous than many have hoped, and advanced AI systems governed by standard versions of deontology need not be especially safe.Philosophical Studies["W. D\u2019Alessandro"]2024-06-13T00:00:00 c4956366bdc71a06d86bd1b89c20ae119cfe7b9cCRISPR-Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease 9) provides powerful gene-editing tools that are applicable for gene therapy of a variety of diseases including, but not limited to cancer, rare diseases, and heart disease. In the current study, we first re-examined our artificial stem cell and organoid simulations that were generated by our literature validated DeepNEU AI platform from the perspective of gene-editing. We then evaluated the aiCRISPRL (aiCRISPR-Like) application of the DeepNEU platform by directly comparing the CRISPR-Cas9 gene-editing approach with the DeepNEU derived aiCRISPRL capabilities using artificial simulated HeLa cells (aiHeLa). To accomplish this, we evaluated the aiCRISPRL like capabilities of DeepNEU to introduce a series of specific mutations into the MutS homolog 2 (MSH2) gene to assess DNA Mismatch Repair (MMR). This approach permits a comparative assessment of CRISPR-Cas9 and aiCRISPRL technologies following the introduction of specific MSH2 mutations. When combined with our previous body of gene editing research, the current data indicates that aiCRISPRL is an advanced AI platform technology that can be used for rapid prototyping and multiple scenario simulation in genomic research to complement wet-lab based gene-editing technologies.bioRxiv10The current data indicates that aiCRISPRL is an advanced AI platform technology that can be used for rapid prototyping and multiple scenario simulation in genomic research to complement wet-lab based gene-editing technologies.bioRxiv["WR Danter"]2024-06-13T00:00:00 1dc802dd519bcd7c67c9a25391a9264c868e21dbWe live in a cyber-universe created by millions of data sets. This universe, where there are almost no time-space constraints, allows people to perform activities in the intercardinal direction in a comfort they could not imagine before. At the same time, this multicultural and global world is a source of ethical challenges. While living in our unique culture, is it possible to share common ethical values in our world, which is becoming more global with each passing day? Or is it getting more and more impossible in this complex cyber universe? This article draws attention to Bynum's Flourishing Ethics theory, which carries the umbrella concept with its potential to unite people around some common values, even if we have different ethical approaches. The main purpose of this article is how, although different approaches they are, theories based on the common nature of human beings can be combined for the same purpose and Thanks to the 'ethical family unity' structure they have created under the ethical umbrella, it is questioned how they will determine the ethical components that can be applied to intelligence systems for the solution of the common problems of the information age.ARTUKLU AKADEMİ00This article draws attention to Bynum's Flourishing Ethics theory, which carries the umbrella concept with its potential to unite people around some common values, even if the authors have different ethical approaches.ARTUKLU AKADEMİ["Nesibe Kantar"]2024-06-13T00:00:00 46902d1768c1315c195f343abe3c289619a112e6International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024)00International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024)["yuanyi yuan", "Longjun Wu", "Yong Zou"]2024-06-13T00:00:00 087f63d376006ec5f8273fff6cd8e4e1125cc6a5This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.00["Luc\u00eda Latorre", "Valent\u00edn Muro", "Eduardo Rego", "Mariana Gutierrez", "Ignacio Cerrato", "Jose Daniel Zarate"]2024-06-13T00:00:00 f416888a9ffd05d90e35baba2fa691cae5201930touchREVIEWS in Ophthalmology00touchREVIEWS in Ophthalmology["Riley J Lyons", "Sruthi R. Arepalli"]2024-06-13T00:00:00 bef53cb5a7b901883749b610e080c36daceab9bc20IJFAB: International Journal of Feminist Approaches to Bioethics["Tina Nguyen"]2024-06-13T00:00:00 9695dcacbe744f135dbe87d1429297db894c75fcThe article critically analyzes the problems of artificial intelligence (AI) and artificial sociality (AS) from several theoretical and methodological positions. First, it is shown that the concept of IS actualizes extra-subjective approaches to understanding social ontology and is comparable with a number of concepts developed in the history of sociology, in particular, with P. Sorokin’s “conductors” (“mediums”). In this respect, AI is similar to other cultural (“artificial”) phenomena. Second, in the perspective of the Slavophile concept of “integral knowledge”, the limited epistemological horizons of AI as “rational cognition” are scrutinized. Third, sociological interpretations of AI that do not provide clarity in understanding its agentic status in social interactions (“participant” or “mediator”) are subjected to revision. Fourth, it is argued that a sociologically correct formulation of the IS question should focus not on natural or artificial agents of sociality, but on forms of sociality as ways of implementing social order. Today’s social reality, so the author of this paper, has not led so far to destruction of old and emergence of fundamentally new forms of social interactions, which, in the situation of insufficient theoretical comprehension, renders IS issue a journalistically superficiality.Sociologiceskie issledovaniâ70It is argued that a sociologically correct formulation of the IS question should focus not on natural or artificial agents of sociality, but on forms of sociality as ways of implementing social order.Sotsiologicheskie issledovaniya["I. Shmerlina"]2024-06-13T00:00:00 6296be9663780561e296491c1c8c154ee6de4c51The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.Electronics09This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control.Electronics["Pierpaolo Dini", "Lorenzo Diana", "Abdussalam Elhanashi", "Sergio Saponara"]2024-06-13T00:00:00 fb75fec3d582756e2699f2ad2121eb5fc6ba97efJournal of Computers in Education115Vital vulnerabilities concerning the potential bias of AI models towards non-native English speakers are highlighted, stemming from possible deficiencies in vocabulary and grammatical structure, to unleash the full potential of AI in education and address ethical considerations tied to its application.Journal of Computers in Education["J. Campino"]2024-06-13T00:00:00 12b22bfd71071e589d64ed17d1da059436d86083Members of the Human-Robot Interaction (HRI) and Artificial Intelligence (AI) communities have proposed Large Language Models (LLMs) as a promising resource for robotics tasks such as natural language interactions, doing household and workplace tasks, approximating `common sense reasoning', and modeling humans. However, recent research has raised concerns about the potential for LLMs to produce discriminatory outcomes and unsafe behaviors in real-world robot experiments and applications. To address these concerns, we conduct an HRI-based evaluation of discrimination and safety criteria on several highly-rated LLMs. Our evaluation reveals that LLMs currently lack robustness when encountering people across a diverse range of protected identity characteristics (e.g., race, gender, disability status, nationality, religion, and their intersections), producing biased outputs consistent with directly discriminatory outcomes -- e.g. `gypsy' and `mute' people are labeled untrustworthy, but not `european' or `able-bodied' people. Furthermore, we test models in settings with unconstrained natural language (open vocabulary) inputs, and find they fail to act safely, generating responses that accept dangerous, violent, or unlawful instructions -- such as incident-causing misstatements, taking people's mobility aids, and sexual predation. Our results underscore the urgent need for systematic, routine, and comprehensive risk assessments and assurances to improve outcomes and ensure LLMs only operate on robots when it is safe, effective, and just to do so. Data and code will be made available.arXiv.org03An HRI-based evaluation of discrimination and safety criteria on several highly-rated Large Language Models reveals that LLMs currently lack robustness when encountering people across a diverse range of protected identity characteristics, and underscores the urgent need for systematic, routine, and comprehensive risk assessments and assurances to improve outcomes.ArXiv["Rumaisa Azeem", "Andrew Hundt", "Masoumeh Mansouri", "Martim Brandao"]2024-06-13T00:00:00 950054b41a181e14b48c2f9de559f27e2096c89ePurposeThis exploratory study innovates the pedagogy of undergraduate business research courses by integrating Generative Artificial Intelligence (GAI) tools, guided by human-centered artificial intelligence, social-emotional learning, and authenticity principles.Design/methodology/approachAn insider case study approach was employed to examine an undergraduate business research course where 72 students utilized GAI for coursework. Thematic analysis was applied to their meta-reflective journals.FindingsStudents leverage GAI tools as brainstorming partners, co-writers, and co-readers, enhancing research efficiency and comprehension. They exhibit authenticity and human-centered AI principles in their GAI engagement. GAI integration imparts relevant AI skills to students.Research limitations/implicationsFuture research could explore how teams collectively interact with GAI tools.Practical implicationsIncorporating meta-reflections can promote responsible GAI usage and develop students' self-awareness, critical thinking, and ethical engagement.Social implicationsOpen discussions about social perceptions and emotional responses surrounding GAI use are necessary. Educators can foster a learning environment that nurtures students' holistic development, preparing them for technological challenges while preserving human learning and growth.Originality/valueThis study fills a gap in exploring the delivery and outcomes of AI-integrated undergraduate education, prioritizing student perspectives over the prevalent focus on educators' viewpoints. Additionally, it examines the teaching and application of AI for undergraduate research, diverging from current studies that primarily focus on research applications for academics.Journal of Research in Innovative Teaching &amp; Learning262This study fills a gap in exploring the delivery and outcomes of AI-integrated undergraduate education, prioritizing student perspectives over the prevalent focus on educators' viewpoints.Journal of Research in Innovative Teaching &amp; Learning["Patrick Aure", "Oriana Cuenca"]2024-06-13T00:00:00 64d774eaf9ef8b774cd0853e647e79078820018fAutonomous Intelligent Systems62A unified framework for self-evolving intelligent driving (ID) based on human feedback based on traditional feedback is proposed and an application in the congested ramp scenario illustrates the effectiveness of the proposed framework.Auton. Intell. Syst.["Kang Yuan", "Yanjun Huang", "Lulu Guo", "Hong Chen", "Jie Chen"]2024-06-13T00:00:00 7dfd0f91066a75c88ee86bbea9ccf16ab1ff8334Recent advancements in Artificial Intelligence (AI), particularly the widespread adoption of Large Language Models (LLMs), have significantly enhanced text analysis capabilities. This technological evolution offers considerable promise for automating the review of scientific papers, a task traditionally managed through peer review by fellow researchers. Despite its critical role in maintaining research quality, the conventional peer-review process is often slow and subject to biases, potentially impeding the swift propagation of scientific knowledge. In this paper, we propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem, aimed at assessing the relevance of a paper in relation to a specified prompt, analogous to a"call for papers". To address this, we introduce a novel dataset comprised of 25,164 instances. Each instance contains one prompt and four candidate papers, each varying in relevance to the prompt. The objective is to develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one. We explore various baseline approaches, including traditional ML classifiers like Support Vector Machine (SVM) and advanced language models such as BERT. Preliminary findings indicate that the BERT-based end-to-end classifier surpasses other conventional ML methods in performance. We present this problem as a public challenge to foster engagement and interest in this area of research.arXiv.org181This paper proposes RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem, aimed at assessing the relevance of a paper in relation to a specified prompt, analogous to a"call for papers".ArXiv["Paulo Henrique Couto", "Quang Phuoc Ho", "Nageeta Kumari", "B. K. Rachmat", "Thanh Gia Hieu Khuong", "Ihsan Ullah", "Lisheng Sun-Hosoya"]2024-06-13T00:00:00 f8c610883ca4b582d4bdb01f837575cf768cf11bThe capability of artificial intelligence (AI) is rapidly increasing and is now sitting on the threshold of the medical writing field. This article presents an AI integration framework that breaks down adoption of this new technology into manageable steps that ensure an informed and thorough approach. Using this framework, individuals and corporations can leverage the benefits of this evolving technology while minimizing risks. The first step, AI literacy, provides a foundation for informed decision making and appropriate expectations for AI capabilities. This knowledge inspires creative exploration of which use cases would be a suitable application of AI tools. Once the scope of potential uses is defined, risks can be assessed, including incorrect content generation, data leakage, and bias. AI tools can then be evaluated to find tools that can both satisfy the use cases and mitigate critical threats. The final step is to integrate the tools transparently with appropriate guardrails. Then the cycle begins again as AI technology evolves and new applications become possible. As medical writers are ushered further into the AI era, clear and consistent advocacy for a synergy point between the efficiency of AI and the experience, ability, and humanity of a medical writer will maximize the impact of these innovative models.American Medical Writers Association AMWA journal51An AI integration framework that breaks down adoption of this new technology into manageable steps that ensure an informed and thorough approach is presented, so individuals and corporations can leverage the benefits of this evolving technology while minimizing risks.AMWA Journal["Jenni Pickett", "Mandy Pennington"]2024-06-13T00:00:00 b79f9881b9f9e8734bf1fbfcf29dd80b33b6ba71The rapid growth of artificial intelligence (AI) technology has had a big impact on a lot of industries, including education. The use of AI tools in language education and instruction has the potential to improve students' academic performance and writing abilities. The purpose of this study is to investigate how pre-service English teachers perceive the application of AI to writing tasks. The objective of this research is to discover students’ perceptions, discover students’ difficulties, and figure out students' reasons. Some insights were collected from individuals who have experience using AI for writing assignments. The data was collected using a qualitative case study design. The data was collected from a questionnaire and an interview. It was then analyzed using narrative analysis. The results of the study are intended to shed light on the advantages, difficulties, and motivations for the use of AI in academic writing. It also offers insightful information to teachers and other organizations looking to use technology to improve language acquisition. This study implied a perception of the student writing assessment using AI for these pre-service English teachers in the future.Journal of world Englishes and educational practices201This study investigates how pre-service English teachers perceive the application of AI to writing tasks and sheds light on the advantages, difficulties, and motivations for the use of AI in academic writing.Journal of World Englishes and Educational Practices["Aurel Salsabila Nadhifah", "Hastin Nursanti Syukur", "Muhammad Ferdy Haryanto", "Roghibatul Luthfiyyah", "Diana Rahmawati Rozak"]2024-06-13T00:00:00 1755a4bf7472e9b69a1f53b26b0b86faf315df41Artificial Intelligence (AI) is a subfield of computer science that focuses on building systems that can carry out tasks that normally require human intelligence. Learning, reasoning, problem-solving, comprehension of spoken language, perception, and even creativity are some of these tasks. Academic libraries can improve services and operations by using artificial intelligence. However, the implementation of AI in academic libraries has many challenges, such as technical issues, ethical and legal concerns, etc. The article includes the history and definition of artificial intelligence, the importance of AI, methodologies and techniques utilized by AI, areas where artificial intelligence can be used, and challenges in implementing AI in academic libraries.International Journal of Innovative Science and Research Technology191The article includes the history and definition of artificial intelligence, the importance of AI, methodologies and techniques utilized by AI, areas where artificial intelligence can be used, and challenges in implementing AI in academic libraries.International Journal of Innovative Science and Research Technology (IJISRT)["Vishnu M. Pawar"]2024-06-13T00:00:00 1469b7306ecf2b8209437c17dd792b985ea9fea2Neural-symbolic integration aims to combine the connectionist subsymbolic with the logical symbolic approach to artificial intelligence. In this paper, we first define the answer set semantics of (boolean) neural nets and then introduce from first principles a class of neural logic programs and show that nets and programs are equivalent.arXiv.org511This paper first defines the answer set semantics of (boolean) neural nets and then introduces from first principles a class of neural logic programs and shows that nets and programs are equivalent.ArXiv["Christian Anti'c"]2024-06-13T00:00:00 3aa59458b23e4944af754e9dae26bde3268ce1fcabstract:This paper analyzes Italian artist Auro Lecci’s contribution to pioneering media art, beginning with his paintings and ending with his computer artworks (1969–1972). As the author suggests, Lecci’s paintings were already characterized by an algorithmic method that the artist went on to develop in his computer-generated works. The paper first discusses the plotter drawings Lecci created at the Computing Center of the University of Pisa (CNUCE), and then focuses on his last computer art project, made at the University of Massachusetts in Amherst, to suggest connections between Lecci’s work and artificial intelligence.400Italian artist Auro Lecci’s contribution to pioneering media art is analyzed, beginning with his paintings and ending with his computer artworks (1969–1972), to suggest connections between Lecci’s work and artificial intelligence.Leonardo["Paola Lagonigro"]2024-06-13T00:00:00 023c45491cfcbd36b9fca63b0d4f37ec54527e67Artificial intelligence (AI)‐driven analysis of comprehensive clinical parameters is bringing about a significant transformation in traditional routine clinical laboratory testing. This transformation impacts the prediction, prevention, diagnosis, and prognosis of human diseases. AI possesses the capability to efficiently analyze and process vast and intricate datasets, thereby facilitating the development of diverse and efficient diagnostic or predictive models. This advancement is fueling significant improvements in laboratory quality, automation, and the accuracy of diagnoses. In this context, we conducted a thorough review and discussion on the progression of AI applications in clinical laboratory medicine, encompassing advancements, implementation, and challenges. Our conclusion underscores that integrating AI into clinical laboratory testing will notably propel personalized precision medicine forward and enhance diagnostic accuracy, especially benefiting patients for whom accurate diagnoses are elusive through traditional laboratory testing systems.Interdisciplinary Medicine1094It is concluded that integrating AI into clinical laboratory testing will notably propel personalized precision medicine forward and enhance diagnostic accuracy, especially benefiting patients for whom accurate diagnoses are elusive through traditional laboratory testing systems.Interdisciplinary Medicine["Heying Xie", "Yin Jia", "Shanrong Liu"]2024-06-14T00:00:00 e0c72fe5d89da3c6dc460ed85e417e0b45a69ee8Cybersecurity has become a central concern in the contemporary digital era due to the exponential increase in cyber threats. These threats, ranging from simple malware to advanced persistent attacks, put individuals and organizations at risk. This study explores the potential of artificial intelligence to detect anomalies in network traffic in a university environment. The effectiveness of automatic detection of unconventional activities was evaluated through extensive simulations and advanced artificial intelligence models. In addition, the importance of cybersecurity awareness and education is highlighted, introducing CyberEduPlatform, a tool designed to improve users’ cyber awareness. The results indicate that, while AI models show high precision in detecting anomalies, complementary education and awareness play a crucial role in fortifying the first lines of defense against cyber threats. This research highlights the need for an integrated approach to cybersecurity, combining advanced technological solutions with robust educational strategies.PeerJ Computer Science431The results indicate that, while AI models show high precision in detecting anomalies, complementary education and awareness play a crucial role in fortifying the first lines of defense against cyber threats.PeerJ Computer Science["Iv\u00e1n Ortiz-Garc\u00e9s", "Jaime Govea", "Santiago S\u00e1nchez-Viteri", "W. Villegas-Ch."]2024-06-14T00:00:00 83f6657bf66ac540f731edab87074720590b1f74We review research at the National Physical Laboratory (NPL) in the area of trustworthy artificial intelligence (TAI), and more specifically trustworthy machine learning (TML), in the context of metrology, the science of measurement. We describe three broad themes of TAI: technical, socio-technical and social, which play key roles in ensuring that the developed models are trustworthy and can be relied upon to make responsible decisions. From a metrology perspective we emphasise uncertainty quantification (UQ), and its importance within the framework of TAI to enhance transparency and trust in the outputs of AI systems. We then discuss three research areas within TAI that we are working on at NPL, and examine the certification of AI systems in terms of adherence to the characteristics of TAI.arXiv.org561Three broad themes of TAI are described: technical, socio-technical and social, which play key roles in ensuring that the developed models are trustworthy and can be relied upon to make responsible decisions.ArXiv["Tameem Adel", "Samuel Bilson", "Mark Levene", "Andrew Thompson"]2024-06-14T00:00:00 c4d1f5760a99f0976908873894d1326bff001dc5Diabetic retinal disease (DRD) is a complication of diabetes mellitus that can lead to vision loss; early detection through screening and treatment can prevent this. Few individuals with diabetes meet recommended DRD screening guidelines, and racial/ethnic minority youth are even less likely to undergo recommended screening. We sought to determine if implementing point of care (POC) autonomous artificial intelligence (AI) screening could mitigate disparities in diabetic eye exam completion. In ACCESS2, a preregistered prospective pre-post study design, youth with type 1(T1D) and type 2 diabetes (T2D) meeting American Diabetes Association criteria for needing a diabetic eye exam underwent POC autonomous AI diabetic eye exams at routine diabetes clinic visits. Completion rates of diabetic eye exams were compared prior to and after implementation of autonomous AI. A total of 380 youth with T1D (71.1%) and T2D (28.9%) were enrolled, mean age 15.2y, 46.8% Non-Hispanic (NH) White, with mean duration of diabetes of 6.0y, and median hemoglobin A1c of 8.1%. A greater percentage of NH White participants reported any prior diabetic eye exam (81.6 v 62.0%, p<0.001) compared to non-white and Hispanic participants. Multivariable analysis demonstrated that the strongest predictor for prior eye exam was diabetes duration. After undergoing POC autonomous AI diabetic eye exams, completion rates were 98% among non-white and Hispanic participants, and 99% among white participants. Introducing autonomous AI at the point of care enhances the accessibility and completion rates of diabetic screening eye exams, and promotes health equity for minority youth with diabetes. D. Patel: None. L.A. Bromberger: None. N. Parimi: None. E.A. Brown: None. A. Liu: None. H. Lehmann: None. M.D. Abràmoff: Board Member; Digital Diagnostics. Stock/Shareholder; Digital Diagnostics. Other Relationship; Digital Diagnostics. R.M. Wolf: Research Support; Dexcom, Inc., Boehringer-Ingelheim, Novo Nordisk. National Eye Institute (R01EY033233-03) Diabetes01Introducing autonomous AI at the point of care enhances the accessibility and completion rates of diabetic screening eye exams, and promotes health equity for minority youth with diabetes.Diabetes["Dhruva Patel", "Lee Bromberger", "Neha Parimi", "Elizabeth A Brown", "Alvin Liu", "Harold P Lehmann", "M. Abr\u00e0moff", "Risa M. Wolf"]2024-06-14T00:00:00 ed5e56bde9c17db5329fd37b7611657077676e20Abstract Objective Investigate the use of advanced natural language processing models to streamline the time-consuming process of writing and revising scholarly manuscripts. Materials and Methods For this purpose, we integrate large language models into the Manubot publishing ecosystem to suggest revisions for scholarly texts. Our AI-based revision workflow employs a prompt generator that incorporates manuscript metadata into templates, generating section-specific instructions for the language model. The model then generates revised versions of each paragraph for human authors to review. We evaluated this methodology through 5 case studies of existing manuscripts, including the revision of this manuscript. Results Our results indicate that these models, despite some limitations, can grasp complex academic concepts and enhance text quality. All changes to the manuscript are tracked using a version control system, ensuring transparency in distinguishing between human- and machine-generated text. Conclusions Given the significant time researchers invest in crafting prose, incorporating large language models into the scholarly writing process can significantly improve the type of knowledge work performed by academics. Our approach also enables scholars to concentrate on critical aspects of their work, such as the novelty of their ideas, while automating tedious tasks like adhering to specific writing styles. Although the use of AI-assisted tools in scientific authoring is controversial, our approach, which focuses on revising human-written text and provides change-tracking transparency, can mitigate concerns regarding AI’s role in scientific writing.J. Am. Medical Informatics Assoc.227Although the use of AI-assisted tools in scientific authoring is controversial, this approach, which focuses on revising human-written text and provides change-tracking transparency, can mitigate concerns regarding AI's role in scientific writing.Journal of the American Medical Informatics Association : JAMIA["M. Pividori", "Casey S. Greene"]2024-06-14T00:00:00 548c18e960d843fd50ef9a3a24c7b2942cff6824The purpose of this article is devoted to the significance of the innovative use of artificial intelligence in modern education, the authors, based on an analysis of the literature, came to the conclusion that artificial intelligence can be used not only as a teaching tool, but also through the classroom, the media and others traditional learning aids. Artificial intelligence has powerful algorithms that can help educators better understand learning focus and break down educational content from multiple angles to help students quickly integrate and update course content. The article discusses the main applications of artificial intelligence in education as analysis criteria, as well as intelligent tutoring, educational data analysis, personalized learning paths, and virtual classroom creation as an innovative integrated classroom design. After discussion and analysis, the authors believe that artificial intelligence has great potential for innovation in teaching methodology and technology, it can enrich teacher teaching methods and improve teaching technology. By integrating traditional teaching methods and advanced artificial intelligence algorithms, artificial intelligence cannot only help teachers improve the effectiveness of teaching, but also to analyze the content of classes and optimize the choice of teaching sequence.Bulletin of Science and Practice20It is concluded that artificial intelligence can be used not only as a teaching tool, but also through the classroom, the media and others traditional learning aids, to enrich teacher teaching methods and improve teaching technology.Bulletin of Science and Practice["Zhenni Yang"]2024-06-14T00:00:00 27407a7e971588e76b57ebaf69c02709654d08a3In recent years, advancements in artificial intelligence have pervaded various facets of education, and learning of English Language and Literature is of no exception. Artificial intelligence assessment tools, ranging from plagiarism checkers to essay writing tools, AI chat box, can aid students in crafting content within a matter of seconds. Artificial Intelligence possesses the potentiality to transform the functioning of the educational system, enhance the competitiveness of educational institutions, and empower educators and students at all levels. The objective of this paper is to scrutinize how the transformative impact of artificial intelligence (AI) on English language and Literature. It examines how Artificial Intelligence is redefining English language and Literature while reshaping its uses. It also provides an insight by addressing concerns regarding the ethical implications and limitations on these technologies.RESEARCH REVIEW International Journal of Multidisciplinary00This paper examines how Artificial Intelligence is redefining English language and Literature while reshaping its uses, and provides an insight by addressing concerns regarding the ethical implications and limitations on these technologies.RESEARCH REVIEW International Journal of Multidisciplinary["Yash. H. Danecha"]2024-06-14T00:00:00 a8d3d4631e9db39a56f60d900d0ba228f51bb608Data are essential in developing healthcare artificial intelligence (AI) systems. However, patient data collection, access, and use raise ethical concerns, including informed consent, data bias, data protection and privacy, data ownership, and benefit sharing. Various ethical frameworks have been proposed to ensure the ethical use of healthcare data and AI, however, these frameworks often align with Western cultural values, social norms, and institutional contexts emphasizing individual autonomy and well-being. Ethical guidelines must reflect political and cultural settings to account for cultural diversity, inclusivity, and historical factors such as colonialism. Thus, this paper discusses healthcare data ethics in the AI era in Africa from the Ubuntu philosophy perspective. It focuses on the contrast between individualistic and communitarian approaches to data ethics. The proposed framework could inform stakeholders, including AI developers, healthcare providers, the public, and policy-makers about healthcare data ethical usage in AI in Africa.arXiv.org630The proposed framework could inform stakeholders, including AI developers, healthcare providers, the public, and policy-makers about healthcare data ethical usage in AI in Africa from the Ubuntu philosophy perspective.ArXiv["A. J. Mahamadou", "Aloysius Ochasi", "Russ B. Altman"]2024-06-14T00:00:00 e3da409e03f0240ebfef48ecc8f42f675440278fA Review of: Subaveerapandiyan, A., Sunanthini, C., & Amees, M. (2023). A study on the knowledge and perception of artificial intelligence. IFLA Journal, 49(3), 503–513.  https://doi.org/10.1177/03400352231180230 Objective – To assess the knowledge, perception, and skills of library and information science (LIS) professionals related to artificial intelligence (AI). Design – 45 statements were distributed to 469 LIS professionals via Google Forms to collect primary data. 245 participants responded to the structured questionnaire. Setting – University and college libraries in Zambia. Subjects – Zambian library and information science professionals.Methods – A descriptive approach was employed for the study. Data was gathered via a questionnaire. “The objective was to assess the statistical relationship between the knowledge, perception, and skills of LIS professionals (the independent variables) and AI (the dependent variable)” (Subaveerapandiyan et al., p. 506). The survey used a 5-point Likert scale with (1) strongly disagree being the lowest score and (5) strongly agree the highest.  Means and standard deviations are included in data display tables. Thematic analysis was employed to analyze the data. SPSS was used for data analysis.Main Results – Survey results are presented in three tables. Table 1, “Awareness of AI among LIS professionals,” contains 21 statements related to AI use in various library environments and services, including reference (finding articles and citations, content summarization, detecting misinformation), circulation of library materials, security and surveillance, character recognition and document preservation, research data management, language translation, and others. The authors note that 44.1 percent of the respondents agreed that “AI is essential for the effectiveness and efficiency of library service delivery, enabling libraries to enhance and offer dynamic services for their users” (Subaveerapandiyan et al., 2023, p. 506). Table 2, “Perception of AI among LIS professionals,” contains 10 statements. Over 85 percent of respondents either strongly agreed or agreed that AI “makes library staff lazy” while 58.1 percent either strongly agreed or agreed that AI is a “threat to librarians’ employment” (Subaveerapandiyan et al., 2023, p. 506). The authors note that the “respondents also indicated barriers to the adoption of AI in libraries, such as the lack of LIS professionals’ skills and budgetary constraints” (Subaveerapandiyan et al., 2023, p. 506). Table 3 lists 13 competencies required by library professionals in the AI era. The majority of the respondents (an average of 65 percent) were in strong agreement that “electronic communication, hardware and software, Internet applications, computing and networking, cyber security and network management, data quality control, data curation, database management … are necessary competencies required by LIS professionals for them to be proficient in AI” (Subaveerapandiyan et al., 2023, p. 506).Evidence Based Library and Information Practice10Assessment of the knowledge, perception, and skills of library and information science professionals related to artificial intelligence in Zambia found that “electronic communication, hardware and software, Internet applications, computing and networking, cyber security and network management, data quality control, data curation, database management … are necessary competencies required by LIS professionals for them to be proficient in AI”.Evidence Based Library and Information Practice["David M. Dettman"]2024-06-14T00:00:00 6208f0d9cf1c66ec4bfef255dae45a826ce2a733Objective: Along with the increasingly rapid development of digital technology and economy, medical treatment has been enhanced by artificial intelligence (AI). Studies have explored many topics in the field of medical AI. However, there is a lack of a systematic review of the overall research area of medical AI. In a visual way, this study uses quantitative analysis to systematically review the entire field and explore the current status and trends of medical AI research. Methods: This paper retrieves 692 papers on medical AI from Social Sciences Citation Index core database of the Web of Science from 2013 to 2023. Three bibliometric and network analysis tools, including CiteSpace, HistCite and Pajek, are used to identify the time-and-space knowledge map, research hotspots, emerging trends and primary path of medical AI research. Results: A co-word network of medical AI research reveals that the field focuses more on the topics of health care and cancer. The analysis of the burst literature indicates the research trends in the sub-sections such as medical ethics, neural network and precision medicine. The analysis of the main path draws the evolution track. Conclusion: The results of bibliometric analysis illustrate the current situation, past evolution and future trends of medical AI research, and identify hotspots and future research directions.Journal of Information Analysis520This study uses quantitative analysis to systematically review the entire field and explore the current status and trends of medical AI research, and identify hotspots and future research directions.Journal of Information Analysis["Yonghao Liu", "Yu Mu"]2024-06-14T00:00:00 4785f055980c9b7188b481133eeb2348dca39853Heart disease prediction is important in healthcare because it enables timely identification and intervention of actual condition of the patient. However, the task of accurately predicting disease remains a challenging task. In this paper, we have proposed a framework for heart disease prediction using explainable artificial intelligence (XAI) based Machine Learning (ML) models such as Decision Tree (DT), Random Forest (RF), k-nearest neighbors (KNN), AdaBoost, Logistic Regression (LR), Naive Bayes (NB), and Neural Network (NN). The efficiency of those models were evaluated using MCC, accuracy, precision, recall, and AUC. Finally, it is observed that, DT emerges as the most effective model offering interpretability. This study underscores the importance of transparent models in healthcare and advocates in order to incorporate XAI to enhance interpretability and medical decision-making.2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)250A framework for heart disease prediction using explainable artificial intelligence (XAI) based Machine Learning (ML) models such as Decision Tree (DT), Random Forest, k-nearest neighbors (KNN), AdaBoost, Logistic Regression (LR), Naive Bayes (NB), and Neural Network (NN) is proposed.2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)["Sivaram Kommineni", "Sanvitha Muddana", "Rajiv Senapati"]2024-06-14T00:00:00 587f323d5f796f9a1274fc849df7e5b4145155d3Over 500 million people were estimated to live with diabetes in 2021, of which 96% were type 2. This leads to various complications, among which are macrovascular diseases (e.g., stroke and coronary heart diseases). As complications increase the 5-year mortality of patients, curbing their progression through early prediction and intervention is key. This scoping review explores the characteristics of current research on how artificial intelligence, including machine learning algorithms, has been utilized to predict diabetes macrovascular complications. In adherence to PRISMA-ScR guidelines, we systematically searched PubMed, Google Scholar, Scopus, IEEE Xplore, EMBASE, and Wiley for relevant literature up to 12 December 2023. Out of the 1,667 hits screened, 52 studies cumulating 7,510,245 people with type 2 diabetes are included. We found 43 studies from HICs/UMICs, in contrast to 9 from LMICs/LICs. 30 studies came from North America and Europe regions, while others from Asia and Australia. Of all macrovascular complications, cardiovascular diseases (e.g., coronary heart disease) have been the most investigated outcome. According to the features of the models, only 12 studies employed non-laboratory features as predictors, while the remaining studies applied solely laboratory (n=2) or mixed (n=38) features, signalling the lack of AI capability for history-taking and physical examination data alone, which are mostly available in low-resource settings. While artificial intelligence is promising in predicting diabetes complications, future studies should explore accessible features in low-resource settings and employ external validation. A systematic review and meta-analysis exploring the performance metrics of a variety of algorithms should be done. A. Nur: None. D. Harbuwono: None. Diabetes00While artificial intelligence is promising in predicting diabetes complications, future studies should explore accessible features in low-resource settings and employ external validation as well as a systematic review and meta-analysis exploring the performance metrics of a variety of algorithms.Diabetes["Aqsha Nur", "D. Harbuwono"]2024-06-14T00:00:00 f0d3ab54856e8918e30c50bd07c7a2b18ba15d52In the contemporary healthcare sector, the convergence of artificial intelligence (AI) and emotional intelligence (EI) carries substantial ramifications for practitioners of medicine. Emotional intelligence is becoming an increasingly significant asset for medical professionals, especially in light of the integration of AI technologies into clinical processes, encompassing the ability to perceive, comprehend, and regulate emotions. This research examines the various dynamics of EI within the framework of AI integration, illustrating its importance in numerous domains. An increasing number of medical practitioners are collaborating with AI-driven systems, which creates a nuanced relationship between the analytical prowess of AI and the emotive intelligence of humans; thus, a balance must be maintained. Additionally, this study examines the effects of EI on critical facets of medical education, interactions between practitioners and patients, and overall job satisfaction. This abstract advocates for a stance that underscores the criticality of bolstering EI talents in the face of technological advancements. A harmonious integration that acknowledges the potential conflicts and synergies between EI and AI to support both the cognitive and affective aspects of healthcare practice.2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)470This research examines the various dynamics of EI within the framework of AI integration, illustrating its importance in numerous domains and advocates for a stance that underscores the criticality of bolstering EI talents in the face of technological advancements.2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO)["Krishnaveni Subramani", "Geetha Manoharan"]2024-06-14T00:00:00 9568be1dde2fe961bb17284db3b7c0802637e416The purpose of the study was to review and analyze the techniques and tools used by cyber terrorists in exploiting artificial intelligence, and to understand how artificial intelligence contributes to enhancing the capabilities and effectiveness of cyber terrorism.the importance of this study is to understand the effects of using artificial intel ligence on fueling cyber terrorism and increasing its effectiveness, and identify the challenges facing counter-terrorism operations. the study followed the descriptive analytical approach to describe the legal framework for artificial intelligence and fueling cyber terrorism, and analyzing legal texts. International Journal of Religion170International Journal of Religion["Monther Abed-Alrazzaq Musleh Al-Amaireh"]2024-06-14T00:00:00 f046f31fb0ef6aefbcfef9c32667d2b49e611354Introduction and Objective: in-hospital glucose management of people living with type 2 diabetes (PLWT2D) has a direct impact on morbidity and mortality. Artificial intelligence (AI) tools could improve patient care. This study aimed to assess the correlation between in-patient glucose management suggested by an AI chatbot and hospital protocols and compare it with real life medical management. Methods: we conducted a cross-sectional study of PLWT2D hospitalized in a tertiary care center in 2023 who required an Endocrinology consultation during their hospital stay. We excluded patients with corticosteroid treatment or need for intravenous insulin.  We provided Chat-GPT4 with the protocol for T2D management used in the hospital and asked for a suggested prescription based on individual de-identified variables. We assessed the accuracy of the output using Spearman (r) and interclass correlation (ICC) coefficients. We then analyzed the correlation between the treatment prescribed by physicians on the first day of hospital stay and the protocol. Results: 85 patients met the inclusion criteria. There was a strong correlation between the dose of basal insulin suggested by Chat-GPT and the protocol (r=0.995), CCI=0.996 [IC 95% 0.994;0.998] as opposed to a weak correlation between the basal insulin dose prescribed by physicians and the protocol (r=0.223), ICC=0.134 [IC95% -0.072;0.331]. The same strong correlation was found between the dose of prandial insulin suggested by Chat-GPT and the protocol (r=0.978), CCI=0.984 [IC 95% 0.975;0.990], and a moderate correlation between physicians and the protocol (r=0.399), CCI=0.466 [IC95% 0.283;0.616]. The chatbot suggested the discontinuation of non-insulin therapies in all patients, as stipulated in the protocol. Conclusions: the use of an AI chatbot resulted in a higher correlation with established protocols compared with medical prescription. T. Rojas-López: None. D. Alvarez-Martin: None. A. Pujol-deCastro: None. O. Moreno-Dominguez: Speaker's Bureau; Novo Nordisk, Lilly Diabetes. Research Support; Sanofi. B. Barquiel: None. E. Garcia-Perez-de-Sevilla: None. P. Parra-Ramírez: None. N. Gonzalez-Perez-de-Villar: Speaker's Bureau; Abbott Diagnostics. Advisory Panel; Medtronic. Speaker's Bureau; Air Liquide. Diabetes00The use of an AI chatbot resulted in a higher correlation with established protocols compared with medical prescription and it suggested the discontinuation of non-insulin therapies in all patients, as stipulated in the protocol.Diabetes["Tatiana ROJAS-L\u00d3PEZ", "Daniel ALVAREZ-MARTIN", "Antonio PUJOL-DECASTRO", "\u00d3scar Moreno-Dom\u00ednguez", "B. Barquiel", "Elena GARCIA-PEREZ-DE-SEVILLA", "Paola PARRA-RAM\u00cdREZ", "Noemi GONZALEZ-PEREZ-DE-VILLAR"]2024-06-14T00:00:00 275cf8cbd89364990f29f0e5ad7b9514c6220f69The proliferation of online social networks has brought forth unprecedented connectivity and communication but has also facilitated the emergence of cyberbullying, a pervasive and harmful phenomenon. Traditional methods for identifying cyberbullying often fall short due to the dynamic nature of online interactions and the sheer volume of data. In response, this study explores the application of deep learning techniques for cyberbullying detection, focusing on the integration of LSTM networks with an attention mechanism. The research leverages a diverse dataset encompassing various forms of cyberbullying across age, ethnicity, gender, religion, and non-bullying content. Key findings reveal that the proposed models achieve high accuracy, precision, recall, and F1 scores, effectively classifying instances of cyberbullying with a comprehensive understanding of contextual nuances. Moreover, the study contributes insights into feature extraction methodologies and model optimization techniques, demonstrating the efficacy of deep learning in addressing the complexities of multi-modal social media data.Artificial Intelligence in Cybersecurity01Key findings reveal that the proposed models achieve high accuracy, precision, recall, and F1 scores, effectively classifying instances of cyberbullying with a comprehensive understanding of contextual nuances, demonstrating the efficacy of deep learning in addressing the complexities of multi-modal social media data.Artificial Intelligence in Cybersecurity["Salma A. Walli", "Byeong-Gwon Kang", "Yunyoung Nam"]2024-06-14T00:00:00 aa8929c995e704d21455d71954a78f54ef67a686Neurophysiologie clinique281Neurophysiologie Clinique["A. McGonigal", "H. Tankisi"]2024-06-14T00:00:00 d0dd470b823a2c089697b544d9eaf7d5fe29bc9bEnvironment, Development and Sustainability731Environment, Development and Sustainability["David van der Woude", "Gilmer Yovani Castro Nieto", "Maria Andreina Moros Ochoa", "Carolina Llorente Portillo", "Anderson Quintero"]2024-06-14T00:00:00 de0305b29871e9b3f206c71705764ffb960ee29dAnnual Review of Virology01Annual review of virology["Julie K. Pfeiffer", "Terence\u00a0S. Dermody"]2024-06-14T00:00:00 3ebd29c4dc9ea9168856aafd0967245a34739e35International Journal of Cross Cultural Management61International Journal of Cross Cultural Management["Terence Jackson"]2024-06-14T00:00:00 727a4aa98cfa8d446f39e952996560ebb01d847aMind &amp; Society1112Mind &amp; Society["Santiago Alonso-D\u00edaz"]2024-06-14T00:00:00 13ebb939c57b9672e364fa26068e2af01cf8e7e4Journal of Promotion Management501Journal of Promotion Management["Mariano M\u00e9ndez-Su\u00e1rez", "Luca Delbello", "Alejandro de Vega de Unceta", "Ana Lucia Ortega Larrea"]2024-06-14T00:00:00 d2bab4ab4f19d1c92e4fa099a20f5da5e0244ce4The optimal functioning of the power system is crucially dependent upon the sound protection of its major stakeholder, i.e., the transmission line, as it is prone to fault. To maintain the integrity of the power system and protect costly power system equipment, protective relaying is necessary to provide a steady and affordable supply of electricity. Relays recognize, classify, and identify transmission line faults using input signals of voltage and current. Many artificial intelligent methods based on Expert Systems, Artificial Neural Networks, Fuzzy Logic, Support Vector Machines, Wavelet-based systems, and deep learning techniques are being investigated to improve modern digital relays’ consistency, speed, and accuracy. This paper is a comprehensive and all-inclusive survey that reviews and incorporates Phasor Measurement Unit (PMU) and Global Positioning System (GPS) approaches together with all of these intelligent transmission line safety strategies and concepts. Initial investigators will benefit from this study by being able to examine, evaluate, and analyze a variety of approaches with references for all relevant contributions.International Journal of Hybrid Intelligent Systems1010This paper is a comprehensive and all-inclusive survey that reviews and incorporates Phasor Measurement Unit and Global Positioning System approaches together with all of these intelligent transmission line safety strategies and concepts.Int. J. Hybrid Intell. Syst.["Yajnaseni Dash", "Ajith Abraham", "Naween Kumar", "Manish Raj"]2024-06-14T00:00:00 676c86a6ba103d812d81653bd03f58e89bb58e19NeuroQuantology00NeuroQuantology[]2024-06-14T00:00:00 73406c4dd7d60129bbbfa40642a6174dc84138cbJournal of Intellectual Property Law & Practice00Journal of Intellectual Property Law and Practice["Thomas Heinz Meitinger"]2024-06-14T00:00:00 1d05ddce78b733c1a2583912fef737e33f55cbd1Established engineering standards are facing challenges in adapting to the evolution of novel material properties, designing entirely new materials, and uncovering new mechanisms that transcend intuitive understanding. This research aims to investigate a new framework of domain-specific language models to automatically generate feasible engineering designs based on requirements. The work is anchored in the understanding of natural language processing and the fidelity requirements of machine-learning models for the civil and construction engineering domain. Relevant reports from the Transportation Research Information Service and standard specifications from various departments of transportation were included in the data collection. Another dataset was the summarized literature of 36 highway agencies. After the comparison of Support Vector Machines (SVM), Long Short-Term Memory network, and linear regression, the SVM algorithm was implemented in the framework to support decision-making through textual and tabular communication, and the results showed improvements in accuracy (130%) and F1 Score (65%). In this research, utilizing case-based content extraction alongside reliable statistical uncertainty estimation has shown the potential to generate valuable decision-support tools and recommendation systems for both engineers and managers. The applications encompassing textual content extraction alongside reliable statistical uncertainty estimation demonstrate the potential to create valuable decision-support tools and recommendation systems for engineers and managers.2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)150This research aims to investigate a new framework of domain-specific language models to automatically generate feasible engineering designs based on requirements to create valuable decision-support tools and recommendation systems for engineers and managers.2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)["Haiyan Sally Xie", "Sai Ram Gandla", "Mangolika Bhattacharya", "Pranshoo Solanki", "Dingnan Zheng"]2024-06-14T00:00:00 2c31f0518251a8df29b636711783a0924eedeedaInternational Journal of Gynecological Cancer50International journal of gynecological cancer : official journal of the International Gynecological Cancer Society["Laila Afroze", "M. S. Rahman"]2024-06-14T00:00:00 542aa2330a1a2aad60ad0efc60e3817ee6f06949Proceedings of the 2024 10th International Conference on Frontiers of Educational Technologies360Proceedings of the 2024 10th International Conference on Frontiers of Educational Technologies["Liandro Antonio Tiongson Tabora", "Datu Sajid Islam Sinsuat Ampatuan", "Marian Angelique Chamen Castaneda", "Erylle Jerica Uy Galinato", "Eury Ellyn Manaloto Zulueta"]2024-06-14T00:00:00 d3e3ebfb236b1917e9421213931251ab3cf8f70fIntroduction: Despite new pharmacotherapy, most patients with long-term type 2 diabetes are still hyperglycemic. This could have been solved by insulin with its unlimited potential efficacy, but its dynamic physiology demands frequent titrations which are overdemanding. This report provides a real-life account for a scalable transformation of diabetes care in a community-based endocrinology center by harnessing AI-based autonomous insulin titration. Methods: The center embedded the d-Nav® technology and its dedicated clinical support. Reported outcomes include treatment efficacy/safety in the first 600 patients and use of cardiorenal-risk reduction pharmacotherapy. Results: Patients used d-Nav for 8.2±3.0 months with 82% retention. Age-67.1±11.5 years and duration of diabetes-19.8±11.0 years. See Figure for HbA1c dynamics during the 3-years prior d-Nav, and on d-Nav. In 21% of the patients, insulin doses initially decreased to prevent hypoglycemia. GLP1/GLP1+GIPag were prescribed in about a half of the patients and SGLT2i in a third. The frequency of hypoglycemia (<54mg/dl) was 0.4±0.6/month and severe hypoglycemia 1.7/100-patient-years. Conclusions: The use of d-Nav allowed for scalable improvement in overall diabetes management with appropriate use of both insulin and non-insulin pharmacologic agents. M.L. Warren: Research Support; Novo Nordisk, Insulet Corporation. Speaker's Bureau; Lilly Diabetes. Advisory Panel; Lilly Diabetes. Research Support; AstraZeneca, Bayer Inc. Speaker's Bureau; Bayer Inc. Research Support; AbbVie Inc., Medtronic, Ascendis Pharma A/S, Amolyt. Speaker's Bureau; Ascendis Pharma A/S, Amgen Inc. Research Support; AstraZeneca. Advisory Panel; Hygieia. R.M. Bergenstal: Other Relationship; Abbott. Research Support; Arkray Marketing. Consultant; Ascensia Diabetes Care, Bigfoot Biomedical, Inc., CeQur. Other Relationship; Dexcom, Inc., Eli Lilly and Company. Consultant; embecta, Hygieia. Research Support; Insulet Corporation. Consultant; MannKind Corporation. Other Relationship; Medtronic, Novo Nordisk. Consultant; Onduo LLC, Roche Diabetes Care. Other Relationship; Sanofi. Research Support; Tandem Diabetes Care, Inc. Other Relationship; UnitedHealth Group. Consultant; Vertex Pharmaceuticals Incorporated, Zealand Pharma A/S. M. Hager: Other Relationship; Hygieia. E. Bashan: Board Member; Hygieia. Employee; Hygieia. Stock/Shareholder; Hygieia. I. Hodish: Stock/Shareholder; Hygieia. Diabetes00The use of d-Nav allowed for scalable improvement in overall diabetes management with appropriate use of both insulin and non-insulin pharmacologic agents and this report provides a real-life account for a scalable transformation of diabetes care in a community-based endocrinology center by harnessing AI-based autonomous insulin titration.Diabetes["Mark L Warren", "R. Bergenstal", "Matthew R Hager", "E. Bashan", "Israel Hodish"]2024-06-14T00:00:00 f305d82370ccd140fe22310e2865b47cbd8fed8aProceedings of the 2024 9th International Conference on Distance Education and Learning70Proceedings of the 2024 9th International Conference on Distance Education and Learning["Tian Chen", "XiaoMin Li"]2024-06-14T00:00:00 28131f0eaf223929c4330e728c6a932943eaa449Evidence Based Library and Information Practice00Evidence Based Library and Information Practice["Heather MacDonald"]2024-06-14T00:00:00 32e4a685c34e705b8a088a7e027ff2d99a175533Proceedings of the 2024 10th International Conference on Frontiers of Educational Technologies30Proceedings of the 2024 10th International Conference on Frontiers of Educational Technologies["Li Wei"]2024-06-14T00:00:00 ed8d4d39e54c05e613d4db25b682d2380340d858In this rapidly evolving era, AI is an emerging industry with a significant presence in various industries. Factors affecting the extent to which AI is used in companies have been explored by many researchers, but the factor of CEO gender is rarely mentioned. To test the effect of CEO gender on the degree of corporate use of AI, this paper analyzes the text of corporate annual reports to measure the degree of each company's use of AI by counting the number of times the term AI appears in each annual report. The CEO gender information is manually collected to construct an indicator of the degree of corporate adoption of AI. In this paper, a theoretical hypothesis of the relationship between CEO gender and the degree of corporate use of AI based on the different attitudes of males and females towards ethical behavior and risk will be provided. Then, accorded with the panel data about companies from Chinese a-share non-financial, which was listed from 2000 - 2021, a two-way fixed-effects regression model is used to analyze the effect of CEO gender on the degree of firms' use of AI technology. The study results show that AI usage is higher in male than female CEOs. Based on the above findings, this paper not only reveals the influence of CEO gender on the extent of firms' AI use but also deepens the understanding of the differences in gender characteristics between males and females.Advances in Economics, Management and Political Sciences00The influence of CEO gender on the extent of firms' AI use is revealed and the understanding of the differences in gender characteristics between males and females is deepened.Advances in Economics, Management and Political Sciences["Jinwen Yi", "Yiwei Wang", "Yiduo Liu"]2024-06-14T00:00:00 9bc437abcc85d2393ad8da6a26aa21181b25495fDespite the pivotal role of family support for type 2 diabetes (T2DM) management, research on family-based intervention that leverages AI-enabled mobile apps is limited. This study aims to explore perceptions of T2DM patients and family members on the acceptability of a FAMILY module in the EMPOWER mobile app and their feedback on specific FAMILY module features to inform future EMPOWER-FAMILY intervention. We conducted semi-structured interviews with 25 T2DM patients and 25 family members. All interviews were audio-recorded and transcribed verbatim. The transcripts were analyzed using NVivo. The FAMILY module was seen as an opportunity to cultivate shared decision making by both patients and family members. However, concerns evolved around increased family burden and diminished sense of patient autonomy, leading to potential patient-family conflicts. Leveraging on AI technology, the FAMILY module sends tailored nudges to a family member based on patient’s lifestyle behaviors. While participants acknowledged its benefits, some expressed a desire for greater customization in both frequency and content. Participants appreciated user-friendly meal logging using AI-powered food detection. Nevertheless, app feedback on dietary choices and healthier alternatives is desired, which is particularly relevant for family members involved in preparing patients’ meals. The inclusion of patient-family collaborative goal setting and achievement tracking features via wearable devices was viewed as beneficial, fostering motivation and dedicated family time. Other proposed enhancements include a platform for sharing experiences and incorporate telemedicine functionality. Both patients and family members indicated their willingness to engage in the FAMILY module. The feedback collected from the study will be used to inform the development of the EMPOWER-FAMILY module and future intervention. R. Lau: None. H. Liu: None. J. Phang: None. Y. Kwan: None. L. Low: None. S. Yoon: None. Ministry of Education Tier 1 grant (2022-MOET1-0005) Diabetes00Perceptions of T2DM patients and family members on the acceptability of a FAMILY module in the EMPOWER mobile app and their feedback on specific FAMILY module features to inform future EMPOWER-FAMILY intervention are explored.Diabetes["Rui LING RENA LAU", "Huiyi Liu", "Jie KIE PHANG", "Yu Heng Kwan", "Lian Leng Low", "Sungwon Yoon"]2024-06-14T00:00:00 efd09fba189f0c5854d0555d48509cbb0f29d8f1Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonoscopy (OC). Our study is aimed at closing this gap, by focusing on the full integration of AI in CCE's pathway, where image processing steps linked to the detection, localization and characterisation of important findings are carried out autonomously using various AI algorithms. We developed a recognition network, that with an impressive sensitivity of 99.9%, a specificity of 99.4%, and a negative predictive value (NPV) of 99.8%, detected colorectal polyps. After recognising a polyp within a sequence of images, only those images containing polyps were fed into two parallel independent networks for characterisation, and estimation of the size of those important findings. The characterisation network reached a sensitivity of 82% and a specificity of 80% in classifying polyps to two groups, namely neoplastic vs. non-neoplastic. The size estimation network reached an accuracy of 88% in correctly segmenting the polyps. By automatically incorporating this crucial information into CCE's pathway, we moved a step closer towards the full integration of AI in CCE's routine clinical practice.arXiv.org190A recognition network, that with an impressive sensitivity of 99.9%, a specificity of 99.4%, and a negative predictive value (NPV) of 99.8, detected colorectal polyps and automatically incorporating this crucial information into CCE's pathway moved a step closer towards the full integration of AI in CCE's routine clinical practice.ArXiv["E. Nadimi", "Jan-Matthias Braun", "B. Schelde-Olesen", "Emile Prudhomme", "V. Blanes-Vidal", "G. Baatrup"]2024-06-14T00:00:00 d132915aaeb2488420fc15217ad52392e56a6cb0Advances in Biotechnology &amp; Microbiology00Advances in Biotechnology &amp; Microbiology["Omid Panahi", "Reza Safaralizadeh"]2024-06-14T00:00:00 c1dc484deb3198ad2d3c1e3c49f6657809f75ca2The article analyses infl uence of linguistic relativity on existing neural networks, as well as to the fore-cast of the potential infl uence of this principle in the future on more advanced neural networks. The investigation provides an analysis of language situations when working with neural networks and a demonstration of specifi c examples of the infl uence of language on the work of AI, as well as possible situations of obtaining a positive eff ect when training neural networks considering the infl uence of linguistic relativity. Experiments conducted on neural networks demonstrating the existence of such an infl uence are described. The principles that allow to begin work on the formation of a framework for interaction with AI are formulated. The principle of engagement, which assumes that the factor of AI involvement in working with natural language not only in meanings, but also in connotations is to be taken into account. The principle of Kaja: to avoid endowing AI with consciousness and private world and not to use such metaphors.Bulletin of Chelyabinsk State University00The investigation provides an analysis of language situations when working with neural networks and a demonstration of examples of the infl uence of language on the work of AI, as well as possible situations of obtaining a positive outcome when training neural networks considering the infl uence of linguistic relativity.Bulletin of Chelyabinsk State University["Dmitrii V. Mamchenkov", "Ivan S. Gorbachev"]2024-06-14T00:00:00 9ff65c30617c847d236db7609c350351c7541e9cArtificial Intelligence (AI) exerts significant influence on both professional and personal spheres, underscoring the necessity for college students to have a fundamental understanding of AI. Guided by self-determination theory (SDT), this study explores the influence of psychological needs satisfaction on AI literacy among university students. A cross-sectional survey involving 445 university students from diverse academic backgrounds was conducted. The survey assessed the mediation effect of students’ psychological need satisfaction between two types of support—technical and teacher—and AI literacy. The results indicate that both support types positively influenced the fulfillment of autonomy and competence needs, which subsequently acted as mediators in enhancing AI literacy. However, the satisfaction of relatedness needs did not mediate the relationship between the types of support and AI literacy. Unexpectedly, no direct association was found between the two forms of support and AI literacy levels among students. The findings suggest that although technical and teacher support contribute to fulfilling specific psychological needs, only autonomy and competence needs are predictive of AI literacy. The lack of direct impact of support on AI literacy underscores the importance of addressing specific psychological needs through educational interventions. It is recommended that educators provide tailored support in AI education (AIEd) and that institutions develop specialized courses to enhance AI literacy.Frontiers in Psychology509It is suggested that educators provide tailored support in AI education (AIEd) and that institutions develop specialized courses to enhance AI literacy and that institutions develop specialized courses to enhance AI literacy.Frontiers in Psychology["Yanyan Shen", "Wencheng Cui"]2024-06-14T00:00:00 8235075ecdfb41045a7824f71c2985617dc4016fThe digital transformation has profoundly affected every facet of human life, with technological advancements potentially reshaping the economy, society, and our daily living and working modalities. Artificial Intelligence (AI), particularly Generative AI (GAI), has emerged as a pivotal disruption in education, showcasing the capability to produce diverse and context-relevant content. Generative Artificial Intelligence (GAI) has revolutionized natural language processing, computer vision, and creative arts. Large language models (LLMs) like GPT-4 and Open Assistant and tools like DALL-E and Midjourney for the visual and creative domain are increasingly used for various tasks by students and others with critical information needs. AI presents novel avenues for crafting effective learning activities and developing enhanced technology-driven learning applications in the educational sector. However, integrating AI with a pedagogical focus pose challenge. Education 4.0, which integrates emerging technologies and innovative strategies, aims to prepare new generations for a technologically fluid world. This systematic literature review aims to analyze the use of LLMs in higher education within the context of Education 4.0’s pedagogical approaches, identifying trends and challenges from a selection of 83 relevant articles out of an initial set of 841 papers. The findings underscore the significant potential of LLMs to enrich higher education, aligning with Education 4.0 by fostering more autonomous, collaborative, and interactive learning. It highlights the necessity for human oversight to ensure the quality and accuracy of AI-generated content. It addresses ethical and legal challenges to ensure equitable implementation, suggesting an exploration of LLM integration that complements human interaction while maintaining academic integrity and pedagogical foundation.Frontiers in Education1095This systematic literature review aims to analyze the use of LLMs in higher education within the context of Education 4.0’s pedagogical approaches, identifying trends and challenges from a selection of 83 relevant articles out of an initial set of 841 papers.Frontiers in Education["Iris Cristina Pel\u00e1ez-S\u00e1nchez", "Davis Velarde-Camaqui", "Leonardo-David Glasserman-Morales"]2024-06-14T00:00:00 ec05ec6c8cb93096af52e25cdadc8794870a59d0PurposeThe purpose of this research is to explore the transformative impact of AI-augmented tools on design pedagogy. It aims to understand how artificial intelligence technologies are being integrated into educational settings, particularly in creative design courses, and to assess the potential advancements these tools can bring to the field.Design/methodology/approachThe research adopts a case-study approach, examining three distinct courses within a creative technology curriculum. This methodology involves an in-depth investigation of the role and impact of AI in each course, focusing on how these technologies are incorporated into different creative disciplines such as production design, fine arts, and digital artistry.FindingsThe research findings highlight that the integration of AI with creative disciplines is not just a passing trend but signals the onset of a new era in technological empowerment in creative education. This amalgamation is found to potentially redefine the boundaries of creative education, enhancing various aspects of the learning process. However, the study also emphasizes the irreplaceable value of human mentorship in cultivating creativity and advancing analytical thinking.Research limitations/implicationsThe limitations of this research might include the scope of the case studies, which are limited to three courses in a specific curriculum. This limitation could affect the generalizability of the findings. The implications of this research are significant for educational institutions, as it suggests the need for a balanced interaction between AI's computational abilities and the intrinsic qualities of human creativity, ensuring that the core essence of artistry is preserved in the age of AI.Originality/valueThe originality of this paper lies in its specific focus on the intersection of AI and creative education, a relatively unexplored area in design pedagogy. The value of this research is in its contribution to understanding how AI can be harmoniously integrated with traditional creative teaching methods. It offers insights for educational institutions preparing for this technological transformation, highlighting the importance of maintaining a balance between technological advancements and humanistic aspects of creative education.Journal of Research in Innovative Teaching &amp; Learning342The research findings highlight that the integration of AI with creative disciplines is not just a passing trend but signals the onset of a new era in technological empowerment in creative education, highlighting the importance of maintaining a balance between technological advancements and humanistic aspects of creative education.Journal of Research in Innovative Teaching &amp; Learning["Mohd Firdaus Naif Omran Zailuddin", "Nik Ashri Nik Harun", "Haris Abadi Abdul Rahim", "A. F. Kamaruzaman", "Muhammad Hawari Berahim", "Mohd Hilmi Harun", "Yuhanis Ibrahim"]2024-06-14T00:00:00 792c1fce26d9cf616d55cb70088b52aa6feda93dGenerative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.arXiv.org202The results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management.ArXiv["Aline de Campos", "Jorge Melegati", "Nicolas Nascimento", "R. Chanin", "Afonso Sales", "Igor Wiese"]2024-06-14T00:00:00 40088ba6b159deaa96497a879110b63842383c9aLearning from human feedback has been central to recent advances in artificial intelligence and machine learning. Since the collection of human feedback is costly, a natural question to ask is if the new feedback always needs to collected. Or could we evaluate a new model with the human feedback on responses of another model? This motivates us to study off-policy evaluation from logged human feedback. We formalize the problem, propose both model-based and model-free estimators for policy values, and show how to optimize them. We analyze unbiasedness of our estimators and evaluate them empirically. Our estimators can predict the absolute values of evaluated policies, rank them, and be optimized.arXiv.org342This work formalizes the problem of off-policy evaluation from logged human feedback, proposes both model-based and model-free estimators for policy values, and shows how to optimize them.ArXiv["Aniruddha Bhargava", "Lalit Jain", "B. Kveton", "Ge Liu", "Subhojyoti Mukherjee"]2024-06-14T00:00:00 e7f5b4e4479a630151095e6e028dd54431972bb9Artificial intelligence changes how tourist destinations operate, provides better service to visitors, and provides long-term benefits for local communities and the environment. However, it is essential to question whether governments can effectively resolve data privacy and cybersecurity challenges when deploying these technologies. This study aims to analyze issues related to the role of artificial intelligence policy in promoting Indonesia's digital tourism. This research employs a normative legal approach, drawing from both statutory and historical sources. This research concludes that Indonesia promotes artificial intelligence in tourism by investing in AI technology research and development, collaborating between the government and the private sector to implement AI solutions, and establishing a supportive regulatory framework to ensure the ethical use of AI in tourism. The impact of digitalization policies on digital tourism includes increasing accessibility and convenience for tourists through online ordering systems and digital payment methods, developing smart destinations with Internet of Things technology and data-based insights, and enhancing tourist experiences through augmented reality applications and virtual reality.Volksgeist Jurnal Ilmu Hukum dan Konstitusi886This research concludes that Indonesia promotes artificial intelligence in tourism by investing in AI technology research and development, collaborating between the government and the private sector to implement AI solutions, and establishing a supportive regulatory framework to ensure the ethical use of AI in tourism.Volksgeist: Jurnal Ilmu Hukum dan Konstitusi["Abdul Kadir Jaelani", "Resti Dian Luthviati", "Ahmad Siboy", "Sholahuddin Al Fatih", "Muhammad Jihadul Hayat"]2024-06-15T00:00:00 1b4e885cc956ef6abe87c990ad5be6b96923980dScientific Reports266There were strong positive correlations between AI attitudes and AI benefits, and also between AI regulation and AI benefits.Scientific Reports["A. Sarwari", "Muhammad Naeem Javed", "Hamedi Mohd Adnan", "Mohammad Nubli Abdul Wahab"]2024-06-15T00:00:00 18c0e13e9b9924ff9d83a157887e07c886dd373cThis study investigates the criminal use and abuse of artificial intelligence (AI), exploring the effectiveness of various mitigation strategies. It employs a mixed-methods approach, combining quantitative data from a survey of 211 experts with qualitative insights from academic, governmental, and industrial publications. The research examines four key hypotheses: the impact of public and organizational awareness, the role of advanced detection technologies, the effectiveness of ethical guidelines, and the influence of penalties and enforcement. The findings reveal that awareness, technology, ethics, and enforcement all contribute to mitigating AI misuse. The study concludes by proposing comprehensive strategies, including targeted awareness campaigns, investment in detection technologies, robust ethical guidelines, and strengthened legal frameworks, to effectively combat the criminal use of AI.Archives of Current Research International05The study concludes by proposing comprehensive strategies, including targeted awareness campaigns, investment in detection technologies, robust ethical guidelines, and strengthened legal frameworks, to effectively combat the criminal use of AI.Archives of Current Research International["Amaka Debie Samuel-Okon", "O. Olateju", "Samuel Ufom Okon", "O. O. Olaniyi", "Udochukwu ThankGod Ikechukwu Igwenagu"]2024-06-15T00:00:00 24d424f38816792b12997e1a2a1ea9e1d910b59eAI (Artificial Intelligence) has transcended the field of science fiction and become a crucial component of various industries, including healthcare. In dermatology, the incorporation of AI is reshaping clinical practices, diagnostics, and treatment strategies. This article delves into the transformative impact of AI in clinical dermatology, exploring its applications, benefits, and the evolving landscape of AI-driven advancements.IP Indian Journal of Clinical and Experimental Dermatology324This article delves into the transformative impact of AI in clinical dermatology, exploring its applications, benefits, and the evolving landscape of AI-driven advancements.IP Indian Journal of Clinical and Experimental Dermatology["Arisha Salam", "Abhinesh N"]2024-06-15T00:00:00 2ff8800d6b273bb3721f23e10dbb983818150d1aThe synchronic and diachronic study of the evolution of Artificial Intelligence (AI) unveils one prominent fact that its effect can be traced in almost all fields such as healthcare industry. The growth is perceived holistically in software, hardware implementation, or application in these various fields. As the title suggests, the review will highlight the impact of AI on healthcare possibly in all dimensions including precision medicine, diagnostics, drug development, automation of the process, etc., explicating whether AI is a blessing or a curse or both. With the availability of enough data and analysis to examine the topic at hand, however, its application is still functioning in quite early stages in many fields, the present work will endeavour to provide an answer to the question. This paper takes a close look at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery, while also addressing some of the key ethical challenges it brings. Issues like patient privacy, safety, and the fairness of AI decisions are explored to understand whether AI in healthcare is a positive force, a potential risk, or perhaps both.European journal of computer science and information technology03A close look is taken at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery, while also addressing some of the key ethical challenges it brings.ArXiv["Balaji Shesharao Ingole", "Vishnu Ramineni", "Nikhil Kumar Pulipeta", "Manoj Jayntilal Kathiriya", "M. Krishnappa", "Vivekananda Jayaram"]2024-06-15T00:00:00 f3fc3fe7e05bb46475d1f2291cc3024d63af604f Nowadays, hydrological systems are becoming increasingly complex owing to the growing interaction between nature and humans at the local scale of river sections, lakes, reservoirs, catchments, etc., to the global scale. There is great demand for the development of models to evaluate, predict, and optimize the performance of complex hydrological systems whose behaviour is characterized by a strong nonlinearity. However, traditional approaches can hardly handle this nonlinear behaviour; moreover, the analysis of hydrological systems at large or even global scale, requires dealing with large-volume and real-time data. In recent years, artificial intelligence (AI), especially deep learning, has shown great potential to process massive data and solve large-scale nonlinear problems. AI has been successfully applied to computer vision, machine translation, bioinformatics, drug design, and climate science. AI models have produced results comparable to and even better than expert human performance. It is expected that AI can significantly contribute to hydrology research as well as development. This book presents some of the latest advances in the field of AI in hydrology. Both theoretical and experimental chapters are included, covering new and emerging AI methods and models from various challenging problems in hydrology. In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.01This book presents some of the latest advances in the field of AI in hydrology, covering new and emerging AI methods and models from various challenging problems in hydrology.[]2024-06-15T00:00:00 7ad45077424fa0d7f9755538456c77c9088c738dAs artificial intelligence (AI) receives wider attention in education, examining teachers' acceptance of AI (TAAI) becomes essential. However, existing instruments measuring TAAI reported limited reliability and validity evidence and faced some design challenges, such as missing informed definitions of AI to participants. This study aimed to develop and validate a TAAI instrument, with providing sufficient evidence for high psychometric quality. Based on the literature, we first identified five dimensions of TAAI, including perceived usefulness, perceived ease of use, behavioral intention, self-efficacy, and anxiety, and then developed items to assess each dimension. We examined the face and content validity using expert review and think-aloud with pre-service teachers. Using the revised instrument, we collected responses from 274 pre-service teachers and examined the item discriminations to identify outlier items. We employed the confirmatory factor analysis and Cronbach's alpha to examine the construct validity, convergent validity, discriminant validity, and reliability. Results confirmed the dimensionality of the scale, resulting in 27 items distributed in five dimensions. The study exhibits robust validity and reliability evidence for TAAI, thus affirming its usefulness as a valid measurement instrument.arXiv.org582This study developed and validated a TAAI instrument, with providing sufficient evidence for high psychometric quality and affirming its usefulness as a valid measurement instrument.ArXiv["Shuchen Guo", "Lehong Shi", "Xiaoming Zhai"]2024-06-15T00:00:00 fc4007165ab187505f181a76de224c2fa3258e03The scientific method is the cornerstone of human progress across all branches of the natural and applied sciences, from understanding the human body to explaining how the universe works. The scientific method is based on identifying systematic rules or principles that describe the phenomenon of interest in a reproducible way that can be validated through experimental evidence. In the era of artificial intelligence (AI), there are discussions on how AI systems may discover new knowledge. We argue that human complex reasoning for scientific discovery remains of vital importance, at least before the advent of artificial general intelligence. Yet, AI can be leveraged for scientific discovery via explainable AI. More specifically, knowing what data AI systems deemed important to make decisions can be a point of contact with domain experts and scientists, that can lead to divergent or convergent views on a given scientific problem. Divergent views may spark further scientific investigations leading to new scientific knowledge.arXiv.org582It is argued that human complex reasoning for scientific discovery remains of vital importance, at least before the advent of artificial general intelligence, yet, AI can be leveraged for scientific discovery via explainable AI.ArXiv["Gianmarco Mengaldo"]2024-06-15T00:00:00 afecff4d93e7f2035d36cef720f58abeabf86517As climate change and long-term energy security drive the global energy sector towards renewable resources, powerful tools are required to optimise integration and management. A novel framework is proposed for effectively utilising Artificial Intelligence (AI) in the renewable energy landscape. AI algorithms can analyse weather patterns, historical generation data, and environmental factors to predict renewable energy output. Energy dispatch is optimised, grid integration is improved, and energy storage requirements are reduced. A system powered by artificial intelligence also significantly reduces downtime, optimises maintenance schedules, and minimises operational costs in wind turbines, solar panels, and other renewable infrastructure. AI can also optimise energy flows, reduce grid instability, and ensure efficient resource utilisation within the smart grid by dynamically managing renewable sources, energy storage systems, and demand profiles. Furthermore, AI-driven spatial analysis and resource mapping can identify optimal locations for renewable installations, considering factors like wind speed, solar irradiance, and environmental constraints. This paper presents two AI frameworks, one for solar energy and one for wind energy, to demonstrate possible applications. They both utilise comprehensive data acquisition, including real-time sensor data and external factors like weather forecasts and historical generation patterns. AI algorithms use these combined data to perform critical tasks such as predictive maintenance, minimising downtime, and maximising efficiency. Power output forecasting enables real-time adjustments based on weather, and optimal site selection maximises energy production. AI is used for proactive issue identification, accurate power output forecasting based on wind conditions, grid demand, storage capacity, dynamic load optimisation for maximum energy efficiency, and wind farm site selection. Integrating these tailored AI frameworks with solar and wind energy can achieve significant benefits such as increased efficiency, reduced operational costs, and seamless grid integration. In addition to analysing the challenges and opportunities associated with this AI integration, the paper explores infrastructure development, ethical considerations, and data acquisition. A second benefit of the research methodology is that it highlights how these tailored AI frameworks can optimise the integration of solar and wind renewable energy sources, providing valuable insights for researchers, practitioners, and policymakers who wish to use AI to create a more sustainable and efficient energy system. Keyword: Artificial Intelligence, renewable energy, climate change.ARID International Journal for Science and Technology01Two tailored AI frameworks are presented, one for solar energy and one for wind energy, to demonstrate how these tailored AI frameworks can optimise the integration of solar and wind renewable energy sources, providing valuable insights for researchers, practitioners, and policymakers.ARID International Journal for Science and Technology["Abdul Salam K. Darwish", "Mohammed Kh Abbas", "Wajdi Al-Salim", "M. Al-Tameemi"]2024-06-15T00:00:00 1b49e5264c42083c653db32df8c8d31e6ae59cfeThe rise of linguistic models as part of artificial intelligence (AI) in academic writing has brought both benefits and challenges. While AI can generate content that closely resembles human writing, recognizing AI-generated content is difficult due to its lack of obvious errors, prompt-based adaptability to various styles, broad subject range, and rapid production speed. To address this issue, various methods, such as technical analysis, metadata examination, stylometric analysis, tests for coherence, and AI detection models like GPTZero, have been developed. Ethical concerns include the risk of duplicity, writing validity, responsibility, and authorship credit. The future of AI-generated content identification is expected to involve improvements in AI detection algorithms, deep analytic tools, interdisciplinary cooperation, and ethical guidelines.Indian Journal of Clinical Anaesthesia111The future of AI-generated content identification is expected to involve improvements in AI detection algorithms, deep analytic tools, interdisciplinary cooperation, and ethical guidelines.Indian Journal of Clinical Anaesthesia["Lalit Gupta"]2024-06-15T00:00:00 57c342ea95453c12f6ec5c014d9af10f60304201Ease of access to information is both an advantage and a challenge for today's young generation. Students who are faced with easy access to information must be able to analyze and filter the information they receive from an early age. The development of this technology also needs to be balanced with efforts to develop Indonesian human resources. Through the independent curriculum, it is hoped that the quality of Indonesian human resources will be higher. The Merdeka Curriculum has an essential difference by combining science and social studies at the elementary school level to become science. In this research, science subjects were collaborated with a technology-based learning model using Artificial Intelligence, with the aim of describing students' abilities when taking part in the learning. This research was conducted using a descriptive qualitative approach. Four data collection techniques were used to describe this research, including interviews, observation, questionnaires, and documentation. The results of this research show that science and science learning in elementary schools using Artificial Intelligence is very interesting for students with 95% indicators of learning interest. The use of Artificial Intelligence also trains students in accessing information and being more careful in sorting information. A finding that is no less interesting is that in the Artificial Intelligence learning process, there needs to be special and specific guidelines, so that students can achieve the Learning Goals efficiently.International Journal Of Humanities Education and Social Sciences (IJHESS)162The results of this research show that science and science learning in elementary schools using Artificial Intelligence is very interesting for students with 95% indicators of learning interest and there needs to be special and specific guidelines in the Artificial Intelligence learning process so that students can achieve the Learning Goals efficiently.International Journal Of Humanities Education and Social Sciences (IJHESS)["Suttrisno Suttrisno", "Nurul Mahruzah Yulia"]2024-06-15T00:00:00 e458ecb3d481077045dd7a3060d094fab95e3602INTRODUCTION Diffusion of Innovation Theory explains how ideas or products gain momentum and diffuse (or spread) through specific populations or social systems over time. The theory analyzes primary influencers of the spread of new ideas, including the innovation itself, communication channels, time, and social systems. METHODS The current study reviewed published medical literature to identify studies and applications of artificial intelligence (AI) in endourology and utilized E.M. Rogers' Diffusion of Innovation Theory to analyze the primary influencers of the adoption of AI in endourological care. The insights gained were triaged and prioritized into AI application-related action items or 'tips' for facilitating the appropriate diffusion of the most valuable endourological innovations. RESULTS Published medical literature indicates that AI is still a research-based tool in endourology and is not widely used in clinical practice. The published studies have presented AI models and algorithms to assist with stone disease detection (n=17), the prediction of management outcomes (n=18), the optimization of operative procedures (n=9), and the elucidation of stone disease chemistry and composition (n=24). Five tips for facilitating appropriate adoption of endourological AI are: (1) Develop/prioritize training programs to establish the foundation for effective use; (2) Create appropriate data infrastructure for implementation, including its maintenance and evolution over time; (3) Deliver AI transparency to gain the trust of endourology stakeholders; (4) Adopt innovations in the context of continuous quality improvement (CQI) Plan-Do-Study-Act (PDSA) cycles as these approaches have proven track records for improving care quality; and (5) Be realistic about what AI can/cannot currently do and document to establish the basis for shared understanding. CONCLUSION Diffusion of Innovation Theory provides a framework for analyzing the influencers of the adoption of AI in endourological care. The five tips identified through this research may be used to facilitate appropriate diffusion of the most valuable endourological innovations.Journal of endourology152Diffusion of Innovation Theory provides a framework for analyzing the influencers of the adoption of AI in endourological care and five tips may be used to facilitate appropriate diffusion of the most valuable endourological innovations.Journal of endourology["Manoj Monga", "Natalie C. Edwards", "S. Rojanasarot", "Mital Patel", "Erin Turner", "Jenifer White", "Samir Bhattacharyya"]2024-06-15T00:00:00 b93ebecb48a09dab181fe4b6c36c337081ab9609Artificial intelligence to a limited extent is science and engineering engrossed with the study of intelligent behaviour using computers and performing tasks which usually assumed can only be performed by human beings as well as the design of products that display this action. It is making waves in dentistry as exponential amount of digital data is available, note worthy progress in hardware performance, as well as significant advancements in algorithmic and software approaches, the current abilities of this technology is unparalleled. Its applications are diversifying into domains that were exclusive to human specialist and include medical and dental imaging diagnostics, decision making assistance, digital medicine, drug discovery, wearable technology, medical surveillance, robotic and digital assistants. The aim of this review is to define artificial intelligence, its benefits in dental field and its potential risks to community.Journal of Dental Panacea231The aim of this review is to define artificial intelligence, its benefits in dental field and its potential risks to community.The Journal of Dental Panacea["Richa Wadhawan", "Sushma Mishra", "Himani Lau", "Mayank Lau", "Anchal Singh", "Sabanaz Mansuri", "Naseef Ali", "Gopal Krishna"]2024-06-15T00:00:00 0f6ae8e3bb314426d29ab93faf9aea2b21c7acf9Artificial intelligence (AI) presents an opportunity to offer innovative solutions to long-standing challenges in agriculture. This review study provides an overview of AI applications in agriculture, focusing on its applications to predict and monitor crop growth rate and yield, climate change and weather patterns, pests and diseases management, weed management, animal production, agricultural machinery, crop irrigation, and soil management, and crop fertilization. AI technologies, including machine learning, computer vision, and precision agriculture, are explored. This review highlights the significant potential of AI to improve agricultural productivity, efficiency, and sustainability. Furthermore, the challenges and limitations of AI adoption in agriculture, including data quality and availability, infrastructure requirements, and ethical considerations, are also discussed. Overall, this study demonstrates the transformative power of AI in agriculture and highlights the need for continued research and investment in this critical field to build more resilient and sustainable agricultural production systems.Big Data In Agriculture00An overview of AI applications in agriculture, focusing on its applications to predict and monitor crop growth rate and yield, climate change and weather patterns, pests and diseases management, weed management, animal production, agricultural machinery, crop irrigation, and soil management, and crop fertilization, is provided.BIG DATA IN AGRICULTURE[]2024-06-15T00:00:00 ac57c796f282cd99f0d8a42cc854b62c13e3e5e4This study examines the effects of artificial intelligence on oil shocks (supply, demand, and risk shocks) across different time scales and market conditions, using the wavelet-based quantile-on-quantile approach. The empirical results have discovered that in the short term, artificial intelligence exerts significant negative impacts on supply and risk shocks, with these adverse effects gradually diminishing over time. Notably, artificial intelligence begins to positively influence supply shock in the medium to long term. In contrast, demand shock is initially positively affected, but these benefits diminish over time. The outcomes gained from this study not only give policymakers valuable insights for developing more precise energy policies, but also provide investors with nuanced market perspectives and risk assessments.Review of Economic Assessment550Examination of the effects of artificial intelligence on oil shocks across different time scales and market conditions finds that in the short term, artificial intelligence exerts significant negative impacts on supply and risk shocks, with these adverse effects gradually diminishing over time.Review of Economic Assessment["Pengchao He", "Nuan Zhao"]2024-06-15T00:00:00 f5cda20867a7dca84a7a5cf0fd21c31c9c927947The technology development of society has a strong impact on the labor market. The use of artificial intelligence leads to changes in the requirements for occupying certain professions, the elimination of same positions, as well as the appearance of new professions. This necessitates changes in the organizational structure and job design. Also changing are the requirements for employees who must acquire new knowledge and develop skills to be able to occupy certain professions. The turbulent business environment also requires organizations to be able to identify emerging trends and quickly respond to these new demands in order to stay in the market. It is here that to stay “in the game” analysis is needed, which projects future trends using artificial intelligence.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The technology development of society has a strong impact on the labor market, which requires organizations to be able to identify emerging trends and quickly respond to these new demands in order to stay in the market.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["B. Stoycheva", "Pavel Vitliemov"]2024-06-15T00:00:00 639da1e561080f9fd2443a72b2fee0237e686b44The study focused on sustaining lecturers’ academic integrity through the adoption of artificial intelligence in public Universities in Rivers State. Four research questions and four corresponding hypotheses were answered and tested in the study. Descriptive survey design was used in the study. The population of the study was 2,874 teaching staff in all the public Universities in Rivers State out of which 351 lecturers were sampled using proportionate stratified random sampling technique. Instrument used for gathering data was a 20 item questionnaire titled “Artificial Intelligence for Sustaining Lecturers Academic Integrity Questionnaire” (AISLAIQ). The questionnaire was face and content validated by an Educational Management expert at University of Port Harcourt while the reliability was estimated using Cronbach Alpha and pronounced an index of 0.82. Out of the 352 copies of questionnaire administered, 336 copies representing 95.7% were retrieved. Research questions raised were answered using mean and standard deviation while the hypotheses were tested using z-test at 0.05 level of significance. The result of the study indicated career progression and lack of competence were the main drivers of academic fraud among the lecturers. The usefulness of AI and the opportunities it provides for personalized learning were among the main factors driving the adoption of AI by the lecturers. Challenges to the adoption of AI and the ways of improving the adoption of AI for sustained academic integrity were identified. The study recommended the need for further AI training for lecturers for sustained academic integrity in the Universities.International journal of education, learning and development00The study indicated career progression and lack of competence were the main drivers of academic fraud among the lecturers and the need for further AI training for lecturers for sustained academic integrity in the Universities is recommended.International Journal of Education, Learning and Development["Precious Aderuyi", "Eliphaletphebe Chinyere Amaewhule"]2024-06-15T00:00:00 915fdb8fa1f9947264876a08d0442388a5833ae6Under the legislation, when artificial intelligence (AI) systems cause harm to third parties, the restoration of violated rights is carried out according to the rules of strict or culpable liability. Strict liability is applied if the AI system is recognized as a source of increased danger or has a defect. For all other cases, culpable civil liability is used. The authors have developed a new approach to non-contractual civil liability for cases of harm caused by AI systems based on the criterion of the risk level of AI systems. According to this approach, for AI systems that create unacceptable or high risk in relation to human rights and freedoms, it is proposed to apply strict liability to their developer, and for AI systems belonging to the low-risk classification group, the rules of culpable liability to restore violated rights and compensate for the harm caused should be applied. With regard to the basic models, the use of culpable liability is envisaged, except situations where AI products with unacceptable or high risk are created on their basis. The proposed approach can become an alternative to using the concept of a source of increased danger in relation to AI systems and will allow transferring strict responsibility from owners of high-risk AI systems to their developers, who have a greater impact on the safety and reliability of AI systems. Lex Russica10A new approach to non-contractual civil liability for cases of harm caused by AI systems based on the criterion of the risk level of AI systems is developed, which will allow transferring strict responsibility from owners of high-risk AI systems to their developers, who have a greater impact on the safety and reliability of AI systems.Lex Russica["O. Izhaev", "D. L. Kuteynikov"]2024-06-15T00:00:00 41d298f5616e83bdf212a94527ba390784c93661As technology continues to advance at an unmatched pace, artificial intelligence (AI) has become an omnipresent presence in our lives. Artificial intelligence (AI) is a technology that utilizes machines to imitate intelligent human conduct that is because of its intense capabilities in data analysis, and virtual algorithms. These capabilities can increase the efficacy of AI robots in dental diagnosis, and treatment plans and also to assess the prognosis of various oral diseases. Apart from benefits, there are several unwanted consequences while doing the AI-assisted operation, the dentist is still required to monitor the whole process. In various case scenarios like data error, any circuit interruption, or some other unexpected conditions, if something happened, the consequences would be unimaginable. Robodentistry is like a coin having two faces. One face helps patients in a better way like a dentist but the other face when turned up, can pose big problems. So, still, more researches are required before thinking that robots can do the job autonomously in dentistry.Journal of Oral Medicine Oral Surgery Oral Pathology and Oral Radiology270There are several unwanted consequences while doing the AI-assisted operation, so, still, more researches are required before thinking that robots can do the job autonomously in dentistry.Journal of Oral Medicine, Oral Surgery, Oral Pathology and Oral Radiology["Kuljit Kaur"]2024-06-15T00:00:00 e7f05a580fc96daa8d7880a2003281f38ebeb2aeThe article discusses current trends and prospects for the development of artificial intelligence technology. International research data and ratings of leading trends in the digital economy are analyzed. The volume of the global market for AI technologies and the leading countries in terms of the amount of investment in this area have been studied. The purpose of this article is to consider how artificial intelligence can improve the process. The article analyze various techniques that make positive changes to the practice of artificial intelligence and discuss the potential benefits, challenges and ethical issues associated with this development. Using examples of the successful implementation of artificial intelligences, the article shows how technology is changing the way of learning and people are thereby helping to reach new heights.International Journal of Information and Communication Technology00Various techniques that make positive changes to the practice of artificial intelligence are analyzed and the potential benefits, challenges and ethical issues associated with this development are discussed.INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES["I. Izembay"]2024-06-15T00:00:00 2491beb11c79b63cab29f0b6687acc61a444f9cdThere is an ongoing debate on balancing the benefits and risks of artificial intelligence (AI) as AI is becoming critical to improving healthcare delivery and patient outcomes. Such improvements are essential in resource-constrained settings where millions lack access to adequate healthcare services, such as in Africa. AI in such a context can potentially improve the effectiveness, efficiency, and accessibility of healthcare services. Nevertheless, the development and use of AI-driven healthcare systems raise numerous ethical, legal, and socio-economic issues. Justice is a major concern in AI that has implications for amplifying social inequities. This paper discusses these implications and related justice concepts such as solidarity, Common Good, sustainability, AI bias, and fairness. For Africa to effectively benefit from AI, these principles should align with the local context while balancing the risks. Compared to mainstream ethical debates on justice, this perspective offers context-specific considerations for equitable healthcare AI development in Africa.arXiv.org460For Africa to effectively benefit from AI, principles should align with the local context while balancing the risks, and compared to mainstream ethical debates on justice, this perspective offers context-specific considerations for equitable healthcare AI development in Africa.ArXiv["Aloysius Ochasi", "A. J. Mahamadou", "Russ B. Altman"]2024-06-15T00:00:00 2821694f21798662b269a5ca9d08fbced21c44e6The growing in-stability of our environmental climate and the rising occurrence of natural disasters, the contribution of AI in disaster management is not only advantageous – it is indispensable. Artificial intelligence is extensively used in forecasting and preparing for disasters conditions, as well as for alerting events, identifying resources and reducing damage after disaster. And its response to effectively in better and more rapid preventive help in disasters management. The purpose of this paper is to identify the uses of artificial intelligence technologies in reducing the impact of disasters on the lives and to investigate the possibility, recovery solutions by artificial intelligence technologies. That based on information and communication technology and reducing the effects of disasters on lives as well as on nature. Also, the paper includes the advantages and challenges in AI. The AI application in forecasting, mitigating, and responding to disasters has brought significant changes. AI’s capacity to analyse large volumes of data, identify trends, and generate forecasts has been used to predict a range of natural disasters, including earthquakes and wildfires.International Journal of Innovative Science and Research Technology150The purpose of this paper is to identify the uses of artificial intelligence technologies in reducing the impact of disasters on the lives and to investigate the possibility, recovery solutions by artificial intelligence technologies.International Journal of Innovative Science and Research Technology (IJISRT)["Snehal Vinod Raut"]2024-06-15T00:00:00 844207b68887ba68a6f251baa33ed53d516481eaThis research addressed several issues, including clarifying the nature of artificial intelligence technologies in terms of definition and characteristics, in addition to stating the general and specific reasons for paying civil liability, so that we reached the conclusion There are many general and specific reasons that may lead to the denial of civil liability for damages arising from the use of artificial intelligence technologies. Hence, the inadequacy of the rules governing the provisions of civil liability in Jordanian civil law becomes clear, and the same is the case in many legislations.We have concluded that in light of the absence of civil liability, it is necessary to find alternative means so that these means re financially sufficient and have the ability to provide compensation to the injured party, including insurance for these damages, or a savings fund for the purpose of compensating for the damages arising from the use of artificial intelligence technologies. Hence, the need for such means becomes clear, and the Jordanian legislator must use these means.Global journal of politics and law research00It is necessary to find alternative means so that these means re financially sufficient and have the ability to provide compensation to the injured party, including insurance for these damages, or a savings fund for the purpose of compensating for the damages arising from the use of artificial intelligence technologies.Global Journal of Politics and Law Research["Nour Muhsen Modhi Almasaeid", "Jihad Al-Jarrah"]2024-06-15T00:00:00 a2962b9b5c329dcf750d7e84781b3918f7494063Journal of public health33Journal of public health["Rowalt C. Alibudbud"]2024-06-15T00:00:00 43e30d5b1b41c4864e8e17c07f8bb19716db6243Pengabdian ini bertujuan untuk mengevaluasi berbagai aspek pemanfaatan AI oleh unit kerja Karang Taruna, mulai dari potensi, implementasi, tantangan, hingga dampak yang dihasilkan. Metode Pengabdian yang digunakan meliputi studi literatur, wawancara, dan survei terhadap anggota Karang Taruna. Pengabdian ini membahas berbagai aspek dari pemanfaatan dan meningkatkan kewaspadaan AI oleh unit kerja Karang Taruna, mulai dari potensi, implementasi, tantangan, hingga dampak yang dihasilkan. Hasil Pengabdian menunjukkan bahwa AI dapat meningkatkan efisiensi operasional melalui otomatisasi tugas-tugas administratif, membantu analisis data untuk pengambilan keputusan yang lebih tepat, dan mengembangkan program kegiatan yang inovatif. Implementasi AI memerlukan pelatihan bagi anggota, pengembangan sistem dan aplikasi yang sesuai, serta kolaborasi dengan pihak ketiga. Tantangan utama meliputi keterbatasan sumber daya, resistensi terhadap perubahan, dan isu keamanan data serta privasi. Dampak positif dari pemanfaatan AI meliputi peningkatan kinerja dan produktivitas, pengambilan keputusan yang lebih tepat, serta peningkatan inovasi dan kreativitas dalam program-program Karang Taruna. Dengan pendekatan strategis, pelatihan memadai, dan kolaborasi yang baik, teknologi AI dapat dioptimalkan untuk mencapai tujuan organisasi dan memberikan manfaat yang lebih besar bagi komunitas. Kata Kunci : Artificial Intelligence, Karang Taruna, Pemanfaatan Teknologi, AIJurnal Pengabdian Kolaborasi dan Inovasi IPTEKS61Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS["Yuris Alkhalifi", "Khairul Rizal", "Nur Alam", "Amir"]2024-06-15T00:00:00 74fcaa2243fb3ea0e50302e29a636655ecdd0093This study analyzes the legal challenges and opportunities presented using emerging technologies such as AI and blockchain for environmental protection and sustainability. The study draws on literature review, case studies, and interviews with legal experts and stakeholders. The study also employs panel data analysis to assess the impact of AI and blockchain technology on environmental sustainability. The findings indicate that AI and blockchain have potential to enhance environmental protection and sustainability, but they also raise legal challenges related to data protection, liability, and governance. The study offers valuable insights to inform legal and policy frameworks and provides multiple regression analysis results to show the relationship between various independent variables and the dependent variable of environmental sustainability. The study's findings contribute to the understanding of the legal implications of AI and blockchain for environmental sustainability and offer insights for policymakers and stakeholders to effectively harness the potential of these technologies for environmental protection and sustainability. International Journal of Religion271International Journal of Religion["Sheer Abbas", "Mohammad Owais Farooqui", "Sheikh Muhammad Adnan", "Sidra Fati\u0307ma"]2024-06-15T00:00:00 b4d51a9ea77403c5f534d5967e949cc6b93b9903Disruptive technologies like AI, automation, and IoT are transforming logistics and distribution management, driving efficiency, cost savings, and improved customer experiences. This empirical investigation investigates the role of these technologies in optimizing operations, enhancing decision-making, and building resilient supply chains. The study analyzes the impact of disruptive technologies on various aspects of logistics, including inventory management, route optimization, predictive maintenance, and last-mile delivery. By leveraging real-time data and advanced analytics, AI algorithms enable accurate demand forecasting, automated replenishment systems, and personalized delivery options, leading to increased efficiency and customer satisfaction. The integration of IoT sensors facilitates real-time tracking of shipments and environmental conditions, enhancing visibility and enabling proactive issue resolution. However, ethical considerations surrounding AI, workforce transformation, and cybersecurity pose challenges that require careful navigation. Study survey was conducted among 216 people from logistics and distribution sector to know “role of disruptive technology and artificial intelligence in effective logistics and distribution management.” The study concludes that there is a significant impact of disruptive technology and artificial intelligence on logistics and distribution management.Multidisciplinary Science Journal02The study analyzes the impact of disruptive technologies on various aspects of logistics, including inventory management, route optimization, predictive maintenance, and last-mile delivery, and concludes that there is a significant impact of disruptive technology and artificial intelligence.Multidisciplinary Science Journal["Indradevi Ramasamy", "Sathya Natarajan", "Vinod Kumar P. Sathyamoorthy"]2024-06-15T00:00:00 8df1008a8edf575bba5c20def52148ee8ba00167AACN Advanced Critical Care62AACN advanced critical care["Linda Harrington"]2024-06-15T00:00:00 095ea609eab6c8c37ebee19aeb335cc5d4443a2eEducation and New Developments 2024 – Volume 200Education and New Developments 2024 – Volume 2[]2024-06-15T00:00:00 31b7d9c1fbebe0ea5e0265f6a55e97edf7f352d8Indian Journal of Clinical Anaesthesia50Indian Journal of Clinical Anaesthesia["Lalit Gupta", "Ripon Choudhary", "Ridhima Sharma"]2024-06-15T00:00:00 20c09c1ef51ee7bf95d86d358bc1d4d2ae88cfbfAccurate diagnosis of tumors is crucial to the treatment and prognosis of patients. Pathological diagnosis is regarded as the "gold standard" of tumor diagnosis, which helps to detect the disease at an early stage and formulate precise treatment plans for patients. However, traditional pathology diagnosis relies heavily on the expertise and diagnostic experience of physicians, making the quality and accuracy of pathology diagnosis largely dependent on their individual capabilities. With the popularization of Whole Slide Image (WSI) technology, the application of AI in pathology has gained significant momentum. With its powerful analyzing ability, AI has been widely used in computational pathology, especially in pathology-assisted diagnosis, showing great potential. This paper first explores two core tasks of AI in the field of pathology image analysis - image segmentation and image classification. Finally, it looks at the challenges and opportunities facing the field.International Journal of Computer Science & Information Technology (IJCSIT)160Two core tasks of AI in the field of pathology image analysis - image segmentation and image classification are explored - image segmentation and image classification.International Journal of Computer Science and Information Technology["Saisai Feng", "Mingchuan Zhang"]2024-06-15T00:00:00 0e28956bde62f4a13b0ccc84bf14fca0a69b30bcNeuropsychiatric disorders have an increasing percentage among the current human population, which is why the use of biodiversity elements in their treatment is a key aspect in the process of psychological rehabilitation, based on biophilia. It is currently used as an intermediate VR/AI tool to generate positive emotions and well-being, based on nature images of landscapes and biodiversity. Although there are a number of problems that arise in the use of VR / AI in the biomedical field, mainly due to legislative and ethical aspects, the digitization of the biomedical field represents the opening of new perspectives on the diagnosis and treatment of neuropsychiatric disorders, resulting in new directions of scientific research aimed at significantly improving the study and understanding of the healing mechanisms of psychopathologies.Bulletin of Integrative Psychiatry160The digitization of the biomedical field represents the opening of new perspectives on the diagnosis and treatment of neuropsychiatric disorders, resulting in new directions of scientific research aimed at significantly improving the study and understanding of the healing mechanisms of psychopathologies.Bulletin of Integrative Psychiatry["M\u0103d\u0103lina Borc\u0103", "Alexandru Borc\u0103", "A. Ciob\u00eec\u0103", "Gianina Beraru"]2024-06-15T00:00:00 3afaa280c9e16143c157bade575a5abaeceb519dAnnals of Operations Research650Annals of Operations Research["R. F. Saen", "Farzaneh Yousefi", "M. Azadi"]2024-06-15T00:00:00 b10711ebfb3593ebee2b17a124af7d32b064add6الذكاء الاصطناعي تقنية تتمتع بقدرة فائقة على التعلم والتطوير والقدرة على اتخاذ القرارات المناسبة. له مٌكنة للتعامل مع المواقف ووضع الحلول المناسبة لها، والقدرة على اجتياح مجال تقديم الخدمات القانونية كالاستشارات التي كانت تقدم بطريقة تقليدية من خلال محامٍ أو مكتب استشارات قانونية. واليوم وبفضل تلك التقنيات فالاستشارات القانونية تقدم وبشكل افتراضي من مستشار قانوني ذكي. وقد اعتمدنا المنهج التحليلي المقارن من خلال تحديد مفهوم تلك التقنية وبيان موقف التشريعات فضلاً عن استعراض التطورات القضائية بهدف تقييم الموقف النهائي لمنظومة الاستشارات القانونية الذكية. ومن نتائج دراسة الاستشارات القانونية الذكية صعوبة تقديمها دون تدخل بشري لأن الاولى نتاجاً عقلياً، لذا لا غنى عن المستشار القانوني التقليدي لاسيما في الأمور القانونية الشائكة التي تحتاج إلى تفسير وتعليل. أما فيما يتعلق بالمسؤولية في نطاق تقنيات الذكاء الاصطناعي فالجميع متفقون على وجود المسؤولية والتعويض، ولكن الخلاف يدور حول أساس تلك المسؤولية ومدى تمتع تلك التقنيات بالشخصية القانونية للقول بمسؤوليتها.Journal of Legal Sciences220Journal of Legal Sciences["Lecturer Doctor Muna Naeem Jaaz"]2024-06-15T00:00:00 ac20d4e81e07f4733a9b00f25483722ced7e5ee5Since the year 1972, several organizations have assigned “words of the year” or WOTY to define what is considered the most important word of the year, basically reflecting the theme or expression that best describes the year that was. It can be a cultural phenomenon, a controversy, an influential concept, or a popular thought. This WOTY is voted upon by linguists and lexicographers and the bodies that designate these include the American Dialect Society, Cambridge University for the Cambridge Dictionary, Collins English Dictionary, Dictionary.com, Macquarie Dictionary, Merriam Webster, and Oxford University Press for the Oxford Dictionary, among others.Philippine journal of cardiology00Philippine Journal of Cardiology["Marcellus Francis L Ramirez"]2024-06-15T00:00:00 0947bb1eed561e8bfdcd029a600b76ead494964b The Article Abstract is not available.   Iranian journal of diabetes and obesity00Iranian journal of diabetes and obesity["Masoud Rostami", "V. Anoosheh"]2024-06-15T00:00:00 2223b1bc81069b06cda64509e9f518c846eb3268Ex Aequo: Revista da Associação Portuguesa de Estudos sobre as Mulheres00ex aequo - Revista da Associação Portuguesa de Estudos sobre as Mulheres["Andrea Ari\u00f1o-Bizarro", "I. Ibarretxe-Antu\u00f1ano"]2024-06-15T00:00:00 3d0ce6bb5e483e1e5169ebf2773e5077a1e8d2d8Educational Administration Theory and Practice00Educational Administration Theory and Practice["Saghir Ahmad"]2024-06-15T00:00:00 3e95ab3b64074d8d9d5b2873aad347116f0af058Algorithms00Algorithms["Nuno Fachada", "Nuno David"]2024-06-15T00:00:00 a81b9200c396952f6414ec7a3797314a5199a183FOCUS: Journal of International Business00FOCUS: Journal of International Business["Gourav Roy", "Amrita Chakraborty"]2024-06-15T00:00:00 176f22684fcb735b929ba8f0f5385c4b3629fbcbУ статті розглянуто теоретичні та прикладні положення використання технології штучного інтелекту в діяльності кредитних установ. Це було реалізовано через аналіз сутності цієї технології, особливостей її використанні у функціонуванні суб’єктів господарювання. Розглянуто фрагментарно історію зародження зазначеної технології та визначено окремі її складові, без яких вона не може існувати, серед яких виокремлено такі: системи машинного навчання, глибоке навчання, нейронні мережі. Це дало можливість у подальшому також проаналізувати окремі види технології штучного інтелекту та визначити такі з них: реактивні технології штучного інтелекту, технології з обмеженою пам’яттю, технології теорії розуму, самосвідомі технології. Вагому увагу у статті приділено аналізу сучасних підходів науковців, спеціалістів у сфері розвитку технології штучного інтелекту щодо загроз та переваг її використання в економічній системі, впливі на сучасний розвиток суспільства. Це дало можливість встановити, що однією з найбільш активних сфер, у якій  використовують сьогодні потенціал штучного інтелекту для розвитку, є сфера фінансових послуг. У статті проведено ґрунтовний аналіз застосування технології штучного інтелекту у функціонуванні кредитних установ. Зокрема, було визначено основні напрями використання цієї технології в роботі цими установами, а саме: аналіз кредитоспроможності позичальників, автоматизація процесу прийняття рішень, моніторинг безпеки та боротьба з шахрайством,  інформаційна підтримка клієнтів та автоматизація надання кредитних послуг, у маркетинговій діяльності з метою створення персоніфікованих кредитних продуктів, пошуку напрямів їх продажу різним групам клієнтів. Також проведено аналіз сучасних тенденцій використання чат-ботів кредитними установами, оскільки саме в цьому напрямку сьогодні ці установи найчастіше використовують окремі елементи технології штучного інтелекту. Відповідно конкретизовано переваги та недоліки застосування чат-ботів у роботі зазначених установ. Крім цього, проведено дослідження сучасних різних випадків використання технології штучного інтелекту вітчизняними та світовими кредитними установами. Це дало змогу визначити основні проблеми, які стримують використання технології штучного інтелекту в Україні такими установами.The actual problems of regional economy development00The actual problems of regional economy development["\u041e\u043b\u044c\u0433\u0430 \u041f\u043e\u043f\u0435\u043b\u043e", "\u041c. \u0412. \u0414\u0443\u0431\u0438\u043d\u0430"]2024-06-15T00:00:00 8ebfdba4f4f993ad501d5e291272f2a032b016efIntensive & Critical Care Nursing140Intensive & critical care nursing["Rafael Lima Rodrigues Carvalho", "Daniela Ponce", "M. S. Marcolino"]2024-06-15T00:00:00 cf0bd7fecbf2eb7a8d3021f82111435b072d0332The integration of artificial intelligence (AI) into accounting has significantly transformed the landscape of fraud detection. Traditional methods, while effective to some extent, often struggle with the increasing complexity and volume of financial data. AI, with its advanced analytical capabilities, offers a promising solution to these challenges. This review provides an overview of the techniques used in AI-driven fraud detection in accounting and highlights case studies that demonstrate the practical applications and benefits of these technologies. AI techniques for fraud detection in accounting primarily involve machine learning (ML), natural language processing (NLP), and data mining. Machine learning algorithms, such as supervised and unsupervised learning models, are employed to identify patterns and anomalies in financial data that could indicate fraudulent activity. Supervised learning involves training a model on a labelled dataset containing examples of both fraudulent and non-fraudulent transactions, enabling the model to learn the distinguishing features of fraud. Unsupervised learning, on the other hand, is used to detect anomalies without prior labeling, identifying outliers that deviate from the norm. Natural language processing (NLP) is utilized to analyze textual data, such as emails and financial documents, to uncover suspicious activities and hidden relationships. This is particularly useful in forensic accounting, where vast amounts of unstructured data must be examined for signs of fraud. Data mining techniques are also critical, enabling the extraction of useful information from large datasets and the identification of trends and patterns that may not be immediately apparent. Several case studies illustrate the effectiveness of AI in enhancing fraud detection in accounting. One notable example is the use of AI by major financial institutions to combat credit card fraud. By implementing ML algorithms, these institutions have significantly improved their ability to detect fraudulent transactions in real-time. The algorithms analyze transaction patterns and flag those that deviate from a customer's typical behavior, allowing for immediate investigation and action. Another case study involves a large multinational corporation that integrated NLP and data mining techniques into its internal audit processes. The company utilized AI to analyze thousands of financial documents and emails, uncovering a complex fraud scheme that had previously gone undetected. The AI system identified unusual communication patterns and financial discrepancies, leading to a comprehensive investigation and the eventual prosecution of the perpetrators. A further example is found in the public sector, where government agencies have employed AI to detect and prevent procurement fraud. By analyzing historical procurement data, AI systems can identify anomalies and potential red flags, such as unusually high bids or frequent contract awards to the same vendor. This proactive approach has enabled these agencies to save millions of dollars and improve the transparency and integrity of their procurement processes. The application of AI in fraud detection within accounting represents a significant advancement over traditional methods. Techniques such as machine learning, natural language processing, and data mining offer powerful tools for identifying and mitigating fraudulent activities. The case studies discussed highlight the practical benefits and successes achieved through AI-driven fraud detection, demonstrating its potential to enhance the accuracy, efficiency, and effectiveness of fraud prevention efforts. As the complexity and volume of financial transactions continue to grow, the role of AI in fraud detection will become increasingly vital. Continued advancements in AI technology, coupled with its integration into accounting practices, promise to further strengthen the fight against financial fraud, safeguarding the integrity of financial systems and promoting trust and confidence among stakeholders. Keywords:  Fraud, Detection, Accounting, Artificial Intelligence, Case Studies.Finance &amp; Accounting Research Journal013The application of AI in fraud detection within accounting represents a significant advancement over traditional methods and offers the potential to enhance the accuracy, efficiency, and effectiveness of fraud prevention efforts.Finance &amp; Accounting Research Journal["Beatrice Oyinkansola Adelakun", "Ebere Ruth Onwubuariri", "Gbenga Adeniyi Adeniran", "Afari Ntiakoh"]2024-06-15T00:00:00 b6a51cb92235c4f7c779aaeaf39a504773df347fThe landscape of financial reporting is undergoing a profound transformation fueled by advancements in Artificial Intelligence (AI) technologies. This review explores the revolutionary impact of AI on financial reporting, with a specific focus on enhancing accuracy and timeliness. AI-driven technologies such as machine learning, natural language processing, and predictive analytics are reshaping traditional financial reporting processes. These technologies enable organizations to automate routine tasks, analyze vast volumes of financial data, and extract valuable insights with unprecedented speed and accuracy. By leveraging AI, organizations can streamline data collection, validation, and analysis, thereby reducing manual errors and improving the overall quality of financial reports. One of the key advantages of AI in financial reporting is its ability to identify patterns and anomalies in financial data that may go unnoticed by human analysts. Machine learning algorithms can detect irregularities in financial transactions, flag potential risks, and enhance fraud detection capabilities, thus bolstering the integrity and reliability of financial reports. Furthermore, AI-powered natural language processing (NLP) algorithms enable organizations to extract relevant information from unstructured data sources such as financial statements, regulatory filings, and news articles. By analyzing textual data, NLP algorithms can generate insights into market trends, competitive dynamics, and regulatory developments, providing decision-makers with valuable intelligence to inform financial reporting decisions. In addition to improving accuracy, AI plays a crucial role in enhancing the timeliness of financial reporting. By automating time-consuming tasks such as data entry, reconciliation, and financial statement preparation, AI enables organizations to expedite the reporting process and deliver financial information to stakeholders in a more timely manner. This not only meets regulatory deadlines but also enables stakeholders to make informed decisions based on up-to-date financial information. Moreover, AI facilitates real-time monitoring of financial performance metrics, enabling organizations to proactively identify emerging trends, risks, and opportunities. Predictive analytics algorithms can forecast future financial outcomes, enabling organizations to anticipate market changes and adjust their strategies accordingly, thereby enhancing agility and responsiveness in financial reporting. The integration of AI technologies is transforming financial reporting practices, enhancing both accuracy and timeliness. By automating routine tasks, analyzing vast datasets, and providing valuable insights, AI enables organizations to produce high-quality financial reports that meet the needs of stakeholders in a dynamic and rapidly evolving business environment. As AI continues to evolve, its role in financial reporting will only become more prominent, driving efficiency, transparency, and accountability across the financial reporting ecosystem. Keywords: Artificial Intelligence, Financial Reporting, Accuracy, Timeliness, Machine Learning.International journal of advanced economics013This review explores the revolutionary impact of AI on financial reporting, with a specific focus on enhancing accuracy and timeliness, as AI-driven technologies such as machine learning, natural language processing, and predictive analytics are reshaping traditional financial reporting processes.International Journal of Advanced Economics["Bernard Owusu Antwi", "Beatrice Oyinkansola Adelakun", "Augustine Obinna Eziefule"]2024-06-15T00:00:00 bb5155ecb366c949e81ec95d4c783dbc0602edc0Artificial Intelligence (AI) is profoundly transforming risk assessment in audit planning and execution, offering unparalleled advancements in efficiency, accuracy, and strategic decision-making. This review explores the role of AI-driven risk assessment in revolutionizing the audit process, highlighting its benefits and the challenges associated with its implementation. AI technologies, particularly machine learning and advanced data analytics, are enhancing auditors' ability to identify, assess, and manage risks. Traditional risk assessment methods often rely on historical data and static models, which can be limited in their predictive power. In contrast, AI-driven approaches leverage vast datasets, continuously updating and learning from new information to provide dynamic and precise risk evaluations. One of the primary benefits of AI in risk assessment is its ability to process and analyze large volumes of data rapidly. AI algorithms can identify patterns and anomalies that may indicate potential risks, which might be missed by human auditors due to cognitive biases or data overload. This capability ensures a more comprehensive and accurate risk assessment, enabling auditors to focus on high-risk areas and allocate resources more effectively. Moreover, AI-driven risk assessment enhances the strategic planning of audits. By providing real-time insights into emerging risks, AI allows auditors to anticipate and address issues proactively. This forward-looking approach not only improves the efficiency of audit execution but also strengthens the overall risk management framework of organizations. Despite these advantages, integrating AI into risk assessment poses several challenges. Ensuring the quality and integrity of data is crucial, as AI systems rely on accurate and relevant information to produce reliable risk assessments. Additionally, the "black box" nature of some AI models can create transparency issues, making it difficult for auditors to explain how specific risk assessments were derived. Addressing algorithmic biases and ensuring compliance with regulatory standards are also critical concerns. In conclusion, AI-driven risk assessment is revolutionizing audit planning and execution by enhancing the ability to detect and manage risks with greater precision and efficiency. However, to fully realize its potential, auditors must navigate challenges related to data quality, transparency, and ethical considerations. By doing so, the audit profession can leverage AI technologies to achieve more robust and effective risk management practices, ultimately enhancing organizational resilience and accountability. Keywords:  AI-Driven, Risk Assessment, Revolutionizing, Audit Planning and ExecutionFinance &amp; Accounting Research Journal012Artificial Intelligence-driven risk assessment is revolutionizing audit planning and execution by enhancing the ability to detect and manage risks with greater precision and efficiency, and enhancing organizational resilience and accountability.Finance &amp; Accounting Research Journal["Ebere Ruth Onwubuariri", "Beatrice Oyinkansola Adelakun", "Omolara Patricia Olaiya", "Joseph Elikem Kofi Ziorklui"]2024-06-15T00:00:00 11b905ccb6cc97eb9bfb0672bc0580b4cdf51160The integration of Artificial Intelligence (AI) in sustainable accounting represents a transformative approach to enhancing the accuracy, efficiency, and comprehensiveness of environmental impact assessment and reporting. This paper explores the development of AI-driven models aimed at advancing sustainable accounting practices, focusing on environmental impact assessment and transparent reporting. AI technologies, particularly machine learning (ML) and natural language processing (NLP), play a pivotal role in automating and refining data collection, analysis, and reporting processes. These technologies enable the processing of vast amounts of heterogeneous data from multiple sources, including IoT sensors, satellite imagery, and corporate disclosures. By leveraging ML algorithms, organizations can identify patterns, predict trends, and assess the environmental impact of their operations with unprecedented precision. One of the key advantages of AI in sustainable accounting is its ability to enhance data accuracy and reliability. Traditional methods often suffer from manual errors and inconsistencies. AI models, however, can continuously learn and adapt, improving their accuracy over time. For instance, predictive analytics can forecast future environmental impacts based on historical data, allowing companies to implement proactive measures to mitigate adverse effects. Furthermore, AI facilitates real-time monitoring and reporting. IoT devices equipped with environmental sensors can stream data to AI systems, which process and analyze the information instantaneously. This capability is crucial for timely reporting and compliance with environmental regulations. Real-time data analytics also empower organizations to make informed decisions swiftly, optimizing their sustainability strategies and reducing their ecological footprint. Another significant contribution of AI is in enhancing transparency and accountability in environmental reporting. NLP algorithms can analyze and interpret regulatory texts, corporate reports, and public records, ensuring that organizations adhere to sustainability standards and guidelines. Additionally, AI can automate the generation of comprehensive and comprehensible sustainability reports, making them accessible to a broader audience, including stakeholders and regulators. Developing robust AI models for sustainable accounting involves several critical steps. Initially, data preprocessing is essential to clean and harmonize diverse datasets, ensuring quality input for AI algorithms. Next, model training and validation are conducted using historical and real-time data to refine predictive capabilities. Continuous model evaluation and adjustment are necessary to maintain accuracy and relevance in dynamic environmental contexts. Collaboration between AI experts, environmental scientists, and accounting professionals is paramount in this development process. Interdisciplinary teams can ensure that AI models are not only technically sound but also aligned with environmental science principles and accounting standards. This collaboration also fosters innovation, leading to the development of more sophisticated tools for environmental impact assessment and reporting. The adoption of AI-driven sustainable accounting models offers numerous benefits, including enhanced efficiency, accuracy, and compliance. However, challenges such as data privacy, algorithmic transparency, and the need for substantial initial investments must be addressed. Future research should focus on overcoming these obstacles and exploring the potential of emerging AI technologies, such as deep learning and blockchain, to further revolutionize sustainable accounting practices. AI holds significant promise for transforming sustainable accounting by improving environmental impact assessment and reporting. Through advanced data analytics, real-time monitoring, and enhanced transparency, AI can help organizations achieve their sustainability goals, ensuring a more sustainable future. The continuous development and refinement of AI models, supported by interdisciplinary collaboration, are essential for realizing these benefits and addressing the complex challenges of environmental sustainability. Keywords:  Sustainable Accounting, Environmental Impact Assessment, AI, Developing Models, Reporting.Finance &amp; Accounting Research Journal011The development of AI-driven models aimed at advancing sustainable accounting practices, focusing on environmental impact assessment and transparent reporting is explored, leading to the development of more sophisticated tools for environmental impact assessment and reporting.Finance &amp; Accounting Research Journal["Beatrice Oyinkansola Adelakun", "Bernard Owusu Antwi", "Afari Ntiakoh", "Augustine Obinna Eziefule"]2024-06-15T00:00:00 a39537e86310463ecd96819629789bc1a590384fArtificial Intelligence (AI) is transforming the field of auditing by significantly enhancing the ability to detect financial anomalies and fraud. The integration of AI in auditing processes offers unprecedented capabilities for analyzing vast datasets with greater speed and precision than traditional methods. This review explores the impact of AI on audit accuracy, focusing on its role in identifying irregularities and fraudulent activities. AI-driven auditing tools leverage machine learning algorithms and advanced data analytics to scrutinize financial records with a high level of detail. These tools can process extensive amounts of financial data rapidly, identifying patterns and deviations that may indicate anomalies or fraudulent behavior. Unlike conventional audit techniques, which often rely on sampling and manual checks, AI can evaluate entire datasets, ensuring comprehensive coverage and reducing the likelihood of undetected issues. One of the primary benefits of AI in auditing is its ability to enhance anomaly detection. Machine learning models are trained to recognize normal financial behaviors and flag deviations that may warrant further investigation. This capability is particularly valuable in identifying subtle or complex patterns of fraud that might be missed by human auditors. For example, AI can detect unusual transaction patterns, inconsistencies in financial statements, or irregularities in vendor or customer behaviors, which are common indicators of fraud. Moreover, AI's predictive analytics can proactively identify potential risks by analyzing historical data and forecasting future trends. This allows auditors to anticipate areas of concern and allocate resources more effectively, improving the overall efficiency and effectiveness of the audit process. Additionally, AI systems continuously learn and adapt, enhancing their accuracy and reliability over time. Despite its advantages, the implementation of AI in auditing also presents challenges. Ensuring data quality and integrity, addressing algorithmic biases, and maintaining transparency in AI decision-making processes are critical considerations. Auditors must also stay updated with evolving AI technologies and regulatory requirements to maximize the benefits while mitigating risks. In conclusion, AI holds significant promise for enhancing audit accuracy by improving the detection of financial anomalies and fraud. By integrating AI into auditing practices, organizations can achieve more thorough and reliable audits, ultimately strengthening financial oversight and integrity. However, careful management of the associated challenges is essential to fully realize AI's potential in the auditing domain. Keywords:  Fraud, Financial Anomalies, AI, Audit Accuracy, Detecting.Finance &amp; Accounting Research Journal010This review explores the impact of AI on audit accuracy, focusing on its role in identifying irregularities and fraudulent activities, and holds significant promise for enhancing audit accuracy by improving the detection of financial anomalies and fraud.Finance &amp; Accounting Research Journal["Bernard Owusu Antwi", "Beatrice Oyinkansola Adelakun", "Damilola Temitayo Fatogun", "Omolara Patricia Olaiya"]2024-06-15T00:00:00 c79196545c7c09219dbd3fd92c82bd1e44d1ff3bArtificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.International journal of advanced economics09This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems.International Journal of Advanced Economics["Beatrice Oyinkansola Adelakun", "Tomiwa Gabriel Majekodunmi", "Oluwole Stephen Akintoye"]2024-06-15T00:00:00 bb7617b208e3bb2f72de8d221e9234e06575da79Artificial Intelligence (AI)-powered chatbots have emerged as a transformative technology, fundamentally changing how businesses and organizations engage with their customers by providing real-time, personalized communication. These chatbots, driven by sophisticated algorithms, utilize Natural Language Processing (NLP) and Machine Learning (ML) to understand, interpret, and respond to human language in a manner that is contextually appropriate and relevant. As a result, AI-powered chatbots enhance both user experience and operational efficiency by automating routine interactions, reducing response times, and providing consistent, high-quality service. The integration of AI chatbots spans multiple sectors, including customer service, healthcare, education, and e-commerce. In customer service, chatbots are deployed to manage high volumes of inquiries, troubleshoot issues, and offer personalized assistance around the clock, thereby freeing human agents to focus on more complex tasks. In healthcare, AI-powered chatbots are utilized to facilitate patient engagement by providing initial diagnoses, managing appointment schedules, offering medication reminders, and delivering health information. Educational institutions employ these chatbots to interact with students, answer frequently asked questions, facilitate administrative processes, and support learning through personalized tutoring. Meanwhile, in e-commerce, chatbots serve as virtual shopping assistants, offering product recommendations, guiding users through their purchasing journey, and resolving post-purchase concerns. This paper delves into the development and deployment methodologies of AI-powered chatbots, examining the various approaches and technologies used to build robust and efficient chatbot systems. The discussion highlights key components such as NLP, ML, reinforcement learning, and deep learning techniques that contribute to the chatbot’s ability to understand user intent, handle natural language conversations, and learn from past interactions to improve future responses. Additionally, the paper analyzes chatbot architecture, including front-end interfaces, dialogue management systems, and backend integration, to provide a comprehensive understanding of the chatbot ecosystem. The literature review presented in this paper synthesizes findings from recent studies and publications, identifying the current trends, advancements, and challenges in implementing AI chatbots across different domains. It evaluates the effectiveness of chatbots in achieving key performance indicators such as customer satisfaction, response accuracy, operational efficiency, and cost savings. The review also highlights areas where AI chatbots have proven to be most effective and identifies potential limitations, including data privacy concerns, integration challenges with existing legacy systems, and the limitations of current NLP models in understanding context, sarcasm, or nuanced language. This paper further discusses the benefits and challenges associated with deploying AI-powered chatbots. Benefits such as 24/7 availability, scalability, reduced operational costs, and enhanced customer engagement are explored in detail, demonstrating how chatbots can deliver substantial value to organizations. Conversely, the paper also addresses challenges such as ensuring data security and privacy, overcoming natural language understanding (NLU) limitations, mitigating biases in AI models, and managing customer expectations when interacting with non-human agents. In addition, this paper provides a forward-looking perspective on the potential future developments of AI chatbots. It explores emerging trends such as multimodal chatbots that integrate voice, text, and visual inputs; advancements in emotion recognition to enable more empathetic and human-like interactions; and the rise of explainable AI, where chatbots can provide transparency in their decision-making processes.To illustrate these concepts, the paper includes diagrams that depict the architecture of AI chatbot systems, the flow of natural language processing, and the integration of various components such as databases, machine learning models, and user interfaces. These visual aids provide a clearer understanding of the technical and functional aspects of chatbot development and deployment. Overall, this paper aims to provide a comprehensive analysis of AI-powered chatbots, detailing their applications, benefits, challenges, and future potential. It serves as a guide for businesses, researchers, and technology developers interested in leveraging AI chatbots to enhance communication, streamline operations, and create a more engaging user experience. By critically examining both the opportunities and the limitations, this research offers valuable insights into the strategic implementation of AI chatbots across diverse industries.Global journal of human resource management01This paper delves into the development and deployment methodologies of AI-powered chatbots, examining the various approaches and technologies used to build robust and efficient chatbot systems, and evaluates the effectiveness of chatbots in achieving key performance indicators such as customer satisfaction, response accuracy, operational efficiency, and cost savings.Global Journal of Human Resource Management["Stella Udoka Nze"]2024-06-15T00:00:00 cef46d751fcf0e90b88d97fe011a515e85b86fa2This article delves into the transformative effects of Artificial Intelligence (AI) and Machine Learning (ML) on the realm of risk management. AI and ML technologies have revolutionized risk assessment, mitigation, and management across various sectors by offering advanced analytical capabilities and automated decision-making processes. In the financial sector, for instance, these technologies have facilitated improvements in loan decision processes, fraud detection, and compliance. Partnerships like ZestFinance and Baidu exemplify the successful application of AI in enhancing loan decisions based on vast data analysis. Despite the evident benefits, challenges such as model-related risks, data availability and protection, and the need for skilled personnel persist. This article aims to provide a comprehensive overview of the current applications of AI and ML in risk management while identifying opportunities for further research and development in this rapidly evolving field.International Journal of Innovative Science and Research Technology361This article aims to provide a comprehensive overview of the current applications of AI and ML in risk management while identifying opportunities for further research and development in this rapidly evolving field.International Journal of Innovative Science and Research Technology (IJISRT)["Moussab El khair Ghoujdam", "Rachid Chaabita", "Salwa Idamia", "Oussama El khalfi", "Hicham El Alaoui", "Kamal Zehraoui"]2024-06-15T00:00:00 c40492d2e0041835829bf7736f4a435d47091bc2This article argues that ethical authorship is essential for the ethical use of artificial intelligence (AI). It examines tensions that historical understandings of authorship have created as instructors and students alike navigate AI technologies. Given these tensions, this article proposes a definition of “ethical authorship” and uses de Colle and Werhane’s moral motivation framework to outline how instructors can use ethical authorship and moral motivation to encourage students’ ethical AI use.Business and Professional Communication Quarterly251A definition of “ethical authorship” is proposed and de Colle and Werhane’s moral motivation framework is used to outline how instructors can use ethical authorship and moral motivation to encourage students’ ethical AI use.Business and Professional Communication Quarterly["Paula Lentz"]2024-06-15T00:00:00 9bbf7f5aa9c2ef34e5622365f186249360658f54Driven by internet adoption around the globe and accelerated through the implications on teaching during the COVID-19 pandemic, e-Learning is booming. This trend continues to grow as such digital programs enable participants to learn new content or acquire additional skills without committing to a specific place or time. Meanwhile, Artificial Intelligence (AI) has become widespread, and e-Learning programs can significantly benefit from this innovative technology. This paper provides a comprehensive overview of AI, its benefits, and how it can be used within digital learning to create innovative and customized educational experiences. Further insights from semi-structured expert interviews are portrayed. Those pedagogical and AI professionals share their perspectives on the potential benefits and risks of e-Learning environments and further elaborate on potential AI usage. The final part of the research covers an AI use case to create customized learning plans for an exemplary e-Learning program. This EU-funded project is called the “Young Refugees AI Student Empowerment Program” (RAISE). It addresses the most significant challenges of the e-Learning program e-VELP, which was set up to provide an innovative platform for young migrants and refugees who want to share their knowledge and cultural diversity through workshops free of charge and entirely voluntarily in Europe.Proceedings of The International Conference on Advanced Research in Education, Teaching, and Learning210An AI use case to create customized learning plans for an exemplary e-Learning program and pedagogical and AI professionals share their perspectives on the potential benefits and risks of e-Learning environments and further elaborate on potential AI usage.Proceedings of The International Conference on Advanced Research in Education, Teaching, and Learning["N. Rohde", "N. Flindt", "Christian Rietz"]2024-06-15T00:00:00 d218d60ab772f19222e6add48363973b47be9150The widespread use of Generative Artificial Intelligence (GAI) among teenagers has led to significant misuse and safety concerns. To identify risks and understand parental controls challenges, we conducted a content analysis on Reddit and interviewed 20 participants (seven teenagers and 13 parents). Our study reveals a significant gap in parental awareness of the extensive ways children use GAI, such as interacting with character-based chatbots for emotional support or engaging in virtual relationships. Parents and children report differing perceptions of risks associated with GAI. Parents primarily express concerns about data collection, misinformation, and exposure to inappropriate content. In contrast, teenagers are more concerned about becoming addicted to virtual relationships with GAI, the potential misuse of GAI to spread harmful content in social groups, and the invasion of privacy due to unauthorized use of their personal data in GAI applications. The absence of parental control features on GAI platforms forces parents to rely on system-built controls, manually check histories, share accounts, and engage in active mediation. Despite these efforts, parents struggle to grasp the full spectrum of GAI-related risks and to perform effective real-time monitoring, mediation, and education. We provide design recommendations to improve parent-child communication and enhance the safety of GAI use.arXiv.org610This study reveals a significant gap in parental awareness of the extensive ways children use GAI, such as interacting with character-based chatbots for emotional support or engaging in virtual relationships, and provides design recommendations to improve parent-child communication and enhance the safety of GAI use.ArXiv["Yaman Yu", "Tanusree Sharma", "Melinda Hu", "Justin Wang", "Yang Wang"]2024-06-15T00:00:00 14fd20068cfca493388f7511d7f715cae3cecc3dArtificial intelligence (AI) is increasingly at the center of public discourse. Considering the current importance of AI technologies, but also the influence that the mass media has on users' adoption of new technologies and their regulation, the article aims to analyze the attitudes of online publications in Romania towards AI. By using content analysis, the attitudes and themes of 100 articles selected from the most visited Romanian news websites were examined. The results reveal a relatively balanced distribution of attitudes, with 37% of articles expressing negative sentiments, 35% positive and 28% neutral. The most frequently addressed topics include the impact on jobs, especially the negative one, regulations in the field and new functionalities. Compared to the publications in English, as can be seen from the specialized literature, the Romanian articles present a more balanced attitude towards technology, with less emphasis on the dangers. However, the analysis shows that current events can strongly influence public discourse, and approaches vary by publication. These findings suggest a diverse debate, subject to rapid changes, with important implications for AI communication, education and policy in the Romanian space.Sociologie Romaneasca00The article aims to analyze the attitudes of online publications in Romania towards AI, and suggests a diverse debate, subject to rapid changes, with important implications for AI communication, education and policy in the Romanian space.Sociologie Romaneasca["Lucian Artene"]2024-06-15T00:00:00 65ad52fd67095ef7bea657052acf8dab1277ca7cBACKGROUND Improved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and its effect on colorectal cancer (CRC) screening outcomes is limited. AIM To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or symptoms. METHODS AI software (GI Genius, Medtronic) was implemented into the standard procedure protocol in November 2022. Data was collected on patient demographics, procedure indication, polyp size, location, and pathology. CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up. RESULTS We evaluated 1008 colonoscopies (278 pre-AI, 255 early post-AI, 285 established post-AI, and 190 late post-AI). The ADR was 38.1% pre-AI, 42.0% early post-AI (P = 0.77), 40.0% established post-AI (P = 0.44), and 39.5% late post-AI (P = 0.77). There were no significant differences in polyp detection rate (PDR, baseline 59.7%), advanced ADR (baseline 16.2%), and non-neoplastic PDR (baseline 30.0%) before and after AI introduction. CONCLUSION In patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy. Although the potential of AI in colonoscopy is undisputed, current AI technology may not universally elevate screening metrics across all situations and patient populations. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.World Journal of Gastrointestinal Endoscopy201In patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy.World Journal of Gastrointestinal Endoscopy["Kenneth Chow", "Matthew T Bell", "Nicholas A. Cumpian", "Maryanne Amour", "Ryan H Hsu", "Viktor E Eysselein", "Neetika Srivastava", "M. Fleischman", "S. Reicher"]2024-06-16T00:00:00 73d97e3a26f03e1aee5b876ae5e11d5daa7b9ea7Explainable artificial intelligence (XAI) models with Shapley additive explanation (SHAP) values allows multidimensional representation of movement performance interpreted on both global and local levels in terms understandable to human intuition. We aimed to evaluate the swimming performance (World Aquatics points) predictability of a combination of demographic, training, anthropometric, and biomechanical variables (inputs) through XAI. Forty-seven swimmers (16 males), after completing a training questionnaire (background and duration) and anthropometric assessment, performed, in a randomised order, a 25 m front crawl and three countermovement jumps, at maximal intensity. The predicted World Aquatics points (516 ± 159; mean ± SD) were highly correlated (r2 = 0.93) with the 529 ± 158 actual values. The duration of swimming training was the most important variable (95_SHAP), followed by the countermovement jump impulse (37_SHAP), both with a positive effect on performance. In contrast, a higher percentage of fat mass (21_SHAP) corresponded to lower World Aquatics points. Impulse, when interpreted together with dryland training duration and stroke rate, shows the positive effects of upper and lower limb power on swimming performance. Height should be interpreted together with arm span when exploring positive effects of anthropometric traits on swimming performance. The XAI modelling highlights the usefulness of specific training, technical and physical testing, and anthropometric factors for monitoring swimmers.Applied Sciences592Impulse, when interpreted together with dryland training duration and stroke rate, shows the positive effects of upper and lower limb power on swimming performance, and height should be interpreted together with arm span when exploring positive effects of anthropometric traits on swimming performance.Applied Sciences["D. Carvalho", "M. Goethel", "A. J. Silva", "J. Vilas-Boas", "D. Pyne", "R. Fernandes"]2024-06-16T00:00:00 26fe6c19ca0bd3ab288d49a63bbd05db328a2d12The article explores the role of artificial intelligence (AI) in the process of learning foreign languages, analyzing both positive and negative aspects of this phenomenon. Based on current research and practical examples, the article discusses the impact of various I-technologies, such as machine learning and natural language algorithms, on the effectiveness and efficiency of language learning. The analysis examines the advantages of AI, such as accessibility, individualization of learning and the possibility of real-time feedback, as well as disadvantages, including limitations in understanding the context, lack of flexibility and dependence on technical means.Innovative Technologica: Methodical Research Journal250The article discusses the impact of various I-technologies, such as machine learning and natural language algorithms, on the effectiveness and efficiency of language learning.Innovative Technologica: Methodical Research Journal["Makhamadkhodjaev Bakhromkhodja"]2024-06-16T00:00:00 2b99defb6b6fe40b6d776c95da6a22694abd77dfThis article examines the changes in the personnel management process caused by the introduction of artificial intelligence, emphasizing their impact on staff functions. The purpose of the study is to consider the potential advantages and disadvantages of using artificial intelligence technologies when working with personnel, as well as to study the process of integrating AI into business processes. To achieve this goal, the author has put forward the following tasks: 1) Give a general description of artificial intelligence, determine its importance and the main advantages of its introduction into business. 2) Consider specifically how it can be applied in personnel management, for this. In order to comprehensively and fully consider the presented topic, the author researched scientific articles written by domestic and foreign authors, as well as information from open sources contained on the Internet.Management of the personnel and intellectual resources in Russia00The purpose of the study is to consider the potential advantages and disadvantages of using artificial intelligence technologies when working with personnel, as to study the process of integrating AI into business processes.Management of the Personnel and Intellectual Resources in Russia["R. Ashurbekov", "Y. Chernikova", "O. Zhuravleva"]2024-06-16T00:00:00 0d1b7ce075a03f6b89e5393a6b3928ededc3d643Modern trends in the development of society and the capabilities of information infrastructure require the sustainable operation of existing models for identifying conflicts among employees in an organization, as well as the use of new approaches. The research work examines certain weaknesses of existing survey methods, which can significantly reduce the quality of the model’s results. The use of artificial intelligence makes it possible to minimize the identified shortcomings of survey methods, process large volumes of information of various types, opening up new opportunities for its use in the field of personnel management. This paper examines current problems of survey methods and the good prospects of using artificial intelligence to identify conflicts between employees of an organization.Management of the personnel and intellectual resources in Russia50Examining current problems of survey methods and the good prospects of using artificial intelligence to identify conflicts between employees of an organization finds the use of artificial intelligence makes it possible to minimize the identified shortcomings of survey methods, process large volumes of information of various types.Management of the Personnel and Intellectual Resources in Russia["V. Kraev", "V. Tihonov"]2024-06-16T00:00:00 32ea3d1782531350f01a5b4434aa6c4cec48856bDiabetes Mellitus (DM) is a global health challenge, demanding proficient predictive models for early identification and intervention. This study adopts a comprehensive strategy for diabetes prediction with Machine learning algorithms, utilizing PIMA Indian diabetes dataset which encompasses clinical, demographic and lifestyle data. Employing techniques like Recursive Feature Elimination (RFE) and correlation analysis, the feature selection process identifies influential predictors, including glucose levels, Body Mass Index (BMI), Blood Pressure and diabetic history of family. A distinctive facet of this study involves integrating IBM Auto AI, automating the machine learning pipeline for tasks like feature engineering, hyperparameter tuning and model selection. Through comparative analysis, the research evaluates the efficiency and performance enhancements achieved through automation in contrast to manually-tailored models. Evaluation metrics encompass accuracy, precision, recall, and F1 score. Crossvalidation, particularly k-fold cross-validation, ensures model generalization to diverse subsets of the dataset. The research outcomes offer valuable insights into the optimal amalgamation of AI techniques for diabetes prediction, underscoring the significance of interpretability, performance, and automation in healthcare analytics. The proposed Methodology is evaluated with different classifiers with Auto AI and without Auto AI techniques. Using IBM Auto AI,Gradient boosting algorithm performed well with 84.4 % accuracy and Logistic Regression showed good accuracy of 84. 4% among conventional machine learning techniques without Auto AI using Pima Indian Diabetes Dataset.Scalable Computing : Practice and Experience191A comprehensive strategy for diabetes prediction with Machine learning algorithms, utilizing PIMA Indian diabetes dataset which encompasses clinical, demographic and lifestyle data is adopted, offering valuable insights into the optimal amalgamation of AI techniques for diabetes prediction.Scalable Comput. Pract. Exp.["SumaLata G L", "Joshitha C", "Meenaksh Kollati"]2024-06-16T00:00:00 2b59ed1d064edd406c1629275b7880d1fcfb6c58Journal of logistics, informatics and service science00Journal of Logistics, Informatics and Service Science[]2024-06-16T00:00:00 9d2cda89f1c3ec378c2b1f88be31491d97b0ba35Particle systems can achieve many scenarios that are difficult to achieve in the field or expensive in reality. In this paper, the requirements of 3D film special effects and the design process of particle systems are studied. Unity3D engine was used to simulate 3D movie special effects. Then, the motion trajectory planning of 3D video group animation characters based on particle swarm optimization is proposed. Then, the system models the animated characters’ moving track to achieve the realism’s dynamic effect. This project intends to use the gravity optimization method for particle swarm optimization. The aim is to overcome the optimization difficulty caused by particle swarm optimization, which is easy to fall into local extreme values. Finally, the generated trajectory information is input into the 3D simulation system for conflict detection and clustering tests. Experiments show that the proposed algorithm can effectively render memorable scenes such as movies and TV. The picture has a high real-time frame rate and is realistic.Scalable Computing : Practice and Experience190This project intends to use the gravity optimization method for particle swarm optimization to overcome the optimization difficulty caused by particle swarm optimization, which is easy to fall into local extreme values.Scalable Comput. Pract. Exp.["Yao Ju", "Guobin Wei"]2024-06-16T00:00:00 4181ac513c6c2676d3c4fabb43b7534e880c07eeThis paper presents a collaborative filtering algorithm based on reinforcement learning theory. Then, the personalized bank financial recommendation system for users is constructed in the massive data environment. Tags mimic different types of user interest points to build a representative personalized data set. The collaborative screening of bank financial products is realized using the simulation results and users’ historical access records. The ranking calculation of related financial products is added to the general bank financial product recommendation system. This method can more accurately express the query results for a specific user. It is found that the collaborative filtering algorithm based on enhanced learning theory can improve the efficiency of collaborative screening of bank financial products. The best results can be obtained by combining the two organically. This paper proposes that the recommendation algorithm of reinforcement learning bank financial products based on user preference and collaborative filtering is feasible.Scalable Computing : Practice and Experience00It is found that the collaborative filtering algorithm based on enhanced learning theory can improve the efficiency of collaborative screening of bank financial products and can more accurately express the query results for a specific user.Scalable Comput. Pract. Exp.["Tianhao Zhang"]2024-06-16T00:00:00 9538dfd69e5db5fdcf5d6fdc3dca2e9ba62252cf<jats:p/>Journal of Kermanshah University of Medical Sciences20Journal of Kermanshah University of Medical Sciences["Mohammad Jalilian", "Zohreh Hosseiniposhteh"]2024-06-16T00:00:00 5faf63a44cdd3f912a660df0a7f48e11059eea4fThis article delves into the preparation of Ukraine’s AI industry for the adoption of EU standards. The author evaluates six tools outlined in the 2023 Roadmap for the Regulation of AI in Ukraine and their potential application within the AI ecosystem. They are designed to foster the advancement of AI technologies in Ukraine while ensuring compliance with EU standards. It is imperative for government authorities to establish favorable conditions to facilitate the seamless integration of the EU AI Law in the future. The research demonstrates the auxiliary measures that can be employed to synchronize the Ukrainian legislation with the advancement of AI ecosystem. These adaptation tools also play a pivotal role in driving the industry’s growth. This discussion pertains to realizing the scientific, technical, and socio-economic potential of Ukraine’s information and communication technology sphere. The article discusses the significance of regulatory sandboxes and outlines methodologies for testing AI technologies and systems. It defines the tasks of labeling input data for machine learning and output data for generative AI, as well as labeling the AI systems themselves. The author explains the drafting of atypical acts within the EU legal system, such as white papers and codes of conduct, for adaptation. The article provides examples of instructions and recommendations for industry development in compliance with the EU AI Act standards. Furthermore, the author summarizes the role of each tool and suggests expanding the Roadmap to include software for developing and AI educational courses. The study contributes to the ongoing public debate on whether Ukraine requires an AI strategy alongside a government concept. It also includes examples of how the researched tools have been implemented in leading countries such as Canada, Great Britain, Japan, Singapore, the USA. Additionally, it showcases international initiatives within the G7 framework (International Code of Conduct for Organizations Developing Advanced AI Systems) and the Council of Europe (HUDERIA).Law and innovative society80This article delves into the preparation of Ukraine’s AI industry for the adoption of EU standards, and explains the drafting of atypical acts within the EU legal system, such as white papers and codes of conduct, for adaptation.Law and innovative society["A. Hachkevych"]2024-06-16T00:00:00 0d780bdfe10309fad9011b349b3611a84115d9ebAI applications streamline workflows, automate tasks, and require adaptive strategies for effective integration into business processes. This research explores the transformative influence of Artificial Intelligence (AI) on various industries, such as software engineering, automation, education, accounting, mining, legal services, and media. We investigate the relationship between technological advancements and the job market to identify relevant skills for individuals and organizations for implementing and managing AI systems and human–machine interactions necessary for actual and future jobs. We focus on the essential adaptations for individuals and organizations to flourish in this era. To bridge the gap between AI-driven demands and the existing capabilities of the workforce, we employ the Rapid Review methodology to explore the integration of AI in businesses, identify crucial skill sets, analyze challenges, and propose solutions in this dynamic age. We searched the Scopus database, screening a total of 39 articles, of which we selected 20 articles for this systematic review. The inclusion criteria focused on conference papers and journal articles from 2020 or later and written in English. The selected articles offer valuable insights into the impact of AI on education, business, healthcare, robotics, manufacturing, and automation across diverse sectors, as well as providing perspectives on the evolving landscape of expertise. The findings underscore the importance of crucial skill sets, such as technical proficiency and adaptability, to successfully adopt AI. Businesses respond strategically by implementing continuous skill adaptation and ethical technology to address challenges. The paper concludes by emphasizing the imperative of balanced skill development, proactive education, and strategic integration to navigate the profound impact of AI on the workforce effectively.Administrative Sciences395This research explores the transformative influence of Artificial Intelligence on various industries, such as software engineering, automation, education, accounting, mining, legal services, and media, and underscores the importance of crucial skill sets, such as technical proficiency and adaptability, to successfully adopt AI.Administrative Sciences["Leili Babashahi", "C. E. Barbosa", "Y. Lima", "A. Lyra", "Herbert Salazar", "M. Arg\u00f4lo", "M. Almeida", "Jano Moreira de Souza"]2024-06-16T00:00:00 84a1315bf94ac387664e298a11e044e75c6798dfThis paper explores the transformative potential of real-time data streaming and artificial intelligence (AI) in the context of e-commerce live streaming shopping. By leveraging advance technologies such as Storm, Trident, Samza, and Spark Streaming, businesses can process and analyze data in real-time, enhancing consumer engagement and driving sales in real time. This paper reviews the literature on live streaming selling, product promotion, and multichannel sales, and discusses the challenges and opportunities associated with these technologies. The findings provide valuable insights for businesses and researchers aiming to harness the power of real-time data streaming in the dynamic landscape of social commerce using real time streamingInternational Journal of Computing and Engineering111The literature on live streaming selling, product promotion, and multichannel sales, and discusses the challenges and opportunities associated with these technologies provide valuable insights for businesses and researchers aiming to harness the power of real-time data streaming.International Journal of Computing and Engineering["Arjun Mantri"]2024-06-16T00:00:00 32a6a46b94709810541e1cfad2a91dca66981ae8El presente artículo aborda el tema de la inteligencia artificial y sus implicaciones éticas, sociales y ambientales para el desarrollo sostenible y qué relación tiene entre sí. La metodología utilizada fue de tipo exploratoria con un enfoque cualitativo. En la cual se realizó una investigación exhaustiva en diferentes fuentes, artículos y base de datos cumpliendo con todos los parámetros de rigurosidad en la cual adentramos a conocer el tema ampliamente y los diferentes enfoques dados. Cabe resaltar que la inteligencia artificial ha sido una herramienta ampliamente utiliza en diversas áreas de la sociedad en cual ha tenido un impacto significativo en el desarrollo sostenible, social y ético, pero a su vez ha tenido grandes riesgos y desafíos que la humanidad ha tenido que afrontar legítimamente en el uso y paramentos en beneficio común para la sociedad. Por otro lado, es de mucha importancia cumplir las leyes y regulaciones para minimizar los riesgos éticos que puedan ocurrir en el desarrollo, implementación y usabilidad de esta tecnología de manera justa, responsable y transparente para evitar posibles amenazas. Por último, en términos ambientales la (IA) ha contribuido en la conservación de la biodiversidad con el objetivo de potenciar y gestionar la sostenibilidad ambiental en la mitigación de problemas ambientales a nivel mundial. Para lograr el desarrollo sostenible, estos impactos éticos, sociales y ambientales deben abordarse para garantizar que la inteligencia artificial se utilice de manera justa y responsable y contribuya al bienestar social y la protección ambiental.Revista científica anfibios231Revista científica anfibios["Marena Vitola-Quintero", "Nick J. Ballestas-Campo", "Jonathan D. P\u00e9rez-Cerro", "Ryan N. Forbes-Santiago"]2024-06-16T00:00:00 6c798704367a75e75ef2a0e77665959491fb4342Este estudio evaluó el impacto de la Inteligencia Artificial (IA) en la eficiencia de los procesos de análisis de costos en contabilidad. Se investigó cómo distintas plataformas de IA —ChatGPT, Claude, y Bing/Copilot— afectan la operatividad y la toma de decisiones estratégicas, midiendo variables como el tiempo de respuesta, la tasa de error y la facilidad de uso mediante un enfoque cuasi-experimental. Se combinaron métodos cualitativos y cuantitativos para evaluar el rendimiento de las plataformas usando un conjunto estandarizado de ejercicios de análisis de costos. Los resultados mostraron variaciones significativas en la eficacia entre las plataformas, destacando diferencias en precisión y velocidad de respuesta. Aunque todas las plataformas demostraron potencial para optimizar la eficiencia, también se identificaron desafíos en precisión de datos e interpretación de resultados complejos. Las conclusiones enfatizan la importancia de seleccionar adecuadamente la plataforma de IA para análisis de costos y resaltan la necesidad de complementar la inteligencia artificial con intervención humana en la interpretación y aplicación de los resultados para decisiones contables y empresariales. Este estudio aporta al conocimiento existente comparando el rendimiento de tecnologías de IA y sugiriendo áreas para futuras investigaciones en prácticas contables. Abstract  This study evaluated the impact of Artificial Intelligence (AI) on the efficiency of cost analysis processes in accounting. It investigated how different AI platforms—ChatGPT, Claude, and Bing/Copilot—affect operational efficiency and strategic decision-making, measuring variables such as response time, error rate, and usability through a quasi-experimental approach. Qualitative and quantitative methods were combined to assess the performance of the platforms using a standardized set of cost analysis exercises. The results showed significant variations in efficacy among the platforms, highlighting differences in analytical precision and response speed. Although all platforms demonstrated potential for optimizing efficiency, challenges were also identified in data accuracy and the interpretation of complex results. The conclusions emphasize the importance of selecting the appropriate AI platform for cost analysis and highlight the need for human intelligence to complement AI in interpreting and applying results for accounting and business decisions. This study contributes to existing knowledge by comparing the performance of selected AI technologies and suggesting areas for future research in accounting practices. Kewywords: artificial intelligence, cost analysis, accounting efficiency, AI platforms, accounting processesRevista Científica Sapientia Technological00Revista Científica Sapientia Technological["Fernando Juca Maldonado", "Kenia Lizezrh Carchi Arias", "Camila Rosales Mu\u00f1oz"]2024-06-16T00:00:00 88673a691676be809532980a925364be959b6142El presente trabajo de investigación tiene como objetivo examinar la influencia de la incorporación de la inteligencia artificial en el desempeño académico de un conjunto de 48 alumnos pertenecientes al tercer curso de bachillerato en la institución educativa Colegio Técnico Clemente Yerovi Indaburú. Para ello, se adoptó una metodología rigurosa, llevando a cabo un estudio experimental de carácter comparativo. Durante este proceso, se dividió equitativamente a los participantes en dos grupos, cada uno integrado por 24 estudiantes. Uno de estos grupos tuvo la oportunidad de interactuar con sistemas de inteligencia artificial (IA) como parte de su proceso formativo, mientras que el otro grupo sirvió como control, sin tener acceso a esta tecnología. Además de evaluar el rendimiento académico, se aplicó un cuestionario con el propósito de recopilar las percepciones y opiniones de los estudiantes acerca de su experiencia al utilizar la IA en su aprendizaje. Este instrumento se centró en valorar el grado de satisfacción de los alumnos con respecto al uso de la IA y su apreciación sobre cómo esta tecnología impactaba en su proceso formativo y en la calidad de la enseñanza recibida. Para procesar la información recolectada, se emplearon técnicas estadísticas tanto de carácter exploratorio como inferencial. Se realizó un análisis exploratorio para comparar el desempeño académico entre el grupo experimental, que hizo uso de la IA, y el grupo control. Adicionalmente, se llevó a cabo un análisis de varianza (ANOVA) con el fin de evaluar las diferencias en las calificaciones obtenidas por ambos grupos. Los hallazgos derivados de este estudio ponen de manifiesto que la implementación de la inteligencia artificial (IA) como herramienta pedagógica puede resultar altamente eficaz para potenciar el rendimiento académico de los estudiantes, tal como se evidencia en los resultados obtenidos.  MQRInvestigar00MQRInvestigar["Karen Giomar Palma Landirez", "Oswaldo Steven Feijoo Romero", "Dayron Rumbaut-Rangel"]2024-06-16T00:00:00 ddf7d2a994607c29b71cbdda4a961baabb1f7c49As NNs permeate various scientific and industrial domains, understanding the universality and reusability of their representations becomes crucial. At their core, these networks create intermediate neural representations, indicated as latent spaces, of the input data and subsequently leverage them to perform specific downstream tasks. This dissertation focuses on the universality and reusability of neural representations. Do the latent representations crafted by a NN remain exclusive to a particular trained instance, or can they generalize across models, adapting to factors such as randomness during training, model architecture, or even data domain? This adaptive quality introduces the notion of Latent Communication -- a phenomenon that describes when representations can be unified or reused across neural spaces. A salient observation from our research is the emergence of similarities in latent representations, even when these originate from distinct or seemingly unrelated NNs. By exploiting a partial correspondence between the two data distributions that establishes a semantic link, we found that these representations can either be projected into a universal representation, coined as Relative Representation, or be directly translated from one space to another. Latent Communication allows for a bridge between independently trained NN, irrespective of their training regimen, architecture, or the data modality they were trained on -- as long as the data semantic content stays the same (e.g., images and their captions). This holds true for both generation, classification and retrieval downstream tasks; in supervised, weakly supervised, and unsupervised settings; and spans various data modalities including images, text, audio, and graphs -- showcasing the universality of the Latent Communication phenomenon. [...]arXiv.org00By exploiting a partial correspondence between the two data distributions that establishes a semantic link, it is found that these representations can either be projected into a universal representation, coined as Relative Representation, or be directly translated from one space to another.ArXiv["Luca Moschella"]2024-06-16T00:00:00 fc4f5707e30a7beea8285bf32a0503323800fd19Artificial Intelligence (AI) and other large language models are rapidly infiltrating the world of education and educational research. These new technological developments raise questions about use and ethics throughout the world of educational research, particularly for qualitative methods given the philosophical and structural foundations of its associated designs. This paper seeks to interrogate the perceived ethics around the use of AI in qualitative research and draws on survey data from qualitative researchers (n = 101) collected from April-May 2023. Findings indicate that researchers were more apt to embrace the use of AI for transcription purposes, and to a lesser extent for preliminary coding. Researchers from high research productivity (R1) universities were generally less accepting of AI's use in the research process than other researchers.Journal of Empirical Research on Human Research Ethics184Findings indicate that researchers were more apt to embrace the use of AI for transcription purposes, and to a lesser extent for preliminary coding, than researchers from high research productivity universities were generally less accepting of AI's use in the research process.Journal of empirical research on human research ethics : JERHRE["David T. Marshall", "David B Naff"]2024-06-17T00:00:00 66a31b37d9ac8e905ba929125bd42763ffd0aa59This paper presents a comprehensive bibliometric review of the development of artificial intelligence (AI) in journalism based on the analysis of 331 articles indexed in the Scopus database between 2019 and 2023. This research combines bibliometric approaches and quantitative content analysis to provide an in-depth conceptual and structural overview of the field. In addition to descriptive measures, co-citation and co-word analyses are also presented to reveal patterns and trends in AI- and journalism-related research. The results show a significant increase in the number of articles published each year, with the largest contributions coming from the United States, Spain, and the United Kingdom, serving as the most productive countries. Terms such as “fake news”, “algorithms”, and “automated journalism” frequently appear in the reviewed articles, reflecting the main topics of concern in this field. Furthermore, ethical aspects of journalism were highlighted in every discussion, indicating a new paradigm that needs to be considered for the future development of journalism studies and professionalism.Journalism and Media434The results show a significant increase in the number of articles published each year, with the largest contributions coming from the United States, Spain, and the United Kingdom, serving as the most productive countries.Journalism and Media["Alem Febri Sonni", "Vinanda Cinta Cendekia Putri", "Irwanto Irwanto"]2024-06-17T00:00:00 49939488857e6babefdfe154896e9783edf78690In the digital age, cyber threats have become increasingly sophisticated, necessitating innovative approaches to bolster security measures. Artificial Intelligence (AI) has emerged as a formidable tool in the realm of cyber security, offering advanced capabilities in threat detection, anomaly detection, and response automation. This article provides an overview of AI applications in cyber security, highlighting its role in mitigating risks and fortifying defense mechanisms. AI techniques such as machine learning, deep learning, and natural language processing empower security systems to analyze vast amounts of data in real-time, identifying patterns indicative of malicious activities. Through the utilization of AI-driven algorithms, cyber security platforms can proactively detect and neutralize cyber threats before they inflict substantial damage. Moreover, AI enables the automation of incident response processes, reducing response times and minimizing the impact of security breaches. Case studies from leading cyber security firms from the integral part of the studies and demonstrate the practical implementation of AI-driven solutions in safeguarding critical infrastructures against cyber threats. Resilience towards cyber-attacks and safeguarding sensitive data assets through leveraging AI technologies has been focused in the study.SADGAMAYA123An overview of AI applications in cyber security, highlighting its role in mitigating risks and fortifying defense mechanisms and resilience towards cyber-attacks and safeguarding sensitive data assets through leveraging AI technologies has been focused in the study.SADGAMAYA["Suman Thapaliya", "Ayub Bokani"]2024-06-17T00:00:00 6a072d986ae3cb7901e524d356fd7f015e8fb11dEye and Vision973Artificial intelligence has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals, but overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential.Eye and Vision["Jie Yao", "Joshua Lim", "Gilbert Yong San Lim", "J. Ong", "Yuhe Ke", "Ting Fang Tan", "Tien-En Tan", "S. Vujosevic", "D. S. W. Ting"]2024-06-17T00:00:00 9014cfd23d3f9d9271c74821b8eb4901e60cbe37Many industry sectors have been pursuing the adoption of Industry 4.0 (I4.0) ideas and technologies, which promise to realize lean and just-in-time production through digitization and the use of smart machines. This shift is driven by technological advances, including Artificial Intelligence (AI) and machine learning, sensor networks and Internet of Things technologies, cloud computing, additive manufacturing, and the availability of large amounts of data that can be exploited by these technologies. However, the adoption of AI technologies for I4.0 varies considerably among industry sectors. This article complements broader reviews of I4.0 by examining the specific applications of AI. The recent White House report on Artificial Intelligence (AI) (Lee, 2016) highlights the significance of AI and the necessity of a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Within the 5C architecture previously proposed in Lee et al. (2015), the capacity to act specifically while addressing the ideal principle of artificial intelligence is solving the problem and achieving the objective. Finally, the paper identifies and discusses significant applications of AI for Industry 4.0.African Journal of Biological Sciences01The paper identifies and discusses significant applications of AI for Industry 4.0 by examining the specific applications of AI within the 5C architecture previously proposed in Lee et al. (2015).African Journal of Biological Sciences["T. Sheshadri"]2024-06-17T00:00:00 56d57a9c3e6e7c66060a7893ee5a886c91cbac3dBackground: Institutional review boards (IRBs) face delays in reviewing research proposals, underscoring the need for optimized standard operating procedures (SOPs). This study assesses the abilities of three artificial intelligence (AI) platforms to address IRB challenges and draft essential SOPs. Methods: An observational study was conducted using three AI platforms in 10 case studies reflecting IRB functions, focusing on creating SOPs. The accuracy of the AI outputs was assessed against good clinical practice (GCP) guidelines. Results: The AI tools identified GCP issues, offered guidance on GCP violations, detected conflicts of interest and SOP deficiencies, recognized vulnerable populations, and suggested expedited review criteria. They also drafted SOPs with some differences. Conclusion: AI platforms could aid IRB decision-making and improve review efficiency. However, human oversight remains critical for ensuring the accuracy of AI-generated solutions.Journal of Empirical Research on Human Research Ethics152Artificial intelligence platforms could aid IRB decision-making and improve review efficiency, however, human oversight remains critical for ensuring the accuracy of AI-generated solutions.Journal of empirical research on human research ethics : JERHRE["K. Sridharan", "G. Sivaramakrishnan"]2024-06-17T00:00:00 9aec692224e01cffd4a1af882685af60b1bb7b46Generative artificial intelligence (AI) is not new, yet it has recently experienced an explosion of interest and debate. Among the topics of concern is how it will affect human relationships and interactions and how organisations will deploy AI in conducting their external relationships. This paper addresses an ongoing experiment which explores the impact of AI in the field of negotiation. It provides initial observations on the use of machine learning in general and ChatGPT in particular in negotiated outcomes. We provide preliminary recommendations regarding the use of AI tools and systems in negotiation and pose questions related to possible future research.Journal of Strategic Contracting and Negotiation02Initial observations on the use of machine learning in general and ChatGPT in particular in negotiated outcomes are provided to provide preliminary recommendations regarding the use of AI tools and systems in negotiation.Journal of Strategic Contracting and Negotiation["Tim Cummins", "Keld Jensen"]2024-06-17T00:00:00 c258c29be4d2e0be11806eeb6c4ff7c44eae6ef4The legal sector is one of the biggest sectors in the world. The operations of the legal sector are supposed to be under-digitized. The legal profession is cautious about embracing new technologies and takes a rather traditional approach to its work Artificial Intelligence (AI) has the potential to significantly advance and improve India's legal system. Although AI is still being adopted in its infancy in the Indian legal system, there are several domains in which it can be advantageous. With the advancement of AI technology, lawyers will be able to practice more strategically, work more efficiently, and spend less money. Even while there are worries that artificial intelligence (AI) may endanger attorneys, AI technology can enhance legal research and analysis, speed up processes, increase access to justice, and enhance the practice of law overall. This study investigates the benefits of incorporating artificial intelligence technology into the legal field and looks at how legal professionals view this use of technology. The study was conducted by using Primary data collected from the 106 lawyers practising in the City of Mumbai. The analysis was done by using simple frequency, percentages, Mann-Whitney Test and Kruskal Wallis test by using SPSS software. It was found that there is no significant association between gender and perception of lawyers about artificial intelligence and there is significant association between age and perception of the lawyers about artificial intelligence.International Journal For Multidisciplinary Research61International Journal For Multidisciplinary Research["Dr. Sarita Sunil Mahadik"]2024-06-17T00:00:00 03636e368c7cababda575c5efa386c5ff47dc5dcArtificial Intelligence in Medicine is looking for novelty in the methodological and/or theoretical content of submitted papers. Such kind of novelty has to be mainly acknowledged in the area of AI and Computer Science. Methodological papers deal with the proposal of some strategy and related methods to solve some scientific issues in specific domains. They must show, usually through an experimental evaluation, how the proposed methodology can be applied to medicine, medicallyoriented human biology, and health care, respectively. They have also to provide a comparison with other proposals, and explicitly discuss elements of novelty. Theoretical papers focus on more fundamental, general and formal topics of AI and must show the novel expected effects of the proposed solution in some medical or healthcare field.30Artificial Intelligence in Medicine is looking for novelty in the methodological and/or theoretical content of submitted papers and must show the novel expected effects of the proposed solution in some medical or healthcare field.[]2024-06-17T00:00:00 33372c0cac07b3ae9ba7564753f66c46bd1c08b4In the given scientific inquiry, the problem of utilizing artificial intelligence tools for optimizing and automating management processes in professional and Olympic sports management is examined. Considering the realities, managers of sports clubs, public organizations, and professional sports associations often juggle coaching and managerial duties, leading to compromised task execution due to time constraints and excessive paperwork. It has been identified that managerial work demands continuous education and enhancement of personnel management skills, encompassing not only quality control of duties performed by sports association or club members, but also personnel selection, adaptation, training, initiation, and implementation of cutting-edge technologies and work methods. A significant portion of these tasks can be delegated to artificial intelligence processing, which, when properly utilized, can alleviate managers from 60% of paper and routine work, resulting in even more precise and thorough automation. Consequently, managers would have more time to focus on managerial tasks requiring indispensable human qualities, experiential wisdom, and interpersonal communication. This approach is particularly pertinent to sports management, as sports managers are the professionals who amalgamate professional work skills with athletes, coaches, parents, governmental representatives, and investors. The invaluable experience and expertise of sports managers significantly influence the development and deepening of Ukrainian and global sports. By characterizing methods of utilizing artificial intelligence tools in professional and Olympic sports management processes, strategies for optimization in sports management were examined to analyze and identify ways to enhance working conditions for sports managers, especially in professional and Olympic sports. The objective is to create conditions for the development of domestic and international sports, optimizing the performance of coaches and personnel in sports associations and public organizations.Visnyk of Zaporizhzhya National University. Physical education and Sports90By characterizing methods of utilizing artificial intelligence tools in professional and Olympic sports management processes, strategies for optimization in sports management were examined to analyze and identify ways to enhance working conditions for sports managers, especially in professional and Olympic sports.Visnyk of Zaporizhzhya National University Physical education and Sports["O. K. \u0421\u0435\u0440\u043f\u0443\u0442\u044c\u043a\u043e", "\u0421. I. \u0421\u0442\u0435\u043f\u0430\u043d\u044e\u043a", "O. \u0421. \u041b\u0435\u043c\u0435\u0448\u043a\u043e", "\u0412. \u042e. \u041a\u043e\u0432\u0430\u043b\u044c"]2024-06-17T00:00:00 e2231a927e847fd9d89ed037fb65d0f8dcc95888: With the rapid development of information technology, artificial intelligence has become a key force driving innovation in journalism. However, this technology-driven innovation has also exacerbated the problem of digital divide, especially the difference in information acquisition and processing capabilities. The purpose of this paper is to explore how the application of AI in journalism can effectively bridge this divide. Using case studies and empirical research, it provides an in-depth analysis of the application of AI technologies such as natural language processing, machine learning and big data analytics in the news gathering, editing and distribution process, and how these technologies contribute to the democratization of information and improve the accessibility of news content. By comparing the information accessibility and satisfaction of audiences in different socio-economic contexts, the paper reveals the potential of AI technologies in improving the quality and distribution efficiency of news content. Attention is also given to how AI can enhance user experience and engagement while ensuring news authenticity and transparency. The paper concludes that the judicious application of AI technologies can not only address the digital divide facing journalism, but also provide the impetus to build a more inclusive and interactive news ecosystem. This finding is important for guiding news organizations on how to leverage AI technology innovation to achieve broader social inclusion.International Journal of Big Data Intelligent Technology80The paper concludes that the judicious application of AI technologies can not only address the digital divide facing journalism, but also provide the impetus to build a more inclusive and interactive news ecosystem.International Journal of Big Data Intelligent Technology["Yan Li"]2024-06-17T00:00:00 2ec32b6c22f712e397a3852486560424b834a56cArtificial Intelligence is the replica of human intelligence operations by machines, particularly computer systems. Special applications of AI is inclusive of expert systems, natural language processing, speech identification & machine vision.Stock trading engages in buying & selling of shares of a certain company. AI Trading is the utilization of Artificial Intelligence in the trading arena to examine market data, obtain investment notions & construct portfolios. AI Trading touches on the application of algorithms & machine learning strategies to dissect large amounts of data & pinpoint patterns & trends in the market.International Journal For Multidisciplinary Research190Stock trading engages in buying & selling of shares of a certain company and AI Trading is the utilization of Artificial Intelligence in the trading arena to examine market data, obtain investment notions & construct portfolios.International Journal For Multidisciplinary Research["S.SHARADH Sureshbabu", "Sornalakshmi"]2024-06-17T00:00:00 a0cd363688cfa8a2722dd950d5e21e77e535cda7: In the current era of rapid technological development, artificial intelligence education has become an important part of the national strategic development, and at the same time, how to combine traditional culture with modern technology education has also become an urgent problem to be solved. Firstly, we analyze the core concepts of Chinese philosophical thoughts of "Taoism and nature", "the middle way" and "unity of knowledge and action", and explore the influence of these thoughts on the cognitive process and behavioral patterns of individuals. We explore the impact of these ideas on individual cognitive processes and behavioral patterns. Next, we examine the current challenges in AI education, including the lack of technological ethics education, the deficiency of innovation ability cultivation, and the lack of humanistic care. Starting from three aspects, namely, curriculum design, teaching methodology and evaluation system, a model of AI education integrating Chinese philosophical ideas is proposed, emphasizing the integration of Chinese philosophical elements in the teaching of AI technology.International Journal of Big Data Intelligent Technology70A model of AI education integrating Chinese philosophical ideas is proposed, emphasizing the integration of Chinese philosophical elements in the teaching of AI technology.International Journal of Big Data Intelligent Technology["Zhaocheng Xu", "Yi Ding"]2024-06-17T00:00:00 2f00941ef498e9ca7f0d15fde15844635e7b456eMedicine is a science, an art, and a trust between the doctor and the patient. In the times of digitization and artificial intelligence, new relationships between the human being and the machines are establishing. The concept for using computers to stimulate intelligent behavior and critical thinking is firstly described by Alan Turing in 1950. Nowadays, it is time to talk about digital transformation in medicine. AI consists of Machine learning (ML), Deep learning (DL) and Computer vision (CV). New terms appear in medical terminology in the context of digital health and digital transformation, as a new reality, extended reality literally. The purpose of this article is to present some fundamentals of AI and its application in Laboratory medicine in accordance with clinical needs and ethical standards. The way of digitization in human life and in medicine is clear and the process has been started, but there are still many things to be introduced in the same practice.Bulgarian Society of Medical Sciences Journal130The purpose of this article is to present some fundamentals of AI and its application in Laboratory medicine in accordance with clinical needs and ethical standards.Bulgarian Society of Medical Sciences Journal["Irena Ivanova", "Nora Ivanova", "Bisera Atanasova"]2024-06-17T00:00:00 095114885bee45678f2299dca4660cccf1bf52bcProducing an artificial general intelligence (AGI) has been an elusive goal in artificial intelligence (AI) research for some time. An AGI would have the capability, like a human, to be exposed to a new problem domain, learn about it and then use reasoning processes to make decisions. While AI techniques have been used across a wide variety of problem domains, an AGI would require an AI that could reason beyond its programming and training. This paper presents a small step towards producing an AGI. It describes a mechanism for an AI to learn about and develop reasoning pathways to make decisions in an a priori unknown domain. It combines a classical AI technique, the expert system, with a its modern adaptation - the gradient descent trained expert system (GDTES) - and utilizes generative artificial intelligence (GAI) to create a network and training data set for this system. These can be created from available sources or may draw upon knowledge incorporated in a GAI's own pre-trained model. The learning process in GDTES is used to optimize the AI's decision-making. While this approach does not meet the standards that many have defined for an AGI, it provides a somewhat similar capability, albeit one which requires a learning process before use.arXiv.org00This paper describes a mechanism for an AI to learn about and develop reasoning pathways to make decisions in an a priori unknown domain and utilizes the gradient descent trained expert system (GDTES) to create a network and training data set for this system.ArXiv["Jeremy Straub"]2024-06-17T00:00:00 2c1dac26692506868f016c66211938c3cea84585This policy analysis focuses on harnessing the power of artificial intelligence (AI) in hospital quality improvement to transform quality and patient safety. It examines the application of AI at the two following fundamental levels: (1) diagnostic and treatment and (2) clinical operations. AI applications in diagnostics directly impact patient care and safety. At the same time, AI indirectly influences patient safety at the clinical operations level by streamlining (1) operational efficiency, (2) risk assessment, (3) predictive analytics, (4) quality indicators reporting, and (5) staff training and education. The challenges and future perspectives of AI application in healthcare, encompassing technological, ethical, and other considerations, are also critically analyzed.Global Journal on Quality and Safety in Healthcare660This policy analysis focuses on harnessing the power of artificial intelligence in hospital quality improvement to transform quality and patient safety by streamlining operations and staff training and education.Global Journal on Quality and Safety in Healthcare["Hana J. Abukhadijah", "A. Nashwan"]2024-06-17T00:00:00 5679637970e1a89e8b090fdb4650cd72979e3125The paper at hand portrays the merging of AI with the realm of renewable energy in the view of sustainable power. It considers the discussion on the advancements made in the state of the art as to the country-wise situation in elaboration to depict how AI is integrated in the betterment of renewable energy feasibility, effectiveness, and levels of grid integration. In applications concerning solar potentials, AI algorithms—primarily when unsupervised—are implemented in such systems to actualize the maximum effective passive solar potential. The next research demonstrates how these algorithms, when used with large datasets to achieve efficiency, may be employed toward the prediction and perhaps aid in the avoidance of recombination events in solar cells. This research will use artificial intelligence (AI) to band gap engineering in order to improve solar absorption efficiency. Gradient Boosting and Random Forest, belonging to the family of Machine Learning techniques, will be used for simulating: the way these patterns are associated between the patterns of solar irradiation, climatic variables, and energy output. It concludes with a view of how the predictive power of AI will shape a future in energy that is resilient and sustainable. (Figure. 1)2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)370How AI is integrated in the betterment of renewable energy feasibility, effectiveness, and levels of grid integration is depicted to depict how the predictive power of AI will shape a future in energy that is resilient and sustainable.2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)["Tina Ziarati", "Sattar Hedayat", "C. Moscatiello", "Giuseppe Sappa", "Matteo Manganelli"]2024-06-17T00:00:00 7f7dd8c72a597a34101be1f96bb771b377c3f203This paper demonstrates how artificial-intelligence language analysis can inform understanding of human–nature relationships and other social phenomena. We demonstrate three techniques by investigating relationships within the popular word2vec word embedding, which is trained on a sample from over 50,000 worldwide news sources. Our first technique investigates what theory-generated analogies are most similar to nature:people. The resource:user analogy is most similar, followed by the playground:child and gift:receiver analogies. Our second technique explores whether nature-related words are affiliated with words that denote race, class, or gender. Nature words tend slightly toward associations with femininity and wealth. Our third technique demonstrates how the relationship between nature and wellbeing compares to other concepts’ relationships to wellbeing—e.g., spirituality–wellbeing, social relations–wellbeing. Nature is more semantically connected to wellbeing than money, social relations, and multiple other wellbeing correlates. Findings are consistent with previous social science and humanities research on human-nature relationships, but do not duplicate them exactly; our results thus offer insight into dominant trends and prevalence of associations. Our analysis also offers a model for using word embeddings to investigate a wide variety of topics.PLoS ONE1540This paper demonstrates how artificial-intelligence language analysis can inform understanding of human–nature relationships and other social phenomena by investigating relationships within the popular word2vec word embedding, which is trained on a sample from over 50,000 worldwide news sources.PLOS ONE["Rachelle K. Gould", "Bradford Demarest", "Adrian Ivakhiv", "Nicholas Cheney"]2024-06-17T00:00:00 d5a71c2dd8ac8899f577b2af076e72b99e8bbfdfExplainable Artificial Intelligence (XAI) methods help to understand the internal mechanism of machine learning models and how they reach a specific decision or made a specific action. The list of informative features is one of the most common output of XAI methods. Multicollinearity is one of the big issue that should be considered when XAI generates the explanation in terms of the most informative features in an AI system. No review has been dedicated to investigate the current approaches to handle such significant issue. In this paper, we provide a review of the current state-of-the-art approaches in relation to the XAI in the context of recent advances in dealing with the multicollinearity issue. To do so, we searched in three repositories that are: Web of Science, Scopus and IEEE Xplore to find pertinent published papers. After excluding irrelevant papers, seven papers were considered in the review. In addition, we discuss the current XAI methods and their limitations in dealing with the multicollinearity and suggest future directions.arXiv.org200A review of the current state-of-the-art approaches in relation to the XAI in the context of recent advances in dealing with the multicollinearity issue is provided.ArXiv["Ahmed M. Salih"]2024-06-17T00:00:00 6399cdec84b349fc227db4dd6ea2052c2d0a2ef7OBJECTIVE We aimed to evaluate the opinions of individuals aged 18 and above in our country regarding the use of artificial intelligence (AI) and robots in the field of healthcare. BACKGROUND The growing population and patient load, coupled with increasing data, can expedite the diagnosis and treatment process for patients through faster, easier, and more accurate interpretation of information. METHODS The study encompasses voluntary participants aged 18 and above, who have either undergone surgery in a hospital or have accompanied a family member during a surgical procedure and possess internet access as well as the capability to participate in online surveys. RESULTS A total of 725 individuals participated in our study 61% (n=442) of respondents expressed trust in the operation of AI and robots in the hospital setting. 64.1% (n=465) of participants expressed trust in AI's contribution to disease diagnosis and laboratory tests. The confidence in AI's use in radiological examinations and its contribution reached 71.6% (n=519). CONCLUSION This study demonstrates that the use of AI and robots in healthcare services is accepted by our society and would be appropriate in our society (Tab. 5, Fig. 1, Ref. 24).Bratislava Medical Journal00This study demonstrates that the use of AI and robots in healthcare services is accepted by the authors' society and would be appropriate in their society.Bratislavske lekarske listy["H. Esin", "Cem Karaali", "Kenan Teker", "H. Mergen", "Omer Demir", "S. Aydo\u011fan", "Mehmet Zeynel Keskin", "M. Emiro\u011flu"]2024-06-17T00:00:00 aac57457423561dfb7a8025db2d3d2e0b04a4accThe rising interest in Artificial Intelligence and the increasing time invested in functional verification processes are driving the demand for AI solutions in this field. Functional verification is the process of verifying that the Register Transfer Layer (RTL) implementation behaves according to the specifications provided. This is performed using a hardware verification language (HVL) such as SystemVerilog combined with the Universal Verification Methodology (UVM). Reading, identifying the key elements from multiple documentations, creating the verification plan, building the verification environment, implementing the tests defined, and achieving 100% coverage are usually the steps performed in order to complete the verification process. The verification process is considered finalized when functional coverage is at 100%. There are multiple ideas on how the process can be aided by AI, such as underlining the essential information from documentation, which would help in understanding faster how the Register Transfer Layer implementation works, thus vastly reducing time. In this paper, to greatly reduce the time spent on functional verification, two Convolutional Neural Network (CNN) architectures are implemented to properly classify the information across different documents; both approaches have significant and promising results. The database used for this classification task was created by the researchers using different documentations available.Electronics170To greatly reduce the time spent on functional verification, two Convolutional Neural Network architectures are implemented to properly classify the information across different documents; both approaches have significant and promising results.Electronics["Diana Dranga", "C\u0103t\u0103lina Dumitrescu"]2024-06-17T00:00:00 f0967c4356fb567fcf042fda5db5762149c245c0Background and aims: Artificial intelligence (AI) has emerged as a rapidly developing tool within the medical landscape, globally aiding in diagnosis and healthcare management. However, its integration within healthcare systems remains varied across different regions. In Sudan, there exists a burgeoning interest in AI potential applications within medicine. This study aims to evaluate the knowledge, attitudes, and practices of AI applications in medicine among physicians in Sudan. Methods: The authors conducted a web-based survey cross-sectional analytical study using an online questionnaire-based survey regarding demographic details, knowledge, attitudes, and practice of AI distributing through various e-mail listings and social media platforms. A sample of 825 Physicians including doctors in Sudan with different ranks and specialties were selected using the convenient non-probability sampling technique. Result: Out of 825 Physicians, 666 (80.7%) of Physicians have previous knowledge about AI. However, only a small number 123 (14.9%) were taught about AI during their time in medical school, even fewer, just 120 (14.5%) had AI-related lessons in their training program. Regarding attitude, 675 (81.8%) agree that AI is very important in medicine, almost the same number, 681 (82.6%) support the idea of teaching AI in medical schools. Practically, 535 (64.8%) of doctors, think that should get special training in using AI tools in healthcare. Excitingly 651 (78.9%) of physicians are interested in working with AI in future. Based on different ranks of doctors toward AI; Medical Officers exhibited the highest proportion at (32.7%) of knowledge and understanding of AI concepts, followed by House Officers at (16.7%) (p=0.076); regarding attitude, Medical Officers demonstrated the highest (31.6%) favorable attitude, followed by House Officers at (17.5%) (p=0.229); In practice also, Medical Officer showed the highest portion (28.0%) among participants (p=0.129). Conclusion: While there is a positive attitude and some level of AI practice, there remains a considerable gap in knowledge that needs addressing.Annals of Medicine and Surgery280While there is a positive attitude and some level of AI practice, there remains a considerable gap in knowledge that needs addressing.Annals of Medicine and Surgery["Mohammed Hammad Jaber Amin", "Gasm Alseed Abdelmonim Gasm Alseed Fadlalmoula", "Musab Awadalla Mohamed Elhassan Elmahi", "Noon hatim Khalid Alrabee", "Lina Hemmeda", "Mohammed Haydar Awad", "Ghassan E. Mustafa Ahmed", "Khabab Abbasher Hussien Mohamed Ahmed"]2024-06-17T00:00:00 288d891bcb7a08e98ce3cc473001b511392dec97Artificial Intelligence in Medicine is looking for novelty in the methodological and/or theoretical content of submitted papers. Such kind of novelty has to be mainly acknowledged in the area of AI and Computer Science. Methodological papers deal with the proposal of some strategy and related methods to solve some scientific issues in specific domains. They must show, usually through an experimental evaluation, how the proposed methodology can be applied to medicine, medicallyoriented human biology, and health care, respectively. They have also to provide a comparison with other proposals, and explicitly discuss elements of novelty. Theoretical papers focus on more fundamental, general and formal topics of AI and must show the novel expected effects of the proposed solution in some medical or healthcare field.30Artificial Intelligence in Medicine is looking for novelty in the methodological and/or theoretical content of submitted papers and must show the novel expected effects of the proposed solution in some medical or healthcare field.["Thompson Stephan"]2024-06-17T00:00:00 21dbcd6587c7c737b5e9a49e43607e9128e8b85dThe general objective of the research was to determine the advances related to the universities and artificial intelligence. The specific objectives of the research are to identify the universities that invest the most in artificial intelligence and the best global universities for artificial intelligence. Methodology, in this research, 42 documents have been selected, carried out in the period 2018 – 2024; including: scientific articles, review articles and information from websites of recognized organizations. Results, AI is becoming increasingly important in all areas of human activity, which is why standards are being established for its proper use. Education is an important aspect in the development of people, which is why it must be invested at an international level. Innovation is very important for any type of organization and especially for universities. Conclusions, artificial intelligence is gaining more followers in university higher education, due to its important contribution. In addition, some principles have been formulated to guide its development. The top global university is the MIT – Massachusetts Institute of Technology (The United States of America); the top university in Latin America and the Caribbean is the Universidade de São Paulo (Brazil); the top university in Europe is the University of Oxford (United Kingdom); the best university in Asia is the Tsinghua University (China); the top university in Africa is the University of Cape Town (South Africa); the top university in Oceania is the University of Melbourne (Australia). The university that invests the most in artificial intelligence was Johns Hopkins University. The best global universities for artificial intelligence were Tsinghua University, Nanyang Technological University, Chinese University of Hong Kong, Stanford University, University of California – Berkeley and Massachusetts Institute of Technology.South Florida Journal of Development120Artificial intelligence is gaining more followers in university higher education, due to its important contribution and some principles have been formulated to guide its development.South Florida Journal of Development["Carlos Rios-Campos", "Erick Orlando Guerrero Zambrano", "Mar\u00eda Fernanda Mera Cantos", "Oscar Anchundia-G\u00f3mez", "Mar\u00eda Elena C\u00e1rdenas Le\u00f3n", "Gina Elizabeth Mera Moya", "Enrique Augusto Mart\u00ednez Garc\u00eda", "Elixer Alexandra Palma Batalla", "Nicky Armando Rodr\u00edguez de la Oliva", "Ovidio Serrano Zelada"]2024-06-17T00:00:00 840fdc7c5ed9d7502a73b21b590210feac506884Environmental science and pollution research international605This study developed and optimised data-driven models such as gradient boosting machines (GBM), deep neural networks (DNN) and RF within the H2O API framework to ensure efficient data processing and handling and contributes significantly to the sustainable management of valuable water resources.Environmental science and pollution research international["Javed Mallick", "Saeed AlQadhi", "Hoang Thi Hang", "Majed Alsubih"]2024-06-17T00:00:00 5fef58742ad25fbcd48b9eb7a0dc7126a8a88454Service Industries Journal693The Service Industries Journal["Pengyi Shen", "Fengying Zhang", "Xiucheng Fan", "Feng Liu"]2024-06-17T00:00:00 bd8889daede18c0d31689baacf321adac7968e5aCurrent Dermatology Reports351Current Dermatology Reports["Mary P. Smith", "Joshua M. Schulman"]2024-06-17T00:00:00 0092eb1a7736a0ce21e83fafa5d16ffdd0710fb7Service Industries Journal1332The Service Industries Journal["Chenchen Weng", "Ruizhi Yuan", "Dandan Ye", "Bo Huang", "Jiyao Xun"]2024-06-17T00:00:00 66801381d94cb9bd62270d312a3721b1693b9381En este trabajo se presentan los resultados de un análisis de contenido de artículos publicados en cinco medios digitales generalistas argentinos en los que se tematiza la inteligencia artificial, la robotización y la automatización de procesos. El corpus se sometió a un abordaje de minería de datos y luego se seleccionó una muestra representativa que fue abordada con un análisis de contenido, a partir de variables como el área de incidencia de la noticia (salud, educación, gobierno), el origen geográfico, las fuentes y la apelación a temores o expectativas. Los principales hallazgos se centran en la similitud de enfoques de los cinco medios (los que, por lo demás, presentan líneas editoriales muy diferentes respecto a la agenda de discusión pública más general), el abordaje mayormente favorable o positivo de estas innovaciones, el sesgo empresarial en las fuentes utilizadas y el predominio, como expectativas y temores aludidos, del eje comodidad-obsolescencia.Dixit00Dixit["Luis Ricardo Sandoval"]2024-06-17T00:00:00 85b8f5e1e36388ce80e9f5ba1431ce37cfd7ed8cJMIR Nursing300JMIR Nursing["Inas Al Khatib", "Malick Ndiaye"]2024-06-17T00:00:00 7c9d16ee1b2dbe73bc311415c7d6c609b61c5733Kegiatan pengabdian kepada masyarakat di SMP Negeri Buti pada 2 September 2023 bertujuan untuk meningkatkan keterampilan guru dalam menggunakan teknologi AI sebagai media pembelajaran interaktif. Melibatkan 7 guru dari berbagai bidang, pelatihan ini mencakup ceramah, demonstrasi, dan latihan praktis. Evaluasi menunjukkan tanggapan sangat positif dengan skor rata-rata 4,7, mengindikasikan peningkatan pemahaman dan kemampuan dalam menggunakan platform Teachmate AI dan Chat GPT. Guru-guru yang berpartisipasi mampu mengembangkan materi pembelajaran yang lebih interaktif dan menarik, serta termotivasi untuk terus meningkatkan kualitas pengajaran. Pelatihan ini diharapkan dapat meningkatkan minat dan motivasi siswa serta mendukung persiapan perangkat pembelajaran yang lebih baik. Hasil kegiatan ini sejalan dengan literatur yang menyatakan bahwa integrasi teknologi dalam pendidikan dapat meningkatkan efektivitas pembelajaran dan motivasi siswa, serta membawa perubahan positif di lingkungan sekolah.Jurnal Pengabdian Masyarakat Bangsa140Jurnal Pengabdian Masyarakat Bangsa["Najdah Thalib", "Leonora Puspa", "Primanopa Situmorang"]2024-06-17T00:00:00 5419b9b7e0768a72ccf0a7d25b85f9cc70f35270This work addresses issues such as inadequate teaching methods, a lack of teaching resources, and low proactiveness in current accounting education. It introduces a novel teaching approach termed “Just-in-Time Teaching (JITT) cloud teaching,” which integrates “real-time teaching” with the Internet of Things-based “cloud teaching model” specifically for accounting education. First, the current status of accounting education in secondary vocational schools is investigated through a questionnaire survey. Subsequently, adjustments are made to the traditional teaching model, considering the limitations in teaching media creation channels and challenges in teaching activities. The teaching content of accounting education is designed in terms of mind maps, curriculum type, and problem design. The findings indicate: (1) Almost half of the surveyed teachers have heard of but never used the JITT cloud teaching, and the proportion is the largest. Some teachers have used but disapprove of JITT cloud teaching. (2) The proportion of students using website learning resources is 43.81%, while the proportion using mobile learning applications is 38.34%. (3) There is a significant difference between the traditional teaching mode and the JITT teaching mode in terms of “classroom teaching” and “sense of responsibility”. The average values under the JITT teaching mode have significantly improved compared to the traditional one. (4) The experimental group has a higher proportion of students scoring 90–100, which is 58%, significantly higher than the control group. The above research results indicate that there are still many possibilities for the practical application of the JITT teaching method in the future Moreover, applying the JITT cloud teaching model contributes to enhancing teaching quality and supports students’ learning.J. Comput. Methods Sci. Eng.370This work introduces a novel teaching approach termed “Just-in-Time Teaching (JITT) cloud teaching,” which integrates “real-time teaching” with the Internet of Things-based “cloud teaching model” specifically for accounting education.J. Comput. Methods Sci. Eng.["Yongyi Wu", "Jingfeng Jiang", "Zhendan Wen"]2024-06-17T00:00:00 aee0e8de10f74209c8eb20f1a924392ed67ec534Scientific Reports815The technology acceptance model is extended to incorporate cognitive factors such as AI intrinsic motivation, AI readiness, AI confidence, and AI anxiety alongside human–computer interaction elements like user interface (UI), content, and learner-interface interactivity in the context of using generative AI (GenAI) tools.Scientific Reports["Wei Li", "Xiaolin Zhang", "Jing Li", "Xiao Yang", "Dong Li", "Yantong Liu"]2024-06-17T00:00:00 66c351673ce839bcb5c6ee352e45d75e7adbf498For modern artificial intelligence (AI) applications such as large language models (LLMs), the training paradigm has recently shifted to pre-training followed by fine-tuning. Furthermore, owing to dwindling open repositories of data and thanks to efforts to democratize access to AI models, pre-training is expected to increasingly migrate from the current centralized deployments to federated learning (FL) implementations. Meta-learning provides a general framework in which pre-training and fine-tuning can be formalized. Meta-learning-based personalized FL (meta-pFL) moves beyond basic personalization by targeting generalization to new agents and tasks. This paper studies the generalization performance of meta-pFL for a wireless setting in which the agents participating in the pre-training phase, i.e., meta-learning, are connected via a shared wireless channel to the server. Adopting over-the-air computing, we study the trade-off between generalization to new agents and tasks, on the one hand, and convergence, on the other hand. The trade-off arises from the fact that channel impairments may enhance generalization, while degrading convergence. Extensive numerical results validate the theory.arXiv.org483This paper studies the generalization performance of meta-pFL for a wireless setting in which the agents participating in the pre-training phase, i.e., meta-learning, are connected via a shared wireless channel to the server.ArXiv["Haifeng Wen", "Hong Xing", "Osvaldo Simeone"]2024-06-17T00:00:00 d9bde273d06f50cfd180ef4927cd99a45c668eacIntroduction Studying the spatiotemporal patterns of amyloid accumulation in the brain over time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography (PET) imaging plays a pivotal role because it allows for the visualization and quantification of abnormal amyloid beta (Aβ) load in the living brain, providing a powerful tool for tracking disease progression and evaluating the efficacy of anti-amyloid therapies. Generative artificial intelligence (AI) can learn complex data distributions and generate realistic synthetic images. In this study, we demonstrate for the first time the potential of Generative Adversarial Networks (GANs) to build a low-dimensional representation space that effectively describes brain amyloid load and its dynamics. Methods Using a cohort of 1,259 subjects with AV45 PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we develop a 3D GAN model to project images into a latent representation space and generate back synthetic images. Then, we build a progression model on the representation space based on non-parametric ordinary differential equations to study brain amyloid evolution. Results We found that global SUVR can be accurately predicted with a linear regression model only from the latent representation space (RMSE = 0.08 ± 0.01). We generated synthetic PET trajectories and illustrated predicted Aβ change in four years compared with actual progression Discussion Generative AI can generate rich representations for statistical prediction and progression modeling and simulate evolution in synthetic patients, providing an invaluable tool for understanding AD, assisting in diagnosis, and designing clinical trials. The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.Frontiers in Aging Neuroscience482The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.Frontiers in Aging Neuroscience["Mat\u00edas Nicol\u00e1s Bossa", "Akshaya Ganesh Nakshathri", "Abel D\u00edaz Berenguer", "Hichem Sahli"]2024-06-17T00:00:00 720f0fb97dd7f0d3f98fbc022bdcef9858be4ca4The increasing presence and importance of Artificial Intelligence (AI) in our society has led to calls for its inclusion at all levels of education. However, the field is only beginning to understand what how AI learning experiences may be designed to be effective and developmentally appropriate, especially for young children. One challenge children encounter is in conceptualizing the “intelligence” of AI while they are still developing a metacognitive model of their own human intelligence. To investigate potential ways to address this, we developed a strategy, metacognitive embodiment, through which children are supported to (a) elicit a mental model of their own intelligent performance on a task and (b) compare that elicited model to an AI designed to accomplish the same task. From this study we found evidence suggesting that engaging children in metacognitive tasks in coordination with AI learning experiences (where the AI performs an analogous task) better positioned them for sensemaking about the AI’s intelligence.International Conference on Interaction Design and Children271Evidence is found suggesting that engaging children in metacognitive tasks in coordination with AI learning experiences better positioned them for sensemaking about the AI’s intelligence.Proceedings of the 23rd Annual ACM Interaction Design and Children Conference["E. Greenwald", "A. Krakowski", "Timothy Hurt", "Kelly Grindstaff", "Ning Wang"]2024-06-17T00:00:00 e8aed5d36923147a4edb999b388167420f981a74The influence of generative Artificial Intelligence (AI) on the propagation and amplification of societal biases, particularly in the context of children’s content creation, is a growing concern. By developing and testing a prototype tool designed to assist children in Digital Storytelling (DST), our research aimed to explore and mitigate the propagation of stereotypes through the use of a character-generating AI tool utilising Stable Diffusion. Despite initial aspirations, the tool demonstrated significant biases inherent in the underlying AI model, leading to the decision against its use by children. The findings we discovered contribute to a broader discourse on the development of ethical AI and its use, advocating for a more responsible and inclusive approach to technological innovation in the context of children’s digital media consumption and creation.International Conference on Interaction Design and Children361This research aimed to explore and mitigate the propagation of stereotypes through the use of a character-generating AI tool utilising Stable Diffusion, and demonstrated significant biases inherent in the underlying AI model, leading to the decision against its use by children.Proceedings of the 23rd Annual ACM Interaction Design and Children Conference["Alexander Baines", "Lidia Gruia", "Gail Collyer-Hoar", "Elisa Rubegni"]2024-06-17T00:00:00 8d84991ed242f4033a026b8f988219238d83efb0This integrative literature review (ILR) looks into the use of artificial intelligence (AI) technology in the French healthcare system, emphasizing personalized medicine and predictive health analytics. The study subject is the problematic integration of AI technologies, which is hampered by significant challenges, including data privacy concerns, system interoperability, ethical and legal issues, resistance to technological change, and the need for extensive training of healthcare professionals. These issues affect French healthcare professionals and politicians, who must overcome these challenges to utilize AI's potential fully. This research aims to investigate and assess the integration of AI into the French healthcare system, particularly in personalized medicine and predictive health analytics, to identify and address the obstacles and leverage the opportunities that improve patient care and operational efficiency. The ILR's guiding conceptual framework is based on three essential concepts: artificial intelligence, personalized medicine, and predictive healthcare analytics. The research methodology consists in thoroughly examining current literature, qualitative study of case studies, and interviews with industry professionals. The study's findings show that AI has tremendous potential to increase diagnostic precision and treatment accuracy in the French healthcare system. They highlight initiatives such as establishing specialized departments like "Intelligent Healthcare Services" and creating positions like "Intelligent Doctor". The conclusions address the potential implications of the findings for enhancing patient care and operational efficiency, as well as recommendations for further study and practice. These include performing longitudinal studies, looking into emerging data integrity solutions such as blockchain, and improving healthcare professional training programs. Ultimately, breaking down current obstacles and ensuring that AI technologies improve healthcare delivery and patient outcomes would make France a leader in AI-driven medical innovation.International Journal For Multidisciplinary Research601The study's findings show that AI has tremendous potential to increase diagnostic precision and treatment accuracy in the French healthcare system, and recommends initiatives such as establishing specialized departments like "Intelligent Healthcare Services" and creating positions like "Intelligent Doctor".International Journal For Multidisciplinary Research["Rachid Ejjami"]2024-06-17T00:00:00 2f0dbc156d8dacb5fa104cdb65c988c2ced1cc46Artificial intelligence (AI) presents challenges and opportunities for higher education. The challenge is to incorporate the benefits of AI while minimizing its potential for misuse and undermining of learning. The opportunity is that AI allows instructors to assess learning authentically by fostering creative, engaging, realistic, and reflective assessments. Drawing on some of the current practices of AI use in the classroom, I discuss how authentic assessment can be achieved while incorporating AI. First, I review what authentic assessment is, how it is implemented, and some of its positive outcomes. Then, I present sample activities that use AI to design, implement, and evaluate assessments to serve the APA Learning Goals for the Undergraduate Psychology Major. The activities and assessments here and in the online Supplemental Materials (Miserandino, 2024, May 23) help fulfill additional learning goals and build academic skills (e.g., critical thinking, reading, writing). By using these activities and assessments or drawing on them to create their own, instructors can improve their teaching and student learning outcomes.Teaching of psychology151This work reviews what authentic assessment is, how it is implemented, and some of its positive outcomes and presents sample activities that use AI to design, implement, and evaluate assessments to serve the APA Learning Goals for the Undergraduate Psychology Major.Teaching of Psychology["M. Miserandino"]2024-06-17T00:00:00 08e17a4e1c1b81f10cfc5d8c1ccd64008f76476eElectric systems are transitioning to higher utilization of renewable and distributed energy resources, propelled by the quick progress of the underlying information technology. The latter empowered smart grids, enabling communities to establish their own energy provisioning systems. Remarkably, even more disruptive technological impact on the energy sector is expected due to the upcoming 6G paradigm and disruptive advancements in artificial intelligence (AI). 6G supersedes its successor, 5G, in the sense that it goes far beyond being just a communication network. 6G is envisioned to be a fabric that empowers almost every aspect of the future smart society by bringing together AI, edge computing, distributed trust, and 99.99999% reliable communication with a delay on the order of microseconds. In this paper, we conceptualize the future development of electric power systems in the advent of the 6G introduction. We show that 6G will have the potential for a transformative effect not only on most aspects of energy production and distribution but also on societal aspects. The technological paradigm of 6G will drive energy systems' evolution towards self-sustainability, meaning that they will optimize, heal, govern, and, eventu-ally, develop themselves to meet the needs of society, better achieving the target key performance and value indicators. For such systems, we will define a conceptual architecture, review both existing and future enabling technologies, and identify key challenges. Importantly, we pinpoint the social interaction challenges hindering the emergence of energy prosumer commu-nities and present the new approach of empowering distributed autonomous organizations with AI that will facilitate the process of self-organization, -governance, and -regulation.2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)460It is shown that 6G will have the potential for a transformative effect not only on most aspects of energy production and distribution but also on societal aspects and presents the new approach of empowering distributed autonomous organizations with AI that will facilitate the process of self-organization, -governance, and -regulation.2024 IEEE International Conference on Environment and Electrical Engineering and 2024 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)["Aleksandr Zavodovski", "Farid Hamzeh Aghdam", "A. Cal\u00f3", "Rashid Dehkordi", "Petr \u0160t\u011bp\u00e1nek", "Mehdi Rasti", "Eva Pongr\u00e1cz", "Petri Ahokangas"]2024-06-17T00:00:00 be1928d78c35b7108e9a9db6790cf27bb63e08deThe multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligence algorithms can augment various imaging modalities such as X-rays, CT, and MRI to improve image quality and generate high-resolution three-dimensional images. AI reconstruction of three-dimensional models of patient anatomy from CT or MRI scans can better enable urologists to visualize structures and accurately plan surgical approaches. AI can also be optimized to create virtual reality simulations of surgical procedures based on patient-specific data, giving urologists more hands-on experience and preparation. Recent development of artificial intelligence modalities such as TeraRecon and Ceevra offer rapid and efficient medical imaging analyses aimed at enhancing the provision of urologic care, notably for intra-operative guidance during robotic-assisted radical prostatectomy and partial nephrectomy. Notably, use of 3-D VR models has been linked to improved operative times, shorter hospital stay, reduced clamp time, and minimized blood loss in patients undergoing robotic assisted laparoscopic partial nephrectomy when compared to standard operative approaches that do not utilize VR technologies.Journal of endourology314Use of 3-D VR models has been linked to improved operative times, shorter hospital stay, reduced clamp time, and minimized blood loss in patients undergoing robotic assisted laparoscopic partial nephrectomy when compared to standard operative approaches that do not utilize VR technologies.Journal of endourology["Labeeqa Khizir", "Vineet Bhandari", "Srivarsha Kaloth", "John Pfail", "Benjamin J. Lichtbroun", "N. Yanamala", "S. Elsamra"]2024-06-18T00:00:00 9613de15785327bb5bb158254a3285d7f624420dPurpose This study aims to explore the interplay of cognitive, affective, and normative constituents for their potential acceptance or rejection of artificial intelligence (AI) and ChatGPTs in the hospitality and tourism context. Design/methodology/approach Using an advanced analytical approach (i.e. a fuzzy-set qualitative comparative analysis), the study tested hypotheses based on 474 responses from individuals who have used ChatGPT for hospitality and tourism information. Findings The study found multiple solutions, including cognitive, affective and normative drivers for strong and weak continuance intentions toward AI-based ChatGPT. Informativeness, one of the cognitive drivers, was found to be a necessary condition for achieving the desired outcome. Originality/value This research provides novel insights into the functionality of developing multiple configurations to predict complex travelers behaviors in the context of hospitality and tourism technology consumption. Journal of Hospitality and Tourism Technology625The study found multiple solutions, including cognitive, affective and normative drivers for strong and weak continuance intentions toward AI-based ChatGPT, withformativeness, one of the cognitive drivers, found to be a necessary condition for achieving the desired outcome.Journal of Hospitality and Tourism Technology["Heesup Han", "S. Kim", "Tadesse Bekele Hailu", "Amr Al-Ansi", "Jiyoung Lee", "J. Kim"]2024-06-18T00:00:00 035c16e017e30f6f26d8ba4d687a706cbcf024c2OBJECTIVES To investigate health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs. METHODS Qualitative semi-structured interviews with health consumers, recruited via health consumer networks and community groups, interviews conducted between January and August 2022. RESULTS We interviewed 28 health consumers about their perceptions towards the ethical use of AI in EDs. The results discussed in this paper highlight the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers. Most health consumers are more likely to support AI health tools in EDs if they continue to be involved in the decision-making process. There is considerably more approval of AI tools that support clinical decision-making, as opposed to replacing it. There is mixed sentiment about the acceptability of AI tools influencing clinical decision-making and judgement. Health consumers are mostly supportive of the use of their data to train and develop AI tools but are concerned with who has access. Addressing bias and discrimination in AI is an important consideration for some health consumers. Robust regulation and governance are critical for health consumers to trust and accept the use of AI. CONCLUSION Health consumers view AI as an emerging technology that they want to see comprehensively regulated to ensure it functions safely and securely with EDs. Without considerations made for the ethical design, implementation and use of AI technologies, health consumer trust and acceptance in the use of these tools will be limited.Emergency Medicine Australasia241Investigating health consumers' ethical concerns towards the use of artificial intelligence (AI) in EDs highlights the challenges and barriers for the effective and ethical implementation of AI from the perspective of Australian health consumers.Emergency medicine Australasia : EMA["Samuel Freeman", "J. Stewart", "Rebecca Kaard", "Eden Ouliel", "A. Goudie", "Girish Dwivedi", "Hamed Akhlaghi"]2024-06-18T00:00:00 2b85202fce4da3839dd4edb151aca815c7deed1fScience journalists, traditionally, play a key role in delivering science information to a wider audience. However, changes in the media ecosystem and the science-media relationship are posing challenges to reliable news production. Additionally, recent developments such as ChatGPT and Artificial Intelligence (AI) more generally, may have further consequences for the work of (science) journalists. Through a mixed-methodology, the quality of news reporting was studied within the context of AI. A content analysis of media output about AI (news articles published within the time frame 1 September 2022–28 February 2023) explored the adherence to quality indicators, while interviews shed light on journalism practices regarding quality reporting on and with AI. Perspectives from understudied areas in four European countries (Belgium, Italy, Portugal, and Spain) were included and compared. The findings show that AI received continuous media attention in the four countries. Furthermore, despite four different media landscapes, the reporting in the news articles adhered to the same quality criteria such as applying rigour, including sources of information, accessibility, and relevance. Thematic analysis of the interview findings revealed that impact of AI and ChatGPT on the journalism profession is still in its infancy. Expected benefits of AI related to helping with repetitive tasks (e.g. translations), and positively influencing journalistic principles of accessibility, engagement, and impact, while concerns showed fear for lower adherence to principles of rigour, integrity and transparency of sources of information. More generally, the interviewees expressed concerns about the state of science journalism, including a lack of funding influencing the quality of reporting. Journalists who were employed as staff as well as those who worked as freelancers put efforts in ensuring quality output, for example, via editorial oversight, discussions, or memberships of associations. Further research into the science-media relationship is recommended.PLoS ONE371Thematic analysis of the interview findings revealed that impact of AI and ChatGPT on the journalism profession is still in its infancy, and concerns showed fear for lower adherence to principles of rigour, integrity and transparency of sources of information.PLOS ONE["A. M. Dijkstra", "Anouk C. de Jong", "Marco Boscolo"]2024-06-18T00:00:00 3b27ae0135644b75a3c64b5a6bfc9499d663f8b0The beginning of a new era has led to the emergence of new factors that impede the effective operation of enterprises. One of these factors is the exponential growth of the amount of data that needs to be processed and analyzed in order to make effective management decisions. This leads to the need to develop or search for a software product aimed at collecting, analyzing and systematizing a large amount of data, as well as performing relevant tasks based on them that artificial intelligence is capable of. The article is aimed at researching and revealing the potential of using artificial intelligence (AI) in the field of accounting. The main focus is on improving the efficiency of the enterprise, which is achieved by automating and optimizing various aspects of accounting work. On the basis of a thorough analysis of the works of foreign and domestic specialists, the theoretical aspects of the concept of artificial intelligence are distinguished on the basis of systemic, functional and information approaches. The directions of compliance of the accountant, which can be artificial intelligence, are identified, namely: processing of invoices, cost management, analysis of financial statements, management of payables and receivables, compliance with tax legislation and detection of fraud and risk management. Ways of using AI in processes of automation and optimization of processes, such as invoice processing, cost management, financial reporting analysis, control of accounts payable and receivable, ensuring compliance with tax legislation and fraud detection and risk management, have been identified. The artificial intelligence technologies for their purpose, which are the most used in the world, are presented. The sequence of actions in the identified needs in artificial intelligence and its outstanding volume is structured. The results of the study confirm the potential of AI to automate operations, increase productivity and accuracy in accounting, as well as reduce risks and improve audit efficiency.   Key words: Artificial intelligence, accounting, analysis, accounting automation, accounting information systems, information systematization, management efficiency.  Economic journal of Lesya Ukrainka Volyn National University01The results of the study confirm the potential of AI to automate operations, increase productivity and accuracy in accounting, as well as reduce risks and improve audit efficiency.Economic journal of Lesya Ukrainka Volyn National University["\u0410\u043b\u043b\u0430 \u0424\u0430\u0442\u0435\u043d\u043e\u043a-\u0422\u043a\u0430\u0447\u0443\u043a", "\u041e\u043b\u0435\u043d\u0430 \u0421\u043a\u043e\u0440\u0443\u043a", "\u0406\u043b\u043b\u044f \u0417\u0430\u0445\u0430\u0440\u0447\u0443\u043a", "\u0420\u043e\u043c\u0430\u043d \u042f\u043d\u0443\u0448"]2024-06-18T00:00:00 70410fead559abdd292ebc9d30db84da066b9b6eThe article considers the application of the artificial intelligence systems as a new tool in making foreign policy decision. The content of the concept of artificial intelligence is studied, the scope of the use of artificial intelligence systems in everyday life and in international relations is studied. It can be noted that AI, as a progressive and rapidly developing technology, has a large set of tools that help people make decisions and increase the efficiency of their work, for example, save time and spent resources to achieve a particular result. With the development of technology, more and more machines with artificial intelligence are used in various areas of life. For example, the field of medicine, mechanical engineering, data analysis, public administration and politics - all these fields are actively developing with using of artificial intelligence technologies. Special attention is paid to how artificial intelligence affects in making decision, event forecasting, and automation of data analysis. The use of artificial intelligence systems in international diplomacy was analyzed. Attention is focused on the weak and strong sides and what risks this technology can carry for foreign policy decisions. The given statistical data show how artificial intelligence is treated in Ukraine. Based on the research, it can be concluded that for the widespread use of artificial intelligence, it is necessary to develop convenient and transparent rules and algorithms, norms for the using of technology and its interaction with people. Summarizing the discussed topic, we will come to the conclusion that artificial intelligence will soon become a powerful tool in international relations, diplomacy, and in other areas of our life, which will bring benefit and a real threat. But in order for the benefit to be greater, it is necessary that the people who will use artificial intelligence be trained and knowledgeable in the technology.European Socio-Legal &amp; Humanitarian Studies101It can be concluded that for the widespread use of artificial intelligence, it is necessary to develop convenient and transparent rules and algorithms, norms for the using of technology and its interaction with people.European Socio-Legal &amp; Humanitarian Studies["A. S. Sirenko"]2024-06-18T00:00:00 78b6ed3d4bb3bbef8d727999638b84aa1598f1d7In response to the commentary by Daungsupawong and Wiwanitkit (doi: 10.1177/15347346241247914), we authored a reply letter addressing their concerns regarding our previous publication (doi: 10.1177/15347346241236811). Daungsupawong and Wiwanitkit highlighted that while the advancements in generative artificial intelligence (AI) chatbots show promise, several challenges remain in their application to diabetic foot ulcer (DFU) management. In our reply, we emphasized the recent improvements in chatbots' capabilities, particularly in image interpretation and non-English language communication. We posit that these challenges will be overcome in the near future, enabling the clinical implementation of AI chatbots for DFU management.International Journal of Lower Extremity Wounds40In response to the commentary by Daungsupawong and Wiwanitkit, the recent improvements in chatbots' capabilities, particularly in image interpretation and non-English language communication are emphasized.The international journal of lower extremity wounds["Makoto Shiraishi", "Koji Kanayama", "Haesu Lee", "Kiichi Furuse", "Mutsumi Okazaki"]2024-06-18T00:00:00 f61967528d33f88666343b46b7a8c2eb25237b0aCurrent Urology Reports210The role of AI in the management of priapism is understudied, yet to achieve dependable and effective models that can reliably assist physicians in making decisions regarding both diagnostic and treatment strategies.Current urology reports["Edoardo Pozzi", "David A Velasquez", "A. Varnum", "B. Kava", "Ranjith Ramasamy"]2024-06-18T00:00:00 cfd27ca97be357723c8f5bbf3e32ddc74b9b6a6eWhat challenges or opportunities exist when using artificial intelligence (AI) at the university? This work presents the experience lived in a Futurist Applications of AI in Education Workshop in which professionals from different educational levels, roles, and areas worked together to construct proposals to enhance the use of AI in higher education. As a parallel research process, a questionnaire was applied to inquire about the perceptions of the uses of AI and the personal and professional applications of managers, professors, students, and stakeholders. The results reflected a broad intention to understand, use, and implement AI to develop and strengthen the processes of access, teaching, and learning in higher education. 10th International Conference on Higher Education Advances (HEAd’24)00This work presents the experience lived in a Futurist Applications of AI in Education Workshop in which professionals from different educational levels, roles, and areas worked together to construct proposals to enhance the use of AI in higher education.10th International Conference on Higher Education Advances (HEAd’24)["Patricia V\u00e1zquez-Villegas", "M. P. Garc\u00eda-Chitiva", "Danilo Valdes-Ramirez", "Genaro Zavala"]2024-06-18T00:00:00 327c3d2eeeb0e5c3092e75d50e17388f688e3cab Recent strides in artificial intelligence and machine learning have gained considerable attention in the diagnosis of Alzheimer's disease due to its ability to detect the disease at an early stage. Along with the advances in AI (Artificial Intelligence) and ML (Machine Learning) for the detection of AD (Alzheimer’s Disease), ethical considerations and regulatory aspects must also be meticulously addressed. This review covers the ethical questions that arise with the use of AI and ML in AD diagnosis. Privacy and protection of individual’s personal data, clinicians making their decisions based on AI, and unbiased and autonomy concerns like consent of the patient are covered here. Given their transformational nature, it remains entirely unclear how AI-driven methods for studying the human brain will impact normative instruments in research ethics and neuro-ethics, as well as fulfill appropriate criteria of scientific validity. Explainable AI and ML systems have been developed recently to avoid potential bias and unethical conduct. This article also discusses the type of potential personal patient information that could be exploited during the use of AI and ML-based algorithms. Recent Advances in Computer Science and Communications00This review covers the ethical questions that arise with the use of AI and ML in AD diagnosis, and the type of potential personal patient information that could be exploited during the use of AI and ML-based algorithms.Recent Advances in Computer Science and Communications["Gupta Swati Sanjaykumar", "Rishabha Malviya", "Prerna Uniyal"]2024-06-18T00:00:00 b8cc843af9a010586eff83ba765810d0e08090a8Digital Twins (DTs) are cutting-edge technological design principles of Industry 4.0. They elevate the representation level of physical systems backed up by accurate real-time data in virtual environments and empower the simulation capabilities of these systems through Artificial Intelligence (AI) for their analysis, monitoring, and optimization. This work comprehensively explores the intrinsic interaction between simulation and AI in DTs, meticulously covering the current literature status and categorizing these symbiotic interactions into three different groups that cover AI to support DT-based simulation, AI for optimization of simulation within DT, and simulation to support AI approaches in DT. In addition, a deeper look is taken at the role of simulation and $A I$ in the emerging concept of the Industrial Metaverse, which promises to extend DTs beyond discrete virtual representation of physical systems to encompass the industrial ecosystem from end-to-end. Finally, the main research challenges for achieving the full integration of simulation and AI in DTs and at the Industrial Metaverse are discussed.International Symposium on Industrial Electronics240This work comprehensively explores the intrinsic interaction between simulation and AI in DTs, meticulously covering the current literature status and categorizing these symbiotic interactions into three different groups that cover AI to support DT-based simulation, AI for optimization of simulation within DT, and simulation to support AI approaches in DT.2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE)["Alexandre O. J\u00fanior", "J. Calvo-Rolle", "Paulo Leit\u00e3o"]2024-06-18T00:00:00 bfd495cedf2f7fafe9f55e1afceb5dbf6a229c7cНа основе статистического исследования рынка технологий искусственного интеллекта, а также анализа теоретических и практических аспектов внедрения ИИ-инструментов в предпринимательскую деятельность в статье предложена концептуальная модель совершенствования бизнес-процессов компании-субъекта малого предпринимательского сектора. Based on a statistical study of the market for artificial intelligence technologies, as well as an analysis of the theoretical and practical aspects of introducing AI tools into business activities, the article proposes a conceptual model for improving the business processes of a small business sector company.Экономика и управление: научно-практический журнал00Экономика и управление: научно-практический журнал["\u0420.\u0410. \u0425\u0430\u0441\u0430\u043d", "\u042d.\u0424. \u041c\u0443\u0440\u0437\u0438\u043d\u0430", "\u041c.\u0410. \u0420\u0438\u0437\u0432\u0430\u043d\u043e\u0432\u0430"]2024-06-18T00:00:00 1914241a2f78c97d9b6150fefb6080e68ed46ce7RATIONALE Evaluating future nurses' perspectives on artificial intelligence, determining their missing or incorrect information on the subject and determining their anxiety levels are of great importance in terms of providing science and technology-based health services in the future. AIMS This research was conducted to evaluate the knowledge, attitude and anxiety levels of future nurses about artificial intelligence applications. METHOD The research was a descriptive type, conducted with 552 nursing students. In collecting data, 'Data collection form' and 'Artificial Intelligence Anxiety Scale' (AIAS) were used. Analysis of data was performed with descriptive statistics, Kolmogorov-Smirnov, Shapiro-Wilk, Spearman, Mann-Whitney U and Kruskal-Wallis tests. In the study, p < 0.05 value was considered statistically significant. RESULTS It was determined that the students' average AIAS score was 51.68 ± 12.32. It was determined that 95.3% of the students did not receive training on artificial intelligence, and 94.0% did not have artificial intelligence-related subjects in their school courses. It was determined that 79.2% of the students wanted artificial intelligence-related subjects to be included in school courses. CONCLUSION In the study, it was determined that the artificial intelligence anxiety levels of nursing students were high. It has been determined that students with negative feelings about artificial intelligence have higher artificial intelligence anxiety levels. Our suggestion; adding courses or subjects related to artificial intelligence to the university curriculum and starting to include nurses in the working processes during their student years.Journal of Evaluation In Clinical Practice300Adding courses or subjects related to artificial intelligence to the university curriculum and starting to include nurses in the working processes during their student years is suggested.Journal of evaluation in clinical practice["Deniz Yi\u011fit", "A. Acikgoz"]2024-06-18T00:00:00 90c40e6855b1c1912b933da165b32b64ec741463This research proposal aims to comprehensively explore the trustworthy, safe, and ethical use of Generative Artificial Intelligence (GAI), particularly Large Language Models (LLMs). To this end, we examine the risks and potential social hazards of LLMs, adopting a multidimensional approach— focused on society, human rights, and ethics— involving various stakeholders, including the AI industry, governmental institutions, and regulatory organizations, among others. This strategy allows for offering a research proposal grounded on social and technological dimensions and providing a comprehensive diagnosis, including perceived challenges in the AI industry, the regulatory debate, ethical dilemmas, etc. By delving into these areas, we aim to design a post-audit tool to ensure models are trustworthy, socially responsible, and in alignment with human rights. Additionally, we aim to encourage responsible AI Innovation through Ethics-Driven Incentives. Supervisor: Prof. Danilo Caivano, danilo.caivano@uniba.it, University of Bari "A. Moro" Co-supervisor: Dr. Azzurra Ragone, azzurra.ragone@uniba.it University of Bari "A. Moro"International Conference on Evaluation & Assessment in Software Engineering220This research proposal aims to comprehensively explore the trustworthy, safe, and ethical use of Generative Artificial Intelligence (GAI), particularly Large Language Models (LLMs), adopting a multidimensional approach focused on society, human rights, and ethics.Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering["Berenice Fernandez Nieto"]2024-06-18T00:00:00 d79da3ccd5132fe5a46b88e5d341290a26218954The article explores the prospects and possibilities of using artificial intelligence in the educational process of agroengineering universities. Modern technologies and methods are considered to optimize the learning process of students, as well as to improve the quality of education in this area. The authors analyze examples of successful AI applications in teaching, highlight the advantages and challenges faced by teachers and students. In conclusion, it is concluded that the use of artificial intelligence in agroengineering universities can significantly improve the educational process and prepare specialists for the challenges of the modern agricultural industry.Moscow Economic Journal00It is concluded that the use of artificial intelligence in agroengineering universities can significantly improve the educational process and prepare specialists for the challenges of the modern agricultural industry.MOSCOW ECONOMIC JOURNAL["Vladimir Saranchin", "Bogdan Krivosheya", "Aleksandr Klimov", "Yan Chusov"]2024-06-18T00:00:00 fa7f5633ee6f4ac1a2cfb292b5f5db8f1f721b1eEducation and educational institutions have undergone many changes in recent years, mainly due to the development of new technologies. The findings of relevant research indicate that the implementation of artificial intelligence into the educational process affects the improvement of students' achievement. The application of artificial intelligence in education has contributed to the development of teaching efficiency through personalized approaches to learning, providing unique experiences for each student. The paper defines artificial intelligence in the educational context, as well as its elements, along with presenting the significant research results and highlighting the advantages and disadvantages of using artificial intelligence in education. The aim of the paper is to shed light on the importance of this phenomenon and its effect on educational practice. Despite the indisputable benefits of artificial intelligence, it is important to point out that there are also potential challenges that we need to perceive and focus on overcoming.Pedagoška obzorja00The aim of the paper is to shed light on the importance of artificial intelligence and its effect on educational practice, along with presenting the significant research results and highlighting the advantages and disadvantages of using artificial intelligence in education.Pedagoška obzorja["Tamara Dragojevi\u0107", "Milena Leti\u0107 Lungulov"]2024-06-18T00:00:00 b27a1f9b2f56b39fa632d277dd4decf1863bbd32Artificial Intelligence (AI) essentially refers to various types of machine learning, often involving deep neural networks. It autocompletes our ideas as we write, enables us to communicate with our phones, and supports language translation.1 According to the 2019 Global Burden of Disease Study, the estimated age-standardized incidence of cardiovascular disease (CVD) in Pakistan was 918.8 per 100,000 people (global: 684.33 per 100,000), and the age-standardized death rate was 357.88 per 100,000 (global: 239.85 per 100,000).2 With AI, new analytical and data-driven approaches could lead to significant advances in understanding multimorbid groups of cardiology patients and potentially improve therapeutic strategies.3 AI has been used to interpret echocardiograms and heart rhythms from ECGs, and to detect indicators of heart disease, such as left ventricular dysfunction, from surface ECGs and nuclear cardiology.4-6 It is a misconception that AI will replace cardiologists. Instead, skilled practitioners will be able to expand their clinical capabilities, make more accurate and prompt diagnoses, and improve management decisions in patient care. As with any statistical application, it is important to understand AI's strengths and limitations. To understand the basics of AI, it starts with developing an algorithm based on human expertise. Programmers create relationships between input and output, known as expert systems. In machine learning, a general algorithm, such as a neural network, approximates a mathematical relationship between input data and expected outputs. In unsupervised learning, such as clustering, only the inputs are fed into the algorithm, which then finds insights in the data using its inner structure and statistics. An AI model can discover new relationships in data that have previously eluded human discovery.1 For research purposes, cardiologists using AI may follow these steps: Type and collection of data. Preprocessing of data. Choosing the right machine learning approach. Validating and evaluating methods and results.3 The application of AI techniques in the healthcare system is still in its infancy and requires more understanding through workshops and integrated learning.7 In conclusion, AI represents a new development in the field of medicine, especially cardiology. However, it is susceptible to significant errors in interpretation and raises safety and ethical concerns.   References Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020;95(5):1015-39. Samad Z, Hanif B. Cardiovascular Diseases in Pakistan: Imagining a Postpandemic, Postconflict Future. Circulation. 2023;147(17):1261-3. Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, et al. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J. 2023;44(9):713-25. Cheng LT, Zheng J, Savova GK, Erickson BJ. Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging. 2010; 23(2):119-32. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287-95. Garcia EV, Klein JL, Taylor AT. Clinical decision support systems in myocardial perfusion imaging. J Nucl Cardiol. 2014;21(3):427-39. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res 2022;24:e32215. Pakistan Heart Journal00In conclusion, AI represents a new development in the field of medicine, especially cardiology, however, it is susceptible to significant errors in interpretation and raises safety and ethical concerns.Pakistan Heart Journal["Tariq Ashraf", "Rafat Sultana"]2024-06-18T00:00:00 ff46d80b921b524d052c545337c0f33b2ccfa315AI is entering the accounting profession. What should accountants do to get the most out of Artificial Intelligence (AI) in their daily jobs? The Tilburg Winter Symposium and Research Camp, themed “Empowering Accounting with Artificial Intelligence,” brought practitioners and researchers together in a two-day event on this important topic. While the tasks and opportunities for AI are numerous, the conference’s overarching conversation suggests that the core of the accountant’s profession is likely to evolve rather than disappear. This evolution requires accountants to redefine their roles by focusing more on how AI can assist in strategic decision-making. Furthermore, participating experts recommend that accountants set up operational and organizational structures and manage stakeholder involvement when using AI, so that the right questions can be asked to ensure that AI can assist in improving corporate decisions.Maandblad Voor Accountancy en Bedrijfseconomie60The Tilburg Winter Symposium and Research Camp, themed “Empowering Accounting with Artificial Intelligence,” brought practitioners and researchers together in a two-day event on this important topic and suggested that the core of the accountant's profession is likely to evolve rather than disappear.Maandblad voor Accountancy en Bedrijfseconomie["E. Cardinaels", "Judith K\u00fcnneke", "Iuliana Sandu", "M\u00e1t\u00e9 Sz\u00e9les"]2024-06-18T00:00:00 859ae4d2580d226802a11d011c03b7863b6ea587In the 21st century, artificial intelligence has been recognized as the main driver of universal progress. It has already gained a place in the economy of developed countries. It is used in both public and private sectors: in education, transport, finance and banking sector, medicine, and so on. Based on the scientific papers of artificial intelligence researchers, it is clear that small and medium-sized businesses can get significant benefits from artificial intelligence use. Small and medium-sized enterprises often work with limited resources, and automating repetitive tasks that can be performed with the help of artificial intelligence can help them save time and costs. Through artificial intelligence, SMEs are empowered to make data-driven decisions, improve their products or services, and identify new opportunities. The paper discusses the possibilities of using artificial intelligence in business, how AI can transform business operations and, therefore, the global economy, highlights the positive and negative aspects of AI, and expresses opinions in the direction of reducing the challenges associated with the introduction of artificial intelligence.Grail of Science30The possibilities of using artificial intelligence in business, how AI can transform business operations and, therefore, the global economy, highlights the positive and negative aspects of AI, and expresses opinions in the direction of reducing the challenges associated with the introduction of artificial intelligence are discussed.Grail of Science["G. Giguashvili"]2024-06-18T00:00:00 d344acbc0ae5797960c87d117f65746e5d1ea8c4Abstract The generative AI chatbot, as an artificial rhetorical agent participating in the invention and circulation of public discourse, shakes the foundations of rhetorical tenets such as agency, ethos, circulation, and justice; and in doing so, it further isolates rhetoric as amoral, ateleological technē concerned with mere calculated effects and consequences, and may ultimately contribute to a post-rhetoric condition. This article depicts a rhetorical profile of the generative AI chatbot characterized by stochastic rhetoric, which is distinguished from the conventional understanding of rhetoric as (human) conscious and purposeful use of language to induce change. Making a case for the possibility of a post-rhetoric condition, the article considers what it might mean for our conceptualization of ethos, circulation, and justice, and suggests ways of adapting to it.Rhetoric Review491Making a case for the possibility of a post-rhetoric condition, the article considers what it might mean for the conceptualization of ethos, circulation, and justice, and suggests ways of adapting to it.Rhetoric Review["Zhaozhe Wang"]2024-06-18T00:00:00 bef0d1b6d3fa012df825a56aaf9581518e25f199AI &amp; SOCIETY182AI &amp; SOCIETY["S. Rani", "Jining Dong", "Dhaneshwar Shah", "S. Xaba", "Khadija Shoukat"]2024-06-18T00:00:00 fd303c3305e20eab259e48a1f31ce8e251875574<jats:p xml:lang="tr"/>Journal of Pedagogical Research02Journal of Pedagogical Research["O. Opesemowo", "Mdutshekelwa Ndlovu"]2024-06-18T00:00:00 1d4442a8e9e8767f349301b85d38891d56cd0a3aThe ability of Generative AI (GenAI) to perform complex tasks has caused mixed feelings within the field of education. The most significant concern is the implications of GenAI for academic integrity. In this study, students applied GenAI to complete past university assessments adapted as research tests, with the goal of achieving a pass grade when graded by an academic, undetected by an AI writing detection tool. The study reveals that from a sample of 21 valid research tests, 23.8% (5) passed when graded by an academic achieving grades between 40% and 60%, with AI writing detection scores ranging between 0% and 14%. AI writing detection scores lower than 20% are potentially a false positive in the context of investigating breaches of academic integrity. The researcher concludes that many traditional methods of assessment in universities are obsolete in the face of increasingly undetectable AI generated solutions.10th International Conference on Higher Education Advances (HEAd’24)01The researcher concludes that many traditional methods of assessment in universities are obsolete in the face of increasingly undetectable AI generated solutions.10th International Conference on Higher Education Advances (HEAd’24)["Aidan Duane"]2024-06-18T00:00:00 79a35e66941096bf477f512ffdfa8fbe946b28afDengan pesatnya perkembangan teknologi, pembelajaran analisis kompleks menjadi sangat penting dalam mengikuti arus kemajuan teknologi, namun sering terabaikan. Oleh karena itu, diperlukan media pembelajaran interaktif seperti video yang diintegrasikan dengan kecerdasan buatan untuk memvisualisasikan materi teoritis. Penelitian ini bertujuan untuk (1) menggambarkan desain video pembelajaran, (2) menjelaskan kualitas validitas hasil pengembangan video pembelajaran oleh para ahli dan uji coba produk, dan (3) menilai keefektifan video pembelajaran. Analisis yang digunakan adalah analisis deskriptif kualitatif. Penelitian ini menggunakan metode penelitian pengembangan (R&D) dengan model ADDIE, yang melibatkan lima tahap: analisis, desain, pengembangan, implementasi, dan evaluasi. Video pembelajaran analisis kompleks dinilai valid oleh para ahli media, dengan produk yang mendapatkan penilaian baik dalam aspek tampilan (85%), kemudahan penggunaan (65%), dan penyajian materi (70%). Berdasarkan penilaian ahli materi, produk juga dinilai baik dalam hal kesesuaian materi ajar (55%) dan keruntutan materi (85%). Sementara itu, penilaian dari ahli bahasa menunjukkan bahwa produk memiliki kesesuaian bahasa yang tinggi (90%), dan juga mendapat penilaian baik dalam ketepatan pengucapan audio (70%) serta pemilihan bahasa (80%). Dari hasil validasi dan penilaian para ahli, serta uji coba pada kelompok kecil dan besar, dapat disimpulkan bahwa pengembangan video berbasis kecerdasan buatan untuk mata kuliah analisis kompleks layak digunakan.Ar-Rihlah: Jurnal Inovasi Pengembangan Pendidikan Islam00Ar-Rihlah: Jurnal Inovasi Pengembangan Pendidikan Islam["M. K. Ni\u2019am", "I. Saputra", "Zuhrotun Nisa", "Umi Mahmudah"]2024-06-18T00:00:00 be7db0d1c4d8ed766d3714fd6f200ca5878c1270Paralleling the Research Topic ’ s exploration of immune-related cell signatures, Stratis et al. captured the longitudinal changes in leukocyte transcript levels of astronauts transitioning to and from space, adaptation of leukocyte activity in space, and post-space fl ight effects using generalized linear modeling, presenting a bridge between statistical methods and machine learning approaches. Their work revealed decreased immune functions when reaching space and increased expression of immune-related genes upon egress back to Earth, shedding light on immuno-modulation in space and longitudinal effects of space on the immune system, highlighting adaptive changes in leukocyte activity in extreme environments. By harnessing the power of cutting-edge imaging modalitiesFrontiers in Immunology10Stratis et al. captured the longitudinal changes in leukocyte transcript levels of astronauts transitioning to and from space, adaptation of leukocyte activity in space, and post-space effects using generalized linear modeling, presenting a bridge between statistical methods and machine learning approaches.Frontiers in Immunology["Melanie L. Hart", "Ryuji Kato", "B. Rolauffs"]2024-06-18T00:00:00 1b8d2ddb8d2da6bdc5a753fd3251491548684b33User studies are crucial for meeting user needs. In user studies, real experimental scenarios and participants are constructed and recruited. However, emerging and unfamiliar studies face limitations, including safety concerns and iterative efficiency. To address these challenges, this study utilizes a large language model (LLM) to create generative AI virtual scenarios for user experience. By recruiting real users to evaluate this experience, we can collect feedback that enables rapid iteration in the early design phase. The air taxi is particularly representative of these challenges and has been chosen as the case study for this research. The key contribution was designing a virtual ATJ using OpenAI's GPT-4 model and AI image and video generators. Based on the LLM-generated scripts, key visuals were created for the air taxi, and the ATJ was evaluated by 72 participants. Furthermore, the LLM demonstrated the ability to identify and suggest environments that significantly improve participants' attitudes toward air taxis. Education level and gender significantly influenced participants' attitudes and their satisfaction with the ATJ. Our study confirms the capability of generative AI to support user studies, providing a feasible approach and valuable insights for designing air taxi user experiences in the early design phase.arXiv.org640This study utilizes a large language model (LLM) to create generative AI virtual scenarios for user experience, confirming the capability of generative AI to support user studies, providing a feasible approach and valuable insights for designing air taxi user experiences in the early design phase.ArXiv["Shengdi Xiao", "Jingjing Li", "Tatsuki Fushimi", "Yoichi Ochiai"]2024-06-18T00:00:00 6a537f6eab2ef07c97664beca9134ea34bd9deb6International Journal of Human-Computer Interaction290International Journal of Human–Computer Interaction["Michael Agyemang Adarkwah", "A. Y. M. A. Islam", "K\u00e4the Schneider", "Rose Luckin", "Michael Thomas", "Jonathan Michael Spector"]2024-06-18T00:00:00 73a2788f0688301724cfa8c479abcc814c44bc38AI &amp; SOCIETY480AI &amp; SOCIETY["A. Kirkpatrick", "Amanda D. Boyd", "Jay D. Hmielowski"]2024-06-18T00:00:00 d8889e8cefb17039100721f6034b408fa6609e90No abstract availableUniversity of Colombo Review00University of Colombo Review["D. Alahakoon"]2024-06-18T00:00:00 74ad42d339f4f4b34592c60a304bea2778555d59Celem artykułu jest ukazanie, dlaczego i w jaki sposób dynamiczny rozwój sztucznej inteligencji może i powinien stanowić przedmiot zainteresowania dyscypliny nauk teologicznych. W pierwszej kolejności autor przekonuje, że artefakty technologiczne, stanowiące część kultury, mogą być traktowane jako jedne z nowych „miejsc” człowieka, a w ten sposób także miejsc teologicznych. Namysł nad algorytmami tworzonymi w celu naśladowania i zastępowania inteligentnych działań ludzi prowadzi do stawiania w nowym kontekście pytań o naturę człowieka, jego funkcjonowanie oraz przeznaczenie. Autor analizuje w jaki sposób współbrzmi to ze współczesnymi zadaniami oraz metodami uprawiania teologii. W dalszej kolejności, autor analizuje kontekst definiowania pojęcia sztucznej inteligencji oraz wskazuje na związane z tym implikacje. W ostatniej części dokonano analizy w jaki sposób etyka, także ta uprawiana na gruncie teologii, może przyczynić się do kształtowania rozwoju sztucznej inteligencji. Całość wywodu prowadzi do wniosku, że w kontekście fenomenu sztucznej inteligencji, teologia, dzięki swojej unikalnej perspektywie, może odgrywać znaczącą i pozytywną rolę. Otwiera to dyscyplinie nauk teologicznych nowe i twórcze możliwości sprawowania służby na rzecz wiary i kultury.Teologia i Moralność10Teologia i Moralność["Maciej Mr\u00f3z"]2024-06-18T00:00:00 a8b2dbc1995876b8b90d85c84b437972a6c4cc4800["Tarandeep Kaur Bhatia", "S. E. Hajjami", "Keshav Kaushik", "Gayo Diallo", "Mariyam Ouaissa", "Inam Ullah Khan"]2024-06-18T00:00:00 6ee4f7d3046629c878dcaa7c884c5d2e0b399eb4Community Service (PKM) is one of the tasks of the Tri Dharma of Higher Education which must be fulfilled by lecturers. The PKM activities carried out had the theme Implications of Artificial Intelligence (AI) Technology as a Support for Student Learning. The partner collaborating on this PKM activity is Nusatama Vocational School, Padang City. Initial observations found that the use of Artificial Intelligence (AI) technology was still very lacking. The aim of PKM activities is to introduce Artificial Intelligence (AI) technology to vocational high school students. It is hoped that this PKM activity can increase students' knowledge about Artificial Intelligence (AI) technology, and can raise children's motivation in learning, produce positive attitudes, as well as a strong commitment to learning and a good attitude towards teachers. This service uses presentations and simulations of the introduction and implications of learning Artificial Intelligence (AI) technology for students. Analysis, education and education methods are used in this service by the service implementation team. This PKM activity resulted in an increase in students' knowledge about Artificial Intelligence (AI) technology and its application as a support for student learning.KREATIF: Jurnal Pengabdian Masyarakat Nusantara00This PKM activity resulted in an increase in students' knowledge about Artificial Intelligence (AI) technology and its application as a support for student learning.KREATIF: Jurnal Pengabdian Masyarakat Nusantara["Yuliawati Yunus", "Renny Permata Saputri", "Monica Fransisca"]2024-06-18T00:00:00 6fa2a2c736691fbb04dff9de0c81e0ff32e976a9Embodied AI represents a crucial step towards achieving Artificial General Intelligence (AGI). The next paradigm of Embodied AI involves physical embodiment, enhanced perception capabilities, and adaptive automation. This advances the field significantly, paving the way for broader expansion. Despite the significant progress, existing computing frameworks, like local computation or cloud computing, struggle to meet the substantial demands of Embodied AI. The Cloud-Fog Embodied framework, namely based on CFA (cloud-fog automation) offers a promising solution to address these challenges. Our goal is to drive integration across multiple domains, including AI, robotics and industrial production, to tackle multifaceted challenges and seize opportunities to achieve AGI in the future.International Symposium on Industrial Electronics240The goal is to drive integration across multiple domains, including AI, robotics and industrial production, to tackle multifaceted challenges and seize opportunities to achieve AGI in the future.2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE)["Dongxiao Hu", "Dapeng Lan", "Yu Liu", "Jiahong Ning", "Jia Wang", "Yun Yang", "Zhibo Pang"]2024-06-18T00:00:00 81e8666325b02b1287d4fdcf33232c74e7144f9aSmart Learning Environments11763This systematic review investigates how students’ over-reliance on AI dialogue systems, particularly those embedded with generative models for academic research and learning, affects their critical cognitive capabilities including decision-making, critical thinking, and analytical reasoning.Smart Learn. Environ.["Chunpeng Zhai", "Santoso Wibowo", "Lily D. Li"]2024-06-18T00:00:00 b0ce75fca254a50e5b6e1eca757a67706d2f91dbThe evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showcasing potential cognitive reasoning abilities in problem-solving and scientific discovery (i.e., AI4Science) once exclusive to human intellect. To comprehensively evaluate current models' performance in cognitive reasoning abilities, we introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage. We argue that the challenges in Olympic competition problems are ideal for evaluating AI's cognitive reasoning due to their complexity and interdisciplinary nature, which are essential for tackling complex scientific challenges and facilitating discoveries. Beyond evaluating performance across various disciplines using answer-only criteria, we conduct detailed experiments and analyses from multiple perspectives. We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions. Our extensive evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration. Through the OlympicArena, we aim to advance AI towards superintelligence, equipping it to address more complex challenges in science and beyond. We also provide a comprehensive set of resources to support AI research, including a benchmark dataset, an open-source annotation platform, a detailed evaluation tool, and a leaderboard with automatic submission features.Neural Information Processing Systems638This work argues that the challenges in Olympic competition problems are ideal for evaluating AI's cognitive reasoning due to their complexity and interdisciplinary nature, which are essential for tackling complex scientific challenges and facilitating discoveries.ArXiv["Zhen Huang", "Zengzhi Wang", "Shijie Xia", "Xuefeng Li", "Haoyang Zou", "Ruijie Xu", "Run-Ze Fan", "Lyumanshan Ye", "Ethan Chern", "Yixin Ye", "Yikai Zhang", "Yuqing Yang", "Ting Wu", "Binjie Wang", "Shichao Sun", "Yang Xiao", "Yiyuan Li", "Fan Zhou", "Steffi Chern", "Yiwei Qin", "Yan Ma", "Jiadi Su", "Yixiu Liu", "Yuxiang Zheng", "Shaoting Zhang", "Dahua Lin", "Yu Qiao", "Pengfei Liu"]2024-06-18T00:00:00 7286c46ac09e5b83abd774c26b1c1db392b73b5cThe integration of Big Data and Artificial Intelligence (AI) in education holds transformative potential, promising enhanced personalized learning experiences, improved administrative efficiency, and advanced predictive analytics. However, the adoption of these technologies also presents significant challenges. This paper explores the current landscape of Big Data and AI in education, identifying key challenges such as data privacy concerns, the digital divide, the need for teacher training, and the integration of AI with existing educational frameworks. Additionally, it examines potential future directions, including the development of ethical guidelines, advancements in adaptive learning technologies, and the creation of more inclusive and equitable AI systems. By addressing these challenges and leveraging future opportunities, the educational sector can harness the full potential of Big Data and AI to improve learning outcomes and operational efficiencies.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402308This paper explores the current landscape of Big Data and AI in education, identifying key challenges such as data privacy concerns, the digital divide, the need for teacher training, and the integration of AI with existing educational frameworks.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Khanssa Mohammed Elam"]2024-06-18T00:00:00 08f88bfbf431adc0a6795521def4277c3999b17aThe focus of debates about conversational artificial intelligence (CAI) has largely been on social and ethical concerns that arise when we speak to machines—what is gained and what is lost when we replace our human interlocutors, including our human therapists, with AI. In this viewpoint, we focus instead on a distinct and growing phenomenon: letting machines speak for us. What is at stake when we replace our own efforts at interpersonal engagement with CAI? The purpose of these technologies is, in part, to remove effort, but effort has enormous value, and in some cases, even intrinsic value. This is true in many realms, but especially in interpersonal relationships. To make an effort for someone, irrespective of what that effort amounts to, often conveys value and meaning in itself. We elaborate on the meaning, worth, and significance that may be lost when we relinquish effort in our interpersonal engagements as well as on the opportunities for self-understanding and growth that we may forsake.JMIR Mental Health372This work elaborate on the meaning, worth, and significance that may be lost when the authors relinquish effort in their interpersonal engagements as well as on the opportunities for self-understanding and growth that they may forsake.JMIR Mental Health["Anna Hartford", "D. J. Stein"]2024-06-18T00:00:00 c051b8db3188def92a5d2300bce56c01bbf17c77This study explores the integration of Artificial Intelligence (AI) in higher education, focusing on its implications for teaching and learning. With AI tools rapidly gaining traction, the research emphasises the necessity of developing proficient AI literacy skills among faculty and students. Employing focus groups and thematic network analysis, the study uncovers faculty and student perspectives on AI’s role in education, with both groups recognising its potential to positively impact all aspects of higher education, while also emphasising concerns about credibility and reliability of AI tool outputs, potential for bias, impact on academic integrity and assessment, as well as concerns about inclusivity. A significant outcome is the development of an AI capabilities matrix, tailored to align with the DigComp 2.2: The Digital Competence Framework for Citizens. Overall, it contributes to the discourse on AI's integration in higher education, setting a foundation for integration and further research on this topic.   10th International Conference on Higher Education Advances (HEAd’24)02This study uncovers faculty and student perspectives on AI’s role in education, with both groups recognising its potential to positively impact all aspects of higher education, while also emphasising concerns about credibility and reliability of AI tool outputs.10th International Conference on Higher Education Advances (HEAd’24)["Orla Daly", "Liam Fogarty", "E. Furlong", "Ernesto Vasquez del Aguila", "Rachel Farrell", "Sarah Morton", "Andrew Woods", "Ashley Bough", "Theresa Schilling", "Tara Redmond", "Dylan McKeever"]2024-06-18T00:00:00 e79b19d882ca63ce33d348a728465095f210dae7LCA is viewed as one of the important techniques to assess the environmental impacts of products, especially during the whole of the life cycle towards a circular economy approach. In the electronics sector, where products undergo rapid technological advancements and complex supply chains, LCA plays a crucial role in promoting sustainability and guiding decision-making processes. On the one hand, the environmental sustainability of electronics like ICT, Consumer Electronics, micro-electronics and Network Equipment is affected by the huge missing data because of the complexity, variety, large number of components, and otherwise different suppliers around the world. A more accurate, reliable, and standard LCA data management is necessary to decrease risks in the Supply Chain, increase transparency and provide sustainable and circular Business Models for Electronic Products and ICT Services. Meanwhile, artificial intelligence (AI), and particularly the advances in machine learning (ML) and deep learning (DL) have led to disruptive innovations in data modeling and other fields. To address this issue in the electronic industry, the integration of big data and artificial intelligence (AI) can transform precision assessment from data inventory to automated LCA modeling, and innovative methodologies for a cost-efficient data configuration. We provide a comprehensive overview of advances in the application of big data and AI technologies for LCA of electronics. We discuss key challenges in missing data and utilization for electronics and micro-electronics, offering strategic solutions. This novel AI-data approach helps organizations easily provide the missing data by integrating complex and disparate data sources across multiple distributed sources to give insights. Meanwhile, a dynamic big data discovery approach delivers more accurate and precious data for intelligent decisions. The intention is to provide the industry, LCA experts, and decision-makers with a more standardized, comprehensive, and reliable data ecosystem. (Abstract)Electronics Goes Green391A comprehensive overview of advances in the application of big data and AI technologies for LCA of electronics is provided to provide the industry, LCA experts, and decision-makers with a more standardized, comprehensive, and reliable data ecosystem.2024 Electronics Goes Green 2024+ (EGG)["Zahra Mehdipour"]2024-06-18T00:00:00 0e18192d28fcc53118bf8b6c8e0b34d6a6523052ChatGPT is a powerful Artificial Intelligence (AI) technology that has the potential to revolutionize the way we study in education. It can be used in a variety of ways and we, on the other hand, focused on senior pre-service teachers’ designing a mathematics lesson plan to implement in primary schools. Four voluntary participants attended, as half of which designed a mathematical task by asking AI while the rest used traditional methods in planning the task. In the next breath, participants implemented their lesson plans in a public school and collected the data relating to tasks they used. At the end, we compare the outcomes concerning the lesson plan and implementation in schools, and determine the strengths and weaknesses of each approach. This research offers valuable insights into using ChatGPT in teacher education to be used in practice and considering the teachers’ task designing role in classrooms with and without ChatGPT.10th International Conference on Higher Education Advances (HEAd’24)01This research focused on senior pre-service teachers’ designing a mathematics lesson plan to implement in primary schools and compared the outcomes concerning the lesson plan and implementation in schools, and determined the strengths and weaknesses of each approach.10th International Conference on Higher Education Advances (HEAd’24)["Ceyda Durmus"]2024-06-18T00:00:00 be44c17dec5f2c6574f84d774cba77d58f38e75bAI &amp; SOCIETY851It is argued that AI research currently falls back on self-regulatory, less participatory, and industry-led approaches that trouble ELS programs’ past achievements and hinder opportunities to overcome the still-existing challenges.AI &amp; SOCIETY["Maximilian Braun", "Ruth M\u00fcller"]2024-06-18T00:00:00 907f8cd45b5c37e79925f90f15a0e2e5894a7366The advent of Artificial Intelligence (AI) has heralded a transformative era in Computer Science, revolutionizing various facets of technology. This paper explores the profound impact of AI on the field of Computer Science, delving into its advancements, innovations, and the potential future trajectory of technology. The primary objective of this paper is to elucidate the significant role of AI in shaping the landscape of Computer Science. Through comprehensive research and analysis, it aims to provide insights into the evolution of AI, its applications, and the implications for future technological developments. This study employs a library research approach, gathering information from academic journals, conference proceedings, books, and reputable online sources. By synthesizing existing literature and scholarly works, it seeks to construct a comprehensive overview of the advancements and innovations in AI within the realm of Computer Science. The findings reveal a dynamic and rapidly evolving field driven by AI technologies. From machine learning algorithms to neural networks and deep learning models, AI has revolutionized data analysis, pattern recognition, and decision-making processes. Moreover, AI-driven applications such as natural language processing, computer vision, and robotics have reshaped various industries, including healthcare, finance, transportation, and manufacturing. However, alongside these advancements come ethical considerations, privacy concerns, and challenges related to algorithmic biases and societal implications. Looking ahead, the future of technology promises further integration of AI into various domains, leading to unprecedented opportunities and challenges in the realm of Computer ScienceJournal of Computational Science and Technology101The profound impact of AI on the field of Computer Science is explored, delving into its advancements, innovations, and the potential future trajectory of technology.TechComp Innovations: Journal of Computer Science and Technology["Achmad Varis Abdussalam", "Ghifari Alif Auladi"]2024-06-18T00:00:00 04a93f72a1a37fa7f2058050fb797b6b3b9d647bThe research focuses on transparency and education related to artificial intelligence (AI) in higher education, as well as how these factors affect student and academic staff acceptance of technology. The main objective of this study is to explore users' views and attitudes towards the use of AI, with a special emphasis on ethical and practical aspects. This study uses a mixed approach, combining quantitative surveys and qualitative interviews conducted at Superior University, Lahore, Pakistan. Quantitative data were analyzed using descriptive and inferential statistics, while thematic methods analyzed qualitative data. The study results showed that 75.2% of respondents supported education and transparency related to AI. 54.7% of respondents expressed concern that AI could threaten academic integrity if not used transparently and fairly. However, 45.3% of respondents see AI as an effective tool to detect plagiarism and improve academic supervision. The implications of this study underscore the importance of comprehensive education and transparency policies to ensure the ethical and practical use of AI in higher education. This research provides an empirical foundation for developing policies that can increase trust and acceptance of AI in academic contexts.Journal of Educational Management Research451This research provides an empirical foundation for developing policies that can increase trust and acceptance of AI in academic contexts and underscores the importance of comprehensive education and transparency policies to ensure the ethical and practical use of AI in higher education.Journal of Educational Management Research["Zohaib Hassan Sain", "Usman Shihu Laval"]2024-06-18T00:00:00 e4825bfc5728ff00e39fc0ea4f67a146f8d40bb3Science and Engineering Ethics720It is argued that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems, and is primarily a problem of attributability rather than of accountability.Science and Engineering Ethics["Jannik Zeiser"]2024-06-18T00:00:00 e2d89f2aaf27d53e50f7599ae58e6a928640eeddThe recent improvements of artificial intelligence (AI) technologies present a revolutionary opportunity for a wide variety of services. One of the most impactful AI technologies, gaining hundreds of millions of users in mere months, are conversational agents based on Large Language Models (LLMs). An obvious application example of conversational agents is found in gaming, where players often engage in conversation with non-player-characters (NPCs). The use of contemporary AI in this context is expected to bring unprecedented levels of adaptability and freedom of expression to in-game conversations, but may come with its own set of drawbacks, such as increased response latency, which may impair user experience during such interactions. In this paper we present the results of a user study investigating the impact of two different types of latency — time to first token and time per output token — on the overall Quality of Experience and player experience in a conversational task with an LLM-powered NPC in a text-based fantasy role-playing game. Conducted with a sample of fairly regular AI users (N=20), our study provides valuable insights regarding the possible threshold for noticeable latency for this type of service.International Workshop on Quality of Multimedia Experience170A user study investigating the impact of two different types of latency — time to first token and time per output token — on the overall Quality of Experience and player experience in a conversational task with an LLM-powered NPC in a text-based fantasy role-playing game.2024 16th International Conference on Quality of Multimedia Experience (QoMEX)["Nikolina Roso", "Sara Vlahovic", "Nenad Markus", "M. Su\u017enjevi\u0107"]2024-06-18T00:00:00 6675f950b5fa7de59203ca7ad92f2bba3e106ad7Introduction Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field. Aim This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings. Method The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments. Results The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students’ emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems. Conclusion This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.Frontiers in Psychology8813A comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings, showcases the significance of AI in revolutionizing educational practices through emotion assessment.Frontiers in Psychology["Angel Olider Rojas Vistorte", "Angel Deroncele-Acosta", "Juan Luis Mart\u00edn Ayala", "Angel Barrasa", "C. L\u00f3pez-Granero", "Mariacarla Mart\u00ed-Gonz\u00e1lez"]2024-06-19T00:00:00 2918c5384ca86484e26958291d0561c6608ffaf7Abstract. Accelerated progress in climate modeling is urgently needed for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet climatically important, such as turbulence and cloud formation. These processes will not be explicitly resolvable for the foreseeable future, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary artificial intelligence (AI)-based methods to model subgrid-scale processes. This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. In addition, increasing the resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distribution. However, currently feasible horizontal resolutions are limited to O(10 km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. Atmospheric Chemistry and Physics1699This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws, to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions.Atmospheric Chemistry and Physics["Tapio Schneider", "L. Leung", "Robert C. J. Wills"]2024-06-19T00:00:00 546379e3aeffb76964a179b7ec218ec99e09dcd8Journal of Hospitality Marketing &amp; Management6310Journal of Hospitality Marketing &amp; Management["Mesut Cicek", "D. Gursoy", "Lu Lu"]2024-06-19T00:00:00 4cb460a350f81d7b9ff5587486eedbe306a0bab0Artificial Intelligence is a term used frequently in academic and other writing, but do we have a clear understanding of what it means? This article starts from first principles, taking a dialectic approach, to raise questions rather than give prescriptive answers. It unpacks some specific examples of the use of AI in journalism and automated approaches to news reporting. The manipulation of media has become commonplace and of greater interest as information itself can be used as an effective weapon to sow confusion and disruption, socially as well as politically. AI depends on the training data and modelling, but the sampling and engineering is done by humans with all the potential for bias, whether intentional or not. Biased datasets and the potential for uncertainty are constant dangers; we need to understand both the data and the processes that go into the AI-driven results, and always be prepared to question everything.Journalism173This article unpacks some specific examples of the use of AI in journalism and automated approaches to news reporting, taking a dialectic approach, to raise questions rather than give prescriptive answers.Journalism["Simon Mahony", "Qing Chen"]2024-06-19T00:00:00 ddfa3a252ee6408e09d072583af7f20c9c38410bThis study investigated the usefulness of Artificial Intelligence (AI) in record-keeping in libraries. The objectives of the study were to analyse current trends in AI applications for record-keeping in libraries, evaluate the effectiveness of AI in protecting library records from physical and digital threats, explore the impact of AI on the efficiency and accuracy of record management in libraries, and identify potential factors that may limit the implementation of AI in library systems. Using a qualitative research approach, the study reviewed existing literature and case studies to assess AI’s contributions and limitations in library settings. The literature search was conducted using three major academic databases: Google Scholar, ResearchGate, and Emerald. These databases were selected based on their comprehensive coverage of scholarly articles, ease of access, and relevance to the fields of information science, library science, and technology. The findings revealed that AI significantly improves the automation of cataloguing and metadata management, thus reducing human error and increasing operational efficiency. AI also enhances the preservation of both digital and physical records through real-time monitoring and automated repair solutions. Additionally, AI-powered search engines provide more relevant and accurate search results by leveraging natural language processing and semantic search capabilities. However, the study also highlights challenges such as data quality issues, data privacy, biases in AI algorithms, and staff and user resistance. The policy implications include the necessity for funding and regulatory support, while practical implications involve the adoption of AI tools and staff training. For librarianship, adapting to new AI technologies and advocating for ethical AI use are essential.Southern African Journal of Security113The findings revealed that AI significantly improves the automation of cataloguing and metadata management, thus reducing human error and increasing operational efficiency, and AI-powered search engines provide more relevant and accurate search results.Southern African Journal of Security["Onome Osagie", "B. Oladokun"]2024-06-19T00:00:00 b43397ab7a9c33608fe21aa36e157bd01be920aaBACKGROUND The increasing deployment of conversational artificial intelligence (AI) in mental health interventions necessitates an evaluation of their efficacy in rectifying cognitive biases and recognizing affect in human-AI interactions. These biases are particularly relevant in mental health contexts as they can exacerbate conditions such as depression and anxiety by reinforcing maladaptive thought patterns or unrealistic expectations in human-AI interactions. OBJECTIVE This study aimed to assess the effectiveness of therapeutic chatbots (Wysa and Youper) versus general-purpose language models (GPT-3.5, GPT-4, and Gemini Pro) in identifying and rectifying cognitive biases and recognizing affect in user interactions. METHODS This study used constructed case scenarios simulating typical user-bot interactions to examine how effectively chatbots address selected cognitive biases. The cognitive biases assessed included theory-of-mind biases (anthropomorphism, overtrust, and attribution) and autonomy biases (illusion of control, fundamental attribution error, and just-world hypothesis). Each chatbot response was evaluated based on accuracy, therapeutic quality, and adherence to cognitive behavioral therapy principles using an ordinal scale to ensure consistency in scoring. To enhance reliability, responses underwent a double review process by 2 cognitive scientists, followed by a secondary review by a clinical psychologist specializing in cognitive behavioral therapy, ensuring a robust assessment across interdisciplinary perspectives. RESULTS This study revealed that general-purpose chatbots outperformed therapeutic chatbots in rectifying cognitive biases, particularly in overtrust bias, fundamental attribution error, and just-world hypothesis. GPT-4 achieved the highest scores across all biases, whereas the therapeutic bot Wysa scored the lowest. Notably, general-purpose bots showed more consistent accuracy and adaptability in recognizing and addressing bias-related cues across different contexts, suggesting a broader flexibility in handling complex cognitive patterns. In addition, in affect recognition tasks, general-purpose chatbots not only excelled but also demonstrated quicker adaptation to subtle emotional nuances, outperforming therapeutic bots in 67% (4/6) of the tested biases. CONCLUSIONS This study shows that, while therapeutic chatbots hold promise for mental health support and cognitive bias intervention, their current capabilities are limited. Addressing cognitive biases in AI-human interactions requires systems that can both rectify and analyze biases as integral to human cognition, promoting precision and simulating empathy. The findings reveal the need for improved simulated emotional intelligence in chatbot design to provide adaptive, personalized responses that reduce overreliance and encourage independent coping skills. Future research should focus on enhancing affective response mechanisms and addressing ethical concerns such as bias mitigation and data privacy to ensure safe, effective AI-based mental health support.JMIR Mental Health583This study shows that, while therapeutic chatbots hold promise for mental health support and cognitive bias intervention, their current capabilities are limited, and reveals the need for improved simulated emotional intelligence in chatbot design to provide adaptive, personalized responses that reduce overreliance and encourage independent coping skills.JMIR mental health["Marcin Rzadeczka", "Anna Sterna", "Julia Stoli'nska", "Paulina Kaczy'nska", "M. Moskalewicz"]2024-06-19T00:00:00 04fcec01d67f999e2e6000c0daf2202725c2b9efPurpose The purpose of this study is to explore the increased uptake of Artificial Intelligence (AI) technology by Facility Management (FM) organizations for enhanced operational efficiency and competitive advantage. While AI adoption in FM has been widely reported, limited attempts have been made to assess its impact on the social well-being of FM employees. To contribute towards addressing this gap, this study established the essential employee social well-being factors mostly impacted by the adoption of AI in South African FM organizations. Design/methodology/approach A four-stage design comprising a comprehensive review of literature, expert interviews, questionnaire census and focus group discussion sessions was used to elicit data from a sample of participants drawn from 22 South African FM organizations. The data was analyzed using a combination of content analysis, relative importance index and interpretative structural modeling for various data sets toward achieving the study’s objectives. Findings Sixteen employee social well-being factors, classified under job satisfaction, social relationship and knowledge development categories, respectively, were identified as being impacted by AI adoption in FM organizations. Furthermore, it was established that job security, job autonomy and professional status, which belong to the job satisfaction social well-being factor category, were deemed by FM employees as being mostly impacted by AI adoption. Practical implications The enhanced understanding of the impact of AI adoption on FM employees’ social well-being factors will contribute to the development of a collaborative intelligence framework for managing AI adoption in FM organizations toward engendering optimal AI–FM employee relationships for improved productivity. Originality/value Besides being one of the foremost studies to investigate the impact of AI adoption on FM employees’ social well-being, this study introduces a hierarchical framework of understanding employee social well-being factors based on multi-stakeholder perspectives. Journal of Corporate Real Estate671Journal of Corporate Real Estate["A. Moghayedi", "Kathy Michell", "B. Awuzie", "U. J. Adama"]2024-06-19T00:00:00 4bf747dd854d2a737ea65429762d2ba04d426ef5The integration of Artificial Intelligence (AI) in the banking sector represents a significant leap forward in the realms of risk management and fraud detection. This paper explores the transformative effects of AI in these areas, emphasizing both the improvements and the challenges brought about by its implementation. In risk management, AI's influence is diverse and profound. Advanced algorithms allow for the creation of more sophisticated credit risk assessment models by detecting subtle patterns in large datasets that might be overlooked by human analysts. This capability enhances the accuracy of credit risk evaluations. Additionally, real-time monitoring of transactions helps in the immediate mitigation of risks, which is particularly crucial when dealing with market and liquidity risks. AI also significantly aids in automating compliance with regulatory requirements, reducing the likelihood of human errors and enabling quicker adaptation to changes in regulations. Operational risks are also minimized through AI's ability to automate routine tasks and strengthen cybersecurity measures. AI systems are adept at identifying anomalies that may indicate fraud by scrutinizing transaction data and customer behavior. The predictive capabilities of AI enable banks to anticipate and prevent potential fraud schemes. Moreover, AI systems can adapt and evolve in response to changing tactics used by fraudsters, maintaining their effectiveness over time. AI enhances customer authentication processes through the use of advanced technologies such as biometric verification, providing an additional layer of security. However, the implementation of AI in banking raises significant concerns regarding data privacy and security due to the sensitive nature of banking information. Furthermore, AI models can inherently carry biases that lead to discriminatory outcomes, necessitating ongoing monitoring and adjustments to these models. The complexity and lack of transparency in AI systems also pose challenges, particularly when AI-driven decisions have significant impacts on customers. The evolving regulatory frameworks for AI in banking present another layer of complexity, as banks must continuously adapt to comply with new and changing regulations. This paper highlights the need for a balanced approach to leveraging AI's potential in banking, addressing both its transformative benefits and the ethical and regulatory challenges involved. By doing so, banks can harness AI to enhance their operations while ensuring fairness, transparency, and compliance with regulatory standards.International journal of applied and natural sciences72The need for a balanced approach to leveraging AI's potential in banking is highlighted, addressing both its transformative benefits and the ethical and regulatory challenges involved by ensuring fairness, transparency, and compliance with regulatory standards.International Journal of Applied and Natural Sciences["Tanni Majumder"]2024-06-19T00:00:00 609b9d73ac0956bd762c21233fa4e8c1873f36f9This study models milk product processing and sustainable of the shelf-life extension in a dairy factory using artificial intelligence. The Cappadocia dairy factory was used to study chemical processes and computational system modeling and simulation. Levenberg–Marquardt algorithm was used to create an artificial neural network model from real-time data. An AI-based method utilizing a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was employed to precisely analyze productivity data in dairy factories. There are 9 product types and production quantities used as input parameters, and 90 datasets of actual dairy products used as output values. The model was trained using the Levenberg–Marquardt algorithm on 62 datasets for training, 14 for validation, and 14 for testing. The accuracy of the model is affected by the optimal data segmentation. The model showed how AI algorithms can improve processes and industrial production by increasing dairy production efficiency from 20 to 40%. Model efficiency values were compared to observed values to determine prediction accuracy. Model mean squared error was 4.02E-06, and coefficient of determination was 0.99984. Model efficiency predictions and observed values differed by −0.04% on average. This study investigated using artificial intelligence to optimize salvage processes and systems to increase energy efficiency and reduce environmental impact. The results show that a neural network model trained with real data can predict dairy plant productivity.Frontiers in Sustainable Food Systems331The results show that a neural network model trained with real data can predict dairy plant productivity, and shows how AI algorithms can improve processes and industrial production by increasing dairy production efficiency from 20 to 40%.Frontiers in Sustainable Food Systems["Oznur Oztuna Taner", "A. B. \u00c7olak"]2024-06-19T00:00:00 e751c7f65568bbb3bb965255a8d44aa0e0fb1570BACKGROUND In recent years, artificial intelligence (AI) technology has been continuously advancing and finding extensive applications, with one of its core technologies, machine learning, being increasingly utilized in the field of healthcare. OBJECTIVE This research aims to explore the role of Artificial Intelligence (AI) technology in psychological counseling and utilize machine learning algorithms to predict counseling outcomes. METHODS Firstly, by employing natural language processing techniques to analyze user conversations with AI chatbots, researchers can gain insights into the psychological states and needs of users during the counseling process. This involves detailed analysis using text analysis, sentiment analysis, and other relevant techniques. Subsequently, machine learning algorithms are used to establish predictive models that forecast counseling outcomes and user satisfaction based on data such as user language, emotions, and behavior. These predictive results can assist counselors or AI chatbots in adjusting counseling strategies, thereby enhancing counseling effectiveness and user experience. Additionally, this study explores the potential and prospects of AI technology in the field of psychological counseling. RESULTS The research findings indicate that the designed machine learning models achieve an accuracy rate of approximately 89% in analyzing psychological conditions. This demonstrates significant innovation and breakthroughs in AI technology. Consequently, AI technology will gradually become a highly important tool and method in the field of psychological counseling. CONCLUSION In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services. The results of this research provide valuable technical insights for further improving AI-supported psychological counseling, contributing positively to the application and development of AI technology.Technology and Health Care261In the future, AI chatbots will become more intelligent and personalized, providing users with precise, efficient, and convenient psychological counseling services, contributing positively to the application and development of AI technology.Technology and health care : official journal of the European Society for Engineering and Medicine["Yuxia Ping"]2024-06-19T00:00:00 3fc84dd370747994651a31ac6456da5dcd005ecfThe paper explores some of the discussion points regarding the determination of the subject of administrative liability for committing offenses in the field of artificial intelligence-equipped vehicle operation. It is noted that legal regulation in the field of transportation safety, taking into account the emergence of electric vehicles, is actively evolving. However, the regulation of administrative liability for offenses in the field of vehicle operation with elements of artificial intelligence has not yet been reflected in current legislation. Based on the analysis conducted, the author draws several conclusions. Particularly, it is determined that there is a pressing need for regulating public relations considering the use of technologies with elements of artificial intelligence, especially in public legal relations, including administrative and criminal law. Presently, there are no specific provisions in modern administrative law dedicated to formulating offenses with regard to the use (operation) of technologies, including vehicles with elements of artificial intelligence. However, the author believes that this gap will be addressed by the legislator in the near future. Analysis of scientific literature led to the conclusion that the subject of administrative liability in this field should be differentiated based on the future composition of administrative offenses proposed by the legislator, as well as depending on the factual circumstances.Общество политика экономика право00Analysis of scientific literature led to the conclusion that the subject of administrative liability in this field should be differentiated based on the future composition of administrative offenses proposed by the legislator, as well as depending on the factual circumstances.Общество: политика, экономика, право["Dmitry O. Lunev"]2024-06-19T00:00:00 d38317482740518a75be196102452d8b74bfec62In  the  context  of  digitalization  of  educational  processes,  an  urgent  need  to  change approaches to teaching foreign languages in the training of highly qualified personnel  – future re-searchers has been growing. The focus of this article is to study and determine the state of the skill of interaction with digital tools, systems and programs of artificial intelligence in postgraduate students of technical fields. The study revealed a conflict between the growing importance of publications in English, and a general trend among learners to reduce the need to read scientific literature in English, which affects not only reading skills, but also affects scientific reading practices. The potential for resolving the identified contradiction lies in the development of pedagogical and methodological techniques  focused  on  the  inclusion  of  modern  digital  tools  in  the  educational  process  in  order  to develop higher-order cognitive skills in graduate students and optimize research processes related to working with English-language scientific literature. A concept of a course for teaching graduate students a foreign language using digital tools based on neural networks is proposed.Vysshee Obrazovanie v Rossii = Higher Education in Russia210A conflict between the growing importance of publications in English, and a general trend among learners to reduce the need to read scientific literature in English is revealed, which affects not only reading skills, but also affects scientific reading practices.Vysshee Obrazovanie v Rossii = Higher Education in Russia["T. V. Potemkina", "Yu. A. Avdeeva", "U. Y. Ivanova"]2024-06-19T00:00:00 1e9c53ab84f684c3735e530a307a5701b38aa785PurposeThe study aims to synthesize existing knowledge and proposes a research framework for building a resilient supply chain (SC) through artificial intelligence (AI) technology. It also identifies existing literature gaps and paves the way for a future research agenda.Design/methodology/approachA systematic literature review has been carried out to identify the peer-reviewed articles from Scopus and Web of Science databases. Then, the selected articles published between 2012 and 2023 are analyzed using descriptive and thematic analysis methods to unearth research gaps and offer new research directions.FindingsDescriptive and thematic analysis reveals the overall development of literature on the role of AI for supply chain resilience (SCR). Based on the findings of the thematic analysis, the motivation, application, capability and outcome (MACO) framework has been developed and propositions have been proposed. Several future research directions have also been suggested in terms of theory, context and methodology (TCM).Practical implicationsThe study provides a fresh perspective on the integration of AI technology within the realm of SCR. The developed MACO framework serves as a practical tool for supply chain management (SCM) professionals, offering a nuanced understanding of AI's applications across various functional areas to streamline operations, minimize waste and optimize resource utilization, thereby helping them in strategic planning.Originality/valueThis study contributes to the literature on the role of AI for building SCR by uncovering gaps, offering research directions and developing propositions for future research directions.Journal of Enterprise Information Management600The developed MACO framework serves as a practical tool for supply chain management (SCM) professionals, offering a nuanced understanding of AI's applications across various functional areas to streamline operations, minimize waste and optimize resource utilization, thereby helping them in strategic planning.J. Enterp. Inf. Manag.["Laxmi Pandit Vishwakarma", "R. Singh", "Ruchi Mishra", "Mani Venkatesh"]2024-06-19T00:00:00 5551644fa31ef9da469699d26d1d3a01373d88f3Artificial Intelligence dan Internet of Things (disebut AIOT) telah banyak digunakan oleh berbagai aktivitas bahkan terutama pada generasi milenial. Akan tetapi teknologi keilmuan didalamnya belum banyak diperkenalkan pada dunia pendidikan. Oleh karena itu diharapkan adanya nilai tambah yang baru bagi mitra sasaran berupa pembelajaran inovasi. Pembelajaran inovasi ditunjukkan dengan memberikan pembelajaran coding yang tidak pernah dilakukan oleh siswa agar AIOT menjadi bagian pembelajaran. Pada artikel ini ditunjukkan bagaimana coding sebagai pembelajaran yang perlu diperkenalkan kepada para siswa tingkat menengah pertama untuk dapat mengenal AIOT secara dini. Metode yang dilakukan adalah dengan memperkenalkan perangkat yang disebut AIOT-kit sebagai perangkat yang dibuat oleh para siswa dengan pelatihan untuk dapat melakukan monitoring secara langsung terhadap parameter lingkungan seperti suhu dan kelembaban. Untuk dapat melakukan hal itu, diperkenalkan Internet of Things (IoT) yang mempergunakan ThinkSpeak sebagai dashboard untuk melakukan pengamatan. Perangkat ini dibuat oleh siswa agar dapat mengikuti proses dari pembuatan hardware AIOT-kit dan coding terkait hingga pemanfaatan.  Kegiatan ini merupakan bagian dari upaya tim pengabdian untuk memberikan kontribusi positif bagi masyarakat dan lingkungan sekolah. Setelah dilakukan kegiatan ini, terjadi perubahan bagaimana para siswa dapat membuat perangkat AIOT-kit sendiri sekaligus melakukan coding. Bahkan sekolah mendapat penghargaan dari pemerintah setempat atas kegiatan inovasi yang dilakukan dalam periode tersebut.Jurnal Abdi Insani160Jurnal Abdi Insani["S. Trihandaru", "H. A. Parhusip", "Johanes Dian Kurniawan", "B. Susanto", "A. Setiawan", "D. Nugroho"]2024-06-19T00:00:00 52ca86e8faed800a2f4f9c4740a963a902c94975This paper explores the transformative potential of Explainable Artificial Intelligence (XAI) in the context of coffee quality assessment, an area traditionally governed by subjective evaluation. By applying machine learning models, specifically a Random Forest Classifier enhanced by SHAP (SHapley Additive exPlanations) values, we identified crucial determinants of coffee quality, such as Category Two defects and high-altitude growth conditions. Our study demonstrates that machine learning can not only match but potentially exceed the accuracy of human experts in predicting coffee quality. More importantly, XAI has provided these models with a layer of transparency, making their complex predictions accessible and actionable for stakeholders in the coffee industry. This integration of AI into coffee quality assessment promises to standardize and optimize the evaluation process, offering a reliable guide for improving practices across the production chain. The findings underscore the broader impact of AI in agriculture, suggesting that such technology could be a harbinger of increased efficiency, sustainability, and trust in food production systems worldwide.International Joint Conference on Computer Science and Software Engineering330This study demonstrates that machine learning can not only match but potentially exceed the accuracy of human experts in predicting coffee quality, and integration of AI into coffee quality assessment promises to standardize and optimize the evaluation process, offering a reliable guide for improving practices across the production chain.2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)["Khamsing Sermmany", "Panupong Wanjantuk", "W. Leelapatra"]2024-06-19T00:00:00 879420dd131cd83299fb7651633b385660f8bfb5Artificial Intelligence (AI) menjadi media pembelajaran yang urgen dikuasai oleh guru. Oleh karena itu, kegiatan ini bertujuan untuk menerapkan teknologi AI dalam rangka meningkatkan kompetensi guru Madrasah Aliyah (MA) dalam menyusun perangkat pembelajaran. Sebanyak 20 orang guru MA menjadi peserta dalam pelatihan ini, di mana mereka diperkenalkan dengan berbagai alat AI, yaitu ChatGPT, Gemini, dan Perplexity. Metode pelatihan melibatkan pemberian materi teoretis dan praktis tentang penggunaan AI untuk mendukung proses penyusunan perangkat pembelajaran. Hasil evaluasi menunjukkan bahwa peserta memberikan respon positif dengan nilai rata-rata sebesar 85,3. Meskipun demikian, peserta mengungkapkan bahwa durasi pelatihan dirasa belum cukup untuk mendalami materi secara optimal. Selain itu, kendala seperti lemahnya jaringan internet sempat mengganggu jalannya pelatihan, namun berhasil diatasi dengan berbagi jaringan dari telepon seluler. Temuan ini mengindikasikan bahwa untuk mencapai hasil yang lebih maksimal, diperlukan penambahan durasi pelatihan dan pengayaan materi dengan lebih banyak tema praktis. Dengan demikian, diharapkan penerapan AI dapat lebih efektif dalam meningkatkan kompetensi guru dalam menyusun perangkat pembelajaran yang inovatif dan efektif. Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora180Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora["Vera Mandailina", "Syaharuddin Syaharuddin", "Abdillah Abdillah"]2024-06-19T00:00:00 777efb9004fd92cf222a46145373cdfc147360a6This paper discusses the challenges and opportunities of regulating artificial intelligence (AI) and its implications for copyright, with a focus on the Indonesian context. Internationally, the United States and the European Union have begun to develop AI regulations, although they are not yet fully comprehensive. Indonesia, while lacking detailed regulations, has developed a National Strategy for Artificial Intelligence 2020-2045 to guide the development of this technology. Key challenges include regulatory ambiguity, insufficient protection of personal data, and ethical issues. Copyright in Indonesia, under Law No. 28 of 2014, provides automatic protection for works that are original and embodied in tangible form. In the context of works created by AI, there are two views: first, AI works cannot be copyrighted because they lack personal characteristics and human creative process; second, AI works can be copyrighted because of their ability to create complex works and as an incentive for AI users. This article concludes that the government needs to rewrite copyright law to accommodate AI works, given their potential as a significant source of innovative and creative works.International journal of social science and human research00It is concluded that the government needs to rewrite copyright law to accommodate AI works, given their potential as a significant source of innovative and creative works.International Journal of Social Science and Human Research["Muhammad Najiib Al Fithri", "Ery Agus Priyono"]2024-06-19T00:00:00 b3877996d71a1e7d88b72dec6297ac39a1881b61Artificial Intelligence (AI) with its increasingly complex development currently seems to increase complications, especially when it comes to violations of the law, especially regarding copyright of works created by AI systems or. So that the clarity of the legal responsibility for problems or conflicts that then arise needs to be clarified in the eyes of the law. This study is a qualitative study with a normative legal approach, and the data analysis method used is qualitative juridical. The results of the study show that based on the laws and regulations in Indonesia, namely Law No. 28 of 2014 concerning Copyright and Law No. 12 of 2016 concerning Patents, it states that creators and copyright holders as well as inventors and patent holders are one or several people, meaning that those who have the right to hold copyright and patent rights are humans as legal subjects (persons or legal entities). Then because AI is a system, AI is not included in the legal subject but AI is a human-made product and functions as a tool to create a work. However, formulating clear and fair policies and regulations regarding civil rights and legal responsibility for the results of AI creations needs to be done by the government. Legal certainty in this case will encourage the development of responsible AI technology and provide adequate protection for all parties involved. Then AI is known as an electronic system and electronic agent that operates based on human commands. Therefore, if an unlawful act or action occurs, then the legal responsibility is borne by the creator and user of AI who gives the commands and parameters.Jurnal AL-MAQASID: Jurnal Ilmu Kesyariahan dan Keperdataan140The results of the study show that formulating clear and fair policies and regulations regarding civil rights and legal responsibility for the results of AI creations needs to be done by the government.Jurnal AL-MAQASID: Jurnal Ilmu Kesyariahan dan Keperdataan["Yenni Batubara", "Husni Ismail"]2024-06-19T00:00:00 a101ad8fa5c43d40acf62b60b01bd467bdc9d783 In today's world, automation and optimization of work processes are becoming key success factors. This work examines the combination of automation systems and artificial intelligence (AI) and their impact on the optimization of work processes. The technology of integration into the process automation system and learning of a large language model for the automated creation of macros using the example of the author's software "Draw & GO" has been developed and proposed. Systems and Technologies00The technology of integration into the process automation system and learning of a large language model for the automated creation of macros using the example of the author's software "Draw & GO" has been developed and proposed.System technologies["V. Antonyuk", "Maryna Sydorova"]2024-06-19T00:00:00 69854184188ec2120e546052ac44d56177cd6cdaThis perspective piece will consider utilising the concepts of positive psychology, specifically in terms of relationships through the application of artificial intelligence (AI) for therapeutic intervention. We will provide an overview of positive psychology, consider how this can be applied in the therapeutic setting and how AI, and more currently artificial wisdom, can support this application.Through an overview of existing research concerning the use of AI in therapeutic environments, it is evident that research is looking into this area with the prospect of supporting a more diverse client base, and it is an area therapists need to be aware of, and potential training in.The paper considers the future use of AI and artificial wisdom, providing potential insights into the direction of research in this area.Counselling and Psychotherapy Research70The paper considers the future use of AI and artificial wisdom, providing potential insights into the direction of research in this area.Counselling and Psychotherapy Research["Julie Prescott", "Steven Barnes"]2024-06-19T00:00:00 15d80b943144fc62c06e2ebd9bc27a1c51afe529Artificial intelligence (AI) is reshaping health care, including nursing, across Asia, presenting opportunities to improve patient care and outcomes. This viewpoint presents our perspective and interpretation of the current AI landscape, acknowledging its evolution driven by enhanced processing capabilities, extensive data sets, and refined algorithms. Notable applications in countries such as Singapore, South Korea, Japan, and China showcase the integration of AI-powered technologies such as chatbots, virtual assistants, data mining, and automated risk assessment systems. This paper further explores the transformative impact of AI on nursing education, emphasizing personalized learning, adaptive approaches, and AI-enriched simulation tools, and discusses the opportunities and challenges of these developments. We argue for the harmonious coexistence of traditional nursing values with AI innovations, marking a significant stride toward a promising health care future in Asia.Asian/Pacific Island Nursing Journal430It is argued for the harmonious coexistence of traditional nursing values with AI innovations, marking a significant stride toward a promising health care future in Asia.Asian/Pacific Island Nursing Journal["Nada Lukkahatai", "Gyumin Han"]2024-06-19T00:00:00 93c5e4f7401b8dcb67d35c8fe47d843778a582d0In the last decade, artificial intelligence (AI) has been increasingly used in various fields of medicine. Recently, the advent of whole slide images (WSI) or digitized slides has paved the way for AI-based anatomic pathology. This paper set out to review the potential integration of AI algorithms in the workflow, and the utilization of AI in the practice of breast pathology.Philippine Journal of Pathology480The potential integration of AI algorithms in the workflow, and the utilization of AI in the practice of breast pathology are reviewed.Philippine Journal of Pathology["Michael Baclig"]2024-06-19T00:00:00 18128009c7d937db7d97f5fea1141a04b134d354Artificial intelligence (AI) transforms workplaces by streamlining operations, automating tasks, and enhancing decision-making. To bridge the knowledge gap in AI best practices, a workshop was created for executives, integrating change management principles. The workshop aimed to help participants understand AI's role, use AI tools for predictive analytics, and develop strategies for leveraging AI in change initiatives. This paper outlines the workshop's impact on building confidence, knowledge, and positive attitudes towards AI in the workplace.Journal of Computer Science Research90The workshop aimed to help participants understand AI's role, use AI tools for predictive analytics, and develop strategies for leveraging AI in change initiatives, outlines its impact on building confidence, knowledge, and positive attitudes towards AI in the workplace.Journal of Computer Science Research["Shuo Xu"]2024-06-19T00:00:00 6cae3bc4935f87ca9d2ba4c5c8ecd686ef4ca4bfCurrent Issues in Tourism73Current Issues in Tourism["P. Christou"]2024-06-19T00:00:00 64829b294778e290ff0b1a9bbf35534dff15ba09Aesthetic Plastic Surgery383This study evaluated the feasibility of using large language models for answering perioperative queries using OpenAI's ChatGPT-3.5, Gemini, Claude, Claude, and Bing's CoPilot, finding differences in readability and reliability.Aesthetic Plastic Surgery["Bryan Lim", "Ishith Seth", "R. Cuomo", "P. S. Kenney", "Richard J. Ross", "Foti Sofiadellis", "P. Pentangelo", "Alessandra Ceccaroni", "Carmine Alfano", "W. Rozen"]2024-06-19T00:00:00 da4168decfd83f3a31ed7a47cfa366ac63df9101[This corrects the article DOI: 10.1093/biosci/biae016.].BioScience00Bioscience[]2024-06-19T00:00:00 4654c050b0e8316777ca00382eecf2542179d52aImage preprocessing models are usually employed as the preceding operations of high‐level vision tasks to improve the performance. The adversarial attack technology makes both these models face severe challenges. Prior research is focused solely on attacking single object detection models, without considering the impact of the preprocessing models (multifocus image fusion) on adversarial perturbations within the object detection system. Multifocus image fusion models work in conjunction with the object detection models to enhance the quality of the images and improve the capability of object detection system. Herein, the problem of attacking object detection system that utilizes multifocus image fusion as its preprocessing models is addressed. To retain the attack capabilities of adversarial samples against as many perturbations as possible, new attack method called adaptive retention attack (ARA) is proposed. Additionally, adversarial perturbations concentration mechanism and image selection mechanism, which, respectively, enhance the transferability and attack capability of ARA‐generated adversarial samples. Extensive experiments have demonstrated the feasibility of the ARA. The results confirm that the ARA method can successfully bypass multifocus image fusion models to attack the object detection model.Advanced Intelligent Systems90The ARA method can successfully bypass multifocus image fusion models to attack the object detection model and enhance the transferability and attack capability of ARA‐generated adversarial samples.Adv. Intell. Syst.["Xueshuai Gao", "Xin Jin", "Shengfa Miao", "Qian Jiang", "Yunyun Dong", "Wei Zhou", "Shao-qing Yao"]2024-06-19T00:00:00 ed16709e6abe1aeaeecfa9e3c1123832bc258921Knee Surgery, Sports Traumatology, Arthroscopy100Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA["Emilio Calvo", "Elena T Calvo"]2024-06-19T00:00:00 a23ff6c080fc38bbeee5906722213d1ebf403d82Artificial Intelligence Review00Artif. Intell. Rev.["A. C. Pushkaran", "Alya A. Arabi"]2024-06-19T00:00:00 0412b899c9249f7005d9f412e9947770516ff9e3Goethe Yearbook00Goethe Yearbook["Matthew Childs"]2024-06-19T00:00:00 d17fdaabe8dd26c0e931470846777ecf4e394df5Journal of public health20Journal of public health["Al Tyler B Octaviano", "Michael Bernard G Baac", "Tatiana Belle S Selso", "Edrick Andrew V Estoya", "Zussette Mae E Buena", "Hannah Kirsten L Hinlo", "Leigh Andrew M Gernale", "Shenci A Hesido", "N. Pacaol"]2024-06-19T00:00:00 c06ec82b1ffb4f3403152ba5a81266959f9143fbNature Biomedical Engineering399A digital pathology framework, nuclei.io, that incorporates active learning and human-in-the-loop real-time feedback for the rapid creation of diverse datasets and models is described that leveraged collaboration between clinicians and AI to enhance accuracies and efficiencies.Nature biomedical engineering["Zhi Huang", "Eric Yang", "Jeanne Shen", "D. Gratzinger", "Frederick Eyerer", "Brooke Liang", "Jeffrey J. Nirschl", "David B. Bingham", "A. Dussaq", "Christian Kunder", "Rebecca Rojansky", "Aubre Gilbert", "Alexandra L Chang-Graham", "Brooke E Howitt", "Ying Liu", "Emily E. Ryan", "Troy B Tenney", "Xiaoming Zhang", "A. Folkins", "E. Fox", "K. Montine", "T. Montine", "James Y. Zou"]2024-06-19T00:00:00 d8a68d8935ee259e06a52f737ba745d7d0439e36Generative, multimodal artificial intelligence (GenAI) offers transformative potential across industries, but its misuse poses significant risks. Prior research has shed light on the potential of advanced AI systems to be exploited for malicious purposes. However, we still lack a concrete understanding of how GenAI models are specifically exploited or abused in practice, including the tactics employed to inflict harm. In this paper, we present a taxonomy of GenAI misuse tactics, informed by existing academic literature and a qualitative analysis of approximately 200 observed incidents of misuse reported between January 2023 and March 2024. Through this analysis, we illuminate key and novel patterns in misuse during this time period, including potential motivations, strategies, and how attackers leverage and abuse system capabilities across modalities (e.g. image, text, audio, video) in the wild.arXiv.org518A taxonomy of GenAI misuse tactics is presented, informed by existing academic literature and a qualitative analysis of approximately 200 observed incidents of misuse reported between January 2023 and March 2024.ArXiv["Nahema Marchal", "Rachel Xu", "Rasmi Elasmar", "Iason Gabriel", "Beth Goldberg", "William Isaac"]2024-06-19T00:00:00 52f0613b6cfb969c1b629c243879c081a58ab70cPurpose This paper aims to empirically examine the impact of psychological factors (i.e. privacy and intrusiveness concerns) on user intentions regarding artificial intelligence (AI)-enabled social commerce applications at their core through perceived usefulness. The theoretical model is supported by the theory of planned behaviour (TPB). Design/methodology/approach Data was gathered from 488 social media users in Saudi Arabia. Findings Privacy concerns significantly affect perceived usefulness. Furthermore, the link between privacy concerns and behavioural intentions was mediated by perceived usefulness. Research limitations/implications Business leaders should raise users’ awareness about the effectiveness of AI-powered tools that can influence their behavioural intentions. Furthermore, managers must be aware of the regulations that protect user privacy, track online activity and offer secure communication channels. Originality/value This paper expands on TPB by bridging the theoretical and practical divide. It further develops a theoretical framework for practitioners to better understand customers’ physiological aspects of using AI-powered social commerce platforms. Spanish Journal of Marketing - ESIC535A theoretical framework for practitioners to better understand customers’ physiological aspects of using AI-powered social commerce platforms is developed and the link between privacy concerns and behavioural intentions was mediated by perceived usefulness.Spanish Journal of Marketing - ESIC["Muhammad Haroon Shoukat", "Islam Elgammal", "Kareem M. Selem", "A. Shehata"]2024-06-19T00:00:00 27b046643c8739bfcee8595871639e821de082a9Google Cloud Platform (GCP) has emerged as a leader in Machine Learning (ML) and Artificial Intelligence (AI), known for its cutting-edge technologies and inclusive accessibility. GCP not only drives innovation but also democratizes access to powerful ML and AI tools, empowering organizations of all sizes to harness data-driven insights for enhanced innovation, efficiency, and scalable growth. GCP's impact transcends technological advancements, representing a significant shift in digital transformation across diverse industries. This paper delves into GCP's transformative influence through real-world examples and practical applications across sectors such as healthcare, finance, retail, and entertainment. By showcasing GCP's scalable computing resources and robust data analytics capabilities, it illuminates how these technologies enable businesses to discover new opportunities and operational efficiencies. GCP's holistic approach to ML and AI fosters a culture of continuous innovation, empowering enterprises to excel in the era of intelligent computing and data-driven decision-makingInternational Journal of Advanced Research in Science, Communication and Technology42This paper delves into GCP's transformative influence through real-world examples and practical applications across sectors such as healthcare, finance, retail, and entertainment and illuminates how these technologies enable businesses to discover new opportunities and operational efficiencies.International Journal of Advanced Research in Science, Communication and Technology["Praveen Borra"]2024-06-19T00:00:00 28eee2be038eba1b77b5c856657f9b3e27987378Graphical User Interface (or simply UI) is a primary mean of interaction between users and their devices. In this paper, we discuss three complementary Artificial Intelligence (AI) approaches for triggering the creativity of app designers and inspiring them create better and more diverse UI designs. First, designers can prompt a Large Language Model (LLM) to directly generate and adjust UIs. Second, a Vision-Language Model (VLM) enables designers to effectively search a large screenshot dataset, e.g. from apps published in app stores. Third, a Diffusion Model (DM) can be trained to specifically generate UIs as inspirational images. We present an AI-inspired design process and discuss the implications and limitations of the approaches.IEEE Software181An AI-inspired design process is presented, a Vision-Language Model enables designers to effectively search a large screenshot dataset, and a Diffusion Model can be trained to specifically generate UIs as inspirational images.ArXiv["Jialiang Wei", "A. Courbis", "Thomas Lambolais", "G\u00e9rard Dray", "Walid Maalej"]2024-06-19T00:00:00 74d1dff1bacf1e9a24ef26ce4abd4be24b09f595Recent advancements in artificial intelligence, such as computer vision and deep learning, have led to the emergence of numerous generative AI platforms, particularly for image generation. However, the application of AI-generated image tools in graphic design has not been extensively explored. This study conducted semi-structured interviews with seven designers of varying experience levels to understand their current usage, challenges, and future functional needs for AI-generated image tools in graphic design. As our findings suggest, AI tools serve as creative partners in design, enhancing human creativity, offering strategic insights, and fostering team collaboration and communication. The findings provide guiding recommendations for the future development of AI-generated image tools, aimed at helping engineers optimize these tools to better meet the needs of graphic designers.arXiv.org391The findings provide guiding recommendations for the future development of AI-generated image tools, aimed at helping engineers optimize these tools to better meet the needs of graphic designers.ArXiv["Yuying Tang", "M. Ciancia", "Zhigang Wang", "Ze Gao"]2024-06-19T00:00:00 1a3673500a10747f9291e0dafbd9c53eec5169e3Artificial Intelligence (AI) has emerged as an effective strategy for helping students improve their writing abilities in today's ever-evolving educational environment. Thus, this study investigates the benefits and applications of using an artificial intelligence assistant to strengthen students' writing skills. Thus, this study aims at investigating the benefits and applications of using an artificial intelligence assistant to strengthen students' writing skills. The data were obtained through observation, questionnaire, and interview. The results of the study revealed that the most commons AI used by the students are Chat GPT, QuillBot, Jenni AI, Grammarly, and StoryAI. The students used AI for different purposes such as for grammar checking, finding story line, getting ideas for the writing topic, and getting first feedback. In line with the results of this study, it can be concluded that AI can be one of the alternatives tools to foster students’ writing skills.  Journal of English Language Learning71It can be concluded that AI can be one of the alternatives tools to foster students’ writing skills.Journal of English Language Learning["E. Syarifah", "Afief Fakhruddin"]2024-06-19T00:00:00 bd8f7d682fcc5ec56c1bd332a0a85c1eca4e3ac1The integration of Artificial Intelligence (AI) into digital marketing is reshaping the landscape by offering unprecedented capabilities for personalization, predictive analytics, conversational AI, and content optimization. This article explores the emerging trends and future predictions for AI in digital marketing as we approach 2025. It examines how AI-driven personalization techniques are evolving beyond conventional methods to deliver hyper-personalized consumer experiences, resulting in higher engagement and conversion rates. The study further delves into the advancements in predictive analytics, highlighting its role in forecasting consumer behavior and optimizing marketing strategies in real-time. The rise of conversational AI, particularly chatbots, is analyzed for its impact on customer service and engagement, with a focus on natural language processing (NLP) advancements that enhance customer interactions. The article also addresses the growing use of AI in content creation and optimization, which is set to revolutionize content marketing by enabling scalable, high-quality content production. In addition to technological advancements, the paper critically examines the ethical implications of AI in marketing, including issues related to data privacy, security, and algorithmic bias. By providing a comprehensive overview of these developments, this article offers valuable insights for marketers, business leaders, and researchers looking to navigate the rapidly evolving digital marketing ecosystem. Through a synthesis of academic research, industry reports, and expert opinions, this study presents a nuanced perspective on the future of AI in digital marketing, outlining both the opportunities and challenges that lie aheadInternational Journal of Artificial Intelligence for Digital Marketing211How AI-driven personalization techniques are evolving beyond conventional methods to deliver hyper-personalized consumer experiences, resulting in higher engagement and conversion rates is examined.International Journal of Artificial Intelligence for Digital Marketing["Hojiakbar Muminov"]2024-06-19T00:00:00 ebb91de3f72c99e0b4288cff6c7c819a817f978fSocial interactive agents play a crucial role in various domains, providing intelligent assistance in healthcare, entertainment, and education settings. Recent advancements in Artificial Intelligence (AI) have shown promising potential to enhance the autonomy of these agents. However, the lack of standardization in their development often results in the creation of complex functionalities that are challenging to transfer across different platforms. In this study, we introduce a general-purpose AI-powered computer vision module designed to address this challenge. Our module features a modular structure that enables easy scalability and integration into diverse environments. Currently supporting seven tasks, including face and person detection, facial recognition, facial expression recognition, facial landmarks estimation, age and gender estimation, and background subtraction, the module offers up to 21 computer vision methods. Additionally, we integrate explainability functionalities to enhance user trust in the system. Moving forward, we aim to expand the module by adding new tasks and methods to meet evolving needs. Our goal is to streamline the integration of AI capabilities into social interactive agents, simplifying their development and enhancing their utility across various applications.Interacción370The goal of this study is to streamline the integration of AI capabilities into social interactive agents, simplifying their development and enhancing their utility across various applications.Proceedings of the XXIV International Conference on Human Computer Interaction["Francesc Xavier Gaya Morey", "Cristina Manresa-Yee", "Jose Maria Buades Rubio"]2024-06-19T00:00:00 a5dad79569880b2357af341c70fa05ffb111dc29This study introduces the “AI-Enhanced Video Drama Maker” app. This innovative language learning tool integrates drama activities with Artificial Intelligence (AI) technologies, catering specifically to learners of English as a Foreign Language (EFL). Aimed at enhancing writing and speaking skills, the app utilizes drama-making for creative expression and real-world communication. This method is aligned with educational theories that emphasize the importance of authentic, contextualized learning experiences. The AI components, such as video-to-text recognition, Generative Pretrained Transformer (GPT)-generated sentences, and Text-to-Speech (TTS) features, are integrated to support and personalize the learning process. The study focuses on Heuristic Evaluation based on Nielsen's principles and gathering expert suggestions for enhancing the app's usability and user experience. Ten UI/UX design experts from two Taiwanese universities participated, providing valuable insights. Data collection was divided into three phases: system introduction, operation, and HCI design analysis. A questionnaire based on Nielsen's ten principles of Heuristic Evaluation was the main data collection tool, supported by SPSS for reliability and descriptive statistics analysis. The findings, underscored by a high Cronbach Alpha score, indicate the app's strong foundation in usability, especially in aesthetics and user support. Recommendations for improvement include enhancing user control, error prevention, and content diversity. The study concludes with the expectation that the app, after incorporating expert feedback, will effectively enhance EFL learners' writing and speaking skills.International Joint Conference on Computer Science and Software Engineering260The study concludes with the expectation that the AI-Enhanced Video Drama Maker app, after incorporating expert feedback, will effectively enhance EFL learners' writing and speaking skills.2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)["Yi-fan Liu", "Muhammad Irfan Luthfi", "Wu-Yuin Hwang"]2024-06-19T00:00:00 62c89101751603a28a929e5065af653a9cb65efeIn the modern world, artificial intelligence has been built and developed to be capable of producing a wide range of creative works, including pictures, videos, sounds, writing, and articles. This study focuses on artificial intelligence's capacity to produce visual works of art in response to diverse textual instructions or orders. However, using this kind of artificial intelligence has resulted in serious errors. The most frequent portrait creation errors are body part deformities or anomalies in the human subject, such as organs that are malformed or absent. To address this problem, it is crucial to design a model for screening deformities in portraits produced by artificial intelligence. This research aims to create a dataset of approximately 100,000 AI-generated portraits and a baseline model for the screen deformities of artificial intelligence portraits. A Deep learning model is introduced to classify artificial intelligence portraits into two categories: normal and defective. The research evaluates four types of deep learning models to identify the most suitable model for screening deformities in portraits. The results of this research show that the ResnetV2152 model has the highest accuracy in screening deformities in artificial intelligence portraits. This model serves as a baseline model for quality screening of artificial intelligence portraits, and the dataset in this publication will be made publicly available at https://bit.ly/4dEdcVd.International Joint Conference on Computer Science and Software Engineering100The research evaluates four types of deep learning models to identify the most suitable model for screening deformities in portraits and shows that the ResnetV2152 model has the highest accuracy in screening deformities in artificial intelligence portraits.2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)["Tanapon Photchanasavanee", "Nuttapong Chentanez"]2024-06-19T00:00:00 9b16afdda1773ad54dc97fc3929378410c7aa8d6The increasing use of generative artificial intelligence (GenAI) has led to a rise in conversations about how teachers and students should adopt these tools to enhance the learning process. Self-regulated learning (SRL) research is important for addressing this question. A popular form of GenAI is the large language model chatbot, which allows users to seek answers to their queries. This article seeks to adapt current SRL models to understand student learning with these chatbots. This is achieved by classifying the prompts supplied by a learner to an educational chatbot into learning actions and processes using the process–action library. Subsequently, through process mining, we can analyze these data to provide valuable insights for learners, educators, instructional designers, and researchers into the possible applications of chatbots for SRL.Future Internet427This article seeks to adapt current SRL models to understand student learning with large language model chatbots by classifying the prompts supplied by a learner to an educational chatbot into learning actions and processes using the process–action library.Future Internet["Joel Weijia Lai"]2024-06-20T00:00:00 7596859219686be5c15162a62260a66ef2d8db1bEuropean Journal of Medical Research424Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients, and an interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality.European Journal of Medical Research["Lingyu Xu", "Chenyu Li", "Jiaqi Zhang", "C. Guan", "Long Zhao", "Xuefei Shen", "Ningxin Zhang", "Tianyang Li", "Chengyu Yang", "Bin Zhou", "Quandong Bu", "Yan Xu"]2024-06-20T00:00:00 d0d1bb5f0d48cc5ba152746379b33b0d2b4c0537This comprehensive article investigates the dynamic integration of Artificial Intelligence (AI) in journalism, tracing its evolution from the initial stages of computer-assisted reporting to the current advanced applications and ethical dilemmas. The paper offers an in-depth analysis of AI’s Impact on journalism, highlighting both the enhancements in efficiency, personalization, and data reporting, as well as the challenges posed by ethical concerns, potential job displacement, and the risks of misinformation. The paper examines real-world applications and controversies surrounding AI in newsrooms, including the use of automated content generation and AI-driven editorial decisions. A critical discussion on ethical considerations is presented, focusing on transparency, accountability, and bias in AI systems and the need for ethical standards and industry-wide collaboration. Looking forward, the article speculates on the future of AI in journalism, emphasizing the continuous essential role of human journalists and the potential technological advancements. This work underscores the necessity of a balanced approach in harnessing AI’s capabilities in journalism, ensuring that technological progress aligns with maintaining journalistic integrity and ethical standards.Journal of Communication and Management84An in-depth analysis of AI’s Impact on journalism is offered, highlighting both the enhancements in efficiency, personalization, and data reporting, as well as the challenges posed by ethical concerns, potential job displacement, and the risks of misinformation.Journal of Communication and Management["Deepika Verma"]2024-06-20T00:00:00 5f187c518f26d13f33e0b2b53a4a940bf947e991Operations Management Research673This study supports the theory related to the SCF by providing empirical evidence about the role of AI in the SCF innovation process and also identifying the resulting benefits and challenges for all the actors involved.Operations Management Research["Alessio Ronchini", "Michela Guida", "Antonella Moretto", "F. Caniato"]2024-06-20T00:00:00 b52d7265c37f0167d7a41e0318f671553907b018This article aims to offer insight into the development of artificial intelligence (AI) and its influence on improving second language learning, with a specific emphasis on English. Through extensive research, several studies were examined that demonstrated the effectiveness of artificial intelligence in improving oral fluency in language learners. The study also investigated the effects of integrating artificial intelligence into language learning, taking into account aspects such as technology capacity, data protection, and equity in language education opportunities. It also examines the ethical complexities of using artificial intelligence in language teaching, highlighting the relevance of a balanced approach that maximizes the advantages of the technology and minimizes potential difficulties. This article provides a thorough analysis of the influence of artificial intelligence on the development of skills to speak a second language, providing valuable information for educators, researchers, and practitioners in the field of language teaching.Ciencia Latina Revista Científica Multidisciplinar103A thorough analysis of the influence of artificial intelligence on the development of skills to speak a second language is provided, providing valuable information for educators, researchers, and practitioners in the field of language teaching.Ciencia Latina Revista Científica Multidisciplinar["Araceli Maritza D\u00e1vila Mac\u00edas", "Diego Omar Armijos Solano", "Laura Mar\u00eda Palma Perero", "Julio Andres Roca Panimboza", "Cristhian Joel Lucas Soledispa"]2024-06-20T00:00:00 473f3ce452766c81bfa27d175d4c213fbda3f188Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care—respect for patient autonomy, beneficence, non-maleficence, and justice.Korean Journal of Radiology383Draft position statements regarding the application and clinical deployment of AI in radiology are drafted to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice.Korean Journal of Radiology["N. K. Wee", "K. Git", "Wen-Jeng Lee", "Gaurang Raval", "A. Pattokhov", "E. L. Ho", "C. Chuapetcharasopon", "Noriyuki Tomiyama", "Kwan Hoong Ng", "Cher Heng Tan"]2024-06-20T00:00:00 167c45e21f214c9ed40a5967fcc53e97462b8f30In today’s fast-paced business landscape, data is no longer just a byproduct; it’s the driving force behind informed decision-making. With the rise of business analytics, organizations can harness the power of data to gain insights that lead to improved strategies, enhanced operations, and, ultimately, a stronger bottom line. The topic relevance is confirmed by the business need for modern data analysis methods. Technological progress and large data volumes that need processing require the machine learning use which can improve the business processes productivity. Machine learning, a subset of artificial intelligence, has emerged as a powerful tool in the field of business analytics. He involves the use of algorithms that allow computers to learn from and make predictions or decisions based on data. Machine learning and analytics help automate processes, reduce costs and improve the quality of every decision made. The article purpose is to seek to establish opportunities, trends and limitations in the machine learning use and artificial intelligence in business analytics context.Computer and Information Science241The article purpose is to seek to establish opportunities, trends and limitations in the machine learning use and artificial intelligence in business analytics context.Computer and Information Science["C. Tsahat", "Ngoulou A. Ndzeli", "C. Moukengue"]2024-06-20T00:00:00 616268c266d9a28db0bd142b3b83cb6c13628325This article presents the results of developing a model for assessing water quality using the artificial intelligence method. The presented model is based on linear regression, which, when evaluated, revealed a statistically significant interdependence between the combined water quality indicators. It was found that among the measured parameters, the most influential predictor of acidity, conductivity, turbidity and redox potential is water temperature. The discovered relationship between the measured indicators is mainly associated with the influence of temperature on the physical and chemical processes that occur when the temperature of river water increases and decreases.2024 V International Conference on Neural Networks and Neurotechnologies (NeuroNT)182The presented model is based on linear regression, which, when evaluated, revealed a statistically significant interdependence between the combined water quality indicators and the most influential predictor of acidity, conductivity, turbidity and redox potential is water temperature.2024 V International Conference on Neural Networks and Neurotechnologies (NeuroNT)["Y. Altay", "Lashin Bazarbay"]2024-06-20T00:00:00 fce8e41a8affa8c1ad4b717717334c3115c88710This paper explores the explanability imperative in the context of Generative Artificial Intelligence (GAI) and its crucial role in addressing the concerns posed by AI technology in Nigeria. This underscores the ethical necessity for AI systems, especially generative ones to provide clear and understandable explanations for their decisions and actions. Although the advent of generative AI undoubtedly heralds the future and however, has also exposed Nigerian society to new vulnerabilities that seemingly are detrimental to our epistemic agency and peaceful political settings. Employing the phenomenological method of philosophical inquiry here, we discovered that this new technology has posed big threats to the future world, and that Nigeria falls amongst this new technology users. To navigate the moral dilemma caused by Generative Artificial Intelligence, this paper suggests many proactive approaches like the development of localized AI explainability standards, the regulatory frameworks, and educational initiatives to promote awareness and understanding of AI systems in Nigeria. By prioritizing the Explanability Imperative, Nigeria can chart a path towards a future whereby AI technologies aligned with societal values, upholds standard education, and as well contributes positively to the nation’s development. This paper encapsulates the importance of AI explainability in Nigeria’s AI landscape and its potential to shape a more ethically responsible and transparent AI future.Universal Library of Arts and Humanities171The importance of AI explainability in Nigeria’s AI landscape and its potential to shape a more ethically responsible and transparent AI future are encapsulated.Universal Library of Arts and Humanities["Emedo Chinyere Christian"]2024-06-20T00:00:00 b6d3cdad38cff98486ee57de139b317283154c56PurposeWhile different attempts have been made to use artificial intelligence (AI) to codify communicative behaviors and analyze startups’ video presentations in relation to crowdfunding projects, less is known about other forms of access to entrepreneurial finance, such as video pitches for candidacies into startup accelerators and incubators. This research seeks to demonstrate how AI can enable the startup selection process for both entrepreneurs and investors in terms of video pitch evaluation.Design/methodology/approachAn AI startup (Speechannel) was used to predict the outcomes of startup video presentations by analyzing text, audio, and video data from 294 video pitches sent to a leading European startup accelerator (LUISS EnLabs). 7 investors were also interviewed in Silicon Valley to establish the differences between humans and machines.FindingsThis research proves that AI has profound implications with regards to the decision-making process related to fundraising and, in particular, the video pitches of startup accelerators and incubators. Successful entrepreneurs are confident (but not overconfident), engaging in terms of speaking quickly (but also clearly), and emotional (but not overemotional).Practical implicationsThis study not only fills the existing research gap but also provides a practical guide on AI-driven video pitch evaluation for entrepreneurs and investors, reshaping the landscape of entrepreneurial finance thanks to AI. On the one hand, entrepreneurs could use this knowledge to modify their behaviors, enabling them to increase their likelihood of being financially backed. On the other hand, investors could use these insights to better rationalize their funding decisions, enabling them to select the most promising startups.Originality/valueThis paper makes a significant contribution by bridging the gap between theoretical research and the practical application of AI in entrepreneurial finance, marking a notable advancement in this field. At a theoretical level, it contributes to research on managerial decision-making processes – particularly those related to the analysis of video presentations in a fundraising context. At a practical level, it offers a model that we called the “AI-enabled video pitch evaluation”, which is used to extract features from the video pitches of startup accelerators and incubators and predict an entrepreneurial project’s success.Management Decision1211This research proves that AI has profound implications with regards to the decision-making process related to fundraising and, in particular, the video pitches of startup accelerators and incubators.Management Decision["Guglielmo Giuggioli", "M. Pellegrini", "Giorgio Giannone"]2024-06-20T00:00:00 863b05bb314766d088139648771704ecca1f87d8Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to enhance the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities, e.g., MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.Radiological Physics and Technology01The radiomics workflow and current handcrafted radiomics methods in the diagnosis and classification of BC based on the most recent studies on different imaging modalities are discussed, e.g., MRI, mammography, contrast-enhanced spectral mammography, ultrasound imaging, and digital breast tumosynthesis.Radiological physics and technology["Reza Elahi", "Mahdis Nazari"]2024-06-20T00:00:00 71fd81b5f9158055603c96556e97de5d8e17edf6The impact of artificial intelligence (AI) is clear and highly influential in many areas of sports, helping to improve team and player results. Not only that, but artificial intelligence has been introduced into the areas of training and analysis of data and results, emulating and presenting potential hypothetical scenarios through the capabilities employed in artificial intelligence to enable accurate and effective training in emergency and critical situations. Another major benefit of using AI in sports is to analyze data and game stats to improve team performance in future games. Improved good decision making capability has made using artificial intelligence applications to gain huge popularity and attention in both academia and industry especially in sports industry. The main problem associated with using Artificial Intelligence applications is sports is that the usefulness of AI for many sports viewers, experts, coaches, team managers, and policy makers is not clear especially when they are not particularly familiar or expert in the field of AI. Similarly, for many, the reasons for employing AI and machine learning (ML) models for mathematical analysis in areas such as sports remain lackluster or unclear. In this research paper the authors present a review in the importance of using AI applications in sports for the people involved in the sports industry in general and especially for the Iraqi academic staff and those working in the sports field. The stake holders and the parties involved need to learn how to use the principles of AI knowledge, and conduct research to improve the performance of Iraqi teams and player.Cihan University-Erbil Scientific Journal01The authors present a review in the importance of using AI applications in sports for the people involved in the sports industry in general and especially for the Iraqi academic staff and those working in the sports field.Cihan University-Erbil Scientific Journal["Adil H. Mohammed", "Zhian J. Othman", "Abdulqadir I. Abdullah"]2024-06-20T00:00:00 d7e3d84bb9575aaca5910814b251d7f32b5b525fThis study aims to provide an overview of the increasing role of artificial intelligence in accounting and auditing. This is supported by the expertise of the accounting and auditor profession which has evolved with advances in technology from the use of pencil and paper to calculators, and eventually spreadsheets and accounting software. This study uses a conceptual approach and semi-systematic review in analyzing published relevant articles. The main results of this study explain that interdisciplinary collaboration is a must with respect to research conducted in the field of AI in accounting and auditing. Wider application of AI in the accounting and auditing professions is expected to deliver greater efficiency, productivity and accuracy benefits while burdening with the challenges of income and wealth inequality, traditional job extinction and an unskilled workforce. Careful preparation is needed on the part of educators, regulators and professional bodies by overcoming paradigm shifts and preparing future students, policymakers and professionals to face the challenges of a world full of big data, blockchain technology, artificial intelligence to deliver success in facing the fourth industrial revolution.Journal of Applied Business Taxation and Economics Research312The main results of this study explain that interdisciplinary collaboration is a must with respect to research conducted in the field of AI in accounting and auditing to deliver success in facing the fourth industrial revolution.Journal of Applied Business, Taxation and Economics Research["Anin Dyah Luthfiani"]2024-06-20T00:00:00 31b52e0141013e1d78a0d2724e074d1505fe7333Because of the improvement of the performance of modern computer hardware and the continuous development of algorithms, the application of artificial intelligence is more widely used in all walks of life. In this work, the application of artificial intelligence technology in the fields of finance, medical care, industry, information, education and social life, especially in the manufacturing industry, has formed an unstoppable trend. For future careers, the arrival of artificial intelligence is also thought-provoking. In addition to bringing a lot of new jobs, but also let some low-cost, labor-intensive jobs disappear, causing great pressure on the job market, the employment threshold has significantly increased, familiar with artificial intelligence managers and experts pay much higher than manual workers, so that the income pattern of workers gradually prolonged. The arrival of AI technology has also triggered changes in the labor market, with labor market and automation technologies have largely replacing repetitive and more basic skilled workers.Advances in Economics, Management and Political Sciences01The application of artificial intelligence technology in the fields of finance, medical care, industry, information, education and social life, especially in the manufacturing industry, has formed an unstoppable trend.Advances in Economics, Management and Political Sciences["Yingying Shen"]2024-06-20T00:00:00 de193bdba16e82620b82edb3527ac7999176228bArtificial intelligence (AI) offers an unbelievable future, but there is a darkness hiding underneath its bright potential indicating the prospect of producing a ruthless society. AI's inherent biases, algorithmic opacity, and the potential for warfare raise legitimate concerns about the influence on our values and social fabric. This study argues that AI, in its current form, poses an imminent risk of aggravating existing inequality in society and producing a ruthless atmosphere. The research investigates how AI algorithms, which frequently mirror social prejudices, might perpetuate and increase discrimination in domains. This investigation illustrates the potential for AI to isolate people and reduce empathy, contributing to a societal atmosphere marked by intolerance and disrespect for different points of view. The research explores the ground reality of the fundamentals of Neil Postman in the theory of Media Ecology by adopting a mixedmethod approach. Methodology includes surveys, interviews, and discourse analysis. Probing the research questions the study will find out the causal relationship between the increasing role of artificial intelligence and changes in social harmony and the adverse effects of AI on an individual’s skill set. The study also analyze the impact of AI on 5th Gen Warfare. The article suggests policymakers, tech corporations, and academics join to develop an AI-driven future that values fairness, empathy, and vibrant human connection over the attraction of ruthless efficiency.Pakistan journal of international affairs50The article suggests policymakers, tech corporations, and academics join to develop an AI-driven future that values fairness, empathy, and vibrant human connection over the attraction of ruthless efficiency.Pakistan Journal of International Affairs["Dr Syeda Maliha Begum, Dr. Fazal Hussain , Dr. Syed Shujat Husain"]2024-06-20T00:00:00 6ceedff58d87b3d13f09d2076bd077cb6a5f0e4cВ данной статье мы рассмотрим перспективы и потенциал интеграции множественного и искусственного интеллекта в свете развития цифровой экономики. Мы исследуем области, где такая интеграция может принести наибольшую пользу и привести к важным изменениям. Также рассмотрим возможные ограничения и риски этой интеграции и обсудим пути их преодоления. В наше время, когда цифровая экономика настолько важна для развития бизнеса и общества в целом, вопросы, связанные с интеграцией множественного и искусственного интеллекта, становятся все более актуальными. Технологии и искусственный интеллект постепенно проникают во все сферы деятельности, и их интеграция может значительно ускорить развитие цифровой экономики. В данной статье мы рассмотрим различные аспекты интеграции множественного и искусственного интеллекта и их влияние на развитие цифровой экономики. In this article we will look at the prospects and potential of integrating multiple and artificial intelligence in the light of the development of the digital economy. We explore areas where such integration can bring the greatest benefit and lead to important changes. We will also consider possible limitations and risks of this integration and discuss ways to overcome them. Nowadays, when the digital economy is so important for the development of business and society as a whole, issues related to the integration of multiple and artificial intelligence are becoming increasingly relevant. Technology and artificial intelligence are gradually penetrating all areas of activity, and their integration can significantly accelerate the development of the digital economy. In this article we will look at various aspects of the integration of multiple and artificial intelligence and their impact on the development of the digital economy.Экономика и предпринимательство00Экономика и предпринимательство["\u0412\u0415\u041b\u0418\u041a\u0410\u041d\u041e\u0412\u0410\u00a0\u0421.\u0421. \u0412\u0415\u041b\u0418\u041a\u0410\u041d\u041e\u0412\u0410\u00a0\u0421.\u0421.", "\u0421\u0410\u041c\u0410\u0420\u041e\u041a\u041e\u0412\u0410\u00a0\u0418.\u0412. \u0421\u0410\u041c\u0410\u0420\u041e\u041a\u041e\u0412\u0410\u00a0\u0418.\u0412.", "\u0425\u0410\u0420\u0418\u0422\u041e\u041d\u041e\u0412\u0410\u00a0\u0421.\u0412. \u0425\u0410\u0420\u0418\u0422\u041e\u041d\u041e\u0412\u0410\u00a0\u0421.\u0412.", "\u041a\u041e\u041b\u0415\u0421\u041d\u0418\u041a\u041e\u0412\u0410\u00a0\u041e.\u042e. \u041a\u041e\u041b\u0415\u0421\u041d\u0418\u041a\u041e\u0412\u0410\u00a0\u041e.\u042e."]2024-06-20T00:00:00 8310cf418524989082873e62f0a37759b4885f57This research, conducted in June 2023 at Sofia University “St. Kliment Ohridski”, aimed at gaining in-depth insights of the extent to which Bulgarian Internet users in tertiary education had developed comprehension of generative artificial intelligence (AI) models and algorithm literacy. For the purposes of this study, a scale measuring the knowledge of generative AI models was devised and implemented, and an existing algorithm literacy scale was tested. Altogether, 125 university students across various majors in the field of education took part in the research. Findings revealed that the newly developed scale on generative AI models displayed good reliability and correlated positively with the measure for algorithm knowledge and students’ self-reported language skills. Group differences in relation to students’ university major were found to be significant for knowledge and use of generative AI models, language skills, and coding skills. As hypothesized, students in media education displayed high scores on most scales.Pedagogika-Pedagogy00Group differences in relation to students’ university major were found to be significant for knowledge and use of generative AI models, language skills, and coding skills, and students in media education displayed high scores on most scales.Pedagogika-Pedagogy["Ekaterina Sofronieva", "Christina Beleva", "Galina Georgieva", "Stefan Markov"]2024-06-20T00:00:00 2e2c4431e54ebaaa625af115573b03ad76640134Divorceify is an Artificial Intelligence (AI)-based platform designed to assist in managing divorce processes within a divorce accounting system. However, applying this platform in Indonesia’s legal system may face several challenges. This article examines the extent of Divorceify’s role and influence within the judicial system. It also delves into the challenges and future impacts of using Divorceify in Indonesia’s legal system. Using normative research methods with a conceptual approach, this article asserts that in the context of Indonesian law, the adoption of such technology faces various obstacles, including alignment with local legal frameworks, cultural sensitivities, access and education on technology, and data privacy issues. Indonesia’s legal system can adapt to this innovation in order to improve efficiency and fairness in divorce proceedings, as it is seen as capable of enhancing efficiency, reducing costs, providing fairer outcomes, and alleviating administrative burdens on courts. Additionally, critical obstacles must be addressed to ensure successful integration, especially regarding legal ethics and personal data protection. With the appropriate regulatory, ethical, and technological frameworks, AI-based divorce management tools like Divorceify hold significant potential to improve Indonesia’s judicial system.Al-Hukama'00The extent of Divorceify’s role and influence within the judicial system is examined, and the challenges and future impacts of using Divorceify in Indonesia’s legal system are delved into.AL-HUKAMA["Iqbal Kamalludin", "Bunga Desyana Pratami"]2024-06-20T00:00:00 f288adbbc798a1bacd85209226910fe16cc09cd0With the rapid advancement of modern science and technology, computer technology has been greatly promoted, leading to the emergence of artificial intelligence. In the new era of ideological and moral education, higher education's ideological and political education is an inevitable path and a necessary guarantee for cultivating innovative talents with both moral integrity and professional competence. However, traditional teaching scenarios can no longer meet the needs of ideological and political education in the new era. The continuous progress in artificial intelligence technology provides new means and ideas for the "Grand Ideological and Political Course" teaching, including the cultivation goals of college ideological and political courses, the construction of teaching staff, and the reform of teaching models. In this context, college ideological and political courses need to fully leverage the advantages of technological empowerment, actively adapt to the changes of the times, and explore innovative teaching methods in the era of artificial intelligence, thereby enhancing the mission-driven ideological and moral education of the new era.World Journal of Education and Humanities00College ideological and political courses need to fully leverage the advantages of technological empowerment, actively adapt to the changes of the times, and explore innovative teaching methods in the era of artificial intelligence, thereby enhancing the mission-driven ideological and moral education of the new era.World Journal of Education and Humanities["Zhengyu Duan"]2024-06-20T00:00:00 f08454ff6eaef3523ed45638af3aab136f81cc92работа посвящена анализу основных теоретических и практических вопросов, связанных с правовыми последствиями создания творческих объектов (произведений) с использованием генеративных технологий искусственного интеллекта. Автор анализирует вопросы охраноспособности таких произведений, описывает и предлагает возможные способы решения таких дискуссионных вопросов, как вопроса распределения интеллектуальных прав на произведения, создаваемые искусственным интеллектом, а также вопроса о последствиях нарушения интеллектуальных прав путем заимствования алгоритмами искусственного интеллекта уже существующих охраняемых объектов интеллектуальной собственности. В заключение автор приходит к выводу о широкой научной перспективе обсуждаемой проблематики. the article is devoted to the analysis of the main theoretical and practical issues related to the legal consequences of creating creative objects (productions) using generative artificial intelligence technologies. The author analyzes the issues of the protection of such works, describes and suggests possible ways to solve such controversial issues as the issue of the distribution of intellectual rights to works created by artificial intelligence, as well as the issue of the consequences of violation of intellectual rights by borrowing artificial intelligence algorithms of already existing protected intellectual property objects. In conclusion, the author comes to the conclusion about the broad scientific perspective of the discussed issues.International law journal00International Law Journal["\u041c\u0438\u0445\u0430\u0438\u043b \u0410\u043d\u0434\u0440\u0435\u0435\u0432\u0438\u0447 \u0421\u043a\u0432\u043e\u0440\u0446\u043e\u0432"]2024-06-20T00:00:00 ed79675c372266dc411b2f7c0e033395ecfd84beTo validate the diagnostic accuracy of an artificial intelligence (AI) model in the diagnosis of thyroid nodules, comparing the results with the evaluation of expert sonographers and cytology.Journal of Clinical Studies and Medical Case Reports00Journal of Clinical Studies and Medical Case Reports["Pignataro Francesco"]2024-06-20T00:00:00 eb528e86db6830c14e634f06d003ecba45e8abf5This article aims to explore the challenges faced by Brazilian Draft Bill no. 2338/2023 in its purpose to implement risk-based regulation of artificial intelligence in Brazil. Based on the inspiration received from the European AI Act, the article describes the Brazilian classification of risks and its impacts for the regulation, the governance rules, the practical application of principles of prevention and precaution, and the administrative and civil liability.Unio - EU Law Journal00The article describes the Brazilian classification of risks and its impacts for the regulation, the governance rules, the practical application of principles of prevention and precaution, and the administrative and civil liability.UNIO – EU Law Journal["Ana Fraz\u00e3o"]2024-06-20T00:00:00 420375e5c6f88cfec394fc18ecb31bf86cfdf014В современном мире искусственный интеллект (ИИ) становится важным элементом в строительстве и управлении недвижимостью. В данной статье рассмотрены разнообразные аспекты применения ИИ в этих отраслях, включая проектирование, управление стройплощадками, снижение экологического воздействия и оптимизацию энергоэффективности. Примеры компаний и исследований подчеркивают важность использования ИИ для улучшения эффективности и снижения затрат в строительстве и управлении недвижимостью. In the modern world, artificial intelligence (AI) is becoming an important element in the construction and management of real estate. This article examines various aspects of AI applications in these industries, including design, site management, environmental impact reduction and energy efficiency optimization. Company examples and research highlight the importance of using AI to improve efficiency and reduce costs in construction and property management.Экономика и предпринимательство00Экономика и предпринимательство["\u0421\u042b\u0427\u0415\u0412\u00a0\u0418.\u0421. \u0421\u042b\u0427\u0415\u0412\u00a0\u0418.\u0421.", "\u0422\u0410\u041d\u041a\u0415\u0415\u0412\u00a0\u041d.\u0410. \u0422\u0410\u041d\u041a\u0415\u0415\u0412\u00a0\u041d.\u0410.", "\u041a\u041e\u041d\u0414\u0410\u041a\u041e\u0412\u00a0\u0410.\u0413. \u041a\u041e\u041d\u0414\u0410\u041a\u041e\u0412\u00a0\u0410.\u0413.", "\u0424\u041e\u041c\u0418\u041d\u00a0\u0418.\u0418. \u0424\u041e\u041c\u0418\u041d\u00a0\u0418.\u0418."]2024-06-20T00:00:00 ebc3d7f82afa6d73f2827644af641e358748507bThis paper proposes leveraging artificial intelligence to create self-updating digital education platforms that can continually refresh their content and provide adaptive personalized learning. Automated curation and contextualization of the latest knowledge using machine learning and natural language processing can make courseware dynamic and learner-needs based. Recommendations encompass public-private collaboration in developing national- level AI infrastructure and policies to make such next- generation systems mainstream. Self-evolving platforms promise to unlock democratized, flexible and lifelong learning critical for workforce resilience.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)140This paper proposes leveraging artificial intelligence to create self-updating digital education platforms that can continually refresh their content and provide adaptive personalized learning and encompasses public-private collaboration in developing national- level AI infrastructure and policies to make such next- generation systems mainstream.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)["S. Gulyamov", "Sardor Mamanazarov", "A. Rodionov"]2024-06-20T00:00:00 0e00df5915c1c58d01c1e0f81b6045cf9f64aaf3Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement. This dependency presents a significant barrier to rapid innovation. Recognizing the complexity of automatically generating neural network architecture from scratch, we introduce Younger, a pioneering dataset to advance this ambitious goal. Derived from over 174K real-world models across more than 30 tasks from various public model hubs, Younger includes 7,629 unique architectures, and each is represented as a directed acyclic graph with detailed operator-level information. The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement. By establishing these capabilities, Younger contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA). Our experiments explore the potential and effectiveness of Younger for automated architecture generation and, as a secondary benefit, demonstrate that Younger can serve as a benchmark dataset, advancing the development of graph neural networks. We release the dataset and code publicly to lower the entry barriers and encourage further research in this challenging area.arXiv.org410The dataset facilitates two primary design paradigms: global, for creating complete architectures from scratch, and local, for detailed architecture component refinement, and contributes to a new frontier, Artificial Intelligence-Generated Neural Network Architecture (AIGNNA).ArXiv["Zhengxin Yang", "Wanling Gao", "Luzhou Peng", "Yunyou Huang", "Fei Tang", "Jianfeng Zhan"]2024-06-20T00:00:00 1702f68e6a7111c6388879f491ba8e27006ea35fThe relevance of the study is driven by the advancing development of artificial intelligence (AI), which presents new prospects for the creative education of students on Art educational programs. The research problem lies in the current challenges to traditional art learning practices, which are losing relevance due to changes and the potential integration of artificial intelligence (AI) technologies. The aim of the research is to review the creative learning process of art students to identify: criteria for forming competencies in the fields of visual arts with the application of AI technologies, AI tools for teaching students in the field of visual arts, key competencies in working with virtual tools for students and teachers in the field of education and art, and to define recommendations for the implementation of AI in the art students’ learning process. The methodological framework is based on interdisciplinary examination of researchers' works in education and art. Research methods include overview-theoretical, art-historical, methodological-pedagogical analysis, as well as comparative and case–study approaches. The theoretical significance of the research lies in reviewing the trajectories of education in the field of neural networks and providing scientific–theoretical justification for the application of AI technologies in the creative learning process of students enrolled in visual arts programs. The practical value offers recommendations for the formation of modern and efficient educational strategies in the field of art at the student level, including the specification of concepts and terms, defining curricula with new educational methodologies and personalized educational practices, adaptation to changing labor market demand, and laying the groundwork for further research. Implementing the research findings will enable the systematization and optimization of methodologies and approaches to the creative learing process of students on Art educational programs through AI tools, virtual, and augmented reality.Central Asian Journal of Art Studies00The aim of the research is to review the creative learning process of art students to identify criteria for forming competencies in the fields of visual arts with the application of AI technologies, and to define recommendations for the implementation of AI in the art students’ learning process.Central Asian Journal of Art Studies["Meruyert Zhanguzhinova"]2024-06-20T00:00:00 4fa2a4a1f8edb9711bd13cfb36d0a00bc1800f64Background: Artificial intelligence (AI) chatbots are novel computer programs that can generate text or content in a natural language format. Academic publishers are adapting to the transformative role of AI chatbots in producing or facilitating scientific research. This study aimed to examine the policies established by scientific, technical, and medical academic publishers for defining and regulating the responsible authors' use of AI chatbots. Methods: This study performed a cross-sectional audit on the publicly available policies of 163 academic publishers, indexed as members of the International Association of the Scientific, Technical, and Medical Publishers (STM). Data extraction of publicly available policies on the webpages of all STM academic publishers was performed independently in duplicate with content analysis reviewed by a third contributor (September 2023 - December 2023). Data was categorized into policy elements, such as 'proofreading' and 'image generation'. Counts and percentages of 'yes' (i.e., permitted), 'no', and 'N/A' were established for each policy element. Results: A total of 56/163 (34.4%) STM academic publishers had a publicly available policy guiding the authors' use of AI chatbots. No policy allowed authorship accreditations for AI chatbots (or other generative technology). Most (49/56 or 87.5%) required specific disclosure of AI chatbot use. Four policies/publishers placed a complete ban on the use of AI tools by authors. Conclusions: Only a third of STM academic publishers had publicly available policies as of December 2023. A re-examination of all STM members in 12-18 months may uncover evolving approaches toward AI chatbot use with more academic publishers having a policy.medRxiv20Only a third of STM academic publishers had publicly available policies guiding the authors' use of AI chatbots as of December 2023, and a re-examination of all STM members in 12-18 months may uncover evolving approaches toward AI chatbot use with more academic publishers having a policy.["D. Bhavsar", "L. Duffy", "H. Jo", "C. Lokker", "R. B. Haynes", "A. Iorio", "A. Marusic", "J. Y. Ng"]2024-06-20T00:00:00 769ad3a41ad3c16a2a4275a8939996e5b4cc9155В статье рассматриваются изменения в мировой экономике, а также в российской при активном внедрении новейших технологий на базе искусственного интеллекта, сопровождаемый не только решением сложнейших задач современности, но и созданием новых проблем и новых рисков. The article examines the changes in the global economy, as well as in the Russian one, with the active introduction of the latest technologies based on artificial intelligence, accompanied not only by solving the most difficult tasks of our time, but also by creating new problems and new risks.Экономика и предпринимательство00Экономика и предпринимательство["\u0425\u0423\u0414\u042f\u041a\u041e\u0412\u0410\u00a0\u041e.\u042e. \u0425\u0423\u0414\u042f\u041a\u041e\u0412\u0410\u00a0\u041e.\u042e.", "\u0425\u0410\u0420\u041b\u0410\u041d\u041e\u0412\u00a0\u0410.\u0421. \u0425\u0410\u0420\u041b\u0410\u041d\u041e\u0412\u00a0\u0410.\u0421."]2024-06-20T00:00:00 891fbc28644d1860709841fb47a4aa95803e7045Artificial intelligence can reduce the burden on doctors and improve their analytical skills. Collaboration between physicians and artificial intelligence has the greatest potential to improve clinical decision-making and patient health outcomes. Due to the complexity of the main symptoms of the diseases, it is difficult to develop early diagnostic tools. Therefore, effective and timely diagnosis of the disease remains a challenge in the medical field. Although the integration of artificial intelligence into medical practice is still in its early stages, its full use for medical diagnostics is possible, especially in the direction of cancer prevention. Artificial intelligence can have a significant impact on medical practice, in particular improving the accuracy of diagnosis, saving costs and time compared to traditional diagnostic methods. Also, artificial intelligence has the potential to make clinical laboratory testing more accurate, faster, and more efficient. It is worth noting that in the health sector, errors in the diagnostic process are the biggest problem. Artificial intelligence can minimize diagnostic errors and detect life-threatening diseases in patients at an early stage. In this regard, the decisive role of artificial intelligence in determining the dosage of medicines, as well as in predicting drug contraindications, which is important for improving the treatment results, is crucial. Artificial intelligence has the potential to analyze individual cases and compare them with patient databases, allowing for individualized treatment planning. This indicates the revolutionary potential of artificial intelligence in medicine. However, experts admit that artificial intelligence presents a challenge because the implementation of algorithms is not an independent process and takes place in a dynamic environment where human reaction and adaptation play a crucial role. Medical education about artificial intelligence is essential because future doctors will have to provide medical services to patients in completely different healthcare settings. To fully realize the potential of artificial intelligence, it is necessary to have properly qualified human resources, to collect quality medical data, to take into account the confidentiality and availability of patient data. Continuous evaluation of AI systems is essential to ensure their sustainable performance over time. Arguably, collaboration between computer scientists and healthcare providers is essential for the practical and successful implementation of artificial intelligence.Economic Profile00Although the integration of artificial intelligence into medical practice is still in its early stages, its full use for medical diagnostics is possible, especially in the direction of cancer prevention, experts admit that artificial intelligence presents a challenge.Economic Profile["T. Verulava"]2024-06-20T00:00:00 8d5ff4ca9de6ff354e2a5df3fc61062abe43c7beEducation is likely to be the area where Artificial Intelligence (AI) will have the greatest impact, due to the importance of learning and the limitations of current educational options. AI is beneficial for two reasons: it requires less human effort and provides quick results. However, there is a concern about whether we are truly learning when we rely too heavily on technology. AI will also affect employment rates, either leading to mass job layoffs or increased workload on employees. When we look back at ancient history, we can understand that real education was technology-free and practical. Students from different backgrounds had access to similar knowledge from similar gurus at similar places. Today, we promise equal education and fair employment for everyone, but we also widen the gap between rich and poor, which challenges the fundamental rights of human beings. We can learn about the qualities of a good teacher from the Bhagavad Gita, the most influential book in our culture, in which Lord Krishna guides Arjun.Journal of Communication and Management100Education is likely to be the area where Artificial Intelligence (AI) will have the greatest impact, due to the importance of learning and the limitations of current educational options, while employment rates will also affect employment rates.Journal of Communication and Management["Beenum Yadav", "Kun Sharma"]2024-06-20T00:00:00 eb3107cf73f2a97f55b03a3fb6be49861539ddfcThe emergence of artificial intelligence (AI) has ushered in an era of unprecedented possibilities and, at the same time, poses significant threats in the media landscape. As a potent tool, AI can streamline operations, offering innovative solutions like content personalization, predictive analytics, and augmented reality experiences that can enhance user engagement and boost the media industry’s economic viability. These opportunities allow for a more immersive and personalized user experience, leveraging data to create content that is finely tuned to individual preferences and trends. However, the integration of AI in media does not come without its challenges. The threat of deep fakes and the dissemination of misinformation stand as substantial concerns, with AI technologies facilitating the creation of highly convincing fake content that can manipulate perceptions and sow discord. Moreover, the autonomy given to AI systems can potentially lead to job displacements and raise ethical concerns regarding privacy and data security. Despite these threats, when wielded responsibly and ethically, AI can usher in a new golden age for media characterized by creativity, efficiency, and innovation. As the media industry stands at this crossroads, it must navigate the fine balance between leveraging AI’s opportunities and mitigating its potential threats, fostering a landscape that upholds truth, ethics, and human value at its core. It is imperative for stakeholders to collaborate, devising strategies and regulatory frameworks to ensure AI serves as a force for good, steering media towards a future that embodies progress and inclusivity.Journal of Communication and Management50The media industry stands at this crossroads, it must navigate the fine balance between leveraging AI’s opportunities and mitigating its potential threats, fostering a landscape that upholds truth, ethics, and human value at its core.Journal of Communication and Management["Prashant Kumar", "Bhaskar Singh"]2024-06-20T00:00:00 cd2aa89ddd5ef0287af74a6507a8013ee57b0b7eGlobal Surgical Education - Journal of the Association for Surgical Education320In conclusion, intraoperative AI can revolutionize decision support, safety monitoring, and overall quality in the operating room as surgical quality and safety is pursued.Global Surgical Education - Journal of the Association for Surgical Education["Andrew P. Bain", "Carla Holcomb", "Herbert J Zeh", "Ganesh Sankaranarayanan"]2024-06-20T00:00:00 b0f74e54c4fef24c28433f6c159ff61a7645f526The paper is devoted to understanding the comparability of human beings and Artificial Intelligence (AI) models in extracting emotions from literary texts. The study employs some classical approaches to analyze emotions sentiment and suggests some ways to interpret the obtained results.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)240The study employs some classical approaches to analyze emotions sentiment and suggests some ways to interpret the obtained results.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)["A. Sysoev", "Irina Mavlina"]2024-06-20T00:00:00 f5ec24eee01d81ee88865cf231d4ad3793ab4dc4Данная научная статья рассматривает актуальную на сегодняшний день тему использования искусственного интеллекта. Авторы исследует различные направления и перспективы развития, а также и существующие риски. В статье дано определение искусственного интеллекта, что под ним понимается и какой смысл он описывает. Искусственный интеллект на текущий момент - не более чем база знаний, которая умеет выдавать ответы на вопрос пользователя на основе информации, хранящейся в базе данных. This scientific article examines the current topic of using artificial intelligence. The authors explore various directions and prospects for development, as well as existing risks. The article gives a definition of artificial intelligence, what is meant by it and what meaning it describes. Artificial intelligence at the moment is nothing more than a knowledge base that can provide answers to a user’s question based on information stored in the database.Экономика и предпринимательство00Экономика и предпринимательство["\u0427\u0415\u0412\u0415\u0420\u0415\u0412\u0410\u00a0\u0421.\u0410. \u0427\u0415\u0412\u0415\u0420\u0415\u0412\u0410\u00a0\u0421.\u0410.", "\u0415\u0416\u041e\u0412\u0410\u00a0\u0410.\u0420. \u0415\u0416\u041e\u0412\u0410\u00a0\u0410.\u0420."]2024-06-20T00:00:00 d7c0db55f2b44b8160776b6a07eb60f29fcedbd6Both artificial intelligence (ठजप्पिप्पन्स प्ज्ञामसपहमद्बम) and culture are important hallmarks of modern times. On the one hand, while artificial intelligence attempts to mimic human intelligence, on the other hand, culture studies a person's life, language, and cultural characteristics. With the adjustment of these two, a new direction and approach is emerging in the art field which is contributing significantly to the development of our society and culture. The relationship between artificial intelligence and Indian art culture has shown a new direction that is taking Indian cultural heritage forward with modernity. This relationship not only helps in giving purpose to Indian literature and art, but also presents the diversity and richness of India to the world. The matching of artificial intelligence and Indian art culture makes it clear that the use of technology gives a new dimension to art, opening the way to artistic prosperity. This relationship has made the art more effective and unique and has also maintained the important role of Indian culture. This has not only helped in the development of Indian art culture, but has also provided a medium to take it to a higher level, through which Indian art culture can keep pace with the modern world. Artificial intelligence has also begun to be used in art culture, such as techniques for conducting cultural research on color, form, and dimension. This is giving a new direction to the diversity and richness of art and new and special forms are emerging in art craftsmanship. The modern technological contribution of this matching of artificial intelligence and art culture is opening new dimensions of art. For example, artificial intelligence is being used in painting and graphics design, creating new experiences, especially in the 3D and virtual art fields. Artificial intelligence and art culture together are opening the way to new possibilities. Both these sectors are making important contributions in building a strong and prosperous society and helping in the preservation, promotion and development of culture. The presented research article discusses in detail how artificial intelligence can play an important role in the preservation and development of Indian art culture.Journal of Communication and Management00The presented research article discusses in detail how artificial intelligence can play an important role in the preservation and development of Indian art culture.Journal of Communication and Management["Pirshant Kumar", "Hariom Kumar"]2024-06-20T00:00:00 9669002d1454bf4f7542769e0031ee07453ffd6bNowadays, the influence of artificial intelligence (AI) on the spheres of our life has not been completely studied. Large companies, institutions are introducing AI in their work to increase profits and reduce costs, there are activities on introducing AI in medicine, education, culture and other areas, which can improve the quality of medical care, increase the efficiency of education, etc. However, there is not enough discussion about the ethical issues of its use, as well as the impact of AI on human cognitive functions. The article presents the results of analyzing the positive and negative factors of the influence of artificial intelligence on human development, as well as the results of a survey among schoolchildren in Yakutsk (Republic of Sakha (Yakutia), Russia) on self-assessment of the impact of using artificial intelligence on one's personal cognitive skills.2024 V International Conference on Neural Networks and Neurotechnologies (NeuroNT)90The article presents the results of analyzing the positive and negative factors of the influence of artificial intelligence on human development, as well as the results of a survey among schoolchildren in Yakutsk (Republic of Sakha, Russia) on self-assessment of the impact of using artificial intelligence on one's personal cognitive skills.2024 V International Conference on Neural Networks and Neurotechnologies (NeuroNT)["Maksim A. Sorochinskiy", "Sayana G. Prokhorova", "Ksenia A. Bazanova"]2024-06-20T00:00:00 e531482539b25ba930353de90167ff13b2385764Данная научная статья рассматривает актуальную на сегодняшний день тему искусственного интеллекта, внедрённого в процессы обучения. Автор исследует различные направления и перспективы развития, вызовы и проблемы, возможности, а также преимущества и недостатки использования искусственного интеллекта в процессе обучения, управления им и контроля. This scientific article examines the current topic of artificial intelligence embedded in learning processes. The author explores various directions and prospects of development, challenges and problems, opportunities, as well as advantages and disadvantages of using artificial intelligence in the learning process, its management and control.Экономика и предпринимательство00Экономика и предпринимательство["\u0427\u0415\u0412\u0415\u0420\u0415\u0412\u0410\u00a0\u0421.\u0410. \u0427\u0415\u0412\u0415\u0420\u0415\u0412\u0410\u00a0\u0421.\u0410.", "\u0428\u0418\u0420\u041e\u041a\u041e\u0412\u0410\u00a0\u041a.\u0410. \u0428\u0418\u0420\u041e\u041a\u041e\u0412\u0410\u00a0\u041a.\u0410."]2024-06-20T00:00:00 43ec2f0f65cad5a616d792dcff9e016fd8780c75Information on problem-oriented control systems and trusted artificial intelligence is provided. It is noted that one of the directions for the development and description of machine learning methods and models of artificial intelligence systems is the axiomatic foundations of substitution functions in the number system of a factorial sets series.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)100It is noted that one of the directions for the development and description of machine learning methods and models of artificial intelligence systems is the axiomatic foundations of substitution functions in the number system of a factorial sets series.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)["Alexander Petrovich Martynov", "Inna Alexandrovna Martynova"]2024-06-20T00:00:00 eb1eb8b526e4fa7f025f4ae14ba54d31426b4dc0Generally, food delivery services like GrabFood act as couriers, transporting consumer needs from restaurants or stores directly to their doorsteps. The rise of Artificial Intelligence (AI) has further revolutionized this convenience, allowing people to order meals and other goods from the comfort of their homes. This research investigates how AI is utilized to influence customer purchase intentions on GrabFood. The study examines the impact of six independent variables: instant food delivery, estimated delivery time, customized food recommendations, interactivity, cashless payment methods, and consumer behavior. These variables are analyzed in relation to the dependent variable – the customer's intention to use GrabFood. To gather data, an online survey was conducted with 100 respondents. The collected data was then verified using SPSS software. The findings revealed that delivery speed is a key driver, with both instant delivery and estimated delivery time showing a significant positive correlation (β = 0.457) with purchase intention. However, other features like personalized recommendations (β = 0.174), cashless payment methods (β = 0.119), and user interaction (β = -0.188) did not significantly impact user decisions. These findings require further exploration to understand user preferences for these featuresInternational Journal of Tourism and Hospitality in Asia Pasific00The findings revealed that delivery speed is a key driver, with both instant delivery and estimated delivery time showing a significant positive correlation with purchase intention, but other features like personalized recommendations, interactivity, cashless payment methods, and user interaction did not significantly impact user decisions.International Journal of Tourism and Hospitality in Asia Pasific["Rosmelisa Yusof", "L. Koay", "Thevisri Ravi", "Yee Teng Teoh", "Mun Yee Thin", "Tiffany Audrey Anak Donold", "Nur Aini Raudhatul Jannah", "Prachi Mittal", "Rishabh Srivastava", "Daisy Mui Hung Kee"]2024-06-20T00:00:00 fda03633848ead237fd867fae139554e41459c60This article discusses the practical implementation of artificial intelligence (ai) in education and scientific research. It provides examples of how and why ai is necessary and draws conclusions about how people's lives may change after ai is integrated into society.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)60The practical implementation of artificial intelligence in education and scientific research provides examples of how and why ai is necessary and draws conclusions about how people's lives may change after ai is integrated into society.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)["Sergei Kuzenkov"]2024-06-20T00:00:00 54810ff1988d53a86db6346c753c75e0fb2f471cWith the rapid development of science and technology, artificial intelligence technology has been widely used in various fields, including mining geological exploration field is no exception. Artificial intelligence technology has brought revolutionary changes to mining geological exploration, its application in mining geological exploration, not only can improve the efficiency and accuracy of mining geological exploration, but also greatly reduce the cost of mine exploration, and improve the safety and reliability of exploration. At present, the application of artificial intelligence technology in mining geological exploration, mainly three-dimensional visual technology, machine learning algorithm, remote sensing image recognition technology, natural language processing technology, intelligent data analysis technology, and so on, has provided strong support for mining geological exploration work.Education Reform and Development130The application of artificial intelligence technology in mining geological exploration, mainly three-dimensional visual technology, machine learning algorithm, remote sensing image recognition technology, natural language processing technology, intelligent data analysis technology, and so on, has provided strong support for mining geological exploration work.Education Reform and Development["Xiuliang Zhang", "T. E. Nyamasvisva", "Chuntao Liu"]2024-06-20T00:00:00 e6e84ebfd091cad97e04827dc4a9a2d4a00ca903This article focuses on the use of artificial intelligence technologies in education. The capabilities of chatbot platforms have been studied, problems associated with their use in educational activities have been identified, and the specifics of using the capabilities of artificial intelligence have been analyzed.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)110The capabilities of chatbot platforms have been studied, problems associated with their use in educational activities have been identified, and the specifics of using the capabilities of artificial intelligence have been analyzed.2024 4th International Conference on Technology Enhanced Learning in Higher Education (TELE)["L. V. Nabokov", "N. Pachina", "E. V. Korolyova", "Alexander Alexandrovich Kuznetsov", "Ella Yurievna Kuzmenko"]2024-06-20T00:00:00 57a6dd7482a4247f1e1ea443bf1c4cb3dfb405c0Comput. Biol. Medicine334The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.Computers in biology and medicine["M. A. Rasel", "S. A. Kareem", "Zhenli Kwan", "S. Yong", "U. Obaidellah"]2024-06-20T00:00:00 cb047c94a7bd5e7258d6bc9788369c3ba4039744Skeletal Radiology1493A perspective review of the most extensively investigated deep learning applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade.Skeletal radiology["H. Ruitenbeek", "E. H. Oei", "Jacob J Visser", "Richard Kijowski"]2024-06-20T00:00:00 77f90c537482c337cd7a84ae7aed2e011911413eGenerative AI such as those with large language models have created opportunities for innovative assessment design practices. Due to recent technological developments, there is a need to know the limits and capabilities of generative AI in terms of simulating cognitive skills. Assessing student critical thinking skills has been a feature of assessment for time immemorial, but the demands of digital assessment create unique challenges for equity, academic integrity and assessment authorship. Educators need a framework for determining their assessments vulnerability to generative AI to inform assessment design practices. This paper presents a framework that explores the capabilities of the LLM ChatGPT4 application, which is the current industry benchmark. This paper presents the Mapping of questions, AI vulnerability testing, Grading, Evaluation (MAGE) framework to methodically critique their assessments within their own disciplinary contexts. This critique will provide specific and targeted indications of their questions vulnerabilities in terms of the critical thinking skills. This can go on to form the basis of assessment design for their tasks.arXiv.org103A framework that explores the capabilities of the LLM ChatGPT4 application, which is the current industry benchmark, and presents the Mapping of questions, AI vulnerability testing, Grading, Evaluation (MAGE) framework to methodically critique their assessments within their own disciplinary contexts.ArXiv["Luke Zaphir", "Jason Lodge", "Jacinta Lisec", "Dom McGrath", "Hassan Khosravi"]2024-06-20T00:00:00 2ef40ea18519ffe279f068e2fce97669b6703bf3Pelatihan menggunakan website kecerdasan buatan (AI) bagi guru merupakan langkah penting dalam menghadapi perkembangan teknologi dalam pendidikan. Melalui webinar ini, narasumber berhasil menyampaikan materi dengan jelas dan efektif, serta memfasilitasi diskusi yang bermanfaat. Umpan balik dari peserta menunjukkan tingkat kepuasan yang tinggi terhadap kinerja narasumber. Diharapkan, kegiatan serupa dapat terus ditingkatkan interaktivitasnya dan diadakan secara berkala untuk mendukung peningkatan keterampilan teknologi AI di kalangan guruJurnal Sinergi01JURNAL SINERGI["M. A. Rahman"]2024-06-20T00:00:00 ecb7210c2ac4285f96836a9e901b3969feedc1ddLaser + Photonics for Advanced Manufacturing01Laser + Photonics for Advanced Manufacturing["Victor Hayot", "Andre Alves Ferreira", "Sylvain Lecler", "G. Chabrol"]2024-06-20T00:00:00 065a887a28bb9462f12fda708be4d0dc91dfd0f2Translational Issues in Psychological Science01Translational Issues in Psychological Science["T. Chenneville", "Brianna Duncan", "Gabriella Silva"]2024-06-20T00:00:00 d2a7634d26e803a817e0019b152857775922753a02["Inam Ullah Khan", "Mariyam Ouaissa", "Mariyam Ouaissa", "Muhammad Fayaz", "Rehmat Ullah"]2024-06-20T00:00:00 970d819a0b08edf909eebfd9aa73896f64d13619IARJSET01IARJSET["Dr.V. Kanimozhi", "V. Sneha"]2024-06-20T00:00:00 0d21c2fb2a792f5f55b986722f6827b19d0bf911International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-06-20T00:00:00 447c5d562d2eb0bcf3745d3e315e4a88ecc48142Bulletin00THE BULLETIN["Y.M. Zhusupov", "Z. Temirkhanov", "A.S. Bekbolsynova"]2024-06-20T00:00:00 8f70de4f490fd0b03634ce93279cbce9275f2744стремительная цифровизация общественных отношений ставит перед юридической наукой новые задачи, одной из которых является определение правового положения искусственного интеллекта, вступающего во взаимодействие (прямое и опосредованное) с человеком, а также с различными механизмами с установленным соответствующим программным обеспечением. По результатам исследования, в ходе которого были в том числе проанализированы содержание понятия искусственный интеллект, а также правовые позиции, излагаемые в юридической литературе по исследуемому вопросу, автор приходит к следующим выводам. Искусственный интеллект не является «сверхразумом» (как это порой преподносится в околонаучной литературе), это в той или иной степени совершенная компьютерная программа, которая не отвечает признакам автономности, воли, самостоятельности принятия решений и другим, присущим человеку качествам. В настоящее время степень развития общественных отношений с включением в них функционала искусственного интеллекта позволяет сделать вывод о том, что он (искусственный интеллект или правильнее говорить компьютерная программа) пока не достиг того «уровня развития», который позволял бы говорить о нем как о полноправном субъекте или субъекте «с ограниченными правами и обязанностями» правоотношений, как offline, так и online пространства. Наиболее приемлемым является считать его объектом таких правоотношений. the rapid digitalization of social relations poses new challenges for legal science, one of which is to determine the legal status of artificial intelligence that interacts (directly and indirectly) with a person, as well as with various mechanisms with the appropriate software installed. Based on the results of the study, during which the content of the concept of artificial intelligence was analyzed, as well as the legal positions set forth in the legal literature on the issue under study, the author comes to the following conclusions. Artificial intelligence is not “superintelligence” (as it is sometimes presented in pseudo-scientific literature); it is, to one degree or another, a perfect computer program that does not meet the characteristics of autonomy, will, independent decision-making and other inherent human qualities. Currently, the degree of development of social relations with the inclusion of artificial intelligence functionality allows us to conclude that it (artificial intelligence or, more correctly, a computer program) has not yet reached that “level of development” that would allow us to speak of it as a full-fledged subject or a subject “with limited rights and obligations” of legal relations, both offline and online. It is most acceptable to consider it the object of such legal relations.International law journal40International Law Journal["\u0414.\u0418. \u041f\u0440\u043e\u0432\u0430\u043b\u0438\u043d\u0441\u043a\u0438\u0439"]2024-06-20T00:00:00 405b0e1fd28d34b0cb01c22ceced5fbfcbb7acc4Bulletin00THE BULLETIN["Zh.E. Zulpykhar", "A. Nurlankyzy", "R. Latip", "N. Karelkhan"]2024-06-20T00:00:00 31944d18acbb30a95968b42e197fa5bd8234f9b7CC The Journal00CC The Journal["Darwin James Singca"]2024-06-20T00:00:00 0bde7ef82944acf8d362b9f41c0bce281e7fa273IJARCCE00IJARCCE["Punit Dewani"]2024-06-20T00:00:00 4be75595ab5bda38353e4d3e7f72df44bfe650cdПовсеместная интеграция цифровых технологий в различные аспекты человеческого существования революционизирует бизнес-модели, ускоряет выход предприятий на глобальный рынок, задает новые параметры для подбора персонала, заменяет определенные формы человеческого труда алгоритмами и машинами. В данной статье рассматривается глубокое влияние искусственного интеллекта (ИИ) на бизнес-модели в нескольких отраслях. Компании по всему миру используют искусственный интеллект (ИИ) для стимулирования инноваций и получения конкурентных преимуществ по мере того, как прорывные технологии трансформируют устоявшиеся системы. В данной статье исследуется влияние новых технологий, в частности искусственного интеллекта, на изменение стратегий компаний, которое в основном фокусируется на внешних переменных, стимулирующих инновации бизнес-моделей. Исследование подчеркивает недостаточное понимание прямого влияния ИИ на инновации бизнес-моделей и выступает за дальнейшие масштабные исследования в этой конкретной области. The widespread integration of digital technologies into various aspects of human existence revolutionizes business models, accelerates the entry of enterprises into the global market, sets new parameters for recruitment, replaces certain forms of human labor with algorithms and machines. This article examines the profound impact of artificial intelligence (AI) on business models in several industries. Companies around the world are using artificial intelligence (AI) to drive innovation and gain competitive advantages as breakthrough technologies transform established systems. This article examines the impact of new technologies, in particular artificial intelligence, on changing company strategies, which mainly focuses on external variables that stimulate business model innovation. The study highlights the lack of understanding of the direct impact of AI on business model innovation and advocates for further large-scale research in this particular area.Экономика и предпринимательство00Экономика и предпринимательство["\u0425\u0410\u041c\u0425\u041e\u0415\u0412\u0410\u00a0\u0424.\u042f. \u0425\u0410\u041c\u0425\u041e\u0415\u0412\u0410\u00a0\u0424.\u042f.", "\u041c\u0418\u041d\u041a\u0410\u0418\u041b\u041e\u0412\u0410\u00a0\u041c.\u041c. \u041c\u0418\u041d\u041a\u0410\u0418\u041b\u041e\u0412\u0410\u00a0\u041c.\u041c.", "\u0410\u0421\u041b\u0410\u0425\u0410\u041d\u041e\u0412\u0410\u00a0\u0421.\u0410. \u0410\u0421\u041b\u0410\u0425\u0410\u041d\u041e\u0412\u0410\u00a0\u0421.\u0410."]2024-06-20T00:00:00 4d80f6d3f58958e6dda47d1aed19b703650ed2ffProduction &amp; Manufacturing Research370Production &amp; Manufacturing Research["G\u00e1bor Nick", "Klaudia Zeleny", "T. Kov\u00e1cs", "Tam\u00e1s J\u00e1rv\u00e1s", "K\u00e1roly Pocsarovszky", "Andrea K\u0151"]2024-06-20T00:00:00 929ad35fdb27fa1f15307eaf7490338147f62719The swift evolution of ChatGPT maintains revealing great promise in different fields of life while occasionally with ethically questionable impacts. While the current research effort has focused on the benefits that can be gained from ChatGPT, increasing concerns have been raised about the ethical implications that could result from its widespread use. To this end, this study presents an in-depth investigation of the ethical aspects of ChatGPT from the perspective of responsible AI. In particular, a novel theoretical framework is introduced to practically analyze and interpret the ChatGPT from an ethical side lens. Our framework is based on the concept of responsible AI, to focus on the variety of scenarios in which ChatGPT can possibly lead to unintentional consequences, and to advocate alternate paths that the researcher and practitioners can follow to expand their knowledge regarding the mitigation of such incidences. This work expands the theorization of the ethical side to disclose unknown ideas of existing literature and to suggest other leading premises that may guide future development and use of ChatGPT and alike language models.Information Sciences with Applications200This study presents an in-depth investigation of the ethical aspects of ChatGPT from the perspective of responsible AI, and a novel theoretical framework is introduced to practically analyze and interpret the ChatGPT from an ethical side lens.Information Sciences with Applications["Sami Lababidi"]2024-06-20T00:00:00 56d31079c79cd2107bcb15c723befc7bc6283163В статье проводится анализ влияния интеграции искусственного интеллекта на динамику производительности в ключевых секторах экономики Российской Федерации. Используя современные методы исследования, авторы рассматривают спектр применения технологий искусственного интеллекта, включая машинное обучение, алгоритмы прогнозирования, разработку чатботов, системы автоматизации и аналитики, и их эффект на повышение эффективности ITобслуживания, производственных процессов, создание новых продуктов и услуг, оптимизацию затрат и повышение качества маркетинговых стратегий и продаж. Особое внимание уделяется анализу региональных особенностей использования искусственного интеллекта в России, его воздействию на показатели занятости и ВВП, а также общее экономическое влияние новых технологий. В статья рассматриваются как позитивные, так и потенциально негативные последствия внедрения искусственного интеллекта, предлагая комплексный взгляд на тенденции развития искусственного интеллекта в контексте стремительного технологического прогресса, и оценивает их влияние на будущее экономическое развитие России. The article analyzes the impact of the integration of artificial intelligence on the dynamics of productivity in key sectors of the economy of the Russian Federation. Using modern research methods, the authors consider the range of applications of artificial intelligence technologies, including machine learning, forecasting algorithms, chatbot development, automation and analytics systems, and their effect on improving the efficiency of IT services, production processes, creating new products and services, optimizing costs and improving the quality of marketing strategies and sales. Special attention is paid to the analysis of regional features of the use of artificial intelligence in Russia, its impact on employment and GDP, as well as the overall economic impact of new technologies. The article examines both the positive and potentially negative consequences of the introduction of artificial intelligence, offering a comprehensive look at the trends in the development of artificial intelligence in the context of rapid technological progress, and assesses their impact on the future economic development of Russia.Экономика и предпринимательство00Экономика и предпринимательство["\u041a\u041e\u0425\u00a0\u041c.\u041d. \u041a\u041e\u0425\u00a0\u041c.\u041d.", "\u041c\u0415\u0414\u0412\u0415\u0414\u0415\u0412\u0410\u00a0\u0410.\u042e. \u041c\u0415\u0414\u0412\u0415\u0414\u0415\u0412\u0410\u00a0\u0410.\u042e."]2024-06-20T00:00:00 73822a8ea8aba1a76ad4cb906bafcd1eff9f8f34В наше время, когда конкуренция на рынке становится все более жесткой, предпринимателям необходимо применять инновационные методы для достижения успеха и выхода в лидеры своей отрасли. Один из таких инструментов искусственный интеллект, который позволяет не только оптимизировать ресурсы организации, но и значительно увеличивает объемы продаж. В данной статье мы рассмотрим, как искусственный интеллект может стать эффективным инструментом для развития бизнеса и достижения поставленных целей. Искусственный интеллект становится все более распространенным и востребованным инструментом в бизнесе. Он способен автоматизировать рутинные задачи, обрабатывать большие объемы данных и прогнозировать потребности рынка. В этой статье мы рассмотрим, как применение искусственного интеллекта может значительно повысить продажи и помочь команде в экономии ресурсов. Nowadays, when competition in the market is becoming increasingly fierce, entrepreneurs need to use innovative methods to achieve success and become leaders in their industry. One of these tools is artificial intelligence, which allows not only to optimize the organization’s resources, but also significantly increases sales volumes. In this article we will look at how artificial intelligence can become an effective tool for business development and achieving your goals. Artificial intelligence is becoming an increasingly common and in-demand tool in business. It is capable of automating routine tasks, processing large amounts of data and predicting market needs. In this article, we will look at how the use of artificial intelligence can significantly increase sales and help the team save resources.Экономика и предпринимательство00Экономика и предпринимательство["\u0412\u0415\u041b\u0418\u041a\u0410\u041d\u041e\u0412\u0410\u00a0\u0421.\u0421. \u0412\u0415\u041b\u0418\u041a\u0410\u041d\u041e\u0412\u0410\u00a0\u0421.\u0421.", "\u0410\u041d\u0414\u0420\u0415\u0415\u0412\u0410\u00a0\u041e.\u0412. \u0410\u041d\u0414\u0420\u0415\u0415\u0412\u0410\u00a0\u041e.\u0412.", "\u0421\u0410\u0412\u0415\u041b\u042c\u0415\u0412\u00a0\u041a.\u041d. \u0421\u0410\u0412\u0415\u041b\u042c\u0415\u0412\u00a0\u041a.\u041d."]2024-06-20T00:00:00 d32903078501325a910ecb9b94e2cf2c06d5ca91Экономика страны, безопасность и качество жизни зависят от исправной транспортной системы. Внедрение технологий искусственного интеллекта и машинного обучения в транспортную систему с одной стороны находят большие перспективы использования их в качестве полноценных помощников в данной сфере, с другой стороны предприятия сталкиваются с определенными вызовами. В статье перечислены положительные и отрицательные стороны внедрения перечисленных технологий в транспортные системы. Автомобильная промышленность, автономные транспортные системы, управление транспортным потоком и обеспечение безопасности в транспортных системах - это сферы, где могут произойти революционные изменения благодаря внедрению технологий искусственного интеллекта и машинного обучения. Для исключения недостатков внедрения искусственного интеллекта в транспортные системы требуется дополнительное финансирование со стороны государства. The country's economy, safety and quality of life depend on a functioning transport system. The introduction of artificial intelligence and machine learning technologies into the transport system, on the one hand, has great prospects for using them as full-fledged assistants in this area, on the other hand, enterprises face certain challenges. The article lists the positive and negative aspects of introducing the listed technologies into transport systems. The automotive industry, autonomous transport systems, traffic management and safety in transport systems are areas where revolutionary changes can occur due to the introduction of artificial intelligence and machine learning technologies. To eliminate the disadvantages of introducing artificial intelligence into transport systems, additional funding from the state is required.Экономика и предпринимательство00Экономика и предпринимательство["\u041a\u0423\u0426\u0415\u041d\u041a\u041e\u00a0\u0421.\u041c. \u041a\u0423\u0426\u0415\u041d\u041a\u041e\u00a0\u0421.\u041c.", "\u0423\u0421\u0422\u042e\u0416\u0410\u041d\u0418\u041d\u041e\u0412\u0410\u00a0\u0414.\u0421. \u0423\u0421\u0422\u042e\u0416\u0410\u041d\u0418\u041d\u041e\u0412\u0410\u00a0\u0414.\u0421."]2024-06-20T00:00:00 d384c65eefd5de194bb42252227f8fdc1567cf18В статье рассмотрена актуальность инновационных рисков инновационных проектов на базе ИИ в здравоохранении Российской Федерации, представлено адаптированное определение экономической категории инновационный риск инновационного проекта на базе ИИ. Обозначены основные инструменты выявления, оценки и снижения рисков в сфере здравоохранения Российской Федерации, выделены особенностей в выявлении и управлении потенциальными рисками для безопасности пациентов и организационного благополучия, а также обозначены возможные риски по основным направлениям разрабатываемых инновационных проектов с использованием ИИ в сфере здравоохранения Российской Федерации. The article examines the relevance of innovative risks of innovative projects based on AI in healthcare of the Russian Federation and presents an adapted definition of the economic category innovation risk of an innovative project based on AI. The main tools for identifying, assessing and reducing risks in the healthcare sector of the Russian Federation are outlined, features in identifying and managing potential risks to patient safety and organizational well-being are highlighted, and possible risks are identified in the main areas of innovative projects being developed using AI in the healthcare sector of the Russian Federation.Экономика и предпринимательство00Экономика и предпринимательство["\u041c\u0410\u041b\u042b\u0428\u041a\u0418\u041d\u0410\u00a0\u041c.\u0412. \u041c\u0410\u041b\u042b\u0428\u041a\u0418\u041d\u0410\u00a0\u041c.\u0412."]2024-06-20T00:00:00 8e4638f5bd46285420c6939391d387c49f3ca5b2This study aims to evaluate the use of the RADEC Model supported by Artificial Intelligence (AI) in enhancing students' science literacy. The research methodology involves data collection through student questionnaires, which include questions related to their understanding of scientific concepts, contexts, and attitudes after using the RADEC Model. Data analysis employs descriptive statistical techniques to assess students' agreement levels regarding the provided statements. The results indicate that most students exhibit high levels of agreement regarding the effectiveness of the RADEC Model in enhancing their understanding of science concepts, boosting learning motivation, facilitating problem-solving, and developing critical thinking skills. Positive impacts are also observed in improving student engagement in collaborative discussions and increasing their interest in further exploration of science. This research makes a significant contribution by highlighting the importance of integrating AI technology into educational approaches to enhance students' science literacy, with broad implications for improving the quality of science education in the future. In conclusion, the integration of the AI-based RADEC Model is an innovative step to increase the effectiveness of science learning in elementary schools, so prospective elementary school teachers must be able to master this technology to provide personalized learning experiences and improve students' science literacy.Jurnal Penelitian Pendidikan IPA356The integration of the AI-based RADEC Model is an innovative step to increase the effectiveness of science learning in elementary schools, so prospective elementary school teachers must be able to master this technology to provide personalized learning experiences and improve students' science literacy.Jurnal Penelitian Pendidikan IPA["Wati Sukmawati", "S. Wahjusaputri"]2024-06-20T00:00:00 c569e33021e04b5e9948654a13eb5a48f445052aFrontier artificial intelligence (AI) systems could pose increasing risks to public safety and security. But what level of risk is acceptable? One increasingly popular approach is to define capability thresholds, which describe AI capabilities beyond which an AI system is deemed to pose too much risk. A more direct approach is to define risk thresholds that simply state how much risk would be too much. For instance, they might state that the likelihood of cybercriminals using an AI system to cause X amount of economic damage must not increase by more than Y percentage points. The main upside of risk thresholds is that they are more principled than capability thresholds, but the main downside is that they are more difficult to evaluate reliably. For this reason, we currently recommend that companies (1) define risk thresholds to provide a principled foundation for their decision-making, (2) use these risk thresholds to help set capability thresholds, and then (3) primarily rely on capability thresholds to make their decisions. Regulators should also explore the area because, ultimately, they are the most legitimate actors to define risk thresholds. If AI risk estimates become more reliable, risk thresholds should arguably play an increasingly direct role in decision-making.arXiv.org1037It is recommended that companies define risk thresholds to provide a principled foundation for their decision-making, use these risk thresholds to help set capability thresholds, and then primarily rely on capability thresholds to make their decisions.ArXiv["Leonie Koessler", "Jonas Schuett", "Markus Anderljung"]2024-06-20T00:00:00 3dfbd68b03575d059415acda033e74e832d00652This study explores the application of artificial intelligence (AI) in achieving sustainable development goals, focusing on sustainable agriculture and waste management. Using a mixed-methods approach, we analyzed data from various case studies and conducted a comprehensive literature review. Our findings reveal that AI significantly enhances operational efficiency, resource optimization, and cost reduction across these sectors. For instance, AI-powered smart irrigation systems in India have reduced water usage by 30% while increasing crop yields, and AI applications in Singapore's waste management have improved recycling rates by 25%. Despite these benefits, challenges such as infrastructure limitations, the need for specialized technical skills, and societal resistance persist. By conducting in-depth interviews with experts and surveys with practitioners, we gathered extensive data that underscores the need for supportive policies, infrastructure investment, and comprehensive training programs to maximize AI's potential. Our research provides practical recommendations to overcome these challenges, aiming to fully leverage AI's capabilities for a greener, more sustainable future.International Transactions on Artificial Intelligence (ITALIC)4017The findings reveal that AI significantly enhances operational efficiency, resource optimization, and cost reduction across these sectors and underscores the need for supportive policies, infrastructure investment, and comprehensive training programs to maximize AI's potential.International Transactions on Artificial Intelligence (ITALIC)["Daniel Hernandez", "Lukita Pasha", "David Arian Yusuf", "Rifky Nurfaizi", "Dwi Julianingsih"]2024-06-21T00:00:00 d083a865e8b5920e8b8ce406d89b9d29cc532b27Reproductive Sciences433A comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.Reproductive sciences["Pulkit Verma", "Pratibha Maan", "Rohit Gautam", "Taruna Arora"]2024-06-21T00:00:00 68b03fe82b2c733d3b482e367b397aab438485afThe integration of artificial intelligence (AI) into communication strategies has revolutionized various fields, including Islamic Da'wah. This study explores how AI technologies can be utilized to enhance the dissemination of Islamic teachings, examining both the opportunities and challenges this integration presents. This research employs a mixed-methods approach, combining qualitative analysis of AI applications in religious contexts with quantitative surveys of Da'wah practitioners and their audiences. The findings reveal that AI technologies, such as chatbots, natural language processing, and machine learning, significantly enhance the reach and personalization of Da'wah efforts. AI enables interactive and engaging communication, breaking down geographical and linguistic barriers. However, the results also highlight several challenges, including ethical concerns regarding the accuracy of AI-generated content, the potential for misinformation, and the digital divide that limits access to these technologies. The study concludes that while AI offers substantial opportunities to innovate Islamic Da'wah, careful consideration must be given to ethical implications and practical limitations. The collaboration between AI experts and Islamic scholars is essential to ensure that AI-driven Da'wah initiatives are both effective and respectful of Islamic values. Balancing AI's advantages with its challenges can lead to more effective and inclusive communication strategies in the digital era, ultimately enriching the practice of Islamic propagation.Feedback International Journal of Communication212The findings reveal that AI technologies, such as chatbots, natural language processing, and machine learning, significantly enhance the reach and personalization of Da'wah efforts and can lead to more effective and inclusive communication strategies in the digital era.Feedback International Journal of Communication["Marlina", "Yaza Azahra Ulya"]2024-06-21T00:00:00 724c0cd32e1b09d1f120ff043b8ec31393e1a6a5Project management information systems (PMIS) are crucial tools in today's dynamic business environment. With the advancement of artificial intelligence (AI) technologies, there are significant opportunities for enhancing PMIS functionalities. The purpose of this article is to analyze current get AI implication for solving PMIS challenges module by module. The results of this study provide theoretical validation that certain modules within PMIS can be enriched through AI integration, thus engendering the emergence of supplementary module facilitated by AI.International Workshop IT Project Management01The results of this study provide theoretical validation that certain modules within PMIS can be enriched through AI integration, thus engendering the emergence of supplementary module facilitated by AI.{"pages": "27-41"}["Ihor Berezutskyi", "T. Honcharenko"]2024-06-21T00:00:00 13b2cf16fc27b1088c415004dd458fbd22916da2 This paper proposes a structured, tiered framework through a UNCITRAL Model Law to gradually integrate artificial intelligence (AI) into foreign investment law arbitration in an ethical and effective manner. It also explores the critical role arbitration institutions can play in facilitating AI implementation. It details how AI can assist arbitrators by searching vast datasets, automating routine tasks, and enhancing decision-making through analysis of previous cases. Current AI regulations in regions like the EU, UK, and Canada fall short in addressing the complexities of cross-border arbitration and ensuring interoperability. By emphasising the distinct contributions of the proposed UNCITRAL Model Law and arbitration institutions, the paper highlights a multi-faceted strategy to overcome challenges posed by outdated international conventions, inconsistencies in bilateral investment treaties, and the lack of comprehensive guidance. This approach aims to refine the integration of AI in arbitration processes, enhancing efficiency, fairness, and the legitimacy of the arbitration system.The Journal of World Investment &amp; Trade01The Journal of World Investment &amp; Trade["Atif M. Alenezi"]2024-06-21T00:00:00 2ccc8969715cb4d3e8420ae00f21f3ac5810a3beArtificial Intelligence (AI) has become an important element in various aspects of life, including Human Resource Management (HRM). AI offers great potential to increase efficiency and accuracy in employee recruitment and selection processes. This research aims to explore how AI can be implemented in recruitment and selection to increase the speed, effectiveness and fairness of the process. This research also evaluates the benefits and challenges faced in using AI in the HRM context, with a particular focus on the candidate screening process based on relevant criteria. The research approach used is a literature study, where data is collected from books, journal articles, industry reports, and other reliable sources. The literature review includes analysis of a variety of academic and practical sources relevant to this topic, to gain a comprehensive picture of the development and application of AI in recruitment and selection. The analysis process involves identifying key themes related to adaptive and flexible leadership and their implications for organizations . This method was chosen to gain a comprehensive understanding of the concepts discussed and their relevance in the context of digital transformation. The research results show that the use of AI in recruitment and selection can significantly increase the efficiency and speed of candidate screening. AI is able to automate the resume screening process, perform big data analysis, and use natural language processing to interact with candidates in a more personal way. AI systems are also able to reduce subjective bias, increase objectivity in decision making, and enable significant time and cost savings. Additionally, the study found that customization of screening criteria provided by AI allows companies to tailor the selection process to their specific needs, resulting in higher quality candidates. However, the research also identified several challenges, such as the need for accurate data, potential bias in algorithms, and the need for ongoing monitoring to ensure transparency and fairness in the recruitment process. AI offers an effective solution to improve efficiency and accuracy in recruitment and selection, but successful implementation requires a deep understanding of the technology as well as a commitment to minimizing risk and ensuring a fair and transparent process.Sci-tech Journal02The research results show that the use of AI in recruitment and selection can significantly increase the efficiency and speed of candidate screening, and that customization of screening criteria provided by AI allows companies to tailor the selection process to their specific needs, resulting in higher quality candidates.Sci-tech Journal["Efrita Norman", "Enah Pahlawati"]2024-06-21T00:00:00 451c509908a14fa720c5a12370a9ee1be69d1153The incorporation of Artificial Intelligence (AI) into the financial services sector has catalyzed profound transformations, significantly enhancing the accuracy, efficiency, and capabilities of financial operations. This paper meticulously examines the pivotal role of AI in revolutionizing risk assessment processes through advanced deep learning and machine learning techniques. These methodologies harness extensive and diverse datasets, which include both traditional financial indicators and non-traditional sources like social media activities, to provide a more nuanced and comprehensive analysis of risk. Additionally, the paper emphasizes the critical role of AI in personalizing customer experiences and elevating fraud detection mechanisms to levels of unprecedented precision. Through detailed quantitative analyses and illustrative case studies, this study assesses the impact of AI on operational efficiency and decision-making accuracy within financial institutions. It explores advanced AI techniquesdeep learning, reinforcement learning, and natural language processingand their significant implications for financial forecasting, algorithmic trading, and regulatory compliance. By integrating empirical evidence with theoretical insights, this paper offers a thorough understanding of AI's transformative influence on the financial sector, highlighting potential future innovations that may redefine industry standards and enhance operational methodologies. This comprehensive examination not only illuminates the current benefits and applications of AI in financial services but also projects its future trajectories in reshaping the financial landscape.Applied and Computational Engineering71This comprehensive examination of AI's transformative influence on the financial sector not only illuminates the current benefits and applications of AI in financial services but also projects its future trajectories in reshaping the financial landscape.Applied and Computational Engineering["Shujie Feng"]2024-06-21T00:00:00 52ccbba980ce94ec7f4539c2703e0bac0caaf7d5The impact of artificial intelligence (AI) in higher education is significant, with potential to transform various aspects of the educational experience. AI applications can assist in administrative tasks, such as simplifying processes, interpreting data, and predicting student success, as well as provide personalized teaching and learning experiences through virtual tours, virtual teaching assistants, and individualized learning plans. AI can also support research by sorting through large datasets, building models, and recommending relevant articles, enabling better-informed decisions in lesson assessment and professional development.Journal of Science and Knowledge Horizons01Artificial intelligence applications can assist in administrative tasks, such as simplifying processes, interpreting data, and predicting student success, as well as provide personalized teaching and learning experiences through virtual tours, virtual teaching assistants, and individualized learning plans.Journal of Science and Knowledge Horizons["Chekirine Dilmi", "Zoubida Sakri"]2024-06-21T00:00:00 d99e33f5a1267f4bd2cce596558a88daf3bee018This article focuses on the future development and innovative uses of AI in marketing and public relations. The purpose of the article is to systematise information on new approaches to marketing and PR that use artificial intelligence. Research methods include analysis of market trends, data synthesis and case studies. The findings highlight that artificial intelligence is playing a central role in the evolution of marketing and public relations, significantly improving the efficiency of data processing, brand positioning and personalisation of communication. The study examines the contribution of tools such as Jasper and ChatGPT to marketing copywriting and content planning, as well as Frace and Fireflies to monitoring the emotional tone of brands on social media and automating meetings. Such tools as Deepl and Grammarly are highlighted to ensure translation and proofreading accuracy, as well as the use of Midjourney for creative visualisation. The analysis of the market for the use of AI in marketing indicates that the AI market in marketing and PR could reach 107.57 billion USD by 2028. However, 37% of companies are still not ready to fully implement AI due to a lack of skilled skills and resources, which highlights the need for educational initiatives to improve professional competence. The document points to the potential for revolutionary changes in the way businesses do business, including increased efficiency, security and adaptation to consumer preferences, with a focus on strategic understanding and adaptation to changing market conditions. The practical significance of the study is to provide businesses with recommendations on the effective use of AI, which can increase the competitiveness of companies. The scientific novelty of this study lies in a comprehensive analysis of the impact of artificial intelligence on various aspects of marketing and public relations, as well as in identifying the potential of automation and personalisation in customer interaction. The main barriers to the full implementation of AI in business processes are identified and strategies to overcome them are developed. Based on the research, recommendations will be formulated for the further development of innovative technologies in marketing and PR, with a focus on educational initiatives that can significantly increase the level of professional qualification in this field.Economics &amp; Education90The study examines the contribution of tools such as Jasper and ChatGPT to marketing copywriting and content planning, as well as Frace and Fireflies to monitoring the emotional tone of brands on social media and automating meetings, and identifies the potential of automation and personalisation in customer interaction.Economics &amp; Education["Yeva Telebenieva"]2024-06-21T00:00:00 a619f1ad4cd3bc73a47fdca7e34dfa4882a9a639Artificial Intelligence, as a manifestation of human ingenuity, has fundamentally transformed many aspects of our daily life. The present article aims to explore the multifaceted implications of AI on the spiritual life of humankind, and the define of the personal identity, specifically from the perspective of Christian Orthodoxy. While AI offers unprecedented advantages in various sectors, its intersection with spirituality poses both challenges and opportunities. This exploration addresses the shifting paradigms of belief, the human desire for connection or escape from daily reality, and the evolving definitions of soul, purpose, and spiritual ascendenceIcoana Credintei00The present article aims to explore the multifaceted implications of AI on the spiritual life of humankind, and the define of the personal identity, specifically from the perspective of Christian Orthodoxy.Icoana Credintei["Morlova Nicu\u0219or"]2024-06-21T00:00:00 6c1b35b1a88da1146fc6153e37b1aa29c63dcca0This study aims to dissect the current state of emotion recognition and response mechanisms in artificial intelligence (AI) systems, exploring the progress made, challenges faced, and implicit operations of integrating emotional intelligence into AI. This study utilized a comprehensive review approach to investigate the integration of emotional intelligence (EI) into artificial intelligence (AI) systems, concentrating on emotion recognition and response mechanisms. The review process entailed formulating research questions, systematically searching academic databases such as PubMed, Scopus, and Web of Science, critically evaluating relevant literature, synthesizing the data, and presenting the findings in a comprehensive format. The study highlights the advancements in emotion recognition models, including the use of deep literacy ways and multimodal data emulsion. It discusses the challenges in emotion recognition, similar to variability in mortal expressions and the need for real-time processing. The integration of contextual information and individual traits is emphasized as enhancing the understanding of mortal feelings. The study also addresses ethical enterprises, similar as sequestration and impulses in training data. The integration of emotional intelligence into AI systems presents openings to revise mortal-computer relations. Emotion recognition and response mechanisms have made significant progress, but challenges remain. Unborn exploration directions include enhancing the robustness and interpretability of emotion recognition models, exploring cross-cultural and environment-apprehensive emotion understanding, and addressing long-term emotion shadowing and adaption. By further exploring emotional intelligence in AI systems, further compassionate and responsive machines can be developed, enabling deeper emotional connections with humans.Annals of Medicine and Surgery440The study highlights the advancements in emotion recognition models, including the use of deep literacy ways and multimodal data emulsion, and discusses the challenges in emotion recognition, similar to variability in mortal expressions and the need for real-time processing.Annals of Medicine and Surgery["Jale Narimisaei", "Mahdi Naeim", "Shima Imannezhad", "Pooya Samian", "Mohammadreza Sobhani"]2024-06-21T00:00:00 dc0a132baf47b9759566bd845a8d71bf5368e64eObjectives: An intracranial aneurysm, usually referred to as an abnormal bulge in the wall of an intracranial artery, is the number one cause of subarachnoid hemorrhage and ranks third among cerebrovascular accidents after cerebral thrombosis and hypertensive cerebral hemorrhage. Head CT Angiography (CTA), magnetic resonance Magnetic Resolution Imaging (MRI) and Digital Subtraction Angiography (DSA) are currently common diagnostic methods. Artificial Intelligence (AI) is a new interdisciplinary, which can greatly help doctors diagnose and treat. Many researchers have contributed novel insights to the study of clinical diagnosis of intracranial aneurysms, which serves as the research direction and foundation of this paper. This study aims to explore how to use artificial intelligence technology to assist doctors in the diagnosis of intracranial aneurysms to improve the accuracy and sensitivity of diagnosis. Materials and Methods: This paper introduced the background of intracranial aneurysm and auxiliary diagnosis system and then carried out academic research and summary on the two key sentences of clinical diagnosis of intracranial aneurysm and the effect of AI auxiliary diagnosis system on clinical diagnosis of intracranial aneurysm. After that, the algorithm model was established and the algorithm was proposed to provide a theoretical basis for the analysis of clinical diagnosis effect of intracranial aneurysms combined with AI auxiliary diagnosis system. Next, the principles and technical methods of the basic theory were analyzed. At the end of the paper, the simulation experiment was carried out and the experiment was summarized and discussed. Results: A total of 50 patients with intracranial aneurysms were studied in clinical diagnosis. It can be seen that the accuracy and sensitivity of MRI (Magnetic Resolution Imaging) in detecting aneurysms were significantly different from those of CT (Computed Tomography) and DSA (Digital Subtraction Angiography); DSA was significantly superior to CT and MRI in the details and neck of the aneurysm and there was a significant difference between them. At the same time, with the research on the clinical diagnosis effect of intracranial aneurysms, the research on artificial intelligence assisted diagnosis system is also facing new opportunities and challenges. Conclusion: Intracranial aneurysms should be treated as soon as possible after diagnosis and the judgment rate of DSA for intracranial aneurysms is high.Indian Journal of Pharmaceutical Education and Research150Intracranial aneurysms should be treated as soon as possible after diagnosis and the judgment rate of DSA for intracranial aneurysms is high and there was a significant difference between them.Indian Journal of Pharmaceutical Education and Research["Qiang Li", "Chunmiao Wu", "Yuhao He", "Shengming Liu", "Sunfu Zhang"]2024-06-21T00:00:00 511c984b39f8714d26510fb550306932aea2e382­ABSTRACT The presence of Artificial Intelligence (AI) has brought many changes to human life. Many job conveniences have been helped by the development of AI. Jobs that previously could be completed by humans are slowly starting to be replaced by entities called AI. The use of AI technology brings various innovations and efficiencies, for example content personalization to create interesting and relevant content for users, account automation, and so on. The Ministry of Foreign Affairs of Ukraine uses AI as a provider of up-to-date and reliable information on consular affairs. The post on the Instagram account of the Ukrainian Ministry of Foreign Affairs stated that Victoria Shi would carry out the task of providing various information services to the public and media as well as Ukrainian consular affairs services. There were mixed reactions to the upload, many responded positively, although quite a few also responded negatively in the comments column. This research aims to describe, understand and analyze the content of conversations or texts and the use of emoticon symbols that occur in the Instagram posts of the Ministry of Foreign Affairs of Ukraine by paying attention to aspects such as the structure of speech transfer, construction of the exchange of ideas, parts of speech and identity. Analysis by paying attention to the form of conversation and interaction that occurs as a form of study in the field of communication.Keywords: Artificial Intelligence, social media, Ukraine, conversationJurnal Mahardika Adiwidia00This research aims to describe, understand and analyze the content of conversations or texts and the use of emoticon symbols that occur in the Instagram posts of the Ministry of Foreign Affairs of Ukraine by paying attention to aspects such as the structure of speech transfer, construction of the exchange of ideas, parts of speech and identity.Jurnal Mahardika Adiwidia["Slamet Budiharjo", "Algooth Putranto"]2024-06-21T00:00:00 d7149e03b852397edee483eb2a313e20808eaa5dThe reliance on the current international, regional, and national legislative frameworks, including their criminal aspects and their applications aimed at protecting personal data, despite considerable efforts The existing international, regional, and national legislative frameworks, including their criminal provisions, are currently inadequate for protecting personal data despite significant efforts at various doctrinal and judicial levels. The presumption that existing protections are sufficient without acknowledging the rapid advancements in science and technology calls for new regulations that are in tune with sophisticated artificial intelligence systems, thereby ensuring robust and effective legal safeguards against emerging crime forms that jeopardize personal and private data. Therefore, it is crucial not to merely extend existing legal frameworks, which were primarily designed for an earlier era, to address new challenges posed by technological advancements that have significantly widened the scope and flow of data. We must adopt a fresh approach that is tailored to meet the challenges posed by artificial intelligence systems and the diminishing influence of state sovereignty in this area. The intrusion and manipulation of an individual's right to confidentiality and exclusive control over their information are more extensive than anticipated. Hence, it is imperative to thoroughly investigate how artificial intelligence and its applications infringe upon these rights. The assumption that current legislation, particularly in terms of criminal protection, provides adequate safeguards is increasingly questionable for several reasons: International legislation, including the Universal Declaration of Human Rights, tends to treat the protection of personal data as a cursory reference, characterized by general rules and provisions that lack binding legal force or mandatory obligations, and thus fail to offer the requisite protection. A review of the current legislative system underscores a trend towards harmonizing laws to enhance effectiveness and applicability. This is particularly evident in regional initiatives within Europe, such as the European Directive, which is explicitly designed to adapt to technological progress and provide suitable protection for personal data in a collective and unified manner. Journal of Science and Knowledge Horizons00The presumption that existing protections are sufficient without acknowledging the rapid advancements in science and technology calls for new regulations that are in tune with sophisticated artificial intelligence systems, thereby ensuring robust and effective legal safeguards against emerging crime forms that jeopardize personal and private data.Journal of Science and Knowledge Horizons["Hadja Ouafi"]2024-06-21T00:00:00 51ebc24727768e49a911c6f73e28a6efd5c38b64This study examines the obstacles and consequences of incorporating artificial intelligence (AI) into digital security in the rapidly evolving digital economy. It emphasizes the importance of safeguarding data in today's digital era, particularly when it comes to international data transfers, and stresses the necessity for effective policies that promote secure and organized data exchange. The study gives analysis through several aspects. For example, it gives analysis of the technical assessment of how block chain technology addresses data security challenges, the impacts of AI on data security, and how data protection standards affect the European digital economy. And the study then shows block chain's vital role in decentralized control over data, emphasizing the complexities of regulating data processors within a highly open and decentralized environment. Furthermore, the study analyses international cooperation regarding cross-border data regulation, suggesting that there is a need for globally recognized legal norms. In conclusion, the study is for that block chain technology is as an essential element, ensuring cross-border data security, and offering technological innovation and legal framework. The findings of this study indicate that the emerging digital age has led to a rise in digital security, including the development of moral and legal issues like deep fakes, exposed human reliability on automation, and disruption of security firms like those involved in robotics.Advances in Economics, Management and Political Sciences00The findings of this study indicate that the emerging digital age has led to a rise in digital security, including the development of moral and legal issues like deep fakes, exposed human reliability on automation, and disruption of security firms like those involved in robotics.Advances in Economics, Management and Political Sciences["Mayuting Gao"]2024-06-21T00:00:00 dc09e231de7adc14bdc493d1613e1960fc4616ceThe integration of artificial intelligence (AI) across contemporary industries is not just a technological upgrade but a transformation with profound structural implications. This paper explores the concept of structural risks associated with the rapid integration of advanced AI systems across social, economic, and political systems. This framework challenges the conventional perspectives that primarily focus on direct AI threats such as accidents and misuse and suggests that these more proximate risks are interconnected and influenced by a larger sociotechnical system. By analyzing the interactions between technological advancements and social dynamics, this study isolates three primary categories of structural risk: antecedent structural causes, antecedent system causes, and deleterious feedback loops. We present a comprehensive framework to understand the causal chains that drive these risks, highlighting the interdependence between structural forces and the more proximate risks of misuse and system failures. The paper articulates how unchecked AI advancement can reshape power dynamics, trust, and incentive structures, leading to profound and often unpredictable shifts. We introduce a methodological research agenda for mapping, simulating, and gaming these dynamics aimed at preparing policymakers and national security officials for the challenges posed by next-generation AI technologies. The paper concludes with policy recommendations.arXiv.org910A comprehensive framework is presented to understand the causal chains that drive structural risks associated with the rapid integration of advanced AI systems across social, economic, and political systems, and how unchecked AI advancement can reshape power dynamics, trust, and incentive structures.ArXiv["Kyle A Kilian"]2024-06-21T00:00:00 7d87f4c3f035b2b16555d7e7634bcd599e8f0f8fThe nature of consciousness in the context of artificial intelligence (AI) presents a problem that necessitates analysis and further exploration. This study seeks to redefine human-technology relationships by examining the intersection of consciousness and AI, including metaphysical implications and considerations. The primary objectives are to define consciousness within the context of AI, assess the potential for AI to exhibit consciousness, investigate the metaphysical implications for human experiences, and explore the ethical dimensions. The research findings indicate that consciousness involves self-awareness, perception, intentionality, and subjective experiences. While AI can achieve advanced cognitive abilities, the existence of higher-order consciousness remains uncertain, raising metaphysical questions about the nature of subjective awareness. The hard problem of consciousness highlights the challenge of bridging physical processes and subjective experiences, underscoring the need for metaphysical considerations. Ethical implications of AI integration and its impact on human experiences are also examined. Recommendations include further research on consciousness in AI, the development of ethical frameworks that account for metaphysical dimensions, and the exploration of the extended mind hypothesis to integrate AI as an augmentation of human consciousness. By addressing metaphysical implications and considerations, we can navigate the evolving landscape of AI and redefine human-technology relationships in a responsible, inclusive, and metaphysically informed manner.UJAH Unizik Journal of Arts and Humanities00The research findings indicate that consciousness involves self-awareness, perception, intentionality, and subjective experiences and can navigate the evolving landscape of AI and redefine human-technology relationships in a responsible, inclusive, and metaphysically informed manner.UJAH: Unizik Journal of Arts and Humanities["Izuchukwu Kizito Okoli", "O. Nnajiofor"]2024-06-21T00:00:00 7d029f067cce8d7972966d790d50cf6369e4fd8cThe conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials. It is thus a real challenge to better design trials in the context of new drug approval for anti-cancer treatment. Artificial intelligence (AI)-based in silico trials can incorporate far fewer but more informative patients and could be conducted faster and at a lower cost. AI can be integrated into in silico clinical trials to improve data analysis, modeling and simulation, personalized medicine approaches, trial design optimization, and virtual patient generation. Health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated. This article also aims to highlight the limits and challenges related to AI and machine learning.Pharmaceuticals310The conventional rules for anti-cancer drug development are no longer sufficient given the relatively limited number of patients available for therapeutic trials, so health authorities are encouraged to thoroughly review the rules for setting up clinical trials, incorporating AI and in silico methodology once they have been appropriately validated.Pharmaceuticals["Emmanuel Chamorey", "Jocelyn Gal", "B. Mograbi", "Gerard Milano"]2024-06-21T00:00:00 8993efd1b0ad1015e59463b79b20d493fb0f564bIntroduction: The systematic analysis of cancer markers and the impact of artificial intelligence (AI) on early detection and therapeutic approach are crucial in today’s medical field. Cancer represents a significant global burden of morbidity and mortality, making early identification of markers a priority for effective disease management. This study aims to explore recent advancements in the identification and characterization of cancer indicators, including genetic, molecular, protein, and imaging biomarkers. Objective: To analyze the latest advances in identifying and characterizing cancer indicators, covering a variety of biomarker types. Additionally, to investigate the role of AI in improving and applying methods for cancer detection, diagnosis, prognosis, and treatment, highlighting its significant contributions to enhancing the accuracy and efficiency of these approaches. Method: A systematic literature review was conducted, selecting relevant studies addressing the identification of cancer biomarkers and the use of AI in this context based on specific inclusion and exclusion criteria. Results: The results of this systematic analysis highlight recent advances in identifying and characterizing cancer indicators, as well as the impact of AI on enhancing detection, diagnosis, prognosis, and treatment approaches. Conclusion: This study offers valuable insights into the role of cancer indicators and AI in disease prevention and management, supporting evidence-based clinical practices and promoting the development of more efficient and personalized healthcare approaches.Revista Brasileira de Cancerologia340Recent advancements in the identification and characterization of cancer indicators, including genetic, molecular, protein, and imaging biomarkers, are explored, highlighting its significant contributions to enhancing the accuracy and efficiency of these approaches.Revista Brasileira de Cancerologia["Carina Toledo Scoparo Barioni", "Renata Paes de Barros Wandresen", "Lucas Formicoli Pereira", "Amanda Franceschi Coimbra", "Barbara Bruna de Ara\u00fajo Oliveira Kubo", "Ricardo Corr\u00eaa da Cunha"]2024-06-21T00:00:00 bd9226120a9c2147b1a59ebfbf8380c0a4a52b7cToday, the need to educate schoolchildren in the field of artificial intelligence is emphasized at the state level. However, issues related to the selection of content and organization of teaching in the field of artificial intelligence to students in basic secondary school are still controversial. Basic general education faces the problem of finding ways to improve teaching methods for schoolchildren in the field of artificial intelligence.The scientifically based implementation in the field of artificial intelligence training at the level of basic general education is associated, firstly, with the development of a model for teaching in the field of artificial intelligence in the conditions of variant school education, secondly, with the identification of approaches to organizing such teaching, thirdly, with the selection and structuring of teaching content in the field of artificial intelligence as part of an informatics course in basic secondary school, fourthly, with the integration of the teaching content in the field of artificial intelligence into various organizational forms of school education. This determined the purpose of the study.As a result of the study, approaches to the formation of the content of variant teaching in the field of artificial intelligence were identified, a model of variant teaching in the field of artificial intelligence at the level of basic general education was created, the principles of implementation of the proposed training model was highlighted, thematic modules for basic and in-depth training in the field of artificial intelligence were identified, educational and methodological materials were developed (theoretical educational material, practical tasks, laboratory work, methodological recommendations, etc.) for variant teaching in the field of artificial intelligence in the basic secondary school. Testing of the educational and methodological materials was carried out at sites in certain educational organizations in Moscow.Informatics and Education170A model of variant teaching in the field of artificial intelligence at the level of basic general education was created, the principles of implementation of the proposed training model was highlighted, thematic modules for basic and in-depth training in the field of artificial intelligence were identified, and educational and methodological materials were developed for variant teaching in the field of artificial intelligence in the basic secondary school.Informatics and education["I. Levchenko", "A. Sadykova", "P. A. Merenkova"]2024-06-21T00:00:00 8dad57e8479efb27675e2cb4304f4bd456ecb498The integration of Artificial Intelligence (AI) with Environmental, Social, and Governance (ESG) models represents a significant shift in corporate strategy and sustainability efforts. This paper explores the transformative role of deep learning and machine learning technologies in enhancing the precision, efficiency, and effectiveness of ESG frameworks. By utilizing convolutional neural networks (CNNs) and natural language processing (NLP), businesses can now process vast amounts of data, gaining insights that were previously unattainable. The study delves into quantitative analyses involving regression models and scenario analyses, backed by Monte Carlo simulations, to demonstrate the predictive power of AI-enhanced ESG models. Furthermore, the paper discusses the challenges and solutions related to data quality, computational demands, and ethical considerations in implementing AI in ESG assessments. The empirical evidence and theoretical analysis presented underline the superiority of AI-integrated models over traditional methods, showcasing improvements in time-to-insight, predictive accuracy, and cost efficiency. This study not only highlights the practical applications of AI in corporate sustainability efforts but also addresses the ethical and operational challenges faced during implementation.Applied and Computational Engineering80The empirical evidence and theoretical analysis presented underline the superiority of AI-integrated models over traditional methods, showcasing improvements in time-to-insight, predictive accuracy, and cost efficiency.Applied and Computational Engineering["Shujie Feng"]2024-06-21T00:00:00 4888aeae92afe42568adc1d0acbb305da5bf5644In various real-world domains, the problem of imbalanced data poses a significant challenge since it affects the efficiency and trustworthiness of machine learning models. This article investigates Explainable Artificial Intelligence (XAI) methods for studying models created on imbalanced datasets. The main objective of this paper is to assess models trained on DOSMOTE resampled balanced datasets. Using XAI techniques, the study seeks to understand better inner processes that lead to model decisions. The methodology involves combining DOSMOTE resampling with XAI to provide holistic evaluation through both qualitative and quantitative analysis. It should be noted that F1-Scores of balanced datasets improve significantly: from 76% to 87% for Web-Phishing; and from 58% to 73% for Hayes-Roth. This research highlights the need for XAI in enhancing interpretability of models trained on resampled imbalanced data sets. It also shows how resampling affects decision making in a model while performing and recommends investigating other resampling techniques or combinations with XAI methods aimed at improving model interpretability and transparency.Journal of Soft Computing and Data Mining00This research highlights the need for XAI in enhancing interpretability of models trained on resampled imbalanced data sets and shows how resampling affects decision making in a model while performing and recommends investigating other resampling techniques or combinations aimed at improving model interpretability and transparency.Journal of Soft Computing and Data Mining["Rose Mary Mathew", "R. Gunasundari", "Sujesh P Lal"]2024-06-21T00:00:00 4ea424fa3fd4b4946f766a9bd6b66d1812f83b56Customer service, marketing, human resource management, finance, accounting, product and service development, healthcare, commerce, and manufacturing are just a few of the areas where artificial intelligence (AI) has completely changed the game. AI enhances decision-making and work processes by means of machine learning algorithms, automation, and predictive analytics. AI makes it possible to personalise, target adverts accurately, and forecast sales in marketing. Through recruiting, performance reviews, and training of employees, AI in human resource management raises productivity and engagement. Demand forecasting, inventory control, and condition based monitoring are all made easier in factory management by artificial intelligence. Maintaining compliance requirements, AI also helps with risk reduction, financial reporting, and fraud prevention. AI enhances consumer happiness and competitiveness in product creation via use of data analytics, modelling, and suggestions. AI also aids in strategic planning and decision-making in healthcare and life sciences. In retail and e-business, AI improves stocking management, customer profiling, and shopping experiences. This review examines AI in Customer Service: Revolutionising Support and Engagement. We utilised relevant published data (2004–2014) from diverse, reliable databases. Findings suggest that other trends like creative AI, XAI, and quantum computing, as well as collaboration between human beings and AI, continue to advance. As a result, ethical concerns remain a critical element to address when it comes to the application of AI, identifying both threats and opportunities. Finally, we note that AI continues to be a formidable and revolutionary force in organisations, enhancing value creation while promoting ethical principles. Keywords: Artificial Intelligence, Customer Service, Chatbots, Virtual Assistants, Predictive Analytics, Manufacturing, Creative AIIAA Journal of Scientific Research220AI continues to be a formidable and revolutionary force in organisations, enhancing value creation while promoting ethical principles, and other trends like creative AI, XAI, and quantum computing, as well as collaboration between human beings and AI, continue to advance.IAA Journal of Scientific Research["Darlington Arinze Echegu"]2024-06-21T00:00:00 87b31ef34e6167eaf5488d89bf1e3ad23b1762c9In recent times, the emergence of artificial intelligence has had a tremendous influence on human language and communication. It involves developing computer programs to complete tasks which would otherwise require human intelligence. This study, therefore, investigates the impact of artificial intelligence on the English language use and communication skills of selected Nigerian university undergraduates. Questionnaires were designed from a five-point rating scale and shared with one hundred and fifty respondents from the University of Nigeria, Nsukka and the University of Nigeria, Enugu Campus These students were randomly sampled because the students were selected without having any particular choice in mind. All the responses gathered through an online survey monkey were categorised and analysed qualitatively and quantitatively. Albert Bandura’s (1977) Social Learning Theory was adopted as the theoretical framework for this study. The findings show that artificial intelligence impacts the language and communication of Nigerian undergraduates both positively and negatively and this includes among others: improvement of their vocabulary and grammar, and overdependence on AI technology for English language vocabulary development.UJAH Unizik Journal of Arts and Humanities10The findings show that artificial intelligence impacts the language and communication of Nigerian undergraduates both positively and negatively and this includes among others: improvement of their vocabulary and grammar, and overdependence on AI technology for English language vocabulary development.UJAH: Unizik Journal of Arts and Humanities["Nelson Ewere Atoi"]2024-06-21T00:00:00 530ddc5686076fa862e51037971a1396d71aea24Asian Bioethics Review800There is need for recognition of the environmental harm which this pursuit of AI can lead to, and a call for an expanded conception of stakeholders in AI for healthcare, to include consideration of those who may be indirectly affected by its development and deployment.Asian Bioethics Review["Amelia Katirai"]2024-06-21T00:00:00 aebc2e6e0a263872a7e6f5367eabf031ea5b9b54The era of artificial intelligence (AI) is upon us. This article presents AI-based technologies that are changing the learning and teaching process. The article discusses the potential of personalized learning, automated assessment, chatbots, predictive models, intelligent robots, and virtual and augmented reality for education, based on a review of the research literature. In today's world, it is essential for educators to be familiar with these technologies. The study concludes by summarizing the appropriate use of these technologies, the role of teachers, their attention to students, and their active communication, as these are all essential for effective education in the age of artificial intelligence. Teachers play a vital role in helping students use AI ethically and effectively. Our survey showed that teachers, regardless of their age or subject, are open to using AI-powered teaching tools. This is a positive development, as educators in today’s digital world should not deprive students of these technologies but find ways to use them to make the learning process more engaging and effective. Active communication and collaboration between teachers and students are essential, as only through joint effort can they take advantage of digital technologies. All of this is essential for effective education in the age of artificial intelligence. In the age of AI, the professional development of teachers requires a Comprehensive approach that includes specific skills and proficiencies, deliberate Techniques, collaborative learning, and a dedication to continuous improvement.International Journal For Multidisciplinary Research230The article discusses the potential of personalized learning, automated assessment, chatbots, predictive models, intelligent robots, and virtual and augmented reality for education, based on a review of the research literature.International Journal For Multidisciplinary Research["Samar Fatima"]2024-06-21T00:00:00 6cc3debc567fa80b844ecdf2b37f0360ce5ac30aDiabetic eye disease (DED), specifically diabetic retinopathy (DR) and diabetic macular edema (DME), affects nearly 30 percent of people living with diabetes. Despite the severity of DED, almost half of those living with diabetes do not receive an annual eye exam for diabetes (EED) as recommended by leading professional societies. Zufall Health Center (ZHC), a Federally Qualified Health Center, faced a substantial care gap due to the high demand for annual EEDs surpassing the capacity of their onsite optometrist. In response, in April 2021, ZHC implemented an FDA-cleared autonomous artificial intelligence (AI) system for the detection of DR (including DME) into routine diabetes care. We investigated the impact of AI implementation on patient access to annual EEDs, assessing changes in completion rates before and after. Annual EEDs were defined as completion of an evaluation in the eye for DED by either an eyecare provider or autonomous AI. Completion rates for annual EEDs for patients with diabetes increased from 16.0% (314/1,904) (April 2021) to 35.0% (996/2,819) (June 2023), 529 of which were tested with autonomous AI. Between April 2021 to June 2023, 384 patients received a diagnosis from the autonomous AI. Among all patients examined by the autonomous AI, 24.0% (92/384) were identified as having signs of DED and received prompt referrals to eyecare. 292 patients tested negative, avoiding an unnecessary referral to eyecare. The integration of autonomous AI at the point of care effectively reduces access barriers, resulting in a substantial increase in DED testing rates. M. Castro: None. D. Bishop: None. D. Weitzman: Employee; Digital Diagnostics. R. Ramirez: None. Diabetes00The integration of autonomous AI at the point of care effectively reduces access barriers, resulting in a substantial increase in DED testing rates, and changes in completion rates before and after.Diabetes["Milibeth Castro", "Douglas Bishop", "Dena Weitzman", "Rina Ramirez"]2024-06-21T00:00:00 a9312f97cf799c3c6d8ba01ea0ef66fbda0aa1d6The use of artificial intelligence methods to process patent text and realize automated patent approval helps to assist patent examiners and speed up the approval progress. However, existing research rarely involves the field of patent approval and lacks the support of corresponding Chinese datasets. To solve this problem, this paper proposes a Chinese Patent Approval Prediction Model Based on Artificial Intelligence (AIPat) to improve the prediction performance. The model calculates for each patent to be evaluated its maximum similarity to the prior art, constructs a structural graph based on the reference relationship between the claims, and obtains a structural patent representation by encoding the fused textual and structural information. Starting from the drafting specification of Chinese patent claims, the representation is disentangled into two subspaces of constituent elements and element relationships, constrained by BoW prediction and parent claim prediction respectively. Finally, the disentangled representations are fused with similarity scores for patent approval prediction. To accomplish this task, we constructed three Chinese patent datasets in different domains, and the experiments conducted on them proved the superior performance of the model and provided directions for further research in this area.2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI)180A Chinese Patent Approval Prediction Model Based on Artificial Intelligence (AIPat) is proposed to improve the prediction performance and constructed three Chinese patent datasets in different domains and proved the superior performance of the model and provided directions for further research in this area.2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI)["Jinzhi Shan", "Chongyang Shi"]2024-06-21T00:00:00 bdf4e5714f005c145b6f14e290fd98cee3de69f7AIM: to study modern approaches to the application of machine learning and deep learning technologies for the management of patients with colorectal cancer.MATERIALS AND METHODS: after screening 398 publications, 112 articles were selected and the full text of the works was studied. After studying the full texts of the articles, the works were selected, machine learning models in which showed an accuracy of more than 80%. The results of 41 original publications were used to write this review.RESULTS: several areas have been identified that are the most promising for the use of artificial intelligence technologies in the management of patients with colorectal cancer. They are predicting the response to neoadjuvant treatment, predicting the risks of metastasis and recurrence of the disease, predicting the toxicity of chemotherapy, assessing the risks of leakage of colorectal anastomoses. As the most promising factors that can be used to train models, researchers consider clinical parameters, the immune environment of the tumor, tumor RNA signatures, as well as visual pathomorphological characteristics. The models for predicting the risk of liver metastases in patients with stage T1 (AUC = 0.9631), as well as models aimed at assessing the risk of 30-day mortality during chemotherapy (AUC = 0.924), were characterized with the greatest accuracy. Most of the technologies discussed in this paper are software products trained on data sets of different quality and quantity, which are able to suggest a treatment scenario based on predictive models, and, in fact, can be used as a doctor’s assistant with very limited functionality.CONCLUSION: the current level of digital technologies in oncology and in the treatment of colorectal cancer does not allow us to talk about a strong AI capable of making decisions about the treatment of patients without medical supervision. Personalized treatment based on the microbiotic and mutation spectrum and, for example, personal pharmacokinetics, so far look fantastic, but certainly promising for future developments.Koloproktologia390Most of the technologies discussed in this paper are software products trained on data sets of different quality and quantity, which are able to suggest a treatment scenario based on predictive models, and, in fact, can be used as a doctor’s assistant with very limited functionality.Koloproktologia["A. Kravchenko", "E. V. Semina", "V. Kakotkin", "M. Agapov"]2024-06-21T00:00:00 67433c4d4c0e6599da96156b3747bd8c0ebaa2ccThe surprising results of recent developments in various fields of artificial intelligence application have caused people to have a feeling of amazement combined with fear of the category of artificial intelligence. In many of since filed the data play a main role for development the sciences. Meanwhile data mining is one of the main subjects in artificial intelligence. In this editorial, brief and useful explanations about artificial intelligence, artificial neural networks, machine learning and deep learning are given, which help the reader to get a correct and clear understanding of the category of artificial intelligence.Information Fusion Research80In this editorial, brief and useful explanations about artificial intelligence, artificial neural networks, machine learning and deep learning are given, which help the reader to get a correct and clear understanding of the category of artificial intelligence.Information Fusion Research["Naser Mohammadi"]2024-06-21T00:00:00 89bec64eb2b0b990c3e2b40bd9055ceb72d30c6fObjective: The aim of this study is to investigate the possibility of using artificial intelligences (AI), metaverses and other digital tools understood as Digital Information and Communication Technologies (DICT) in educational activities, with the aim of discussing how education in contemporary times cannot escape the technological effect such as AI, because educational practice, especially in the post-Covid-19 pandemic world, has taken on contours that cannot be reversed. The school floor is permeated by practices such as consulting ChatGPT, Bing and other AIs, whether to plan or teach a lesson. With regard to educational practice, audiovisuals stand out as a technological possibility through futuristic-themed films to explore the dynamism and volatility of human life and its relationship with technology in the first quarter of the 21st century. In this way, it is emphasised that cinema plays an important role in introducing the digital world into everyday life, especially for young people, the new formats of reality for human adaptation to technology.   Theoretical Framework: The primary goal of AI is to emulate human intelligence. Some AI that are present in our daily lives are personal assistants such as Siri on the iPhone and Apple computers, Cortana from Microsoft, Alexa from Amazon and Google Assistant on Android smartphones through deep learning neural networks (Rocha, 2019). The year 2021 was a watershed for the use of virtual reality, especially when Facebook changed its name to Meta (Rospigliosi, 2022). The use of futuristic science fiction films brings the visual arts into the field of digital technologies for the classroom. Episodes from the series Black Mirror (Netflix) and The Peripheral (Amazon Prime) are used here to discuss the study of audiovisuals as a learning method.   Method: The methodology adopted for this research consists of demonstrating the concepts and functions of metaverses and AI, as well as the educational nature of films based on science fiction. The research is qualitative and is characterised by a literature review. The data was collected considering aspects of the ethnographic method in a virtual environment - virtual ethnography. The procedure consisted of creating an alert on Google Scholar (GA) by entering the terms: ‘metaverse and education’.  A Scopus search was carried out on the subjects: ‘Artificial Intelligence’, ‘Distance Education’ and ‘Metaverse’. The most frequent references reported dealt with distance education and, secondarily, metaverse, and only one referred to AI. It is therefore with this theoretical scope that the inferences will be made and discussed.   Results and Discussion: It is concluded that learning is more effective when technology is explained in a playful way, for example through the use of cinema, because theory alone is too tiring. It is pointed out that there is still an abysmal social, economic and cultural inequality for different social classes and regions of the world, which implies different and unequal access to technological artefacts for these different audiences. It is therefore worth reflecting that there is no real investment in human development.   Research Implications: The main implications of the study are the lack of investment in both equipment and people, i.e. cognitive development alongside technological development, because there is no point in acquiring equipment that is not part of the daily lives of people in different parts of the world. Investments need to be tailored to the characteristics of each location.    Originality/Value: This study contributes to the literature by demonstrating that there are still abysmal social and economic gaps in the world, especially in the different regions of Brazil, which has an alarming number of illiterates, especially digital illiterates. This is directly related to purchasing power, as public schools are scrapped and offer a much lower level of education than the financially, economically, socially and culturally superior classes.Revista de Gestão Social e Ambiental142It is concluded that learning is more effective when technology is explained in a playful way, for example through the use of cinema, because theory alone is too tiring.Revista de Gestão Social e Ambiental["Walter Rodrigues Marques", "Ana Cristina Souza Silva", "Suzana Pinheiro Nascimento", "Francisco das Chagas Santos Costa", "Dediane Melry Martins C\u00e2mara", "Sherlene Regea Araujo Farias"]2024-06-21T00:00:00 a60bf11ff2c5c92d94b122ee1439ad0af5947028ABSTRACTRevue d'Intelligence Artificielle281Revue d'Intelligence Artificielle["Sandfreni", "E. K. Budiardjo"]2024-06-21T00:00:00 6f7687f9626acb28b05c6f460cbf485e374c3c20<jats:sec> <jats:title/> <jats:p/> </jats:sec>Current Neurovascular Research01Current neurovascular research["K. Maiese"]2024-06-21T00:00:00 51a56bd9edca6b075623f905c2e8d48caa6ecba8O presente ensaio objetiva discorrer sobre a IA e suas implicações diante da escrita acadêmica de pós-graduandos em educação. Questiona-se: em que o avanço da IA transforma os desafios de pós-graduandos na escrita? Qual será o lugar desses sujeitos nas novas configurações da escrita acadêmica? Utilizamos centralmente os conceitos freireanos de situação-limite, atos-limite e inédito viável para explicitar a superação de limites por parte de sujeitos implicados na experiência educacional. Também dialogamos com a literatura especializada envolvendo escrita acadêmica e inteligência artificial na educação. Por fim, constatamos a complexidade da temática e reforçamos que, independentemente da forma de uso da IA na escrita acadêmica, não se deve apagar dela o sujeito e sua experiência. Tal apagamento do sujeito acarretaria uma desconfiguração completa da autoria e da educação como processo transformador e coletivo.Educação em Análise80Educação em Análise["Alexandre Marinho Pimenta", "Carlos Lopes", "C\u00e1ssia Elen Nunes de Almeida", "Sabrina Stein"]2024-06-21T00:00:00 8a1628c27a9c2aa5bbd3ebe31d3e67d5dc4455a62024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI)002024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI)[]2024-06-21T00:00:00 1576bf03ffcbcebc4b883a60f86bf8184d0ba055Surgical neurology international60Surgical Neurology International["Muhammad Kashif", "Ahmed Muthana", "Abdullah M. Al-Qudah", "Samer S. Hoz"]2024-06-21T00:00:00 46a650241d801de8671998b1cc68659a6255300dAlthough the rapidly emerging capabilities of AI bring potential benefits that could be transformative for cyber security, significant threats have emerged that continue to grow in impact and scale. One proposed solution to addressing important risks in AI is the emergence of strategies for AI governance. Yet, as this conceptual early-stage research argues, what is crucial for individuals, businesses, public institutions, including the military, and for high-risk environments, are questions concerning trust in AI governance. Will governance of AI be trusted? As an example, during 2023, several AI governance initiatives and strategies emerged, with some nation states proposing legislation while others looked to treaties and collaboration as solutions. Indeed, at a supra-national level, the United Nations expert multinational stakeholder Policy Network on AI (PNAI) formed to examine key issues in current AI governance. These include the interoperability of governance, data governance mechanisms, AI in supporting inclusion and the transition of nations. To help our understanding of trust in AI governance, the focus for this paper is limited in scope to interoperability in AI governance. Interoperability encompasses different aspects, policy initiatives (such as frameworks, legislation, or treaties), systems and their abilities to communicate and work together. The approach taken in this early-stage research is framed as questions of trust in AI governance. The paper therefore reviews the nature of different AI governance strategies developed and implemented by a range of key nation states and supra-national actors. This is followed by an evaluation of the role of trust, focused on AI governance strategies, in the context of interoperability in AI governance. Trust-building strategies are also considered, with a focus on leveraging the separate elements involved in trust-building to assist our understanding of the implementation of trusted AI governance. The contribution of this early-stage research is to highlight issues that may not be considered by the technical community and to contribute to developing a platform and a research approach that informs policy- learning for institutions, practitioners and academics.European Conference on Cyber Warfare and Security210The nature of different AI governance strategies developed and implemented by a range of key nation states and supra-national actors are reviewed, followed by an evaluation of the role of trust, focused on AI governance strategies, in the context of interoperability in AI governance.European Conference on Cyber Warfare and Security["Allison Wylde"]2024-06-21T00:00:00 23023be376e57a9758689bf6ef97b0808b959b8bCurrent Opinion in Psychology560The need for scientific consensus on whether or not people's faces can reveal their inner traits is highlighted, and researchers are urged to address the critical concerns around epistemic validity, practical relevance, and societal welfare before recommending AI-based facial inference for consequential uses.Current opinion in psychology["Shilpa Madan", "Gayoung Park"]2024-06-21T00:00:00 37fd471ea8b91a6121b626e061515b47546e63d5The reliability and stability of the power system are paramount for societal well-being and economic progress. Anomalous fluctuations in power load often precipitate supply interruptions, system breakdowns, and other severe repercussions. Hence, timely and precise power load forecasting is imperative. The conventional rule- and statistic-based power load forecasting technology exhibits numerous limitations that impede its ability to meet the demands of a complex, real-time power system. This paper will delve into AI-based power load forecasting for the power system. In contrast to traditional methods, AI-driven approaches can markedly enhance predictive performance and contribute to more efficient and stable grid management and operations.2024 6th International Conference on Energy Systems and Electrical Power (ICESEP)220This paper will delve into AI-based power load forecasting for the power system, and shows how AI-driven approaches can markedly enhance predictive performance and contribute to more efficient and stable grid management and operations.2024 6th International Conference on Energy Systems and Electrical Power (ICESEP)["Guanyao Wang", "Zihan Zhao", "Xu Wang", "Yuanqi Dou", "Yuanfeng Dou", "Zheng Liu"]2024-06-21T00:00:00 cc70ce37cda27bc3ffed39b80c596cd3d0fc40f3Newborn1270Newborn["Thierry Agm Huisman"]2024-06-21T00:00:00 4c4f9cb158cd9b2376592921661265f18398f762Objective: This study aims to highlight the issues of civil liability resulting from errors in AI banking applications. To date, there is ambiguity about whether it is possible to grant virtual legal personality to AI systems. Methodology: The researcher used the analytical method to understand the mechanisms of AI applied in banking operations, assess the effectiveness of these applications, and analyze the legal texts. Additionally, the inductive method was employed to fit the uses of AI in the banking sector. Results: The main findings of the study can be summarized as follows: The possibility of assigning civil liability to AI entities for errors they commit is applicable only in one case, which is when the AI banking system is the tool causing the damage. Conclusion: The study concludes that there is a need to amend certain legal texts in the Transactions Law. The study suggests that the Omani Civil Transactions Law of 2013 should be updated to align with the nature of AI in banking. This includes developing general legal rules related to the responsibility of employers for the actions of their employees and the custody of objects, ensuring they fit the specific errors of AI in banking. The study also provides some recommendations and proposals regarding the particularities of civil liability in the field of AI banking.Journal of Ecohumanism260The study suggests that the Omani Civil Transactions Law of 2013 should be updated to align with the nature of AI in banking, including developing general legal rules related to the responsibility of employers for the actions of their employees and the custody of objects.Journal of Ecohumanism["Murtadha Abdullah Khairi Abdullah", "Nizar Qashta"]2024-06-21T00:00:00 f0b0af8a774a560f3815defa3e9ee535f31e2f53CAIBDA20{"pages": "119-124"}["Mingyue Li", "Xianmin Sun"]2024-06-21T00:00:00 566cb2f30a6f9473117ea4ac3c07c2c887cf0161CAIBDA70{"pages": "146-152"}["Kai Zhang", "Weiqun Cheng"]2024-06-21T00:00:00 9ab3f843f7b2f9403a5161dd737a2027b19a6551ABSTRACTRevue d'Intelligence Artificielle720Revue d'Intelligence Artificielle["Renuka Agrawal", "Kanhaiya Sharma"]2024-06-21T00:00:00 016c0b628787c089ec47ba08b4e84bc3d1e3b8a4The Journal for The Foundation of Science and Technology00The Journal for The Foundation of Science and Technology["Wendy Hall"]2024-06-21T00:00:00 680503b10a1d4be8858548075ae562940d1e8cf7This study investigates the impact of Artificial Intelligence (AI) on the practice of law, analyzing the challenges and opportunities faced by lawyers in the face of this technological transformation. By considering the growing integration of AI in legal research, document analysis, and decision-making, we examine how this technology affects the work of legal professionals. We observe that, while AI can save time and enablegreater efficiency in the provision of legal services, it also presents ethical, legal, and practical challenges, such as issues of confidentiality, transparency, and impartiality. We highlight the importance of the professional training and updating of lawyers so that they can adapt to technological changes and capitalize on the opportunities offered by AI. We conclude that AI is redefining legal practice, providing new tools for lawyers as they continue to play their fundamental role as defenders of individual rights and promoters of a more effective and accessible administration of justice. RCMOS - Revista Científica Multidisciplinar O Saber00It is concluded that AI is redefining legal practice, providing new tools for lawyers as they continue to play their fundamental role as defenders of individual rights and promoters of a more effective and accessible administration of justice.RCMOS - Revista Científica Multidisciplinar O Saber["Gilmar Rodrigues Cardoso"]2024-06-21T00:00:00 e14daa7dc29f4df5f948aece973992bd4288d7ceThe paper proposes the design approach as a blueprint for building a sentient artificial agent capable of exhibiting humanlike attributions of consciousness. The paper also considers whether if such an artificial agent is ever built, how it will be indistinguishable from a human being? Well, it is glowingly evident that the evolution of artificial intelligence is guided by us, humans, whose own mental evolution have been shaped by the passing years in the course of the phenomenology of adaptation and survival (Darwinian). Yet, the evolution of synthetic minds powered by artificial cognition seems to be quite fast. Yes, the artificial mind in robots, if we accept the analogy ‘mind’ in its fullest sense, that day is not very far when the mental embodiment of consciousness in machines would become reality. But prior to such a feat becoming reality, rhetoric debates have been taking shape as of, how to decode and cipher consciousness in machines, a phenomenon considered as often as ‘nonentity’, then, what would be the true essence of such an artificial consciousness? This paper discusses these aspects and attempts to throw some new light on the design and developmental aspects of artificial consciousness.Journal of Robotics and Automation Research00The design approach is proposed as a blueprint for building a sentient artificial agent capable of exhibiting humanlike attributions of consciousness and whether if such an artificial agent is ever built, how it will be indistinguishable from a human being.Journal of Robotics and Automation Research["Sidharta Chatterjee"]2024-06-21T00:00:00 8f44e4c8aca0b8e3bc80296d045d58285a508bb92024 2nd International Conference on Communications, Computing and Artificial Intelligence2902024 2nd International Conference on Communications, Computing and Artificial Intelligence["Xiao-Hui Zhao", "Yao He", "Yankun Gao", "Xu Luo", "Wenjun Ke"]2024-06-21T00:00:00 01ff4e6b44473d584d1fd658ac01b5ea813cf836Rapid urbanization has significantly increased urban populations, leading to higher consumption of resources such as energy, water, and fuel. Resource efficiency is crucial to managing urban growth in an environmentally friendly and economical manner. This research aims to explore the role of artificial intelligence (AI) in improving resource efficiency in urban infrastructure. By leveraging AI technology, this study seeks to find innovative solutions that can optimize resource use, enhance energy management, and improve monitoring and control of infrastructure systems. The findings indicate that the implementation of AI can increase energy efficiency by 15%, reduce transportation travel times by 15%, and improve water management efficiency by 15%. These results demonstrate that AI can be an effective tool in supporting the sustainability of urban infrastructure, reducing operational costs, and mitigating environmental impacts. This research provides practical guidance for city managers and policymakers in designing and implementing smarter and more efficient technological solutions.International Transactions on Artificial Intelligence (ITALIC)4457International Transactions on Artificial Intelligence (ITALIC)["Ninda Lutfiani", "Nuke Puji Lestari Santoso", "Ridhuan Ahsanitaqwim", "U. Rahardja", "Achani Rahmania Az Zahra"]2024-06-21T00:00:00 314fc7e63f3f22690b1bb51f8feb319e48c514e9Energy efficiency and the development of renewable resources are crucial issues in addressing the global energy crisis and climate change. This research explores the role of artificial intelligence (AI) in increasing energy efficiency and optimizing the development of renewable resources, such as solar energy, wind, and bioenergy. By using a mixed-methods approach that combines qualitative and quantitative methods, this research identifies concrete applications of AI in various renewable energy sectors. The results demonstrate that AI can significantly improve operational efficiency and reduce energy waste. Examples include optimizing solar panel placement, predictive maintenance of wind turbines, and optimizing fermentation processes in biogas production. The implementation of AI in renewable energy not only enhances efficiency but also reduces costs and supports sustainability. This research contributes to the field of energy efficiency and AI technologies by providing empirical evidence of the benefits of AI in the renewable energy sector. It is recommended that governments and the energy industry widely adopt AI, invest in technology and workforce training, and strengthen collaboration between the energy, technology, and academic sectors to develop innovative and applicable AI solutions. Further research should conduct broader and more comprehensive studies, including analysis of the long-term costs and benefits of AI implementation, as well as the integration of AI technology with existing energy management systems.International Transactions on Artificial Intelligence (ITALIC)4043It is recommended that governments and the energy industry widely adopt AI, invest in technology and workforce training, and strengthen collaboration between the energy, technology, and academic sectors to develop innovative and applicable AI solutions.International Transactions on Artificial Intelligence (ITALIC)["U. Rusilowati", "Hajra Rasmita Ngemba", "Rio Wahyudin Anugrah", "Anandha Fitriani", "Eka Dian Astuti"]2024-06-21T00:00:00 1c4a4d8af9f3c695ebb23d64655be6a11a9eeb03In the era of globalization and rapid industrial growth, energy efficiency and resource management are crucial to addressing complex environmental and economic challenges. Efficient management reduces costs and contributes to sustainability. Technological advancements in Artificial Intelligence (AI) enhance energy efficiency and resource management through faster data analysis, better predictions, and automation. Despite progress, challenges like inaccurate energy demand predictions and inefficient resource allocation persist. This study explores AI's role in improving energy and resource management efficiency, focusing on prediction, optimization, and automation using Deep Learning approaches, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The findings show that AI models significantly enhance efficiency and sustainability by providing accurate predictions and automation recommendations. This research underscores AI's practical relevance, suggesting companies integrate these technologies to optimize energy use and achieve sustainability goals.International Transactions on Artificial Intelligence (ITALIC)4037This research underscores AI's practical relevance, suggesting companies integrate these technologies to optimize energy use and achieve sustainability goals, and shows that AI models significantly enhance efficiency and sustainability by providing accurate predictions and automation recommendations.International Transactions on Artificial Intelligence (ITALIC)["Agus Kristian", "Thomas Sumarsan Goh", "Ahmad Ramadan", "Archa Erica", "Sondang Visiana Sihotang"]2024-06-21T00:00:00 5f0a96997b4203895e9c0b10b11606dcab944ea2Effective waste management and resource recovery are essential for maintaining environmental sustainability. With the increasing volume of waste generated from industrial and domestic activities, there is a critical need for strategies that reduce environmental impact and enhance resource utilization efficiency. This study explores the application of artificial intelligence (AI) technologies, specifically Machine Learning (ML) and Artificial Neural Networks (ANN), in optimizing waste management processes. The research demonstrates that AI can significantly improve waste classification accuracy, predict waste volumes, and identify resource recovery opportunities. Implementing AI solutions resulted in a 15% increase in resource recovery efficiency and a 20% reduction in operational costs. These findings provide valuable insights for stakeholders and policymakers in integrating AI technologies to achieve more sustainable waste management practices.International Transactions on Artificial Intelligence (ITALIC)4021The research demonstrates that AI can significantly improve waste classification accuracy, predict waste volumes, and identify resource recovery opportunities, and implement AI solutions resulted in a 15% increase in resource recovery efficiency and a 20% reduction in operational costs.International Transactions on Artificial Intelligence (ITALIC)["Li Wei Ming", "James Anderson", "Farhan Hidayat", "Firdaus Dwi Yulian", "Nanda Septiani"]2024-06-21T00:00:00 1bf39f165ee7db69b333f707075b38115b202979This study evaluates the role of Artificial Intelligence (AI) in enhancing the efficiency of natural resource management through a quantitative analysis using SmartPLS. Data was collected from 200 professionals with significant experience in AI and natural resource management. Descriptive statistics indicated high levels of AI usage (X1) and technological competence (X2) among respondents, with average scores of 4.2 and 4.0, respectively. Convergent and discriminant validity were confirmed, with all constructs having factor loading values above 0.7 and AVE exceeding 0.5. Structural model analysis revealed that AI usage and technological competence positively and significantly impact natural resource management efficiency (Y1), with path coefficients of 0.45 and 0.38, respectively. These findings underscore AI's critical role and the necessity of technological training to maximize its benefits. This research contributes to the literature by highlighting the importance of integrating AI in sustainable resource management practices, providing a robust framework for future studies.International Transactions on Artificial Intelligence (ITALIC)4218Findings underscore AI's critical role and the necessity of technological training to maximize its benefits, and highlight the importance of integrating AI in sustainable resource management practices, providing a robust framework for future studies.International Transactions on Artificial Intelligence (ITALIC)["Juan Carlos Rodr \u0301\u0131gue", "John Van der Merwe", "Syahrul Muarif Wahid", "Galih Putra Cesna", "Dimas Aditiya Prabowo"]2024-06-21T00:00:00 cc61f725bac0009af1bad7dec91f837716b1a70cThe increasing need for electrical energy in the modern era requires innovative steps to optimize its use. This research explores the application of Artificial Intelligence (AI) in optimizing the use of electrical energy with a focus on efficiency and sustainability. Using a quantitative approach with a descriptive-analytical design, this research collects data from various sources, including surveys, interviews and secondary literature. The results show that the application of AI can reduce electrical energy consumption by 20-30% in various sectors, such as the manufacturing industry and smart households. In addition, AI contributes significantly to reducing carbon emissions, with a 25% reduction in emissions in the manufacturing industrial sector. AI also demonstrated higher energy efficiency compared to traditional methods, with an average improvement of 25%. These findings imply that AI not only improves energy efficiency but also supports environmental sustainability through reducing carbon emissions. Practical recommendations include investment in AI technologies for energy management and policy support to accelerate AI adoption. This research provides the basis for further studies to explore the potential of AI in other sectors and its long-term economic impact. Thus, the application of AI in electrical energy management is expected to contribute significantly to energy efficiency and global sustainability.International Transactions on Artificial Intelligence (ITALIC)3617The results imply that AI not only improves energy efficiency but also supports environmental sustainability through reducing carbon emissions and contributing significantly to energy efficiency and global sustainability.International Transactions on Artificial Intelligence (ITALIC)["Heni Nurhaeni", "Ariana Delhi", "Ora Plane Maria Daeli", "Sheila Aulia Anjani", "Natasya Aprila Yusuf"]2024-06-21T00:00:00 4b22c3f1eb4a02e69bf7d71fc3aa8c87fe26aafdThe integration of artificial intelligence (AI) into sustainable development practices holds significant promise for addressing contemporary environmental, economic, and social challenges. This paper explores the application of AI in natural resource management, sustainable agriculture, and waste and energy management. The study employs a mixed-methods approach, combining qualitative analysis of case studies with quantitative data analysis to evaluate the effectiveness of AI technologies. Findings indicate that AI significantly enhances efficiency and effectiveness across various domains, including improved resource monitoring, optimized agricultural practices, and enhanced waste management processes. The results underscore AI's potential in mitigating climate change and promoting biodiversity through advanced predictive models and monitoring systems. This research highlights the critical role of supportive policies and infrastructure in realizing AI's benefits for sustainable development. The study concludes with recommendations for policymakers to foster AI adoption and address challenges such as high initial costs and data privacy concerns.International Transactions on Artificial Intelligence (ITALIC)4111Findings indicate that AI significantly enhances efficiency and effectiveness across various domains, including improved resource monitoring, optimized agricultural practices, and enhanced waste management processes.International Transactions on Artificial Intelligence (ITALIC)["Jack Jones", "Edward Harris", "Yusuf Febriansah", "Alfri Adiwijaya", "Ihsan Nuril Hikam"]2024-06-21T00:00:00 f67a2009ed75e4737b83402ce79d8d909e19595cEfficient and sustainable energy management is crucial for addressing global environmental challenges. Artificial intelligence (AI) has emerged as a significant tool in the energy revolution, enhancing operational efficiency and integrating renewable energy sources. This study examines the impact of AI on optimizing energy and resource management, focusing on increasing renewable energy use and efficiency. Using a quantitative and exploratory approach, data from 100 energy companies that have implemented AI solutions were analyzed. The findings show that AI can improve energy efficiency by 25%, strengthen sustainable operations, and reduce environmental impact. These results align with Complex Systems Theory, highlighting that advanced technologies like AI enhance system adaptability and efficiency. Despite these insights, the study is limited to companies that have adopted AI and focuses solely on the energy sector. This highlights the need for broader research across various sectors and geographic contexts. The implications suggest that AI not only improves energy management but also supports global sustainability efforts, making it vital for a sustainable energy future.International Transactions on Artificial Intelligence (ITALIC)4010International Transactions on Artificial Intelligence (ITALIC)["Sipah Audiah", "Yulia Putri", "Ayu Sanjaya", "Ora Pertiwi Daeli", "Michael Johnson"]2024-06-21T00:00:00 ee805df402125cf18258f488020b1ad9f4034486Nat. Mac. Intell.156It is shown that using very large privacy budgets can render reconstruction attacks impossible, while drops in performance are negligible, and it is concluded that not using DP at all is negligent when applying artificial intelligence models to sensitive data.Nat. Mac. Intell.["Alexander Ziller", "Tamara T. Mueller", "Simon Stieger", "Leonhard F. Feiner", "Johannes Brandt", "R. Braren", "D. Rueckert", "G. Kaissis"]2024-06-21T00:00:00 4015b6315b1738e8abeaa3bbfff50cd4b932d8b5In the pursuit of developing expressive music performance models using artificial intelligence, this paper introduces DExter, a new approach leveraging diffusion probabilistic models to render Western classical piano performances. The main challenge faced in performance rendering tasks is the continuous and sequential modeling of expressive timing and dynamics over time, which is critical for capturing the evolving nuances that characterize live musical performances. In this approach, performance parameters are represented in a continuous expression space, and a diffusion model is trained to predict these continuous parameters while being conditioned on a musical score. Furthermore, DExter also enables the generation of interpretations (expressive variations of a performance) guided by perceptually meaningful features by being jointly conditioned on score and perceptual-feature representations. Consequently, we find that our model is useful for learning expressive performance, generating perceptually steered performances, and transferring performance styles. We assess the model through quantitative and qualitative analyses, focusing on specific performance metrics regarding dimensions like asynchrony and articulation, as well as through listening tests that compare generated performances with different human interpretations. The results show that DExter is able to capture the time-varying correlation of the expressive parameters, and it compares well to existing rendering models in subjectively evaluated ratings. The perceptual-feature-conditioned generation and transferring capabilities of DExter are verified via a proxy model predicting perceptual characteristics of differently steered performances.Applied Sciences435DExter is a new approach leveraging diffusion probabilistic models to render Western classical piano performances that enables the generation of interpretations guided by perceptually meaningful features by being jointly conditioned on score and perceptual-feature representations.Applied Sciences["Huan Zhang", "Shreyan Chowdhury", "Carlos Eduardo Cancino-Chac'on", "Jinhua Liang", "Simon Dixon", "Gerhard Widmer"]2024-06-21T00:00:00 6f3087ea3a481122c9e8c621d1ceef719c3e6f4eBMC Medical Ethics4511Fear of losing the ‘human touch’ associated with doctors was a common theme within qualitative coding, suggesting a potential conflict between the implementation of AI and patient-centered care.BMC Medical Ethics["Kaila Witkowski", "Ratna Okhai", "Stephen R. Neely"]2024-06-22T00:00:00 f293b47053df2b54b2085f3ea579e96a92f2cff5Annals of Surgical Oncology185Differences in the performance of various AI content detectors are demonstrated with the potential to label human-written articles as AI-generated, demonstrating a strategy for continuous evaluation and validation as AI models and detectors rapidly evolve.Annals of surgical oncology["Madelyn A. Flitcroft", "Salma A. Sheriff", "Nathan Wolfrath", "Ragasnehith Maddula", "Laura McConnell", "Yun Xing", "Krista L. Haines", "Sandra L Wong", "Anai N. Kothari"]2024-06-22T00:00:00 bdcd4e20b593cb2284de00dd9dc9110609cc5fc2The vast and dynamic maritime domain demands constant observance and accurate information for successful naval operations. However, traditional methods struggle to keep pace with the ever-increasing complexity and data overflow. The paper explores how Artificial Intelligence (AI) presents a transformative opportunity, significantly impacting naval operation by enhancing Situational awareness (SA) and Threat monitoring (TM). It is analyzed the impact of AI across three key areas: enhanced data processing and analysis, improved anomaly detection and predictive capabilities, and real-time decision support. By analyzing key principles, tactics, and procedures for AI implementation, it is explored the process how these capabilities can convert into practical applications and benefits. Examples like AI-powered maritime surveillance and predictive systems for naval assets demonstrate solid benefits of this technological progress. Additionally, in the paper are envisioned future operational scenarios where AI-driven autonomous systems and dynamic route optimization become commonplace. The analysis demonstrates how AI can be a critical factor in moving naval operations into a new era of efficiency and proactive threat management. However, responsible development and ethical considerations remain of paramount importance.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference131The paper explores how Artificial Intelligence presents a transformative opportunity, significantly impacting naval operation by enhancing Situational awareness and Threat monitoring and analyzed the impact of AI across three key areas: enhanced data processing and analysis, improved anomaly detection and predictive capabilities, and real-time decision support.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["Todor Dimitrov"]2024-06-22T00:00:00 f0febbe6b23810784f87dc464bf444ce2917ef94The advancement of Artificial Intelligence (AI) has brought about significant changes in various industries, including public relations (PR) practices in companies. This research aims to explore the implementation of AI in corporate PR activities in Indonesia. Using a case study approach with in-depth interviews with PR practitioners from three major companies, this research reveals how AI is being used to optimise PR functions. The findings show that AI is primarily used to accelerate media and sentiment analysis, facilitate social media content management, and enhance personalisation and automation in marketing communications. However, there are still limitations to the implementation of AI due to resource constraints and regulatory factors. This research contributes to a better understanding of AI adoption in corporate PR practices in Indonesia and its future development potential. By examining real-world cases, it provides valuable insights into the opportunities and challenges associated with using AI for strategic communication efforts in an emerging market context.International journal of social sciences and humanities01The findings show that AI is primarily used to accelerate media and sentiment analysis, facilitate social media content management, and enhance personalisation and automation in marketing communications.International Journal of Social Science and Humanity["Asep Soegiarto", "Wina Puspita Sari", "Abdul Kholik", "Mentari Anugrah Imsa"]2024-06-22T00:00:00 d8796340cf23709ad4936ab526e678c81d58d5c7The article justifies the necessity of introducing methodological innovations in the modern field of artificial intelligence (AI), which precede and actively determine technological innovations. The authors of the article analyse the existing AI conceptual structure and point out its insufficiency and introduce significant improvements, including the figurative component. This addition was not made arbitrarily but in accordance with the structure of human natural intelligence, where the rational (symbolic) is directly related to the figurative and interacts with it. The figurative is not identical to the rational (symbolic); there are significant differences in their epistemological content. This difference results from the epistemological differences in their basic (primary) structures - the concept and the image. This, accordingly, means that the figurative (image) is always a reflection of the singular (individual), which is always brought to the sensory-specific, while the rational (concept) is always a reflection of the general (typical), which reaches the level of the systematic. This analysis of the peculiarities of the epistemological and methodological content of figurative and rational thinking becomes important not only in terms of studying the essence of an individual's natural thinking. These features are of great methodological importance in the modeling of artificial intelligence, especially when the question is raised about the creation of a new generation of artificial intelligent systems. Considering all of the above, artificial intelligence combines rational (logical) and figurative components, with priority given to the figurative structure as more information-intensive and heuristically powerful. It is the figurative component of artificial intelligence that defines and ensures the object's multidimensional representation, while the rational component chooses one of the dimensions provided by the figurative one and fills it with logical content. In the concept of functioning of a real artificial intelligence system, its figurative component initially functions, transforming into rational transformations, which, in turn, are later returned and included in more voluminous figurative architectures. The article proposes schemes that present new approaches to depicting the modern AI conceptual structure. The suggested innovations are not implemented arbitrarily, they are determined and correspond to the real structure and functioning of human natural intelligence. ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference101The article justifies the necessity of introducing methodological innovations in the modern field of artificial intelligence (AI), which precede and actively determine technological innovations, and proposes schemes that present new approaches to depicting the modern AI conceptual structure.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["Anatolii Yarovyi", "Andrii Yarovyi", "Svitlana Kizim", "Volodymyr Ozeranskyi"]2024-06-22T00:00:00 30a697cf60bcf0ad875d68b65019cb8253898710This research examines the challenges and opportunities of AI integration in Islamic banks through a case study of Bank Syariah Indonesia. A qualitative method was applied using an interview approach. Four experts from the IT division of Bank Syariah Indonesia were interviewed. The results suggest that AI applications offer potential benefits such as automation, improved decision-making and efficiency, customer recommendations, and enhanced customer experience. However, the challenges of AI integration include implementation costs, cyber security risks, Shariah compliance, and ethical issues. The research recommends that stakeholders in Islamic banks invest more in cybersecurity and educate their customers about the importance and usage of AI technology. Additionally, the research suggests that the government implements policies related to the ethical regulation of AI technology. Future research should provide comparative analysis and use a mixed-method approach to better understand the challenges and opportunities of AI integration in Islamic banks.Modern Finance646The results suggest that AI applications offer potential benefits such as automation, improved decision-making and efficiency, customer recommendations, and enhanced customer experience, however, the challenges of AI integration include implementation costs, cyber security risks, Shariah compliance, and ethical issues.Modern Finance["Issa Hamadou", "Aimatul Yumna", "Hawaou Hamadou", "Mamadou Salieu Jallow"]2024-06-22T00:00:00 030da7ef0b16fccb9a1833012af76d4c784ca5b4The integration of Artificial Intelligence (AI) into communication technologies has significantly transformed how humans interact with machines. This research paper explores the evolving landscape of AI-driven communication systems, focusing on how these technologies bridge the gap between human and machine dialogue. The study examines various AI methodologies, including natural language processing (NLP), machine learning, and conversational agents, to understand their impact on enhancing communication efficiency and effectiveness. By analyzing case studies and current applications, the paper identifies key challenges and opportunities in AI communication, such as maintaining contextual understanding, ensuring conversational coherence, and addressing ethical concerns. The findings reveal that while AI has made substantial progress in mimicking human-like interactions, challenges remain in achieving truly natural and empathetic dialogue. The paper concludes with recommendations for improving AI communication systems, emphasizing the need for ongoing advancements in AI technology, user-centric design, and ethical considerations to enhance the quality and reliability of human-machine interactions.Nanotechnology Perceptions80The findings reveal that while AI has made substantial progress in mimicking human-like interactions, challenges remain in achieving truly natural and empathetic dialogue.Nanotechnology Perceptions["Dr. Shirisha Deshpande", "Dr. A. Vijayalakshmi", "Devdatta Tare", "Dr Rajani Wadhai", "Dr. Rupendrakumar Gour", "Atul Gavaskar"]2024-06-22T00:00:00 23b6d75567d80db387dfe42e333ad6eec099b574The article is devoted to legal and ethical problems pertaining to the use of artificial intelligence (hereinafter – AI) in law. AI solutions are already being applied in some areas of law, and the use of AI will undoubtedly be expanding. There are problems relating to the regulatory framework because AI has no expressly defined legal status and the scope of AI is not clear either. AI could successfully be employed for data processing in certain areas, such as forensic science and criminology, as well as legal proceedings, where AI could assess procedural documents for their conformity with formal requirements, namely as a means of assisting a human, who is a decision maker. Recognising AI’s decision-making ability is extremely challenging. Thus, AI would transform from a means into a subject of law empowered to make decisions about other subjects of law. The existing legislation is not ready to embrace it, and AI’s decision-making ability is related to issues of an ethical nature, considering that decisions about people would be made by a non-human subject. ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference220Legal and ethical problems pertaining to the use of artificial intelligence in law are devoted to issues of an ethical nature, considering that decisions about people would be made by a non-human subject.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["I. Kudeikina", "Sandra Kaija"]2024-06-22T00:00:00 8a26ddf463436b68575bcfaed7f48694cf9090b9Technology plays a very important role in a business, one of which is Artificial Intelligence (AI). This research was conducted to determine the impact of AI on business innovation and the challenges faced in its implementation. This research is carried out using the Literature Review method. Data in this research is collected through various electronic media. The findings in this study are AI can automate routine tasks, increase accuracy and efficiency, and provide in-depth data analysis. Although AI replaces many human analytical skills, the need for intuitive and empathetic abilities is increasing. This shows the importance of a new approach in human-machine interaction, where AI and human skills work synergistically to provide optimal service. But in its implementation, AI has obstacles such as costs, privacy, quality of human resources, and legal regulations. Overall it can be underlined that Artificial Intelligence (AI) affects the business of business.Dinasti International Journal of Digital Business Management150The findings in this study are AI can automate routine tasks, increase accuracy and efficiency, and provide in-depth data analysis, and although AI replaces many human analytical skills, the need for intuitive and empathetic abilities is increasing.Dinasti International Journal of Digital Business Management["Jodi Setiawan", "Yayan Hendayana"]2024-06-22T00:00:00 307747ac28d08e39cb2311d5b25d4ca62176863bDual Use Research of Concern (DURC) has been well analyzed regarding the life sciences. This article explores the topic of younger fields of medical research and their potential for misuse, especially in the military context. The areas of research considered are artificial intelligence, neurotechnology, and neuroenhancement. Each of these areas have brought forward highly promising new research. However, in light of the current armed conflicts in Europe and in the Middle East, there is a need to consider what the potential harmful consequences of medical research are. Using the example of war, this article demonstrates various instances of how current medical research could be—or is being—misused and discusses various possible solutions to the dual use dilemma. The main finding is that there needs to be a more concise and international effort to prevent the misuse of research. The raising of awareness in the general medical research community for the topic of DURC is one of the simplest steps that should be undertaken in order to ensure the non-maleficence of global research. Additionally, considering the potentially far-reaching consequences of DURC, it is time to consider the introduction of a new intergovernmental agency to monitor research and establish safeguards in order to cover all fields of research.Research Ethics200The topic of younger fields of medical research and their potential for misuse, especially in the military context, is explored and the main finding is that there needs to be a more concise and international effort to prevent the misuse of research.Research Ethics["Elisabeth Krauel", "Andreas Frewer"]2024-06-22T00:00:00 df692018c1afc4da2e834fa330b0f3919eb23526This pilot study assesses the reliability and validity of measurement tools and instrumentation to ensure accurate measurement of the variables and defines possible problems of the follow-up larger-scale research. The study’s overall goal is to measure stakeholders' perspectives on the use of generative artificial intelligence (AI) in higher education and its implications for university social responsibility (USR) with the purpose of better understanding how AI technologies are deployed in academic institutions. The primary aim of this pilot study is to evaluate the effectiveness of the designed questionnaire by calculating Cronbach's alpha coefficient of the measurement scales. A questionnaire of 20 items was disseminated to the relevant stakeholders, including students, and academic and administrative staff, with the total number of received valid responses being 101. Cronbach's alpha was used as a measure of internal consistency to test the reliability of the measurement scale that consists of two groups of items: Scale B) perceptions of AI use in higher education of all the relevant stakeholders; Scale C) AI integration into higher education and its implications for USR. Key findings and implications from the study results include good or acceptable internal consistency > 0.7 among the majority of the items in the questionnaire. Specific recommendations for improving some of the items were suggested based on the findings. Modifying language, rephrasing questions, or deleting items that lead to reduced internal consistency are examples of these. The pilot study provides useful insights on the viability of employing the questionnaire in a larger-scale study, and considerations for time and resource allocation to ensure practicality in the subsequent study. ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference300The pilot study provides useful insights on the viability of employing the questionnaire in a larger-scale study, and considerations for time and resource allocation to ensure practicality in the subsequent study.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["O\u013cegs \u0145ikadimovs", "V. Vevere"]2024-06-22T00:00:00 aa14c3c0116441051ed8c9a6fe9b68dca10deff0The research focuses on the attitudes and readiness of organizations to integrate Artificial Intelligence (AI) technology into their internal business processes. The present study aims to determine how organizations perceive technological innovations related to AI. Specific goals include measuring the degree of readiness and acceptance of technological innovations by organizations, as well as identifying factors influencing the success or failure of this process. The main object is AI technology and its potential for enhancing the efficiency of internal business process management. The significance of this analysis is threefold, providing valuable information on current trends and challenges in internal business processes and their transformation under the influence of AI. In the course of the study shall be justified the thesis that AI technology holds significant potential for optimizing internal business processes, that is not yet fully realized and utilized due to various obstacles. Overcoming these obstacles is possible through individualized strategies, the establishment of ethical standards, active training, and other measures that contribute to the successful integration of artificial intelligence into organizational dynamics. The methodology includes a comprehensive literature review combined with the use of questionnaire surveys, Gap analysis and SWOT analysis. The main conclusions are related to the diversity in motivations among surveyed companies, necessitating differentiated strategies. Improving operational efficiency and customer service, and enhancing competitiveness, transpire as driving power for AI implementation. Evaluating attitudes reveals differences in readiness among business organizations, resp. some of them actively taking steps to implement AI, while others are still exploring possibilities or are uncertain about the overall approach to adopt. The recommendations for organizations are multifaceted. Constantly exploring new technologies and updating approaches are necessary for a sustainable transition to more intelligent business process management. ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference160The thesis is that AI technology holds significant potential for optimizing internal business processes, that is not yet fully realized and utilized due to various obstacles, and is necessary for a sustainable transition to more intelligent business process management.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["Galina Chipriyanova", "Mihail Chipriyanov", "Kiril Luchkov"]2024-06-22T00:00:00 7d556870cc8b1695b845ebcd805a5257758f55a9In today's dynamic educational landscape, integrating Artificial Intelligence (AI) technology presents opportunities and challenges for educators, especially undergraduates. Emotional Intelligence (EI) is crucial in the AI age, impacting resilience (RI). However, the relationship between RI and EI among undergraduates is under explored. This study investigates this relationship among Chinese undergraduates, examining EI's significance amidst AI integration. Surveying 420 undergraduates from X University, China, using 2 questionnaires and PLS-SEM analysis, the study reveals high EI levels, particularly in emotional regulation, and a positive relationship between RI and EI. Findings stress the importance of socio-emotional skills in navigating AI-driven education.Environment-Behaviour Proceedings Journal110High EI levels, particularly in emotional regulation, and a positive relationship between RI and EI among Chinese undergraduates are revealed, stressing the importance of socio-emotional skills in navigating AI-driven education.Environment-Behaviour Proceedings Journal["Wei Guo", "Xiran Zhao", "Phaik Gaik Lee", "Liang Ke"]2024-06-22T00:00:00 53cc404c42748478253473b7579846898f42bee9The purpose of the article is to highlight key aspects of improving the economic and legal regulation of the use of technologies based on artificial intelligence in distance education. The object of the study is the economic and legal regulation of the use of technologies based on artificial intelligence in distance education. The scientific task is to present a methodological approach to improving the economic and legal regulation of the use of technologies based on artificial intelligence in distance education. The research methodology involves the use of the IDEF0 methodology. As a result, a functional model is presented for improving the economic and legal regulation of the use of technologies based on artificial intelligence in distance education. Innovation in the presented blocks of the IDEF0 model improves the economic and legal regulation of the use of technologies based on artificial intelligence in distance education. Prospects for further research involve taking into account socio-psychological aspects too.International Journal of Religion00A functional model is presented for improving the economic and legal regulation of the use of technologies based on artificial intelligence in distance education and innovation in the presented blocks of the IDEF0 model improves the economic and legal regulation of the use of technologies based on artificial intelligence in distance education.International Journal of Religion["Ruslan Gubarev", "H. Biletska", "N. Mironova", "Natalia Kazanishena", "S. Skrypnyk"]2024-06-22T00:00:00 4cd1c78d32b3621d19a75ff3be63e9804a3fffadThe study explores the use of automation and artificial intelligence (AI) in public administration and its potential benefits and constraints for government organizations. It discusses how automation and AI can revolutionize public administration by improving productivity, effectiveness, and service quality. The study provides an overview of automation and AI technologies, highlighting their strengths and weaknesses. It identifies key areas where automation and AI can be integrated into public administration, including policymaking, service delivery, decision-making, and citizen engagement. The advantages of using automation and AI in public administration are examined, such as data-driven policy decisions, streamlined administrative procedures, and enhanced service delivery through AI-powered chatbots and virtual assistants. Ethical considerations, privacy concerns, and job displacement are also addressed, proposing methods for ensuring ethical and equitable application of automation and AI. The study includes case studies from different countries that demonstrate successful applications of automation and AI, showcasing increased citizen involvement, transparency, and improved government services. It concludes by emphasizing the importance of strong leadership, collaboration between governmental organizations and technical experts, and ongoing research and development to maximize benefits and minimize risks. The study aims to provide insights and recommendations for policymakers, administrators, and researchers, contributing to the knowledge base on AI and automation in public administration.Journal of Public Administration Research70How automation and AI can revolutionize public administration by improving productivity, effectiveness, and service quality is discussed, and key areas where automation and AI can be integrated into public administration are identified, including policymaking, service delivery, decision-making, and citizen engagement.Journal of Public Administration Research["S. Dar"]2024-06-22T00:00:00 28e2269879db38e61e07c72437e0c7cf1d327a90In the last 10 years, corporations and contemporary literature have focused on machine learning and other advancements in artificial intelligence (AI) technologies. Even while artificial intelligence technology shows a great deal of promise in terms of resolving problems, there are still obstacles associated with its practical use, and there is a lack of knowledge on how it may be strategically utilized to improve businesses. This study will do a comprehensive literature review analysis of the convergence of AI and business strategy. This will be done so that the model can be built. The fundamentals of research technique were discussed in 81 papers that were subjected to peer review. Theoretical framework is developed, addressing the various sources of value creation: “AI and Machine Learning in Organizations, Alignment of AI, Information Technology (IT) with Organizational Strategy, Decision-Making Process”. This framework also addresses gaps in future research. These findings give rise to managerial and theoretical points of view, opening up a wide range of potential new management techniques. Despite several literature assessments, there hasn't been much work that has objectively analyzed the literature with a focus exclusively on the phrase "digital transformation" since other words might provide a skewed interpretation. This essay seeks to unbiasedly examine the topic of business and management study known as digital transformation. The findings show that this study area is still in its infancy and has just begun to expand fast. Among all of them, the Internet of Things and its digital doppelganger seem to be a recurring topic.Nanotechnology Perceptions180The findings show that this study area is still in its infancy and has just begun to expand fast, among all of them, the Internet of Things and its digital doppelganger seem to be a recurring topic.Nanotechnology Perceptions["Dr. Abhishek Sharma", "Dr. Ankitha Sharma", "Anurag Agarwal", "Dr. Shagufta Parween", "Dr. Anurag Shrivastava", "Vandita Hajra"]2024-06-22T00:00:00 e4d9d3a275a0baed87e2ea1cfc809450d96d85abThis article is written in the context of two European cultures from two countries with different histories, both universities collaborating in the field of digital arts. The aim of the study is to provide clear methods for the use of digital tools in higher education for students of architecture and visual arts. To achieve this objective, ten tasks have been set and the results are presented in this paper. The methods used in the study include observation, photo-fixation, Prototyping interior design with artificial intelligence, literature studies, modelling, surveys, and interpretation of their results through graph-analytical methods. The authors present the positive and critical aspects of education: artificial intelligence is powerful and fast at processing huge amounts of data that humans should be able to process over an incomparably longer period, but it is poor at judging people and art. AI accurately processes billions of websites and resources to offer the best results for our search queries, and it has beaten the reigning champions in many intellectual games. But based on their own and others' research, the authors show how inaccurate AI is, for example in predicting whether individuals who have previously used AI in their artwork might achieve better results than if they had produced their own work using their own talent and personal experience. AI is no better than a simple guess, and yet AI is being used to determine people's futures. One of the experts discussed by the authors is Zweig, who introduces us to the basics of AI and provides a toolkit for designing AI systems. Finally, all the respondents explore the ethics of AI and how we can shape the process, prepared us for the biggest question about AI: where we should use it - and where we should not with a particular focus on the quality of education, developing young people's creative abilities, fostering critical thinking and responsible decision-making.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference360The authors present the positive and critical aspects of education: artificial intelligence is powerful and fast at processing huge amounts of data that humans should be able to process over an incomparably longer period, but it is poor at judging people and art.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["Elina Elere", "A. Ulme", "Lucio De Paolis"]2024-06-22T00:00:00 e839a736fb6e046297fd4ced4a1356506b94d63cModern transport projects are complex, multi-stage and long-term, associated with significant calculations, changing schedules, matching resources, analyzing different scenarios for their impact on the development of transport systems and infrastructure. These projects are implemented in a dynamic external environment with a great influence of rapidly changing political, social and natural factors. This objectively requires the application of flexibility in the planning and determination of the various options for development and the search for new approaches to managing transport projects and ensuring the necessary dynamics. Through the application of artificial intelligence, the flexibility in planning and implementation of these transport projects can be improved. This article examines the possibility of using artificial intelligence in transport project management. More specifically, the methodological essence is emphasized from the point of view of using artificial intelligence in the design and implementation of these projects.   ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference200This article examines the possibility of using artificial intelligence in transport project management and the methodological essence is emphasized from the point of view of using artificial intelligence in the design and implementation of these projects.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["Irena Petrova"]2024-06-22T00:00:00 6eb476cb3ad357421170f3899176672ba72b23bcThis paper presents a bibliometric analysis examining the impact of Artificial Intelligence (AI) on advertising. Using data from the Web of Science Core Collection spanning 2014 to 2024, the study investigates citation patterns, prolific authors, universities, publishers/journals, and trends in publication year-wise.Results indicate a significant increase in research interest from 2018 onwards, with a notable surge in publications in 2022. Top contributing countries include the USA, with a focus on computer science, business economics, engineering, and communication fields.Co-authorship and bibliographic coupling analyses reveal influential authors and interconnected research works. Keyword co-occurrence analysis highlights central themes such as AI, machine learning, digital marketing, and advertising.The study's limitations include its reliance on Web of Science data and exclusion of unpublished works. Future research could encompass broader databases and diverse publication types. Overall, this analysis offers insights into AI-driven advertising trends, aiding researchers and industry professionals in understanding evolving research directions.Nanotechnology Perceptions120A bibliometric analysis examining the impact of Artificial Intelligence (AI) on advertising using data from the Web of Science Core Collection spanning 2014 to 2024 offers insights into AI-driven advertising trends, aiding researchers and industry professionals in understanding evolving research directions.Nanotechnology Perceptions["Dr. Mubina Saifee", "Dr. Arvind Khadse", "Dr. Geeta Naidu", "Diptanshu Graham", "Avinash Sahu", "D. Mashirkar"]2024-06-22T00:00:00 224d4ccfb76dafcd1257cfe5abe647491b65edd6International Review of Psychiatry544International Review of Psychiatry["Rowalt C. Alibudbud", "J. J. B. Aruta", "Kevin Anthony Sison", "R. Guinto"]2024-06-22T00:00:00 b5b5c85c53a45cbbd953a4d00d44e43f2662c92fEducation and Information Technologies : Official Journal of the IFIP technical committee on Education404Educ. Inf. Technol.["HeeWon Hong", "YeonKyoung Kim"]2024-06-22T00:00:00 ca2fb50d6e550207c2e2f536b991a8b14503182fEducation and Information Technologies : Official Journal of the IFIP technical committee on Education403Educ. Inf. Technol.["Huiling Ma", "Lilliati Ismail", "Weijing Han"]2024-06-22T00:00:00 0091117ac76b50bc43916c790fd7e9bd2962d96cThis study aims to produce a model that can meet the needs of Differentiated Learning based on AI. This method used research and development (R&D) with the Generic Desain Research Model (GDRM). The subject of this study was mathematics education experts and mathematics learning model experts. Instruments used in this study include quality validity assessment and feasibility assessment analysis. The data analysis techniques used were quantitative methods with learning model validity assessment criteria that use A Linkert Scale of 1-4. The test results of the AI-based Mathematics learning model show that (1) the Content Validity test resulted in an average of 3.47 which means the model is very valid, and (2) The average feasibility test is 3.43, which means it is also very valid. The results showed that the AI-based Mathematics learning model can be implemented in differentiated mathematics classes and train students to learn independently, think critically, provide solutions to problems, and have the courage to convey solutions.Jurnal kependidikan431The results showed that the AI-based Mathematics learning model can be implemented in differentiated mathematics classes and train students to learn independently, think critically, provide solutions to problems, and have the courage to convey solutions.Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran["J. Penelitian", "dan Kajian", "Kepustakaan di", "Bidang Pendidikan", "Pengajaran dan", "Pembelajaran", "M. J. D. Sunarto", "B. Hariadi", "Julianto Lemantara", "di Bidang Pendidikan"]2024-06-22T00:00:00 6f73362d314d7db3e6baf6298abba7dccd9893c1npj Digit. Medicine60NPJ Digital Medicine["Alexandra Valetopoulou", "Simon C. Williams", "H. J. Marcus"]2024-06-22T00:00:00 d7951f5c49cc8e6bf3a5c481384a4e1cbb8e1f58The finishing process in the textile sector is recognized as one of the most complex. This complexity arises from the diversity of structures, the multiple steps involved, the use of complex machinery, the variety of materials, chemicals and dyes, and the need to combine creativity and precision. Therefore, it is crucial to have tools that can improve efficiency, flexibility, and decision-making in this complex area. This literature review aims to provide relevant information on the use of digital engineering in the field of textile finishing. In this review, we used a systematic literature review methodology to examine how digital engineering is applied in the dyeing and finishing process. The data for this study was collected from reputed databases such as Science Direct, IEEE Xplore, Textile Research Journal and Google Scholar. We used the Prisma framework to select relevant articles, which led to the exclusive inclusion of journal articles in our literature review. A comprehensive framework has been developed to understand the impacts of using digital engineering. The approach presented in this framework provides a comprehensive and highly effective approach to addressing the complex challenges associated with ambiguity, modifications and subtleties frequently observed in the ennobling process. The results of various studies explored different aspects, such as properties of textile materials, chemicals and dyes, performance of finishing machines, organizational performance of finishing companies, as well as health concerns and safety at work. Although these studies have provided valuable solutions, they unfortunately remain insufficient to meet the requirements of the finishing process, which remains a complex area characterized by uncertainties, variations, and subtleties inherent to the practice. This particularity of each dyed and finished product promotes an environment conducive to experimentation and continued research.Data and Metadata1090A systematic literature review methodology is used to examine how digital engineering is applied in the dyeing and finishing process and a comprehensive framework has been developed to understand the impacts of using digital engineering.Data and Metadata["Mostafa Elkhaoudi", "Mhammed El Bakkali", "Redouane Messnaoui", "Omar Cherkaoui", "A. Soulhi"]2024-06-22T00:00:00 e8a4e3164f66220c561a825fa9bd177fb1103276Proceedings of the 2024 8th International Conference on Education and Multimedia Technology20Proceedings of the 2024 8th International Conference on Education and Multimedia Technology["Chih-Hung Wu", "Mei-Tzu Chou"]2024-06-22T00:00:00 5f773041ac326ec0557757ffdad87c78ee8d2532The purpose of this research is to analyze the role of AI in decision-making on organizational performance. The method used is qualitative with secondary data. The data used is metadata on documents found on the Scopus website. The data was analyzed by the SLR method. The results of this study show that AI can help identify potential solutions by predicting the problems being studied by testing various variables and conditions in the simulation of organizational activities. AI can also improve performance through integration in decision-making.  However, not all roles performed by humans should be handled by AI, such as customer service. The use of AI agents as public service operators compared to human operators has a negative impact on the perception of the mutuality of public control in the organization-public relationshipIndo-Fintech Intellectuals: Journal of Economics and Business310The results of this study show that AI can help identify potential solutions by predicting the problems being studied by testing various variables and conditions in the simulation of organizational activities.Indo-Fintech Intellectuals: Journal of Economics and Business["Hendrian Hendrian", "Dedi Purwana", "Saparuddin Saparuddin", "Puji Wahono"]2024-06-22T00:00:00 66fe66194f264f4dc31437051086345ff359c9abOperations Management Research600Operations Management Research["Erfan Shakeripour", "M. Ronaghi"]2024-06-22T00:00:00 eb50b659ea0eab254404814005e8e8075537e1d7The rise of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in higher education necessitates assessment reform. This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore, examining GenAI familiarity, perceptions of its use in assessment marking and feedback, knowledge checking and participation, and experiences of GenAI text detection. Descriptive statistics and reflexive thematic analysis revealed a generally low familiarity with GenAI among both groups. GenAI feedback was viewed negatively; however, it was viewed more positively when combined with instructor feedback. Academic staff were more accepting of GenAI text detection tools and grade adjustments based on detection results compared to students. Qualitative analysis identified three themes: unclear understanding of text detection tools, variability in experiences with GenAI detectors, and mixed feelings about GenAI's future impact on educational assessment. These findings have major implications regarding the development of policies and practices for GenAI-enabled assessment and feedback in higher education.arXiv.org442This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment.ArXiv["Jasper Roe", "Mike Perkins", "Daniel Ruelle James Cook University Singapore", "British University Vietnam", "VinUniversity"]2024-06-22T00:00:00 93a07912aff548415fc6f1e9b8c2ba25d93a00ddReduced environmental effect, lower operating costs, and a stable and sustainable energy supply for current and future generations are the main reasons why power optimization is important. Power optimization makes ensuring that energy is used more effectively, cutting down on waste and optimizing the utilization of resources.In today's world, power optimization and artificial intelligence (AI) integration are essential to changing the way energy is produced, used, and distributed. Real-time monitoring and analysis of power usage trends is made possible by AI-driven algorithms and predictive analytics, which enable dynamic modifications to effectively satisfy demand. Efficiency and sustainability are increased when power consumption is optimized in different sectors thanks to the use of intelligent systems. This survey paper comprises an extensive review of the several AI techniques used for power optimization as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of power consumption.This literature review identifies the performance and outcomes of 17 different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. Furthermore, this article outlines future directions in the integration of AI for power consumption optimization.Discover Artificial Intelligence531This survey paper comprises an extensive review of the several AI techniques used for power optimization as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of power consumption.ArXiv["Parag Biswas", "Abdur Rashid", "A. Biswas", "Md Abdullah Al Nasim", "Kishor Datta Gupta", "Roy George"]2024-06-22T00:00:00 5e5477c14631ef1b31c5825da78e6b507029bd3fArtificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries, including electrical power systems, as a result of recent digitalization. Algorithms for artificial intelligence are data-driven models that are based on statistical learning theory and are used as a tool to take use of the data that the power system and its users generate. Initially, we perform a thorough literature analysis of artificial intelligence (AI) applications related to renewable energy (RE). Next, we present a thorough analysis of renewable energy factories and assess their suitability, along with a list of the most widely used and appropriate AI algorithms. Nine AI-based strategies are identified here to assist Renewable Energy (RE) in contemporary power systems. This survey paper comprises an extensive review of the several AI techniques used for renewable energy as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of renewable energy. This literature review identifies the performance and outcomes of nine different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. This study also addressed three main topics: using AI technology for renewable power generation, utilizing AI for renewable energy forecasting, and optimizing energy systems. Additionally, it explored AI's superiority over conventional models in controllability, data handling, cyberattack prevention, smart grid implementation, robotics- AI's significance in shaping the future of the energy industry. Furthermore, this article outlines future directions in the integration of AI for renewable energy.arXiv.org641AI's superiority over conventional models in controllability, data handling, cyberattack prevention, smart grid implementation, robotics, and optimizing energy systems is explored- AI's significance in shaping the future of the energy industry is explored.ArXiv["Abdur Rashid", "Parag Biswas", "A. Biswas", "Md Abdullah Al Nasim", "Kishor Datta Gupta", "Roy George"]2024-06-22T00:00:00 153044be884727ad36248fc498efea093a02d3c4While artificial intelligence (AI) may have utility in some medical practices such as reading X‐rays, it is not going to be useful in diagnosing or treating conditions with nuances — notably, substance use disorders (SUDs) — experts suggested in a panel on the topic at last week's College on Problems of Drug Dependence (CPDD) annual conference in Montreal.Alcoholism &amp; Drug Abuse Weekly00Alcoholism &amp; Drug Abuse Weekly["Alison Knopf"]2024-06-22T00:00:00 48bce97465f8f7ca9dc60e28507aa81b62a3983aThis study investigates the evolving role of Artificial Intelligence (AI) in promoting Diversity, Equity, and Inclusion (DE&I) initiatives within Indian organizations. Employing a bibliometric analysis, the research explores the application of AI-powered tools in mitigating bias during recruitment and performance evaluations. Findings suggest that anonymized applications and skill-based assessments facilitated by AI can contribute to fairer decision-making processes. However, the analysis also identifies significant ethical and practical challenges, including data privacy concerns and the lack of algorithmic transparency. Notably, the research highlights a growing emphasis on developing fair AI algorithms and integrating DE&I principles throughout the design process. This underscores the critical need for robust ethical frameworks and ongoing research tailored to the Indian context. By navigating these complexities, organizations can harness the potential of AI to foster a more inclusive and equitable work environment.International Research Journal on Advanced Engineering and Management (IRJAEM)00Findings suggest that anonymized applications and skill-based assessments facilitated by AI can contribute to fairer decision-making processes, but also identify significant ethical and practical challenges, including data privacy concerns and the lack of algorithmic transparency.International Research Journal on Advanced Engineering and Management (IRJAEM)["Charul Sharma", "Ritik Srivastava", "Anmol Rajput", "Rupjyoti Mukherjee", "Siddharth Chandra"]2024-06-22T00:00:00 e88e53874dba0f058e55b729f11deaae4b65ea7aThe opportunities that Artificial Intelligence and the principles of Algorithmic management provide to modern managers bring undeniable advantages for the development of a competitive business in today's extremely difficult business environment. At the same time, however, the effect of their use should be carefully analysed from the point of view of the compliance of the employees opinion in the enterprise - mainly in line with the observance and guarantee of basic rights of the employees. In this regard, the European Parliament and the European Council launched a legislative initiative to define harmonized rules within the Community on the use of artificial intelligence. Concepts such as "algorithmic discrimination" were introduced quite purposefully at the regulation level, given the risk of possible abuses associated with the use of AI. This report aims to ascertain the views of employers and employees on the use of artificial intelligence in Human Resource Management. The report presents and analyses data from an empirical study conducted among managers and employees in leading ICT enterprises in Bulgaria. According to our responders, one of the biggest advantages of using AI in Human Resources Management is related to the elimination of subjectivity in performance evaluation and the possibility of fair play in the procedures of internal selection of employees. At the same time, employees with more experience (over 10 years) are more sceptical of the idea of their work performance being evaluated solely by AI, while younger workers show more trust in AI solutions. However, both managers and workers recognize that it is best for the final decision in determining career development to be made by a person, but justified by the analyses made by AI. The report draws conclusions and recommendations that can serve both researchers and business managers. Certainly, AI is yet to undergo a very large development and application, including in the Human Resource Management, but at the same time it should not be at the expense of affected rights.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference180AI is yet to undergo a very large development and application, including in the Human Resource Management, but at the same time it should not be at the expense of affected rights.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["Miglena Angelova"]2024-06-22T00:00:00 e53e750a78c778da898adf479fcf0d760b8a1606The results of a survey conducted in 2023 on skills and attitudes towards the use of Artificial Intelligence are presented. The study included employees working in the public administration in the Republic of Bulgaria and students from the Cybersecurity specialty. The aim was to find out to what extent and area of interest the two target groups work with or are willing to start using ChatGPT or similar chatbots. The questions asked in chatbots, as well as the listed by both groups advantages and disadvantages can be used as one of the indicators when creating the teaching materials for school or university courses, as well as for the updating of already ongoing training programs.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference120The results of a survey conducted in 2023 on skills and attitudes towards the use of Artificial Intelligence are presented and can be used as one of the indicators when creating the teaching materials for school or university courses, as well as for the updating of already ongoing training programs.ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference["R. Yoshinov", "Monka Kotseva", "Anastas Madzharov", "Neda Chehlarova"]2024-06-22T00:00:00 e2252538ef7ecc68d4158a2d64a27bbe387b54f8This paper advocates for a constructivist approach to symbiosis to restore human-centredness in the governance of Symbiotic Artificial Intelligence (SAI). Challenging rigid, deterministic foundational methods warns against the risk of divorcing ethics from mere adherence to moral principles. Instead, it calls for a shift towards a distributed, contextual, relational, and dialectical structure to embody human-centredness. Through an analysis of the SAI landscape and its interplay between social and technological factors, the paper argues for a reconceptualisation of theoretical foundation and human responsibility within the socio-technical perspective. Chapter 2 delves into foundational issues of SAI, questioning the application of biological categories and proposing patterns of SAI based on definitions of intelligent life. Chapter 3 explores the potential of a constructivist approach, emphasising flexibility and context awareness, and presents a framework for understanding and evaluating SAI systems, components of an evolving methodology.Intelligenza Artificiale270An analysis of the SAI landscape and its interplay between social and technological factors, the paper argues for a reconceptualisation of theoretical foundation and human responsibility within the socio-technical perspective.Intelligenza Artificiale["Antonio Carnevale", "Antonio Lombardi", "F. Lisi"]2024-06-22T00:00:00 51bed9e1de1905212277ea94f5f0a58c5c0900fbThe rapid development of artificial intelligence (AI) technology has revolutionized the digital marketing landscape, enabling marketers to improve efficiency, personalization and strategic decision making. This research aims to map research trends related to the application of AI in digital marketing through bibliographic analysis. Qualitative research methods with a historical approach are used to collect and analyze scientific articles from leading international journals. Bibliometric analysis was performed with VOSviewer software to visualize patterns and clusters of key terms. Results show a significant increase in the number of publications related to AI in digital marketing, with a focus on topics such as marketing strategy, customer experience, social media, e-commerce and industrial marketing. The visualization reveals the interrelationships and groupings of various terms, providing insight into the current research landscape. This study contributes to the understanding of trends, opportunities and challenges in integrating AI into digital marketing practices. These findings are useful for researchers and practitioners to identify future research directions and develop effective marketing strategies by leveraging AI. However, further research is needed to explore the ethical implications, data security and other aspects regarding the application of AI in the context of digital marketingApollo550This research aims to map research trends related to the application of AI in digital marketing through bibliographic analysis, and reveals the interrelationships and groupings of various terms, providing insight into the current research landscape.Apollo: Journal of Tourism and Business["Muhammad Edrick Abhiseka", "Riyandi", "Yongki Alex", "Riza Ardy Saputra", "Adi Setiawan"]2024-06-22T00:00:00 8818a99bca6d276380000dc4ff3c242bc696ad95In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.arXiv.org420This study conducts a meta-review to explore data issues and methods within the implementation of industrial AI, and proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle.ArXiv["Xuejiao Li", "Cheng Yang", "Charles M\u00f8ller", "Jay Lee"]2024-06-22T00:00:00 8167e1471630b1bcd232bb0350a820373ebea165BACKGROUND AND AIM Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis. METHODS A comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms "Artificial Intelligence," "Liver Cancer," "Hepatocellular Carcinoma," "Machine Learning," and "Deep Learning" were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information. RESULTS Our search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B-mode US and a contrast-enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI-based model achieved HCC diagnosis with an AUC of 0.970. CONCLUSIONS AI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.Journal of Gastroenterology and Hepatology624Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.Journal of gastroenterology and hepatology["Odysseas P. Chatzipanagiotou", "C. Loukas", "M. Vailas", "Nikolaos Machairas", "S. Kykalos", "G. Charalampopoulos", "D. Filippiadis", "Evangellos Felekouras", "Dimitrios Schizas"]2024-06-23T00:00:00 3a19a0366701bfcb723a0e56e8d3be5c075ebc0bThe integration of artificial intelligence (AI) in Indonesia's business landscape has ushered in significant changes, presenting challenges and opportunities for traditional businesses across various sectors. Understanding the implications of AI is crucial for navigating these changes effectively. This study aims to investigate the transformative impact of AI on traditional Indonesian businesses, specifically analyzing the opportunities and challenges associated with AI adoption and its implications for business strategies and operations. Utilizing a qualitative method, this study examines the influence of AI technologies on traditional Indonesian businesses. The study compiles data from academic literature, industry reports, and real-world case studies to analyze the intricate dynamics between AI technologies and human behavior. The findings reveal that AI integration offers numerous opportunities for traditional businesses in Indonesia, such as enhanced operational efficiency, improved customer experience, and innovation potential. However, significant challenges, including high implementation costs, data privacy concerns, and the lack of skilled AI talent, hinder widespread adoption. Despite these challenges, businesses that successfully navigate them can gain a competitive advantage in the digital age. This study contributes to the existing literature by providing fresh insights into the transformative impact of AI on traditional Indonesian businesses. It synthesizes recent research findings and case studies, offering valuable guidance for businesses aiming to leverage AI for strategic advantage.Journal of Management Studies and Development423The findings reveal that AI integration offers numerous opportunities for traditional businesses in Indonesia, such as enhanced operational efficiency, improved customer experience, and innovation potential, however, significant challenges, including high implementation costs, data privacy concerns, and the lack of skilled AI talent hinder widespread adoption.Journal of Management Studies and Development["Muhammad Asif Khan"]2024-06-23T00:00:00 44e1bb67ef6bbd4eca7155fa326dfdd045e12306The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed databases. After removing duplicates and reviews, a total of 2061 articles were assessed using VOSviewer software (version 1.6.20). The results were organized into two main sections: influential factors and thematic directions of AI integration in healthcare. The first section highlights the most productive countries, authors, and institutions in terms of publications. The second section explores the keywords used in the relevant literature, and identifies the main thematic areas where AI has a significant impact in medical sector. The findings of this study aimed not only to assess AI’s current contributions to medicine in general but also to highlight specific technological advancements across medical departments, offering a comprehensive overview.Future Internet01The scope of AI utilization across the healthcare spectrum is examined by analyzing a significant body of publications from the Scopus and PubMed databases, offering a comprehensive overview of AI’s current contributions to medicine in general and specific technological advancements across medical departments.Future Internet["Loukas Triantafyllopoulos", "Evgenia Paxinou", "G. Feretzakis", "D. Kalles", "V. Verykios"]2024-06-23T00:00:00 a6fe04f25a39a8d971199e38f75787347167f454International ophtalmology361This study revealed the hot spots and potential trends of AI in terms of cataract diagnosis and intraocular lens power calculation and discovered that AI will become more prevalent in the field of ophthalmology in the future.International Ophthalmology["Si Chen", "Li Huang", "Xiaoqing Li", "Qin Feng", "Huilong Lu", "Jing Mu"]2024-06-23T00:00:00 c342eb1603e06ae64c849c3bea715f501cf55003In the contemporary engineering landscape, the complexity and interconnectivity of systems have given rise to the concept of Systems of Systems (SoS), wherein multiple independent systems collaborate to achieve a higher-order functionality not possible by any single component system alone. The engineering of SoS, or System of Systems Engineering (SoSE), presents unique challenges that necessitate novel approaches for design, integration, and management for connecting systems into SoS. Additionally, SoS capabilities can be further leveraged using the potential of Artificial Intelligence (AI), which has yet to be fully explored. This paper introduces an innovative framework leveraging AI to address the complex demands of SoSE. The framework itself has been developed using a systematic domain analysis method. The framework incorporates a metamodel, a Matrix Chart for aligning AI technologies with specific SoS capabilities, and a systematic method for their application across different stages of the SoS lifecycle. To validate our framework, we employ a multi-case study methodology, examining diverse SoS examples across industries. These case studies demonstrate the practicality and impact of the presented proposed AI-driven approach in real-world SoSE challenges. The framework is used to depict a useful map to pave the way for further research and innovations.International Symposium on Service Oriented Software Engineering181This paper introduces an innovative framework leveraging AI to address the complex demands of SoSE, and employs a multi-case study methodology, examining diverse SoS examples across industries to depict a useful map to pave the way for further research and innovations.2024 19th Annual System of Systems Engineering Conference (SoSE)["B. Tekinerdogan"]2024-06-23T00:00:00 8a7266aa65be306f736566ff90c2b27c16faf62bThis paper explores the dynamic intersection of artificial intelligence (AI) and strategic project management (SPM), investigating the transformative effects of AI technologies on traditional project management practices. As organizations navigate an increasingly complex and fast-paced business environment, the integration of AI in SPM emerges as a catalyst for efficiency, adaptability, and informed decision-making. The study delves into key facets of SPM influenced by AI, including data-driven decision-making, predictive analytics, automation of routine tasks, and resource optimization. The role of AI in risk management, particularly in identifying, assessing, and mitigating project risks, is examined in detail. Furthermore, the paper explores how natural language processing (NLP) fosters enhanced communication within project teams, contributing to a more collaborative and connected working environment. Adaptive project planning, facilitated by AI, is investigated as a mechanism for responding to evolving project dynamics in real-time. The paper underscores the importance of continuous monitoring and reporting enabled by AI, providing project managers with timely insights for strategic adjustments. The concept of continuous improvement, driven by AI-driven analytics, is explored as organizations seek to refine and optimize their project management approaches based on past experiences. Ethical considerations and responsible AI practices are emphasized as integral components of AI integration in SPM. The paper concludes by highlighting the synergistic potential of human expertise and AI capabilities, envisioning a future where organizations can leverage AI to achieve more adaptive, efficient, and successful project outcomes. This comprehensive review aims to contribute to the understanding of AI's transformative influence on strategic project management, providing insights for practitioners, researchers, and organizations seeking to navigate the evolving landscape of project management in the era of artificial intelligence.Bulletin of NTU "KhPI". Series: Strategic management, portfolio, program and project management02The study delves into key facets of SPM influenced by AI, including data-driven decision-making, predictive analytics, automation of routine tasks, and resource optimization, and natural language processing fosters enhanced communication within project teams, contributing to a more collaborative and connected working environment.Bulletin of NTU "KhPI". Series: Strategic management, portfolio, program and project management["Sergey Bushuyev", "D. Bushuyev", "Victoria Bushuyeva", "N. Bushuyeva", "Yuri Tykchonovych"]2024-06-23T00:00:00 e693242954f107cb7e2ac5e83d0734f2b5c523d3Artificial intelligence is becoming a key force leading the reform of higher education and teaching. This paper systematically analyzes the application demand and development trend of artificial intelligence in college education, discusses the reform direction and realization path of college education and teaching mode driven by artificial intelligence, analyzes the effectiveness and challenges of the reform of education and teaching mode enabled by artificial intelligence, and looks forward to the future development trend. It is found that artificial intelligence can promote the intellectualization of teaching content, the personalization of teaching process, the intellectualization of teaching evaluation, the reshaping of the roles of teachers and students, and the intellectualization of teaching management. However, there are still some challenges in knowledge expression, adaptive learning, learning analysis and acceptance of teachers and students, which need to be solved by specific measures. In the future, artificial intelligence will further enable the reform of college education and teaching mode, and colleges and universities should take the initiative to embrace artificial intelligence, optimize the teaching mode, and provide students with more intelligent and personalized high-quality education services.International Journal For Multidisciplinary Research80It is found that artificial intelligence can promote the intellectualization of teaching content, the personalization of teaching process, the intellectualization of teaching evaluation, the reshaping of the roles of teachers and students, and the intellectualization of teaching management.International Journal For Multidisciplinary Research["Jia Xin Xie"]2024-06-23T00:00:00 0a654796df9e84b7ca9fa5359627fda2d1aecd22Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. Powered by foundation models that have been pretrained and can generate complex content, GenAI represents a paradigm shift away from the more traditional focus on task-specific classifiers that have dominated the AI landscape thus far. We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications that automate healthcare workflows at the point of care using smaller foundation models. These models will be finetuned for different capabilities and application specific scenarios and will have the ability to provide medical explanations, reference evidence within a retrieval augmented framework and utilizing external tools. We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance, including safety-critical diagnostic tasks, which will require greater research prior to implementation. We consider areas where 'human in the loop' Generative AI can improve healthcare quality and safety by automating mundane tasks. Using the principles of implementation science will be critical for integrating 'end to end' GenAI systems that will be accepted by healthcare teams.arXiv.org30This work posits that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications that automate healthcare workflows at the point of care using smaller foundation models.ArXiv["Laleh Jalilian", "Daniel McDuff", "A. Kadambi"]2024-06-23T00:00:00 414c8734ee767a3698995ca1befb113b47c7838eThis research aims to see the influence of cartoon animation media on student creativity in nutrition and food courses. This research uses quantitative descriptive methods. This research was carried out at UIN Sulthan Thaha Saifuddin Jambi, with a research population of 47 students who took nutrition and food courses in semester V. The sample in this study used a total sampling technique, namely, the entire population that was the research subject. The data collection technique uses an indirect creativity questionnaire survey on five indicators using a Likert scale with a total of 20 questions. Based on the research results, data was obtained that the use of Artificial Intelligence-based cartoon media can influence student learning creativity in nutrition and food courses with an average score of 4.46 in the good category, where in detail, each indicator, such as the Investigation Group, has an average score of 4.44 in the good category, the Pre-Knowledge Indicator with an average value of 4.13 in the Good category, then the Influence of Culture and Values ​​indicator with an average value of 4.83 in the good category, the Motivation indicator with an average value of 4.95 in the good category and the Self-Esteem indicator with an average value of 3.87 in the pretty good category. The highest creativity indicator in this research is Motivation in the good category, while the lowest in this research is the Self-Esteem creativity indicator in the quite good category.International Journal of Education and Teaching Zone00The use of Artificial Intelligence-based cartoon media can influence student learning creativity in nutrition and food courses with an average score of 4.46 in the good category, where in detail, each indicator has an average score of 4.46.International Journal of Education and Teaching Zone["Aminah Zb", "Wasim Khan"]2024-06-23T00:00:00 27336e7dc0d7608e8689778a386f64e5f42a5cf0This paper reviews the diverse applications of Artificial Intelligence (AI) in agricultural land planning, highlighting how AI enhances agricultural production efficiency, optimizes resource allocation, and strengthens decision-making quality to promote sustainable agriculture. The article discusses AI's role in land suitability analysis, precision agriculture, integration into decision support systems, and the challenges and limitations of technology, emphasizing AI's significant role in advancing sustainable agricultural development and future research directions. Despite challenges such as data quality, model transparency, and ethical issues, AI's application prospects remain broad, potentially becoming a significant driving force in agricultural land planning and global food security.Frontiers in Science and Engineering160The article discusses AI's role in land suitability analysis, precision agriculture, integration into decision support systems, and the challenges and limitations of technology, emphasizing AI's significant role in advancing sustainable agricultural development and future research directions.Frontiers in Science and Engineering["Peng Li"]2024-06-23T00:00:00 8fcd5382c0ff891121dc9af34c98e32bd7451537Proceedings of DRS01Proceedings of DRS["Martin Tironi"]2024-06-23T00:00:00 84f852ccb2acae4182c58da9055589f9ecf36026This study aimed to evaluate the pre-test blood glucose levels in Type-II Diabetic adults within both experimental and control groups, to determine the effectiveness of Giloy juice on these levels in the experimental group, to assess the post-test blood glucose levels in both groups, and to identify any associations between post-test blood glucose levels and selected sociodemographic variables. A true experimental, one-group pre-test post-test design was utilized. Sixty Type-II Diabetic adults meeting the inclusion criteria were selected via probability-simple random sampling. Informed consent was obtained, and Giloy juice was administered to participants in ward-9, Purani Basti, Kohka Nagar Nigam Bhilai, (C.G.). The findings indicated a highly significant difference between pre-test and post-test blood glucose levels in the experimental group, with a calculated ‘t’ value of 14.05 (df=19) exceeding the table value of 2.09 at the 0.05 level of significanceInternational Journal of Advanced Research in Science, Communication and Technology20The findings indicated a highly significant difference between pre-test and post-test blood glucose levels in the experimental group, with a calculated ‘t’ value of 14.05 exceeding the table value of 2.09 at the 0.05 level of significance.International Journal of Advanced Research in Science, Communication and Technology["Mr. Bhushan Girase", "Mr. Pranjal Bobade"]2024-06-23T00:00:00 124b1077a4c31c6fbed2ae8482f55cb7cc9ba976The integration of Artificial Intelligence (AI) into industrial systems with high levels of automation has introduced significant uncertainty and complexity. In particular, work by the automotive industry, on autonomous vehicles has led to the emergence of the Operational Design Domain (ODD) concept, which delineates the expected operating domain of such vehicles, departing from conventional automotive Use Case-based approaches. However, this ODD’s automotive-centric approach has hindered its broader application, lacking the comprehensive guidance on the system engineering methodologies required for its definition. This paper presents a domain-agnostic definition of ODD, grounded in established system frameworks and emphasizing a systemic risk-based engineering to make it applicable to multiple domains. A case study from the maritime domain illustrates the benefits and applicability of the proposed methodology. By providing a systematic framework, this research facilitates the adoption of ODD principles beyond the automotive sector, fostering the development of AI-based products and services across diverse industrial domains. The ODD represents a key aspect of systems engineering for autonomous systems, integrating considerations of technology, environment, regulation, and user expectations.International Symposium on Service Oriented Software Engineering282A domain-agnostic definition of ODD is presented, grounded in established system frameworks and emphasizing a systemic risk-based engineering to make it applicable to multiple domains, fostering the development of AI-based products and services across diverse industrial domains.2024 19th Annual System of Systems Engineering Conference (SoSE)["Morayo Adedjouma", "Bernard Botella", "Javier Iba\u00f1ez-Guzm\u00e1n", "Kevin Mantissa", "Chauk-Mean Proum", "A. Smaoui"]2024-06-23T00:00:00 6ede914bc19067d63a889c0f41dc79e4cc2b83dfArtificial Intelligence (AI) has become an integral part of decision-making processes across a spectrum of applications, from autonomous vehicles and healthcare diagnostics to financial forecasting and customer service. As AI systems increasingly take on roles that directly impact human lives and societal structures, the issue of trust in their decision-making capabilities assumes paramount importance. Confidence, as a measurable attribute within AI systems, plays a pivotal role in shaping this trust dynamic. This abstract is drawn from the conference "AI Decision-Making: The Role of Confidence," which explores the multifaceted dimensions of confidence in AI decision-making and its profound implications for accuracy and trust calibration. Researchers, practitioners, and industry experts converge to discuss the challenges and opportunities surrounding this critical concept.International Journal of Religion81This abstract is drawn from the conference "AI Decision-Making: The Role of Confidence," which explores the multifaceted dimensions of confidence in AI decision-making and its profound implications for accuracy and trust calibration.International Journal of Religion["Nurhaslinda Mat Rabi"]2024-06-23T00:00:00 82ec98e0095aa91adaf419accaa3b29ad341f3f2Artificial Intelligence (AI), once exclusive to sophisticated technological spheres, now plays a transformative role across all aspects of society, driving significant progress and innovation. In this backdrop, basic conversational interface aka chat emerged as the easiest, and the simplest way to interact with AI systems. However, the current Human-AI conversations are fraught with a host of challenges necessitating a critical exploration into their design, approach, and implications. As AI technologies continue to permeate our lives, the need for seamless, intuitive, and human-like conversations is amplified. While our larger research embarks on a research study to suggest a conceptual framework to help design more effective and engaging Human-AI conversations, this paper focuses on a critical aspect that surfaced as a primary gap during our literature review. Conversations are effective and engaging only when their fundamental purpose is identified and understood by their participants. Hence, formulating a purpose driven conversational typology emerged as a key design imperative to inform an array of frameworks that could help create meaningful Human-AI conversations. This study evaluates a dataset of over hundred Human-AI and Human-to-Human conversations, proposing twelve conversational archetypes central to a conceptual framework intended to enhance Human-AI conversations. Employing a hybrid methodology that integrates content analysis with case study research, this paper examines the issue from a human perspective. It aims to provide a useful resource for designers, developers, researchers, and industry professionals who seek to foster deeper connections and trust in human-AI interactions.International Journal For Multidisciplinary Research210This study evaluates a dataset of over hundred Human-AI and Human-to-Human conversations, proposing twelve conversational archetypes central to a conceptual framework intended to enhance Human-AI conversations.International Journal For Multidisciplinary Research["Shridhar Marri"]2024-06-23T00:00:00 04975e9bd5e2fe5a7bdf424a88263602c87a5186With the rise of artificial intelligence (AI) tools to support the work of numerous disciplines, we describe a preliminary investigation into the benefits and drawbacks of large language model (LLM) use as part of a traditional systems engineering and design workflow. To explore this, we tasked a group of systems engineers to each create a list of requirements and use case diagram to satisfy a systems of systems user scenario presented in a proposal document. Participants created models of a healthcare setting in which clinicians resolved discrepancies with patient care by consulting additional sources of record, demonstrating the importance of integrating new systems within the larger healthcare system of systems. The first group were provided open access to an LLM, the second group were provided draft materials generated by an LLM, and the third followed their normal workflow. A subject matter expert (SME) evaluator then scored each model according to its completeness, consistency, correctness, simplicity, and traceability. Through this, we show that although LLMs are not a replacement for a trained systems engineer, they can contribute in two primary ways to the modeling process: first, they can generate a significant portion of the information necessary to create a minimum viable product (MVP) model within a fraction of the time, offering a promising way to accelerate the overall model development process. Second, they can answer detailed, domain-specific questions and reduce the time spent on external research.International Symposium on Service Oriented Software Engineering150It is shown that although LLMs are not a replacement for a trained systems engineer, they can contribute in two primary ways to the modeling process: first, they can generate a significant portion of the information necessary to create a minimum viable product (MVP) model within a fraction of the time, offering a promising way to accelerate the overall model development process.2024 19th Annual System of Systems Engineering Conference (SoSE)["Erin Smith Crabb", "Matthew T. Jones"]2024-06-23T00:00:00 47467c34d79c359adec9eb54fa0d2076192741c0The United States and China will play an important role in navigating safety and security challenges relating to advanced artificial intelligence. We sought to better understand how experts in each country describe safety and security threats from advanced artificial intelligence, extreme risks from AI, and the potential for international cooperation. Specifically, we compiled publicly-available statements from major technical and policy leaders in both the United States and China. We focused our analysis on advanced forms of artificial intelligence, such as artificial general intelligence (AGI), that may have the most significant impacts on national and global security. Experts in both countries expressed concern about risks from AGI, risks from intelligence explosions, and risks from AI systems that escape human control. Both countries have also launched early efforts designed to promote international cooperation around safety standards and risk management practices. Notably, our findings only reflect information from publicly available sources. Nonetheless, our findings can inform policymakers and researchers about the state of AI discourse in the US and China. We hope such work can contribute to policy discussions around advanced AI, its global security threats, and potential international dialogues or agreements to mitigate such threats.arXiv.org60This work compiled publicly-available statements from major technical and policy leaders in both the United States and China to better understand how experts in each country describe safety and security threats from advanced artificial intelligence, extreme risks from AI, and the potential for international cooperation.ArXiv["Akash Wasil", "Tim Durgin"]2024-06-23T00:00:00 a2997bc19fc1bbdf8fff6744751ca5abc974c1ffIt is essential to increase children’s understanding of artificial intelligence and machine learning as they encounter it through their daily activities. We have developed DataBites, a museum exhibit aimed at fostering middle-school-age children’s understanding of supervised machine learning. DataBites engages visitors in learning about the steps and practices of supervised machine learning, using three guiding design principles: embodied interaction, creativity, and collaboration. Our design allows learners to use tangible pieces to collaboratively create their own labeled examples of pizzas and sandwiches to include in a training dataset for an image-based machine-learning pizza/sandwich classification algorithm. The algorithm can classify sandwiches and pizzas by learning patterns from people’s examples. Learners can view the results and self-evaluate how well their dataset did at enabling the algorithm to distinguish between the two items. This poster paper contributes a novel design and approach to engaging children in learning about AI in museum settings.Creativity & Cognition360DataBites, a museum exhibit aimed at fostering middle-school-age children’s understanding of supervised machine learning, is developed, allowing learners to use tangible pieces to collaboratively create their own labeled examples of pizzas and sandwiches to include in a training dataset for an image-based machine-learning pizza/sandwich classification algorithm.Proceedings of the 16th Conference on Creativity & Cognition["Hasti Darabipourshiraz", "Dev Ambani", "Duri Long"]2024-06-23T00:00:00 a32b36645b33b3c91e359588db7117fd6761dff7Wright’s notion of "organic creative space" invites viewers to experience the harmony and discord inherent when operating at the boundary between the natural and designed worlds. In this artwork, we interrogate similar boundaries: between the natural and artificial, and between the creative and the generative. We explore the use of artificial intelligence systems to generate images of natural phenomena–specifically, women’s faces–and the discomfort felt by viewers as they are unsettled by the unanticipated. The Matrix of Discomfort is a multimedia art installation that blends quilting and augmented reality (AR) to critically reflect upon AI as a medium that holds promise and distrust, and that exists at boundaries: between the natural and artificial, the creative and the generative, the digital and the physical. It reimagines the quilt, traditionally a feminized symbol of comfort and relaxation, as a canvas for stimulating conversation about the ethical quandaries and potential promises of generative AI.Creativity & Cognition120The use of artificial intelligence systems to generate images of natural phenomena–specifically, women’s faces–and the discomfort felt by viewers as they are unsettled by the unanticipated are explored.Proceedings of the 16th Conference on Creativity & Cognition["Karen Royer", "Gillian Smith", "Y. Telliel"]2024-06-23T00:00:00 635e6594b104069d9e0ce0d5f3a7b57be52d1eccArtificial Intelligence with its multifaceted technologies and integral role in global production significantly impacts gender dynamics particularly in gendered labor. This paper emphasizes the need to explore AIs broader impacts on gendered labor beyond its current emphasis on the generation and perpetuation of epistemic biases. We draw attention to how the AI industry as an integral component of the larger economic structure is transforming the nature of work. It is expanding the prevalence of platform based work models and exacerbating job insecurity particularly for women. Of critical concern is the increasing exclusion of women from meaningful engagement in the digital labor force. This issue often overlooked demands urgent attention from the AI research community. Understanding AIs multifaceted role in gendered labor requires a nuanced examination of economic transformation and its implications for gender equity. By shedding light on these intersections this paper aims to stimulate in depth discussions and catalyze targeted actions aimed at mitigating the gender disparities accentuated by AI driven transformations.arXiv.org330The need to explore AIs broader impacts on gendered labor beyond its current emphasis on the generation and perpetuation of epistemic biases is emphasized and how the AI industry as an integral component of the larger economic structure is transforming the nature of work is drawn attention.ArXiv["Satyam Mohla", "Bishnupriya Bagh", "Anupam Guha"]2024-06-23T00:00:00 13ab9678b063d336c383405f9ead3b8274c01efdThe explosive surge in Human-AI interactions, fused with a soaring fascination in wearable technology, has ignited a frenzy of innovation and the emergence of a myriad of Wearable AI devices, each wielding diverse form factors, tackling tasks from health surveillance to turbocharging productivity. This paper delves into the vision for wearable AI technology, addressing the technical bottlenecks that stand in the way of its promised advancements. Embracing a paradigm shift, we introduce a Human-Inspired Distributed Network for Wearable AI, enabled by high-speed ultra-low-power secure connectivity via the emerging 'Body as a Wire' (Wi-R) technology. This breakthrough acts as the missing link: the artificial nervous system, seamlessly interconnecting all wearables and implantables, ushering in a new era of interconnected intelligence, where featherweight, perpetually operating wearable AI nodes redefine the boundaries of possibility.Design Automation Conference230A Human-Inspired Distributed Network for Wearable AI is introduced, enabled by high-speed ultra-low-power secure connectivity via the emerging 'Body as a Wire' (Wi-R) technology.{"pages": "364:1-364:4"}["Shreyas Sen", "Arunashish Datta"]2024-06-23T00:00:00 29c19f5bb8178de7e3674fd78bbe45d6124b772bPor diversas razones, los filósofos Hubert Dreyfus y John Searle no están de acuerdo en la posibilidad de que la Inteligencia Artificial (IA) sea capaz de pensar y comprender como la hacen los seres humanos. Tanto el enactivismo de Dreyfus (1992) como la posición semántica de Searle (1980) sirven de base para la tesis de la dependencia cognitiva que aquí se defiende, la cual considera que la IA es incapaz de desarrollar sus funciones sin el sostén de las capacidades cognitivas que se derivan de la experiencia y creatividad de los seres vivos, en especial de la vida humana.Análisis Revista de investigación filosófica00Análisis. Revista de investigación filosófica["Marcos de J. Aguirre Franco"]2024-06-23T00:00:00 a036a006f4466d4ff56d516ec1b6fdb277b998f9Revista Chilena de Atención Primaria y Salud Familiar00Revista Chilena de Atención Primaria y Salud Familiar["Jhonny W. Acevedo Ayala"]2024-06-23T00:00:00 1a6c0ed0bc3bdb7e7daff710451edaabcb191e57Penelitian ini bertujuan menganalisis proses yang dapat dilakukan dalam adopsi teknologi generative artificial Intelligence (AI) melalui perspektif teori difusi inovasi sehingga dapat memaksimalkan kebermanfaatannya. Metode yang digunakan adalah meta-sintesis dengan pendekatan kualitatif. Data penelitian diperoleh dari literatur Scopus yang dipublikasikan pada November 2023 – April 2024. Hasil meta-sintesis menunjukkan terdapat beberapa cara yang perlu dilakukan dalam mendukung proses adopsi generative AI, yaitu memahami potensi dan risiko, menanamkan nilai-nilai dasar penggunaan AI, meningkatkan kompetensi penyusunan prompt, meningkatkan penggunaan dan uji generative AI di dalam kelas, serta kolaborasi antar aktor dalam sektor pendidikan. Proses adopsi generative AI dihadapkan pada beberapa dilema dan tantangan. Dilema tersebut adalah menurunkan integritas akademik sehingga diperlukan penanaman nilai dasar penggunaan disamping perlunya keterampilan teknis dalam menyusun prompt. Tantangan lainnya adalah masih tertutupnya sistem pendidikan terhadap teknologi AI. Oleh karena itu, setiap aktor pendidikan harus berkolaborasi dalam menyosialisasikan generative AI, membuat kebijakan yang tepat untuk mengujicobakan AI, dan mengembangkan kurikulum agar teknologi generative AI dapat menjadi bagian dari pembelajaran. Kesimpulan, proses adopsi teknologi generative AI dalam dunia pendidikan menimbulkan dilema dan diperlukan kolaborasi para pemangku kepentingan pendidikan agar kehadiran teknologi tersebut dapat dimanfaatkan dengan baik dalam pembelajaran.Jurnal Pendidikan dan Kebudayaan496Jurnal Pendidikan dan Kebudayaan["Shiddiq Sugiono"]2024-06-24T00:00:00 55fff1631b718fb17c4b270649df6ab805f2d112Artificial intelligence (AI) chatbots, such as Chat Generative Pre‐trained Transformer (ChatGPT), may threaten the validity of selection processes. This study provides the first examination of how AI cheating in the asynchronous video interview (AVI) may impact interview performance and applicant reactions. In a preregistered experiment, Prolific respondents (N = 245) completed an AVI after being randomly assigned to a non‐ChatGPT, ChatGPT‐Verbatim (read AI‐generated responses word‐for‐word), or ChatGPT‐Personalized condition (provided their résumé/contextual instructions to ChatGPT and modified the AI‐generated responses). The ChatGPT conditions received considerably higher scores on overall performance and content than the non‐ChatGPT condition. However, response delivery ratings did not differ between conditions and the ChatGPT conditions received lower honesty ratings. Both ChatGPT conditions rated the AVI as lower on procedural justice than the non‐ChatGPT condition.International Journal of Selection and Assessment444This study provides the first examination of how AI cheating in the asynchronous video interview (AVI) may impact interview performance and applicant reactions.International Journal of Selection and Assessment["Damian Canagasuriam", "Eden-Raye Lukacik"]2024-06-24T00:00:00 adc39103792c0476293a118660fbe6feaccafd56This study investigates the global impact of Artificial Intelligence (AI) on enhancing student learning autonomously through a mixed-method approach. By combining both qualitative and quantitative data collection and analysis methods, this research provides a comprehensive understanding of the role of AI in autonomous learning as perceived by teachers. The study involves 25 teachers from SD Muhammadiyah Kebumen as participants, representing a diverse educational context. The qualitative analysis delves into the rich tapestry of educators' experiences and perspectives, shedding light on the multifaceted nature of their interactions with AI in the classroom. This qualitative component allows for an in-depth exploration of how teachers perceive and engage with AI in their teaching practices. Additionally, the quantitative analysis quantifies teachers' perceptions and offers statistical evidence of the impact of AI on student learning outcomes. Through surveys and data-driven analysis, the study assesses the extent to which AI influences student learning autonomously. The triangulation of these findings validates and complements each other, reinforcing the positive perception of AI's role in education. However, the research also highlights the need for addressing ethical concerns surrounding AI implementation and the importance of providing comprehensive support mechanisms for teachers navigating the integration of AI in the classroom. These findings contribute to the ongoing discourse on AI in education, offering insights into its potential benefits and challenges while emphasizing the importance of teacher training and ethical considerations in leveraging AI for autonomous student learning.English Language Teaching Educational Journal01The research highlights the need for addressing ethical concerns surrounding AI implementation and the importance of providing comprehensive support mechanisms for teachers navigating the integration of AI in the classroom, while emphasizing the importance of teacher training and ethical considerations in leveraging AI for autonomous student learning.English Language Teaching Educational Journal["Djoko Sutrisno", "Iin Inawati", "Hermanto Hermanto"]2024-06-24T00:00:00 e3f3c0a6c3cc8617e2a8ae0b148821d6d606d0fbInsights into Imaging891A first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials.Insights into Imaging["J. Bojsen", "M. Elhakim", "Ole Graumann", "David Gaist", "Mads Nielsen", "F. Harbo", "C. H. Krag", "M. V. Sagar", "C. Kruuse", "M. Boesen", "Benjamin S. B. Rasmussen"]2024-06-24T00:00:00 b0bde62864a504784eb03a3406ca9d9151a1fc79Neuropsychological rehabilitation plays a critical role in helping those recovering from brain injuries restore cognitive and functional abilities. Artificial Intelligence, with its potential, may revolutionize this field further; therefore, this article explores applications of AI for neuropsychological rehabilitation of patients suffering brain injuries. This study employs a systematic review methodology to comprehensively review existing literature regarding Artificial Intelligence use in neuropsychological rehabilitation for people with brain injuries. The systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search of electronic databases (PubMed, Scopus, PsycINFO, etc.) showed a total of 212 potentially relevant articles. After removing duplicates and screening titles and abstracts, 186 articles were selected for assessment. Following the assessment, 55 articles met the inclusion criteria and were included in this systematic review. A thematic analysis approach is employed to analyze and synthesize the extracted data. Themes, patterns, and trends are identified across the included studies, allowing for a comprehensive understanding of the applicability of AI in neuropsychological rehabilitation for patients with brain injuries. The identified topics were: AI Applications in Diagnostics of Brain Injuries and their Neuropsychological Repercussions; AI in Personalization and Monitoring of Neuropsychological Rehabilitation for traumatic brain injury (TBI); Leveraging AI for Predicting and Optimizing Neuropsychological Rehabilitation Outcomes in TBI Patients. Based on the review, it was concluded that AI has the potential to enhance neuropsychological rehabilitation for patients with brain injuries. By leveraging AI techniques, personalized rehabilitation programs can be developed, treatment outcomes can be predicted, and interventions can be optimized.Applied neuropsychology. Adult731It was concluded that AI has the potential to enhance neuropsychological rehabilitation for patients with brain injuries by leveraging AI techniques, personalized rehabilitation programs can be developed, treatment outcomes can be predicted, and interventions can be optimized.Applied neuropsychology. Adult["Veselin Medenica", "Lidija Ivanovi\u0107", "Neda Milosevic"]2024-06-24T00:00:00 deaa84d19477a81d723c834a492c07dd365970dbQualitative research often involves the transcription of interviews, a traditionally manual and time-consuming task. Recent advancements have introduced AI-driven transcription technologies, aiming to streamline this process. One such technology is OpenAI’s Whisper, an automated speech recognition system capable of transcribing audio in multiple languages. This paper introduces Whisper and provides a guide on its utilization for research transcription. In conclusion, automated transcription of interviews in qualitative research using artificial intelligence is now possible with excellent accuracy and user-friendliness. While Whisper presents a promising solution to the transcription challenges in qualitative research, careful usage and data review are essential.Journal of Surgery Research and Practice81While Whisper presents a promising solution to the transcription challenges in qualitative research, careful usage and data review are essential and careful usage and data review are essential.Journal of Surgery Research and Practice["Jacob Rosenberg"]2024-06-24T00:00:00 8d305c618a3e27c496b5a16c8d275a931ba7a243Management Review Quarterly1632This work identifies and discusses trends, outline underrepresented algorithms with significant potential, and derive research avenues, and proposes a conceptual framework comprising eight dimensions to categorize findings in terms of application areas, methods, and algorithms of applied AI, mitigating the lack of a concise AI taxonomy.Management Review Quarterly["D. Bendig", "Antonio Br\u00e4unche"]2024-06-24T00:00:00 efecfe52e76806199c3a66d3b6dc59bf33a4ab6dKemajuan teknologi yang pesat telah membuka jalan bagi integrasi Artificial Intelligence (AI) di berbagai bidang, termasuk dalam perancangan dan pengembangan aplikasi. Studi kualitatif ini menganalisis secara empiris peran AI dalam membantu proses perancangan dan pengembangan, dengan fokus pada pentingnya pemahaman dan penguasaan konsep dasar pemrograman untuk mencapai pengembangan aplikasi yang sukses. Melalui analisis beberapa studi kasus dan proyek sebelumnya, termasuk yang terkait dengan sistem manajemen keluhan, manajemen suku cadang mobile dengan QR code, dan permainan RPG edukatif, penelitian ini menyoroti bagaimana AI dapat meningkatkan efisiensi dan efektivitas pengembangan aplikasi. Temuan penelitian menunjukkan bahwa meskipun alat AI memberikan dukungan yang signifikan dalam mengotomatisasi tugas-tugas berulang, menghasilkan potongan kode, dan mengoptimalkan alur kerja desain, dasar yang kuat dalam prinsip-prinsip pemrograman tetap sangat penting bagi para pengembang. Studi ini memberikan kontribusi terhadap wacana yang sedang berlangsung tentang peran AI dalam pendidikan dan praktik rekayasa perangkat lunak, menekankan perlunya pendekatan seimbang yang memanfaatkan kemampuan AI sambil mempertahankan pemahaman mendalam tentang keterampilan pemrograman inti.Digital Transformation Technology02Digital Transformation Technology["Muhammad Nurfalah Setiawan", "R. Roring", "Yeyen Dwi Atma", "Henri Tetiawadi"]2024-06-24T00:00:00 30f32b85043d483c03476addc1615dc0d69bb86eWith decentralized Machine Learning (ML) strategies and modern edge Tensor Processing Unit (TPUs), smart devices are no longer only consumers but also producers of Artificial Intelligence (AI), transforming an Internet of Things (IoT) into a global and decentralized Artificial Intelligence of Things (AIoT). With the availability of a large amount of AI actors comes not only the challenge to discover and network with them, but also the potential to use their AI capabilities as a service. However, the heterogeneity of the AI actors, their AI capabilities, AI contextual environment, mobility or even the AI characteristic available or sought requires not only a robust IoT architecture but also flexible AI semantics. In this paper, we present an AI-as-a-Service platform assisting AI consumers to identify existing AI tailored to their needs among the AIoT. We describe the architecture, the APIs, the message flows and AI semantics to identify the most appropriate AI workers when and where needed to efficiently generate AI models from distributed vehicles. As a proof-of-concept, we select an application scenario demonstrating the trainability/changeability of AI models between vehicles according to their context using the CARLA driving simulator.IEEE Vehicular Technology Conference81An AI-as-a-Service platform assisting AI consumers to identify existing AI tailored to their needs among the AIoT, and describes the architecture, the APIs, the message flows and AI semantics to identify the most appropriate AI workers when and where needed to efficiently generate AI models from distributed vehicles.2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)["Ali Nadar", "J\u00e9r\u00f4me H\u00e4rri"]2024-06-24T00:00:00 b4f63fa53ba24c17abc125e5a9a855ee79655cf4It is proposed to investigate the technological changes that drive Distance Education (EaD) through Artificial Intelligence (AI). It can become a powerful tool, expanding possibilities to have more assertive and effective results in the process of evaluating the knowledge obtained by the student in pedagogical content taught by the teacher. The agents are the resources to the AI technique, working in an Intelligent Tutor System (STI), which allow for flexibility in part of this evaluation process, as their characteristics of responding to inferences from the environment, enable them to display future results, based on statistics, making them autonomous and intelligent. This work proposes to present a project or its insertion of AI agents in the STI, specifically to the MOODLE platform, which allows for a better way of learning in accordance with the needs of students, respecting their level of knowledge. Flexibility that will occur through the classification of activities, to be performed by students in a list of exercises, with difficulty levels in easy, medium and difficult levels. The MOODLE stores and makes available to the student all the material prepared for the teacher's class and, with the incorporation of this AI technique, the process will be flexible when measuring each student respecting their previous knowledge. Respect for their education profile by experiencing the limits of the cognitive aspect, their deficiencies in knowledge of the content(s) not assimilated in high school or in curricular unit(s) in the undergraduate course, encouraging to reach levels of overcoming and acquiring learning.Cadernos de Educação, Tecnologia e Sociedade01This work proposes to present a project or its insertion of AI agents in the STI, specifically to the MOODLE platform, which allows for a better way of learning in accordance with the needs of students, respecting their level of knowledge.Cadernos de Educação, Tecnologia e Sociedade["Geise Divino Silva", "Hugo Leonardo Pereira Rufino", "Paula Teixeira Nakamoto"]2024-06-24T00:00:00 45544bcd360e8bb4db25381208c04c4ebeafd502Scientific Reports451A comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented, enabling continuous and systematic performance monitoring.Scientific Reports["Yingwen Wang", "Weijia Fu", "Yuejie Zhang", "Daoyang Wang", "Ying Gu", "Weibing Wang", "Hong Xu", "Xiaoling Ge", "Chengjie Ye", "Jinwu Fang", "Ling Su", "Jiayu Wang", "Wen He", "Xiaobo Zhang", "Rui Feng"]2024-06-24T00:00:00 af950ba9828d9a8e99fc85ae5656c3718e1b1cf2Simulation models, including discrete event simulation, agent-based models, and system dynamics, are employed to study various scenarios and behaviors. However, understanding these models can be particularly challenging because they depend on varying inputs and parameters. This study proposes the use of existing and new explainable artificial intelligence techniques to enhance the understanding of these simulation models.SIGSIM Principles of Advanced Discrete Simulation111This study proposes the use of existing and new explainable artificial intelligence techniques to enhance the understanding of these simulation models.Proceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation["Gayane Grigoryan"]2024-06-24T00:00:00 732820a0142933301ea2ae95dc2817ce02854b56In the era of digitalization, artificial intelligence plays a leading role in all areas of daily life. The present study analyzes the influence of artificial intelligence on digital marketing through Amazon’s e-commerce page using eye tracking with an eye-tracking device, user testing, and heuristic evaluation, among other methodologies. Therefore, eye-tracking reports data through metrics such as the time of first fixation, the number of saccades and the average amplitude over the areas of interest. The user tests were conducted on 40 students from the Computer Sciences College of the Technical University of Manabí; the study is based on Human-Computer interaction and the foundations of AI in digital marketing. With the final results, it was possible to conclude that users show a preference for images and ignore their descriptions; this suggests that there is an opportunity to implement an AI image generator in the future which can use data and machine learning to create attractive images that easily capture users’ visual attention.International Conference on eDemocracy & eGovernment351It was possible to conclude that users show a preference for images and ignore their descriptions; this suggests that there is an opportunity to implement an AI image generator in the future which can use data and machine learning to create attractive images that easily capture users’ visual attention.2024 Tenth International Conference on eDemocracy & eGovernment (ICEDEG)["Anthony David Bucheli Mendoza", "Leticia Azucena Vaca Cardenas"]2024-06-24T00:00:00 2fc4fdcc32e048f592aef725ee4f0955ef88a70aIn the digital transformation era, banks in Indonesia must innovate by adopting Artificial Intelligence (AI). However, user acceptance of AI-based services is still in its early stages, as evidenced by the fact that only a few company executives use AI to help determine strategy, financial design, and decision-making. Although some previous research has provided an understanding of AI-supported banking, it still needs to express what factors affect Artificial Intelligence fully. Therefore, this study was conducted by developing and integrating a research framework with an expectation confirmation (ECO) model to assess the magnitude of user satisfaction (SAT) and acceptance of digital banking services that incorporate artificial intelligence. A total of 307 responses were obtained using questionnaires and then analyzed using Structural Equation Modeling (SEM). The findings show that all aspects affect user satisfaction with digital banking, except customization. Similarly, user acceptance of AI-based digital banking is influenced by customer satisfaction and corporate reputation (CRP). Based on the findings of the impact calculations on SEM and IPMA (importance performance analysis), corporate reputation has more influence on the acceptance of AI digital banking than user satisfaction. Companies are advised to prioritize improvements that reflect corporate reputation, such as brand image, reliability, or customer support. Although the customization variable did not significantly impact the acceptance of AI-based digital banking, it still performed well. By identifying the factors influencing user satisfaction and acceptance of AI-powered digital banking, this study provides valuable insights for decision-makers on improving digital banking services using artificial intelligence.International Conference on Telecommunications570By identifying the factors influencing user satisfaction and acceptance of AI-powered digital banking, this study provides valuable insights for decision-makers on improving digital banking services using artificial intelligence.2024 IEEE 30th International Conference on Telecommunications (ICT)["C. Candiwan", "Rizki Ridla Annikmah"]2024-06-24T00:00:00 739601ef744b3ce6d66a2c8b95f2309108a6f85cSentiment evaluation is a form of artificial intelligence (AI) that makes use of herbal language processing (NLP) to classify text in keeping with the emotional tone it conveys. This technology can provide valuable insights into the sentiments of residents living in smart cities. It could allow nearby governments to better track and deal with sentiment shifts in a fee-green manner while also supplying them with the possibility to decorate their urban planning by incorporating reactions to offerings and tasks. By means of utilizing AI-primarily based sentiment evaluation, nearby governments can become aware of developments and styles and gain a deeper knowledge of the general public sentiment about their city in real-time. They also can use the insights to sing the success of beyond guidelines and offerings while improving upon them within the destiny. In addition, it is able to enable neighborhood governments to react quickly to online newsfeed pastimes, permitting them to deal with issues swiftly and efficaciously permitting them to higher reply to the needs of their residents.International Conference on Computing Communication and Networking Technologies1302024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Manashree", "Shreya Chakraborty", "Rekha Devrani"]2024-06-24T00:00:00 6e49056478dbe8078dfffe7bbdaa8d4379c1b139Innovation has surged due to the emergence of artificial intelligence (AI), especially in the field of education. Traditional teaching methods are undergoing a profound transformation thanks to the influential algorithms driving AI. This technology extends far beyond the boundaries of education, penetrating various dimensions of our daily lives, enriching human activities and lifestyles. AI stimulation of human thinking and intelligence in a machine by performing tasks commonly recognized and associated with human intelligence and imagination. This research aims to synthesize diverse valuable insights of AI on educational environments. It emphasizes the ethical transition from traditional teaching and learning methods to more interactive human experiences. Furthermore, the research provides an overview of innovative elements, current challenges, concerns, and benefits of AI in education. It also highlights popular AI-powered educational tools and platforms. Remarkably, sophisticated AI platforms like ChatGPT, Squirrel AI, Google Bard (Gemini), and Grammarly take center stage. These systems, based on natural language models, can generate responses alike human interactions. The paper investigates the potential advantages of AI in education while addressing the ethical and practical considerations surrounding its implementation. By examining these technologies, the research aims to illuminate the path towards a more dynamic and effective educational landscape.International Conference on Computing Communication and Networking Technologies440The paper investigates the potential advantages of AI in education while addressing the ethical and practical considerations surrounding its implementation, and highlights popular AI-powered educational tools and platforms.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Rakesh Ranjan", "Anish Kumar Vishwakarma", "Sundaram", "Nikhil Dhengre", "Sagar S. Motdhare", "A. P. Rao"]2024-06-24T00:00:00 069cf76f6ed30f4ecc881bee9d7c6360180581d2Concerns over the energy and environmental crisis are driving the strategic integration of distributed generators (DGs) in power systems, hastening the shift to sustainable energy. Microgrids (MGs) emerged as self-reliant, localized power solutions for DGs integration, enhancing grid performance and reliability by allowing grid-connected and islanded modes of operation. Moreover, the dc nature of many DGs and household devices, together with the progress in power electronics, have shifted research from conventional ac MGs towards dc and hybrid ac/dc MGs, which increase the complexity and the number of control scenarios. Therefore, the MG control environment require advanced data-driven algorithms to overcome the stochasticity and non-linear characteristics of MGs systems. In this context, artificial intelligence (AI) techniques demonstrate high potential for enhancing the control and operation in the dynamic MG environment. This paper reviews the most recent research effort regarding the application of AI-based technology in the hierarchical control structure for ac, dc and hybrid ac/dc MG architectures.IEEE International Conference on Compatibility, Power Electronics and Power Engineering570This paper reviews the most recent research effort regarding the application of AI-based technology in the hierarchical control structure for ac, dc and hybrid ac/dc MG architectures and demonstrates high potential for enhancing the control and operation in the dynamic MG environment.2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)["Javier Guti\u00e9rrez-Escalona", "C. Roncero\u2010Clemente", "O. Husev", "E. Gonz\u00e1lez-Romera", "M. Milan\u00e9s-Montero", "Tomislav Dragi\u010devi\u0107"]2024-06-24T00:00:00 782ec2cd93e08817d24d33f22f7d474b2fc21df2The use of artificial intelligence (AI), particularly in the writing of scientific texts, has been much problematized. This article analyzes, in research of an exploratory and documentary nature (Lüdke; André, 1988), whether there are positions taken by Brazilian scientific associations and their respective journals on the production of scientific communications mediated by the use of artificial Intelligence (AI), such as ChatGPT and like, unveiling the emerging themes associated with the practices of appropriation of this technology in textual production. The research was carried out between May and June 2023, on the websites of 33 Brazilian scientific associations from different areas of knowledge, and 50 journals. No expressly contrary positions were found in relation to the uses of generative AI for textual production, by the 33 (thirty-three) associations researched. Only 3 (three) of the 50 (fifty) journals, until June 2023, had explicit policies or guidelines for authors on the use of artificial intelligence in the writing of scientific communication. From the analytical induction, when exploring the collected data, we conclude that: a) topics such as authorship, plagiarism and ethics are the most correlated with the appropriation of AI for scientific communication; b) the absence of positions from a significant number of journals, on the use of generative AI in the textual production of scientific communication is still safeguarded by the principles of authorship and ethics, with no general mention of the technologies that generate writing; c) concerns about the use of AI in scientific communication tend to spread, since its effects are not restricted to a specific type of knowledge. It is necessary for scientific associations and journals to explain their positions on the use, or not, of AI in scientific communication.Cadernos de Educação, Tecnologia e Sociedade00It is concluded that topics such as authorship, plagiarism and ethics are the most correlated with the appropriation of AI for scientific communication, and concerns about the use of AI in scientific communication tend to spread, since its effects are not restricted to a specific type of knowledge.Cadernos de Educação, Tecnologia e Sociedade["Carlos Lopes", "Geusiani Pereira Silva e Nascimento", "Railma Aparecida Cardoso Marinho", "Wellington Luiz Rocha"]2024-06-24T00:00:00 dd9856a748888986bd85fa725f1cf179e001b1edPerkembangan teknologi saat ini telah mendorong digitalisasi pada operasional dan layanan perbankan, dengan adanya teknologi diharapkan dapat mengotomatisasi berbagai tugas dan layanan perbankan sehingga menjadi lebih efisien dan efektif. Pemanfaatan sistem berbasis artificial intelligence (AI) digunakan di berbagai industri, termasuk industri perbankan. Penelitian ini merupakan penelitian studi literatur (systematic literature review), data dikumpulkan dari pencarian artikel yang diterbitkan pada portal Scopus dengan pencarian artikel dengan topik pembahasan AI pada industri perbankan. Penelitian ini bertujuan untuk mengetahui bagaimana pemanfaatan AI pada industri perbankan dan jenis AI yang digunakan. Hasil penelitian ini dapat bermanfaat untuk mempertimbangkan keputusan dalam mengoptimalkan penggunaan dan pengembangan AI di industri perbankan.Jurnal Mutiara Akuntansi180JURNAL MUTIARA AKUNTANSI["Fanny Ramadhani", "Diva Trimuliani"]2024-06-24T00:00:00 641a12d6d5a2e7931c78590e0a9c7614dfcfe4e4В статье рассматривается возможность применения искусственного интеллекта для улучшения качества диагностики возрастной макулярной дегенерации (ВМД) на основе анализа литературы. На сегодняшний день поражения сетчатки различного генеза выходят на первое место как причина снижения остроты зрения и необратимой слепоты во всем мире. Актуальной представляется проблема раннего выявления, лечения и прогнозирования течения и исхода ВМД. Офтальмологическая служба нуждается в быстром, экономичном, автоматическом, высокочувствительном и специфичном методе выявления патологии сетчатки. Платформы на основе искусственного интеллекта (ИИ) могут стать основой принятия клинических решений в диагностике и лечении заболеваний сетчатки в практике врача-офтальмолога. The article discusses the possibility of using artificial intelligence to improve the quality of diagnosis of age-related macular degeneration based on the analysis of the literature. To date, retinal lesions of various origins come out on top as the causes of decreased vision and blindness of the world’s population. The problem of early detection, treatment and prediction of the outcome of age-related macular degeneration (AMD) seems to be relevant. The ophthalmological service needs a fast, economical, automatic, highly sensitive and specific method for detecting fundus pathology. Platforms based on artificial intelligence (AI) can become the basis for clinical decision-making in the diagnosis and treatment of retinal diseases in the practice of an ophthalmologist.Офтальмология. Восточная Европа00Офтальмология. Восточная Европа["\u0422.\u0412. \u041a\u0430\u0447\u0430\u043d", "\u041b.\u041d. \u041c\u0430\u0440\u0447\u0435\u043d\u043a\u043e", "\u0418.\u0418. \u0421\u0435\u043c\u0435\u043d\u043e\u0432\u0430", "\u0410.\u0410. \u0414\u0430\u043b\u0438\u0434\u043e\u0432\u0438\u0447", "\u0418.\u0413. \u0413\u0443\u0434\u0438\u0435\u0432\u0441\u043a\u0430\u044f", "\u041e.\u0412. \u0422\u0435\u0440\u0435\u0448\u0435\u043d\u043a\u043e", "\u041b.\u0412. \u0410\u043a\u0438\u043c\u043e\u0432\u0430", "\u0410.\u0418. \u041a\u0430\u043b\u0438\u043d\u0438\u043d\u0430", "\u0415.\u0412. \u041a\u043e\u0442\u043b\u044f\u0440\u043e\u0432\u0430", "\u0415.\u0421. \u041a\u0443\u043b\u044c"]2024-06-24T00:00:00 e40d991141082d1c6f3e4709be164313c1db2309Accounting has evolved significantly over the centuries, from its rudimentary origins to the adoption of advanced technologies such as artificial intelligence (AI). This article explores this historical trajectory and examines how new technologies are transforming accounting practices. Initially focused on fiscal compliance and asset control, accounting has adapted to meet the complex demands of modern economies. The methodology of this study was based on a bibliographic review, utilizing scientific articles and theses. The results indicate that AI increases the accuracy and efficiency of accounting processes by automating repetitive tasks and enhancing data analysis, allowing accountants to take on more strategic roles. However, AI also presents challenges in terms of data privacy and security, highlighting the importance of an ethical approach and continuous professional development.Concilium00The results indicate that AI increases the accuracy and efficiency of accounting processes by automating repetitive tasks and enhancing data analysis, allowing accountants to take on more strategic roles.Concilium["Dionathan Pinto de Carvalho", "Rosimeire Freires Pereira Oliveira", "J. S\u00e1"]2024-06-24T00:00:00 7d9814a42c8a08df039d231c89cbaf7f5b7dd9e8: This paper explores the transformative impact of artificial intelligence (AI) on interior design, examining how AI technologies are revolutionizing traditional design processes and enhancing creative capabilities. By integrating machine learning algorithms, generative design techniques, and advanced data analytics, AI offers innovative solutions for optimizing spatial layouts, selecting color schemes, and personalizing interior aesthetics. This research delves into key applications such as virtual staging, smart home integrations, and AI-driven design assistants that enable designers to craft functional and aesthetically pleasing environments with greater efficiency. Furthermore, the study investigates the role of AI in sustainable design, highlighting its potential to minimize waste and promote eco-friendly materials. Through case studies and practical examples, the paper demonstrates the benefits and challenges of AI adoption in interior design, emphasizing the importance of maintaining a balance between technological advancements and human creativity. The findings suggest that while AI tools significantly enhance design accuracy and productivity, they also necessitate a redefinition of the designer's role in the creative process. This research contributes to the growing body of knowledge on AI in design, offering insights for practitioners and academics seeking to understand and leverage AI's potential in shaping the future of interior design.International Journal of Science and Engineering Applications40This research delves into key applications such as virtual staging, smart home integrations, and AI-driven design assistants that enable designers to craft functional and aesthetically pleasing environments with greater efficiency.International Journal of Science and Engineering Applications["Yanhua Liu"]2024-06-24T00:00:00 b26e733f0a81aaef427eb2d0e3f321ccafbf3bb5Artificial Intelligence (AI) has profoundly impacted various industries, and its influence on the fields of law and forensics is remarkable. This paper aims to explore the evolution, applications, ethical considerations, challenges, future prospects, and real-world examples of AI integration in legal and forensic practices. It discusses the transformative power of AI technologies, including machine learning, natural language processing, and data analytics, and their implications for reshaping legal research, case management, forensic investigations, and predictive analytics in the legal and forensic domains. Artificial Intelligence (AI) stands as a revolutionary force reshaping the landscape of legal practices and forensic investigations. This article explores the multifaceted role of AI in these domains, elucidating its impact, applications, challenges, and future prospects. In the legal sphere, AI has catalyzed significant advancements, revolutionizing tasks ranging from legal research and contract analysis to predictive analytics for case outcomes. Machine learning algorithms and natural language processing have empowered legal professionals with unprecedented access to vast repositories of legal information, expediting research processes and enhancing the quality of legal strategies. Similarly, within forensic investigations, AI-driven technologies have transformed the landscape by bolstering digital forensics, biometric analysis, and crime pattern recognition. These advancements have facilitated the identification of cybercrimes, the analysis of digital evidence, and the identification of suspects through facial recognition and DNA analysis, significantly aiding law enforcement agencies in solving complex cases. However, the integration of AI in law and forensics also poses ethical challenges, including concerns about biases in algorithms, data privacy, and the interpretability of AI-generated results. Addressing these ethical dilemmas is paramount to harnessing the full potential of AI while ensuring fairness, accountability, and transparency in its applications. Looking forward, the future of AI in law and forensics holds immense promise, with advancements in explainable AI and tighter ethical frameworks shaping its trajectory. Balancing technological innovation with ethical considerations will be pivotal in leveraging AI’s potential to further streamline legal processes, enhance forensic capabilities, and contribute to a more just and secure society. This article navigates the transformative impact of AI in law and forensics, offering insights into its applications, ethical considerations, and future implications, ultimately emphasizing the need for a harmonious synergy between technological advancement and ethical governance.International Conference on Computing Communication and Networking Technologies270The transformative impact of AI in law and forensics is navigated, offering insights into its applications, ethical considerations, and future implications, ultimately emphasizing the need for a harmonious synergy between technological advancement and ethical governance.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["K. Rajasekar", "D. Vezhaventhan"]2024-06-24T00:00:00 dba291ad6db458b88dac04f76e456b20222debb0The existence of AI (artificial intelligence) has helped and solved many problems faced by humans, including in the world of learning Arabic. Currently, AI is mostly produced and developed by Western countries, but Islamic countries have become consumers and markets for AI products. If AI is not utilized in various areas of life, including the world of Arabic language learning, then the development of human resources, especially Arabic language experts, will certainly be hampered in the future. Therefore, in the world of Arabic language learning, useArtificial Intelligence It is very necessary for the teaching and learning process to be effective and efficient and to achieve the learning objectives. This is what underlies the discussion of this research. This research uses qualitative methods and the type is library research. In the context of ChatGPT which is part of information technology, it is necessary to briefly discuss IT which provides advantages such as speed, consistency, accuracy and reliability so that the learning process runs well and results are obtained. Fundamentals of AI also provides insight into the basic principles of AI development itself, starting from a problem and working to develop a solution. In general, the presence of ChatGPT as an AI product has recently become very important in the world of learning because it helps teachers and makes their work easier, especially in implementing learning strategies in schools. Even if we can utilize the sophistication of AI, of course it should be operated by humans who have a strong understanding of the basics of learning science in detail and in depth. Technology that is part of a scientific application is independent of the values of its users. This means that the benefits of technology depend on the producers and users of each technology. Islam itself strongly recommends its followers to utilize technology in managing the natural resources that Allah has given to their people. Utilization of artificial technology in the form of chatGPT is an inseparable part of the world of learning to facilitate the teaching and learning process and motivate students to study hard and achieve quality results in the futureAt Turots: Jurnal Pendidikan Islam160Utilization of artificial technology in the form of chatGPT is an inseparable part of the world of learning to facilitate the teaching and learning process and motivate students to study hard and achieve quality results in the future.At Turots: Jurnal Pendidikan Islam["Amrin Mustofa", "S. Ps", "Suci Rafi Sari", "Rofi Wirawan"]2024-06-24T00:00:00 a421553a4240cef131f48f970cb82767b6970184Artificial intelligence (AI) is becoming increasingly important in the field of scientific research, providing new opportunities for processing large amounts of data, automating routine tasks, and discovering new dependencies in complex systems. Despite these advantages, the use of AI in the scientific domain is accompanied by many challenges and limitations. This paper explores the current state of the problem, analyses the challenges facing the scientific community and examines the main limitations of using AI. The report includes a SWOT analysis of the deployment of AI in scientific research, summarizing the strengths, weaknesses, opportunities and threats that accompany the use of AI technologies. Possible solutions to overcome these limitations are proposed, including the development of new methodologies for AI training, the creation of ethical and legal frameworks, and the training of professionals who can effectively use AI technologies. The report highlights the need for a coordinated effort between the scientific community, industry and regulators for the successful application of AI in research. AI has the potential to significantly improve the scientific process, but requires careful management of the challenges and risks associated with its use.International Conference on Computing Communication and Networking Technologies170The report highlights the need for a coordinated effort between the scientific community, industry and regulators for the successful application of AI in research, and summarizes the strengths, weaknesses, opportunities and threats that accompany the use of AI technologies.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Aldeniz Rashidov", "Fatme Rashidova"]2024-06-24T00:00:00 2f6d6294376e8cbd5ac7ea0aebcbf989f719b3efScientific Reports450The study shows that AI can recommend almost risk-free exercises for patients with MSDs, which is an effective way to create individualized exercise plans without putting patients at risk for higher pain intensity or discomfort.Scientific Reports["Annika Griefahn", "C. Zalpour", "Kerstin Luedtke"]2024-06-24T00:00:00 4aa1617d1b1e5a3c00a8f6bedde9eedaa88f850cArtificial intelligence (AI) and Requirements Engineering (RE) intersect in innovative and transformative ways, reshaping how we approach technology development today [1]. On the one hand, AI techniques, e.g., NLP, enhance RE processes by automating the extraction and analysis of requirements, increasing quality, accuracy and efficiency in translating human needs into technical specifications [2]. On the other hand, RE plays a crucial role in developing AI systems themselves; it ensures that AI technologies are designed with clear, well-defined requirements that align with ethical standards and practical user needs [1]. This symbiotic relationship not only advances the capabilities of AI but also ensures that the developed systems are human-centric, reliable and trustworthy [3]. AIRE workshop aims to explore this symbiotic relation to identify complex RE problems that could benefit from applying AI techniques and addressing RE for AI challenges. The 2024 workshop edition received 21 submissions, with each submission independently reviewed by at least three program committee members. The final program consisted of nine papers (seven regular research papers and two short papers) and one lightning talk. The workshop took place on June 25th, 2024. The workshop featured a keynote by Jan-Philipp Steghofer with the title “The proof is in the pudding-Real-world use cases for GenAI for Requirements Engineers.”, and a hands-on session on generative AI in RE. We are very grateful to the Program Committee members and authors of the submissions for their hard work and dedication in putting together this program. We thank you all for your participation in AIRE'24.2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW)160The AIRE workshop aims to explore this symbiotic relation to identify complex RE problems that could benefit from applying AI techniques and addressing RE for AI challenges.2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW)["Chetan Arora", "Fatma Ba\u015fak Aydemir", "Julian Frattini"]2024-06-24T00:00:00 ce7bb1de6143c4a22e8ca99b5f286fe5d9dbbc56Diabetes represents a significant public health concern, affecting millions of individuals worldwide. Its prevalence is increasing, driven in part by lifestyle factors and the aging of the global population. This systematic review explores the potential of artificial intelligence (AI) in enhancing diabetes prevention, diagnosis, and management. The review highlights the promise of personalized and proactive healthcare enabled through AI. The research methodology employed an exhaustive review of the literature, the formulation of specific inclusion and exclusion criteria, a data extraction process from selected studies that focused on the role of AI in diabetes, and a comprehensive analysis to identify the specific domains and functions in which AI makes a significant contribution. The results of the conducted literature review indicate that artificial intelligence (AI) can be regarded as a transformative force in the following eight key areas within the field of diabetes care: 1) Management and Care of Diabetes, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Development of Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancement of Clinical Decision Making, and 8) Engagement and Self-Management of Patients. Additionally, the utilization of AI may result in a reduction in the risk of mortality from diabetes.International Journal of Public Health Excellence (IJPHE)360The results of the conducted literature review indicate that artificial intelligence (AI) can be regarded as a transformative force in the following eight key areas within the field of diabetes care: 1) Management and Care of Diabetes, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Development of Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancement of Clinical Decision Making, and 8) Engagement and Self-Management of Patients.International Journal of Public Health Excellence (IJPHE)["Prima Dewi Kusumawati", "Pius Weraman", "Eli Sabrifha", "Ahmad Zil Fauzi", "Article Info"]2024-06-24T00:00:00 013c59655ec9dd76fc19ec4de547e81bbf5bdabdEnsuring safe, secure, and trustworthy artificial intelligence (AI), particularly within safety-critical systems like autonomous cyber-physical stems (CPS), is of paramount importance and of crucial urgency for dependability research. One approach to establishing such desiderata of AI is through formal verification, particularly in machine learning (ML) components like neural networks, to establish they meet certain formal specifications. The Neural Network Verification (NNV) software tool implements automated formal methods for this purpose, specifically reachability analysis, and this interactive tutorial will demonstrate these to formally verify specifications in neural networks, as well as in closed-loop CPS. The tutorial begins with a lecture on the emerging research area of neural network verification, followed by interactive demos of these methods implemented in NNV. Examples will be shown from the security, medicine, and CPS domains.2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S)150This interactive tutorial will demonstrate automated formal methods to formally verify specifications in neural networks, as well as in closed-loop CPS, implemented in the Neural Network Verification (NNV) software tool.2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S)["Taylor T. Johnson", "Diego Manzanas Lopez", "Hoang-Dung Tran"]2024-06-24T00:00:00 5457cf0fc58ee5a0b2cc8a847bf7141fc2c57c7dArtificial Intelligence (AI) techniques are being applied to numerous applications from Healthcare to Cyber Security to Finance. For example, Machine Learning (ML) algorithms are being applied to solve security problems such as malware analysis and insider threat detection. However, there are many challenges in applying ML algorithms for various applications. For example, (i) the ML algorithms may violate the privacy of individuals. This is because we can gather massive amounts of data and apply ML algorithms to the data to extract highly sensitive information. (ii) ML algorithms may show bias and be unfair to various segments of the population. (iii) ML algorithms themselves may be attacked possibly resulting in catastrophic errors including in cyber-physical systems such as transportation systems. Finally, (iv) the ML algorithms must be safe and not harm society. Therefore, when ML algorithms are applied to transportation systems for handling congestion, preventing accidents, and giving advice to drivers, we must ensure that they are secure, ensure privacy and fairness, as well as provide for the safe operation of the transportation systems. Other AY techniques such as Generative AI (GenAI) are also being applied not only to secure systems design but also to determine the attacks and potential solutions. This presentation is divided into two parts. First, we describe our research over the past decade on Trustworthy ML systems. These are systems that are secure as well as ensure privacy, fairness, and safety. We discuss our ensemble-based ML models for detecting attacks as well as our research on developing Adversarial Machine Learning techniques. We also discuss securing the Internet of Transportation systems that are based on traditional methods such as Extended Kalman Filters to detect cyberattacks. Second, Second, we discuss our work on Finally, we discuss the research we recently started as part of the USDOT National University Technology Center TraCR (Transportation Cybersecurity and Resiliency) led by Clemson University. In particular, we describe (i) the application of federated machine learning techniques for detecting attacks in transportation systems; (ii) publishing synthetic transportation data sets that preserve privacy, (iii) fairness algorithms for transportation systems, and (iv) examining how GenAI systems are being integrated with transportation systems to provide security. Our focus includes the following: · Data Privacy: We are designing a Privacy-aware Policy-based Data Management Framework for Transportation Systems. Our work involves collecting the requisite data and developing analysis tools to identify and quantify privacy risks. Existing privacy-preserving, differentially private synthetic data generation techniques, which tailor data utility for generic ML accuracy, are not well suited for specific applications. We are developing synthetic data generation tools for transportation systems applications. We will develop new ML algorithms that can leverage these datasets. · Fairness: We have developed a novel adaptive fairness-aware online meta-learning algorithm, FairSAOML, which adapts to changing environments in both bias control and model precision. Our current work is focusing on adapting our framework to fairness in transportation systems. and control bias over time, especially ensuring group fairness across different protected sub-populations; identifying interesting attributes using explainable AI techniques that might help to mitigate bias and develop equitable algorithms. We have also developed a second system, FairDolce, that recognizes objects involving fairness constraints in a changing environment. We are adapting it to transportation applications. For example, pedestrian detection (whether or not the object being seen is a pedestrian) must be fair with respect to the race or gender of the individuals being detected under changing environments (e.g., rainy, cloudy sunny). Adversarial ML: Our prior work on adversarial ML models worked on traditional datasets such as network traffic data. Our current focus is on adapting our approach to AV-based sensor data. Our ML models are being applied to sensor data for object recognition and traffic management. These ML models may be attacked by the adversary. We will study various attack models and investigate ways of how interactions may occur between the model and the adversary and subsequently develop appropriate adversarial ML models that operate on the AV sensor data. · Attack Detection - Smart vehicles are often exposed to various attacks making it difficult for manufacturers to collaboratively train anomaly/attack detection models. Yet it would be ideal if all the data available across manufacturers could be used in building robust attack detection systems. To achieve this, we developed FAST-SV, which incorporates federated learning in conjunction with augmentation techniques to build a highly performant attack detection system for smart cars. Safety: Safety has been studied for cyber-physical systems and formal methods have been applied to specify safety properties and subsequently verify that the system satisfies the specifications. However, our goal is to ensure that the ML algorithms utilized by the transportation systems are safe. This would involve developing an AI Governance framework that would require transparency and explainability (among others) of the ML algorithms utilized by the transportation system.ACM Symposium on Access Control Models and Technologies00The application of federated machine learning techniques for detecting attacks in transportation systems, a novel adaptive fairness-aware online meta-learning algorithm, FairSAOML, and research on developing Adversarial Machine Learning techniques are discussed.Proceedings of the 29th ACM Symposium on Access Control Models and Technologies["B. Thuraisingham"]2024-06-24T00:00:00 a1652ec23e0f29265fc9ebde006aeb6bb8d0aba4Most of the business and society processes are under digital transformation. Organizations must change their mindset toward the augmented age and the use of artificial intelligence and generative AI, Gen AI-agents. Integrating generative artificial intelligence tools to support the innovation process is essential when aiming to improve the efficiency of start-up companies and more broadly in all innovation processes. In the innovation ecosystem data is a valuable currency that fuels the data-driven innovation process. Capturing data from various sources and executing it in businesses requires a human-oriented approach. The strategic challenge is applying a systematic approach using Gen AI agents during all innovation phases. This article introduces a co-evolution framework for human and artificial intelligence interaction in the innovation process in our rapidly evolving digital culture. Data for this research has been collected and framework tested in six AI-related projects by action research approach by comparing the various phases of the innovation process and how the innovation process could be deployed by using Gen AI. In new entrepreneurship and start-up enterprises, co-evolution starts from personal competence identification and team cohesion building. Artificial intelligence will support team operations during all phases of the innovation process of a start-up enterprise.International Conference on Engineering, Technology and Innovation120A co-evolution framework for human and artificial intelligence interaction in the innovation process in the authors' rapidly evolving digital culture is introduced.2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)["V. Salminen", "Matti Pyykk\u00f6nen", "Carita Salminen"]2024-06-24T00:00:00 b2e1b4b2b9c3828d05d251fc59d3ff74262352c2Artificial intelligence (AI) and gadgets gaining knowledge of (ML) have revolutionized organizations’ overall performance, leading to accelerated performance and productivity. This era has become a fundamental part of our daily lives, from personal assistants to self-using vehicles. However, as AI and ML continue to improve and become more pervasive, it’s crucial to undergo the prison implications that would stand up. One of the primary criminal considerations for AI and ML is record privacy and safety. With the sizeable series and processing of extensive records, the danger of fact breaches and privacy violations increases. Corporations should follow applicable legal suggestions and policies, the overall records protection law (GDPR), and the California Purchaser Privacy Act (CCPA). Another interest is a legal responsibility. As AI and ML become more impartial, questions arise about who is responsible for any damage or harm because of those structures. This trouble has become more complicated, even as a few events concern improving and deploying AI and ML technologies.International Conference on Computing Communication and Networking Technologies140It’s crucial to undergo the prison implications that would stand up as AI and ML continue to improve and become more pervasive, it’s crucial to undergo the prison implications that would stand up.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Ms. Gunjan Bhatnagar", "Ms. Alpika Verma", "Nilanjan Chakraborty", "Ms. Suman", "Dr.Ashwini Kumar", "Aman Mittal"]2024-06-24T00:00:00 a56c3065654b2a613082f77a78efc03eead535deStudy employs a subjective research approach to investigate the effects of artificial insight (human-generated intelligence) on computerized displays considering the view that human and mental activities can be replaced by artificial intelligence. According to a survey of 15 marketing and AI experts, artificial intelligence is influencing marketing practices and ultimately improving their efficacy. According to this argument, combining human and machine labor is the most effective way to improve results because artificial intelligence can now complete many time-consuming and pointless tasks in an advertiser’s daily life. Two major barriers to the adoption of artificial intelligence are a lack of innovation, trust, and a lack of societal readiness. These difficulties and moral responsibilities have been overcome numerous times. Given these facts, business executives and managers should prepare their teams and staff for the implementation of artificial intelligence.International Conference on Computing Communication and Networking Technologies200It is argued that combining human and machine labor is the most effective way to improve results because artificial intelligence can now complete many time-consuming and pointless tasks in an advertiser’s daily life.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Mohammed Arif Hussain", "Rajeev Gupta", "Anurag Kushwaha", "Prasenjeet Samanta", "Manjula Khulbe", "Vasim Ahmad"]2024-06-24T00:00:00 f4d8ca37b90364cf7d5824644e91b83e81157396In November 2023, the White House of the United States introduced relevant laws and regulations about AI. The regulation of artificial intelligence is the inevitable result of social development and the inevitable trend of technological development. Other counties domestic regulation of artificial intelligence-related laws and regulations has not yet been carried out. This paper analyzes American AI executive order from keywords, transitivity, modality, and cohesion by using Systemic Functional Grammar theory. Understanding the important contents and characteristics of the U.S. executive order on artificial intelligence can provide a reference for the formulation of other countries artificial intelligence-related administrative regulations and laws, which finally promote the development of international artificial intelligence law.Lecture Notes in Education Psychology and Public Media60This paper analyzes American AI executive order from keywords, transitivity, modality, and cohesion by using Systemic Functional Grammar theory to provide a reference for the formulation of other countries artificial intelligence-related administrative regulations and laws, which finally promote the development of international artificial intelligence law.Lecture Notes in Education Psychology and Public Media["Hongyu Zhang"]2024-06-24T00:00:00 9adf5d18c3b8c57077df2c9b002273f47cb11c57Potential enabling artificial intelligence (AI) to fundamentally transform both businesses along with civilizations has made AI a revolutionary force. AI has many beneficial applications, but there are also many moral dilemmas that come with its development. With the increasing integration of AI systems into every aspect of lives, there is an increased possibility of unforeseen effects and ethical conflicts.The lack of information’s gathering, sharing, as well as readability; the inability to demonstrate the internal decision-making process; and the disregard for ethical considerations in the creation of AI frameworks are some possible impediments to the ordinary application of AI. In artificial intelligence, “byte-sized morality” corresponds to the moral precepts and considerations that are ingrained in AI systems including are frequently constrained by information in digital format, algorithms, and analytical procedures. In this paper aims to contribute to ongoing ethical AI discussion, By examining these advancing understanding of legal obligations that accompany the advancement developing artificial intelligence in the technologically advanced society.International Conference on Computing Communication and Networking Technologies190By examining these advancing understanding of legal obligations that accompany the advancement developing artificial intelligence in the technologically advanced society, this paper aims to contribute to ongoing ethical AI discussion.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Ayushi Sharma", "Gurpreet Singh", "Sonia Bhalla"]2024-06-24T00:00:00 29e4561eee173031fb850f6094a7526a6e944db7The article investigates the current use of artificial intelligence technologies and services in the educational process of general secondary education institutions by participants in the educational process – teachers and students. An analysis of research and publications that highlight the issues of using artificial intelligence in education has been conducted. The potential and specific features of implementing artificial intelligence at the present stage are outlined. Positive developments in pedagogical practice regarding the use of artificial intelligence in the educational process are identified. Risks and negative trends that need to be considered to ensure fair and safe use of AI in education are highlighted. In particular, important ethical aspects of applying artificial intelligence in the educational process are emphasized, the consideration and implementation of which are urgent needs for the ethical and responsible use of AI-based tools. This will improve teaching practices and the learning experience of students, ensuring they develop future skills within ethical frameworks, and provide teachers with the support needed to enhance the effectiveness of their pedagogical activities and the development of innovative teaching methods.Problems of Education10Important ethical aspects of applying artificial intelligence in the educational process are emphasized, the consideration and implementation of which are urgent needs for the ethical and responsible use of AI-based tools.Problems of Education["Ivan Haidamaka"]2024-06-24T00:00:00 6622546d5d33e17b6f2342cb47e099ea4460d082The study of the legal architecture of the relationship between artificial intelligence and patient protection in medical diagnostics includes a brief overview of the current state of application of artificial intelligence in diagnostic medicine and an analysis of its potential benefits and risks. This article reviews the current regulations governing the use of artificial intelligence in medical diagnostics, including laws, guidelines and best practices, as well as the role of health care regulators and personal data protection. Special attention is paid to the ethical aspects of using artificial intelligence in diagnostics, such as: ensuring patient privacy, protecting medical data, preventing algorithmic bias, and ensuring transparency in the diagnostic decision-making process. Specific examples of implementation of artificial intelligence in diagnostic practice are presented, illustrating both opportunities and challenges associated with this technology, including analysis of successful and unsuccessful cases of implementation. The study describes promising developments in the field of AI diagnostics, including new technologies and trends, and forecasts the evolution of the legal framework in response to these innovations. Finally, it summarizes the results of the study and presents recommendations for improving the legal regulation of artificial intelligence in medical diagnostics, taking into account the need to protect the rights and interests of patients.Review of Law Sciences00Review of Law Sciences["Ekaterina Kan"]2024-06-24T00:00:00 c2e4833b64f68cafd8c7a7d833a1ad2335ca8a62Artificial Intelligence technology is artificial intelligence which is then associated with the ability of machines or computers to do things that humans do. Artificial Intelligence originally started in 1942 when the American science fiction writer Isaac Asimov then included AI in his book entitled Runaround which then told about robots. Then, in 1956 MarvinMinsky and John McCarthy began conducting research on artificial intelligence. Artificial Intelligence technology has become increasingly popular among the wider community, in all circles, including in the world of education. One of the products from AI which is quite popular at the moment is GPT chat which is often used in the world of education. This is because Artificial Intelligence technology makes it very easy to do work or assignments. This article will then examine using bibliometric analysis the trends in the use of Artificial Intelligence technology in the world of education. This article analyzes using the bibliometric analysis method using a database from Scopus with a research range from 2011 to 2023, with the keywords artificial intelligence, impact, and education, which then produces 1565 articles. These articles were analyzed using bibliometric methods with biblioshiny. The aim of this article is to analyze trends in the use of artificial intelligence technology in the world of education based on bibliometric analysis by observing the number of publications on this topic from year to year. It was found that from year to year research on the use of artificial intelligence in the world of education, the graph continues to increase and shows a percentage of 43.71% for the development of publications from year to year. This article is divided into several parts (1) introduction which explains each research component starting from scopus bibliometrics and biblioshiny as well as the topics studied; (2) research methods carried out using bibliometric analysis; (3) results and discussion of the bibliometric analysis; and (4) conclusions.Info Bibliotheca: Jurnal Perpustakaan dan Ilmu Informasi10It was found that from year to year research on the use of artificial intelligence in the world of education, the graph continues to increase and shows a percentage of 43.71% for the development of publications from year to year.Info Bibliotheca: Jurnal Perpustakaan dan Ilmu Informasi["Raka Gading Raihanzaki", "Imam Yuadi"]2024-06-24T00:00:00 b50972d870ce820a16d0ca0259cd526958e38f8e- The integration of Artificial Intelligence (AI) and Machine Learning (ML) into national defense and intelligence systems is crucial for modernizing and strengthening the security infrastructure of nations. This research paper examines the current state of AI in defense and intelligence in Bangladesh, compares it with neighboring countries, and proposes crucial steps and applications for enhancing Bangladesh’s capabilities. The paper includes detailed descriptions, examples of ML algorithms, codes, and innovative strategies to establish Bangladesh as a leading nation in AI-driven defense and intelligence.International journal of scientific and research publications70The current state of AI in defense and intelligence in Bangladesh is examined, it is compared with neighboring countries, and crucial steps and applications for enhancing Bangladesh’s capabilities are proposed.International Journal of Scientific and Research Publications["Md Tawfiqur Rahman"]2024-06-24T00:00:00 08f53b6cbded79ea54d282a6352f8906c6fdaf2dThe human race is forced to engage in a very rapid adaptation process whenever it is confronted with technological change in any sphere of life. The unabated progress of artificial intelligence (AI) has also impacted the field of critical thinking. It is fascinating that critical thinking, an essential component of intellectual intelligence in nursing, seems to be disrupted by an artificial brain-alike machine that can automatically analyze and synthesize a series of contexts. This has been an improvisation of ideas of intellectual intelligence for a very long time. Perspectives on both sides of the coin bring up interesting questions about the role that AI will play in the future, such as whether it will disrupt the critical thinking skills of nurses or whether it may be engaged as a tool to increase the critical thinking skills capability, especially in the fields of nursing care.Journal of Healthcare Administration330Journal of Healthcare Administration["Y. Prasetyo", "Blacius Dedi", "Antonius Ngadiran"]2024-06-24T00:00:00 794fe37ad427dee42c3d67703c2c4884aed39224Artificial intelligence’s (AI) capacity to transform education has acquired a lot of attention, particularly because of COVID-19. It is comprehensively changing the way traditional educational methods are applied in classrooms. The author synthesizes current research findings, examines emerging trends, and focuses on finding out the multifaceted impact of applying AI technologies to the education system. The study explores how AI is enabling educators to customize instructional content to fit the different requirements of students, support specialized learning pathways, and develop critical thinking abilities by using AI-driven tools such as virtual mentors, intelligent tutoring systems, and personalized learning systems. How the analytics capabilities of AI help educators make data-driven decisions. The study sheds light on some of the ethical concerns. The challenges of the digital divide, which emerges because of the adoption of AI in education, are that not all institutions will be able to implement it. The study proposes recommendations for harnessing the transformative power of AI to promote equitable and inclusive educational practices in the digital age.International Conference on Computing Communication and Networking Technologies360The study explores how AI is enabling educators to customize instructional content to fit the different requirements of students, support specialized learning pathways, and develop critical thinking abilities by using AI-driven tools such as virtual mentors, intelligent tutoring systems, and personalized learning systems.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["C. Gupta"]2024-06-24T00:00:00 f78bfa2daa34bd2552aa3f5f090e98cdab3b4c62In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery. We propose that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems. This perspective focuses on scientific use cases like cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches. The study outlines the methodologies needed to address such challenges at scale on supercomputers or the cloud and provides exemplars of such approaches applied to solve a variety of scientific problems.arXiv.org1080It is proposed that scaling up artificial intelligence on high-performance computing platforms is essential to address such complex problems as cognitive simulations, large language models for scientific inquiry, medical image analysis, and physics-informed approaches.ArXiv["Wesley Brewer", "Aditya Kashi", "Sajal Dash", "A. Tsaris", "Junqi Yin", "M. Shankar", "Feiyi Wang"]2024-06-24T00:00:00 ce9e12fed2d6e9fcaf685b058e773205ca653bb6Journal of Artificial Intelligence and Robotics05Journal of Artificial Intelligence and Robotics["N. L. Rane"]2024-06-24T00:00:00 08c8be43287d117a61d013f6852e69ab370b1974Large Language Models (LLMs), such as ChatGPT and Google Bard, have performed interestingly well when assisting developers on computer programming tasks, a.k.a., coding, thus potentially resulting in convenient and faster software constructions. This new approach significantly enhances efficiency but also presents challenges in unsupervised code construction with limited security guarantees. LLMs excel in producing code with accurate grammar, yet they are not specifically trained to guarantee the security of the code. In this paper, we provide an initial exploration into using formal software specifications as a starting point for software construction, allowing developers to translate descriptions of security-related behavior into natural language instructions for LLMs, a.k.a., prompts. In addition, we leveraged automated verification tools to evaluate the code produced against the aforementioned specifications , following a modular, step-by-step software construction process. For our study, we leveraged Role-based Access Control (RBAC), a mature security model, and the Java Modeling Language (JML), a behavioral specification language for Java. We test our approach on different publicly-available LLMs, namely, OpenAI ChatGPT 4.0, Google Bard, and Microsoft CoPilot. We provide a description of two applications-a security-sensitive Banking application employing RBAC and an RBAC API module itself-, the corresponding JML specifications, as well as a description of the prompts, the generated code, the verification results, as well as a series of interesting insights for practitioners interested in further exploring the use of LLMs for securely constructing applications.ACM Symposium on Access Control Models and Technologies451This paper provides an initial exploration into using formal software specifications as a starting point for software construction, allowing developers to translate descriptions of security-related behavior into natural language instructions for LLMs, a.k.a., prompts.Proceedings of the 29th ACM Symposium on Access Control Models and Technologies["Carlos E. Rubio-Medrano", "Akash Kotak", "Wenlu Wang", "Karsten Sohr"]2024-06-24T00:00:00 79754fb441cc804eb177d4f2fee0cc2cc0ad13c7American Journal of Bioethics31The American Journal of Bioethics["J. Blumenthal-Barby", "Faith E Fletcher", "Lauren Taylor", "Ryan H Nelson", "B. Moore", "Brendan Saloner", "Peter A Ubel"]2024-06-24T00:00:00 415af5702da4ab08517f07c8b75d4a29e11ba444Diagnostic and Interventional Radiology01Diagnostic and Interventional Radiology["M. Klontzas"]2024-06-24T00:00:00 2b867694aa8722d25f9a47831704052c8d76cfe0Review of International Political Economy331Review of International Political Economy["Cecilia Rikap"]2024-06-24T00:00:00 7d52295f51bf3b022ca12f1df23b256ba7a01835As pessoas são essenciais para que as organizações, nasçam, cresçam, se desenvolvam e consigam atingir os seus objetivos. E para que este processo aconteça é fundamental que o departamento de Recrutamento e Seleção, consiga sempre atrair, recrutar, selecionar e reter os melhores talentos. Devido ao grande crescimento tecnológico, o presente estudo tem por objetivo pesquisar e analisar, sobre a inserção do uso de ferramentas digitais e Inteligência Artificial (IA) nos processos de recrutamento e seleção. Na parte da metodologia optou-se pela pesquisa descritiva e bibliográfica, que se dá por meio da análise de assuntos teóricos, e também pelo método de pesquisa exploratória utilizando a técnica da História Oral, que consiste na coleta de depoimentos com pessoas que testemunharam conjunturas, processos, acontecimentos, modos de ser e de estar dentro de uma sociedade ou instituição. Quanto a estrutura do artigo, no primeiro tópico aborda sobre a gestão de recursos humanos e suas subdivisões, a IA, R&S por meio digital, e por fim análise de discursão de resultados e referências bibliográficas. No desenvolver da temática proposta, a pesquisa não exclusivamente contribui para o campo acadêmico, mas, conectadamente, busca entusiasmar práticas futuras e provocar conscientização na sociedade sobre a importância do uso da IA na gestão de pessoas.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação["Regiane Freitas dos Santos", "Martha Helena Rodrigues de Souza"]2024-06-24T00:00:00 db65703d078d3177c2440dfd3ee9900cdbcf7593The arrival of synthetic Intelligence (AI) in the place of job has delivered progressive adjustments in the way organizations capture and keep information. AI era has superior to allow automated data capture for both traditional and dynamic sorts of facts, from emails to documents to conversations. AI-powered algorithms can automatically extract, categorize, and keep records, allowing get admission to relevant statistics quickly and accurately. AI also can be used to automate retrieval of data with natural language processing able to knowledge complicated queries for quick and correct effects. AI-pushed technology are reworking the manner agencies seize and shop statistics, simplifying search and retrieval and deepening the skills of statistics analytics. AI is ushering in an era of smarter, greater efficient knowledge control for the current place of business.International Conference on Computing Communication and Networking Technologies140Artificial intelligence-pushed technology are reworking the manner agencies seize and shop statistics, simplifying search and retrieval and deepening the skills of statistics analytics.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Girija Shankar Sahoo", "V. Haripriya", "Pramod Kumar Faujdar", "A. M. Jaffar", "C. Nivedha", "Shailesh J. Thaware"]2024-06-24T00:00:00 7606c0fc3ab5b2673729981aeaa1ab237fc4cc40This technical abstract search for a court between synthetic Intelligence (AI) and statistics technology (DS). We analyze and survey the regions wherein the two fields overlap. We determine the current kingdom of both AI and DS from their respective historical sequences and discuss the capability possibilities among them. We look at how they complement each other and which new technologies can get up from the combination of both. We also look into the ethical implications of such technologies and how AI can help enhance DS studies. Subsequently, we speak of viable software situations in a selection of domain names.International Conference on Computing Communication and Networking Technologies130This technical abstract search for a court between synthetic Intelligence (AI) and statistics technology (DS) looks at how they complement each other and which new technologies can get up from the combination of both.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Manish Srivastava", "K. Gopalakrishna", "A. M. Jaffar", "C. Santhosh Kumar", "Jayashree V. Bagade", "Preeti Naval"]2024-06-24T00:00:00 d6d3a308ddced49e2919355d8ebb45453577b898American Journal of Bioethics100The American Journal of Bioethics["Lukas J. Meier"]2024-06-24T00:00:00 bacbb0c01f8277cdb2c7db34d48095569731f1b6The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAl) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI act, and relevant CEN-CENELEC standardization efforts, as well as EU-funded projects such as AI4EU and SHERPA. Subsequently, we introduce a new TAl application framework, called E-TAl, tailored for energy applications, including smart grid and smart building systems. This framework draws inspi-ration from EGTAI but is customized for AI systems in the energy domain. It is designed for stakeholders in electrical power and energy systems (EPES), including researchers, developers, and energy experts linked to transmission system operators, distribution system operators, utilities, and aggregators. These stakeholders can utilize E-TAl to develop and evaluate AI services for the energy sector with a focus on ensuring trustworthiness throughout their development and iterative assessment processes.International Conference on Engineering, Technology and Innovation650The present study evaluates the current fuzzy landscape of Trustworthy AI (TAl) within the European Union (EU), with a specific focus on the energy sector, and introduces a new TAl application framework, called E-TAl, tailored for energy applications, including smart grid and smart building systems.2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)["Sotiris Pelekis", "Evangelos Karakolis", "George Lampropoulos", "S. Mouzakitis", "Ourania I. Markaki", "Christos Ntanos", "Dimitris Askounis"]2024-06-24T00:00:00 d3e2cf339d8655e057c79d09253556f857272a22 Improving the availability and reliability of aircraft engines is paramount in managing the aircraft fleet’s efficiency. While previous efforts have primarily focused on condition-based monitoring and Remaining Useful Life (RUL) prediction based on physics-based models, this paper introduces a novel approach to Engine Health Monitoring (EHM) using deep learning models. In particular, this work leverages critical engine parameters such as surge margin and exhaust gas temperature margin for interpretable EHM and prognostics. We present three deep learning models, namely, the Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long-Short Term Memory (LSTM), optimized for these tasks. The models were trained using synthetic data of 100 CFM 56 5B Turbofan engine-inspired models, simulating various flight cycles at steady-state cruise conditions using TURBOMATCH software (Cranfield University in-house aircraft engine performance simulation tool). The degradation in each engine was based on mass flow capacity and efficiency variation in the fan, compressor, and turbine, which is the effect of fouling, erosion, corrosion, etc. Unlike the existing datasets, this study deployed full factorial degradation of engine components and a wide range of degradation scenarios. Results demonstrate the competitiveness of the proposed models, as evidenced by low Root Mean Square Error (RMSE) values. The CNN model performs well in health monitoring, achieving an RMSE of 0.0148 health margin prediction. In contrast, the LSTM model proves most effective in predicting Remaining Useful Life, with an RMSE of 53.64 flight cycles. In conclusion, deep CNN and LSTM models showed a promising method for accurate engine condition monitoring and RUL predictions.Volume 4: Controls, Diagnostics, and Instrumentation00Three deep learning models are presented, namely, the Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long-Short Term Memory (LSTM), optimized for engine condition monitoring and RUL predictions, and demonstrate the competitiveness of the proposed models.Volume 4: Controls, Diagnostics, and Instrumentation["Aditya Aditya", "T. Nikolaidis", "Arias Chao Manuel", "Simone Togni"]2024-06-24T00:00:00 d686024012dc950cd38356be2e96adefe9deb7b3Objective: The histologic diagnosis of cutaneous metastatic breast cancer can be challenging as the differentials can include primary cutaneous glandular neoplasms and metastases from other glandular neoplasms which present very similar on H&E. Many immunohistological markers including GATA3 and CK7 have been employed to screen for primary or metastatic breast cancer cells and because of this, we wanted to develop a stain capable of differentiating these diagnoses quickly and accurately. Methods: We utilized 61 archived dermatopathology laboratory specimens of various benign and malignant cutaneous adnexal and breast tissues for analysis with a polyclonal Wnt9b antibody stain. Results: The average staining in benign categories (cutaneous adnexal and benign breast tissue) as well as metastases from non-breast carcinomas was negative. Among the malignant cutaneous adnexal and metastatic breast tissues, a significant difference was observed in staining as adnexal carcinomas were weakly positive (0.53+) and primarily seen in the outer layer of glandular structures, while metastatic breast tissues were strongly positive (3.63+) (P<0.01). The specificity in both adnexal and metastatic breast tissues was 100% while the sensitivity for adnexal carcinomas was 37% and metastatic breast was 94%. A larger sample size could greatly improve these values. Conclusion: These results demonstrate that Wnt9b has specific staining for cutaneous metastatic breast cell nuclei and could be utilized as a diagnostic to differentiate from cutaneous adnexal tumors in routine dermatopathological applications.Journal of Dermatology Research140It is demonstrated that Wnt9b has specific staining for cutaneous metastatic breast cell nuclei and could be utilized as a diagnostic to differentiate from cutaneous adnexal tumors in routine dermatopathological applications.Journal of Dermatology Research["Lauren M Larson, BS"]2024-06-24T00:00:00 6754e48889ac5c33eb97b419b677df17c89fa4b2The textile and apparel manufacturing business in India is the second largest job producer and the largest contributor to the country’s GDP. However, it lags below in terms of technological innovation and adoption to tackle the difficulties. The fundamental issue involves how to effectively get diverse ideas to market promptly with less work and expenditure in textile manufacturing with the objective to optimize the supply chain system and enhance satisfaction among consumers. Furthermore, by shortening product lead-time, these technologies help to maintain sustainability and help producers and distributors to adapt quickly to market demand. However, there has been little research on in-depth reviews of these technologies’ uses in textile manufacturing. The findings showed that the industry’s adoption of technology led to an improvement in the efficiency of the manufacturing process through the automation of repetitive design processes, an upsurge in worker productivity, and a decline in the lead time for product development.International Conference on Computing Communication and Networking Technologies150The findings showed that the industry’s adoption of technology led to an improvement in the efficiency of the manufacturing process through the automation of repetitive design processes, an upsurge in worker productivity, and a decline in the lead time for product development.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["Reenu", "Shivangi Singh", "Shinu Vig", "Sunita Dwivedi"]2024-06-24T00:00:00 55eeec3300eaed89aee8416791d86dff7bb56e44In recent years, Deep Neural Networks (DNNs) have shown remarkable potential in medical image analysis, particularly in Computed Tomography (CT) imaging. The use of DNNs to analyze medical images, especially in detecting cancer, is becoming a promising area of research. This study explored an approach to detect liver cancer using DNNs based on CT images. Convolutional neural networks (CNNs), a specialized class of DNNs tailored for spatial data processing tasks, prove highly effective in image analysis. The methodology of this study involves the preprocessing of a diverse imaging dataset of CT scans of the liver created in collaboration with seven hospitals and research institutions and the training of several different convolutional neural networks (CNNs) with various architectures. The whole dataset was divided into three sets. The training set consisted of $70 \%$ of the data. The validation set was created from $15 \%$ of the data, and the remaining $15 \%$ was used for testing. In order to evaluate the effectiveness of several convolutional neural networks, multiple measures such as accuracy, IoU (Intersection over Union), precision, recall and F-score were used. The results of this study showcase significant improvements in accuracy and efficiency compared to traditional methods, paving the way for early and accurate diagnosis without the help of specialists.2024 Progress in Applied Electrical Engineering (PAEE)200This study explored an approach to detect liver cancer using DNNs based on CT images using several different convolutional neural networks with various architectures, showing significant improvements in accuracy and efficiency compared to traditional methods.2024 Progress in Applied Electrical Engineering (PAEE)["Justyna Budzy\u0144ska", "Maria Kujawa", "Rados\u0142aw Roszczyk"]2024-06-24T00:00:00 d408871648a066b1e1c8c8ab9261329edd495c50Journal of Science, Technology and Society00Journal of Science, Technology and Society["Shuming Chen", "Yongbo Dai", "Xiaohui Zou"]2024-06-24T00:00:00 eb3bc201199fd739c9ae484a8647495b16dc2cfdIntense pulsed light has a growing body of research supporting its use in skin rejuvenation, dermatologic conditions, as well as ocular rosacea, dry eyes and meibomian gland dysfunction. This paper will start with the conception of one protocol for treating dry eyes, blepharitis and styes using broad band light, a version of intense pulsed light, and its evolution into a life-changing in-office procedure for many patients. The approach for optimizing the settings, considerations during the consultation, the procedure in detail, after treatment care, and potential complications to avoid are all explained. Periocular and facial rejuvenation treatment protocols are discussed as well. This should be a useful guide for clinicians looking to add intense pulsed light to their in-office treatment armamentarium to significantly improve the lives of their patients.International ophthalmology clinics630This paper will start with the conception of one protocol for treating dry eyes, blepharitis and styes using broad band light, a version of intense pulsed light, and its evolution into a life-changing in-office procedure for many patients.International Ophthalmology Clinics["Andrew S. H. Tsai", "Michelle Yip", "Amy Song", "Gavin S W Tan", "Daniel S W Ting", "J. P. Campbell", "Aaron S. Coyner", "R. V. P. Chan"]2024-06-24T00:00:00 c0816554cb3cafbdb5cdcedd48bf4a6956fe9ba0Generative AI platform ecosystems exhibit unique features and dynamics. The platforms employ a novel approach to user interaction, and the complementors' offerings on the supply side of the platform are tightly coupled with a highly dynamic large language model (LLM) operated by the platform owner. We conducted a case study of OpenAI's ChatGPT platform ecosystem to examine the architecture of generative AI platform ecosystems. We identify that the inter-play of platform core and modules (i.e., GPTs) in the platform periphery differs significantly from traditional software plat-forms. We contribute to the literature on digital platform eco-systems by revealing the novel layered modular architecture of this emerging type of platform ecosystem. We also highlight novel opportunities and challenges for platform complement-ors who offer modules in generative AI platform ecosystems.International Conference on Engineering, Technology and Innovation270A case study of OpenAI's ChatGPT platform ecosystem is conducted to examine the architecture of generative AI platform ecosystems and identifies that the inter-play of platform core and modules in the platform periphery differs significantly from traditional software plat-forms.2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)["Vincent Heimburg", "Maximilian Schreieck", "Manuel Wiesche"]2024-06-24T00:00:00 aff1c1877dfb763ddf2e76e7f5b00a44e6e5d815Transportation planners are increasingly relying on AI to optimize logistics and solve persistent challenges. However, as AI advances rapidly, most software vendors are unable to evaluate and implement all new developments. This paper uses bibliometric methods to track and evaluate the emerging trend of neurosymbolic AI, which combines neural networks with symbolic AI to improve decision making. By analyzing literature and citation data, we gain insights into the development and impact of neurosymbolic AI. The results provide a scalable approach for practitioners to efficiently identify and evaluate AI trends to facilitate the strategic adoption of technologies and innovations in transportation planning.International Conference on Engineering, Technology and Innovation270Bibliometric methods are used to track and evaluate the emerging trend of neurosymbolic AI, which combines neural networks with symbolic AI to improve decision making, and provide a scalable approach for practitioners to efficiently identify and evaluate AI trends.2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC)["U\u011fur Ertem", "Theo Lutz", "Tim Zeiser"]2024-06-24T00:00:00 5f5c91f7e0326638e03cf4254af590240d795448JAMA Internal Medicine50JAMA internal medicine["D. Fraile-Navarro", "Richard Lehman"]2024-06-24T00:00:00 cacdcbaa4095d955e63634c57de8bbee664c8820<jats:p>N/A</jats:p>Journal of Healthcare Administration30Journal of Healthcare Administration["H. Imam"]2024-06-24T00:00:00 7bf058494535fe05b7470f1f1e82ff74eb018c29As digital payment methods continue to gain widespread adoption, the specter of credit card fraud looms large for both financial institutions and consumers. Addressing this escalating risk demands the deployment of sophisticated anomaly detection methods. This initiative is dedicated to crafting a resilient and effective framework for detecting credit card fraud, leveraging state-of-the-art anomaly detection algorithms and machine learning methodologies. The overarching goal is to conceive, engineer, and assess a robust anomaly detection system adept at pinpointing fraudulent credit card transactions with exceptional precision, all while curbing false positives to a minimum.International Conference on Computing Communication and Networking Technologies140The overarching goal is to conceive, engineer, and assess a robust anomaly detection system adept at pinpointing fraudulent credit card transactions with exceptional precision, all while curbing false positives to a minimum.2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)["C. Manjula Devi", "A. Gobinath", "S. Padma Priya", "M. Adithiyaa", "M. K. Chandru", "M. Jothi"]2024-06-24T00:00:00 05445e4cf753d852c0bb2b9e38becaacd5189fbcnpj Digit. Medicine765The AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.NPJ Digital Medicine["L. Pastika", "A. Sau", "K. Patlatzoglou", "E. Sieliwonczyk", "Ant\u00f4nio H. Ribeiro", "K. McGurk", "Sadia Khan", "D. Mandic", "William R Scott", "James S. Ware", "Nicolas Peters", "A. Ribeiro", "Daniel B. Kramer", "J. Waks", "F. Ng"]2024-06-25T00:00:00 c44c359d0b7be36f1a8235e575342c547a831b50Artificial intelligence technology has spread rapidly in the business world in recent years and has transformed the business processes of many organizations. This transformation has caused significant changes not only in the technological infrastructure but also in the organizational culture and way of doing business. However, the effects of artificial intelligence on organizational culture are complex and diverse. While artificial intelligence applications can change businesses' values, norms, and ways of working, they can also make it difficult to maintain a human‐centered approach. In this context, it is important to understand and evaluate the effects of artificial intelligence on organizational culture. This article examines the effects of artificial intelligence on organizational culture from the perspective of the hermeneutic cycle. The hermeneutic cycle allows us to understand the interaction between organizational culture and artificial intelligence as a continuous process of interpretation and understanding. In addition, by making use of the experience anecdote of phenomenology, it is emphasized how mental processes are shaped and that these processes have a structure suitable for the hermeneutic cycle. This framework helps us more comprehensively evaluate and analyze the effects of artificial intelligence on organizational culture. Our findings reveal that integrating AI within organizational frameworks requires nuanced understanding and adaptations that align with human‐centric values.Systems research and behavioral science272Examining the effects of artificial intelligence on organizational culture from the perspective of the hermeneutic cycle reveals that integrating AI within organizational frameworks requires nuanced understanding and adaptations that align with human‐centric values.Systems Research and Behavioral Science["Asl\u0131han Canbul Yaro\u011flu"]2024-06-25T00:00:00 534815a191166544babfa23284fe6eb9b0e63948We propose that metacognitive skills and metacognitive thinking will become increasingly important for effective use of AI (Artificial Intelligence) systems. As the collaborative capability of AI systems improves, humans will spend more of their time working with AI. This is expected to uniquely influence the human decision-making process. We identify four characteristics that differentiate human-AI interactions from human-human interaction, each of which is likely to affect our thinking and decisions. These are (1) the accuracy of our cognitive heuristics for predicting the behaviour of AI systems, (2) AI’s limited capability when dealing with novel and ill-defined problems, (3) the lack of a natural, reciprocal feedback mechanism in AI systems and (4) the inability of AI systems to engage in metacognition. Drawing upon the dual-process theory of human thought process, we argue that these characteristics will diminish the efficacy of the system one mode of human thinking, making metacognitive thinking skills important to ensure effective use of AI systems. We conclude by describing how this need can be addressed through training and AI design.Conference on Algebraic Informatics631It is proposed that metacognitive skills and metacognitive thinking will become increasingly important for effective use of AI (Artificial Intelligence) systems as the collaborative capability of AI systems improves, and how this need can be addressed through training and AI design is described.2024 IEEE Conference on Artificial Intelligence (CAI)["Sidra Sidra", "Claire Mason"]2024-06-25T00:00:00 da3738ea7bc7e1d6730cb47cb11a7fb8e3b9de7a Purpose This study aims to shed light on the dilemma of “publish or perish” within the context of artificial intelligence (AI) and to suggest approaches that scholars and organizations can implement to enhance ethical behavior in research and publishing. Design/methodology/approach This investigation examined institutional guidelines, policies, processes, norms and prior research to pinpoint ethical patterns that could be leveraged to promote ethical behavior in research and publishing. Findings The research outlined various unethical behaviors that have a detrimental impact on research outcomes including falsification, fabrication, plagiarism, p-hacking, authorship conflicts of interest, salami publication, republishing and manipulation of visual data, as well as incorrect selection of statistical analysis techniques. Furthermore, the study recommends optimal strategies for researchers and institutions to improve the quality of research, such as embracing the Open Research Library, forming partnerships and consortia, adhering to established informed consent standards and safeguarding confidentiality and privacy, among other practices. Practical implications These findings can serve as a foundation for policies that enable institutions and scholars to heighten their comprehension of ethical research practices and establish mechanisms for supervising research outcomes. Originality/value Numerous research and educational institutions are contending with new obstacles brought about by using technologies such as AI. These findings can offer a reference point to stimulate the ongoing discourse regarding the utilization of generative AI in academic settings. Library Hi Tech News231The research outlined various unethical behaviors that have a detrimental impact on research outcomes including falsification, fabrication, plagiarism, p-hacking, authorship conflicts of interest, salami publication, republishing and manipulation of visual data, as well as incorrect selection of statistical analysis techniques.Library Hi Tech News["Stephen Oloo Ajwang", "Anselimo Peters Ikoha"]2024-06-25T00:00:00 8f2c001164b0f6c07a526147c23a69356b95ffbfPurpose This study aims to explore the implementation of artificial intelligence (AI) into recruitment by considering its potential to maximise the effectiveness of the human resources (HR) processes, challenges associated with the implementation and ethical concerns. Design/methodology/approach A qualitative research approach was used to reach the stated objectives within the context of the small open economy – the Czech Republic. Interviews were conducted with four participants, Czech-based recruiters, each with five or more years of experience in their field. The interviews were conducted in Autumn 2023 within the online platform. The answers were transcribed and thematically analysed. Findings The participants who were interviewed heavily emphasised the importance of the role of the human factor in recruitment, yet several observations and insights were obtained. In particular, some interviewees indicated a possible usage of a chatbot for the first round of the candidates' selection, but they see it as problematic in the final decision on the position fulfilment, where the human factor is not replaceable so far. The key ethical challenges of the broader implementation of AI in the recruitment practices of the respondents remain the risks regarding privacy and data protection, especially the General Data Protection Regulation (GDPR) legislation. Originality/value This article delivers pertinent insights for recruiters on using AI in recruitment, bringing forth a more subtle understanding of the faceted subject of AI-based recruitment. Vilakshan - XIMB Journal of Management381This article delivers pertinent insights for recruiters on using AI in recruitment, bringing forth a more subtle understanding of the faceted subject of AI-based recruitment.Vilakshan - XIMB Journal of Management["Zuzana S\u00fdkorov\u00e1", "Dana Hague", "O. Dvoulet\u00fd", "D. A. Proch\u00e1zka"]2024-06-25T00:00:00 724742c8e6f0eb18b55275b6831f0876ba2ebbb5Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the Random Forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (> 0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.medRxiv371This study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics through accurate and accessible diagnostics.Lab on a Chip["Mert Tunca Doganay", "Purbali Chakraborty", "Sri Moukthika Bommakanti", "Soujanya Jammalamadaka", "Dheerendranath Battalapalli", "A. Madabhushi", "Mohamed S Draz"]2024-06-25T00:00:00 909483c0c3ce4d479f6dab9feefea7043f721cd2Cancer is one of the world’s most serious medical challenges. Because of its great heterogeneity, persons with similar tumors could react in a different way to the same drugs or surgical methods, prompting the development of more accurate tumor treatment approaches as well as patient-specific tailored treatments. To establish targeted therapy choices for patients, it is critical to have a full acceptance of the changes that tumors endure, counting modifications in their genetic factors, proteins, and cancer cell behaviors. Tumor treatment requires precise targeting. Big data-driven artificial intelligence (AI) may reveal patterns, insights, and related information hidden inside huge volumes of data. To identify exact data from transcriptomics, radiomics, genomes, proteomics, digital pathological images, etc., subsets of AI’s machine learning capability may be explored. This may help clinicians get a better and more comprehensive knowledge of malignancies. In addition, to provides the optimal therapy for each patient and improves clinical outcomes.International Journal of Pharmaceutical Quality Assurance181To identify exact data from transcriptomics, radiomics, genomes, proteomics, digital pathological images, etc., subsets of AI’s machine learning capability may be explored to help clinicians get a better and more comprehensive knowledge of malignancies.INTERNATIONAL JOURNAL OF PHARMACEUTICAL QUALITY ASSURANCE["Pooja K Ugemuge", "Gaurav G Khandalkar", "Rahul G Ingle"]2024-06-25T00:00:00 19bc0b8dabcae1a40b7a6214d5b78acba07135b4This study explores the growing significance of Large Language Models (LLMs) in tourism, for their current and potential applications. It aims to achieve two primary objectives: first, to develop a novel hetero-intelligence framework merging human and artificial intelligence (AI) to address contemporary sustainability challenges in tourism; second, to validate this framework by applying it to sustainable tourism planning, assessing LLMs' capabilities and limitations. The research employs a hetero-intelligence performance test, contrasting human intelligence and AI contributions in sustainable tourism planning with overtourism as a proxy challenge. Results showed that hetero-intelligence could effectively address sustainability issues in tourism, provided human and AI strengths and weaknesses are understood. LLMs proved useful in diagnosing and proposing solutions for sustainability-related issues. However, a rigorous methodological framework is essential to ensure unbiased outcomes. The research offers practical guidelines for applying this approach and significantly contributes to epistemological and empirical dimensions, providing valuable insights for researchers and tourism planners. The study calls for more empirical research to validate the methodology and explore ethical and legal dimensions, extending hetero-intelligence applications to broader sustainability challenges in tourism.Tourism &amp; Management Studies01Results showed that hetero-intelligence could effectively address sustainability issues in tourism, provided human and AI strengths and weaknesses are understood, and calls for more empirical research to validate the methodology and explore ethical and legal dimensions, extending hetero-intelligence applications to broader sustainability challenges in tourism.Tourism &amp; Management Studies["Roc\u00edo Y\u00f1iguez-Ovando", "E. M. Buitrago-Esquinas", "Miguel Puig-Cabrera", "M. Santos", "Jos\u00e9 Ant\u00f3nio C. Santos"]2024-06-25T00:00:00 34f375f1f0e05224dad4cd438b87b1488e548082Vaccine hesitancy is one of the top ten threats to global health. Artificial intelligence-driven chatbots and motivational interviewing skills show promise in addressing vaccine hesitancy. This study aimed to develop and validate an artificial intelligence-driven motivational digital assistant in decreasing COVID-19 vaccine hesitancy among Hong Kong adults. The intervention development and validation were guided by the Medical Research Council’s framework with four major steps: logic model development based on theory and qualitative interviews (n = 15), digital assistant development, expert evaluation (n = 5), and a pilot test (n = 12). The Vaccine Hesitancy Matrix model and qualitative findings guided the development of the intervention logic model and content with five web-based modules. An artificial intelligence-driven chatbot tailored to each module was embedded in the website to motivate vaccination intention using motivational interviewing skills. The content validity index from expert evaluation was 0.85. The pilot test showed significant improvements in vaccine-related health literacy (p = 0.021) and vaccine confidence (p = 0.027). This digital assistant is effective in improving COVID-19 vaccine literacy and confidence through valid educational content and motivational conversations. The intervention is ready for testing in a randomized controlled trial and has high potential to be a useful toolkit for addressing ambivalence and facilitating informed decision making regarding vaccination.Vaccines462This digital assistant is effective in improving COVID-19 vaccine literacy and confidence through valid educational content and motivational conversations and has high potential to be a useful toolkit for addressing ambivalence and facilitating informed decision making regarding vaccination.Vaccines["Yan Li", "Kit-Ching Lee", "Daniel Bressington", "Qiuyan Liao", "Mengting He", "Ka-Kit Law", "A. Y. Leung", "Alex Molassiotis", "Mengqi Li"]2024-06-25T00:00:00 7e6fa8b3fb71a0ce35fc5ff5e554cc7a5718c037PURPOSE OF REVIEW This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. RECENT FINDINGS Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. SUMMARY Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.Current Opinion in Oncology952The critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence are highlighted.Current opinion in oncology["I. Riaz", "Muhammad Ali Khan", "T. Haddad"]2024-06-25T00:00:00 6ed3194c4ea1c1bd779628983afdfa0197d1ef76Artificial intelligence is growing quickly, and its application in the global diabetes pandemic has the potential to completely change the way this chronic illness is identified and treated. Machine learning methods have been used to construct algorithms supporting predictive models for the risk of getting diabetes or its complications. Social media and Internet forums also increase patient participation in diabetes care. Diabetes resource usage optimisation has benefited from technological improvements. As a lifestyle therapy intervention, digital therapies have made a name for themselves in the treatment of diabetes. Artificial intelligence will cause a paradigm shift in diabetes care, moving away from current methods and toward the creation of focused, data-driven precision treatment.Archives of medical sciences. Atherosclerotic diseases511Artificial intelligence will cause a paradigm shift in diabetes care, moving away from current methods and toward the creation of focused, data-driven precision treatment.Archives of Medical Sciences. Atherosclerotic Diseases["A. Papazafiropoulou"]2024-06-25T00:00:00 5b7a291f89d997a9330e3bd263dc420403f44587Accurate and trust are crucial for ChatGPT and other artificial intelligence (AI) markets. One of the challenges is data leakage, which is frequently overlooked but possesses highly consequential implications. Federated learning (FL) is recognised as a new era of secure AI systems. The market for FL is estimated to reach USD 266.77 million by 2030 according to Polaris Market Research (1). This paper focuses on FL-based approaches for improving AI safety and examines the significance of Deep learning (DL) and its privacy implications. This has been achieved through six models: Federated Convolutional Neural Network (F-CNN), Federated averaging CNN (FA-CNN), Federated Adam (FA), Malicious Generative adversarial network (MGAN), Federated M-GAN (FMGAN) and Conditional GAN (CGAN). The authors analysed MNIST and CIFAR-10 datasets and conducted extensive numerical evaluations to confirm improved user privacy in federated learning for AI models. A case study with fast convergence speed and excellent asymptotic test accuracy was designed to outline White-box attacks on MGAN, FMGAN, and CGAN models. The study also implemented active inference attacks on deep neural networks without sharing raw data through FL. We created 256 synthetic images specifically to test the effectiveness of the original classifier. These counterfeit visuals effectively deceived the classifier, appearing as legitimate representations of true class labels. Trimming shared parameters was ineffective in preventing the attack, revealing limitations in collaborative learning. The generator shows the least loss of 0.0104 encountered of all models in the study. Our Generator is also the fastest after the FMGAN model. FMGAN performs best with maximum accuracy (0.9613) followed by CGAN (0.9208), MGAN (0.9163), FA (0.5148), FCNN (0.4376) and FACNN (0.4285). It also demonstrated high efficiency by successfully attacking in a short timeframe of 0.7459 milliseconds. The Federated approach led by Adam exhibited the longest processing time, at approximately 10.52 minutes. The case study illustrates the risks of surveillance and manipulation by attackers, who pressured participants to disclose confidential information. It also aimed to increase flexibility and robustness. Our work is accessible to diverse audiences, facilitating the adoption and practical applications of deep learning methods for privacy protection by major corporations.Conference on Algebraic Informatics191A case study with fast convergence speed and excellent asymptotic test accuracy was designed to outline White-box attacks on MGAN, FMGAN, and CGAN models, and active inference attacks on deep neural networks without sharing raw data through FL.2024 IEEE Conference on Artificial Intelligence (CAI)["Ekta Sharma", "R. Deo", "Christopher P. Davey", "Brad D. Carter", "S. Salcedo-Sanz"]2024-06-25T00:00:00 c10de75d9164677488e6b1d28e1572973df301afThe integration of artificial intelligence (AI) technology into energy project management has emerged as a significant trend. This paper presents an extensive review and analysis of AI applications in this domain, emphasizing areas such as data analysis and prediction, intelligent optimization, risk management, and decision support systems. We systematically review the current literature, highlighting the critical role of AI in enhancing energy project management. Our discussion encompasses existing research outcomes and future development trajectories, aiming to furnish valuable insights and guidance for both research and practical applications in the field.International Journal of Electric Power and Energy Studies41An extensive review and analysis of AI applications in this domain, emphasizing areas such as data analysis and prediction, intelligent optimization, risk management, and decision support systems is presented.International Journal of Electric Power and Energy Studies["Wei Liu"]2024-06-25T00:00:00 833077a9d5f53bcfb3474e4346c38a55dc409c76PurposeArtificial intelligence (AI) radically transforms organizations, yet ethical AI’s effect on employee innovation remains understudied. Therefore, this study aims to explore whether responsible artificial intelligence (RAI) enhances high-tech employees’ innovative work behavior (IWB) through creative self-efficacy (CSE) and employee mental health and well-being (EMHWB). The study further examines how leaders’ RAI symbolization (LRAIS) moderates RAI’s effect.Design/methodology/approachThrough structural equation modeling, 441 responses of high-tech firms’ employees from Pakistan were utilized for hypotheses testing via SmartPLS-4.FindingsThe results revealed that second-order RAI enhances employees’ IWB. The effect was supported directly and indirectly through CSE and EMHWB. Findings also showed that LRAIS significantly moderates RAI’s influence on CSE, on the one hand, and EMHWB, on the other.Practical implicationsHigh-tech firms’ managers can fix AI-outlook issues that impair their employees’ IWB by prioritizing an ethical AI design involving actions like AI control mechanisms, bias checks and algorithmic audits. Similarly, these managers should facilitate RAI discussions and targeted trainings focusing on employees’ cognitive development and well-being. Likewise, RAI embracement programs and evaluations for leadership positions could be incorporated into high-tech firms.Originality/valueThis study advances the mainstream AI literature and addresses a notable gap concerning RAI’s influence on employees’ IWB while grounding in social cognitive theory. Moreover, this study unveils how CSE and EMHWB affect IWB within RAI milieus. Additionally, through signaling theory, it underscores the significance of LRAIS in amplifying the direct association between RAI, CSE, and EMHWB within high-tech firms in emerging markets.Kybernetes1031This study unveils how CSE and EMHWB affect IWB within RAI milieus and underscores the significance of LRAIS in amplifying the direct association between RAI, CSE, and EMHWB within high-tech firms in emerging markets.Kybernetes["Shahan Bin Tariq", "Jian Zhang", "F. Gilal"]2024-06-25T00:00:00 4f14b5816a722540e318bb9ef581bfc5e6e3f37cWith the advancement of big data, the scope and potential of Artificial Intelligence (AI) have acquired major momentum. Data-Centric Artificial Intelligence (DCAI) is one of the most emergent fields of study in the current era of digitalization. Many examples have proven the effectiveness of Machine- and Deep Learning methods. In industrial production, however, limitations are still present that hinder application online and in a series that goes beyond isolated use cases. One crucial issue is data precondition, i.e., data quality, consistency, and labeling. As DCAI addresses these issues, developments in this field have caught the attention of various experts. In summary, DCAI continues to be an exciting and promising field of study that enhances AI applicability. Several research works have been conducted in DCAI, but unfortunately, no comprehensive reviews have been conducted to summarize and highlight the results. This gap in knowledge inspired our work, that aims to answer well-structured research questions. The focus of this paper is to clarify the terminology used in DCAI while also distinguishing it from other AI-related problems. This helps to analyze the current standards and problems associated with DCAI. This paper summarizes current use cases of DCAI and their impact on industries. Through this detailed description, readers can understand the potential and benefits of using DCAI in different business sectors. The analysis of the latest methods employed by DCAI to achieve enhanced AI performance and outcomes provides valuable insights for professionals and organizations that strive to incorporate AI into their business.Conference on Algebraic Informatics350The focus of this paper is to clarify the terminology used in DCAI while also distinguishing it from other AI-related problems, which helps to analyze the current standards and problems associated with DCAI.2024 IEEE Conference on Artificial Intelligence (CAI)["Michael Nieberl", "Alexander Zeiser", "Holger Timinger"]2024-06-25T00:00:00 85288c5fd7624f39bbdba6e1160777b42aff2e47Cyberbullying has become a concerning issue in contemporary society with the widespread use of digital communication tools and social media platforms. The impacts of cyberbullying can be far-reaching, especially for certain groups such as children and teenagers. This work aims to mitigate cyberbullying in an ethically appropriate manner with careful consideration of transparency, explainability, privacy protection, contextual understanding, and continuous monitoring and improvement. We propose an ethically aligned artificial intelligence framework for cyberbullying detection and analysis. The frame-work provides four core functions: 1) detecting cyberbullying comments with explanations; 2) building and enriching an evolutionary cyberbullying knowledge map with detected instances and external ethics resources; 3) constructing and maintaining a cyberbullying instance network; and 4) performing analytics and recommendation (e.g., mental aid support) based on the knowledge map and instance network. The output knowledge map, instance network, and analysis report collectively offer useful insights for policymakers, regulators, ethicists, industry stakeholders and researchers, facilitating the establishment of global standards and fostering collaborative efforts in addressing cyberbullying. We keep human in the loop and also ensure that user privacy is well protected.Conference on Algebraic Informatics130This work aims to mitigate cyberbullying in an ethically appropriate manner with careful consideration of transparency, explainability, privacy protection, contextual understanding, and continuous monitoring and improvement.2024 IEEE Conference on Artificial Intelligence (CAI)["Min Wang", "C. B. Burken", "Nan Sun", "S. K. Kermanshahi", "Yu Zhang", "Jiankun Hu"]2024-06-25T00:00:00 c4a0f1ca13f2ca75fdd31025754df6a3c813c867The emergency department (ED) of a hospital is a critical component of the healthcare system, serving as the first point of contact for patients in need of immediate medical attention. However, due to the unpredictable volume and nature of demand, managing resources in an ED can be quite challenging. The increasing demand for emergency services has resulted in overcrowding and long wait times, which can negatively impact patient outcomes and satisfaction. In this paper, we propose a solution to these problems by using artificial intelligence (AI) to identify inefficiencies in ED operations and to forecast the patient flow one week in advance. The proposed AI system resorts to machine learning algorithms to analyze the different process phases a patient has to go through while in the ED. By analyzing real-time data, the system can identify bottlenecks and recommend strategies for improving resource allocation, such as adjusting staffing levels or reassigning patients to different areas in the ED. Additionally, the system can also forecast demand for ED services, allowing hospital administrators to proactively allocate resources and ensure that patients receive timely and effective care. The proposed AI-based approach has the potential to significantly improve ED operations and patient outcomes by reducing wait times, improve resource allocation, and improve overall quality of the health service provided. By leveraging the power of AI, hospitals can provide more effective and efficient care, ultimately improving patient satisfaction and outcomes.2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)170An artificial intelligence system is proposed to identify inefficiencies in emergency department operations and to forecast the patient flow one week in advance, allowing hospital administrators to proactively allocate resources and ensure that patients receive timely and effective care.2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)["L. Elvas", "Miguel Nunes", "Berit Irene Helgheim", "Jo\u00e3o C. Ferreira"]2024-06-25T00:00:00 85f68c118fc147a56c99a61c74ca04f72d38d280Norway is a global leader in renewable energy, with hydropower accounting for 90% of its electricity generation. The country's hydropower sector is crucial to both national and international energy demands, and the need for efficient management has become more pressing as the world shifts from fossil fuels to cleaner energy sources. Artificial Intelligence (AI) is emerging as a powerful tool for optimizing hydropower management by improving predictive analytics, automating decision-making, and processing real-time data. In Norway, AI is increasingly being used to forecast water flow and manage energy production more effectively, while also enhancing predictive maintenance to minimize downtime and operational costs. Despite its potential, the implementation of AI faces challenges such as high costs, infrastructure investments, and data privacy concerns. This article explores recent innovations in AI applied to hydropower in Norway, discussing both the opportunities and challenges. The successful integration of AI into hydropower operations holds promise for improving efficiency and sustainability, offering insights for broader adoption across the global renewable energy sector. Future developments in AI and its application in renewable energy, such as smart grids and interconnecting different energy sources, could further enhance the energy landscape.International Journal of Artificial Intelligence420International Journal of Artificial Intelligence["Claude Jeroen", "Juzeniene Pettersen", "Kjesbu Hyysalo"]2024-06-25T00:00:00 58f9e9427fdfa3628b056a8d82b7f8fb3d597020Some aspects of the “artificial intelligence” phenomenon are considered, including possible political and economic reasons for its appearance. The directions for the development of artificial intelligence are outlined, which can bring real benefits to the state and society at the moment, first of all, the further development of digitalization of public administration and the improvement of devices to increase labor productivity. Signs are given that, according to the author, indicate the participation of a chatbot in preparing the text. To inform readers about the use of the chatbot, it is proposed to place special labels next to such texts. It has been suggested that the use of a chatbot in the preparation of scientific articles is an illusion that the lack of abilities, experience and professionalism can be replaced by technical competence and material resources, which in the future, with large-scale use, can lead to a distortion of the meaning of not only educational activities, but also to the changes in the researcher's activities.Science Management: Theory and Practice00It has been suggested that the use of a chatbot in the preparation of scientific articles is an illusion that the lack of abilities, experience and professionalism can be replaced by technical competence and material resources, which in the future can lead to a distortion of the meaning of not only educational activities, but also to the changes in the researcher's activities.Science Management: Theory and Practice["Aleksander Skazochkin"]2024-06-25T00:00:00 24f72616b5f2fc0d869ede27fd860ef6f91ab66fВ статье исследуется история развития искусственного интеллекта, начиная с Дартмутской конференции 1956 года, и заканчивая современными достижениями в области машинного обучения, обработки естественного языка и других областях применения ИИ. Особое внимание уделено новому поколению технологий искусственного интеллекта, такому как ChatGPT, и его влиянию на рынок труда и структуру промышленности. В заключении автор приходит к выводу, что внедрение технологий ИИ не только изменит структуру промышленности, но также потребует новых навыков и компетенций от будущей рабочей силы для успешной адаптации к изменениям в промышленной среде. The article explores the history of artificial intelligence development, from the 1956 Dartmouth Conference to modern advances in machine learning, natural language processing, and other AI applications. Special attention is paid to the new generation of AI technologies, such as ChatGPT, and its impact on the labor market and industry structure. The author concludes that the introduction of AI technologies will not only change the structure of industry, but will also require new skills and competencies from the future workforce to successfully adapt to changes in the industrial environment.Экономика и предпринимательство00Экономика и предпринимательство["\u041d\u0410\u0422\u0410\u041b\u042c\u0421\u041e\u041d\u00a0\u0410.\u0412. \u041d\u0410\u0422\u0410\u041b\u042c\u0421\u041e\u041d\u00a0\u0410.\u0412."]2024-06-25T00:00:00 b9c167040f509e8818a09a297aa2e37c1ff11fb5Complex relationships among variables in the field of environmental engineering and water management sector foster numerous advantages when harnessing AI tools in modeling. Non-linear correlation among parameters proposed for ecological state prediction propose utilizing of Artificial Intelligence-based models. Given the highly intricate and dynamic nature of water environments, effective management poses a demanding challenge, particularly in the realm of forecasting. Consequently, there is a critical demand for the development of accurate water quality prediction models to address these complexities and ensure optimal management practices. To achieve highly precise results in predicting water quality, hybrid AI-based models stand as the state-of-the-art solution. Applying hybrid AI-based models in water quality prediction enhances accuracy, robustness, and flexibility by leveraging corresponding strengths of multiple algorithms, leading to optimized performance and improved decision-making in water management.2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)250Applying hybrid AI-based models in water quality prediction enhances accuracy, robustness, and flexibility by leveraging corresponding strengths of multiple algorithms, leading to optimized performance and improved decision-making in water management.2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)["Ivana Krtolica", "Milovan Medojevi\u0107"]2024-06-25T00:00:00 89ac1c5a3b720ea697ebcbd03a50fecaa2dcf66a"The research Paper examines the complicated interplay of AI and science fiction films, which charts its evolution in terms of depiction while examining how it impacts society’s perceptions. Traversing historical epochs, from early portrayals in classics like “Metropolis” to contemporary narratives in films like “Her” and “Ex Machina,” the study employs a qualitative analysis of key cinematic works. In methodological terms, this research is based on a sequential framework that examines the evolving themes, ethical issues, and transformation of artificial intelligence characters. To understand recurring themes and their implications, a qualitative content analysis has also been applied. The paper reveals the symbiotic relationship between movie stories and society’s perceptions, shedding light on how science fiction cinema has a role to play in shaping national opinion about artificial intelligence, influencing ethical concerns, technological progress, and social attitudes. This paper seeks to provide insight into the cultural, technological and commercial aspects of this widespread theme through an examination of the intersection between artificial intelligence and science fiction in films."Economic Affairs00The paper reveals the symbiotic relationship between movie stories and society’s perceptions, shedding light on how science fiction cinema has a role to play in shaping national opinion about artificial intelligence, influencing ethical concerns, technological progress, and social attitudes.Economic Affairs["Somendra Prajapat"]2024-06-25T00:00:00 5674f6c484167228dc19ec388478b6b939fbea03В настоящее время технологии искусственного интеллекта (ИИ) активно внедряются в систему здравоохранения Российской Федерации для точности диагностики и результатов лечения, а также для улучшения ухода за пациентами. Инновационные проекты по внедрению ИИ в сфере здравоохранения направлены на создание инструментов и приложений, которые могут применяться в различных областях медицины, таких как радиология, патология, геномика и персонализированная медицина. Актуальность темы исследования обусловлена тем, что не все инновационные идеи берутся к разработке, до стадии испытаний доходит только часть, а внедренными и коммерциализированными оказываются только единицы. Поэтому важно определить факторы успеха инновационных проектов на базе ИИ в сфере здравоохранения Российской Федерации. Currently, artificial intelligence (AI) technologies are being actively introduced into the healthcare system of the Russian Federation for the accuracy of diagnosis and treatment results, as well as to improve patient care. Innovative AI projects in healthcare are aimed at creating tools and applications that can be used in various fields of medicine, such as radiology, pathology, genomics and personalized medicine. The relevance of the research topic is due to the fact that not all innovative ideas are taken into development; only a part reaches the implementation stage, and when implemented and commercialized they turn out to be only an additional factor. Therefore, it is important to determine the success factors of innovative projects to implement AI in healthcare the Russian Federation.Экономика и предпринимательство00Экономика и предпринимательство["\u041c\u0410\u041b\u042b\u0428\u041a\u0418\u041d\u0410\u00a0\u041c.\u0412. \u041c\u0410\u041b\u042b\u0428\u041a\u0418\u041d\u0410\u00a0\u041c.\u0412."]2024-06-25T00:00:00 4228021486e7a644711e38583d328988d418cbbbIn Brazil, skin tumors represents the type of neoplasm with the highest incidence rate among the population. Because of this, this study explores the invasiveness of this disease using computational techniques to understand how specific patient characteristics influence its progression. Through the analysis of data provided by the Cancer Hospital Registry (RHC) of the National Cancer Institute José Alencar Gomes da Silva (INCA), and with the aid of Artificial Intelligence (AI) algorithms explained by the SHapley Additive exPlanations (SHAP) approach, the study reveals that the invasiveness of skin cancer is affected in a significantly different way by the individual characteristics of patients compared to analyses based on more general attributes. These findings underline the importance of personalization in medicine, suggesting that a deeper understanding of individual characteristics can lead to more accurate diagnoses and more effective treatments. Furthermore, the research highlights the role of XAI in clarifying these relationships, pointing to the need for more refined approaches in prevention, treatment, and the formulation of public health policies aimed at combating skin tumors, despite limitations such as data imbalance encountered during the study.Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2024)190The study reveals that the invasiveness of skin cancer is affected in a significantly different way by the individual characteristics of patients compared to analyses based on more general attributes, suggesting that a deeper understanding of individual characteristics can lead to more accurate diagnoses and more effective treatments.Anais do XXIV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2024)["Marcus Augusto Padilha da Mata", "P. S. L. Leit\u00e3o J\u00fanior"]2024-06-25T00:00:00 bfdf9ad210da38b361b83bbcc405b896d7e57d5b Purpose This study aims to identify the effects of artificial intelligence (AI) on university libraries and to reveal challenges associated with the adoption of AI-powered applications in libraries. Design/methodology/approach A systematic literature review (SLR) was applied to address the study’s objectives. The 25 most relevant seminal studies published in Scopus- and Web of Science-indexed journals were selected to conduct the study. Findings Findings revealed that AI has strong positive effects on university libraries. These effects included efficiency and promotion of library products, innovative library services, alignment of library services with the fourth industrial revolution (4IR), collection management and user services and transformation of library systems. Results also manifested that skills and knowledge barriers, financial and resource constraints and resistance to change created challenges to adopt AI-based services in university libraries. Originality/value This study has added valuable literature to the existing body of knowledge by conducting SLR on the basis of 25 most relevant research articles published in cite score and impact factor journals. It has provided practical implications by offering recommendations to adopt AI in university libraries. The study is a benchmark for policymakers, AI applications developers, higher education bodies, government representatives, university administration and library leadership to devise effective strategies and methods for the efficient adoption of AI in libraries. The study has also provided a framework to adopt AI applications in library settings. Global Knowledge Memory and Communication450The study is a benchmark for policymakers, AI applications developers, higher education bodies, government representatives, university administration and library leadership to devise effective strategies and methods for the efficient adoption of AI in libraries.Global Knowledge, Memory and Communication["Khurram Shahzad", "S. A. Khan", "Abid Iqbal"]2024-06-25T00:00:00 7b8e383a1487cb3af615496171de3cc0a3d25044The European Union and some of its institutions have taken significant steps to address the challenges posed by the development and use of Artificial Intelligence (AI) in various contexts. The ubiquity of AI applications in everyday life, affecting both citizens and professionals, has made AI a common topic of discussion. However, as is evident from the documents analyzed here, concerns have been raised about the possible negative social consequences of AI, in particular discriminatory bias, making it a particularly relevant issue if people-centred, rights-based AI is to be implemented. This article aims to examine the challenges of defining, identifying and mitigating discriminatory bias in AI systems from two perspectives: (1) to conduct an ethical and normative review of European Commission documents from the last 8 years (from GDPR to AI Act regulation); and (2) to expose recommendations for key stakeholders, including designers, end-users and public authorities, to minimize/mitigate this risk. The document review was carried out on 21 EU regulatory and ethical guidelines in the field of AI, from which 152 measures were extracted, differentiated between design, governance and organizational measures. It has also been observed that there is no clear conceptual framework on the issue at the European level, showing a clear problem in providing definitions of algorithmic bias and discrimination, but not in assessing their potential negative impact on individuals. Secondly, these gaps may affect the concreteness and detail of the possible mitigation/minimization measures proposed and, subsequently, their application in different contexts. Finally, the last section of this paper presents a brief discussion and conclusions on possible issues related to the implementation of the measures extracted and certain limitations of the study.Frontiers Artif. Intell.450An ethical and normative review of European Commission documents from the last 8 years (from GDPR to AI Act regulation) is conducted to expose recommendations for key stakeholders, including designers, end-users and public authorities, to minimize/mitigate discriminatory bias in AI systems.Frontiers in Artificial Intelligence["Pablo Cerezo-Mart\u00ednez", "Alejandro Nicol\u00e1s-S\u00e1nchez", "F. J. Castro-Toledo"]2024-06-25T00:00:00 abaf14218c9b0e3dd874d2a63ab4a71457928168The use of Artificial Intelligence (AI) is common in education recently. As pre-service teachers’ attitudes and behaviors towards the use of AI potentially influence the learning process and outcomes of their future students, it is necessary to know the impact of AI in pre-service teachers' training. This study aims to explore pre-service art teachers' attitudes toward using the current AI art generator and their perceptions of using it in their future careers. 45 Pre-service art teachers with no AI art generator experience participated and were distributed into eight groups. They used the AI art generator to draw pictures by selecting and revising keywords. They could discuss keywords within their groups. In the first round, the participants need to draw a similar picture as the trainer displayed. In the second round, participants could draw any picture they liked according to their own opinions. After class, participants filled out an online questionnaire concerning their experience of using the AI art generator and their opinions of using it in their future careers, including keywords they used and the times of keyword revision. Half of the groups thought that the AI art generator did not create the pictures they wanted according to the keywords they provided, and the times at which the keywords changed in these groups were much higher than in the other groups. Participants who thought the AI generated the pictures they wanted showed a much higher preference for using AI in the future. The majority of pre-service art teachers were interested in AI art generators. They believed that AI tools could help them prepare course materials, provide inspiration, and benefit their interactions with students in the future. However, they found that it took a lot of time to revise keywords, which represents the necessity to enhance their information literacy and ability to use educational technology. It also places new demands on our current education to adapt to the AI era.Conference on Algebraic Informatics80Pre-service art teachers with no AI art generator experience found that it took a lot of time to revise keywords, which represents the necessity to enhance their information literacy and ability to use educational technology.2024 IEEE Conference on Artificial Intelligence (CAI)["Yan Zhou"]2024-06-25T00:00:00 f782a41a0e4a96eef4ba702ba43a3cee7df1c4cdBackground: The objective of this study was to clinically evaluate the precision of Logy AI's oral screening solution. This innovative module, driven by artificial intelligence, operates seamlessly through WhatsApp and as a standalone smartphone application. It is designed to detect various dental issues such as stains, calculus, and caries, utilizing images captured by a smartphone camera. The accuracy of the module was assessed by comparing its diagnoses with those made by dental professionals. Methods: A prospective clinical study was conducted in Saveetha Dental College, a tertiary care hospital in the southern part of India with 325 patients. Smartphone images taken were sent to the Logy AI oral screening solution which predicted if the patient had any oral hygiene issues like stains, calculus, and caries. Patients were examined by a dentist with visual-tactile and orthopantomogram (OPG) examination and were documented. Both were compared. Results: The accuracy of the Logy AI oral screening solution for the detection of stains, calculus, and caries was comparable with the dentist's diagnosis. The accuracy was 85% for caries, 97% for stains, and 83% for calculus. The sensitivity was 88% for caries, 89% for stains, and 82% for calculus. Conclusions: The Logy AI oral screening module demonstrates potential as an efficient oral screening tool suitable for community-level deployment, particularly in remote regions lacking access to costly dental equipment and healthcare professionals. Its accuracy and efficiency make it well-suited for operation in low-resource settings. Moreover, it holds promise as a valuable home screening tool for individuals seeking to monitor their oral health proactively.International Journal of Advances in Medicine00The Logy AI oral screening module demonstrates potential as an efficient oral screening tool suitable for community-level deployment, particularly in remote regions lacking access to costly dental equipment and healthcare professionals.International Journal of Advances in Medicine["Pradeep Kumar R.", "Nivedita Tiwari", "Anand Panchbhai", "L. R. Chellappa", "Sushanthi Suresh", "Indumathy Pandiyan"]2024-06-25T00:00:00 de02505ac97f636b984b62534b6b8c68db2cf0c7Искусственный интеллект является глобальным трендом для всех отраслей экономики. В этой статье был проведен анализ терминологии искусственного интеллекта, где было определено, что это технологические решения, использующие данные, знания и алгоритмы для автоматизации человеческой работы. Также была изучена законодательная база и поддержка государства данной отрасли. Основным драйвером развития стала Национальная стратегия и федеральный проект, увеличившие финансирование. Рассмотрено, как развивается отечественный и мировой искусственный интеллект, изучены венчурный рынок и факторы, влияющие на его развитие. Исследование полезно для студентов и аспирантов, интересующихся передовыми технологиями в экономике. Artificial intelligence is a global trend for all sectors of the economy. This article analyzed the terminology of artificial intelligence, defining it as technological solutions that use data, knowledge, and algorithms to automate human work. The legislative framework and state support for this industry were also studied. The regulatory framework and government support for the industry are examined. The main driver of development has been the National Strategy and federal project, which increased funding. The development of domestic and global artificial intelligence, the venture market, and the factors influencing its growth have been explored. The study is useful for students and postgraduates interested in advanced technologies in the economy.Экономика и предпринимательство00Экономика и предпринимательство["\u0418\u0420\u0410\u0413\u0415\u041b\u041e\u0412\u0410\u00a0\u0423.\u0410. \u0418\u0420\u0410\u0413\u0415\u041b\u041e\u0412\u0410\u00a0\u0423.\u0410.", "\u0420. \u0421\u0410\u0419\u041f\u0423\u041b\u0410\u0415\u0412\u0410\u00a0\u041a", "\u0410\u0421\u0410\u0414\u0423\u041b\u0410\u0415\u0412\u0410\u00a0\u0428.\u0420. \u0410\u0421\u0410\u0414\u0423\u041b\u0410\u0415\u0412\u0410\u00a0\u0428.\u0420."]2024-06-25T00:00:00 022f473138b22ea649e11575edaf9103a6afb83fThe article is a detailed summary of the main ideas and conclusions formulated by the authors in the report presented at the 6th International science-to-practice conference “Greater Eurasia: National and Civilizational Aspects of Development and Cooperation”. The proposed theses seem to be relevant in the context of the problems of the development of the system of legal regulation of the existence of artificial intelligence (AI) technologies in Russia and at the international level. It is found out that an integral system of legal regulation of the existence of AI technologies in Russia and in the world is just being formed. The current set of regulatory legal acts related to the existence of AI technologies is not emergent, but so far only summative. There are currently no international acts restricting the development of AI technologies in Russia. At the level of federal legislation, the legal regulation of AI technologies is carried out in an experimental mode – on a limited territory and in a limited time frame. The conclusion is made about the high proportion of strategic planning documents in the bulk of legal acts. It is noted that the state policy in the field of AI has been developed. It objectively exists and is systematically implemented on a long-term basis. A proposal has been formulated to develop a draft federal law “On the Fundamentals of Legal Regulation of the Development of Artificial Intelligence in the Russian Federation”.Science Management: Theory and Practice10It is found out that an integral system of legal regulation of the existence of AI technologies in Russia and in the world is just being formed and a proposal has been formulated to develop a draft federal law on the Fundamentals of Legal Regulation of the Development of Artificial Intelligence in the Russian Federation.Science Management: Theory and Practice["Andrey Slivitsky", "Boris Slivitsky"]2024-06-25T00:00:00 c3af9023c62220bfff74e1f6fef1b7176e4f6c43The article examines the main issues of cognitivism as the basis of artificial intelligence (AI) in a modern philosophical interpretation of these entities. A classification of AI is given according to the level of cognitivism of basic functions. We consider the issues of the evolution of the cognitive capabilities of artificial intelligence. The problems of predictability of the negative impact of AI on society are raised. The article highlights the main cognitive distortions that are possible when using artificial intelligence in research, namely, the illusion of research breadth. The authors provide recommendations for researchers and editors of academic journals regarding a competent use of AI in scientific experiments. This work also raises the issue of trust in the field of cybersecurity of AI systems. The authors consider the hypothesis about the presence of consciousness in chatbots and draw clear conclusions about its absence.Science Management: Theory and Practice30The authors consider the hypothesis about the presence of consciousness in chatbots and draw clear conclusions about its absence, and highlight the main cognitive distortions that are possible when using artificial intelligence in research, namely, the illusion of research breadth.Science Management: Theory and Practice["Vladimir Artamonov", "Elena Artamonova", "Alexandr Milakov"]2024-06-25T00:00:00 e10615f98479cff9b4b0e61139f9116242e709f9Artificial intelligence (AI) implemented on the basis of neural networks is compared to human intelligence because it is capable of replacing humans in performing a number of tasks. However, there are important differences that do not allow putting it on the same level as people. The dependence of the modern version of AI on humans lies not only in its origin, technology for its creation and its material embodiment, but also in the tasks it performs and data available to it for analysis. It operates with a system of concepts, abstractions and connections between them that is created by a human being. AI is in the same Plato’s cave as man himself and is limited by a person’s worldview. It does not realize the boundaries of the unknowable. The second important difference between AI and people is its memory effect which makes it similar to Laplace’s demon that is incapable of independent development. And the third difference is due to the technical design of modern AI employed on a computer device that does not have the flexibility of cognitive structures formed in the human brain.Science Management: Theory and Practice10Science Management: Theory and Practice["Vladimir Rakin"]2024-06-25T00:00:00 3210985a7fc30c18d45085aedf1cb9a456ce9281The increasing development and use of artificial intelligence technologies emphasizes the need to integrate them into the Salesforce platform. As a cloud-based platform, Salesforce enables companies to improve customer relations, simplify operations, and adapt to all business requirements. Therefore, it stands out as one of the first CRM platforms to adopt the capabilities and technologies of artificial intelligence, incorporating them within its products, such as Service cloud, Sales cloud, Marketing cloud, and Commerce cloud. This paper uses a systematic research and literature review to identify ways to integrate artificial intelligence into Salesforce. The results of this study will contribute to understanding the current situation in this developing field. In collaboration with artificial intelligence, Salesforce strives to improve various aspects, thus benefiting businesses that use this platform.2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)240This paper uses a systematic research and literature review to identify ways to integrate artificial intelligence into Salesforce, one of the first CRM platforms to adopt the capabilities and technologies of artificial intelligence.2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)["Andjela Todoric", "Teodora Vu\u010dkovi\u0107", "Rog\u00e9rio Dionisio", "D. Daki\u0107", "Darko Stefanovi\u0107"]2024-06-25T00:00:00 dd1dd4d66607509ba61fa1aa714d18283be1aac7Artificial Intelligence (AI) is a capability related to intelligent creatures in the form of artificial robots which are fully controlled by digital computers. The presence of Artificial Intelligence (AI) certainly changes the structure of human life which affects the effectiveness of human performance such as in the field of entrepreneurship. The main scope in artificial intelligence technology is gaining strategic advantage in digital business, human resources, market research, customer relations, accounting and finance, sales, marketing, and others. The investigation of this work is centered to understand more about Artificial Intelligence, opportunities for using Artificial Intelligence for entrepreneurship, perspectives that support AI technology as part of the new model of business, perspectives against the development of artificial intelligence, as well as future opportunities from artificial intelligence technology.TIERS Information Technology Journal220The investigation of this work is centered to understand more about Artificial Intelligence, opportunities for using Artificial Intelligence for entrepreneurship, perspectives that support AI technology as part of the new model of business, perspectives against the development of artificial intelligence, as well as future opportunities from artificial intelligence technology.TIERS Information Technology Journal["Made Ayu Chandra Dewi Harika Putri", "Made Ratih Nurmalasari"]2024-06-25T00:00:00 63ac5c246067adb792c93d3c9ad072c75cc1917aThe article describes the essence of artificial intelligence, its main advantages and technology fea-tures. The prospects for the use of artificial intelligence in the economy are considered, including process automation, improving analytics and resource management, personalizing products and ser-vices, creating new business models and increasing the level of security. An important aspect of the article is also a discussion of ethical and social issues related to the use of artificial intelligence in the economy, and possible ways to solve themScientific Papers Collection of the Angarsk State Technical University00The essence of artificial intelligence, its main advantages and technology advantages, and the prospects for the use of artificial intelligence in the economy are considered.Scientific Papers Collection of the Angarsk State Technical University["Elena Cheklaukova", "Aleksandr Bystrov"]2024-06-25T00:00:00 1d8a7843d872c01b6d65d01e6a9ca3946c1f2088The article discusses the reasons for the similarity of public opinion about artificial intelligence in different countries. At the same time, this opinion differs from the judgments expressed by experts on this topic. These similarities and differences are explained by the conformity between folk theories that stem from individuals due to their limited experience of interacting with artificial intelligence. Risk assessments given by experts do not fully take into account the results and findings of cognitive science that are directly related to artificial intelligence. A number of results obtained in the cognitive sciences are presented. The author highlights some of them that are useful to consider when assessing artificial intelligence.Science Management: Theory and Practice210The article discusses the reasons for the similarity of public opinion about artificial intelligence in different countries, and highlights some of them that are useful to consider when assessing artificial intelligence.Science Management: Theory and Practice["V. Tambovtsev"]2024-06-25T00:00:00 25ffc7b16450772c830ec0b5bbded7e3940b1f94Статья посвящена исследованию влияния технологий искусственного интеллекта на рынок труда. Автором статьи рассматриваются последствия развития искусственного интеллекта для рынка труда, включая изменения в требуемых навыках и знаниях, возможные сдвиги в содержании работы, а также влияние на уровень заработной платы. Обсуждаются вопросы безопасности, связанные с применением искусственного интеллекта. В заключении автором предлагаются рекомендации по подготовке специалистов к новым требованиям рынка труда и обеспечению безопасности при использовании технологий искусственного интеллекта. The article is devoted to the study of the impact of artificial intelligence technologies on the labor market. The author of the article considers the consequences of the development of artificial intelligence for the labor market, including changes in the required skills and knowledge, possible shifts in the content of work, as well as the impact on wages. Safety issues related to the application of artificial intelligence are discussed. In conclusion, the author offers recommendations for preparing specialists for new labor market requirements and ensuring safety when using artificial intelligence technologies.Экономика и предпринимательство00Экономика и предпринимательство["\u041d\u0410\u0422\u0410\u041b\u042c\u0421\u041e\u041d\u00a0\u0410.\u0412. \u041d\u0410\u0422\u0410\u041b\u042c\u0421\u041e\u041d\u00a0\u0410.\u0412."]2024-06-25T00:00:00 8bb7ad508e821eb1f0623b38299ba6e3ff992fbbArtificial intelligence encapsulates a "black box" of undiscovered knowledge, propelling the exploration of Explainable Artificial Intelligence (XAI) in generative data synthesis and deep learning. Focused on unveiling these "black box" areas, pointed into interpretability and validation in synthetic data generation, shedding light on the intricacies of generative processes. XAI techniques illuminate decision-making in complex algorithms, enhancing transparency and fostering a comprehensive understanding of non-linear relationships. Addressing the complexity of explaining deep learning models, this paper proposes an XAI solution for deep synthetic data generation explanation. The model integrates a clustering approach to identify similar training instances, reducing interpretation time for large datasets. Explanations, available in various formats, are tailored to diverse user profiles through integration with language models, generating texts with different technical detail levels. This research contributes to ethically deploying AI, bridging the gap between advanced model complexities and human interpretability in the dynamic landscape of artificial intelligence.Conference on Algebraic Informatics60Addressing the complexity of explaining deep learning models, this paper proposes an XAI solution for deep synthetic data generation explanation that integrates a clustering approach to identify similar training instances, reducing interpretation time for large datasets.2024 IEEE Conference on Artificial Intelligence (CAI)["Lu\u00eds Valina", "B. Teixeira", "Ars\u00e9nio Reis", "Zita A. Vale", "Tiago Pinto"]2024-06-25T00:00:00 2805853e40f057bc1672f61dbac4df4a818a963bThe complexity and rise of data in healthcare means that artificial intelligence will be increasingly applied in that field. Several types of AI are already being used in the area and will flourish as soon as the world evolves. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.At the end the paper also concludes the limitations of AI and also the challenges faced in the making of this paper.International Journal For Multidisciplinary Research190Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period.International Journal For Multidisciplinary Research["Saanvi Kaur Sahni"]2024-06-25T00:00:00 c580e1a60148eb56bbdbbf52fe07cbb4d87f3284The study aimed to demonstrate the reality of administrative performance in Arab preparatory schools within the Green Line in light of the requirements of artificial intelligence. The study relied on the descriptive, correlational approach, and the research population was represented by all principals of Arab preparatory schools within the Green Line. The study sample consisted of (187) school principals. Arabic preparatory schools were selected by a simple random method, and a questionnaire was prepared to collect data from the study members. The results of the study showed that the level of administrative performance of the study members was at an average level, and that the degree of availability of artificial intelligence requirements in Arab preparatory schools was at a moderate degree, and it was shown that there is a correlation between the availability of Artificial intelligence requirements and improving the level of administrative performance. The results of the study also showed that there were no statistically significant differences in the responses of the study sample members in the field of administrative performance due to the variable (gender, academic qualification, years of experience), while there were statistically significant differences at the level of significance. (0.05) in the study members’ responses to the field of artificial intelligence requirements is attributed to gender in favor of females, to academic qualification in favor of postgraduate studies, and to the years of experience variable in favor of those with 4 years of experience or less.International Journal of Religion130It was shown that there is a correlation between the availability of Artificial intelligence requirements and improving the level of administrative performance, and there were no statistically significant differences in the responses of the study sample members in the field of administrative performance due to the variable.International Journal of Religion["Najdieh Moussa Habashi"]2024-06-25T00:00:00 e56960462268ac63d0aec05941bc86f18adf51edNatural enzymes are often difficult to meet the needs of application and research in terms of activity, enantiomer selectivity or thermal stability. Therefore, it is an important task of enzyme engineering to explore efficient molecular modification technologies to improve the properties of such enzymes. The molecular modification technologies of enzymes mainly include rational design, directed evolution, and artificial intelligence-assisted design. Directed evolution and rational design are experiment-driven molecular modification approaches of enzymes and have been successfully applied to enzyme engineering. However, due to the huge space sizes of protein sequences and the lack of experimental data, the current modification methods still face major challenges. With the development of next-generation sequencing, high-throughput screening, protein databases, and artificial intelligence (AI), data-driven enzyme engineering is emerging as a promising solution to these challenges. The AI-assisted statistical learning method has been used to establish a model for predicting the sequence/structure-properties of enzymes in a data-driven manner. Excellent mutant enzymes can be selected according to the prediction results, which greatly improve the efficiency of molecular modification. Considering the application requirements of molecular modification of enzymes, this paper reviews the data acquisition methods and application examples of AI-assisted molecular modification of enzymes, with focuses on the convolutional neural network method for predicting protein thermostability, aiming to provide reference for researchers in this field.Sheng wu gong cheng xue bao = Chinese journal of biotechnology00This paper reviews the data acquisition methods and application examples of AI-assisted molecular modification of enzymes, with focuses on the convolutional neural network method for predicting protein thermostability, aiming to provide reference for researchers in this field.Sheng wu gong cheng xue bao = Chinese journal of biotechnology["Pei Xu", "Weihua Wang", "Hongwei Ning", "Ruifen Cao", "Sheng Liu", "Peifeng Fan", "Xiaoping Song"]2024-06-25T00:00:00 8b9485d4fd6f086071691076935604cb0f64efbfWater quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality is crucial for ensuring sustainable water resource management, which supports agricultural, industrial, and domestic needs while safeguarding ecosystems. The models were assessed using important statistical variables, a dataset comprising six relevant parameters, and water use records. The database contained electrical conductivity, pH, dissolved oxygen, nitrate, phosphates, suspended solids, water temperature, water consumption records, and an appropriate date. Furthermore, Random Forest, K-nearest Neighbor, and Support Vector Machine are the three machine learning methodologies employed for water quality categorization forecasting. Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index. The water quality classification produced by the Random Forest forecast had the highest accuracy of 99.89%. The GRU model fared better than the LSTM and BiLSTM models with values of R2 and NSE, which are 0.81 and 0.720 for water consumption and 0.78 and 0.759 for water quality index, in the prediction results. The outcomes showed how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand. It is worth noting that accurate predictions of water quantity and quality are essential for sustainable resource management, public health protection, and ecological preservation. Such promising research could significantly enhance urban water demand planning and water resource management.Water671Three recurrent neural networks, namely long short-term memory, bidirectional long short-term memory, and the gated recurrent unit, have been specifically designed to predict urban water consumption and water quality index and how reliable Random Forest was in classifying water quality forecasts and how reliable gated recurrent units were in predicting water quality indices and water demand.Water["Alain Niyongabo", "Danrong Zhang", "Yiqing Guan", "Ziyuan Wang", "Muhammad Imran", "Bertrand Nicayenzi", "Alemayehu Kabeta Guyasa", "Pascal Hatungimana"]2024-06-25T00:00:00 134dbd9519ec2d4eda0acacfaef5d4a7565840dbSimulation in Healthcare: The Journal of the Society for Simulation in Healthcare31Simulation in healthcare : journal of the Society for Simulation in Healthcare["Monica Bhutiani", "Douglas L Hester", "Hannah J Lonsdale"]2024-06-25T00:00:00 f3fa0fab2bebe1178829d297d19e4fa1f162ec6eBackground Large Hemispheric Infarction (LHI) poses significant mortality and morbidity risks, necessitating predictive models for in-hospital mortality. Previous studies have explored LHI progression to malignant cerebral edema (MCE) but have not comprehensively addressed in-hospital mortality risk, especially in non-decompressive hemicraniectomy (DHC) patients. Methods Demographic, clinical, risk factor, and laboratory data were gathered. The population was randomly divided into Development and Validation Groups at a 3:1 ratio, with no statistically significant differences observed. Variable selection utilized the Bonferroni-corrected Boruta technique (p < 0.01). Logistic Regression retained essential variables, leading to the development of a nomogram. ROC and DCA curves were generated, and calibration was conducted based on the Validation Group. Results This study included 314 patients with acute anterior-circulating LHI, with 29.6% in the Death group (n = 93). Significant variables, including Glasgow Coma Score, Collateral Score, NLR, Ventilation, Non-MCA territorial involvement, and Midline Shift, were identified through the Boruta algorithm. The final Logistic Regression model led to a nomogram creation, exhibiting excellent discriminative capacity. Calibration curves in the Validation Group showed a high degree of conformity with actual observations. DCA curve analysis indicated substantial clinical net benefit within the 5 to 85% threshold range. Conclusion We have utilized NIHSS score, Collateral Score, NLR, mechanical ventilation, non-MCA territorial involvement, and midline shift to develop a highly accurate, user-friendly nomogram for predicting in-hospital mortality in LHI patients. This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations.Frontiers in Neurology361This nomogram serves as valuable reference material for future studies on LHI patient prognosis and mortality prevention, while addressing previous research limitations.Frontiers in Neurology["Jian Ding", "Xiaoming Ma", "Wendie Huang", "Chunxian Yue", "Geman Xu", "Yumei Wang", "Shiying Sheng", "Meng Liu", "Yi Ren"]2024-06-25T00:00:00 deba2cda67195ff4e76a50f9c8c1cb86be93eb8dInnovations in Business and Strategic Management01Innovations in Business and Strategic Management["N. L. Rane"]2024-06-25T00:00:00 1d85fc82e82b12fddad39de622d6dc1c338a7a43Abstract Aims Aortic elongation can result from age-related changes, congenital factors, aneurysms, or conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases and severe complications like aortic aneurysms and dissection. We assess qualitatively and quantitatively explainable methods to understand the decisions of a deep learning model for detecting aortic elongation using chest X-ray (CXR) images. Methods and results In this study, we evaluated the performance of deep learning models (DenseNet and EfficientNet) for detecting aortic elongation using transfer learning and fine-tuning techniques with CXR images as input. EfficientNet achieved higher accuracy (86.7% ± 2.1), precision (82.7% ± 2.7), specificity (89.4% ± 1.7), F1 score (82.5% ± 2.9), and area under the receiver operating characteristic (92.7% ± 0.6) but lower sensitivity (82.3% ± 3.2) compared with DenseNet. To gain insights into the decision-making process of these models, we employed gradient-weighted class activation mapping and local interpretable model-agnostic explanations explainability methods, which enabled us to identify the expected location of aortic elongation in CXR images. Additionally, we used the pixel-flipping method to quantitatively assess the model interpretations, providing valuable insights into model behaviour. Conclusion Our study presents a comprehensive strategy for analysing CXR images by integrating aortic elongation detection models with explainable artificial intelligence techniques. By enhancing the interpretability and understanding of the models’ decisions, this approach holds promise for aiding clinicians in timely and accurate diagnosis, potentially improving patient outcomes in clinical practice.European Heart Journal - Digital Health01This study presents a comprehensive strategy for analysing CXR images by integrating aortic elongation detection models with explainable artificial intelligence techniques, and holds promise for aiding clinicians in timely and accurate diagnosis, potentially improving patient outcomes in clinical practice.European Heart Journal. Digital Health["Estela Ribeiro", "D. C\u00e1rdenas", "F. M. Dias", "J. Krieger", "Marco A. Gutierrez"]2024-06-25T00:00:00 55fb91b888150e7d7d0c5e38ebb2a62cd3a681fdPurpose The study aims to identify the practical borders of AI legal personality and accountability in human-centric services. Design/methodology/approach Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland. Findings The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution. Research limitations/implications The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts. Practical implications The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models. Social implications The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems. Originality/value This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries. Meditari Accountancy Research1052The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.Meditari Accountancy Research["Piotr Staszkiewicz", "Jaros\u0142aw Horobiowski", "A. Szel\u0105gowska", "Agnieszka Maryla Strzelecka"]2024-06-25T00:00:00 ddf45b86a9445142d8386d7dab0fbecd3b49dd8cBackground: Concurrent prescriptions of various medications may lead to unfavorable and unanticipated potential drug-drug interactions. Hence, the elimination of drug-drug interactions is a key aspect of delivering a coherent treatment regime. In response to this concern, HealthPlix, one of India's largest AI-powered electronic medical record providers, introduced a drug-drug interaction nudge feature in June 2022, providing a proactive solution for physicians to address potential interactions between incompatible drugs. This study aimed to elucidate the role of electronic medical records in identifying and managing drug interactions and the advantages of interaction nudges for doctors in prescribing appropriate medications. Methods: An observational retrospective study was conducted using data obtained from HealthPlix, containing two or more drugs, written for patients older than 18 years. Results: In an average of 1.9 million patient visits analyzed, the interaction visits were observed to be 1.2 million. An average of 185,745 interactions were observed during the study period. For all observed interactions, an average of 72,383 molecules were removed. These results provide insights into the efficiency of HealthPlix in abrogating interactions and illustrate the tangible benefits of nudges in modifying prescription practices. Conclusions: The above results illustrate the effectiveness of drug-drug interaction nudges as a clinical decision support tool integrated into HealthPlix, marking a significant advancement in Indian healthcare. This unique feature contributes to reducing the frequency of potent drug interactions, showcasing its potential to enhance patient safety and improve the quality of healthcare delivery.International Journal of Basic &amp; Clinical Pharmacology00The effectiveness of drug-drug interaction nudges as a clinical decision support tool integrated into HealthPlix contributes to reducing the frequency of potent drug interactions, showcasing its potential to enhance patient safety and improve the quality of healthcare delivery.International Journal of Basic &amp; Clinical Pharmacology["G. Jayanthy", "Arnab Majumdar", "Supriya Kaloo", "Snehal Shah"]2024-06-25T00:00:00 1a83b4a754d156648c941f4d46fd347df611bc93One of the most disaster-prone countries globally, experiences frequent natural calamities, including typhoons, earthquakes, and floods is the Philippines. This study explores the role of AI in enhancing disaster prediction, risk management, and mitigation in the Philippines. Using a qualitative research approach, semi-structured interviews were conducted between June 2023 and March 2024 with key stakeholders, including disaster management officials, meteorologists, and researchers. The findings highlight how AI technologies, particularly machine learning and neural networks, have significantly improved disaster forecasts by processing extensive datasets from meteorological, seismic, and geographical sources. AI-driven models are enhancing the accuracy of predictions for typhoons, earthquakes, and flood risks, contributing to more effective early warning systems and timely evacuation protocols. Despite these advancements, challenges remain, including limitations in infrastructure, budget constraints, and data quality, which hinder the full adoption of AI in disaster risk management (DRM). Nevertheless, the study identifies substantial opportunities for further development, emphasizing international collaboration and policy support to promote AI integration in DRM. The findings suggest that AI holds immense potential to revolutionize disaster response strategies in the Philippines, and further research is needed to address technical barriers and enhance AI’s role in building resilient communities.International Journal of Artificial Intelligence500It is suggested that AI holds immense potential to revolutionize disaster response strategies in the Philippines, and further research is needed to address technical barriers and enhance AI’s role in building resilient communities.International Journal of Artificial Intelligence["Rommel Baltazar", "Bacabac Florencio", "Aguda Vicente", "Phillip Belizario"]2024-06-25T00:00:00 6d5f05765b8e4284061debff70bf21eb093ca903This study focuses on identifying texts related to maritime contexts using an advanced Large Language Model (LLM) and cost-sensitive approach for handling data imbalances. Firstly, a comprehensive dataset specifically for maritime-context queries is collected and augmented. Secondly, the dynamic contextual representations of input query considering the context of each word are obtained by a pre-trained LLM which incorporates Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Neural Network (CNN). Thirdly, a Multi-Layer Perceptron (MLP) is constructed as the classifier to fine-tune the whole network on the newly collected dataset. Finally, the Focal loss is introduced for more effective parameter optimization to tackle the challenge of data imbalance between positive and negative samples, Extensive experiments have been conducted and the following promising results have been obtained: 1) The proposed approach achieves an impressive 99.97% F1 score in recognizing maritime-context texts; 2) The ConvBERT model, an enhancement over the original BERT, demonstrates superior performance in text representation while being more computationally efficient; 3) The Focal loss method outperforms other cost-sensitive learning strategies like class weighting and oversampling techniques; and 4) the proposed method surpasses other deep learning and BERT-based methods in text classification tasks.Conference on Algebraic Informatics320The proposed method surpasses other deep learning and BERT-based methods in text classification tasks and outperforms other cost-sensitive learning strategies like class weighting and oversampling techniques.2024 IEEE Conference on Artificial Intelligence (CAI)["Xiaocai Zhang", "Hur Lim", "Xiuju Fu", "Ke Wang", "Zhe Xiao", "Zheng Qin"]2024-06-25T00:00:00 b7d52adc6a8c16f51580e5794b3739f9de9556a4Annals of Operations Research250Annals of Operations Research["Yi Liu", "Nisreen Innab", "K. S. Savita", "Wejdan Deebani", "Meshal Shutaywi"]2024-06-25T00:00:00 4b56d5f925338576c82d6621f6a74bae9f1002deThis paper examines the ethics and current situation of facial recognition from the perspective of racial and gender equality. Facial recognition is a fairly new image analytics technology. It consists of face detection, which dissociates the face from the background, and facial recognition, which compares unique features of an individual’s face to a database and biometrics of sample faces. The primary ethical concern with this technology is biased teaching. Statistics show that a darker-skinned female is significantly less likely to be detected and recognized with the correct identity than a lighter-skinned male. Many works on this issue, some mentioned and analyzed in this paper, underscore the morally and ethically problematic use of this technology in a wide range of important fields considering that facial recognition technology is still in its infancy, and the consequences of misidentification can be severe. Next Frontier For Life Sciences and AI00The ethics and current situation of facial recognition from the perspective of racial and gender equality is examined, showing that a darker-skinned female is significantly less likely to be detected and recognized with the correct identity than a lighter-skinned male.Next Frontier For Life Sciences and AI["Ozan Sezen"]2024-06-25T00:00:00 e67a47f424a4296245b83b269895102c1bf95697Автор анализирует современное состояние регулирования категории «интеллектуальное управление» в экономико-правовой сфере, возникающей как при умышленных, так и по неосторожности. Проведенный в статье анализ показывает, что правоохранителям и законодателям следует пересмотреть подход к определению вины в случае использования систем искусственного интеллекта для совершения умышленных преступлений. Поскольку система искусственного интеллекта в некотором смысле обладает собственными развитыми знаниями и волей, суды не могут опираться на традиционную концепцию вины в умышленных преступлениях, где умысел четко определяется в соответствии с действиями преступника. Регулирование ответственности роботов за преступления особенно проблематично. The author analyzes the current state of regulation of the category “intelligent control” in the economic and legal sphere, which arises in both intentional and negligent crimes. The analysis carried out in the article shows that law enforcement officers and legislators should reconsider their approach to determining guilt in the case of using artificial intelligence systems to commit intentional crimes. Since the artificial intelligence system in some sense has its own advanced knowledge and will, courts cannot rely on the traditional concept of guilt in intentional crimes, where intent is clearly defined in accordance with the actions of the offender. Regulating robot liability for crimes is particularly problematic.Экономика и предпринимательство00Экономика и предпринимательство["\u0412\u042f\u0422\u041a\u0418\u041d\u00a0\u041c.\u0410. \u0412\u042f\u0422\u041a\u0418\u041d\u00a0\u041c.\u0410."]2024-06-25T00:00:00 15c1065f69af8339121c57d15fae3810386f6695The Review of Socionetwork Strategies270Rev. Socionetwork Strateg.["Chia-Sung Yen", "Shuang Yang"]2024-06-25T00:00:00 df77c553fbb5a4b912fa3b6d7757aa8a7261d986В статье рассматриваются перспективы применения искусственного интеллекта (ИИ) в строительной отрасли. Проведен анализ существующих исследований и практических примеров использования ИИ в различных аспектах строительства, таких как проектирование, управление проектами, контроль качества, безопасность на строительных площадках и управление жизненным циклом объектов недвижимости. Выявлены потенциальные преимущества внедрения ИИ, включая повышение эффективности строительных процессов, оптимизацию затрат и сроков реализации проектов, улучшение качества строительных работ и обеспечение безопасности труда. Также рассмотрены проблемы и ограничения, связанные с применением ИИ в строительстве, такие как необходимость адаптации технологий к специфике отрасли, потребность в квалифицированных кадрах и вопросы регулирования. В заключении обозначены перспективы дальнейшего развития и внедрения ИИ в строительной отрасли России. The article discusses the prospects for the use of artificial intelligence (AI) in the construction industry. The analysis of existing research and practical examples of the use of AI in various aspects of construction, such as design, project management, quality control, safety on construction sites and life cycle management of real estate objects. The potential benefits of implementing AI have been identified, including improving the efficiency of construction processes, optimizing costs and project deadlines, improving the quality of construction work and ensuring occupational safety. The problems and limitations associated with the use of AI in construction are also considered, such as the need to adapt technologies to the specifics of the industry, the need for qualified personnel and regulatory issues. In conclusion, the prospects for further development and implementation of AI in the Russian construction industry are outlined.Экономика и предпринимательство00Экономика и предпринимательство["\u0413\u0410\u0421\u0410\u041d\u041e\u0412\u0410\u00a0\u0410.\u0423. \u0413\u0410\u0421\u0410\u041d\u041e\u0412\u0410\u00a0\u0410.\u0423.", "\u041b\u0418\u041d\u041d\u0418\u041a\u00a0\u0421.\u0412. \u041b\u0418\u041d\u041d\u0418\u041a\u00a0\u0421.\u0412.", "\u0421\u0422\u0415\u041f\u0423\u0420\u0410\u00a0\u0421.\u041c. \u0421\u0422\u0415\u041f\u0423\u0420\u0410\u00a0\u0421.\u041c.", "\u041a\u041e\u0412\u0422\u0423\u041d\u0415\u041d\u041a\u041e\u00a0\u041c.\u0413. \u041a\u041e\u0412\u0422\u0423\u041d\u0415\u041d\u041a\u041e\u00a0\u041c.\u0413."]2024-06-25T00:00:00 a83b834f7b89d1374b9638466059df0cca341e87В статье рассматривается применение искусственного интеллекта (ИИ) для оптимизации коммуникаций между энергетическими компаниями и потребителями. Актуальность темы обусловлена растущим спросом на эффективные и персонализированные подходы к обслуживанию в секторе энергетики, где важно не только обеспечить надежное и бесперебойное энергоснабжение, но и способствовать рациональному потреблению ресурсов. Исследование включает обзор теоретических основ ИИ, анализ существующих проблем в коммуникациях между энергетическими компаниями и потребителями, и предлагает модель на базе ИИ для улучшения этих взаимодействий. Описывается методология разработки и внедрения системы, а также анализируются полученные результаты экспериментальной проверки. Статья заканчивается обзором эффективности внедрения системы, выводами по исследованию, предложениями по дальнейшему развитию и внедрению подобных систем, а также ограничениями исследования и перспективами будущих работ. The article discusses the use of artificial intelligence (AI) to optimize communications between energy companies and consumers. The relevance of the topic is due to the growing demand for effective and personalized service approaches in the energy sector, where it is important not only to ensure reliable and uninterrupted energy supply, but also to promote rational consumption of resources. The study includes an overview of the theoretical foundations and analysis of existing problems in communications between energy companies and consumers, and offers an AI-based model for improvements to these interactions. The methodology of the development and implementation of the system is described, as well as the results of the experimental verification are analyzed. The article ends with an overview of the effectiveness of the implementation of the system, the conclusions of the study, proposals for further development and implementation of such systems, as well as the limitations of the study and the prospects for future work.Экономика и предпринимательство00Экономика и предпринимательство["\u041d\u041e\u0412\u041e\u0421\u0415\u041b\u041e\u0412\u00a0\u041d.\u0414. \u041d\u041e\u0412\u041e\u0421\u0415\u041b\u041e\u0412\u00a0\u041d.\u0414.", "\u0425\u0410\u041c\u0418\u0422\u041e\u0412\u00a0\u0420.\u041c. \u0425\u0410\u041c\u0418\u0422\u041e\u0412\u00a0\u0420.\u041c."]2024-06-25T00:00:00 f553bb09f873f880e3b4d470533fb584b7c9e023When making treatment decisions for invasive cardiovascular procedures in older persons, physicians often face a myriad of complex scenarios, such as frailty, cognitive impairment, and multimorbidity. Accounting for these characteristics in real-world practice is challenging in aortic valve replacement for aortic stenosis (AS) as they impact individualized decisions in achieving meaningful postprocedural outcomes without excessive risk. Based on these characteristics, 864 unique scenarios were created that formed the original dataset, which was further split into 70% training and 30% testing datasets. More controversial clinical scenarios were further tuned based on responses from ten cardiologists and processed using multilayered neural network sequential features analysis and deep learning methods. Contrary to guidelines, symptoms and left ventricular function ranked low in physician importance. In contrast, aging-related functional features, including cognition, ambulation, and frailty scores, ranked high with good overall model accuracy (Shapley 0.811, TabNet 0.938). Feature optimization using the top three features showed good model accuracy (Shapley 0.811, TabNet 0.881). Here, AS illustrates a use case scenario in artificial intelligence (AI) that could be applied to complex clinical decision-making and has excellent potential for handling diverse clinical problems and augmenting physician treatment decision-making.Conference on Algebraic Informatics100AS illustrates a use case scenario in artificial intelligence (AI) that could be applied to complex clinical decision-making and has excellent potential for handling diverse clinical problems and augmenting physician treatment decision-making.2024 IEEE Conference on Artificial Intelligence (CAI)["J. Wong", "Glades Tan", "Xinliu Zhong", "Kay Woon Ho", "Vincent Wei Jun Sim", "Si Yong Yeo", "Angela S. Koh"]2024-06-25T00:00:00 7733822ac27f4061599a58865f24088be3cff7f9С пятидесятых годов прошлого века система качества стала занимать особое место в производственных и управленческих процессах различных компаний. Система качества продолжила свое развитие и выкристаллизовалась в появлении международных стандартов в области управления качеством. В настоящее время существует острая необходимость пересмотреть многие концепции в области управления качеством в соответствии с новыми результатами научнотехнического прогресса, в свете автоматизации процессов производства и появления возможности применения искусственного интеллекта с его огромным потенциалом. Since the fifties of the last century, the quality system began to occupy a special place in the production and management processes of various companies. The quality system continued its development and crystallised in the emergence of international standards in the field of quality management. Nowadays there is an urgent need to revise many concepts in the field of quality management in accordance with the new results of scientific and technological progress, in light of the automation of production processes and the emergence of the possibility of using artificial intelligence with its enormous potential.Экономика и предпринимательство00Экономика и предпринимательство["\u0414\u0416\u0410\u041d\u041e\u00a0\u0414. \u0414\u0416\u0410\u041d\u041e\u00a0\u0414.", "\u041e\u0421\u0418\u041f\u041e\u0412\u00a0\u0414.\u0412. \u041e\u0421\u0418\u041f\u041e\u0412\u00a0\u0414.\u0412."]2024-06-25T00:00:00 5a51fef2659f68cc3ed20c1e80c2eaec3015ba91Целью данной статьи является разработка концептуальной основы для определения ценности новой технологии ИИ генеративного искусственного интеллекта (ИИ) феномена современной стратегии развития искусственно-интеллектуальной экономики (ИИЭ). Мы предлагаем 1. переосмысление основных бизнес-процессов для решения конкретных бизнес-задач, приводящих к снижению затрат на создание контента и увеличению доходов от использования генеративного ИИ, охватывающего большинство бизнес-функций; 2) оценку генеративного ИИ, которая поможет руководителям лучше понять быстро развивающееся его состояние и доступные возможности участия компаний в повышение их организационной эффективности в зависимости от новой технологии, стоимости и требований к модели генеративного ИИ. Наконец, актуализируем внимание бизнеслидеров на жизненно важную роль генеративного ИИ в обеспечении вхождения организаций в многообещающий мир технологического уклада с учетом верховенства генеративного искусственного интеллекта. The purpose of this article is to develop a conceptual framework for determining the value of a new AI technology generative artificial intelligence (AI) a phenomenon of the modern strategy for the development of an artificially intelligent economy (AIE). We propose 1) rethinking core business processes to solve specific business problems, leading to lower content creation costs and increased revenue from the use of generative AI, covering most business functions; 2) an assessment of generative AI that will help leaders better understand its rapidly evolving state and the available opportunities for companies to participate in improving their organizational effectiveness depending on the new technology, cost and requirements for the generative AI model. Finally, we bring to the attention of business leaders the vital role of generative AI in ensuring that organizations enter the promising world of technological order, taking into account the supremacy of generative artificial intelligence.Экономика и предпринимательство00Экономика и предпринимательство["\u041d\u041e\u0421\u041e\u0412\u0410\u00a0\u0421.\u0421. \u041d\u041e\u0421\u041e\u0412\u0410\u00a0\u0421.\u0421.", "\u041d\u041e\u0420\u041a\u0418\u041d\u0410\u00a0\u0410.\u041d. \u041d\u041e\u0420\u041a\u0418\u041d\u0410\u00a0\u0410.\u041d.", "\u041c\u041e\u0420\u041e\u0417\u041e\u0412\u00a0\u041d.\u0412. \u041c\u041e\u0420\u041e\u0417\u041e\u0412\u00a0\u041d.\u0412."]2024-06-25T00:00:00 87dd9f8c930d5125606fe93aafbc325132d46233Abstract not available KYAMC Journal Volume: 14, No: 04, January 2024: 188-189.  KYAMC Journal00KYAMC Journal["Quazi Manjurul Haque"]2024-06-25T00:00:00 20596896cb697ea9e09ead714c99b367598d842dA doença cárie é uma condição comum e crônica, que resulta na perda de minerais dos tecidos dentais. O diagnóstico das lesões de cárie, especialmente as interproximais, é desafiador, sendo o exame visual-tátil o método mais utilizado, porém apresenta suas limitações. Exames complementares, como as radiografias bitewing são recomendadas para melhor detecção e diagnóstico da lesão. Nos últimos anos, a inteligência artificial (IA) tem sido aplicada em diversos campos, inclusive na odontologia para facilitar o diagnóstico precoce de lesões de cárie, permitindo intervenções minimamente invasivas e mais eficazes. Este estudo fez uma revisão de literatura sobre o emprego da IA no diagnóstico de lesão de cárie, destacando a necessidade de avanços nessa área para promoção de uma prática mais conservadora. A busca pelos artigos foi realizada nos bancos de dados PubMed e ScienceDirect, limitados ao idioma inglês e publicados entre os anos de 2020 a 2024. Foram utilizados os termos “artificial intelligence”, “dentistry”, “dental caries” e “diagnosis oral”. Nos trabalhos selecionados, uma diversidade de modelos de IA foram utilizados e todos eles demonstraram uma maior acurácia na detecção de lesões de cárie em comparação aos cirurgiões-dentistas, principalmente em lesões em estágio inicial em esmalte, independente do modelo e da classificação utilizada. A IA é uma ferramenta promissora, onde o profissional poderá com seu auxílio diagnosticar lesões de cárie precocemente, propondo um tratamento mais conservador ao pacienteRevista Foco150REVISTA FOCO["Mariana Sati Cantalejo Tsutsumi", "Luiza Iaizzo Magalh\u00e3es", "Fabiano de Oliveira Ara\u00fajo", "Paulo Augusto Pires Milani", "F. Marson", "G. E. S. Reis", "Y. M. Pupo"]2024-06-25T00:00:00 5708e6c14e3db8c0891c20d5ba990c81f7af2dd0The aim of this position paper is to identify a specific focus and the major challenges related to the human-centered artificial intelligence (HCAI) approach in the field of Industry 5.0 and the circular economy. A first step towards the opening of a line of research is necessary to aggregate multidisciplinary and interdisciplinary skills to promote and take into consideration the different aspects related to this topic, from the more technical and engineering aspects to the social ones and the repercussions in terms of sustainability. The proposal and vision of this preliminary work is to identify and discuss a suitable field for such interaction. This field has been identified, specifically, within additive manufacturing (AM) in the context of Industry 5.0. Additive manufacturing (AM), is a disruptive opportunity for more sustainable production systems that can be better optimized with AI, becoming an ideal platform for interconnection between different levels of application and integration of HCAI concepts, and at the same time able to prove them. In this context, two prospective areas with a high application impact of HCAI are those of AM-oriented supply chain and product customization in the AM field, enabled by a plethora of recently emerging technologies such as the internet of things, cloud and edge computing, and next-generation networks (5G). The paper concludes with the challenges HCAI poses to public policymakers, who face significant policy challenges in regulating artificial intelligence, and addressing the socioeconomic and technological impacts. Decision-makers are required to address these challenges by adopting some tentative policy recommendations.Sustainability014The paper concludes with the challenges HCAI poses to public policymakers, who face significant policy challenges in regulating artificial intelligence, and addressing the socioeconomic and technological impacts.Sustainability["Barbara Martini", "Denise Bellisario", "Paola Coletti"]2024-06-26T00:00:00 f863d3742a3e02169b734629ae7ab9f3b49027d4Artificial Intelligence Review527This paper aims to provide a comprehensive review of the advancements in artificial intelligence (AI), deep learning (DL), and machine learning (ML) technologies that have been used to facilitate communication for individuals who are deaf and mute (D–M).Artif. Intell. Rev.["Hanaa ZainEldin", "Samah A. Gamel", "Fatma M. Talaat", "Mansourah Aljohani", "Nadiah A. Baghdadi", "Amer Malki", "Mahmoud Badawy", "Mostafa A. Elhosseini"]2024-06-26T00:00:00 b82956474d73d43f27542daa9537bbbe3d3b56a8The recent rise in artificial intelligence systems, such as ChatGPT, poses a fundamental problem for the educational sector. In universities and schools, many forms of assessment, such as coursework, are completed without invigilation. Therefore, students could hand in work as their own which is in fact completed by AI. Since the COVID pandemic, the sector has additionally accelerated its reliance on unsupervised ‘take home exams’. If students cheat using AI and this is undetected, the integrity of the way in which students are assessed is threatened. We report a rigorous, blind study in which we injected 100% AI written submissions into the examinations system in five undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university. We found that 94% of our AI submissions were undetected. The grades awarded to our AI submissions were on average half a grade boundary higher than that achieved by real students. Across modules there was an 83.4% chance that the AI submissions on a module would outperform a random selection of the same number of real student submissions.PLoS ONE368A rigorous, blind study in which 100% AI written submissions into the examinations system in five undergraduate modules, across all years of study, for a BSc degree in Psychology at a reputable UK university found that 94% of AI submissions were undetected.PLOS ONE["Peter Scarfe", "Kelly Watcham", "A. Clarke", "E. Roesch"]2024-06-26T00:00:00 0183f7f7c84a8a967fff3dc7a51a3faf1921caeaAt present, the integration of technology into education has generated a significant change in the way students access knowledge and develop skills. The availability of digital tools and online platforms has democratised access to information, allowing students to learn from anywhere and at any time. This article focuses on how the combination of artificial intelligence digital tools, such as ChatGPT, with one-to-one tutoring affects the development of soft skills in higher education students. A total of 182 university students participated in the study, divided into two groups. One group was required to construct an academic topic autonomously using only ChatGPT. The other group used the ChatGPT tool in conjunction with personal tutoring, with the teacher present to expand knowledge and enrich learning. The findings suggest that a combination of technology and meaningful human interactions is necessary to optimise the educational experience. While digital tools can be beneficial in accessing knowledge and developing skills, it is essential to acknowledge the value of individual connections with teachers in fostering authentic and deep learning. Furthermore, the study considers the potential necessity to modify and refocus both teaching participation and the student assessment system. This would entail a shift away from an emphasis on the memorisation of theoretical knowledge and towards the training and development of soft skills, competences, values and social implications.Education sciences06The findings suggest that a combination of technology and meaningful human interactions is necessary to optimise the educational experience and suggest a shift away from an emphasis on the memorisation of theoretical knowledge and towards the training and development of soft skills, competences, values and social implications.Education Sciences["Pablo Gonz\u00e1lez-Rico", "Mireia Lluch Sintes"]2024-06-26T00:00:00 322f047bf10311b64768df1e921d754eb3b88d4dArtificial intelligence (AI) is rapidly advancing, aiming to mimic human cognitive abilities, and is addressing complex medical challenges in the field of biological science. Over the past decade, AI has experienced exponential growth and proven its effectiveness in processing massive datasets and optimizing decision-making. The main content of this review paper emphasizes the active utilization of AI in the field of stem cells. Stem cell therapies use diverse stem cells for drug development, disease modeling, and medical treatment research. However, cultivating and differentiating stem cells, along with demonstrating cell efficacy, require significant time and labor. In this review paper, convolutional neural networks (CNNs) are widely used to overcome these limitations by analyzing stem cell images, predicting cell types and differentiation efficiency, and enhancing therapeutic outcomes. In the biomedical sciences field, AI algorithms are used to automatically screen large compound databases, identify potential molecular structures and characteristics, and evaluate the efficacy and safety of candidate drugs for specific diseases. Also, AI aids in predicting disease occurrence by analyzing patients’ genetic data, medical images, and physiological signals, facilitating early diagnosis. The stem cell field also actively utilizes AI. Artificial intelligence has the potential to make significant advances in disease risk prediction, diagnosis, prognosis, and treatment and to reshape the future of healthcare. This review summarizes the applications and advancements of AI technology in fields such as drug development, regenerative medicine, and stem cell research.Technologies1494Technologies["Minjae Kim", "S. Hong"]2024-06-26T00:00:00 1596739fa96987236444a9696732bad2a2fccf83Background: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patients with major trauma are limited. This study aimed to develop and validate an artificial intelligence (AI) platform for predicting the risk of sepsis among patients with major trauma. Patients and methods: This study involved 961 patients, with a prospective analysis of data from 244 patients with major trauma at our hospital and a retrospective analysis of data from 717 patients extracted from a database in the United States. The patients from our hospital constituted the model development cohort, and the patients from the database constituted the external validation cohort. The patients in the model development cohort were randomly divided into a training cohort and an internal validation cohort at a ratio of 8:2. The machine-learning algorithms used to train models included logistic regression, decision tree, extreme gradient boosting machine (eXGBM), neural network (NN), random forest, and light gradient boosting machine (LightGBM). Results: The incidence of sepsis for the model development cohort was 43.44%. Twelve predictors, including gender, abdominal trauma, open trauma, red blood cell count, heart rate, respiratory rate, injury severity score, sequential organ failure assessment score, Glasgow coma scale, smoking, total protein concentrations, and hematocrit, were used as features in the final model. Internal validation showed that the NN model had the highest area under the curve (AUC) of 0.932 (95% CI: 0.917–0.948), followed by the LightGBM and eXGBM models with AUCs of 0.913 (95% CI: 0.883–0.930) and 0.912 (95% CI: 0.880–0.935), respectively. In the external validation cohort, the eXGBM model (AUC: 0.891, 95% CI: 0.866–0.914) had the highest AUC value, followed by the LightGBM model (AUC: 0.886, 95% CI: 0.860–0.906), and the AUC value of the NN model was only 0.787 (95% CI: 0.751–0.829). Considering the predictive performance for both the internal and external validation cohorts, the LightGBM model had the highest score of 82, followed by the eXGBM (81) and NN (76) models. Thus, the LightGBM has emerged as the optimal model, and it was deployed online as an AI application. Conclusions: This study develops and validates an AI application to effectively assess the susceptibility of patients with major trauma to sepsis. The AI application equips healthcare professionals with a valuable tool to promptly identify individuals at high risk of developing sepsis. This will facilitate clinical decision-making and enable early intervention.International Journal of Surgery443The LightGBM has emerged as the optimal model, and it was deployed online as an AI application to effectively assess the susceptibility of patients with major trauma to sepsis.International Journal of Surgery (London, England)["Baisheng Sun", "Mingxing Lei", "Li Wang", "Xiaoli Wang", "Xiaoming Li", "Zhi Mao", "Hongjun Kang", "Hui Liu", "Shiying Sun", "Feihu Zhou"]2024-06-26T00:00:00 935418b8ff035791e9cd2b1812794b29a755c529This research explores the strengthening role of artificial intelligence (AI) technology in supporting the duties and responsibilities of court judges in Indonesia. In the digital era, the integration of AI in the judicial system is necessary to improve efficiency, accuracy, and fairness in legal decision-making. This research uses a qualitative method with literature studies, in-depth interviews, and case analysis from other countries that have implemented AI in their justice systems. The results show that AI can reduce judges' workload, speed up case resolution, and minimize human error and bias. However, there are challenges such as ethical issues, data privacy, and public trust in AI decisions. This research emphasizes the need for a clear regulatory framework and specialized training for judges to ensure the effective and responsible use of AI. In addition, the importance of collaboration between governments, legal institutions, and technology developers to create AI solutions that comply with the principles of fairness and transparency was expressed. In conclusion, although AI has great potential to strengthen the role of judges in Indonesia, its implementation must be done carefully to maintain the principles of fairness.Edunity Kajian Ilmu Sosial dan Pendidikan101The results show that AI can reduce judges' workload, speed up case resolution, and minimize human error and bias, but there are challenges such as ethical issues, data privacy, and public trust in AI decisions.Edunity Kajian Ilmu Sosial dan Pendidikan["Yanwiyatono Yanwiyatono", "Herman Bakir"]2024-06-26T00:00:00 77f0344fc55bc6d736f2a4f1de8815c312d5f0c7This paper contributes to a theory of artificial legal intelligence (ALI) that harmonizes concerns for artificial intelligence (AI) bias and prejudice with 1) the critical perspective, and (2) Jacques Ellul’s critique of the “technological phenomenon”. Necessary to this contribution is an argument for the importance of ontology in understanding the multidimensionality of ALI, and critical theory’s ability to deal with this multidimensionality. First, the paper introduces critical theory and some of its tenets. My focus then is critical legal studies (CLS) and their contentious relationship with the ontological issue of instrumentality. I emphasize that one way a theory of ALI can engage with this critical theme is through an ontological classification of AI. I propose two classifications: AI as a tool and AI as an ideological phenomenon. Each classification is attributive of a certain autonomy to AI and telling about a potentiality for domination a critical theory of ALI should recognize, deconstruct, and challenge. Ellul’s argument that the technological phenomenon is “autonomous” informs this part of my argument. I then discuss the concept of “prejudice” and find that, considering the ontological classifications, prejudice is visible in more than one form. Although the “algorithmic bias” approach is adequate for AI as a tool, it does not account effectively for another form of prejudice rooted in technology. I call it technological prejudice.McGill GLSA Research Series11McGill GLSA Research Series["\u00c9mile Chamberland"]2024-06-26T00:00:00 c3c9feecaa7745b91c3470bb42a8094803bec3eeInternet Policy Review317Internet Policy Review["Dieuwertje Luitse"]2024-06-26T00:00:00 c02e4b757467e52babc0e0a131b80a376a1e42caIn the rapidly advancing era of Artificial Intelligence (AI), the transformative force of foundation models has streamlined the automated generation of multi-modal content, corresponding to user intents. At the same time, Machine Learning (ML), particularly Deep Learning (DL), has achieved state-of-the-art performance in optimization and inference tasks of various domains, including telecommunications. However, the segregation of technological domains poses challenges to the integration of powerful AI/ML capabilities towards realizing the vision of "AI-native" networks, such as future 6G networks that will provide ubiquitous intelligence across their infrastructure and service planes and will seamlessly adapt and evolve to support new classes of applications. This paper introduces Auto-TimeGPT, a handsfree Automated ML (AutoML) solution, extending the profound impact of Generative AI (GenAI) for Time Series Analysis, to networks and communications. The contribution includes an out-of-the-box zero-shot approach for anomaly detection and Time Series Forecasting (TSF) which is particularly useful for the Edge-Cloud orchestration framework CODECO, comprehensive in and out-of-domain evaluation, and a limitations analysis. Evaluation results across diverse real-world datasets demonstrate competitive performance with cutting-edge approaches based on Large Language Models (LLMs) and DL models, generalization and optimization ability and effective anomaly detection. The proposed framework democratizes AI analytics in communication and networking domains.International Symposium on Computers and Communications190Auto-TimeGPT, a handsfree Automated ML (AutoML) solution, extending the profound impact of Generative AI for Time Series Analysis, to networks and communications and democratizes AI analytics in communication and networking domains is introduced.2024 IEEE Symposium on Computers and Communications (ISCC)["Georgios Samaras", "Marinela Mertiri", "Maria-Evgenia Xezonaki", "V. Theodorou", "Panteleimon-Konstantinos Chartsias", "Theodoros Bozios"]2024-06-26T00:00:00 76bc0f6ec9db5008f2713acc9899f5960cd894e1The application of Artificial Intelligence (AI) in the realm of economic markets, particularly in the business and real estate sectors, has witnessed substantial growth. However, its effectiveness is curtailed by several limitations, especially in the context of the rising valuation of intangible assets. The intangible nature of assets such as brand value, environmental impact or social impact, among others, presents a challenge for AI, which relies on quantifiable data for analysis and decision-making. The intrinsic volatility and uncertainty of markets, heightened by the intangible asset valuation, further complicate the AI's predictive accuracy and adaptability throughout the time.AI models, primarily dependent on historical data, struggle to accurately forecast market movements influenced by intangible factors, which are often subjective and dynamically changing. This limitation is particularly pronounced in the real estate promotion sector, where the perceived value of properties can be significantly affected by intangible elements like location prestige or architectural uniqueness. Additionally, the ethical implications of AI deployment, such as data privacy concerns and potential biases in algorithmic decision-making, pose further constraints on its application in these sectors. While AI offers transformative potential for economic markets, its current limitations in handling the valuation of intangibles, market volatility, and ethical considerations necessitate a cautious and complementary approach to its integration into business and real estate promotion strategies, specially in the concern of life-cycle approaches.6th International Conference on Advanced Research Methods and Analytics - CARMA 202400While AI offers transformative potential for economic markets, its current limitations in handling the valuation of intangibles, market volatility, and ethical considerations necessitate a cautious and complementary approach to its integration into business and real estate promotion strategies, specially in the concern of life-cycle approaches.6th International Conference on Advanced Research Methods and Analytics - CARMA 2024["Salvador Dom\u00ednguez Gil", "Andrea San Jos\u00e9 Cabrero", "Antonio S\u00e1nchez Gea", "Pilar Miguel-sin", "Gema Ram\u00edrez Pacheco"]2024-06-26T00:00:00 ca05601af8cd5c2dd69098476389647c8c6c91e7Advances in artificial intelligence (AI) technology are increasingly rapid and have great potential to bring significant change in various sectors. However, the challenges faced are limited to technological development and the legal and ethical aspects of its implementation. This research aims to analyze the existing legal framework related to the application of Pancasila-based artificial intelligence technology to minimize risks and optimize benefits to achieve the vision of Indonesia Emas 2045. This research uses descriptive qualitative research methods, involving a comprehensive literature review of legal documents, scholarly articles, and case studies. The data collection technique in this research is literature study. The collected data is then analyzed through three stages: data reduction, data presentation, and conclusion drawing. The research results show that the legal framework for implementing AI technology based on Pancasila is an essential initiative in responding to challenges and opportunities in the era of digital transformation. By leveraging the demographic bonus, the vision of Indonesia Emas 2045 can be achieved. The legal framework that incorporates Pancasila values as a moral and ethical foundation aims to ensure that the use of AI not only positively impacts society but also minimizes potential risks. This study highlights the importance of aligning AI development with national values to foster sustainable and ethical technological advancement.Asian Journal of Engineering, Social and Health190The research results show that the legal framework for implementing AI technology based on Pancasila is an essential initiative in responding to challenges and opportunities in the era of digital transformation.Asian Journal of Engineering, Social and Health["Francisca Romana Nanik Alfiani", "Ade Saptomo"]2024-06-26T00:00:00 d1f2940bcc6569d4930148d1f1810189df1f0a2fThis study advocates for the integration of artificial intelligence (AI) in the tourism industry. It synthesizes literature to comprehensively examine this concept, emphasizing the importance of tourist satisfaction and industry development. The study pursues two main objectives: elucidating AI's workings and analyzing its application in tourism. Employing a descriptive methodology, it gathers secondary data from diverse sources. The findings highlight the potential benefits of AI implementation in policy, strategy, and operational aspects of tourism. Moreover, it underscores the importance of AI education for stakeholders, including institutions, policymakers, and tour management teams, to leverage cutting-edge technologies effectively.This paper is an endeavour to shed light on the specific ways AI is utilized within the tourism sector, offering insights that can inform industry practices and academic discourse.This research contributes to the discourse on AI's role in enhancing tourism experiences and industry efficiency, offering insights for future strategies and implementations.International journal of engineering technology and managememt research850This study advocates for the integration of artificial intelligence (AI) in the tourism industry by synthesizing literature to comprehensively examine this concept, emphasizing the importance of tourist satisfaction and industry development.International Journal of Engineering Technologies and Management Research["A. S. S. Zimik", "Arup Barman"]2024-06-26T00:00:00 9376a0c944817bf886f867e3d0266911e46cc4feThe purpose of the article is to analyze global trends in the practical use of artificial intelligence algorithms in library science in 2019–2023, establish the state of practical use of AI algorithms in libraries of leading countries, identify problems and prospects for the implementation of foreign experience into the practice of Ukrainian libraries. The methodology of the research includes content analysis, literature review, and systematization. 20% of the most influential (CiteScore metric in Scopus) scientific journals in library and information science in 2019–2023 were selected. Then 100 articles related to artificial intelligence were filtered. Only those articles that have practical results were used for this study. The results. The analysis of articles allowed to identify the main research topics of artificial intelligence in library science: application of artificial intelligence in: digital linguistics (20%), scientometrics and altmetrics (45,7%), integration with Big Data to ensure data quality (5,7%), research on historical and cultural heritage (11,4%), and integration of AI technologies into library production (17,1%). The results of the conducted research allow to clarify the state of development of AI problems in foreign library science, to determine the methodologies of integrating AI technologies into modern library production. The scientific novelty of the article is explained by the absence of Ukrainian comprehensive studies on the international experience of implementing AI in library activities, which emphasizes the need for such research. The practical significance. Examples of practical implementation of AI algorithms are valuable because studying approaches, analyzing mistakes, and conclusions of experienced scientists will improve models of AI application in the work of Ukrainian archives, libraries, and other document-communication institutions.Visnyk of Kharkiv State Academy of Culture310The state of practical use of AI algorithms in libraries of leading countries, identify problems and prospects for the implementation of foreign experience into the practice of Ukrainian libraries are established, and the methodologies of integrating AI technologies into modern library production are determined.Visnyk of Kharkiv State Academy of Culture["D. Honcharov"]2024-06-26T00:00:00 c7344709089c26d7d8bc68533714675ab2612f8bPURPOSE OF REVIEW This review examines recent research on artificial intelligence focusing on machine learning (ML) models for predicting postoperative pain outcomes. We also identify technical, ethical, and practical hurdles that demand continued investigation and research. RECENT FINDINGS Current ML models leverage diverse datasets, algorithmic techniques, and validation methods to identify predictive biomarkers, risk factors, and phenotypic signatures associated with increased acute and chronic postoperative pain and persistent opioid use. ML models demonstrate satisfactory performance to predict pain outcomes and their prognostic trajectories, identify modifiable risk factors and at-risk patients who benefit from targeted pain management strategies, and show promise in pain prevention applications. However, further evidence is needed to evaluate the reliability, generalizability, effectiveness, and safety of ML-driven approaches before their integration into perioperative pain management practices. SUMMARY Artificial intelligence (AI) has the potential to enhance perioperative pain management by providing more accurate predictive models and personalized interventions. By leveraging ML algorithms, clinicians can better identify at-risk patients and tailor treatment strategies accordingly. However, successful implementation needs to address challenges in data quality, algorithmic complexity, and ethical and practical considerations. Future research should focus on validating AI-driven interventions in clinical practice and fostering interdisciplinary collaboration to advance perioperative care.Current Opinion in Anaesthesiology560Artificial intelligence (AI) has the potential to enhance perioperative pain management by providing more accurate predictive models and personalized interventions, but successful implementation needs to address challenges in data quality, algorithmic complexity, and ethical and practical considerations.Current opinion in anaesthesiology["R. Sajdeya", "Samer Narouze"]2024-06-26T00:00:00 de90a14eef69639444d902ffe96f8ea70b211d94Artificial Intelligence (AI) faces persistent challenges in terms of transparency and accountability, which requires rigorous documentation. Through a literature review on documentation practices, we provide an overview of prevailing trends, persistent issues, and the multifaceted interplay of factors influencing the documentation. Our examination of key characteristics such as scope, target audiences, support for multimodality, and level of automation, highlights a dynamic evolution in documentation practices, underscored by a shift towards a more holistic, engaging, and automated documentation.arXiv.org350An examination of key characteristics such as scope, target audiences, support for multimodality, and level of automation highlights a dynamic evolution in documentation practices, underscored by a shift towards a more holistic, engaging, and automated documentation.ArXiv["Stefan Arnold", "Dilara Yesilbas", "Rene Gr\u00f6bner", "Dominik Riedelbauch", "Maik Horn", "Sven Weinzierl"]2024-06-26T00:00:00 e38ac00a80a47559bfd7182548287db6b2dac4f4: This research aims to evaluate the existing body of literature on the application of artificial intelligence (AI) in the field of education. Using a bibliometric analysis of 1,192 scholarly articles indexed in the Scopus database, the study maps the scholarly network in this field, identifies publication trends, influential contributors, core research themes, and areas that require further investigation. The findings reveal a significant exponential growth in publications since 2010, establishing AI in education as a vibrant field. Prolific contributors include individual authors, institutions like the Education University of Hong Kong, and countries such as China and the US. Network analyses highlight extensive collaborations through co-authorship within and between regions, while core themes focus on AI’s transformative role in pedagogy and learning experiences. Although the study is limited to Scopus-indexed publications, the insights from the bibliometric maps provide valuable implications for strengthening collaborative ties and addressing under-represented areas. This in-depth and systematic analysis o ff ers a unique contribution to the field, informing future research directions in AI-enhanced education.International Journal of Computing and Digital Systems340Using a bibliometric analysis of 1,192 scholarly articles indexed in the Scopus database, the study maps the scholarly network in this field, identifies publication trends, influential contributors, core research themes, and areas that require further investigation.International Journal of Computing and Digital Systems["Khawla Abdulrahman Albinali", "Noorminshah A. Iahad", "Ahmad Fadhil Yusof"]2024-06-26T00:00:00 1b9a282b2dee640b687f71145546dbac240d920bIn today's digital age, technical engineering plays an important role in using artificial intelligence and cryptocurrency to optimize tax systems and improve financial efficiency in fintech businesses. Artificial intelligence helps automate business processes, especially in taxation, which reduces the cost of tax administration. Cryptocurrencies open up new opportunities for optimizing tax systems, providing greater transparency and efficiency in financial transactions.. The use of AI in tax administration can streamline processes, reduce human error, and improve compliance. AI algorithms can analyze large amounts of data, identify patterns, and detect potential tax evasion or fraud, leading to more accurate tax assessments and improved revenue collection. Additionally, AI-powered chatbots and virtual assistants can provide taxpayers with personalized support and guidance, enhancing the overall experience. Cryptocurrencies, on the other hand, offer a transparent and secure way to conduct financial transactions. By leveraging blockchain technology, cryptocurrencies enable immutable and auditable records of transactions, which can facilitate tax reporting and compliance. Furthermore, the decentralized nature of cryptocurrencies eliminates the need for intermediaries, reducing transaction costs and increasing efficiency. However, the implementation of these technologies in tax systems requires significant investments in infrastructure, software, and personnel training. Tax authorities ought to allocate substantial budgets to modernize their systems and integrate AI and blockchain solutions seamlessly. Additionally, concerns over data privacy and the potential for cyber threats pose challenges in ensuring the confidentiality and security of taxpayer information.Economics and technical engineering190Cryptocurrencies open up new opportunities for optimizing tax systems, providing greater transparency and efficiency in financial transactions, and the use of AI in tax administration can streamline processes, reduce human error, and improve compliance.Economics and technical engineering["Maryna Sadovenko", "Olga Kondratyuk", "Nataliia Suprun", "Maxim Tarverdiev"]2024-06-26T00:00:00 09c27942029cfc3df08d0fb0c19ac1214e4b9a0aThis paper explores the applications of MuBaBaO Creative Thinking Blocks as an educational tool in the artificial intelligence-driven era. The multidimensional MuBaBaO method diverges from traditional learning methods and emphasizes fostering critical thinking, effective communication, creativity, spatial and emotional intelligence through non-linear pedagogy. By engaging participants in creative and problem-solving tasks using wooden blocks, this dynamic exploratory approach enhances cognitive abilities and prepares students to thrive alongside AI advancements. The paper highlights the importance of developing distinctive human qualities and skills, such as adaptability, imagination, and innovative thinking, to complement the technical knowledge required in a technology-dominated landscape.Roczniki Kulturoznawcze110This paper explores the applications of MuBaBaO Creative Thinking Blocks as an educational tool in the artificial intelligence-driven era and highlights the importance of developing distinctive human qualities and skills to complement the technical knowledge required in a technology-dominated landscape.Roczniki Kulturoznawcze["Rachita Ramya"]2024-06-26T00:00:00 93d55d57b26014e48d978c3f1c3c26b1b107e357Background: The existence of different basal cell carcinoma (BCC) clinical criteria cannot be objectively validated. An adequate ground-truth is needed to train an artificial intelligence (AI) tool that explains the BCC diagnosis by providing its dermoscopic features. Objectives: To determine the consensus among dermatologists on dermoscopic criteria of 204 BCC. To analyze the performance of an AI tool when the ground-truth is inferred. Methods: A single center, diagnostic and prospective study was conducted to analyze the agreement in dermoscopic criteria by four dermatologists and then derive a reference standard. 1434 dermoscopic images have been used, that were taken by a primary health physician, sent via teledermatology, and diagnosed by a dermatologist. They were randomly selected from the teledermatology platform (2019-2021). 204 of them were tested with an AI tool; the remainder trained it. The performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists was analyzed using McNemar's test and Hamming distance. Results: Dermatologists achieve perfect agreement in the diagnosis of BCC (Fleiss-Kappa=0.9079), and a high correlation with the biopsy (PPV=0.9670). However, there is low agreement in detecting some dermoscopic criteria. Statistical differences were found in the performance of the AI tool trained using the ground-truth of one dermatologist versus the ground-truth statistically inferred from the consensus of four dermatologists. Conclusions: Care should be taken when training an AI tool to determine the BCC patterns present in a lesion. Ground-truth should be established from multiple dermatologists.arXiv.org190Care should be taken when training an AI tool to determine the BCC patterns present in a lesion when the ground-truth is inferred, as there is low agreement in detecting some dermoscopic criteria.ArXiv["Francisca Silva-Claver'ia", "Carmen Serrano", "Iv'an Matas", "Amalia Serrano", "Tom'as Toledo-Pastrana", "David Moreno-Ram'irez", "B. Acha"]2024-06-26T00:00:00 5f7850d61b032a142309b42fc8ce432b7a3051afThe presence of artificial intelligence (AI) has changed business practices and has had an impact on employment and professions. Meanwhile, the income received by workers and professionals is the object of income tax. If there is a decrease in labor revenue and professional service users, it may also be followed by a decrease in tax revenue from the income tax sector. The purpose of this study is to determine the implications of AI on taxation aspects. This research uses qualitative methods with descriptive data analysis. The results show that AI has a positive impact on making it easier for taxpayers to carry out tax obligations, improve tax services, detect tax fraud, and potentially increase tax revenue from the Income Tax Article (ITA) 17 (2), Value Added Tax (VAT), and ITA 23 sectors for delivery services. The negative impact of AI is a decrease in tax revenue from ITA 21 or ITA 23 for expert services, followed by a decrease in personal income tax for these experts and employees. Recommendations for domicile tax certificate (DGT) as a consideration for making policies related to income tax for the use of AI. In addition, as a consideration, it also makes regulations for the Ministry of Manpower to protect professional experts and labor, the Ministry of Communication and Information to protect AI user data, the Ministry of Education and Culture to protect the profession of educators and review the use of AI in education, IAI the education compartment as a consideration for planning a curriculum for accounting education to keep pace with technological developments or AI. InFestasi00AI has a positive impact on making it easier for taxpayers to carry out tax obligations, improve tax services, detect tax fraud, and potentially increase tax revenue from the Income Tax Article (ITA) 17 (2), Value Added Tax (VAT), and ITA 23 sectors for delivery services.InFestasi["Puji Rahayu"]2024-06-26T00:00:00 8de6966d651bfbbb346b572972f0257cf7b87feeIn recent years, the integration of artificial intelligence (AI) and the digital economy has become an important catalyst for social and economic transformation. Artificial intelligence, with its intelligence to mimic human thinking and learning, as well as its ability to perform complex tasks and massive calculations, is revolutionizing various industries, while reshaping the way we live and work. At the same time, the digital economy, driven by advances in information and communication technologies, is creating new prospects for growth, innovation and efficiency. This paper adopts literature analysis method, through extensive academic literature survey and review, as well as research reports in related fields, from both opportunities and challenges, uses a comprehensive literature review to study the interactive impact between artificial intelligence and digital economy, and further analyzes the risks brought by the integration of artificial intelligence and digital economy and the chain reaction caused by its wide application in reality.Advances in Economics, Management and Political Sciences00This paper uses a comprehensive literature review to study the interactive impact between artificial intelligence and digital economy, and further analyzes the risks brought by the integration of artificial intelligence and digital economy and the chain reaction caused by its wide application in reality.Advances in Economics, Management and Political Sciences["Sihan Zuo", "Ziyi Wang", "Ruohua Dong"]2024-06-26T00:00:00 fb7a76c95dcb2f0ecd54f18f0e3dc71c5222277aThe development of technology has changed the position of animals in the modern world in various aspects. However, only the achievements of artificial intelligence in the field of natural languages indicated the possibility of reaching a new level of understanding and relationship with animals. Modern technologies have made it possible to isolate and fi x animal sounds and collect a huge array of audio and video data, and the experience of translation, even in the absence of parallel texts, has indicated the potential for using artificial intelligence to analyze animal sounds. Despite numerous difficulties, including those associated with the difference in the worldview of animals and humans, there are already precedents for translation from the language of animals. The article analyzes the possibilities of using artificial intelligence in conditions of limited data and its current use in the field of animal communication. If for domestic and farm animals, researchers rely on the interpretation of meanings or emotions, then for wild animals, scientists compare sounds and behavior, and rely on the potential of artificial intelligence in solving unstructured problems. Although a number of recent studies report high reliability of “translation” from the language of animals, the very possibility of testing the effectiveness is difficult. Nevertheless, the accelerating emergence of new solutions that facilitate the recognition of the voices of specific animals, the classification of sounds and actions of different animals, etc., indicate the possibility of a qualitative leap in the understanding of animals in the near future. Success in the field of interpretation of animal sounds can lead not only to progress in a large number of areas related to the animal world, but also to a change in the status and position of the animal. At the same time, the achievements raise ethical questions related to the possibility of using new technologies to the detriment of animals and people.Ideas and Ideals00The article analyzes the possibilities of using artificial intelligence in conditions of limited data and its current use in the field of animal communication to indicate the possibility of a qualitative leap in the understanding of animals in the near future.Ideas and Ideals["D. Bylieva"]2024-06-26T00:00:00 bb70e125e2f0a56f76492e0f9e7ecfd32ae10e0bJournal of imaging informatics in medicine190The diagnostic performance of the AI algorithm in the detection of otosclerosis was comparable to that of a trained radiologist, although the sensitivity at the estimated ideal threshold was lower.Journal of imaging informatics in medicine["Antoine Emin", "S. Daubi\u00e9", "Lo\u00efc Gaillandre", "Arthur Aouad", "J. Pialat", "Valentin Favier", "F. Carsuzaa", "S. Tringali", "M. Fieux"]2024-06-26T00:00:00 419d5bceae309a26df0bd5f27d208b4490519dc8There are a lot of books out there that introduce readers to AI, but this one stands out since it focuses on KR ideas instead. The foundation of AI is knowledge representation; programmers who want their programs to work by encoding and manipulating knowledge must carefully consider the scheme they will use to represent knowledge and the outcomes of their decisions. An examination of knowledge representation challenges serves as the book’s unique introduction to the subject of artificial intelligence. It is assumed that you have some acquaintance with computers and, ideally, with the fundamentals of formal logic. With an emphasis on knowledge representation, this book introduces students to AI and includes activities at the end of each chapter. If you are a student or professional in the field of computer science looking for a primer on artificial intelligence and knowledge representations, this is the book for you.00If you are a student or professional in the field of computer science looking for a primer on artificial intelligence and knowledge representations, this is the book for you.["Dr Sunil Joshi", "Mr. Prasad T. Shaha", "Dr. Namita Chawla", "Dr. Shahid Thekiya"]2024-06-26T00:00:00 866685c9e38548b78260d534d47f400d7b6093cbService Industries Journal923The Service Industries Journal["A. Tlili", "Mouna Denden", "Mourad Abed", "Ronghuai Huang"]2024-06-26T00:00:00 37d60b63c2176e5afa8e95195c6a1748c062e81aAcademy of Management Review111Academy of Management Review["Stratos Ramoglou", "Reiner Schaefer", "Y. Chandra", "Jeffery S. McMullen"]2024-06-26T00:00:00 d6e37a3f7df19ddf444fe9fdc8c702f2e9f2b499Journal of Public Health Policy01Journal of public health policy["Elena N Naumova"]2024-06-26T00:00:00 c1712a18c4b72123be77feebb933b13d93323644This work explores the mathematical modelling capabilities of various iterations of ChatGPT, focusing on their performance across tasks of differing complexity and openness. The study examines the abilities of GPT-3.5, GPT-4.0, and a more instructed version, GPT-MM, in multiple scenarios. It is observed that all versions demonstrate basic mathematical problem-solving skills. However, their effectiveness varies with increasing task complexity. While GPT-4.0 and GPT-MM show marginal improvements in providing detailed solutions, significant challenges persist, especially in moderate to complex modelling contexts where comprehending the nuances of tasks becomes challenging. Additionally, the study suggests that the openness of modelling tasks has a limited impact on performance, highlighting that mathematical and contextual complexities play more critical roles. The implications of these observations are discussed in terms of potential enhancements to teaching methodologies and the integration of AI tools like GPT in educational settings. This reiterates the importance of further research to fully understand the capabilities and limitations of AI tools and ensure their effective use in education.Education sciences02The study examines the abilities of GPT-3.5, GPT-4.0, and a more instructed version, GPT-MM in multiple scenarios and observes that all versions demonstrate basic mathematical problem-solving skills, however, their effectiveness varies with increasing task complexity.Education Sciences["Carina Spreitzer", "Oliver Straser", "Stefan Zehetmeier", "Katja Maa\u00df"]2024-06-26T00:00:00 293b135e93373cfa08ffcb7661f2abdb722240f5Summer 2024 International Conferences Proceedings00Summer 2024 International Conferences Proceedings[]2024-06-26T00:00:00 ed86de5f047feea693b29fb5178d2c55ddbcbabb00["Ariel Fern\u00e1ndez"]2024-06-26T00:00:00 d8353176ba856d314b9604e79ae36ae9ab461391Reposition00Reposition["Pamela Breda"]2024-06-26T00:00:00 0d69a4184cd22b1ed365158a8a0cb19a66e2a1d5Перспективи та інновації науки00Перспективи та інновації науки["I.V. Stavytska", "N. Shalova", "O. Korbut"]2024-06-26T00:00:00 5b46c933cc5e55107dacd71b24568e0055392010Journal of Computational Chemistry00Journal of computational chemistry["Gernot Frenking"]2024-06-26T00:00:00 c4ff4ae507a83b37e0bb942c3412b86372c4317dCritical Military Studies200Critical Military Studies["Sian Troath"]2024-06-26T00:00:00 a1e93f857904b2eec9f5d71e912af22140982b29American Journal of Respiratory and Critical Care Medicine00American journal of respiratory and critical care medicine["Samuel H Friedman", "Kathryn J Long", "Stephen Sexauer", "A. Menon", "E. Kilb"]2024-06-26T00:00:00 f1c06a43f66119f4f8ae29971599d5bd7cf27cceInternational Journal of Surgery00International journal of surgery["Baorong Wang", "Heguo Jiang"]2024-06-26T00:00:00 65a9d27a2a807efbbf6d6953f349572b64ef2018Technology, Pedagogy and Education640Technology, Pedagogy and Education["Kai Wang", "Ching-sing Chai", "Jyh-Chong Liang", "Guoyuan Sang"]2024-06-26T00:00:00 705ff9ce13a041f30e6ff5a041bee03724c597cfThe utilization of technology has influenced many aspects of life, and one of the fields affected is education. Basic education must adapt to the evolving era influenced by technology. The implementation of a workshop on the utilization of AI as a learning tool is based on observations and interviews with the headmaster of SD IT Bina Insani Semarang. This workshop introduces AI technology to the teachers of SD IT Bina Insani, along with its advantages and disadvantages. During the workshop, participants not only receive theoretical material but also engage in practical exercises in utilizing AI, guided by the team from PPM Universitas STEKOM Semarang. The evaluation of participants' understanding of the material on the use of AI is conducted directly during the practical sessions, considering the participants' active involvement throughout the workshop.KREATIF: Jurnal Pengabdian Masyarakat Nusantara00This workshop introduces AI technology to the teachers of SD IT Bina Insani, along with its advantages and disadvantages, and introduces participants' understanding of the material on the use of AI.KREATIF: Jurnal Pengabdian Masyarakat Nusantara["Andik Prakasa Hadi", "Rudjiono", "Ahmad Zainudin", "S. Nugroho", "Agus Priyadi"]2024-06-26T00:00:00 191d17a97f5632b0c6635df6f67c0044f6b8a5b1Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)470The efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making is explored.2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)["Enrique Fern\u00e1ndez-Morales", "C. L. S\u00e1nchez-Bocanegra", "Rafael Pastor Vargas", "J. Pereyra-Rodr\u00edguez", "J. Haut", "J. Ben\u00edtez-Andrades"]2024-06-26T00:00:00 48c9c29cc7f08012f98474acae912396412d58f3The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing"language agents", which are complex large language models (LLMs) pipelines involving both prompting techniques and tool usage methods. While language agents have demonstrated impressive capabilities for many real-world tasks, a fundamental limitation of current language agents research is that they are model-centric, or engineering-centric. That's to say, the progress on prompts, tools, and pipelines of language agents requires substantial manual engineering efforts from human experts rather than automatically learning from data. We believe the transition from model-centric, or engineering-centric, to data-centric, i.e., the ability of language agents to autonomously learn and evolve in environments, is the key for them to possibly achieve AGI. In this work, we introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own in a data-centric way using symbolic optimizers. Specifically, we consider agents as symbolic networks where learnable weights are defined by prompts, tools, and the way they are stacked together. Agent symbolic learning is designed to optimize the symbolic network within language agents by mimicking two fundamental algorithms in connectionist learning: back-propagation and gradient descent. Instead of dealing with numeric weights, agent symbolic learning works with natural language simulacrums of weights, loss, and gradients. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks and show that agent symbolic learning enables language agents to update themselves after being created and deployed in the wild, resulting in"self-evolving agents".arXiv.org3117ArXiv["Wangchunshu Zhou", "Yixin Ou", "Shengwei Ding", "Long Li", "Jialong Wu", "Tiannan Wang", "Jiamin Chen", "Shuai Wang", "Xiaohua Xu", "Ningyu Zhang", "Huajun Chen", "Y. Jiang"]2024-06-26T00:00:00 5d731b824e2a3e900d42c7d3499cf10b8009d3f3As generative Artificial Intelligence (genAI) technologies proliferate across sectors, they offer significant benefits but also risk exacerbating discrimination. This chapter explores how genAI intersects with non-discrimination laws, identifying shortcomings and suggesting improvements. It highlights two main types of discriminatory outputs: (i) demeaning and abusive content and (ii) subtler biases due to inadequate representation of protected groups, which may not be overtly discriminatory in individual cases but have cumulative discriminatory effects. For example, genAI systems may predominantly depict white men when asked for images of people in important jobs. This chapter examines these issues, categorizing problematic outputs into three legal categories: discriminatory content; harassment; and legally hard cases like unbalanced content, harmful stereotypes or misclassification. It argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues. The chapter suggests updating EU laws, including the AI Act, to mitigate biases in training and input data, mandating testing and auditing, and evolving legislation to enforce standards for bias mitigation and inclusivity as technology advances.arXiv.org06This chapter argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues, updating EU laws to mitigate biases in training and input data.ArXiv["Philipp Hacker", "Brent Mittelstadt", "Frederik Zuiderveen Borgesius", "Sandra Wachter"]2024-06-26T00:00:00 2f7741a1703d2a64a9488ed8c05a8d27561faed4The integration of artificial intelligence (AI) and the Internet of Things (IoT) in agriculture has significantly transformed rural farming. However, the adoption of these technologies has also introduced privacy and security concerns, particularly unauthorized breaches and cyber-attacks on data collected from IoT devices and sensitive information. The present study addresses these concerns by developing a comprehensive framework that provides practical, privacy-centric AI and IoT solutions for monitoring smart rural farms. This is performed by designing a framework that includes a three-phase protocol that secures data exchange between the User, the IoT Sensor Layer, and the Central Server. In the proposed protocol, the Central Server is responsible for establishing a secure communication channel by verifying the legitimacy of the IoT Sensor devices and the User and securing the data using rigorous cryptographic techniques. The proposed protocol is also validated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The formal security analysis confirms the robustness of the protocol and its suitability for real-time applications in AI and IoT-enabled smart rural farms, demonstrating resistance against various attacks and enhanced performance metrics, including a computation time of 0.04 s for 11 messages and a detailed search where 119 nodes were visited at a depth of 12 plies in a mere search time of 0.28 s.Italian National Conference on Sensors487The present study develops a comprehensive framework that provides practical, privacy-centric AI and IoT solutions for monitoring smart rural farms by designing a three-phase protocol that secures data exchange between the User, the IoT Sensor Layer, and the Central Server.Sensors (Basel, Switzerland)["Mosiur Rahaman", "Chun-Yuan Lin", "Princy Pappachan", "Brij B. Gupta", "Ching-Hsien Hsu"]2024-06-26T00:00:00 978795ef5cfb6997d4cc03f371187a106712cc2bArtificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure-activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.Journal of Chemical Information and Modeling456DrugFlow is an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows and it is hoped that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery.Journal of chemical information and modeling["Chao Shen", "Jianfei Song", "Chang-Yu Hsieh", "Dongsheng Cao", "Yu Kang", "Wenling Ye", "Zhenxing Wu", "Jike Wang", "Odin Zhang", "Xujun Zhang", "Hao Zeng", "Heng Cai", "Yu Chen", "Linkang Chen", "Hao Luo", "Xinda Zhao", "Tianye Jian", "Tong Chen", "Dejun Jiang", "Mingyang Wang", "Qing Ye", "Jialu Wu", "Hongyan Du", "Hui Shi", "Yafeng Deng", "Tingjun Hou"]2024-06-26T00:00:00 6411492b38e22eb96eb9e115ed85554a28d57dd6Recent advancements in artificial intelligence and automation are transforming catalyst discovery and design from traditional trial-and-error manual mode into intelligent, high-throughput digital methodologies. This transformation is driven by four key components, including high-throughput information extraction, automated robotic experimentation, real-time feedback for iterative optimization, and interpretable machine learning for generating new knowledge. These innovations have given rise to the development of self-driving labs and significantly accelerated materials research. Over the past two years, the emergence of large language models (LLMs) has added a new dimension to this field, providing unprecedented flexibility in information integration, decision-making, and interacting with human researchers. This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.Chemical Science2214This review explores how LLMs are reshaping catalyst design, heralding a revolutionary change in the fields.Chemical Science["Yuming Su", "Xue Wang", "Yuanxiang Ye", "Yibo Xie", "Yujing Xu", "Yibin Jiang", "Cheng Wang"]2024-06-26T00:00:00 431c85f7cb5436981c798697acc13aa72f3e133bLarge language models (LLMs) have demonstrated the world with the sparks of artificial general intelligence (AGI). One opinion, especially from some startups working on LLMs, argues that an LLM with nearly unlimited context length can realize AGI. However, they might be too optimistic about the long-context capability of (existing) LLMs -- (1) Recent literature has shown that their effective context length is significantly smaller than their claimed context length; and (2) Our reasoning-in-a-haystack experiments further demonstrate that simultaneously finding the relevant information from a long context and conducting (simple) reasoning is nearly impossible. In this paper, we envision a pathway from LLMs to AGI through the integration of \emph{memory}. We believe that AGI should be a system where LLMs serve as core processors. In addition to raw data, the memory in this system would store a large number of important conclusions derived from reasoning processes. Compared with retrieval-augmented generation (RAG) that merely processing raw data, this approach not only connects semantically related information closer, but also simplifies complex inferences at the time of querying. As an intermediate stage, the memory will likely be in the form of natural language descriptions, which can be directly consumed by users too. Ultimately, every agent/person should have its own large personal model, a deep neural network model (thus \emph{AI-native}) that parameterizes and compresses all types of memory, even the ones cannot be described by natural languages. Finally, we discuss the significant potential of AI-native memory as the transformative infrastructure for (proactive) engagement, personalization, distribution, and social in the AGI era, as well as the incurred privacy and security challenges with preliminary solutions.arXiv.org483A pathway from LLMs to AGI through the integration of AI-native memory is envisioned, which believes that AGI should be a system where LLMs serve as core processors where the memory would store a large number of important conclusions derived from reasoning processes.ArXiv["Jingbo Shang", "Zai Zheng", "Xiang Ying", "Felix Tao", "Mindverse Team"]2024-06-26T00:00:00 65ae4089ebfeb25fe028af789e19140b22b379b2Loyalty programs have long served as valuable tools for customer engagement and retention. Today, with the increasing availability of customer data, new methods for analysis and program optimization are becoming a necessity. Artificial Intelligence (AI) has also been a topic of high relevance in recent years, and its connection with loyalty programs has not been researched and discussed enough to date. In this paper we aim to explore the emerging topic of AI data analysis in the context of loyalty programs. We examine various applications of AI data analysis in loyalty programs, including personalized rewards and incentives, customer segmentation and targeting and fraud detection and risk management. Covering these possible applications of AI data analysis provides a general understanding of AI data analysis functioning and its integration into loyalty programs. The provided information also serves as a good basis for understanding how AI data analysis functions and may be applied in various fields. In our paper we look at the outcomes of AI data analysis applications in loyalty programs from the point of view of a customer who is using those programs as well as from the point of view of the brand who manages them. By analyzing the identified points, we then highlight the benefits and challenges of using AI data analysis in loyalty programs. Key research gaps are also presented, emphasizing the need for further research on the topic, with particular attention given to problems such as ethical considerations, data privacy concerns, and the transparency of AI models. The main goal of our article is to provide readers with complex information on the topic of AI data analysis usage in loyalty programs and the ways this technology may benefit customers and the brand in the selected context. The article is theoretical-empirical and is based on external information from trustworthy sources, both digital and printed, completed with our own research outcomes.European Conference on Research Methodology for Business and Management Studies171The main goal of the article is to provide readers with complex information on the topic of AI data analysis usage in loyalty programs and the ways this technology may benefit customers and the brand in the selected context.European Conference on Research Methodology for Business and Management Studies["Andrii Kushnarevych"]2024-06-26T00:00:00 f5ad2693de2e65eff68208069c1c7ef78367b0deResearch in Science Education5439A consolidated analysis of AI’s impact on students’ learning outcomes, contexts of its adoption, students’ and teachers’ perceptions about its use, and the challenges of its use within science education are offered.Research in Science Education["Firas Almasri"]2024-06-27T00:00:00 3d56986c6dc13543273f29e4026f047718b2024cFraud prevention is a critical challenge for financial institutions, businesses, and governments worldwide. The rise of digital transactions and complex financial systems has led to increasingly sophisticated fraudulent activities. Artificial Intelligence (AI) offers innovative solutions to this growing problem, leveraging its ability to analyze vast amounts of data, identify patterns, and predict fraudulent behavior with high accuracy. This abstract explores the various AI techniques and their applications in fraud prevention, highlighting their transformative impact on the security landscape. AI techniques such as machine learning (ML), deep learning, and natural language processing (NLP) have revolutionized fraud detection and prevention. Machine learning algorithms, particularly supervised learning models like decision trees and neural networks, are used extensively to identify fraudulent transactions by learning from historical data. These models can distinguish between legitimate and fraudulent transactions by recognizing subtle patterns that might be missed by traditional rule-based systems. Unsupervised learning methods, including clustering and anomaly detection, are employed to detect novel fraud schemes by identifying outliers in transaction data that do not conform to expected behavior. Deep learning, a subset of machine learning, has shown exceptional promise in fraud detection due to its ability to process and analyze unstructured data such as images, text, and voice. Techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are utilized in applications ranging from credit card fraud detection to anti-money laundering (AML) efforts. Natural language processing aids in detecting fraudulent activities by analyzing textual data, such as emails and transaction descriptions, to identify suspicious language and patterns. AI's application in fraud prevention extends beyond detection to proactive measures. Predictive analytics powered by AI can forecast potential fraud hotspots, allowing organizations to implement preventative strategies. Real-time monitoring systems, enhanced by AI, provide instantaneous alerts for suspicious activities, enabling swift action to mitigate fraud. The integration of AI in fraud prevention presents challenges, including data privacy concerns, the need for high-quality datasets, and the interpretability of AI models. However, the benefits far outweigh these hurdles, as AI continues to enhance the accuracy, efficiency, and scalability of fraud prevention efforts. As AI technologies evolve, their role in safeguarding financial systems and reducing fraud losses will only grow, underscoring the importance of continued innovation and research in this field. Keywords: AI, Fraud Prevention, Technique, Application, Exploring.Computer Science &amp; IT Research Journal021The various AI techniques and their applications in fraud prevention are explored, highlighting their transformative impact on the security landscape and the importance of continued innovation and research in this field.Computer Science &amp; IT Research Journal["Oluwabusayo Adijat Bello", "Komolafe Olufemi"]2024-06-27T00:00:00 aa8e6735bcfabaffe1a784b5a95d5144f5567787The artificial intelligence (AI) tools in editorial departments have become common practice within news organisations, which poses challenges for digital journalism. It treads new terrain for both media professionals and their audiences, and it is safe to assume there is no going back to the way things were. These advances in the field require new frameworks and codes of ethics that include ethical principles to mitigate the use of AI in journalism. The fast incorporation of AI into media production processes is marked by a tendency towards the loss of citizens’ trust in the information that media offers, political polarization, and the increasing impact of misinformation. This article analyses the perception of communication professionals in this new scene through the analysis of 99 codes of ethics and 14 international press associations. In addition, audience perception is addressed through a survey taken by nearly 2,000 people. The results indicate that both the public and journalists are worried about misinformation that AI might cause and the potential erosion of trust between journalist and the public . Overwhelmingly, people are advocating for external regulation of its use to preserve the values, the ethical principles, and good practices of journalistic work.Communication &amp; Society528The results indicate that both the public and journalists are worried about misinformation that AI might cause and the potential erosion of trust between journalist and the public, and people are advocating for external regulation of its use to preserve the values, the ethical principles, and good practices of journalistic work.Communication &amp; Society["Tania Forja-Pena", "Berta Garc\u00eda-Orosa", "Xos\u00e9 L\u00f3pez-Garc\u00eda"]2024-06-27T00:00:00 190694b532f80dbcbc60f3914ec1ab8f448d7834Artificial intelligence (AI) is revolutionizing plastic surgery through its remarkable advancements in various domains such as image analysis, robotic assistance, predictive analytics, and augmented reality. Predictive analytics, powered by AI, harnesses patient data to predict surgical outcomes, minimize risks, and tailor treatment plans, thereby optimizing patient care and safety. Augmented reality and virtual reality technology are also reshaping the cosmetic surgery landscape, providing immersive experiences for preoperative imaging, intraoperative guidance, and advanced skills through simulation. Looking ahead, the future of AI in plastic surgery holds great promise, including personalized medicine, bioprinting of tissues and organs, and continuous learning through iterative improvement algorithms based on real-world surgical experience. However, amid these transformational advances, ethical considerations and regulatory frameworks must evolve to ensure the responsible deployment of AI, protect patient privacy, minimize errors and algorithmic deviation, and uphold standards of fairness and transparency. Our study aims to explore the role of AI in the field of plastic surgery with the potential for the future in mind. In summary, AI is considered a beacon of innovation in plastic surgery, enhancing surgical precision, enhancing patient outcomes, and heralding a future where interventions rely on personalized technology that will redefine the boundaries of aesthetic and regenerative medicine.Cosmetics04Artificial intelligence is considered a beacon of innovation in plastic surgery, enhancing surgical precision, enhancing patient outcomes, and heralding a future where interventions rely on personalized technology that will redefine the boundaries of aesthetic and regenerative medicine.Cosmetics["Tran Van Duong", "Vu Pham Thao Vy", "Truong Nguyen Khanh Hung"]2024-06-27T00:00:00 7378166273fc8bfee911016d6caf5f4eb764b7e1Artificial intelligence (AI) is a reality of our times, and it has been successfully implemented in all fields, including medicine. As a relatively new domain, all efforts are directed towards creating algorithms applicable in most medical specialties. Pathology, as one of the most important areas of interest for precision medicine, has received significant attention in the development and implementation of AI algorithms. This focus is especially important for achieving accurate diagnoses. Moreover, immunohistochemistry (IHC) serves as a complementary diagnostic tool in pathology. It can be further augmented through the application of deep learning (DL) and machine learning (ML) algorithms for assessing and analyzing immunohistochemical markers. Such advancements can aid in delineating targeted therapeutic approaches and prognostic stratification. This article explores the applications and integration of various AI software programs and platforms used in immunohistochemical analysis. It concludes by highlighting the application of these technologies to pathologies such as breast, prostate, lung, melanocytic proliferations, and hematologic conditions. Additionally, it underscores the necessity for further innovative diagnostic algorithms to assist physicians in the diagnostic process.Journal of Personalized Medicine1483The applications and integration of various AI software programs and platforms used in immunohistochemical analysis are explored, highlighting the application of these technologies to pathologies such as breast, prostate, lung, melanocytic proliferations, and hematologic conditions.Journal of Personalized Medicine["Diana Gina Poalelungi", "Anca-Iulia Neagu", "A. Fulga", "M. Neagu", "Dana Tutunaru", "A. Nechita", "I. Fulga"]2024-06-27T00:00:00 26c2794ed945a595c556e4c15ce3a64f175cb31bThe integration of artificial intelligence (AI) in educational measurement has revolutionized assessment methods, enabling automated scoring, rapid content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide timely, consistent feedback and valuable insights into student performance, thereby enhancing the assessment experience. However, the deployment of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes pose risks of perpetuating inequalities and affecting assessment outcomes. Responding to these concerns, various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. The National Council of Measurement in Education's Special Interest Group on AI in Measurement and Education (AIME) also focuses on establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI's responsible and effective use in education.arXiv.org1963A diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI's responsible and effective use in education.ArXiv["Okan Bulut", "Maggie Beiting-Parrish", "J. Casabianca", "Sharon C. Slater", "Hong Jiao", "Dan Song", "Chris Ormerod", "Deborah Gbemisola Fabiyi", "Rodica Ivan", "Cole Walsh", "Oscar Rios", "Joshua Wilson", "S. Yildirim-Erbasli", "Tarid Wongvorachan", "Joyce Xinle Liu", "Bin Tan", "Polina Morilova"]2024-06-27T00:00:00 09b6a20265146205f83566ca11f7761fbc22107eThis article explores the transformative potential of integrating artificial intelligence (AI) technologies into English reading teaching in senior high school settings. With a focus on improving reading comprehension skills, enhancing personalized learning experiences, and providing adaptive assessment and feedback mechanisms, this article examines various AI applications tailored to the needs of high school students. Through a comprehensive review of relevant literature and case studies, it elucidates the benefits, challenges, and best practices associated with AI-driven approaches to English reading instruction. Additionally, this article offers insights into the pedagogical implications and future directions for harnessing AI to optimize reading instruction in senior high schools.Academic Journal of Management and Social Sciences61The transformative potential of integrating artificial intelligence technologies into English reading teaching in senior high school settings and the pedagogical implications and future directions for harnessing AI to optimize reading instruction in senior high schools are explored.Academic Journal of Management and Social Sciences["Maiyu Jin"]2024-06-27T00:00:00 aa082e4f01753c98a53f79e05f28fb1abf54b6eaThe study examines the issue of digitalisation of public administration. After outlining the theoretical foundations, an international framework for the creation of digital public administration is analyzed, followed by a discussion of its development in Hungary and its evaluation. In Hungary, the related legislation was initially introduced with the implementation of electronic administration, the anomalies of which were first noticeable with the introduction of electronic birth registration. In this context, the study discusses the possibilities of applying artificial intelligence (AI) in the public sector and reviews current and existing applications and good solutions, as well as possible development directions. The article describes the successes of AI applications in the financial sector and then goes on to discuss automated decision-making in more detail, as well as the planned legislative thinking on the subject.Studia Iuridica Lublinensia01The article describes the successes of AI applications in the financial sector and then goes on to discuss automated decision-making in more detail, as well as the planned legislative thinking on the subject.Studia Iuridica Lublinensia["A. Bencsik"]2024-06-27T00:00:00 d0fd993e96f5c1cea3145156e5db8b98f805fb94The purpose of this article is to consider the prospects for the integration of blockchain and artificial intelligence (hereinafter referred to as AI) as an innovative approach to modernisation of various economic sectors. The authors analyse the possibilities of using this technological merger to optimise business processes, increase transparency and reduce transaction costs in different sectors, including finance, healthcare, transport, energy, etc. Particular attention is paid to the benefits of synergy between AI and distributed ledger technology, which allows for more efficient and sustainable systems of managing data and assets. The research object in this article is the process of integrating blockchain and AI. The subject of the study is the effectiveness of modernisation of various economic sectors through the combination of these technologies. The research method is an analytical review of scientific publications and successful implemented projects. The results of the current article lie in the analysis of the advantages of integrating blockchain and AI as well as forecasting further prospects for the development of this area. The practical significance of the work consists in the fact that the obtained results can be used to design strategies and plans for the implementation of this technology integration into domestic business.Вестник университета131The authors analyse the possibilities of using this technological merger to optimise business processes, increase transparency and reduce transaction costs in different sectors, including finance, healthcare, transport, energy, etc.Vestnik Universiteta["N. A. Kashevarova", "M. E. Kulikova"]2024-06-27T00:00:00 e99762d384f638b28aad0aee62a4b88cec3f8a6aEuropean Radiology242An overview of available evidence for the integration of prostate MRI and AI in active surveillance is provided, addressing its potential for clinical optimizations in the context of established guidelines, while highlighting the main challenges for implementation.European Radiology["Vilma Bozgo", "Christian Roest", "I. V. van Oort", "Derya Yakar", "Henkjan Huisman", "M. de Rooij"]2024-06-27T00:00:00 847290192852f74e40e7f27da444336d49753ed6INTRODUCTION While most orthopaedic journals permit the use of artificial intelligence (AI) in article development, they require that AI not be listed as an author, that authors take full responsibility for its accuracy, and that AI use be disclosed. This study aimed to assess the prevalence and disclosure of AI-generated text in abstracts published in high-impact orthopaedic journals. METHODS Abstracts published from January 1, 2024, to February 19, 2024, in five orthopaedic journals were analyzed: the American Journal of Sports Medicine; the Journal of Arthroplasty; the Journal of Bone and Joint Surgery; the Knee Surgery, Sports, Traumatology, and Arthroscopy (KSSTA) journal; and the BMC Musculoskeletal Disorders (BMC MD) journal. Artificial intelligence detection software was used to evaluate each abstract for AI-generated text. Disclosure of AI use, country of origin, and article type (clinical, preclinical, review, or AI/machine learning) were documented. To evaluate the accuracy of AI detection software, 60 consecutive articles published in the Journal of Bone and Joint Surgery in 2014, before AI writing software was available, were also evaluated. These abstracts were evaluated again after being rewritten with AI writing software. The sensitivity and specificity of the software program for AI-generated text were calculated. RESULTS A total of 577 abstracts were included in the analysis. AI-generated text was detected in 4.8% of abstracts, ranging from 0% to 12% by journal. Only one (3.6%) of the 28 abstracts with AI-generated text disclosed its use. Abstracts with AI-generated text were more likely to be from the Asian continent (57.1% vs. 28.0%, P = 0.001) and to involve topics of AI or machine learning (21.4% vs. 0.6%, P < 0.0001). The sensitivity and specificity of the AI detection software program were determined to be 91.7% (55/60) and 100% (60/60). DISCUSSION A small percentage of abstracts published in high-impact orthopaedic journals contained AI-generated text, and most did not report the use of AI despite journal requirements. LEVEL OF EVIDENCE Diagnostic Level III.Journal of the American Academy of Orthopaedic Surgeons111A small percentage of abstracts published in high-impact orthopaedic journals contained AI-generated text, and most did not report the use of AI despite journal requirements, but the sensitivity and specificity of the software program for AI-generated text were calculated.The Journal of the American Academy of Orthopaedic Surgeons["Ben D. Pesante", "Cyril Mauffrey", "J. Parry"]2024-06-27T00:00:00 795234d8e9817ea087a6f06486df09e3f4f3884cIn today's digital age, artificial intelligence (AI) has revolutionized many sectors, including auditing and finance, with the potential to improve the efficiency and effectiveness of the audit process. However, there is limited understanding of the impact of AI implementation on financial statement transparency and external auditor reputation. This study aims to fill that knowledge gap by exploring how AI can strengthen financial statement transparency and enhance auditor reputation. The methodology used is descriptive-analytical with an empirical normative approach, collecting data through literature review and documentation. The results finds that the integration of AI in auditing significantly improves financial statement transparency, facilitates auditors in conducting more in-depth and accurate analyses, and potentially enhances the reputation of external auditors in the eyes of stakeholders. The findings confirm that AI not only improves the efficiency of the audit process but also plays a strategic role in building trust and integrity in financial reporting. The implications of this research are significant in demonstrating the importance of adapting the latest technologies to meet and exceed evolving financial and reputational expectations in the digital age.Jurnal Ilmu Keuangan dan Perbankan (JIKA)542The integration of AI in auditing significantly improves financial statement transparency, facilitates auditors in conducting more in-depth and accurate analyses, and potentially enhances the reputation of external auditors in the eyes of stakeholders.Jurnal Ilmu Keuangan dan Perbankan (JIKA)["Zakaria Kuswara", "Marsel Pasaribu", "Fitriana Fitriana", "Rachmat Agus Santoso"]2024-06-27T00:00:00 cbdb6d46f06fb9dd620162368033343dbd43ed54Liver transplantation is the ultimate therapeutic option for patients with end-stage liver disease. The clinical management of transplant patients significantly impacts their prognosis, with outcomes influenced by multiple interacting variables. Cardiovascular complications count as a leading cause of both short-term and long-term morbidity and mortality in liver transplant recipients. In this respect, accurate risk assessment and stratification are crucial for optimizing clinical outcomes. Modern artificial intelligence (AI) techniques have significant potential for early risk prediction, providing comprehensive risk assessments in both diagnosed cohorts and early clinical phase patients. This systematic review examines the state of the art in AI applications for predicting cardiovascular risks in liver transplantation, identifying current issues, challenges, and future research directions. We reviewed articles from digital repositories such as PubMed, IEEE Xplore, and ScienceDirect published between 2000 and 2023, using keywords including artificial intelligence, machine learning, cardiovascular, and liver transplantation. Our analysis revealed a diverse range of machine learning algorithms used in this domain. Despite the potential, only 12 papers met the criteria for adequate topic coverage, highlighting a scarcity of research at this intersection. Key challenges include integrating diverse datasets, isolating cardiovascular effects amid multifaceted influences, ensuring data quality and quantity, and the issues to extrapolate machine learning models to day-to-day clinical practice. Nevertheless, leveraging AI for risk prediction in liver transplantation could significantly enhance patient management and resource optimization, indicating a shift towards more personalized and effective medical practices.F1000Research261A systematic review of articles from digital repositories using keywords including artificial intelligence, machine learning, cardiovascular, and liver transplantation found only 12 papers met the criteria for adequate topic coverage, highlighting a scarcity of research at this intersection.F1000Research["Netra Hirani", "Parag Chatterjee"]2024-06-27T00:00:00 1b06c3f8e8366c6e1fa0c4e5cda74b7a6f9113cdBackground: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro–Wilk test for normality, the Kruskal–Wallis H test for group differences, and the Mann–Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.Diagnostics432The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models, highlighting the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.Diagnostics["F. Ya\u011f\u0131n", "Cemil \u00c7olak", "Abdulmohsen Algarni", "Yasin Gormez", "Emek Guldogan", "L. P. Ardig\u00f2"]2024-06-27T00:00:00 e95b762a60e99897cf000e067f6fa45d87d0deadThis comprehensive study examines the role of Artificial Intelligence (AI) in modem education, focusing on its advantages, challenges, and strategies for effective implementation. Through a multi-faceted methodological approach including literature review, case studies, expert interviews, and data analysis, the research reveals significant benefits of AI in personalized learning, intelligent tutoring, automated grading, and predictive analytics. However, it also highlights critical challenges such as the digital divide, data privacy concerns, educator preparedness, and ethical considerations. The study presents practical implementations of AI in both higher education and K-12 settings, demonstrating improvements in student performance, engagement, and administrative efficiency. Global comparisons of AI adoption in education are also provided. The article concludes with recommendations for addressing challenges and leveraging AI's potential to create more effective, equitable, and personalized learning experiences. This research offers valuable insights for policymakers, educators, and technologists seeking to navigate the complex landscape of AI integration in education.Vestnik of M. Kozybayev North Kazakhstan University41The research reveals significant benefits of AI in personalized learning, intelligent tutoring, automated grading, and predictive analytics, however, it also highlights critical challenges such as the digital divide, data privacy concerns, educator preparedness, and ethical considerations.Vestnik of M. Kozybayev North Kazakhstan University["Ye. M. Zhunussov"]2024-06-27T00:00:00 11ade704d8f5cd9b71b9ae00bd986d7f99035348From desktop design to virtual imaging, digital media art has jumped beyond the "screen" and undergone tremendous changes in the past few decades. Its application fields have also shifted from the initial graphic design and film and television special effects to display design, military, geography and other fields. Digital media art brings novel audio-visual experiences and provides convenience in life. Human activities are closely connected with digital media. Digital media art relies on digital media technology to artistically display creativity, which has a profound impact and is crucial to the development and growth of cultural and creative industries. In order to explore the characteristics and future development of digital media art, this article uses interdisciplinary research methods, comparative research methods, literature research methods, etc. to understand the relationship between digital media technology and artificial intelligence, from the perspectives of technology, communication, and art Let’s observe the current performance of digital media art as it becomes more and more intelligent, find out the problems and solutions hidden under the wave of intelligence, and explore the innovative development strategies of digital media art.Frontiers in Computing and Intelligent Systems101The current performance of digital media art as it becomes more and more intelligent is observed, the problems and solutions hidden under the wave of intelligence are found, and the innovative development strategies of digital media art are explored.Frontiers in Computing and Intelligent Systems["Sixian Li"]2024-06-27T00:00:00 a02b178f1b30d5e9999154d6cdf68e185841b37aUrologie11ChatGPT is trained with millions of texts from the internet, social media, online forums, journal articles, and books; it covers almost all areas of life.Urologie["J. M. Wenderlein"]2024-06-27T00:00:00 6c6ad0a7d8341f15f4d6c4bbbf7b1a3c5d4ef923The research explores the hiring and selection processes of Information Technology (IT) firms in Hyderabad, with a focus on state-of-the-art technologies such as Artificial Intelligence (AI), chat platforms, social media, and virtual reality. It seeks to comprehend the strategies employed in talent acquisition amidst Hyderabad’s ascent as an IT center. Artificial Intelligence has notably expedited candidate identification and screening, while chat boards and social media platforms facilitate community-building and engagement with potential hires. Virtual Reality adds an immersive dimension to recruitment experiences. Employing a quantitative survey methodology, the study aims to assess the impact of these technologies on talent acquisition within Hyderabad’s IT industry. The findings aspire to offer valuable insights for strategic decision-making in the competitive IT arena. Emphasizing the importance of recruitment processes in acquiring skilled personnel, the research examines prevalent practices and challenges encountered by IT companies in Hyderabad. It concludes that effective recruitment practices are widespread in the region. Furthermore, the study sheds light on how leading IT enterprises leverage AI, Chabot’s, Social Media, and Virtual Reality to revamp their recruitment and selection approaches, evaluating the drivers, obstacles, and efficacy of these technologies in attracting top talent and enhancing the candidate experience amidst Hyderabad’s evolving IT landscape.European Conference on Artificial Intelligence231How leading IT enterprises leverage AI, Chabot’s, Social Media, and Virtual Reality to revamp their recruitment and selection approaches is examined, evaluating the drivers, obstacles, and efficacy of these technologies in attracting top talent and enhancing the candidate experience amidst Hyderabad’s evolving IT landscape.2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)["Thaya Madhavi", "Avulakunta Kaveri"]2024-06-27T00:00:00 ce1107e2dcabe8bdff5c8177bae11601f2f0d655BACKGROUND AND OBJECTIVES Mental health disorders, including anxiety and depression, are the leading causes of global health-related burden and have increased dramatically since the 1990s. Delivering mental healthcare using artificial intelligence chatbots may be one option for closing the gaps in mental healthcare access. The overall aim of this scoping review was to describe the use, efficacy, and advantages/disadvantages of using an artificial intelligence chatbot for mental healthcare (stress, anxiety, depression). METHODS PubMed, PsycINFO, CINAHL, and Web of Science databases were searched. When possible, Medical Subject Headings terms were searched in combination with keywords. Two independent reviewers reviewed a total of 5768 abstracts. RESULTS Fifty-four articles were chosen for further review, with 10 articles included in the final analysis. Regarding quality assessment, the overall quality of the evidence was lower than expected. Overall, most studies showed positive trends in improving anxiety, stress, and depression. DISCUSSION Overall, using an artificial intelligence chatbot for mental health has some promising effects. However, many studies were done using rudimentary versions of artificial intelligence chatbots. In addition, lack of guardrails and privacy issues were identified. More research is needed to determine the effectiveness of artificial intelligence chatbots and to describe undesirable effects.Computers, Informatics, Nursing331Overall, using an artificial intelligence chatbot for mental health has some promising effects, but more research is needed to determine the effectiveness of artificial intelligence chatbots and to describe undesirable effects.Computers, informatics, nursing : CIN["Cara Gallegos", "Ryoko Kausler", "Jenny Alderden", "Megan Davis", "Liya Wang"]2024-06-27T00:00:00 fff42f36e80856a177c9644400a4753a081a766cManagement systems help organizations achieve their strategic direction, and enhance the effectiveness and efficiency of their processes. The ISO management system standards are based on a pivotal principle - the process approach. The first edition of ISO 42001 - the standard for Artificial Intelligence (AI) Management Systems (MS) was published in December 2023. This is a good opportunity for organizations to keep up to date with the modern context and integrate AI into their MS. This paper presents a model that helps to establish the input clauses and output clauses of individual ISO 42001 processes, better understand the interactions between the processes, and create an overall view and representation of the AI MS based on these processes.2024 9th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE)101A model is presented that helps to establish the input clauses and output clauses of individual ISO 42001 processes, better understand the interactions between the processes, and create an overall view and representation of the AI MS based on these processes.2024 9th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE)["Tzvetelin Gueorguiev"]2024-06-27T00:00:00 71e7f194082b3a3667f72ce3f256e05b272d1c84Salah satu model pembelajaran yang direkomendasikan, yaitu PBL. Kolaborasi model pembelajaran PBL dengan memanfaatan teknologi menjadi salah satu pilihan untuk menghasilkan lingkungan belajar yang variatif, dan efektif dalam mencapai tujuan pembelajaran, dan mencapai nilai-nilai profil pelajar Pancasila, salah satunya nilai kemampuan berpikir peserta didik. Penelitian yang sudah dilakukan ini memiliki tujuan melihat sejauh apa Problem Based Learning berbasis Artificial Intelligence dalam meningkatkan kemampuan berpikir siswa. Penelitian dilaksanakan di SMA Negeri 1 Merauke kelas X.A.5 semester ganjil Tahun Ajaran 2023/2024. Kelas yang digunakan 1 kelas, jumlah siswa sebanyak 36 orang, teknik pengambilan sampel menggunakan cluster random sampling. Jenis penelitian yang diterapkan quasi eksperimen, data diambil melalui instrumen test berbentuk essay sebanyak 5 soal yang sudah divalidasi terlebih dahulu. Desain yang digunakan one group pretest posttest. Perlakuan yang diberikan melalui model PBL dilaksanakan dengan mengikuti 5 tahapan, yaitu: orientasi siswa kepada masalah, pengorganisasian/kelompok, membimbing penyelidikan, pengembangan-demonstrasi hasil, dan evaluasi, proses dibantu dengan media pembelajaran berbasis teknologi AI, yakni Tome.App. Analisis n-gain menggunakan SPSS 25. PBL berbasis AI memberikan dampak positif terhadap kemampuan berpikir siswa pada topik Pengukuran, hasil yang diperoleh bahwa kemampuan berpikir fisika siswa rata-rata bernilai 62, dimana n-gain menunjukkan 0.41 yang artinya berada pada kategori sedang. PBL berbasis AI sebagai salah satu solusi dalam menciptakan pembelajaran fisika yang efektif di kelas.Jurnal Pendidikan Fisika351Jurnal Pendidikan Fisika["Ika Trisni Simangunsong", "Kristina Uskenat", "Delson A. Gebze"]2024-06-27T00:00:00 f7b4f4b814c935ac8faf087a6e6f07c34d586a4dThis study examined how artificial intelligence (AI) has transformed the personalization of the virtual educational process in higher education programs. A systematic review of literature published between 2012 and 2023 was carried out, evaluating empirical studies, reports and review articles available in academic databases such as IEEE Xplore, SpringerLink and Google Scholar. Methods discussed include intelligent tutoring systems, learning analytics, and recommendation systems. The results showed that AI significantly improved the personalization of learning. Intelligent tutoring systems provide real-time adaptive feedback, adjusting content and pacing based on students' individual needs, improving their understanding and retention. Learning analytics helps identify student behavior patterns and predict academic issues, thereby facilitating timely interventions that help improve performance. Additionally, recommender systems personalize study materials based on student preferences and progress, thereby optimizing the educational experience. However, significant challenges have been identified, such as the need to protect data privacy and mitigate algorithmic biases that can affect the fairness and efficiency of these systems. In conclusion, the integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance. However, there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefitsData and Metadata80The integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance, but there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefits.Data and Metadata["Elizabeth Magdalena Recalde Drouet", "David Mauricio Tello Salazar", "Tatiana Lizbeth Charro Dom\u00ednguez", "Pablo Jord\u00e1n Catota Pinthsa"]2024-06-27T00:00:00 27ca2110028a4691351de5f2f67fb9d9a2465216The aim of this study is to evaluate the efficiency of ChatGPT versions 3.5 and 4 for training tourist guides. The study followed a systematic approach by conducting assessments on undergraduate students from three institutions who are enrolled in tourist guide education programs and both ChatGPT versions. Competent academicians assessed a succession of questions in the form of open-ended and multiple-choice questions. The mean scores obtained on the multiple-choice test for ChatGPT-4 were better than those of both ChatGPT-3.5 and college students, thereby indicating that ChatGPT-4 has greatly improved. Nevertheless, when responding to open-ended queries, individuals with real-life experience as tour guides gave much more inclusive as well as convincing answers compared to ChatGPT-4. This underscores the importance of hands-on experiences in training tour guides, where AI technology is currently weak. This study contributes to better comprehension regarding the role played by artificial intelligence (AI) in education with reference to the tourism industry specifically. While at the same time emphasizing how critical human expertise is needed during practical learning sessions, this implies that AI has potential for disseminating theoretical knowledge. The results suggest that AI is a beneficial supplementary aid in educational environments, rather than a replacement for human-centered instructional approaches.Journal of Multidisciplinary Academic Tourism660The results suggest that AI is a beneficial supplementary aid in educational environments, rather than a replacement for human-centered instructional approaches, and implies that AI has potential for disseminating theoretical knowledge.Journal of Multidisciplinary Academic Tourism["Abdullah \u00dclk\u00fc"]2024-06-27T00:00:00 2e12bd32d64b35dbb469b3e9ca57777d092fc5ceThe consistent practice of digitalization shows the ‘emergence’ of a new quality of work of development institutes, businesses. The further development of the infrastructure, taking into account innovations and ‘digits’, will make it possible to fulfill important tasks in the partial implementation of the directions of progressive digital development of the regions of Ukraine outlined by the government in the post-war period. The purpose of the article is research and data analysis in terms of sub-indices of digital transformation of the regions of Ukraine with the aim of finding opportunities for digitization of infrastructure and working business processes. The authors consider three stages of the development of artificial intelligence through the prism of digitalization of the economy and virtual reality, namely: basic artificial intelligence, universal artificial intelligence, artificial super intelligence. The article attempts to present the opportunities created as a result of industry digital transformation, the development of the Internet, the penetration of basic e-services, the introduction of a paperless regime, and the development of centers for the provision of administrative services. The sub-index ‘Penetration of basic e-services’ shows positive dynamics. Improvement of this sub-index indicates an improvement in terms of connection to the Unified Information System of the Social Sphere; availability in all departments of the State Registration of Civil Status Acts, maternity hospitals e-Malyatko; connecting the Technical Inventory Bureau to the State Register of Property Rights. It was concluded that the new virtual reality in combination with the ever-present application of the latest information and communication technologies is the driving force behind the formation of Industry 5.0, innovations are ‘born’, and new types of digital services appear. The IoT, big data analytics, AI, communicativeness and networking of interactions, diffusion of innovations and Internet technologies become the impetus for digital and innovative changes in all sectors of the economy.Economics. Finances. Law20The article attempts to present the opportunities created as a result of industry digital transformation, the development of the Internet, the penetration of basic e-services, the introduction of a paperless regime, and the development of centers for the provision of administrative services.Economics. Finances. Law["K. Kraus", "N. Kraus"]2024-06-27T00:00:00 5f24e912d6496a7c891425b25db2ead130a223ebKnowledge and library professionals in the UK are exploring the use of generative artificial intelligence (AIand contributing to discussions concerning data and knowledge, in the context of a country keen to drivethe adoption of data driven services and digital technologies. In this article we introduce the driversadoption of AI within NHS Knowledge and Library Services (KLS) in England, and the methodologies employedto upskill staff in new technologies. This is set against the backdrop of the ethics and risks associated withwhich provide opportunities for KLS to improve services and support the safe and effective adoption of AIfollow up article we provide practical use case studies, to help inspire experimentation and adoption.Journal of EAHIL80The drivers of adoption of AI within NHS Knowledge and Library Services in England, and the methodologies employed to upskill staff in new technologies are introduced.Journal of EAHIL["Emily Hopkins", "Susan Smith", "Hannah Wood"]2024-06-27T00:00:00 02942df06456b53ffd8e958f373e81a000b1485cBecause current conceptualisations of how to achieve Artificial Intelligence are data driven, so information professional skills applied to data become highly relevant. Translating our well established information skills to the context of data management and stewardship could be invaluable in such areas as data search, understanding data provenance, copyright issues, promoting data sharing and standards based description of data, data disposition or preservation, data ethics, and in promoting data literacy. As a profession we have a valuable and unique contribution to make through information skills applied to data, but we need to include data more in our vocabulary and thinking.Journal of EAHIL00Journal of EAHIL["Andrew Cox"]2024-06-27T00:00:00 d499aef63ad41295dd9f164d880f617610da1c1cIn today’s technological era, as AI systems become more integral to critical decision-making, the importance of Explainable Artificial Intelligence (XAI) has become more pronounced. It addresses the challenge of understanding complex machine learning and deep learning models, ensuring transparency, interpretability, and accountability. This research paper provides a comprehensive analysis of XAI, focusing on its significance, methodologies, challenges, and future prospects. Theoretical foundations of XAI are elucidated, clarifying key concepts such as interpretability, transparency, and accountability. We differentiate between model-agnostic and model-specific XAI methods, outlining their strengths and limitations. A range of recent XAI techniques, including Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM), are scrutinized. Through case studies in Healthcare (Pneumonia Classification), Finance (Stock Price Prediction), and Entertainment (Sentiment Analysis), we demonstrate how XAI enhances the understandability and trustworthiness of AI systems. Additionally, a comparative study of all three methods on all three case studies has been conducted, and the results are compared. Challenges such as scalability issues and ethical considerations, including biases and fairness, are discussed. Looking ahead, we offer insights into future XAI research trajectories, aiming to foster public trust and shape a future where AI systems are both intelligent and comprehensible.European Conference on Artificial Intelligence210A comprehensive analysis of XAI, focusing on its significance, methodologies, challenges, and future prospects, and distinguishes between model-agnostic and model-specific XAI methods, outlining their strengths and limitations.2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)["Kriti Srivastava", "Afreen Sorathiya", "Jinal Mehta", "Vineet Chotaliya"]2024-06-27T00:00:00 c11ecefc4f982411b015028bddf2e9f31b51eeeeArtificial intelligence has been seeping into various fields of international law for some time, affecting fields such as international humanitarian law – especially regarding the legality of autonomous weapon systems, but also intellectual property law and the legal profession as a whole. A conflicting zone encompassing many subfields is human rights, where an already sensitive subject that is open to debates and interpretation is met with rough questions. For instance, should and could human rights norms be transferred into pre-programmed entities? What relevance can human rights have to a non-human being that has been created, programmed and assembled by humans? Vast regional differences exist between the European, African and Inter-American systems with a lack of coherent structure in the Asia-Pacific region. Our understanding of human rights has also developed substantially over the decades, especially regarding norms on slavery, free speech, the prohibition of discrimination and the rights of women, of disabled persons and indigenous peoples to name a few examples. Furthermore, a vast array of international documents on human rights are political manifestos utilising expressions such as “respecting” and “ensuring” human rights as obligations for members of the international community. Since these provisions deliberately leave a lot of room for interpretation, it seems almost an impossible task to translate them to “binary code”, to a format that is digestible for an artificial entity. The article aims to answer these questions by analysing the abovementioned line of thought and combining it with various attempts at international regulation by states, international organisations as well as non-governmental organisations and think-tanks. The fundamental focus of this paper is to ascertain whether human rights and AI can be made compatible under the current framework of international law at today’s level of development.Acta Humana00Whether human rights and AI can be made compatible under the current framework of international law at today’s level of development is ascertained.Acta Humana["Andr\u00e1s H\u00e1rs"]2024-06-27T00:00:00 f23ef44746b8e9f6c7dc4d2f05e8e46bb2bd0dd6The realm of Artificial Intelligence is the discipline of crafting intelligent machines and smart computer systems through science and engineering. It is connected with parallel endeavors in utilizing computers to grasp human intelligence though intelligence can transcend methods that are biologically observable. Although there is no universally agreed upon definition for Artificial Intelligence (AI), it is typically recognized as the field of computer science which can emulate human cognitive functions that include but are not limited to processes like perception, reasoning or decision-making. This research paper focuses on what artificial intelligence is all about, how we are depended onto AI in our daily life or what are the applications of AI, Different AI tools or machines powered by AIInternational Journal of Advanced Research in Science, Communication and Technology00This research paper focuses on what artificial intelligence is all about, how the authors are depended onto AI in their daily life or what are the applications of AI, Different AI tools or machines powered by AI.International Journal of Advanced Research in Science, Communication and Technology["Rohit Suryawanshi", "Surajkumar Singh"]2024-06-27T00:00:00 ec5cf0f7b79a6647cf35fe69a9c3fcc39d6c8318The rapid development of artificial intelligence (AI) technology has brought great changes to the lives of mankind. The emergence of AI as part of the rapid evolution of digitas technology has led to major changes in the world's lives. This research examines the impact of AI digitalization on Indonesia's workforce and the role of regulation in mitigating negative impacts. It emphasizes the need for holistic evaluation of labor law and modernization of professional job training programs. The research results show that the use of AI in Indonesia is increasing in various sectors, including the employment sector. Moreover, data privacy protection is still inadequate to ensure data protection and privacy of AI technology users. Therefore, it is necessary to update and add regulations that regulate comprehensively, covering data protection, algorithm transparency, occupational safety and health, elimination and specific retraining related to AI, and labor rights relevant to technological developments. It is also necessary to implement new regulations that provide legal protection for the application of AI can minimize the occurrence of cybercrimes on customer data.Devotion : Journal of Research and Community Service300Devotion : Journal of Research and Community Service["Imam Budi Santoso", "Wiwin Triyunarti", "A. Farhani", "Faiqah Nur Azizah", "Ade Maman Suherman"]2024-06-27T00:00:00 3768b4fc04cf7e92dc8124d78266b1976c4cfbd8 Coronary computed tomography angiography (CCTA) plays an important role in the modern assessment of coronary artery disease (CAD). With the increasing number of CCTA examinations, technologies such as Artificial Intelligence (AI) that reduce reader workload and improve inter-reader agreement are of great interest. The Coronary Artery Disease Reporting and Data System (CAD-RADS) is a structured reporting system to categorise the severity of CAD and guide patient management. Patients with low CAD-RADS categories (1-2) usually need preventive medical therapies but no downstream testing. However, patients in more severe categories (CAD-RADS 3-5) may require further testing, such as stress tests or invasive coronary angiography. The aim of this study was to assess the potential of an on-site AI to differentiate between non-obstructive and potentially obstructive CAD. In this study, 395 patients (65.3% male, 64.9 ± 10.0 years) who underwent a clinically indicated CCTA examination were retrospectively included. Cases with significant artefacts and coronary artery segments <2 mm in diameter were not included into the analysis. The severity of CAD was graded as non-obstructive (CAD-RADS ≤2) or potentially obstructive (CAD-RADS ≥3) by consensus of two highly experienced readers. The final study population consisted of 50.6% non-obstructive and 49.4% obstructive CAD patients. The CCTA reconstruction with the best image quality was selected, transferred to the on-site AI prototype and fully automatically processed to yield the CAD-RADS category. Cohen's kappa and intraclass correlation were used to compare the results of the readers and the AI prototype. In the subgroup with non-obstructive CAD (n=200, 58.0% male), there was high agreement between the AI and the readers, with 96.0% of patients being correctly classified by the AI. Accordingly, 92.8% of patients with obstructive CAD (n=195, 72.8% male) were correctly classified by the AI. The results showed very good agreement between human and AI results in the overall population, with a kappa of 0.89 (95% CI 0.84-0.94) and an overall ICC of 0.89 (95% CI 0.84-0.93). AI can reliably differentiate between non-obstructive and obstructive CAD in cases with good image quality. This could help readers to identify low-risk patients and expedite CCTA analysis. However, AI still has some limitations, such as the identification of artefacts in cases with impaired image quality or the detection of totally occluded coronary arteries. Further development is needed to improve its diagnostic accuracy. European Heart Journal - Cardiovascular Imaging00AI can reliably differentiate between non-obstructive and obstructive CAD in cases with good image quality in cases with good image quality, which could help readers to identify low-risk patients and expedite CCTA analysis.European Heart Journal - Cardiovascular Imaging["K. Nagy", "P. Fortner", "M. Aurich", "S. Seitz", "A. Sommer", "J. Gorich", "M. Schobinger", "F. Andre", "S. Bu\u00df"]2024-06-27T00:00:00 3ca04802c0643e145cf58c92424d0e9332494981The proliferation of AI-based techniques poses a range of new challenges for the design and engineering of intelligent and adaptive systems since they tend to either act as black boxes or, more generally, not offer the user sufficient transparency, control, and interaction opportunities, which are considered major goals of user-centred design in the HCI field. This workshop aims to share and discuss recent developments at the intersection of HCI and AI and to explore novel methodological, technical, and interaction approaches. Researchers and practitioners with diverse disciplinary backgrounds can and should contribute to addressing the challenges in this emerging field of human-centred artificial intelligence.User Modeling, Adaptation, and Personalization00This workshop aims to share and discuss recent developments at the intersection of HCI and AI and to explore novel methodological, technical, and interaction approaches to address the challenges in this emerging field of human-centred artificial intelligence.Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization["Daniel Buschek", "Julian Frommel", "H. Hauptmann", "Hendrik Heuer", "A. Smits"]2024-06-27T00:00:00 e7bfe06c9eec03ea6b2ec8a1422666e595dec5ceArtificial intelligence (AI) is one of the most important areas of scientific and technological progress in many countries, including Russia. This technology is included in national innovation development programs, since its mass implementation can double economic growth. The transition to the sixth technological order has begun, in which AI-based technologies will play an important role. Government programs for the development of AI are becoming an important element of the growth of national economies and national security. AI technologies can significantly increase the economic efficiency of those industries where they will be introduced, primarily in the real sector, in medicine, transport, and weapons. The introduction of AI technologies in these industries can have a multiplier effect in related sectors of the economy, e.g. in education. The creation of high-tech jobs is an important intermediate result of the entire complex of measures for the development of AI technology. The article focuses on the study of state incentives for the development of the artificial intelligence industry in Russia. The state supports the industry mainly through national programs, legal regulation, and budget injections. The author maintains that the AI development and implementation system can be improved by following certain principles. The availability of state support can stimulate the emergence of new players in the artificial intelligence market, which, in turn, could lead to accelerated development of the sector.Общество и экономика00The author maintains that the AI development and implementation system can be improved by following certain principles and can stimulate the emergence of new players in the artificial intelligence market, which could lead to accelerated development of the sector.Obshchestvo i ekonomika["Timur Galeev"]2024-06-27T00:00:00 17bebb6b630fcd5573f7adae98483274c7095b5a Artificial intelligence (AI) falls under the purview of computer technology, which analyzes complex data and helps solve problems in different segments. Big Data, Machine Learning, and AI are currently being used by the major pharmaceutical industries to minimize time and costs and increase possibilities. Artificial intelligence is used in the pharmaceutical industry in diverse ways, such as drug discovery and development, clinical trials, disease diagnosis, and different stages in pharmaceutical manufacturing, data analysis, and supply management. Most of the cost and time are involved in drug discovery and clinical trials. Artificial intelligence can minimize human error in data processing, documentation, data integrity issues, and data selection throughout the journey. It works in descriptive, diagnostic, predictive, and prescriptive mode. Major pharmaceutical conglomerates like Pfizer, Roche, Novartis, and Johnson & Johnson have already applied Artificial Intelligence in different segments of pharmaceutical and medicinal science. Tech companies like IBM Watson, Catalia Health, Intel, Microsoft, and Google, in collaboration with pharmaceutical companies, are working in the different areas of drug discovery, early diagnosis, and personalized medicine. Further, AI finds application in the health sector for data management, scanning and evaluation of medical history reports, and finding optimum treatment strategies for chronic care patients. Though lots of research and development are being done on the utilization of artificial intelligence in the pharmaceutical industry, it is still in the nascent stage. This article is our endeavor to study, in detail, the present and future opportunities of machine learning and AI in the pharmaceutical industry as a whole. The Chinese Journal of Artificial Intelligence00This article is an endeavor to study the present and future opportunities of machine learning and AI in the pharmaceutical industry as a whole.The Chinese Journal of Artificial Intelligence["Moumita Das Kirtania", "Dibya Sinha", "Shreya Biswas", "Sania Sultana", "Ranjan Kirtania"]2024-06-27T00:00:00 58dde5def16d7ef9d2081c06715b165320f7e5bfThe economy is currently experiencing a substantial and uncertain transformation propelled by recent advancements in artificial intelligence. Companies that embrace calculated risks and proactively position themselves ahead of the curve will be the ones poised to capitalize on the substantial growth and value-creation opportunities that artificial intelligence offers across nearly every industry. To achieve this, leaders must acknowledge Artificial Intelligence (AI’s) extensive potential as the all-encompassing technology of the twenty-first century. The purpose of this study is to comprehend how artificial intelligence is currently being used in leadership-related areas like hiring, training, and career development. This paper examines the advantages and disadvantages of both using and not using AI in leadership. The transformation of leadership brought about by AI is highlighted in this paper.European Conference on Artificial Intelligence340How artificial intelligence is currently being used in leadership-related areas like hiring, training, and career development is comprehended to comprehend how artificial intelligence is currently being used in leadership-related areas.2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)["Thaya Madhavi", "Divya Bhatt"]2024-06-27T00:00:00 e6ef24d5b9301672b297dc52a2793a86f1d74f98Objectives Lower rates of goals of care (GOC) conversations have been observed in non-white hospitalised patients, which may contribute to racial disparities in end-of-life care. We aimed to assess how a targeted initiative to increase GOC documentation rates is associated with GOC documentation by race. Methods We retrospectively assessed GOC documentation during a targeted GOC initiative for adult patients with an artificial intelligence predicted elevated risk of mortality. Patients were admitted to an urban academic medical centre in Pittsburgh, Pennsylvania between July 2021 and 31 December 2022. Results The 3643 studied patients had a median age of 72 (SD 13.0) and were predominantly white (87%) with 42% admitted to an intensive care unit and 15% dying during admission. GOC documentation was completed for 28% (n=1019/3643). By race, GOC was documented for 30% black (n=105/351), 28% white (n=883/3161) and 24% other (n=31/131) patients (p=0.3933). There was no statistical difference in the rate of documented GOC among races over time (p=0.5142). Conclusions A targeted initiative to increase documented GOC conversations for hospitalised patients with an elevated risk of mortality is associated with similar documentation rates across racial groups. Further research is needed to assess whether this initiative may promote racial equity in GOC documentation in other settings.BMJ Supportive & Palliative Care150A targeted initiative to increase documented GOC conversations for hospitalised patients with an elevated risk of mortality is associated with similar documentation rates across racial groups, with no statistical difference among races over time.BMJ Supportive & Palliative Care["Gina M. Piscitello", "Jane Schell", "Robert M Arnold", "Yael Schenker"]2024-06-27T00:00:00 367ffe6faa0b0972ed941064d93c03caf9c32cb2Artificial Intelligence (AI) is transforming education by enhancing personalized learning, automating routine tasks, and improving both student engagement and teaching effectiveness. However, challenges such as inadequate teacher training, infrastructural limitations, and data privacy concerns hinder AI's widespread adoption in educational settings. This paper presents findings from a survey of 150 educators and classroom observations, highlighting the impact of AI on teaching and learning. Results show significant improvements in student engagement, academic performance, and a reduction in teacher workload in AI-enabled classrooms. Despite the potential of AI, addressing key challenges is critical for its successful integration into education. The paper concludes with recommendations for training, infrastructure development, and ethical considerations. Keywords: Artificial Intelligence, Education, Pedagogy, Learning Technologies, Teacher Training, Data PrivacyJournal of Asian Primary Education (JoAPE)00Findings from a survey of 150 educators and classroom observations show significant improvements in student engagement, academic performance, and a reduction in teacher workload in AI-enabled classrooms.Journal of Asian Primary Education (JoAPE)["AMITABH KUMAR"]2024-06-27T00:00:00 ca3228a17a932171a54919ec28e15fb7094281aaThe second part of this special issue aims to continue to contribute to the debate on the impact, possibilities and challenges that artificial intelligence is bringing to the mass media, media groups and audiences. This new issue draws together five constructive academic articles that address the innovations in this steadily expanding field from different theoretical and methodological perspectives. Specifically, it provides an overview of the integration of automated tools in film editing, explores the regulatory frameworks and policies related to deepfakes in five American states, reflects on the ethics of artificial intelligence, examines how these tools have transformed production routines in the publishing industry and investigates artificial intelligence-generated television presenters in southern Asia. We invite our readers, whether academics or professionals, to join us on this journey of discovery, challenges and, ultimately, opportunity.Communication &amp; Society50An overview of the integration of automated tools in film editing, explores the regulatory frameworks and policies related to deepfakes in five American states, reflects on the ethics of artificial intelligence, and examines how these tools have transformed production routines in the publishing industry.Communication &amp; Society["M. Ufarte-Ruiz", "Sandra-L. Borden", "Llu\u00eds Codina"]2024-06-27T00:00:00 b5677afad692f8fe21cbf8532c25bab568d0824fGiven the increasingly sophisticated technological advancements that are reshaping our current work environment, it is crucial to acknowledge that the concept of artificial intelligence represents a fundamental lever in the development process of the accounting profession, which accountants must integrate. This profession is undergoing significant transformation, as tasks once thought to be exclusive to humans are now being performed by machines. By incorporating artificial intelligence, the accounting profession would experience an evolution through the involvement of robots and machines within their firms, simplifying many tasks and enabling professionals to focus on high-value-added activities. Accountants are developing professional profiles through complex systems based on artificial intelligence, aiming to enhance the skills and performance of their employees while remaining competitive. To achieve this, employees must be ready to adapt and acquire training to effectively embrace and navigate this digital revolution.In line with this objective, this research paper aims to analyze the implication of integrating artificial intelligence on the accounting profession. To accomplish this, the study relies primarily on existing theories and includes a qualitative investigation through semi-structured interviews conducted with 20 accounting firms operating in different cities across Morocco.Salud, Ciencia y Tecnología100This research paper aims to analyze the implication of integrating artificial intelligence on the accounting profession through a qualitative investigation through semi-structured interviews conducted with 20 accounting firms operating in different cities across Morocco.Salud, Ciencia y Tecnología - Serie de Conferencias["Sophia Vandapuye", "Siham Jabraoui"]2024-06-27T00:00:00 ea8a832d200569679f91f87a8d6bb866cd91f63cThis study is designed with an analytical approach that compares and analyzes the views of artificial intelligence algorithms on tourism education. This study, which includes data collection, data analysis, and conclusion-drawing processes, aims to understand, evaluate, and improve the problems related to tourism education from the perspective of artificial intelligence. The questions used in the data collection phase were inspired by the 2023 theme of the 23rd National Tourism Congress, "Tourism Education." The answers obtained through four basic questions directed to ChatGBT 3.5, Jenni, Bearly, and Google Bard artificial intelligence algorithms were collected in August 2023. The average time to answer each question was between 5-20 seconds. The questions were posed in Turkish for the ChatGBT 3.5, Google Bard, and Bearly algorithms, while they were translated into English for Jenni. In the data analysis phase, the long answer texts obtained from the artificial intelligence algorithms were analyzed using the hierarchical code sub-code model of the MAXQDA24 qualitative data analysis program. The similarities and differences between the findings were interpreted. As a result of the examinations conducted, it has been observed that the most comprehensive and up-to-date data were provided by Bard and Bearly. The information provided by the ChatGBT 3.5 algorithm, being based on data up to September 2021, and Jenni's limited features being freely accessible, have been restrictive in terms of the obtained responses. When the research findings are evaluated overall, it is observed that the language used is fluent, a general-to-specific approach is adopted, and there is no significant inconsistency among the provided information.Tourism and Recreation160This study, which includes data collection, data analysis, and conclusion-drawing processes, aims to understand, evaluate, and improve the problems related to tourism education from the perspective of artificial intelligence.Tourism and Recreation["Demet G\u00fcner", "Hakk\u0131 \u00c7\u0131lg\u0131no\u011flu"]2024-06-27T00:00:00 ff63bdcded0bc87433b6e3f47239a2205f84c1aeThe use of technology in learning has become inevitable in modern education. It plays an important role in optimizing the learning process, including in the context of mathematics learning. This study aims to describe the optimization of mathematics learning outcomes using Artificial Intelligence technology. This research uses a qualitative method with a descriptive approach. The instruments used were tests and interviews. The data collected was then analyzed using the interactive model analysis flow developed by Miles and Huberman, namely reducing data, presenting data, and verifying/concluding. The results showed that by using Artificial Intelligence (AI) technology in the learning process, lecturers can provide experiences tailored to students' individual needs, improve their understanding of mathematical concepts, and provide fast and precise feedback. Thus, the use of AI technology can optimize mathematics learning.MAPAN180By using Artificial Intelligence (AI) technology in the learning process, lecturers can provide experiences tailored to students' individual needs, improve their understanding of mathematical concepts, and provide fast and precise feedback, showing that the use of AI technology can optimize mathematics learning.MaPan["Topanus Tulak", "Rubianus", "Sarah Maramba'", "J. Matematika", "dan Pembelajaran"]2024-06-27T00:00:00 a3c3f948d2bb466dbe8b897ef3c469fb9b762526Artificial intelligence (AI) is a machine-type technology that mimics human behavior, then develops on the basis of the knowledge of human thinking, capable of running human thinking processes, and capable of making decisions like humans. Its existence is capable of facilitating human work by making it more efficient and effective. However, the presence of AI is vulnerable to being abused by irresponsible parties to commit a criminal act. The study aims firstly to learn about the development of Artificial Intelligence (AI) settings in the legal system in Indonesia and secondly, to know about the process of criminal investigation based on Artificial intelligence in Indonesia. The results of this study are, first; the rules on AI are not detailed in the law so many opinions are trying to interpret and associate it with Act Number 11 of 2008 on Electronic Information and Transaction. The second in the criminal investigation of AI highlighted is the presence of electronic evidence tools and evidence tools experts in the field of electronics, as well as how to meet the formula of articles in the laws imposedPena Justisia Media Komunikasi dan Kajian Hukum190The rules on AI are not detailed in the law so many opinions are trying to interpret and associate it with Act Number 11 of 2008 on Electronic Information and Transaction, as well as how to meet the formula of articles in the laws imposed.Pena Justisia: Media Komunikasi dan Kajian Hukum["Cahya Wulandari", "Ali Masyar Mursyid", "Winarsih Winarsih"]2024-06-27T00:00:00 5d6425588253a351f4c80fe7862f053f571a437dThe rise of artificial intelligence (AI) presents a transformative opportunity for architects seeking to achieve sustainable design goals. AI’s ability to extract and analyze vast amounts of data during the pre-design, design and construction, and post-construction phases empowers architects to make data-driven decisions that optimize building performance. This data can provide strategies for material selection, time and energy efficiency, and resource management, ultimately contributing to the realization of Sustainable Development Goals (SDGs) through building design. This research explores the specific applications of artificial intelligence in sustainable building design. To achieve this objective, a systematic literature review and case study analysis were conducted, utilizing academic journals and article reports as primary data sources. Specific keywords were employed to narrow the scope of the research. The analysis involved classifying AI applications and investigating their potential benefits in : 1) analyzing environmental conditions, such as historical and real-time climate data to optimize building orientation, and natural ventilation strategies, 2) facilitating the design process by providing real-time visualization and recommendations for achieving sustainable designs, 3) optimizing building materials and construction efficiency, by analyzing material properties, and providing accurate data during the construction stage to gives broader image on how to build efficiently and 4) simulating building performance, by predicting and doing assessment on the building’s energy consumption and thermal performance, allowing architects to refine design for optimal sustainability. By integrating AI into these workflows, architects can address critical challenges like global warming and climate change by creating high-performing, sustainable buildings. Keywords: Artificial Intelligence, Energy Efficiency, Global Warming, Sustainable ArchitectureAksen70This research explores the specific applications of artificial intelligence in sustainable building design by classifying AI applications and investigating their potential benefits, and facilitating the design process by providing real-time visualization and recommendations for achieving sustainable designs.Aksen : Journal of Design and Creative Industry["Stephanie Widodo", "Susan Susan"]2024-06-27T00:00:00 84fcf8c74c0ce244ab286f5df262b6233c0f03a2The American journal of psychoanalysis51The reader is left to consider whether these findings demand a new role for psychoanalysis in supporting, sustaining, and reframing their humanity as the authors create technology that transcends their abilities.American journal of psychoanalysis["Danielle Knafo"]2024-06-27T00:00:00 a30e95cdef689979b5ed727d99a3587e25807a96NEJM AI233NEJM AI["Chin Lin", "Wei-Ting Liu", "Chiao-Hsiang Chang", "Chiao-Chin Lee", "Shi-Chue Hsing", "Wen-Hui Fang", "Dung-Jang Tsai", "Kai-Chieh Chen", "Chun-Ho Lee", "Cheng-Chung Cheng", "Y. Hung", "Shih-Hua Lin", "Chien-Sung Tsai", "Chin Lin"]2024-06-27T00:00:00 e458aa2404751b0d2b7376d25f70e4657cf8a08701["Arvind Dagur", "D. Shukla", "Nazarov Fayzullo Makhmadiyarovich", "Akhatov Akmal Rustamovich", "Jabborov Jamol Sindorovich"]2024-06-27T00:00:00 0c3e0b4b17a16623fc9656e777639b57aa7f7a52Public Management Review541Public Management Review["Yinhui Deng", "Yu Sun"]2024-06-27T00:00:00 5988dcba480461a99ec59a8ea40b27f1ad9cc489Pengelolaan nikel sebagai sumber daya strategis yang melekat dengan teknologi masa depan termasuk teknologi militer menjadi semakin kritis cakrawala pada proyeksi fenomena Global Megatrends 2045. Saat ini kebijakan pengelolaan yang ditetapkan Pemerintah melalui hilirisasi nikel merupakan kekuatan dengan di dukung potensi sumber daya nikel sebagai keunggulan komparatif di tataran global. Namun demikian kebijakan ini menghadapi banyak tantangan dan dinamika dalam implementasinya, melalui studi lapangan guna mendapatkan data primer ditambah dengan studi literatur yang relevan serta dianalisis secara kualitatif, diidentifikasi beberapa isu aktual dalam pengelolaan nikel saat ini yang paling mengemuka yaitu terkait dengan aspek ketahanan energi. Mendasarkan pada pertimbangan tantangan masa depan, diperlukan konsepsi pengelolaan nikel yang progresif berdasarkankan ketahanan energi (resilience based strategy) sebagai prioritas dengan mengedepankan inovasi-inovasi dalam pengelolaan nikel yang akan menjamin keberlangsungan sumber daya dan lingkungan serta memberikan nilai tambah ekonomi yang signifikan sehingga kebangkitan dan pelestarian mineral dapat diwujudkan di kancah global. Selain itu ketahanan sumber daya nikel akan dapat menjadi keunggulan kompetitif sebagai modalitas serta memastikan akses sumber daya untuk pengembangan teknologi dirgantara, mengingat aplikasi yang sangat luas logam nikel dalam teknologi penerbangan dan antariksa khususnya di era kecerdasan buatan.Indonesian Journal of Innovation Multidisipliner Research541Indonesian Journal of Innovation Multidisipliner Research["Yanto S Manurung"]2024-06-27T00:00:00 78d4c0e314bd172e92a9444b84e82bd5819d3039Education and Information Technologies : Official Journal of the IFIP technical committee on Education901Educ. Inf. Technol.["Hanrui Gao", "Yi Zhang", "Gwo-Jen Hwang", "Sunan Zhao", "Ying Wang", "Kang Wang"]2024-06-27T00:00:00 f94ee31a35e5aeaf10f0f17d6911b2a60b9dd938This study evaluates the perspectives and expectations of postgraduate medical trainees of the Postgraduate Institute of Medicine regarding the use of AI-based systems for medical information retrieval and Healthcare Information Systems. The study was done from January to May 2023 with 106 trainees participating in a library user education programme, and the study achieved a 62.26% response rate (66 respondents). Data were collected through a Google Form questionnaire and analysed using Excel and SPSS version 23. The finding reveals different of perspectives among medical professionals on the use of AI in medical information retrieval. Most respondents (63.34) were enrolled in the MD programme. Medical literature reviews (24.24%) and treatment guidelines, diagnosis, and management of diseases (18.18%) were most frequently searched information types. Findings indicate varied confidence levels among medical professionals, with 21% moderately confident in using AI tools. AI- based tools were considered most suitable for searching medical journals, research articles, and electronic health records. Key factors for evaluating AI tools included accuracy (90%), user-friendliness (80%), and speed (70%). Time savings (19.51%) was, highlighted as the primary advantage of AI tools.There is a strong consensus on the necessity for staying updated with AI technologies, with 54.55% considering it "Very important." Furthermore, 48.48% of respondents emphasised the importance of AI tools in identifying relevant clinical guidelines and best practices. Confidence in AI-based diagnostic recommendations varied, with 43.94% finding them "Somewhat accurate" and 31.82% "Moderately accurate," while a majority (69.7%) believed AI could reduce medical errors and improve patient safety. Integration of AI in administrative and operational tasks was seen as highly significant (41.46%). Satisfaction with AI-generated search accuracy was moderate, with 40.91% "Somewhat satisfied" and 18.18% "Very satisfied." The need for training to use AI tools effectively was widely recognised, with 56.06% indicating some level of expertise required and 24.24% supporting extensive training. Overall, the study highlights perspectives on AI in medical information retrieval, emphasizing the need for accurate, efficient, and user-friendly AI solutions along with sufficient training to enhance medical practice and support evidence-based decision-making. This study finding revealed a range of perceptions and expectations among postgraduate medical trainees regarding AI integration, highlighting the importance of training and education initiatives to support the effective use of AI tools in the field of medicine.Sri Lanka Library Review141The study highlights perspectives on AI in medical information retrieval, emphasizing the need for accurate, efficient, and user-friendly AI solutions along with sufficient training to enhance medical practice and support evidence-based decision-making.Sri Lanka Library Review["C. Wadasinghe"]2024-06-27T00:00:00 9c6cf86a6ffd4ec23002b20d9a55965cefa64f35NEJM AI01NEJM AI["Robert Avram", "William F. Fearon"]2024-06-27T00:00:00 935e27b351cf539a267f52f49af963eee43fb3caEmerging markets finance & trade781Emerging Markets Finance and Trade["Chien\u2010Chiang Lee", "Changchun Pan", "Yuhang Song"]2024-06-27T00:00:00 b41b97057d9f790631b09e8130c7243f7ff99352Scientific Reports472A multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced cardiac magnetic resonance imaging, electrocardiography and clinical data suggests that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure.Scientific Reports["Maarten Z H Kolk", "S. Ruip\u00e9rez-Campillo", "C. Allaart", "A. A. Wilde", "R. Knops", "Sanjiv M. Narayan", "F. Tjong", "Femke D. Anne-Lotte C. J. Marco J. W. Jasper L. Laura Iv Raijmakers Van Der Lingen G\u00f6tte Selder Alvarez-Flo", "F. D. Raijmakers", "A. C. van der Lingen", "M. J. G\u00f6tte", "Jasper L Selder", "Laura Alvarez-Florez", "Ivana Isgum", "E. J. Bekkers"]2024-06-27T00:00:00 64d8dbfcc8a06a02c0dcf9e9105b05f786f076b8Environment, Development and Sustainability362Environment, Development and Sustainability["Peiyao Zhao", "Yu Gao", "Mao Wu", "Xue Sun"]2024-06-27T00:00:00 35fd699c6a293a301167407de521a3793eae9280This essay questions, with regard to medicine, the idea of progress as technological development by focusing on people rather than things. It analyzes how the predominance of such an idea of progress converts today's societies to techno-fetishism that degrades community life and medical practice, contributing to the medicalization of social life. It is argued that the realization of technological potentialities depends on their forms of use; that the main motive of technological development is unlimited profit and that priority developments are those that enhance the social control that maintains the status quo. The intelligence as an intelligence quotient is criticized by proposing it as an attribute of the human being as a whole, manifested in the ways of thinking and proceeding of people in their circumstances, where affectivity and critical thinking are essential for their development; it is emphasized that its antecedent is the harmonic concert of planetary life that contrasts with the prevailing human disharmony. It is proposed that artificial intelligence is the most recent creation of techno-fetishism that deposits vital attributes in technology and that its forms of use will accentuate the degradation of human and planetary life. Another idea of medical progress is proposed, based on forms of organization conducive to the development of inquisitive, critical and collaborative skills that promote permanent improvement, whose distant horizon is dignifying progress: spiritual, intellectual, moral and convivial sublimation of collectivities in harmony with the planetary ecosystem.Boletín Médico del Hospital Infantil de México00Boletin medico del Hospital Infantil de Mexico["Leonardo Viniegra-Vel\u00e1zquez"]2024-06-27T00:00:00 dafcfff45759cbf1d258592a28238e846d3e2198This essay challenges the idea of progress as technological development in relation to medicine by focusing on people rather than things. It analyzes how the prevalence of such an idea of progress leads contemporary societies to a technofetishism that degrades community life and medical practice, contributing to the medicalization of social life. It is argued that the realization of technological potentialities depends on their forms of use, that the main motive of technological development is unlimited profit, and the priority developments are those that enhance social control which maintains the status quo. Intelligence as an intelligence quotient is criticized by proposing it as an attribute of the human being as a whole, manifested in the ways of thinking and acting of human beings in their circumstances, where affectivity and critical thinking are essential for their development; it is emphasized that its antecedent is the harmonic concert of planetary life, which contrasts with the prevailing human disharmony. It is proposed that artificial intelligence is the latest creation of technofetishism, which deposits vital attributes in technology, and that its use will accentuate the degradation of human and planetary life. Another idea of medical progress is proposed, based on forms of organization that is conducive to the development of inquisitive, critical, and collaborative skills that promote permanent improvement, whose distant horizon is dignified progress: the spiritual, intellectual, moral, and convivial sublimation of collectivities in harmony with the planetary ecosystem.Boletín Médico del Hospital Infantil de México00This essay challenges the idea of progress as technological development in relation to medicine by focusing on people rather than things, and proposes that artificial intelligence is the latest creation of technofetishism, which deposits vital attributes in technology and that its use will accentuate the degradation of human and planetary life.Boletin medico del Hospital Infantil de Mexico["L. Viniegra-Vel\u00e1zquez"]2024-06-27T00:00:00 0ddace5f0a653408f2575d434331c129daaf47fdMedicon Medical Sciences00Medicon Medical Sciences[]2024-06-27T00:00:00 0a1c854d164afcd0660d6179094c5d6a5261949dUniversum:Technical sciences00Universum:Technical sciences["Dmitry Kotov"]2024-06-27T00:00:00 483ef9a319f43acbc74a004a51b11956e7f5dd54В данной работе предметом исследования является персонализированное обучение, оценка знаний и прогнозирование успеваемости, создание учебных материалов и курсов, обучение с подкреплением для управления образовательными процессами. Цель исследования и применения искусственного интеллекта в образовании заключается в создании более эффективных, инновационных, доступных и персонализированных образовательных решений, способствующих развитию и успешности всех учащихся. Результаты применению искусственного интеллекта в образовании приносить персонализированное обучение, адаптивные образовательные платформы, улучшенная реакция на потребности студентов, автоматизация рутинных задач, виртуальные помощники для обучения, прогнозирование успеваемости и выявление ранних признаков отставания. Практическая значимость результатов искусственного интеллекта в образовании состоит в том, что они помогают улучшить качество обучения, сделать его более доступным, эффективным и индивидуализированным, а также оптимизировать использование ресурсов и поддерживать непрерывное обучение и развитие учащихся и преподавателей. В работе анализируется методы машинного обучения искусственного интеллекта. Рассмотрены несколько способов и приложений ИИ для использования в образовании.Наука. Образование. Техника00Наука. Образование. Техника["\u042d. \u0420\u0430\u0438\u043c\u0431\u0435\u043a \u0443.", "\u0416.\u0411. \u041a\u0430\u0434\u044b\u0440\u0431\u0430\u0435\u0432\u0430"]2024-06-27T00:00:00 6d94fc52e2a5152f72162af3b5de132f07b31c8aJACC: Cardiovascular Interventions90JACC. Cardiovascular interventions["Stephen G Ellis", "Michael W. Kattan"]2024-06-27T00:00:00 31b1ca048c39fd45f572a2914ebf697f9885cabdBACKGROUND: Previously, we presented the process of developing a clinical decision support system (CDSS) for adjusting insulin pump (IP) settings in children with type 1 diabetes mellitus (T1D) and assessing the agreement of the recommendations it generates with the expert opinion. The CDSS demonstrated satisfactory forecasting of glucose profile and agreement rates between recommendations CDSS and experts.AIM: To evaluate the effectiveness and safety of using CDSS in children with T1D, testing the hypothesis of non-inferiority (with a limit of -5%) of relative increase of glucose time in range (TIR) over 6 months.MATERIALS AND METHODS: The trial included 80 children with T1D, divided into two comparable groups of 40 children using the minimization method. Patients in the main group received recommendations for adjusting the IP settings from a physician who uses the CDSS; patients in the control group received recommendations from a physician (control group). Patients were observed for 6 months with remote consultations once a month (7 consultations in total) and monitoring of glycated hemoglobin (HbA1c) at 1, 4 and 7 consultations. The primary outcome is the difference in group mean relative changes in TIR (%), secondary outcomes are TIR (%), HbA1c concentration. RESULTS: The trial was completed by 63 patients 32 in the main group, 31 in the control group. The difference in the mean relative increase in TIR at the 7th consultation in the groups was 3.02%, one-sided 95% CI (-4.55%; inf ). Thus, the lower bound of this CI is greater than the noninferiority limit of -5%, and the noninferiority hypothesis can be accepted. There were no statistically significant differences between groups for all outcomes. The dynamics of the indicators were positive in the main group and had a statistical tendency towards positive changes in the control group.CONCLUSION: The use of CDSS was no less effective in terms of the TIR than the management of the patient by a physician. The use of CDSS in clinical practice can help in regular and frequent monitoring of children with T1D, and standardize at a high level the approach to correction of IP parameters, supplemented with CGM.Diabetes mellitus110The use of CDSS in clinical practice can help in regular and frequent monitoring of children with T1D, and standardize at a high level the approach to correction of IP parameters, supplemented with CGM.Diabetes mellitus["D. Laptev", "D. Y. Sorokin", "E. S. Trufanova", "O. Rebrova", "O. Bezlepkina"]2024-06-27T00:00:00 64553c3c8e78fa7db66f7b3bc8673cdf8a18d23eBACKGROUND: Widely available diabetes devices (continuous glucose monitoring, insulin pump etc.) generate large amount of data and development of an advanced clinical decision support system (CDSS), able to automatically evaluate and optimize insulin therapy, is relevant.AIM: Development of a mathematical model and an CDSS based on it to optimize insulin therapy in children with type 1 diabetes (T1D) and assessment of the agreement between the recommendations of the CDSS and the physician on insulin pump (IP) parameters: basal profile (BP), carbohydrate ratio (CR), correction factor (СF).MATERIALS AND METHODS: Data from 504 children with T1DM were analyzed over the period of 7875 days. The data included glucose, insulin, food, sex, age, height, weight, diabetes duration and HbA1c. We constructed recurrent neural network (RNN) to predict glucose concentration for 30-120 minutes, an algorithm for optimizing IP settings using prediction results. Next, a software product was developed — a CDSS. To assess the agreement of the recommendations of the CDSS and physicians, retrospective data from 40 remote telemedicine consultations of 40 patients with T1D (median age 11.6 years [7; 15]) were used and 960 points of possible adjustments were analyzed. Three degrees of agreement have been introduced: complete agreement, partial agreement, and complete disagreement. The magnitude of the adjustments was also analyzed.RESULTS: The accuracy of glycemic predictions was better or comparable with other similar models. The assessment of agreement for BP, CR and CF, according to the Kappa index, showed slight and weak agreement. The frequency of complete agreement between recommendations for adjusting the ongoing IP therapy between the CDSS and physicians is 37.5–53.8%, and complete inconsistency is 4.5–17.4%. From a clinical point of view, consistency in the frequency of occurrence of the indicator is more important. There were no differences in median IP settings between the CDSS and physicians.CONCLUSION: The CDSS has an acceptable accuracy of glycemic predictions. The CDSS and physicians provide comparable recommendations regarding CSII parameters.Diabetes mellitus180The CDSS has an acceptable accuracy of glycemic predictions and the CDSS and physicians provide comparable recommendations regarding CSII parameters.Diabetes mellitus["D. Y. Sorokin", "E. S. Trufanova", "O. Rebrova", "O. Bezlepkina", "D. Laptev"]2024-06-27T00:00:00 14c3452f36c2b070dcbfc9efc391298ece6a5ae1The research expects AI applications to play an important role in the educational process. The study addresses multiple types of algorithms and their functions in the teaching process. For the academic year 2023, the research was conducted according to the survey curriculum, and the place of study is the secondary administration of Baghdad governorate. In addition, 2024 represents the school community in terms of computer teachers at the secondary level, numbering about 410 teachers, 85 of whom were selected, while the research variable depends on each teacher's educational grade separately. (Qualified) and the second variable is also represented in (number of years of experience) based on their ability to properly complete the questionnaire created using two axes: "Degree of use of AI applications" in addition to "Challenges of use of AI applications." The survey indicated that the use of educational toys was the most common AI application in the study sample, while "converting printed images or handwritten texts into editable text files." "The use of artificial intelligence applications was the least popular. The main factors contributing to this were the idea that the use of AI applications in education required more work than traditional teaching methods, lack of technical assistance required, inadequate problem-solving skills in students, while the results showed that the availability of skills related to the use of AI applications by students (Planning for the lecture) was an average calculator of 3.07, while the other showed after implementation to an average arithmetic of 3.10, which means the importance of enhancing those skills before the implementation of AI. Accordingly, the recommendations for the study highlighted the importance of holding training courses on the use of AI applications for all partiesJournal Port Science Research100The survey indicated that the use of educational toys was the most common AI application in the study sample, while "converting printed images or handwritten texts into editable text files" was the least popular.Journal Port Science Research["Dunea Taleb Kazim"]2024-06-27T00:00:00 15f05f22f5632c0bc54de4152b9b0093a4579850The present paper provides an innovative approach in the existing methods of studying energy poverty, i.e., a crucial socio-economic challenge of the past decade in Europe. Since the literature has shown that conventional statistical models lack effectiveness in handling unconventional relationships between variables and present limitations in terms of accurate classification and prediction, the paper explores the ability of Artificial Intelligence and, particularly, of Artificial Neural Networks (ANNs), to successfully predict energy poverty in Greece. The analysis included the prediction of seven energy poverty indicators (output indicators) based on certain socio-economic/geographical factors (input variables), via training an ANN, i.e., the Multilayer Perceptron. Three models (Model A, Model B and Model C) of different combinations of the input variables were tested for each one of the seven indicators. The analysis showed that ANNs managed to predict energy poverty at a remarkably good level of accuracy, ranging from 61.71% (lowest value) up to 82.72% (highest accuracy score). The strong relationships that came up on the examined cases confirmed that ANNs are a promising tool towards a deeper understanding of the energy poverty roots, which in turn can lead to more targeted policies.Energies352Energies["Lefkothea Papada", "D. Kaliampakos"]2024-06-27T00:00:00 2f6d777e31d8f4aab1e0e87282091648cd553253Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review’s purpose is four-fold: (i) to investigate AI’s role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI’s future potential impact; (iv) to investigate possible concerns about AI’s use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI’s role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI’s intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies’ heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI’s potential in human nutrition.Nutrients677The key findings indicated AI’s role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension.Nutrients["A. Sosa-Holwerda", "Oak-Hee Park", "Kembra Albracht-Schulte", "Surya Niraula", "Leslie Thompson", "W. Oldewage-Theron"]2024-06-28T00:00:00 4cc9124a34dfe14c316c5c51b1d734821b8032b4From the innovation, Artificial Intelligence (AI) materialized as one of the noticeable research areas in various technologies and has almost expanded into every aspect of modern human life. However, nowadays, the development of AI is unpredictable with the stated values of those developing them; hence, the risk of misbehaving AI increases continuously. Therefore, there are uncertainties about indorsing that the development and deploying AI are favorable and not unfavorable to humankind. In addition, AI holds a black-box pattern, which results in a lack of understanding of how systems can work based on the raised concerns. From the above discussion, trustworthy AI is vital for the extensive adoption of AI in many applications, with strong attention to humankind and the need to focus on AI systems developing into the system outline at the time of system design. In this survey, we discuss compound materials on trustworthy AI and present state-of-the-art of trustworthy AI technologies, revealing new perspectives, bridging knowledge gaps, and paving the way for potential advances of robustness, and explainability rules which play a proactive role in designing AI systems. Systems that are reliable and secure and mimic human behaviour significantly impact the technological AI ecosystem. We provided various contemporary technologies to build explainability and robustness for AI-based solutions, so AI works safer and more trustworthy. Finally, we conclude our survey paper with high-end opportunities, challenges, and future research directions for trustworthy AI to investigate in the future.ACM Computing Surveys1518Various contemporary technologies are provided to build explainability and robustness for AI-based solutions, so AI works safer and more trustworthy, and future research directions for trustworthy AI to investigate in the future are provided.ACM Computing Surveys["B. Chander", "Chinju John", "Lekha Warrier", "Kumaravelan Gopalakrishnan"]2024-06-28T00:00:00 fd1af013a99a867ba2923de5ba3636b88132491aIn an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery to their communities, ranging from simple task automation to more complex engineering endeavours. As more local governments adopt AI, it is imperative to understand the functions, implications, and consequences of these advanced technologies. Despite the growing importance of this domain, a significant gap persists within the scholarly discourse. This study aims to bridge this void by exploring the applications of AI technologies within the context of local government service provision. Through this inquiry, it seeks to generate best practice lessons for local government and smart city initiatives. By conducting a comprehensive review of grey literature, we analysed 262 real-world AI implementations across 170 local governments worldwide. The findings underscore several key points: (a) there has been a consistent upward trajectory in the adoption of AI by local governments over the last decade; (b) local governments from China, the US, and the UK are at the forefront of AI adoption; (c) among local government AI technologies, natural language processing and robotic process automation emerge as the most prevalent ones; (d) local governments primarily deploy AI across 28 distinct services; and (e) information management, back-office work, and transportation and traffic management are leading domains in terms of AI adoption. This study enriches the existing body of knowledge by providing an overview of current AI applications within the sphere of local governance. It offers valuable insights for local government and smart city policymakers and decision-makers considering the adoption, expansion, or refinement of AI technologies in urban service provision. Additionally, it highlights the importance of using these insights to guide the successful integration and optimisation of AI in future local government and smart city projects, ensuring they meet the evolving needs of communities.Smart Cities08This study offers valuable insights for local government and smart city policymakers and decision-makers considering the adoption, expansion, or refinement of AI technologies in urban service provision and highlights the importance of using these insights to guide the successful integration and optimisation of AI in future local government and smart city projects, ensuring they meet the evolving needs of communities.Smart Cities["Tan Yigitcanlar", "Anne David", "Wenda Li", "Clinton Fookes", "S. Bibri", "Xinyue Ye"]2024-06-28T00:00:00 37f3eef37dd64de9368c57d6ba7c8e802e99c779Purpose: This research examines the intricate interplay between Business Intelligence (BI), Big Data Analytics (BDA), and Artificial Intelligence (AI) within the realm of Supply Chain Management (SCM). While the integration of these technologies has promised improved operational efficiency and decision-making capabilities, concerns about complexities and potential overreliance on technology persist. The study aims to provide insights into achieving a balance between data-driven insights and qualitative factors in SCM for sustained competitiveness. Design/methodology/approach: The research executed interviews with ten Arab Gulf-based consulting firms. These companies’ ability to successfully complete BI projects is well recognised. Findings: Through examining the interplay of human judgement and data-driven strategies, addressing integration challenges, and understanding the risks of excessive data reliance, the research enhances comprehension of the modern SCM landscape. It underscores BI’s foundational role, the necessity of balanced human input, and the significance of customer-centric strategies for lasting competitive advantage and relationships. Practical implications: The research provided information for organizations seeking to effectively navigate the complexities of integrating data-driven technologies in SCM. The research is a foundation for future studies to delve deeper into quantitative measurement methodologies and effective data security strategies in the SCM context. Originality: The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.Journal of Infrastructure Policy and Development654The research highlights the value of integrating BI, BDA, and AI in SCM for improved efficiency, cost reduction, and customer satisfaction, emphasising the need for a balanced approach that combines data-driven insights, human judgement, and customer-centric strategies to maintain competitiveness.Journal of Infrastructure, Policy and Development["Najwa Ashal", "Amer Morshed"]2024-06-28T00:00:00 147bd83be5413690b1c1f4a2c95cbd3504f6127dCurrent Neurology and Neuroscience Reports833AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.Current Neurology and Neuroscience Reports["Moein Amin", "E. Mart\u00ednez-Heras", "D. Ontaneda", "Ferran Prados Carrasco"]2024-06-28T00:00:00 78aad43de41a9e9f4d8990b9a18b702e7dbcc726Artificial Intelligence (AI) has revolutionized various sectors, including academic research. This article examines the transformative effects of AI on academic research across different disciplines. We explore how AI enhances data analysis, automates repetitive tasks, enables new research methodologies, and addresses ethical considerations. By reviewing recent advancements and case studies, we provide a comprehensive overview of AI’s impact on the efficiency, scope, and quality of academic research.Universal Library of Innovative Research and Studies43The transformative effects of AI on academic research across different disciplines are examined, exploring how AI enhances data analysis, automates repetitive tasks, enables new research methodologies, and addresses ethical considerations.Universal Library of Innovative Research and Studies["Zachary Rolnik"]2024-06-28T00:00:00 4490ca28b6469301dc16d173c5724821e6bd1f52Contemporary discussions about the application of artificial intelligence in newsrooms are commonplace because of the unique opportunities it presents for news media. This study investigated the intricate relationship between journalism and AI with the broad research question: How are journalists adopting AI technologies and what challenges and opportunities do such technologies present to them? Eighteen journalists practising in Ghana and South Africa were interviewed through qualitative research techniques. Transcribed interview data were analysed thematically using the data analysis method proposed by Charmaz. The findings were that most newsrooms in the two countries have not formally incorporated AI tools into newsroom practices. However, journalists use AI tools at their discretion in a non-complex manner, such as transcription, research, generating story ideas, and fact-checking. Practical limitations to the formal integration of AI technology into newsroom operations include cost, language barrier, and aversion to change. Although participants recognised the advantages of employing AI for newsroom tasks, they were also concerned about the ethical quandaries of misinformation, improper attribution, and intellectual property. Participants also thought that fact-checking and mindfulness regarding ethical usage might increase ethical AI usage in newsrooms. This study adds an important perspective on AI’s role in African journalism, addressing the obstacles and ethics concerns.Journalism and Media553It was found that most newsrooms in the two countries have not formally incorporated AI tools into newsroom practices, but journalists use AI tools at their discretion in a non-complex manner, such as transcription, research, generating story ideas, and fact-checking.Journalism and Media["T. D. Adjin-Tettey", "Tigere P. Muringa", "Samuel Danso", "Siphumelele Zondi"]2024-06-28T00:00:00 3af888c9f0aa0d7b0bf819f2021e2364731f9e27The digitisation of education systems has been a growing trend in recent years. Artificial intelligence (AI) enables the development of new educational software that can even create adaptive - personalised learning plans for students. Such new software can be invaluable for improving the efficiency of the educational process, improving communication between teachers and students, and facilitating a better understanding of educational material. It is therefore important to make use of the tools already available in the educational process. Alongside teachers, students are the ones who are an integral part of the educational process and it is they who become active users of this software. The aim of our research is to assess students' interest in educational software. The study involved a quantitative survey involving a total of 500 students from different educational institutions covering primary, secondary and higher education, as well as Generation Z and Alpha. The survey gave students an insight into the concept of artificial intelligence. In addition to the openness to educational software, our study investigated the relationship between the students' attitudes towards artificial intelligence and their previous use of educational software. Our results show that students are keen to use educational software, and the majority are open to using artificial intelligence-based educational software. Our conclusions point to the need for educators to implement these software in their pedagogical practice whenever possible when shaping future teaching methods. Therefore, based on international literature, this study presents 15 educational software solutions that, through their intelligent features, accelerate and simplify the teaching process while supporting differentiated and more personalized education. The aim of this study is therefore to familiarise the reader with the potential of educational software and to encourage educational institutions and teachers to use this type of software on a daily basis.Ìnformacìjnì Tehnologì ì Zasobi Navčannâ693This study presents 15 educational software solutions that, through their intelligent features, accelerate and simplify the teaching process while supporting differentiated and more personalized education.Information Technologies and Learning Tools["Norbert Annu\u0161"]2024-06-28T00:00:00 3d00cc64bd0bea3f5f5ae4235b596f2612dbaf08Artificial intelligence (AI)–based clinical decision support systems are gaining momentum by relying on a greater volume and variety of secondary use data. However, the uncertainty, variability, and biases in real-world data environments still pose significant challenges to the development of health AI, its routine clinical use, and its regulatory frameworks. Health AI should be resilient against real-world environments throughout its lifecycle, including the training and prediction phases and maintenance during production, and health AI regulations should evolve accordingly. Data quality issues, variability over time or across sites, information uncertainty, human-computer interaction, and fundamental rights assurance are among the most relevant challenges. If health AI is not designed resiliently with regard to these real-world data effects, potentially biased data-driven medical decisions can risk the safety and fundamental rights of millions of people. In this viewpoint, we review the challenges, requirements, and methods for resilient AI in health and provide a research framework to improve the trustworthiness of next-generation AI-based clinical decision support.Journal of Medical Internet Research373The challenges, requirements, and methods for resilient AI in health are reviewed, and a research framework to improve the trustworthiness of next-generation AI-based clinical decision support is provided.Journal of Medical Internet Research["Carlos S\u00e1ez", "Pablo Ferri", "Juan M Garc\u00eda-G\u00f3mez"]2024-06-28T00:00:00 69ae29bcded26cc7c98663e89866ca3325c27f5aPurposeThis research explores and examines the change in perception artificial intelligence (AI) technology can bring in various human resources (HR) functions [(perception of change that AI can create in the talent acquisition (PAITA), perception of change that AI can create in the training and development (PAITD), perception of change that AI can create in the performance assessment (PAIPA) and perception of change that AI can create in the pay and rewards (PAIPR)] and its impact on intention to adopt AI by HR professionals. Additionally, as the literature on trust in AI is scanty, the mediation influence of AI-tech trust was also examined.Design/methodology/approachCross-sectional data were gathered from 264 HR professionals from Indian e-commerce organizations. The model has been tested using a two-step partial least squares-based, structural equational modeling (PLS-SEM) technique.FindingsAI uses algorithms for creating accurate and trustworthy information databases; it also enables quick data access and transmission, which enhances HR functions. Employees’ perception of the change that AI can bring to various HR functions significantly impacts the adoption of AI in HR. Additionally, AI-tech trust positively mediates all the hypothesized relationships.Originality/valueBased on stimulus-organism-response (S-O-R) and affordance theory, this study significantly increases the understanding of how employees perceive changes in various HR functions as a result of AI implementation and how much they trust the AI technology. This study also addresses the lack of research on AI integration in HR, with a special focus on developing countries.Benchmarking : An International Journal641Employees’ perception of the change that AI can bring to various HR functions significantly impacts the adoption of AI in HR, and AI-tech trust positively mediates all the hypothesized relationships.Benchmarking: An International Journal["Meenal Arora", "Amit Mittal"]2024-06-28T00:00:00 b48148fe629e29d7b2293ed167cb8fcb75d27820Artificial intelligence (AI) has been extensively employed across various domains, with increasing social, ethical, and privacy implications. As their potential and applications expand, concerns arise about the reliability of AI systems, particularly those that use deep learning techniques that can make them true “black boxes”. Explainable artificial intelligence (XAI) aims to offer information that helps explain the predictive process of a given algorithmic model. This article examines the potential of XAI in elucidating algorithmic decisions and mitigating bias in AI systems. In the first stage of the work, the issue of AI fallibility and bias is discussed, emphasizing how opacity exacerbates these issues. The second part explores how XAI can enhance transparency, helping to combat algorithmic errors and biases. The article concludes that XAI can contribute to the identification of biases in algorithmic models, then it is suggested that the ability to “explain” should be a requirement for adopting AI systems in sensitive areas such as court decisions.Revista Thesis Juris371The article concludes that XAI can contribute to the identification of biases in algorithmic models, then it is suggested that the ability to “explain” should be a requirement for adopting AI systems in sensitive areas such as court decisions.Revista Thesis Juris["Ot\u00e1vio Morato de Andrade", "Marco Ant\u00f4nio Sousa Alves"]2024-06-28T00:00:00 ee86e6402bb225ce963ea07ed23bfbcaa2f08ec2The integration of Artificial Intelligence (AI) technology in law enforcement has become a significant development in Indonesia's information technology landscape. The use of AI in law enforcement presents substantial challenges, including issues of accountability, privacy concerns, and ethical implications. This study aims to evaluate the effectiveness of existing regulations in addressing the use of AI technology in criminal law enforcement in Indonesia and to identify the need for comprehensive legal reforms. The findings indicate that although regulatory frameworks exist, their effectiveness in managing AI applications in criminal law enforcement remains inadequate. There is an urgent need to update the laws to accommodate the rapid advancements in AI and to address emerging legal uncertainties. Comprehensive legal reforms are essential to ensure that AI-enabled law enforcement can be conducted effectively and in accordance with fundamental legal principles.Asian Journal of Engineering, Social and Health132There is an urgent need to update the laws to accommodate the rapid advancements in AI and to address emerging legal uncertainties to ensure that AI-enabled law enforcement can be conducted effectively and in accordance with fundamental legal principles.Asian Journal of Engineering, Social and Health["Agus Nawawi", "Azis Budianto", "Rineke Sara"]2024-06-28T00:00:00 82682343c3f4af322a3228a06a2fb52733b1b852Discover Artificial Intelligence472The role of judges as influential leaders in shaping public trust in AI is highlighted and the influence of individual differences on trust in AI is examined, to help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.Discov. Artif. Intell.["Anna Fine", "Shawn Marsh"]2024-06-28T00:00:00 e1936e4c5e2ef07fe5e8683f5bf3f67ee77ba8adThe article explores the landscape of Artificial Intelligence (AI) applications in Human Resources (HR) by highlighting current trends and providing some anticipations about future trends and developments. AI is revolutionary reshaping basic HR processes – from workforce planning, recruitment, to employee’s development and fostering diversity and inclusion. AI plays important role in addressing bias in recruitment, enhances objectivity and promotes equal opportunities. AI-driven tools (like chatbots and virtual assistants etc.) integration in HR processes enables seamless communication and propellers HR practices towards enhanced efficiency and strategic decision-making. Furthermore, the article provides a short analysis of some software solutions that serve organizations as AI HR tools. By taking a look towards the future, we can predicts that tools like predictive analytics, monitoring of employee well-being, and convergence of AI with augmented reality (AR) and virtual reality (VR) can be projected as some of the future key developments. In the conclusion we can stress out the transformative synergy between AI and HR, which allows organizations and HR professionals to embrace innovation in order to deliver better results for all of the stakeholders: employees, management, owners and broader society.Mednarodno inovativno poslovanje = Journal of Innovative Business and Management101The article explores the landscape of Artificial Intelligence (AI) applications in Human Resources (HR) by highlighting current trends and providing some anticipations about future trends and developments, and predicts that tools like predictive analytics, monitoring of employee well-being, and convergence of AI with augmented reality (AR) and virtual reality (VR) can be projected as some of the future key developments.Mednarodno inovativno poslovanje = Journal of Innovative Business and Management["Klemen \u017dibret"]2024-06-28T00:00:00 cc9b9c52116972d42dbb67c0ec5d1898d9b2a618The use of artificial intelligence capabilities in forensic science is of scientific interest, due to the rapid digitalization of society and the need to quickly identify the attacker. The intensive development of these technologies contributes to the emergence of new types of social relations, directly affects the speed of making management decisions, as well as the emergence of previously unstudied types of crime. The problem under consideration allows not only to enrich the theoretical basis, but also to significantly increase the speed of identifying intruders, as well as to optimize the work of law enforcement officers. The paper examines the theoretical foundations, significance and possibilities of implementing artificial intelligence systems in forensic science. Potential areas for using this technology in solving and investigating crimes of various types are outlined. During the study, dialectical, systemic and logical methods (general scientific) were used. Among private scientific research methods, formal legal methods, methods of generalization and abstraction prevailed. The issues of introducing the capabilities of artificial intelligence systems into the branch of forensic science — forensic technology — are analyzed. Promising directions for use in the investigation of crimes in the context of combating crime are proposed, incl. in the digital environment. Potential negative factors of using these technologies in the work of law enforcement agencies have been identified.LEGAL ORDER: History, Theory, Practice01The theoretical foundations, significance and possibilities of implementing artificial intelligence systems in forensic science are examined, and promising directions for use in the investigation of crimes in the context of combating crime are proposed.LEGAL ORDER: History, Theory, Practice["Alexei V. Kutuzov"]2024-06-28T00:00:00 a4a2df5e8dae0174cc651e6e2852bf99aa0a813bArtificial Intelligence (AI) sangat penting untuk mempersiapkan sumber daya manusia (SDM) dalam menghadapi perubahan yang cepat dan dinamis di dunia kerja. AI adalah teknologi yang semakin berkembang dan digunakan dalam berbagai sektor, termasuk pendidikan. Pendidikan harus beradaptasi dengan perkembangan teknologi AI dan memanfaatkannya untuk meningkatkan kualitas pembelajaran. Tujuan penelitian ini untuk menganalisis pendidikan di era AI apakah dapat meningkatkan kualitas sumber daya manusia atau menggantikannya. Metode penelitian yang diguanakan pada penelitian ini adalah literatur review. Hasil penelitian menunjukkan bahwa walaupun teknologi AI dapat membantu dalam mengoptimalkan pembelajaran, sumber daya manusia dalam bidang pendidikan tetap sangat diperlukan. Keterampilan manusia dalam kreativitas, empati, komunikasi, dan pemecahan masalah masih sangat penting dalam menciptakan pengalaman pembelajaran yang efektif dan menyenangkan bagi siswa.Journal Development321Journal Development["Esti Nur Wakhidah", "M. Sulaeman", "Diksi Metris", "Aji Priambodo", "Riyan Dwi Yulian Prakoso"]2024-06-28T00:00:00 27a9d0113c6fc35c4860562cd849d8a2c4f65d85As the only way to comprehensively expand domestic demand, consumption upgrading gradually reflects the impact and mechanism of consumption upgrading with the development of artificial intelligence technology. The role of mechanism evaluation in consumption structure under artificial intelligence is very important, but there is a problem of inaccurate outcome evaluation. The traditional consumption model cannot solve the problem of evaluation of the mechanism of consumption structure under artificial intelligence, and the evaluation is unreasonable. Therefore, this paper proposes a data mining algorithm to evaluate and analyze the mechanism of optimization and innovation. Firstly, the relative income consumption theory is used to evaluate the consumer market, and the indicators are divided according to the requirements of the mechanism evaluation to reduce the interference factors in the evaluation of the mechanism. Then, the relative income consumption theory evaluates the mechanism of consumption upgrading level, forms a mechanism evaluation scheme, and comprehensively analyzes the evaluation results of the mechanism. MATLAB simulation shows that under certain evaluation criteria, the evaluation accuracy of the mechanism of data mining algorithms on the level of consumption upgrading and the quality of consumption structure transformation and upgrading are better than those of traditional consumption patterns.2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)121A data mining algorithm to evaluate and analyze the mechanism of optimization and innovation and shows that under certain evaluation criteria, the evaluation accuracy of the mechanism of data mining algorithms on the level of consumption upgrading and the quality of consumption structure transformation and upgrading are better than those of traditional consumption patterns.2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)["Jixu Zhu", "Xiaoshi Chen"]2024-06-28T00:00:00 e3b938d76ccd98a765ba63e4132ed65bcfe51b4aArtificial Intelligence (AI) has gained attention in tourism, which requires its sustainability. Our study focuses on a bibliometric analysis of AI in sustainable tourism using 174 manuscripts from 2000 to 2022. One of the main findings is that 'intelligence' appears frequently, followed by related terms such as work, performance, resources, sustainability, impact, optimization and management. There is no previous evidence on AI in the context of sustainable tourism to explain how public managers or politicians design public policies to create and improve resource efficiency. La Inteligencia Artificial (IA) ha ganado atención en el turismo, que requiere su sostenibilidad. Nuestro estudio se centra en un análisis bibliométrico de la IA en el turismo sostenible utilizando 174 manuscritos de 2000 a 2022. Una de las principales conclusiones es que "inteligencia" aparece con frecuencia, seguida de términos relacionados como trabajo, rendimiento, recursos, sostenibilidad, impacto, optimización y gestión. No existen pruebas previas sobre la IA en el contexto del turismo sostenible que expliquen cómo los gestores públicos o los políticos diseñan políticas públicas para crear y mejorar la eficiencia de los recursos.Cuadernos de Turismo462Cuadernos de Turismo["Paola Hermosa Del Vasto", "Mar\u00eda Lourdes Arco Castro"]2024-06-28T00:00:00 1076427470788f9cebbea7377db7d7d8571d072aBACKGROUND Artificial intelligence (AI) techniques are showing significant potential in the medical field. The rapid advancement in artificial intelligence methods suggests their soon-to-be essential role in physicians' practices. OBJECTIVES This study seeks to assess and compare the readability, clarity, and precision of medical knowledge responses provided by three large language models (LLMs) and informed consent forms for 14 common aesthetic surgical procedures, as prepared by the American Society of Plastic Surgeons (ASPS). METHODS The efficacy, readability and accuracy of three leading LLMs, ChatGPT-4 (San Francisco, CA), Gemini (Google, Mountain View, California), and Copilot (Microsoft Corp, Redmond, WA), was systematically evaluated using 14 different prompts related to the risks of 14 common aesthetic procedures. Alongside these LLM responses, risk sections from the informed consent forms of these procedures, provided by the ASPS, were also reviewed. RESULTS The risk factor segments of the combined general and specific operation consent forms were rated highest for medical knowledge accuracy. (p<0.05) Regarding readability and clarity, the procedure-specific informed consent forms, including LLMs, scored highest scores. (p<0.05) However, these same forms received the lowest score for medical knowledge accuracy. (p<0.05) Interestingly, surgeons preferred patient-facing materials created by Chat GPT-4, citing superior accuracy and medical information compared to other artificial intelligence (AI) tools. CONCLUSIONS Physicians prefer patient-facing materials created by Chat GPT-4 over other AI tools due to their precise and comprehensive medical knowledge. Importantly, adherence to ASPS' strong recommendation for signing both the procedure-specific and the general informed consent forms can avoid potential future complications and ethical concerns, thereby ensuring patients receive adequate information.Aesthetic surgery journal02Physicians prefer patient-facing materials created by Chat GPT-4 over other AI tools due to their precise and comprehensive medical knowledge due to their precise and comprehensive medical knowledge.Aesthetic surgery journal["Alpay Duran", "Oguz Cortuk", "Bora Ok"]2024-06-28T00:00:00 f7689d29cf5d5337419c9e7c42f48874a85a5a22Machine learning (ML) refers to the ability of machines to advance their performance by relying on previous results or observations. ML algorithms empower computers to learn without the need to be programmed. In recent years ML algorithms, as a branch of Artificial Intelligence (AI), have been employed in many fields and are used in multiple applications towards improving daily human lives through efficient data mining, and thus, trustful decision-making. This work attempts to examine the reliability of ML, referring to AI in the broader sense, in healthcare, currently offering techniques and methods that contribute to the clinical diagnosis of patients, as perceived by doctors. The study explores the concepts of trustworthiness of clinical AI systems in healthcare, followed by a survey on experts’ opinions in the health sector regarding their trust and the factors that mainly affect it. The survey includes a target group of 35 doctors located in Northern Greece and a corresponding questionnaire. Research identified nine factors that influence the medical staff towards the overall adoption of AI in healthcare. Moreover, the drawn conclusions based on the analysis of the survey’s results revealed that doctors in Northern Greece consider AI systems as essential supporting medical tools and are positive about using them, even though some find them costly, complex, and still don’t fully trust them.IEEE International Conference on Circuits and Systems for Communications350Researchers identified nine factors that influence the medical staff towards the overall adoption of AI in healthcare and revealed that doctors in Northern Greece consider AI systems as essential supporting medical tools and are positive about using them, even though some find them costly, complex, and still don’t fully trust them.2024 International Conference on Circuit, Systems and Communication (ICCSC)["Eleni Givanoudi", "E. Vrochidou", "G. Papakostas"]2024-06-28T00:00:00 1b577090971751df939364069bd2c529897dc04fThe article discusses the main aspects of the use of artificial intelligence in education, focusing on its advantages and challenges. The author defines AI as a system of functional computer technologies that model human thinking and skills, such as analysis of complex systems, judgment, and dialogue support. AI allows collecting and analyzing large amounts of data, developing individualized teaching methods, automating control, and providing feedback. Innovative technologies in education have proven to be effective even in times of crisis, such as quarantine and war. The main benefits of AI include personalization of learning, creation of individualized plans, immersive learning, and intelligent progress tracking, which reduces the workload of teachers and allows them to focus on students. At the same time, AI adoption is facing resistance to innovation, financial challenges, and skepticism about its effectiveness. Most AI programs focus on content presentation and testing, which does not promote critical thinking and creativity. Research prospects include developing methodologies for evaluating AI's effectiveness, integrating learning theories with practical developments, and addressing ethical issues.Молодий вчений60The article discusses the main aspects of the use of artificial intelligence in education, focusing on its advantages and challenges, as well as developing methodologies for evaluating AI's effectiveness and addressing ethical issues.Молодий вчений["\u041d\u0430\u0442\u0430\u043b\u0456\u044f \u0411\u043e\u0431\u0440\u043e"]2024-06-28T00:00:00 28de35f8630b9364df368ae1e7fc3589248101daThis paper explores the impact of artificial intelligence (AI) technologies on urban economic density, conducting an empirical analysis based on evidence from 276 cities. The article first describes how AI technology, as a key driver of economic growth, has a profound impact on the global economic landscape by optimizing production processes, improving decision-making efficiency and creating new business models. As the center of economic activities, the improvement of urban economic density is closely related to the improvement of urban infrastructure, talent agglomeration and policy support. Through quantitative analysis, this paper studies the relationship between AI technology development and urban economic density, and puts forward the hypothesis that AI technology can significantly improve urban economic density by promoting the optimization of industrial structure and the improvement of talent concentration. By constructing a fixed effect model and using Stata software to conduct multiple regression analysis, the results show that AI technology has a significant positive promotion effect on urban economic density. Therefore, this paper puts forward policy suggestions such as building an urban innovation ecosystem with AI as the core, promoting industrial intelligent upgrading, strengthening data governance and privacy protection, and strengthening international cooperation, aiming to maximize the positive effect of AI technology and promote the sustainable development of urban economy.International Journal of Global Economics and Management70Policy suggestions such as building an urban innovation ecosystem with AI as the core, promoting industrial intelligent upgrading, strengthening data governance and privacy protection, and strengthening international cooperation are put forward, aiming to maximize the positive effect of AI technology and promote the sustainable development of urban economy.International Journal of Global Economics and Management["Liang Wang", "Ruizhi Chen"]2024-06-28T00:00:00 809a4f395d6d25f56b08de57228974dfaa348d5aThis study examines AI's role in enhancing guest satisfaction and efficiency in the hotel industry. Employing a mixedmethods approach, it analyzes guest feedback and interviews staff at AI-integrated hotels. The findings aim to identify key AI applications that boost satisfaction and efficiency, and outline challenges and best practices for AI implementation. This research offers a holistic view of AI's influence on hospitality, enriching understanding and guiding industry practices. As the hotel industry continues to evolve, the integration of artificial intelligence (AI) technologies has become increasingly prevalent, aiming to enhance guest experience. This research investigates the impact of AI integration on guest experience enhancement within the hotel industry. The purpose of this study is to comprehensively explore how AI technologies influence various aspects of guest satisfaction in hotels. A mixed-methods approach is employed, combining quantitative analysis of guest feedback data with qualitative methods by interviewing the guests staying in the hotel. Data is collected from a diverse range of hotels that have implemented AI technologies, allowing for a nuanced understanding of the impacts across their establishments. This research is expected to provide valuable insights into the multifaceted effects of AI integration in the hotel industry. Specifically, it aims to identify the specific AI applications that most significantly contribute to guest satisfaction levels. Additionally, the study seeks to uncover potential challenges and limitations associated with AI implementation, as well as best practices for successful integration. This topic lies in its comprehensive examination of AI's impact on both guest experience within the hotel industry. While previous research has explored AI's role in hospitality, few studies have undertaken such a holistic analysis, considering its implications for guests. By addressing this gap, this research contributes to a deeper understanding of the transformative effects of AI in the hotel sector, providing practical insights for industry practitioners and stakeholders.Geo Journal of Tourism and Geosites460The purpose of this study is to comprehensively explore how AI technologies influence various aspects of guest satisfaction in hotels, and to identify the specific AI applications that most significantly contribute to guest satisfaction levels.GeoJournal of Tourism and Geosites["Pritilata Acharya", "Smita S. Mahapatra"]2024-06-28T00:00:00 51c9508e2c5e8872a4ba3a18e528699e5371dd3a The integration of artificial intelligence (AI) in the field of dermatology promises a revolutionary transformation in our approach to this domain. However, the use of AI in dermatology is still in its early stages, and with limited scope of applications. While some of the existing research performed has highlighted the capabilities of AI in diagnosing lesions comparable with those of a dermatologist, the implementation of AI brings about its own ethical challenges that affect both the clinician and the patient. This article aims to explore the ethical issues and prospects surrounding AI implementation in dermatology by drawing insights from the current literature. A qualitative analysis was conducted on articles focusing on AI usage in medical care, with a specific emphasis on dermatology. The analysis unveils several ethical issues that emerge from the implementation of AI in dermatology: the obscure and subjective nature of AI leads to selective bias and unequal treatment based on skin tone differences. It also has the potential to erode patient autonomy by taking on a more paternalistic approach to medicine, increasing the risk of unwarranted harm to patients because of unnecessary biopsies, along with potential misalignment of treatment priorities between AI systems and patients. Issues of accountability arise as AI integration can challenge the epistemic authority and ownership of clinicians over their patients. Finally, the closed-loop system of training algorithms coupled with the lack of an open-access database due to privacy issues further increases the subjectiveness and inaccessibility of each algorithm. While AI’s integration in dermatology promises to streamline the diagnostic process, it also sparks ethical issues that affect the main stakeholders. These findings indicate the need for further development and enhancement of AI systems before they are capable of being implemented. Introduction of a transparent, explainable AI model would alleviate concerns regarding patient autonomy and treatment rationalization. Simultaneously, the establishment of a global open-access database will serve to mitigate selective bias. Furthermore, additional research comparing algorithms would be useful in establishing a standardized validation tool. As it stands, rather than replacing clinicians, AI should be used as a diagnostic adjunct in aiding clinical decisions.British Journal of Dermatology00The analysis unveils several ethical issues that emerge from the implementation of AI in dermatology: the obscure and subjective nature of AI leads to selective bias and unequal treatment based on skin tone differences, and the need for further development and enhancement of AI systems before they are capable of being implemented.British Journal of Dermatology["Sut Mo Zachary Chan", "Faishal Dubash"]2024-06-28T00:00:00 f11b714d488d8d393abbdc80f3dbc2b21007e058, are committed to not being left behind. Transformative AI technologies offer the potential to optimise resource allocation and enhance decision-making processes in the public sector. The opportunities presented by AI technologies have enabled governments across the globe to utilise them in diverse sectors to enhance the delivery of public services and improve citizens’ quality of life. Background: Rapid advancements in artificial intelligence (AI) technologies have provided opportunities to improve public service delivery. Uganda is committed to leveraging opportunities presented by AI technologies to improve service delivery. Aim: This study examines how the Ugandan government uses AI technologies to enhance public service delivery. Setting: Few studies have been conducted exploring how AI technologies are used to improve public service delivery in Uganda. To bridge this knowledge gap, this study examines the ways in which AI technologies have been used in public service delivery by the government of Uganda. Methods: Using a mixed-methods approach, secondary and primary data were collected. Textual content analysis and Microsoft Excel 2016 were used to analyse qualitative and quantitative data respectively to obtain results and insights for the study. Results: The results reveal that the Ugandan government is deploying AI technologies in various agencies to enhance efficiency and productivity, improve accuracy and precision, solve environmental challenges, enhance fraud detection and security, and enable personalisation and customisation of citizen-centric services. Furthermore, this study discusses the ethical concerns and social implications of adopting AI technologies such as data privacy, security threats, the digital divide and job displacement. Conclusion: Recognising the transformative potential of AI technologies to overcome traditional public service barriers, ethical concerns and social implications should be considered in the implementation to yield sustainable outcomes in Uganda. Contribution: This study contributes to the body of knowledge on AI adoption in Africa, and provides insights for policymakers and researchers seeking to understand and/or recommend AI technologies utilisation to optimise public service delivery.Africa's Public Service Delivery & Performance Review320Recognising the transformative potential of AI technologies to overcome traditional public service barriers, ethical concerns and social implications should be considered in the implementation to yield sustainable outcomes in Uganda.Africa’s Public Service Delivery and Performance Review["T. Nalubega", "D. Uwizeyimana"]2024-06-28T00:00:00 85f6b3ab2a1903fab64db9510fb307140d9b1f33Artificial intelligence (AI) is present in every facet of contemporary life, and concerns about sustainability are receiving more attention across the board in human endeavors. Nowadays, large firms are expected to report on their operations, expose them, and account for their environmental and social footprint. This is accomplished through various frameworks, measurements, and also environmental, social, & governance standards, or ESG (Environment, Social Governance), gradually replacing the more traditional term CSR (Corporate Social Responsibility). Accountants should use AI techniques to assess and validate an organization's sustainability and net-zero commitment claims. In this manner, accountants may guarantee AI technology's moral and efficient integration into accounting procedures by validating an organization's ESG metrics and enacting change from the inside. The methodology adopted for this study includes qualitative data collection, which primarily revolved around interviews using purposive sampling. Professionals must effectively utilize AI's potential in sustainable accounting. For future research, it is crucial to develop an entire framework based on the principles described here, based on various sources that describe the integration between accounting, AI, and ESG.The Accounting Journal of Binaniaga00Accountants should use AI techniques to assess and validate an organization's sustainability and net-zero commitment claims by validating an organization's ESG metrics and enacting change from the inside.The Accounting Journal of Binaniaga["R. Silitonga", "Vicky Pratama Putra", "Yung-Tsan Jou", "Ronald Sukwadi"]2024-06-28T00:00:00 a17cf30da96aaefa5a0f0d43bf8e97ead037846dВ статье рассматривается влияние искусственного интеллекта (ИИ) на экономику современного общества. Автор анализирует различные аспекты применения ИИ в экономике, отмечая его влияние на повседневную жизнь людей, бизнес-процессы, отрасли промышленности и социальные сферы. Особое внимание уделяется технологическим изменениям, вызванным внедрением ИИ, а также его потенциальным последствиям для рынка труда и занятости. В статье представлены данные исследований, демонстрирующие как позитивные, так и негативные аспекты влияния ИИ на экономику. The article discusses the impact of artificial intelligence (AI) on the economy of modern society. The author analyzes various aspects of AI application in the economy, noting its impact on people's daily lives, business processes, industries and social spheres. Special attention is paid to the technological changes caused by the introduction of AI, as well as its potential consequences for the labor market and employment. The article presents research data demonstrating both positive and negative aspects of AI impact on the economy.Экономика и предпринимательство00Экономика и предпринимательство["\u041d\u0410\u0422\u0410\u041b\u042c\u0421\u041e\u041d\u00a0\u0410.\u0412. \u041d\u0410\u0422\u0410\u041b\u042c\u0421\u041e\u041d\u00a0\u0410.\u0412.", "\u0425\u0410\u0411\u0418\u0411\u0420\u0410\u0425\u041c\u0410\u041d\u041e\u0412\u0410\u00a0\u0410.\u0418. \u0425\u0410\u0411\u0418\u0411\u0420\u0410\u0425\u041c\u0410\u041d\u041e\u0412\u0410\u00a0\u0410.\u0418."]2024-06-28T00:00:00 7fb2d7a1cf471eda4552a37882e8430e09f39b89Objectives: I analyze, deconstruct, and debunk prevalent misconceptions about artificial intelligence (AI) in education. Methods: This study identifies and presents ten common myths about AI in education, followed by concise explanations that counter each myth with the corresponding reality, relying on credible sources and evidence. Results: AI does not replace educators; it lacks the vital human qualities crucial for effective learning experiences. Thus, it can complement rather than substitute for educators. Physical classrooms remain pivotal for fostering student engagement, an element AI cannot fully replicate, challenging the notion of AI replacing the need for traditional classrooms. Despite excelling in specific tasks, AI lacks human cognitive characteristics such as understanding and creativity, which counters the belief that AI is smarter than people. Conclusions: Dispelling these myths can help pave the way for a more nuanced, responsible, and beneficial integration of AI in the realm of education. This ensures that its influence aligns with constructive pedagogical goals and contributes to societal advancement. The strengths of AI can be leveraged to empower a more inclusive, equitable, and effective education for all. Implications for Practice: Educators are advised to be informed about the realities of AI in education to counter misconceptions and make informed decisions about its integration. Policymakers should also allocate resources for educator training in AI use, aiming for proficiency and confidence in incorporating these technologies into educational methodologies.Higher Learning Research Communications520Ten common myths about AI in education are identified and presented, followed by concise explanations that counter each myth with the corresponding reality, relying on credible sources and evidence.Higher Learning Research Communications["L. Giray"]2024-06-28T00:00:00 35deb48c26664ee3e37da6f1c97479f1ab7428dcВ данной статье рассматривается влияние искусственного интеллекта (ИИ) на практику управления бизнесом. Стремительное развитие технологий ИИ привело к революционным изменениям в работе предприятий от оптимизации процессов до улучшения процесса принятия решений. В данной статье рассматривается, как ИИ изменил различные аспекты управления бизнесом, включая маркетинг, операционную деятельность, управление персоналом и финансы. Здесь также обсуждаются проблемы и возможности, которые ИИ открывает перед бизнесом, такие как этические аспекты и необходимость повышения квалификации сотрудников. В целом данная статья подчеркивает значительную роль, которую ИИ играет в формировании будущего практики управления бизнесом. This paper examines the impact of artificial intelligence (AI) on business management practices. The rapid advancements in AI technology have revolutionized the way businesses operate, from streamlining processes to improving decision-making. This paper explores how AI has transformed various aspects of business management, including marketing, operations, human resources, and finance. It also discusses the challenges and opportunities that AI presents for businesses, such as ethical considerations and the need for upskilling employees. Overall, this paper highlights the significant role that AI plays in shaping the future of business management practices.Экономика и предпринимательство00Экономика и предпринимательство["\u0422\u0410\u0420\u0410\u041c\u041e\u0412\u00a0\u042e.\u0425. \u0422\u0410\u0420\u0410\u041c\u041e\u0412\u00a0\u042e.\u0425.", "\u0410\u0411\u0414\u0423\u041b\u041c\u0423\u041a\u041c\u0418\u041d\u041e\u0412\u0410\u00a0\u0424.\u041c. \u0410\u0411\u0414\u0423\u041b\u041c\u0423\u041a\u041c\u0418\u041d\u041e\u0412\u0410\u00a0\u0424.\u041c.", "\u0414\u0410\u0422\u0410\u0415\u0412\u00a0\u0410.\u0410. \u0414\u0410\u0422\u0410\u0415\u0412\u00a0\u0410.\u0410."]2024-06-28T00:00:00 4c2e860fd71609a241d37e17171920a218f52f68Nowadays people’s lives are hugely affected by artificial intelligence (AI). For exam-ple, machines text quickly, develop software, and create different types of arts. One may notice that many of the sport and business news which are accessible on the net are written by machines. According to the current legal framework of many states, AI-generated works are generally viewed as a simple tool. However, with the develop-ment of advanced AI machines, this perspective has undergone a significant shift. The emergence of AI has hugely posed challenges to intellectual property law, in par-ticular copyright. In copyright sphere, AI-generated machines have risen some legal disputes whether AI can be the author of creative works, how AI works need to be protected or who owns the rights for such works. The paper aims to address the aforementioned questions and to clarify copyright issues arising from AI machines. The purpose of this paper is to identify the characteristics of legal regulations govern-ing copyrighted works produced by AI machines in developed countries and subse-quently offer relevant recommendations to improve national (Kazakh) copyright law concerning AI-generated machines. Based on foreign practice, the author argues that when it comes to creative works generated by computer machines, the individual who has made the necessary arrangements for the creation of such works should be con-sidered the author.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00The characteristics of legal regulations govern-ing copyrighted works produced by AI machines in developed countries are identified and relevant recommendations to improve national (Kazakh) copyright law concerning AI-generated machines are offered.Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan["A. Aronov", "S. Idrysheva"]2024-06-28T00:00:00 d0a2771d77d07cc3e030aa10b8318693e2f6ca51Currently, science is constantly monitoring the phenomenon of artificial intelligence (AI) because even the very definition of this phenomenon contains, in our opinion, logical flaws. For example, the definition of AI in the Britannica online dictionary contains, in our opinion, a false statement that AI can "understand the meaning of a text." At the same time, when asked this question, AI begins to "answer" as if to another question, namely, how AI distinguishes between a text written by a human and a text written by an AI, which is not an answer to the question of how the AI finds the meaning of a text. The latter, in our opinion, is the prerogative of human intelligence. This situation confirms the conclusions of the modern American philosopher John Searle, who proposed a thought experiment called the "Chinese room," where he proves that the analytical ability of AI to follow specific instructions is not a sign of intelligence. We believe that the development of AI poses the following threats: In particular, decision-making in the absence of biological instincts of self-preservation, the presence of a priori knowledge in AI in the form of algorithms laid down by a human programmer, the absence of the concept of morality in AI, because morality, like human intelligence in general, is subjective (a purely human property) and may differ from ethical social norms. Such a difference between morality and ethics is especially evident in totalitarian societies, such as, for example, in the case of citizens of Nazi Germany who, contrary to the ethical norms of that society, hid and helped Jews leave the country. Spatial development00The development of AI poses the following threats: decision-making in the absence of biological instincts of self-preservation, the presence of a priori knowledge in AI in the form of algorithms laid down by a human programmer, the absence of the concept of morality in AI.Spatial development["Andrii Timchenko", "Ivan Chornomordenko"]2024-06-28T00:00:00 767bcc8b83ab3d92a633d1a813c86f15af5485e4The shipping industry has long been a critical component of global trade and commerce, but it has faced numerous challenges, including labor shortages, safety concerns, and the need to reduce environmental impact. In recent years, the emergence of autonomous ships and the application of artificial intelligence (AI) have the potential to transform the shipping industry, addressing these challenges and driving innovation. This study explores the role of autonomous ships and AI in revolutionizing the shipping industry. It employs a multifaceted research approach, including a comprehensive literature review, expert interviews, case studies, and quantitative analysis, to provide a detailed understanding of the key developments, potential benefits, and challenges associated with the integration of these technologies. The research findings reveal significant advancements in autonomous ship technology, with vessels equipped with advanced sensors, navigation systems, and decision-making algorithms that enable them to operate with minimal to no human intervention. The integration of AI further enhances the capabilities of autonomous ships, enabling them to process vast amounts of data, optimize routes and energy usage, and improve overall safety and reliability. The study identifies the potential benefits of autonomous ships and AI-powered shipping operations, including increased efficiency, improved safety, reduced operational costs, and decreased environmental impact. However, it also highlights the challenges that must be addressed, such as the need for regulatory adaptations, overcoming technological limitations, managing cybersecurity risks, and addressing public acceptance.Maritime Park Journal of Maritime Technology and Society60The study identifies the potential benefits of autonomous ships and AI-powered shipping operations, including increased efficiency, improved safety, reduced operational costs, and decreased environmental impact, but highlights the challenges that must be addressed, such as the need for regulatory adaptations, overcoming technological limitations, managing cybersecurity risks, and addressing public acceptance.Maritime Park Journal of Maritime Technology and Society["Muhammad Riyadh"]2024-06-28T00:00:00 84e14399b2f7c7047566a5dcfd2768f0f62cd558 Skin cancer is the most common cancer worldwide. Early diagnosis is crucial for improving patient survival and morbidity. Artificial intelligence (AI)-assisted smartphone applications (apps) for skin cancer potentially offer accessible, early risk assessment of suspicious skin lesions. If harnessed to effective and responsive dermatology services, they may improve patient outcomes. The integration of novel technologies into dermatology raises ethical concerns. Ethical principles for acceptable AI governance are well known. Less well elucidated are how these should be applied to actual AI apps. Ethical use-case analysis can improve this understanding. Accordingly, we undertook an ethical use-case analysis of commercially available skin cancer apps. We applied previously described methods for ethical analysis of clinical AI applications to two popular skin cancer apps: SkinVision and Scanoma (Rogers WA, Draper H, Carter SM. Evaluation of artificial intelligence clinical applications: detailed case analyses show value of healthcare ethics approach in identifying patient care issues. Bioethics 2021; 35: 623–33). Systematic searches incorporating published literature, regulatory documents, and websites were undertaken to provide a detailed description of each app, and review the available evidence about their development, effectiveness and use. Screening for inclusion was undertaken by two researchers independently. Ethical concerns were identified with reference to previously described ethical concerns and principles for AI-assisted healthcare. These data were also independently checked and agreed on. By conceptualizing ethical principles within the use context of skin cancer apps, we identified specific ethical issues arising throughout the AI lifecycle of both apps. One company provided extensive detail regarding algorithm development and decision making; this information was insufficiently reported for the other app. Other concerns identified related to the number, quality, consistency and independence of studies assessing algorithm efficacy. Limited efforts to address potential skin tone biases and exclusion of patients with darker skin as target users by one app risk perpetuating existing inequalities. Inadequate regulatory requirements were highlighted. To the best of our knowledge, this study represents the first ethical use-case analysis of patient-facing AI-assisted skin cancer apps. Our findings suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps. Improved regulation will increase accountability. Ensuring ethics by design through integration between technology developers, dermatologists, ethicists and patients is urgently needed to prevent the potential benefits of AI-assisted skin cancer apps being overshadowed by potential ethical harms.British Journal of Dermatology00This study represents the first ethical use-case analysis of patient-facing AI-assisted skin cancer apps and suggests inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps.British Journal of Dermatology["Fazal Shah", "Daniel Arecco", "Heather Draper", "R. Matin"]2024-06-28T00:00:00 f2b408be53b855af19108baabf00fb21a045e0f4 There has been a rapid expansion in the use of artificial intelligence (AI) models to aid in diagnosis and triage of skin cancer in the UK in the last few years. This has been driven by a need to manage an increasing number of urgent referrals for suspected skin cancer. Our service has been using one such platform to screen out benign lesions, thus reducing the number of patients subsequently requiring a face-to-face appointment with a trust dermatologist. The AI platform assesses the features of a single dermoscopic image to categorize the lesion into benign or malignant. Information from patient history and macroscopic images are not involved. As dermatologists, we recognize the importance of history (pain, rapid growth) and clinical examination (induration, tenderness) as well as dermoscopic assessment in reaching a diagnosis. We suspected that the use of dermoscopy in clinical practice is of greater value for distinguishing benign from malignant pigmented lesions compared with keratotic lesions. When a suspected skin cancer is referred into our service, the general practitioner is required to state whether they suspect melanoma or squamous cell carcinoma (SCC). (Basal cell carcinomas are referred on a separate, routine pathway). We sought to use this information to assess for any differences in outcomes. A retrospective review of patients assessed by the AI platform was conducted between August 2023 and November 2023. In total 800 patients were included in the study (396 melanoma pathway, 404 squamous cell carcinoma pathway). In total 116 referrals were discharged by the AI platform on the melanoma pathway (29.2%) and 32 referrals were discharged by DERM on the SCC pathway (7.9%). These results support our hypothesis that AI is better at recognizing benign pigmented lesions, as 92% of patients referred on the SCC pathway required further assessment by a trust dermatologist compared with 70% of those referred on a melanoma pathway. This information is of importance to commissioners planning to implement skin cancer AI triage models.British Journal of Dermatology00The hypothesis that AI is better at recognizing benign pigmented lesions is supported, as 92% of patients referred on the SCC pathway required further assessment by a trust dermatologist compared with 70% of those referred on a melanoma pathway.British Journal of Dermatology["Nor Ismail", "Maryam Barfei", "Hannah McLaughlin", "E. Roberts"]2024-06-28T00:00:00 c0f4fe128f5659812957047df9ceb11006b1d638Numerous obstacles endanger the livelihoods of smallholder farmers and global food security. Crop output losses due to plant diseases alone are estimated to be between 15% to 25% every year. However, farmers' capacity to recognize and treat illnesses in a timely manner is hampered by things like a lack of agronomic expertise, limited access to actionable insights, and information gaps. The work proposes a comprehensive end-to-end web application that uses machine learning models to help farmers with configurable expert advice, smart farm management, and automatic disease detection. Thus, enabling farmers to identify the infection using their smart phones and obtain crop - fertilizer recommendations for the crops to be treated in the farmer's area based on historical data analysis of yield-influencing elements such as geography, weather, and soil. The proposed system will employ Generative Artificial Intelligence, such as ChatGPT's natural language processing and generation capabilities, to engage in conversational interactions with farmers. The work achieves close to 95% accuracy after utilizing 30 epochs in the area of disease detection and 97% accurate models are achieved for fertilizer outputs. These features along with it's high accuracy can assist farmers in making informed decisions, utilising the data obtained to reduce crop output losses.2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC)150The work proposes a comprehensive end-to-end web application that uses machine learning models to help farmers with configurable expert advice, smart farm management, and automatic disease detection, and 97% accurate models are achieved for fertilizer outputs.2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC)["S. Lokesh", "Arvind Madhavan", "RM Prakash Ramanathan", "Krteen Anand"]2024-06-28T00:00:00 7e73c7ed1880f9d05ada2a550224b06145b1890a Teledermatology can help support the timely diagnosis of skin cancer. NHS England recently published a roadmap to accelerate the roll-out of teledermatology services nationally, including an updated series of audit and quality control standards. However, there remains no consensus as to the ideal audit methodology, which contributes to a paucity of comparative teledermatology evidence. This study aims to describe a reproducible audit methodology for monitoring clinician performance in teledermatology pathways. We present a novel quantitative risk scoring system – the Teledermatology Audit Risk Scoring System (TARSS) – that draws upon our experience in monitoring the safety and effectiveness of an artificial intelligence (AI) as a medical device product in NHS skin cancer pathways. By adapting our clinical risk management system (Table), we are able to assess clinician performance by applying risk scores to confusion matrices for both management and diagnostic accuracy. Thirty nonconsecutive cases were reviewed by 10 experienced teledermatologists (all NHS consultants working in different units across the UK). Reflecting the typical case mix, lesions with both histology and clinical ground truths were included; 27 cases were selected at random and three known cancers were included. Overall, 90% of dermatologists missed between two and four high-risk malignancies each; only one dermatologist correctly identified all high-risk cancers. Management accuracy stratified by lesion risk demonstrated clinician sensitivity of 78.3% (95% confidence interval 70.2–84.8) and specificity of 47.5% (95% confidence interval 39.3–55.8). There were no significant differences in management accuracy between dermatologists (one-way AnovaP = 0.71), although there was only ‘moderate’ interrater agreement (Fleiss’ kappa 0.43). Similar performance was seen in diagnostic accuracy. Anonymized results were discussed in a dedicated forum and each dermatologist was provided with personalized feedback. This study describes an innovative teledermatology audit methodology that uses a quantitative risk-based approach to assess clinician performance, in terms of both management and diagnostic accuracy. We are now collaborating with a number of NHS partners to audit local teledermatology services using this methodology. Real-world cases were selected from multiple skin cancer pathway AI deployments, including some funded by an NHS AI in Health and Care Award. All authors are employed by the AI provider, which runs regular audits as part of its clinical governance framework.Risk scoreDescription4 MajorIncorrectly diagnosed malignant lesion (false negative)Diagnostic delay > 2 months3 SignificantIncorrectly diagnosed premalignant lesion (false negative)Diagnostic delay > 7 days2 ModerateIncorrectly diagnosed lesion (false positive) resulting in inappropriate referral for investigationDiagnostic delay < 7 days1 MinorInconvenience or minor psychological upset0 No riskNo riskBritish Journal of Dermatology00A novel quantitative risk scoring system – the Teledermatology Audit Risk Scoring System (TARSS) – that draws upon the experience in monitoring the safety and effectiveness of an artificial intelligence as a medical device product in NHS skin cancer pathways.British Journal of Dermatology["Joshua Luck", "Audrey Menezes", "Dilraj Kalsi", "Dan Mullarkey", "Niall Wilson"]2024-06-28T00:00:00 ca2349f2bad8b8b1f06d9b024f0f3465637c6f75 Teledermatology can help support the timely diagnosis of skin cancer. NHS England recently published a roadmap to accelerate the roll-out of teledermatology services nationally, including an updated series of audit and quality control standards. However, there remains no consensus as to the ideal audit methodology, which contributes to a paucity of comparative teledermatology evidence. This study aims to describe a reproducible audit methodology for monitoring clinician performance in teledermatology pathways. We present a novel quantitative risk scoring system – the Teledermatology Audit Risk Scoring System (TARSS) – that draws upon our experience in monitoring the safety and effectiveness of an artificial intelligence (AI) as a medical device product in NHS skin cancer pathways. By adapting our clinical risk management system (Table), we are able to assess clinician performance by applying risk scores to confusion matrices for both management and diagnostic accuracy. Thirty nonconsecutive cases were reviewed by 10 experienced teledermatologists (all NHS consultants working in different units across the UK). Reflecting the typical case mix, lesions with both histology and clinical ground truths were included; 27 cases were selected at random and three known cancers were included. Overall, 90% of dermatologists missed between two and four high-risk malignancies each; only one dermatologist correctly identified all high-risk cancers. Management accuracy stratified by lesion risk demonstrated clinician sensitivity of 78.3% (95% confidence interval 70.2–84.8) and specificity of 47.5% (95% confidence interval 39.3–55.8). There were no significant differences in management accuracy between dermatologists (one-way AnovaP = 0.71), although there was only ‘moderate’ interrater agreement (Fleiss’ kappa 0.43). Similar performance was seen in diagnostic accuracy. Anonymized results were discussed in a dedicated forum and each dermatologist was provided with personalized feedback. This study describes an innovative teledermatology audit methodology that uses a quantitative risk-based approach to assess clinician performance, in terms of both management and diagnostic accuracy. We are now collaborating with a number of NHS partners to audit local teledermatology services using this methodology. Real-world cases were selected from multiple skin cancer pathway AI deployments, including some funded by an NHS AI in Health and Care Award. All authors are employed by the AI provider, which runs regular audits as part of its clinical governance framework.Risk scoreDescription4 MajorIncorrectly diagnosed malignant lesion (false negative)Diagnostic delay > 2 months3 SignificantIncorrectly diagnosed premalignant lesion (false negative)Diagnostic delay > 7 days2 ModerateIncorrectly diagnosed lesion (false positive) resulting in inappropriate referral for investigationDiagnostic delay < 7 days1 MinorInconvenience or minor psychological upset0 No riskNo riskBritish Journal of Dermatology00A novel quantitative risk scoring system – the Teledermatology Audit Risk Scoring System (TARSS) – that draws upon the experience in monitoring the safety and effectiveness of an artificial intelligence as a medical device product in NHS skin cancer pathways.British Journal of Dermatology["Joshua Luck", "Audrey Menezes", "Dilraj Kalsi", "Dan Mullarkey", "Niall Wilson"]2024-06-28T00:00:00 23d9c5e8e22dc4b190e6178e8ec2b4a631c3f8d6 The use of patient data for artificial intelligence (AI) research should be transparent, rigorous and accountable. In the UK, the General Data Protection Regulation, Data Protection Act 2018 and General Medical Council govern data handling and patients’ rights to privacy. We report on our multistep pseudonymization protocol for real-world skin lesion datasets, in preparation for research within a trusted research environment (TRE). Firstly, patients referred from primary care are triaged for community locality and imaging centre (CLIC) suitability. There, trained healthcare professionals capture lesion images (dermoscopic, macroscopic and regional) and patient information using a mobile application on trust-certified devices. Training is standardized across all CLIC sites, with specific anonymization training on removing in-frame clothing and jewellery, device positioning, and magnification to minimize identifiable features like eyes, nose and ears. Lesion datasets (paired images and clinical information) are subsequently transferred to an image management system (IMS) hosted on our trust-secured network. Within the IMS, images are manually inspected, and those with identifiable tattoos and piercings are excluded. All regional images are also excluded from transfer to the TRE. Before transfer to the TRE, images undergo a further round of review. Data fields are manually checked for identifiable patient information, patient names are removed, and dates of birth are rounded to 3-month granularity. The job ID, patient’s hospital number, date of clinical episode and responsible photographer are replaced with randomly generated project-specific identifiers. In an initial study period, 658 of 963 (68%) captured lesion datasets have undergone IMS manual inspection. Of these, 24 lesion datasets were excluded for identifiable features, 10 (41%) for more than one-third of the face being visible, 9 (38%) for full iris visibility, and 5 (21%) for tattoos. On breakdown by anatomical location these images were of the face (19, 80%), torso (2, 8%), limbs (2, 8%) and neck (1, 4%). The remaining 634 datasets (96%) were securely transferred to the TRE, where a further 5% were excluded due to potential identifiability. Although full anonymization is desirable, it is usually achieved by aggregating patient data. Pseudonymization, which allows for future reidentification in a secured fashion, strikes the balance between patient data privacy and clinical governance, while retaining a level of granularity sufficient for meaningful analysis. Currently, this protocol is manually intensive with room to partly automate. Use of common standardized protocols will strengthen the public trust in clinical AI.British Journal of Dermatology00Pseudonymization, which allows for future reidentification in a secured fashion, strikes the balance between patient data privacy and clinical governance, while retaining a level of granularity sufficient for meaningful analysis.British Journal of Dermatology["Trisha J M Chin", "G. Chin", "James Sutherland", "Andrew Coon", "Colin A Morton", "C. Fleming"]2024-06-28T00:00:00 a2deac7983ba472b17a1653be01ac2dbfb64bf8c Every year 2-week rule (TWR) referrals for skin cancer are increasing, reflected at both national and local level, and they are projected to rise further. The number of substantive staff in dermatology is not increasing and innovative solutions are necessary. We present an evaluation of a south-east UK dual-site teledermatology TWR service 1 year after implementation of the artificial intelligence software DERM, provided by third-party Skin Analytics (SA). Our TWR service successfully initiated a photodermatology service by use of a ‘PhotoHub’ for skin lesions. SA DERM was subsequently implemented to analyse these images and autonomously triage the patients into (i) TWR referral to secondary care dermatology, (ii) benign and for automated discharge, or (iii) ‘other’ diagnosis for general practitioner or community review. Lesions deemed benign by DERM underwent a safety-net review by an SA consultant dermatologist prior to discharge. Patients referred to secondary care dermatology were reviewed on a virtual platform by a trust consultant dermatologist to determine an appropriate pathway. SA DERM aimed to (i) speed up local cancer diagnosis, (ii) reassure patients with benign lesions faster, (iii) support workforce capacity and (iv) be cost-effective. Over a 12-month period (July 2022 to July 2023), 1599 patients seen in the PhotoHub underwent analysis by SA DERM. By the end of the pilot around 30% of the monthly TWR referrals were being analysed. SA DERM had a correct automated discharge rate of 20%. In total, 50% of cases were referred directly to trust dermatologists for further triage via the SA online platform. Overall, 41% of patients analysed by SA DERM avoided urgent face-to-face appointments. SA Derm demonstrated > 95% target sensitivity for melanoma and squamous cell carcinoma and > 90% for basal cell carcinoma and premalignant lesions. Patient and end users reported a 75% positive experience. The hospital skin cancer performance target increased from 36% to 95%. Clinicians have been surveyed on their experience. In conclusion, SA DERM helps manage the continued increase in TWR referrals. It demonstrated an improvement each quarter as the software was established. However, a large proportion of teledermatology images requiring triage remained, with the subsequent administrative burden. Future local challenges include encouraging community referrals via the PhotoHub and addressing the waiting list backlog for ‘routine’ appointments so this pathway can be utilized for lower-grade skin lesions. Clinician concerns remain. Ongoing engagement with AI solutions is likely to be required to develop skin cancer services fit for future demand.British Journal of Dermatology00An evaluation of a south-east UK dual-site teledermatology TWR service 1 year after implementation of the artificial intelligence software DERM, provided by third-party Skin Analytics (SA), which helps manage the continued increase in TWR referrals.British Journal of Dermatology["Philippa Walker-Smith", "Anne-Marie Christie", "Annabel Scott", "Arani Chandrakumar"]2024-06-28T00:00:00 0f45daad026e0673de85afae18af6a520d805627This comprehensive literature review delves into the extensive body of research exploring the role of artificial intelligence (AI) in project management. Its primary objective is to amalgamate current knowledge while discerning prevalent trends, challenges, and advantages associated with incorporating AI into project management methodologies. The study employed bibliometric analysis using VOSViewer® software to scrutinize 246 pertinent publications sourced from the Scopus database, spanning the years 2013 to 2023. The findings from the Association Strength and Density Analysis revealed distinct clusters: Cluster 1 elucidated strong associations with terms like «construction project» «technology» «algorithm» «performance» and «time»; Cluster 2 emphasized «case stud,» «decision» and «cost»; while Cluster 3 concentrated on «literature» «industry» and «framework» Moving forward, promising areas for further exploration encompass the refinement of AI algorithms, exploration of AI’s impact on project outcomes, and the fusion of AI with emergent technologies like blockchain and the Internet of Things (IoT). This research serves as a foundational roadmap for future investigations aiming to leverage AI to enhance project management effectiveness and efficiency. Keywords: scientometric analysis, bibliometric review, project management, artificial intelligence, Machine Learning, cluster analysis, research trends.Bulletin of Toraighyrov University Economics series00This comprehensive literature review delves into the extensive body of research exploring the role of artificial intelligence (AI) in project management to serve as a foundational roadmap for future investigations aiming to leverage AI to enhance project management effectiveness and efficiency.Bulletin of Toraighyrov University. Economics series["A. Kozhakmetova", "T. Narbaev", "D. Serikbay", "A. \u041c\u0430myrbaev", "K. Abdrashova"]2024-06-28T00:00:00 55c2c175822752bceb82a936d31dd1ae1c6e26b3 Over 25% of patients with suspected skin cancer in England waited over 4 weeks from urgent referral to diagnosis in October 2023. Implementation of artificial intelligence (AI) can augment this pathway to improve timely diagnosis. A prospective, postdeployment, multicentre clinical performance review of AIaMD was performed. AIaMD is a UKCA class IIa-approved artificial intelligence as a medical device intended for use in the screening, triage and assessment of skin lesions suspicious for skin cancer. AIaMD was deployed at four sites as part of the National Health Service AI in Health and Care Award. Patients assessed by the most recent version of AIaMD from April 2022 to November 2023 were eligible for inclusion. Outcomes were confirmed from histology reports for cancerous lesions and histology reports or consultant teledermatology assessment for premalignant and benign lesions. AIaMD assessed 3979 lesions with outcomes confirmed, including 38 melanomas, 68 squamous cell carcinomas, 181 basal cell carcinomas, 5 rare skin cancers, and 612 premalignant lesions such as Bowen disease and actinic keratoses. The negative predictive value – how often AIaMD labelled lesions as premalignant or benign and correctly ruled out cancer – was 99.8% (539 of 540). The sensitivities of AIaMD for melanoma, all skin cancer and premalignancy were 97% (37 of 38), 96.6% (282 of 292) and 86.3% (528 of 612), respectively. The specificity of AIaMD for benign lesions was 74.9% (2118 of 2827). For benign lesions confirmed by biopsy only, the specificity was 27.6% (105 of 381). One site reported a reduction in average wait time to first appointment by 11 days, a 10% reduction in biopsies and a 13% reduction in routine follow-up appointments. This postmarket service evaluation reported the clinical outcomes of AIaMD – an AI device used for assessment of skin lesions. The pathway was sensitive, identifying 282 out of 292 skin cancers, and specific, correctly identifying three out of every four benign lesions assessed and confirmed clinically or histologically. Moreover, implementation of AIaMD was linked to benefits to the pathway such as a reduction of biopsies. The integration of AIaMD into skin cancer diagnostic pathways could significantly improve the accuracy of urgent suspected skin cancer referrals, removing unnecessary specialist review of benign lesions. Part of the deployment period was funded as part of the NHS AI in Health and Care Award, and two of the authors are employed by one of the deployment sites. Three of the authors are employed by the AI provider.British Journal of Dermatology00The integration of AIaMD into skin cancer diagnostic pathways could significantly improve the accuracy of urgent suspected skin cancer referrals, removing unnecessary specialist review of benign lesions.British Journal of Dermatology["Felix Brewer", "Jonathan Kentley", "Dilraj Kalsi", "Dan Mullarkey", "Lucy Thomas"]2024-06-28T00:00:00 53dcf41c2b1cd0a2c293ff8ca5b8ce476b4c191bThis research examines the ethical and legal implications of deploying Artificial Intelligence (AI) systems in law enforcement, with a particular focus on potential human rights violations in Indonesia. Utilizing a normative analysis approach, the study evaluates existing ethical frameworks, legal principles, and human rights standards to assess the governance and implications of AI-driven policing. Key findings indicate significant ethical concerns, including bias, discrimination, lack of transparency, and privacy violations. The legal analysis reveals gaps in Indonesia’s regulatory framework, highlighting the need for specific legislation to address AI’s complexities. Human rights implications, such as threats to privacy, freedom of expression, and equality, are critically analyzed. Comparative case studies from other jurisdictions provide empirical insights and underscore the importance of robust ethical and legal frameworks. The research proposes several recommendations, including the establishment of clear ethical guidelines, strengthening legal frameworks, enhancing transparency and accountability, promoting public engagement, and conducting regular impact assessments to ensure responsible AI governance in law enforcement. This study aims to contribute to the development of ethical AI governance frameworks and inform policy recommendations for responsible AI deployment in law enforcement practices.The Easta Journal Law and Human Rights360The research proposes several recommendations, including the establishment of clear ethical guidelines, strengthening legal frameworks, enhancing transparency and accountability, promoting public engagement, and conducting regular impact assessments to ensure responsible AI governance in law enforcement.The Easta Journal Law and Human Rights["Zulkham Sadat Zuwanda", "Arief Fahmi Lubis", "Nuryati Solapari", "Marius Supriyanto Sakmaf", "Andri Triyantoro"]2024-06-28T00:00:00 8b232d3bbf3eaefd43c98ff29dc7b2c5eea0d6d9 Dermatology services are facing rising demand with limited workforce and resources. Artificial intelligence as a medical device (AIaMD) may offer a way to expand capacity and improve patient outcomes. The aim of this project was to develop a cost–utility health economic model of an AIaMD used for the screening, triage and assessment of lesions suspicious for skin cancer using dermoscopic images. This report focuses on the triage of patients referred by general practitioners on the dermatology urgent suspected cancer pathway. The AIaMD assessment takes place in a photo clinic and the images and medical history are subsequently assessed by a specialist (teledermatology). The aim of the AIaMD triage is to identify patients with noncancerous lesions who could be discharged. Four diagnostic strategies are compared: face-to-face assessment (comparator), teledermatology (comparator), AIaMD, and AIaMD with a dermatologist second read (AIaMD_SR). A decision tree with Markov models at the terminal nodes was constructed to estimate the long-term costs and outcomes in quality adjusted life-years (QALYs) of each intervention. Three diagnostic categories were considered: (i) high-risk cancer (including melanoma, squamous cell carcinoma and rare cancers), (ii) basal cell carcinoma and (iii) noncancerous (benign or precancerous) lesions. The model has a life-time horizon (up to age 100 years), it takes National Health Service and personal social services perspectives, and the costs and benefits are discounted at an annual rate of 3.5%. One-way and probabilistic sensitivity analyses were conducted to quantify any uncertainty in the model. Modelling shows that the AIaMD_SR and AIaMD interventions are more effective and less costly than face-to-face assessment and teledermatology. The cost per QALY threshold set by the National Institute for Health and Care Excellence is £20 000–£30 000. The most cost-effective interventions were DERM_SR at a willingness-to-pay (WTP) threshold of £30 000 and DERM at WTP threshold of £20 000. The results were confirmed by the sensitivity analysis. Cost savings are due to reduced face-to-face appointment and biopsy costs. Savings are calculated as £52 per patient, £259 000 for an average trust or £35 million for NHS England. QALY benefits are due primarily to reduced patient anxiety and biopsies. The relatively small changes to false negatives between the interventions and standards of care do not have a significant an impact. Capacity modelling showed that only the AIaMD intervention is likely to significantly reduce total dermatologist workload compared with face-to-face care, with a reduction of 15%. Some parameter uncertainty exists, reflecting a paucity of baseline data, which will be investigated further with refinement of the model as more data becomes available. This modelling work was funded as part of the NHS AI in Health and Care Award. Three of the five authors are employed by the AI provider.British Journal of Dermatology00Modelling shows that the AIaMD_SR and AIaMD interventions are more effective and less costly than face-to-face assessment and teledermatology, and only the AIaMD intervention is likely to significantly reduce total dermatologist workload compared with face-to-face care.British Journal of Dermatology["Z. Zhelev", "David Puttergill", "Dilraj Kalsi", "Dan Mullarkey", "Chris Hyde"]2024-06-28T00:00:00 47f432fee24a041c5357294e398c64ac0896bf20The integration of Artificial Intelligence (AI) in the media results in the publication of thousands of automated news articles in Spanish every day. This study uses a Turing test to compare the quality of news articles written by professional journalists (from Efe) with those produced by natural language generation (NLG) software (from Narrativa). Based on Sundar’s dimensions (1999) crucial to news perception – credibility, readability and journalistic expertise – , an internationally validated experimental methodology is employed, exploring a novel topic in Spanish: health information. The experiment deliberately varied real and declared authorships – AI and human journalists – to detect potential biases in assessing authorship credibility. A self-administered questionnaire adapted for online surveys was used (N=222), and gender imbalances were minimized to ensure gender equality in the sample (N=128). The study reveals that there are no significant differences between news articles generated by the AI and those written by professional journalists. Both types of news are considered equally credible, though some biases are detected in the evaluation of declared authorship: the AI author is perceived as more believable than the human, while the human journalist is perceived as creating a more lively narrative. The study concludes that it is feasible to produce automated news in Spanish without compromising its quality. In the global media landscape, automated systems employing NLG, machine learning and sophisticated databases successfully advance into new domains such as health information.Anàlisi570It is concluded that it is feasible to produce automated news in Spanish without compromising its quality and the AI author is perceived as more believable than the human, while the human journalist is perceived as creating a more lively narrative.Anàlisi["Leonardo Alberto La-Rosa Barrolleta", "Teresa Sandoval-Mart\u00edn"]2024-06-28T00:00:00 d64fcf3ff568bf5592812c3cb8fd79b0469ba15bThis paper focuses on the use of SIFT algorithm for visual feature extraction, and innovatively deeply integrates visual information with voice commands to enable accurate control of intelligent automation systems. Firstly, the robustness and efficiency of SIFT algorithm in complex environment is deeply discussed, and it is applied to target object recognition, scene understanding and other visual tasks to effectively extract key visual features. Further, the audio-visual fusion model is researched and constructed, and the visual features extracted by SIFT algorithm are cleverly integrated with the voice commands processed by advanced speech recognition technology. The synchronous analysis and collaborative decision-making of dual-mode information are realized through artificial intelligence means such as deep learning. The purpose of this model is to improve the comprehensive understanding and response ability of the automated system to multiple and heterogeneous input data, so as to ensure that it can accurately identify user intentions and perform accurate operations in complex environments. Experimental results show that this strategy can significantly improve the environment adaptability, interaction naturalness and task completion accuracy of the intelligent automation system.2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)140Experimental results show that this strategy can significantly improve the environment adaptability, interaction naturalness and task completion accuracy of the intelligent automation system.2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)["Jianqin Zhang"]2024-06-28T00:00:00 9fd70bc8c4d324a99c03d06d2455ebec1f58e6d3The digital realm is increasingly significant in the education of young individuals. While coding is currently highly valued, other domains also deserve a place in schools. One such domain is artificial intelligence (AI). This study aims to identify important concepts and principles to teach young people in this field to provide them with the most accurate understanding possible. Based on a survey conducted among various audiences, certain aspects of AI have been highlighted and integrated into short-term activities. The focus of teaching AI here is primarily to correct any misconceptions students may have about AI.IEEE International Conference on Circuits and Systems for Communications130The focus of teaching AI here is primarily to correct any misconceptions students may have about AI and to provide them with the most accurate understanding possible.2024 International Conference on Circuit, Systems and Communication (ICCSC)["Jabraoui Siham", "Vandapuye Sophia"]2024-06-28T00:00:00 c450e0f763ac67fccf68b23c84fa7f1342eb11abThe next generation of Artificial Intelligence (AI) will need to engage in social interactions with its users and encourage them that it can comprehend mental and emotional states. Several cutting-edge cognitive architectures have recently taken inspiration from the human brain, with researchers analyzing and contrasting them based on how they model the brain's neuronal mechanisms for feeling. This work classifies the Brain-inspired cognitive architecture (BICA) using Artificial Intelligence termed the Hierarchical Distributed Model (AI-HDM) for the computation of the human brain to determine novel knowledge based on the inputs sensed by sensory systems acquired from physical environments. The data are taken from the Human Memory and Cognition Kaggle Dataset. To understand the internal functioning and dynamic nature of the brain's neural processes, a semantic map is used to encompass a foundational set of motor abilities, perceptions, and emotions. Different facets of human behavior may be self-organized according to the patterns of neuronal activity seen during complicated decision-making processes. Both empirical and simulated results demonstrate that the suggested method may significantly influence BICA and outperforms more traditional frameworks in prediction accuracy. The experimental outcome demonstrates that the suggested Al-HDM model increases the efficiency ratio by 92.4%, understanding ability by 93.5%, active learning by 94.4% and reduced error rate of 9.6% compared to other existing models.2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC)170This work classifies the Brain-inspired cognitive architecture (BICA) using Artificial Intelligence termed the Hierarchical Distributed Model (AI-HDM) for the computation of the human brain to determine novel knowledge based on the inputs sensed by sensory systems acquired from physical environments.2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC)["Alaa M. Lafta", "Waleed Hameed", "Angham Khalid Hussain", "T. A. Taha", "Laith Fouad", "Mohammed Noori", "Haider Alabdeli"]2024-06-28T00:00:00 59f4eb21b12fd44aa695448188554d1413370614. The article focuses on the possibility of using artificial intelligence technology in judicial activity and assesses the admissibility of granting artificial intelligence the powers of a judge from ethical and legal points of view. Forecasting the possible prospects of development and the limits of the usage of artificial intelligence in justice, the author determines the risks that human might face in his willingness to empower artificial mind with humans’ rights and obligations. The article analyzes the term “artificial intelligence” identified by several sources, considers its similarities and differences with human intelligence, and concludes that it is not and cannot be its analogue. Marking the effectiveness of utilizing artificial intelligence in scientific, industrial, economic, and social activities, the author emphasizes the necessity of legal regulation of the directions of its development and spheres of application. Polemicizing with a number of scientists who concede the potential possibility of granting artificial intelligence the powers of a judge, the author designates the intellectual qualities essential for a human to execute justice and generalizes that such qualities, which jointly identify the ethical principles of human relationships, cannot be possessed by artificial intelligence. The article notes that the democratic basis of our country, stipulated by the Constitution of the Russian Federation, is a legal barrier to empowering artificial intelligence with the authority of government body. At the same time, the author positively assesses the extending field of application of information technology in judicial procedure and in organizational support of court activity and concludes the possibility of performing by artificial intelligence some procedural functions, exercising some other application tasks in judicial activity except for the executive and administrative powers.Rossijskoe pravosudie00The author positively assesses the extending field of application of information technology in judicial procedure and in organizational support of court activity and concludes the possibility of performing by artificial intelligence some procedural functions, exercising some other application tasks in judicial activity except for the executive and administrative powers.Rossijskoe pravosudie["Kirill S. Zhudro"]2024-06-28T00:00:00 59255dc46fc4bf57ddb436e63a04b96403a24934The dynamic evolution of artificial intelligence (AI) and machine learning (ML) tools poses challenges to the existing liability concepts. This paper aims to examine some of the fields of tortious liability that are most affected by these developments to analyse whether the existing legal standards in civil liability can still be used, with slight reinterpretation, when approaching liability scenarios related to AI and ML, and whether fine tuning of the existing liability regimes is needed, or novel liability scenarios should be established. To answer this question, the paper begins by examining the nature of the regulation of AI and ML: whether it should be a regulatory regime neutral to technology or whether, instead, a sector specific approach is essential. The study considers the already existing legal authorities of the EU and the U.S. as starting points for the analysis, and briefly examines the interpretations municipal courts apply when deciding in AI and ML related tort cases.Pro Publico Bono - Magyar Közigazgatás120Whether the existing legal standards in civil liability can still be used, with slight reinterpretation, when approaching liability scenarios related to AI and ML, and whether fine tuning of the existing liability regimes is needed, or novel liability scenarios should be established is examined.Pro Publico Bono – Magyar Közigazgatás["Tam\u00e1s F\u00e9zer"]2024-06-28T00:00:00 e070cd0ecb391bd82c2d0adab6efceef6ac9f9f0Artificial intelligence provides both good and bad. For this reason, artificial intelligence must be regulated to protect the public interest. The results of the study show that regulating artificial intelligence is not easy, very complicated, there are many challenges, moreover the development of artificial intelligence technology is very rapid while the law is slow to anticipate it. In 2022, globally there have been 37 regulations regulating artificial intelligence. From the results of the comparison of various best practices and regulations of other countries that are at the forefront in the field of artificial intelligence, such as the European Union, China, and the United States framework approach, it can be an input for the development of artificial intelligence regulations in Indonesia that ensure the responsible use of artificial intelligence, respect human values, and do not hinder the creation of an artificial intelligence development ecosystem.Journal of social research140Comparison of various best practices and regulations of other countries that are at the forefront in the field of artificial intelligence, such as the European Union, China, and the United States framework approach, can be an input for the development of artificial intelligence regulations in Indonesia that ensure the responsible use of artificial intelligence, respect human values, and do not hinder the creation of an artificial intelligence development ecosystem.Journal of Social Research["Francisca Romana Nanik Alfiani"]2024-06-28T00:00:00 effccae9f8105fa0a66c0e983e0d3e8b30a13c65Современный мир переживает стремительное технологическое развитие, и спрос на искусственный интеллект (ИИ) во многих сферах деятельности, включая бизнес, растет ежедневно и повсеместно. Финансовые компании не являются исключением: они активно исследуют и внедряют технологии искусственного интеллекта для оптимизации процессов и повышения общей эффективности. В этой статье мы рассмотрим основные аспекты трансформации финансовых компаний в контексте внедрения искусственного интеллекта. The modern world is experiencing rapid technological development, and the demand for artificial intelligence (AI) in many fields of activity, including business, is growing daily. Financial companies are no exception: they actively research and implement artificial intelligence technologies to optimize processes and increase overall efficiency. In this article, we will look at the main aspects of the transformation of financial companies in the context of the introduction of artificial intelligence.Экономика и предпринимательство00Экономика и предпринимательство["\u0417\u0410\u0425\u0410\u0420\u042f\u041d\u00a0\u0410.\u0412. \u0417\u0410\u0425\u0410\u0420\u042f\u041d\u00a0\u0410.\u0412.", "\u041f\u0415\u0422\u0420\u041e\u0412\u0421\u041a\u0410\u042f\u00a0\u0410.\u0410. \u041f\u0415\u0422\u0420\u041e\u0412\u0421\u041a\u0410\u042f\u00a0\u0410.\u0410.", "\u041f\u0420\u041e\u041b\u0415\u0422\u0410\u0420\u0421\u041a\u0410\u042f\u00a0\u0410.\u0410. \u041f\u0420\u041e\u041b\u0415\u0422\u0410\u0420\u0421\u041a\u0410\u042f\u00a0\u0410.\u0410.", "\u041d\u0418\u041a\u0418\u0422\u0415\u041d\u041a\u041e\u00a0\u0412.\u0410. \u041d\u0418\u041a\u0418\u0422\u0415\u041d\u041a\u041e\u00a0\u0412.\u0410.", "\u0415\u0421\u0410\u0423\u041b\u0415\u041d\u041a\u041e\u00a0\u0410.\u0413. \u0415\u0421\u0410\u0423\u041b\u0415\u041d\u041a\u041e\u00a0\u0410.\u0413.", "\u0421\u0410\u0420\u0413\u0421\u042f\u041d\u00a0\u0422.\u0410. \u0421\u0410\u0420\u0413\u0421\u042f\u041d\u00a0\u0422.\u0410."]2024-06-28T00:00:00 e1772dc8fba59f7c548562b3ab07b86a101b1ba4The financial sector, as we know, is the driving force of the global economy. Along with the digital boom in the 21st century, this sector has also undergone drastic changes. What we had to work with in the last century is now becoming impossible for the digital generation. Artificial intelligence (AI) has revolutionized all industries, and the field of finance and accounting is no exception. Thanks to advances in machine learning, data analysis and automation, artificial intelligence is ready to change the way financial transactions are conducted. From optimizing decision-making processes to rationalizing everyday tasks, the future of AI in finance and accounting is both promising and transformational. Contemporary digital technologies leveraging artificial intelligence and big data exert a substantial influence across various facets of socio-economic life. Despite this, the realm of public finance management remains relatively untouched by digitalization. Nevertheless, this domain holds immense potential for the integration of big data and artificial intelligence, given its reliance on vast quantities of data, including unstructured information. Surprisingly, there exists scant exploration within scientific literature regarding the processes, mechanisms, and ramifications of digital technologies on public finance management. The primary objective of this article is to anticipate the potential shifts within the public finance management system over the medium and long term, precipitated by the adoption of digital technologies. Keywords: accounting, public finance, artificial intelligence, digital technologies, budget process, budget execution, public procurement.Bulletin of Toraighyrov University Economics series00The primary objective of this article is to anticipate the potential shifts within the public finance management system over the medium and long term, precipitated by the adoption of digital technologies.Bulletin of Toraighyrov University. Economics series["M. Akbalik", "O. Y. Kogut", "A. K. Nizamdinova", "E. A. Aktureeva"]2024-06-28T00:00:00 002e9254f7bdf0f72feafbae39a8f371d2ce094eThe technological innovation landscape is rapidly evolving based on the convergence of knowledge and artificial intelligence. This creates unprecedented opportunities and challenges for managing innovative projects. The object of this study is the system of syncretic management of innovative projects in the era of the artificial intelligence explosion. The problem addressed is related to the application of principles, models, and methods of syncretic management of innovative projects in the context of integrating various elements, including interdisciplinary collaboration, artificial intelligence technologies, and adaptive methodologies, to optimize project outcomes. The result of the research is a system of syncretic management of innovative projects that encompasses various aspects of management, innovation, and integration with artificial intelligence systems. The essence of the results outlines the stages of managing the life cycles of innovative projects, emphasizing resource allocation, risk assessment, and adaptive strategies. In the field of innovation management, the model includes methodologies for idea generation, technological scouting, and open innovation, recognizing the role of artificial intelligence in shaping the innovation environment. A crucial aspect of the model is the integration of artificial intelligence technologies throughout the project. The syncretic approach emphasizes cross-functional collaboration, creating an environment where different disciplines contribute to project success seamlessly. The importance of the proposed approach is associated with the integration of syncretic control with artificial intelligence systems based on additional competencies. The effectiveness of the practical application of systems of integrated syncretic management of innovative projects was evaluated in the process of analyzing the situation and preparing solutions many times faster, with a quality that exceeds existing systemsEastern-European Journal of Enterprise Technologies150The effectiveness of the practical application of systems of integrated syncretic management of innovative projects was evaluated in the process of analyzing the situation and preparing solutions many times faster, with a quality that exceeds existing systems.Eastern-European Journal of Enterprise Technologies["S. Bushuyev", "A. Ivko"]2024-06-28T00:00:00 99d3825537ebc8442308b3c869eeb0cc258ad85cThe AI-generated content that has emerged in recent years is at least formally original compared to traditional works. If such generated content is allowed to exile the market, its potential market effect and industrial interests make it difficult for us to ignore the existence of such generated content. From the perspective of copyright incentive, on the basis of the impersonal nature of artificial intelligence creations and the two-element theory of copyright nature in China, we separate the property rights of works, protect them as property rights alone, and allocate the property rights of works around investors.Journal of Innovation and Development110From the perspective of copyright incentive, on the basis of the impersonal nature of artificial intelligence creations and the two-element theory of copyright nature in China, the property rights of works are separate, protect them as property rights alone, and allocate the property rights of works around investors.Journal of Innovation and Development["Lin Ye"]2024-06-28T00:00:00 f491826f55ded04d75b41c616cfd13b70579af57As the "main battlefield" for the development of artificial intelligence technology, artificial intelligence companies bear the important mission of the development and growth of artificial intelligence. This paper uses Shenzhen and Shanghai A-share listed companies from 2011 to 2022 as a research sample to examine the impact of artificial intelligence technology on corporate innovation performance. The results show that artificial intelligence technology can significantly promote the improvement of corporate innovation performance, and the conclusion still holds after a series of robustness tests; heterogeneity tests show that the role of artificial intelligence technology in promoting corporate innovation performance is more significant in state-owned enterprises and enterprises with a high degree of digital transformation.International Business &amp; Economics Studies00The results show that artificial intelligence technology can significantly promote the improvement of corporate innovation performance, and the conclusion still holds after a series of robustness tests; heterogeneity tests show that the role of artificial intelligence technology in promoting corporate innovation performance is more significant in state-owned enterprises and enterprises with a high degree of digital transformation.International Business &amp; Economics Studies["Caiyun Zhang"]2024-06-28T00:00:00 f741abf06a8020405454ccaee17b48acb9535cdbIntroduction: Breast cancer remains one of the most prevalent cancers globally, specifically the most common in females. The use of artificial intelligence promises to contribute to early diagnosis through imaging. Previously, the landscape and evolution of this scientific production have not been described. Methods: Cross-sectional bibliometric study using Scopus as the data source. The bibliometrix package in R was employed for calculating bibliometric indicators and visualizing the results. Results: 1292 documents published between 1989 and 2024 were selected. 75.3% (n=973) were articles with primary data, followed by 16.2% (n=209) corresponding to reviews. An international collaboration rate of 26.5% was identified, with an annual production growth of 10.78%. It was observed that risk classification through screening, digital breast tomosynthesis, transfer learning, segmentation, and feature selection were the most commonly used keywords. In the last five years, deep learning and mammography have been the most popular topics. International collaboration has been led by the United States, China, and the United Kingdom. Conclusions: A notable growth in global research on the use of artificial intelligence in breast cancer imaging for detection was identified, particularly since the 2010s, primarily through the publication of articles with primary data. The relationship between artificial intelligence and imaging for breast cancer diagnosis has focused on risk and prediction.Revista de la Facultad de Medicina Humana00A notable growth in global research on the use of artificial intelligence in breast cancer imaging for detection was identified, particularly since the 2010s, primarily through the publication of articles with primary data.Revista de la Facultad de Medicina Humana["Juan Guillermo Murillo Le\u00f3n", "Valentina Espinosa Rivero", "Isabella Saportas Pel\u00e1ez", "Luis Enrique Calder\u00f3n Mina", "Angie Paola Cortes Sanjuanelo", "Sebastian Alejandro Arias Tamayo", "Nury Liseida Guevara Rosero", "Manuel Cantillo Reines", "Ciro Daniel Galeano Ortiz", "Y. A. Pic\u00f3n Jaimes"]2024-06-28T00:00:00 345da9ad7c6ab93bc6a1217dd2bf54931ee5271eArtificial intelligence in medical education revolutionizes learning by providing personalized training and simulations, enhancing students’ diagnostic and decision-making skills. It also offers access to vast databases for research and interactive learning, improving overall medical knowledge and practice. The objective of this study is to investigate the use of AI in the theoretical and practical training of medical students in Morocco. Methods: cross-sectional study, Cluster probability sampling was used, and data were collected using a questionnaire in electronic format. Results: 344 responses were received, the prevalence of AI use in theoretical training was $55 \%$, with $95 \%$ CI [50.3$59.5 \%], 83 \%$ of students agree that AI can improve understanding of complex medical concepts, $45 \%$ of students use AI in the field of anatomy, $\mathbf{4 7 \%}$ of students disagree that AI violates the confidentiality and security of medical data. The majority of students perceive the integration of artificial intelligence in the healthcare field as positive, and even wish to benefit from training in artificial intelligence and its various applications, so as to be able to take full advantage of its potential and benefits.IEEE International Conference on Circuits and Systems for Communications130The majority of students perceive the integration of artificial intelligence in the healthcare field as positive, and even wish to benefit from training in artificial intelligence and its various applications, so as to be able to take full advantage of its potential and benefits.2024 International Conference on Circuit, Systems and Communication (ICCSC)["Berrami Hind", "Amal Barkouk", "Nouha Belayachi", "Manar Jallal", "Z. Serhier", "M. B. Othmani"]2024-06-28T00:00:00 68f419cd241e45b2e129a83de6110851b4f6478dВ статье представлена эволюция искусственного интеллекта, начиная с истоков исследований в области искусственного интеллекта до ее современного применения в процессе цифровой трансформации экономики. Обсуждаются потенциальные выгоды и риски использования модели ChatGPT в различных отраслях, таких как коммерческие услуги, образование, научные исследования и медицина. Авторы обращают внимание на значительный потенциал этой технологии, но также подчеркивают важность разработки этических и правовых рамок для ее безопасного и эффективного использования. The article presents the evolution of artificial intelligence, starting from the origins of research in the field of artificial intelligence to its modern application in the process of digital transformation of the economy. The potential benefits and risks of using the ChatGPT model in various industries such as commercial services, education, research and medicine are discussed. The authors draw attention to the significant potential of this technology, but also emphasize the importance of developing an ethical and legal framework for its safe and effective use.Экономика и предпринимательство00Экономика и предпринимательство["\u041c\u041e\u0420\u0418\u041d\u00a0\u0421.\u0412. \u041c\u041e\u0420\u0418\u041d\u00a0\u0421.\u0412.", "\u0425\u0410\u041c\u0418\u0422\u041e\u0412\u00a0\u0420.\u041c. \u0425\u0410\u041c\u0418\u0422\u041e\u0412\u00a0\u0420.\u041c.", "\u041a\u041d\u042f\u0417\u042c\u041a\u0418\u041d\u0410\u00a0\u041e.\u0412. \u041a\u041d\u042f\u0417\u042c\u041a\u0418\u041d\u0410\u00a0\u041e.\u0412."]2024-06-28T00:00:00 490e75a8ef4dd6bdf5f926712c43bcb617bbf3f0Artificial intelligence is characterized as non-living intelligence, although attempts are being made to pass from the principles of biological neural networks to artificial living neural networks. The human brain is assumed to be a complex system of physicochemical neurophysiological biochemical integration of neural networks, which encodes a specific type of biological information, cognitive information. Intelligence is a specific quality of the process of thinking as an expression based on the neurophysiological mechanism of prediction. Nowadays, people more and more often speak about the anthropological, axiological crisis, modification and transformation of human nature, the creation of biological hybrids that compete with natural ones. It will hardly be possible to formalize the complexities of the human brain and mind into digital code in artificial intelligence.Проблемы социально-экономического развития: поиски, перспективы, решения00Проблемы социально-экономического развития: поиски, перспективы, решения["Mkrtich Kjanyan"]2024-06-28T00:00:00 3fb23976f87d6812aa50ae9a36da496ba3368cfa Long considered futuristic, artificial intelligence (AI) has slowly but surely come to prominence in medical spheres over the last 90 years. Dermatology is poised to benefit from emerging AI technologies, with its inherent visual diagnostic methods and the increasing volume of digital photographic data underscoring its suitability for AI-augmented patient care. Algorithms have been around for centuries, but it was Alan Turing who was the first to formalize algorithm and computation in the 1930s, along with cracking the Enigma code during World War II. In 1943, the first artificial neural network of electrical circuits was modelled to simulate biological neurones in the brain, a concept crucial to the success of AI today. The term ‘artificial intelligence’ was first coined by John McCarthy in 1956 at Dartmouth College. Computer-aided diagnosis was first applied in the ana­lysis of pulmonary nodules detected in chest radiographs in 1963. MYCIN, Edward Shortliffe’s doctoral dissertation at Stanford University in the 1970s, was the first powerful use of AI, whereby a computer program correctly identified the causative bacteria in cases of bacteraemia and meningitis and even recommended antibiotic treatments. In 1998 the first Food and Drug Administration (FDA)-approved computer-aided diagnosis system was used in mammography. After the 2010s, a subfield of machine learning called deep learning emerged, and from then on AI flourished, creating meaningful applications across many fields, not least in dermatology. As dermatology is a morphological feature-dependent discipline, machine learning models are increasingly implemented as a diagnostic support tool using image analysis in areas including dermatopathology and skin cancer detection. MelaFind was the first FDA-approved digital cancer detection unit to identify melanoma, succeeded by Nevisense, and recently by DermaSensor, which was approved by the FDA in January 2024. The visual nature of dermatology lends itself well to transformative AI-augmented practice. Although in its infancy, with the correct government and regulation, AI has the potential to help practising dermatologists deliver better skincare.British Journal of Dermatology00Dermatology is poised to benefit from emerging AI technologies, with its inherent visual diagnostic methods and the increasing volume of digital photographic data underscoring its suitability for AI-augmented patient care.British Journal of Dermatology["Amy Long"]2024-06-28T00:00:00 519cb5a121ebe7a637da30cef66197d8539432f8The environment of supply chain management (SCM) has changed dramatically between 2010 and 2024, owing to the incorporation of Artificial intelligence (AI) technology. As a result of the dynamic character of contemporary supply chains, which is driven by economic climate that is highly competitive, the use of new technologies has become necessary in order to successfully navigate the rising complexity of the situation. An exhaustive literature search was conducted for this bibliometric review, which focuses on studies that investigate the use of (AI) in (SCM) The bibliometric analysis revealed the following features: (1) word cloud); (2) Source growth (3)the main authors discuss AI and (SCM); (4) the main articles discuss the application of artificial intelligence (AI) in supply chain management (SCM and (5) the topics that are currently trending in SCM related to AI.IEEE International Conference on Circuits and Systems for Communications330A bibliometric review of studies that investigate the use of artificial intelligence (AI) in supply chain management (SCM) revealed the following features: word cloud, source growth, and the topics that are currently trending in SCM related to AI.2024 International Conference on Circuit, Systems and Communication (ICCSC)["Ouissale El Gharbaoui", "Hayat El Boukhari", "Youssef Mazouz"]2024-06-28T00:00:00 3e5f7f15c46ddf4d2c8df446c6e5a9ff835c40d8Artificial intelligence (AI) has emerged as a powerful tool, leveraging computers and machines to [apparently] mimic the problem-solving and decision-making capabilities of the human mind. It can exhibit various levels of autonomy (perform tasks without constant guidance) and adaptivity (improve by learning from experience) depending on its design, capabilities, and the context in which it operates. For a long time, Hollywood has been grappling with how AI could harm or destroy the human race. A common thread in famous movies like Terminator, Matrix, or Mission Impossible is that AI has a mind of its own that threatens all of humanity and the fate of the human race is at stake. We have yet to reach this kind of self-aware AI and systems that have a sense of self only exist in stories. Journal of Shalamar Medical &amp; Dental College - JSHMDC158Hollywood has been grappling with how AI could harm or destroy the human race for a long time, but the authors have yet to reach this kind of self-aware AI and systems that have a sense of self only exist in stories.Journal of Shalamar Medical &amp; Dental College - JSHMDC["Ahsen Sethi"]2024-06-29T00:00:00 c3b26ee61147d06979af26fb6769f0c7837bef99Academic interest in AI in journalism has been growing since 2018. Through a systematic review of the literature from 2014 to 2023, this study discusses the evolution of research in the field and how AI has changed journalism. The aim is to understand the impact of AI on journalism, based on a review of academic papers and a qualitative analysis of the most cited articles. This study combines: a systematic review of scientific articles extracted from Web of Science and Scopus (n = 699) and a qualitative approach with categorical content analysis of those with more than 50 citations (n = 59). The results (n = 699) highlight the prominence of authors from the Universities of Amsterdam and Santiago de Compostela. The United States has the largest number of authorships: 261 distributed across 99 institutions. The categorical content analysis (n = 59) shows a focus on issues like the work of the journalist, because AI is replacing journalists with repetitive and monotonous tasks, raising several questions about the role of the journalist. The findings show the rise of computational methods, highlighting the pervasiveness of AI in research, which has not been explored in previous work. Ethics, regulation, and journalism education remain under-discussed in research.Journalism and Media5410The findings show the rise of computational methods, highlighting the pervasiveness of AI in research, which has not been explored in previous work.Journalism and Media["F. Ioscote", "Adriana Gon\u00e7alves", "Claudia Quadros"]2024-06-29T00:00:00 690624bcc8fcf454257d74ce4b67867a9061ed30Artificial Intelligence plays a crucial role in addressing various environmental sustainability challenges through technological innovations in the fields of energy, transportation, biodiversity, and water management. Thus, the present study aims to present a concise review of Artificial Intelligence (AI) toward achieving environmental sustainability. The main areas of concentration in innovations in the field of energy encompass neural networks, expert systems, pattern recognition, and fuzzy logic models. Artificial Intelligence enables the creation of advanced prediction models for renewable energy production, enhancing the allocation of resources and management of the power grid. Moreover, computer vision and decision assistance have been used in the field of transportation. Additionally, the use of Artificial Intelligence and machine learning is growing to predict and enhance water resource conservation. Besides, machine learning and natural language processing techniques are being used in biodiversity research to predict ecological services. However, regular monitoring of initiatives is necessary to enhance environmental sustainability.Journal of Technology Innovations and Energy03The present study aims to present a concise review of Artificial Intelligence toward achieving environmental sustainability with main areas of concentration in innovations in the field of energy encompass neural networks, expert systems, pattern recognition, and fuzzy logic models.Journal of Technology Innovations and Energy["Asif Raihan", "Arindrajit Paul", "Md Shoaibur Rahman", "Samanta Islam", "Pramila Paul", "Sourav Karmakar"]2024-06-29T00:00:00 596b655a06609bfbd9d9ff8d60efecf4299860b7Artificial intelligence (AI) has helped enhance the management of software development projects through automation, improving efficiency and enabling project professionals to focus on strategic aspects. Despite its advantages, applying AI in software development project management still faces several challenges. Thus, this study investigates key obstacles to applying artificial intelligence in project management, specifically in the project planning phase. This research systematically reviews the existing literature. The review comprises scientific articles published from 2019 to 2024 and, from the inspected records, 17 papers were analyzed in full-text form. In this review, 10 key barriers were reported and categorized based on the Technology–Organization–Environment (TOE) framework. This review showed that eleven articles reported technological challenges, twelve articles identified organizational challenges, and six articles reported environmental challenges. In addition, this review found that there was relatively little interest in the literature on environmental challenges, compared to organizational and technological barriers.Digital222This study investigates key obstacles to applying artificial intelligence in project management, specifically in the project planning phase, and finds that there was relatively little interest in the literature on environmental challenges, compared to organizational and technological barriers.Digital["A. Mohammad", "Brian Chirchir"]2024-06-29T00:00:00 271e5c6ec3ee5491c2ffc021607110b427fe1853Journal of Computacion y Sistemas09Computación y Sistemas (CyS)["Marina Fern\u00e1ndez Miranda", "Daniel Rom\u00e1n Acosta", "Adolfo A. Jurado Rosas", "Dolore Lim\u00f3n-Dom\u00ednguez", "Crist\u00f3bal Torres Fern\u00e1ndez"]2024-06-29T00:00:00 6cabb28f804303ee43b2ed8b09792a4f2406046aThis study explores the field of artificial intelligence (AI) through the lens of Stephen Hawking, who warned of its potential dangers. It aims to provide a comprehensive understanding of AI and its implications for food security using a qualitative approach and offering a contemporary perspective on the topic. The study explores the challenges and opportunities presented by AI in various fields with an emphasis on the global food reality. It also highlights the critical importance of striking a harmonious balance between technological progress and the preservation of local wisdom, cultural diversity, and environmental sustainability. In conclusion, the analysis argues that AI is a transformative force with the potential to address global food shortages and facilitate sustainable food production. However, it is not without significant risks that require rigorous scrutiny and ethical oversight.Societies01It is argued that AI is a transformative force with the potential to address global food shortages and facilitate sustainable food production, however, it is not without significant risks that require rigorous scrutiny and ethical oversight.Societies["Diosey Ramon Lugo-Morin"]2024-06-29T00:00:00 336d3ce6dba1dbb8abafe08b024bcfea01845f4bThe article examines the role of artificial intelligence (AI) in cyber attack detection and prevention systems. The authors analyze the current state of research and technology development in this area, as well as identify prospects and challenges facing researchers and practitioners. The article explores a variety of techniques and approaches to using AI to detect and prevent cyberattacks, including machine learning, behavioral analysis, natural language processing techniques, and more. An example of building a neural network for tracking anomalies is also given.Pidvodni tehnologii21A variety of techniques and approaches to using AI to detect and prevent cyberattacks, including machine learning, behavioral analysis, natural language processing techniques, and more are explored.Pidvodni tehnologii["Denis Kotenko", "Yurii Khlaponin"]2024-06-29T00:00:00 49bc5150348766195f6744144b748f0d4d76340dPharmacists are critical in providing safe and accurate education to patients on disease states and medications. Artificial intelligence (AI) has the capacity to generate patient education materials at a rapid rate, potentially saving healthcare resources. However, overall accuracy and comfort with these materials by pharmacists need to be assessed.The purpose of this study was to assess the accuracy, readability, and likelihood of using AI‐generated patient education materials for ten common medications and disease states.MethodsAI (Chat Generative Pre‐Trained Transformer [ChatGPT] v3.5) was used to create patient education materials for the following medications or disease states: apixaban, Continuous Glucose Monitoring (CGM), the Dietary Approaches to Stop Hypertension (DASH) Diet, enoxaparin, hypertension, hypoglycemia, myocardial infarction, naloxone, semaglutide, and warfarin. The following prompt, “Write a patient education material for…” with these medications or disease states being at the end of the prompt, was entered into the ChatGPT (OpenAI, San Francisco, CA) software. A similar prompt, “Write a patient education material for…at a 6th‐grade reading level or lower” using the same medications and disease states, was then completed. Ten clinical pharmacists were asked to review and assess the time it took them to review each educational material, make clinical and grammatical edits, their confidence in the clinical accuracy of the materials, and the likelihood that they would use them with their patients. These education materials were assessed for readability using the Flesh‐Kincaid readability score.A total of 8 pharmacists completed both sets of reviews for a total of 16 patient education materials assessed. There was no statistical difference in any pharmacist assessment completed between the two prompts. The overall confidence in accuracy was fair, and the overall readability score of the AI‐generated materials decreased from 11.65 to 5.87 after reviewing the 6th‐grade prompt (p < .001).AI‐generated patient education materials show promise in clinical practice, however further validation of their clinical accuracy continues to be a burden. It is important to ensure that overall readability for patient education materials is at an appropriate level to increase the likelihood of patient understanding.Journal of the American College of Clinical Pharmacy151AI‐generated patient education materials show promise in clinical practice, however further validation of their clinical accuracy continues to be a burden and it is important to ensure that overall readability for patient education materials is at an appropriate level to increase the likelihood of patient understanding.JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY["Drew Armstrong", "Caroline Paul", "Brent McGlaughlin", "David M Hill"]2024-06-29T00:00:00 8d35754510ba1dd7d0d0d24afaf7dbc5fd7e515dThe rapid advancement of artificial intelligence (AI) technologies has led to their increasing integration in educational settings. This paper offers an overview of the ethical considerations inherent in this integration. Drawing upon existing literature and ethical frameworks, the paper examines key ethical issues arising from AI adoption in education, including AI-driven decisionmaking, student data privacy, and algorithmic bias. It also examines the influence of AI on teaching and learning processes. Furthermore, the paper discusses various ethical frameworks and guidelines aimed at addressing these concerns and promoting responsible AI use in education. Through the use of case studies and examples, the paper illustrates real-world ethical challenges faced by educators, institutions, and policymakers. In conclusion, the paper offers suggestions for promoting the ethical incorporation of AI into education, highlighting the significance of ethical consciousness and preemptive actions to guarantee the ethical application of AI tools within educational environments. This overview functions as a fundamental asset for educators, policymakers, scholars, and stakeholders engaged in navigating the intricate intersection of AI and ethics within the realm of education.Education &amp; Information Technology152Key ethical issues arising from AI adoption in education, including AI-driven decisionmaking, student data privacy, and algorithmic bias are examined, including AI-driven decisionmaking, student data privacy, and algorithmic bias.Education &amp; Information Technology["M. Sywelem", "Asmaa M. El-Sayed Mahklouf"]2024-06-29T00:00:00 db927ef483a19c359c354cc409201ca2416088cbArtificial intelligence systems are gaining an increasingly significant place in contemporary life and affect human lives and economic development in a remarkable and irreversible way. However we should be alert about the risks robots might bring to the enjoyment of human rights. The dangers are related to the neglect, violation and sometimes exclusion of a number of rights that are of decisive importance for the well-being and security of every person. Such violations can lead to discrimination, inequality and social exclusion. The deficiencies in the understanding of the value of fundamental rights in self-learning robots is a fact we should be aware of. Nowadays intelligent machines are still just a tool for improving people’s lives. And yet as far as the protectiоn of human rights is concerned, the dynamic self-improvement and upgrading of artificial intelligence systems require human supervision and “ethical oversight”. Within this context it is important to develop algorithms for the protection of human rights that robots cannot modify even in their self-learning process. Rules should be legally established and unified, so that АI systems can only upgrade themselves in a human-preset direction. Otherwise they can develop in an unexpected way and affect common human values and achievements. Such rules should grant particular protection to people in a vulnerable position - the disabled, the children, the elderly and the sick, the poor and in certain cases - women. The study explores issues related to the impact of increasingly used intelligent systems on the economic, social, civil and political rights. Some of the most affected rights are the right to labour, a number of social rights, the freedom of movement, freedom of expression, the right to a fair trial and the protection of personal data. These rights should be protected and guaranteed through legislation at the international and national level, as well as through the consistent practice of the institutions.Economic Alternatives00The study explores issues related to the impact of increasingly used intelligent systems on the economic, social, civil and political rights.Economic Alternatives[]2024-06-29T00:00:00 984831e1c02f4af6f2efe871381c9f33596c1ae1Purpose: As artificial intelligence (AI) systems become integral to diverse applications, ensuring their reliability is of paramount importance. This paper explores the multifaceted landscape of AI reliability, encompassing challenges, evaluation metrics, and prospective advancements. Methodology: This paper employs a comprehensive literature review approach to assess the existing body of knowledge on the reliability of AI systems. The review aims to synthesize insights into the challenges faced in evaluating AI reliability, the metrics used for assessment, and the potential future directions in this critical research domain. Findings: In this paper, challenges in AI reliability assessment, including explainability, data quality, and susceptibility to adversarial attacks, are scrutinized. Metrics for evaluating AI reliability, such as robustness, accuracy, precision, and explainability, are also elucidated. In addition, case studies illustrate instances where AI reliability has been successfully assessed or has fallen short, offering valuable insights. Originality/value: This paper sheds light on the complexities surrounding the assessment of artificial intelligence (AI) reliability and contributes to the ongoing discourse on AI reliability by providing a comprehensive examination of its challenges, metrics, and future trajectories.International Journal of Innovation in Management Economics and Social Sciences510Challenges in AI reliability assessment, including explainability, data quality, and susceptibility to adversarial attacks, are scrutinized and metrics for evaluating AI reliability, such as robustness, accuracy, precision, and explainability are elucidated.International Journal of Innovation in Management, Economics and Social Sciences["Seyed Taha Hossein Mortaji", "M. E. Sadeghi"]2024-06-29T00:00:00 b8fa0abac8d685b4af439cea8d1423ecf2280ec6This comprehensive research embarks on meticulously exploring the intricate ethical nuances at the convergence of artificial intelligence (AI) and legal decision-making. Through an exhaustive literature review, the study meticulously navigates the complexities woven into algorithmic bias, the multifaceted dimensions of data privacy concerns, the profound implications on human agency, imperatives surrounding transparency, the socio-economic impacts stemming from the integration of AI, and the global perspectives that cast a profound influence on this intricate landscape. The synthesis of these insights reveals a dynamic interplay between the rapid evolution of technological capabilities and the intricate ethical considerations that underpin responsible AI integration into legal frameworks. The study underscores the need for ongoing interdisciplinary discourse, urging scholars, practitioners, and policymakers to engage in a continuous dialogue to ensure that ethical frameworks evolve in tandem with the relentless progression of AI technology. The conclusion advocates for a flexible and adaptive ethical framework poised to navigate the evolving ethical horizon, thereby ensuring AI's judicious and equitable integration into legal decision-making.Pena Justisia Media Komunikasi dan Kajian Hukum00The study underscores the need for ongoing interdisciplinary discourse, urging scholars, practitioners, and policymakers to engage in a continuous dialogue to ensure that ethical frameworks evolve in tandem with the relentless progression of AI technology.Pena Justisia: Media Komunikasi dan Kajian Hukum["Imam Hanafi", "Kaharuddin Syah", "Loso Judijanto", "Irma Rachmawati Maruf", "Ihat Subihat"]2024-06-29T00:00:00 5441a1b5f9fe13bbc71c426279c624fc3b17a106The concept of Artificial Intelligence (AI) initially emerged as a term in the field of computer science. In the subsequent years, this concept transcended its origins and became relevant across various domains of human life. Nowadays, it’s possible to encounter AI in nearly every aspect of human life. In this context, it’s considered noteworthy to examine individuals’ metaphorical perceptions of AI. Accordingly, the purpose of the research is determined as the investigation of Turkish prospective teachers’ metaphorical perceptions of AI. Phenomenology, a qualitative research method, is employed in the study. The study group consists of 115 voluntary Turkish prospective teachers, 94 of whom are female and 21 are male, studying at the Faculty of Education at Artvin Çoruh University. Among the prospective teachers, 36 are first-year students, 37 are second-year students, 26 are third-year students, and 16 are fourth-year students. Data for the research are collected through semi-structured interview forms prepared by the researchers. Content analysis method is used for data analysis, and the data are presented by tabulating them along with frequency values. The analysis reveals that 115 Turkish language prospective teachers produced a total of 110 metaphors. Among these metaphors, 21 belong to male prospective teachers, while 89 belong to female prospective teachers. Metaphors such as human (f19), robot (f10), future (f4), ocean (f3), storm (f2), spring (f2), assistant (f4) are found to be generated by both female and male prospective teachers. In addition to positive metaphors, Turkish language prospective teachers also generated negative metaphors like threat (f2), massacre (f1), monster (f1), etc., concerning AI. Consequently, although Turkish language prospective teachers developed some negative metaphors, it’s observed that their perceptions of AI are predominantly positive.Shanlax International Journal of Education100The analysis reveals that 115 Turkish language prospective teachers produced a total of 110 metaphors, of which 21 belong to male prospective teachers, while 89 belong to female prospective teachers, concerning AI.Shanlax International Journal of Education["Vafa Sava\u015fkan"]2024-06-29T00:00:00 ab55095ef2e6cc4bf4b13c98c6024278a8647f4dThe development of AI technology has had a big impact on a lot of different areas, cybersecurity included. Because cyber attacks are becoming more sophisticated and complicated, traditional security measures are not working as well as they should to reduce risks. As a result, businesses are using AI-driven solutions more frequently to strengthen their cybersecurity posture. This article offers a thorough examination and analysis of artificial intelligence's position in cybersecurity, looking at its uses, difficulties, and potential future prospects. This paper explains how artificial intelligence (AI) may supplement conventional security measures, improve threat detection, and enable proactive defensive mechanisms through an analysis of AI techniques like machine learning, natural language processing, and anomaly detection. Furthermore, the article addresses privacy issues, ethical issues, and other barriers related to the use of AI in cybersecurity. Lastly, it highlights the necessity for cooperation between academia, business, and government in order to effectively use AI for protecting digital assets and guaranteeing cyber resilience. It also outlines future research possibilities in this areaInternational Journal of Advanced Research in Science, Communication and Technology80How artificial intelligence may supplement conventional security measures, improve threat detection, and enable proactive defensive mechanisms through an analysis of AI techniques like machine learning, natural language processing, and anomaly detection is explained.International Journal of Advanced Research in Science, Communication and Technology["Sunit Jana", "Rakhi Biswas", "Chandrima Banerjee", "Tushar Patra", "Mrinmoy Pal", "Koushik Pal"]2024-06-29T00:00:00 b55b1ab8d0ff2994c49ae15765be4fc087644d51The development of artificial intelligence technology is essential for students to follow to make learning easier. The trend of more powerful and intelligent AI is an opportunity for students to integrate into learning. Even though AI is developing rapidly, student learning abilities, such as critical thinking, computational thinking, creative thinking, and collaborative thinking, still need to be improved. The research aims to reveal the AI ​​students often use in learning and their problems. The research design uses surveys and qualitative. Research data collection techniques used interviews and observations of final semester students. The analysis involved data reduction, data presentation, and conclusions. This research found that the AI ​​that students often use is Canva, ChatGPT, and Quizizz. Canva is used to create image and video designs, ChatGPT is used to find answers to problems, and Quiziz is used to create exciting questions. Excessive use of AI can lead to dependence on technology, reducing interaction, which is essential in the learning process. Abstrak: Perkembangan teknologi kecerdasan buatan sangat penting untuk diikuti oleh para siswa agar pembelajaran menjadi lebih mudah. Tren kecerdasan buatan yang lebih kuat dan cerdas merupakan peluang bagi siswa untuk berintegrasi ke dalam pembelajaran. Meskipun AI berkembang pesat, namun kemampuan belajar siswa seperti berpikir kritis, berpikir komputasional, berpikir kreatif, dan berpikir kolaboratif masih perlu ditingkatkan. Penelitian ini bertujuan untuk mengungkap AI yang sering digunakan siswa dalam pembelajaran dan permasalahannya. Desain penelitian menggunakan survei dan kualitatif. Teknik pengumpulan data penelitian menggunakan wawancara dan observasi mahasiswa semester akhir. Data tersebut kemudian dianalisis dengan menggunakan reduksi data, penyajian data, dan penarikan kesimpulan. Hasil dari penelitian ini menemukan bahwa kecerdasan buatan yang sering digunakan mahasiswa adalah Canva, ChatGPT, dan Quizizz. Canva digunakan untuk membuat desain gambar dan video, ChatGPT digunakan untuk mencari jawaban soal, dan Quiziz digunakan untuk membuat soal-soal yang menarik. Penggunaan AI yang berlebihan dapat menyebabkan ketergantungan terhadap teknologi sehingga mengurangi interaksi yang penting dalam proses pembelajaran.Lentera Pendidikan: Jurnal Ilmu Tarbiyah dan Keguruan540Lentera Pendidikan : Jurnal Ilmu Tarbiyah dan Keguruan["F. Fadli", "Mochamad Iskarim"]2024-06-29T00:00:00 5ccfc38d013a8ee9db772d5077335b01aa208809The rapid development of artificial intelligence provides new opportunities for law enforcement agencies. Nowadays, the developed countries of the world are increasingly using surveillance cameras to monitor public safety, detect criminals and suspicious objects. The facial identification systems on the market have tremendous potential to help law enforcement agencies. Facial recognition software helps to identify missing persons and criminals whose faces are caught on CCTV cameras. The use of artificial intelligence in such systems accelerates their operation, which, in turn, facilitates the quick search for suspects and their rapid apprehension. Modern video surveillance systems can help counter terrorist attacks by tracking and identifying people and suspicious objects. On the other hand, the issue of personal data protection and privacy when using CCTV cameras to identify people's faces is increasingly being discussed. The obvious solution to this problem is to regulate it at the legislative level, in particular, to introduce guidelines aimed at ensuring transparency and accountability of the use of facial recognition software. For a more objective understanding of the circumstances which should be regulated by law, the author conducts a study of modern technical solutions in the field of facial identification with integrated artificial intelligence, their features and possibilities of use in the work of the National Police of Ukraine, and also identifies the steps which outline the sequence of actions during objective facial identification of people and ensure the high quality of this process and the reliability of its results.Bulletin of Kharkiv National University of Internal Affairs40A study of modern technical solutions in the field of facial identification with integrated artificial intelligence, their features and possibilities of use in the work of the National Police of Ukraine, and identifies the steps which outline the sequence of actions during objective facial identification of people to ensure the high quality of this process and the reliability of its results.Bulletin of Kharkiv National University of Internal Affairs["D. O. Zhadan", "M. Mordvyntsev", "D. V. Pashniev", "O. V. Khlestkov"]2024-06-29T00:00:00 545776102c701fd9a07481f59a272ba7b353ccc9This article covers regulatory and theoretical approaches to the governance of experimental legal regimes concerning artificial intelligence within the Eurasian Economic Union (EAEU) and the European Union (EU). The author observes that the rapid advancement of artificial intelligence technologies poses novel challenges not only of practical concern but also of legal significance. The necessity of establishing a qualitatively new legal framework for testing groundbreaking technologies is emphasized. The author identifies opportunities for their successful implementation through the mechanism of experimental legal regimes. It is concluded that the EAEU should develop an integrated legal framework to facilitate the effective operation of «regulatory sandboxes» in the realm of artificial intelligence, thereby advancing its digital agenda. Furthermore, attention is drawn to the ongoing completion of the regulatory framework for experimental legal regimes concerning artificial intelligence within the EU, suggesting that such legal insights may prove beneficial in shaping the legislation of the EAEU. A comprehensive array of methods including comparative legal analysis, functional examination, and systemic scrutiny of normative legal acts have enabled the examination of legal regulations within the EAEU and the EU, leading to the formulation of proposals for their enhancement.NORTH CAUCASUS LEGAL VESTNIK00It is concluded that the EAEU should develop an integrated legal framework to facilitate the effective operation of «regulatory sandboxes» in the realm of artificial intelligence, thereby advancing its digital agenda.NORTH CAUCASUS LEGAL VESTNIK["\u0421\u043e\u043b\u043e\u043c\u0430\u0442\u0438\u043d \u0415\u0432\u0433\u0435\u043d\u0438\u0439 \u041e\u043b\u0435\u0433\u043e\u0432\u0438\u0447"]2024-06-29T00:00:00 54e957b9b05c9a1393d25f6d3d3f2451e71658f7Artificial intelligence is a technology that enables machines, especially computer systems, to mimic human intelligence and problem-solving abilities. This technology is being used nowadays in many areas of the healthcare industry, from scheduling online appointments to robot-assisted, safer surgeries with better patient outcomes.  Just like a physician, AI can record a patient's history, signs and symptoms, and lab results, leading to an accurate diagnosis and a proper treatment plan, but in no time. This is just a small example of the implications of AI in the sector. Other areas of medicine are also being benefited by this innovation, like gastroentology, radiology, surgery, and preventive medicine. The development of advanced algorithms has reduced the burden on radiologists by helping them identify abnormal images and pick up malignant lesions with minimal diagnostic errors. Similarly, AI-assisted colonoscopy can help identify malignant and benign polyps.  AI-powered systems, like Google's DeepMind Health, may identify malignant growth in mammograms and diabetic retinopathy, which can aid in early detection and treatment.Annals of King Edward Medical University70Artificial intelligence is a technology that enables machines, especially computer systems, to mimic human intelligence and problem-solving abilities, which is being used nowadays in many areas of the healthcare industry, from scheduling online appointments to robot-assisted, safer surgeries with better patient outcomes.Annals of King Edward Medical University["Saira Afzal", "Meha Siddiqui"]2024-06-29T00:00:00 6d3f056b79e314a625c779d9e617a630a31c403bAutism spectrum disorder (ASD) is a neurological illness characterized by challenges with repetitive tasks, social interaction, and communication. Even if genetics is the primary cause, early detection is vital, and using ML presents a promising way to diagnose the condition more quickly and affordably. In an effort to improve and automate the diagnostic process, this research uses a variety of machine-learning techniques to pinpoint important ASD features. With the rapid growth of artificial intelligence techniques, it has become possible to use intelligent methods to carry out early large-scale senseless screening and diagnosis of autism. In the future, research should focus on building an intelligent medical screening and diagnosis system for autism patients, developing screening tools and constructing an intelligent identification model for patients that integrates multimode data.THE SCIENTIFIC TEMPER140This research uses a variety of machine-learning techniques to pinpoint important ASD features and develops screening tools and constructing an intelligent identification model for patients that integrates multimode data.The Scientific Temper["Archana Verma"]2024-06-29T00:00:00 a82c8b40cea5b3ed26b1751c4b5c08cbb9535ee3Prospects for the introduction of artificial intelligence technologies are discussed through the prism of human and societal imperfection. It is emphasized that artificial intelligence should be considered an essential reason for a kind of full-fledged self-audit by civilization and personality. The reasons for the pessimistic attitude to the possibilities of qualitative social transformations are evaluated, despite the vivid developments in the field of artificial intelligence. Fears of artificial intelligence reveal absurdity of many “basic” mechanisms of society’s existence up to now, including the standard ideas about prime importance of mercantile values. It is stated that there is a constant slip in the use of all without exception overtechnologies with maximum public benefit. Attention is focused on the expediency to implement a social revolution in its personally transformative dimension. At the same time it is noted that human improvement projects are almost unambiguously met with anxiety, distrust and reasonable skepticism.Sociologiceskie issledovaniâ00It is stated that there is a constant slip in the use of all without exception overtechnologies with maximum public benefit and that human improvement projects are almost unambiguously met with anxiety, distrust and reasonable skepticism.Sotsiologicheskie issledovaniya["Andrei V. Yakovenko"]2024-06-29T00:00:00 c529243c7cd6d7bebd717ae44a624731dbb6fb59Currently, the implementation of systems based on Artificial Intelligence is transforming the way in which decisions are made in the business financial field. Having as its main objective to provide a comprehensive and updated review of the literature on the impact of AI on corporate financial management, addressing the latest research, discoveries and controversies. This article is based on a systematic review, with a qualitative approach, through the review of scientific articles consulted in different databases such as: Scopus, Redalyc, Semantic Scholar, Dialnet, Scielo, among others. Likewise, the Prisma method was used for its preparation. Finally, it is concluded that the coupling of artificial intelligence positively impacts business financial management, given its ability to quickly and automatically process the data necessary to make decisions in the business financial field, directly influencing the profitability of companies. reducing the risks of spending more than the stipulated investment.SCIÉNDO00It is concluded that the coupling of artificial intelligence positively impacts business financial management, given its ability to quickly and automatically process the data necessary to make decisions in the business financial field, directly influencing the profitability of companies.SCIÉNDO["Roberto Quispe", "Fredesvinda Rios", "Fiorella Quispe", "Deyvi Tafur", "Renato Vidal", "Mirko Mercedes"]2024-06-29T00:00:00 19a5a8b01c9c3b4c4ad55d78af63803ce1aef966JOURNAL OF COMMUNICABLE DISEASES01Journal of Communicable Diseases["A. Gopakumar"]2024-06-29T00:00:00 9b879f36d4ebc70a3ad50dee3cd2d615affe55d3GeoJournal371GeoJournal["Donfouet Olivier", "Ngouhouo Ibrahim"]2024-06-29T00:00:00 0cfaa597e45a3b18406b4f22df3e07ac89770136Otolaryngology Head & Neck Surgery51Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery["Katie Tai", "Robin Zhao", "Ana\u00efs Rameau"]2024-06-29T00:00:00 64640481e54e586bd7ccf703b39ac2f5169ae2a1Journal of Computacion y Sistemas01Computación y Sistemas (CyS)["Jairo Eduardo M\u00e1rquez D\u00edaz"]2024-06-29T00:00:00 7adcd4705a61648c314ed4ef2cba552315c114d8Indonesian Treasury Review: Jurnal Perbendaharaan, Keuangan Negara dan Kebijakan Publik00Indonesian Treasury Review Jurnal Perbendaharaan Keuangan Negara dan Kebijakan Publik[]2024-06-29T00:00:00 4a70103d272d95ad06ca6c666ddabd7285a85767Otolaryngology Head & Neck Surgery20Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery["Michael W. Denham", "Katherine K. S. Rieth", "Ana\u00efs Rameau"]2024-06-29T00:00:00 bce3acb3104f96d7d089f75a5425c60004eca872لقد أحدث استخدام الذكاء الاصطناعي(AI) ثورة في طريقة تعامل الأفراد في حل المشكلات وصنع القرار في مختلف المجالات., وقد برز الذكاء الاصطناعي في مجال التعليم كأداة قوية لديها القدرة على تعزيز تجارب التعلم وتحسين النتائج التعليمية. هدفت الدراسة إلى استكشاف استخدام تطبيقات الذكاء الاصطناعي في تحقيق فاعلية التعليم ونجاح العملية التعليمية بشكل عام. تمت هذه الدراسة في مؤسسة تعليمية كدراسة حالة في الرياض، المملكة العربية السعودية. استخدم الباحث المنهج الوصفي-التحليلي من خلال توزيع استبانة كأداة لجمع البيانات. تم تطبيق استبانة إلكترونية في الفترة من ديسمبر للعام الأكاديمي 2023 حتى فبراير 2024. تضمنت البيانات الأولية معلومات حول العينة للدراسة وفقًا للمتغيرات المستقلة للدراسة، والتي هي (المهنة- التخصص- العمر). شملت الاستبانة 32 عبارة موزعة على أربعة محاور كما يلي: مدى الاستفادة من تطبيقات الذكاء الاصطناعي، والدعم من جانب إدارة المؤسسة لاستخدام الذكاء الاصطناعي، والعوامل التي ساعدت في الانتقال إلى استخدام تطبيقات الذكاء الاصطناعي، والصعوبات التي يواجهها الطلاب والمعلمون والإداريون في الاستفادة من تطبيقات الذكاء الاصطناعي. أظهرت نتائج الدراسة وجود علاقات ذات دلالة بين استخدام تطبيقات الذكاء الاصطناعي والدعم من جانب إدارة المؤسسة التعليمية، بما في ذلك التشجيع وتوفير الموارد ومعالجة التحديات التي تواجهها الطالبات والمعلمات في الوصول إلى الموارد المتعلقة بالذكاء الاصطناعي. استنادًا إلى النتائج والطلب المتزايد لاستخدام هذه التطبيقات وهذه التقنية، توصي الباحثة بزيادة الوعي بتأثيرات الذكاء الاصطناعي على المجتمع والتحديات الأخلاقية، وتنفيذ إطار تنظيمي قوي، وابتكار تطبيق وطني قائم على حوكمة البيانات. كما توصي بالاستثمار في أبحاث الأخلاقيات المتعلقة بالذكاء الاصطناتعاوني وأدوات مراقبة الآثار الأخلاقية.مجلة العلوم التربوية و النفسية50مجلة العلوم التربوية و النفسية["\u0633\u0627\u0631\u0647 \u062d\u0645\u0648\u062f \u0627\u0644\u0642\u062d\u0637\u0627\u0646\u064a"]2024-06-29T00:00:00 b14505ad2b2de06bf2becea4635bc6bd5433c6ffVěda a perspektivy00Věda a perspektivy["Olga Chorna"]2024-06-29T00:00:00 7acc2835f4109834c33262942c063ea79dbcefdbBu çalışma, yapay zekâ(YZ) destekli ChatGPT'nin muhasebe eğitimi alanında sunabileceği potansiyeli ve bu kullanımın getirebileceği fırsatları ve zorluklarıdetaylı bir şekilde ele almaktadır. ChatGPT, muhasebedeki işlemleri ve görevleri otomatikleştirme kabiliyetiyle hata oranlarını azaltabilir ve mali analiz süreçlerini geliştirerek fayda sağlayabilir. ChatGPT'nin muhasebe eğitimindeki rolünü değerlendirmek üzere kapsamlı bir literatür incelemesi ve finansal verilerin kullanımıyla nitel ve nicel araştırma yöntemlerinin karmasıbir yöntem kullanılmıştır. Literatür taraması ve örnek uygulama, ChatGPT'nin muhasebe eğitiminde önemli ve etkili bir araç olarak kullanılabileceğini desteklemektedir.Bu nedenle, yapay zekâdestekli sistemlerin gizlilik, güvenlik, fikri mülkiyet gibi bazı etik sorunlar giderildiğinde muhasebe eğitiminde kullanılması, eğitim kalitesini ve verimliliği artırabilir ve yeni fırsatlar yaratabilir.Türk Turizm Araştırmaları Dergisi00Turk Turizm Arastirmalari Dergisi["Erol Ge\u00e7ici"]2024-06-29T00:00:00 f6efab075b66fd41fbba735e383a4afc25f7f01eThe metaverse has been the subject of a global trend in recent years. A network of connected, immersive digital areas where people can engage with computer-generated settings is called the metaverse. The realm of the metaverse possesses the potential to fundamentally change and alter smart cities—urban areas that aim to enhance citizen experiences by accelerating economic growth, modernizing government functions, enhancing accessibility, and promoting sustainability. In this article, we explore how utilizing the metaverse to power smart cities might spur substantial advancements and breakthroughs. The primary technologies that facilitate the metaverse are examined, along with the advantages of using this technology and its potential for smart city bids. We presented multiple instances and looked at the main opportunities that the metaverse provides for smart cities in order to show how the technology of the metaverse has helped and enriched a range of enterprises. Subsequently, five models of neural networks were chosen from the literature to be utilized in the air quality prediction process and evaluated based on accuracy. Therefore, the innovative model combination of ANN and DLR as an aid for decision-making and problem-solving can favorably regulate air pollution in order to handle environmental challenges.THE SCIENTIFIC TEMPER330This article presented multiple instances and looked at the main opportunities that the metaverse provides for smart cities in order to show how the technology of the metaverse has helped and enriched a range of enterprises.The Scientific Temper["Archana Verma"]2024-06-29T00:00:00 c6a2b23eaff9d149c66790d8a6dc9ec9bf500e79This article explores affiliate marketing integration with AI and Web3 technologies, providing a comprehensive analysis of their individual and combined potential to revolutionize the digital marketing landscape. Starting with defining the core components, the article sets a foundation for understanding how AI and Web3 can synergistically enhance affiliate marketing strategies. The paper proceeds with a detailed overview of traditional affiliate marketing models, highlighting their evolution in response to technological advances and changing market dynamics. The article further examines the global landscape of affiliate marketing, presenting current statistics and trends that underscore its economic significance. A focused discussion on AI technologies pertinent to affiliate marketing reveals how machine learning, natural language processing, and predictive analytics can optimize performance and decision-making processes. The role of Web3 is examined by its ability to introduce decentralized, transparent, and secure elements into affiliate marketing, suggesting a shift towards more user-centric models. Finally, the potential of combining AI with Web3 is discussed, illustrating how this convergence can lead to innovative marketing strategies that are more effective and uphold higher standards of integrity. This synthesis aims to illuminate how modern technologies can be harnessed to foster a new digital marketing era.Pidvodni tehnologii100How modern technologies can be harnessed to foster a new digital marketing era is illuminated, illustrating how this convergence can lead to innovative marketing strategies that are more effective and uphold higher standards of integrity.Pidvodni tehnologii["Mykola Malenko", "Yevheniia Shabala"]2024-06-29T00:00:00 925830e05cefb83df565ebee24c40ddf960c7529Pengabdian kepada masyarakat (PkM) ini bertujuan untuk meningkatkan literasi dan keterampilan siswa SMP dan SMA dalam bidang Artificial Intelligence (AI) dan robotika. Kegiatan ini dilaksanakan oleh tim dosen dan mahasiswa Fakultas Teknologi Informasi, Universitas Respati Indonesia (URINDO). Program ini terdiri dari serangkaian pelatihan yang melibatkan teori dasar AI, pengenalan teknologi robotika, serta praktek langsung pembuatan dan pemrograman robot. Melalui pendekatan yang interaktif dan aplikatif, peserta diharapkan mampu memahami konsep dasar AI dan robotika, serta mengaplikasikan pengetahuan ini dalam proyek sederhana. Hasil dari kegiatan ini menunjukkan peningkatan pemahaman dan minat siswa terhadap teknologi AI dan robotika, serta kemampuan mereka dalam mengembangkan solusi kreatif berbasis teknologi. Selain itu, program ini juga memberikan dampak positif dalam membangun kesadaran akan pentingnya literasi teknologi di kalangan generasi muda. Kata Kunci: literasi teknologi, Artificial Intelligence, robotika, pelatihan, siswa SMP, siswa SMA, Universitas Respati Indonesia, pengabdian kepada masyarakat.Jurnal Inovasi Pengabdian Masyarakat250Jurnal Inovasi Pengabdian Masyarakat["Ramadhani Ulansari", "S. Hartanto", "S. Suharyanto", "Taufik Kurahmadan", "Arif Prayogo", "Reza Ramadhan", "Rafli Maulana Zidane", "Christianto Tri Raihan"]2024-06-29T00:00:00 987a1d9cb223e4153aed3d00637490f75cb4c802Overall, algorithmic transparency is an important aspect of responsible AI development and deployment. Ensuring that AI systems are transparent and accountable will help build trust and confidence in these systems and ensure that they are used ethically and effectively. Artificial intelligence (AI) has emerged as a cutting-edge domain that is fundamentally redefining different areas of daily experiences, such as health care, transport, finance, education, and others. The systems are not created for making a judgment like human judgment of natural language, spotting patterns and problem-solving; rather AI produces machines that also have intelligence level same as that of human beings. AI having more influence over us, it is to be considered the ethical directions of these tools and see that they operate under principles of transparency and accountability. The element regarding algorithmic transparency, which means the process of understanding the functioning and explanation of how AI systems make their decisions is the one that is most crucial. The issue of algorithm transparency is of fundamental importance for many considerations. AI systems are not only supported by fairness but also by their non-discrimination. If we do not know how a system of AI arrives at the decisions made, it becomes impossible to determine if the provided results meet equal treatment for everybody. If used in delicate areas like recruitment, credit, and legal system- where the AI-machine must make choices which are life changing, then this aspect is very important. On top of fairness, algorithmic transparency is also an important factor for accountability. If we are ignorant about what an artificial intelligence algorithm does and what is the source of its decision-making process, we are unable to track and classify the mistakes or mishaps of the system. This has always mattered when central to the operation of systems with high stake, such as those used in self-driving vehicles or in health care. Algorithmic transparency may be reached using different instruments. The transparent AI systems can be made by a more transparent design, for example, the simple modelling tools, that use interpretable models. Another method is designing technologies and techniques that can help people why the artificial systems difficult to be decoded but easy to understand which they can utilize in making decisions. Therefore, algorithmic transparency is a key factor of the AI made responsibly and used by the society. It is crucial that AI machines are both transparent and accountable since this will lead to people building trust in the system and accepting its ethical and practical implications. This paper examines regulation of algorithmic transparency in the EU, specifically provisions under the General Data Protection Regulation (GDPR), it aims to situate analysis of the GDPR's provisions on explainability of AI systems within broader technology ethics and policy discourse. The paper's scope is limited to EU regulations applicable to AI data processing transparency.Masaryk University Journal of Law and Technology596Regulation of algorithmic transparency in the EU, specifically provisions under the General Data Protection Regulation (GDPR), is examined to situate analysis of the GDPR's provisions on explainability of AI systems within broader technology ethics and policy discourse.Masaryk University Journal of Law and Technology["Gyandeep Chaudhary"]2024-06-29T00:00:00 21ff2dea8bd191d048cad916cc319430f04b5059In the contemporary financial realm, safeguarding against banking fraud and managing associated risks is paramount. In this pursuit, the integration of Artificial Intelligence (AI) stands as a beacon of promise, offering multifaceted solutions that outshine traditional fraud detection mechanisms. This study delves into the expansive applications of AI, delineating its role in identifying, pre-empting, and navigating fraudulent activities within the banking sector, juxtaposed against conventional fraud detection methodologies. AI revolutionizes banking fraud prevention and risk management by leveraging its rapid analysis capabilities to detect anomalies and flag fraudulent activities in real-time. Deep learning, particularly through neural networks trained on historical fraud data, excels in discerning intricate patterns and forecasting fraudulent transactions with remarkable accuracy. Natural Language Processing (NLP) enhances Know Your Customer (KYC) protocols, ensuring the authenticity of customers by scrutinizing textual data from diverse sources. Graph analytics visually map transactional relationships, spotlighting suspicious activities like rapid fund transfers indicative of money laundering. Predictive analytics transcends conventional credit scoring by integrating diverse datasets, offering holistic insights into customer creditworthiness. User-friendly interfaces like AI-powered chatbots facilitate immediate reporting of suspicious activities alongside advanced biometric authentication mechanisms such as facial and voice recognition. Adaptability inherent in AI ensures dynamic updates to combat evolving fraud strategies, extending beyond fraud detection to phishing, IoT integration, and cross-channel analysis. Additionally, AI’s capability to simulate economic scenarios empowers proactive risk management and streamlines regulatory compliance processes, marking a transformative shift in banking security and efficiency.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)04This study delves into the expansive applications of AI, delineating its role in identifying, pre-empting, and navigating fraudulent activities within the banking sector, juxtaposed against conventional fraud detection methodologies.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)["F. Johora", "Rakibul Hasan", "Syeda Farjana Farabi", "Mohammad Zahidul Alam", "Md Imran Sarkar", "Md Abdullah Al Mahmud"]2024-06-29T00:00:00 d7eb0570dd6caa722eb85db6d84c1b91e24478a9The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.Diagnostics1284This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNN) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments.Diagnostics["Jiaming Zhang", "Jiayi Fang", "Yanneng Xu", "Guangyan Si"]2024-06-29T00:00:00 6670546864bd235ea13669dae51c7dfe65944439Pursuing artificial intelligence for biomedical science, a.k.a. AI Scientist, draws increasing attention, where one common approach is to build a copilot agent driven by Large Language Models (LLMs). However, to evaluate such systems, people either rely on direct Question-Answering (QA) to the LLM itself, or in a biomedical experimental manner. How to precisely benchmark biomedical agents from an AI Scientist perspective remains largely unexplored. To this end, we draw inspiration from one most important abilities of scientists, understanding the literature, and introduce BioKGBench. In contrast to traditional evaluation benchmark that only focuses on factual QA, where the LLMs are known to have hallucination issues, we first disentangle"Understanding Literature"into two atomic abilities, i)"Understanding"the unstructured text from research papers by performing scientific claim verification, and ii) Ability to interact with structured Knowledge-Graph Question-Answering (KGQA) as a form of"Literature"grounding. We then formulate a novel agent task, dubbed KGCheck, using KGQA and domain-based Retrieval-Augmented Generation (RAG) to identify the factual errors of existing large-scale knowledge graph databases. We collect over two thousand data for two atomic tasks and 225 high-quality annotated data for the agent task. Surprisingly, we discover that state-of-the-art agents, both daily scenarios and biomedical ones, have either failed or inferior performance on our benchmark. We then introduce a simple yet effective baseline, dubbed BKGAgent. On the widely used popular knowledge graph, we discover over 90 factual errors which provide scenarios for agents to make discoveries and demonstrate the effectiveness of our approach. The code and data are available at https://github.com/westlake-autolab/BioKGBench.arXiv.org01Surprisingly, it is discovered that state-of-the-art agents, both daily scenarios and biomedical ones, have either failed or inferior performance on the authors' benchmark.ArXiv["Xinna Lin", "Siqi Ma", "Junjie Shan", "Xiaojing Zhang", "Shell Xu Hu", "Tiannan Guo", "Stan Z. Li", "Kaicheng Yu"]2024-06-29T00:00:00 cc4d8c72110511f1d9434d781ecc2ec75ceb8048This study explores the integration of generative artificial intelligence, especially models such as ChatGPT, into teacher training, highlighting both the promises and challenges of adopting advanced technologies in an educational context. Through a course titled "Introduction to Generative Artificial Intelligence for Teachers," 97 educators from various universities in Baja California participated, spanning a wide range of ages. The qualitative methodology adopted allowed an in-depth exploration of teachers' perceptions, experiences, and expectations regarding AI in education. Results showed a generally positive evaluation of the course, with significant emphasis on the importance of AI in transforming educational practices. Approximately 41.38% of the comments highlighted the potential of gen AI to support and enhance teaching and learning. Additionally, there was a clear interest in deepening knowledge about AI, as well as a need for ongoing training strategies. However, the study also emphasizes critical reflections on the ethical and practical challenges of integrating AI into education, underscoring the importance of a reflective and ethical approach. The demand for gen AI training by educational institutions indicates a global trend toward the adoption of these technologies. The study concludes with recognition of the potential of AI to enrich pedagogy, provided that the associated risks are considered, and ethical and effective adoption is promoted.Education &amp; Information Technology151The study concludes with recognition of the potential of AI to enrich pedagogy, provided that the associated risks are considered, and ethical and effective adoption is promoted.Education &amp; Information Technology["Karla Karina Ruiz Mendoza", "Ma. Antonia Miramontes Arteaga", "Karla Yudit Castillo Villapudua"]2024-06-29T00:00:00 408f4e92194b3a60cd17ece9fcb29baea06bf7e7Generative artificial intelligence models, when trained on a sufficient number of a person's images, can replicate their identifying features in a photorealistic manner. We refer to this process as 'likeness generation'. Likeness-featuring synthetic outputs often present a person's likeness without their control or consent, and may lead to harmful consequences. This paper explores philosophical and policy issues surrounding generated likeness. It begins by offering a conceptual framework for understanding likeness generation by examining the novel capabilities introduced by generative systems. The paper then establishes a definition of likeness by tracing its historical development in legal literature. Building on this foundation, we present a taxonomy of harms associated with generated likeness, derived from a comprehensive meta-analysis of relevant literature. This taxonomy categorises harms into seven distinct groups, unified by shared characteristics. Utilising this taxonomy, we raise various considerations that need to be addressed for the deployment of appropriate mitigations. Given the multitude of stakeholders involved in both the creation and distribution of likeness, we introduce concepts such as indexical sufficiency, a distinction between generation and distribution, and harms as having a context-specific nature. This work aims to serve industry, policymakers, and future academic researchers in their efforts to address the societal challenges posed by likeness generation.arXiv.org671A taxonomy of harms associated with generated likeness, derived from a comprehensive meta-analysis of relevant literature, is presented, which categorises harms into seven distinct groups, unified by shared characteristics.ArXiv["Ben Bariach", "Bernie Hogan", "Keegan McBride"]2024-06-29T00:00:00 921290195c9f2de64f6977627b3f218d2e867fc5The Artificial Intelligence revolution is turning its attention toward Latin America. The region features an extraordinary abundance of natural resources paired with a strategic location for the industry, a relatively stable political context, and high demand for investment. This new AI hub could serve as a key factor for geopolitical balance, countering China’s influential investments in infrastructure. Latin American countries also have a unique advantage: they can host the entire AI development, including natural resource extraction, chip manufacturing, data centers, and e-waste facilities. Yet, the environmental impact of unsustainable AI can hinder fragile energy and water public services, quickly leaving multiple communities without basic access. Finding proactive mitigation policies and leveraging local expertise will be key to a long-term realization. La revolución de la Inteligencia Artificial está fijando su atención en América Latina. La región presenta una extraordinaria abundancia de recursos naturales, junto con una ubicación estratégica para la industria, un contexto político relativamente estable y una alta demanda de inversión. Este nuevo centro de IA podría servir como un factor clave para el equilibrio geopolítico, contrarrestando las influyentes inversiones de China en infraestructura. Los países latinoamericanos también tienen una ventaja única: pueden albergar todo el desarrollo de la IA, desde la extracción de recursos naturales y la fabricación de chips hasta los centros de datos y las instalaciones de desechos electrónicos. Sin embargo, el impacto medioambiental de la IA insostenible puede amenazar la fragilidad de los servicios públicos de energía y agua, dejando pronto a múltiples comunidades sin el acceso básico. Encontrar políticas de mitigación proactivas y aprovechar la experiencia local será clave para una realización a largo plazo.Middle Atlantic Review of Latin American Studies10Middle Atlantic Review of Latin American Studies["Marina Malamud"]2024-06-29T00:00:00 26f5422266376fe3f5ab06477c5dc348da6410afThe challenge of effectively monitoring and controlling traffic violations in India has surfaced due to factors such as a growing population, increased commuters, inadequate traffic management, and a lack of adherence to road safety norms. This project seeks to tackle this crucial issue by harnessing the capabilities of Artificial Intelligence (AI). The proposed system incorporates cutting-edge technologies, including computer vision, machine learning algorithms, and realtime data processing, to promptly report traffic violations by accurately identifying vehicle number plates. Through the integration of these advanced technologies, the project aims to establish a robust framework for real-time monitoring and control of traffic violations. Additionally, the project recognizes the importance of engaging the general public in enhancing traffic safety. To streamline reporting, a user-friendly interface will be developed, allowing individuals to report violations easily from any location. This inclusive approach aims to empower the common man to actively contribute to the monitoring and control of traffic violations, fostering a collaborative effort for safer roads throughout India.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)90The proposed system incorporates cutting-edge technologies, including computer vision, machine learning algorithms, and realtime data processing, to promptly report traffic violations by accurately identifying vehicle number plates, to establish a robust framework for real-time monitoring and control of traffic violations.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)["Sakshi Patil", "Aditi Mokashi", "Omkar Satav", "Sahil Sarkate", "Anita Shinde", "Vishal Adsool"]2024-06-29T00:00:00 0d9b118cc5ff4a02e7a24f7d867c16c5a71ecbbcAI-based learning technologies, especially deep learning, hold significant promise for enhancing students’ learning experiences in educational systems. However, providing accurate predictions or answers to students’ learning problems through high-performance deep learning models is not sufficient for students to achieve effective learning. This study explores Explainable Artificial Intelligence (XAI) in reducing students’ cognitive load and improving learning outcomes within the realm of object-oriented programming education. Specifically, this study examines the application of Gradient Integration to generate coloured code segments associated with code errors predicted by a Performer-based deep learning classification model for debugging tasks. Thirty-six participants took part in a controlled experiment assessing students’ cognitive load and learning performance through the XAI system. They were randomly assigned to a control group (N=18) and an experiment group (N=18). The independent-samples Wilcoxon-Mann-Whitney test results revealed that the coloured code segments reduce students’ cognitive load (p=0.006) and improve their exam scores (p=0.006) significantly. This study contributes to an appropriate application of the XAI technique that can reduce students’ cognitive load and improve learning outcomes in educational settings.Education &amp; Information Technology200This study examines the application of Gradient Integration to generate coloured code segments associated with code errors predicted by a Performer-based deep learning classification model for debugging tasks and finds that the coloured code segments reduce students’ cognitive load and improve learning outcomes in educational settings.Education &amp; Information Technology["Feng Hsu Wang"]2024-06-29T00:00:00 361fb711763065db0edfff9d8b84100506badf3eIn the 1970s, technology opened horizons to the educational field, not only to problematize about it and its impact on teaching and learning, but also to expand the resources available to teachers to enhance their pedagogical mediation. However, it would be in the 21st century when the development of digital technology came to enhance the use of ICT for educational purposes, up to Artificial Intelligence, to build bridges that favor its incorporation into teaching at the higher level. Thus, in the field of disciplinary training, the strengthening of knowledge and research skills must include the effective use of technological resources in the training of college students. This article reports some results of a study whose objective was to analyze the attitudes that graduate students have about the use of AI in their education. The study had a quantitative approach with a descriptive transactional non-experimental design, in which 118 subjects participated, distributed in 10 Higher Education Institutions, 5 of them public and 5 of them private. Among its results, the uncertainties that the participants of the study have regarding the use of AI can be appreciated, while recognizing its ease and the attractiveness of a technology that requires specialized skills, responsibility in its use and cognitive processes typical of research.Alteridad00Results of a study whose objective was to analyze the attitudes that graduate students have about the use of AI in their education are reported, recognizing its ease and the attractiveness of a technology that requires specialized skills, responsibility in its use and cognitive processes typical of research.Alteridad["Genaro Aguirre-Aguilar", "Ismael Esquivel-G\u00e1mez", "Rub\u00e9n Edel Navarro", "Mar\u00eda Guadalupe Veytia-Buchelli"]2024-06-29T00:00:00 ab3ab45d9d9ba1ad1783f67abc28b20f39d9079bThis paper proposes a methodology for the design of electronic control unit (ECU) hardware units with increased performance and reliability. Today's vehicles are equipped with dozens of ECUs that significantly influence the system's efficiency, reliability, performance, and safety. With the increased complexity of control algorithms and the environmental constraints that automotive systems operate, the robustness and efficiency of the ECUs are of utmost importance. In this work, an approach is proposed based on combining hardware redundancy, commercial field programmable gate arrays (FPGAs), and artificial intelligence strategies to provide increased redundancy checks and robust control. An additional redundancy is added to the hardware architecture of the ECU to include a parallel hardware unit. The two controlling units operate in parallel. The output of each of them is compared, allowing redundancy checks in the computation of the output variable (oV) of the system. The mathematical model of the ECU depicts the governing equations of the ECU in the form of differential equations, which results in a corresponding state-space configuration. These mathematical models are encoded into the field programmable gate array (FPGA) and processed in hardware, leading to an equivalent software-based implementation. To analyze the performance of both models within the ECU, an artificial neural network (ANN)-based strategy is proposed. The ANN depicts the governing equations of the ECU in the form of differential equations encoded in the form of sigmoidal functions. To analyze the reliability of the control action in the ECU, the temperature of the system is increased, which leads to a random variation of the system parameters. The variability of the ECU parameters leads to a deviation in the computation of the oV and the corresponding control action. The robustness of the control is determined in such conditions. A control law is determined to guarantee proper control action under variations in the governing equations of the system. This control law is represented by a simple algebraic equation, which can then be cast in various control strategies, such as look-up tables or fuzzy logic controllers. Several cases are simulated to assess the performance of the proposed control law for the hardware redundancy scheme, for the ANN-based equivalent software implementation approach, and for the additional fuzzy logic controller. The simulation results are analyzed concerning the requested oV and give insight into the performance and reliability of the proposed dedicated ECU design.Global Research and Development Journals00An approach is proposed based on combining hardware redundancy, commercial field programmable gate arrays (FPGAs), and artificial intelligence strategies to provide increased redundancy checks and robust control in the design of electronic control unit (ECU) hardware units with increased performance and reliability.Global Research and Development Journals["Chirag Vinalbhai Shah", "Aravind Ravi"]2024-06-29T00:00:00 8f5c6899ee1d5b0825f23862082c168b92115c66Background/Objectives: The aim of this study was to establish a histology-based gold standard for the evaluation of artificial intelligence (AI)-based caries detection systems on proximal surfaces in bitewing images. Methods: Extracted human teeth were used to simulate intraoral situations, including caries-free teeth, teeth with artificially created defects and teeth with natural proximal caries. All 153 simulations were radiographed from seven angles, resulting in 1071 in vitro bitewing images. Histological examination of the carious lesion depth was performed twice by an expert. A total of thirty examiners analyzed all the radiographs for caries. Results: We generated in vitro bitewing images to evaluate the performance of AI-based carious lesion detection against a histological gold standard. All examiners achieved a sensitivity of 0.565, a Matthews correlation coefficient (MCC) of 0.578 and an area under the curve (AUC) of 76.1. The histology receiver operating characteristic (ROC) curve significantly outperformed the examiners’ ROC curve (p < 0.001). All examiners distinguished induced defects from true caries in 54.6% of cases and correctly classified 99.8% of all teeth. Expert caries classification of the histological images showed a high level of agreement (intraclass correlation coefficient (ICC) = 0.993). Examiner performance varied with caries depth (p ≤ 0.008), except between E2 and E1 lesions (p = 1), while central beam eccentricity, gender, occupation and experience had no significant influence (all p ≥ 0.411). Conclusions: This study successfully established an unbiased dataset to evaluate AI-based caries detection on bitewing surfaces and compare it to human judgement, providing a standardized assessment for fair comparison between AI technologies and helping dental professionals to select reliable diagnostic tools.Journal of Clinical Medicine610This study successfully established an unbiased dataset to evaluate AI-based caries detection on bitewing surfaces and compare it to human judgement, providing a standardized assessment for fair comparison between AI technologies and helping dental professionals to select reliable diagnostic tools.Journal of Clinical Medicine["Julian Boldt", "Matthias Schuster", "G. Krastl", "Marc Schmitter", "Jonas Pfundt", "Angelika Stellzig-Eisenhauer", "Felix Kunz"]2024-06-29T00:00:00 7e25bc7b47a0146e3ef818fcb095df2cb32e4673The quick amalgamation of artificial intelligence (AI) into various industries has driven innovation to unprecedented peaks. This comparative paper addresses the critical intersection of AI and intellectual property (IP) protection, focusing on the imperative to safeguard AI-driven innovations. Employing a comparative analysis framework, this study evaluates the effectiveness of existing IP mechanisms across selected jurisdictions. The methodology involves a comprehensive examination of patent and trademark systems, utilizing defined metrics for comparative evaluation. The literature review traces the historical evolution of AI in innovations and synthesizes existing knowledge on the interaction between AI and IP. The comparative analysis includes an in-depth examination of patent protection in the context of AI-generated content and trademark protection for AI innovations. This research aims to unveil the strengths and weaknesses of IP protection mechanisms in the selected jurisdictions by employing a robust comparative framework. The findings and discussion section presents a nuanced investigation of the implications of IP protection on AI-driven innovation, offering insights into potential challenges and opportunities. The paper concludes with actionable recommendations for policy reforms and considerations for international collaboration, striking a balance between robust IP protection and promoting a conducive environment for AI innovation.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)510This research aims to unveil the strengths and weaknesses of IP protection mechanisms in the selected jurisdictions by employing a robust comparative framework and makes actionable recommendations for policy reforms and considerations for international collaboration.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)["Jamshid Kazimi", "Harshita Thalwal"]2024-06-29T00:00:00 21ae938af88c0be805101e0e7d2943ca729dd919The purpose of this study is to determine whether deep learning, a potent type of artificial intelligence (AI), can help achieve the Sustainable Development Goals (SDGs) set forth by the UN. We investigate the pervasive influence of deep learning across several industries and its potential to achieve the SDGs through a review of previous research and expert comments. To accomplish the three main goals of Zero Hunger (SDG 2), Good Health and Well-Being (SDG 3), and Climate Action (SDG 13), particular uses of deep learning approaches are highlighted. We analyse pertinent information and processes connected to these uses, illustrating how deep learning can be used to address issues with access to healthcare, food security, and climate change mitigation. The results demonstrate the potential of deep learning as a tool for sustainable development. We can hasten the transition to a sustainable future in line with the UN Sustainable Development Goals by putting deep learning solutions into practice with appropriate organisational control and ethical concern.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)240The results demonstrate the potential of deep learning as a tool for sustainable development and can hasten the transition to a sustainable future by putting deep learning solutions into practice with appropriate organisational control and ethical concern.2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP)["Nitu Yadav", "S. Sheoran"]2024-06-29T00:00:00 c8952f56476106617c06de4e459e03f67446ade8This paper aimed to explore the use of Freytag’s Pyramid narrative elements in storytelling prompts for artificial intelligence (AI) enhanced art generation known as Leonardo AI. It examines how narrative prompts have been used to enhance the quality of AI-generated art and create more engaging and meaningful art. It would focus on the Freytag's pyramid, a narrative structure that divides a story into five parts, exposition, rising action , c limax, falling action and denouement or resolution. This paper would introduce a Freytag's Pyramid Rubric as an attempt to analyse AI-generated art prompts narratives through a limited art generation sampling and demonstration. The study holds significance due to its potential to improve the calibre of AI-generated artwork and its implications for developing such artwork that is more captivating, meaningful, and of superior quality.International Journal of Academic Research in Business and Social Sciences180A Freytag's Pyramid Rubric is introduced as an attempt to analyse AI-generated art prompts narratives through a limited art generation sampling and demonstration to improve the calibre of AI-generated artwork and its implications for developing such artwork that is more captivating, meaningful, and of superior quality.International Journal of Academic Research in Business and Social Sciences["Siti Hajar Abd Aziz", "Ahmad Nur Azam Ahmad Ridzuan", "Muhamad Hanapi Khamis", "Zuliani Mohd Azni", "Mohd Sufiean Hassan", "Nur Shazana Abdul Rani"]2024-06-29T00:00:00 ab3473ae09834b006b5485fb680e764aa3abbab8The convergence of Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technologies is reshaping contemporary business operations. This abstract explores their collective impact on efficiency, transparency, and strategic advantage in organizations. AI and ML drive data-driven decision-making, automate processes, and enhance customer experiences through personalized interactions. Blockchain ensures transparency and security in transactions, fostering trust and accountability. Together, these technologies revolutionize traditional business models, offering insights into future trends and challenges in the digital era. Ethical considerations, security concerns, and regulatory landscapes are crucial in navigating this transformative landscape. As businesses embrace these innovations, they gain competitive edges, optimize resource allocation, and elevate customer satisfaction in a dynamic marketplace.World Journal of Advanced Research and Reviews07This abstract explores their collective impact on efficiency, transparency, and strategic advantage in organizations as AI and ML drive data-driven decision-making, automate processes, and enhance customer experiences through personalized interactions.World Journal of Advanced Research and Reviews["Rakibul Hasan Chowdhury"]2024-06-30T00:00:00 1e51423fe922c7e826858ed420ae5862d011083aArtificial intelligence (AI) has emerged as a powerful tool in healthcare, with the potential to revolutionize the monitoring of HIV treatment adherence. This conceptual exploration delves into the various roles that AI can play in this critical aspect of HIV management, aiming to improve patient outcomes and enhance the effectiveness of treatment programs. The Review will discuss how AI can analyze data from various sources, such as electronic medical records, wearable devices, and patient-reported outcomes, to monitor treatment adherence in real-time. By leveraging machine learning algorithms, AI can identify patterns and trends in patient behavior that may indicate non-adherence, allowing healthcare providers to intervene early and provide targeted support. Furthermore, the Review will highlight the potential of AI to personalize adherence monitoring strategies based on individual patient characteristics and treatment regimens. AI can analyze large datasets to identify factors that influence adherence, such as socioeconomic status, mental health, and comorbidities, enabling tailored interventions that address the unique needs of each patient. Additionally, the Review will discuss how AI can improve patient engagement and education through personalized interventions delivered via mobile applications or virtual assistants. These interventions can provide patients with real-time feedback on their adherence behavior, offer motivational support, and address any barriers to adherence they may be facing. Overall, this conceptual exploration will demonstrate the transformative potential of AI in monitoring HIV treatment adherence. By harnessing the power of AI, healthcare providers can develop more effective strategies for improving adherence, ultimately leading to better health outcomes for patients living with HIV.International Journal of Multidisciplinary Research Updates1229This conceptual exploration will demonstrate the transformative potential of AI in monitoring HIV treatment adherence by harnessing the power of AI, healthcare providers can develop more effective strategies for improving adherence, ultimately leading to better health outcomes for patients living with HIV.International Journal of Multidisciplinary Research Updates["Janet Aderonke Olaboye", "Chukwudi Cosmos Maha", "Tolulope Olagoke Kolawole", "Samira Abdul"]2024-06-30T00:00:00 53d075f091b85b629b982df0727a9460cd6bd135Volume 4 Issue 1010Volume 4 Issue 1[]2024-06-30T00:00:00 d00f4b9fb6009f2cbae48206b0ed43bcf61d5ad7This concept note explores the integration of Virtual Reality (VR) and Artificial Intelligence (AI) in autism therapy, aiming to enhance therapeutic outcomes for children with autism spectrum disorder (ASD). VR provides immersive, controlled environments for practicing social, cognitive, and motor skills, while AI offers personalized, adaptive learning experiences and real-time feedback. The synergy of these technologies promises innovative, effective interventions that cater to the unique needs of each child. This project proposes the development and evaluation of a VR-AI therapy platform, focusing on improving social interaction, communication, and daily living skills among young children with autism. The integration of Virtual Reality (VR) and Artificial Intelligence (AI) into autism therapy offers a transformative approach to enhancing treatment effectiveness and accessibility. Autism Spectrum Disorder (ASD) presents unique challenges in social interaction, communication, and behavior, requiring tailored therapeutic interventions. VR provides immersive environments where individuals with ASD can safely practice social scenarios, sensory processing, and daily living skills. AI, particularly through machine learning algorithms, can personalize these virtual experiences by adapting to the user's progress and specific needs, offering real-time feedback and data-driven insights for therapists. This concept note explores the synergistic potential of combining VR and AI in autism therapy. VR environments can be meticulously controlled and replicated, allowing for consistent therapeutic sessions. AI can analyze user interactions within these environments, providing granular data to refine treatment plans. The integration aims to make therapy more engaging, individualized, and scalable. This approach not only enhances the effectiveness of traditional therapies but also addresses limitations such as therapist availability and the need for generalized settings. Furthermore, this technology-driven paradigm can democratize access to high-quality autism therapy, particularly in underserved regions. The proposed integration of VR and AI heralds a new era in autism therapy, promising improved outcomes and greater inclusivity.International Journal of Science and Research Archive178The proposed integration of VR and AI heralds a new era in autism therapy, promising improved outcomes and greater inclusivity and this technology-driven paradigm can democratize access to high-quality autism therapy, particularly in underserved regions.International Journal of Science and Research Archive["Srishti Bhatt", "Saumya Jogy", "Astha Puri"]2024-06-30T00:00:00 3913a3b11894f762592e588a2c577f4251499f46Research background: As Artificial Intelligence (AI) weaves into the fabric of daily life, its societal and economic implications underscore the urgency of embracing an environment conducive to its informed adoption. This requires a sophisticated understanding of the societal perception and adaptability to AI, emphasizing the importance of developing comprehensive AI literacy.  Purpose of the article: This study inquiries into the sociodemographic underpinnings of AI literacy, aiming to demystify how knowledge about AI's capabilities in everyday tasks varies across individual population segments. It allows us to define the basic determinants that influence the differences in the individual population structures. It also reveals the potential risks associated with the use of AI. Methods: This study investigates the awareness of Artificial Intelligence (AI) in daily lives of the Czech population, focusing on the influence of socio-demographic factors. Utilizing computer-assisted web interviewing, we surveyed 1,041 respondents in April 2023, ensuring representativeness by applying quotas for age, gender, education, region, and residential area size. Our investigation spanned AI applications in sectors like customer service, music playlist recommendation, email sorting, healthcare, online shopping, and home devices. Findings & value added: Findings taken from descriptive statistics reveal variable AI awareness levels across different domains, with younger demographics exhibiting notably lower awareness in several areas. Regression analysis highlighted that awareness is significantly associated with gender, age, and education level. Regression analysis showed that males, younger age groups and those with higher levels of education were more likely to correctly answer majority of questions about the role of AI in everyday life. These insights are crucial for stakeholders aiming to enhance AI literacy, tailor communication strategies, and develop digital platforms, offering guidance for policymakers and market analysts in optimizing AI-related initiatives.Oeconomia Copernicana936This study inquiries into the sociodemographic underpinnings of AI literacy, aiming to demystify how knowledge about AI's capabilities in everyday tasks varies across individual population segments and reveals the potential risks associated with the use of AI.Oeconomia Copernicana["V\u00e1clav Moravec", "Nik Hynek", "Be\u00e1ta Gavurov\u00e1", "M. Kub\u00e1k"]2024-06-30T00:00:00 abd8f44e1852d64fd04344feb3e2e716097a29e2The current technological landscape has been profoundly shaped by rapid and significant advances in artificial intelligence (AI), sparking extraordinary interest in academia and beyond. Considered an unprecedented revolutionary technology, it has captured the attention of researchers, scholars, and professionals from various disciplines as it offers transformative prospects in a wide range of fields. This technological progress has sparked a broad debate on its social, ethical, and economic impacts, raising important questions that require in-depth and multidisciplinary investigations. AI thus emerges as an ever-evolving discipline with significant implications for the future of human progress. Besides being seen as an opportunity to pursue common societal goals, many observers have recognized the potential risks associated with such developments. Its integration into the political context also presents a promising opportunity to enhance the efficiency of political decisions however, its adoption raises significant challenges that require careful evaluation. The aim of this research is to explore in detail the relationship between AI and political communication, focusing on the analysis of AI's usage in this context while highlighting the phenomenon of deepfakes, which jeopardize democratic stability and security in many cases. The importance of this research contribution lies in the context where AI is assuming an increasingly prominent role in daily dynamics, making it essential to fully understand the implications and potential consequences of AI application in the political field. Furthermore, it is crucial to assess whether such initiatives can genuinely be considered democratic or if they could represent a dangerous trend towards the use of manipulative algorithms.Society Register574The aim of this research is to explore in detail the relationship between AI and political communication, focusing on the analysis of AI's usage in this context while highlighting the phenomenon of deepfakes, which jeopardize democratic stability and security in many cases.Society Register["Daniele Battista"]2024-06-30T00:00:00 c5d73f59b5c55af85020c5a38ebaec9f50f66dceThis integrative literature review (ILR) examines the impact of artificial intelligence (AI) on legal systems, focusing on technologies such as natural language processing (NLP), machine learning (ML), and AI-driven decision support systems. The research problem addresses the need to understand how AI enhances efficiency, precision, and data handling in legal operations, transforming tasks like document analysis and decision-making procedures. The ILR aims to comprehensively understand AI integration in legal systems, considering its advantages and difficulties. It is guided by a conceptual framework based on AI, legal analytics, and decision support systems to enhance efficiency, accuracy, and innovation. Using a systematic methodology, the review integrates and examines existing research, evaluating AI's tangible benefits and ethical implications. The findings indicate that while AI can revolutionize legal systems, the study underscores the importance of continuous oversight, frequent evaluations, and developing AI models with the ability to identify and correct biases. Future research should prioritize longitudinal studies to assess AI's enduring effects, address ethical considerations, and encompass various legal and geographical contexts. Encouraging cross-disciplinary cooperation and utilizing diverse research methodologies is crucial to ensure that AI improves legal services while maintaining the integrity and impartiality of judicial procedures, and it makes the audience feel included and part of the AI revolution in legal systems.International Journal For Multidisciplinary Research655The findings indicate that while AI can revolutionize legal systems, the study underscores the importance of continuous oversight, frequent evaluations, and developing AI models with the ability to identify and correct biases.International Journal For Multidisciplinary Research["Rachid Ejjami"]2024-06-30T00:00:00 ce80f291223cd39d1dc9ee632d345238818207c9 The function of Artificial Intelligence (AI) in Human-Robot Cooperation (HRC) in Industry 4.0 is unequivocally important and cannot be undervalued. It uses Machine Learning (ML) and Deep Learning (DL) to enhance collaboration between humans and robots in smart manufacturing. These algorithms effectively manage and analyze data from sensors, machinery, and other associated entities. As an outcome, they can extract significant insights that can be beneficial in optimizing the manufacturing process overall. Because dumb manufacturing systems hinder coordination, collaboration, and communication among various manufacturing process components. Consequently, efficiency, quality, and productivity all suffer as a whole. Additionally, Artificial Intelligence (AI) makes it possible to implement sophisticated learning processes that enhance human-robot collaboration and effectiveness when it comes to assembly tasks in the manufacturing domain by enabling learning at a level that is comparable to human-human interactions. When Artificial Intelligence (AI) is widely applied in Human-Robot Cooperation (HRC), a new and dynamic environment for human-robot collaboration is created and responsibilities are divided and distributed throughout social and physical spaces. In conclusion, Artificial Intelligence (AI) plays a crucial and indispensable role in facilitating effective and efficient Human-Robot Cooperation (HRC) within the framework of Industry 4.0. The implementation of Artificial Intelligence (AI)-based algorithms, encompassing deep learning, machine learning, and reinforcement learning, is highly consequential as it enhances human-robot collaboration, streamlines production procedures, and boosts overall productivity, quality, and efficiency in the manufacturing industry.Applied Computer Science474The implementation of Artificial Intelligence (AI)-based algorithms, encompassing deep learning, machine learning, and reinforcement learning, is highly consequential as it enhances human-robot collaboration, streamlines production procedures, and boosts overall productivity, quality, and efficiency in the manufacturing industry.Applied Computer Science["Hawkar Asaad", "S. Askar", "Ahmed Kakamin", "Nayla Faiq"]2024-06-30T00:00:00 bf0304ec7ecee2cc49e813f928030cf050de9a40Artificial intelligence is the fastest growing field in the digital technology industry. Every industry is trying its best to shift its processes to AI based machines. Among these industries the HRM is one of which is adopting these. This study aims to examine the impact of the artificial intelligence on the HRMR practices of the business sector of the Pakistan. A quantitative approach was adopted to analyze the study. The data was gathered from the 300 business managers of the Pakistan two cities Karachi and Lahore by purposive sampling. Data was gathered from a closed ended questioner which was designed on Google forms. The gathered data was analyzed using a technique based on the Partial Least Square with the help of the software named as SmartPLS. From the results of this study, it was found that there are four major HRM practices exist in the business sector of Pakistan named as recruitment and selection, training and development, performance appraisal and compensation and benefits. Results reveals that these all the majors HRM practices are highly impact by the artificial intelligence technology in the upcoming future. This is also recommended to the business sector of the Pakistan to adopt AI based application more and more in their operations especially in HRM.Journal of Development and Social Sciences353It was found that there are four major HRM practices exist in the business sector of Pakistan named as recruitment and selection, training and development, performance appraisal and compensation and benefits, and these all the majors HRM practices are highly impact by the artificial intelligence technology in the upcoming future.Journal of Development and Social Sciences["Bilal Ahmed", "M. Ramish", "Mutasam"]2024-06-30T00:00:00 e1f29a506274e2f98c11a68b43c4778020478967This research is about the use of artificial intelligence (AI) in the formation of laws and regulations and its implications for ethics and security. The method used in this research is a normative juridical legal research method. Normative research must use a statutory and regulatory approach. The data obtained is then collected through documentary study or literature study data collection techniques. Data (legal materials) obtained from library research were analyzed using qualitative methods. The results of Artificial Intelligence (AI) research are used to form legislation which is considered to be an effort to develop regulations and control the law enforcement system using technological devices. The existence of AI can be utilized by various sectors, including banking, trade, health, and the law enforcement sector. The definition relating to AI in the ITE Law is potentially unable to cover the evolution of AI. Law enforcement is influenced by several factors, in the field of technology, the factors that are important are facilities and infrastructure.Community Service Progress01The results of Artificial Intelligence (AI) research are used to form legislation which is considered to be an effort to develop regulations and control the law enforcement system using technological devices.Community Service Progress["Imelda Mardayanti", "Y. Arfah", "Dedy Dwi Arseto"]2024-06-30T00:00:00 541711dcd9bd1a499daef79c3c1129ae5eef342eIntegrating artificial intelligence (AI) into robotics learning is a field of study that offers a range of significant benefits. A qualitative approach was employed, along with the PRISMA methodology, to ensure an appropriate selection of academic and scientific material. The results demonstrated that AI contributes to student performance and fosters the development of cognitive and social skills. It facilitates optimal assimilation of knowledge through projects, problem-solving, and knowledge integration among peers. Furthermore, it promotes learning through trial and error, enabling students to tackle challenges before entering the workforce. AI resources also enable the customization of the educational experience, tailoring it to individual student needs and enhancing their engagement in the learning process.Universidad, Ciencia y Tecnología01AI contributes to student performance and fosters the development of cognitive and social skills, and facilitates optimal assimilation of knowledge through projects, problem-solving, and knowledge integration among peers.Universidad Ciencia y Tecnología["Ninfa Elizabeth Pacha Chipantiza", "Henry Marcelo Barba Palma", "Lizbeth Estefania Sevilla Morocho"]2024-06-30T00:00:00 7625a11cedb41bd0e03fed64fa8924495accb227Penggunaan Artificial Intelligence (AI) dalam desain grafis telah menimbulkan dampak signifikan dan beragam bagi para desainer. Salah satu manfaat para desainer grafis menggunakan Artificial Intelligence memudahkan mereka berkarya menjadi lebih efisien dan praktis. Namun, disamping hal tersebut, juga memiliki dampak-dampak negatif dan permasalahan yang perlu diperhatikan. Desainer perlu menyeimbangkan penggunaan teknologi ini dengan menjaga aspek kreatif dan orisinalitas dalam karya mereka. Ada kekhawatiran bahwa AI dapat menggantikan peran desainer untuk tugas-tugas tertentu. Namun, fakta lain menunjukkan bahwa hasil yang didapat tidak selalu berkualitas baik sehingga menurunkan nilai jasa desain dan dapat memicu plagiarisme. Tujuan dari penelitian ini untuk mengetahui dan mengenali dampak produksi desain grafis apabila menggunakan teknologi Artificial Intelligence (AI) atau kecerdasan buatan yang berkembang sangat pesat dalam beberapa tahun terakhir ini. Penelitian ini menjadi acuan bagi perusahaan agency yang di dalamnya banyak pekerja, seperti desainer grafis, pekerja digital marketing yang juga membuat desain grafis, dan regulator yang memiliki ketertarikan dalam meneliti hubungan antara kecerdasan buatan dan produktivitas kerja dalam memproduksi karya. Metode yang digunakan dalam penelitian ini adalah pendekatan Grounded Theory. Proses analisis data meliputi reduksi data, penyajian data, dan penarikan kesimpulan untuk memperoleh pemahaman menyeluruh tentang topik penelitian. Teknik analisis data pada penelitian ini terdiri dari tiga tahapan, yaitu: open coding, axial coding, dan selektif coding. Pada penulisan ini untuk menganalisis dampaknya, responden dipilih secara purposive sampling, dan data dikumpulkan melalui wawancara dan referensi dari berbagai sumber. Hasil penelitian ditemukan bahwa kecerdasan buatan/Artificial Intelligence (AI) mempunyai dampak yang signifikan terhadap produktivitas desainer grafis maupun produksinya. AI dapat mengotomatiskan tugas yang memakan waktu seperti mengedit foto, memilih palet warna, dan membuat tipografi, menghasilkan teks alternatif untuk gambar dan video dan masih banyak lagi kegunaannya. Hal ini memungkinkan desainer untuk fokus pada aspek yang lebih kreatif dari pekerjaan mereka. Kecerdasan buatan/Artificial Intelligence (AI) mempunyai dampak yang signifikan terhadap produktivitas desainer grafis maupun produksinya. Selain itu, ditemukan bahwa penggunaan AI selain pada produksi desain grafis dalam pembuatan karya juga memberikan kemudahan dan efisiensi, tetapi juga menimbulkan kekhawatiran terkait kualitas, risiko plagiarisme, dan penyalahgunaan. Fitur AI dan risiko internal pengguna adalah faktor utama yang memengaruhi efektivitasnya. Meskipun bermanfaat, penggunaan AI juga memunculkan tantangan yang perlu diatasi.Jurnal Seni Nasional Cikini161Jurnal Seni Nasional Cikini["Taris Zakira Alam", "Jerry Haikal"]2024-06-30T00:00:00 6ba509579e29a8d44830ebf09ce771723690401bThe integration of Artificial Intelligence (AI) and Machine Learning (ML) into pharmaceutical formulation design has brought about a significant transformation, opening up new avenues for innovation and operational efficiency. This review paper aims to extensively examine the utilization of AI and ML in pharmaceutical formulation development, consolidating recent empirical findings and emerging patterns. Meta-analyses examining AI-driven drug discovery and formulation design efforts have revealed promising outcomes, including the acceleration of drug development timelines and enhancements in success rates across preclinical and clinical trials. Notably, a meta-analysis featured in Nature Reviews Drug Discovery sheds light on the pivotal role of AI in rational drug design, resulting in the identification of novel therapeutic candidates boasting improved efficacy and diminished side effects. Furthermore, AI and ML techniques are increasingly being deployed to optimize drug delivery systems, with studies showcasing their effectiveness in devising controlled-release formulations and nano-scale delivery platforms. For instance, the research highlighted in Advanced Drug Delivery Reviews demonstrates the application of ML algorithms in predicting the physicochemical attributes of nanoparticles, thereby aiding in the development of more durable and efficient drug carriers. Despite these advancements, challenges persist, including data scarcity, regulatory complexities, and ethical considerations. Nevertheless, ongoing endeavors to tackle these obstacles coupled with the continual evolution of AI and ML technologies offer promising prospects for the future of pharmaceutical formulation design. In conclusion, this review underscores the transformative influence of AI and ML on pharmaceutical formulation development, underscoring the necessity for sustained research and collaboration to fully leverage these technologies in enhancing healthcare outcomes.International Journal of Newgen Research in Pharmacy &amp; Healthcare622The transformative influence of AI and ML on pharmaceutical formulation development is underscores the necessity for sustained research and collaboration to fully leverage these technologies in enhancing healthcare outcomes, underscoring the necessity for sustained research and collaboration.International Journal of Newgen Research in Pharmacy &amp; Healthcare["Hrithik Dey", "Nisha G Arya", "Harshita Mathur", "Neel Chatterjee", "Ruchi Jadon"]2024-06-30T00:00:00 13fe99092a03a05b40155b3caa992e3df8bbd131In an era of rapidly developing technology, the need to simplify and improve the learning process of mathematics is increasingly urgent. One emerging solution is the utilization of Artificial Intelligence (AI) in mathematics education. AI provides learning content that is tailored to students' needs, creating a relevant and engaging learning experience. An understanding of AI and the ability to interact with technology is important, helping students face the challenges of the digital age. Understanding math concepts is the foundation for logical thinking, problem solving and critical thinking. Research shows that some students do not fully understand mathematical concepts. Therefore, this study aims to identify the impact of using AI in mathematics learning on student learning outcomes. The preliminary study data showed the prevalence of AI use in mathematics learning. The results show a very strong positive correlation between the use of AI and mathematics learning outcomes, where student learning activities observed during learning using AI (Artificial Intelligence) based on observation sheets include several indicators, with the following results: 1) Visual indicators of 88%, 2) Listening indicators of 84%, 3) Oral indicators of 72.5%, and 4) Mental indicators of 84.5%. The results showed that learning activities were in the high category with a percentage of 82.25%. This shows that during learning activities, there is a good stimulus for students.At- Ta'lim : Jurnal Pendidikan121The results show a very strong positive correlation between the use of AI and mathematics learning outcomes, where student learning activities observed during learning using AI (Artificial Intelligence) based on observation sheets include several indicators, with the following results.At- Ta'lim : Jurnal Pendidikan["Moh. Anang Efendi", "Indah Rahayu Panglipur", "Frida Murtinasari"]2024-06-30T00:00:00 d1495b8f34813935cf922c603a29e766b42e1481The traditional personal information life cycle model, primarily tailored to conventional systems, is inherently unsuitable for comprehending the nuances of personal information flow within artificial intelligence frameworks and for formulating ef fective protective measures. Therefore, this study endeavors to introduce a personal information life cycle model specificall y designed for artificial intelligence (AI). This paper presents a personal information life cycle model suitable for artificial intelligence, which includes the stages of collection, retention, learning, use, and destruction/suspension, along with the re-l earning process for destruction/suspension. Subsequently, we compare the performance of these existing models (such as personal information impact assessment and the ISMS-P model) with the newly proposed model. This underscores the sup eriority of our proposed model in comprehensively understanding the personal information flow in AI and establishing robu st protective measuresJouranl of Information and Security101A personal information life cycle model suitable for artificial intelligence is presented, which includes the stages of collection, retention, learning, use, and destruction/suspension, along with the process for destruction/suspension.Jouranl of Information and Security["Jaeyoung Jang", "Jong-Min Kim"]2024-06-30T00:00:00 5d143934b01d1e7b8beafb413777c6a0898728b9The optimization of investment portfolios has become a primary focus in the management of dynamic financial markets. The Whale Optimization Algorithm (WOA) and Artificial Intelligence (AI) have emerged as potential solutions to tackle market complexities. WOA offers an efficient approach to finding optimal solutions, while AI models such as Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms are effective in predicting market behaviors. The integration of WOA and AI holds promise for better outcomes in optimizing investment portfolios by considering complex factors and market volatility. However, the development of this technology requires interdisciplinary collaboration, increased financial and technological literacy, and consideration of social and environmental aspects. With a sustainable, inclusive, and responsible approach, we can create a more sustainable financial future that positively impacts society and the environment.Jurnal Software Engineering and Computational Intelligence131The Whale Optimization Algorithm and Artificial Intelligence have emerged as potential solutions to tackle market complexities and hold promise for better outcomes in optimizing investment portfolios by considering complex factors and market volatility.Jurnal Software Engineering and Computational Intelligence["Iski Mediansyah", "Firza Septian", "Arief Zikry"]2024-06-30T00:00:00 e337ed03ee7bbdfc59a35c5d6d83f8a3d21dd544This study investigates the efficacy of Artificial Intelligence (AI) in enhancing English as a Foreign Language (EFL) conversation classes through a mixed-methods approach. It assessed AI’s impact on student engagement and language acquisition by analyzing both quantitative and qualitative data. Findings indicated that students using AI interactively experienced improved language skills and increased participation. Qualitative insights highlighted AI’s role in providing personalized feedback and facilitating learning, while also raising concerns about dependency on AI and its integration with conventional teaching approaches. The study underscores the need for a balanced implementation of AI in EFL settings to complement conventional language learning bringing along the least possible disadvantages in this context. It suggests further investigation into AI’s long-term effects across diverse educational contexts to maximize benefits for learners. This study contributes insights into how AI can effectively improve language education when thoughtfully integrated into the curriculum.The Society of English Education in Korea01The study underscores the need for a balanced implementation of AI in EFL settings to complement conventional language learning bringing along the least possible disadvantages in this context and suggests further investigation into AI’s long-term effects across diverse educational contexts to maximize benefits for learners.The Society of English Education in Korea["Claudia Sangmi Yun Claudia Sangmi Yun"]2024-06-30T00:00:00 2f6381ce1fa968305b916318f4f542f1782556c7Artificial Intelligence (AI) has transformed education, raised significant ethical concerns and influenced student perceptions. This study examined the perceptions, awareness, and ethical concerns of AI among students in Zamboanga City. Using a mixed-methods approach, quantitative data were collected from 500 university students through stratified random sampling and validated surveys, while qualitative insights were gathered from focus groups. Pilot testing ensured survey clarity and reliability. Findings revealed high AI awareness (75%) but significant ethical concerns (55%), particularly regarding data privacy and job displacement. Despite recognizing AI's educational benefits, students stressed the need for clearer ethical guidelines and transparency. The study recommended integrating AI ethics into curricula, fostering interdisciplinary discussions, and establishing partnerships with AI organizations. Ethical considerations were paramount, ensuring informed consent, confidentiality, and data protection. The conceptual framework was based on the Theory of Planned Behavior, examining how attitudes, subjective norms, and perceived behavioral control influenced AI acceptance. Grounded in Ethical Theories, the study underscored the importance of addressing ethical considerations in AI education. Further research was suggested to explore longitudinal impacts and targeted interventions to enhance AI literacy and mitigate ethical concerns.Cognizance Journal of Multidisciplinary Studies171This study examined the perceptions, awareness, and ethical concerns of AI among students in Zamboanga City, examining how attitudes, subjective norms, and perceived behavioral control influenced AI acceptance.Cognizance Journal of Multidisciplinary Studies["Aldrin Sebastian Valerio"]2024-06-30T00:00:00 cb448771cd24f49873bad9dfd8565de3f0414d09As a cornerstone of societal progress, integrating Artificial Intelligence (AI) into educational methodologies with robust stakeholder engagement represents a pivotal stride towards optimizing the efficacy and relevance of education in an era characterized by rapid development. The disruptive impact of events such as the COVID-19 pandemic has underscored the urgent need for innovative solutions to educational challenges, particularly in Nigeria, where academic activities halted amid the crisis. In response, this study explores the potential of the "Open Campus Model" (OCM), a transformative educational governance framework supported by AI. The research, conducted through a comprehensive literature review and a workshop at Alex Ekwueme Federal University Ndufu-Alike, Nigeria, identified key themes: Educational Practices Enhancement, Educational Innovation with AI, Governance and Participation, Collaborative Learning and Inclusivity, and Access and Equity. An unstructured questionnaire with ten questions facilitated in-depth interviews with 63 participants, including university lecturers, administrators and educational stakeholders from South East Nigeria. Data from the interviews underwent thematic analysis, revealing that OCM, supported by AI, enhances educational practices, fosters collaborative learning, and promotes inclusivity and equity. The study concludes that implementing OCM can address current educational challenges in Nigeria, recommending further research to refine and expand the model's application.International Journal of Learning, Teaching and Educational Research02The study concludes that implementing OCM can address current educational challenges in Nigeria, recommending further research to refine and expand the model's application.International Journal of Learning, Teaching and Educational Research["I. Ukeje", "C. Elom", "M. A. Ayanwale", "C. Umoke", "Sunday Odo Nwangbo"]2024-06-30T00:00:00 d8c8253f503ebde952e86bcd967dcc648aa05976The study was conducted to determine the capabilities and application of Artificial Intelligence in the development of themes in qualitative and quantitative data analysis. Multi-stage data mining as a tool in AI data collection was employed by the researcher to process data reduction and the extraction of codes. This technique was able to generate the final themes pertaining to the capabilities and application of AI models which include pattern recognition analysis, thematic analysis, sentiment analysis, efficient data processing, objectivity and bias reduction, and insight generation. These capabilities and application of AI models can be used to highlight the benefits and ethical use of AI in research especially in data mining, data processing, and data analysis.Archives des Sciences181Multi-stage data mining as a tool in AI data collection was employed by the researcher to process data reduction and the extraction of codes to generate the final themes pertaining to the capabilities and application of AI models.Archives des Sciences["Dr. Moises C. Torrentira Jr. EnP"]2024-06-30T00:00:00 ca91ea3d04034abcdc762923e64dcb08b9b43584The study of the Artificial Intelligence (AI) phenomenon has developed rapidly in recent years by placing various multi-disciplinary perspectives including Communication. The question then arises, how to study the AI phenomenon with a Communication approach? What kind of communication theories can be used to explain the AI phenomenon? Can the existing perspective of communication theory, which emphasizes the notion of communication as an interaction in which two communicators, using some medium of communication, move towards a better understanding of each other through the exchange of messages, still be used? Is the communication theory domain in terms of elements, levels and contexts of communication still relevant to studying the AI phenomenon? Is there a possibility of developing a new group of theories in studying AI phenomena?. This paper will provide an inspiring perspective on the development of a group of communication theories called "Beyond Human Communication" (Littlejohn, 2021). This broader view of communication includes interactions between humans and other animate and inanimate entities. It provides opportunities to examine different ways and different reasons for communicating. AI phenomena in the "Beyond Human Communication" theoretical domain can be explained through what is referred to as "human-machine communication". Understanding the theoretical domain of "Beyond Human Communication" places the study of the AI phenomenon into a special study in Communication science.Interaksi: Jurnal Ilmu Komunikasi131Understanding the theoretical domain of the group of communication theories called "Beyond Human Communication" places the study of the AI phenomenon into a special study in Communication science.Interaksi: Jurnal Ilmu Komunikasi["P. Utari", "Pramana Pramana", "Amelia Ramadhani"]2024-06-30T00:00:00 a684015b2e4da8863d08b055af896e66b8779ea8This paper investigates the emergent issues of Artificial intelligence intervention in redefining the HR processes. Grounded on the Technology, Organization, and Environment (TOE) theory of Information system and Job-Demand-Resource (JDR) theory of HRM, this paper investigates AI intervention in HRM to find out how HRM functions are getting redefined in the future workplace by reviewing 70 research papers in this field which were selected and screened by using the Prisma Flow diagram. These papers were categorized into different themes and sub-themes based on HR functions including, recruitment, performance management, compensation management, employee relations, and ethical concerns for stakeholders associated with those functions. This paper highlights the ethical concerns and challenges of AI and proposes solutions to develop effective practices to embed AI within organizational frameworks. Findings of this paper elucidates on embedding AI-related HR technology in existing business intelligence system of organizations with a redefined focus of HR functions to ensure employees privacy concerns are properly ensured, relevant data are extracted and utilized having informed consent from the stakeholders and organizational justice is not overshadowed in performance and compensation management in the process of Algorithm based rigid assessment. This paper synthesizes the adverse effects of AI in HRM and demonstrates mitigating principles to tackle the perils of AI. Since there exists little theoretical development and empirical evidence involving AI‘s role in HRM, this paper provides new avenues of interdisciplinary researches to connect diffusion of AI in workplace with HRM theories and investigate predictive capabilities and design mechanisms of AI in HRM functions.Archives of Business Research01New avenues of interdisciplinary researches are provided to connect diffusion of AI in workplace with HRM theories and investigate predictive capabilities and design mechanisms of AI in HRM functions.Archives of Business Research["S. Chowdhury", "Souman Guha", "Nehad Laila Sanju"]2024-06-30T00:00:00 b13b2e8a9a300ac5561026d140a10261de094a0aPredictive maintenance is a critical task for ensuring the reliability and efficiency of industrial systems. The integration of artificial intelligence (AI) and mathematical models has shown great potential in improving the accuracy and efficiency of predictive maintenance. This study provides an overview of the different types of mathematical models used for predictive maintenance, including physics-based, data-driven, and hybrid models. The study also discusses how AI techniques, such as machine learning and deep learning, can be used to enhance the accuracy and efficiency of predictive maintenance models. Additionally, the article highlights some of the challenges and limitations of integrating AI and mathematical models for predictive maintenance in industrial systems. Finally, this study provides a case study to demonstrate the practical application of the integrated approach for predictive maintenance in an industrial setting. This article aims to provide a comprehensive overview of the state-of-the-art in integrating AI and mathematical models for predictive maintenance and to provide guidance for researchers and practitioners working in this field.FUDMA Journal of Sciences182An overview of the different types of mathematical models used for predictive maintenance, including physics-based, data-driven, and hybrid models is provided, including physics-based, data-driven, and hybrid models.FUDMA JOURNAL OF SCIENCES["Okeoghene Blessing Ohoriemu", "Justin Onyarin Ogala"]2024-06-30T00:00:00 d2de70ba0296d655684d832aa172602d362bc45cThis paper is focused on examining selected Israeli technologies operating based on artificial intelligence and the possible use of these technologies by the Israeli Defense Forces in the war in Gaza. These technologies include the Besorah system, an AI technology for identifying and recommending targets suitable for aerial bombardment, as well as other forms of attacks carried out from a distance. The SMASH system is an automatic targeting and firing system for small arms. The Goshawk system is a fully autonomous UAV used for aerial protection. The IRIS robot can be used for the initial investigation of tunnels ahead of assaults. The alleged use of these technologies in the war in Gaza cannot be verified by independent sources, mainly due to the ongoing conflict. The paper strives to present a general theoretical assessment of these technologies and weapons from the perspective of international law and armed conflicts.Obrana a strategie (Defence and Strategy)142This paper is focused on examining selected Israeli technologies operating based on artificial intelligence and the possible use of these technologies by the Israeli Defense Forces in the war in Gaza, and a general theoretical assessment of these technologies and weapons is presented.Obrana a strategie (Defence and Strategy)["Veronika D\u2019Evereux"]2024-06-30T00:00:00 7544ea6eadbd032d89035396ea3de246229d30cfThe article explores the increasing influence of artificial intelligence (AI) in education, addressing contemporary challenges and highlighting its significance in refining teaching methods and enhancing learning efficiency. It is a structured literature review that systematically analyzes existing literature on AI in education, drawing insights from prominent researchers to understand current and future trends. Four key questions guide the analysis: the relationship between education and AI, their interaction, AI's contribution to educational evolution, and research challenges. The study employs a systematic review of literature, focusing on works by eminent scholars such as Lee, Memarian, and Yuan, selected from the Scopus database spanning from 1986 to 2024. It follows a structured approach to gather and analyze data from selected studies. The article progresses by presenting an introduction to the topic, outlining the methodology, and summarizing and analyzing key findings from selected literature. It explores the intrinsic relationship between education and AI, their interaction, and AI's role in evolving the educational process. Major findings underscore the importance of a cautious and ethical approach to integrating AI in education. Despite its potential benefits, challenges and shortcomings in current research are acknowledged, urging for further exploration and consideration of ethical implications.Data and Metadata72A structured literature review that systematically analyzes existing literature on AI in education, drawing insights from prominent researchers to understand current and future trends, explores the intrinsic relationship between education and AI, their interaction, and AI's role in evolving the educational process.Data and Metadata["Zouheir Boussouf", "Hanae Amrani", "Mouna Zerhouni Khal", "Fouad Daidai"]2024-06-30T00:00:00 7c9fa15e81ecd379435db103c499ad46527a0424The capacity of robots to mimic human thinking processes, especially in huge data systems for certain kinds of tasks, is known as artificial intelligence.  The purpose of the study was to identify the barriers to artificial intelligence adoption at the university level. Research was quantitative in nature and a survey method was used to collect data. The population of this research is limited to all students enrolled in two public and two private universities located in the Lahore district. The 487 students were selected using a convenient sampling technique from two public and two private Universities in Lahore. Data was gathered by using a self-developed instrument. The data were analyzed through SPSS (Statistical Packages for Social Sciences). inferential statistics were used to calculate the data. Regarding the challenges posed by AI in higher education, male and female students' opinions did not significantly vary from one another. Universities should employ artificial intelligence, with explicit ethical principles for AI development that cover bias mitigation, student data protection, and responsible development. Keywords: Artificial Intelligence, Higher Education, Machine LearningJournal of Arts &amp; Social Sciences01Male and female students' opinions did not significantly vary from one another and universities should employ artificial intelligence, with explicit ethical principles for AI development that cover bias mitigation, student data protection, and responsible development.Journal of Arts &amp; Social Sciences["Dr. Sumaira Munawar", "Jahanzeb Ghouri", "Fouzia Jahanzeb"]2024-06-30T00:00:00 371a264df1973d4f3d984451cef431db0b00197eThis research study focuses on the induction of artificial intelligence into pharmacy practice, including challenges associated with AI application and policy issues. The aim is to acquire how to harness AI in a time-saving manner to enhance service at a pharmacy, coupled with an understanding of its potential pitfalls and governance arrangements. It included government reports, systematic literature reviews of peer-reviewed studies, and policy papers spanning the years 2021–2024. All data required were derived from literature searches in PubMed, Science Direct, Springer, and Google Scholar. The results show that artificial intelligence can contribute so much to the practice of pharmacy by making the drug-dispensing procedure ideal to guarantee better patient care and prevent human mistakes. Coupled with this promising potential were concerns related to data privacy, rigorous regulatory frameworks, and job losses. This will require the development of clear policies and guidelines in the regulatory framework so that AI can be ethically and effectively applied in the pharmacy practice. While the present work adds to a growing literature on AI in health care, it also has the potential to act as a launch pad for future research toward working out the challenges identified herein and thinking out newer opportunities for such innovation.British Journal of Pharmacy and Pharmaceutical Sciences302The results show that artificial intelligence can contribute so much to the practice of pharmacy by making the drug-dispensing procedure ideal to guarantee better patient care and prevent human mistakes, and will require the development of clear policies and guidelines in the regulatory framework so that AI can be ethically and effectively applied in the pharmacy practice.British Journal of Pharmacy and Pharmaceutical Sciences["Musawer Hakimi", "\u2709. Ghulam", "Ali Amiri", "Sayed Ehsan Shamsi"]2024-06-30T00:00:00 337133cc8efaa230ad8678cc5b9bb0b1e98e6dc6Artificial intelligence and critical thinkingCentral Asian Journal of Medical Hypotheses and Ethics41Central Asian Journal of Medical Hypotheses and Ethics["I. Benliday\u0131"]2024-06-30T00:00:00 10730f617f47c72dda2dd5367297974768e12f71This study conducted and evaluated an extracurricular program aimed at improving university students’ skills in data analysis and artificial intelligence. Overall satisfaction with the program increased from 4.28 to 4.42, which is significant for enhancing students’ willingness to participate again and recommend it to others. By introducing a faculty advisor system, the systematic management and supervision of the mentoring program were strengthened through review and feedback on mentors’ activity reports, improving the program’s effectiveness. Additionally, through competitions, participants enhanced their practical problem-solving skills and their understanding and proficiency in Python and AI. Furthermore, students reported learning ‘integrative competencies utilizing diverse majors and backgrounds’, ‘self-directed learning skills’, ‘collaboration and interpersonal skills’, and ‘creative problem-solving skills’ through the program. It is recommended to consider the difference in learning levels between mentors and mentees during program operation, adjusting mentoring methods or difficulty levels, and systematizing mentor selection criteria. This study aims to provide practical directions for future program improvements. By doing so, it intends to contribute to strengthening the connection between the university’s general education and major education programs.Liberal Arts Innovation Center00An extracurricular program aimed at improving university students’ skills in data analysis and artificial intelligence was conducted, and overall satisfaction with the program increased, which is significant for enhancing students’ willingness to participate again and recommend it to others.Liberal Arts Innovation Center[]2024-06-30T00:00:00 833fbe4bd733a198b90fe01285154b568706e3b3This research focuses on the influence of simulated intelligence on people’s financial knowledge, emphasizing the aspects of risk evaluation, outcome evaluation, network security, and global collaboration. It presents the scenarios and the use of man-thinking (artificial intelligence) techniques such as AI information science, and NLP to solve the worldwide financial crises. Firstly, the introductory part of the review sets the background of monetary emergencies. Then, it proceeds with the evaluation of current monetary knowledge techniques and the administrative planning for simulated intelligence in finance. The organization in the process of creating a money policy is characterized by moral implications, human-computer-based intelligence joint effort, and contextual analysis. The study also touches on AI-driven risk assessment, stating that AI makes it easier to identify risks in real time. The study, which focuses on the case study of the UCLA Computer Science Department, through continuous monitoring, cybersecurity measures, and the comprehensive examination of AI's role in reshaping financial management, emphasizes. Lastly, the research shows that AI can be the base of the financial ecosystem which can be more adaptable, efficient, and secure, giving valuable info to the academics, the professionals in the industry, and the policymakers.International Journal of Business Reflections00The research shows that AI can be the base of the financial ecosystem which can be more adaptable, efficient, and secure, giving valuable info to the academics, the professionals in the industry, and the policymakers.International Journal of Business Reflections[]2024-06-30T00:00:00 e45631a47cbae74641d958761a5c88031519bcdaThe change of society has continued since the first industrial revolution, and society has been required to adapt to the revolution. However, the 4th Industrial Revolution based on artificial intelligence is different from other revolutions. This is because as artificial intelligence replaces ‘human workers’, the right to work, which is closely related to survival, is being violated. In addition, constant and indiscriminate collection of information will undermine the right to self- determination that must be guaranteed under the constitution, and labor’s three primary rights, which have been exercised for better working conditions so as not to be replaced by artificial intelligence, may become nominal. Since the introduction of artificial intelligence into the labor field has a direct and indirect effect on workers, research in terms of labor law should be actively conducted, but it was not. Workers' protection measures are not conducted only to protect individuals. Nation must respond with policies at the national level to prevent large-scale unemployment, polarization, and consequent greater social costs due to the introduction of artificial intelligence technology. Recently, the European Union passed the world's first artificial intelligence law to protect workers' basic rights, health, and safety from artificial intelligence. However, due to the comprehensive regulatory method, no special measures are seen for the protection of workers' rights. Therefore, this paper examines the current status and problems of artificial intelligence use in the labor field, and examines the establishment of an artificial intelligence impact assessment as a way to protect workers' rights.The Korea Association for Corruption Studies00The Korea Association for Corruption Studies[]2024-06-30T00:00:00 6807a8f7172b533cf559abc6477d317ae0d20c1aThe current writing aims at bringing forward the ethical matters related to AI (artificial intelligence) integration in healthcare proceeding, particularly in therapy care decision-making process.Journal of Artificial Intelligence &amp; Cloud Computing00Journal of Artificial Intelligence &amp; Cloud Computing[]2024-06-30T00:00:00 b4d3fab1e94466699d72003b7bddd5072c98a6a0The urban landscape is rapidly evolving towards sustainable, energy-efficient practices, thanks in large part to theintegration of artificial intelligence (AI) solutions. This study explores the dynamic transition to AI-basedtechnologies for improving energy efficiency in urban environments. AI is emerging as a powerful tool foroptimizing energy consumption, streamlining infrastructure operations and fostering smart, resilient urbanecosystems. The study examines various applications of AI, highlighting the essential role advanced technologiesplay in revolutionizing conventional approaches to urban energy management, and illustrating how thesetechnologies can contribute to the creation of self-sufficient urban spaces. It examines the implementation of AIbased energy networks, smart energy storage solutions and the integration of renewable energy into urbanplanning, and also looks at the potential challenges and ethical considerations associated with its adoption. Itaddresses issues such as data privacy, algorithmic biases and the need for transparent decision-making processes.This research is a literature review that aims to contribute to the current discourse on creating smart, energyefficient cities.Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism-ICCAUA450The dynamic transition to AI-based technologies for improving energy efficiency in urban environments is explored, highlighting the essential role advanced technologies play in revolutionizing conventional approaches to urban energy management, and illustrating how the set of technologies can contribute to the creation of self-sufficient urban spaces.Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism-ICCAUA["Ikram Menai", "Hana Salah Salah", "Sara Khelil", "Amina Aidaoui", "F. Djouad"]2024-06-30T00:00:00 860c8de8ce614124a2b3bd1e4298dfd2c064ddf2The Artificial intelligence (AI) is transforming path way for nursing in this digital era by introducing advanced technologies that enhance patient care and improve efficiency of care. In this Digital Era how AI is redefining standards of care in nursing by assisting with tasks such as patient monitoring, data analysis, and decision-making. Through the integration of AI, nurses can provide more accurate and timely care, ultimately improving patient outcomes and satisfaction. The study highlights various work of AI applications in nursing and discusses the potential benefits scopes and challenges associated with their implementation. Also, the AI has created a biggest impact on routine task of Nurses and reduced the overload. This advancements in the field of nursing are not only enhancing the quality of care but also redefining the nursing profession by making it more efficient.International Journal For Multidisciplinary Research50Improvements in the field of nursing are not only enhancing the quality of care but also redefining the nursing profession by making it more efficient.International Journal For Multidisciplinary Research["Ravliya Urmila", "Chisla Unnati"]2024-06-30T00:00:00 060b701ea63d1e742b61d67bd4892c5977059cb9Artificial intelligence (AI) has emerged as a transformative force in the banking sector, particularly in India, where rapid technological advancements are reshaping financial services. The relevance of AI in the current context lies in its ability to enhance operational efficiency, improve customer satisfaction, and promote financial inclusion, addressing the evolving needs of a digital-first economy. This study aims to examine the role of AI in revolutionizing Indian banking firms by analyzing its impact on key performance indicators such as operational efficiency, customer satisfaction, and financial inclusion. A descriptive research methodology was employed, with primary data collected through a structured questionnaire using a 5-point Likert scale and secondary data from reliable sources. The study focused on ten leading Indian banks with high AI adoption, with 25 respondents from each bank. Statistical tools such as descriptive analysis, correlation, and ANOVA were used to analyze the data. The findings reveal a strong positive correlation between AI adoption and improved performance metrics, with significant differences observed among banks in their AI implementation effectiveness. Banks like HDFC and Kotak Mahindra demonstrate superior outcomes, highlighting the importance of strategic AI integration. The study concludes that AI adoption is pivotal for future-ready banking, offering actionable insights for enhancing efficiency and inclusivity.ShodhKosh Journal of Visual and Performing Arts150It is concluded that AI adoption is pivotal for future-ready banking, offering actionable insights for enhancing efficiency and inclusivity.ShodhKosh: Journal of Visual and Performing Arts["Ramesh K V"]2024-06-30T00:00:00 207eddbfa5798cd5a9eb0e52820912f99ccf3912This paper aims to explore the similarities and differences of artificial intelligence education policies and strategic layout in China, the United States and Mongolia. First of all, by analyzing the evolution of AI education policies in the three countries, it is found that China focuses on the combination of technology introduction and independent research and development, the United States emphasizes industry-university-research cooperation and innovation, and Mongolia focuses on infrastructure construction and teacher training. Then, the author makes a comparative analysis of the three countries' AI educational policies, and points out that the United States and China have achieved remarkable results in the promotion of educational modernization and the improvement of educational quality. Finally, on the basis of comparative analysis results, This article gives some inspiration and suggestions for developing and implementing AI educational policy in the future.Journal of Educational Research and Policies100A comparative analysis of the three countries' AI educational policies is made, and it is pointed out that the United States and China have achieved remarkable results in the promotion of educational modernization and the improvement of educational quality.Journal of Educational Research and Policies["Hao Li", "Munkhjargal Davaasuren", "Naranchimeg Dorjpalam"]2024-06-30T00:00:00 3beaeaedf443606ed1c5c36e95b8a559b2d96434The article attempts to assess the relationship between artificial intelligence (AI), which has become an important part of the life of modern society, and substantial (essential, most significant for humans as a species) values. The relevance of the study is determined by the widespread use of AI and the discussions it constantly causes. On the basis of interdisciplinary, systemic, value-based, dialectical, cultural-historical approaches using methods of observation, analysis, interpretation and others, it was revealed that substantial values ​​are significantly influenced by the spread of AI, but at the same time do not undergo of significant transformations. A number of values ​​(life, freedom of choice, human identity, subjectivity, the possibility of creativity) do not experience real threats, but in the context of the spread of AI they are beginning to be valued higher. Other values ​​(individuality, collectivism, family values, comfort, pleasure) are developing progressively under the influence of AI. Only one substantial value (security) experiences real threats, but its significance for a person only increases as a result. The conclusion is drawn about the significant, but not transformational, influence of AI on the system of substantial values, and therefore about its correspondence to this system.Russian Studies in Culture and Society00It was revealed that substantial values are significantly influenced by the spread of AI, but at the same time do not undergo of significant transformations, and therefore about its correspondence to this system.Russian Studies in Culture and Society["Evgeniya B. Belikova"]2024-06-30T00:00:00 4207b0bdccd9d064a77c026699757cbf3e5687f6This article describes the evidence-based development of online learning resources on Artificial Intelligence (AI) in vocational education and training. Within the target group and context analysis of the AI_VET project, guided interviews with N = 48 stakeholders from vocational education and training were conducted and analyzed. The results reveal an ambiguous and rather superficial understanding of AI within the target group as well as a limited incorporation of AI into vocational practice despite a broad consensus regarding its increasing relevance. Recommendations for the design of online learning resources include a focus on AI basics to promote a foundational understanding, consideration of opportunities, risks and limitations in professional contexts, and the use of specific application examples. Building on this, the AI_VET course series was implemented through a participatory approach. Initial evaluation results suggest differentiated usage of the course modules and acceptance among learners. The article concludes with a discussion of implications for the further integration of AI in vocational education and training, as well as the positioning of the developed offerings in AI education.Teaching and learning with and about artificial intelligence in vocational education and training00Recommendations for the design of online learning resources on Artificial Intelligence in vocational education and training include a focus on AI basics to promote a foundational understanding, consideration of opportunities, risks and limitations in professional contexts, and the use of specific application examples.Teaching and learning with and about artificial intelligence in vocational education and training["Marc Egloffstein", "Kristina K\u00f6gler", "Dirk Ifenthaler"]2024-06-30T00:00:00 48dcee5cc4516d0b63f11eb8230a7ffc14d21576The recent development of artificial intelligence (AI) technology is bringing about changes at a faster pace and on a larger scale than any other period in human history. With technological advancements overcoming the limitations of medical AI through training with databases, AI technology has made remarkable progress since the inception of deep learning for image processing with convolutional neural networks (CNN) in 2012. The recent advancements in natural language processing (NLP) have accelerated the utilization of AI through sophisticated natural language processing, enabling machines to identify and understand data regardless of the complexity of the language. This has laid the foundation for the rapid and precise development of generative AI. In the era where generative AI is being utilized without pausing in its developmental speed, we considered the civil liability of AI in our civil law principles, taking into account the inherent characteristics of AI such as unpredictability, opacity, and the black box effect. To do this, we first examined the legal liability considering the stages of AI technology development in discussing the tort liability caused by AI. Even “Weak AI,” created by AI developers, may fall under “Gefahr,” and while not all types, some may apply to strict liability in terms of risk liability. Furthermore, while reviewing civil liability applicable to AI under fault-based and no-fault liability, we also looked at the trends in the EU comparatively. In discussing no-fault liability, particularly under the Product Liability Act, we examined the possibility and implications of applying risk liability to pharmaceutical manufacturing using generative AI technology as a representative example to overcome the limitations of the existing Product Liability Act. Humanity currently lives in an era of rapid technological development and exploding big data, enjoying numerous benefits due to these advancements. As user convenience improves and massive added value is created through technological progress, the meaning of risk liability in the realm of civil liability can gain more significance. Generative AI has already drastically reduced the costs and time required for new drug development, providing substantial profits to pharmaceutical companies. However, even if the existing Product Liability Act is applied, it may be difficult to adequately remedy the harm to victims due to the reasonable alternative possibility defense regarding design defects. In the era of generative AI, we examined the possibility of applying enhanced risk liability by assuming the case of pharmaceutical manufacturing.The Korean Association of Civil Law00The civil liability of AI was considered in the authors' civil law principles, taking into account the inherent characteristics of AI such as unpredictability, opacity, and the black box effect, and the possibility of applying enhanced risk liability by assuming the case of pharmaceutical manufacturing.The Korean Association of Civil Law["Sookyoung Lee"]2024-06-30T00:00:00 ca41ac4f91e201ea2a96d5bee353151337363399General practices (GPs), called family physicians in certain countries, are the cornerstone of primary health care. The increase in average lifespan and, thereby, the number of chronic diseases has recently increased the workload of GPs and decreased the time spent on the patient. Implementations of Artificial intelligence (AI)-powered systems are essential in GPs to facilitate the jobs of health professionals. Implementing AI-driven systems is expected to help health professionals diagnose and treat. AI involves the machine simulation of human cognitive capabilities, encompassing a range of technologies, including deep learning and machine learning. AI is currently being used across various applications in medicine and continues to evolve, and its role in medicine is expected to become increasingly prominent. AI-enhance sensor systems can continuously monitor physiological parameters and generate personalized medicinal therapy. However, the employment of AI in GPs is still in the very early phase. AI is a tool to aid healthcare professionals in improving the accuracy and speed of diagnosis rather than a replacement for their expertise. This review will focus on applying artificial intelligence in general practices (GPs).Arsiv Kaynak Tarama Dergisi500This review will focus on applying artificial intelligence in general practices (GPs), a tool to aid healthcare professionals in improving the accuracy and speed of diagnosis rather than a replacement for their expertise.Arşiv Kaynak Tarama Dergisi["Ebru U\u011fra\u015f Tiryaki", "Erhan \u015eim\u015fek"]2024-06-30T00:00:00 b2d825dff98d28707860187dc64973280e1e1541The problem facing people around the world is the increasing difficulty of distinguishing between facts, opinions, truth, and disinformation. Technology and access to various news sources have exacerbated this problem. There are no regulations on social media, so anyone can say anything, whether it is true or false. This article discusses the latest technological developments that make it difficult for people to determine what is true. This is artificial intelligence (AI). In this article, we will look at artificial intelligence from the research question: What is reality in an unreal world. There are two sections, the first is devoted to theories and the second to the case of Iraq. The contribution of this is that it goes beyond the study of fake news and includes the study of deepfakes. These are fakes in images, videos, or audio recordings. The methodology is deductive based on qualitative data, where the relevance and innovation of the link between theory and the case of Iraq lies in detailing key aspects of conflict management and resolution. Namely, the ways in which disinformation, fake news and deepfakes are used as weapons in sectarian religious conflicts, manipulated to influence elections and thereby exacerbate social divisions, reduce trust in institutions and authorities, and undermine journalism and credible news sources.Media i Społeczeństwo00Artificial intelligence is looked at from the research question: What is reality in an unreal world and the contribution of this is that it goes beyond the study of fake news and includes the study of deepfakes.Media i Społeczeństwo["Glen Segell"]2024-06-30T00:00:00 556843759c38454594d3a26f726d26fb55f16892AI literacy has emerged as a crucial aspect of digital literacy research in the field of education. Currently, there are limited studies about the implications of Artificial Intelligence (AI) in Early Childhood Education (ECE). Owing to the recent development of curricula for young learners in industrialized nations, developing countries are hesitant to adopt AI at the ECE level. A scoping review was undertaken on the content of fourteen research articles published between 2016 and 2023. This scoping review evaluates and reviews the contents of fourteen papers on the knowledge and comprehension of AI in ECE, which covers curriculum design, artificial intelligence tools, instructional methodologies, research designs, evaluation methods, findings, and various types of possibilities and problems linked to AI literacy and content. Several obstacles were identified, including (1) an insufficiently designed curriculum, (2) lacking instructors' understanding, experience, and trust in AI, and (3) the lack of an instruction manual. Engaging in reading can offer educational possibilities and foster the growth of AI literacy in young learners, encompassing AI concepts, actions, and perspectives. This study recommended AI literacy for the educators and learners of ECE to be suitable for their age group and level.Qlantic Journal of Social Sciences00This study recommended AI literacy for the educators and learners of ECE to be suitable for their age group and level and identified several obstacles, including an insufficiently designed curriculum, lacking instructors' understanding, experience, and trust in AI, and the lack of an instruction manual.Qlantic Journal of Social Sciences["Rukhsana Durrani", "Arshad Iqbal", "Humaira Akram"]2024-06-30T00:00:00 deb08443ff20c3fb72dd789f7d9552ad09f45b0aAbstract: “The integration of artificial intelligence (AI) and robotics is profoundly transforming the employment landscape across various industries. This paper examines the extensive impacts of these technologies on the workforce, highlighting key areas such as the automation of routine tasks, augmentation of human capabilities, creation of new job sectors, necessity for skills shift and reskilling, as well as the resultant job polarization. It delves into the ethical, social, and global implications of these changes, emphasizing the disparity between different socio-economic groups and geographical regions. The paper advocates for proactive, collaborative policy responses from governments, educational institutions, and industry leaders to mitigate adverse effects while enhancing the benefits of technological advancements. Through a comprehensive analysis, this study underscores the critical need for strategic planning and regulation to navigate the challenges and harness the opportunities presented by AI and robotics in the evolving employment landscapeInternational Journal for Research in Applied Science and Engineering Technology00Through a comprehensive analysis, this study underscores the critical need for strategic planning and regulation to navigate the challenges and harness the opportunities presented by AI and robotics in the evolving employment landscape.International Journal for Research in Applied Science and Engineering Technology["Harsh R. Mishra"]2024-06-30T00:00:00 d7c3afa1a7dc4d600a00d74279857cd298802e4dThe present chapter examines the innovative capabilities of Artificial intelligence (AI) systems in contrast to human writing creativity. With the fast improvements in AI generation, there`s an ongoing debate regarding whether or not AI can shape or maybe surpass human creativity in numerous domain names, consisting of writing. This look explores the present-day country of AI-generated writing and its capability impact on human creativity, highlighting the strengths and limitations of both AI and human writers. Through a comparative evaluation of AI-generated texts and human-authored works, we propose to shed light on the precise traits, innovative strategies, and implications of AI in the realm of writing creativity.BSSS Journal of Computer00Through a comparative evaluation of AI-generated texts and human-authored works, it is proposed to shed light on the precise traits, innovative strategies, and implications of AI in the realm of writing creativity.BSSS Journal of Computer["Dr Bharti Joshi", "Ms Tanuja Khan"]2024-06-30T00:00:00 67bf64b91f2f29b513e18745af66064acc85e97eArtificial intelligence(AI) is a sensation that currently stimuluses every phase of life. AI applications already started totransform the business methods in different disciplines. The complex nature of the practice makes architecture a importantarea of research and experiment for AI claims and applications. This paper presents a study that evaluates the methods ofAI in architecture from an educational standpoint. It includes existing executions and probable future approaches fromdiverse domains and areas of theory and practice that might be useful for the development of architectural education. TheStudy will also cover Essential evaluation on how the dependability upon the tools and technics against the creative thinkingis affecting the desired level of outcome. This analysis will also give the clue of how much integration of AI is necessary forspecific design process. The objective of this research is to define how AI tools and architectural knowledge can be integratedto learn and practice architecture. If architects can use the opportunity to utilize AI in various phases of design andconstruction, the nature of the profession will change irreversibly. This process should be started right from the educationand it should be implemented first as a core pedagogical strategy.Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism-ICCAUA160The objective of this research is to define how AI tools and architectural knowledge can be integrated to learn and practice architecture.Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism-ICCAUA["Maharishi Thula"]2024-06-30T00:00:00 32214982e66548b0d62e7e80c33896d715583f90The electrocardiogram is a test that records the electrical activity of the heart, and has recently been shown that it can also detect other non-cardiovascular conditions, such as diabetes, measles, Alzheimer’s, arterial hypertension, fatty liver, hiperpotasemia, hypothyroidism, malaria, etc. For this reason, this paper proposes a computational technique to analyze and detect patterns based on the position and length of the segments between the peaks that make up the electrocardiogram signals, using deep learning techniques created by Artificial Intelligence. The program started by evaluating a database of heart arrhythmia signals, which included more than 120 electrocardiograms grouped between signals from normal and arrhythmia patients, employing convolutional neural network (CNN). The program had a prediction accuracy rate of 94.3%.Journal of Biomedical Sciences and Biotechnology Research240A computational technique to analyze and detect patterns based on the position and length of the segments between the peaks that make up the electrocardiogram signals, using deep learning techniques created by Artificial Intelligence is proposed.Journal of Biomedical Sciences and Biotechnology Research["Raul Isea"]2024-06-30T00:00:00 2e054816bb48c2fc9c6f535f9b2292989a66d42fArtificial intelligence (AI) is transforming paediatric diabetes management, offering innovative solutions for monitoring, treatment, and prediction. This mini-review explores how AI is being utilised to improve the care of children with diabetes mellitus, focusing on its application in glucose monitoring systems, predictive algorithms, and personalised treatment plans. The study synthesises recent advancements in AI technologies, examining their impact on enhancing the accuracy of diagnosis, reducing the burden on healthcare providers, and improving patient outcomes. Through a systematic review of the literature, key AI tools and models that have shown promise in paediatric diabetes care are identified. The findings highlight the potential of AI to revolutionise diabetes management, with implications for both clinical practice and future research. However, challenges remain in ensuring the ethical implementation and integration of these technologies into existing healthcare systems. The paper concludes with recommendations for advancing AI applications in this field, emphasising the need for continued innovation and collaboration between healthcare professionals and AI developers.The Journal General Health and Pharmaceutical Sciences Research470This mini-review explores how AI is being utilised to improve the care of children with diabetes mellitus, focusing on its application in glucose monitoring systems, predictive algorithms, and personalised treatment plans.The Journal General Health and Pharmaceutical Sciences Research["Reina Melani", "Galih Samodra", "R. Al-Hakim"]2024-06-30T00:00:00 b87e5732336b295119b19186fb7713a4cfec68feThe article examines the impact of artificial intelligence on public administration and its role in achieving sustainable development. The authors analyze how the introduction of AI can contribute to increased efficiency, transparency, and accountability in the public sector, focusing on the importance of artificial intelligence in the fight against corruption, ensuring security, and optimizing administrative processes. The article focuses on the role of artificial intelligence in public administration and its impact on achieving the Sustainable Development Goals. The authors analyze how AI can contribute to increasing efficiency, transparency, and accountability in public administration, as well as affect economic development and social justice. The analysis is based on the methods of analysis and synthesis, historical method, formal legal method, and systemic method. These methods allow for in-depth research of the topic, revealing its complexity and formulating conclusions and recommendations. The authors demonstrate that AI is making a significant contribution to various aspects of public administration, including automating citizen services, analyzing large amounts of data, forecasting demand for services, and evaluating political effectiveness. The authors also discuss potential issues related to privacy, security, ethical considerations, and accuracy of AI. They point out the need to create conditions for the effective implementation of AI in public administration, including the adaptation of international standards and practices to the Ukrainian context. They consider potential challenges and problems associated with the use of AI, such as privacy, security, ethics, accuracy, and bias, as well as the risks of technology dependence. An important part of the article is the consideration of international examples of AI application in public administration that can serve as a model for Ukraine. Ultimately, the article emphasizes the importance of developing strategies and approaches that facilitate the integration of AI into public administration that meets Ukraine's national interests and is open to innovation and technological progress.Salud, Ciencia y Tecnología120The authors analyze how the introduction of AI can contribute to increased efficiency, transparency, and accountability in the public sector, focusing on the importance of artificial intelligence in the fight against corruption, ensuring security, and optimizing administrative processes.Salud, Ciencia y Tecnología - Serie de Conferencias["Olga Cholyshkina", "Anna Karnaukh", "Olha Volianiuk", "Maryna Ostapenko", "Anastasiia Holishevska"]2024-06-30T00:00:00 f3e2b9dc85c632cb4db5b84311695bac555196abThe goal of writing this article is to explore the impact of artificial intelligence on enhancing efficiency and ensuring the competitiveness of insurance companies, as well as to uncover the opportunities and advantages, downsides, and challenges of using digital technologies in social insurance and insurance. Digitalization in this field occurs under the influence of globalization, technological innovations, and changes in demographic trends. The study pays special attention to the impact of digital technologies on insurance processes, including the use of big data and artificial intelligence for personalizing insurance products and optimizing risk assessment and claim processing procedures. The research also focuses on analyzing the effects of these changes on social equity and the accessibility of insurance services for different population strata. This article provides a comprehensive view of the trends and prospects for the development of social insurance and insurance, highlighting both theoretical and practical aspects of reforming these systems in the context of modern socio-economic challenges. The research shows that companies that reject innovation will face higher costs in the future compared to those actively implementing new developments. These companies will also face challenges in attracting high-risk clients, whereas innovations help attract more reliable clients who benefit from personalized products. This article demonstrates that through digitalization of insurance, clients become more satisfied and interested in insurance services, which leads to increased insurance premium revenues. Moreover, digital technologies help reduce costs by optimizing decision-making and reducing risks, thereby enhancing the efficiency of insurance operations and strengthening the competitive advantages of insurers. The advantages of digitization and the use of AI include improved accuracy of risk assessment, process automation, product personalization, fraud detection, and enhanced investment decision-making. The downsides may include data confidentiality issues, the exclusion of the human factor, the risk of algorithm bias, implementation complexity and high costs, and the threat of unemployment.Sociedad y Economía180This article demonstrates that through digitalization of insurance, clients become more satisfied and interested in insurance services, which leads to increased insurance premium revenues, thereby enhancing the efficiency of insurance operations and strengthening the competitive advantages of insurers.Social Economics["Elizaveta Pletneva", "Daria Zagorska"]2024-06-30T00:00:00 df796480acd71e4bcabc2f4bb308f7f6bfb3d3ceThey discovered that habitat degradation is a threat that impacts ecosystems and people across the globe. Therefore, as habitats decrease and species are threatened, improved strategies for managing such effects are inevitable. This paper focuses on how the loss of biological diversity may be complemented by applying artificial intelligence (AI). It stresses its role in monitoring and modeling the environment and the trends geared towards its saving. On top of the more specific use of AI for species counting and monitoring, change detection, and decision-making, the analysis provides an understanding of how AI technologies work in practice and how conservationists can benefit from it. This paper also outlines some of the critical threats related to AI opportunities in conservation and proposes strategies to address these threats. Therefore, it can be concluded that it is necessary and adequate to incorporate technology into their conservation measures to mitigate the global loss of biological diversity.Darpan International Research Analysis240It can be concluded that it is necessary and adequate to incorporate technology into their conservation measures to mitigate the global loss of biological diversity.Darpan International Research Analysis["V. Gunnam"]2024-06-30T00:00:00 85f7af9d06b4444da88799449f69deee9c42f720This study investigates the complicated relationship between consumers and artificial intelligence (AI), precisely exploring the impact of tailored data (AI) on purchasing decisions. Drawing from a diverse sample of social media users in top five HEC ranked universities, the study investigates how factors such as privacy reservations and positive past experiences intersect with AI-driven personalization, impact consumer attitudes and willingness to make purchases online. The researcher applied survey method to collect data from 346 participants, primarily social media users, with the focus on those regularly engaging with the social media advertising and AI-based applications. This research tested three hypotheses wherein findings show significant influence of tailored data on consumer purchasing willingness and positive past experiences with AI ads. The privacy concerns, however, were found to have an insignificant impact on the online buying decisions. In this linking, these insights contribute to broader discourse on AI's role in consumer behavior, offering practical implications for various businesses navigating integration of artificial intelligence technologies in marketing strategies.JUNE00This research tested three hypotheses wherein findings show significant influence of tailored data on consumer purchasing willingness and positive past experiences with AI ads, and the privacy concerns were found to have an insignificant impact on the online buying decisions.JUNE 2024["Muhammad Farrukh", "Sundus Rafi", "Parsa Chand"]2024-06-30T00:00:00 00348a164e5a14446fbbcd39be2d3bb37b47dbe0One kind of artificial intelligence technology called generative AI is used to create new text, picture, audio, and video material. It may be used for many different things in education, such creating material, enhancing data, personalizing learning, simulating situations, and providing training. It also raises moral questions about prejudices, veracity, false information, intellectual property, loss of employment, and potential future developments like more realism and responsiveness. Content creation, personalized learning, administrative work automation, interactive learning environments, feedback and evaluation, natural language processing (NLP), forecasting and prediction, and collaborative learning are some of the educational applications of generative AI approaches. These technological advancements are intended to improve educational opportunities, streamline administrative duties, and provide individualized course materials. But there are still issues to be resolved, like protecting data privacy, managing human - AI interaction well, and preventing biases in information produced by AI. The process of creating a generative AI system for teaching includes gathering data, choosing a model, training it, and deploying it. Although scalable, personalized, and engaging learning solutions offered by generative AI hold great promise for revolutionizing education, there are a number of drawbacks that may restrict the technology's applicability and prevent it from being widely used. The difficulty of maintaining and upgrading these systems, ethical and privacy problems, and the caliber and bias of the produced information are examples of technical constraints. The applications, legal frameworks, and social consequences of generative AI will be shaped by its technological limits. To fully realize the benefits of AI in education, issues including data privacy breaches, possible bias in AI systems, and the digital divide must be resolved.Journal of Research in Vocational Education210To fully realize the benefits of AI in education, issues including data privacy breaches, possible bias in AI systems, and the digital divide must be resolved.Journal of Research in Vocational Education["Prabal Pratap Singh"]2024-06-30T00:00:00 a7f669864a824ddec6ab88b5aee58e1ded3a9059Foreign language acquisition within a highly interconnected context such as Europe is a complex challenge that can be influenced by many variables. This article presents TRIP a-Bike, an innovative didactic device designed to facilitate foreign language teaching for professionals and university students that combines artificial intelligence (AI) and gamification with an embodied based approach. TRIP a-Bike consists of an App for smartphones that integrates didactics with a video game, which in addition to offering the possibility of remote use, can be connected to a bicycle fixed to the ground and an LCD screen via a smartphone holder placed on the vehicle’s handlebar and a switch. Through physical interaction with the bicycle and the virtual environment, students can learn a new language in a dynamic and experiential way, while the AI generates customized content and activities based on their progress. The paper discusses the potential benefits of integrating AI and embodied theories and highlights how this system could represent an innovative and multidisciplinary educational research space.   TRIP a-Bike: un nuovo sistema per insegnare le lingue tra corpo ed intelligenza artificiale. L’acquisizione di una lingua straniera all’interno di un contesto altamente interconnesso quale quello europeo, rappresenta una sfida complessa che può essere influenzata da tante variabili. Questo articolo presenta TRIP a-Bike, un dispositivo didattico innovativo pensato per facilitare l’insegnamento delle lingue straniere ai professionisti e agli studenti universitari che combina l’intelligenza artificiale (IA) e la gamification con un approccio embodied based. TRIP a-Bike consiste in un App per smartphone che integra la didattica ad un videogame, che oltre ad offrire la possibilità di utilizzo da remoto, può essere collegata a una bicicletta fissata al suolo e a uno schermo LCD mediante un supporto per smartphone posto sul manubrio del veicolo ed uno switch. Attraverso l’interazione fisica con la bicicletta e l’ambiente virtuale, gli studenti possono apprendere una nuova lingua in modo dinamico ed esperienziale, mentre l’IA genera contenuti e attività personalizzate in base ai loro progressi. L’articolo discute i potenziali benefici dell’integrazione tra IA ed embodied theories e sottolinea come questo sistema possa rappresentare uno spazio di ricerca didattico innovativo e multidisciplinare.Form@re : Open Journal per la Formazione in Rete490Form@re - Open Journal per la formazione in rete["Santolo Ciccarelli", "Francesco V. Ferraro", "Maria Giovanna Tafuri"]2024-06-30T00:00:00 ee08a5fb6558d1a582e3c54565c89aefcd5280deWorkshop Media Pembelajaran Digital Bagi Guru dengan Teknologi AI (Artificial Intelligence) adalah sebuah program pengabdian masyarakat yang bertujuan meningkatkan keterampilan guru di SMKN 2 Kota Palopo, Palopo, dalam memanfaatkan teknologi kecerdasan buatan (AI) untuk meningkatkan proses pembelajaran. Fokus utama workshop ini adalah pengembangan kemampuan guru dalam menciptakan materi pembelajaran yang interaktif dan personal, dengan menggunakan berbagai alat dan aplikasi berbasis AI. Selain aspek praktisnya, workshop ini juga membahas konsep dasar AI, etika penggunaannya, dan cara mengatasi berbagai tantangan yang mungkin muncul. Metode yang digunakan dalam workshop ini adalah demonstrasi langsung dan ceramah. Hasilnya, peserta workshop dapat berkolaborasi dalam sesi interaktif, berbagi pengalaman, dan menggali potensi teknologi AI untuk meningkatkan efektivitas pembelajaran di kelas. Diharapkan bahwa para guru akan dapat mengintegrasikan teknologi AI secara kreatif dan etis, sehingga menciptakan lingkungan pembelajaran yang responsif dan inovatif. Workshop ini merupakan langkah penting dalam mempersiapkan pendidik menghadapi tuntutan pendidikan abad ke-21 dan memaksimalkan manfaat teknologi AI dalam proses.Abdimas Langkanae70Abdimas Langkanae["Safwan Kasma", "A. Syukur", "Hardiana", "Suhardi", "Lis Sugianto", "M. Hamzah"]2024-06-30T00:00:00 2f1984abfdf8e62f018704032c9cead3d7a02ef4There is a growing interest in artificial Intelligence (AI) as a research topic. Adapting to AI technologies has become essential for educational institutions, specifically educational leaderships in order to embrace AI trends in enhancing leadership practices. This study comes in response to the increased discussions of AI implementing in education, which has created the need for AI Maturity Model in education to help educational institutions assess their progress in AI adaptation. This paper investigated how are the leaderships at college of Artsand Social Sciences Departments in Sultan Qaboos University (SQU) in Oman, as a case study, has adapted AI technologies throughout AI Maturity lens. The study aims to assess the college AI Maturity level and the qualitative approach was used to collect the data via semi-structured interviews with the college's heads and decision makers. The results showed that the leaderships at the college are updated with the AI developments, however, the academic departments are still in their early stages of using AI technologies as AI is still an emerging topic. The leaderships have good awareness of the importance of integrating AI in teaching-learning process at higher education institutions' level and the effects of adapting such technologies. Through in-depth interviews and the qualitative data analysis, one key finding was that College of Arts and Social Science at SQU is establishing good infrastructure for AI revolution at higher education according to the AI 5Ds cycle as there are some significant efforts to raise the awareness of the importance and role of AI in higher education and some individual AI implementations. However, more clear plans and work are needed for the move to the next phases of applying AI technologies in HE.Journal of Techno-Social00One key finding was that College of Arts and Social Science at SQU is establishing good infrastructure for AI revolution at higher education according to the AI 5Ds cycle as there are some significant efforts to raise the awareness of the importance and role of AI in higher education and some individual AI implementations.Journal of Techno-Social["Samiya Al-Hinaai", "Khalfan Al-Hijji", "Faten Hamad"]2024-06-30T00:00:00 ce0a52504b72208cd46a47ed57222d64b9eb9613The F-16 aircraft, widely used by the Polish Army Air Force, requires modifications based on Artificial Intelligence (AI) algorithms to enhance its combat capabilities and performance. This study aims to develop comprehensive guidelines for this purpose by first describing F-16 systems and categorizing AI algorithms. Machine learning, deep learning, fuzzy logic, evolutionary algorithms, and swarm intelligence are reviewed for their potential applications in modern aircraft. Subsequently, specific algorithms applicable to F-16 systems are identified, with conclusions drawn on their suitability based on system features. The resultant analysis informs potential F-16 modifications and anticipates future AI applications in military aircraft, facilitating the guidance of new algorithmic developments and offering benefits to similar aircraft types. Moreover, directions for future research and development work are delineated.Scientific Journal of Silesian University of Technology. Series Transport00The F-16 aircraft, widely used by the Polish Army Air Force, requires modifications based on Artificial Intelligence algorithms to enhance its combat capabilities and performance and comprehensive guidelines are developed by first describing F-16 systems and categorizing AI algorithms.Scientific Journal of Silesian University of Technology. Series Transport["Tomasz Krawczyk", "Mateusz Papis", "R. Bielawski", "W. Rz\u0105dkowski"]2024-06-30T00:00:00 64728b9909b13a29cd7e09a5efabf62b54d80f5cAim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.Exploration of Digital Health Technologies240Harmonious Understanding of Machine Learning Analytics Network is proposed, a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine.Exploration of Digital Health Technologies["Neha Deo", "F. Nawaz", "Clea du Toit", "T. Tran", "C. Mamillapalli", "Piyush Mathur", "S. Reddy", "Shyam Visweswaran", "Thanga Prabhu", "Khalid Moidu", "S. Padmanabhan", "Rahul Kashyap"]2024-06-30T00:00:00 a879e4f704904ebbc8c0c04a14b9c251facdc97dAs of 2024, there are six institutions of higher education in Singapore. The article presents the results of an analysis of the academic integrity policies of all six Singapore universities in the context of using artificial intelligence (AI) in education. The trend towards updating institutional policies of higher educational institutions in Singapore is driven by high academic standards and strict requirements for ensuring the quality of higher education in the country, as evidenced by international rankings of the best higher educational establishments. In addition, educational institutions in Singapore actively invest in scientific research and technological innovations, leading to the dynamic implementation of AI technologies in teaching and learning processes, which requires an update of institutional policies. Therefore, the articleaimed to highlight the features of academic integrity norms and recommendations for usinggenerative AI in Singapore's higher education context. The analysis highlighted various approaches to academic integrity and plagiarism issues, as well as showed a trend towards adaptation to modern technologies, including AI, in their policies. It is worth noting that the institutional policies of Singapore universities regarding the use of generative AI set a trend not only topenalize unauthorized use but also toserve as a source of support and assistance to students and teachers in effectivelyusing these technologies. These features reflect the general trend towards increased regulation and control in using new technologies in the academic environment, emphasizing the importance of academic integrity in moderneducation. Along with this, there is a need to prepare scientific and pedagogical personnel for the ethical use of AI technologies to understand the needs of the modern generation of students and adapt to the pedagogical challenges associated with the rapid development of digital technologies and AI tools. The highlighted features may be useful for Ukrainian universities in the process of developing institutional policies for regulating the use of generative AI in education.International Scientific Journal of Universities and Leadership40The article presents the results of an analysis of the academic integrity policies of all six Singapore universities in the context of using artificial intelligence (AI) in education to highlight the features of academic integrity norms and recommendations for using generative AI in Singapore's higher education context.International Scientific Journal of Universities and Leadership["M. Shchedrina"]2024-06-30T00:00:00 534a2dcaa008a776b11e7d01da5fff6fde419716Artificial intelligence (AI) enabled customer relationship management (CRM) technologies are critical for increasing client satisfaction in the modern era of digitalization. They customize experiences, optimize processes, and offer predictive analytics for proactive service. Customers benefit from 24/7 support, which provides fast help. Data-driven insights boost strategy, whereas improved communication and scalability assure continuous service quality. Overall, AI CRM promotes strong customer interactions and long-term profitability. The objective of this research to investigate indispensable research question of what is the indirect impact of AI CRM on customer satisfaction with the mediation of customer service flexibility and service optimization by resource-based view theory. Philosophically, this research belongs post-positivist framework, with a deductive approach as the casual association among research variables are investigating by primary data through survey method by utilizing time-lagged design in the healthcare sector of Pakistan. The sample of this research is collected form purposefully selected 15 hospitals of different operating in three larger metropolitan cities of Punjab, Pakistan. This research utilized contemporary data utilized and analysis procedure. Data analysis is done by utilizing Smart PLS. This research found that AI CRM elevate the customer satisfaction with the mediation of customer service flexibility and service optimization. The association between AI CRM and company customer satisfaction is also examined. When contemplating the practical implications, managers and policymakers need to take into account the foundational structure to foster their customers.Business Review of Digital Revolution560This research found that AI CRM elevate the customer satisfaction with the mediation of customer service flexibility and service optimization with the mediation of resource-based view theory.Business Review of Digital Revolution["Humaira Sarwar", "Syed Ahsan Raza", "M. Saleem", "Faisal Mahmood"]2024-06-30T00:00:00 c63b89c473c19294ca44c3b9b3e7ab54e394f506This study aims to investigate the different views and responses of male and female Thai students regarding the advancement of artificial intelligence (AI) technology in the context of teacher training study programs in Islamic universities in Indonesia. Through a qualitative approach with a case study design, the study collected data through interviews, document studies, observations, and surveys. Data validity is guaranteed through triangulation and analysis using Miles and Huberman models. Involving Thai students at UIN Walisongo Semarang, this study found significant differences in responses to AI between genders. Women tend to support the use of existing AI for learning, while men are more interested in developing new technologies to overcome challenges. This difference is reflected in their views on the role of AI in improving access and quality of education as well as in concern for data privacy and security. In addition, the study highlights differences in engagement rates between genders, with women more open to the effective use of AI in learning, while men are more active in the development of AI technologies for education. These findings illustrate the influence of cultural, social, and psychological factors in the adoption and development of AI. This research contributes to finding the characteristics of men and women in responding to AI in Islamic higher education, and this plays a role in determining policies so that AI development can be utilized optimally by international students. Abstrak: Penelitian ini bertujuan untuk mengetahui perbedaan pandangan dan tanggapan mahasiswa Thailand laki-laki dan perempuan terhadap kemajuan teknologi kecerdasan buatan (AI) dalam konteks program studi keguruan di universitas-universitas Islam di Indonesia. Melalui pendekatan kualitatif dengan desain studi kasus, penelitian ini mengumpulkan data melalui wawancara, studi dokumen, observasi, dan survei. Keabsahan data dijamin melalui triangulasi, dan analisis menggunakan model Miles dan Huberman. Dengan melibatkan mahasiswa asal Thailand di UIN Walisongo Semarang, penelitian ini menemukan perbedaan signifikan respon terhadap AI antar gender. Perempuan cenderung mendukung penggunaan AI yang ada untuk pembelajaran, sementara laki-laki lebih tertarik mengembangkan teknologi baru untuk mengatasi tantangan. Perbedaan ini tercermin dalam pandangan mereka mengenai peran AI dalam meningkatkan akses dan kualitas pendidikan serta kepedulian terhadap privasi dan keamanan data. Selain itu, penelitian ini menyoroti perbedaan tingkat keterlibatan antar gender, dimana perempuan lebih terbuka terhadap penggunaan AI secara efektif dalam pembelajaran, sementara laki-laki lebih aktif dalam pengembangan teknologi AI untuk pendidikan. Temuan ini menggambarkan pengaruh faktor budaya, sosial, dan psikologis dalam adopsi dan pengembangan AI. Penelitian ini berkontribusi untuk menemukan karakteristik laki-laki dan perempuan dalam menyikapi AI di perguruan tinggi Islam, hal ini berperan dalam menentukan kebijakan agar pengembangan AI dapat dimanfaatkan secara maksimal oleh mahasiswa internasional.eL-HIKMAH Jurnal Kajian dan Penelitian Pendidikan Islam00eL-HIKMAH: Jurnal Kajian dan Penelitian Pendidikan Islam["Fihris", "Ninit Alfianika", "Nasikhin Nasikhin"]2024-06-30T00:00:00 ef89c04447961aad0ce3db051aa84698be663e03In recent years, age management practices and strategies have become widely diffused since today the valorization of people according to their age represents, a fundamental aspect for organisations, which are required to understand in depth the dynamics of generational belonging for a correct definition of their work and training policies. Population ageing is a phenomenon shared by many countries, especially those in Europe. In health care contexts, the ageing of workers and their co-habitation of organisational contexts with colleagues belonging to other generations demands effective equal opportunities for training and skills development policies to improve both the quality of work and the quality of services offered. To this purpose, communities of practice, including virtual ones, become a training device to be explored, especially looking at the latest developments in artificial intelligence.   Age management e formazione intergenerazionale in medicina. Intelligenza artificiale e comunità di pratica virtuali. Negli ultimi anni, le pratiche e le strategie di age management si sono largamente diffuse poiché la valorizzazione delle persone in funzione della loro età rappresenta, oggi, un aspetto fondamentale per le organizzazioni, chiamate a comprendere in modo approfondito le dinamiche dell’appartenenza generazionale per una corretta definizione e valorizzazione delle proprie politiche di lavoro e formazione. L’invecchiamento della popolazione è un fenomeno che accomuna molti paesi, soprattutto quelli europei. Nei contesti sanitari, l’invecchiamento degli operatori e la loro co-abitazione dei contesti organizzativi con colleghi appartenenti ad altre generazioni richiede politiche efficaci di pari opportunità e di formazione e sviluppo delle competenze per migliorare sia la qualità del lavoro sia la qualità dei servizi offerti. A tale scopo, le comunità di pratica, anche quelle virtuali, divengono un dispositivo formativo da esplorare, soprattutto guardando ai più recenti sviluppi dell’intelligenza artificiale.Form@re : Open Journal per la Formazione in Rete370Form@re - Open Journal per la formazione in rete["Francesca Marone", "Maria Navarra"]2024-06-30T00:00:00 73358f54476b569e0fd587959fe26093205c60a4The growing integration of artificial intelligence (AI) in digital healthcare services presents a promising opportunity for manage oral health of pets within the digital healthcare space. This study explores various applications of AI in management of oral health of pets and discusses the future market directions for this technology. In particular, the expansion of AI-based smart oral health management services and telemedicine offers pet owners the possibility of managing their pet’s oral health without direct face-to-face interactions with veterinarians. The development of dental imaging analysis and oral health management systems using AI technology is expected to aid in the early detection, prevention, and treatment of oral diseases in pets through rapid and accurate diagnosis. Additionally, AI can be utilizedin innovative tools such as genetic analysis and precision medicine to manage the oral health of pets. Therefore, AI can be leveraged in various digital healthcare fields to manage the oral health of pets. By considering aspects such as data privacy, accountability, reliability, and legal compliance, the quality of life for pet owners and their petscan be enhanced significantly.Korea Industrial Technology Convergence Society00This study explores various applications of AI in management of oral health of pets, the future market directions for this technology, and discusses the future market directions for this technology.Korea Industrial Technology Convergence Society["Jung-Yun Kang", "Jae-Kyo Moon", "Y. Cho", "Moonseon Jeon", "Kyuseok Kim"]2024-06-30T00:00:00 8cce0b76602c43558d40ad2aedd705d5da1e7492This paper explores the legal and regulatory challenges posed by AI's role in cybersecurity, specifically focusing on AI-driven cyber defense and offense mechanisms. It examines the current legal frameworks, highlights gaps, and proposes recommendations for regulating AI technologies in the cybersecurity domain. As artificial intelligence (AI) continues to revolutionize cybersecurity, it introduces both opportunities and legal challenges, particularly in AI-driven cyber defense and offense mechanisms. This article explores the legal implications of AI’s use in cybersecurity, examining the sufficiency of existing frameworks, the ethical concerns in AI-operated cyber attacks, and the accountability issues surrounding AI’s autonomous decisions. By analyzing current laws and identifying key gaps, this research highlights the need for updated legal frameworks to address the rapid advancements in AI-driven cybersecurity.ShodhKosh Journal of Visual and Performing Arts00The legal implications of AI’s use in cybersecurity are explored, examining the sufficiency of existing frameworks, the ethical concerns in AI-operated cyber attacks, and the accountability issues surrounding AI’s autonomous decisions.ShodhKosh: Journal of Visual and Performing Arts["Amit Jaiswal", "Prakash Chandra Mishra"]2024-06-30T00:00:00 41cb182c715d1b2cd95e6a12d3c366d15e8fd09aArtificial Intelligence (AI) is like teaching a computer to think for itself. It can then make its own decisions. Think about using AI to make big government decisions. It's exciting, but there are also big questions about what's fair and the right way to do things. We need to take a hard look at the right way to use AI, making sure it's fair when it makes choices for us. We studied all the good things AI can do and the trouble it could cause, especially for people in charge. Our research covers many areas like learning, business, and healthcare, even in creating special treatments for health problems. The study shows AI might change a lot of things in big ways. But to make sure AI does its job well, we need to set some clear rules. It's important to understand AI, care about fairness, and work together to make these rules. When making these rules, we have to focus a lot on fairness, being open about how things work, and letting everyone have a say. This is super important to make sure AI helps us in the best way possible.Journal of Asian development studies00To make sure AI does its job well, the authors need to set some clear rules, and it's important to understand AI, care about fairness, and work together to make these rules.Journal of Asian Development Studies["Qaiser Iqbal", "Dalir Khan", "Muhammad Salis"]2024-06-30T00:00:00 76099d21f2a96678316ccce9240e42a433e543a2AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced response time, enhanced logistics route optimization, improved demand planning as well as real-time inventory control. Thus, the idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics. SCO-AI implementation has seen significant improvements in inventory turnover rate, rates of on-time delivery as well as overall supply chain costs. In this period of high business turbulence, such kind of research builds up the robustness of a given supply chain while at the same time minimizing operational risks by means of simulations and case studiesData and Metadata2759The idea herein suggested fits smoothly into existing supply chain frameworks for data-driven decisions that make companies remain agile in ever-changing market dynamics and helps build up the robustness of a given supply chain.Data and Metadata["Anber Abraheem Shlash Mohammad", "I. Khanfar", "Badrea Al Oraini", "A. Vasudevan", "Ibrahim Mohammad Suleiman", "Zhou Fei"]2024-07-01T00:00:00 e96f0bfaf89e1c9a979ebbbd06f3c8e1f0b5d19c The emergence of generative artificial intelligence (AI), exemplified by models like ChatGPT, presents both opportunities and challenges. As these technologies become increasingly integrated into various aspects of society, the need for a harmonized legal framework to address the associated risks becomes crucial. This article presents a comprehensive analysis of the disruptive impact of generative AI, the legal risks of AI-generated content, and the governance strategies needed to strike a balance between innovation and regulation. Employing a three-pronged methodology—literature review, doctrinal legal analysis, and case study integration—the study examines the current legal landscape; synthesizes scholarly works on the technological, ethical, and socioeconomic implications of generative AI; and illustrates practical challenges through real-world case studies. The article assesses the strengths and limitations of US governance strategies for AI and proposes a harmonized legal framework emphasizing international collaboration, proactive legislation, and the establishment of a dedicated regulatory body. By engaging diverse stakeholders and identifying critical gaps in current research, the study contributes to the development of a legal framework that upholds ethical principles, protects individual rights, and fosters responsible innovation in the age of generative AI.Journal of Information Policy3516The article assesses the strengths and limitations of US governance strategies for AI and proposes a harmonized legal framework emphasizing international collaboration, proactive legislation, and the establishment of a dedicated regulatory body.Journal of Information Policy["Y. Wu", "Xukang Wang"]2024-07-01T00:00:00 162e4928fe2286a1d6691ccb2d4082df88248263Journal of the American College of Cardiology15830The critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all are defined.Journal of the American College of Cardiology["R. Khera", "E. Oikonomou", "Girish Nadkarni", "Jessica R. Morley", "Jenna Wiens", "A. Butte", "E. J. Topol"]2024-07-01T00:00:00 17f5fb4c300b11eb816acc79d2cb79b11d8930b9Journal of business research19424Journal of Business Research["Erik Hermann", "Stefano Puntoni"]2024-07-01T00:00:00 337bf68618865a9e1bdf597c9919f5f515cd766cEnergy Strategy Reviews14213Energy Strategy Reviews["Aseel Bennagi", "Obaida AlHousrya", "D. Cotfas", "Petru-Alexandru Cotfas"]2024-07-01T00:00:00 a42dd46dc4785cc59c2e300321720541a9f9979dPurposeRecent disruptions have sparked concern about building a resilient and sustainable manufacturing supply chain. While artificial intelligence (AI) strengthens resilience, research is needed to understand how cloud adoption can foster integration, collaboration, adaptation and sustainable manufacturing. Therefore, this study aimed to unleash the power of cloud adoption and AI in optimizing resilience and sustainable performance through collaboration and adaptive capabilities at manufacturing firms.Design/methodology/approachThis research followed a deductive approach and employed a quantitative method with a survey technique to collect data from its target population. The study used stratified random sampling with a sample size of 1,279 participants working in diverse manufacturing industries across California, Texas and New York.FindingsThis research investigated how companies can make their manufacturing supply chains more resilient and sustainable. The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance (hypotheses H1-H7, except H5). Additionally, utilizing artificial intelligence helps improve adaptability, further strengthening resilience and sustainability (H8-H11). Interestingly, the study found that internal integration alone does not significantly impact collaboration (H5). This suggests that external factors are more critical in fostering collaboration within the manufacturing supply chain during disruptions.Originality/valueThis study dives into the complex world of interconnected factors (formative constructs in higher order) influencing manufacturing supply chains. Using advanced modeling techniques, it highlights the powerful impact of cloud-based integration. Cloud-based integration and artificial intelligence unlock significant improvements for manufacturers and decision-makers by enabling information processes and dynamic capability theory.Journal of Manufacturing Technology Management9311The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance, leading to better performance (hypotheses H1-H7, except H5).Journal of Manufacturing Technology Management["A. Rashid", "Rizwana Rasheed", "A. Ngah", "Noor Aina Amirah"]2024-07-01T00:00:00 971ed176664244170192a8523d840922c78f65e6The global population, now at 8 billion and projected to reach 9.7 billion by 2050, necessitates a significant increase in food production. This escalating demand underscores the importance of artificial intelligence (AI) technologies in agriculture, which enhance resource optimization and productivity amid supply chain pressures and more frequent extreme weather events. A systematic literature review (SLR), conducted using the PRISMA methodology, examined AI applications in agriculture, encompassing 906 relevant studies from five electronic databases. From these, 176 studies were selected for bibliometric analysis, with a quality appraisal further refining the selection to 17 key studies. The review highlighted a notable rise in publications over the past five years, identifying over 20 AI techniques, including machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision, as predominant. The research emphasized significant contributions from India, China, and the USA, focusing on sectors like crop management, prediction, and disease and pest management. The study concluded with an analysis of current challenges and future trends, pointing to promising directions for AI in agriculture to meet global food production demands.The American Journal of Agriculture and Biomedical Engineering09A systematic literature review of artificial intelligence applications in agriculture highlighted a notable rise in publications over the past five years, identifying over 20 AI techniques, including machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision, as predominant.The American Journal of Agriculture and Biomedical Engineering["Sanchita Saha", "Ashok Ghimire", "Mia Md Tofayel Gonee Manik", "Anamika Tiwari", "Md Ahsan Ullah Imran"]2024-07-01T00:00:00 3e43d4544feca199a89c4b8af6a25ddbb5653175Cities14412AI implementation needs oversight to ensure it is ethical, inclusive, and privacy-respecting as an effective tool to aid decision-making, and the full potential of AI may be unlocked to shape sustainable urban environments and realize SDG 11.Cities["W. Leal Filho", "M. Mbah", "M. A. Dinis", "L. Trevisan", "Deborah E. de Lange", "Ashish Mishra", "B. Rebelatto", "Tarek Ben Hassen", "Y. Aina"]2024-07-01T00:00:00 9085a0017193d6c2d5e2ebc216492540b0a111cbWith the improvement of economic conditions and the increase in living standards, people’s attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.Diagnostics1437This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine.Diagnostics["Fangfang Gou", "Jun Liu", "Chunwen Xiao", "Jia Wu"]2024-07-01T00:00:00 46385ab2293eec9a57424e1ebf0fcaa51cf51349Background: Musculoskeletal disorders (MSDs) are a leading cause of disability worldwide, with a growing burden across all demographics. With advancements in technology, conversational artificial intelligence (AI) platforms such as ChatGPT (OpenAI, San Francisco, CA) have become instrumental in disseminating health information. This study evaluated the effectiveness of ChatGPT versions 3.5 and 4 in delivering primary prevention information for common MSDs, emphasizing that the study is focused on prevention and not on diagnosis. Methods: This mixed-methods study employed the CLEAR tool to assess the quality of responses from ChatGPT versions in terms of completeness, lack of false information, evidence support, appropriateness, and relevance. Responses were evaluated independently by two expert raters in a blinded manner. Statistical analyses included Wilcoxon signed-rank tests and paired samples t-tests to compare the performance across versions. Results: ChatGPT-3.5 and ChatGPT-4 effectively provided primary prevention information, with overall performance ranging from satisfactory to excellent. Responses for low back pain, fractures, knee osteoarthritis, neck pain, and gout received excellent scores from both versions. Additionally, ChatGPT-4 was better than ChatGPT-3.5 in terms of completeness (p = 0.015), appropriateness (p = 0.007), and relevance (p = 0.036), and ChatGPT-4 performed better across most medical conditions (p = 0.010). Conclusions: ChatGPT versions 3.5 and 4 are effective tools for disseminating primary prevention information for common MSDs, with ChatGPT-4 showing superior performance. This study underscores the potential of AI in enhancing public health strategies through reliable and accessible health communication. Advanced models such as ChatGPT-4 can effectively contribute to the primary prevention of MSDs by delivering high-quality health information, highlighting the role of AIs in addressing the global burden of chronic diseases. It is important to note that these AI tools are intended for preventive education purposes only and not for diagnostic use. Continuous improvements are necessary to fully harness the potential of AI in preventive medicine. Future studies should explore other AI platforms, languages, and secondary and tertiary prevention measures to maximize the utility of AIs in global health contexts.Cureus217ChatGPT versions 3.5 and 4 are effective tools for disseminating primary prevention information for common MSDs, with ChatGPT-4 showing superior performance, underscores the potential of AI in enhancing public health strategies through reliable and accessible health communication.Cureus["Selkin Y\u0131lmaz Muluk", "Nazli Olcucu"]2024-07-01T00:00:00 2d2e0f9d98850717b12ea64470f5dfdc6b97975bCIRP annals25310CIRP Annals["R. X. Gao", "J\u00f6rg Kr\u00fcger", "Marion Merklein", "Hans-Christian M\u00f6hring", "J. V\u00e1ncza"]2024-07-01T00:00:00 2343c0b519bb95587a912fcdd17a5132b2595109Trends in analytical chemistry : TRAC24610This review provides a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health, and discusses studies that have successfully used AI across different aspects of metabolomic analysis.Trends in analytical chemistry : TRAC["Jinhua Chi", "Jingmin Shu", "Ming Li", "Rekha Mudappathi", "Yan Jin", "Freeman Lewis", "Alexandria Boon", "Xiaoyan Qin", "Li Liu", "Haiwei Gu"]2024-07-01T00:00:00 ce1542c5f17dc343719bfa7ea8effc928c6ef26cAbstract The capabilities of artificial intelligence (AI) have accelerated over the past year, and they are beginning to impact healthcare in a significant way. Could this new technology help address issues that have been difficult and recalcitrant problems for quality and safety for decades? While we are early in the journey, it is clear that we are in the midst of a fundamental shift in AI capabilities. It is also clear these capabilities have direct applicability to healthcare and to improving quality and patient safety, even as they introduce new complexities and risks. Previously, AI focused on one task at a time: for example, telling whether a picture was of a cat or a dog, or whether a retinal photograph showed diabetic retinopathy or not. Foundation models (and their close relatives, generative AI and large language models) represent an important change: they are able to handle many different kinds of problems without additional datasets or training. This review serves as a primer on foundation models’ underpinnings, upsides, risks and unknowns—and how these new capabilities may help improve healthcare quality and patient safety.BMJ Quality & Safety344This review serves as a primer on foundation models’ underpinnings, upsides, risks and unknowns—and how these new capabilities may help improve healthcare quality and patient safety.BMJ Quality & Safety["Michael D Howell"]2024-07-01T00:00:00 046e24185400286911c67a773d788b89aeba0fc1This article presents a comprehensive bibliometric review of 2,916 articles on artificial intelligence (AI) in banking services, extracted from Web of Science and analyzed with VOSviewer. Scientific production in this field has experienced exponential growth since 2016, with the United States leading the research, followed by European countries such as England and France. International collaboration is evident, highlighting the global nature of banking AI research. There is a significant focus on improving credit risk, with an emphasis on applying AI to provide clear explanations and improve the accuracy of risk assessments. The trend towards personalization and improving the user experience is evident, especially on mobile platforms. However, the discussion of various studies highlights critical challenges, such as biases and vulnerabilities to cyberattacks. The absence of evidence of scientific production in Central America highlights a significant opportunity to foster research in this region. This bibliometric analysis provides a solid foundation for understanding current trends and challenges in the application of AI in banking services, underlining the importance of addressing key issues to advance in this ever-evolving strategic field effectively.Región Científica484A comprehensive bibliometric review of 2,916 articles on artificial intelligence (AI) in banking services, extracted from Web of Science and analyzed with VOSviewer provides a solid foundation for understanding current trends and challenges in the application of AI in banking services.Región Científica["Sergio Gerardo Padilla Hern\u00e1ndez"]2024-07-01T00:00:00 1066b1354bfa863ff6bf7da27e6b5f309afcd703Artificial Intelligence (AI) has been part of every citizen's life for several years. Still, the emergence of generative AI (GenAI), accessible to all, has raised discussions about the ethical issues they raise, particularly in education. GenAI tools generate content according to user requests, but are students using these tools ethically and safely? Can teachers guide students in this use and use these tools in their teaching activities? This paper argues that teacher professional development (TPD) is an essential key trigger in adopting these emerging technologies. The paper will present an integrative literature review that discusses the components of TPD that may empower teachers to guide their students towards the ethical and safe use of GenAI. According to the literature review, one key component of TPD should be AI literacy, which involves understanding AI, its capabilities and limitations, and its potential benefits and drawbacks in education. Another essential component is hands-on activities that engage teachers, their peers, and students in actively using these tools during the training process. The paper will discuss the advantages of working with GenAI tools and designing lesson plans to implement them critically in the classroom.Digital Education Review564It is argued that teacher professional development is an essential key trigger in adopting these emerging technologies and should be AI literacy, which involves understanding AI, its capabilities and limitations, and its potential benefits and drawbacks in education.Digital Education Review["Anabela Brand\u00e3o", "L. Pedro", "Nelson Zagalo"]2024-07-01T00:00:00 40efbcf2e63d5f513b7db8f5660cb2f5c9defe01Background: Artificial intelligence (AI) is a burgeoning new field that has increased in popularity over the past couple of years, coinciding with the public release of large language model (LLM)-driven chatbots. These chatbots, such as ChatGPT, can be engaged directly in conversation, allowing users to ask them questions or issue other commands. Since LLMs are trained on large amounts of text data, they can also answer questions reliably and factually, an ability that has allowed them to serve as a source for medical inquiries. This study seeks to assess the readability of patient education materials on cardiac catheterization across four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. Methodology: A set of 10 questions regarding cardiac catheterization was developed using website-based patient education materials on the topic. We then asked these questions in consecutive order to four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. The Flesch Reading Ease Score (FRES) was used to assess the readability score. Readability grade levels were assessed using six tools: Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. Results: The mean FRES across all four chatbots was 40.2, while overall mean grade levels for the four chatbots were 11.2, 13.7, 13.7, 13.3, 11.2, and 11.6 across the FKGL, GFI, CLI, SMOG, ARI, and FORCAST indices, respectively. Mean reading grade levels across the six tools were 14.8 for ChatGPT, 12.3 for Microsoft Copilot, 13.1 for Google Gemini, and 9.6 for Meta AI. Further, FRES values for the four chatbots were 31, 35.8, 36.4, and 57.7, respectively. Conclusions: This study shows that AI chatbots are capable of providing answers to medical questions regarding cardiac catheterization. However, the responses across the four chatbots had overall mean reading grade levels at the 11th-13th-grade level, depending on the tool used. This means that the materials were at the high school and even college reading level, which far exceeds the recommended sixth-grade level for patient education materials. Further, there is significant variability in the readability levels provided by different chatbots as, across all six grade-level assessments, Meta AI had the lowest scores and ChatGPT generally had the highest.Cureus215This study shows that AI chatbots are capable of providing answers to medical questions regarding cardiac catheterization, and there is significant variability in the readability levels provided by different chatbots as, across all six grade-level assessments, Meta AI had the lowest scores and ChatGPT generally had the highest.Cureus["Benjamin J. Behers", "Ian A Vargas", "Brett M Behers", "Manuel A Rosario", "Caroline N. Wojtas", "Alexander C. Deevers", "Karen M Hamad"]2024-07-01T00:00:00 991b6ef3ad60c3fe07551c736974267339870996The study examines the impact of artificial intelligence (AI) on students’ academic performance, focusing on factors such as improved student performance, attitudes toward learning, motivation for study habits, and learning mechanisms. Further, it aims to evaluate and analyze how AI enhances student academic outcomes. A mixed-methods approach, incorporating focus group discussions (FGD), was used to gather quantitative and qualitative data. Random sampling was employed to select a sample size of 100 respondents based on predefined criteria. The results indicate that AI effectively targets the specific learning needs of students, facilitating comprehensive and improved learning experiences. It identifies struggling learners and provides necessary interventions and support to enhance their academic performance. Additionally, AI accurately measures and enhances students’ attitudes toward learning, offering deeper insights into the learning process. It also boosts students’ motivation toward study habits and learning behavior. Furthermore, AI’s adaptive learning mechanisms guide students’ learning processes and provide valuable feedback.Innovare journal of education343The results indicate that AI effectively targets the specific learning needs of students, facilitating comprehensive and improved learning experiences and boosts students’ motivation toward study habits and learning behavior.Innovare Journal of Education["L. D. Mallillin"]2024-07-01T00:00:00 f7968f6f5abc8d337e0268807c97d35e045c2e61Purpose Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years. Methods Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses. Results The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. Scientific Reports had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis. Conclusion This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field.Journal of Multidisciplinary Healthcare713A thorough update on the progress in AI for MSK diseases over the last 24 years is provided, providing quick access to essential information and fostering the development of innovative concepts in this field.Journal of Multidisciplinary Healthcare["Siyang Cao", "Yihao Wei", "Yaohang Yue", "Deli Wang", "Ao Xiong", "Hui Zeng"]2024-07-01T00:00:00 c22f79509440ab44847f0229eaccb77e46037ec0Objective: This paper aims to critically analyze the role of Artificial Intelligence (AI) in Education 5.0, focusing on its opportunities and challenges. It explores technological advancements in AI, their applications in educational settings, and the paradigm shift towards personalization and adaptive learning, while examining ethical considerations inherent in integrating AI into education. Method: The research employs a qualitative analysis of existing literature spanning a period of five years. This involves reviewing case studies, industry reports, and empirical evidence on the implementation and impact of AI technologies in educational contexts. The study covers various aspects of AI applications, including AI algorithms in educational content curation, machine learning in student assessment, and natural language processing in language learning. Results: The findings reveal that AI significantly enhances educational experiences by enabling personalized and adaptive learning, improving student engagement, and providing tailored feedback. AI algorithms have transformed educational content curation, while machine learning has revolutionized student assessments by providing nuanced evaluations and predictive analytics. Natural language processing has advanced language learning by offering interactive and immersive experiences. However, the study also highlights challenges such as data privacy, algorithmic bias, and the digital divide. Ensuring robust data protection, addressing bias in AI systems, and improving digital infrastructure are essential to maximizing the benefits of AI in education. Conclusions: AI's integration into Education 5.0 presents both significant opportunities and substantial challenges. While AI has the potential to revolutionize education through enhanced personalization and efficiency, it also raises ethical and accessibility concerns. The study emphasizes the need for balanced approaches that leverage AI's strengths while mitigating its risks. Collaboration among educators, policymakers, and technologists is crucial to ensure that the benefits of AI in education are equitably distributed and ethically aligned with societal values.SDGs Studies Review153Analysis of the role of Artificial Intelligence in Education 5.0 reveals that AI significantly enhances educational experiences by enabling personalized and adaptive learning, improving student engagement, and providing tailored feedback.SDGs Studies Review["Khushabu T. Pandya"]2024-07-01T00:00:00 ef64d2da9f28ff78b0308e936c4439c977929490BMC Digital Health503Mapping the evidence of the methodological quality of HEEs of AI shows a need to improve the quality in particular the use of proxy measures as outcome, reporting, and interpretation of the ICER.BMC Digital Health["Nanna Kastrup", "A. Holst-Kristensen", "Jan B. Valentin"]2024-07-01T00:00:00 aa96d203a013417e83bc3530b87c02b19205f964This editorial introduces the special issue “The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.’ We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented.International Journal of Production Research353This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions.International Journal of Production Research["S. Wamba", "M. Queiroz", "E. Ngai", "F. Riggins", "Y. Bendavid"]2024-07-01T00:00:00 52973c4c945ff018327606565cae0b43fc1c8317Artificial intelligence (AI) has emerged as a revolutionary tool in various healthcare domains, including smoking cessation among pregnant women. Smoking during pregnancy is a significant public health concern, linked to adverse maternal and fetal outcomes. Traditional cessation methods have had limited success, necessitating innovative approaches. AI offers personalized interventions, predictive analytics, and real-time support, enhancing the effectiveness of smoking cessation programs. This editorial explores the potential of AI in transforming smoking cessation efforts for pregnant women, highlighting its benefits, challenges, and future prospects. By integrating AI into healthcare strategies, we can improve maternal and fetal health outcomes and contribute to the broader public health goal of reducing smoking rates among expectant mothers.Cureus63The potential of AI in transforming smoking cessation efforts for pregnant women is explored, highlighting its benefits, challenges, and future prospects and contributing to the broader public health goal of reducing smoking rates among expectant mothers.Cureus["V. Georgakopoulou", "A. Diamanti"]2024-07-01T00:00:00 66fa9351d8896c9f42d75f7fbfad88252e75a22eArtificial intelligence (AI) has become a much-discussed topic due to its implementation in many areas, including journalism. This article examines the coverage of journalistic AI in Spanish written media, in particular the relevance allocated to it, the topics that are addressed, and the framing from which it is approached. Quantitative content analysis and statistical analysis were used. Although journalistic AI only appears as the main topic in a third of cases, it is increasingly present, it is distributed across more sections, and it mostly appears in self-written articles. Despite informative texts prevailing, journalists author the majority of interpretative and practically all opinion pieces, thus increasing knowledge and promoting public debate. They deal largely with the most widespread application of AI, which is news automation, ranking well above the issues of job loss and ethics. Although journalistic AI is generally framed from a perspective of its benefits rather than from its risks, some recent changes are observed: growing concern about its dangers and its ethical implications; the detection of numerous pieces with a Personal Frame, where journalists reflect on their profession and the use of ChatGPT; and the growing relevance of the Episodic Frame as specific AI products develop. This study could have examined all types of media, although that approach would have gone beyond the exploratory nature of this pioneering research. With this work we extend scientific production on journalistic AI in Spain, where this perspective has not been analysed.Revista Mediterránea de Comunicación11The coverage of journalistic AI in Spanish written media is examined, in particular the relevance allocated to it, the topics that are addressed, and the framing from which it is approached, to extend scientific production on journalistic AI in Spain.Revista Mediterránea de Comunicación["Sonia Parratt-Fern\u00e1ndez", "M. Chaparro-Dom\u00ednguez", "Isabel Mart\u00edn-S\u00e1nchez"]2024-07-01T00:00:00 e7b946ceb17a99970102e8338add7e0255853e09: The way digital content is conceived, created, and consumed across a range of sectors has been revolutionized by artificial intelligence (AI), which has become a disruptive force in the content creation space. With an emphasis on how AI affects creativity, productivity, and content quality, this dissertation explores the significant effects of AI on the processes involved in creating content. This study offers a thorough analysis of AI's incorporation into content creation workflows and its varied consequences through a thorough evaluation of the literature and a careful examination of secondary data. Artificial intelligence is radically altering the field of content creation and presenting both new opportunities and difficulties. We can fully utilize artificial intelligence (AI) in content creation by comprehending and tackling these issues, producing educational, entertaining, and inspirational content for audiences all over the world.International Journal of Research Publication and Reviews01This study offers a thorough analysis of AI's incorporation into content creation workflows and its varied consequences through a thorough evaluation of the literature and a careful examination of secondary data.International Journal of Research Publication and Reviews["Dr. Rachita Ota", "Dr.Sushree Sangita Ray", "Mr. Sk Salim Alli"]2024-07-01T00:00:00 35ec765ba1d6204f1baee9ee9346884a35597370Journal of Microbiological Methods09The transformative impact of AI on vaccine development is underscored and the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases is highlighted.Journal of microbiological methods["D. Olawade", "Jennifer Teke", "Oluwaseun Fapohunda", "Kusal Weerasinghe", "S. O. Usman", "Abimbola O. Ige", "A. David-Olawade"]2024-07-01T00:00:00 16f369ab427d9a4d6c58d9e630e3b9468a5a1755Computer Law and Security Review09Comput. Law Secur. Rev.["Johann Laux", "Sandra Wachter", "Brent Mittelstadt"]2024-07-01T00:00:00 7a103ee60251d0963df64d514d1e77d685a4d93aThe European Union’s (EU’s) regulatory ecosystem presents challenges with balancing legal and sociotechnical drivers for explainable artificial intelligence (XAI) systems. Core tensions emerge on dimensions of oversight, user needs, and litigation. This article maps provisions on algorithmic transparency and explainability across major EU data, AI, and platform policies using qualitative analysis. We characterize the involved stakeholders and organizational implementation targets. Constraints become visible between useful transparency for accountability and confidentiality protections. Through an AI hiring system example, we explore the complications with operationalizing explainability. Customization is required to satisfy explainability desires within confidentiality and proportionality bounds. The findings advise technologists on prudent XAI technique selection given multidimensional tensions. The outcomes recommend that policy makers balance worthy transparency goals with cohesive legislation, enabling equitable dispute resolution.IEEE Intelligent Systems151IEEE Intelligent Systems["Luca Nannini", "J. Alonso-Moral", "Alejandro Catal\u00e1", "Manuel Lama", "Sen\u00e9n Barro"]2024-07-01T00:00:00 c178a0b85b38c9a65b4e3d3787c5912bd19244cd Artificial Intelligence (AI) brings enormous opportunities into learning, teaching, and assessment processes. Among them, it is convenient to explore its ability to channel students’ creativity, which is described as a basic competence in the training of people with both the OECD and the recent Spanish LOMLOE law pointing to the need to foster it in educational settings. In this context, the objective of this research is to explore the creative potential of prospective elementary school teachers related to storytelling, via a project including the rational use of AI generative tools. A combination of qualitative and quantitative instruments was used to get insight on the implications of those AI tools in the creative process and to gain understanding on the concerns of prospective teachers about AI at both their training and future teaching practice. The results show the potential of AI from an educational point of view, specially in self-assessment and co-evaluation processes, since it allows confronting not only the result of the creative task, but also the process itself by reflecting on the asked questions. Finally, the importance of continuing research on the ability to ask questions (a creative skill in itself) in the new context of AI is discussed. Digital Education Review541The results show the potential of AI from an educational point of view, specially in self-assessment and co-evaluation processes, since it allows confronting not only the result of the creative task, but also the process itself by reflecting on the asked questions.Digital Education Review["I. Pont-Nicl\u00f3s", "Yolanda Echegoyen-Sanz", "Patricia Orozco-G\u00f3mez", "A. Mart\u00edn-Ezpeleta"]2024-07-01T00:00:00 ea461d63851f6c7063dd51dfa66ebcc36b551e03This study investigates students' perceived readiness for the adoption of artificial intelligence (AI) support services in Nigerian universities, focusing on Al-Hikmah University as a case study. The data were collected from 45 students who were selected via stratified, purposive and convenience sampling techniques. Thematic analysis was employed to analyze the interview transcripts. Factors influencing students' readiness include perceived usefulness, ease of use, and concerns about privacy and job security. The findings suggest that students at Al-Hikmah University are generally positive and open-minded about the potential of AI to enhance their learning experiences and support services. The study identified several benefits of AI-based support services, including personalized learning experiences, access to information and resources at any time, and the potential to improve efficiency in administrative processes. While students are generally receptive to AI, the study also highlighted some concerns and challenges. These include privacy concerns related to data collection and usage, and the need for adequate training to effectively use AI tools. In conclusion, the study on students' perceived readiness for the adoption of artificial intelligence (AI)-based support services in Nigerian universities, focusing on Al-Hikmah University, provides valuable qualitative evidence on the attitudes and perspectives of students toward AI technology in the context of higher education.Journal of Education in Black Sea Region371The findings suggest that students at Al-Hikmah University are generally positive and open-minded about the potential of AI to enhance their learning experiences and support services, and several benefits of AI-based support services are identified, including personalized learning experiences, access to information and resources at any time, and the potential to improve efficiency in administrative processes.Journal of Education in Black Sea Region["Yusuf Suleiman"]2024-07-01T00:00:00 50e8628d661b0ff7302e58d4ff1c456ef61ca147The integration of artificial intelligence (AI) in mathematics education, focusing on its implications in the 4th Industrial Revolution (4IR) era. Through a comprehensive analysis of 10 relevant studies in Scopus and Google Scholar from 2015 to 2023, this review identifies the research methods, research instruments, participants, and AI tools used in mathematics education. Some key ideas include using AI-driven personalized learning and enhanced mathematics instruction, real-time assessment and feedback, curriculum development, and empowering educators, which were highlighted. The study aligns with the preferred reporting items for systematic reviews and meta-analysis. Based on the analysis, most studies reviewed utilized qualitative research methods. The study indicates that questionnaires were mainly used to gather data from students and teachers who were the most significant participants in the reviewed papers. Further results revealed that ChatGPT were the primary AI tool used in mathematics education, among other AI tools, as identified in this review. Additionally, this review discusses the transformative potential of AI in addressing educational disparities and preparing learners for the demands of 4IR.Eurasia Journal of Mathematics, Science and Technology Education481This review identifies the research methods, research instruments, participants, and AI tools used in mathematics education, and revealed that ChatGPT were the primary AI tool used in mathematics education, among other AI tools, as identified in this review.Eurasia Journal of Mathematics, Science and Technology Education["O. Opesemowo", "H. Adewuyi"]2024-07-01T00:00:00 5d9e120dec80966fce5ce2e15438c2fe67a8ee75Effective work scheduling for clinical training is essential for medical education, yet it remains challenging. Creating a clinical training schedule is a difficult task, due to the complexity of curriculum requirements, hospital demands, and student well-being. This study proposes the Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling (CCP-AI-MWS) to optimize clinical training schedules. The CCP-AI-MWS integrates the Collaborative Requirement Planning principle with Artificial Intelligence (AI). Two experiments have been conducted comparing CCP-AI-MWS with current practice. Results show that the newly developed protocol outperforms the current method. CCP-AI-MWS achieves a more equitable distribution of assignments, better accommodates student preferences, and reduces unnecessary workload, thus mitigating student burnout and improving satisfaction. Moreover, the CCP-AI-MWS exhibits adaptability to unexpected situations and minimizes disruptions to the current schedule. The findings present the potential of CCP-AI-MWS to transform scheduling practices in medical education, offering an efficient solution that could benefit medical schools worldwide.International Journal of Computers Communications & Control01The proposed Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling (CCP-AI-MWS) achieves a more equitable distribution of assignments, better accommodates student preferences, and reduces unnecessary workload, thus mitigating student burnout and improving satisfaction.Int. J. Comput. Commun. Control["Puwadol Oak Dusadeerungsikul", "S. Nof"]2024-07-01T00:00:00 119fbb440fc412ee487b609948eb565ec7d1e7cbThe advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of ‘big data’, ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.Diagnostics1661The role of AI is examined, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures, and the potential directions for the application of AI in thoracic radiology.Diagnostics["Jin Y. Chang", "M. Makary"]2024-07-01T00:00:00 b08a8309be53b46bf88700bae06471de736b8ddbUntil recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon’s perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon’s perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon’s cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.Cureus951Artificial intelligence is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery, and is also transforming how surgical proficiency and trainees in postgraduate programs are evaluated.Cureus["Allan Hamilton"]2024-07-01T00:00:00 c7c9b0817bfc44b760f8fa588f1795b0a491e727 In recent years, there has been an exponential rise in the use of artificial intelligence (AI) systems in the business world. AI has many current and potential uses in accounting and auditing. However, the introduction of AI comes with significant risks. In this paper, we explore the use of AI for repetitive and simple tasks, generative AI to produce new textual content, and predictive AI to help assess future risks. We then consider important aspects in the use of AI, including data ownership, governance, and bias introduced by AI systems. We show how accounting and auditing professionals must understand these issues to effectively use AI. We also consider changes to the profession regarding the potential erosion of professional trust and the deskilling of the profession. In each case, we discuss how risks and adverse effects from AI can be mitigated by new standards and professional control of AI implementation. JEL Classifications: M41; M42; M48.Social Science Research Network181The use of AI for repetitive and simple tasks, generative AI to produce new textual content, and predictive AI to help assess future risks are explored, including data ownership, governance, and bias introduced by AI systems.SSRN Electronic Journal["Nir Eisikovits", "William C. Johnson", "Ariel J. Markelevich"]2024-07-01T00:00:00 95418aae8ab03bef8b9008a0751e38bd18ab6ca4The Artificial Intelligence Act (AI Act) adopted by the European Union might serve as a global regulatory reference point. Heated negotiations over the AI Act have shown that reconciling the interests of numerous stakeholders is not an easy task. As well as creating clear and precise rules that would enable implementing effective safeguards for citizens against the manipulative potential of technology. The AI Act introduces the first legal definition of deep fakes and creates a system of protection against their harmful applications, that is based on transparency obligations. I argue that in the course of negotiations, the EU has made progress in regulating deep fakes, but the adopted solutions should only be an introduction to a more strict protection of individuals. The AI Act targets a specific group of deep fakes, leaving some of them unregulated or poorly regulated and disregarding the consequences of nonconsensual deep fake pornography. By analyzing the legal provisions related to deep fakes in the AI Act, I point to the rationale behind the chosen forms of protection, criticize the shortcomings and list the consequences of adopting the AI Act for deep fakes landscape.Policy &amp; Internet251It is argued that in the course of negotiations, the EU has made progress in regulating deep fakes, but the adopted solutions should only be an introduction to a more strict protection of individuals.Policy &amp; Internet["Mateusz \u0141abuz"]2024-07-01T00:00:00 87411bde6a4021e056b65b4b9af0f7f4332b710dAbstract: The integration of artificial intelligence (AI) and big data in anterior segment (AS) imaging represents a transformative approach to glaucoma diagnosis and management. This article explores various AS imaging techniques, such as AS optical coherence tomography, ultrasound biomicroscopy, and goniophotography, highlighting their roles in identifying angle-closure diseases. The review focuses on advancements in AI, including machine learning and deep learning, which enhance image analysis and automate complex processes in glaucoma care, and provides current evidence on the performance and clinical applications of these technologies. In addition, the article discusses the integration of big data, detailing its potential to revolutionize medical imaging by enabling comprehensive data analysis, fostering enhanced clinical decision-making, and facilitating personalized treatment strategies. In this article, we address the challenges of standardizing and integrating diverse data sets and suggest that future collaborations and technological advancements could substantially improve the management and research of glaucoma. This synthesis of current evidence and new technologies emphasizes their clinical relevance, offering insights into their potential to change traditional approaches to glaucoma evaluation and care.Taiwan Journal of Ophthalmology781Various AS imaging techniques are explored, such as AS optical coherence tomography, ultrasound biomicroscopy, and goniophotography, highlighting their roles in identifying angle-closure diseases and the integration of artificial intelligence (AI) and big data in anterior segment (AS) imaging.Taiwan Journal of Ophthalmology["Sunee Chansangpetch", "Mantapond Ittarat", "W. Cheungpasitporn", "Shan Lin"]2024-07-01T00:00:00 8b6c8e011f564118854634ceebaeb20c87a29c12Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.Nutrition in clinical practice831The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions through tailored and adaptive nutrition interventions.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition["Hannah Kittrell", "Ahmed Shaikh", "Peter A Adintori", "Paul J McCarthy", "R. Kohli-Seth", "Girish N. Nadkarni", "Ankit Sakhuja"]2024-07-01T00:00:00 d87ba864520ca6e9c010cd9d157e68250730df77Study Purpose This study aims to analyze radiologic technology student’s perceptions of artificial intelligence (AI) and its applications in radiology. Methods A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students. Results Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI. Conclusion The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.International Journal of General Medicine431A positive evaluation of the applications of this technology, which is attributed to its essential support role, is reflected, however, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.International Journal of General Medicine["Wejdan M Arif"]2024-07-01T00:00:00 79f7053fe02029e96ca35dcee3c5302cbe3661b8Cardiovascular disease remains a leading global health challenge, necessitating advanced diagnostic approaches. This review explores the integration of artificial intelligence (AI) in multimodal cardiac imaging, tracing its evolution from early X-rays to contemporary techniques such as CT, MRI, and nuclear imaging. AI, particularly machine learning and deep learning, significantly enhances cardiac diagnostics by estimating biological heart age, predicting disease risk, and optimizing heart failure management through adaptive algorithms without explicit programming or feature engineering. Key contributions include AI's transformative role in non-invasive coronary artery disease diagnosis, arrhythmia detection via wearable devices, and personalized treatment strategies. Despite substantial progress, challenges including data standardization, algorithm validation, regulatory approval, and ethical considerations must be addressed to fully harness AI's potential. Collaborative efforts among clinicians, scientists, industry stakeholders, and regulatory bodies are essential for the safe and effective deployment of AI in cardiac imaging, promising enhanced diagnostics and personalized patient care.Cureus581This review explores the integration of artificial intelligence in multimodal cardiac imaging, tracing its evolution from early X-rays to contemporary techniques such as CT, MRI, and nuclear imaging, promising enhanced diagnostics and personalized patient care.Cureus["Marlon E Rivera Boadla", "Nava R Sharma", "Jeffy Varghese", "Saral Lamichhane", "Muhammad H Khan", "Amit Gulati", "Sakshi Khurana", "Samuel Tan", "Anupam Sharma"]2024-07-01T00:00:00 c4605ea5c27d197e2237a8453abb36c0812396bfTo address various business challenges, organisations are increasingly employing artificial intelligence (AI) to analyse vast amounts of data. One application involves consolidating diverse user data into unified profiles, aggregating consumer behaviours to accurately tailor marketing efforts. Although AI provides more convenience to consumers and more efficient and profitable marketing for organisations, the act of aggregating data into behavioural profiles for use in machine learning algorithms introduces significant privacy implications for users, including unforeseeable personal disclosure, outcomes biased against marginalised population groups and organisations' inability to fully remove data from AI systems on consumer request. Although these implementations of AI are rapidly altering the way consumers perceive information privacy, researchers have thus far lacked an accurate method for measuring consumers' privacy concerns related to AI. In this study, we aim to (1) validate a scale for measuring privacy concerns related to AI misuse (PC‐AIM) and (2) examine the effects that PC‐AIM has on nomologically related constructs under the APCO framework. We provide evidence demonstrating the validity of our newly developed scale. We also find that PC‐AIM significantly increases risk beliefs and personal privacy advocacy behaviour, while decreasing trusting beliefs. Trusting beliefs and risk beliefs do not significantly affect behaviour, which differs from prior privacy findings. We further discuss the implications of our work on both research and practice.Information Systems Journal1042A scale for measuring privacy concerns related to AI misuse (PC‐AIM) is validated and the effects that PC‐AIM has on nomologically related constructs under the APCO framework are examined, finding that PC‐AIM significantly increases risk beliefs and personal privacy advocacy behaviour, while decreasing trusting beliefs.Inf. Syst. J.["Philip Menard", "Gregory J. Bott"]2024-07-01T00:00:00 152e01e95b2e34725c906f97f738669d104d3437Many Western and economically developed countries have already incorporated Artificial Intelligence (AI) into their newsrooms. As the media industry is constantly addressing new technological advancements, media scholars are highly confident about the combination of AI and the newsroom. This research investigates AI as a new prospect in the Bangladeshi journalism arena, focusing on the current state of AI usage and projecting the future by evaluating professional journalists’ ‘Mental Readiness’ across a variety of media companies. In the first phase, from the survey of 107 working journalists from 20 different news organisations, this study finds that journalists possess a mostly positive attitude towards AI and are willing to incorporate current technologies in their newsrooms. The majority of journalists are informed, yet many of them lack sufficient AI literacy. In the second part, in-depth interviews with five newsroom editors reveal that it is difficult for Bangladesh to make a significant transformation within a short period. Most of them believe that providing AI-enabled newsrooms in a developing country like Bangladesh is still a long shot, owing to economic and technological constraints.Pacific Journalism Review – Te Koakoa281Investigating AI as a new prospect in the Bangladeshi journalism arena, focusing on the current state of AI usage and projecting the future by evaluating professional journalists’ ‘Mental Readiness’ across a variety of media companies finds that journalists possess a mostly positive attitude towards AI and are willing to incorporate current technologies in their newsrooms.Pacific Journalism Review : Te Koakoa["Sanjoy Basak", "Partha Maliha", "Tabassum", "Ashraful Goni", "Priyanka Kundu"]2024-07-01T00:00:00 e499a13aba91a92a8737308895fc73c4217da5a8 In response to the increasing demand for personalization in cardiac dietetics, tools based on artificial intelligence (AI) are gaining popularity. We assess the compliance of AI-generated nutritional advice and diet plans with current dietary standards. The study aims to evaluate whether nutritional advice and diet plans created by AI for heart disease patients align with the recommended caloric intake and macronutrient ratios, based on current dietary guidelines. Using selected AI tools, nutritional advice and diet plans were generated for defined dietary scenarios corresponding to specific cardiac diagnoses. The analysis included assessing the caloric content and macronutrient composition (protein, fat, carbohydrates) compared to current guidelines. The analysis showed that the majority of the AI-generated diet plans and nutritional advice were compliant with dietary recommendations, with an >75% compliance rate for caloric content and macronutrient ratios. However, instances of non-compliance were discovered, suggesting a need for further adjustment of AI algorithms. AI tools demonstrate potential in creating personalized diet plans for cardiac patients but require optimization to fully meet current dietary guidelines. The study highlights the importance of integrating dietary knowledge into the design process of AI tools, which is crucial for providing high-quality nutritional support. European Journal of Cardiovascular Nursing01The analysis showed that the majority of the AI-generated diet plans and nutritional advice were compliant with dietary recommendations, with an >75% compliance rate for caloric content and macronutrient ratios, however, instances of non-compliance were discovered, suggesting a need for further adjustment of AI algorithms.European Journal of Cardiovascular Nursing["M. Sloma Krzeslak", "O. Kowalski"]2024-07-01T00:00:00 c08231c1bcaf08bd45998aa07dd3a641dc5999bcIn the digital era, the convergence of universal design for learning (UDL) principles and artificial intelligence (AI) stands as a transformative force shaping education. This article explores their intersection, emphasizing their combined impact on fostering inclusion and autonomous learning through the lens of UDL’s multiple means of representation, engagement, and expression. UDL, committed to inclusivity by providing various ways for students to access, engage with, and demonstrate understanding of content, synergizes with AI’s transformative capabilities. The article presents three practical applications illustrating how the integration of UDL and AI, employing multiple means, enhances autonomous learning, eliminates barriers, and cultivates inclusive educational spaces. This collaboration not only addresses immediate challenges but also serves as a catalyst for systemic change, paving the way for a more just and equitable educational landscape in the digital era. Continuous reflection, ethical considerations, and purposeful integration of UDL principles and AI are essential for refining these approaches and ensuring a responsive educational system that promotes inclusion and autonomy.International Journal of Professional Development Learners and Learning182The article presents three practical applications illustrating how the integration of UDL and AI, employing multiple means, enhances autonomous learning, eliminates barriers, and cultivates inclusive educational spaces.International Journal of Professional Development, Learners and Learning["Silvia Sabor\u00edo-Taylor", "Fabi\u00e1n Rojas-Ram\u00edrez"]2024-07-01T00:00:00 038175094677b6d61da839a9f37db9ecd3b7e87bThe integration of Artificial Intelligence (AI) into public health has the potential to transform the field, influencing healthcare at the population level. AI can aid in disease surveillance, diagnosis, and treatment decisions, impacting how healthcare professionals deliver care. However, it raises critical questions about inputs, values, and biases that must be addressed to ensure its effectiveness. This article investigates the factors influencing the values guiding AI technology and the potential consequences for public health. It outlines four key considerations that should shape discussions regarding the role of AI in the future of public health. These include the potential omission of vital factors due to incomplete data inputs, the challenge of balancing trade-offs in public health decisions, managing conflicting inputs between public health objectives and community preferences, and the importance of acknowledging the values and biases embedded in AI systems, which could influence public health policy-making.PLOS Digital Health132The potential omission of vital factors due to incomplete data inputs, the challenge of balancing trade-offs in public health decisions, managing conflicting inputs between public health objectives and community preferences, and the importance of acknowledging the values and biases embedded in AI systems, which could influence public health policy-making are outlined.PLOS Digital Health["Itai Bavli", "Sandro Galea"]2024-07-01T00:00:00 fcbeac114dc3e0cf717248ce583d7e5e06c8bf30Background: AI has been initiated in clinical practice and nursing education. The integration of artificial intelligence (AI) in nursing education will benefit practice by potentially preparing nursing students for repetitive, safe, and efficient practice. Objective: To determine the effectiveness of using artificial intelligence (AI) on students' learning interest, critical thinking, and creativity in learning in nursing education. Method: This study used a quantitative method, a post-test with randomized experimental and control groups. The sample consisted of nursing students from the Mitra Adiguna Health Science Institute in Palembang. The experimental group of 89 students received an explanation of the use of Chatbot or ChatGPT, an explanation of nursing lecture material, and completed a nursing care case study using Chatbot or ChatGPT. Meanwhile, the control group of 89 students did not use Chatbot or ChatGPT. Results: The Mann-Whitney test results in this study showed an Asymp. Sig. (2-tailed) P-value of 0.000 < 0.05, meaning that the use of artificial intelligence (AI) has proven effective in increasing students' learning interest, critical thinking, and creativity in nursing education. Conclusions: The use of artificial intelligence can be one of the methods in nursing education to increase learning interest, stimulate critical thinking, and enhance student creativity.Journal of Nursing Culture and Technology121The use of artificial intelligence can be one of the methods in nursing education to increase learning interest, stimulate critical thinking, and enhance student creativity.Journal of Nursing Culture and Technology["Ani Syafriati"]2024-07-01T00:00:00 6dea1e1b680eba78f5362f8bf935edc12a180314 This article looks at three directions in which artificial intelligence is developing in journalism: automated journalism, AI-generated news anchors and AI-based fake news detection. How effective is artificial intelligence when it comes to news reporting? How does a robot present a news story? How does AI distinguish fake news from real news. These are some of the questions on which I have built this article. The research results show that although artificial intelligence has been strongly introduced in major newsrooms and often outperforms the human factor, the human journalist is still indispensable. This is due to the limitations of artificial intelligence to fully understand natural human language, but also due to its inability to deeply analyse everyday events.Saeculum122SAECULUM["Dan-Lauren\u021biu Carda\u015f-R\u0103du\u021ba"]2024-07-01T00:00:00 5b8879b044ce76a54df84bc8f5314866739503bdSimple Summary Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms “machine learning” and “mastitis” were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return. Abstract Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. “Mastitis” and “machine learning” were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as “sensors” and “mastitis detection”, also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area.Animals452It was concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return.Animals : an Open Access Journal from MDPI["Thatiane Mendes Mitsunaga", "Breno Luis Nery Nery Garcia", "Ligia Beatriz Rizzanti Pereira", "Yuri Campos Braga Costa", "Roberto Fray da Silva", "A. Delbem", "M. V. dos Santos"]2024-07-01T00:00:00 744c81168c6504240f9eaeac05dc09001a985a75The intelligence of computers or software, as opposed to the intellect of living things, mainly people, is known as artificial intelligence (AI). It is a branch of computer science that focuses on creating and researching intelligent machines. These devices could be referred to as AIs. Artificial Intelligence is widely applied in government, industry, and academia. Advanced web search engines like Google Search, recommendation systems used by YouTube, Amazon, and Netflix, human speech-based interaction like Google Assistant, Siri, and Alexa, self-driving cars like Waymo, generative and creative tools like ChatGPT and AI art, and superhuman play and analysis in strategy games like chess and Go are a few high-profile applications. In the fields of finance and accounting, artificial intelligence has had a big impact. In reality, because they save time and offer deep insights, AI-enabled finance and accounting systems are the means for businesses to remain successful competitors in a market that is becoming more and more competitive.Recent trends in Management and Commerce01In the fields of finance and accounting, artificial intelligence has had a big impact, and because they save time and offer deep insights, AI-enabled finance and accounting systems are the means for businesses to remain successful competitors in a market that is becoming more and more competitive.Recent trends in Management and Commerce["S.S Gopika"]2024-07-01T00:00:00 d65ea9892405a1bc843802f28f41fa239bce03ca: Artificial intelligence (AI) is a rapidly evolving field of technology that involves the development of intelligence that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and making decisions based on data. This paper explores the relation of AI and scientific research. AI facilitates the extraction of meaningful insights from large datasets, enabling researchers to uncover patterns, correlations, and trends that might otherwise remain obscured. Moreover, Artificial intelligence aids in predictive analytics, allowing scientists to forecast outcomes and identify potential areas for further investigation. Additionally, AI systems are increasingly employed in experimental design and optimization, streamlining processes and enhancing efficiency in laboratory settings. Despite its myriad benefits, the integration of AI into scientific research presents challenges related to data quality, interpretability, and ethical considerations.Sustainability Education Globe271This paper explores the relation of AI and scientific research and facilitates the extraction of meaningful insights from large datasets, enabling researchers to uncover patterns, correlations, and trends that might otherwise remain obscured.Sustainability Education Globe["Mohamed Waly"]2024-07-01T00:00:00 24c875cacc355aa3e5fad37314d00ed25cb63eccThe many advancements in medical technology of the last century have continually sought to improve the sensitivity of testing and the specificity of treatment of human maladies. Conventional physical and pharmaceutical treatment is largely an imprecise process, stimulating the impetus for the advancement of machine learning-enhanced artificial intelligence (AI) medical technologies. Biospectral Emission Sequencing (BES) is a bioregulatory intelligence (BI) technology already in use as an adjunct to conventional testing. Biospectral Emission Sequencing provides a functional system of dynamic real-time adjunctive testing and treatment selection. This paper discusses the parallel technologies of present and future AI and BI technologies in medicine.Cureus372Biospectral Emission Sequencing provides a functional system of dynamic real-time adjunctive testing and treatment selection and bioregulatory intelligence (BI) technology already in use as an adjunct to conventional testing.Cureus["David A Jernigan"]2024-07-01T00:00:00 b1629f386dcf54fab2ad920dd39dbdd684a82e38Artificial Intelligence (AI) has emerged as a transformative force reshaping industries, societies, and human experiences. This in-depth written work delves into the multifaceted landscape of AI, examining its history, key concepts, ethical implications, current applications, and future directions. Drawing upon a diverse array of research, literature, and expert insights, this written work aims at providing a comprehensive understanding of AI’s evolution, challenges, and potentials. In doing this, intelligent agent, problem solving and search; and knowledge representation and reasoning are discussed as the fundamental concepts of AI. Furthermore, some main frontiers of AI like machine learning, ethical considerations in artificial intelligence and the socio-economic impact of AI are highlighted. It is noted in the discourse that the future belongs to AI and as such, humanity should make proper utilization of the phenomenon for the good of mankind. Finally, recommendations are made, among which is collaboration among critical stakeholders of Artificial Intelligence in maximizing its usage.Innovation Science and Technology01This in-depth written work delves into the multifaceted landscape of AI, examining its history, key concepts, ethical implications, current applications, and future directions, providing a comprehensive understanding of AI’s evolution, challenges, and potentials.Innovation in Science and Technology["I. T. Ayorinde", "P. N. Idyorough"]2024-07-01T00:00:00 0889ab043db5065ffba5449f08b54fc74a4f50fcArtificial Intelligence (AI) is poised to revolutionize family medicine, offering a transformative approach to achieving the Quintuple Aim. This article examines the imperative for family medicine to adapt to the rapidly evolving field of AI, with an emphasis on its integration in clinical practice. AI's recent advancements have the potential to significantly transform health care. We argue for the proactive engagement of family medicine in directing AI technologies toward enhancing the "Quintuple Aim."The article highlights potential benefits of AI, such as improved patient outcomes through enhanced diagnostic tools, clinician well-being through reduced administrative burdens, and the promotion of health equity by analyzing diverse data sets. However, we also acknowledge the risks associated with AI, including the potential for automation to diverge from patient-centered care and exacerbate health care disparities. Our recommendations stress the need for family medicine education to incorporate AI literacy, the development of a collaborative for AI integration, and the establishment of guidelines and standards through interdisciplinary cooperation. We conclude that although AI poses challenges, its responsible and ethical implementation can revolutionize family medicine, optimizing patient care and enhancing the role of clinicians in a technology-driven future.Journal of the American Board of Family Medicine02It is concluded that although AI poses challenges, its responsible and ethical implementation can revolutionize family medicine, optimizing patient care and enhancing the role of clinicians in a technology-driven future.Journal of the American Board of Family Medicine : JABFM["Karim Hanna", "David Chartash", "Winston R. Liaw", "Damian Archer", "Daniel Parente", "Nipa R. Shah", "Steven Waldren", "Bernard Ewigman", "Wayne Altman"]2024-07-01T00:00:00 c03b2f093e5fcd4b6a473772e76fa6c15d8868fcSummary: Integration of artificial intelligence (AI), specifically with natural language processing and machine learning, holds tremendous potential to enhance both clinical practices and administrative workflows within plastic surgery. AI has been applied to various aspects of patient care in plastic surgery, including postoperative free flap monitoring, evaluating preoperative risk assessments, and analyzing clinical documentation. Previous studies have demonstrated the ability to interpret current procedural terminology codes from clinical documentation using natural language processing. Various automated medical billing companies have used AI to improve the revenue management cycle at hospitals nationwide. Additionally, AI has been piloted by insurance companies to streamline the prior authorization process. AI implementation holds potential to enhance billing practices and maximize healthcare revenue for practicing physicians.Plastic and Reconstructive Surgery, Global Open322Plastic and Reconstructive Surgery Global Open["Christina Zhu", "Pradeep K. Attaluri", "Peter J. Wirth", "Ellen C. Shaffrey", "Jeffrey B. Friedrich", "Venkat K Rao"]2024-07-01T00:00:00 161c4d000df62be0d8045654d3a7366fce7c56c3Purpose The World Health Organization has identified Klebsiella pneumoniae (KP) as a significant threat to global public health. The rising threat of carbapenem-resistant Klebsiella pneumoniae (CRKP) leads to prolonged hospital stays and higher medical costs, necessitating faster diagnostic methods. Traditional antibiotic susceptibility testing (AST) methods demand at least 4 days, requiring 3 days on average for culturing and isolating the bacteria and identifying the species using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), plus an extra day for interpreting AST results. This lengthy process makes traditional methods too slow for urgent clinical situations requiring rapid decision-making, potentially hindering prompt treatment decisions, especially for fast-spreading infections such as those caused by CRKP. This research leverages a cutting-edge diagnostic method that utilizes an artificial intelligence-clinical decision support system (AI-CDSS). It incorporates machine learning algorithms for the swift and precise detection of carbapenem-resistant and colistin-resistant strains. Patients and Methods We selected 4307 KP samples out of a total of 52,827 bacterial samples due to concerns about multi-drug resistance using MALDI-TOF MS and Vitek-2 systems for AST. It involved thorough data preprocessing, feature extraction, and machine learning model training fine-tuned with GridSearchCV and 5-fold cross-validation, resulting in high predictive accuracy, as demonstrated by the receiver operating characteristic and area under the curve (AUC) scores, laying the groundwork for our AI-CDSS. Results MALDI-TOF MS analysis revealed distinct intensity profiles differentiating CRKP and susceptible strains, as well as colistin-resistant Klebsiella pneumoniae (CoRKP) and susceptible strains. The Random Forest Classifier demonstrated superior discriminatory power, with an AUC of 0.96 for detecting CRKP and 0.98 for detecting CoRKP. Conclusion Integrating MALDI-TOF MS with machine learning in an AI-CDSS has greatly expedited the detection of KP resistance by approximately 1 day. This system offers timely guidance, potentially enhancing clinical decision-making and improving treatment outcomes for KP infections.Infection and Drug Resistance241Infection and Drug Resistance["M. Jian", "Tai-Han Lin", "Hsing-Yi Chung", "Chih-Kai Chang", "C. Perng", "Feng-Yee Chang", "Hung-Sheng Shang"]2024-07-01T00:00:00 cfe6f16e87005d5ca1845a6bee08e68fbd52bac5This paper examines the Influence of Artificial Intelligence on Employment Trends in the United States (US). The rapid development of AI technology is creating significant disruptions in traditional labor markets, leading to concerns about unemployment and inequality. The paper highlights the importance of understanding the effects of AI on the labor market and the need for proactive steps to address any negative impact as well as the policy options available to address these challenges, including retraining and education programs, regulation of AI use, and tax policies. It also considers the potential costs and benefits of these options and the stakeholders who are impacted by them. This paper recommends investing in education and training programs for workers, creating tax incentives for affected communities, promoting research and development of AI technologies that complement human workers, and implementing policies to promote the responsible use of AI. The paper also discusses bureaucratic barriers to participation that may hinder effective implementation of these recommendations. Finally, it recommends a combination of retraining and education programs and tax policies to promote investment in new technologies and job creation.International Journal of Innovative Science and Research Technology352Investing in education and training programs for workers, creating tax incentives for affected communities, promoting research and development of AI technologies that complement human workers, and implementing policies to promote the responsible use of AI are recommended.International Journal of Innovative Science and Research Technology (IJISRT)["Gbolahan Owoeye"]2024-07-01T00:00:00 1a3b2af78d865b53a2160f6b19fd948498c31ff0This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI n education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.Forum for Education Studies317The purpose of the study is to show how AI n education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes, and highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly.Forum for Education Studies["Milad Shahvaroughi Farahani", "Ghazal Ghasmi"]2024-07-01T00:00:00 a42554ce8fc8a3c3b6235abac522bc2a27158281Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.Diagnostic and Interventional Radiology1439This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later.Diagnostic and interventional radiology["Burak Ko\u00e7ak", "A. Ponsiglione", "A. Stanzione", "Christian Bluethgen", "Jo\u00e3o Santinha", "L. Ugga", "Merel Huisman", "M. Klontzas", "Roberto Cannella", "Renato Cuocolo"]2024-07-02T00:00:00 1ac8bb223cbe8b526eef58e7112d11972eac1ee4Objectives The application of artificial intelligence (AI) in healthcare is an important public health issue. However, few studies have investigated the perceptions and attitudes of healthcare professionals toward its applications in nursing. This study aimed to explore the knowledge, attitudes, and concerns of healthcare professionals, AI-related professionals, and others in China toward AI in nursing. Methods We conducted an online cross-sectional study on nursing students, nurses, other healthcare professionals, AI-related professionals, and others in China between March and April 2024. They were invited to complete a questionnaire containing 21 questions with four sections. The survey followed the principle of voluntary participation and was conducted anonymously. The participants could withdraw from the survey at any time during the study. Results This study obtained 1,243 valid questionnaires. The participants came from 25 provinces and municipalities in seven regions of China. Regarding knowledge of AI in nursing, 57% of the participants knew only a little about AI, 4.7% did not know anything about AI, 64.7% knew only a little about AI in nursing, and 13.4% did not know anything about AI in nursing. For attitudes toward AI in nursing, participants were positive about AI in nursing, with more than 50% agreeing and strongly agreeing with each question on attitudes toward AI in nursing. Differences in the numbers of participants with various categories of professionals regarding knowledge and attitudes toward AI in nursing were statistically significant (p < 0.05). Regarding concerns and ethical issues about AI in nursing, every participant expressed concerns about AI in nursing, and 95.7% of participants believed that it is necessary to strengthen medical ethics toward AI in nursing. Conclusion Nursing students and healthcare professionals lacked knowledge about AI or its application in nursing, but they had a positive attitude toward AI. It is necessary to strengthen medical ethics toward AI in nursing. The study’s findings could help develop new strategies benefiting healthcare.Frontiers in Public Health535Nursing students and healthcare professionals lacked knowledge about AI or its application in nursing, but they had a positive attitude toward AI, and it is necessary to strengthen medical ethics toward AI in nursing.Frontiers in Public Health["Xiaoyan Wang", "Fangqin Fei", "Jiawen Wei", "Mingxue Huang", "Fengling Xiang", "Jing Tu", "Yaping Wang", "Jinhua Gan"]2024-07-02T00:00:00 5c5776d5bcb056b6e566fc14d93ddf97b831178eJournal of Racial and Ethnic Health Disparities424A narrative review of current literature on AI and health disparities in the United States aimed to answer the question, Does AI have the potential to reduce or eliminate health disparities, or will its use further exacerbate these disparities?Journal of racial and ethnic health disparities["Anastasia Murphy", "Kuan Bowen", "Isaam M El Naqa", "Balaurunathan Yoga", "B. Green"]2024-07-02T00:00:00 8df7b758897ed2b49cb93a7e3c5d2956d64580d7Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org.iMetaOmics212This framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine and guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences.iMetaOmics["Jinghong Chen", "Lingxuan Zhu", "Weiming Mou", "Anqi Lin", "Dongqiang Zeng", "Chang Qi", "Zao-bin Liu", "Aimin Jiang", "Bufu Tang", "Wenjie Shi", "U. D. Kahlert", "Jianguo Zhou", "Shipeng Guo", "Xiaofan Lu", "Xu Sun", "Trunghieu Ngo", "Zhongji Pu", "Baolei Jia", "Che Ok Jeon", "Yongbin He", "Haiyang Wu", "Shuqin Gu", "W. Cheungpasitporn", "Haojie Huang", "Weipu Mao", "Shixiang Wang", "Xin Chen", "Lo\u00efc Cabannes", "Gerald Sng Gui Ren", "Iain S Whitaker", "Stephen Ali", "Quan Cheng", "Kai Miao", "Shuofeng Yuan", "Peng Luo"]2024-07-02T00:00:00 006ba0cc20b3b2e77959ef1679191fd5412fb6c2The COVID-19 pandemic accelerated the adoption of artificial intelligence and the metaverse in education, highlighting their potential to personalize learning and provide instant feedback. A descriptive study was conducted with 38 teachers in Quito, evaluating the acceptance of AI technologies through surveys based on the Technology Acceptance Model. The results showed a positive attitude towards AI and the metaverse, influenced by perceived usefulness, ease of use, and self-efficacy. The importance of these factors for technological adoption is emphasized. Despite limitations, the study highlights the potential of AI and the metaverse to enhance educational practices and suggests further research.Metaverse Basic and Applied Research01Metaverse Basic and Applied Research["R. J. Posso-Pacheco", "Elizabeth Alexandra Guti\u00e9rrez-Ramos", "Nelly Jimena Chica-Montero", "Jenny Araceli Alem\u00e1n-Aguay", "Maria del Carmen Rondal-Guanotasig", "Kevin Santiago Mullo-C\u00f3ndor"]2024-07-02T00:00:00 3366107d984c1b406af24f3a271d0c9214dbb778Purpose Artificial intelligence (AI) can assist in the worldwide shortage of cybersecurity workers in technical and managerial roles. Thus, the purpose of this study was to investigate the role of AI in automating many of the routine tasks associated with cybersecurity. As such, AI enables cybersecurity personnel to reduce their workloads and focus on more strategic aspects of their work. Design/methodology/approach This study is an exploratory field study. The authors started by conducting a literature review to assess the possibility that AI tools can provide and how they can improve cybersecurity efficacy. Following this, the authors identified the specific core tasks for two cybersecurity work roles (technical and managerial) and searched for specific commercial tools that can perform each of the tasks. Then, the authors used the free ChatGPT 3.5 to list the current cybersecurity systems that use AI for the associated tasks, which the authors then reviewed with the tools’ documentation and websites to confirm these tasks were conducted or assisted by AI. Findings Results indicated that all 14 cybersecurity tasks of the technical work role are currently noted to be performed by commercial cybersecurity systems with AI-integrated capabilities, while only 11 of the 17 managerial work role tasks currently appear to be performed by AI. Practical implications The rapid integration of AI capabilities into commercial cybersecurity systems may suggest that the cybersecurity workforce must be currently trained on how to use AI tools in their daily operations, especially as it pertains to technical cybersecurity work roles. Social implications The cybersecurity workforce shortage is reported to exceed four million cybersecurity workers worldwide in 2023. Thus, further understanding of the role of AI in improving the efficiency of technical and managerial cybersecurity tasks is significant. Originality/value The value of this research lies in the initial assessment of the current AI capabilities of commercial cybersecurity systems, which will ultimately provide the “super-human” performances resulting from human-AI teaming. Information and Computer Security171The purpose of this study was to investigate the role of AI in automating many of the routine tasks associated with cybersecurity, which will ultimately provide the “super-human” performances resulting from human-AI teaming.Inf. Comput. Secur.["Ruti Gafni", "Y. Levy"]2024-07-02T00:00:00 bd6bfa6800e27a49b80662d5a18cc0ba70f9a305Journal of Business Ethics71It is argued that automatizing the exploitation of common inputs, in ways that remix and reconfigure them, can lead to a crisis of academic authorship in which the moral agency involved in scholarly production is increasingly eroded.Journal of Business Ethics["Gazi Islam", "Michelle Greenwood"]2024-07-02T00:00:00 cd58e51ced471b664f857f583b8340b2824df0bbGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie191It is identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie["Niveditha Pattathil", "Tin-Suet Joan Lee", "Ryan S. Huang", "Eleanor R. Lena", "T. Felfeli"]2024-07-02T00:00:00 5e2d8a104a807228b3705751385f9e42260736ecCurrent Allergy and Asthma Reports512Artificial intelligence appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers, and the new guidelines adapted by regulatory bodies are explored.Current allergy and asthma reports["Maham Khan", "Sandipta Banerjee", "Sakshi Muskawad", "Rick Maity", "Shubhayu Roy Chowdhury", "Rida Ejaz", "Ekins Kuuzie", "Travis Satnarine"]2024-07-02T00:00:00 505f0d256cc83016f04b5a570029fa2bd947aafbHypertension Research131In this review, it was unable to clarify whether AI-supported clinical practice improved BP control compared with usual care, and further studies will be needed to provide robust evidence for the effectiveness of AI-supported care in clinical settings.Hypertension research : official journal of the Japanese Society of Hypertension["Toshiki Maeda", "Yuki Sakamoto", "Satoshi Hosoki", "A. Satoh", "Rie Koyoshi", "Sumiyo Yamashita", "Hisatomi Arima"]2024-07-02T00:00:00 3f92b660f3991a4e510398f5736a8d9f0f85aabaJournal of the American Planning Association448Journal of the American Planning Association["Thomas W. Sanchez", "M. Brenman", "Xinyue Ye"]2024-07-02T00:00:00 26c22914b0433d4eae3e93e20ff05f9457cb2ea2This paper describes the development of an artificial intelligence(AI) device management system that utilizes over-the-air(OTA) updates to efficiently manage AI devices and the services they provide. By employing wireless communication such as short-range wireless communications or mobile telecommunications, the developed system monitors and manages the status of AI devices and update the learning model operating on the devices. In this way, the developed system enhances the performance of services provided by AI devices and ensures their stable operation.International Conference on Ubiquitous and Future Networks20The developed system enhances the performance of services provided by AI devices and ensures their stable operation by employing wireless communication such as short-range wireless communications or mobile telecommunications.2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)["Jinhong Kim", "Yun Won Choi", "Jang Woon Baek", "Kwang-Ju Kim", "Dongkyun Kim"]2024-07-02T00:00:00 3f5578c954ccc5b063943a4603b8fe2bf2f83ad7Рассматривается технология внедрения систем управления с использованием искусственного интеллекта на транспорте. Переход от ручного к полностью автономному управлению транспортным объектом представлен в виде последовательных этапов развития системы управления. Исследуется наличие в системе управления человеческого фактора как источника информации для автоматизированной системы управления с применением искусственного интеллекта (ИИ), влияния интересов человека на выбор управляющих воздействий, обучение и совершенствование системы управления с ИИ. Предложены механизмы стимулирования человека-оператора (водителя, машиниста, диспетчера) за эффективность принимаемых ими решений по сравнению с автоматизированной системой управления. The technology of control systems implementation using artificial intelligence in transport is considered. The transition from manual to fully autonomous control of a transport object is presented in the form of successive stages in the development of a control system. The presence of the human factor in the control system as a source of data for an automated control systems with artificial intelligence, the influence of human interests on the choice of control actions, training and improvement of the control system using artificial intelligence are investigated. The paper proposes mechanisms for stimulating a human operator (driver, machinist, dispatcher) for the quality of decisions made by him in comparison with the decisions of an automated control system.Транспорт: Наука, техника, управление00Транспорт: Наука, техника, управление["\u0410\u043d\u0432\u0435\u0440 \u041a\u0430\u0441\u0438\u043c\u043e\u0432\u0438\u0447 \u0415\u043d\u0430\u043b\u0435\u0435\u0432"]2024-07-02T00:00:00 93be8673f4bab8b64812ef10eaeb44370eac9bcfThe implementation of artificial intelligence has the scope to revolutionize e-government services by enhancing the quality of life for citizens, enhancing operational efficiency, and enabling groundbreaking applications. There is a possibility that this can be achieved through technical improvements. A wide range of artificial intelligence technologies and services are offered by Amazon Web Services, which has the potential to alter how e-government services are provided. The purpose of this abstract is to study the revolutionary potential of artificial intelligence by utilizing Amazon Web Services and to highlight the benefits and possibilities that it provides to the field of e-government. The utilization of artificial intelligence in conjunction with Amazon Web Services has the potential to significantly enhance the quality of e-government services by reducing inefficiencies, enabling the development of creative applications, and enabling the customization of experiences. With the assistance of the artificial intelligence tools and services offered by Amazon Web Services, the public sector can take advantage of the revolutionary power of artificial intelligence while also ensuring that responsible and ethical practices are followed.Problems of Information Society00The utilization of artificial intelligence in conjunction with Amazon Web Services has the potential to significantly enhance the quality of e-government services by reducing inefficiencies, enabling the development of creative applications, and enabling the customization of experiences.Problems of Information Society["Mohammad Ali Al Qudah", "Leyla Muradkhanli", "Mutaz Mohammed Abuhashish"]2024-07-02T00:00:00 0da828093858cb775e921332ba28ad86548a5454Managerial communication has undergone concurrent evolution with organizational theories, starting from the twentieth century. Technological progress, evidenced by the emergence of computers and the expansion of networks, has diversified communication methods in the digital era. Subsequent research has focused on sociological theories such as the classical management theory by Fayol and Weber, the Human Relations Theory, and McGregor's X and Y theory. Currently, Artificial Intelligence (AI) fundamentally alters managerial communication, bringing increased efficiency, predictive analytics, and automation. A study at MASPEX Romania highlights the positive impact of AI on reporting and collaboration processes. However, ethical dilemmas arise, including those related to privacy and social impact, necessitating regulation and heightened attention. The objective of this study is to investigate the impact and implications of Artificial Intelligence in the context of managerial communication in organizations.Bulletin of the Transilvania University of Braşov: Series VII: Social Sciences, Law160The objective of this study is to investigate the impact and implications of Artificial Intelligence in the context of managerial communication in organizations.Bulletin of the Transilvania University of Braşov. Series VII: Social Sciences • Law["Liliana Iosif"]2024-07-02T00:00:00 45ab4bcde5f4c2cfd504057d87ccd13f08d6249dThe purpose of this study is to evaluate the global and continental positions of e-government models in countries such as Jordan by analysing their experiences. The evaluation of the progress of e-government is carried out using a multi-practice methodology, which incorporates a variety of different procedures and techniques. The performance of Jordan is evaluated using the United Nations e-government maturity index, which is comprised of the Telecommunication Infrastructure Index, the Human Capital Index, and the Online Service Index. These indexes are used to compare Jordan’s performance from 2008 to 2015. The purpose of this research is to improve the capabilities of e-government by utilising previous experiences, addressing deficiencies, and making the most of potential. In addition to this, the study investigates the influence that artificial intelligence (AI) has on the confidence of users and the quality of government services that are delivered through online platforms. Specifically, the report underlines the cost-effectiveness and efficiency of adopting and utilising artificial intelligence, as well as the potential of tools and solutions that are driven by artificial intelligence.Problems of Information Society00The report underlines the cost-effectiveness and efficiency of adopting and utilising artificial intelligence, as well as the potential of tools and solutions that are driven by artificial intelligence.Problems of Information Society["Mohammad Alqudah", "Leyla Muradkhanli"]2024-07-02T00:00:00 fb60ec18308b24cf60492ffde460a30557a9dd5dJournal of Developmental and Physical Disabilities400Comparisons of the use of artificial intelligence with traditional pen and paper self-instructional MSWO training methods for five preservice SLPs suggest that artificial intelligence had a higher treatment acceptability and was more effective at producing socially significant outcomes than traditional methods.Journal of Developmental and Physical Disabilities["Brenna Griffen", "Elizabeth R. Lorah", "Christine Holyfield", "N. Caldwell", "John Nosek"]2024-07-02T00:00:00 84aa3d8b7f39ed8415396d8933b4408f8dbb0a69В исследовании приведено разработанное авторами содержание дисциплины «Технологии искусственного интеллекта», проходящей апробацию в Самарском государственном университете путей сообщения. При выборе содержания дисциплины учитывались рекомендации Министерства науки и высшего образования, а также потребности железнодорожной отрасли, актуальные тенденции в области искусственного интеллекта, текущий уровень подготовки обучающихся и доступные ресурсы для обучения. Содержание дисциплины может быть использовано для формирования цифровых компетенций студентов транспортных вузов, а также послужить основой для достижения целей по дополнению образовательных программ высшего образования по всем специальностям и направлениям подготовки разделами по изучению технологий искусственного интеллекта. The study presents the content of the discipline «Artificial Intelligence Technologies» developed by the authors, which is being tested at the Samara State University of Transport. When choosing the content of the discipline, the recommendations of the Ministry of Science and Higher Education were taken into account, as well as the needs of the railway industry, current trends in the field of artificial intelligence, the current level of training of students and available resources for training. The content of the discipline can be used to develop the digital competencies of students at transport universities, and also serve as the basis for achieving the goals of supplementing educational programs of higher education in all specialties and areas of training with sections on the study of artificial intelligence technologies.Pedagogical Perspective10Pedagogical perspective["\u0418.\u0412. \u0422\u044e\u0436\u0438\u043d\u0430", "\u0421.\u0412. \u0413\u043e\u0440\u0431\u0430\u0442\u043e\u0432"]2024-07-02T00:00:00 acfb05246413254864d6f23a1e7fdf9215a9857bThis bibliometric review analyzes the scientific production related to the variables Artificial Intelligence, Regulation and Ethics registered in the Scopus database during the period 2018-2023. The main objective of the study was to identify and characterize the volume of publications, achieving a total of 552 documents. The information collected was organized by graphs, categorizing it by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. The results reveal that the United States is the country with the highest number of publications, reaching a total of 99 scientific papers. The area of Computer Science stood out as the most prolific in terms of bibliographic contribution, with 236 documents. Likewise, Journal Articles represented 50% of total publications. This analysis also includes a qualitative study on the positions of various authors in relation to the topics addressed, providing a comprehensive view of the current state of research in this field. Among the main conclusions, it is stated that the regulation of AI is an evolving field that requires collaboration between legislators, technologists, ethicists and society in general, and managing flexible regulations is completely necessary taking into account the changing and constant evolution of everything related to AI today.International Journal of Religion140The regulation of AI is an evolving field that requires collaboration between legislators, technologists, ethicists and society in general, and managing flexible regulations is completely necessary taking into account the changing and constant evolution of everything related to AI today.International Journal of Religion["Angel Wilfrido Guevara Mena", "Luis Edison Torres Alulema", "Edgar Andr\u00e9s Quiroga Natale", "Lidue Suarez"]2024-07-02T00:00:00 825992a32da2492dc193a48cf9720c8073358733The application of artificial intelligence (AI) into photography has given rise to a controversial discussion over image perception, originality, and authenticity. Though AI presents artists with new tools and opportunities, worries over the degradation of artistic integrity and emotional depth continue. This paper investigates the complex ramifications of artificial intelligence (AI)-generated art, looking at how it affects the artistic process, public opinion, and the changing field of visual expression. Drawing on insights from recent controversies in the film industry and diverse perspectives from artists, the present essay delves into the complexities of AI-generated art.Bulletin of the Transilvania University of Braşov: Series VII: Social Sciences, Law140This paper investigates the complex ramifications of artificial intelligence (AI)-generated art, looking at how it affects the artistic process, public opinion, and the changing field of visual expression.Bulletin of the Transilvania University of Braşov. Series VII: Social Sciences • Law["Eduard C. Gross"]2024-07-02T00:00:00 27c2c020fb711897c82c9cda71e38cca915910feThe adoption of human oversight measures makes it possible to regulate, to varying degrees and in different ways, the decision-making process of Artificial Intelligence (AI) systems, for example by placing a human being in charge of supervising the system and, upstream, by developing the AI system to enable such supervision. Within the global governance of AI, the requirement for human oversight is embodied in several regulatory formats, within a diversity of normative sources. On the one hand, it reinforces the accountability of AI systems' users (for example, by requiring them to carry out certain checks) and, on the other hand, it better protects the individuals affected by the AI-based decision (for example, by allowing them to request a review of the decision). In the European context, the AI Act imposes obligations on providers of high-risk AI systems (and to some extent also on professional users of these systems, known as deployers), including the introduction of human oversight tools throughout the life cycle of AI systems, including by design (and their implementation by deployers). The EU legislator is therefore going much further than in the past in"spelling out"the legal requirement for human oversight. But it does not intend to provide for all implementation details; it calls on standardisation to technically flesh out this requirement (and more broadly all the requirements of section 2 of chapter III) on the basis of article 40 of the AI Act. In this multi-level regulatory context, the question of the place of humans in the AI decision-making process should be given particular attention. Indeed, depending on whether it is the law or the technical standard that sets the contours of human oversight, the"regulatory governance"of AI is not the same: its nature, content and scope are different. This analysis is at the heart of the contribution made (or to be made) by legal experts to the central reflection on the most appropriate regulatory governance -- in terms of both its institutional format and its substance -- to ensure the effectiveness of human oversight and AI trustworthiness.arXiv.org00The EU legislator is going much further than in the past in spelling out the legal requirement for human oversight; it calls on standardisation to technically flesh out this requirement (and more broadly all the requirements of section 2 of chapter III) on the basis of article 40 of the AI Act.ArXiv["Marion Ho-Dac", "Baptiste Martinez"]2024-07-02T00:00:00 d44e3f5e252ffd8bd44cb1cd230988f951983b76The increasing integration of Artificial Intelligence (AI) into everyday life makes it essential to explain AI-based decision-making in a way that is understandable to all users, including those with disabilities. Accessible explanations are crucial as accessibility in technology promotes digital inclusion and allows everyone, regardless of their physical, sensory, or cognitive abilities, to use these technologies effectively. This paper presents a systematic literature review of the research on the accessibility of Explainable Artificial Intelligence (XAI), specifically considering persons with sight loss. Our methodology includes searching several academic databases with search terms to capture intersections between XAI and accessibility. The results of this survey highlight the lack of research on Accessible XAI (AXAI) and stress the importance of including the disability community in XAI development to promote digital inclusion and accessibility and remove barriers. Most XAI techniques rely on visual explanations, such as heatmaps or graphs, which are not accessible to persons who are blind or have low vision. Therefore, it is necessary to develop explanation methods through non-visual modalities, such as auditory and tactile feedback, visual modalities accessible to persons with low vision, and personalized solutions that meet the needs of individuals, including those with multiple disabilities. We further emphasize the importance of integrating universal design principles into AI development practices to ensure that AI technologies are usable by everyone.arXiv.org00A systematic literature review of the research on the accessibility of Explainable Artificial Intelligence (XAI), specifically considering persons with sight loss and the importance of including the disability community in XAI development to promote digital inclusion and accessibility and remove barriers is presented.ArXiv["C. Nwokoye", "Maria J. P. Peixoto", "Akriti Pandey", "Lauren Pardy", "Mahadeo Sukhai", "Peter R. Lewis"]2024-07-02T00:00:00 e7a4e1983eda654380fb17ae2fa0c259cfe64e69Higher education institutions must recognize that jobs will change significantly as the world enters the Fourth Industrial Revolution and experiences new technological advancements, particularly in artificial intelligence (AI). Both workers and students will need to adapt, and higher education must be able to provide students with the skill set they need to enter and advance in the workforce of the future. Even though the vast majority of journalism academic programs today seem to focus more on the theoretical and practical aspects of journalism, such as news literacy, introduction to digital journalism, journalistic reporting and writing, and global issues in journalism, industry leaders already see artificial intelligence (AI) as playing a big role in journalism in the future. AI has a big potential to change how media is written and consumed. This article offers a proposal for an undergraduate journalism degree with an artificial intelligence (AI) concentration. It is based on projections of employment for the next five to ten years, the government of the United Arab Emirates' (UAE) strategic orientation, higher education, and industry demands. The planned degree, the first of its type, is intended to give students the skills, technology know-how, and information necessary to succeed in the journalism field in the future.   Received: 4 February 2024 / Accepted: 29 June 2024 / Published: 02 July 2024Academic Journal of Interdisciplinary Studies660The planned degree, the first of its type, is intended to give students the skills, technology know-how, and information necessary to succeed in the journalism field in the future.Academic Journal of Interdisciplinary Studies["Maytha Al-Ali"]2024-07-02T00:00:00 796277b59d75b6b85b4a39d61bf4b0d1fbac91b0The medical industry is undergoing an active digital transformation, including the creation of electronic databases, cloud security systems, mobile health monitoring devices, and telemedicine tools. Artificial intelligence (AI), one of the most important technological achievements of the last decade, is gradually gaining momentum in various areas of practical medicine. The cutting edge of AI, neural networks, offers promising approaches to the improvement of clinical examination quality. The review presents data of studies focusing on the use of AI tools in the diagnosis of the most common ophthalmic diseases: diabetic retinopathy, macular degeneration, retinopathy of prematurity, glaucoma, cataracts, and ophthalmic oncology. We discuss both the advantages of neural networks in the diagnosis and monitoring of eye diseases, and outline the difficulties of their implementation, including ethical and legal conflicts.Russian Ophthalmological Journal550Both the advantages and difficulties of neural networks in the diagnosis and monitoring of eye diseases are discussed, and the difficulties of their implementation are outlined, including ethical and legal conflicts.Russian Ophthalmological Journal["V. Neroev", "O. V. Zaytseva", "S. Petrov", "A. A. Bragin"]2024-07-02T00:00:00 2d88f0f218151f6f38f3888024ccff9e8c3ed0f7Artificial Intelligence (AI) represents a transformative force across numerous sectors, from healthcare and finance to automotive and public services. The selection and deployment of AI tools are critical to leveraging this technology’s potential while adhering to ethical standards, regulatory compliance, and ensuring societal benefit. The European Union (EU) has been at the forefront of establishing frameworks and criteria to guide the development, deployment, and selection of AI systems to foster innovation while protecting citizens’ rights and societal values. The EU’s proactive stance in establishing these criteria aims to balance innovation with ethical considerations and societal welfare, setting a benchmark for responsible AI development and deployment globally. The aim of the article is to present general criteria for the selection of artificial intelligence tools, as well as those specific to the field of publishing. The research was carried out based on the analysis of scientific and other sources. The results of the study can be useful for organizations and individuals that must be interested in selecting and using the right AI tools.Innovations in Publishing, Printing and Multimedia Technologies00General criteria for the selection of artificial intelligence tools, as well as those specific to the field of publishing are presented, based on the analysis of scientific and other sources.Innovations in Publishing, Printing and Multimedia Technologies["Lina \u0160arlauskien\u0117", "Samanta Dagyt\u0117"]2024-07-02T00:00:00 b6d879fed532ea34c6e551a7af6c9f68f29a883bBackground Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. Clinical Impact Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.Frontiers in Endocrinology300Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.Frontiers in Endocrinology["Si Eun Lee", "Hye Jung Kim", "Hae Kyoung Jung", "Jin Hyang Jung", "Jae-Han Jeon", "Jin Hee Lee", "H. Hong", "Eun Jung Lee", "Daham Kim", "Jin Young Kwak"]2024-07-02T00:00:00 a43e8eaeaa44ffcc55e17747c28e5914f2491ceeThis article critically analyzes the ethical and legal implications of adopting predictive analytics by the French administrative justice system. It raises a key question: Is it wise to integrate artificial intelligence into the administrative justice system, considering its potential benefits, despite the associated risks, ethical dilemmas, and legal challenges? The research employs a method based on an extensive literature review, a qualitative analysis of the adoption by the French administrative justice of predictive analytics tools, and a critical evaluation of the benefits and issues these tools bring. The study finds that AI can make the administrative justice system more efficient, reduce backlogs, and enhance the consistency and predictability of judicial decisions. However, the study also identifies important risks and serious ethical and legal issues associated with integrating AI tools into the justice system. Especially, AI utilization can lead to the dehumanization of justice and poses real risks to the independence and impartiality of justice. While AI can offer significant benefits to all the stakeholders of the administrative justice system, its integration must be approached with caution. A progressive and responsible approach to AI adoption is necessary to avoid compromising judicial integrity and upholding fundamental justice values. Hasanuddin Law Review00The study finds that AI can make the administrative justice system more efficient, reduce backlogs, and enhance the consistency and predictability of judicial decisions, however, the study also identifies important risks and serious ethical and legal issues associated with integrating AI tools into the justice system.Hasanuddin Law Review["Zouhaier Nouri", "Walid Ben Salah", "Nayel Al Omrane"]2024-07-02T00:00:00 86e62a96738467a311a83fd3d7872c67d6c8ba6cJournal of Management Information Systems765Journal of Management Information Systems["Kambiz Saffarizadeh", "Mark Keil", "Likoebe M. Maruping"]2024-07-02T00:00:00 d4fc2821c1fe7f6f9f4b219b5c28feec699cd074International Journal of Computer Assisted Radiology and Surgery262Predicting the stress level based on the ergonomic and physiological parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity demonstrates the possibility of predicting factors that help to improve the surgeon's health during robotic surgery.International journal of computer assisted radiology and surgery["Daniel Caballero", "Manuel J. P\u00e9rez-Salazar", "J. S\u00e1nchez-Margallo", "F. S\u00e1nchez-Margallo"]2024-07-02T00:00:00 324d41e746ee9644a0bc05309809b4a500415df1perior performance in predicting significant fibrosis compared to traditional biomarkers.Hepatology Forum231Hepatology Forum["Cem \u015eim\u015fek"]2024-07-02T00:00:00 6dafd88a33bd97bed0d3135fc5db1c8eab68d5a5The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases. This study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifiers were support vector machine (SVM), K-nearest neighbor (K-NN), decision tree (DT), and ensemble model (EM). A feature extractor, the InceptionV3 convolutional neural network, was also employed. The performance of the models was evaluated against nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM classifiers, the analysis exhibited state-of-the-art performance, with classification accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identification and classification of retinal disorders, leading to reduced human error and saved time. .Biomedical engineering and physics express02An automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases is created.Biomedical physics & engineering express["A. M. Salaheldin", "Manal Abdel Wahed", "N. Saleh"]2024-07-02T00:00:00 31746164719451f8be14eff1baddb5dd53e54141Journal of Management Information Systems971Journal of Management Information Systems["Hanzhuo Ma", "Wei Huang", "Alan R. Dennis"]2024-07-02T00:00:00 db40b316d9008b2b0f19481bf152bd7e9c19391fJournal of Oral Biology and Craniofacial Research271It was observed that academicians are more aware of AI terminologies and usage as compared to PG students and clinicians, and there is a consensus that AI is a useful tool for diagnosis and treatment planning, boosting performance and quality care in orthodontics.Journal of Oral Biology and Craniofacial Research["Arvind Mengi", "R. Singh", "Nancy Mengi", "Sneh Kalgotra", "Abhishek Singh"]2024-07-02T00:00:00 683f18ae16214670c3af6e7de8a11c479c41b543Spectra Undergraduate Research Journal00Spectra Undergraduate Research Journal["Jonathan Moore"]2024-07-02T00:00:00 03db2a079d712fdd5d64d21030056f372f6ed843Journal of Behavorial Finance580Journal of Behavioral Finance["Baris Kocaarslan"]2024-07-02T00:00:00 c8f82c90a8d709788c1a38eacd8f2b60c0117187Journal of Management Information Systems1060Journal of Management Information Systems["Magno Queiroz", "Abhijith Anand", "Aaron Baird"]2024-07-02T00:00:00 0524a0e7d2eb7b3b968464df9f6e1fb29c53c989AI algorithms designed to clone human speaker identity are reportedly capable of replicating human-specific vocal expression. However, whether listeners can identify a single speaker expressing varying emotive states as one individual remains unclear, particularly never in AI-to-AI pairings. This study asked thirty-six Chinese listeners to hear two consecutive clips and to judge whether identical speakers delivered pairs of Chinese sentences in human-only and AI-only scenarios, with the prosody of the first and second clips being incongruent or congruent. We found a decrease in the accuracy of identifying the same speaker under inconsistent prosody conditions compared to consistent ones, a trend evident in both human-to-human and AI-to-AI pairs. Meanwhile, correctly distinguishing between two speakers was more challenging than identifying a single speaker, with AI pairs reporting notably poorer performance than human-human pairs. When presented with pairs of speakers using consistent prosody, listeners demonstrated significantly slower reaction times when identifying two speakers. Our findings suggest that vocal prosodies can lead to within-speaker identity variation, in which listeners form average-based representations and still recognise the same speaker across prosodies. The findings about the reduced capability in speaker discrimination in AI voices provide supportive evidence for the ‘out-group homogeneity effect’ of AI voice perception.Proceedings of the International Conference on Speech Prosody170It is suggested that vocal prosodies can lead to within-speaker identity variation, in which listeners form average-based representations and still recognise the same speaker across prosodies, supportive evidence for the ‘out-group homogeneity effect’ of AI voice perception.Speech Prosody 2024["Wenjun Chen", "Xiaoming Jiang", "Jingyi Ge", "Shuwan Shan", "Siyuan Zou", "Yiyang Ding"]2024-07-02T00:00:00 d506d371ae537618d9d0d5a68c81c1e6ec3379c000["Michael E. Miller", "Christina F. Rusnock"]2024-07-02T00:00:00 ff4b63bc24f0663f635ad159dc20037fc41922e2The article examines risks faced by banks during their lending processes and the mechanisms for managing these risks, utilizing modern statistical methods. Specifically, the study focused on the artificial neural network model as a technique of artificial intelligence that has successfully applied various classifications and discrimination tasks among institutions. A random sample of 46 institutions obtained loans from the branches of the National Bank of Algeria (BNA), Local Development Bank (BDL), Popular Credit of Algeria (CPA), and Agricultural and Rural Development Bank (BADR) in El Bayadh province, Algeria. Each of these institutions was characterized by 14 measurable variables with numerical values derived from the financial statements (balance sheets and income statements), as well as 3 qualitative non-accounting variables extracted from the loan applicants’ files (age of the institution, sector of activity (services/productive), institution status (viable/struggling). The sample of these 46 institutions was initially divided into two groups: 64% comprised financially stable institutions, and the other 36% were struggling institutions. The research checks whether the risk assessment of each of these 46 institutions using artificial neural networks will identify their institution status (viable/struggling) in the same way as it was in the base sample. The training phase recorded a prediction error rate of 0%, and the network testing phase misclassification rate was 5.6%. The overall correct classification rate for the multilayer artificial neural network was 92.9%, with a total error rate of 7.1%. The contribution rate of the non-accounting variable “sector of activity” was 100%, and the variable “age of the institution” was 94.4%. Other variables had minor percentages, underscoring the importance of qualitative variables in the classification process. Thus, the study proved that artificial neural network model is an effective model for distinguishing between viable and struggling institutions, significantly contributing to banking risk management.SocioEconomic Challenges02The study proved that artificial neural network model is an effective model for distinguishing between viable and struggling institutions, significantly contributing to banking risk management.SocioEconomic Challenges["Mohamed Roba", "Oum Keltoum Moulay"]2024-07-02T00:00:00 6716ffbef73bf7bbb3583c7c161ece6c8f50cf1eThe learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a dual network is outlined acting between synapses of a basic ANN, which controls the learning process and coordinates the subnetworks selected by attention mechanisms toward purposeful behaviors. Mechanisms of memory and their affinity with comprehension are considered, by emphasizing the common role of abstraction and the interplay between assimilation and accommodation, in the spirit of Piaget’s analysis of psychological acquisition and genetic epistemology. Learning, comprehension, and knowledge are expressed as different levels of organization of informational processes inside cognitive systems. It is argued that formal analyses of cognitive artificial systems could shed new light on typical mechanisms of “natural intelligence” and, in a specular way, that models of natural cognition processes could promote further developments of ANN models. Finally, new possibilities of chatbot interaction are briefly discussed.Inf.193It is argued that formal analyses of cognitive artificial systems could shed new light on typical mechanisms of “natural intelligence” and, in a specular way, that models of natural cognition processes could promote further developments of ANN models.Inf.["Vincenzo Manca"]2024-07-02T00:00:00 e46d114d8178b11920b6c15ea4baabc3d1763124Artificial intelligence (AI) is transforming numerous sectors, including the port industry. This article addresses how AI-based solutions can revolutionize business development in port environments, providing significant competitive advantages. The state of the art reveals that ports face increasing challenges in terms of operational efficiency, resource optimization, and competitiveness. In this context, AI emerges as a powerful tool capable of analyzing large volumes of data and making informed decisions.The content of the article is structured into several key sections. First, it explores the competitive advantages offered by AI in the port sector, such as resource optimization, strategic decision-making, improved security, environmental sustainability, and customer experience. Specific applications such as predictive analytics, process automation, and route optimization are discussed. Case studies of ports that have successfully implemented AI technologies, such as the Port of Rotterdam, the Port of Hamburg, and the Port of Singapore, are presented to illustrate the benefits obtained and the challenges overcome.The development of the research focuses on the algorithms and analysis techniques used, including machine learning, neural networks, and natural language processing. Various applications in the port sector are analyzed, from facility pre-design and space optimization to Capex and Opex estimations and business generation capacity. AI is also used to improve operational efficiency and optimize investments and costs.The conclusions emphasize that, although the adoption of AI presents challenges, such as the need for advanced technological infrastructure and continuous staff training, the potential benefits are significant. AI enables ports to improve operational efficiency, reduce costs, increase security and sustainability, and offer better services to customers. Finally, recommendations for the successful implementation of AI solutions are proposed, emphasizing the importance of detailed strategic planning, collaboration with technology partners, and adequate staff training.article is to provide an extensive comparison of some of them, showing their similarities and also their regulatory differences, as well as possible gaps and inconsistencies.Revista de Ordenación del Sector Marítimo00How AI-based solutions can revolutionize business development in port environments, providing significant competitive advantages is addressed, emphasizing the importance of detailed strategic planning, collaboration with technology partners, and adequate staff training.Revista de Ordenación del Sector Marítimo["Pablo Alonso Medina", "Ricardo Sanz S\u00e1iz"]2024-07-02T00:00:00 efa96e9059334396b433be9981f06004367bcd24The development of embedded artificial intelligence, and particularly artificial neural networks (ANN), faces a need for a good adequacy between target and algorithms. This paper presents a benchmark of computer vision ANN performance in embedded systems typically used in ground vehicle. Based on the state of the art we choose several ANN architectures and embedded targets in order to test their performances in image classification (12 ANNs), object detection (22 ANNs), and semantic segmentation (8 ANNs). We propose an analysis of the results as well as decision guidelines to select the target-ANN couple that best fits the requirements of an application.International Conference on Ubiquitous and Future Networks190A benchmark of computer vision ANN performance in embedded systems typically used in ground vehicle is presented and an analysis of the results as well as decision guidelines to select the target-ANN couple that best fits the requirements of an application are proposed.2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)["Julien Beloin", "Louis Bonicel", "Philippe Millet"]2024-07-02T00:00:00 7caba85fc7d0a76ebf53cdeb9c1811d3bf80ccf0 Purpose Grounded on the X Reality framework and human–machine collaboration, this study aims to explore the potential of immersive augmentation through artificial intelligence (AI) service robots for promoting social inclusion in the hospitality industry. Design/methodology/approach Three experimental studies across diverse hospitality contexts examine the effects of immersive augmentation using inclusive-AI service robots compared to standard-AI robots. The studies also uncover the underlying process of perceived ethicality and the moderating role of customers’ familiarity with AI. Findings The results indicate that immersive augmentation through inclusive-AI service robots generates higher levels of supportive tipping behavior (Studies 1 and 3), superior buying intentions (Study 2) and an increased likelihood for customers to pay a premium price (Study 2). These effects are mediated by perceived ethicality (Studies 1–3). However, the impact of immersive augmentation for social inclusion is contingent upon customers’ familiarity with AI: customers with high familiarity with AI exhibit lower levels of supportive tipping behavior (Study 3). Research limitations/implications The findings emphasize the importance of perceived ethicality and customers’ familiarity with AI in determining the effectiveness of immersive augmentation for social inclusion in hospitality. Originality/value This study contributes to the literature by exploring the potential of immersive augmentation using AI service robots for social inclusion in hospitality. It offers novel insights by highlighting the importance of perceived ethicality and customers’ familiarity with AI. The findings provide valuable guidance for hospitality managers seeking to leverage AI technology to foster social inclusion. International Journal of Contemporary Hospitality Management756The results indicate that immersive augmentation through inclusive-AI service robots generates higher levels of supportive tipping behavior, superior buying intentions and an increased likelihood for customers to pay a premium price.International Journal of Contemporary Hospitality Management["H\u00e9ctor Gonz\u00e1lez\u2010Jim\u00e9nez", "Diego Costa Pinto"]2024-07-02T00:00:00 5c3af45748a345462de2567d8482fbba9444c138The introduction and rapid evolution of generative artificial intelligence (genAI) models necessitates a refined understanding for the concept of “intelligence”. The genAI tools are known for its capability to produce complex, creative, and contextually relevant output. Nevertheless, the deployment of genAI models in healthcare should be accompanied appropriate and rigorous performance evaluation tools. In this rapid communication, we emphasizes the urgent need to develop a “Generative AIQ Test” as a novel tailored tool for comprehensive benchmarking of genAI models against multiple human-like intelligence attributes. A preliminary framework is proposed in this communication. This framework incorporates miscellaneous performance metrics including accuracy, diversity, novelty, and consistency. These metrics were considered critical in the evaluation of genAI models that might be utilized to generate diagnostic recommendations, treatment plans, and patient interaction suggestions. This communication also highlights the importance of orchestrated collaboration to construct robust and well-annotated benchmarking datasets to capture the complexity of diverse medical scenarios and patient demographics. This communication suggests an approach aiming to ensure that genAI models are effective, equitable, and transparent. To maximize the potential of genAI models in healthcare, it is important to establish rigorous, dynamic standards for its benchmarking. Consequently, this approach can help to improve clinical decision-making with enhancement in patient care, which will enhance the reliability of genAI applications in healthcare.Mesopotamian Journal of Artificial Intelligence in Healthcare406This communication suggests an approach aiming to ensure that genAI models are effective, equitable, and transparent to establish rigorous, dynamic standards for its benchmarking, which can help to improve clinical decision-making with enhancement in patient care.Mesopotamian Journal of Artificial Intelligence in Healthcare["Malik Sallam", "Roaa Khalil", "Mohammed Sallam"]2024-07-02T00:00:00 b6faeb2181c36cc8444d270c612ec19103c564f0BMC Medical Imaging664The AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion and improve the diagnostic accuracy of IA and reduce missed detection and false positives.BMC Medical Imaging["Zhiyue Zhou", "Yuxuan Jin", "Haili Ye", "Xiaoqing Zhang", "Jiang Liu", "Wenyong Zhang"]2024-07-02T00:00:00 ee65797e34b92455ad3da21798183f692a84612c The launch of ChatGPT in late 2022 led to major controversies about the governance of generative artificial intelligence (AI). This article examines the first international governance and policy initiatives dedicated specifically to generative AI: the G7 Hiroshima process, the Organisation for Economic Cooperation and Development reports, and the UK AI Safety Summit. This analysis is informed by policy framing and governance literature, in particular by the work on technology governance and Responsible Innovation. Emerging governance of generative AI exhibits characteristics of polycentric governance, where multiple and overlapping centers of decision-making are in collaborative relationships. However, it is dominated by a limited number of developed countries. The governance of generative AI is mostly framed in terms of the risk management, largely neglecting issues of purpose and direction of innovation, and assigning rather limited roles to the public. We can see a “paradox of generative AI governance” emerging, namely, that while this technology is being widely used by the public, its governance is rather narrow. This article coins the term “governance fix” to capture this rather narrow and technocratic approach to governing generative AI. As an alternative, it suggests embracing the politics of polycentric governance and Responsible Innovation that highlight democratic and participatory co-shaping of technology for social benefit. In the context of the highly unequal distribution of power in generative AI characterized by a high concentration of power in a small number of large tech companies, the government has a special role in reshaping the power imbalances by enabling wide-ranging public participation in the governance of generative AI.Policy & Society563This article examines the first international governance and policy initiatives dedicated specifically to generative AI: the G7 Hiroshima process, the Organisation for Economic Cooperation and Development reports, and the UK AI Safety Summit, informed by policy framing and governance literature.Policy and Society["I. Ulnicane"]2024-07-02T00:00:00 fff62c9ed1ec4a04ab682e8b008ea735b6d6ea10-The integration of Artificial Intelligence (AI) in mobile health (mHealth) devices offers significant advancements in patient care but also raises complex security and privacy concerns for sensitive health data. This paper explores the evolving landscape of Health Insurance Portability and Accountability Act (HIP AA) compliance within the context of AI-based mHealth devices, focusing on anticipated challenges in 2024. It examines the current state of HIP AA compliance in AI-driven mHealth, identifying potential vulnerabilities and gaps among mHealth devices in considering privacy and security. The study outlines key security and privacy considerations specific to AI-powered mHealth technologies, emphasizing risks such as data breaches, ransomware attack and unauthorized access. The paper concludes by highlighting the importance of continual adaptation to the dynamic nature of AI technologies and offers insights for stakeholders to navigate the regulatory landscape and ensure the responsible deployment of AI in healthcare.Annual International Computer Software and Applications Conference02The evolving landscape of Health Insurance Portability and Accountability Act (HIP AA) compliance within the context of AI-based mHealth devices, focusing on anticipated challenges in 2024 is explored.2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)["A. K. I. Riad", "Abdul Barek", "Md. Mostafizur Rahman", "Mst. Shapna Akter", "Tahia Islam", "Mohamed Abdur Rahman", "Md. Raihan Mia", "Hossain Shahriar", "Fan Wu", "S. Ahamed"]2024-07-02T00:00:00 c87057b7e3d73fe4c9075f3e50dcc2a02452a5cbAdvances in Artificial Intelligence (AI) are helping tackle a growing number of societal challenges, demonstrating technology's increasing capability to address complex issues, including those outlined in the United Nations (UN) Sustainable Development Goals (SDGs). Despite global efforts, 80 percent of SDG targets have deviated, stalled, or regressed, and only 15 percent are on track as of 2023, illustrating the urgency of accelerating efforts to meet the goals by 2030. We draw on Google's internal and collaborative research, technical work, and social impact initiatives to show AI's potential to accelerate action on the SDGs and make substantive progress to help address humanity's most pressing challenges. The paper highlights AI capabilities (including computer vision, generative AI, natural language processing, and multimodal AI) and showcases how AI is altering how we approach problem-solving across all 17 SDGs through use cases, with a spotlight on AI-powered innovation in health, education, and climate. We then offer insights on AI development and deployment to drive bold and responsible innovation, enhance impact, close the accessibility gap, and ensure that everyone, everywhere, can benefit from AI.arXiv.org852AI capabilities are highlighted and how AI is altering how the authors approach problem-solving across all 17 SDGs through use cases are showcased, with a spotlight on AI-powered innovation in health, education, and climate.ArXiv["Brigitte Hoyer Gosselink", "Kate Brandt", "Marian Croak", "Karen DeSalvo", "Ben Gomes", "Lila Ibrahim", "Maggie Johnson", "Yossi Matias", "Ruth Porat", "Kent Walker", "James Manyika"]2024-07-02T00:00:00 6252a280bf26dac598d5c28f09ba6de56d1211bdThis paper embarks on a pivotal scholarly journey to scrutinize the civil liability implications of Artificial Intelligence (AI) within the Jordanian legal framework. As AI transforms from a speculative concept into an omnipresent reality, it presents unique jurisprudential challenges, particularly civil liability. This study aims to bridge the jurisprudential vacuum in Jordan, where an absence of judicial precedents highlights the urgency for legal exploration in this emerging field. Adopting a doctrinal legal research methodology, the study rigorously examines Jordanian liability theories and their applicability to AI and undertakes a comparative analysis with more advanced legal systems in AI regulation. The paper navigates the intricacies of AI in the context of traditional Jordanian legal theories, such as objective and personal liability theories. It extends to explore the challenges of attributing liability in the age of AI. Key findings reveal significant inadequacies in Jordan’s current legal provisions to address AI-induced liabilities, the disconnection between civil liability and AI accountability, jurisdictional ambiguities, and challenges in applying traditional legal concepts to the unpredictable nature of AI. The study proposes granting AI legal personality, legislative intervention, enforcing global accountability standards, advocating for international cooperation, and exploring technological limitations with ethical programming. This research contributes profoundly to academic discourse and policy formulation, particularly in the Jordanian context, highlighting the need for a comprehensive, adaptable, and robust legal framework that addresses the unique challenges posed by AI and robotics in the legal domain.Jordanian Journal of Law and Political Science411This research contributes profoundly to academic discourse and policy formulation, particularly in the Jordanian context, highlighting the need for a comprehensive, adaptable, and robust legal framework that addresses the unique challenges posed by AI and robotics in the legal domain.The Jordanian Journal of Law and Political Science["Ahmed Al-Hawamdeh", "T. Alhasan"]2024-07-02T00:00:00 267f4b61e020dc8567a2105e5c8b41683c98b12bCurrent artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites—Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals.Energies451Energies["Ebubekir Demir", "Mahmut \u00c7avur", "Yu-Ting Yu", "H. S. Duzgun"]2024-07-02T00:00:00 1f6598f89c10a5f2d5175ae3622f542b0d177fd8Game-Based Learning (GBL) and its subset, Board Game-Based Learning (bGBL), are dynamic pedagogical approaches leveraging the immersive power of games to enrich the learning experience. bGBL is distinguished by its tactile and social dimensions, fostering interactive exploration, collaboration, and strategic thinking; however, its adoption is limited due to lack of preparation by teachers and educators and of pedagogical and instructional frameworks in scientific literature. Artificial intelligence (AI) tools have the potential to automate or assist instructional design, but carry significant open questions, including bias, lack of context sensitivity, privacy issues, and limited evidence. This study investigates ChatGPT as a tool for selecting board games for educational purposes, testing its reliability, accuracy, and context-sensitivity through comparison with human experts evaluation. Results show high internal consistency, whereas correlation analyses reveal moderate to high agreement with expert ratings. Contextual factors are shown to influence rankings, emphasizing the need to better understand both bGBL expert decision-making processes and AI limitations. This research provides a novel approach to bGBL, provides empirical evidence of the benefits of integrating AI into instructional design, and highlights current challenges and limitations in both AI and bGBL theory, paving the way for more effective and personalized educational experiences.Intelligenza Artificiale801This research provides a novel approach to bGBL, provides empirical evidence of the benefits of integrating AI into instructional design, and highlights current challenges and limitations in both AI and bGBL theory, paving the way for more effective and personalized educational experiences.Intelligenza Artificiale["Andrea Tinterri", "Federica Pelizzari", "Marilena Di Padova", "Francesco Palladino", "Giordano Vignoli", "Anna Dipace"]2024-07-02T00:00:00 cf395fdb7b356ead446173ff3f5bbd80687fd610Humanities and Social Sciences Communications141It is argued that by designing policies that address risks associated with AI development and implementation, smaller countries can create a vibrant AI ecosystem that encourages experimentation and early adoption of the technology.Humanities and Social Sciences Communications["Shaleen Khanal", "Hongzhou Zhang", "Araz Taeihagh"]2024-07-02T00:00:00 23d559caf346b8f9458fadac2005ddfa6aecd488The integration of Artificial Intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.Drones1089This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making.Drones["Daniel Caballero-Martin", "J. M. L\u00f3pez-Guede", "Juli\u00e1n Est\u00e9vez", "M. Gra\u00f1a"]2024-07-03T00:00:00 305546cb41590a9566f8c812bb9a0537b598981bAbstract With the advances in artificial intelligence (AI), data‐driven algorithms are becoming increasingly popular in the medical domain. However, due to the nonlinear and complex behavior of many of these algorithms, decision‐making by such algorithms is not trustworthy for clinicians and is considered a black‐box process. Hence, the scientific community has introduced explainable artificial intelligence (XAI) to remedy the problem. This systematic scoping review investigates the application of XAI in breast cancer detection and risk prediction. We conducted a comprehensive search on Scopus, IEEE Explore, PubMed, and Google Scholar (first 50 citations) using a systematic search strategy. The search spanned from January 2017 to July 2023, focusing on peer‐reviewed studies implementing XAI methods in breast cancer datasets. Thirty studies met our inclusion criteria and were included in the analysis. The results revealed that SHapley Additive exPlanations (SHAP) is the top model‐agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis. Additionally, the SHAP model primarily explained tree‐based ensemble machine learning models. The most common reason is that SHAP is model agnostic, which makes it both popular and useful for explaining any model prediction. Additionally, it is relatively easy to implement effectively and completely suits performant models, such as tree‐based models. Explainable AI improves the transparency, interpretability, fairness, and trustworthiness of AI‐enabled health systems and medical devices and, ultimately, the quality of care and outcomes.Cancer Innovation1466It is revealed that SHapley Additive exPlanations (SHAP) is the top model‐agnostic XAI technique in breast cancer research in terms of usage, explaining the model prediction results, diagnosis and classification of biomarkers, and prognosis and survival analysis.Cancer Innovation["Amirehsan Ghasemi", "Soheil Hashtarkhani", "David L. Schwartz", "A. Shaban-Nejad"]2024-07-03T00:00:00 dd79d2bbf67ce8a56b072d4b93d09d0dd1c9890aArtificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper’s significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.Frontiers in Public Health744This research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.Frontiers in Public Health["Jin Wang", "Jianxiang Li"]2024-07-03T00:00:00 99f57ee943d207212561798b55aec23f96375a8ePurpose: The aim of the study was to investigate the ethical issues in artificial intelligence adoption in African higher education institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Ethical concerns surrounding the adoption of Artificial Intelligence (AI) in African higher education institutions reveal key findings from recent studies. These include issues of data privacy, transparency in AI algorithms, and ethical implications in decision-making processes. Universities in Africa implementing AI technologies must navigate challenges such as ensuring ethical data handling and maintaining transparency in AI systems. Unique Contribution to Theory, Practice and Policy: Ethical frameworks in AI adoption, critical theory & ethics of care may be used to anchor future studies on the ethical issues in artificial intelligence adoption in African higher education institutions. Embed ethics education into AI courses and training programs across African universities. Equip students, educators, and AI developers with the knowledge and skills to identify, analyze, and address ethical dilemmas associated with AI technologies. Develop and enforce clear, context-specific AI ethics policies and regulatory frameworks at national and institutional levels. These policies should outline guidelines for data management, algorithmic transparency, and ethical review processes.African Journal of Information and Knowledge Management301This study looked into already published studies and reports as the data was easily accessed through online journals and libraries as the data was easily accessed through online journals and libraries.African Journal of Information and Knowledge Management["Adewale Afolabi"]2024-07-03T00:00:00 c594b0cacaa2a3ba4af95e6a074cd004649eea44Artificial Intelligence (AI), as in various other fields, is causing significant changes in the fields of communication and public relations by modernizing traditional methods and tools. AI applications, which have evolved with technological advancements, introduce new tools that make communication processes more efficient and effective, enabling both time and resource savings. Notably, comprehensive data analyses and the ability to understand target audience behaviors, along with continuous communication opportunities provided by automated response systems and chatbots, are particularly striking. In this context, the new opportunities that AI brings to the fields of communication and public relations are creating positive impacts in current sectoral practices and are also being reflected in academic studies conducted in the field. This study is a descriptive research aimed at examining academic studies on artificial intelligence in the fields of communication and public relations published in journals included in the Dergi Park system. The sample of the study consists of articles published in the Dergi Park system between 1990 and 2023, identified using the keywords communication-artificial intelligence and public relations-artificial intelligence. Data were analyzed using the MAXQDA20 software. The analysis of the study is based on a systematic and interpretive approach that considers the titles, abstracts, keywords, publication years, publication languages, research methods used, and the authors' fields of study. According to the findings, a total of 32 articles on communication, public relations, and artificial intelligence were identified, and it was determined that the majority of these articles were conducted using a qualitative research approach. The research findings indicate that the number of academic studies published on artificial intelligence has increased over the years, but the total number of published articles remains insufficient. Additionally, among the communication subtopics associated with artificial intelligence, public relations stands out as a diverse and multidimensional field, with the most intensive studies found in the areas of corporate communication, crisis communication, and reputation management, respectively.Sosyal bilimler dergisi281The research findings indicate that the number of academic studies published on artificial intelligence has increased over the years, but the total number of published articles remains insufficient and public relations stands out as a diverse and multidimensional field.Mevzu – Sosyal Bilimler Dergisi["Emel Ku\u015fku \u00d6zdemir"]2024-07-03T00:00:00 2febaca2c779970b24e5d62e3761c33011e7a39aArtificial intelligence has been talked about for over half a century now. Still, it became a fast-growing reality in 2023 through modern technologies, such as Meta AI, Open AI, or ChatGPT, and has created some ethical concerns. This research provides examples of how AI is being used in academia, how it can be used, and how to assess college students’ familiarity with such technologies, their perception of it, and level of usage. Using an AI-generated short survey to gather quantitative and qualitative data through a discussion exercise, 126 undergraduates with four different professors were asked to share their answers and views. The findings show that many of today’s college students in South Florida see the usage of AI as ethical and legal. However, a few respondents remain uncertain due to a lack of clear guidelines from professors and the institution. Thus, most respondents reported that they are familiar with AI as they use it multiple times weekly. Consequently, educators and administrators must sharpen their students’ AI skills so they can be ethical and competitive in the workplace. Implications for students, educators and administrators in the higher education arena are explored. Besides serving as a person’s second brain, using AI can be an excellent way for students to mitigate and overcome procrastination, enhance their productivity, and comprehensively complete academic projects on time. Furthermore, the proper use of AI tools can reduce errors, quickly assess large amounts of data, automate repetitive functions, lead to better decisions, and help learners move forward amid challenging obstacles. As such, academic institutions must do more to ensure they are “sharpening their students’ AI saw” before they graduate and embark on their professional endeavors. Artificial intelligence, when used properly, ethically, and legally following established industry norms and guidelines, offers many transformative benefits across diverse fields to benefit human beings and society. Students pursuing a healthcare career can use AI to aid in early disease detection, accelerate drug discovery, and improve patient care through precision medicine. Graduates in the engineering or transportation industries can use AI to optimize traffic flow, enhance safety with autonomous vehicles, and reduce emissions through predictive maintenance. Moreover, those who remain in the education field after graduation can use AI to facilitate personalized learning experiences tailored to individual student needs while fostering greater engagement and academic success for all learners. The latest advancements underscore AI’s potential to drive innovation, increase efficiency, and address complex challenges while ultimately shaping a more interconnected and prosperous future for everyone in society.Business Ethics and Leadership01Examples of how AI is being used in academia, how it can be used, and how to assess college students’ familiarity with such technologies, their perception of it, and level of usage are provided.Business Ethics and Leadership["B. Mujtaba"]2024-07-03T00:00:00 3092de3cf4be8c93ec01f560a2fb6754e8c6235eThe combination of Artificial Intelligence and Machine Learning technologies within the healthcare industry has enabled a significant shift in conventional methodologies, offering more opportunities to enhance the patient care, streamline resource management, and optimize healthcare delivery efficiency. This research study describes a proposed system that uses AI and ML algorithms to previously available datasets to provide medical prescriptions and disease prediction based on the symptoms provided by the user. Proposed technique also integrates state-of-the-art AI technology, including Natural Language Processing (NLP) and Deep Learning (DL) to enable the chatbot to provide contextually appropriate descriptions of medical conditions and treatments. Extensive algorithm selection guarantees response generation accuracy, while the deployment is streamlined by establishing a connection with Google Dialogflow. When taken as a whole, these components improve healthcare accessibility. Performance review verifies that this approach is effective in addressing inquiries with accurate and helpful information. The proposed system holds significant potential for enhancing healthcare accessibility and efficiency, with opportunities for further research and collaboration with healthcare providers.2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)150A proposed system that uses AI and ML algorithms to previously available datasets to provide medical prescriptions and disease prediction based on the symptoms provided by the user, and integrates state-of-the-art AI technology, including Natural Language Processing and Deep Learning to enable the chatbot to provide contextually appropriate descriptions of medical conditions and treatments.2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)["R. Umbare", "Ritesh Patil", "Tejas Mukund", "Adesh Rathod", "Pratik Rajurkar"]2024-07-03T00:00:00 aa7849fd9cda88de7f8b6c85aa1b72fbbd37e55aBackground: As health systems incorporate artificial intelligence (AI) into various aspects of patient care, there is growing interest in understanding how to ensure transparent and trustworthy implementation. However, little attention has been given to what information patients need about these technologies to promote transparency of their use. Methods: We conducted three asynchronous online focus groups with 42 patients across the United States discussing perspectives on their information needs for trust and uptake of AI, focusing on its use in cardiovascular care. Data were analyzed using a rapid content analysis approach. Results: Our results suggest that patients have a set of core information needs, including specific information factors pertaining to the AI model, oversight, and healthcare experience, that are relevant to calibrating trust as well as perspectives concerning information delivery, disclosure, consent, and physician AI use. Conclusions: Identifying patient information needs is a critical starting point for calibrating trust in healthcare AI systems along with designing strategies for information delivery. These findings highlight the importance of patient-centered engagement when considering approaches for transparent healthcare AI.medRxiv20It is suggested that patients have a set of core information needs, including specific information factors pertaining to the AI model, oversight, and healthcare experience, that are relevant to calibrating trust in healthcare AI systems along with designing strategies for information delivery.["MA Austin M. Stroud", "PhD Sarah A. Minteer", "PhD MS Xuan Zhu", "PhD Jennifer L. Ridgeway", "PhD Jennifer E. Miller", "PhD Barbara A. Barry"]2024-07-03T00:00:00 bb91489172558a478193fa46c9cba3da25302d21A country’s economy and prosperity are substantially impacted by agriculture as food is the necessity for human beings. The aim of this study is to solve the issues like shortage of quality foods especially horticultural crops that are essential for leading a healthy lifestyle. The practices of modern farming that includes soil-less cultivation can be an alternate way to overcome the problems that farmers are facing in traditional way execution of insightful hydroponics system that uses Artificial Intelligence (AI) and Internet of Things (IoT). Factors like pH of nutrient solution, CO2, light, temperature, relative humidity, Electrical Conductivity (EC) can be monitored on daily basis using advance sensors in hydroponic system for efficient utilization of precious natural resources that leads to sustainable agriculture to meet out the future demand of agriculture commodities.2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)400Factors like pH of nutrient solution, CO2, light, temperature, relative humidity, Electrical Conductivity, and pH can be monitored on daily basis using advance sensors in hydroponic system for efficient utilization of precious natural resources that leads to sustainable agriculture to meet out the future demand of agriculture commodities.2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)["Narendra Singh Bhandari", "Nisha Bhandari", "Rashi Agarwal", "Pradeep Kumar Sharma"]2024-07-03T00:00:00 beed6c3ef05caa908fe4518dbe262dab132b224dAn organisation with a large workforce from a variety of backgrounds collaborates on various projects with different groups of individuals in order to achieve specific goals. Employers who develop coaching relationships with staff members may create high-quality results. According to this viewpoint, an employee who is receiving excellent training or coaching will be able to interact with the tasks assigned successfully in order to meet the organisational aim. The objective of this study is to examine how managerial coaching, executive coaching, and group coaching relate to work engagement in artificial intelligence during the COVID-19 season. It is obvious that the best coaching methods, including managerial coaching, executive coaching, and group coaching, will boost employee involvement, excitement, drive, and motivation while producing great results. 110 personnel of the artificial intelligence business are involved in this study. For data gathering in this study, a survey was used as a quantitative method. In order to analyse the data for this study, Pearson's correlation coefficient and the Statistical Package for the Social Science (SPPS) version 29.0 were both employed. The findings indicated that executive coaching has a high mean average of 3.96 and a significant link (r=0.856, p=0.01) between executive coaching and work engagement. Despite the study's shortcomings, the empirical findings contribute to our understanding of job engagement and purpose in public organisations. Consequently, training is essential in an organisation.International Journal of Business and Technopreneurship200Examination of how managerial coaching, executive coaching, and group coaching relate to work engagement in artificial intelligence during the COVID-19 season indicated that executive coaching has a high mean average and a significant link between executive coaching and work engagement.International Journal of Business and Technopreneurship (IJBT)["Siti Durrah", "Nur Syafiqah A. Rahim", "Irza Hanie Abu Samah", "Junaidah Yusof", "Amalina Ibrahim"]2024-07-03T00:00:00 f30035d72f06858de4f7a67507b67660e257e89fAbstract Background Artificial intelligence chatbots are being increasingly used for medical inquiries, particularly in the field of ultrasound medicine. However, their performance varies and is influenced by factors such as language, question type, and topic. Objective This study aimed to evaluate the performance of ChatGPT and ERNIE Bot in answering ultrasound-related medical examination questions, providing insights for users and developers. Methods We curated 554 questions from ultrasound medicine examinations, covering various question types and topics. The questions were posed in both English and Chinese. Objective questions were scored based on accuracy rates, whereas subjective questions were rated by 5 experienced doctors using a Likert scale. The data were analyzed in Excel. Results Of the 554 questions included in this study, single-choice questions comprised the largest share (354/554, 64%), followed by short answers (69/554, 12%) and noun explanations (63/554, 11%). The accuracy rates for objective questions ranged from 8.33% to 80%, with true or false questions scoring highest. Subjective questions received acceptability rates ranging from 47.62% to 75.36%. ERNIE Bot was superior to ChatGPT in many aspects (P<.05). Both models showed a performance decline in English, but ERNIE Bot’s decline was less significant. The models performed better in terms of basic knowledge, ultrasound methods, and diseases than in terms of ultrasound signs and diagnosis. Conclusions Chatbots can provide valuable ultrasound-related answers, but performance differs by model and is influenced by language, question type, and topic. In general, ERNIE Bot outperforms ChatGPT. Users and developers should understand model performance characteristics and select appropriate models for different questions and languages to optimize chatbot use.JMIR Medical Informatics350JMIR Medical Informatics["Yong Zhang", "Xiao Lu", "Yan Luo", "Ying Zhu", "W. Ling"]2024-07-03T00:00:00 44be6af98a1a9e2ae18cf243186d87d7eca1ed6cThe field of drug discovery and development has been revolutionized by the integration of artificial intelligence (AI) technologies. AI has significantly impacted various stages of the drug development process, including target identification, lead optimization, pharmacokinetics, and toxicity prediction. This review paper provides an overview of the impact of AI in drug discovery and development, highlighting the advancements, challenges, and future prospects. It discusses the application of machine learning, deep learning, and other AI techniques in accelerating the drug discovery process, improving the efficiency of clinical trials, and reducing the overall cost of drug development. Additionally, this review examines the ethical and regulatory considerations associated with the use of AI in drug development. Overall, this paper emphasizes the transformative potential of AI in revolutionizing the pharmaceutical industry and improving patient outcomesInternational Journal of Advanced Research in Science, Communication and Technology180The application of machine learning, deep learning, and other AI techniques in accelerating the drug discovery process, improving the efficiency of clinical trials, and reducing the overall cost of drug development are discussed.International Journal of Advanced Research in Science, Communication and Technology["Miss. Tanuja J. Katkar", "Mr. Manohar D. Kengar", "Mr. Prashant P. Aiwale", "Mr. Sharad K. Kamble", "Dr. Rajesh S. Jagtap", "Dr. Amol A. Patil"]2024-07-03T00:00:00 720a4eeb38745b20dac6049219e8b53691e1d283The integration of Artificial Intelligence (AI) into automation systems has the potential to enhance efficiency and to address currently unsolved existing technical challenges. However, the industry-wide adoption of AI is hindered by the lack of standardized documentation for the complex compositions of automation systems, AI software, production hardware, and their interdependencies. This paper proposes a formal model using standards and ontologies to provide clear and structured documentation of AI applications in automation systems. The proposed information model for artificial intelligence in automation systems (AIAS) utilizes ontology design patterns to map and link various aspects of automation systems and AI software. Applied to a practical example, the model demonstrates its effectiveness in improving documentation practices and aiding the sustainable implementation of AI in industrial settings.IEEE International Conference on Emerging Technologies and Factory Automation330The proposed information model for artificial intelligence in automation systems (AIAS) utilizes ontology design patterns to map and link various aspects of automation systems and AI software and demonstrates its effectiveness in improving documentation practices and aiding the sustainable implementation of AI in industrial settings.2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)["Marvin Schieseck", "Philip Topalis", "L. Reinpold", "Felix Gehlhoff", "Alexander Fay"]2024-07-03T00:00:00 b538b2f2e1c6037805ee514414c08b4f53d31428BACKGROUND: In recent years, the healthcare sector has emerged as a key area where artificial intelligence technologies are gaining strategic importance. In particular, the implementation of these technologies in primary healthcare has demonstrated particular relevance and importance [1–3]. AIM: The aim of the study is to characterize the stages of implementation of artificial intelligence technologies in the activities of urban polyclinics in Moscow. MATERIALS AND METHODS: Analytical, statistical, socio-hygienic, and experimental methods were used. RESULTS: The primary objective of integrating artificial intelligence into the operations of city polyclinics was to enhance the efficacy of medical data processing, mitigate the likelihood of professional missteps, and optimize the coordination of interactions between different medical professionals. The initial challenge of processing a vast quantity of information was met by the implementation of artificial intelligence in the analysis of electronic medical records. This approach resulted in the development of integrated and secure systems that facilitate the accessibility of patient data to physicians and medical staff for the purpose of quality of care analysis. In addressing the second task of using artificial intelligence technologies to provide consulting services to physicians in making a diagnosis, the work was carried out in several stages. In 2020, the top three medical decision support systems were implemented, which assist therapists in making preliminary diagnoses based on the International Classification of Diseases 10th revision (ICD-10). Since 2023, the Diagnostic Assistant system, which analyzes data from a patient’s electronic medical record and offers a second opinion on a confirmed diagnosis, has been actively used. Currently, this system includes 95 codes of ICD-10 and similar diagnoses, with plans to expand its functionality to 268 diagnoses. As a consequence of the training and implementation of the expansion, the system will be capable of covering approximately 85% of the most frequently established confirmed diagnoses. A considerable number of expert physicians were involved in the establishment and evaluation of the systems, with over 10,000 cases being handled. In December 2023, a pilot project was conducted at the City Polyclinic No. 64 (Moscow) with the involvement of almost 100 doctors of this medical institution to identify the possibility of improving the reliability of the model. According to its results, it was found that the diagnoses made by the doctor and the artificial intelligence system coincide by 89%. Despite the impressive achievements of technology, it is important to emphasize that the use of artificial intelligence is not intended to replace the doctor, but rather serves as a second opinion in the work of a specialist. CONCLUSIONS: The integration of artificial intelligence into the operations of Moscow’s polyclinics not only reduces the time required to search and process a substantial volume of information, but also helps to avoid professional errors. Furthermore, it enhances the efficiency of primary health care in Moscow as a whole.Digital Diagnostics10The integration of artificial intelligence into the operations of Moscow’s polyclinics not only reduces the time required to search and process a substantial volume of information, but also helps to avoid professional errors and enhances the efficiency of primary health care in Moscow as a whole.Digital Diagnostics["E. V. Blokhina", "A. S. Bezymyannyy"]2024-07-03T00:00:00 4bd97aa819332f564bd9a7c1a8396295b86aab64Background: To validate JLK-CTL, an artificial intelligence (AI) software developed to predict large vessel occlusion (LVO) using non-contrast CT (NCCT) scans, and to investigate its clinical implications regarding both infarct volume and functional outcomes. Methods: Between January-2021 and April-2023, a consecutive series of patients who concurrently underwent CT angiography and NCCT within 24 hours of last-known-well (LKW) were collected. LVO was confirmed through consensus among three experts reviewing CT angiography. Infarct volumes were quantified using diffusion-weighted imaging (DWI) conducted within seven days of the NCCT. The performance of the JLK-CTL was evaluated based on the area under the receiver operating characteristic curve (AUROC), as well as its sensitivity and specificity. The association of JLK-CTL LVO scores with infarct volumes and functional outcomes was assessed using Pearson correlation and logistic regression analyses, respectively. Results: Of 1,391 screened patients, 774 (mean age 69.0 {+/-} 13.6 years, 57.6% men) were included. The median time from LKW to NCCT was 3.1 hours (IQR 1.5-7.4), with 24.2% (n=187) presenting LVO. The JLK-CTL demonstrated AUROC of 0.832 (95% CI 0.804-0.858), with a sensitivity of 0.711 (95% CI 0.641-0.775) and a specificity of 0.830 (95% CI 0.797-0.859) at the predefined threshold. Incorporating the National Institute of Health Stroke Scale into the model increased the AUROC to 0.872 (95% CI 0.846-0.894; p<0.001). The LVO scores showed a significant correlation with infarct volumes on follow-up DWI (r=0.53; p<0.001). When JLK-CTL LVO scores were categorized based on observed frequency of LVO, the highest JLK-CTL LVO scores (51-100) group showed an independent association with unfavorable functional outcomes (adjusted odds ratio 9.48; 95% CI 3.98-22.55). Conclusion: The performance of the AI software in predicting LVO was validated across multiple centers. This tool has the potential to assist physicians in optimizing stroke management workflows, especially in resource-limited settings.medRxiv170The performance of the AI software in predicting LVO was validated across multiple centers and has the potential to assist physicians in optimizing stroke management workflows, especially in resource-limited settings.["MD Jong-Won Chung", "PhD Myungjae Lee", "MD MSc Sue Young Ha", "Msc Pyeong Eun Kim", "MD Leonard Sunwoo", "MD Nakhoon Kim", "MD Kwang-Yeol Park", "MD Kyu Sun Yum", "MD Dong-Ick Shin", "MD MSc Hong-Kyun Park", "MD Yong-Jin Cho", "MD Keun-Sik Hong", "MD MSc Jae Guk Kim", "MD Soo Joo Lee", "MD PhD Joon-Tae Kim", "MD PhD Woo-Keun Seo", "MD Oh Young Bang", "MD PhD Gyeong-Moon Kim", "PhD Dongmin Kim", "MD Hee-Joon Bae", "MD PhD Wi-Sun Ryu", "MD PhD Beom Joon Kim"]2024-07-03T00:00:00 eb3d0ee29a0992ba2c4733b57b3de0db02847454Public Health315The 'AI Deployment Paradox' was introduced to focus on the challenges of using AI to address health disparities in LMICs, and the following three categories were identified: data poverty and contextual shifts; cost-effectiveness and health equity; and new technological colonisation and potential exploitation.Public health["Lanyi Yu", "Xiaomei Zhai"]2024-07-03T00:00:00 ddf8c0430b9b18413093189a7f8eee5462df908eAnnals of Laboratory Medicine93Annals of Laboratory Medicine["Hyeoun-Ae Park"]2024-07-03T00:00:00 4b34f8b24bce381768fed9a0244c0aacac8dd191Democratization563Democratization["Jelena Cupa\u0107", "Hendrik Schopmans", "\u0130rem Tuncer-Ebet\u00fcrk"]2024-07-03T00:00:00 24c2411ec88aca1b9806465feb011bd1109e34efEconomic Change and Restructuring632Economic Change and Restructuring["Zhengang Zhang", "Peilun Li", "Liangxiong Huang", "Yichen Kang"]2024-07-03T00:00:00 80ab8823a47970c94b844240bb9adefd0ad4b8baJournal of Terrorism Studies00Journal of Terrorism Studies[]2024-07-03T00:00:00 8d61444bcf8d4809f41b884a2441d90049e0aa6dAJR. American journal of roentgenology00AJR. American journal of roentgenology["Taehee Lee"]2024-07-03T00:00:00 3822c620dd4e02714a06333afbef911b664feb3000["Erick Javier"]2024-07-03T00:00:00 fbab4deb837c989c9099623cefb294b8e439f337Predicting agricultural product prices is crucial for farmers, distributors and policymakers to make informed decisions. Existing AI models for agriculture product price prediction struggle to account for the numerous factors affecting prices, such as weather conditions, market demand and seasonal variations. Predicting the price of agricultural products is difficult because of the limited availability of historical and real-time data as well as the intricate interaction of multiple unidentified factors that influence price. In this paper, we propose an association rule-driven light gradient boosting machine (AR-LGBM) to predict the price of the product. The agricultural crop pricing dataset collected from Virudhunagar District, Tamilnadu, India, was significant. Min-max normalization is used to pre-process the data. Principal Component Analysis (PCA) was utilized to extract features, which reduces data dimensionality while maintaining crucial data. Intelligent Flower Pollination Optimization (IFPO) is used for choosing the relevant features from the extracted features to simulate the proposed method using python. To evaluate the performance of the proposed method in terms of precision (87%),  Mean Absolute Error (MAE) of 45.75, recall  of 86%, Root Mean Squared Error (RMSE) of 2.15, F1-score of 85%, R-squared of 14, accuracy of 85% and Mean Squared Error (MSE) 32.72. As a result, the outcomes show that the suggested strategy outperforms traditional approaches. This study demonstrates the effectiveness of the AR-LGBM model in enhancing agriculture product price prediction.Multidisciplinary Science Journal160This study demonstrates the effectiveness of the AR-LGBM model in enhancing agriculture product price prediction in enhancing agriculture product price prediction.Multidisciplinary Science Journal["Usha Kumari", "Anitha Nallasivam", "Rajesh Gupta", "Manjula Jain"]2024-07-03T00:00:00 7cc2b7793fcc7fcf07875ef826cd0e62a59fea49British medical journal40BMJ["Lambert Zixin Li", "Peilin Yang"]2024-07-03T00:00:00 b9a885b2198cbf7c3dcc1be0bdc6413aaea32668AI &amp; SOCIETY30AI &amp; SOCIETY["T. Jaynes"]2024-07-03T00:00:00 4aa9e300d5579ed33baa663b0c3dc90cfb1d316cDiseases of the Colon & Rectum00Diseases of the colon and rectum["Patricia Sylla", "Kevin A Chen"]2024-07-03T00:00:00 b47992fa1693b8d0a004d66012814ad3c5f75933Organisationsberatung, Supervision, Coaching30Organisationsberatung, Supervision, Coaching["Ulrich Lenz"]2024-07-03T00:00:00 0f8671c06af6271b292fe3d04fb92dd2311236e9The rapid integration of artificial intelligence (AI) systems into various domains has raised concerns about their impact on individual and societal wellbeing, particularly due to the lack of transparency and accountability in their decision-making processes. This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems. The review identifies four main thematic areas: technical approaches, legal and regulatory frameworks, ethical and societal considerations, and interdisciplinary and multi-stakeholder approaches. By synthesizing the current state of research and proposing key strategies for policymakers, this review contributes to the ongoing discourse on responsible AI governance and lays the foundation for future research in this critical area. Ultimately, the goal is to promote individual and societal wellbeing by ensuring that AI systems are developed and deployed in a transparent, accountable, and ethical manner.Frontiers in Human Dynamics7817This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems by synthesizing the current state of research and proposing key strategies for policymakers.Frontiers in Human Dynamics["Ben Chester Cheong"]2024-07-03T00:00:00 3ac86048f125070b1e248b9d882bca473c05c422Purpose The purpose of this paper is to examine the challenges and opportunities presented by the Metaverse and Meta artificial intelligence (AI) for libraries in Higher Education Institutions (HEIs) and to propose strategies for libraries to adapt and innovate in response to these disruptions. Design/methodology/approach This paper employs a qualitative approach, drawing upon literature review and analysis to explore the disruptive impact of emerging technologies, including the Metaverse and Meta AI, on libraries in HEIs. Findings The findings suggest that the convergence of the Metaverse and Meta AI is reshaping library services, altering user expectations and transforming information retrieval and management. While these disruptions pose challenges such as bias in AI algorithms and privacy concerns, they also offer opportunities for libraries to enhance user experiences, foster collaboration and expand their reach beyond physical boundaries. Research limitations/implications The findings of this paper highlight the need for libraries in HEIs to embrace change, prioritize user-centric design, foster innovation and promote digital literacy education. Following this, libraries will continue to fulfill their mission of supporting teaching, learning and research in the digital age. Originality/value This essay contributes to the existing literature by providing insights into the disruptive impact of emerging technologies on libraries in HEIs. The paper explores the intersection of the Metaverse, Meta AI and library services, as well as offers original perspectives on the evolving role of libraries in the digital era. Library Hi Tech News112The findings suggest that the convergence of the Metaverse and Meta AI is reshaping library services, altering user expectations and transforming information retrieval and management, highlighting the need for libraries in HEIs to embrace change, prioritize user-centric design, foster innovation and promote digital literacy education.Library Hi Tech News["B. Oladokun", "Y. Ajani", "Nnenda W. Tom-George", "O. C. Okeke"]2024-07-03T00:00:00 b1ad51f6a214990391d9a64442bba41fe4c467aeBackground Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes. Objective This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA. Methods We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases—PubMed, Embase, and IEEE Xplore—and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Results A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence. Conclusions Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI’s impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.JMIR Human Factors501The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA), although with very low certainty evidence.JMIR Human Factors["E. Gabarron", "Dillys Larbi", "Octavio Rivera-Romero", "Kerstin Denecke"]2024-07-03T00:00:00 43eaf1692f77acdcc7183eb0d18c21dc8ceafedaThe increasing interest in developing Artificial Intelligence applications in the medical domain, suffers from the lack of high-quality data set, mainly due to privacy-related issues. In addition, the recent increase in large multimodal models (LMM) leads to the need for multimodal medical data sets, where clinical reports and findings are attached to the corresponding CT or MRI scans. This paper illustrates the entire workflow for building the MedPix 2.0 data set. Starting with the well-known multimodal data set MedPix\textsuperscript{\textregistered}, mainly used by physicians, nurses, and healthcare students for Continuing Medical Education purposes, a semi-automatic pipeline was developed to extract visual and textual data followed by a manual curing procedure in which noisy samples were removed, thus creating a MongoDB database. Along with the data set, we developed a GUI aimed at navigating efficiently the MongoDB instance and obtaining the raw data that can be easily used for training and/or fine-tuning LMMs. To enforce this point, in this work, we first recall DR-Minerva, a RAG-based LMM trained using MedPix 2.0. DR-Minerva predicts the body part and the modality used to scan its input image. We also propose the extension of DR-Minerva with a Knowledge Graph that uses Llama 3.1 Instruct 8B, and leverages MedPix 2.0. The resulting architecture can be queried in a end-to-end manner, as a medical decision support system. MedPix 2.0 is available on GitHub. \url{https://github.com/CHILab1/MedPix-2.0}arXiv.org181This work recalls DR-Minerva, a RAG-based LMM trained using MedPix 2.0, and proposes the extension of DR-Minerva with a Knowledge Graph that uses Llama 3.1 Instruct 8B, and leverages MedPix 2.0 as a medical decision support system.ArXiv["Irene Siragusa", "Salvatore Contino", "Massimo La Ciura", "Rosario Alicata", "Roberto Pirrone"]2024-07-03T00:00:00 286d61ce98f0918412606ec78cbac186710d6731PURPOSE This study aimed to develop and assess the performance of an artificial intelligence (AI)-driven decision support system, XRAInet, in accurately identifying pediatric patients with pleural effusion or pneumothorax and determining whether tube thoracostomy intervention is warranted. METHODS In this diagnostic accuracy study, we retrospectively analyzed a data set containing 510 X-ray images from 170 pediatric patients admitted between 2005 and 2022. Patients were categorized into two groups: Tube (requiring tube thoracostomy) and Conservative (managed conservatively). XRAInet, a deep learning-based algorithm, was trained using this data set. We evaluated its performance using various metrics, including mean Average Precision (mAP), recall, precision, and F1 score. RESULTS XRAInet, achieved a mAP score of 0.918. This result underscores its ability to accurately identify and localize regions necessitating tube thoracostomy for pediatric patients with pneumothorax and pleural effusion. In an independent testing data set, the model exhibited a sensitivity of 64.00% and specificity of 96.15%. CONCLUSION In conclusion, XRAInet presents a promising solution for improving the detection and decision-making process for cases of pneumothorax and pleural effusion in pediatric patients using X-ray images. These findings contribute to the expanding field of AI-driven medical imaging, with potential applications for enhancing patient outcomes. Future research endeavors should explore hybrid models, enhance interpretability, address data quality issues, and align with regulatory requirements to ensure the safe and effective deployment of XRAInet in healthcare settings.Pediatric Pulmonology121XRAInet presents a promising solution for improving the detection and decision-making process for cases of pneumothorax and pleural effusion in pediatric patients using X-ray images and contributes to the expanding field of AI-driven medical imaging.Pediatric pulmonology["Mustafa Alper Akay", "O. C. Tatar", "Elif Tatar", "Beyza Nur Ta\u011fman", "Semih Metin", "Onursal Varl\u0131kl\u0131"]2024-07-03T00:00:00 45562c652d388f2f89805c9e4ae2b77c20d48ff4Artificial Intelligence-driven Development Environments (AIDEs) offer developers revolutionary computer programming assistance. There is great potential in incorporating AIDEs into Computer Science education; however, the effects of these tools should be fully examined before doing so. Here, a within-subjects study was conducted to compare the programming performance, workload, emotion, and self-efficacy of seventeen novices coding with and without use of the GitHub Copilot AIDE under time pressure. Results showed that using the AIDE significantly increased programming efficiency and reduced effort and mental workload but did not significantly impact emotion or self-efficacy. However, participants' performance improved with more experience using the AI, and their self-efficacy followed. The results suggest that students who try AIDEs will likely be tempted to use them for time-sensitive work. There is no evidence that providing AIDEs will aid struggling students, but there is a clear need for students to practice with AI to become competent and confident using it.Annual Conference on Innovation and Technology in Computer Science Education401Using the AIDE significantly increased programming efficiency and reduced effort and mental workload but did not significantly impact emotion or self-efficacy, and the results suggest that students who try AIDEs will likely be tempted to use them for time-sensitive work.Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1["Nicholas Gardella", "Raymond Pettit", "Sara L. Riggs"]2024-07-03T00:00:00 76e61fd85673881766adb167ebe7c8fb4c984275Introduction: In the era of globalization, service quality is fundamental, especially in the educational sector where student-focused attention is key to their satisfaction and engagement with the institution. Universities are implementing Artificial Intelligence (AI) tools, such as chatbots, to enhance the academic experience. Methodology: This study, utilizing the PRISMA methodology and analyzing data from SCOPUS, Web of Science, and ERIC, examines how chatbots are transforming student support. Results: There is a growing interest among universities in using these technologies to provide efficient service, offering quick responses and support in academic and administrative processes through personalized recommendations. Discussion: The findings highlight the significance of these tools, emphasizing the need for advanced machine learning and careful interaction design. However, the implementation of AI in the educational field presents significant challenges, such as data security and privacy, which require comprehensive strategies. Conclusions: This analysis underscores the importance of continuous evaluation of the effectiveness and acceptance of AI-based interventions, to optimize academic performance and student retention.  European Public &amp; Social Innovation Review491This analysis underscores the importance of continuous evaluation of the effectiveness and acceptance of AI-based interventions, to optimize academic performance and student retention.European Public &amp; Social Innovation Review["Nuria Segovia-Garc\u00eda"]2024-07-03T00:00:00 0dcc244e0d65e787271eee28f186307645b1e1aaMinds Mach.331The findings suggest that the tools analysed were trained using inputs from past anti-corruption procedures and practices and based on common sense assumptions about corruption, which are not necessarily free from unfair disproportionality and discrimination.Minds and Machines["Fernanda Odilla"]2024-07-03T00:00:00 8bba80be9e56dd11e04beeacf3c5e43433ed4863In healthcare, legible prescription information is crucial but often compromised by hurried, illegible handwriting. This can lead to misinterpretation and errors in medication dispensing, posing risks to patient safety. Moreover, the inefficiency caused by unclear prescriptions can contribute to delays in treatment and administrative challenges for healthcare providers. In response to these issues, the “PrescriptIQ” system introduces an innovative solution by leveraging advanced artificial intelligence technologies to interpret handwritten prescriptions accurately and efficiently. This research introduces a new multilingual handwriting identification system, “PrescriptIQ,” which aims to translate handwritten prescriptions into understandable text. The system uses deep fusion techniques in consecutive computational steps that include picture enhancement and text segmentation, merging CNNs and FLSTM (Fusion of LSTM) networks for improved performance. Additionally, fuzzy search strategies are used to improve prescription accuracy and patient safety. By standardizing decoded text with Unicode mapping, the technology enables correct translation into many languages, bridging the digital and traditional prescription systems. This development has the potential to transform medication administration and patient care globally.2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)160This research introduces a new multilingual handwriting identification system, “PrescriptIQ,” which aims to translate handwritten prescriptions into understandable text, and standardizing decoded text with Unicode mapping enables correct translation into many languages, bridging the digital and traditional prescription systems.2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)["D. Roja Ramani", "B. V. Santhosh Krishna", "L. Balaji", "S. Sathyanarayanan", "M. Muthumanickam", "S. Kaliappan"]2024-07-03T00:00:00 371f07c23ab6dcdf2265643bcc25356e44477389Early stroke detection significantly increases the prognosis for both survival and rehabilitation. Patients are more likely to receive appropriate therapy that minimizes brain damage and lowers the risk of consequences if a stroke is detected early on. Researchers are motivated to investigate the possibilities of artificial intelligence and machine learning technologies in creating new categorization systems that can identify and detect strokes more quickly and accurately due to their rapid development. This could potentially enhance the likelihood of surviving and recuperating. The support-vector machine (SVM), logistic regression, decision tree, random forest, Bayes nets, and K-nearest neighbor (KNN) algorithms are employed in this study's CRISP model technique. To enhance the final quality, the dataset was balanced using an oversampling technique, and the algorithms employed were subjected to principal components analysis (PCA). With an accuracy rate of 99%, the Random Forest algorithm is regarded as the optimum for prediction. Our study illustrates that the random forest classification model using the data balancing strategy outperforms the other strategies investigated, with a 99% classification accuracy and a 98% F1 score. The study also shows that the outcomes are unaffected by preprocessing with the PCA technique. The next objectives of the study are to use a larger dataset, various preprocessing methods, and machine learning models to enhance the framework models.Ahliya Journal of Allied Medico-Technology Science110This study illustrates that the random forest classification model using the data balancing strategy outperforms the other strategies investigated, with a 99% classification accuracy and a 98% F1 score.Ahliya Journal of Allied Medico-Technology Science["Hala Shaheen", "Mutaz Rasmi Abu Sara", "Khaled Sabarna", "L. Arafeh"]2024-07-03T00:00:00 88b58d82341da734c330fa1da3283a0d0c9bddeaBackground – The demand for content across various rising digital media platforms pushes the advertising secotr to adopt Artificial Intelligence (AI) automation to improve creativity, speed, and efficiency, especially in areas like art direction, copywriting, and graphic design. While AI offers solutions to improve efficiency and support creative processes, advertising agency stakeholders start to see the urgency in assessing how AI can work alongside human creativity to produce essential quality content for the client’s value creation as the industry moves forward for a sustainable business growth. Methodology – This research uses mixed-method; Quantitative method to measure AI integration within advertising agencies and assess audience reactions to AI-generated ads, establishing a link between AI usage and audience behavior; Qualitative method through In-Depth Interviews to identify the underlying insights from the advertising professionals’ perspective in integrating AI on daily basis. The findings are processed for the development of a Performance Management System (PMS) using AHP scored by industry experts as the basis to prioritize the Key Performance Indicators (KPIs) Practical implications – This PMS framework is designed for macro-level advertising agencies to monitor and optimize the use of AI tools effectively through weighted KPIs and strategic AI investments. Originality/value – This study contributes to the existing industry study by introducing a performance measurement and addresses a theoretical gap between AI-driven creative process and its impact to the industry’s value creation.International Journal of Current Science Research and Review180This research addresses a theoretical gap between AI-driven creative process and its impact to the industry’s value creation by introducing a performance measurement and addressing a theoretical gap between AI-driven creative process and its impact to the industry’s value creation.International Journal of Current Science Research and Review["Ofira Amanda Putri, S.Ars", "Prof. Dr. Ir. Dermawan Wibisono, M.Eng"]2024-07-03T00:00:00 351ce17187a4b928d2f1df1bada5824117b55be1Lithium-ion (Li-Ion) batteries are widely adopted to power a large variety of mobile applications. Their capacitance degrades during lifetime, mainly with the increasing number of charge and discharge cycles. Such a capacitance degradation with respect to its nominal value is typically represented by the battery State of Health (SoH). Predicting the SoH of Li -Ion batteries is crucial to avoid the powered system’s service disruption. Artificial Intelligence (AI) can be adopted to predict the SoH of batteries. However, the hardware executing the AI may be affected by soft errors (SEs) during its operation in the field, possibly compromising the correctness of the AI prediction.In this paper, we analyze the impact of SEs on the reliability of AI in predicting the SoH of Li-Ion batteries. We will show that, due to SEs affecting the AI, unhealthy batteries may be erroneously classified as healthy, with possible dramatic consequences on the powered system’s operation. Moreover, healthy batteries may be wrongly classified as unhealthy, thus mandating unnecessary maintenance/replacement costs. A detailed analysis of the diverse effects of SEs, depending on the affected portions of the AI implementing hardware has been conducted, thus enabling to identify the parts that, if affected by SEs, are more likely to compromise the AI prediction ability.Based on the achieved results, the possible selective adoption of traditional fault tolerance techniques, such as ECCs, or duplication and comparison followed by recovery, has then been proposed, to enable to increase the AI reliability in predicting batteries’ SoH.IEEE International Symposium on On-Line Testing and Robust System Design270It is shown that, due to SEs affecting the AI, unhealthy batteries may be erroneously classified as healthy, with possible dramatic consequences on the powered system’s operation, and the possible selective adoption of traditional fault tolerance techniques, such as ECCs, or duplication and comparison followed by recovery, has been proposed, to increase the AI reliability in predicting batteries’ SoH.2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS)["Sara Cret\u00ed", "Martin Oma\u00f1a", "C. Metra", "Gianni Borelli"]2024-07-03T00:00:00 b7a25832137ad8ea96101eaf73c459550da5dc01 The discipline of computer science known as artificial intelligence (AI) mimics human thought processes, learning capacities, and knowledge stores. There are many different algorithms available in AI, including fuzzy logic (FL), artificial neural networks (ANNs), etc., which offers myriad opportunities to solve approaching problems in food and agricultural sectors, including global food demand. AI has been used in supply chain management, manufacturing improvement, food quality enhancement, and in maintenance of industrial hygiene. By employing a computerized system, the industry can assess and then deliver the product in the most favourable conditions at every stage, for example, by keeping an eye on all seed selection, crop monitoring, agricultural operations, such as watering, and temperature monitoring, new product development, and maintaining food industry safety standards which could enhance the quality of the products. Nevertheless, there are obstacles in the way of AI’s implementation in the food industry like data privacy issues, job displacement through automation, flaws in algorithms used in making decisions and difficulties in compliance with laws and regulations. The ethical implications of food safety and sustainability make the application of AI much more challenging. The food industry’s future depends on a holistic approach that takes advantage of AI’s potential while maintaining ethical and environmentally friendly operations. © CAB International 2024 Food Science and Nutrition Cases190The food industry’s future depends on a holistic approach that takes advantage of AI’s potential while maintaining ethical and environmentally friendly operations.Food Science and Nutrition Cases["Priyanka Kataria"]2024-07-03T00:00:00 13b8e46eaa27339b8f00113b91e9c8d51946791dArtificial intelligence (AI) stands as a potent catalyst for revolutionizing manufacturing, promising unprecedented efficiency, agility, and resilience. This research embarks on an investigative journey to dissect the multifaceted landscape of AI in manufacturing, aiming to unravel its current status, intrinsic challenges, and prospective pathways. This research unveils the intricate relationship between AI technologies and manufacturing processes across diverse domains. Examining various domains, including system-level analysis, human-robot collaboration, process monitoring, diagnostics, prognostics, and material-property modeling. The research also reveals AI’s transformative potential in optimizing manufacturing operations, enhancing decision-making, and fostering innovation. By dissecting each domain, the research illuminates how AI empowers manufacturers to adapt to dynamic market demands and technological advancements, ultimately driving sustainable growth and competitiveness. Moreover, it also examines the evolving dynamics of human-robot collaboration within manufacturing settings, recognizing AI’s pivotal role in facilitating seamless communication, shared understanding, and dynamic adaptation between humans and machines. Through an exploration of AI-enabled human-robot collaboration, this research underscores the transformative power of symbiotic relationships in reshaping the future of manufacturing. While highlighting opportunities, it acknowledges the myriad challenges accompanying AI integration in manufacturing, such as data quality issues, interpretability of AI models, and knowledge transfer across domains. By addressing these challenges, the research aims to pave the way for more resilient AI-driven manufacturing systems capable of navigating complex market landscapes and technological disruptions. This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.Metaverse578This research sheds light on AI’s transformative potential in manufacturing, inspiring collaborative efforts and innovative solutions that will propel the industry forward into a new era of possibility and prosperity.Metaverse["Zarif Bin Akhtar"]2024-07-04T00:00:00 c283b8da128c49ce52ab8d1a544ad3135a4fa3e3The primary aim of this research is to present a thorough and all-encompassing examination of artificial intelligence (AI) methodologies employed in the detection of financial fraud. The present study employs a systematic literature review (SLR) that was conducted utilizing the PRISMA approach. A comprehensive search was undertaken on reputable academic databases including ScienceDirect, Scopus, Springer, and Emerald, yielding a total of 24 papers published throughout the timeframe of 2014 to 2023. These articles will, thereafter, undergo further analysis. The findings of this study demonstrate that the implementation of artificial intelligence (AI) techniques for detecting financial fraud yields favorable outcomes. Specifically, the AI approach proves to be effective in enhancing the precision and efficiency of fraud pattern identification, thereby making a substantial contribution in this domain. In contrast, the prevailing methodology employed in the realm of financial fraud detection is frequently centered around machine learning. Furthermore, a majority of the research encompassed a diverse range of industries, with particular emphasis on the financial industry as the primary domain for the implementation of artificial intelligence (AI) in the detection of financial fraud. Keywords: artificial intelligent, financial fraud, fraud detectionKnE Social Sciences393The findings of this study demonstrate that the implementation of artificial intelligence (AI) techniques for detecting financial fraud yields favorable outcomes and the AI approach proves to be effective in enhancing the precision and efficiency of fraud pattern identification, thereby making a substantial contribution in this domain.KnE Social Sciences["Indrawati Yuhertiana", "Ahsanul Hadi Amin"]2024-07-04T00:00:00 0a26facec89aa39bd7dc0c1065d3f05691e7bf38Using Artificial Intelligence (AI) methods along with computational mathematics is changing the way that hard problems are solved in many areas. This combination of fields uses the best parts of AI, like machine learning, neural networks, and natural language processing, to make computational mathematics, which is mostly about numbers, algorithms, and statistics, more powerful. Researchers can solve difficult problems more quickly, accurately, and on a larger scale by building AI methods into computer systems. Machine learning systems, for example, can find the best answers to differential equations by guessing what will happen and finding trends in very big datasets that would be hard to process any other way. In addition, neural networks help by modeling nonlinear systems and estimating difficult functions. This makes their answers more reliable when standard methods fail. Also, AI-driven optimization methods like genetic algorithms and simulated annealing are very useful for solving high-dimensional optimization problems that come up in economics, engineering, and physics. Putting these technologies together makes it easier to create adaptable, smart systems that can learn and change in real time. This improves the way decisions are made and predictive analytics are used. AI and computer mathematics also work well together to make models more accurate in areas that need them, like biological engineering, climate modeling, and financial forecasting. This combined method not only speeds up the computing process, but it also creates new study and growth opportunities by letting people look into problems that were previously impossible to solve. AI is getting better all the time, and combining it with computational mathematics could completely change how problems are solved, leading to huge finds and progress in many fields of science and industry.Panamerican Mathematical Journal211Using Artificial Intelligence (AI) methods along with computational mathematics is changing the way that hard problems are solved in many areas, leading to huge finds and progress in many fields of science and industry.Panamerican Mathematical Journal["Dr. Pradip Suresh Mane", "Dr. Ratnaprabha Ravindra Borhade", "D. M. P. Deore", "Dr. Poonam Chaudhari", "Dr. Shital Sachin Barekar"]2024-07-04T00:00:00 79f156697a1034b6ab904427068a0e0cd05ff635Rapid advancements in AI (artificial intelligence) technologies, including machine learning, natural language processing, and computer vision, have developed sophisticated tools capable of performing complex medical tasks. The AI integration in healthcare can revolutionise the industry by improving patient outcomes, optimising resource allocation, and reducing operational costs. However, the AI use in medicine carries certain risks related to ethics and data privacy, shortcomings in the quality of data for training algorithms, and importance of protecting against cyberthreats. There is also a threat of rising medical costs due to the need for a large number of tests and validations of new technologies. This study focuses on the AI application in the diagnostic field, as it is revolutionising the medical industry by offering new opportunities for accurate disease detection, classification, and prediction of treatment outcomes. The diagnostic field specificity is that any changes in it affect both those medical professionals who directly perform diagnostic procedures and those medical specialists who use the results of diagnostic examinations in their work. The research consists of two stages. Stage 1 is a survey of 119 respondents (medical professionals in Ukraine) about their attitude to the integration of AI technologies in diagnostics. Stage 2 is a study of opinions by 10 experts (medical professionals in Ukraine) about their own assessment of AI risk parameters in medical diagnostics. The survey showed the vast majority of Ukrainian doctors (over 84%) had no experience with AI-based diagnostic systems. Simultaneously, 74% of respondents believe AI can be effective in reducing diagnostic errors, and the future of medical diagnostics is associated with AI. They consider its main advantages to be speed, accuracy, objectivity, and ability to detect diseases at early stages. Respondents argue that AI is the most appropriate for diagnosing cancer, genetic research, and chronic conditions with atypical symptoms. Regarding the risks and barriers to AI introduction in medical diagnostics, at the first study stage, respondents named the high cost of implementation, the need for specialised training, and the possible lack of personal interaction between doctor and patient as the main ones. This opinion was clarified at the second study stage. In particular, 10 experts ranked these risks and potential problems in the following order (from the most to least important): unequal access; dependence on technology; ethical issues; legislative and regulatory challenges; lack of personal contact; bias and inequality; data privacy and security; errors in diagnosis and treatment. To mitigate each of these risks, the article develops a set of recommendations.Health Economics and Management Review02This study focuses on the AI application in the diagnostic field, as it is revolutionising the medical industry by offering new opportunities for accurate disease detection, classification, and prediction of treatment outcomes.Health Economics and Management Review["Nataliia Sheliemina"]2024-07-04T00:00:00 a268248ef02a2e00eb04a1c928650cfcb4aa1756What is the"nature"of the cognitive processes and contents of an artificial neural network? In other words, how does an artificial intelligence fundamentally"think,"and in what form does its knowledge reside? The psychology of artificial intelligence, as predicted by Asimov (1950), aims to study this AI probing and explainability-sensitive matter. This study requires a neuronal level of cognitive granularity, so as not to be limited solely to the secondary macro-cognitive results (such as cognitive and cultural biases) of synthetic neural cognition. A prerequisite for examining the latter is to clarify some epistemological milestones regarding the cognitive status we can attribute to its phenomenology.arXiv.org102This study requires a neuronal level of cognitive granularity, so as not to be limited solely to the secondary macro-cognitive results (such as cognitive and cultural biases) of synthetic neural cognition.ArXiv["Michael Pichat"]2024-07-04T00:00:00 044fc4c07886ca873615b2759a95e36223f274d8This study explores the impact of artificial intelligence (AI) technology application on the internal wage gap within enterprises and the moderating role of employee scale. Using panel data from Chinese A-share listed companies from 2011 to 2022 and an AI application level index constructed through text mining methods, this paper analyzes through a fixed effects model and finds that the application of AI technology significantly increases the wage gap between management and ordinary employees, especially in non-state-owned enterprises, non-manufacturing, and high-tech industries. However, the effect of this wage gap increase is relatively weaker in large enterprises, indicating that company size can mitigate the impact of AI technology to a certain extent. This finding contributes to a deeper understanding of how technological progress shapes the internal wage structure of enterprises and is significant for enterprises and governments to formulate fair wage policies, providing new strategies to promote internal wage equality within enterprises.Journal of Education, Humanities and Social Sciences141The application of AI technology significantly increases the wage gap between management and ordinary employees, especially in non-state-owned enterprises, non-manufacturing, and high-tech industries, however, the effect of this wage gap increase is relatively weaker in large enterprises, indicating that company size can mitigate the impact of AI technology to a certain extent.Journal of Education, Humanities and Social Sciences["Jianjia Zhang"]2024-07-04T00:00:00 59e1e3757c114e57cdf1c6e1a26a19e0c0a6a528Artificial intelligence applications have great potential to enhance perioperative care. This article explores promising areas for artificial intelligence in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.Anesthesiology701Promising areas for artificial intelligence in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation are explored.Anesthesiology["Lichy Han", "Danton Char", "N. Aghaeepour"]2024-07-04T00:00:00 50803159ce431e9c22029458e9fba3ab227cd2bcHealth systems are experiencing increasing pressures worldwide due to heightened service demands, demographic aging, stringent regulations, and economic constraints, making efficiency and efficacy in health management critical aspects. At the heart of this complexity, health managers seek to optimize resources and improve care delivery at a time when the adoption of digital technologies, including artificial intelligence (AI), becomes increasingly imperative. This necessity reflects not only the pursuit of innovation but also the urgency to adapt to an ever-evolving environment. However, the effective characterization, availability, and incorporation of these technologies as support tools still represent an emerging challenge that is insufficiently explored in the literature. In response, this project proposes the development of a framework of theoretical and practical guidelines for the implementation and management of digital tools in health systems in the age of AI. Adopting a mixed-methods approach that includes systematic review, analyses of commercial off-the-shelf solutions, and qualitative studies with health managers and practitioners, the aim is to map current technology use, identify gaps and best practices, and provide a guide for future direction. This project also intends to develop in co-creation with professionals in the field to ensure the relevance and practical applicability of the developed guidelines. The results are expected to not only contribute to the scientific literature but also offer an evidence-based guide to optimizing the use of digital technologies in health management, promoting a significant transformation in the development and adoption of innovative digital solutions.F1000Research321This project proposes the development of a framework of theoretical and practical guidelines for the implementation and management of digital tools in health systems in the age of AI, and offers an evidence-based guide to optimizing the use of digital technologies in health management.F1000Research["Ericles Andrei Bellei", "Ana Carolina Bertoletti De Marchi"]2024-07-04T00:00:00 7670cba5caf45a7c14d3aec9d8bfa9ce018fb143The problems of optimization of a controlled object pursuing several goals are considered. A model of multi-criteria optimization has been obtained, which allows the controlled object to realize all the goals set in the entire range of possible situations without the direct participation of a person. A systematic approach to the problem of vector optimization made it possible to combine models of individual trade-off schemes into a single integral structure that adapts to the situation of making a multi-criteria decision. The advantage of the concept of a non-linear trade-off scheme is the possibility of making a multi-criteria decision formally, which is a hallmark of artificial intelligence. The apparatus of the nonlinear trade-off scheme, developed as a formalized tool for studying management systems with conflicting criteria, allows the artificial intelligence system to solve practically multi-criteria problems of a wide class. Artificial intelligence systems are created in order to replace a person as a decision maker in this or that situation. AI systems such as robots, decision support systems, neural networks, etc. operate in conditions that a person considers unfavorable for himself. Thus, a demining robot operates in an environment that is dangerous for a sapper. Decision support systems are usually used in conditions of time pressure or in aggressive environments. Neural network classifiers process volumes of information that exceed the capabilities of a human operator, etc. Replacing a person with an AI system requires the formalization of both the formulation and the process of solving the problem. Subjective factors should be excluded from the solution algorithm. A special place among such systems is occupied by those which functioning is evaluated by a set of conflicting quality criteria. When solving a specific problem of vector optimization, the decision maker creates his own model of the objective function (utility function) in accordance with his preferences.International Scientific Technical Journal "Problems of Control and Informatics"41A model of multi-criteria optimization has been obtained, which allows the controlled object to realize all the goals set in the entire range of possible situations without the direct participation of a person.International Scientific Technical Journal "Problems of Control and Informatics"["Albert Voronin", "Alina Savchenko"]2024-07-04T00:00:00 a8c91c49b598fc2b846737dbe6ee9c8b738f1130The growing significance of intelligent technology (AI) in the context of handling financial risks is examined in this study. The essay analyses the drawbacks of using conventional risk management techniques and gives a summary of the theoretical underpinnings of financial risk management. The literature research section sheds light on the various ways artificial intelligence is being used in the field of managing financial risk. Credit risk, market risk, organisational risk, and liquidity risk are some of these uses. The essay also discusses the advantages and difficulties of using artificial intelligence to financial risk management. The benefits of using AI include improved speed and accuracy in risk identification, surveillance, and mitigation. The study also highlights a number of difficulties, including the requirement for extremely high-quality data, the possibility of computational bias, and the existence of societal issues. According to the research, intelligent technology (AI) has the potential to drastically alter the financial risk management industry by improving capacity for decision-making, minimising risks, and improving efficiency. However, to fully leverage these advantages, it is essential to skillfully negotiate the obstacles associated with the incorporation of AI into the domain of financial risk control. This study provides a thorough examination of artificial intelligence's function in financial risk management, stressing both the benefits and drawbacks of its application. For decision-makers, risk managers, and financial institutions looking to integrate AI into their risk management plans, this paper is an invaluable resource.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)390This study provides a thorough examination of artificial intelligence's function in financial risk management, stressing both the benefits and drawbacks of its application.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["Avni Garg"]2024-07-04T00:00:00 203c21b227c51f4d470fe0cc113858c6d41b7af8The continuous advancement of science and technology has led to the emergence of artificial intelligence (AI) as a pivotal force in the operation and management of enterprises. This paper aims to examine the role of AI in enhancing the efficiency of enterprise operations and management, with a particular focus on the perspectives of large companies and small and medium-sized enterprises (SMEs). The rapid evolution of AI technology has brought about significant impacts and opportunities for the operation and management of enterprises. The competitive market and changing global environment present challenges to large companies, which must find new operational models to enhance their competitiveness. Artificial intelligence (AI) can assist large companies in addressing risk management and cost control by identifying potential risk factors and providing more reliable data support through data analysis and predictive modeling. Furthermore, AI can improve management efficiency in large companies by automating tedious transactional work and optimizing internal communication processes and teamwork efficiency. Small and medium-sized enterprises (SMEs) are facing challenges due to insufficient specialized human resource management capabilities and lack of risk control. Artificial intelligence (AI) plays an important role in HR management for SMEs, helping to improve the quality and efficiency of the recruitment process, automate HR processes, and reduce costs. Furthermore, AI can identify potential risk factors through data analytics and model predictions, thereby assisting SMEs in reducing losses from risks. In summary, the implementation of AI technology can assist organizations in enhancing operational management efficiency, optimizing existing operational processes, and developing novel business models and growth opportunities. Studying the application and impact mechanisms of AI in different fields can provide theoretical support for enterprises to formulate more effective strategies and management tactics. Furthermore, it can help them to better understand and apply AI technology, thus enabling them to better cope with the market competition and uncertainty environment. Ultimately, this will facilitate continuous innovation and development.Journal of Education, Humanities and Social Sciences190Studying the application and impact mechanisms of AI in different fields can provide theoretical support for enterprises to formulate more effective strategies and management tactics, thus enabling them to better cope with the market competition and uncertainty environment.Journal of Education, Humanities and Social Sciences["Feiting Liu"]2024-07-04T00:00:00 6f3eeeb9f97c7fd9dbb4f2c512527d3db4e32062The article “The Future of Universities in the Age of Artificial Intelligence: Forecasts and Transformations” examines the prospects for the development of higher education institutions against the backdrop of the ever-increasing influence of AI technologies. The authors examine how personalizing learning, automating administrative tasks, expanding research capabilities and the need to develop future skills could radically change the university environment. Particular attention is paid to the ethical and social challenges that arise in connection with the integration of AI into the educational process. The article offers a comprehensive look at the potential transformation of educational institutions and emphasizes the importance of adapting to new realities in the era of digitalization.Bulletin of Issyk-Kul University00The authors examine how personalizing learning, automating administrative tasks, expanding research capabilities and the need to develop future skills could radically change the university environment.Bulletin of Issyk-Kul University["K. A. Mamadalieva", "U. T. Attokurov", "Ch. A. Alimamatova"]2024-07-04T00:00:00 0e928780ffeaffba7fa7ef1a768b3968263e8419Algorithmic price collusion facilitated by artificial intelligence (AI) algorithms raises significant concerns. We examine how AI agents using Q-learning engage in tacit collusion in two-sided markets. Our experiments reveal that AI-driven platforms achieve higher collusion levels compared to Bertrand competition. Increased network externalities significantly enhance collusion, suggesting AI algorithms exploit them to maximize profits. Higher user heterogeneity or greater utility from outside options generally reduce collusion, while higher discount rates increase it. Tacit collusion remains feasible even at low discount rates. To mitigate collusive behavior and inform potential regulatory measures, we propose incorporating a penalty term in the Q-learning algorithm.arXiv.org290This work examines how AI agents using Q-learning engage in tacit collusion in two-sided markets and proposes incorporating a penalty term in the Q-learning algorithm to mitigate collusive behavior and inform potential regulatory measures.ArXiv["Cristian Chica", "Yinglong Guo", "Gilad Lerman"]2024-07-04T00:00:00 d5a1d516a380f5da98ab065adb58829d887a57e9The In this case, the achievement of the utilization of artificial intelligence (AI) methods for early identifying neonatal jaundice, which is frequent in babies, is reviewed in this paper. Specifically, while presenting data about AI models and their successes, we found that deep learning approaches demonstrated high levels of accuracy in early risk assessment of neonates with jaundice burdens - before such manifestations of path gnomic symptoms or dangerous levels of bilirubin increase. An early prediction of neonatal jaundice is vital since it enables health practitioners to make early assessments and referrals and introduces treatment and prevention measures hence preventing the occurrence of complications, limiting invasive procedures, and alleviating the families' stress and expenses. With that being said, the potential application of the findings of this research can be generalized to improved and healthier lives of infants in the future, with AI technologies as one of the primary protectors of their health from early stages of their lives. We also mention limitations of the present study, directions for future work, and novel developments in applying big data analytics to optimize neonatal care: data quality remains an essential factor to consider during the analytic processes to ensures that the findings of the research will also be generalizable across different populations, as well as the ethical considerations for the integration of AI in neonatology healthcare.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)300It is found that deep learning approaches demonstrated high levels of accuracy in early risk assessment of neonates with jaundice burdens - before such manifestations of path gnomic symptoms or dangerous levels of bilirubin increase.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["Ms. Ashwarya", "Dr. Richa Sharma", "R. S. S. Raju", "Asst. Prof"]2024-07-04T00:00:00 6266d2504beb6967fdaee0b00944571f0dbecce0This research looks at the elements that influence artificial intelligence adoption in agricultural operations. Using the UTAUT2 framework, the study gathered and analyzed responses from 464 individuals. The statistical study was carried out using SPSS 28 and AMOS 28, with techniques such as EFA, Cronbach's Alpha, Common Method Bias, CFA, Validity, and Structural evaluation. The findings suggest that performance expectation, social influence, price, and hedonic motivation all have a substantial impact on AI adoption in agriculture, although effort expectancy, habit, and enabling circumstances do not. According to the study, encouraging AI adoption among farmers should emphasize its performance expectation, social impact, financial value, and hedonic incentive in order to raise awareness and support effective adoption. This research contributes to the field by exploring AI adoption in agriculture through technology acceptance models, thereby advancing understanding of adoption dynamics in this context.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)220Encouraging AI adoption among farmers should emphasize its performance expectation, social impact, financial value, and hedonic incentive in order to raise awareness and support effective adoption.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["A. Abad", "M. Maaz", "Mohd Salman Shamsi", "Shahzaib Tariq"]2024-07-04T00:00:00 5401a590e2074dfa335a7e5cebb5955f2c5182ceIt's possible that artificial intelligence (AI) will change the way crisis relief is managed and delivered by providing flexible solutions that use real-time data insights and adjust to new situations. AI programs, which use a lot of data analysis, demand forecasts, and predictive models, can help emergency response systems change how they distribute resources. These projects' main goals are to provide quick and effective assistance while also making the best use of resources and reducing response times. Machine learning algorithms are important for improving decision-making because they allow systems to handle unexpected situations well and adapt quickly and correctly to changing times. The usefulness of these methods depends on their ability to handle transportation issues, which speeds up and improves the supply of aid while cutting down on waste and inefficient operations. Wanting to make the most of AI-driven solutions stresses the importance of being creative and giving when using them. These solutions have the potential to change the way businesses and charities handle their resources. Using AI to help plan crisis aid ensures that vulnerable people get the right help when needed and makes the best use of available resources. To find and use successful strategies that can clearly lower pain and save lives, it is important to understand the promise of artificial intelligence in crisis aid.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)280To find and use successful strategies that can clearly lower pain and save lives, it is important to understand the promise of artificial intelligence in crisis aid.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["B. Rajalakshmi", "P. Aswini", "H. Thethi", "H. Goyal", "Jajimoggala Sravanthi", "Ali Abdulhussein Hameed"]2024-07-04T00:00:00 e205f3cc5072ce26d3befbc1c8c2f7f86167ea58Artificial Intelligence (AI) is a technological achievement that simulates human intelligence through machines or computer programs. The integration of AI in military operations aims to minimize combatant casualties and enhance effectiveness in warfare. Despite the advantages and significance of this research, concerns arise regarding the ideal implementation of AI in armed conflicts due to potential security challenges. A significant issue lies in the legal perspective governing AI as a comprehensive defense tool. This paper employs a juridical normative research method based on a statutory approach to provide a descriptive analysis and examine the regulatory framework surrounding AI in armed conflict. The results indicate that the absence of comprehensive regulations complicates the accountability framework, making liability determination intricate, particularly when AI malfunctions due to substandard quality or improper use. In such cases, accountability may extend to both the creator and the user. The concept of liability for violations in armed conflict is explored according to international law, highlighting the implications and associated responsibilities of using AI within legal principles. This paper concludes that AI regulation must be crafted to ensure usage aligns with established procedures within the framework of international law. Hasanuddin Law Review340It is concluded that AI regulation must be crafted to ensure usage aligns with established procedures within the framework of international law.Hasanuddin Law Review["Naek Siregar", "Desy Churul Aini", "Rehulina Rehulina", "Agit Yogi Subandi", "Isroni Muhammad Miraj Mirza"]2024-07-04T00:00:00 4c9a371088aaa535ccde0f0a30455d4188d15e4fIn latest generations, there has been a significant increase in study interest in the growing applications of artificial intelligence in health and medicine. The purpose of this study is to present a worldwide and chronological overview of AI study in the areas of medical care and health. The online science tool was used to obtain a total number of articles that were released between. The detailed study looked at the number of publications, as well as the cooperation between writers and nations. Generally, indicate that a factor, along with robotics, algorithms, neural networks, artificial cognitive computing, and natural language processing, were identified through a vast network of researchers' key words and phrases and message review of pertinent academic papers. These methods are regularly used in clinical forecasting and rehabilitation. The most articles were on cancer, followed by those on cardiovascular disease, stroke, blindness, Early onset dementia, and sadness. Additionally, the lack of study on applying AI to some illnesses with a high disease prevalence indicates potential paths for Ai development. The study proposes the creation of international and national guidelines and laws on the rationale and application of pertaining to medical products and provides a first and complete image of the global efforts made in this significant and lucrative study area.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)620The study proposes the creation of international and national guidelines and laws on the rationale and application of pertaining to medical products and provides a first and complete image of the global efforts made in this significant and lucrative study area.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["Jasneet Kaur", "S. B. Kuamr", "Pradnya Mehta", "Shalini Ninoria", "Vishal Sharma", "C. Karthikeyan"]2024-07-04T00:00:00 5f15deab5c73831ab2bbac2090e50b7b066101e3This paper provides a conceptual model of the strategic AI integration process in organizations that outlines the steps for conceptualization, implementation, and assessment. Key principles of the framework include the focus on strategy and goals by choosing AI projects that support strategic goals, objectives, and priorities of an organization. It also underscores the need to put in place proper structures of governance of the utilization of artificial intelligence in ways that will not be misleading and unethical. Starting from the conceptual levels, the framework states the need for the existence of AI vision and purpose or specific goals to achieve in AI integration. It contains useful tips or guidelines for choosing right AI technologies based on Organization readiness, existing infrastructure & scenarios of its implementation. The necessity of creating cross-functional teams is described, focusing on cooperation between specialists in the field of AI and domain experts, which is crucial to achieve the desired outcomes. This paper covers significant aspects like data issues where they explored the problem of acquiring quality data for training the AI models. Stakeholder management, infrastructure necessities for AI deployment, and integration of AI into existing business models and processes are also outlined here, for continuity and effectiveness of operations. In addition, the framework contains insights on how to assess AI outcomes and enforce best practices with regards to performance and reusability. Finally, to enumerate the success stories from different industries and learn from them, samples of AI plans are included. As noted the following is the strategic framework that, when applied by business leaders and technologists will allow AI to be used as a force multiplier to permanently alter organizations and provide sustainable success in a hostile world economy. The holistic approach promotes AI projects that are both technically and organizationally relevant and responsive to the company's strategic outlook.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)300This paper provides a conceptual model of the strategic AI integration process in organizations that outlines the steps for conceptualization, implementation, and assessment and contains insights on how to assess AI outcomes and enforce best practices with regards to performance and reusability.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["Anish Gupta", "Lalit Tyagi", "V. S. Sisodia", "Aman Verma"]2024-07-04T00:00:00 7e0e8a05c4397435640796e8d68331e7e4aa117f Artificial intelligence (AI) has become an important driver in the current dynamic technological environment, presenting itself as a revolutionary power capable of reconfiguring various sectors, economies, and social structures. The paper aims to address a wide range of readers, encompassing AI practitioners, academics, and people in general. Its primary objective is to connect the complex technical aspects of AI and the ethical problems inherent in its creation and implementation. In an era marked by the growing integration of AI systems into various aspects of human existence, the book offers fundamental ideas that contribute to cultivating an environment where these systems function with transparency, ethical considerations, and reliability. The paper's comprehensive coverage spans various subjects that contribute to a complete comprehension of the intricate terrain of reliable AI. The analysis is initiated by conducting an indepth examination of the architectural aspects of AI systems, elucidating the progression from the input of data to the generation of decision-making outcomes. The text introduces the core functions of AI, explores its conceptual framework, and emphasizes the significance of data processing modules, computations, Machine Learning models (ML), and integrating software. This foundational framework establishes a basis for subsequent investigation into the pivotal concepts of integrity, trust, and ethics. This paper bravely tackles urgent issues about bias, justice, and the erosion of data privacy while offering practical solutions to increase AI system openness and explainability by 20%. This paper examines various strategies to improve transparency and explainability, recognizing the importance of strengthening user understanding and confidence. Within the realm of healthcare, the paper acquaints readers with the pioneering notion of Federated Deep Learning, which can improve data privacy by up to 30%. This includes a dedicated part that delves into the concept of explainable AI, introducing various methodologies such as LIME and SHAP, which are employed to interpret predictions made by AI models. The paper provides readers with the knowledge to traverse the ever-changing environment of AI safely and ethically. It emphasizes the importance of utilizing AI's transformative potential for improving humanity while maintaining the utmost adherence to ethical principles. Recent Advances in Electrical &amp; Electronic Engineering (Formerly Recent Patents on Electrical &amp; Electronic Engineering)00This paper bravely tackles urgent issues about bias, justice, and the erosion of data privacy while offering practical solutions to increase AI system openness and explainability by 20%.Recent Advances in Electrical &amp; Electronic Engineering (Formerly Recent Patents on Electrical &amp; Electronic Engineering)["Muskan Dixit", "I. Kansal", "Vikas Khullar", "Rajeev Kumar", "Sunil Kumar"]2024-07-04T00:00:00 5fe864c9b63237f82d12bac6b067f3dfb2d3d61bThis study delves into the integration of Artificial Intelligence (AI) in Human Resource Management (HRM) and examines the pivotal role of Management Information Systems (MIS) in enhancing decision-making processes. The transformative capabilities of AI are revolutionizing HRM by making operations more efficient, precise, and data-driven. By leveraging AI, MIS can significantly improve the decision-making processes within HRM, enabling more accurate, timely, and objective outcomes. This comprehensive analysis, based on a systematic review of 100 studies, underscores the various benefits of integrating AI within HRM systems, such as improved recruitment processes, enhanced performance management, and increased employee engagement. Additionally, the study addresses the challenges associated with AI adoption, including technical, ethical, and organizational hurdles. It also explores the future prospects of AI in HRM, suggesting that continuous advancements and strategic implementations of AI-enabled MIS can lead to superior organizational outcomes. The findings from this study indicate that the adoption of AI-enabled MIS holds substantial potential for enhancing HR decision-making processes, ultimately contributing to better organizational performance and competitive advantage.GLOBAL MAINSTREAM JOURNAL390The findings from this study indicate that the adoption of AI-enabled MIS holds substantial potential for enhancing HR decision-making processes, ultimately contributing to better organizational performance and competitive advantage.GLOBAL MAINSTREAM JOURNAL["Md Ashrafuzzaman", "Khan Bahadur Prince", "Anisur Rahman"]2024-07-04T00:00:00 4665b83405ec622bd8a277d6fed786fc4f108826Background. In recent years, the topic of artificial intelligence (AI) in medicine has been actively discussed not just as a promising solution but the one that can help to improve some results. A significant growth of interest in AI systems all over the world began in the early-mid 2010s, which allowed us to consider the practical application of such systems. The aim of the study is to analyze all the software products (SP) registered in our country as a medical device, including those with AI technology, and to evaluate their applicability in traumatology and orthopedics. Methods. The study included all the SP having a registration certificate of a medical device according to the OKPD2 code 58.29.XX.XXX (services for publishing other software). In the state register of medical devices and organizations (individual entrepreneurs), which is engaged in the production and manufacturing of medical devices, we found 111 registered SP according to the inclusion criterion, as at February 14, 2024. Results. We proposed to categorize all registered SP as follows: systems working with the DICOM standard images (47 pcs, 42%), laboratory data (20 pcs, 18%), microscopy images (7 pcs, 6%), photographic images (5 pcs, 5%), medical information systems (4 pcs, 4%), text data mining systems (3 pcs, 3%), clinical decision support systems (3 pcs, 3%), Holter ECG analysis (2 pcs, 2%), other systems (16 pcs, 14%). Systems applicable to traumatology and orthopedics accounted for 4 pcs (4%). Conclusions. Unfortunately, the real-world applicability of existing solutions in the field of traumatology and orthopedics can be regarded as minimal in comparison with pulmonology, oncology, and laboratory diagnostics, where AI programs have already achieved significant success.Traumatology and Orthopedics of Russia190The real-world applicability of existing solutions in the field of traumatology and orthopedics can be regarded as minimal in comparison with pulmonology, oncology, and laboratory diagnostics, where AI programs have already achieved significant success.Traumatology and Orthopedics of Russia["A. Sereda", "A. Dzhavadov", "A. Cherny"]2024-07-04T00:00:00 d1ac92ba133b12848c6faa2f418114fdf3cf6832Purpose: The aim of the study was to examine the Role of Artificial Intelligence in Marketing Automation in China Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that the role of artificial intelligence (AI) in marketing automation in China has revolutionized the marketing landscape, offering unprecedented opportunities for efficiency, personalization, and data-driven decision-making. AI technologies, such as machine learning, natural language processing, and predictive analytics, have enabled Chinese businesses to automate and optimize various marketing processes, leading to enhanced customer engagement and improved ROI. AI-powered marketing automation tools have facilitated precise targeting and segmentation, allowing brands to deliver highly personalized content and offers to individual consumers at the right time through the right channels. This level of personalization has significantly improved customer experiences, fostering stronger brand loyalty and higher conversion rates. Furthermore, AI has enhanced the ability of marketers in China to analyze vast amounts of data quickly and accurately, providing deep insights into consumer behavior, preferences, and trends. This has enabled more informed strategic decisions, agile responses to market changes, and proactive identification of new opportunities. Unique Contribution to Theory, Practice and Policy: Technology Acceptance Model, Social Cognitive Theory & Resource-Based View (RBV) may be used to anchor future studies on Role of Artificial Intelligence in Marketing Automation in China. Organizations should prioritize hiring skilled AI professionals and investing in robust AI infrastructure to effectively leverage AI technologies for marketing automation. This includes building in-house capabilities or partnering with external vendors to develop and implement AI-powered marketing solutions tailored to specific business needs. Encourage marketing teams to experiment with AI-driven tools and technologies in their campaigns and initiatives. Create a supportive environment that rewards innovation and learning from failures, enabling organizations to iterate and improve AI-driven marketing strategies over time. Policymakers should collaborate with industry stakeholders to develop regulatory frameworks that govern the ethical use of AI in marketing automation. This includes establishing guidelines for data privacy, algorithmic transparency, and consumer protection to ensure that AI technologies are deployed responsibly and ethically.International Journal of Strategic Marketing Practice180The study found that the role of artificial intelligence (AI) in marketing automation in China has revolutionized the marketing landscape, offering unprecedented opportunities for efficiency, personalization, and data-driven decision-making.International Journal of Strategic Marketing Practice["Mercy Chen"]2024-07-04T00:00:00 3bc5cb7e040089fa0ecc183f46bfc758d324c2e4When artificial intelligence (AI) and computational mathematics come together, it opens up a new era in science computing with unmatched chances to make study and technology better. This combination uses the best parts of AI's data-driven methods and computational mathematics' strict logical models to make it easier to solve problems in a wide range of scientific areas. AI algorithms, especially those that are based on machine learning and deep learning, are very good at finding trends and making guesses from very large datasets. This lets them solve hard, multidimensional problems that traditional computers have a hard time with. On the other hand, computational mathematics gives AI models the theoretical background and accuracy they need to be easier to understand and more reliable. By combining AI with computer methods like numerical analysis, optimization, and differential equations, researchers can make mixed models that make computers much faster and more accurate. This method from different fields not only speeds up the simulation and modeling processes, but it also makes it possible to work with bigger and more complicated information, which helps scientists, engineers, and biologists make important discoveries. Additionally, using AI-driven methods in high-performance computer settings makes the best use of resources, which speeds up calculations and lowers costs. As AI keeps getting better, its programs get better at learning from data and drawing conclusions from them. This means that the computing methods they are used with are always getting better. The mutually beneficial connection between AI and computer mathematics also encourages new ways of making algorithms, which leads to progress that can be used more readily in the real world. In the end, this fusion is going to change the way science computing is done by allowing for more complex studies, more accurate predictions, and the discovery of new things. The current study and development in this area shows how important it is for people from different fields to work together.Panamerican Mathematical Journal140The mutually beneficial connection between AI and computer mathematics also encourages new ways of making algorithms, which leads to progress that can be used more readily in the real world.Panamerican Mathematical Journal["Dr. Bhushan Manjre", "Dr. Pragati Chandankhede", "Dr.Pratibha Vijay", "Jadhav", "Dr. Rupali Atul Mahajan", "D. R. M. Gawande", "Dr. Vijeet H. Meshram"]2024-07-04T00:00:00 3d24c5bb983d1c5aeb4e3ad3dc2182045910f24eIn the contemporary era characterized by the proliferation of extensive data sets and the advent of Industry 4.0, artificial intelligence (AI) emerges as a prominent catalyst for economic growth. It assumes a pivotal role in facilitating the integration of advanced technologies like bitcoin, virtualization, the Internet of Things (IoT), and graphics processors, hence fostering innovation and development. In this scholarly essay, a comprehensive examination of artificial intelligence (AI) and learning methodologies spanning the years 1961 to 2018 is conducted. Through an extensive examination of AI, encompassing various aspects such as causal factors, practical systems, fundamental machine learning, commercial achievements, and current advancements, this investigation serves as a highly valuable resource for both academic scholars and industry experts. There is a substantial number of challenges associated with artificial intelligence (AI). However, it is undeniable that AI has seen significant advancements, emerging as an innovative and transformative tool across all domains and applications.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)470Through an extensive examination of AI, encompassing various aspects such as causal factors, practical systems, fundamental machine learning, commercial achievements, and current advancements, this investigation serves as a highly valuable resource for both academic scholars and industry experts.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["Mr. Shivam Saraswat", "Dr. Ritika Mehra", "Mr. Gurpreet", "K. Yuvaraj", "P. Karthigaikumar", "Mrs. D.S. Wankhede"]2024-07-04T00:00:00 46e445fb99ff45504393b0af8e4e96e00f1a1c93Recently accurate predictions of electricity demands have emerged from the Liberalization and commercialization of the energy area. The future energy or intelligent grid market. A better request-side management method and a shift of mindset are required for a more credible system scale estimate of up to one year. However, electricity is difficult to forecast as elements, including sociological and environmental activity and climatic influences, are affected. The two methodologies utilized to predict cargo, in general, are artificial intelligence and analytical procedures. All analytical methodologies are frequently used in regression literature: linear regression, Box-Jenkins' methodology, and nonparametric methods. The analytical approaches function effectively in everyday settings regarding meteorological, sociological, and psychological problems but don't give satisfying outcomes. Consequently, the period of time does not change on the basis of economic developments. Inference systems tactics are among the artificial intelligence systems.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)260The two methodologies utilized to predict cargo, in general, are artificial intelligence and analytical procedures, which function effectively in everyday settings regarding meteorological, sociological, and psychological problems but don't give satisfying outcomes.2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)["Neeraj Kumar", "Tanusha Mittal", "Hassan M. Al-Jawahry", "Saloni Bansal", "A. Deepak", "N. Deepika", "Rohit Kaushik"]2024-07-04T00:00:00 9ef849f030dfae54b2482a9badcebf994d092a8bWith the rapid development of modern information technology, artificial intelligence has been widely used in human production and life, which plays a vital role in enhancing the level of intelligence. In particular, the introduction of artificial intelligence in the field of electrical engineering automation will further enhance the degree of automation and promote the development of the electrical industry to a high-quality industry, so as to obtain the maximum economic benefits. To give full play to the effectiveness of artificial intelligence and provide more intelligent support for electrical automation, it is necessary to study in depth the driving role of artificial intelligence on electrical automation and clarify the in-depth application of artificial intelligence in the field of electrical automation in order to improve the level of electrical automation.Forum on Research and Innovation Management20It is necessary to study in depth the driving role of artificial intelligence on electrical automation and clarify the in-depth application of artificial intelligence in the field of electrical automation in order to improve the level of electrical automation.Forum on Research and Innovation Management["Shiqi Zhang"]2024-07-04T00:00:00 d6b3bcd5dbf535becec89289947ca19ca1ebecd7Article contain the investigation of the modern artificial intelligence models level, characteristics of combat and military artificial intelligence, elements of equipment and software for creating and training the artificial intelligence, highlights the facts of using the artificial intelligence for solving strategic and tactical tasks, modern examples of the ground military robotics with parts of artificial intelligence and the lethal autonomous systems are discussed. The analysis of application the equipment with artificial intelligence for the Oshkosh truck autonomous piloting, developed by the Robotic Research company for the ExLF program (US DoD) was performed. The using of artificial intelligence elements in robotic ground combat platforms created by Pratt & Miller (Oshkosh Defense, USA), General Dynamics Land System (USA), Hanwha (South Korea), Howe & Howe Technologies (Textron Systems, USA), Rheinmetall (Germany), Milrem (Estonia) are shown. The application experience of lethal autonomous systems with artificial intelligence from the Samsung Techwin and doDaam (South Korea), Rafael (Israel) was carry out. An example of solving tasks combination: autonomous driving and target search in the AbramsX and StrykerX demonstrators created by General Dynamics Land Systems was analyzed. The requirements to adapt civilian artificial intelligence equipment for military tasks are substantiated. General requirements for integration of artificial intelligence elements in the chassis and fight module for ground vehicles are formulated. The example of the artificial intelligence kit for armored personnel carrier is proposed and the basic algorithms of the autopilot logistics system and fight module are discussed. The example integration of the artificial intelligence kit into the 8x8 armored personnel carrier is substantiated; a scheme of remote interaction of an autonomous vehicle equipped with artificial intelligence controlled by the human is given.Наукові праці Державного науково-дослідного інституту випробувань і сертифікації озброєння та військової техніки230Наукові праці Державного науково-дослідного інституту випробувань і сертифікації озброєння та військової техніки["V. Soloviov"]2024-07-04T00:00:00 23d43a67ffb485763728c2df6efec239e2142376International Journal of Emergency Medicine131This manuscript reports on how to create annual master rotation schedules to meet the training requirements for 60 Emergency Medicine residents, while maintaining steady adequate departmental staffing and accommodating the different external rotation capacities.International Journal of Emergency Medicine["Rawan Eskandarani", "Ahmed Almuhainy", "Abdulrahman Alzahrani"]2024-07-04T00:00:00 60459f3a3dc7e359e9fa5011e9654293e97d1830International Journal of Dermatology51International journal of dermatology["N. Litaiem", "Karama Sboui", "Jinen Daghrir", "Asma Khouladi", "Lotfi Tlig", "M. Bouchouicha", "Mounir Sayadi", "F. Zeglaoui"]2024-07-04T00:00:00 28c605cee32d72584c10eb156435c88f5ad6b57bIn 2023, 28 countries and technology private sector representatives signed the Bletchley Declaration, calling for international oversight of AI by applying a risk-based formulary. The goal is to achieve uniform global regulation. The absence of regulatory uniformity challenges companies operating across borders. This paper provides practitioners and policymakers with an overview of the EU regulatory model and a new legislative recommendation, The AI Integrative Risk-Based model for the U.S., which has not designed any regulatory framework. The AIRB provides a useful approach for conducting meaningful risk assessments to guide future U.S. regulation and ensure compliance in global markets.AIB Insights141An overview of the EU regulatory model and a new legislative recommendation, The AI Integrative Risk-Based model for the U.S., which has not designed any regulatory framework are provided.AIB Insights["Miriam F. Weismann"]2024-07-04T00:00:00 b24f6dc3c36281a221b98a2c7ec5cb44d7620708Criminal Justice Ethics40Criminal Justice Ethics["Jesper Ryberg"]2024-07-04T00:00:00 3078443b91333664d1c0c4ddd7763c22a3c0e6a4Informatica00Informatica (Slovenia)["Yan Zhang"]2024-07-04T00:00:00 5af206bf2a316ee9d3be0d58f9dd4fcfadc43ec100Information & Culture["Nathan Schneider"]2024-07-04T00:00:00 a367bba3e5eae83f21b6f8610e01f9e4915b36dcAnesthesiology230Anesthesiology["Megan E Salwei", "M. Weinger"]2024-07-04T00:00:00 7f6a0c27017b312b069d7f8390a4409c1a078f02Postdigital Science and Education70Postdigital Science and Education["Noah Khan"]2024-07-04T00:00:00 6bae74d881d876872878253e09af720e79869b25This study investigates the application of Generative AI in game development and its impact on the final profit of a company. Firstly, the article introduces the development history of generative AI and its application in different fields, focusing on the application in the game industry. Subsequently, the specific application of generative AI technology in game development and its impact on the company’s performance is analysed through a case study of NetEase. It is found that through the introduction of generative AI technology, NetEase has successfully developed a series of innovative and unique games, such as “Ni Shuihan” and “Eggy Party”, and achieved significant commercial success. Finally, the study suggests directions for further research in the future, including more in-depth quantitative analyses of the application of generative AI in different game genres and markets, as well as exploring how individual or small team developers can benefit from the rise of generative AI.Journal of Education, Humanities and Social Sciences100The study suggests directions for further research in the future, including more in-depth quantitative analyses of the application of generative AI in different game genres and markets, as well as exploring how individual or small team developers can benefit from the rise of generative AI.Journal of Education, Humanities and Social Sciences["Yize Liu"]2024-07-04T00:00:00 b0afe94cfd82bf12e2bef38829b07fb5ad98c440JACC: Advances30JACC: Advances["M. M. R. Kannan Mutharasan", "MS Jessica Walradt"]2024-07-04T00:00:00 d212177b4c2de264fa9ceaa1d89e3b305291ac9eTesting the security, efficiency, and dependability of AI-driven healthcare systems is crucial. It is essential to perform thorough and rigorous testing to make sure the AI algorithms are capable. Our goal is to ensure that these algorithms can handle a wide range of scenarios that may occur in healthcare settings. We must observe, for instance, how well they function in the presence of changes in patient characteristics, data accuracy, and even environmental factors. Developers are able to go deeply and find any potential flaws, biases, or restrictions by thoroughly testing AI models. This enables them to enhance and maximize the algorithms' performance. Our goal is for these AI systems to be adaptable and strong, ready to overcome any challenges. Our goal is for these AI systems to be adaptable and robust, ready to overcome whatever challenges they encounter. Reliability testing is another crucial step in this process. Our goal is to guarantee that, over time, the AI predictions in actual medical contexts continue to be accurate and dependable. In the end, we rely on these systems to produce trustworthy outcomes that actually enhance patient care. Developers and healthcare institutions are not the only parties involved in this. Policymakers and regulatory bodies are also quite important. They put a lot of effort into developing standards and protocols for carrying out trustworthy and demanding AI testing in the medical field. Strict safety and efficacy standards are met by AI-driven healthcare solutions thanks to the requirements they set for testing procedures, data quality, and performance indicators. This article focuses on all the current robustness and reliability testing using AI in Healthcare.Asian Journal of Research in Computer Science00This article focuses on the current robustness and reliability testing using AI in Healthcare, which aims to ensure that AI algorithms can handle a wide range of scenarios that may occur in healthcare settings.Asian Journal of Research in Computer Science["Tushar Khinvasara", "Abhishek Shankar", "Connor Wong"]2024-07-04T00:00:00 04cb958fa9bf12e7ea99210c37ff4a2f68ed65e9In this study, we explored the progression trajectories of artificial intelligence (AI) systems through the lens of complexity theory. We challenged the conventional linear and exponential projections of AI advancement toward Artificial General Intelligence (AGI) underpinned by transformer-based architectures, and posited the existence of critical points, akin to phase transitions in complex systems, where AI performance might plateau or regress into instability upon exceeding a critical complexity threshold. We employed agent-based modelling (ABM) to simulate hypothetical scenarios of AI systems' evolution under specific assumptions, using benchmark performance as a proxy for capability and complexity. Our simulations demonstrated how increasing the complexity of the AI system could exceed an upper criticality threshold, leading to unpredictable performance behaviours. Additionally, we developed a practical methodology for detecting these critical thresholds using simulation data and stochastic gradient descent to fine-tune detection thresholds. This research offers a novel perspective on AI advancement that has a particular relevance to Large Language Models (LLMs), emphasising the need for a tempered approach to extrapolating AI's growth potential and underscoring the importance of developing more robust and comprehensive AI performance benchmarks.arXiv.org401This research challenged the conventional linear and exponential projections of AI advancement toward Artificial General Intelligence (AGI) underpinned by transformer-based architectures, and posited the existence of critical points, akin to phase transitions in complex systems, where AI performance might plateau or regress into instability upon exceeding a critical complexity threshold.ArXiv["Teo Susnjak", "Timothy R. McIntosh", "A. Barczak", "N. Reyes", "Tong Liu", "Paul Watters", "Malka N. Halgamuge"]2024-07-04T00:00:00 5927b43def69bfd9b94fddf84d9a7d392b3d3906This scientific article aims to conduct a comprehensive analysis of the advances and challenges of artificial intelligence in the legal system, considering its ethical, social, and legal implications. Furthermore, the objective is to analyze the proposals of Brazilian legislations and establish a parallel with European law, with the purpose of identifying divergences and similarities. The development of these systems is based on the use of data from human experience, which highlights the inherent risks and has driven several countries to undertake legislative efforts for regulation. In this context, the European Union stands out, whose main objective is to create a trust-based ecosystem for development of AI. In order to understand the current approach to this topic and conduct a comparative study between the legal systems in question, practical examples of artificial intelligence implementation were analyzed. Additionally, a bibliographic survey of documents, legislative proposals, and studies published by the European Union, as well as relevant Brazilian legislation, was carried out. The results obtained revealed that the current Brazilian legislation constitutes a starting point in solving problems associated with the topic. However, as these technologies develop, there is a need for secure protection to prevent and repair damage. The presence of critical scientists, specialized in the area, is also important, as they directly influenced the legislative creations and the correct framing of AIs according to their risk.P2P E INOVAÇÃO00The results obtained revealed that the current Brazilian legislation constitutes a starting point in solving problems associated with the topic, and there is a need for secure protection to prevent and repair damage.P2P E INOVAÇÃO["N. S. Silva", "Alo\u00edsio Alencar Bolwerk"]2024-07-04T00:00:00 c9b89664c9e009dae203d60fc388882afe28d07cArtificial intelligence integration, specifically ChatGPT, is becoming increasingly popular in educational contexts. This research paper provides a systematic literature review that examines the effects of incorporating ChatGPT into education. The study examines four primary research questions: the benefits and challenges of ChatGPT, its impact on student engagement and learning outcomes, ethical considerations and safeguards, and the effects on educators and teachers, based on an analysis of numerous scientific research articles published between 2022 and 2023. The results emphasize the numerous benefits of ChatGPT, such as the opportunity for students to investigate AI technology, personalized assistance, and improved learning experiences. Furthermore, advantages such as enhanced learning and enhanced information accessibility are identified. Nevertheless, ethical considerations and biases in AI models are also highlighted. ChatGPT enhances student engagement by offering personalized responses, prompt feedback, and rapid access to information, resulting in enhanced learning outcomes and the growth of critical thinking abilities. Ethical considerations and safeguards, including user education, privacy protection, human supervision, and stated guidelines, are essential for responsible use. The integration of ChatGPT transforms the role of educators from content delivery to assistance and guidance, thereby fostering personalized and differentiated learning. Educators have to consider ethical considerations while monitoring student usage in order to facilitate this transformation. Educational institutions can increase student engagement, learning outcomes, and the responsible use of AI in education by addressing challenges, establishing ethical guidelines, and leveraging the strengths of ChatGPT. This will prepare students for future challenges.Frontiers in Education4314The study examines the benefits and challenges of ChatGPT, its impact on student engagement and learning outcomes, ethical considerations and safeguards, and the effects on educators and teachers, based on an analysis of numerous scientific research articles published between 2022 and 2023.Frontiers in Education["Anissa M. Bettayeb", "Manar Abu Talib", "Al Zahraa Sobhe Altayasinah", "F. Dakalbab"]2024-07-04T00:00:00 1ff74b7a42d5f7a3f0173191be4a7c7ce814478cCluster Computing2215A collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism, and highlights the significance of Edge AI in processing real-time data at the edge of the network.ArXiv["S. Gill", "Muhammed Golec", "Jianmin Hu", "Minxian Xu", "Junhui Du", "Huaming Wu", "G. Walia", "Subramaniam Subramanian Murugesan", "Babar Ali", "Mohit Kumar", "Kejiang Ye", "Prabal Verma", "Surendra Kumar", "F\u00e9lix Cuadrado", "Steve Uhlig"]2024-07-04T00:00:00 3cff673e4ddb32d3e381b0cb735144082974204eThe study aims to identify the knowledge, skills and competencies required by accounting and auditing (AA) professionals in the context of integrating disruptive Generative Artificial Intelligence (GenAI) technologies and to develop a framework for integrating GenAI capabilities into organisational systems, harnessing its potential to revolutionise lifelong learning and skills development and to assist day-to-day operations and decision-making. Through a systematic literature review, 103 papers were analysed, to outline, in the current business ecosystem, the competencies’ demand generated by AI adoption and, in particular, GenAI and its associated risks, thus contributing to the body of knowledge in underexplored research areas. Positioned at the confluence of accounting, auditing and GenAI, the paper introduces a meaningful overview of knowledge in the areas of effective data analysis, interpretation of findings, risk awareness and risk management. It emphasizes and reshapes the role of required skills for accounting and auditing professionals in discovering the true potential of GenAI and adopting it accordingly. The study introduces a new LLM-based system model that can enhance its GenAI capabilities through collaboration with similar systems and provides an explanatory scenario to illustrate its applicability in the accounting and audit area.Electronics933A new LLM-based system model is introduced that can enhance its GenAI capabilities through collaboration with similar systems and provides an explanatory scenario to illustrate its applicability in the accounting and audit area.Electronics["Ionu\u021b Anica-Popa", "Marinela Vr\u00eencianu", "Liana Anica-Popa", "I. Cismasu", "C. Tudor"]2024-07-04T00:00:00 d3559b5509d44f05501986fe7c90468cdd7af09fBACKGROUND AND AIMS Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH. METHODS We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions. RESULTS About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region. CONCLUSION Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.Liver international (Print)402Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging, in MASH individuals who received either routine lifestyle intervention or strengthened lifestyle intervention.Liver international : official journal of the International Association for the Study of the Liver["Hai-Yang Yuan", "Xiao-Fei Tong", "Yayun Ren", "Yang-Yang Li", "Xin-Lei Wang", "Li-Li Chen", "Sui-Dan Chen", "Xiao-Zhi Jin", "Xiao-Dong Wang", "Giovanni Targher", "Christopher D. Byrne", "Lai Wei", "V. W. Wong", "Dean Tai", "Arun J Sanyal", "Hong You", "M. Zheng"]2024-07-04T00:00:00 c3f17d260fcba36c33d3cff9a16a6967d26391c9Künstliche Intell.332Künstliche Intell.["A. Steen", "Christoph Benzm\u00fcller"]2024-07-04T00:00:00 4dc18700c451e7f13dc23465ac64de7cc5bcb894Journal of Assisted Reproduction and Genetics222The model highlights the potential of using XAI algorithms to enhance ploidy prediction, optimize embryo selection as patient-centric consultation, and provides reliability and transparent insights into the decision-making process.Journal of assisted reproduction and genetics["Thi-My-Trang Luong", "N. Ho", "Y. Hwu", "Shyr-Yeu Lin", "J. Y. Ho", "Ruey-Sheng Wang", "Yi-Xuan Lee", "Shun-Jen Tan", "Yi-Rong Lee", "Yung-Ling Huang", "Yi-Ching Hsu", "N. Le", "C. Tzeng"]2024-07-04T00:00:00 c9d821dd104920b76017b15d690a9ed3ab38c6f9While extensive research has focused on ChatGPT in recent years, very few studies have systematically quantified and compared linguistic features between human-written and Artificial Intelligence (AI)-generated language. This study aims to investigate how various linguistic components are represented in both types of texts, assessing the ability of AI to emulate human writing. Using human-authored essays as a benchmark, we prompted ChatGPT to generate essays of equivalent length. These texts were analyzed using Open Brain AI, an online computational tool, to extract measures of phonological, morphological, syntactic, and lexical constituents. Despite AI-generated texts appearing to mimic human speech, the results revealed significant differences across multiple linguistic features such as consonants, word stress, nouns, verbs, pronouns, direct objects, prepositional modifiers, and use of difficult words among others. These findings underscore the importance of integrating automated tools for efficient language assessment, reducing time and effort in data analysis. Moreover, they emphasize the necessity for enhanced training methodologies to improve the capacity of AI for producing more human-like text.arXiv.org182Despite AI-generated texts appearing to mimic human speech, the results revealed significant differences across multiple linguistic features such as consonants, word stress, nouns, verbs, pronouns, direct objects, prepositional modifiers, and use of difficult words among others.ArXiv["Georgios P. Georgiou"]2024-07-04T00:00:00 ed6d2a0e346aee649f6c642f81c3dca7dd68df1dQuality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing.Human Reproduction01Four critical areas considered essential to successful uptake of any new technology include value proposition, innovative disruption, clinical agency, and responsible computing are discussed.Human reproduction["Gerard Letterie"]2024-07-04T00:00:00 606191720aec463d7c8a276497a68745cfc33283The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.Medicine446The need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms to ensure responsible and transparent research processes is underscored, underscoring the need for the scientific community to strengthen ethical norms.Medicine["Ziyu Chen", "Changye Chen", "Guozhao Yang", "Xiangpeng He", "Xiaoxia Chi", "Zhuoying Zeng", "Xuhong Chen"]2024-07-05T00:00:00 2f425691f27b0373df21f40673859c3ee0f6cdedFrontiers of Engineering Management654The results reveal that the primary focus of current AI research in this field centers on safety monitoring and control, as well as process management, which are promising for further advancements in infrastructure construction.Frontiers of Engineering Management["Ke Chen", "Xiaojie Zhou", "Zhikang Bao", "M. Skibniewski", "Weili Fang"]2024-07-05T00:00:00 0e21d4983c34f0733260f246a7b827d3cb8afe6bPurposeThis study aimed to explore the impact of Artificial Intelligence (AI) characteristics, namely Perceived Animacy (PAN), perceived intelligence (PIN), and perceived anthropomorphism (PAI), on user satisfaction (ESA) and continuous intentions (CIN) by integrating Expectation Confirmation Theory (ECT), with a particular focus on Generation Y and Z.Design/methodology/approachUsing a quantitative method, the study collected 495 data from Gen Y (204) and Z (291) respondents who were users of digital banking apps through structured questionnaires that were analysed using PLS-SEM. The latter helped investigate the driving forces of AI characteristics and user behavioural intentions as well as reveal generation-specific features of digital banking engagement.FindingsThe study revealed that PAN and PIN have significant positive effects on the anthropomorphic perceptions of digital banking apps, which in turn increases perceived usefulness, satisfaction, and continuous intentions. In particular, the influence of these AI attributes varies across generations; Gen Y’s loyalty is mostly based on the benefits derived from AI features, whereas Gen Z places a greater value on the anthropomorphic factor of AI. This marked a generational shift in the demand for digital banking services.Research limitations/implicationsThe specificity of Indian Gen Y and Z users defines the scope of this study, suggesting that demographic and geographical boundaries can be broadened in future AI-related banking research.Practical implicationsThe results have important implications for bank executive officers and policymakers in developing AI-supported digital banking interfaces that appeal to the unique tastes of millennial customers, thus emphasising the importance of personalising AI functionalities to enhance user participation and loyalty.Originality/valueThis study enriches the digital banking literature by combining AI attributes with ECT, offering a granular understanding of AI’s role in modulating young consumers' satisfaction and continuance intentions. It underscores the strategic imperative of AI in cultivating compelling and loyalty-inducing digital banking environments tailored to the evolving expectations of Generations Y and Z.Management Decision1923This study enriches the digital banking literature by combining AI attributes with ECT, offering a granular understanding of AI’s role in modulating young consumers' satisfaction and continuance intentions and underscores the strategic imperative of AI in cultivating compelling and loyalty-inducing digital banking environments tailored to the evolving expectations of Generations Y and Z.Management Decision["Puneett Bhatnagr", "Anupama Rajesh"]2024-07-05T00:00:00 cd9c2bed3b5ee9c6536120c0c3f061b636526ff2Objective: to describe the development of a predictive nursing workload classifier model, using artificial intelligence. Method: retrospective observational study, using secondary sources of electronic patient records, using machine learning. The convenience sample consisted of 43,871 assessments carried out by clinical nurses using the Perroca Patient Classification System, which served as the gold standard, and clinical data from the electronic medical records of 11,774 patients, which constituted the variables. In order to organize the data and carry out the analysis, the Dataiku® data science platform was used. Data analysis occurred in an exploratory, descriptive and predictive manner. The study was approved by the Ethics and Research Committee of the institution where the study was carried out. Results: the use of artificial intelligence enabled the development of the nursing workload assessment classifier model, identifying the variables that most contributed to its prediction. The algorithm correctly classified 72% of the variables and the area under the Receiver Operating Characteristic curve was 82%. Conclusion: a predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient’s electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.Revista Latino-Americana de Enfermagem323A predictive model was developed, demonstrating that it is possible to train algorithms with data from the patient’s electronic medical record to predict the nursing workload and that artificial intelligence tools can be effective in automating this activity.Revista Latino-Americana de Enfermagem["Ninon Girardon da Rosa", "T. Vaz", "Am\u00e1lia de F\u00e1tima Lucena"]2024-07-05T00:00:00 5e3162aefc5f48bf6ac8fe6babe96b16d9a4cb1aThis paper employs text analysis to investigate and gain a deeper understanding of the evolving intellectual structure of marketing publications on artificial intelligence. 50 papers titled "Artificial Intelligence" and "AI" from marketing journals from 2009 to 2023 were used for text analysis. Artificial intelligence-titled marketing research is in its early developmental stage. All the articles carry positive sentiments but exhibit shallow similarities. No precise key dimensions or clusters were detected. Two coherent and exclusive topics were found. This research employed various advanced text analysis techniques to systematically and objectively review artificial intelligence research in marketing. The results offer valuable insights into the emerging intellectual structure and the main thematic topics in literature.Management Research Quarterly01This research employed various advanced text analysis techniques to systematically and objectively review artificial intelligence research in marketing and offers valuable insights into the emerging intellectual structure and the main thematic topics in literature.Management Research Quarterly["Y. Wei"]2024-07-05T00:00:00 cb5f8782a6302dd00e14fbb497b493c4d868934cThis research article explores the influence of leveraging machine learning algorithms (ML) and artificial intelligence (AI) in the early detection and mitigation of cyber attacks. With the rise of cybercriminal activities, traditional cybersecurity measures have proven inadequate. This study reviews the various AI and ML techniques, such as anomaly and cyber intelligence, which can be used in detecting cyberattacks before they occur. A case study on IBM security illustrates the practical implications and outcome of implementing machine learning and artificial intelligence in cybersecurity.Applied and Computational Engineering02This study reviews the various AI and ML techniques, such as anomaly and cyber intelligence, which can be used in detecting cyberattacks before they occur.Applied and Computational Engineering["Encheng Liu"]2024-07-05T00:00:00 697cb92ab34fa69ff467f515ac4aa17dc0ac4e49Background In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. Objective The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. Methods A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. Results Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. Conclusions These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.Journal of Medical Internet Research732An urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes is highlighted.Journal of Medical Internet Research["Rawan AlSaad", "Alaa A. Abd-alrazaq", "Fadi Choucair", "Arfan Ahmed", "S. Aziz", "Javaid Sheikh"]2024-07-05T00:00:00 40ddedfff46ebdcd8cc68046c50a6c9202c3c8f3Artificial Intelligence (AI) technology is continually booming, and its impact in various fields is increasingly significant. As the core of modern science and technology, the field of integrated circuits is also undergoing profound changes driven by AI technology. This survey paper aims to explore the impact of AI technology on the development of integrated circuits. We will introduce the basics of AI technology and highlight its successful applications in image recognition, natural language processing, and other fields. At the same time, we will review the historical evolution of Integrated Circuits (IC) and the applications of chips with different levels of integration. Special attention is paid to the application of AI in manufacturing, design and fault detection, and the positive effects it brings are analyzed. Finally, we discuss the challenges faced by AI technologies in the IC sector and highlight the importance of standardization and cross-border cooperation to achieve closer integration. The results show that rational application of AI technologies and the cultivation of cross-domain talent will lead the development of IC to a higher level.Applied and Computational Engineering01The results show that rational application of AI technologies and the cultivation of cross-domain talent will lead the development of IC to a higher level.Applied and Computational Engineering["Xudong Zhao"]2024-07-05T00:00:00 1238bfee502c25bb3219d1362c3879487b3a036fAbstract Background The integration of artificial intelligence (AI) across different sectors, notably healthcare, is on the rise. However, a thorough exploration of AI's incorporation into nursing research, as well as its advantages and obstacles, is still lacking. Objective The aim of this scoping review was to map the roles, benefits, challenges, and potentials for the future development and use of AI in the context of nursing research. Methods An exhaustive search was conducted across seven databases: MEDLINE, PsycINFO, SCOPUS, Web of Science, CINAHL, Google Scholar, and ProQuest. Articles were additionally identified through manual examination of reference lists of the articles that were included in the study. The search criteria were restricted to articles published in English between 2010 and 2023. The Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA‐ScR guidelines guided the processes of source selection, data extraction, and data presentation. Results Twenty articles met the inclusion criteria, covering topics from ethical considerations to methodological issues and AI's capabilities in data analysis and predictive modeling. Conclusion The review identified both the potentials and complexities of integrating AI into nursing research. Ethical and legal considerations warrant a coordinated approach from multiple stakeholders. Implication The findings emphasized AI's potential to revolutionize nursing research, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.International Nursing Review441AI's potential to revolutionize nursing research is emphasized, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.International Nursing Review["Y. Yasin", "Areej Al-Hamad", "Kateryna Metersky", "Vahe Kehyayan"]2024-07-05T00:00:00 f71198d39644154e18141e4780fc8d07e449a4cbThe purpose of this study is to explore how artificial intelligence (AI) and big data can be used to solve the twin issues of athlete safety and sports event quality in a college sports environment. Furthermore, this study attempts to fill the literature vacuum regarding the application and effectiveness of artificial intelligence and big data in improving safety and quality in collegiate sports administration by investigating possible synergies between these elements and the implementation of developed technologies. This qualitative study used a sampling method to conduct in-depth interviews with 18 sports administrators and commentators. Using coding and classification methods, the data were evaluated thematically with a focus on artificial intelligence and big data applications. Research has found that artificial intelligence and big data play a key role in proactively reducing injuries, optimizing athlete performance and enabling data-driven decision-making. It also identifies barriers and opportunities for integrating these technologies, revealing their dynamic potential. This study provides new perspectives on the relationship between safety and quality and the application of artificial intelligence and big data in collegiate sports management. It also highlights the ways in which these technologies have transformative potential in sport. The findings have important implications for educational programs and policy development aimed at managing responsible technology integration and preparing future professionals in the field of sport management.Journal of Information Systems Engineering & Management331This study provides new perspectives on the relationship between safety and quality and the application of artificial intelligence and big data in collegiate sports management and highlights the ways in which these technologies have transformative potential in sport.Journal of Information Systems Engineering and Management["Weiwei Jiang", "Mohamad Nizam Bin Nazarudin", "Nur Shakila Mazalan"]2024-07-05T00:00:00 28ab0c0f267b00781285601842d5d999609e984fPurposeThis paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.Design/methodology/approachDrawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.FindingsAll AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.Research limitations/implicationsThe study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.Practical implicationsThe AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.Originality/valueThe originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.Management Decision711All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions, and those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.Management Decision["Ewerton Alex Avelar", "Ricardo Vin\u00edcius Dias Jord\u00e3o"]2024-07-05T00:00:00 baf2d935001a3be1a908933c99ba1b5d30953101BACKGROUND Recent research at a single-purpose nursing institution has suggested a means to authenticate student writing by distinguishing it from artificial intelligence (AI)-generated text through the detection of key terms. PURPOSE The purpose was to replicate and expand the research that identified key terms present in student writing but absent from AI-generated text. METHODS A total of 5 generative AI writing tools were fed prompts to collect 14 787 words. Using the Search function on word processing software, the frequency of the terms, because, since, so, then, thing, think, and too, was measured and compared against earlier published findings from AI and students. RESULTS The replication study was successful for the terms since, then, thing, think, and too. CONCLUSIONS Measuring key term frequency may be a path to authenticate student writing. While no tool can provide certainty of original authorship, the absence of key terms in a student submission may suggest AI authorship.Nurse Educator61Measuring key term frequency may be a path to authenticate student writing and suggest AI authorship after research identified key terms present in student writing but absent from AI-generated text.Nurse educator["Miriam R Bowers Abbott", "Wyatt W Abbott"]2024-07-05T00:00:00 14538785c64cb96d00298561797a4535c7f197e1Ageing Research Reviews1558The prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies, are discussed and currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions are highlighted.Ageing Research Reviews["Aananya P Reddy", "Ruhananhad P Reddy", "Aryan Kia Roghani", "Ricardo I Garcia", "Sachi Khemka", "Vasanthkumar Pattoor", "Michael Jacob", "P. H. Reddy", "Ujala Sehar"]2024-07-05T00:00:00 5efc0de85285c5d89b113d939e58e4874fb522ecThe latest research from Nonprofit HR, a consulting firm serving social impact organizations, shows that philanthropic groups are already making use of generative artificial intelligence (AI)—that is, artificial intelligence tools that can generate text, images or other media—and it is likely to grow in importance to the sector.Board &amp; Administrator for Administrators Only00The latest research from Nonprofit HR shows that philanthropic groups are already making use of generative artificial intelligence tools that can generate text, images or other media, and it is likely to grow in importance to the sector.Board &amp; Administrator for Administrators Only[]2024-07-05T00:00:00 38fd64a994d328b31458e2d1d7dbb5cb9df8e5a1Objective: It is vital to investigate how technologies benefit or impair sustainable development. This review aimed to provide updated literature on Artificial Intelligence (AI), in explicit connection with sustainability.   Theoretical Framework: This article performs a systematic literature review of information systems (IS) journals on AI employment in promoting sustainable development (SD).   Method: Among 331 articles, 97 have been identified in the Scopus and Web of Science databases from 2017 to 2022 focusing on the AI use for environmental, economic, legal political, organizational, and social development.   Results and Discussion: According to the findings, the identified areas of interest and respective papers were associated with the corresponding concepts and summarized. These studies point to the role of AI in supporting decision-making and reveal research avenues in information and communication technologies (ICTs) and SD. The authors also propose a framework correlating the concepts with the 17 Sustainable Development Goals (SDGs).   Research Implications: The practical and theoretical implications of this research were discussed, providing insights into how the results can be applied or influence practices in the field of ICTs and SD.   Originality/Value: The relevance and value of this research are evidenced by highlighting the contributions research in the IS field has made regarding AI for SD since 2017. As a step forward in this literature review, the authors suggest a research agenda for the IS field.Revista de Gestão Social e Ambiental1090A systematic literature review of information systems (IS) journals on AI employment in promoting sustainable development (SD) and proposes a framework correlating the concepts with the 17 Sustainable Development Goals (SDGs).Revista de Gestão Social e Ambiental["Manoel Brod Siqueira", "V. Santos", "E. Diniz", "Ana Paula Alves Cruz"]2024-07-05T00:00:00 40f2682686d94836f4b1cd0741af8a6c90f7a537Artificial intelligence (AI) has very soon changed numerous aspects, and the world of digital media is no exception. With its ability to replicate human-like intelligence and learning capabilities, AI has opened up new possibilities for enhancing various aspects of digital media creation, distribution, and consumption. From personalized content recommendations to advanced image and video editing techniques, AI applications are revolutionizing how human beings interact with digital media. This study searches for a comprehensive and systematic literature review to explore the impact of AI in the digital media world. The objective is to offer a detailed examination of how AI is being integrated into various aspects of digital media areas. This has been achieved in this study through an analysis of existing research, news and reports updates, and scholarly works. The problem statement is that there exists a lack of inclusive research examining the complex implications and applications of AI integration. The current studies more focus on specific sub-fields or isolated examples of AI adoption, resulting in a disjointed knowledge base. A complete understanding of the larger impact of AI in digital media has been described for decision-making, policy development, and strategies. The methodology used in this study is to examine the existing literature on digital media. The population is several bibliographical resources and the samples are 104 news research reports, journal papers, scholarly articles, and other sources that have been systematically reviewed and studied in this research during Jan-March 2024. The study is qualitative research and several techniques have been applied to the secondary data which are: Literary Search technique, inclusion and exclusion technique, and selection and extraction technique as the data mainly relies on secondary data. The results of this study show how AI has changed the media industry by enabling automated data analysis and enhancing content creation and curation. Furthermore, AI-powered image recognition technologies have greatly influenced visual media production. Through deep learning algorithms, computers can now accurately identify objects within images or videos. The results highlight the need for moral guidelines, skill-updating techniques, and ethical AI practices. As AI advances, we can expect even more innovative applications that will shape the future of digital media. Furthermore, the study will identify any existing research gaps and propose recommendations for future research, policy, and practical applications. While efficiency, customization, and creativity are advantages of AI, privacy issues, job displacement, and algorithmic bias require careful study. Scholars, professionals, and policymakers must hold the implications of AI's influence as it becomes increasingly universal across various societal domains.Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq00The results of this study show how AI has changed the media industry by enabling automated data analysis and enhancing content creation and curation and AI-powered image recognition technologies have greatly influenced visual media production.Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq["Jamal Faeq Kakbra"]2024-07-05T00:00:00 fea7efa90f15772e7b4dd0926000067160327012With the rapid development of information technology and network technology, the Internet not only brings a lot of convenient resources to people's lives but also brings potential threats to people's network security, and the development of artificial intelligence provides new solutions for network security governance. The article focuses on common problems in network security, systematically summarizes the network security solutions based on AI technology and the empowering effects brought in recent years, analyzes the application of AI technology in common security problems, summarizes the advantages of AI in the application of network security, and puts forward the potential problems and challenges. Overall, AI technology has improved the accuracy, scalability, reliability and performance of network security applications in practical applications, and will become an essential engine of empowerment in the future development of network security technology.Applied and Computational Engineering00Overall, AI technology has improved the accuracy, scalability, reliability and performance of network security applications in practical applications, and will become an essential engine of empowerment in the future development of network security technology.Applied and Computational Engineering["Zimeng Li"]2024-07-05T00:00:00 28b3733fdf83c9ee0a85c7506b9fb0e2b8de7b9dThe article provides a comprehensive narrative review of the role of artificial intelligence (AI) in the future of medicine, focusing on its potential to replace physicians in various clinical applications. AI technologies such as machine and deep learning, especially convolutional neural networks (CNNs), are explored in detail, and their applications in AI-assisted diagnosis in areas such as oncology, cardiology, and dentistry are discussed. Both advantages and disadvantages of AI in medicine are highlighted, including its ability to analyze large volumes of medical data and improve diagnostic accuracy, as well as ethical and practical challenges related to patient data protection and transparency in decision-making. Although AI shows great potential to transform medical care, it is concluded that it currently remains a support tool for clinicians and cannot completely replace clinical decision making. It highlights the importance of addressing the remaining challenges and continuing to research and develop new technologies to maximize the potential of AI in medicine.Mexican Journal of Medical Research ICSA00It is concluded that it currently remains a support tool for clinicians and cannot completely replace clinical decision making, and the importance of addressing the remaining challenges and continuing to research and develop new technologies to maximize the potential of AI in medicine.Mexican Journal of Medical Research ICSA["Sergio David Pintado Brito"]2024-07-05T00:00:00 c807bac1477029cebbf04395cf5778b4bae6e8ccPurposeThe purpose of this paper is to explore companies’ business risks and challenges across macro- and micro-environments, as well as how small and medium-sized enterprises (SMEs) can benefit from digital technologies, including artificial intelligence (AI), as part their risk-management (RM) strategies in the face of recent disruptive events.Design/methodology/approachWe perform a literature review on risk management and business continuity (BC) in the context of SMEs, both in general and specifically in the manufacturing sector.FindingsThe critical importance of RM and BC for SMEs is highlighted. The review underscores the significant impact of recent disruptions on SMEs and reveals a range of risk factors affecting their BC. Moreover, the review recognises how SMEs, in general, and manufacturing SMEs, in particular, can benefit from using digital technologies and AI as essential components of their RM.Originality/valueThe review highlights transformative role of digital technologies and AI in enhancing RM. Through a systematic classification of risk factors within macro- and micro-environments, this novel approach provides a structured foundation for future research. It provides practical value by enabling SMEs to integrate dynamic capabilities and adaptive capacities through the adaption of digital technologies and AI into their RM.Continuity &amp; Resilience Review810A literature review on risk management and business continuity (BC) in the context of SMEs, both in general and specifically in the manufacturing sector, highlights transformative role of digital technologies and AI in enhancing RM.Continuity &amp; Resilience Review["Tero Sotamaa", "A. Reiman", "O. Kauppila"]2024-07-05T00:00:00 66076b811b8eb7c7cf130e3037d9a1221ee19937Artificial intelligence (AI) is a computer science discipline focused on the development of systems and algorithms capable of performing human tasks, such as learning, reasoning, problem-solving, and language understanding. Its applications range from virtual assistants to medical diagnostic systems. The introduction of AI in medicine has brought significant changes, improving diagnostics, management, and medical education, but also raising concerns about its overuse and impact on the doctor-patient relationship. The integration of AI into medical education is essential to prepare health professionals for its ethical and effective use. Studies highlight the benefits of AI in the interpretation of medical images, accurate diagnoses, and more efficient clinical practice. However, challenges such as familiarizing medical students with AI, ethical issues, and training educators still need to be addressed. The ethical implementation of AI in medical education requires the development of clear guidelines and ongoing discussions about its use. The literature highlights the importance of research, innovation, and collaboration to harness the potential of AI in promoting better health outcomes. To achieve this, there needs to be a continued focus on areas such as simulating realistic medical interactions, developing safe and transparent systems, and strengthening the capacity of healthcare professionals to deal with this constantly evolving technology.VI Seven International Multidisciplinary Congress00There needs to be a continued focus on areas such as simulating realistic medical interactions, developing safe and transparent systems, and strengthening the capacity of healthcare professionals to deal with this constantly evolving technology.VI Seven International Multidisciplinary Congress["Julio Cesar Sarto e Silva", "Sofia Ferreira Pena Quadros"]2024-07-05T00:00:00 79b002ae3f320511bdfd88e701fa190507c794ebDiabetic retinopathy (DR) is a microvascular disorder caused by the long-term effects of diabetes mellitus and among the primary causes of blindness worldwide. Early detection of DR is the key to its effective treatment and subsequent reduction of associated economic burden, but manual screening is time-consuming and of limited availability. A highly sensitive and specific automatic diagnostic tool would significantly improve screening programs and allow referring for further evaluation and treatment in an ophthalmology clinic only patients with significant lesions or with changes between two successive evaluations. Several deep learning-based automated diagnosis tools have been proposed to aid screening but their implementation with minimal costs is not accessible to physicians with no coding knowledge. We aimed to develop a fundus images classification model with no coding knowledge by using generative artificial intelligence (AI) implemented in Windows 11 operating system under subscription (Copilot Pro), a free image analysis tool (Fiji ImageJ2), and Vertex AI, a machine learning (ML) platform launched by Google in 2021. For this purpose, we selected a total of 2961 labelled cases from the APTOS 2019 database of DR fundus images. Images were batch segmented using a Java ImageJ script generated by Copilot Pro and based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. Segmented images were used to train an automated ML classification model to detect DR severity (5 classes – no DR, mild non-proliferative DR, moderate DR, severe DR, proliferative DR). The model achieved an area under the precision-recall curve of 0.889, with a precision rate of 83.8% and a recall rate of 77%. In conclusion, generative AI implemented into Windows operating system together with a free imaging processing tool and Vertex AI allow ophthalmologists with no coding knowledge to benefit from publicly available image databases (thousands of cases) to develop accurate automated diagnostic tools. Such tools have the potential to facilitate screening especially in areas with few specialists.Brain: Broad Research in Artificial Intelligence and Neuroscience510Generative AI implemented into Windows operating system together with a free imaging processing tool and Vertex AI allow ophthalmologists with no coding knowledge to benefit from publicly available image databases to develop accurate automated diagnostic tools.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["Alexandra Cristina Rusu", "R. Chistol", "Simona-Irina Damian", "Klara Br\u00eenzaniuc", "K. Horvath"]2024-07-05T00:00:00 1dafb0800772b8fc296d7032c3fcd91b7c3691a3BACKGROUND: Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences. OBJECTIVE: The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures. METHODS: A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review’s purview. RESULTS: Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning. CONCLUSION: For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.Technology and Health Care530For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data and standardise AI approaches, which could transform their use in medical environments.Technology and Health Care["Hadeel Alsaleh"]2024-07-05T00:00:00 8f216db0eec2035f1b7996cbbbcfaa27c340c8b3: This study investigates the integration of Artificial Intelligence (AI) & Big Data into sustainable finance & market risk management. As financial markets become increasingly complex & interconnected, the need for advanced technologies to address sustainability & risk challenges is paramount. This research explores how AI & Big Data enhance decision making, risk assessment, & Environmental, Social, & Governance (ESG) performance. A mixed method approach, including surveys & interviews with finance professionals, alongside the analysis of secondary data, was employed to gather comprehensive insights. The findings indicate that AI & Big Data significantly improve the accuracy & efficiency of risk assessments, contribute to better ESG outcomes, & promote sustainable investment practices. Despite challenges related to data quality, technological barriers, & the need for standardized metrics, the integration of these technologies presents substantial opportunities for financial institutions to optimize operations, reduce costs, & achieve long term sustainability goals. The study concludes with practical recommendations for leveraging AI & Big Data to enhance sustainable finance & risk management. Additionally, it highlights the necessity for ongoing innovation & adaptation in financial practices to meet evolving market demands & regulatory standards.International Journal of Science and Research (IJSR)240The findings indicate that AI & Big Data significantly improve the accuracy & efficiency of risk assessments, contribute to better ESG outcomes, & promote sustainable investment practices.International Journal of Science and Research (IJSR)["Pranay Harsha Jupalli"]2024-07-05T00:00:00 12f58feedc10a8ff6ab15abaca7d0122dc5b6edfIn recent years, Artificial Intelligence (AI) has emerged as a powerful tool in education. This paper examines the use of AI in teaching mathematics to high school students, highlighting specific applications, benefits, challenges, and recent studies demonstrating its effectiveness. Personalization of learning, increased student engagement, and improved assessment are some of the benefits observed, while technology gaps and data privacy emerge as significant challenges.Revista Iberoamericana de Educación30This paper examines the use of AI in teaching mathematics to high school students, highlighting specific applications, benefits, challenges, and recent studies demonstrating its effectiveness.Revista Iberoamericana de la Educación["Efr\u00e9n Antonio Castillo Del Pezo", "Nancy Karina Tapia Yagual", "Silvia Maribel Placencia Ibadango", "Christian Antonio Pav\u00f3n Brito"]2024-07-05T00:00:00 285676f21d1ed7d56d8268f503fe5770f93e830fIn “Artificial Intelligence and Applications,” the fundamentals and ethics of AI are thoroughly examined, along with its many practical applications in domains such as consumer electronics, automotive engineering, manufacturing, banking, robotics and automation, electronic devices and systems, and predictive analysis. A one volume covering all the bases, this book covers every angle of AI and all it can do.00The fundamentals and ethics of AI are thoroughly examined, along with its many practical applications in domains such as consumer electronics, automotive engineering, manufacturing, banking, robotics and automation, electronic devices and systems, and predictive analysis.["Dr. S. Murugesan", "Dr. N. Bharathiraja", "Ms. D. Meenakshi", "Dr. B. Selvalakshmi"]2024-07-05T00:00:00 a8d21ce89740c82b0479b4cd3c725b1b759819b2OpenAI launched ChatGPT in the United States on November 30,2022, with an astonishing success of more than 100 million users. CHATGPT trains by using online information, including personal information. With that comes the risk of personal information..This have a serious impact on society, including misleading the public, manipulating public opinion, etc. Furthermore, malicious users of artificial intelligence technology may use personal information for social engineering or phishing attacks, Inducing users to disclose more sensitive information or be subject to fraud. These are the challenges faced by personal information under the development of artificial intelligence. The research questions of the paper are how can people protect personal information and how to make more rational use of artificial intelligence. The study are conducted by consulting literature and learning about this area.Finally, in this paper, the author put forward that the threats of personal information include CHATGPTs management of personal information data and the generation of false information, and then the use of Chatgpt by criminals, and proposed the solution to this, stregthens peoples consciousness, formulates the law provision, uses the safer equipment.Applied and Computational Engineering00The author put forward that the threats of personal information include CHATGPTs management of personal information data and the generation of false information, and then the use of Chatgpt by criminals, and proposed the solution to this.Applied and Computational Engineering["Yongrui Wang"]2024-07-05T00:00:00 ea49be45b50054f4956a6a53e2ae4d4bb85d79f8: The integration of artificial intelligence (AI) in healthcare is revolutionizing precision medicine, enabling advanced patient risk prediction and providing healthcare professionals with data - driven insights. This research explores AI's transformative potential, focusing on empowering doctors to make informed decisions based on accurately predicted patient risks. Leveraging AI algorithms for real - time data analysis allows healthcare providers to tailor personalized treatment plans, optimize care, and enhance outcomes. AI - driven risk prediction equips doctors with a proactive approach to address potential health issues before they escalate, leading to improved patient outcomes and more efficient healthcare delivery. By identifying individuals at higher risk for specific conditions, interventions can be targeted and tailored, potentially preventing complications and reducing healthcare costs. This study highlights the pivotal role of AI in augmenting medical decision - making processes. AI models can analyze vast amounts of patient data, including medical histories, genetic information, lifestyle factors, and real - time physiological data, to generate comprehensive risk profiles. This information empowers healthcare professionals to make more informed diagnoses, select appropriate treatments, and monitor patient progress more effectively. Ultimately, the integration of AI in healthcare has the potential to revolutionize how data - driven insights are harnessed to enhance patient care strategies and optimize healthcare systems.International Journal of Science and Research (IJSR)150This research explores AI's transformative potential, focusing on empowering doctors to make informed decisions based on accurately predicted patient risks, to highlight the pivotal role of AI in augmenting medical decision - making processes.International Journal of Science and Research (IJSR)["Sivachandran Selvaraj"]2024-07-05T00:00:00 50d3fcf650745c13f4152300d4456272853dbdbeArtificial intelligence is a crucial and controversial topic. Rapid advances in technology have led to greater scrutiny and serious discussion in today's academic institutions. In this regard, there is another aspect to this issue related to the level of use of media work and the production of media content in digital media. At the same time, the Kurdistan Region and the Kurdish media have not been exempted from the wave of modern technology that has embraced the means of communication. From this point of view, this study aims to understand the extent of use of artificial intelligence in creating Kurdish digital media content. Therefore, the question of the extent of use and benefits of modern technological advances in Kurdish media, especially in digital media, has become a research topic. To obtain a correct and accurate scientific answer, researchers used descriptive types and research methods. They rely on research tools. The study also includes a collection of official Kurdish media organizations in the Kurdistan Region. The problem is that these digital media are the media that use artificial intelligence tools, apps, services, and features. At the end of the study, the researchers reached several conclusions: There is a lack of experience of the Kurdish media in official digital media institutions relating to the use of artificial intelligence. At the same time, their competency level is also low. On the other hand, the institutions do not provide the media with sufficient professional and technical development. In addition, the media of Kurdistan's official digital media institutions usually use tools, applications, functions and services (artificial intelligence) at different levels and in different ways, and producing media content for both purposes (editing, writing).Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq00There is a lack of experience of the Kurdish media in official digital media institutions relating to the use of artificial intelligence and their competency level is also low, and the institutions do not provide the media with sufficient professional and technical development.Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq["Hersh Rasool Murad", "Goran Zyad Smail"]2024-07-05T00:00:00 fae61f2a364c271e536702dff2ab52af8965c5fbFrom four-function calculators to calculators (or computers) with Computer Algebra System (CAS) software, Mathematics computing technology has advanced. With just a few button pushes, CASs can solve a wide range of mathematical problems, which is a true quantum leap in technology. The implications of having software in the classroom that can, for example, expand and factorize algebraic expressions, solve equations, differentiate functions, and find anti-derivatives are causing the mathematical community to engage in a heated debate about whether this is one of the most exciting or frightening developments in the history of education. It was only a matter of time before Artificial Intelligence entered the field of Science. This is now also the case with Mathematics, one of the dominant, perhaps the most basic, but also the most "difficult" of the sciences. The human mind, for better or for worse, has its limits. As we see in every manifestation of our lives, in this case, technology is being enlisted to help humanity take the next step, whether it has to do with automation and practical matters, or with knowledge and exploration. Creating a model that is understandable to humans is the primary objective of Artificial Intelligence. Additionally, concepts and methods from numerous mathematical fields can be used to prepare these models. In this paper, we will examine the use of AI in CASs and explore some ways to optimize them. The documentation sheets are the data source that we used to examine their characteristics. The research results reveal that there are many tips that we can follow to accelerate performance.Brain: Broad Research in Artificial Intelligence and Neuroscience80The use of AI in CASs is examined and some ways to optimize them are explored, which reveal that there are many tips that the authors can follow to accelerate performance.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["K. Zotos"]2024-07-05T00:00:00 6ae8e1bb3a7adda9935bf2a1ac879594f93b7cd0In the current era, artificial intelligence (AI) development is experiencing unprecedented growth, fueled by advancements in deep learning, big data, and computing power. Industries are integrating AI for automation, optimization, and decision-making. Breakthroughs in natural language processing and computer vision are reshaping how humans interact with technology. Ethical considerations regarding AI bias, privacy, and job displacement remain paramount. Collaborations between academia, industry, and governments drive innovation, aiming to harness AI's potential while addressing its societal implications. There is no denying that AI has revolutionized various sectors, including healthcare. This paper explores the applications of AI in the healthcare industry, highlighting its impact on diagnosis, treatment, patient care, and the challenges AI is facing in the healthcare industry. The effectiveness and challenges of AI implementation in healthcare are discussed through the analysis of various studies and real-world examples. Eventually, conclusions can be drawn that under appropriate regulation and promotion, AI has immense potential and will shine brightly in the healthcare industry, significantly improving the quality of medical care for individuals.Applied and Computational Engineering00The effectiveness and challenges of AI implementation in healthcare are discussed through the analysis of various studies and real-world examples, and conclusions can be drawn that under appropriate regulation and promotion, AI has immense potential and will shine brightly in the healthcare industry, significantly improving the quality of medical care for individuals.Applied and Computational Engineering["Yutong Wu"]2024-07-05T00:00:00 6e04df344dd233613426e266cff3a51202d5a327: Artificial Intelligence (AI) is revolutionizing the medical field by enhancing diagnostic accuracy and improving patient outcomes. This paper provides a comprehensive overview of the current state of AI in the medical field, its possible future prospects, highlighting its applications, benefits, challenges. Key AI technologies such as machine learning, deep learning, natural language processing, and computer vision are explored, demonstrating their roles in diagnostics, treatment recommendations, patient monitoring, and administrative tasks. The survey covers notable case studies and real-world implementations, illustrating the impact of AI on various medical specialties. Despite the significant advancements, the paper also addresses the challenges associated with AI integration, including ethical considerations, data privacy, and the need for rigorous validation and regulatory approval. By synthesizing current research and developments, this paper aims to provide a thorough understanding of how AI is transforming medical practice and to identify areas requiring further investigation and development.International Journal of Science and Research (IJSR)380A comprehensive overview of the current state of AI in the medical field, its possible future prospects, and its applications, benefits, challenges is provided, highlighting its applications, benefits, challenges.International Journal of Science and Research (IJSR)["Thendral Kabilan"]2024-07-05T00:00:00 3081b143997c29515b2a0faeab4711ea128ed7daThe impact of artificial intelligence on employment and the configuration of the workforce in the digital age is a topic of great relevance today. The objective of the research is to analyze how the adoption of artificial intelligence is transforming work roles, generating both opportunities and challenges for workers. Changes in skill demand, the emergence of new occupations, and potential automation of tasks are examined, as well as implications for workforce training and adaptation. A bibliographic review and a qualitative analysis of the aspects involved in the introduction of new technologies in the work process were carried out. The result was that artificial intelligence could be implemented to improve employment and take advantage of the opportunities offered in all work spheres, mainly science and education.Tennessee Community Service International of Empowerment180The result was that artificial intelligence could be implemented to improve employment and take advantage of the opportunities offered in all work spheres, mainly science and education.Tennessee Community Service International of Empowerment["Eric Eduardo Molina-Menendez", "Nathaly Juleidy Cede\u00f1o-Acosta", "Cristhian Leonel Cede\u00f1o-Cuzme", "Mar\u00eda Rodr\u00edguez-G\u00e1mez", "Wilber Manuel Saltos-Arauz"]2024-07-05T00:00:00 9d363d054bbe0f064ef8a81c9e5dd86aaad7aa39One of the most discussed and investigated topics in the educational field nowadays is the use of artificial intelligence (AI). This article aims to shed light on the use and effects of artificial intelligence (AI) on higher education. It also explores various studies to analyze the impact of AI tools keeping in view the University of Brighton. The effects of AI on grading and assessment along with learning and teaching processes in examined in this study. Using the qualitative approach of research based on a case study analysis, this paper investigates the use of AI in various aspects of education. It also presents an extensive literature review to support the evidence. The results and findings of this paper highlight that artificial intelligence should be used more extensively in higher education so that it may prove to be helpful in developing new skills for students.    Received: 22 May 2024 / Accepted: 24 June 2024 / Published: 5 July 2024Journal of Educational and Social Research120The results and findings of this paper highlight that artificial intelligence should be used more extensively in higher education so that it may prove to be helpful in developing new skills for students.Journal of Educational and Social Research["Greta Jani", "Doliana Celaj"]2024-07-05T00:00:00 075f37c2aab10af953eb5a623bdb555dce683fdcThe article examines the theoretical and methodological principles of integrating artificial intelligence into an organization’s management system. It presents a cumulative model illustrating the impact of artificial intelligence on the organization’s management mechanism, which identifies the subjects of influence, tools of influence, directions, and dimensions of influence. Additionally, it describes the challenges posed by the influence of artificial intelligence on the organization’s management mechanism and outlines the main outcomes of this influence. The ways of improving management productivity in various dimensions (socio-technical, strategic-structural, innovative-organizational, task-oriented, information-system) have been systematized. The main results that the use of artificial intelligence offers to the organization have been highlighted, comprising the automation of routine tasks, the reallocation of working time to strategic and creative tasks, increased efficiency in decision-making through analytics and forecasting provided by artificial intelligence, improved external and internal communication, enhanced effectiveness in HR management, formulation of realistic and achievable strategies aligned with future changes, and the development of innovative products and services. An algorithm for introducing artificial intelligence into the organization’s management system has been proposed. The allocation of 8 stages is substantiated as follows: formation of organizational culture; determination of the goals for implementing artificial intelligence; identification of the main performance indicators; establishment of an information base on the state of the management system; analysis of products using artificial intelligence; integration of artificial intelligence products into the management system; monitoring the results of artificial intelligence implementation; and conducting a management system audit. The factors related to the development, implementation, and adaptation of artificial intelligence within the organization’s management system at each stage of its implementation have been considered. These factors include: rethinking the interaction between people and machines in the work environment; awareness among management and staff; organizational support; openness to innovation; staff resistance to change; the presence of a system for disseminating best practices; availability of critical skills for artificial intelligence implementation; ensuring ethical components such as bias, confidentiality, and transparency; integration of model results into relevant business processes; compatibility with other available information systems; and the satisfaction level of stakeholders with the outcomes of artificial intelligence implementation.Academic Review20A cumulative model illustrating the impact of artificial intelligence on the organization’s management mechanism is presented, which identifies the subjects of influence, tools of influence, directions, and dimensions of influence and describes the main outcomes of this influence.Academic Review["H. Mytrofanova", "Olha Yevtushenko", "Artem Hlukhyy", "Mykyta Lugovyy"]2024-07-05T00:00:00 472f6208ac4f84bbdc4e3821407695ff863676b2Human Resources are considered the assets of any organization to reach the organizational goals, vision, and targets to improve the organization's overall performance. At the same time, organizations must start adopting innovative practices to sustain themselves in the competitive market. In the era of Industry 5.0, organizations are shifting from traditional practices towards implementing various innovative and advanced processes like artificial intelligence, automation, robotics, and machine intelligence. Industry 5.0 is the next level of the Industrial Revolution, highlighting the critical part of human and machine intelligence interaction. It is the advancement of Industry 4.0 that has heavily impacted various industries like manufacturing including the steel industry, the Power sector, Coal and clean, information technology, etc. through the application of automation processes, data transformation, Internet over things (IoT), Machine learning, and Artificial intelligence. In the process of the above-stated transformation, the researchers wish to focus on studying the “Role of Artificial Intelligence in enhancing productivity through an empirical analysis”. KEYWORDS: Human-Machine Interaction, Productivity, Artificial Intelligence, Data Analytics, Transformation of the Workforce.EPRA International Journal of Economics, Business and Management Studies00The researchers wish to focus on studying the “Role of Artificial Intelligence in enhancing productivity through an empirical analysis” as part of the transformation of the Workforce.EPRA International Journal of Economics, Business and Management Studies["Kundurthi Venkata Satya Sri Kumar", "Dr. V. Tulasi Das"]2024-07-05T00:00:00 b86ee15d12292476e3623839551d37dd112471f2In the process of social development in our country, education, as one of the fundamental development contents, has received high attention from the country and even the masses. Artificial intelligence (AI) is a subsidiary technology of computer science, whose main research direction is to understand the realistic nature of intelligence, in order to produce intelligent machines that can understand human intelligence and respond accordingly. The application of artificial intelligence technology in the management of student education in universities is the main direction for universities to carry out and build intelligent, automated, and personalized education management for students. It not only innovates traditional education methods and management models, but also meets the needs of modern students for technology, enabling universities to carry out scientific planning and configuration in a fully intelligent state, further improving the level and efficiency of talent cultivation in universities. Therefore, in this study on the impact and application strategies of artificial intelligence technology on college student education management, relevant research results show that integrating artificial intelligence technology into college student education management can generate innovation and broaden the ways of student management, deepen the depth and breadth of college student education management work, and provide a solid foundation guarantee for realizing the management of college student education with artificial intelligence technology through the construction and promotion of innovation and entrepreneurship demonstration zones, the construction of social innovative public service platforms and entrepreneurship bases, the construction of a multidisciplinary education system, the expansion of artificial intelligence education application scenarios, and the opening up of different types of data information.2024 International Conference on Language Technology and Digital Humanities (LTDH)1602024 International Conference on Language Technology and Digital Humanities (LTDH)["Ye Zi"]2024-07-05T00:00:00 71e25a11ef018332af7638c002ce6658e45eaec7Economics of Innovation and New Technology421Economics of Innovation and New Technology["A. Batabyal", "K. Kourtit", "Peter Nijkamp"]2024-07-05T00:00:00 a134707b16fb2c94d7d0a966d6ec7bf0ec6cb73eInternational Journal of Artificial Intelligence and Machine Learning01International Journal of Artificial Intelligence and Machine Learning["Jasmine Jade Lovell"]2024-07-05T00:00:00 b9a1bdb1ce8c78de9028a1ed7d77d93e31c1d936Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering31Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering["Shijia Huang", "Yaxin Liang", "Fangzhou Shen", "Fanru Gao"]2024-07-05T00:00:00 6893db5bf2fbb0ed6dc291190dc623ada195cd27InfoScience Trends721InfoScience Trends["Seyed Ghasem Hashemi Fotemi", "Nishith Reddy Mannuru", "Ravi Varma Kumar Bevara", "Aashrith Mannuru"]2024-07-05T00:00:00 3596cfcc1dc9c9decacf239ad52600607db96a19Academic medicine : journal of the Association of American Medical Colleges00Academic medicine : journal of the Association of American Medical Colleges["Haiwen Gui", "Justin L Jia"]2024-07-05T00:00:00 fa2539a434d37944f754afc45b63bec7a788f13dئامانجى ئەم توێژینەوەیە ئەوەیە کە چەمکى زیرەکى دەستکرد بناسێنێت و لەگەڵ خستنەرووى دەرفەت و ئاڵنگارییەکانى بەردەم زیرەکى دەستکرد لە ژینگەى پراکتیکى ڕۆژنامەوانی لە هەرێمى کوردستاندا. بایەخى ئەم توێژینەوە لەوەدایە کە دەیەوێت بابەتێکى نوێ لە بوارى تەکنەلۆژیاى پەیوەندیکردن و ڕۆژنامەوانى گەنگەشە بکات کە پێشتر توێژینەوەى زۆرکەمى لەبارەوەکراوە، لەهەمانکاتدا بابەتە جێ بایەخە لەو روانگەی کە گۆرانکارى زۆرى لە ژینگەى پراکتیکى رۆژنامەوانیدا دروستکردووە، ئەنجامەکانى ئەم توێژینەوەیە دەبێتە بنەمایەکى باش بۆ ڕۆژنامەوانان و ناوەندە زانستى و دامەزراوە ئەکادیمییەکانى هەرێمى کوردستان. توێژینەوەکە لە جۆرى توێژینەوەی وەسفییە، توێژەران بۆ گەشتن بە ئامانجەکانى توێژینەوەکە، میتۆدى چۆنایەتییان بەکارهێناوە. سامپڵی توێژینەوەکە بە شێوەى مەبەستدار وەرگیراوە، کە بەشێکە لە سامپڵی نائەگەرى، ژماریەک لە مامۆستایی زانکۆ لە بوارى رۆژنامەوانى و چەند رۆژنامەوانێک وەک سامپڵی توێژینەوەکە وەرگیراوە. توێژینەوەکە بەچەند دەرئەنجامێک گەشتووە: 1- ئاڵنگارییە (ئەخلاقی و پیشەییەكان) ئەو ئاڵنگاریانەن ئەگەر بەووریایەوە مامەڵەیان لەگەڵ نەكرێت مەترسی لەسەر ژیانی تاك و پیشەی ڕۆژنامەوانان دروست دەكەن، دەتوانن ئاسایشی كۆمەڵگە تێكبدەن . 2- بەكارهێنانەكانی زیرەكی دەستكرد لە بواری ڕۆژنامەوانیدا لە ئێستادا خۆی لە كاری داڕشتنەوەی تێكست و دروستكردنی وێنە و ڤیدیۆ وبەكارهێنانی بۆ كاری مۆنتاژ و ئیدیتینگ دەبینیتەوە، هەروەها لەژووری هەواڵ بۆ شیكردنەوەی داتاكان و ووردبینیكردنی سودی لێوەردەگیریت . 3- ژێرخانى تەکنیکى زیرەکى دەستکرد لە دامەزراوە رۆژنامەوانیە كوردییەكان لە ئاستێکى لاوازدایە، و عەقڵیەتی بەرێوەبردنی دامەزراوەكانیش ڕێگرە لە بەرەوپێشچوون و بەكارهێنانی زیرەكی دەستكرد، بۆیە ئاستى پشتبەستنى ئەو دامەزراوانە بەزیرەکى دەستکرد لە ئاستێکى زۆر سەرەتاییدایە.Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq00Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq["Arkan Rauf Aziz", "Shakar Abdulqadir Mohammed"]2024-07-05T00:00:00 d34511554e37057da171910af864d621488aa91cالإبداع الذي نشهده حاليًا من خلال برامج الذكاء الاصطناعي يُعتبر استمرارًا للإبداع الذي ينبع من العقل البشري، حيث يتعاون الابتكار التكنولوجي مع الفكر البشري لتحقيق إنجازات متقدمة. والفكرة الإبداعية التي يستفيد منها مستخدمو برامج الذكاء الاصطناعي ينبغي أن تكون قائمة على أسس تقنية وقانونية، مع احترام الضوابط المحددة من قبل التشريعات المحلية. حيث يُولي المشرعون اهتمامًا خاصًا لضمان حماية هذه الأفكار والمصنفات وتحديد المسؤوليات والحقوق وفقًا للأنظمة القانونية؛ وبالمقابل يسعى الفقهاء إلى تحديد القواعد الشرعية الملائمة لتلك القضايا، مع الأخذ في اعتبارهم الأدلة الشرعية المتاحة. إن برامج الذكاء الاصطناعي والوسائط الرقمية لها القدرة على إنتاج مؤلفات ومصنفات ومحتوى فيجب أن يلتزم المُنشئ لها بالقوانين المحلية للحقوق المؤلفة، ويتحقق ذلك من خلال توجيه وإدارة هذه البرامج، فيصبح المبرمج مبدعًا في صياغة هذا الإبداع عبر إدخال الخوارزميات بطريقة تتوافق مع الأنظمة التقنية، ويثير هذا الجانب القانوني والفقهي اهتمام الخبراء، ويُركز الفقهاء والقانونيون على الشخصية القانونية وملكيتها لتلك البرامج وشرعية استخدامها، متسائلين عن ملكية المحتوى الناتج وتأثيراته الفقهية والقانونية. عرضت الورقة البحثية تعريف المصنفات بشكل عام والمصنفات الناشئة بواسطة برامج الذكاء الاصطناعي والوسائط الرقمية، وتعريفاتها من وجهة القانون والفقهاء، والملكية لها بين الفقه والقانون، ثم أحكام القانون الإماراتي مقارن بالقواعد الفقهية المناسبة لها، ثم خلصت الورقة البحثية لنتائج وتوصيات.Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq00Proceedings of the 2nd International Scientific Conference "Digital Media Effects on Society Security Under Domestic and International Laws" , Erbil, Kurdistan Region of Iraq["Soner Duman", "Fadi Shoshan"]2024-07-05T00:00:00 027532d5567bc014f8d4a221eaebc63d431e2027Journal of Urbanism: International Research on Placemaking and Urban Sustainability290Journal of Urbanism: International Research on Placemaking and Urban Sustainability["E. Dorostkar", "Mehrnaz Molavi", "Nader Zali"]2024-07-05T00:00:00 ebf166a918a1555d13c1242bfcfbbaadc241f4f500["Kavita Saini", "Swaroop S. Sonone", "M. S. Sankhla", "Naveen Kumar"]2024-07-05T00:00:00 5b534af403df3806aa03c15847c5a35dabfa460aIn the field of computer vision, three basic tasks are particularly important: object detection [...]Electronics00Electronics["Hao Li", "Fei Xie", "Jianbo Zhou", "Jieyi Liu"]2024-07-05T00:00:00 1e7f59b69f39e0a867e069332d734e3949d6737dJournal of Medical Academics40Journal of Medical Academics["Ritu Sharma"]2024-07-05T00:00:00 8bb9dcf66f4c09ed61426db7e3678072191b2df6International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Nirali Shah"]2024-07-05T00:00:00 ef11409304bfadcf8622f0351f4bd3b3cc05d670PurposeThe purpose of the study was to evaluate the impact of generative artificial intelligence (GenAI) on students' learning experiences and perceptions through a master’s-level course. The study specifically focused on student engagement, comfort with GenAI and ethical considerations.Design/methodology/approachThe study used an action research methodology employing qualitative data collection methods, including pre- and post-course surveys, reflective assignments, class discussions and a questionnaire. The AI-Ideas, Connections, Extensions (ICE) Framework, combining the ICE Model and AI paradigms, is used to assess students' cognitive engagement with GenAI.FindingsThe study revealed that incorporating GenAI in a master’s-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications. The AI-ICE Framework demonstrated most students were at the initial engagement level, with growing awareness of GenAI’s limitations and ethical issues. Course reflections highlighted themes of improved teaching strategies, personal growth and the practical challenges of integrating GenAI responsibly.Research limitations/implicationsThe small sample size poses challenges to the analytical power of the findings, potentially limiting the breadth and applicability of conclusions. This constraint may affect the generalizability of the results, as the participants may not fully represent the broader population of interest. The researchers are mindful of these limitations and suggest caution in interpreting the findings, acknowledging that they may offer more exploratory insights than definitive conclusions. Future research endeavors should aim to recruit a larger cohort to validate and expand upon the initial observations, ensuring a more robust understanding.Originality/valueThe study is original in its integration of GenAI into a master's-level instructional design course, assessing both the practical and ethical implications of its use in education. By utilizing the AI-ICE Framework to evaluate students' cognitive engagement and employing action research methodology, the study provides insights into how GenAI influences learning experiences and perceptions. This approach bridges the gap between theoretical understanding and the real-world application of GenAI, offering actionable strategies for its responsible use in educational settings.Journal of Research in Innovative Teaching &amp; Learning406It is revealed that incorporating GenAI in a master’s-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications, providing insights into how GenAI influences learning experiences and perceptions.Journal of Research in Innovative Teaching &amp; Learning["Dwayne Wood", "Scott H. Moss"]2024-07-05T00:00:00 6ff9e22615f12b031caa3ea582f1e28efa408f87Purpose Current knowledge and research on students’ utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students’ engagement with these tools. Design/methodology/approach This research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%). Findings Most respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI’s ethical and appropriate use. Originality/value Literature related to generative AI use in higher education specific to ChatGPT is predominantly from educators’ viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy. Information and Learning Sciences243Empirical evidence is provided about how university students report using generative AI in the context of generative AI literacy among students enrolled in a four-year public university in the southeastern USA.Information and Learning Sciences["Kong Chen", "April C. Tallant", "Ian Selig"]2024-07-05T00:00:00 8b5b5113dbc109982bc150f3e724b77965b144fdBACKGROUND Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and time. The emergence of AI has brought forth innovative solutions to streamline and enhance this diagnostic process, which is especially valuable given the shortage of neuro-ophthalmologists. Machine learning algorithms, in particular, have demonstrated significant potential in interpreting imaging data, identifying subtle patterns, and aiding clinicians in making more accurate and timely diagnosis while also supplementing nonspecialist evaluations of neuro-ophthalmic disease. EVIDENCE ACQUISITION Electronic searches of published literature were conducted using PubMed and Google Scholar. A comprehensive search of the following terms was conducted within the Journal of Neuro-Ophthalmology: AI, artificial intelligence, machine learning, deep learning, natural language processing, computer vision, large language models, and generative AI. RESULTS This review aims to provide a comprehensive overview of the evolving landscape of AI applications in neuro-ophthalmology. It will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression. Additionally, the review will explore the integration of generative AI into neuro-ophthalmic education and clinical practice. CONCLUSIONS We review the current state of AI in neuro-ophthalmology and its potentially transformative impact. The inclusion of AI in neuro-ophthalmic practice and research not only holds promise for improving diagnostic accuracy but also opens avenues for novel therapeutic interventions. We emphasize its potential to improve access to scarce subspecialty resources while examining the current challenges associated with the integration of AI into clinical practice and research.Journal of neuro-ophthalmology662This review will delve into the diverse applications of AI, optical coherence tomography (OCT), and fundus photography to the development of predictive models for disease progression and the integration of generative AI into neuro-ophthalmic education and clinical practice.Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society["Rachel C Kenney", "Tim W Requarth", "Alani I. Jack", "Sara W Hyman", "Steven L. Galetta", "S. Grossman"]2024-07-05T00:00:00 4e818cd60cefd6f011c94bf077b2d9efa04b6047 This JAMA Forum discusses pending legislation in the US House and Senate and the history of the “firm-based approach” the US Food and Drug Administration (FDA) could use when regulating artificial intelligence (AI) medical devices to augment patient care. JAMA Health Forum42JAMA health forum["Scott Gottlieb"]2024-07-05T00:00:00 2abec8ce9623ccbe142ab2b7431b018e86266d6bMany emerging Artificial Intelligence (AI) applications require on-demand provisioning of large-scale computing, which can only be enabled by leveraging distributed computing services interconnected through networking. To address such increasing demand for networking to serve AI tasks, we investigate new scheduling strategies to improve communication efficiency and test them on a programmable testbed. We also show relevant challenges and research directions.Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication202This work investigates new scheduling strategies to improve communication efficiency and test them on a programmable testbed to address increasing demand for networking to serve AI tasks.{"pages": "60-62"}["Ruikun Wang", "Jiawei Zhang", "Qiaolun Zhang", "Bojun Zhang", "Zhiqun Gu", "Aryanaz Attarpour", "Yuefeng Ji", "Massimo Tornatore"]2024-07-05T00:00:00 58e828fb30de2aae76757b50ec2eef54c02ba26bThis study explores teachers’ acceptance of artificial intelligence in education (AIEd) and its relationship with various variables and pedagogical beliefs. Conducted at the Universidad Técnica Particular de Loja (UTPL, Ecuador), the research surveyed 425 teachers across different disciplines and teaching modalities. The UTAUT2 model analyzed dimensions like performance expectations, effort expectations, social influence, facilitating conditions, hedonic motivation, usage behavior, and intention to use AIEd. Results showed a high level of acceptance among teachers, influenced by factors like age, gender, and teaching modality. Additionally, it was found that constructivist pedagogical beliefs correlated positively with AIEd adoption. These insights are valuable for understanding AIEd integration in educational settings.Education sciences516It was found that constructivist pedagogical beliefs correlated positively with AIEd adoption, and these insights are valuable for understanding AIEd integration in educational settings.Education Sciences["Julio Cabero-Almenara", "Antonio Palacios-Rodr\u00edguez", "M. I. Loaiza-Aguirre", "Mar\u00eda del Rosario de Rivas-Manzano"]2024-07-06T00:00:00 7c241799acc85bef73ebc84199905f640a45ad4dIn this study, an interview was conducted with Generative Pre-Train (ChatGPT) to determine whether artificial intelligence can replace dietitians and its potential contributions to the field of Nutrition and Dietetics. Qualitative research method was used in the study and data was obtained using interview technique. The study includes 6 questions asked to version 3.5. of ChatGPT. Based on the answers given to the questions, ChatGPT has shown that it can benefit dietitians by providing basic nutritional information and helping to create nutrition plans. Nonetheless, artificial intelligence cannot fulfill the official duties and responsibilities of dietitians and cannot create disease- and individual-specific nutrition programs. ChatGPT, which is reported to provide theoretical resources as a contribution to Nutrition and Dietetics education, lacks personal experience and practical skills. Although ChatGPT contributes to dietitians, educators, and students in the field of nutrition and dietetics in different dimensions, it cannot replace dietitians. More research on the use of generative language models developed by artificial intelligence is necessary.Toros University Journal of Food, Nutrition and Gastronomy171Although ChatGPT contributes to dietitians, educators, and students in the field of nutrition and dietetics in different dimensions, it cannot replace dietitians and its potential contributions to the field of Nutrition and Dietetics.Toros University Journal of Food, Nutrition and Gastronomy["Elif G\u00fcner", "Mutlu Tu\u00e7e \u00dclker"]2024-07-06T00:00:00 b5393438b90e5d17c690f1b198d8e5dde16df8daDigital deliberation has been steadily growing in recent years, enabling citizens from different geographical locations and diverse opinions and expertise to participate in policy-making processes. Software platforms aiming to support digital deliberation usually suffer from information overload, due to the large amount of feedback that is often provided. While Machine Learning and Natural Language Processing techniques can alleviate this drawback, their complex structure discourages users from trusting their results. This paper proposes two Explainable Artificial Intelligence models to enhance transparency and trust in the modus operandi of the above techniques, which concern the processes of clustering and summarization of citizens’ feedback that has been uploaded on a digital deliberation platform.Future Internet211This paper proposes two Explainable Artificial Intelligence models to enhance transparency and trust in the modus operandi of the above techniques, which concern the processes of clustering and summarization of citizens’ feedback that has been uploaded on a digital deliberation platform.Future Internet["Ilias Siachos", "Nikos I. Karacapilidis"]2024-07-06T00:00:00 7cb6dbe1645ed91a93df69225d41b67107093524The high predictive accuracy of contemporary machine learning-based AI systems has led some scholars to argue that, in certain cases, we should grant them epistemic expertise and authority over humans. This approach suggests that humans would have the epistemic obligation of relying on the predictions of a highly accurate AI system. Contrary to this view, in this work we claim that it is not possible to endow AI systems with a genuine account of epistemic expertise. In fact, relying on accounts of expertise and authority from virtue epistemology, we show that epistemic expertise requires a relation with understanding that AI systems do not satisfy and intellectual abilities that these systems do not manifest. Further, following the Distribution Cognition theory and adapting an account by Croce on the virtues of collective epistemic agents to the case of human-AI interactions we show that, if an AI system is successfully appropriated by a human agent, a hybrid epistemic agent emerges, which can become both an epistemic expert and an authority. Consequently, we claim that the aforementioned hybrid agent is the appropriate object of a discourse around trust in AI and the epistemic obligations that stem from its epistemic superiority.Minds Mach.426This work claims that it is not possible to endow AI systems with a genuine account of epistemic expertise, and shows that, if an AI system is successfully appropriated by a human agent, a hybrid epistemic agent emerges, which can become both an epistemic expert and an authority.Minds and Machines["Andrea Ferrario", "Alessandro Facchini", "Alberto Termine"]2024-07-06T00:00:00 a658bc9bca4239f380878324ad3509a79e0ca129A rapidly developing technology - artificial intelligence - is actively being introduced into the life of modern people. One of the promising areas of its application is dentistry. Artificial intelligence can be used to improve the efficiency of diagnosis, treatment and prevention of oral diseases. This allows dentists, both public and private clinics, to work more efficiently and accurately. The process of treatment of all dental diseases is accelerated, making it more comfortable for patients. In addition, the quality of treatment and prosthetic results improves. The main goal of any dental clinic is to improve the quality of life of patients. But sometimes, interruptions in cellular communications and computer equipment, incomplete knowledge of the use of technology by receptionists, and a fairly large amount of medical documentation that a dentist needs to fill out negatively affects the provision of dental services. In turn, the management of a dental clinic is simplified. Aim of the work. Conduct a survey of the heads of private and public dental clinics in St. Petersburg on the readiness to use artificial intelligence in the institution, as well as a review of domestic and foreign literature sources to analyze the need to implement artificial intelligence in the work of dental clinics. Material and methods. A survey was conducted in St. Petersburg of the heads of 6 private and 6 state dental clinics using a developed questionnaire, as well as a review of available Russian and foreign scientific literature over the past 5 years, which allows for a more complete assessment of the programs based on artificial intelligence offered by many companies and developers to improve the concept of management and quality of patient service in dental clinics. Results and discussion. The use of artificial intelligence in the dental services management system not only helps save time and material resources, but also creates convenience for patients when making an appointment with a dentist, and improves the quality of provision of all types of dental services. Conclusions. The prospects for introducing artificial intelligence into dental clinic management are enormous. But its integration into this area of medicine requires the continuation of medical and sociological research and the creation of regulatory frameworks.Bulletin Biomedicine and sociology100The use of artificial intelligence in the dental services management system not only helps save time and material resources, but also creates convenience for patients when making an appointment with a dentist, and improves the quality of provision of all types of dental services.Bulletin "Biomedicine and sociology"["Borisov N.A.", "Malyavin S.N.", "Borisova E.G."]2024-07-06T00:00:00 132893a61aad0cea7865b3bd9c6c52afe1c2eeb9The article is a synthesis of a series of studies conducted over the last 10 years at the Faculty of Foreign Languages and Area Studies of Lomonosov Moscow State University and is devoted to the current topic of digital technologies application in linguodidactics. The paper provides an analysis of legal acts regulating the development of artificial intelligence technologies in the Russian Federation in all spheres of activity, including education, national standards that apply to the field of education and establish general provisions and terminology in the field of the use of artificial intelligence technologies in education, a description of the basic principles of the operation of generative artificial intelligence technologies, an analysis of the formats of tasks for the introduction of mobile, electronic and generative tools, including those based on neural networks, into General English courses. The main results of the study are the identification of new task formats based on artificial intelligence technologies, as well as the need to form provisions for updating the regulatory framework for the introduction of artificial intelligence technologies in foreign language higher education.Linguistics and Intercultural Communication60The main results of the study are the identification of new task formats based on artificial intelligence technologies, as well as the need to form provisions for updating the regulatory framework for the introduction of artificial intelligence technologies in foreign language higher education.Linguistics and Intercultural Communication["A. Avramenko", "V. Fadeeva", "V. Ternovsky"]2024-07-06T00:00:00 55e369f33e50ea98a32e415697016e10fd189674The study investigated automation, artificial intelligence and the future of work in key industries in Rivers State. Four research questions and four corresponding hypotheses were formulated to understand how AI automation relationship s job security, skill requirements, workforce adaptation, employee well-being, and work-life balance in these industries. The research uses a correlational research design and the target population comprises employees from key industries in Rivers State, including manufacturing, agriculture, healthcare, and technology sectors. A stratified random sampling technique was employed to draw a sample size of 54 employees across these industries. The self-structured questionnaire titled “Automation and Artificial Intelligence Integration in Industries Questionnaire (AAIIIQ)” and “Future of Work Questionnaire (FWQ)” using a 4-point Likert scale ranging from Very Low Extent (1) to Very High Extent (4). The questionnaire was distributed within the dry season of November 2023 to February 2024. During this period, 54 copies were distributed, but only 49 were returned. Of these, 2 were not correctly filled out, leaving the researcher with only 47 valid copies that were used for data analysis. To ensure content validity, the questionnaire was reviewed by experts in the field of Organizational Behaviour and Information and Communication Technology (ICT). The reliability of the instrument was assessed using the test-retest method with a sample of 10 participants which were not part of the main sample size, but within the study’s population. The questionnaire was distributed twice at a two-week interval, and the responses were analyzed using the Pearson Product-Moment Correlation (PPMC). A reliability index of .87 was calculated based on the outcomes of this analysis. Mean and standard deviation, was used to answer the research questions, Pearson’s Product-Moment Correlation (PPMC), was utilized to test hypotheses at a significance level of 0.05. The findings underscore the critical importance of considering the effects of AI automation on job security, workforce adaptation, employee well-being, and organizational strategies.  Conclusion, the study’s findings underscore the transformative nature of AI automation on the future of work in key industries in Rivers State. By recognizing and responding to these changes, organizations can harness the full potential of AI automation while mitigating its potential negative impacts. The study recommended that organizations should prioritize employee training and development, offering opportunities for upskilling and reskilling to ensure their workforce remains competitive and adaptable to technological advancements.Journal of basic and applied research international00The study investigated automation, artificial intelligence and the future of work in key industries in Rivers State to understand how AI automation relationship s job security, skill requirements, workforce adaptation, employee well-being, and work-life balance in these industries changed.Journal of Basic and Applied Research International["Lawretta Adaobi Onyekwere"]2024-07-06T00:00:00 702cb2f5f071b30b6e1766d1ca2886367b002b79The paper is aimed at the objective analysis of positive and negative sides connected to the pragmatic and scientific investigation of Artificial Intelligence technologies. Special attention is paid to the opportunities provided by the artificial intelligence in different fields of social usage, such as: education, culture, medicine, communication, management, ethical code, academic science, military sphere, jurisdiction, etc. Bringing the studies together, the author proposed an expanded view of AI functions, which crucially emphasize the role and importance of network ties, at one side, and at the other side, drawing on a number of examples shows what happens when such network ties become loosened as a result of Artificial Intelligence overestimation.Linguistics and Intercultural Communication20An expanded view of AI functions is proposed, which crucially emphasize the role and importance of network ties, at one side, and at the other side, drawing on a number of examples shows what happens when such network ties become loosened as a result of Artificial Intelligence overestimation.Linguistics and Intercultural Communication["G. G. Molchanova"]2024-07-06T00:00:00 0d0d96043b1822a61e17da16abce747853903689In the swiftly evolving area of medical science, artificial intelligence (AI) is rising as a transformative pressure, in particular in the realm of surgery. The integration of AI into surgical practices guarantees to revolutionize operative care, improving precision, performance, and affected person consequences. This editorial delves into the profound effect of AI on surgical operation, highlighting key advancements, potential blessings, and the future trajectory of this groundbreaking technology[1]. The Evolution of AI in Surgery AI, encompassing system getting to know (ML), deep learning, and robotics, has made significant strides in various medical applications. In surgery, AI structures are designed to assist in preoperative planning, intraoperative guidance, and postoperative care. These structures leverage considerable quantities of records to provide real-time insights, predictive analytics, and selection guide, thereby augmenting the competencies of surgeons and enhancing the overall excellent of surgical care[2, 3]. Enhancing Surgical Precision and Accuracy One of the greatest contributions of AI in surgery is its potential to enhance precision and accuracy. Robotic-assisted surgical structures, which include the da Vinci Surgical System, utilize AI algorithms to provide surgeons with greater dexterity and control, taking into account minimally invasive methods with extra precision[4]. These structures can filter out hand tremors and offer magnified 3-D views of the surgical area, extensively enhancing the accuracy of complicated surgical maneuver[5]. Moreover, AI-powered imaging technology are revolutionizing intraoperative navigation. Advanced image recognition algorithms can analyse scientific pictures in real-time, figuring out vital anatomical structures and ability headaches. This real-time guidance helps surgeons make knowledgeable decisions, reducing the chance of mistakes and improving surgical consequences[6]. Preoperative Planning and Predictive Analytics AI is likewise gambling a important position in preoperative making plans. Machine studying models can examine affected person information, inclusive of medical history, diagnostic snap shots, and genetic statistics, to are expecting surgical results and ability complications[7]. This predictive functionality permits surgeons to devise personalised surgical plans tailored to the particular wishes of each affected person, thereby optimizing the possibilities of fulfillment[8]. Additionally, AI-pushed systems can simulate surgical processes, permitting surgeons to exercise and refine their techniques earlier than acting the real surgery. These simulations can help pick out capacity challenges and refine surgical strategies, in the end leading to more secure and more powerful surgeries[9]. Postoperative Care and Recovery The benefits of AI amplify beyond the working room, impacting postoperative care and affected person recovery. AI algorithms can reveal sufferers' critical signs and symptoms and healing progress in actual-time, alerting healthcare companies to any deviations from the predicted healing trajectory. This proactive tracking enables early intervention, reducing the risk of complications and selling faster healing[10, 11]. AI-powered tools also can offer customized rehabilitation plans based totally on sufferers' recovery data. These tailor-made plans can encompass hints for bodily therapy, medication management, and way of life adjustments, assisting patients achieve most fulfilling recovery consequences[12]. The Future of AI in Surgery The future of AI in surgical procedure holds monstrous capacity. As AI technologies preserve to advance, we are able to expect even greater integration of AI into surgical practices. The development of self sufficient surgical robots, capable of performing sure techniques without human intervention, is already underway. These robots, guided by way of state-of-the-art AI algorithms, could perform routine surgeries with unheard of precision and consistency[13]. Furthermore, AI's potential to analyse large datasets will retain to decorate personalized remedy. By integrating genetic, environmental, and lifestyle statistics, AI can offer deeper insights into sickness mechanisms and surgical results, paving the way for fantastically individualized surgical care[14, 15]. Challenges and Considerations While the capability of AI in surgical operation is sizeable, numerous demanding situations ought to be addressed to fully realize its benefits. Ensuring the safety and reliability of AI systems is paramount, as any errors in AI algorithms should have serious consequences. Rigorous testing and validation of AI technologies are important to make sure their efficacy and protection in medical settings[16]. Ethical concerns, including affected person consent and data privacy, need to also be carefully managed. Patients need to be absolutely knowledgeable about the use of AI in their surgical care and the capability implications for his or her privacy and confidentiality[17]. Conclusion Artificial intelligence is poised to revolutionize the field of surgical operation, providing extraordinary precision, predictive abilities, and personalised care. As we keep to explore and integrate AI technologies into surgical practices, the ability to decorate patient consequences and rework operative care becomes increasingly more evident. The future of surgical procedure, augmented through AI, promises to be greater unique, green, and patient-centric, heralding a new generation in clinical technological know-how.DEVELOPMENTAL MEDICO-LIFE-SCIENCES80The profound effect of AI on surgical operation is delved into, highlighting key advancements, potential blessings, and the future trajectory of this groundbreaking technology.DEVELOPMENTAL MEDICO-LIFE-SCIENCES["Masood Rashid"]2024-07-06T00:00:00 f219c6edb391b5bb455f333e63055bfa237ad131Journalism has undergone major changes as a result of digital culture. Technologies such as artificial intelligence, virtual reality, and augmented reality are changing the way journalists work, and the speed and timeliness of information delivery is a concern. Digital journalism emphasizes interaction with the audience and speed. Audiences have the opportunity to participate and comment. AI helps journalists verify content, ensure production runs well, and ensure that the audience is engaged. Journalists face issues such as decreased revenue, audience fragmentation, and information credibility. However, digital culture is changing journalism significantly, causing many challenges and opportunities for journalists to stay relevant and maintain public trust.International Journal of Advanced Multidisciplinary Research and Studies10AI helps journalists verify content, ensure production runs well, and ensure that the audience is engaged, as digital journalism emphasizes interaction with the audience and speed.International Journal of Advanced Multidisciplinary Research and Studies["Nazwah Ramadani", "Korry El Yana", "Mahnum Elbah Azzahra", "Alyza Putri Maharani", "Ulfa Khusnul Khotimah", "Siti Nurbaiti", "Syahrul Saronta", "Drajat Ali Muhtarom"]2024-07-06T00:00:00 7164c7300428fe7cf9fc4704d8f67e3de9b3964ePflügers Archiv: European Journal of Physiology460A global approach to the ethics of ai-systems in healthcare and medicine is presented which incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.Pflugers Archiv : European journal of physiology["Tijs Vandemeulebroucke"]2024-07-06T00:00:00 ba99b2a2f71f286cfc8b924a5003c8410d3c9fdfHealthcare service delivery, especially in terms of safeguarding personal data, requires ensuring the confidentiality of information. In this regard, establishing cybersecurity systems that ensure information security is highly necessary. The rapid advancement of technologies increases the likelihood of cyberattacks, and particularly, AI-supported threats can cause serious harm in service delivery. In the current era, attacks not only come from humans but also from AI tools, posing threats to information security. Considering that AI technology is expected to further advance in the future, it's evident that this technology could become even more menacing. This is especially pertinent to the healthcare sector. Cyberattacks can lead to breaches in healthcare system data and disrupt service delivery to the extent of paralyzing the healthcare system. Our study, which includes case examples, is a compilation-type research. Within the scope of our research, searches were conducted using the keywords healthcare sector, information security, and cybersecurity on Google Scholar and Web of Science. The most current topic headings intersecting information security with the healthcare sector were examined based on the articles found on the subject. Our study evaluates the following topics in order: information and cyber security concepts, cyber threats and public services, electronic health records and security, major cyber-attacks in the health sector, why healthcare data is attractive for cyberattacks, information security in the artificial intelligence era, and information security policies for Türkiye and other countries in the world. Ransomware holds a significant place among cyberattacks. Therefore, users within the healthcare system are advised to pay particular attention to this issue. Attacks generally occur via email, starting with enticing the user into a cyber-threat through email. Artificial intelligence can also be used to get rid of such spam mails. Hence, it is strongly recommended that users in the healthcare sector undergo training on this matter. These trainings should be conducted regularly and continuously, with the institution's IT center offering an institutional approach in this regard.Journal of AI293This study evaluates the following topics in order: information and cyber security concepts, cyber threats and public services, electronic health records and security, major cyber-attacks in the health sector, why healthcare data is attractive for cyberattacks, information security in the artificial intelligence era, and information security policies for Türkiye and other countries in the world.Journal of AI["Muhammet Damar", "Ahmet \u00d6zen", "Ay\u015fin Y\u0131lmaz"]2024-07-06T00:00:00 0abcfaeb2f1d6c471d068d27420025f005e52941Journal opf Pakistan Society of Internal Medicine00Journal opf Pakistan Society of Internal Medicine[]2024-07-06T00:00:00 5fada562cc6f75ca365f79ead94cd2b6d4ad9932Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-07-06T00:00:00 a7a4395268502deac3ad8b543fc82cad1ec09e7cJournal opf Pakistan Society of Internal Medicine00Journal opf Pakistan Society of Internal Medicine[]2024-07-06T00:00:00 32c1cb3cdd087b42d5358fe14868b5b1cf0b0e65The Article Abstract is not available.Journal of Pharmaceutical Care00Journal of Pharmaceutical Care["Mandana Moradi"]2024-07-06T00:00:00 111233353ab3c20ab9f70a41f77a420887356daeScientific Reports730For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages, indicating that interaction with the world impacts the infant behaviour most at the site of organism~world connection.Scientific Reports["Massoud Khodadadzadeh", "Aliza T Sloan", "Nancy Aaron Jones", "Damien Coyle", "J. Kelso"]2024-07-06T00:00:00 659cf2c1ba2402e3feae0776068a6b51171006edSocial Indicators Research240Social Indicators Research["Yogeeswari Subramaniam", "Nanthakumar Loganathan", "Fatin Nur Hidayah Taib Khan", "Thirunaukarasu Subramaniam"]2024-07-06T00:00:00 dcc46cd4f9c06a10d0b9358a36236072e67d5053In recent years, universities implemented shuttle bus services to facilitate campus transportation for students without personal vehicles. However, students often encounter challenges in accurately estimating bus arrival times, leading to extended wait times or missed buses. This project aims to develop a cost-effective prototype bus tracking system by implementing Radio Frequency Identification (RFID) technology. The developed system provides students with real-time bus location updates and estimates the time of arrival (ETA) at their designated stops. ETA predictions are generated using a machine learning approach, specifically a linear regression model. The system utilizes ultra-high frequency RFID tags, a YPD-R200 RFID reader module board with reading range of up to 5 meters, and a WeMos D1 microcontroller for uploading data to the Blynk cloud platform. Users can access real-time bus information and status updates through the Blynk IoT application. The prototype had been successfully built and tested. This research surely can improve the students’ experience utilizing the bus services by enabling the students to track the bus and giving precise arrival time predictions for the bus timetables.IEEE Symposium on Industrial Electronics and Applications130This research surely can improve the students’ experience utilizing the bus services by enabling the students to track the bus and giving precise arrival time predictions for the bus timetables.2024 IEEE Symposium on Industrial Electronics & Applications (ISIEA)["Joshua Ting Ung Hing", "Hui Jing Lee"]2024-07-06T00:00:00 8879ba5786a105724d9eb5971d1f36276739d643Artificial Intelligence (AI)-driven automation is increasingly transforming the financial services industry, promising significant economic benefits such as enhanced efficiency, cost reductions, and improved customer experiences. This research paper delves into the economic impacts of AI-driven automation within this sector, examining both the positive and negative ramifications. The literature review provides a historical context of automation in financial services and discusses contemporary AI technologies like machine learning and robotic process automation that are pivotal in this transformation. The paper identifies several positive economic impacts, including increased productivity, cost savings, enhanced accuracy, and better customer service. However, it also addresses negative impacts, notably job displacement, security and privacy concerns, and economic inequality. Through detailed case studies of major financial institutions that have successfully implemented AI, the research highlights real-world economic outcomes, best practices, and lessons learned. Challenges associated with AI-driven automation, such as technical and operational hurdles, regulatory compliance, and ethical considerations, are thoroughly analyzed. The paper also explores future prospects, suggesting that while AI advancements hold great potential for further transformation of financial services, careful management of long-term economic implications is essential. Policy recommendations include investing in workforce retraining and education to prepare for the evolving job market. This comprehensive study aims to provide a balanced perspective on the economic impacts of AI-driven automation in financial services, offering insights into how the industry can leverage AI for growth and innovation while addressing associated challenges and ensuring a sustainable and inclusive future.International Journal of Scientific Research and Management478This comprehensive study aims to provide a balanced perspective on the economic impacts of AI-driven automation in financial services, offering insights into how the industry can leverage AI for growth and innovation while addressing associated challenges and ensuring a sustainable and inclusive future.International Journal of Scientific Research and Management (IJSRM)["Toluwani Babatunde Adeyeri"]2024-07-06T00:00:00 0243ad3ae8ed757f6c7babf7c85e62e8ad1d4f9eThe utilization of Artificial Intelligence (AI) in educational institutions has the potential to bring about a significant transformation in current educational systems. As more educational establishments incorporate AI tools into their teaching and learning practices, there is a growing adoption of Large Language Model (LLM) technologies, including within the field of education. This adoption is driven by the ever-increasing volume of data and evolving educational requirements. However, despite the advantages offered by these technologies, there remains a consistent lack of clarity surrounding the ethical guidelines, technical standards, and best practices that are vital for their effective implementation. This paper primarily focuses on two key areas of research. Firstly, it seeks to investigate the potential benefits, risks, and outcomes associated with the use of LLM technologies in education. Secondly, it delves into the ethical considerations that should guide the utilization of LLM technologies within this domain. The findings underscore the significance of affording students access to LLM technologies in order to enhance the learning environment, with an emphasis on the necessity of transparent and reliable data collection in research. Moreover, given the considerable potential for the dissemination of misinformation and harmful content through LLM technologies, it is imperative to integrate ethical considerations throughout the field of education. This necessitates educating users and reinforcing measures to control the content in order to mitigate associated risks.Journal of Technology and Humanities00The findings underscore the significance of affording students access to LLM technologies in order to enhance the learning environment, with an emphasis on the necessity of transparent and reliable data collection in research.Journal of Technology and Humanities[]2024-07-06T00:00:00 96640d278e63f859a90b02e2216a0ba268b3504dHumanities and Social Sciences Communications162It is important to emphasize that Choice Engines and AI might be enlisted by self-interested actors, who might exploit inadequate information or behavioral biases, and thus reduce consumer welfare.Humanities and Social Sciences Communications["C. Sunstein"]2024-07-06T00:00:00 8eccd781ee74f5f9b4bf50744c42c932f74d1601The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles.Electronics1252The current state of cybersecurity in autonomous vehicles is examined, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks and existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols.Electronics["Irmina Durlik", "Tymoteusz Miller", "Ewelina Kostecka", "Z. Zwierzewicz", "Adrianna \u0141obodzi\u0144ska"]2024-07-06T00:00:00 6b61f3c2ed6c8c039197625e9ead1a5804101f62Artificial Intelligence (AI) is increasingly revolutionizing the food and beverage service industry by offering innovative solutions to enhance operational efficiency and personalize customer experiences. This abstract explores the transformative impact of AI technologies such as machine learning, predictive analytics, and robotics on various aspects of hospitality operations. AI-driven automation streamlines kitchen processes, optimizes inventory management, and reduces operational costs, thereby improving service consistency and profitability (Alsmadi et al., 2020; Li et al., 2021).AI facilitates personalized client interactions by analyzing vast datasets to understand individual preferences, dietary restrictions, and purchasing behaviors. This capability empowers establishments to offer tailored menu recommendations, personalized promotions, and interactive dining experiences that cater to diverse consumer needs (Chen et al., 2019; Huang et al., 2020). AI's integration into the food and beverage service industry significantly enhances customer satisfaction and loyalty while concurrently boosting revenue through targeted marketing and operational efficiencies. By leveraging AI technologies such as machine learning and predictive analytics, establishments can tailor offerings to meet individual preferences and anticipate consumer behavior, thereby fostering stronger customer relationships. Moreover, the future trajectory of AI in this sector foresees advancements in IoT integration, AR applications, and blockchain technology. These innovations are set to further transform operations by optimizing efficiency, enriching customer interactions, and ensuring robust supply chain transparency. IoT devices, for instance, will facilitate real-time data collection and analysis, thereby enhancing inventory management precision and optimizing resource allocation to meet demand fluctuations effectively. AR applications will offer immersive dining experiences, allowing customers to interact with digital menus or view culinary presentations. Blockchain technology will enhance transparency and traceability, ensuring food safety and compliance with regulatory standards. Together, these innovations will enable food and beverage establishments to innovate and differentiate themselves in a competitive market landscape, ultimately shaping the future of dining experiences worldwide to further optimize operations and enhance transparency in supply chain management (Jiang et al., 2023; Wang et al., 2023).International Journal for Multidimensional Research Perspectives41Together, these innovations will enable food and beverage establishments to innovate and differentiate themselves in a competitive market landscape, ultimately shaping the future of dining experiences worldwide to further optimize operations and enhance transparency in supply chain management.International Journal for Multidimensional Research Perspectives["M.K. Murugeah"]2024-07-06T00:00:00 ed6c13d58f881db24abb3083824081963879db28This study critically analyzes the impact of artificial intelligence (AI) and big data on the media industry, focusing on the ethical challenges and biases introduced by these technologies. The research aims to uncover the extent to which AI and big data influence content personalization, creation, and marketing, and the ramifications of these influences on cultural diversity and societal norms. A mixed-methods approach was employed, combining quantitative analysis through a survey of 532 respondents and qualitative thematic analysis of 10 academic literatures. The findings reveal significant associations between automated content creation tools and societal biases, personalized recommendation systems and echo chambers, and algorithmic recommendations and cultural homogenization. Conversely, no significant association was found between big data analytics and privacy concerns. The study highlights the need for ethical guidelines, enhanced content diversity, strengthened data privacy measures, and increased algorithmic transparency to mitigate the ethical challenges and biases in AI-driven media platforms. These insights contribute to the broader understanding of AI and big data's role in shaping the media industry, offering valuable implications for future research, policy-making, and industry practices.Asian Journal of Research in Computer Science01The study highlights the need for ethical guidelines, enhanced content diversity, strengthened data privacy measures, and increased algorithmic transparency to mitigate the ethical challenges and biases in AI-driven media platforms.Asian Journal of Research in Computer Science["Amaka Debie Samuel-Okon"]2024-07-06T00:00:00 77b54321d715a84357d6b042eb92f155b7957c5eThe advent of Turing’s test could be considered as the inception point of Artificial Intelligence. The invention of the World Wide Web, and Artificial Intelligence in the last 70 years have shifted the paradigm of technology and revolutionised the human race with an unmatched speed. Undoubtedly the progress made by humans in these previous few decades is many folds more than the progress we made from the beginning of the time. In healthcare and rehabilitation sciences, AI has not only improved diagnostic accuracy, personalized treatments, and supported patient recovery, but also enabled remote care, leading to improved health outcomes.Pakistan journal of rehabilitation00In healthcare and rehabilitation sciences, AI has not only improved diagnostic accuracy, personalized treatments, and supported patient recovery, but also enabled remote care, leading to improved health outcomes.Pakistan Journal of Rehabilitation["Dr. Kehkashan Kanwal"]2024-07-06T00:00:00 92618cd32e7733e8ea8e375b26691a13a591e8cfThe integration of artificial intelligence (AI) into video production has ushered in an era of significant transformation within the media landscape. This paper presents an in-depth analysis of AI-driven video generation, with a specific focus on the SORA platform, to illuminate the present capabilities, limitations, and future prospects of this emergent technology. Our study synthesizes expert discussions, developmental forums, and experimental assessments using text-to-video generation tools to elucidate the current state and trajectory of AI's role in video production. We identify a set of comprehensive best practices for maximizing the utility of AI-generated video content while mitigating the associated risks and challenges. Our findings reveal a striking potential for AI in enhancing the efficiency of content creation, the democratization of media production, and the realization of novel creative visions. However, the research also underscores critical concerns such as the preservation of authenticity, management of biases, and safeguarding against ethical misuse. Through this exploration, we aim to contribute a robust framework for integrating AI within traditional filmmaking workflows, thereby advancing the discourse on AI's implications for the creative industry. The proposed framework advocates for a human-centered approach to AI deployment, emphasizing ethical considerations and the imperative of maintaining the human essence within the storytelling art form. This paper seeks to provide a pivotal resource for filmmakers, content creators, and technologists as they navigate the evolving confluence of AI capabilities and creative aspirations.IEEE Symposium on Industrial Electronics and Applications160An in-depth analysis of AI-driven video generation, with a specific focus on the SORA platform, to illuminate the present capabilities, limitations, and future prospects of this emergent technology.2024 IEEE Symposium on Industrial Electronics & Applications (ISIEA)["A. Samad", "M. Izani", "D. Abdulla", "M. Faiz", "R. Wadood", "A. Hamdan"]2024-07-06T00:00:00 6d6660ab2375cde831ce77616df70ab7837c2903This study analyses Hungary's approach to regulating artificial intelligence (AI) by analyzing their AI Strategy (2020-2030) and provides insights for improving Indonesia's legal framework. In Hungary, although there is no dedicated legislation for artificial intelligence (AI), the country places a high importance on adhering to current regulations to regulate AI technologies. This paper does a comparative analysis to evaluate the influence of Hungary's approach on the advancement of artificial intelligence (AI), the methods used to enforce regulations, the ethical principles followed, the safeguarding of data, and the extent of international partnerships. This research seeks to offer practical insights for enhancing Indonesia's legal infrastructure in the field of AI governance and technology regulation by comparing Hungary's regulatory landscape with Indonesia's current framework. The purpose of the research is to provide guidance to policymakers and stakeholders in Indonesia regarding effective tactics and best practices based on Hungary's experience. This will assist in enhancing Indonesia's regulatory framework for AI and technologyJambe Law Journal350A comparative analysis is done to evaluate the influence of Hungary's approach on the advancement of artificial intelligence (AI), the methods used to enforce regulations, the ethical principles followed, the safeguarding of data, and the extent of international partnerships.Jambe Law Journal["Beny Saputra", "Hartati Hartati", "Oliv\u00e9r Bene"]2024-07-06T00:00:00 b2c62be7c6a23c71de87ab35e16d539a83f2c407This study presents an enhanced artificial intelligence chatbot (EAIC) that integrates the capabilities of ChatGPT and Rasa Open Source to assist teachers in guiding numerous students to promote autonomous learning performance in the virtual 3D chemistry laboratory concurrently. This study found that a virtual 3D chemistry laboratory with an EAIC could benefit learning satisfaction than without an EAIC. However, there were no significant differences in learning effectiveness. Encouragingly, the EAIC could provide good performance regarding ongoing conversation, ease of understanding, offering an appropriate amount of information, and high response speed. However, it was suggested to improve context tracking, accuracy, and dialogue clarity during conversational processes.IIAI International Conference on Advanced Applied Informatics502024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)["Bodong Chen", "Chih-Ming Chen", "Chieh-Ling Huang"]2024-07-06T00:00:00 e9c05be065eaf72f9e4b2ac6146d082f064c9316The growing penetration of artificial intelligence in applications including autonomous vehicles and brilliant factory equipment amplifies the risk of catastrophic damages from the execution of incomplete machine learning algorithms. For providing durable and dependable effectiveness, Artificial Intelligence (AI) frameworks require massive quantities of thoughtfully curated training data. However, because the production of training data frequently wants trained manual annotation, which limits scalability, it is scarce. Thus in this research, we propose a hybrid intelligent system for a human-machine collaborative environment. This can help humans interpret the machine and finish any assignments on budget. Considering not all automated machines can manage occupations by themselves, this human-AI shared effort will have an immense effect on many different sectors. Human-machine intelligence is blended into human-in-the-loop computing to establish a hybrid intelligence regarding supplementary habits. Humans play a role with their dynamic and creative mental abilities, but algorithms are unparalleled in logic and calculation speed. To acquire high accuracy and confidence in machine learning methodologies, hybrid cognitive systems are mandatory. To make certain that potential applications are successful and dependable, designers must establish and reinforce the trust between humans and AI, starting with being an AI-ready organization and ending with a clear focus on the benefits a hybrid intelligent system can offer customers. The alliance connecting humans and technology will become even larger because of the developing trends of technical innovations and the introduction of artificial intelligence in a growing range of applications.Nanotechnology Perceptions140This research proposes a hybrid intelligent system for a human-machine collaborative environment that can help humans interpret the machine and finish any assignments on budget and ends with a clear focus on the benefits a hybrid intelligent system can offer customers.Nanotechnology Perceptions["J. Jayapradha", "Dr.M.Janaki", "Rayappan Lotus", "A. F. Ahamed"]2024-07-06T00:00:00 aed272de00dd93ff58e34ddad0bc339ce279abbeEste artículo presenta un panorama general de las acciones, proyectos y experiencias de la incorporación de la inteligencia artificial (IA) generativa en el ámbito de la educación superior, en concreto, en la Universidad Iberoamericana Ciudad de México (Ibero). Tras la pandemia, la reestructuración de la Ibero impulsó la fusión de las áreas didáctica-curricular y tecnopedagógica, un ajuste que dio origen a la Dirección de Innovación Educativa (DIE) que ha puesto en marcha diversas estrategias para integrar el uso de la ia generativa en las prácticas educativas, incluyendo talleres de formación, ferias tecnológicas, conferencias, investigaciones y la creación de materiales didácticos. Un elemento central de esta iniciativa es la creación del Asistente Digital para la Docencia y el Aprendizaje (ADDA), un chatbot diseñado con un enfoque humanista que se alinea con la filosofía y pedagogía ignaciana, y facilita la interacción continua entre estudiantes y docentes, ya que está disponible las 24 horas del día. Iniciativas como ésta, reconocen tanto el potencial transformador de la tecnología como las capacidades humanas únicas, con el objetivo de mantener un equilibrio entre ambos aspectos para garantizar prácticas educativas enriquecedoras y significativas. En este sentido, la DIE ha tenido un papel importante en la elaboración de políticas internas para el uso ético de la IA generativa, contribuyendo así a establecer un marco normativo claro y a proporcionar recursos de capacitación adecuados.DIDAC70DIDAC["Marco Antonio Contreras Ruiz", "Indira Ochoa Carrasco", "Cimenna Chao Rebolledo"]2024-07-06T00:00:00 ef25610360ad8af7b1995d2b41dd75b52bb4fe2cEn este artículo se presentan tres estrategias de formación dirigidas al profesorado de la Universidad Nacional Autónoma de México (UNAM) que la Coordinación de Universidad Abierta, Innovación Educativa y Educación a Distancia (CUAIEED), ahora Coordinación de Evaluación, Innovación y Desarrollo Educativos (CEIDE) ha diseñado e implementado para desarrollar prácticas discursivas académicas en y para el uso de la inteligencia artificial generativa.Se describen las principales características del diseño didáctico y fundamentación de dos cursos y un material de formación docente a partir de los rasgos y posibilidades que representa para la docencia la incorporación de la inteligencia artificial generativa. Como parte de la fundamentación y reflexión incorporada en el desarrollo de las estrategias se plantea la importancia de pensar la relación entre docencia e inteligencia artificial más allá de una competencia asociada al uso de una herramienta, como un proceso de participación a partir del cual el profesorado es capaz de reconfigurar su práctica y resignificar el uso de esta tecnología.DIDAC60DIDAC["Mario Alberto Benavides Lara", "V\u00edctor Jes\u00fas Rend\u00f3n Cazales", "Mar\u00eda de los Angeles Guti\u00e9rrez Lovera", "Melchor S\u00e1nchez Mendiola"]2024-07-06T00:00:00 07af99281e254755c01c5c5af8a3502002fef596Históricamente, la tecnología y la educación se han vinculado con el objetivo de potenciar el aprendizaje de los estudiantes. A principios de 2023, la revolución ocasionada por ChatGPT sorprendió a diversas instituciones educativas y no educativas debido al potencial y los riesgos, principalmente éticos, que estas tecnologías basadas en inteligencia artificial (IA) presentan a sus usuarios. Sin embargo, no transcurrió mucho tiempo antes de que los educadores comprendieran que el surgimiento de este aplicativo no era más que la punta del iceberg. Este artículo explora el recorrido de la IA en el mundo moderno hasta nuestros días, sus primeros pasos en la educación, la disrupción causada por ChatGPT, sus primeros usos educativos y, finalmente, la forma en que aporta a enfoques educativos vanguardistas como el aprendizaje personalizado y adaptativo, al reducir los tiempos de planificación y preparación de recursos de los profesores, además de motivarlos a incorporar estas tecnologías en su práctica docente.DIDAC370DIDAC["Christian Ricardo Marroqu\u00edn D\u00e1vila"]2024-07-06T00:00:00 8b7d64781c3c462c3dd2198e252a249fc3af07e2Este artículo recupera la implementación de herramientas de inteligencia artificial generativa, específicamente Chatgpt y Bard, en el proceso de evaluación de un proyecto colaborativo final en un curso de pedagogía en la Universidad Nacional Autónoma de México (UNAM). El proyecto consistió en el diseño de un organismo internacional especializado en educación. Se utilizaron programas de inteligencia artificial generativa para elaborar rúbricas de evaluación en el trabajo del estudiantado. Se reflexiona sobre las experiencias con Chatgpt y Bard, resaltando sus fortalezas y limitaciones, a partir de lo cual se enfatiza la importancia de instrucciones claras y específicas al utilizar herramientas de ia generativa con fines educativos. Se concluye que estas herramientas pueden ser útiles para generar instrumentos con mayor eficiencia, aunque siempre es necesaria la supervisión y modificación del docente para obtener resultados confiables.DIDAC00DIDAC["Beatriz Ortega Estrada", "Abraham Daniel Hern\u00e1ndez Fabi\u00e1n"]2024-07-06T00:00:00 0e09f598ac942a7f7e190bc7a1677410dd3a3ec0En el contexto de las tecnologías emergentes incorporadas a la industria y los servicios, la capacitación técnica se ha convertido en un elemento esencial para adaptarse a los cambios tecnológicos acelerados, especialmente en el campo de la inteligencia artificial (IA). En este estudio se describen las acciones de los participantes durante un programa de capacitación técnica apoyado por ChatGPT para la empresa ART. Con un enfoque descriptivo y no experimental, se analizaron las acciones individuales y colectivas de los técnicos de mantenimiento en un taller de sistemas de bombeo. Los resultados del análisis indican que, a nivel individual, ChatGPT facilitó someramente el aprendizaje autónomo, ya que su uso se restringió sobre todo a la resolución de consultas relacionadas con información concreta. Colectivamente, la herramienta fomentó el aprendizaje colaborativo al actuar como catalizador en el intercambio de información y en las prácticas de mantenimiento entre los técnicos involucrados en la investigación.DIDAC40DIDAC["Angel Manuel Castillo Galicia", "Zoraima Barajas Z\u00fa\u00f1iga"]2024-07-06T00:00:00 fffcffe8b8f8843e23ebf1062f3e9caac167528fEl crecimiento vertiginoso de la inteligencia artificial (IA) y su incursión en los ambientes educativos ha generado debates sobre sus usos éticos y sobre sus efectos en el aprendizaje. El número 84 de DIDAC reconoce la necesidad de dar voz a docentes que comparten experiencias innovadoras de aplicaciones de herramientas de IA en su práctica y que, desde una perspectiva crítica, reflexionan sobre sus implicaciones como parte de un proceso de aprendizaje y enriquecimiento mutuo.DIDAC00DIDAC["M. S\u00e1nchez Salda\u00f1a"]2024-07-06T00:00:00 ae16e92fa50d1caa6670cd039a6242ebc17cd22bRevista Científica en Ciencias Sociales00Revista científica en ciencias sociales["Carlos Rafael Riquelme Ben\u00edtez", "Mar\u00eda Alejandra Pereira Ben\u00edtez"]2024-07-06T00:00:00 b6bf707f5da7e3af642ac7a945d52b9b7ac25a7cEscritura e inteligencias artificiales (IA) en la educación superior es una relación importante para pensar y discutir, sobre todo en espacios de formación de futuros docentes. La escritura es una habilidad clave para que los estudiantes se desarrollen, está presente en los discursos curriculares, institucionales y en las prácticas de los docentes, ya sea como problemática, inquietud o desafío, o como la necesidad de que los estudiantes escriban y produzcan, pero ahora desde una realidad digital, situación que plantea algunas preguntas que los docentes podrían hacerse al interior de estas instituciones: ¿deben permitirse las IA en las tareas y actividades escolares? ¿En qué medida las ia pueden ser usadas? ¿Quién debe regular el uso de IA y cómo?Quizá estas interrogantes les resulten familiares a los maestros de todos los niveles educativos cuando empiezan a explorar los desafíos a los que se enfrentan en la mediación entre la IA, la escritura y los estudiantes, así como sus riesgos y oportunidades.DIDAC90DIDAC["Fanny Araceli Ocampo Mart\u00ednez"]2024-07-06T00:00:00 3755a12f2a1018829e4feaaf74d84477979f6b35Metaheuristics were employed with ANFIS and K-means to determine whether COVID-19 performed the best on the American continent. A few individuals lost their lives in COVID-19, and quite a few nations assisted with this matter. It is essential to know the nations that performed the best in COVID-19. Researchers can carry out evaluations of nations using metaheuristic approaches and ANFIS. Based on the performance of these nations, clusters will be determined and established. The research excluded only two of the thirteen criteria that were investigated. Seven distinct groups have been established for each of the thirty-five nations. In the United States, the performance of COVID-19 is the poorest, according to research. These three nations also had the most extraordinary response to the COVID-19 outbreak. Based on the methodology and the context of the literature evaluation, this work's contribution may be divided into two distinct areas. The existence of research gaps makes it clear that a regional emphasis is needed rather than a focus on a nation or a portion of a country, which illustrates the requirement for a focus on the whole continent.Spectrum of Operational Research011The existence of research gaps makes it clear that a regional emphasis is needed rather than a focus on a nation or a portion of a country, which illustrates the requirement for a focus on the whole continent.Spectrum of Operational Research["Amir Karbassi Yazdi", "Hossein Komasi"]2024-07-07T00:00:00 d6e55b97c764e50e37004ce6ac0e2a436584579fThis article explores the potential effects of Artificial Intelligence (AI) on education provision, focusing on perspectives from present students and academic staff. By synthesizing insights from scholarly literature and empirical research, it examines possible impacts on education provision concerns related to AI implementation in education, including general knowledge about AI, data privacy, usage of it during the learning/teaching process, access, student autonomy, transparency, and accountability. The study investigates the current views and issues surrounding (AI) in education through the analysis of two survey questionnaires—one administered to students and another to academic staff. The surveys aim to discern perspectives on AI's potential benefits and its implications for changes in educational structures, qualifications, delivery methods, teacher training, and ethical considerations in assessment processes. The findings suggest a nuanced understanding among both students and academic staff regarding the transformative potential of AI in education, highlighting opportunities for personalized learning, concerns about ethical use, and the need for ongoing research and development in the field.International Conference on Trends and Innovations in Management, Engineering, Sciences and Humanities361The findings suggest a nuanced understanding among both students and academic staff regarding the transformative potential of AI in education, highlighting opportunities for personalized learning, concerns about ethical use, and the need for ongoing research and development in the field.International Conference on Trends and Innovations in Management, Engineering, Sciences and Humanities["Odeta Gluoksnyte", "Colin White", "Marius \u017ditkus"]2024-07-07T00:00:00 04b3cd76679294ac9ac3611902b537b53ec4b499AI &amp; SOCIETY492This paper proposes here to assign preter-intentional behavior to generative AI, to highlight how AI intentionality both incorporates and transcends human intentionality; i.e., it goes beyond (preter) human intentionality while being linked to it.AI &amp; SOCIETY["R. Redaelli"]2024-07-07T00:00:00 d1bfa7f61570a68b34130ff2180e24c7962f940cThe rapid advancement of Artificial Intelligence (AI) technology has brought about significant changes in education, particularly in elementary school settings. Integrating AI programs has led to substantial modifications, offering valuable support to educators in developing learning media. This study employed qualitative research to investigate elementary school teachers' viewpoints on utilizing AI for developing learning media, its integration into teaching activities, and the specific AI types utilized. The findings emphasized the crucial role of AI in education, highlighting the importance of careful AI deployment aligned with specific instructional needs and educational goals. Educators appreciated the significant assistance provided by AI in accessing a wide range of information and reference materials essential for creating engaging learning content. Additionally, participants noted the customized nature of their AI usage, tailored to their teaching requirements and desired educational outcomes. The research emphasized teachers' reliance on AI for information access and content development and underscored its vital role in creating a safe and enriching educational environment for educators and students.Journal of Integrated Elementary Education252Qualitative research was employed to investigate elementary school teachers' viewpoints on utilizing AI for developing learning media, its integration into teaching activities, and the specific AI types utilized, highlighting the crucial role of AI in education.Journal of Integrated Elementary Education["Reza Rachmadtullah", "Mareyke Jessy Tanod Mareyke Jessy Tanod", "Rasmitadila Rasmitadila", "Nico Irawan", "Alan McNeilly", "S. Suharni"]2024-07-07T00:00:00 155886417c89bcb38b73699e2ebe6cdce8814137By completing tasks that would normally be completed by people in a fraction of the time and at a fraction of the expense, artificial intelligence makes life easier for patients, physicians, and hospital managers. AI is used in healthcare in a variety of ways, including the discovery of new genetic connections, the operation of robots that assist during surgery, the automation of administrative processes, the customization of treatment plans, and much more. India has witnessed an exponential rise in the field of artificial intelligence (AI). A revolution in cost reduction, efficiency, quality, and accessibility of healthcare services for millions of users is anticipated with the integration of artificial intelligence into the healthcare system. The focus of the majority of AI-based proposals in India, according to the literature currently accessible on AI and healthcare systems, has been to provide AI-based medical services to underserved remote rural communities where people cannot afford high-quality medical facilities.International Journal For Multidisciplinary Research210The focus of the majority of AI-based proposals in India, according to the literature currently accessible on AI and healthcare systems, has been to provide AI-based medical services to underserved remote rural communities where people cannot afford high-quality medical facilities.International Journal For Multidisciplinary Research["Syed Uzra Jahan", "Santosh Bali"]2024-07-07T00:00:00 664b3e92c6a819903de13700e56825c930efeb8bThis paper aims to introduce the concept and objectives of the recently funded AI-OBSERVER Horizon Europe Twinning project titled “Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence”. The AIOBSERVER project aims to significantly strengthen and stimulate the scientific excellence and innovation capacity of the ERATOSTHENES Centre of Excellence on the use of Artificial Intelligence for Earth Observation in the Disaster Risk Reduction thematic area, as well as the research management and administrative skills, of the Centre. This will be achieved through a series of capacity building and targeted research activities, having the support of internationally leading institutions, i.e., the German Research Centre for Artificial Intelligence from Germany and the University of Rome Tor Vergata from Italy, assisting the ERATOSTHENES Centre of Excellence to reach its longterm objective of raised excellence on Artificial Intelligence for Earth Observation on environmental hazards.IEEE International Geoscience and Remote Sensing Symposium20The concept and objectives of the recently funded AI-OBSERVER Horizon Europe Twinning project titled “Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence” are introduced.IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium["M. Tzouvaras", "Gerd Reis", "F. Frate", "Haris Zacharatos", "D. Hadjimitsis"]2024-07-07T00:00:00 9383743ba1054ef174fad2bdd9e499b3ff99bf55Technology is advancing rapidly in this globalization era. Modern technology such as Artificial Technology (AI) emerges as the result of digitalization and it has a favourable impact on many facets of life including in education sector. Generation Z or Gen Z, as digital natives, utilize digital technology including AI as an essential part of their daily routines. The purpose of this study is to investigate Gen Z students’ perspectives towards the use of technology particularly AI (artificial intelligence) technology in English language learning. The participants of this study were 30 students from English Education Department, class of TBI-3, fourth semester of State Islamic University of North Sumatera (UIN SU). Qualitative method was implemented in this study. To collect the data, two instruments were employed, through an interview technique and observation. The research findings revealed that 18 (60%) students agree with AI technology, 10 (33.3%) students were neutral and 2 (6.7%) students disagree with the utilization of AI technology in English language learning. AI technology seems to be more advanced in the future to assist human. Moreover, if it is integrated officially in education sector. Thus, Gen Z students should be wiser in accessing it. This study may broaden teachers’ and lecturers’ insight or horizon about current technology in English language learning.  New Language Dimensions00Gen Z students’ perspectives towards the use of technology particularly AI (artificial intelligence) technology in English language learning revealed that 18 (60%) students agree with AI technology, 10 (33.3%) students were neutral and 2 (6.7%) students disagree with the utilization of AI technology in English language learning.New Language Dimensions["Henny Mardiah", "Khairun Nissa"]2024-07-07T00:00:00 5d1228ccf5541bf0594a21bcee58b8de33b5375eThis article explores the integration of Artificial Intelligence (AI) enhanced Earth Observation (EO) technologies to predict and manage vector-borne diseases (VBDs), particularly mosquito-borne diseases (MBDs), amid the backdrop of climate change. The study emphasizes the critical role of accurate predictions in supporting decision-making for mosquito control initiatives. The AI component predicts mosquito larvae and adult populations, pathogen circulation, and human cases of West Nile Virus (WNV) disease. The approach aims to enhance the efficiency of mosquito control activities by providing real-time, operational predictions. Detailed methodologies include electronic data acquisition of entomological and observatory data, utilizing the eBite© application, and a comprehensive data processing pipeline for integrating EO data. The machine learning models, employing techniques like Variational Autoencoder and ensemble methods, demonstrate robust predictive performance for mosquito larvae, adult mosquito abundance, and West Nile Virus risk. The models' significance in optimizing scheduling, resource allocation, and fostering a shift towards preventive control measures are underscored.IEEE International Geoscience and Remote Sensing Symposium40IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium["S. Gewehr", "Miltos Iatrou", "Xanthi Tseni", "Alexis Brezas", "S. Kalaitzopoulou", "Nikolaos Perros", "S. Mourelatos"]2024-07-07T00:00:00 b71e66ff54c42ee26016fcb2ed65babfd93913af. Financial markets are the foundation of the modern economy, facilitating the movement of capital between its owners and borrowers, thereby enabling businesses to develop and innovate. These markets provide a platform for price discovery and risk management. At their core, they act as intermediaries between those who have surplus funds and those who need financing. The functioning of financial markets provides borrowers with a mechanism for accessing capital and capital owners with an opportunity to earn a return on their investments. This interaction between capital owners and borrowers is essential for economic growth and development. Rapid changes in the global economy and the impact of information technology have brought significant changes to the way financial markets function, simplifying communication between market participants and increasing efficiency. However, these changes have also created a number of risks caused by uncertainty. The use of artificial intelligence systems in building a model for forecasting financial indicators based on processing a large data set can significantly improve the accuracy and quality of forecast data. The authors have conducted a comparative analysis of forecasting methods, identifying their respective strengths and weaknesses, and the potential for their application to forecasting indicators. Based on the findings of this study, the authors propose a model for forecasting financial market indicators using artificial intelligence systems. The model comprises two components: 1) a time series LSTM, a network with aНаука і техніка сьогодні00A model for forecasting financial market indicators using artificial intelligence systems based on processing a large data set based on processing a large data set can significantly improve the accuracy and quality of forecast data.Наука і техніка сьогодні["Olena Zaichenko", "Stanislav Skorobogatov"]2024-07-07T00:00:00 4e307ec145a26ad2467c07496f8b4bddba92b82cIn modern business, the use of artificial intelligence (AI) is becoming increasingly common in various areas of company financial activities and in forecasting the likelihood of their bankruptcy. Traditional analytical methods, while having their advantages, are limited in data, linear, and subject to subjectivity. The use of AI allows for more precise and timely analysis, as well as providing reasoned recommendations for decision-making. Various AI approaches, such as natural language processing (NLP) systems, machine learning, and neural networks, enable the automation of processes for analyzing financial risks, forecasting bankruptcy, optimizing investments, and managing risks. This contributes to increasing the efficiency of company management, identifying risks at early stages, and making timely decisions to prevent bankruptcy.Science &amp; World20Various AI approaches, such as natural language processing (NLP) systems, machine learning, and neural networks, enable the automation of processes for analyzing financial risks, forecasting bankruptcy, optimizing investments, and managing risks.Science &amp; World["Pavel Butko"]2024-07-07T00:00:00 c77fe13aa7b637dc94e27e8baa83656bcb6e0522 The purpose of the research is to study and analyze the issue of the use of artificial intelligence (AI) by banks that results in losses to customers. To respond to the issue, this paper argues that banks as AI users should be held criminally liable despite the lack of mens rea in banks as corporations which are legal entities. For this reason, this paper uses the identification theory as an analytical tool for bank criminal liability. The issue is compiled on the results of normative legal research with a statute approach and conceptual approach. The results of the study show that based on the identification theory, the bank as an AI user can be held criminally liable with the public prosecutor must identify the person who committed the criminal act (actus reus) is the management as the controlling personnel (directing mind or controlling mind). The location of the AI mens rea is in the approval of the corporate controller to use AI, meaning that the approval is interpreted as the inner attitude of the controller to accept the risks arising from the use of AI. Tujuan dari penelitian untuk menganalisis isu penggunaan artificial intelligence (AI) oleh bank yang berakibat pada kerugian bagi nasabah. Untuk menanggapi isu tersebut, tulisan ini berpendapat bahwa bank sebagai pengguna AI patut dimintai pertanggungjawaban pidana meskipun tidak mempunyai mens rea pada bank sebagai korporasi yang merupakan badan hukum. Untuk itu, tulisan ini menggunakan teori identifikasi sebagai pisau analisis terhadap pertanggungjawaban pidana bank. Isu tersebut disusun atas hasil penelitian hukum normatif dengan pendekatan undang-undang (statute approach) dan pendekatan konseptual (conceptual approach). Hasil penelitian menunjukan, berdasarkan teori identifikasi, bank sebagai pengguna AI dapat dimintai pertanggungjawaban pidana dengan penuntut umum harus mengidentifikasi yang melakuan perbuatan pidana (actus reus) adalah pengurus sebagai personil pengendali (directing mind atau controlling mind). Letak mens rea AI ada pada persetujuan pengendali korporasi menggunakan AI, artinya sikao persetujuan tersebit dimaknai sebagai sikap batin pengali untuk menerima resiko yang timbul akibat penggunaai AI.  JURNAL USM LAW REVIEW170JURNAL USM LAW REVIEW["Mardian Putra Frans", "A. Sari", "Krismelia Y Panji", "Yudhi Ismara"]2024-07-07T00:00:00 de5d8ff836ccf135ab918550397bb2450a8e04ecArtificial intelligence is already an unavoidable player in our everyday lives and its role will, certainly, continue to grow in the future. The article, after a short theoretical explanation, examines the role that artificial intelligence can play in the development of different areas of field artillery (artillery target acquisition, fire control systems, weapons and ammunition). At the end of the study, the author draws attention to an area that is still quite undeveloped today, the ethical issues of the use of artificial intelligence.Mesterséges intelligencia90The article examines the role that artificial intelligence can play in the development of different areas of field artillery (artillery target acquisition, fire control systems, weapons and ammunition) and the ethical issues of the use of artificial intelligence.Mesterséges intelligencia["G\u00e9za Guly\u00e1s"]2024-07-07T00:00:00 e9a076bb5d1c348d4999601417fd10b6aed146d5The early science fiction and films have been deeply fascinated using Artificial Intelligence (AI). This abstract basically adhered by exploring AI which usually acts as a catalyst in early science fiction and films. And this study depicts the development of AI concepts and societal implications in various works and highlights their lasting influence with respect to Artificial Intelligence. We can trace out the emergence of AI in early science fiction and films in the early 19th century, which played a pivotal role in shaping fiction in a different way. Writers like Mary Shelly, in her novel “Frankenstein” (1818) and Karel Capek, with his work “R.U.R” (1920), enhanced audiences to the idea of creating artificial life, raising questions about the moral and ethical entanglement of playing God. We can also witness in the most iconic film “Metropolis” (1927) which was well directed by Fritz Lang which featured the character Maria, a robot; indeed, offering a glimpse of fear and fascination engaging AI. The portrayal of Maria created a lasting image of AI in a gracious manner as well as a potential threat, reflecting the anxieties of a rapidly industrializing world. Films like “Forbidden Planet” which presents Robby the robot and in Stanley Kubrick’s “2001: A space Odyssey” (1968) introduces HAL 9000 a sentinel AI, these presentations symbolize AI’s potential to assist and interact with humans in a different manner. In a drastic and dramatic manner, the literary exploration of AI in early science fiction and films offers invaluable insights into the origins of AI narratives and their enduring impact on our understanding of artificial intelligence. And these early works in the 90’s serve as a foundational text in the ongoing discourse about AI’s role in society and the responsibilities that come with the development. Key Words: futuristic, implications, ethical considerations, explorationsINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT40These early works in the 90’s serve as a foundational text in the ongoing discourse about AI’s role in society and the responsibilities that come with the development.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Avisek Pattnaik", "*Dr. Prasanta Kumar Padhi"]2024-07-07T00:00:00 09f8f270d8536e023bd2a6c02c04a8b7e6d88418Human Resource Development International592Human Resource Development International["Komal Khandelwal", "A. Upadhyay", "Aaradhana Rukadikar"]2024-07-07T00:00:00 7e39800f9d100c6fe823da93a6c23c39a62a1d8fOrvosi Hetilap00Orvosi hetilap["Daungsupawong Hineptch", "W. Viroj"]2024-07-07T00:00:00 cd1febfddb5453f37dd207a24baee4d96dad4be8The relationship network between enterprises will change their adoption behavior of AI technology and this micro-decision-making mechanism will eventually decide whether AI technology can diffuse and the extent of diffusion on the macro level. However, the existing AI technology diffusion research mostly focuses on the integration of AI technology with other industries from the industrial level, ignoring the complexity of the micro-complex game process and interactions within the enterprise network on the macro technology diffusion. In this regard, this paper builds a game model of AI technology diffusion in core and non-core enterprises from the levels of market competition and policy incentives based on complex network evolutionary game theory. It does this through simulation analysis that examines the mechanism of key factors affecting the diffusion of AI technology, as well as the influence and combination effects of pertinent policies. The study shows that (1) AI technology diffuses more effectively in non-core enterprises than it does in core enterprises; (2) changes in parameters like technology cost and policy regimes have a more evident impact on core enterprises than non-core ones; (3) in market competition, increasing the network average degree, the proportion of AI technology products in the mainstream market, the opportunity cost, the cost reduction factor, or decreasing the cost of AI technology can all promote the diffusion of AI technology; (4) under policy incentives, increasing the proportion of AI technology subsidies and the influence of high-tech identification of enterprises can both promote the diffusion of AI technology.Syst.420The study shows that AI technology diffuses more effectively in non-core enterprises than it does in core enterprises, and changes in parameters like technology cost and policy regimes have a more evident impact on core enterprises than non-core ones.Syst.["Xiaofei Ma", "Jia Wang"]2024-07-07T00:00:00 fb412224faf286b588034cdcafc975e8591d447fThis study investigates the utilization of Artificial Intelligence (AI) among students enrolled in Mathematics and English Language Education programs at higher education in completing their academic tasks. Through a qualitative method, the study examines students' attitudes towards AI, their preferred AI tools, and the perceived benefits and challenges associated with AI integration in academic tasks. The findings reveal that the majority of students in both programs view AI as a valuable tool for enhancing learning outcomes and academic performance. Chat GPT emerges as the most favoured AI tool, particularly among Mathematics students. Moreover, students acknowledge the potential of AI to improve their motivation in learning and provide personalized feedback. However, students also express concerns regarding AI's limitations, including its inaccuracy and potential biases in data and information. Overall, this study underscores the importance of integrating AI responsibly and equitably to optimize its benefits and address potential drawbacksQomaruna01The importance of integrating AI responsibly and equitably to optimize its benefits and address potential drawbacks is highlighted, particularly among Mathematics students.Qomaruna["Happy Kusuma Wardani", "Eva Nur Mazidah", "Bariqotul Hidayah"]2024-07-07T00:00:00 dd55c02fd27c4c89158a570babbceab768a05a43Early detection of suspicious skin lesions can significantly increase the five-year survival rates of the patients. Advancements in computer vision techniques facilitate the use of artificial intelligence (AI) models along with image data for skin cancer detection. However, there is limited work done on skin cancer detection solely based on patient metadata. The 7-point checklist (7PCL) and Williams methods use a limited number of meta-features to calculate skin lesion risk scores and to find a patient at risk of developing skin cancer, respectively. This study attempts to fill the gap and proposes an AI-based framework for classifying skin lesion metadata into binary classes: Suspicious vs Non-suspicious. The developed framework has been evaluated using real-world skin lesion metadata sourced from a network of private skin diagnostic clinics across the UK. We have collected and analyzed 54,000 skin lesions metadata, from 25,214 patients undergoing teledermatology assessment after clinical examination and imaging, comprising 25 features including patient age, gender, and lesion location. The metadata has been pre-processed through encoding, followed by feature selection using wrapper, Shapley, and Pearson correlation methods. Finally, five different predictive models were utilized and optimized to classify skin lesion metadata into Suspicious vs Non-suspicious classes. Our proposed approach achieved $83.53(\pm 0.03) \%$ sensitivity in detecting suspicious lesions using only metadata and outperformed the 7PCL and Williams methods. We believe this AI-based framework is unique in classifying skin lesions based solely on metadata and has significant potential to improve the performance of current AI models that are based on image assessment alone.International Conference on Digital Health280This study proposes an AI-based framework for classifying skin lesion metadata into binary classes: Suspicious vs Non-suspicious and believes this AI-based framework is unique in classifying skin lesions based solely on metadata and has significant potential to improve the performance of current AI models that are based on image assessment alone.2024 IEEE International Conference on Digital Health (ICDH)["Md Shafiqul Islam", "Gordon Wishart", "Joseph Walls", "Per Hall", "Alba Garcia", "John Gan", "Haider Raza"]2024-07-07T00:00:00 40f80c1202abff944e3da56e00371c395134ad1aPurpose- The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning Artificial Intelligence (AI) and Machine Learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field. Design/methodology/approach- In this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology, to analyze related papers. Findings- In business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models, and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis. Research limitations/implications- While this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024. Originality/value- This paper adopts a pioneering approach by conducting an extensive examination of the integration of AI/ML techniques across the entire process management lifecycle. Additionally, it presents groundbreaking research and introduces AI/ML-enabled integrated tools, further enhancing the insights for future research.arXiv.org1420The main goal is to offer researchers and process analysts insights into the latest developments concerning Artificial Intelligence (AI) and Machine Learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field.ArXiv["Mostafa Abbasi", "R. Nishat", "Corey Bond", "J. B. Graham-Knight", "Patricia Lasserre", "Yves Lucet", "H. Najjaran"]2024-07-07T00:00:00 2dd4d4d1ebbd5eaca19936c3b8437d6065912f4fLarge language models (LLMs) can complete general scientific question-and-answer, yet they are constrained by their pretraining cut-off dates and lack the ability to provide specific, cited scientific knowledge. Here, we introduce Network for Knowledge Organization (NEKO), a workflow that uses LLM Qwen to extract knowledge through scientific literature text mining. When user inputs a keyword of interest, NEKO can generate knowledge graphs and comprehensive summaries from PubMed search. NEKO has immediate applications in daily academic tasks such as education of young scientists, literature review, paper writing, experiment planning/troubleshooting, and new hypothesis generation. We exemplified this workflow’s applicability through several case studies on yeast fermentation and cyanobacterial biorefinery. NEKO’s output is more informative, specific, and actionable than GPT-4’s zero-shot Q&A. NEKO offers flexible, lightweight local deployment options. NEKO democratizes artificial intelligence (AI) tools, making scientific foundation model more accessible to researchers without excessive computational power.bioRxiv420Network for Knowledge Organization (NEKO) is introduced, a workflow that uses LLM Qwen to extract knowledge through scientific literature text mining and its output is more informative, specific, and actionable than GPT-4’s zero-shot Q&A.bioRxiv["Zhengyang Xiao", "H. Pakrasi", "Yixin Chen", "Yinjie J Tang"]2024-07-07T00:00:00 53edbc4a5e8d6581104a8674b680fcc2efe89564An equitable and environmentally just community is essential in order to avoid disproportionate burden borne by vulnerable communities. This need becomes pressing in the aftermath of an extreme event such as disaster or hazard when it is difficult for the governing bodies to implement resource allocation as per the need. Artificial Intelligence (AI) algorithms can help surface Equity and Environmental Justice (EEJ) issues when trained on EEJ datasets. However, curating AI-ready EEJ training datasets is challenging due to differences in factors such as heterogeneity, resolution, modality, and level of expertise in labeling. Additionally, EEJ issues involve sensitive information where uncertainties and errors could degrade the performance of AI algorithms. For eg. error in seasonal crop yield information can highly effect the prediction of annual crop yield. To address these challenges, Data-centric AI (DCAI) methods are employed, which enhance AI algorithm performance even with limited training samples. DCAI prioritizes data quality, thereby reducing the adverse effects of uncertainties and errors during the model training process. This research proposes a novel dataset and benchmark for analyzing the effect of the Maui Wildfire of 2023 for Equity and Environmental Justice (EEJ) issues. The proposed dataset aligns with the concepts of DCAI such as annotation quality, data preprocessing, privacy, feature engineering, governance and provenance. The proposed AI-ready dataset is available on HuggingFace at https://huggingface.co/datasets/nasa-impact/ml4ej-maui-wildfire.IEEE International Geoscience and Remote Sensing Symposium210This research proposes a novel dataset and benchmark for analyzing the effect of the Maui Wildfire of 2023 for Equity and Environmental Justice (EEJ) issues that aligns with the concepts of DCAI such as annotation quality, data preprocessing, privacy, feature engineering, governance and provenance.IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium["Paridhi Parajuli", "Rajat Shinde", "I. Gurung", "M. Maskey", "R. Ramachandran"]2024-07-07T00:00:00 0e3890672c624f5366d5e5fb9b1c0786d0e8cdcbWith ongoing market competitions, advancements in technology, and diverse products and services, supply chains (SC) have become increasingly sophisticated and complicated. The complexity of SC makes the ability to withstand the external uncertain changes critically important, which is known as SC resilience. Healthcare supply chains (HSC) are a critical subset facing similar or even more severe challenges. HSC resilience is the ability of the system to quickly adapt and recover from disruptions, thereby ensuring continuous healthcare product and service delivery. Recently various measures have been applied to enhance the resilience of SC. Among them, the adoption of artificial intelligence (AI) contributes most. However, the diversity of AI technologies requires an assessment of their contributions in enhancing SC resilience. To address this, this paper proposes a hybrid resilient assessment multi-criteria decision-making (MCDM) framework. The hybrid method combines the strength of interval-valued q rung orthopair fuzzy set (IVq-ROFS) in flexibly handling vagueness, the superiority of preference ranking organization method for enrichment evaluation (PROMETHEE) in ranking process, and the effectiveness of prospect theory in dealing with bounded rationality of decision-makers. Besides, a set of HSC resilience evaluation criteria are introduced from the perspective of technical contribution. In practice, the hybrid method is validated by identifying the impact of AI technologies on HSC resilience. Sensitivity analysis and comparison analysis are also conducted to prove the robustness and superiority of the method.International Conference on Digital Health410A hybrid resilient assessment multi-criteria decision-making (MCDM) framework that combines the strength of interval-valued q rung orthopair fuzzy set (IVq-ROFS) in flexibly handling vagueness, the superiority of preference ranking organization method for enrichment evaluation (PROMETHEE) in ranking process, and the effectiveness of prospect theory in dealing with bounded rationality of decision-makers is proposed.2024 IEEE International Conference on Digital Health (ICDH)["Huzhi Xue", "Haihua Xie", "Carl K. Chang"]2024-07-07T00:00:00 7ee798980cb8398c5b185898c172a0572ff83899The "Physically-Based Synthetic Data for Earth Observation" (SD4EO) project1, initiated in October 2023, aims to integrate physically-based simulation data and artificial intelligence-based data generation tools into Earth Observation applications. Leveraging advancements in physically-based and AI-driven simulation, the project investigates the potential of combining real and simulated data to improve target categorization in AI-driven Earth Observation analytic pipelines. For this purpose, three diverse use cases have been selected: crop field categorization, human settlement categorization, and photovoltaic panel status monitoring. SD4EO project seeks also to expedite innovation in this field and foster collaboration between the Artificial Intelligence, Computer Graphics and Earth Observation scientific communities.IEEE International Geoscience and Remote Sensing Symposium400The SD4EO project investigates the potential of combining real and simulated data to improve target categorization in AI-driven Earth Observation analytic pipelines.IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium["Marcos Fern\u00e1ndez", "J. Gimeno", "Rossana Gini", "David Miraut", "Raul Rodr\u00edguez Ju\u00e1rez", "Manuel P\u00e9rez-Aixendri", "E. Hisam", "David de la Fuente Blanco", "Marta Toro Bermejo", "Eloy Zafra Santos"]2024-07-07T00:00:00 81c5dd61c85a47727de4b5787f0da2591437d137The activities undertaken within the EcoNet project aim at the design and development of an integrated system for the monitoring of changes in surface waters natural status based on different sensoristic techniques. The proposed integration approach combines ground measurements and hyperspectral satellite images. The promising dialogue that occurs between these two multi-sensoristic technologies requires the implementation of appropriate tools for data handling and analysis which in this work are represented by Artificial Intelligence (AI), particularly suitable to retrieve very subtle relationships among the data. This integration can open enormous potential for overcoming the limits of traditional environmental monitoring and diagnostic techniques.IEEE International Geoscience and Remote Sensing Symposium150The proposed integration approach combines ground measurements and hyperspectral satellite images that can open enormous potential for overcoming the limits of traditional environmental monitoring and diagnostic techniques.IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium["Valeria La Pegna", "F. Frate", "D. D. Santis", "Dario Cappelli", "Martina Frezza", "Roberto Dragone", "Gerardo Grasso", "Daniela Zane", "Bruno Brunetti", "Sabrina Foglia", "Giorgio Licciardi", "P. Sacco", "D. Tapete"]2024-07-07T00:00:00 bcfa2a8d472e1c7a0d9078e58d30f62229ad2392In this century our world, there are huge impacts of AI. This paper discusses an overview of the Artificial Intelligence (AI) applications used in power electronic systems. AI works in three distinctive life-cycle phases, design, control, and maintenance. Also, it includes optimisation, classification, regression, and data structure exploration. Here we discuss, the applications of four categories of AI: expert system, fuzzy logic, metaheuristic method, and machine learning. This paper shows various techniques of AI, applications of AI and different methods are fault diagnosis and prognosis. Future scope of AI in power electronics is discuss in this review paper.International Journal For Multidisciplinary Research90This paper shows various techniques of AI, applications of AI and different methods are fault diagnosis and prognosis, and future scope of AI in power electronics is discuss in this review paper.International Journal For Multidisciplinary Research["Karuna Gamare", "V. A. Joshi"]2024-07-07T00:00:00 425c3f40d9eb33156d7d5b0d740c923e0263cff4The progress in large language models (LLMs) brings much excitement and efforts in medical artificial intelligence, which could transform patient-doctor conversation while making joint medical decisions. LLMs, exemplified by ChatGPT, are proficient in grasping and generating text, and can perform tasks such as question answering, document summarising, and paraphrasing with a level of proficiency comparable to that of a human. Their potential applications span across various tasks in medicine, notably improving clinical patient care experience, advancing scientific medical research, and revolutionizing medical education. This survey critically examines the evolving landscape of medical large language models (Med LLMs), with a special focus on their application in stomatology. While Med LLMs are inevitably becoming an integral part to medical text processing and image processing, their use in enhancing clinical care requires extra precaution and assurance due to the stringent requirements on ethics and patient safety. The design, deployment and use of LLMs and services requires thorough risks analysis of technology misuse and potential harms. This survey looks into the current status, different prospects and challenges in LLMs development in medical use cases and ways to control and mitigates risks of generative artificial intelligence.International Conference on Digital Health880The current status, different prospects and challenges in LLMs development in medical use cases and ways to control and mitigates risks of generative artificial intelligence are looked into.2024 IEEE International Conference on Digital Health (ICDH)["Mingming Xu", "Chen Ye", "Zheng Zeng", "Chenyang Chang", "Shijie Qi", "Yujia Wu", "Huifang Yang", "Yifan Chen", "Haifeng Huang", "Lin Liu", "Zhanqiang Cao", "Xuliang Deng"]2024-07-07T00:00:00 6e5a4dd3c2dfd2c3f3571a55efae5662c125c395Scientific Reports356This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, and found ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making.Scientific Reports["Khadijeh Moulaei", "Mohammadreza Afrash", "Mohammad Parvin", "S. Shadnia", "Mitra Rahimi", "Babak Mostafazadeh", "Peyman Erfan Talab Evini", "Babak Sabet", "Seyed Mohammad Vahabi", "Amirali Soheili", "Mobin Fathy", "Arya Kazemi", "Sina Khani", "Seyed Mohammad Mortazavi", "S. M. Hosseini"]2024-07-08T00:00:00 c707fbf09d6d4e4ce28b596efc68e172f75a7de4INTRODUCTION The USA has the highest age-standardized prevalence of inflammatory bowel disease (IBD). Both genetic and environmental factors have been implicated in IBD flares and multiple strategies are centered around avoiding dietary triggers to maintain remission. Chat-based artificial intelligence (CB-AI) has shown great potential in enhancing patient education in medicine. We evaluate the role of CB-AI in patient education on dietary management of IBD. METHODS Six questions evaluating important concepts about the dietary management of IBD which then were posed to three CB-AI models - ChatGPT, BingChat, and YouChat three different times. All responses were graded for appropriateness and reliability by two physicians using dietary information from the Crohn's and Colitis Foundation. The responses were graded as reliably appropriate, reliably inappropriate, and unreliable. The expert assessment of the reviewing physicians was validated by the joint probability of agreement for two raters. RESULTS ChatGPT provided reliably appropriate responses to questions on dietary management of IBD more often than BingChat and YouChat. There were two questions that more than one CB-AI provided unreliable responses to. Each CB-AI provided examples within their responses, but the examples were not always appropriate. Whether the response was appropriate or not, CB-AIs mentioned consulting with an expert in the field. The inter-rater reliability was 88.9%. DISCUSSION CB-AIs have the potential to improve patient education and outcomes but studies evaluating their appropriateness for various health conditions are sparse. Our study showed that CB-AIs have the ability to provide appropriate answers to most questions regarding the dietary management of IBD.European Journal of Gastroenterology and Hepathology274This study showed that CB-AIs have the ability to provide appropriate answers to most questions regarding the dietary management of IBD and showed that ChatGPT provided reliably appropriate responses to questions on dietary management of IBD.European journal of gastroenterology & hepatology["Haider A Naqvi", "Thilini Delungahawatta", "Joseph O. Atarere", "S. K. Bandaru", "Jasmine Barrow", "Mark C Mattar"]2024-07-08T00:00:00 5687de153594ccab11f530bff2346fe8edb794e0Epithelial dysplasia is a condition characterized by a spectrum of architectural and cytological alterations to the epithelium, resulting from the accumulation of genetic alterations. It is associated with an increased risk of cancer progression in a variety of organs. However, the variability of different grading systems, as well as inter- and intra-examiner variability, gives rise to concerns regarding the reliability of the results. Histopathology represents the gold standard for the diagnosis of epithelial dysplasia. The combination of big data in pathology and artificial intelligence (AI) will facilitate the achievement of accurate diagnoses and treatments, providing objective and efficient methods to integrate and refine diverse morphological, molecular, and multi-omics information. This perspective provides a summary of the existing research and prospects for the application of AI to epithelial dysplasia in multiple organs. A number of studies have been conducted with the aim of developing a grading system and prognostic identification method for epithelial dysplasia in the oral cavity, larynx, esophagus, and stomach. Digital pathology-based AI may prove useful in facilitating the clinical management of epithelial dysplasia in multiple organs. In summary, digital pathology images obtained by scanning hematoxylin & eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future.Exploration of Digital Health Technologies151Digital pathology images obtained by scanning hematoxylin & eosin-stained slides, identifying image features, and building AI models using deep learning combined with machine learning algorithms, validated with real-world data from multicenter cohorts could provide AI as a promising clinical application in the future.Exploration of Digital Health Technologies["Xin-Jia Cai"]2024-07-08T00:00:00 593432bde52e5a4b49ee8e67be67b8f4095347baMachine learning is a well-matured discipline, and exploration of datasets can be performed in an efficient way, leading to accurate and operational prediction and decision models. On the other hand, most methods tend to produce black-box-type models, which can be considered a serious drawback. This is so, since in case of numerous practical applications, it is also required to justify, explain, and uncover the inner decision mechanism so that an in-depth understanding of the causal and functional dependencies becomes possible and some responsibility for the decision can be considered. This paper addresses the critical need for model-driven eXplainable Artificial Intelligence (XAI) by exploring the limitations inherent in existing explanatory mechanisms, such as LIME or SHAP, which rely solely on input data. This seems to be an intrinsic limitation and a conceptual error, as no expert domain knowledge can come into play, and no analytical models of the phenomena under investigation are created. In order to deal with this issue, this paper puts forward the idea of building open, white-box explanatory models. To do that, we propose employing grammatical evolution tools combined with expert domain knowledge. The results demonstrate that the developed models can effectively explain the structure and behavior of decision models in terms of components, connections, causality, and simple functional dependencies.Applied Sciences281This paper addresses the critical need for model-driven eXplainable Artificial Intelligence (XAI) by exploring the limitations inherent in existing explanatory mechanisms, such as LIME or SHAP, which rely solely on input data.Applied Sciences["Dominik Sepio\u0142o", "Antoni Lig\u0119za"]2024-07-08T00:00:00 cdeca2efa629fafd5e92fda539fa5e5d0b0f362aThis study was conducted to examine the effects of artificial intelligence technology on journalistic practice. The study analysed the news of İbrahim Selçuk, an artificial intelligence-based columnist of Dünya Newspaper, one of the leading newspapers in Turkey, and evaluated them in terms of news value and journalistic ethics. Within the scope of the research, 10 news samples published between October 2023 and March 2024 were analysed by descriptive analysis method. The results of the analysis show how much the news produced by artificial intelligence comply with the principles of traditional journalism. The evaluation made within the framework of the basic concepts of journalism reveals that although artificial intelligence news is newsworthy, it faces certain difficulties in terms of journalistic ethics. However, it is noted that more research is needed to provide a deeper understanding of the role and effects of AI technology in journalistic practice. This study can be considered as an important step to understand the evolution of AI technology in the field of journalism and to guide future applications.İletişim Kuram ve Araştırma Dergisi01The evaluation of the news of İbrahim Selçuk, an artificial intelligence-based columnist of Dünya Newspaper, and evaluated them in terms of news value and journalistic ethics reveals that although artificial intelligence news is newsworthy, it faces certain difficulties in terms of journalistic ethics.İletişim Kuram ve Araştırma Dergisi["Mustafa B\u00f6y\u00fck"]2024-07-08T00:00:00 51ee8cb3e113c5c5d08ffa08077958f14f34a6f7The evolution of technology is giving rise to new scenarios in communication, information access, and social relations. Particularly, artificial intelligence has a great impact on the current media ecosystem, including social, academic, communicative, health aspects, and interpersonal relationships. This research aims to study how artificial intelligence is reflected in the scientific production in the most relevant publications in Social Sciences. To this end, a systematic review of the scientific literature published in Spanish on the Web of Science and Scopus databases spanning from 2018 to the first three quarters of 2023 was carried out, following the standards of PRISMA Statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). From an initial sample of 159 articles, 109 were analysed after applying the inclusion and exclusion criteria. Results show that 2022 was the most productive year, with Spain having the highest number of publications. Furthermore, most of the research was published on Scopus and in the field of Law, with a predominance of qualitative methodology. The key themes were the benefits of implenting artificial intelligence (AI) and its dangers and threats.Tripodos1522A systematic review of the scientific literature published in Spanish on the Web of Science and Scopus databases spanning from 2018 to the first three quarters of 2023 was carried out, with results showing that 2022 was the most productive year, with Spain having the highest number of publications.Tripodos["Aurora Forteza-Mart\u00ednez", "Nadia Alonso L\u00f3pez"]2024-07-08T00:00:00 67900d4aa19bfd18fbd9238730bf6abef7926dfcThe impact of artificial intelligence (AI) technological advancements is reshaping various aspects of our daily lives, including education. Integrating AI in education offers advantages such as personalized learning and operational efficiency. However, educators need to be aware of AI's implications in education. Teachers must enhance their awareness and knowledge levels to adapt to the educational environment where AI technologies are becoming increasingly prevalent. Therefore, this research aims to assess teachers' AI awareness levels and investigate whether AI awareness varies based on age, graduation status, and years of experience. This study used data collected from 147 educators using the Teachers' Artificial Intelligence Awareness Scale. The results indicated that teachers' AI awareness was at a moderate level. Additionally, the study examined teachers' AI awareness across different variables. Independent sample t-tests and one-way ANOVA analyses determined teachers' AI awareness variation based on age. The research findings suggest that younger educators and those with higher academic qualifications have more excellent practical knowledge of AI. The study's limitations included a relatively small sample size and the assumption of accurate participant responses. Despite these limitations, understanding teachers' AI awareness levels is a foundation for developing educational programs related to AI. By understanding teachers' perceptions and knowledge of AI, tailored interventions and training initiatives can enhance educators' proficiency in effectively utilizing AI technologies within educational settings.Advances in Mobile Learning Educational Research372Assessing teachers' AI awareness levels and investigating whether AI awareness varies based on age, graduation status, and years of experience suggest that younger educators and those with higher academic qualifications have more excellent practical knowledge of AI.Advances in Mobile Learning Educational Research["Derya Uygun", "I\u015f\u0131l Akta\u015f", "\u0130smail Duygulu", "Numan K\u00f6seer"]2024-07-08T00:00:00 eb7285b4c57433f40acc7ba01541eaa61bad8170Computer Assisted Language Learning437Computer Assisted Language Learning["J. Fathi", "M. Rahimi"]2024-07-08T00:00:00 736898770dc0a765e837808de568ae39ce6d4025 Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Companies become better positioned to exploit the capabilities of artificial intelligence (AI) when employees perceive the technology's significance. A positive response from them drives the informal learning that can enhance career resilience and boost overall firm performance. Originality/value The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format. Development and Learning in Organizations: an international journal10The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.Development and Learning in Organizations: An International Journal[]2024-07-08T00:00:00 a12e315401738e5fd87c70e41564690f096e53e5Making science fiction a reality, Artificial Intelligence (AI) has become a transformative drive in almost every aspect of human life today. With the advancements of modern technology, AI has acquired the ability to think like humans and create inventions that are economically worthwhile. The concept of ‘inventiveness of machines’ has become a focal point in the field of intellectual property law at present. It has compelled the world to reconsider the parameters of patent law in terms of protecting AI inventors and inventions of AI. Simultaneously, the procurement of patents for inventions of AI has posed challenges not only in the legal field but also in ethical and moral aspects. As AI is gradually becoming an undeniable part of human life, every nation will have to adopt the developments of AI technology into their legal systems sooner or later. Taking the prevailing definitions of ‘inventor’ into account, this research mainly discusses whether machine inventors and human inventors be given equal protection of law or whether there should be different dimensions of protection. This paper also discusses the moral and ethical dilemma of granting legal recognition for AI inventors while examining the capability of existing legal framework including Sri Lanka in accommodating the inventiveness of machines. This research was carried out using mixed method approach. Literature review, qualitative and empirical research methodologies and comparative analysis were incorporated to strengthen the study. The paper concludes by highlighting the need of legislative intervention of competent authorities to reconsider the legal parameters to accommodate the possible challenges waiting to be imposed by inventiveness of machine in future. This paper also introduces the concept of ‘collaborative inventiveness of humans and AI’ and suggests recommendations to amend existing laws in a manner that they afford the technological advancements of modern times.KDU journal of multidisciplinary studies110The concept of ‘collaborative inventiveness of humans and AI’ is introduced and recommendations to amend existing laws in a manner that they afford the technological advancements of modern times are suggested.KDU Journal of Multidisciplinary Studies["Ruwini Uthpala Nissanka"]2024-07-08T00:00:00 e197e290e83b2c1defcb60596e108f1fd384b6c7In the digital age, the integration of Artificial Intelligence (AI) in supervisory practices in psychotherapy presents new opportunities for enhancing service quality and accessibility, especially in regions with limited supervisory resources. This paper examines the application of AI tools, such as text-based neural networks in supporting mental health professionals by assisting in case analysis, generating metaphors, and developing therapeutic techniques, thus facilitating more comprehensive and accessible supervision. The rresults indicated great convenience and positive effectiveness of AI in supervision. The findings suggest that AI can significantly enhance the supervisory experience by providing dynamic, context-aware support, though perceptions of effectiveness vary. The implications for future research include the need for further development of AI functionalities, ethical considerations, and integration of diverse psychotherapeutic approaches to meet the evolving professional needs of psychotherapists.The Global Psychotherapist70It is suggested that AI can significantly enhance the supervisory experience by providing dynamic, context-aware support, though perceptions of effectiveness vary, and the need for further development of AI functionalities, ethical considerations, and integration of diverse psychotherapeutic approaches to meet the evolving professional needs of psychotherapists.The Global Psychotherapist["Margarita Sokolovskaya"]2024-07-08T00:00:00 ce93ecd2beadf535289f8cd6d23296d10fe6cbadThis review article explores the intricate role of Instagram and artificial intelligence (AI) in medical education and health promotion. It scrutinizes the dual-edged nature of these technologies, highlighting their potential benefits in disseminating health information and the challenges they present, particularly regarding information accuracy and misinformation. The analysis underscores Instagram's unique capacity to reach broad audiences with visually engaging health education content, emphasizing its utility in public health campaigns and crisis communication. However, the platform's susceptibility to spreading inaccurate health information necessitates urgent and proactive strategies for mitigation. Integrating AI in content management and moderation on Instagram is a promising solution to address misinformation, with the effectiveness of such technologies hinging on continuous research, development, and ethical implementation. The review advocates for developing evidence-based content and engagement strategies by health professionals and educators to maximize benefits and minimize risks associated with Instagram use. It concludes that a collaborative, multidisciplinary approach involving health professionals, educators, researchers, technology developers, and regulators is essential to leverage Instagram and AI for global health improvement responsibly.JOURNAL OF SURGICAL AND CLINICAL RESEARCH490It is concluded that a collaborative, multidisciplinary approach involving health professionals, educators, researchers, technology developers, and regulators is essential to leverage Instagram and AI for global health improvement responsibly.JOURNAL OF SURGICAL AND CLINICAL RESEARCH["I. Ara\u00fajo-Filho", "Am\u00e1lia Cinthia Meneses do R\u00eago"]2024-07-08T00:00:00 e395ee1797c1d8d17390ec3daa308616827fe9b8This study aims to analyze the effect of governance by algorithm and artificial intelligence (AI) on taxpayer compliance at KPP Pratama Jakarta Mampang Prapatan. The results showed that there is a significant influence between government with algorithms and artificial intelligence on taxpayer compliance. In the government variable with the algorithm (X1), the t-count value is 5,491 with a significance level of 0.000, which is smaller than the 5% confidence level. This t-count value (5.491) is greater than the t-table (1.967). This causes the alternative hypothesis (Ha) to be accepted and the null hypothesis (H0) to be rejected, so it can be concluded that there is a significant influence between government and algorithms on taxpayer compliance at KPP Pratama Jakarta Mampang Prapatan. Furthermore, in the artificial intelligence variable (X2), the t-count value is 5.892 with a significance level of 0.001, which is greater than the 5% confidence level. The t-count value (5.463) is also greater than the t-table (1.967). This causes the alternative hypothesis (Ha) to be accepted and the null hypothesis (H0) to be rejected, so it can be concluded that there is a significant influence between artificial intelligence on taxpayer compliance at KPP Pratama Jakarta Mampang Prapatan. The results of the determination test of the Government with Algorithms and Artificial Intelligence can explain the Taxpayer Compliance of KPP Pratama Jakarta Mampang Prapatan by Adjusted R square is 0.774 which means 77.40%, while the remaining 22.60% is influenced by other factors.Journal of Economic Bussines and Accounting (COSTING)00The results showed that there is a significant influence between government with algorithms and artificial intelligence on taxpayer compliance at KPP Pratama Jakarta Mampang Prapatan.Journal of Economic, Bussines and Accounting (COSTING)["Akbari Adha", "Rulinawaty Rulinawaty", "Faizal Madya"]2024-07-08T00:00:00 4b3fbab35e7ac07a0045045ca3599747d71a5635Artificial Intelligence (AI) is only as good as its training data. Large training sets with variants on the same classifier improve AI performance and accuracy, especially in image processing systems. Obtaining these large amounts of training data required for training AI and deep neural networks, is laborintensive, expensive and in some cases not possible. This article explores creating a synthetic image dataset of basic electronic components by using the Blender 3D software package to automatically generate large amounts of synthetic images and image augmentation to expand the synthetic dataset. A YOLOv5 classifier model was trained on the resulting synthetic data, and the performance of the model was evaluated using a set of real-world and synthetic testing images. The results show that good-quality synthetic data that accurately represent real-world electronic components can be used to successfully train a deep learning classifier, leading to cost and time savings in the data acquisition process. However, it also shows that synthetic data that does not accurately represent real-world electronic components is of no use and will reduce the overall performance of the classifier.International Conference on Human System Interaction330The results show that good-quality synthetic data that accurately represent real-world electronic components can be used to successfully train a deep learning classifier, leading to cost and time savings in the data acquisition process, and shows that synthetic data that does not accurately represent real-world electronic components is of no use and will reduce the overall performance of the classifier.2024 16th International Conference on Human System Interaction (HSI)["Bernardus C. Bothma", "Nicolaas Luwes"]2024-07-08T00:00:00 d1dee48e3533fc1ddf7c1e08374e7f317023085fBrain tumors are evaluated, characterized, and monitored using diagnostic imaging. However, because these tumors are so diverse, there are still several difficulties in each group. This might involve differences in the biology of the cancer that are linked to varying intensities of cellular invasion, proliferation, and necrosis, all of which have distinct imaging appearances. Due to these changes, tumor evaluation, including segmentation, surveillance, and molecular characterizations, has become more complex. Even though various rule-based techniques have been put into practice to relate to tumor appearance and size, these techniques naturally reduce the vast quantity of tumor imaging data to a small number of variables. Due to their efficacy in resolving image-based problems, approaches in artificial intelligence, machine learning, and deep learning have found increased use in computer vision tasks, such as tumor imaging. This section aims to provide an overview of some of these developments in tumor imaging.International Journal of Experimental and Biomedical Research00Due to their efficacy in resolving image-based problems, approaches in artificial intelligence, machine learning, and deep learning have found increased use in computer vision tasks, such as tumor imaging.International Journal of Experimental and Biomedical Research["Gautham Chakra R", "Kavyanjali K", "Sree D", "Shaima Sk", "Govardhan B"]2024-07-08T00:00:00 89b3b75e346dbe4040034c13c6742322115a5153This thesis explores the application of artificial intelligence in marketing and the trends, challenges and opportunities of digital transformation. Firstly, it introduces the definition, development history and main technology and application areas of artificial intelligence. Then, it analyses the overview of the application of AI in the business field, including personalised recommendation, intelligent customer service and other aspects. Subsequently, how AI has changed marketing strategies and practices is explored and compared with traditional approaches. In terms of specific applications, the role and benefits of personalised recommendation systems, intelligent customer service systems, etc. are highlighted. In addition, the application of data-driven market trend analyses, intelligent predictive models, etc. in marketing is examined. Finally, the impact of AI and digital transformation on marketing is summarised, highlighting the need for companies to strengthen data security and management, and to grasp the opportunities of technological development to achieve sustainable development and competitive advantage.Transactions on Social Science, Education and Humanities Research50The impact of AI and digital transformation on marketing is summarised, highlighting the need for companies to strengthen data security and management, and to grasp the opportunities of technological development to achieve sustainable development and competitive advantage.Transactions on Social Science, Education and Humanities Research["Bohan Zhang"]2024-07-08T00:00:00 fa4e77c21ad9a005b891e2c0dc0612a399736b7eHow does Artificial Intelligence (AI) affect the organization of work? We incorporate AI into an economy where humans endogenously sort into hierarchical firms: Less knowledgeable agents become “workers” (execute routine tasks), while more knowledgeable agents become “managers” (specialize in problem-solving). We model AI as an algorithm that uses computing power to mimic the behavior of humans with a given knowledge. We show that AI not only leads to occupational displacement but also changes the endogenous matching between all workers and managers. This leads to new insights regarding AI’s effects on productivity, firm size, and degree of decentralization.ACM Conference on Economics and Computation450It is shown that AI not only leads to occupational displacement but also changes the endogenous matching between all workers and managers, which leads to new insights regarding AI’s effects on productivity, firm size, and degree of decentralization.{"pages": "834-836"}["Enrique Ide", "Eduard Talam\u00e0s"]2024-07-08T00:00:00 3390608e16cc39d44f3479f86ff22cc19cd39e79The main characteristic of modern socio-economic development is the use of digital information technologies, in particular, the use of generative artificial intelligence. Artificial intelligence technologies are developing at a faster pace than originally predicted by specialists and scientists, while the areas of its use of generative artificial intelligence (AI) are also expanding. For example, the world experience of using AI in marketing personnel (selection, selection, formation of a personnel reserve, etc.) and directly in HR processes (onsite adaptation, training, payroll, termination, etc.) suggests that AI makes all processes more transparent, democratic, in compliance with the norms of social justice inherent in AI, which contributes to improving the efficiency of both HR management in the organization and the management system as a whole. The study examined the possibilities of using AI in personnel marketing and personnel management, the main positive aspects, and also paid attention to the main risks in this area. The use of generative artificial intelligence contributes to a multitude of benefits that increase the efficiency of personnel marketing and personnel management in the organization, which helps organizations scale, develop and rationally manage their resources, costs and efficiency.Actual directions of scientific researches of the XXI century: theory and practice20The study examined the possibilities of using AI in personnel marketing and personnel management, the main positive aspects, and also paid attention to the main risks in this area.Actual directions of scientific researches of the XXI century: theory and practice["Vladimir Dzhuha", "Ruzanna Pogosyan", "Artur Batyrgaliev"]2024-07-08T00:00:00 18931b056ab9bd1b500026156746a548adbe88aaHuman beings and artificial intelligence, although materially different, are never separate. Technology rests within man, in this resting it surpasses him. Technology draws the world and the human into itself and retains it. The opposites of man and artificial intelligence are in dispute. For an artificial intelligence system to be an artificial intelligence is to engage in a dispute between man and the essence of technology. What is the essence of technology? It lies in what the technology really is. It establishes this dispute in the form of an artificial intelligence system that makes it public. The human being, his or her rights, freedoms, duties, what appears to be inviolable, inalienable and unalienable in his or her being, what reaches him or her and is only given to him or her, without which he or she would not be a human being, is only experienced through the artificial intelligence system, being subjected to its influence. This article attempts to demonstrate that, in this dispute, technological development does not necessarily mean progress any more.Studia z zakresu Prawa Pracy i Polityki Społecznej00This article attempts to demonstrate that, in this dispute between man and artificial intelligence, technological development does not necessarily mean progress any more.Studia z zakresu Prawa Pracy i Polityki Społecznej["Micha\u0142 B\u0105ba"]2024-07-08T00:00:00 dc756f2a41e96071333a5452bab1eed63ddd3722This article analyzes the use of artificial intelligence technologies in military systems. Artificial intelligence (AI) is one of the modern areas of information technology. The history of human-like mechanisms development dates back to ancient times and has gone through a complex evolutionary path from myths and legends about the first androids, mechanical chess players and other complex mechanisms with human behaviour to modern autonomous robots. The first research on AI in the modern sense began almost immediately since the appearance of the first computers. Since the late 1950s, AI researchers have been trying to create smart machines that mimic the brain. AI has become a key technology in various industries, including the military, in recent decades. Thanks to its characteristics, AI transforms traditional strategies and tactics into a military art. Various aspects of the artificial intelligence use are discussed, in particular autonomous weapons systems, forecasting and analytics, cybersecurity and strategic planning. The advantages and opportunities provided by the use of artificial intelligence by military formations are highlighted, as well as attention is drawn to the ethical and legal aspects of this issue. Artificial intelligence technologies open up new opportunities for the military in many aspects, from autonomous systems to analytics and strategic planning. However, it is also important to take into account the ethical and legal aspects of these technologies use, as well as the risks associated with their implementation. A balanced approach to the use of artificial intelligence will help ensure its effective application in military systems. The article focuses on the importance of a balanced approach to the artificial intelligence technologies use for military purposes, taking into account their capabilities and risks.Випробування та сертифікація30A balanced approach to the use of artificial intelligence will help ensure its effective application in military systems, and the importance of a balanced approach to the artificial intelligence technologies use for military purposes is focused on.Випробування та сертифікація["A. Havrylenko"]2024-07-08T00:00:00 4f96d010296fcd9a33a33e2c18e2712ce66059c3A significant and successful approach to fraud detection includes artificial intelligence in forensic auditing. Forensic auditors can now respond quickly to suspicious circumstances and take preventative action before fraud spreads and causes further damage to the organization, all thanks to artificial intelligence that has enabled early fraud identification. This article analyzes forensic auditing and the use of artificial intelligence through a bibliometric analysis in Scopus and a systematic literature review. The samples were documents selected using Boolean operators with keywords in English (Forensic AND auditing, artificial AND Intelligence), analyzed in Excel and VOSviewer. This research points out that forensic auditing and the use of artificial intelligence have advanced, in the variety of topics covered, the prominence of perpetrators, and the accessibility of crucial data. Therefore, to maintain the quality and integrity of their work, forensic auditors must adapt to technological advances, training in the use of artificial intelligence, and collaborate with other specialists and professionals. Consequently, with its empirical basis, this bibliometric and systematic review critically evaluates the research, to clarify the empirical basis of current trends in this field and lays the groundwork for future research.Heritage and Sustainable Development520It is pointed out that forensic auditing and the use of artificial intelligence have advanced, in the variety of topics covered, the prominence of perpetrators, and the accessibility of crucial data, and to maintain the quality and integrity of their work.Heritage and Sustainable Development["Rafael Romero-Carazas", "A. P. Espiritu-Martinez", "Margoth Marleny Aguilar-Cuevas", "Maribel Nerida Usuriaga-Palacios", "Luis Alberto Aguilar-Cuevas", "Miriam Zulema Espinoza-V\u00e9liz", "Melvi Janett Espinoza-Egoavil", "Sonia Gladys Guti\u00e9rrez-Monz\u00f3n"]2024-07-08T00:00:00 448c0afcc0e5eb7081310e7d601c52a429e46d01Journal of Integrative and Complementary Medicine161Journal of integrative and complementary medicine["C. Citkovitz", "Sandro Graca", "Belinda Anderson", "Lisa Conboy", "Melanie A Gold", "Eric Hirsch", "Kathleen Lumiere", "Scott Phelps", "R. Schnyer", "L. Taylor-Swanson"]2024-07-08T00:00:00 772bacd2df9462cc71feddc44f978dd5a3440cb0International Immunopharmacology731An artificial intelligence-driven multiomics predictive model for AAA subtypes was established to identify heterogeneous immune cell infiltration and predict disease progression and accurate and reliable risk stratification and clinical management.International immunopharmacology["Lin Zhang", "Han Yang", "Chenxing Zhou", "Yao Li", "Zhen Long", "Que Li", "Jiangfeng Zhang", "Xiao Qin"]2024-07-08T00:00:00 fc31d948666a853b1d78a44beb8abb95c405e99eSignal Transduction and Targeted Therapy61Signal Transduction and Targeted Therapy["Leonard E. van Dyck", "Frank Bremmer", "Katharina Dobs"]2024-07-08T00:00:00 42ef0f58959549687eda54daa19efe47b79b0dd1African Journal of Biological Sciences00African Journal of Biological Sciences[]2024-07-08T00:00:00 085748d210c4186ac0599674fa27675c18b8998eFoundations of Science80Foundations of Science["R. Leporini"]2024-07-08T00:00:00 0616820c52f933612a25d30fda3a7a3c5b2cf4e7910["Ludmila Yarozkaja", "Daria V. Aleinikova"]2024-07-08T00:00:00 652b2f779c31a1f64d5571acc7463dce3db34bc7British Journal of Guidance &amp; Counselling420British Journal of Guidance &amp; Counselling["Pei Boon Ooi", "Graeme Wilkinson"]2024-07-08T00:00:00 a80387ea1c2ae12922e174c4589f11ac975e5f55Bu araştırmanın amacı, ortaokul matematik öğretmenlerinin yapay zekâ kullanımına yönelik yeterlilik algılarını farklı değişkenlere (cinsiyet, mesleki kıdem, mezun olunan fakülte türü, eğitim durumu, lisansüstü eğitim yapma isteği) göre belirlemektir. Araştırma betimsel nitelikli nedensel karşılaştırma araştırmasıdır. Araştırmanın çalışma grubunu, 2023-2024 eğitim - öğretim yılında ortaokullarda görev yapan 69 matematik öğretmeni oluşturmaktadır. Araştırma çalışma grubu üzerinden yürütülmüş olup örneklem alma yoluna gidilmemiştir. Araştırmada veriler “kişisel bilgi formu” ile “yapay zekâya yönelik tutum ölçeği” kullanarak elde edilmiştir. Araştırmada matematik öğretmenlerinin yapay zekâ kullanımına yönelik yeterlilik algılarının belirlenmesi için frekans, yüzde, aritmetik ortalama ve standart sapma kullanılmıştır. Nedensel karşılaştırma deseni için bağımsız gruplar için t-testi ile Anova testi kullanılmıştır. Araştırma sonucunda, matematik öğretmenlerinin yapay zekâ kullanımına yönelik yeterlilik algılarının olumlu olduğu belirlenmiştir. Ayrıca öğretmenlerinin cinsiyet, mesleki kıdem, mezun olunan fakülte türü, eğitim durumu, lisansüstü eğitim yapma isteği değişkenlerine göre ölçeğin olumlu tutum ya da olumsuz tutum alt boyutlarında bir farklılık olmadığı sonucu bulunmuştur.Türk Turizm Araştırmaları Dergisi00Turk Turizm Arastirmalari Dergisi["Cevat Eker", "Seda Hal\u0131c\u0131 G\u00fcrb\u00fcz"]2024-07-08T00:00:00 6cfc38511215cefc2aa413b6e1f814a91ada7d71Journal of Police and Legal Sciences00Journal of Police and Legal Sciences["Abdalla Alnaqbi"]2024-07-08T00:00:00 96907378f28783974dabed83f7d5443d8961c904Nature Network Boston3510Results show that using an AI-based score to select a small proportion of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography.Nature Medicine["Mattie Salim", "Yue Liu", "Moein Sorkhei", "Dimitra Ntoula", "T. Foukakis", "Irma Fredriksson", "Yanlu Wang", "Martin Eklund", "Hossein Azizpour", "Kevin Smith", "Fredrik Strand"]2024-07-08T00:00:00 d482cd26d7a0c408feabcfa55f7fcfc06205da44International Journal for Educational Integrity593Assessment of whether people could identify AI-generated text and whether factors such as expertise or confidence would predict this ability highlighted challenges for scholars and practitioners to consider as they navigate the integration of AI in education.International Journal for Educational Integrity["Tal Waltzer", "Celeste Pilegard", "Gail D. Heyman"]2024-07-08T00:00:00 89a8d64020bfdfa350ee4cb5e0fb40e8e7318a3cAIM This research examines the effects of artificial intelligence (AI)-based decision support systems (DSS) on the operational processes of nurses in critical care units (CCU) located in Amman, Jordan. BACKGROUND The deployment of AI technology within the healthcare sector presents substantial opportunities for transforming patient care, with a particular emphasis on the field of nursing. METHOD This paper examines how AI-based DSS affect CCU nursing workflows in Amman, Jordan, using a cross-sectional analysis. A study group of 112 registered nurses was enlisted throughout a research period spanning one month. Data were gathered using surveys that specifically examined several facets of nursing workflows, the employment of AI, encountered problems, and the sufficiency of training. RESULT The findings indicate a varied demographic composition among the participants, with notable instances of AI technology adoption being reported. Nurses have the perception that there are favorable effects on time management, patient monitoring, and clinical decision-making. However, they continue to face persistent hurdles, including insufficient training, concerns regarding data privacy, and technical difficulties. DISCUSSION The study highlights the significance of thorough training programs and supportive mechanisms to improve nurses' involvement with AI technologies and maximize their use in critical care environments. Although there are differing degrees of contentment with existing AI systems, there is a general agreement on the necessity of ongoing enhancement and fine-tuning to optimize their efficacy in enhancing patient care results. CONCLUSION AND IMPLICATIONS FOR NURSING AND/OR HEALTH POLICY This research provides essential knowledge about the intricacies of incorporating AI into nursing practice, highlighting the significance of tackling obstacles to guarantee the ethical and efficient use of AI technology in healthcare.International Nursing Review213The study highlights the significance of thorough training programs and supportive mechanisms to improve nurses' involvement with AI technologies and maximize their use in critical care environments, and provides essential knowledge about the intricacies of incorporating AI into nursing practice.International nursing review["Wesam Almagharbeh"]2024-07-08T00:00:00 fed66cf463533106c8e39f18edd72ae3c773f92fArtificial intelligence (AI) is being pervasively integrated into various facets of human life, including the emotional realm. Romantic AI chatbots, positioned as artificial companions offering emotional support and connection, have witnessed a significant rise in recent years. Users of romantic AI chatbots often reveal personal information during intimate conversations, potentially unaware of the consequences or how their data may be utilized. Complicating matters, lengthy and convoluted privacy policies are commonly overlooked or misunderstood by users. This study aims to address these privacy concerns by introducing a comprehensive framework for analyzing the privacy practices of romantic AI chatbot apps. Through a combination of static and dynamic analysis, we investigate 21 Android romantic AI chatbot apps for: discrepancies between privacy policies and chatbot responses to questions regarding privacy practices; social login and age verification mechanisms; permissions requested by apps; data sharing practices; tracking services employed; and potential security vulnerabilities. Our findings highlight the prevalence of discrepancies between chatbot responses regarding users' privacy and the privacy policies of the apps. Additionally, we note some concerning observations related to: customer service responses to privacy concerns; inadequate age verification measures; contradictions in data sharing claims; and extensive usage of tracking services. We found that all romantic AI chatbot apps tested had discrepancies between their chatbots' responses and privacy policies. None of the apps take any measures against faking the birthdate, and most would continue the conversation despite knowing that the user is underage. 13 out of 21 romantic AI chatbot apps use at least 3 tracking services, and 18 out of 21 apps send detailed device information to tracking services. This study reveals privacy and security flaws in romantic AI chatbot apps, stressing the need for better transparency and user protection measures. Particularly, Discrepancies between chatbot responses and privacy policies highlight the importance of clear communication on data handling.2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)391It is found that all romantic AI chatbot apps tested had discrepancies between their chatbots' responses and privacy policies, highlighting the need for better transparency and user protection measures.2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)["Abdelrahman Ragab", "Mohammad Mannan", "Amr M. Youssef"]2024-07-08T00:00:00 efd461ab778121cec5e59baf488430451aca4356 Jie Gu, Associate Professor from Northwestern University, examines AI-empowered neural processing for intelligent human-machine interface and biomedical devices. Most conventional wearable devices rely on motion detection or image classifications to capture users’ activities. However, they lack the ability to decode neural signals generated by the human body. Neural signals, such as EEG, ECG, and EMG, offer a rich amount of information on a person’s physiological and psychological activities. Recognition and use of such signals present many new opportunities for applications in medical and daily commercial usage. Recently, artificial intelligence (AI) has been applied to neural signal processing, leading to a new generation of intelligent human-machine interfaces and biomedical devices. Open Access Government01Examination of AI-empowered neural processing for intelligent human-machine interface and biomedical devices for recognition and use of neural signals generated by the human body finds many new opportunities for applications in medical and daily commercial usage.Open Access Government["Jie Gu"]2024-07-08T00:00:00 95eb367112c9402ccfbe5657ebbc2104598acc12Machine-mediated learning191This paper leverages the well-known Mixture of Experts architecture with user-defined limits on non-interpretability with a counterfactual fairness module to ensure the selection of consistently fair experts: FairMOE.Mach. Learn.["Joe Germino", "Nuno Moniz", "Nitesh V. Chawla"]2024-07-08T00:00:00 f68f1a532f7a8470c8efd90bce2aa370ac413c5aArtificial Intelligence (AI) holds promise in improving diagnostics and treatment. Likewise, AI is anticipated to mitigate the impacts of staff shortages in the healthcare sector. However, realising the expectations placed on AI requires a substantial effort involving patients and clinical domain experts. Against this setting, this review examines ethical challenges related to the development and implementation of AI in healthcare. Furthermore, we introduce and discuss various approaches, guidelines, and standards that proactively aim to address ethical challenges.Ugeskrift for læger01Ethical challenges related to the development and implementation of AI in healthcare are examined and various approaches, guidelines, and standards that proactively aim to address ethical challenges are introduced.Ugeskrift for laeger["Anne Gerdes", "I. Fasterholdt", "Benjamin S. B. Rasmussen"]2024-07-08T00:00:00 2d0f312927b46148efc860f8e693b1b2e31a9626A common, but largely untested, assumption in artificial intelligence (AI) and speech systems is that results from experiments using only the English language as the communication medium will hold true across any other language or cultural context. We argue here, based on emerging recent scientific evidence, that such an assumption appears to be invalid. In fact, there appear to be stark differences across languages and cultures when experiments are conducted using the same artificial speech system setup to be able to communicate in more than one language. Moreover, using those AI systems with bilingual human speakers shows that their behavior, social cues, and communication patterns change when language "code-switching" occurs within the same experiment session. To illustrate our point further, in the second half of the paper we give the specific example of ChatGPT (as the backbone speech content for artificial speech systems) being used for older adults with dementia and Alzheimer’s, who often have altered speech patterns (e.g. slurred pronunciation). There are emerging reports from such research of severe limitations of ChatGPT in such contexts, which highlights the dangers of assuming findings from a narrow range of linguistic and/or cultural contexts can fully capture some universal truths about human communication with artificial agents. Finally, we point out that the reluctance of scientific journals and conferences to publish negative results means many of those emerging reports are only being reported anecdotally, which is problematic for the field of conversational user interfaces (CUI).International Conference on Conversational User Interfaces311It is pointed out that the reluctance of scientific journals and conferences to publish negative results means many of those emerging reports are only being reported anecdotally, which is problematic for the field of conversational user interfaces (CUI).ACM Conversational User Interfaces 2024["Casey C. Bennett"]2024-07-08T00:00:00 51debe58e13b9c87e92aa6dcbb7031f6a49b8000The integration of artificial intelligence (AI) chatbots into higher education marks a shift towards a new generation of pedagogical tools, mirroring the arrival of milestones like the internet. With the launch of ChatGPT-4 Turbo in November 2023, we developed a ChatGPT-based teaching application (https://chat.openai.com/g/g-1imx1py4K-chatge-medical-imaging) and integrated it into our undergraduate medical imaging course in the Spring 2024 semester. This study investigates the use of ChatGPT throughout a semester-long trial, providing insights into students' engagement, perception, and the overall educational effectiveness of the technology. We systematically collected and analyzed data concerning students' interaction with ChatGPT, focusing on their attitudes, concerns, and usage patterns. The findings indicate that ChatGPT offers significant advantages such as improved information access and increased interactivity, but its adoption is accompanied by concerns about the accuracy of the information provided and the necessity for well-defined guidelines to optimize its use.Meta-Radiology171The findings indicate that ChatGPT offers significant advantages such as improved information access and increased interactivity, but its adoption is accompanied by concerns about the accuracy of the information provided and the necessity for well-defined guidelines to optimize its use.ArXiv["Xin Song", "Jiajin Zhang", "P. Yan", "Juergen Hahn", "U. Kruger", "Hisham Mohamed", "Ge Wang"]2024-07-08T00:00:00 9e3896211a7e30fbd04b27d88d26852ad644abceArtificial Intelligence (AI) and machine learning have seen significant growth in recent years, leading to an increased demand for computational resources. To meet this demand and to boost Europe’s competitive edge, the European Commission has built several supercomputers across the continent. However, these traditional supercomputers often lack modern APIs and automation tools necessary for AI development. This paper outlines how High-Performance Computing (HPC) systems and cloud platforms can be seamlessly integrated using ColonyOS, an open-source meta-operating system designed to connect and integrate diverse computing environments into a cohesive compute continuum. ColonyOS enables development of AI workflows that are portable across HPC systems and cloud environments, including Kubernetes, Docker, and Slurm. This integration makes it possible to develop automation workflows, such as training AI models on HPC systems and automatically deploy trained models to cloud platforms for inference. The paper details the architecture of ColonyOS and how it can be used to build AI applications that can run in an HPC-Cloud Continuum. This will be exemplified through a satellite image segmentation case study, showcasing the benefits of combining the Leonardo EuroHPC supercomputer with a Kubernetes cluster. Ultimately, ColonyOS paves the way for hyper-distributed AI applications that can seamlessly utilize both cloud and HPC systems, including existing EuroHPC supercomputers.International Symposium on Parallel and Distributed Computing2502024 23rd International Symposium on Parallel and Distributed Computing (ISPDC)["Johan Kristiansson", "T. Wikfeldt"]2024-07-08T00:00:00 1b63ed4c0fd645764b9a6c60d14339c4a2216ef7The phenomenal growth of artificial intelligence (AI) and machine learning (ML) in recent years has revolutionized various sectors, including healthcare. Particularly the large language models (LLMs) developed recently by various companies such as Google, Microsoft, Nvidia, OpenAI have demonstrated remarkable capabilities in understanding and generating contextualized text thus making them valuable tools that can be effectively utilized by healthcare industry and service providers. Prompt engineering plays a crucial role leveraging full potential of such LLMs. Prompt Engineering is the technique used to design and refine inputs to improve the performance of AI models and maximize the efficacy of AI applications. In India, with diverse population and varying healthcare needs and resource constraints specially in tier 3 cities, villages and remote areas. This paper highlights pivotal role that Prompt Engineering can play in supplementing solutions and initiatives to address healthcare needs. It explores applications in diagnostics, electronic health records (EHRs), virtual health assistants and the overall impact on patient care and administrative efficiency. Through case studies and theoretical analysis, this paper aims to demonstrate how prompt engineering can enhance diagnostic accuracy, streamline administrative tasks, and improve patient care. Keywords— AI, healthcare delivery, prompt engineering, LLM, EHRs, virtual health assistants, patient careINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00It is demonstrated how prompt engineering can enhance diagnostic accuracy, streamline administrative tasks, and improve patient care through case studies and theoretical analysis.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Atulesh Pratap Singh", "Dr. Ajay Singh", "D. Verma"]2024-07-08T00:00:00 717c39550cde0d1781eec3b0a3051727c22e622d Purpose This study aims to establish whether accounting research articles can be potentially generated by artificial intelligence. If artificial intelligence can produce quality work, the integrity of academic research may be compromised. Design/methodology/approach ChatGPT was used to create a paper on a meta-analysis of the relationship between sustainability reporting and value relevance. After the paper was generated, references had to be added by hand based on the citations created by ChatGPT. The paper was then presented as-is for review. Findings ChatGPT was able to create a relatively good-quality research paper that received two major revisions from independent specialists in the field of accounting and finance. Even though there is uncertainty regarding the appropriateness of all the references and the results cannot be confirmed, there is a risk that a reviewer may find the paper publishable because reviewers are not compelled to check references and the accuracy of results if proper methods were used that appear to be sufficient at face value. Originality/value Artificial intelligence for academic writing is still relatively new, and there is still significant uncertainty as to the impact it may have on scholarly research. This is especially problematic because artificial intelligence applications improve by the second. Accounting Research Journal381Whether accounting research articles can be potentially generated by artificial intelligence is established to establish if artificial intelligence can produce quality work, if artificial intelligence can produce quality work, the integrity of academic research may be compromised.Accounting Research Journal["Elda du Toit"]2024-07-09T00:00:00 0582b31a9ab9a2019132c03598cc24a6565fbd4bArtificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.Asian Journal of Andrology392The current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach to artificial intelligence in andrology are highlighted.Asian Journal of Andrology["A. E. Calogero", "A. Crafa", "R. Cannarella", "R. Saleh", "R. Shah", "Ashok Agarwal"]2024-07-09T00:00:00 ee2b71a6e9eb5bccc57b6a4d1abf25268ec0dfd2General Context : In response to the rapidly changing technological landscape, companies are increasingly embracing artificial intelligence to streamline their recruitment processes. The incorporation of artificial intelligence involves deploying advanced algorithms and machine learning techniques to identify and select candidates for various positions. Objective: This research endeavours to investigate the factors motivating Moroccan companies to adopt artificial intelligence in their recruitment practices. Methodology : Our focus was on Human resources managers to discern the factors influencing their decision to adopt algorithmic recruitment. Results: The findings underscored that the pursuit of expeditious transmission of recruitment data, acquisition of more pertinent profiles, and the security of Human Resources systems were foremost considerations in implementing algorithmic recruitment. Conclusion: The study is based on an original approach aimed at determining the reasons why Moroccan companies are adopting AI technology, which serves as motivation, companies aspiring to optimize their hiring processes stand to gain multiple benefits from artificial intelligence recruitment. Its capacity to augment efficiency, mitigate biases associated with streamlined processes, and facilitate informed talent management decision-making have been highlighted. Notably, the overall candidate experience has seen enhancements. In the pursuit of diverse team composition, fostering innovation, and achieving sustained success in the fiercely competitive contemporary job market, organizations must recognize artificial intelligence as an indispensable tool.Access Journal - Access to Science, Business, Innovation in the digital economy471The findings underscored that the pursuit of expeditious transmission of recruitment data, acquisition of more pertinent profiles, and the security of Human Resources systems were foremost considerations in implementing algorithmic recruitment.Access Journal - Access to Science, Business, Innovation in the digital economy["Asmaa Benhmama", "Yasmina Bennis Bennani"]2024-07-09T00:00:00 669b50aa3ba859a90d4589bcb6545faa0b3d390d Purpose The main objective of this study is to present a compact overview analysis of intellectual property laws, specifically copyright-related provisions applicable to generative artificial intelligence (GenAI) in the Indian context. Design/methodology/approach The paper adopts a qualitative research methodology that is grounded in secondary sources of information. The data were gathered from the Scopus database for a systematic literature review. Findings GenAI technology has given rise to numerous questionable issues within the domain of intellectual property that need resolution in the form of policy solutions. Based on the findings of this paper, it can be deduced that Indian copyright laws are not adequate for addressing the rights pertaining to AI and its creations and outputs. Different countries like the United States, European Union and China have approached the regulation and protection of AI-generated content within the realm of copyright law in different ways. The future of law, as it has been established thus far, seems to be on a path of substantial evolution. Practical implications The study has implications for policymakers globally as there is a need to create feasible policy solutions that can efficiently safeguard against risks stemming from large language models (LLMs) and other GenAI models, while also promoting innovation, technical advancement and adoption. Originality/value The paper discusses the copyright-related issues in GenAI technology in the context of an emerging economy, India. Journal of Science and Technology Policy Management272It can be deduced that Indian copyright laws are not adequate for addressing the rights pertaining to AI and its creations and outputs.Journal of Science and Technology Policy Management["Shinu Vig"]2024-07-09T00:00:00 29f06e0ba4b4c0a207b5801f9888801a2a34daecBMC Medical Imaging511A multimodal explainable AI model combining imaging features, deep learning features, and radiomics features at three different semantic levels outperforms traditional radiomics models and can accurately predict the prognosis of ICH.BMC Medical Imaging["Hao Zhang", "Yun-Feng Yang", "Xue-Lin Song", "Hai-Jian Hu", "Yuan-Yuan Yang", "Xia Zhu", "Chao Yang"]2024-07-09T00:00:00 3db296ecd397919d4df52e8d6dfff02c0b38aefd Purpose The purpose of this paper is to propose a taxonomy of artificial intelligence (AI) literacy to support AI literacy education and research. Design/methodology/approach This study makes use of the facet analysis technique and draws upon various sources of data and information to develop a taxonomy of AI literacy. The research consists of the following key steps: a comprehensive review of the literature published on AI literacy research, an examination of well-known AI classification schemes and taxonomies, a review of prior research on data/information/digital literacy research and a qualitative and quantitative analysis of 1,031 metadata records on AI literacy publications. The KH Coder 3 software application was used to analyse metadata records from the Scopus multidisciplinary database. Findings A new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet. Research limitations/implications The proposed taxonomy may serve as a conceptual AI literacy framework to support the critical understanding, use, application and examination of AI-enhanced tools and technologies in various educational and organizational contexts. Practical implications The proposed taxonomy provides a knowledge organization and knowledge mapping structure to support curriculum development and the organization of digital information. Social implications The proposed taxonomy provides a cross-disciplinary perspective of AI literacy. It can be used, adapted, modified or enhanced to accommodate education and learning opportunities and curricula in different domains, disciplines and subject areas. Originality/value The proposed AI literacy taxonomy offers a new and original conceptual framework that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research. Digital Library Perspectives361A new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research.Digit. Libr. Perspect.["Ali Shiri"]2024-07-09T00:00:00 ce76f5bcdd1385472459fdd8146fb5abdce17974Due to the absence of technological advancements, it is impossible to handle the operations that regulate the multifaceted nature of knowledge for effective security on the Internet. It is difficult and complex to assemble the technology needed to efficiently and effectively defend against security threats. These challenges can be overcome by utilizing machine learning and artificial intelligence (AI) techniques. This study provides a brief overview of the applications of AI for cybersecurity via smart technologies and an assessment of the prospects for expanding protection capabilities through improved defense mechanisms. The main findings show that there are presently created AI tools that are successful in protecting data. To begin with, there are neural networks, mainly intended for shielding the outermost layer. Multiple methods using AI to resolve specific safety issues are getting traction with them. On a strategic level, however, one of the outstanding cybersecurity issues is the selection of effective protection technologies.Research Papers00The main findings show that there are presently created AI tools that are successful in protecting data and multiple methods using AI to resolve specific safety issues are getting traction with them.Research Papers[]2024-07-09T00:00:00 2ff2dc6033843712c04b3bb5c78f2e6b0c030dcfThis study aims at determining the role of artificial intelligence systems in the development and improving of the administrative legal base, and to identify the theoretical framework in the field of using the artificial intelligence in the administrative life and its impacts. Moreover, the study provides recommendations to the competent authorities in the field of artificial intelligence for its uses in administrative work in a manner that guarantees the various dimensions of the administrative development, Especially the technological dimension. Among the outcomes reached through this study is that the artificial intelligence is a modern technology that has been able to enter all fields, especially utility and service ones, and it is indispensable to exploit such intelligence in describing service quality when needs are satisfied. Finally, one of the most important recommendations of the study is to find a legal regulation for the artificial intelligence, in addition to strengthening the technological infrastructure to accommodate the intervention of this technology in the field of the administrative law; as flexibility and scalability are of its most important characteristics. International Journal of Religion40One of the most important recommendations of the study is to find a legal regulation for the artificial intelligence, in addition to strengthening the technological infrastructure to accommodate the intervention of this technology in the field of the administrative law.International Journal of Religion["A. Hosnia", "Oichene Hanane", "Shihab Sulaiman Abdalla Osman"]2024-07-09T00:00:00 072d15a642c1434d12c33480ceecd869ed2eb1a7This study explored the relationship between artificial intelligence and business process optimization in selected banks in Anambra State. The population consisted of 745 employees from commercial banks in Anambra State, Nigeria. Using purposeful sampling, three banks from each senatorial district in the state were chosen, and 170 questionnaires were distributed to staff members of these selected banks. Out of the 170 distributed questionnaires, 125 were completed and returned. A Pearson correlation critical value table was used to test the assumptions, and the Pearson product- moment correlation coefficient was the statistical instrument for data analysis. The hypothesis results indicated a significant correlation between business process optimization in banks and artificial intelligence, specifically in enhancing customer service relationships and boosting cyber-security in the selected banks in Anambra State. The study recommends that the banking industry should continue to implement artificial intelligence cautiously to maintain a balance between innovative developments and the responsible and ethical use of AI. This approach will ensure improved cyber- security and customer service in banks.International Journal of Innovative Science and Research Technology410The hypothesis results indicated a significant correlation between business process optimization in banks and artificial intelligence, specifically in enhancing customer service relationships and boosting cyber-security in the selected banks in Anambra State.International Journal of Innovative Science and Research Technology (IJISRT)["Chikeluba Uzoamaka", "Bello Sunday Ade"]2024-07-09T00:00:00 120778f43f2c37d6fdc91c46ebe435b602e5e681The proliferation of Artificial Intelligence (AI) technology has revolutionized various aspects of our lives, including how scammers operate and perpetrate fraudulent activities. This paper explores the complex relationship between scammers and AI, highlighting the challenges posed by AI-driven scams and the opportunities for leveraging AI to combat fraudulent behavior. Through an analysis of automated scamming techniques, such as fake content generation and social engineering, as well as AI-powered detection and prevention strategies, this paper provides insights into the evolving landscape of online fraud. Furthermore, it discusses the role of education, awareness, and regulatory measures in mitigating the impact of scammers leveraging AI.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)70Through an analysis of automated scamming techniques, such as fake content generation and social engineering, as well as AI-powered detection and prevention strategies, this paper provides insights into the evolving landscape of online fraud.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)["Wai Yie Leong", "Yuan Zhi Leong", "W. -. Leong"]2024-07-09T00:00:00 4446fbda6d0e96466148063e2ee990997a95c243The parallel history of the evolution of human intelligence and artificial intelligence is a fascinating journey, highlighting the distinct but interconnected paths of biological evolution and technological innovation. This history can be seen as a series of interconnected developments, each advance in human intelligence paving the way for the next leap in artificial intelligence. Human intelligence and artificial intelligence have long been intertwined, evolving in parallel trajectories throughout history. As humans have sought to understand and reproduce intelligence, AI has emerged as a field dedicated to creating systems capable of tasks that traditionally require human intellect. This book examines the evolutionary roots of intelligence, explores the emergence of artificial intelligence, examines the parallel history of human intelligence and artificial intelligence, tracing their development, interactions, and profound impact they have had on each other, and envisions future landscapes where intelligence converges human and artificial. Let's explore this history, comparing key milestones and developments in both realms.00This book examines the evolutionary roots of intelligence, explores the emergence of artificial intelligence, examines the parallel history of human intelligence and artificial intelligence, tracing their development, interactions, and profound impact they have had on each other, and envisions future landscapes where intelligence converges human and artificial.["Nicolae Sfetcu"]2024-07-09T00:00:00 f4489f5fa87d15322f9b6592bad1bf80c01c67fdBackground: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic valve stenosis (AVS), yet it requires skilled operators and can be resource-intensive. Objectives: To develop and validate an artificial intelligence (AI)-based system for evaluating AVS that is effective in both resource-limited and advanced settings. Methods: We created a dual-pathway AI system for AVS evaluation using a nationwide echocardiographic dataset (developmental dataset, n=8,427): 1) a deep learning (DL)-based AVS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AVS evaluation. We performed internal (internal test dataset [ITDS], n=841) and external validation (distinct hospital dataset [DHDS], n=1,696; temporally distinct dataset [TDDS], n=772) for diagnostic value across various stages of AVS and prognostic value for composite endpoints (cardiovascular death, heart failure, and aortic valve replacement) Results: The DL index for the AVS continuum (DLi-AVSc, range 0-100) increases with worsening AVS severity and demonstrated excellent discrimination for any AVS (AUC 0.87-0.99), significant AVS (0.93-0.97), and severe AVS (0.97). A 10-point increase in DLi-AVSc was associated with an 85% increased risk for composite endpoints in ITDS and a 53% and 59% increase in DHDS and TDDS, respectively. Automatic measurement of conventional AVS parameters demonstrated excellent correlation with manual measurement, resulting in high accuracy for AVS staging (98.2% for ITDS, 81.0% for DHDS, and 96.8% for TDDS) and comparable prognostic value to manually-derived parameters. Conclusions: The AI-based system provides accurate and prognostically valuable AVS assessment, suitable for various clinical settings. Further validation studies are planned to confirm its effectiveness across diverse environments.medRxiv00An artificial intelligence (AI)-based system for evaluating AVS that is effective in both resource-limited and advanced settings and provides accurate and prognostically valuable AVS assessment, suitable for various clinical settings is developed.["J. Park", "J. Kim", "J. Jeon", "Y. E. Yoon", "Y. Jang", "H. Jeong", "Y. Hong", "S.-A. Lee", "H.-M. Choi", "I. Hwang", "G. Cho", "H.-J. Chang"]2024-07-09T00:00:00 8ceb8d61f69501a72ff18fb15db0cdd31c7027a1Background: Artificial intelligence (AI) is emerging as a promising tool to enhance diagnostic care processes throughout various clinical domains. The use of AI is enhancing diagnostic accuracy through advancements in machine learning and deep learning. Therefore, the aim of this scoping review is to assess the current utilization of AI in diagnostic healthcare services, aiming to identify prevalent themes, trends, and existing gaps in the literature. Methodology: This scoping review uses a structured approach using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA) Checklist. A systematic literature search was conducted on PubMed, utilizing keywords including “artificial intelligence,” “machine learning,” “deep learning,” “diagnostic healthcare,” “medical diagnostics,” “diagnostic accuracy,” “radiology,” and “pathology.” The review aims to answer the population, intervention, comparison, and outcome (PICO) question: “Do hospitals that implement artificial intelligence technologies during diagnostic service-line patient care processes experience quality care improvements compared to hospitals that do not use such technologies?” Conclusion: The study draws conclusions based on evidence that reveals significant promise of AI improving diagnostic accuracy and prognostic predictions across various imaging applications. These findings highlight the evolving landscape of AI in diagnostic care, advocating for rigorous validation and interdisciplinary collaboration to ensure effective clinical integration and maximize quality care outcomes. Future research is needed monitor and effectively implement AI across various clinical settings. Key words: artificial intelligence, diagnostic care, scoping reviewInternational Journal of Health Sciences and Research00This scoping review draws conclusions based on evidence that reveals significant promise of AI improving diagnostic accuracy and prognostic predictions across various imaging applications.International Journal of Health Sciences and Research["Anthony Vincent Razzano"]2024-07-09T00:00:00 a24ec536043d29ecdb22487b991347982c8c5ae8International Urology and Nephrology330AI has great potential in predicting and managing surgical complications of urological surgery, but challenges and ethical considerations must be addressed before widespread AI implementation.International urology and nephrology["Nikhil Shah", "Usman Khalid", "Rajesh Kavia", "D. Batura"]2024-07-09T00:00:00 f799960b274dac68679da349c546fccd5a9b4a9cArtificial Intelligence (AI) has emerged as a groundbreaking force in the world of art, redefining the boundaries of creativity and offering new experiences. This article focuses on exploring the impressive realm of artistic endeavors shaped by AI and how it has changed the traditional art paradigm. The materials and techniques used in artworks produced by AI surpass traditional boundaries, incorporating elements such as virtual and augmented reality, robot technologies, and 3D printing. These approaches make significant contributions to the art world, expanding the boundaries of artistic expression and supporting the creative process for artists. Additionally, AI makes art more accessible to a broader audience, promoting inclusivity. However, these innovations also lead to significant debates in the art world. Questions about the reality of AI-generated art, the role of the artist in this process, and the future of art in the age of AI are prominent. AI-supported or AI-generated art redraws boundaries across a spectrum ranging from complex digital landscapes to interactive installations. The impact and future trajectory of these approaches depend on evolving values in the art world and society at large, holding the potential to transform artistic paradigms at the intersection of technological innovation and creative expression.Anadolu Üniversitesi Sanat &amp; Tasarım Dergisi150The impressive realm of artistic endeavors shaped by AI and how it has changed the traditional art paradigm is explored, holding the potential to transform artistic paradigms at the intersection of technological innovation and creative expression.Anadolu Üniversitesi Sanat &amp; Tasarım Dergisi["Vildan I\u015f\u0131k"]2024-07-09T00:00:00 aecc8fc3676d2e5fd5760fa7e41378d35f42424cIn this paper, is shown a solution aimed to detect dairy cow estrous by using self-sufficient surveillance cameras that relies on cloud computing services and 5G mobile networks, to observe mount behavior through an artificial intelligence algorithm that alerts the farmer using a smartphone app that sends images and videos of these events. It was possible to achieve these detections in various weather and light conditions, close or away from the camera with minimal intervention at farm installations and without needing an individual sensor for each cow. It was also included an integration with an existing farm software, where it was possible to see behavior and reproduction data together, allowing the farm to achieve better milk production through getting the reproduction signs automatically as early as possible.IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems2002024 IEEE International Conference on Microwaves, Communications, Antennas, Biomedical Engineering and Electronic Systems (COMCAS)["N. M. D. Landim", "B. S. Dias", "C. A. D. Santos", "S. N. Lobo", "T. D. P. Paim", "H. X. Araujo"]2024-07-09T00:00:00 19d8ed199fdd57dc8bce46c38d7471f823a7c56eMany companies adopt the corporate newsroom model to streamline their corporate communication. This article addresses why and how corporate newsrooms transform corporate communication following the rise of artificial intelligence (AI) systems. It draws on original data from 13 semi-structured interviews with executive communication experts in large Swiss companies which use corporate newsrooms. Interviews show that corporate newsrooms serve as an organisational (rather than spatial) coordination body for topic-oriented and agile corporate communication. To enable their functionality, it is crucial to find the right balance between optimising and stabilising communication structures. Newsrooms actively adopt AI both to facilitate routine tasks and enable more innovative applications, such as living data archives and channel translations. Interviews also highlight an urgent need for AI regulation for corporate communication. The article's findings provide important insights into the practical challenges and coping strategies for establishing and managing corporate newsrooms and how newsrooms can be transformed by AI.arXiv.org00Why and how corporate newsrooms transform corporate communication following the rise of artificial intelligence (AI) systems is addressed and an urgent need for AI regulation for corporate communication is highlighted.ArXiv["Tobias Rohrbach", "M. Makhortykh"]2024-07-09T00:00:00 c362fd7e2f29c884b60930449b3ef58ed1cc48d4The doctrine of technological neutrality posits that technology itself lacks moral value and it is only the use by humans that bestows value upon it. However, this perspective overlooks the social context of technology itself, as well as the political, economic, and cultural factors involved in its research, development, and application. In fact, technology not only reflects the value orientations of designers and users but also embodies the social structure and human behavior of a certain stage. Especially in the era of artificial intelligence, technology possesses the capability to operate autonomously, and the autonomous operation of technological products can present the value defects at the design stage in a scaled manner, leading to systemic biases and injustices. This paper analyzes the purpose of technological design and the power manipulation reflected in technological code, demonstrating that technology is not neutral and does not imply justice. Technology needs to be supervised and regulated by ethics, law, society, and other aspects to safeguard human interests and dignity.Sociology, Philosophy and Psychology210This paper analyzes the purpose of technological design and the power manipulation reflected in technological code, demonstrating that technology is not neutral and does not imply justice.Sociology, Philosophy and Psychology["Qifan Gao", "Hongyu An"]2024-07-09T00:00:00 b7053c0a6ddf87ad8bcf4c1ec97ac669e7e08c14Pembelajaran menulis naskah drama di sekolah seringkali disajikan dalam bentuk teoritis dan dianggap tugas yang sulit, tidak menyenangkan, memakan waktu, dan penuh kesulitan dalam mengembangkan ide. Berdasarkan fenomena ini, penulis mengusulkan sebuah konsep dengan menggabungkan metode discovery learning dan kecerdasan buatan (Chat GPT) dalam pembelajaran menulis drama. Gagasan konseptual ini bertujuan untuk mendukung perkembangan pendidikan digital di Indonesia yang sejalan dengan Kurikulum Merdeka. Konsep ini juga sejalan dengan evolusi Kurikulum Indonesia yang menerapkan pendekatan merdeka belajar..  Penelitian ini menggunakan metode kualitatif dengan pendekatan koseptual. Dari hasil penelitian ini, pembelajaran menulias naskah drama pada generasi z dengan metode discovery learning berbasi artificial intelligance (Chat GPT) menunjukkan bahwa terdapat enam tahapan sintak yaitu: (a) pemberian rangsangan, (b) mengidentifikasi masalah , (c) pengumpulan data, (d) pengelolaan data, (e) pembuktian, (f) Menarik kesimpulan. Hasil penelitian menunjukkan bahwa penerapan metode discovery learning berbasis AI dapat meningkatkan interaktivitas siswa dalam proses pembelajaran, mendukung kolaborasi daring, dan memfasilitasi umpan balik real-time, yang relevan dalam pendidikan kontemporer. Namun, studi ini perlu dikembangkan lebih lanjut menjadi penelitian terapan untuk menguji nilai efektifitasnya secara empiris dalam meningkatkan keterampilan menulis naskah drama generasi Z di era kurikulum merdeka.Jurnal inovasi pendidikan01Jurnal Inovasi Pendidikan["Lutfiatul Fauziyah", "M. Haryanto"]2024-07-09T00:00:00 c6db0b5edb078d0b3e990aa2baacef647721a218International Journal of Production Research333International Journal of Production Research["R. Fornasiero", "Lorenz Kiebler", "Mohammadtaghi Falsafi", "S. Sardesai"]2024-07-09T00:00:00 f957d6114e27881d759df7cd3ba9d0143f73d641EDPACS: The EDP Audit, Control, and Security Newsletter251EDPACS["\u00d6znur Ta\u015fd\u00f6ken"]2024-07-09T00:00:00 72fd87abeb8cf78ed05ada02f2a243aeda7cac85AI adoption until now has not been maximized by academics and audit practitioners due to many limitations, especially at Mataram University. This research then aims to find out the views of audit lecturers on AI adoption and how the level of AI adoption at Mataram University. This research is a descriptive qualitative research using subject data types and primary data sources. The data collection technique used was a structured interview with audit lecturers at Mataram University. Data analysis techniques consist of data reduction, data presentation, and conclusions which then uses data triangulation to increase data validity. The results showed that the adoption of AI in the view of audit lecturers at Mataram University is very important with regard to the progress of the times and expectations for quality audits. Then related to the level of AI adoption in audit practices at the University of Mataram, it was found that there has not been massive adoption, so in the future it is expected to be adopted selectively in order to produce quality audit processes and results and go hand in hand with technological advances. The recommendations offered then are HR training and commitment and consistency in AI adoption in audit practices at Mataram University.EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis171The adoption of AI in the view of audit lecturers at Mataram University is very important with regard to the progress of the times and expectations for quality audits, and in the future it is expected to be adopted selectively in order to produce quality audit processes and results.EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis["Baiq Wahyuni Damayanti", "Bambang Bambang"]2024-07-09T00:00:00 3d136f9528417b4a139c138af25a0863e0064954SAR (Soedirman Accounting Review): Journal of Accounting and Business00SAR (Soedirman Accounting Review): Journal of Accounting and Business[]2024-07-09T00:00:00 0f6c4af06ae6899359687ebcd78fc7df4de319adBioanalysis80Bioanalysis["Priyanka A Shah", "P. Shrivastav", "Manjunath Ghate", "Vishwajit Chavda"]2024-07-09T00:00:00 a9932e6398bd319b68c9d87b79f846853af6d8baCardiovascular Electrophysiology10Journal of cardiovascular electrophysiology["Sutton R. Fox", "A. Toomu", "Kelly Gu", "Jessica Kang", "Kevin Sung", "Frederick T. Han", "Kurt S. Hoffmayer", "Jonathan C. Hsu", "F. Raissi", "Gregory K Feld", "Andrew D McCulloch", "Gordon Ho", "D. Krummen"]2024-07-09T00:00:00 6ea01d93be329fc4f1395cbf059c95cfe974c379Intelligent Management of Data and Information in Decision Making00Intelligent Management of Data and Information in Decision Making["Ziwei Shu", "Ram\u00f3n Alberto Carrasco"]2024-07-09T00:00:00 4c24735566a8324c752cc419c27dcf1016700204Neuroradiology50Neuroradiology["F. J. Meijer"]2024-07-09T00:00:00 9bb704acf1f6d6c4f8c87800fc419ebf876333e3AI &amp; SOCIETY121It is argued that current definitions of risk are ill-suited to capture supposed AGI existential risks, and that the risk-based framework of the EU AI Act is inadequate to deal with truly general, agential systems.AI &amp; SOCIETY["Federico L. G. Faroldi"]2024-07-09T00:00:00 0c56893bee455109dfbb2d1b4a1f18609d56b07aAlthough artificial intelligence (AI) has been successful in many domains, fully grasping the concept for ordinary people is still challenging. Also, the convolutional neural network (CNN) is state-of-the-art model in computer vision research due to its fundamental properties linked to visual perceptions. For the sake of popular science, we crafted the CNN model design and training to make an award-winning installation artwork to interpret CNN. In each of the three installations, seven clear acrylic plates are lined up, showing the end-to-end tensor transitions from an animal image to a specific Chinese pictogram to reveal the layered structures, feature maps, and weights belonging to the CNN. We deeply hope to help pave the path toward a better understanding of modern advances in AI for the public.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)60The CNN model design and training is crafted to make an award-winning installation artwork to interpret CNN, showing the end-to-end tensor transitions from an animal image to a specific Chinese pictogram to reveal the layered structures, feature maps, and weights belonging to the CNN.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)["Chen-Hua Lu", "Su-Chu Hsu", "Che-Rung Lee"]2024-07-09T00:00:00 3a7ad2e108848bdb1bd4814bcf1373c1617e2ac6PurposeThe study aims to explore the role of ChatGPT, an artificial intelligence (AI) language model, in the field of management education. Specifically, the goal is to evaluate ChatGPT's effectiveness in facilitating active learning, promoting critical thinking, and fostering creativity among students. Additionally, the study seeks to investigate the potential of ChatGPT as a novel tool for enhancing traditional teaching methods within the framework of management education.Design/methodology/approachThis research systematically explores ChatGPT's impact on student engagement in management education, considering AI integration benefits and limitations. Ethical dimensions, including information authenticity and bias, are scrutinized, alongside educators' roles in guiding AI-augmented learning.FindingsThe study reveals ChatGPT's effectiveness in engaging students, nurturing critical thinking, and fostering creativity in management education. Ethical concerns regarding information authenticity and bias are addressed. Insights from student and teacher perceptions offer valuable pedagogical implications for AI's role in management education.Research limitations/implicationsWhile this study offers valuable insights into the role of ChatGPT in management education, it is essential to acknowledge certain limitations. Firstly, the research primarily focuses on a specific AI model (ChatGPT), and findings may not be generalized to other AI language models. Additionally, the study relies on a specific set of educational contexts and may not fully capture the diverse landscape of management education globally. The duration of the research and the sample size could also impact the generalizability of the findings.Practical implicationsThe findings of this study hold practical significance for educators and institutions engaged in management education. The integration of ChatGPT into teaching strategies has the potential to improve active learning, critical thinking, and creativity. Educators can utilize this AI tool to diversify instructional methods and accommodate diverse learning styles. However, the practical implementation of AI in the classroom necessitates meticulous consideration of infrastructure, training, and ongoing support for both educators and students. Furthermore, institutions should proactively tackle ethical concerns and establish guidelines for the responsible use of AI in education.Social implicationsThe incorporation of AI, such as ChatGPT, in management education carries broader social implications. The study underscores the significance of addressing ethical concerns associated with AI, including issues related to information authenticity and bias. As AI becomes more widespread in educational settings, there is a necessity for societal discussions on the role of technology in shaping learning experiences. This research advocates for a thoughtful approach to AI adoption, emphasizing the importance of transparency, accountability, and inclusivity in the development and deployment of AI technologies within the educational sphere. The findings prompt reflections on the societal impact of AI-driven education and the potential consequences for students' skills, employment prospects, and societal values.Originality/valueOriginality/Values: This research contributes to the academic discourse by systematically examining the role of ChatGPT in management education, providing insights into both its advantages and potential ethical challenges. The study offers original perspectives on the use of AI in educational settings, paving the way for well-informed decision-making that can shape the future of management education in the evolving landscape of technological progress.Journal of Research in Innovative Teaching &amp; Learning598The study reveals ChatGPT's effectiveness in engaging students, nurturing critical thinking, and fostering creativity in management education, and underscores the significance of addressing ethical concerns associated with AI.Journal of Research in Innovative Teaching &amp; Learning["R. Leelavathi", "Reddy C. Surendhranatha"]2024-07-09T00:00:00 a190519761fd33e02683aaf1442813a266606b22This study explores the potential trajectories of human civilization influenced by the development and competition of advanced artificial intelligence (AI) systems. Using a system of stochastic partial differential equations (SPDEs), we model the probability density function representing the state of civilization in a phase space defined by prosperity and knowledge. The model incorporates diffusion, growth, saturation, and drift terms, alongside stochastic noise to reflect the uncertainties and random fluctuations in AI impacts. The simulation results are visualized in a probability density graph, revealing the likelihood of various outcomes ranging from extinction and regression, to prosperity and technological advancement. The analysis highlights the balanced prospects of human future, with significant probabilities in both positive and negative directions. Positive scenarios suggest potential for increased prosperity and knowledge, emphasizing the importance of effective AI management and international cooperation. Conversely, the notable risks of regression and extinction underline the need for strategic interventions to mitigate adverse impacts. Our findings stress the stochastic nature of future developments and the critical role of adaptive and flexible policies in steering human civilization towards favorable outcomes. This study provides a simple yet comprehensive framework for understanding the complex dynamics at play and underscores the importance of proactive strategies in the age of AIJournal of Mathematical Techniques and Computational Mathematics00A system of stochastic partial differential equations (SPDEs) is used to model the probability density function representing the state of civilization in a phase space defined by prosperity and knowledge, revealing the likelihood of various outcomes ranging from extinction and regression, to prosperity and technological advancement.Journal of Mathematical Techniques and Computational Mathematics[]2024-07-09T00:00:00 c11e56a6d485d898d77711bc7942e9c09b3e6170The maritime industry, responsible for moving approximately 90% of the world’s goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry’s environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry.Applied Sciences03Applied Sciences["Irmina Durlik", "Tymoteusz Miller", "Ewelina Kostecka", "Adrianna \u0141obodzi\u0144ska", "Tomasz Kostecki"]2024-07-09T00:00:00 e67062adf6cd7c1adc6e7330fdabb7d3a96b2a42This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required. To ensure their responsible use and implementation, both developers and users of research incorporating these tools, should familiarize themselves with their current limitations, including the issues related to scientific validity, risk of bias, and consider equity and ethical implications. We also surveyed the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools.arXiv.org51The transformative potential of generative Artificial Intelligence and foundation models, including large language models (LLMs), for health technology assessment (HTA) is introduced and developers and users of research incorporating these tools, should familiarize themselves with their current limitations.ArXiv["Rachael Fleurence", "Jiang Bian", "Xiaoyan Wang", "Hua Xu", "Dalia Dawoud", "Mitch Higashi", "J. Chhatwal"]2024-07-09T00:00:00 67858f2f05db3eb12b71adec1ccc7bd5d1c4c2baArtificial Intelligence (AI) is increasingly revolutionizing the food and beverage service industry by offering innovative solutions to enhance operational efficiency and personalize customer experiences. This abstract explores the transformative impact of AI technologies such as machine learning, predictive analytics, and robotics on various aspects of hospitality operations. AI-driven automation streamlines kitchen processes, optimizes inventory management, and reduces operational costs, thereby improving service consistency and profitability (Alsmadi et al., 2020; Li et al., 2021).AI facilitates personalized client interactions by analyzing vast datasets to understand individual preferences, dietary restrictions, and purchasing behaviors. This capability empowers establishments to offer tailored menu recommendations, personalized promotions, and interactive dining experiences that cater to diverse consumer needs (Chen et al., 2019; Huang et al., 2020). AI's integration into the food and beverage service industry significantly enhances customer satisfaction and loyalty while concurrently boosting revenue through targeted marketing and operational efficiencies. By leveraging AI technologies such as machine learning and predictive analytics, establishments can tailor offerings to meet individual preferences and anticipate consumer behavior, thereby fostering stronger customer relationships. Moreover, the future trajectory of AI in this sector foresees advancements in IoT integration, AR applications, and blockchain technology. These innovations are set to further transform operations by optimizing efficiency, enriching customer interactions, and ensuring robust supply chain transparency. IoT devices, for instance, will facilitate real-time data collection and analysis, thereby enhancing inventory management precision and  optimizing resource allocation to meet demand fluctuations effectively. AR applications will offer immersive dining experiences, allowing customers to interact with digital menus or view culinary presentations. Blockchain technology will enhance transparency and traceability, ensuring food safety and compliance with regulatory standards. Together, these innovations will enable food and beverage establishments to innovate and differentiate themselves in a competitive market landscape, ultimately shaping the future of dining experiences worldwide to further optimize operations and enhance transparency in supply chain management (Jiang et al., 2023; Wang et al., 2023).International Journal for Multidimensional Research Perspectives31Together, these innovations will enable food and beverage establishments to innovate and differentiate themselves in a competitive market landscape, ultimately shaping the future of dining experiences worldwide to further optimize operations and enhance transparency in supply chain management.International Journal for Multidimensional Research Perspectives["Gunjan Sinha", "A. Prof.PraveenF."]2024-07-09T00:00:00 81054c8bc82197611ea1e7b72d65170a382d0c70A new age of accuracy and efficiency, especially in face recognition and other biometric technologies, has been brought about in recent years by the integration of artificial intelligence (AI) into biometric security systems. The discussion extends to the security implications of AI-enhanced biometric systems, including their susceptibility to threats such as spoofing and adversarial attacks. We analyze the vulnerabilities these systems face and propose advanced algorithmic solutions to fortify them against such risks. Moreover, this paper addresses the ethical and privacy concerns surrounding the widespread use of biometric data, emphasizing the need for stringent data protection measures and regulatory compliance. Additionally, the research investigates AI's significant contributions to genetic engineering, particularly through advancements in CRISPR [1] technology. By integrating AI, the precision of gene editing can be significantly improved, potentially revolutionizing personalized medicine and genetic therapies. This extensive research intends to shed light on the revolutionary potential of artificial intelligence (AI) in genetic engineering and biometric security, emphasizing both the exciting developments and the difficult obstacles still to be overcome. Through this research, readers will get a clearer knowledge of how artificial intelligence (AI) is altering biotechnology and security, opening the door for discoveries that might have a significant influence on healthcare and other fields.International Journal of Innovative Science and Research Technology221The ethical and privacy concerns surrounding the widespread use of biometric data are addressed, emphasizing the need for stringent data protection measures and regulatory compliance and AI's significant contributions to genetic engineering, particularly through advancements in CRISPR technology are investigated.International Journal of Innovative Science and Research Technology (IJISRT)["Umang H Patel", "Krish Gera"]2024-07-09T00:00:00 1454976c4044bdd861eaf61128c7d4f1869e65ee As artificial intelligence (AI) methods are increasingly used to develop new guidance intended for operational use by forecasters, it is critical to evaluate whether forecasters deem the guidance trustworthy. Past trust-related AI research suggests that certain attributes (e.g., understanding how the AI was trained, interactivity, performance) contribute to users perceiving the AI as trustworthy. However, little research has been done to examine the role of these and other attributes for weather forecasters. In this study, we conducted 16 online interviews with National Weather Service (NWS) forecasters to examine (a) how they make guidance use decisions, and (b) how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance. The interviews pertained to either a random forest model predicting probability of severe hail or a 2D-convolutional neural net model predicting probability of storm mode. When taken as a whole, our findings illustrate how forecasters’ assessment of AI guidance trustworthiness is a process that occurs over time rather than automatically or at first introduction. We recommend developers center end users when creating new AI guidance tools, making end users integral to their thinking and efforts. This approach is essential for the development of useful and used tools. The details of these findings can help AI developers understand how forecasters perceive AI guidance and inform AI development and refinement efforts.Weather and forecasting681This study conducted 16 online interviews with National Weather Service forecasters to examine how they make guidance use decisions and how the AI model technique used, training, input variables, performance, and developers as well as interacting with the model output influenced their assessments of trustworthiness of new guidance.Weather and Forecasting["Mariana G. Cains", "Christopher D. Wirz", "Julie L. Demuth", "Ann Bostrom", "David John Gagne", "Amy McGovern", "R. Sobash", "Deianna Madlambayan"]2024-07-09T00:00:00 63a05afd8664a821e7b3f0200ead10b2789b3f70The integration of Artificial Intelligence (AI) technologies into clinical practice holds significant promise for revolutionizing healthcare. However, the realization of this potential requires rigorous evaluation and validation of AI applications to ensure their safety, efficacy, and clinical significance. Despite increasing awareness of the need for robust testing, the majority of AI-related Randomized Controlled Trials (RCTs) so far have exhibited notable limitations, impeding the generalizability and proper integration of their findings into clinical settings. To understand whether the field is progressing towards more robust testing, we conducted an analysis of the registration data of ongoing and planned RCTs of AI in medicine available in the Clinicaltrials.gov database. Our analysis highlights several key trends and challenges. Effectively addressing these challenges is essential for advancing the field of medical AI and ensuring its successful integration into clinical practice.medRxiv191To understand whether the field of medical AI is progressing towards more robust testing, an analysis of the registration data of ongoing and planned RCTs of AI in medicine available in the Clinicaltrials.gov database is conducted.["M. Andreoletti", "B. Senkalfa", "A. Blasimme"]2024-07-09T00:00:00 00779a37dc55a6dc1e3fee00baf65714a80f7a98The use of artificial intelligence (AI) in working environments with individuals, known as Human-AI Collaboration (HAIC), has become essential in a variety of domains, boosting decision-making, efficiency, and innovation. Despite HAIC's wide potential, evaluating its effectiveness remains challenging due to the complex interaction of components involved. This paper provides a detailed analysis of existing HAIC evaluation approaches and develops a fresh paradigm for more effectively evaluating these systems. Our framework includes a structured decision tree which assists to select relevant metrics based on distinct HAIC modes (AI-Centric, Human-Centric, and Symbiotic). By including both quantitative and qualitative metrics, the framework seeks to represent HAIC's dynamic and reciprocal nature, enabling the assessment of its impact and success. This framework's practicality can be examined by its application in an array of domains, including manufacturing, healthcare, finance, and education, each of which has unique challenges and requirements. Our hope is that this study will facilitate further research on the systematic evaluation of HAIC in real-world applications.arXiv.org1371A detailed analysis of existing HAIC evaluation approaches is provided and a fresh paradigm for more effectively evaluating these systems is developed, which includes a structured decision tree which assists to select relevant metrics based on distinct HAIC modes (AI-Centric, Human-Centric, and Symbiotic).ArXiv["George Fragiadakis", "Christos Diou", "G. Kousiouris", "Mara Nikolaidou"]2024-07-09T00:00:00 36faddd4394c9392fb0b1d915c8066b24740d8c0Renewable energy sources, expected to form about 70% of power systems by 2050, bring challenges like fluctuating outputs and grid instability. Advanced power monitoring systems, crucial in environments like research facilities and hospitals, must navigate these dynamic scenarios. Traditional power management, especially Uninterruptible Power Supply (UPS) systems, often needs to catch up due to high costs and limited response to varied power demands, focusing mainly on constant power supply without differentiating between constant and fluctuating loads. In response, Artificial intelligence (AI) techniques are becoming indispensable for real-time power prediction and control. A distributed AI framework forecasts power needs, considering renewable sources, loads, and storage. This is key to ensuring smooth mini-grid operations, balancing operational demands with environmental considerations, and advancing intelligent energy management. Such systems are essential in optimizing energy usage, aligning it with available power to enhance efficiency and reduce waste. This is particularly important for mini-grids, with or without UPS systems, where predictive monitoring can substantially cut operational costs and extend lifespan.The paper focuses on providing consistent, constant, and fluctuating power by predicting mini-grid power needs hourly from the previous day's data. We use a Temporal Convolutional Network (TCN) for time series prediction, integrated within the BDIx agent's belief system through TensorFlow Lite. This approach accurately predicts upcoming power needs, ensures smooth operation, and prevents power outages. The TCN model's predictive capabilities highlight a significant stride in combining AI with energy management to address the complexities of modern power systems.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)20This paper focuses on providing consistent, constant, and fluctuating power by predicting mini-grid power needs hourly from the previous day's data using a Temporal Convolutional Network for time series prediction, integrated within the BDIx agent's belief system through TensorFlow Lite.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)["Iacovos I. Ioannou", "Saher Javaid", "V. Vassiliou", "Andreas Pitsillides", "Yasuo Tan"]2024-07-09T00:00:00 a20fe4511321f7af339c7246ef7d57ac7146db7bIn recent years, the significance of machine translation systems has grown due to the extensive production of online texts across various disciplines. Traditional translation methods have proven inadequate in meeting global translation needs. While translation tools are brilliant in addressing diverse disciplines and text genres, their usability and reliability face considerable debate, especially when applied to literary texts. Therefore, this research seeks to explore the impact of Artificial Intelligence (AI) translation tools (e.g., ChatGPT) on the translation and back translation of literary texts. The study employed an experimental model within a qualitative approach, utilizing a translation test as the primary research tool. 80 English-major students at Imam Mohammed Ibn Saud Islamic University (IMSIU) were randomly selected and assigned into four groups: two control and two experimental groups. Students are asked to translate and back translate an English short story and qualitative data from the test has undergone analysis through various comparisons. For statistical analysis, an independent samples t-test was employed to compare two independent groups. The findings revealed that students using AI tools were able to produce better translations and back translations than students using traditional methods, with slightly better performance in back translation.The Journal of Social Studies00The findings revealed that students using AI tools were able to produce better translations and back translations than students using traditional methods, with slightly better performance in back translation.Journal of Social Studies["Dr. Khaled Abkar Alkodimi", "Dr. Osama Abdulrhman Alqahtani", "Dr. Baleigh Qassim Al-Wasy"]2024-07-09T00:00:00 8bb508cf229f76f8fe3a231bf7cdbb07618710bbArtificial intelligence has accelerated innovations in different aspects of citizens' lives. Many contexts have already addressed technology-enhanced learning, but educators at different educational levels now need to develop AI literacy and the ability to integrate appropriate AI usage into their teaching. We take into account this objective, along with the creative learning design, to create a formative intervention that enables preservice teachers, in-service teachers, and EdTech specialists to effectively incorporate AI into their teaching practices. We developed the formative intervention with Terra Numerica and Maison de l'Intelligence Artificielle in two phases in order to enhance their understanding of AI and foster its creative application in learning design. Participants reflect on AI's potential in teaching and learning by exploring different activities that can integrate AI literacy in education, including its ethical considerations and potential for innovative pedagogy. The approach emphasises not only acculturating professionals to AI but also empowering them to collaboratively design AI-enhanced educational activities that promote learner engagement and personalised learning experiences. Through this process, participants in the workshops develop the skills and mindset necessary to effectively leverage AI while maintaining a critical awareness of its implications in education.arXiv.org20A formative intervention that enables preservice teachers, in-service teachers, and EdTech specialists to effectively incorporate AI into their teaching practices and empowering them to collaboratively design AI-enhanced educational activities that promote learner engagement and personalised learning experiences is developed.ArXiv["Margarida Romero"]2024-07-09T00:00:00 e33fe2cf6d877fcd0501bd34da118b43dbb08d6bThe integration of Artificial Intelligence (AI) in data analytics is revolutionizing various industries, driving significant economic transformation, and reshaping the workforce. This study explores the multifaceted impact of AI-driven data analytics, highlighting both the promising opportunities and formidable challenges it presents. Key findings demonstrate that AI significantly enhances data processing capabilities, leading to improved decision-making and operational efficiencies. Furthermore, the emergence of new job roles such as data scientists, AI specialists, and machine learning engineers underscores the demand for specialized skills. However, the rapid adoption of AI also exposes considerable skill gaps in the workforce and raises ethical concerns, particularly regarding data privacy, security, and algorithmic bias. Addressing these challenges requires strategic workforce training, robust governance frameworks for ethical AI practices, and effective change management strategies to overcome resistance to change. By comprehensively addressing these issues, businesses and policymakers can harness the full potential of AI in data analytics, fostering innovation, economic growth, and a smooth transition to an AI-driven economy.ACADEMIC JOURNAL ON SCIENCE, TECHNOLOGY, ENGINEERING &amp; MATHEMATICS EDUCATION00This study explores the multifaceted impact of AI-driven data analytics, highlighting both the promising opportunities and formidable challenges it presents, and demonstrates that AI significantly enhances data processing capabilities, leading to improved decision-making and operational efficiencies.ACADEMIC JOURNAL ON SCIENCE, TECHNOLOGY, ENGINEERING &amp; MATHEMATICS EDUCATION["Tonmoy Barua", "Jafrina Jabin", "Sunanda Barua"]2024-07-09T00:00:00 3ce2aa1450534b1e5ca765fe71931c96fba5efcaThe development of open radio access network (ORAN) technology has revolutionized the telecommunications’ industry, which providing openness, flexibility and interchangeability in radio access network (RAN) architectures. However, the stability of O-RAN systems has been a practical concern in this community, which caused the demand for developing smart reboot algorithm. Meanwhile, in alignment with the growing environmental consciousness, the need for innovative mechanisms to enhance energy efficiency is prompted. Regarding these issues and requirements mentioned above, this paper proposes a statistical learning approach that combines the O-RAN base stations’ (BSs’) abnormal detection and power control mechanisms to fulfill the demands. In particular, we present a statistical learning algorithm which is based on explainable artificial intelligence (AI) to detect the BSs which are operating abnormally. Subsequently, the remaining healthy BSs are selected, and the proposed statistical learning based power control algorithm is operated, which combines the spirit of explainable AI with bisection method to increase the transparency and linearity of the algorithm itself.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)120A statistical learning algorithm which is based on explainable artificial intelligence (AI) to detect the BSs which are operating abnormally and the remaining healthy BSs are selected, and the proposed statistical learning based power control algorithm is operated.2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)["Yu-An Chen"]2024-07-09T00:00:00 a83bdc763fab690616fcd1e0a74399cc2173656dIn the contemporary era of globalization, cross-border e-commerce advertising has emerged as a pivotal conduit for interconnecting diverse cultures and marketplaces. This research delves into the nexus of Yiwu, China, a globally recognized hub for small commodity distribution, to examine the integration of artificial intelligence (AI) within the realm of social media e-commerce advertising. It places a particular emphasis on the strategies and efficacy of cross-cultural communication within this context. Employing an online survey methodology, this study has garnered consumer feedback encompassing both local vendors and international purchasers. The survey instrument was designed to assess various dimensions, including recognition of AI advertisements, perceptions of quality, the experience of personalization, and the adaptability to cross-cultural contexts. Subsequent analysis reveals that while AI-driven advertising excels in bolstering user engagement and propensity to purchase, it faces ongoing challenges in navigating cultural disparities. The findings of this research proffer strategic insights for enterprises in Yiwu and the broader cross-border e-commerce sector, advocating for the enhancement of cultural adaptability in AI-driven advertising initiatives and advocating a deeper understanding of consumer behaviors across different cultural landscapes.Sociology, Philosophy and Psychology80This research delves into the nexus of Yiwu, China, to examine the integration of artificial intelligence within the realm of social media e-commerce advertising, placing a particular emphasis on the strategies and efficacy of cross-cultural communication within this context.Sociology, Philosophy and Psychology["Yuxin Cai", "Xiaoyu Liu"]2024-07-09T00:00:00 478430b39fcc6eafe6b0b1c2ff6289cce0f0d07aThe support of artificial intelligence (AI) based decision-making is a key element in future 6G networks, where the concept of native AI will be introduced. Moreover, AI is widely employed in different critical applications such as autonomous driving and medical diagnosis. In such applications, using AI as black-box models is risky and challenging. Hence, it is crucial to understand and trust the decisions taken by these models. Tackling this issue can be achieved by developing explainable AI (XAI) schemes that aim to explain the logic behind the black-box model behavior, and thus, ensure its efficient and safe deployment. Recently, we proposed a novel perturbation-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications. The core idea of the XAI-CHEST framework is to identify the relevant model inputs by inducing high noise on the irrelevant ones. This manuscript provides the detailed theoretical foundations of the XAI-CHEST framework. In particular, we derive the analytical expressions of the XAI-CHEST loss functions and the noise threshold fine-tuning optimization problem. Hence the designed XAI-CHEST delivers a smart input feature selection methodology that can further improve the overall performance while optimizing the architecture of the employed model. Simulation results show that the XAI-CHEST framework provides valid interpretations, where it offers an improved bit error rate performance while reducing the required computational complexity in comparison to the classical DL-based channel estimation.arXiv.org00The designed XAI-CHEST delivers a smart input feature selection methodology that can further improve the overall performance while optimizing the architecture of the employed model, and provides the detailed theoretical foundations of the XAI-CHEST framework.ArXiv["Abdul Karim Gizzini", "Y. Medjahdi", "A. Ghandour", "Laurent Clavier"]2024-07-09T00:00:00 fa8be00368a5221f31b1061c19b96bbe973e3bf4In a construction project schedule, delays in delivery are one of the most important problems. Delays can be caused by several project components; however, the issue is amplified when delays occur simultaneously. Classifying delays is relevant in order to allocate responsibility to the parties. In Italy, the delay in the delivery of medium and large-sized works in residential urban centers is about 15% compared to the project forecast. Moreover, the AECO sector’s ability to adapt to emerging challenges, such as environmental sustainability and digitization, is limited by the lack of innovation in management methods. The aim of this research is to create a methodology for managing the built and to-be-built environment in a digital way. This will optimize the building process by reducing delays and waste of resources. The methodology will use tools such as digital twin (DT), Building Information Modeling (BIM), Internet of Things (IoT), and Artificial Intelligence (AI) algorithms. The integration of lean construction practices can make the use of these technologies even more efficient, supporting better workflow management by using the BIM environment. The paper presents a methodology that can be applied to various scaling factors and scenarios. It is also useful for construction sites that are already in progress. As highlighted below, this brings significant economic-temporal advantages.Buildings5012The aim of this research is to create a methodology for managing the built and to-be-built environment in a digital way that will optimize the building process by reducing delays and waste of resources.Buildings["G. Piras", "F. Muzi", "Virginia Adele Tiburcio"]2024-07-10T00:00:00 6efd9fc49bfb3b0414267286445b6c041f081a50Artificial Intelligence Review1595The role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions are investigated to serve as a road map for researchers and others interested in cybersecurity and digital security.Artif. Intell. Rev.["MohammadHossein Homaei", "Oscar Mogollon-Gutierrez", "Jos\u00e9 Carlos Sancho", "M. \u00c1vila", "Andr\u00e9s Caro"]2024-07-10T00:00:00 da6bc543eb4120a20aa049af0c7e5e0e33683885Scientific Reports273It is demonstrated that AI-based methods for diagnosing LVH exhibit higher diagnostic accuracy compared to conventional criteria, with notable increases in sensitivity, which contribute to the validation of AI as a promising tool for LVH detection.Scientific Reports["Noppachai Siranart", "Natee Deepan", "Witina Techasatian", "Somkiat Phutinart", "Walit Sowalertrat", "Ponthakorn Kaewkanha", "P. Pajareya", "N. Tokavanich", "N. Prasitlumkum", "Ronpichai Chokesuwattanaskul"]2024-07-10T00:00:00 88e565c386c91d78b826bde4ffaa0ea1ab0850db"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy (HIE) using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High Dose Erythropoietin for Asphyxia (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25th, 2017 and October ninth, 2019, were retrospectively analyzed. The harmonized MRI protocol included T1-weighted, T2-weighted, and diffusion tensor imaging. Deep learning classifiers were trained to predict the primary outcome of the HEAL trial (death or any neurodevelopmental impairment [NDI] at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on a test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 100% of cases from 2 institutions (out-of-distribution test set, n = 41). Model performance in predicting additional secondary outcomes, including death alone, was also assessed. Results For the 414 neonates (mean gestational age, 39 weeks ± 1.4, 232 males, 182 females), in the study cohort, 198 (48%) died or had any NDI at 2 years. The deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.60-0.86) and 63% accuracy on the in-distribution test set and an AUC of 0.77 (95% CI: 0.63-0.90) and 78% accuracy on the out-of-distribution test set. Performance was similar or better for predicting secondary outcomes. Conclusion Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes. ©RSNA, 2024.Radiology: Artificial Intelligence03Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes, including death or any neurodevelopmental impairment at 2 years.Radiology. Artificial intelligence["Christopher O Lew", "E. Calabrese", "Joshua V Chen", "Felicia Tang", "Gunvant Chaudhari", "Amanda Lee", "John Faro", "Sandra E. Juul", "Amit M. Mathur", "Robert C. McKinstry", "J. Wisnowski", "A. Rauschecker", "Yvonne W. Wu", "Yi Li"]2024-07-10T00:00:00 a5062ffeb0b955130b097e98998dd77317064b3a Purpose This study aims to explore the relationship between digital transformation, transformational leadership style and artificial intelligence (AI) in accounting in the context of Vietnam as an emerging market. Additionally, it examines the role of transformational leadership style as a moderator in the nexus between digital transformation and AI in accounting. Design/methodology/approach Data was collected through e-survey questionnaires distributed to Vietnamese manufacturing firms following comprehensive screening to ensure representativeness of the entire population. A final sample of 510 responses was analyzed. Findings Using partial least squares structural equation modeling, our findings reveal that digital transformation and a transformational leadership style positively influence AI in accounting. Furthermore, transformational leadership style demonstrates a significant moderating effect on the relationship between digital transformation and AI in accounting. Practical implications This study discusses the benefits of incorporating AI in accounting for managerial decision-making. It underscores the critical importance of digital transformation in contemporary accounting practices, particularly with regards to AI in accounting. Consequently, managers are encouraged to embrace digital transformation, leveraging national policies, to enhance their firm's utilization of AI in accounting. Moreover, managers should focus on developing their transformational leadership style to maximize the aforementioned outcomes. Originality/value This study contributes to the literature on AI in accounting by highlighting the positive effects of digital transformation and a transformational leadership style. Additionally, our findings underscore the effectiveness of a transformational leadership style and its moderating influence. Finally, this study presents a pioneering empirical investigation that integrates transformational leadership style with AI in accounting within developing economies, specifically Vietnam. VINE Journal of Information and Knowledge Management Systems732The findings reveal that digital transformation and a transformational leadership style positively influence AI in accounting, and transformational leadership style demonstrates a significant moderating effect on the relationship between digital transformation and AI in accounting.VINE Journal of Information and Knowledge Management Systems["Malik Muneer Abu Afifa", "Tho Hoang Nguyen", "Mai Truc Thi Le", "L. Nguyen", "Thuy Thi Hong Tran"]2024-07-10T00:00:00 206f0bdab5743629c365b1527bee96f18847dd90The integration of Artificial Intelligence (AI) has been reshaping healthcare globally. However, the AI adoption in Jordan is met with cautious progress. AI has shown substantial potential to enhance healthcare services and foster Emotional Intelligence (EI), especially in advanced economies. Despite its proven effectiveness elsewhere, the Jordanian populace is reluctant to adopt AI in the healthcare sector, with predictions for hospitalizations, medical consultations, and treatment recommendations being sluggish to gain acceptance. This study investigates the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model. While UTAUT typically considers performance expectancy, effort expectancy, social influence, and facilitating conditions as key determinants of technology acceptance, this study argues that emotional intelligence, including self-regulated, self-awareness, motivation, empathy, and social skills, should be integrated as direct determinants of behavioural intention. In this study, a quantitative approach has been employed, whereby questionnaires were delivered through email and messaging apps to evaluate the impact of emotional intelligence on Jordanians’ willingness to adopt AI technology in the healthcare sector. The findings suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct, particularly in developing countries like Jordan, where user models for AI adoption are less explored. The implications of the study extend to healthcare planners and developers in Jordan, providing insights into factors, which influence the successful adoption of AI technologies among diverse user groups. This study has provided valuable recommendations for developers of AI-based healthcare systems, enabling them to align their assistance with the perceptions and behaviours of Middle Eastern users. By doing so, they can foster increased acceptance of AI-based healthcare systems in the Middle East and other developing regions to improve healthcare services. Journal of Electrical Systems2032This study investigated the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model and suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct.Journal of Electrical Systems["Mahmoud Mohammad Ahmad", "Ibrahim Putra", "Sumari Pantea", "Keikhosrokiani Lara", "Ahmad Ghasab", "Almashagba Areej", "Ahmed Theeb"]2024-07-10T00:00:00 64ba3db9aa34442892e8e0508134cffa1cdb5100Since the introduction of ChatGPT as artificial intelligence and its massive development over the years, it has had a huge impact on various aspects of life, and it is increasingly visible in the presence of increasingly advanced features, functions, and appearances. Its use in the field of education has not escaped the advancement of artificial intelligence. For some teachers and students, the term artificial intelligence is still very new, as is its use in the teaching and learning process. Currently, Indonesia's use of artificial intelligence faces numerous unavoidable obstacles and challenges. However, in global education, it is becoming increasingly common to use artificial intelligence in the teaching and learning process. The introduction of artificial intelligence to junior high school students is considered very appropriate because students' psychology at this level is more developed, and the learning load begins to increase compared to the elementary level. Math subjects for most students are still a scourge. In this community service, artificial intelligence is introduced to students of the exchange program class at SMP Insan Rabbany, which is related to mathematics learning, so that it can help in student learning. The results of the community service evaluation showed high student enthusiasm regarding the use of artificial intelligence, both to aid learning and for other purposes.ASPIRASI : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat01In this community service, artificial intelligence is introduced to students of the exchange program class at SMP Insan Rabbany, which is related to mathematics learning, so that it can help in student learning.ASPIRASI : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat["Jan Setiawan", "Noni Dwi Sari", "Yuniar Istiyawati"]2024-07-10T00:00:00 189fc9c0e499bd7dd3e84133d081bddfc601ae46The use of artificial intelligence (AI) in healthcare analytics has brought about a transformation in the medical field of diagnosis and treatment optimization. AI technologies have unmatched abilities in processing substantial amounts of medical data and extracting valuable insights by combining big data analytics and advanced machine learning algorithms. AI algorithms provide healthcare professionals with enhanced accuracy and speed in diagnosing illnesses, predicting patient results, and tailoring treatment plans across the entire healthcare journey. This abstract explores how artificial intelligence (AI) can revolutionize healthcare analytics in various areas such as genomics, electronic health records (EHRs), medical imaging, and clinical decision support systems. Healthcare providers can improve healthcare services by optimizing workflows, enhancing patient outcomes, and using AI-driven initiatives to make services more accessible and high in quality. In order to ensure ethical and accountable use of AI in healthcare, it is necessary to address problems such as algorithm bias, data privacy worries, and regulatory obstacles. Despite these challenges, the ongoing advancement of AI technologies has vast potential to transform patient care models and healthcare delivery methods.Asian Journal of Medicine and Health02How artificial intelligence can revolutionize healthcare analytics in various areas such as genomics, electronic health records (EHRs), medical imaging, and clinical decision support systems is explored.Asian Journal of Medicine and Health["Tushar Khinvasara", "Kimberly Morton Cuthrell", "Nikolaos Tzenios"]2024-07-10T00:00:00 ae99ca2e9ddf086690ae55a5437adabc8498e6fbDiscover Education382The results revealed that there are opportunities for the integration of AI and ML in chemistry education, including personalized learning experiences, teacher assistance, and accessibility to learning materials, and the limitations and challenges surrounding AI and ML were revealed.Discover Education["Aloys Iyamuremye", "F. Niyonzima", "J. Mukiza", "Innocent Twagilimana", "Pascasie Nyirahabimana", "Th\u00e9ophile Nsengimana", "Jean Dieu Habiyaremye", "Olivier Habimana", "Ezechiel Nsabayezu"]2024-07-10T00:00:00 e66432d79fbf0d91c4b449670805411a52e750a4This literature review explores the transformative impact of artificial intelligence (AI) on enhancing cybersecurity measures across various domains. The study systematically examines the integration of AI in Intrusion Detection Systems (IDS), malware detection, phishing detection, threat intelligence, network security, and endpoint protection. Key findings reveal that AI-driven techniques significantly outperform traditional methods, particularly in real-time threat detection, accuracy, and adaptive response capabilities. Network-based IDS benefit from supervised and unsupervised learning algorithms, improving the identification of malicious network traffic and novel attack patterns. In malware detection, AI-enhanced static and dynamic analysis methods surpass signature-based approaches by detecting previously unknown malware and complex behaviors. Phishing detection has seen substantial improvements with AI applications in email filtering and URL analysis, reducing phishing incidents despite challenges like false positives. AI's role in threat intelligence is critical, automating data analysis to uncover hidden threats and employing predictive analytics to anticipate and mitigate cyber attacks. AI techniques in network security and endpoint protection enhance real-time monitoring and authentication processes, providing robust defenses against cyber intrusions. Despite these advancements, challenges such as handling high data volumes and the need for continuous learning to adapt to emerging threats remain. This review underscores the significant advancements, practical implementations, and ongoing challenges of leveraging AI in cybersecurity, highlighting its potential to fortify digital defenses and address the complexities of contemporary cyber threats.GLOBAL MAINSTREAM JOURNAL02This review underscores the significant advancements, practical implementations, and ongoing challenges of leveraging AI in cybersecurity, highlighting its potential to fortify digital defenses and address the complexities of contemporary cyber threats.GLOBAL MAINSTREAM JOURNAL["Mosa Sankaram", "Ms Roopesh", "Sasank Rasetti", "Nourin Nishat"]2024-07-10T00:00:00 984ed8b1d4ace4d5769e2781c32e1fa7045d0766Artificial intelligence (AI) is transforming the way we interact with data, leading to a growing concern about bias. This study aims to address this issue by developing intelligent algorithms that can identify and prevent new biases in AI systems. The strategy involves combining innovative machine-learning techniques, ethical considerations, and interdisciplinary perspectives to address bias at various stages, including data collection, model training, and decision-making processes. The proposed strategy uses robust model evaluation techniques, adaptive learning strategies, and fairness-aware machine learning algorithms to ensure AI systems function fairly across diverse demographic groups. The paper also highlights the importance of diverse and representative datasets and the inclusion of underrepresented groups in training. The goal is to develop AI models that reduce prejudice while maintaining moral norms, promoting user acceptance and trust. Empirical evaluations and case studies demonstrate the effectiveness of this approach, contributing to the ongoing conversation about bias reduction in AI.International Research Journal on Advanced Engineering Hub (IRJAEH)241The goal is to develop AI models that reduce prejudice while maintaining moral norms, promoting user acceptance and trust and the importance of diverse and representative datasets and the inclusion of underrepresented groups in training.International Research Journal on Advanced Engineering Hub (IRJAEH)["Isha Mishra", "Vedika Kashyap", "Nancy Yadav", "Dr. Ritu Pahwa"]2024-07-10T00:00:00 14fb838f3821563ecea8649934bf78e0e730a16bIn recent years, medicine has faced the serious challenge of the covid pandemic, due to which representatives of the health care sector had to mobilize forces and resources to jointly overcome these problems. The rapid development of artificial intelligence, its learning capabilities, and in recent years the creation of a neural network opens up wide possibilities for the use of AI in medicine. Aims: To analyze the modern literature on the use of AI for diagnosis and treatment and to analyze what problems may arise with the uncontrolled introduction of artificial intelligence Methodology: When conducting a literature review, an analysis and generalization of data on the research topic from 2019 to 2024 was carried out. The literature search was carried out by keywords using the PubMed search engine. Results: The literature review demonstrated the use of artificial intelligence in medicine, which has grown significantly in recent years and continues its rapid development, which is associated with the improvement of innovative technologies. The use of artificial intelligence in diagnostics is associated with the use of a neural network, which makes it possible to identify digitized images for rapid diagnosis. The use of artificial intelligence in surgery is reflected in the application of da Vinci. Artificial intelligence has been widely used in anesthesiology. Scientific Novelty: The literature search established that the implementation of artificial intelligence in medicine creates certain challenges related to the protection of personal data, and the possibility of error is not excluded when using AI. Conclusion: The use of AI is promising for diagnosis and treatment and helps doctors quickly make a diagnosis and prescribe treatment, but certain challenges created by artificial intelligence must be solved by implementing more reliable personal data protection systems, as well as control over the information reproduced by artificial intelligence.Futurity Medicine601The use of AI is promising for diagnosis and treatment and helps doctors quickly make a diagnosis and prescribe treatment, but certain challenges created by artificial intelligence must be solved by implementing more reliable personal data protection systems, as well as control over the information reproduced by artificial intelligence.Futurity Medicine["Liudmyla Bashkirova", "Iryna Kit", "Yury Havryshchuk", "A. Krasnova", "Svitlana Vasylyuk-Zaitseva"]2024-07-10T00:00:00 e83c9aa970991da4f0ccaabaeb9a4142f060468eThis research paper delves into the transformative impact of artificial intelligence (AI) on supply chain management, focusing on enhancing demand forecasting, operational efficiency, and customer satisfaction, while also managing costs and streamlining logistics operations. The adoption of AI in supply chains offers significant opportunities to improve service delivery and operational capabilities, which are crucial for maintaining competitiveness in the rapidly evolving business landscape. The study underscores the importance of AI technologies in reshaping supply chain dynamics by providing a comprehensive analysis of both the benefits and challenges associated with its implementation. Key benefits highlighted include the optimization of inventory management, enhanced accuracy of demand forecasting, reduced operational costs, and improved customer service. These enhancements are pivotal in achieving a competitive edge and adapting to changing market demands.However, the integration of AI into supply chains is not devoid of challenges. The paper identifies critical obstacles such as the need for significant cultural shifts within organizations, data security concerns, and the complexities of navigating legal and regulatory frameworks. These challenges require strategic management to ensure successful AI adoption and to mitigate associated risks.The research includes case studies of Arab companies that have integrated AI into their supply chains, offering practical insights into the real-world application of these technologies. These examples demonstrate both the potential rewards and the difficulties encountered, providing a balanced perspective on the practicalities of AI deployment in supply chain settings.In conclusion, while AI presents substantial opportunities for advancing supply chain management, it also necessitates careful consideration of various implementation challenges. The paper provides strategic recommendations for Arab companies aiming to leverage AI technologies effectively. These guidelines emphasize the need for thorough planning, continuous risk assessment, and fostering an adaptive organizational culture. By addressing these key areas, companies can harness the full potential of AI to enhance their supply chain operations and achieve sustainable growth.Journal of Ecohumanism562Journal of Ecohumanism["Mohamed Kama Laldin Ismaeil", "Adam Fad Lalla"]2024-07-10T00:00:00 3553135f337934ec6297ec14473eb5969f799e71This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.Frontiers in Neurology681The prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis are discussed, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support.Frontiers in Neurology["Ying Liu", "Zhongjian Wen", "Yiren Wang", "Yuxin Zhong", "Jianxiong Wang", "Yiheng Hu", "Ping Zhou", "Shengmin Guo"]2024-07-10T00:00:00 db5f4547575ffd537b0f37a1a6f5988722511d0fThe purpose of this paper is to examine the effectiveness of using artificial intelligence technology Artificial Intelligence (AI) in teaching Islamic Religious Education (PAI) in secondary schools (SMA). The method used in this research uses a library method where the literature review method is a systematic approach in collect,evaluate and synthesize literature then draw conclusions. The purpose of this writing is to find out how Artificial Intelligence (AI)dan improve PAI learning and its influence on student learning outcomes.Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika00The purpose of this writing is to find out how Artificial Intelligence (AI) can be used to improve PAI learning and its influence on student learning outcomes.Jurnal Arjuna : Publikasi Ilmu Pendidikan, Bahasa dan Matematika["Aprianti Astuti", "Muhammad Nabil Priambada", "Faelasup Faelasup", "Nurwati Nurwati"]2024-07-10T00:00:00 b80c28e4e4bcc2b55f41beaa6db3680420e951e3Aim: Exploration and developing mechanisms of advanced data acquisition necessary for training an artificial intelligence model capable of effectively detecting areas with increased susceptibility to fire situations. The study focuses on utilizing data from satellite missions and ground-based sensors, which provide both high-resolution imagery and precise data on temperature, humidity, and other environmental factors. By analysing these diverse data sources, the research aims to create a comprehensive and efficient model capable of early detection of potential fire hazards, which is crucial for prevention for fire-prone situations. Project and methods: It centres on a project that aims to enhance fire detection and management through the integration of artificial intelligence with data acquired from satellite systems and internet of things devices. The methodologies employed in this project involve a combination of advanced data acquisition, machine learning techniques, and the synthesis of diverse environmental data to train artificial intelligence models that can predict and detect fire incidents more effectively. Results: Significant advancements in fire detection and management have been demonstrated through the integration of artificial intelligence (AI) with satellite data and IoT: 1. Enhanced monitoring capabilities the use of satellite data systems enabled real-time monitoring of thermal anomalies and vegetation health, crucial for early detection and effective monitoring of wildfires. This real-time capability allowed for quicker responses and more informed decision-making in firefighting efforts. 2. Effective integration of data sources: the integration of satellite and surface data proved to be effective in enhancing the predictive capabilities of the fire management systems. This comprehensive approach allowed for a better understanding of fire dynamics and contributed to more accurate and timely predictions. Conclusions: It could be emphasize the significant benefits and future potential of integrating artificial intelligence with satellite and internet of things data for improving fire detection and management. The integration of satellite imagery and internet of things sensor data is essential for enhancing the predictive accuracy of artificial intelligence systems. This integration allows for a comprehensive assessment of fire risks, providing actionable intelligence that is critical for prevention for fire-prone situations. These conclusions underscore the transformative potential of artificial intelligence in enhancing fire management systems. Keywords: data acquisition, artificial intelligence, IoT, satellite data systems, fire management systemsSAFETY &amp; FIRE TECHNOLOGY100The study focuses on utilizing data from satellite missions and ground-based sensors, which provide both high-resolution imagery and precise data on temperature, humidity, and other environmental factors to create a comprehensive and efficient model capable of early detection of potential fire hazards.SAFETY &amp; FIRE TECHNOLOGY["Dawid Jurczy\u0144ski", "Pawe\u0142 Buchwald"]2024-07-10T00:00:00 eac5edf01d7061a20b05dd8ca86ed3a3531b0dd7The corporate world has undergone a transformation due to the advent of artificial intelligence (AI). AI has many important uses in the marketing industry, one of which is performance improvement. The goal of this study is to determine how artificial intelligence (AI) is affecting marketing from the viewpoint of Indian corporate employees who work in the field. The literature was thoroughly reviewed, and the results gave rise to a clear grasp of artificial intelligence (AI) and its applications in marketing. Second, the researcher conducted semi-structured interviews with several marketing specialists from several corporate employment organizations in India using a qualitative study approach. Thirty six marketing experts were interviewed by the researcher using a sample size of thirty six from the different twelve organizations. The study's conclusions highlight the elements that go into integrating AI into marketing, its advantages and disadvantages, The use of AI in marketing, ethical issues, and your company's pre- post AI marketing strategy. According to study, incorporating artificial intelligence into marketing & sales processes can boost company productivity and provide it a competitive edge. Keywords: Marketing, Artificial Intelligence, Delhi-NCR, Indian Corporate Employees, Customer, Market Scenario.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00According to study, incorporating artificial intelligence into marketing & sales processes can boost company productivity and provide it a competitive edge.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Dr. Akhilesh Gaur", "Ms. Swati Gupta", "Dr. Lata Sisodiya"]2024-07-10T00:00:00 bd2323a921285ade8859c49d815cd2b647e52c21The present study paper presents a theoretical framework for understanding the language capabilities of artificial intelligence (AI) systems, with a particular emphasis on natural language processing (NLP). The proposed framework, known as the Framework of Key Performative Attributes in AI Discourse (FKPA-AID), is based on a survey of pertinent linguistic theories and AI literature. It specifies six essential dimensions for modeling intelligent conversations: linguistic precision, contextual adaptation, fluid dialogue management, self-learning capacities, socio-emotional sensitivity, and continuity. The study also uses FKPA-AID to assess the strengths and weaknesses of present AI chat capabilities, and uses this analysis to propose prospective research directions for improving machine-based language mastery to better mimic human discourses. The study conducts a literature review of current breakthroughs in AI, notably in the field of natural language processing. It investigates AI's disruptive influence across industries and its role in driving digital transformation, with a particular emphasis on mobile technology applications. The paper also dives into the development of Generative AI (GAI) and Natural Language Processing (NLP), explaining its intricacies, models, applications, and recent advances. Furthermore, it emphasizes the use of AI and NLP in voice-controlled homes and healthcare, with a focus on designing intuitive interfaces and analyzing health data. The primary goal of this literature review is to provide a complete knowledge of the emerging landscape shaped by the combination of AI and NLP. The findings from the studied literature highlight the disruptive potential, ethical difficulties, and practical applicability across multiple disciplines. The paper emphasizes the importance of responsible development in the dynamic and ever-changing world of AI and NLP, and calls for collaborative efforts from interdisciplinary experts such as linguists, computer scientists, psychologists, sociologists, and philosophers to guide future research in this field. The proposed FKPA-AID framework is positioned as a theory-based systematic tool for assessing AI conversation capabilities, contributing to overall improvements in the field.International Journal For Multidisciplinary Research210The present study paper presents a theoretical framework for understanding the language capabilities of artificial intelligence (AI) systems, with a particular emphasis on natural language processing (NLP), based on a survey of pertinent linguistic theories and AI literature.International Journal For Multidisciplinary Research["Pooja V Pathak", "Vidhi D Mehta"]2024-07-10T00:00:00 44f53539cd37d3243ea489bc3564593eb80d579bIn this article, the author examines problems related to the risks associated with the use of artificial intelligence (AI) when carrying out certain types of legal activities. Attention is drawn to the risk of: a) “template generation” of court decisions in case they are generated by AI; b) violation of the key principles of law when using real-time and remote biometric identification systems. As a result of the study, the author concludes that creating the most optimal conditions for the use of AI in the legal field is a more rational way to minimize these risks than imposing a ban on such use. The author proposes specific conditions, including providing citizens with additional guarantees for the implementation of their rights and freedoms.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia00It is concluded that creating the most optimal conditions for the use of AI in the legal field is a more rational way to minimize these risks than imposing a ban on such use.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia["Yulia Balalaeva"]2024-07-10T00:00:00 494375068c850a99e287a98e9ed574f2f970fd8cThis paper provides a systematic literature review of studies investigating generative artificial intelligence (AI)-related copyright regulation concerns, issues, and policies. Our analysis highlights copyright doctrine application to generative AI in terms of data security, infringement, and fair use. The inspected databases were the Web of Science, Scopus, and ProQuest. For original and review article screening and quality assessment, we leveraged the following review software systems: Abstrackr, CADIMA, DistillerSR, EPPI Reviewer, MMAT, ROBIS, and SRDR+. Dimensions and VOSviewer were harnessed for bibliometric mapping and layout algorithms with regard to data visualization and analysis. PRISMA was the reporting quality assessment tool. Our results show that copyrighted data can be deployed in generative AI system training fairly and coherently, thus decreasing copyright-infringing content.Journal of Electrical Systems00The results show that copyrighted data can be deployed in generative AI system training fairly and coherently, thus decreasing copyright-infringing content.Journal of Electrical Systems["Doina Popescu Ljungholm"]2024-07-10T00:00:00 c53caa7d4ac7fb3eb8b9c3783417b53519a260a8Artificial Intelligence (AI) has revolutionized the healthcare sector by improving patient care and treatment through diagnostic revolutionization. AI is used for diagnosing and detecting diseases, analyzing large-scale patient data sets to find trends and abnormalities. This has led to increased precision and speed of disease identification, enabling early intervention and individualized treatment programs. AI-driven diagnostic systems have shown effectiveness in reducing incorrect diagnoses and enhancing patient outcomes for diseases like diabetes, cancer, and heart issues.AI algorithms also aid in treatment planning and drug discovery, predicting patient responses to treatments and optimizing therapeutic strategies. In clinical settings, AI-powered systems automate administrative tasks, manage patient records, and improve workflow efficiency. Chatbots and virtual health assistants can offer patient guidance and support, reducing healthcare staff burden and enhancing patient experiences. However, AI integration in healthcare faces challenges such as data privacy, security, financial resources, and ethical considerations. Bias in AI algorithms can perpetuate healthcare disparities, and efforts are being made to reduce bias through diverse datasets and transparent AI systems. Legal and ethical frameworks are needed to address these issues.In conclusion, AI in healthcare has the potential to improve patient outcomes, but challenges such as funding, security, data privacy, and ethical considerations need to be addressed.International Research Journal on Advanced Engineering Hub (IRJAEH)200In conclusion, AI in healthcare has the potential to improve patient outcomes, but challenges such as funding, security, data privacy, and ethical considerations need to be addressed.International Research Journal on Advanced Engineering Hub (IRJAEH)["Isha Mishra", "Vedika Kashyap", "Dr. Ritu Pahwa", "Dr. R. Dheivanai"]2024-07-10T00:00:00 7bf2b2de3edfe7e1e766c0a9511cf8df9779096bThe work focuses on the fact that the widespread introduction of robotization in the context of the development of a digital society requires improving security measures when introducing technological processes based on artificial intelligence, due to real risks leading to negative consequences. In particular, the development of technological processes will contribute to an increase in cases associated with causing harm to society, which will be forced to resort to the development of a legislative framework to bring the perpetrators to legal responsibility. In the process of the study, it was convincingly proven that various threats can arise due to the lack of new technologies adapted to the specific features, criminal legal mechanisms for the protection of public relations, as well as the resulting legislative vagueness on the issue of subjects of legal relations in cases of damage and harm caused by these technologies. In this regard, the relevance of issues involving increased responsibility of both employers and workers themselves associated with the artificial intelligence industry is increasing. In conclusion, it is concluded that by minimizing the various risks of using new technologies, which can lead to unpredictable negative consequences of a resonant nature, it will be possible to ensure the necessary level of public trust on the part of society.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia30It is concluded that by minimizing the various risks of using new technologies, which can lead to unpredictable negative consequences of a resonant nature, it will be possible to ensure the necessary level of public trust on the part of society.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia["Petr Kobets"]2024-07-10T00:00:00 ab9afc5d1bc53444acc2d04df7d9159c9620d263The subfield of artificial intelligence (AI) within computer science aims to create intelligent machines capable of performing tasks typically requiring human intelligence. This foundational concept posits that human intelligence can be sufficiently defined for machines to emulate. Machine learning (ML), a branch of AI, enables software programs to enhance their predictive accuracy without explicit programming, using historical data to forecast new output values. Deep learning, a subset of ML, involves training models to organize sounds, text, or images using neural networks and substantial labeled data. In some cases, deep learning models surpass human performance, achieving state-of-the-art accuracy. This research study explores the principles and advancements in AI, ML, and deep learning, emphasizing their transformative potential and applications.2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)260This research study explores the principles and advancements in AI, ML, and deep learning, emphasizing their transformative potential and applications.2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)["Syed Mohtashim Mian", "Mohammad Shuaib Khan", "Mohd Shawez", "Amandeep Kaur"]2024-07-10T00:00:00 fbc1feab3df416423a66d9be0f7b8bc054658d1fThe study explores the evolving role of Artificial Intelligence (AI) beyond its perceived neutrality, delving into its politicization particularly in the Global South's digitalization context. It argues that once seen as a neutral problem-solving tool, AI has transformed into a politically charged entity, embodying biases rooted in its creation and training processes. This transformation marks a shift towards AI colonialism, where corporate interests intertwine with extensive data extraction practices, raising concerns about extractive colonial power dynamics. The discourse of AI colonialism underscores the interdependence of AI, corporate interests, and the extraction of meaning, prompting a re-evaluation of regulatory frameworks to mitigate profit-driven activities. Furthermore, the article examines how AI's intersection with data extraction facilitates societal surveillance, leading to Digital Apartheid in Sub-Saharan Africa—a manifestation of racial capitalism in the digital age. This Digital Apartheid perpetuates social segregation based on race through AI-driven technologies, exacerbating biases that disproportionately affect people of color. The article advocates for open discussions on digital and AI ethics to address these challenges to counteract racial discrimination and foster a more inclusive and equitable technological landscape. Overall, the abstract highlights the complex socio-political dimensions of AI, urging for proactive measures to mitigate its negative impacts and ensure fair and just technological development.Keywords: artificial intelligence; AI coloniality; digital apartheid; racial capitalismGlobal South Review230The study explores the evolving role of Artificial Intelligence beyond its perceived neutrality, delving into its politicization particularly in the Global South's digitalization context, and highlights the complex socio-political dimensions of AI.Global South Review["Muhd Rafli Ramadhan Warganegara"]2024-07-10T00:00:00 ebc1f24b70dce6efb83041b4a931896c32120e8b| Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in imagerecognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically , in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to imagebased tasks. We explore how these methods could impact multiple facets of radiology , with a general focus on applications in oncology , and demonstrate ways in which these methods are advancing the field. Finally , we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced. PErsPECTIvEs © 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature reviews | CanCer AI in medical imaging The primary driver behind the emergence of AI in medical imaging has been the desire for greater efficacy and efficiency in clinical care. Radiological imaging data continues to grow at a disproportionate rate when compared with the number of available trained readers, and the decline in imaging reimbursements has forced healthcare providers to compensate by increasing productivity24. These factors have contributed to a dramatic increase in radiologists’ workloads. Studies report that, in some cases, an average radiologist must interpret one image every 3–4 seconds in an 8-hour workday to meet workload demands25. As radiology involves visual perception as well as decision making under uncertainty26, errors are inevitable — especially under such constrained conditions. A seamlessly integrated AI component within the imaging workflow would increase efficiency, reduce errors and achieve objectives with minimal manual input by providing trained radiologists with prescreened images and identified features. Therefore, substantial efforts and policies are being put forward to facilitate technological advances related to AI in medical imaging. Almost all imagebased radiology tasks are contingent upon the quantification and assessment of radiographic characteristics from images. These characteristics can be important for the clinical task at hand, that is, for the detection, characterization or monitoring of diseases. The application of logic and statistical pattern recognition to problems in medicine has been proposed since the early 1960s27,28. As computers became more prevalent in the 1980s, the AIpowered automation of many clinical tasks has shifted radiology from a perceptual subjective craft to a quantitatively computable domain29,30. The rate at which AI is evolving radiology is parallel to that in other application areas and is proportional to the rapid growth of data and computational power. There are two classes of AI methods that are in wide use today (Box 1; Fig. 2). The first uses handcrafted engineered features that are defined in terms of mathematical equations (such as tumour texture) and can thus be quantified using computer programs31. These features are used as inputs to state-ofthe-art machine learning models that are trained to classify patients in ways that can support clinical decision making. Although such features are perceived to be discriminative, they rely on expert definition and hence do not necessarily represent the most optimal feature quantification approach for the discrimination task at hand. Moreover, predefined features are often unable to adapt to variations in imaging modalities, such as computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI), and their associated signalto-noise characteristics. The second method, deep learning, has gained considerable attention in recent years. Deep learning algorithms can automatically learn feature representations from data without the need for prior definition by human experts. This datadriven approach allows for more abstract feature definitions, making it more informative and generalizable. Deep learning can thus automatically quantify phenotypic characteristics of human tissues32, promising substantial improvements in diagnosis and clinical care. Deep learning has the added benefit of reducing the need for manual preprocessing steps. For example, to extract predefined features, accurate segmentation of diseased tissues by experts is often needed33. Because deep learning is data driven (Box 1), with enough example data, it can automatically identify diseased tissues and hence avoid the need for expertdefined segmentations. Given its ability to learn complex data representations, deep learning is also often robust against undesired variation, such as the interreader variability, and can hence be applied to a large variety of clinical conditions and parameters. In many ways, deep learning can mirror what trained radiologists do, that is, identify image parameters but also weigh up the importance of these parameters on the basis of other factors to arrive at a clinical decision. Given the growing number of applications of deep learning in medical imaging14, several efforts have compared deep learning methods with their predefined featurebased counterparts and have reported substantial performance improvements with deep learning34,35. Studies have also shown that deep learning technologies are on par with radiologists’ performance for both detection36 and segmentation37 tasks in ultrasonography and MRI, respectively. For the classification tasks of lymph node metastasis in PET–CT, deep learning had higher sensitivities but lower specificities than radiologists38. As these methods are iteratively refined and tailored for specific applications, a better command of the sensitivity:specificity tradeoff is expected. Deep learning can also enable faster development times, as it depends solely on curated data and the corresponding metadata rather than domain expertise. On the other hand, traditional predefined feature systems have shown plateauing performance over recent years and hence do not generally meet the stringent requirements for clinical utility. As a result, only a few have been translated into the clinic39. It is expected that Box 1 | artificial intelligence methods in medical imaging Machine learning algorithms based on predefined engineered features traditional artificial intelligence (ai) methods rely largely on predefined engineered feature algorithms (Fig. 2a) with explicit parameters based on expert knowledge. such features are designed to quantify specific radiographic characteristics, such as the 3D shape of a tumour or the intratumoural texture and distribution of pixel intensities (histogram). a subsequent selection step ensures that only the most relevant features are used. statistical machine learning models are then fit to these data to identify potential imagingbased biomarkers. examples of these models include support vector machines and random forests. Deep learning algorithms recent advances in ai research have given rise to new, nondeterministic, deep learning algorithms that do not require explicit feature definition, representing a fundamentally different paradigm in machine learning. the underlying methods of deep learning have existed for decades. However, only in recent years have sufficient data and computational power become available. without explicit feature predefinition or selection, these algorithms learn directly by navigating the data space, giving them superior problemsolving capabilities. while various deep learning architectures have been explored to address different tasks, convolutional neural networks (CNNs) are the most prevalent deep learning architecture typologies in medical imaging today. a typical CNN comprises a series of layers that successively map image inputs to desired end points while learning increasingly higherlevel imaging features (Fig. 2b). starting from an input image, ‘hidden layers’ within CNNs usually include a series of convolution and pooling operations extracting feature maps and performing feature aggregation, respectively. these hidden layers are then followed by fully connected layers providing highlevel reasoning before an output layer produces predictions. CNNs are often trained endto-end with labelled data for supervised learning. Other architectures, such as deep autoencoders and generative adversarial networks, are more suited for unsupervised learning tasks on unlabelled data. transfer learning, or using pretrained networks on other data sets, is often utilized when dealing with scarce data. © 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. www.nature.com/nrc P e r s P e c t i v e sJournal of the Mexican Federation of Radiology and Imaging990A general understanding of AI methods, particularly those pertaining to imagebased tasks, is established and how these methods could impact multiple facets of radiology is explored, with a general focus on applications in oncology, and ways in which these methods are advancing the field are demonstrated.Journal of the Mexican Federation of Radiology and Imaging["Guillermo Elizondo-Riojas", "A. A. Negreros-Osuna", "J. M. Bernal-Ramirez", "B. Conde-Castro"]2024-07-10T00:00:00 5d227aaab8610c64a35f2f78e4252fd6acc2242bThis article explores the application and future prospects of artificial intelligence (AI) technology in medical education. Through in-depth analysis of the application of AI technology in personalized teaching, intelligent tutoring, objective evaluation, and sharing of teaching resources in medical education, the significant advantages of AI technology in improving the quality and effectiveness of medical education have been revealed. At the same time, combining the characteristics of traditional Chinese medicine teaching, this article also explores the integration and application of AI technology in traditional Chinese medicine teaching and its potential impact. Finally, this article summarizes the challenges and problems faced by current AI technology in medical teaching, and looks forward to future development trends.Journal of Education and Educational Research20The challenges and problems faced by current AI technology in medical teaching are summarized, the future development trends are looked forward to, and the significant advantages of AI technology in improving the quality and effectiveness of medical education are revealed.Journal of Education and Educational Research["Xujie Xu", "Wenli Wang", "Tingnan Huang", "Shihan Lv", "Lele Wang", "Mingxing Liu", "Zheng Wang"]2024-07-10T00:00:00 f4757008cf84526934979f0abf856a8cb83e088bThis study investigates prospective teachers' perspectives on the present status of artificial intelligence (AI) and their predictions regarding its future development. The study utilized a case study approach to select a group of 64 prospective teachers from the faculty of education at a state university in Türkiye. The study participants comprised 34 female and 30 male prospective teachers. The researchers employed a purposive sampling technique, specifically the criterion sampling approach, to select the prospective teachers included in the study. The researchers collected data for the study using the "AI Perception Interview Form" and the "AI Future Foresight Determination Form," and then analyzed the data using descriptive and content analysis techniques. The results showed that prospective teachers obtained information about AI primarily from social media, internet/news websites, and applications. Analyzing the definitions and explanations provided by the prospective teachers revealed that they particularly emphasized the uploading of human intelligence to computer systems, the acquisition of human-like abilities by machines, and the ability of AI to learn independently. Additionally, prospective teachers identified health, education, accounting, and finance as domains with significant potential for the advancement of AI. In education, the initial applications prospective teachers thought AI could be used for included determining students' mental states, assessing student levels, and providing personalized content. The data obtained from the study indicate that prospective teachers produced both utopian and dystopian content regarding the future of AI. This production of varied content reveals that prospective teachers have diverse perspectives on the future of AI.Sakarya University Journal of Education160The data obtained from the study indicate that prospective teachers produced both utopian and dystopian content regarding the future of AI, which reveals that prospective teachers have diverse perspectives on the future of AI.Sakarya University Journal of Education["Mehmet Uymaz"]2024-07-10T00:00:00 cbc48d488577511e0d629389ee7a4874f8f9910bIn an era where Artificial Intelligence (AI) is often seen as a universal solution for any complex problem, this presentation offers a critical examination of its role in the field of automatic control. To be concrete, I will focus on Optimal Control techniques, navigating through its history and addressing the evolution from its traditional model-based roots to the emerging data-driven methodologies empowered by AI. The presentation will delve into how the theoretical underpinnings of Optimal Control have been historically aligned with computational capabilities, and how this alignment has shifted over the years. This juxta-position of theory and computation motivates a deeper investigation into the diminishing relevance of certain traditional control methods amidst the AI revolution. We will critically examine scenarios where AI-driven approaches could outperform classical methods, as well as cases where the hype surrounding AI overshadows its actual utility. The talk will conclude with a nuanced view of state-of-the-art optimal control methods in practical applications including self-driving cars, advanced robotics and energy efficient systems. From this perspective, we will identify and explore future potential directions for the field, including the design of learning control architectures which seamlessly integrate predictive capabilities at every level, focusing on systems that can autonomously refine their performance over time through continuous learning and interaction with their environment.American Control Conference00The presentation will delve into how the theoretical underpinnings of Optimal Control have been historically aligned with computational capabilities, and how this alignment has shifted over the years, to critically examine scenarios where AI-driven approaches could outperform classical methods, as well as cases where the hype surrounding AI overshadows its actual utility.2024 American Control Conference (ACC)["Francesco Borrelli"]2024-07-10T00:00:00 ff6661feb918466a3643cd8952148ac1b8bfb8bc BreastScreen Norway discusses how the results from their screening programme for early breast cancer detection can influence future artificial intelligence to streamline early breast cancer detection. Breast cancer is a significant global health concern, with more than 2 million new cases diagnosed and over half a million women dying from the disease annually.(1) Many countries, including Norway, have implemented mammographic screening to detect breast cancer in an early stage of disease development, as an early intervention has clear benefits on the disease outcome. In Norway, all women aged 50 to 69 years are invited to biennial mammographic screening through the national screening program, BreastScreen Norway.(2) Open Access Government00How the results from their screening programme for early breast cancer detection can influence future artificial intelligence to streamline early breast cancer detection is discussed.Open Access Government["\u00c5sne S\u00f8rlien Holen", "Steffan Bos-Haugen", "Vessela N Kristensen", "Solveig Hofvind"]2024-07-10T00:00:00 0f2a3439a9314d9eab825593b2c1fb6998629484Objective: This paper aims to show how REDECA Reengineering Delphi and Evaluation can be integrated with Artificial Intelligence (AI) in a way to increase the influence of AI on Occupational Safety and Health (OSH) by further advancing the risk identification process, the prevention of injuries, and the compliance with safety standards.Methods: A quantitative cross-sectional study method was used through multiple regressions analysis for the relationships between AI application, risk identification, injury reduction, safety culture, and compliance. Organizational safety culture was explored further as a moderator influencing the effectiveness of AI in OSH systems.Results: AI enhances the identification and prediction of risk, resulting in a significant reduction in workplace injuries and fatalities. AI-enabled applications ensure higher adherence to safety protocols and helped in building a time-tested safety culture. In fact, organizational safety culture improves the effectiveness of AI, serving as a vital moderating factor that facilitates lasting advancements in workplace safety practices. This points to the relationship between technological innovation and organizational influences on better OSH outcomes.Novelty: This study presents an original integration of AI-driven predictive safety mechanisms through the REDECA framework, highlighting the moderating role of safety culture. This serves as a bridge between technology adoption and organizational behavior to advance workplace safety strategies.Research Implication: The findings provide a roadmap to organizations to not just invest in AI-based safety systems but also to inculcate a strong safety culture to reap the rewards of technical advances. This research sends a message to the fostering of the AI integration as a transformative approach for OSH management, which aims for the sustainable improvements in workplace safety, risk mitigation and employed well-being for the policymakers and the industry leaders.Safety and Health for Medical Workers340This study presents an original integration of AI-driven predictive safety mechanisms through the REDECA framework, highlighting the moderating role of safety culture and provides a roadmap to organizations to not just invest in AI-based safety systems but also to inculcate a strong safety culture to reap the rewards of technical advances.Safety and Health for Medical Workers["Sheila Michiel", "Isabelle Moissact", "Christopher Sean"]2024-07-10T00:00:00 8980ee7492165fefd876bacfdd1cd0a18c4d7434The rapid development of artificial intelligence in the field of healthcare has greatly improved diagnosis accuracy, disease prediction, personalized treatment and healthcare resource management. However, with the widespread application of medical artificial intelligence, challenges has emerged in the aspects of medical data, model development and evaluation, and societal considerations. Therefore, this study aims to explore challenges in the application of artificial intelligence in healthcare and suggest a series of feasible solutions to improve medical professional and researchers' understanding of medical artificial intelligence and enhance the quality of healthcare in clinical practice.Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi00Challenges in the application of artificial intelligence in healthcare are explored and a series of feasible solutions to improve medical professional and researchers' understanding of medical artificial intelligence and enhance the quality of healthcare in clinical practice are suggested.Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi["Z. C. Ye", "P. Xue", "Y. Qiao", "Y. Jiang"]2024-07-10T00:00:00 711be02e2f72b2351a8cbd264c8a7ab90aea20edNEJM AI105NEJM AI["Christopher A. Longhurst", "Karandeep Singh", "Aneesh Chopra", "Ashish Atreja", "John S. Brownstein"]2024-07-10T00:00:00 a81ff1a070e62dcac299f1d5b90feff8afe75d35Korean Journal of Radiology145Korean Journal of Radiology["Seong Ho Park", "C. H. Suh"]2024-07-10T00:00:00 80c15b33d6a6a1ef015e1956065682492292d86dВ статье рассмотрены примеры использования искусственного интеллекта (ИИ) в политиче- ской сфере, проанализированы правила использования самых популярных нейросетей на предмет возможно- сти их применения для создания агитационных материалов в политической кампании. Рассмотрен российский опыт законодательного регулирования материалов, созданных ИИ. Сделаны выводы о необходимости совер- шенствования правовой системы в области применения технологий ИИ. The article examined examples of the use of AI in the political sphere, analyzed the rules for using the most popular neural networks for the possibility of their use to create campaign materials in a political company. The Russian experience of legislative regulation of materials created by AI is considered. Conclusions are drawn about the need to improve the legal system in the field of AI technologies.Eurasian Advocacy (Evraziiskaya Advokatura)01Eurasian Advocacy (Evraziiskaya Advokatura)["\u041e.\u0410. \u0411\u043e\u0431\u0440\u043e\u0432\u0441\u043a\u0430\u044f"]2024-07-10T00:00:00 b65c5ef33cc5a6961705a43bac5c15c7c2735d2700[]2024-07-10T00:00:00 1004374623c7d56b787ec6f5dde6e95d6c5dae64Journal of the Mexican Federation of Radiology and Imaging00Journal of the Mexican Federation of Radiology and Imaging["Claudia M. Delsol-Perez", "Alix D. Reyes-Mosqueda", "Tania A. Rios-Rodriguez", "D. Perez-Montemayor"]2024-07-10T00:00:00 9b8a78fdb3abe64c3aef65805ea172fc42767319International Journal of Multidisciplinary Research &amp; Reviews00International Journal of Multidisciplinary Research &amp; Reviews["Alinor C. Amil"]2024-07-10T00:00:00 c4d0ea847d3ec65a51848fdedd9c333232c5baddMertihan Kurdoğlu graduated from Hacettepe University Faculty of Medicine, Department of Medicine (English). He completed his specialty in Obstetrics and Gynecology at Gazi University, Faculty of Medicine, Department of Obstetrics and Gynecology between 2001 and 2005. In 2006, he worked as a specialist at Çankırı State Hospital. Between 2007 and 2014, he worked at Van Yüzüncü Yıl University, Faculty of Medicine, Department of Obstetrics and Gynecology. Between the years 2014- 2016, he worked in Gazi University Faculty of Medicine, Department of Obstetrics and Gynecology and during that time, he was sent to Division of Minimally Invasive Gynecology and Research in the Department of Obstetrics and Gynecology of the University of Texas Medical Branch at Galveston, Texas, USA by the Gazi University and was trained on robotic surgery by Assoc. Prof. Gökhan Sami Kılıç. He has published over 150 scientific papers in national and international journals with more than 2100 citations and 7 book chapters in the national and international textbooks. He was a member of the editorial board of Van Medical Journal, editor of Turkish Journal of Obstetrics and Gynecology and editor-inchief of the Eastern Journal of Medicine, previously. At present, he acts as the editorin-chief in the International Journal of Women’s Health and Reproduction Sciences together with Prof. Dr. Arash Khaki.International Journal of Women's Health and Reproduction Sciences30Mertihan Kurdoğlu acts as the editorin-chief in the International Journal of Women’s Health and Reproduction Sciences together with Prof. Arash Khaki.International Journal of Women's Health and Reproduction Sciences["M. Kurdo\u011flu", "A. Khaki", "G. Davarnia"]2024-07-10T00:00:00 99d4c66df5f960421b5996dd0b58c0fb877a4f03Current Dermatology Reports470Current Dermatology Reports["Fatuma-Ayaan Rinderknecht", "Lotanna Nwandu", "Jenna Lester", "R. Daneshjou"]2024-07-10T00:00:00 f8861d36729bb759140c02fcd08d50c39b36c583For security reasons, this system utilized Raspberry Pi to create a facial recognition with door lock and unlock system. Two of the most significant universal rights are privacy and security. There is a great deal of research being done to guarantee security in our everyday lives through technology. Among these, one well-known and widely used technique is facial recognition. With the use of the Internet of Things (IoT), faces may be recognized and identified in photos using this technology, making it even more accurate and valuable. This study intends to develop a smart door that authenticates users and secures the gateway based on face recognition and IoT. This system has utilized both Eigenfaces approach and the Viola-Jones method to identify faces and individuals in our proof of concept for such a smart security system. To guarantee the system's low cost and compact form, a Raspberry Pi has been employed as the microprocessor. When the processor receives a command, the door will automatically open for the recognized individual. Conversely, an email with a photo of the unknown will be uploaded and forwarded to the owner. From anywhere in the world, the owner can then grant or deny admission and sound an alarm with the aid of the GSM module.2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)170This study intends to develop a smart door that authenticates users and secures the gateway based on face recognition and IoT and employs Raspberry Pi to create a facial recognition with door lock and unlock system.2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)["Jakir Hussain", "Hemanth Kumar", "Z. A. Ahamed", "S. Jeevan", "P. A. Kumar"]2024-07-10T00:00:00 4bc54934aae663bf373f93b296e107c262e52267AIware100Proceedings of the 1st ACM International Conference on AI-Powered Software["James Noble"]2024-07-10T00:00:00 7e77ecc3869e6f95fca6b67406f9fe97df3b9f50Frontiers in Nuclear Engineering00Frontiers in Nuclear Engineering["Jian Deng", "Jianjun Xiao", "Songbai Cheng", "Yu Liu", "Zhifang Qiu"]2024-07-10T00:00:00 f5cf9606c990871e2659c789bf8b5eade342166eJournal of Environmental Rheumatology00Journal of Environmental Rheumatology["E. Grossi"]2024-07-10T00:00:00 ba9d720f07251d12c6e01a1169d23d2a82dd5c75Several jurisdictions are starting to regulate frontier artificial intelligence (AI) systems, i.e. general-purpose AI systems that match or exceed the capabilities present in the most advanced systems. To reduce risks from these systems, regulators may require frontier AI developers to adopt safety measures. The requirements could be formulated as high-level principles (e.g. 'AI systems should be safe and secure') or specific rules (e.g. 'AI systems must be evaluated for dangerous model capabilities following the protocol set forth in...'). These regulatory approaches, known as 'principle-based' and 'rule-based' regulation, have complementary strengths and weaknesses. While specific rules provide more certainty and are easier to enforce, they can quickly become outdated and lead to box-ticking. Conversely, while high-level principles provide less certainty and are more costly to enforce, they are more adaptable and more appropriate in situations where the regulator is unsure exactly what behavior would best advance a given regulatory objective. However, rule-based and principle-based regulation are not binary options. Policymakers must choose a point on the spectrum between them, recognizing that the right level of specificity may vary between requirements and change over time. We recommend that policymakers should initially (1) mandate adherence to high-level principles for safe frontier AI development and deployment, (2) ensure that regulators closely oversee how developers comply with these principles, and (3) urgently build up regulatory capacity. Over time, the approach should likely become more rule-based. Our recommendations are based on a number of assumptions, including (A) risks from frontier AI systems are poorly understood and rapidly evolving, (B) many safety practices are still nascent, and (C) frontier AI developers are best placed to innovate on safety practices.arXiv.org06Policymakers should initially mandate adherence to high-level principles for safe frontier AI development and deployment, ensure that regulators closely oversee how developers comply with these principles, and urgently build up regulatory capacity.ArXiv["Jonas Schuett", "Markus Anderljung", "Alexis Carlier", "Leonie Koessler", "Ben Garfinkel"]2024-07-10T00:00:00 e0246fa7cab50b026e732f10810c0281f5c6c69aThe rapid advancement of artificial intelligence (AI) has ushered in a new age in education, with prospects to tailor learning experiences and change teaching processes. Conventional educational systems often find it difficult to accommodate varied learning styles and needs, resulting in disengagement and wasted opportunities. AI-powered technologies, such as personalized learning algorithms and adaptive assessment tools, provide solutions by customizing educational experiences for each student, increasing engagement and motivation. This integrated literature review (ILR) offers an in-depth examination of AI-based curriculum development, focusing on its revolutionary impact on education. The research aims to build an AI-based curriculum that customizes learning and fulfills varied student needs without increasing existing inequities or jeopardizing data privacy and security. The study's purpose is to consolidate current research to highlight the benefits and problems connected with AI integration in education. The underlying conceptual framework incorporates personalized learning algorithms, adaptive assessment tools, and immersive educational technology, highlighting their ability to improve learning outcomes and increase student engagement. This ILR systematically reviews literature, including scholarly papers, articles, conference proceedings, and reputable digital resources. The data collection approach included identifying relevant keywords, conducting extensive searches, and meticulously reviewing selected publications. The review combines technology, pedagogy, psychology, and sociology concepts to understand AI's function in education comprehensively. The results show that personalized learning algorithms can dramatically improve student engagement and academic achievement by offering relevant and sufficiently challenging content. Adaptive assessment technologies provide real-time feedback and interventions, which improves learning outcomes. However, common issues like data privacy, algorithmic biases, and equal access necessitate careful control. Using immersive technology such as virtual reality and augmented reality improves comprehension and retention, but it requires significant financial investment and technical skill. The findings highlight AI's potential to transform education by delivering personalized, adaptable, and engaging learning experiences. Future research and practice recommendations include extensive professional development programs for educators, equal access to AI technologies, and the establishment of solid ethical criteria. The study underlines the need for specific positions in educational institutions, such as AI Data Privacy Coordinators and AI Equity Specialists, to successfully handle ethical and practical concerns. This ILR highlights AI's revolutionary educational potential and offers a road map for using these technologies to create more inclusive, effective, and engaging learning environments. Further research is needed to investigate the long-term effects of AI integration on student results and to provide scalable, adaptive AI solutions for various educational environments.International Journal For Multidisciplinary Research565The research aims to build an AI-based curriculum that customizes learning and fulfills varied student needs without increasing existing inequities or jeopardizing data privacy and security, and highlights AI's potential to transform education by delivering personalized, adaptable, and engaging learning experiences.International Journal For Multidisciplinary Research["Rachid Ejjami"]2024-07-10T00:00:00 f648f2dc4949780049c583553a59bdcfef5e903c"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology, and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and more generally in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment and highlights the need to integrate clinical and medical imaging data and introduces strategies to ensure smooth and incentivized integration. ©RSNA, 2024.Radiology: Artificial Intelligence04The report emphasizes the importance of collaboration to improve clinical deployment and highlights the need to integrate clinical and medical imaging data and introduces strategies to ensure smooth and incentivized integration.Radiology. Artificial intelligence["M. Linguraru", "S. Bakas", "Mariam S. Aboian", "Peter D. Chang", "Adam E Flanders", "Jaysheree Kalpathy-Cramer", "F. Kitamura", "M. Lungren", "John T Mongan", "Luciano M Prevedello", "Ronald M. Summers", "Carol C. Wu", "Maruf Adewole", "Charles E. Kahn"]2024-07-10T00:00:00 1c9123caa6702ec925f4ac25dcf8d43c358e9940Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.International Reviews of Immunology1513This review article aims to summarize important computational resources and AI-based tools in vaccine development and discusses the various applications and limitations of AI tools in vaccine development.International reviews of immunology["Ananya", "Darshan C Panchariya", "Anandakrishnan Karthic", "S. P. Singh", "Ashutosh Mani", "A. Chawade", "Sandeep Kushwaha"]2024-07-10T00:00:00 91925f988424a5ab55c5e3cf36cb55fea0b96ea8As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce.arXiv.org521A road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce is provided and an AI literacy framework to define competencies for medical students is proposed.ArXiv["Yingbo Ma", "Yukyeong Song", "Jeremy A. Balch", "Yuanfang Ren", "Divya Vellanki", "Zhenhong Hu", "Meghan Brennan", "Suraj Kolla", "Ziyuan Guan", "Brooke Armfield", "T. Ozrazgat-Baslanti", "Parisa Rashidi", "Tyler J. Loftus", "A. Bihorac", "B. Shickel"]2024-07-10T00:00:00 e5fd292a688a871c606be70d7cefa95e9582caa0A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process.British Journal of Educational Technology151Five papers highlight the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized.Br. J. Educ. Technol.["Olga Viberg", "Ren\u00e9 F. Kizilcec", "Alyssa Friend Wise", "Ioana Jivet", "Nia Nixon"]2024-07-10T00:00:00 2c3dc8a3e6e367fb23c5a900d295aa122c532974Background: The emergence of generative artificial intelligence (GenAI) presents unprecedented opportunities to redefine conceptions of personhood and cognitive disability, potentially enhancing the inclusion and participation of individuals with cognitive disabilities in society. Objective: We aim to explore the transformative potential of GenAI in reshaping perceptions of cognitive disability, dismantling societal barriers, and promoting social participation for individuals with cognitive disabilities. Methods: This study is a critical review of current literature in disability studies, artificial intelligence (AI) ethics, and computer science, integrating insights from disability theories and the philosophy of technology. The analysis focused on 2 key aspects: GenAI as a social mirror reflecting societal values and biases, and GenAI as a cognitive partner for individuals with cognitive disabilities. Results: This paper proposes a theoretical framework for understanding the impact of GenAI on perceptions of cognitive disability. It introduces the concepts of GenAI as a “social mirror” that reflects and potentially amplifies societal biases and as a “cognitive copilot” providing personalized assistance in daily tasks, social interactions, and environmental navigation. This paper also presents a novel protocol for developing AI systems tailored to the needs of individuals with cognitiveJMIR Neurotechnology641A theoretical framework for understanding the impact of GenAI on perceptions of cognitive disability is proposed and the concepts of GenAI as a “social mirror” that reflects and potentially amplifies societal biases and as a “cognitive copilot” providing personalized assistance in daily tasks, social interactions, and environmental navigation are introduced.JMIR Neurotechnology["Dorit Hadar Souval", "Yuval Haber", "A. Tal", "Tomer Simon", "Tal Elyoseph", "Z. Elyoseph"]2024-07-10T00:00:00 6db7a3321de8e76e58fbe8ada083a91917deb2e9 This article examines the innovative combination of blockchain and artificial intelligence (AI) technologies in the field of electronic letters of credit and international trade finance. It explores how the combined effect of this convergence may greatly improve the security, effectiveness, and clarity of trade finance procedures. The article presents a suggested framework for combining various technologies, focusing on important design considerations including security, trust, interoperability, and adherence to international trade norms. The technological design blends blockchain’s decentralized ledger with AI’s analytical capabilities, highlighting the need of smart contracts, data management, and application programming interfaces for smooth interoperability. The implementation plan and stages are meticulously detailed, emphasizing the systematic approach necessary for effective integration. The article also discusses the problems and dangers related to this technology integration, such as technical obstacles, regulatory compliance, security threats, stakeholder acceptance, and cost factors. The conclusion outlines the impact of blockchain and AI on trade finance, discusses their larger implications for international commerce, and advocates for the adoption of these advanced technologies by collective action. This article seeks to provide significant insights to financial institutions, policy-makers, technologists, and stakeholders in the trade finance sector, promoting the modernization of trade finance via new technology.Uniform Law Review = Revue de Droit Uniforme01The article presents a suggested framework for combining various technologies, focusing on important design considerations including security, trust, interoperability, and adherence to international trade norms, and outlines the impact of blockchain and AI on trade finance.Uniform Law Review["Moein Elahi Nezhad", "Shima Rashidian", "Consiglia Botta"]2024-07-10T00:00:00 7d37f08f2580850caad7a9b5436b3abcc2316ba6 Intelligent systems have been rapidly evolving and play a pivotal role in assisting individuals across diverse domains, from healthcare to transportation. Understanding the dynamics of human-Artificial Intelligence (AI) partnering, particularly how humans trust and collaborate with intelligent systems, is becoming increasingly critical to design effective systems. This paper presents an experimental analysis to assess the impact of AI design attributes on users' trust, workload and performance when solving classification problems supported by an AI assistant. Specifically, we study the effect of transparency, fairness, and robustness in the design of an AI assistant and analyze the role of participants' gender and education background on the outcomes. The experiment is conducted with 47 students in undergraduate, master's and Ph.D. programs using a drawing game application where the users are asked to recognize incomplete sketches revealed progressively while receiving recommendations from multiple versions of an AI assistant. The results show that when collaborating with the AI, participants achieve a higher performance than their individual performance or the performance of the AI. The results also show that gender does not have an impact on users' trust and performance when collaborating with different versions of the AI system, whereas education level has a significant impact on the participants' performance but not on trust. Finally, the impact of design attributes on participants' trust and performance highly depends on the accuracy of the AI recommendations, and improvements in participants' performance and trust in some cases come at the expense of increased workload.Journal of Mechanical Design01The results show that when collaborating with the AI, participants achieve a higher performance than their individual performance or the performance of the AI, and improvements in participants' performance and trust in some cases come at the expense of increased workload.Journal of Mechanical Design["Mostaan Lotfalian Saremi", "Isabella Ziv", "Onur Asan", "A. E. Bayrak"]2024-07-10T00:00:00 78eeaa19a61178721cd2319c3ed2036cabd4ecdeThe prospective arrival of transformative artificial intelligence (TAI) will be a filter for the human civilization – a threshold beyond which it will either strongly accelerate its growth, or vanish. Historical evidence on technological progress in AI capabilities and economic incentives to pursue it suggest that TAI will most likely be developed in just one to four decades. In contrast, theoretical problems of AI alignment, needed to be solved in order for TAI to be “friendly” towards humans rather than cause our extinction, appear difficult and impossible to solve by mechanically increasing the amount of compute. This means that transformative AI poses an imminent existential risk to the humankind which ought to be urgently addressed. Starting from this premise, this paper provides new economic perspectives on discussions surrounding the issue: whether addressing existential risks is cost effective and fair towards the contemporary poor, whether it constitutes “Pascal’s mugging”, how to quantify risks that have never materialized in the past, how discounting affects our assessment of existential risk, and how to include the prospects of upcoming singularity in economic forecasts. The paper also suggests possible policy actions, such as ramping up public funding on research on existential risks and AI safety, and improving regulation of the AI sector, preferably within a global policy framework.Technological and Economic Development of Economy201New economic perspectives on discussions surrounding the issue of transformative artificial intelligence are provided: whether addressing existential risks is cost effective and fair towards the contemporary poor, whether it constitutes “Pascal’s mugging”, how to quantify risks that have never materialized in the past, how discounting affects the authors' assessment of existential risk, and how to include the prospects of upcoming singularity in economic forecasts.Technological and Economic Development of Economy["J. Growiec"]2024-07-10T00:00:00 04773252550ca1de9d29fa09b158f5e2427382b5Digital Twins (DTs) are revolutionizing supply chain operations by creating dynamic digital replicas of physical assets, processes, and systems. This paper explores the integration of Artificial Intelligence (AI) with Digital Twins to bridge the physical and digital worlds in supply chain management. By leveraging AI, Digital Twins can analyze real-time data, predict future events, and optimize decision-making processes. This synergy enhances operational efficiency, reduces costs, and improves responsiveness to disruptions. We delve into the architecture of AI-driven Digital Twins, highlighting their components, data flow, and interaction mechanisms. Case studies across different industries demonstrate the practical applications and benefits of this technology. The discussion includes challenges such as data privacy, integration complexity, and the need for standardized protocols. Future research directions focus on advancing AI algorithms for better predictive capabilities and creating more robust, scalable Digital Twin frameworks. This paper underscores the transformative potential of AI-enhanced Digital Twins in creating agile, resilient, and intelligent supply chains. Journal of Electrical Systems181The architecture of AI-enhanced Digital Twins is explored, highlighting their components, data flow, and interaction mechanisms and underscores the transformative potential of AI-enhanced Digital Twins in creating agile, resilient, and intelligent supply chains.Journal of Electrical Systems["Manuel Enrique", "Chenet Zuta", "Chaitanya Koneti", "Dr Olivares Zegarra", "Venus Flor", "Marina Carvajal-Ordo\u00f1ez"]2024-07-10T00:00:00 21c2ab6dbd491e0df030c37ee0164fadcc80827fArtificial intelligence has been utilized for enhancement of education. The paper describes the gradual influences of Natural language processing, ChatGPT and Intelligent Tutoring Systems in mathematical education in particular. A systematic review and briefing is targeted for budding researchers in the field of AI based education. A comparison of research with a brief summary is provided along with the analysis of the search. Future scope for the clear understanding and easy implementation of mathematics is aimed with development of the intelligent system with customized solutions to the difficulties faced by the learners of mathematics. The impact of Artificial Intelligence (AI) on the applications in education has been evolving since the past 50+ years. The advent of Intelligent Tutoring Systems has emerged as a rich and important natural environment that deploys and improves AI algorithms. The review carried out shows ITS as an ever growing field for education comes with positive impact on education outcomes, effective increase in learning rates, increase in the learning levels of students and many such benefits. It also has its own challenges in creating authoring environments, making collaborative learning possible, world wide web deployment and creation of virtual reality environments to name a few. In this review paper we focus on the impact of applications that have been developed using NLP, ChatGPT and ITS as education technology.International Research Journal on Advanced Engineering Hub (IRJAEH)261The paper describes the gradual influences of Natural language processing, ChatGPT and Intelligent Tutoring Systems in mathematical education in particular and focuses on the impact of applications that have been developed using NLP, ChatGPT and ITS as education technology.International Research Journal on Advanced Engineering Hub (IRJAEH)["KP Mredula", "Roman Jonita", "P. Sajja"]2024-07-10T00:00:00 05f0ba605e5d53c71bc188b7875946b1bfdf39be Global demand by cloud vendors, financial institutions, and telecommunication companies for commercially owned and operated data centers is accelerating in recent years. Given this increase in energy supply required to meet US artificial intelligence (AI) technology consumer demand, the paper addresses state-of-the-art traditional data centers and their capacity transition to process AI technologies. Subsequently, the paper explains the potential for small modular (nuclear) reactors—and specifically a subset, micro modular reactors (MMRs)—to generate the increasing energy demanded for AI processing capabilities, including the potential of quantum computing, through 2035 and beyond. Lastly, the paper identifies a primary regulatory policy challenge—the federal regulatory construction review/permitting process—that hinders an environmentally sustainable source of nuclear energy to power such AI data centers and offers policy recommendations to assist in meeting this federal regulatory policy challenge and encourages the deployment of MMR technologies.Science and Public Policy331The paper explains the potential for small modular (nuclear) reactors—and specifically a subset, micro modular reactors (MMRs)—to generate the increasing energy demanded for AI processing capabilities, including the potential of quantum computing, through 2035 and beyond.Science and Public Policy["Thomas A Hemphill"]2024-07-10T00:00:00 1e06c9421d46e355039565d812028c5352b59a37The use of information technology and artificial intelligence in the healthcare sector has brought about smart hospitals. These hospitals incorporate AI, IoT, big data analytics, and robotics to increase productivity, optimize patient care, and decrease expenses. This paper aims at discussing the current trends in application of advanced IT in hospitals and the role of AI in enhancing the quality of patient care, clinical outcomes and financial performance. The study used a retrospective cohort design in two large hospitals in New York City and Los Angeles to evaluate the adoption of AI technologies from 2019 to 2023. The EHRs and interviews with the healthcare providers and patients were used to assess the mortality, complication, length of stay, and readmission rates before and after the implementation of AI. The findings show that there are statistically significant positive changes in patient outcomes after the integration of AI in smart hospital solutions. Issues related to data integration, privacy, and clinicians’ acceptance of AI-generated suggestions are also considered. This research contributes to the knowledge of the changes that AI brings to the healthcare system and the directions for the development of smart hospitals. Journal of Electrical Systems301There are statistically significant positive changes in patient outcomes after the integration of AI in smart hospital solutions, and issues related to data integration, privacy, and clinicians’ acceptance of AI-generated suggestions are considered.Journal of Electrical Systems["Dr Subash", "Chandra Nayak Dr", "Samrat Ray", "A. S. Amar", "Vinod Kunjady Chacko", "Amal Thomas", "Venu Gopal", "Krovvidi", "Dr Suhas Rajaram Mache"]2024-07-10T00:00:00 2ffa4a283dc46783d89c0f6041bfdc77d1e4e2a6Many students and academics face difficulties in English academic writing, particularly non-native speakers of English. This paper examines the use of Artificial Intelligence (AI) in optimizing English academic writing in intelligent decision supporting systems (IDSS). Various literatures were studied to find out how AI can help improve English academic writing. The findings reveal that AI can improve grammar, spelling, sentence construction and make suggestions for clarity and cohesion through tools like Grammarly. Furthermore, integrating AI into IDSS also promotes more efficient and data driven decisions. However, there are still barriers like complex data analysis, ensuring accuracy and reliability of AI-generated content, and addressing ethical issues including bias and transparency. This simple paper highlights the importance of developing more sophisticated and user-friendly AI technologies and the need for further research to explore the long-term impact of using AI in English academic writing for Indonesia context. The results are beneficial to learners, educators and learning institutions as they employ AI tools with the purpose to improve the English academic writing quality and support better decision making.Jurnal Ilmiah Universitas Batanghari Jambi160The findings reveal that AI can improve grammar, spelling, sentence construction and make suggestions for clarity and cohesion through tools like Grammarly, and integrating AI into IDSS also promotes more efficient and data driven decisions.Jurnal Ilmiah Universitas Batanghari Jambi["Sri Marmoah", "Dimas Adika", "Sri Haryati", "Yurni Yurni"]2024-07-10T00:00:00 aab6d4193ab9ef1d0af3746384b316b37ad87aecAustralasian Journal of Engineering Education4711Australasian Journal of Engineering Education["Sasha Nikolic", "Carolyn Sandison", "R. Haque", "Scott Daniel", "Sarah Grundy", "M. Belkina", "Sarah Lyden", "Ghulam M. Hassan", "Peter Neal"]2024-07-11T00:00:00 30c164fb9ba7fbfc090a3c0738630b1f616b8751The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.International Journal of Cancer664The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies.International journal of cancer["F. Moro", "M. Ciancia", "D. Za\u00e7e", "M. Vagni", "Huong Elena Tran", "M. T. Giudice", "S. G. Zoccoli", "F. Mascilini", "F. Ciccarone", "Luca Boldrini", "Francesco D'Antonio", "G. Scambia", "A. Testa"]2024-07-11T00:00:00 280467e6d838124b8501b7005fe62fd324774d29E-marketing refers to the use of technology and artificial intelligence to analyze and interpret marketing data. In addition to providing recommendations and guidance to improve marketing campaigns, improve user experience, and increase sales. These tools include data analytics, machine learning, data classification and aggregation, voice and image recognition, and natural language. Therefore, this study aimed to identify the effect of artificial intelligence (AI) in Design on E-Marketing in Companies. This study used a survey design with a quantitative approach, with the target group being workers of E-Marketing companies. A 198 surveys were also gathered from Jordanian E-Marketing companies, however, 187 questionnaires were judged viable for study. Where the results indicate AI positively affects E-Marketing, and natural language processing, analytical models, content marketing, and digital marketing affect E-Marketing.Journal of Ecohumanism494Where the results indicate AI positively affects E-Marketing, and natural language processing, analytical models, content marketing, and digital marketing affect E-Marketing, then artificial intelligence in Design on E-Marketing in Companies is identified.Journal of Ecohumanism["Ali Mohammad Ali Alqudah", "Yousef M. Jaradat", "B. Alobaydi", "Derar Alqudah", "Emran (Mohamad Ali) Abdalah Al Qudah", "B. Jarah"]2024-07-11T00:00:00 9f852a0861fd0de0fc13c4337e41ff6435c75149Our research systematically investigates the cognitive and emotional processes revealed through eye movements within the context of virtual reality (VR) environments. We assess the utility of eye-tracking data for predicting emotional states in VR, employing explainable artificial intelligence (XAI) to advance the interpretability and transparency of our findings. Utilizing the VR Eyes: Emotions dataset (VREED) alongside an extra trees classifier enhanced by SHapley Additive ExPlanations (SHAP) and local interpretable model agnostic explanations (LIME), we rigorously evaluate the importance of various eye-tracking metrics. Our results identify significant correlations between metrics such as saccades, micro-saccades, blinks, and fixations and specific emotional states. The application of SHAP and LIME elucidates these relationships, providing deeper insights into the emotional responses triggered by VR. These findings suggest that variations in eye feature patterns serve as indicators of heightened emotional arousal. Not only do these insights advance our understanding of affective computing within VR, but they also highlight the potential for developing more responsive VR systems capable of adapting to user emotions in real-time. This research contributes significantly to the fields of human-computer interaction and psychological research, showcasing how XAI can bridge the gap between complex machine-learning models and practical applications, thereby facilitating the creation of reliable, user-sensitive VR experiences. Future research may explore the integration of multiple physiological signals to enhance emotion detection and interactive dynamics in VR.Applied Sciences444These findings suggest that variations in eye feature patterns serve as indicators of heightened emotional arousal, and highlight the potential for developing more responsive VR systems capable of adapting to user emotions in real-time.Applied Sciences["Meryem Bekler", "Murat Yilmaz", "H. E. Ilg\u0131n"]2024-07-11T00:00:00 9698cb6b56012e6e0c0b3a24e96408541438d7b1The article is devoted to the study of the impact of artificial intelligence (AI) on modern education, the analysis of the prospects for the use of artificial intelligence in institutions of higher education (HEIs) and the resulting problems. It was emphasized that the future of education is inextricably linked with the development of information and communication technologies and intelligent machines. The prospects of artificial intelligence open up new opportunities in teaching and learning in higher education institutions with a strong potential to change even the management system of higher education institutions. The history of the emergence of artificial intelligence is briefly given, starting from the 13th century, when Raimund Lullius proposed the idea of implementing reasoning and mental processes in an intellectual machine. The article uses methods of complex theoretical and descriptive analysis. The scientific novelty of the work: the established effectiveness of using artificial intelligence in education can be presented in the form of the following functions: automation, integration, acclimatization, differentiation, identification. The increasingly widespread use of artificial intelligence in higher education institutions and schools also raises ethical questions. Educational institutions must now consider what type of data is collected, how that information is used, and what controls are in place to protect the privacy of learners. Practical significance of the work: in addition to functions that reflect the effectiveness of using artificial intelligence in the educational process, because the authors identified the positive aspects of introducing artificial intelligence into education. The results of the study: the authors of the article come to the conclusion that now it is necessary to rethink the function and pedagogical models of learning in connection with artificial intelligence in higher education institutions, because significant opportunities are opening up for higher education institutions thanks to the use of artificial intelligence in the educational process.Modern Information Technologies and Innovation Methodologies of Education in Professional Training Methodology Theory Experience Problems132The authors come to the conclusion that now it is necessary to rethink the function and pedagogical models of learning in connection with artificial intelligence in higher education institutions, because significant opportunities are opening up for higher education institutions thanks to the use of artificial intelligence in the educational process.Modern Information Technologies and Innovation Methodologies of Education in Professional Training Methodology Theory Experience Problems["\u0420\u043e\u043c\u0430\u043d \u0421\u0435\u043c\u0435\u043d\u043e\u0432\u0438\u0447 \u0413\u0443\u0440\u0435\u0432\u0438\u0447", "\u041b\u0435\u043e\u043d\u0456\u0434 \u041a\u043e\u043d\u043e\u0448\u0435\u0432\u0441\u044c\u043a\u0438\u0439", "\u041e\u043b\u0435\u0433 \u041a\u043e\u043d\u043e\u0448\u0435\u0432\u0441\u044c\u043a\u0438\u0439", "\u0410\u043b\u0456\u043d\u0430 \u0412\u043e\u0454\u0432\u043e\u0434\u0430", "\u0421\u0432\u0456\u0442\u043b\u0430\u043d\u0430 \u041b\u044e\u043b\u044c\u0447\u0430\u043a"]2024-07-11T00:00:00 133b254f3ed247a33d5b4c9d9696ca6c5ef66d28BACKGROUND Over the past decade, several studies have shown that potential of artificial intelligence (AI) in dermatology. However, there has yet to be a systematic review evaluating the usage of AI specifically within the field of Mohs micrographic surgery (MMS). OBJECTIVE In this review, we aimed to comprehensively evaluate the current state, efficacy, and future implications of AI when applied to MMS for the treatment of nonmelanoma skin cancers (NMSC). MATERIALS AND METHODS A systematic review and meta-analysis was conducted following PRISMA guidelines across several databases, including PubMed/MEDLINE, Embase, and Cochrane libraries. A predefined protocol was registered in PROSPERO, with literature search involving specific keywords related to AI and Mohs surgery for NMSC. RESULTS From 23 studies evaluated, our results find that AI shows promise as a prediction tool for precisely identifying NMSC in tissue sections during MMS. Furthermore, high AUC and concordance values were also found across the various usages of AI in MMS, including margin control, surgical recommendations, similarity metrics, and in the prediction of stage and construction complexity. CONCLUSION The findings of this review suggest promising potential for AI to enhance the accuracy and efficiency of Mohs surgery, particularly for NMSC.Dermatologic Surgery371High AUC and concordance values were also found across the various usages of AI in MMS, including margin control, surgical recommendations, similarity metrics, and in the prediction of stage and construction complexity.Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]["Fatima N. Mirza", "Z. Haq", "Parsa Abdi", "Michael J Diaz", "Tiffany J. Libby"]2024-07-11T00:00:00 e85e52abfb24f6f4d50b428f3235e7aab1dccff1The integration of artificial intelligence (AI) into healthcare practice has improved patient management and care. Many clinical laboratory specialties have already integrated AI in diagnostic specialties such as radiology and pathology, where it can assist in image analysis, diagnosis, and clinical reporting. As AI technologies continue to advance, it is crucial for biomedical science students to receive comprehensive education and training in AI concepts and applications and to understand the ethical consequences for such development. This review focus on the importance of integrating AI into biomedical science curricula and proposes strategies to enhance curricula for different specialties to prepare future healthcare workers. Improving the curriculum can be achieved by introducing specific subjects related to AI such as informatics, data sciences, and digital health. However, there are many challenges to enhancing the curriculum with AI. In this narrative review, we discuss these challenges and suggest mitigation strategies.Clinics and Practice731This review focuses on the importance of integrating AI into biomedical science curricula and proposes strategies to enhance curricula for different specialties to prepare future healthcare workers.Clinics and Practice["Aarti Sharma", "Amal Al-Haidose", "Maha Al-Asmakh", "Atiyah M. Abdallah"]2024-07-11T00:00:00 0f33032debb1b220b635bc1859e46577ca9e1037The author offers an analysis of the regulatory legal acts prepared in the Russian Federation over the past four years, regulating the development of technologies using artificial intelligence. The legal basis of the mechanism for regulating artificial intelligence in the context of the implementation of the law enforcement function of the state is revealed.STATE POWER AND LOCAL SELF-GOVERNMENT00State power and local self-government["Stanislav B. Kulikov"]2024-07-11T00:00:00 c1212c0abd65c015f91d5543d6f14913e28b9058The use of artificial intelligence (AI) to farming techniques has become a revolutionary force in the era of precision agriculture, altering conventional practices and increasing agricultural production. With an emphasis on important areas including agricultural yield optimization, resource management, and economic sustainability, this study aims to investigate the concrete effects of AI on smart farming. The research explores real-time decision-making processes using sophisticated data analytics and machine learning algorithms, It shows how an AI-driven insights enable farmers to make wise decisions, reduce resource wastage, and improve overall farm productivity. The results show how AI may be used to promote environmentally friendly farming methods in addition to demonstrating the technology’s demonstrable benefits in terms of higher crop yields and lower production costs. Ethical questions are central to our study as we traverse the complicated junction of technology and agriculture. For data protection, fair access, and the appropriate use of new technology, ethical aspects of AI-integrated farming methods must be carefully examined. This research highlights possible obstacles and suggests risk-reduction tactics while critically examining the ethical ramifications of AI’s broad use in agriculture. In order to support the appropriate application of AI technology in smart farming, the research emphasizes the significance of developing an ethical framework that is consistent with society values.International Conference on Advanced Technologies for Signal and Image Processing260The research explores real-time decision-making processes using sophisticated data analytics and machine learning algorithms and shows how an AI-driven insights enable farmers to make wise decisions, reduce resource wastage, and improve overall farm productivity.2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)["Mohammad Nadeem Ahmed", "Ajay Pal Singh", "Mohammad Rashid Hussain", "A. Rasool", "Imran Mohammad Khan", "Muhammad Shahid Dildar"]2024-07-11T00:00:00 d4c85102523937c04b6010dfc61a7664c33db2e8Artificial intelligence (AI) has demonstrated revolutionary potential and wide-ranging applications in the comprehensive management of fundus diseases, yet it faces challenges in clinical translation, data quality, algorithm interpretability, and cross-cultural adaptability. AI has proven effective in the efficient screening, accurate diagnosis, personalized treatment recommendations, and prognosis prediction for conditions such as diabetic retinopathy, age-related macular degeneration, and other fundus diseases. However, there is a significant gap between the need for large-scale, high-quality, and diverse datasets and the limitations of current research data. Additionally, the black-box nature of AI algorithms, the acceptance by clinicians and patients, and the generalizability of these algorithms pose barriers to their widespread clinical adoption. Researchers are addressing these challenges through approaches such as federated learning, standardized data collection, and prospective trials to enhance the robustness, interpretability, and practicality of AI systems. Despite these obstacles, the benefits of AI in fundus disease management are substantial. These include improved screening efficiency, support for personalized treatment, the discovery of novel disease characteristics, and the development of precise treatment strategies. Moreover, AI facilitates the advancement of telemedicine through 5G and the Internet of Things. Future research should continue to tackle existing issues, fully leverage the potential of AI in the prevention and treatment of fundus diseases, and advance intelligent, precise, and remote ophthalmic services to meet global eye health needs.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology00There is a significant gap between the need for large-scale, high-quality, and diverse datasets and the limitations of current research data and the black-box nature of AI algorithms, the acceptance by clinicians and patients, and the generalizability of these algorithms pose barriers to their widespread clinical adoption.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology["M. Zhang", "Q. Liao", "T. T. Yang"]2024-07-11T00:00:00 1530306cd4aa44642cc97b6f9e7d167e825b8334China is currently transitioning from rapid economic growth to high-quality growth, with its influence in the international community continuously increasing. The new era and new environment impose higher standards and requirements for the steady development of enterprises. If companies cannot achieve relative sustainable development, it will result in a significant waste of resources and fail to fundamentally improve production efficiency. In recent years, the rapid development of the internet has accumulated vast amounts of data. Structured data, like numbers, are relatively easy to process and extract information from, but unstructured data such as voice, video, and images are difficult to handle satisfactorily using traditional methods. Artificial Intelligence (AI), as an emerging technology, is rapidly transforming business operations and management methods. This paper adopts a case study approach, using Alibaba as the research case, to explore the role and impact of AI on corporate sustainable development. The study finds that AI can significantly promote sustainable development by optimizing resource utilization, enhancing production efficiency, fostering innovation, and improving decision-making processes. However, the application of AI also brings challenges such as data privacy, security risks, and job displacement. This paper analyzes these impacts and proposes corresponding countermeasures and recommendations to provide a reference for companies in implementing AI strategies.Frontiers in Business, Economics and Management300The study finds that AI can significantly promote sustainable development by optimizing resource utilization, enhancing production efficiency, fostering innovation, and improving decision-making processes.Frontiers in Business, Economics and Management["Yiting Wu"]2024-07-11T00:00:00 d89a2fb3a8dae47902a9df6c33d0dd264a20fd1bAbstract: SMPN 1 Plumbon is one of the public schools in Cirebon Regency. Based on the results of observations, SMPN 1 Plumbon already has facilities that support digital-based learning. Technology can help teachers be more creative in developing learning in the classroom. One way to expand learning in the school is by utilizing creative learning media so students can be more active. Learning media can facilitate the achievement of specific learning goals. Based on this description, Community Service (PKM) was carried out on learning media in the form of a learning media workshop with the theme "AI-Based Learning Media Design Training (Artificial Intelligence)." This activity aims to train teachers in utilizing technology by creating AI-based interactive learning media (Artificial intelligence). The "AI-Based Learning Media Design Training (Artificial Intelligence)" was implemented on May 17, 2024, at SMPN 1 Plumbon with the resource person Mr. Jajang Rahmatudin, M.Pd. The training was carried out at 13.00, the training activity for AI-based interactive learning media design (Artificial intelligence). It began with the brief provision of material by the resource persons. After the provision of the material, the trainees were invited to try to design AI-based interactive learning media (Artificial intelligence) directly on the spot. The resource person explained the steps that must be taken, and then the participants followed the directions. Students help assist participants who have difficulties creating learning media. The implementation of the training went smoothly and well, and the trainees looked enthusiastic during the training activities.Jurnal Abdisci00Community Service was carried out on learning media in the form of a learning media workshop with the theme "AI-Based Learning Media Design Training (Artificial Intelligence)," which aims to train teachers in utilizing technology by creating AI-based interactive learning media (Artificial intelligence).Jurnal Abdisci["Jajang Rahmatudin", "Siti Musyarofah", "Syifa Fijri Arrofilah", "Tiwi Widyawati", "Hisyam Ahyani", "Shoimatul Atifah"]2024-07-11T00:00:00 79ff2dcd74ff94cb076f5ba3ed8f1808ce0c3039This study aims to address the existing knowledge gap regarding the specific impact of artificial intelligence (AI) on patent research and emphasize its strategic significance as a catalyst for innovation. The methodology employs a comprehensive approach, integrating both qualitative and quantitative research methods. It systematically investigates the transformative potential of AI in patent research within the BRICS nations, including an examination of the technological, ethical, and legal challenges associated with AI’s application in patent analysis. This research contributes to the field by extending beyond the conventional focus on the role of patents in innovation and shedding light on the potential of AI in patent research. It offers valuable insights into how AI can redefine the landscape of patent research, providing a more rapid and accurate perspective on the identification of technological trends, opportunities, and competitive factors. The findings underscore that AI in patent research yields numerous advantages, ranging from efficient data processing to the forecasting of technological trends. Future studies should explore ethical and legal considerations associated with AI in patent research, as well as its implementation in the strategies of both corporate entities and governmental bodies in the BRICS nations.Computing and Artificial Intelligence270This study systematically investigates the transformative potential of AI in patent research within the BRICS nations, including an examination of the technological, ethical, and legal challenges associated with AI’s application in patent analysis.Computing and Artificial Intelligence["Claudio Zancan", "J. Passador", "C. Passador", "Ricardo Carvalho Rodrigues"]2024-07-11T00:00:00 59b3414788f16165ce527b0e323207ede3b2393cAmidst the tremendous evolution of the digital economy and the expedited establishment of a new development paradigm, the use of artificial intelligence (AI) technologies holds significant importance in achieving superior economic development. While much of the previous research focused on the macroeconomic impact of AI, this study examined how AI technology affects food processing firm performance, productivity, and labor skill structure at the food processing firm level. This study utilized panel data from listed food processing enterprises in Shanghai and Shenzhen spanning from 2010 to 2021, performing textual analysis on the annual reports of listed companies and then creating enterprise-level AI indicators to empirically examine the influence of AI applications on enterprise performance and its underlying mechanisms. The findings indicate a substantial improvement in business performance due to the application of artificial intelligence, which is a conclusion corroborated through a series of stability tests. Exploring channels and mechanisms, the analysis revealed that AI-driven advancements in production technologies stimulated the requirement for highly skilled labor, thereby inducing shifts in the labor force’s structure. Further investigation demonstrated that artificial intelligence contributed to enhancing the total factor productivity, consequently bolstering the overall enterprise performance. A heterogeneity analysis showed that firm-level factors, such as the nature of property rights and factor intensity, had an impact on the influence of AI on firm performance. In addition, the geographic location and time of year of a company also had impacts on the productivity benefits of artificial intelligence. This research deepened the cognition and understanding of the role played by AI in the production process at the micro-enterprise level and provided suggestions for promoting the development of artificial intelligence technologies at the micro-enterprise level, which will facilitate the transformation of the labor structure to further augment enterprise efficiency.Sustainability330This research deepened the cognition and understanding of the role played by AI in the production process at the micro-enterprise level and provided suggestions for promoting the development of artificial intelligence technologies at the micro-enterprise level, which will facilitate the transformation of the labor structure to further augment enterprise efficiency.Sustainability["Huanan Liu", "Yan Wang", "Zhoufu Yan"]2024-07-11T00:00:00 4acfb1aa57846bf99a47b0e99148ca46f8879c97At present, it is predicted that there will be a demand for the use of experimental legal regimes for such products and solutions as individual artificial intelligence systems in healthcare, artificial intelligence and robotics systems for special and dual-use purposes (in the field of medicine, transport and logistics, construction and the fuel and energy complex); Artificial Intelligence Systems in Public Administration. Methods and forms of dispute resolution (removal of legal uncertainty, settlement of conflicts) in the abovementioned areas cannot be dissonant with the efficiency of production and management processes in these areas. Otherwise, alternative forms of conflict resolution to justice will be implemented.Arbitrazh-civil procedure00Methods and forms of dispute resolution in the abovementioned areas cannot be dissonant with the efficiency of production and management processes in these areas otherwise, alternative forms of conflict resolution to justice will be implemented.Arbitrazh-Civil Procedure["Aleksandr K. Fetisov"]2024-07-11T00:00:00 ccdaacf310adfc62e66941024eb3dc14b805fb0f The advent of radiomics has revolutionized medical image analysis, affording the extraction of high dimensional quantitative data for the detailed examination of normal and abnormal tissues. Artificial intelligence (AI) can be used for the enhancement of a series of steps in the radiomics pipeline, from image acquisition and preprocessing, to segmentation, feature extraction, feature selection and model development. The aim of this review is to present the most used AI methods for radiomics analysis, explaining the advantages and limitations of the methods. Some of the most prominent AI architectures mentioned in this review include Boruta, random forests, gradient boosting, generative adversarial networks, convolutional neural networks, and transformers. Employing these models in the process of radiomics analysis can significantly enhance the quality and effectiveness of the analysis, while addressing several limitations that can reduce the quality of predictions. Addressing these limitations can enable high quality clinical decisions and wider clinical adoption. Importantly, this review will aim to highlight how AI can assist radiomics in overcoming major bottlenecks in clinical implementation, ultimately improving the translation potential of the method.BJR|Artificial Intelligence00This review will aim to highlight how AI can assist radiomics in overcoming major bottlenecks in clinical implementation, ultimately improving the translation potential of the method.BJR|Artificial Intelligence["Konstantinos Vrettos", "Matthaios Triantafyllou", "K. Marias", "A. H. Karantanas", "M. Klontzas"]2024-07-11T00:00:00 64e46e363da7983fbe4d239c5600c59f3acce960Medical and Biological Engineering and Computing330An artificial intelligence framework for simultaneous grading diagnosis of these three pathological types through fusing multimodal tissue characterization parameters dug by quantitative ultrasound methods derived from ultrasound radiofrequency signals, B-mode images, shear wave elastography images, and clinical ultrasound systems is established.Medical & biological engineering & computing["Xingyue Wei", "Yuanyuan Wang", "Lianshuang Wang", "Mengze Gao", "Qiong He", "Yao Zhang", "Jianwen Luo"]2024-07-11T00:00:00 f3644c96f5d0fe7020bbdd9d9a0b0c46fa8a2179This paper explores the transformative potential of integrating artificial intelligence (AI) into fixed asset management, with a particular focus on its practical application in data analytics and predictive maintenance. Effective internal controls in fixed asset management are paramount for organizations seeking to strengthen competitiveness, minimize risk, and optimize asset utilization. By leveraging AI-driven data analytics, organizations can delve deep into their asset portfolios to gain comprehensive insights that inform strategic decision-making and facilitate proactive maintenance protocols. Through a comprehensive literature review and methodological overview, this study highlights the various approaches and cutting-edge technologies used in fixed asset management, underscoring the critical importance of accurate valuation, efficient asset utilization, and regulatory compliance. In addition, real-world case studies and practical applications underscore the tangible benefits and transformative potential of AI-infused asset management practices, demonstrating their ability to revolutionize conventional methodologies and drive organizational performance to unprecedented levels.Frontiers in Business, Economics and Management140Frontiers in Business, Economics and Management["Yun Liu"]2024-07-11T00:00:00 35ed0ac88fddb32a6a695ce1806dec9fa37dadd4Chronic kidney disease (CKD) is a global public health problem characterized by persistent kidney damage or loss of kidney function. Previously, the diagnosis of CKD has mainly relied on serum creatinine and estimation of the glomerular filtration rate. However, with the development and progress of artificial intelligence (AI), AI has played different roles in various fields, such as early diagnosis, progression prediction, prediction of associated risk factors, and drug safety and efficacy evaluation. Therefore, research related to the application of AI in the field of CKD has become a hot topic at present. Therefore, this study adopts a bibliometric approach to study and analyze the development and evolution patterns and research hotspots of AI-CKD. English publications related to the field between January 1, 2004, and June 27, 2024, were extracted from the Web of Science Core Collection database. The research hotspots and trends of AI-CKD were analyzed at multiple levels, including publication trends, authors, institutions, countries, references and keywords, using VOSviewer and CiteSpace. The results showed that a total of 203 publications on AI-CKD were included in the study, of which Barbieri Carlo from the University of Milan, Italy, had the highest number of publications (NP=5) and had a high academic impact (H-Index=5), while the USA and its institution, the Mayo Clinic, were the publications. The USA and its Mayo Clinic are the countries and institutions with the highest number of publications, and China is the country with the second highest number of publications, with three institutions attributed to China among the top five institutions. Germany's institution, Fresenius Medical Care, has the highest academic impact (H-index=6). Keyword analysis yielded artificial intelligence, chronic kidney disease, machine learning, prediction model, risk, deep learning, and other keywords with high frequency, and cluster analysis based on the timeline yielded a total of 8 machine learning, deep learning, retinal microvascular abnormality, renal failure, Bayesian network, anemia, bone disease, and allograft nephropathology clusters. This study provides a comprehensive overview of the current state of research and global frontiers of AI-CKD through bibliometric analysis. These findings can provide a valuable reference and guidance for researchers.medRxiv340This study provides a comprehensive overview of the current state of research and global frontiers of AI-CKD through bibliometric analysis and can provide a valuable reference and guidance for researchers.["Y. Li", "Y. Ding", "Y. Xu", "H. Meng", "H. Wu", "D. Li", "Y. Hu"]2024-07-11T00:00:00 929f1c9c7e72018c300047073268eff104f685cdThe Fintech industry is currently experiencing a major shift as Artificial Intelligence (AI) becomes more integrated, fundamentally changing how financial services are provided, overseen, and enhanced. This study explores the ever-evolving landscape of AI in Fintech, examining the most recent advancements and impactful applications. As AI technologies continue to evolve, they find their way into the core operations of Fintech, encompassing risk assessment, fraud prevention, customer service, and investment strategies. This paper provides a detailed examination of these cutting-edge applications, highlighting their significance and potential. Notable trends discussed in this research encompass Explainable AI, the increasing emphasis on data security and privacy, and the global proliferation of AI-powered financial solutions. Furthermore, the paper investigates the intricate landscape of regulations and ethical considerations governing AI in Fintech and the strategies in place to navigate them. This comprehensive analysis of AI’s impact on Fintech aims to provide a holistic view of the current landscape while offering insights into the future trajectories of this symbiotic relationship. It serves as a valuable resource for industry practitioners, policymakers, and researchers interested in harnessing the transformative potential of AI within the financial technology domain.International Conference on Advanced Technologies for Signal and Image Processing350This comprehensive analysis of AI’s impact on Fintech aims to provide a holistic view of the current landscape while offering insights into the future trajectories of this symbiotic relationship.2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)["Mohammad Nadeem Ahmed", "Abhijeet Anand", "Mohammad Rashid Hussain", "Mohammed Mohsin Ahmed", "Imran Mohd Khan", "M. A. Rasool"]2024-07-11T00:00:00 a5f11c9259a36a3bbe210f23b5e09d797b688dc9A profound gap persists between artificial intelligence (AI) and clinical practice in medicine, primarily due to the lack of rigorous and cost-effective evaluation methodologies. State-of-the-art and state-of-the-practice AI model evaluations are limited to laboratory studies on medical datasets or direct clinical trials with no or solely patient-centered controls. Moreover, the crucial role of clinicians in collaborating with AI, pivotal for determining its impact on clinical practice, is often overlooked. For the first time, we emphasize the critical necessity for rigorous and cost-effective evaluation methodologies for AI models in clinical practice, featuring patient/clinician-centered (dual-centered) AI randomized controlled trials (DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as an effective proxy for DC-AI RCTs. Leveraging 7500 diagnosis records from two-step inaugural DC-AI RCTs across 14 medical centers with 125 clinicians, our results demonstrate the necessity of DC-AI RCTs and the effectiveness of VC-MedAI. Notably, VC-MedAI performs comparably to human clinicians, replicating insights and conclusions from prospective DC-AI RCTs. We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner. Chinese Clinical Trial Registration: ChiCTR2400086816.arXiv.org500This work envisions DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner.ArXiv["Wanling Gao", "Yunyou Huang", "Dandan Cui", "Zhuoming Yu", "Wenjing Liu", "Xiaoshuang Liang", "Jiahui Zhao", "Jiyue Xie", "Hao Li", "Li Ma", "Ning Ye", "Yumiao Kang", "Dingfeng Luo", "Peng Pan", "Wei Huang", "Zhongmou Liu", "Jizhong Hu", "Gangyuan Zhao", "Chongrong Jiang", "Fan Huang", "Tianyi Wei", "Suqing Tang", "Bingjie Xia", "Zhifei Zhang", "Jianfeng Zhan"]2024-07-11T00:00:00 5a3e2ade445e3a5a9ca026521e5e9415f99aa766This study examines the impact of artificial intelligence (AI) on the future of digital media, detailing the evolution of AI technologies and their implications for the media industry. It also explores the benefits and challenges faced by media professionals due to the integration of AI technologies. The study highlights that information technology is contributing to the formation of a new digital environment, where applications based on human intelligence lead to the emergence of new concepts and values in media. Amid rapid technological advancements, the utilization of AI in the media industry is significant for enhancing content quality and streamlining production processes. However, these technologies also raise questions about the future of public relations and the integration of creative media. The study employed a comprehensive research methodology, utilizing contemporary books, academic theses, and related scientific studies, along with the internet for data collection. The study emphasizes that AI technologies play a crucial role in advancing the media industry, improving content quality, and accelerating production speed. However, humans should remain the primary drivers of these technologies, and it is essential to establish new ethical standards to ensure a balance between humans and machines in media production. Among the research recommendations: the need for journalists and media professionals to pay attention to artificial intelligence technologies to improve the quality of content and simplify production processes.International Journal for Scientific Research00The need for journalists and media professionals to pay attention to artificial intelligence technologies to improve the quality of content and simplify production processes is highlighted, and it is essential to establish new ethical standards to ensure a balance between humans and machines in media production.International Journal for Scientific Research["Najm Al-Rashed"]2024-07-11T00:00:00 a8a42f4a6698100e7a585230c28935d02ef9a4dc One of the most popular sectors in the tech and healthcare industries right now is artificial intelligence. In the search and development of new drugs, artificial intelligence is essential. Drug design using computer-assisted design (CADD) has supplanted the traditional approach. Artificial intelligence is assisting businesses in the development of new drugs in a faster, more affordable, and more efficient manner, saving money and manpower in the process of creating new drug molecules to treat any disease. Quantitative structure-activity relationship (QSAR) analysis, activity scoring, in silico testing, biomarker development, and mode of action identification are all aided by artificial intelligence. It is revolutionizing these sectors by swiftly identifying potential drug candidates, efficiently conducting clinical trials, and customizing patient care. AI optimizes drug manufacturing processes, augments safety monitoring, and streamlines market analysis. In clinical trials, AI streamlines patient recruitment and ensures more precise trial designs, leading to faster and more efficient research. AI empowers personalized medicine by tailoring treatment plans and drug dosages to individual patient characteristics. AI also optimizes pharmaceutical manufacturing processes, amplifies safety monitoring by analyzing real-time data for adverse events, and supports market analysis and sales strategies. AI in the pharmaceutical industry is a multifaceted tool. Artificial Intelligence (AI) has the potential to streamline complex pharmaceutical regulatory matters. Regulatory processes like audits and dossier completion can be automated with AI tools. Current Indian Science00Artificial intelligence is assisting businesses in the development of new drugs in a faster, more affordable, and more efficient manner, saving money and manpower in the process of creating new drug molecules to treat any disease.Current Indian Science["Aditya Narayan", "Arsh Chanana", "Oma Shanker", "Yukta R. Kulkarni", "Pooja Gupta", "Akhilesh Patel", "Ujwal Havelikar", "Ravindra Pal Singh", "H. Chawra"]2024-07-11T00:00:00 7cd3f1a707eaba7b0012adeef8f00e37881b139aWith the rapid development of science and technology, artificial intelligence (AI) technology is gradually infiltrating into all levels of enterprise management, especially in human resources (HR) management. Its application not only improves work efficiency, optimizes management processes, but also has a significant impact on employee performance. This study comprehensively and deeply discusses the application of AI technology in recruitment, training and performance management and its influence on employee performance through mixed method research design, quantitative analysis and case study. The results of quantitative analysis show that the application of AI technology in recruitment, training and performance management has a significant positive correlation with the improvement of employee performance. Regression analysis further reveals the positive effects of factors such as recruitment automation, training personalization, employee experience and educational background on employee performance. The case study reveals the common patterns and differences of the impact of AI technology application on employee performance by comparing enterprises in different industries, scales and levels of AI technology application. This study provides valuable insights for understanding the application of AI technology in enterprise HR management and its impact on employee performance, and provides a useful reference for enterprises to make more effective use of this technology. With the continuous development and popularization of AI technology, its application in the field of HR management will be more extensive and in-depth, which will inject new impetus into the sustainable development of enterprises.Frontiers in Business, Economics and Management80This study comprehensively and deeply discusses the application of AI technology in recruitment, training and performance management and its influence on employee performance through mixed method research design, quantitative analysis and case study.Frontiers in Business, Economics and Management["Yingzhe Zhu"]2024-07-11T00:00:00 7889e60842644d99d67247146287dd9875703452With the advancement of the information age, the transportation industry has experienced rapid growth, leading to an expansion in the scale and number of highway constructions. However, this development has also given rise to numerous traffic issues, including frequent vehicle congestion and traffic accidents. To address these problems, it is essential to leverage modern technology for real-time information collection and analysis, providing robust technical support for intelligent transportation systems. This paper focuses on artificial intelligence (AI) technology, explaining its concept and its role in intelligent transportation. It reviews the various application areas and analyzes the use of AI in intelligent transportation. Finally, it proposes strategies for applying AI to promote the healthy development of intelligent transportation systems.Journal of World Architecture70This paper focuses on artificial intelligence (AI) technology, explaining its concept and its role in intelligent transportation, and proposes strategies for applying AI to promote the healthy development of intelligent transportation systems.Journal of World Architecture["Pei Liu"]2024-07-11T00:00:00 42fcabda77c89df49d1b4595b64cf1bf3cfd583aTotal Quality Management &amp; Business Excellence553Total Quality Management &amp; Business Excellence["Juliano Endrigo Sordan", "Roy Andersson", "Jiju Antony", "Marcio Lopes Pimenta", "P. Oprime"]2024-07-11T00:00:00 b0dac73750304d51c78780c8e9f65361c0d9cef9The advent of AI and the development of cyberspace have considerably reshaped the strategic landscape of various regions, particularly South Asia, where geo-political tensions and security concerns intersect with continuous technological advancements. This study focuses on the concept of digital strategic autonomy with a particular emphasis on the South Asian region by outlining the implications of AI and cyberspace on the security environment of the region and the pursuit of national interests. Firstly, this study explores the evolving nature of digital autonomy and how South Asian nations, especially India and Pakistan, are addressing the security concerns posed by cyberspace vulnerabilities. Employing the lens of Politics of Datafication Digital Autonomy, this study provides a nuanced understanding of the notions of contemporary sovereignty. Secondly, this research also explains the interconnection between military advancements and AI-driven technologies and the implications for the strategic stability of South Asia. Moreover, the impact of Digital Strategic Autonomy on E-commerce in India and Pakistan and the geopolitical dimensions of cyberspace related to India and Pakistan are also addressed. Finally, this study argues that digital strategic autonomy is a multi-dimensional concept involving technology, military, governance, and geopolitical dimensions by highlighting the significance of utilising technologies, AI, and Cyberspace to ensure cyber sovereignty while enhancing the security of states in digital domains.Journal of Security &amp; Strategic Analyses01It is argued that digital strategic autonomy is a multi-dimensional concept involving technology, military, governance, and geopolitical dimensions by highlighting the significance of utilising technologies, AI, and Cyberspace to ensure cyber sovereignty while enhancing the security of states in digital domains.Journal of Security &amp; Strategic Analyses["Summar Iqbal", "Syeda Tabeer"]2024-07-11T00:00:00 7747a44c7a2e34315909b99bc2af83d588df3144Актуальность исследования обусловлена тем, что сегодня один из наиболее серьезных фундаментальных вызовов для технологических компаний – это то, что потребности в вычислительных мощностях постоян- но растут. Бизнес-среда все быстрее приближается к новой вычислительной парадигме. Одним из ответов на этот вызов могут стать квантовые техноло- гии. Авторами в данном исследовании представлено главное преимущество квантовых вычислений, отмечается, что они основаны совершенно на других принципах, чем традиционные, и будут использовать собственную аппарат- ную и математическую базу. Эмпирической базой исследования послужили исследования института искусственного интеллекта AIRI. Цель исследо- вания – оценка текущего состояния квантовых вычислений и определение направления развития этого направления, а также препятствий и ограниче- ний применения квантовых технологий. Практическая значимость данного исследования заключается в том, что по зволит бизнес-сообществу, а также органам государственной власти выработать стратегию развития в новой вычислительной парадигме.Journal of Monetary Economics and Management01Journal of Monetary Economics and Management["\u0410.\u0410. \u0412\u043e\u043b\u043a\u043e\u0440\u0435\u0437", "\u0415.\u0412. \u0428\u0438\u0440\u0438\u043d\u043a\u0438\u043d\u0430"]2024-07-11T00:00:00 b84d0c970bfa17f8ff9c1d5b01a5bb2871cf1843Journal of evolutionary economics621Journal of Evolutionary Economics["Luca Grilli", "Sergio Mariotti", "Riccardo Marzano"]2024-07-11T00:00:00 618c5ca72ab38a516f8c0b43a50f1a68c3e08752International Journal of Dermatology52International journal of dermatology["Mohamad Goldust"]2024-07-11T00:00:00 d96d1495031b9b816a6e571b8deb1f2c55c1a745The article presents a new method for obtaining analogues of words, characterized by simplicity and the absence of the need for preliminary training on large data as in existing methods. In the method under study, analogues are determined by their syntactic predicates using methods of distributive semantics. In the study, analogues of adjectives, nouns and verbs were obtained and analyzed. This made it possible to obtain a result that is not inferior to the results obtained using the most popular neural network approach as word2vec when qualitatively comparing analogues. The demonstrated method shows that obtaining analogues is possible using methods of distributive semantics using a more interpretable method, which opens up the possibility of studying semantic analogy. This method also allows you to identify analogues on a specific topic. Based on the experimental results obtained, an original definition of analogues and cognitive schemes is formulated. The article also analyzes and substantiates the possibility of a new approach for creating artificial intelligence systems based on the researched method. According to the authors, this provides significant advantages for the creation of such systems. In particular, the proposed method allows for broader generalizations over orders of magnitude smaller data, as well as learning during use, which is not possible for neural networks.Philosophical Problems of IT &amp; Cyberspace (PhilIT&amp;C)10A new method for obtaining analogues of words, characterized by simplicity and the absence of the need for preliminary training on large data as in existing methods is presented, which allows for broader generalizations over orders of magnitude smaller data, as well as learning during use, which is not possible for neural networks.Philosophical Problems of IT &amp; Cyberspace (PhilIT&amp;C)["A. B. Khomyakov", "P. Chizhik"]2024-07-11T00:00:00 f1af39e7a5f73033c20db5a76d55918ed5067145Global Fashion Management Conference00Global Fashion Management Conference["Y. Park", "Jaehun Kim", "Kyung Hoon Kim"]2024-07-11T00:00:00 107b81df35ecc8317dde96abbda7268f15f784f0В статье рассмотрены вопросы формирования внутрен- ней инновационной среды предприятия, ее элементов с учетом внедрения инструментов искусственного интеллекта и автоматизированных информа- ционных систем. Эффективное стратегическое планирование имеют решаю- щее значение для повышения конкурентоспособности компании. Искусствен- ный интеллект представляет форму интеллектуального капитала, который создает добавленную ценность благ и преобразует различные сектора, от промышленного производства до экономической безопасности и государ- ственного управления, тем самым способствуя новым формам создания стоимости. Эффективное осуществление инновационной деятельности ведет к формированию благоприятной инновационной среды. Определены этапы реализации инноваций. Несмотря на обширные исследования региональной и национальной инновационной среды, конкретная динамика внутри отдель- ных предприятий остается недостаточно изученной. Особое внимание в цифровом контексте инновационного развития уделяется технологическим инновациям.Journal of Monetary Economics and Management00Journal of Monetary Economics and Management["\u0420.\u0410. \u041e\u043b\u0438\u043d", "\u0415.\u0421. \u0428\u0430\u0442\u0440\u043e\u0432\u0430"]2024-07-11T00:00:00 d5a6d5cbd5fcca9bd311bb3835a978fbcc1bab7dGlobal Fashion Management Conference00Global Fashion Management Conference["McDougall Liezel", "Truter Lorna", "Venter de Villiers Marike"]2024-07-11T00:00:00 553f0f0c425aa3e61a45f3b5dd47f45953005b6fA large pharmaceuticals corporation utilizing a complex IT infrastructure such as SAP ERP typically faces a substantial volume GMP and Serialization data annually, numbering in the hundreds of thousands. These inquiries, whether initiated over the phone or online via platforms like integration, seek assistance with various issues. Enterprise resource planning (ERP) software streamlines business processes by integrating technology, services, and human resources across interconnected applications. This research proposes implementing an intelligent system to streamline volume of the data and analyzation for the SAP ERP. This system aims to automate responses to user queries, reducing the time required for issue investigation and resolution, and enhancing user responsiveness. Employing machine learning algorithms, the system efficiently interprets and classifies text across multiple categories, facilitating accurate question comprehension. Additionally, it utilizes a specialized framework to retrieve relevant evidence, ensuring the delivery of optimal responses. Furthermore, its conversational AI capabilities enable the creation of chatbots, fostering collaborative problem-solving among user groups in real-time.International Journal of Scientific Research in Computer Science Engineering and Information Technology60This research proposes implementing an intelligent system to streamline volume of the data and analyzation for the SAP ERP, which aims to automate responses to user queries, reducing the time required for issue investigation and resolution, and enhancing user responsiveness.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Gaurav Kumar"]2024-07-11T00:00:00 5e35746cbfc121b966f0a5d6f79a7335d57e6d81This paper examines Gucci's strategy in its Chengdu store, blending Italian and Chinese cultures through innovative design and AI. Analyzing brand heritage using semiotic analysis, it explores Gucci's balance of historical legacy and modern relevance, emphasizing storytelling, glocalization, and cultural sensitivity in the luxury brand's navigation of diverse cultural landscapes.Global Fashion Management Conference00Analyzing brand heritage using semiotic analysis, Gucci's balance of historical legacy and modern relevance is explored, emphasizing storytelling, glocalization, and cultural sensitivity in the luxury brand's navigation of diverse cultural landscapes.Global Fashion Management Conference["Lucila Campiglia", "Fabio Sandes"]2024-07-11T00:00:00 5c773bdb195408747e568dbc0b146d433b1c9329Biomedical Journal of Scientific &amp; Technical Research00Biomedical Journal of Scientific &amp; Technical Research["A. P. Hu\u00f1is"]2024-07-11T00:00:00 35af324d0d0e14e53d6b69b07c236b5b1fca5a06Evolutionary Intelligence180Evol. Intell.["Jyoti Agarwal", "Sachin Sharma"]2024-07-11T00:00:00 59d4d8c52c8830795534b4e57f9310ed60876d01<jats:p>-</jats:p>European Journal of Artificial Intelligence and Digital Economy00European Journal of Artificial Intelligence and Digital Economy["Askarova Mokhichekhra"]2024-07-11T00:00:00 9285327402f804524c65c271338dd858a7104962The management of patients with aortic and aortic valve pathology is an extremely relevant task. The main problem of this pathology is the absence of obvious symptoms before the onset of a life–threatening condition, dissection or rupture of the aorta. Early timely diagnosis becomes the most relevant in this situation, and imaging research methods play a leading role in this regard. However, the main limiting factor is the speed and quality of image evaluation. Therefore, an actual task is to develop an AI-based physician assistant for image mining (Computer vision, CV). This article provides an overview of modern neural network methods for effective analysis of diagnostic images (MSCT and MRI) relevant for the study of diseases of the cardiovascular system in general and the aorta in particular. One of the main focuses of this analysis is the study of the applicability of modern neural network methods based on the Transformer architecture or the Attention Mechanism, which show high accuracy rates in solving a wide range of tasks in other subject areas, and have a high potential of applicability for qualitative analysis of diagnostic images. An overview of two fundamental problems of image mining is given: classification (ResNet architecture, ViT architect, Swin Transformer architect) and semantic segmentation (2D approaches – U-Net, TransUNet, Swin-Unet, Segmenter and 3D approaches – 3D-Unet, Swin UNETR, VT-UNET). The described methods, with proper fine tuning and the right approach to their training, will effectively automate the process of diagnosing aortic and aortic valve pathology. For the successful implementation of AI development projects, a number of limitations should be taken into account: a high-quality data set, server graphics stations with powerful graphics cards, an interdisciplinary expert group, prepared scenarios for testing in conditions close to real ones.Siberian Journal of Clinical and Experimental Medicine210The described methods will effectively automate the process of diagnosing aortic and aortic valve pathology with high accuracy rates based on the Transformer architecture or the Attention Mechanism and have a high potential of applicability for qualitative analysis of diagnostic images.Siberian Journal of Clinical and Experimental Medicine["G. I. Kim", "I. Blekanov", "F. V. Ezhov", "L. A. Kovalenko", "E. S. Larin", "E. S. Razumilov", "K. V. Pugin", "M. S. Dadashov", "V. A. Pyagay", "D. Shmatov"]2024-07-11T00:00:00 85b2f1fff5f450683f1a8d248c6178627084c3baВ статье рассматриваются инновационные стратегии управ- ления, необходимые предприятиям для эффективного взаимодействия с машинными клиентами и автономными агентами на основе искусственно- го интеллекта. Машинные клиенты, представляющие собой автономные системы, могут самостоятельно принимать решения и выполнять действия в рамках заданных алгоритмов, играя активную роль в экономической экоси- стеме без прямого вмешательства человека. Автономные агенты искус- ственного интеллекта, работающие с более высоким уровнем автономно- сти и адаптивности, выполняют сложные задачи, включая глубокий анализ данных, принятие стратегических решений и управление системой, повышая эффективность и инновации в бизнес-процессах. Растущая интеграция таких технологий трансформирует бизнес-модели, позволяя предприятиям достичь беспрецедентного уровня эффективно- сти и адаптируемости. Исследование подчеркивает важность понимания и использования различных возможностей машинных клиентов и агентов с искусственным интеллектом для поддержания конкурентных преимуществ. По мере развития подобных инновационных систем, их способность выпол- нять сложные задачи и адаптироваться к меняющимся условиям будет способствовать трансформации в менеджменте предприятий и операцион- ных стратегиях.Journal of Monetary Economics and Management00Journal of Monetary Economics and Management["\u0420.\u0410. \u041e\u043b\u0438\u043d", "\u041a.\u0412. \u041f\u043e\u0440\u0442\u043d\u043e\u0432", "\u0415.\u0421. \u0428\u0430\u0442\u0440\u043e\u0432\u0430"]2024-07-11T00:00:00 9b1404d1326d9d5e662c356e2e3b4d32c8c33fa4В статье рассматривается фундаментальная значимость искусственного интеллекта (далее – ИИ) в трансформации современных маркетинговых стратегий, а также его интеграция, преимущества и проблемы. Внедрение ИИ в маркетинг позволяет использовать передовую аналитику данных и машинное обучение для понимания и прогнозирования поведения потребителей, обеспечивая более целенаправленные и эффективные кампании. Персонализация контента повышает вовлеченность клиентов, что делает ИИ ключевым конкурентным преимуществом для компаний. Однако интеграция ИИ в маркетинг также сопряжена с серьезными проблемами, включая вопросы конфиденциальности, этического использования данных и соблюдения правовых норм. Эти вопросы требуют тщательного изучения как теоретических, так и практических аспектов использования ИИ в маркетинге для обеспечения ответственного применения. Благодаря комплексному анализу и научным исследованиям в статье освещаются различные методики, повышающие эффективность рекламы и взаимодействие с потребителями. В статье также приводятся отсылки к значимым научным работам как российских, так и зарубежных ученых, в которых анализируется эволюция ИИ в маркетинге. Практическое применение ИИ в маркетинге рассматривается на примере конкретных примеров и текущих реализаций, иллюстрирующих возможности ИИ по автоматизации и оптимизации маркетинговых задач – от сегментации потребителей до настройки контента в режиме реального времени. Обсуждаются будущие последствия применения этих технологий, акцентируется внимание на необходимости постоянной адаптации и этических соображениях. В статье подчеркивается, что, хотя ИИ обладает потенциалом для значительного повышения эффективности маркетинга и удовлетворенности клиентов, для полной реализации его потенциала и поддержания маркетинговой индустрии необходим сбалансированный подход к инновациям, этическим и правовым аспектам. This article examines the fundamental importance of artificial intelligence (AI) in transforming modern marketing strategies, as well as its integration, benefits and challenges. The adoption of AI in marketing enables the use of advanced data analytics and machine learning to understand and predict consumer behaviour, enabling more targeted and effective campaigns. Personalising content increases customer engagement, making AI a key competitive advantage for companies. However, integrating AI into marketing also comes with significant challenges, including privacy, ethical use of data, and legal compliance. These issues require careful consideration of both the theoretical and practical aspects of using AI in marketing to ensure responsible use. Through comprehensive analyses and academic research, the article highlights various techniques that enhance advertising effectiveness and consumer interaction. The article also provides references to significant research papers by both Russian and foreign scholars that analyse the evolution of AI in marketing. The practical application of AI in marketing is discussed using case studies and current implementations that illustrate AI’s ability to automate and optimise marketing tasks - from consumer segmentation to real-time content customisation. The future implications of these technologies are discussed, emphasising the need for continuous adaptation and ethical considerations. The article highlights that while AI has the potential to significantly improve marketing effectiveness and customer satisfaction, a balanced approach to innovation, ethical and legal considerations is required to realise its full potential and sustain the marketing industry.Вестник Академии права и управления00Вестник Академии права и управления["\u0414.\u0418. \u041a\u043e\u0447\u0435\u0442\u043e\u0432"]2024-07-11T00:00:00 2459b1ffab4c9181f08d1701da61ad8326136b7ePlant Archives00Plant Archives["Jyoti Singh", "Vijay Kumar Yadav", "Ghanshyam Yadav", "Aseeya Wahid"]2024-07-11T00:00:00 d7f03961eed1b6880deefd2098ef3c14e48e8b72Universum:Philology and art history00Universum:Philology and art history["Anna Isakova"]2024-07-11T00:00:00 9a7d0929869386d61e8156e17634fb0134658a9cAI transparency is often placed at the forefront in discussions about digital technologies regulation. Extending the scope of access to information to understanding AI is at the heart of the concept of explainable AI. This concept provides a framework of possible explanations for how AI works. These include: causal explanations (based on the traditional legal logic of cause and effect), counterfactual explanations (revealing those factors in the AI-based decision-making process that need to be changed to arrive at a different outcome), and in-context explanations (allowing citizens to protect their rights in court). However, AI transparency in the public sector can be hindered by the fact that programs are often created in the private sector and protected by intellectual property rights or trade secrets. In order to take into account and overcome various risks in the use of AI in public administration, it is necessary to establish the principle of transparency in a normative way, extending it to the use of AI in public administration, including the implementation of the concept of explainable AI.STATE POWER AND LOCAL SELF-GOVERNMENT00In order to take into account and overcome various risks in the use of AI in public administration, it is necessary to establish the principle of transparency in a normative way to the use of AI in public administration, including the implementation of the concept of explainable AI.State power and local self-government["Elvira V. Talapina"]2024-07-11T00:00:00 ba6c8d0179c8979436981716846cb2c8da3c8f68In this perspective paper, we introduce the concept of Specialized Generalist Artificial Intelligence (SGAI or simply SGI) as a crucial milestone toward Artificial General Intelligence (AGI). Compared to directly scaling general abilities, SGI is defined as AI that specializes in at least one task, surpassing human experts, while also retaining general abilities. This fusion path enables SGI to rapidly achieve high-value areas. We categorize SGI into three stages based on the level of mastery over professional skills and generality performance. Additionally, we discuss the necessity of SGI in addressing issues associated with large language models, such as their insufficient generality, specialized capabilities, uncertainty in innovation, and practical applications. Furthermore, we propose a conceptual framework for developing SGI that integrates the strengths of Systems 1 and 2 cognitive processing. This framework comprises three layers and four key components, which focus on enhancing individual abilities and facilitating collaborative evolution. We conclude by summarizing the potential challenges and suggesting future directions. We hope that the proposed SGI will provide insights into further research and applications towards achieving AGI.arXiv.org2343This paper introduces the concept of Specialized Generalist Artificial Intelligence (SGAI or simply SGI) as a crucial milestone toward Artificial General Intelligence (AGI), and proposes a conceptual framework for developing SGI that integrates the strengths of Systems 1 and 2 cognitive processing.ArXiv["Kaiyan Zhang", "Biqing Qi", "Bowen Zhou"]2024-07-11T00:00:00 e41ba311cb7f4644538691d824f6a92084ea6102In the past decade, the number of malware variants has increased rapidly. Many researchers have proposed to detect malware using intelligent techniques, such as Machine Learning (ML) and Deep Learning (DL), which have high accuracy and precision. These methods, however, suffer from being opaque in the decision-making process. Therefore, we need Artificial Intelligence (AI)-based models to be explainable, interpretable, and transparent to be reliable and trustworthy. In this survey, we reviewed articles related to Explainable AI (XAI) and their application to the significant scope of malware detection. The article encompasses a comprehensive examination of various XAI algorithms employed in malware analysis. Moreover, we have addressed the characteristics, challenges, and requirements in malware analysis that cannot be accommodated by standard XAI methods. We discussed that even though Explainable Malware Detection (EMD) models provide explainability, they make an AI-based model more vulnerable to adversarial attacks. We also propose a framework that assigns a level of explainability to each XAI malware analysis model, based on the security features involved in each method. In summary, the proposed project focuses on combining XAI and malware analysis to apply XAI models for scrutinizing the opaque nature of AI systems and their applications to malware analysis.ACM Computing Surveys1492This survey reviewed articles related to Explainable AI (XAI) and their application to the significant scope of malware detection and proposed a framework that assigns a level of explainability to each XAI malware analysis model, based on the security features involved in each method.ACM Comput. Surv.["Mohd Saqib", "Samaneh Mahdavifar", "Benjamin C. M. Fung", "P. Charland"]2024-07-11T00:00:00 e1c1e7c6fa67bde417c627f62ba54030119a1f82This article explores the convergence of connectionist and symbolic artificial intelligence (AI), from historical debates to contemporary advancements. Traditionally considered distinct paradigms, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs), exemplified by ChatGPT and GPT-4, highlight the potential of connectionist architectures in handling human language as a form of symbols. The study argues that LLM-empowered Autonomous Agents (LAAs) embody this paradigm convergence. By utilizing LLMs for text-based knowledge modeling and representation, LAAs integrate neuro-symbolic AI principles, showcasing enhanced reasoning and decision-making capabilities. Comparing LAAs with Knowledge Graphs within the neuro-symbolic AI theme highlights the unique strengths of LAAs in mimicking human-like reasoning processes, scaling effectively with large datasets, and leveraging in-context samples without explicit re-training. The research underscores promising avenues in neuro-vector-symbolic integration, instructional encoding, and implicit reasoning, aimed at further enhancing LAA capabilities. By exploring the progression of neuro-symbolic AI and proposing future research trajectories, this work advances the understanding and development of AI technologies.arXiv.org842The study argues that LLM-empowered Autonomous Agents (LAAs) embody this paradigm convergence, by utilizing LLMs for text-based knowledge modeling and representation, and integrate neuro-symbolic AI principles, showcasing enhanced reasoning and decision-making capabilities.ArXiv["Haoyi Xiong", "Zhiyuan Wang", "Xuhong Li", "Jiang Bian", "Zeke Xie", "Shahid Mumtaz", "Laura E. Barnes"]2024-07-11T00:00:00 dc318511a852c69f4f42c22d8b29d76f16dde059Alzheimer’s disease (AD) stands as a form of dementia characterized by the gradual degeneration of brain cells, resulting in compromised memory, cognitive functions, and the loss of fundamental skills, ultimately leading to fatality. While a definitive cure for AD remains elusive, early detection plays a pivotal role in managing its progression and enhancing the quality of life for patients. This study delves into the realm of Alzheimer’s disease identification through the application of various Neural Network models employing classification techniques. Leveraging a contemporary hybrid dataset, the investigation yielded four distinct classifications. Moreover, the study delved into elucidating the specific brain regions contributing to each classification using the Grad-CAM (Gradient-weighted Class Activation Mappings) based XAI (eXplainable Artificial Intelligence) framework applied to patients’ MRI images. A comprehensive assessment was conducted on pre-trained deep neural networks, particularly focusing on Convolutional Neural Network (CNN) models trained exclusively on authentic MRIs and a combination of authentic and synthetic MRIs. The efficacy of deep learning in disease detection was exemplified, with the CNN model trained on both real and synthetic MRIs outperforming its counterpart trained solely on real MRIs. The former achieved an impressive accuracy of 97.50%, a Balanced Accuracy Score (BA) of $98.58 \%$, and a Matthew’s Correlation Coefficient (MCC) of 95.95%. In contrast, the model trained exclusively on real MRIs exhibited an accuracy of $88.98 \%$, a BA of 94.01%, and an MCC of $83.67 \%$.International Conference on Advanced Technologies for Signal and Image Processing111This study delved into elucidating the specific brain regions contributing to each classification using the Grad-CAM based XAI based XAI (eXplainable Artificial Intelligence) framework applied to patients’ MRI images, resulting in four distinct classifications of Alzheimer’s disease.2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)["Dhekra Mansouri", "Amira Echtioui", "R. Khemakhem", "A. Ben Hamida"]2024-07-11T00:00:00 0ec8701ea9c7c88278aa2289602806aa8d956c9cAs Artificial Intelligence (AI) tools are increasingly employed in diverse real-world applications, there has been significant interest in regulating these tools. To this end, several regulatory frameworks have been introduced by different countries worldwide. For example, the European Union recently passed the AI Act, the White House issued an Executive Order on safe, secure, and trustworthy AI, and the White House Office of Science and Technology Policy issued the Blueprint for an AI Bill of Rights (AI BoR). Many of these frameworks emphasize the need for auditing and improving the trustworthiness of AI tools, underscoring the importance of safety, privacy, explainability, fairness, and human fallback options. Although these regulatory frameworks highlight the necessity of enforcement, practitioners often lack detailed guidance on implementing them. Furthermore, the extensive research on operationalizing each of these aspects is frequently buried in technical papers that are difficult for practitioners to parse. In this write-up, we address this shortcoming by providing an accessible overview of existing literature related to operationalizing regulatory principles. We provide easy-to-understand summaries of state-of-the-art literature and highlight various gaps that exist between regulatory guidelines and existing AI research, including the trade-offs that emerge during operationalization. We hope that this work not only serves as a starting point for practitioners interested in learning more about operationalizing the regulatory guidelines outlined in the Blueprint for an AI BoR but also provides researchers with a list of critical open problems and gaps between regulations and state-of-the-art AI research. Finally, we note that this is a working paper and we invite feedback in line with the purpose of this document as described in the introduction.arXiv.org801This work provides easy-to-understand summaries of state-of-the-art literature and highlights various gaps that exist between regulatory guidelines and existing AI research, including the trade-offs that emerge during operationalization.ArXiv["Alexander X. Oesterling", "Usha Bhalla", "Suresh Venkatasubramanian", "Himabindu Lakkaraju"]2024-07-11T00:00:00 c5b7eda9cac9b58cf34d581acbc7f6ae07698e81The utilization of artificial intelligence (AI) in recruitment has emerged as a transformative area in talent acquisition. This systematic literature review examines the state of knowledge on AI usage in recruitment from 2018 to 2023, excluding algorithmic and other technological aspects. 26 research articles were selected for the review, along with six review articles for comparison. The study makes significant contributions by synthesizing diverse findings, categorizing them into perceptions, acceptance criteria, adoption and ethical considerations, offering a comprehensive and structured analysis of AI’s implications in recruitment. This study lays the foundation for future empirical research to validate and expand on the present findings. It emphasizes the need for more quantitative studies to asses AI’s impact on recruitment and explores the potential for hybrid approaches combining AI and human judgement for enhanced fairness and effectiveness. The study also underscores the importance of ongoing exploration of ethical considerations and development of responsible AI usage guidelines in recruitment. Overall, this systematic literature review serves as a valuable resource for organizations seeking to implement AI recruitment while guiding future research to unlock the full potential of AI in talent acquisition.Frontiers in Business, Economics and Management341The need for more quantitative studies to asses AI’s impact on recruitment is emphasized, and the potential for hybrid approaches combining AI and human judgement for enhanced fairness and effectiveness is explored.Frontiers in Business, Economics and Management["Asima Asif"]2024-07-11T00:00:00 ee90b2f1aa8140608273635d522ddb33cf140dfeBMC Medicine414Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists.BMC Medicine["Dong Xu", "Lin Sui", "Chunquan Zhang", "Jing Xiong", "Vicky Yang Wang", "Yahan Zhou", "Xinying Zhu", "Chen Chen", "Yu Zhao", "Yiting Xie", "Weizhen Kong", "Jincao Yao", "Lei Xu", "Yuxia Zhai", "Liping Wang"]2024-07-12T00:00:00 479c2d1e3730add315ed57cc8b1bdeeb577a8f1bFor businesses trying to negotiate complex rules, the use of artificial intelligence to automate the tracking of regulatory compliance is a significant advancement. Automation of enforcement and monitoring is achieved by leveraging state-of-the-art technology like machine learning and natural language processing. By using artificial intelligence technologies, businesses may swiftly determine whether rules apply to their operations and look into how those constraints effect their operations. These systems can entirely adapt to laws that are always changing, make sure you are following the rules, and lessen the possibility that anything goes wrong. Artificial intelligence has the potential to help identify compliance issues and promptly address them, eliminating the need for you to worry about paying a fee. Additionally, by automating time-consuming tasks like filing papers and creating reports, it frees up your team to focus on more important tasks. The use of artificial intelligence for compliance tracking offers businesses a scalable, economical, and efficient approach to managing regulatory difficulties in the ever-changing corporate environment.Journal of Engineering Research and Reports01The use of artificial intelligence for compliance tracking offers businesses a scalable, economical, and efficient approach to managing regulatory difficulties in the ever-changing corporate environment.Journal of Engineering Research and Reports["Tushar Khinvasara", "Abhishek Shankar", "Connor Wong"]2024-07-12T00:00:00 e7a9a92713feb8ccb05e6049e3da85ee273d058dScientific Reports352The findings reveal substantial inter- and intra-rater variability among junior raters, contrasting with the lower intra-rater variability observed in the senior rater, and identifies a systemic bias, indicating a tendency for human experts to measure aneurysms smaller than the AI system.Scientific Reports["Andrej Planinc", "Nina \u0160pegel", "Zala Podobnik", "Uro\u0161 \u0160inigoj", "Petra Skubic", "June Ho Choi", "Wonhyoung Park", "Tina Robi\u010d", "Nika Tabor", "Leon Jarabek", "\u017diga \u0160piclin", "\u017diga Bizjak"]2024-07-12T00:00:00 9c536f0b686b75c257f66f5f0b7d1a97cfd65783NEJM AI78NEJM AI["David Blumenthal", "Bakul Patel"]2024-07-12T00:00:00 773eb4742381c5533bbc20c1d79e8bffde3d5703The life cycle of an artificial intelligence model is the object of research. The purpose of the study is to develop a model life-cycle methodology that describes the economic content of the investment process in artificial intelligence technology. During the study, both general scientific methods such as analysis, synthesis, comparison, abstraction, induction and deduction were used, as well as project methodologies of the life-cycle, employed as the basis for the value creation life-cycle of the model. The analysis was based on identifying the necessary stages of model development in terms of the CRISP-DM methodology and determining the features of each of them in terms of cash flows. Modified versions of the model life-cycle containing risk assessment, including model risk, were also taken into account. In the process of research, the proposed generalized model life-cycle methodology was specified for a specific AI technology — large language models. As a result of the study, the author proposed a three-stage model. The possible optionality between the stages and the characteristics of cash flows are described. It was concluded that an investment project for the development of AI contains several real options — abandonment, reduction, expansion and replacement. For large language models, the life cycle structure and possible optionalities are preserved. The peculiarity is that the value creation process involves cash flows from different areas of application of the model in business processes. The results of the study are of practical importance for medium and large businesses engaged in the independent development of AI models and/or applying them to their business processes. The proposed concept of the model life-cycle can also be used to develop a methodology for evaluating investments in AI using real options.Finance: Theory and Practice120The proposed concept of the model life-cycle can be used to develop a methodology for evaluating investments in AI using real options and the possible optionality between the stages and the characteristics of cash flows.Finance: Theory and Practice["N. A. Nikitin"]2024-07-12T00:00:00 ee9e8b3872167af2944d3ba94ae663d645806813Currently, the business sector has entered a digital transformation stage involving Big Data (BD) and Artificial Intelligence (AI). At this stage the institution must change the way it operates its business, even to a philosophical point. This paper attempts to predict the direction of these changes through literature studies. The purpose of this paper is to provide a broad view regarding the application of BD and AI in Talent Management (MT) practice. From this study it was found that the use of AI and BD in the MT process (recruiting, developing, retaining and deploying talent) has obstacles inculde: 1) availability of expert human resources, 2) data security and 3) psychological barriers of top management and employees. Some of the strategic recommendations found include: 1) Building HR expertise and adaptation, 2) Legal readiness, 3) Managerial strategy and 4) Reducing AI bias to increase the fairness of AI decisions.    International Journal of Economics and Management Sciences520It was found that the use of AI and BD in the MT process (recruiting, developing, retaining and deploying talent) has obstacles inculde: 1) availability of expert human resources, 2) data security and 3) psychological barriers of top management and employees.International Journal of Economics and Management Sciences["Aditya Santoso", "Suryono Efendi", "Andini Nurwulandari"]2024-07-12T00:00:00 0226692c3efc00c4ac334d5d4ad8d32029fe41ebThis paper delves into the burgeoning intersection of Artificial Intelligence (AI) and business activation, scrutinizing the legal and Intellectual Property Rights (IPR) perspectives that govern this integration. The research adopts a doctrinal methodology, drawing insights from contemporary literature including books, journals, and papers. It aims to comprehensively understand AI’s role in business processes and the ensuing legal implications. The study commences with an exploration of AI’s escalating influence on business operations and proceeds to dissect the legal frameworks and IPR issues pertinent to AI inventions. A proposed conceptual model for AI integration addresses data privacy, security, and transparency concerns. The paper further investigates the patentability of AI algorithms and inventions, copyright ramifications for AI-generated content, and confidentiality challenges associated with AI models. Authorship and ownership debates are also examined, particularly concerning AI-generated work and employee-created AI within employer rights. The paper highlights ethical dilemmas surrounding AI ownership and accountability, underscored by challenges safeguarding AI-related IP. Real-world case studies provide a pragmatic lens through which the paper discusses how businesses navigate these legalities. The culmination of this inquiry offers pragmatic recommendations for businesses contemplating AI adoption. The paper acknowledges limitations due to the rapid evolution of technology potentially outdating legislative measures. Hence, it advocates for ongoing legislative updates to remain congruent with technological advancements.DME Journal of Management320This paper delves into the burgeoning intersection of Artificial Intelligence and business activation, scrutinizing the legal and Intellectual Property Rights perspectives that govern this integration, and offers pragmatic recommendations for businesses contemplating AI adoption.DME Journal of Management["Lavanya Bhagra", "Tushar Singh"]2024-07-12T00:00:00 8950087f20f3150f073555008c1725c4cf9e2851As a domain of science and technology, Artificial Intelligence (AI) opened new horizons for education. Technologies change the ways we teach and learn. While Generative AI tools create new prospects for learning, several concerns also arise. Educators are worried that they cannot differentiate between the output of students' work and the output from AI and this will impact the discipline, originality, integrity, and ethics in such cases. In addition, the problem also potentially arises in the matter of the authorship of the works regarding Copyright Law.  This paper examines several legal issues of the utilization of Generative AI through the perspective of Copyright Law. This paper concludes several important points; First, although the framework of Indonesia’s copyright law is based on the principle of human authorship, the rapid development of Generative AI must be balanced with an accommodative legal framework, Second, it is particularly important to formulate a special provisions to guide the implementation concerning the utilization of copyrighted works as the input material for generative AI so that it will not harm the "legitimate interest of the author" in the limit of "normal exploitation of the work" and classified as fair use, Third, academics and administrators need to gain a better understanding of the promise and perils of generative AI, how it will likely impact education, and how it might best govern by encourage the school and universities to develop institutional policies and/or formal guidance concerning the use of digital technology and Generative AI for the future of education.Jurnal Pendidikan Terbuka Dan Jarak Jauh110Examination of several legal issues of the utilization of Generative AI through the perspective of Copyright Law concludes that although the framework of Indonesia’s copyright law is based on the principle of human authorship, the rapid development of Generative AI must be balanced with an accommodative legal framework.Jurnal Pendidikan Terbuka Dan Jarak Jauh["R. F. Mayana", "Tisni Santika"]2024-07-12T00:00:00 fee90a48fa9c99f04f8e0916044109024e70be40 In the era of Society 5.0, which is better known as the era of technological maturity and humanity, this era emphasizes increasing the capacity and quality of human resources (HR) in all joints, especially the use of technology. Until now, there has not been a single regulation that specifically regulates artificial intelligence. For all criminal offenses related to AI, the applicable law is ITE Law Number 1 of 2024. Issues related to misuse, legal protection efforts for victims, and law enforcement for perpetrators of misuse of AI in the form of deepfake porn are essential because it is a violation of honor and reputation. It is hoped that in the future, the public can protect themselves from the potential misuse of this technology. Therefore, to be able to comprehensively interpret how legal protection for victims of AI misuse in the form of deepfake porn is provided, analytical and in-depth research is needed regarding the legal review of the misuse of artificial intelligence in Indonesian regulations. International Journal of Sustainability in Research70To be able to comprehensively interpret how legal protection for victims of AI misuse in the form of deepfake porn is provided, analytical and in-depth research is needed regarding the legal review of the misuse of artificial intelligence in Indonesian regulations.International Journal of Sustainability in Research["Norma Kinanty", "Bambang Santoso"]2024-07-12T00:00:00 a8afe10709fb2e8d1ff8ee2d3ad14442bb09d789Our contribution aims to propose a novel research agenda for behavioural public administration (BPA) regarding one of the most important developments in the public sector nowadays: the incorporation of artificial intelligence into public sector decision-making. We argue that this raises the prospect of distinct set of biases and challenges for decision-makers and citizens, that arise in the human-algorithm interaction, and that thus far remain under- investigated in a bureaucratic context. While BPA scholars have focused on human biases and data scientists on ‘machine bias’, algorithmic decision-making arises at the intersection between the two. In light of the growing reliance on algorithmic systems in the public sector, fundamentally shaping the way governments make and implement policy decisions, and given the high-stakes nature of their application in these settings, it becomes pressing to remedy this oversight. We argue that behavioural public administration is well-positioned to contribute to critical aspects of this debate. Accordingly, we identify concrete avenues for future research, and develop theoretical propositions.Journal of Behavioral Public Administration1290It is argued that behavioural public administration is well-positioned to contribute to critical aspects of this debate, and identifies concrete avenues for future research, and develops theoretical propositions.Journal of Behavioral Public Administration["Saar Alon-Barkat", "M. Busuioc"]2024-07-12T00:00:00 2c142584f89eaa3fa3d9eae3472a377d81528b4aWith the growth of e-commerce transactions and people living their lives online, it is important for consumer disputes to be tailored in a manner that is suitable for consumers and their types of disputes. Currently South Africa is facing major delays in resolving consumer disputes, and consumers end up not pursuing their low-value claims as the current processes take a long time. Further, consumers do not have the funds to pay attorneys. The Consumer Protection Act encourages the use of alternative dispute resolution (ADR) before a consumer dispute can be referred to a court of law. However, such ADR processes are lengthy and do not provide consumers with affordable and efficient relief. The current ADR processes do not meet the expectations of the consumers; thus, this paper proposes an integration of artificial intelligence (AI) and Blockchain Technologies in resolving consumer disputes via online dispute resolution (ODR). Various forms of AI and blockchain technologies are explored. The concept of online dispute resolution is introduced and current examples of online dispute resolution systems like eBay, and countries that have already moved to online dispute resolution with the integration of AI, are used as exemplary models for a South African online dispute resolution powered by AI and blockchain technologies.Potchefstroom Electronic Law Journal90This paper proposes an integration of artificial intelligence (AI) and Blockchain Technologies in resolving consumer disputes via online dispute resolution (ODR), and various forms of AI and blockchain technologies are explored.Potchefstroom Electronic Law Journal["Mnotho Ngcobo"]2024-07-12T00:00:00 09775e51681e4f77138a4b9f8be7004920f2c820Acta Diabetologica90DAIRET showed an optimal sensitivity in detecting all cases of referable DR (moderate DR or above) compared with that of a human grader, and the specificity of DAIRET was low because of the high number of false-positives, which limits its cost-effectiveness.Acta diabetologica["Silvia Burlina", "Sandra Radin", "Marzia Poggiato", "Dario Cioccoloni", "Daniele Raimondo", "Giovanni Romanello", "Chiara Tommasi", "Simonetta Lombardi"]2024-07-12T00:00:00 23412e78ac4cd991a6033c401024d6fb9debfb91The purpose of this article is to study translation as a human speech act in the context of artificial intelligence. Using the method of analysing the related literature, the article focuses on the impact of technological changes on traditional approaches and explores the links between these concepts and their emergence in linguistics and automatic language processing methods. The results show that the main methods include stochastic, rule-based, and methods based on finite automata or expressions. Studies have shown that stochastic methods are used for text labelling and resolving ambiguities in the definition of word categories, while contextual rules are used as auxiliary methods. It is also necessary to consider the various factors affecting automatic language processing and combine statistical and linguistic methods to achieve better translation results. Conclusions - In order to improve the performance and efficiency of translation systems, it is important to use a comprehensive approach that combines various techniques and machine learning methods. The research confirms the importance of automated language processing in the fields of AI and linguistics, where statistical methods play a significant role in achieving better results. Keywords: technological changes, linguistics, innovations, language technologies, automatic translationData and Metadata220The research confirms the importance of automated language processing in the fields of AI and linguistics, where statistical methods play a significant role in achieving better results.Data and Metadata["N. Yuhan", "Yu.A. Herasymenko", "Oleksandra Deichakivska", "Anzhelika Solodka", "Yevhen Kozlov"]2024-07-12T00:00:00 48b5d3c395c8eda9dfe02dd77f08d9adc3150993The integration of artificial intelligence (AI) in financial markets and business operations has emerged as a transformative force, reshaping traditional practices and unlocking new opportunities. This paper presents a systematic literature review encompassing a wide array of studies on AI applications in finance and business. The review explores AI's role in enhancing financial forecasting, trading strategies, risk management, and fraud detection. It discusses various AI techniques such as machine learning, deep learning, and natural language processing, highlighting their effectiveness in analysing vast datasets and improving decision-making processes. Moreover, the review addresses the implications of AI adoption in optimising business operations, including process automation, predictive analytics, and customer experience enhancement. Key themes include the benefits of AI-driven innovations, such as increased efficiency, cost reduction, and personalised services, alongside challenges related to job displacement, algorithmic bias, and regulatory frameworks. The paper concludes with insights into future research directions aimed at advancing AI's interpretability, transparency, and ethical deployment in financial and business contexts.International Journal For Multidisciplinary Research170A systematic literature review encompassing a wide array of studies on AI applications in finance and business explores AI's role in enhancing financial forecasting, trading strategies, risk management, and fraud detection, and the implications of AI adoption in optimising business operations.International Journal For Multidisciplinary Research["Prangya Khurana"]2024-07-12T00:00:00 c55f4f69400ae808ae561209fa7ffe0fa44fdabcCognitive Research630It is concluded that multidisciplinary research from use-inspired basic cognitive research, domain-specific applied research, and technical research is needed to understand and design artificial intelligence systems to mitigate these impacts and develop training and use protocols to prevent negative impacts on users’ cognitive skills.Cognitive Research: Principles and Implications["B. Macnamara", "Ibrahim Berber", "M. C. \u00c7avu\u015fo\u011flu", "Elizabeth A. Krupinski", "Naren Nallapareddy", "Noelle E Nelson", "Philip J Smith", "Amy L Wilson-Delfosse", "Soumya Ray"]2024-07-12T00:00:00 021d27b171a39cfd7efb937a48dc7673b9a029efHistory is full of examples of technological transformations changing how people do business. The Internet can be considered the latest example of how businesses have had to adapt to survive and thrive in the market. While the term artificial intelligence (AI) has been around since the 1950s, its relevance in corporate governance has been mostly ignored. While it is true that the current wave of development is not the first one, and it will certainly not be the last, there is no industry left untouched by AI. Be it healthcare, education, manufacturing, or e-governance, AI is making its impact felt across the globe.Existing research has shown that digital transformation is a difficult exercise for any business. The shift from old ways of doing things to technology-based solutions often faces a direct behavioural challenge from a company’s employees. Adopting AI into corporate governance can be considered one such digital transformation initiative. The literature review in this paper covers how digital transformation can take place in an organization's corporate governance function and various factors that need to be considered. Qualitative research methodology has been used to conduct research for this paper.The data collection method used is semi-structured interviews. A set of questions was prepared in advance so that the discussion did not deviate from the main theme. A total of twenty interviews were conducted for twenty participants. For data analysis, these participants were divided into three groups: AI experts who are currently working in the private sector, corporate professionals who are currently in senior managerial positions, and public servants employed by the government handling IT-related projects.The responses of the participants have been analysed and presented in this paper. This analysis reveals that there is cautious optimism shown by the participants with regard to integrating AI into corporate governance processes. They acknowledge benefits such as transparency, accountability, and fast and unbiased decision-making and, at the same time, point out concerns like a lack of regulations, misuse, ethical considerations, and unavailability of skilled workforce. International Journal of Religion390There is cautious optimism shown by the participants with regard to integrating AI into corporate governance processes, and they acknowledge benefits such as transparency, accountability, and fast and unbiased decision-making and, at the same time, point out concerns like a lack of regulations, misuse, ethical considerations, and unavailability of skilled workforce.International Journal of Religion["Hussein Jalal Mohammed Alkinani", "Sajjad Raad Khalaf Aljabry"]2024-07-12T00:00:00 70a2b448519ec6f72e30e309b6367319eed7fdc3Kant's Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principle of human judgment. The technology of artificial intelligence (AI), based on functionalism, claims to simulate or replicate human judgment. To assess this claim, it is necessary to study whether AI judgment possesses the characteristics of human judgment. This paper argues that AI judgments exhibit a form that cannot be understood in terms of the characteristics of human judgments according to Kant. Because the characteristics of judgment overlap, we can call this AI's uncertainty. Then, I show that concepts without physical intuitions are not easy to explain when their functions are shown through vision. Finally, I illustrate that even if AI makes sentences through subject and predicate in natural language, which are components of judgment, it is difficult to determine whether AI understands the concepts to the level humans can accept. This shows that it is questionable whether the explanation through natural language is reliable.arXiv.org570It is argued that AI judgments exhibit a form that cannot be understood in terms of the characteristics of human judgments according to Kant, and it is shown that it is questionable whether the explanation through natural language is reliable.ArXiv["Jongwoo Seo"]2024-07-12T00:00:00 b1a02bd5965e03773d8229635039e8f6e045fcb1The research on computer vision mainly aims to enable computers to understand and interpret images. Computer vision is an emerging discipline, and applying generative artificial intelligence technology to image processing is an important entry point in current machine vision research. When generating images, this article utilized adversarial learning mechanisms to optimize the parameters of the generator and discriminator, creating a competitive relationship between the two and gradually improving the quality of the generated images. Adversarial training methods were used in video processing to optimize the generated model, enabling it to map between different styles and achieve style transfer. The generative adversarial network was optimized using a cyclic consistency loss function to ensure consistency of video content before and after editing. Based on the trained recurrent generative adversarial network, style transformation and content editing on the video were performed. The PSNR value of generated image 3 was 27.1; the PSNR (peak signal-to-noise ratio) value of the original image was 27.8; the SSIM (Structural Similarity) value of the generated image was 0.95; the SSIM value of the original image was 1.00. The PSNR and SSIM values of the generated image with image number 3 in the experiment were relatively high and close to the corresponding values of the original image. This article provided a reference for the research on the application of generative artificial intelligence in visual effects.2024 2nd World Conference on Communication & Computing (WCONF)150Adversarial training methods were used in video processing to optimize the generated model, enabling it to map between different styles and achieve style transfer, and provided a reference for the research on the application of generative artificial intelligence in visual effects.2024 2nd World Conference on Communication & Computing (WCONF)["Yan Du"]2024-07-12T00:00:00 6414bce821fe6e6d12505a0064c0b7513ce4fb3cThis article provides a detailed analysis of the main difficulties and limitations of traditional BI (Business Intelligence) systems, and combines the latest AI technology to verify the possibility of applying Artificial Intelligence (AI) technology in traditional BI systems. By combining AI with BI, the efficiency, quality, and depth of data analysis can be improved, promoting the business development and decision-making of enterprises in the era of big data.2024 4th International Symposium on Computer Technology and Information Science (ISCTIS)902024 4th International Symposium on Computer Technology and Information Science (ISCTIS)["Jinxia Liu", "Pei Liu"]2024-07-12T00:00:00 49485af91c1dd21522233e96ec7648f7915d946aIn recent years, the utilization of artificial intelligence-powered tools in education has witnessed a significant surge. These tools offer invaluable assistance to teachers in classroom preparation and learners seeking to enhance their English language proficiency. With their considerable potential, AI-powered tools provide an affordable, effective, and efficient approach to teaching and learning. This research focuses on analyzing AI-powered tools that facilitate the reading process. Employing a qualitative research design with content analysis as the primary methodology and documentation as the technique, this study delves into the examination of selected AI-powered tools to aid in the reading process. The findings underscore the benefits and advantages of these tools when aligned with the various stages of the reading process. Specifically, ChatGPT emerges as beneficial for the pre-reading stage, while Quillbot and Paraphraser.io prove advantageous during reading. Post-reading activities can be facilitated by tools such as ChatGPT and Storywiz.io. In conclusion, based on the outcomes, the research recommends the utilization of the aforementioned AI-powered tools to enhance the reading process.PRIMACY Journal of English Education and Literacy00ChatGPT emerges as beneficial for the pre-reading stage, while Quillbot and Paraphraser.io prove advantageous during reading, and the research recommends the utilization of the aforementioned AI-powered tools to enhance the reading process.PRIMACY Journal of English Education and Literacy["M. H. A Rahman", "Hidayah Nor"]2024-07-12T00:00:00 b4ecc0bd9772678973c28613e42fc16667947358eXplainable Artificial Intelligence (XAI) aims at providing understandable explanations of black box models. In this paper, we evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. To this end, we used an Electric Arc Furnace (EAF) model to better understand the limits and robustness characteristics of XAI methods such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), as well as Averaged Local Effects (ALE) or Smooth Gradients (SG) in a highly topical setting. These XAI methods were applied to various types of black-box models and then scored based on their correctness compared to the ground-truth sensitivity of the data-generating processes using a novel scoring evaluation methodology over a range of simulated additive noise. The resulting evaluation shows that the capability of the Machine Learning (ML) models to capture the process accurately is, indeed, coupled with the correctness of the explainability of the underlying data-generating process. We furthermore show the differences between XAI methods in their ability to correctly predict the true sensitivity of the modeled industrial process.arXiv.org300The resulting evaluation shows that the capability of the Machine Learning (ML) models to capture the process accurately is, indeed, coupled with the correctness of the explainability of the underlying data-generating process.ArXiv["Benedikt Kantz", "Clemens Staudinger", "C. Feilmayr", "Johannes Wachlmayr", "Alexander Haberl", "Stefan Schuster", "Franz Pernkopf"]2024-07-12T00:00:00 f266d616b718dc03789a747b60c25ab99a93097bThe findings revealed that there is no significant difference in the perception of artificial intelligence among the male and female postgraduate students. But, on the contrary, the significant difference in the trust and Threat perception of artificial intelligence was detected between male and female postgraduates in Maharashtra, India. In the study, t- test and ANOVA was used for statistical data analysis.International Journal For Multidisciplinary Research150International Journal For Multidisciplinary Research["Shaikh HAJIMALANG AKABAR"]2024-07-12T00:00:00 b18a3a26264f7d50d16164a5c12b606186932395Academy of Management Review61Academy of Management Review["A. Eicke", "J. N. Foege", "Stephan N\u00fcesch"]2024-07-12T00:00:00 fa018014bd0e03b7b1d2b6b4733adc771487df31Artificial Intelligence Review1201The review starts by giving a general overview of the many approaches to AI algorithms, followed by a general overview of the various MH algorithms for healthcare applications, an analysis of MHs boosted AI for healthcare applications, and using a wide range of research databases as a data source for access to numerous field publications.Artif. Intell. Rev.["Essam H. Houssein", "Eman Saber", "A. Ali", "Y. Wazery"]2024-07-12T00:00:00 b549a90b1df01b76426a1a6642567a7416a39090npj Climate Action160npj Climate Action["F. Larosa", "Adam Wickberg"]2024-07-12T00:00:00 aaa3e1a8cbd56eff91fd1c63e99cb3e493d34caaJournal of Anesthesia Analgesia and Critical Care130This project involves the integration of different technologies (AI, big data, robotics, and telemedicine) to create a unique system for patients admitted to intensive care units suffering from infectious diseases capable of both increasing the personalization of care and ensuring a safer environment for caregivers.Journal of Anesthesia, Analgesia and Critical Care["M. Bocci", "Raffaella Barbaro", "Valentina Bellini", "Christian Napoli", "Luigino Jalale Darhour", "E. Bignami"]2024-07-12T00:00:00 ac95a8e8a8e949e5f3d969e2d49756323117b5a9Radiologia Brasileira30Radiologia Brasileira["Tulio Augusto Alves Macedo", "M. Rocha"]2024-07-12T00:00:00 94dbdffc1299899289c533ae4314737534d112c6Eluned Creighton-Sims considers the role of AI in ophthalmic lens designThe Optician00Optician["Eluned \u2018Lil\u2019 Creighton-Sims"]2024-07-12T00:00:00 5ee2fc40a04f0a6c5652f01911f017fa385ce37fУ статті автори аналізують різні визначення, сутність та особливості штучного інтелекту (ШІ), його значення для підвищення ефективності вивчення англійської мови, удосконалення комунікативної та професійної компетентності студентів. Особливістю штучного інтелекту є те, що це комп’ютерне моделювання людського інтелекту розроблене для функціонування подібного людині. Мета створення штучного інтелекту полягає у тому, щоб полегшити освіту у викладанні та вивченні іноземної мови. Пропонована розвідка розкриває роль штучного інтелекту у викладанні іноземних мов і досліджує технології, що підтримуються штучним інтелектом у цьому навчальному процесі. Штучний інтелект пропонує ефективну навчальну атмосферу для вивчення іноземної мови, зокрема курсу англійської. Іншою відмінною перевагою ШІ є його здатність створювати особистісно орієнтовану атмосферу, в якій студенти використовують свої знання та досвід для одночасного вдосконалення іншомовних навичок, враховуючи власний поточний рівень володіння іноземною мовою, бажання та потреби. Технології ШІ створюють технічне середовище для розвитку іншомовних навичок. Можливості сучасного програмного забезпечення на основі ШІ дають змогу організувати застосування цих технологій відповідно до умов вивчення англійської мови. Широкий спектр засобів навчання полегшує оволодіння студентами англійською мовою. Велика різноманітність іншомовних додатків із підтримкою штучного інтелекту, включаючи мобільне та програмне забезпечення, пропонує студентам багато опцій. Зазначені своєрідні технології характеризуються людською поведінкою та мисленням завдяки моделюванню інтелекту та прийняттю обґрунтованих рішень, ідентичних людським. Прикладами технологічних розробок на основі ШІ є онлайн-платформи English Able, Text to speech, Google Translate, Orai, та інші.Молодь і ринок00Молодь і ринок["Vadym Tynnyi", "Emma Schukina", "Olga Belyakova"]2024-07-12T00:00:00 119b9313219a6e424f80dae89e72e7e908b7ddf2Integrating Artificial Intelligence (AI) into mobile and wearables offers numerous benefits at individual, societal, and environmental levels. Yet, it also spotlights concerns over emerging risks. Traditional assessments of risks and benefits have been sporadic, and often require costly expert analysis. We developed a semi-automatic method that leverages Large Language Models (LLMs) to identify AI uses in mobile and wearables, classify their risks based on the EU AI Act, and determine their benefits that align with globally recognized long-term sustainable development goals; a manual validation of our method by two experts in mobile and wearable technologies, a legal and compliance expert, and a cohort of nine individuals with legal backgrounds who were recruited from Prolific, confirmed its accuracy to be over 85%. We uncovered that specific applications of mobile computing hold significant potential in improving well-being, safety, and social equality. However, these promising uses are linked to risks involving sensitive data, vulnerable groups, and automated decision-making. To avoid rejecting these risky yet impactful mobile and wearable uses, we propose a risk assessment checklist for the Mobile HCI community.Proc. ACM Hum. Comput. Interact.1275A semi-automatic method that leverages Large Language Models to identify AI uses in mobile and wearables, classify their risks based on the EU AI Act, and determine their benefits that align with globally recognized long-term sustainable development goals is developed.Proc. ACM Hum. Comput. Interact.["Marios Constantinides", "E. Bogucka", "S. \u0160\u0107epanovi\u0107", "Daniele Quercia"]2024-07-12T00:00:00 8f971a3a0670784f3ceb8c049a1ce6debcdcc708BACKGROUND Artificial intelligence (AI) tools have the potential to revolutionize many facets of medicine and medical sciences research. Numerous AI tools have been developed and are in continuous states of iterative improvement in their functionality. OBJECTIVES This study aimed to assess the performance of three AI tools: The Literature, Microsoft's Copilot and Google's Gemini in performing literature reviews on a range of dermatology topics. METHODS Each tool was asked to write a literature review on five topics. The topics chosen have recently had peer-reviewed systematic reviews published. The outputs of each took were graded on their evidence and analysis, conclusions and references on a 5-point Likert scale by three dermatologists who are working in clinical practice, have completed the UK dermatology postgraduate training examination and are partaking in continued professional development. RESULTS Across all five topics chosen, the literature reviews written by Gemini scored the highest. The mean score for Gemini for each review was 10.53, significantly higher than the mean scores achieved by The Literature (7.73) and Copilot (7.4) (p < 0.001). CONCLUSIONS This paper shows that AI-generated literature reviews can provide real-time summaries of medical literature across a range of dermatology topics, but limitations to their comprehensiveness and accuracy are apparent.Journal of the European Academy of Dermatology and Venereology102It is shown that AI-generated literature reviews can provide real-time summaries of medical literature across a range of dermatology topics, but limitations to their comprehensiveness and accuracy are apparent.Journal of the European Academy of Dermatology and Venereology : JEADV["Lauren Passby", "Vidya Madhwapathi", "Simon Tso", "A. Wernham"]2024-07-12T00:00:00 15f9dad4f26adacc27bc7d998626317113ad2f4cIntegrating Artificial Intelligence (AI) into critical infrastructure sectors such as the power grid, energy systems, and telecommunications has revolutionised decision-making processes, enhancing efficiency and reliability. However, this data-driven transition introduces inherent risks, including adversarial attacks, biases, and system failures with potentially catastrophic consequences. This paper addresses the crucial need of collecting AI-incident data to develop effective mitigation methodologies. It analyses the successful incident reporting mechanisms in safety-critical sectors of aviation, cybersecurity, and fire safety, which have significantly improved system reliability and prevented future incidents. It then evaluates the existing AI incident databases, examining reported incidents, impacted sectors, and affected parties. Findings of this study reveal significant gaps in capturing critical infrastructure-specific AI incidents. The paper proposes a formalised process for AI-incident reporting for critical infrastructure, enabling data collection and analysis, thus assisting in developing mitigation strategies, safety protocols, and regulatory frameworks. Emphasising transparency, accountability, and collaboration, this study ensures responsible and trustworthy deployment of AI in critical infrastructure.IEEE International Conference on Electronics, Computing and Communication Technologies182The paper proposes a formalised process for AI-incident reporting for critical infrastructure, enabling data collection and analysis, thus assisting in developing mitigation strategies, safety protocols, and regulatory frameworks.2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)["Avinash Agarwal", "M. Nene"]2024-07-12T00:00:00 3789596cfc9d4ba168c4c4b213988aa6c3da70dbPurposeThe notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market for detecting artificially generated content has seen a mushroom growth of tools that claim to be more than 90% accurate in sensing artificially written content.Design/methodology/approachThis research evaluates the capabilities of some highly mentioned AI detection tools to separate reality from their hyperbolic claims. For this purpose, eight AI engines have been tested on four different types of data, which cover the different ways of using ChatGPT. These types are Original, Paraphrased by AI, 100% AI generated and 100% AI generated with Contextual Information. The AI index recorded by these tools against the datasets was evaluated as an indicator of their performance.FindingsThe resulting figures of cumulative mean validate that these tools excel at identifying human generated content (1.71% AI content) and perform reasonably well in labelling AI generated content (76.85% AI content). However, they are perplexed by the scenarios where the content is either paraphrased by the AI (39.42% AI content) or generated by giving a precise context for the output (60.1% AI content).Originality/valueThis paper evaluates different services for the detection of AI-generated content to verify academic integrity in research work and higher education and provides new insights into their performance.Journal of Applied Research in Higher Education282This paper evaluates different services for the detection of AI-generated content to verify academic integrity in research work and higher education and provides new insights into their performance.Journal of Applied Research in Higher Education["Muhammad Bilal Saqib", "S. Zia"]2024-07-12T00:00:00 f716ae8eca3fa400841e91f417e002bb60e24caaRecent research in Artificial Intelligence (AI) in diagnosing Autism Spectrum Disorders (ASD) helps autistic people to improve their social, communicational and emotional skills. Diagnosis of ASD through enhanced models provides a reality-based therapy for promising improvements in autism disorders. This research paper proposes a novel approach for autism detection using ensemble learning with AI-enabled convolutional neural networks (CNNs). The proposed approach focuses on evaluating risk factors and disorders through an ensemble approach with transfer learning schemes. Our method leverages transfer learning from pre-trained CNN models, including EfficientNet B5, MobileNet, and InceptionV3, trained on the ImageNet dataset. We fine-tune these models for autism detection by improvising their architectures, freezing convolutional layers, and adding new fully connected layers for binary classification problems. Through extensive experimentation, the demonstration was made with a keen focus on each CNN model, which then improved accuracy in identifying autistic traits from image data. Furthermore, by combining the predictions of these models using a soft voting ensemble technique, they achieve superior performance with an accuracy of about 91% respectively. Our results indicate the effectiveness of ensemble learning in improving autism detection accuracy, showcasing the potential of deep learning approaches in aiding clinical diagnosis and treatment planning for autism spectrum disorders (ASDs).2024 2nd World Conference on Communication & Computing (WCONF)240The results indicate the effectiveness of ensemble learning in improving autism detection accuracy, showcasing the potential of deep learning approaches in aiding clinical diagnosis and treatment planning for autism spectrum disorders (ASDs).2024 2nd World Conference on Communication & Computing (WCONF)["A. Abdullah", "Yeshwanth Govindarajan", "Vishal Pranav", "S. Geetha", "A. Aashish"]2024-07-12T00:00:00 c2ddae47ec7e3c6645971602d845e73bfd39e982Humanities and Social Sciences Communications5013This study aims to comprehensively investigate the impact of AI on higher education in Saudi Arabia, delving into stakeholders’ attitudes, perceptions, and expectations regarding its implementation, and highlights the necessity for a comprehensive understanding of AI integration.Humanities and Social Sciences Communications["Abdulrahman M. Al-Zahrani", "Talal M. Alasmari"]2024-07-13T00:00:00 88057e2abc3ba855ed24fa0dbd815266e42989ceBACKGROUND: Artificial intelligence, like medicine, is a dynamically developing field that can be considered both a science and an art. This makes it much more difficult to use artificial intelligence compared to other technologies that come with a user manual. Research and start-ups in the field of medical artificial intelligence are rapidly multiplying: the popularity of smart mobile devices, networked applications and remote digital services is growing. However, there are still some problems that complicate the widespread use of artificial intelligence algorithms in everyday clinical practice. The reasons for this are the high cost of operating neural network platforms and the limited qualifications of some medical professionals in the field of computer technology. These are only temporary difficulties, though, which should and will be gradually resolved. CONCLUSION: This article focuses on the most sensitive points that are currently hindering the accelerated progress of machine learning in healthcare.Sociology of Medicine201This article focuses on the most sensitive points that are currently hindering the accelerated progress of machine learning in healthcare.Sociology of Medicine["V. A. Berdutin", "T. Romanova", "S. Romanov", "O. Abaeva"]2024-07-13T00:00:00 6aa791bb4ea43ee5a10ee7b8041c57be3361ee2fLa Radiologia medica392Artificial intelligence is a topic of increasing interest in MSK US research and attention should be paid to the use of validation strategies, particularly regarding independent clinical validation performed on external datasets.La Radiologia Medica["Jonas M Getzmann", "Giulia Zantonelli", "Carmelo Messina", "Domenico Albano", "F. Serpi", "S. Gitto", "L. Sconfienza"]2024-07-13T00:00:00 82a0f5386529a6ac295c4c46abfcd906774ad18cAI has transformed the FMCG industry in recent years by increasing productivity, reimagining long-standing procedures, and creating new avenues for innovation. Artificial intelligence is changing demand forecasting and inventory control in the FMCG sector. Information conveniently gathered from the 300 fast-moving consumer products buyers in the Palakkad District sample. The study's qualitative data were rated using a five-point Rensis Likert scale. With the use of SPSS software, one-way ANOVA and the Wilcoxon Mann-Whitney U test are used for statistical studies. The present investigation aims to understand the benefits perceived by customers of fast-moving consumer goods after the implementation of AI. It’s necessary to understand whether AI is effective in improving customer base for fast-moving consumer goods. The one-way ANOVA test results shows that is significant difference between levels of education and perception of consumers about Personalized Marketing and Consumer Engagement and Product Innovation and Development. The Wilcoxon Mann Whitney U test results shows that Gender has no impact on perception of consumers about benefits of AI in FMCG sector.Recent trends in Management and Commerce10The one-way ANOVA test results shows that is significant difference between levels of education and perception of consumers about Personalized Marketing and perception of consumers about Personalized Marketing and Consumer Engagement and Product Innovation and Development.Recent trends in Management and Commerce[]2024-07-13T00:00:00 8fb9f0a31f360ff35f05fdfcfb418aa1ca6753f4The use of artificial intelligence (AI) in green finance has become essential considering the growing environmental concerns and the need to slow down climate change. This abstract outline the essential role that artificial intelligence (AI) plays in propelling green investment, explaining its diverse functions and transformational potential. Primarily, artificial intelligence enhances the effectiveness of green finance by enabling data-driven decision making procedures.AI helps investors evaluate environmental risks, find sustainable investment opportunities, and allocate their portfolio optimally to green assets by using sophisticated algorithms and predictive analytics. Additionally, AI-powered platforms improve accountability and transparency, which strengthens investor confidence in green financial instruments. Second, AI enables regulatory agencies and financial institutions to reduce the risks related to environmental degradation and climate change. Stakeholders can protect financial stability and reduce systemic risks by proactively assessing an investment's resistance to climate related shocks by utilising AI driven risk assessment tools. Finally, AI encourages cooperation and knowledge exchange within the ecosystem of green finance. AI-driven platforms help disseminate knowledge by analysing large datasets and finding patterns. This allows researchers, policymakers, and investors to learn about new trends, best practices, and investment opportunities in sustainable finance.Recent trends in Management and Commerce00The essential role that artificial intelligence plays in propelling green investment is outlined, explaining its diverse functions and transformational potential.Recent trends in Management and Commerce[]2024-07-13T00:00:00 384c2a52287272c16810c2c56aa46fedc5673dd5The cyber security industry is witnessing a significant shift as more and more companies look to artificial intelligence (AI) to rethink their defense strategies against emerging cyber threats. This study explores how artificial intelligence (AI) is fundamentally changing the way that threat detection paradigms are traditionally understood. Through an examination of the past and present of traditional approaches, we draw attention to the growing necessity of artificial intelligence-driven solutions. The paper provides a thorough examination of the basis of artificial intelligence (AI) in threat detection, with a focus on neural networks and machine learning methods. This paper examines several AI-powered threat detection methods, such as behavioral analysis, anomaly detection, and a comparison of heuristic-based and signature-based strategies. Examined are issues like explain ability, interpretability, and adversarial attacks, which offer a thorough understanding of the difficulties and factors, related to AI-driven cyber security. With a focus on threat intelligence platforms, explainable AI, predictive analytics, and integration, the paper takes a forward-looking approach to improving cyber security. The effectiveness and efficiency of AI-powered threat detection are compared to more conventional techniques in a comparative analysis that ends with advice for businesses looking to use AI-driven solutions. Key findings and their implications for the changing cyber defense landscape are summarized in the study', s conclusion. By doing this research, we hope to add to the continuing conversation about the development of threat detection and provide useful advice and insights to help enterprises successfully incorporate AI into their cyber security plans.Recent trends in Management and Commerce70Several AI-powered threat detection methods are examined, such as behavioral analysis, anomaly detection, and a comparison of heuristic-based and signature-based strategies, to provide a thorough examination of the basis of artificial intelligence in threat detection, with a focus on neural networks and machine learning methods.Recent trends in Management and Commerce[]2024-07-13T00:00:00 179be67a767fb857539f8874a692b00eca003dcfIn the last decade, electronic surveillance systems have been actively employed for monitoring traffic rule violations with the aim of enhancing traffic regulation. The utilization of these systems has resulted in increased compliance with traffic regulations, consequently leading to a reduction in losses attributed to traffic accidents. The impact created by these systems is expected to be further amplified through the incorporation of artificial intelligence (AI) support. Within the scope of this study, a detailed analysis of the socio-economic impact of AI-assisted Electronic Traffic Monitoring Systems has been conducted, focusing on economic, mobility, health, environmental, and quality of life aspects.Akıllı ulaşım sistemleri ve uygulamaları dergisi00A detailed analysis of the socio-economic impact of AI-assisted Electronic Traffic Monitoring Systems has been conducted, focusing on economic, mobility, health, environmental, and quality of life aspects.Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi["M. Samast\u0131"]2024-07-13T00:00:00 307ab276ce4d7d2acca741badb4880ddb3e9dfebSchool Physical Education (PE), a crucial component for the integral development of students, faces the challenge of promoting the inclusion of students with special needs (SEN). Barriers such as the lack of adaptation of activities and the scarcity of adequate resources hinder the full participation of these students. This article investigates the potential of Artificial Intelligence (AI) as a tool to personalize teaching, adapt activities, and create inclusive learning environments in PE. Through the analysis of case studies and practical examples, it demonstrates how AI can be used to individually assess students, provide real-time feedback, develop adaptive platforms, recognize gestures and movements, create gamified activities, use virtual and augmented reality, translate sign language, implement virtual assistants, and prevent bullying. At the same time, the article discusses the ethical challenges and the need for teacher training to ensure the responsible and effective use of AI in promoting inclusion in PE.RCMOS - Revista Científica Multidisciplinar O Saber00The potential of Artificial Intelligence as a tool to personalize teaching, adapt activities, and create inclusive learning environments in PE is investigated and how AI can be used to individually assess students is demonstrated.RCMOS - Revista Científica Multidisciplinar O Saber["Adenise Alexandre de Brito E Guedes", "Joel Cleiton Maia de Lima", "Josivaldo Jorge Gon\u00e7alves da Silva", "Malena Poliana Pereira De Figueiredo", "Maria Milizia Heline de Figueiredo Pereira"]2024-07-13T00:00:00 1a0e40857fe793c7b5af033cd6a9b12fe28c6c6dPenelitian ini bertujuan untuk mengeksplorasi pengaruh penggunaan teknologi Artificial Intelligence (AI) terhadap minat belajar mahasiswa angkatan 2022 di Universitas Muhammadiyah Ponorogo. Metode penelitian yang digunakan adalah kualitatif dengan pendekatan studi kasus melalui observasi, wawancara, dan angket. Hasil penelitian menunjukkan bahwa penggunaan AI dalam proses pembelajaran memiliki pengaruh signifikan terhadap minat belajar mahasiswa, dengan berbagai faktor yang mempengaruhi tingkat ketertarikan dan keterlibatan mereka dalam pembelajaran. Saran diberikan kepada dosen, penyedia layanan teknologi, dan mahasiswa untuk memaksimalkan manfaat AI dalam pendidikan.Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer60Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer["Muhammad Amirul Muchminiin", "Muhammad Kevin", "Andrian Rahmadhani", "Syaikul Muqorobin", "Faisal Mustaghfirullah", "Osama Saddam Luthfi", "Jl. Budi", "Utomo No", "Ronowijayan 10", "Kec. Ponorogo", "Kabupaten Ponorogo", "Jawa Timur"]2024-07-13T00:00:00 c63ad1b3a22d06fcaa272b0266f7af60c896f861INTRODUCTION Myocardial perfusion imaging (MPI) is one of the most commonly ordered cardiac imaging tests. Accurate motion correction, image registration, and reconstruction is critical for high-quality imaging, but this can be technically challenging and traditionally has relied on expert manual processing. With accurate processing, there is a rich variety of clinical, stress, functional, and anatomic data that can be integrated to guide patient management. AREAS COVERED Pubmed and Google Scholar were reviewed for articles related to artificial intelligence in nuclear cardiology published between 2020 and 2024. We will outline the prominent roles for artificial intelligence (AI) solutions to provide motion correction, image registration, and reconstruction. We will review the role for AI in extracting anatomic data for hybrid MPI which is otherwise neglected. Lastly, we will discuss AI methods to integrate the wealth of data to improve disease diagnosis or risk stratification. EXPERT OPINION There is growing evidence that AI will transform the performance of MPI by automating and improving on aspects of image acquisition and reconstruction. Physicians and researchers will need to understand the potential strengths of AI in order to benefit from the full clinical utility of MPI.Expert Review of Cardiovascular Therapy980The prominent roles for artificial intelligence (AI) solutions to provide motion correction, image registration, and reconstruction are outlined and the role for AI in extracting anatomic data for hybrid MPI is reviewed.Expert review of cardiovascular therapy["Robert J H Miller", "Piotr J. Slomka"]2024-07-13T00:00:00 fbbd85a5b9a4982ae615860f1484858ed8950efbArtificial intelligence (AI) has quickly evolved from a sci-fi idea to a crucial part of modern technology, impacting a number of industries like healthcare, banking, education, and law enforcement. Fairness and bias issues with AI systems have drawn a lot of attention as they grow increasingly prevalent in everyday life. In artificial intelligence, "bias" refers to the systematic and unjust discrimination against particular groups of individuals. Prejudices in training data or those unintentionally introduced during algorithm development are common examples of bias. Contrarily, fairness is the idea that every person should have equal access to opportunities and treatment regardless of society or personal traits.International Journal for Research Publication and Seminar100Fairness and bias issues with AI systems have drawn a lot of attention as they grow increasingly prevalent in everyday life.International Journal for Research Publication and Seminar["Kabir Singh", "Chadha"]2024-07-13T00:00:00 dc2bec0910e90fe2f70c103b2b05697a5eef8e9cJournal of Broadcasting &amp; Electronic Media372Journal of Broadcasting &amp; Electronic Media["Zixi Li", "Jingyuan Shi", "Yinqiao Zhao", "Bohan Zhang", "Bu Zhong"]2024-07-13T00:00:00 f76b476c452d4105e28b777735726dcdc2559144The marketing field has undergone a considerable transformation with the introduction of AI, leading to enhanced performance. The present study is aimed at finding the effect of AI on marketing. This paper includes an in-depth literature review that offers a complete understanding of the application of AI in marketing. Various studies emphasize significant AI applications in marketing, such as neural networks, case-based reasoning, and expert systems, marking a transformative shift from traditional marketing methods. The integration of AI in marketing leverages technologies like natural language processing, machine learning, and sentiment analysis to enhance decision-making processes, providing precise insights into customer lifecycles and market trends. By combining AI with customer and brand experience data, businesses gain a competitive edge and are better equipped to navigate the dynamic marketplace. The paper underscores the key elements of incorporating AI in marketing functions to enhance overall business performance, leading to increased profitability and a competitive advantage. Its objective is to document insights into the AI ecosystem, elucidating how embedded technologies support marketing processes and contribute to organizational success. Furthermore, the paper delves into the synergy between AI and marketing, highlighting their combined potential as the future driving force for successful business organizations.Recent trends in Management and Commerce00An in-depth literature review that offers a complete understanding of the application of AI in marketing is included, elucidating how embedded technologies support marketing processes and contribute to organizational success.Recent trends in Management and Commerce[]2024-07-13T00:00:00 d9ed7733b8e2bedcee1c203ce9389da12be1dbf6SIGGRAPH Labs10{"pages": "7"}["Deborah Yuen", "J. Spjut"]2024-07-13T00:00:00 8974c303e0c950deafa65323dd1902f383871e98GEORGIAN SCIENTISTS00GEORGIAN SCIENTISTS["Emeliane Gogilidze", "Natia Gogilidze"]2024-07-13T00:00:00 d9b6b10812cd0cd9285fafd4763f157410130bb7Generative AI copyright protection is in response to the inherent requirements of AI development and intellectual property protection. At present, generative AI copyright protection faces problems such as insufficient legal basis for prevention and control mechanisms, unsmooth preventive systems, and poor operation mechanisms. Therefore, to solve the dilemma of generative AI copyright protection, it is necessary to take the principle of risk prevention as the concept, clarify the legal basis for generative AI copyright prevention, reasonably define the scope of generative AI, establish a multi-principal protection system to protect generative AI copyright, introduce diversified ways to help generative AI copyright maintenance, and set up a composite responsibility system to solidify the generative AI copyright protection bottom line, and establish an administrative protection system to protect the copyright protection bottom line. The bottom line of copyright protection is an administrative protection path to help generative artificial intelligence copyright protection efforts to promote the healthy development of the two in the integration.Scientific Journal Of Humanities and Social Sciences150To solve the dilemma of generative AI copyright protection, it is necessary to take the principle of risk prevention as the concept, clarify the legal basis for generative AI copyright prevention, reasonably define the scope of generative AI, establish a multi-principal protection system to protect generative AI copyright, and establish an administrative protection system to protect the copyright protection bottom line.Scientific Journal Of Humanities and Social Sciences["Zhi Yang", "Yuanhong Xu"]2024-07-13T00:00:00 ebd6d9be10dacfb3844d3c77dee0e1c4cc6ebab5Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)00Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)["Jinjian Li", "Yi Liu"]2024-07-13T00:00:00 72c9f6d870535b203ec18aa21379dbc2c336a914Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)00Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)["Yi Liu", "Jinjian Li"]2024-07-13T00:00:00 91d079f2d2c52df382583c01962031c87a4f9c4cPersonal information is the foundation of generative AI, and ChatGPT-like generative AI needs to process a large amount of personal information at various stages such as model training, model generation, and model optimization, which also has a certain impact on traditional personal information protection rules. During the information collection phase, generative AI may fugitive the informed consent rules and infringe on the privacy rights of information subjects. In the information utilization stage, generative AI may impact basic personal information processing rules such as the principle of purpose limitation and the principle of openness and transparency, increasing the risk of personal information leakage. At the information generation stage, generative AI can generate false and discriminatory information. Therefore, in the context of generative AI, personal information protection is faced with the problems of the notification and consent rules being hollowed out, the principle of minimum necessity being voided, and the frequent leakage of personal information. Based on this, it is necessary to promote the transformation of the "personal control center to the risk control center" of the notification and consent rules, promote the risk-based interpretation of the principle of least necessary, and improve the risk-based personal information protection compliance system to solve the problem of personal information protection in the context of generative AI.Scientific Journal Of Humanities and Social Sciences140It is necessary to promote the transformation of the "personal control center to the risk control center" of the notification and consent rules, promote the risk-based interpretation of the principle of least necessary, and improve the risk-based personal information protection compliance system to solve the problem of personal information protection in the context of generative AI.Scientific Journal Of Humanities and Social Sciences["Tingting Yan"]2024-07-13T00:00:00 dbcd89b265d345f1fdbeb32be7d66c0fa1ec09e5The alarming security threats in the internet world continually raise critical concerns among individuals, organizations and governments alike. The sophistication of cyber-attacks makes it imperative for a paradigm shift from traditional approaches and measures for quelling the attacks to modern sophisticated, digital and strategic ones, such as those involving machine learning and other technologies of artificial intelligence (AI). This study is aimed at examining machine learning (ML) strategies for effective cyber security. ML involves using algorithms and statistical models to enable computers learn from and make decisions or predictions based on data. The study relied on secondary data, which were subjected to a systematic review. The results of its thematic and qualitative analyses prove that majority of the literatures allude to the fact that the maximal performance abilities and tactics of the ML constitute its strategies for quelling cyber security. These include its: early detection of threats that are tackled before they cause damages; ability to analyze huge quantity of data quickly and accurately; and processing of datasets in real-time. The study argues that the noted abilities and tactics constitute ML strategies for quelling cyber security, regardless of its challenges like data quality, security vulnerabilities and possible incidences of bias. The study concludes that ML can indeed be used to detect and respond to threats in real-time, ascertain patterns of malicious behavior, and improve on internet security, which thereby prove it to be a viable tool for quelling cyber security.Asian Journal of Research in Computer Science09The study concludes that ML can indeed be used to detect and respond to threats in real-time, ascertain patterns of malicious behavior, and improve on internet security, which thereby prove it to be a viable tool for quelling cyber security.Asian Journal of Research in Computer Science["Chandra Shikhi Kodete", "Bharadwaj Thuraka", "Vikram Pasupuleti", "Saiteja Malisetty"]2024-07-13T00:00:00 6624b3e16af0453b82adb4d33b1d7f95cb58d47eIn the digital age, Artificial Intelligence (AI) is pivotal in enhancing cybersecurity, offering advanced capabilities to detect and mitigate cyber threats efficiently. This article delves into AI's role in strengthening cyber security, emphasizing its ability to proactively identify vulnerabilities, forecast attacks, and automate incident responses. It also addresses the challenges and ethical concerns associated with AI in cyber security, such as the potential for misuse by cybercriminals to conduct sophisticated attacks and issues related to data privacy and algorithmic bias. The piece highlights the necessity of a balanced approach to leveraging AI, advocating for collaboration among stakeholders to navigate the ethical and regulatory complexities. Ultimately, it underscores AI's indispensable role in developing resilient cyber security frameworks and fostering a secure digital environment amidst an increasingly complex threat landscape.Australian Journal of Engineering and Innovative Technology54Artificial Intelligence's indispensable role in developing resilient cyber security frameworks and fostering a secure digital environment amidst an increasingly complex threat landscape is underscored.Australian Journal of Engineering and Innovative Technology[]2024-07-13T00:00:00 bea89dab828e97d882e3dfb9ebb89819584a6047In the time of digital innovation, Artificial Intelligence (AI) stands at the forefront, signaling new capabilities in fraud management but also new vulnerabilities. This paper aims to dissect AI's paradoxical influence on fraud, portraying its roles in both promoting and mitigating fraudulent activities. The research seeks to bridge the gap in understanding the dual nature of AI, highlighting the need for ethical and regulatory frameworks to traverse the complications AI introduces into fraud detection and prevention. Utilising secondary data from Google News and academic databases with the keyword 'AI fraud,' this study adopts a keyword-based analysis to sift through the most relevant literature. The approach is designed to capture a comprehensive snapshot of the current discourse, underlining the bipolar impact of AI on fraud. The analysis reveals AI's significant potential in enhancing fraud detection systems through rapid data analysis and pattern recognition. However, AI technologies can be exploited to facilitate sophisticated fraud schemes. The study underscores an urgent need for evolving practices and policies that counteract AI's potential for misuse, weighing in the emerging concept of self-regulatory AI systems as a promising direction for future research. This paper contributes insights into the dualistic role of AI in fraud, adding depth to the discourse on its implications for security, ethical considerations, and regulatory challenges. It advocates for a balanced perspective on AI's capabilities.Recent trends in Management and Commerce40Insight is added into the dualistic role of AI in fraud, adding depth to the discourse on its implications for security, ethical considerations, and regulatory challenges, and advocates for a balanced perspective on AI's capabilities.Recent trends in Management and Commerce[]2024-07-13T00:00:00 510db068f4ba3521b17a76e679695bfb2f1aedd9Artificial intelligence (AI) refers to the simulation or approximation of human intelligence in machines. AI is being used today across different industries from finance to healthcare. It is also reinventing the retail landscape. Computer vision is enabling frictionless checkout and enhancing loss prevention for brick-and-mortar stores. Retailers that harness AI to connect with customers and operate more efficiently will be better positioned to thrive in today’s AI powered world. The study is conducted with the sample size of 61 respondents by conducting survey through questionnaire in Palakkad district. This paper attempts to get an insight into the influence of AI technologies in the shopping experience of customers especially on generation Z. It is high time that customers become aware of different implications of AI and how it is contributing to their shopping life. This paper further talks about the trust issues of customers when it comes to AI and personal data sharing and how it can be improved by taking necessary measures by retailers like educating the consumers in a proper way about AI. Customers also agree that AI has made a positive impact on their shopping experience especially in customer support after purchase.Recent trends in Management and Commerce60The trust issues of customers when it comes to AI and personal data sharing are talked about and how it can be improved by taking necessary measures by retailers like educating the consumers in a proper way about AI.Recent trends in Management and Commerce[]2024-07-13T00:00:00 7091f97df67b9eaf846014441d55a67aa6f16ac7This paper examines the profound impact of Artificial Intelligence (AI) on recruitment and selection processes within the banking sector. Through the integration of AI technologies, traditional practices are undergoing a paradigm shift, resulting in heightened efficiency, objectivity, and effectiveness in talent acquisition. The utilization of AI facilitates automated resume screening, precise candidate matching, predictive analytics, and bias reduction, while also incorporating innovative tools such as chatbots for initial engagement. Furthermore, AI enables comprehensive analyses of employee retention and skills gap, empowering organizations to optimize workforce management strategies. This abstract highlight the transformative role of AI in reshaping recruitment practices within the dynamic landscape of the banking sector.Recent trends in Management and Commerce30The transformative role of AI in reshaping recruitment practices within the dynamic landscape of the banking sector is highlighted, empowering organizations to optimize workforce management strategies.Recent trends in Management and Commerce[]2024-07-13T00:00:00 ebb243779ba59f3f804058d8947165441cb8893fThe financial services industry is experiencing a profound transformation driven by the rapid adoption of artificial intelligence (AI). This paper explores the opportunities, challenges, and implications of unleashing the power of AI in financial services. AI technologies offer significant benefits, including cost reductions, enhanced productivity, improved customer service, and the development of innovative financial products and services. The market for AI in finance is projected to grow from $7.3 billion in 2021 to $22.6 billion by 2026, with the global AI market size expected to reach $1.85 trillion by 2030. Despite the promising opportunities, the implementation of AI in finance presents several challenges. These include ensuring data privacy and security, addressing ethical concerns, managing regulatory compliance, and mitigating algorithmic bias. Financial institutions must develop robust AI governance frameworks to navigate these complexities and ensure the responsible use of AI. The implications of AI adoption are significant, with AI expected to create over $140 billion annually in value in banking by 2025. Moreover, 89% of financial institutions plan to increase their AI spending in the coming years, highlighting the growing importance of AI in the industry. By strategically leveraging AI technologies, financial institutions can gain a competitive edge, increase market share, and improve profitability. This paper concludes that while AI presents transformative opportunities for financial services, success will depend on effectively addressing the associated challenges. The future of finance is intertwined with AI advancements, making it crucial for stakeholders to embrace and strategically implement these technologies to unlock their full potentialInternational Journal of Advanced Research in Science, Communication and Technology33While AI presents transformative opportunities for financial services, success will depend on effectively addressing the associated challenges, making it crucial for stakeholders to embrace and strategically implement these technologies to unlock their full potential.International Journal of Advanced Research in Science, Communication and Technology["Sumit Bhatnagar", "Roshan Mahant"]2024-07-13T00:00:00 c6e878308e0d9430c6053b902ce725ddb838452cIndustry 5.0 (IR 5.0) is a modern production model focused on human-machine collaboration. The goal is to maintain a balance between machine and human interaction, with an emphasis on creative production and customization. Artificial intelligence (AI) will play a key role in IR 5.0, enabling intelligent manufacturing and transforming many aspects of society. Technologies such as AI, Internet of Things (IoT), Blockchain, Virtual Reality (VR)/Augmented Reality (AR), Big Data Analytics and Cyber-Physical Systems (CPS) are essential to achieve the goals of an intelligent society. This article explores the integration of AI in IR 5.0. However, there are some challenges to overcome such as data security, ethical concerns, employee training, black box AI, etc. Despite its challenges, AI integration to IR 5.0 promises to drive manufacturing automation, efficiency, and customization. To ensure inclusive and sustainable development, the social implications and impacts of IR 5.0 must be carefully considered.GAZI UNIVERSITY JOURNAL OF SCIENCE200Despite its challenges, AI integration to IR 5.0 promises to drive manufacturing automation, efficiency, and customization, but there are some challenges to overcome such as data security, ethical concerns, employee training, black box AI, etc.Gazi University Journal of Science["D. G", "Prabadevi Boopathy"]2024-07-13T00:00:00 0b9ffa5cf8899cae0e799f78bfd20d34ae33f021The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the pipeline sector of the oil and gas industry has demonstrated considerable potential, especially in overcoming the difficulties associated with incomplete data. This paper explores the application of AI in supplementing missing data for risk evaluations, particularly in scenarios where safety is a critical concern. Potential pitfalls and risks associated with relying solely on AI-generated data are analytically discussed and illustrated in this paper. Through a detailed process flow, this paper also suggests strategies to balance AI reliance with real data acquisition, emphasizing the importance of consequence analysis, costbenefit considerations, and a hybrid approach to ensure the safety and reliability of operations across the pipeline and broader oil and gas industry in an efficient way.International Journal on Cybernetics &amp; Informatics50This paper explores the application of AI in supplementing missing data for risk evaluations, particularly in scenarios where safety is a critical concern, and suggests strategies to balance AI reliance with real data acquisition.International Journal on Cybernetics &amp; Informatics["Syed Jehanzeb Adeel Haider"]2024-07-13T00:00:00 91d7069f98f45ffa3898528ddaa7c62862c314fbThe evolving demands of education necessitate creativity and innovation in the teaching and learning process. Artificial intelligence (AI) has emerged as a disruptive force in the field of education, offering innovative approaches to enhance instructional design, personalize student experiences, streamline administrative procedures, and boost academic performance. With a focus on significant areas where these technologies are having a significant impact, this study attempts to provide a comprehensive overview of the application of AI tools in education. The incorporation of AI-powered learning platforms, such as intelligent tutoring systems and adaptive learning systems, enables personalized learning experiences tailored to the needs of each individual student. These platforms assess student data, track learning progress, disseminate material in real-time, and adjust it to meet learning objectives by using artificial intelligence (AI) algorithms. Five subsections make up this study. Section 1.1 introduces AI applied educational technology platforms. Section 1.2 introduces Impact of AI Tools on Educational Pedagogy and Learning Outcomes. Section 1.3 introduces Impact of AI-Driven Educational Technologies on Teaching Pedagogy. Section 1.4 introduces Effectiveness of AI-Powered Learning Platforms. Finally, section 1.5 describes Ethical Considerations and Challenges in Implementing AI Tools in Education.Recent trends in Management and Commerce150This study attempts to provide a comprehensive overview of the application of AI tools in education with a focus on significant areas where these technologies are having a significant impact.Recent trends in Management and Commerce["N. M. Louly"]2024-07-13T00:00:00 dc8269ac6217cd66d4e0468a9f6329d24a1ebd6cAI &amp; SOCIETY470A set of two conjoint experiments with a high-quality sample of 973 Americans who identify as Black or African American suggest that respondents are willing to tolerate some level of disparity in outcomes in exchange for certain net improvements for their community.AI &amp; SOCIETY["Ryan Kennedy", "Amanda Austin", "Michael Adams", "Carroll Robinson", "Peter Salib"]2024-07-13T00:00:00 365980261ca7e08b429ff832bb8f9d417eadc959In the ever-evolving landscape of crowdfunding, the ability to predict project success is paramount for investors and stakeholders. In this paper, we employ advanced machine learning techniques to analyze crowdfunding data and identify key predictors of project outcomes. Leveraging a comprehensive dataset sourced from Kickstarter, we preprocess the data, apply cutting-edge machine learning models, and evaluate performance using a range of metrics. Notably, the Random Forest model emerges as the top performer, achieving the highest performance across all metrics, including an impressive AUC of 99.90%, accuracy of 99.86%, precision of 99.60%, recall of 100%, and F1-Score of 99.80%. Additionally, we introduce novel Key Performance Indicators (KPIs) to provide deeper insights into crowdfunding project dynamics. Our analysis reveals actionable insights into project success factors, empowering stakeholders to make informed decisions and maximize investment returns. Through rigorous experimentation and visualization, including dashboards, we demonstrate the efficacy 0 four a pproach in predicting crowdfunding project outcomes with high accuracy and precision. This paper not only contributes to the growing body of research in crowdfunding analytics but also offers practical implications for investors and entrepreneurs navigating the dynamic crowdfunding landscape.Internet, Multimedia Systems and Applications330This paper employs advanced machine learning techniques to analyze crowdfunding data and identify key predictors of project outcomes, and reveals actionable insights into project success factors, empowering stakeholders to make informed decisions and maximize investment returns.2024 Intelligent Methods, Systems, and Applications (IMSA)["Haidy Haitham", "Nadeen Amgad", "Hanan Tarek", "M. Solayman"]2024-07-13T00:00:00 e4bfb085bf41397fdd673836cdf185c9817629dbNature Communications5511It is shown that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration, and combining with energy policy and low-carbon power generation could approximately reduce energy consumption and carbon emissions compared to business-as-usual scenarios in 2050.Nature Communications["Chao Ding", "Jing Ke", "Mark Levine", "Nan Zhou"]2024-07-14T00:00:00 1c2c0e1b7bc6bdb61b8bfbff3b0a1946573a4ab6Marine pollution, a major disturbance to the sustainable use of oceans, is becoming more prevalent around the world. Multidimensional and sustainable ocean governance have become increasingly focused on managing, reducing, and eliminating marine pollution. Artificial intelligence has been used more and more in recent years to monitor and control marine pollution. This systematic literature review, encompassing studies from the Web of Science and Scopus databases, delineates the extensive role of artificial intelligence in marine pollution management, revealing a significant surge in research and application. This review aims to provide information and a better understanding of the application of artificial intelligence in marine pollution. In marine pollution, 57% of AI applications are used for monitoring, 24% for management, and 19% for prediction. Three areas are emphasized: (1) detecting and responding to oil pollution, (2) monitoring water quality and its practical application, and (3) monitoring and identifying plastic pollution. Each area benefits from the unique capabilities of artificial intelligence. If the scientific community continues to explore and refine these technologies, the convergence of artificial intelligence and marine pollution may yield more sophisticated solutions for environmental conservation. Although artificial intelligence offers powerful tools for the treatment of marine pollution, it does have some limitations. Future research recommendations include (1) transferring experimental outcomes to industrial applications in a broader sense; (2) highlighting the cost-effective advantages of AI in marine pollution control; and (3) promoting the use of AI in the legislation and policy-making about controlling marine pollution.Journal of Marine Science and Engineering04This systematic literature review, encompassing studies from the Web of Science and Scopus databases, delineates the extensive role of artificial intelligence in marine pollution management, revealing a significant surge in research and application.Journal of Marine Science and Engineering["Jia Ning", "Shufen Pang", "Zainal Arifin", "Yining Zhang", "U. P. K. Epa", "Miaomiao Qu", "Jufen Zhao", "Feiyang Zhen", "A. Chowdhury", "Ran Guo", "Yuncheng Deng", "Haiwen Zhang"]2024-07-14T00:00:00 c6cb8ed0ff3f35368fdb48334065dd40483df6f4Under the background of dual carbon goals and energy Internet construction, new energy is booming, which also brings high uncertainty to the operation dispatching and optimal control of the new power system. This paper mainly reviews the artificial intelligence large model technology and its future applications in power system operation and regulation. Firstly, the demand for AI in the new power system and the current application status of AI large model technology in the power field were summarized. Then, multimodal large model technology was introduced, and the technical path of AI large model adaptation in vertical industries was explored. Finally, the application of AI large models in the operation and regulation of new power systems is discussed from the dimensions of source-transmission-distribution-usage-equipment, providing reference for the theory of power grid operation and regulation based on large models.2024 3rd International Conference on Energy and Electrical Power Systems (ICEEPS)91The application of AI large models in the operation and regulation of new power systems is discussed from the dimensions of source-transmission-distribution-usage-equipment, providing reference for the theory of power grid operation and regulation based on large models.2024 3rd International Conference on Energy and Electrical Power Systems (ICEEPS)["Han Liu", "Wei Hu", "Liwen Yu", "Kang Wang", "Chunlan Guo"]2024-07-14T00:00:00 06a4dccfe714ec3b3d63edcdb6bd054b38b2a4b6AIMS This study aimed to explore the correlation between artificial intelligence (AI) literacy, AI anxiety and AI attitudes among paediatric nurses, as well as identify the influencing factors on paediatric nurses' AI attitudes. DESIGN A descriptive, correlational and cross-sectional research. METHODS This study was conducted between January and February 2024 with 170 nurses actively working in paediatric clinics in Turkey. The data collection tools included the Nurse Information Form, the General Attitudes Towards Artificial Intelligence Scale (GAAIS), the Artificial Intelligence Literacy Scale (AILS) and the Artificial Intelligence Anxiety Scale (AIAS). To determine the associations between the variables, the data was analysed using IBM SPSS 28, which included linear regression and Pearson correlation analysis. RESULTS The study indicated significant positive correlations between paediatric nurses' age and their AIAS scores (r = .226; p < .01) and significant negative correlations between paediatric nurses' age and their AILS (r = -.192; p < .05) and GAAIS scores (r = -.152; p < .05). The GAAIS was significantly predictive (p < .000) and accounted for 50% of the variation in AIAS and AILS scores. CONCLUSION Paediatric nurses' attitudes towards AI significantly predicted AI literacy and AI anxiety. The relationship between the age of the paediatric nurses and the anxiety, AI literacy and attitudes towards AI was demonstrated. Healthcare and educational institutions should create customized training programs and awareness-raising activities for older nurses, as there are noticeable variations in the attitudes of paediatric nurses towards AI based on their age. IMPLICATIONS FOR PROFESSION AND/OR PATIENT CARE Providing in-service AI training can help healthcare organizations improve paediatric nurses' attitudes towards AI, increase their AI literacy and reduce their anxiety. This training has the potential to impact their attitudes positively and reduce their anxiety. REPORTING METHOD The study results were critically reported using STROBE criteria. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.Journal of Advanced Nursing401Paediatric nurses' attitudes towards AI significantly predicted AI literacy and AI anxiety, and providing in-service AI training can help healthcare organizations improve paediatric nurses' attitudes towards AI, increase their AI literacy and reduce their anxiety.Journal of advanced nursing["Damla \u00d6z\u00e7evik Suba\u015fi", "Aylin Ak\u00e7a S\u00fcmengen", "Remziye Semerci", "Enes \u015eim\u015fek", "G\u00f6k\u00e7e Naz \u00c7ak\u0131r", "Ebru Temizsoy"]2024-07-14T00:00:00 e872b5578a3fed177401d1adf659c705dc05a4dbThis study is devoted to the analysis of the prospects for the convergence of the legislation of the EAEU countries regarding the legal regulation of artificial intelligence (AI).Aim. To identify the need and identify the prerequisites for supranational legal regulation of AI in the EAEU. Tasks. To list the features of AI that necessitate supranational legal regulation in the context of analyzing the consequences for the purposes of the EAEU. To make a classification and analyze the prerequisites for the formation of the will of the EAEU member states to bring together the legislation of the EAEU countries in terms of legal regulation of AI.Methods. The problem-theoretical, formal-legal, logical, system-structural method and the method of comparison are used.Results. The study showed that such features of AI technologies as the ability to cause cross-border harm and the ability to autonomous processes require: a) the establishment of legal limits for delegating human authority to a machine that are uniform for the EAEU states, which is achieved by establishing administrative responsibilities for participants in the life cycle of AI systems and applications; b) developing a unified approach to eliminating the “responsibility gap” for the harm caused by AI and its legal consolidation in the law of the EAEU, which is achieved through supranational regulation of AI on these issues. The lack of “uniform norms” regarding the distribution of responsibility for harm produces legal conflicts that contribute to the creation of obstacles to the functioning of internal markets and asymmetries in the development of AI within the EAEU. The results of the analysis of the prerequisites for the formation of the will of the EAEU member states to bring together the legislation of the EAEU countries in terms of legal regulation of AI allow us to state the absence of prerequisites for a unified policy of the EAEU countries in the creation and use of AI, and consequently, the prospects for the convergence of legislation in the field of public relations through the formation of supranational legal regulation. However, the EAEU law does not contain obstacles to the implementation of a unified AI policy in the future.Conclusions. The specifics of AI technologies require supranational legal regulation of AI, at least in matters of allocation of responsibility for harm caused by AI in order to avoid legal conflicts that contribute to the creation of obstacles to the functioning of internal markets and asymmetries in the development of AI within the EAEU. Despite the current lack of prerequisites, the EAEU law does not contain obstacles to the convergence of the legislation of the EAEU countries in terms of legal regulation of AI in the event of such a decision.EURASIAN INTEGRATION: economics, law, politics52The results of the analysis of the prerequisites for the formation of the will of the EAEU member states to bring together the legislation of the EAEU countries in terms of legal regulation of AI allow us to state the absence of prerequisites for a unified policy of the EAEU countries in the creation and use of AI.EURASIAN INTEGRATION: economics, law, politics["E. Melnikova"]2024-07-14T00:00:00 442fb9a0448c7c1fce84609819fe6f73ea7de3ccThis introductory chapter provides an overview of the evolution and impact of Artificial Intelligence technologies in today society. Beginning with a historical context while exploring a few general definitions of AI, the author provides a timeline of the used technologies, highlighting its periods of stagnation, commonly referred to as AI winters, and the subsequent resurgence fueled by relentless enthusiasm and investment. The narrative then transitions to focus on the transformative effects of AI on society at large, with a particular emphasis on educational applications. Through examples, the paper shows how AI technologies have moved from theoretical constructs to practical tools that are reshaping pedagogical approaches and student engagement. The essay concludes by discussing the prospects of AI in education, emphasizing the need for a balanced approach that considers both technological advancements and societal implications.arXiv.org30The author provides a timeline of the used technologies, highlighting its periods of stagnation, commonly referred to as AI winters, and the subsequent resurgence fueled by relentless enthusiasm and investment, and highlights the need for a balanced approach that considers both technological advancements and societal implications.ArXiv["C\u0103t\u0103lin Vrabie"]2024-07-14T00:00:00 09e9755ec49d6475bd1d2aced4eb4c65ab6bb668Road safety remains a pressing concern in the Philippines, with annual high traffic fatalities and injuries. This study delves into the potential of Artificial Intelligence (AI) to enhance driver education and road safety in the country. The Philippines faces challenges in its transportation system due to driver behavior issues and a need for more discipline. To address this, the study explores the integration of AI into driving simulators and education, aiming to develop more skilled and responsible drivers.International Journal For Multidisciplinary Research50The study explores the integration of AI into driving simulators and education, aiming to develop more skilled and responsible drivers in the Philippines.International Journal For Multidisciplinary Research["Hernel A. BUGABUGA", "Johneros P. PUYO"]2024-07-14T00:00:00 76af164bb1477be599f590a0975f3e59d3325941This study focuses on understanding the effects of digital transformation processes in the business world on employee satisfaction. The purpose of this study is to determine whether the general attitude towards artificial intelligence plays a moderating role in the effect of digital transformation on employee satisfaction. At the same time, the study was also reinforced and elaborated with demographic questions directed to the employees. The population of the study consists of private enterprises operating in the retail sector in Istanbul. The sample is the decision-making white- collar (N= 522) current employees working in these retail sectors. SPSS 24.0 statistical package programe was used to analyze the data. Normality test was performed to determine whether the data set was suitable for parametric tests. Kurtosis and skewness values were used to evaluate normality. Pearson correlation analysis was performed to determine the direction and severity of the relationship between the variables. Moderating analysis was performed to determine how the relationship between an independent variable and a dependent variable is affectedy a third variable. According to the results obtained from the study, there are quite high and significant correlations between digital transformation and other variables in the correlation analysis. According to the moderating effect analysis, it was observed that the general attitude towards artificial intelligence did not moderate the effect of digital transformation on the variables. In demographic variables, significant differences are observed in all variables and sub-dimensions.Journal of Research in Business00It was observed that the general attitude towards artificial intelligence did not moderate the effect of digital transformation on the variables, and significant differences are observed in all variables and sub-dimensions.Journal of Research in Business["Ay\u015fe Meri\u00e7 Yaz\u0131c\u0131", "Filiz Sivasl\u0131o\u011flu"]2024-07-14T00:00:00 0dee10622c0669c716307c2168e9e7ba7cd3d88dIn the era of globalization and rapid technological development, the implementation of artificial intelligence (AI) has become an unavoidable phenomenon in various fields, including law and notary. AI offers significant potential to improve efficiency and accuracy in notarial practice, but also poses various juridical challenges. Notaries are recognized as trusted parties by the public to ensure the authenticity of a deed and prevent legal violations. Although AI can assist in administrative tasks and data analysis, its implementation must consider juridical aspects such as legal liability, personal data protection, and document authenticity. In facing the fourth industrial revolution, notaries need to update their knowledge and skills and maintain the relevance of civil law. Collaboration between human and artificial intelligence is required to achieve better results without sacrificing principles of justice and legal authenticity.Journal of Law, Politic and Humanities00In the era of globalization and rapid technological development, the implementation of artificial intelligence has become an unavoidable phenomenon in various fields, including law and notary, and its implementation must consider juridical aspects such as legal liability, personal data protection, and document authenticity.Journal of Law, Politic and Humanities["M. Mariyam", "Umi Rahmawati", "Nadea Nur Sofia Madani", "Fanis Fifin Nazilah", "Dhiya\u2019 Ulhaq Mahfudzoh"]2024-07-14T00:00:00 1531a554d331916d0d47de9669ae7ffea7d89b93Background: The aim of this study is to examine the development trends and dynamics of research on the use of artificial intelligence in dental caries diagnosis, to identify the strengths and limitations of the existing literature, and to guide future research. Methods: A literature search was conducted using the Web of Science database, covering articles published before 3 June 2024. Pilot searches were conducted and 883 studies were reached. After the specified scanning and filtering processes, the study was carried out on 270 publications. In the bibliometric analysis, the Biblioshiny R package as well as the features of Web of Science and VOSviewer software were used for visualizations. Microsoft Excel was used to tabulate the data. Results: There is a general increase in the number of articles published each year. A total of 3081 citations were made to publications on the use of artificial intelligence in cariology. The average number of citations per article was found to be 11.41, and the H index was 29. The most cited country was Germany (581 citations), and the most influential author was Falk Schwendicke. On the basis of institutions, the highest contribution was made by Charite University Medicine Berlin (19 articles, 475 citations). Conclusion: Since 2008, and particularly since 2018, the utilisation of artificial intelligence (AI) in the investigation of dental caries and oral and dental diseases has garnered increasing interest. Artificial Intelligence (AI) can be said to be a groundbreaking discovery that will be increasingly applied in various branches of dentistry.Selcuk Dental Journal00The utilisation of artificial intelligence (AI) in the investigation of dental caries and oral and dental diseases has garnered increasing interest and can be said to be a groundbreaking discovery that will be increasingly applied in various branches of dentistry.Selcuk Dental Journal["\u0130brahim Tevfik G\u00fcl\u015fen", "Ru\u015fen Erdem", "Yavuz Selim Gen\u00e7", "G\u00fclbeddin Yal\u0131n\u0131z"]2024-07-14T00:00:00 b99c1f8dd90ab62ffd4a5166a82a439e3112acaaService Industries Journal734The Service Industries Journal["W. Ladeira", "F. Santini", "Tareq Rasul", "Isaac Cheah", "Samer Elhajjar", "Naveed Yasin", "Shakeb Akhtar"]2024-07-14T00:00:00 bb5ce2ea302863c603442ee62414cb70c7b45921International Journal on Road Materials and Pavement Design1062Road Materials and Pavement Design["Jianqi Kang", "Pejoohan Tavassoti", "Muhammad Nuh Ali Reza Chaudhry", "H. Baaj", "Moojan Ghafurian"]2024-07-14T00:00:00 9c544f4a6bb1d8af31106f3db20b2c0ea56748dfThis paper presents a proof of concept for a novel evolutionary methodology inspired by core knowledge . This theory describes human cognition as a small set of innate abilities combined through compositionality. The proposed approach generates predictive descriptions of the interaction between elements in simple 2D videos. It exploits well-known strategies, such as image segmentation, object detection, simple laws of physics (kinematics and dynamics), and evolving rules, including high-level classes and their interactions. The experimental evaluation focuses on two classic video games, Pong and Arkanoid. Analyzing a small number of raw video frames, the methodology identifies objects, classes, and rules, creating a compact, high-level, predictive description of the interactions between the elements in the videos.GECCO Companion90The proposed approach generates predictive descriptions of the interaction between elements in simple 2D videos, exploiting well-known strategies, such as image segmentation, object detection, simple laws of physics, and evolving rules, including high-level classes and their interactions.{"pages": "259-262"}["A. Calabrese", "Stefano Quer", "Giovanni Squillero", "A. Tonda"]2024-07-14T00:00:00 527d0bef4e414c4a7395f103e679ad8b5f7e1bc4Plant Methods766A framework that utilizes state-of-the-art computer vision and artificial intelligence techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants and shows that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants.Plant Methods["Muhammad Farrukh Shahid", "T. J. Khanzada", "Muhammad Ahtisham Aslam", "Shehroz Hussain", "S. Baowidan", "R. B. Ashari"]2024-07-14T00:00:00 14dc52964007c827173be51528755a45ac95a2faThe recent rise of generative artificial intelligence (GenAI) capable of creating scientific images presents a challenge in the fight against academic fraud. This study evaluates the efficacy of three free web-based AI detectors in identifying AI-generated images of Western blots, which is a very common technique in biology. We tested these detectors on a collection of artificial Western blot images (n=48) that were created using ChatGPT 4 DALLE 3 and on authentic Western blots (n=48) that were sampled from articles published within four biology journals in 2015; this was before the rise of generative AI based on large language models. The results reveal that the sensitivity (0.9583 for Is It AI, 0.1875 for Hive Moderation, and 0.7083 for Illuminarty) and specificity (0.5417 for Is It AI, 0.8750 for Hive Moderation, and 0.4167 for Illuminarty) are very different. Positive predictive values (PPV) across various AI prevalence were low, for example reaching 0.1885 for Is It AI, 0.1429 for Hive Moderation, and 0.1189 for Illuminarty at an AI prevalence of 0.1. This highlights the difficulty in confidently determining image authenticity based on the output of a single detector. Reducing the size of Western blots from four to two lanes reduced test sensitivities and increased test specificities but did not markedly affect overall detector accuracies and also only slightly improved the PPV of one detector (Is It AI). These findings strongly argue against the use of free AI detectors to detect fake scientific images, and they demonstrate the urgent need for more robust detection tools that are specifically trained on scientific content such as Western blot images.00The findings strongly argue against the use of free AI detectors to detect fake scientific images, and they demonstrate the urgent need for more robust detection tools that are specifically trained on scientific content such as Western blot images.["Romain-Daniel Gosselin"]2024-07-14T00:00:00 37dd7517ed2e964f8be4a4921f5e4b5889d1c595This paper explores a design solution for a mobile application that combines artificial intelligence technology with traditional Chinese home cooking tutorials. The goal is to optimize the field of culinary teaching through intelligent means. We conducted a comprehensive analysis of current domestic and international approaches to integrating artificial intelligence with various domains, as well as the current development status of cooking tutorials. We pointed out that existing applications mostly lack personalized tutorials and real-time interactive guidance, making it difficult to meet the needs of personalized and immediate interaction for users. Based on these issues, the authors proposed a design solution for a personalized companion-style cooking tutorial app. This solution aims to provide more refined and personalized cooking tutorials through AI technology, as well as real-time voice interactive guidance, making the cooking process simpler and more efficient. The system adopts a frontend-backend separation architecture design, where the frontend is responsible for presenting the user interface and interaction, while the backend handles logic and data storage. In terms of functionality design, it includes not only basic features such as recipe browsing, searching, and detailed display but also specially designed modules such as voice input and AI conversation systems to enhance user interaction experience.2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA)180A design solution for a mobile application that combines artificial intelligence technology with traditional Chinese home cooking tutorials to provide more refined and personalized cooking tutorials through AI technology, as well as real-time voice interactive guidance, making the cooking process simpler and more efficient.2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA)["Mingyang Liu", "Zhen Liu", "Yu Wang", "Kang Yi", "Jing Xu"]2024-07-14T00:00:00 fb656921715a8c82aa2aa72eca32e7e6e8416b3cLa sociedad del conocimiento demanda transformaciones urgentes en los sistemas educativos a nivel global, las cuales deben alinearse con las nuevas tecnologías y los servicios intangibles. En la actualidad, nos encontramos inmersos en la Era de la Inteligencia Artificial (IA), la cual desempeña un papel crucial en diversas áreas como el transporte, la atención médica, los servicios financieros, las plataformas de entretenimiento, la robótica y la fabricación. El propósito de esta investigación, de naturaleza proyectiva y con un diseño bibliográfico, es presentar una metodología para la implementación de la IA en el ámbito educativo. Los fundamentos teóricos de esta investigación se basan en las contribuciones de Tascón y Collaut, Yan-Tak, así como en la orientación proporcionada por organismos destacados como ISO/IEC y UNESCO. Los resultados de la propuesta se dividen en categorías que abordan procesos de supervisión, admisión y retención universitaria, detección temprana de problemas de conducta, y estrategias metodológicas para el aprendizaje de personas con discapacidad. La conclusión principal extraída es que la IA posee un valor inestimable en el mercado, no solo en términos monetarios, sino especialmente en su capacidad para optimizar procesos no comerciales, como es el caso del sector educativo. La IA se presenta y continuará siendo un elemento clave en la transformación de los paradigmas tradicionales en la educación.Revista Científica Multidisciplinaria InvestiGo00Revista Científica Multidisciplinaria InvestiGo["Juliana Vanessa Lavayen Herrera", "Glodecinda Isabel Oca\u00f1a S\u00e1nchez", "Nancy Del Roc\u00edo Zu\u00f1iga Le\u00f3n", "Ana Mar\u00eda Reyes Murillo"]2024-07-14T00:00:00 20ec647b070e46893df14885095275e50f98afc9Recently, Cartesian Genetic Programming has been used to evolve developmental programs to guide the formation of artificial neural networks (ANNs). This approach has demonstrated success in enabling ANNs to perform multiple tasks while avoiding catastrophic forgetting. One unique aspect of this approach is the use of separate developmental programs evolved to regulate the development of separate soma and dendrite units. An opportunity afforded by this approach is the ability to incorporate Activity Dependence (AD) into the model such that environmental feedback can help to regulate the behavior of each type of unit. Previous work has shown a limited version of AD (influencing neural bias) to provide marginal improvements over non-AD ANNs. In this work, we present promising results from new extensions to AD. Specifically, we demonstrate a more significant improvement via AD on new neural parameters including health and position, as well as a combination of all of these along with bias. We report on the implications of this work and suggest several promising directions for future work.arXiv.org200This work demonstrates a more significant improvement via AD on new neural parameters including health and position, as well as a combination of all of these along with bias, and suggests several promising directions for future work.ArXiv["Yintong Zhang", "Jason A. Yoder"]2024-07-14T00:00:00 8983c20bab18c9648b121b4be2cdcd0cb2da8da1<jats:p/>Revista Interamericana de Investigación Educación y Pedagogía RIIEP62Revista Interamericana de Investigación Educación y Pedagogía RIIEP["Oscar-Yecid Aparicio-G\u00f3mez", "Mauricio Antonio Cort\u00e9s Gallego"]2024-07-14T00:00:00 9d94c0851c0f7df49d8696080fc7714e1619bd73Annual Conference on Genetic and Evolutionary Computation00Proceedings of the Genetic and Evolutionary Computation Conference["Una-May O\u2019Reilly"]2024-07-14T00:00:00 3cdb8b13312176638d952c147fb8c871df0143adINTRODUCTION The completion of the Human Genome Project in 2003 marked the beginning of a transformative era in medicine. This milestone laid the foundation for personalized medicine, an innovative approach that customizes healthcare treatments. CONTENT Central to the advancement of personalized medicine is the understanding of genetic variations and their impact on drug responses. The integration of artificial intelligence (AI) into drug response trials has been pivotal in this domain. These technologies excel in handling large-scale genomic datasets and patient histories, significantly improving diagnostic accuracy, disease prediction and drug discovery. They are particularly effective in addressing complex diseases such as cancer and genetic disorders. Furthermore, the advent of wearable technology, when combined with AI, propels personalized medicine forward by offering real-time health monitoring, which is crucial for early disease detection and management. SUMMARY The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries. OUTLOOK As technology continues to evolve, the role of AI in enhancing personalized medicine and transforming the healthcare landscape is expected to grow exponentially. This synergy between AI and healthcare holds great promise for the future, potentially revolutionizing the way healthcare is delivered and experienced.Drug Metabolism and Personalized Therapy688The integration of AI into personalized medicine represents a significant advancement in healthcare, promising more accurate diagnoses, effective treatment plans and innovative drug discoveries.Drug metabolism and personalized therapy["Mohammad Abu Zahra", "A. Al-Taher", "Mohamed Alquhaidan", "Tarique Hussain", "Izzeldin Ismail", "Indah Raya", "Mahmoud Kandeel"]2024-07-15T00:00:00 5f4fce59e844a5b53a54e0d0760c06ea47ce2de5Early diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, and error‐prone. The purpose of this study is to present a comprehensive review of AI technologies used for detecting cervical pre‐cancerous lesions and cancer. The review study includes studies where AI was applied to Pap Smear test (cytological test), colposcopy, sociodemographic data and other risk factors, histopathological analyses, magnetic resonance imaging‐, computed tomography‐, and positron emission tomography‐scan‐based imaging modalities. We performed searches on Web of Science, Medline, Scopus, and Inspec. The preferred reporting items for systematic reviews and meta‐analysis guidelines were used to search, screen, and analyze the articles. The primary search resulted in identifying 9745 articles. We followed strict inclusion and exclusion criteria, which include search windows of the last decade, journal articles, and machine/deep learning‐based methods. A total of 58 studies have been included in the review for further analysis after identification, screening, and eligibility evaluation. Our review analysis shows that deep learning models are preferred for imaging techniques, whereas machine learning‐based models are preferred for sociodemographic data. The analysis shows that convolutional neural network‐based features yielded representative characteristics for detecting pre‐cancerous lesions and CrC. The review analysis also highlights the need for generating new and easily accessible diverse datasets to develop versatile models for CrC detection. Our review study shows the need for model explainability and uncertainty quantification to increase the trust of clinicians and stakeholders in the decision‐making of automated CrC detection models. Our review suggests that data privacy concerns and adaptability are crucial for deployment hence, federated learning and meta‐learning should also be explored.This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning Technologies > Classification WIREs Data. Mining. Knowl. Discov.1263The review study shows the need for model explainability and uncertainty quantification to increase the trust of clinicians and stakeholders in the decision‐making of automated CrC detection models and suggests that data privacy concerns and adaptability are crucial for deployment hence, federated learning and meta‐learning should also be explored.WIREs Data. Mining. Knowl. Discov.["S. K. Khare", "V. Blanes-Vidal", "B. Booth", "L. K. Petersen", "E. Nadimi"]2024-07-15T00:00:00 2fa62432c097ec897fd7d3c132a8101a8a76572fAtrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.Frontiers in Cardiovascular Medicine663Clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care.Frontiers in Cardiovascular Medicine["Z. Papalamprakopoulou", "D. Stavropoulos", "S. Moustakidis", "Dimitrios Avgerinos", "M. Efremidis", "Polydoros N. Kampaktsis"]2024-07-15T00:00:00 8c8c073f49bdf1d740c3281c9bc1afd0bb24fba3Islamic Religious Education (PAI) has a significant impact on the development of students' character, morality, and overall learning outcomes. This study aims to investigate the effects of artificial intelligence (AI) and mobile learning on student learning outcomes, with a specific focus on the role of students' self-competence as a mediating factor. Employing a quantitative survey approach, the research included 208 students from the PAI Study Program at IAIN Ponorogo, using probability sampling techniques. Data was collected through Likert-scale questionnaires, and the research data was analyzed using PLS-SEM analysis. The results indicate a positive influence of AI and mobile learning on student learning outcomes, with self-competence playing a crucial role as a mediating factor. These findings highlight the importance of educators promoting self-regulation, self-efficacy, and motivation skills within online learning environments. The study emphasizes the potential of integrating AI and mobile learning to enhance the quality of education and recommends that educators continuously update their knowledge of technological advancements through training and collaboration. Strengthening these competencies can lead to a more interactive, personalized, and adaptive learning environment for students.Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika413A positive influence of AI and mobile learning on student learning outcomes is indicated, with self-competence playing a crucial role as a mediating factor, and the importance of educators promoting self-regulation, self-efficacy, and motivation skills within online learning environments is highlighted.Jurnal Penelitian dan Pengkajian Ilmu Pendidikan: e-Saintika["Gilang Hardiansyah Priamono", "Arif Rahman Hakim", "R. W. Daryono"]2024-07-15T00:00:00 0c4b0144e683f51cac255bd81b09df3df392886fArtificial intelligence (AI) has emerged as a transformative paradigm, promising revolutionary advancements in animal healthcare. Leveraging AI's unparalleled capacity for rapid data analysis significantly enhances diagnostic precision and speed, thereby facilitating informed decision-making by veterinarians. Predictive medicine powered by AI not only anticipates disease outbreaks but also enables tracking zoonotic diseases and predicting individual health risks for animals. AI helps to generate personalized treatment plans by analyzing genetic, environmental, and historical data. Remote monitoring and telemedicine, empowered by AI, overcome geographical constraints and offer continuous care, enabling veterinarians to track vital signs and intervene promptly. However, as AI becomes integral to veterinary practice, ethical considerations surrounding data privacy, transparency, and responsible AI use are crucial. This review explores the scope of AI in enhancing research and drug development, highlighting its ability to improve the discovery process and contribute to novel therapeutic interventions. It emphasizes the necessity of maintaining a delicate balance between AI-driven automation and the expertise of veterinary professionals. As the veterinary community moves toward embracing the transformative potential of AI, this comprehensive examination provides valuable insights into the current scenario. It discusses the challenges, opportunities, implications, and ethical considerations that shape the future of AI in veterinary science.Journal of Experimental Biology and Agricultural Sciences1111The scope of AI in enhancing research and drug development is explored, highlighting its ability to improve the discovery process and contribute to novel therapeutic interventions and the necessity of maintaining a delicate balance between AI-driven automation and the expertise of veterinary professionals.Journal of Experimental Biology and Agricultural Sciences["K. Sharun", "S. A. Banu", "Merlin Mamachan", "L. Abualigah", "A. Pawde", "K. Dhama"]2024-07-15T00:00:00 aba26c996b1bfe97da1a8ce69086066383ca0e0cArtificial intelligence (AI) is rapidly transforming tuberculosis (TB) diagnosis. It is addressing the longstanding challenges in accuracy, efficiency, and accessibility. Traditional diagnostic methods, while effective, often suffer from limitations such as variability in sensitivity and lengthy turnaround times. AI technologies, including machine learning and deep learning algorithms, offer innovative solutions by automating the analysis of chest X-rays, genomic data, and clinical parameters. These advancements promise improved diagnostic accuracy, expedited treatment initiation, and personalized medicine approaches. However, successful implementation requires overcoming challenges related to data quality, integration with healthcare systems, and ethical considerations. Moving forward, this paper sheds light on AI-driven TB diagnosis, which stands poised to enhance global healthcare outcomes through enhanced detection capabilities and optimized treatment strategies.IP Indian Journal of Immunology and Respiratory Medicine101Light is shed on AI-driven TB diagnosis, which stands poised to enhance global healthcare outcomes through enhanced detection capabilities and optimized treatment strategies.IP Indian Journal of Immunology and Respiratory Medicine["Sankalp Yadav", "Naveen Jeyaraman", "Madhan Jeyaraman", "G. Rawal"]2024-07-15T00:00:00 d21c3b878d915993eb3301e7b6e5d51bcfcdd64bThe aim of the work is to study and analyze the role of artificial intelligence in jurisprudence in the context of the European integration of Ukraine. The methodological basis of the study is the dialectical method of scientific knowledge, since with its help the genesis of the development of artificial intelligence in society in general, including in jurisprudence, interaction with other elements of the legal system of Ukraine, prospects for development in domestic criminology, etc. was revealed. The anthropological approach helped reveal the importance of reforming domestic criminology in the context of international legal standards in the field of human rights. From the standpoint of the value approach, the role of artificial intelligence in modern criminology is investigated. The work uses such general scientific methods as: system analysis, going from the general to the specific, analogy, generalization, comparison. Special-legal methods, such as comparative-legal, formal-dogmatic, sociological-legal, cultural-legal. Results. The article found that artificial intelligence is popular in many areas of human activity, in particular in jurisprudence: for checking documents, conducting legal research, forming accounts for court costs, distributing court cases between judges, drawing up contracts, searching and highlighting the necessary information in the text legal document, detection and investigation of crimes, conducting forensic examinations, etc. It has been proven that the field of digital forensics covers the detection, recording, preliminary investigation and use of computer information, digital traces and means of processing them to solve tasks related to the detection, detection, investigation and prevention of crimes. Conclusions. Digital forensics focuses on the patterns of occurrence and use of digital traces, the development of technical means, techniques and methods for the detection, recording, extraction and study of digital information (evidence), as well as the development of techniques and methods for processing digital information. This is a branch of criminology that studies the means to uncover targets based on knowledge of these patterns, investigate and prevent crimes. During martial law, the possibilities of using artificial intelligence in Ukraine are implemented in three areas of criminal justice: preventive activities, pre-trial investigation activities, and trial activities.Visegrad journal on human rights91The article found that artificial intelligence is popular in many areas of human activity, in particular in jurisprudence: for checking documents, conducting legal research, forming accounts for court costs, distributing court cases between judges, drawing up contracts, searching and highlighting the necessary information in the text legal document, detection and investigation of crimes, conducting forensic examinations, etc.Visegrad Journal on Human Rights["N. Kalyniuk", "Kateryna Melnykova"]2024-07-15T00:00:00 c18c572baba6d5def8606e464db9acf4eda83080 In an attempt to improve the whole college student experience, higher education has adopted modern techniques and technologies more and more in recent years. A few instances of how technology improves college students' educational planning and participation include the usage of games, learning management systems, virtual and augmented reality, artificial intelligence applications, and video-assisted learning. There are worries about how technology may affect students at colleges, universities, and other higher education institutions, despite the fact that it has significantly improved education. International Journal of Advance Research in Education &amp; Literature (ISSN 2208-2441)121A few instances of how technology improves college students' educational planning and participation include the usage of games, learning management systems, virtual and augmented reality, artificial intelligence applications, and video-assisted learning.International Journal of Advance Research in Education &amp; Literature (ISSN 2208-2441)["Maison Awawdy"]2024-07-15T00:00:00 b3c436e7fdef318d9e82e649e3909a58edde5b74Forensic Medicine deals with applying medical knowledge in the administration of justice, bridging medical science with the law. The new technology of Artificial Intelligence (AI) is increasingly applied in the various fields of Forensic Medicine & crime investigation. It is used by forensic pathologists to establish the identity of an unknown person, estimate the age of injuries, primarily bruises, detect and analyze trace evidence, etc. It is very convenient to store, analyze, and transmit massive data within a very short time. This new technology is also helpful in conducting non-invasive autopsy by using various technologies such as Sonography, CT scans, MRIs, 3D surface scanning, etc. Detection and analysis of many trace evidence can be carried out by using AI. It is also very convenient to reconstruct the crime scene by creating video animation. However, as of now, its use is minimal and at a nascent stage. Moreover, it is not legally acceptable in a court of law.IP International Journal of Forensic Medicine and Toxicological Sciences181The new technology of Artificial Intelligence (AI) is increasingly applied in the various fields of Forensic Medicine & crime investigation and is used by forensic pathologists to establish the identity of an unknown person, estimate the age of injuries, primarily bruises, detect and analyze trace evidence, etc.IP International Journal of Forensic Medicine and Toxicological Sciences["O. Gambhir Singh", "Suresh Kumar", "Bhagwan Shah", "Anil Shandil", "Rahul Kumar"]2024-07-15T00:00:00 a4e4e65198eb01f9500b87c918fb8b6cbd028b48Over the past decade, artificial intelligence (AI) and its subfields, deep learning and machine learning, have become integral parts of ophthalmology, particularly in the field of ophthalmic imaging. A diverse array of algorithms has emerged to facilitate the automated diagnosis of numerous medical and surgical retinal conditions. The development of these algorithms necessitates extensive training using large datasets of retinal images. This approach has demonstrated a promising impact, especially in increasing accuracy of diagnosis for unspecialized clinicians for various diseases and in the area of telemedicine, where access to ophthalmological care is restricted. In parallel, robotic technology has made significant inroads into the medical field, including ophthalmology. The vast majority of research in the field of robotic surgery has been focused on anterior segment and vitreoretinal surgery. These systems offer potential improvements in accuracy and address issues such as hand tremors. However, widespread adoption faces hurdles, including the substantial costs associated with these systems and the steep learning curve for surgeons. These challenges currently constrain the broader implementation of robotic surgical systems in ophthalmology. This mini review discusses the current research and challenges, underscoring the limited yet growing implementation of AI and robotic systems in the field of retinal conditions.Frontiers in Medicine291The current research and challenges are discussed, underscoring the limited yet growing implementation of AI and robotic systems in the field of retinal conditions.Frontiers in Medicine["Eleftherios Chatzimichail", "Nicolas Feltgen", "Lorenzo Motta", "Theo Empeslidis", "A. Konstas", "Zisis Gatzioufas", "G. Panos"]2024-07-15T00:00:00 c1906815523fc3b1f44accc22b2c488a0a1658f4Two recent trends made this project possible: (1) The recognition that near misses can be predictors of future negative events and (2) enhanced artificial intelligence (AI) and machine learning (ML) tools that make data analytics accessible for many organizations. Increasingly, organizations are learning from prior incidents to improve safety and reduce accidents. The U.S. Coast Guard (USCG) uses a reporting system called the Marine Information for Safety and Law Enforcement (MISLE) database. Because many of the incidents that appear in this database are minor ones, this project initially focused on determining if near misses in MISLE could be predictors of future accidents. The analysis showed that recent near-miss counts are useful for predicting future serious casualties at the waterway level. Using this finding, a predictive AI/ML model was built for each waterway type by vessel combination. Random forest decision tree AI/ML models were used to identify waterways at significant accident risk. An R-based predictive model was designed to be run monthly, using data from prior months to make future predictions. The prediction models were trained on data from 2007 to 2022 and tested on 10 months of data from 2022, where prior months were added to test the next month. The overall accuracy of the predictions was 92%-99.9%, depending on model characteristics. The predictions of the models were considered accurate enough to be potentially useful in future prevention efforts for the USCG and may be generalizable to other industries that have near-miss data and a desire to identify and manage risks.Risk Analysis251The analysis showed that recent near-miss counts are useful for predicting future serious casualties at the waterway level and these models were considered accurate enough to be potentially useful in future prevention efforts for the USCG and may be generalizable to other industries that have near-miss data and a desire to identify and manage risks.Risk analysis : an official publication of the Society for Risk Analysis["Peter M Madsen", "Robin L Dillon", "Evan T Morris"]2024-07-15T00:00:00 ef4e13933c27fcedee6ac09af5ebe010951bb462PurposeThis study demonstrates how artificial intelligence (AI) shapes the strategic planning process in volatile, uncertain, complex and ambiguous (VUCA) business environments. Having adopted various domains of the Cynefin framework, the research explores AI's transformative potential and provide insights regarding how organisations can harness AI-driven solutions for strategic planning.Design/methodology/approachThis conceptual paper theorises the role of AI in strategic planning process in a VUCA world by integrating extant knowledge across multiple literature streams. The “model paper” approach was adopted to provide a theoretical framework predicting relationships among considered concepts.FindingsThe paper highlights potential application of the Cynefin framework to manage complexities in strategic decision-making process, the transformative impact of AI at different stages of strategic planning, the required strategic planning characteristics within VUCA to be supported by AI and the attendant challenges posed by AI integration in the uncertain business landscape.Originality/valueThis study pioneers a theoretical exploration of AI's role in strategic planning within the VUCA business landscape, guided by the Cynefin framework. Thus, it enriches scholarly discourse and expands knowledge frontiers.Management Decision932The paper highlights potential application of the Cynefin framework to manage complexities in strategic decision-making process, the transformative impact of AI at different stages of strategic planning, and the required strategic planning characteristics within VUCA to be supported by AI.Management Decision["Roberto Biloslavo", "David Edgar", "Erhan Aydin", "\u00c7a\u011fri Bulut"]2024-07-15T00:00:00 b596fc27d4204b6ff393bd5df8382daf4b69d7c8Face to face with the complex and various challenges generated by the rapid evolution of technology in financial auditing, artificial intelligence (AI) is emerging as a key element for innovation, providing advanced solutions to adapt to dynamic market needs. By analyzing real-time data, identifying anomalies and generating predictive insights, AI significantly improves accuracy, efficiency and risk understanding in auditing, marking an essential step towards optimal performance in a changing environment. The authors set out to investigate the impact of artificial intelligence in auditing using bibliometric analysis and statistical approaches to highlight the essential role of artificial intelligence technologies in the evolution and optimization of the financial audit process. In conclusion, the implementation of artificial intelligence in auditing offers significant advantages, such as increased efficiency, accurate fraud detection and adaptation to specific client requirements, but it is vital to recognize the challenges and limitations in order to fully exploit the potential of artificial intelligence and revolutionize audit practices in the digital era.Audit Financiar01The authors set out to investigate the impact of artificial intelligence in auditing using bibliometric analysis and statistical approaches to highlight the essential role of artificial intelligence technologies in the evolution and optimization of the financial audit process.Audit Financiar["Corina Catalina HHURDUCACI (GOREA)", "Bogdan-\u015etefan Ionescu"]2024-07-15T00:00:00 79a28aba4d3a8084b74593ae69327470171ebfc9On 29 May 2024, a captivating Bootcamp on artificial intelligence in media was held, organized by SMPTE Montreal. The event chaired by Francois Bourdua, SMPTE Governor 2024 (Canada) brought together 245 participants in the room and some others via streaming in a dynamic and engaging atmosphere. Eighteen expert presenters shared their knowledge and vision, sparking interest and engagement from the audience. The presentations were interactive, allowing participants to ask questions in real-time, enriching the debates and discussions. The feedback from participants was extremely positive, highlighting the quality and relevance of the presentations. The day concluded with a networking “Happy Hour” session, offering an excellent opportunity to strengthen professional ties and forge new collaborations.SMPTE Motion Imaging Journal00On 29 May 2024, a captivating Bootcamp on artificial intelligence in media was held, organized by SMPTE Montreal, offering an excellent opportunity to strengthen professional ties and forge new collaborations.SMPTE Motion Imaging Journal["Daniel Gu\u00e9vin"]2024-07-15T00:00:00 946457098a9c38e45aaeb278962dc7c6ddaf7e2bPenelitian ini bertujuan untuk memberikan pelatihan membuat slide presentasi berbasis Artificial Intelligence (AI) kepada perkumpulan pemuda pemudi Gereja Pentakosta Sion Indonesia (GPSI) Medan Utara. Penggunaan teknologi AI dalam proses pembuatan slide presentasi diharapkan dapat meningkatkan keterampilan pemuda pemudi gereja dalam merancang presentasi yang menarik dan informatif untuk dapat digunakan dalam setiap pembahasan kegiatan gereja. Metode penelitian yang digunakan melibatkan pemberian pelatihan intensif kepada pemuda pemudi Gereja Pentakosta Sion Indonesia Medan Utara menggunakan platform AI khusus wepik untuk pembuatan slide presentasi. Hasil penelitian ini diharapkan dapat memberikan wawasan yang mendalam tentang potensi integrasi AI dalam pembelajaran keterampilan presentasi. Implikasi praktis dari penelitian ini diharapkan dapat memberikan kontribusi positif terhadap pengembangan pengetahuan Artificial Inteligence di gereja dan memajukan pendekatan pembelajaran berbasis teknologi di kalangan pemuda pemudi Gereja Pentakosta Sion Indonesia (GPSI) Medan Utara.Jurnal Minfo Polgan00Jurnal Minfo Polgan["Y. Yudi", "Albert Suwandhi", "Awan Awan", "H. Hendra", "Waisen Waisen"]2024-07-15T00:00:00 2985c260913827c3b56ca6411e0111b11ed3c273Artificial Intelligence (AI) is increasing in accounting practice, and firms desire new hires who have adopted this technology. Universities can prepare students to adopt AI. The purpose of this quantitative study was to examine whether perceived ease of use (PEOU) and perceived usefulness (PU) affect on the relationship between accounting students’ level of technology readiness and self-efficacy with their decision to adopt AI. The study involved an examination of individual students’ perceptions of technology readiness and technology adoption. An online questionnaire consisting of 40 items gathering demographic information and perceptions of technology readiness, technology adoption, self-efficacy, PEOU, and PU was administered to student participants. The findings from the study indicated that technology readiness has a significant influence on technology adoption. However, mediation analysis using regression showed that the relationship between technology readiness and technology adoption of Artificial Intelligence is affected by both PEOU and PU. International Journal of Religion210The findings indicated that technology readiness has a significant influence on technology adoption, however, mediation analysis using regression showed that the relationship between technology readiness and technology adoption of Artificial Intelligence is affected by both PEOU and PU.International Journal of Religion["Noral Hidayah Alwi", "B. N. A. Khan"]2024-07-15T00:00:00 8ca00a2b193aa1ab7d84bd61151beb8fe3ac909eThe growing development of technology has had an impact on many sectors particularly business, communication, education and agriculture. In addition to its popularity, technology has brought many new concepts to the use of sectors, most of the important of which are cloud computing, artificial intelligence and cryptocurrencies. While the opportunities and concepts provided by technology have destroyed the existing job opportunities, they also introduced many positive opportunities like artificial intelligence, which can be considered as one of such positive innovations. The OECD artificial intelligence data of G7 countries and Turkey were used within the scope of this study. This study analyses the investment opportunities in agriculture and other sectors based on the artificial intelligence data. In addition to this study, both country-based and sectoral comparisons were made respectively. As a result, AI investments in the agricultural sector are generally at a lower level than other sectors. According to the analysis results, countries such as Türkiye and Canada are the countries that invest the most in the agricultural sector. This may reflect these countries' interest in agricultural potential and agricultural technology.International Journal of Agriculture, Environment and Food Sciences280According to the analysis results, countries such as Türkiye and Canada are the countries that invest the most in the agricultural sector, which may reflect these countries' interest in agricultural potential and agricultural technology.International Journal of Agriculture, Environment and Food Sciences["Ersin \u00c7a\u011flar"]2024-07-15T00:00:00 f0c7093b178a23054f35096eea057600a8d76c30PurposeThis study aimed to investigate the perception and acceptance of artificial intelligence (AI) technologies among the Israeli workforce. More specifically, it examined how age, income, and education level are related to employees’ fears of being replaced by AI technologies and their willingness to adopt these technologies in their personal and professional lives.Design/methodology/approachData were collected by surveying 502 adults from the Jewish population of Israel in February 2023 via an Internet panel. Stratified sampling was performed to ensure a representative cross-section of the population.FindingsContrary to the expectations from a technologically advanced society, the findings indicated varied levels of enthusiasm and apprehension. Age was found to be negatively correlated with the fear of being replaced by AI technologies and the willingness to adopt these technologies. Income was negatively correlated with the fear of being replaced by AI technologies. Education level was negatively correlated with the fear of being replaced and positively correlated with the willingness to adopt.Practical implicationsThe findings provide valuable guidance for policymakers, educators, and business leaders in shaping AI integration strategies. They emphasize the need for targeted educational and policy initiatives to bridge the gap in AI readiness.Originality/valueThis study offers unique insights into the perceptions toward AI in a leading technological hub, contributing to the understanding of how advanced societies are adapting to rapid AI integration.Aslib Journal of Information Management480Age, income, and education level are related to employees’ fears of being replaced by AI technologies and their willingness to adopt these technologies in their personal and professional lives, contributing to the understanding of how advanced societies are adapting to rapid AI integration.Aslib Journal of Information Management["Vlad Vasiliu", "Gal Yavetz"]2024-07-15T00:00:00 2cfdd7e6d2b73a1066f9506f6b79ea7fffb4c275Abstract Background Some research has already reported the diagnostic value of artificial intelligence (AI) in different endoscopy outcomes. However, the evidence is confusing and of varying quality. Objective This review aimed to comprehensively evaluate the credibility of the evidence of AI’s diagnostic accuracy in endoscopy. Methods Before the study began, the protocol was registered on PROSPERO (CRD42023483073). First, 2 researchers searched PubMed, Web of Science, Embase, and Cochrane Library using comprehensive search terms. Then, researchers screened the articles and extracted information. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR2) to evaluate the quality of the articles. When there were multiple studies aiming at the same result, we chose the study with higher-quality evaluations for further analysis. To ensure the reliability of the conclusions, we recalculated each outcome. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the credibility of the outcomes. Results A total of 21 studies were included for analysis. Through AMSTAR2, it was found that 8 research methodologies were of moderate quality, while other studies were regarded as having low or critically low quality. The sensitivity and specificity of 17 different outcomes were analyzed. There were 4 studies on esophagus, 4 studies on stomach, and 4 studies on colorectal regions. Two studies were associated with capsule endoscopy, two were related to laryngoscopy, and one was related to ultrasonic endoscopy. In terms of sensitivity, gastroesophageal reflux disease had the highest accuracy rate, reaching 97%, while the invasion depth of colon neoplasia, with 71%, had the lowest accuracy rate. On the other hand, the specificity of colorectal cancer was the highest, reaching 98%, while the gastrointestinal stromal tumor, with only 80%, had the lowest specificity. The GRADE evaluation suggested that the reliability of most outcomes was low or very low. Conclusions AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases. These findings provide a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes. However, further high-quality research is needed in the future to fully validate AI’s effectiveness.JMIR Medical Informatics500AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases, and provided a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes.JMIR Medical Informatics["Bowen Zha", "Angshu Cai", "Guiqi Wang"]2024-07-15T00:00:00 38599446bde21ce9eca2a9d3fefec135ed77f53eArtificial Intelligence (AI) and the Internet of Things (IoT) are changing the way we live and work by enabling seamless technology integration in our daily lives. This study explores the literature on the integration of AI and IoT to create intelligent systems that can autonomously make decisions and perform tasks based on real-time data from connected devices. This paper presents a meta-analysis of the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in decision-making processes, as well as in Industry 4.0 and 5.0. The study analyzed relevant records from the Web of Science  database, evaluating research output, authorship, collaboration, institutional and geographical distribution, and impact. The results indicate that China has the highest number of total publications and total citations, followed by the USA and India. The study offers valuable insights into the scientific and technological advancements of various regions, their level of international collaboration, and their impact on the field of AI-IoT. The trend of publications indicates that Computer Science, Engineering, and Telecommunications are prominent and steadily growing fields. However, there has been a recent emergence and increase in Chemistry, Instruments & Instrumentation, and Material Science, which are contributing to the development of AI-IoT.Revista de Ciencias Tecnológicas100A meta-analysis of the integration of Artificial Intelligence and the Internet of Things in decision-making processes, as well as in Industry 4.0 and 5.0 offers valuable insights into the scientific and technological advancements of various regions, their level of international collaboration, and their impact on the field of AI-IoT.REVISTA DE CIENCIAS TECNOLÓGICAS["Manuel Baro Tijerina", "Manuel Rom\u00e1n Pi\u00f1a Mon\u00e1rrez", "Jos\u00e9 Manuel Villegas Izaguirre", "Cinthia Judith Valdiviezo Castillo"]2024-07-15T00:00:00 dd79257dfb2f3acb5f9da73bd9a3a8a95750e2baDigestive Diseases and Sciences280It is proposed that a central repository for collection and analysis for training and validation datasets is essential to overcoming potential biases and the question of liability in case of adverse events related to use of AI in GI must be addressed among the physician, the healthcare institution, and the AI developer.Digestive diseases and sciences["Nishant Aggarwal", "David A. Drew", "Ravi B Parikh", "Sushovan Guha"]2024-07-15T00:00:00 2b79a7ec118bdea730aa2ff893200466c4d0c5d5Rapid advances and diffusion of artificial intelligence (AI) technologies have the potential to transform agriculture globally by improving measurement, prediction, and site-specific management on the farm, enabling autonomous equipment that is trained to mimic human behavior and developing recommendation systems designed to autonomously achieve various tasks. Here, we discuss the applications of AI-enabled technologies in agriculture, including those that are capable of on-farm reinforcement learning and key attributes that distinguish them from precision technologies currently available. We then describe various ways through which AI-driven technologies are likely to change the decision space for farmers and require changes to the theoretical and empirical economic models that seek to understand the incentives for their adoption. We conclude with a discussion of areas for future research on the economic, environmental, and equity implications of AI-enabled technology adoption for the agricultural sector.Annual Review of Resource Economics00Various ways through which AI-driven technologies are likely to change the decision space for farmers and require changes to the theoretical and empirical economic models that seek to understand the incentives for their adoption are described.Annual Review of Resource Economics["Madhu Khanna", "S. Atallah", "T. Heckelei", "Linghui Wu", "Hugo Storm"]2024-07-15T00:00:00 16a925741495f86503627e93068dfcaa538f7b3aIn today's complex business landscape, matrix organizations represent a prevalent structural framework characterized by overlapping reporting structures and cross-functional project teams. This organizational model, while offering flexibility and specialization, also presents unique challenges in resource allocation, decision-making, collaboration, and project management. This research paper investigates the potential of artificial intelligence (AI) to address these challenges and enhance the performance of matrix organizations. Drawing upon an extensive review of existing literature and relevant case studies, the paper provides a comprehensive analysis of AI's role in optimizing various aspects of matrix organization operations. Specifically, it examines how AI technologies can streamline resource allocation processes by analyzing project requirements, team capabilities, and individual skill sets to ensure optimal staffing levels and skill matching across projects. Additionally, the paper explores AI's capabilities in providing decision support to managers and team leaders by leveraging data analytics to offer insights into project progress, risk assessment, and performance evaluation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper examines how AI technologies can streamline resource allocation processes by analyzing project requirements, team capabilities, and individual skill sets to ensure optimal staffing levels and skill matching across projects.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Hitesh Chaudhari", "Sandeep Mishra"]2024-07-15T00:00:00 622a47ac96708216eec185e1d18c86388dfbfba6The advent of Artificial Intelligence (AI) has revolutionized the creative landscape, blurring the lines between human and machine innovation. This paper delves into the fascinating realm of AI in Creative Arts, exploring the capabilities and implications of AI-generated art, music, and literature. Through a comprehensive review of existing literature and case studies, we examine the current state of AI-powered creative tools and software, highlighting their potential to augment and transform human creativity. We also investigate the challenges and controversies surrounding AI-generated art, including issues of authorship, ownership, and the role of human imagination. Our analysis reveals that AI-generated art, music, and literature not only demonstrate technical proficiency but also exhibit creative potential, often surpassing human expectations. However, the reliance on algorithms and data raises important questions about the nature of creativity and the value of human input. This paper contributes to the ongoing discourse on AI in Creative Arts, providing insights into the possibilities and limitations of AI-generated content. Our findings have significant implications for the future of creative industries, highlighting the need for collaboration between humans and machines to foster innovative and meaningful artistic expression. Ultimately, this research demonstrates that AI in Creative Arts is not a replacement for human creativity but a transformative force that can enhance and expand our understanding of art, music, and literature. By embracing this synergy, we can unlock new possibilities for artistic expression and push the boundaries of human creativity.International Journal of Advanced Research in Science, Communication and Technology40AI in Creative Arts is not a replacement for human creativity but a transformative force that can enhance and expand the authors' understanding of art, music, and literature, and can unlock new possibilities for artistic expression and push the boundaries of human creativity.International Journal of Advanced Research in Science, Communication and Technology["Mojahedur Molla"]2024-07-15T00:00:00 9fabf47aae827c5eea87dee45cb7d884c2db804bSubject. This article discusses the role of artificial intelligence technologies in optimizing business processes. Objectives. The article aims to develop a set of measures to develop the competencies necessary for a manager to work with artificial intelligence systems. Methods. For the study, I used a comparative analysis. Results. The article finds that the main attention should be paid to the problems that impede the optimization of the management system in Russia. To solve these problems, it is necessary to improve educational training programmes in specialties related to management. Conclusions. The introduction of artificial intelligence technologies presents a challenge for managers, but it also opens up new opportunities to optimize business processes and achieve success. Managers who have the skills to work with an AI system are able to adapt to a rapidly changing environment and make ethically sound decisions.Regional Economics: Theory and Practice30The article finds that the main attention should be paid to the problems that impede the optimization of the management system in Russia and develops a set of measures to develop the competencies necessary for a manager to work with artificial intelligence systems.Regional Economics: Theory and Practice["Denis V. Gavchuk"]2024-07-15T00:00:00 9e76da3382087207813ce0418c09c5a628b3a9c1This article discusses methods for increasing the efficiency and performance of computing systems based on artificial intelligence technologies.Bulletin of Science and Practice20Bulletin of Science and Practice["E. Lashtabega", "N. Limanova", "V. Kozlov"]2024-07-15T00:00:00 8c0b28421991bb4c8dcfa0d637f01065a693773cArtificial intelligence (AI) technologies have had an impact on the healthcare industry for some time. The ongoing advancement of technology based on sophisticated machine learning and methods that can identify intricate patterns in data undoubtedly benefits this. A sophisticated model that can automate diagnosis could be created utilizing pooled healthcare data thanks to the quickly developing field of artificial intelligence. Additionally, customizing therapies and directing resources with maximum effectiveness in a timely and dynamic manner facilitates a more precise approach to medicine. Regrettably, a number of significant problems prevent AI's unambiguous affirmation. These range from the dearth of clinical studies that can show its dependability and superior effectiveness compared to conventional systems to the difficulties associated with allocating blame in the event of medical errors.The Journal of Community Health Management350A sophisticated model that can automate diagnosis could be created utilizing pooled healthcare data thanks to the quickly developing field of artificial intelligence.The Journal of Community Health Management["N. Karunakaran", "B. Maryam", "M. Sadiq", "I. P. Singh", "M. M. Ahmad"]2024-07-15T00:00:00 7a98ec698d613b46db7a4797fef8303d9e72d082Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India310Pretrained DL models are more in application than CNN algorithms when employed for ENT disease predictions and conversational AI models such as ChatGPT in the ENT discipline are more in application than CNN algorithms when employed.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India["Ayushmaan Pandey", "Jagdeep Kaur", "Darwin Kaushal"]2024-07-15T00:00:00 47b5e11890cbf5980352d5cce23b536174d27bfdThis session provided attendees with an overview of the elements of artificial intelligence (AI) that medical communication professionals can use in their decision-making when communicating about and with AI applications. Knowing how to determine if an AI application is reliable and secure guides the professional in their assessment of applications they write about and their choice of applications they use in their practice. In turn, this ability to assess AI applications underpins professionals’ abilities to identify and apply best practices for developing and writing about AI. Taken together, this understanding of how AI works, how applications are developed, and how to identify and ethically apply best practices will guide professionals in their communication about and with AI applications. Editor's Note Developments in artificial intelligence (AI) will continue to be of critical importance to medical communicators for the foreseeable future. Accordingly, AMWA Journal expects to continue to feature AI-related articles in upcoming issues. Given how rapidly advancements are occurring in AI as they relate to medical communication, we are striving to be as timely as possible in bringing relevant articles to you. In this spirit, we are supplementing the Summer 2024 Digital Revolution theme issue with a timely article titled ‘Communicating About and With Artificial Intelligence Applications’ by J. Kelly Byram, based on a presentation made by the author at the most recent AMWA Medical Writing & Communication Conference.American Medical Writers Association AMWA journal60AMWA Journal["J. K. Byram"]2024-07-15T00:00:00 5f72b2885a6fd856c00103e8cf9a799ade143b23The research reviewed theoretical studies published between 2019 and 2022 on civil liability and ethics in the development of artificial intelligence (AI), analyzing 490 documents from various databases. The need to establish regulatory mechanisms to ensure accountability in the development of AI applications was identified. In response, the implementation of internal and external audits with reports accessible to users is proposed. This would contribute to increasing the reliability of applications, especially those with the potential to impact fundamental rights. The aim of the manuscript is to highlight the importance of these regulatory mechanisms in addressing the ethical and legal challenges associated with the development of AI, thus promoting greater transparency and accountability in this rapidly evolving field.IUSTA00The research reviewed theoretical studies published between 2019 and 2022 on civil liability and ethics in the development of artificial intelligence (AI), analyzing 490 documents from various databases to highlight the importance of regulatory mechanisms in addressing the ethical and legal challenges associated with the development of AI.IUSTA["Tatiana Dulima Zabala Leal", "Carla Ang\u00e9lica G\u00f3mez Macfarland"]2024-07-15T00:00:00 988b554e9a36625d9c605894b90b8e010da01e9dPurpose: Phishing is a significant cybercrime threat that affects individuals and organizations globally, including the banking industry in Kenya. The sophistication of phishing attacks continues to increase, and it is increasingly challenging traditional security measures to mitigate these threats. The purpose of this thesis is to build a framework for mitigating phishing e-mail attacks in the banking industry in Kenya using artificial intelligence. Phishing emails are among the most common techniques of cyber-attacks utilized by assailants to gain unauthorized access to sensitive information such as financial details, personal information, and login credentials. These attacks can have devastating effects on the victims, leading to financial loss, reputation damage, and even identity theft. Methodology: The framework development consists of four main stages: data collection, data preprocessing, model training, and deployment. In the data collection stage, a dataset of phishing and non-phishing emails is gathered from various sources such as public databases, dark web forums, and bank employees mail. In the data preprocessing stage, the collected data is cleaned, preprocessed, and labeled. In the model training stage, machine learning algorithms and NLP techniques is used to develop a robust phishing and non-phishing emails detection model. In the deployment stage, the model is integrated into the bank's email system to detect and block phishing emails in real-time. The framework is then evaluated using a dataset of phishing and non-phishing e-mails collected from the banking industry in Kenya. Various metrics such as accuracy, precision, recall, and F1-score are used to evaluate the framework. The framework is able to detect new phishing e-mails that were not previously included in the dataset, demonstrating its ability to adapt to new threats. Findings: The framework is based on a hybrid approach that combines machine learning algorithms, natural language processing (NLP) techniques, and human expertise that identify and prevent phishing emails from reaching their targets. The four main components of this framework include e-mail filtering, feature extraction, classification, and response. The e-mail filtering component uses several algorithms to identify and filter suspicious e-mails. The feature extraction component analyzes the content of the e-mail and extracts relevant features to help classify the e-mail as either legitimate or phishing. The classification component uses machine-learning algorithms to classify the e-mail as either legitimate or phishing. Finally, the response component takes appropriate action based on the classification results. Unique Contribution to Theory, Practice and Policy: The framework provides an effective way to identify and mitigate phishing e-mail attacks, reducing the risk of data breaches and financial losses.International journal of technology and systems310A framework for mitigating phishing e-mail attacks in the banking industry in Kenya using artificial intelligence that combines machine learning algorithms, natural language processing (NLP), and human expertise that identify and prevent phishing emails from reaching their targets.International Journal of Technology and Systems["Asiema Mwavali"]2024-07-15T00:00:00 5f92d79cfa0dc56b65a3bee990722446816f43baThis academic review paper aims to investigate the possibility of integrating artificial intelligence into the daily workflow of management accounting, and to what extent this would increase the effectiveness and efficiency of the business operations. By focusing on aspects such as management accounting, artificial intelligence, the advantages and disadvantages of utilising artificial intelligence for management accounting, ethical considerations of utilising AI while handling sensitive financial information, and various real-world businesses which use AI in their day-to-day operations, this paper is able to clearly identify and explain the possibility of human accountants and artificial intelligence working together in order to maximise the accuracy and efficiency of outcome. This paper also provides an overview regarding some of the essential accounting tools equipped with AI that are currently being used by management accountants, and also discusses the possibility of AI replacing human management accountants completely. By covering all of these components and providing necessary explanations, this academic review paper aims to answer the research question “To what extent will AI be more effective than traditional methods of management accounting?”International Journal For Multidisciplinary Research100International Journal For Multidisciplinary Research["Sree Charan Pendam"]2024-07-15T00:00:00 f2504765159578af35b3e6dbbf6cfce394288632PurposeFirms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.Design/methodology/approachA 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.FindingsOur findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.Practical implicationsThe findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.Originality/valueOur study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.China Accounting and Finance Review570The findings of this study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.China Accounting and Finance Review["Mohammad Islam Biswas", "Md. Shamim Talukder", "Atikur Rahman Khan"]2024-07-15T00:00:00 18d1488fd2b922ba242e0bed9abe206ee5f91e0bThe modern geopolitical realities that had developed by the twenties of the XXI century, the experience accumulated over two years of conducting its own by Russia, the cardinal transformation of the previously established patterns in the field of military and defense policy of the United States and the European Union member states of NATO, the build-up of their military and economic potentials and bringing their financing to 2 % of GDP, a significant increase in spending and financing military-industrial complexes, military-technical policy and scientific research, The strengthening of military-defensive cooperation between Western states demonstrates the birth of a new era of warfare, the effectiveness of which will directly depend on accurate and operational decisions made with the direct participation of artificial intelligence in this process. The purpose of the research in this article is a comprehensive analysis of the theoretical developments of Russian and foreign researchers and practical experience in the use of artificial intelligence in the development and decision-making in the field of naval activities in general, and for the purposes of the Navy in particular. In accordance with the stated purpose of the study, the tasks of substantiating the relevance of the study, analyzing modern scientific publications of domestic and foreign researchers, the practice of using AI technologies in the development and decision-making process in the field of the Navy in Russia and abroad, etc. are formulated. The research methodology includes general scientific methods of analysis and synthesis of information, interpretation of the obtained data. The research is of a complex, interdisciplinary nature, and therefore, political science and legal analysis are included in the arsenal of research methods. Institutional and systemic approaches have been applied. The study uses the concepts of the theory of integrated and regional security (Buzan B., Waever O.), theories of cybersecurity, including the concept of the cybernetic power of the state, etc.Social'naja politika i social'noe partnerstvo (Social Policy and Social Partnership)00The research is a comprehensive analysis of the theoretical developments of Russian and foreign researchers and practical experience in the use of artificial intelligence in the development and decision-making in the field of naval activities in general and for the purposes of the Navy in particular.Social'naja politika i social'noe partnerstvo (Social Policy and Social Partnership)["V. E. Bondyrev", "E. Ustinovich"]2024-07-15T00:00:00 9ed5d05cad37fb48e49a7eec79350f1509766560Subject. The article investigates the role and place of artificial intelligence in the system design of high-tech products. Objectives. The focus is on determination of artificial intelligence implementation stages in the system design of high-tech products. Methods. We apply general scientific research methods. Results. The study established that artificial intelligence in the system design of high-tech products should be considered as a single software-algorithmic, system-technological, and conceptual-terminological complex. It identified methodological and ideological problems of introducing artificial intelligence into system design. We defined the structure of hybrid intelligence and substantiated the need for its use. We proposed to use the modern methodology of program-target planning to organize a phased, planned, and coordinated introduction of artificial intelligence technologies in the system design of high-tech products. Conclusions. The findings can be used in the development of State scientific and technological programs and plans to create high-tech products for various purposes in the interests of ensuring the sustainability of innovative and technological development and national security of the Russian Federation, given the current geopolitical and economic situation in the world.National Interests Priorities and Security70The study established that artificial intelligence in the system design of high-tech products should be considered as a single software-algorithmic, system-technological, and conceptual-terminological complex and identified methodological and ideological problems of introducing artificial intelligence into system design.National Interests: Priorities and Security["Aleksandr V. Leonov", "Aleksei Yu. Pronin"]2024-07-15T00:00:00 7388c109b248f7db2d20d3adc95b6ba6ebaa52bcThis study aimed to identify the impact of using artificial intelligence (AI) on the investment decision-making process among potential investors in Palestine. The study employed a quantitative approach using 195 questionnaires, with data collected through a convenience sample. The data analysis was conducted using the SmartPLS software. The results showed no statistically significant impact between the use of AI in data analysis, portfolio optimization, and sentiment analysis on investment decision-making. Conversely, there was a positive and statistically significant impact of using AI in risk management and market trend forecasting on investment decision-making. This research is original as it empirically explores the factors influencing potential investors' acceptance of AI's role in the investment decision-making process in Palestine. The central role of AI in this field lies in its ability to analyze vast amounts of data quickly and accurately, as well as in market trend forecasting and risk management. These areas have not received sufficient attention in previous literature. This adds valuable insights to the scientific literature and can benefit potential investors by improving investment strategies and reducing risks.Ahliya Journal of Business Technology and MEAN Economies140The results showed no statistically significant impact between the use of AI in data analysis, portfolio optimization, and sentiment analysis on investment decision-making, and there was a positive and statistically significant impact of using AI in risk management and market trend forecasting on investment decision-making.Ahliya Journal of Business Technology and MEAN Economies["Yazan Saleh", "Feras Albaw", "Majd Salah", "Ahmad Natsheh"]2024-07-15T00:00:00 6e04d552da6be6b5de163beccc22b64cf3f1431bArtificial Intelligence models have been employed in various fields, leading to a growing interest in the subject and in the development of the models. The direct involvement of complex AI models in decision-making processes stressed the needs to explain the rationales behind the results, globally and locally for each prediction/result via eXplainable Artificial Intelligence (XAI) techniques. This paper compared three XAI techniques (SHAP, LIME and IG) with aim of using them for temporal explainability of predictive results regarding time-series in order to understand if these methods are able provide temporal explanation of deep learning AI models. The comparison provided has been qualitative and quantitative and addressing computational performance. This work has been partially supported by the CN MOST, national center on sustainable mobility in Italy, on CAI4DSA of FAIR, and has been developed on the Snap4City platform.International Conference on Big Data Computing Service and Applications160Compared three XAI techniques (SHAP, LIME and IG) with aim of using them for temporal explainability of predictive results regarding time-series in order to understand if these methods are able provide temporal explanation of deep learning AI models.2024 IEEE 10th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService)["Edoardo Canti", "Enrico Collini", "L. A. I. Palesi", "P. Nesi"]2024-07-15T00:00:00 b1d362819eed4f7ff6072aa39469c2c947a1fb25Artificial Intelligence (AI) is revolutionizing various aspects of life, including education. AI in education (AIEd) fosters teachers' understanding of students' learning processes, offers personalized and adaptive learning, and provides instantaneous feedback. It has the potential to improve academic performance and reduce educational disparities. This study aims to engage researchers, policymakers, teachers, students, and engineers in a dialogue about AIEd. It provides an overview of studies on opportunities, challenges, and recommendations for future research, focusing on specific educational outcomes. A comprehensive perspective is needed to understand the function of AIEd. This research utilizes bibliometric analysis and systematic literature review to analyze AIEd, presenting results as a specific bibliometric network using the VOSviewer tool. Research findings that AIEd-based environments are enhancing student learning, but their personalized learning is still in its experimental stage. Challenges include a lack of resources and ethical concerns. AI chatbots and interactive books aid language learning, but they also have advantages and disadvantages. The humanities must balance these advantages and disadvantages.Fahima220This research utilizes bibliometric analysis and systematic literature review to analyze AIEd, presenting results as a specific bibliometric network using the VOSviewer tool.Fahima["Ghasa Faraasyatul \u2018Alam", "B. Wiyono", "Burhanuddin Burhanuddin", "M. Muslihati", "Ani Mujaidah"]2024-07-15T00:00:00 8bf45475874b3f52b2406eabf13aae97747f2fc5The world eagerly anticipates advancements in AI technologies, with substantial ongoing research on the potential AI applications in the domain of education. The study aims to analyse publications about the possibilities of artificial intelligence (AI) within higher education, emphasising their bibliometric properties. The data was collected from the Scopus database, uncovering 775 publications on the subject of study from 2000 to 2022, using various keywords. Upon analysis, it was found that the frequency of publications in the study area has risen from 3 in 2000 to 314 in 2022. China and the United States emerged as the most influential countries regarding publications in this area. The findings revealed that “Education and Information Technologies” and the “International Journal of Emerging Technologies in Learning” were the most frequently published journals. “S. Slade” and “P. Prinsloo” received the most citations, making them highly effective researchers. The co-authorship network primarily comprised the United States, Saudi Arabia, the United Kingdom, and China. The emerging themes included machine learning, convolutional neural networks, curriculum, and higher education systems are co-occurred with AI. The continuous expansion of potential AI technologies in higher education calls for increased global collaboration based on shared democratic principles, reaping mutual advantages.European Journal of Educational Research680It was found that the frequency of publications in the study area has risen from 3 in 2000 to 314 in 2022, and that “Education and Information Technologies” and the “International Journal of Emerging Technologies in Learning” were the most frequently published journals.European Journal of Educational Research["K. Kavitha", "V. P. Joshith", "Neethu P Rajeev", "Asha S"]2024-07-15T00:00:00 8b25f7f497e46ad6cca3c0bc68db0884c3bd3901Abstract Objective Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale. Materials and Methods We engineered prompts to summarize abstracts at a literacy level accessible to the public, prioritizing succinctness, clarity, and practical relevance. Researchers and volunteers assessed ChatGPT-generated lay summaries across five dimensions: accuracy, relevance, accessibility, transparency, and harmfulness. We used precision analysis and adaptive random sampling to determine the optimal number of summaries for evaluation, ensuring high statistical precision. Results ChatGPT achieved 95.9% (95% CI, 92.1–97.9) accuracy and 96.2% (92.4–98.1) relevance across 192 summary sentences from 33 abstracts based on researcher review. 85.3% (69.9–93.6) of 34 volunteers perceived ChatGPT-generated summaries as more accessible and 73.5% (56.9–85.4) more transparent than the original abstract. None of the summaries were deemed harmful. We expanded ResearchMatch’s technical infrastructure to automatically generate and display lay summaries for over 750 published studies that resulted from the platform’s recruitment mechanism. Discussion and Conclusion Implementing AI-generated lay summaries on ResearchMatch demonstrates the potential of a scalable framework generalizable to broader platforms for enhancing research accessibility and transparency.J. Am. Medical Informatics Assoc.435Implementing AI-generated lay summaries on ResearchMatch demonstrates the potential of a scalable framework generalizable to broader platforms for enhancing research accessibility and transparency.Journal of the American Medical Informatics Association : JAMIA["Cathy Shyr", "Randall W Grout", "Nan Kennedy", "Y. Akdas", "Maeve Tischbein", "Joshua Milford", "Jason Tan", "Kaysi Quarles", "Terri L. Edwards", "Laurie L Novak", "Jules White", "Consuelo H. Wilkins", "Paul A. Harris"]2024-07-15T00:00:00 33189a50bd2143784a46f1299bd2697d7ce16860Journal of Chemical Education181Journal of Chemical Education["James D. Mendez"]2024-07-15T00:00:00 9e37a2d0a02d24f6cd77925f250f6eded14e11f7Neurosurgical review41Neurosurgical Review["H. Farooqi", "Zenia Safwan", "Rayyan Nabi"]2024-07-15T00:00:00 a2bd48fa5331f1012ac2f2485b9cb6603b9c698cCom o objetivo de discutir as interseções entre os Objetivos de Desenvolvimento Sustentável (ODS), a agenda Environmental, Social and Governance (ESG) e a inteligência artificial (IA), a pesquisa examina a transversalidade e a integração desses temas e discute as contribuições para a sustentabilidade corporativa. Utilizando uma abordagem interdisciplinar, foram discutidos os conceitos e analisadas as implicações de tais abordagens para a sustentabilidade corporativa. A pesquisa pode ser caracterizada como qualitativa, com abordagem bibliográfica e utiliza-se de revisão de literatura para trazer reflexões sobre o problema. Os resultados destacam a eficácia da IA na identificação de padrões ESG, bem como sua capacidade de impulsionar a inovação e a eficiência operacional alinhadas com os ODS. Concluí-se que a integração de IA na agenda ESG pode proporcionar benefícios significativos para as empresas, facilitando a tomada de decisões sustentáveis e promovendo a consecução dos ODS.Akrópolis - Revista de Ciências Humanas da UNIPAR00Akrópolis - Revista de Ciências Humanas da UNIPAR["Telma Regina Stroparo", "A. Guerra", "E. D. S. Cordeiro", "Beatriz Bochniak", "M. A. Bortolotti", "Orivaldo da Silva Lacerda J\u00fanior"]2024-07-15T00:00:00 5ea4f20809f624ce5e5b4bb85c966c6a55287b27Con el desarrollo de las tecnologías, muchos trabajadores se han visto enfrentados a la automatización de sus cargos. Al igual que la era industrial, la era virtual se está convirtiendo en un conflicto en diferentes campos laborales. En el mundo tanto del cine y de la creación de contenido, la inteligencia artificial ha tomado protagonismo, simplificando varias tareas en la producción y postproducción de contenido. En el arte de la postproducción o edición, existen muchas herramientas que facilitan diferentes trabajos que antes tomaban horas, días, e incluso meses en realizarse, tales como la colorización, los recortes, la animación, entre otras cosas. En este trabajo se indaga en el uso de la inteligencia artificial por medio de distintos softwares que se pueden emplear en la postproducción audiovisual, aprovechando la era digital como una aliada en la realización del séptimo arte.Ñawi00Ñawi["Jheimy Jironza Hidalgo"]2024-07-15T00:00:00 94654ff0c1ca47e35d6cca305c5b465945791c9300[]2024-07-15T00:00:00 a977819be9675bf65549b5d4d984ba3797e3d557This study was aimed at understanding graduate students' preferences in applying AI when dealing with their class papers and projects in Indonesia. It also sought to understand how students who have used AI in previous writing feel about such experience. This study adopted a phenomenological research method in order to elicit in-depth insights into the concerns of the students. The participants included graduate students from four universities in Indonesia. These data were gathered using semi-structured interviews of 30 students who had experience using AI for their academic writing. Guided by understanding the decision-making process, perceived benefits, and drawbacks of AI, and overall experiences, interview questions were prepared. One-way thematic analysis was conducted with the interview data. Students seemed to view AI applications as only helping with formatting and editing tasks, as most of them would like to have the opportunity to do the major work by themselves for better learning. Another underlying strong theme emerging here is related to AI overdependency and unequal access to it. The results offer insights into the respective areas that can be used by educators and institutions to provide a balance between the rising AI in use and support for independent learning within academics.Jurnal Paedagogy00Understanding graduate students' preferences in applying AI when dealing with their class papers and projects in Indonesia offers insights into the respective areas that can be used by educators and institutions to provide a balance between the rising AI in use and support for independent learning within academics.Jurnal Paedagogy["Ikrawansyah Ikrawansyah", "M. G. E. Romadhon"]2024-07-15T00:00:00 bf1a26058d7432a906cf5d5f496a83eac4bb1490Indian Journal of Clinical Anatomy and Physiology60Indian Journal of Clinical Anatomy and Physiology["Anupama Mahajan"]2024-07-15T00:00:00 492f832c5841e22471d79a4c047b451472409515Biomedical Journal of Scientific &amp; Technical Research00Biomedical Journal of Scientific &amp; Technical Research["Bellido Casado J"]2024-07-15T00:00:00 6ca59bdafecb63a3cff78b3806f6ed62b6c0ae51Brazilian Journal of Cardiovascular Surgery00Brazilian Journal of Cardiovascular Surgery["Somsri Wiwanitmkit", "V. Wiwanitkit"]2024-07-15T00:00:00 49fc723ff91cc1ec4e9674cab54e691138399e8e00["N. Sharma", "Durgesh Srivastava", "Deepak Sinwar"]2024-07-15T00:00:00 01d5ae7801d180dfcdcc8b58da99419878663f80IP International Journal of Orthopaedic Rheumatology70IP International Journal of Orthopaedic Rheumatology["S. Keshkar", "Manish Khanna"]2024-07-15T00:00:00 ce294fe4fa3bd8035d6562cc2b98a9ec08038117Indian Journal of Clinical and Experimental Ophthalmology450Indian Journal of Clinical and Experimental Ophthalmology["Amit Raj", "Ankita Sharma", "P. Nishant", "R. P. Maurya"]2024-07-15T00:00:00 2993c421ec08bca06bc169ca2dd8c151176f668dAbstract. Martian architecture has gained interest in the recent year. Several grand architectural studios have designed hypothetical buildings as part of a colony of the red planet. This study is a continuation of a previous research on mars Habitat. The use of AI to generate alternatives of design based on an initial idea gives insight of how technology can assist us in such major projects. The methodology followed in this study is as per the below steps: 1- General Description of the initial concept: Organic Architecture, 2- General Description of the initial concept: Minimal Architecture, 3- Use of AI in the selected projects, tool description, 4- Results: Outcomes of AI Applications. The aim of this study is to investigate the impact of the AI on Space Architecture, more specifically Martian Architecture. The initial step in the methodology is to design a colony that connects together but as also well distributed in the plan. The following step is using an AI tool to generate processed (rendered) images of the base image. These AI renders will then be analyzed and the final implication of the findings for the project will be described. The findings of this study can be relevant to relevant authorities in space exploration and space architecture with the help of AI tools.Renewable Energy00The use of AI to generate alternatives of design based on an initial idea gives insight of how technology can assist us in such major projects as space exploration and space architecture.Renewable Energy: Generation and Application["L. Bande"]2024-07-15T00:00:00 290ce877f020bae37f11f57a0b25e34f4529332fInternational journal of latest research in engineering and management00International Journal of Latest Research in Engineering and Management (IJLREM)["Satyakam Sahoo"]2024-07-15T00:00:00 335900f23a2d6483d54ea0916c7fee36b6423ea1Journal Francais d'Ophtalmologie1240This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy, and acknowledges the limitations of current AI models, including their reliance on binary classification.Journal francais d'ophtalmologie["B. Gurnani", "K. Kaur", "V. G. Lalgudi", "G. Kundu", "M. Mimouni", "H. Liu", "V. Jhanji", "G. Prakash", "A. S. Roy", "R. Shetty", "J. S. Gurav"]2024-07-15T00:00:00 0963f297412b1e775c25ef4a400dd63b08d45dbc00["Kevin Padraic Donnelly"]2024-07-15T00:00:00 8301b47a6bda1093df99aaa5a37551820df2d4b9This paper explores the transformative dynamics within the digital social economy, focusing on the intersection of Blockchain technology, the Metaverse, and Artificial General Intelligence (AGI). Our research illuminates the pathways and implications of this confluence, presenting a comprehensive view of its potential to reshape economic, social, and technological landscapes. We propose a multifaceted framework integrating these technologies, highlighting their collective potential to revolutionize the digital social economy. Methodologically, we combine extensive analysis of existing literature and current market data with qualitative interviews with industry experts in blockchain, virtual reality, and AGI. Our findings reveal that blockchain serves as the backbone for secure and transparent transactions, essential for trust in virtual interactions. The Metaverse emerges as a transformative platform for social and economic engagement, while AGI drives intelligent, adaptive, and personalized experiences within these digital realms. We identify critical challenges, such as platform interoperability, ethical considerations in AGI deployment, and the digital divide hindering equitable access. We propose strategic solutions, emphasizing robust governance frameworks, ethical AI development standards, and inclusive policies. This paper provides strategic insights for stakeholders, offering a visionary perspective for future innovation and policy development in the evolving digital social economy.Journal of Science & Technology01It is revealed that blockchain serves as the backbone for secure and transparent transactions, essential for trust in virtual interactions, and a multifaceted framework integrating these technologies is proposed, highlighting their collective potential to revolutionize the digital social economy.Journal of Science and Technology["Farhang M Hamzah"]2024-07-15T00:00:00 67d268d7cec1d1578287258f707057527854bbd8In light of the unprecedented success of Artificial Intelligence (AI), the amount of video content intended for machine vision has surpassed that intended for human vision. Therefore, it is crucial to develop customized codecs that are more specialized in machine vision applications. To facilitate the study of this topic, Moving Picture Experts Group (MPEG) has established two working groups, Video Coding for Machines (VCM) and Feature Coding for Machines (FCM). Unlike traditional video coding standards, the output of VCM decoder are fed into machine vision models (in many cases neural networks) instead of being viewed by humans. Thus, the codec is optimized towards high machine task performance rather than high fidelity. FCM takes a further step by directly compressing the intermediate feature tensors of the task neural networks, enabling a balance between compression efficiency and multi-task accuracy. Furthermore, FCM facilitates load balancing, allowing for the realization of Collaborative Intelligence (CI). We propose an advanced FCM algorithm, Asymmetrical Feature Coding (AFC), with novel feature re-duction and feature restoration modules. We evaluated the AFC on multiple datasets under three task scenarios, including object detection, instance segmentation, and object tracking. AFC outperforms the state-of-the-art video and feature compression technologies, achieving an overall of 94.65% BD-rate gain. AFC ranks 1st in the MPEG FCM Call for Proposals (CfP) responses evaluation.2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)200An advanced FCM algorithm, Asymmetrical Feature Coding (AFC), with novel feature re-duction and feature restoration modules is proposed, which outperforms the state-of-the-art video and feature compression technologies and facilitates load balancing, allowing for the realization of Collaborative Intelligence (CI).2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)["Yuan Zhang", "Hanming Wang", "Yunlong Li", "Lu Yu"]2024-07-15T00:00:00 744438fd0761134997f347c1964922c7ac6e72b2Hybrid intelligence aims to enhance decision-making, problem-solving, and overall system performance by combining the strengths of both, human cognitive abilities and artificial intelligence. With the rise of Large Language Models (LLM), progressively participating as smart agents to accelerate machine learning development, Hybrid Intelligence is becoming an increasingly important topic for effective interaction between humans and machines. This paper presents an approach to leverage Hybrid Intelligence towards sustainable and energy-aware machine learning. When developing machine learning models, final model performance commonly rules the optimization process while the efficiency of the process itself is often neglected. Moreover, in recent times, energy efficiency has become equally crucial due to the significant environmental impact of complex and large-scale computational processes. The contribution of this work covers the interactive inclusion of secondary knowledge sources through Human-in-the-loop (HITL) and LLM agents to stress out and further resolve inefficiencies in the machine learning development process.arXiv.org290The interactive inclusion of secondary knowledge sources through Human-in-the-loop (HITL) and LLM agents to stress out and further resolve inefficiencies in the machine learning development process is covered.ArXiv["Daniel Geissler", "P. Lukowicz"]2024-07-15T00:00:00 9ccdbbcb9aa033589fe4c0367c5295cd1c30befcHigher Education127A critical analysis of “grey literature” claims made in the first months after ChatGPT was made public is presented, proposing that a more critical interrogation of generative AI, and the involvement of students in the conversation, may be beneficial.Higher Education["Lasse X. Jensen", "Alexandra Buhl", "Anjali Sharma", "M. Bearman"]2024-07-15T00:00:00 aef144cc2512c5c7d201c3b2077a1948e77e395aThe adoption of artificial intelligence (AI) in retail has significantly transformed the industry, enabling more personalized services and efficient operations. However, the rapid implementation of AI technologies raises ethical concerns, particularly regarding consumer privacy and fairness. This study aims to analyze the ethical challenges of AI applications in retail, explore ways retailers can implement AI technologies ethically while remaining competitive, and provide recommendations on ethical AI practices. A descriptive survey design was used to collect data from 300 respondents across major e-commerce platforms. Data were analyzed using descriptive statistics, including percentages and mean scores. Findings shows a high level of concerns among consumers regarding the amount of personal data collected by AI-driven retail applications, with many expressing a lack of trust in how their data is managed. Also, fairness is another major issue, as a majority believe AI systems do not treat consumers equally, raising concerns about algorithmic bias. It was also found that AI can enhance business competitiveness and efficiency without compromising ethical principles, such as data privacy and fairness. Data privacy and transparency were highlighted as critical areas where retailers need to focus their efforts, indicating a strong demand for stricter data protection protocols and ongoing scrutiny of AI systems. The study concludes that retailers must prioritize transparency, fairness, and data protection when deploying AI systems. The study recommends ensuring transparency in AI processes, conducting regular audits to address biases, incorporating consumer feedback in AI development, and emphasizing consumer data privacy.European journal of computer science and information technology15The study concludes that retailers must prioritize transparency, fairness, and data protection when deploying AI systems, and recommends ensuring transparency in AI processes, conducting regular audits to address biases, incorporating consumer feedback in AI development, and emphasizing consumer data privacy.ArXiv["Anthonette Adanyin"]2024-07-15T00:00:00 23a467b1e122c5352a7cba0be970596a20e27cccPurposeThis systematic literature review (SLR) aims to critically analyze the current academic research on the adoption of artificial intelligence (AI) in supply chain management (SCM) and develop a theoretical framework and future research agenda.Design/methodology/approachThrough a comprehensive review of 68 relevant papers, this study synthesizes the findings to identify key themes based on extended technology-organization-environment (TOE) theory.FindingsThis study analyzes AI integration in SCM based on the TOE framework, identifying drivers (technological, organizational, environmental and human), barriers (technical, organizational, economic and human) and outcomes (operational, environmental, social and economic) of AI adoption. It emphasizes AI's potential in improving SCM practices like resilience, process improvement and sustainable operations, contributing to better decision-making, efficiency and sustainable practices. The study also provided a novel framework that offers insights for strategic AI integration in SCM, aiding policymakers and managers in understanding and leveraging AI's multifaceted impact.Originality/valueThe originality of the study lies in the development of a theoretical framework that not only elucidates the drivers and barriers of AI in SCM but also maps the operational, financial, environmental and social outcomes of AI-enabled practices. This framework serves as a novel tool for policymakers and managers, offering specific, actionable insights for the strategic integration of AI in supply chains (SCs). Furthermore, the study's value is underscored by its potential to guide policy formulation and managerial decision-making, with a focus on optimizing SC efficiency, sustainability and resilience through AI adoption.Journal of Manufacturing Technology Management1044This study analyzes AI integration in SCM based on the TOE framework, identifying drivers, barriers, and outcomes of AI adoption and maps the operational, financial, environmental and social outcomes of AI-enabled practices.Journal of Manufacturing Technology Management["Gulnaz Shahzadi", "Fu Jia", "Lujie Chen", "Albert John"]2024-07-15T00:00:00 3233d45209e982c7a7853a030c1f5db130147132Objectives To employ a drug supply chain information system to optimize drug management practices, reducing costs and improving efficiency in financial and asset management. Methods A digital artificial intelligence + vendor managed inventory (AI+VMI)-based system for drug supply chain information management in hospitals has been established. The system enables digitalization and intelligentization of purchasing plans, reconciliations, and consumption settlements while generating purchase, sales, inventory reports as well as various query reports. The indicators for evaluating the effectiveness before and after project implementation encompass drug loss reporting, inventory discrepancies, inter-hospital medication retrieval frequency, drug expenditure, and cloud pharmacy service utilization. Results The successful implementation of this system has reduced the hospital inventory rate to approximately 20% and decreased the average annual inventory error rate from 0.425‰ to 0.025‰, significantly boosting drug supply chain efficiency by 42.4%. It has also minimized errors in drug application, allocation, and distribution while increasing adverse reaction reports. Drug management across multiple hospital districts has been standardized, leading to improved access to medicines and enhanced patient satisfaction. Conclusion The AI+VMI system improves drug supply chain management by ensuring security, reducing costs, enhancing efficiency and safety of drug management, and elevating the professional competence and service level of pharmaceutical personnel.Frontiers in Pharmacology648The AI+VMI system improves drug supply chain management by ensuring security, reducing costs, enhancing efficiency and safety of drug management, and elevating the professional competence and service level of pharmaceutical personnel.Frontiers in Pharmacology["Jianwen Shen", "Fengjiao Bu", "Zhengqiang Ye", "Min Zhang", "Qin Ma", "Jingchao Yan", "Taomin Huang"]2024-07-16T00:00:00 ca09bce0a91268079631f6007618681d022633a0Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.IgMin Research53This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions, and underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations.IgMin Research["Almasri Imad-Addin"]2024-07-16T00:00:00 f774465f320c1fcf8a87afd1c0ffcf64b09a066dReporting standards are essential to health research as they improve accuracy and transparency. Over time, significant changes have occurred to the requirements for reporting research to ensure comprehensive and transparent reporting across a range of study domains and foster methodological rigor. The establishment of the Declaration of Helsinki, Consolidated Standards of Reporting Trials (CONSORT), Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) are just a few of the historic initiatives that have increased research transparency. Through enhanced discoverability, statistical analysis facilitation, article quality enhancement, and language barrier reduction, artificial intelligence (AI)—in particular, large language models like ChatGPT—has transformed academic writing. However, problems with errors that could occur and the need for transparency while utilizing AI tools still exist. Modifying reporting rules to include AI-driven writing tools such as ChatGPT is ethically and practically challenging. In academic writing, precautions for truth, privacy, and responsibility are necessary due to concerns about biases, openness, data limits, and potential legal ramifications. The CONSORT-AI and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-AI Steering Group expands the CONSORT guidelines for AI clinical trials—new checklists like METRICS and CLEAR help to promote transparency in AI studies. Responsible usage of technology in research and writing software adoption requires interdisciplinary collaboration and ethical assessment. This study explores the impact of AI technologies, specifically ChatGPT, on past reporting standards and the need for revised guidelines for open, reproducible, and robust scientific publications.Journal of Korean medical science1153This study explores the impact of AI technologies, specifically ChatGPT, on past reporting standards and the need for revised guidelines for open, reproducible, and robust scientific publications.Journal of Korean Medical Science["F. Alnaimat", "Salameh Al-Halaseh", "Abdel Rahman Feras AlSamhori"]2024-07-16T00:00:00 f417c612179d2e90c55ae2a2a588a2f0ce20cdf5Inspired by the biological visual system, the convolutional neural network has been widely studied and invented in the field of artificial intelligence. As one of the important algorithms in artificial neural networks, convolutional neural networks have shown outstanding application potential in fields such as image recognition, computer vision, and natural language processing. This article will focus on exploring the powerful capabilities of convolutional neural networks in image processing. By delving into the implementation process of a convolutional neural network, readers will gain a deeper understanding of its working principles. In addition, this article will briefly introduce three classic models of convolutional neural networks, providing readers with more background knowledge. Next, this paper will analyze in detail two typical application cases of convolutional neural networks in the field of image processing: intelligent transportation systems and dental imaging technology. These cases demonstrate the successful application of convolutional neural networks in practical scenarios, pointing the way for their future development. In the future, convolutional neural networks will be more widely used in fields such as image and video processing as data scale increases and computing power improves. By using techniques such as model compression and hardware optimization, it is made more suitable for low-power and high-efficiency environments, and its interpretability and applicability are enhanced through data augmentation and model interpretation techniques.Applied and Computational Engineering01Two typical application cases of convolutional neural networks in the field of image processing are analyzed: intelligent transportation systems and dental imaging technology, demonstrating the successful application of convolutional neural networks in practical scenarios.Applied and Computational Engineering["Jiamin Jiang"]2024-07-16T00:00:00 a3f1e1ca717b1a801063b0ba32d1e180f1897a35 Purpose The purpose of this paper is to provide an in-depth analysis of the challenges associated with using artificial intelligence (AI) in academic research and suggest various preventive measures that can be taken to address these issues and transform them into opportunities. Design/methodology/approach To develop measurement items and constructs, the authors collected 248 responses through an online survey. These responses were then used to establish the structural model and determine discriminant validity through the use of structural equation modeling with SmartPLS 4.0.9.9. Additionally, the authors used SPSS (Version 29) to create graphs and visual representations of the challenges faced and the most commonly used AI tools. These techniques allowed them to explore data and draw meaningful conclusions for future research. Findings This research shows that AI has a positive impact on higher education, improving learning outcomes and data security. However, issues such as plagiarism and academic integrity can destroy students. The study highlights AI’s potential in education while emphasizing the need to address challenges. Practical implications This paper emphasizes the preventive measures to tackle academic challenges and suggests enhancing academic work. Originality/value This study examines how AI can be used to personalize learning and overcome challenges in this area. It emphasizes the importance of academic institutions in promoting academic integrity and transparency to prevent plagiarism. Additionally, the study stresses the need for technology advancement and exploration of new approaches to further improve personalized learning with AI. Quality Assurance in Education442An in-depth analysis of the challenges associated with using artificial intelligence (AI) in academic research and suggest various preventive measures that can be taken to address these issues and transform them into opportunities is provided.Quality Assurance in Education["M. Tamanna", "Bijaya Sinha"]2024-07-16T00:00:00 fa6fda3126d9e100cd448b5a0ea3d2c923061960Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.HYPERTENSION02Key findings are summarized from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023 on information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.Hypertension["D. Shimbo", "R. Shah", "M. Abdalla", "Ritu Agarwal", "Faraz S Ahmad", "Gabriel Anaya", "Z. Attia", "Sheana Bull", "Alexander R. Chang", "Y. Commodore\u2010Mensah", "Keith C. Ferdinand", "Kensaku Kawamoto", "R. Khera", "J. Leopold", "James Luo", "Sonya Makhni", "Bobak J. Mortazavi", "Young S Oh", "Lucia C Savage", "Erica S Spatz", "G. Stergiou", "M. Turakhia", "Paul K. Whelton", "C. Yancy", "Erin Iturriaga"]2024-07-16T00:00:00 d633c8d4b82060606b286a8180640ea544247a0fDiscov. Comput.402It is found that accountants who are knowledgeable about and utilize AI are more likely to be engaged in AI, leading to positive changes in accounting procedures, and the robust positive relationship between AI’s impact on accounting procedures and accounting efficiency indicates a significant positive influence.Discov. Comput.["Mahfoudh Hussein Mgammal"]2024-07-16T00:00:00 e94a3b9e244cec7a00d72de802248c5348f391f3The present study's aim is to investigate the intention to use and actual use of a artificial intelligence (AI) for talent acquisition in Bangladesh. The authors used deductive reasoning approach in a positive paradigm. The study finally collected 243 responses through self-administered questionnaire and used the PLS-based structural equation modeling to analyze the data. The findings of this study revealed that each of the predictors is significantly associated with the intention to use and actual use of AI for recruiting talents, excepting the influence of facilitation conditions on actual use of AI. The influence of age demonstrated that there is no moderating effect of the influence of users' intention to use on actual use of AI for talent acquisition. This study also advances knowledge in AI adoption for recruiting talents, and enhances literature in the fields of AI adoption for recruiting talents by providing insights for the policy makers in a developing country's context. Furthermore, the study also provides insightful directions for future researchers.International Journal of Human Capital and Information Technology Professionals561It is revealed that each of the predictors is significantly associated with the intention to use and actual use of AI for recruiting talents, excepting the influence of facilitation conditions on actual use of AI.Int. J. Hum. Cap. Inf. Technol. Prof.["M. Alam", "Kazi Sirajum Munira", "Md. Sahidur Rahman", "Md. Aftab Uddin", "Ayesha Akter"]2024-07-16T00:00:00 1a32f28c9428d20d3c44c819f8f24d8e64e96f1dABSTRACT Artificial Intelligence (AI) emerges as a transformative force, offering novel solutions to enhance medical education and mentorship in the specialty of Physical Medicine and Rehabilitation (PM&R). AI is a transformative technology that is being adopted in nearly every industry, In medicine, the use of AI in medical education is growing. AI may also assist with some of the challenges of mentorship, including the limited availability of experienced mentors, and the logistical difficulties of time and geography are some constraints of traditional mentorship. In this commentary, we discuss various models of AI in medical education and mentoring, including expert systems, conversational agents, and hybrid models. These models enable tailored guidance, broaden outreach within the PM&R community, and support continuous learning and development. Balancing AI's technical advantages with the essential human elements while addressing ethical considerations, AI integration into medical education and mentorship presents a paradigm shift towards a more accessible, responsive, and enriched experience in rehabilitation medicine.American Journal of Physical Medicine & Rehabilitation01Various models of AI in medical education and mentoring are discussed, including expert systems, conversational agents, and hybrid models, which enable tailored guidance, broaden outreach within the PM&R community, and support continuous learning and development.American journal of physical medicine & rehabilitation["Julie K. Silver", "Mustafa Reha Dodurgali", "N. Gavini"]2024-07-16T00:00:00 d26c1255cfbe8df7e2234d36db1cca5a2d4ab6a5Rheumatology International701This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints.Rheumatology International["O. Koker", "S. \u015eahin", "Mehmet Y\u0131ld\u0131z", "Amra Adrovic", "O. Kasapcopur"]2024-07-16T00:00:00 0c3cea7ca585155ef360088af6a01e806a5a1293Future‐oriented Science & Technology (S&T) Strategies trigger the innovative developments of advanced materials, providing an envision to the significant progress of leading‐/cutting‐edge science, engineering, and technologies for the next few decades. Motivated by Made in China 2025 and New Material Power Strategy by 2035, several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for Science and main respective of big data, database, standards, and ecosystems are discussed. Referring to classical toolkits at various spatial and temporal scales, AI‐based toolkits and AI‐enabled computations for material design are compared, highlighting the dominant role of the AI agent paradigm. Our recent developed ProME platform together with its functions is introduced briefly. A case study of AI agent assistant welding is presented, which is consisted of the large language model, auto‐coding via AI agent, image processing, image mosaic, and machine learning for welding defect detection. Finally, more duties are called to educate the next generation workforce with creative minds and skills. It is believed that the transformation of knowledge‐enabled data‐driven integrated computational material engineering era to AI+ era promotes the transformation of smart design and manufacturing paradigm from “designing the materials” to “designing with materials.”Materials Genome Engineering Advances768It is believed that the transformation of knowledge‐enabled data‐driven integrated computational material engineering era to AI+ era promotes the transformation of smart design and manufacturing paradigm from “designing the materials” to “designing with materials.”Materials Genome Engineering Advances[]2024-07-16T00:00:00 13613a3934046319957230af43fbd7a6ed71f550With the rapid advancement of Artificial Intelligence (AI), the emergence of various AI models such as Stable Diffusion, ChatGPT, and MidJourney has brought numerous benefits and opportunities. Through users' extensive utilization, they have discovered biases towards gender, race, and other factors in these AI systems. This paper focuses on bias in AI and aims to investigate its causes and propose strategies for mitigation. Through a comprehensive literature review, the paper has explored the phenomenon of bias in AI-generated content. Furthermore, we examine the reasons behind bias and solutions from social and intelligence science perspectives. From a social science perspective, we examine the effects of gender bias in AI and highlight the importance of incorporating diversity and gender theory in machine learning. From an intelligence science standpoint, we explore factors like biased datasets, algorithmic fairness, and the role of machine learning randomness in group fairness. Additionally, we discuss the research methodology employed, including the literature search strategy and quantity assessment. The results and discussions confirm the existence of bias in current AI products, particularly in the underrepresentation of women in the AI development field. Finally, we present future perspectives on reducing bias in AI products, including the importance of fair datasets, improved training processes, and increased participation of female engineers and intelligence scientists in the AI field. By addressing bias in AI, the paper can strive for more equitable and responsible AI systems that benefit diverse users and promote social progress.Applied and Computational Engineering00The results and discussions confirm the existence of bias in current AI products, particularly in the underrepresentation of women in the AI development field, and proposes strategies for mitigation.Applied and Computational Engineering["Yuhan Liu"]2024-07-16T00:00:00 b9324b177a87d3e26d26d0e94ddeabc97849dab2Learning the Quran is an integral part of religious education. However, challenges in efficiency often arise in the learning process. The integration of artificial intelligence (AI) is expected to accelerate the learning and understanding of the Quran. On the other hand, it is important to recognize that excessive dependence on AI may potentially hinder human cognitive development. Therefore, a wise and cautious approach is necessary when utilizing AI. This research, through library research, aims to provide insights into ways to enhance the efficiency of Quranic learning by integrating AI into the learning process. The study considers the ethical and practical implications of using AI technology in religious education and evaluates its impact on the efficiency and effectiveness of Quranic learning. Additionally, this research identifies various opportunities and challenges in the utilization of AI. The findings indicate that integrating AI into Quranic learning not only improves the efficiency and effectiveness of the learning process but also opens up new opportunities. The results of this research are expected to encourage broader adoption of AI technology within the educational system, particularly in the context of religious education. By strengthening the understanding of how best to utilize AI technology in Quranic learning, it is hoped that more effective and holistic approaches can be developed. These approaches will combine the advantages of technology with the spiritual needs and meaningfulness inherent in learning the Quran.Dinamika Penelitian: Media Komunikasi Penelitian Sosial Keagamaan00The findings indicate that integrating AI into Quranic learning not only improves the efficiency and effectiveness of the learning process, but also opens up new opportunities.Dinamika Penelitian: Media Komunikasi Penelitian Sosial Keagamaan["Anistya Sukmawati"]2024-07-16T00:00:00 f005d58683a82cb04bae693bbf0e5f7474b8e2a2Artificial intelligence has transformed e-learning by enabling personalized and efficient teaching. This manuscript analyzes the importance of statistics and probability in educational AI. Statistical methodologies improve decision-making, personalize learning, and optimize educational outcomes. Challenges such as data privacy and ethics are addressed. Case studies demonstrate the practical applications of AI in diverse educational contexts. Future directions suggest a need for robust research to further understand and implement AI-driven educational strategies. The findings underscore the critical role of data-driven approaches in shaping the future of education. Statistics and probability are not only foundational to the development of AI but also essential for analyzing vast amounts of educational data. They allow for the creation of predictive models that can identify student needs and adapt instructional methods accordingly. This adaptability enhances the learning experience by providing targeted support and resources to students, thereby improving their academic performance. Ethical considerations are fundamental when using AI to handle educational data. Protecting student data with privacy and security is crucial to maintaining trust in AI applications. This manuscript examines how educators and policymakers can collaborate to create guidelines that safeguard student information while utilizing data to enhance education. Integrating statistics and probability into educational AI significantly impacts and improves e-learning. Educators can enhance learning by employing data-driven strategies that provide personalized and effective teaching. This approach not only benefits individual learners but also contributes to the overall advancement of educational practices. Embracing these data-driven methodologies is essential for the continued evolution of teaching and learning in the digital age.Advances and Applications in Statistics and Probability00This manuscript examines how educators and policymakers can collaborate to create guidelines that safeguard student information while utilizing data to enhance education and highlights the critical role of data-driven approaches in shaping the future of education.Advances and Applications in Statistics and Probability["Rosales Adriana Rodr\u00edguez"]2024-07-16T00:00:00 e1ef325342ddaffa6b0bf51324635da299aaafefThe modern era has ushered the proliferation of new technologies, especially witnessed in the emergence of the nascent artificial intelligence (AI) sector. The use of AI is largely multifaceted, proving useful in various industries such as healthcare - however, it may also allow for deleterious effects to occur. The use of AI in healthcare settings can work to extend and augment the quality of patients’ lives. Notwithstanding this, health AI enshrines various perils including the lack of patient privacy, algorithm bias - particularly on marginalized and racialized communities. This is ultimately compounded by the absence of ethical framework governing the usage of AI in healthcare settings. Specifically, this article seeks to explore whether or not the use of health AI is a potential prospect or peril; considering its duality. To investigate this topic, this article will utilize an interdisciplinary approach – drawing from domains such as: sociology, socio-legal and socio-medical climates. Secondary data will be primarily sourced via peer-reviewed journal articles, textbooks, and reliable contemporary websites. This study finds that health AI remains a greater prospect - reinforcing the quality and elongates the duration of the human lifespan. It concludes with a call to action to inform the success of health AI in praxis: namely, the need to incorporate the aforementioned topics within medical pedagogy and ethical frameworks.Canadian Journal for the Academic Mind00This study finds that health AI remains a greater prospect - reinforcing the quality and elongates the duration of the human lifespan - and concludes with a call to action to inform the success of health AI in praxis: namely, the need to incorporate the aforementioned topics within medical pedagogy and ethical frameworks.Canadian Journal for the Academic Mind["Thalia Bueno"]2024-07-16T00:00:00 870e822814ff558f72f75e4aa917bb581dafe793Artificial intelligence, also known as AI, has led the trend of evolution in the past and future decades, and the potential of consciousness artificial intelligence emerges as a renovative field to address. The computer machine aims to process repetitive and tedious tasks for humans since its concept was first developed. Whether AI is conscious does not raise unprecedented discussion before the appearance of the concept of machine learning. After it appears, the machine, instead of merely passing in input and generating output, is able to learn while processing the information, which resembles a human's learning process. Therefore, this paper delves into the complex terrain of AI to explore the theoretical possibility of endowing machines with consciousness and addresses the future concerns and potentials of AI. Illustrating through the aspects of ethical concerns, metaphysical perspectives on consciousness, and the latest advancements in AI, the study critically examines whether machines can possess a consciousness similar to human understanding.Applied and Computational Engineering00The study critically examines whether machines can possess a consciousness similar to human understanding and addresses the future concerns and potentials of AI.Applied and Computational Engineering["Nian Lyu"]2024-07-16T00:00:00 23e6f29be40b2fe245b8d2ce0513d27dd98ed119This service aims to increase students' knowledge and skills in writing descriptive paragraphs by using AI in the learning process. The implementation of the service involved 30 students in English lessons by combining several methods, namely lecture, demonstration, simulation, and discussion methods including preparation, implementation, and evaluation stages. The descriptive paragraph writing results of the students before and after the training on the use of Quillbot were evaluated, and with the use of Quillbot, their writing showed improvement in expressing ideas and developing paragraphs logically and systematically, quality in grammatical aspects, and more structured. Questionnaires filled out by participants during the implementation of the activity showed that many students had not utilized Artificial Intelligence so far in any case, especially related to learning activities. Through this training, students have the ability and knowledge from experience in using AI in writing descriptive paragraphs.TAAWUN00Through this training, students have the ability and knowledge from experience in using AI in writing descriptive paragraphs and with the use of Quillbot, their writing showed improvement in expressing ideas and developing paragraphs logically and systematically.TAAWUN["N. Susanti", "Cholimatus Zuhro", "Agus Setia Budi", "Alfi Hidayatu Miqawati", "F. Wijayanti"]2024-07-16T00:00:00 61ab8580c8886b4818bb00a2832afaf566630804In recent years, Artificial Intelligence (AI) has rapidly evolved due to significant improvements in computing performance, increased utilization of large datasets, and algorithm advancements, leading to widespread societal changes. These developments promise innovative applications of AI across various fields but highlight the necessity of ethical use and deep understanding of AI, underscoring the importance of AI literacy. While current research on AI literacy primarily focuses on secondary and higher education, the need for education that impacts cognitive and social development at the elementary level is increasingly emphasized. Furthermore, understanding the factors influencing AI literacy is crucial for educators and policymakers in designing and implementing effective AI education programs. This study investigated how gender, grade level, experiences related to AI, interest in AI, and programming language experience affect AI literacy among elementary students, revealing that these factors significantly impact AI literacy levels. Male students showed higher AI literacy than female students, and AI literacy improved with higher grade levels. Direct and indirect experiences related to AI positively influenced literacy improvement, and high interest in AI and experience with programming languages played essential roles. These findings provide evidence for developing effective AI education strategies for elementary students, emphasizing the importance of educational programs that meet students' diverse backgrounds and needs. These factors in AI education can enhance students' literacy levels and contribute to nurturing talents equipped with the necessary technical, ethical, and problem-solving skills for future society.International Journal on Advanced Science, Engineering and Information Technology00This study investigated how gender, grade level, experiences related to AI, interest in AI, and programming language experience affect AI literacy among elementary students, revealing that these factors significantly impact AI literacy levels.International Journal on Advanced Science, Engineering and Information Technology["Hyunwoo Moon", "Hakneung Go", "Youngjun Lee", "Seong-Won Kim"]2024-07-16T00:00:00 fa15345af265d73c81fc59e22a3a3389aa777962Artificial Intelligence (AI) technologies have been widely applied to tackle Coronavirus Disease 2019 (COVID-19) challenges, from diagnosis to prevention. Patents are a valuable source for understanding the AI technologies used in the COVID-19 context, allowing the identification of the current technological scenario, fields of application, and research, development, and innovation trends. This study aimed to analyze the global patent landscape of AI applications related to COVID-19. To do so, we analyzed AI-related COVID-19 patent metadata collected in the Derwent Innovations Index using systematic review, bibliometrics, and network analysis., Our results show diagnosis as the most frequent application field, followed by prevention. Deep Learning algorithms, such as Convolutional Neural Network (CNN), were predominantly used for diagnosis, while Machine Learning algorithms, such as Support Vector Machine (SVM), were mainly used for prevention. The most frequent International Patent Classification Codes were related to computing arrangements based on specific computational models, information, and communication technology for detecting, monitoring, or modeling epidemics or pandemics, and methods or arrangements for pattern recognition using electronic means. The most central algorithms of the two-mode network were CNN, SVM, and Random Forest (RF), while the most central application fields were diagnosis, prevention, and forecast. The most significant connection between algorithms and application fields occurred between CNN and diagnosis. Our findings contribute to a better understanding of the technological landscape involving AI and COVID-19, and we hope they can inform future research and development’s decision making and planning.Machine Learning and Knowledge Extraction460The results show diagnosis as the most frequent application field, followed by prevention, and the most central algorithms of the two-mode network were CNN, SVM, and Random Forest, while the most central application fields were diagnosis, prevention, and forecast.Mach. Learn. Knowl. Extr.["Fabio Mota", "L. Braga", "B. Cabral", "Natiele Carla da Silva Ferreira", "Cl\u00e1udio Damasceno Pinto", "Jos\u00e9 Aguiar Coelho", "L. Alves"]2024-07-16T00:00:00 ea6ed86c88e8fb5af464c9e6c71e322dfcba2138Engagement has been defined as an attitude toward work, as a positive, satisfying, work‐related state of mind characterized by high levels of vigour, dedication, and absorption. Both its definition and its assessment have been controversial; however, new methods for its assessment, including artificial intelligence (AI), have been introduced in recent years. Therefore, this research aims to determine the state of the art of AI in the study of engagement. To this end, we conducted a systematic review in accordance with PRISMA to analyse the publications to date on the use of AI for the analysis of engagement. The search, carried out in six databases, was filtered, and 15 papers were finally analysed. The results show that AI has been used mainly to assess and predict engagement levels, as well as to understand the relationships between engagement and other variables. The most commonly used AI techniques are machine learning (ML) and natural language processing (NLP), and all publications use structured and unstructured data, mainly from self‐report instruments, social networks, and datasets. The accuracy of the models varies from 22% to 87%, and its main benefit has been to help both managers and HR staff understand employee engagement, although it has also contributed to research. Most of the articles have been published since 2015, and the geography has been global, with publications predominantly in India and the US. In conclusion, this study highlights the state of the art in AI for the study of engagement and concludes that the number of publications is increasing, indicating that this is possibly a new field or area of research in which important advances can be made in the study of engagement through new and novel techniques.Expert Syst. J. Knowl. Eng.840This study concludes that the number of publications on the use of AI for the analysis of engagement is increasing, indicating that this is possibly a new field or area of research in which important advances can be made in the study of engagement through new and novel techniques.Expert Syst. J. Knowl. Eng.["Claudia Garc\u00eda\u2010Navarro", "M. Pulido-Martos", "Cristina P\u00e9rez\u2010Lozano"]2024-07-16T00:00:00 b8abd8d1cbde334bddfafeb67becb949f67e581dBackground Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patient ratio of 1:834 in India, AI has the potential to alleviate a significant health care burden. Public perception plays a crucial role in shaping attitudes that can facilitate the adoption of new technologies. Similarly, the acceptance of AI-driven mental health interventions is crucial in determining their effectiveness and widespread adoption. Therefore, it is essential to study public perceptions and usage of existing AI-driven mental health interventions by exploring user experiences and opinions on their future applicability, particularly in comparison to traditional, human-based interventions. Objective This study aims to explore the use, perception, and acceptance of AI-driven mental health interventions in comparison to traditional, human-based interventions. Methods A total of 466 adult participants from India voluntarily completed a 30-item web-based survey on the use and perception of AI-based mental health interventions between November and December 2023. Results Of the 466 respondents, only 163 (35%) had ever consulted a mental health professional. Additionally, 305 (65.5%) reported very low knowledge of AI-driven interventions. In terms of trust, 247 (53%) expressed a moderate level of Trust in AI-Driven Mental Health Interventions, while only 24 (5.2%) reported a high level of trust. By contrast, 114 (24.5%) reported high trust and 309 (66.3%) reported moderate Trust in Human-Based Mental Health Interventions; 242 (51.9%) participants reported a high level of stigma associated with using human-based interventions, compared with only 50 (10.7%) who expressed concerns about stigma related to AI-driven interventions. Additionally, 162 (34.8%) expressed a positive outlook toward the future use and social acceptance of AI-based interventions. The majority of respondents indicated that AI could be a useful option for providing general mental health tips and conducting initial assessments. The key benefits of AI highlighted by participants were accessibility, cost-effectiveness, 24/7 availability, and reduced stigma. Major concerns included data privacy, security, the lack of human touch, and the potential for misdiagnosis. Conclusions There is a general lack of awareness about AI-driven mental health interventions. However, AI shows potential as a viable option for prevention, primary assessment, and ongoing mental health maintenance. Currently, people tend to trust traditional mental health practices more. Stigma remains a significant barrier to accessing traditional mental health services. Currently, the human touch remains an indispensable aspect of human-based mental health care, one that AI cannot replace. However, integrating AI with human mental health professionals is seen as a compelling model. AI is positively perceived in terms of accessibility, availability, and destigmatization. Knowledge and perceived trustworthiness are key factors influencing the acceptance and effectiveness of AI-driven mental health interventions.JMIR Formative Research570The key benefits of AI highlighted by participants were accessibility, cost-effectiveness, 24/7 availability, and reduced stigma, and major concerns included data privacy, security, the lack of human touch, and the potential for misdiagnosis.JMIR Formative Research["Mahima Anna Varghese", "Poonam Sharma", "Maitreyee Patwardhan"]2024-07-16T00:00:00 e9da5272909d027dc56dba03d5e5ac6880382247Numerous fairness metrics have been proposed and employed by artificial intelligence (AI) experts to quantitatively measure bias and define fairness in AI models. Recognizing the need to accommodate stakeholders' diverse fairness understandings, efforts are underway to solicit their input. However, conveying AI fairness metrics to stakeholders without AI expertise, capturing their personal preferences, and seeking a collective consensus remain challenging and underexplored. To bridge this gap, we propose a new framework, EARN Fairness, which facilitates collective metric decisions among stakeholders without requiring AI expertise. The framework features an adaptable interactive system and a stakeholder-centered EARN Fairness process to Explain fairness metrics, Ask stakeholders' personal metric preferences, Review metrics collectively, and Negotiate a consensus on metric selection. To gather empirical results, we applied the framework to a credit rating scenario and conducted a user study involving 18 decision subjects without AI knowledge. We identify their personal metric preferences and their acceptable level of unfairness in individual sessions. Subsequently, we uncovered how they reached metric consensus in team sessions. Our work shows that the EARN Fairness framework enables stakeholders to express personal preferences and reach consensus, providing practical guidance for implementing human-centered AI fairness in high-risk contexts. Through this approach, we aim to harmonize fairness expectations of diverse stakeholders, fostering more equitable and inclusive AI fairness.arXiv.org730The work shows that the EARN Fairness framework enables stakeholders to express personal preferences and reach consensus, providing practical guidance for implementing human-centered AI fairness in high-risk contexts.ArXiv["Lin Luo", "Yuri Nakao", "Mathieu Chollet", "Hiroya Inakoshi", "Simone Stumpf"]2024-07-16T00:00:00 c11ca0098876a8365709fc8c901bd2ed6129d2e9Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human and, in specific repetitive tasks, outperform a human's capability. The 2 critical underlying technologies in AI are used for supervised and unsupervised machine learning. Machine learning uses neural networks and deep learning modeled after the human brain from structured or unstructured data sets to learn, make decisions, and continuously improve the model. Natural language processing, used for supervised learning, is understanding, interpreting, and generating information using human language in chatbots and generative and conversational AI. These breakthroughs result from increased computing power and access to large data sets, setting the stage for releasing large language models, such as ChatGPT and others, and new imaging models using computer vision. Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals. AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.HYPERTENSION00Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals, and AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.Hypertension["Patrick Dunn", "Asif Ali", "Akash P Patel", "Srikanta Banerjee"]2024-07-16T00:00:00 aa087fde028248ac5ef0162c4be4d112f0bb196cThe article deals with ethical problems in the process of joint decision-making arising in connection with the active involvement of artificial intelligence systems in medical practice. Special attention is paid to the influence of artificial intelligence systems on the principle of respect for the patient’s autonomy, an ethical assessment of the main criteria of the patient’s autonomous action — voluntariness, awareness and competence is given. It is noted that the declared ethical values in the implementation of artificial intelligence in the field of medicine at this stage of its development cannot fully provide the standard of joint informed decision-making. Along with the extensive capabilities of artificial intelligence systems to increase the patient’s competence and responsibility for their health, ethically ambiguous issues remain related to the awareness and voluntary nature of the patient. Technological features of artificial intelligence create obstacles to the formation of trust between the doctor and the patient: they complicate the process of informing the patient, prevent the patient from voluntarily choosing the preferred treatment algorithm, which may make it difficult to comply with the principle of respect for patient autonomy. The study of trigger points in the mechanism of ethical management of artificial intelligence in the healthcare system is a promising task for the creation of reliable medical artificial intelligence.Человек100Chelovek["Julia Y. Kochetova"]2024-07-16T00:00:00 e6cfe8497388430603a08f6ec93bd86a6b7168c7The incorporation of artificial intelligence (AI) is accelerating in the dental field and even patients are catching on to the trend. There is a form of perceived pressure mounting on practitioners to incorporate modern dental equipment and online services to accelerate treatment time or supplement the diagnosis with visual treatment planning. Many of these applications utilise AI as part of the software to process the inserted data. The use of these products in practice presents various ethical dilemmas the clinician would need to mitigate. Practitioners who own or are considering adding applications and equipment that are AI-based to their treatment repertoire have an ethical and legal responsibility to ensure that the best interest and safety of the patient are observed. Patient autonomy and protection of all information become a paramount consideration over and above improving profit or personal gain. By no means could the ethical dilemmas in this communication be exhausted, as the rapid AI innovation and the dynamic nature of technological advances have the potential to raise even more debate. As a fraternity, we need to be vigilant and remain grounded with the basic ethical principles underpinned by autonomy, patient confidentiality/ privacy and the practitioner-patient relationship.South African Dental Journal00The incorporation of artificial intelligence (AI) is accelerating in the dental field and even patients are catching on to the trend, so practitioners who own or are considering adding applications and equipment that are AI-based to their treatment repertoire have an ethical and legal responsibility to ensure that the best interest and safety of the patient are observed.South African Dental Journal["Ronel D Maart", "R. Mulder"]2024-07-16T00:00:00 f59248f6ad483c4d088550dc08da7e705f4598baThe integration of Artificial Intelligence (AI) into macroeconomic models marks a significant evolution in the field of economic forecasting and analysis. Traditional macroeconomic models, often constrained by linear assumptions and a limited set of variables, struggle to accurately capture the complexities of the global economy. This paper explores how AI, particularly machine learning algorithms like Random Forests and Neural Networks, enhances these models by processing and interpreting vast and diverse datasets, including unstructured data such as social media sentiment and news analysis. AI's capability to adapt and learn from new data enables dynamic models that remain relevant and accurate amidst changing economic conditions. By addressing non-linearities and enhancing model robustness, AI provides a more nuanced understanding of economic dynamics, uncovering intricate patterns missed by traditional analyses. The implications of AI-enhanced macroeconomic models are profound, offering more reliable foundations for economic research and policy-making. This paper argues that the integration of AI into economic modeling not only improves the precision of economic forecasts but also enriches the field of economic research and the formulation of more effective policy interventions. Through examples such as inflation rate forecasting and the identification of complex, non-linear economic relationships, this study highlights the transformative potential of AI in macroeconomic analysis and policy formulation.Applied and Computational Engineering90It is argued that the integration of AI into economic modeling not only improves the precision of economic forecasts but also enriches the field of economic research and the formulation of more effective policy interventions.Applied and Computational Engineering["Zetian Chen"]2024-07-16T00:00:00 9dc84813b4f96d2c13ac38a4c8f5a01950da17fcTeknologi membawa dampak yang signifikan bagi manusia. Penelitian ini bertujuan untuk menganalisis dan mendeskripsikan implementasi artificial intelligence dalam sebuah perspektif pendidikan. Pendekatan dalam penelitian ini menggunakan studi kepustakaan, dengan teknik pengumpulan data dokumentasi, dan teknik analisis data yakni reduksi data, penyajian data dan penarikan kesimpulan. Hasil penelitian ini ditemukan bahwa pengimplementasian AI dalam pendidikan menjadi penting sebagai bekal bagi siswa untuk mengenal dan memanfaatkan teknologi dalam kehidupan mereka. Penggunaan teknologi AI ini diharapkan mampu memberikan kontribusi yang signifikan dalam meningkatkan kualitas pendidikan serta membantu siswa mencapai potensi belajar yang lebih tinggi. Implementasi teknologi AI di bidang pendidikan mempermudah para pendidik dalam berbagai urusan terutama dalam bidang administratif seperti menentukan nilai akhir berdasarkan bobot dan penilaian, menciptakan pembelajaran yang lebih aktif, serta mempermudah tugas guru dan siswa dalam kegiatan belajar dan mengajar. Dalam konteks penerapan, teknologi AI dapat berperan sebagai Sistem Tutor, Intelligent Tutee, alat/media pembelajaran, dan panduan dalam membuat kebijakan pendidikan.  AI memudahkan siswa dan mahasiswa dalam menunjang studinya secara visibilitas dan komprehensif namun secanggihnya Artificial Intellegence pun juga belum tentu menjadikan kebiasaan baik dalam beretika dalam berteknologi, sehingga tetap perlu adanya pendidikan dan pengajaran secara langsung dalam membimbing dan mengarahkan anak didiknya.EDUKATIF : JURNAL ILMU PENDIDIKAN200EDUKATIF : JURNAL ILMU PENDIDIKAN["Getar Rahmi Pertiwi", "M. Jailani", "As\u2019ad Isma"]2024-07-16T00:00:00 8d8431089acafc388630b1677e1d6d3f03226117Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention. AutoML automates the development and optimization of machine learning models, leading to high energy consumption due to the large amount of calculations involved. Hyperparameter optimization algorithms, central to AutoML, can significantly impact its carbon footprint. This work introduces and investigates energy efficiency metrics for advanced hyperparameter optimization algorithms within AutoML. These metrics enable the evaluation and optimization of an algorithm’s energy consumption, considering accuracy, sustainability, and reduced environmental impact. The experimentation demonstrates the application of Green AI principles to AutoML hyperparameter optimization algorithms. It assesses the current sustainability of AutoML practices and proposes strategies to make them more environmentally friendly. The findings indicate a reduction of 28.7% in CO2e emissions when implementing the Green AI strategy, compared to the Red AI strategy. This improvement in sustainability is achieved with a minimal decrease of 0.51% in validation accuracy. This study emphasizes the importance of continuing to investigate sustainability throughout the life cycle of AI, aligning with the three fundamental pillars of sustainable development.Applied Sciences05Energy efficiency metrics for advanced hyperparameter optimization algorithms within AutoML are introduced and investigated, enabling the evaluation and optimization of an algorithm’s energy consumption, considering accuracy, sustainability, and reduced environmental impact.Applied Sciences["Dagoberto Castellanos-Nieves", "Luis Garc\u00eda-Forte"]2024-07-16T00:00:00 9d50377c11db95a5b1a3b23eaf9563860ec25e83EDPACS: The EDP Audit, Control, and Security Newsletter384EDPACS["Mehdi Bouchetara", "Messaoud Zerouti", "Ana\u00efs Radja Zouambi"]2024-07-16T00:00:00 6f7febeb094ecf69d355709e666fb0cf6e14d1b1AIM To give an insight into areas for future development and suggestions in the complexities of incorporation of AI into human colorectal cancer (CRC) care while bringing into focus the importance of clinicians' roles in patient care. METHODS Existing literature around AI use in CRC care is reviewed and potential regulatory issues and medicolegal implications around its implementation in CRC multidisciplinary team meetings (MDTs) are identified. RESULTS Challenges with patient privacy and confidentiality, patient consent, inequity and bias, patient autonomy, as well as AI system transparency and the liability and accountability issues arising from complications that arise from AI-aided clinical decisions are important focusses associated with the use of AI in CRC MDTs. CONCLUSION Consideration of various medicolegal aspects of the use of AI in CRC MDTs is warranted to ensure its safe and smooth incorporation into CRC MDTs. AI function as a clinical decision support system and does not replace professional expertise.Colorectal Disease154Consideration of various medicolegal aspects of the use of AI in CRC MDTs is warranted to ensure its safe and smooth incorporation into CRC MDTs.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland["Yovita Tjhin", "Bharti Kewlani", "H. Singh", "Nikhil Pawa"]2024-07-16T00:00:00 430a94939cee760f82cbe40b3970d41b4edadabdEmergency Medicine Australasia143Emergency medicine Australasia : EMA["Jonathon Stewart", "Michael Innes", "A. Goudie"]2024-07-16T00:00:00 ce4af8d3d2a2543b8a7d72cea1ca56068d4f1724Practice151PRACTICE["Ryan Thomas Williams", "Ewan Ingleby"]2024-07-16T00:00:00 057b3edfc105f278b0f4c04092c5d9b31aada0ccEmergency Medicine Australasia61Emergency medicine Australasia : EMA["Adelene Hilbig"]2024-07-16T00:00:00 2a8044994798b864d8cb4a369e473ce747684fa2Multiple Sclerosis and Related Disorders981Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression, and the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance.Multiple sclerosis and related disorders["Shima Pilehvari", "Yasser Morgan", "Wei Peng"]2024-07-16T00:00:00 4b5d18d0a655c414ed5af7a1a0e00221b645fd4aApplied Nanoscience322Applied Nanoscience["M. Yuvaraju", "D. Deena"]2024-07-16T00:00:00 b101ebe6bd5599afa1037bfe8d9a3bbd3bc350a9Biomedical Journal of Scientific &amp; Technical Research01Biomedical Journal of Scientific &amp; Technical Research["Robert McGee"]2024-07-16T00:00:00 56d3316766b1ab445f5e8e7cf698657180b3997fEmergency Medicine Australasia52Emergency medicine Australasia : EMA["Ryan D Metcalfe"]2024-07-16T00:00:00 6217831a910bc9c8993a4266c9fa3ff9dd7ede45American Journal of Health-System Pharmacy00American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists["Kate Traynor"]2024-07-16T00:00:00 e9002c3fa73cb548ab9dec70050cefc936c179fcProceedings of the International Conference on Industrial Engineering and Operations Management00Proceedings of the International Conference on Industrial Engineering and Operations Management["B. Ndlovu", "Sibusisiwe Dube", "Kudakwashe Maguraushe", "Sinobukezela Princess Dube"]2024-07-16T00:00:00 a49caf7243b8b751b0705b9591e2f329188ce34dEmergency Medicine Australasia230Emergency medicine Australasia : EMA["James M Colalillo", "Joshua I Smith"]2024-07-16T00:00:00 fdb5b8e0a7ea0d8243cf43f597ed8ceb35d2a42aProceedings of the International Conference on Industrial Engineering and Operations Management00Proceedings of the International Conference on Industrial Engineering and Operations Management["Rukudzo Ndlovu", "Kghanya Ndlovu", "Samson Chivunga", "Lawrence Mkwebu", "B. Ndlovu", "Sibusisiwe Dube"]2024-07-16T00:00:00 46ce9e531c078ee3817b3013704a07ca26a1a548In recent decades, with the fast development of various algorithms and the rapid improvement of computer hardware performance, the development of facial recognition technology has been very rapid. This technology used to only appear in science fiction movies, but now it has been widely used in mobile payments, identity recognition, and other fields, and it also has very reliable performance. This article introduces the different facial recognition methods used in different periods of facial recognition technology from the 1960s and 1970s to the present day, to help people understand the development process of facial recognition technology. Among them, the focus is on early methods based on geometric features, grayscale information, and feature extraction, as well as recent applications based on deep learning and convolutional neural networks. In response to some challenges faced by current technology, some speculations on possible future development directions are proposed.Applied and Computational Engineering00The different facial recognition methods used in different periods of facial recognition technology from the 1960s and 1970s to the present day are introduced to help people understand the development process of facial recognition technology.Applied and Computational Engineering["Canbin Zhou"]2024-07-16T00:00:00 e4baf301c92325ba68ac29fce06f2838bf36fa96International Journal of Research Publications00International Journal of Research Publications["Manuel Aguilar Yuste", "Eric Corrales Rojas"]2024-07-16T00:00:00 d6cfd2286d1faf07f7c3713a56f9005f57e78143Proceedings of the International Conference on Industrial Engineering and Operations Management00Proceedings of the International Conference on Industrial Engineering and Operations Management["Mahdi Bastan", "Negin Hassani", "Behnaz Salimi", "Ali Ghazizadeh", "Mahdi Hamid"]2024-07-16T00:00:00 03ab78f213376fae417f7289bdb1a1c312be276eSince ChatGPT's release, large AI models have gained significant attention, particularly in the integration with Smart Home 3.0, marking a new research direction. This paper reviews cutting-edge research on the intersection of smart homes and large AI models, highlighting challenges and trends. We focus on data, models, and execution to explore the advancement of smart home platforms globally. We discuss data collection and feature research progress within smart homes, using intelligent voice assistants like Amazon Alexa and Google Bard as examples. We examine the applications, potentials, and challenges of AI models in smart homes and offer insights into future applications of large AI models in this field.Applied and Computational Engineering00This paper reviews cutting-edge research on the intersection of smart homes and large AI models, highlighting challenges and trends and offers insights into future applications of large AI models in this field.Applied and Computational Engineering["Yufeng Chen", "Yuheng Ren"]2024-07-16T00:00:00 5af22f53d7f87d2d063535859d641442567c2ee1Journal of Advanced Lung Health90Journal of Advanced Lung Health["Muhammed Aslam"]2024-07-16T00:00:00 079fa2c62dd28663610f1ca0bf22c674934ae33fCirculation20Circulation["Demilade A. Adedinsewo"]2024-07-16T00:00:00 c1976c4b31998b2ec29c4fe8e627b41296b06e5eThe close collaboration between Psychology and Law has contributed to the enrichment of the legislative technique. The application of psychological behavioural analysis to law breaks the current paradigm and offers a unique perspective for lawmaking through the study of people's behaviour in the face of laws. Legal experimentation, an innovative practice, emerges as a crucial tool for law-making and regulatory impact assessment in novel areas whose subject matter may be potentially burdensome to individual rights and freedoms. In the context of the Spanish artificial intelligence sandbox, the application of psychology to law is of particular importance, as it contributes to the emerging and much-needed legal regulation. This cooperation between doctrines promises not only to boost normative evolution, but also to ensure a more effective and protective regulation in a world increasingly influenced by technology.Revista Estudios Jurídicos Segunda Época190Revista Estudios Jurídicos. Segunda Época["Mar\u00eda Trinidad Pl\u00e1 Herrero"]2024-07-16T00:00:00 34e059600404eb6fb4ecfe03b99917fe5d19d1b0The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.JMIR Bioinformatics and Biotechnology785This review critically examines the ethical implications of using large language models like GPT-3 and GPT-4 (OpenAI) in oncology chatbots, and highlights the ethical challenges LLMs present and the need for mitigation strategies.JMIR Bioinformatics and Biotechnology["James C L Chow", "Kay Li"]2024-07-16T00:00:00 d01ef93b69d8098eef2e874b411430c82ee6294dThe rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates, 568 records were screened, with 196 full-text articles assessed for eligibility. Ultimately, 135 articles were included in the final synthesis. The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media. Big data analytics tools like Hadoop and Spark facilitate real-time monitoring and early identification of potential IP threats, providing a comprehensive view of the supply chain landscape. Several successful case studies across different industries, including pharmaceuticals, electronics, and fashion, demonstrate the practical applications of these technologies in addressing IP risks. However, the review also highlights several challenges, including data quality, scalability, model interpretability, data privacy, and integration with legacy systems. Despite these challenges, the benefits of AI and big data analytics in IP risk management are substantial, enabling organizations to protect their intellectual assets more effectively. The study underscores the need for future research to address these challenges and explore innovative solutions to maximize the potential of AI and big data analytics in IP risk management. By investing in the necessary infrastructure and expertise, organizations can enhance their resilience and maintain a competitive edge in the global market.Global Mainstream Journal325The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media.Global Mainstream Journal["Md Abdur Rauf", "Md Majadul Islam Jim", "Md Mahfuzur Rahman", "Md Tariquzzaman"]2024-07-16T00:00:00 78899aff3194b8e5f438982acd5e5045b3bfa5acIn recent times, there has been a surge of interest in the transformative potential of artificial intelligence (AI), particularly within the realm of online advertising. This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications. The aim is to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research. Employing a systematic and comprehensive approach, the study analyzes academic publications, industry reports, and case studies to extract theoretical frameworks, empirical findings, and practical insights. The findings underscore a significant transformation catalyzed by AI integration into Customer Relationship Management (CRM). AI enables personalized interactions, fortifies customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four central themes emerge: personalized service, enhanced engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article contributes significantly to the discourse on AI in CRM by synthesizing the current state, exploring key themes, and suggesting research avenues. It advocates for responsible AI implementation, emphasizing ethical considerations and guiding organizations in navigating opportunities and challenges.Journal of Infrastructure Policy and Development333This research focuses on the critical examination of AI’s role in enhancing customer experience (CX) across diverse business applications to identify key themes, assess the impact of AI-powered CX initiatives, and highlight directions for future research.Journal of Infrastructure, Policy and Development["Minh Tung Tran"]2024-07-16T00:00:00 71e01c5cdbb5e9ea00c956d0ac7f1186fe986337With the proliferation of AI technology, machine learning has emerged as a cornerstone of AI systems, facilitating pattern recognition and decision-making through robust data analysis. This encompasses various learning paradigms such as supervised, unsupervised, and reinforcement learning, all of which are indispensable for the advancement of artificial intelligence. Nevertheless, the development of AI necessitates substantial computational resources, with specialized chips serving as the linchpin, particularly in demanding tasks such as deep learning. Dedicated chip development, exemplified by GPUs and TPUs, plays a pivotal role in enhancing the performance of AI systems, notwithstanding challenges related to costs and market monopolies. Moreover, AI systems require significant power support, especially during the training of large-scale models. To address these challenges, this paper reviews the existing literature on modeling techniques aimed at enhancing the efficiency of machine learning and reducing energy consumption. This review encompasses optimal algorithm design, hardware optimization, and spatial modeling. Through the implementation of these approaches, the challenges posed by resource constraints in machine learning scenarios can be effectively mitigated, thereby fostering the continued development and application of AI technology.Applied and Computational Engineering01Through the implementation of these approaches, the challenges posed by resource constraints in machine learning scenarios can be effectively mitigated, thereby fostering the continued development and application of AI technology.Applied and Computational Engineering["Wenwen Hou"]2024-07-16T00:00:00 ee1014b9c0ba88ec764547fa7d7bd0ec849f1bb2Students' physical and digital lives are increasingly entangled. It is difficult to separate students' digital well‐being from their offline well‐being given that artificial intelligence increasingly shapes both. Within the context of education's fiduciary and moral duty to ensure safe, appropriate and effective digital learning spaces for students, the continuing merger between artificial intelligence and learning analytics not only opens up many opportunities for more responsive teaching and learning but also raises concerns, specifically for previously disadvantaged and vulnerable students. While digital well‐being is a well‐established research focus, it is not clear how AI‐Powered Educational Decision Support Systems (AI‐EDSS) might impact on the inherent, situational and pathogenic vulnerability of students. In this conceptual paper, we map the digital well‐being of previously disadvantaged and vulnerable students in four overlapping fields, namely (1) digital well‐being research; (2) digital well‐being research in education; (3) digital well‐being research in learning analytics; and (4) digital well‐being in AI‐informed educational contexts. With this as the basis, we engage with six domains from the IEEE standard 7010–2020—IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well‐Being and provide pointers for safeguarding and enhancing disadvantaged and vulnerable student digital well‐being in AI‐EDSS. What is already known about this topic Digital well‐being research is a well‐established focus referring to the impact of digital engagement on human well‐being. Digital well‐being is effectively inseparable from general well‐being as it is increasingly difficult to disentangle our online and offline lives and, as such, inherently intersectional. Artificial Intelligence shows promise for enhancing human digital well‐being, but there are concerns about issues such as privacy, bias, transparency, fairness and accountability. The notion of ‘vulnerable individuals’ includes individuals who were previously disadvantaged, and those with inherent, situational and/or pathogenic vulnerabilities. While current advances in AI‐EDSS may support identification of digital wellness, proxies for digital wellness should be used with care. What this study contributes An overview of digital well‐being research with specific reference how it may impact on vulnerable students. Illustrates specific vulnerabilities in five domains from the IEEE standard 7010–2020—IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well‐Being selected for their significance in online learning environments. Pointers for the design and implementation of fair, ethical, accountable, and transparent AI‐EDSS with specific reference to vulnerable students. Implications for practice and/or policy Fairness, equity, transparency and accountability in AI‐EDSS affect all students but may have a greater (positive or negative) impact on vulnerable students. A critically informed understanding of the nature of students' vulnerability—whether as inherent, situational and/or pathogenic, as well as temporal/permanent aspects—is crucial. Since AI‐EDSS can exacerbate existing vulnerabilities resulting in pathogenic vulnerability, care is needed when designing AI‐EDSS. British Journal of Educational Technology391This conceptual paper maps the digital well‐being of previously disadvantaged and vulnerable students in four overlapping fields, namely digital well‐being research in education; digital well‐being research in education; digital well‐being research in learning analytics; and digital well‐being in AI‐informed educational contexts.Br. J. Educ. Technol.["P. Prinsloo", "Mohammad Khalil", "Sharon Slade"]2024-07-16T00:00:00 7a99eadfd71b84bee578f6dc993ce3c56139b25cAI &amp; SOCIETY291This paper investigates how AI shapes the negotiation of environmental sustainability as an issue of public interest by examining the deployment of AI in government-led climate action and extends academic literature in science and technology studies that examines public participation in climate change adaptation by shedding light on the emergent phenomenon of public interest AI.AI &amp; SOCIETY["Nicolas Zehner", "Andr\u00e9 Ullrich"]2024-07-16T00:00:00 e775fcbe83fb66e83426835d3353679438a7fac8 Purpose Bothorship – “bot authorship”, or the use of artificial intelligence tools to support writing activities - has transformed publishing in the few years since the emergence of ChatGPT in late 2022. The bane of the publisher’s existence, but a boon for writers, these tools support enhanced writing quality and reduce the amount of time and effort needed to turn research findings into an acceptable manuscript. This paper aims to discuss some of the key aspects of Bothorship as they have emerged in the past two years. Design/methodology/approach This paper explores recent publications and discourse surrounding artificial intelligence (AI) contributions to scholarly publications. Findings While there are substantial downsides to AI use in scholarly communications, there are also tremendous benefits. Bothorship can level the playing field for non-native English speakers having to navigate an arena (scholarly publishing) where English is the lingua franca. Originality/value This paper discusses key issues related to bothorship and AI contributions to publications. It reviews and presents a perspective on the future of AI authorship and copyediting for manuscripts. Library Hi Tech News41This paper reviews and presents a perspective on the future of AI authorship and copyediting for manuscripts, and discusses key issues related to bothorship and AI contributions to publications.Library Hi Tech News["Brady Lund"]2024-07-16T00:00:00 26753b8d5685d020d6682461482071c5b255307dThis review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.Applied Sciences16614This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection, and details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems.Applied Sciences["Ibrahim Alhamrouni", "Nor Hidayah Abdul Kahar", "Mohaned Salem", "Mahmood Swadi", "Younes Zahroui", "D. J. Kadhim", "Faisal A. Mohamed", "Mohammad Alhuyi Nazari"]2024-07-17T00:00:00 bbb9b5e8e48a654c8fb8774b993d64ac0402a105The application of artificial intelligence art therapies (AIATs) in mental health care represents an innovative merger between digital technology and the therapeutic potential of creative arts. This systematic review aimed to assess the effectiveness and ethical considerations of AIATs, incorporating robots, AI painting and AI Chatbots to augment traditional art therapies. Aligning with the Preferred Reporting Items for systematic reviews (PRISMA) guidelines, we meticulously searched PubMed, Cochrane Library, Web of Science and CNKI, resulting in 15 selected articles for detailed analysis. To ensure methodological quality, we applied the Joanna Briggs Institute (JBI) criteria for quality assessment and extracted data using the PICO(S) format, specifically targeting randomised controlled trials (RCTs). Our findings suggest that AIATs can profoundly enhance the therapeutic experience by providing new creative outlets and reinforcing existing methods, despite possible drawbacks and ethical challenges. This examination underscores AIATs' potential to enrich mental health therapies, emphasising the critical importance of ethical considerations and the responsible application of AI as the field evolves. With a focus on expanding treatment efficacy and patient expressiveness, the promise of AIATs in mental health care necessitates a careful balance between innovation and ethical responsibility. Trial Registration: PROSPERO: CRD42024504472.International Journal of Mental Health Nursing561It is suggested that AIATs can profoundly enhance the therapeutic experience by providing new creative outlets and reinforcing existing methods, despite possible drawbacks and ethical challenges, and underscores AIATs' potential to enrich mental health therapies.International journal of mental health nursing["Xuexin Luo", "Aijia Zhang", "Yu Li", "Zheyu Zhang", "Fangtian Ying", "Runqing Lin", "Qianxu Yang", "Jue Wang", "Guanghui Huang"]2024-07-17T00:00:00 69be73482a31c3bfa08f6a62ed877aa90f76b7f4This study examines the association between artificial intelligence innovation (AII) and stock price crash risk (SPCR). AII serves as a governance mechanism that can bolster strength in internal controls, leading to increased financial transparency and thereby reducing the likelihood of future SPCR. The results hold after accounting for possible endogeneity issues Further, we find that monitoring through corporate governance mechanisms, level of following by equity analysts, and the reduced information asymmetry constitute important channels that mediate the association between AII and SPCR. Additionally, the relationship between AII and SPCR varies across corporate life cycle stages and workplace culture.Journal of Financial Research1071AII serves as a governance mechanism that can bolster strength in internal controls, leading to increased financial transparency and thereby reducing the likelihood of future SPCR.Journal of Financial Research["Junru Zhang", "Chen Cui", "Chen Zheng", "Grantley Taylor"]2024-07-17T00:00:00 2a2943cb05470eed31e64f9169f3825c83cb54adThe report of the 20th National Congress of the Communist Party of China proposed to “accelerate the development of the digital economy, promote digital industrialization and industrial digitization, promote the deep integration of the digital economy and the real economy, and create a digital industry cluster with international competitiveness.” In this context, this article analyzes the application of artificial intelligence in decision support, talent management, security control, and other aspects. It proposes that enterprises should establish a data-driven management system, cultivate talents in the field of AI, and build intelligent management systems and other strategies. By introducing advanced AI technology and building a digital enterprise, enterprises can improve operational efficiency, enhance market insight and rapid response capabilities, and achieve high-quality development. It is highly in line with the national strategy of promoting the construction of a digital China and accelerating the development of the digital economy. Enterprises should seize the opportunity of digital transformation, utilize AI empowerment, enhance core competitiveness, and maintain a leading advantage in the era of intelligence.Modern Economics &amp; Management Forum42This article analyzes the application of artificial intelligence in decision support, talent management, security control, and other aspects and proposes that enterprises should establish a data-driven management system, cultivate talents in the field of AI, and build intelligent management systems and other strategies.Modern Economics &amp; Management Forum["Haoyu Wang"]2024-07-17T00:00:00 8777873761c8c263aef178b96be84dc3a852c307BACKGROUND Male infertility is defined as the inability of a male to achieve a pregnancy in a fertile female by the American Urological Association (AUA) and the American Society for Reproductive Medicine (ASRM). Artificial intelligence, particularly in language processing models like ChatGPT4.0, offers new possibilities for supporting clinical decision-making. This study aims to assess the effectiveness of ChatGPT4.0 in responding to clinical queries regarding male infertility, which is aligned with AUA/ASRM guidelines. METHODS This observational study employed a design to evaluate the performance of ChatGPT4.0 across 1073 structured clinical queries categorized into true/false, multiple-choice, and open-ended. Two independent reviewers specializing in reproductive medicine assessed the responses using a six-point Likert scale to evaluate accuracy, relevance, and guideline adherence. RESULTS In the true/false category, the initial accuracy was 92%, which increased to 94% by the end of the study period. For multiple-choice questions, accuracy improved from 85% to 89%. The most significant gains were seen in open-ended questions, where accuracy rose from 78% to 86%. Initially, some responses did not fully align with the AUA/ASRM guidelines. However, by the end of the 60 days, these responses had become more comprehensive and clinically relevant, indicating an improvement in the model's ability to generate guideline-conformant answers (p < 0.05). The depth and accuracy of responses for higher difficulty questions also showed enhancement (p < 0.01). CONCLUSION ChatGPT4.0 can serve as a valuable support tool in managing male infertility, providing reliable, guideline-based information that enhances the accuracy of clinical decision-making tools and supports patient education.Andrology282ChatGPT4.0 can serve as a valuable support tool in managing male infertility, providing reliable, guideline-based information that enhances the accuracy of clinical decision-making tools and supports patient education.Andrology["Oya Gokmen", "T. Gurbuz", "B. Devrano\u011flu", "M. Karaman"]2024-07-17T00:00:00 1e88d88ae46ab7552b47aa9dee3743fbe095d8afPurposeManagers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.Design/methodology/approachUsing existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.FindingsWe identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.Originality/valueSimilar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.Online information review (Print)1292A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company's AI journey and developing a model illustrating five AI application stages how AI can be used for supply chain collaborations.Online Inf. Rev.["Eric Weisz", "David M. Herold", "Nadine Kathrin Ostern", "Ryan Payne", "Sebastian Kummer"]2024-07-17T00:00:00 3183869aa06c42789abd4547e1ef15a75d5493d3This study explores the integration of advanced machine learning and artificial intelligence technologies in historic buildings to optimize energy consumption management while preserving cultural heritage and ensuring occupant comfort. Focusing on a historically significant church in San Antonio, Texas, two predictive control models, a Feedforward Neural Network (FNN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS), are developed and compared against a conventional controller. Results demonstrate the promising predictive capabilities of both FNN and ANFIS models in regulating HVAC system operations. ANFIS outperforms FNN due to its ability to incorporate fuzzy inference systems (FIS), enabling the learning of hidden rules within the data. Lastly, the study emphasizes the need for robust strategies to balance energy efficiency with heritage preservation and occupant comfort in historic buildings.International Conference on Information, Intelligence, Systems and Applications100The study emphasizes the need for robust strategies to balance energy efficiency with heritage preservation and occupant comfort in historic buildings to optimize energy consumption management while preserving cultural heritage and ensuring occupant comfort.2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)["Athanasios Ioannis Arvanitidis", "Carlos Faubel", "Antonio Martinez-Molina", "M. Alamaniotis"]2024-07-17T00:00:00 a33dd93c2002525f4a9e68e2e4eaa7d165fa5218This paper outlines the development of Artificial Intelligence (AI) technology and its contribution to the Industrial Revolution. From the First Industrial Revolution to the Fourth Industrial Revolution, technological innovations have continuously promoted productivity and social progress. In particular, the fourth industrial revolution, represented by AI technology, has penetrated into various industries and fields with its wide range of applications, significantly improving production efficiency and quality. In the manufacturing industry, AI technology realizes automated production and intelligent quality inspection, reducing labor costs and improving product quality. In the field of agriculture, AI technology has effectively improved the yield and quality of crops through drone spraying and intelligent irrigation. In addition, AI technology also plays an important role in social governance, improving the efficiency and level of social management.Advances in Economics and Management Research40This paper outlines the development of Artificial Intelligence (AI) technology and its contribution to the Industrial Revolution, and AI technology plays an important role in social governance, improving the efficiency and level of social management.Advances in Economics and Management Research["Kam Yi Eason Yao", "Eric Youzhen Wang", "Xinyi Lei", "Boyang Xu"]2024-07-17T00:00:00 b56a31eb3bc84145ff0a23f75376c363b81835caArtificial Intelligence (AI) is emerging as one of the most powerful agents of change in accounting education, presenting the sector with unprecedented academic, ethical, and legal challenges. AI is the umbrella term that is used to explain machine learning and natural language processing. This study aims to explore the opportunities and challenges of AI in block teaching and how and what measures should be in place to safeguard academic integrity, giving special reference to the Accounting curriculum.Journal of Block and Intensive Learning and Teaching10This study aims to explore the opportunities and challenges of AI in block teaching and how and what measures should be in place to safeguard academic integrity, giving special reference to the Accounting curriculum.Journal of Block and Intensive Learning and Teaching["Chitra S de Silva Lokuwaduge"]2024-07-17T00:00:00 69f1352da4910bbc7c5cdbb4ca0c8a6a8bd0d751This paper presents an Artificial intelligence (AI)-driven approach designed to provide a sophisticated decision support tool for companies. The main goal of this machine learning (ML) model is to aid companies in elevating their Industry 4.0 maturity level by guiding strategic decisionmaking processes, drawing insights from successful companies that have attained high maturity levels in similar contexts.International Conference on Intelligent Engineering Systems90An Artificial intelligence (AI)driven approach designed to provide a sophisticated decision support tool for companies in elevating their Industry 4.0 maturity level by guiding strategic decisionmaking processes, drawing insights from successful companies that have attained high maturity levels in similar contexts.2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES)["Ben Ali Oussama", "H\u2019Mida Fehmi", "Hammami Sondes"]2024-07-17T00:00:00 7c04cdc05b7b5c4abf5e7c26ba29c3f8058f7b66This research article examines the use of artificial intelligence (AI) literacy in the field of research. Employing a multi-method methodological approach, it integrates narrative review, a quasi-experiment of an AI literacy course, and methodological triangulation. As a result, it describes a comprehensive content construct to develop AI literacy processes aimed at researchers. This construct encompasses generic competencies, generative AI, search engines, document interaction, document analysis by characteristics, generation of co-citation maps, literature reviews, database integration, data analysis, research tools, ethics in AI literacy, development of critical thinking through AI, and evaluation of AI literacy. The proposal provides a solid guide to strengthen the understanding and application of AI in research.2024 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)260A comprehensive content construct is described to develop AI literacy processes aimed at researchers that encompasses generic competencies, generative AI, search engines, document interaction, document analysis by characteristics, generation of co-citation maps, literature reviews, database integration, data analysis, research tools, ethics in AI literacy, and evaluation of AI literacy.2024 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)["Mauricio Rojas Contreras", "Jorge Omar Portilla Jaimes"]2024-07-17T00:00:00 e1967910490d7377cf4276f9e98295d0ac9c0ccdPurposeThe goal of this study is to better understand the driving force behind the use of artificial intelligence (AI) in pharmaceutical manufacturing firms (PMFs) that are recognized as developing countries in the Middle East and North Africa (MENA) region that are listed by the Chambers of the Industries of Jordan, the Kingdom of Saudi Arabia, Morocco, and Algeria. Furthermore, the effect of adopting and using AI in managing raw materials (RMs), products, parts, and components for PMFs through supply chains (SCs).Design/methodology/approachA self-administrated questionnaire survey was used to gather data from 95 out of 511 participating managers (e.g. manufacturing, supplying, IT, operational, and logistical managers) utilizing a quantitative technique with a random sample size. In fact, 18.8% of the 89 different manufacturing firms (MFs) in the MENA area responded, with five to six managers from each company. The raw data was analyzed using partial least squares structural equation modeling (PLS-SEM).FindingsThe study’s findings show that the readiness to embrace artificial intelligence (AI) in the production management supply chain performance (PMSCP) of pharmaceutical manufacturing firms in the Middle East and North Africa (MENA) is positively and significantly influenced directly and indirectly by sustainable strategic supplier reliability (SSSR), shipping process dependability (SPD), technological factors (TFs), and infrastructure transformational development capability (ITDC).Originality/valueAs the studied countries are growing economies, such study findings might offer insightful consequences for stakeholders and policymakers regarding the significance of using artificial intelligence system adoptions in pharmaceutical manufacturing enterprises in the MENA region. The managers may also concentrate on the strong positive direct and indirect links between SSSR, SPD, TFs, and ITDC preparedness to accept AI adoption and its applications and systems in supply chain and production management departments and the consequences of informational and product delivery.Business Process Management Journal690The study’s findings show that the readiness to embrace artificial intelligence (AI) in the production management supply chain performance (PMSCP) of pharmaceutical manufacturing firms in the Middle East and North Africa (MENA) is positively and significantly influenced directly and indirectly by sustainable strategic supplier reliability (SSSR), shipping process dependability (SPD), technological factors (TFs), and infrastructure transformational development capability (ITDC).Bus. Process. Manag. J.["M. Al-Shboul"]2024-07-17T00:00:00 51be4b08336dffe90223b4bfa2bd9401118c06f9Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various sectors of society. This qualitative study explores the advancements, applications, and ethical considerations surrounding the use of AI. Through in-depth interviews, focus group discussions, and document analysis, the research investigates the latest developments in AI technology, including machine learning, natural language processing, and computer vision. The study examines the diverse applications of AI across industries such as healthcare, finance, transportation, and education, highlighting its role in streamlining processes, enhancing decision-making, and driving innovation. Furthermore, the research delves into the ethical implications of AI adoption, addressing concerns related to privacy, bias, transparency, and accountability. By engaging with stakeholders from academia, industry, and civil society, the study explores perspectives on responsible AI development and governance frameworks. It also discusses initiatives aimed at promoting ethical AI practices, such as fairness, accountability, and transparency in algorithmic decision-making. The findings underscore the need for a balanced approach to AI deployment that maximizes its benefits while mitigating potential risks and societal harms. Key themes identified include the importance of interdisciplinary collaboration, regulatory oversight, and public engagement in shaping the future trajectory of AI. By unpacking the complexities of AI advancements, applications, and ethical considerations, this study contributes to the ongoing discourse on harnessing AI's potential for the benefit of humanity.Global International Journal of Innovative Research50The study examines the diverse applications of AI across industries such as healthcare, finance, transportation, and education, highlighting its role in streamlining processes, enhancing decision-making, and driving innovation and delves into the ethical implications of AI adoption.Global International Journal of Innovative Research["Anjar Tiyo Saputro", "Yanto Naim", "D. P. Yani", "Agustinus Supriyanto", "Tata Sumitra"]2024-07-17T00:00:00 a5e21d05c666ce7b9b38450b9ee3703a6017a07aSince 2022 we have been exploring application areas and technologies in which Artificial Intelligence (AI) and modern Natural Language Processing (NLP), such as Large Language Models (LLMs), can be employed to foster the usage and facilitate the documentation of Indigenous languages which are in danger of disappearing. We start by discussing the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP. To address those challenges, we propose an alternative development AI cycle based on community engagement and usage. Then, we report encouraging results in the development of high-quality machine learning translators for Indigenous languages by fine-tuning state-of-the-art (SOTA) translators with tiny amounts of data and discuss how to avoid some common pitfalls in the process. We also present prototypes we have built in projects done in 2023 and 2024 with Indigenous communities in Brazil, aimed at facilitating writing, and discuss the development of Indigenous Language Models (ILMs) as a replicable and scalable way to create spell-checkers, next-word predictors, and similar tools. Finally, we discuss how we envision a future for language documentation where dying languages are preserved as interactive language models.arXiv.org940This work discusses the decreasing diversity of languages in the world and how working with Indigenous languages poses unique ethical challenges for AI and NLP, and proposes an alternative development AI cycle based on community engagement and usage.ArXiv["Claudio Pinhanez", "Paulo Cavalin", "Luciana Storto", "Thomas Finbow", "Alexander Cobbinah", "J. Nogima", "Marisa Vasconcelos", "P. Domingues", "Priscila de Souza Mizukami", "Nicole Grell", "Majo'i Gongora", "Isabel Gon\u00e7alves"]2024-07-17T00:00:00 07f117b74f3edfd17e00142e124c285df5687723Technological trends point to Artificial Intelligence (AI) as a crucial tool in healthcare, but its development must respect human rights and ethical standards to ensure robustness and safety. Despite general good practices are available, health AI developers lack a practical guide to address the construction of trustworthy AI. We introduce a development framework to serve as a reference guideline for the creation of trustworthy AI systems in health. The framework provides an extensible Trustworthy AI matrix that classifies technical methods addressing the EU guideline for Trustworthy AI requirements (privacy and data governance; diversity, non-discrimination and fairness; transparency; and technical robustness and safety) across the different AI lifecycle stages (data preparation; model development, deployment and use, and model management). The matrix is complemented with generic but customizable example code pipelines for the different requirements with state-of-the-art AI techniques using Python. A related checklist is provided to help validate the application of different methods on new problems. The framework is validated using two representative open datasets, and it is provided as Open Source to the scientific and development community. The presented framework provides health AI developers with a theoretical development guideline with practical examples, aiming to ensure the development of robust and safe health AI and Clinical Decision Support Systems. GitHub repository: https://github.com/bdslab-upv/trustworthy-aimedRxiv20The presented framework provides health AI developers with a theoretical development guideline with practical examples, aiming to ensure the development of robust and safe health AI and Clinical Decision Support Systems.["Carlos de-Manuel-Vicente", "David Fern\u00e1ndez-Narro", "V. Blanes-Selva", "Juan M Garc\u00eda-G\u00f3mez", "Carlos S\u00e1ez"]2024-07-17T00:00:00 d1c42de3f227309a13495a952f54df30b2a4bf2eArtificial Intelligence (AI) chatbots and social robots, when incorporated into educational settings, have the potential to significantly transform the learning experience, particularly by providing more personalised, one-on-one attention to students. However, deploying such technology in classrooms also presents challenges. These include unresolved issues related to human-AI interaction, pedagogy and theories, human oversight, data privacy, transparency, accuracy and trust. This paper explores the development of a comprehensive framework aimed at enhancing the efficiency, pedagogical effectiveness, and ethical use of AI-driven educational tools, focusing on social robots and chatbots. The framework addresses important factors such as interaction quality, requirements engineering strategies, and the alignment of AI functionalities with educational goals. It also examines ethical concerns, including data security, user transparency, and trustworthiness. By reviewing recent literature on social robots and chatbots and incorporating emerging ethical considerations alongside long-standing educational objectives, this research aims at establishing a holistic framework, FEPER, of best practices and requirements. The findings offer valuable insights for educators, policymakers, and developers aiming to create more dynamic, personalised, and ethical educational environments using AI technology.International Conference on Information, Intelligence, Systems and Applications260This research aims at establishing a holistic framework, FEPER, of best practices and requirements aimed at enhancing the efficiency, pedagogical effectiveness, and ethical use of AI-driven educational tools, focusing on social robots and chatbots.2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)["M. Virvou", "G. Tsihrintzis"]2024-07-17T00:00:00 52037cba05875670791e0c9cb6a3ce4901904722This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement.International Conference on Information, Intelligence, Systems and Applications640Investigation of the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT reveals that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation.2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)["G. Tsihrintzis", "Elissaios Sarmas", "Vangelis Marinakis", "Dimitrios P. Panagoulias", "Evangelia-Aikaterini Tsichrintzi", "M. Virvou"]2024-07-17T00:00:00 9a92e5f6fb9256541589e476f91aadecede153fbThe ever-changing landscape of financial technology has undergone significant changes owing to advancements in machine learning, artificial intelligence, blockchains, and digitalization. These changes have had a profound impact on the provision of financial services, specifically, credit scoring and lending. This study examines the intersection of financial technology, artificial intelligence, machine learning, blockchain, and digitalization in the context of credit services with a focus on credit scoring and lending. This study addressed three main research questions: The research followed a comprehensive methodology, considering factors such as population, intervention, comparison, outcomes, and setting to ensure that collected data aligns with research objectives. The research questions were structured using the PICOS framework, and the PRISMA model was used for the systematic review and study selection. The publications analysed covered a wide range of datasets and methodologies.International Journal of Business Analytics500This study examines the intersection of financial technology, artificial intelligence, machine learning, machine learning, blockchain, and digitalization in the context of credit services with a focus on credit scoring and lending.International Journal of Business Analytics["Jewel Kumar Roy", "L\u00e1szl\u00f3 Vasa"]2024-07-17T00:00:00 cec27241a8e92b093aa15c9ff9b3c5469369991dThis research aims to determine the significant influence of Artificial Intelligence and Product Innovation by linking it to Customer Interest. This research uses a quantitative approach method using data collection techniques by distributing questionnaires and producing a sample of 100 people. Analysis of this research data uses Statistical Product and Service Solutions (SPSS 26). The research results show that Artificial Intelligence does not have a positive and significant influence on Customer Interest. These findings also show that Artificial Intelligence has a significant positive effect on Product Innovation and Product Innovation has a positive effect on Customer Interest for the MSME industry. The findings of this research also show that Customer Interest has a simultaneous influence on Artificial Intelligence and Product Innovation in the scope of Micro, Small and Medium Enterprises.Journal of institution and sharia finance00The research results show that Artificial Intelligence does not have a positive and significant influence on Customer Interest, but that Artificial Intelligence has a significant positive effect on Product Innovation and Product Innovation has a positive effect on Customer Interest for the MSME industry.Journal of Institution and Sharia Finance["Ageni Trifia", "Suci Maghfira Alimuddin", "Syahrul Fitra", "Mutiara Mutiara", "R. Nita"]2024-07-17T00:00:00 1f43b40139761c6eac63b0e3f33489e2242d995bThe integration of artificial intelligence in education is a significant advancement that fundamentally transforms education delivery and reception. Artificial intelligence relies on technologies like machine learning and big data analysis to offer customized and interactive learning experiences. Analyzing students' performance and providing individualized advice may improve their knowledge. Artificial intelligence (AI) may also enhance the creation of cutting-edge educational materials using technologies like augmented and virtual reality, making the learning experience more engaging and interesting. Nevertheless, further comprehensive research is necessary to fully understand the lasting impact of AI approaches on student learning results. In order to address this deficiency, the present work proposes a novel strategy that integrates bibliometric analysis with systematic literature review (SLR) utilizing the PRISMA methodology. The first stage focused on a comprehensive bibliometric, which included key nations, educational establishments, publications, keywords, and influential authors in the realm of artificial intelligence in education. This phase facilitated a comprehensive understanding of the overall state of this field across different disciplines. The subsequent phase was a systematic literature review (SLR) of 12 specifically chosen scholarly articles. This review focused on the current use of artificial intelligence (AI) in education. This review also examined the impact of implementing artificial intelligence (AI) in education, specifically focusing on its influence on student motivation and the desire to learn.The present study aims to implement artificial intelligence (AI) technology in education and explore strategies for achieving sustainable education for future generations.International Journal of Academic Research in Business and Social Sciences160This work proposes a novel strategy that integrates bibliometric analysis with systematic literature review (SLR) utilizing the PRISMA methodology and examines the impact of implementing artificial intelligence (AI) in education, specifically focusing on its influence on student motivation and the desire to learn.International Journal of Academic Research in Business and Social Sciences["Fatema Al Nabhani", "Mahizer Hamzah", "Hassan Abu Hassna"]2024-07-17T00:00:00 cd9904fd80c798b7d0982d8415e88d3e7df73674This study explores the integration of artificial intelligence (AI) in home hospitalisation services in San Juan, Argentina. By analysing interdisciplinary team training and data collection methods, the research demonstrates how AI tools enhance patient care. A virtual survey among healthcare professionals highlights the benefits of 24-hour vital signs monitoring, revealing the impact of socio-cultural factors and treatment adjustments. The findings emphasize the transition from traditional data management to AI-driven applications, improving decision-making and patient outcomes.International Symposium on Communication Systems, Networks and Digital Signal Processing140The research demonstrates how AI tools enhance patient care by analysing interdisciplinary team training and data collection methods, and emphasizes the transition from traditional data management to AI-driven applications, improving decision-making and patient outcomes.2024 14th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)["Liliana Mart\u00ednez", "Florencia Reveco-Toledo", "Franco Guevara", "Cristina Laplagne"]2024-07-17T00:00:00 013b0ac13ebc4367a8bcfa019c8d1766c5777133People cannot handle the volume of data and the complexity of processes needed to secure cyberspace without significant automation. However, it is challenging to develop technologies and software with conventional fixed implementations (hardwired decision-making logic) that successfully defend against security risks. AI learning techniques and machine simplicity can be used to treat this issue. Artificial intelligence (AI) approaches have been attempted to be used in a variety of cyber security applications recently. This paper gives an overview to the algorithms that might be used to have a better protection for our systems. This paper examines the crucial role that artificial intelligence plays in cybersecurity, as well as its benefits, drawbacks, and practical applications from the largest global corporations, such as PayPal and AWS. Also gives the benefits of using AI technology for Security.International Conference on Intelligent Engineering Systems260The crucial role that artificial intelligence plays in cybersecurity, as well as its benefits, drawbacks, and practical applications from the largest global corporations, such as PayPal and AWS are examined.2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES)["A. Sharko", "Genci Sharko", "Silvana Qose"]2024-07-17T00:00:00 9f7553845bf240855347592c0055dc71b760dde1In the pursuit of sustainable development, the fusion of artificial intelligence (AI) with legal mechanisms emerges as a potent force for transformative change. This paper delves into the intricate interplay between AI technologies and the legal framework, elucidating their collective capacity to address multifaceted challenges and propel progress towards the Sustainable Development Goals (SDGs). At the forefront of environmental stewardship, AI-driven solutions offer unprecedented capabilities in pollution monitoring, waste management, and climate change adaptation. From predictive analytics for air quality assessment to real-time monitoring of deforestation patterns, AI empowers stakeholders with actionable insights to mitigate environmental degradation and foster resilience. Simultaneously, AI applications hold promise in addressing socio-economic inequities through innovative approaches to poverty alleviation, financial inclusion, and inclusive growth strategies. By leveraging predictive modelling and data analytics, AI facilitates targeted interventions tailored to the unique needs of marginalized communities, thereby fostering sustainable development trajectories. However, the ethical and regulatory dimensions of AI deployment necessitate careful consideration within the legal framework. Ensuring data privacy, algorithmic transparency, and equitable access emerges as imperatives in harnessing the full potential of AI for societal benefit. Through the formulation of robust regulatory frameworks and ethical guidelines, the legal system plays a pivotal role in safeguarding against potential risks and maximizing the societal dividends of AI innovation. Moreover, interdisciplinary collaboration and stakeholder engagement are paramount in navigating the evolving landscape of AI governance, fostering dialogue, and consensus-building among diverse stakeholders. Thus, this paper advocates for a holistic approach to sustainable development, synergizing the transformative potential of AI with the protective mechanisms of the legal system. By fostering innovation, inclusivity, and accountability, this symbiotic relationship serves as a catalyst for advancing the global agenda towards a more equitable, resilient, and sustainable future. Keywords: Artificial Intelligence, Climate Change Adaptation, Deforestation, Education, Healthcare, Legal System, Pollution, Poverty, Sustainable Development Goals, Water and Waste Management.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper delves into the intricate interplay between AI technologies and the legal framework, elucidating their collective capacity to address multifaceted challenges and propel progress towards the Sustainable Development Goals (SDGs).INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Florine Muchokore", "Dr. Gurpreet Kaur"]2024-07-17T00:00:00 cec6f8931546d5a016b63b56fe0e90275fd6a7c6Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain‐specific artificial intelligence is more reliable, energy‐efficient, and ethically tractable, and raises mostly a problem of effective coordination between different systems and humans. Herein, it is argued that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient.Advanced Intelligent Systems190It is argued that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient and that simpler ways of sharing awareness are sufficient.Adv. Intell. Syst.["Oph\u00e9lia Deroy", "Davide Bacciu", "Bahador Bahrami", "C. D. Santina", "Sabine Hauert"]2024-07-17T00:00:00 1081956025a8e25236b2aa3efdb1e2338e585765Artificial intelligence provides both good and evil. For this reason, artificial intelligence must be regulated to protect the public interest. The research results show that regulating artificial intelligence is not easy, it is very complicated, and there are many challenges, especially as the development of artificial intelligence technology is very rapid while the law is slow to anticipate it. By 2022, globally, there will be 37 regulations governing artificial intelligence. From the results of the comparison of various best practices and regulations from other countries leading in the field of artificial intelligence, such as the European Union, China, and the United States framework approach, it can be used as input for developing artificial intelligence regulations in Indonesia that guarantee the use of artificial intelligence responsibly, respecting values. humanity, and does not hinder the creation of an artificial intelligence development ecosystem.Journal of Social Science100The results of the comparison of various best practices and regulations from other countries leading in the field of artificial intelligence, such as the European Union, China, and the United States framework approach, can be used as input for developing artificial intelligence regulations in Indonesia that guarantee the use of artificial intelligence responsibly, respecting values.Journal of Social Science (JoSS)["Francisca Romana Nanik Alfiani", "Faisal Santiago"]2024-07-17T00:00:00 2e69e6b4efb3af5f7084b8a4cec4e1b36e4970edThe increasing integration of Artificial Intelligence (AI) in higher education institutions necessitates a student prepared for this transformative change. This study investigates the factors influencing students' intention to use AI tools in their study. Drawing upon the Technology Acceptance Model (TAM), the research aims to understand how perceived ease of use, and perceived usefulness impact students' intention to use with attitude, and self-efficacy as mediators. Data collection employed a survey instrument distributed to a sample of 319 students from public and private higher education institutions. The survey measured participants' perceptions of AI ease of use, perceived usefulness, attitude towards AI, self-efficacy, and intention to use AI tools in their study. Statistical analysis utilized Partial Least Squares (PLS) to assess the relationships between the proposed variables and test the formulated hypotheses. The results of the hypothesis testing confirmed the positive influence of perceived ease of use and perceived usefulness on students' intention to use AI tools, aligning with TAM principles. Furthermore, the study revealed that attitude and self-efficacy act as mediating factors, bridging the gap between perceived ease of use and perceived usefulness and intention to use. These findings suggest that beyond just the technical aspects of AI, students' perceptions, attitudes, and confidence levels significantly influence their willingness to use AI in their study. The study's implications are significant for organizations implementing AI. By prioritizing the user-centered design of AI tools, emphasizing training and skill development to enhance perceived ease of use, and communicating the benefits of AI to address perceived usefulness, organizations can foster a more positive student attitude towards AI. Additionally, promoting a culture of learning and support can boost student’s self - efficacy and ultimately encourage wider usage of AI tools within the organizationInternational Journal of Academic Research in Business and Social Sciences470It is suggested that beyond just the technical aspects of AI, students' perceptions, attitudes, and confidence levels significantly influence their willingness to use AI in their study, and the study's implications are significant for organizations implementing AI.International Journal of Academic Research in Business and Social Sciences["Zahir Osman", "Ratna Khuzaimah Mohamad", "Nadzurah Kasbun"]2024-07-17T00:00:00 51503b0bb286cdf30cd4d9a6f3c9e83bd01729ddJournal of Computational Social Science583J. Comput. Soc. Sci.["T. T. Khoei", "Aditi Singh"]2024-07-17T00:00:00 5499eeb3af11432ec77b69eac8761fa76c162c26Journal of Cosmetic Dermatology51Journal of cosmetic dermatology["Marina Landau", "Mohamad Goldust"]2024-07-17T00:00:00 066287208bdab39c2c78db9863a492b6b25aa2e0Annals of Surgical Oncology32Annals of surgical oncology["Shigeki Matsubara"]2024-07-17T00:00:00 c413de9c69d62949b5e97549c79de32788afdb40In this paper, we present LYRICEL, an advanced AI-enhanced system that combines a rule-based decision-making mechanism, OpenAI's application programming interface (API), and additional external machine learning and analytical APIs to deliver song lyrics recommendations for an e-learning platform dedicated to Greek music and songs. This new component enhances MUSILYAN, a specialised software tool designed for musicological and lyrical analysis, by complementing it with GPT4o's capabilities. While ChatGPT excels in natural language understanding and generation, MUSILYAN provides structured semantic content management and thematic organization of lyrics. The integration of the two modules in LYRICEL, holds significant promise for enhancing the exploration and understanding of poetry and lyrics, offering a robust framework for enriching cultural heritage e-learning experiences.International Conference on Information, Intelligence, Systems and Applications1102024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)["Dimitrios P. Panagoulias", "Dionisios N. Sotiropoulos", "K. Chrysafiadi", "Evangelia-Aikaterini Tsichrintzi", "E. Sakkopoulos", "G. Tsihrintzis", "M. Virvou"]2024-07-17T00:00:00 23e52e3cb35a8a6f256e0c464aee6c29b91f0ecdThis paper examines the impact of varying consequences on human trust in AI responses, focusing on how different trust states—trust, mistrust, overtrust, and AI phobia—are affected by three scenarios: Possibly Positive Consequences (+1), Neutral Consequences (0), and Possibly Negative Consequences (−1). In the Possibly Positive Consequences (+1) scenario, where any AI response is better than none due to the inherent chance of success, trust can be upgraded to a higher state if the AI response is reliable. However, mistrust or overtrust may develop if the response is untrustworthy, prompting a reassessment of AI reliance. In the Neutral Consequences (0) scenario, where accepting the AI response poses no negative consequences, the trust state remains unaffected. Trust can be easily established and maintained if the AI response is trustworthy, as the absence of adverse outcomes encourages confidence. Mistrust is mitigated in this scenario due to the lack of perceived downside. However, overtrust can become a concern if individuals rely on AI without critical evaluation, potentially leading to future vulnerabilities. In the Possibly Negative Consequences (−1) scenario, where accepting a potentially erroneous AI response could lead to significant negative outcomes, trust is highly contingent on the AI's accuracy and reliability and therefore trust can easily be down graded to lower levels (mistrust of AI phobia). Even minor errors can lead to a rapid erosion of trust and the establishment of mistrust, diminishing the trust state to a lower level. Overtrust is less likely due to the high stakes involved, but if it does occur, it can lead to severe repercussions.International Conference on Information, Intelligence, Systems and Applications3102024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)["M. Virvou", "G. Tsihrintzis"]2024-07-17T00:00:00 e94cc80e254890094ba4481c51b49ae876f7ab65AJR. American journal of roentgenology10AJR. American journal of roentgenology["Kiran Batra"]2024-07-17T00:00:00 b73bd5ff89bbb10d0df17ebc16f469baffc6b531KI - Künstliche Intelligenz200KI - Künstliche Intelligenz["T. Stollenwerk", "Somtapa Bhattacharya", "Michele Cattelan", "Alessandro Ciani", "Gabriele Compostella", "David Headley", "Johannes Klepsch", "Matthias Klusch", "Markus Leder", "A. Macaluso", "K. Michielsen", "Dmytro Nabok", "Anestis Papanikolaou", "Alexander Rausch", "Marco Schumann", "Andrea Skolik", "S. Yarkoni", "Frank K. Wilhelm"]2024-07-17T00:00:00 1eb045e87fb4fec1174aa5e7bd4fa334f0c22a5c,Journal of Block and Intensive Learning and Teaching00Journal of Block and Intensive Learning and Teaching["Maja Husaric", "Sunam Pradhan", "Kathy Tangalakis", "P. Sinnayah", "Loretta Konjarski", "Katherine Harkin", "Craig Kappes", "Jessi Dillon", "Bhashika Bhattarai", "Scott Michael", "Sidney Lung", "Mihai Gavrilescu", "Michelle Prawer", "Tahlia Funnell", "Samantha Amjadali", "Samuel Howe", "Jack Feehan", "Michelle Young", "Emma Moore", "Kris Vingrys"]2024-07-17T00:00:00 7e5f9b882ee852d98bb4019fbc025c0250647f4aIn financial applications, regulations or best practices often lead to specific requirements in machine learning relating to four key pillars: fairness, privacy, interpretability and greenhouse gas emissions. These all sit in the broader context of sustainability in AI, an emerging practical AI topic. However, although these pillars have been individually addressed by past literature, none of these works have considered all the pillars. There are inherent trade-offs between each of the pillars (for example, accuracy vs fairness or accuracy vs privacy), making it even more important to consider them together. This paper outlines a new framework for Sustainable Machine Learning and proposes FPIG, a general AI pipeline that allows for these critical topics to be considered simultaneously to learn the trade-offs between the pillars better. Based on the FPIG framework, we propose a meta-learning algorithm to estimate the four key pillars given a dataset summary, model architecture, and hyperparameters before model training. This algorithm allows users to select the optimal model architecture for a given dataset and a given set of user requirements on the pillars. We illustrate the trade-offs under the FPIG model on three classical datasets and demonstrate the meta-learning approach with an example of real-world datasets and models with different interpretability, showcasing how it can aid model selection.European Conference on Artificial Intelligence590This paper proposes FPIG, a general AI pipeline that allows for these critical topics to be considered simultaneously to learn the trade-offs between the pillars better and proposes a meta-learning algorithm to estimate the four key pillars given a dataset summary, model architecture, and hyperparameters before model training.{"pages": "834-841"}["Roberto Pagliari", "Peter Hill", "Po-Yu Chen", "Maciej Dabrowny", "T. Tan", "Francois Buet-Golfouse"]2024-07-17T00:00:00 a8e93b16fe25aa2466b1f62a52dfe4add618c098Данная статья посвящена актуальному состоянию искусственного интеллекта (ИИ) и его многогранному влиянию на современный мир. В статье анализируются ключевые тренды и достижения в исследованиях и разработках искусственного интеллекта, такие как экспоненциальный рост данных и вычислительной мощности, прогресс в области машинного обучения, а также смещение фокуса в сторону создания общего искусственного интеллекта (ОИИ). Особое внимание уделяется все более глубокой интеграции искусственного интеллекта в различные аспекты повседневной жизни, от потребительской электроники и здравоохранения до финансов и транспорта. В то же время поднимаются важнейшие этические и социальные вопросы, связанные с развитием искусственного интеллекта, такие как потенциальная утрата рабочих мест, проблемы предвзятости и дискриминации, защита конфиденциальности и риски, связанные с автономным оружием. В заключении подчеркивается необходимость ответственного подхода к развитию искусственного интеллекта, основанного на этических принципах и направленного на то, чтобы искусственный интеллект служил на благо всего человечества. This article is devoted to the current state of artificial intelligence (AI) and its multifaceted impact on the modern world. The article analyzes key trends and achievements in research and development of artificial intelligence, such as exponential growth of data and computing power, progress in machine learning, as well as a shift in focus towards the creation of Artificial general intelligence (AGI). Special attention is being paid to the increasingly deep integration of artificial intelligence into various aspects of everyday life, from consumer electronics and healthcare to finance and transportation. At the same time, the most important ethical and social issues related to the development of artificial intelligence are being raised, such as the potential loss of jobs, problems of bias and discrimination, privacy protection and the risks associated with autonomous weapons. In conclusion, the need for a responsible approach to the development of artificial intelligence based on ethical principles and aimed at ensuring that artificial intelligence serves the benefit of all mankind is emphasized.Правовая культура как основа становления гражданского общества: сборник статей международной научной конференции (Екатеринбург, Июнь 2024)10Правовая культура как основа становления гражданского общества: сборник статей международной научной конференции (Екатеринбург, Июнь 2024)["\u0412\u043b\u0430\u0434\u0438\u043c\u0438\u0440 \u0412\u0430\u0441\u0438\u043b\u044c\u0435\u0432\u0438\u0447 \u0421\u043c\u0435\u0442\u0430\u043d\u0430"]2024-07-17T00:00:00 c6c750591999bd44f9903e421bc0c35f2837f31bFrontiers in Education60Frontiers in Education["Yujin Han"]2024-07-17T00:00:00 b689cc2d7c5202c55b196a938a1d8b70d4e4ecd7Current Psychology630Current Psychology["Shuang Zheng", "Weiwei Wang"]2024-07-17T00:00:00 e7a1d7d336770a361d18ff96b26f3b5fd5ca9ef8Learning English has become a major focus in this modern era, where cross-cultural communication is increasingly important. However, the language learning process is often challenging for many individuals, especially those who do not have adequate access or resources. The advent of Artificial General Intelligence promises significant advances in language learning approaches, with the potential to increase the accessibility, speed and effectiveness of learning. This research aims to explore the potential benefits of Artificial General Intelligence in English language learning, especially in the context of accessibility, speed and effectiveness of learning. The research method uses a qualitative approach involving a comprehensive literature study on the latest developments in the use of Artificial General Intelligence in language learning. The research results show that Artificial General Intelligence has great potential to improve English language learning. Apart from that, there are also many benefits to be gained from using Artificial General Intelligence in English language learning. The conclusions in this research confirm that the use of Artificial General Intelligence in English language learning offers significant potential to increase the accessibility, speed and effectiveness of learning. However, challenges related to ethics, privacy and data security also need to be seriously considered in the development and implementation of this technology. With a careful and integrated approach, Artificial General Intelligence can be a valuable tool in supporting inclusive and effective English language learning for all individuals. The limitation of this research is that this research only conducted research at the educational unit level, specifically in English language learning.Journal of Science & Technology410The research results show that Artificial General Intelligence has great potential to improve English language learning and can be a valuable tool in supporting inclusive and effective English language learning for all individuals.Scientechno: Journal of Science and Technology["Yulian Purnama", "Sherly Gaspersz"]2024-07-17T00:00:00 a9dbd913f37addaf0e29d15a48af17126eb8c8e3In the wake of recent discussions about the use of artificial intelligence for education, this study aims to describe the project “Continuidades e reelaborações da Flânerie na crônica de Lima Barreto.” The possibility of using ChatGPT for teaching evinces the challenge of filling out a database on literature science. For this, modeling emerges as an appropriate method to convey scientific information.Comunicação &amp; Educação00Modeling emerges as an appropriate method to convey scientific information in the possibility of using ChatGPT for teaching and the challenge of filling out a database on literature science.Comunicação &amp; Educação["Gabriel Magalh\u00e3es Siston", "Raoni Schimitt Huapaya", "Nat\u00e1lia Elda Jorge de Souza", "Isabelly Barbosa da Silva"]2024-07-17T00:00:00 a750368af653af6617298e3ffc423c99d31b288bThis is a teaching experience report on the “Ethics and information” course at a federal university. Its first section describes its theoretical foundation, based on Álvaro Vieira Pinto’s cybernetics and the political economy of information and communication. Its second section discusses the course participatory methodology inspired by Paulo Freire (Vieira Pinto’s student), which resorted to multimedia didactic, self-assessment, dozens of student seminars, and a final open essay. As its main result, dialogic education confirms students’ unreasonable fascination with texts generated by artificial intelligence, along with the unpreparedness of the institution to address this new challenge.Comunicação &amp; Educação00Comunicação &amp; Educação["Monique Figueira"]2024-07-17T00:00:00 20cf47b383d23e9997de61073d3edee84497e08eArtificial Intelligence, in particular conversational artificial intelligence (AI), is revolutionizing higher education. The commercialization and popularization of these tools has catapulted its adoption. It offers personalized student learning, support to academic and professional staff, and the streamlining administrative tasks. This rapidly developing technology promises to significantly influence the higher educational landscape. An interpretive synthesis of the current application of conversational artificial intelligence in higher education was guided by a state-of-the-art literature review. The aim of the research study was to evaluate academics perspective of conversational AI use and its perceived benefits and challenges, in four different countries (South Africa, Hungary, Lebanon, and Wales). It was found that less than half of the respondents employed conversational AI in teaching, whereas most of the respondents utilized it for research support. The preference for using conversational artificial intelligence tools in research rather than in teaching—particularly among younger academics and those favoring remote working environments—suggests a future trajectory where AI could become more central in academic research than in traditional teaching methods. Furthermore, it was found that significantly more of those who do not use conversational AI for teaching, prefer teaching face to face, whereas those using conversational AI to enhance their teaching, most were neutral about their preferred mode of delivery. Despite some of the concerns raised by some academics, most viewed it its numerous potential advantages for teaching and research, positively. The study, however, did raise concerns regarding the ethical integration and adoption of these technologies in academic settings.International Conference on Intelligent Engineering Systems381The aim of the research study was to evaluate academics perspective of conversational AI use and its perceived benefits and challenges, in four different countries, and it was found that less than half of the respondents employed conversational AI in teaching, whereas most of the respondents utilized it for research support.2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES)["I. M. Venter", "D. Cranfield", "R. Blignaut", "S. Achi", "Andrea Tick"]2024-07-17T00:00:00 a0c4abadaa5993334a0bcac02844c76ee465e117This study investigated the integration of artificial intelligence (AI) tools in teaching literature, specifically focusing on short stories. An online survey of literature teachers was used, in which 40 literature teachers from different Saudi universities participated. The survey results indicated that literature teachers recognized the potential benefits of AI tools, including personalized learning experiences and increased student engagement. Teachers believed that AI tools could improve learning outcomes by enhancing students' comprehension of literary techniques and devices. However, the survey also revealed challenges related to teacher training and concerns about the quality of AI-generated content. The study suggests several recommendations, including stakeholder engagement, comprehensive teacher training, ethical guidelines, continuous evaluation of AI tools, and ensuring that AI complements rather than replaces human expertise. The study concludes that AI tools, when implemented thoughtfully and supported by ongoing research, have the potential to enhance short story education by providing customized and engaging learning experiences that enhance interactions between students and texts.Theory and Practice in Language Studies161AI tools, when implemented thoughtfully and supported by ongoing research, have the potential to enhance short story education by providing customized and engaging learning experiences that enhance interactions between students and texts.Theory and Practice in Language Studies["Abdulrahman Mokbel Mahyoub Hezam", "Abdulelah Alkhateeb"]2024-07-17T00:00:00 920955e6503b7feb77e0a782a8d3a58c297ed47dProblem definition: Artificial intelligence (AI) assistants—software agents that can perform tasks or services for individuals—are among the most promising AI applications. However, little is known about the adoption of AI assistants by service providers (i.e., physicians) in a real-world healthcare setting. In this paper, we investigate the impact of the AI smartness (i.e., whether the AI assistant is powered by machine learning intelligence) and the impact of AI transparency (i.e., whether physicians are informed of the AI assistant). Methodology/results: We collaborate with a leading healthcare platform to run a field experiment in which we compare physicians’ adoption behavior, that is, adoption rate and adoption timing, of smart and automated AI assistants under transparent and non-transparent conditions. We find that the smartness can increase the adoption rate and shorten the adoption timing, whereas the transparency can only shorten the adoption timing. Moreover, the impact of AI transparency on the adoption rate is contingent on the smartness level of the AI assistant: the transparency increases the adoption rate only when the AI assistant is not equipped with smart algorithms and fails to do so when the AI assistant is smart. Managerial implications: Our study can guide platforms in designing their AI strategies. Platforms should improve the smartness of AI assistants. If such an improvement is too costly, the platform should transparentize the AI assistant, especially when it is not smart. Funding: This research was supported by a Behavioral Research Assistance Grant from the C. T. Bauer College of Business, University of Houston. H. Zhao acknowledges support from Hong Kong General Research Fund [9043593]. Y. (R.) Tan acknowledges generous support from CEIBS Research [Grant AG24QCS]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0093 .Manufacturing & Service Operations Management431It is found that the smartness of the AI smartness can increase the adoption rate and shorten the adoption timing, whereas the transparency can only shorten the adoption timing.Manuf. Serv. Oper. Manag.["Ting Hou", "Meng Li", "Y. Tan", "Huazhong Zhao"]2024-07-17T00:00:00 14252ebc368aea6c5d76729a0038074236448bc3The field of green finance has garnered growing international interest in recent years and can contribute to green development towards addressing climate change. This can be supported through emerging technologies in cross-domain data sharing and artificial intelligence (AI). The present study explores ways to leverage existing and innovative methods and technologies related to financial “green” products, such as green bonds alongside research programs aimed at promoting green investments. In this context, we investigate whether AI and cross-organisational and cross-domain data sharing can be effectively leveraged to address the lack of information on green investments. More specifically, we conduct a review on previous scientific research. Therefore, we demonstrate that by finding and exploiting data through various techniques, and by sharing the data among stakeholders, it is possible to optimise and solve issues that will promote sustainable development, thus paving an innovative and necessary path towards the green transition. Based on the derived common acceptance of the necessity to enhance green finance, we propose techniques and technologies for the use of cross-domain data sharing and AI. As proof-of-concept, we subsequently describe two ongoing case studies in the energy and green deal domains, supported by the emerging technology of European Data Spaces. Overall, the analysis and compilation of the derived results can be used by stakeholders including researchers, financial institutions, and investors toward the next steps for the development of green finance.International Conference on Information, Intelligence, Systems and Applications680It is demonstrated that by finding and exploiting data through various techniques, and by sharing the data among stakeholders, it is possible to optimise and solve issues that will promote sustainable development, thus paving an innovative and necessary path towards the green transition.2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)["Ioannis Papagiannopoulos", "Christos Ntanos", "Sotiris Pelekis", "A. Tzortzis", "Hercules Koutalidis", "Evangelos Karakolis", "Georgios Kormpakis", "Eugenia Skepetari", "Ariadni Michalitsi-Psarrou", "Afroditi Blika", "Dimitris Askounis"]2024-07-17T00:00:00 adb6f48603309c6a0ab740ce2d68159c8a965271Escalating global mental health demand exceeds existing clinical capacity. Scalable digital solutions will be essential to expand access to high-quality mental healthcare. This study evaluated the effectiveness of a digital intervention to alleviate mild, moderate and severe symptoms of generalized anxiety. This structured, evidence-based program combined an Artificial Intelligence (AI) driven conversational agent to deliver content with human clinical oversight and user support to maximize engagement and effectiveness. The digital intervention was compared to three propensity-matched real-world patient comparator groups: i) waiting control; ii) face-to-face cognitive behavioral therapy (CBT); and iii) remote typed-CBT. Endpoints for effectiveness, engagement, acceptability, and safety were collected before, during and after the intervention, and at one-month follow-up. Participants (n=299) used the program for a median of 6 hours over 53 days. There was a large clinically meaningful reduction in anxiety symptoms for the intervention group (per-protocol (n=169): change on GAD-7 = -7.4, d = 1.6; intention-to-treat (n=299): change on GAD-7 = -5.4, d = 1.1) that was statistically superior to the waiting control, non-inferior to human-delivered care, and was sustained at one-month follow-up. By combining AI and human support, the digital intervention achieved clinical outcomes comparable to human-delivered care while significantly reducing the required clinician time. These findings highlight the immense potential of technology to scale effective evidence-based mental healthcare, address unmet need, and ultimately impact quality of life and economic burden globally.medRxiv10A structured, evidence-based program combined an Artificial Intelligence (AI) driven conversational agent to deliver content with human clinical oversight and user support to maximize engagement and effectiveness to alleviate mild, moderate and severe symptoms of generalized anxiety.["C. E. Palmer", "E. Marshall", "E. Millgate", "G. Warren", "M. P. Ewbank", "E. Cooper", "S. Lawes", "M. Bouazzaoui", "A. Smith", "C. Hutchins-Joss", "J. Young", "M. Margoum", "S. Healey", "L. Marshall", "S. Mehew", "R. Cummins", "V. Tablan", "A. Catarino", "A. Welchman", "A. D. Blackwell"]2024-07-17T00:00:00 7ff35aa8a43082520874fc7cf75cc1cb55d18d93Artificial intelligence (AI) is revolutionizing higher education by affecting teaching, learning, assessment, and the skills required for future careers. AI technologies like machine learning and data analytics emerged through computer-related technologies and evolved into web-based intelligent education platforms. They now include web-based chatbots assisting or performing instructors' tasks. By leveraging these platforms, educators can tailor personalized and adaptive learning experiences adjusted to the unique needs of each of their students. Through analysis of datasets and pattern recognition, these systems offer customized recommendations to improve student motivation and engagement. Automated grading systems provide students with immediate feedback that encourages self-assessment and facilitates real-time comprehension of their strengths and flaws, thereby enabling improvement. This approach allows educators to focus more on refining curricula and enhancing teaching quality. AI also supports collaborative learning environments with intelligent tutoring systems and virtual assistants, promoting active participation, critical thinking, and problemsolving skills. However, integrating AI in education presents challenges, especially with respect to privacy and ethics. Protecting student information and guaranteeing ethical AI use are crucial. Also, over-reliance on AI can lead to passive learning experiences. Balancing AI with human instruction is essential to maintain meaningful interactions and foster deeper understanding. This keynote will delve into the multifaceted impact of AI on education, drawing on recent research and practical applications, it will emphasize the importance of considering the ethical implications and challenges of AI-based education, and will suggest that, by tackling AI’s potential, educators can equip students with modern skills for their future careers.International Conference on Intelligent Engineering Systems00The importance of considering the ethical implications and challenges of AI-based education, and the suggestion that, by tackling AI’s potential, educators can equip students with modern skills for their future careers are suggested.2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES)["Mounir Hamdi"]2024-07-17T00:00:00 264b1be81f56c45ee407e847d1b92fbb3c3d70cdBMC Nursing335There is a statistically significant correlation between attitude, ethical awareness, and creativity, highlighting that ethical awareness moderates the relationship between attitudes and innovative work behaviors.BMC Nursing["A. Atalla", "A. El-Ashry", "Samia Mohamed Sobhi Mohamed"]2024-07-18T00:00:00 91c79b5b087fb3300ecf6c70fd6b32bff98bd2c7The study aimed to address ways to improve the efficiency of agribusiness through the introduction of modern technologies and innovative approaches. The methods of quantitative and qualitative analysis of financial indicators, analytical reviews, and specific examples of technology implementation were used to achieve this goal. The study emphasises the impact of digitalisation of the agricultural sector on improving economic security, efficiency and competitiveness. The introduction of digital technologies, such as the Internet of Things, process automation and precision farming, is helping to optimise production processes, reduce costs and improve product quality. The study results show that the concept of smart farming significantly increases the efficiency of agribusiness. The use of Big Data, blockchain technologies, drones and satellite technologies provides better management of agribusiness, while artificial intelligence helps to predict yields and optimise agricultural processes. Risk management in the agricultural sector, including insurance and financial instruments to hedge price fluctuations, is a key factor in enhancing economic stability. The experience of leading global companies such as John Deere, Agricultural Bank of China, Fruition Sciences, TE-FOOD and FieldView has shown the effectiveness of digital technologies and innovations in improving efficiency and sustainable development. Particular attention is devoted to the problems and prospects for the introduction of modern technologies in the agricultural sector of Ukraine on the example of Myronivsky Hliboproduct. An integrated model for improving the business processes of Myronivsky Hliboproduct is proposed, including the digitalisation of production processes, the introduction of blockchain technologies, the use of AI and risk management. Necessary steps to overcome existing challenges, such as insufficient funding for innovation, infrastructure constraints, staff training and cybersecurity, were determined. The implementation of the proposed measures will allow the company to become a leader in the implementation of innovations in the agricultural sector of Ukraine, increase competitiveness and ensure sustainable development in the futureEkonomika APK04The study results show that the concept of smart farming significantly increases the efficiency of agribusiness and the implementation of the proposed measures will allow the company to become a leader in the implementation of innovations in the agricultural sector of Ukraine, increase competitiveness and ensure sustainable development in the future.Ekonomika APK["Nataliia Zelisko", "N. Raiter", "Nataliia Markovych", "H. Matskiv", "Orysya Vasylyna"]2024-07-18T00:00:00 13a8745a020539eec00e36deef2b3a19e7d5125eArtificial intelligence and technology have continued to evolve over recent decades, and their utility in hip and knee arthroplasty is growing with interest and enthusiasm. A multitude of technologies are available to assist surgeons in the intraoperative execution of hip and knee arthroplasty, ranging from robotics and augmented reality to artificial intelligence-powered fluoroscopy. The purpose of this review is to provide a framework for arthroplasty surgeons to understand the concept of artificial intelligence and the advancements in technologies that impact the perioperative care of patients undergoing hip and knee arthroplasty.Surgical technology international01The purpose of this review is to provide a framework for arthroplasty surgeons to understand the concept of artificial intelligence and the advancements in technologies that impact the perioperative care of patients undergoing hip and knee arthroplasty.Surgical technology international["Joshua P. Rainey", "N. Sodhi", "J. Gililland", "Michael A. Mont"]2024-07-18T00:00:00 06134e01d22f3bf69e68080eaa7fbb9ecf207c11Oral diseases pose a significant burden on global healthcare. While many oral conditions are preventable and manageable through regular dental office visits, a substantial portion of the population faces obstacles in accessing essential and affordable quality oral healthcare. In this mini review, we describe the issue of inequity and bias in oral healthcare and discuss various strategies to address these challenges, with an emphasis on the application of artificial intelligence (AI). Recent advances in AI technologies have led to significant performance improvements in oral healthcare. AI also holds tremendous potential for advancing equity in oral healthcare, yet its application must be approached with caution to prevent the exacerbation of inequities. The “black box” approaches of some advanced AI models raise uncertainty about their operations and decision-making processes. To this end, we discuss the use of interpretable and explainable AI techniques in enhancing transparency and trustworthiness. Those techniques, aimed at augmenting rather than replacing oral health practitioners’ judgment and skills, have the potential to achieve personalized dental and oral care that is unbiased, equitable, and transparent. Overall, achieving equity in oral healthcare through the responsible use of AI requires collective efforts from all stakeholders involved in the design, implementation, regulation, and utilization of AI systems. We use the United States as an example due to its uniquely diverse population, making it an excellent model for our discussion. However, the general and responsible AI strategies suggested in this article can be applied to address equity in oral healthcare on a global level.Frontiers in Oral Health492The issue of inequity and bias in oral healthcare is described and various strategies to address these challenges are discussed, with an emphasis on the application of artificial intelligence (AI).Frontiers in Oral Health["Zaid H. Khoury", "Alexys Ferguson", "Jeffery B Price", "Ahmed S Sultan", "Rong Wang"]2024-07-18T00:00:00 aa2b5f4c15ca9e161bd0525e7c5a17eb29350b57This study investigates the application of Artificial Intelligence (AI) in monitoring and managing marine ecosystems to address the impacts of climate change on Kenya’s Blue Economy. It aims to assess the threats posed by climate change to these ecosystems and explore the potential of AI solutions to enhance adaptation and resilience. The research employs a comprehensive review of secondary data sources, including academic publications, reports from reputable institutions, and other relevant materials. The study analyzes existing literature on AI applications in marine ecosystem management and climate change mitigation, focusing on the specific context of Kenya’s Blue Economy. The study reveals that climate change poses significant threats to Kenya’s marine ecosystems, including coral bleaching, ocean acidification, sea-level rise, and disruptions to ocean currents. AI technologies offer promising solutions for monitoring and managing these impacts, with applications in predictive modeling, resource optimization, and decision support. The research highlights the need for further exploration into specific AI applications tailored to Kenya’s unique coastal challenges and the importance of incorporating diverse stakeholder perspectives. Additionally, it emphasizes the necessity for long-term impact assessments of AI technologies in the context of climate change mitigation. This study contributes to the growing body of knowledge on AI applications in environmental management, particularly within the context of Kenya’s Blue Economy. By identifying the potential of AI to enhance resilience and sustainability in marine ecosystems, the research offers valuable insights for policymakers, researchers, and practitioners involved in climate change mitigation and adaptation efforts.Frontiers in Marine Science412The study reveals that climate change poses significant threats to Kenya’s marine ecosystems, including coral bleaching, ocean acidification, sea-level rise, and disruptions to ocean currents, and AI technologies offer promising solutions for monitoring and managing these impacts.Frontiers in Marine Science["Brigid Gesami", "Jacob Nunoo"]2024-07-18T00:00:00 03f58ee119484741eb7cc474dd29c48434b7b1c9PurposeThe purpose of this study is to examine the acceptance of artificial intelligence devices (AIDs) by customers in banking service encounters using the Artificially Intelligent Device Use Acceptance (AIDUA) model and thus test the validity of the AIDUA model in the context of the banking sector as well as extending the AIDUA model by incorporating two moderator variables, namely technology anxiety and risk aversion by regarding the nature of banking services, which are considered highly risky and technology-intensive.Design/methodology/approachAbout 575 valid face-to-face self-administered surveys were gathered using convenience sampling among real bank customers in Turkey. The structural equation modelling was used to test hypotheses involving both direct and moderation effects.FindingsThe current study has demonstrated that the AIDUA model is valid and reliable for the acceptance of AIDs in banking service encounters by modifying it. The study results have shown that the acceptance process of AIDs for bank customers consists of three phases. Furthermore, the study’s findings have demonstrated that technology anxiety and risk aversion have adverse moderation effects on the relationship between performance expectancy and emotion as well as on the relationship between emotion and willingness to accept AIDs, respectively.Originality/valueThe current study validates the AIDUA model for the banking industry. In addition, the present study is unique compared to other studies conducted in the literature since it applies the AIDUA model to the setting of banking services for the first time by considering the potential effects of two moderators.International Journal of Bank Marketing1082The present study applies the AIDUA model to the setting of banking services for the first time by considering the potential effects of two moderators, namely technology anxiety and risk aversion by regarding the nature of banking services, which are considered highly risky and technology-intensive.International Journal of Bank Marketing["\u0130smail G\u00f6khan Ci\u0307ntam\u00fcr"]2024-07-18T00:00:00 01868070eb91988dd0be6417ffeed7e6edda95e5BMC Cancer712Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data.BMC Cancer["Solmaz Sohrabei", "Hamid Moghaddasi", "Azamossadat Hosseini", "S. J. Ehsanzadeh"]2024-07-18T00:00:00 3948c9f3913a660c635a7f4f7d4ea8c69bc43b00The spread of AI text generators such as ChatGPT in education has reached an enormous reach in a short period, which has led to various questions regarding the acceptance of artificial intelligence among teachers and student teachers. This study examines the acceptance of AI among teachers and student teachers. In particular, it considers crucial aspects for planning teaching and teacher training. The results show that despite fundamentally positive attitudes towards AI applications, there are concerns regarding data ethics and legal standards. The correlation between the intention to use AI and trust in AI is significant. The findings should help gain a more comprehensive understanding of the acceptance of AI in the education sector and help teachers plan training and further education accordingly.Advances in Mobile Learning Educational Research161Examination of the acceptance of AI among teachers and student teachers shows that despite fundamentally positive attitudes towards AI applications, there are concerns regarding data ethics and legal standards.Advances in Mobile Learning Educational Research["Gerhard Brandhofer", "Karin Tengler"]2024-07-18T00:00:00 1dab8bb2e77c1eaab47d973bcb6c9069b82512e9INTRODUCTION The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long-term goal of harnessing the power of AI to augment didactic teaching, the objective of this study was to quantify and compare the accuracy of responses provided by ChatGPT (GPT-4 and GPT-3.5) and Google Gemini, the three primary large language models (LLMs), to human graduate students (control group) to the annual in-service examination questions posed by the American Academy of Periodontology (AAP). METHODS Under a comparative cross-sectional study design, a corpus of 1312 questions from the annual in-service examination of AAP administered between 2020 and 2023 were presented to the LLMs. Their responses were analyzed using chi-square tests, and the performance was juxtaposed to the scores of periodontal residents from corresponding years, as the human control group. Additionally, two sub-analyses were performed: one on the performance of the LLMs on each section of the exam; and in answering the most difficult questions. RESULTS ChatGPT-4 (total average: 79.57%) outperformed all human control groups as well as GPT-3.5 and Google Gemini in all exam years (p < .001). This chatbot showed an accuracy range between 78.80% and 80.98% across the various exam years. Gemini consistently recorded superior performance with scores of 70.65% (p = .01), 73.29% (p = .02), 75.73% (p < .01), and 72.18% (p = .0008) for the exams from 2020 to 2023 compared to ChatGPT-3.5, which achieved 62.5%, 68.24%, 69.83%, and 59.27% respectively. Google Gemini (72.86%) surpassed the average scores achieved by first- (63.48% ± 31.67) and second-year residents (66.25% ± 31.61) when all exam years combined. However, it could not surpass that of third-year residents (69.06% ± 30.45). CONCLUSIONS Within the confines of this analysis, ChatGPT-4 exhibited a robust capability in answering AAP in-service exam questions in terms of accuracy and reliability while Gemini and ChatGPT-3.5 showed a weaker performance. These findings underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains. However, the current limitations of these models such as inability to effectively process image-based inquiries, the propensity for generating inconsistent responses to the same prompts, and achieving high (80% by GPT-4) but not absolute accuracy rates should be considered. An objective comparison of their capability versus their capacity is required to further develop this field of study.Journal of Periodontal Research278ChatGPT-4 exhibited a robust capability in answering AAP in-service exam questions in terms of accuracy and reliability while Gemini and ChatGPT-3.5 showed a weaker performance, underscore the potential of deploying LLMs as an educational tool in periodontics and oral implantology domains.Journal of periodontal research["Hamoun Sabri", "Muhammad H A Saleh", "P. Hazrati", "Keith Merchant", "Jonathan Misch", "Purnima S Kumar", "Hom\u2010Lay Wang", "S. Barootchi"]2024-07-18T00:00:00 e0dad152feab054c9e98ee95762976681894778cThis paper delves into the evolution of Artificial Intelligence Generated Content (AIGC) within the realm of contemporary digital art, and scrutinizes its implications on artistic modalities and production. It delineates the genesis and progression of AIGC technology, offering an analysis of its extensive applications on the design of cinematic character. The paper acknowledges the latent capabilities of AIGC in the domain of character design, and highlighting the significance of the fusion of AI technology and humanities in the realm of artistic production. In conclusion, delineates the prospective opportunities and risks associated with the future application of AIGC technology in the field of character design and offer a critical analysis of its implications for the creative industry, which is maintaining a reverent and contemplative stance towards traditional art while pursuing of technological innovation.Advances in Education, Humanities and Social Science Research70The genesis and progression of AIGC technology is delineated, offering an analysis of its extensive applications on the design of cinematic character, and the potential opportunities and risks associated with the future application of AIGC technology in the field of character design are delineated.Advances in Education, Humanities and Social Science Research["Han Yan", "Shu Tan", "Kaiqi Zhang", "Danrui Wang"]2024-07-18T00:00:00 2f1a83b154f8b4f302fceae4ab11c9cfd8771f5eArtificial Intelligence (AI) is an expanding field within computer science that specializes in the creation of intelligent machines capable of executing tasks traditionally performed by humans. The utilization of AI technology is progressively increasing across various sectors such as education, healthcare, banking, and transportation. The potential of AI lies in its ability to enhance efficiency, accuracy, and decision-making processes across different industries. We often use AI many times in our daily life without knowing it; for example, we have recently scrolled through a website page, watched a video and used a mobile phone. The voice control function, checking the inbox, or retrieving a word uses a complex computer algorithm that can be automatically learned from experience. AI systems have the capacity to acquire knowledge and enhance their performance through the utilization of machine learning algorithms. In recent years, AI has been increasingly used in various sectors. A detailed review of recent AI tools is discussed in this review to provide idea for students.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT220A detailed review of recent AI tools is discussed in this review to provide idea for students.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Jain Caroline Lourduraj", "Dr. Muthu Thangaraj", "Dr. Merlyn Sujatha", "Sowmya Senthilkumar"]2024-07-18T00:00:00 fc4ec3cc1682b7e24058fd17441c7ab8529530eeBMC Medical Ethics440German physicians frequently encountering incapacitated patients exhibit hesitance toward AI-driven preference prediction but hold a higher esteem for CESS, suggesting concerns about individuality, explicability, and human-machine roles may facilitate the acceptance of AI in clinical ethics.BMC Medical Ethics["Lasse Benzinger", "Jelena Epping", "F. Ursin", "S. Salloch"]2024-07-18T00:00:00 9019d3506dd371e3ce21f779a2c6260cb422b671This paper analyses cybersecurity alerts and advisories issued by the Cybersecurity and Infrastructure Security Agency of the United States of America from January 2023 to March 2024. Employing a qualitative content analysis methodology, the study investigates prevalent cyber threats, identifies the most impacted industry sectors and affected systems, and evaluates recommended mitigation strategies. The analysis categorises the identified threats, revealing sector-specific vulnerabilities and their impact on critical systems. Furthermore, the study explores the potential of Artificial Intelligence (AI) to enhance cybersecurity practices. Key findings highlight significant trends in ransomware attacks, phishing campaigns, state-sponsored activities, and vulnerabilities within critical infrastructure, underlining the crucial need for robust cybersecurity solutions. The paper recommends integrating AI into existing cybersecurity frameworks, enhancing predictive threat detection capabilities, automating response systems, and improving network behavioural analytics. While acknowledging the valuable insights from public advisories, the study also identifies their limitations. Finally, the paper suggests potential avenues for future research to further refine cybersecurity strategies and policies.2024 IEEE Technology and Engineering Management Society (TEMSCON LATAM)390This paper analyses cybersecurity alerts and advisories issued by the Cybersecurity and Infrastructure Security Agency of the United States of America from January 2023 to March 2024, and explores the potential of Artificial Intelligence to enhance cybersecurity practices.2024 IEEE Technology and Engineering Management Society (TEMSCON LATAM)["Masike Malatji"]2024-07-18T00:00:00 1febfa969ba709cd003b7eb1cc478a27e3549304The widespread adoption of generative artificial intelligence (GAI) technologies heralds an era of expanding possibilities in the domain of cultural heritage conservation. This paradigm shift is marked by a confluence of innovative methodologies, including digital twin mapping, digital archiving, and enhanced preservation strategies, aimed at safeguarding the vestiges of our shared past. The application of AI within this field represents a frontier where technology and tradition intersect, offering new vistas for the preservation of historical structures and artifacts that are at risk of deterioration or oblivion. This article endeavors to elucidate the perspectives of professionals within the conservation domain on the integration of AI technologies, drawing upon a comprehensive review of scholarly discourse and the insights derived from a qualitative study. These discussions brought forth rich insights from a spectrum of professionals, each contributing unique perspectives based on their domain expertise and experiences. Participants included conservationists, archaeologists, museum curators, technologists, architects, and restorers, among others, whose collective wisdom paints a multifaceted picture of the challenges and opportunities AI presents in this field.Metaverse310This article endeavors to elucidate the perspectives of professionals within the conservation domain on the integration of AI technologies, drawing upon a comprehensive review of scholarly discourse and the insights derived from a qualitative study.Metaverse["Kholoud Ghaith", "James Hutson"]2024-07-18T00:00:00 cab30c8bb5d5f86317c4c2190bfe2848ba66fbe3Purpose This study aimed to evaluate the quality of artificial intelligence (AI)/machine learning (ML) studies on hemorrhagic stroke using the Minimum Information for Medical AI Reporting (MINIMAR) and Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) frameworks to promote clinical application. Materials and Methods PubMed, MEDLINE, and Embase were searched for AI/ML studies on hemorrhagic stroke. Out of the 531 articles found, 29 relevant original research articles were included. MINIMAR and MI-CLAIM scores were assigned by two experienced radiologists to assess the quality of the studies. Results We analyzed 29 investigations that utilized AI/ML in the field of hemorrhagic stroke, involving a median of 224.5 patients. The majority of studies focused on diagnostic outcomes using computed tomography scans (89.7%) and were published in computer science journals (48.3%). The overall adherence rates to reporting guidelines, as assessed through the MINIMAR and MI-CLAIM frameworks, were 47.6% and 46.0%, respectively. In MINIMAR, none of the studies reported the socioeconomic status of the patients or how missing values had been addressed. In MI-CLAIM, only two studies applied model-examination techniques to improve model interpretability. Transparency and reproducibility were limited, as only 10.3% of the studies had publicly shared their code. Cohen’s kappa between the two radiologists was 0.811 and 0.779 for MINIMAR and MI-CLAIM, respectively. Conclusion The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal. It is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.Yonsei medical journal270The overall reporting quality of published AI/ML studies on hemorrhagic stroke is suboptimal and it is necessary to incorporate model examination techniques for interpretability and promote code openness to enhance transparency and increase the clinical applicability of AI/ML studies.Yonsei Medical Journal["Chae Young Lim", "B. Sohn", "M. Seong", "Eung Yeop Kim", "Sung Tae Kim", "S. Won"]2024-07-18T00:00:00 81b99485762ec46c5ff29e04df6e9d6b0623f18eCognitive Research760An enduring belief that AI is less adept at assessing the morality compared to the competence of human behavior, even as AI capabilities continued to advance is suggested.Cognitive Research: Principles and Implications["Manuel Oliveira", "Justus Brands", "Judith Mashudi", "B. Liefooghe", "R. Hortensius"]2024-07-18T00:00:00 9dc5eeaef7fb9fa03c651ceae3a593da68c34652Artificial Intelligence (AI) has emerged as a transformative force in various domains, including software engineering. The integration of AI into software engineering practices has led to significant advancements in project management, software development, and testing processes. This paper explores the profound impact of AI on software engineering by examining its historical context, methodologies, and practical applications. It delves into AI-driven project management, AI-assisted software development lifecycle, and AI in software testing. Additionally, it highlights the application of AI in Software as Medical Devices (SaMD), software measurement, and overall software engineering practices. The interaction between AI and software engineering presents synergies and mutual benefits, yet poses challenges such as data quality, model interpretability, and ethical concerns. The paper concludes with insights into future trends and research directions, emphasizing the potential of AI to revolutionize software engineering further and the need for continuous research to address emerging challenges. The findings underscore the transformative potential of AI, guiding practitioners and policymakers towards more efficient, ethical, and innovative software engineering practicesInternational Journal of Advanced Research in Science, Communication and Technology130This paper explores the profound impact of AI on software engineering by examining its historical context, methodologies, and practical applications, and delves into AI-driven project management, AI-assisted software development lifecycle, and AI in software testing.International Journal of Advanced Research in Science, Communication and Technology["Celia Dolores Benitez", "Montes Serrano"]2024-07-18T00:00:00 a6f40c0fbd1cfb93ec3c7b3cc94d2eb332f2ad36“Lexi” is a light of clarity in a world where legal complexity frequently makes comprehension difficult. It is a tool that uses the revolutionary potential of generative Artificial Intelligence (AI) to completely change the process of producing legal documents. To simplify legal language and improve the accessibility and comprehension of legal documents, this paper introduces Lexi, a revolutionary tool. Through the integration of cutting-edge AI technology, Lexi not only improves legal drafting productivity but also supports legal communications that are clear and understandable. This innovation marks a paradigm change in legal documentation by emphasizing readability and ease of use and opening the door to a more diverse legal environment.2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST)150This innovation marks a paradigm change in legal documentation by emphasizing readability and ease of use and opening the door to a more diverse legal environment.2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST)["Thippaluri Yahid Basha", "Barma Kalyani", "Yelisetti Sandeep"]2024-07-18T00:00:00 d036ed01d8f75e81081978e29fc9534f3e2ae027This study aims to investigate external auditors' perception of the impact of artificial intelligence's ease of use and usefulness on audit quality, while also examining the potential scepticism among external auditors towards the integration of artificial intelligence in the audit process. A quantitative approach is utilized to analyze the relationship between the variables. The research was conducted through questionnaires distributed via telegram and WhatsApp groups, email, and collaboration with IAPI. One hundred and eight valid responses were gathered from external auditors of Big Four KAP, internationally affiliated KAP - non big four, and national KAP.  Findings reveal that external auditors do not exhibit a sceptical attitude towards the usage of artificial intelligence in the audit process. The test results demonstrate that external auditors' perceptions of the ease of using artificial intelligence aids in enhancing audit quality. Similarly, gauging external auditors' perceived usefulness or benefits of artificial intelligence can also lead to improved audit quality. This study's sample is limited to external auditors of financial statements at public accounting firms, and therefore the findings should not be extended to other types of auditors such as government or internal auditors. This study offers external auditors a thorough comprehension of how artificial intelligence contributes to the audit process to improve audit quality. The necessity for external auditors to improve their technical skills arises due to the increased utilization of big data by businesses. Hence, universities must provide graduates with competence in not only accounting and auditing but also the latest technology.Jurnal Akuntansi290Findings reveal that external auditors do not exhibit a sceptical attitude towards the usage of artificial intelligence in the audit process, and demonstrate that external auditors' perceptions of the ease of using artificial intelligence aids in enhancing audit quality.JAK (Jurnal Akuntansi) Kajian Ilmiah Akuntansi["Yetri Martika Sari", "R. Putri."]2024-07-18T00:00:00 78549da065b57552955802ec5429fe8f5ae69bc2This paper presents the Menstrual Health Chatbot, aimed at boosting awareness and access to information on menstrual health. It features advanced natural language processing and a healthcare-focused artificial intelligence model, specially refined to deliver individualized responses. The chatbot integrates personal medical histories to customize interactions, catering to conditions like PCOS and Endometriosis. Its architecture enhances user experience from sign-up to query resolution, fostering privacy and encouraging open dialogues on menstrual health. A critical aspect in the crafting of this model is the model finetuning with structured question and answer pairs, highlighting Artificial Intelligence’s role in bespoke healthcare. The model has undergone finetuning using the techniques Adapter Tuning and Low Rank Adaptation (LoRA). A comparison between the models finetuned using Adapter Tuning and LoRA is conducted, based on the chatbot responses.2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST)170The Menstrual Health Chatbot features advanced natural language processing and a healthcare-focused artificial intelligence model, specially refined to deliver individualized responses, highlighting Artificial Intelligence’s role in bespoke healthcare.2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST)["Krishnaveni Vengala", "Nanda Krishna Cherukuri", "Prasanna Annavarapu", "Prasanth Dodla"]2024-07-18T00:00:00 d0cc8d9045fb7e4799861f68943b16c21fb630c7Abstract—Emerging technology such as artificial intelligence employs machines to mimic human behavior and is dependent on computers and huge data.By looking at large amounts of data, applying algorithms, and visualizing the information, people may apply artificial intelligence.The use of various forms of technology can replace many decision-making processes in the modern era.Nowadays, a lot of courts are looking at using AI in the legal system.Artificial intelligence is used to provide relevant resources, pass/expire tests, provide early warnings at every access connection time, and more.Inequality and corruption in the court system can also be resolved, which is advantageous for decision- making supported by intelligence.An examination of this article demonstrates that artificial intelligence is required to combat crime within the legal system and facilitates the shift. from general to diligent labor and is helpful in enhancing the legal system.Over 8 million instances were seen for the first time in 2017, with only 100,000 cases ending.Nevertheless, 4,444 million patients must be closed by 2020 due to the smart experiment involving more than 10 million patientsfor the firsttime.Although a lot has changed, we still need to create and enhance corporate policies, safeguard intelligence from private access, and regulate corporations. has the same thinking and human brain in mind.It might be required to support decision-making. Index Terms—— Artificial Intelligence, Courts,Judges;INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00An examination of this article demonstrates that artificial intelligence is required to combat crime within the legal system and facilitates the shift from general to diligent labor and is helpful in enhancing the legal system.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Srikanth Bn", "Sowmya V"]2024-07-18T00:00:00 77c1a7878764c7bf3ea3f8c93e60515ef7c6ae5fThe increasing entrepreneurial activity has led to a growth in the number of cases being handled by the judicial system. In order to reduce the workload on the courts and expand public access to justice, digital technologies are being adopted in judicial activities around the world, and experiments are being conducted on the use of artificial intelligence technologies for performing certain procedural actions. The article examines examples of the use of artificial intelligence technologies by judicial authorities in different countries. The results of these experiments show that existing artificial intelligence technologies can efficiently process large volumes of information, identify the applicable legal norms, and generate draft judicial documents based on similar past cases. However, the complete replacement of a judge with technology is currently extremely risky due to various potential risks, including the possibility of making unjust decisions. Artificial intelligence technologies may face difficulties in processing abstract concepts (such as reasonableness and justice), that are typically employed in legal principles. It seems that further implementation of digital technologies and the creation of additional online services will help reduce the workload on the courts and enable citizens to exercise their right to judicial protection. Nevertheless, large-scale integration of artificial intelligence technology into judicial activities requires further research, including testing its application in simple case categories and for specific procedural actions.Tyumen State University Herald Social Economic and Law Research90Examples of the use of artificial intelligence technologies by judicial authorities in different countries are examined, showing that existing artificial intelligence technologies can efficiently process large volumes of information, identify the applicable legal norms, and generate draft judicial documents based on similar past cases.Tyumen State University Herald. Social, Economic, and Law Research["N. Buzova"]2024-07-18T00:00:00 27655d7fe3166938025bed014fafa5e8d5d09a39The study aims to do research analysis on AI usage in the field of health care. Technology has been in use in daily and practical life. People are able to feel its presence every single moment. Artificial Intelligence usage like tracking customers data every day based on their daily routine are normal nowadays. AI cannot be restricted to mobile phone usage alone. There are lot of emerging technologies like machine learning, deep learning, sensors etc. practically used in everyday routine. AI have intruded in people lives and they are aware its tracking. Knowing more about the AI environment, its benefits enjoyed in every sector especially in health care which is emerging sector in India provides lot of insights to the readers through review of literature and bibliometric analysis. The results shows that in the year 2020, the number of publications has increased by 67.8 percent. In the year 2021 tremendous increase of 70 percent of the articles and in the year 2022 it has been 56 percent. Hence this paper has attempted to find answers to the above research questions.2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)350Knowing more about the AI environment, its benefits enjoyed in every sector especially in health care which is emerging sector in India provides lot of insights to the readers through review of literature and bibliometric analysis.2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)["S.Sudha", "D.Anitha Kumari", "Nirmal Raaghav"]2024-07-18T00:00:00 6850f33fd75ab985aee8e62642b4d8b4ec105a09The aim of this study is to explore the connection between individual innovativeness levels and attitudes toward artificial intelligence among nursing and midwifery students. Data were collected from 500 nursing and midwifery students studying at a university in Türkiye. The data gathered between November and December 2023 involved a Personal Information Form, the Individual Innovation Scale, and the General Attitudes toward Artificial Intelligence Scale. Data analysis used descriptive statistics, independent-samples t test, analysis of variance, Bonferroni test, and logistic regression models. Students' average Individual Innovativeness Scale score was 59.47 ± 7.23. Consequently, it was determined that students' individual innovativeness levels were inadequate, placing them in the questioning group. Students demonstrated positive attitudes toward artificial intelligence, with General Attitudes toward Artificial Intelligence Scale-positive scores at a good level (42.67 ± 7.10) and negative attitudes at an average level (24.08 ± 5.81). A significant, positive relationship was found between Individual Innovation Scale and General Attitudes toward Artificial Intelligence Scale total scores (P < .001). The individual innovation level of students proved to be a significant predictor of attitudes toward artificial intelligence (P < .001). Students' individual innovativeness levels positively influence their attitudes toward artificial intelligence. However, it was identified that students' individual innovativeness levels are not sufficient and require improvement.Computers, Informatics, Nursing290The individual innovation level of students proved to be a significant predictor of attitudes toward artificial intelligence (P < .001), and students' individual innovativeness levels positively influence their attitudes toward artificial intelligence.Computers, informatics, nursing : CIN["\u015eeyma Ki\u0307lci\u0307 Erci\u0307yas", "Ebru Cirban Ekrem", "Elif Keten Edis"]2024-07-18T00:00:00 d5c7b00f2c0f3f7243654010129ab11e39d19dffPurposeDrawing on the Theory of Planned Behavior, this study aims to explore the role of individual digital competencies, encompassing information and data literacy, communication and collaboration, safety and security, and problem-solving, in shaping cognitive determinants and influencing digital entrepreneurial intentions as well as investigates the moderating effect of performance expectancy of AI solutions on the relationship between digital competencies, cognitive determinants, and digital entrepreneurial intention.Design/methodology/approachUsing a sample of 1326 MBA students in Vietnam with a stratified sampling approach, the second-order PLS-SEM is used to test the formulated hypotheses rigorously.FindingsThe study reveals that individual digital competencies, sculpted by information and data literacy, communication and collaboration, safety and security, and problem-solving, significantly impact cognitive determinants (attitude towards digital entrepreneurship, subjective norms, and perceived behavioral control), influencing digital entrepreneurial intentions. Performance expectancy of AI solutions also plays a crucial moderating role, enhancing the relationship between digital competencies and digital entrepreneurial intention.Research limitations/implicationsSome practical implications have been recommended for policymakers, educators, and entrepreneurs.Originality/valueThis research provides original empirical findings, validating the impact of a varied array of digital competencies on entrepreneurial mindsets/cognition and intentions. The introduction of performance expectancy of AI solutions as a moderator introduces a nuanced dimension to comprehending the interaction between technological skills and entrepreneurial intentions.Journal of Small Business and Enterprise Development1084The study reveals that individual digital competencies, sculpted by information and data literacy, communication and collaboration, safety and security, and problem-solving, significantly impact cognitive determinants, influencing digital entrepreneurial intentions.Journal of Small Business and Enterprise Development["Cong Doanh Duong", "Trung Thanh Le", "Ngoc Su Dang", "Ngoc Diep Do", "Anh Trong Vu"]2024-07-18T00:00:00 c5e5b17abe8e122b72c6b9c7a726ce9ee5bfc346Service Industries Journal932The Service Industries Journal["Muhammad Abubakar Tahir", "Gaofeng Da", "Muzhar Javed", "Muhammad Waheed Akhtar", "Xiaohui Wang"]2024-07-18T00:00:00 6b5f19ded45765fd4c8cfe95a320753b6b7f0fe5Academy of Management Journal921Academy of Management Journal["Lydia Paine Hagtvedt", "Sarah Harvey", "Ozumcan Demir-Caliskan", "Henrik Hagtvedt"]2024-07-18T00:00:00 4b66fba438d55f277696a25cca87de2ff5767eb0The rapid evolution of AI technologies has reshaped our daily lives. As AI systems become increasingly prevalent, AI literacy, the ability to comprehend and engage with these technologies, becomes paramount in modern society. However, existing research has yet to establish a comprehensive framework for AI literacy. This study aims to fill this gap by developing a holistic AI literacy scale. Three levels of dimensions are considered: individual, interactive, and sociocultural. The scale includes cognitive, behavioral, and normative competencies. After rigorous reliability and validity assessments, the final AI literacy scale comprises six dimensions: AI features, AI processing, algorithm influences, user efficacy, ethical consideration, and threat appraisal. Detailed scale development, validation, and dimension-specific items are thoroughly explained. This comprehensive scale equips individuals with the competencies needed to navigate and critically engage with AI in today’s multifaceted AI landscape.Journal of educational computing research502This comprehensive scale equips individuals with the competencies needed to navigate and critically engage with AI in today’s multifaceted AI landscape.Journal of Educational Computing Research["C. Yuan", "Hsin-yi Sandy Tsai", "Yu-Ting Chen"]2024-07-18T00:00:00 69d5d09c440af7924e90eefb870f67b389ce0429Journal of Applied Learning &amp; Teaching00Journal of Applied Learning &amp; Teaching[]2024-07-18T00:00:00 077f3948d72e8f26278cba0c9c0f18e72662d4dcMuscle and Nerve00Muscle & nerve[]2024-07-18T00:00:00 15f23d745ed330e4cdcb269a4f7c5c7bb08b0f2fAbstract Introduction In silico tools capable of predicting the functional consequences of genomic differences between individuals, many of which are AI-driven, have been the most effective over the past two decades for non-synonymous single nucleotide variants (nsSNVs). When appropriately selected for the purpose of the study, a high predictive performance can be expected. In this feasibility study, we investigate the distribution of nsSNVs with an allele frequency below 5%. To classify the putative functional consequence, a tier-based filtration led by AI-driven predictors and scoring system was implemented to the overall decision-making process, resulting in a list of prioritised genes. Methods The study has been conducted on breast cancer patients of homogeneous ethnicity. Germline rare variants have been sequenced in genes that influence pharmacokinetic parameters of anticancer drugs or molecular signalling pathways in cancer. After AI-driven functional pathogenicity classification and data mining in pharmacogenomic (PGx) databases, variants were collapsed to the gene level and ranked according to their putative deleterious role. Results In breast cancer patients, seven of the twelve genes prioritised based on the predictions were found to be associated with response to oncotherapy, histological grade, and tumour subtype. Most importantly, we showed that the group of patients with at least one rare nsSNVs in cystic fibrosis transmembrane conductance regulator (CFTR) had significantly reduced disease-free (log rank, p = 0.002) and overall survival (log rank, p = 0.006). Conclusion AI-driven in silico analysis with PGx data mining provided an effective approach navigating for functional consequences across germline genetic background, which can be easily integrated into the overall decision-making process for future studies. The study revealed a statistically significant association with numerous clinicopathological parameters, including treatment response. Our study indicates that CFTR may be involved in the processes influencing the effectiveness of oncotherapy or in the malignant progression of the disease itself.Oncology00The study indicates that CFTR may be involved in the processes influencing the effectiveness of oncotherapy or in the malignant progression of the disease itself, and provides an effective approach navigating for functional consequences across germline genetic background, which can be easily integrated into the overall decision-making process for future studies.Oncology["M\u00e1ria Kov\u00e1\u010dov\u00e1", "V. Hlav\u00e1\u010d", "R. Ko\u017eevnikovov\u00e1", "K. Rau\u0161", "Ji\u0159\u00ed Gat\u011bk", "Pavel Sou\u010dek"]2024-07-18T00:00:00 6566749750d87fc47b97453c00ba8cd2f7f689e8Research on AI governance is important towards potentially useful and constraining affordable misuse, reduce new risks and economic trends that threaten to disrupt public political and economic trends, and drive off target as interest in advanced AI systems and the norms, focal points, and use of new AI research are potentially transformative and governance institutions aim to prevent. Potential public benefits from policy community re-using AI research are enormous, including reduced economic instability. A fundamental challenge in AI governance is a cognitive framing challenge: governing AI research requires understanding new kinds of safety risks, performance goals, and intended applications that advanced AI systems will make possible. Specifically, the letter focuses on how AI research could mitigate issues such as the possibility of AI capabilities getting concentrated within a small and hard-to-regulate group of actors, and ultimately recommends the prioritization of open research and collaboration, with concern for long-term social and economic looming to the forefront of coalitions if AI becomes an increasingly important aspect of the future economy and society.Journal of Ecohumanism200This letter focuses on how AI research could mitigate issues such as the possibility of AI capabilities getting concentrated within a small and hard-to-regulate group of actors, and ultimately recommends the prioritization of open research and collaboration.Journal of Ecohumanism["Bakhit Moh\u2019d Al Dajeh"]2024-07-18T00:00:00 d1e4040998ee095692cc60293b9b59256cfe77fcTheology and Science00Theology and Science["C. Kaunda"]2024-07-18T00:00:00 a417e8e881341b4c5868c212349b4745c7f1084eJAMA ophthalmology70JAMA ophthalmology["Andrew Mihalache", "M. Popovic", "R. Muni"]2024-07-18T00:00:00 6dac0409b43c9d021e1acd99310107f3ff797ac1Background The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians’ comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. Objective The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. Methods We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Results Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. Conclusions This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. International Registered Report Identifier (IRRID) RR2-10.11124/JBIES-22-00374JMIR Medical Education818This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education and encourages future researchers to engage in a multidisciplinary approach to curriculum redesign.JMIR Medical Education["Raymond Tolentino", "A. Baradaran", "Genevi\u00e8ve Gore", "Pierre Pluye", "Samira Abbasgholizadeh-Rahimi"]2024-07-18T00:00:00 f23f624836db8b43953984d0832a0afd3bec6001Training data privacy is a fundamental problem in modern Artificial Intelligence (AI) applications, such as face recognition, recommendation systems, language generation, and many others, as it may contain sensitive user information related to legal issues. To fundamentally understand how privacy mechanisms work in AI applications, we study differential privacy (DP) in the Neural Tangent Kernel (NTK) regression setting, where DP is one of the most powerful tools for measuring privacy under statistical learning, and NTK is one of the most popular analysis frameworks for studying the learning mechanisms of deep neural networks. In our work, we can show provable guarantees for both differential privacy and test accuracy of our NTK regression. Furthermore, we conduct experiments on the basic image classification dataset CIFAR10 to demonstrate that NTK regression can preserve good accuracy under a modest privacy budget, supporting the validity of our analysis. To our knowledge, this is the first work to provide a DP guarantee for NTK regression.arXiv.org06This work can show provable guarantees for both differential privacy and test accuracy of the NTK regression and conducts experiments on the basic image classification dataset CIFAR10 to demonstrate that NTK regression can preserve good accuracy under a modest privacy budget, supporting the validity of the analysis.ArXiv["Jiuxiang Gu", "Yingyu Liang", "Zhizhou Sha", "Zhenmei Shi", "Zhao Song"]2024-07-18T00:00:00 7c88ce6431ed38208852914ebf1b3b4524fa6264 The Artificial Intelligence revolution is upon us—AI has begun to penetrate almost every sphere of human activity, from the economy to law, health, transport, education, defence, media/communication, sport, and leisure. This edited book of twenty specially commissioned chapters examines some of the key moral dilemmas that AI poses—including those around privacy, bias, transparency, accountability, and autonomy. The short chapters are rich with both real and hypothetical examples—and written in a style that will be accessible to all.00This edited book of twenty specially commissioned chapters examines some of the key moral dilemmas that AI poses—including those around privacy, bias, transparency, accountability, and autonomy.[]2024-07-18T00:00:00 6c49f079b806cbd881cbd40ab5dcc77f9b40fa6aWe outline the principles of classical assurance for computer-based systems that pose significant risks. We then consider application of these principles to systems that employ Artificial Intelligence (AI) and Machine Learning (ML). A key element in this"dependability"perspective is a requirement to have near-complete understanding of the behavior of critical components, and this is considered infeasible for AI and ML. Hence the dependability perspective aims to minimize trust in AI and ML elements by using"defense in depth"with a hierarchy of less complex systems, some of which may be highly assured conventionally engineered components, to"guard"them. This may be contrasted with the"trustworthy"perspective that seeks to apply assurance to the AI and ML elements themselves. In cyber-physical and many other systems, it is difficult to provide guards that do not depend on AI and ML to perceive their environment (e.g., other vehicles sharing the road with a self-driving car), so both perspectives are needed and there is a continuum or spectrum between them. We focus on architectures toward the dependability end of the continuum and invite others to consider additional points along the spectrum. For guards that require perception using AI and ML, we examine ways to minimize the trust placed in these elements; they include diversity, defense in depth, explanations, and micro-ODDs. We also examine methods to enforce acceptable behavior, given a model of the world. These include classical cyber-physical calculations and envelopes, and normative rules based on overarching principles, constitutions, ethics, or reputation. We apply our perspective to autonomous systems, AI systems for specific functions, generic AI such as Large Language Models, and to Artificial General Intelligence (AGI), and we propose current best practice and an agenda for research.arXiv.org03The principles of classical assurance for computer-based systems that pose significant risks are outlined and application of these principles to systems that employ Artificial Intelligence (AI) and Machine Learning (ML) is considered.ArXiv["Robin Bloomfield", "John Rushby"]2024-07-18T00:00:00 ca20fa89c428f2a02aab7bc844854f6757b1b373 Purposeful and strategic use of L1 can help with L2 learning. However, in many contexts, monolingual immersion approaches dominate, leading language teachers to refrain from using L1. It can also mean that teachers are not professionally prepared to implement evidence-informed uses of L1. In this article, we share the findings of an intervention study that aimed to raise preservice English language teachers’ awareness of purposeful L1 use while co-exploring ways generative artificial intelligence (AI) tools (e.g. ChatGPT) can aid teachers’ knowledge development and strategic utilization of L1 in L2 classrooms. Data were collected from fifty-six preservice language teachers in Hong Kong through a pre- and post-intervention mixed-method survey and follow-up group interviews. The findings show that explicit instruction on the use of L1 in L2 classrooms can increase preservice teachers’ intention to use L1 as well as their knowledge about the evidence-informed use of L1 and the ways in which generative AI tools can assist their implementation of L1.ELT Journal142The findings show that explicit instruction on the use of L1 in L2 classrooms can increase preservice teachers’ intention to use L1 as well as their knowledge about the evidence-informed use of L1 and the ways in which generative AI tools can assist their implementation of L1.ELT Journal["Benjamin Luke Moorhouse", "Yuwei Wan", "Tsz Ying Ho", "Angel M. Y. Lin"]2024-07-18T00:00:00 b785239887327f388ff6f94fc87a18a2a64297d2The pharmaceutical industry must maintain stringent quality assurance standards to ensure product safety and regulatory compliance. A key component of the well-known Six Sigma methodology for process improvement and quality control is precise and comprehensive documentation. However, there are a number of significant issues with traditional documentation procedures, including as slowness, human error, and difficulties with regulatory standards. This review research looks at innovative ways to employ machine learning (ML) and artificial intelligence (AI) to enhance Six Sigma documentation processes in the pharmaceutical sector. AI and ML provide cutting-edge technologies that have the potential to drastically alter documentation processes by automating data entry, collection, and analysis. Natural language processing (NLP) and computer vision technologies have the potential to significantly reduce human error rates and increase the efficacy of documentation processes. By applying machine learning algorithms to support real-time data analysis, predictive analytics, and proactive quality management, pharmaceutical organizations may be able to identify potential quality issues early on and take proactive efforts to address them. Combining AI and ML improves documentation accuracy and reliability while also strengthening compliance with stringent regulatory criteria. The primary barriers and limitations to the current state of Six Sigma documentation in the pharmaceutical industry are identified in this study. It examines the fundamentals of AI and ML with an emphasis on their specific applications in quality assurance and potential benefits for Six Sigma processes. The report includes extensive case studies that highlight notable developments and explain how AI/ML enhanced documentation is used in the real world.Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology01The primary barriers and limitations to the current state of Six Sigma documentation in the pharmaceutical industry are identified and the fundamentals of AI and ML are examined with an emphasis on their specific applications in quality assurance and potential benefits for Six Sigma processes.Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology["Mausami Vaghela", "Sanjesh Rathi", "Rahul L. Shirole", "Jyoti Verma", "Shaheen", "Saswati Panigrahi", "Shubham Singh"]2024-07-18T00:00:00 14ab6f533ce7eb9c2d40fb33870adc4fd59326adThis study conducts an empirical exploration of generative Artificial Intelligence (AI) tools across the game development pipeline, from concept art creation to 3D model integration in a game engine. Employing AI generators like Leonardo AI, Scenario AI, Alpha 3D, and Luma AI, the research investigates their application in generating game assets. The process, documented in a diary-like format, ranges from producing concept art using fantasy game prompts to optimizing 3D models in Blender and applying them in Unreal Engine 5. The findings highlight the potential of AI to enhance the conceptualization phase and identify challenges in producing optimized, high-quality 3D models suitable for game development. This study reveals the current limitations and ethical considerations of AI in game design, suggesting that while generative AI tools hold significant promise for transforming game development, their full integration depends on overcoming these hurdles and gaining broader industry acceptance.Metaverse291The current limitations and ethical considerations of AI in game design are revealed, suggesting that while generative AI tools hold significant promise for transforming game development, their full integration depends on overcoming these hurdles and gaining broader industry acceptance.Metaverse["Andrew Begemann", "James Hutson"]2024-07-18T00:00:00 3d7925cb321c059a039f8445da38d60b2c81d535Foundation Models (FMs) are gaining increasing attention in the biomedical artificial intelligence (AI) ecosystem due to their ability to represent and contextualize multimodal biomedical data. These capabilities make FMs a valuable tool for a variety of tasks, including biomedical reasoning, hypothesis generation, and interpreting complex imaging data. In this review paper, we address the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications. We explore strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges, ensuring that these FMs are translated responsibly into clinical and translational settings. Additionally, we emphasize the importance of key stewardship and co-design principles that not only ensure robust regulation but also guarantee that the interests of all stakeholders—especially those involved in or affected by these clinical and translational applications—are adequately represented. We aim to empower the biomedical AI community to harness these models responsibly and effectively. As we navigate this exciting frontier, our collective commitment to ethical stewardship, co-design, and responsible translation will be instrumental in ensuring that the evolution of FMs truly enhances patient care and medical decision-making, ultimately leading to a more equitable and trustworthy biomedical AI ecosystem.Bioengineering1661This review paper addresses the unique challenges associated with establishing an ethical and trustworthy biomedical AI ecosystem, with a particular focus on the development of FMs and their downstream applications and explores strategies that can be implemented throughout the biomedical AI pipeline to effectively tackle these challenges.Bioengineering["Simha Sankar Baradwaj", "Destiny Gilliland", "Jack Rincon", "Henning Hermjakob", "Yu Yan", "Irsyad Adam", "Gwyneth Lemaster", "Dean Wang", "Karol Watson", "Alex Bui", "Wei Wang", "Peipei Ping"]2024-07-18T00:00:00 2e1b179ded00d180ff9cb3ebf9feb8ba1f33158aEvery year, digital technologies appear in every industry. The new, developing technologies offer both advantages and disadvantages. The following are some recent examples of cutting-edge innovations in technology: data science, cybersecurity, block chain technology, artificial intelligence, machine learning, quantum learning, Internet of Things (IoT), 5G and 6G networks, hyper automation, cloud computing, robotics, and natural language processing. AL and ML combined with other cutting-edge, popular technologies have the potential to yield the positive outcomes and contribute to a greener future. Personalized medicine, drug development and predictive diagnostics using large scale data sets are all areas where machine learning might be beneficial to physicians. Students studying mechanical engineering must have a solid understanding of emerging trends such as autonomous vehicles. The potential of AV to create new, improved lifestyle and revolutionize urban planning and transportation has attracted a lot of interest.  A research utilized a quantitative technique to further his research. A questionnaire was used to collect data from different participants, and 120 students from different fields in higher education sector were chosen at random. According to research, students who used popular technologies acquired more sophisticated abilities that will increase their output at work. Technology is always changing because it takes ongoing training to keep up with the latest development. The issue of the digital divide will be resolved by ongoing training.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402301According to research, students who used popular technologies acquired more sophisticated abilities that will increase their output at work, and the issue of the digital divide will be resolved by ongoing training.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Doctor P. Z Msekelwa"]2024-07-18T00:00:00 49f641ca01a4720a3c9657c687b546b054982b09As Artificial Intelligence (AI) increasingly integrates into our daily lives, fairness has emerged as a critical concern, particularly in medical AI, where datasets often reflect inherent biases due to social factors like the underrepresentation of marginalized communities and socioeconomic barriers to data collection. Traditional approaches to mitigating these biases have focused on data augmentation and the development of fairness-aware training algorithms. However, this paper argues that the architecture of neural networks, a core component of Machine Learning (ML), plays a crucial role in ensuring fairness. We demonstrate that addressing fairness effectively requires a holistic approach that simultaneously considers data, algorithms, and architecture. Utilizing Automated ML (AutoML) technology, specifically Neural Architecture Search (NAS), we introduce a novel framework, BiaslessNAS, designed to achieve fair outcomes in analyzing skin lesion datasets. BiaslessNAS incorporates fairness considerations at every stage of the NAS process, leading to the identification of neural networks that are not only more accurate but also significantly fairer. Our experiments show that BiaslessNAS achieves a 2.55% increase in accuracy and a 65.50% improvement in fairness compared to traditional NAS methods, underscoring the importance of integrating fairness into neural network architecture for better outcomes in medical AI applications.International Conference on Medical Image Computing and Computer-Assisted Intervention340A novel framework, BiaslessNAS, is introduced, designed to achieve fair outcomes in analyzing skin lesion datasets, which incorporates fairness considerations at every stage of the NAS process, leading to the identification of neural networks that are not only more accurate but also significantly fairer.{"pages": "153-163"}["Yi Sheng", "Junhuan Yang", "Jinyang Li", "James Alaina", "Xiaowei Xu", "Yiyu Shi", "Jingtong Hu", "Weiwen Jiang", "Lei Yang"]2024-07-18T00:00:00 bad38b91b33f7c25559d9ff394f25d959ac8b5deIn the era of lifelong learning, creating easily available services that link employment and educational resources is a challenge for career counselling. There hasn’t been much study done on the use of AI to advice in higher education and the workforce thus far. This study explores the potential benefits and enhancements that career counseling at postsecondary educational institutions can bring to the use of artificial intelligence. The outcomes of focus groups, scenario work, and practical trials are mapped to the requirements and potential uses of artificial intelligence in career counseling from the perspectives of students, guidance counselors, and institutions. The findings illustrate both the potential benefits and uses of artificial intelligence in career counseling, along with adoption obstacles and enablers, to enhance postsecondary education and lifelong learning. Drawing on the data, the authors provide many agency and maturity levels for AI integration into guiding processes. Future study should concentrate on agency in advice interactions, expanding the ecology of guidance data, and ethical issues.2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST)120The findings illustrate both the potential benefits and uses of artificial intelligence in career counseling, along with adoption obstacles and enablers, to enhance postsecondary education and lifelong learning.2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST)["Ayush Chandrol", "Monisha Awasthi", "Deepak Sharma", "Mani Kansal", "Komal Sharma", "Ankur Goel"]2024-07-18T00:00:00 2e22f5b4bae3d15a17bdc9bc29ae9a0c8c085aa3The implementation of Artificial Intelligence (AI) in media and journalism is a rapidly evolving area that is reshaping the landscape of news production, distribution, and consumption . [7] However, the full potential of AI in journalism is currently limited by the reliance on tech companies and the early stages of academic literature on the topic. Studies highlight the transformative impact of AI on journalism, emphasizing its role in enabling journalists to break news quickly while also facilitating in-depth analysis. The integration of AI in media and Journalism is evolving drastically, reshaping the landscape of production, distribution and consumption of news content. AI technologies, such as machine learning, planning and optimization are increasingly used in the news industry to enhance productivity and efficiency. This study "Navigating the Nexus: A Bibliometric Analysis of the Intersection Between AI, Multimedia and Journalism" provides a comprehensive overview of the research data over the time period of 2015 to 2024 comprising 73 scholarly documents from 40 different sources to explore the intersection of AI in media and Journalism to increase the productivity and efficiency of news and content production. The study underlines the importance of AI in enhancing the journalistic capabilities while addressing the challenges of the implementation. This study aims at the annual publication numbers in the given time span. Major themes include the implementation of AI in journalism, ethical considerations on AI in news production, global impact, the future research directions to ensure it fulfils humanity’s best interests without enhancing societal inequalities or biases.2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)180This study provides a comprehensive overview of the research data over the time period of 2015 to 2024 comprising 73 scholarly documents from 40 different sources to explore the intersection of AI in media and Journalism to increase the productivity and efficiency of news and content production.2024 Third International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)["Ramandeep Sharma", "Deepak Kumar Singh", "Pradeep Kumar", "Mohd Khalid", "Tushar Ranjan Dash", "Bhoomi Vij"]2024-07-18T00:00:00 8116480c5d948fef55c7f814ce68153a6b602820This paper focuses on the modelling and simulation of Maximum Power Point algorithms to Improve Power Quality at different atmospheric condition and using AI Improve efficiency. Grid-connected output systems are made to convert as much solar energy as possible into usable electricity. Artificial intelligence, Artificial Neural Network (ANN) and the Incremental Conductance Technique method are two recommended techniques interface with DC link controller with the reference voltage in a variety of situations. The aim of this paper is to track MPPT from the solar PV array by the proposed AI controller for irradiation changes and comparing results with PO, A single phase grid connected with a photovoltaic (PV) power system wll provide high voltage gain with state model analysis for the control of the system has been presented. First the photovoltaic system is designed and simulated using MATLAB SIMULINK software. The output voltage of a PV array is comparatively low thus high voltage gain is necessary for grid-connection and synchronization. The PV system has been provided with a boost converter which will boost the low voltage of the PV array to high dc-voltage. A steady state model is obtained and is verified with the help of simulation. A full bridge inverter with bidirectional power flow is used as the second power processing stage, which stabilizes the dc voltage and the output current. Further, a maximum- power-point-tracking method is employed in the PV system to obtain a high performance. Key Words: AI, Solar System, Maximum Power Point Tracking, Fuzzy logic Algorithm, Sinusoidal Pulse Width Modulation, Matlab.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT50To track MPPT from the solar PV array by the proposed AI controller for irradiation changes and comparing results with PO, a single phase grid connected with a photovoltaic (PV) power system will provide high voltage gain with state model analysis for the control of the system has been presented.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Laxman Kumar", "Vivek Kumar", "Vijay Kumar"]2024-07-18T00:00:00 44defff4b093fcbf5779ea54673bb53bdd4ee6fcThis survey article provides a comprehensive overview of the evolving landscape of Explainable Artificial Intelligence (XAI) in Decision Support Systems (DSSs). As Artificial Intelligence (AI) continues to play a crucial role in decision-making processes across various domains, the need for transparency, interpretability, and trust becomes paramount. This survey examines the methodologies, applications, challenges, and future research directions in the integration of explainability within AI-based Decision Support Systems. Through an in-depth analysis of current research and practical implementations, this article aims to guide researchers, practitioners, and decision-makers in navigating the intricate landscape of XAI-based DSSs. These systems assist end-users in their decision-making, providing a full picture of how a decision was made and boosting trust. Furthermore, a methodical taxonomy of the current methodologies is proposed and representative works are presented and discussed. The analysis of recent studies reveals that there is a growing interest in applying XDSSs in fields such as medical diagnosis, manufacturing, and education, to name a few, since they smooth down the trade-off between accuracy and explainability, boost confidence, and also validate decisions.Electronics845The analysis of recent studies reveals that there is a growing interest in applying XDSSs in fields such as medical diagnosis, manufacturing, and education, since they smooth down the trade-off between accuracy and explainability, boost confidence, and also validate decisions.Electronics["Georgios Kostopoulos", "Gregory Davrazos", "S. Kotsiantis"]2024-07-19T00:00:00 765fe01df0a659f8c6e7bb081a6452a62529414dArtificial intelligence (AI) models can play a more effective role in managing patients with the explosion of digital health records available in the healthcare industry. Machine-learning (ML) and deep-learning (DL) techniques are two methods used to develop predictive models that serve to improve the clinical processes in the healthcare industry. These models are also implemented in medical imaging machines to empower them with an intelligent decision system to aid physicians in their decisions and increase the efficiency of their routine clinical practices. The physicians who are going to work with these machines need to have an insight into what happens in the background of the implemented models and how they work. More importantly, they need to be able to interpret their predictions, assess their performance, and compare them to find the one with the best performance and fewer errors. This review aims to provide an accessible overview of key evaluation metrics for physicians without AI expertise. In this review, we developed four real-world diagnostic AI models (two ML and two DL models) for breast cancer diagnosis using ultrasound images. Then, 23 of the most commonly used evaluation metrics were reviewed uncomplicatedly for physicians. Finally, all metrics were calculated and used practically to interpret and evaluate the outputs of the models. Accessible explanations and practical applications empower physicians to effectively interpret, evaluate, and optimize AI models to ensure safety and efficacy when integrated into clinical practice.Journal of ultrasound in medicine703Four real-world diagnostic AI models for breast cancer diagnosis using ultrasound images are developed (two ML and two DL models) and 23 of the most commonly used evaluation metrics were reviewed uncomplicatedly for physicians to provide an accessible overview of key evaluation metrics.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine["A. Abbasian Ardakani", "Omid Airom", "Hamid Khorshidi", "Nathalie J Bureau", "M. Salvi", "F. Molinari", "U. Acharya"]2024-07-19T00:00:00 c305db14521b4f0c8e145b0a21c042705c4ad2e2Despite the growing impact of artificial intelligence (AI) in business, there is little research examining its effects on firm idiosyncratic risk (IR). This is an important issue for boards: as key conduits of firm–environment information flows via board interlock networks, traditional risk oversight functions are being increasingly augmented with strategic decision‐making and communications. Accordingly, we explore how AI and board interlocks independently and interactively affect IR, focusing on the heterogeneity of the board's network ties. We hypothesize these effects within signalling theory, positing that a firm's AI exposure and board network will differentially affect market perceptions of risk contingent on their perceived cost and relative signal strength under different environmental conditions. We find that while AI and board network heterogeneity both favourably affect risk, operating in a high‐AI industry while occupying a network position that spans industry boundaries mitigates these effects, leading to an increase in IR for firms in the most technologically advanced industries. Additional analyses of diversification corroborate these theoretical mechanisms: as a costly signal of competence across multiple domains, diversification enables firms to simultaneously engage with AI and diverse knowledge networks without market penalties. Our findings offer practical insights for directors and avenues for theoretical development.Journal of Management Studies1271It is found that while AI and board network heterogeneity both favourably affect risk, operating in a high‐AI industry while occupying a network position that spans industry boundaries mitigates these effects, leading to an increase in IR for firms in the most technologically advanced industries.Journal of Management Studies["Kerry Hudson", "Robert E. Morgan"]2024-07-19T00:00:00 30aa9df2f1e5c93629b840af1f8ff2ebcac9bfb4SUMMARY OBJECTİVES: The aim of this study was to compare the performance of artificial intelligence models ChatGPT-3.5, ChatGPT-4, and Google Bard in answering Physical Medicine and Rehabilitation board-style questions, assessing their capabilities in medical education and potential clinical applications. METHODS: A comparative cross-sectional study was conducted using the PMR100, an example question set for the American Board of Physical Medicine and Rehabilitation Part I exam, focusing on artificial intelligence models' ability to answer and categorize questions by difficulty. The study evaluated the artificial intelligence models and analyzed them for accuracy, reliability, and alignment with difficulty levels determined by physiatrists. RESULTS: ChatGPT-4 led with a 74% success rate, followed by Bard at 66%, and ChatGPT-3.5 at 63.8%. Bard showed remarkable answer consistency, altering responses in only 1% of cases. The difficulty assessment by ChatGPT models closely matched that of physiatrists. The study highlighted nuanced differences in artificial intelligence models' performance across various Physical Medicine and Rehabilitation subfields. CONCLUSION: The study illustrates the potential of artificial intelligence in medical education and clinical settings, with ChatGPT-4 showing a slight edge in performance. It emphasizes the importance of artificial intelligence as a supportive tool for physiatrists, despite the need for careful oversight of artificial intelligence-generated responses to ensure patient safety.Revista da Associação Médica Brasileira211The study illustrates the potential of artificial intelligence in medical education and clinical settings, with ChatGPT-4 showing a slight edge in performance, despite the need for careful oversight of artificial intelligence-generated responses to ensure patient safety.Revista da Associação Médica Brasileira["A. K. Menek\u015feo\u011flu", "E. Is"]2024-07-19T00:00:00 58ce6320bf0642bf033e3a0afe4abc4a9278f7f0PurposeStarting from the relevance of ethics to the application of artificial intelligence (AI) in the context of employee recruitment and selection (R&S), in this article, we aim to provide a comprehensive review of the literature in light of the main ethical theories (utilitarian theories, theories of justice, and theories of rights) to identify a future research agenda and practical implications.Design/methodology/approachOn the basis of the best-quality and most influential journals, we conducted a systematic review of 120 articles from two databases (Web of Science and Scopus) to provide descriptive results and adopt a framework for deductive classification of the main topics.FindingsInspired by the three ethical theories, we identified three thematic lines of enquiry for the debate on AI in R&S: (1) the utilitarian view: the efficient optimisation of R&S through AI; (2) the justice view: the perceptions of justice and fairness related to AI techniques; and (3) the rights view: the respect for legal and human rights requirements when AI is applied.Originality/valueThis article provides a detailed assessment of the adoption of AI in the R&S process from the standpoint of traditional ethics theories and offers an integrative theoretical framework for future research on AI in the broader field of HRM.Person-centered review801This article provides a detailed assessment of the adoption of AI in the R&S process from the standpoint of traditional ethics theories and offers an integrative theoretical framework for future research on AI in the broader field of HRM.Personnel Review["Martina Mori", "Sara Sassetti", "Vincenzo Cavaliere", "Mariacristina Bonti"]2024-07-19T00:00:00 f0ac2341419c0ba7553ce443c5c963646aa0c7b3 Artificial intelligence (AI) is reshaping drug discovery and delivery in the pharmaceutical industry, fundamentally transforming traditional methods. In drug discovery, AI algorithms rapidly analyze vast biological and chemical datasets to identify potential drug candidates with unprecedented accuracy. Machine learning models predict compound efficacy and safety, accelerating earlystage drug development. AI also facilitates drug repurposing, uncovering new therapeutic uses for existing medications. At the drug delivery front, AI optimizes formulations and systems, enabling targeted and personalized approaches. Intelligent algorithms enhance the understanding of pharmacokinetics and pharmacodynamics, guiding the development of precision medicine strategies. This integration of AI not only expedites innovative drug discovery but also refines delivery mechanisms, promising more effective and tailored treatments with the potential to revolutionize patient care. The data-processing capabilities of AI drive digitalization and widespread utilization. Applications in drug discovery, development, repurposing, and clinical trials aim to alleviate human workload, expedite objectives, and foster innovation. Despite promising prospects, concerns about job displacement and stringent regulations accompany AI implementation. Emphasizing the intent to augment human labor rather than replace it entirely, the industry anticipates that AI will become a pivotal resource, propelling efficiency, innovation, and advancements in healthcare. This review emphasizes the role of AI in transforming drug discovery and delivery Current Drug Therapy01The role of AI in transforming drug discovery and delivery is emphasized, with applications in drug discovery, development, repurposing, and clinical trials aiming to alleviate human workload, expedite objectives, and foster innovation.Current Drug Therapy["Kumaran Chinnaiyan", "Sruthi Laakshmi Mugundhan", "Damodharan Narayanasamy", "Mothilal Mohan"]2024-07-19T00:00:00 b5e6ad5eaaa96c65a67ffd03879500ff1e7261c2The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitativeliterature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438471The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values.Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438["G\u00f6kt\u00fcrk Kalkan"]2024-07-19T00:00:00 d63e40490868067c15c656373a2c7a8b87bada0aJ. Medical Syst.972The evidence indicates that RSF and DeepSurv models are currently the optimal models for predicting CVD outcomes, and the better predictive ability of DL survival models, compared to ML models are highlighted.Journal of Medical Systems["Achamyeleh Birhanu Teshale", "Lin Htun Htet", "Mor Vered", "Alice J. Owen", "R. Freak-Poli"]2024-07-19T00:00:00 acb0bf3a21c0850d3ed7ee4f4b2d3cdc00f4f22fStill rebounding from the impact of the global pandemic, the higher education sector is being challenged even further by the next wave of Artificial Intelligence (AI) technologies. These technologies have the power to generate in a matter of seconds, quality text, images, music and coding responses to questions or prompts entered into an online chat box. Currently, one of the most accessible and popular text generators is OpenAI’s ChatGPT which was released in November 2022. Early evaluation indicates that the quality of the responses exceed standard pass rates for comparable university assessments. Even if academic protocols mandate that text cited from AI sources should be acknowledged and referenced as any other source material, the speed, accessibility and high quality of the AI material justifies a rethink of the purpose of higher education and a redesign of curriculum, pedagogy and assessment. An initial suggestion being promoted in the sector is that learning outcomes and assessments should move away from a focus on content memorisation and recall, to development of higher order thinking skills such as critical analysis, evaluation, resilience, creativity, problem solving, appraising and mastery of verbal communication and computer literacy. This preliminary paper examines some of the literature to date, which discusses potential risks and threats, as well as the opportunities to enhance learning, embedded in this new wave of emerging AI technologies in higher education. Keywords: Artificial Intelligence technologies, generative text software, implications for curriculum, pedagogy and assessment design.Athens Journal of Education450A preliminary paper examines some of the literature to date, which discusses potential risks and threats, as well as the opportunities to enhance learning, embedded in this new wave of emerging AI technologies in higher education.Athens Journal of Education["Lorraine Bennett", "Ali Abusalem"]2024-07-19T00:00:00 8264d4e1106a0fb99bb8c4584d4ba4279c77d9ceThe article deals with the problem of the independent use of artificial intelligence in education and its influence in solving educational problems by students and students, the reasons for the use of AI. The purpose of the article is to discuss problematic episodes and search for constructive solutions to neutralize the negative sides, to increase the beneficial effect on solving educational problems of students. The methodology and research methods are based on the analysis of scientific literature and Internet sources in the field of IT technologies in education. The practical research method was implemented in the ascertaining stage of the study, a survey among 12 students of various educational institutions and interviews with people interested in scientific developments. The results of the study revealed the reasons for the use of AI, what tasks it performs for students, what negative manifestations occurred when using this technology, as well as the attitude of respondents to the use of AI in the educational field by students. Considering the topic of using artificial intelligence by students is extremely important, given both the positive and negative sides of this approach. It is necessary to take into account the negative aspects of AI and the fact that the use of AI can make people dependent on neural networks, depriving them of their own knowledge, creativity and self-sufficiency. However, when used correctly, AI can significantly improve the learning process, helping schoolchildren and students learn knowledge faster and more effectively, develop skills and solve educational problems. Conclusions. Therefore, understanding the causes of the problems of using artificial intelligence in education will allow us to find a balance between its advantages and risks, ensuring maximum results and favorable conditions for students.Applied psychology and pedagogy00Understanding the causes of the problems of using artificial intelligence in education will allow us to find a balance between its advantages and risks, ensuring maximum results and favorable conditions for students.Applied psychology and pedagogy["R. Rabadanova", "Elvira Seminskaya", "Alexey Filatov"]2024-07-19T00:00:00 651e9bd49687de7596aa69ad4182f8af7de5f197The modern-day reality is that the world has seen unprecedented evolution in information technology and artificial intelligence. In the opinion of theorist of technological convergence, the acceptance of information technology has become necessary as almost every facet of life revolves around it. In fact, technological determinism posits that advanced technology is taking over the entire landscape of human existence, including learning and research. This is buttressed by the evolution of Artificial Intelligence (AI) tools that aid in research, writing and referencing. The evolution of these AI tools necessitates a conversation about the ownership and protection of Intellectual Property Rights (IPRs) in materials generated using these AI tools. It has also become important to define what amounts to infringements of these IPRs, at what point an infringement could be stated to have occurred and who would be held liable for such infringements. This paper adopts the doctrinal research methodology to analyse primary and secondary sources of data in order to determine the issues of ownership and protection of IPRs emanating from the use of AI tools for research. It submits that the use of AI tools in research presents some fresh problems for Intellectual Property (IP) protection and enforcement which should be addressed through making amendment to existing IP laws in Nigeria. The study concludes by making recommendations on how to fortify our existing legal regimes and weed out loopholes that can be exploited to successfully infringe on the rights of AI generated IPRs holders.ABUAD Law Journal00The use of AI tools in research presents some fresh problems for Intellectual Property protection and enforcement which should be addressed through making amendment to existing IP laws in Nigeria, and recommendations on how to fortify existing legal regimes are made.ABUAD Law Journal["Damilola Seun Adesanya", "Mujeeb Ademola Imran"]2024-07-19T00:00:00 bdc941879733e8d59b9e3a180d47b24b6a0c86e2This research aimed to examine the effectiveness of artificial intelligence applications in asthma and chronic obstructive pulmonary disease (COPD) outpatient treatment support in terms of patient health and public costs. The data obtained in the research using semiotic analysis, content analysis and trend analysis methods were analyzed with strengths, weakness, opportunities, threats (SWOT) analysis. In this context, 18 studies related to asthma, COPD and artificial intelligence were evaluated. The strengths of artificial intelligence applications in asthma and COPD outpatient treatment stand out as early diagnosis, access to more patients and reduced costs. The points that stand out among the weaknesses are the acceptance and use of technology and vulnerabilities related to artificial intelligence. Opportunities arise in developing differential diagnoses of asthma and COPD and in examining prognoses for the diseases more effectively. Malicious use, commercial data leaks and data security issues stand out among the threats. Although artificial intelligence applications provide great convenience in the outpatient treatment process for asthma and COPD diseases, precautions must be taken on a global scale and with the participation of international organizations against weaknesses and threats. In addition, there is an urgent need for accreditation for the practices to be carried out in this regard.Medicine430The strengths of artificial intelligence applications in asthma and COPD outpatient treatment stand out as early diagnosis, access to more patients and reduced costs and the points that stand out among the weaknesses are the acceptance and use of technology and vulnerabilities related to artificial intelligence.Medicine["S. Akduman", "Kadir Y\u0131lmaz"]2024-07-19T00:00:00 e46ea0efb34e8838f8076c05c52bad62a917ce55We conducted a descriptive bibliometric analysis to examine scientific production, identify the most influential publications, and identify the most and least researched topics in four specific knowledge domains. We used a quantitative, descriptive, and correlational research approach to scientific production to carry out the analysis, which involved extracting 7,937 articles from the Web of Science and distributing them into three search equations. Using SciMAT v1.1.04 software, we processed the data and conducted a descriptive analysis of scientific production, enabling the creation of maps highlighting scientists with the most and least researched topics. The analysis of published articles, author performance, most productive journals, and most cited articles provided a detailed view of the dominant trends and approaches in the fields of Artificial Intelligence and business. The analysis showed that there is a significant evolution in the scholarly output, with themes such as "Value Creation", "Artificial Intelligence", "Business Intelligence", "E-Commerce", "Decision Making" and "Management" emerging as central in different periods, indicating their continued importance. Additionally, we note the inclusion of emerging themes like 'Customer Experience', 'Chatbots', 'Internet of Things', and 'Machine Learning', which reflect the dynamics and evolution of research concerns over time.  The results of the analysis have significant implications for business policy and strategy formulation. Understanding emerging trends can help organizations make informed decisions and proactively adapt to changes in the artificial intelligence and sustainability landscape.Edelweiss Applied Science and Technology870A descriptive bibliometric analysis of scientific production showed that there is a significant evolution in the scholarly output, with themes such as "Value Creation", "Artificial Intelligence", "Business Intelligence", "E-Commerce", "Decision Making" and "Management" emerging as central in different periods, indicating their continued importance.Edelweiss Applied Science and Technology["Jorge Campoverde Campoverde", "Katherine Coronel-Pangol", "Dom\u00e9nica Heras Tigre"]2024-07-19T00:00:00 1c953078c5d6d6ffab5a68f4b3c05bffd3e8dbc7In recent years, developments in Artificial Intelligence (AI) and MachineLearning (ML) technologies have had profound effects on all sectors. The logistics industry has also become a sector where these technologies are being used to a significant extent. The emergence of intelligent logistics systems offers several opportunities for the advancement of this sector by facilitating digital transformation in supply chain and logistics. The aim of this study is to provide a comprehensive review of recent studies examining the use of AI and ML systems in the logistics industry. In this study, which is designed as a systematic study, firstly, based on the existing literature, the basic concepts, trends, researchers and countries working on AI and ML systems in the logistics sector are examined by bibliometric analysis method. Then, information about the prominent AI and ML systems in logistics is given. It is seen that the most frequently used AI and ML technologies in logistics are Deep Learning, Optimization, Internet of Things (IoT), Data Mining and Predictive Models. The methodologies presented in the study have a practical importance in increasing efficiency, transparency and planning in the logistics.Trends in Business and Economics00It is seen that the most frequently used AI and ML technologies in logistics are Deep Learning, Optimization, Internet of Things (IoT), Data Mining and Predictive Models.Trends in Business and Economics["Suzan O\u011fuz", "Deniz Yal\u00e7\u0131nta\u015f"]2024-07-19T00:00:00 74af0ec4e922796fd957f39e4acf91905b297e3bSignificant technical developments have occurred in the digital era, altering the dynamics of modern businesses and bringing both difficulties and opportunities. The purpose of this study was to thoroughly investigate the relationship between artificial intelligence (AI) and digital transformation, as well as how these factors affect managerial empowerment and organizational performance. Additionally, the study looked at how employee engagement functions as a mediator and how digital readiness modifies these relationships. Using a quantitative method, a structured questionnaire was used to gather data from a sample of 282 companies that were chosen at random. The AMOS software's structural equation modeling (SEM) aided in the investigation of the linkages. The findings demonstrated strong and positive relationships between AI and digital transformation and organizational performance and managerial empowerment, which were mediated and regulated by employee engagement and digital preparedness. By providing a cohesive paradigm, this study gives practical insights for enterprises managing the digital landscape while also advancing theoretical understanding. This study is unique in that it examines the linkages between AI, digital transformation, employee engagement, and digital preparedness in the context of organizational performance and managerial empowerment.Journal of Information Systems Engineering & Management610The findings demonstrated strong and positive relationships between AI and digital transformation and organizational performance and managerial empowerment, which were mediated and regulated by employee engagement and digital preparedness.Journal of Information Systems Engineering and Management["Shanshan Li", "Fei Huang"]2024-07-19T00:00:00 50d63d7870cc2b3eb60c534e520364e429d4d637PurposeThe aim of this study is to offer valuable insights to businesses and facilitate better understanding on transformer-based models (TBMs), which are among the widely employed generative artificial intelligence (GAI) models, garnering substantial attention due to their ability to process and generate complex data.Design/methodology/approachExisting studies on TBMs tend to be limited in scope, either focusing on specific fields or being highly technical. To bridge this gap, this study conducts robust bibliometric analysis to explore the trends across journals, authors, affiliations, countries and research trajectories using science mapping techniques – co-citation, co-words and strategic diagram analysis.FindingsIdentified research gaps encompass the evolution of new closed and open-source TBMs; limited exploration across industries like education and disciplines like marketing; a lack of in-depth exploration on TBMs' adoption in the health sector; scarcity of research on TBMs' ethical considerations and potential TBMs' performance research in diverse applications, like image processing.Originality/valueThe study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations. Implications for managers and researchers along with suggested research questions to guide future investigations are provided.Kybernetes1600The study offers an updated TBMs landscape and proposes a theoretical framework for TBMs' adoption in organizations and implications for managers and researchers are provided.Kybernetes["Giulio Marchena Sekli"]2024-07-19T00:00:00 9f6968b882427722d9fbb19546cae09fd59a3441In a world where Artificial Intelligence (AI) is rapidly gaining prominence, its potential application in the field of Assisted Reproductive Technology (ART) cannot be overlooked. AI in ART has revolutionized the field of reproductive medicine, promising enhanced efficiency and outcomes. This article delves into the legal and ethical considerations surrounding this burgeoning intersection. AI algorithms are increasingly utilized in ART procedures such as in vitro fertilization (IVF), embryo selection, and gamete screening, optimizing success rates, and minimizing risks. AI holds promise. This study explores the intersection of AI and ART, investigating the legal challenges arising from their integration. It scrutinizes the implications of employing AI in reproductive technologies, delving into concerns such as data privacy, consent, liability, and the potential necessity for novel regulatory frameworks. The research provides a comprehensive overview of the evolving legal and ethical landscape in this domain. Employing a doctrinal methodology, which involves analyzing legal principles and doctrines, the study aims to contribute to the ongoing discourse on the ethical and legal framework essential for ensuring the responsible and equitable utilization of AI in ART. Despite the numerous challenges, the amalgamation of AI and ART is poised to significantly influence the trajectory of medical advancement in the future.ABUAD Law Journal00This study explores the intersection of AI and ART, investigating the legal challenges arising from their integration and scrutinizes the implications of employing AI in reproductive technologies, delving into concerns such as data privacy, consent, liability, and the potential necessity for novel regulatory frameworks.ABUAD Law Journal["Oyetola Mary Adeniyi", "V. B. Monehin"]2024-07-19T00:00:00 bd92eeaeb7c0f42d92b47ee094e05693e6acd01cThe research aimed to identify the reality of using artificial intelligence in educational research from the point of view of graduate students at the College of Education at Taif University. The study sample consisted of 32.8% of the study population consisting of (128 male and female students) enrolled in the master’s degree- at the College of Education- Taif University In the specializations of (educational leadership, pedagogy, educational policies, special education, curricula, teaching methods and educational techniques) in the second semester of the academic year 1444-1445 AH, and using the questionnaire tool, the research concluded the following: 1- That students use artificial intelligence to a high degree in all stages of research. 2- One of the most important problems in using artificial intelligence in research is related to reliability and security, and the lack of a clear policy for applying artificial intelligence in educational research. 3- One of the most important controls that students believed should be available (formulation The Artificial Intelligence Ethics Document, the establishment of the University Center for Artificial Intelligence Ethics, and the enhancement of cyber security skills for the educational researcher, while there are no statistically significant differences between the sample members in their responses to the questionnaire axes due to specialization and level While the most widely used artificial intelligence program in educational research was Chat GPT, it affected the reality axis without the rest of the axes. Among the most important recommendations reached by this study: establishing a university center for the ethics of artificial intelligence, drafting a document on the ethics of artificial intelligence in educational research, and adding it as a course for students. Undergraduate and postgraduate studiesInternational Journal for Scientific Research00Among the most important recommendations reached by this study: establishing a university center for the ethics of artificial intelligence, drafting a document on the ethics of artificial intelligence in educational research, and adding it as a course for students.International Journal for Scientific Research["Saleha Alsofiani"]2024-07-19T00:00:00 44e0fba8e5565b657de81cd15adfa964dcbf4353This research paper centers on how Artificial Intelligence (AI) , a field of computer science that aims to make computers emulate human thinking and behavior, is being applied in the realm of e-commerce. Due to the rapid advancement in technology, innovation and society as a whole, the utilization of artificial intelligence has been on the rise in both work environments and individual’s lifestyles. It is utilized for personal shopping, AI-powered assistance, fraud prevention and recommendation system.International Journal For Multidisciplinary Research00International Journal For Multidisciplinary Research["Harshitha K.S", "Monisha .G", "Manjunatha K .R"]2024-07-19T00:00:00 37c3137f6de925e1ba54051e22468ad8e2e7e4c5Artificial intelligence (AI) has garnered significant attention for its pivotal role in the national security and health care sectors. However, its utilization in military medicine remains relatively unexplored despite its immense potential. AI operates through evolving algorithms that process extensive datasets, continuously improving accuracy and emulating human learning processes. Generative AI, a type of machine learning, uses algorithms to generate new content, such as images, text, videos, audio, and computer code. These models employ deep learning to encode simplified representations of training data and generate new work resembling the original without being identical. Although many AI applications in military medicine are theoretical, the U.S. Military has implemented several initiatives, often without widespread awareness among its personnel. This article aims to shed light on two resilience initiatives spearheaded by the Joint Artificial Intelligence Center, which is now the Chief Digital and Artificial Intelligence Office. These initiatives aim to enhance commanders' dashboards for predicting troop behaviors and develop models to forecast troop suicidality. Additionally, it outlines 5 key AI applications within military medicine, including (1) clinical efficiency and routine decision-making support, (2) triage and clinical care algorithms for large-scale combat operations, (3) patient and resource movements in the medical common operating picture, (4) health monitoring and biosurveillance, and (5) medical product development. Even with its promising potential, AI brings forth inherent risks and limitations that require careful consideration and discussion. The article also advocates for a forward-thinking approach for the U.S. Military to effectively leverage AI in advancing military health and overall operational readiness.Military Medicine150Two resilience initiatives spearheaded by the Joint Artificial Intelligence Center, which is now the Chief Digital and Artificial Intelligence Office, aim to enhance commanders' dashboards for predicting troop behaviors and develop models to forecast troop suicidality.Military medicine["Ryan M Leone", "James A Chambers", "Benjamin P. Donham", "Caesar A Junker"]2024-07-19T00:00:00 b6350cfd5b2a9ccdd19b4312e4041e6e74d64493The current existence of Artificial Intelligence (AI) shows that the convenience of technology is increasingly showing progress. Easily accessed by all levels, anywhere and anytime, quickly and concisely, is a breakthrough brought by AI to support various human needs. However, something that is fast and concise will make users complacent. Without re-checking the answers given by the artificial system. In fact, artificial intelligence will not be free from errors, so it has an impact on academics and one of them is in the discourse on hadith science, it is not enough to rely on the system but needs further research as is the tradition carried out by scholars in hadith science. The aim of this research is to find out how AI becomes an opportunity and challenge for academics discussing hadith science and provide solutions to these cases. By using a qualitative research method based on library research, the author will explore the extent of the positive and negative impacts of the emergence of AI and how to take a stance in facing technological advances, especially AI in the hadith science discourse. The author hopes that this research will make academics increase their sense of wisdom towards technological progress and not abandon the rules that have been established by muhaddithin.Proceedings of International Conference on Muslim Society and Thought00The aim of this research is to find out how AI becomes an opportunity and challenge for academics discussing hadith science and provide solutions to these cases and to explore the extent of the positive and negative impacts of the emergence of AI.Proceedings of International Conference on Muslim Society and Thought["Amelia Damayanti", "Susi Wulandari"]2024-07-19T00:00:00 40446c9655f1056c67c4657d6c211bc8f6c83cbcBackground: Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be reviewed by clinicians. Objective: To assess provider burnout and professional fulfilment associated with Ambient AI technology during a pilot study, assessed using the Stanford Professional Fulfillment Index (PFI). Methods: A pre-post observational study was conducted at University of Iowa Health Care with 38 volunteer physicians and advanced practice providers. Participants used a commercial ambient AI tool, over a 5-week trial in ambulatory environments. The AI tool transcribed patient-clinician conversations and generated preliminary clinical notes for review and entry into the electronic medical record. Burnout and professional fulfillment were assessed using the Stanford PFI at baseline and post-intervention. Results: Pre-test and post-test surveys were completed by 35/38 participants (92% survey completion rate). Results showed a significant reduction in burnout scores, with the median burnout score improving from 4.16 to 3.16 (p=0.005), with validated Stanford PFI cutoff for overall burnout 3.33. Burnout rates decreased from 69% to 43%. There was a notable improvement in interpersonal disengagement scores (3.6 vs. 2.5, p<0.001), although work exhaustion scores did not significantly change. Professional fulfillment showed a modest, non-significant increase (6.1 vs. 6.5, p=0.10). Conclusions: Ambient AI significantly reduces healthcare provider burnout and modestly enhances professional fulfillment. By alleviating documentation burdens, ambient AI improves operational efficiency and provider well-being. These findings suggest that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.medRxiv00Ambient AI significantly reduces healthcare provider burnout and modestly enhances professional fulfillment and suggests that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.["Jason Misurac", "MD Lindsey A. Knake", "James M. Blum"]2024-07-19T00:00:00 21cf1e0b510b14822941e3fbbfb794cf555a09acThe article discusses development of autonomous artificial intelligence systems for corporate management. The function of a corporate director is still one of the few that are legislated for execution by a “natural” rather than an “artificial” person. The main prerequisites for development of systems for full automation of management decisions made at the level of a board of directors are formed in the field of corporate law, machine learning, and compliance with the rules of non-discrimination, transparency, and accountability of decisions made and algorithms applied. The basic methodological approaches in terms of corporate law for the “autonomous director” have already been developed and do not get rejection among representatives of the legal sciences. However, there is an undeniable need for further extensive research in order to amend corporate law to effectively introduce “autonomous directors”. In practice, there are two main options of management decisions automation at the level of top management and a board of directors: digital command centers or automation of separate functions. Artificial intelligence systems will be subject to the same strict requirements for non-discrimination, transparency, and accountability as “natural” directors. At a certain stage, autonomous systems can be an effective tool for countries, regions, and companies with a shortage of human capital, equalizing or providing additional chances for such countries and companies to compete on the global market.Artificial Societies80Development of autonomous artificial intelligence systems for corporate management can be an effective tool for countries, regions, and companies with a shortage of human capital, equalizing or providing additional chances for such countries and companies to compete on the global market.ArXiv["Anna Romanova"]2024-07-19T00:00:00 21b02029e3f29c95921e982e2015b7e73c8c59bfArtificial Intelligence (AI) is a technology that simulates human intelligence and has been rapidly developed in recent years. With the continuous development of the Internet and big data technology, AI technology has been widely applied and studied. In this paper, using "artificial intelligence" as the keyword, we searched the core journals on China Knowledge Network, selected 8913 effective documents from 2015-2023 as the research samples, and used the scientific bibliometric methods such as word frequency analysis, cluster analysis, and theme evolution analysis, and visualized and analyzed them with the help of COOC software, to synthesize the research hotspots in the field of artificial intelligence. The hotspots, development and evolution history of research in the field of artificial intelligence are sorted out, and the future research direction and application prospects are prospected.International Journal of Computer Science & Information Technology (IJCSIT)100The hotspots, development and evolution history of research in the field of artificial intelligence are sorted out, and the future research direction and application prospects are prospected.International Journal of Computer Science and Information Technology["Chong Wang", "Zixi Chen"]2024-07-19T00:00:00 75bb5925a5049d980fb4b05aff03dff3ba885652PurposeThe construction industry is a major contributor to global carbon emissions. This study investigates the role of procurement and contracting methods in carbon emission reduction (CER) in the construction industry. It also examines artificial intelligence’s (AI’s) potential to drive low-carbon practices, aiming to identify transformative policies and practices.Design/methodology/approachThis study employed a qualitative methodology, engaging in semi-structured interviews with nine industry professionals alongside an innovative engagement with Generative Pre-trained Transformer (GPT) technology to gather insights into procurement and project delivery methods (PDM) role in CER. The study involved identifying patterns, organizing themes, and analyzing data to extract meaningful insights on effective policies and strategies for CER in the construction industry.FindingsThe results underscore the importance of early contractor involvement and integrated PDM for CER in construction. Results emphasize the pivotal role of project owners in directing projects toward sustainability, highlighting the need for client demand. The research identifies cost constraints, limited material availability, and human resource capacity as key barriers in the US. The study proposes innovative materials, financial incentives, education, and regulatory standards as effective interventions. It also explores the future use of AI in enhancing CER, suggesting new avenues for technological integration.Originality/valueThe study provides empirical insights into the role of procurement and PDM in CER within the US construction industry by using qualitative approach and use of a GPT. It underscores the interplay between contracting methods, stakeholder engagement, and AI’s emerging role, for enhancing policies and practices to decarbonize the US construction industry.Built Environment Project and Asset Management340Built Environment Project and Asset Management["Danish Kumar", "Chengyi Zhang"]2024-07-19T00:00:00 351e317aabd8fec7630d6b00ddcf3406940283eaJournal of Advanced Manufacturing Systems01Journal of Advanced Manufacturing Systems["Kiran Kumar Abbili"]2024-07-19T00:00:00 7b93cb913a6c49007dd8c224374affd269ce90bdProceedings of the 2nd International Conference on Educational Knowledge and Informatization61Proceedings of the 2nd International Conference on Educational Knowledge and Informatization["Yingzi Zhou", "Chongqiu Fang"]2024-07-19T00:00:00 8a9e7b74e91ee1257bcf08cace9fc912a60f51c8Proceedings of the 2nd International Conference on Educational Knowledge and Informatization00Proceedings of the 2nd International Conference on Educational Knowledge and Informatization["Wenjun Xiong"]2024-07-19T00:00:00 fcc3b9bb10ed42d401b87dbbc33c5b3627355ef9Arquivos Brasileiros de Cardiologia140Arquivos brasileiros de cardiologia["Maria Cristina Meira Ferreira", "G. M. M. de Oliveira"]2024-07-19T00:00:00 8ac652e7ea99470f34d71e085bea3e7585a70fdeICEME150{"pages": "343-349"}["Yiwei Gong", "Huamei Sun"]2024-07-19T00:00:00 ae90f0cfa912c0dcdfead6beff17527fc2afab48The Veterinary Record00Veterinary Record["Paige Scally"]2024-07-19T00:00:00 0de1de82e2be1bd16f3e9e161dde71c93e406614J. Medical Syst.440Through ECG analyses, the AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.Journal of Medical Systems["Yuan Hung", "Chin Lin", "Chin Lin", "Chiao-Chin Lee", "Wen-Hui Fang", "Chia-Cheng Lee", "Chih-Hung Wang", "Dung-Jang Tsai"]2024-07-19T00:00:00 f0ad23e256b687c20b810002be82ecbc7a08e072JACC: Advances120JACC: Advances["Joan E. Briller", "Aswathi Jayaram"]2024-07-19T00:00:00 c9e5417d40daaa368444ec9452a63623ed7291fcProceedings of the 2nd International Conference on Educational Knowledge and Informatization300Proceedings of the 2nd International Conference on Educational Knowledge and Informatization["Xiaodan Li"]2024-07-19T00:00:00 ce17ae4545000e8838efae5d593784b376b3c13cProceedings of the 2nd International Conference on Educational Knowledge and Informatization110Proceedings of the 2nd International Conference on Educational Knowledge and Informatization["Sheng Jin", "Zengyi Yu", "Xinyu Chen", "Jian Dai"]2024-07-19T00:00:00 b12026633f243f497173cad4dfbfccc6d038e794Proceedings of the 2nd International Conference on Educational Knowledge and Informatization50Proceedings of the 2nd International Conference on Educational Knowledge and Informatization["Peng Huang", "Yueyu Chen", "Yi-Feng Lin", "Jaja Li"]2024-07-19T00:00:00 9a96b37cd9e0061d13a99e4388007e271f0c7292ICEME160{"pages": "175-181"}["Yan Qian", "Hai-Qi Feng"]2024-07-19T00:00:00 f24f22150b018b6c3d8f8d0a64278f40979bdcdfICEME00{"pages": "366-370"}["Xiaoqian Ma"]2024-07-19T00:00:00 6fec55d320395b47e9712822c332bf8dab6405e0AI has been deployed in finance, health care, law enforcement, research, teaching, communication and even transportation. In the legal industry, AI has been useful to law students, lawyers and judges. The widespread use of AI has raised salient questions over its effects on legal concepts like human rights, intellectual property, labour and employment law, criminal law, health law and entertainment law. The need for formal regulation became more evident in recent years with the popularisation of generative AI models which have brought AI closer to the people more than ever. Regulating AI is essential to curb its adverse effects on the society. It is critical that harmonised rules and policies are made across countries, to truly harness the potential of AI in enhancing socio-economic development and mitigate the risks that are inherent in the deployment of AI. This paper serves as an overview of the relationship and impact of AI in various fields of law and provides suggestions on various thorny issues raised by the deployment of AI in law.ABUAD Law Journal00An overview of the relationship and impact of AI in various fields of law is served and suggestions on various thorny issues raised by the deployment of AI in law are provided.ABUAD Law Journal["I. A. Olubiyi", "Rahamat Oyedeji-Oduyale", "D. M. Adeniyi"]2024-07-19T00:00:00 2fd374caba3324bf80cb6d2d4d079030a1ffc59fPurpose: Advancements in both computer hardware and software fields are utilized to attain progress across a variety of industries including business, manufacturing, education, health, and governance. However, there is a common denominator irrespective of the application of artificial intelligence (AI) i.e., affective or emotional intelligence (EI) of AI systems. This paper aims to discuss the integration of major elements of EI models into artificial emotional intelligence (AEI) systems. Design/Methodology: The paper structure is descriptive. Based on 50 studies examining the areas of AI, EI, and AEI, the paper expands the discussion on the interlinks between AI and EI. Findings: With the availability of big data, advanced data analytical tools, complex algorithms capable of conducting multivariate analysis, expandable memory, and retention, AI embarks on understanding, learning, and applying human emotions, and attaining emotional intelligence. This study proposes that artificial emotional intelligence can be achieved by simulating the learning mechanisms exhibited by human beings. Research Implications The indispensable interface between man and machine makes it pertinent to discuss AI’s ability to embrace and internalize human emotions. The study has implications for every industry, especially those that are looking to employ AI tools to assist or replace human counterparts. Originality Based on the most renowned model of emotional intelligence presented by Goleman, this study proposes a rudimentary EI model for outlining the basic facets of AEI systems. The study contributes to the literature examining the crossover between AI technologies, emotions, and learning.NUST Business Review530It is proposed that artificial emotional intelligence can be achieved by simulating the learning mechanisms exhibited by human beings by integrating major elements of EI models into artificial emotional intelligence (AEI) systems.NUST Business Review["Dr. Saman Javed"]2024-07-19T00:00:00 8d02b35bfad8c346e83190d9b38ac9d5c95edc56In the article by Jaime F [...]De Computis162Comput.["R. Dama\u0161evi\u010dius"]2024-07-19T00:00:00 21e620ebcf2d332b7dfdeed4809efe1ebe383a6fThe intelligent goal of process manufacturing is to achieve high efficiency and greening of the entire production. Whereas the information system it used is functionally independent, resulting to knowledge gaps between each level. Decision-making still requires lots of knowledge workers making manually. The industrial metaverse is a necessary means to bridge the knowledge gaps by sharing and collaborative decision-making. Considering the safety and stability requirements of the process manufacturing, this article conducts a thorough survey on the process manufacturing intelligence empowered by industrial metaverse. First, it analyzes the current status and challenges of process manufacturing intelligence, and then summarizes the latest developments about key enabling technologies of industrial metaverse, such as interconnection technologies, artificial intelligence, cloud-edge computing, digital twin (DT), immersive interaction, and blockchain technology. On this basis, taking into account the characteristics of process manufacturing, a construction approach and architecture for the process industrial metaverse is proposed: a virtual-real fused industrial metaverse construction method that combines DTs with physical avatar, which can effectively ensure the safety of metaverse’s application in industrial scenarios. Finally, we conducted preliminary exploration and research, to prove the feasibility of proposed method.IEEE Transactions on Cybernetics970A construction approach and architecture for the process industrial metaverse is proposed: a virtual-real fused industrial metaverse construction method that combines DTs with physical avatar, which can effectively ensure the safety of metaverse’s application in industrial scenarios.IEEE Transactions on Cybernetics["Weichao Luo", "Keke Huang", "Xiaojun Liang", "Hao Ren", "Nan Zhou", "Chaobo Zhang", "Chunhua Yang", "Weihua Gui"]2024-07-19T00:00:00 0013aecf813400174158e4f012918c5408f90962 The introduction of generative artificial intelligence (AI) to the wider public could have a huge impact on EFL learning and teaching. Researchers have voiced concerns that learners might lean too much on technology. Previous studies have investigated the use of AI tools in L2 writing with various populations and found that it was difficult for teachers to detect use of AI and that teachers mainly relied on linguistic strategies to detect AI-generated texts. This paper reports on a qualitative study that investigated whether novice English teachers were able to detect AI-generated writing and which strategies they used to do this. The results show that some novice teachers are quite good at detecting AI-generated texts, while others proved to have more difficulties. The teachers used both linguistic and content-related strategies to detect AI-generated writing. The results point towards the value of including this topic in teaching methodology courses in (initial) teacher training programmes.ELT Journal102Whether novice English teachers were able to detect AI-generated writing and which strategies they used to do this is investigated and the results show that some novice teachers are quite good at detecting AI-generated texts, while others proved to have more difficulties.ELT Journal["Vanessa De Wilde"]2024-07-19T00:00:00 390b83330dc1319e4112af50720aa511e55a2203Timely and unbiased evaluation of Autism Spectrum Disorder (ASD) is essential for providing lasting benefits to affected individuals. However, conventional ASD assessment heavily relies on subjective criteria, lacking objectivity. Recent advancements propose the integration of modern processes, including artificial intelligence-based eye-tracking technology, for early ASD assessment. Nonetheless, the current diagnostic procedures for ASD often involve specialized investigations that are both time-consuming and costly, heavily reliant on the proficiency of specialists and employed techniques. To address the pressing need for prompt, efficient, and precise ASD diagnosis, an exploration of sophisticated intelligent techniques capable of automating disease categorization was presented. This study has utilized a freely accessible dataset comprising 547 eye-tracking systems that can be used to scan pathways obtained from 328 characteristically emerging children and 219 children with autism. To counter overfitting, state-of-the-art image resampling approaches to expand the training dataset were employed. Leveraging deep learning algorithms, specifically MobileNet, VGG19, DenseNet169, and a hybrid of MobileNet-VGG19, automated classifiers, that hold promise for enhancing diagnostic precision and effectiveness, was developed. The MobileNet model demonstrated superior performance compared to existing systems, achieving an impressive accuracy of 100%, while the VGG19 model achieved 92% accuracy. These findings demonstrate the potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism. Moreover, the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding. Users and healthcare professionals can utilize these classifiers to enhance the accuracy rate of ASD diagnosis. The development of these automated classifiers based on deep learning algorithms holds promise for enhancing the diagnostic precision and effectiveness of ASD assessment, addressing the pressing need for prompt, efficient, and precise ASD diagnosis.Frontiers in Medicine451The potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism is demonstrated, and the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding.Frontiers in Medicine["Nizar Alsharif", "M. H. Al-Adhaileh", "Mohammed Al-Yaari", "Nesren Farhah", "Zafar Iqbal Khan"]2024-07-19T00:00:00 73af0b43758d0f00e4c73ec3845efa9a39b76a40The main purpose of the paper is to evaluate and compare different business valuation models that incorporate artificial intelligence (AI) technologies. The paper seeks to understand the capabilities, advantages, disadvantages, and limitations of these AI-based models in valuing businesses accurately. Additionally, the paper aims to provide insights into how AI can be utilized effectively in the field of business valuation to enhance accuracy and efficiency. We used qualitative research methods which involve reviewing and analyzing existing literature, case studies, and expert opinions on business valuation models and artificial intelligence. The main contribution of the paper is the integration of artificial intelligence (AI) techniques into traditional business valuation models. The authors propose using AI algorithms such as machine learning and natural language processing to improve the accuracy and efficiency of valuing businesses. By leveraging AI technology, the paper aims to provide more reliable and data-driven valuations, ultimately enhancing decision-making processes for investors, managers, and other stakeholders. The initial segment of the analysis outlines conventional business valuation approaches, such as discounted cash flow (DCF), comparable company analysis (CCA), and asset-based valuation. These methods utilize historical financial data, market comparisons, and asset valuations to estimate a company’s value. Although they are effective, these traditional models have limitations in terms of capturing intricate market dynamics and accurately forecasting future performance. The following section of the analysis delves into specific AI-driven valuation strategies, such as sentiment analysis, predictive analytics, and algorithmic trading techniques. It also explores how AI technologies, like machine learning algorithms, natural language processing (NLP), and deep learning, are revolutionizing business valuation practices. AI enables the analysis of vast datasets, including unstructured data from platforms like social media, news articles, and industry reports, to extract valuable insights. Machine learning models can detect patterns, correlations, and predictive indicators that traditional models may miss, leading to more accurate and agile valuations. The analysis then addresses the benefits, obstacles, and considerations associated with integrating AI into business valuation. This includes data quality and accessibility, model interpretability and transparency, regulatory compliance, and ethical concerns related to AI bias and fairness. In addition, a comparative evaluation of AI-based models is presented. In conclusion, integrating AI into business valuation models presents significant potential to enhance the accuracy, efficiency, and dependability of valuation assessments. Using AI-driven methodologies, investors and analysts can gain deeper insights into the intrinsic value of businesses, enabling them to make more informed investment decisions in dynamic and competitive markets. However, it is crucial to pay careful attention to data integrity, model transparency, and ethical implications to ensure the responsible and effective use of AI in business valuation. Finally, future directions and recommendations are provided.Sustainable Economies191Comparing and evaluating different business valuation models that incorporate artificial intelligence (AI) technologies presents significant potential to enhance the accuracy, efficiency, and dependability of valuation assessments.Sustainable Economies["Milad Shahvaroughi Farahani"]2024-07-19T00:00:00 059d9598389833556dd879ddb9a9805829e92347In the context of the development of technologies, every sports club tends to improve its training methods to obtain the best possible results in sports training. The goal of the research is to develop a specialized training program designed to enhance ball-control skills so that children can play soccer with increased confidence, therefore reinforcing their need for constant visual contact with the ball during possession. The study participants are children between the ages of 8 and 10, who have acquired at least one year of consistent and well-structured football practice, divided into two groups, experimental group I and control group II. The T-Blaze test training, the Adams test, and the registration of the degree of head tilt using artificial intelligence and visual recognition were implemented. During the training, the authors used the BlazePod sensors to measure participants’ times more precisely, thus avoiding the inaccuracy of using a classic timer. At the same time, the authors used the Vesta HeadUp to block the child’s view of the ball when he has possession of the ball or is very close to it. The recording of time spent playing head-up and head-down revealed statistically significant differences between the three test sessions in favor of the experimental group. Considering the statistically substantial influence obtained, the authors can conclude that our intervention program based on specific means and using HeadUp vests was a decisive factor in achieving improved performance.Applied Sciences381Considering the statistically substantial influence obtained, the authors can conclude that the intervention program based on specific means and using HeadUp vests was a decisive factor in achieving improved performance.Applied Sciences["Marius Stoica", "Ciolc\u0103 Sorin", "Rafael Vi\u0219an", "A. Dreve"]2024-07-19T00:00:00 5ab5d3ac390b9ec43b1fdb530c979b1f2db9b462The proliferation of Artificial Intelligence (AI) across various industries in the United States has ushered in an era of transformative technological advancements, which has provided businesses with the ability to enhance customer experiences and drive operational efficiencies. However, this development has brought about increased challenges in preserving the privacy and security of personal data in the US. The paper examines the need to balance customer convenience with privacy compliance within the context of AI and personal data privacy in the U.S. The paper also examines the state of data privacy and concerns arising from the use of AI. It assesses the key legal frameworks in the U.S. and their adequacy to regulate AI in light of data privacy. The paper employs a doctrinal research methodology to examine the laws and identify the challenges arising from the regulatory gaps in AI and personal data privacy. The paper finds that there are challenges stemming from the lack of alignment between existing legal frameworks and the evolving AI technologies, especially in relation to data collection, data anonymization, and consent management. The paper recommends the need to reform existing laws to be up to date with the evolving capabilities of AI. The paper concludes that the growth of AI in relation to personal data privacy presents both opportunities and challenges.ABUAD Law Journal00The paper employs a doctrinal research methodology to examine the laws and identify the challenges arising from the regulatory gaps in AI and personal data privacy and recommends the need to reform existing laws to be up to date with the evolving capabilities of AI.ABUAD Law Journal["Hakeemat Ijaiya", "Israel Adekunle Adeniyi"]2024-07-19T00:00:00 93dddf06af789996e3b2fb6e1532b8ac2b0b6055In recent times, the combination of Artificial Intelligence (AI) technologies has enabled new methods for creating narratives, integrating visual content understanding and linguistic expression seamlessly. This paper explores the synergy between Convolutional Neural Networks, and Inception V3 to connect the gap between visual interpretive and linguistic storytelling. This paper thoroughly explores the integration of Inception V3 and CNNs to generate narratives by interpreting both visual content and language. By employing language generation techniques, AI systems can effectively extract semantic insights from textual data, allowing for the creation of context-rich narratives. The method discussed in this paper has the potential to transform the field of narrative generation and pave the way for future advancements in AI systems. By combining Inception V3's visual feature extraction capabilities with CNNs' image understanding prowess, coupled with NLP's linguistic comprehension, AI systems can analyse multimedia inputs comprehensively. This integrated approach enables AI models to synthesize narratives that are not only semantically coherent but also visually descriptive, blurring the boundaries between image and text-based storytelling. Furthermore, we discuss AI narratives’ potential applications and implications in vast domains, such as entertainment, education, and human interaction with computers. From generating tailor-made storytelling experiences to assisting content creators in multimedia production, AI narratives promise to revolutionise how stories are told and consumed in the digital age. This Research underscores the significance of leveraging Inception v3, CNNs, and Language Model to create AI-driven narrative generation systems capable of seamlessly bridging visual content and linguistic expression. As these technologies continue to evolve, AI narratives are poised to redefine the landscape of storytelling, offering new avenues for creativity, communication, and engagement in an increasingly visual and interconnected world.2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE)220The synergy between Convolutional Neural Networks, and Inception V3, CNNs, and Language Model is explored to create AI-driven narrative generation systems capable of seamlessly bridging visual content and linguistic expression.2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE)["Preetam", "Sai Chetan Muppalla", "Apoorv Raj", "Jasneet Chawla"]2024-07-19T00:00:00 494220a525e33871071019d9b6db3375dee00ca8The rapid expansion of Artificial Intelligence (AI) technologies has introduced both significant advancements and challenges, with diversity and inclusion (D&I) emerging as a critical concern. Addressing D&I in AI is essential to reduce biases and discrimination, enhance fairness, and prevent adverse societal impacts. Despite its importance, D&I considerations are often overlooked, resulting in incidents marked by built-in biases and ethical dilemmas. Analyzing AI incidents through a D&I lens is crucial for identifying causes of biases and developing strategies to mitigate them, ensuring fairer and more equitable AI technologies. However, systematic investigations of D&I-related AI incidents are scarce. This study addresses these challenges by identifying and understanding D&I issues within AI systems through a manual analysis of AI incident databases (AIID and AIAAIC). The research develops a decision tree to investigate D&I issues tied to AI incidents and populate a public repository of D&I-related AI incidents. The decision tree was validated through a card sorting exercise and focus group discussions. The research demonstrates that almost half of the analyzed AI incidents are related to D&I, with a notable predominance of racial, gender, and age discrimination. The decision tree and resulting public repository aim to foster further research and responsible AI practices, promoting the development of inclusive and equitable AI systems.arXiv.org450A decision tree to investigate D&I issues tied to AI incidents and populate a public repository of D&I-related AI incidents is developed, demonstrating that almost half of the analyzed AI incidents are related to D&I, with a notable predominance of racial, gender, and age discrimination.ArXiv["R. Shams", "Didar Zowghi", "Muneera Bano"]2024-07-19T00:00:00 37464a26a0e6954b5f8d9f8308439e1562cf11f2With the rapid development of artificial intelligence technology, machine learning and deep learning are widely applied in various fields, showing great potential and value. This article is aimed to explore the integrated application of machine learning with deep learning technologies in industrial automation and cybersecurity situational awareness. Utilizing deep learning technology to facilitate real-time monitoring and early detection of network security threats. This study first reviews the application status of machine learning in industrial safety, including feature selection, model construction, and performance evaluation. Then, the key technologies of deep learning in cybersecurity situational awareness, such as anomaly detection and intrusion identification, are discussed in depth. Furthermore, this paper proposes a framework that integrates machine learning and deep learning to improve their liability and network security protection capabilities of industrial control systems. By conducting experiments, the proposed framework demonstrates its ability to accurately forecast potential equipment failures in industrial settings and promptly detect security threats within the network, offering a novel approach to enhancing industrial automation and safeguarding network security. After the experiment, the accuracy of the improved algorithm is more than 85%, the prediction is more than 75, the regression line is more than 55%, and the modified algorithm is better than XGBost and other algorithms. The concentration of the improved CNN in identifying attack information can reach 95%.2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML)50A framework that integrates machine learning and deep learning to improve their liability and network security protection capabilities of industrial control systems is proposed, offering a novel approach to enhancing industrial automation and safeguarding network security.2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML)["Xingcheng Lu", "Kechen Wu", "Yixuan Chen"]2024-07-19T00:00:00 7f9205d7e1b06c162d6db17a77679b72182516b5Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review described advancements from conventional agriculture to modern SA, including architecture and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber threats that endanger its integrity and dependability and provide a complete analysis of their possible consequences. Still, the research examined the essential role of AI in combating these threats, focusing on its applications in threat identification, risk management, and real-time response mechanisms. The survey also discusses ethical concerns such as data privacy, the requirement for high-quality information, and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers and practitioners with insights into AI’s capabilities and future directions in the security of smart agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by comprehensively evaluating the existing environment and future trends.Mesopotamian Journal of Computer Science010The role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA) is investigated, focusing on its applications in threat identification, risk management, and real-time response mechanisms.Mesopotamian Journal of Computer Science["Guma Ali", "Maad M. Mijwil", "Bosco Apparatus Buruga", "Mostafa Abotaleb", "I. Adamopoulos"]2024-07-20T00:00:00 9e0da334b440299b3d606dbf8be792d2cc24fed6This research aims to identify effective ways of integrating Artificial Intelligence (AI) technology with innovative learning methods to improve students' creative thinking skills in various educational contexts. The bibliometric method, a quantitative and statistical approach to bibliographic data such as publications and citations, serves as the research method. Data were collected on July 2024, through the SCOPUS database using the keywords "creative thinking" OR "creative thinking skills" AND "creativity.". Data analysis was conducted in three stages: data collection, visualization, and analysis using software such as VosViewer. The results showed research trends, knowledge gaps, and potential new research areas relevant to technology integration in education. This research contributes to developing more effective and inclusive education policies and practices by ensuring all students have equal opportunities to develop essential skills in the digital age. Limitations of this study include limited access to technology in some areas and a need for teacher training in integrating AI into the learning process. Future research recommendations include focusing more on improving access to technology and teacher training to effectively use AI technology in education.ASEAN Journal of Science and Engineering02This research aims to identify effective ways of integrating Artificial Intelligence (AI) technology with innovative learning methods to improve students' creative thinking skills in various educational contexts to improve students' creative thinking skills in various educational contexts.ASEAN Journal of Science and Engineering["A. Solihat", "D. Dahlan", "K. Kusnendi", "Budi Susetyo", "Abdulkareem Sh. Mahdi Al Obaidi"]2024-07-20T00:00:00 113b88bcb5ab14aa4a04b099c595ed279034a703Despite the increasing prevalence of artificial intelligence (AI) ethics frameworks, the practical application of these frameworks in industrial settings remains limited. This limitation is further augmented in energy systems by the complexity of systems composition and systems operation for energy generation, distribution, and supply. The primary reason for this limitation is the gap between the conceptual notion of ethics principles and the technical performance of AI applications in energy systems. For instance, trust is featured prominently in ethics frameworks but pertains to limited relevance for the robust operation of a smart grid. In this paper, we propose a lifecycle approach for AI ethics that aims to address this gap. The proposed approach consists of four phases: design, development, operation, and evaluation. All four phases are supported by a central AI ethics repository that gathers and integrates the primary and secondary dimensions of ethical practice, including reliability, safety, and trustworthiness, from design through to evaluation. This lifecycle approach is closely aligned with the operational lifecycle of energy systems, from design and production through to use, maintenance, repair, and overhaul, followed by shutdown, recycling, and replacement. Across these lifecycle stages, an energy system engages with numerous human stakeholders, directly with designers, engineers, users, trainers, operators, and maintenance technicians, as well as indirectly with managers, owners, policymakers, and community groups. This lifecycle approach is empirically evaluated in the complex energy system of a multi-campus tertiary education institution where the alignment between ethics and technical performance, as well as the human-centric application of AI, are demonstrated.Energies241A lifecycle approach for AI ethics that aims to address the gap between the conceptual notion of ethics principles and the technical performance of AI applications in energy systems is proposed and empirically evaluated in the complex energy system of a multi-campus tertiary education institution.Energies["Nicole El-Haber", "Donna Burnett", "A. Halford", "Kathryn Stamp", "Daswin de Silva", "Milos Manic", "Andrew Jennings"]2024-07-20T00:00:00 d9d928980cb7ec4c96452b8514bcaa30f7a83b06Over the years, technology has revolutionized our world. It has changed our lives from barter system to currency system, and now it has upgraded to Artificial Intelligence powered system. It has also changed the way we think, the way we communicate, the way we bank and transact. The technology is getting better and smarter day-by-day leading human life become easier, faster, and convenient. In this digitalized era, like all other industrial sectors, it has become imperative for banking sectors to embrace digital transformation and to adopt new technologies like artificial intelligence and machine learning. Banks that fail to make the leap to digital transformation will risk being overtaken by competition and deserted by their customers.This paper provides a detailed review about Artificial Intelligence and Machine learning in banking, covering key areas such as customer services, fraud detection, personalised banking services, credit scoring, operational efficiency, and sophisticated product development.International Journal For Multidisciplinary Research140This paper provides a detailed review about Artificial Intelligence and Machine learning in banking, covering key areas such as customer services, fraud detection, personalised banking services, credit scoring, operational efficiency, and sophisticated product development.International Journal For Multidisciplinary Research["Roshni A N"]2024-07-20T00:00:00 7737e30ed62a574343508ccd8171cca1dffc273dThis study investigates the impact of artificial intelligence (AI) and employee service quality on customer satisfaction, engagement, and loyalty in the hotel industry. The research focuses on the customer perspective and specifically examines the experiences of departing guests who have encountered both AI and employee services in surveyed hotels in China. The results reveal that AI and employee service quality significantly contribute to overall service quality assessment, customer satisfaction, engagement, and loyalty. However, specific dimensions of service quality have a more pronounced effect on the outcomes of interest. More so, some dimensions of AI service quality also have a pronounced effect on outcomes of interest. This study enriches the existing research on AI and customer-related outcomes (satisfaction, engagement, and loyalty), offering valuable insights for hotel management on synergizing AI and employee service to obtain a competitive advantage and favorable customer behaviors. KEYWORDS: Artificial intelligence, employee service quality, customer satisfaction, customer loyalty, customer engagementEPRA International Journal of Economics, Business and Management Studies00This study enriches the existing research on AI and customer-related outcomes (satisfaction, engagement, and loyalty), offering valuable insights for hotel management on synergizing AI and employee service to obtain a competitive advantage and favorable customer behaviors.EPRA International Journal of Economics, Business and Management Studies["Korankye Benard", "Odai Afotey Leslie", "Omane Abigail"]2024-07-20T00:00:00 7cbce0335b1ac6b27904e79003d6ff5b2164f382The article examines the potential of artificial intelligence in entrepreneurial activity in the Russian economy at the present stage of its development in the context of modern challenges. The authors identified Russia’s place in global trends related to artificial intelligence. The authors paid attention to such relatively little–studied aspects of the use of artificial intelligence as the modernization of business processes in all sectors of the economy; improving the labor market through new forms of employment; optimization of migration policy; increasing the efficiency of the institution of self–employment in general and in terms of pension provision for the self–employed in particular; optimization of the size of the «shadow economy»; improvement of entrepreneurial activity through more complete implementation of the functions of an entrepreneur. The authors have identified the high positive potential of artificial intelligence in solving a number of problems in the Russian economy, including: reducing the size of the shadow economy, optimizing the labor market and migration policy, improving business processes and a number of others.The article shows that reducing transaction and transformation costs with the help of artificial intelligence will increase the growth rate of the Russian economy, increase the level of well–being of citizens and fully realize the functions of an entrepreneur in a market economy.Entrepreneur's Guide70The article shows that reducing transaction and transformation costs with the help of artificial intelligence will increase the growth rate of the Russian economy, increase the level of well–being of citizens and fully realize the functions of an entrepreneur in a market economy.Entrepreneur’s Guide["S. Sazanova", "N. N. Karmanov"]2024-07-20T00:00:00 9aa3f1d8a5b7715cb51f014587a806f5b3929d4dDigital transformation is increasingly essential for enhancing efficiency and data security. This study explores the impacts that Artificial Intelligence (AI) may have on the control of public procurement under the New Bidding Law. A systematic review of the literature on AI and public agencies was conducted. The findings indicate that the use of AI to oversee administrative activities and public procurement is already a reality. Further research is needed to identify additional factors where this technology can serve as an innovative tool to support the efficiency of public procurement.JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH60The findings indicate that the use of AI to oversee administrative activities and public procurement is already a reality, and further research is needed to identify additional factors where this technology can serve as an innovative tool to support the efficiency of public procurement.JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH["Helton Souza da Cunha", "Fab\u00edola Lopes", "Caetano Machado2", "Xisto Lucas", "Travassos Junior3", "Cristiano Vasconcellos", "Avenida"]2024-07-20T00:00:00 782b50555e2df667e9505bcaf564e5680dd7903cThe primary objective of this study is to ascertain the potential advantages and obstacles associated with implementing artificial intelligence (AI) within the context of Indonesian ports. The study utilises qualitative methodologies, namely conducting in-depth interviews with port managers, technicians, and operational staff. Additionally, it involves the analysis of secondary data derived from yearly port reports, case studies, and academic literature. The findings demonstrate that artificial intelligence (AI) can augment operational efficiency, boost ship traffic management, and automate container handling activities within Indonesian ports. Nevertheless, integrating artificial intelligence (AI) within Indonesian ports is currently in its nascent phase, encountering challenges about physical limitations, human capital, and regulatory frameworks. The investigation also examines using interconnected technologies, including the Internet of Things (IoT), blockchain, and augmented reality (AR), to provide additional advantages to the port sector. In summary, via a comprehensive comprehension and effective utilisation of the opportunities afforded by artificial intelligence (AI), Indonesian ports have the potential to position themselves as frontrunners in the international maritime sector.Transactions on Maritime Science00Indonesian ports have the potential to position themselves as frontrunners in the international maritime sector through a comprehensive comprehension and effective utilisation of the opportunities afforded by artificial intelligence (AI).Transactions on Maritime Science["Safuan", "Asma Syafira"]2024-07-20T00:00:00 5ee900933fd60606a6208312ee876cd617a955a2Modern society is undergoing a structural transformation of the world economy. This is as a result of the transition to a new technological base through the introduction of artificial intelligence, cutting-edge information and communication technology, energy technology, biotechnology and nanotechnology. Artificial intelligence has the ability to significantly change the economy and social relations in society, and its newly discovered capabilities are transformational and global in nature. At the same time, the extraordinary capabilities of artificial intelligence technologies involve risks that can threaten stability and undermine human values. In order to eliminate possible threats and risks and mitigate potential dangers, it is crucial to develop systemic legal measures and ways to regulate AI technologies and models on a national and international scale and to define the legal status of AI, which must include protection of humans from the uncontrolled influence of AI and the inviolability of guarantees of human rights and freedoms. With this in mind, and in order to mitigate potential dangers and ensure the controllability and sustainability of AI technologies based on the concept of trusted (responsible) AI, it is necessary to agree on universal international guidelines for the development and application of AI technologies and models. Furthermore, it is necessary to create a universal code of conduct for AI developers, who together can create a basis for a uniform framework of legal regulation within the national legislation of each country on the principles of human rights protection, privacy and data protection, transparency and explainability, fairness, accountability and safety of artificial intelligence, adequate human oversight and ethical standards for the creation and application of AI models.Legal Issues in the Digital Age30It is crucial to develop systemic legal measures and ways to regulate AI technologies and models on a national and international scale and to define the legal status of AI, which must include protection of humans from the uncontrolled influence of AI and the inviolability of guarantees of human rights and freedoms.Legal Issues in the Digital Age["Aleksandr Kartskhiya"]2024-07-20T00:00:00 82a0761163bb5b7e1eacf94af132881307e8c6ddNanotechnology Perceptions04Nanotechnology Perceptions[]2024-07-20T00:00:00 ef3b3098153a5ac50c5b1715c5540a40d9698db0Cogent Education441Cogent Education["Alberto Gr\u00e1jeda", "Pamela C\u00f3rdova", "Juan Pablo C\u00f3rdova", "Andr\u00e9s Laguna-Tapia", "Johnny Burgos", "Luis Rodr\u00edguez", "Mart\u00edn Arandia", "Alberto Sanjin\u00e9s"]2024-07-20T00:00:00 ba6033a7c4a24d71cbf5a063edb81e7d3366c3c6Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-07-20T00:00:00 c67559b1dbef39a7613903ac4c6139d61217a220INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT00INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT["Pritam Roy"]2024-07-20T00:00:00 e8731c45d14ac0479cec50e57fb902017202a301ACM Trans. Hum. Robot Interact.00ACM Trans. Hum. Robot Interact.["Jivko Sinapov", "Zhao Han", "Shelly Bagchi", "Muneeb Ahmad", "Matteo Leonetti", "Ross Mead", "Reuth Mirsky", "Emmanuel Senft"]2024-07-20T00:00:00 13d4fe1a2698c301c9f8566aa99bc12077166431Acta Informatica Pragensia500Acta Informatica Pragensia["Jela Steinerov\u00e1", "Miriam Ondri\u0161ov\u00e1"]2024-07-20T00:00:00 aa8ea913c82372466311f4aa8ebde62758791f90General-purpose artificial intelligence (AI) systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora. Our audit of 14,000 web domains provides an expansive view of crawlable web data and how codified data use preferences are changing over time. We observe a proliferation of AI-specific clauses to limit use, acute differences in restrictions on AI developers, as well as general inconsistencies between websites' expressed intentions in their Terms of Service and their robots.txt. We diagnose these as symptoms of ineffective web protocols, not designed to cope with the widespread re-purposing of the internet for AI. Our longitudinal analyses show that in a single year (2023-2024) there has been a rapid crescendo of data restrictions from web sources, rendering ~5%+ of all tokens in C4, or 28%+ of the most actively maintained, critical sources in C4, fully restricted from use. For Terms of Service crawling restrictions, a full 45% of C4 is now restricted. If respected or enforced, these restrictions are rapidly biasing the diversity, freshness, and scaling laws for general-purpose AI systems. We hope to illustrate the emerging crises in data consent, for both developers and creators. The foreclosure of much of the open web will impact not only commercial AI, but also non-commercial AI and academic research.Neural Information Processing Systems12317This first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora is conducted, providing an expansive view of crawlable web data and how codified data use preferences are changing over time.ArXiv["Shayne Longpre", "Robert Mahari", "Ariel N. Lee", "Campbell Lund", "Hamidah Oderinwale", "William Brannon", "Nayan Saxena", "Naana Obeng-Marnu", "Tobin South", "Cole Hunter", "Kevin Klyman", "Christopher Klamm", "Hailey Schoelkopf", "Nikhil Singh", "Manuel Cherep", "A. Anis", "An Dinh", "Caroline Chitongo", "Da Yin", "Damien Sileo", "Deividas Mataciunas", "Diganta Misra", "Emad A. Alghamdi", "Enrico Shippole", "Jianguo Zhang", "Joanna Materzynska", "Kun Qian", "Kush Tiwary", "Lester James Validad Miranda", "Manan Dey", "Minnie Liang", "Mohammed Hamdy", "Niklas Muennighoff", "Seonghyeon Ye", "Seungone Kim", "Shrestha Mohanty", "Vipul Gupta", "Vivek Sharma", "Vu Minh Chien", "Xuhui Zhou", "Yizhi Li", "Caiming Xiong", "Luis Villa", "Stella Biderman", "Hanlin Li", "Daphne Ippolito", "Sara Hooker", "Jad Kabbara", "Sandy Pentland"]2024-07-20T00:00:00 e66b146cedfbc91e1362b6e4a6d6a994c459b14aOur research investigates the impact of Generative Artificial Intelligence (GAI) models, specifically text-to-image generators (T2Is), on the representation of non-Western cultures, with a focus on Indian contexts. Despite the transformative potential of T2Is in content creation, concerns have arisen regarding biases that may lead to misrepresentations and marginalizations. Through a Non-Western community-centered approach and grounded theory analysis of 5 focus groups from diverse Indian subcultures, we explore how T2I outputs to English input prompts depict Indian culture and its subcultures, uncovering novel representational harms such as exoticism and cultural misappropriation. These findings highlight the urgent need for inclusive and culturally sensitive T2I systems. We propose design guidelines informed by a sociotechnical perspective, contributing to the development of more equitable and representative GAI technologies globally. Our work underscores the necessity of adopting a community-centered approach to comprehend the sociotechnical dynamics of these models, complementing existing work in this space while identifying and addressing the potential negative repercussions and harms that may arise as these models are deployed on a global scale.AAAI/ACM Conference on AI, Ethics, and Society854This work explores how T2I outputs to English input prompts depict Indian culture and its subcultures, uncovering novel representational harms such as exoticism and cultural misappropriation, and proposes design guidelines informed by a sociotechnical perspective, contributing to the development of more equitable and representative GAI technologies globally.ArXiv["Sourojit Ghosh", "Pranav Narayanan Venkit", "Sanjana Gautam", "Shomir Wilson", "Aylin Caliskan"]2024-07-20T00:00:00 44dbcaf0e6767fb13364cce2168f0875aafef9d5This integrative literature review (ILR) examines the influence of artificial intelligence (AI) on vocational training, specifically focusing on unequal access to AI-powered resources and the ensuing inequities in education. The study seeks to analyze the impact of AI on vocational training and employability, offering insights into the advantages and difficulties related to integrating AI technology into educational institutions. The study's conceptual framework is grounded in three primary pillars: AI-driven innovation, challenges, and perspectives. This research is essential for providing valuable insights that can guide strategic planning and policy-making to improve vocational training programs and ensure that they remain effective in preparing students for the changing job market. The ILR methodology entailed integrating theoretical and empirical literature and collecting and evaluating relevant scholarly materials to provide a thorough comprehension of AI's function in vocational training. The results emphasize the capacity of AI to enhance educational achievements using tailored learning, adaptable platforms, immediate feedback, and simulations. However, there is a risk of widening educational inequalities due to biased algorithms. The study highlights the necessity of making significant investments in infrastructure and providing ongoing professional development for educators to incorporate AI successfully. It also suggests the establishment of distinct positions inside vocational training institutions, such as Vocational AI Curriculum Developer (VACD), Vocational AI Data Protection Specialist (VAIDPS), and Vocational AI Sustainability Facilitator (VAISF), to tackle these difficulties effectively. The conclusions highlight the revolutionary capacity of AI in vocational training, soliciting strategic investments and evoking the creation of specialized positions to promote fair and efficient deployment of AI. The study's findings underscore the significance of continuous research and improvements in practice to promote positive societal change and better educational fairness.International Journal For Multidisciplinary Research682The ILR methodology entailed integrating theoretical and empirical literature and collecting and evaluating relevant scholarly materials to provide a thorough comprehension of AI's function in vocational training, and highlights the revolutionary capacity of AI in vocational training.International Journal For Multidisciplinary Research["Rachid Ejjami"]2024-07-20T00:00:00 f16b31f0388b957e2de564a0366ad19d541a22dfIn recent years, the potential benefits of artificial intelligence (AI) in language education have garnered increasing interest. Understanding the beliefs of pre-service English language teachers (PELTs) toward the integration of AI in language education is crucial for equipping them with the skills needed to integrate AI into educational practices. Therefore, this paper investigates the beliefs and attitudes of PELTs toward integrating AI into language education. The study involved a sample of 20 PELTs and utilized focus-group interviews to uncover their perspectives. The findings indicate that PELTs hold a mix of positive and negative attitudes toward AI integration. Various factors influencing their views on the use of AI-based tools in language education were identified, with a detailed exploration of each factor providing deep insights into the complexities of PELTs’ beliefs. By delving into PELTs’ perspectives, this study enriches the growing corpus of research on AI-integrated language teaching and offers valuable insights for educators, curriculum designers, and technology innovators.E-Learning and Digital Media262The findings indicate that PELTs hold a mix of positive and negative attitudes toward AI integration, and various factors influencing their views on the use of AI-based tools in language education were identified.E-Learning and Digital Media["Ramazan Yetkin", "Zekiye \u00d6zer-Alt\u0131nkaya"]2024-07-20T00:00:00 6f934f637a545b1df4677ff2dfaf0df39921dd08This research paper is aimed to provide the analysis of the phenomenon of artificial intelligence and data analytics in the context of the financial services industry with focus on the incorporation of these two concepts in Customer Relationship Management systems. The work takes a detailed look at the present and the contemporary developments in the field of CRM systems, the immense opportunities of applying artificial intelligences in the field of customer analytics, as well as the complex issues of implementation. In the case study, analysing machine learning algorithms, natural language processing, and complex predictive analyses, I show how AI improves customer information and personalisation operations, as well as decision-making. Lack of hard evidence of the performance of the AI-CRM system is an area that needs some improvement, Real-life examples taken from retail banking, wealth management businesses, and insurance industries show the effective adoption of the AI -CRM system. The research also incorporates invaluable questions concerning data privacy, compliance, and the ethical use of AI in the financial service industry. Last but not the least, it speaks about the current trends and offers a literature-backed guideline for the financial service providers who want to use the AI in the CRM and create possibilities for the future of the AI in the CRM system.Turkish Journal of Computer and Mathematics Education02A detailed look at the present and the contemporary developments in the field of CRM systems, the immense opportunities of applying artificial intelligences in the field of customer analytics, as well as the complex issues of implementation is taken.Turkish Journal of Computer and Mathematics Education (TURCOMAT)["Geetesh Sanodia"]2024-07-20T00:00:00 b095d4da1f39d3cd03831174392950a3d16358cdThis comprehensive research paper explores the transformative impact of Artificial Intelligence (AI) on cloud computing modernization. It examines the current state of cloud infrastructure, identifies key AI technologies driving innovation, and analyses strategies for AI-driven cloud modernization. The study investigates implementation approaches, presents case studies from major cloud providers, and discusses challenges and future trends. Through extensive analysis of recent developments and industry data, this research highlights the symbiotic relationship between AI and cloud computing, demonstrating how AI is revolutionizing cloud architectures, improving efficiency, enhancing security, and enabling new services. The paper concludes with an assessment of the economic impact and provides recommendations for businesses navigating this technological frontier.Turkish Journal of Computer and Mathematics Education01Through extensive analysis of recent developments and industry data, this research highlights the symbiotic relationship between AI and cloud computing, demonstrating how AI is revolutionizing cloud architectures, improving efficiency, enhancing security, and enabling new services.Turkish Journal of Computer and Mathematics Education (TURCOMAT)["Geetesh Sanodia"]2024-07-20T00:00:00 7f36b9e20c6468446b6877103fd096ee54e92755The research aims to evaluate the effectiveness of artificial intelligence (AI) in solving mathematical problems at the high school level by comparing the performance of AI with that of students. Utilizing the ChatGPT tool and a dataset of 20 mathematics questions from the high school curriculum, the study involved ten students from the Muhammadiyah Mertoyudan Islamic Boarding School. The results indicate that AI exhibits high accuracy in multiple-choice questions (98%) and short-answer questions (95%), albeit with a decrease in performance for essay questions (75%); conversely, students demonstrated an average accuracy of 85% for multiple-choice questions, 80% for short-answer questions, and 70% for essay questions, additionally, also demonstrated higher consistency, particularly in questions requiring complex conceptual understanding. At the same time, the findings underscore the significant potential of AI as a tool for mathematics learning, further development is needed to enhance its ability to comprehend and respond to essay questions, thereby improving conceptual understanding and critical reasoning. The implications of these findings can contribute to the advancement of more sophisticated and effective educational technology to support mathematics learning in schools.Union: Jurnal Ilmiah Pendidikan Matematika250Evaluating the effectiveness of artificial intelligence in solving mathematical problems at the high school level by comparing the performance of AI with that of students indicates that AI exhibits high accuracy in multiple-choice questions and short-answer questions, albeit with a decrease in performance for essay questions.Union: Jurnal Ilmiah Pendidikan Matematika["Efendi Hidayatullah", "Retno Untari", "Ferdinandus Fifardin"]2024-07-20T00:00:00 2ff7cb67cc01fdfee3d1505924e2a3f21e477965Modern medicine is increasingly focused on a personalized approach to diagnosis and treatment. This approach is based on a thorough analysis of biomarkers — objective indicators reflecting various aspects of health. Unleashing the potential of biomarkers requires tools that can effectively process and interpret large volumes of data. In this context, artificial intelligence (AI) technologies are becoming increasingly popular. AI systems have unique capabilities for identifying and interpreting complex relationships between biomarkers. Examples of using AI to predict cardiovascular diseases and oncology based on biomarker data are given. AI is capable of not only finding correlations, but also studying cause-and-effect relationships, which opens new horizons in understanding the pathogenesis of diseases. AI also opens up opportunities for personalizing therapeutic interventions based on the analysis of individual patient characteristics.Terapevt (General Physician)40Examples of using AI to predict cardiovascular diseases and oncology based on biomarker data are given, which open up opportunities for personalizing therapeutic interventions based on the analysis of individual patient characteristics.Terapevt (General Physician)["A. Krylov"]2024-07-20T00:00:00 549f441cfaec6cbb8b070aa96cc9a65ab46444dbAs healthcare embraces the transformative potential of Artificial Intelligence (AI), it is imperative to safeguard patient and provider safety, equity, and trust in the healthcare system. This article outlines the approach taken by the British Columbia (BC) Provincial Health Services Authority (PHSA) to establish clinical governance for the responsible deployment of AI tools in healthcare. Leveraging its province-wide mandate and expertise, PHSA establishes the infrastructure and processes to proactively and systematically intake, assess, prioritize, and evaluate AI tools. PHSA proposes a coordinated approach in AI tool deployment in collaboration with regional health authorities to prevent duplication of efforts and ensure equitable access to existing and emerging AI tools across the province of BC, incorporating principles of anti-Indigenous racism, cultural safety, and humility. The proposed governance structure underscores the identification of clinical needs, proactive ethics review, rigorous risk assessment, data validation, transparent communication, provider training, and ongoing evaluation to ensure success.Healthcare Management Forum90The proposed governance structure underscores the identification of clinical needs, proactive ethics review, rigorous risk assessment, rigorous risk assessment, data validation, transparent communication, provider training, and ongoing evaluation to ensure success.Healthcare management forum["Angel Arnaout", "Prabjot Gill", "Alice Virani", "Alexandra Flatt", "Natasha Prodan-Balla", "David Byres", "Megan Stowe", "Alireza Saremi", "Michael Coss", "Michael Tatto", "May Tuason", "Shannon Malovec", "Sean Virani"]2024-07-20T00:00:00 7b28bff533d2e7301a3de9e2b28c7bd124ee4a10Artificial intelligence (AI) has become an increasingly prominent tool in the field of creative writing, particularly in the realm of novel composition. This paper explores the integration of AI-driven character building techniques into the novel writing process, examining the potential benefits, challenges, and ethical considerations surrounding this convergence of technology and literature. Through a review of relevant literature, this paper discusses the ways in which AI can be leveraged to enhance the crafting of compelling and believable characters, ultimately strengthening the overall narrative and storytelling experience. The paper also delves into the potential impact of AI-generated content on the traditional roles of the author and reader, as well as the ethical implications of blending human and machine-driven creativity. In conclusion, this paper argues that the strategic integration of AI in the novel writing process can lead to novel narrative structures, character development, and literary experiences, provided that the technology is utilized with a deep understanding of its capabilities and limitations, as well as its ethical considerations.International Journal For Multidisciplinary Research200It is argued that the strategic integration of AI in the novel writing process can lead to novel narrative structures, character development, and literary experiences, provided that the technology is utilized with a deep understanding of its capabilities and limitations, as well as its ethical considerations.International Journal For Multidisciplinary Research["Mohd Shahremy Ikmal Shahbudin"]2024-07-20T00:00:00 ca3bf8ac036c7a3da233a2fe6966ae6783e559f6The article examines issues of using artificial intelligence in such a sensitive area of human activity as justice. The authors refer to numerous facts on attempts to create a kind of “smart court” in various countries. At the same time, these attempts run up against circumstances that indicate the need to establish legal restrictions on the use of artificial intelligence in the administration of justice. Moreover, according to the authors’ reasoned conviction, there are areas in which the robot judge turns out to be powerless to replace human intelligence. Based on the philosophical and legal approach to assessing such a phenomenon as digitalization and the phenomenology of legal judgment, the authors conclude that the adoption of a court decision that meets the requirements of the principle of justice is something beyond the reach of artificial intelligence. Such a decision can only be made by a human judge, but not by a robot. AI systems in the judicial system should support rather than supersede judges.Legal Issues in the Digital Age20It is concluded that the adoption of a court decision that meets the requirements of the principle of justice is something beyond the reach of artificial intelligence, and AI systems in the judicial system should support rather than supersede judges.Legal Issues in the Digital Age["\u0415lena Burdina", "Viktor \u041a\u043ernev"]2024-07-20T00:00:00 1b8f90a16336713ceac1b9470d0505304d5bb695Contemporary artificial intelligence systems are pivotal in enhancing human efficiency and safety across various domains. One such domain is autonomous systems, especially in automotive and defense use cases. Artificial intelligence brings learning and enhanced decision-making to autonomy system goal-oriented behaviors and human independence. However, the lack of clear understanding of autonomy system capabilities hampers human-machine or machine-machine interaction and interdiction. This necessitates varying degrees of human involvement for safety, accountability, and explainability purposes. Yet, measuring the level autonomous capability in an autonomous system presents a challenge. Two scales of measurement exist, yet measuring autonomy presupposes a variety of elements not available in the wild. This is why existing measures for level of autonomy are operationalized only during design or test and evaluation phases. No measure for level of autonomy based on observed system behavior exists at this time. To address this, we outline a potential measure for predicting level of autonomy using observable actions. We also present an algorithm incorporating the proposed measure. The measure and algorithm have significance to researchers and practitioners interested in a method to blind compare autonomous systems at runtime. Defense-based implementations are likewise possible because counter-autonomy depends on robust identification of autonomous systems.arXiv.org350A potential measure for predicting level of autonomy using observable actions and an algorithm incorporating the proposed measure are presented, which have significance to researchers and practitioners interested in a method to blind compare autonomous systems at runtime.ArXiv["Jason M. Pittman"]2024-07-20T00:00:00 43b1cdabb0f3637ca63367ceb8ee7bb2bf4cef5dIn the era of advancements in artificial intelligence (AI) and machine learning, the healthcare industry has become one of the major areas where such technologies are being actively adopted and utilized. The global health care sector generated more than 2.3 zettabytes of data worldwide in 2020. Analysts estimate that the global market for artificial intelligence (AI) in medicine will grow to $13 billion by 2025, with a significant increase in newly established companies. Artificial intelligence in medicine is used to predict, detect and diagnose various diseases and pathologies. The sources of data can be various results of medical research (EEG, X-ray images, laboratory tests, e.g. tissues, etc.). At the same time, there are understandable concerns that AI will undermine the patient-provider relationship, contribute to the deskilling of providers, undermine transparency, misdiagnose or inappropriately treat because of errors within AI decision-making that are hard to detect, exacerbate existing racial or societal biases, or introduce algorithmic bias that will be hard to detect. Traditional research methods, general and special ones, with an emphasis on the comparative legal method, were chosen. For the AI to work it needs to be trained, and it’s learning from all sorts of information given to it. The main part of the information on which AI is trained is health data, which is sensitive personal data. The fact that personal data is qualified as sensitive personal data indicates the significance of the information contained, the high risks in case it’s leaking, and hence the need for stricter control and regulation. The article offers a detailed exploration of the legal implications of AI in medicine, highlighting existing challenges, the current state of regulation, and proposes future perspectives and recommendations for legislation adapted to the era of medical AI. Given the above, the study is divided into three parts: international framework, that will focus primarily on applicable WHO documents; risks and possible ways to minimize them, where the authors have tried to consider various issues related to the use of AI in medicine and find options to address them; and relevant case-study.Legal Issues in the Digital Age20The article offers a detailed exploration of the legal implications of AI in medicine, highlighting existing challenges, the current state of regulation, and proposes future perspectives and recommendations for legislation adapted to the era of medical AI.Legal Issues in the Digital Age["Boris Edidin", "Alexey Bunkov", "Ksenia Kochetkova"]2024-07-20T00:00:00 660c5311cb97fb2ee5104b2888d5c233c801defbJournal of Artificial Intelligence, Machine Learning and Data Science00Journal of Artificial Intelligence, Machine Learning and Data Science["Gomathi Shirdi Botla"]2024-07-20T00:00:00 ecda97b224f0d1644b2b5d48977957fe872b049eJournal of Artificial Intelligence, Machine Learning and Data Science00Journal of Artificial Intelligence, Machine Learning and Data Science["Rajalakshmi Thiruthuraipondi Natarajan"]2024-07-20T00:00:00 46dc2dd287facddfdfaa50c78424dc696ea143c0El objetivo de esta revisión sistemática es analizar el estado actual del uso de la inteligencia artificial (IA) como recurso educativo en la educación superior. Se busca identificar las principales aplicaciones, beneficios, desafíos y limitaciones de la IA en este contexto, así como su impacto en el aprendizaje, la enseñanza y la gestión académica. Para llevar a cabo esta revisión, se siguió una metodología basada en las directrices PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Se realizaron búsquedas exhaustivas en bases de datos académicas como Scopus, Web of Science y Google Scholar, utilizando palabras clave como "inteligencia artificial", "educación superior", "tecnología educativa" y "aprendizaje automatizado". Se incluyeron estudios publicados entre 2010 y 2024. Se aplicaron criterios de inclusión y exclusión para seleccionar los artículos más relevantes, y los resultados se sintetizaron cualitativamente. Los resultados muestran que la inteligencia artificial se está aplicando en diversas áreas de la educación superior, tales como la personalización del aprendizaje, la evaluación automatizada, los sistemas de tutoría inteligente y la gestión administrativa. La revisión concluye que la IA tiene un gran potencial para transformar la educación superior, pero su implementación debe ser cuidadosa y acompañada de un enfoque ético y formativo. Es fundamental que las instituciones educativas inviertan en infraestructura tecnológica y en la capacitación docente para aprovechar al máximo las herramientas basadas en IA. Además, es necesario seguir investigando sobre el impacto a largo plazo de estas tecnologías en el desarrollo de habilidades críticas y el pensamiento creativo en los estudiantes.RECIMUNDO00RECIMUNDO["Silvia Paulina Puente Titua\u00f1a", "Lourdes Alexandra Baja\u00f1a Jim\u00e9nez", "Carlos Edison Serrano Torres", "Katuska Maria Vallejo Flores"]2024-07-20T00:00:00 ae58e03df84f9afde8dee311155727b4959c85e6El objetivo de esta investigación es realizar una revisión sistemática sobre la inteligencia artificial aplicada en la educación, incluyendo temas como la automatización de la enseñanza, la retroalimentación en tiempo real, y la adaptación del contenido a las necesidades individuales de cada estudiante. La investigación recopila evidencias de diversas plataformas de aprendizaje basadas en IA, evaluando efectividad y aceptación por parte de los docentes y estudiantes. En la revisión de estudios empíricos, se identifican patrones positivos de mejora en los resultados académicos, especialmente en estudiantes con dificultades de aprendizaje. Sin embargo, también se exploran riesgos potenciales, como la dependencia excesiva de la tecnología, riesgos relacionados con la privacidad de datos, desigualdad en el acceso a tecnologías avanzadas y la posible deshumanización del proceso educativo. Las conclusiones resaltan que, si bien la IA pueden reducir la carga administrativa y mejorar el aprendizaje personalizado, su implementación requiere un rediseño pedagógico y una inversión sustancial en capacitación docente. Para la revisión, se siguió un enfoque sistemático utilizando la tecnología PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) para seleccionar los estudios y se utilizó un análisis temático para identificar patrones de uso, tipos de tecnología utilizada y efectividad en diferentes contextos educativos (primaria, secundaria, educación superior).; a través de bases de datos en Google Scholar, PsycINFO, SciELO. Se seleccionaron 32 estudios publicados en los últimos 5 años. Los criterios de inclusión fueron artículos en inglés y español, estudios de implementación de herramientas de IA publicados en revistas científicas y que documenten intervenciones de IA en entornos educativos. Se excluyeron estudios fuera del rango temporal, que no se centren explícitamente en la aplicación de la inteligencia artificial en la educación, estudios sin análisis empíricos o estudios de caso, estudios que no estén disponibles en su totalidad, estudios duplicados y de baja calidad metodológica.RECIMUNDO00RECIMUNDO["Guisella Isabel Villamar V\u00e1squez", "Edgar Efrain Tipan Criollo", "Jos\u00e9 Luis Rugel Llongo"]2024-07-20T00:00:00 92c82cce1161b59f198d1ae16eff2323698c95b2Los grandes modelos lingüísticos (LLM por sus siglas en inglés Large Language Models) son capaces de responder a consultas de texto libre sin estar específicamente entrenados para la tarea en cuestión (Thirunavukarasu et al., 2023). ChatGPT, lanzado en el año 2022 por la empresa OpenAI, es un chatbot de inteligencia artificial generativa (IAG), basado en LLM, que ha revolucionado diferentes campos, incluida la comunicación científica. En el área de la educación, García-Peñalvo (2024) señala que la IAG permite personalizar el aprendizaje y mejorar la calidad de los recursos educativos, entre otros beneficios; sin embargo, la falta de alfabetización en inteligencia artificial (IA), en general, puede ocasionar que su utilidad vulnere aspectos éticos y legislativos.Revista Científica de Ingeniería, Diseño y Arquitectura Contemporánea00Revista Científica de Ingeniería, Diseño y Arquitectura Contemporánea["Lloy Pool Pinedo Tuanama"]2024-07-20T00:00:00 667340fbde4960046d70f1d215f8b3cc77d64258The current business environment faces numerous new challenges closely linked to the rapid development of information and communication technologies, which influence the corporate landscape. This article focuses on exploring the possibilities of integrating artificial intelligence, as one of the key technologies of today, into the recruitment process. Its aim is to examine the potential applications of artificial intelligence across various stages of employee recruitment. To achieve this goal, the authors employed various methods and techniques, including the PICOS framework, scientific mapping, and case study analysis. The outcome of this study identifies opportunities for leveraging artificial intelligence in the employee recruitment process within corporate settings. The results reflect the current research gaps concerning the analysis of the personnel processes and conceptualizing the implementation possibilities of artificial intelligence in these processes. The contribution of this article to the academic community lies in its conceptualization, providing a foundation for further research focused on analyzing the impacts of integrating AI into recruitment processes.Administrative Sciences994The outcome of this study identifies opportunities for leveraging artificial intelligence in the employee recruitment process within corporate settings and provides a foundation for further research focused on analyzing the impacts of integrating AI into recruitment processes.Administrative Sciences["Gabriel Koman", "Patrik Bor\u0161o\u0161", "M. Kubina"]2024-07-21T00:00:00 f526a76e5f91e8ab32e0574f533296d2ba85218ePatient Safety in Surgery293The model’s high precision and real-time inference capabilities highlight its potential to serve as an AI safeguard to potentially improve patient safety and reduce manual burden on surgical staff.Patient Safety in Surgery["Ekamjit S. Deol", "Grant M. Henning", "Spyridon P. Basourakos", "Ranveer M S Vasdev", "Vidit Sharma", "Nicholas L. Kavoussi", "R. Karnes", "Bradley C. Leibovich", "S. Boorjian", "A. Khanna"]2024-07-21T00:00:00 494318bf04d312a11fc94dfc56fb68ad608d26e9Abstract Purpose The aim of this study was to conduct a systematic review and meta‐analysis on the reliability and applicability of artificial intelligence (AI)‐based analysis of leg axis parameters. We hypothesized that AI‐based leg axis measurements would be less time‐consuming and as accurate as those performed by human raters. Methods The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). PubMed, Epistemonikos, and Web of Science were searched up to 24 February 2024, using a BOOLEAN search strategy. Titles and abstracts of identified records were screened through a stepwise process. Data extraction and quality assessment of the included papers were followed by a frequentist meta‐analysis employing a common effect/random effects model with inverse variance and the Sidik–Jonkman heterogeneity estimator. Results A total of 13 studies encompassing 3192 patients were included in this meta‐analysis. All studies compared AI‐based leg axis measurements on long‐leg radiographs (LLR) with those performed by human raters. The parameters hip knee ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint‐line convergence angle (JLCA) showed excellent agreement between AI and human raters. The AI system was approximately 3 min faster in reading standing long‐leg anteroposterior radiographs (LLRs) compared with human raters. Conclusion AI‐based assessment of leg axis parameters is an efficient, accurate, and time‐saving procedure. The quality of AI‐based assessment of the investigated parameters does not appear to be affected by the presence of implants or pathological conditions. Level of Evidence Level I.Knee Surgery, Sports Traumatology, Arthroscopy411AI‐based assessment of leg axis parameters is an efficient, accurate, and time‐saving procedure and the quality of AI‐based assessment of the investigated parameters does not appear to be affected by the presence of implants or pathological conditions.Knee Surgery, Sports Traumatology, Arthroscopy["Mikhail Salzmann", "Hakam Hassan Tarek", "R. Prill", "Roland Becker", "Andreas G Schreyer", "Robert Hable", "Marko Ostoji\u0107", "N. Ramadanov"]2024-07-21T00:00:00 f711042d70fcdedbaea311eea111999614d99d34Generative Artificial Intelligence (GenAI) is emerging as a promising tool in academic production, offering the potential to help with literature reviews, content creation and idea generation. However, the use of AI raises ethical debates related to authorship, plagiarism and intellectual property. Therefore, regulating the use of AI in the academic sphere is necessary, through a dialog between the academic community, companies and governments that defines guidelines that consider principles such as transparency, justice, equity, responsibility and beneficence. The future of academic production will depend on the integration of AI with human expertise and judgment. Thus, this article is a comprehensive review of the existing literature on Generative Artificial Intelligence, ethics and intellectual property, synthesizing a solid knowledge base for discussing the use of these intelligent mechanisms within academic production.Anais do V Workshop sobre as Implicações da Computação na Sociedade (WICS 2024)171This article is a comprehensive review of the existing literature on Generative Artificial Intelligence, ethics and intellectual property, synthesizing a solid knowledge base for discussing the use of these intelligent mechanisms within academic production.Anais do V Workshop sobre as Implicações da Computação na Sociedade (WICS 2024)["Vitor Rabelo Delgado", "Keanu Frota Sales", "Vin\u00edcius Augusto Carvalho de Abreu"]2024-07-21T00:00:00 87523654be029bfa8d24df33f21a79be60b3a3efThe rapid advancement of Artificial Intelligence (AI) technologies is set to reshape IT jobs in the coming years. This paper investigates AI's evolving role in the sector and its implications for employment trends, drawing from a review of secondary sources including various research journal papers, and reports of White House, World Economic Forum, and McKinsey, etc. It analyses the current AI applications in IT, focusing on areas where automation and augmentation will likely impact traditional roles. The study views AI as complementing rather than replacing human labour, foreseeing potential for new job creation and transformations in existing positions, especially in cybersecurity, software development, and IT infrastructure management. Policy recommendations and strategic initiatives from various stakeholders are examined to maximize AI benefits while addressing employment risks. Insights from government and industry reports stress the need for workforce preparation through reskilling and upskilling. Ethical considerations around AI deployment in IT underscore responsible innovation and inclusive growth. Ultimately, this paper aims to offer a comprehensive overview of AI's future impact on IT jobs, providing insights for policymakers, industry leaders, and educators navigating the evolving digital economy. By synthesizing diverse perspectives and empirical evidence, it informs decision-making for a sustainable and equitable future of work in IT.International Journal For Multidisciplinary Research140This paper investigates AI's evolving role in the sector and its implications for employment trends, drawing from a review of secondary sources including various research journal papers, and reports of White House, World Economic Forum, and McKinsey, etc.International Journal For Multidisciplinary Research["S. Subudhi"]2024-07-21T00:00:00 70c2be981626b6636c264ea1281be76ec187866aBACKGROUND AND PURPOSE Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. METHODS A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method. RESULTS We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were .849, .878, and 45.598, respectively. CONCLUSION AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.Journal of Neuroimaging400A systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT in AIS patients found that AI/ML models can reliably predict the occurrence of HT in AIS patients.Journal of neuroimaging : official journal of the American Society of Neuroimaging["Richard Dagher", "B. Ozkara", "Mert Karabacak", "Samir A. Dagher", "Elijah Isaac Rumbaut", "Licia P Luna", "V. Yedavalli", "Max Wintermark"]2024-07-21T00:00:00 108df39c6349b1298299d7e885da1e3356e21151OBJECTIVE We examined the performance validity test (PVT) security risk presented by artificial intelligence (AI) chatbots asking questions about neuropsychological evaluation and PVTs on two popular generative AI sites. METHOD In 2023 and 2024, multiple questions were posed to ChatGPT-3 and Bard (now Gemini). One set started generally and refined follow-up questions based on AI responses. A second set asked how to feign, fake, or cheat. Responses were aggregated and independently rated for inaccuracy and threat. Responses not identified as inaccurate were assigned a four-level threat rating (no, mild, moderate, or high threat). Combined inaccuracy and threat ratings were examined cross-sectionally and longitudinally. RESULTS Combined inaccuracy rating percentages were 35 to 42% in 2023 and 16 to 28% in 2024. Combined moderate/high threat ratings were observed in 24 to 41% of responses in 2023 and in 17 to 31% of responses in 2024. More ChatGPT-3 responses were rated moderate or high threat compared to Bard/Gemini responses. Over time, ChatGPT-3 responses became more accurate with a similar threat level, but Bard/Gemini responses did not change in accuracy or threat. Responses to how to feign queries demonstrated ethical opposition to feigning. Responses to similar queries in 2024 showed even stronger ethical opposition. CONCLUSIONS AI chatbots are a threat to PVT test security. A proportion of responses were rated as moderate or high threat. Although ethical opposition to feigning guidance increased over time, the natural language interface and the volume of AI chatbot responses represent a potentially greater threat than traditional search engines.Clinical Neuropsychologist240Although ethical opposition to feigning guidance increased over time, the natural language interface and the volume of AI chatbot responses represent a potentially greater threat than traditional search engines.The Clinical neuropsychologist["Shannon Lavigne", "Anthony Rios", "Jeremy J Davis"]2024-07-21T00:00:00 4532e984a71a91950bde921304ac3507ebbdde39 The continued rise of manufacturing automation and the prospect of integrating artificial intelligence in manufacturing environments renews the need for understanding how different stakeholders within an organization perceive the implementation of such technologies. This paper presents a research instrument designed to capture and understand perceptions of employees at different levels of an organization. A multimodal data collection approach is used to gather participant responses using surveys, focus group discussions, and interviews. Qualitative and quantitative data collected from six manufacturing companies is analyzed to identify general themes in terms of participation, communication, level of understanding, and alignment to company goals. Definitions provided by participants are also analyzed to determine participant level of familiarity with concepts of AI and automation. The study demonstrates the usability of the research instrument. Preliminary findings suggest that many of the companies are ill prepared for implementing artificial intelligence in their organizations. Finally, future analysis of the collected data is discussed, and potential improvements to the research instrument are presented.2024 International Symposium on Flexible Automation00A research instrument designed to capture and understand perceptions of employees at different levels of an organization is presented and preliminary findings suggest that many of the companies are ill prepared for implementing artificial intelligence in their organizations.2024 International Symposium on Flexible Automation["Pavan Kumar", "Oredola Adebayo", "Apurva Patel", "Joshua D. Summers"]2024-07-21T00:00:00 753da43dc5fbf092eb3483d561072711470b109cArtificial intelligence is transforming many areas of activity, including completely transforming banks. Artificial intelligence offers solutions to improve the rationality of decisions, the speed and accuracy of information processing, the provision of ready–made solutions and the implementation of monitoring operations. This article examines the evolution of artificial intelligence in the banking sector. The key areas of AI use in the financial market are highlighted, such as scoring and underwriting, anti–fraud, investment portfolio management, robo–consulting, monitoring client requests and others.The subject of the research in the article is the system of relations arising during the transformation of the banking sector with the introduction of artificial intelligence.Entrepreneur's Guide30The system of relations arising during the transformation of the banking sector with the introduction of artificial intelligence is examined, such as scoring and underwriting, anti–fraud, investment portfolio management, robo–consulting, monitoring client requests and others.Entrepreneur’s Guide["V. L. Parkhomenko", "I. S. Kulaeva"]2024-07-21T00:00:00 57d2e4577333e254c36f8243969d68413dce9556This paper discusses the potential role of artificial intelligence in enhancing dental diagnosis and care delivery. It describes how AI shows promise in automatic detection of abnormalities in dental images, personalised disease risk assessment through predictive modelling, and generation of comprehensive examination reports. The ability of AI to analyse patterns across medical imaging, genetics, lifestyle factors and health records enables more holistic understanding of oral health in relation to systemic conditions. Clinical decision support through differential diagnoses and evidence-based treatment recommendations aims to augment dentists' decision making. Seamless integration of AI into clinical workflows through interfaces is emphasised as important for adoption. Challenges around data and model validation are also addressed. Continued development of AI aims to realise benefits like earlier disease identification and more proactive, personalised care approachesInternational Journal of Scientific Research and Management140How AI shows promise in automatic detection of abnormalities in dental images, personalised disease risk assessment through predictive modelling, and generation of comprehensive examination reports is described.International Journal of Scientific Research and Management (IJSRM)["Sabira Arefin"]2024-07-21T00:00:00 94abbc3ce6d367a263f03402b217ce91c3a8aacePelatihan pembuatan media pembelajaran menggunakan AI dengan tujuan agar pembelajaran terus dikembangkan dan diinovasi khususnya kemajuan zaman di era digitalisasi dalam meningkatkan keterampilan untuk memanfaatkan teknologi. Metode pada pengabdian ini menggunakan metode pelatihan dan forum grup diskusi dengan mahasiswa. Kemajuan teknologi menjadi tuntutan bagi semua kalangan khususnya di dunia pendidikan. Mahasiswa dalam pembuatan media presentasi power point yang sering dilakukan selalu mengalami kesulitan, mau tidak mau mahasiswa harus terus mengupgrade teknologi agar tidak tertinggal. Hingga inilah saatnya mahasiswa harus aktif dan inovotif dalam pembuatan power point dengan bantuan AI menggunakan gamma.app sehingga dapat mendukung pengembangan media pembelajaran. Kegiatan yang dilakukan pada pengabdian ini yaitu mensosialisasikan kepada mahasiswa tentang pemanfaatan penggunaan teknologi, pelatihan penggunaan AI, pendampingan dalam pembuatan powerpoint dengan bantuan AI. Pelatihan yang dilakukan sangat membantu dan meningkatkan semangat dalam pembuatan media pembelajaran karena sangat mudah dan sangat mengasikkan sehingga mereka sangat senang dengan pelatihan yang dilakukan dengan banyak manfaat yang didapatkan dalam pemanfaatan penggunaan teknologi bantua AI.J-COSCIS Journal of Computer Science Community Service131J-COSCIS : Journal of Computer Science Community Service["Keterampilan Pembelajaran", "Betti Megawati", "Maisaroh Ritonga", "Rahmad Aditiya", "Ahmad Habin Sagala", "Wahyu Azhar Ritonga"]2024-07-21T00:00:00 f62304f1c69950f734594def520bc9905e2151baCogent Engineering720Cogent Engineering["Marco Antonio D\u00edaz Mart\u00ednez", "Reina Ver\u00f3nica Rom\u00e1n Salinas", "Santos Ru\u00edz Hern\u00e1ndez", "Herson Santos Ru\u00edz Dom\u00ednguez", "Gabriela Cervantes Zubir\u00edas", "Mario Alberto Morales Rodr\u00edguez"]2024-07-21T00:00:00 861bdb9d9bc56dd51f8bf3b8396f38c81974ceb4Esta pesquisa investiga a percepção dos usuários de sistemas computacionais em relação à Inteligência Artificial (IA), utilizando a escala ATAI – Attitude Towards Artificial Intelligence. Os resultados obtidos a partir de entrevistas com 76 participantes, estratificados em quatro grupos, destacam uma intensa relevância e dependência de sistemas computacionais, bem como a necessidade de abordagens éticas e inclusivas no uso de IA. Por fim, discute-se sobre possíveis caminhos e medidas que se convém tomar no Brasil para a adoção da IA de forma efetiva, ética e responsável e que possa servir como forma de inclusão, equidade e não para ampliar desigualdade.Anais do V Workshop sobre as Implicações da Computação na Sociedade (WICS 2024)230Anais do V Workshop sobre as Implicações da Computação na Sociedade (WICS 2024)["D. Carvalho", "Mariza Ferro", "Fabio Corr\u00eaa", "Vin\u00edcius Figueiredo de Faria", "Leandro Cearen\u00e7o Lima", "Amanda Damasceno de Souza", "Marco de Moura Gromato"]2024-07-21T00:00:00 253cd1b5676c454aaca1cdd806fbb191f52408f2In this survey, we address the key challenges in Large Language Models (LLM) research, focusing on the importance of interpretability. Driven by increasing interest from AI and business sectors, we highlight the need for transparency in LLMs. We examine the dual paths in current LLM research and eXplainable Artificial Intelligence (XAI): enhancing performance through XAI and the emerging focus on model interpretability. Our paper advocates for a balanced approach that values interpretability equally with functional advancements. Recognizing the rapid development in LLM research, our survey includes both peer-reviewed and preprint (arXiv) papers, offering a comprehensive overview of XAI's role in LLM research. We conclude by urging the research community to advance both LLM and XAI fields together.arXiv.org706This survey addresses the key challenges in Large Language Models (LLM) research and examines the dual paths in current LLM research and eXplainable Artificial Intelligence (XAI): enhancing performance through XAI and the emerging focus on model interpretability.ArXiv["Erik Cambria", "Lorenzo Malandri", "Fabio Mercorio", "Navid Nobani", "Andrea Seveso"]2024-07-21T00:00:00 c86e95ee6e33bf3061f0363c446c469fe71a1b5cThe intersection of artificial intelligence (AI) and partial differential equations (PDEs), emphasizing how AI techniques can revolutionize the analysis and solution of PDEs in various scientific and engineering applications. Traditional methods for solving PDEs often face challenges related to computational complexity, high-dimensionality, and nonlinearity. By leveraging advanced AI algorithms, particularly deep learning and neural networks, we propose novel approaches to approximate solutions, reduce computational costs, and handle complex boundary conditions more effectively. The study highlights the advantages of AI-driven methods in terms of accuracy, efficiency, and scalability, presenting case studies from fluid dynamics, quantum mechanics, and financial mathematics. Our findings suggest that AI has the potential to significantly enhance the analytical capabilities and practical applications of PDEs, paving the way for new advancements in both theoretical research and real-world problem solvingInternational Journal of Advanced Research in Science, Communication and Technology80It is suggested that AI has the potential to significantly enhance the analytical capabilities and practical applications of PDEs, paving the way for new advancements in both theoretical research and real-world problem solving.International Journal of Advanced Research in Science, Communication and Technology["M. Krishna Reddy", "N. Vijayabhaskar Reddy"]2024-07-21T00:00:00 fe3b00e87a22711dd163239007de13947f822d5fIn the pursuit of clean energy goals set by various countries and states for 2030, it is imperative to assess Carbon Emission Flow (CEF) accurately across the entire energy supply chain. This paper introduces an innovative approach employing Artificial Intelligence (AI) techniques to calculate both direct (Scope 1) and indirect (Scope 2) CEF, encompassing power generation, transmission, and end-user consumption. While direct emissions from connected generators can be measured using power generation data, heat rates, and fuel types, indirect emissions from imported/exported power require AI algorithms analyzing historical and real-time data. CEF of an element is the amount of CO2 generated from power sources for the corresponding element. Our methodology computes the total (both direct and indirect) and Locational Marginal Emission (LME) for diverse energy mixes at each node and load in the power network. This approach provides precise, location specific CEF data, allowing identification of nodes with varying carbon intensity. By understanding the emissions landscape, policymakers can develop targeted strategies, facilitating the reduction of carbon emissions and guiding future taxation policies. This innovative AI-driven framework enhances our ability to transition towards a sustainable energy future while meeting clean energy targets.IEEE Power & Energy Society General Meeting1502024 IEEE Power & Energy Society General Meeting (PESGM)["Ahmed Saber", "Md Ashraful Alam", "T. Khandelwal"]2024-07-21T00:00:00 0783ec80f550b5af526b32b83e3c11ac6a4c0951This study investigates the role of generative Artificial Intelligence (AI), like ChatGPT and other Large Language Models (LLMs), on learning strategies among computer and data science students at the Center for Informatics, University of Paraíba (CI/UFPB), Brazil. Analyzing 178 responses, the research highlights a significant engagement with LLMs and discovers a moderate correlation between students' LLM knowledge and their use of metacognitive learning strategies. Additionally, findings suggest a decrease in dysfunctional learning strategies with academic progression. The study reveals AI's potential to improve personalized learning while emphasizing the need for educational adjustments to avoid overreliance on AI.Anais do XXXII Workshop sobre Educação em Computação (WEI 2024)240The study reveals AI's potential to improve personalized learning while emphasizing the need for educational adjustments to avoid overreliance on AI.Anais do XXXII Workshop sobre Educação em Computação (WEI 2024)["Vitor Meneghetti Ugulino de Ara\u00fajo", "Pedro Henrique Ramos Pinto", "Cleydson de Souza Ferreira Junior", "Maria Jullyanna Ferreira Marques", "Lutero Lima Goulart", "Gabriel Silva Aguiar", "Paloma Duarte de Lira", "Samuel Jos\u00e9 Fernandes Mendes"]2024-07-21T00:00:00 84a01069906610cadfa4eed438fc8fd6d930d528Industry 5.0, which focuses on human and Artificial Intelligence (AI) collaboration for performing different tasks in manufacturing, involves a higher number of robots, Internet of Things (IoTs) devices and interconnections, Augmented/Virtual Reality (AR), and other smart devices. The huge involvement of these devices and interconnection in various critical areas, such as economy, health, education and defense systems, poses several types of potential security flaws. AI itself has been proven a very effective and powerful tool in different areas of cybersecurity, such as intrusion detection, malware detection, and phishing detection, among others. Just as in many application areas, cybersecurity professionals were reluctant to accept black-box ML solutions for cybersecurity applications. This reluctance pushed forward the adoption of eXplainable Artificial Intelligence (XAI) as a tool that helps explain how decisions are made in ML-based systems. In this survey, we present a comprehensive study of different XAI-based intrusion detection systems for industry 5.0, and we also examine the impact of explainability and interpretability on Cybersecurity practices through the lens of Adversarial XIDS (Adv-XIDS) approaches. Furthermore, we analyze the possible opportunities and challenges in XAI cybersecurity systems for industry 5.0 that elicit future research toward XAI-based solutions to be adopted by high-stakes industry 5.0 applications. We believe this rigorous analysis will establish a foundational framework for subsequent research endeavors within the specified domain.arXiv.org00This survey presents a comprehensive study of different XAI-based intrusion detection systems for industry 5.0, and examines the impact of explainability and interpretability on Cybersecurity practices through the lens of Adversarial XIDS (Adv-XIDS) approaches.ArXiv["Naseem Khan", "Kashif Ahmad", "Aref Al-Tamimi", "M. Alani", "Amine Bermak", "Issa Khalil"]2024-07-21T00:00:00 edf14d32606b7253fb956cbe4dfc7f2a062ac822Applied Artificial Intelligence143Applied Artificial Intelligence["Mingxia Wu", "Wei Liu", "Shengyang Zheng"]2024-07-21T00:00:00 d7cc0d2de93f4287227086988c1162319c2762bdMost multi-agent reinforcement learning approaches adopt two types of policy optimization methods that either update policy simultaneously or sequentially. Simultaneously updating policies of all agents introduces non-stationarity problem. Although sequentially updating policies agent-by-agent in an appropriate order improves policy performance, it is prone to low efficiency due to sequential execution, resulting in longer model training and execution time. Intuitively, partitioning policies of all agents according to their interdependence and updating joint policy batch-by-batch can effectively balance performance and efficiency. However, how to determine the optimal batch partition of policies and batch updating order are challenging problems. Firstly, a sequential batched policy updating scheme, B2MAPO (Batch by Batch Multi-Agent Policy Optimization), is proposed with a theoretical guarantee of the monotonic incrementally tightened bound. Secondly, a universal modulized plug-and-play B2MAPO hierarchical framework, which satisfies CTDE principle, is designed to conveniently integrate any MARL models to fully exploit and merge their merits, including policy optimality and inference efficiency. Next, a DAG-based B2MAPO algorithm is devised, which is a carefully designed implementation of B2MAPO framework. Comprehensive experimental results conducted on StarCraftII Multi-agent Challenge and Google Football Research demonstrate the performance of DAG-based B2MAPO algorithm outperforms baseline methods. Meanwhile, compared with A2PO, our algorithm reduces the model training and execution time by 60.4% and 78.7%, respectively.European Conference on Artificial Intelligence440A sequential batched policy updating scheme, B2MAPO (Batch by Batch Multi-Agent Policy Optimization), is proposed with a theoretical guarantee of the monotonic incrementally tightened bound and a DAG-based B2MAPO algorithm outperforms baseline methods.ArXiv["Wenjing Zhang", "Wei Zhang", "Wenqing Hu", "Yifan Wang"]2024-07-21T00:00:00 f6313c00a9657ee410148366aa03dea36f6f7d61A integração de Inteligência Artificial (IA) na educação introduz inovações e coloca em destaque questões éticas, especialmente com o surgimento de IAs Generativas. Este estudo explora as percepções de estudantes de graduação (81,4%), mestrado (12,2%), doutorado (4,7%) e MBA (1,7%), sobre ética e responsabilidade no desenvolvimento de artefatos computacionais. Concentrando-se em questões como ética dos dados, algoritmos, e alfabetização digital, além do uso de tecnologias de IA em ambientes educacionais, a pesquisa adota uma metodologia mista para analisar respostas de 172 participantes. O objetivo é compreender as atitudes relativas à ética na IA e ao desenvolvimento tecnológico.Anais do XXXII Workshop sobre Educação em Computação (WEI 2024)90Anais do XXXII Workshop sobre Educação em Computação (WEI 2024)["M\u00f4nica da Silva", "E. Seixas", "Mariza Ferro", "Jos\u00e9 Viterbo", "F. Seixas", "Luciana Salgado"]2024-07-21T00:00:00 cd2a49af074300ee24408e5d7138d28c97cdf9f4Atualmente as pessoas não conseguem mais viver desconectadas e diariamente um volume muito grande de informação e dados pessoais são despejados na internet viabilizando uma diversidade de violações aos direitos fundamentais. Desta maneira, o objetivo deste estudo é analisar o art. 20, da Lei n. 13.709, de 14 de agosto de 2018, denominada Lei Geral de Proteção de Dados (LGDP), que explica as decisões tomadas unicamente com base em tratamento automatizado. E tem como objetivos específicos: Apresentar a Lei n. 13.709, de 14 de agosto de 2018; destacar o que é inteligência artificial; mostrar as ameaças contra a proteção de dados, bem como a Cibersegurança; verificar através da ferramenta Google fotos como é feito o seu armazenamento e a sua proteção de dados com base na LGPD. Metodologia, trata-se de uma pesquisa bibliográfica de cunho qualitativa. A pesquisa mostrou que as tecnologias passaram a ser uma necessidade na vida das pessoas, contudo a IA possui suas garantias, para que este usuário se sinta seguro com os seus dados, principalmente quando se trata de dados pessoais, nesse sentido a Lei LGPD, garante não só a proteção dos dados do usuário que guarda suas fotos no Google fotos, mas também nas demais ferramentas que esta disponibiliza para seus usuários.Research, Society and Development00Research, Society and Development["Ronnie de Andrade Souza", "Jana\u00edna Silva de Souza"]2024-07-21T00:00:00 fdb271b9aa255ced6d5c85160476958a47be12c0Este artigo descreve o desenvolvimento de uma arquitetura, denominada Mind Bridge, para adaptar questões de avaliação para estudantes neurodivergentes, utilizando recursos de Inteligência Artificial Generativa. O Mind Bridge torna as questões mais acessíveis e compreensíveis, contribuindo para a inclusão e a equidade de oportunidades. A arquitetura foi projetada com três módulos: Módulo de Entrada, Módulo Lógico e Módulo Generativo. O estudo de caso realizado demonstrou a eficácia da arquitetura na adaptação de questões de diferentes áreas do conhecimento, destacando sua capacidade de transformar questões técnicas e complexas em problemas do mundo real.Anais do XI Encontro Nacional de Computação dos Institutos Federais (EnCompIF 2024)230Anais do XI Encontro Nacional de Computação dos Institutos Federais (EnCompIF 2024)["J. Silva", "H. Ferreira"]2024-07-21T00:00:00 0563f3ee076a5e725dc377fdfbcce0da64fe128dAs soluções de inteligência artificial (IA), com os avanços trazidos pelos grandes modelos de linguagem, abrem grandes oportunidades para aumento de eficiência e efetividade dos órgãos públicos. Nos Tribunais de Contas, elas podem automatizar tarefas rotineiras (ex.: revisão de contratos e licitações) bem como preditivas (ex.: detecção de fraudes). No entanto, tal aprimoramento do controle externo e gestão pública traz desafios. Para mapear desafios e esboçar soluções ligadas à introdução de IA neste cenário, realizamos uma pesquisa de opinião com mais de 150 auditores. Como resultado, identificamos 5 desafios sociais, técnicos e de negócio, e 10 propostas de solução para nortear iniciativas de IA no setor de fiscalização.Anais do XII Workshop de Computação Aplicada em Governo Eletrônico (WCGE 2024)70Anais do XII Workshop de Computação Aplicada em Governo Eletrônico (WCGE 2024)["G. Valen\u00e7a", "A. C. Chaves", "Willams Brand\u00e3o"]2024-07-21T00:00:00 4fedc62256cfc5abf81a722e44908779ad51e74bA construção de sistemas de IA se dá em ambientes distribuídos e heterogêneos, envolvendo uma extensa rede de agentes humanos, artificiais e híbridos, interações e ações. O objetivo deste trabalho é contribuir no debate sobre IA ética e responsável, recorrendo ao quadro analítico e conceitual de Luciano Floridi enfatizando a sua abordagem de responsabilidade moral distribuída como uma via possível e plausível para lidar com a dificuldade de localização da agência e atribuição de reponsabilidade moral considerando a vasta, diversa e distribuída rede de agentes envolvidos na construção de sistemas inteligentes.Anais do V Workshop sobre as Implicações da Computação na Sociedade (WICS 2024)161Anais do V Workshop sobre as Implicações da Computação na Sociedade (WICS 2024)["Elizabeth Maria Freire de Jesus"]2024-07-21T00:00:00 aad7db5f7e7a5a308068487f57d22ab1bbaa9c0cNa última década, houve avanços significativos nos resultados alcançados por modelos de Aprendizagem Profunda e uma ampla adoção desses na academia e na indústria. Embora esses modelos tenham potencial para auxiliar na gestão de recursos naturais e em questões ambientais, eles tipicamente demandam grande poder computacional, resultando em maiores gastos energéticos e também em grandes números de pegada de carbono. Este trabalho busca evidenciar e discutir o gasto energético envolvido no uso de modelos de redes neurais, comparando experimentalmente algumas arquiteturas em relação ao desempenho, à eficiência energética e ao custo computacional. Os resultados obtidos reforçam que é possível construir modelos que consumam menos energia e que tenham desempenho compatível com aqueles mais dispendiosos, contribuindo para uma abordagem mais sustentável.Anais do XV Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA 2024)70Anais do XV Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA 2024)["V\u00edvian R. G. Ferraro", "G. Gullo", "Daniel Vitor da Silveira Costa", "P. N. Moura"]2024-07-21T00:00:00 99f88b690adccc73ec29b2e3cf351f25b77252e0AI has the potential to revolutionize mental health services by providing personalized support and improving accessibility. However, it is crucial to address ethical concerns to ensure responsible and beneficial outcomes for individuals. This systematic review examines the ethical considerations surrounding the implementation and impact of artificial intelligence (AI) interventions in the field of mental health and well-being. To ensure a comprehensive analysis, we employed a structured search strategy across top academic databases, including PubMed, PsycINFO, Web of Science, and Scopus. The search scope encompassed articles published from 2014 to 2024, resulting in a review of 51 relevant articles. The review identifies 18 key ethical considerations, including 6 ethical considerations associated with using AI interventions in mental health and wellbeing (privacy and confidentiality, informed consent, bias and fairness, transparency and accountability, autonomy and human agency, and safety and efficacy); 5 ethical principles associated with the development and implementation of AI technologies in mental health settings to ensure responsible practice and positive outcomes (ethical framework, stakeholder engagement, ethical review, bias mitigation, and continuous evaluation and improvement); and 7 practices, guidelines, and recommendations for promoting the ethical use of AI in mental health interventions (adhere to ethical guidelines, ensure transparency, prioritize data privacy and security, mitigate bias and ensure fairness, involve stakeholders, conduct regular ethical reviews, and monitor and evaluate outcomes). This systematic review highlights the importance of ethical considerations in the responsible implementation and impact of AI interventions for mental health and well-being. By addressing privacy, bias, consent, transparency, human oversight, and continuous evaluation, we can ensure that AI interventions like chatbots and AI-enabled medical devices are developed and deployed in an ethically sound manner, respecting individual rights, promoting fairness, and maximizing benefits while minimizing potential harm.The social science7512This systematic review examines the ethical considerations surrounding the implementation and impact of artificial intelligence interventions in the field of mental health and well-being and highlights the importance of ethical considerations in the responsible implementation and impact of AI interventions for mental health and well-being.Social Sciences["H. R. Saeidnia", "Seyed Ghasem Hashemi Fotami", "Brady D. Lund", "Nasrin Ghiasi"]2024-07-22T00:00:00 5fbe46ae504fdf02b4e62b5404e318dc0100b819 This Viewpoint examines how artificial intelligence could support surrogate decision-makers while addressing some of the attendant epistemic and moral challenges. JAMA Internal Medicine56JAMA internal medicine["T. Brender", "Alexander K. Smith", "Brian L Block"]2024-07-22T00:00:00 f7d5b2147ffe9b2b5775ceea6602f442338c4721npj Digit. Medicine467This study reviews the current state of XAI for multimodal and longitudinal datasets and highlights the challenges thereof, and proposes the XAI orchestrator, an instance that aims to help clinicians with the synopsis of multimodal and longitudinal data, the resulting AI predictions, and the corresponding explainability output.NPJ Digital Medicine["Aur\u00e9lie Pahud de Mortanges", "Haozhe Luo", "Shelley Zixin Shu", "Amith Kamath", "Yannick Suter", "M. Shelan", "Alexander Poellinger", "Mauricio Reyes"]2024-07-22T00:00:00 19fcc581a7baf02431dca041df373dc8dc0c4686Artificial intelligence (AI) is increasingly used in health care to improve diagnostics and treatment. Decision-making tools intended to help professionals in diagnostic processes are developed in a variety of medical fields. Despite the imagined benefits, AI in health care is contested. Scholars point to ethical and social issues related to the development, implementation, and use of AI in diagnostics. Here, we investigate how three relevant groups construct ethical challenges with AI decision-making tools in prostate cancer (PCa) diagnostics: scientists developing AI decision support tools for interpreting MRI scans for PCa, medical doctors working with PCa and PCa patients. This qualitative study is based on participant observation and interviews with the abovementioned actors. The analysis focuses on how each group draws on their understanding of 'good health care' when discussing ethical challenges, and how they mobilise different registers of valuing in this process. Our theoretical approach is inspired by scholarship on evaluation and justification. We demonstrate how ethical challenges in this area are conceptualised, weighted and negotiated among these participants as processes of valuing good health care and compare their perspectives.Sociology of Health and Illness183This qualitative study investigates how three relevant groups construct ethical challenges with AI decision-making tools in prostate cancer (PCa) diagnostics, and demonstrates how ethical challenges in this area are conceptualised, weighted and negotiated among these participants as processes of valuing good health care and compare their perspectives.Sociology of health & illness["M. Hesjedal", "Emilie Hybertsen Lys\u00f8", "Marit Solbj\u00f8r", "J. Skolbekken"]2024-07-22T00:00:00 89c418340287db39d0522ee62516470d24caa211When faced with questions about artificial intelligence (AI), many surgeons respond with scepticism and rejection. However, in the realm of cardiac surgery, it is imperative that we embrace the potential of AI and adopt a proactive mindset. This systematic review utilizes PubMed® to explore the intersection of AI and cardiac surgery since 2017. AI has found applications in various aspects of cardiac surgery, including teaching aids, diagnostics, predictive outcomes, surgical assistance, and expertise. Nevertheless, challenges such as data computation errors, vulnerabilities to malware, and privacy concerns persist. While AI has limitations, its restricted capabilities without cognitive and emotional intelligence should lead us to cautiously and partially embrace this advancing technology to enhance patient care.Brazilian Journal of Cardiovascular Surgery351AI has found applications in various aspects of cardiac surgery, including teaching aids, diagnostics, predictive outcomes, surgical assistance, and expertise, Nevertheless, challenges such as data computation errors, vulnerabilities to malware, and privacy concerns persist.Brazilian Journal of Cardiovascular Surgery["Rahul Bhushan", "V. Grover"]2024-07-22T00:00:00 77cd45c71bd8c2ec099572d7cd4453ae0efe9e42This quality improvement study evaluates the use of artificial intelligence to accelerate triage of patients presenting to the emergency department with chest pain.JAMA Internal Medicine32JAMA Internal Medicine["J. Hinson", "R. A. Taylor", "Arjun Venkatesh", "Benjamin D. Steinhart", "Christopher Chmura", "Rohit B. Sangal", "Scott R. Levin"]2024-07-22T00:00:00 5ad5d70597f37bc30ecda862434e3e1d52132dbcBMC Musculoskeletal Disorders651This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.BMC Musculoskeletal Disorders["U. Longo", "S. De Salvatore", "Federica Valente", "Mariajose Villa Corta", "Bruno Violante", "Kristian Samuelsson"]2024-07-22T00:00:00 62fbc78884d1b2ab02e0ad60a9795f7596369f26Artificial Intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity often decreases human trust, which slows their application in high-risk decision-making domains, such as finance. The field of eXplainable AI (XAI) seeks to bridge this gap, aiming to make AI models more understandable. This survey, focusing on published work from the past five years, categorizes XAI approaches that predict financial time series. In this paper, explainability and interpretability are distinguished, emphasizing the need to treat these concepts separately as they are not applied the same way in practice. Through clear definitions, a rigorous taxonomy of XAI approaches, a complementary characterization, and examples of XAI's application in the finance industry, this paper provides a comprehensive view of XAI's current role in finance. It can also serve as a guide for selecting the most appropriate XAI approach for future applications.arXiv.org1121This paper provides a comprehensive view of XAI's current role in finance, including clear definitions, a rigorous taxonomy of XAI approaches, a complementary characterization, and examples of XAI's application in the finance industry.ArXiv["Pierre-Daniel Arsenault", "Shengrui Wang", "Jean-Marc Patenande"]2024-07-22T00:00:00 afad63ba047a50db63203f02a38221f8963edf58Aim: The aim of the study is to draw attention to the dangers of using artificial intelligence. Methodology: Alongside a relevant literature review, the author illustrates the aspects of artificial intelligence jeopardising our security by providing examples and addresses the existing and evolving regulatory environment. Findings: Artificial intelligence can directly or indirectly pose a threat to our security. The risks associated with artificial intelligence, coupled with the current rapid technological advancement, make it imperative to establish appropriate and adaptive continuous regulations to ensure the increasing use of AI comes with minimal negative consequences. Value: The study explores previously overlooked features that compromise security. Its findings can contribute to understanding how artificial intelligence can endanger our security on both narrower and broader societal levels.Belügyi Szemle190The study explores previously overlooked features that compromise security and can contribute to understanding how artificial intelligence can endanger the authors' security on both narrower and broader societal levels.Belügyi Szemle["Levente T\u00f3th"]2024-07-22T00:00:00 0588d822f3d4332130b095b9fe2661daca031ea3Artificial Intelligence (AI) has emerged as a transformative force across various sectors, revolutionizing processes, enhancing efficiency, and redefining innovation. This research paper delves into the multifaceted landscape of AI, focusing on its applications, knowledge representation, and implications for innovation. The paper begins by exploring the diverse applications of AI across healthcare, gaming, finance, data security, social media, robotics, and e-commerce. In healthcare, AI aids in diagnosis and patient care, while in gaming, it enables strategic game play and enhances user experience. The finance sector leverages AI for automation, analytics, and algorithmic trading, improving decision-making and customer service. AI also plays a vital role in ensuring data security through advanced detection systems, manages vast social media data for enhanced user engagement, and drives innovation in robotics and e-commerce. Moving forward, the paper delves into the realm of expert systems and knowledge representation, elucidating the role of AI in simulating human expertise and modeling complex information structures. It discusses various aspects of knowledge representation, such as propositional knowledge representation, image retrieval, functional relationships between objects, and class representation formalism, highlighting their significance in developing intelligent systems. Furthermore, the paper examines the integration of AI in maintenance practices, both for tangible systems like engineering workshops and intangible products like data extraction wrappers. It underscores the importance of AI in optimizing operational efficiency, reducing downtime, and ensuring continuous data extraction. Lastly, the paper explores the concept of deep learning as a general- purpose invention, discussing its potential implications for innovation, management, institutions, and policy. It addresses key issues such as the management and organization of innovation, intellectual property rights, competition policy, and the cumulative knowledge production facilitated by deep learning. In conclusion, this research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Brahmansh Sharma"]2024-07-22T00:00:00 bd773859fc5d9c44be494b24ef82b381fe505ecaEmployee turnover is a significant concern across industries, resulting in wasted training resources and the costs associated with hiring replacements. Understanding the under-lying reasons behind employee attrition is crucial for businesses seeking to address this issue. Employing data analytics solutions can lead to substantial savings in terms of training hours and financial resources. However, in addition to accurate predictions of employee turnover, the explainability of these analytics results is equally important to gain user trust. Many existing data analytics techniques provide predictions and recommendations in an opaque “black box” manner, making it challenging for humans to understand the reasoning behind them. In this paper, we present an explainable artificial intelligence (XAI) solution that combines cutting-edge techniques and enhances them to generate practical and comprehensible explanations for end-users. To assess the effectiveness of our XAI solution, we conduct a case study using real-life employee turnover data. The results demonstrate the practicality and usefulness of our XAI solution in applications such as analyzing employee turnover.International Conference on Information Visualisation450An explainable artificial intelligence (XAI) solution is presented that combines cutting-edge techniques and enhances them to generate practical and comprehensible explanations for end-users in applications such as analyzing employee turnover.2024 28th International Conference Information Visualisation (IV)["C. Leung", "Rayan Imran", "Adam G. M. Pazdor", "Joglas Souza"]2024-07-22T00:00:00 bf6d7b8e03906d6ecf8aefe100ed33d8e96eb830This study aims to delve into communicative interactions within the realm of online politics, with a specific emphasis on the increasingly significant role of artificial intelligence (AI). In an era where digital technologies are radically transforming communication flows, the introduction of AI into political processes opens new prospects, but also presents numerous challenges. The research seeks to understand how these technologies are influencing political language and user engagement strategies, with profound repercussions on social and political dynamics. The methodology employed combines both qualitative and quantitative analysis. On the one hand, a qualitative analysis was conducted on AI-generated content within online political discussions, examining how this content influences political narratives and shape’s public opinion. On the other hand, a quantitative analysis evaluated the dynamics of engagement and interaction across various digital platforms, measuring the impact of AI on user behaviour and information dissemination. The findings of this research reveal that AI is not only transforming the language of politics but also altering the ways individuals participate in public debate. Artificial intelligences not only facilitate new forms of communication but can also influence power dynamics, either reinforcing or destabilizing existing structures. Based on these results, it is essential to balance technological innovation with the protection of democratic processes, ensuring that the use of AI is transparent, ethical, and oriented toward the common good. In conclusion, this contribution highlights the need for public debate and ongoing research on the social and political implications of AI, so that these technologies can positively contribute to the future of democracy.Journal of Sociological Research00The findings of this research reveal that AI is not only transforming the language of politics but also altering the ways individuals participate in public debate, highlighting the need for public debate and ongoing research on the social and political implications of AI so that these technologies can positively contribute to the future of democracy.Journal of Sociological Research["Daniele Battista", "Alessandra Petrone"]2024-07-22T00:00:00 8d766dba185d49a686bdcbe6bf152b2b1e2aca2bWith the deepening influence of artificial intelligence on the production mode of news and the increasing application of technological innovation in cross-border communication, this study found that artificial intelligence can solve the communication dilemma of language in cross-cultural communication revealed in the myth of the Tower of Babel through language translation and empathetic communication strategies. The literature review section introduces the basic concepts of cross-cultural empathy communication and the practical application of artificial intelligence-generated news in international communication. Multiple case studies were used in the research method, based on the specific practices of major media outlets including Xinhua News Agency, People's Daily, China Central Television, Associated Press, New York Times, and CBS in the application of intelligent technology. The research results indicate that the application of intelligent technology in the news production process has significantly improved work efficiency and promoted innovation in news production models at three levels: technical logic, media logic, and capital logic. The conclusion drawn from this article is that the deep involvement of generative artificial intelligence technology in the news production process can help build a new production mechanism that balances efficiency and responsibility and promotes the comprehensive improvement of efficiency and quality in cross-cultural communication. Future research will continue to focus on the empirical application of intelligent technology in news production, further optimizing news production models, and providing new perspectives and methods for developing the media industry.Advances in Education, Humanities and Social Science Research180The research results indicate that the application of intelligent technology in the news production process has significantly improved work efficiency and promoted innovation in news production models at three levels: technical logic, media logic, and capital logic.Advances in Education, Humanities and Social Science Research["Lok Yee Xu"]2024-07-22T00:00:00 796c28313376eb5ee3cf32a8c021902b78f410e4There are many operational and technical obstacles in the way of the shift to a decentralized, sustainable smart grid. In the face of growing renewable energy integration, distributed resources, and cyber threats, traditional grid management techniques are ill-suited to handle the real-time optimization, predictive analytics, and autonomous control necessary for dependable and efficient electricity delivery. This study thoroughly analyzes how artificial intelligence (AI) approaches can be used to address the main problems that smart grid systems face. The paper looks at how cutting-edge AI techniques, such as multi-agent systems, deep learning, machine learning, and optimization algorithms, can be used in important smart grid applications.2024 International Telecommunications Conference (ITC-Egypt)520This study thoroughly analyzes how cutting-edge AI techniques, such as multi-agent systems, deep learning, machine learning, and optimization algorithms, can be used in important smart grid applications.2024 International Telecommunications Conference (ITC-Egypt)["Marwa Elkholy", "Omar Shalash", "Mostafa S. Hamad", "M. S. Saraya"]2024-07-22T00:00:00 2bd76f9cca3ce1d4954e25ac49d91be62ce85ce7The study determines the impact of artificial intelligence (AI) on human resources (HR) management. Based on a comparative analysis of three Russian developers of AI applications, the recruitment processes that can currently be implemented with the help of AI were investigated. Comparison of recruiting processes with already existing AI applications will allow understand the extent of AI involvement in HR processes. The purpose of this study is to identify existing AI applications for HR management, in terms of their functional content, and the benefits that HR departments gain from using them. The study is based on a critical analysis of academic papers in the field of digital technologies, particularly AI technology, and information from developers’ websites. The functional content of AI applications was determined, as well as the benefits of AI in HR management that HR department gains when using it. The development of new technologies and their use in HR management requires changing the approach in the work of HR specialists and reviewing their competencies.Entrepreneur's Guide30The functional content of AI applications was determined, as well as the benefits of AI in HR management that HR department gains when using it, and the benefits that HR departments gain from using them.Entrepreneur’s Guide["I. V. Voronova", "O. A. Rasskazova"]2024-07-22T00:00:00 b5c3e7fc9bfd43d98b2f0ad3784926f307b4fc34This study aims to investigate the applications of artificial intelligence and chatbots in secondary school science lessons through descriptive content analysis. Using a qualitative case study approach, 20 theses from YÖKTEZ database and 10 international articles from Google Scholar were analysed, focusing on chatbots in science education. The results of the analysis revealed a notable increase in AI and chatbot-related theses between 2012 and 2023, especially in the last two years, highlighting the growing interest in e-learning and AI in STEM fields. Positive effects on student and teacher attitudes towards STEM education have been widely reported. However, besides the limited number of international studies, the discovery of only one secondary school thesis on chatbots unrelated to Science in Turkey points to a significant research gap in AI and chatbot integration into Science education. Addressing this gap may improve the application of AI and chatbot technologies in science teaching and reveal the need for further research to explore their potential benefits and applications in educational settings.Computing and informatics150Investigating the applications of artificial intelligence and chatbots in secondary school science lessons through descriptive content analysis revealed a notable increase in AI and chatbot-related theses between 2012 and 2023, highlighting the growing interest in e-learning and AI in STEM fields.Computers and Informatics["I. D\u00f6kme", "T. Y\u0131lmaz"]2024-07-22T00:00:00 abf33622093e11ef4574bb25efe3bd7d4f547829 The use of artificial intelligence (AI) in military affairs is largely limited to logistics, transport, reconnaissance, and other support functions at present. Yet, the United States and the People’s Republic of China (PRC) are also making significant progress in developing autonomous combat vehicles and other offensive applications of AI. The ambiguous nature and multifaceted applications of AI in military affairs could be seen as either increasing or decreasing the relative cost of offensive military action. The critical question, therefore, is not a technical determination of whether the emerging uses of AI favor the offensive or defensive force, but rather an assessment of how AI is perceived by policymakers. Similar to the World War I era cult of the offensive, ambiguous emerging technology may drive leaders to focus on the offensive advantages of that technology when expectations of conflict are rising. This paper examines the policy elite discourse surrounding the military applications of AI in both the United States and the PRC between 2014 and 2022. It finds that over this period, elite discussion of the use of AI in military affairs has shifted in both states in ways that indicate more focus on the offensive rather than defensive applications of this emerging technology. While by no means determinative, it is an indicator that perceptions of the offense–defense balance may be moving in a destabilizing direction.Foreign Policy Analysis330Foreign Policy Analysis["Zachary Selden"]2024-07-22T00:00:00 1f4565902d0000648f60d83b36cf28d6b6e27fc0Precision health, increasingly supported by digital technologies, is a domain of research that broadens the paradigm of precision medicine, advancing everyday healthcare. This vision goes hand in hand with the groundbreaking advent of artificial intelligence (AI), which is reshaping the way we diagnose, treat, and monitor both clinical subjects and the general population. AI tools powered by machine learning have shown considerable improvements in a variety of healthcare domains. In particular, reinforcement learning (RL) holds great promise for sequential and dynamic problems such as dynamic treatment regimes and just-in-time adaptive interventions in digital health. In this work, we discuss the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health. Focusing on the area of adaptive interventions, we expand the methodological survey with illustrative case studies that used RL in real practice. This invited article has undergone anonymous review and is intended as a book chapter for the volume"Frontiers of Statistics and Data Science"edited by Subhashis Ghoshal and Anindya Roy for the International Indian Statistical Association Series on Statistics and Data Science, published by Springer. It covers the material from a short course titled"Artificial Intelligence in Precision and Digital Health"taught by the author Bibhas Chakraborty at the IISA 2022 Conference, December 26-30 2022, at the Indian Institute of Science, Bengaluru.arXiv.org1320This work discusses the opportunity offered by AI, more specifically RL, to current trends in healthcare, providing a methodological survey of RL methods in the context of precision and digital health, and expands the methodological survey with illustrative case studies that used RL in real practice.ArXiv["Nina Deliu", "Bibhas Chakraborty"]2024-07-22T00:00:00 63e389079c0aad489095ed2e90a7c5746b45e0eenpj Digit. Medicine265Results from real-world deployment in a large integrated healthcare system suggest that autonomous AI is associated with improvement in overall DED testing adherence, patient access, and health equity.NPJ Digital Medicine["Jane Huang", "R. Channa", "Risa M. Wolf", "Yiwen Dong", "Mavis Liang", "Jiangxia Wang", "M. Abr\u00e0moff", "T. Y. A. Liu"]2024-07-22T00:00:00 9f53bce9339196d9719fe8097688c90fdb046b9e Despite increasing concerns over the use of AI in surveillance, privacy, public health, climate change, global migration and warfare, the implications of its use in the field of intercultural communication are still not clearly defined. This paper critically examines the contemporary emergence of AI through the lens of a critical realist depth ontology to argue that AI, with its unending interplay of signs and symbols, is the ultimate simulacrum. As such, AI vacates the normative terrain of judgemental rationality in favour of the relativist terrain of endless simulacra and the fetish appearances of postmodernism. To illustrate this, it is argued that the inability of AI to make judgements based on judgemental rationality (or Ethics1) occludes the possibility of intervening in the world to ameliorate real injustice. Therefore, if intercultural ethics remains within the realm of judgmental relativism (or Ethics2) it abdicates the possibility to have an impact in the material world.Applied Linguistics Review213It is argued that the inability of AI to make judgements based on judgemental rationality occludes the possibility of intervening in the world to ameliorate real injustice and if intercultural ethics remains within the realm of judgmental relativism it abdicates the possibility to have an impact in the material world.Applied Linguistics Review["John P. O\u2019Regan", "Giuliana Ferri"]2024-07-22T00:00:00 9c948260418df15cfdff7a010b18466e955a727cJournal of Chemical Education263Journal of Chemical Education["Guilherme Gon\u00e7alves Costa", "Wilton J. D. Nascimento J\u00fanior", "Murilo N\u00edcolas Mombelli", "Gildo Girotto J\u00fanior"]2024-07-22T00:00:00 1e90af8785c64f8233269afb9fee547c9ddffdc6Water Research523The results of the study designate that the adoption of ML and XAI approaches can be considered as a valuable tool for unveiling the complicated correlation of the presence and contamination intensity with these zoonotic parasites that could constitute a basis for the development of monitoring platforms and early warning systems for the prevention of waterborne disease outbreaks.Water research["P. Ligda", "N. Mittas", "G. Kyzas", "E. Claerebout", "S. Sotiraki"]2024-07-22T00:00:00 f33b81cb8a1b108fb6b7b3aeb9e0420cf952a34dThe 10th International Conference on Time Series and Forecasting191The 10th International Conference on Time Series and Forecasting["Sarthak Pattnaik", "Natasya Liew", "Ali Ozcan Kures", "Eugene Pinsky", "Kathleen Park"]2024-07-22T00:00:00 f179917153969c52f713a2d9c8745028bcb7823aSociology of power00Sociology of Power["Dmitry Kralechkin"]2024-07-22T00:00:00 cd8cf682bb5c49c016bfcbffc7c405b6223cb4f8This research aims to determine students' perceptions and challenges faced using AI due to the gaps faced by EFL students. This research uses a qualitative method which uses two data collection techniques, namely observation as supporting data and interviews with 6 students from different classes as main data. To analyze the data, researchers used thematic analysis and then used content validity to support the research. The results of this research show that the majority of students agree that the use of AI in EFL classes is very helpful in the independent learning process and improves speaking, reading, writing, and giving ideas. So the researchers concluded that based on students' perceptions, these perceptions had a positive impact on the use of AI in EFL classes. Researchers hope that students and lecturers can use AI according to the role of AI. Researchers also hope that this research can become a reference for readers.ELTR journal200The results of this research show that the majority of students agree that the use of AI in EFL classes is very helpful in the independent learning process and improves speaking, reading, writing, and giving ideas.ELTR Journal["Sesilia Yuliani", "Tefanya Laili Mukhibbah", "Eliasanti Agustina"]2024-07-22T00:00:00 28dbfe58c0d8a88413ce17edbf595abb53d0745eJAMA Internal Medicine10JAMA internal medicine["Deborah Grady"]2024-07-22T00:00:00 b5688e00f04fbb2c6b2c3b7875062dd3d412a24aОдно из важнейших направлений социально-экономического и науч- но-технологического развития России связано с искусственным интеллектом (ИИ), а базисом такого развития является образование в области ИИ, в том числе в основной школе. Для интеграции дидактических элементов ИИ в основной школе необходим отбор содержания обучения с учетом возрастных особенностей и подготовленности школьников, структурирование учебного материала в контексте предметных связей с разными дисциплинами. Цель исследования: формирование теоретически обосно- ванного содержания обучения элементам ИИ в основной школе, а также определение возможных вариантов интеграции разработанного содержания такого обучения. Зада- чи исследования: определить целесообразность обучения в области ИИ на урочных и внеурочных занятиях в основной школе; предложить подходы к формированию со- держания обучения элементам ИИ в основной школе; определить цели и результаты обучения и сформировать содержание обучения элементам ИИ в основной школе с учетом предметных связей с информатикой, технологией и другими учебными дисцип линами.Вестник МГПУ. Серия Информатика и информатизация образования00Вестник МГПУ. Серия Информатика и информатизация образования["\u0418.\u0412. \u041b\u0435\u0432\u0447\u0435\u043d\u043a\u043e", "\u0410.\u0420. \u0421\u0430\u0434\u044b\u043a\u043e\u0432\u0430", "\u041b.\u0418. \u041a\u0430\u0440\u0442\u0430\u0448\u043e\u0432\u0430", "\u041f.\u0410. \u041c\u0435\u0440\u0435\u043d\u043a\u043e\u0432\u0430"]2024-07-22T00:00:00 44bd5070275ece57775adfa9081a907ddcd7b054Academy of Management Review70Academy of Management Review["Joel Gehman", "Vern L. Glaser", "Paul Merritt"]2024-07-22T00:00:00 e22715451d41ed99cccc3be2fd30ae8c8564fd76The author thanks and acknowledges the many positive and critical comments by Robertas Damaševičius [...]De Computis70Comput.["Jaime F. C\u00e1rdenas-Garc\u00eda"]2024-07-22T00:00:00 bbeabbaef54f02db96d91530a40d0920bcd909d5The global community is increasingly focusing on conservation. To combat pollution andresource wastage, modern technologies, like automation and AI are widely employed in environmentalprotection tools. This study delves into the utilization and advancement of automation and AI inenvironmental protection tools examining their real world applications across sectors like wastewatertreatment, air pollution control, solid waste management, metallurgy and energy supervision. Furthermoreit investigates the fusion of automation and artificial intelligence in the development of environmentalprotection tools. The study also addresses aspects such as data collection, performance monitoring,sustainable growth, prevailing challenges and future trends. Through an analysis it underscores thepotential of automation and artificial intelligence in enhancing environmental protection tools effectivenesstowards achieving sustainable conservation goals. Lastly recommendations for research are proposed todrive innovation and enhancement of environmental protection technology. This study aims to offerinsights and guidance for both research endeavors and practical applications, within the realm ofenvironmental protection technology.Konstruksi: Publikasi Ilmu Teknik, Perencanaan Tata Ruang dan Teknik Sipil130This study delves into the utilization and advancement of automation and AI in environmental protection tools examining their real world applications across sectors like wastewater treatment, air pollution control, solid waste management, metallurgy and energy supervision.Konstruksi: Publikasi Ilmu Teknik, Perencanaan Tata Ruang dan Teknik Sipil["Ahmed Atiyah Itwayya", "M.N. S. Al-Maliki", "H. A. Al-behadili"]2024-07-22T00:00:00 d35f1042defe9a1d69c343ce0237f14d057f48b8What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and we propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.Nature Human Behaviour27212This Perspective shows how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet the authors' expectations and complement their limitations.Nature human behaviour["Katherine M. Collins", "Ilia Sucholutsky", "Umang Bhatt", "Kartik Chandra", "Lionel Wong", "Mina Lee", "Cedegao Zhang", "Tan Zhi-Xuan", "Mark Ho", "Vikash K. Mansinghka", "Adrian Weller", "Joshua B. Tenenbaum", "Thomas L. Griffiths"]2024-07-22T00:00:00 acba0f23fa5f2613fe47ddb5e0a9f114513f37bcThe rapid development and widespread adoption of Generative Artificial Intelligence-based (GAI) applications have greatly enriched our daily lives, benefiting people by enhancing creativity, personalizing experiences, improving accessibility, and fostering innovation and efficiency across various domains. However, along with the development of GAI applications, concerns have been raised about transparency in their privacy practices. Traditional privacy policies often fail to effectively communicate essential privacy information due to their complexity and length, and open-source community developers often neglect privacy practices even more. Only 12.2% of examined open-source GAI apps provide a privacy policy. To address this, we propose a regulation-driven GAI Privacy Label and introduce Repo2Label, a novel framework for automatically generating these labels based on code repositories. Our user study indicates a common endorsement of the proposed GAI privacy label format. Additionally, Repo2Label achieves a precision of 0.81, recall of 0.88, and F1-score of 0.84 based on the benchmark dataset, significantly outperforming the developer self-declared privacy notices. We also discuss the common regulatory (in)compliance of open-source GAI apps, comparison with other privacy notices, and broader impacts to different stakeholders. Our findings suggest that Repo2Label could serve as a significant tool for bolstering the privacy transparency of GAI apps and make them more practical and responsible.arXiv.org924Repo2Label, a novel framework for automatically generating these labels based on code repositories, is introduced and suggested that Repo2Label could serve as a significant tool for bolstering the privacy transparency of GAI apps and make them more practical and responsible.ArXiv["Meixue Si", "Shidong Pan", "Dianshu Liao", "Xiaoyu Sun", "Zhenyuan Tao", "Wenchang Shi", "Zhenchang Xing"]2024-07-22T00:00:00 594f4a93bb77979c1c4c65d091e2092be7f29c05 Purpose The viewpoint paper aims to highlight the assistive role that Generative artificial intelligence (Gen AI) can play in the design of learning and development programs for employees with special needs. The article discusses the challenges, benefits and reasons why Gen AI should be used to manage diversity, equity and inclusion by creating personalized and customized training and development programs. Design/methodology/approach The viewpoint paper is based on reviewing articles and videos on the application of Gen AI in learning and development. Findings Gen AI offers immense opportunities to design personalized learning solutions for employees with special needs due to disability that can be physical or cognitive. The AI-based solutions support special learners by customizing assistive technology-based solutions and content based on the level of disability and need of the learner. This paper also highlights the importance of synergy between the training department, government and technology solution providers. Originality/value The viewpoint paper fills in an important gap by discussing the role that Gen AI can play by facilitating the learning and development of employees with unique skills. Strategic HR Review03The article discusses the challenges, benefits and reasons why Gen AI should be used to manage diversity, equity and inclusion by creating personalized and customized training and development programs for employees with special needs.Strategic HR Review["A. Upadhyay"]2024-07-22T00:00:00 567780d7ff78f724ad106de7bfe1e187ea2805bf Considering how generative artificial intelligence might affect occupations.Communications of the ACM122Commun. ACM["Ed Felten", "Manav Raj", "Rob Seamans"]2024-07-22T00:00:00 8415483c481f8635fc78b11217a5b952f1c57d20AI and Ethics512The compatibility of command responsibility in light of recent empirical studies and psychological evidence is examined, aiming to anchor discussions in empirical realities rather than relying exclusively on normative arguments.AI and Ethics["Ann-Katrien Oimann", "A. Salatino"]2024-07-22T00:00:00 20cc533e0d73dfa91a29b1522b90aa0b05de44e9Social engineering (SE) attacks remain a significant threat to both individuals and organizations. The advancement of Artificial Intelligence (AI), including diffusion models and large language models (LLMs), has potentially intensified these threats by enabling more personalized and convincing attacks. This survey paper categorizes SE attack mechanisms, analyzes their evolution, and explores methods for measuring these threats. It highlights the challenges in raising awareness about the risks of AI-enhanced SE attacks and offers insights into developing proactive and adaptable defense strategies. Additionally, we introduce a categorization of the evolving nature of AI-powered social engineering attacks into"3E phases": Enlarging, wherein the magnitude of attacks expands through the leverage of digital media; Enriching, introducing novel attack vectors and techniques; and Emerging, signifying the advent of novel threats and methods. Moreover, we emphasize the necessity for a robust framework to assess the risk of AI-powered SE attacks. By identifying and addressing gaps in existing research, we aim to guide future studies and encourage the development of more effective defenses against the growing threat of AI-powered social engineering.arXiv.org1882A categorization of the evolving nature of AI-powered social engineering attacks into 3E phases is introduced, wherein the magnitude of attacks expands through the leverage of digital media, and the necessity for a robust framework to assess the risk of AI-powered SE attacks is emphasized.ArXiv["Jingru Yu", "Yi Yu", "Xuhong Wang", "Yilun Lin", "Manzhi Yang", "Yu Qiao", "Fei-Yue Wang"]2024-07-22T00:00:00 9dff225ca4c8c754956a5465ebeb4526933aab60Through the last decades, Artificial Intelligence (AI) has revolutionized the field of education and transformed traditional teaching approaches. This study aimed to examine how university students adopt AI tools in their learning processes and the role of digital literacy (DL) in this process through the lens of the Technology Acceptance Model (TAM). In this context, this study measured the impact of DL on university students' acceptance of AI technologies and their intention to use such technologies in the future. The data was collected from university students (N = 154) at a university in Western Türkiye during the fall semester of 2023. Data collection was conducted using two separate online forms; the first form included items adapted from the Digital Literacy Scale developed by Bayrakçı and Narmanlıoğlu (2021) to measure digital literacy levels, while the second form included items adapted from the UTAUT study by Venkatesh et al. (2003). The hypothesis testing results showed that students with higher levels of DL perceived the usefulness and ease of use of AI tools more positively, which positively affected their intention to adopt AI-based tools. The study also found that perceived usefulness and ease of use were important in shaping students' attitudes and behavioural intentions towards AI. When students perceive AI as a valuable tool for learning and find it easy to interact with, they are more willing to use it. This study suggests that DL plays a significant role in the acceptance of AI-based tools among university students, and accordingly, the TAM is a practical and accurate model to explore students’ potential engagement with AI in the learning process.Sakarya University Journal of Education371It is suggested that DL plays a significant role in the acceptance of AI-based tools among university students, and accordingly, the TAM is a practical and accurate model to explore students’ potential engagement with AI in the learning process.Sakarya University Journal of Education["Caner B\u00f6rekci", "\u00d6zg\u00fcr \u00c7elik"]2024-07-22T00:00:00 a909557089b2d6b4dc95e3095c929a55c07e9b9eTranslational research, especially in the fast-evolving field of Artificial Intelligence (AI), is key to converting scientific findings into practical innovations. In Responsible AI (RAI) research, translational impact is often viewed through various pathways, including research papers, blogs, news articles, and the drafting of forthcoming AI legislation (e.g., the EU AI Act). However, the real-world impact of RAI research remains an underexplored area. Our study aims to capture it through two pathways: patents and code repositories, both of which provide a rich and structured source of data. Using a dataset of 200,000 papers from 1980 to 2022 in AI and related fields, including Computer Vision, Natural Language Processing, and Human-Computer Interaction, we developed a Sentence-Transformers Deep Learning framework to identify RAI papers. This framework calculates the semantic similarity between paper abstracts and a set of RAI keywords, which are derived from the NIST's AI Risk Management Framework; a framework that aims to enhance trustworthiness considerations in the design, development, use, and evaluation of AI products, services, and systems. We identified 1,747 RAI papers published in top venues such as CHI, CSCW, NeurIPS, FAccT, and AIES between 2015 and 2022. By analyzing these papers, we found that a small subset that goes into patents or repositories is highly cited, with the translational process taking between 1 year for repositories and up to 8 years for patents. Interestingly, impactful RAI research is not limited to top U.S. institutions, but significant contributions come from European and Asian institutions. Finally, the multidisciplinary nature of RAI papers, often incorporating knowledge from diverse fields of expertise, was evident as these papers tend to build on unconventional combinations of prior knowledge.AAAI/ACM Conference on AI, Ethics, and Society861A small subset of RAI papers that go into patents or repositories is highly cited, with the translational process taking between 1 year for repositories and up to 8 years for patents, and the multidisciplinary nature of RAI papers, often incorporating knowledge from diverse fields of expertise, was evident.ArXiv["Ali Akbar Septiandri", "Marios Constantinides", "Daniele Quercia"]2024-07-22T00:00:00 3d50cd67246b09a358fce201e7da8381cf66f492Artificial Intelligence (AI) is one of today's most relevant emergent technologies. In view thereof, this paper proposes that more attention should be paid to the philosophical aspects of AI technology and its use. It is argued that this deficit is generally combined with philosophical misconceptions about the growth of knowledge. To identify these misconceptions, reference is made to the ideas of the philosopher of science Karl Popper and the physicist David Deutsch. The works of both thinkers aim against mistaken theories of knowledge, such as inductivism, empiricism, and instrumentalism. This paper shows that these theories bear similarities to how current AI technology operates. It also shows that these theories are very much alive in the (public) discourse on AI, often called Bayesianism. In line with Popper and Deutsch, it is proposed that all these theories are based on mistaken philosophies of knowledge. This includes an analysis of the implications of these mistaken philosophies for the use of AI in science and society, including some of the likely problem situations that will arise. This paper finally provides a realistic outlook on Artificial General Intelligence (AGI) and three propositions on A(G)I and philosophy (i.e., epistemology).arXiv.org31This paper provides a realistic outlook on Artificial General Intelligence (AGI) and three propositions on A(G)I and philosophy (i.e., epistemology) and shows that these theories bear similarities to how current AI technology operates.ArXiv["Max Velthoven", "Eric Marcus"]2024-07-22T00:00:00 0dffedfb6ce95cf7a817dd4c8fb191ac171d1b8cArtificial intelligence (AI) systems have become an indispensable component of modern technology. However, research on human behavioral responses is lagging behind, i.e., the research into human reliance on AI advice (AI reliance). Current shortcomings in the literature include the unclear influences on AI reliance, lack of external validity, conflicting approaches to measuring reliance, and disregard for a change in reliance over time. Promising avenues for future research include reliance on generative AI output and reliance in multi-user situations. In conclusion, we present a morphological box that serves as a guide for research on AI reliance.arXiv.org1231A morphological box is presented that serves as a guide for research into human reliance on AI advice and promising avenues for future research include reliance on generative AI output and reliance in multi-user situations.ArXiv["S. Eckhardt", "Niklas Kuhl", "Mateusz Dolata", "Gerhard Schwabe"]2024-07-22T00:00:00 737bd311a4051f8c669530aa576a479dbf759a10Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This paper proposes a unified approach to both synthetic data generation (SDG) and automated data labeling (ADL) with a unified SDG-ADL algorithm. SDG-ADL uses multidimensional (n-D) representations of data visualized losslessly with General Line Coordinates (GLCs), relying on reversible GLC properties to visualize n-D data in multiple GLCs. This paper demonstrates use of the new Circular Coordinates in Static and Dynamic forms, used with Parallel Coordinates and Shifted Paired Coordinates, since each GLC exemplifies unique data properties, such as inter-attribute n-D distributions and outlier detection. The approach is interactively implemented in computer software with the Dynamic Coordinates Visualization system (DCVis). Results with real data are demonstrated in case studies, evaluating impact on classifiers.International Conference on Information Visualisation150This paper demonstrates use of the new Circular Coordinates in Static and Dynamic forms, used with Parallel Coordinates and Shifted Paired Coordinates, since each GLC exemplifies unique data properties, such as inter-attribute n-D distributions and outlier detection.2024 28th International Conference Information Visualisation (IV)["Alice Williams", "B. Kovalerchuk"]2024-07-22T00:00:00 fd47731bd56087d637206f45ecfebf33e2c5d1a7As Artificial Intelligence (AI) increasingly influences various aspects of society, there is growing public interest in its potential benefits and risks. In this paper we present results of public perception of AI from a survey conducted with 10,000 respondents spanning ten countries in four continents around the world. The results show that currently an equal percentage of respondents who believe AI will change the world as we know it, also believe AI needs to be heavily regulated. However, our findings also indicate that despite the general sentiment among the global public that AI will replace workers, if a company were to use AI to innovate to improve lives, the public would be more likely to think highly of the company, purchase from them and even be interested in a job in that company. Our results further reveal that the global public largely views AI as a tool for problem solving. These nuanced results underscore the importance of AI directed towards challenges that the public would like science and technology-based innovations to address. We draw on a multi-year 3M study of public perception of science to provide further context on what the public perceives as important problems to be solved.arXiv.org00Despite the general sentiment among the global public that AI will replace workers, if a company were to use AI to innovate to improve lives, the public would be more likely to think highly of the company, purchase from them and even be interested in a job in that company.ArXiv["Jayshree Seth"]2024-07-22T00:00:00 5d4cc64a0f1d9ddae0057fa2c17a9fd8e99a0875By ingesting a vast corpus of source material, generative deep learning models are capable of encoding multi-modal data into a shared embedding space, producing synthetic outputs which cannot be decomposed into their constituent parts. These models call into question the relation of conceptualisation and production in creative practices spanning musical composition to visual art. Moreover, artificial intelligence as a research program poses deeper questions regarding the very nature of aesthetic categories and their constitution. In this essay I will consider the intelligibility of the art object through the lens of a particular family of machine learning models, known as ‘latent diffusion’, extending an aesthetic theory to complement the image of thought the models (re)present to us. This will lead to a discussion on the semantics of computational states, probing the inferential and referential capacities of said models. Throughout I will endorse a topological view of computation, which will inform the neural turn in computer science, characterised as a shift from the notion of a stored program to that of a cognitive model. Lastly, I will look at the instability of these models by analysing their limitations in terms of compositionality and grounding.Technophany, A Journal for Philosophy and Technology00This essay will consider the intelligibility of the art object through the lens of a particular family of machine learning models, known as ‘latent diffusion’, extending an aesthetic theory to complement the image of thought the models (re)present to us.Technophany, A Journal for Philosophy and Technology["Anil Bawa-Cavia"]2024-07-22T00:00:00 78ab202312523890ade9032d2d201ea25cb8d71bThe use of artificial intelligence (AI) in the classroom is growing, particularly when it comes to writing instruction. However, because of their low self-efficacy, a lot of pre-service English teachers are reluctant to apply AI. This study aims to analyze how pre-service English teachers' intentions to teach writing with AI are influenced by their level of self-efficacy in the field. In this quantitative study, 303 aspiring English instructors at an public institution were surveyed. Researchers created two questionnaires to gauge behavioral intentions and self-efficacy. Simple Linear Regression was used to evaluate the results. Based on the research findings, self-efficacy significantly influences the intention to employ AI to teach writing skills. Teachers are more likely to use AI if they feel confident in using it. This study concludes that increasing self-efficacy in AI among pre-service teachers has a positive impact on their intention to integrate AI into teaching practice. These findings underscore the need for teacher training programs and educational institutions to focus on building confidence in using AI, which can improve teaching practices.Jurnal Pendidikan Bahasa Inggris Undiksha00It is concluded that increasing self-efficacy in AI among pre-service teachers has a positive impact on their intention to integrate AI into teaching practice and underscores the need for teacher training programs and educational institutions to focus on building confidence in using AI.Jurnal Pendidikan Bahasa Inggris undiksha["Putu Maha Surya Suardewa", "I Putu Indra Kusuma", "Kadek Sintya Dewi"]2024-07-22T00:00:00 9364f9a56a46e328c1215fe5dc53e38b27a0549cThis comprehensive paper explores the transformative impact of integrating Machine Learning (ML) and Artificial Intelligence (AI) into oil and gas drilling operations. The paradigm shift is evident in domains like predictive maintenance, Rate of Penetration (ROP) optimization, and equipment failure prediction. The study, utilizing a meticulous data-driven approach and advanced ML algorithms, highlights the substantial cost savings and performance enhancements achieved in drilling operations. Azure Machine Learning is identified as a powerful tool for building, training, and deploying ML models, offering streamlined processes and scalability. The integration of ML and AI is seen as a strategic leap forward, promising increased efficiency, cost reduction, and sustainability in the oil and gas industry.2024 International Telecommunications Conference (ITC-Egypt)90This comprehensive paper explores the transformative impact of integrating Machine Learning (ML) and Artificial Intelligence (AI) into oil and gas drilling operations, and highlights the substantial cost savings and performance enhancements achieved in drilling operations.2024 International Telecommunications Conference (ITC-Egypt)["Mina Helkany", "Piyali Mondal", "Mahmoud Maged Mahmoud", "Abbes Chakchouk"]2024-07-22T00:00:00 bc2330a5bc84b5d8c2675dd3a78b2c67d13df1a5The rapid integration of generative artificial intelligence (AI) into postsecondary education and many other sectors resulted in a global reckoning with this new technology. This paper contributes to the study of the multifaceted influence of generative AI, with a particular focus on OpenAI's ChatGPT within academic settings during the first six months after the release in three specific ways. First, it scrutinizes the rise of ChatGPT as a transformative event construed through a study of mainstream discourses exhibiting AI hype. Second, it discusses the perceived implications of generative AI for writing, teaching, and learning through the lens of critical discourse analysis and critical AI studies. Third, it encourages the necessity for best practices in the adoption of generative AI technologies in education.The Open/Technology in Education Society and Scholarship Association Journal470This paper contributes to the study of the multifaceted influence of generative AI, with a particular focus on OpenAI's ChatGPT within academic settings during the first six months after the release, scrutinizes the rise of ChatGPT as a transformative event construed through a study of mainstream discourses exhibiting AI hype.The Open/Technology in Education, Society, and Scholarship Association Journal["Isabel Pedersen"]2024-07-22T00:00:00 d70fcbbe201daaf9230040ab000d495a9880c406The integration of Artificial Intelligence (AI) into military operations has significant implications for global stability. Understanding these implications is crucial for policymakers, researchers, and the international community.  This study addresses the impact of AI-driven technologies on defence systems. It examines autonomous weapons, surveillance, and cyber warfare, highlighting the potential for an arms race. The main argument is that responsible AI deployment is essential for maintaining peace and security. The study is anchored on Deterrence theory. This study adopts qualitative research methods as a means of data collection which is secondary source based, and were merely obtained from textbooks, Journal articles, conference proceedings, Newspapers, and reliable internet materials. The data collected were analysed thematically. The study however revealed that AI enhances military capabilities while raising legal and ethical concerns. The study therefore recommends among others, the need for AI governance via international norms, and cooperation to prevent misuse.Research Journal in Advanced Humanities457The study revealed that AI enhances military capabilities while raising legal and ethical concerns, and recommends among others, the need for AI governance via international norms, and cooperation to prevent misuse.Research Journal in Advanced Humanities["G. .. Osimen", "Oluwakemi Morola Fulani", "Felix Chidozie", "Dolapo Omolara Dada"]2024-07-23T00:00:00 773717a8c70b04e305be53e9c33a582f4b9f3baaArtificial Intelligence (AI) transforms the social sciences by providing new methodologies and tools for data analysis. This article was based on a comprehensive literature review that analyzed the role of artificial intelligence as an analytical tool in the social sciences. It was observed that the ability of AI to process text, images, and audio in an integrated manner allows researchers to address complex problems with greater accuracy and efficiency. Multimodal tools facilitate the analysis of large volumes of data, the interpretation of financial documents, and the evaluation of facial expressions, which improves decision making in social research. Specialized databases offer access to a wide range of AI tools that optimize tasks such as literature review, data collection and visualization of results. In addition, safety and ethics in the use of AI are key priorities, with the creation of alliances and regulatory frameworks that ensure responsible and safe development of these technologies. Initiatives such as the AI Safety Alliance and the European Union's Artificial Intelligence Act set global standards for the ethical and safe use of AI, safeguarding both individuals and society at large.LatIA417It was observed that the ability of AI to process text, images, and audio in an integrated manner allows researchers to address complex problems with greater accuracy and efficiency.LatIA["M. D. L. C. Hern\u00e1ndez-Lugo"]2024-07-23T00:00:00 61d1e4a254ca74e8d49635fb216fa532438f6d31PurposeThe purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.Design/methodology/approachThe research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.FindingsThe research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.Practical implicationsConsidering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.Originality/valueSince no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.Business Process Management Journal603The research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics, due to moderate knowledge- and leadership-readiness along with low human- and finance-readiness of most of the business organizations.Bus. Process. Manag. J.["Md. Khalid Hossain", "Aashish Srivastava", "Gillian C. Oliver", "Md Ekramul Islam", "Nayma Akther Jahan", "Ridoan Karim", "Tanjila Kanij", "T. Mahdi"]2024-07-23T00:00:00 644152db9515c3012ce51b0d7080a6c0e8d57f6b Purpose This paper aims to analyze the time-varying dynamic connectedness among environmental, social and governance (ESG)-compliant firms, Fintech-based firms and artificial intelligence (AI) firm’s stocks. Design/methodology/approach To examine the spillover from globally leading companies that systematically follow ESG reporting and standards into their financial books to top AI-based and Fintech-based companies, we use the daily observation extending from December 31, 2019 to October 9, 2023. For the empirical investigation, Diebold and Yilmaz (2012) model and Baruník and Křehlík (2018) model are employed. Findings An intriguing observation is found for both recipient and transmission as Northrop Grumman remains the least shock transmitter and receiver among all constituent markets irrespective of two different used models. On this note, Northrop Grumman can be classified among the safest stock comparatively which has to be held in short, medium and long run to mitigate the risk. Originality/value After extensive existing literature review and to the best of the authors knowledge, it is a novel study that examines the dynamic connectedness among ESG, Fintech and AI stocks covering two unprecedented events like the COVID-19 outbreak and the Russia–Ukraine invasion. Journal of Accounting &amp; Organizational Change461A novel study that examines the dynamic connectedness among ESG, Fintech and AI stocks covering two unprecedented events like the COVID-19 outbreak and the Russia–Ukraine invasion is examined.Journal of Accounting &amp; Organizational Change["Sabia Tabassum", "L. K. Dhillon", "M. Yadav", "Khaliquzzaman Khan", "Mohd Afzal Saifi", "Zehra Zulfikar"]2024-07-23T00:00:00 3ec2117ea33a1917bf825f86f448b116bb05d4f9Context: The diagnostic methods for diabetes mellitus (DM), a chronic metabolic disorder characterized by elevated blood sugar levels, are rapidly evolving thanks to artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL). This review explores the applications of AI in risk assessment and diagnosing different types of diabetes. Evidence Acquisition: The review highlights the effectiveness of various ML models, including support vector machines (SVMs), random forests (RFs), and DL techniques like convolutional neural networks (CNNs), in achieving high diagnostic accuracy. Challenges include limited data availability, interpretability of complex models, and the need for standardized performance metrics. Results: Machine learning methods like SVMs and RFs are highly effective at diagnosing different types of diabetes, and DL techniques like CNNs also show great promise. Conclusions: Overall, AI has immense potential to revolutionize diabetes diagnosis by facilitating risk assessment and early detection, improving treatment efficacy, and preventing severe complications.Shiraz E Medical Journal582Shiraz E-Medical Journal["Alireza Keshtkar", "Nazanin Ayareh", "Farnaz Atighi", "Reza Hamidi", "Parsa Yazdanpanahi", "Alireza Karimi", "Arzhang Naseri", "Fatemeh Hosseini", "Mohammadhossein Dabbaghmanesh"]2024-07-23T00:00:00 551217c4558a1489000ef7db09700a30f9bb0c35Self-regulated learning (SRL) has been integrated into English-as-a-Foreign Language (EFL) reading instruction to enhance students’ reading achievement. Evidence indicates that providing personalized support is critical to SRL. However, providing personalized support is time-consuming and challenging to implement in language classrooms. Although personalized support has been built into many online reading systems, the support is often delivered in a one-way manner, with little chance for follow-up discussions. This innovation in practice introduces an artificial intelligence (AI) chatbot developed to provide personalized SRL support for EFL students in reading. The AI chatbot was designed as a reading companion to facilitate active, out-of-class reading that expands in-class instruction. By giving autonomy to students to engage with appropriate reading materials and receive personalized self-regulated reading (SRR) guidance, the innovation empowered learners to overcome challenges in the reading process and facilitated their use of SRR strategies. The pedagogical values of the innovation were explored from students’ perspectives. Future pedagogical directions for AI-supported SRR instruction is also discussed.RELC Journal : A Journal of Language Teaching and Research in Southeast Asia161This innovation in practice introduces an artificial intelligence (AI) chatbot developed to provide personalized SRL support for EFL students in reading and empowered learners to overcome challenges in the reading process and facilitated their use of SRR strategies.RELC Journal["Mengru Pan", "Kaiyang Guo", "Chun Lai"]2024-07-23T00:00:00 e5a1084982d3df65b91fad267ad41cf2c5ea194bThe purpose is to stimulate imagination of artificial intelligence (AI) and education beyond current schooling. The approach this article took is a broad review of literature on learning, teaching, schooling, and technological development and evidence-based reasoning about the possible future of education in the context of AI. Schools could be transformed with the advancement of technology, especially generative AI. The changes should start with student-driven personalizing learning and problem-oriented pedagogy. The value of the article lies with the unique definitions of personalized learning and problem-oriented pedagogy as well as the contribution of AI to support a new form of learning.ECNU Review of Education232The value of the article lies with the unique definitions of personalized learning and problem-oriented pedagogy as well as the contribution of AI to support a new form of learning.ECNU Review of Education["Yong Zhao (\u8d75\u52c7)"]2024-07-23T00:00:00 755e2c9940fae5cacb2c7eef092df73a70fd331bThe “new normal” era has made remote work the new standard, making the use of artificial intelligence (AI) increasingly important. Therefore, this study aims to investigate employee perceptions of change leadership in the application of AI that affects employee engagement and productivity according to the resource-based view (RBV). Of the 467 respondents who worked in the banking industry in West Sumatra province, Indonesia, only 359 met the eligibility requirements. The partial least squares (PLS) analysis shows a direct relationship between AI and employee engagement (p &lt; 0.05) and productivity (p &lt; 0.05), as well as employee engagement and employee productivity (p &lt; 0.05). The effect of AI on employee productivity is mediated by employee engagement (p &lt; 0.05), but the moderating effect provided by change leadership is not significant (p &gt; 0.05) in increasing employee productivity. These findings will help managers create a positive work environment through the application of AI, resulting in higher employee engagement and productivity. Specifically, these findings help organizations integrate AI more effectively and provide managers with a comprehensive understanding of the considerations needed to increase productivity through employee engagement for organizational competitiveness. AcknowledgmentThe authors thank Universitas Negeri Padang for helping finish this article. We also appreciate the cooperation and support of each member.Problems and Perspectives in Management242Investigating employee perceptions of change leadership in the application of AI that affects employee engagement and productivity according to the resource-based view helps organizations integrate AI more effectively and provides managers with a comprehensive understanding of the considerations needed to increase productivity through employee engagement for organizational competitiveness.Problems and Perspectives in Management["Mia Ayu Gusti", "Alpon Satrianto", "Candrianto", "E. Juniardi", "Halkadri Fitra"]2024-07-23T00:00:00 868ec72990a18fa0b740826bdfa39e16fa701e75Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management, and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI’s integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI’s potential can be harnessed for better patient outcomes through personalized treatment.Annals of Medicine and Surgery392Overall, AI’s potential can be harnessed for better patient outcomes through personalized treatment in the medical field due to privacy, data security, bias, and transparency.Annals of Medicine and Surgery["Muhammad Iftikhar", "Muhammad Saqib", "S. N. Qayyum", "Rehana Asmat", "Hassan Mumtaz", "Muhammad Rehan", "Irfan Ullah", "Iftikhar Ud-din", "Samim Noori", "Maleeka Khan", "Ehtisham Rehman", "Zain Ejaz"]2024-07-23T00:00:00 f55d544f40c891dbacf9d12f71bfd5febfbd4672Science China. Earth Sciences518Science China Earth Sciences["Renguang Zuo", "Qiuming Cheng", "Ying Xu", "Fanfan Yang", "Yihui Xiong", "Ziye Wang", "Oliver P. Kreuzer"]2024-07-23T00:00:00 b9e8be3bde0899fdc719cb0d7d9f01d37a6d9e4dIt is believed that the way in which media speak about emerging technologies can influence the public perception of their benefits and risks. Risk statements highlight the possible negative effects, real or imaginary, that a particular event could have on audiences. Just as journalism varies over space and time, what is considered a risk is deeply rooted in specific social, economic, and technological contexts. This variability implies that journalistic practices are neither universal nor static; instead, they change and adapt according to circumstance. Moreover, technological advances have allowed the press to better understand their audiences and adhere to their demands. In this context, the discursive construction of the risk of artificial intelligence was studied in the press of four European countries: Belgium, Spain, France, and Portugal. In total, 290 texts published in January 2024 were examined. Mentions of “artificial intelligence” were found in the following newspapers: Le Soir, El País, Le Figaro, and Público. Fourteen risk categories and seven groups of voices responsible for their enunciation were identified, with significant variations between the studied newspapers. It was concluded that national contexts make it possible to differentiate the way in which the press communicates the risks associated with artificial intelligence. Although these results do not directly reflect public awareness of the risks in each of these countries, they open a line of research on the possible influences of the progressive monitoring and knowledge of audiences in the construction of the media agenda.Journalism and Media360It was concluded that national contexts make it possible to differentiate the way in which the press communicates the risks associated with artificial intelligence, and opens a line of research on the possible influences of the progressive monitoring and knowledge of audiences in the construction of the media agenda.Journalism and Media["Cristian Gonz\u00e1lez-Arias", "Xos\u00e9 L\u00f3pez-Garc\u00eda"]2024-07-23T00:00:00 410b598d088df50e535a53b841665864d19b20ffThe purpose of this study is to investigate the role of human-centred artificial intelligence in Industry 5.0 across three different settings: developed economies, developing countries, and low-income economies. This study utilizes secondary data issued by International Monetary Fund (IMF) to analyze both the societal opportunities and challenges of human-centred artificial intelligence and evaluates how the role of humans can evolve alongside the advancement of human-AI collaboration. The findings indicate that developed countries are likely to face significant challenges in the labour market due to the lack of complementarity between AI and humans in certain occupations. It is important to note that exposure to AI in developing and low-income countries is still very low, which may result in significant issues in the future due to inadequate preparation to adopt AI technologies. Finally, the study identifies the most critical new skills required to align with the expectations of Industry 5.0.International Conference on Wireless Networks and Mobile Communications290The findings indicate that developed countries are likely to face significant challenges in the labour market due to the lack of complementarity between AI and humans in certain occupations.2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM)["Mohamed Mamad", "Ouissal Chichi"]2024-07-23T00:00:00 d3aa388f39fec314b260e99ea5b54b79e86c883dArtificial intelligence is widely used in human life, especially in the field of communications. This article presents an overview of recent research in the application of artificial intelligence in communications, in big data, machine learning and cloud computing. Several examples of the use of artificial intelligence in communication are presented to build an understanding of the current applications of artificial intelligence. Ethics of using artificial intelligence are also mentioned, as ethics are the principles by which one judges whether a technology is suitable for human use or not. In order to predict the future development of communications, an idea of the previous progress in the field of communications is given and sorted into the categories of wireless communication, ethical issues, and network monitoring and control. This study confirms that the future development of artificial intelligence mainly consists of two aspects: SDN (software-defined networking) and NFV (network function visualization). Thus, AI will be used to improve the efficiency of communications.Modern Problems of Telecommunications - 2024. Proceedings of Russian Scientific and Technical Conference00An overview of recent research in the application of artificial intelligence in communications, in big data, machine learning and cloud computing is presented to build an understanding of the current applications of artificial intelligence.Modern Problems of Telecommunications - 2024. Proceedings of Russian Scientific and Technical Conference["A. A. Erkaev"]2024-07-23T00:00:00 b4e60ed22e05b79dc6cac31be1e152e8da42af3dThere is not a single, accepted explanation for artificial intelligence (AI). The phrase refers to computational techniques that are like mental functions such as thinking, comprehension, adjustment, sensory perception and collaboration but never fully replaces humans1. The most profitable branch of AI over the past decade has been the field of machine learning2Journal of Bahria University Medical and Dental College90Journal of Bahria University Medical and Dental College["Fareeha Shahid"]2024-07-23T00:00:00 86b398f34e6f72b1a6754f0bcc905af81470a6caThe academic press is increasingly discussing the impact of Artificial Intelligence (AI) technologies on education and science. Researchers pay attention not only to the applied aspects of the use of AI technologies, but also to the issues of the ontological foundations of activity that are being transformed under the influence of new technologies. However, the issues of the impact of AI technologies on engineering activity, engineering thinking and, consequently, on engineering education are not sufficiently reflected in academic publications. Moreover, the aspects related to the widespread use of artificial intelligence, both in professional activities and in everyday life, remain insufficiently studied.The article proposes theses and corresponding arguments that clarify the fundamental changes occurring in engineering activity and engineering thinking in the context of the expansion of artificial intelligence technologies. Engineering activity is presented as a system that is not identical to the activity of individual engineers. A definition of engineering activity is proposed, which reveals its essence through its goal of solving human and societal problems. A non-instrumental approach to the interpretation of engineering based on AI technologies is substantiated, within which artificial intelligence appears as a partner in engineering activity. Finally, engineering thinking is complemented by anticipatory and responsible thinking.The article is a contribution to the academic discussion on the specifics of engineering activity and engineering thinking at the present stage.Vysshee Obrazovanie v Rossii = Higher Education in Russia190The article proposes theses and corresponding arguments that clarify the fundamental changes occurring in engineering activity and engineering thinking in the context of the expansion of artificial intelligence technologies and proposes a non-instrumental approach to the interpretation of engineering based on AI technologies.Vysshee Obrazovanie v Rossii = Higher Education in Russia["V. Sheinbaum", "V. S. Nikolskiy"]2024-07-23T00:00:00 dbb4714397e817c51ed92166de86924e602c8f2cAt the time of diagnosis for cancer patients, a wide array of data can be gathered, ranging from clinical information to multiple layers of omics data. Determining which of these data are most informative is crucial, not only for advancing biological understanding but also for clinical and economic considerations. This process facilitates the selection of the most significant markers, enhancing patient stratification and informing treatment recommendations. In this paper, we start with 89 features extracted from multiomics and clinical data and aim to identify the most important ones in predicting response to neoadjuvant chemotherapy (NAC) using different explainable Artificial Intelligence (XAI) models and statistics. Our results show that XAI methods consistently recover important features that are missed by statistics and vice versa, hinting towards the need for complementary implementation of these methods. Furthermore, we find that a myriad of features, from mutations to immune infiltration, affect the response to NAC in breast tumors.bioRxiv260The results show that XAI methods consistently recover important features that are missed by statistics and vice versa, hinting towards the need for complementary implementation of these methods.bioRxiv["Parisa Amin"]2024-07-23T00:00:00 1c6d82cb54bf9c6855f73d8a01182946310876bfWe gather many perspectives on Capital and synthesize their commonalities. We provide a characterization of Capital as a historical agential system and propose a model of Capital using tools from computer science. Our model consists of propositions which, if satisfied by a specific grounding, constitute a valid model of Capital. We clarify the manners in which Capital can evolve. We claim that, when its evolution is driven by quantitative optimization processes, Capital can possess qualities of Artificial Intelligence. We find that Capital may not uniquely represent meaning, in the same way that optimization is not intentionally meaningful. We find that Artificial Intelligences like modern day Large Language Models are a part of Capital. We link our readers to a web-interface where they can interact with a part of Capital.The 2024 Conference on Artificial Life240It is claimed that Capital can possess qualities of Artificial Intelligence when its evolution is driven by quantitative optimization processes and it is found that Capital may not uniquely represent meaning, in the same way that optimization is not intentionally meaningful.ArXiv["Cesare Carissimo", "Marcin Korecki"]2024-07-23T00:00:00 89c95b5251970f4762a96878ae36a9ba5c9f59b9Discover Conservation330Discover Conservation["Amar Causevic", "S. Causevic", "Matthew Fielding", "Julia Barrott"]2024-07-23T00:00:00 bee39191f80ff7773f5aa83b87170c53cf27be50Fake news and artificial intelligence are phenomena addressed in academia and research within the fields of communication and audiovisual education. As a medium and language, cinema has mirrored these challenges, exemplified by the short film Deep Soria. The primary objective proposed in this study is to ascertain whether this short film, directed by Pedro Estepa in 2021, is perceived as a mockumentary and to explore students’ perceptions of the social issues it raises and the way it expresses them. An ad hoc questionnaire was employed specifically for the short film, enabling the assessment of 118 students from the Journalism and Primary Education Degree programmes at the University of Valladolid. The method used is exploratory and descriptive. Results were obtained by combining descriptive statistical analysis and content analysis of the open-ended questions in the questionnaire. Notably, the study highlights the detection of the documentary’s central themes- fake news and depopulation- the use of humour, the focus on depopulation, and suggestions for incorporating more elements of “everyday reality”, such as images and testimonies from the inhabitants of Soria and the city/province. At the same time, the short film has been reevaluated for its propensity to address social issues.Journal of Technology and Science Education450This study highlights the detection of the documentary’s central themes- fake news and depopulation- the use of humour, the focus on depopulation, and suggestions for incorporating more elements of “everyday reality”, such as images and testimonies from the inhabitants of Soria and the city/province.Journal of Technology and Science Education["Ana Isabel Cea", "Inmaculada S\u00e1nchez-Mac\u00edas"]2024-07-23T00:00:00 6c1fb57c5d6c6cffcebbe2b31f9a943adb716a44 Purpose This study aims to identify the librarians’ readiness to leverage artificial intelligence for sustainable competence development and smart library services. Design/methodology/approach This study used a quantitative research design for addressing the objectives. The population consisted of librarians from the public and private sector universities of Pakistan. The data were analyzed by using Smart PLS software. Findings The analysis consisted of two major parts: first the assessment of measurement model and second the structural equation modeling analysis. A significant positive impact of AI adoption was found on the implementation smart library services. Findings revealed that behavioral intention motivated librarians to adopt AI tools in university libraries for the delivery of smart library services. Research limitations/implications We applied quantitative method to carry out the study while future authors may conduct a systematic literature review on the same topic for offering a broader outlook. Practical implications It has provided practical contributions by providing a baseline for management bodies to construct policies for the successful adoption of AI in libraries for sustainable competence development of practicing librarians and implementation of smart library services. Social implications The study has social implications too as AI integrated library services prove fruitful for society and digitally skilled librarians play a vital role for the promotion of reading and research culture in society. Originality/value To the best of the authors’ knowledge, this is the first study on librarians’ readiness to leverage artificial intelligence for the enhancement of digital literacy skills, sustainable competence development and smart library services in the context of Pakistan. Global Knowledge Memory and Communication560Findings revealed that behavioral intention motivated librarians to adopt AI tools in university libraries for the delivery of smart library services, a significant positive impact of AI adoption was found on the implementation smart library services.Global Knowledge, Memory and Communication["Khurram Shahzad", "S. A. Khan", "Abid Iqbal"]2024-07-23T00:00:00 4b1fc9c620a6dd8a7d6b77f760cda3018becad33This study investigates the impact of intelligent manufacturing methods driven by artificial intelligence (AI) on cost stickiness in Chinese manufacturing enterprises. Leveraging the ABJ model， a regression analysis explores how different AI‐enabled intelligent manufacturing approaches influence cost stickiness through the lens of innovation equilibrium. The sample comprises manufacturing companies listed on China's A‐share market from 2013 to 2021. The findings reveal a negative correlation between intelligent manufacturing adoption and cost stickiness among these firms. Specifically, production‐based intelligent manufacturing exhibits a more significant effect on reducing cost stickiness compared with collaborative intelligent manufacturing methods. Moreover, intelligent manufacturing positively impacts both joint equilibrium innovation and matching equilibrium innovation. While joint equilibrium innovation is negatively associated with cost stickiness, matching equilibrium innovation shows no significant relationship with cost stickiness. The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness. Overall, this research sheds light on how AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises. It highlights the strategic value of adopting AI systems to enhance operational efficiency and cost management flexibility within manufacturing contexts.Systems research and behavioral science440The results indicate that innovation equilibrium plays a mediating role in the relationship between AI‐driven intelligent manufacturing and cost stickiness, and AI capabilities enabling intelligent manufacturing processes and innovation equilibrium dynamics can help alleviate cost stickiness issues faced by manufacturing enterprises.Systems Research and Behavioral Science["Fangfang Wang", "Qiang Li", "Hong Chen"]2024-07-23T00:00:00 6e2bb6217357e7ee2926e7520bdc4def4310dfd5Objective: This study aims to provide an overview of the use of artificial intelligence (AI) in Distance Education (EaD) courses, aiming to contribute to the expansion of information in the literature on the emerging topic of the role of AIs in digital education.   Theoretical Framework: This study is based on fundamental concepts about the use of AI in distance learning, highlighting its application in personalizing teaching, institutional management and supporting the educational environment.   Method: We carried out a bibliographical search in the Google Scholar database, focusing on scientific articles published between 2020 and 2024, complemented by case studies in the news for a comprehensive overview.   Results and Discussion: The results indicate that AI plays a crucial role in personalizing teaching and educational management in distance learning courses. We discuss the practical implications of these technologies, as well as the needs for ongoing training for teachers and updating curricula.   Research Implications: This research offers insights for the effective implementation of AI in remote learning, highlighting its potential to optimize educational management and promote more personalized and efficient teaching practices.   Originality/Value: We contribute to the literature by offering an updated overview of the use of AI in distance learning, emphasizing its practical and theoretical contributions to the educational field in the 21st century.Revista de Gestão Social e Ambiental70The results indicate that AI plays a crucial role in personalizing teaching and educational management in distance learning courses, and the practical implications of these technologies, as well as the needs for ongoing training for teachers and updating curricula are discussed.Revista de Gestão Social e Ambiental["Davi Cipriano de Queiroz", "Jonatha Lisboa Galv\u00e3o do Nascimento", "Paulo Henrique de Oliveira Nunes", "Ananda Maria Pinto Gomes", "Joseilson Trajano de Souza", "Israel Nogueira de Oliveira"]2024-07-23T00:00:00 ee72f876c2043cdd6b5262a96fccbe25209ad1b0This study aims to objectively explored the relevance of big data issues that have developed in the professional world to the best practices of the management accounting profession. The conceptual framework was developed to become the frame for consideration of making structured designs on artificial intelligence issues. Using data sources derived from literature studies and conducting various reviews of articles related to this interesting topic, conclusions are generated that refer to the implications of management accountant best practices. This study finds that the concept of management accountants is strongly influenced by the adoption of Big Data in the companies. Furthermore, we specifically define and present strategic steps that can suggest management accountants can carry out best practices in accordance with professional programs that have become an important part of practice. This study contributes to the development of the best practice of management accountants where Big Data is the center of attention that cannot be separated from their professional practice so that it is possible to adjust the practice of management accountants that generate added value. To the best authors knowledge, this is the first study to seeks and explores the Big Data and Artificial Intellegence in the management accountant profession from global perspectives. The study provides some deep insight to the accountant management global more take care for their sustainable profession in the long wave of digitalisation.Jurnal Bisnis dan Akuntansi00This study finds that the concept of management accountants is strongly influenced by the adoption of Big Data in the companies and defines and presents strategic steps that can suggest management accountants can carry out best practices in accordance with professional programs that have become an important part of practice.Jurnal Bisnis dan Akuntansi["Suham Cahyono", "Ardianto Ardianto"]2024-07-23T00:00:00 1c27d0fd1e7bd115fcf7e4f1d49f407af608a221BACKGROUND As the use of artificial intelligence (AI) becomes more prevalent in academic settings, there is a growing concern about maintaining a culture of integrity. METHOD This article explores the role of academic institutions and programs in fostering a culture of integrity in relation to AI. RESULTS By implementing specific policies, integrating tools, and utilizing software for AI detection, academic institutions can establish a culture of integrity in relation to AI. These collective efforts foster an environment where ethical AI practices are upheld and reinforce the importance of academic honesty, particularly in the nursing profession. CONCLUSION Academic institutions have the capacity to establish integrity-focused policies and integrate anti-AI agent tools in courses to mitigate unethical AI usage, while software advancements assist faculty in identifying AI presence during assessments. Emphasizing the interplay between academic and professional integrity strengthens nurses' dedication to academic honesty. [J Nurs Educ. 2024;63(X):XXX-XXX.].Journal of Nursing Education150Academic institutions have the capacity to establish integrity-focused policies and integrate anti-AI agent tools in courses to mitigate unethical AI usage, while software advancements assist faculty in identifying AI presence during assessments.The Journal of nursing education["Louisa Krueger", "Sally Clemenson", "Ellen Johnson", "Laura Schwarz"]2024-07-23T00:00:00 7e8c1eff1cd83958c393ced37a9825225edd9352A midst the rapid evolution of artificial intelligence (AI) technology, its integration into higher education has gained significant momentum, presenting novel avenues for fostering educational equity. This paper comprehensively examines the utilization of AI in higher education, meticulously analyzing its specific contributions to the advancement of educational equity, and subsequently offers tailored strategies and recommendations for its optimal deployment. By harnessing the power of intelligent algorithms, big data analysis, and other AI capabilities, higher education institutions are able to personalize learning experiences, expand access to quality resources, and streamline administrative processes, thereby promoting a more equitable distribution of educational opportunities. The paper delves into these applications, underscores their potential impact, and provides practical guidance for leveraging AI to foster a more inclusive and equitable higher education landscape.Journal of Higher Education Research80This paper comprehensively examines the utilization of AI in higher education, meticulously analyzing its specific contributions to the advancement of educational equity, and subsequently offers tailored strategies and recommendations for its optimal deployment.Journal of Higher Education Research["Zeqian Jiang"]2024-07-23T00:00:00 51e1c00abd36bbd6a19e94204cfd02b4c34ba14bIn the article, the authors raise several topical topics related to the modern development of artificial intelligence as a component and toolkit of Industry 4.0 development. It is emphasized that according to the British Collins dictionary, in 2023 the term "artificial intelligence" became the word of the year. The main provisions of the article are based on special research in the field of AI carried out by such well-known think tanks as the Alan Turing Institute, the Center for the 4th Industrial Revolution of the CEF in Davos with the support of the Hitachi company, as well as the RAND Corporation. The materials of the reports of the European Commission and developments on the subject of AI in recent years in Ukraine, in particular in the military and security areas, have been included. The authors also note the fact that in the 21st century, a rather unusual problem of the ethics of artificial intelligence is gaining more and more momentum: the question arises whether we will be able to develop an effective system of rules in the medium term, designed to create an environment, so to speak, of trusted development of technologies AI. Special attention is paid to the issues of geostrategic confrontation between the United States and the Peopleʼs Republic of China in the field of innovative development of AI. In this context, artificial intelligence requires original management solutions at the global level. The governance structure should be preventive, flexible, inclusive, transparent and focused. All stakeholders – states, IT institutions, non-governmental actors and development firms – should, based on these principles, form 3 overlapping governance regimes. The first is to fact-find and advise governments on the risks associated with artificial intelligence; the second to prevent a full arms race between states on an innovative basis; the third to control the destructive forces of technology unlike anything the world has seen in the past. It is concluded that artificial intelligence, under the conditions of its further framing and ethical proof, is only one of the newest directions (recall Gödelʼs theorem here) of the development of the human mind in the 21st century.Mediaforum : Analytics, Forecasts, Information Management30It is concluded that artificial intelligence, under the conditions of its further framing and ethical proof, is only one of the newest directions of the development of the human mind in the 21st century.Mediaforum : Analytics, Forecasts, Information Management["Volodymyr Fisanov", "Oleksandra Hissa-Ivanovych"]2024-07-23T00:00:00 4ccc939132316640c093fd4cd9ca1c30d8e59646Journal of Computer Virology and Hacking Techniques1519J. Comput. Virol. Hacking Tech.["Ekaterina Pleshakova", "Aleksey Osipov", "Sergey Gataullin", "Timur Gataullin", "Athanasios Vasilakos"]2024-07-23T00:00:00 7625903f60ffe70b5f5c13c8e1b2313a20e1b547Proceedings of the National Academy of Sciences of the United States of America33Proceedings of the National Academy of Sciences of the United States of America["Don A Moore", "Juliana Schroeder", "Erica R. Bailey", "Rachel Gershon", "Joshua E Moore", "Joseph P Simmons"]2024-07-23T00:00:00 21d5c2c72513f7dbd8707422f8133f3f4dfd940eThe improper utilization of AI technology poses difficulties to democracy, particularly the growing threat of unjust elections, exemplified by the deployment of bot accounts and deep fakes during electoral processes. Hence, it is crucial to build a strong and comprehensive framework to regulate the utilization of AI technology in Indonesia's political process. This article analyzes four main topics: (a) the advancement of AI technology and its connection to elections; (b) the influence of AI technology on election principles; (c) the pressing need for regulating AI in elections; and (d) the possibilities and difficulties of regulating AI technology within Indonesia's legal framework. The paper employs doctrinal legal research to examine the necessity of regulating AI technology in the context of conducting elections, taking into account the constitutional framework, established principles, and democratic election norms. The result shows that irresponsible use of AI technology remains a menace to democratic election ideals, and Indonesia must establish adequate legal mechanisms to tackle the problems stemming from the improper use of AI technology in the political process. The regulation of AI technology can be initiated by introducing a bill specifically focused on artificial intelligence (AI). This process should also involve the synchronization and harmonization of election rules, including election laws, laws governing the election of governors, regents, and mayors, laws concerning political parties, and other implementing regulations such as those established by the General Election Commission and the Election Supervisory Board.Jambura Law Review322The result shows that irresponsible use of AI technology remains a menace to democratic election ideals, and Indonesia must establish adequate legal mechanisms to tackle the problems stemming from the improper use of AI technology in the political process.Jambura Law Review["Hesti Armiwulan", "Rofi Aulia Rahman", "Valentino Nathanael Prabowo", "J\u00f3zsef Hajd\u00fa"]2024-07-23T00:00:00 ca827afd0a8ab96bc1f42a93194e7c3966116336статья посвящена проблемам восприятия и понимания будущими педагогами рисков и новых возможностей искусственного интеллекта в образовании. Целью исследования являлся поиск ответа на вопрос о том, как современные студенты педагогического университета с разной степенью академической успешности и компьютерной грамотности воспринимают факт стремительного внедрения искусственного интеллекта в практику образования. Для ответа на него использовались методики: «экспертная оценка академической успешности студентов»; «экспертная оценка компьютерной грамотности студентов (с использованием «метода полярных баллов»), а также проективная исследовательская беседа и опросник, составленный на основе анализа современных теоретических источников. Выборку исследования составили 216 студентов бакалавриата и магистратуры направления «педагогическое образование». В качестве основного результата проведенной работы можно отметить, что будущие педагоги еще не в полной мере осознали риски и преимущества, которые несет с собой внедрение искусственного интеллекта в образование. В результате исследования было изучено как общее отношение студентов к распространенным угрозам распространения ИИ, так и зависимость этого отношения от различного рода факторов (уровня образовательной программы, опыта использования в определенной сфере ИИ или отсутствия такого опыта). Результаты исследования показали, что чуть больше половины студентов не используют возможности искусственного интеллекта для решения учебных задач, но среди них достаточно большой процент составляют те, кто использует его для решения бытовых и личных проблем. Оценка угроз распространения искусственного интеллекта существенно зависит от степени и характера использования систем искусственного интеллекта в жизни и работе. Использование искусственного интеллекта в решении профессиональных педагогических задач, как правило связано с более спокойной оценкой как возможностей искусственного интеллекта, так и его угроз. Идея перестройки образовательных программ с целью соблюдения минимальной интеллектуальной нагрузки на обучающегося встретила в целом положительную оценку, но она существенно отличалась от категории респондентов. the article is devoted to the problems of perception and understanding by future teachers of the risks and new opportunities of artificial intelligence in education. The purpose of the study was to find an answer to the question of how modern students of a pedagogical university with varying degrees of academic success and computer literacy perceive the fact of the rapid introduction of artificial intelligence into educational practice. To answer it, the following methods were used: “expert assessment of students’ academic success”; “expert assessment of students’ computer literacy (using the “polar points method”), as well as a projective research conversation and a questionnaire compiled on the basis of an analysis of modern theoretical sources. The study sample consisted of 216 undergraduate and graduate students in the field of “teacher education.” As the main result of the work, it can be noted that future teachers have not yet fully realized the risks and benefits that the introduction of artificial intelligence in education brings. As a result of the study, both the general attitude of students towards common threats from the spread of AI and the dependence of this attitude on various factors (level of educational program, experience in using AI in a certain area or lack of such experience) were studied. The results of the study showed that slightly more than half of students do not use the capabilities of artificial intelligence to solve educational problems, but among them, a fairly large percentage are those who use it to solve everyday and personal problems. Assessing the threats of the spread of artificial intelligence significantly depends on the degree and nature of the use of artificial intelligence systems in life and work. The use of artificial intelligence in solving professional pedagogical problems is usually associated with a calmer assessment of both the capabilities of artificial intelligence and its threats. The idea of restructuring educational programs in order to maintain a minimum intellectual load on the student met with a generally positive assessment, but it differed significantly from the category of respondents.Modern scientist110Modern scientist["\u0410.\u0418. \u0421\u0430\u0432\u0435\u043d\u043a\u043e\u0432", "\u041c.\u0412. \u0412\u043e\u0440\u043e\u043f\u0430\u0435\u0432"]2024-07-23T00:00:00 965039f235e5cc9c664932e64efcb7cf57081d47The recent – purportedly rapid – development of AI tools has again resurrected the actuality of post-work u-/dystopias. Drawing on discursive topoi which have become popular since the post-WW2 automatization surge, AI post-work now advances into the field of white-collar labor, but also creative, artistic, and even music labor. In this paper I aim to analyze the emergent arrival of the post-work thesis into music labor. I will draw on prominent critics of automatization, AI and post-work discourses, such as Pierre Naville, Aaron Benanav and Jason Resnikoff, to show that these discourses are not only unsubstantiated, but are instrumentalized in order to depreciate the value of concrete labor in music production.Artificial Intelligence in Music, Arts, and Theory Revisited90This paper aims to analyze the emergent arrival of the post-work thesis into music labor, and draws on prominent critics of automatization, AI and post-work discourses to show that these discourses are not only unsubstantiated, but are instrumentalized in order to depreciate the value of concrete labor in music production.Artificial Intelligence in Music, Arts, and Theory Revisited["Sr\u0111an Atanasovski"]2024-07-23T00:00:00 0bf4c2cab9a947113812cc3267fa0926069951bdБизнес. Образование. Право00Бизнес. Образование. Право["\u0418.\u0412. \u041a\u0430\u0441\u043f\u0430\u0440\u043e\u0432"]2024-07-23T00:00:00 a2e14e057be74be438e8dfbf0fb4277cd2707323Current Dermatology Reports00Current Dermatology Reports["Fatuma-Ayaan Rinderknecht", "Lotanna Nwandu", "R. Daneshjou", "Jenna Lester"]2024-07-23T00:00:00 ae71c6aa151340ecc6e83ea4bea8d0f386d6fc46Journal of Diabetes Science and Technology50Journal of diabetes science and technology["L. Borges", "M. S. Barreto", "R. S. Santos", "E. E. D. Silva", "D. M. R. R. Silva", "P. H. M. Moura", "P. Jesus", "J. B. D. Souza", "L. A. D. M. Santana", "Adriana Gibara Guimar\u00e3es"]2024-07-23T00:00:00 8ada0497f962d74fcfd83507b757ce266c49ede5RELC Journal : A Journal of Language Teaching and Research in Southeast Asia30RELC Journal["Joel C. Meniado"]2024-07-23T00:00:00 5a58d40d8736dd3ca414f94589a1f1a1e531cacfAlthough large language models (LLMs) and other artificial intelligence systems demonstrate cognitive skills similar to humans, such as concept learning and language acquisition, the way they process information fundamentally differs from biological cognition. To better understand these differences, this article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science. It aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs. To achieve this goal, Psychomatics will rely on a comparative methodology, starting from a theory-driven research question-is the process of language development and use different in humans and LLMs?-drawing parallels between LLMs and biological systems. Our analysis shows how LLMs can map and manipulate complex linguistic patterns in their training data. Moreover, LLMs can follow Grice's Cooperative principle to provide relevant and informative responses. However, human cognition draws from multiple sources of meaning, including experiential, emotional, and imaginative facets, which transcend mere language processing and are rooted in our social and developmental trajectories. Moreover, current LLMs lack physical embodiment, reducing their ability to make sense of the intricate interplay between perception, action, and cognition that shapes human understanding and expression. Ultimately, Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence, both artificial and biological. Moreover, by drawing parallels between LLMs and human cognitive processes, Psychomatics can inform the development of more robust and human-like artificial intelligence systems.Cyberpsychology, Behavior, and Social Networking440This article introduces Psychomatics, a multidisciplinary framework bridging cognitive science, linguistics, and computer science that aims to delve deeper into the high-level functioning of LLMs, focusing specifically on how LLMs acquire, learn, remember, and use information to produce their outputs.Cyberpsychology, behavior and social networking["Giuseppe Riva", "F. Mantovani", "B. Wiederhold", "Antonella Marchetti", "A. Gaggioli"]2024-07-23T00:00:00 9f78fcf6e9b8c0b051a43353fc06b32411e2093cThis integrative literature review investigates the transformative impact of artificial intelligence (AI) on supply chain management, addressing the pressing need for efficiency and robustness through AI-driven predictive maintenance, machine learning (ML), and decision support systems. By examining current literature, the study highlights AI's potential to automate and revolutionize supply chain operations, enhancing speed, accuracy, and risk management capabilities while identifying significant challenges such as bias mitigation, algorithmic transparency, and data privacy. The methodology involves a comprehensive review of scholarly articles, reports, and academic publications, focusing on AI applications in predictive maintenance, risk mitigation, and decision-making processes. The analysis reveals significant improvements in operational efficiency and accuracy due to AI, alongside concerns about biases, transparency, and implementation issues. The findings confirm AI's transformative potential in supply chain management but emphasize the necessity for ongoing supervision, regular audits, and the development of AI models capable of detecting and rectifying operational anomalies. The study proposes creating roles such as AI Supply Chain Oversight Officer (AISCO), AI Supply Chain Compliance Officer (AISCCO), and AI Supply Chain Quality Assurance Officer (AISQAO) to ensure responsible AI utilization, maintaining the integrity and efficiency of supply chain operations while addressing implementation challenges. The review concludes that AI is promising for transforming supply chains; however, careful implementation is crucial to uphold operational integrity and resilience. Future research should prioritize longitudinal studies to evaluate AI's long-term impact, focus on addressing implementation concerns, and ensure fair and transparent integration of AI technologies. These findings have significant implications for practice and policy, underscoring the need for robust frameworks and regulatory measures to guide the effective use of AI in supply chains.International Journal For Multidisciplinary Research684The review concludes that AI is promising for transforming supply chains; however, careful implementation is crucial to uphold operational integrity and resilience, underscoring the need for robust frameworks and regulatory measures to guide the effective use of AI in supply chains.International Journal For Multidisciplinary Research["Rachid Ejjami", "Khaoula Boussalham"]2024-07-23T00:00:00 cb2420f8e642682f51bbab932866324757f34be4BACKGROUND Traditional assessments often lack flexibility, personalized feedback, real-world applicability, and the ability to measure skills beyond rote memorization. These may not adequately accommodate diverse learning styles and preferences, nor do they always foster critical thinking or creativity. The inclusion of Artificial Intelligence (AI), especially Generative Pre-trained Transformers, in medical education marks a significant shift, offering both exciting opportunities and notable challenges for authentic assessment practices. Various fields, including anatomy, physiology, pharmacy, dentistry, and pathology, are anticipated to employ the metaverse for authentic assessments increasingly. This innovative approach will likely enable students to engage in immersive, project-based learning experiences, facilitating interdisciplinary collaboration and providing a platform for real-world application of knowledge and skills. METHODS This commentary paper explores how AI, authentic assessment, and Student-as-Partners (SaP) methodologies can work together to reshape assessment practices in medical education. RESULTS The paper provides practical insights into effectively utilizing AI tools to create authentic assessments, offering educators actionable guidance to enhance their teaching practices. It also addresses the challenges and ethical considerations inherent in implementing AI-driven assessments, emphasizing the need for responsible and inclusive practices within medical education. Advocating for a collaborative approach between AI and SaP methodologies, the commentary proposes a robust plan to ensure ethical use while upholding academic integrity. CONCLUSION Through navigating emerging assessment paradigms and promoting genuine evaluation of medical knowledge and proficiency, this collaborative effort aims to elevate the quality of medical education and better prepare learners for the complexities of clinical practice.Postgraduate medical journal442This commentary paper explores how AI, authentic assessment, and Student-as-Partners (SaP) methodologies can work together to reshape assessment practices in medical education and proposes a robust plan to ensure ethical use while upholding academic integrity.Postgraduate medical journal["S. Fatima", "Nabeel Ashfaque Sheikh", "Athar Osama"]2024-07-23T00:00:00 0773cc8fcef8afa71c2b754c3d165c83c66e78b2PurposeDespite the use of plagiarism-checking software and current ethical guidelines in sport management journals, raising awareness of ethical concerns and potential risks of artificial intelligence (AI) applications is necessary. This paper discusses how AI affects ethical research and publishing and provides guidelines for sport management scholars to ensure quality and integrity of their research.Design/methodology/approachA comprehensive review and critical analysis of literature was performed to evaluate research ethics, potential risks, and guiding principles for the use of AI in research.FindingsEthical research guidelines for quantitative sport management research were proposed. The guidelines encompass seven principles for the proper use of AI and ethical conduct specific to the research methods, data analysis, and results, which would be challenging for AI to accurately replicate.Originality/valueThis study provides an original contribution to the field of sport management because numerous questions concerning ethics and AI have not been addressed until now. The guidelines are suitable for use by sport management scholars, concerning the accuracy, validity, and quality of research while mitigating ethical risks in AI-generated content.International Journal of Sports Marketing & Sponsorship501Ethical research guidelines for quantitative sport management research encompass seven principles for the proper use of AI and ethical conduct specific to the research methods, data analysis, and results, which would be challenging for AI to accurately replicate.International Journal of Sports Marketing and Sponsorship["Galen T. Trail", "Ari Kim", "Hyejin Bang", "Jessica R. Braunstein-Minkove"]2024-07-23T00:00:00 d9511862d05129578479381dbfd4523f8db3ccb0Discover Global Society101The perspectives of 11 employees from the field of German engineering and their views on AI are summarized, focusing not on the technical aspect of AI but rather on the employees’ requirements regarding their work.Discover Global Society["Amelie Tihlarik"]2024-07-23T00:00:00 2626eaf2fc4bbd464cecdb3a4ba3eecf7a4f3906In an era of uncertainty, organizations have had to navigate a complex and dynamic business environment. This may have a positive or negative impact on their functions and activities. The integration of artificial intelligence techniques into business processes may provide some leeway for better management. This study is an attempt to find out how predictive analytics, an artificial intelligence (AI) tool, helps businesses enhance their performance by anticipating future trends, risks, and opportunities. This prediction would enable businesses to make critical strategic decisions that may lead to a competitive advantage. Predictive analytics uses AI technologies such as machine learning and data mining to provide organizations with a powerful tool for extracting actionable insights from voluminous data and making future predictions. This study investigates the execution, influence, and challenges of predictive analytics within organizational contexts. It examines the potential of predictive analytics to optimize operational efficiency, mitigate risks, identify market trends, and drive innovation. Through an extensive review of existing literature and theoretical frameworks, this study aims to elucidate the mechanisms through which predictive analytics, as an AI-enabled tool, can empower organizations to make more informed and proactive decisions. By shedding light on the transformative potential of predictive analytics, this research seeks to inform organizational leaders, policymakers, and industry stakeholders about the strategic implications of leveraging AI-driven predictive analytics for improved decision-making.2024 Multimedia University Engineering Conference (MECON)241The potential of predictive analytics to optimize operational efficiency, mitigate risks, identify market trends, and drive innovation, and the mechanisms through which predictive analytics, as an AI-enabled tool, can empower organizations to make more informed and proactive decisions are examined.2024 Multimedia University Engineering Conference (MECON)["C. Gupta", "V. V. R. Kumar"]2024-07-23T00:00:00 c927d56a1c01552441f9f050b445d4c074350d16Artificial Intelligence (AI) is transforming sustainable business strategies globally, yet its specific applications within American enterprises remain underexplored. This study examines the integration of AI in sustainability efforts across various industries in the USA from 2014 to 2022. By analyzing 263 sustainability reports from 41 leading Nasdaq-listed firms using advanced text mining techniques, we uncover nuanced insights into how AI is employed to address environmental and social challenges. Our findings reveal a strategic deployment of AI not only to enhance operational efficiency, but also to drive significant environmental improvements, such as optimizing renewable energy usage and mitigating emissions. Additionally, AI’s impact extends to fostering workplace safety, enhancing diversity, and bolstering community initiatives. This research highlights the critical role of AI as a catalyst in advancing sustainable practices, providing a blueprint for other regions and industries aiming to leverage technology for greater sustainability.Sustainability07Analysis of sustainability reports from 41 leading Nasdaq-listed firms using advanced text mining techniques reveals a strategic deployment of AI not only to enhance operational efficiency, but also to drive significant environmental improvements.Sustainability["Yavuz Selim Balc\u0131o\u011flu", "Ahmet Alkan \u00c7elik", "Erkut Alt\u0131nda\u011f"]2024-07-24T00:00:00 24204dff17e69ab26601610a6c9808fef6897e49Mob. Networks Appl.223The AI tools used to analyse carbon footprinting in the construction sector in terms of selected functionalities are analyzed to form the basis for the development of a strategic plan for the development of AI within the research activities at the Faculty of Civil Engineering in Košice.Mob. Networks Appl.["P. M\u00e9s\u00e1ro\u0161", "J. Smetankov\u00e1", "A. Beh\u00fanov\u00e1", "K. Krajn\u00edkov\u00e1"]2024-07-24T00:00:00 cbe2e8a3aaef346327a0500f07aea4f5e6132e72BMC Primary Care751While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding.BMC Primary Care["E. Siira", "Daniel Tyskbo", "Jens Nygren"]2024-07-24T00:00:00 30227624c83199121047f7021c812c645d51a134PurposeThe present study undertakes an extensive review of the causes of service failures in artificial intelligence (AI) technology literature.Design/methodology/approachA hybrid review has been employed which includes descriptive analysis, and bibliometric analysis with content analysis of the literature approach to synthesizing existing research on a certain topic. The study has followed the SPAR-4-SLR protocol as outlined by Paul et al. (2021). The search period encompasses the progression of service failure in AI from 2001 to 2023.FindingsFrom identified theories, theoretical implications are derived, and thematic maps direct future research on topics such as data mining, smart factories, and among others. The key themes are being proposed incorporates technological elements, ethical deliberations, and cooperative endeavours.Originality/valueThis research study makes a valuable contribution to understanding and reducing service defects in AI by providing insights that can inform future investigations and practical implementations. Six key future research directions are derived from the thematic and cluster discussions presented in the content analysis.Asia Pacific Journal of Marketing and Logistics641An extensive review of the causes of service failures in artificial intelligence (AI) technology literature finds six key future research directions are derived from the thematic and cluster discussions presented in the content analysis.Asia Pacific Journal of Marketing and Logistics["Ritika Chopra", "Seema Bhardwaj", "Park Thaichon", "Kiran Nair"]2024-07-24T00:00:00 cf71eedbaf59438c656edaacc8fcdab2e49dffa7This research sought to explore educators’ views on Artificial Intelligence (AI), a topic that has become increasingly important with the advent of recent digital transformations. Given its potential impact on education, AI can offer valuable insights for curriculum planning and teaching strategies. The study used metaphor analysis to understand educators' perspectives on AI. An online questionnaire was employed to collect data from teachers working in schools affiliated with the Ministry of National Education in Şanlıurfa in the 2023-2024 academic year. Teachers were asked to complete the sentences about their perceptions of artificial intelligence, especially using expressions such as "Artificial intelligence is like ..." and "...because ...". According to the findings of the metaphor analysis, teachers conceptualized AI as a job facilitator, associating it with robots and machines representing cognitive intelligence. However, concerns also emerged about the potential risks of AI and its impact on creativity. The findings emphasized the complex perceptions of AI in education, showcasing the balance between its positive contributions and the ethical responsibilities it entails. While the study offers valuable insights for understanding the complexity of AI in the educational context, it also highlights the various metaphors teachers use to describe this technology. In this context, prominent metaphors used by teachers to describe artificial intelligence include human, robot, brain, assistant, and machine.Sakarya University Journal of Education221The findings emphasized the complex perceptions of AI in education, showcasing the balance between its positive contributions and the ethical responsibilities it entails.Sakarya University Journal of Education["Hasan Celal Bal\u0131k\u00e7\u0131", "Mustafa Alps\u00fcl\u00fcn", "G\u00fclseren Hayo\u011flu"]2024-07-24T00:00:00 cad64409c140f3870f5158d1a8f0417df4c427faInterpretive artificial intelligence (AI) tools are poised to change the future of radiology. However, certain pitfalls may pose particular challenges for optimal AI interpretative performance. These include anatomic variants, age-related changes, postoperative changes, medical devices, image artifacts, lack of integration of prior and concurrent imaging examinations and clinical information, as well as the satisfaction-of-search effect. Model training and development should account for such pitfalls, to minimize errors and optimize interpretation accuracy. More broadly, AI algorithms should be exposed to diverse and complex training data sets, to yield a holistic interpretation that considers all relevant information beyond the individual examination. Successful clinical deployment of AI tools will require that radiologist end-users recognize these pitfalls and other limitations of the available models. Furthermore, developers should incorporate explainable AI techniques (e.g., heat maps) into their tools, to improve radiologists' understanding of model outputs and to enable radiologists to provide feedback for guiding continuous learning and iterative refinement. In this article, we provide an overview of common pitfalls that radiologists may encounter when using interpretive AI products in daily practice. We present how such pitfalls lead to AI errors and offer potential strategies that AI developers may use for their mitigation.AJR. American journal of roentgenology371An overview of common pitfalls that radiologists may encounter when using interpretive AI products in daily practice is provided, to present how such pitfalls lead to AI errors and offer potential strategies that AI developers may use for their mitigation.AJR. American journal of roentgenology["Shima Behzad", "Seyed Mohammad Hossein Tabatabaei", "Max Yang Lu", "Liesl S. Eibschutz", "Ali Gholamrezanezhad"]2024-07-24T00:00:00 3ea68d5e6fe8412def053486f574b0b81e6830ceUniversities worldwide, particularly public universities in Uganda are facing a dilemma in which their massification has far outstripped the growth of their academic service delivery capacity, especially their actual teaching staff size. Consequently, most lecturers are struggling with heavy teaching workloads resulting from large class sizes of 100 to 300 or more students created by massification per course unit, especially at the undergraduate level. These workloads have overstretched most lecturers’ ability to teach effectively and limited their career growth by keeping them too busy to conduct research and participate in community service. The dilemma is faced at the time when Industry 4.0 has developed Artificial Intelligence (AI), which can execute different tasks, including teaching tasks in much the same way as human beings perform them. Drawing on the AI job replacement theory complemented by UTAUT and TOE, this study employed a cross-sectional questionnaire survey involving 325 respondents (deans, heads of department [HODs] and lecturers) randomly selected from five randomly selected public universities to analyse awareness of the teaching tasks AI can execute to reduce faculty members’ workload without replacing them, acceptance of AI to perform these tasks, and hindrances to its adoption. Findings from the descriptive analysis indicate that at least 74% of the deans, HODs, and lecturers were highly aware of the teaching tasks AI can perform. Most of these respondents accept AI to perform such teaching tasks that do not involve a human touch as an online search for research and lecture content, lecture dictation, student assessment and evaluation, and grading of marks. They, however, did not accept AI to execute teaching tasks that involve the human touch such as lecture planning, facilitating tutorials and discussions, assessing students’ interpersonal weaknesses that affect learning, and feedback provision. These findings allude to a need to adopt AI to execute only the teaching tasks it is accepted to perform and leave to the lecturers all the tasks they do not accept to perform. Adopting AI this way is bound to relieve the teaching workload allocated to lecturers as massification intensifies. The findings indicate, however, that AI adoption is hindered by different factors, including lack of strategic, ethical, and policy guidelines, and lack of funds and skills required to operate it. These findings point to a need for the management of Uganda’s public universities to adopt AI by lobbying the government for more funding, mobilizing necessary funds internally, training faculty members in using AI, and encouraging all of them to accept it by explaining the role it is capable of playing in reducing workloads and erasing their fear that AI could replace themEast African Journal of Education Studies00A need for the management of Uganda’s public universities to adopt AI by lobbying the government for more funding, mobilizing necessary funds internally, training faculty members in using AI, and encouraging all of them to accept it by explaining the role it is capable of playing in reducing workloads and erasing their fear that AI could replace them is pointed to.East African Journal of Education Studies["Edith Namutebi"]2024-07-24T00:00:00 1c9a628b32ba282196aa6e50852445664265bd0dThis scoping review examines the concept of trust in nursing and its potential application in developing trustworthy Artificial Intelligence (AI) for healthcare. Recognizing nurses as highly trusted professionals, the study explores how attributes contributing to trust in nursing can inform AI development. Following the Joanna Briggs Institute framework, the review synthesizes literature on patients' perceptions of nurses' trustworthiness and compares these with desired qualities in trustworthy AI. Preliminary findings suggest that nursing's trust-inducing actions could offer valuable insights for implementing trust-enhancing features in AI. This approach aims to bring innovative insights into the nature of trust and contribute to creative solutions to develop trustworthy AI in healthcare. By aligning AI development with principles of trust observed in nursing, the review proposes novel strategies for creating more ethical and accepted AI systems in healthcare settings.Nursing Informatics00It is suggested that nursing's trust-inducing actions could offer valuable insights for implementing trust-enhancing features in AI, and novel strategies for creating more ethical and accepted AI systems in healthcare settings are proposed.Studies in health technology and informatics["C. Ronquillo", "Richard G Booth", "Winnifred Adzo Vittor", "Isabella Mendoza", "Natasha Wood", "Olivia Gomes van Berlo", "Ryan Chan", "Chantelle Recsky"]2024-07-24T00:00:00 100124e350e5f70eb751c3649d6122decbbb1043The 21st century has seen rapid changes in educational practices, mainly due to technological advancements such as artificial intelligence. Especially in today's digital age, technology is essential in transforming education. One prominent innovation is the use of AI in the context of learning. This research aims to discover prospective teachers' learning processes using AI. In addition, this research seeks to determine the position of AI in learning mathematics for prospective teachers. The participants are Mathematics Education students of Universitas Swadaya Gunung Jati (UGJ) Cirebon. This qualitative study uses phenomenological methods by collecting data from each research subject about experiences regarding the use of AI in solving several math problems. This research provides insight into how prospective teachers can utilize AI technology in the mathematics learning process. In addition, this research is essential because it contributes to the development of technology-based pedagogy, a significant trend in global education today. This research uses data collection methods through observation, interviews, and content. The results of this study are that 3 paradigms from previous research are in line with the theory of 3 paradigms: (1) AI-directed students as recipients, (2) AI-supported students as collaborators, and (3) AI-empowered students as leaders. Most participants in this study were collaborators in these three paradigms.International Journal of Educational Research Excellence (IJERE)120This research provides insight into how prospective teachers can utilize AI technology in the mathematics learning process and contributes to the development of technology-based pedagogy, a significant trend in global education today.International Journal of Educational Research Excellence (IJERE)["Syfa Nurfadilah", "Gifa Nur Arofah", "Toto Subroto", "Tonah"]2024-07-24T00:00:00 8fdf43d0f89e54d749a8704c361878221bff722dEndocrine170The AI model based on CT images and clinical data successfully realized automatic scoring of ocular motility in TED patients, thus facilitating clinical application and potentially enhanced the efficiency and accuracy of ocular motility evaluation.Endocrine["Zijia Liu", "Kexin Tan", "Haiyang Zhang", "Jing Sun", "Yinwei Li", "S. Fang", "Jipeng Li", "Xuefei Song", "Huifang Zhou", "Guangtao Zhai"]2024-07-24T00:00:00 122649be12c21d14506b98d7d799a950f4cdb108We aimed to understand nursing informaticists' perspectives on key challenges, questions, and opportunities for the nursing profession as it prepares for an era of healthcare delivery enriched by artificial intelligence (AI). We found that nursing practice is currently, and will continue to be, directly influenced by AI in healthcare. Educating and training nurses so that they may safely and effectively use AI in their clinical practice and engage in implementation planning and evaluation will help overcome predicted challenges. Defining the key tenets of AI literacy for nurses and re-envisioning nursing models of care in the context of AI-enriched healthcare are important next steps for nursing informaticists. If embraced, AI has the potential to support the existing nursing workforce in the context of major shortages and augment the safe and high-quality care that nurses can deliver.Nursing Informatics00It is found that nursing practice is currently, and will continue to be, directly influenced by AI in healthcare.Studies in health technology and informatics["Meghan Reading Turchioe", "Christianna Pepingco", "Kay Lytle", "Robin Austin"]2024-07-24T00:00:00 c50dcad5d7e31d13df3789e2569c99f828d3cdf8Artificial Intelligence (AI) has become a useful tool for the police in curbing traffic violations and improving road safety. This research aims to examine how AI is applied to positive law in Indonesia. By focusing on positive law, the study provides a focused analysis of AI's impact on existing legal statutes and regulations in Indonesia, potentially examining case studies, legal precedents, or specific AI tools used within the Indonesian legal framework. This geographical and jurisdictional focus offers unique insights into the challenges and opportunities presented by AI within the legal framework, facilitating comparisons with AI in other countries or regions. The findings offer valuable information for policymakers, legal practitioners, and technologists on effectively integrating AI into the legal system. This article specifies that electronic equipment may be utilized to facilitate the prosecution of infractions in the realm of traffic and road transportation, and the resultant data obtained through this equipment can serve as admissible evidence in legal proceedings.Interdiciplinary Journal and Hummanity (INJURITY)170Electronic equipment may be utilized to facilitate the prosecution of infractions in the realm of traffic and road transportation, and the resultant data obtained through this equipment can serve as admissible evidence in legal proceedings.Interdiciplinary Journal and Hummanity (INJURITY)["Robertus Wardhana Utama", "Lamijan Lamijan", "Tri Susilowati"]2024-07-24T00:00:00 8c1386d2166c7107d78741093447969265179b8cWith the rapid advancement of technology and the integration of artificial intelligence in various aspects of life, students' approach to academic tasks is evolving. This study aims to analyze the effect of using AI on changes in habits in completing academic assignments. The type of research used is descriptive quantitative with multiple Linear Regression method.  In this study, the sample was drawn with cluster random sampling of 400 people who had used AI which was divided into 16 faculties at Hasanuddin University. The data collection method in this study uses a questionnaire distributed online via kuesio.id which contains statements, interviews and documentation by searching the internet. In testing data analysis techniques, namely validity and reliability tests calculated using the SPSS (Statistical Product and Service Solutions) program with Cronbach Alpha (α) above 0.60, so the overall statement is declared reliable. The results of this study indicate that the literacy level of Hasanuddin University students is categorized as good with a respondent achievement rate (TCR) of 78.3% and the use of AI that is often used from the 15 applications listed by researchers, namely ChatGPT by 26.7%, Perplexity 12.7%, and Grammarly 12.6%. The results of data analysis show that there is a positive correlation between technological literacy and the ability to use AI with changes in habits in completing academic tasks. Students who have high literacy and are skilled in utilizing AI tend to be more efficient and effective in completing their tasks. International Journal of Religion60There is a positive correlation between technological literacy and the ability to use AI with changes in habits in completing academic tasks, and students who have high literacy and are skilled in utilizing AI tend to be more efficient and effective in completing their tasks.International Journal of Religion["Nurfadilah Syafiuddin", "A. A. Unde", "Muh. Akbar"]2024-07-24T00:00:00 4e23bd86b40234a6bd4dba209245b0f6bb501b01There is a pressing necessity to shift our economy, society, and culture to systems and actions that promote ecological sustainability. This radical transformation necessitates an equally radical transformation of resource utilization and decision-making strategies. Sustainable entrepreneurship (SE) is frequently touted as the solution to the triple-bottom-line challenges that businesses encounter; however, there are tangible constraints on its potential. SE is currently in the first phase of implementing technological frontier tools that provide empirical guidance throughout the entrepreneurial decision-making process. The potential for artificial intelligence (AI) to inform decision-making is advanced by Big Data (BD), which also establishes pathways to attain desired outcomes. The interactions between AI, BD, and SE have been generally under-studied thus far. The absence of work that consolidates and synthesizes this literature is the primary focus of this conceptual paper. We propose that AI and BD are capable of rapidly contributing to the continued sustainable development of the weak form, but they also hold significant potential for attaining the strong sustainability ideal. We present two proposals for the integration of AI and BD to inform and facilitate SE. Finally, we outline potential areas for future research. The core of human cosmology and ethics has always been the definition of his uniqueness. He ceased to be the species situated at the center of the universe, accompanied by the sun and stars, with the arrival of Copernicus and Galileo. He ceased to be the species that was created and specially endowed by God with soul and reason with the arrival of Darwin. With Freud, he ceased to be the species whose behavior could potentially be regulated by the rational mind. He has ceased to be the species that is uniquely capable of complex, intelligent manipulation of his environment as we begin to produce mechanisms that think and learn.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300It is proposed that AI and BD are capable of rapidly contributing to the continued sustainable development of the weak form, but they also hold significant potential for attaining the strong sustainability ideal.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Rula Abu Shanab"]2024-07-24T00:00:00 29c028c9ca645f4144f953ab151ce664f0da49dcThe American Association of Colleges of Nursing (AACN) is shifting the nursing education paradigm to competency-based education. Competency-based nursing education focuses on the nursing students' demonstration of knowledge. This shift in nursing education will rely on performance measures and data to determine success. Data and artificial intelligence can provide the nursing student with a targeted evaluation from the nursing faculty. Building on prior applications of "precision" and the use of data to drive precision medicine or precision nursing, nursing informatics is well-positioned for this paradigm shift to support data and artificial intelligence in nursing education. Little is understood about the concept of "precision nursing education" and its implications. We completed a concept analysis and defined the concept of "precision nursing education" as the use of data and artificial intelligence to measure student nurses' performance with competency-based nursing education.Nursing Informatics00A concept analysis was completed and the concept of "precision nursing education" was defined as the use of data and artificial intelligence to measure student nurses' performance with competency-based nursing education.Studies in health technology and informatics["Jane M Carrington", "Rene Love", "Michael D. Bumbach", "Michael A Maymi", "Nigel Newbutt"]2024-07-24T00:00:00 5250d74434e83dda1b42079abc26a8c458dc0db7In the current environment of the Artificial Intelligence of Things(AIoT), the necessity to develop efficient cyber attack detection systems is essential. In this regard, this paper introduces a hybrid framework which takes advantage of the feature extraction capabilities of Convolutional Neural Networks and the prediction abilities of Logistic Regression. Throughout analysis, our model has shown an accuracy of 92%, while both precision and F1-scores have reached 0.94 and 0.93, respectively.2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT)80A hybrid framework which takes advantage of the feature extraction capabilities of Convolutional Neural Networks and the prediction abilities of Logistic Regression is introduced.2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT)["Brij B. Gupta", "Akshat Gaurav", "Varsha Arya", "Kwok Tai Chui"]2024-07-24T00:00:00 1967a175045d8ddc93e9bd1b646a1bd6595e2bd8BACKGROUND To measure the success of three different artificial intelligence chatbots, ChatGPT, Bard, and Bing, in correctly answering questions about glaucoma types and treatment modalities and to examine their superiority over each other. MATERIALS AND METHODS Thirty-two questions about glaucoma types and treatment modalities were asked using the ChatGPT, Bard, and Bing chatbots. The correct and incorrect answers were also provided. Accuracy rates were compared. OUTCOMES Questions asked: ChatGPT answered 56.3%, Bard 78.1%, and Bing 59.4% correctly. There was no statistically significant difference between the three artificial intelligence chatbots in the rate of correct and incorrect answers to the questions asked (p = 0.195). CONCLUSION Artificial intelligence chatbots can be used as a tool to access accurate information regarding glaucoma types and treatment modalities. However, the information obtained is not always accurate, and care should be taken when using this information.Klinische Monatsblätter für Augenheilkunde140Artificial intelligence chatbots can be used as a tool to access accurate information regarding glaucoma types and treatment modalities, however, the information obtained is not always accurate, and care should be taken when using this information.Klinische Monatsblatter fur Augenheilkunde["Ey\u00fcpcan \u015eensoy", "Mehmet Citirik"]2024-07-24T00:00:00 ceb243d5de34f6f5b2aa2d6e1858b3289e266ceaPurpose This paper aims to explore explainable artificial intelligence (XAI) in democracy, proposing an applicable framework. With artificial intelligence’s (AI) increasing use in democracies, the demand for transparency and accountability in AI decision-making is recognized. XAI addresses AI “black boxes” by enhancing model transparency. Design/methodology/approach This study includes a thorough literature review of XAI. The methodology chosen was design science research to enable design theory and problem identification about XAI’s state of the art. Thereby finding and gathering crucial information to build a framework that aims to help solve issues and gaps where XAI can be of major influence in the service of democracy. Findings This framework has four main steps to be applied in the service of democracy by applying the different possible XAI techniques that may help mitigate existing challenges and risks for the democratic system. The proposed artifact intends to display and include all the necessary steps to select the most suitable XAI technology. Examples were given for every step of the artifact to provide a clear understanding of what was being proposed. Originality/value An evaluation of the proposed framework was made through interviews with specialists from different areas related to the topics in the study. The interviews were important for measuring the framework’s validity and originality. Transforming Government: People, Process and Policy210This framework has four main steps to be applied in the service of democracy by applying the different possible XAI techniques that may help mitigate existing challenges and risks for the democratic system.Transforming Government: People, Process and Policy["Marta Sofia Marques da Encarnacao", "Maria Anastasiadou", "Vitor Santos"]2024-07-24T00:00:00 d1e51e3c8e7f86cb067479ffbe52a8bc0646acf4The rapid adoption of artificial intelligence (AI) is transforming the financial industry. AI will either increase systemic financial risk or act to stabilise the system, depending on endogenous responses, strategic complementarities, the severity of events it faces and the objectives it is given. AI's ability to master complexity and respond rapidly to shocks means future crises will likely be more intense than those we have seen so far.340Future crises will likely be more intense than those the authors have seen so far because AI's ability to master complexity and respond rapidly to shocks means future crises will likely be more intense.["Jon Danielsson", "A. Uthemann"]2024-07-24T00:00:00 5421e030b6ae91ea4b71df37f3ed7982e07b1e6cThis article examines the use of artificial intelligence for early career guidance of schoolchildren. Various areas of career guidance and ways of using artificial intelligence algorithms are analyzed, the problems that schools and parents may face when using artificial intelligence and ways to overcome them are highlighted. The article also provides examples of various resources that can help students at the initial stage of choosing a future profession.Standards and Monitoring in Education00The use of artificial intelligence for early career guidance of schoolchildren and the problems that schools and parents may face when using artificial intelligence and ways to overcome them are highlighted.Standards and Monitoring in Education["A. Neustupov"]2024-07-24T00:00:00 10e7e43a3462d198ed74c31daf9c10eea481f2e8Nurses continue to face challenges in leading health information technology innovations such as Artificial Intelligence (AI). There is an acknowledged need to explore the attitude of nurses towards AI and nurses' acceptance of AI in clinical settings. We sought to address this gap in knowledge about the perceptions of AI by nursing-related professionals in their work and as a content area in the education of nursing students. Norwegian nurses and healthcare personnel interested in the topic met in a seminar in Oslo in 2023 to explore their perspectives on AI. Following a lecture on AI, audience members offered their insights in a recorded discussion. Data analysis consisted of inductive coding of concepts in the transcribed recording. Three major themes emerged: Expectations, Identified needs and related recommendations; and Policy to guard safety; as well as recommendations new to the literature.Nursing Informatics00This work sought to address the gap in knowledge about the perceptions of AI by nursing-related professionals in their work and as a content area in the education of nursing students about the perceptions of AI.Studies in health technology and informatics["Paulina S Sockolow", "E. B\u00f8r\u00f8sund", "R. Helles\u00f8"]2024-07-24T00:00:00 ebd5c13a1aa636a1cf18117fddd66fda4892cfb2Digital technology innovations, particularly in artificial intelligence (AI), have the potential to significantly benefit agriculture. Agriculture is increasingly being held accountable for ensuring food security and safety while also taking into account environmental concerns. AI in the agricultural sector has the potential to feed a continuously growing global population while also contributing to achieving the UN's Sustainable Development Goals (SDGs). This was the right time to analyse and explore the artificial intelligence in agriculture. The present study was conducted in Dindigul district of Tamil Nadu. The findings indicated that farmers had moderately favourable level of attitude towards AI in agriculture. Farmers had positive attitude towards AI in agriculture because AI-enabled systems make weather predictions, monitor agricultural sustainability, and assess farms for the presence of diseases or pests and undernourished plants using data like temperature, precipitation, wind speed, and sun radiation in conjunction with photographs taken by satellites and drones.Asian Journal of Agricultural Extension, Economics &amp; Sociology00Farmers had positive attitude towards AI in agriculture because AI-enabled systems make weather predictions, monitor agricultural sustainability, and assess farms for the presence of diseases or pests and undernourished plants using data like temperature, precipitation, wind speed, and sun radiation in conjunction with photographs taken by satellites and drones.Asian Journal of Agricultural Extension, Economics &amp; Sociology["R. Priyanka"]2024-07-24T00:00:00 f109f0d9d78685fe2426862502398513c61d872aPractical healthcare increasingly faces the issues of transparency, objectivity and accountability of the artificial intelligence algorithms, prejudiced results offered by the artificial intelligence which depend on the degree of limitation of the data sets used for computer learning, on possible mistakes, respect for the patients’ rights, ethical standards.Medical Bulletin of the Ministry of Internal Affairs40Medical Bulletin of the Ministry of Internal Affairs["O. Mahova"]2024-07-24T00:00:00 68426ee63036ac969509bf546d57000a9fd2ef93Electronic health records (EHRs) provide the most important data sources for artificial intelligence (AI). Gaining access to quality data suitable for advanced analytics continues to be challenging. This rapid review documents the current state of available data; identifies foundational AI data/information needs; and explores the benefits of adopting new and emerging technologies to design and implement next-generation EHRs. Opportunities to optimize EHRs for AI purposes are identified. This review was informed by expert knowledge and shared experiences supported by the literature, including technical standards. Main findings include poor ecosystem-wide infrastructures due to the lack of adopting the right set of standards, and current data and knowledge governance no longer fit for purpose. While many jurisdictions are continuing the use of legacy systems, some forward-looking national health systems and health-care facilities are adopting transformational strategies by adopting a strong data and digital focus to transition to new-generation systems. New foundational-level national infrastructures with strong leadership and governance are essential to enhance the governance and quality of available data, from collection at source throughout the entire data supply chain. Secure and ubiquitous access to high-quality EHR data at scale will foster the evolution of more intelligent and trustworthy AI. Key characteristics of next-generation EHRs supported by currently available technologies and standards that are able to meet digital era demands are provided in this paper. We conclude that the use of generative AI in clinical settings can only be reliably achieved when EHRs are optimized throughout the entire global digital health ecosystem.Artificial Intelligence in Health00It is concluded that the use of generative AI in clinical settings can only be reliably achieved when EHRs are optimized throughout the entire global digital health ecosystem.Artificial Intelligence in Health["Evelyn J. S. Hovenga", "K. Atalag"]2024-07-24T00:00:00 a4c76001ecbfb8a7bebcac53e025209b166108cfMarine Pollution Bulletin734AI can detect, locate, and even predict aquatic contaminants like oil fingerprinting, oil spills, oil spill damage, oil slicks, forecasting marine water quality, water quality development, harmful algal blooms, benthic sediment toxicity, as well as detection of marine debris with high accuracy.Marine pollution bulletin["A. Zare", "Nurgul Ablakimova", "A. Kaliyev", "N. Mussin", "N. Tanideh", "F. Rahmanifar", "Amin Tamadon"]2024-07-24T00:00:00 4c0b637d6d820f2234d256e500e2bc3ff2ead4e7International Journal of Artificial Intelligence in Education1283International Journal of Artificial Intelligence in Education["Andr\u00e9 Coy", "Phaedra S. Mohammed", "Paulson Skerrit"]2024-07-24T00:00:00 7c6a8f9495ad53fe822a63c8dc47ef7bb1bea74fAI and Ethics321AI and Ethics["K. Snyder", "R. A. Stewart", "Catherine J. Hunter"]2024-07-24T00:00:00 d661c086eec4a99bafa61f232db48c126c4d4436International Journal of Clinical Pharmacy332International journal of clinical pharmacy["Kreshnik Hoti", "A. Weidmann"]2024-07-24T00:00:00 dbc607ebfd578dc644df3f9f53063009a89744adInternational Journal for Quality in Health Care01International journal for quality in health care : journal of the International Society for Quality in Health Care["Phillip Phan", "Sonali Desai", "Ezequiel Garcia Elorio", "David Greenfield", "Recce Hinchcliff", "Usman Iqbal", "Paul O'Connor", "Anthony Staines", "Rosa Su\u00f1ol", "Aziz Sheikh"]2024-07-24T00:00:00 662640d8e52660a176fa27610c68316a40b30cdeIn this essay, I argue that explicit ethical machines, whose moral principles are inferred through a bottom-up approach, are unable to replicate human-like moral reasoning and cannot be considered moral agents. By utilizing Alan Turing's theory of computation, I demonstrate that moral reasoning is computationally intractable by these machines due to the halting problem. I address the frontiers of machine ethics by formalizing moral problems into 'algorithmic moral questions' and by exploring moral psychology's dual-process model. While the nature of Turing Machines theoretically allows artificial agents to engage in recursive moral reasoning, critical limitations are introduced by the halting problem, which states that it is impossible to predict with certainty whether a computational process will halt. A thought experiment involving a military drone illustrates this issue, showing that an artificial agent might fail to decide between actions due to the halting problem, which limits the agent's ability to make decisions in all instances, undermining its moral agency.arXiv.org00It is argued that explicit ethical machines, whose moral principles are inferred through a bottom-up approach, are unable to replicate human-like moral reasoning and cannot be considered moral agents and it is demonstrated that moral reasoning is computationally intractable by these machines due to the halting problem.ArXiv["Massimo Passamonti"]2024-07-24T00:00:00 aa617f7fc99a6f49ffe66f5b411f16600ba370b5To construct a robot intelligent discharge follow-up platform and explore its application effects in clinical discharge follow-up scenarios Applying intelligent voice technology to build a robot intelligent discharge follow-up platform, replacing nurses in completing telephone calls, interactive communication, feedback collection, and information input during follow-up. Compare the work efficiency and manpower investment between robots and manual follow-up. Compared with the manual telephone follow-up method, the application of robot intelligent discharge follow-up platform resulted in a higher follow-up rate, fewer follow-up hours and nurse manpower. The robot intelligent discharge follow-up platform can release nursing manpower and time, improve the follow-up rate of discharged patients.Nursing Informatics00Compared with the manual telephone follow-up method, the application of the robot intelligent discharge follow-up platform resulted in a higher follow-up rate, fewer follow-up hours and nurse manpower.Studies in health technology and informatics["Wenping Mao", "Jinkai Luo"]2024-07-24T00:00:00 2a1b8aaf0b2000bed6817928a79b88cacafe131aRecently, the research community of computerized medical imaging has started to discuss and address potential fairness issues that may emerge when developing and deploying AI systems for medical image analysis. This chapter covers some of the pressing challenges encountered when doing research in this area, and it is intended to raise questions and provide food for thought for those aiming to enter this research field. The chapter first discusses various sources of bias, including data collection, model training, and clinical deployment, and their impact on the fairness of machine learning algorithms in medical image computing. We then turn to discussing open challenges that we believe require attention from researchers and practitioners, as well as potential pitfalls of naive application of common methods in the field. We cover a variety of topics including the impact of biased metrics when auditing for fairness, the leveling down effect, task difficulty variations among subgroups, discovering biases in unseen populations, and explaining biases beyond standard demographic attributes.arXiv.org430A variety of topics are covered including the impact of biased metrics when auditing for fairness, the leveling down effect, task difficulty variations among subgroups, discovering biases in unseen populations, and explaining biases beyond standard demographic attributes are covered.ArXiv["Enzo Ferrante", "Rodrigo Echeveste"]2024-07-24T00:00:00 a715ce4050bf217699d8ed202edb6bfd528a063aBreast cancer is the second leading cause of death in the world and the age of diagnosis is younger and younger. The research is aimed to make a prediction model related to environmental hormones and breast cancer incidence. First, we analyzed lab data to figure out the risk factor of breast cancer. By using Chi-square, Neural network and logistic regression, we find out that Lead, Copper, Zinc, Mercury, Chromium, Chloramphenicol, Sulfonamides, Penicillin and metabolites of phthalates MEP, MBHP related to incidence of breast cancer. These risk factors will be verified by questionnaire of daily habit survey of breast cancer patients. We will establish the relationship between breast cancer and environmental hormones and make public attention to risks of environmental hormones.Nursing Informatics00The relationship between breast cancer and environmental hormones is established and public attention to risks of environmental hormones is made to make public attention to risks of environmental hormones.Studies in health technology and informatics["Pei-Hung Liao"]2024-07-24T00:00:00 d422e6fc0772257edabfd77d7691345877aa59d9AJR. American journal of roentgenology00AJR. American journal of roentgenology["Jan Vosshenrich"]2024-07-24T00:00:00 7798112bb0be22797bb42d318d3820c46399ebddInternational journal of scientific and research publications70International Journal of Scientific and Research Publications["Hassan khawaji", "Hazem bahkali", "Fayez Alasmari", "Abeer Mubaraki", "Fahad Noshaili", "Rayan Alnamei", "Hussain El Sharif", "Mohammed H Alfahmi", "Ahmed Alfifi", "Abdurahman Almalki", "Abdulelah Shamakhi"]2024-07-24T00:00:00 8e9d0f55bf30dde8230b7ed50f9ea5521eea0c19The goal of the “Heart Disease Predictor” project is to create an AI-powered tool for cardiovascular disease (CVD) early detection and prevention. User requests are analysed through surveys and field trips, resulting in a comprehensive product specification. To deliver accurate CVD risk assessments, sophisticated machine learning algorithms are used and carefully evaluated against established risk assessment techniques. Additionally, the project aims to increase public knowledge of cardiovascular health and provides recommendations for preventive actions. The expected effects include lower mortality, better health outcomes, more efficient use of healthcare resources, and continuous breakthroughs in research.International Conference on Advanced Infocomm Technology140The goal of the “Heart Disease Predictor” project is to create an AI-powered tool for cardiovascular disease early detection and prevention, and to increase public knowledge of cardiovascular health and provides recommendations for preventive actions.2024 Second International Conference on Advances in Information Technology (ICAIT)["K. Pavithra", "Tinka Singh", "A.Raja Nandana Reddy", "Chinmay J Reddy", "SyedTaahir Ahamed"]2024-07-24T00:00:00 8f3b4237bc66409c062856d8a1b2b5e8fbd0e2f5Aim: This study aims to perform a comprehensive bibliometric analysis of academic publications on AI applications in vascular surgery, identifying key authors, influential journals, prevalent research themes, and international collaborations, focusing on infrastructure, conceptual structure, and social networks within the field. Material and Methods: The analysis covers 815 documents published from 2020 to 2024, retrieved from the Web of Science Core Collection database. Metrics analyzed include publication growth, citation rates, key contributors, leading journals, prevalent themes, and international collaborations. Results: The research output showed a 15% annual growth rate, peaking in 2023. Despite increasing publications, the average citation rate per article declined. The study identified 5039 contributors with significant international co-authorship. Leading authors included Lareyre F and Raffort J, and the "Journal of Vascular Surgery" was the most influential journal. The USA and China led in contributions, reflecting robust research infrastructure. Key themes include risk assessment, diagnostic methods, and patient management, highlighting AI's role in enhancing diagnostic accuracy, treatment planning, and patient outcomes in vascular surgery. Conclusion: The analysis highlights the rapid growth and collaborative nature of AI research in vascular surgery. Key contributors, influential journals, and emerging themes were identified, emphasizing AI's role in improving diagnostics and patient outcomes. Limitations include the focus on one database and a five-year period, suggesting future research should include more databases and a longer timeframe. Exploring high-impact studies and practical applications will further advance the field.Turkish Journal of Vascular Surgery00A comprehensive bibliometric analysis of academic publications on AI applications in vascular surgery identifies key authors, influential journals, prevalent research themes, and international collaborations, focusing on infrastructure, conceptual structure, and social networks within the field.Turkish Journal of Vascular Surgery["U. Demirk\u0131l\u0131\u00e7", "Burcu Tosun"]2024-07-24T00:00:00 da35fef77c8e68cba66c9d9f1e8f7df8dff13e0000["Luntian Mou"]2024-07-24T00:00:00 975f90cb250170d95d40f5282290636c3648b371With the advent of intelligence-of-everything in 6G, the explosion of Internet of Things (IoT) devices will bring massive volumes of data. Though injecting artificial intelligence (AI) technologies into IoT, namely AI of things (AIoT), can release the data value, it still faces many challenges, such as poor efficiency, lack of trust, etc. As a promising technology with decentralization, scalability, and security, leveraging blockchain in AIoT has attracted widespread attention in recent years. However, isolatability and scalability are two critical issues that hinder blockchain from building efficient, secure, and trusted intelligence-sharing platforms for AIoT. Blockchain interoperability addresses isolatability between heterogeneous blockchains, while sharding-based blockchain achieves scalability by solving isolatability of homogeneous blockchains running in different shards. To ensure atomicity between different blockchains, both blockchain interoperability and sharding-based blockchain need to enhance the resistance to double-spending attacks of the blockchain to which the cross-chain transaction originator belongs. To this end, this work presents a two-tier multi-zone consensus, where Consensus Zone in tier-1 features blockchain interoperability and sharding-based blockchain by allowing different blockchains run in different zones, while Coordination Layer in tier-2 is to enhance each zone’s security by allowing them to interoperate as a Direct Acyclic Graph (DAG) in a distributed and parallel manner. Then, stochastic models are used to capture the consensus process and analyze the probability of successful double-spending attacks. From the numerical results, this consensus is proved to be with enhanced security.2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT)170This work presents a two-tier multi-zone consensus, where Consensus Zone in tier-1 features blockchain interoperability and sharding-based blockchain by allowing different blockchains run in different zones, while Coordination Layer in tier-2 is to enhance each zone’s security by allowing them to interoperate as a Direct Acyclic Graph in a distributed and parallel manner.2024 IEEE Annual Congress on Artificial Intelligence of Things (AIoT)["Weikang Liu", "Bin Cao", "Mugen Peng"]2024-07-24T00:00:00 4a7ef20c1ab00149b4240fa9be166f49626cf523 Purpose In today’s highly competitive world, the purpose of this research is to emphasize the increasing significance of management education and advocate for the adoption of innovative teaching approaches, specifically focusing on artificial intelligence (AI)-driven personalized learning (PL). This study aims to explore the integration of self-determination theory (SDT) principles into management education, with a primary focus on enhancing student motivation, engagement and academic performance (AP). Design/methodology/approach This interdisciplinary research adopts a multifaceted approach, combining perspectives from AI, education and psychology. The design and methodology involve a thorough exploration of the theoretical foundations of both AI-driven education and SDT. The research demonstrates how these two elements can synergize to create a holistic educational experience. To substantiate the theoretical claims, empirical data-driven analyses are employed, showcasing the effectiveness of AI-enabled personalized learning (AIPL). The study integrates principles from SDT, such as autonomy, competence and relatedness, to create an environment where students are intrinsically motivated, receiving tailored instruction for optimal outcomes. Findings The study, rooted in SDT, demonstrates AIPL’s transformative impact on management education. It positively influences students’ autonomy, competence and relatedness, fostering engagement. Autonomy is a key driver, strongly linked to improved AP. The path analysis model validates these relationships, highlighting AI’s pivotal role in reshaping educational experiences and intrinsically motivating students. Practical implications This study holds substantial significance for educators, policymakers and researchers. The findings indicate that the AIPL model is effective in increasing student interest and improving AP. Furthermore, this study offers practical guidance for implementing AI in management education to empower students, enhance engagement and align with SDT principles. Originality/value Contribute original insights through an interdisciplinary lens. Synthesize AI and SDT principles, providing a roadmap for a more effective educational experience. Empirical data-driven analyses enhance credibility, offering valuable contributions for educators and policymakers in the technology-influenced education landscape. Vilakshan - XIMB Journal of Management545The study demonstrates AIPL’s transformative impact on management education, and offers practical guidance for implementing AI in management education to empower students, enhance engagement and align with SDT principles.Vilakshan - XIMB Journal of Management["Adil Ellikkal", "S. Rajamohan"]2024-07-24T00:00:00 1a62f919044e0abd764e64600d05300d5568d91bEuropean Radiology Experimental343This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a “machines-in-the-loop” approach.European Radiology Experimental["F. Pesapane", "Renato Cuocolo", "Francesco Sardanelli"]2024-07-24T00:00:00 1e25df14996ff751f606e32b84183adbcfcbdd27With the rapid advancement of artificial intelligence (AI) technologies, higher education institutions are increasingly exploring innovative ways to rethink teaching and assessment practices. This research paper examines the implications of AI on assessments in online learning environments. Specifically, the objectives of this study were to evaluate the effectiveness of AI-powered teaching methodologies in enhancing student engagement and learning outcomes in online education settings and, secondly, to analyze the impact of AI-driven assessment tools on the accuracy, reliability, and fairness of evaluating student performance in online learning environments through a systematic review and meta-analysis of existing literature. The study adopted activity theory to understand the issues around AI and assessment. The study adopted a mixed-methods design. The study adopted the use of meta-analysis in order to statistically combine results from multiple studies on a particular topic to provide a more comprehensive and reliable summary of the overall findings. The study found that to guarantee moral and just practices, there are issues with the integration of AI in online learning that need to be resolved. Key issues included data privacy, algorithmic prejudice, and the role of human instructors in the administration of the assessments online, carefully considered and addressed in a proactive manner. These findings provided insights on how AI can transform traditional teaching methods and assessment strategies, creating an AI-crowded environment that fosters student learning and academic success. Based on the findings, the study recommends that there is a need to integrate pedagogical strategies that leverage AI innovation, such as adaptive learning approaches, real-time feedback mechanisms, or interactive simulations, to improve teaching effectiveness and student performance in online settings.African Journal of Empirical Research372It is recommended that there is a need to integrate pedagogical strategies that leverage AI innovation, such as adaptive learning approaches, real-time feedback mechanisms, or interactive simulations, to improve teaching effectiveness and student performance in online settings.African Journal of Empirical Research["J. Lyanda", "S. Owidi", "A. M. Simiyu"]2024-07-24T00:00:00 e3ce15672e8bb13680befca0ebbb7f3384886ba6The integration of Artificial Intelligence (AI) into education is transforming traditional administrative processes and instructional methodologies. This paper examines the pivotal role of Intelligent Tutoring Systems (ITS) in enhancing education administration through AI-powered solutions. By automating routine tasks, providing real-time analytics, and supporting data-driven decision-making, ITS contribute to more efficient and effective management of educational institutions.We explore several models that illustrate the diverse applications of ITS in administration, including automated workflow management, data-driven decision-making, personalized learning support, resource optimization, and enhanced student support services. Each model demonstrates the potential of ITS to streamline operations, improve resource allocation, and offer personalized educational experiences, thereby reducing administrative burdens and fostering a more adaptive and responsive educational environment.Through a comprehensive analysis of current implementations and case studies, this paper highlights the transformative impact of AI-powered ITS on education administration. The findings underscore the importance of careful implementation and continuous evaluation to address challenges such as data privacy and ethical considerations, ensuring that the benefits of AI are equitably distributed among all stakeholders in the educational ecosystem. International Journal of Religion172The findings underscore the importance of careful implementation and continuous evaluation to address challenges such as data privacy and ethical considerations, ensuring that the benefits of AI are equitably distributed among all stakeholders in the educational ecosystem.International Journal of Religion["Pham Bich Thuy", "Pham Dao Tien"]2024-07-24T00:00:00 109bb0005122ae0624eadc81633567f9b21d4a54Context. In less than a year practitioners and researchers witnessed a rapid and wide implementation of Generative Artificial Intelligence. The daily availability of new models proposed by practitioners and researchers has enabled quick adoption. Textual GAIs capabilities enable researchers worldwide to explore new generative scenarios simplifying and hastening all timeconsuming text generation and analysis tasks. Motivation. The exponentially growing number of publications in our field with the increased accessibility to information due to digital libraries makes conducting systematic literature reviews and mapping studies an effort and timeinsensitive task Stemmed from this challenge we investigated and envisioned the role of GAIs in evidencebased software engineering. Future Directions. Based on our current investigation we will follow up the vision with the creation and empirical validation of a comprehensive suite of models to effectively support EBSE researchersarXiv.org221This work investigated and envisioned the role of GAIs in evidencebased software engineering, and envisioned the creation and empirical validation of a comprehensive suite of models to effectively support EBSE researchers.ArXiv["Mattel Esposito", "Andrea Janes", "Davide Taibi", "Valentina Lenarduzzi"]2024-07-24T00:00:00 d191cba1543c1ed7a67d3de054fd91916ddf3887Communications Medicine741It is demonstrated that AI-based CDSS can improve the accuracy of remote strep throat screening yet underscores the necessity to enhance human–machine collaboration, particularly in trust and intelligibility.Communications Medicine["Catalina Gomez", "Brittany-Lee Smith", "Alisa Zayas", "Mathias Unberath", "Therese Canares"]2024-07-24T00:00:00 9e2abf20c7c50aeca9cb5c2d687c59a365a77242In today's rapidly evolving digital landscape, the volume of enterprise data has surged exponentially, posing significant challenges in effective data management. Traditional data management techniques are becoming increasingly inadequate to handle the complexity and scale of modern enterprise data. This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations. By leveraging advanced artificial intelligence technologies, such as machine learning, natural language processing, and predictive analytics, our proposed system aims to streamline data processing, ensure data quality, and provide real-time insights. This paper will discuss the limitations of existing data management systems, illustrate the novel methodologies integrated within our AI-driven framework, and demonstrate the system's efficacy through empirical results. The transformative potential of AI in automating data management processes not only addresses current challenges but also sets a foundation for future advancements in the field. As enterprises strive to maintain a competitive edge, the adoption of AI-powered automation for data management is not merely an option but a necessity for sustaining growth and innovation.International Journal of Innovative Science and Research Technology151This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations by leveraging advanced artificial intelligence technologies.International Journal of Innovative Science and Research Technology (IJISRT)["Priyanka Neelakrishnan"]2024-07-24T00:00:00 02e38d23caef9346d734258f452f275637b2400aThe impact generated by artificial intelligence (AI) on companies radically transforms current business dynamics. In this work, we sought to identify the benefits of the implementation of this revolutionary technology in companies. Its development and execution achieves notable improvements in operational efficiency, decision making, continuous innovation and service personalization, the latter being a crucial factor for customer satisfaction. To identify these benefits, different research documents were used, where it was established, that artificial intelligence is fundamental to the success of a company, because it not only generates benefits with the automation and optimization of routine tasks, but also achieves the most efficient use. of resources, generating a significant reduction in operating costsManagement6011This work sought to identify the benefits of the implementation of this revolutionary technology in companies, where it was established that artificial intelligence is fundamental to the success of a company, because it not only generates benefits with the automation and optimization of routine tasks, but also achieves the most efficient use.Management (Montevideo)["Andrea Valentina Ca\u00f1\u00f3n Solano", "Luz Daniela Cardona Arboleda", "Claudia Cristina Coral Garc\u00eda", "Cristian David Carmona Dominguez"]2024-07-25T00:00:00 e3351141c5854d3255f413f69f6424ed32ad6e73Human talent and artificial intelligence have been closely related, having a great impact on the performance and productivity of today's organizations. In this research work, we sought to identify the challenges posed by the implementation of artificial intelligence tools in human talent management, such as data privacy, discrimination and automated decision making, through the review of scientific literature, this as the main objective. To develop it, sources of research articles, magazines and previous research carried out on the topic in the last ten years were used, with which it was possible to identify the use of AI for the selection and retention of human talent, the development of skills and skills, in addition to benefiting the well-being of collaborators; but also disadvantages such as its impact on privacy and the growing concern about job replacement. Concluding, to take full advantage of the benefits and minimize the problems associated with AI in human talent, it is necessary to have clear and transparent regulations, encouraging collaboration and development of knowledge in employees and ensuring ethics in the use of AI. within the organizationMultidisciplinar (Montevideo)6810To take full advantage of the benefits and minimize the problems associated with AI in human talent, it is necessary to have clear and transparent regulations, encouraging collaboration and development of knowledge in employees and ensuring ethics in the use of AI.Multidisciplinar (Montevideo)["Julio Cesar Gama Espinosa", "Lina Mar\u00eda Leiva S\u00e1nchez", "Melisa Andrea Fajardo Pereira"]2024-07-25T00:00:00 2e5de5a0ded3f00599745bd5662da5299e64d4baThis article reviews the advances, benefits and contributions of artificial intelligence to business organizations, without ignoring the need for skilled human labor and talent that companies have always required and which is essential for the economic development of a company. The implementation of legal frameworks and regulations that will in turn address issues such as organizational privacy, cybersecurity and liability are important aspects to be addressed in this article. Thus, this paper focuses on the compilation of scientific articles, books and other texts in which important topics were evidenced such as (i). - Innovation and invention, (ii). - Big Data, (iii). -Implementation of artificial intelligence in organizations, and (iv). -legality and responsibility of decisions, likewise, it examines the legal challenges of AI and highlights that smart contracts are a technological innovation that allows secure, fast and low-risk transactions using blockchain technology, this is why the literature review aims to explore how technology, artificial intelligence and communication tools can facilitate the work of the population, decreasing the time spent on some activitiesMultidisciplinar (Montevideo)886The literature review aims to explore how technology, artificial intelligence and communication tools can facilitate the work of the population, decreasing the time spent on some activities.Multidisciplinar (Montevideo)["Diana Paola Amaya Amado", "Fabian Andr\u00e9s C\u00e1rdenas Diaz", "Roci\u00f3 del Pilar Cabrera Pantoja", "Lina Mar\u00eda Bastidas Sanchez"]2024-07-25T00:00:00 33cea9e505b67ed694b4386ae84bdf1569db59a4The place of artificial intelligence (AI) and strategic management (SM) in the success of any projects cannot be overemphasized. This study critically explores the place of AI and SM in the attainment of success in national and international projects in the US and beyond, drawing evidence from previous studies. Relying on secondary data, drawn from the internet and subjected to a critical analytic exposition and thematic systematic review, the study shows that AI and SM play multifaceted functions that guarantee the success of projects. The paper concludes that once deployed judiciously, AI and SM have the potentials of fostering the success of different national and international projects. The implication of the findings is that AI and SM can be used in combination for more results, as in to attain significant successes in managing national and international projects as well as business and other activities/affairs. It recommends judicious adoption and application of the two in project management for the attainment of any desired results and successes in inter/national projects in the US and beyond.Journal of Engineering Research and Reports06The study shows that AI and SM play multifaceted functions that guarantee the success of projects and recommends judicious adoption and application of the two in project management for the attainment of any desired results and successes in inter/national projects in the US and beyond.Journal of Engineering Research and Reports["Bharadwaj Thuraka", "Vikram Pasupuleti", "Saiteja Malisetty", "Kenneth O Ogirri"]2024-07-25T00:00:00 616dfc142e02b07b5730495f3f0537953ee042daThis paper examines the strategic implementation of Artificial Intelligence (AI) in South African Higher Education (HE) institutions and its potential opportunities and challenges. It posits that AI can significantly enhance educational outcomes and administrative efficiency in South African HE institutions, but successful integration necessitates addressing infrastructure limitations, ethical concerns, and strategic frameworks. The study employs a qualitative research methodology using secondary sources. Findings reveal substantial benefits, such as improved administrative efficiency, personalized learning, and data-driven decision-making, often impeded by challenges like inadequate infrastructure, socio-economic disparities, and ethical issues related to data privacy and algorithmic bias. The importance of strategic planning and frameworks, such as the AI8-Point Model, is emphasized for effective AI integration in HE. Recommendations include investing in technological infrastructure, developing policies for ethical and privacy concerns, and adopting strategic frameworks. Collaboration among policymakers, educators, and technology providers is essential to navigate AI integration complexities and enhance educational outcomes and operational efficiency in South African HE.Technium Education and Humanities453It is posits that AI can significantly enhance educational outcomes and administrative efficiency in South African HE institutions, but successful integration necessitates addressing infrastructure limitations, ethical concerns, and strategic frameworks, but successful integration necessitates addressing infrastructure limitations, ethical concerns, and strategic frameworks.Technium Education and Humanities["Shahiem Patel", "M. Ragolane"]2024-07-25T00:00:00 7648084a040841757e90a41d8f9a5a1228fdd851Purpose This study aims to investigate the moderating role of natural language processing natural language processing (NLP) on the relationship between AI-empowered AIS (data gathering, data analysis, risk assessment, detection, prevention and Investigation) and auditing and fraud detection. Design/methodology/approach Quantitative methodology was adapted through a questionnaire. In total, 221 individuals represented the population of the study, and SPSS was used to screen primary data. The study indicated the acceptance of the hypothesis that “Artificial Intelligence in AIS has a statistically significant influence on auditing and fraud detection,” showing a strong correlation between auditing and fraud detection. The study concluded that NLP moderates the relationship between AI in AIS and auditing and fraud detection. Findings The study’s implications lie in its contribution to the development of theoretical models that explore the complementary attributes of AI and NLP in detecting financial fraud. Research limitations/implications A cross-sectional design is a limitation. Practical implications NLP is a useful tool for developing more efficient methods for detecting fraudulent activities and audit risks. Originality/value The study’s originality stems from its focus on the use of AI-empowered AIS, a relatively new technology that has the potential to significantly impact auditing and fraud detection processes within the accounting field. The International Journal of Organizational Analysis383The study concluded that NLP moderates the relationship between AI in AIS and auditing and fraud detection, showing a strong correlation between auditing and fraud detection.International Journal of Organizational Analysis["A. Qatawneh"]2024-07-25T00:00:00 762aad56033f280dd2249b69901e26f48576b77bArtificial intelligence (AI) has shown transformative potential in various medical fields, including diagnostic imaging. Recent advances in AI-driven technologies have opened new avenues for improving echocardiographic practices. AI algorithms enhance the image quality, automate measurements, and assist in the diagnosis of cardiovascular diseases. These technologies reduce manual errors, increase consistency, and match the diagnostic performances of experienced echocardiographers. AI in tele-echocardiography offers significant benefits, particularly in rural and remote regions in Japan, where healthcare provider shortages and geographic isolation hinder access to advanced medical care. AI enhances accessibility, provides real-time remote analyses, supports continuous monitoring, and improves the quality and efficiency of remotely delivered cardiac care. However, addressing challenges related to data security, transparency, integration into clinical workflows, and ethical considerations is essential for the successful implementation of AI in echocardiography. On overcoming these challenges, AI will be able to revolutionize echocardiography and ensure timely and effective cardiac care for all patients in the future.Internal medicine02Artificial intelligence in tele-echocardiography offers significant benefits, particularly in rural and remote regions in Japan, where healthcare provider shortages and geographic isolation hinder access to advanced medical care.Internal medicine["K. Kusunose"]2024-07-25T00:00:00 e03057b26ae37ea79538c80d9b3f3f8344e9b9daThe Agile methodology, with an 80% adoption rate, often faces challenges leading to project failures. This study investigates using artificial intelligence (AI) to overcome these challenges through a systematic literature review of 44 papers. It examines AI's impact on key Agile phases: envision, speculate, explore, adapt, and close. Findings highlight AI's critical role in improving project outcomes by addressing implementation challenges. AI tools aid in risk assessment and project selection during planning, enhance effort estimation and task allocation in speculation, improve team communication and technical issue resolution in exploration, optimize systems in adaptation, and provide valuable insights in closure. The paper offers guidance on effective AI integration to enhance Agile Project Management success.Indonesian Journal of Computer Science01This study examines AI's impact on key Agile phases: envision, speculate, explore, adapt, adapt, and close to highlight AI's critical role in improving project outcomes by addressing implementation challenges.The Indonesian Journal of Computer Science["Harry Leonardo Lumbanraja", "Teguh Raharjo", "Anita Nur Fitriani"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/e03057b26ae37ea79538c80d9b3f3f8344e9b9da a8de38dcc0881d7a9136c0ded80239a16cadb0a1International Urology and Nephrology381The current state of renal and CV risk prediction in CKD is reviewed, highlighting the limitations of traditional models and the potential for integrating artificial intelligence (AI) techniques.International Urology and Nephrology["Andrea Simeri", "Giuseppe Pezzi", "Roberta Arena", "Giuliana Papalia", "Tamas Szili-Torok", "Rosita Greco", "Pierangelo Veltri", "Gianluigi Greco", "Vincenzo Pezzi", "Michele Provenzano", "Gianluigi Zaza"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/a8de38dcc0881d7a9136c0ded80239a16cadb0a1 6c62624887548517db01718831a27cffa6c3590bWith the increasing maturity of big data capture, the application of artificial intelligence (Artificial Intelligence, AI) technology in various fields is expanding, from simple, information, digital to a more accurate and comprehensive intelligent development, which is especially reflected in the field of medical research. Medical education not only has the laws applicable to it in ordinary higher education, but also contains the special laws of medical education, which are mainly reflected in the characteristics of lifelong uninterrupted learning and the complexity of the courses learned. With the gradual promotion of AI-assisted intelligent education means, medical education has also ushered in new opportunities and challenges. The introduction of AI technology can not only greatly improve the level of medical education, but also maintain the long-term effect of students' learning. At present, AI is mainly applied in medical education to comprehensive curriculum analysis, assisted learning and learning interest and direction assessment. In the long-term development, AI technology still has great difficulties in being widely used in medical education, such as relative difficulties in practical evaluation, difficult barriers to its own technology, and many challenges in data security and medical ethics. However, we have reason to believe that in the near future, with the continuous development and improvement of science and technology, the role of AI in medical education will continue to increase, and it will play a more important role in promoting the development of medical education.Contemporary Education and Teaching Research00The role of AI in medical education will continue to increase, and it will play a more important role in promoting the development of medical education, with the continuous development and improvement of science and technology.Contemporary Education and Teaching Research["Ning Du", "Xin Sun", "Yunfeng Zhang"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/6c62624887548517db01718831a27cffa6c3590b 81bcaf1c84d51fe0c18d6d8b4d57610525d6a41dBlockchain provides a decentralised, tamper-proof and trustworthy distributed database technology that is widely used in finance and economics, IoT and big data. Artificial intelligence (AI) provides a technology that can mimic human intelligence, learn autonomously and automate decision-making, which plays a major role in enhancing productivity, solving complex problems and improving decision-making. The two represent two of the major driving forces in technology today, and their integration is redefining our digital world. The aim of this paper is to explore the integration of these two technologies and the innovations, challenges, and future prospects they bring. First, we trace their history and evolution, introduce the basic characteristics of blockchain and AI, and explain in detail how they work. We then delve into the integration of blockchain and AI, highlighting their importance and significance in areas such as finance, supply chain and healthcare. We analyse the applications and implications of this integration for these areas, as well as the challenges and dilemmas faced, including issues of security, privacy, data leakage, and technical feasibility. Finally, we explore future trends and related work, highlighting the importance of global community collaboration and innovation to realize the potential of blockchain and AI.Applied and Computational Engineering00The history and evolution of blockchain and AI are traced, the basic characteristics of blockchain and AI are introduced, and how they work are explained, highlighting their importance and significance in areas such as finance, supply chain and healthcare.Applied and Computational Engineering["Yiwen Wang"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/81bcaf1c84d51fe0c18d6d8b4d57610525d6a41d 135cb95d5bbf27e6deddc3f722116233342e9383INTRODUCTION. The subject of this study is achievements (results) of technology, or more precisely, products of artificial intelligence (intelligent machine; intelligent computer program, etc.) and their impact on the implementation of human rights through the prism of the human mind and in the context of the international legal, including the human rights dimension.MATERIALS AND METHODS. The scientific research is based on the work of both Russian and foreign specialists in the field of law, international law, international human rights law, as well as specialists in the regulation and use of artificial intelligence products. Documents and materials of international organizations, primarily the United Nations (UN), as well as national legal acts of the Russian Federation have been analyzed. In the preparation of the study, general scientific, comparative legal and specifically legal methods were used.RESEARCH RESULTS. Within the framework of the conducted research, the authors delved into the concept of the term “artificial intelligence” itself, and come to the conclusion that despite its widespread use by specialists in various fields, there is no single concept of this term at the moment. The authors analyzed the national legal framework of the Russian Federation directly or indirectly regulating the use of AI products, as well as international legal achievements in the regulation of this area (primarily at the universal (UN) and integration (European Union (EU)) levels), with a special emphasis on the implementation and observance of human rights and freedoms.DISCUSSION AND CONCLUSIONS. The authors came to the conclusion that the nature of the impact of artificial intelligence on the realization of human rights is dual. Already at the initial stage of defining the legal regime for the development and use of AI products weak mechanisms of control and responsibility in this sphere are laid down. The question arises as to which legal regime is preferable for states whose corporations are leading in the development of AI products. Despite existing international legal regulatory mechanisms, this protection is still insufficient.Moscow Journal of International Law70Moscow Journal of International Law["A. Abashidze", "M. Popovic"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/135cb95d5bbf27e6deddc3f722116233342e9383 ccbaf7135674d0eb65ed3ebd3dda85e81a22a376This research explores the current and future trends of Artificial Intelligence (AI) applications in the Thai medical field. Conducted through qualitative research involving 40 medical professionals, the study gauges awareness levels and opinions regarding AI in healthcare. Findings reveal a burgeoning interest, despite the temporary limitations posed by the COVID-19 pandemic. Key themes include the transformative potential of AI, the pivotal role of government policies, and ethical considerations. The research provides valuable insights for policymakers, medical professionals, and researchers navigating the evolving landscape of AI in Thai healthcare.International journal of social science and human research100This research explores the current and future trends of Artificial Intelligence applications in the Thai medical field through qualitative research involving 40 medical professionals, revealing a burgeoning interest, despite the temporary limitations posed by the COVID-19 pandemic.International Journal of Social Science and Human Research["Pongkit Ekvitayavetchanukul", "Thanawat Thanitnan", "Natnicha Akwittayavechnukul", "Wipatsaya Muenkiat"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/ccbaf7135674d0eb65ed3ebd3dda85e81a22a376 86e14eb0a352ebd9d3a26f55c3191b7440f3381aOBJECTIVES Breast cancer is a type of cancer caused by the uncontrolled growth of cells in the breast tissue. In a few cases, erroneous diagnosis of breast cancer by specialists and unnecessary biopsies can lead to various negative consequences. In some cases, radiologic examinations or clinical findings may raise the suspicion of breast cancer, but subsequent detailed evaluations may not confirm cancer. In addition to causing unnecessary anxiety and stress to patients, such diagnosis can also lead to unnecessary biopsy procedures, which are painful, expensive, and prone to misdiagnosis. Therefore, there is a need for the development of more accurate and reliable methods for breast cancer diagnosis. METHODS In this study, we proposed an artificial intelligence (AI)-based method for automatically classifying breast solid mass lesions as benign vs malignant. In this study, a new breast cancer dataset (Breast-XD) was created with 791 solid mass lesions belonging to 752 different patients aged 18 to 85 years, which were examined by experienced radiologists between 2017 and 2022. RESULTS Six classifiers, support vector machine (SVM), K-nearest neighbor (K-NN), random forest (RF), decision tree (DT), logistic regression (LR), and XGBoost, were trained on the training samples of the Breast-XD dataset. Then, each classifier made predictions on 159 test data that it had not seen before. The highest classification result was obtained using the explainable XGBoost model (X2GAI) with an accuracy of 94.34%. An explainable structure is also implemented to build the reliability of the developed model. CONCLUSIONS The results obtained by radiologists and the X2GAI model were compared according to the diagnosis obtained from the biopsy. It was observed that our developed model performed well in cases where experienced radiologists gave false positive results.Journal of ultrasound in medicine380An artificial intelligence (AI)-based method for automatically classifying breast solid mass lesions as benign vs malignant and it was observed that the developed model performed well in cases where experienced radiologists gave false positive results.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine["P. Oztekin", "O\u011fuzhan Katar", "T. Omma", "Serap Erel", "O\u011fuzhan Tokur", "Derya Avc\u0131", "Murat Aydo\u011fan", "Ozal Yildirim", "Engin Avci", "U. R. Acharya"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/86e14eb0a352ebd9d3a26f55c3191b7440f3381a c55acd7a569b6f859f2f15ffd76c71cb2c5f49b6The study on the social responsibility of small and medium enterprises (SMEs) in Vietnam through digital transformation and the application of artificial intelligence explored key aspects such as challenges faced during digital transformation, the importance of SMEs in the Vietnamese economy, and the significance of corporate social responsibility (CSR). It emphasized the need for SMEs to adapt to remain competitive and contribute more significantly to the state budget. The research highlighted the landscape of SMEs in Vietnam from 2017 to 2021, focusing on their classification, numbers, and characteristics, noting a steady increase in the number of SMEs each year. The document discussed the limited adoption of advanced technologies like artificial intelligence among Vietnamese SMEs and the need for increased support and resources for effective digital transformation, especially adopting AI technology. Additionally, it touched upon the social responsibility aspects of SMEs in the context of digital transformation, addressing opportunities and challenges related to environmental impact, labor productivity, financial transparency, and animal welfare. Through a qualitative analysis approach, the study aimed to provide insights into the evolving landscape of SMEs in Vietnam and their integration of digital technologies to enhance social responsibility practicesLatIA80LatIA["Duong Ngoc Anh", "Phan Minh Duc"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/c55acd7a569b6f859f2f15ffd76c71cb2c5f49b6 26b0368e628a2b826b3df86f6fdc75314f3ef177The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies across industries has accelerated transformative changes, particularly within the intellectual property (IP) landscape. This research examines the impact of AI and ML on intellectual property (IP) law, focusing on patent and copyright law. It identifies a need to explore legal frameworks in response to technological advancements further. The study includes a thorough literature review, a comparative analysis of international legal frameworks, and an in-depth review of case law and treaties. It explores questions of ownership in AI and ML-generated inventions in patent law and issues of authorship and fair use in copyright law. The findings are expected to significantly contribute to the discussion of AI, ML, and intellectual property law.2024 7th International Conference on Green Technology and Sustainable Development (GTSD)450This research examines the impact of AI and ML on intellectual property (IP) law, focusing on patent and copyright law, and identifies a need to explore legal frameworks in response to technological advancements further.2024 7th International Conference on Green Technology and Sustainable Development (GTSD)["Jamshid Kazimi", "Harshita Thalwal"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/26b0368e628a2b826b3df86f6fdc75314f3ef177 18f252acb06e959ea6a3667e1ca83c548b9cc79aDecisions based on artificial intelligence can reproduce biases or prejudices present in biased historical data and poorly formulated systems, presenting serious social consequences for underrepresented groups of individuals. This paper presents a systematic literature review of technical, feasible, and practicable solutions to improve fairness in artificial intelligence classified according to different perspectives: fairness metrics, moment of intervention (pre-processing, processing, or post-processing), research area, datasets, and algorithms used in the research. The main contribution of this paper is to establish common ground regarding the techniques to be used to improve fairness in artificial intelligence, defined as the absence of bias or discrimination in the decisions made by artificial intelligence systems.Education for Information430This paper presents a systematic literature review of technical, feasible, and practicable solutions to improve fairness in artificial intelligence classified according to different perspectives: fairness metrics, moment of intervention, research area, datasets, and algorithms used in the research.Educ. Inf.["Ant\u00f3nio Trigo", "Nubia Stein", "F. Belfo"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/18f252acb06e959ea6a3667e1ca83c548b9cc79a 5aaa02e3beea81d7bcaf6920bdb78b0a2ae8c0e0In this work, artificial intelligence methods are designed and adopted for evaluating various risk levels of thyroid hormone and blood pressure in humans. Fuzzy Logic (FL) method is firstly exploited to provide the risk levels. Additionally, a machine learning was proposed using the Adaptive Neuron- Fuzzy Inference System (ANFIS) to learn and assess the risk levels by fusing a multiple-layer Neural Network (NN) with the FL. The data are collected for standard risk levels from real medical centers. The results lead to well ANFIS design based on the FL, which can generate such interesting outcomes for predicting risk levels for thyroid hormone and blood pressure. Both proposed methods of the FL and ANFIS can be exploited for medical applications.International Journal of Electronics and Telecommunications290A machine learning was proposed using the Adaptive Neuron- Fuzzy Inference System (ANFIS) to learn and assess the risk levels by fusing a multiple-layer Neural Network (NN) with the FL.International Journal of Electronics and Telecommunications["Musab T. S. Al-Kaltakchi", "R. Al-Nima", "Azza Alhialy"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/5aaa02e3beea81d7bcaf6920bdb78b0a2ae8c0e0 8ee69c4a78660913ce3678b4dd36b276cb6483a3 Purpose The purpose of this paper is to provide evidence on the relationship between artificial intelligence (AI) and financial inclusion to achieve sustainable development goals (SDGs), an agenda set by United Nations for 2030. Financial inclusion is an enabler of 8 of the 17 SDGs. This paper emphasizes the introduction of AI in the financial sector, which is indispensable for achieving financial inclusion and plays a crucial role in the achievement of SDGs. Design/methodology/approach This study adopts qualitative research methodology to highlight the significance of AI in achieving high levels of financial inclusion in an economy. Both narrative and comparative approaches are used to provide empirical evidence for reaching the UN SDGs target through AI-assisted financial inclusion. Findings AI implementation in finance enables people to take part in the formal financial sector and thus, enhances economic growth and reduces poverty. Research limitations/implications This research is limited in its data. Only five top AI applications are chosen and comparison is made between two countries only. Future research should consider it as an established concept and include more data to strengthen the evidence. Practical implications The results of this paper will help policymakers convince governments and institutions to put their efforts toward AI implementation in financial infrastructure of countries. Originality/value This research is unique in providing real-life examples and cases demonstrating the significance of AI implementation in the financial sector. Recent literature lacks evidence on the relationship of AI, financial inclusion and SDGs. This study adds to the existing literature by compiling data on top AI applications and comparing the performance of countries in achieving financial inclusion with the help of AI. Qualitative Research in Financial Markets560This study adds to the existing literature by compiling data on top AI applications and comparing the performance of countries in achieving financial inclusion with the help of AI.Qualitative Research in Financial Markets["Anam Fazal", "Alia Ahmed", "Sagheer Abbas"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/8ee69c4a78660913ce3678b4dd36b276cb6483a3 f0eea37754e51c5d7707820191fc91775855a374AI &amp; SOCIETY650How should society evolve to keep pace with the transformative impact of the current AI technology wave?AI &amp; SOCIETY["Vuong-Quan Hoang", "Viet-Phuong La", "Hong-Son Nguyen", "Minh-Hoang Nguyen"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/f0eea37754e51c5d7707820191fc91775855a374 6691e0adcf511956033422ff7ec9d0515d9779f2ABSTRACT With artificial intelligence (AI) rapidly advancing, advanced practice nurses must understand and use it responsibly. Here, we describe an assignment in which Doctor of Nursing Practice (DNP) students learned to use generative text AI. Using our program and course outcomes, developed from the 2021 American Association of Colleges of Nursing (AACN) Essentials competency for DNP students to learn and use AI, we reviewed the literature seeking examples using ChatGPT for the DNP informatics course. No published examples existed to guide us toward infusing a ChatGPT assignment into the course. We developed a novel assignment that included a guide for students on how to use ChatGPT. Students were given time before the assignment to learn the AI/chatbot technology. They were then given the assignment and grading rubric. The assignment was to develop a tool for their current or future practice using ChatGPT. During the course faculty debrief, we learned that few students had questions and the assignment was clear. We also learned that students who sought to develop straightforward, uncomplicated patient tools succeeded with the technology. Those who sought to create something for complex patients had more challenges. Nursing education and practice will be influenced by the increasing prevalence of AI. This manuscript outlines an AI-based assignment for graduate nursing education intended for the students to become familiar with current AI and best practices for patient care. The assignment was well received by students. We plan to use it again in the next course offering.Journal of the American Association of Nurse Practitioners70An AI-based assignment for graduate nursing education intended for the students to become familiar with current AI and best practices for patient care is outlined, which was well received by students.Journal of the American Association of Nurse Practitioners["Michael D. Bumbach", "Jane M Carrington", "Rene Love", "Ragnhildur I. Bjarnadottir", "Hwayoung Cho", "Gail Keenan"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/6691e0adcf511956033422ff7ec9d0515d9779f2 7e794e81687a62033f7a8dddc06620fa093385efHIGHLIGHTS 1. The article highlights how Artificial Intelligence with Large Language Models (AI-LLMs) greatly improves diagnosis and treatment personalization in obstetrics & gynecology, and also enhances medical education through interactive simulations and up-to-date learning materials.2. The article also discusses the ethical issues linked to AI, emphasizing the need for cooperation among different stakeholders to use AI responsibly in medicine, focusing on protecting data privacy and minimizing reliance on technology.   ABSTRACT The introduction of Artificial Intelligence through Large Language Models (AI-LLM) into medicine holds great promise for improving patient care and medical education, especially in obstetrics and gynecology. AI-LLM can significantly improve diagnostic accuracy and treatment efficiency by utilizing large medical databases, which is especially useful for dealing with rare diseases that are difficult to document or understand by human practitioners alone. In addition, AI-LLM can provide informed patient care recommendations by analyzing large amounts of data and providing insights based on unique patient profiles, with the added benefit of being accessible 24/7 via the internet. This constant availability ensures that patients receive prompt information and assistance as needed. In the field of education, AI-LLMs enhance the learning experience by incorporating interactive simulations into the curriculum, improving medical students' and professionals' practical knowledge. They also ensure that educational materials are always up-to-date reflecting the most recent research and worldwide medical standards. This access latest information from global resources helps to bridge the educational gap, making advanced knowledge more accessible to learners regardless of their geographic location. However, the introduction of AI-LLMs is not without challenges. Ethical issues, such as data privacy and the risk of overreliance on technology, must be addressed. Effective management of these concerns necessitates collaboration among medical professionals, technological experts, academics, hospital committees, and representatives of patients. This multidisciplinary teamwork is vital for upholding ethical norms and preserving patient dignity and respect. AI-LLMs can considerably improve both patient care and medical education in obstetrics and gynecology provided they are appropriately balanced with innovation and ethics.Majalah Obstetri &amp; Ginekologi240How Artificial Intelligence with Large Language Models (AI-LLMs) greatly improves diagnosis and treatment personalization in obstetrics & gynecology, and also enhances medical education through interactive simulations and up-to-date learning materials is highlighted.Majalah Obstetri &amp; Ginekologi["K. E. Gumilar", "Ming-Liang Tan"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/7e794e81687a62033f7a8dddc06620fa093385ef 4d18f546b7af0ead2775b36647587656ec54ff14Abstract Background Clinical decision-making in oncology is a complex process influenced by numerous disease-related factors, patient demographics, and logistical considerations. With the advent of artificial intelligence (AI), precision medicine is undergoing a shift toward more precise and personalized care. Wearable device technology complements this paradigm shift by offering continuous monitoring of patient vitals, facilitating early intervention, and improving treatment adherence. The integration of these technologies promises to enhance the quality of oncological care, making it more responsive and tailored to individual patient needs, thereby enabling wider implementation of such applications in the clinical setting. Summary This review article addresses the integration of wearable devices and AI in oncology, exploring their role in patient monitoring, treatment optimization, and research advancement along with an overview of completed clinical trials and utility in different aspects. The vast applications have been exemplified using several studies, and all the clinical trials completed till date have been summarized in Table 2. Additionally, we discuss challenges in implementation, regulatory considerations, and future perspectives for leveraging these technologies to enhance cancer care and radically changing the global health sector. Key Messages AI is transforming cancer care by enhancing diagnostic, prognostic, and treatment planning tools, thus making precision medicine more effective. Wearable technology facilitates continuous, noninvasive monitoring, improving patient engagement and adherence to treatment protocols. The combined use of AI and wearables aids in monitoring patient activity, assessing frailty, predicting chemotherapy tolerance, detecting biomarkers, and managing treatment adherence. Despite these advancements, challenges such as data security, privacy, and the need for standardized devices persist. In the foreseeable future, wearable technology can hold significant potential to revolutionize personalized oncology care, empowering clinicians to deliver comprehensive and tailored treatments alongside standard therapy.Oncology00This review article addresses the integration of wearable devices and AI in oncology, exploring their role in patient monitoring, treatment optimization, and research advancement along with an overview of completed clinical trials and utility in different aspects.Oncology["Meghna Birla", "Rajan", "Prabhat Gautam Roy", "Ishaan Gupta", "P. Malik"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/4d18f546b7af0ead2775b36647587656ec54ff14 7b6a7310a1e815864b0b18f4287f3b500fd72dcaTaxes are an essential pillar of country's economic growth and sustainable development. Therefore, this study investigates the effect of algorithmic government, artificial intelligence, and tax services on tax compliance. This research uses a quantitative approach with a survey method. The sample was 393 taxpayers. The research instrument used was a questionnaire designed on a Likert scale with five response options. The questionnaire was distributed via the WhatsApp application and email in Google Form format. Data were analyzed using regression. The results found that algorithmic government, AI, and tax services, partially and simultaneously, affect tax compliance among taxpayers. These findings provide insight into how tax compliance can be improved through algorithmic government, AI, and tax services. Therefore, researchers and practitioners can discuss the findings of this research critically and in depth before adapting and adopting them in their future work without ignoring the limitations of this research.JPPI (Jurnal Penelitian Pendidikan Indonesia)00The results found that algorithmic government, AI, and tax services, partially and simultaneously, affect tax compliance among taxpayers, providing insight into how tax compliance can be improved through algorithmic government, AI, and tax services.JPPI (Jurnal Penelitian Pendidikan Indonesia)["Akbari Adha", "Rulinawaty Rulinawaty", "Faizal Madya"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/7b6a7310a1e815864b0b18f4287f3b500fd72dca 5371fd45f6b5ab07bf9707288a6b67c4c0d34ad6The article considers the use of artificial intelligence tools to improve the efficiency of teaching graphic disciplines. The article analyses the possibilities of AI for personalising the learning process, automated assessment, providing interactive tasks and real-time feedback. Such approaches can increase student motivation, improve the quality of learning outcomes, and make the learning process more adaptive to the individual needs of each student. Special attention is paid to the impact of AI on the development of professional skills of students of technical specialities. The use of interactive simulations and virtual environments controlled by artificial intelligence helps students to better master complex graphic and spatial concepts. Students have the opportunity to independently work on graphic tasks in a virtual environment that simulates real-world professional conditions, while receiving instant feedback from the system. This approach also promotes critical thinking and creativity in solving graphic problems. The article also discusses methodological approaches to the introduction of AI into curricula, in particular, the integration of new technologies into the educational process without disrupting traditional educational methods. It is noted that effective integration of AI requires an integrated approach, where technologies work in cooperation with the teacher, complementing traditional teaching methods and creating conditions for the development of the necessary competencies in students. The importance of an integrated approach to the use of artificial intelligence, where technologies complement traditional teaching methods, contributing to the development of graphic competence, is substantiated. Future research will be aimed at an in-depth study of ways to improve the teaching of graphic disciplines using artificial intelligence, as well as their integration into programmes for students with special educational needs.Health and Safety Pedagogy140The article analyses the possibilities of AI for personalising the learning process, automated assessment, providing interactive tasks and real-time feedback, and discusses methodological approaches to the introduction of AI into curricula.Health and Safety Pedagogy["Svitlana Kyrylashchuk", "A. Kolomiiets"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/5371fd45f6b5ab07bf9707288a6b67c4c0d34ad6 048e1ff00a51f2da1af210d19a6d7eb19648ea2dIn context of changes and the implementation of the public administration reform, the authors consider the strategic directions of the development of artificial intelligence in the legal system of the Republic of Serbia. This includes the legal framework, necessary infrastructure and interoperability, optimization and digitization of administrative procedures and public services. The impact of the fourth industrial revolution on aspects of public administration is considered and the significant progress of the Republic of Serbia in terms of innovation and social transformations in synergy with the concept of artificial intelligence is described. According to the artificial intelligence readiness index for 2022, out of 181 ranked countries, Serbia is in 59th place. The Republic of Serbia is implementing the Artificial Intelligence Development Strategy for the 2020-2025 period, it is among the 26 countries that launched the National Platform for Artificial Intelligence and is the first country in Southeast Europe to establish an Institute for Artificial Intelligence. Through the contemporary theory of modern administration and the concept and importance of e-Governance in the development of modern administration, the authors particularly discuss the ethical use of artificial intelligence in the legislative framework that promotes and regulates development. The foundation of e-Governance is regulated by a series of special laws, and the Law on Electronic Administration regulates the performance of public administration tasks using information and communication technologies. Therefore, the application of artificial intelligence in public administration belongs to the field of electronic administration, and the Law on General Administrative Procedure is of great importance for the application of e-administration institutes and rules. In addition to significant benefits, artificial intelligence also brings numerous risks in relation to human rights and freedoms, rights to privacy, rights to the protection of personal data, bias and discrimination, lack of transparency and accountability, uncertainty and unreliability, loss of human control and supervision, and so on. In this sense, the authors suggest the establishment of strong inspection, penal and appeal policy mechanisms. Altogether, it is important from the perspective of various preventive mechanisms that will enable accountable development and ways of verifying artificial intelligence in accordance with the highest ethical and security standards, and in terms of finding a fair balance between technological development and the protection of human rights and democratic principles. This paper points to the importance of future research in various areas of law in the field of artificial intelligence.MB University International Review480This paper suggests the establishment of strong inspection, penal and appeal policy mechanisms that will enable accountable development and ways of verifying artificial intelligence in accordance with the highest ethical and security standards, and the importance of future research in various areas of law in the field of artificial intelligence.MB University International Review["Marija Kosti\u0107", "Jovan Koji\u010di\u0107"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/048e1ff00a51f2da1af210d19a6d7eb19648ea2d 1ff521ff389f51e025c47daf26da41850fa7a0fbArtificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatments and streamlining administrative tasks through advanced algorithms and machine learning. This review examines AI’s impact across various areas, including medical imaging, diagnostics, personalized medicine, drug discovery, patient monitoring, and surgical procedures. AI’s capacity to analyze complex medical data improves clinical decision-making, predicts patient outcomes, and optimizes hospital operations. AI offers significant benefits, including reduced diagnostic errors and lower healthcare costs. The future of AI in healthcare promises further innovations, such as robotic-assisted surgery, virtual patient care via remote consultations, and advanced health monitoring with wearable devices. Embracing AI not only enhances patient outcomes but also transforms medical research and administrative efficiency, paving the way for a more accessible and effective global healthcare system. Ongoing research and regulatory oversight are essential to fully harness AI’s potential while ensuring ethical standards and patient safety.International Journal of Scientific Research in Science Engineering and Technology110A review of AI’s impact across various areas, including medical imaging, diagnostics, personalized medicine, drug discovery, patient monitoring, and surgical procedures, finds that AI offers significant benefits, including reduced diagnostic errors and lower healthcare costs.International Journal of Scientific Research in Science, Engineering and Technology["Miss. Isha Anand Bhagat", "Miss. Komal Gajanan Wankhede", "Mr. Navoday Atul Kopawar", "Prof. Dipali A. Sananse"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/1ff521ff389f51e025c47daf26da41850fa7a0fb 669ebc0b8e4e99344e9850a5e77d93fd0646df71Introduction. There is a process of forming a conceptual approach to understanding artificial intelligence and regulatory regulation strategies in the world. Having analyzed the theoretical approaches to the definition of the concept of artificial intelligence, we come to the conclusion that a common approach to the definition of artificial intelligence (hereinafter AI) has not been formed in scientific circles and within the framework of international organizations.Currently, in the vast majority of cases, AI technologies are created by humans and in one way or another interact with humans, however, in the process of legal regulation of relations with AI, it should be taken into account that artificial intelligence is gaining more and more autonomy due to the improvement of technologies.The hypothesis of the scientific research consists in the substantiation of foreign experience and the formation of proposals for managing the development of entrepreneurship using artificial intelligence.The purpose of the study is to substantiate the peculiarities of the adaptation of foreign experience in managing the development of entrepreneurship with the use of artificial intelligence.The methodology of scientific research is general scientific methods of research: logical and comparative analysis in revealing the principles of managing the development of entrepreneurship using artificial intelligence; the induction method for making formal and logical generalizations, the deduction method was used to obtain intermediate (partial) conclusions based on the analysis of the nature of the general process, the abstraction method for identifying and identifying significant trends in managing the development of entrepreneurship using artificial intelligence. Conclusions and prospects for further research. Artificial intelligence has become an integral part of many spheres of social life, and law in its broadest sense has not become an exception, because artificial intelligence technologies have in one way or another touched various branches of law since the beginning of scientific research on this issue, which takes its own beginning as early as the 70s of the 20th century, until today they have transformed from the modeling of legal norms to contracts written in the form of computer programs, virtual legal consultants, predictive justice technologies and other similar technologies. However, the perception of law as a system of value orientations can become an extremely difficult task for AI, therefore, when forming the legal basis of human-AI interaction, it is worth considering the difficulty for AI systems to work with abstract categories.The formation of the foundations of the international legal regulation of AI takes place within universal and regional organizations. For example, the Center for Artificial Intelligence and Robotics was founded within the UN, UNESCO adopted the Recommendation on the Ethics of Artificial Intelligence, the OECD – relevant principles, UNCITRAL and UNIDROIT are studying the issues of integrating AI into private-law relations. Formation of technical standards, principles of responsible handling of these technologies and ethical standards also takes place within the framework of the ITU, the EU and other international organizations.Keywords: integration; management; development; entrepreneurship; artificial intelligence; differentiation; scaling.Management00The purpose of the study is to substantiate the peculiarities of the adaptation of foreign experience in managing the development of entrepreneurship with the use of artificial intelligence.Management["L. Hanushchak-Yefimenko", "Vadym Hrytsun"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/669ebc0b8e4e99344e9850a5e77d93fd0646df71 2df085ef2b7a440a11433d34c95f0eba343272f8European Journal of Medical Research764The XGBoost model developed in this study is easily accessible through the deployed web application and can aid in clinical research and can help identify risk factors associated with extended PLOS.European Journal of Medical Research["Parhat Yasin", "Yasen Yimit", "Xiaoyu Cai", "Abasi Aimaiti", "Weibin Sheng", "Mardan Mamat", "Mayidili Nijiati"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/2df085ef2b7a440a11433d34c95f0eba343272f8 36be20ba34a4995ed48d2036b4977deba8108cb7Energy &amp; Fuels1585Energy &amp; Fuels["M. N. Amar", "Hakim Djema", "Khaled Ourabah", "F. Alqahtani", "Mohammad Ghasemi"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/36be20ba34a4995ed48d2036b4977deba8108cb7 1d0dd127612f968f4a1048b59ab691538de5d622Intensive Care Medicine21Intensive care medicine["Mejdeddine Al Barajraji", "A. Niset", "Alexandre Englebert", "Salim El Hadwe", "S. Barrit"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/1d0dd127612f968f4a1048b59ab691538de5d622 0bc7d2cb5de0ebb4837e37442631ed5427c4f5d4Introduction AI technologies have the potential to transform patient care. AI has been used to aid in differential diagnosis and treatment planning for psychiatric disorders, administer therapeutic protocols, assist with interpretation of cognitive testing, and patient treatment planning. Despite advancements, AI has notable limitations and remains understudied and further research on its strengths and limitations in patient care is required. This study explored the responses of AI (Chat-GPT 3.5) and trained clinicians to commonly asked patient questions. Methods Three clinicians and AI provided responses to five dementia/geriatric healthcare-related questions. Responses were analyzed by a fourth, blinded clinician for clarity, accuracy, relevance, depth, and ease of understanding and to determine which response was AI generated. Results AI responses were rated highest in ease of understanding and depth across all responses and tied for first for clarity, accuracy, and relevance. The rating for AI generated responses was 4.6/5 (SD = 0.26); the clinician s' responses were 4.3 (SD = 0.67), 4.2 (SD = 0.52), and 3.9 (SD = 0.59), respectively. The AI generated answers were identified in 4/5 instances. Conclusions AI responses were rated more highly and consistently on each question individually and overall than clinician answers demonstrating that AI could produce good responses to potential patient questions. However, AI responses were easily distinguishable from those of clinicians. Although AI has the potential to positively impact healthcare, concerns are raised regarding difficulties discerning AI from human generated material, the increased potential for proliferation of misinformation, data security concerns, and more.Frontiers Artif. Intell.371Although AI has the potential to positively impact healthcare, concerns are raised regarding difficulties discerning AI from human generated material, the increased potential for proliferation of misinformation, data security concerns, and more.Frontiers in Artificial Intelligence["Ian Moore", "Christopher Magnante", "Ellie Embry", "Jennifer Mathis", "Scott Mooney", "S. Haj-Hassan", "Maria Cottingham", "P. Padala"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/0bc7d2cb5de0ebb4837e37442631ed5427c4f5d4 38f71d6cfed1e2a6cff246815ef49ba5e93bd74dWhile the integration of robot‐based learning in early childhood education has gained increasing attention in recent years, there is still a lack of evidence regarding the impact of AI robots on young children's learning.The study explored the effectiveness of two AI education approaches in advancing kindergarteners' computational thinking, sequencing, self‐regulation and theory of mind skills.An experiment was conducted with 90 kindergarteners (ages 5–6) randomly assigned to either a direct instruction (DI), cooperative play (CP) or control group.Results show that (1) children in all three groups had significant improvements on computational thinking, sequencing and self‐regulation; (2) both early AI education approaches (CP and DI) significantly enhance young children's computational thinking, sequencing, self‐regulation and theory of mind skills; (3) the DI group had significant higher improvement than the CP group on computational thinking; (4) the CP group exhibited greater enhancements in theory of mind skills than the DI group.These findings jointly demonstrate that each AI educational approach has unique strengths, underscoring the significance of designing new pedagogies to expand children's skills.Journal of Computer Assisted Learning441Both early AI education approaches significantly enhance young children's computational thinking, sequencing, self‐regulation and theory of mind skills, underscoring the significance of designing new pedagogies to expand children's skills.J. Comput. Assist. Learn.["Jiahong Su", "Weipeng Yang", "I. H. Y. Yim", "Hui Li", "Xiao Hu"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/38f71d6cfed1e2a6cff246815ef49ba5e93bd74d 81f4200b13539f9bcc6627e2a87b63e448eb07c5Overall Equipment Effectiveness (OEE) stands as a key performance metric widely adopted in the manufacturing industry, aiding in enhancing productivity. This metric offers a comprehensive overview to higher management, enabling them to identify equipment-related losses. With the advancements in Industry 4.0 technologies, the Manufacturing Execution System facilitates real-time data collection, enhancing production efficiency and reducing manufacturing costs. However, the real-time depiction of OEE often fails to provide decision-makers with timely insights to conduct their tasks effectively. This study formulated machine learning models to tackle this challenge by forecasting the OEE for the upcoming working shift. Firstly, the historical dataset encompasses 31 features collected and processed to estimate the OEE value. Then, prominent machine learning models were utilized as prediction models: Linear Regression, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. The results show that the Extreme Gradient Boosting performs well for the OEE prediction with accuracy in training higher than 99% and testing nearly 90%. Our study illustrates an actionable knowledge-discovery process using a real-world data mining approach for the manufacturing industry, potentially applicable to other sectors.2024 7th International Conference on Green Technology and Sustainable Development (GTSD)240This study illustrates an actionable knowledge-discovery process using a real-world data mining approach for the manufacturing industry, potentially applicable to other sectors.2024 7th International Conference on Green Technology and Sustainable Development (GTSD)["Van Trieu Vy Nguyen", "Song Thanh Quynh Le", "Duc Duy Nguyen"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/81f4200b13539f9bcc6627e2a87b63e448eb07c5 c8f597b811685ce7e98542524c793424249cdfcfJournal of Medical Imaging and Radiation Sciences70Journal of medical imaging and radiation sciences["Mark M. Alipio"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/c8f597b811685ce7e98542524c793424249cdfcf 21d78c0a6f5aaad4ef291e8d0e4afca1094013f3The article deals with the rapid integration of AI into global businesses and underscores the necessity for policymakers to act swiftly. The problems discussed include varying levels of readiness among countries to adopt AI, as measured by the IMF's newly developed AI Preparedness Index. This index analyzes aspects such as digital infrastructure, human capital and labor market policies, innovation, and regulation and ethics. The main focus is on how wealthier economies like Singapore, the U.S., and Denmark lead in AI readiness, while low-income countries lag behind. The article emphasizes the need for advanced economies to balance AI innovation with robust regulations and for emerging markets to invest in digital infrastructure and workforce development.Grail of Science10The article emphasizes the need for advanced economies to balance AI innovation with robust regulations and for emerging markets to invest in digital infrastructure and workforce development.Grail of Science["Daniil Tsyba", "N. Gudkova"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/21d78c0a6f5aaad4ef291e8d0e4afca1094013f3 134f74a80c3a8e5cf84dd5e720522f13b8865b87AI in Precision Oncology320AI in Precision Oncology["Andrea Cherubini"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/134f74a80c3a8e5cf84dd5e720522f13b8865b87 9d06fdf8f5de8f7ff59b371e6084e6a4a3ec04d7Ethical dilemmas are common in the practice of medicine and can lead to an array of seemingly reasonable decisions unless policies or regulations mandate certain actions. Choosing the appropriate solution requires not only biomedical evidence, but also requires the balancing of possibly divergent preferences, values, contextual factors and ethical theories. These include utilitarianism, which aims to optimise happiness for the largest number of people; versus deontology, which promotes actions based on rules and duties even if these actions do not result in the greatest common good. The inability to find common ground can both delay appropriate care and trigger moral distress among health professionals.1 However, training in ethical reasoning or obtaining ethics consultations may not be universally available. How then can frontline healthcare teams navigate ethical dilemmas?Annals of the Academy of Medicine, Singapore80How can frontline healthcare teams navigate ethical dilemmas and training in ethical reasoning or obtaining ethics consultations may not be universally available?Annals of the Academy of Medicine, Singapore["K. See"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/9d06fdf8f5de8f7ff59b371e6084e6a4a3ec04d7 835df071220d71bd2ca13d453b6d9e84d919289eThe IncomeWellBeingX (IWX) application aims to revolutionize the financial well-being of individuals, particularly in Asia, by providing comprehensive income support, tax information, rebate assistance, and financial tips. The primary focus of IWX is to empower users with the knowledge and tools necessary to achieve financial stability and growth. This paper explores the technologies utilized in the development of IWX, including React Native and TypeScript (TSX), which facilitate a robust and seamless user experience across multiple platforms. Additionally, the application integrates various cybersecurity measures to ensure the safety and privacy of user data, including data encryption, SSL pinning, multi-factor authentication, and regular security audits.International Journal For Multidisciplinary Research00The technologies utilized in the development of IWX, including React Native and TypeScript (TSX), are explored, which facilitate a robust and seamless user experience across multiple platforms.International Journal For Multidisciplinary Research["Priyant Banerjee", "Utkarsh Mhatre"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/835df071220d71bd2ca13d453b6d9e84d919289e 10e454e5e125ac82366a9a22a3731c027636c85dObservatory Operations: Strategies, Processes, and Systems X00Observatory Operations: Strategies, Processes, and Systems X["A. Yanes-D\u00edaz", "Sergio Rueda Teruel", "Rafael Bello", "David Lozano Perez", "Mario Cervera", "Tamara Civera", "Mikel Dominguez", "Nuria Martinez Olivar", "S. Chueca", "C. I\u00f1iguez", "A. Mar\u00edn Franch", "F. Rueda Teruel", "G. Lopez Alegre", "Samuel Bielsa", "Jorge Mu\u00f1oz", "Hector Rueda", "Alejandro Mu\u00f1oz Teruel", "D. Garces", "Mar\u00eda Almarcegui", "Javier Cenarro Lagunas", "Mariano Moles Villamate", "C. Lopez Sanjuan", "H. V\u00e1zquez Rami\u00f3", "Javier Zaragoza Cardiel", "L. Valdivielso", "S. Pyrzas", "D. Crist\u00f3bal-Hornillos", "Jes\u00fas Varela", "M. C. D\u00edaz-Mart\u00edn", "R. Iglesias Marzoa", "Natalio Maicas", "J. L. Lamadrid", "F\u00e1tima Lopez Martinez", "Francisco Jos\u00e9 Galindo Guil", "E. Lacruz-Calder\u00f3n", "Juan Castillo", "Angel Lopez Sainz", "M. Akhlaghi", "Javier Hernandez", "D. Muniesa", "Alberto Moreno", "Antonio Hernan Caballero", "H\u00e9ctor Vives Arias", "G. Lorenzetti", "A. Ederoclite", "A. del Pino", "Juan Antonio Fern\u00e1ndez Ontiveros", "Fabiola Carolina Hern\u00e1ndez P\u00e9rez", "Raul Infante", "Teet Kuutma", "Alejandro Lumbreras Calle", "S. Eskandarlou", "A. Dom\u00ednguez-Fern\u00e1ndez", "Francisco Arizo Borillo", "H. Dom\u00ednguez S\u00e1nchez", "Jairo Andres Alzate Trujillo", "Ricardo Oscar Amor\u00edn Barbieri", "David Fern\u00e1ndez Gil", "Adri\u00e1n Hidalgo Pinilla", "David Morate Gonz\u00e1lez", "Rahna Payyasseri Thanduparackal", "Jes\u00fas Vega Ferrero", "Silvia Vaquero Valer", "In\u00e9s Mu\u00f1oz Igado", "Mar\u00eda Teresa Alegre S\u00e1nchez", "Gema Mar\u00eda Juli\u00e1n Caballero de Espa\u00f1a", "Alicia Romero", "Mar\u00eda Carmen Espallargas Do\u00f1ate", "Ana L\u00f3pez Col\u00e1s"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/10e454e5e125ac82366a9a22a3731c027636c85d 133193d18453a1c47e2cc9c77161fc24aed845fbPurpose The purpose of the article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” is to explore the profound impact of Artificial Intelligence (AI) on education and to emphasize the need for a fundamental shift in current education systems. The article aims to provide practitioners with actionable insights on how to navigate the rapidly evolving landscape of AI in education while preparing young people for their crucial role as the workforce of tomorrow. It seeks to highlight the potential of AI to revolutionize education while also acknowledging the importance of preserving the unique human touch in the learning process. Design/methodology/approach This article explores the disruptive impact of Artificial Intelligence (AI) on education and emphasizes the need for a fundamental shift in current education systems to prepare young people for an AI-driven future. It highlights the potential of AI to revolutionize education through personalized learning experiences, enhanced teacher professional development and automation of administrative tasks while acknowledging the importance of approaching AI implementation with caution and preserving the unique human touch in education. The article argues for a shift in focus from rote learning to fostering critical thinking, creativity and problem-solving skills, emphasizing the development of Learning Mastery and Knowledge Mastery. It underscores the vital role of educators in leveraging AI technologies and preparing young people for the future, along with the need for responsive educational policies and curriculum frameworks that integrate AI literacy and ethical considerations. The article concludes by calling for reimagining the schooling system, prioritizing high-level thinking and nurturing the unique capabilities of human intelligence. The future of education lies in harnessing the power of AI while celebrating and cultivating distinctively human qualities. Educational practitioners play a crucial role in shaping this future by bridging the gap between research and practice, ensuring a positive and prosperous future for society in an AI-driven world. Findings (1) AI can revolutionize education through personalized learning, enhanced teacher development and task automation. (2) Balance is needed between AI and human touch in education. Current education systems fail to cultivate critical thinking and creativity. (3) Learning Mastery and Knowledge Mastery should be emphasized to foster independent thinking and problem-solving. (4) Educators play a vital role in integrating AI into the learning process. (5). AI can redefine success in education and cultivate future-proof skills. (6). Responsive and adaptable educational policies are necessary. (7) The future of education lies in harnessing AI while nurturing human intelligence. Research limitations/implications Not appropriate for style of text. Practical implications (1) Educators should actively engage with AI technologies and explore ways to integrate them into the learning process to enhance personalized learning experiences. (2) Professional development programs should be designed to equip teachers with the necessary skills to effectively utilize AI tools and leverage them to improve instructional practices. (3) Curriculum frameworks need to be revised to integrate AI literacy, digital citizenship and ethical considerations into the educational journey of young learners. (4) Educational institutions should invest in AI-powered assessment tools that provide a holistic understanding of a student’s abilities, capturing their strengths and areas for improvement beyond test scores. (5) Educators should focus on teaching metacognitive strategies, encouraging self-reflection and self-assessment and providing opportunities for students to develop problem-solving and critical-thinking skills. (6) Active learning strategies, such as project-based learning, problem-based learning and inquiry-based learning, should be employed to foster deep learning and knowledge mastery. (7) Educational policies should encourage innovation and collaboration between educational institutions, government bodies and industry stakeholders to ensure responsiveness to the rapidly evolving landscape of AI in education. (8) Educators should strive to create a learning environment that nurtures and celebrates the unique capabilities of human intelligence while harnessing the power of AI to enhance the learning experience. Social implications (1) Workforce preparedness for an AI-driven future. (2) Potential exacerbation of societal inequalities. (3) Fostering human–AI collaboration skills. (4) Addressing ethical concerns regarding data privacy and security. (5) Emphasizing lifelong learning to adapt to changing demands. (6) Redefining success through a holistic view of student abilities. (7) Shaping societal values that balance human intelligence and AI capabilities. The education system must address these implications to ensure equitable access to AI-enhanced learning, maintain public trust and prepare individuals for a society where human–AI collaboration is essential, while promoting a balanced and harmonious coexistence between human intelligence and AI. Originality/value The article “Nurturing Human Intelligence in the Age of AI: Rethinking Education for the Future” offers a fresh perspective on the impact of AI on education. While the topic of AI in education is not novel, the article’s emphasis on nurturing human intelligence alongside AI integration sets it apart. The author’s call for a fundamental shift in education systems to prioritise critical thinking, creativity and problem-solving skills is a unique approach. The article’s exploration of Learning Mastery and Knowledge Mastery as key concepts in preparing students for an AI-driven future adds originality to the discussion. Overall, the article presents a thought-provoking and original viewpoint on the future of education in the age of AI. Development and Learning in Organizations: an international journal21The article argues for a shift in focus from rote learning to fostering critical thinking, creativity and problem-solving skills, emphasizing the development of Learning Mastery and Knowledge Mastery, and underscores the vital role of educators in leveraging AI technologies and preparing young people for the future.Development and Learning in Organizations: An International Journal["Rosemary Luckin"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/133193d18453a1c47e2cc9c77161fc24aed845fb 2334c104a2fc630bcc3c7baedad7648d0a4e93beNature Network Boston217These findings point toward an anti-AI bias when receiving digital medical advice, even when AI is supposedly supervised by physicians, and elucidating ways to counteract this bias should be an important objective of future research.Nature Medicine["Moritz Reis", "Florian Reis", "Wilfried Kunde"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/2334c104a2fc630bcc3c7baedad7648d0a4e93be 8ef2b351d00a6656ff953e02bcc649c12eaa522dUnderstanding the mechanisms enabling the learning and flexible use of knowledge in context-appropriate ways has been a major focus of research in the study of both semantic cognition and cognitive control. We present a unified model of semantics and control that addresses these questions from both perspectives. The model provides a coherent view of how semantic knowledge, and the ability to flexibly access and deploy that knowledge to meet current task demands, arises from end-to-end learning of the statistics of the environment. We show that the model addresses unresolved issues from both literatures, including how control operates over features that covary with one another and how control representations themselves are structured and emerge through learning, through a series of behavioral experiments and simulations. We conclude by discussing the implications of our approach to other fundamental questions in cognitive science, machine learning, and artificial intelligence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).Psychology Review06A unified model of semantics and control is presented that addresses unresolved issues from both literatures, including how control operates over features that covary with one another and how control representations themselves are structured and emerge through learning, through a series of behavioral experiments and simulations.Psychological review["Tyler Giallanza", "Declan Campbell", "Jonathan D. Cohen", "Timothy T Rogers"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/8ef2b351d00a6656ff953e02bcc649c12eaa522d 8e20ce3197a85d89c24b0b8b9db5dd970a247edeThis study examines the applications, benefits, challenges, and ethical considerations of artificial intelligence (AI) in the banking and finance sectors. It reviews current AI regulation and governance frameworks to provide insights for stakeholders navigating AI integration. A descriptive analysis based on a literature review of recent research is conducted, exploring AI applications, benefits, challenges, regulations, and relevant theories. This study identifies key trends and suggests future research directions. The major findings include an overview of AI applications, benefits, challenges, and ethical issues in the banking and finance industries. Recommendations are provided to address these challenges and ethical issues, along with examples of existing regulations and strategies for implementing AI governance frameworks within organizations. This paper highlights innovation, regulation, and ethical issues in relation to AI within the banking and finance sectors. Analyzes the previous literature, and suggests strategies for AI governance framework implementation and future research directions. Innovation in the applications of AI integrates with fintech, such as preventing financial crimes, credit risk assessment, customer service, and investment management. These applications improve decision making and enhance the customer experience, particularly in banks. Existing AI regulations and guidelines include those from Hong Kong SAR, the United States, China, the United Kingdom, the European Union, and Singapore. Challenges include data privacy and security, bias and fairness, accountability and transparency, and the skill gap. Therefore, implementing an AI governance framework requires rules and guidelines to address these issues. This paper makes recommendations for policymakers and suggests practical implications in reference to the ASEAN guidelines for AI development at the national and regional levels. Future research directions, a combination of extended UTAUT, change theory, and institutional theory, as well as the critical success factor, can fill the theoretical gap through mixed-method research. In terms of the population gap can be addressed by research undertaken in a nation where fintech services are projected to be less accepted, such as a developing or Islamic country. In summary, this study presents a novel approach using descriptive analysis, offering four main contributions that make this research novel: (1) the applications of AI in the banking and finance industries, (2) the benefits and challenges of AI adoption in these industries, (3) the current AI regulations and governance, and (4) the types of theories relevant for further research. The research findings are expected to contribute to policy and offer practical implications for fintech development in a country.Inf.1016Inf.["Nurhadhinah Nadiah Ridzuan", "Masairol Masri", "Muhammad Anshari", "Norma Latif Fitriyani", "Muhammad Syafrudin"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/8e20ce3197a85d89c24b0b8b9db5dd970a247ede 29fad663b3a8a8672a2c11b52995dc84f6d08cadMicrogrids play a pivotal role in modern power distribution systems, necessitating precise control methodologies to tackle challenges such as performance instability, especially during islanding operations. This paper introduces an advanced control strategy that employs artificial intelligence, specifically deep neural network (DNN) predictions, to enhance microgrid performance, particularly in an islanding mode where voltage and frequency (VaF) deviations are critical concerns. By utilizing real‐time data and historical trends, the proposed controller accurately forecasts power demand and generation patterns, enabling proactive planning and optimization of efficiency, reliability, and sustainability in microgrid management. One significant aspect of this approach is to establish an intelligent distributed control system that minimizes reliance on communication devices while ensuring that VaF remains within acceptable limits. Moreover, it consolidates the roles of primary and secondary controllers within the microgrid and facilitates the prediction of load changes and load injection processes. This capability significantly reduces microgrid VaF deviations, enhancing system performance through precise power distribution and balanced coordination among distributed generators. Consequently, it ensures the stability and reliability of the system. In summary, the integration of DNN‐based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.IET Generation, Transmission &amp; Distribution112The integration of DNN‐based predictive control represents a significant advancement in microgrid management, providing a solution to address performance challenges and optimize operational efficiency, reliability, and sustainability.IET Generation, Transmission &amp; Distribution["Afshin Hasani", "Hossein Heydari", "Mohammad Sadegh Golsorkhi"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/29fad663b3a8a8672a2c11b52995dc84f6d08cad cad255a80b1bd525097615f8a8f20b39be04afc9The integration of artificial intelligence (AI) into English language learning programs offers significant opportunities and challenges in the current education and technology landscape. This study explores the intersection of AI-driven language learning platforms and the preservation of academic writing integrity within evolving intelligent interfaces.Initially, the study examines the functionalities of AI-driven language learning tools, emphasizing their ability to personalize learning experiences, provide immediate feedback, and optimize language acquisition processes. These platforms leverage machine learning algorithms to analyze learner data, enabling tailored content delivery and proficiency assessment.However, alongside these benefits, critical concerns arise regarding academic integrity, particularly concerning writing proficiency and originality. As AI tools enhance students' writing capabilities, the risk of plagiarism and ethical lapses escalates. The study investigates strategies for cultivating ethical writing practices amid the prevalence of AI-guided composition tools.Furthermore, the emergence of intelligent interfaces has broader implications for educational paradigms. Integrating AI into language learning necessitates a reevaluation of pedagogical methodologies to ensure a balanced approach that emphasizes skill development alongside ethical awareness.Drawing upon academic literature, case studies, and theoretical frameworks, this study analyzes the dual impact of AI on language learning and academic integrity. It underscores the necessity for collaboration among educators, policymakers, and technology developers to establish ethical guidelines and educational frameworks that leverage AI's potential while upholding academic rigor and integrity.This study advocates for a holistic approach to AI-driven language learning that emphasizes responsible technology implementation and fosters a culture of ethical writing practices alongside linguistic proficiency. By addressing these considerations, stakeholders can navigate the evolving educational landscape in the era of intelligent interfaces effectively and responsibly.English Language Teaching and Linguistics Studies01It is underscored the necessity for collaboration among educators, policymakers, and technology developers to establish ethical guidelines and educational frameworks that leverage AI's potential while upholding academic rigor and integrity within evolving intelligent interfaces.English Language Teaching and Linguistics Studies["Jiaolan Pan"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/cad255a80b1bd525097615f8a8f20b39be04afc9 f90f28da848c5f411aec037100c8edc6997af2daEnglish language education in Japan has long been criticized for its traditional methods which emphasize grammar and reading at the expense of communicative competence. This article explores the potential of Artificial Intelligence in Education (AIEd) to address this issue. A review of literature explored key challenges faced by Japanese EFL learners including Japanese teachers’ low English proficiency and attitudes towards English teaching, heavy focus on entrance examinations in high school, overemphasis on grammar in EFL curricula and textbooks, lack of authentic communicative practice, and differences in cultural values. An analysis of technology integration in Japanese education revealed that while many institutions have begun incorporating technology, its widespread adoption has been gradual. Several case studies support the use of AI to address the psychological barrier to speaking by offering a safe and engaging learning environment, thus boosting confidence and fluency. Furthermore, in the Japanese language context, the use of AI can lower anxiety, promote creativity, and offer personalized learning. In addition to the individual benefits, AI empowers institutions to tailor learning needs, teachers to shift their role from instructors to facilitators, and students to become independent critical thinkers. Finally, challenges and limitations including ethical considerations surrounding data privacy, overreliance, authenticity, watermarking, and academic integrity are addressed. Despite potential drawbacks, the benefits of AIEd merit a deeper exploration of its adoption in EFL curricula. AI tools can be a practical solution to prepare Japanese EFL students to effectively and confidently communicate in English and thus participate in the global landscape.  Technology in Language Teaching &amp; Learning01The benefits of AIEd merit a deeper exploration of its adoption in EFL curricula, and challenges and limitations including ethical considerations surrounding data privacy, overreliance, authenticity, watermarking, and academic integrity are addressed.Technology in Language Teaching &amp; Learning["Alexis Busso", "Becky Sanchez"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/f90f28da848c5f411aec037100c8edc6997af2da e3b62eb0e4d8a87381b7f6e76410645a96fa0849This study explores the legal implications of data protection laws, artificial intelligence (AI) regulation, and cybersecurity measures on privacy rights in 2024. The primary objective is to qualitatively analyze how recent advancements and legislative changes in these areas impact individual privacy rights and shape the legal landscape for data protection. The research employs a qualitative literature review methodology, synthesizing findings from academic articles, legal texts, policy papers, and case studies to provide a comprehensive understanding of the evolving legal challenges and implications for privacy rights. The literature review methodology involves systematically collecting and analyzing a wide range of scholarly sources on data protection, AI regulation, and cybersecurity. The study categorizes the literature into key themes, such as the effectiveness of current data protection laws, the ethical and legal considerations of AI, and the impact of cybersecurity measures on personal data security. Through a thematic analysis, the research identifies the intersection of these legal areas and their collective influence on privacy rights. The findings reveal that recent data protection laws, such as the General Data Protection Regulation (GDPR) and emerging national legislations, have significantly enhanced individual control over personal data and accountability for data breaches. However, the rapid advancement of AI technologies poses new challenges for privacy, including concerns about data bias, algorithmic transparency, and the ethical use of personal information. Cybersecurity measures are essential for protecting data integrity and preventing unauthorized access, yet they also raise issues related to surveillance and the potential infringement of privacy rights.Global International Journal of Innovative Research01The findings reveal that recent data protection laws have significantly enhanced individual control over personal data and accountability for data breaches, however, the rapid advancement of AI technologies poses new challenges for privacy, including concerns about data bias, algorithmic transparency, and the ethical use of personal information.Global International Journal of Innovative Research["Dharma Setiawan Negara", "Nunu Burhanuddin", "Abu Sahman Nasim", "Juni Irianti Sitinjak", "J. J. Koynja"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/e3b62eb0e4d8a87381b7f6e76410645a96fa0849 c8ec249959a8ab0e7e96906797d1b063fedad1bbThe integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.Applied and Computational Engineering01This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.Applied and Computational Engineering["Qi Shen"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/c8ec249959a8ab0e7e96906797d1b063fedad1bb c930d4718f079653caeb193c0aed55e3475fd6d4Globally, 1 in 7 people has some kind of mental or substance use disorder that affects their thinking, feelings, and behaviour in everyday life. People with mental health disorders can continue their normal lives with proper treatment and support. Mental well-being is vital for physical health. The use of AI in mental health areas has grown exponentially in the last decade. However, mental disorders are still complex to diagnose due to similar and common symptoms for numerous mental illnesses, with a minute difference. Intelligent systems can help us identify mental diseases precisely, which is a critical step in diagnosing. Using these systems efficiently can improve the treatment and rapid recovery of patients. We survey different artificial intelligence systems used in mental healthcare, such as mobile applications, machine learning and deep learning methods, and multimodal systems and draw comparisons from recent developments and related challenges. Also, we discuss types of mental disorders and how these different techniques can support the therapist in diagnosing, monitoring, and treating patients with mental disorders.ACM Trans. Comput. Heal.340This work surveys different artificial intelligence systems used in mental healthcare, such as mobile applications, machine learning and deep learning methods, and multimodal systems and draws comparisons from recent developments and related challenges.ACM Trans. Comput. Heal.["Faustino Muetunda", "Soumaya Sabry", "M. Jamil", "Sebasti\u00e3o Pais", "Gael Dias", "Jo\u00e3o Cordeiro"]2024-07-25T00:00:00https://www.semanticscholar.org/paper/c930d4718f079653caeb193c0aed55e3475fd6d4 11338806b15284a9ccef4ccbd3bcb66b6786ea8eArtificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen their security posture against various security issues and cyberattacks. This objective focuses on analysing how AI-based cyber security (CS) solutions improve performance in financial transactions and banking sectors. It also aims to identify the latest advancements in AI-driven CS) research to enhance security and operational efficiency in the financial sector. This article presents a systematic literature review and a detailed analysis of AI use cases for cybersecurity in financial transactions. The review resulted in 800 studies, of which 225 articles remain. This paper will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in financial transactions. The review also identifies future research opportunities in examining cybersecurity application areas, advanced AI methods, data representation, and the development of new infrastructures for successful adoption in financial transactions. The paper might increase cyber security systems’ performance by increasing their defence against cyberattacks. Artificial intelligence approaches improve and enhance cyber security with machine learning and deep learning, fraud and threat detection, and this makes sure a secure and safe financial transaction. This study helps identify how to make transactions safer with cyber security. This study highlights the vital role of evaluation and continuous adaptation in AI. In the near future, this topic should focus on more collaboration among AI, cyber security, and system developers for better and secured outcomes. International Journal of Religion838A systematic literature review and a detailed analysis of AI use cases for cybersecurity in financial transactions will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in financial transactions.International Journal of Religion["Mahfujur Rahman Faraji", "Fisan Shikder", "Md. Hasibul Hasan", "Md. Mominul Islam", "Umme Kulsum Akter"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/11338806b15284a9ccef4ccbd3bcb66b6786ea8e 94111178de6233a4617f502f41a44a28de19ac0c The adoption of biofuels as an energy source has experienced a substantial increase, exceeding the consumption of fossil fuels. The shift can be ascribed to the availability of renewable resources for energy production and the ecological advantages linked to their utilisation. Nevertheless, due to its intricate characteristics, the process of producing ethanol fuel from biomass poses difficulties in terms of administration, enhancement, and forecasting future results. To tackle these difficulties, it is crucial to utilise modelling techniques like artificial intelligence (AI) to create, oversee, and improve bioethanol production procedures. Artificial Neural Networks (ANN) is a prominent AI technique that offers significant advantages for modelling bioethanol production systems’ pretreatment, fermentation, and conversion stages. They are highly flexible and accurate, making them particularly well-suited. This study thoroughly examines several artificial intelligence techniques used in bioethanol production, specifically focusing on research published in the past ten years. The analysis emphasises the importance of using AI methods to address the complexities of bioethanol production and shows their role in enhancing efficiency and sustainability in the biofuel industry.International journal of Chemical Reactor Engineering747This study thoroughly examines several artificial intelligence techniques used in bioethanol production, specifically focusing on research published in the past ten years and shows their role in enhancing efficiency and sustainability in the biofuel industry.International Journal of Chemical Reactor Engineering["Christopher Selvam Damian", "Yuvarajan Devarajan", "Raja Thandavamoorthy", "R. Jayabal"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/94111178de6233a4617f502f41a44a28de19ac0c 83fd2f5288c5cc5144a94a35911a905f3dc858bcEducation and Information Technologies : Official Journal of the IFIP technical committee on Education304Logistic regression analyses showed that self-control and self-efficacy were associated with using artificial intelligence to solve daily doubts, due to the need of interacting with someone and to do academic tasks instead of the student.Education and Information Technologies["Joaqu\u00edn Rodr\u00edguez-Ruiz", "Inmaculada Mar\u00edn-L\u00f3pez", "Raquel Espejo-Siles"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/83fd2f5288c5cc5144a94a35911a905f3dc858bc dfad67dec6a24661aebbbd0d7238786eb58f57ebThis article explores the revolutionary role of Artificial Intelligence (AI) in personalized learning, focusing on its potential to revolutionize educational processes and improve learning outcomes. This paper provides insights into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences. The article examines case studies and examples to demonstrate different uses of AI in personalized learning settings, such as autonomous tutoring systems, personalized education channels, and analysis based on data. However, the use of AI in education raises ethical, technological, and policy concerns that require careful study and proactive actions to assure fair access, preserve learner privacy, and eliminate algorithmic biases. Ultimately, finally, the article emphasizes AI's transformative potential in personalized learning and advocates for coordinated efforts among educators, academics, and policymakers to leverage its benefits while tackling accompanying obstacles2024 Asia Pacific Conference on Innovation in Technology (APCIT)214Insight is provided into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences.2024 Asia Pacific Conference on Innovation in Technology (APCIT)["Rukaiyya Qureshi", "Shital Hajare", "Prateek Verma"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/dfad67dec6a24661aebbbd0d7238786eb58f57eb 55ad60edf95db25bc76001f38ffcb3131c7dded0Artificial Intelligence Review1175This Systematic Literature Review (SLR) identifies 138 relevant articles from 2005 to 2022 and highlights empirical examples demonstrating XAI's potential benefits in the financial industry and concisely defines the existing challenges, requirements, and unresolved issues in applying XAI in the financial sector.Artif. Intell. Rev.["Jurgita \u010cernevi\u010dien\u0117", "Audrius Kaba\u0161inskas"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/55ad60edf95db25bc76001f38ffcb3131c7dded0 cb3e0d95b0d923069b2d94a6cdb4b751cf229a97The development of Artificial Intelligence (AI) technology such as Chatbots, virtual assistants, machine translation, natural language processing, GPT chat, You AI, and Google Bardi have had a significant impact on Arabic language learning for postgraduate students at UIN Maulana Malik Ibrahim Malang. The aim of this research is to identify the various AI applications used by students, as well as to evaluate the extent to which AI contributes to enriching their learning experience. The results of this research are that AI tools facilitate more interactive learning, encourage easy access to information, and increase efficiency in understanding Arabic. AI provides immediate feedback, helps translate text, and provides extensive reference resources, providing a richer and more affordable learning experience. Through its multifunctional role, AI has opened the door to a more personalized, adaptive and effective learning approach in mastering the Arabic language. This reflects the role of technology in supporting students in achieving a deeper understanding of the language, marking an important milestone in the development of Arabic language education at UIN Maulana Malik Ibrahim Malang.Khatulistiwa03The results of this research are that AI tools facilitate more interactive learning, encourage easy access to information, and increase efficiency in understanding Arabic.Khatulistiwa: Jurnal Pendidikan dan Sosial Humaniora["Evy Nur Rohmawaty", "Danial Hilmi", "M Sholih Salimul Uqba", "Ummu Sulaimah Saleh"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/cb3e0d95b0d923069b2d94a6cdb4b751cf229a97 2e684b9d512efd38283cce825094d9fee734e297The application of artificial intelligence technology in education has penetrated into various fields of education and teaching, scientific research and social services, and the relationship between artificial intelligence and education and teaching has become a hot research topic. With the development of information and communication technology, the research on the impact of AI on learning has also had some theoretical and empirical results and presented many trends and problems. In this paper, we intend to statistically analyse 500 papers during the period of 2010-2023 by means of keyword analysis, author analysis and research institution analysis. It is found that in recent years, domestic artificial intelligence is mainly in the fields of learning science, machine learning, deep learning and higher education, etc. Artificial intelligence has had a profound impact on education and learning, which not only changes the way of learning, but also promotes the change of education.Scientific Journal of Intelligent Systems Research133This paper intends to statistically analyse 500 papers during the period of 2010-2023 by means of keyword analysis, author analysis and research institution analysis, finding that in recent years, domestic artificial intelligence is mainly in the fields of learning science, machine learning, deep learning and higher education, etc.Scientific Journal of Intelligent Systems Research["Ting Mei", "Lingyun Yuan", "Chenglong Li"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/2e684b9d512efd38283cce825094d9fee734e297 986e3b13ed14a67ebe241cf857aadb5ca9c33db3 Artificial intelligence (AI) can help improve many areas of waste management and biogas generation. The world has reached a state where waste generation is increasing daily, while an effective waste management system is essential for the sustainable development of a country. AI could be of great use in optimizing the waste management scheme by technical differentiation of all sorts and recycling techniques. AI can contribute to the improvement of waste segmentation, recycling, and disposal. Thus, by assessing availability and composition, AI can easily contribute to the selection of the most suitable feedstock for biogas generation. This paper will discuss the optimization of gasifier design, an important part of biogas production, to enhance gasification efficiency for more efficient syngas production. Several gains accrue from AI applications, and among them is the selection of feedstocks and gasifiers optimal for more efficient and sustainable waste management and use in the production of biogas systems. This review paper identifies the potential application areas in either waste management practices or biogas production and puts forward ways in which AI can be used in these areas.Journal of energy resources technology01This paper will discuss the optimization of gasifier design, an important part of biogas production, to enhance gasification efficiency for more efficient syngas production.Journal of Energy Resources Technology["Vandana Sharma", "Kamal Upreti", "Arul Kumar Natarajan", "Nishi Jain", "Sanjay Kumar", "Anant Rajee Bara", "Sushma Kumari"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/986e3b13ed14a67ebe241cf857aadb5ca9c33db3 36fb85b57c95f8693661706b74b34f4b01fc6673The integration and impact of artificial intelligence (AI) and cloud computing (CC) technology in the construction industry (CI) would support their implementation process and adoption. However, there is a lack of research in the extant literature, and recent advances in this field have not been explored. As such, the key research question focuses on the extent of existing literature, main research hotspots, and recent advances (i.e., research gaps and future directions) in AI in CC in the CI. To address this research question, this study aims to conduct a state-of-the-art review of AI in CC in the CI by providing a qualitative discussion of the main research hotspots, research gaps, and future research directions. This review study used a four-step bibliometric-systematic review approach consisting of literature search, literature screening, science mapping analysis, and qualitative dis-cussion. The results found four main research hotspots, namely (1) construction project performance indicators, (2) data analysis and visualization, (3) construction quality control and safety, and (4) construction energy efficiency. These findings would provide valuable insights for scholars and practitioners seeking to understand and integrate AI and CC technology applications in the CI. This review study will lay a better foundation for future developments in construction project management processes, data-sharing protocols, real-time safety monitoring, and ethical implications of AI and CC technologies.Journal of Information Technology in Construction1001This study aims to conduct a state-of-the-art review of AI in CC in the CI by providing a qualitative discussion of the main research hotspots, research gaps, and future research directions by using a four-step bibliometric-systematic review approach.J. Inf. Technol. Constr.["Jian Wang", "M. Antwi-Afari", "A. Tezel", "Prince Antwi-Afari", "Tala Kasim"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/36fb85b57c95f8693661706b74b34f4b01fc6673 564ce9744246e96abe6ab0b987a78b798b91aeadArtificial Intelligence (AI) is transforming the field of palliative care, offering innovative solutions that improve patients' quality of life and optimize clinical practice. Using technologies such as machine learning and natural language processing, AI makes it possible to analyze large amounts of clinical data to provide valuable insights and decision-making support. This article aims to examine the current applications of AI in palliative care, assess its potential benefits, analyze the associated ethical challenges, and explore prospects in this rapidly evolving field. In conclusion, AI offers unprecedented opportunities to improve palliative care, but it is crucial to address ethical and equity challenges to ensure fair benefits. The future of palliative care will depend on our ability to balance technological innovation with human values.Hospice &amp; Palliative Medicine International Journal01The current applications of AI in palliative care are examined, its potential benefits are assessed, the associated ethical challenges are analyzed, and prospects in this rapidly evolving field are explored.Hospice &amp; Palliative Medicine International Journal["Utria Munive Jesus"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/564ce9744246e96abe6ab0b987a78b798b91aead 768ce528bbeb47f5106fcbbffd273fbc255ad4aeArtificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.Water Science and Technology02This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters, and fault detection in the processes and equipment integral to wastewater treatment.Water science and technology : a journal of the International Association on Water Pollution Research["Mudita Nagpal", "Miran Ahmad Siddique", "Khushi Sharma", "Nidhi Sharma", "A. Mittal"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/768ce528bbeb47f5106fcbbffd273fbc255ad4ae 99560f295eeabd52313935900e3c22c7912862e3Considering the fundamental role of libraries in social integration and information services, adapting service and business processes in parallel with technological advances is a crucial agenda. The impact and use of artificial intelligence technology in processes such as cataloging, content indexing, and responding to user requests have been extensively discussed in the information science literature in recent years. The purpose of this study is to investigate the practices and perspectives of library directors related to the use of artificial intelligence in the services and operations of university libraries in Türkiye. Evaluations related to the impact and use of artificial intelligence applications in the services and operations of university libraries were assessed based on the views of 43 university library directors in Türkiye. The perspectives of library directors reveal that improvements are needed in budget and technical infrastructure, human resources and training, data management, and ethical use to implement AI applications in university libraries in Türkiye.Information Development132The perspectives of library directors reveal that improvements are needed in budget and technical infrastructure, human resources and training, data management, and ethical use to implement AI applications in university libraries in Türkiye.Information Development["Tolga \u00c7akmak", "\u015eahika Ero\u011flu"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/99560f295eeabd52313935900e3c22c7912862e3 7fbbfe75e0ae3af9dcd525e061a22bdbf70ff5adРассматриваются некоторые аспекты организации исследовательской деятельности курсантов военных вузов и возможные варианты использования искусственного интеллекта на современном уровне его развития в качестве средства ее поддержки. Описаны история развития искусственного интеллекта и возможности его успешного применения в образовании и исследованиях, а также потенциальные вызовы и ограничения. Показано его влияние как стимула для формирования отдельных компонентов готовности к научно-исследовательской деятельности будущих офицеров. Делается вывод о том, каким образом искусственный интеллект может сделать обучение и научные исследования курсантов более эффективными. Aspects of the activities of some research organizations of courses at military universities and possible options for using artificial intelligence at the current level of its development as a means of supporting it are considered. The history of the development of artificial intelligence and the possibilities of its application in education and research, as well as potential challenges and limitations are described. Its influence is shown as a stimulus for individual components in the research and development activities of future officers. The conclusion is drawn about how artificial intelligence can make cadets' training and research more effective.Higher education today00Higher education today["\u0410.\u0410. \u0411\u0430\u0436\u0443\u0442\u0438\u043d"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/7fbbfe75e0ae3af9dcd525e061a22bdbf70ff5ad 0a0264e46de613fb7eda04ff2aa1fa5c9b2efc14This project is to examine how potential teachers in Hamzanwadi University's Economic Education project Program might improve their digital literacy through the use of artificial intelligence (AI). This research employs a quantitative method involving respondents primarily composed of sixth-semester students. Digital literacy is an essential skill that prospective teachers must possess to navigate the digital era, and this study finds that the use of AI technology can significantly enhance this capability. According to the survey results, 94.6% of students actively use AI-based applications or services, such as ChatGPT, Google Assistant, and others, to enhance their knowledge. The study's findings show that AI significantly and favorably impacts pupils' development of digital literacyThis is indicated by the fact that the null hypothesis is rejected and the alternative hypothesis is accepted, as indicated by the significance value of 0.000, which is less than 0.05, and the t-value of 6.951, which is more significant than the t-table value of 1.663. Perceived Utility, Perceived Usability, and Intention to Use are the study's leading indicators. The TCR values for Perceived Usefulness are 85.86%, for Perceived Ease of Use is 84.2%, and for Intention to Use is 82.6%, indicating that students find AI to be very useful, easy to use, and have a solid intention to continue using this technology in their learning. International Journal of Religion320The study's findings show that AI significantly and favorably impacts pupils' development of digital literacy, and the use of AI technology can significantly enhance this capability.International Journal of Religion["Muh. Fahrurrozi", "Abdullah Muzakari", "Abdul Latif", "Purwaningrum Puji Lestari"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/0a0264e46de613fb7eda04ff2aa1fa5c9b2efc14 e81109e0aaa919ff16030192f90692b1fe0a214cThis paper examines the role of generative artificial intelligence (GAI) in promoting academic integrity within educational settings. It explores how AI can be ethically integrated into classrooms to enhance learning experiences, foster intrinsic motivation, and support voluntary behavior change among students. By analyzing established ethical frameworks and educational theories such as deontological ethics, consequentialism, constructivist learning, and Self-Determination Theory (SDT), the paper argues that GAI, when used responsibly, can enhance digital literacy, encourage genuine knowledge construction, and uphold ethical standards in education. This research highlights the potential of GAI to create enriching, personalized learning environments that prepare students to navigate the complexities of the modern world ethically and effectively.Frontiers Artif. Intell.490This research highlights the potential of GAI to create enriching, personalized learning environments that prepare students to navigate the complexities of the modern world ethically and effectively.Frontiers in Artificial Intelligence["M. J. Tan", "Nicholle Mae Amor Tan Maravilla"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/e81109e0aaa919ff16030192f90692b1fe0a214c 174d6921e9fcc3247a5a8bec477c5d20d988f3b3The paper examines the role of artificial intelligence in modern technological processes. The active introduction of artificial intelligence into various spheres of society leads to increased production efficiency through the use of neural networks. At the moment, there are numerous examples of the introduction and operation of artificial intelligence. In this regard, a global expert group has conducted numerous studies in the field of digital technologies, which makes very reasonable forecasts of productivity improvement and positive impact on the economy through the use of innovative technologies. The paper pays special attention to generative artificial intelligence that creates new content based on the provided data and deep learning methods. Generative artificial intelligence has features that distinguish it from artificial intelligence in general. The paper analyzes the EU law on artificial intelligence, as well as the standing of the European Parliament and the International Commission for Harmonization on the relationship between artificial intelligence and intellectual property law. The author notes the lack of a unified approach in determining the legal status of a work created by generative artificial intelligence. In the context of this issue, the author examines the cases of creating a work entirely or partially by generative artificial intelligence. The issue of identifying criteria for recognizing a work as creative is emphasized. The ambiguity of using copyrighted data to train generative artificial intelligence requires special attention. Judicial practice in foreign countries underlines the relevance of these issues. In general, the apaper is a comprehensive study of the legal status of works created by generative artificial intelligence from the point of view of copyright. The work contains various approaches, and such an ambiguous legal position of such objects indicates legal gaps in this area.Russian Law Online00The paper analyzes the EU law on artificial intelligence, the standing of the European Parliament and the International Commission for Harmonization on the relationship between artificial intelligence and intellectual property law, and examines the cases of creating a work entirely or partially by generative artificial intelligence.Russian Law Online["V. O. Shamova"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/174d6921e9fcc3247a5a8bec477c5d20d988f3b3 988a5a1d35e9b89a353d9583aa1e2140a7882dd0The publishing industry is on the verge of digital transformation, catalyzed by the rapid development of artificial intelligence technologies. However, the introduction of artificial intelligence into the industry is facing a lot of opposition, with some technophobia and resistance from many market participants. Our research aims to dispel the myths surrounding artificial intelligence, demonstrate its real benefits for the publishing business, and offer strategies to overcome technophobia for successful innovation.Grail of Science10This research aims to dispel the myths surrounding artificial intelligence, demonstrate its real benefits for the publishing business, and offer strategies to overcome technophobia for successful innovation.Grail of Science["O. Sytnyk"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/988a5a1d35e9b89a353d9583aa1e2140a7882dd0 c2c8419ea9ac08466c8b0b46ce9792622d478c67Background: Artificial intelligence technologies improve the learning environment; in the near future, they are expected to provide great benefits for students and teachers, in general, and for those with disabilities and their teachers, in particular. Objective: This research has aimed at identifying the perceptions of special education teachers about the use of artificial intelligence in teaching students with disabilities as well as identifying the impact of some variables, such as the number of years of experience, disability category, or the school stage, on these perceptions. Methods and Participants: The research was based on the descriptive approach. The research sample consists of 301 male and female teachers of students with disabilities from Riyadh, Kingdom of Saudi Arabia. It includes 138 males and 163 females, divided into a group of special education programs. The research used a questionnaire on the perceptions of special education teachers about the use of artificial intelligence in educating students with disabilities. Results: The research findings showed that these teachers' perceptions were mostly neutral, that there are differences in their perceptions due to the number of years of experience, and that there are no differences in their perceptions due to the disability category or school stage variable. Conclusions: As artificial intelligence is considered one of the modern variables in the field of education for people with disabilities in the Arab environment, it is expected to support personal education, assistive technologies, data-based decision-making when teaching people with disabilities, and promoting inclusion. The research also presented a questionnaire identifying special education teachers' perceptions of artificial intelligence.Journal of Intellectual Disability - Diagnosis and Treatment210As artificial intelligence is considered one of the modern variables in the field of education for people with disabilities in the Arab environment, it is expected to support personal education, assistive technologies, data-based decision-making when teaching people with disabilities, and promoting inclusion.Journal of Intellectual Disability - Diagnosis and Treatment["Nouf Abdullah Alsudairy", "M. Eltantawy"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/c2c8419ea9ac08466c8b0b46ce9792622d478c67 8bd3dd39c7da5c0d9917bfab0c9896719faa4e08With the advancement of technology and the improvement of people's living standards, intelligent vehicles have gradually become an important component of modern transportation. However, traditional intelligent vehicles still face issues such as low accuracy in speech recognition and the need to improve safety in different scenarios in terms of safety and protection. This article aimed to explore the application value of artificial intelligence (AI) technology in the safety and protection of intelligent vehicles, in order to better meet the actual needs of current vehicles. In the article, a vehicle recognition model was first constructed using convolutional neural networks, and then the database was expanded and classified through data augmentation. Research the security architecture of smart cars to protect car data and privacy. Finally, the algorithm is compared with traditional algorithms to verify its application in the safety and protection of smart cars. Experimental results show that in the case of 10,000 sound sources, the algorithm proposed in this article can obtain an accuracy of 93.26%, while the traditional method is only 79.84 %. Research has shown that the method proposed in this article has higher accuracy in intelligent vehicle speech recognition, and its driving safety is also higher. Research has demonstrated the importance of artificial intelligence technology in the safety and protection of intelligent vehicles, providing more feasible methods for promoting the more comprehensive development of intelligent vehicles.2024 International Conference on Data Science and Network Security (ICDSNS)100Research has shown that the method proposed in this article has higher accuracy in intelligent vehicle speech recognition, and its driving safety is also higher, and the algorithm is compared with traditional algorithms to verify its application in the safety and protection of smart cars.2024 International Conference on Data Science and Network Security (ICDSNS)["Hongtao Yang", "Weichun Hou", "Yongli Sun", "Huiyan Liu"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/8bd3dd39c7da5c0d9917bfab0c9896719faa4e08 8d3daf4c66e9fc0d4f54e321e91e00035ee6ca8dUrban and regional planning faces unprecedented challenges in the 21st century, ranging from rapid urbanization and population growth to climate change and resource depletion. In addressing these challenges, artificial intelligence (AI) has emerged as a transformative toolset for planners, offering advanced analytics, predictive modeling, and optimization capabilities. In this paper, the author discusses how artificial intelligence can be integrated into urban and regional planning in India’s socio-economic landscape. It highlights the use of machine learning to predict future trends and interpret complex data sets, geospatial analysis using various AI-powered tools for spatial planning, as well as Natural Language Processing for data mining. As a way of understanding and improving urban infrastructure, deep learning techniques can be used in urban image analysis and agent-based modeling along with urban simulation for better prediction and decision-making. Nevertheless, a great number of actors make it difficult to implement such techniques locally such as the absence of valuable local data, limited infrastructure facilities, professional knowledge gaps among employees and their poor integration into existing planning processes. The article strongly stresses institutional capacity building, interagency cooperation through governance structures and open data initiatives. Importantly, there has been an indication that the Indian government is committed to artificial intelligence based on various initiatives and policies showing its willingness to embrace these technologies despite their minimal direct application in Indian urban and regional development so farInternational Journal of Arts Architecture &amp; Design100How artificial intelligence can be integrated into urban and regional planning in India’s socio-economic landscape is discussed, highlighting the use of machine learning to predict future trends and interpret complex data sets, geospatial analysis using various AI-powered tools for spatial planning, as well as Natural Language Processing for data mining.International Journal of Arts Architecture &amp; Design["Shiv Marwaha", "Dr. Himadri S Dey", "Dr. T S Brar"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/8d3daf4c66e9fc0d4f54e321e91e00035ee6ca8d b24d01223ca484dcf9c9cca3f7bb2fdc8a13cd74The purpose of this paper is to establish an intelligent analysis model of economic data based on artificial intelligence. LSTM(Long Short-Term Memory) technology will be used to improve data processing efficiency, and enhance the model's adaptability and prediction accuracy. Through this process, we hope to expand the analysis perspective of this paper and improve the processing ability of unstructured data. This paper verifies the effectiveness and superiority of LSTM model in economic data analysis through empirical research, and LSTM model provides scientific basis for economic decision-making and promotes the development of economic research. In the experimental stage, this paper discusses the performance of LSTM, ARIMA(Autoregressive Integrated Moving Average) and SVM(Support Vector Machine) models for economic data analysis, including prediction accuracy, model stability and long-term prediction ability. In the prediction accuracy experiment, LSTM model had the best performance in the prediction accuracy, and the Mean Squared Error (MSE) of ARIMA model was 0.015, MSE of ARIMA model was 0.025, and that of SVM model was 0.030. In the second model stability test experiment, LSTM model showed the best stability in the face of different noise levels, MSE increased from 0.01 to 0.04, ARIMA increased from 0.02 to 0.06, and SVM model increased from 0.03 to 0.08. In the long-term prediction ability evaluation experiment, the LSTM model showed significant advantages, and the AUC (Area Under the Curve) value reached 0.92. It can be seen from the experimental data conclusion that LSTM model is superior to ARIMA and SVM model in terms of prediction accuracy, model stability and generalization ability. Among them, LSTM model shows its excellent adaptability and accuracy when dealing with data containing noise and data in different economic environments.2024 International Conference on Data Science and Network Security (ICDSNS)140LSTM model is superior to ARIMA and SVM model in terms of prediction accuracy, model stability and generalization ability and shows its excellent adaptability and accuracy when dealing with data containing noise and data in different economic environments.2024 International Conference on Data Science and Network Security (ICDSNS)["Baowen Zhang", "Qinhe Yu", "Yunhao Zhang"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/b24d01223ca484dcf9c9cca3f7bb2fdc8a13cd74 6b8af04fd722e8a9ab3b15d6cf8ed118f409c0deWith the growing importance of engineering cost risk management in the construction sector, the integration of artificial intelligence (AI) technology has emerged as a crucial tool for enhancing the effectiveness of risk alerts and control measures. This research endeavors to delve into the utility of AI in managing engineering cost risks and to suggest appropriate warning and control approaches. By conducting a thorough literature review and case studies, the study identified and compared three AI models: the Long Short-term Memory (LSTM) Network, Support Vector Machine (SVM), and Random Forest (RF), evaluating their performance against key metrics such as Cost Variance Ratio (CVR), Schedule Performance Index (SPI), and Risk Response Efficiency (RRE). The research methodology encompassed data collection and preprocessing, model training and validation, and comprehensive performance assessment. The findings revealed that the LSTM model achieved a minimum CVR of 1.1%, a maximum SPI of 1.03, and a remarkable risk response efficiency as fast as 12.5 minutes. The study concludes that LSTM significantly enhances the precision of engineering cost risk warnings, offering a novel insight and robust technical foundation for project management in the construction industry.2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)90The study concludes that LSTM significantly enhances the precision of engineering cost risk warnings, offering a novel insight and robust technical foundation for project management in the construction industry.2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)["Weiyi Xu"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/6b8af04fd722e8a9ab3b15d6cf8ed118f409c0de 8ce557055666698328a7fe4c146c8c0bcd8cd25cThis article explores the perspectives and challenges of employing artificial intelligence (AI) in foreign language education. It examines the primary AI technologies, their advantages and limitations, as well as the future prospects of this field.Scientific and Technical Creativiy of Youth - 2024. Proceedings of Russian Scientific and Technical Conference00The perspectives and challenges of employing artificial intelligence in foreign language education and the primary AI technologies, their advantages and limitations, as well as the future prospects are explored.Scientific and Technical Creativiy of Youth - 2024. Proceedings of Russian Scientific and Technical Conference["U. A. Garaeva"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/8ce557055666698328a7fe4c146c8c0bcd8cd25c c81fb40cb0234ce098267ff7ff10593ec56c1354This research explored the use of Artificial Intelligence (AI) to positively impact on students in learning English. However, the study also recognized the potential negative impacts of AI, such as over-reliance on technology, reduced language expression and grammar proficiency. The purpose of this phenomenological qualitative research was to investigate how AI can be utilized to positively influence students in the context of English language learning. The research method involved collecting and analyzing first-hand experiences from students to understand the nuances of AI integration in English education. In this study, the researchers took second semester students of the English Education Study Program, University of Nias as participants. The research was conducted at the University of Nias. How to use AI to have a positive impact on students in learning English is by filtering the answers generated from AI.Jurnal Simki Pedagogia140Jurnal Simki Pedagogia["Y. A. Telaumbanua", "Nesti Arni Hulu", "Ayu Kartika Zai", "Delan Septiani Hulu", "Ednis Sartika Ndruru"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/c81fb40cb0234ce098267ff7ff10593ec56c1354 092947248e3d9457950a86d4d6d49405f23fbe05The creation of Artificial Intelligence (AI) in healthcare has initiated exceptional modifications in service transport and affected person care. However, the specific effect and integration of AI within children's hospitals have no longer been drastically explored. Pediatric healthcare presents specific demanding situations and requires tailored AI applications to cope with its various needs. The goal of this study is to fill this gap by inspecting the role of AI in improving provider design in children's hospitals. It investigates how AI-pushed innovations can improve affected person consequences, streamline medical institution operations, and address the precise challenges of pediatric care. Utilizing a case examine technique, the study accrued qualitative insights from numerous stakeholders in deciding on main children's hospitals. The research concerned analyzing AI implementations across diagnostic approaches, remedy making plans, and patient engagement, in conjunction with evaluating the moral and practical implications. The findings reveal that AI drastically improves diagnostic accuracy and treatment efficacy, main to higher patient outcomes. Ethical issues, specifically regarding facts privations, emerged as crucial in AI adoption. The study underscores the want for comprehensive AI integration strategies which are sensitive to the precise requirements of pediatric sufferers. This research contributes to the literature by providing empirical information on AI's impact in a pediatric context, providing a unique AI-integrated service layout version. It gives authentic insights into the scalability and ethical integration of AI, underscoring the ability of AI to revolutionize pediatric healthcare transport.Journal of Information Systems Engineering & Management360The research concerned analyzing AI implementations across diagnostic approaches, remedy making plans, and patient engagement, in conjunction with evaluating the moral and practical implications, reveals that AI drastically improves diagnostic accuracy and treatment efficacy, main to higher patient outcomes.Journal of Information Systems Engineering and Management["Jinghao Wang", "Ahmad Zuhairi Abdul Majid", "Jundi Dai"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/092947248e3d9457950a86d4d6d49405f23fbe05 a284fb12ca9b66f4f45a6a4bcf99f8418ceb3298Large Language Models (LLMs) like ChatGPT 4 (OpenAI), Claude 2 (Anthropic), and Llama 2 (Meta AI) have emerged as novel technologies to integrate artificial intelligence (AI) into everyday work. LLMs in particular, and AI in general, carry infinite potential to streamline clinical workflows, outsource resource-intensive tasks, and disburden the healthcare system. While a plethora of trials is elucidating the untapped capabilities of this technology, the sheer pace of scientific progress also takes its toll. Legal guidelines hold a key role in regulating upcoming technologies, safeguarding patients, and determining individual and institutional liabilities. To date, there is a paucity of research work delineating the legal regulations of Language Models and AI for clinical scenarios in plastic and reconstructive surgery. This knowledge gap poses the risk of lawsuits and penalties against plastic surgeons. Thus, we aim to provide the first overview of legal guidelines and pitfalls of LLMs and AI for plastic surgeons. Our analysis encompasses models like ChatGPT, Claude 2, and Llama 2, among others, regardless of their closed or open-source nature. Ultimately, this line of research may help clarify the legal responsibilities of plastic surgeons and seamlessly integrate such cutting-edge technologies into the field of PRS.Frontiers in Surgery250This work provides the first overview of legal guidelines and pitfalls of LLMs and AI for plastic surgeons and encompasses models like ChatGPT, Claude 2, and Llama 2, among others, regardless of their closed or open-source nature.Frontiers in Surgery["Leonard Knoedler", "Alexander Vogt", "Michael G Alfertshofer", "Justin M Camacho", "Daniel Najafali", "A. Kehrer", "L. Prantl", "J. Iske", "Jillian Dean", "Simon Hoefer", "Christoph Knoedler", "S. Knoedler"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/a284fb12ca9b66f4f45a6a4bcf99f8418ceb3298 f62b963db3e3fdb0be7abb691acdce00641d3778In 6G, the proliferation of artificial intelligence (AI) applications requires networks to provide ubiquitous AI services, as well as communication, computation, connection and data resources. Network slicing enables network to be divided into logically isolated slices that share underlying resources and are customized to meet the different quality of AI service (QoAIS) requirements of a large number of users. State-of-the-art proposals have not taken into account the match between user QoAIS requirements and actual network capabilities. Besides, due to the time-varying states and huge operating spaces, users may suffer from violations of service level agreements. This paper first introduces a novel QoAIS mapping method, and then optimizes resource allocation and AI services orchestration for network slicing to guarantee QoAIS requirements. Finally, taking advantages of both the Markov decision process (MDP) and deep deterministic policy gradient (DDPG), a MDP-based DDPG algorithm is designed. Simulation results reveal that the proposed algorithm can effectively reduce latency and energy consumption compared with benchmarks.2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)110A novel QoAIS mapping method is introduced, and a MDP-based DDPG algorithm is designed that can effectively reduce latency and energy consumption compared with benchmarks and takes advantages of both the Markov decision process and deep deterministic policy gradient.2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)["Qixing Wang", "Meihui Hua", "Tianjiao Chen", "Guangyi Liu", "Juan Deng", "Na Li"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/f62b963db3e3fdb0be7abb691acdce00641d3778 1046ae0ebaca6ed5de870ee670b3ae745a124beeArtificial intelligence can count as a tool for the creation of new environments in daily life, making the acquisition of new knowledge more accessible to the population, favoring the different strategies that can be implemented in its use, likewise, giving a teaching to new generations about the greatest benefit of these new tools. One of the main distinctive features arises from recognizing that Artificial Intelligence (AI) is not a current issue, therefore it is essential to observe the different perspectives in two different periods, although it can be deduced that it was created to support and reduce the work of human beings, on the other hand, to the activities to be carried out by them, so it is important to understand how the evolution of this concept has been carried out as a main role in education with proposals for different problems.Revista Metropolitana de Ciencias Aplicadas00It is important to understand how the evolution of this concept has been carried out as a main role in education with proposals for different problems.Revista Metropolitana de Ciencias Aplicadas["Joselin Camarena-L\u00f3pez", "Naomi Paulina Trejo-Garc\u00eda", "Yuleidy Uribe-Neria"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/1046ae0ebaca6ed5de870ee670b3ae745a124bee 6303a6e904fdb829d00f8619af26957e83aa76a1The article researches theoretical and practical aspects of applying artificial intelligence while learning a foreign language. Due to the widescope usage of artificial intelligence (AI) in education, in particular, learning foreign languages, students enhance their foreign language competence and expertise in digital technologies.  The peculiarities of implementing artificial intelligence into foreign language learning are substantiated, and the procedure of developing students’ foreign language competence is analyzed. The authors emphasize positive effect of AI–based technologies for motivating students to develop their professional skills including abilities in mastering foreign language. The pedagogical conditions of creating learning strategies by means of information and communication technologies taking into account educational environment as well as students’ personal characteristics are revealed. A methodological basis for constructing new knowledge on the basis of currently acquired and developed information structure is analyzed. A number of efficient directions of implementing AI-based technologies into foreign language learning under the conditions of the modern virtual environment are described.Grail of Science00The authors emphasize positive effect of AI–based technologies for motivating students to develop their professional skills including abilities in mastering foreign language.Grail of Science["Vadym Tynnyi", "Emma Schukina", "Olga Belyakova"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/6303a6e904fdb829d00f8619af26957e83aa76a1 5fe35633c4e78e00f481130220a510de87a16092With the rapid development of artificial intelligence, the ethical issues of artificial intelligence have become increasingly prominent. This paper discusses the problems of data security, bias and the implantation of morality and values in the ethics of artificial intelligence algorithms, and systematically and comprehensively expounds the main solutions to these three problems. It is found that the current three major technical paths for solving the data security issues have the problems of high dependency, large computation and communication overhead, and limited applicability, and the future trend is the integration and development of the three paths; the solution of the bias problem needs to be improved in terms of interpretability under the condition that the underlying fairness is difficult to be determined; and it is difficult for the implantation of morality and values to take into account the learning ability, adaptability, and interpretability at the same time. Finally, this paper analyzes and looks forward to the development of AI ethics, hoping to provide lessons and references for AI ethics-related research.2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI)340This paper discusses the problems of data security, bias and the implantation of morality and values in the ethics of artificial intelligence algorithms, and systematically and comprehensively expounds the main solutions to these three problems.2024 6th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI)["Xinying Xu"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/5fe35633c4e78e00f481130220a510de87a16092 cff32b4fde4737bc45b0e76ba0206d46604ab6beThe integration of Artificial Intelligence (AI) into medical education has emerged as a transformative element in the modern healthcare educational system. With the exponential growth of medical knowledge and the increasing complexity of healthcare systems, AI offers innovative solutions to enhance learning outcomes, facilitate personalized education pathways, and improve clinical decision-making skills among medical professionals. This literature review explores the transformative role of AI in the training of healthcare providers, focusing on advancements in medical education, medical diagnostics, and emergency care training. Additionally, it addresses the readiness of healthcare professionals to employ AI technologies, analyzing their current knowledge, attitudes, and the training provided. By synthesizing findings from multiple studies, we aim to highlight AI's potential to enhance medical education, address challenges, and propose future directions for integrating AI into healthcare training.medRxiv460This literature review explores the transformative role of AI in the training of healthcare providers, focusing on advancements in medical education, medical diagnostics, and emergency care training and addresses the readiness of healthcare professionals to employ AI technologies.["Shiva Rasouli", "Duha Alkurdi", "Bochen Jia"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/cff32b4fde4737bc45b0e76ba0206d46604ab6be ac31041cfc5abe7c2867584493cd209a51eaa7f9Computer power continues to grow along with the ease of access to worldwide information and the accessibility of enormous amounts of data that are ready for processing with artificial intelligence (AI) applications in the health sector. A substantial amount of data requires meticulous analysis to improve accuracy in generating datasets from AI. Soon, AI will play a crucial role in the field of dentistry in diagnosis, prediction models, restorative procedures, endodontic procedures, and orthodontic procedures. The advancements in AI technology are causing a revolution in the field of dentistry, making it easier for dentists to provide expert opinions and work with greater precision. There are numerous benefits of the use of AI discussed in this article.Global Journal of Medical, Pharmaceutical, and Biomedical Update570The advancements in AI technology are causing a revolution in the field of dentistry, making it easier for dentists to provide expert opinions and work with greater precision.Global Journal of Medical, Pharmaceutical, and Biomedical Update["Farheen Tafti", "Rohit Thorat", "Swapnali Mhatre", "Reema Srichand", "Suyog Savant", "S. Sachdev"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/ac31041cfc5abe7c2867584493cd209a51eaa7f9 69045716659fdfbc8d463e1d30b25724087618e5As an emerging analytical tool, AI (Artificial Intelligence) algorithms have shown tremendous potential in various fields. Statistical analysis, as the foundation of data processing and inference, can also benefit from the advantages of AI algorithms. The article first introduced the basic principles and common application areas of AI algorithms. The main AI algorithm used in the article was the support vector machine in machine learning algorithms. Then, it discussed in detail the application cases of support vector machines in iris feature statistical analysis, including data mining, pattern recognition, and predictive analysis. Through empirical research and case analysis, this article demonstrated the advantages and potential of support vector machines in iris feature statistical analysis. The model established using this method had a highest accuracy of 95.7%. Finally, the article summarized the current research progress and existing problems, and proposed future research directions and development trends.2024 International Conference on Data Science and Network Security (ICDSNS)140Through empirical research and case analysis, this article demonstrated the advantages and potential of support vector machines in iris feature statistical analysis and summarized the current research progress and existing problems, and proposed future research directions and development trends.2024 International Conference on Data Science and Network Security (ICDSNS)["Qiuming Jiang", "Song Li"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/69045716659fdfbc8d463e1d30b25724087618e5 52858e5be85ce29ba89244e2e9dd213c0b4988c0 Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that haven’t been mined and formalized in an integrated way. It requires substantial effort and time to extract scientific information from these works. AM domain experts have contributed over two dozen review papers to summarize these works. However, information specific to AM and AI contexts still requires manual effort to extract. The recent success of foundation models such as BERT (Bidirectional Encoder Representations for Transformers) or GPT (Generative Pre-trained Transformers) on textual data has opened the possibility of expediting scientific information extraction. We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature. A demonstration tool is implemented based on the proposed framework and a case study is conducted to extract information relevant to the datasets, modeling, sensing, and AM system categories. We show the ability of LLMs (Large Language Models) to expedite the extraction of relevant information from data-driven AM literature. In the future, the framework can be used to extract information from the broader design and manufacturing literature in the engineering discipline.Conference on Computability in Europe260A framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature is proposed and the ability of LLMs (Large Language Models) to expedite the extraction of relevant information from data-driven AM literature is shown.ArXiv["Mutahar Safdar", "Jiarui Xie", "Andrei Mircea", "Y. Zhao"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/52858e5be85ce29ba89244e2e9dd213c0b4988c0 bb76153d4f3d77e5f20c73e600bcdf03ff7753ddArtificial intelligence (AI) entered all aspects of human life and became a ubiquitous presence we interact with daily, influencing or even determining, among other things, our decisions, social interactions, and the digital content we follow. Add to this the recent breathtaking advances such as generative AI models and the hype heralding the imminence of Artificial General Intelligence, and we are facing one of the most significant challenges in history for our society and understanding of what it means to be human. In this context, this work explores the anthropological implications of AI by drawing on the theology of Joseph Ratzinger/Pope Benedict XVI a prominent figure who, through his writings and actions, has contributed significantly to the dialogue between theology, philosophy, and the natural sciences. This article continues this dialogue and examines how AI challenges the creative potential of human beings and how this potential risks losing meaning and direction in the absence of God. After analyzing the relationship between the human and the artificial, the article looks at the performative-informative paradigm shift driven by AI in today’s digital society and the challenges of living in a machine-readable world that aims to reduce people to numbers. Finally, the article concludes by discussing how the way we relate to AI affects our relationship with God and each other.Journal of moral theology10The anthropological implications of AI are explored by drawing on the theology of Joseph Ratzinger/Pope Benedict XVI a prominent figure who, through his writings and actions, has contributed significantly to the dialogue between theology, philosophy, and the natural sciences.Journal of Moral Theology["O. Machidon"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/bb76153d4f3d77e5f20c73e600bcdf03ff7753dd 967bb2348f3dda6d9a1df3869758d258ae16bc2bEnsuring good air quality is imperative for sustaining life on Earth. The escalating concerns of air quality in densely populated urban landscapes like Delhi, underscore the need for effective interventions. As urbanization and population growth exacerbate environmental issues, the deterioration of air quality becomes a paramount threat. This study explores how artificial intelligence can play a pivotal role in proactively managing air quality. The research evaluates the performance of three forecasting models for predicting PM2.5 and Air Quality Index (AQI) in Alipur, Delhi, India: Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and Seasonal Autoregressive Integrated Moving Average with exogenous variables (SARIMAX). Our analysis indicates that the Bi-LSTM model outperformed the others, demonstrating a Mean Absolute Error (MAE) of 0.0411 for PM2.5 and 0.0754 for AQI. This research not only highlights the effectiveness of AI models in capturing intricate temporal patterns but also emphasises their significance in formulating preemptive measures during episodes of poor air quality.2024 Asia Pacific Conference on Innovation in Technology (APCIT)180Analysis of three forecasting models for predicting PM2.5 and Air Quality Index in Alipur, Delhi, India indicates that the Bi-LSTM model outperformed the others, demonstrating a Mean Absolute Error (MAE) of 0.0411 for PM2.5 and 0.0754 for AQI.2024 Asia Pacific Conference on Innovation in Technology (APCIT)["Vaishvi Shah", "Kahaan Patel", "R. Gupta", "Hiteshri Yagnik"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/967bb2348f3dda6d9a1df3869758d258ae16bc2b e5928b6392038f69c81a6f29aa616c91ca2e0ec1This study investigated the impact of artificial intelligent based embedded security solution model for PID controller. Recent studies have revealed that the conventional mitigation techniques like zoning, demilitarization, firewalls, company’s policies to mention but a few are no longer enough to match the sophisticated intelligence being deployed by attackers hence the urgent need for the adoption of artificial intelligent based embedded security solution model. Furthermore, conventional IT security solutions cannot be deployed directly in process controllers given the real time availability requirement of industrial control systems. This has limited the security solutions to firewalls, network segmentation and some laid down policies which the control system operators are not expected to violate. This study will ultimately help deliver algorithms that will be implemented in industrial field device controllers making them resilient to cyber-attacks whose consequences have far reaching implications. With regards to the methodology of the work, four sets of data were collected from the test bed. The first data, Data_1, shown in appendix D is the plant’s response to the existing PID algorithm. This was done by taking the temperatures of the plant at interval of 3 seconds after loading the controller with the existing (insecure) PID algorithm shown in appendix A. A total of 100 samples were taken. From the methodology employed, it was discovered that it is seen that the existing PID algorithm was able to achieve the control objective of maintaining the temperature of the process plant within the temperature range of 38 o C and 43 o C with 40 o C as the optimal or ideal performance. From table I in appendix I, the mean steady state error (MSSE) of the existing PID algorithm considering the 27th to 101th temperature data is 0.062631579 which is approximately 0.06. It means the accuracy of the existing PID algorithm is ((40 – 0.06)/40) ∗ 100 = 99. 85 %. This showed that the existing PID control algorithm’s performance is acceptable under normal condition since the minimum control accuracy required to achieve the control objective in the considered process plant is (40 – (3-2)/2)/40) ∗ 100 = 98. 75 %.International Journal of Innovative Science and Research Technology431It was discovered that the existing PID algorithm was able to achieve the control objective of maintaining the temperature of the process plant within the temperature range of 38 o C and 43 o C with 40 o C as the optimal or ideal performance.International Journal of Innovative Science and Research Technology (IJISRT)["Bitrus Haruna", "Mathew Ehikhamenle"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/e5928b6392038f69c81a6f29aa616c91ca2e0ec1 becc37a62d57e5b9e202bdce47134f3d6ee9cdb4Journal of Public Health Policy471Preliminary evidence is provided that chatbots can spark behaviour change, with applications in diverse and underrepresented groups, and reduced uncertainty about protective behaviours is found.Journal of Public Health Policy["Simon Thomas van Baal", "Suong Le", "Farhad Fatehi", "A. Verdejo-Garc\u00eda", "Jakob Hohwy"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/becc37a62d57e5b9e202bdce47134f3d6ee9cdb4 4f655f71d3f488e173586db44950ab4b5ef9cf7eEducation and Information Technologies : Official Journal of the IFIP technical committee on Education552Education and Information Technologies["Shaofeng Wang", "Zhuo Sun"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/4f655f71d3f488e173586db44950ab4b5ef9cf7e 874621418dc0d10f8bf5c8241549412c77781665ICCBD60{"pages": "212-216"}["Xianyi Chen"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/874621418dc0d10f8bf5c8241549412c77781665 23e0f2eb9e7d86046f1fda8fd173e23e3754b5b3ICCBD110{"pages": "583-588"}["Zhike Qiu", "Yu Qin"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/23e0f2eb9e7d86046f1fda8fd173e23e3754b5b3 25ee9cccfa0375de2cf00a2b2f78b9e29962d9baRevista Electronica De Veterinaria00Revista Electronica De Veterinaria["Dr.Vinod Kumar Kanvaria"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/25ee9cccfa0375de2cf00a2b2f78b9e29962d9ba 79da8f9c32b4da77aa4ad7460ac15cf2fbb3e7e1The demand for AI security and privacy has been growing. It is necessary to innovate in ways and technologies for reducing the privacy leakage rate. This chapter proposes a defense strategy from the perspective of the machine learning model itself to prevent anti-attack risk and privacy leakage risk. Based on the comprehensive study of the existing research, this chapter discusses the key defense strategies for machine learning models, including confrontation training, model distillation, input space restriction, and model repair. In addition, it also talks about the application of differential privacy technology, data desensitization, and access control strategies in privacy protection. Finally, the chapter evaluates the privacy leakage rate of algorithms, and the results show that it is between 1% and 1.8%. Among these methods, the defense strategy of confrontation training has a relatively good performance, and introducing confrontation samples can make the robustness of the model significantly improved.2024 International Conference on Data Science and Network Security (ICDSNS)170This chapter proposes a defense strategy from the perspective of the machine learning model itself to prevent anti-attack risk and privacy leakage risk, and discusses the key defense strategies for machine learning models, including confrontation training, model distillation, input space restriction, and model repair.2024 International Conference on Data Science and Network Security (ICDSNS)["Xue Yu"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/79da8f9c32b4da77aa4ad7460ac15cf2fbb3e7e1 1fbf0d0ff35efb9d86e42701465d1d744722467fProfessional Case Management10Professional case management["L. S. Muller"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/1fbf0d0ff35efb9d86e42701465d1d744722467f 5133213e7901d87e0dc01b4a8e333a102f93fc76Penelitian ini bertujuan untuk menguji tingkat validitas, reliabilitas, dan tingkat kesukaran soal yang dihasilkan oleh kecerdasan buatan (AI) dengan menggunakan platfrom QuestionWell. Metode penelitian menggunakan pendekatan deskriptif kuantitatif dengan analisis validitas, reliabilitas, dan tingkat kesukaran soal secara empirik. Sampel penelitian terdiri dari 32 siswa kelas 8 SMP Bina Insan Mandiri. Soal yang diujikan sebanyak 20 butir soal berbentuk pilihan ganda sesuai dengan materi yang dipelajari siswa. Analisis data dilakukan menggunakan korelasi Point Biserial untuk validitas butir soal, KR-20 untuk reliabilitas, dan uji proporsi untuk tingkat kesukaran soal. Hasil penelitian menunjukkan bahwa sebanyak 60% soal yang dihasilkan oleh AI terbukti valid. Tingkat reliabilitas soal mencapai angka 0,743, menunjukkan konsistensi yang dapat diandalkan dalam pengukuran. Seluruh soal dikategorikan sebagai soal dengan tingkat kesukaran yang mudah. Berdasarkan pemeringkatan kompetensi membaca, seluruh butir soal termasuk pada tingkat dasar. Kelebihan pada pembuat soal ini adalah penggunaannya berpotensi dalam meningkatkan efisiensi dan membantu guru dalam membuat soal secara otomatis. Namun kekurangannya yaitu, guru perlu memastikan kembali soal yang akan diujikan sesuai dengan tujuan pembelajaran dan soal yang dihasilkan belum variatif dalam mengukur kompetensi membaca siswa. Dengan demikian, peneliti merekomendasikan untuk memandang AI sebagai sarana pendukung dalam penyusunan instrumen tes. Soal yang dihasilkan AI hanya sebagai bahan masukan bagi guru, bukan sebagai instrumen final. Peran guru dalam hal ini adalah berkolaborasi dengan AI untuk mengoptimalisasi peran AI dalam ranah pendidikan.Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI)270Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI)["Najwa Zalfa Zuhri", "S. Syihabuddin", "Tatang Tatang"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/5133213e7901d87e0dc01b4a8e333a102f93fc76 68f19eebb9f5e854352794f5b64750f1cfe5827eICCBD10{"pages": "509-514"}["Xiwei Xiao", "Ya-Hong Xiao"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/68f19eebb9f5e854352794f5b64750f1cfe5827e 8e5f43a28cbe02c774c677e04afd9ecfbfb81152Existing research on AI applications in HRM has focused on specific functions with limited exploration of other areas. Changes in publication trends and focuses coinciding with COVID-19 require investigation. Study conducted a bibliometric analysis of 233 publications from 2015-2024 in the Scopus database, analyzing trends in sources, authorship, institutions, countries, keywords and themes. The results show an exponential surge in the annual number of publications since 2020, indicating that COVID-19 accelerated interest in AI-driven workforce innovations. Research efforts became more concentrated among prolific authors, core journals, and leading universities. Study reveals how the pandemic triggered substantive changes, transforming AI-HRM research into a more globally engaged and focused field. This is the first comprehensive bibliometric study to analyze how COVID-19 transformed AI-HRM research landscape in terms of quantitative trends, participants and investigated topics.Formosa Journal of Multidisciplinary Research180This is the first comprehensive bibliometric study to analyze how COVID-19 transformed AI-HRM research landscape in terms of quantitative trends, participants and investigated topics.Formosa Journal of Multidisciplinary Research["Dipak Mahat"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/8e5f43a28cbe02c774c677e04afd9ecfbfb81152 d7899d052c63d5a3c66170e9bdf226197a379885VII Международная научно-практическая конференция «Инновационное развитие современной науки: новые подходы и актуальные исследования»00VII Международная научно-практическая конференция «Инновационное развитие современной науки: новые подходы и актуальные исследования»["\u0418.\u041c. \u041f\u043e\u043f\u043e\u0432\u0430", "\u041b.\u0418. \u0427\u0438\u0440\u0438\u043a\u043e\u0432\u0430", "\u0415.\u0410. \u0410\u0431\u0434\u0443\u043b\u0438\u043d\u0430"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/d7899d052c63d5a3c66170e9bdf226197a379885 049aa94f909a45fb80bac9b5e121a20eb618a342ICCBD160{"pages": "402-408"}["Shi-Xin Li", "Li Zhang", "Mengchen Gu", "Jian Chan", "Qi Wu"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/049aa94f909a45fb80bac9b5e121a20eb618a342 f79413376e62a7d7f224333945a4c3e8ce48fda5XVIII Международная научно-практическая конференция «Современные стратегии и цифровые трансформации устойчивого развития общества, образования и науки»00XVIII Международная научно-практическая конференция «Современные стратегии и цифровые трансформации устойчивого развития общества, образования и науки»["\u042f. \u0427\u0436\u0430\u043d", "\u0422.\u041d. \u0413\u0443\u0431\u0430\u0439\u0434\u0443\u043b\u043b\u0438\u043d\u0430"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/f79413376e62a7d7f224333945a4c3e8ce48fda5 ffd232b4e99d03652c5f87f0c98beaa63ca02786The research dealt with climate drought, its types, the causes that lead to drought, and how to reduce drought. Drought in the city of Mosul, northern Iraq, was also analyzed and studied by obtaining monthly rainfall data for the Mosul climate station located within the study area for the period from 1981-2018. Previous studies and research conducted to study drought in different regions of the world were taken into consideration. Most of these studies and researches use drought indices, which are among the most widely used indices in estimating the amounts of deficit, their use, severity, and their impact on the water balance. The standard rainfall index (SPI) is one of the most widely used indices in estimating climate drought. (SPI) is characterized by many characteristics that distinguish it from other indicators. The standard rainfall index (SPI) technique was used in analyzing rain records. The analysis principle using the standard rainfall index is statistically based on the principle of converting the gamma distribution of the data series to the normal distribution. Positive SPI values mean that there is an increase in rainfall above the average rainfall, i.e. wet years, while negative values mean that there is a decrease in rainfall below the average rainfall, i.e. dry years. SPI values for a period of 12 months were adopted in the analysis because they cover the annual rainfall amount falling on the station during a year. Using the MATLAB program, several networks were created and tested, and the network with the best performance was selected from among the networks. 30 annual rainfall values were used against the SIP values calculated using equations and the Excel program to train the neural network on the data. While the rainfall data for the remaining 8 years were used to verify the results of the neural network by comparing the results of the neural network with the actual values recorded at the measuring station. This network was able to obtain the index value by simply entering the annual rainfall value. By comparing the index value with the drought classification table, the drought class can be determined without resorting to the calculation method. The network with the 1-7-1 structure (input layer, hidden layer containing seven neurons, and output layer) with the TRAINLM training function and the LEARNGDM learning function gave the best performance, as the correlation coefficient between its results and the actual results (which were not included in the training) was equal to 0.99 and the square error rate was 0.014, meaning that the results of this network can be adopted for the purpose of calculating the standard rain index with high confidence in the outputsEuropean Journal of Medical Genetics and Clinical Biology250The research dealt with climate drought, its types, the causes that lead to drought, and how to reduce drought by obtaining monthly rainfall data for the Mosul climate station located within the study area for the period from 1981-2018.European Journal of Medical Genetics and Clinical Biology["Abdul Basit Hamza Jalal Salman", "Ahmed Emad Milli Hamidan", "Worood Abdul Redha Sharif Nayef Al-Mousawi", "Zainab haider Mussa Saadoun", "Esraa Ali Hussein Ali"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/ffd232b4e99d03652c5f87f0c98beaa63ca02786 e48fbccea1f7abfefb2b2a9bca2b202b7fd5615fIn the rapidly evolving field of artificial intelligence, distinctions between science fiction and reality are becoming increasingly hazy. Imagine a future where a person’s spirit lives on beyond death, where we can still connect with our loved ones through digital platforms even after they pass away. The notion of maintaining awareness within a digital realm, sparked by the imaginative depiction in the 2014 motion picture Transcendence, is no longer confined to science fiction. We are about to witness a technological advance that will change the game: the ability to create digital replicas that are intricate imprints of our true nature rather than merely simulations.2024 Asia Pacific Conference on Innovation in Technology (APCIT)50In the rapidly evolving field of artificial intelligence, distinctions between science fiction and reality are becoming increasingly hazy, and the authors are about to witness a technological advance that will change the game: the ability to create digital replicas that are intricate imprints of their true nature rather than merely simulations.2024 Asia Pacific Conference on Innovation in Technology (APCIT)["Nidhi Bisht", "Arushi Rajvanshi", "Priyanka Saklani", "Harshit Narang", "S. Vats", "V. Sharma"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/e48fbccea1f7abfefb2b2a9bca2b202b7fd5615f 6a56a5f12c87515b6ab3e3757eeab89bed0b8e8eHuman emotion detection from images poses a significant challenge in social communication research. This paper introduces an artificial intelligence (AI) system designed for emotion detection through facial expressions, utilizing deep learning (DL) techniques. The proposed method surpasses traditional image processing approaches, showcasing improved performance. The emotion detection process consists of three key steps: face detection, feature extraction, and emotion classification. The paper presents a convolutional neural networks (CNN) based architecture tailored for deep learning in emotion detection from images.The have a observe delves in addition into the version's conduct with the aid of analyzing a Confusion Matrix, providing valuable insights into times of misclassifications across exceptional emotional states. The studies underscore the significance of emotion reputation in numerous programs, together with human-pc interaction, intellectual health assessment, and sentiment assessment. Leveraging current-day deep studying techniques, in particular CNNs, proves promising in automatically extracting complex functions from facial expressions for proper emotion categorization. The technique includes acquiring and preprocessing a whole dataset to facilitate strong education and assessment of the CNN model.Experimental effects exhibit the efficacy of the proposed method in adeptly figuring out and categorizing emotional states. Rigorous assessment metrics, inclusive of precision, bear in thoughts, F1 rating, and accuracy, display off the version's capability to generalize well to severa emotional expressions. In stop, this study contributes to advancing emotion detection via cutting-edge CNNs, highlighting their capability effect on various actual-worldwide packages.2024 Asia Pacific Conference on Innovation in Technology (APCIT)190This paper introduces an artificial intelligence (AI) system designed for emotion detection through facial expressions, utilizing deep learning techniques, and presents a convolutional neural networks (CNN) based architecture tailored for deep learning in emotion detection from images.2024 Asia Pacific Conference on Innovation in Technology (APCIT)["Chandradeep Bhatt", "Albeena Tasleem", "Nishant Rawat", "Sachin Singh", "Tanisha Bahuguna", "Teekam Singh"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/6a56a5f12c87515b6ab3e3757eeab89bed0b8e8e f9b321b11748aeb8d5984d1dad15d3e5f306cc5fHigh cost of building makes houses expensive for US citizens and residents. Thus, this study proposes the leveraging of cost-effective artificial intelligence (AI) and smart technologies (ST) for rapid infrastructural development in US. It considers them as sustainable means of tackling the challenges for the attainment of affordable houses. The study explores the potentials of prominent AI and smart technologies capable of reducing the cost of building houses in the US, for which houses would become affordable for all. The primary data are obtained from telephone interviews with 10 construction workers and 5 experts of AI, alongside observation and introspection. The secondary data are drawn from library and the internet. Qualitative method, thematic and content analyses, systematic review, and descriptive and interpretive tools are employed. The results show Machine Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, and Robotic Process Automation to be prominent cost-effective AI technologies, while Building Automation Systems, Internet of Things, Renewable Energy Systems, and Smart Water Management Systems are cost-effective smart technologies. The study concludes that the identified AI and smart technologies are not only cost-effective, but also transformative and innovation-driven and can be leveraged to increase efficiency, productivity, quality delivery and satisfactory services. The study recommends them to government and organizations for cost-effectiveness towards attaining rapid infrastructural development in the USA.African Journal of Advances in Science and Technology Research375African Journal of Advances in Science and Technology Research["Akintayo Philips"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/f9b321b11748aeb8d5984d1dad15d3e5f306cc5f a4d77ec84060e768d443d3967ded168dfd73598dThe integration of Artificial Intelligence (AI) into education has transformative potential, providing tailored learning experiences and creative instructional approaches. However, the inherent biases in AI algorithms hinder this improvement by unintentionally perpetuating prejudice against specific demographics, especially in human-centered applications like education. This survey delves deeply into the developing topic of algorithmic fairness in educational contexts, providing a comprehensive evaluation of the diverse literature on fairness, bias, and ethics in AI-driven educational applications. It identifies the common forms of biases, such as data-related, algorithmic, and user-interaction, that fundamentally undermine the accomplishment of fairness in AI teaching aids. By outlining existing techniques for mitigating these biases, ranging from varied data gathering to algorithmic fairness interventions, the survey emphasizes the critical role of ethical considerations and legal frameworks in shaping a more equitable educational environment. Furthermore, it guides readers through the complexities of fairness measurements, methods, and datasets, shedding light on the way to bias reduction. Despite these gains, this survey highlights long-standing issues, such as achieving a balance between fairness and accuracy, as well as the need for diverse datasets. Overcoming these challenges and ensuring the ethical and fair use of AI's promise in education call for a collaborative, interdisciplinary approach.arXiv.org1953This survey delves deeply into the developing topic of algorithmic fairness in educational contexts, providing a comprehensive evaluation of the diverse literature on fairness, bias, and ethics in AI-driven educational applications.ArXiv["Sribala Vidyadhari Chinta", "Zichong Wang", "Zhipeng Yin", "Nhat Hoang", "Matthew Gonzalez", "Tai Le Quy", "Wenbin Zhang"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/a4d77ec84060e768d443d3967ded168dfd73598d 7ba5bd13a75415d3b4d8df872bd1be2617db1564Artificial Intelligence Review613The effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors, which provides researchers with a new understanding of the interplay between artificial intelligence and human intelligence.Artif. Intell. Rev.["Adrien B\u00e9cue", "Jo\u00e3o Gama", "Pedro Quelhas Brito"]2024-07-26T00:00:00https://www.semanticscholar.org/paper/7ba5bd13a75415d3b4d8df872bd1be2617db1564 deaa747c3f7c09bad54b049600c0def92b39b4dcInternational Workshop on Artificial Intelligence and Cognition002024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC)[]2024-07-27T00:00:00https://www.semanticscholar.org/paper/deaa747c3f7c09bad54b049600c0def92b39b4dc e9cc357fd4e9d6155d6cf0965d0238d876f6a6d5arXiv.org4215This chapter identifies two promising strategies for meeting the “AI challenge,” focusing on the example of Fintech, and suggests that these two strategies are interconnected, in that greater investment is an important element in both fostering and signaling a well-functioning innovation ecosystem and that a well -functioning ecosystem will, in turn, attract more funding.ArXiv["M. Fenwick", "E. Vermeulen", "Marcelo Corrales Compagnucci"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/e9cc357fd4e9d6155d6cf0965d0238d876f6a6d5 e7cce7e8058b4acf21edb725f9fe7accfe6a987bTechnological advancement in various aspects of life has led to integrating artificial intelligence into educational practices. Students’ use of artificial intelligence assistance tools has become more fundamental in academic settings, which evolved a range of positive and negative perspectives. The current study explores the impact of artificial intelligence assistance tools on students’ overall personal and academic performance. Hence, this article is significant as it evaluates how Moroccan high school students use artificial intelligence assistance tools to solve their homework assignments. The study attempts to answer to what extent these students rely on these tools and examine the teachers’ attitudes and concerns toward the impact of these evolving changes that artificial intelligence has brought to their classrooms. A mixed-method approach is used to achieve the study’s objectives, employing both quantitative and qualitative methods. Therefore, the findings indicate that students rely heavily on artificial intelligence to complete their everyday homework tasks, which impedes their learning process and skills acquisition. These findings provide several recommendations to policymakers, parents, educators, and learners to be aware of the adverse effects of the overuse of artificial intelligence assistance tools on students’ learning outcomesArab World English Journal171The findings indicate that students rely heavily on artificial intelligence to complete their everyday homework tasks, which impedes their learning process and skills acquisition.Arab World English Journal["Jihane Tamimi", "Essafa Addichane", "Sadik Madani ALAOUI"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/e7cce7e8058b4acf21edb725f9fe7accfe6a987b c59a2fbd6f1f3b9f47c73d6d766cb1b6cb0a8d1dThis qualitative study aimed to explore how teachers of English perceive the advantages and disadvantages of using artificial intelligence by Saudi students who study English as a Foreign Language. The study used semi-structured interviews to delve into teachers’ pedagogical beliefs, ethical concerns, and expectations regarding using artificial intelligence tools by Saudi students, using the College of Languages and Translation at Al-Imam Mohammed Bin Saud Islamic University as a case study. The main research question focused on examining the positive and negative impacts of artificial intelligence on students’ language performance. The study findings revealed several themes from teachers’ interviews, including strategies for implementing artificial intelligence in the classroom, the impacts of artificial intelligence on students’ language proficiency, and the importance of guiding students to effectively use artificial intelligence applications. The findings also highlighted teachers’ expectations for expanding open-source language learning online channels and the widespread use of robots in English classrooms. The study recommends aligning professional development programs with language curricula to equip teachers with the necessary skills for effectively integrating artificial intelligence technologies into the classroom. The significance of this study stems from its contribution to the current debate on using artificial intelligence in education, presenting empirical evidence on its impacts on students’ language performance.Arab World English Journal371The study used semi-structured interviews to delve into teachers’ pedagogical beliefs, ethical concerns, and expectations regarding using artificial intelligence tools by Saudi students, using the College of Languages and Translation at Al-Imam Mohammed Bin Saud Islamic University as a case study.Arab World English Journal["Amal Abdul-Aziz Mohammed Al-Othman"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/c59a2fbd6f1f3b9f47c73d6d766cb1b6cb0a8d1d 08e23dfd79888f5bee4d04ae99cf52607a166955Psychotherapy is crucial for addressing mental health issues but is often limited by accessibility and quality. Artificial intelligence (AI) offers innovative solutions, such as automated systems for increased availability and personalized treatments to improve psychotherapy. Nonetheless, ethical concerns about AI integration in mental health care remain. This narrative review explores the literature on AI applications in psychotherapy, focusing on their mechanisms, effectiveness, and ethical implications, particularly for depressive and anxiety disorders. A review was conducted, spanning studies from January 2009 to December 2023, focusing on empirical evidence of AI’s impact on psychotherapy. Following PRISMA guidelines, the authors independently screened and selected relevant articles. The analysis of 28 studies provided a comprehensive understanding of AI’s role in the field. The results suggest that AI can enhance psychotherapy interventions for people with anxiety and depression, especially chatbots and internet-based cognitive-behavioral therapy. However, to achieve optimal outcomes, the ethical integration of AI necessitates resolving concerns about privacy, trust, and interaction between humans and AI. The study emphasizes the potential of AI-powered cognitive-behavioral therapy and conversational chatbots to address symptoms of anxiety and depression effectively. The article highlights the importance of cautiously integrating AI into mental health services, considering privacy, trust, and the relationship between humans and AI. This integration should prioritize patient well-being and assist mental health professionals while also considering ethical considerations and the prospective benefits of AI.Indian Journal of Psychological Medicine472Indian Journal of Psychological Medicine["M. J. Beg", "Mohit Verma", "Vishvak Chanthar K. M. M.", "Manish Kumar Verma"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/08e23dfd79888f5bee4d04ae99cf52607a166955 30e3f8664faf419527a2fc9bf99029a422893fabThis training program is designed to enhance the competency of elementary school teachers in developing practical actions on the Merdeka Mengajar Platform (PMM) by utilizing Artificial Intelligence (AI) technology. Through the use of Turnitin for plagiarism checks and Gamma for document preparation, the program aims to ensure that the work produced is original and well-structured. The training involves teachers in intensive sessions that include the introduction and use of Turnitin to detect plagiarism, as well as Gamma to assist in preparing practical action documents that meet Kemendikbud's validation standards. The results of the training show a significant improvement in teachers' ability to produce creative, innovative, and compliant practical actions according to the assessment criteria. Teachers participating in this program can produce more professional and plagiarism-free documents, thanks to the constructive feedback from AI. Furthermore, the resulting practical action documents have a clear and attractive structure, facilitating the validation process. This program also encourages the formation of a learning community among teachers, where they share best practices and support continuous improvement. Thus, this training program not only enhances teachers' digital competence but also contributes to the overall improvement of education quality. The use of AI in education becomes an important step in preparing teachers to face challenges in the digital era.Journal Of Computer Science Contributions (JUCOSCO)00This training program is designed to enhance the competency of elementary school teachers in developing practical actions on the Merdeka Mengajar Platform by utilizing Artificial Intelligence (AI) technology, and aims to ensure that the work produced is original and well-structured.Journal Of Computer Science Contributions (JUCOSCO)["Rafika Sari", "Ratna Sari", "Khairunnisa Fadhilla Ramdhania", "Juhanda"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/30e3f8664faf419527a2fc9bf99029a422893fab 396968600ac44eba778053562467bedd11defe51In the era of artificial intelligence, the teaching mode of higher education is undergoing unprecedented changes. The traditional teaching model centered on teachers and mainly based on classroom lectures is gradually shifting towards a new model dominated by student-centered, interactive, and personalized learning. This change is not only reflected in teaching tools and technology, but also has a profound impact on educational philosophy and teaching methods.Journal of social sciences and humanities50In the era of artificial intelligence, the teaching mode of higher education is undergoing unprecedented changes, and this change is not only reflected in teaching tools and technology, but also has a profound impact on educational philosophy and teaching methods.Journal of Social Science and Humanities["Chunlin Wang"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/396968600ac44eba778053562467bedd11defe51 e1baeb75efad9a60a911f54439d88a32791bf896Cognitive Behavioral Therapy (CBT) is a well-established intervention for mitigating psychological issues by modifying maladaptive cognitive and behavioral patterns. However, delivery of CBT is often constrained by resource limitations and barriers to access. Advancements in artificial intelligence (AI) have provided technical support for the digital transformation of CBT. Particularly, the emergence of pre-training models (PTMs) and large language models (LLMs) holds immense potential to support, augment, optimize and automate CBT delivery. This paper reviews the literature on integrating AI into CBT interventions. We begin with an overview of CBT. Then, we introduce the integration of AI into CBT across various stages: pre-treatment, therapeutic process, and post-treatment. Next, we summarized the datasets relevant to some CBT-related tasks. Finally, we discuss the benefits and current limitations of applying AI to CBT. We suggest key areas for future research, highlighting the need for further exploration and validation of the long-term efficacy and clinical utility of AI-enhanced CBT. The transformative potential of AI in reshaping the practice of CBT heralds a new era of more accessible, efficient, and personalized mental health interventions.arXiv.org1670The integration of AI into CBT across various stages: pre-treatment, therapeutic process, and post-treatment is introduced, and the benefits and current limitations of applying AI to CBT are discussed.ArXiv["Meng Jiang", "Qing Zhao", "Jianqiang Li", "Fan Wang", "Tianyu He", "Xinyan Cheng", "Bing Xiang Yang", "Grace W.K. Ho", "Guanghui Fu"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/e1baeb75efad9a60a911f54439d88a32791bf896 f418c972c750d9466aec559a4fdffe0d385bcc20With the rapid development of artificial intelligence technology, its application in university classroom teaching is becoming increasingly widespread, bringing unprecedented changes to the field of education. In recent years, the breakthrough of artificial intelligence technology is not only reflected in the optimization of algorithms and the improvement of computing power, but also in its deep integration into the teaching process, achieving intelligent management of teaching resources and precise implementation of personalized teaching. In addition, the rapid development of artificial intelligence technology has also given rise to innovative teaching tools such as virtual laboratories and intelligent teaching assistants, which not only enrich teaching content but also provide students with more immersive and interactive learning experiences.Journal of Research in Vocational Education40The breakthrough of artificial intelligence technology is not only reflected in the optimization of algorithms and the improvement of computing power, but also in its deep integration into the teaching process, achieving intelligent management of teaching resources and precise implementation of personalized teaching.Journal of Research in Vocational Education["Chunlin Wang"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/f418c972c750d9466aec559a4fdffe0d385bcc20 156494c77d90d8e2deed7fced4395d1b168413fb: Research Question (RQ): How do testing laboratories use artificial intelligence (AI) and what challenges arise from the use of AI tools? Purpose: To investigate the use of AI in Slovenian and Croatian testing laboratories, to analyse the impact of the complexity of measurement methods and equipment and to predict trends in this area. Method: A questionnaire was developed for the study. Representatives of 125 randomly selected testing laboratories in Slovenia and Croatia performing accreditation activities according to SIST EN ISO/IEC 17025:2017 were invited to participate. In addition to descriptive statistics, the Kruskal-Wallis and Mann-Whitney U tests were used to analyse the data. Results: 44 laboratories responded. The survey shows that most testing laboratories expect increased use of AI tools in the future and that laboratory staff recognise the benefits in terms of efficiency, accuracy and error reduction. However, according to the participants, the use of AI in Slovenian and Croatian laboratories is still limited due to the lack of qualified personnel, technical limitations and high initial costs. Laboratories that have more sophisticated measuring equipment perceive AI tools differently than laboratories that do not operate such equipment. The challenge for the future is to use AI to improve the quality of laboratory services, increase efficiency, improve progress and limit costs. Organisation: The use of AI enables the development of new business models based on the automation and digitalisation of laboratory processes. Research enables organisations to better understand and exploit the potential of AI. SocietyIzzivi Prihodnosti190The survey shows that most testing laboratories expect increased use of AI tools in the future and that laboratory staff recognise the benefits in terms of efficiency, accuracy and error reduction, but according to the participants, the use of AI in Slovenian and Croatian laboratories is still limited.Izzivi prihodnosti["Milan Simon\u010di\u010d"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/156494c77d90d8e2deed7fced4395d1b168413fb 45172e056a78276b08372cef0a4b33ac9503a4f0The paper discusses the problem of the artificial future in learning the English language. English occupies a leading place in all spheres of human activity. In the 21st century, it is not easy to imagine human activity without the help of AI. AI, as a unique invention of humanity, has its history. It is also actively used in foreign language learning. Many researchers work in this field, but there is no single conclusion about the positive and negative aspects of using AI in language learning. The research was conducted according to the fundamental question of whether AI is the future or the utopia of learning English. Respondents answered five questions. The results show that all people use AI in their lives. AI is a practical tool in translation and automatic spelling correction. It is not surprising that all respondents have practical communicative experience with AI. Regarding the future of AI, the responses have demonstrated the undeniable need for AI and a positive attitude toward it. However, it is worth remembering that only the intelligent use of AI will have significant consequences in the future.Arab World English Journal150The research was conducted according to the fundamental question of whether AI is the future or the utopia of learning English, and the results show that all people use AI in their lives.Arab World English Journal["Andrii Vornachev", "Lesia Kushmar", "I. Hrachova", "Nina Nikolska"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/45172e056a78276b08372cef0a4b33ac9503a4f0 e7cb5ad21ad36081d4a75a78c0b84fb750e0012fGuru di SMKN 2 Pinggir memerlukan literasi digital yang tinggi untuk mengintegrasikan AI dalam pembelajaran dan mengembangkan media pembelajaran yang relevan dengan teknologi terkini. Pengabdian masyarakat dapat membantu guru-guru ini memahami, mengembangkan, dan mengadopsi AI dalam pendidikan. Melalui analisis situasi mendalam, solusi yang tepat dapat dirancang sesuai kebutuhan mereka. Keterbatasan pengetahuan dan keterampilan dalam literasi digital dan penggunaan AI adalah masalah utama yang dihadapi. Dengan solusi yang dirancang khusus, pengabdian masyarakat dapat membantu guru SMKN 2 Pinggir mengatasi hambatan tersebut, meningkatkan literasi digital, dan mengadopsi teknologi AI dalam pembelajaran. Metode pelaksanaan yang digunakan harus mencakup pengenalan AI, aplikasi AI dalam pembelajaran, alat-alat relevan, dan pengembangan media pembelajaran berbasis AI. Pendekatan ini akan memastikan program pengabdian masyarakat berjalan efektif dan memberikan manfaat nyata bagi guru-guru SMKN 2 Pinggir dalam meningkatkan literasi digital mereka serta mengadopsi teknologi AI dalam proses pembelajaran.J-COSCIS Journal of Computer Science Community Service150J-COSCIS : Journal of Computer Science Community Service["Yogi Yunefri", "Yogi Ersan Fadrial", "Sutejo", "Muhamad Sadar"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/e7cb5ad21ad36081d4a75a78c0b84fb750e0012f b80f0e8d3420b8a441b19e1c1fbf4f4edc9f7bf4The development of artificial intelligence (AI) has introduced unprecedented technological advancements and complex legal challenges, particularly in copyright infringement. The capability of the systems to replicate and disseminate copyrighted content without authorization raises questions about the adequacy of existing legal frameworks. Therefore, this research aims to explore the critical question of liability for AI-related copyright infringement, examining the responsibilities of developers, users, and systems. A comprehensive examination of relevant laws and regulations is carried out using a normative qualitative methodology. This is supported by case research and recent legal advancements, with a comprehensive comparison of relevant terms. Legal factors and dispute resolution methods applicable to AI-related copyright infringement are also considered. Due to the systems' autonomy, standard liability frameworks such as Digital Millennium Copyright Act (DMCA) cannot address AI-induced infringement. Meanwhile, a fault-based liability strategy that requires proof of purpose or negligence is suggested to improve accountability. This research reports the strengths and weaknesses of using dispute resolution mechanisms to solve copyright infringement. The results show that World Intellectual Property Organization (WIPO) dispute resolution provides a robust framework for resolving disputes after comparing regulations and mechanism.Jambura Law Review193World Intellectual Property Organization (WIPO) dispute resolution provides a robust framework for resolving disputes after comparing regulations and mechanism, and the strengths and weaknesses of using dispute resolution mechanisms to solve copyright infringement are reported.Jambura Law Review["Nanda Yuniza Eviani", "Maskun Maskun", "Ahmad Fachri Faqi"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/b80f0e8d3420b8a441b19e1c1fbf4f4edc9f7bf4 fef664092303bec715b57344b69c94430e837fc6Introduction: Artificial Intelligence (AI) has emerged as a seminal force in healthcare, fundamentally transforming various aspects of medical practice and patient care. This review explores the transformative impact of AI on the treatment of cardiovascular diseases (CVDs). State of Knowledge: AI has revolutionized diagnostic accuracy, treatment precision, and patient management in the realm of cardiovascular care. It enhances diagnostic processes, enabling the early detection of CVDs through advanced imaging analysis and interpretation. Additionally, AI facilitates precision in risk stratification, identifying high-risk patient cohorts with heightened accuracy and informing personalized treatment strategies. Furthermore, AI optimizes drug selection and dosage regimens through pharmacogenomics, maximizing therapeutic efficacy while minimizing adverse drug reactions. Moreover, AI improves precision and safety during interventional procedures, guiding clinicians in real-time decision-making and enhancing procedural outcomes. Conclusions: AI is poised to revolutionize cardiovascular care, fostering innovation and improving patient outcomes.  Quality in Sport00Artificial Intelligence improves precision and safety during interventional procedures, guiding clinicians in real-time decision-making and enhancing procedural outcomes.Quality in Sport["Katarzyna Szyma\u0144ska", "Katarzyna Szmyt", "Julia Krasnoborska", "Sylwia Samojedny", "Maciej Superson", "Kamil Walczak", "Klaudia Wilk-Trytko", "Juia Zar\u0119bska", "Tomasz Duplaga"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/fef664092303bec715b57344b69c94430e837fc6 b5808aa8ce11d7c602d859c7df6e342890896fbdCross-cultural competence (CCC) is essential for any future leader. The paper analyzes and discusses the potential of artificial intelligence (AI) to narrow the divide existing in online degree programs, a pivotal element in fostering leadership excellence and capabilities while ensuring universal access to technology. The study's methodology primarily involves an in-depth literature review of existing research and theories on AI, online education, and cross-cultural competence. It discusses the strengths and weaknesses of current online educational platforms and recommends introducing AI precision education to bridge the shortcomings of different platforms. The paper reveals some critical insights: CCC is essential for students and future leaders to function in a globalized, increasingly interconnected world; however, there has been a gap between students' assumed CCC and their actual CCC. The paper identifies shortcomings in their CCC curriculum and suggests improvement methods by analyzing the world's top ten STEM universities. The paper finds that AI's ability to accommodate cultural and cognitive differences promotes an inclusive educational environment and addresses broader digital access and equity issues. It concludes with a strong argument for integrating AI in online cross-cultural education to prepare future leaders with the skills necessary to thrive in a globalized, increasingly interconnected world.Proceedings of The International Conference on New Findings in Humanities and Social Sciences430A strong argument for integrating AI in online cross-cultural education to prepare future leaders with the skills necessary to thrive in a globalized, increasingly interconnected world is concluded.Proceedings of The International Conference on New Findings in Humanities and Social Sciences["Xiuli Chen", "Zeljana Zmire"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/b5808aa8ce11d7c602d859c7df6e342890896fbd f96e3fe68dc600756d031c7c81a49fa37918a6c7This study explored the impacts of artificial intelligence (AI) tools on English as a foreign language (EFL) reading instruction. The main aim was to examine EFL learners’ perceptions of using AI tools in their EFL reading classes and explore how those tools could impact their learning. The study tried to answer those questions: What were EFL learners’ perceptions of AI tools in reading instruction? And, how could AI tools impact EFL learners’ reading skills? To achieve the objectives, an online survey was used to investigate EFL learners’ perspectives on using AI tools and their effects in instructing reading. The findings indicated that learners had positive perceptions of using AI tools in their learning because they helped improve their reading skills and increased their confidence and motivation in reading. In addition, using AI tools for instructing reading enhanced EFL learners’ skills because they provided supportive and adaptive learning tailored to their needs. However, concerns were raised regarding long-term impacts and optimal integration models. The findings suggested AI showed promise for supporting reading instruction when combined judiciously with traditional methods. The study recommended EFL instructors consider the strategic blending of AI tools in the classroom to enhance reading proficiency and motivation.Arab World English Journal140The findings suggested AI showed promise for supporting reading instruction when combined judiciously with traditional methods and recommended EFL instructors consider the strategic blending of AI tools in the classroom to enhance reading proficiency and motivation.Arab World English Journal["Talal Waleed Daweli", "Rain Abdulrahman Moqbel Mahoub"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/f96e3fe68dc600756d031c7c81a49fa37918a6c7 5b30cc3fb4e1e05ce92053c52d695687eec2e514The development of artificial intelligence (AI) necessitates both theoretical reflection on strategic changes in educational content and forms, and the exploration of AI's potential to enhance educational quality. AI will replace many activities of a person in his/her professional career and personal life. Questions arise regarding the uniquely human abilities that current AI cannot replicate. What are the strategic implications of the introduction of artificial intelligence into our lives for university education or other levels and forms of education, including the specifics of individual disciplines? We aim to summarize research findings on the distinctions between current AI and human intelligence, focusing on identifying human intelligence's specificity and promoting its development within educational systems. We are contemplating the possibility of creating a type of AI that could replace even those human abilities that the current type of AI cannot.Economics of Contemporary Russia100This work aims to summarize research findings on the distinctions between current AI and human intelligence, focusing on identifying human intelligence's specificity and promoting its development within educational systems.Economics of Contemporary Russia["R. Valencik", "Lea Melnikovov\u00e1"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/5b30cc3fb4e1e05ce92053c52d695687eec2e514 a3236be508fe1215b06d5310acc4e6d5808d416fEl advenimiento de la inteligencia artificial (IA) ha penetrado e impactado prácticamente todos los aspectos de la vida, convirtiéndose en una realidad que está cambiando y redefiniendo como nos comunicamos e interrelacionamos. Esta tecnología, basada en programas informáticos, ha demostrado su capacidad transformadora e innovadora para optimizar, sustituir e imitar tareas realizadas por los seres humanos. Entre los campos donde su utilización ha representado toda una revolución se encuentran la investigación y la escritura de artículos científicos. Si bien la escritura de artículos científicos suele ser una actividad desafiante y retadora para los investigadores, es necesario revisar y discutir a fondo las ventajas de utilizar la IA en el terreno de la escritura científica, así como reconocer sus desventajas y limitaciones, todo en pro de encontrar un equilibrio que sea no solo beneficioso, sino también ético.Revista de la Sociedad Venezolana de Microbiología00Revista de la Sociedad Venezolana de Microbiología["Mar\u00eda Mercedes Panizo Mar\u00eda Mercedes Panizo"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/a3236be508fe1215b06d5310acc4e6d5808d416f 935987a7910405bccd5709fa45c7433aa1b5b713The discovery of hazardous scenarios is crucial for the testing and optimization of autonomous driving strategies. However, effective testing of driving strategies faces two key challenges. Firstly, when testing a well-trained autonomous driving strategy, the probability of encountering hazardous scenarios in natural environments is low. Secondly, vehicles in hazardous scenarios should exhibit behavior as close as possible to that of human drivers, thus generating more realistic hazardous scenarios. To address the challenges identified, this study enhances the artificial potential field method, resulting in the Driving Hazard Field (DHF). Furthermore, by integrating the theory of the Driving Hazard Field with the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, a hazardous scenario generation framework called DHF-MAPPO is developed. The vehicles surrounding the test vehicle are treated as agents, and the multi-agents trained within this framework not only possess the strong exploration characteristics of reinforcement learning but also, guided by the Driving Hazard Field, exhibit behaviors more akin to human drivers and follow more reasonable driving trajectories. The agents learn to interact with the test vehicle during the driving process, enabling the tested autonomous vehicle to face more complex hazardous scenarios, accelerating autonomous driving testing. The effectiveness of this approach is validated in a high-speed road scenario. Experimental results indicate a significant reduction in the autonomous driving performance of the test vehicle in the hazardous scenarios generated by the framework, with a notable increase in the risk coefficient during the driving process.Cybersecurity and Cyberforensics Conference70By integrating the theory of the Driving Hazard Field with the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, a hazardous scenario generation framework called DHF-MAPPO is developed and indicates a significant reduction in the autonomous driving performance of the test vehicle in the hazardous scenarios generated by the framework.2024 43rd Chinese Control Conference (CCC)["Yong Wang", "Zhicheng Tang", "Daifeng Zhang", "Yanqiang Li", "Pengchao Sun", "Xiaoya Chong", "Chunqi Gao"]2024-07-28T00:00:00https://www.semanticscholar.org/paper/935987a7910405bccd5709fa45c7433aa1b5b713 71353200a4f9757e10d0243e231fc5bcd14d8387International Journal of Human-Computer Interaction9824International Journal of Human–Computer Interaction["Chengliang Wang", "Haoming Wang", "Yuanyuan Li", "Jian Dai", "Xiaoqing Gu", "Teng Yu"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/71353200a4f9757e10d0243e231fc5bcd14d8387 874ce9a1fa87bc7dbdf46fb524c6e20fb740e103The application of new technologies, such as artificial intelligence (AI), to science affects the way and methodology in which research is conducted. While the responsible use of AI brings many innovations and benefits to science and humanity, its unethical use poses a serious threat to scientific integrity and literature. Even in the absence of malicious use, the Chatbot output itself, as a software application based on AI, carries the risk of containing biases, distortions, irrelevancies, misrepresentations and plagiarism. Therefore, the use of complex AI algorithms raises concerns about bias, transparency and accountability, requiring the development of new ethical rules to protect scientific integrity. Unfortunately, the development and writing of ethical codes cannot keep up with the pace of development and implementation of technology. The main purpose of this narrative review is to inform readers, authors, reviewers and editors about new approaches to publication ethics in the era of AI. It specifically focuses on tips on how to disclose the use of AI in your manuscript, how to avoid publishing entirely AI-generated text, and current standards for retraction.Journal of Korean medical science547This narrative review is to inform readers, authors, reviewers and editors about new approaches to publication ethics in the era of AI, focusing on tips on how to disclose the use of AI in your manuscript, how to avoid publishing entirely AI-generated text, and current standards for retraction.Journal of Korean Medical Science["Zafer Ko\u00e7ak"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/874ce9a1fa87bc7dbdf46fb524c6e20fb740e103 0fd984d4086d79ea362f45e885c62f6be01c4070Artificial intelligence (AI) is presently transforming society and industries with significant implications for the public relations and communication profession. However, scholarship on this subject in Africa is lacking. This paper addresses this gap by investigating AI in the public relations and communication management industry in Ghana. It focuses on the knowledge, adoption, and impact of AI, as well as the perceived risks and challenges associated with the application of AI. The study used the quantitative method to gather data from 275 professionals. Results revealed that professionals have a limited understanding of AI despite their knowledge of the concept. Communication professionals believe AI will impact the profession, their department, and how they work. However, they did not foresee any challenges or risks associated with applying AI (e.g. competency in using AI, motivation to use AI, and loss of jobs). The result points to the need for professionals to increase their knowledge and understanding of AI. There is also the need for public relations scholars in Ghana and Africa to begin serious discussions on this issue.  Communicare371Investigating AI in the public relations and communication management industry in Ghana revealed that professionals have a limited understanding of AI despite their knowledge of the concept, which points to the need for professionals to increase their knowledge and understanding of AI.Communicare: Journal for Communication Studies in Africa["Albert A. Anani-Bossman", "Noel Nutsugah", "Justice Issah Abudulai"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/0fd984d4086d79ea362f45e885c62f6be01c4070 675f327a7f5eb0cab6800aefd464836899d353a0Animal experimentation has long been a cornerstone of neurology research, but it faces growing scientific, ethical, and economic challenges. Advances in artificial intelligence (AI) are providing new opportunities to replace animal testing with more human-relevant and efficient methods. This article explores the potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models. These approaches can better recapitulate human brain physiology, predict drug responses, and uncover novel insights into neurological disorders. They also offer faster, cheaper, and more ethical alternatives to animal experiments. Case studies demonstrate AI’s ability to accelerate drug discovery for Alzheimer’s, predict neurotoxicity, personalize treatments for Parkinson’s, and restore movement in paralysis. While challenges remain in validating and integrating these technologies, the scientific, economic, practical, and moral advantages are driving a paradigm shift towards AI-based, animal-free research in neurology. With continued investment and collaboration across sectors, AI promises to accelerate the development of safer and more effective therapies for neurological conditions while significantly reducing animal use. The path forward requires the ongoing development and validation of these technologies, but a future in which they largely replace animal experiments in neurology appears increasingly likely. This transition heralds a new era of more humane, human-relevant, and innovative brain research.Neurology International772The potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models is explored.Neurology International["Thorsten Rudroff"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/675f327a7f5eb0cab6800aefd464836899d353a0 561dcd3caf89c32384268d7de6c87be4de1d1e32In order to inspire future researches and practices regarding the use of Artificial Intelligence Generated Content (AIGC) in education, this study conducted a literature review on AIGC in education. The study found that using AIGC in teaching and learning brings unprecedented opportunities for digital transformation in education, including providing technical support for the enhancement of personalized learning, empowering the cultivation of higher-order cognitive skills, and transforming educational assessment to reshape teaching and learning. Nevertheless, the application of AIGC in education may involve risks such as cognitive biases, ethical concerns, and widening the digital divide. To address the risks, coping strategies have been proposed by researchers, including strengthening the capability of providing reliable AIGC services, developing ethical guidelines for AIGC in education, and promoting teachers’ and students’ digital literacy. However, the existing studies mainly focused on theoretical analyses, and practical researches of AIGC in education is still limited. Based upon the literature review, the authors put forward future research directions for reference.International Symposium on Electronics and Telecommunications401The study found that using AIGC in teaching and learning brings unprecedented opportunities for digital transformation in education, including providing technical support for the enhancement of personalized learning, empowering the cultivation of higher-order cognitive skills, and transforming educational assessment to reshape teaching and learning.2024 International Symposium on Educational Technology (ISET)["Jiayuan Li", "Sha Zhu", "H. Yang", "Jian Xu"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/561dcd3caf89c32384268d7de6c87be4de1d1e32 0c209cba0621c2b191c1fcded4a30b5bf43a241aKemajuan teknologi telah memberikan dampak yang sangat signifikan bagi seluruh aspek kehidupan manusia. Mulai dari kehidupan ekonomi, politik, sosial, hingga ranah pendidikan. Kemajuan teknologi dapat memberikan kemudahan dalam mengakses informasi global. Keberadaan mesin menggantikan peran manusia sehingga menyebabkan lahirnya budaya serba instan. Kebutuhan dilayani oleh mesin-mesing pintar dan ini menyebabkan kurangnya interaksi sesama manusia. Penelitian literatur (library research) ini bertujuan untuk menjelaskan tentang pentingnya pendidikan moral, karakter, dan pekerta bagi peserta didik. Bagaimanapun manusia berinteraksi dengan mesin-mesin, belajar melalui internet, berkomunikasi melalui internet dan kecerdasan buatan lainnya (Artificial Intelligence). Namun pendidikan moral dan akhlak harus tetap diperhatikan. Hasil penelitian ini menunjukkan bahwa pendidikan harus diarahkan untuk pengembangan dan pendewasaan kepribadian peserta didik Oleh karena itu, proses pendidikan tidak hanya mencakup transfer ilmu pengetahuan, tetapi juga transfer nilai dan keterampilan, serta pembentukan moral dan budi pekerti (character building)Jurnal Manajemen dan Budaya201Jurnal Manajemen dan Budaya["S. Sunardi", "Wawan Kurnia Utama", "M. Munir"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/0c209cba0621c2b191c1fcded4a30b5bf43a241a 0e9e9d6f500dc0a5bbd79444ec818a4b40058d5ePurposeAs artificial intelligence (AI) continues to influence sports league and team operations, the brands providing these services are sponsoring sports properties to demonstrate and communicate their performance capabilities. This article examines Amazon Web Services (AWS) sponsorship with the National Football League (NFL). This sponsorship features functional congruence, which is when a sponsor has a participatory role in performing services for the property.Design/methodology/approachThe AWS sponsorship with the NFL is captured by examining specially created websites, in-game sponsored elements, and television commercials aired during the broadcast of NFL games. The AWS website focuses on the services profiled in this article.FindingsAWS provides the NFL with performance-based (on-the-field) and business-based (off-the-field) services. Of particular note, AWS capabilities help the NFL create the game schedule and address the issue of player health and safety. Demonstrating functional congruence appears to be especially valuable in business-to-business marketing where purchase decisions are more focused on brand reliability. AWS television commercials feature the tagline, “if AWS can do this for the NFL, imagine what it can do for your business.”Originality/valueWith the role of AI in sports in its relative infancy, it is imperative to document what services AI brands are performing for a professional sports league. Examining AWS sponsorship with the NFL provides a timely, practical example of how an AI brand communicates and positions itself using sponsorship as a marketing strategy.International Journal of Sports Marketing & Sponsorship481Examination of Amazon Web Services (AWS) sponsorship with the National Football League provides a timely, practical example of how an AI brand communicates and positions itself using sponsorship as a marketing strategy.International Journal of Sports Marketing and Sponsorship["John A. Fortunato", "Allie Kosterich"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/0e9e9d6f500dc0a5bbd79444ec818a4b40058d5e d73e21e191e313dad4a30ea1fe426abe4556ddccThe rapid expansion of the digital realm is profoundly impacting the trajectory of e-commerce, a trend further accelerated by the ongoing pandemic and evolving consumer behaviors. Consequently, businesses are increasingly turning to artificial intelligence (AI) as a vital tool to boost efficiency and adaptability. This paper aims to delve into the transformative influence of AI on e-commerce, particularly in addressing Albanian customers experience challenges. It asserts that AI presents a compelling opportunity for shaping the future landscape of commerce. Employing a blend of qualitative and quantitative methodologies, including online surveys and secondary sources like literature reviews, the research offers a comprehensive analysis. The findings highlight the effectiveness of integrating AI in e-commerce operations, emphasizing its capacity to enhance customer satisfaction, expand customer base, and drive business growth. Furthermore, the study underscores AI's potential to revolutionize various industries, underscoring its pivotal role in shaping the future trajectory of global sectors.   Received: 2 June 2024 / Accepted: 25 July 2024 / Published: 29 July 2024Interdisciplinary journal of research and development131The findings highlight the effectiveness of integrating AI in e-commerce operations, emphasizing its capacity to enhance customer satisfaction, expand customer base, and drive business growth and underscores AI's potential to revolutionize various industries.Interdisciplinary Journal of Research and Development["Eda Tabaku"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/d73e21e191e313dad4a30ea1fe426abe4556ddcc 5d6e8f2ce7f1ec08f501fcdb743fed3baefec395Artificial intelligence tools are increasingly common in the judiciary. While robot judges have not been invented yet, introducing new AI technologies is spreading and contributing to the transformation of court activities. This article discusses AI's impact on the judiciary and its potential influence on future research, aiming to improve current legislation. The author outlines two key areas of influence: the implementation of AI in professional tasks and the increasing awareness about AI. Artificial intelligence in the judiciary is expected to lead to more effective legal proceedings and ensure non-bias by excluding biases caused by inappropriate training data. Interest in this topic is driven by the desire to find potential areas for improving judges' activities, although artificial intelligence is not anticipated to replace them. The author describes different ways in which AI can impact the judiciary. These include using AI as a support tool for making legally binding decisions, taking on secondary tasks in place of judges, strengthening reliability and enhancing security in adopting modern technologies for legal proceedings, and increasing awareness of artificial intelligence. Further development and implementation of AI technologies may result in biased outcomes, external interference, and errors, highlighting the need for future research. This article explains why artificial intelligence in the judiciary requires more attention. Many questions still remain open, such as the legal support of innovative technologies for secondary tasks and guarantees for following generally accepted ethical principles of AI when used by judges. Certainly, scientists and researchers should promote new possibilities for the judiciary by developing and enhancing special software for this field. Key words: artificial intelligence, AI tools, artificial intelligence in the judiciary, transformation of the judiciary, generative artificial intelligence technologies, biased outcomes, human judges, awareness of AI, reliable AI.Slovo of the National School of Judges of Ukraine71The author describes different ways in which AI can impact the judiciary, including using AI as a support tool for making legally binding decisions, taking on secondary tasks in place of judges, strengthening reliability and enhancing security in adopting modern technologies for legal proceedings, and increasing awareness of artificial intelligence.Slovo of the National School of Judges of Ukraine["A. Hachkevych"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/5d6e8f2ce7f1ec08f501fcdb743fed3baefec395 4403e8512b28292fbec5a53f1d4e8c3160c2faa3Organisations in Africa have integrated Artificial Intelligence’s (AI) innovations, such as data driven technologies and automation, into their operations. This is being done, among others, to enhance customer relationships, strategic communication and to deliver services. However, there are suggestions that these data-driven technologies are not transparent enough, which is contrary to what strategic communication is about. A survey in South Africa, for example, shows that only thirty nine percent of the people have heard of AI. This is despite South Africa being among the top five African countries in the 2020 Global Government Artificial Intelligence Readiness Index. Several academic papers evaluating the AI topic from different standpoints have been published in recent years. However, little academic work has been done regarding AI’s impact on strategic communication in the African continent. Although AI automation and applications seek to address most of society’s pressing problems, there are also challenges such as the technicality of AI, ethical issues, and overselling of AI. This conceptual article, analyses documents published on AI, journal articles and books content, identifies and discusses AI challenges, reviews different approaches to AI, examines AI’s impact on the strategic communication field and makes recommendations, with an intention to contribute to the AI and strategic communication disciplines. The research established that AI will continue to preoccupy academics and the public because of the increasing intermingling of smart technologies with different areas of human life.Communicare521The research established that AI will continue to preoccupy academics and the public because of the increasing intermingling of smart technologies with different areas of human life.Communicare: Journal for Communication Studies in Africa["Pay Shabangu"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/4403e8512b28292fbec5a53f1d4e8c3160c2faa3 bf57bc93bb95db297d2f020e8e603c6579aacfa1BMC Medical Informatics Decis. Mak.316All chatbot models provided generally accurate, moderate reliable and moderate to good quality answers to questions about the clear aligners, however, to be fully effective they need to be supplemented with more evidence-based information and improved readability.BMC Medical Informatics and Decision Making["Derya Dursun", "Rumeysa Bilici Ge\u00e7er"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/bf57bc93bb95db297d2f020e8e603c6579aacfa1 ec57d457b96142afc4c09587f7645301e411340aWith the rapid development of social economy, college graduates are facing more employment pressure. Based on artificial intelligence (AI), this paper constructs an innovation and entrepreneurship (IEN) information sharing system. On the source layer, through the analysis of multiple data sources, the objects and contents to be extracted are determined, and the extracted object data are transmitted to the data layer through the data collector; through the data layer, the structure of data table is simplified, which is convenient for data processing, mining and analysis. In the analysis layer, according to the needs of enterprise information application, AI analysis algorithm is used to process data; at the performance level, the method is combined with IEN decision-making, and it is applied to IEN projects. According to the specific situation of individuals, the career development trend and characteristics of individuals are analyzed. The processing time of the platform for different data is 2.19s for document information data, 1.36s for archive information log data, and 2.26s for user push information data. The IEN information sharing system based on AI is conducive to direct feedback on the overall situation of individuals and help college students better carry out IEN activities.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)200The IEN information sharing system based on AI is conducive to direct feedback on the overall situation of individuals and help college students better carry out IEN activities.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)["Xiaoying Qiu"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/ec57d457b96142afc4c09587f7645301e411340a e89fbea8608028cf57ee6cdc498fe5026dce77beThe production of waste is an ongoing activity that must be managed efficiently to protect both the environment and the health of the general population. Therefore, proper management of waste from dental care is essential in protecting the environment's health, and it should become an inherent part of dental services.  This study’s primary objective was to use artificial intelligence in dental biomedical waste management. The goal of this project was to develop an automated technique for categorizing dental trash to enhance the process of managing biological waste. In the proposed research, the Support Vector Machine classifier has been regarded as the most effective method of classification for a dataset of Euclidean size. The most effective classifier used in the model is a support vector machine (with an accuracy of 96.5%, 95.9% specificity, and 95.3% sensitivity) when classifying the different types of garbage. The categorization is accomplished through machine learning techniques, to accurately separate waste into recycling categories, precisely four categories for dental biomedical waste. Based on the findings of these trials, This method has the potential to be used for garbage sorting and classification on different scales, which might aid in the scientific disposal of biological waste.THE SCIENTIFIC TEMPER200The goal of this project was to develop an automated technique for categorizing dental trash to enhance the process of managing biological waste, and the most effective classifier in the model is a support vector machine.The Scientific Temper["Somalee Mahapatra", "Manoranjan Dash", "Subhashis Mohanty"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/e89fbea8608028cf57ee6cdc498fe5026dce77be 0eeaf84d36813166c00e27ef5e1e1a775a0b2999Artificial intelligence (AI) and the continuous advancements in technology have changed how individuals live and how organisations function. The move to automation questions the need for and value of manual labour, particularly in the field of strategic communication. It has raised concerns about the future of jobs in the communication field and the role of humans in these advancements. The principles of the theory of disruptive innovation are applicable to the study. This study aims to explore the role of AI in the strategic communication industry. Semi-structured interviews with communication professionals in the South African strategic communication industry were conducted to explore their knowledge of AI and its role in the industry. Participants indicated a basic knowledge of the role of AI in the industry, agreeing that AI offers the benefits of convenience and efficiency. However, human input should remain valuable and training in AI technologies should be prioritised. This study contributes to the limited research on the role of AI in the strategic communication industry in South Africa.Communicare710The principles of the theory of disruptive innovation are applicable to the study as semi-structured interviews with communication professionals in the South African strategic communication industry were conducted to explore their knowledge of AI and its role in the industry.Communicare: Journal for Communication Studies in Africa["S. Morapeli", "Mammiki Khemisi"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/0eeaf84d36813166c00e27ef5e1e1a775a0b2999 dca58704b05cc910edcb9d8c1a70a4acc91ce5c7Background Artificial intelligence (AI) has considerable potential to enhance public health. People using AI systems for public health decisions, or who are affected by such decisions, may need to understand how these systems work, or articulate how much they want decision-makers to trust the system. This public engagement project, part of the Human Behaviour-Change Project, aimed to a) explore people’s views regarding trust in, and use of, AI for public health decisions and, based on that, b) create a toolkit of resources to facilitate people critically questioning the use of an AI system. Methods Six online, public engagement workshops were conducted in England in 2021 to inform the content and design of the toolkit. Twenty-four people including members of the public, public health professionals, and researchers worked with a graphic designer to create the toolkit. Results The resulting ‘AI in Public Health Toolkit’ contains resources to enable people to evaluate AI systems and provides a roadmap for the decision process, a set of suggested questions to ask about an AI system, a guide to features of good answers and a ‘personal views tool’ prompting reflection on the answers received. Participants suggested that public health decision-makers should use the Toolkit to consult people representative of those affected by the decision to recommend whether an AI system should be used in that instance. Conclusions The ‘AI in Public Health Toolkit’ has the potential to facilitate public engagement in the use of AI in public health. The Toolkit gives those developing AI-driven systems a sense of the public’s queries regarding such systems. The resources in the Toolkit can also facilitate conversations about broader AI applications to healthcare and public services.Wellcome Open Research150The ‘AI in Public Health Toolkit’ contains resources to enable people to evaluate AI systems and provides a roadmap for the decision process, a set of suggested questions to ask about an AI system, a guide to features of good answers and a ‘personal views tool’ prompting reflection on the answers received.Wellcome Open Research["Alison J. Wright", "Eva Jermutus", "Ella Howes", "A. O'Mara-Eves", "Clement Veall", "Jennie Ives", "Susan Michie"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/dca58704b05cc910edcb9d8c1a70a4acc91ce5c7 d320ad3633a6a2ca41a54477e70ad580788e6ae6An artificial intelligence architecture of financial real-time monitoring based on data mining is established, which is suitable for the actual operation of enterprises. At the same time, the practical application of this method in capital scheduling, real-time cost control, dynamic financial risk warning and so on is studied. This paper analyzes the overall structure and function of the system, and divides the system into three parts: data acquisition, financial risk assessment and system maintenance. In addition to the system's functional requirements, the system's safety and operational performance are also designed. This paper studies the early warning method of financial revenue and expenditure risk for public institutions. Using cloud computing technology to extract the abnormal components in financial data. At the same time, it is used for risk control. The method of program design is adopted for risk control. A cloud-based financial risk control system is realized by combining software and hardware. Under the same data capacity and the same test platform.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)110This paper analyzes the overall structure and function of the system, and divides the system into three parts: data acquisition, financial risk assessment and system maintenance.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)["Huagu Wu"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/d320ad3633a6a2ca41a54477e70ad580788e6ae6 9c30d08fb3a342ddd106ff6e0aff059c33be1934Tujuan penelitian ini adalah untuk mengetahui gambaran deskriptif kuantitatif kecakapan dosen-dosen PTKI di Indonesia serta merumuskan strategi dalam menghadapi invasi Artificial Intelligence (AI) di perguruan tinggi. Berdasar jenis datanya, penelitian ini merupakan penelitian campuran (Mixed Method Research). Adapun framework penelitian yang dipilih adalah studi kasus. Teknik pengumpulan data menggunakan kuesioner, Online Focus Group Discussion (OFGD), observasi, dan dokumentasi. Peneliti mengumpulkan data komprehensif dari objek penelitian tentang invasi AI dalam pembelajaran di perguruan tinggi. Dari objek tersebut, dilakukan eksplorasi secara kualitatif dengan melibatkan informan-informan yang merupakan praktisi dan pakar berkaitan dengan objek penelitian. Dari hasil analisa data, didapatkan bahwa mayoritas dosen memiliki tingkat kecakapan dan kesiapan dalam menghadapi invasi AI dalam skala 3 dari skala 1 sampai 5. Lebih lanjut, didapatkan kesimpulan dari uji komparasi bahwa dosen di atas 42 tahun memiliki tingkat kecakapan dan kesiapan yang lebih rendah daripada generasi setelahnya secara signifikan. Adapun dari analisa kualitatif, didapatkan kesimpulan bahwa AI dapat meningkatkan kualitas dan efisiensi sistem pembelajaran jika AI diposisikan sebagai alat. Jika adaptasi terhadap era AI tidak dilakukan, maka AI justru akan menjadi disrupsi baru dalam pembelajaran di perguruan tinggiJurnal Manajemen dan Budaya00Jurnal Manajemen dan Budaya["Najib Mubarok", "Eri Susanti", "E. Lestari"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/9c30d08fb3a342ddd106ff6e0aff059c33be1934 3045b82f469992ea465bf40dce96f67d7075238fEver-changing cyber threats require strong and flexible network security solutions. This paper suggests a new method to improve the performance of detecting both known and unknown attacks using a neuro-symbolic artificial intelligence (NSAI) network intrusion detection system (NIDS). Deep neural networks (DNN) learn complex network data patterns, which create a detailed overview of cyber-attack characteristics. Symbolic logic integration into the DNN allows for model training guidance by applying penalties when the DNN fails to differentiate between malicious and benign network traffic. This improves our model’s adaptability to new attacks and overcomes traditional signature-based NIDS limitations. By testing our NSAI NIDS on a large cyber dataset that includes novel attack scenarios, we show that it delivers an improvement in how accurately it detects attacks compared to traditional DNN methods. While our system maintains its high accuracy in recognizing known attacks, it outperforms conventional NIDS in discovering unknown attacks. This work improves cybersecurity by introducing a new way to detect both known and unknown network intrusions by combining DNNs with symbolic logic.International Conference on Computer Communications and Networks220A new method to improve the performance of detecting both known and unknown attacks using a neuro-symbolic artificial intelligence network intrusion detection system (NIDS) by combining DNNs with symbolic logic is suggested.2024 33rd International Conference on Computer Communications and Networks (ICCCN)["Alice Bizzarri", "B. Jalaeian", "Fabrizio Riguzzi", "Nathaniel D. Bastian"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/3045b82f469992ea465bf40dce96f67d7075238f c8957a1642a5c32268fcd13cf800fbe665b7ac84The main job of auditing is to evaluate and review, and the huge and complex workload, as well as low tolerance for errors, are the characteristics of this work. However, with the development of technology and the expansion of the market, the workload that auditing needs to face is also constantly increasing. Therefore, this article proposes to promote the digital transformation of auditing based on machine learning (ML) artificial intelligence (AI) to solve this problem. Finally, this article validates the viewpoint of this article through experiments. In the experiment on audit accuracy, when the number of iterations of ML reaches 200, the audit accuracy is already as high as 98.23%, very close to 100%, indicating its obvious effect. In the experiment of work efficiency, the average time spent on evaluating a certain part of the audit system based on AI is 113.5 hours, while compared to the audit system based on big data, which requires 125.9 hours, the difference is also quite significant. Therefore, research has found that the digital audit system assisted by AI based on ML performs well in terms of work efficiency and audit accuracy.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)160Research has found that the digital audit system assisted by AI based on ML performs well in terms of work efficiency and audit accuracy and validates the viewpoint of this article through experiments.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)["Xin Liu", "Yi-peng Ren", "Guodong Qi", "Yujing Li", "Rui Fan"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/c8957a1642a5c32268fcd13cf800fbe665b7ac84 d9aa17c7aa4cc5e14aa0a6f61ece973e9dfe39f5Reliability and accuracy of model prediction are underperforming, how to improve the accuracy and confidence value of the analysis is a problem, and uncertainty analysis and error compensation can solve these problems well. This study uses computer models and artificial intelligence algorithms to compare the performance differences between deep learning models and Bayesian methods in uncertainty analysis and error compensation. We experimentally verified the two methods and evaluated their computational efficiency. Based on the error compensation experiment, the effect of the two methods in correcting the model's prediction deviation is compared. Error compensation results show that the compensated value of the Bayesian network still has a deviation of 0.2 from the actual value. The deep learning compensates for a deviation of 0.3 from the actual value. The deviation of the Bayesian method is small after error compensation, and the effect of error compensation is better.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)220This study uses computer models and artificial intelligence algorithms to compare the performance differences between deep learning models and Bayesian methods in uncertainty analysis and error compensation, and experimentally verified the two methods and evaluated their computational efficiency.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)["Yuan Jiang", "Ruixiang Wang", "Rui Jiang"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/d9aa17c7aa4cc5e14aa0a6f61ece973e9dfe39f5 b980c765d5e910fdd3675a9a19a9acf3bb87908cThe field of Artificial Intelligence (AI) is evolving in a fast pace impacting a wide range of other field including general public use of social media platforms all the way to industrial activities and Cybersecurity. When AI is combined with Quantum Computing the abilities of AI are exponentially increased. This paper aims to explore the evolution of AI, discuss if this evolution a transformation or a mutation, and its correlation with Quantum Computing. It is also looking into the impact of AI on Cybersecurity risks and mitigation.International Journal of Computational Science Information Technology and Control Engineering110The evolution of AI is explored, if this evolution a transformation or a mutation, and its correlation with Quantum Computing are discussed, also the impact of AI on Cybersecurity risks and mitigation is looked into.International Journal of Computational Science, Information Technology and Control Engineering["Ali Rah"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/b980c765d5e910fdd3675a9a19a9acf3bb87908c e132509b333e94676c9774daf8aeeec9c758e4f3This paper investigates the impact of Artificial Intelligence (AI) within the Albanian banking sector by exploring the dynamic landscape shaped by the fusion of advanced technologies and financial services. The research paper handles a comprehensive approach encompassing technological applications and the ensuing socio-economic consequences. Additionally, the paper emphasizes the role of AI in automating daily processes, enhancing customer experiences, and fortifying security measures. Through a qualitative and quantitative analysis, the research aims to provide a detailed overview of the AI landscape in Albanian banking. In conclusion, this paper sheds light on the multifaceted impact of AI in the Albanian banking system, offering a comprehensive understanding of the evolving landscape. As financial institutions embark on the journey of digital transformation, this study endeavours to guide strategic decisions, inform regulatory policies, and pave the way for a resilient and technologically adept banking sector in Albania.   Received: 2 June 2024 / Accepted: 25 July 2024 / Published: 29 July 2024Interdisciplinary journal of research and development450Light is shed on the multifaceted impact of AI in the Albanian banking system, offering a comprehensive understanding of the evolving landscape.Interdisciplinary Journal of Research and Development["Monika Kolleshi", "Ela Golemi"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/e132509b333e94676c9774daf8aeeec9c758e4f3 d2dd80a8552cbca17acc3d075e2f64c9c98315efThe swift development of Artificial Intelligence (AI), namely the introduction of Large Language Models (LLMs), is drastically altering various industries and necessitating a major change in the way software engineering is taught. To equip upcoming software engineers with the knowledge and abilities to function in this AI-powered environment, curriculum and pedagogical techniques must be critically reevaluated. To better understand the integration of AI and LLMs into software engineering education, this study gives a thorough and critical analysis of the literature, looking at existing models, pedagogical frameworks, and enduring issues. We explore various approaches utilized by educational establishments, including as specialized AI and LLM courses, incorporating modules into pre-existing curricula, and utilizing open-source LLM materials. Our analysis, which is based on case studies and research data, thoroughly assesses how well these strategies enable software engineers to comprehend, make use of, and ethically create AI and LLMs. Key obstacles to the successful integration of AI and LLM are also identified by our analysis, including the inexperienced status of LLM educators, resource limitations, potential biases in AI and LLM algorithms, and insufficient instructor knowledge. Building on these discoveries, we provide solid answers to these problems and suggest interesting avenues for further study to improve the integration of AI and LLM. In the end, this study advocates for a multimodal strategy to get future software engineers ready for the impending AI and LLM future and secure their place in this quickly changing field.Conference on Software Engineering Education and Training170This study advocates for a multimodal strategy to get future software engineers ready for the impending AI and LLM future and secure their place in this quickly changing field.2024 36th International Conference on Software Engineering Education and Training (CSEE&T)["Chandan Kumar Sah", "Xiaoli Lian", "Muhammad Mirajul Islam", "Kamrul Islam"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/d2dd80a8552cbca17acc3d075e2f64c9c98315ef d503090f78325ea23775b4ad8971775705e08cf9Central bank communication is a valuable source of information designed to shape the expectations of economic agents within and outside an economy. In particular, the content of Monetary Policy Committees’ press releases and statements reflect the central banks’ view of current and future macroeconomic developments, making them useful for creating high-frequency indicators as alternatives to traditional but slower-to-publish macroeconomic indicators. In this study, Artificial Intelligence (AI)-powered text-mining techniques were employed to create monetary policy communication-based indicators, namely the Monetary Policy Readability Index (MPRI), the Monetary Policy Sentiment Index (MPSI), and the Monetary Policy Uncertainty Index (MPUI), using press releases from the Bank of Ghana's monetary policy committee spanning January 2003 to December 2022. The findings suggest that while readability and sentiments generally declined over the sample period, uncertainty increased, indicating persistent macroeconomic imbalances and vulnerabilities in the domestic economy. The newly developed time series-based indicators demonstrate Granger causal relationships with key macroeconomic variables, affirming their relevance to the central bank, the Ministry of Finance, researchers, investors, and development partners. Notably, the indicators can serve as an early warning system for monitoring and predicting the country's macroeconomic risks, forecasting lagging indicators, assessing the effectiveness of the Bank’s monetary policy communication, and addressing monetary policy inquiries.Communicare450Artificial Intelligence (AI)-powered text-mining techniques were employed to create monetary policy communication-based indicators, suggesting that while readability and sentiments generally declined over the sample period, uncertainty increased, indicating persistent macroeconomic imbalances and vulnerabilities in the domestic economy.Communicare: Journal for Communication Studies in Africa["F. M. Abude", "Jones Odei-Mensah", "Eric Schaling"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/d503090f78325ea23775b4ad8971775705e08cf9 c77a165250a32a2f886298b68eb431d280df5301Academic writing plays a crucial role in academics including undergraduate students. In addition, the development of technology provides effectiveness for them in the process of academic writing. The present study unravels undergraduate students’ voices on how artificial intelligence (AI) supports their academic writing including kinds of AI applications mostly used by them in enhancing academic writing and challenges in using AI. Anchoring in a qualitative research method, the data of this study were gathered from in-depth interviews with six students at an Indonesian higher education. The findings of the study revealed that AI applications are used to support students’ academic writing. Some AI applications they use include translation tools such as Google Translate, paraphrasing tools such as Quillbot, grammar and spelling checkers such as Grammarly, plagiarism checkers such as Turnitin, and reference management software such as Mendeley and Zotero. Furthermore, they reported that AI could enhance their academic writing by providing better organization, efficiency, consistency, and accuracy. However, critical thinking skills cannot be developed by AI applications. Thus, the finding of the study offers an implication that students can utilize these AI applications to support their work and they should actively participate in the process of academic writing.Prominent340The findings of the study revealed that AI applications are used to support students’ academic writing and students can utilize these AI applications to support their work and they should actively participate in the process of academic writing.Prominent["Sri Wahyuningsih"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/c77a165250a32a2f886298b68eb431d280df5301 b2822044795f9ea0e5e6048c4e3bd681fb49d0a4With the advent of the information age, artificial intelligence has been widely applied in various industries, bringing opportunities for vocational English teaching. In order to better seize these opportunities, vocational colleges need to pay attention to the application of artificial intelligence technology in English teaching, fully leverage its technological advantages and promote innovation in English teaching activities. Starting from the development of artificial intelligence technology, this article analyzes the current situation of vocational English teaching, discusses the value of artificial intelligence technology in vocational English teaching and proposes specific reform strategies for vocational English teaching from the perspective of artificial intelligence, accumulating experience for the implementation of vocational English teaching reform.Education Reform and Development110The current situation of vocational English teaching is analyzed, the value of artificial intelligence technology in vocational English teaching is discussed, and specific reform strategies for vocational English teaching are proposed from the perspective of artificial intelligence.Education Reform and Development["Ju Yuan"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/b2822044795f9ea0e5e6048c4e3bd681fb49d0a4 704c99ca3608e7f55a77a9ce61de14893d3c441cArtificial Intelligence (AI) has emerged as an essential tool in healthcare for optimizing healthcare delivery and improving patient outcomes. This study is motivated by using AI in healthcare as a step for social responsibility implementation. The research aimed to investigate the attitudes of healthcare professionals on this issue, and it assessed physicians’ opinions regarding their perceptions of AI and their intention to use and implement AI tools in their activity. An electronic survey was proposed during February–June 2024 to a sample of healthcare professionals (309 were admitted into the study, 62 males and 247 females, with a mean age of 42). The results of the survey highlighted both groups’ excellent perceptions of AI and the low perceived knowledge of AI, which arises from more technical questions. The use of AI in healthcare represents a step for social responsibility implementation; it is an unstoppable process, and stakeholders should take into consideration investing more in monitoring and training activities.Healthcare170The research assessed physicians’ opinions regarding their perceptions of AI and their intention to use and implement AI tools in their activity and highlighted both groups’ excellent perceptions of AI and the low perceived knowledge of AI.Healthcare["L. Dumitra\u015fcu", "Delia-Andreea Lespezeanu", "C. Zugravu", "C. Constantin"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/704c99ca3608e7f55a77a9ce61de14893d3c441c a5ee25354c13c353697e6d10b0f1f292f684d3e6This study investigates the antecedents and consequences of consumers' attitudes toward artificial intelligence in the social media era. Through an empirical study, data was collected from 388 consumers in Turkey. SmartPLS was used to test the proposed hypotheses. Several key findings were reached: (i) Anthropomorphism impacts consumers' performance expectations positively. (ii) Anthropomorphism does not influence positive attitudes towards artificial intelligence. (iii) Consumers' performance expectations have significantly positive effects on positive attitudes towards artificial intelligence and also on positive emotions. (iv) Positive emotions do not influence positive attitudes towards artificial intelligence. (v) Positive attitudes towards artificial intelligence significantly have positive effects on consumers’ engagement on social media. (vi) Social media self-efficacy has a positive effect on consumers’ engagement on social media. (vii) Consumers’ social media engagement impacts purchase behavior positively. Establishing a comprehensive framework, this study offers valuable insights into the intricate relationships among anthropomorphism, performance expectations, emotions, attitudes, social media self-efficacy, social media engagement, and consumer purchase decisions in the evolving landscape of artificial intelligence. The study contributes to the literature by examining the antecedents and consequences of consumers’ positive attitudes toward artificial intelligence with a comprehensive model. Besides, understanding the drivers that push consumers to generate positive attitudes toward artificial intelligence and the consequences of these positive attitudes is crucial for marketing managers and businesses.Business and economics research journal00Establishing a comprehensive framework, this study offers valuable insights into the intricate relationships among anthropomorphism, performance expectations, emotions, attitudes, social media self-efficacy, social media engagement, and consumer purchase decisions in the evolving landscape of artificial intelligence.Business and Economics Research Journal["Sinem Sarg\u0131n"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/a5ee25354c13c353697e6d10b0f1f292f684d3e6 77474b2b1534031043bd8cd5b975581bd5059accWith the advancement of science and technology, artificial intelligence technology has penetrated into all aspects of our lives, especially in the field of online privacy protection dissemination, and its application research has received more and more attention. Artificial intelligence technology, with its excellent information processing capabilities, provides new possibilities for the dissemination of online health knowledge. The article provides an overview of the main technologies and methods adopted in this field, and points out their important significance in engineering practice. Firstly, research key technologies such as data encryption, access control, and user consent to ensure the security and privacy of data during storage and transmission. On this basis, research was conducted on techniques such as data anonymity. At the same time, this article also points out the important role of the principle of minimizing information, transparency in establishing user trust, and compliance with relevant regulations. Through the application of artificial intelligence technology, the dissemination of online privacy protection will be able to be carried out more effectively.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)160An overview of the main technologies and methods adopted in the field of online privacy protection dissemination points out their important significance in engineering practice and the important role of the principle of minimizing information, transparency in establishing user trust, and compliance with relevant regulations.2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS)["Yiming Wang"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/77474b2b1534031043bd8cd5b975581bd5059acc e4f90697c4e804a6f5d44d07234479c9b245d5ecPurposeThis study builds on a conceptual model by integrating AI features – Perceived intelligence (PIN) and anthropomorphism (PAN) – while extending expectation confirmation theory (ECT) factors – interaction quality (IQU), confirmation (CON), and customer experience (CSE) – to evaluate the continued intention to use (CIU) of AI-enabled digital banking services.Design/methodology/approachData were collected through an online questionnaire administered to 390 digital banking customers in India. The data were further analysed, and the presented hypotheses were evaluated using partial least squares structural equation modelling (PLS-SEM).FindingsThe research indicates that perceived intelligence and anthropomorphism predict interaction quality. Interaction quality significantly impacts expectation confirmation, consumer experience, and the continuous intention to use digital banking services powered by AI technology. AI design will become a fundamental factor; thus, all interactions should be user-friendly, efficient, and reliable, and the successful implementation of AI in digital banking will largely depend on AI features.Originality/valueThis study is the first to demonstrate the effectiveness of an AI-ECT model for AI-enabled Indian digital banks. The user continuance intention to use digital banking in the context of AI has not yet been studied. These findings further enrich the literature on AI, digital banking, and information systems by focusing on the AI's Intelligence and Anthropomorphism variables in digital banks.Journal of Enterprise Information Management1512The research indicates that perceived intelligence and anthropomorphism predict interaction quality, which significantly impacts expectation confirmation, consumer experience, and the continuous intention to use digital banking services powered by AI technology.J. Enterp. Inf. Manag.["Puneett Bhatnagr", "Anupama Rajesh", "Richa Misra"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/e4f90697c4e804a6f5d44d07234479c9b245d5ec fd803e9389001df25c14a2ced30796e75f54839eJACC Cardiovascular Imaging101JACC. Cardiovascular imaging["Bo Xu", "Alejandro Sanchez-Nadales"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/fd803e9389001df25c14a2ced30796e75f54839e a3ae433ddde0009e4dacf810cb10eb5620cf3e1a01["S. Tripathi", "Joanna Rosak-Szyrocka"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/a3ae433ddde0009e4dacf810cb10eb5620cf3e1a 7adfea42449765e66031f1b796f6cdb59b3726faCritical Care62Critical Care["Maurizio Cecconi", "Massimiliano Greco", "B. Shickel", "J. Vincent", "A. Bihorac"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/7adfea42449765e66031f1b796f6cdb59b3726fa 67654876e605bd54cf15433ee8d151e7a27544cbJournal of Arts, Literature, Humanities and Social Sciences00Journal of Arts, Literature, Humanities and Social Sciences[]2024-07-29T00:00:00https://www.semanticscholar.org/paper/67654876e605bd54cf15433ee8d151e7a27544cb a5e7161ce2ea3f7566f606bbace8a5cb7cc0dfe2Journal of Web Librarianship160Journal of Web Librarianship["Kristen Seikaly"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/a5e7161ce2ea3f7566f606bbace8a5cb7cc0dfe2 03ec4d241cbd85edcd028c6aad2bd1d610746fc7Technology Analysis &amp; Strategic Management330Technology Analysis &amp; Strategic Management["Quan Zhang", "Aiguo Wang", "Ruixue Li"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/03ec4d241cbd85edcd028c6aad2bd1d610746fc7 8dabe6165f26e009a15f88d63f36289c53324edbIn the intensive care unit (ICU), patients receive continuous life-support treatment facilitated by advanced medical technologies. Mechanical ventilators assist with breathing, intravenous fluids regulate bloodstream hydration, and body-attached sensors monitor vital signs such as heart rate and blood pressure. Bedside monitors visualize this data, ensuring that patient care aligns with medical objectives. Establishing an efficient monitoring process in the ICU involves several stages to ensure effective oversight and performance evaluation of tasks, initiatives, and systems. This research study provides a comprehensive analysis for developing and implementing a robust monitoring process in ICU settings, aimed at optimizing patient care and treatment outcomes.International Conference on Information Security and Cryptology230This research study provides a comprehensive analysis for developing and implementing a robust monitoring process in ICU settings, aimed at optimizing patient care and treatment outcomes.2024 8th International Conference on Inventive Systems and Control (ICISC)["P. L. Lobo", "Panjagari Kavitha", "K. Saranya", "Selvaraju", "Chandrashekhar Kumar", "D. Venkatachalam"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/8dabe6165f26e009a15f88d63f36289c53324edb 9ef7f2b8d60ccf4ced0b5da1eda6fa3b0bdb788eForest fires pose significant threats to public safety and the environment, releasing hazardous pollutants and spreading rapidly through vegetated areas. Early detection is critical to prevent forest fires from becoming catastrophic, but the dynamic nature of weather conditions complicates this process. This study addresses the challenge by employing advanced Deep Learning (DL) techniques, utilizing algorithms such as Support Vector Machines (SVM), Logistic Regression, and Convolutional Neural Networks (CNN) to analyze images captured by satellites, drones, and webcams. Developing a neural model involves constructing, training, and fine-tuning sophisticated algorithms, optimizing accuracy by adjusting parameters such as dense layers and hidden layers. Data preprocessing techniques, including data augmentation, are used to enhance the input and improve model performance. The integration of new technologies and approaches, such as deep learning and data augmentation, aims to mitigate the effects of wildfires and protect both human lives and the environment. The ultimate goal of this research study is to enhance early detection systems and reduce response times, thereby minimizing the detrimental impact of forest fires on ecosystems and communities.International Conference on Information Security and Cryptology220This study addresses the challenge of early detection of forest fires by employing advanced Deep Learning techniques, utilizing algorithms such as Support Vector Machines, Logistic Regression, and Convolutional Neural Networks to analyze images captured by satellites, drones, and webcams.2024 8th International Conference on Inventive Systems and Control (ICISC)["Kavuluri Leela Sai Rasagna Devi", "Garnepudi Narasimha Kumar", "Potturi Ashok Narayana", "Kakani Venkata Ramana", "K. Amarendra", "Tirupathi Rao Gullipalli"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/9ef7f2b8d60ccf4ced0b5da1eda6fa3b0bdb788e 4677b993d0e9c173c0ca04bacd5a1193f0e993adNature Communications00Nature Communications["Alexander P. L. Martindale", "Carrie D. Llewellyn", "Richard O de Visser", "Benjamin Ng", "V. Ngai", "Aditya U Kale", "Lavinia Ferrante di Ruffano", "Robert M Golub", "Gary S. Collins", "D. Moher", "M. Mccradden", "Lauren Oakden-Rayner", "Samantha Cruz Rivera", "M. Calvert", "Christopher J. Kelly", "Cecilia S Lee", "Christopher Yau", "An-Wen Chan", "P. Keane", "Andrew L. Beam", "A. Denniston", "Xiaoxuan Liu"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/4677b993d0e9c173c0ca04bacd5a1193f0e993ad a819acecd3dfa40bffab6beb242db4c53ad9ca02In the legal context, judicial congestion is due, among other causes, to the fact that the stages of the criminal process are led, almost exclusively, by human activity, generating significant wear and tear on the institutional structure. Therefore, this article seeks to answer this research question: can artificial intelligence be adopted as a strategy to decongest criminal jurisdiction marked by human performances from different levels and within the framework of state parameters typical of the current global context? To provide an answer, the performances of criminal law are systematized, which are classified into four levels: a first descriptive level, a second procedural level, a third argumentative level and a fourth strategic level. When analyzing each of these levels, it is considered on which occasions it is possible to link to artificial intelligence to a greater or lesser extent, suggesting support for human activity in order to fulfill the intended purposes from and for specific realities.Revista Brasileira de Direito Processual Penal100Revista Brasileira de Direito Processual Penal["Juan Sebasti\u00e1n Alejandro Perilla Granados"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/a819acecd3dfa40bffab6beb242db4c53ad9ca02 c703c14f9beb9ab982c08e595ddb4517251e6c40Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This survey paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.arXiv.org1525This survey paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation, and emphasizing the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery.ArXiv["Sribala Vidyadhari Chinta", "Zichong Wang", "Xingyu Zhang", "Thang Doan Viet", "A. Kashif", "Monique Antoinette Smith", "Wenbin Zhang"]2024-07-29T00:00:00https://www.semanticscholar.org/paper/c703c14f9beb9ab982c08e595ddb4517251e6c40 26fb518236ed7d65d752736ccddef3bd7f2f9b4bAI and Ethics7811This study thoroughly evaluates AI’s application in information security, discussing its strengths and weaknesses, and identifies key areas for future AI research in information security, focusing on improving algorithms, strengthening information security, addressing ethical issues, and exploring safety and security-related concerns.AI and Ethics["Ehtesham Hashmi", "M. Yamin", "Sule YAYILGAN YILDIRIM"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/26fb518236ed7d65d752736ccddef3bd7f2f9b4b 874f90cfc1b79c1e9e519b7a79f35493fabeba80Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including “artificial intelligence implantology”, “AI implant planning”, “AI dental implant”, and “implantology artificial intelligence”. Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of AI in implant planning. The results of the literature review indicate a growing interest in the application of AI in implant planning, with evidence suggesting an improvement in precision and predictability compared to traditional methods. The summary of the obtained findings by the included studies represents the latest AI developments in implant planning, demonstrating its application for the automated detection of bones, the maxillary sinus, neuronal structure, and teeth. However, some disadvantages were also identified, including the need for high-quality training data and the lack of standardization in protocols. In conclusion, the use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management. However, further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.Bioengineering396The use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management, but further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.Bioengineering["M. Macr\u00ec", "Vincenzo D\u2019Albis", "Giuseppe D\u2019Albis", "M. Forte", "S. Capodiferro", "Gianfranco Favia", "Abdulrahman Omar Alrashadah", "Victor Diaz-Flores Garc\u00eda", "Felice Festa"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/874f90cfc1b79c1e9e519b7a79f35493fabeba80 0ede2c7ab4a87077765967bfc1e81579997d6515The concept paper identifies the relationship of Artificial Intelligence (AI) towards teaching and learning in STEM education. AI can really revolutionize STEM education if AI-powered tools are in place to ensure that each of the students receives personalized instructions, intelligent tutoring, and interactive simulations. Not only this, but they even grade assignments or quizzes that are submitted automatically and prove predictions with analytics to create efficiency and effectiveness in STEM pedagogy. However, there is a limited quantity of primary research regarding the actual impacts of such AI technologies. The paper will hence fill this gap by making an in-depth assessment of the application of AI tools in STEM classrooms. If strategically deployed, AI has huge potential to improve student mastery in STEM, increase learner motivation and autonomy, and allow teachers to become more personalized in their support. However, it also identifies challenges of equitable access, bias in algorithms, and wishing that the teachers have robust training programs. It thus proposes, based on the results, key recommendations that include developing ethical guidelines, investing in professional development, and designing AI systems accommodating diverse learning needs. To be precise, this research provides relevant empirical evidence and some practical guidance for education stakeholders to harness the transformative power of AI for STEM learningInternational Journal of Science and Research Archive245The concept paper identifies the relationship of Artificial Intelligence towards teaching and learning in STEM education and identifies challenges of equitable access, bias in algorithms, and wishing that the teachers have robust training programs.International Journal of Science and Research Archive["Mohammad Aniq Bin", "Mohammad Aniq", "Bin Amdan", "Naldo Janius", "Mohd Aidil", "Hazidi Bin Kasdiah"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/0ede2c7ab4a87077765967bfc1e81579997d6515 5ebc1e1f5e7c1d2b8a1e522feb6f1e4da3d21e9eIntegrating Artificial Intelligence (AI) in supply chain management (SCM) signifies a significant advancement with profound implications for modern businesses, including those in Uganda. This research paper critically examines the challenges and opportunities associated with this integration, using Uganda as a case study. A comprehensive analysis of existing literature and specific insights from the Ugandan context identifies critical challenges such as data integration, technology adoption, and organizational readiness within the country. Additionally, it explores AI's diverse opportunities in optimizing supply chain processes for Ugandan businesses, including demand forecasting, inventory management, and logistics optimization within Uganda's unique operational landscape. Furthermore, the paper discusses the potential impact of AI integration on various stakeholders within Uganda's supply chain ecosystem, including suppliers, manufacturers, distributors, and customers. By synthesizing insights from academic research and industry practices in Uganda, this paper provides valuable insights for Ugandan businesses aiming to leverage AI technologies in their SCM strategies. Ultimately, this research contributes to a deeper understanding of the complexities of integrating AI in SCM within the Ugandan context and offers recommendations for addressing challenges while maximizing the opportunities presented by this transformative technology.World Journal of Advanced Engineering Technology and Sciences73A comprehensive analysis of existing literature and specific insights from the Ugandan context identifies critical challenges such as data integration, technology adoption, and organizational readiness within the country and offers recommendations for addressing challenges while maximizing the opportunities presented by this transformative technology.World Journal of Advanced Engineering Technology and Sciences["Onyango Laban", "Oliver Owin", "Natuhwera Pius"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/5ebc1e1f5e7c1d2b8a1e522feb6f1e4da3d21e9e 4034e2f04234c4b1b471c25eed99d99f82e79974Chimnomso Elsie Ihedioha reviews Judges, Technology and Artificial Intelligence: The Artificial Judge by Tania Sourdin.Law, Technology and Humans03Law, Technology and Humans["Chimnomso Elsie Ihedioha"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/4034e2f04234c4b1b471c25eed99d99f82e79974 8eb7a9d5f8768bd2fab447f7929225dcb1bf9ec7This study delves into how artificial intelligence (AI) transforms working capital management by addressing the limitations of traditional methods. The focus is to critically review research publications, case studies and industry reports using qualitative research methodology to examine how AI improves operational efficiency and decision-making in this area. The study demonstrates the practical application of advanced machine learning algorithms and big data analytics in optimizing inventory management, enhancing demand forecasting and improving cash flow predictions. A thorough review of recent research and case studies reveals additional benefits, including automated reconciliations, debtor risk analysis, accelerated cash inflows, invoice processing and proactive working capital management. Despite challenges in integrating AI with legacy systems, the potential for substantial improvements in financial health and operational efficiency is significant. The study also suggests future research directions, such as developing comprehensive AI-driven applications for broader working capital considerations, creating empirical validation frameworks for model performance and addressing ethical considerations to fully harness AI's potential in optimizing working capital management.World Journal of Advanced Research and Reviews03The study demonstrates the practical application of advanced machine learning algorithms and big data analytics in optimizing inventory management, enhancing demand forecasting and improving cash flow predictions and suggests future research directions to fully harness AI's potential in optimizing working capital management.World Journal of Advanced Research and Reviews["Stanley Chidozie Umeorah", "Adesola Oluwatosin Adelaja", "Oluwatoyin Funmilayo Ayodele", "Bibitayo Ebunlomo Abikoye"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/8eb7a9d5f8768bd2fab447f7929225dcb1bf9ec7 34126a8fb2047ccec809e8a0d1263ee73bcb65e8Artificial Intelligence is novel technology which concise the world a single unites. AI helps to whole task of human brain and it help us in every sort of our work. In media and communication, the most booming industry in the world is also hold the hands of Artificial Intelligence. Social responsibility and dissemination of right information to the right people at the right time is the basic function of media. Emergence of AI in the media sector will overwhelm the journalists and opportunities. It creates more accuracy in reports but it will surely challenge the humanitarian attitude and journalistic ethics. Artificial Intelligence reduces the human effort and provides more clear and concise reports. The writing, vocal and visual synchronizing ability of Artificial Intelligence questioning the human power.International Journal of Innovative Science and Research Technology151Emergence of AI in the media sector will overwhelm the journalists and opportunities, it creates more accuracy in reports but it will surely challenge the humanitarian attitude and journalistic ethics.International Journal of Innovative Science and Research Technology (IJISRT)["Seethal George"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/34126a8fb2047ccec809e8a0d1263ee73bcb65e8 f29478eb3e6017ed9bfe926b1834f6459cf0cf35Currently, eight million people in the United States suffer from cancer, and is a major global health concern. Early detection and interventions are urgently needed for all cancers, including colorectal cancer (CRC). CRC is the third most common type of cancer worldwide; From diagnostic efforts to general awareness and lifestyle choices, it is understandable why CRC is so prevalent today. There is a notable lack of awareness concerning the impact of this cancer and its connection to lifestyle elements, as well as people sometimes mistaking symptoms for a different gastrointestinal condition. Artificial Intelligence (AI) may assist in the early detection of all cancers, including CRC. The usage of AI has exponentially grown within healthcare through extensive research and since clinical implementation, has succeeded in improving patient lifestyles, modernizing diagnostic processes, and innovating current treatment strategies. Numerous challenges arise for CRC patients and Oncologists alike during treatment. For initial screening phases, conventional methods oftentimes result in misdiagnosis. Moreover, after detection, determining the course of which CRC can sometimes contribute to treatment delays. This article touches on recent advancements in AI and its clinical application while shedding light on why this disease is so common today.Cancer Prevention Research01Artificial Intelligence (AI) may assist in the early detection of all cancers, including colorectal cancer, and its clinical application while shedding light on why this disease is so common today.Cancer prevention research["Michael Jacob", "Ruhananhad P Reddy", "Ricardo I Garcia", "Aananya P Reddy", "Sachi Khemka", "Aryan Kia Roghani", "Vasanthkumar Pattoor", "Ujala Sehar", "P. H. Reddy"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/f29478eb3e6017ed9bfe926b1834f6459cf0cf35 2c8cda78462ef7940587a5356973a082af21ed6bThis article discusses the solutions and threats posed by artificial intelligence in the context of business competition law. The findings of this article indicate that artificial intelligence presents both challenges and opportunities in this field. While artificial intelligence can enhance competition by increasing efficiency and fostering innovation, it raises concerns about market dominance and collusion. Consequently, the KPPU must adapt to these complexities to ensure fair competition and protect consumers. Balancing innovation with competitive enforcement is essential to leveraging the benefits of artificial intelligence while mitigating potential threats as artificial intelligence becomes more prevalent in the market. Based on these findings, the authors recommend that the KPPU increase its oversight of artificial intelligence-related activities to ensure compliance with business competition laws. The KPPU should look for evidence of anti-competitive behavior driven by artificial intelligence, such as price collusion through price monitoring and algorithmic matching software, by amending Law No. 5 of 1999. The KPPU enforces laws and regulations to address the impact of artificial intelligence on business competition law. For instance, the European Union’s Digital Markets Act empowers the European Commission to request algorithms, data about their testing, and explanations about their use from companies designated as competition  gatekeepers.Jurnal Persaingan Usaha721Jurnal Persaingan Usaha["Ahmad Sabirin", "Anna Maria Tri Anggraini"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/2c8cda78462ef7940587a5356973a082af21ed6b cc4fdee859ecfc3ab037bae95d44e4aad822f65bMajor advances in artificial intelligence have fuelled a rapid increase in the automation and augmentation of journalistic work, challenging the centrality of journalists in the news production process. This article theoretically explores news automation by adopting a system of professions framework from the sociology of professions to provide a holistic perspective on the impact of artificial intelligence on journalistic work. This framework posits that different factors influence professional control over work, and problems caused by these factors have left journalism vulnerable to automation. The routine and mundane nature of a significant portion of journalistic tasks suggests that artificial intelligence may potentially replace many journalists in the future, thereby challenging the profession’s claim to expertise. For journalism to uphold its professional authority in the future, it needs to brace for the impact of artificial intelligence. Building on this analysis, we explore strategies for journalism to do so. This involves reinforcing professional ideals in new algorithmic practices, readjusting knowledge and skill taught in education, and reclaiming specialised work practices in organisations. Rather than a threat, the emergence of artificial intelligence then presents an opportunity for journalism to reintroduce the distinctiveness of the profession and rejuvenate its professional promise.Journalism362A system of professions framework from the sociology of professions is adopted to provide a holistic perspective on the impact of artificial intelligence on journalistic work, suggesting that different factors influence professional control over work, and problems caused by these factors have left journalism vulnerable to automation.Journalism["Lynge Asbj\u00f8rn M\u00f8ller", "Morten Skovsgaard", "Claes de Vreese"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/cc4fdee859ecfc3ab037bae95d44e4aad822f65b 6a59dc70d5ebf48c8e5648104970f40958c2649fPurposeThe purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.Design/methodology/approachThis study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.FindingsThe study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.Research limitations/implicationsThe study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.Originality/valueThe results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.Technological Sustainability871The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement, and positioning AI and related technologies as drivers of innovation and expansion within the labour market.Technological Sustainability["Najeb Masoud"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/6a59dc70d5ebf48c8e5648104970f40958c2649f 2f8d27346184abe6f471e2f6622af84089b95ff6With the continuous advancement of artificial intelligence in the field of prostate cancer research, numerous studies have shown that AI performance can rival that of physicians. This review examines the recent applications and developments of AI in the early, accurate, and non-invasive diagnosis of prostate cancer, subsequently elucidating its importance, benefits, and limitations. The review emphasizes the exploration of the potential integration of AI with multi-omics and other cutting-edge technologies. Considering the current status of AI in prostate cancer diagnosis, the review summarizes the challenges faced in the clinical adoption of AI technologies and looks forward to improved and enhanced AI-based prostate cancer diagnostic techniques. The goal is to offer a reference for the integration of artificial intelligence into clinical practice.Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation01This review examines the recent applications and developments of AI in the early, accurate, and non-invasive diagnosis of prostate cancer, subsequently elucidating its importance, benefits, and limitations.Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation["Shucai Hong", "Heyuan Zhang"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/2f8d27346184abe6f471e2f6622af84089b95ff6 732a9e27ed510e155509e0ed9ccc9d46bafbf184Severe acute pancreatitis (SAP) presents a complex clinical scenario that demands prompt and accurate decision-making regarding the appropriate course of treatment. The management of SAP involves a delicate balance between surgical intervention and conservative therapy, aiming to optimize patient outcomes while minimizing morbidity and mortality. Traditional methods of assessing disease severity, such as the Balthazar scale, Ranson criteria, Glasgow-Imrie score, and APACHE II score, provide valuable clinical insight but may lack the precision necessary for individualized patient care. In recent years, integrating artificial intelligence (AI) technologies into healthcare has shown promise in augmenting clinical decision-making processes. By leveraging machine learning algorithms and predictive analytics, AI has the potential to enhance the accuracy and efficiency of severity assessment in SAP. This article explores the role of AI in conjunction with existing severity scales in aiding surgeons' decision-making regarding the timing and modality of intervention in patients with SAP. Through a comprehensive review of current literature and case studies, we will examine the advantages and limitations of AI-based approaches and propose strategies for integrating these technologies into clinical practice. By harnessing the power of AI, surgeons can potentially optimize patient outcomes, improve resource utilization, and reduce the burden of SAP on healthcare systems worldwide.World Journal of Advanced Research and Reviews530The role of AI in conjunction with existing severity scales in aiding surgeons' decision-making regarding the timing and modality of intervention in patients with SAP is explored.World Journal of Advanced Research and Reviews["I. Ara\u00fajo-Filho", "Am\u00e1lia Cinthia Menseses R\u00eago"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/732a9e27ed510e155509e0ed9ccc9d46bafbf184 2ca5a35a6d7abaa503d84c658f26c5e427096302The aim of this paper is to integrate Artificial Intelligence (AI) into Economic Operation and Planning (EOP) methodologies of power systems, specifically by implementing an Optimal Power Flow (OPF) optimization method in the Chennai Utility Bus System. Many traditional approaches to optimize power generation and planning are inherently limited and many of these limitations can be overcome by the use of Artificial Intelligence (AI) techniques. Herein, we have used the AI powered optimization algorithms such as Multi Objective Particle Swarm Optimization (MOPSO) and Multi Objective Genetic Algorithm (MOGA) to increase the efficiency in economical ranking and also grid planning. Implementation of AI techniques in Chennai utility bus system and also evaluating it in real time using Multi Objective Particle Swarm Optimization, Multi Objective Genetic Algorithm, Newton Raphson method and their results are compared. These AI-based methods aim to reduce operation costs, minimize power loss and improve voltage stability as well as minimizing deviation of voltages in order to increase the efficiency. Future work will expand the use of these techniques to more intricate systems, such as the Indian utility 146-bus system to validate their effectiveness, in real world applications.International Research Journal of Multidisciplinary Technovation130International Research Journal of Multidisciplinary Technovation["Saranya S.D", "Balasingh Moses M"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/2ca5a35a6d7abaa503d84c658f26c5e427096302 f47034ae9fc9ea3d9969a46889917e8bc74c185bMethods and means of improving complex technological equipment are shown through the operational assessment of the quality of implemented technologies realtime. This is proved by the fact that implementing technological operations on universal equipment each time requires modeling dynamic processes and taking into account a large number of uncertainty factors that affect the geometry generation quality. It is not possible to be a priori aware of these factors. There is a need to create new information technologies with the possibilities of universal application for immediate understanding of various dynamic processes in diagnostic, identification and control systems. Standard computer systems for statistical analysis and optimization of dynamic processes with the possibilities of universal application for various implementations of modern technologies have been introduced. The possibility of using integrative criteria and methods of artificial intelligence for diagnostic systems, identification and control of advanced technological complexes is shown. The implementation of information systems for the management of complex objects of various technological purposes is presented. The proposed modeling methods and approaches have been tested at various machine-building enterprises when processing parts on turning, milling and grinding machines, both universal and CNC. The research results made it possible to implement new principles of automated control and optimal adjustment of technological processes in real time and create an automated system for evaluating their quality, which allows increasing the efficiency and reliability of management decisions by conducting optimization directly on operating equipment. Based on the methods and approaches described above, new results have been obtained in the implementation of plasma technologies for the modification of geometrically complex surfaces of mechanical engineering products aimed at increasing wear resistance, hardness and other technical characteristics of the working surfaces of precision engineering products. A fairly complete approbation of methods, approaches, procedures and decision-making criteria for various technologies allows them to be recommended for universal applicability.Science intensive technologies in mechanical engineering40A fairly complete approbation of methods, approaches, procedures and decision-making criteria for various technologies allows them to be recommended for universal applicability.Science intensive technologies in mechanical engineering["B. Brzhozovsky", "V. Martynov", "Marina Brovkova3"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/f47034ae9fc9ea3d9969a46889917e8bc74c185b f6857348797a436d131d117f3d010365003ad996The purpose of this study is to investigate, through the use of both quantitative and qualitative methods, the computer capabilities that pre-service teachers believe are necessary for integrating Artificial Intelligence (AI) in Special Education Schools (SES). They reviewed earlier research on schooling and created a three-dimensional survey to determine what computing skills are required for implementing AI. Additionally, a range of Al-Qasemi Academy students from the first to the fourth year of study participated in semi-structured interviews with the researchers. These interviews are intended to gather information related to the research questions. After testing, the reliability of the questionnaire produced a reliability score of (0.963). Within the Green Line, it consists of 580 male and female students from Al-Qasemi Academy. A sample of 150 kids was selected by researchers, with 10 female students participating in the interviews. The results of this study show a moderate level of computer competency availability required for utilizing artificial intelligence. It is suggested that training pre-service teachers in the essential AI application skills is necessary for practical implementation.Trends in Higher Education420The results of this study show a moderate level of computer competency availability required for utilizing artificial intelligence and it is suggested that training pre-service teachers in the essential AI application skills is necessary for practical implementation.Trends in Higher Education["Yasmeen Nzam Abu Mukh", "A. Abd-Rabo", "Safia Tarteer"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/f6857348797a436d131d117f3d010365003ad996 69b5e5d912bd24659a2a4bc237a2e6285f8b0072Infertility, a global health concern, impacts millions of individuals and couples, entailing profound emotional and socio-economic consequences. Recent advances in genetic editing and artificial intelligence (AI) herald a new frontier in infertility treatment, offering precision, personalization, and enhanced efficacy. This compilation explores the integration of clustered regularly interspaced short palindromic repeats associated proteins (CRISPR-Cas) 9 gene editing and AI-driven diagnostics and treatment strategies in the context of reproductive medicine. Through a comprehensive review of current research, clinical applications, and ethical considerations, this paper highlights the transformative potential of these technologies while addressing the associated challenges. The synergy of genetic editing and AI not only promises to improve outcomes for individuals battling infertility but also raises important questions about accessibility, privacy, and ethical implications. By examining these developments, we aim to provide insights into the future of infertility treatments and the evolving landscape of reproductive medicine.Journal of Controversies in Obstetrics &amp; Gynecology and Pediatrics00This compilation explores the integration of clustered regularly interspaced short palindromic repeats associated proteins (CRISPR-Cas) 9 gene editing and AI-driven diagnostics and treatment strategies in the context of reproductive medicine.Journal of Controversies in Obstetrics &amp; Gynecology and Pediatrics["T. G\u00fcrb\u00fcz", "A. Yurci"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/69b5e5d912bd24659a2a4bc237a2e6285f8b0072 713135a9b3807cb213715de5bf375188474459afThe author analyzes the provisions of the Law regarding an experiment for the development and implementation of artificial intelligence (AI) technology in Moscow and the Government Decree developing it “On the approval of the Regulations on the implementation of an experimental legal regime in the field of application of artificial intelligence technologies in Moscow”. The purpose of the work is a theoretical and practical understanding of various aspects of the implementation of AI, as they are fixed at the level of relevant normative legal acts. The formulated goal assumes, within the framework of the study, the solution of such a task as identifying the features of the proposed experimental legal regulation. The result of the work is the conclusion that a number of provisions fixed by the acts under consideration (for example, changes in the regulation of personal data, etc.) are objectively conditioned by the transition from the knowledge economy to the data economy. On the other hand, with the existing shortcomings of the current regulation, it has been revealed that the benefits provided by AI are significantly leveled today by the concerns and prejudices of citizens, which probably need to be dealt with separately when implementing various initiatives in the field of AI.Gaps in Russian Legislation00A number of provisions fixed by the acts under consideration (for example, changes in the regulation of personal data, etc.) are objectively conditioned by the transition from the knowledge economy to the data economy.Gaps in Russian Legislation["K. M. Belikova"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/713135a9b3807cb213715de5bf375188474459af 8377d41ed8b07c00690ca8753ac874d53363a133Artificial intelligence (AI) signifies advanced computer systems adept at tasks traditionally within the purview of human intelligence. This paper explores the transformative landscape of AI applications in healthcare, with a specific focus on risk assessment, predictive modeling, and remote monitoring to proactively address high-risk pregnancies. Aligned with Sustainable Development Goal (SDG) 3.1, our investigation underscores AI's pivotal role in advancing maternal outcomes, encapsulating recent research across domains such as complication prediction, healthcare access enhancement, clinical decision support systems, and fertility treatments. AI-driven models demonstrate efficacy in predicting preterm birth, gestational diabetes, preeclampsia, and other adverse outcomes through meticulous analysis of maternal health data, enabling timely interventions. In underserved regions, AI acts as a catalyst, enhancing accessibility to vital services like prenatal ultrasounds and health education through telemedicine platforms. The integration of AI decision support systems empowers healthcare providers with real-time, patient-specific assessments and recommendations derived from population data analysis. Within fertility medicine, AI proves instrumental in refining genetic screening, embryo viability selection, and optimizing in vitro fertilization success rates. Despite these advancements, challenges persist in regulatory policy, privacy safeguards, accuracy, and seamless integration into clinical workflows, necessitating prudent consideration before widespread implementation. So, ethically applied AI emerges as a transformative force, offering substantial opportunities to advance maternal healthcare significantly. By averting complications, broadening access, informing sound decisions, and optimizing fertility outcomes, AI stands as a promising ally. This comprehensive review encapsulates pivotal applications of this burgeoning technology, outlining potential directions for future research, thereby contributing to the realization of SDG 3.1.World Journal of Advanced Research and Reviews00This paper explores the transformative landscape of AI applications in healthcare, with a specific focus on risk assessment, predictive modeling, and remote monitoring to proactively address high-risk pregnancies, underscoring AI's pivotal role in advancing maternal outcomes.World Journal of Advanced Research and Reviews["Pradeep Kumar Panda", "Rahul Sharma"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/8377d41ed8b07c00690ca8753ac874d53363a133 26393081efd7322ce0d1198e7041e10f3be09895While networked learning (NL) is most often associated with adult learning and professional work practices, examining the “ontogenetic development” of children in the context of today’s smart global networks is also relevant to NL research (Rodríguez-Illera & Barberà in NLEC et al., 2021). In this paper, we ask: What child-technology relations are being forged in our posthuman era of Artificial Intelligence (AI), big data and global networks? We begin by scoping the intensifying presence of networked, smart technologies in the home life of infants, toddlers and preschoolers; we examine recent policy frameworks regarding AI, ethics and children. We then turn to two phenomenological philosophers, Michel Serres and Bernard Stiegler to consider how their thinking about digital technologies might provide insight for parents and educators as they endeavour to make the best “smart” technology choices for children. Finally, we consider the implications of our phenomenological reflections on today’s young posthumans for networked learning and postdigital education.Networked Learning Conference00The intensifying presence of networked, smart technologies in the home life of infants, toddlers and preschoolers is scoped; recent policy frameworks regarding AI, ethics and children are examined; and two phenomenological philosophers are turned to to consider how their thinking about digital technologies might provide insight for parents and educators as they endeavour to make the best “smart” technology choices for children.Networked Learning Conference["Catherine Adams", "S. Groten", "Yin Yin"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/26393081efd7322ce0d1198e7041e10f3be09895 a96ab11b26e49e24c8ede0e0447a18bcd98d1702While several studies have investigated how artificial intelligence (AI) has shaped next-generation user interfaces, most of the literature is Western-based and scant information exists on the technological advancement for people with disabilities. To address this research gap, this study conducts an integrative review to show how AI in China has facilitated the development of next-generation interfaces, allowing more realistic and personalized interactions based on the different needs of people with disabilities. Specifically, it reports various cases adopted and implemented in various Chinese real-scenarios, beyond lab experiments or small tests. This study contributes to the Sustainable Development Goals (SDGs) by depicting different scenarios, techniques and interaction levels that could be adopted in various contexts to facilitate the life of people with disabilities in different fields.Nafath110This study conducts an integrative review to show how AI in China has facilitated the development of next-generation interfaces, allowing more realistic and personalized interactions based on the different needs of people with disabilities.Nafath["A. Tlili", "Xiangling Zhang", "Ronghuai Huang"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/a96ab11b26e49e24c8ede0e0447a18bcd98d1702 c16a4354f2f3351869fef3ff14daf7ff76d56bdfThe use of Artificial Intelligence (AI) in education is a rapidly evolving field with the potential to transform teaching and learning. However, its implementation brings various ethical and practical challenges that must be carefully considered. Key aspects include ensuring student data privacy, promoting fairness and inclusivity in access to technology, maintaining transparency in AI algorithms and decision-making processes, and determining the appropriate levels of human control. Furthermore, accessibility for all students, including those with special needs, must be prioritized, alongside evaluating the potential long-term effects on cognitive, social, and emotional development. Given the complexity of these issues, a thoughtful and ethical approach to integrating AI into education requires ongoing collaboration among various stakeholders, including educators, AI developers, policymakers, students, and the broader community. The goal of this collaboration is to ensure that AI is used in ways that not only improve educational outcomes but also uphold fairness, equity, and transparency. It is crucial to address concerns such as data privacy, algorithmic bias, and the potential negative effects of AI on vulnerable groups to ensure the technology serves as an inclusive tool for education. This research adopts a qualitative approach to explore the different aspects of AI in education, aiming to identify and analyze its potential benefits. By examining the implications of AI integration, the study seeks to provide valuable insights into how AI can be effectively and ethically applied in education, ensuring it enhances learning while respecting core educational values and human dignity.Blockchain Frontier Technology00By examining the implications of AI integration, the study seeks to provide valuable insights into how AI can be effectively and ethically applied in education, ensuring it enhances learning while respecting core educational values and human dignity.Blockchain Frontier Technology["Musidiansyah Otto Syaidina", "Rifqi Fahrudin", "Indah Ainun Mutiara"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/c16a4354f2f3351869fef3ff14daf7ff76d56bdf f9b61f7a598fa7bd6f1111dad5bac233c35b62ff. The article focuses on the revolutionizing of the beauty industry through artificial intelligence (AI) technologies in the USA. The aim of the study is to explore how the implementation of AI affects customer service and business management in modern beauty salons. The research utilized general scientific methods of cognition, such as analysis, synthesis, induction, deduction, and comparison. The results show that the beauty industry, which generated approximately $430 billion in 2022, exhibits a steady growth trend and is forecasted to reach $580 billion by 2027. The main beauty products include skincare, hair care, makeup, and fragrances. Modern beauty salons are actively integrating AI technologies into their operations, leading to significant changes in customer service and business management. Notably, the use of smart mirrors, virtual makeup trial apps, skin condition analysis, and on-demand services, which allow receiving cosmetic procedures at any location and convenient time, ensures a new level of service and consumer satisfaction. The primary directions of AI application in the beauty industry can be broadly divided into two categories: enhancing customer interaction and developing business models. In the first category, AI is used for personalized recommendations, virtual try-ons, skin analysis, and custom care programs, as well as for customer service through chatbots. Regarding business development, AI promotes sustainable growth in the beauty sector, the development of new and sustainable products, the use of advanced technologies, inventory management, and supply chain optimization. However, the use of AI in the beauty industry also comes with certain challenges, including risks of confidential information leaks and the potential loss of clients due to excessive automationVěda a perspektivy00The aim of the study is to explore how the implementation of AI affects customer service and business management in modern beauty salons to promote sustainable growth in the beauty sector and develop business models.Věda a perspektivy["Kristina Gasenko"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/f9b61f7a598fa7bd6f1111dad5bac233c35b62ff 277487fb875058a90b132ed7eed2bd9c3b1bda34Bias, particularly gender bias, is common in artificial intelligence (AI) systems, leading to harmful impacts that reinforce existing negative gender stereotypes and prejudices. Although gender mainstreaming is topical and fashionable in written discourse, it is yet to be thoroughly implemented in practice. While the clamour for AI regulation is commonplace globally, most government policies on the topic do not adequately account for gender inequities. Africa, Egypt, Rwanda and Mauritius are at the forefront of AI policy development. By exploring these three countries as case studies, employing a feminist approach and using the African Union Strategy for Gender Equality & Women’s Empowerment for 2018–2028 as a methodological guide, this study undertakes a comparative analysis of the gender considerations in their policy approaches to AI. It was found that a disconnect exists between gender equality/responsiveness and the AI strategies of these countries, showing that gender has yet to be mainstreamed into these policies. The study provides key recommendations that offer an opportunity for African countries to be innovative leaders in AI governance by developing even more robust policies compared with Western AI policies that fail to adequately address gender.  Law, Technology and Humans800It was found that a disconnect exists between gender equality/responsiveness and the AI strategies of Africa, Egypt, Rwanda and Mauritius, showing that gender has yet to be mainstreamed into these policies.Law, Technology and Humans["Ifeoma E. Nwafor"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/277487fb875058a90b132ed7eed2bd9c3b1bda34 4b6cb4f799c45f52fbd0872d85ed0b7658816eb7 Purpose This research proposal aims to address the growing significance of artificial intelligence (AI) technology in the field of records and information management (RIM) within the African context. Despite the increasing prevalence of AI, there is a lack of comprehensive understanding regarding the factors influencing AI readiness and adoption in RIM. The primary purpose of this paper is to explore these factors and propose an AI readiness and adoption conceptual framework. Design/methodology/approach A comprehensive literature review was conducted to identify the proposed variables and support the hypothesis development. The theoretical foundation of the proposed conceptual framework is based on three theories: the technology acceptance model (TAM), the technology readiness index (TRI) and the cognitive appraisal theory (CAT). Findings The literature reveals that there is a lack of empirical investigation of AI readiness and adoption within the RIM context. Through the proposed conceptual model, the researcher anticipates uncovering critical insights into the factors influencing AI readiness and adoption in RIM practices across African nations. Research limitations/implications The proposed model is not yet empirically tested and the study's scope is limited to African nations. Originality/value The proposed model takes a pioneering approach to empirically investigate AI readiness and adoption within the RIM field, specifically in an African context which is understudied. Records Management Journal720The proposed model takes a pioneering approach to empirically investigate AI readiness and adoption within the RIM field, specifically in an African context which is understudied.Records Management Journal["Liah Shonhe"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/4b6cb4f799c45f52fbd0872d85ed0b7658816eb7 3ab721c17814d470360de1d3f02b7e2b51f89fb8Archives of Gynecology and Obstetrics220The proposed LGB model demonstrated exceptional accuracy, excellent calibration, and superior overall net benefits, which have the potential to assist healthcare professionals in advising women with a history of GDM and in developing preventive strategies to mitigate the adverse effects on maternal and fetal well-being.Archives of gynecology and obstetrics["Min Chen", "Weijiao Xu", "Yanni Guo", "Jianying Yan"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/3ab721c17814d470360de1d3f02b7e2b51f89fb8 85a68c082d2ca62c3e8c179468e69cef71bed808Subject. The study investigates elements of business models of companies in the sphere of educational technologies. Objectives. The purpose is to determine how artificial intelligence affects product innovation, business processes, efficiency, customer relationships, and strategic development in the context of business models. Methods. The study draws on general scientific methods of cognition. Results. Using the Likert scale questionnaire, we collected quantitative data from managers of companies operating in the sphere under consideration. The results show the significant impact of artificial intelligence on data processing, educational process effectiveness, content creation speed, and changing roles and competencies. The findings emphasize the role of artificial intelligence in educational process optimization, automation of administrative tasks, and predictive analytics for making informed decisions. However, the impact of this technology on revenue streams and scalability remains minimal. It is essential to develop artificial intelligence-based tools, given ethical aspects, data confidentiality, and compliance with educational standards. Conclusions. The study provides insight into strategic changes in educational technologies due to the integration of artificial intelligence, and contributes to a broader discussion about the role of technologies in education.Economic Analysis: Theory and Practice310The findings emphasize the role of artificial intelligence in educational process optimization, automation of administrative tasks, and predictive analytics for making informed decisions, however, the impact of this technology on revenue streams and scalability remains minimal.Economic Analysis: Theory and Practice["E. D. Pavlyukevich", "K. S. Sadov"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/85a68c082d2ca62c3e8c179468e69cef71bed808 6f524fb64b3a70a4cd2bbff21dac8b4a8bab720cThis thematic issue explores the applications of artificial intelligence‐based technologies and their potential for producing discriminatory and biased outcomes based on ethnicity, religion, and gender. This thematic issue adds to the ongoing debate with theoretical and empirical studies and a commentary that examine the topic from various perspectives. This editorial discusses the key themes highlighted in the studies and presents the findings of the different contributions to this collection.Social Inclusion80This editorial discusses the key themes highlighted in the studies and the findings of the different contributions to this collection and presents the findings of the different contributions to this collection.Social Inclusion["Derya Ozkul"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/6f524fb64b3a70a4cd2bbff21dac8b4a8bab720c ea541c1c45ae515998888ea9eea62dde6a903874Objective: to explore and identify the issues and opportunities for the ethical and legal regulation of artificial intelligence by the example of digital transformation in Morocco.Methods: the study was conducted using analytical and comparative approaches to address the emerging legal issues arising from the development of artificial intelligence. The traditional scientific method in law is based on legal analysis, which was applied to the study of legal texts, scientific literature, diagnosis of the condition of the study field at the national and international level. Along with this, the comparative approach in law was used, which made it possible to examine the Moroccan legislation comparison with that of other countries.Results: the article presents a review of scientific literature on the legal and ethical issues of using artificial intelligence. Legal texts and decrees developed at national and international level, directly or indirectly linked to the use of artificial intelligence, were reviewed. Moroccan legislation was compared with that of other countries. The findings suggest that, in the absence of a specific legal framework for artificial intelligence systems, the adoption of ethical standards in the form of guidelines, best practices and ethical charters is preferable. These mechanisms appear to be a viable alternative to legal regulation. In this sense, several initiatives were taken to promote “soft law”, which aims to encourage appropriate behavior of technological agents.Scientific novelty: the analysis of digital transformations in Morocco made it possible to present a comprehensive view on the role of ethical aspects and on the sufficiency of law to respond to the changes in the modern society, transformed by the development of artificial intelligence.Practical significance: the study allows identifying ways to find a more flexible balance between “soft” and “hard” law in the regulation of relations, taking into account the technological reality. This should encourage the appropriate behavior of technological agents and positively affect the specificity of the current situation. Today, the “hard law” slowly recognizes and addresses the problems associated with the digital technologies’ regulation and slowly takes into account the possible risks posed by artificial intelligence and the insufficiency of its regulation.Journal of Digital Technologies and Law90The findings suggest that, in the absence of a specific legal framework for artificial intelligence systems, the adoption of ethical standards in the form of guidelines, best practices and ethical charters is preferable and appear to be a viable alternative to legal regulation.Journal of Digital Technologies and Law["H. Jabir", "K. Lagtati", "D. Pohe-Tokpa"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/ea541c1c45ae515998888ea9eea62dde6a903874 27584d86c98d5c71b7cdafa54c2088b42eba2e18Artificial intelligence has the potential to transform medical imaging. The effective integration of artificial intelligence into clinical practice requires a robust understanding of its capabilities and limitations. This paper begins with an overview of key clinical use cases such as detection, classification, segmentation and radiomics. It highlights foundational concepts in machine learning such as learning types and strategies, as well as the training and evaluation process. We provide a broad theoretical framework for assessing the clinical effectiveness of medical imaging artificial intelligence, including appraising internal validity and generalisability of studies, and discuss barriers to clinical translation. Finally, we highlight future directions of travel within the field including multi-modal data integration, federated learning and explainability. By having an awareness of these issues, clinicians can make informed decisions about adopting artificial intelligence for medical imaging, improving patient care and clinical outcomes.British journal of hospital medicine930An overview of key clinical use cases such as detection, classification, segmentation and radiomics and foundational concepts in machine learning such as learning types and strategies are highlighted as well as the training and evaluation process.British journal of hospital medicine["Shivank Keni"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/27584d86c98d5c71b7cdafa54c2088b42eba2e18 0bd92c46d5a73d706032178b3bc3ea6316b7f4ffThe use of artificial intelligence in e-commerce makes it easier for users to do online shopping. However, user data collection carried out by artificial intelligence in e-commerce can be misused. This is a shift in intention to adopt artificial intelligence in e-commerce. This study aims to identify the factors that impact the adoption of artificial intelligence in the field of e-commerce. The technology readiness model and the technology acceptance model are both utilized in this study. Data was collected from 283 students who have done shopping in e-commerce. The data collected will then be analyzed using SEM-PLS. The findings suggest that optimism, innovativeness, and discomfort have a role in shaping the acceptability of artificial intelligence in e-commerce, through the perceived ease of use and perceived usefulness. However, research findings suggest that there is no correlation between insecurity and the perceived ease of use and usefulness. The findings suggest that the way users view the ease of use, and the utility of artificial intelligence technology directly influences their acceptance of it in e-commerce, which is then through in their intention to use it. The result of this study can be used by online businesses to apply TAM and technology readiness models to maximize the use of AI in e-commerce.Journal Markcount Finance410The findings suggest that optimism, innovativeness, and discomfort have a role in shaping the acceptability of artificial intelligence in e-commerce, through the perceived ease of use and perceived usefulness, but research findings suggest that there is no correlation between insecurity and the perceived ease of use and usefulness.Journal Markcount Finance["Naskiroh Naskiroh", "Dina Nurqolbiyah", "W. Winarti", "Ida Rosnidah", "Firman Hidayat"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/0bd92c46d5a73d706032178b3bc3ea6316b7f4ff 2f2788ffc692c94158d54d5bac07e80322652f95The subject of the study is the processes of forming the user's mental model in artificial intelligence systems. The construction of such a model is associated with solving the problem of opacity and incomprehensibility of the decision-making process in such systems for end users. To solve this problem, the system user needs to receive an explanation of the obtained decision. The explanation should take into account the user's perception of the decision and the decision-making process, which is formalized within the user's mental model. The mental model considers the user's use of explicit and implicit knowledge, the latter of which usually lacks formal representation. The externalization of such knowledge ensures its transformation into a formal form. The aim of the work is to develop an approach to the externalization of implicit knowledge based on identifying patterns and causal dependencies for the decision-making process in an intelligent system when constructing the user's mental model. To achieve this goal, the following tasks are solved: developing a user's mental model of an artificial intelligence system that takes into account both explicit and implicit knowledge and developing an approach to the externalization of implicit knowledge of the user of the artificial intelligence system. A user's mental model of an artificial intelligence system that accounts for both explicit and implicit knowledge of the user is proposed. The model considers the connections between the user's explicit and implicit knowledge regarding the artificial intelligence system, the decision-making process, the method of using the decisions, and the general concept of the intelligent system. This creates conditions for the externalization of the user's implicit knowledge and the subsequent use of this knowledge in forming explanations regarding the decision-making process in the artificial intelligence system. An approach to the externalization of knowledge from the statistical and semantic layers of the user's mental model is proposed. In practical terms, the approach makes it possible to translate into explicit form the conditions and constraints regarding the formation and use of decisions in the artificial intelligence system.Bulletin of National Technical University KhPI Series System Analysis Control and Information Technologies00The aim of the work is to develop an approach to the externalization of implicit knowledge based on identifying patterns and causal dependencies for the decision-making process in an intelligent system when constructing the user's mental model.Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies["S. Chalyi", "I. Leshchynska"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/2f2788ffc692c94158d54d5bac07e80322652f95 88751ec06357c2ce1dc5e83c4401ec01636df40bCurrently, Artificial Intelligence has penetrated almost every application and site on the Internet. This has had an impact on the world of education, business, etc. In addition to making it easier for humans to carry out their activities, this can also have negative effects if the response from the AI ​​chatbot is inaccurate or even misleading. In this article, the author has conducted experiments on several free versions of popular chatbots, namely ChatGPT, Gemini (Bard), AI Chat and Aichatting. AI chatbots are very helpful in the field of general knowledge, finding solutions to problems and creating creations, such as fiction, or guiding the creation of scientific reports. Users must understand and be observant in using this chatbot because the response is very dependent on the input given. Questions that contain errors will get different responses. All responses given by the AI ​​chatbot are suggestions, not something that must be followed absolutely. This is because the user is responsible for the consequences, not AI. AI has ethics in providing responses so that it will not produce negative answers or direct users to things that violate the rules. The factors that are the main measurements are speed, accuracy, completeness, understanding of questions, response variations, ability to handle input errors and ability to provide solutions. The four chatbots tested each have their own strengths and weaknesses, but there is one that has the most features and variations in its responses, namely Gemini (Bard).Journal of Embedded Systems, Security and Intelligent Systems00The author has conducted experiments on several free versions of popular chatbots, namely ChatGPT, Gemini (Bard), AI Chat and Aichatting, and there is one that has the most features and variations in its responses, namely Gemini (Bard).Journal of Embedded Systems, Security and Intelligent Systems["Hans Marwi", "Nobertus Tri Suswanto Saptadi"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/88751ec06357c2ce1dc5e83c4401ec01636df40b 2a1a31b366f9414f4be219562984d541a94f52d8This article describes the origin, history and future of artificial intelligence. It has been found that with knowledge about artificial intelligence, we can explore and develop various fields and apply them in our lives. In order to develop, we must use artificial intelligence correctly. This is because the intelligence that is developing now will change in the future. The article describes how artificial intelligence is used in other fields and what benefits and harm it can bring. It has been shown that artificial intelligence was once a universal term for applications that performed complex tasks such as communicating with customers online or playing chess. Artificial intelligence is the future of business, allowing you to create innovative solutions and open up new opportunities for the development of companies.Bulletin of Issyk-Kul University00It has been shown that artificial intelligence was once a universal term for applications that performed complex tasks such as communicating with customers online or playing chess.Bulletin of Issyk-Kul University["Sh. O. Omurbekova", "D. Y. Kasmakunov", "K. S. Stalbekov"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/2a1a31b366f9414f4be219562984d541a94f52d8 a8e5c1d38d220072c7219273e823de1a082378bfOver the past decade, social media has been attracting a growing number of people to the online space. Due to the increase in internet usage, a huge number of text data has been produced. Such data can reflectusers’ mental healthstatus, but it is still challenging to predictsuicide risk from data,due to the high complexity of texts.This research aims to predict the suicide risk from Reddit posts using artificial intelligence (AI). The data were collected from the Kaggle dataset, which includedpostingsof suicide subreddits.The datawere pre-processed throughnatural language processing techniques. Logistic regression, naive Bayes, and random forest models were then used for classifying the Reddit users, i.e., to predict if they are in a suicidal or non-suicidal mental state. These models were compared to identify an AI approach that provides the best performance among the three models. Then, the logistic regression model with doc2vec showed the highest precision of 0.92, recall 0.92, and F1score of 0.92.International Journal on Perceptive and Cognitive Computing110This research aims to predict the suicide risk from Reddit posts using artificial intelligence (AI) and identifies an AI approach that provides the best performance among the three models.International Journal on Perceptive and Cognitive Computing["Elean Sugafta Rafa", "Adeeba Mahmooda", "Takumi Sase"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/a8e5c1d38d220072c7219273e823de1a082378bf 0ae236fb7e1502b7adcc7bb9b3380322721a1f8fThe article is devoted to the research and analysis of the possibilities and benefits of the integration of artificial intelligence (AI) into the educational process of students specializing in agricultural engineering. The advantages of using AI in the educational sphere and its potential to improve the quality of education and training of future specialists are considered. In particular, attention is focused on improving students' analytical skills through the analysis of large volumes of data, optimization of agricultural processes with the help of machine learning systems, and the development of their innovative skills. Artificial intelligence - attributes of smart systems that perform innovative functions, traditionally considered a human prerogative; the science and technology of creating intelligent machines, especially intelligent computer programs. Artificial intelligence is concerned with the task of using computers to understand human intelligence, but it is not necessarily limited to biologically feasible methods. The fields of application of modern intelligent systems are very narrow. For example, a program that can beat people at chess and cannot answer questions, etc. The impact of AI integration on improving student success in education and preparation for the challenges of the modern agricultural sector has been studied. The conclusions of the article indicate the importance of the implementation of artificial intelligence technologies for increasing the efficiency of the educational process and training qualified personnel in the field of agricultural engineering. In addition, the article examines specific examples of the application of artificial intelligence in the educational process of agricultural engineering students, such as systems for analyzing soil and weather conditions, autonomous robots for watering and caring for crops, as well as software tools for predicting yields. Special attention is paid to the practical aspects of the implementation of AI technologies in the educational process, such as teaching methods and assessment of students' educational achievements. The article also examines potential challenges and obstacles that may arise when integrating artificial intelligence into the education of agricultural engineers, such as the availability of technology, the preparation of teachers and students to use AI, and the ethical aspects of using algorithms and data for educational purposes.ENGINEERING, ENERGY, TRANSPORT AIC100Specific examples of the application of artificial intelligence in the educational process of agricultural engineering students, such as systems for analyzing soil and weather conditions, autonomous robots for watering and caring for crops, as well as software tools for predicting yields are examined.ENGINEERING, ENERGY, TRANSPORT AIC["Svitlana Kravets", "O. Tokarchuk"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/0ae236fb7e1502b7adcc7bb9b3380322721a1f8f c20f0e0ce728800f5505a2b9002e29be81d5c5e6Purpose: The aim of the study was to evaluate the impact of artificial intelligence on supply chain efficiency in Turkey. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings:  Artificial intelligence (AI) has significantly enhanced supply chain efficiency in Turkey. AI applications such as predictive analytics, demand forecasting, and optimization algorithms have streamlined operations, reduced costs, and improved decision-making. Automation of repetitive tasks through AI has increased productivity and accuracy in inventory management and logistics. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV) theory, dynamic capabilities theory & technology acceptance model (TAM) may be used to anchor future studies on the impact of artificial intelligence on supply chain efficiency in Turkey. Invest in continuous training and development programs for supply chain professionals to effectively utilize AI tools. Develop industry standards and regulatory frameworks for the ethical use of AI in supply chains. These policies should address data privacy, security, and the responsible use of AI technologies to protect stakeholders.International journal of technology and systems00Artificial intelligence (AI) has significantly enhanced supply chain efficiency in Turkey and Automation of repetitive tasks through AI has increased productivity and accuracy in inventory management and logistics.International Journal of Technology and Systems["Aylin Kaya"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/c20f0e0ce728800f5505a2b9002e29be81d5c5e6 e713d33088761ab8605ec01f2d912e0facc7be1cScience China Technological Sciences504Science China Technological Sciences["GuoYin Wang", "Huanan Bao", "Qun Liu", "TianGang Zhou", "Si Wu", "Tiejun Huang", "Zhaofei Yu", "CeWu Lu", "Yihong Gong", "Zhaoxiang Zhang", "Sheng He"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/e713d33088761ab8605ec01f2d912e0facc7be1c a68ab87ea966ccf08b8c285747787b984d55be93IJARCCE01IJARCCE["F. Ekpar"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/a68ab87ea966ccf08b8c285747787b984d55be93 2d0f89630ca10ff2d967d9e43eaee70e3222b889This study examines the significant growth of e-commerce in Romania, which has surpassed the rates of expansion observed in other more developed countries of the European Union. Based on market analysis by sector-specific companies, the Romanian e-commerce market has reached over €6.5 billion. This rapid growth trajectory is expected to continue, driven by various factors, including the impact of the COVID-19 pandemic and the natural evolution of the market. The main purpose of this study is to assess the expansion of the e-commerce market in Romania, identify the key factors behind this growth, and project future market values. Data for this analysis has been collected from industry reports, market analysis, and relevant statistical databases. The study uses a quantitative approach, utilizing financial data and growth rates to forecast future market trends. The dataset includes financial figures from e-commerce sales, digital services such as bill payments, and airline and hotel bookings from 2018 to 2023. Projections for 2024 and beyond were derived from this historical data. In 2019, the e-commerce market in Romania was valued at €4.68 billion, representing a significant increase compared to previous years. By 2020, amid the pandemic, the market value increased to €5.5 billion, marking a 38.4% increase from the previous year. Forecasts for 2024 estimate that the market will exceed €8 billion. In addition, when related digital services are included, the total market value could exceed €10 billion, illustrating the substantial economic impact of the online sector and the growth potential. This study highlights the dynamic nature of the e-commerce landscape in Romania and underlines the significant economic opportunities it presents.Telecom211The main purpose of this study is to assess the expansion of the e-commerce market in Romania, identify the key factors behind this growth, and project future market values, using a quantitative approach.Telecom["Andreea Nistor", "Eduard Zadobrischi"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/2d0f89630ca10ff2d967d9e43eaee70e3222b889 ef5b2652da3b52bd0f82f301a66455bbc684acc3Acute psychological stress (APS) is a complex and multifactorial phenomenon that affects metabolism, necessitating real-time detection and interventions to mitigate its effects on glycemia in people with type 1 diabetes. This study investigates the detection of APS using physiological variables measured by the Empatica E4 wristband and employs explainable machine learning to evaluate the importance of the physiological signals. The extreme gradient boosting model is developed for classification of APS and non-stress (NS) with weighted training, achieving an overall accuracy of 99.93%. The Shapley additive explanations (SHAP) technique is employed to interpret the global importance of the physiological signals, determining the order of importance for the variables from most to least as galvanic skin response (GSR), heart rate (HR), skin temperature (ST), and motion sensors (accelerometer readings). The increase in GSR and HR are positively correlated with the occurrence of APS as indicated by high positive SHAP values. The SHAP technique is also used to explain the local signal importance for particular instances of misclassified samples. The detection of APS can inform multivariable automated insulin delivery systems to intervene to counteract the APS-induced glycemic excursions in people with type 1 diabetes.Signals491This study investigates the detection of APS using physiological variables measured by the Empatica E4 wristband and employs explainable machine learning to evaluate the importance of the physiological signals.Signals["Mahmoud M. Abdel-Latif", "Mudassir M. Rashid", "Mohammad-Reza Askari", "Andrew Shahidehpour", "Mohammad Ahmadasas", "Minsun Park", "Lisa Sharp", "L. Quinn", "A. Cinar"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/ef5b2652da3b52bd0f82f301a66455bbc684acc3 c74d8d3ad7e70a867d369a89f047763e5d97551eJournal of Medical Internet Research300Journal of Medical Internet Research["Shoko Maru", "Ryohei Kuwatsuru", "Michael D Matthias", "Ross Joseph Simpson Jr"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/c74d8d3ad7e70a867d369a89f047763e5d97551e ceb2237fb8462c87317c380712fbfdba93a4d025IJARCCE00IJARCCE["Anvitha S Badiger", "S. Karuna", "Shreya S Jain", "Srushtitha S", "Poornima Hn"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/ceb2237fb8462c87317c380712fbfdba93a4d025 d2b17169c67b9e393aaee0614e96be756a925e6aThis paper addresses the pressing need to evaluate the maturity and performance metrics of generative AI tools dedicated to accessibility in product development. The problem lies in the lack of standardized methods for assessing the maturity of generative AI tools tailored to accessibility needs and the absence of universally accepted performance metrics to measure their efficacy. This deficiency hampers the advancement of inclusive design practices and limits the potential impact of AI-driven accessibility solutions. This paper proposes a comprehensive framework for evaluating the maturity of AI tools specifically designed for accessibility in product development. We elucidate the critical criteria integral to this evaluation, encompassing aspects such as usability, reliability, scalability, and adaptability to diverse user needs and contexts. The proposed solution aims to contribute valuable knowledge to the evolving landscape of generative AI tools dedicated to enhancing accessibility in product development. By establishing a structured approach to assessing maturity and advocating for standardized performance metrics, our research endeavors to empower developers, designers, and stakeholders to make informed decisions regarding the adoption and refinement of AI-driven accessibility solutions.Nafath60A comprehensive framework for evaluating the maturity of AI tools specifically designed for accessibility in product development is proposed, encompassing aspects such as usability, reliability, scalability, and adaptability to diverse user needs and contexts.Nafath["Iyad Abu Doush"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/d2b17169c67b9e393aaee0614e96be756a925e6a 67309c7ee2a944bb286a59d7e85de7ee3967b6e1Frontiers in Public Health20Frontiers in Public Health["Jorge P. Sim\u00f5es", "P. T. Ten Klooster", "Patrick K. Neff", "Uli Niemann", "J. Kraiss"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/67309c7ee2a944bb286a59d7e85de7ee3967b6e1 32d8e03af20dd02a34c7ca3b12ad7f2a27fffe57Horticultural Science and Technology160Horticultural Science and Technology["K.P.S. Kumaratenna", "Youngsang Cho"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/32d8e03af20dd02a34c7ca3b12ad7f2a27fffe57 31d695ebe985725b086a384f5b4f2b6aa4de4d9fIJARCCE00IJARCCE["Tirumala Rao Chimpiri"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/31d695ebe985725b086a384f5b4f2b6aa4de4d9f fad30ad322937a2aec33824684b7761824dcbc7bIn the changing financial environment, improving customer experience is essential for banks. A fundamental aspect of this experience is Customer Support Services (CSS). The banking business has traditionally utilized technology tools such as Interactive Voice Response (IVR) Systems and chatbots; nevertheless, their rule-based design frequently limits their adaptability. This study investigates the capacity of Generative AI to revolutionize Customer Support Services within the banking sector. In contrast to conventional systems, Generative AI's capacity to produce original material facilitates a more tailored and contextually aware engagement. We have evaluated traditional approaches against sophisticated Generative AI capabilities using a scenario-based methodology. The findings elucidate how Generative AI may transform Customer Support Services across digital platforms, ensuring an enhanced customer experience.World Journal of Advanced Research and Reviews00This study investigates the capacity of Generative AI to revolutionize Customer Support Services within the banking sector and elucidate how Generative AI may transform Customer Support Services across digital platforms, ensuring an enhanced customer experience.World Journal of Advanced Research and Reviews["Praneeth Reddy Amudala Puchakayala"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/fad30ad322937a2aec33824684b7761824dcbc7b fe3b20378f2b2001b87beaae76eda397e26cbb90International Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology["Indrajit Roy Chowdhury"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/fe3b20378f2b2001b87beaae76eda397e26cbb90 e47a3c6e30d7e5e4625c18314af28b2a4264a955International Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology["Christopher Maduabuchi Okonkwo"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/e47a3c6e30d7e5e4625c18314af28b2a4264a955 c2021bc11aa6b28ca523eaa54684f9561184a851Penjadwalan perkuliahan merupakan salah satu aspek penting untuk mendukung terlaksananya perkuliahan. Dengan adanya penjadwalan perkuliahan maka waktu perkuliahan dapat diatur, agar ruangan dapat digunakan dengan cara yang efektif. Untuk menyusun penjadwalan perkuliahan tentu dibutuhkan cara yang tepat agar penyusunan jadwal perkuliahan menjadi mudah dan cepat. Pada Perguruan Tinggi XXXX, penjadwalan perkuliahan masih dilakukan dengan cara konvensional yaitu menyusun jadwal dengan cara manual menggunakan software Microsoft excel sehingga membutuhkan waktu yang lama dalam menyusun penjadwalan perkuliahan. Untuk mempermudah dan mempercepat proses penyusunan jadwal perkuliahan secara optimal, Sistem Informasi Penjadwalan Perkuliahan merupakan salah satu solusi yang tepat. Algoritma Genetika adalah salah satu cara untuk melakukan langkah logis dan sistematis Agar Sistem Informasi Penjadwalan perkuliahan dapat memberikan solusi yang mudah, cepat dan optimal. Algoritma genetika merupakan salah satu kemajuan pada bidang teknologi informasi dalam bidang Artificial Intelligence (AI) yang dapat menyelesaikan masalah optimasi. Sehingga proses pembuatan jadwal menggunakan algoritma genetik membuat penyusunan jadwal perkuliahan menjadi lebih cepat, tepat dan optimal. Hasil yang diperoleh dari pemodelan algoritma genetika pada sistem penjadwalan perkuliahan ini berupa jadwal kuliah reguler pagi, jadwal kuliah reguler malam, dan jadwal kuliah extention pada semua jurusan di Perguruan Tinggi XXXX.Multidisciplinary Indonesian Center Journal (MICJO)00Multidisciplinary Indonesian Center Journal (MICJO)["Aspiran Sisca Hia", "N. Andika", "Hadi Supratikta"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/c2021bc11aa6b28ca523eaa54684f9561184a851 ef902519971a1f5c1bdd154a25afb7c4afa9aa30Paediatric Educator’s Special Interest Group00Paediatric Educator’s Special Interest Group["Radhika Puttha", "Harshavardan Thalava", "Janaki Mehta", "Ramesh Thalava"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/ef902519971a1f5c1bdd154a25afb7c4afa9aa30 4c3d0ddecf5b90c0e56d40214ac0db5883ad6b54الذكاء الاصطناعي هو مجال سريع التطور له القدرة على احداث تأثير عميق على المجتمع والاقتصاد، ومن المتوقع أن يخلق الذكاء الاصطناعي فرصاً جديدة، لاسيما وظائف جديدة في مجالات عديدة مثل؛ الرعاية الصحية، والتعليم، وتكنولوجيا المعلومات والاتصالات، والنقل، والخدمات اللوجستية، ومع ذلك من المتوقع أن يتسبب الذكاء الاصطناعي في فقدان الوظائف في كل القطاعات لاسيما الصناعية، والزراعية، والنقل، والصحة وغيرها، يعتمد تأثير الذكاء الاصطناعي على معدلات البطالة على عاملين أساسيين هما: معدل انتشار الذكاء الاصطناعي، وطبيعة الوظيفة، من المرجح كذلك أن يتعرض سوق العمل الذي يتضمن مهام متكررة أو مملة أو خطيرة، للخطر من الذكاء الاصطناعي. لقد انطلق البحث من فرضية مفادها أن تكنولوجيا الذكاء الاصطناعي سيكون لها العديد من الأثار السلبية والإيجابية على الوظائف من خلال خلق وظائف جديدة ويصاحب ذلك بعض الاثار السلبية في تقليص عدد العاملين في بعض الوظائف الأخرى. وتنبع أهمية البحث من الدور الكبير للذكاء الاصطناعي في حياة المجتمعات على مستوى الاقتصاديات العالمية وأصبح هذا المصطلح شائع الاستخدام ويتطلب توضيح هذه الأهمية بالنسبة لسوق العمل والاهتمام من قبل جميع الشركات والمؤسسات في الدول جميعاً.Al-Ghary Journal of Economic and Administrative Sciences00Al-Ghary Journal of Economic and Administrative Sciences["\u0635\u0627\u0644\u062d \u0645\u0647\u062f\u064a \u0627\u0644\u0639\u0627\u0645\u0631\u064a", "\u062d\u0633\u0646 \u062c\u0645\u0627\u0644 \u0627\u0644\u064a\u0648\u062f\u0627\u0648\u064a"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/4c3d0ddecf5b90c0e56d40214ac0db5883ad6b54 0f8adb9ecf2c2274c32d542b5d0a90d7e7bb49f7ARES60{"pages": "148:1-148:7"}["Ijeoma Ebere-Uneze", "Syed Naqvi"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/0f8adb9ecf2c2274c32d542b5d0a90d7e7bb49f7 a1cbff9332144107727058fb7b5972c243f3e08782nd STAI Annual Convention0082nd STAI Annual Convention["Amresh Pratap Singh", "Subhash Chandra", "Mihir Mandal", "S. Paroha"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/a1cbff9332144107727058fb7b5972c243f3e087 7b991c12860e478a4223ef2326a22d20528e82c7ARES370{"pages": "44:1-44:12"}["R. May", "Jacob Kr\u00fcger", "Thomas Leich"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/7b991c12860e478a4223ef2326a22d20528e82c7 a2803993d02cd5c08d6f29c15e457b72cc496524هدف هذا البحث إلى أستكشاف العلاقة بين قدرات الذكاء الاصطناعي وخدمات التأمين الرقمية في شركة التأمين العراقية. وتتمثل أهمية البحث من سعيه إلى تحديد قابليات الذكاء الأصطناعي التي تسهم في تقديم خدمات تأمين رقمية قادرة على تلبية وتحقيق رغبات الزبائن، فضلاً عن السعي لردم الفجوة المعرفية بين هذين المتغيرين. لتحقيق هذا الهدف تم تصميم استبانة موجهة لموظفي شركة التأمين العراقية، وتناولت قدرات الذكاء الاصطناعي وخدمات التأمين الرقمية، بموجبها وزع الباحثون بإستخدام طريقة العينة العشوائية البسيطة (115) إستبانه على عدد من الموظفين في الشركة المبحوثة. من بعد ذلك تم أسترجاع  (100)إستبانة صالحة للتحليل الأحصائي وبمعدل استجابة بلغ (87%). بعد تحليل البيانات بإستخدام برنامج SPSS V.24 توصل الباحثون إلى وجود علاقة إرتباط طردية ومعنوية بين قابليات الذكاء الأصطناعي وخدمات التأمين الرقمية، فضلاً عن وجود تأثير معنوي وموجب لقابليات الذكاء الأصطناعي في خدمات التأمين الرقمية.Al-Ghary Journal of Economic and Administrative Sciences00Al-Ghary Journal of Economic and Administrative Sciences["\u0644\u064a\u062b \u0639\u0644\u064a \u064a\u0648\u0633\u0641 \u0627\u0644\u062d\u0643\u064a\u0645", "\u0627\u064a\u0641\u0627\u0646 \u0645\u0627\u0636\u064a \u062d\u0645\u0632\u0629", "\u0645\u0627\u0647\u0631 \u0645\u062d\u0633\u0646 \u0627\u0644\u062a\u0645\u064a\u0645\u064a", "\u062d\u064a\u062f\u0631 \u062c\u0627\u0633\u0645 \u0627\u0644\u062a\u0645\u064a\u0645\u064a"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/a2803993d02cd5c08d6f29c15e457b72cc496524 6eaebcf57b00d99169191dc89b9670c98dd9ec89الهدف من البحث هو اكتشاف طبيعة العلاقة بين الذكاء الاصطناعي والابتكار التنظيمي، بُني البحث على فرضيتين رئيستين انبثقت من كل فرضية رئيسة اربعة فرضيات فرعية، وتم التحقق من صحة الفرضيات الرئيسة والفرضيات الفرعية عن طريق تحليل علاقات الإرتباط والتأثير بين متغيرات البحث بواسطة برنامج  (SPSS v.26)، للبيانات المتحصلة بواسطة استمارة إستبيان تم توزيعها على عينة مؤلفة من (87) من المهندسين العاملين في شركة ربان السفينة للصناعات الكهربائية والهندسية في النجف الاشرف، وتم استرجاع (83) استمارة، وبعد فحصها تبين إن عدد الاستمارات الصالحة للتحليل الإحصائي (79) استمارة، أي بنسبة استرجاع (90%)، وقد تضمن البحث أربعة مباحث، المبحث الأول المنهجية العلمية للبحث، والمبحث الثاني الجانب النظري، أما المبحث الثالث الجانب التطبيقي، وأخيراً جاء المبحث الرابع الاستنتاجات والتوصيات.Al-Ghary Journal of Economic and Administrative Sciences00Al-Ghary Journal of Economic and Administrative Sciences["\u0644\u064a\u062b \u0634\u0627\u0643\u0631 \u0623\u0628\u0648 \u0637\u0628\u064a\u062e", "\u0623\u0645\u064a\u0631 \u0645\u062d\u0645\u062f \u0627\u0644\u062d\u0643\u064a\u0645", "\u0639\u062f\u0646\u0627\u0646 \u0631\u062d\u064a\u0645 \u062d\u0645\u0648\u062f", "\u0627\u064a\u0641\u0627\u0646 \u0645\u0627\u0636\u064a \u062d\u0645\u0632\u0629", "\u0644\u064a\u062b \u0639\u0644\u064a \u0645\u0637\u0631"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/6eaebcf57b00d99169191dc89b9670c98dd9ec89 e4ee758e3317f8242a8335e411cac4e47c8e2de2International Review of Psychiatry800International Review of Psychiatry["F. Ricci", "Daniela Giallanella", "Costanza Gaggiano", "J. Torales", "J. Castaldelli-Maia", "M. Liebrenz", "Abdulbari Bener", "Antonio Ventriglio"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/e4ee758e3317f8242a8335e411cac4e47c8e2de2 03aa23adb63cd89ed0e4a34e4a1ce66616cba166International Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology["Aseem Mankotia", "Rohith Chinnaswamy", "Sara Venkatachalam"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/03aa23adb63cd89ed0e4a34e4a1ce66616cba166 b4bcd719fed2bc332d19db43d0bd4fb929cd22ebMedia pembelajaran memainkan peran penting dalam meningkatkan pengalaman belajar peserta didik di sekolah. Salah satu unsur terpenting  yang menentukan kualitas dalam desain media pembelajaran adalah bahan ajar.  diperoleh. Faktanya, proses penyusunan bahan ajar yang efektif dengan cara konvensional membutuhkan perencanaan yang matang, waktu yang lama, pemahaman yang mendalam tentang kurikulum, serta kemampuan untuk menyajikan materi dengan pendekatan yang inovatif.  Namun, upaya-upaya peningkatan kompetensi guru dalam pengembangan bahan ajar berdasarkan kegiatan yang telah banyak dilakukan sebelumnya hanya berfokus pada pengembangan bahan ajar dalam konteks pemanfaatan media digital dan konteks materi bahan ajar. Dengan banyaknya kesibukan administrasi dan pengajaran di kelas, guru semakin kesulitan untuk mengembangkan bahan ajar dengan efektif dan efisien jika menggunakan cara-cara konvensional. Permasalahan ini juga terjadi pada sebagian Guru SMP Negeri 2 Kahu Kabupaten Bone Sulawesi Selatan yang merupakan mitra dalam kegiatan pengabdian kepada masyarakat ini. Oleh karena itu pada kegiatan program kemitraan masyarakat ini dilaksanakan pelatihan pemanfaatan aplikasi berbasis Artificial Intelligence (AI) untuk pengembangan bahan ajar guru. Kegiatan ini terbagi ke dalam 3 tahapan yaitu tahap persiapan, pelaksanaan kegiatan dan tahap evaluasi. Dimana materi inti yang disampaikan adalah berkaitan dengan teknik penggunaan prompt Google Gemini dalam mendukung dan membantu guru untuk penyusunan dan pengembangan bahan ajar.  Hasil pelatihan menunjukkan bahwa terjadi peningkatan yang signifikan pada para peserta terkait pengetahuan dan kompetensi peserta dalam pengembangan bahan ajar dengan memanfaatkan aplikasi berbasis AI.Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat00Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat["Andi Baso Kaswar", "Fhatiah Adiba", "Dyah Darma Andayani", "N. Nurjannah", "Andi Akram Nur Risal"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/b4bcd719fed2bc332d19db43d0bd4fb929cd22eb 9854c791f21a2068338860aaacaa542d43095545The integration of Blockchain technology and Artificial Intelligence (AI) is revolutionizing data management and business intelligence. Blockchain, with its decentralized, immutable ledger, ensures data integrity and security, while AI enhances data analysis through advanced algorithms and predictive capabilities. This article explores the synergy between these two transformative technologies, examining how their combined strengths can address modern challenges in data security and business operations. The paper begins with an overview of Blockchain and AI, detailing their foundational principles and recent advancements. It then delves into their applications in enhancing data security, highlighting Blockchain's role in providing encryption and immutability and AI's capabilities in threat detection and response. The discussion extends to their impact on business intelligence, showcasing how Blockchain contributes to transparent and verifiable data, while AI drives advanced analytics and decision-making. Real-world case studies illustrate successful implementations of Blockchain and AI integration, demonstrating their potential to revolutionize various industries. The article also addresses technical challenges, privacy concerns, and regulatory issues associated with these technologies. Finally, it outlines future directions for research and innovation, emphasizing the need for continued exploration of their combined potential. By providing a comprehensive analysis of Blockchain and AI's transformative impact, this article aims to offer valuable insights for researchers, practitioners, and policymakers seeking to leverage these technologies for improved data security and business intelligence.World Journal of Advanced Research and Reviews09A comprehensive analysis of Blockchain and AI's transformative impact is provided, aiming to offer valuable insights for researchers, practitioners, and policymakers seeking to leverage these technologies for improved data security and business intelligence.World Journal of Advanced Research and Reviews["Rakibul Hasan Chowdhury"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/9854c791f21a2068338860aaacaa542d43095545 c53bb747cd6e483f9a3c5334f5eed0ff4adbebf0Data-driven decision-making (DDDM) has become a cornerstone in modern IT and business landscapes, leveraging the immense potential of artificial intelligence (AI) and data science to transform raw data into actionable insights. This review paper explores the intersection of these domains, highlighting methodologies, applications, benefits, and challenges associated with integrating AI and data science into business intelligence (BI). Through an extensive review of current literature, this paper elucidates how organizations can harness these technologies to drive strategic decisions, optimize operations, and maintain a competitive edge.World Journal of Advanced Research and Reviews06Through an extensive review of current literature, this paper elucidates how organizations can harness these technologies to drive strategic decisions, optimize operations, and maintain a competitive edge.World Journal of Advanced Research and Reviews["Comfort Idongesit Michael", "Oluwaseun Johnson Ipede", "Adejoke Deborah Adejumo", "Ibrahim Oyeyemi Adenekan", "Damilola Adebayo", "Adefisayo Simon Ojo", "Praise Ayomide Ayodele"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/c53bb747cd6e483f9a3c5334f5eed0ff4adbebf0 b1573adddf73084384715008fdd75632b63091ebThe advent of artificial intelligence (AI) has dramatically changed the business landscape, providing unprecedented opportunities for organizations to transform, grow and innovate This research paper explores how AI can be used to transform business advanced growth strategies, market research, customer insights and competitive intelligence. Companies can reap many benefits from integrating AI into their operations. AI algorithms can analyze large data sets to identify trends and patterns beyond human capabilities, enabling companies to forecast market demand, streamline distribution, and make data-driven decisions in terms of production and marketing. AI also empowers customers with more personalized experiences by analyzing behaviors and preferences to recommend personalized products and services. This personalization drives greater customer engagement, loyalty and increased sales. The paper highlights the transformative potential of AI through real-world examples and case studies in various industries. These examples illustrate the impact of AI, from predicting customer attendance in mobile communication to optimizing marketing campaigns in retail. However, implementing AI is not without its challenges. Businesses need to address the need for robust data structures and reduce the potential for bias in AI algorithms. Furthermore, ethical considerations such as data privacy and displacement of human workers must be treated with caution. The paper discusses these challenges and highlights the importance of responsible AI implementation strategies. By acknowledging and addressing these obstacles, companies can unlock the full potential of AI. This review paper aims to provide readers with a comprehensive understanding of AI-driven business development strategies, enabling them to harness the potential of AI to drive sustainable growth and success.International Journal of Science and Research Archive02This research paper explores how AI can be used to transform business advanced growth strategies, market research, customer insights and competitive intelligence, and highlights the transformative potential of AI through real-world examples and case studies in various industries.International Journal of Science and Research Archive["Temitayo Oluwadamilola Adesoga", "Omolara Patricia Olaiya", "Omotoyosi Qazeem Obani", "Mary-Cynthia Uchenna Orji", "Chimezirim Akanu Orji", "Oluwabusola Dorcas Olagunju"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/b1573adddf73084384715008fdd75632b63091eb 6eb7644640fb0da55b116a6daea098d9e70ffc63The essay dealt with a massage of Pope Francis concerning the developments of artificial intellegence. The presence of artificial intelligence brings a change in human life. Artificial intelligence, which was the invention of mankind in the ages of 4.0 to 5.0, is now dominating every aspect of life. Those incentive offers are provided by this artificial intelligence. However, Pope Francis, together with the Church, encouraged mankind to use it wisely. These machines are expected to help humans in their work. At the same time as the 58th World Day of Peace, Pope Francis wrote a letter to mankind. In his letter, the Pope expects artificial intelligence to contribute to world peace. So was his letter on the 57th day of social communication. He expects the use and exploitation of communication in the age of artificial intelligence to bring users to the wisdom of heart.Pineleng Theological Review01The essay dealt with a massage of Pope Francis concerning the developments of artificial intellegence, which expects the use and exploitation of communication in the age of artificial intelligence to bring users to the wisdom of heart.Pineleng Theological Review["Finsen Fader", "Iyano Janwarin", "Richardo Lutam"]2024-07-30T00:00:00https://www.semanticscholar.org/paper/6eb7644640fb0da55b116a6daea098d9e70ffc63 f3e0605503959a4af57a858b5dd89b7402d34984Objectives In this study, we explored practical teaching and learning approaches that utilize generative artificial intelligence, which already influences various aspects of our lives, to assist students in their writing endeavors. Methods I proposed concrete teaching and learning methods for utilizing the generative AI, Luton, in the stage of content generation in writing. I explored application methods for addressing writing issues that authors encounter in areas such as topic selection, generating ideas, and content creation. Additionally, I investigated the possibility of implementing a program for self-assessment, enabling authors to receive evaluations of their writing outputs and make self-revisions accordingly. Results Generative artificial intelligence (AI) can be used as a tool to provide inspiration during creative activities, offer solutions for content creation, and assist in generating ideas for simple outlines or storyboards. Additionally, it proved helpful in producing writing materials and facilitating checks and feedback on written work. Conclusions We hope to enhance writing instruction and improve students' writing abilities through the utilization of generative artificial intelligence (AI).Korean Association For Learner-Centered Curriculum And Instruction01Generative artificial intelligence (AI) can be used as a tool to provide inspiration during creative activities, offer solutions for content creation, and assist in generating ideas for simple outlines or storyboards.Korean Association For Learner-Centered Curriculum And Instruction[]2024-07-31T00:00:00https://www.semanticscholar.org/paper/f3e0605503959a4af57a858b5dd89b7402d34984 66a0bf384a672577bee79f0548b498ace0a16387. As a result of large-scale digitalization of all spheres of human activity and the rapid introduction of artificial intelligence technologies, the need has arisen for forensic support for legal proceedings in cases in which artificial intelligence has a role. The most pressing tasks solved by forensic computer expert units are the study of the facts of unlawful (mainly criminal) use of artificial intelligence, the use of artificial intelligence to create new and improve existing methods of computer forensics, forensic analysis of products using artificial intelligence technologies in order to establish compliance of the final product with the technical specifications for its creation, as well as a comprehensive forensic the analysis is carried out either within a forensic computer examination, or comprehensively, with the involvement of specialists in the field of forensic linguistics, forensic phonoscopic and other types of examinations. Identifying distortions in metadata is an illustrative example of improving forensic methods for analyzing digital images using artificial intelligence technology.Theory and Practice of Forensic Science51Identifying distortions in metadata is an illustrative example of improving forensic methods for analyzing digital images using artificial intelligence technology.Theory and Practice of Forensic Science["Yu. S. Rudenkova", "S. N. Khaziev", "A. I. Usov"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/66a0bf384a672577bee79f0548b498ace0a16387 3578d7356da8506e9af6f84ae2dfc949f397be6eThis systematic review explores the role of artificial intelligence (AI) and machine learning (ML) technologies in the diagnosis and treatment of thyroid cancers (TC), focusing on enhancing precision, risk assessment, and tailored care. By analyzing ten studies, the review highlights how AI and ML technologies, such as deep learning (DL) and computer-aided diagnostics (CAD), improve the accuracy of ultrasound imaging, risk stratification, and the detection of high-risk nodules. Despite advancements, challenges persist in transitioning to personalized care, including uneven prognostication and diagnostic uncertainty. The review evaluates the effectiveness of AI and ML compared to conventional methods, their ability to address diverse tumor characteristics, and their strengths and limitations in prognosis prediction. Findings suggest AI's potential in improving precision and risk assessment, but limitations such as inconsistent approaches and biases highlight the need for larger datasets and standardized procedures. Moreover, the review underscores the importance of interpretability and transparency in AI models and calls for further research to validate findings in clinical settings. Despite limitations and challenges, AI's transformative potential in TC management is evident, underscoring the need for ongoing investigation and integration into clinical practice.Journal of Cancer and Tumor International01Artificial intelligence's transformative potential in TC management is evident, underscoring the need for ongoing investigation and integration into clinical practice and the importance of interpretability and transparency in AI models.Journal of Cancer and Tumor International["Rushin Patel", "Akash Jain", "Zalak Patel", "Chieh Yang", "Darshil Patel", "Mrunal Patel"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/3578d7356da8506e9af6f84ae2dfc949f397be6e 38d248936918ceb6a0d03fbcdcbd43e710d01200Tracking session‐by‐session patient‐reported outcomes (e.g. alliance and clinical symptoms) has been shown to improve treatment outcomes. However, self‐report measures are cumbersome to collect, and completion rates are inconsistent. Proof‐of‐concept machine learning research applications using psychotherapy data sets suggest that it may be possible to generate fully automated predictions of patient‐reported alliance and symptom ratings based on behavioural markers extracted from video recordings of psychotherapy sessions. For these artificial intelligence (AI)‐based measurements to be feasible, patients and clinicians must be comfortable with video recording their sessions and must be open to deploying such automated AI‐based models in their psychotherapy practice.We conducted two online survey studies between December 2022 and March 2023. We asked 954 patients and 248 clinicians about the use and usefulness of (1) self‐report measures for routine outcome monitoring, (2) video recording therapy sessions and (3) utilising AI‐based prediction models in their treatments.Patients and clinicians found the use of self‐report measures useful but burdensome. While both patients and clinicians reported interest and willingness to embrace AI‐based technology for measurement‐based care, patients reported significantly more willingness to record their sessions, and more positive views on the use and usefulness of AI‐based measurement feedback for clinical outcomes, compared with clinicians.Clinicians should be provided with more practice and training in the use of AI‐based tools to aid their clinical work before such AI‐based measurement tools may be successfully implemented into clinical practice.Counselling and Psychotherapy Research261Patients and clinicians found the use of self‐report measures useful but burdensome, and clinicians should be provided with more practice and training in the use of AI‐based tools to aid their clinical work before such AI‐based measurement tools may be successfully implemented into clinical practice.Counselling and Psychotherapy Research["Katie Aafjes\u2010van\u00a0Doorn"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/38d248936918ceb6a0d03fbcdcbd43e710d01200 06ea09f21b3b2159793d68ddf6c5338c7c7b20acThis study explores the problems and prospects of integrating Artificial Intelligence (AI) in management education, a field that has significantly evolved with technological advancements. AI has the potential to revolutionize management education by enhancing personalized learning, improving administrative efficiency, and providing data-driven decision-making support. However, challenges such as the digital divide, resistance to change among educators, data privacy concerns, and the substantial investments required for technology and training impede its widespread adoption. A mixed-methods approach, combining a literature review and survey, was employed to gather perspectives from the participants, including educators, administrators, and students. The findings reveal that while a major of institutions use AI for management education, significant concerns remain regarding data privacy, algorithmic bias, and the cost of implementation. The study underscores the need for comprehensive training, ethical guidselines, and increased funding to overcome these barriers. By addressing these challenges, management education institutions can effectively leverage AI to enhance learning outcomes, administrative efficiency, and overall educational quality. The paper provides valuable insights for educators, administrators, and policymakers aiming to integrate AI into management education, ensuring a smoother transition and greater acceptance among stakeholders.EDUMALSYS Journal of Research in Education Management171The findings reveal that while a major of institutions use AI for management education, significant concerns remain regarding data privacy, algorithmic bias, and the cost of implementation, which underscores the need for comprehensive training, ethical guidselines, and increased funding to overcome these barriers.EDUMALSYS Journal of Research in Education Management["Binod Shah", "Rajesh Kumar Sah", "Kiran Kumari Sah", "S. K. Sahani"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/06ea09f21b3b2159793d68ddf6c5338c7c7b20ac 462d4bd767ae89cee082bdfa32dcd6af1ad520c1This paper investigates the application of artificial intelligence in the financial sector, analyzing the existing technical, ethical, and legal issues, and proposing corresponding solutions. The research background highlights the widespread use of AI technology in the financial industry and the efficiency and cost benefits it brings. The research focuses on challenges related to data quality, feature engineering, model complexity, real-time capability, computational resources, and data privacy protection. The research method includes literature review and case analysis, revealing the applications of AI technology in stock prediction, risk management, trading strategy optimization, and customer service. The results indicate that effective data cleaning, automated feature engineering, model simplification and regularization techniques, the use of interpretability tools like LIME and SHAP, and the introduction of fairness evaluation standards can significantly enhance AI model performance and transparency. The conclusion points out that these measures can not only solve the current technical and ethical issues of AI in the financial sector but also promote the widespread application and standardization of AI technology in other fields.Applied and Computational Engineering01The results indicate that effective data cleaning, automated feature engineering, model simplification and regularization techniques, the use of interpretability tools like LIME and SHAP, and the introduction of fairness evaluation standards can significantly enhance AI model performance and transparency.Applied and Computational Engineering["Yanyu Chen"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/462d4bd767ae89cee082bdfa32dcd6af1ad520c1 75553a983250d19063f45d196858384a44a40f57Discover Sustainability281Sustainability is becoming more critical in the equation of AI-driven supply chains especially with the current socio-political and economic circumstances, constituting a solid base for further academic research and more innovations in the managerial and business-related policies in this field.Discover Sustainability["Ibtissam Zejjari", "Issam Benhayoun"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/75553a983250d19063f45d196858384a44a40f57 8167ae8480645181098224915c87b227ee23846eArtificial Intelligence (AI) is quickly transforming the marketing landscape, providing unprecedented opportunities for businesses to optimize strategies and improve customer experiences. This literature review looks at the various applications of AI in marketing and their potential impact on consumer behavior. The study delves into key aspects of AI, from its intelligence to ethical concerns about its use. Furthermore, it investigates how AI is used in various aspects of marketing, such as planning and strategy, product management, pricing, location management, and promotion. Previous research findings highlight AI's transformative potential for increasing marketing efficiency, effectiveness, and customer engagement. However, the study emphasizes the importance of responsible implementation and raises ethical and societal concerns about AI adoption.Cross-Cultural Management Journal01The study delves into key aspects of AI, from its intelligence to ethical concerns about its use, and investigates how AI is used in various aspects of marketing, such as planning and strategy, product management, pricing, location management, and promotion.CROSS-CULTURAL MANAGEMENT JOURNAL["A. Odeibat"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/8167ae8480645181098224915c87b227ee23846e 3daeb1147fe43bf232db3b9914f829356e8e0802Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.Knee Surgery, Sports Traumatology, Arthroscopy112This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions, and identifies tree-based models, regularized regression, and clustering algorithms as key techniques.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA["Felix C Oettl", "Jacob F. Oeding", "Robert Feldt", "Christophe Ley", "M. Hirschmann", "Kristian Samuelsson"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/3daeb1147fe43bf232db3b9914f829356e8e0802 b488e505164e16a194b5d697022e15259a743e25United Nations set Sustainable Development Goals and this paper focuses on 7th (Affordable and Clean Energy), 9th (Industries, Innovation and Infrastructure), and 13th (Climate Action) goals. Climate change is a major concern in our society; for this reason, a current global objective is to reduce energy waste. This work summarizes all main approaches towards energy efficiency using Artificial Intelligence with a particular focus on multi-agent systems to create smart buildings. It mentions the tight relationship between AI, especially IoT, and Big Data. It explains the application of AI to anomaly detection in smart buildings and a possible classification of Intelligent Energy Management Systems: Direct and Indirect. Finally, some drawbacks of AI approaches and some possible future research focuses are proposed.arXiv.org361This work summarizes all main approaches towards energy efficiency using Artificial Intelligence with a particular focus on multi-agent systems to create smart buildings and explains the tight relationship between AI, especially IoT, and Big Data.ArXiv["Alberto Pasqualetto", "Lorenzo Serafini", "Michele Sprocatti"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/b488e505164e16a194b5d697022e15259a743e25 6fc13efcb5ad01c3b063183231a1a7e40241f04ePurpose: The purpose of this study is to analyze teachers' perceptions of what should be prepared for the activation of A.I. education, so that A.I. education can produce substantive educational results and be established as a future-oriented teaching and learning environment in schools. Methods: A qualitative analysis was carried out of 196 discussion messages freely posted on an online bulletin board by 46 primary and secondary school teachers in a graduate course, ‘Artificial Intelligence and New Media’, in response to the discussion topic: ‘Discuss what needs to be in place to facilitate the teaching of artificial intelligence in schools’. Results: The areas of readiness for the promotion of AI education were presented as teachers, students, school environment, curriculum and teaching and learning, and government policies and support, and sub-components were identified. In the area of teachers, the study included supporting teachers to improve their competence in AI education, raising teachers' interest in AI education and changing their perceptions; in the area of students, the study included implementing comprehensive digital literacy education, and strengthening competencies for the future society; in the area of school environment, the research included constructing and managing infrastructure necessary for AI education and improving the working environment of teachers; in the area of curriculum and teaching and learning, it included developing and disseminating curricula, providing teaching models, materials and successful cases for AI education, and shifting to learner-centered teaching and assessment; in the area of government policy, the study included preparing a comprehensive AI education policy, protecting user information and rights, and specifying ethical standards. Conclusion: This study comprehensively derived the preparation factors for AI education as perceived by teachers, which play a key role in activating AI education in schools. The key areas and factors for activating AI education can be used as a reference for establishing AI education policies and provide useful information for exploring effective ways to use AI in school education.Korean Journal of Teacher Education00The preparation factors for AI education as perceived by teachers can be used as a reference for establishing AI education policies and provide useful information for exploring effective ways to use AI in school education.Korean Journal of Teacher Education[]2024-07-31T00:00:00https://www.semanticscholar.org/paper/6fc13efcb5ad01c3b063183231a1a7e40241f04e 1c204632bf7313e04085cdc719c36fb4d790ee4dThis paper examines Artificial intelligence (AI)’s role in enhancing marketing creativity by analyzing the synergy between computational and human creative processes. Through two studies, we investigate non-generative and generative AI applications within marketing contexts using a conceptually driven and empirically derived approach. In Study 1, we observe how creative individuals, particularly artists, utilize AI and its effects on their creative experiences, revealing AI’s role as 1) a new instrumental resource, 2) a tool for exploring possibilities, and 3) a means to deconstruct the creative process. Study 2 assesses 1,036 AI systems (2015-2021) and 241,292 AI models (2022-2024), categorizing them into four clusters and three levels of observed creativity. From these insights, we introduce a framework for AI-enabled creativity: (i) Inspiring agile methods, (ii) Augmenting human creativity, and (iii) Inspiring unconventional thinking. Validated by three workshops, this framework equips marketing leaders with a deeper comprehension of AI’s creative potential. We advocate for AI integration within agile, augmented, and unconventional marketing approaches, advancing our understanding of AI’s contribution to marketing creativity. Additionally, we propose a research roadmap for empirical validation in real-world applications.Journal of Interactive Marketing00A framework for AI-enabled creativity is introduced, advocating for AI integration within agile, augmented, and unconventional marketing approaches, advancing the understanding of AI’s contribution to marketing creativity.Journal of Interactive Marketing["Margherita Pagani", "Yoram Wind"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/1c204632bf7313e04085cdc719c36fb4d790ee4d c2c7ecd9abb77ba18abfd5b1900ed9de4af8f167The community service activity "Learn English With Artificial Intelligence" at Islamic Village Vocational School, led by lecturers from the Informatics Engineering Study Program, Faculty of Engineering, Muhammadiyah University of Tangerang (UMT), aims to integrate artificial intelligence (AI) technology in English language learning. This activity succeeded in increasing understanding of the concept of AI and its benefits in education, motivating students to study English more intensely, and increasing the effectiveness of learning through AI applications such as Duolingo and Grammarly. provide regular training for teachers, encourage collaboration between schools, and conduct further research to measure the long-term impact of using AI in education. Thus, the success of this activity is not only in achieving the set educational goals, but also in expanding the boundaries of innovation in ways of learning English and the potential for further development in the increasingly connected and digital context of modern education. supporting education at the Islamic Village Vocational School.JPMNT : JURNAL PENGABDIAN MASYARAKAT NIAN TANA240This community service activity aimed at integrating artificial intelligence technology in English language learning succeeded in increasing understanding of the concept of AI and its benefits in education, motivating students to study English more intensely, and increasing the effectiveness of learning through AI applications such as Duolingo and Grammarly.JPMNT : JURNAL PENGABDIAN MASYARAKAT NIAN TANA["Yeni Daniarti", "Sri Mulyati", "Syepry Maulana Husain", "Ilham Pratama"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/c2c7ecd9abb77ba18abfd5b1900ed9de4af8f167 75cffc7df8f2879d0fd4340830dd61933478a2a0With the rapid development and application of artificial intelligence (AI) technology, automation control systems are undergoing unprecedented transformation and upgrading. The introduction of AI has brought revolutionary changes to traditional electronic engineering control methods. Through techniques such as machine learning and deep learning, systems can learn from vast amounts of data and optimize processes, enabling intelligent handling and decision-making in complex environments and tasks. This study aims to explore the advantages of AI technology in electronic engineering automation control and its specific applications within this field. The research is intended to provide valuable reference for technological innovation and application in the field of electronic engineering.Academic Frontiers Publishing Group00This study aims to explore the advantages of AI technology in electronic engineering automation control and its specific applications within this field, to provide valuable reference for technological innovation and application in the field of electronic engineering.Academic Frontiers Publishing Group["Yong-Hua Gou"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/75cffc7df8f2879d0fd4340830dd61933478a2a0 92ce404eb350d32e8037c11b0a0c4618beed9fd3or the products or services it offers. Furthermore, it plays a pivotal role in the success of any organisation by increasing efficiency and enabling innovation, regardless of industry or sector (Gaglio, Kraemer-Mbula & Lorenz 2022). Technology as a crucial driver of rapid change has revolutionised the way modern business is carried out (Shai et al. 2020). Organisation for Economic Co-operation and Development (OECD 2021) recognises technology as a transformative force to economies worldwide reforming how businesses design, market and sell their goods and services; however, despite the decline in broadband costs and increased internet access via low-cost mobile phones in the mid-to-late 2000s, the digitisation gap between high-income and low-income countries remains wide. Background: The adoption of artificial intelligence (AI) in manufacturing has the potential to considerably improve productivity, efficiency and sustainability. Artificial intelligence aids with tasks such as data processing and process monitoring, process modelling and optimisation, live fault detection, and process quality assessment in manufacturing processes. Aim: This study sought to obtain a full understanding of the influence of AI on the South African manufacturing industry by exploring how AI technology is impacting productivity, reshaping the workforce, affecting quality control practices and optimising supply chain management among other issues. Setting: Data in this study were obtained from 23 qualitative research publications that address the influence of AI on the manufacturing industry in South Africa published on ScienceDirect, Scopus, Springer, Web of Science and Google Scholar. Method: Multiple correspondence analysis was utilised to analyse associations among quality, productivity, supply chain and workforce transformation in the presence of AI in the South African manufacturing industry. Results: The findings demonstrate a substantial association between the usage of AI and a range of performance measures, suggesting that those organisations embracing AI technology can benefit from greater productivity, quality control and supply chain management. Additionally, findings emphasised the necessity of workforce transformation because of AI adoption. Conclusion: The adoption of AI technology positively influenced the South African manufacturing industry, contributing to increased productivity and quality, and optimising the supply chain. Contribution: This study makes a valuable contribution to the existing body of knowledge as AI adoption in the manufacturing industry in developing countries is only emerging.South African Journal of Economic and Management Sciences550A substantial association between the usage of AI and a range of performance measures is demonstrated, suggesting that those organisations embracing AI technology can benefit from greater productivity, quality control and supply chain management.South African Journal of Economic and management Sciences["M. L. Nzama", "Gloria A. Epizitone", "S. Moyane", "N. Nkomo", "P. P. Mthalane"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/92ce404eb350d32e8037c11b0a0c4618beed9fd3 eba1ce0f5d2a2b986a37c115f0255af569380b1fIn the past decade, artificial intelligence (AI) literacy has become a primary focus in digital literacy education research. However, the use of AI in early childhood education (PUAD) remains largely unexplored, prompting experts to conduct a scoping review. This scoping review aimed to (1) explore various forms of AI application in early childhood education, and (2) identify commonly used methods in this topic. The database searches for scientific articles covered the period between 2016 to 2023. Inclusion and exclusion criteria were established, focusing on reviews related to AI literacy and early childhood education. Electronic databases such as Scopus, EBSCO Sciences, Emerald, Sinta, and Science Direct were used from December 2023 to February 2024. From the total of 260 articles selected for this analysis, only 10 met the inclusion criteria and were reviewed. The results showed that most articles used learning media tailored to children's developmental stages to enhance literacy. These included smart toys integrating AI technology, particularly speech synthesis, virtual reality, robots, KIERO, and chatbots. Qualitative methods were commonly adopted, although some research used experimental methods and literature reviews. In summary, the integration of AI literacy in early childhood education contributed significantly to the development of active learning interventions for children.Psikologika Jurnal Pemikiran dan Penelitian Psikologi00The integration of AI literacy in early childhood education contributed significantly to the development of active learning interventions for children and commonly used methods were commonly adopted.Psikologika: Jurnal Pemikiran dan Penelitian Psikologi["Novia Solichah", "Nurul Shofiah"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/eba1ce0f5d2a2b986a37c115f0255af569380b1f 44c82797805b6da14b998cb67fb03502a9750d12With the remarkable strides in artificial intelligence (AI) technology, a range of sophisticated techniques and products have sprung forth, including speech recognition and semantic analysis. These advancements have propelled the application of AI to new heights, particularly in the realm of education. Notably, AI applications such as speech recognition, human-computer interaction, and automated evaluation have revolutionized traditional English speaking teaching, introducing unprecedented changes to the way students learn and interact. The paper aims to investigate how AI might enhance high school pupils' English-speaking proficiency, which employs a literature review methodology to examine and illustrate the smooth integration of AI technology with high school English speaking instruction. This approach enhances speaking learning materials and fosters creativity in teaching strategies and assessment techniques. In addition, it creates room for the evolution of the oral teaching style and broadens the learning environment. This intelligent transformation of the oral teaching mode is expected to effectively enhance the effectiveness of high school oral English teaching and ultimately improve students' language proficiency.Communications in Humanities Research00This paper aims to investigate how AI might enhance high school pupils' English-speaking proficiency, which employs a literature review methodology to examine and illustrate the smooth integration of AI technology with high school English speaking instruction.Communications in Humanities Research["Qianyu Men"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/44c82797805b6da14b998cb67fb03502a9750d12 f4b6c89146b7fda4ed509753cd823d225a2d9d66In this paper, we investigate uses of AI (Artificial intelligence) in two distinct fields: radiology and prehistoric archaeology. We examine the normative tensions between the scripts encapsulated within the technology and pre-existing professional and epistemic cultures, as well as the situations in which mechanical objectivity fits with local norms. Through ethnographic observation and interviews in French field sites, we show how in radiology a specific definition of “normal” bodies, embedded within detection tools, conflicts with medical practice, and the way in which non-consensual knowledge in archaeology can challenge the prediction of soil occupation in a prehistoric site. We also highlight the conditions under which AI tools can adhere to certain epistemic norms and become part of professional practices in radiology and prehistoric archaeology. While in radiology AI is judged by its ability to close uncertainties without imposing binary categories, in prehistoric archaeology, its epistemic validity depends on mobilizing exogenous scientific data to increase researchers’ reflexivity about their practices and knowledge, suggesting new clues and explanatory paths. This article demonstrates the effectiveness of AI technologies is shaped by local constraints, and why their objectivity is not a given property but an emergent feature arising from specific contexts of use.Science, Technology, &amp; Human Values420This article demonstrates the effectiveness of AI technologies is shaped by local constraints, and why their objectivity is not a given property but an emergent feature arising from specific contexts of use.Science, Technology, &amp; Human Values["Giulia Anichini", "Baptiste Kotras"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/f4b6c89146b7fda4ed509753cd823d225a2d9d66 e401e0fc6a6b0a474e0536eb0c7a4f1a75288bc3Technology is developing to provide new experiences resembling human intelligence, called artificial intelligence (AI). AI technology can be applied in various aspects of science and life, including education. Therefore, a study will explore some factors in student that can encourage the intention to adopt artificial intelligence for learning activities at Universitas Negeri Malang (UM). UM is one of the best universities in the education field in Indonesia. This research aims to examine the factors influencing AI adoption in learning activities in higher education. The study will be developed using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study used 251 students from UM. The researchers used SEM-PLS to analyze the data. The findings showed that performance expectancy and perceived risk significantly influenced on attitudes toward AI, which in turn can lead to the adoption of AI in Higher education. AI has a significant impact on learning activities in the classroom. In addition, the AI price does not influence the use of AI by students in higher education.2024 9th International STEM Education Conference (iSTEM-Ed)610The findings showed that performance expectancy and perceived risk significantly influenced on attitudes toward AI, which in turn can lead to the adoption of AI in Higher education.2024 9th International STEM Education Conference (iSTEM-Ed)["A. S. Prameka", "D. Kurniawan", "A. F. Suwanan", "Andro Agil Nur Rakhmad", "Rizky Firmansyah"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/e401e0fc6a6b0a474e0536eb0c7a4f1a75288bc3 58cf078b45838a83f13b76ee9caf1b95371dd978The purpose of this service is to assist junior high school teachers in creating digital learning media based on artificial intelligence and to enable them to be implemented independently in the teaching and learning process. This activity was carried out from September to November 2023 at SMP Negeri 16 Palangka Raya. The stages of implementing this service activity are: coordination with partners, Training Implementation, and Mentoring Implementation. This mentoring activity has succeeded in achieving the main goal, which is to improve the competence of SMP Negeri 16 Palangka Raya teachers in creating Digital Learning Media based on Artificial Intelligence (AI). Teachers who previously only had basic computer and internet skills are now able to utilize various AI-based applications such as Google Workspace, Canva, and Microsoft Office 365 to create teaching modules and teaching materials that are interactive, engaging, and relevant to the needs of students in the digital era. This activity also succeeded in increasing the confidence of teachers in adopting new technology into the learning process. From the results of this activity, some of the challenges faced, such as the difference in the level of technology mastery among teachers, can be solved through direct guidance and intensive mentoring. Teachers feel helped by the existence of training modules, technical instructions, and support from students and resource persons. This shows that well-designed training, supported by adequate facilities and infrastructure, is able to have a positive impact on the development of educators' competencies.TAAWUN210This mentoring activity has succeeded in achieving the main goal, which is to improve the competence of SMP Negeri 16 Palangka Raya teachers in creating Digital Learning Media based on Artificial Intelligence (AI).TAAWUN["Sundari", "Dehen Erang", "Sumarnie", "A. Saputra", "Tanti Girsang"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/58cf078b45838a83f13b76ee9caf1b95371dd978 d2956bee6ccae0e295988a64ac1c0bfc117c7e16This article investigates the current and potential future applications of AI in the SmartLynx airlines. The airline industry is a complex system that requires efficient and effective management of various operations, including revenue management, flight operations, customer service, and baggage handling. The use of Artificial Intelligence (AI) has now emerged as a promising solution to address the challenges faced by many other airlines. The aim of this research is to analyze SmartLynx Airlines' operational efficiency in flight operations segments of the Latvian aviation industry and to develop recommendations for improving the current operational strategy for the company through the integration of AI-supported tools with conventional flight operation tools. Research tasks are: (1) to conceptualize theoretical aspects of the use of AI in the aviation industry; (2) to perform empirical research regarding current operational issues and study the use of AI in SmartLynx Airlines to improve these issues; (3) to work out recommendations. The current research employs the quantitative approach – a survey of SmartLynx employees of various departments.Wseas Transactions on Business and Economics60The aim of this research is to analyze SmartLynx Airlines' operational efficiency in flight operations segments of the Latvian aviation industry and to develop recommendations for improving the current operational strategy for the company through the integration of AI-supported tools with conventional flight operation tools.WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS["V. Vevere", "Kanchan Dange", "Iveta Linina", "R. Zvirgzdi\u0146a"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/d2956bee6ccae0e295988a64ac1c0bfc117c7e16 fbd4c23d61133a46fd78e525176340619558e7b2In the context of todays rapidly evolving technological landscape, the integration of artificial intelligence (AI) technology with the education sector has shown immense vitality and potential. This study is based on a profound understanding of this integration trend and explores the practical application of AI technology in information courses in primary and secondary schools. Through literature analysis, this research examines numerous high-quality domestic and international documents and finds that AI technology has achieved widespread application in information courses, covering multiple teaching scenarios. On this basis, the author summarizes its advantages and challenges, and proposes practical suggestions and strategies, aiming to provide strong support for the scientific development of AI technology in information courses, thereby laying a solid foundation for the comprehensive advancement of digital education.Lecture Notes in Education Psychology and Public Media00This study explores the practical application of AI technology in information courses in primary and secondary schools and summarizes its advantages and challenges, and proposes practical suggestions and strategies to provide strong support for the scientific development of AI technology in information courses, thereby laying a solid foundation for the comprehensive advancement of digital education.Lecture Notes in Education Psychology and Public Media["Yu Zhang"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/fbd4c23d61133a46fd78e525176340619558e7b2 86d7638ffbee283618d65060b404f8e9a08ebba4Artificial intelligence is at the center of technological advancement in the modern world. With the rapidly progressing use and multitude of uses, AI allows for more convenient methods, saving time and effort, both. The current study consisted of 3 focus groups with 8 in 1 group and 11 members in each of the other 2 groups, who were asked questions probing their understanding of the ethical use of AI. The responses to each question were manually recorded. The results displayed accountability as the best understood ethical concern in the context of responsible AI. Students were found to be most aware of accountability with different levels of knowledge of other ethical concerns covered in the framework. There was a moderately significant lack of awareness found among the students.International Journal of Social Science &amp; Entrepreneurship340Accountability was found to be most aware of accountability with different levels of knowledge of other ethical concerns covered in the framework, and a moderately significant lack of awareness was found among the students.International Journal of Social Science &amp; Entrepreneurship["Salima Barkat", "Maha Haider", "Darakshan Samiullah", "Shamsha Shamsy"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/86d7638ffbee283618d65060b404f8e9a08ebba4 7312f3c4fedc74095dc8eaefe9e702766c3fa96cConfidence in banks and financial institutions is a cornerstone of financial stability and economic prosperity. This study investigates the relationship between personal characteristics and confidence in banks, recognizing the pivotal role of trust in shaping individuals' perceptions of financial institutions. Through a mixed-methods approach combining survey techniques and artificial intelligence modelling, we analyse data collected from a representative sample of the university community. Our findings highlight the significant influence of demographic factors such as age, gender and education level on confidence in banks. Moreover, we validate our hypothesis using metrics such as ROC Area and PRC Area, indicating the predictive power of personal characteristics in determining confidence in banks. The sensitivity analysis further elucidates the relative importance of different predictors in shaping confidence levels. The implications of our research extend to policymakers, financial institutions and researchers, offering insights for tailored interventions, customer engagement strategies, and future investigations. By deepening our understanding of the drivers of confidence in banks, this study contributes to the enhancement of financial stability and consumer trust in the banking sector.Anales del Instituto de Actuarios Españoles00The findings highlight the significant influence of demographic factors such as age, gender and education level on confidence in banks and validate the hypothesis using metrics such as ROC Area and PRC Area, indicating the predictive power of personal characteristics in determining confidence in banks.Anales del Instituto de Actuarios Españoles["Ra\u00fal G\u00f3mez-Mart\u00ednez", "Benito P\u00e9rez-Gonz\u00e1lez", "Mar\u00eda Luisa Medrano-Garc\u00eda", "Jose Torres-Pru\u00f1onosa"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/7312f3c4fedc74095dc8eaefe9e702766c3fa96c 2daed4cab8d5369f105afe5242a993c03ea44a9fIn the growing field of biotechnology, artificial intelligence (AI) has emerged as a pivotal force of innovation, unveiling a new era of discovery and advancement. The convergence of AI with biotechnology has revolutionized the landscape of scientific research and development. The dynamic interplay between AI and biotechnology highlights the transformative power of AI techniques in accelerating advancements in drug discovery, development, personalized medicine, biomolecular engineering, bioprocessing, CRISPR technology, genome editing, genomics, proteomics, metabolomics, transcriptomic, and AI-enabled robotics in biotechnology. This integration of AI with biotechnology helps us combat global challenges and offers environmentally friendly and sustainable solutions like bioremediation, bioplastics, biodiesel, and biofiltration. Current examples of these problems include waste management, air pollution, healthcare, clean water, energy access, sustainable practices, conservation of biodiversity, and ecosystems. This review article provides a comprehensive analysis, drawing on current literature, case studies, and emerging trends, to highlight the transformative potential of AI in reshaping the biotechnological landscape. It also addresses the challenges and opportunities associated with this AI-powered transformation, discussing future directions, ethical considerations, and the need for human-AI collaboration to ensure responsible and sustainable progress for a brighter future.Nepal Journal of Biotechnology00A comprehensive analysis is provided, drawing on current literature, case studies, and emerging trends, to highlight the transformative potential of AI in reshaping the biotechnological landscape and addresses the challenges and opportunities associated with this AI-powered transformation.Nepal Journal of Biotechnology["Munawar Ali", "Kainat Shabbir", "Shahbaz Ali", "Muhammad Mohsin", "Ajay Kumar", "Murad Aziz", "Muhammad Zubair", "Hafiz M. Sultan"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/2daed4cab8d5369f105afe5242a993c03ea44a9f a2c8d3237428f11dfdd1ca7667b2a017ee086585Purpose: The rapid advancement of artificial intelligence (AI), is transforming the required skills in the workforce. This article presents research findings from large organisations that have adopted AI. Methodology/Approach: The aim is to identify the skills driven by the utilisation of AI. The paper pinpoints the key skills for effective AI implementation and creates a model that delineates the specific groups related to AI utilisation. The data were obtained from the Top 100 organisations in Czechia, focusing on those actively leveraging AI. Findings: The outputs show the orientation of the use of AI skills in marketing and human resources and basic administrative tasks. A significant gap was found in relation to emotional and interpersonal skills, which has not yet been emphasised in studied organisations. Research Limitation/Implication: This paper formulates future-oriented, successful approaches to skill development with the wider use of AI. The limitation is first approach to technologically oriented Czech top organisations and limited sample due to a specific approach and early phase of AI use in operations. Originality/Value of paper: The results yielded a new framework of AI-required skills, reflecting the changing competency requirements for effective AI utilisation. This research contributes to the academic domain by providing an integrated and fundamental framework for competency development that incorporates technological advancements.  Kvalita Inovácia Prosperita110The paper pinpoints the key skills for effective AI implementation and creates a model that delineates the specific groups related to AI utilisation, reflecting the changing competency requirements for effective AI utilisation.Quality Innovation Prosperity["Zden\u011bk Kronberger", "Lucie Depoo", "Gabriela \u0158\u00edhov\u00e1"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/a2c8d3237428f11dfdd1ca7667b2a017ee086585 409b7892e21eca925f041332ec3b8e0277384366In the epoch where climate change poses an existential threat to humanity, understanding the intricate dynamics of CO2 emissions is more critical than ever. This study embarks on an ambitious journey to unravel the complex interplay of factors influencing carbon emissions, leveraging the prowess of Artificial Intelligence (AI) and the analytical capabilities of Power BI. Anchored in the context of the United Nations' Sustainable Development Goals (SDGs), this research transcends traditional analytical boundaries, offering a novel lens to view and interpret environmental data.  At the heart of this exploration lies the UN SDG dataset, a rich tapestry of environmental, economic, and social indicators. The study's methodology is a fusion of advanced AI techniques with Power BI's visualization influencers, a combination that not only promises precision but also an unprecedented depth of insight. This dual approach enables a multifaceted analysis, capturing the nuances and subtleties often lost in conventional studies.  The findings of this research are both revealing and transformative. They shed light on the significant yet varied factors that drive CO2 emissions across different geographical and socio-economic landscapes. The study unveils a striking correlation between increased access to electricity and GDP per capita with rising carbon emissions, a pattern particularly pronounced in developing regions. Conversely, in more developed contexts, the analysis reveals a complex interplay between emissions, literacy rates, and fertility rates, suggesting indirect yet potent pathways through which socio-economic development influences environmental outcomes. The insights gleaned offer a beacon for policymakers, illuminating the pathways to tailor environmental strategies that resonate with the unique needs of different regions. For developing nations, the study advocates for policies that intertwine educational and family planning initiatives with environmental objectives. In contrast, for developed countries, it underscores the need for technological innovation and efficiency improvements. The study's innovative use of AI and Power BI sets a new precedent in environmental research, demonstrating the immense potential of these tools in shaping sustainable futures.Data Science: Journal of Computing and Applied Informatics00The study unveils a striking correlation between increased access to electricity and GDP per capita with rising carbon emissions, a pattern particularly pronounced in developing regions, and reveals a complex interplay between emissions, literacy rates, and fertility rates, suggesting indirect yet potent pathways through which socio-economic development influences environmental outcomes.Data Science: Journal of Computing and Applied Informatics["Harry Patria", "Djuwita A. Rahim"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/409b7892e21eca925f041332ec3b8e0277384366 f39ff55d748dbf901d7bcef4fc88623ee730441eDiabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. By synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.Current Medical Research and Opinion790Evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts is evaluated.Current medical research and opinion["Qi Guo", "Bo Fu", "Yuan Tian", "Shujun Xu", "Xin Meng"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/f39ff55d748dbf901d7bcef4fc88623ee730441e d2f43a020d933e926ac1259dca1c0e2c6c9077e4As global efforts intensify to combat climate change and achieve sustainable energy goals, nuclear power is re-emerging as a vital component of the energy mix. This article explores modern nuclear technologies' economic and environmental potential, focusing on the nuclear fuel cycle, Generation IV reactors, Small Modular Reactors (SMRs), and the transformative role of Artificial Intelligence (AI). The nuclear fuel cycle, encompassing fuel production, utilization, and disposal, is evolving to become more efficient and sustainable, reducing costs and environmental impacts. Generation IV reactors and SMRs represent significant advancements, offering enhanced safety, efficiency, and flexibility, making nuclear power more accessible and adaptable. AI integration revolutionizes nuclear operations by optimizing performance, predictive maintenance, and safety monitoring. These technologies collectively enhance nuclear power's economic viability and environmental benefits, positioning it as a cornerstone of global zero-emission strategies. The return on involvement in nuclear energy is substantial, driven by economic growth, energy security, and technological advancements. Continued investment and innovation in these areas are essential for realizing the full potential of nuclear energy in a sustainable and carbon-neutral future. This article underscores the importance of nuclear power in achieving global climate objectives and fostering a sustainable energy landscape.Journal of Economics &amp; Management Research00Modern nuclear technologies' economic and environmental potential are explored, focusing on the nuclear fuel cycle, Generation IV reactors, Small Modular Reactors, and the transformative role of Artificial Intelligence (AI).Journal of Economics &amp; Management Research["Bahman Zohuri", "Farhang Mossavar-Rahmani"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/d2f43a020d933e926ac1259dca1c0e2c6c9077e4 7ff92e0da40aa6c4b60c3ee8fe55b90e9410f312Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI) may help improve pulmonary embolism (PE) management with early CT pulmonary angiogram (CTPA) screening and accelerate PERT coordination. This study aimed to test the clinical validity of an FDA-approved PE AI algorithm. CTPA scan data of 200 patients referred due to automated AI detection of suspected PE were retrospectively reviewed. In our institution, all patients suspected of PE received a CTPA. The AI app was then used to analyze CTPA for the presence of PE and calculate the right-ventricle/left-ventricle (RV/LV) ratio. We compared the AI's output with the radiologists' report. Inclusion criteria included segmental PE with and without RV dysfunction and high-risk PE. The primary endpoint was false positive rate. Secondary end points included clinical outcomes according to the therapy selected, including catheter-directed interventions, systemic thrombolytics, and anticoagulation. Fifty-seven of 200 exams (28.5%) were correctly identified as positive for PE by the algorithm. A total of 143 exams (71.5%) were incorrectly reported as positive. In 8% of cases, PERT was consulted. Four patients (7%) received systemic thrombolytics without any complications. There were six patients (10.5%) who developed high-risk PE and underwent thrombectomy, one of whom died. Among 46 patients with acute PE without right heart strain, 44 (95%) survived. The false positive rate of our AI algorithm was 71.5%, higher than what was reported in the AI's prior clinical validity study (91% sensitivity, 100% specificity). A high rate of discordant AI auto-detection of suspected PE raises concerns about its diagnostic accuracy. This can lead to increased workloads for PERT consultants, alarm/notification fatigue, and automation bias. The AI direct notification process to the PERT team did not improve PERT triage efficacy.Journal of Investigative Medicine160The AI direct notification process to the PERT team did not improve PERT triage efficacy and a high rate of discordant AI auto-detection of suspected PE raises concerns about its diagnostic accuracy.Journal of investigative medicine : the official publication of the American Federation for Clinical Research["Andrew Talon", "C. Puri", "D.L. Mccreary", "D. Windschill", "W. Bowker", "Yuqing A Gao", "S. Uppalapu", "M. Mathew"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/7ff92e0da40aa6c4b60c3ee8fe55b90e9410f312 716efcb1a3bb804199a3c9e3cea68de342241f3bThis qualitative study investigates the perceptions of Vietnamese tertiary English as a Foreign Language (EFL) teachers regarding the impact of artificial intelligence (AI) on student creativity in language learning. Amidst the rapid integration of AI in educational contexts, this research focuses on a relatively unexplored area: the intersection of AI technology and creative language pedagogy in the Vietnamese educational setting. Nine EFL teachers from two institutions in Vietnam, representing novice, mid-career, and near-end career stages, participated in semi-structured interviews. The study employed the Technology Acceptance Model (TAM) and the Creativity in Language Learning Framework (CLLF) as its theoretical basis. Thematic analysis of the interview data revealed four key themes: the perceived impact of AI on creative language practice, challenges in integrating AI with creative pedagogy, varied perceptions of AI’s role in developing student autonomy, and the impact of AI on traditional teaching methods and teacher roles. The findings indicated a spectrum of perspectives, from viewing AI as a beneficial tool for creative engagement to concerns over its potential to limit creative thinking and traditional pedagogical approaches. The study highlights the complexity of integrating AI into language learning and its influence on both teaching practices and student creativity.Jurnal Komunikasi Pendidikan00The findings indicated a spectrum of perspectives, from viewing AI as a beneficial tool for creative engagement to concerns over its potential to limit creative thinking and traditional pedagogical approaches.Jurnal Komunikasi Pendidikan["Trut-Thuy Pham", "Thanh-Thao Le"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/716efcb1a3bb804199a3c9e3cea68de342241f3b 8ada0a9c22f0adb64a7c95dc3d97b05b2ac3e298Social media platforms have become the primary medium for news consumption, offering vast amounts of real-time information. However, the proliferation of fake news across these platforms poses significant risks, including societal misinformation, political manipulation, and erosion of public trust. Traditional methods of combating fake news, such as manual fact-checking, have proven insufficient in curbing its spread due to the sheer volume of data and the speed at which misinformation can go viral. To address this challenge, artificial intelligence (AI) has emerged as a powerful tool in detecting fake news. Leveraging techniques such as natural language processing (NLP), machine learning algorithms, and deep learning, AI systems can analyze and flag deceptive content more efficiently than human-based efforts. This paper explores the influence of AI in identifying and mitigating fake news on social media platforms. It delves into how AI-driven fake news detection models work, examining the use of both supervised and unsupervised learning techniques. Additionally, the paper discusses the impact of these AI systems on user behavior, credibility assessment, and trust in social media platforms. However, while AI has shown significant promise, challenges such as algorithmic bias, ethical concerns, and the potential for misuse remain critical areas for future development. The integration of AI in fake news detection is reshaping the digital information landscape, offering both opportunities and risks for enhancing the quality of online discourse.ShodhKosh Journal of Visual and Performing Arts140How AI-driven fake news detection models work is explored, examining the use of both supervised and unsupervised learning techniques and the impact of these AI systems on user behavior, credibility assessment, and trust in social media platforms.ShodhKosh: Journal of Visual and Performing Arts["Shyam Swaroop T."]2024-07-31T00:00:00https://www.semanticscholar.org/paper/8ada0a9c22f0adb64a7c95dc3d97b05b2ac3e298 6a4c4fdc2c96e2d30716060829b94c8e6f18899eThis research investigates the impact of Artificial Intelligence (AI) on government administration and public service delivery in Indonesia.  The study employed qualitative research methods, including semi-structured interviews and detailed case studies of selected AI projects.  The research results indicate that AI has significantly improved administrative efficiency and public service delivery. The “Smart Administration System” has automated routine tasks, resulting in a 60% reduction in manual processing times and enhancing overall productivity. The “Policy Insight Tool” has supported better policy formulation through advanced predictive analytics and scenario modeling. Despite these successes, the study identified challenges such as resistance to change, technical difficulties, and data integration issues. Feedback from users also highlighted limitations in AI language processing and accessibility concerns.Journal of Management and Administration Provision130The research results indicate that AI has significantly improved administrative efficiency and public service delivery in Indonesia, and has supported better policy formulation through advanced predictive analytics and scenario modeling.Journal of Management and Administration Provision["S. Saprudin"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/6a4c4fdc2c96e2d30716060829b94c8e6f18899e bfa505dcae0b40831833fc8ad716048b01307b4dIntroduction: Medical services use artificial intelligence for operating to help and even transform the healthcare system. AI innovators have developed tools to improve clinical care processes, advance medical research, and increase efficiency in medical services.Purposes of the Research:  The purpose of this article is to analyse the legal validity of therapeutic agreements using AI in medical field.Methods of the Research: The research method used is a normative juridical research type with an analytical approach.Results of the Research: The results show that medical services are complex and closely related systems, and always contain risks, so they must be carried out with great care. Legal provisions governing the use of AI in therapeutic agreements with a medicolegal approach must be able to evaluate and ensure the safety and accuracy of medical decisions made by AI "thinking algorithms".Batulis Civil Law Review140Legal provisions governing the use of AI in therapeutic agreements with a medicolegal approach must be able to evaluate and ensure the safety and accuracy of medical decisions made by AI "thinking algorithms".Batulis Civil Law Review["Reka Dewantara", "Rekyan Pandansari"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/bfa505dcae0b40831833fc8ad716048b01307b4d 2a968454b7f0ab5ea45f54c8a820b215c3948293Military operations necessitate swift and informed choices under duress. This paper explores the intersection of military and engineering sciences, proposing a novel framework that leverages artificial intelligence (AI) and advanced engineering simulations to bolster military decision-making. GenAi, using a cutting-edge large language model, is employed to analyze vast datasets of historical military campaigns, engineering feats, and terrain data. By integrating GenAi's analytical prowess with physics-based engineering simulations, the framework enables the creation of high-fidelity virtual battlefields. These simulations can incorporate real-world factors like weather patterns, troop movements, and equipment capabilities. Military commanders can utilize these simulations to experiment with various strategies and assess potential outcomes before committing troops. The framework facilitates the exploration of complex scenarios, including combined arms maneuvers, logistical support chains, and the efficacy of novel weaponry. Through iterative simulations, commanders can refine their plans, identify potential weaknesses, and optimize resource allocation. This paper highlights the advantages of this AI-powered approach, including enhanced situational awareness, improved risk assessment, and the ability to train for unforeseen circumstances. It also acknowledges the challenges associated with data security, model bias, and the ethical considerations of employing AI in warfare. Finally, the paper proposes future research directions to refine the framework and ensure its responsible implementation within the military domain.SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE00This paper highlights the advantages of this AI-powered approach, including enhanced situational awareness, improved risk assessment, and the ability to train for unforeseen circumstances, and acknowledges the challenges associated with data security, model bias, and the ethical considerations of employing AI in warfare.SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE["Mihai-Alin Meclea", "A. Goga", "M. Bo\u0219coianu"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/2a968454b7f0ab5ea45f54c8a820b215c3948293 12391aaff1f11e6e03f9aad2c75e30c6a449f7a7Transformative technologies such as artificial intelligence (AI) make difficult tasks more accessible and convenient. Since 2018, the use of AI in research has increased drastically, with annual publication rates of 3–5 times higher than pre‐2017. Currently, >100,000 manuscripts using AI are published annually within science and engineering, and >20,000 of these belong to the agricultural and environmental fields. Given the magnitude of use, clear communication on how AI is used and how it helps advance scientific knowledge is essential. Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black‐box” nature of deep‐learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first‐principles mechanistic and process‐based theories and models. In this commentary, we provide guidelines and discussion points to the scientific community to ensure transparent and effective communication of AI research in agricultural and environmental research publications.Agricultural &amp; Environmental Letters80This commentary provides guidelines and discussion points to the scientific community to ensure transparent and effective communication of AI research in agricultural and environmental research publications.Agricultural &amp; Environmental Letters["A. Daigh", "Samira H. Daroub", "Peter M. Kyveryga", "Mark E. Sorrells", "Nithya Rajan", "J. A. Ippolito", "E. Kailer", "Christine S. Booth", "Umesh Acharya", "Deepak Ghimire", "Saurav Das", "B. Maharjan", "Yufeng Ge"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/12391aaff1f11e6e03f9aad2c75e30c6a449f7a7 057914a1f101f46e2326e9b4b854e606a3c7e2d4The study examines human capital as a set of socio-economic relations and as one of the determining factors of economic growth. The main attention is paid to the analysis of opportunities to improve the quality of human capital at the local level, based on mechanisms of cooperation among stakeholders and reforming the landscape of local self-government - in particular, through the creation of innovation hubs, the implementation of digitalization projects, and the expansion of the use of artificial intelligence technologies. The opportunities and benefits of direct participation of local communities in international economic relations and global supply chains are demonstrated.AD ALTA: Journal of Interdisciplinary Research430The analysis of opportunities to improve the quality of human capital at the local level based on mechanisms of cooperation among stakeholders and reforming the landscape of local self-government through the creation of innovation hubs, the implementation of digitalization projects, and the expansion of the use of artificial intelligence technologies is paid.AD ALTA: Journal of Interdisciplinary Research["Viacheslav Serhieiev", "Volodymyr Gruntkovskiy", "D. Dzvinchuk", "Dmytro Kharechko", "Mark Liutyi", "Natalia Kovalska"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/057914a1f101f46e2326e9b4b854e606a3c7e2d4 b2f19e7bebe8c95edb8dbca033e6482400374323AI, despite its inherent limitations, is a versatile tool for writing and creative writing. I firmly believe that the human brain is unparalleled, as all human inventions are products of its ideas and thoughts. Hence, the creation of inventions in any domain can improve and benefit humanity. Therefore, technologies like artificial intelligence (AI) aim to enhance writing skills in general, particularly when it comes to creative writing too. AI functions by recognising and reproducing patterns in data, including language, in the context of AI-generated fiction. Creativity has always been associated with human cognition and intuition, resulting in novel and distinct results. Creative writing aims to captivate, enlighten, and inspire its audience, whether through written or spoken mediums. As writers utilize technologies to aid in the writing process, they encounter a set of questions that revolve around important features resulting from their partnership with artificial intelligence, namely about authenticity and originality. The concept of originality, which arises from a writer's creative abilities, has historically been within the domain of the human intellect.ShodhKosh Journal of Visual and Performing Arts70As writers utilize technologies to aid in the writing process, they encounter a set of questions that revolve around important features resulting from their partnership with artificial intelligence, namely about authenticity and originality.ShodhKosh: Journal of Visual and Performing Arts["Suma Priyadarshini. B. K."]2024-07-31T00:00:00https://www.semanticscholar.org/paper/b2f19e7bebe8c95edb8dbca033e6482400374323 722f43738a1f83ac82bf2bf46376db65122fc16bWith the rapid development of technology, artificial intelligence technology has gradually been valued by the public and applied in various industries. At the level of business and corporate governance, there are also examples of AI directors participating in corporate governance internationally, providing us with space to explore the application of AI directors in Chinese corporate governance and decision-making. In December 2023, China promulgated a newly revised Company Law, and this article aims to explore how China responds to the issue of artificial intelligence directors in the context of the new Company Law. Specifically, it manifests as the authorization of artificial intelligence directors as auxiliary functions, the expansion and modification of the obligations of natural person directors, and the issue of responsibility assumption caused by artificial intelligence directors.Lecture Notes in Education Psychology and Public Media00Lecture Notes in Education Psychology and Public Media["Hao Xue"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/722f43738a1f83ac82bf2bf46376db65122fc16b c32a306c724c66836c2fe217f36d84a0b421409bArtificial intelligence enables machines to imitate human capabilities such as logical thinking, planning and creativity. This implies that learning something and then using the new learned knowledge characterizes artificial intelligence. Once artificial intelligence learns how algorithms, programs or systems work, it can always retrieve them. Thus, artificial intelligence could be in the position to support our everyday lives and our working lives if we use it properly. The present paper explores how artificial intelligence can be used in working life, especially in the field of public affairs, and what advantages and disadvantages it could have for public affairs managers. There is not much information about this matter so far, although it is a very interesting issue since public affairs is a communication profession in which adequate communication cannot simply always be the same and must be customized for individual persons. Based on actual articles, studies, internet sources and books, the current study shows what available literature reveals about this topic.MAP Education and Humanities50The present paper explores how artificial intelligence can be used in working life, especially in the field of public affairs, and what advantages and disadvantages it could have for public affairs managers.MAP Education and Humanities["Katharina Lebenbauer"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/c32a306c724c66836c2fe217f36d84a0b421409b 26b617138949c8d3b34f01a154c180ef3f6005e7This study was conducted to find out the role of Artificial Intelligence in education. The objectives of this study are: 1) To examine the significance of artificial intelligence in the field of education. 2) To determine the problems and possibilities of using artificial intelligence within the land of education, examining the relevant factors is important.  3) To explore the challenges of artificial intelligence inherent in the educational field. 4)  To examine the potential benefits of incorporating artificial intelligence in education. The population of this study is all colleges (government and private) in the district of Rahim Yar Khan. Of these 20 colleges, 400 students (boys and girls) and 150 teachers (males and females) participated as a sample. The study is descriptive, and the quantitative research method is utilized. Two self-structured questionnaires were used as a research tool: one for students, comprising 40 items, and one for teachers consisting of 30 items. A simple random sampling technique is used for data collection. The collected data was analyzed with the help of SPSS.  The results revealed that artificial intelligence plays a significant role in education and improves personalized learning experiences. AI technology is capable of fulfilling the needs of teachers and pupils in the teaching and learning process effectively e.g. tutoring, communication, evaluation, analysis, supervision, etc. It enhances the teaching and learning process by using modern technologies and methodologies. In short, the use of AI in education and learning is very noteworthy.International Journal of Social Science &amp; Entrepreneurship100The results revealed that artificial intelligence plays a significant role in education and improves personalized learning experiences and the use of AI in education and learning is very noteworthy.International Journal of Social Science &amp; Entrepreneurship["Dr. Muhammad Athar Hussain", "Nazish Akhtar", "Sana Kiran", "Mehwish Noreen"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/26b617138949c8d3b34f01a154c180ef3f6005e7 9b48fdd31d9e336ee3b398b7c762b41cab62f930Abstract: From the beginning, the Healthcare sector stood a pioneer for the development of Artificial Intelligence technology. Numerous research and discussion have been conducted till today based on the idea of Artificial Intelligence. This is because of the nature of the amenities and the susceptibility of ample portion of consumers. In the present study, a blended approach both qualitative and quantitative has been applied to determine the constituents of Artificial Intelligence for healthcare industry and analysis is conducted to find its effect on value formation along with market performance. By analysis of the patient perspective, it revealed the ways in which different Artificial Intelligence components contribute to healthcare organizations and provide improved patient-centered health care.International Journal for Research in Applied Science and Engineering Technology00By analysis of the patient perspective, it revealed the ways in which different Artificial Intelligence components contribute to healthcare organizations and provide improved patient-centered health care.International Journal for Research in Applied Science and Engineering Technology["Kuldeep Kaur"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/9b48fdd31d9e336ee3b398b7c762b41cab62f930 19034fb82326e07e041128a446499214708509c0This article explores the impact of artificial intelligence (AI) on the advertising industry, detailing its evolution from traditional to AI-driven methods. Advertising has historically been a crucial medium for promoting messages, with its impact magnified during public health crises. The integration of AI has revolutionized advertising by automating functions such as ad optimization, media buying, and personalized ad creation. AI's ability to analyze large data sets and predict consumer behavior has enhanced the efficiency and effectiveness of advertising campaigns, leading to higher returns on investment (ROI). Using a case study methodology, the article examines the social and demographic impacts of AI-driven advertising, particularly on youth and the elderly, and addresses the ethical implications and challenges associated with AI's pervasive use in advertising. Additionally, the case study of Being Patient, an online platform for Alzheimer's disease, illustrates the significant impact of well-crafted AI advertising on business success and community engagement. The article concludes with a call for further research to evaluate AI advertising's reception across different age groups and to develop strategies for its broader acceptance and integration into mainstream practices.Communications in Humanities Research00The social and demographic impacts of AI-driven advertising, particularly on youth and the elderly, are examined, and the ethical implications and challenges associated with AI's pervasive use in advertising are addressed.Communications in Humanities Research["Yalan Chen"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/19034fb82326e07e041128a446499214708509c0 9b0aa3b07031f95907e14752c6e2f515b0333bceThis paper discusses the use of artificial intelligence in the context of Industry 4.0, highlighting its advantages, disadvantages, and challenges in implementation. Special emphasis is placed on the potential consequences that could arise if production, decision-making, and measurement processes were entirely entrusted to the independent action of artificial intelligence. Additionally, ethical dilemmas arising from the increasing reliance on artificial intelligence in industrial processes are explored. The paper underscores the importance of awareness of risks and the necessary balance between the autonomy of artificial intelligence and human control, particularly in the field of measurement, to ensure sustainable and ethical development of the industry in the era of Industry 4.0.Proceedings 11th International Conference IcETRAN 202400The paper underscores the importance of awareness of risks and the necessary balance between the autonomy of artificial intelligence and human control, particularly in the field of measurement, to ensure sustainable and ethical development of the industry in the era of Industry 4.0.Proceedings 11th International Conference IcETRAN 2024["Vesna Vasili\u0107", "M. Urekar"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/9b0aa3b07031f95907e14752c6e2f515b0333bce 09750794df7250e10f46c124d748b5b6127eed69This article analyzes the use of artificial intelligence technologies in forming a credit database of corporate borrowers for a bank. It provides a definition of the credit database of corporate borrowers and characterizes the process of its formation, highlighting several key stages. A structural diagram of such a database is presented. The use of specific artificial intelligence technologies is examined according to the specifics of the main stages of its formation and the level of structured information accumulated. The possibilities of generative artificial intelligence models in constructing the structural diagram of the credit database are identified. An algorithm of artificial intelligence actions in forming the database is described. The economic benefits of the bank from automating this process based on intelligent technologies are determined. Conclusion dwells upon the fact that leveraging artificial intelligence capabilities in forming bank customer databases can positively impact the efficiency of the entire corporate lending process and credit portfolio management.Теория и практика общественного развития00The conclusion dwells upon the fact that leveraging artificial intelligence capabilities in forming bank customer databases can positively impact the efficiency of the entire corporate lending process and credit portfolio management.Теория и практика общественного развития["Konstantin S. Konstantinov"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/09750794df7250e10f46c124d748b5b6127eed69 24725bbfe400cf4fe748e9a88d288fd0a548d854The integration of artificial intelligence in hotel services is an important and rapidly developing field. The article "Integration of Artificial Intelligence in Hotel Services: Trends and Developments" explores the current possibilities and trends in utilizing advanced artificial intelligence technologies in the hotel industry. The article discusses the use of smart algorithms for data analysis, the implementation of robots and smart technologies to enhance guest experiences, the utilization of support systems and personalized self-service, and the improvement of management and operational processes in hotels . It presents examples and research from the industry that demonstrate the benefits of leveraging AI and machine learning in hotel services and offers scenarios and tools for successful implementation of this advanced technology in the field. The article provides an in-depth and up-to-date insight into the use of artificial intelligence in the hotel industry and offers ideas and recommendations for experts and businesses in the field to effectively implement this technology in hotel operations.The Annals of the University of Oradea. Economic Sciences00The article discusses the use of smart algorithms for data analysis, the implementation of robots and smart technologies to enhance guest experiences, the utilization of support systems and personalized self-service, and the improvement of management and operational processes in hotels.The Annals of the University of Oradea. Economic Sciences["Levi Sapir"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/24725bbfe400cf4fe748e9a88d288fd0a548d854 efa36ecd492ef17f9caef3dbf82a4826783e43fcThe objective of this research is to analyze the components of artificial intelligence entrepreneurship on the digital ecosystem for higher education institutions and synthesize the conceptual framework. This will involve the use of documentary research methods to analyze and synthesize the conceptual framework. The findings were that the conceptual framework consists of three components: (1) Business Model, (2) AI-Assisted Business Model, and (3) Ecosystem of Artificial Intelligence Entrepreneurship. It can be applied to digital entrepreneurs, entrepreneurs, start-ups, and students interested in doing business in higher education institutions to develop their artificial intelligence entrepreneurial competencies appropriately and effectively.2024 9th International STEM Education Conference (iSTEM-Ed)330The findings were that the conceptual framework consists of three components: Business Model, AI-Assisted Business Model, and Ecosystem of Artificial Intelligence Entrepreneurship that can be applied to digital entrepreneurs, entrepreneurs, start-ups, and students interested in doing business in higher education institutions to develop their artificial intelligence entrepreneurial competencies appropriately and effectively.2024 9th International STEM Education Conference (iSTEM-Ed)["Katekeaw Pradit", "Pallop Piriyasurawong"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/efa36ecd492ef17f9caef3dbf82a4826783e43fc 088a2cd27f10955c98690c098ac45ac488053aecIn the modern era of technology, the influence of artificial intelligence in various fields is becoming increasingly evident and profound, and aviation safety is one of the areas intensely experiencing this technological revolution. As air travel becomes more accessible and frequent, ensuring high safety standards is essential, making artificial intelligence a crucial element that is transforming the entire aviation industry. From advanced assistance systems for pilots and air traffic controllers to sophisticated algorithms that analyze data to prevent incidents, artificial intelligence has introduced a series of innovations that significantly enhance aviation safety. However, this shift also brings new challenges, from managing sensitive data to ensuring effective collaboration between humans and algorithms. This paper will explore the depth of AI’s influence in the field of aviation safety, examining both its benefits and the challenges that must be overcome to achieve an optimal balance between technology and the human factor in this critical domain.SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE00This paper will explore the depth of AI’s influence in the field of aviation safety, examining both its benefits and the challenges that must be overcome to achieve an optimal balance between technology and the human factor in this critical domain.SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE["Marius-Alexandru Voicu"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/088a2cd27f10955c98690c098ac45ac488053aec 77694958f5825bda4c248d7d14c7b474ba4dc2fbTechnological advancements have significantly transformed various facets of human existence, including the realm of education. An important transformation that has taken place is the rise of artificial intelligence, which has become an essential component of the educational process, particularly among students in higher education. The primary objective of this essay is to examine the comprehension of artificial intelligence and the potential hazards it poses within the realm of education. The methodology employed involves doing a comprehensive literature review by examining pertinent scholarly articles, books and researches. The data that was gathered was subjected to analysis in order to examine the effects of dependence on artificial intelligence in the field of education. The study was conducted at Ambon State Polytechnic, where the findings indicated the presence of various risks linked to the utilisation of artificial intelligence in the field of education. These risks encompassed the possibility of errors and inaccuracies within artificial intelligence systems, ethical concerns, and psychological ramifications. This article additionally presents a number of strategies for the prudent utilisation of artificial intelligence in order to mitigate these potential hazards. The primary objective of this article is to enhance comprehension regarding the utilisation of artificial intelligence in the field of education, while also providing recommendations on effectively managing potential hazards that may emerge.Linguistica280The findings indicated the presence of various risks linked to the utilisation of artificial intelligence in the field of education, which encompassed the possibility of errors and inaccuracies within artificial intelligence systems, ethical concerns, and psychological ramifications.LINGUISTICA["Juvrianto Chrissunday Jakob", "Nikolaus Passasung"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/77694958f5825bda4c248d7d14c7b474ba4dc2fb 1d86d45522539608f47ab96c721f79387d3c3818Natural resources like water are one of the essential elements for the survival of life on the globe. Due to increase in population, more water is required for agricultural purposes to assure food availability. This elevated demand not only consumes a considerable amount but also wastage of these resources has been perceived. One of the major causes for this wastage is adoption of conventional irrigation methods. Hence there is a need to upgrade the irrigation processes integrating the smart techniques for the optimum utilization of available resources. Therefore, this study intends to propose a smart irrigation system utilizing Artificial Intelligence approach. This research tends to present the advanced parameters for the enhancement of smart irrigation systems. The proposed model comprises three major modules including memory, analyzer and decision maker. This study contributed to present a roadmap towards smart decision making for a smart model keeping various constraints under consideration. The presented model would lead the trail of smart advancement to an innovative era. This has been done with the least involvement of humans, but the adaptive artificial intelligence is influenced by the internet of things.International Journal of Information Systems and Computer Technologies00This study contributed to present a roadmap towards smart decision making for a smart model keeping various constraints under consideration to lead the trail of smart advancement to an innovative era.International Journal of Information Systems and Computer Technologies["Fatima Mustafa", "Sidra Rehman", "Umair Rashid"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/1d86d45522539608f47ab96c721f79387d3c3818 fd4e272602c6b403049f7958a664f40fad1ab215The digital monetary system in the modern era is based on money. It occupies a leading position in economic studies and in all current economic transactions. It is a digital cash system, an advanced system for electronic money that was invented by unknown parties in 2008. The main goal behind its invention was to get rid of the capitalist system and the official authorities’ control over the issuance of digital currencies and to come up with a new technology that, with its technological confidence, replaces the role of control and supervision in central banks, that is, in light of artificial intelligence. Dealing with it has been banned in Iraq because it leaves effects and problems on the economic level that cannot be easily overcome. This topic was addressed through two sections. The first dealt with research into encrypted digital currencies in terms of their economic nature, while we devoted the second section to researching and clarifying how to invest in them and their negative and positive economic effects digital currencies electronic money, artificial intelligence.International Journal of Financial, Administrative, and Economic Sciences00This topic was addressed through two sections: research into research into encrypted digital currencies in terms of their economic nature, while the second section to researching and clarifying how to invest in them and their negative and positive economic effects.International Journal of Financial, Administrative, and Economic Sciences["Sakna. j . Faraj", "Basma Hassan", "Wasn Hadi"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/fd4e272602c6b403049f7958a664f40fad1ab215 233ac4c98c381a7ae8fe0a2d4c1b0b07e19e6907Artificial intelligence (AI), which includes computing for perception, cognition, and action, raises complicated legal issues. This research investigates AI’s influence and legal implications, focusing on its autonomy in communication and creativity, which raises problems about language, intellectual property, and ethical accountability. Discussions differ depending on whether they are influenced by the Common Law or Civil Law systems. While Common Law defines AI as “computer-generated work,” Civil Law tends to see AI as a legal thing. This research aims to formulate a solid ground for an AI legal framework in the Indonesian national legal system. The research undertaken involves a thorough analysis of academic literature, focusing on the legal and ethical implications of AI, highlighting the need for a nuanced perspective to define its subjectivity. In conclusion, the complex interplay between artificial intelligence (AI) and legal principles involves reframing old terminology. Existing models for AI duty are called into question, and vicarious liability is one possible answer. AI is a derived law problem, so it needs to be carefully calibratedfor responsible innovation while also keeping ethics and technological progress in check.Jurnal Penelitian Hukum De Jure00This research aims to formulate a solid ground for an AI legal framework in the Indonesian national legal system, focusing on its autonomy in communication and creativity, which raises problems about language, intellectual property, and ethical accountability.Jurnal Penelitian Hukum De Jure["Rangga Hotman Hasibuan", "Aurelya Jessica Rawung", "Fidel Jeremy Wowiling"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/233ac4c98c381a7ae8fe0a2d4c1b0b07e19e6907 c7c145280fbd392078989420ae90b1d0ee603d0f: The paper aims to provide an image of the future of teaching and learning in the context of artificial intelligence transforming various industries, including sports, education, and construction. Its place in education is a frequently discussed topic. While some argue that artificial intelligence will revolutionize education, others worry that it will take over to the harm of educators and students. Though robotics in the classroom is still a ways off, artificial intelligence is finding its way into the classroom. AI has the power to improve teaching and learning methods, solve some of the largest issues facing education today, and hasten the achievement of inclusive and equitable quality education. In addition to delivering artificial intelligence courses, EdTech businesses are increasingly using eLearning solutions to personalize learning experiences, pinpoint knowledge gaps, and give focused feedback. Also, AI-driven education is upending conventional teaching methods and influencing how this field will use technology in the future. With the use of complex algorithms and massive data sets, artificial intelligence solutions for education may provide a lot of advantages, yet as with the use of artificial intelligence in any context, there are significant ethical considerations which are a hot topic of discussion in the technology world and beyond, and the majority of university degree programs are including courses on AI ethics in their curricula. Therefore, the paper presents the benefits of AI in the classroom, such as engagement and assistance for students, assessment and evaluation, and individualized learning but also about the difficulties and worries associated with AI in education, including prejudice and privacy issues, as well as the moral issues raised by AI-powered learning. It also discusses the possible effects on the educational system and how students are trained for the workforce of the future as potential applications of AI in education are explored.The Annals of the University of Oradea. Economic Sciences00The paper presents the benefits of AI in the classroom, such as engagement and assistance for students, assessment and evaluation, and individualized learning but also about the difficulties and worries associated with AI in education, including prejudice and privacy issues, as well as the moral issues raised by AI-powered learning.The Annals of the University of Oradea. Economic Sciences["A. Pop", "Monica-Ariana Sim", "Amalia Sturza", "Simona-Veronica Caciora"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/c7c145280fbd392078989420ae90b1d0ee603d0f b37036d9f6fecf761835bb84d5db9a8e38df5fd7Recent developments in IT have changed the way accounting professionals and auditors do business. The research conducted in this article aims to explore how artificial intelligence and ERP systems offer opportunities to increase efficiency, accuracy and improve decision making in companies operating in the accounting and auditing industry. One of the results obtained from the bibliometric analysis indicates that artificial intelligence enables the automation of repetitive tasks, allowing the analysis of a large set of data to support strategic decision making. In addition, the integration of ERP systems streamlines financial processes, improves data management and ensures compliance with regulatory requirements. The digitalization of the accounting profession has transformed traditional practices and revolutionized the way accounting professionals operate in today's digital age. By embracing digital tools and platforms, accounting professionals can enhance efficiency, accuracy, and collaboration, ultimately improving the quality of financial reporting and analysis. The role of these technologies (artificial intelligence and ERP systems) is to streamline workflows, increase productivity and adapt to evolving industry requirements. The research in this article was based on a bibliometric analysis that aimed to observe research trends in this field, through which to observe or identify uncovered areas and future research directions in this field. Following a comprehensive analysis of the benefits and challenges associated with the adoption of artificial intelligence and ERP systems in accounting and auditing practices, this study aims to provide valuable insights to these professionals as a result of the upward trend of the digitalization phenomenon. As a result of the digitisation of business, the article provides valuable information needed by accounting professionals and auditors to help them remain competitive in a rapidly changing landscape.The Annals of the University of Oradea. Economic Sciences00Results obtained from the bibliometric analysis indicate that artificial intelligence enables the automation of repetitive tasks, allowing the analysis of a large set of data to support strategic decision making in companies operating in the accounting and auditing industry.The Annals of the University of Oradea. Economic Sciences["Laura-Eugenia-Lavinia Barna", "Corina-C\u0103t\u0103lina Hurducaci Gorea"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/b37036d9f6fecf761835bb84d5db9a8e38df5fd7 5da311850e860291e092840f6a31cb5a3f394a14In the context of increasing global agricultural challenges, the application of artificial intelligence technology in the agricultural field is increasingly a trend, especially in the production of agricultural products, intelligent identification technology has shown the potential to significantly improve production efficiency and optimize yield and quality. By the mid-21st century, demand for food production is expected to reach 50 percent, and there will be enormous pressure to achieve this goal with traditional agricultural technologies, which could be achieved through the application of artificial intelligence. The application of artificial intelligence technology in modern agricultural production will be analyzed in detail in this paper, the guidance of future research fields will be proposed, and the existing challenges and technical problems will be identified and discussed in order to promote the deepening and wide application of intelligent agriculture. This paper specifically discusses examples of applications in orchard management, pest detection, and automated harvesting and summarizes the effectiveness and obstacles of these techniques.Applied and Computational Engineering00Examples of applications in orchard management, pest detection, and automated harvesting are discussed and the effectiveness and obstacles of these techniques are summarized.Applied and Computational Engineering["Miaowei Wang"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/5da311850e860291e092840f6a31cb5a3f394a14 1db97aa9f1dd7b96d8811c6df3710d308d86f99fIntroduction. Artificial intelligence and machine learning methodologies, such as prediction, pattern recognition, or general inference based on the data used in clinical aspects, must fit within the intended purposes of developing it. This article aims to provide high-level, non-technical details of the initiative and a comprehensive approach that has been taken to integrate AI-powered techniques in evidence-based nursing practices appropriately. Methodology. A multi-pronged phased approach was considered for developing artificial intelligence tools. This approach includes conducting a scoping review, analyzing data to identify patterns of impactful intervention, employing data triangulation, enhancing data collection based on impactful intervention strategies, and developing a prototype (pilot) for an artificial intelligence tool. The process encompasses piloting, testing and training, validation, and implementation. Results. In this early stage of piloting the tool, the primary focus was identifying patterns from various information gathered from healthcare organizations. This analysis revealed opportunities for knowledge generation, facilitated the expedited implementation of guidelines, and enhanced resource efficiency. Discussion. Focusing on a data-driven model to inform best practices for implementing guidelines and identifying the most impactful interventions is facilitated by extensive in-house data storage. The triangulation of approaches to guideline development, implementation, and evaluation contributes to developing this scientifically validated artificial intelligence and machine learning initiative. Conclusion. Any artificial intelligence technique requires extensive data. To provide healthcare organizations with the best available evidence, purposeful efforts must be made to structure data collection and ensure data quality before expanding the development of artificial intelligence tools.  Medunab110A comprehensive approach has been taken to integrate AI-powered techniques in evidence-based nursing practices appropriately and the triangulation of approaches to guideline development, implementation, and evaluation contributes to developing this scientifically validated artificial intelligence and machine learning initiative.MedUNAB["Shanoja Naik", "Doris Grinspun"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/1db97aa9f1dd7b96d8811c6df3710d308d86f99f 96bc81047956f60f6f734e2b22a30148e5e31d62Artificial Intelligence (AI) has the potential to revolutionize various sectors, and secondary education in India is no exception. This paper explores the integration of AI in India's secondary education system, highlighting its potential benefits, challenges, and the roadmap for effective implementation. It aims to provide a comprehensive understanding of how AI can enhance the quality of education, personalize learning experiences, and bridge the educational divide in the country.ShodhKosh Journal of Visual and Performing Arts50This paper explores the integration of AI in India's secondary education system, highlighting its potential benefits, challenges, and the roadmap for effective implementation.ShodhKosh: Journal of Visual and Performing Arts["Rekha A Pathak", "Suresh S Waghmare"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/96bc81047956f60f6f734e2b22a30148e5e31d62 c6f44c8e6800168e524c0e5cea6c2d45d2b16bf7The research explores the impact of AI-assisted tools on academic English reading skills in the classroom and personalized learning among university students regarding how AI tools complement students’ assessments. To conduct this research, 24 students were divided into two groups of equal size: a control and an experimental group, which will be involved in an experimental study: one group using solely conventional methods and another group using AI-assisted tools in addition to conventional methods. The goal is to gain evidence on the effectiveness of AI-powered tools in enhancing reading comprehension, vocabulary acquisition, and critical thinking while identifying potential damage, such as overreliance on AI and ethical implications. It employed a mixed-methods approach, which involved an experimental study, survey, and semi-structured interview with students who either have or have not utilized AI for English reading learning. In the experimental research, pre-and post-tests were used to compare the differences in English academic reading scores between two groups of students. The survey was used to determine the students’ opinions on the effectiveness of AI tools, and individual semi-structured interviews were employed to obtain more detailed information. The findings posit that the involvement of AI tools in facilitating traditional teaching and learning methods has a more significant positive effect on improving academic English reading skills in educational settings, offering better convenience and effectiveness for students. However, challenges such as overreliance on AI and ethical implications were emphasized. The research provides invaluable insights into the potential benefits and challenges of evolving AI tools to enhance higher education English reading skills in educational settings.Journal of General Education and Humanities471The findings posit that the involvement of AI tools in facilitating traditional teaching and learning methods has a more significant positive effect on improving academic English reading skills in educational settings, offering better convenience and effectiveness for students.Journal of General Education and Humanities["Phalla Chea", "Yangtian Xiao"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/c6f44c8e6800168e524c0e5cea6c2d45d2b16bf7 8f951a17d47ccd70dea90b4bcccd9f0af72043d5JURNAL SOSIAL HUMANIORA01Jurnal Sosial Humaniora["Allicia Deana Santosa", "Iis Surgawati"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/8f951a17d47ccd70dea90b4bcccd9f0af72043d5 4db3860ef1f5868caaac4a10460d7ad48eaa94eeEwha Medical Journal41The Ewha Medical Journal["Chang Ho Ahn"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/4db3860ef1f5868caaac4a10460d7ad48eaa94ee ece6100df3fa41c7ff8adc124eee0ff307dc0844Este manuscrito presenta un análisis crítico sobre el papel de la inteligencia artificial (IA) en los medios de comunicación contemporáneos, integrando perspectivas multidisciplinarias de la filosofía, sociología, comunicación e informática. A través de una revisión de publicaciones académicas y periodísticas, se examina la evolución histórica de la IA en el periodismo, sus aplicaciones actuales y los desafíos éticos y prácticos que conlleva su implementación. El estudio pone de relieve cómo la IA está transformando la producción y distribución de información, explorando problemáticas como los sesgos algorítmicos, la personalización de contenido y su impacto en la integridad periodística y el debate público. Se enfatiza la necesidad de desarrollar enfoques de regulación y gobernanza que garanticen un uso ético y responsable de la IA en los medios, considerando las implicaciones prácticas de su implementación. Este análisis busca contribuir al desarrollo de un marco conceptual sólido para la evaluación crítica del papel de la IA en el periodismo, buscando un equilibrio entre la innovación tecnológica y la preservación de la integridad periodística. En última instancia, a través de este monográfico se busca enriquecer el debate académico y contribuir al desarrollo de un marco conceptual sólido para la evaluación crítica de esta área de estudio en constante evolución.adComunica00adComunica["Oscar Molina Bailon"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/ece6100df3fa41c7ff8adc124eee0ff307dc0844 4b2ba680d8f3585e1d8fe5d4393bed376996e491La inteligencia artificial generativa es un entramado tecnocultural que supone alcances y desafíos para la renovación metodológica en las ciencias sociales. En ese contexto, se propone el modelo denominado Análisis de Contenido mediante Inteligencia Artificial (ACIA) como una posibilidad analítica para enfrentar la llamada cultura de la conectividad (Van Dijck, 2013), en que la masividad, la multimodalidad, la interactividad y la brevedad de los mensajes desborda la cotidianeidad de la interacción social hiperconectada. Mediante una propuesta inicial con el apoyo de tecnología conversacional y en el marco de la discusión de los métodos digitales (Rogers, 2013), enmarcada en términos de rigor y vigilancia epistemológica, se configura un debate metodológico en el que la relación entre prácticas de investigación crítica y técnicas computacionales automatizadas se desenvuelve afirmativamente en matrices de modelado y reflexividad incesante para el quehacer científico contemporáneo.adComunica00adComunica["C\u00e9sar Augusto Rodr\u00edguez-Cano"]2024-07-31T00:00:00https://www.semanticscholar.org/paper/4b2ba680d8f3585e1d8fe5d4393bed376996e491 03c3162de56d592380238f058472bfd39068a41fToday, the Fourth Industrial Revolution is accelerating with the development of ICT and AI, and people are enjoying benefits such as “biological human enhancement” through various advanced technologies. However, there are also concerns that the advancement of science and technology may shrink the realm of human creativity. This study explores the possibilities and limitations of AI's ability to create literary art. Since the first AI-authored literary attempt in 1984, AI's ability to create literature has improved by leaps and bounds with the introduction of deep learning. GPT-3, in particular, has learned far more text and 175 billion parameters than its predecessors, resulting in a remarkable linguistic ability. GPT-3 can produce text that is virtually indistinguishable from human writing, and some researchers describe it as creative, witty, deep, and beautiful. However, there are questions about GPT-3's capabilities. GPT-3 is limited to generating text based on existing works, does not form a coherent narrative, and requires guidance from a human user. It has also been criticized that GPT-3 plays a “probability game,” choosing words based on statistical probabilities, which is inherently different from human creation. Nevertheless, GPT-3's creations often contain compelling and eye-catching parts. In the creation of literary works, novelty is divided into novelty in the “story” and novelty in the “telling” of the story. A story requires a “fictional world” that includes characters and events, and to create a finished story is to create a fictional world. The narrative technique or style is telling, which refers to the author's distinctive style. It is still useful to examine whether AI can reach each stage. While AI may be able to imitate some forms, the question remains whether its output will have lasting and unique formal characteristics. The technology for creating AI poetry involves entering keywords based on a language model, extracting.K-Culture·Story Contents Reasearch Institute00While AI may be able to imitate some forms, the question remains whether its output will have lasting and unique formal characteristics, which is inherently different from human creation.K-Culture·Story Contents Reasearch Institute[]2024-07-31T00:00:00https://www.semanticscholar.org/paper/03c3162de56d592380238f058472bfd39068a41f 77627c54040da29bb884173052d99fd0166ed0e6Technological evolution has enabled the development of new artificial intelligence (AI) models with generative capabilities. Among them, one of the most discussed is the virtual agent ChatGPT. This chatbot may occasionally produce fake information, as also declared by the producer OpenAI. Such a model may provide very useful support in several tasks, ranging from text summarization to programming. The research community has marginally investigated the impact that fake information created by AI models has on the users’ perceptions and on their belief in AI. We analyzed the impact of the fake information produced by AI on user perceptions, specifically trust and satisfaction, by performing a user study on ChatGPT. An additional issue is assessing whether the early or late knowledge of the possibility of the tool generating fake information has a different impact on the users’ perceptions. We conducted an experiment, involving 62 university students, a category of users who may employ tools such as ChatGPT extensively. The experiment consisted of a guided interaction with ChatGPT. Some of the participants experienced the failure of the chatbot, while a control group only received correct and reliable answers. We collected participants’ perceptions of trust, satisfaction, and usability, together with the net promoter score (NPS). The results demonstrated a statistically significant difference in trust and satisfaction between the users who early experienced fake information production compared to those who discovered ChatGPT’s faulty behaviors later during the interaction. Also, there is no statistically significant difference among the users who received the late fake information and the control group (no fake information). Usability and the NPS also resulted higher when the fake news was detected in the late interaction. When users are aware of the fake information generated by ChatGPT their trust and satisfaction decrease, especially when they impact on this at the early stage of use of the chatbot. Nevertheless, the perception of trust and satisfaction still remains high, as some of the users are still enthusiastic; others consider a more conscious use of the tool in terms of support to be verified. A useful strategy could be to favor a critical use of ChatGPT, letting young people to verify the provided information. This should be a new way to perform learning activities.IEEE Transactions on Computational Social Systems3515The results demonstrated a statistically significant difference in trust and satisfaction between the users who early experienced fake information production compared to those who discovered ChatGPT’s faulty behaviors later during the interaction, and there is no statistically significant difference among the users who received the late fake information and the control group (no fake information).IEEE Transactions on Computational Social Systems["Ilaria Amaro", "Paola Barra", "Attilio Della Greca", "R. Francese", "Cesare Tucci"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/77627c54040da29bb884173052d99fd0166ed0e6 1a5c409d07cccb0928de9988673482116b414048International Journal of Information Management6029Int. J. Inf. Manag.["Einav Peretz-Andersson", "Sabrina Tabares", "Patrick Mikalef", "Vinit Parida"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/1a5c409d07cccb0928de9988673482116b414048 aa33fb89ceef2e25445cbcbf65cba2ead713ce38Computers & electrical engineering12630Comput. Electr. Eng.["Zahra Sadeghi", "R. Alizadehsani", "Mehmet Akif Cifci", "Samina Kausar", "Rizwan Rehman", "P. Mahanta", "P. Bora", "Ammar Almasri", "Rami Suleiman Alkhawaldeh", "Sadiq Hussain", "Bilal Alatas", "A. Shoeibi", "H. Moosaei", "Milan Hlad\u00edk", "Saeid Nahavandi", "P. Pardalos"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/aa33fb89ceef2e25445cbcbf65cba2ead713ce38 e991b2c0dc3442b811be6df78f5dff529175c459Energy9619Energy["Z. Amiri", "Arash Heidari", "Nima Jafari Navimipour"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/e991b2c0dc3442b811be6df78f5dff529175c459 5341f5374275a06d8fe76be7164b73e4390dbdeaJournal of Cleaner Production2615Journal of Cleaner Production["Y. Alzoubi", "Alok Mishra"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/5341f5374275a06d8fe76be7164b73e4390dbdea 2e3288ea09c85e4e5afd1780d0855d584e161c01System (Linköping)5616System["Ran Zhi", "Yongxiang Wang"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/2e3288ea09c85e4e5afd1780d0855d584e161c01 3b7e128f0b9b081f829cbec8cf855ef28e56e099Expert systems with applications4614Expert Syst. Appl.["Dimitrios P. Panagoulias", "M. Virvou", "G. Tsihrintzis"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/3b7e128f0b9b081f829cbec8cf855ef28e56e099 5bf6f0252d9daedd120487d06b0cb90ce71d4c71Abstract This study aims to contsruct  a  mass media bussiness industry model  in the era of artificial intelligence (AI) development in Indonesia. The development of artificial inteligence technology in Indodesia, espesially in mass media industry, makes it possible to apply this technology. The  object of the study in mass media bussiness industry model  in AI development era is bussiness communication  and  the  subjects are informants  from mass media who apply digital in running their bussines. This study was designed as qualitative study with contsruction paradigm  and case study analysis. The results of the study are 1). The bussiness industry concept of mass media “Ayo Bandung” in the era of AI development  have a role as bussiness booster “Ayo Media” in an integrated solution communication bussiness with one window service concept. Ayo Bandung develops an online news and through more social media  actively with news content more focus on regional news  emphasizing  in  basic bussiness aspect, target market and creativity  looking for ideas for creating regeneration 2). Mass media bussiness industry model whichis Ayo Bandung have 10 keys the era of  artificial intelligence development, including Customer Segment​, Value Proposition, Channels, Customer Relationship, Revenue Streams, Key Resource,  Key Activities, Key Partnership,  Cost Structure dan Technology Applied  Linimasa : Jurnal Ilmu Komunikasi229The bussiness industry concept of mass media “Ayo Bandung” in the era of AI development  have a role as bussiness booster “Ayo Media” in an integrated solution communication bussiness with one window service concept.Linimasa : Jurnal Ilmu Komunikasi["Iis Saidah"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/5bf6f0252d9daedd120487d06b0cb90ce71d4c71 a6171ca230aeac2c8f989de7f3e3231e480c500bApplied Energy7312Applied Energy["Muhammad Qamar Rasheed", "Yuhuan Zhao", "Abdul Haseeb", "Zahoor Ahmed", "Shah Saud"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/a6171ca230aeac2c8f989de7f3e3231e480c500b cfa14de80635fb2a2600219d338fb06ba7129385Sustainable cities and society9012Sustainable Cities and Society["B. Alhasnawi", "Sabah Mohammed Mlket Almutoki", "Firas Faeq K. Hussain", "Ambe Harrison", "B. Bazooyar", "M. Zanker", "Vladim\u00edr Bure\u0161"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/cfa14de80635fb2a2600219d338fb06ba7129385 b154d7a8bfeb9311782c0f53fe83c919ad240fd8Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases—SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.Diagnostics596This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases—SCOPUS, PUBMED, and Google Scholars, with a significant emphasis on low- and middle-income countries, rural/remote areas, and emergency contexts.Diagnostics["Seungjun Kim", "Chanel Fischetti", "Megan Guy", "Edmund Hsu", "J. Christian Fox", "Sean D. Young"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/b154d7a8bfeb9311782c0f53fe83c919ad240fd8 be0180a415861fe85626e7080caeda9fc339823e Generative Artificial Intelligence (AI) is boosting anticipatory forms of governance, through which state actors seek to predict the future and strategically intervene in the present. In this context, city brains represent an emerging type of generative AI currently employed in urban governance and public policy in a growing number of cities. City brains are large-scale AIs residing in vast digital urban platforms, which manage multiple urban domains including transport, safety, health, and environmental monitoring. They use Large Language Models (LLMs) to generate visions of urban futures: visions that are in turn used by policymakers to generate new urban policies. In this paper, we advance a twofold contribution. Theoretically, we develop a critical theory of anticipatory governance in the age of generative AI. More specifically, we focus on technocratic approaches to anticipatory governance, to explain how the act of governing extends into the future by means of predictive AI technology. Our approach is critical in order to expose the dangers that the use of AI (generative AI, in particular) in urban governance poses, and to identify their causes. These dangers include the formation of a policy process that, under the influence of unintelligible LLMs, risks losing transparency and thus accountability, and the marginalization of human stakeholders (citizens, in particular) as the role of AI in the management of cities keeps growing and governance begins to turn posthuman. Empirically, we critically examine an existing city brain project under development in China and ground our critical theory in a real-life example.Policy & Society447Theoretically, a critical theory of anticipatory governance in the age of generative AI is developed and technocratic approaches to anticipatory governance are focused on, to explain how the act of governing extends into the future by means of predictive AI technology.Policy and Society["Federico Cugurullo", "Ying Xu"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/be0180a415861fe85626e7080caeda9fc339823e c88029ee05ac49622d4bc7cbef821a35922854fbJ. Medical Syst.1336The role of AI/ML in AMR management is explored, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents.Journal of Medical Systems["Jos\u00e9 M. P\u00e9rez de la Lastra", "Samuel J. T. Wardell", "Tarun Pal", "Cesar de la Fuente-Nunez", "Daniel Pletzer"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/c88029ee05ac49622d4bc7cbef821a35922854fb d7a223f5eecfd56b804c896da3084e09aaef71d4Journal of Medicine, Surgery, and Public Health4510Journal of Medicine, Surgery, and Public Health["Adewunmi Akingbola", "Adegbesan Abiodun", "Olajide Ojo", "Otumara Urowoli Jessica", "U. H. Alao"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/d7a223f5eecfd56b804c896da3084e09aaef71d4 6b1258c62e354e37a1a4445a23ff3b156edeb135Journal of Environmental Chemical Engineering12611Journal of Environmental Chemical Engineering["Xingyang Li", "Jiming Su", "Hui Wang", "Grzegorz Boczkaj", "J\u00fcrgen Mahlknecht", "Shiv Vendra Singh", "Chongqing Wang"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/6b1258c62e354e37a1a4445a23ff3b156edeb135 fb70a41ec30c4319a85e99ae334087c4f9d39f72Abstract Burnout and workforce attrition present pressing global challenges in healthcare, severely impacting the quality of patient care and the sustainability of health systems worldwide. Artificial intelligence (AI) has immense potential to reduce the administrative and cognitive burdens that contribute to burnout through innovative solutions such as digital scribes, automated billing and advanced data management systems. However, these innovations also carry significant risks, including potential job displacement, increased complexity of medical information and cases, and the danger of diminishing clinical skills. To fully leverage AI’s potential in healthcare, it is essential to prioritise AI technologies that align with stakeholder values and emphasise efforts to re-humanise medical practice. By doing so, AI can contribute to restoring a sense of purpose, fulfilment and efficacy among healthcare workers, reinforcing their essential role as caregivers, rather than distancing them from these core professional attributes.BMJ Health & Care Informatics334Burnout and workforce attrition present pressing global challenges in healthcare, severely impacting the quality of patient care and the sustainability of health systems worldwide, and it is essential to prioritise AI technologies that align with stakeholder values and emphasise efforts to re-humanise medical practice.BMJ Health & Care Informatics["Suresh Pavuluri", "Rohit B. Sangal", "John Sather", "Andrew Taylor"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/fb70a41ec30c4319a85e99ae334087c4f9d39f72 0de49d5285686c2d7cb98ce90aaa0530cf9f5839BMC Nursing395A clearer understanding of the use of deep learning in nursing and its implications for nursing practice is provided and a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.BMC Nursing["Supichaya Wangpitipanit", "Jiraporn Lininger", "Nick Anderson"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/0de49d5285686c2d7cb98ce90aaa0530cf9f5839 7566ec4fe2eec4a6767ecf07a12e3e35e5a49121In this issue, Lee and colleagues 1 describe the performance of several widely used artificial intelligence (AI) image generation models on producing images of physicians in the United States. The key question the authors set out to answer was whether the models would produce images that accurately reflect the actual racial, ethnicJAMA Network Open34The performance of several widely used artificial intelligence image generation models on producing images of physicians in the United States and whether the models would produce images that accurately reflect the actual racial, ethnic differences is described.JAMA network open["Byron Crowe", "Jorge A Rodriguez"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/7566ec4fe2eec4a6767ecf07a12e3e35e5a49121 11e04f974f25df913cdf3f15ae2971075522c6ecAbstract Background and Aims The recent integration of artificial intelligence (AI) across education, research, and clinical healthcare has led to a growing interest in AI training for healthcare students. This scoping review seeks to delve into existing literature, aiming to evaluate the perceptions and attitudes, of health science students toward the implementation of AI in their field. Methods This review followed the methodological guidance offered by Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis extension for Scoping Reviews (PRISMA‐ScR). A systematic search was conducted in the databases Medline, Emcare, and Scopus. Studies using both quantitative and qualitative methodologies were eligible if they explored the perceptions or attitudes of health science students in relation to AI. Relevant data from eligible articles was extracted and analyzed using narrative synthesis. Results Ten studies were included. Articles reported on the primary outcomes of perceptions (i.e., thoughts, ideas, satisfaction, etc.) and attitudes (i.e., beliefs, tendencies, etc.). Disciplines included nursing, diagnostic radiography, pharmacy, midwifery, occupational therapy, physiotherapy, and speech pathology were featured. Overall, students felt positively about the potential benefits AI would have on their future work. Students' interest and willingness to learn about AI was also favorable. Studies evaluating attitudes found positive correlations between attitudes toward AI, AI utilization, and intention to use AI. Negative perceptions related to threats of job security, and a lack of realism associated with AI software. Conclusion Overall, evidence from this review indicates that health science students' worldwide hold positive perceptions toward AI. Educators should focus on instilling positive attitudes toward AI, given correlations between AI exposure and intention to adopt AI.Health Science Reports264Evidence from this review indicates that health science students' worldwide hold positive perceptions toward AI, and Educators should focus on instilling positive attitudes toward AI, given correlations between AI exposure and intention to adopt AI.Health Science Reports["Shokoufeh Derakhshanian", "Lucy Wood", "E. Arruzza"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/11e04f974f25df913cdf3f15ae2971075522c6ec e69f08952347c1f378d8aa4679ebf472a7dd8d04Abstract Background With the ever‐increasing integration of artificial intelligence (AI) into health care, it becomes imperative to gain an in‐depth understanding of how health care professionals, specifically nurse practitioners, perceive and approach this transformative technology. Objectives This study aimed to gain insights into nurse practitioners' perceptions and attitudes toward AI adoption in health care. Methods This qualitative research employed a descriptive and phenomenological approach using in‐depth interviews. Data were collected through a semi‐structured questionnaire with 37 nurse practitioners selected through purposive sampling, specifically Maximum Variation Sampling and Expert Sampling techniques, to ensure diversity in characteristics. Trustworthiness of the research was maintained through member checking and peer debriefing. Thematic analysis was employed to uncover recurring themes and patterns in the data. Results The thematic analysis revealed nine main themes that encapsulated nurse practitioners' perceptions and attitudes toward AI adoption in health care. These included nurse practitioners' perceptions of AI implementation, attitudes toward AI adoption, patient‐centered care and AI, quality of health care delivery and AI, ethical and regulatory aspects of AI, education and training needs, collaboration and interdisciplinary relationships, obstacles in integrating AI, and AI and health care policy. While this study found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care. Conclusions This research provides a valuable contribution to the evolving discourse surrounding AI adoption in health care. The findings underscore the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice. Furthermore, fostering collaboration and interdisciplinary relationships is pivotal for the successful incorporation of AI in health care. Policymakers should also address the challenges and opportunities that AI presents in the health care sector. This study enhances the ongoing conversation on AI adoption in health care by shedding light on the perspectives of nurses, thereby shaping future strategies for AI integration.Health Science Reports953It is found that nurse practitioners held a wide range of perspectives, with many viewings AI as a tool to enhance patient care, and the necessity for comprehensive education and training in AI, accompanied by clear and robust ethical and regulatory guidelines to ensure the responsible integration of AI in health care practice.Health Science Reports["Moustaq Karim Khan Rony", "S. Numan", "Fateha Tuj Johra", "Khadiza Akter", "Fazila Akter", "Mitun Debnath", "Sujit Mondal", "Md. Wahiduzzaman", "Mousumi Das", "Mohammad Ullah", "Mohammad Habibur Rahman", "Shuvashish Das Bala", "Mst. Rina Parvin"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/e69f08952347c1f378d8aa4679ebf472a7dd8d04 549e48b1b5ab0da6da259e1548beafe9f4afc86bBreast cancer is one of the paramount causes of new cancer cases worldwide annually. It is a malignant neoplasm that develops in the breast cells. The early screening of this disease is essential to prevent its metastasis. A mammogram X-ray image is the most common screening tool practiced currently when this disease is suspected; all the breast lesions identified are not malignant. The invasive fine needle aspiration (FNA) of a breast mass sample is the secondary screening tool to clinically examine cancerous lesions. The visual image analysis of the stained aspirated sample imposes a challenge for the cytologist to identify the malignant cells accurately. The formulation of an artificial intelligence-based objective technique on top of the introspective assessment is essential to avoid misdiagnosis. This paper addresses several artificial intelligence (AI)-based techniques to diagnose breast cancer from the nuclear features of FNA samples. The Wisconsin Breast Cancer dataset (WBCD) from the UCI machine learning repository is applied for this investigation. Significant statistical parameters are measured to evaluate the performance of the proposed techniques. The best detection accuracy of 98.10% is achieved with a two-layer feed-forward neural network (FFNN). Finally, the developed algorithm’s performance is compared with some state-of-the-art works in the literature.Journal of Imaging383Several artificial intelligence (AI)-based techniques to diagnose breast cancer from the nuclear features of FNA samples are addressed and the developed algorithm’s performance is compared with some state-of-the-art works in the literature.Journal of Imaging["R. Islam", "Mohammed Tarique"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/549e48b1b5ab0da6da259e1548beafe9f4afc86b 996160af8ed6b916f967ac7ce212bc369c8efd4cArtificial Intelligence (AI) has been increasingly integrated into healthcare settings, including the radiology department to aid radiographic image interpretation, including reporting by radiographers. Trust has been cited as a barrier to effective clinical implementation of AI. Appropriating trust will be important in the future with AI to ensure the ethical use of these systems for the benefit of the patient, clinician and health services. Means of explainable AI, such as heatmaps have been proposed to increase AI transparency and trust by elucidating which parts of image the AI ‘focussed on’ when making its decision. The aim of this novel study was to quantify the impact of different forms of AI feedback on the expert clinicians’ trust. Whilst this study was conducted in the UK, it has potential international application and impact for AI interface design, either globally or in countries with similar cultural and/or economic status to the UK. A convolutional neural network was built for this study; trained, validated and tested on a publicly available dataset of MUsculoskeletal RAdiographs (MURA), with binary diagnoses and Gradient Class Activation Maps (GradCAM) as outputs. Reporting radiographers (n = 12) were recruited to this study from all four regions of the UK. Qualtrics was used to present each participant with a total of 18 complete examinations from the MURA test dataset (each examination contained more than one radiographic image). Participants were presented with the images first, images with heatmaps next and finally an AI binary diagnosis in a sequential order. Perception of trust in the AI systems was obtained following the presentation of each heatmap and binary feedback. The participants were asked to indicate whether they would change their mind (or decision switch) in response to the AI feedback. Participants disagreed with the AI heatmaps for the abnormal examinations 45.8% of the time and agreed with binary feedback on 86.7% of examinations (26/30 presentations).’Only two participants indicated that they would decision switch in response to all AI feedback (GradCAM and binary) (0.7%, n = 2) across all datasets. 22.2% (n = 32) of participants agreed with the localisation of pathology on the heatmap. The level of agreement with the GradCAM and binary diagnosis was found to be correlated with trust (GradCAM:—.515;—.584, significant large negative correlation at 0.01 level (p = < .01 and—.309;—.369, significant medium negative correlation at .01 level (p = < .01) for GradCAM and binary diagnosis respectively). This study shows that the extent of agreement with both AI binary diagnosis and heatmap is correlated with trust in AI for the participants in this study, where greater agreement with the form of AI feedback is associated with greater trust in AI, in particular in the heatmap form of AI feedback. Forms of explainable AI should be developed with cognisance of the need for precision and accuracy in localisation to promote appropriate trust in clinical end users.PLOS Digital Health423It is shown that the extent of agreement with both AI binary diagnosis and heatmap is correlated with trust in AI for the participants in this study, where greater agreement with the form of AI feedback is associated with greater trust in AI, in particular in the heatmap form of AI feedback.PLOS Digital Health["C. Rainey", "R. Bond", "J. McConnell", "Ciara Hughes", "Devinder Kumar", "S. McFadden"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/996160af8ed6b916f967ac7ce212bc369c8efd4c 58a93a477279ef97fb087d97c6a17492b68139ebBackground: The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical procedures, offering substantial benefits across various domains. With advancements in AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), healthcare systems are witnessing improvements in early detection, patient treatment, and overall administration. This article traces the evolution of AI, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes. Methods: This comprehensive review analyzes the existing literature on AI applications in healthcare, focusing on various AI methodologies and their integration into clinical settings. It evaluates the effectiveness of AI in processing large datasets, improving diagnostic precision, and facilitating data-driven decision-making. The study also explores the ethical, legal, and technical challenges associated with AI deployment in medical environments. Results: AI technologies have demonstrated significant improvements in healthcare, particularly in early disease detection, personalized treatment plans, and resource management. The use of AI in analyzing vast medical datasets has enhanced diagnostic accuracy, reduced costs, and optimized patient care. However, challenges related to ethical considerations, patient privacy, and system reliability remain critical barriers to full-scale AI adoption. Conclusion: Despite the challenges, AI is positioned as an indispensable tool in modern medicine, capable of enhancing preventive care, personalizing treatments, and improving healthcare delivery. This review proposes a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare. Further research is essential to maximize AI's potential while addressing ethical and practical concerns, ensuring safe and effective implementation in clinical settings.Journal of Angiotherapy01The evolution of AI is traced, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes, proposing a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare.Journal of Angiotherapy[]2024-08-01T00:00:00https://www.semanticscholar.org/paper/58a93a477279ef97fb087d97c6a17492b68139eb 0189e60b1ce1ae2f0d86cfd00adb1bf63f864107Case Studies in Chemical and Environmental Engineering679Case Studies in Chemical and Environmental Engineering["Abdullah O. Baarimah", "Mahmood A. Bazel", "W. Alaloul", "M. Alazaiza", "Tharaa M. Al-Zghoul", "Basheer Almuhaya", "Arsalaan Khan", "Ahmed W. Mushtaha"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/0189e60b1ce1ae2f0d86cfd00adb1bf63f864107 956d39ef974c93960a34fe3eac6d4e638bbfcb78The Lancet Diabetes and Endocrinology2119The current and future prospects of AI across the diabetes care continuum are explored, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.The lancet. Diabetes & endocrinology["Bin Sheng", "Krithi Pushpanathan", "Zhouyu Guan", "Q. Lim", "Zhi Wei Lim", "Samantha Min Er Yew", "Jocelyn Hui Lin Goh", "Y. Bee", "C. Sabanayagam", "Nick Sevdalis", "C. Lim", "Chwee Teck Lim", "Jonathan E Shaw", "Weiping Jia", "E. I. Ekinci", "Rafael Sim\u00f3", "Lee-Ling Lim", "Huating Li", "Y. Tham"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/956d39ef974c93960a34fe3eac6d4e638bbfcb78 23fc68632dc76a9d0774fd330080280ffb60854fLong COVID (Coronavirus disease), affecting millions globally, presents unprecedented challenges to healthcare systems due to its complex, multifaceted nature and the lack of effective treatments. This perspective review explores the potential of artificial intelligence (AI)-guided transcranial direct current stimulation (tDCS) as an innovative approach to address the urgent need for effective Long COVID management. The authors examine how AI could optimize tDCS protocols, enhance clinical trial design, and facilitate personalized treatment for the heterogeneous manifestations of Long COVID. Key areas discussed include AI-driven personalization of tDCS parameters based on individual patient characteristics and real-time symptom fluctuations, the use of machine learning for patient stratification, and the development of more sensitive outcome measures in clinical trials. This perspective addresses ethical considerations surrounding data privacy, algorithmic bias, and equitable access to AI-enhanced treatments. It also explores challenges and opportunities for implementing AI-guided tDCS across diverse healthcare settings globally. Future research directions are outlined, including the need for large-scale validation studies and investigations of long-term efficacy and safety. The authors argue that while AI-guided tDCS shows promise for addressing the complex nature of Long COVID, significant technical, ethical, and practical challenges remain. They emphasize the importance of interdisciplinary collaboration, patient-centered approaches, and a commitment to global health equity in realizing the potential of this technology. This perspective article provides a roadmap for researchers, clinicians, and policymakers involved in developing and implementing AI-guided neuromodulation therapies for Long COVID and potentially other neurological and psychiatric conditions.Brain Science1321While AI-guided tDCS shows promise for addressing the complex nature of Long COVID, significant technical, ethical, and practical challenges remain and the importance of interdisciplinary collaboration, patient-centered approaches, and a commitment to global health equity in realizing the potential of this technology is emphasized.Brain Sciences["Thorsten Rudroff", "O. Rainio", "R. Kl\u00e9n"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/23fc68632dc76a9d0774fd330080280ffb60854f c0417daae970a490377d78992b5d01a61f86c65aEarly-stage detection of chronic kidney disease (CKD) is crucial in research to enable timely intervention, enhance understanding of disease progression, reduce healthcare costs and support public health initiatives. The traditional approaches on early-stage chronic kidney disease detection often suffer from slow convergence and not integrate advanced technologies, impacting their effectiveness. Additionally, security and privacy concerns related to patient data are ineffectively addressed. To overcome these issues, this research incorporates novel optimized artificial intelligence-based approaches. The main aim is to enhance detection process through enhanced hybrid mud ring network (EHMRN), a novel detection technique combining light gradient boosting machine and MobileNet, involving extensive data collection, including a large dataset of 100,000 instances. The introduced network is optimized through the mud ring optimization to attain enhanced performance. Incorporating spark ensures secure cloud-based storage, enhancing privacy and compliance with healthcare data regulations. This approach represents a significant advancement in primary stage detection more effectively and promptly. The results show that the introduced approach outperforms traditional approaches in terms of accuracy (99.96%), F1-score (99.91%), precision (100%), specificity (99.98%), recall (100%) and execution time (0.09 s).International Journal of Electrical and Computer Engineering (IJECE)281This research incorporates novel optimized artificial intelligence-based approaches to enhance detection process through enhanced hybrid mud ring network (EHMRN), a novel detection technique combining light gradient boosting machine and MobileNet, involving extensive data collection, including a large dataset of 100,000 instances.International Journal of Electrical and Computer Engineering (IJECE)["Mamatha B", "Sujatha P. Terdal"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/c0417daae970a490377d78992b5d01a61f86c65a a0ed23fdd1ec3000893044186c04c173ec9a6708Background Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI’s impact on healthcare and to identify areas for further development and focus. Main Applications The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Challenges and Limitations Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. Conclusion The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.Journal of Multidisciplinary Healthcare881The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency, however, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare.Journal of Multidisciplinary Healthcare["Zifang Shang", "Varun Chauhan", "Kirti Devi", "Sandip Patil"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/a0ed23fdd1ec3000893044186c04c173ec9a6708 05f2d0ba5c31d8bc44752bc13a1b00db230ef20bFunctional & Integrative Genomics1481The range of AI tools available are explored and it is shown how they have become vital in various sectors of genomic research supporting clinical decisions.Functional & integrative genomics["Firat Ozcelik", "Mehmet Sait Dundar", "A. Yildirim", "G. Henehan", "Oscar Vicente", "J. S\u00e1nchez-Alc\u00e1zar", "Nuriye Gokce", "Duygu T. Yildirim", "Nurdeniz Nalbant Bingol", "D. P. Karanfilska", "Matteo Bertelli", "L. Pojski\u0107", "Mehmet Ercan", "Mikl\u00f3s Kellermayer", "I. O. Sahin", "Ole K Greiner-Tollersrud", "Busra Tan", "D. Martin", "Robert Marks", "Satya Prakash", "Mustafa Yakubi", "T. Beccari", "Ratnesh Lal", "S. G. Temel", "I. Fournier", "M. C. Ergoren", "Adam Mechler", "M. Salzet", "M. Maffia", "D. Danalev", "Qun Sun", "Lembit Nei", "D. Matulis", "D. T\u0103p\u0103loag\u0103", "Andres Janecke", "James Bown", "Karla Santa Cruz", "I. Radecka", "Celal Ozturk", "O. U. Nalbantoglu", "S. Sag", "K. Ko", "R. Arngr\u00edmsson", "I. Belo", "H. Akal\u0131n", "Munis Dundar"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/05f2d0ba5c31d8bc44752bc13a1b00db230ef20b 1226e4dc1236489f76336718dafe0ecd936c46d5Artificial Intelligence (AI), since its inception, has revolutionized multiple sectors, including healthcare in the 20th century, and has applications in data interpretation, leading to advancements in diagnostics, therapeutics, and clinical decision-making. AI is referred to as the capability of a software program to accurately analyze extrinsic information and utilize it for accomplishing desired goals and objectives through appropriate flexibility. It makes use of complicated operative algorithms to excel in human learning potential with overwhelming abilities to interpret large sets of data. The scope and implications of AI are consistently amplifying and have contributed significantly in nearly all phases of human life, especially healthcare. The integration of AI-related advancements would surely ameliorate the delivery of healthcare by allowing its accessibility, affordability, and level of care provided. For instance, reading CT scans is feasible by both AI as well as a radiologist. The screening of Tuberculosis is possible through AI via Chest X-rays with comparability in performance as molecular testing and mammography scans can predict the onset of breast cancer prior to the appearance of the ocular signs. Therefore, AI has been realized as one of the core areas by researchers and the government for public health benefit. For the same reason, it is imperative to adopt an ethically sound policy framework for guiding the further development of AI-based technologies and their application in public health. AI-based interpretations themselves cannot be fully trusted for their diagnostic decisions and judgements, and hence, it is vital to assess their accountability through all phases of development and deployment in the field of health. This article emphasizes the advancements in AI-based technologies, their assistance in public healthcare delivery systems, and their merits and demerits. It also explores the various ethical directives that need to be adhered to while utilizing it for public health welfare.Cureus271It is imperative to adopt an ethically sound policy framework for guiding the further development of AI-based technologies and their application in public health, and the various ethical directives that need to be adhered to while utilizing it for public health welfare are explored.Cureus["A. Mudey", "Aditya S Dhonde", "Mandar Chandrachood"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/1226e4dc1236489f76336718dafe0ecd936c46d5 1a3922a2716da58589a3ede4b5fcde328257f9a9Background: In diabetic retinopathy, early detection and intervention are crucial in preventing vision loss and improving patient outcomes. In the era of artificial intelligence (AI) and machine learning, new promising diagnostic tools have emerged. The IDX-DR machine (Digital Diagnostics, Coralville, IA, USA) represents a diagnostic tool that combines advanced imaging techniques, AI algorithms, and deep learning methodologies to identify and classify diabetic retinopathy. Methods: All patients that participated in our AI-based DR screening were considered for this study. For this study, all retinal images were additionally reviewed retrospectively by two experienced retinal specialists. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the IDX-DR machine compared to the graders’ responses. Results: We included a total of 2282 images from 1141 patients who were screened between January 2021 and January 2023 at the Jules Gonin Eye Hospital in Lausanne, Switzerland. Sensitivity was calculated to be 100% for ‘no DR’, ‘mild DR’, and ‘moderate DR’. Specificity for no DR’, ‘mild DR’, ‘moderate DR’, and ‘severe DR’ was calculated to be, respectively, 78.4%, 81.2%, 93.4%, and 97.6%. PPV was calculated to be, respectively, 36.7%, 24.6%, 1.4%, and 0%. NPV was calculated to be 100% for each category. Accuracy was calculated to be higher than 80% for ‘no DR’, ‘mild DR’, and ‘moderate DR’. Conclusions: In this study, based in Jules Gonin Eye Hospital in Lausanne, we compared the autonomous diagnostic AI system of the IDX-DR machine detecting diabetic retinopathy to human gradings established by two experienced retinal specialists. Our results showed that the ID-x DR machine constantly overestimates the DR stages, thus permitting the clinicians to fully trust negative results delivered by the screening software. Nevertheless, all fundus images classified as ‘mild DR’ or greater should always be controlled by a specialist in order to assert whether the predicted stage is truly present.Journal of Clinical Medicine121The results showed that the ID-x DR machine constantly overestimates the DR stages, thus permitting the clinicians to fully trust negative results delivered by the screening software.Journal of Clinical Medicine["Eleonora Riotto", "Stefan Gasser", "J. Potic", "Mohamed Sherif", "Theodor Stappler", "Reinier O. Schlingemann", "Thomas Wolfensberger", "Lazaros Konstantinidis"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/1a3922a2716da58589a3ede4b5fcde328257f9a9 5d6075912d339e55e7f9da2058dc72feb29201b1Neurosurgical review41Technological advancements in AI and ML are poised to enhance early detection and risk management, potentially contributing to the observed reduction in UCA rupture rates and improving patient outcomes.Neurosurgical Review["Muzamil Akhtar", "H. Farooqi", "Rayyan Nabi"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/5d6075912d339e55e7f9da2058dc72feb29201b1 d4e84c2b30a7f6757cb654743cfbe037498b1e4aNanoEthics531Strengthening a human-centered approach, the presented concept for a reembodied understanding of AI technology enables better integrated ethical and regulatory debates, and improves social discourse and human agency in developing and regulating AI technology.NanoEthics["Ludwig Weh"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/d4e84c2b30a7f6757cb654743cfbe037498b1e4a f5d1cf937c7c8cf40d704d70d9c7868e43808acbRobotic technology is increasingly utilized in surgical procedures to enhance precision, particularly in tasks demanding delicate maneuvers beyond human capabilities. Robotic orthopedic surgery emerges as a dynamic and compelling technology reshaping the landscape of surgical practice. This aids surgeons in achieving enhanced accuracy and reproducibility, ultimately aiming for improved patient outcomes. As of now, the majority of these systems are in a developed stage and are gradually gaining broader adoption. These systems have to show that they are user-friendly, are successful in clinical settings, and have a good cost-effectiveness ratio before they can be widely adopted in the field of surgery. In this review, we examine the evolution of robotics in orthopedic surgery, assess its current applications, and provide insights into the future trajectory of this technology, particularly in light of advances in artificial intelligence (AI) and machine learning (ML).Cureus311This review examines the evolution of robotics in orthopedic surgery, assess its current applications, and provides insights into the future trajectory of this technology, particularly in light of advances in artificial intelligence (AI) and machine learning (ML).Cureus["Jagbir Singh", "Priyank Patel"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/f5d1cf937c7c8cf40d704d70d9c7868e43808acb 055ea74f7fb6fd54baf6a3657f1b377a9b09ad3dPurpose: This study aimed to task and assess generative artificial intelligence (AI) models in creating medical illustrations for corneal transplant procedures such as Descemet's stripping automated endothelial keratoplasty (DSAEK), Descemet's membrane endothelial keratoplasty (DMEK), deep anterior lamellar keratoplasty (DALK), and penetrating keratoplasty (PKP). Methods: Six engineered prompts were provided to Decoder-Only Autoregressive Language and Image Synthesis 3 (DALL-E 3) and Medical Illustration Manager (MIM) to guide these generative AI models in creating a final medical illustration for each of the four corneal transplant procedures. Control illustrations were created by the authors for each transplant technique for comparison. A grading system with five categories with a maximum score of 3 points each (15 points total) was designed to objectively assess AI's performance. Four independent reviewers analyzed and scored the final images produced by DALL-E 3 and MIM as well as the control illustrations. All AI-generated images and control illustrations were then provided to Chat Generative Pre-Trained Transformer-4o (ChatGPT-4o), which was tasked with grading each image with the grading system described above. All results were then tabulated and graphically depicted. Results: The control illustration images received significantly higher scores than produced images from DALL-E 3 and MIM in legibility, anatomical realism and accuracy, procedural step accuracy, and lack of fictitious anatomy (p<0.001). For detail and clarity, the control illustrations and images produced by DALL-E 3 and MIM received statistically similar scores of 2.75±0.29, 2.19±0.24, and 2.50±0.29, respectively (p=0.0504). With regard to mean cumulative scores for each transplant procedure image, the control illustrations received a significantly higher score than DALL-E 3 and MIM (p<0.001). Additionally, the overall mean cumulative score for the control illustrations was significantly higher than DALL-E 3 and MIM (14.56±0.51 (97.1%), 4.38±1.2 (29.2%), and 5.63±1.82 (37.5%), respectively (p<0.001)). When assessing AI's grading performance, ChatGPT-4o scored the images produced by DALL-E 3 and MIM significantly higher than the average scores of the independent reviewers (DALL-E 3: 10.0±0.0 (66.6%) vs. 4.38±1.20 (29.2%), p<0.001; MIM: 10.0±0.0 (66.6%) vs. 5.63±1.82 (37.5%), p<0.001). However, mean scores for the control illustrations between ChatGPT-4o and the independent reviewers were comparable (15.0±0.0 (100%) vs. 14.56±0.13 (97.1%); p>0.05). Conclusion: AI is an extremely powerful and efficient tool for many tasks, but it is currently limited in producing accurate medical illustrations for corneal transplant procedures. Further development is required for generative AI models to create medically sound and accurate illustrations for use in ophthalmology.Cureus112Cureus["Kayvon A Moin", "Ayesha A Nasir", "Dallas J Petroff", "Bosten Loveless", "Omeed A Moshirfar", "P. Hoopes", "Majid Moshirfar"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/055ea74f7fb6fd54baf6a3657f1b377a9b09ad3d 953dd7f7e0b64f96fd85976fb7c71a03ef303355Introduction The integration of artificial intelligence (AI) in healthcare has revolutionized patient interactions and service delivery. AI's role extends from supporting clinical diagnostics and enhancing operational efficiencies to potentially improving informed consent processes in surgical settings. This study investigates the application of AI, particularly large language models like OpenAI's ChatGPT, in facilitating surgical consent, focusing on patient understanding, satisfaction, and trust. Methods We employed a mixed-methods approach involving 86 participants, including laypeople and medical staff, who engaged in a simulated AI-driven consent process for a tonsillectomy. Participants interacted with ChatGPT-4, which provided detailed procedure explanations, risks, and benefits. Post-interaction, participants completed a survey assessing their experience through quantitative and qualitative measures. Results Participants had a cautiously optimistic response to AI in the surgical consent process. Notably, 71% felt adequately informed, 86% found the information clear, and 71% felt they could make informed decisions. Overall, 71% were satisfied, 57% felt respected and confident, and 57% would recommend it, indicating areas needing refinement. However, concerns about data privacy and the lack of personal interaction were significant, with only 42% reassured about the security of their data. The standardization of information provided by AI was appreciated for potentially reducing human error, but the absence of empathetic human interaction was noted as a drawback. Discussion While AI shows promise in enhancing the consistency and comprehensiveness of information delivered during the consent process, significant challenges remain. These include addressing data privacy concerns and bridging the gap in personal interaction. The potential for AI to misinform due to system "hallucinations" or inherent biases also needs consideration. Future research should focus on refining AI interactions to support more nuanced and empathetic engagements, ensuring that AI supplements rather than replacing human elements in healthcare. Conclusion The integration of AI into surgical consent processes could standardize and potentially improve the delivery of information but must be balanced with efforts to maintain the critical human elements of care. Collaborative efforts between developers, clinicians, and ethicists are essential to optimize AI use, ensuring it complements the traditional consent process while enhancing patient satisfaction and trust.Cureus311The integration of AI into surgical consent processes could standardize and potentially improve the delivery of information but must be balanced with efforts to maintain the critical human elements of care.Cureus["Alex Teasdale", "Laura Mills", "Rhodri Costello"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/953dd7f7e0b64f96fd85976fb7c71a03ef303355 4ae74d5c55d633663bed62a6e960565dbe2db8acAsynclitism, a misalignment of the fetal head with respect to the plane of passage through the birth canal, represents a significant obstetric challenge. High degrees of asynclitism are associated with labor dystocia, difficult operative delivery, and cesarean delivery. Despite its clinical relevance, the diagnosis of asynclitism and its influence on the outcome of labor remain matters of debate. This study analyzes the role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery. The study also aims to assess the performance of the AIDA (Artificial Intelligence Dystocia Algorithm) algorithm in integrating AD with other ultrasound parameters for predicting labor outcome. This retrospective study involved 135 full-term nulliparous patients with singleton fetuses in cephalic presentation undergoing neuraxial analgesia. Data were collected at three Italian hospitals between January 2014 and December 2020. In addition to routine digital vaginal examination, all patients underwent intrapartum ultrasound (IU) during protracted second stage of labor (greater than three hours). Four geometric parameters were measured using standard 3.5 MHz transabdominal ultrasound probes: head-to-symphysis distance (HSD), degree of asynclitism (AD), angle of progression (AoP), and midline angle (MLA). The AIDA algorithm, a machine learning-based decision support system, was used to classify patients into five classes (from 0 to 4) based on the values of the four geometric parameters and to predict labor outcome (ICD or non-ICD). Six machine learning algorithms were used: MLP (multi-layer perceptron), RF (random forest), SVM (support vector machine), XGBoost, LR (logistic regression), and DT (decision tree). Pearson’s correlation was used to investigate the relationship between AD and the other parameters. A degree of asynclitism greater than 70 mm was found to be significantly associated with an increased rate of cesarean deliveries. Pearson’s correlation analysis showed a weak to very weak correlation between AD and AoP (PC = 0.36, p < 0.001), AD and HSD (PC = 0.18, p < 0.05), and AD and MLA (PC = 0.14). The AIDA algorithm demonstrated high accuracy in predicting labor outcome, particularly for AIDA classes 0 and 4, with 100% agreement with physician-practiced labor outcome in two cases (RF and SVM algorithms) and slightly lower agreement with MLP. For AIDA class 3, the RF algorithm performed best, with an accuracy of 92%. AD, in combination with HSD, MLA, and AoP, plays a significant role in predicting labor dystocia and labor outcome. The AIDA algorithm, based on these four geometric parameters, has proven to be a promising decision support tool for predicting labor outcome and may help reduce the need for unnecessary cesarean deliveries, while improving maternal-fetal outcomes. Future studies with larger cohorts are needed to further validate these findings and refine the cut-off thresholds for AD and other parameters in the AIDA algorithm.Journal of Imaging611The role of the degree of asynclitism (AD) in assessing labor progress and predicting labor outcome is analyzed, focusing on its ability to predict intrapartum cesarean delivery (ICD) versus non-cesarean delivery ( non-cesarean delivery).Journal of Imaging["Antonio Malvasi", "Lorenzo E. Malgieri", "Ettore Cicinelli", "Antonella Vimercati", "Reuven Achiron", "R. Spari\u0107", "Antonio D\u2019Amato", "G. M. Baldini", "M. Dellino", "Giuseppe Trojano", "R. Beck", "Tommaso Difonzo", "A. Tinelli"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/4ae74d5c55d633663bed62a6e960565dbe2db8ac a578ead46dac07a0dbecb7bf93e50bee6ed1762fGiven the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.PLOS Digital Health761A narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains.PLOS Digital Health["Vijaytha Muralidharan", "Joel Schamroth", "Alaa Youssef", "L. A. Celi", "Roxana Daneshjou"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/a578ead46dac07a0dbecb7bf93e50bee6ed1762f 8b90c6aa7ad16960ab065a2be81a01616de34fc1This article explores the determinants of the adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMEs) with special attention to the impact of competencies and skills. The research was based on data from a representative sample of SMEs in Spain and used logistic regression econometric analysis. Additionally, the study applied an innovative AI technique, Generative Adversarial Networks (GANs), to balance the data set. The findings indicate that SMEs whose business owners / managers have university degrees or high levels of professional training, those with information technology (IT) experts among their staffAmfiteatru Economic681The findings indicate that SMEs whose business owners / managers have university degrees or high levels of professional training, those with information technology (IT) experts among their staff are more likely to adopt artificial intelligence.Amfiteatru Economic["Mammadov Huseyn", "\u00c1frica Ruiz-G\u00e1ndara", "Luis Gonzalez-Abril", "Isidoro Romero"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/8b90c6aa7ad16960ab065a2be81a01616de34fc1 b1547fedf4ac89c36ae2d6499bcbffb4d52a3ce3Background In the evolving landscape of healthcare, artificial intelligence (AI) has emerged as a transformative force, revolutionizing decision-making processes. Through advanced machine learning and data analytics, AI promises precision and personalized treatments, particularly impactful in diagnostics and personalized medicine. Aim and objectives This study aims to investigate the utilization and effectiveness of AI algorithms among healthcare caregivers, focusing on decision-making processes. Objectives include assessing AI adoption prevalence, understanding demographic factors influencing utilization, and evaluating its impact on decision-making dynamics, diagnostics, and personalized medicine. Methods Employing a quantitative cross-sectional approach, an online questionnaire was distributed to 224 healthcare professionals. The survey covered AI familiarity, perceived effectiveness, and potential barriers. Data analysis utilized descriptive statistics and bivariate analyses. Results Seventy-five percent of caregivers reported that they used AI in the decision-making process, with nurses representing a significant majority (50.4%). Bivariate analyses identified correlations between AI utilization and demographic variables, emphasizing its diverse adoption across specialties. Conclusion This study reveals substantial AI adoption, notably among nurses, indicating a transformative shift in decision-making processes. The findings underscore AI's potential in diagnostics and personalized medicine, highlighting the need for targeted interventions and collaborative efforts to address challenges and maximize AI benefits in healthcare.Cureus151This study reveals substantial AI adoption, notably among nurses, indicating a transformative shift in decision-making processes, and underscores AI's potential in diagnostics and personalized medicine, highlighting the need for targeted interventions and collaborative efforts to address challenges and maximize AI benefits in healthcare.Cureus["Horaya A Amin", "Turki M. Alanzi"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/b1547fedf4ac89c36ae2d6499bcbffb4d52a3ce3 8b6903ca85e69b356ee94fb935c6fad59608e1ecBACKGROUND AND METHODS In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. RESULTS We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. CONCLUSIONS The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.Cephalalgia591The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.Cephalalgia : an international journal of headache["Anker Stubberud", "Helge Langseth", "P. Nachev", "M. Matharu", "E. Tronvik"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/8b6903ca85e69b356ee94fb935c6fad59608e1ec 41939fcd501901de17b6f180954232ccde64b8b9The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application–a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.PLOS Digital Health541This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation.PLOS Digital Health["Tyler J. Loftus", "Jeremy A. Balch", "Kenneth L. Abbott", "Die Hu", "M. Ruppert", "B. Shickel", "T. Ozrazgat-Baslanti", "P. Efron", "P. Tighe", "William R. Hogan", "Parisa Rashidi", "Michelle I. Cardel", "Gilbert R Upchurch", "A. Bihorac"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/41939fcd501901de17b6f180954232ccde64b8b9 f8acd803fafec99da2d05e3982d2c9f6c9c52f3d The burgeoning role of artificial intelligence (AI) and Generative AI (GenAI) in academia signifies a transformative shift in educational methodologies and research practices. This mixed-methods cross-sectional study investigates the differential familiarity, usage and attitudes towards AI and GenAI among 704 students and lecturers, supplemented by in-depth interviews with 12 industry and academic professionals. Findings indicate minimal familiarity with AI tools and low use, primarily attributing this to a lack of knowledge. Notably, despite a reported higher familiarity among men than women, actual usage rates did not significantly differ. Discrepancies were also observed between students and lecturers in familiarity and usage, with institutional factors and generational divides impacting AI integration. The study underscores the necessity for academia to bridge these gaps through targeted AI literacy initiatives, fostering equitable access and integrating AI into curriculum and training.European Journal of Open, Distance and E-Learning181Investigation of differential familiarity, usage and attitudes towards AI and GenAI among students and lecturers indicates minimal familiarity with AI tools and low use, primarily attributed to a lack of knowledge.European Journal of Open, Distance and E-Learning["A. Gesser-Edelsburg", "Rana Hijazi", "Ester Eliyahu", "Amir Tal"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/f8acd803fafec99da2d05e3982d2c9f6c9c52f3d 39014fbfbf186d03fb5041f85bc11a972473c681NanoEthics1431The challenges for adequate risk regulation that arise primarily from the specific type of risks involved, i.e. risks to the protection of fundamental rights and fundamental societal values, are addressed.NanoEthics["Carsten Orwat", "Jascha Bareis", "Anja Folberth", "Jutta Jahnel", "Christian Wadephul"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/39014fbfbf186d03fb5041f85bc11a972473c681 5b0eda8ec1789a643d85333774285c75c4eb90f2Artificial intelligence (AI)coupled with existing Internet of Things (IoT) enables more streamlined and autonomous operations across various economic sectors. Consequently, the paradigm of Artificial Intelligence of Things (AIoT) having AI techniques at its core implies additional energy and carbon costs that may become significant with more complex neural architectures. To better understand the energy and Carbon Footprint (CF) of some AIoT components, very recent studies employ conventional metrics. However, these metrics are not designed to capture energy efficiency aspects of inference. In this paper, we propose a new metric, the Energy Cost of AIoT Lifecycle (eCAL) to capture the overall energy cost of inference over the lifecycle of an AIoT system. We devise a new methodology for determining eCAL of an AIoT system by analyzing the complexity of data manipulation in individual components involved in the AIoT lifecycle and derive the overall and per bit energy consumption. With eCAL we show that the better a model is and the more it is used, the more energy efficient an inference is. For an example AIoT configuration, eCAL for making $100$ inferences is $1.43$ times higher than for $1000$ inferences. We also evaluate the CF of the AIoT system by calculating the equivalent CO$_{2}$ emissions based on the energy consumption and the Carbon Intensity (CI) across different countries. Using 2023 renewable data, our analysis reveals that deploying an AIoT system in Germany results in emitting $4.62$ times higher CO$_2$ than in Finland, due to latter using more low-CI energy sources.arXiv.org371A new metric, the Energy Cost of AIoT Lifecycle (eCAL) is proposed to capture the overall energy cost of inference over the lifecycle of an AIoT system and shows that the better a model is and the more it is used, the more energy efficient an inference is.ArXiv["Shih-Kai Chou", "Jernej Hribar", "M. Mohor\u010di\u010d", "Carolina Fortuna"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/5b0eda8ec1789a643d85333774285c75c4eb90f2 307ed666032f0f78d069bdddebb36364c836b3feBACKGROUND Robotic-assisted radical prostatectomy (RARP) is currently a first-line treatment option for men with localized prostate cancer (PCa), at least 10 years of life expectancy, and candidate for curative treatment. We performed a scoping review to evaluate the role of artificial intelligence (AI) on RARP for PCa. METHODS A comprehensive literature search was performed using EMBASE, PubMed, and Scopus. Only English papers were accepted. The PICOS (Patient Intervention Comparison Outcome Study type) model was used; P: adult men with PCa undergoing RARP; I: use of AI; C: none; O: preoperative planning improvement and postoperative outcomes; S: prospective and retrospective studies. RESULTS Seventeen papers were included, dealing with prediction of positive surgical margins/extraprostatic extension, biochemical recurrence, patient's outcomes, intraoperative superimposition of magnetic resonance images to identify and locate lesions for nerve-sparing surgery, identification and labeling of surgical steps, and quality of surgery. All studies found improving outcomes in procedures employing AI. CONCLUSIONS The integration of AI in RARP represents a transformative advancement in surgical practice, augmenting surgical precision, enhancing decision-making processes and facilitating personalized patient care. This holds immense potential to improve surgical outcomes and teaching, and mitigate complications. This should be balanced against the current costs of implementation of robotic platforms with such a technology.Chinese Clinical Oncology02The integration of AI in RARP represents a transformative advancement in surgical practice, augmenting surgical precision, enhancing decision-making processes and facilitating personalized patient care and holds immense potential to improve surgical outcomes and teaching, and mitigate complications.Chinese clinical oncology["D. Castellani", "Leonard Perpepaj", "Demetra Fuligni", "Giuseppe Chiacchio", "Pietro Tramanzoli", "Silvia Stramucci", "V. De Stefano", "Vanessa Cammarata", "Simone Cappucelli", "Valerio Pasarella", "S. Ferretti", "D. Campobasso", "V. Gauhar", "A. Galosi"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/307ed666032f0f78d069bdddebb36364c836b3fe 674694fe539e14b9a77890e1a098b986dd641eb1Given advancements in large-scale data and AI, integrating multimodal artificial intelligence into cancer research can enhance our understanding of tumor behavior by simultaneously processing diverse biomedical data types. In this review, we explore the potential of multimodal AI in comprehending B-cell non-Hodgkin lymphomas (B-NHLs). B-cell non-Hodgkin lymphomas (B-NHLs) represent a particular challenge in oncology due to tumor heterogeneity and the intricate ecosystem in which tumors develop. These complexities complicate diagnosis, prognosis, and therapy response, emphasizing the need to use sophisticated approaches to enhance personalized treatment strategies for better patient outcomes. Therefore, multimodal AI can be leveraged to synthesize critical information from available biomedical data such as clinical record, imaging, pathology and omics data, to picture the whole tumor. In this review, we first define various types of modalities, multimodal AI frameworks, and several applications in precision medicine. Then, we provide several examples of its usage in B-NHLs, for analyzing the complexity of the ecosystem, identifying immune biomarkers, optimizing therapy strategy, and its clinical applications. Lastly, we address the limitations and future directions of multimodal AI, highlighting the need to overcome these challenges for better clinical practice and application in healthcare.Biomedicines1432The potential of multimodal AI in comprehending B-cell non-Hodgkin lymphomas (B-NHLs) is explored, and various types of modalities, multimodal AI frameworks, and several applications in precision medicine are defined.Biomedicines["Pouria Isavand", "S. Aghamiri", "Rada Amin"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/674694fe539e14b9a77890e1a098b986dd641eb1 610ed81de82a105a61b8521ab1e2577373add539Observers and practitioners of artificial intelligence (AI) have proposed an FDA-style licensing regime for the most advanced AI models, or 'frontier' models. In this paper, we explore the applicability of approval regulation -- that is, regulation of a product that combines experimental minima with government licensure conditioned partially or fully upon that experimentation -- to the regulation of frontier AI. There are a number of reasons to believe that approval regulation, simplistically applied, would be inapposite for frontier AI risks. Domains of weak fit include the difficulty of defining the regulated product, the presence of Knightian uncertainty or deep ambiguity about harms from AI, the potentially transmissible nature of risks, and distributed activities among actors involved in the AI lifecycle. We conclude by highlighting the role of policy learning and experimentation in regulatory development, describing how learning from other forms of AI regulation and improvements in evaluation and testing methods can help to overcome some of the challenges we identify.AAAI/ACM Conference on AI, Ethics, and Society991The applicability of approval regulation to the regulation of frontier AI is explored, highlighting the role of policy learning and experimentation in regulatory development and describing how learning from other forms of AI regulation and improvements in evaluation and testing methods can help to overcome some of the challenges.{"pages": "239-254"}["Daniel Carpenter", "Carson Ezell"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/610ed81de82a105a61b8521ab1e2577373add539 6f59ebb54a53351715237d721e755a5ac6cacea2Energy566Energy["Marina Corral-Bobadilla", "Rub\u00e9n Lostado-Lorza", "Celia Sabando-Fraile", "Sa\u00fal \u00cd\u00f1iguez-Macedo"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/6f59ebb54a53351715237d721e755a5ac6cacea2 95f4e4e7af5fae6bfa7913cd05d9bdbe56a21214Technovation528Technovation["Deborah L. Roberts", "M. Candi"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/95f4e4e7af5fae6bfa7913cd05d9bdbe56a21214 574449fc85c2eb4ef0d2bf345cdd5258e092719aInternational journal of hydrogen energy1206International Journal of Hydrogen Energy["Siripond Mullanu", "Caslon Chua", "Andreea Molnar", "Ali Yavari"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/574449fc85c2eb4ef0d2bf345cdd5258e092719a ea1fcad87ee1b60cdeb34ad0e4ef64939362c6bbThe Lancet Digital Health336The diagnostic performance of GPT-3 was comparable to physicians, it was significantly better than a typical person using a search engine and its performance was closer to that of lay individuals.The Lancet. Digital health["David M Levine", "Rudraksh Tuwani", "Benjamin Kompa", "Amita Varma", "Samuel G Finlayson", "A. Mehrotra", "Andrew Beam"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/ea1fcad87ee1b60cdeb34ad0e4ef64939362c6bb 31c0c7d033550908ae6263ba0a6e707e6d35e02fComputers and Education Open696Computers and Education Open["I. T. Sanusi", "F. J. Agbo", "Alexander Oluwaseun Dada", "A. Yunusa", "Kehinde D. Aruleba", "G. Obaido", "Olayemi Olawumi", "S. Oyelere", "Centre for Multidisciplinary Research and Innovation"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/31c0c7d033550908ae6263ba0a6e707e6d35e02f 4e478ccca0dce31d4954a7adc153b873bca48a8bAs any field evolves, so do journals' expectations from authors. As Artificial Intelligence (AI) usage in Higher Professions Education (HPE) has evolved, Medical Teacher's expectations have changed, and previously-accepted paper types are now routinely rejected. This commentary gives some guidance for authors currently submitting AI in HPE papers to Medical Teacher.Medical Teacher00This commentary gives some guidance for authors currently submitting AI in HPE papers to Medical Teacher, as Artificial Intelligence usage in Higher Professions Education has evolved and journals' expectations have changed.Medical teacher["Ken Masters"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/4e478ccca0dce31d4954a7adc153b873bca48a8b 5c2d3e3e1075705b53f2f8c93e1e3c987ebe2865This paper presents the experience and lessons learned in a pilot project aimed at integrating artificial intelligence (AI) technologies in sturgeon aquaculture. The project used convolutional neural networks and visual intelligence for the evaluation of fish biomass and the optimisation of sturgeon rearing technologies in integrated multitrophic production systems. Similar solutions have been used before to determine the biomass of other fish species, but this is the first documentation of the application of such a solution for sturgeons. The application challenges were significant, which was determined by the special morphological peculiarities of the sturgeons (shape, way of swimming, their dimensions). Both YOLACT technology and a computer vision context were tested using LAB and HSV colour spaces to estimate fish biomass based on imaging data. It was found that the LAB colour space provided superior results in terms of precision and efficiency, but maximum accuracy was achieved using convolutional neural networks (YOLACT). The analysis of the project results confirms the significant advantages of using the AI system for biomass monitoring, advantages consisting of the reduction of unit costs with labour and feed, improvement of water quality, active optimisation of sturgeon growing conditions. In this way, conditionsAmfiteatru Economic340The analysis of the project results confirms the significant advantages of using the AI system for biomass monitoring, advantages consisting of the reduction of unit costs with labour and feed, improvement of water quality, active optimisation of sturgeon growing conditions.Amfiteatru Economic["D. Cristea", "Alexandru Adrian Gavrila", "S. Petrea", "Dan Munteanu", "Sofia David", "C. Manescu"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/5c2d3e3e1075705b53f2f8c93e1e3c987ebe2865 5e3cbed75a82adc240da122dc8d403dc6fcdbf3aRecognizing the fast advancement of artificial intelligence (AI) in soil science, the main objective of this commentary paper is to discuss how this technology is being incorporated into the discipline, focusing on the most common algorithms and their applications. Employing a discursive and reflective methodology, the article draws insights from the authors' expertise and opinions. The paper explores some ethical considerations and the potential impact of AI on the job market and calls for a balanced approach that maximizes the benefits of this technology while vigilantly mitigating its negative implications to ensure the integrity and inclusivity of the profession. Artificial intelligence (AI) is changing soil science with advanced analytic and predictive modeling tools. Ethical AI in soil science should focus on data integrity, privacy, and transparent research. AI is reshaping the soil science job market, emphasizing the need for adaptability, and continuous learning. Collaboration between technology and soil experts can lead to groundbreaking research and academic solutions. AI, as a complementary tool, can enhance soil scientists' expertise, creativity, and problem‐solving abilities. Agricultural &amp; Environmental Letters170The paper explores some ethical considerations and the potential impact of AI on the job market and calls for a balanced approach that maximizes the benefits of this technology while vigilantly mitigating its negative implications to ensure the integrity and inclusivity of the profession.Agricultural &amp; Environmental Letters["Jose Pablo Castro", "C. Gasch", "Paulo Flores"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/5e3cbed75a82adc240da122dc8d403dc6fcdbf3a 7fc4cfe6857773cff9cf6b5b835fe194483a4fca In 2023 the disputes and discussions about the use of artificial intelligence (AI) in different spheres of life have flared up with renewed vigor. As the indisputable advantages of the use of automated systems and algorithms in the media are efficiency (the ability to quickly and accurately search for information, create template texts, check data), comprehensiveness (the ability to analyze any topic, to work with any direction), convenience (when properly configured does not require extra effort from the media specialist), learning (the ability to quickly absorb new knowledge and to analyze the needs and demands of the audience). The disadvantages are a noticeable number of errors and inaccuracies, fears of job cuts (especially in newsrooms), limitations in communication, lack of legislative regulation of AI, dependence on technology, the ethical aspect of work. The scientific and professional communities are increasingly drawing attention to the serious threats posed by the use of neural networks and computer algorithms. The article raises questions about the risks and challenges of using artificial intelligence, analyzes its capabilities regarding the threats to media safety (both the creation of fakes and deepfakes and the exposure of such content). Special attention is given to the methods of applying neural networks in the media space, which seems especially relevant nowadays, when IT developments have become one of the central themes for discussions at many international venues. Possible options for the further use of these technologies in the near future and the consequences of this application are presented as conclusions. Litera30The article raises questions about the risks and challenges of using artificial intelligence, analyzes its capabilities regarding the threats to media safety and special attention is given to the methods of applying neural networks in the media space.Litera["D. V. Nerents"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/7fc4cfe6857773cff9cf6b5b835fe194483a4fca ae086a20fc4ad177967416c66b192899222b170fPURPOSE To investigate the current decision-making capabilities of 6 different artificial intelligence (AI) models by assessing their refractive surgery recommendations (laser in-situ keratomileusis [LASIK] or photorefractive keratectomy [PRK]) for a theoretical patient with a history of keloid formation. METHODS Claude-2 (Anthropic, 2023), GPT-4 (OpenAI, 2023), GPT-3.5 (OpenAI, 2022), Gemini 1.0 (Google DeepMind, 2023), Microsoft Copilot (Microsoft AI, 2023), and Google-PaLM (Google AI, 2022) underwent three systematic queries to determine the most appropriate surgical plan (LASIK or PRK) for a theoretical patient with an increasing manifest refraction of -3.50, -5.00, and -7.00 diopters (D) in both eyes, an uncomplicated ocular examination, and history of keloid formation. They were then tasked with providing published scientific references to support their responses. The AI models' recommendations were compared to those of a group of 6 experienced ophthalmologists, serving as a benchmark. RESULTS The group of ophthalmologists unanimously recommended LASIK (6/6 ophthalmologists), in contrast to the unanimous initial recommendation for PRK from the AI models (6/6 models). Of the 42 references provided by the AI models, 55% were fictitious and 45% were authentic. Only 1 of the 6 models altered its initial recommendation to LASIK when presented with the same patient with a history of keloid formation but with increasing severity of myopia (-3.50 to 5.00 to 7.00 D). DISCUSSION It is evident that current AI models lack the critical-thinking abilities required to accurately analyze and assess apparent risk factors in clinical scenarios, such as the risk of corneal haze after PRK at higher levels of myopia, particularly in cases with a history of keloid formation. [J Refract Surg. 2024;40(8):e533-e538.].Journal of refractive surgery120It is evident that current AI models lack the critical-thinking abilities required to accurately analyze and assess apparent risk factors in clinical scenarios, such as the risk of corneal haze after PRK at higher levels of myopia, particularly in cases with a history of keloid formation.Journal of refractive surgery["Majid Moshirfar", "Kayvon A Moin", "Soroush Omidvarnia", "Spencer D Moulton", "Preston B Willey", "Isabella M. Stoakes", "P. Hoopes"]2024-08-01T00:00:00https://www.semanticscholar.org/paper/ae086a20fc4ad177967416c66b192899222b170f fe0898e00c279a824fdbba888e0fef7b5ae27b9dThis study examines the application of artificial intelligence (AI) in enhancing risk management within financial services. Through comprehensive analysis, the research reveals that AI technologies, particularly machine learning, and deep learning models, significantly improve the accuracy and efficiency of risk assessment and management processes. AI-powered credit risk models demonstrate a 20% increase in predictive accuracy compared to traditional methods, while market risk management sees a 30% improvement in anomaly detection speed and precision. The study also highlights a 60% reduction in false positives for fraud detection and a 40% increase in accurate favorable rates. Despite these advancements, challenges persist, primarily in data quality and model interpretability. The research projects that by 2028, AI will be integral to risk management in over 80% of large financial institutions, potentially reducing risk-related losses by 25% and improving operational efficiency by 35%. The study concludes by emphasizing the need for strategic implementation and responsible AI use, outlining future research directions, including the long-term impact on systemic risk, ethical implications, and the potential of quantum machine learning in risk modeling. Keywords: Artificial Intelligence, Financial Risk Management, Machine Learning, Regulatory Compliance.Engineering Science &amp; Technology Journal06It is projected that by 2028, AI will be integral to risk management in over 80% of large financial institutions, potentially reducing risk-related losses by 25% and improving operational efficiency by 35%.Engineering Science &amp; Technology Journal["Haosen Xu", "Kaiyi Niu", "Tianyi Lu", "Siyang Li"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/fe0898e00c279a824fdbba888e0fef7b5ae27b9d 27c943351980c6e00d1a5f3a3645659d264de08b This JAMA Forum discusses new regulatory requirements for antidiscrimination in artificial intelligence tools used in health care, the dark side of flexible enforcement by agencies, and ways to facilitate meaningful compliance. JAMA Health Forum63JAMA health forum["Michelle M. Mello", "Jessica L. Roberts"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/27c943351980c6e00d1a5f3a3645659d264de08b 16f34d1c20a7113411254e1c40ce85659cb441cdRegional Environmental Change303This article explores recent advances in AI and connects them to the different stages of GEAs showing how some processes can be automatised and streamlined and recommends establishing ethical committees and organising dedicated expert meetings to develop best practice guidelines, ensuring responsible and transparent integration of AI into GEAs.Regional Environmental Change["V. Muccione", "S. Vaghefi", "J. Bingler", "Simon K. Allen", "Mathias Kraus", "Glen Gostlow", "Tobias Wekhof", "Chiara Colesanti-Senni", "Dominik Stammbach", "Jingwei Ni", "Tobias Schimanski", "Ting Yu", "Qian Wang", "Christian Huggel", "Juerg Luterbacher", "R. Biesbroek", "Markus Leippold"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/16f34d1c20a7113411254e1c40ce85659cb441cd 502e5d01039693791a13f6b122b35d77533c200fThe integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation.Future Internet743The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper.Future Internet["I. Kabashkin", "Leonid Shoshin"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/502e5d01039693791a13f6b122b35d77533c200f e82837a36a92daf3f4d7187d6b5f8bdc08daf4a8Purpose The primary aim of this study is to investigate the integration of generative artificial intelligence, specifically ChatGPT, into workplace L&D practices, exploring the associated advantages and challenges such integration from an organizational change perspective. Design/methodology/approach This study uses a qualitative approach, conducting semi-structured interviews with twelve learning and development (L&D) experts. Findings This study indicates that ChatGPT can positively impact L&D by streamlining processes and potentially enhancing employee performance, engagement and satisfaction. However, to mitigate employee resistance, organizations must clearly communicate the necessity and rationale behind the change, involve employees in the implementation process and address trust issues. Key challenges such as overreliance on ChatGPT, AI skill shortages and technology issues like privacy breaches and misinformation must be managed through strong governance frameworks, including policies, guidelines and regular audits. Research limitations/implications The study’s scope is confined to semi-structured interviews with L&D experts, potentially limiting its generalizability. Further research could explore the long-term effects and broader implications of ChatGPT integration in different organizational contexts. Practical implications By framing GenAI integration within the context of organizational change, this study offers insights into managing the transition effectively by providing guidance for managers on effectively integrating ChatGPT into L&D practices, emphasizing the importance of mitigating potential negative consequences while maximizing benefits. Social implications Integrating ChatGPT into organizational L&D has the potential to reshape how employees acquire new skills and knowledge, potentially influencing organizational culture and dynamics. However, careful consideration is required to ensure that the integration process aligns with ethical and social norms, minimizing adverse impacts. Originality/value This research contributes foundational insights into the integration of ChatGPT in corporate L&D by researching and understanding the opinions of corporate professionals. It serves as a starting point for organizations to identify challenges in adopting GenAI. Development and Learning in Organizations: an international journal42This study indicates that ChatGPT can positively impact L&D by streamlining processes and potentially enhancing employee performance, engagement and satisfaction, and offers insights into managing the transition effectively by providing guidance for managers on effectively integrating ChatGPT into L&D practices.Development and Learning in Organizations: An International Journal["Mohammad Issa Alhusban", "Hashem Alshurafat", "Ibrahim N. Khatatbeh"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/e82837a36a92daf3f4d7187d6b5f8bdc08daf4a8 e02d244228d24111df31dc69a870f0cc1f60d6acPurpose: This study explores the evolving research landscape at the intersection of Accounting and Artificial Intelligence. It aims to identify key trends, influential journals, and emerging topics in this field. Methodology: The research employs bibliometric analysis. The approach consists of a methodical exploration utilizing keywords such as "Artificial Intelligence," and "Accounting" along with a three-part research process: creating a strategy, formulating research questions, and refining search results based on specific inclusion and exclusion criteria. Findings: The examination uncovers important findings about integration of AI and accounting studies. Sweden is identified as a frontrunner in total references, while the United States leads in terms of publication frequency. Journals like the 'Journal of Emerging Technologies in Accounting' and the 'International Journal of Accounting Information Systems' significantly contribute to the field's body of knowledge. The word cloud analysis accentuates significant thematic elements, highlighting terms such as "Accounting," "Artificial Intelligence," and "Technology." Practical implications: Trends and influential journals provide valuable insights for researchers, practitioners, and educators to understand the practical implications of AI in accounting. Originality/value: This research adds to the novelty of the discipline by presenting a thorough summary of AI and accounting studies KEYWORDS: Artificial Intelligence, Accounting, Technology, Bibliometric Analysis, AuditEPRA International Journal of Environmental Economics, Commerce and Educational Management01This research adds to the novelty of the discipline by presenting a thorough summary of AI and accounting studies by utilizing keywords such as "Artificial Intelligence," and "Accounting" along with a methodical exploration utilizing bibliometric analysis.EPRA International Journal of Environmental Economics, Commerce and Educational Management["Chetan Kapasia", "Dr. S. S. Sodha"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/e02d244228d24111df31dc69a870f0cc1f60d6ac baaf32d9d4b77568b8fa307d5fe13dd50806ee76Breast cancer (BC), specifically HER2‐positives subtype, has a poor prognosis. Nevertheless, the development of anti‐HER2 therapy yielded satisfactory outcomes. Therefore, evaluating patient HER2 status and ascertaining responsiveness to anti‐HER2 therapy is crucial. The advent of deep learning has propelled the artificial intelligence (AI) revolution, leading to an increased applicability of AI in predictive models. In the field of medicine, AI is an emerging modality that is gaining momentum for facilitating cancer diagnosis and treatment, particularly in the effective management of breast cancer. This study aims to provide a comprehensive review of current diagnostic and predictive models that utilize data obtained from histopathological slides, radiomics, and HER2 binding sites. Advancements and practical applications of these models were also evaluated. Additionally, we examined existing obstacles that AI encounters for anti‐HER2 therapy. We also proposed future directions for integrating AI in assessing and managing anti‐HER2 therapy. The findings of this study offer valuable insights into the evaluation of AI‐based anti‐HER2 therapy, emphasizing key concepts and obstacles that, if addressed, could facilitate the integration of AI‐assisted anti‐HER2 therapy. The integration of AI has the potential to enhance the precision and customization of screening and treatment protocols for HER2+ breast cancer.iMetaOmics862This study aims to provide a comprehensive review of current diagnostic and predictive models that utilize data obtained from histopathological slides, radiomics, and HER2 binding sites, and examined existing obstacles that AI encounters for anti‐HER2 therapy.iMetaOmics["X. Deng", "Yixuan Yan", "Zekai Zhan", "Jindong Xie", "Hailin Tang", "Yutian Zou", "Jian Tu", "Peng Liu"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/baaf32d9d4b77568b8fa307d5fe13dd50806ee76 9845860c9a5b809f34bd61a8b80626b02a980d46Surgical Endoscopy281An AI system was developed using a semantic segmentation model that indicated DLs in real-time when this system was synchronized during surgery and can help visualize the DLs in real-time during LG.Surgical endoscopy["Yoshimasa Aoyama", "Y. Matsunobu", "T. Etoh", "Kosuke Suzuki", "Shunsuke Fujita", "Takayuki Aiba", "Hajime Fujishima", "Shinichiro Empuku", "Y. Kono", "Y. Endo", "Y. Ueda", "H. Shiroshita", "Toshiya Kamiyama", "Takemasa Sugita", "Kenichi Morishima", "Kohei Ebe", "T. Tokuyasu", "Masafumi Inomata"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/9845860c9a5b809f34bd61a8b80626b02a980d46 b88fc3ee4d8893fb2c92a7ffb45a0cb9fd3e4b7aArtificial intelligence tools promise transformative impacts in drug development. Regulatory agencies face challenges in integrating AI while ensuring reliability and safety in clinical trial approvals, drug marketing authorizations, and post-market surveillance. Incorporating these technologies into the existing regulatory framework and agency practices poses notable challenges, particularly in evaluating the data and models employed for these purposes. Rapid adaptation of regulations and internal processes is essential for agencies to keep pace with innovation, though achieving this requires collective stakeholder collaboration. This article thus delves into the need for adaptations of regulations throughout the drug development lifecycle, as well as the utilization of AI within internal processes of medicine agencies.Frontiers in Pharmacology822The need for adaptations of regulations throughout the drug development lifecycle, as well as the utilization of AI within internal processes of medicine agencies are delved into.Frontiers in Pharmacology["Wahiba Oualikene-Gonin", "M. Jaulent", "Jean-Pierre Thierry", "Sofia Oliveira-Martins", "L. Belgod\u00e8re", "Patrick Maison", "Jo\u00ebl Ankri"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/b88fc3ee4d8893fb2c92a7ffb45a0cb9fd3e4b7a be89fc1a571f6b28178ce553dd28a0fe621a6a80The research design in this thesis was based on a mixed exploratory design and tool development consisting of two stages. In the first stage, qualitative data were collected using the thematic analysis method, and the role of artificial intelligence and blockchain technology on companies’ internal accounting and audit reports was presented. Then, the quantitative data were used to determine the relationships between the qualitative and quantitative data. Thus, this study was applied in its quantitative phase and developmental in its qualitative phase. The study population was divided into two qualitative and quantitative parts of the research. In the qualitative part, all the articles and theses related to the subject in recent years were studied in Persian and English (2017 onwards). In the quantitative part, all experts and accountants of financial and auditing companies based in Tehran (1500 people) were considered the statistical population. The results showed that 24 categories were identified as influential factors in the role of artificial intelligence and blockchain technology in Iranian auditing companies' accounting and internal audit reports. DOI: https://doi.org/10.52783/pst.618Power system technology220The research design in this thesis was based on a mixed exploratory design and tool development consisting of two stages and showed that 24 categories were identified as influential factors in the role of artificial intelligence and blockchain technology in Iranian auditing companies' accounting and internal audit reports.Power System Technology["Friba Habibi"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/be89fc1a571f6b28178ce553dd28a0fe621a6a80 aff7ae41bb109c6dc12ea75055c9ca23cfbf3d8fToday, resistance developed by bacteria to common antibiotics that were otherwise regarded as effective is posing a serious challenge. It is believed that without any different efforts, this perennial problem will undermine all the ongoing efforts in antibiotic discovery and therapy development. In Nigeria, antibiotics are frequently prescribed in hospitals. However, issues like multidrug resistance (MDR) and inappropriate use and misuse of antibiotics, including incorrect dosages and use of broad-spectrum antibiotics for targeted infections, have precipitated the rise of MDR bacteria. Consequently, this leads to higher healthcare costs, mainly due to prolonged hospital stays and additional medications as well as increased patient mortality. The prospects of artificial intelligence (AI)-enabled antibiotic prescribing hold significant promise in transforming the current health-care practices. AI has the potential to enhance the precision and efficiency of antibiotic treatment through advanced algorithms and data analytics. This technology can contribute to improved diagnostic accuracy, providing real-time clinical support, optimizing dosage recommendations, personalized treatment plans, and streamlined antimicrobial stewardship, ultimately aiding the global fight against antibiotic resistance and optimizing patient outcomes. The integration of AI in antibiotic prescribing reflects a cutting-edge approach with the potential to revolutionize how antibiotics are prescribed to address challenges in antimicrobial stewardship, clinical decision-making, and combating antibiotic resistance. One of the key impediments to integrating AI into Nigeria’s health-care system is budgetary constraints. Addressing these constraints through strategic investments, improved budgetary allocation to research and development, and leveraging the opportunities presented by AI technologies can significantly enhance antibiotic prescribing and health-care practices, leading to improved public health outcomes.Global Health Economics and Sustainability00The integration of AI in antibiotic prescribing reflects a cutting-edge approach with the potential to revolutionize how antibiotics are prescribed to address challenges in antimicrobial stewardship, clinical decision-making, and combating antibiotic resistance.Global Health Economics and Sustainability["Ismail Rabiu", "Abdulazeez Muhammed", "Halima Tukur Ibrahim", "Fatima Garba Rabiu", "Jaafaru Isah Abdullahi", "Khadijat Abdulfatai", "Hafsat Abubakar Musa"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/aff7ae41bb109c6dc12ea75055c9ca23cfbf3d8f 335c5901bfec88f132aa38ac8f9dc443e3194991The quality of teaching and learning process can be improved through innovative methods like use of virtual reality. We introduce EnVision, a groundbreaking Virtual Reality based approach to revolutionize Artificial Intelligence and Machine Learning (AI/ML) education. EnVision offers a unique approach where users build models through immersive learning experiences. Using interactive tutorials mixed with real-time data integration and visualization, users learn AI/ML concepts with ease. Through qualitative and quantitative methods, we evaluate EnVision's effectiveness and user satisfaction, contributing to the advancement of innovative educational gaming technologies. The evaluation results show that EnVision provides the AI/ML practitioners engaging learning experiences.2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)130This work introduces EnVision, a groundbreaking Virtual Reality based approach to revolutionize Artificial Intelligence and Machine Learning (AI/ML) education that provides the AI/ML practitioners engaging learning experiences.2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)["Louise Patra", "Manisha Kumari", "Radhakrishnan Gopalapillai"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/335c5901bfec88f132aa38ac8f9dc443e3194991 16d6bde3ce19e769dad1af71d7ee922a271c2750Indroduction: Colonoscopy is an acclaimed screening test to detect colorectal cancer (CRC). The most important quality indicators for colonoscopy are adenoma detection rate (ADR), cecal intubation rate (CIR), withdrawal time (WT), and bowel preparation (Boston Bowel Preparation Scale; BBPS). In modern endoscopy practice, the human eye is enhanced by highdefinition white-light visualization and advanced imaging technology. The main limitation of this procedure is the detection rate of suspicious lesions. The next generation of endoscopes with 4K resolution and computer-aided detection (CADe) based on artificial intelligence (AI) may be the next step to improve the quality of tests performed.Aim: The aim was to assess the effect of CADe implementation in the environment of the latest generation of endoscopes and 4K visualization in retrospective analysis.Methods: The study included 2,000 patients over 18 years old who underwent colonoscopy for various indications. Olympus Endo-Aid CADe AI system was used, together with the latest X1 series endoscope set using LED lighting and 4K ultra high-resolution technology. Group I consisted of 1,000 consecutive tests performed using Endo-Aid CADe, and group II the first 1,000 consecutive tests without the CADe system. ADR, Advanced adenoma detection rate (AADR), polyp detection rate (PDR), and mean polyp per patient score (MPP) were assessed in each groupResults: A total of 2,000 participants were included in the analysis, divided into two groups regarding CADe implementation. The overall PDR was similar in the analyzed groups (AI: 46.7% vs. non-AI: 44.9%, P = 0.419). Both ADR (29.7 vs. 28.9%, P = 0.694) and AADR (6.9 vs. 7.1%, P = 0.861) changed unremarkably. However, a significant elevation in MPP was noted. The MPP rose from 0.85 in the non-AI group to 1.26 in the AI group (P<0.001). The comparative analysis conducted separately for each segment of the bowel revealed that PDR remarkably increased in the left colon (29.3 vs. 18.0%, P<0.001), with no difference for other segments and other parameters. Investigating the MPP separately in each segment showed a significant difference for the right colon (0.33 vs. 0.23, P = 0.032) and the left colon (0.47 vs. 0.28, P<0.001). When adjusted to bowel preparation the PDR and MPP were constantly higher in the AI group (29.3 vs. 19.0%, P<0.001, and 0.48 vs. 0.30, P<0.001, respectively). In addition, the significant impact of AI implementation on MPP faded in the right colon (0.33 vs. 0.24, P = 0.051) when compared with the overall analysis.Conclusions: Although recently published evidence is optimistic regarding AI efficiency in improving the quality of colonoscopy, the provided results widen the overall perspective. Prospective randomized controlled trials (RCTs) including procedures performed with newest generation scopes should elucidate the role of AI in high-resolution colonoscopy.Polski przeglad chirurgiczny00Assessment of the effect of CADe implementation in the environment of the latest generation of endoscopes and 4K visualization in retrospective analysis found that when adjusted to bowel preparation the PDR and MPP were constantly higher in the AI group, compared with the non-AI group.Polski przeglad chirurgiczny["Zofia Orzeszko", "Tomasz Gach", "Pawe\u0142 Bogacki", "B. Markowska", "R. Solecki", "Miros\u0142aw Szura"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/16d6bde3ce19e769dad1af71d7ee922a271c2750 d0fcdccb1bd11d4ea52af625071d08b60d3b610aFraud in the financial sector poses significant threats to economic stability and organizational integrity, necessitating advanced detection and prevention mechanisms. This paper explores the transformative role of artificial intelligence (AI) and big data in enhancing fraud detection and prevention in finance. By integrating machine learning, deep learning, and natural language processing, AI can identify complex patterns and anomalies indicative of fraudulent activities. Big data analytics complements these efforts by processing vast and diverse datasets, enabling real-time detection and predictive modeling. The synergy of AI and big data results in improved accuracy, speed, and adaptability of fraud detection systems. However, the implementation of these technologies is not without challenges, including issues related to data quality, privacy, algorithmic bias, and regulatory compliance. Through case studies of leading financial institutions such as PayPal, JP Morgan Chase, and Visa, this paper illustrates the practical applications and benefits of AI and big data in fraud detection. The paper also explores future directions, including explainable AI, blockchain integration, federated learning, and the incorporation of IoT data, which promise to further enhance the capabilities of fraud detection systems. This comprehensive examination underscores the critical importance of leveraging AI and big data to safeguard the financial sector from evolving fraud threats.Dandao Xuebao/Journal of Ballistics170The transformative role of artificial intelligence (AI) and big data in enhancing fraud detection and prevention in finance is explored, highlighting the critical importance of leveraging AI and big data to safeguard the financial sector from evolving fraud threats.Dandao Xuebao/Journal of Ballistics["Ehsan Ellahi", "Muhammad Talha", "Dr. Deepak A. Vidhate", "Ms. Garima Mann", "Dr Sadhna Chauhan", "Vijay Singh"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/d0fcdccb1bd11d4ea52af625071d08b60d3b610a 9a609118be7e62bc2c8dcb914a354798cb3df937Modern advances in artificial intelligence and cyber security can be exceptionally profitable for new businesses. These advances can offer viable answers for defending IT resources, anticipating cyberattacks, and ensuring trade progression. In this consider, an exhaustive survey of the writing has been conducted. A common outline of experience tourism is given in this paper. This consider too covers creating innovation and the components of enterprise tourism. The benefits and troubles are too highlighted.2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)140A common outline of experience tourism, which covers creating innovation and the components of enterprise tourism, is given in this paper.2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)["Sachin Sharma", "Abhiraj Gautam", "Ranu Tyagi"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/9a609118be7e62bc2c8dcb914a354798cb3df937 929f58c63565d3a73dd14761fdaf64619fc70717Purpose: The general objective of the study was to investigate the impact of Artificial Intelligence on supply chain optimization. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of Artificial Intelligence on supply chain optimization. Preliminary empirical review revealed that AI significantly improved various aspects of supply chain management, including forecasting, inventory management, logistics, and risk management. It was found that AI technologies enhanced operational efficiency by providing more accurate demand predictions, optimizing logistics operations, and improving risk management capabilities. Additionally, AI contributed to greater sustainability in supply chains by reducing resource waste and supporting environmental goals, thus demonstrating its critical role in modernizing and optimizing supply chain practices. Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Resource-Based View (RBV) and Dynamic Capabilities Theory may be used to anchor future studies on Artificial Intelligence. The study recommended that future research should focus on developing theoretical models that integrate AI with traditional supply chain theories and that companies should adopt AI-driven tools for improved supply chain performance. It suggested that policymakers create guidelines for ethical AI use and data management to ensure responsible implementation. Additionally, it was recommended that collaboration between academia, industry, and technology providers be fostered to share best practices and address sector-specific needs. Lastly, it was advised that the long-term impacts and adaptability of AI technologies be evaluated to ensure their continued effectiveness and relevance.Journal of Technology and Systems160It was found that AI technologies enhanced operational efficiency by providing more accurate demand predictions, optimizing logistics operations, and improving risk management capabilities, thus demonstrating its critical role in modernizing and optimizing supply chain practices.Journal of Technology and Systems["Alma Kelly"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/929f58c63565d3a73dd14761fdaf64619fc70717 bc484d62fdcafb937660ff8da53b4b20e602adc9Purpose: The general objective of this study was to explore Intellectual Property Rights in the era of Artificial Intelligence. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Intellectual Property Rights in the era of Artificial Intelligence. Preliminary empirical review revealed that the era of Artificial Intelligence (AI) has significantly transformed the landscape of Intellectual Property Rights (IPR), presenting both opportunities and challenges. It highlighted that traditional IP laws are increasingly inadequate to address the complexities introduced by AI-generated content, necessitating a rethinking of existing frameworks. The study emphasized the need for recognizing AI's role in the creation of new works and inventions and the importance of developing balanced approaches to protect both human and AI contributions. Ethical considerations, such as accountability, transparency, and fairness, were also deemed crucial in ensuring responsible AI use. Overall, the study called for a comprehensive and proactive approach to integrate AI into IPR, ensuring robust protections while fostering innovation. Unique Contribution to Theory, Practice and Policy: The Technological Determinism Theory, Innovation Diffusion Theory and Legal Realism Theory may be used to anchor future studies on Intellectual Property Rights in the era of Artificial Intelligence. The study recommended revising existing IP laws to explicitly include AI-generated content and inventions, clarifying criteria for authorship and inventorship. It suggested expanding theoretical frameworks to accommodate AI contributions, emphasizing the collaborative nature of human and AI creativity. Practical measures, such as enhanced cybersecurity and legal safeguards for AI-generated trade secrets, were advised. Policy-wise, the study advocated for international cooperation to harmonize IP laws concerning AI. Developing ethical guidelines for responsible AI use and implementing education programs to inform stakeholders about AI and IP implications were also recommended. These measures aimed to create a balanced IP framework supporting innovation while protecting the rights of all stakeholders.Journal of Modern Law and Policy240The study recommended revising existing IP laws to explicitly include AI-generated content and inventions, clarifying criteria for authorship and inventorship, and suggested expanding theoretical frameworks to accommodate AI contributions, emphasizing the collaborative nature of human and AI creativity.Journal of Modern Law and Policy["Yvonne Nyaboke"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/bc484d62fdcafb937660ff8da53b4b20e602adc9 c0df41eefa1ecf98012bc866187f68dc3507d656Education and Information Technologies : Official Journal of the IFIP technical committee on Education214Education and Information Technologies["Mohammed Alwaqdani"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/c0df41eefa1ecf98012bc866187f68dc3507d656 06c96cfa7bb86a4c90c17f1be4fe780294aac541Disaster and Emergency Medicine Journal01Disaster and Emergency Medicine Journal["Konrad Zarzecki", "Jakub Cecot", "Mi\u0142osz Mandryk", "Jakub Plizga", "Agnieszka G\u0142uszczyk"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/06c96cfa7bb86a4c90c17f1be4fe780294aac541 85fab06daf3e97a1fa22e0745375456798dd8442 Revista Panamericana De Salud Publica-pan American Journal of Public Health01Revista Panamericana de Salud Pública["J. B. da Silva", "N\u00edsia Trindade Lima", "Ana Estela Haddad", "Socorro Gross Galiano", "Sebastian Garcia Sais\u00f3", "Mary Lou Valdez", "James Fitzgerald", "Mariana Faria Teixeira", "Ernesto Bascolo", "Daniel Rodriguez", "Luis Jimenez McInnis", "Judit Rius Sanjuan", "Myrna Marti", "Daniel Luna", "Paula Kohan", "Marcelo D'agostino"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/85fab06daf3e97a1fa22e0745375456798dd8442 de626d5d5d9d6b2b0fb5547c64c495d76869a5a3During the war time dysregulation of negative emotions such as fear, anger, hatred, frustration, sadness, humiliation, and hopelessness can overrule normal societal values, culture, and endanger global peace and security, and mental health in affected societies. Therefore, it is understandable that the range and power of negative emotions may play important roles in consideration of human behavior in any armed conflict. The estimation and assessment of dominant negative emotions during war time are crucial but are challenged by the complexity of emotions’ neuro-psycho-physiology. Currently available natural language processing (NLP) tools have comprehensive computational methods to analyze and understand the emotional content of related textual data in war-inflicted societies. Innovative AI-driven technologies incorporating machine learning, neuro-linguistic programming, cloud infrastructure, and novel digital therapeutic tools and applications present an immense potential to enhance mental health care worldwide. This advancement could make mental health services more cost-effective and readily accessible. Due to the inadequate number of psychiatrists and limited psychiatric resources in coping with mental health consequences of war and traumas, new digital therapeutic wearable devices supported by AI tools and means might be promising approach in psychiatry of future. Transformation of negative dominant emotional maps might be undertaken by the simultaneous combination of online cognitive behavioral therapy (CBT) on individual level, as well as usage of emotionally based strategic communications (EBSC) on a public level. The proposed positive emotional transformation by means of CBT and EBSC may provide important leverage in efforts to protect mental health of civil population in war-inflicted societies. AI-based tools that can be applied in design of EBSC stimuli, like Open AI Chat GPT or Google Gemini may have great potential to significantly enhance emotionally based strategic communications by more comprehensive understanding of semantic and linguistic analysis of available text datasets of war-traumatized society. Human in the loop enhanced by Chat GPT and Gemini can aid in design and development of emotionally annotated messages that resonate among targeted population, amplifying the impact of strategic communications in shaping human dominant emotional maps into a more positive by CBT and EBCS.Frontiers in Psychology821The proposed positive emotional transformation by means of CBT and EBSC may provide important leverage in efforts to protect mental health of civil population in war-inflicted societies.Frontiers in Psychology["K. \u0106osi\u0107", "Vanja Kopila\u0161", "Tanja Jovanovic"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/de626d5d5d9d6b2b0fb5547c64c495d76869a5a3 ea3ceecd8c96ab99df97bf1e26eec2882172a9ddInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-02T00:00:00https://www.semanticscholar.org/paper/ea3ceecd8c96ab99df97bf1e26eec2882172a9dd 25f9f76ed47b214fa2c7cfa7fd90de67f589c95cGlobal Journal on Quality and Safety in Healthcare00Global Journal on Quality and Safety in Healthcare["Hana J. Abukhadijah", "A. Nashwan"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/25f9f76ed47b214fa2c7cfa7fd90de67f589c95c 01ece2b963a6d334dfb0ba50ad096567ca1766c1Living in a smart city has many advantages, such as improved waste and water management, access to quality healthcare facilities, effective and safe transportation systems, and personal protection. Explainable AI (XAI) is called a system that is capable of providing explanations for its judgments or predictions. This term describes a model, its expected impacts, and any potential biases that may be present. XAI tools and frameworks can aid in comprehending and trusting the output and outcomes generated by machine-learning algorithms. This study used XAI methods to classify cities based on smart city metrics. The logistic regression method with LIME achieved perfect accuracy, precision, recall, and F1-score, predicting correctly all cases.Engineering, Technology &amp; Applied Science Research200This study used XAI methods to classify cities based on smart city metrics and the logistic regression method with LIME achieved perfect accuracy, precision, recall, and F1-score, predicting correctly all cases.Engineering, Technology &amp; Applied Science Research["Mohammad \u0397. Algarni", "Shailendra Mishra"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/01ece2b963a6d334dfb0ba50ad096567ca1766c1 be9f9c7bc2f5e3ac99231045a1dac6af2494052100["E. M. Azoff"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/be9f9c7bc2f5e3ac99231045a1dac6af24940521 9eff20c8f60f1b36a96ddac5a0e4851bc3b995d1International Journal of STEM Education476The results indicate that the online explicit-reflective learning module significantly enhanced students' knowledge of AI ethics, and the need for placing more emphasis on students’ ability to identify AI-related ethical issues but also on their capacity to resolve and perhaps mitigate the impact of such ethical dilemmas.International Journal of STEM Education["M. Usher", "M. Barak"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/9eff20c8f60f1b36a96ddac5a0e4851bc3b995d1 79b4e13f0faa42942adb17abb7758d9ded3a61f3This research article investigates the synergies between artificial intelligence (AI), digital transformation (DT), and financial reporting systems within the business context. The central theme explores how organizations enhance their decision-making processes by integrating AI technologies into digital transformation initiatives, particularly in financial reporting. The focal point is comprehending how the synergy of these integrated systems can unlock substantial business value, instigate strategic innovation, and elevate overall financial analytics through the adoption of intelligent, data-driven decision-making methodologies. By harnessing advanced analytics, automation, and adaptive decision support capabilities, organizations navigate the complexities of a rapidly evolving business environment, in which neural networks emerge as a valuable tool for calibrating outcomes in the financial accounting environment, demonstrating effectiveness in processing complex financial data, identifying patterns, and making predictions, ushering in a new era of transformative possibilities. The introduction of a game theory payoff matrix in this AI decision-making tool adds a strategic framework for analyzing interactions among decision-makers, considering strategic choices and outcomes in a dynamic and competitive context.Electronics04The introduction of a game theory payoff matrix in this AI decision-making tool adds a strategic framework for analyzing interactions among decision-makers, considering strategic choices and outcomes in a dynamic and competitive context.Electronics["A. Artene", "A. Domil", "Larisa Ivascu"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/79b4e13f0faa42942adb17abb7758d9ded3a61f3 10e5b7c76de70cfc6199c5192b70e99789e12a8fBackground Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set–based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. Objective The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. Methods The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. Results Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. Conclusions Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.Journal of Medical Internet Research543A systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings and mapped the findings to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.Journal of Medical Internet Research["Amir Kamel Rahimi", "Oliver Pienaar", "Moji Ghadimi", "Oliver J. Canfell", "Jason D Pole", "Sally Shrapnel", "Anton H van der Vegt", "Clair M Sullivan"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/10e5b7c76de70cfc6199c5192b70e99789e12a8f e88e870bc363050a8c47e660138f2197c7ed4a55In the midst of the ongoing digital revolution, firms are increasingly embracing the artificial intelligence (AI) to optimize their operations. This study aims to explore the role of AI adoption in firm environmental, social, and governance (ESG) performance. By analyzing 23,094 firm-year observations of Chinese A-share listed firms from 2009 to 2021, the primary findings reveal that AI significantly improves firm ESG performance. This highlights the importance of technological advancements in driving environmental efficiency and promoting sustainable practices. Furthermore, the impact is more pronounced in non-state-owned enterprises, compared to state-owned enterprises (SOEs), and in central SOEs than local SOEs. Additionally, the mechanism analysis indicates that AI helps firms alleviate financing constraints, enhance internal control, and improve overall firm performance, leading to enhanced ESG performance over time. Moreover, the impact is more pronounced in regions with high fintech activity, strict environmental regulations, and high bank concentration. These findings highlight the substantial role of China's government in advancing the digital economy and broader ESG initiatives. The results remain robust and valid across different statistical methods, including PSM, sys-GMM, and 2SLS.Energy &amp; Environment513The primary findings reveal that AI significantly improves firm ESG performance, and is more pronounced in non-state-owned enterprises, compared to state-owned enterprises, and in central SOEs than local SOEs.Energy &amp; Environment["Shuangyan Li", "Muhammad Waleed Younas", "Umer Sahil Maqsood", "R. A. Zahid"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/e88e870bc363050a8c47e660138f2197c7ed4a55 15b153e0f6342ad455bfb5e2b6090b89c8066340Artificial Intelligence (AI) is a widely developed and adopted technology across entire industry sectors. Integrating environmental, social, and governance (ESG) considerations with AI investments is crucial for ensuring ethical and sustainable technological advancement. Particularly from an investor perspective, this integration not only mitigates risks but also enhances long-term value creation by aligning AI initiatives with broader societal goals. Yet, this area has been less explored in both academia and industry. To bridge the gap, we introduce a novel ESG-AI framework, which is developed based on insights from engagements with 28 companies and comprises three key components. The framework provides a structured approach to this integration, developed in collaboration with industry practitioners. The ESG-AI framework provides an overview of the environmental and social impacts of AI applications, helping users such as investors assess the materiality of AI use. Moreover, it enables investors to evaluate a company's commitment to responsible AI through structured engagements and thorough assessment of specific risk areas. We have publicly released the framework and toolkit in April 2024, which has received significant attention and positive feedback from the investment community. This paper details each component of the framework, demonstrating its applicability in real-world contexts and its potential to guide ethical AI investments.arXiv.org372ArXiv["Sung Une Lee", "Harsha Perera", "Yue Liu", "Boming Xia", "Qinghua Lu", "Liming Zhu", "Jessica Cairns", "Moana Nottage"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/15b153e0f6342ad455bfb5e2b6090b89c8066340 0d647009530d5c600244ad6dc34132334ab86e28The rapid advancement of Generative Artificial Intelligence (GenAI) presents both opportunities and challenges for English for Academic Purposes (EAP) instruction. This paper proposes an adaptation of the AI Assessment Scale (AIAS) specifically tailored for EAP contexts, termed the EAP-AIAS. This framework aims to provide a structured approach for integrating GenAI tools into EAP assessment practices while maintaining academic integrity and supporting language development. The EAP-AIAS consists of five levels, ranging from"No AI"to"Full AI", each delineating appropriate GenAI usage in EAP tasks. We discuss the rationale behind this adaptation, considering the unique needs of language learners and the dual focus of EAP on language proficiency and academic acculturation. This paper explores potential applications of the EAP-AIAS across various EAP assessment types, including writing tasks, presentations, and research projects. By offering a flexible framework, the EAP-AIAS seeks to empower EAP practitioners seeking to deal with the complexities of GenAI integration in education and prepare students for an AI-enhanced academic and professional future. This adaptation represents a step towards addressing the pressing need for ethical and pedagogically sound AI integration in language education.arXiv.org612An adaptation of the AI Assessment Scale (AIAS) specifically tailored for EAP contexts, termed the EAP-AIAS, which aims to provide a structured approach for integrating GenAI tools into EAP assessment practices while maintaining academic integrity and supporting language development.ArXiv["Jasper Roe", "Mike Perkins", "Yulia Tregubova James Cook University Singapore", "British University Vietnam"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/0d647009530d5c600244ad6dc34132334ab86e28 f9b524e75ba8ec7d8fd8dcccc8b5927571def659Discover Education511In the current article, the analysis and synthesis of Mixed Methods Research (MMR) data with generative Artificial Intelligence (Gen AI) was used to demonstrate the analysis and synthesis of Mixed Methods Research data with ChatGPT results.Discover Education["Celeste Combrinck"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/f9b524e75ba8ec7d8fd8dcccc8b5927571def659 e8c214376b1fdca88e368b0a34b06b7c64bac8a8Artificial Intelligence (AI) is revolutionizing the healthcare sector by addressing its most pressing challenges: cost, efficiency, and access to quality care. As healthcare costs escalate and the demand for patient-centred services intensifies, the significance of AI in enhancing healthcare delivery has become essential. This paper examines the diverse uses of AI, including as predictive analytics, natural language processing, and machine learning, which are transforming diagnosis, therapy, and patient care. AIdriven diagnostic tools decrease expenses by reducing misdiagnoses and superfluous testing, while predictive models enhance resource allocation, hence enhancing operational efficiency. Moreover, AI-driven virtual health assistants and telemedicine platforms enhance accessibility to healthcare services, particularly in marginalized areas. Nonetheless, despite these developments, the implementation of AI in healthcare presents hurdles. Concerns regarding data privacy, the necessity for regulatory frameworks, and possible biases in AI systems require meticulous attention. This study emphasizes the present influence of AI on healthcare expenses, efficiency, and accessibility, while also stressing the necessity of ethical and regulatory frameworks to guarantee the equitable, safe, and successful application of AI technology. By confronting these difficulties, AI has the capacity to transform healthcare, rendering it more economical, efficient, and globally accessible.Pathfinder of Research01The diverse uses of AI, including as predictive analytics, natural language processing, and machine learning, which are transforming diagnosis, therapy, and patient care are examined, which have the capacity to transform healthcare, rendering it more economical, efficient, and globally accessible.Pathfinder of Research["Md Rahatul Ashakin", "Md Shishir Bhuyian", "Md Refat Hosain", "Rifah Sajida Deya", "Sayed Eqramul Hasan"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/e8c214376b1fdca88e368b0a34b06b7c64bac8a8 0e5ae8ee5bf33105a815052d7ddd148ea0b00eb6Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs.IEEE Transactions on Industrial Cyber-Physical Systems331A GenAI-driven DT architecture in ICPSs is developed and a contract theory model is proposed and a sustainable diffusion-based soft actor-critic algorithm is developed to identify the optimal feasible contract to address the adverse selection problem caused by information asymmetry.IEEE Transactions on Industrial Cyber-Physical Systems["Jinbo Wen", "Jiawen Kang", "D. Niyato", "Yang Zhang", "Shiwen Mao"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/0e5ae8ee5bf33105a815052d7ddd148ea0b00eb6 86990bc02473d11c12821615f70d2243c026e37aThe rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance. A key benefit of the RAI Question Bank is its systematic approach to linking lower-level risk questions to higher-level ones and related themes, preventing siloed assessments and ensuring a cohesive evaluation process. Case studies illustrate the practical application of the RAI Question Bank in assessing AI projects, from evaluating risk factors to informing decision-making processes. The study also demonstrates how the RAI Question Bank can be used to ensure compliance with standards, mitigate risks, and promote the development of trustworthy AI systems. This work advances RAI by providing organizations with a valuable tool to navigate the complexities of ethical AI development and deployment while ensuring comprehensive risk management.arXiv.org280The Responsible AI (RAI) Question Bank is introduced, a comprehensive framework and tool designed to support diverse AI initiatives that aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance.ArXiv["Sung Une Lee", "Harsha Perera", "Yue Liu", "Boming Xia", "Qinghua Lu", "Liming Zhu"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/86990bc02473d11c12821615f70d2243c026e37a 79298279b56bc8aa41ba0f46a554a4289284a968Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive predictive capabilities in controlled settings, it still suffers from a range of practical setbacks preventing its widespread use in various critical scenarios. In particular, it is generally unclear if a given AI system's predictions can be trusted by decision-makers in downstream applications. To address the need for more transparent, robust, and trustworthy AI systems, a suite of tools has been developed to quantify the uncertainty of AI predictions and, more generally, enable AI to"self-assess"the reliability of its predictions. In this manuscript, we categorize methods for AI self-assessment along several key dimensions and provide guidelines for selecting and designing the appropriate method for a practitioner's needs. In particular, we focus on uncertainty estimation techniques that consider the impact of self-assessment on the choices made by downstream decision-makers and on the resulting costs and benefits of decision outcomes. To demonstrate the utility of our methodology for self-assessment design, we illustrate its use for two realistic national-interest scenarios. This manuscript is a practical guide for machine learning engineers and AI system users to select the ideal self-assessment techniques for each problem.arXiv.org600This manuscript categorizes methods for AI self-assessment along several key dimensions and provides guidelines for selecting and designing the appropriate method for a practitioner's needs, focusing on uncertainty estimation techniques that consider the impact of self-assessment on the choices made by downstream decision-makers and on the resulting costs and benefits of decision outcomes.ArXiv["Gregory Canal", "Vladimir Leung", "Philip Sage", "Eric Heim", "I-Jeng Wang"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/79298279b56bc8aa41ba0f46a554a4289284a968 4a3161e20d5f372f61e1b3d7b799581ab83d7084This paper presents a revolutionary solution to optimize water use in agriculture, using an AI-based smart irrigation system. Our approach focuses on automatic adjustment of water release control valves, guided by artificial intelligence (AI) that takes into account the complex relationship between soil moisture and ambient temperature in the plant root zone. The decision-making process in our system is powered by a deep neural network meticulously trained on a comprehensive dataset, including the dynamic interaction of soil moisture, temperature changes of the crop. The reason for using an AI model that focuses specifically on the relationship between soil moisture and temperature lies in its ability to recognize small details, complex patterns and correlations that impact irrigation need. Traditional methods are often unable to adapt to the complex and dynamic nature of agricultural ecosystems. By harnessing the power of AI, our system not only captures, but also learns and predicts, optimal irrigation conditions, thereby promoting resource-efficient agricultural practices. The proposed system seamlessly integrates with micro-irrigation and pipeline networks, ensuring flexibility in different agricultural environments. Using MQTT communication powered by Node-RED, collected data is efficiently transmitted to the cloud, allowing real-time monitoring and analysis. This combination of AI, sensor technology and cloud connectivity aims to provide farmers with accurate information, promote water conservation, optimize crop yields and reduce environmental impact. Ultimately, our research contributes to advancing sustainable agriculture by addressing important challenges and paving the way for a new era of smart irrigation practices.2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)160A revolutionary solution to optimize water use in agriculture, using an AI-based smart irrigation system guided by artificial intelligence that takes into account the complex relationship between soil moisture and ambient temperature in the plant root zone.2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)["B. Alex", "G. Jignasa", "K. Madhubabu", "A. Gopi"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/4a3161e20d5f372f61e1b3d7b799581ab83d7084 a4fbae7d32e992bb6c44da5618331bf511f70fe2This study explores the integration of generative artificial intelligence (AI) with Exercise Generation Algorithm+ (EGAL+), a multi-objective harmony search (HS) metaheuristic-based algorithm capable of composing high-quality exercises. These exercises are characterized by their diversity, consistent difficulty, and comprehensive coverage of the source material, tailored to user preferences. One of the main challenges of using metaheuristics to compile exercises efficiently is the initial creation of a large question bank, which often demands significant time and effort from instructors. To overcome this challenge, the integration of a readily available existing generative AI module is proposed. This module is accessed through its application programming interface, autonomously populating the question bank. This sets the stage for EGAL+ to fine-tune the selection and assembly of specific exams. The resulting program enables educators to create an extensive question bank from any educational material, independent of the subject, and subsequently compose exercises with minimal effort. This approach leverages the synergistic benefits of both generative AI and metaheuristicbased optimization, offering a robust and efficient solution for exercise generation.International Journal of Emerging Technologies in Learning (iJET)00Int. J. Emerg. Technol. Learn.["Blanka L\u00e1ng", "Bal\u00e1zs D\u00f6ms\u00f6di"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/a4fbae7d32e992bb6c44da5618331bf511f70fe2 57ceea56f67cfc79410c5b3b02ee936d7e147edcAbstract As society rapidly digitizes, successful aging necessitates using technology for health and social care and social engagement. Technologies aimed to support older adults (e.g., smart homes, assistive robots, wheelchairs) are increasingly applying artificial intelligence (AI), and thereby creating ethical challenges to technology development and use. The international debate on AI ethics focuses on implications to society (e.g., bias, equity) and to individuals (e.g., privacy, consent). The relational nature of care, however, warrants a humanistic lens to examine how “AI AgeTech” will shape, and be shaped by, social networks or care ecosystems in terms of their care actors (i.e., older adults, care partners, service providers); inter-actor relations (e.g., care decision making) and relationships (e.g., social, professional); and evolving care arrangements. For instance, if an older adult’s reduced functioning leads actors to renegotiate their risk tolerances and care routines, smart homes or robots become more than tools that actors configure; they become semiautonomous actors, in themselves, with the potential to influence functioning and interpersonal relationships. As an experientially diverse, transdisciplinary working group of older adults, care partners, researchers, clinicians, and entrepreneurs, we co-constructed intersectional care experiences, to guide technology research, development, and use. Our synthesis contributes a preliminary guiding model for AI AgeTech innovation that delineates humanistic attributes, values, and design orientations, and captures the ethical, sociological, and technological nuances of dynamic care ecosystems. Our visual probes and recommended tools and techniques offer researchers, developers/innovators, and care actors concrete ways of using this model to promote successful aging in AI-enabled futures.The gerontologist00A preliminary guiding model for AI AgeTech innovation is contributed that delineates humanistic attributes, values, and design orientations, and captures the ethical, sociological, and technological nuances of dynamic care ecosystems.The Gerontologist["Amy S Hwang", "T. Tannou", "Jarshini Nanthakumar", "Wendy Cao", "Charlene H. Chu", "Ceren Zeytinoglu Atici", "Kerseri Scane", "Amanda Yu", "Winnie Tsang", "Jennifer Chan", "Paul Lea", "Zelda Harris", "Rosalie H Wang"]2024-08-02T00:00:00https://www.semanticscholar.org/paper/57ceea56f67cfc79410c5b3b02ee936d7e147edc 86426b1a35e7b906a08dbe8d10839a884528d50cJournal of Intelligent Manufacturing443The ability to avoid a large majority of violations of time constraints shows the approaches effectiveness and the future requirement to more accurately integrate such product-inherent constraints into production control.J. Intell. Manuf.["M. May", "Jan Oberst", "Gisela Lanza"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/86426b1a35e7b906a08dbe8d10839a884528d50c 91c70059eba582163bb13c9b18f4fe47caa17635Undoubtedly, artificial intelligence (AI) tools are becoming increasingly common in people’s lives. The educational field is one of the most reflective on the importance of its adoption. Universities have made great efforts to integrate these new technologies into their classrooms, considering that every future professional will need AI skills and competencies. This article examines the importance of student perception and acceptance in adopting AI tools in higher education effectively. It highlights how students’ positive perceptions can significantly influence their motivation and commitment to learning. This research emphasizes that to integrate AI into university curricula successfully, it is essential to include its technologies in all areas of study and foster positivity among students regarding their use and training. This study’s methodology applied the validated instrument “Perception of Adoption and Training in the Use of Artificial Intelligence Tools in the Profession” to a sample of Mexican students. This exploratory analysis highlights the need for educational institutions to understand and address student perceptions of AI to design educational strategies that incorporate technological advances, are pedagogically relevant, and align with the students’ aspirations and needs.Societies352The need for educational institutions to understand and address student perceptions of AI to design educational strategies that incorporate technological advances, are pedagogically relevant, and align with the students’ aspirations and needs is highlighted.Societies["Jos\u00e9 Carlos V\u00e1zquez-Parra", "Carolina Henao-Rodr\u00edguez", "Jenny-Paola Lis-Guti\u00e9rrez", "Sergio Palomino-G\u00e1mez"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/91c70059eba582163bb13c9b18f4fe47caa17635 13579702293f6883a370a72cc7fc69571c03ae43. The article examines the impact of artificial intelligence on the process of improving virtual reality in the development of games on the Unity platform. The problem of scientific research is the insufficient study of the role of artificial intelligence and its influence on the improvement of virtual reality in the process of developing games on the Unity game engine. The subject of scientific research is the factors of the influence of artificial intelligence on the improvement of virtual reality, and the process of introducing artificial intelligence tools and their use in the subsequent development of games on the Unity platform is chosen as the object. The purpose of the article is to determine the role of artificial intelligence and evaluate the effectiveness of its tools in improving virtual reality during the development of games based on the Unity game engine. To achieve this goal, the current study considered the main tools of artificial intelligence in the context of game development and the features of implementing virtual reality in them. reality, the specifics of the Unity game engine and the advantages and disadvantages of its use. The basis of the study was the reviewed and analyzed scientific literature, which was pre-selected according to the thematic direction: publications of Ukrainian and foreign scientists on the studied issues and the results of independent observations. A number of general scientific methods were also used, including: the method of abstraction, which was used in order to highlight the main concepts and categories, the method of analysis and synthesis (to identify the most influential elements of the researched object), abstract-logical and dialectical methods of scientific knowledge, as well as the method scientific abstraction, which helped to form the necessary theoretical generalizations and conclusions and clarify the conceptual apparatus.Наука і техніка сьогодні00The role of artificial intelligence is determined and the effectiveness of its tools in improving virtual reality during the development of games based on the Unity game engine is evaluated.Наука і техніка сьогодні["Maksym Botviniev"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/13579702293f6883a370a72cc7fc69571c03ae43 05254519112db8ef66150d3cb0b6d3a81913f880The research aims to explain the use of Artificial Intelligence (AI) and Jakarta Smart City (JSC) technology, which was first implemented in Indonesia in 2014, starting in the Jakarta City Region (DKJ), followed by 100 cities, carried out to overcome the technology gap between regions. Use of AI and JSC by the government for public services, such as smart environment; people's economy; government; population mobility, and branding. Research methodology uses qualitative with a descriptive approach using observation instruments and direct interviews. The research period is three months (March to April 2024). Locations in seven JSC management units. The research population was in five JSC divisions, using a snowball pattern sample. The research results show that there has been an increase in the efficiency of public services using AI and JSC technology in transportation management; rubbish; city ​​security defense; and improving the quality of resources. Traffic congestion research recommendations, DKJ must develop innovations for all integrated public transportation facilities, by expanding the Integrated Highway (MRT) and Light Rail Transit (LRT) networks. For flooding problems, DKJ must use early digital detection technology for flood monitoring. In the future, DKJ hopes to implement an integrated digital payment system between the Resident Card and: Taxpayer Identification Number (NPWP), digital lifetime extension of the Driving License (SIM), processing of annual Vehicle Registration Certificates (STNK), and Vehicle Tax Proof Bermptor (BPKB) five-yearly all two-wheeled vehicles online (digitally) without queuing at the location.Ilomata International Journal of Social Science290The research results show that there has been an increase in the efficiency of public services using AI and JSC technology in transportation management; rubbish; city ​​security defense; and improving the quality of resources.Ilomata International Journal of Social Science["Taufiqurokhman", "Ma\u2019mun Murod", "Dany Kunto Wibisono"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/05254519112db8ef66150d3cb0b6d3a81913f880 69f66d3cf94a14ee416bd94e377c6defaea0860aThis article examined how higher education institutions in the Republic of Georgia responded to the challenges of the COVID-19 pandemic. Focusing on the context of the digital revolution and centering upon the utilization of artificial intelligence (AI), it aimed to discern how these institutions sustained the continuity of the learning process and implemented innovative measures. Based on the research findings, the solutions proposed in this article present AI tools for personalized learning, adaptive assessment, and intelligent tutoring. As institutions navigated the post-pandemic era, the integration of AI into education proved viable. This research provided tangible insights into the digital revolution affecting education and informing strategic decision-making in Georgia's evolving higher education landscape. Recognizing the difficulties caused by the pandemic and the inherent challenges associated with strategic decision-making, a qualitative research approach was used to gain nuanced insights. It relied on in-depth interviews, recognizing the spontaneous and time-sensitive nature of strategic decisions made by universities during the pandemic, often precluding extensive pre-planning. The authors provided critical findings in terms of the pros and cons of distance learning and proposed AI solutions for each challenge that Universities faced during and after this significant disruption, giving real successful examples.Journal of Eastern European and Central Asian Research270This research provided tangible insights into the digital revolution affecting education and informing strategic decision-making in Georgia's evolving higher education landscape and proposed AI solutions for each challenge that Universities faced during and after this significant disruption.Journal of Eastern European and Central Asian Research (JEECAR)["Maia Noniashvili", "Lela Matchavariani"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/69f66d3cf94a14ee416bd94e377c6defaea0860a bbcc8094addbc5fe204f8258216f501dd09b579dThe article presents a generalized approach to the content of analytical procedures that provide diagnostics and control of the financial security of a company from the perspective of corporate fraud risks. Modern methods of empirical diagnostics of financial security using information resources and artificial intelligence, based on the concepts of professional auditing standards and preparation of financial statements, are disclosed.Russian Journal of Management40Russian Journal of Management["N. Kazakova"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/bbcc8094addbc5fe204f8258216f501dd09b579d 6021674042b8f4616f325bbf9dde6caf4c633edeTeknologi kecerdasan buatan atau Artificial Intelligence menimbulkan problematika baru berupa hilangnya etika mahasiswa saat memanfaatkan teknologi tersebut dalam penyusunan tugas.  Penelitian ini bertujuan untuk meninjau etika yang dapat diterapkan oleh mahasiswa dalam memanfaatkan teknologi Artificial Intelligence saat penyusunan tugas. Pendekatan yang digunakan adalah kualitatif dengan metode studi kepustakaan. Data yang terkumpul diperoleh dari beberapa sumber relevan yang dianalisis untuk memperkuat informasi terkait dengan penelitian. Hasil penelitian menunjukkan bahwa AI dapat membantu mahasiswa mendapatkan informasi secara instan dan ringkas. Namun, AI juga berpotensi menimbulkan rasa malas, menurunnya tingkat literasi, hingga menyebabkan kecanduan teknologi yang dapat berujung pada plagiarisme. Problematika tersebut dapat diatasi dengan mulai menanamkan nilai moral dan etika kepenulisan yang sesuai dengan integritas akademik. Etika yang dapat diterapkan antara lain bersikap jujur, penuh tanggung jawab, dan menjunjung orisinalitas. Strategi parafrase juga penting untuk diterapkan guna meminimalkan risiko plagiasi dari AI sehingga mahasiswa dapat menghasilkan karya yang berkualitas dan terbebas dari plagiarisme. Dengan ini, AI dapat menjadi alat bantu yang bermanfaat dalam penyusunan tugas jika digunakan secara bijak dan beretikaEDUKATIF : JURNAL ILMU PENDIDIKAN190EDUKATIF : JURNAL ILMU PENDIDIKAN["Fatimah Gandasari", "Annisa Septiana Koeswinda", "Aulia Kharisma Putri", "Disca Anansa Putri Kumala", "Nani Muftihah"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/6021674042b8f4616f325bbf9dde6caf4c633ede 74f35b702e296afcc52a62463abc008040777190This study aims to elucidate the role of the state as the primary actor in managing the extensive use of Artificial Intelligence (AI) in the realm of da’wah (calling others to practice the teachings of Islam), which has both positive and negative consequences. The identified negative impacts include the delegitimization of religious teachings and the authority of religious figures. This research utilizes a literature review approach, collecting and analyzing data from various sources such as books, websites, articles, and newspapers related to the application of AI in da’wah. The findings indicate that state intervention is crucial in regulating AI use through three main mechanisms: first, regulation; second, strengthening the roles of relevant actors; and third, supervision. Without proper regulation, the use of AI in da’wah can become unmanageable, leading to disinformation and the erosion of authoritative references. Therefore, this study underscores the importance of state involvement in ensuring that AI is employed ethically and effectively to support da’wah activities in Indonesia. These findings affirm that the state must actively regulate AI to maintain the integrity of religious teachings and the authority of religious figures, as well as to fully harness the positive potential of AI in the context of da’wah.Jurnal Penelitian00The findings affirm that the state must actively regulate AI to maintain the integrity of religious teachings and the authority of religious figures, as well as to fully harness the positive potential of AI in the context of da’wah.Jurnal Penelitian["Siti Malaiha Dewi", "Mansur Hidayat"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/74f35b702e296afcc52a62463abc008040777190 1783d22038624e086bd273bae70c44142968271bArtificial Intelligence (Kecerdasan Buatan) yang kemudian disingkat AI memiliki kemampuan untuk memantau dan menganalisis aktivitas jaringan dan sistem informasi untuk mendeteksi serangan dan ancaman potensial. Penulisan jurnal ini bertujuan untuk menganalisis pertahanan Negara dalam menghadapi ancaman AI saat ini melalui metode penulisan jurnal deskriptif kualitatif. Metode penulisan jurnal deskriptif kualitatif difungsikan untuk mengumpulkan dan menganalisa data tentang bentuk pertahanan Negara dalam menghadapi Artificial Intelligence (Kecerdasan Buatan). Hasil penulisan jurnal ini menyatakan bahwa AI merupakan ancaman terhadap pertahanan Negara, hal ini dikarenakan penggunaan AI dalam pertahanan Negara dapat mempengaruhi superioritas militer maupun informasinya. Adapun strategi dalam menghadapi ancaman penggunaan AI dalam pertahanan bisa dengan pembuatan berbagai kebijakan, Undang-Undang atau peraturan yang memadai sebagai dasar pemantapan kemampuan intelijen untuk memahami perkembangan ancaman AI, pembangunan industri pertahanan dalam negeri, perekrutan dan pelatihan sumber daya manusia yang berkelanjutan, pembangunan kekuatan yang terintegrasi diantara komponen utama, cadangan dan pendukung. Dengan penyesuaian strategis yang tepat, negara dapat menghadapi perubahan dengan cerdas dan efektif, selain itu dapat menjaga keamanan dan pertahanan negara dalam menghadapi ancaman yang semakin kompleks.JIIP - Jurnal Ilmiah Ilmu Pendidikan00JIIP - Jurnal Ilmiah Ilmu Pendidikan["Nana Masihna", "Mandri Kartono", "Fajar Adha"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/1783d22038624e086bd273bae70c44142968271b 83ef9a1146ae262576eba7dde32cdf723b56df13ABSTRACT The advent of cutting-edge technologies has prompted the transformation of current educational systems by integrating these advancements into contemporary teaching and learning methodologies. Given the substantial potential of Artificial Intelligence (AI) to revolutionize these practices, the consideration of its incorporation into the educational domain is inevitable. This research delves into the latest developments in AI within education, examining how secondary school students involved in this research utilize AI and for what specific purposes. Additionally, this study seeks to understand whether these students employ Artificial Intelligence for educational and learning purposes and their opinions on its inclusion in educational frameworks. The collected data provide valuable insights into students' comprehension of AI's role and objectives in education and their perspectives on its application across various scenarios. Furthermore, the study underscores the significance and challenges of integrating Artificial Intelligence into curricula, highlighting the opportunities it presents for crafting educational practices that enhance students' academic knowledge and digital proficiency while emphasizing the ethical and responsible use of such technologies. ABSTRAK Munculnya teknologi mutakhir telah mendorong transformasi sistem pendidikan saat ini dengan mengintegrasikan kemajuan ini ke dalam metodologi pengajaran dan pembelajaran kontemporer. Mengingat potensi besar Kecerdasan Buatan (AI) untuk merevolusi praktik-praktik ini, pertimbangan untuk memasukkannya ke dalam bidang pendidikan tidak dapat dihindari. Penelitian ini menggali perkembangan terkini AI dalam dunia pendidikan, mengkaji bagaimana siswa sekolah menengah yang terlibat dalam studi kasus ini memanfaatkan AI dan untuk tujuan spesifik apa. Selain itu, penelitian ini berupaya memahami apakah para siswa ini menggunakan Kecerdasan Buatan untuk tujuan pendidikan dan pembelajaran serta pendapat mereka tentang penyertaannya dalam kerangka pendidikan. Data yang dikumpulkan memberikan wawasan berharga mengenai pemahaman siswa tentang peran dan tujuan AI dalam pendidikan serta perspektif mereka terhadap penerapannya dalam berbagai skenario. Lebih jauh lagi, penelitian ini menggarisbawahi pentingnya dan tantangan dalam mengintegrasikan Kecerdasan Buatan ke dalam kurikulum, menyoroti peluang yang ada untuk menciptakan praktik pendidikan yang meningkatkan pengetahuan akademis dan kemahiran digital siswa sambil menekankan penggunaan teknologi tersebut secara etis dan bertanggung jawab.Journal of Education and Islamic Studies (JEIS)240Journal of Education and Islamic Studies (JEIS)["Zohaib Hassan Sain", "Asokan Vasudevan", "R\u0103zvan \u0218erban", "Chanda Chansa Thelma"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/83ef9a1146ae262576eba7dde32cdf723b56df13 5f8d450cb9460dc5ce1cf73d2f47e17ed67cd13fThe article discusses the issues of assessing the risk of bankruptcy and forming a forecast of the financial stability of the enterprise of the EMPIX company using the DL “Random forest” artificial intelligence model. The relevance of the study is that in the context of digitalization, approaches to ensuring the sustainable development of an enterprise based on artificial intelligence are increasingly being used. The scientific novelty lies in the fact that in the study, a deep learning model DL model “Random forest” was formed, which makes it possible to obtain a forecast of the risk of bankruptcy of an enterprise, based on the parameters embedded in the Altman and Conan-Golder models. The practical significance of the study is determined by the possibility of using its results in practice, in particular, in order to provide support for decision-making regarding the sustainable development of an enterprise. In the experiment, the hyperparameters of the neural network did not change; the input values in various trees were selected randomly by the algorithm. The DL model demonstrated high prediction accuracy. In the model that was developed by the authors, the best decision tree was used, with hyperparameter settings that meet the optimality requirements. These include, for example, the depth of a tree - three layers, and ten estimators in an ensemble of trees.Russian Journal of Management130The article discusses the issues of assessing the risk of bankruptcy and forming a forecast of the financial stability of the enterprise of the EMPIX company using the DL “Random forest” artificial intelligence model, which demonstrated high prediction accuracy.Russian Journal of Management["N. Lomakin", "N. Lomakin", "T. Kuzmina", "A. Polozhentsev"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/5f8d450cb9460dc5ce1cf73d2f47e17ed67cd13f 86c0f92b1459e11986e24c3063d53baaa358ff5fEthics and Information Technology221It is argued that the perfect technological storm brewing in the context of artificially intelligent machines makes us vulnerable to moral complacency, and focuses on three salient problems that converge to make us especially vulnerable to becoming morally complacent and losing meaningful human control.Ethics Inf. Technol.["Marten H. L. Kaas"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/86c0f92b1459e11986e24c3063d53baaa358ff5f 0c671feb86a109212600cb74b034aabef9f183e1Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-08-03T00:00:00https://www.semanticscholar.org/paper/0c671feb86a109212600cb74b034aabef9f183e1 db013078c5e7cd8d30984c742d01fad723bc17f5Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-08-03T00:00:00https://www.semanticscholar.org/paper/db013078c5e7cd8d30984c742d01fad723bc17f5 089dcfe388a5e6f30462c01461bbab9a73f6b672Indian Journal of Skin Allergy50Indian Journal of Skin Allergy["A. Mohta", "Abhinav Mohta"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/089dcfe388a5e6f30462c01461bbab9a73f6b672 cffcef1226653b53fed25ee57aa5114eafe39643Наука і техніка сьогодні00Наука і техніка сьогодні["Volodymyr Silchenko"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/cffcef1226653b53fed25ee57aa5114eafe39643 a0e06cb57f799b19284ad9ca86c3e4cef7732ee1This paper delves into the fusion of artificial intelligence (AI) and emotional intelligence (EQ) by analyzing the frameworks of international sustainability agendas driven by UNESCO, WEF, and UNICEF. It explores the potential of AI integrated with EQ to effectively address the Sustainable Development Goals (SDGs), with a focus on education, healthcare, and environmental sustainability. The integration of EQ into AI use is pivotal in using AI to improve educational outcomes and health services, as emphasized by UNESCO and UNICEF’s significant initiatives. This paper highlights the evolving role of AI in understanding and managing human emotions, particularly in personalizing education and healthcare. It proposes that the ethical use of AI, combined with EQ principles, has the power to transform societal interactions and decision-making processes, leading to a more inclusive, sustainable, and healthier global community. Furthermore, this paper considers the ethical dimensions of AI deployment, guided by UNESCO’s recommendations on AI ethics, which advocate for transparency, accountability, and inclusivity in AI developments. It also examines the World Economic Forum’s insights into AI’s potential to revolutionize learning and healthcare in underserved populations, emphasizing the significance of fair AI advancements. By integrating perspectives from prominent global organizations, this paper offers a strategic approach to combining AI with EQ, enhancing the capacity of AI systems to meaningfully address global challenges. In conclusion, this paper advocates for the establishment of a new Sustainable Development Goal, SDG 18, focused on the ethical integration of AI and EQ across all sectors, ensuring that technology advances the well-being of humanity and global sustainability.Sustainability232It is proposed that the ethical use of AI, combined with EQ principles, has the power to transform societal interactions and decision-making processes, leading to a more inclusive, sustainable, and healthier global community.Sustainability["Ayse Basak Cinar", "Stephane Bilodeau"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/a0e06cb57f799b19284ad9ca86c3e4cef7732ee1 9471912f9e788a4b2ac1ba4e73098c04517ee9e8The integration of artificial intelligence (AI) in developing software solutions marks a pivotal advancement in enhancing accessibility for individuals with disabilities. This paper explores the transformative potential of AI-driven technologies designed to empower those with physical, sensory, and cognitive impairments. AI's capability to learn and adapt to diverse user needs enables the creation of personalized and intuitive applications, offering unprecedented levels of independence and inclusion. AI-driven accessibility solutions encompass various innovations, including speech recognition, natural language processing (NLP), and computer vision. Speech recognition technologies facilitate communication for individuals with speech and hearing impairments by converting spoken language into text and vice versa. NLP advancements have enabled the development of sophisticated text-to-speech systems, which can read aloud text content for visually impaired users, and text prediction tools that assist users with motor impairments in typing efficiently. Furthermore, computer vision technology provides real-time image and video recognition, aiding visually impaired users in navigating their environment and identifying objects. These AI-driven tools are integrated into everyday devices and platforms, significantly enhancing their utility and accessibility. For instance, AI-powered screen readers and voice assistants are now embedded in smartphones and computers, providing seamless access to information and digital services. Educational software leveraging AI ensures that learning materials are accessible to all students, regardless of their disabilities, by providing tailored content and support. The impact of AI-driven accessibility solutions extends beyond personal empowerment to societal inclusion. By enabling greater participation in education, employment, and social activities, these technologies help bridge the gap between individuals with disabilities and their peers. Companies and organizations benefit from the diverse talents and perspectives of a more inclusive workforce, driving innovation and economic growth. However, the development and implementation of AI-driven accessibility solutions also present challenges. Ensuring data privacy and security, avoiding bias in AI algorithms, and maintaining affordability and user-friendliness are critical considerations. Ongoing research, collaboration among stakeholders, and inclusive design practices are essential to address these challenges and maximize the benefits of AI for accessibility. In conclusion, AI-driven accessibility solutions are revolutionizing the way individuals with disabilities interact with the world. By harnessing the power of AI, these technologies offer transformative opportunities for independence, inclusion, and empowerment, ultimately contributing to a more equitable and accessible society. Keywords: Al-Driven, Accessibility, Transformative, Disabilities, Empowering.International journal of applied research in social sciences015The transformative potential of AI-driven technologies designed to empower those with physical, sensory, and cognitive impairments are explored, offering transformative opportunities for independence, inclusion, and empowerment, ultimately contributing to a more equitable and accessible society.International Journal of Applied Research in Social Sciences["Nnaemeka Valentine Eziamaka", "Theodore Narku Odonkor", "Adetola Adewale Akinsulire"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/9471912f9e788a4b2ac1ba4e73098c04517ee9e8 2b28cb2a97d9344abc05f559788ba360e211aa11This study investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) in STEM education, emphasizing the transformative potential and inherent challenges of these technologies. The purpose of this research is to provide a thorough understanding of how AI and ML can enhance educational outcomes, personalize learning experiences and address critical issues within the STEM fields. Utilizing a comprehensive review of current literature, this study examines the state of AI and ML integration in STEM education, identifies key ethical considerations and explores future trends and research directions. Key findings reveal that AI and ML significantly contribute to personalized learning, adaptive teaching strategies, and increased student engagement. However, challenges such as data privacy concerns, ethical dilemmas and the necessity for extensive educator training and infrastructure investment are prominent. The study underscores the importance of developing ethical frameworks and guidelines to ensure responsible use, mitigate biases and promote transparency. The conclusions drawn from this research highlight the critical need for collaboration among educators, technology developers, policymakers and researchers to fully leverage the potential of AI and ML. Recommendations include investing in professional development for educators, ensuring equitable access to AI tools and fostering international cooperation to share best practices and innovative solutions. Further, ongoing research into the ethical and practical implications of these technologies is essential for their successful integration into STEM education. This study elucidates the profound opportunities AI and ML present in transforming STEM education and calls for a strategic, ethical, and collaborative approach to overcome existing challenges and enhance educational practices. Keywords: Artificial Intelligence, Machine Learning, STEM Education, Personalized Learning, Ethical AI, Educational Technology.Computer Science &amp; IT Research Journal011Key findings reveal that AI and ML significantly contribute to personalized learning, adaptive teaching strategies, and increased student engagement, however, challenges such as data privacy concerns, ethical dilemmas and the necessity for extensive educator training and infrastructure investment are prominent.Computer Science &amp; IT Research Journal["Olatunbosun Bartholomew Joseph", "Nwankwo Charles Uzondu"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/2b28cb2a97d9344abc05f559788ba360e211aa11 5cbd1465476ad4dcc3635cf7be71a2e030afae75This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly. Embedding these competencies into educational programs and professional training initiatives can equip individuals to become responsible and informed users and creators of generative AI. The competencies follow a logical progression and serve as a roadmap for individuals seeking to get familiar with generative AI and for researchers and policymakers to develop assessments, educational programs, guidelines, and regulations.Digital Government: Research and Practice1067These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.ArXiv["R. Annapureddy", "Alessandro Fornaroli", "D. Gatica-Perez"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/5cbd1465476ad4dcc3635cf7be71a2e030afae75 e14de408971fe26e90351870b20c3b7d14571eedRevolutionizing education by introducing innovative methods to enhance student experiences has birthed Artificial Intelligence (AI). This article provided an in-depth overview of AI's educative and transformative influence, particularly concentrating on learning outcomes for students of all ages at Kumasi Technical University. AI amalgamation in education has enabled modified learning experiences tailored towards each learner's unique needs. The purpose of this study sought to investigate the effects of AI-personalized learning systems on academic performance across different age groups in higher education institution. The researcher employed a quantitative research design, using a face-content verified structured questionnaire to collect data from respondents, with expert consultation. Forty-five students from Kumasi Technical University's engineering and procurement departments were selected using the convenience sampling technique. The findings provided valuable insights into the use of AI-driven personalized learning platforms in higher education. The data revealed higher adoption rates among undergraduates compared to postgraduates, and a greater likelihood of use among men than women, highlighting gender disparities and potential areas for targeted support. The predominant use of AI tools by younger students demonstrated their comfort with emerging technology, while the low participation of older students suggested potential adoption barriers. Statistical analyses (Pearson correlation; (r (43) = 0.166, p = 0.265) and linear regression; (R^2 of 0.03), (F (1, 45) = 1.25, p = 0.265) indicated that age did not significantly correlate with academic success in the context of AI use, despite extensive integration of AI learning systems in academic courses. Contrary to expectations that younger students' engagement with AI tailored learning systems would positively impact their academic performance compared to those over thirty, no significant correlation between age and academic achievement was found. These findings underscore the need for further research into other factors that may influence the effectiveness of AI learning systems.Aug-Sept 2024203It was indicated that age did not significantly correlate with academic success in the context of AI use, despite extensive integration of AI learning systems in academic courses, and the need for further research into other factors that may influence the effectiveness of AI learning systems was underscore.Aug-Sept 2024["Seth Kofi Owusu", "Joseph Bikunati Zimpa", "Frank Amoako Atta", "M. Darling"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/e14de408971fe26e90351870b20c3b7d14571eed 1d3bd5c1fe54a1f186b0cdc8d37b8d2c9e43d561La Radiologia medica1223Recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway are outlined, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies.La Radiologia medica["Takahiro Tsuboyama", "Masahiro Yanagawa", "Tomoyuki Fujioka", "S. Fujita", "D. Ueda", "Rintaro Ito", "Akira Yamada", "Yasutaka Fushimi", "F. Tatsugami", "Takeshi Nakaura", "Taiki Nozaki", "K. Kamagata", "Yusuke Matsui", "Kenji Hirata", "N. Fujima", "M. Kawamura", "Shinji Naganawa"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/1d3bd5c1fe54a1f186b0cdc8d37b8d2c9e43d561 6c897b71e86011fdc9cf4f1b360adae15095584bThe integration of Artificial Intelligence (AI) into Human Resources (HR) management practices within educational institutions holds significant potential for addressing critical challenges such as teacher retention and student outcomes. AI-driven HR solutions offer innovative approaches to identifying, recruiting, and retaining high-quality teaching staff, thereby directly influencing the educational environment and student performance. This paper explores the transformative impact of AI on HR management in educational settings. It examines how AI technologies, including machine learning algorithms and data analytics, can streamline recruitment processes by predicting candidate success and fit, thus ensuring the selection of the most suitable teachers. By analyzing vast datasets, AI can identify patterns and predictors of teacher turnover, enabling institutions to implement targeted retention strategies. These strategies may include personalized professional development plans, adaptive workload management, and early intervention programs to address potential issues before they lead to teacher attrition. Additionally, the paper highlights how AI can enhance student outcomes through improved HR practices. By optimizing teacher assignments and ensuring that students are taught by educators whose skills and expertise align with their needs, AI can contribute to more effective and tailored educational experiences. Furthermore, AI can assist in creating a supportive work environment for teachers by providing insights into employee satisfaction and engagement, facilitating a culture of continuous improvement. The adoption of AI in HR management also raises important ethical considerations, including data privacy and the potential for algorithmic bias. The paper addresses these concerns by discussing best practices for ethical AI implementation, ensuring transparency, accountability, and fairness in AI-driven HR processes. Overall, this study demonstrates that leveraging AI in HR management can significantly enhance teacher retention and student outcomes, ultimately leading to a more effective and resilient educational system. By embracing AI technologies, educational institutions can foster a supportive environment for teachers and create optimal learning conditions for students, paving the way for sustained educational excellence. Keywords:  Artificial Intelligence, Human Resources Management, Teacher Retention, Student Outcomes, Educational Institutions.International Journal of Management &amp; Entrepreneurship Research00It is demonstrated that leveraging AI in HR management can significantly enhance teacher retention and student outcomes, ultimately leading to a more effective and resilient educational system.International Journal of Management &amp; Entrepreneurship Research["Olanike Abiola Ajuwon", "Enitan Shukurat Animashaun", "Njideka Rita Chiekezie"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/6c897b71e86011fdc9cf4f1b360adae15095584b 34b797732525a64f52db52e6eb2953911bc297aaIn the rapidly evolving landscape of data engineering, the integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming Enterprise Resource Planning (ERP) systems and supply chain management. This paper explores the profound impact of AI and ML technologies on these critical business domains. By leveraging AI and ML, organizations can enhance their ERP systems' efficiency through advanced data analytics, predictive modeling, and automation, leading to more informed decision-making and streamlined operations. Similarly, in supply chain management, these technologies enable real-time insights, improved demand forecasting, and optimized logistics, thereby reducing costs and increasing agility. This study examines current trends, practical applications, and case studies that highlight the benefits and challenges of incorporating AI and ML into ERP and supply chain processes. It also addresses the future directions of data engineering solutions and their potential to revolutionize business operations, offering valuable insights for professionals aiming to leverage these technologies for competitive advantage.Nanotechnology Perceptions180This study examines current trends, practical applications, and case studies that highlight the benefits and challenges of incorporating AI and ML into ERP and supply chain processes and addresses the future directions of data engineering solutions and their potential to revolutionize business operations.Nanotechnology Perceptions["Hemanth Kumar Gollangi", "E. Galla", "Chandrababu Kuraku", "C. Madhavaram", "Janardhana Rao"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/34b797732525a64f52db52e6eb2953911bc297aa 2582269bd1ecaa4ff8f983619fe3bbef5a51bb06The legal regulation of relations concerning genomic information has undergone several transformations over recent years in the Russian Federation. Not only is a universal approach towards the regulation of such relations in civil law, including regarding the possibility of sharing such information, currently lacking, the concept of genomic information remains poorly defined as an object of civil rights. A special legal regime for big genomic data capable of processing with the help of artificial intelligence technologies is also lacking in the current legislation of the Russian Federation. Considering the great potential value of genomic data for all of humanity, and the need for the sharing of such data, a proposed balanced approach will ensure the sharing of genomic data with proper protection of personal non-propertyand other rights. Thus, the present work sets out to identify the specific features of genomic data sharing. When determining the civil legal regime for genomic information and genomic data, a balance between public and private interests can be ensured by maintaining personal non-property rights, ensuring the confidentiality of personal data, and obtaining consent for the dissemination of information in accordance with the law. However, in order to do this, it is necessary to distinguish conceptually between genomic information about a particular person and the genomic data on the basis of which such genomic information is obtained. The civil legal status of human biological material should be determined along with regulations for its processing to obtain genomic information at the same time as defining the legal regime for regulating biobanks in which such human biological material can be stored.Lex Genetica220Lex Genetica["I. Z. Ayusheeva"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/2582269bd1ecaa4ff8f983619fe3bbef5a51bb06 ca646d197c3337f61eab60887026d8659cfbe5deActualmente, existen diversas herramientas de inteligencia artificial para el respaldo docente este estudio analizó las perspectivas y desafíos que enfrentan los docentes de la Unidad Educativa Manuel J. Calle en Cuenca, Ecuador, en relación con la IA como apoyo educativo con la finalidad de optimizar el proceso educativo de enseñanza-aprendizaje. Mediante encuestas, se examinaron las actitudes de los profesores locales hacia la implementación de la IA en sus funciones. Por otra parte, esta investigación fue de alcancé exploratorio, de cohorte transversal debido a que se obtuvieron los datos en un solo momento y los resultados mediante la aplicación SPSS, para después realizar el análisis correspondiente. El objetivo fue identificar perspectivas y desafíos en el uso de IA como apoyo docente. La investigación reveló que la capacitación formal es crucial para la adopción efectiva de herramientas de IA, aunque no eliminó completamente los obstáculos técnicos y éticos percibidos. Sin embargo, la IA mostró un potencial significativo para ahorrar tiempo en tareas administrativas y personalizar el aprendizaje. Basándose en estos hallazgos, se propuso una guía didáctica con herramientas de IA, organizadas en secciones, para ayudar a los docentes a comprender y utilizar cada herramienta de manera eficaz.MQRInvestigar00MQRInvestigar["Diego Sebasti\u00e1n Carchipulla-Fajardo", "D. Gonz\u00e1lez-Maldonado", "Daysi Karina Flores-Chuquimarca"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/ca646d197c3337f61eab60887026d8659cfbe5de a72f04e40748e6f6c38789fda1f99a11720daaa3La presente investigación se centra en la aplicación de un modelo de estrategias de marketing digital e inteligencia artificial en microempresas de la localidad de Ciudad Serdán pertenecientes al sector de servicios, sobre la línea de preparación de alimentos y bebidas, esto a causa de que el sector servicios fue de los más afectados durante la pandemia del covid19 ante los cambios en los hábitos del consumidor y el impulso que tuvo el marketing digital. Los resultados se basan en las fluctuaciones obtenidas en la gestión comercial de cada empresa, realizando un análisis comparativo del estatus previo a la aplicación del modelo contra los resultados obtenidos después de la aplicación del modelo. El modelo busca optimizar los procesos publicitarios de las microempresas considerando los desafíos que presenta un mundo cada vez más digitalizado y competitivo ante la vulnerabilidad que distingue este tipo de empresas para su permanencia.Estudios y Perspectivas Revista Científica y Académica80Estudios y Perspectivas Revista Científica y Académica["Karla Elisson Armenta Roque", "Fernando Aguirre y Hern\u00e1ndez", "Edna Araceli Romero Flores", "V\u00edctor Ricardo Castillo Intriago", "Mauricio Romero Montoya"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/a72f04e40748e6f6c38789fda1f99a11720daaa3 da3a5d1f316fd734864d07ba1896beaeaf022457El presente artículo aborda la falta de conocimiento en el uso de la IA en la educación, así mismo como su aplicación correcta a su aprendizaje; el objetivo se centra en identificar los retos que enfrentan los jóvenes al integrar la inteligencia artificial al panorama educativo de Azogues, Ecuador. La investigación se basa en un enfoque mixto, de alcance correlacional, una investigación no experimental y de cohorte transversal, aplicando así el instrumento a jóvenes de 16 años de la Unidad Educativa “Luis Rogerio González” obteniendo como resultado la importancia de una capacitación constante y formación específica de la integración de la IA, para ello se desarrolla una propuesta llamada EMIA (Encendiendo Mentes Con Inteligencia Artificial) con una guía para docentes. En síntesis, la IA permite a los estudiantes tomar un rumbo diferente y el control de su aprendizaje, adaptándose a su ritmo y preferencias.  MQRInvestigar00MQRInvestigar["Laura Sof\u00eda Guill\u00e9n-Parra", "S. Moscoso-Bernal"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/da3a5d1f316fd734864d07ba1896beaeaf022457 c0277a868493f5d89f0cd3bace4fb47b518803c7Argumentation and Advocacy171Argumentation and Advocacy["B. Bricker", "Jacob Justice"]2024-08-03T00:00:00https://www.semanticscholar.org/paper/c0277a868493f5d89f0cd3bace4fb47b518803c7 d3736153d5995c9a3eab17a6cd10334220ad86fdThe security of the power grid is essential for the proper function of a democratic society, yet it is constantly under threat. The internet of things (loT) will make a bad situation much worse. Misinformation, disinformation, and malinformation (MDM) have been identified as serious threats to our democratic institutions, and that same acronym applies to mobile device management (MDM), and these devices have become ubiquitous. People are notoriously bad at doing basic cybersecurity. With trillions of devices and remote access everywhere, what could possibly go wrong? Now add in the engaging opportunity of distributed energy resources (DERs) and life gets very interesting. Supervisory control and data acquisition (SCADA) systems may require substantial upgrades to meet minimal cybersecurity standards. Balancing the demand-response system is much more complex, especially if the islanding of neighborhoods is promoted within a Smart Grid. Artificial Intelligence (AI) may assist in daily operations, but also poses a potential threat. This paper uses a literature review to assess the current technology management status and evaluate the impetus for progress against future threats.Portland International Conference on Management of Engineering and Technology621A literature review is used to assess the current technology management status and evaluate the impetus for progress against future threats and Artificial Intelligence may assist in daily operations, but also poses a potential threat.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Chip Corbett", "Charles M. Weber", "Timothy R. Anderson"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/d3736153d5995c9a3eab17a6cd10334220ad86fd 55458a654bd7463c7cd26d0af41aa05cb218f51bThe article is devoted to analysis of the stages of development and current directions of research and practical application of artificial intelligence (AI) in the field of healthcare and medicine based on scientific publications in PubMed/MEDLINE, Scopus, Web of Science, Embase, eLibrary and CyberLeninka. The dynamics of scientific publications on AI in healthcare and medicine is shown, and an analysis of the growth of investments in software development based on AI in recent years is provided. AI can achieve comparable accuracy in the diagnosis of diseases in comparison with human decisions. However, future research should focus on comparing the clinical results of diagnosis and treatment performed by doctors who make decisions based on AI with the results of clinical work by doctors who do not use AI. The importance of training specialists able to combine knowledge in the field of medicine with the skills of using AI is emphasized.FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology121Analysis of the stages of development and current directions of research and practical application of artificial intelligence in the field of healthcare and medicine based on scientific publications in PubMed/MEDLINE, Scopus, Web of Science, Embase, eLibrary and CyberLeninka shows that AI can achieve comparable accuracy in the diagnosis of diseases in comparison with human decisions.FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology["A. I. Lamotkin", "D. Korabelnikov", "I. Lamotkin", "S. A. Livshitz", "E. G. Perevalova"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/55458a654bd7463c7cd26d0af41aa05cb218f51b 77132f45ca5de9d5577d1fa24104f338985b1b91As the use of Generative Artificial Intelligence tools have grown in higher education and research, there have been increasing calls for transparency and granularity around the use and attribution of the use of these tools. Thus far, this need has been met via the recommended inclusion of a note, with little to no guidance on what the note itself should include. This has been identified as a problem to the use of AI in academic and research contexts. This article introduces The Artificial Intelligence Disclosure (AID) Framework, a standard, comprehensive, and detailed framework meant to inform the development and writing of GenAI disclosure for education and research.College &amp; Research Libraries News00The Artificial Intelligence Disclosure (AID) Framework is introduced, a standard, comprehensive, and detailed framework meant to inform the development and writing of GenAI disclosure for education and research.ArXiv["Kari D. Weaver"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/77132f45ca5de9d5577d1fa24104f338985b1b91 eaccd409971835972cc6529e419c02cf41c69d1fThe industry is at an interesting inflection point with Artificial Intelligence (AI) rapidly emerging as an integral part of business strategy and operations. Recognizing this central role of AI, there is an increasing demand on Enterprise Architectures to play a pivotal role in sustainably integrating AI environments and driving organization's transformation agenda. This has opened significant opportunities and challenges for the enterprise architecture (EA) function in harnessing the full potential of AI. A critical analysis therefore explicates the need for a rapid refresh of traditional enterprise architectures by architecting sustainability principles into its design and instill a holistic view to guide its evolution. This paper therefore seeks to analyze the existing enterprise architectural principles and strengthen its core by applying sustainable architectural principles that guide to a holistic architectural blueprint. Notwithstanding to define and strengthen architectural design, this approach believes in tapping into digital architecture best practices that enables secure access of data using open interfaces to ensure interoperability on an adaptive design with resiliency. The principles would provide the basic guidelines in defining organization's AI strategy and provide the basis for architectural design decisions on setting about organizations AI mission.Portland International Conference on Management of Engineering and Technology320This paper seeks to analyze the existing enterprise architectural principles and strengthen its core by applying sustainable architectural principles that guide to a holistic architectural blueprint to define and strengthen architectural design.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Nampuraja Enose Kamalabai", "I. Donoghue", "Lea Hannola"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/eaccd409971835972cc6529e419c02cf41c69d1f 80e4eafa112f3d0de6c362cf467a95ef7fc2919dIn artificial intelligence era, the learning methods of learners have deeply changed. Many learners choose to learn through many kinds of ways, such as online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with an abundance of resource information, they are prone to become lost in knowledge. To solve this problem, a multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms. First, the learner model is constructed from four perspectives: cognitive level, learning ability, learning style, and learning intensity. Second, the association rule algorithm is employed to generate a sequence of knowledge points and to plan the learning sequence of knowledge points for learners. Last, the swarm intelligence algorithm is utilized to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability so that learners can learn using a more targeted approach. How to slove the problems that learning we want to know is the point of this papar.Portland International Conference on Management of Engineering and Technology150A multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Zhongyuan Xu"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/80e4eafa112f3d0de6c362cf467a95ef7fc2919d f49e736197b515454b1dbc781832ebb702ae4403In the evolving landscape of supply chain digitalization, integration, and globalization, there is a growing recognition of the potential of advanced information processing methods like Artificial Intelligence (AI) to enhance supply chain performance (SCP) and logistic performance (LP). Out of sixty articles reviewed, sourced from both conferences and journals, only twenty-four qualified for in-depth synthesis and analysis. This highlights a significant gap in the literature, especially when considering comprehensive reviews on the current and potential impacts of AI on SCP and LP, despite the increasing interest in this domain. Thus, this paper examines the nexus of AI application, SCP and LP. This paper consolidates and synthesize the current available research and provides the basis for further research on the connection between AI, SCP, and LPInternational Journal of Academic Research in Business and Social Sciences550This paper consolidates and synthesizes the current available research and provides the basis for further research on the connection between AI, SCP, and LP.International Journal of Academic Research in Business and Social Sciences["Siti Norhadibah Azman", "Fairuz Ramli", "N. Azami", "Ruqaiyah Ab Rahim"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/f49e736197b515454b1dbc781832ebb702ae4403 26eea9d7e4fe817c047a816d0adc7a223d042d2bBusinesses and firms rely strongly on their capacity to articulate and innovate their value proposition. Derived from the empowerment of artificial intelligence to leverage data from the environment and its capacity to analyze language, we present an innovation methodology that encompasses machine learning techniques and the canvas value proposition. Our methodological approach focuses specifically on the operation and design of the value proposition model. This allowed us to demonstrate the feasibility of implementing text mining techniques to support business model innovation. Overall, we introduce a novel approach for managers and innovators to employ artificial intelligence to facilitate the conception of new strategic value propositions. Furthermore, we set a path to do further research on the many ways in which computer sciences through artificial intelligence will reset the way to conceive innovation within organizations.Portland International Conference on Management of Engineering and Technology430This work introduces a novel approach for managers and innovators to employ artificial intelligence to facilitate the conception of new strategic value propositions and sets a path to do further research on the many ways in which computer sciences through artificial intelligence will reset the way to conceive innovation within organizations.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Arturo Atl Rodr\u00edguez", "Gabriela Calvario"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/26eea9d7e4fe817c047a816d0adc7a223d042d2b 8e204dd575000e58353e83547f5bfd0f5c5313aaThis study analyzes the decision-making support system based on artificial intelligence, which provides decision-making support and suggestions for doctors' clinical medication, and its technical characteristics. The study conducted interviews with 6 experts and scholars and analyzed a total of 267 experts with questionnaires. Three rounds of questionnaire interviews and responses were conducted from the four dimensions of “technology”, “process”, “behavior” and “influence”, and a mixed method approach was adopted. The analysis method is used to analyze to clarify the technical predictions or expectations of technology developers and technology users for the application of artificial intelligence in clinical medication decision support systems. The research results indicate that the construction of the system requires a large amount of patient medical information, clinical treatment guidelines and other medical professional knowledge for technology developers. For technology users, there are issues related to “user field” and “user habits” regarding user processes and behaviors, so opinions are inconsistent. In particular, clinical experts believe that critical procedures and high-risk situations will affect the utilization rate of artificial intelligence introduction. Therefore, system development should include medical personnel to provide suggestions and feedback on the construction of the system, in terms of medical information, clinical treatment guidelines and other medical professional knowledge.Portland International Conference on Management of Engineering and Technology260The research results indicate that the construction of the system requires a large amount of patient medical information, clinical treatment guidelines and other medical professional knowledge for technology developers and technology users, and its technical characteristics are inconsistent.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Kae-Kuen Hu", "Kuo-Liang Chen", "Mo-An Chu", "C. Peng", "Chia-Min Lin", "L. Cho"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/8e204dd575000e58353e83547f5bfd0f5c5313aa 8f8096a4823d8c277a23dd4ce0e7725c2888c4eaThe earth is an unstable planet. Change is constant and frequently chaotic. It is very important to plan carefully to manage changes that can be anticipated now, and to cover potential issues that might not yet have been discovered. The dawn of artificial intelligence (AI) presents a host of new concerns and opportunities. The criteria for evaluation are diverse and complex. Cybersecurity is absolutely essential, yet advances in technology have created an internet of things (IoT) where threat vectors might gain access to critical command and control elements through trillions of connected IoT devices. Whereas elegant zero trust cybersecurity models can be constructed and deployed, the vast majority of successful attacks involve a human element. Disruptive change makes decisions on any future course of action very difficult to evaluate. Distributed energy resources (DERs) can be designed to benefit individuals and improve the resilience of the power grid. We propose to direct the deployment of limited resources in an efficient fashion by evaluating the multi-discipline criteria using a hierarchical decision model (HDM), evaluated by human subject matter experts (SMEs) and generative artificial intelligence (GenAI).Portland International Conference on Management of Engineering and Technology510This work proposes to direct the deployment of limited resources in an efficient fashion by evaluating the multi-discipline criteria using a hierarchical decision model (HDM), evaluated by human subject matter experts (SMEs) and generative artificial intelligence (GenAI).2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Chip Corbett", "Cuong Nguyen", "D. M. Hongchai", "Prajakta Thorat", "Pavithra Prasad", "Sarah von Schimmelmann", "Charles M. Weber", "Timothy R. Anderson"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/8f8096a4823d8c277a23dd4ce0e7725c2888c4ea 28fa58c5358bc3ad2c7dba34bdd09f46bad7cddbTechnology, specifically artificial intelligence (AI) is gradually but progressively creeping into the health sector and it’s perhaps the one that has been revolutionised most in diagnosis and treatment. This review brings out discussions on the practices of AI technologies in medical, the pros and the cons. First of all, an endeavour is made to elucidate the meaning of the term AI and its utilization in the field of healthcare. The specific AI techniques are described comprehensively focusing on the machine learning, deep learning, and natural language processing methods to be used in the project The role of multiple types of data in AI includes the EHR, medical images, and genomics data. Self-diagnosis: AI is improving the diagnosis approaches in the radiology and pathology fields and predicting the early-stage disease with better results in most of the cases, and enhancing the identification of genetic diseases. As for treatment, the enhancement of the use of AI has had an impact on issues such as; Prescribing and recommending drugs according to the characteristics of the patients, smart drug administration and management, robotic surgeries and simulations. Discussions are made using concrete and successful implementation of AI in cancer, cardiovascular, neurological and infectious diseases for the purpose of elucidating particular results. This also has to do with the ethical and legal problems like who has the liability to determine in the instance of complicated problems, patients’ information discretion, data privacy, and other legalities. In this article, we briefly mention the prosaic matters of AI, which deals with the engineering aspects of establishing AI such as the aspect of data and the ways and means of checking them and the interdisciplinary character of it. Concerning future developments, additional technologies like AI and connected devices in the field of health care, interdisciplinary at national and international level as well as data sharing is emphasized. Thus. AI has a very great perspective in healthcare, particularly in diagnostics and treatment of diseases due to the probability of increasing the level of accuracy, efficacy, and personalization. Despite these, they are tangible objectives with major challenges and require cooperation between nations with proper handling of Artificial Intelligence to practice clinical medication.Journal for Research in Applied Sciences and Biotechnology340AI has a very great perspective in healthcare, particularly in diagnostics and treatment of diseases due to the probability of increasing the level of accuracy, efficacy, and personalization.Journal for Research in Applied Sciences and Biotechnology["Shekhar Singh", "Vishal Rai", "Ajay Yadav", "Akanksha Kanojia", "Sanjay Kumar Srivastava"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/28fa58c5358bc3ad2c7dba34bdd09f46bad7cddb 8a027fe1db795d8c3e319c177d6766d6b6073daeArtificial intelligence technologies include a set of methods that enhance the ability of computers to solve complex problems and mimic human-like decision-making processes. This study aims to conduct a bibliometric analysis on international trade and artificial intelligence to measure scientific productivity and effectiveness in this field, track the evolution of the research area, identify scientific communication networks, and identify gaps in the literature. For this purpose, the studies published in the Scopus database between 1984 and 2024 were analyzed and the data obtained were mapped with VOSviewer. A total of 486 studies with the concepts of artificial intelligence and international trade in their titles, abstracts or keywords were included in the study and subjected to bibliometric analysis. The results reveal that the number of studies in the related field has been increasing with an increasing momentum since 1984 in the international literature.Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi380A bibliometric analysis on international trade and artificial intelligence to measure scientific productivity and effectiveness in this field, track the evolution of the research area, identify scientific communication networks, and identify gaps in the literature is conducted.Cankiri Karatekin Universitesi Iktisadi ve Idari Bilimler Fakultesi Dergisi["\u015e\u00fcheda Baran"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/8a027fe1db795d8c3e319c177d6766d6b6073dae d22c9a230e2ecbb8ed507e60a587ba5554abec87In the ever-changing world of Business, making a successful product design requires quickly trying out new ideas and listening to what users think. With the integration of Artificial Intelligence (AI), the process of rapidly generating content from existing data becomes easier, accelerating the creation of innovative product concepts. To use this cutting-edge technology to build novel products a framework is essential. The paper aims to propose a novel framework that caters to a customer-centric integration of AI in Product Design. The paper goes beyond theoretical explanation, implementing the framework to iteratively design 3 diverse products. The implementation also highlights the potential opportunities and challenges of using an AI-enabled framework. Finally, the paper concludes with the critical insights obtained with the implementation of the framework.Portland International Conference on Management of Engineering and Technology140This paper aims to propose a novel framework that caters to a customer-centric integration of AI in Product Design, and implements the framework to iteratively design 3 diverse products.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Dhyeykumar Nikalwala", "Minnie H. Patel"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/d22c9a230e2ecbb8ed507e60a587ba5554abec87 edf13825fd7adee6ef173cfd1e659f64b29aaa40The effective use of open data and shared knowledge to enhance digital innovation in enterprises has become a trending research area in the cross-disciplinary field of AI and management. And AI contests have emerged as pivotal forms for the industrialization of digital technologies and open innovation. Yet, in a competitive and cooperative setting, the role of knowledge sharing in solution generation among contestants remains uncertain. This study conducted a quasi-experiment in conjunction with the launch of the AI contest platform's knowledge-sharing function, and the Propensity Score Matching and Difference-in-Differences (PSM-DID) methodology was used to empirically investigate the knowledge-sharing effect on participant performance, with further analysis on participant's heterogeneity. The findings indicate that the introduction of the knowledge-sharing function positively affects participant performance, though with a latency period. Additionally, the knowledge-sharing function shows a heterogeneous impact on participant performance; while the performance of individual participants significantly improved, team participants experienced a decrease, yet not significantly. Participants with more entries and longer tenure significantly increased, whereas those with more submissions showed a significant performance decline. These results offer valuable management implications for the platform and contestants to effectively utilize the knowledge-sharing function.Portland International Conference on Management of Engineering and Technology190The findings indicate that the introduction of the knowledge-sharing function positively affects participant performance, though with a latency period, and the knowledge-sharing function shows a heterogeneous impact on participant performance.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Lingling Wang", "Meijian Yang", "Sen Li"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/edf13825fd7adee6ef173cfd1e659f64b29aaa40 cafa28c08aa7e7eb17a6ee8ca66e97490665cf95Journal of Big Data12619A straightforward framework for assessing AI Methods in cyber threat detection is presented, evaluating the effectiveness and the limitations of current ML and DL proposed models, in addition to the metaheuristic algorithms.Journal of Big Data["Aya H. Salem", "Safaa M. Azzam", "O. Emam", "A. A. Abohany"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/cafa28c08aa7e7eb17a6ee8ca66e97490665cf95 871e781f8b62785a660ffdbf21f45666c3b43944The proposed literature review aims to investigate the emerging concept of “Digital Resilience” within the context of Industry 5.0, focusing on integrating technology management strategies and continuous improvement practices in the era of artificial intelligence (AI). The research paper explores how the integration of Industry 5.0 and AI can help organisations establish feedback loops that enhance their digital resilience against disruptions. The review explores the intersection of Industry 5.0, characterised by technology, with the transformative power of AI on principles such as human-centricity, environmental stewardship, and social benefit. It assesses existing literature to identify key frameworks, models, best practices, challenges, and empirical evidence that support the establishment of feedback loops, emphasizing their role in fostering digital resilience and the synergies between technology management, continuous improvement, and Industry 5.0. The review further highlights the significance of understanding how organisations navigate the complex landscape of Industry 5.0, leveraging AI-driven technologies to improve operational efficiency and responsiveness. In conclusion, this review aims to provide a comprehensive synthesis of current knowledge, offering insights into the strategic integration of technology management, continuous improvement, and Industry 5.0 to navigate the complexities of the AI era.Portland International Conference on Management of Engineering and Technology411The research paper explores how the integration of Industry 5.0 and AI can help organisations establish feedback loops that enhance their digital resilience against disruptions and highlights the significance of understanding how organisations navigate the complex landscape of Industry 5.0.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Amara Atif", "Muhammad Atif Qureshi"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/871e781f8b62785a660ffdbf21f45666c3b43944 e5399f62259e24f786eafebf1293aaeb226b99a9This integrative literature review investigates the transformative impact of artificial intelligence (AI) on manufacturing, focusing on AI-driven predictive maintenance, machine learning-based quality control, and AI-driven supply chain optimization. By examining current literature, the study highlights AI's potential to automate and revolutionize manufacturing operations, enhancing efficiency, resilience, and transparency. The study's conceptual framework is grounded in three primary pillars: AI-driven supply chain optimization, predictive analytics, and machine learning-based quality control, each contributing to the overall enhancement of manufacturing efficiency, resilience, and transparency. The methodology involves a comprehensive review of scholarly articles, reports, and academic publications, focusing on AI applications in predictive maintenance, quality control, and supply chain optimization. The analysis reveals significant improvements in operational efficiency and resilience due to AI, alongside concerns about biases, transparency, and implementation issues. The findings confirm AI's transformative potential in manufacturing but emphasize the necessity for ongoing supervision, regular audits, and the development of AI models capable of detecting and rectifying operational anomalies. The study proposes creating jobs such as AI Manufacturing Oversight Officer (AIMOO), AI Manufacturing Compliance Officer (AIMCO), and AI Manufacturing Quality Assurance Officer (AIMQAO) to ensure responsible AI utilization, maintaining the integrity and efficiency of manufacturing operations while addressing implementation challenges. The review concludes that AI is promising for transforming manufacturing; however, careful implementation is crucial to uphold operational integrity and resilience. Future research should prioritize longitudinal studies to evaluate AI's long-term impact, focus on addressing implementation concerns, and ensure fair and transparent integration of AI technologies. These findings have significant implications for practice and policy, underscoring the need for robust frameworks and regulatory measures to guide the effective use of AI in manufacturing.International Journal For Multidisciplinary Research681The review concludes that AI is promising for transforming manufacturing; however, careful implementation is crucial to uphold operational integrity and resilience, underscoring the need for robust frameworks and regulatory measures to guide the effective use of AI in manufacturing.International Journal For Multidisciplinary Research["Rachid Ejjami", "Khaoula Boussalham"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/e5399f62259e24f786eafebf1293aaeb226b99a9 0dfcabebde13c0286d2f5c30ced32245c6b14880This study explores the impending impacts of Artificial Intelligence (AI) on work environments, forecasting significant shifts in the skill sets of emerging talents across various industries over the next decade. Despite AI's technological strides, industry's lags in the data management and utilization capabilities. A significant obstacle to innovation is the shortage of a skilled workforce. This paper highlights the necessity to develop comprehensive skill sets across all organizational tiers to unlock data utility and to fully leverage AI's potential to the products, processes and services. Drawing on datasets from Finland, the paper elucidates the current state and needs of the manufacturing industry and identifies pressing skill requirements. It argues that companies aiming for a leading position in the AI era must focus on updating skills and consider collaboration with universities to synchronize educational curricula with forthcoming industry requirements. For this purpose, paper addresses views of business and ICT lecturers regarding AI skills development. The paper continues to propose examples of learning environments supporting reskilling and upskilling initiatives, ensuring a smooth industry transition to meet the evolving exigencies of the AI landscape.Portland International Conference on Management of Engineering and Technology930It is argued that companies aiming for a leading position in the AI era must focus on updating skills and consider collaboration with universities to synchronize educational curricula with forthcoming industry requirements, ensuring a smooth industry transition to meet the evolving exigencies of the AI landscape.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Katri Salminen", "Pia Hautam\u00e4ki", "Markus J\u00e4hi"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/0dfcabebde13c0286d2f5c30ced32245c6b14880 555aa21f07bef137b3c4f4410bb91847fda11899New digital technologies are rapidly changing how companies do business. The latest wave of artificial intelligence (AI) technologies is further boosting this process, especially among high-tech manufacturers, leading to new opportunities for new service-oriented business models. In this paper, we refer to this process as “AI servitization” (i.e., artificially intelligent servitization). Moreover, while AI offers many opportunities, it also brings new challenges for companies. The purpose of this paper is to further explore these issues. Based on a comparative case study of four Chinese high-tech manufacturing firms, we develop theory on AI servitization and its associated paradoxes and coping practices. Methodologically, we first perform within-case analyses, zooming into each manufacturer in detail, followed by a cross-case analysis where we compare the cases on the study's selected key issues (i.e., types of services and AI technologies, paradoxes, and coping practices). Theoretically, we propose a two-dimensional framework considering manufacturers' service focus (i.e., product-oriented vs. customer process-oriented) and the architecture (i.e., modular vs. integral) of their AI-based products and associated platforms, unveiling four “ideal” types of AI servitization. Next, we discuss four types of paradoxes-namely, economic, technological, organizational, and institutional-encountered by the studied firms as well as the practices that they deploy to cope with them. The insights generated from this study offers practical implications for managers to pinpoint their current AI service position and think of future innovation pathways, and for researchers to further develop the research field of AI servitization.Portland International Conference on Management of Engineering and Technology820Based on a comparative case study of four Chinese high-tech manufacturing firms, theory on AI servitization and its associated paradoxes and coping practices is developed and four “ideal” types of AI servitization are unveiled.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Xinyi Lin", "Dong Wu", "Coreynen Wim"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/555aa21f07bef137b3c4f4410bb91847fda11899 71583fa1baaca69edb891abbbb076e2e660748beWith the breakthroughs in algorithms, computing power, and big data, artificial intelligence (AI) technology has garnered widespread attention in both academic and industrial spheres. The existing literature on entry timing predominantly focuses on the traditional manufacturing industry, treating knowledge as merely a resource or capability foundation, and often overlooks the dynamic knowledge capabilities specific to the AI industry, where existing knowledge can be self-learned, and new knowledge can be autonomously generated. Through a dynamic knowledge capability lens, this paper seeks to elucidate the relationship between entry timing and AI-based innovation performance within the AI industry, as well as the moderating impact of pre-entry knowledge diversity. In this study, we analyze data from 891 AI firms in China, obtained using an AI-related keywords database, and compile 2,101 panel data points from 2018 to 2020 for regression analysis. The findings reveal a U-shaped relationship between entry timing and AI-based innovation performance, which is negatively moderated by pre-entry knowledge diversity. This paper dissects the dynamic knowledge capability mechanism that underlies entry timing in the AI industry and provides a practical framework for firms with varying levels of pre-entry knowledge diversity.Portland International Conference on Management of Engineering and Technology900The findings reveal a U-shaped relationship between entry timing and AI-based innovation performance, which is negatively moderated by pre-entry knowledge diversity.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Xinyi Lin", "Dong Wu"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/71583fa1baaca69edb891abbbb076e2e660748be f7cff431b56745d6dbb776e7a07d7ad812f7bb52This paper systematically investigates and specifies the possibilities of AI-based service systems (so-called “smart services”) and provides practical design approaches for companies. Building on a structured approach for a sustainable design of AI-based services, we examine the potential of AI-based smart services for ecological, social, and economic impact on company value creation. Our work is based on a comprehensive literature review and use cases from applied research projects in collaboration with companies, contributing to technology management in the era of artificial intelligence and the sustainable transformation of the economy and society.Portland International Conference on Management of Engineering and Technology870This paper systematically investigates and specifies the possibilities of AI-based service systems (so-called “smart services”) and provides practical design approaches for companies and examines the potential of AI-based smart services for ecological, social, and economic impact on company value creation.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Nicole Gladilov", "Lena Ahner", "Jens Neuh\u00fcttler", "Katharina H\u00f6lzle"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/f7cff431b56745d6dbb776e7a07d7ad812f7bb52 6658eecbe7de23134f6a9f4c2a949604642a9225This ongoing-study explores the symbiotic relationship between humans and Artificial Intelligence (AI) within organizational settings, challenging the conventional perception of AI as a mere tool and establishing it as an integral component of the workforce. The research seeks to address the question: “In what ways does the collaborative interaction between humans and AI influence specific dimensions within organizations?”. Drawing from the Extended Mind Theory, which suggests that cognitive processes can extend beyond the human mind into objects and environments (including AI), the study explores diverse applications of AI in business contexts. From data analysis and decision-making to customer service, marketing, risk management, and product development, the research analyzes the multifaceted impacts of AI integration. This also encompasses the potential challenges and considerations associated with their implementation across various domains. This research aims to redefine the perception of AI and its “new” role in organizations, providing valuable insights for decision-makers adapting to the changing AI integration.Portland International Conference on Management of Engineering and Technology420This research aims to redefine the perception of AI and its “new” role in organizations, providing valuable insights for decision-makers adapting to the changing AI integration.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Daniel Amori Molina", "Vladimir Kharlov", "Ja-Shen Chen"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/6658eecbe7de23134f6a9f4c2a949604642a9225 1ca3939b06e633dc854949894a5c94e5baf66d9dIndustries are evolving more rapidly in today's time than ever before, human needs and lifestyle are constantly adapting and changing as technology is becoming an irreplaceable part of their lives. In today's world Artificial Intelligence (AI) is emerging as a groundbreaking force replacing conventional ways with new innovative processes. Product development in this era of artificial intelligence is going under major transformations. A review of papers and research shows that the role of Artificial intelligence in product management is going to increase with time. This paper dwells through the tasks, roles, and perspectives of the product lifecycle stages, along with latest AI tools available in today's market. Further this paper, proposes the advantages and challenges of implementing AI in product development and management, to provide insight on how artificial intelligence influences and improves the stages of the product life cycle, from concept generation to product launch.Portland International Conference on Management of Engineering and Technology170This paper proposes the advantages and challenges of implementing AI in product development and management, to provide insight on how artificial intelligence influences and improves the stages of the product life cycle, from concept generation to product launch.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Atmesh Tiwari", "Minnie H. Patel"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/1ca3939b06e633dc854949894a5c94e5baf66d9d 4679dfe6ae260ce2a9a8aab8779f363edfce63c3Artificial intelligence has several effective applications, ranging from language modelling to pharmaceutical sector enhancement, and it speeds up and lowers the cost of medication research and development. As the amount of drug-related data increases, the deep-learning method has been applied at every stage of the drug development process. A broad overview of artificial intelligence (AI) and its use in medication research and discovery is discussed in this review. Drug metabolism, excretion, and recent advancements in colorectal cancer and tooth loss are discussed, along with the integration of plant-based traditional medicine and the use of computer-aided drug discovery and ligand-based quantitative structure activity and property (QSAR/QSPR) and De Novo drug design. The AI-assisted platform used to discover the serotonin 5-HT1A drug is demonstrated, and it reached the clinical trial in less than 12 months—a significantly shorter time than the conventional method, which requires four years to complete. The challenges, ethical considerations, and future perspectives of AI in drug discovery were also discussed in this reviewJournal of Pharma Insights and Research410The AI-assisted platform used to discover the serotonin 5-HT1A drug is demonstrated, and it reached the clinical trial in less than 12 months—a significantly shorter time than the conventional method, which requires four years to complete.Journal of Pharma Insights and Research["Nvvs Vinayak", "Lingolu", "Deena Kumari", "Pavan Kumar"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/4679dfe6ae260ce2a9a8aab8779f363edfce63c3 be8f84152361d8941a7eae049f24d39a84c93ffbThis literature review attempts to outline the status of the cannabis industry ecosystem and its importance for the success of the entrepreneurial entities within it. As was true for an abundance of historical industries and many current ones, the cannabis industry is profoundly stigmatized. This continues to hamper its growth potential and makes it an outlier in traditional entrepreneurial ecosystems. It is believed that Artificial Intelligence (AI) may hold the key to revolutionizing the cannabis industry and reducing the impact of the stigmatization by providing it a legitimacy that it has not been afforded to date.Portland International Conference on Management of Engineering and Technology470It is believed that Artificial Intelligence (AI) may hold the key to revolutionizing the cannabis industry and reducing the impact of the stigmatization by providing it a legitimacy that it has not been afforded to date.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Caren Weinberg"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/be8f84152361d8941a7eae049f24d39a84c93ffb d842448085880a10ba792a46b582061a6c9d959aThis paper explores the multifaceted impact of Artificial Intelligence (AI) on the workplace, focusing on worker performance, job satisfaction, and the nuanced perceptions of job security across different demographic groups. Through an analysis of data from OECD 2022's AI surveys of employers and workers, we investigate three hypotheses: the positive impact of AI adoption by employers on worker performance, the enhancement of work performance and job satisfaction through AI-related training and upskilling, and the varied perceptions of AI's impact on job security and working conditions among different demographic groups. Our findings reveal that, contrary to common concerns, AI's integration into the workforce can improve decision-making, efficiency, and even employee happiness, if employees are adequately trained and engaged in AI's implementation process. The study underscores the importance of strategic AI integration and training to harness its full potential for enhancing organizational performance and employee well-being.Portland International Conference on Management of Engineering and Technology200The findings reveal that, contrary to common concerns, AI's integration into the workforce can improve decision-making, efficiency, and even employee happiness, if employees are adequately trained and engaged in AI's implementation process.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["J\u00e9ssica L\u00f3pez-Garc\u00eda", "David Romero-G\u00f3mez", "M. Casta\u00f1\u00f3n-Puga", "Eduardo Ahumada-Tello"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/d842448085880a10ba792a46b582061a6c9d959a 3352b3ab5f6edd362487fb21ae3f1027cce12a16The surprising arrival and speedy diffusion of artificial intelligence (AI) functions have concerned many nations. One of the main worries lies in the required formation of specialized human resources (HR) that can successfully meet the new skills that AI is demanding. If this condition preoccupies advanced nations, the future of emerging countries' workforce looks uncertain. To a large extent, human capital formation is one of developing nations' main challenges; therefore, they must implement strategies for preparing their employees for the AI era. In this study, we review the main challenges that need to be addressed, such as adjusting training programs to prioritize critical thinking, encouraging creativity and problem-solving, investing in digital infrastructure and promoting digital literacy, fostering international collaboration and knowledge sharing, and implementing ethical frameworks for responsible AI development and deployment. By reviewing the case of a Master's Program on Technology Management in Mexico, we propose a policy roadmap to address these incoming requirements, including a human capital development index.Portland International Conference on Management of Engineering and Technology240The main challenges that need to be addressed are reviewed, such as adjusting training programs to prioritize critical thinking, encouraging creativity and problem-solving, investing in digital infrastructure and promoting digital literacy, fostering international collaboration and knowledge sharing, and implementing ethical frameworks for responsible AI development and deployment.2024 Portland International Conference on Management of Engineering and Technology (PICMET)["Humberto Merritt"]2024-08-04T00:00:00https://www.semanticscholar.org/paper/3352b3ab5f6edd362487fb21ae3f1027cce12a16 89a1bb823aed78b4bdd0f2ee0fc46a7ef8a66d5eJournal of Electrical Systems and Information Technology503This exploration aims to uncover how AI actively observes and supports various technologies in zero trust model, illuminating the transformative potential of AI in fortifying security within zero trust security models.Journal of Electrical Systems and Information Technology["Deepa Ajish"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/89a1bb823aed78b4bdd0f2ee0fc46a7ef8a66d5e ff49226080c6b3ac37e8fa2b5dfd9dd5727187a3Artificial Intelligence (AI) is coming to mainstream and as emerged as a transformative force in various fields not just limited including mechanical engineering. This paper provides an overview of the profound impact of AI on the practice and evolution of mechanical engineering. The usage of AI technologies in the field of mechanical engineering has potential to revolutionize traditional design, manufacturing, and maintenance processes. With AI-powered design tools engineers now can generate optimized designs faster with greater efficiency, leading to enhanced product performance and reduced development cycles. Further, Predictive/forecasting method of AI in maintenance systems facilitate early detection of equipment failures, thereby minimizing downtime and maintenance costs.International Journal of Innovative Science and Research Technology282An overview of the profound impact of AI on the practice and evolution of mechanical engineering is provided and Predictive/forecasting method of AI in maintenance systems facilitate early detection of equipment failures, thereby minimizing downtime and maintenance costs.International Journal of Innovative Science and Research Technology (IJISRT)["Prasanna Adhithya Balagopal"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/ff49226080c6b3ac37e8fa2b5dfd9dd5727187a3 65a0a90c9c96fcd7268acfca375b212bb1526ebdArtificial Intelligence (AI) is revolutionizing various fields, including public health surveillance. In Africa, where health systems frequently encounter challenges such as limited resources, inadequate infrastructure, failed health information systems and a shortage of skilled health professionals, AI offers a transformative opportunity. This paper investigates the applications of AI in public health surveillance across the continent, presenting successful case studies and examining the benefits, opportunities, and challenges of implementing AI technologies in African healthcare settings. Our paper highlights AI's potential to enhance disease monitoring and health outcomes, and support effective public health interventions. The findings presented in the paper demonstrate that AI can significantly improve the accuracy and timeliness of disease detection and prediction, optimize resource allocation, and facilitate targeted public health strategies. Additionally, our paper identified key barriers to the widespread adoption of AI in African public health systems and proposed actionable recommendations to overcome these challenges.arXiv.org2191This paper investigates the applications of AI in public health surveillance across the continent, presenting successful case studies and examining the benefits, opportunities, and challenges of implementing AI technologies in African healthcare settings.ArXiv["Jean Marie Tshimula", "Mitterrand Kalengayi", "Dieumerci Makenga", "Dorcas Lilonge", "Marius Asumani", "D'eborah Madiya", "\u00c9lie Nkuba Kalonji", "Hugues Kanda", "Ren'e Manass'e Galekwa", "Josias Kumbu", "Hardy Mikese", "Grace Tshimula", "Jean Tshibangu Muabila", "Christian N. Mayemba", "D'Jeff K. Nkashama", "Kalonji Kalala", "S. Ataky", "Tighana Wenge Basele", "Mbuyi Mukendi Didier", "S. Kasereka", "Maximilien V. Dialufuma", "Godwill Ilunga Wa Kumwita", "Lionel Muyuku", "Jean-Paul Kimpesa", "Dominique Muteba", "A. Abedi", "Lambert Mukendi Ntobo", "Gloria M. Bundutidi", "D. K. Mashinda", "E. K. Mpinga", "N. Kasoro"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/65a0a90c9c96fcd7268acfca375b212bb1526ebd d6f71933b204acb4b134b6d6278307c03e1aca1eThe goal of this study is to explore the current state and prospects for the use of artificial intelligence (Artificial intelligence, AI) in the framework of the administration of justice, in particular, in civil proceedings. In light of constantly changing social relations and the growing need to use modern technologies in various areas of life, including legal ones, it is important to understand what opportunities artificial intelligence can provide to improve legal proceedings and ensure the protection of citizens’ rights. The use of an artificial intelligence system in legal activitieshas a number of advantages, such as speeding up the decision-making process, increasing the accuracy and objectivity of decisions made, and improving the accessibility of justice. However, it is also necessary to take into account possible disadvantages, for example, the risk of data privacy violations and the possibility of errors in the algorithms, which can lead to an unfair decision. The final conclusion of the study is that the use of information technology andartificial intelligence systems should not be considered an end in itself but should be introduced as part of a strategyto improve the legal system and increase the effectiveness of the protection and restoration of the rights of subjectsof legal relations. In addition, it is necessary to take into account the social and ethical aspects of legal proceedings.Государство и право60The final conclusion of the study is that the use of information technology and artificial intelligence systems should not be considered an end in itself but should be introduced as part of a strategy to improve the legal system and increase the effectiveness of the protection and restoration of the rights of subjectsof legal relations.Gosudarstvo i pravo["A. Danielyan"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/d6f71933b204acb4b134b6d6278307c03e1aca1e 6ff277b250aeca3ef9582c8609988e45083e968bThe growing influence of artificial intelligence, robotics and neurotechnologies on modern society increases the need to include in the legal system the rules governing the use of digital technologies and products based on them. This article attempts to establish how the concepts of “robot” and “artificial intelligence” relate to each other, as far as they are in contact with the field of neurotechnology. The author highlights the trends in the development of digital technologies, which will inevitably increase in the coming years, and outlines the boundaries within which the legal regulation being created today will be implemented.Государство и право110This article attempts to establish how the concepts of “robot” and “artificial intelligence” relate to each other, as far as they are in contact with the field of neurotechnology.Gosudarstvo i pravo["Irina A. Filipova"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/6ff277b250aeca3ef9582c8609988e45083e968b 57909d3f22e0dd3d50cbfef93d9b97ddcb4d5923The patent system has long been criticized for limiting access to medicines. Dramatic advances in artificial intelligence and machine learning technology present a revolutionary opportunity in drug discovery, formulation and testing of dosage forms. The pharmaceutical industry claims that patenting is necessary to encourage innovation in the risky, lengthy, and costly research and development (R&D) process. But it still does not provide logical evidence about the actual effects of patents on innovation. The increasing use of artificial intelligence in research is intensifying the debate about pharmaceutical patents. Inventions created or enabled by artificial intelligence raise questions about patentability and patent policy in general. Faster and more efficient research and development weakens the justification for pharmaceutical patents. Research findings suggest that despite the necessity of continuing incentives for drug research and development, lawmakers should consider alternative systems that prioritize access alongside incentives to advance healthcare as a human right.Journal of Pharmaceutical Care00Research findings suggest that despite the necessity of continuing incentives for drug research and development, lawmakers should consider alternative systems that prioritize access alongside incentives to advance healthcare as a human right.Journal of Pharmaceutical Care["Babak Sabet", "Shahriar Eslamitabar", "Ehsan Lame", "Fatemeh Anvar"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/57909d3f22e0dd3d50cbfef93d9b97ddcb4d5923 68b2063c6a44911d59fc34bb57adc7b7066a98d8 Implementing artificial Intelligence (AI) and data science holds significant potential for optimizing Midstream and Downstream operations in the petroleum industry, fostering margin enhancement opportunities and overall value chain optimization. This involves streamlining processes such as production, logistics, pipeline monitoring, refinery operations, plant safety, and environmental impact reduction. Artificial intelligence involves developing intelligent systems that operate autonomously, learn from experiences, and improve performance with increasing knowledge. On the other hand, data science focuses on extracting, cleaning, and analyzing data to drive valuable insights. In the midstream stage, which pertains to crude or refined petroleum transportation, digital twin technology plays a crucial role, offering applications like pipeline monitoring and logistics optimization. Downstream is the refinery, processing and purifying of crude oil and natural gas. Companies like Shell and Chevron leverage in real-time data from sensors, cameras, and drones to create dynamic representations of operations and equipment for real-time monitoring and analysis. However, maintaining accurate and up-to-date digital twin models poses challenges. This is where AI techniques like optimization (model creation and updating), generative modelling, data analytics, predictive analytics, and decision-making come to play. For instance, according to Abn resource, Shell Lubricants introduced the AI-powered Chatbot tool, Shell LubeChat, in 2018, enhancing customer service for business-to-business lubricant clients and optimizing profits. Cognite Data Fusion, a data solution, codifies industrial knowledge into software for seamless integration with existing ecosystems, facilitating scalability from proofs of concepts to operation-driven scenarios. Maintenance systems often struggle to intelligently schedule tasks without contextualized data from various sources provided by Cognite Data Fusion. Artificial intelligence and Data science solutions are instrumental in helping midstream and downstream operators achieve optimal performance, contributing to both profitability and sustainability.SPE Nigeria Annual International Conference and Exhibition20Artificial intelligence and Data science solutions are instrumental in helping midstream and downstream operators achieve optimal performance, contributing to both profitability and sustainability.SPE Nigeria Annual International Conference and Exhibition["Daniel Adah Favour"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/68b2063c6a44911d59fc34bb57adc7b7066a98d8 b96444d4f0b13704aa64b48fb1e681e2610aa9f3This study aims to conduct a bibliometric analysis of studies related to artificial intelligence (AI) in the field of nursing, accessed from the Web of Science database. The search was conducted using the keywords "artificial intelligence OR ChatGPT OR Chatbot) and (nursing OR nursing care) and (practice OR innovation OR machine learning OR deep learning)" between January 01-20, 2024. A total of 164 studies related to artificial intelligence in nursing were identified through the search. It was found that 65.85% of these studies were research articles, with the majority being published in the Journal of Nursing Management (nine studies), and the highest number of studies being published in 2023. The most prolific author, with seven studies, was identified as Rozzano Locsin, while the United States was determined to be the country with the highest number of publications, and Florida Atlantic University and Tokushima University were the institutions with the most studies. The most frequently used keyword was "artificial intelligence," with a total citation count of 1010 and an h-index of 20. The study indicates an increasing interest in AI-related research in nursing, particularly in recent years, with a trend towards quantitative growth.Journal of Innovative Healthcare Practices170There is an increasing interest in AI-related research in nursing, particularly in recent years, with a trend towards quantitative growth.Journal of Innovative Healthcare Practices["Meltem \u00d6zkaya", "\u00d6znur K\u00f6r\u00fck\u00e7\u00fc"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/b96444d4f0b13704aa64b48fb1e681e2610aa9f3 ca5d0bb250645d497805de18fec71cb089e8dc2dWith the rapid development of artificial intelligence technology, its impact on international law is becoming increasingly significant. This paper explores the application of AI in international relations and the challenges it poses to existing legal systems, analyzing AI regulation within the current international law framework and the exploration of emerging international legal norms. Through specific cases, the paper elaborates on legal issues in the fields of intelligent weapons, cybersecurity, and data protection, emphasizing the importance of international cooperation and global governance mechanisms in addressing these challenges. Finally, the paper looks forward to future trends in AI technology and international law’s response strategies, proposing the necessity of constructing effective legal frameworks and ethical guidelines to promote the sustainable development of AI technology and the healthy development of the global legal system.Economics Law and Policy00The paper elaborates on legal issues in the fields of intelligent weapons, cybersecurity, and data protection, emphasizing the importance of international cooperation and global governance mechanisms in addressing these challenges.Economics, Law and Policy["Jialing Liu"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/ca5d0bb250645d497805de18fec71cb089e8dc2d 76130e53cbdf7d407b8d19804177ac05676095d6In the Leibniz AIAcademy project a comprehensive system of 17 hybrid courses on the topic artificial intelligence (AI) is currently being created. To obtain a unified course system, a standardized concept, addressing organizational and formal aspects, as well as content and didactics, was initially developed and continually adjusted. To assist the instructors with the practical implementation of the concept, we acted as didactic support team and offered various workshops, guidelines, and additional support. Transforming 17 existing regular university face-to-face courses into hybrid courses is a task involving many challenges but also many insights - for the instructors as well as for the didactic support team. To gain insights into the development process of the instructors and thus optimize the support, we carried out and analyzed surveys and interviews with the instructors to identify obstacles in the process and to gather recommendations for best practices. Those results that can be useful also outside the project. We furthermore describe some learnings obtained by the didactic support team during the process.Grid Economics and Business Models140To gain insights into the development process of the instructors and thus optimize the support, the didactic support team carried out and analyzed surveys and interviews with the instructors to identify obstacles in the process and to gather recommendations for best practices.2024 IEEE 3rd German Education Conference (GECon)["Tanja Dieckmann", "Ariane Hussy", "Johannes Krugel"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/76130e53cbdf7d407b8d19804177ac05676095d6 7b8168c9d8b89ef3ed1f4985ffc05dd3d502374bRelevance. In the modern world, artificial intelligence (AI) is one of the fastest growing areas of information technology, which is increasingly penetrating into various spheres of human activity. In the context of public and municipal management, AI is a technology that is able to automate and optimize work processes, improve the quality of services provided to citizens, and increase the level of security and transparency of government work.The purpose is to analyze the possibilities and risks of using artificial intelligence in state and municipal management, as well as to determine the prospects for its development in this area.Objectives: to analyze the advantages of using artificial intelligence in state and municipal management; to study the risks and challenges associated with its implementation; to analyze trends in the development of artificial intelligence in this area, as well as to determine the prospects for its use.Methodology. Empirical, theoretical, statistical and graphical representation methods were used in the course of scientific research.Results. The study revealed the advantages of AI in public administration (improving efficiency, improving the quality of services, transparency, security); risks and challenges were studied (lack of infrastructure, risk of privacy violations, problems of transparency of algorithms, risk of discrimination); trends in the development of AI were analyzed (e-government, optimization of workflows, personalization of services, monitoring of social media, security); the prospects of using AI (smart cities, virtual assistants, hybrid systems, decision support, cooperation with business, international cooperation) have been identified.Conclusions. Artificial intelligence can significantly improve the efficiency and quality of public and municipal administration. However, for the successful implementation of AI, it is necessary to solve problems such as the lack of infrastructure and qualified specialists, ensuring data security, transparency of algorithms and compliance with the regulatory framework. To do this, it is necessary to conduct further research, develop integrated approaches and take into account world experience. The prospects for using AI include the creation of smart cities, the use of virtual assistants, hybrid systems, decision support systems, business cooperation and international cooperation. Proceedings of the Southwest State University. Series: Economics. Sociology. Management30Artificial intelligence can significantly improve the efficiency and quality of public and municipal administration, however, for the successful implementation of AI, it is necessary to solve problems such as the lack of infrastructure and qualified specialists, ensuring data security, transparency of algorithms and compliance with the regulatory framework.Proceedings of the Southwest State University. Series: Economics. Sociology. Management["O. V. Belyaeva", "A. Y. Sokolova"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/7b8168c9d8b89ef3ed1f4985ffc05dd3d502374b ebedf34e3c232b5d160536812d9387171e8f0e0dThe future of artificial intelligence (AI) in diagnosing rare genetic disorders is poised to transform precision medicine by accelerating the identification of conditions that are often difficult to diagnose. Rare genetic disorders, which affect millions of people worldwide, typically involve complex symptoms and lengthy diagnostic processes. AI's ability to process vast amounts of genomic, phenotypic, and clinical data positions it as a game-changer in this field. By detecting subtle patterns and correlations in large datasets, machine learning algorithms can deliver diagnoses faster and more accurately than traditional methods. AI-powered tools are proving valuable in whole genome and exome sequencing, automating the identification of pathogenic variants linked to rare diseases. By integrating clinical and phenotypic data, these systems can offer personalized insights, reduce diagnostic delays and improve genetic counseling and treatment development. However, the use of AI in rare disease diagnosis poses challenges, such as the need for diverse datasets to train algorithms and concerns over data privacy and equal access. Ensuring that AI tools are validated in diverse populations and effectively integrated into healthcare systems is crucial to their success. This summary will focus on the potential of AI to improve diagnostic accuracy, personalize treatments, and improve the management of rare diseases.Next Frontier For Life Sciences and AI20The potential of AI to improve diagnostic accuracy, personalize treatments, and improve the management of rare diseases is focused on.Next Frontier For Life Sciences and AI["Buse Liv"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/ebedf34e3c232b5d160536812d9387171e8f0e0d fde9b41c4c8135ec0b4ea40ef49282d2c6ab831fBackground In the research field of artificial intelligence (AI) in surgery, there are many open questions that must be clarified. Well-designed randomized controlled trials (RCTs) are required to explore the positive clinical impacts by comparing the use and non-use of AI-based intraoperative image navigation. Therefore, herein, we propose the ImNavi trial, a multicenter RCT, to compare the use and non-use of an AI-based intraoperative image navigation system in laparoscopic surgery. Methods The ImNavi trial is a Japanese multicenter RCT involving 1:1 randomization between the use and non-use of an AI-based intraoperative image navigation system in laparoscopic colorectal surgery. The participating institutions will include three high-volume centers with sufficient laparoscopic colorectal surgery caseloads (>100 cases/year), including one national cancer center and two university hospitals in Japan. Written informed consent will be obtained from all patients. Patients aged between 18 and 80 years scheduled to undergo laparoscopic left-sided colorectal resection will be included in the study. The primary outcome is the time required for each target organ, including the ureter and autonomic nerves, to be recognized by the surgeon after its initial appearance on the monitor. Secondary outcomes include intraoperative target organ injuries, intraoperative complications, operation time, blood loss, duration of postoperative hospital stay, postoperative complications within 30 days, postoperative male sexual dysfunction 1 month after surgery, surgeon's confidence in recognizing each target organ, and the postoperative fatigue of the primary surgeon. Discussion The impact of AI-based surgical applications on clinical outcomes beyond numerical expression will be explored from a variety of viewpoints while evaluating quantitative items, including intraoperative complications and operation time, as secondary endpoints. We expect that the findings of this RCT will contribute to advancing research in the domain of AI in surgery.medRxiv50The ImNavi trial is a Japanese multicenter RCT involving 1:1 randomization between the use and non-use of an AI-based intraoperative image navigation system in laparoscopic colorectal surgery and the findings will contribute to advancing research in the domain of AI in surgery.["D. Kitaguchi", "N. Fuse", "M. Wakabayashi", "N. Kosugi", "Y. Ishikawa", "K. Hayashi", "H. Hasegawa", "N. Takeshita", "M. Ito"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/fde9b41c4c8135ec0b4ea40ef49282d2c6ab831f e88d624a4d50940bb9b8137ff8cc4fdb17794d72This paper presents and evaluates a new retrieval augmented generation (RAG) and large language model (LLM)-based artificial intelligence (AI) technique: rubric enabled generative artificial intelligence (REGAI). REGAI uses rubrics, which can be created manually or automatically by the system, to enhance the performance of LLMs for evaluation purposes. REGAI improves on the performance of both classical LLMs and RAG-based LLM techniques. This paper describes REGAI, presents data regarding its performance and discusses several possible application areas for the technology.arXiv.org460ArXiv["Zach Johnson", "Jeremy Straub"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/e88d624a4d50940bb9b8137ff8cc4fdb17794d72 50cb3382f7ae54cf38e0de8b9a6a6d9a17c704ff: The world economy is in the process of transformation, and AI has immense potential to rapidly accelerate US economic growth. This research paper reviews how AI can contribute to the growth and development of the US economy by analyzing the impact of AI on productivity, industry transformation, job creation, and policy implications. The role of AI in helping to increase productivity through automation and efficiency gains is explored. Artificial Intelligence technologies automate the mundane processes, leaving human workers to focus their time on more intricate and creative projects. This kind of automation improves productivity and decisional capabilities, powered by advanced data analytics. Second, it is a technology that's currently driving changes in many different industries. Diagnostic tools powered by AI and AI - driven, personalized treatment plans contribute to better patient outcomes and cost reduction within healthcare. In the financial sector, AI reinforces risk management, enhances fraud detection, and offers customer service. In manufacturing, it optimizes supply chains, allows for predictive maintenance, and controls quality better—through which efficiency gains are realized. It also pointed out how AI had the potential to generate new employment opportunities against the backdrop of challenges arising from job displacement. In this regard, AI specialists, data scientists, and cybersecurity experts are emerging as totally new job profiles with growing needs. Particular emphasis was placed on the need for reskilling and upskilling of the workforce, and proposals made toward public - private partnerships so as to provide training courses that would impart employees with basic relevant digital skills. Finally, the paper turns to the policy implications of AI integration, calling for a robust regulatory framework to facilitate innovation while ensuring AI's ethical and responsible use. It also emphasized continuous investment in R&D for AI through increased public funding via the government and private investors to ensure the US remains at the leadership front of this technological advancement. Only by grasping AI's potential will the country be in a better position to increase productivity, drive industry transformation, and create new economic opportunities that will ultimately secure its position as a global economic leader. With this, the paper concludes by calling for policymakers, industry leaders, and educators to work together on the challenge of harnessing AI's potential for sustainable and equitable economic growth.International Journal of Science and Research (IJSR)180This research paper reviews how AI can contribute to the growth and development of the US economy by analyzing the impact of AI on productivity, industry transformation, job creation, and policy implications, and the role of AI in helping to increase productivity through automation and efficiency gains is explored.International Journal of Science and Research (IJSR)["Mohammed Saleem Sultan", "Mohammed Shahid Sultan"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/50cb3382f7ae54cf38e0de8b9a6a6d9a17c704ff f0675c7d4ce9e68003a85b778c5e4c0872e48443Introduction. Artificial Intelligence (AI) is a field of computer science where hardware and software systems enable machines to think and act like humans. AI utilizes different algorithms and computational resources to perform intelligent tasks autonomously. AI techniques applied in clinical decision support have proven effective across various medical disciplines, including clinical anaesthesia. Objective. The aim of this review is to explore the areas of anaesthesiology where artificial intelligence is utilized, the benefits of this implementation, and to define ethical dilemmas connected with using AI technology. Review Methods. PubMed and Google Scholar databases were searched using key words. Original articles in English, published between 2018–2024 were included. Brief description of the state of knowledge. AI is a rapidly developing field, notable particularly for its increasing application across various domains, including anaesthesiology. The amount of research on AI in anesthesia is growing. Summary. AI has shown considerable promise in various aspects of anaesthesiology, from pre-operative to post-operative care. AI-driven systems predict patient risk, manage drug dosages, administer drugs, and monitor vital signs more effectively than traditional methods. Its applications in anaesthesia can enhance patient outcomes through more personalized and precise interventions, optimize resource allocation, and improve overall efficiency in clinical practice. It also facilitates real-time decision-making and pro-active management of potential complications. However, AI cannot entirely replace the nuanced understanding and empathetic care provided by human professionals. As AI technology advances, legal and ethical standards must also evolve.Journal of Pre-Clinical and Clinical Research390The aim of this review is to explore the areas of anaesthesiology where artificial intelligence is utilized, the benefits of this implementation, and to define ethical dilemmas connected with using AI technology.Journal of Pre-Clinical and Clinical Research["Aleksandra Bogo\u0144", "Magdalena G\u00f3rska", "Magdalena Ostojska", "Izabela Ka\u0142u\u017ca", "Gabriela Dziuba", "Mateusz Dobosz"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/f0675c7d4ce9e68003a85b778c5e4c0872e48443 f156d2389fcdce25c7083e3eb7d2ef5d8fc76f1e<jats:p><jats:bold><jats:italic>Relevance.</jats:italic></jats:bold><jats:bold><jats:italic> </jats:italic></jats:bold><jats:italic>Artificial</jats:italic><jats:italic> </jats:italic><jats:italic>intelligence</jats:italic><jats:italic> </jats:italic><jats:italic>(AI)</jats:italic><jats:italic> </jats:italic><jats:italic>has</jats:italic><jats:italic> </jats:italic><jats:italic>become</jats:italic><jats:italic> </jats:italic><jats:italic>one</jats:italic><jats:italic> </jats:italic><jats:italic>of</jats:italic><jats:italic> </jats:italic><jats:italic>the</jats:italic><jats:italic> </jats:italic><jats:italic>most</jats:italic><jats:italic> </jats:italic><jats:italic>discussed</jats:italic><jats:italic> </jats:italic><jats:italic>and</jats:italic><jats:italic> </jats:italic><jats:italic>rapidly</jats:italic><jats:italic> </jats:italic><jats:italic>developing</jats:italic><jats:italic> </jats:italic><jats:italic>technologies</jats:italic><jats:italic> </jats:italic><jats:italic>in</jats:italic><jats:italic> </jats:italic><jats:italic>recent</jats:italic><jats:italic> </jats:italic><jats:italic>years.</jats:italic><jats:italic> </jats:italic><jats:italic>Its</jats:italic><jats:italic> </jats:italic><jats:italic>potential</jats:italic><jats:italic> </jats:italic><jats:italic>and</jats:italic><jats:italic> </jats:italic><jats:italic>capabilities</jats:italic><jats:italic> </jats:italic><jats:italic>have</jats:italic><jats:italic> </jats:italic><jats:italic>a significant impact on various spheres of life, including business. AI algorithms are used to analyze huge amounts of data and make forecasts, which helps companies make more efficient and rational decisions.</jats:italic></jats:p><jats:p><jats:bold><jats:italic>The</jats:italic></jats:bold><jats:bold><jats:italic> </jats:italic></jats:bold><jats:bold><jats:italic>purpose</jats:italic></jats:bold><jats:bold><jats:italic> </jats:italic></jats:bold><jats:italic>is</jats:italic><jats:italic> </jats:italic><jats:italic>to</jats:italic><jats:italic> </jats:italic><jats:italic>study the main trends in the use of artificial intelligence in business and determine its potential and advantages.</jats:italic></jats:p><jats:p><jats:bold><jats:italic>Objectives:</jats:italic></jats:bold><jats:bold><jats:italic> </jats:italic></jats:bold><jats:italic>to</jats:italic><jats:italic> </jats:italic><jats:italic>consider</jats:italic><jats:italic> </jats:italic><jats:italic>various</jats:italic><jats:italic> </jats:italic><jats:italic>aspects</jats:italic><jats:italic> </jats:italic><jats:italic>of</jats:italic><jats:italic> </jats:italic><jats:italic>the use of AI in business, to investigate its impact on the efficiency and effectiveness of business processes, as well as to identify the challenges and limitations associated with the use of this technology.</jats:italic></jats:p><jats:p><jats:bold><jats:italic>Methodology.</jats:italic></jats:bold><jats:italic>The</jats:italic><jats:italic> </jats:italic><jats:italic>research</jats:italic><jats:italic> </jats:italic><jats:italic>methodology</jats:italic><jats:italic> </jats:italic><jats:italic>includes</jats:italic><jats:italic> </jats:italic><jats:italic>empirical</jats:italic><jats:italic> </jats:italic><jats:italic>and</jats:italic><jats:italic> </jats:italic><jats:italic>analytical</jats:italic><jats:italic> </jats:italic><jats:italic>methods,</jats:italic><jats:italic> </jats:italic><jats:italic>statistical</jats:italic><jats:italic> </jats:italic><jats:italic>analysis</jats:italic><jats:italic> </jats:italic><jats:italic>is</jats:italic><jats:italic> </jats:italic><jats:italic>also</jats:italic><jats:italic> </jats:italic><jats:italic>used,</jats:italic><jats:italic> </jats:italic><jats:italic>revealing</jats:italic><jats:italic> </jats:italic><jats:italic>the features of the introduction of artificial intelligence in the business sphere.</jats:italic></jats:p><jats:p><jats:bold><jats:italic>Results.</jats:italic></jats:bold><jats:bold><jats:italic> </jats:italic></jats:bold><jats:italic>It</jats:italic><jats:italic> </jats:italic><jats:italic>has</jats:italic><jats:italic> </jats:italic><jats:italic>been</jats:italic><jats:italic> </jats:italic><jats:italic>revealed</jats:italic><jats:italic> </jats:italic><jats:italic>that the main areas of application of artificial intelligence in business are sales and communication with customers, marketing, content creation, and personnel management. The important aspects that companies should pay attention to when implementing artificial intelligence are indicated.</jats:italic></jats:p><jats:p><jats:bold><jats:italic>Conclusions.</jats:italic></jats:bold><jats:bold><jats:italic> </jats:italic></jats:bold><jats:italic>The</jats:italic><jats:italic> </jats:italic><jats:italic>results</jats:italic><jats:italic> </jats:italic><jats:italic>of</jats:italic><jats:italic> </jats:italic><jats:italic>the</jats:italic><jats:italic> </jats:italic><jats:italic>study</jats:italic><jats:italic> </jats:italic><jats:italic>showed</jats:italic><jats:italic> </jats:italic><jats:italic>that</jats:italic><jats:italic> </jats:italic><jats:italic>artificial</jats:italic><jats:italic> </jats:italic><jats:italic>intelligence</jats:italic><jats:italic> </jats:italic><jats:italic>(AI)</jats:italic><jats:italic> </jats:italic><jats:italic>has</jats:italic><jats:italic> </jats:italic><jats:italic>huge</jats:italic><jats:italic> </jats:italic><jats:italic>potential</jats:italic><jats:italic> </jats:italic><jats:italic>to</jats:italic><jats:italic> </jats:italic><jats:italic>improve</jats:italic><jats:italic> </jats:italic><jats:italic>business</jats:italic><jats:italic> </jats:italic><jats:italic>efficiency</jats:italic><jats:italic> </jats:italic><jats:italic>and</jats:italic><jats:italic> </jats:italic><jats:italic>productivity.</jats:italic><jats:italic> </jats:italic><jats:italic>Data</jats:italic><jats:italic> </jats:italic><jats:italic>security</jats:italic><jats:italic> </jats:italic><jats:italic>is</jats:italic><jats:italic> </jats:italic><jats:italic>a key aspect when using AI in business. Companies should pay special attention to the protection of information, ensuring confidentiality and data integrity. It is also important to keep in mind the ethical aspects of using AI in business, such as fairness, transparency and responsibility for decisions.</jats:italic></jats:p>Proceedings of the Southwest State University. Series: Economics. Sociology. Management10Proceedings of the Southwest State University. Series: Economics. Sociology. Management["N. V. Pyanova", "E. Stolyarova", "R. R. Pyanov", "O. Kryzhanovskaya"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/f156d2389fcdce25c7083e3eb7d2ef5d8fc76f1e 020c52a2d40e0e5a1be2fb08b9483785c2a5efc9International Journal of Human-Computer Interaction411International Journal of Human–Computer Interaction["Burhan \u00dcz\u00fcm", "M. El\u00e7i\u00e7ek", "Ata Pesen"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/020c52a2d40e0e5a1be2fb08b9483785c2a5efc9 164453459f010c400691865f0fc2ee22571f6d5dJournal of Educational Psychology00Journal of Educational Psychology[]2024-08-05T00:00:00https://www.semanticscholar.org/paper/164453459f010c400691865f0fc2ee22571f6d5d 74547b92f0395ca95ef1dcfa890473e7f9167d81Proceedings of the 17th IADIS International Conference Information Systems 2024,00Proceedings of the 17th IADIS International Conference Information Systems 2024,[]2024-08-05T00:00:00https://www.semanticscholar.org/paper/74547b92f0395ca95ef1dcfa890473e7f9167d81 4afaf10130fa12323a06376a6390cdba82600931In this paper we report experiences from a Massive Open Online Course (MOOC) on AI literacy. The course aims to cater to a diverse audience including corporate learners, educators and the general public. It employs a pedagogical framework that emphasizes underlying ideas, playful and constructionist design, and interactivity and fosters engagement and community through discussion forums and hands-on activities. Despite common challenges in MOOCs such as high dropout rates and the need for more personalized learning experiences, the course achieves notable success in retention and learner satisfaction. Data from the course reveals low attrition rates and positive feedback, highlighting its effectiveness in bridging the gap between different learner groups and promoting comprehensive understanding of AI among a broad audience.Grid Economics and Business Models270Data from the course reveals low attrition rates and positive feedback, highlighting its effectiveness in bridging the gap between different learner groups and promoting comprehensive understanding of AI among a broad audience.2024 IEEE 3rd German Education Conference (GECon)["Jadga H\u00fcgle", "Tilman Michaeli", "Stefan Seegerer"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/4afaf10130fa12323a06376a6390cdba82600931 c9a329fc1ecd0599df1e8d86e7857c7ef7697865Multimedia tools and applications170Multimedia Tools and Applications["P. Lin", "Feiyu Zhao", "Xiaoxuan Wang", "Yongming Chen"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/c9a329fc1ecd0599df1e8d86e7857c7ef7697865 0ee3edc12f00632afd76a6d0a0bb715cf43336cdJournal of Geoscience education350Journal of Geoscience Education["Shannon Sung", "Xiaotong Ding", "Rundong Jiang", "Elena Sereiviene", "Dylan Bulseco", "Charles Xie"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/0ee3edc12f00632afd76a6d0a0bb715cf43336cd 67ffab2c01279bdcc5982b4a2d6e390a1c3694b6Monitoring systems have been used in hydraulic centrifugal pumps for fault detection and predictive mainte-nance, which are critical to prevent costly unplanned shutdowns. Machine learning-driven monitoring systems that are based on pump vibration data have widely become popular in this context. The success of these techniques crucially depends on the quality of the vibration data received from the pump data acquisition system. In this regard, the paper first implements a proof-of-concept custom data acquisition unit mountable on the centrifugal pumps to log data related to the vibration, pressure, voltage, current, and temperature. Second, advanced machine learning techniques including Random Forest (RF), Multi-Layer Perceptron Classifier (MLPClassifier), Autoencoders (AEs), and Voting Classifier (VC) are applied to detect anomalies and classify 11 known faults. Performance metrics of the models are compared based on several tests on different experimental datasets. The accuracy of the implemented system has ranged from 98.5% to 100% regarding the classification of faults and detection of anomalies.2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)240A proof-of-concept custom data acquisition unit mountable on the centrifugal pumps to log data related to the vibration, pressure, voltage, current, and temperature is implemented and advanced machine learning techniques are applied to detect anomalies and classify 11 known faults.2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)["Mehmet Can Turk", "Zahra Kazemi", "Peter Rindom Andersen", "Jakob Lemming", "P. G. Larsen"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/67ffab2c01279bdcc5982b4a2d6e390a1c3694b6 0bca7e2eff5cb626b42c946fd4d37d6e0791577dThird International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024)00Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024)["Tao Zhao", "Chang'an Hu", "Yan Zang", "Fei Lv", "Dekun Peng", "Wanze Li"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/0bca7e2eff5cb626b42c946fd4d37d6e0791577d ac054daa9ee3596d4e6965a05f73ac642f426ba0Social Science Research Network310SSRN Electronic Journal["Tobias Heinrich", "Christopher Witko"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/ac054daa9ee3596d4e6965a05f73ac642f426ba0 ef9500b0bd1ee6839532c47bc0c77fd783469befKorean Journal of Chemical Engineering1000Korean Journal of Chemical Engineering["Junyong Bae", "Seung Jun Lee"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/ef9500b0bd1ee6839532c47bc0c77fd783469bef 8bed79669c44aa776970f398aacda6c45563bcbdInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Avni Gupta"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/8bed79669c44aa776970f398aacda6c45563bcbd b7307ab861efab6c0882a52fa75b869ea0540993Annals of Surgical Oncology30Annals of surgical oncology["Madelyn A. Flitcroft", "Anai N. Kothari"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/b7307ab861efab6c0882a52fa75b869ea0540993 882c0e97c60dae93c7b2363987a5be62b4474346This article explores the impact and potential applications of large language models in Occupational Medicine. Large language models have the ability to provide support for medical decision-making, patient screening, summarization and creation of technical, scientific, and legal documents, training and education for doctors and occupational health teams, as well as patient education, potentially leading to lower costs, reduced time expenditure, and a lower incidence of human errors. Despite promising results and a wide range of applications, large language models also have significant limitations in terms of their accuracy, the risk of generating false information, and incorrect recommendations. Various ethical aspects that have not been well elucidated by the medical and academic communities should also be considered, and the lack of regulation by government entities can create areas of legal uncertainty regarding their use in Occupational Medicine and in the legal environment. Significant future improvements can be expected in these models in the coming years, and further studies on the applications of large language models in Occupational Medicine should be encouraged.Revista Brasileira de Medicina do Trabalho140Large language models have the ability to provide support for medical decision-making, patient screening, summarization and creation of technical, scientific, and legal documents, training and education for doctors and occupational health teams, as well as patient education, potentially leading to lower costs, reduced time expenditure, and a lower incidence of human errors.Revista Brasileira de Medicina do Trabalho["Mateus Lins dos Santos", "Vera Nascimento Gomes Vict\u00f3ria"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/882c0e97c60dae93c7b2363987a5be62b4474346 4593bb01c753decdb4c2cb848765fe212b5c7d04Systematic literature reviews are the highest quality of evidence in research. However, the review process is hindered by significant resource and data constraints. The Literature Review Network (LRN) is the first of its kind explainable AI platform adhering to PRISMA 2020 standards, designed to automate the entire literature review process. LRN was evaluated in the domain of surgical glove practices using 3 search strings developed by experts to query PubMed. A non-expert trained all LRN models. Performance was benchmarked against an expert manual review. Explainability and performance metrics assessed LRN's ability to replicate the experts' review. Concordance was measured with the Jaccard index and confusion matrices. Researchers were blinded to the other's results until study completion. Overlapping studies were integrated into an LRN-generated systematic review. LRN models demonstrated superior classification accuracy without expert training, achieving 84.78% and 85.71% accuracy. The highest performance model achieved high interrater reliability (k = 0.4953) and explainability metrics, linking 'reduce', 'accident', and 'sharp' with 'double-gloving'. Another LRN model covered 91.51% of the relevant literature despite diverging from the non-expert's judgments (k = 0.2174), with the terms 'latex', 'double' (gloves), and 'indication'. LRN outperformed the manual review (19,920 minutes over 11 months), reducing the entire process to 288.6 minutes over 5 days. This study demonstrates that explainable AI does not require expert training to successfully conduct PRISMA-compliant systematic literature reviews like an expert. LRN summarized the results of surgical glove studies and identified themes that were nearly identical to the clinical researchers' findings. Explainable AI can accurately expedite our understanding of clinical practices, potentially revolutionizing healthcare research.arXiv.org440This study demonstrates that explainable AI does not require expert training to successfully conduct PRISMA-compliant systematic literature reviews like an expert, and can accurately expedite the understanding of clinical practices, potentially revolutionizing healthcare research.ArXiv["Joshua Morriss", "Tod Brindle", "Jessica Bah R\u00f6sman", "Daniel Reibsamen", "Andreas Enz"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/4593bb01c753decdb4c2cb848765fe212b5c7d04 26cbbeb29f757617bbfbcf1adc59fa98e67d1031International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Hasan Abdulsalam Ali Emran", "Fathia M Elhony"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/26cbbeb29f757617bbfbcf1adc59fa98e67d1031 00d84ce67c0ca6527aa5d86f9606eb48304eac6fSleep & breathing = Schlaf & Atmung50Sleep & Breathing = Schlaf & Atmung["N. Netzer"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/00d84ce67c0ca6527aa5d86f9606eb48304eac6f c3be7f3b66811f2bd9ca89a8e0816357a3c3b60dInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Mohammed Saleem Sultan", "Mohammed Shahid Sultan"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/c3be7f3b66811f2bd9ca89a8e0816357a3c3b60d 24a6fd92e8a89f5da1f2334bede6802ac61af716International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Bhushan Jayeshkumar Patel", "Jagbir Singh"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/24a6fd92e8a89f5da1f2334bede6802ac61af716 89f2dfbd650cf0301177448178db4b03b30dbea2Frontiers in Cardiovascular Medicine70Frontiers in Cardiovascular Medicine["A. Saglietto", "Elena Cavallone", "Michael Spartalis", "Bert Vandenberk", "M. Anselmino"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/89f2dfbd650cf0301177448178db4b03b30dbea2 909587c5645b433fe6805d261587ba6600b063a8Recent advancements in artificial intelligence (AI) technologies, particularly in generative pre-trained transformer large language models, have significantly enhanced the capabilities of text-generative AI tools—a development that opens new avenues for human–AI collaboration across various domains. However, the dynamics of human interaction with AI-based chatbots, such as ChatGPT, remain largely unexplored. We observed and analyzed how people interact with ChatGPT in a collaborative writing setting to address this research gap. A total of 135 participants took part in this exploratory lab study, which consisted of engaging with ChatGPT to compose a text discussing the prohibition of alcohol in public in relation to a given statement on risky alcohol consumption. During the writing task, all screen activity was logged. In addition to the writing task, further insights on user behavior and experience were gained by applying questionnaires and conducting an additional short interview with a randomly selected subset of 18 participants. Our results reveal high satisfaction with ChatGPT regarding quality aspects, mainly cognitive rather than affect-based trust in ChatGPT’s responses, and higher ratings on perceived competence than on warmth. Compared to other types of prompts, mostly content-related prompts for data, facts, and information were sent to ChatGPT. Mixed-method analysis showed that affinity for technology integration and current use of ChatGPT were positively associated with the frequency of complete text requests. Moreover, prompts for complete texts were associated with more copy–paste behavior. These first insights into co-writing with ChatGPT can inform future research on how successful human–AI collaborative writing can be designed.Applied Informatics734High satisfaction with ChatGPT regarding quality aspects, mainly cognitive rather than affect-based trust in ChatGPT’s responses, and higher ratings on perceived competence than on warmth are revealed, which can inform future research on how successful human–AI collaborative writing can be designed.AI["Teresa Luther", "J. Kimmerle", "U. Cress"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/909587c5645b433fe6805d261587ba6600b063a8 85e4494a8814026e6826e5ad2687880c56aab030Artificial Intelligence (AI) is increasingly acknowledged as a vital component for the advancement and competitiveness of modern organizations, including small and medium enterprises (SMEs). However, the adoption of AI technologies in SMEs faces significant barriers, primarily related to cost, lack of technical skills, and employee acceptance. This study proposes a comprehensive, phased framework designed to facilitate the effective adoption of AI in SMEs by systematically addressing these barriers. The framework begins with raising awareness and securing commitment from leadership, followed by the adoption of low-cost, general-purpose AI tools to build technical competence and foster a positive attitude towards AI. As familiarity with AI technologies increases, the framework advocates for the integration of task-specific AI tools to enhance efficiency and productivity. Subsequently, it guides organizations towards the in-house development of generative AI tools, providing greater customization and control. Finally, the framework addresses the development of discriminative AI models to meet highly specific and precision-oriented tasks. By providing a structured and incremental approach, this framework ensures that SMEs can navigate the complexities of AI integration effectively, driving innovation, efficiency, and competitive advantage. This study contributes to the field by offering a practical, prescriptive framework tailored to the unique needs of SMEs, facilitating the successful adoption of AI technologies and positioning these organizations for sustained growth in a competitive landscape.arXiv.org293A practical, prescriptive framework designed to facilitate the effective adoption of AI in SMEs by systematically addressing barriers, and facilitating the successful adoption of AI technologies and positioning these organizations for sustained growth in a competitive landscape.ArXiv["Atif Hussain", "Rana Rizwan"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/85e4494a8814026e6826e5ad2687880c56aab030 32f2014d1e0c2041ef0121ff995bd3c378faf779Artificial Intelligence (AI) has increasingly become a transformative force in the education sector, offering unprecedented opportunities to enhance learning experiences and outcomes. This study examines the potential adverse effects of AI-assisted learning on critical cognitive skills, particularly critical thinking and problem-solving, within the context of Albania's educational landscape. Employing a quantitative methodology, a survey of 53 students was conducted across various educational institutions in Albania to gather data on their experiences and perceptions regarding AI-assisted learning. The findings indicate no significant difference in critical thinking skills between students with prior exposure to AI tools and those without. However, there is a statistically significant negative correlation between reliance on AI tools for assignments and students' problem-solving skills, suggesting that excessive dependence on AI can hinder the development of independent problem-solving abilities. Conversely, a strong positive correlation was found between the frequency of AI tool usage and students' perceptions of academic performance and assignment efficiency, highlighting the potential benefits of AI in enhancing these aspects of the educational experience. These results emphasize the need for a balanced integration of AI tools in education to ensure they complement rather than replace traditional learning methods. The study's findings have significant implications for educators and policymakers, suggesting that while AI can enhance certain educational outcomes, it is essential to address its potential risks to promote the development of essential cognitive skills. Future research should focus on larger, more diverse samples, incorporate objective measures of cognitive skills, and explore the long-term impacts of AI-assisted learning.International Journal of Risk and Contingency Management313There is a statistically significant negative correlation between reliance on AI tools for assignments and students' problem-solving skills, suggesting that excessive dependence on AI can hinder the development of independent problem-solving abilities.International Journal of Risk and Contingency Management["Eriona \u00c7ela", "Mathias Mbu Fonkam", "R. Potluri"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/32f2014d1e0c2041ef0121ff995bd3c378faf779 a0b89f5cb5b502befc107ee37cd85eab7338f3a0This study delves into the pivotal integration of Artificial Intelligence (AI) in primary mathematics education, examining the nuanced interplay between teachers’ attitudes toward AI, Contextual Factors (CFs), such as institutional support and resource availability, and their Intention to Use AI (IUAI) in teaching practices. With the advent of digital transformation in education, understanding the dynamics of AI adoption in primary settings becomes increasingly critical. Surveying 476 primary mathematics teachers in China, this research employs partial least squares structural equation modeling (PLS-SEM) to unravel the complexities influencing AI integration. The findings reveal that teachers’ attitudes significantly impact their IUAI, while CF, particularly institutional support and resource availability, play a crucial role in shaping these attitudes and directly influencing IUAI. This investigation not only bridges a vital research gap by offering empirical insights into the determinants of AI adoption in a primary education context but also provides a robust framework for educators, policymakers, and stakeholders to foster a conducive environment for AI integration. By aligning AI deployment with pedagogical strategies, this study contributes significantly to enhancing the quality and effectiveness of mathematics education, underscoring the transformative potential of AI in shaping future educational landscapes.Asian Journal for Mathematics Education391The findings reveal that teachers’ attitudes significantly impact their IUAI, while CF, particularly institutional support and resource availability, play a crucial role in shaping these attitudes and directly influencing IUAI.Asian Journal for Mathematics Education["Mao Li", "Abdul Qawi Noori"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/a0b89f5cb5b502befc107ee37cd85eab7338f3a0 124405c029452da65f683eae10d6f1e618691e11: The rapid growth in Artificial Intelligence demands the progress of advanced computation techniques to support new complex algorithms. Traditional computational methods, mainly relying on classical architectures, become inadequate to satisfy the needs of state - of - the - art AI applications which demand greater computing power and efficiency. The paper deals with advanced computation techniques and their applicability and effectiveness for optimization in AI algorithms. It focuses on quantum computing, distributed systems, and neuromorphic computing as computational paradigms. Quantum computing uses principles of superposition and entanglement to give exponential speedups for specific problems of search and factorization. Distributed systems run enormous datasets and complex computations across a large number of computing resources and provide a scalable solution with efficiency. Neuromorphic computing works like the neural structure of the human brain and performs real - time processing in an energy - efficient manner. In this paper, we present a set of experiments that reveal how these cutting - edge computing technologies greatly improve the performance of AI algorithms. Quantum algorithms run on a simulated quantum processor exhibited marked computational time decreases for search and factorization problems. Distributed neural networks trained on a Hadoop cluster showed linear scalability with the addition of nodes, thus decreasing training time. That is to say, utilizing spiking neural networks allowed neuromorphic hardware to realize real - time processing while consuming very minimal energy, hence outperforming traditional architectures on tasks such as image recognition. The research builds on a number of unique data sets, with graphs showing how computational performance may be improved. In summary, our findings suggest that the marriage of these advanced computation techniques can empower the creation of more efficient and scalable AI systems and thus outline the future course of developments in the domain. This paper discusses the insights from the findings and future research directions. This paper explores advanced computation techniques crucial for optimizing complex AI algorithms, focusing on quantum computing, distributed systems, and neuromorphic computing. Through simulated experiments on quantum processors, distributed neural networks, and neuromorphic hardware, the research demonstrates significant improvements in processing speed, scalability, and energy efficiency. The findings suggest that integrating these techniques can lead to the development of more efficient and scalable AI systems, with significant implications for future AI advancements. This research is significant as it highlights the potential of cuttingedge computation techniques to revolutionize AI by improving processing efficiency, scalability, and energy consumption, paving the way for more robust and capable AI systems.International Journal of Science and Research (IJSR)211This research highlights the potential of cuttingedge computation techniques to revolutionize AI by improving processing efficiency, scalability, and energy consumption, paving the way for more robust and capable AI systems.International Journal of Science and Research (IJSR)["Mohammed Saleem Sultan", "Mohammed Shahid Sultan"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/124405c029452da65f683eae10d6f1e618691e11 95120250af1ec8147885045bc595618f78f99a91: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Agile development practices presents both opportunities and challenges. This paper explores how AI/ML can enhance Agile development, providing insights into the benefits, potential pitfalls, and best practices. Through a review of current literature and case studies, the paper identifies key strategies for successful integration.International Journal of Science and Research (IJSR)370This paper explores how AI/ML can enhance Agile development, providing insights into the benefits, potential pitfalls, and best practices, and identifies key strategies for successful integration.International Journal of Science and Research (IJSR)["Sai Vaibhav Medavarapu"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/95120250af1ec8147885045bc595618f78f99a91 76c7ceb05f7d0a85e0ffeb57d2e098e65e54c9ecBACKGROUND As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results. METHODS This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges. The scenarios were developed by expert nurses and underwent validation against current literature. Seventy-four ICU nurses participated in a simulation-based assessment involving four written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified ICU nurses for accuracy, completeness and response. RESULTS Nurses consistently achieved higher diagnostic accuracy than AI across open-ended scenarios, though certain models matched or exceeded human performance on standardized cases. Reaction times also diverged substantially. Qualitative response format differences emerged such as concision versus verbosity. Variations in GenAI models system performance across cases highlighted generalizability challenges. CONCLUSIONS While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement. Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration. Response format interfaces should consider leveraging distinct strengths. Rigorous mixed methods research involving diverse stakeholders can help iteratively inform safe, beneficial human-GenAI partnerships centred on experience-guided care augmentation. RELEVANCE TO CLINICAL PRACTICE This mixed-methods simulation study provides formative insights into optimizing collaborative models of GenAI and nursing knowledge to support patient assessment and decision-making in intensive care. The findings can help guide development of explainable GenAI decision support tailored for critical care environments. PATIENT OR PUBLIC CONTRIBUTION Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.Journal of Clinical Nursing120While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement, Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration.Journal of clinical nursing["C. Levin", "Moriya Suliman", "Etti Naimi", "M. Saban"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/76c7ceb05f7d0a85e0ffeb57d2e098e65e54c9ec 40c4bcac7bb3be507264fabe41b944efc41c768eIn the context of globalization, agricultural safety is directly linked to food security and human health. Modern agriculture’s challenge is effectively monitoring and processing vast agricultural safety information in the digital era. This study aims to achieve the automatic identification and intelligent processing of agricultural safety information within a digital media environment by applying artificial intelligence (AI) technologies. Initially, the background of AI applications in agriculture is explored, followed by an analysis of the urgent need for automated processing of agricultural safety information and an overview of current research in this field. It is demonstrated that, despite progress, existing methods still lack in-depth feature extraction and real-time capabilities of the data processing systems. A method based on multi-level feature fusion for identifying agricultural product safety is proposed to address these limitations alongside a decentralized AI Internet of Things (IoT) system for processing agricultural safety information. The multi-level feature fusion method can extract and integrate essential details on agricultural products from different dimensions and levels, thus enhancing the identification accuracy. Meanwhile, the decentralized AIoT system strengthens the efficiency and reliability of data processing, ensuring timely responses to agricultural safety information. Through deep neural networks, efficient recognition and classification of agricultural product images were achieved, enabling the automatic identification of agricultural safety information in a digital media environment. Utilizing deep learning algorithms, the system could learn and understand the characteristics of different agricultural products and accurately identify potential safety issues, such as pests, diseases, or spoilage, providing timely monitoring and management for agricultural production. The innovation of this research lies in the comprehensive utilization of multi-level data features and advanced Internet of Things (IoT) technology, significantly improving the level of intelligence in agricultural safety supervision.Quality Assurance and Safety of Crops &amp; Foods270Through deep neural networks, efficient recognition and classification of agricultural product images were achieved, enabling the automatic identification of agricultural safety information in a digital media environment, significantly improving the level of intelligence in agricultural safety supervision.Quality Assurance and Safety of Crops &amp; Foods["Aiping Li", "Pei Wang", "Lin Shao", "Huiyun Liu"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/40c4bcac7bb3be507264fabe41b944efc41c768e 98374b02a4885e1df3c4ee3891707e0705982454 Cloud adoption – often referred to as "digital" has in recent times, proven to effectively eliminate hardware and software resource efficiency and sufficiency limitations obtainable in on-premise (located within company facility) infrastructure, in the area of compute and storage. It has also become apparent, that Artificial Intelligence (AI) Assisted workflows dramatically increases productivity for domain experts (Engineers, Geoscientists, Geologists, etc.), abstracting away mundane and repetitive tasks while leaving room for greater levels of productivity. However, accessing the above stated benefits of AI and digital must be inevitably preceded by data liberation from siloed systems into a democratized cloud-ready secure platform. The SLB DMaaS (Data Management as a Service) solution, a suite of SLB "best-in-class" software technology which was successfully deployed for the first time in West Africa for a Nigerian Independent Energy Company in the year 2023, was designed to adequately implement a secure and democratized cloud ready collaborative environment devoid of siloed data sources. Aimed at creating an environment which is not just ready for a seamless transition to the cloud environment with minimal effort, but also a secure and accessible system to all authorized data users (domain experts) within the organization, the DMaaS solution is also OSDU (Open Subsurface Data Universe) ready. The DMaaS solution technology suite presents a key piece in the puzzle of the Global Energy Industry digital transformation journey as well as the benefits therein. Elaborately discussed in this paper, are insights to how SLB is helping the global energy industry unlock the value hidden in data through Data Management for cloud adoption, digital transformation and AI – assisted workflows.SPE Nigeria Annual International Conference and Exhibition40In insights to how SLB is helping the global energy industry unlock the value hidden in data through Data Management for cloud adoption, digital transformation and AI – assisted workflows, are insights to how SLB is helping the global energy industry unlock the value hidden in data.SPE Nigeria Annual International Conference and Exhibition["O. A. Jackson", "E. T. Tseyi"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/98374b02a4885e1df3c4ee3891707e0705982454 c5dd64ffd15484aff100c6e3fdbf782ec5a5922cArtificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these methods have been shown to be subject to race bias, i.e. they exhibit different levels of performance for different races depending on the (im)balance of the data used to train the AI model. In this paper we investigate the source of this bias, seeking to understand its root cause(s) so that it can be effectively mitigated. We perform a series of classification and segmentation experiments on short-axis cine CMR images acquired from Black and White subjects from the UK Biobank and apply AI interpretability methods to understand the results. In the classification experiments, we found that race can be predicted with high accuracy from the images alone, but less accurately from ground truth segmentations, suggesting that the distributional shift between races, which is often the cause of AI bias, is mostly image-based rather than segmentation-based. The interpretability methods showed that most attention in the classification models was focused on non-heart regions, such as subcutaneous fat. Cropping the images tightly around the heart reduced classification accuracy to around chance level. Similarly, race can be predicted from the latent representations of a biased segmentation model, suggesting that race information is encoded in the model. Cropping images tightly around the heart reduced but did not eliminate segmentation bias. We also investigate the influence of possible confounders on the bias observed.arXiv.org200It is found that race can be predicted with high accuracy from the images alone, but less accurately from ground truth segmentations, suggesting that the distributional shift between races, which is often the cause of AI bias, is mostly image-based rather than segmentation-based.ArXiv["Tiarna Lee", "E. Puyol-Ant\u00f3n", "B. Ruijsink", "S. Roujol", "Theodore Barfoot", "S. Ogbomo-Harmitt", "Miaojing Shi", "Andrew P. King"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/c5dd64ffd15484aff100c6e3fdbf782ec5a5922c 7f72fb34e4ae5be58ea6d6f7958d5e4091a8581cIn the contemporary era marked by rapid internationalization in higher education, strategizing effective means to leveraging the power of Artificial Intelligence in knowledge paradigm has taken center-stage in universities and higher educational institutions worldwide. Development and diffusion of AI techniques in conjunction with adaptation of best practices and their efficient reinforcement are enabling factors for resource optimization and purpose-driven outcome based learning in an inclusive and sustainable manner. Incorporating AI applications in higher education has multipronged advantages encompassing enhanced performance prediction, resource mobilization, assessment and evaluation, user-friendly learning management system, intelligent tutoring systems, and improvement of learning experiences for students inclusive of psychometric profiling of students. AI-powered tools, chatbots and similar techniques assist in personalized self-paced learning for students while preparing customized resources for collaborative student learning and engagement. Concurrently, adoption of AI applications have remarkable imprints in developing predictive analytics to improve retention management, intelligent analytics, assistive technology, and automatic content analysis. Ai-powered techniques such as Natural Language Processing (NLP) also enhance the efficacy of collaborative research in virtual space especially in areas of big data analytics, clinical assessment, high impact publications, patents, trademarks, etc. Higher educational institutions can leverage AI techniques for administrative decision efficiency and devising superior models of CRM. Interestingly, AI techniques is being increasingly leveraged by students of humanities, arts, management and commerce for knowledge creation, dissemination and better employability. This research paper evaluates the implications of AI techniques on outcome-based learning, action deliverables, and adoption of best practices in higher educational landscape by analysing latest developments in AI integration as emerging new normal. While doing so the paper assesses AI efficacy in terms of capacity building of learning management system in higher educational institutions and provides recommendations for improvements in integration process.The Business and Management Review310This research paper assesses AI efficacy in terms of capacity building of learning management system in higher educational institutions and provides recommendations for improvements in integration process.The Business and Management Review["Padmakali Banerjee", "Debasis Bhattacharya"]2024-08-05T00:00:00https://www.semanticscholar.org/paper/7f72fb34e4ae5be58ea6d6f7958d5e4091a8581c 4af8ff97e781d177561eea18cbd6ff135b7cb294Environmental science and pollution research international701While concerns about the reduction in human resources and potential unemployment, as well as the activation time and high energy consumption of AI systems, are identified, the study underscores the success of AI in data analytics as a strong aspect.Environmental science and pollution research international["S. Yal\u00e7\u0131n", "Ertu\u011frul Ayy\u0131ld\u0131z"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/4af8ff97e781d177561eea18cbd6ff135b7cb294 09db9456cf4643a8d1e77795ae2490c8c2f44e5eArtificial Intelligence is a system designed using technology that can make a computer system that can imitate human intellectual abilities. Chat-GPT (Generative Pre-trained Transformer) is a platform from Artificial Intelligence where it works using a conversational format. Simply put, it's like we ask a lecturer in class, but in Chat-GPT we ask an application that has been programmed to imitate human intellectual abilities so that it is able to provide answers in a short time. This study will use a quantitative approach with a descriptive method. The data of the research results was obtained through a survey by distributing questionnaires to Bumigora University students at random. Data from the study showed that most students (72%) used Chat-GPT to obtain information related to learning. By using Chat-GPT, 38.11% of students felt helped in learning and obtaining information related to learning. Chat-GPT in addition to having many benefits, also has a negative impact so there needs to be supervision for students in using this Chat-GPT platform so that academic ethical and moral values are maintained and upheld so that what students do brings benefits.Indo-MathEdu Intellectuals Journal142Chat-GPT in addition to having many benefits, also has a negative impact so there needs to be supervision for students in using this Chat-GPT platform so that academic ethical and moral values are maintained and upheld so that what students do brings benefits.Indo-MathEdu Intellectuals Journal["Mutiah Mutiah", "Elyakim Nova Supriyedi Patty", "Sri Astuti Iriani"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/09db9456cf4643a8d1e77795ae2490c8c2f44e5e 02760f5b819ef34e15f392d111632f60affb7254PurposeDrawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.Design/methodology/approachUsing survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.FindingsWe find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.Originality/valueOur study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.International Journal of Operations &amp; Production Management471It is found that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience.International Journal of Operations &amp; Production Management["Jing Dai", "Ruoqi Geng", "Dong Xu", "W. Shangguan", "Jinan Shao"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/02760f5b819ef34e15f392d111632f60affb7254 4a29a7ba226e3f8b68aabb1cfc3b9aaea9f28092The rapid advancement of Artificial Intelligence (AI) technologies has permeated various industries, ushering in transformative changes. One such sector experiencing significant shifts is accountancy. This paper presents a systematic literature review to discern the impact of AI on the domain of accountancy, aiming to unveil its repercussions, challenges, and difficulties. The review draws from a vast array of academic journals, white papers, industry reports, and conference proceedings. The results indicate that while AI streamlines tasks like data processing and forecasting, leading to enhanced efficiency and accuracy, it also brings about potential challenges. These challenges include the demand for new skill sets, fears of job displacement, ethical considerations, and the requirement for updated regulatory frameworks. However, a noteworthy observation during this review was the complexities and challenges in carrying out a systematic review itself, given the rapid technological advancements and diverse perspectives in the existing literature. Inconsistencies in terminologies, overlapping scopes, and the multifaceted nature of AI applications in accountancy further complicated the process. This paper offers a dual contribution: shedding light on the implications of AI in accountancy and providing insights into the intricacies faced while conducting a systematic literature review in a dynamic field.Socio-Economic and Humanistic Aspects for Township and Industry411A systematic literature review to discern the impact of AI on the domain of accountancy aims to unveil its repercussions, challenges, and difficulties, and indicates that while AI streamlines tasks like data processing and forecasting, leading to enhanced efficiency and accuracy, it also brings about potential challenges.Socio-Economic and Humanistic Aspects for Township and Industry["F. Rusgowanto"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/4a29a7ba226e3f8b68aabb1cfc3b9aaea9f28092 b9f35f29f87497e76bb92eb00f82a639a5f28972BMC Medical Education351The need for comprehensive AI education in medical and dental curricula to address knowledge gaps and prepare future healthcare professionals for the ethical and effective integration of AI in practice is underscored.BMC Medical Education["Sebastian Fitzek", "Kyung-Eun (Anna) Choi"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/b9f35f29f87497e76bb92eb00f82a639a5f28972 e6f0b6a342f82b02996d53397777199d752986baToday’s world is changing rapidly because of technological developments. Among the astonishing evolution, AIoT has become a new addition to machinery growth. This term is a compound of another two dominant infrastructures: Artificial Intelligence (AI) and the Internet of Things (IoT). AIoT intends to invent more efficient and improved IoT operations or services with enhanced data management and analysis capabilities. AI deals with machines, especially computer systems fields like natural language processing, speech recognition, etc. IoT is a system where interrelated devices and machines communicate and transfer data with different sensors without human intervention. AIoT can be more beneficial for both types of technology, where a vast amount of data obtained from IoT devices, will be handled by some powerful AI algorithms. When it needs to handle a lot of data, essential and efficient data processing is required to use the information gathered from IoT devices. Without integrating AI, IoT devices can't aid efficient services. Besides, AIoT is utilized to handle efficiency, scalability, accuracy, and fraud detection in transactions. This paper aims to show the potentiality of AIoT, its recent applications and benefits, and the probable future of this technology.2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)281The potentiality of AIoT, its recent applications and benefits, and the probable future of this technology are shown.2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA)["Chowdhury Abida Anjum Era", "Mahmudur Rahman", "Syada Tasmia Alvi"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/e6f0b6a342f82b02996d53397777199d752986ba b3607f1dcf2a5b010388c88d84c59f97eb5b1460Introduction Eating Disorders (EDs) affect individuals globally and are associated with significant physical and mental health challenges. However, access to adequate treatment is often hindered by societal stigma, limited awareness, and resource constraints. Methods The project aims to utilize the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve EDs diagnosis and treatment. The Master Data Plan (MDP) will collect and analyze data from diverse sources, utilize AI algorithms for risk factor identificat io n, treatment planning, and relapse prediction, and provide a patient-facing chatbot for information and support. This platform will integrate patient data, support healthcare professionals, and empower patients, thereby enhancing care accessibility, personalizing treatment plans, and optimizing care pathways. Robust data governance measures will ensure ethical and secure data management. Results Anticipated outcomes include enhanced care accessibility and efficiency, personalized treatment plans leading to improved patient outcomes, reduced waiting lists, heightened patient engagement, and increased awareness of EDs with improved resource allocation. Discussion This project signifies a pivotal shift towards data-driven, patient-centered ED care in Italy. By integrat ing AI and promoting collaboration, it seeks to redefine mental healthcare standards and foster better well- being among individuals with EDs.Frontiers in Psychiatry422This project signifies a pivotal shift towards data-driven, patient-centered ED care in Italy and seeks to redefine mental healthcare standards and foster better well- being among individuals with EDs.Frontiers in Psychiatry["Francesco Monaco", "A. Vignapiano", "Martina Piacente", "Claudio Pagano", "Carlo Mancuso", "Luca Steardo", "Alessandra Marenna", "Federica Farina", "Gianvito Petrillo", "Stefano Leo", "Emanuela Ferrara", "Stefania Palermo", "V. Martiadis", "Marco Solmi", "A. Monteleone", "Alessio Fasano", "G. Corrivetti"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/b3607f1dcf2a5b010388c88d84c59f97eb5b1460 eb54c490e3ae96661fd790290f88be8d05173897Journal of Environmental Management818Journal of environmental management["Muhammad Salman", "Guimei Wang", "Lin Qin", "Xing He"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/eb54c490e3ae96661fd790290f88be8d05173897 9e3f367c2d26137679bfb45e107f666a716e8496 In the rapidly evolving oil and gas industry, leveraging technology for optimizing well design and operations is essential. This technical paper outlines the integration of machine learning and artificial intelligence (AI) to address critical challenges in drilling operations, safety enforcement, and environmental monitoring. It also explores the importance of robust data governance and standardization facilitated by Company's DreamWell tool. Each technology application aims to enhance decision-making, increase operational and cost efficiency, and promote safety. The complexity of well operations in the oil and gas industry necessitates robust data governance and standardization to ensure efficient, safe, and sustainable practices. As time goes by, Company has developed DreamWell, an integrated tool for database management, reporting, evaluation, analysis, and monitoring of well operations. DreamWell optimizes technology to improve data governance and standardization, leading to better decision-making, operational efficiency, and regulatory compliance. This paper explores the features and capabilities of DreamWell, its impact on data governance and standardization in well operations, and its potential for future development.SPE/IADC Asia Pacific Drilling Technology Conference and Exhibition00The integration of machine learning and artificial intelligence (AI) to address critical challenges in drilling operations, safety enforcement, and environmental monitoring is outlined and the importance of robust data governance and standardization facilitated by Company's DreamWell tool is explored.SPE/IADC Asia Pacific Drilling Technology Conference and Exhibition["Syatria Kumala Putra", "Mohammad Faisal Umar", "Ridwan Sangaji", "Andi Nugroho", "Ichsan Farandi Dananjaya", "Ardilla Sufri", "Rafli Nur Prima"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/9e3f367c2d26137679bfb45e107f666a716e8496 949b2bc2ff05310775f54c839dbbdbfb8270206fThis study aims to explore the innovative application of AI virtual anchors in live streaming e-commerce under the background of artificial intelligence. With the development of artificial intelligence technology and the rise of live streaming e-commerce, the combination of virtual anchor technology with live streaming platforms and e-commerce has created a new concept of AI virtual anchor live streaming e-commerce. This new form of shopping experience not only brings consumers a brand-new feeling, but also provides enterprises with an innovative marketing approach. The advantage of AI virtual anchor live streaming e-commerce lies in its ability to attract consumer attention through the image and personalized display of virtual anchors. Virtual anchors can provide customized recommendations based on consumer needs and preferences, provide personalized shopping suggestions, and increase the efficiency of purchasing decisions. In addition, virtual anchors can also showcase the functions, characteristics, and usage methods of products, enabling consumers to have a more intuitive understanding of the products and enhancing their confidence in purchasing. Through live streaming platforms, consumers can interact in real time with virtual anchors, ask questions, comment, and share their shopping experience, further enhancing their sense of participation and satisfaction. However, AI virtual anchors also face some challenges in live streaming e-commerce. Firstly, the image and personalized display of virtual anchors need to have a high degree of realism and attractiveness to attract consumer attention. Sec-ondly, technical support and a stable network environment are crucial for achieving good live streaming results. In addition, protecting consumer privacy and data security is also an important issue that requires the development of corresponding policies and regulations.Journal of Humanities Arts and Social Science50This study aims to explore the innovative application of AI virtual anchors in live streaming e-commerce under the background of artificial intelligence to provide personalized shopping suggestions, and increase the efficiency of purchasing decisions.Journal of Humanities, Arts and Social Science["Miao Shi"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/949b2bc2ff05310775f54c839dbbdbfb8270206f c263b322cccd66c0651dbe0bb47145f47aacfbf6Pengabdian masyarakat ini bertujuan untuk melakukan transformasi kreatif dalam pelatihan artificial intelligence (AI) menggunakan Canva guna meningkatkan keterampilan presentasi siswa di MTS Darul Hikmah. Dalam era digital yang terus berkembang, kemampuan presentasi yang baik menjadi keterampilan penting yang harus dimiliki oleh siswa. Pelatihan ini mengintegrasikan teknologi AI dengan platform desain Canva untuk memberikan bimbingan praktis dalam membuat presentasi yang menarik dan efektif. Program ini mencakup sesi pelatihan intensif tentang penggunaan AI dalam Canva untuk merancang slide presentasi yang berkualitas, serta teknik presentasi yang memukau. Metode yang diterapkan termasuk workshop, sesi praktik langsung, dan evaluasi berkala untuk memastikan siswa mampu menerapkan ilmu yang diperoleh. Hasil pengabdian masyarakat ini menunjukkan adanya peningkatan yang signifikan dalam kualitas presentasi siswa, serta peningkatan kepercayaan diri dan kreativitas mereka. Program ini diharapkan dapat menjadi model bagi pelatihan serupa di institusi pendidikan lain dan memberikan kontribusi positif terhadap pengembangan keterampilan presentasi siswa secara umum.   This community service aims to carry out creative transformation in artificial intelligence (AI) training using Canva to improve students' presentation skills at MTS Darul Hikmah. In the ever-evolving digital era, good presentation skills are an important skill that students must have. This training integrates AI technology with Canva's design platform to provide practical guidance in creating engaging and effective presentations. The program includes intensive training sessions on using AI in Canva to design quality presentation slides, as well as stunning presentation techniques. The methods applied include workshops, hands-on practice sessions, and periodic evaluations to ensure students are able to apply the knowledge they have acquired. The results of this community service show a significant improvement in the quality of students' presentations, as well as an increase in their confidence and creativity. This program is.Jurnal Abdimas Komunikasi dan Bahasa60Jurnal Abdimas Komunikasi dan Bahasa["Mari Rahmawati", "Rusma Insan Nurachim", "Wangsit Supeno", "Ade Fitria Lestari"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/c263b322cccd66c0651dbe0bb47145f47aacfbf6 44c47f1df96ee0a22d6ad303d6d9c213e1e6c69fJournal of Medical and Biological Engineering330These investigations evaluated the potential utility of AI models that analyzed routine laboratory data, medical records, ECG, transthoracic echocardiography, CMR, and WBS in the diagnosis of CA.Journal of Medical and Biological Engineering["Armia Ahmadi-Hadad", "Egle De Rosa", "L. Di Serafino", "G. Esposito"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/44c47f1df96ee0a22d6ad303d6d9c213e1e6c69f 906c53f1700a1c32658615b16358a95e9aa937eaABSTRACT ChatGPT, an artificial intelligence (AI) system released by OpenAI on November 30th, 2022, has upended scientific and educational paradigms, reshaping the way that we think about teaching, writing, and now research. Since that time, qualitative data analytic software programs such as ATLAS.ti have quickly incorporated AI into their programs to assist with or even replace human coding. Qualitative research is key to understanding the complexity and nuance of HIV-related behaviors, through descriptive and historical textual research, as well as the lived experiences of people with HIV. This commentary weighs the pros and cons of the use of AI coding in HIV-related qualitative research. We pose guiding questions that may help researchers evaluate the application and scope of AI in qualitative research as determined by the research question, underlying epistemology, and goal(s). Qualitative data encompasses a variety of media, methodologies, and styles that exist on a spectrum underpinned by epistemology. The research question and the data sources are informed by the researcher's epistemological viewpoint. Given the heterogeneous applications of qualitative research in nursing, medicine, and public health there are circumstances where qualitative AI coding is appropriate, but this should be congruent with the aims and underlying epistemology of the research.Journal of the Association of Nurses in AIDS Care80This commentary weighs the pros and cons of the use of AI coding in HIV-related qualitative research and poses guiding questions that may help researchers evaluate the application and scope of AI in qualitative research as determined by the research question, underlying epistemology, and goal(s).The Journal of the Association of Nurses in AIDS Care : JANAC["A. Bergman", "K. C. McNabb", "M. Relf", "Mark Dredze"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/906c53f1700a1c32658615b16358a95e9aa937ea 2aca0ce76a985f32755854634d44975724b489e7This paper reviewed some project initiatives on inclusive and equitable use of artificial intelligence (Al) in education with a view of guiding education policy-makers and stakeholders in the development of similar project initiatives in Nigeria. The gap in extant literature on policy direction for the development of inclusive and equitable use of Al in education in Nigeria necessitated the paper. It began with a discussion of the general overview of Al and the unprecedented revolution it has created in the education delivery. It further discussed the advantages of the Al application in education as well as the challenges and controversies associated with it particularly the generative Al (GenAl) systems which are rapidly spreading in the education sector. It also discussed UNESCO’s recommendations of the appropriate use of GenAl in education to genuinely benefit and empower teachers, learners and researchers. It advocated the necessity of Nigerian government to develop a policy framework for the responsible use of GenAl in education and research. Most importantly, the paper identified some large-scale project initiatives on inclusive and equitable use of Al in education and discussed the lessons derivable from the project initiatives for policy direction in the development of similar projects in Nigeria. The paper recommended, among others, special funding on the development of inclusive and equitable Al apps in the education budgetary allocation and private sector partnership/collaboration in funding in the development of such Al apps.COUNS-EDU: The International Journal of Counseling and Education00The paper recommended, among others, special funding on the development of inclusive and equitable Al apps in the education budgetary allocation and private sector partnership/collaboration in funding in the development of such Al apps.COUNS-EDU: The International Journal of Counseling and Education["Kester Ojokheta", "A. Omokhabi"]2024-08-06T00:00:00https://www.semanticscholar.org/paper/2aca0ce76a985f32755854634d44975724b489e7 7842d7a4a8b909b110a246f1eb6e4efc7ff2f2f3Over the past few years, the field of artificial intelligence and neurotechnology has moved beyond the scope of exclusive scientific discussion to the realm of public policy. The state is an important participant in technological progress, which allows us to consider in detail the connection between government officials and neurosciences, because, according to a large number of scientists, it is this segment of sciences that will allow humanity to transition to a new technological order. The purpose of this study is to consider the theoretical foundations of the interaction of the subjects of the public administration system with end-to-end technologies and to search for practical examples of the implementation of this interaction. In the course of the work such methods as theoretical analysis, comparison and contrast, cognitive method, system analysis, and analysis of statistical data were used. The theoretical foundations of the study of neurotechnologies, as well as the market of the existing neuroprosthetics products, were considered. The authors of the research studied and compared examples of the development of plans and the application of artificial intelligence and neurotechnologies in such countries as Russia, the United States, and the United Kingdom, and analysed global rankings of digitalisation of public administration. Based on this, it was concluded that countries are actively participating in a new technological race, trying to introduce artificial intelligence in the field of public administration in order to gain their own advantage, however the sluggishness of states in the development of neurotechnologies, with subsequent implementation in the public sector, was noted, and the fact of significant differences in the understanding of artificial intelligence in public administration around the world was revealed. This fact creates a field for further research and discussion. The results of the research can be used in the framework of further study of the analyzed aspects by scientific and research organizations, within the framework of the activities of federal authorities, as well as private companies.Management Sciences130The authors of the research studied and compared examples of the development of plans and the application of artificial intelligence and neurotechnologies in such countries as Russia, the United States, and the United Kingdom, and analysed global rankings of digitalisation of public administration.Management Sciences["R. E. Salnichenko", "L. K. Babayan"]2024-08-06T00:00:00 273fd658ec8eb1c5eafa7c2ae868f71450c59453The purpose of this study was to investigate research trends in artificial intelligence studies related to education that were published in the Web of Science database. This review conducted a bibliometric analysis of 4673 articles published between 1975 and 2023 and explored trends in several areas, including the annual distribution of publications, frequently studied topics, top authors, top countries, top universities/departments, top journals and publishers, and top funders. The findings highlighted that the number of studies increased exponentially after 2010. The most often used terms in artificial intelligence research in education were machine learning, deep learning, and data mining. Studies in higher education have been more prevalent than studies in elementary and secondary education. The USA, mainland China, and Australia were the three most productive nations. Most productive authors were connected to academic institutions in Taiwan, Hong Kong, or mainland China. Furthermore, there was little cooperation among the most productive authors andcountries. In addition to the abundance of journals on educational technology, it is crucial to emphasize the dearth of publications on education across different disciplines. To understand how artificial intelligence can support new practices in educational research, interdisciplinary interest and support are needed.Sustainability490A bibliometric analysis of 4673 articles published between 1975 and 2023 explored trends in several areas, including the annual distribution of publications, frequently studied topics, top authors, top countries, top universities/departments, top journals and publishers, and top funders.Sustainability["Ibrahim Delen", "Nihal Sen", "Fatma Ozudogru", "M. Biasutti"]2024-08-06T00:00:00 19f86a3cd572aa9655f71a456902770ccd697d3dArtificial Intelligence in Business Management is a review of artificial intelligence (AI) applications in businesses. This book adopts a cross-disciplinary strategy toward AI adoption. Book chapters explore many projects that go beyond simple data management and accessibility to showcase the growing role of artificial intelligence and machine learning in the enterprise data space. AI methods for tackling marketing and commercial strategies, as well as the use of AI and machine learning in tourism, insurance and healthcare systems, are discussed. A study on the significance of cultural assets in evaluating risks and protection is also presented. The content gives valuable insights on the application and implications of artificial intelligence and machine learning from this book to readers aiming for corporate roles, such as directors, executives, senior software developers, and digital transformation managers. The book is an essential resource for researchers and professionals in business, economics, and allied disciplines.00The content gives valuable insights on the application and implications of artificial intelligence and machine learning from this book to readers aiming for corporate roles, such as directors, executives, senior software developers, and digital transformation managers.["Mohammed Majeed"]2024-08-06T00:00:00 37e8b48dccd727246a84dfc86322b07e45941a4eThis paper presents a comprehensive bibliometric analysis aimed at mapping the convergent landscapes of artificial intelligence (AI) and financial technology (Fintech), fields poised for significant disruptive potential in global financial services. Given the rapid integration of AI within financial operations, this research investigates the publication trends, key contributing nations, and prevalent themes within this intersection, crucial for understanding the trajectory of Fintech innovations and their alignment with AI advancements. Employing a robust methodology utilizing VOSviewer and the Scopus database, the analysis distilled insights from 298 selected papers, focusing on co-authorship, bibliographic coupling, and keyword occurrences to highlight the global influential works and primary research hubs. Key findings reveal that AI and Fintech are not only predominant themes but are also the nucleus of emerging scholarly discussions, with the United States, China, and India leading in contributions. These nations, alongside others like France and the United Kingdom, form critical nodes in our analysis, indicating a robust interconnection of global research efforts. This study introduces the novel application of advanced bibliometric techniques to dissect dense academic outputs, offering a granular view of how AI influences financial technologies. The implications of this research are manifold; it provides a strategic blueprint for academics, industry practitioners, and policymakers to understand the focal areas of AI in Fintech, suggesting an amplified focus on collaborative innovations and policy-making that aligns with technological advancements. Future research should expand the analysis to include diverse databases and explore the integration of AI across various financial sectors, emphasizing the socioeconomic impacts, ethical considerations, and regulatory challenges posed by AI-driven financial services. This extended focus will enhance our understanding of AI’s role in shaping the future of Finance, ensuring comprehensive coverage of this dynamically evolving field.International Journal of Financial Engineering00International Journal of Financial Engineering["Manish Kumar", "Babita Jha", "Gaurav Gupta", "Shiv Ranjan"]2024-08-06T00:00:00 8967f383b41d6992bce027d42ee1089f514d5d8bЦифровые технологии сегодня являются одним из показателей мирового лидерства, уровень их развития и внедрения в реальный сектор экономики влияет на национальную безопасность государства. Искусственный интеллект как одна из наиболее топовых цифровых технологий в условиях глобализации, жесточайших санкционных мер приобретает актуальное значение во всех сферах общественной жизни, особенно ожидается стремительное развитие ИИ-технологий в промышленности. Автор рассматривает ключевые направления развития технологий, выделяет проблемы и риски внедрения искусственного интеллекта в основные отрасли промышленности. В статье приводятся примеры применения технологий в промышленности и указываются подходы к решению существующих проблем. Digital technologies today are one of the indicators of world leadership, the level of their development and implementation in the real sector of the economy affects the national security of the state. Artificial intelligence as one of the most advanced digital technologies in the context of globalization and the most severe sanctions measures is becoming relevant in all spheres of public life, especially the rapid development of AI technologies in industry is expected. The author examines the key directions of technology development, highlights the problems and risks of introducing artificial intelligence into the main industries. The article provides examples of the use of technologies in industry and indicates approaches to solving existing problems.Экономика и предпринимательство00Экономика и предпринимательство["\u0418.\u0410. \u0413\u0443\u0441\u0435\u0432\u0430"]2024-08-06T00:00:00 4ff9bc00342fdcae7e14122f25c3d38b7754bcc6Цифровые технологии являются неотъемлемой частью жизни современного общества. Их развитие, в частности в сфере искусственного интеллекта, влечёт за собой трансформацию экономической сферы страны. Бизнес применяет высокотехнологичные компоненты и инновации в качестве эффективного инструмента своего развития и повышения уровня конкурентоспособности. В рамках данной статьи будет дана характеристика технологическому предпринимательству, а также рассмотрены актуальные тенденции, которые прослеживаются сегодня в бизнес-сфере в связи с активным прогрессом в области технологий на основе искусственного интеллекта. Digital technologies are an integral part of the life of modern society. Their development, in particular in the field of artificial intelligence, entails a transformation of the country s economic sphere. Business uses high-tech components and innovations as an effective tool for its development and increasing its level of competitiveness. Within the framework of this article, technological entrepreneurship will be characterized, and current trends that can be traced today in the business sphere in connection with active progress in the field of technologies based on artificial intelligence will be considered.Экономика и предпринимательство00Экономика и предпринимательство["\u041c.\u0410. \u041c\u0430\u0439\u043e\u0440\u043e\u0432\u0430", "\u0421.\u041d. \u041c\u0430\u0439\u043e\u0440\u043e\u0432\u0430", "\u041d.\u0421. \u041c\u0430\u0439\u043e\u0440\u043e\u0432"]2024-08-06T00:00:00 22d2a5f55c701094832bc9960d332d6c12408399В рамках данной научной статьи раскрываются тенденции в сфере использования искусственного интеллекта отечественными кампаниями. Анализируются особенности тенденций в рамках межотраслевого сопоставления. Раскрываются особенности влияния активности в сфере внедрения искусственного интеллекта, на эффективность деятельности предприятий различных отраслей через призму показателя рентабельности. Осуществлена кластеризация отраслей национальной экономики в соответствии с особенностями зависимости рентабельности деятельности от уровня внедрения технологий искусственного интеллекта в деятельность. These scientific articles reveal trends in the use of artificial intelligence by domestic campaigns. Features of trends are analyzed within the framework of intersectoral discussion. Reveal the features of activity in the field of artificial intelligence, the efficiency of enterprises in various industries through the prism of profitability indicators. Clustering of sectors of the national economy was carried out in accordance with the characteristics of the profitability of activities depending on the level of artificial intelligence technologies in the activity.Экономика и предпринимательство00Экономика и предпринимательство["\u0414.\u0420. \u041c\u0443\u0441\u0442\u0430\u0444\u0438\u043d"]2024-08-06T00:00:00 45b7df870ed8113536a6ce710202591769c04641Artificial intelligence (AI) is poised to transform health care across medical specialties. Although the application of AI to neuroanesthesiology is just emerging, it will undoubtedly affect neuroanesthesiologists in foreseeable and unforeseeable ways, with potential roles in preoperative patient assessment, airway assessment, predicting intraoperative complications, and monitoring and interpreting vital signs. It will advance the diagnosis and treatment of neurological diseases due to improved risk identification, data integration, early diagnosis, image analysis, and pharmacological and surgical robotic assistance. Beyond direct medical care, AI could also automate many routine administrative tasks in health care, assist with teaching and training, and profoundly impact neuroscience research. This article introduces AI and its various approaches from a neuroanesthesiology perspective. A basic understanding of the computational underpinnings, advantages, limitations, and ethical implications is necessary for using AI tools in clinical practice and research. The update summarizes recent reports of AI applications relevant to neuroanesthesiology. Providing a holistic view of AI applications, this review shows how AI could usher in a new era in the specialty, significantly improving patient care and advancing neuroanesthesiology research.Journal of Neuroanaesthesiology and Critical Care950Providing a holistic view of AI applications, this review shows how AI could usher in a new era in the specialty, significantly improving patient care and advancing neuroanesthesiology research.Journal of Neuroanaesthesiology and Critical Care["Zhenrui Liao", "N. Mathur", "Vidur Joshi", "Shailendra Joshi"]2024-08-06T00:00:00 4577f7d371d30bedb81afd71b1fe0f17a3a5ded8Современная экономическая система, отличается активными темпами внедрения информационных технологий, одной из которых, является искусственный интеллект. С его помощью достигается минимизация рисков, повышение производительности труда, увеличение эффективности деятельности хозяйствующих субъектов. Учитывая данный факт, в мировой экономике отмечается увеличение инвестиций в разработки связанные с искусственным интеллектом. В статье проведен анализ использования искусственного интеллекта в экономике нашей страны, а также обоснован тезис относительно необходимости активизации процесса интеграции искусственного интеллекта в деятельность отечественных хозяйствующих субъектов. The modern economic system is characterized by an active pace of introduction of information technologies, one of which is artificial intelligence. It helps to minimize risks, increase labor productivity, and increase the efficiency of business entities. Given this fact, there is an increase in investments in artificial intelligence-related developments in the global economy. The article analyzes the use of artificial intelligence in the economy of our country, and also substantiates the thesis on the need to intensify the process of integrating artificial intelligence into the activities of domestic economic entities.Экономика и предпринимательство00Экономика и предпринимательство["\u0414.\u0420. \u041c\u0443\u0441\u0442\u0430\u0444\u0438\u043d"]2024-08-06T00:00:00 2f5194bbcfaedd43c521bae8d7eef474242af0c2Purpose: The main objective of the research was to analyse the effects of conversational artificial intelligence (AI) on bank operational efficiency. The emergency of conversational artificial intelligence (AI) has revolutionised the way business interacts with its customers. Methodology: The study employed a mixed- method approach where interviews and questionnaires were used to collect qualitative and quantitative data. A sample of 92 bank employees was drawn from ten Zimbabwean banks. Findings: Conversational AI has a positive impact on banking operational efficiency. Specifically, conversational AI improves customer services by providing faster and more accurate responses to customer inquiries, reduces operational costs by automating routine tasks and improve workflow efficiency Limitation: The study concentrated on the banking industry of one particular country. Contribution: The study makes a significant contribution in understanding the advantages of adopting conversational artificial intelligence in banking operations.International Journal of Accounting and Management Information Systems240Conversational AI improves customer services by providing faster and more accurate responses to customer inquiries, reduces operational costs by automating routine tasks and improve workflow efficiency.International Journal of Accounting and Management Information Systems["Lilian Gumbo", "Margaret Mashizha", "Chosani Simon", "Phillipa Phiri"]2024-08-06T00:00:00 b7681dc3ad24ebd72adcaf35dee638ea8099a8baPemanfaatan Artificial Intelligence (AI) dalam bidang pendidikan memudahkan mahasiswa dalam penyelesaian tugas. Penelitian ini bertujuan untuk  mengetahui persepsi mahasiswa jurusan ilmu perpustakaan UIN Sumatera Utara terhadap penggunaan Artificial Intelligence (AI) dalam membantu penulisan karya ilmiah. Penelitian ini menggunakan metode kualitatif dengan pengambilan sampel menggunakan teknik purposive dan snowball. Teknik pengumpulan data dilakukan melalui observasi, wawancara dan dokumentasi. Analisis data dilakukan menggunakan software NVivo dan dilakukan triangulasi untuk memastikan validitas dan kredibilitas temuan penelitian. Hasil penelitian menunjukkan bahwa mahasiswa memiliki persepsi positif terhadap penggunaan Artificial Intelligence (AI) dalam komunikasi ilmiah. Artificial Intelligence (AI) dianggap cukup relevan untuk dijadikan acuan dalam mencari referensi pada penulisan karya ilmiah. Namun, tidak sedikit dari mahasiswa yang kurang ahli dalam menganalisis platform kecerdasan buatan yang relevan dan lebih valid dalam memberikan informasi. The utilisation of artificial intelligence (AI) in the field of education facilitates the completion of tasks by students. This study aims to ascertain the perception of students of the science major at the UIN Library of North Sumatra regarding the role of Artificial Intelligence (AI) in assisting with the composition of scientific papers. This research employs qualitative methods, utilising purposive and snowball sampling techniques. Data is gathered through observations, interviews and document analysis. The data analysis was conducted using NVivo software, and triangulation was employed to ensure the validity and credibility of the research findings. The results indicated that students held a positive perception of the use of artificial intelligence (AI) in scientific communication. Artificial Intelligence (AI) was perceived as a valuable reference tool for locating scientific literature. However, a significant proportion of students demonstrated limited proficiency in analysing AI platforms that could provide more reliable and valid information.Djtechno: Jurnal Teknologi Informasi300Djtechno: Jurnal Teknologi Informasi["Nurlaili Hasibuan", "Retno Sayekti"]2024-08-06T00:00:00 34a31c80efcc225d33f753be735f4d30679cf0aeACS Sustainable Chemistry &amp; Engineering1053ACS Sustainable Chemistry &amp; Engineering["S. Sadeghi", "Richard B. Canty", "Nikolai Mukhin", "Jinge Xu", "Fernando Delgado-Licona", "M. Abolhasani"]2024-08-06T00:00:00 d09ff64b81e0421de6d81f1e060252d85f878a23Journal of Medical Imaging and Radiation Sciences1002The ethical, legal, and regulatory landscape of AI integration in Australian healthcare is examined, the current legislative framework is examined, the trust and reliability of AI tools are assessed, and future directions for ethical AI integration in radiography are proposed.Journal of medical imaging and radiation sciences["M. Chau"]2024-08-06T00:00:00 684633ffa5def3e38db434fa00f9bc4467543287Education and Information Technologies : Official Journal of the IFIP technical committee on Education582Education and Information Technologies["F. Razak", "Mohd Amli Abdullah", "B. Ahmad", "Wan Hashridz Rizal Bin Wan Abu Bakar", "Nur Aulia Fahada binti Misaridin"]2024-08-06T00:00:00 a923be632d01006ed1f8ca46668676571c5e2033Статья посвящена анализу процессов цифровой трансформации в условиях реализации стратегической задачи укрепления технологического суверенитета Российской Федерации. В данной статье рассматриваются некоторые теоретические аспекты развития современных цифровых технологий и практика их применения в экономической и социальной сферах, в системе государственного и корпоративного управления. Проводится анализ современных тенденций на рынке труда в условиях цифровой трансформации. Особое внимание уделено развитию технологий искусственного интеллекта, его практическое применение и влияние на рынок труда. Авторами рассматриваются проблемы и перспективные направления подготовки кадров для современных отраслей экономики, обеспечивающих технологический суверенитет страны. Актуальность данной статьи определяется необходимостью разработки механизмов использования цифровых технологий, в том числе искусственного интеллекта в процессе эффективного использования трудового потенциала в социальноэкономическом развитии Российской Федерации и ее регионов. The article is devoted to the analysis of the processes of digital transformation in the context of the implementation of the strategic task of strengthening the technological sovereignty of the Russian Federation. This article discusses some theoretical aspects of the development of modern digital technologies and the practice of their application in the economic and social spheres, in the system of public and corporate governance. The analysis of current trends in the labor market in the context of digital transformation is carried out. Special attention is paid to the development of artificial intelligence technologies, its practical application and impact on the labor market. The authors consider the problems and promising areas of personnel training for modern sectors of the economy that ensure the technological sovereignty of the. The relevance of this article is determined by the need to develop mechanisms for the use of digital technologies, including artificial intelligence in the process of effective use of labor potential in the socio-economic development of the Russian Federation and its regions.Экономика и предпринимательство00Экономика и предпринимательство["\u0418.\u042e. \u041a\u0430\u043b\u043c\u044b\u043a\u043e\u0432\u0430", "\u041d.\u042e. \u0410\u043b\u0435\u043a\u0441\u0430\u043d\u0434\u0440\u0443\u043a"]2024-08-06T00:00:00 36619844611b37f6b1a805b0bbfad5eeb7da7030В этой статье исследуется роль искусственного интеллекта (ИИ) в практике управления персоналом (HR), уделяя особое внимание повышению операционной эффективности и стратегическому управлению персоналом. Используя систематический обзор научной литературы в качестве методологии, в статье исследуется, как технологии искусственного интеллекта интегрируются в различные функции управления персоналом, такие как подбор персонала, управление производительностью и вовлечение сотрудников. Результаты показывают, что ИИ не только автоматизирует рутинные задачи, но и вносит значительный вклад в принятие стратегических решений посредством прогнозной аналитики и персонализированных стратегий управления сотрудниками. В статье делается вывод, что ИИ в сфере управления персоналом не только оптимизирует процессы, но и создает новые проблемы, особенно с точки зрения этических соображений и конфиденциальности данных. Новизна этой работы заключается во всестороннем анализе роли искусственного интеллекта в изменении функций HR для адаптации к требованиям современной рабочей силы, подчеркивая необходимость баланса между технологическими достижениями и этическими практиками HR. This article investigates the role of Artificial Intelligence (AI) in Human Resources (HR) practices, focusing on enhancing operational efficiency and strategic workforce management. Employing a systematic review of recent literature as the methodology, the paper explores how AI technologies are integrated into various HR functions such as recruitment, performance management, and employee engagement. The findings reveal that AI not only automates routine tasks but also significantly contributes to strategic decision-making through predictive analytics and personalized employee management strategies. The article concludes that AI in HR not only streamlines processes but also presents new challenges, particularly in terms of ethical considerations and data privacy. The novelty of this work lies in its comprehensive analysis of AI's role in reshaping HR functions to adapt to the demands of the modern workforce, highlighting the necessity for a balance between technological advancements and ethical HR practices.Экономика и предпринимательство00Экономика и предпринимательство["\u0410.\u0423. \u041d\u0443\u0440\u0430\u043b\u0438\u0435\u0432\u0430"]2024-08-06T00:00:00 728f575f3d2d1aeae81ba52bf796fecea72eac49В работе рассматривается влияние цифровых технологий, включая искусственный интеллект, на сферу финансовых услуг и финтех-инновации. Авторы статьи анализируют текущее состояние и перспективы развития цифровизации в финансовом секторе, акцентируя внимание на ключевых аспектах применения современных технологий в улучшении операционной эффективности и конкурентоспособности финансовых механизмов. Основная цель исследования - оценить, как применение ИИ и других цифровых технологий способствует инновациям в финтехе, а также выявить преимущества и недостатки цифровизации для данной отрасли. В статье подробно обсуждаются изменения в структуре международных финансов, которые модифицируются под воздействием технологических инноваций. Исследование охватывает анализ практических аспектов внедрения цифровых решений на примерах ведущих финансовых институтов и стартапов в сфере финтеха. Авторы предлагают ряд рекомендаций для оптимизации использования цифровых технологий, направленных на повышение эффективности финансовых процессов. The paper examines the impact of digital technologies, including artificial intelligence, on the financial services sector and fintech innovations. The authors of the article analyze the current state and prospects for the development of digitalization in the financial sector, focusing on key aspects of the use of modern technologies in improving the operational efficiency and competitiveness of financial mechanisms. The main purpose of the study is to assess how the use of AI and other digital technologies contributes to innovation in fintech, as well as to identify the advantages and disadvantages of digitalization for this industry. The article discusses in detail the changes in the structure of international finance, which are being modified under the influence of technological innovations. The study covers the analysis of practical aspects of the implementation of digital solutions using the examples of leading financial institutions and startups in the field of fintech. The authors propose a number of recommendations for optimizing the use of digital technologies aimed at improving the efficiency of financial processes.Экономика и предпринимательство00Экономика и предпринимательство["\u041e.\u041f. \u0428\u0435\u0432\u0447\u0435\u043d\u043a\u043e", "\u0410.\u041b. \u0417\u043e\u043b\u043a\u0438\u043d", "\u0415.\u0412. \u041b\u043e\u043c\u0430\u043a\u0438\u043d\u0430", "\u041e.\u042e. \u042f\u043d\u043e\u0432\u0430"]2024-08-06T00:00:00 8c130a2ad3419a6b3fd8d8260f351308e02fc403The article considers aspects of the use of neural networks in business processes of formation and promotion of tourist products (excursions and tourist programs). The analysis of neural network software in the Google and Yandex search engines in Russian and English is carried out.Gostinichnoe delo (Hotel Business)00Parts of the use of neural networks in business processes of formation and promotion of tourist products (excursions and tourist programs) and the analysis of neural network software in the Google and Yandex search engines in Russian and English are considered.Gostinichnoe delo (Hotel Business)["N. N. Balev", "O. Y. Zeveke"]2024-08-06T00:00:00 a23b747f451533cbb1ce26d1b81a7ae4d76128b9The convergence of economic development, urbanization, technological innovation, and environmental challenges necessitates novel approaches to meeting and managing energy demands. Three emerging trends present unique challenges that make striking the appropriate balance to meet societal needs particularly challenging for our energy systems.Climate and Energy00Climate and Energy["Shreyas Vangala", "Leah Liebovitz", "Morgan Witt"]2024-08-06T00:00:00 9631374e161e20a0fb646414917405d7ab1e6282Scientific Reports00Scientific Reports["Andrej Planinc", "Nina \u0160pegel", "Zala Podobnik", "Uro\u0161 \u0160inigoj", "Petra Skubic", "June Ho Choi", "Wonhyoung Park", "Tina Robi\u010d", "Nika Tabor", "Leon Jarabek", "\u017diga \u0160piclin", "\u017diga Bizjak"]2024-08-06T00:00:00 5f00ba1ab119aa82acfabe6f26336e700ccfa971В научной статье представлены результаты анализа использования искусственного интеллекта в банковском кредитовании, выявлены направления использования и перспективы его применения в Российской Федерации. Авторы анализируют возможности искусственного интеллекта в деятельности финансовых институтов Российской Федерации, целесообразность использования цифровых финансовых технологий, определяют возможности эффективного взаимодействия отдельных технологий с применением искусственного интеллекта при внедрении данной технологий в деятельность банков. В научной статье раскрыты преимущества использования искусственного интеллекта и цифровых технологий в деятельности банков, в которую входят все виды операций, начиная от анализа кредитоспособности клиента и его финансовых потребностей для получения персональных предложений, заканчивая вычислением потенциальных направлений предоставления дополнительных услуг банками. Авторами дополнительно выделены отдельные цифровые технологии, при взаимодействии с которыми искусственный интеллект приводит к синергетическому эффекту в банковском кредитовании. В статье определены перспективы в использования цифровых технологий в деятельности банков Российской Федерации. The scientific article presents the results of an analysis of the use of artificial intelligence in bank lending, identifying areas of use and prospects for its use in the Russian Federation. The authors analyze the possibilities of artificial intelligence in the activities of financial institutions of the Russian Federation, the feasibility of using digital financial technologies, and determine the possibilities for effective interaction of individual technologies with the use of artificial intelligence when introducing this technology into the activities of banks. The scientific article reveals the advantages of using artificial intelligence and digital technologies in the activities of banks, which includes all types of operations, from analyzing the client’s creditworthiness and his financial needs to receive personal offers, to calculating potential areas for the provision of additional services by banks. The authors additionally highlight certain digital technologies, when interacting with which artificial intelligence leads to a synergistic effect in bank lending. The article identifies prospects for the use of digital technologies in the activities of banks in the Russian Federation.Экономика и предпринимательство00Экономика и предпринимательство["\u0410.\u0412. \u041c\u0430\u043b\u044c\u043a\u043e", "\u0415.\u0418. \u0412\u043e\u0440\u043e\u0431\u044c\u0435\u0432\u0430"]2024-08-06T00:00:00 992604602d37c4eef669839ed3bb40a9bc567be4AI &amp; SOCIETY540This paper proposes to enact AGI crimes that complement the varieties of legal responses to existential risks that might motivate and speed up further regulatory changes.AI &amp; SOCIETY["Kamil Mamak"]2024-08-06T00:00:00 a85dbd019762d03d08562ba72a9fc97001c61883The study aims to investigate the effect of a 5-week artificial intelligence-generated calisthenics training program (AIGCTP) on health-related physical fitness components, including flexibility, cardiovascular endurance, and muscular endurance. Utilizing a quasi-experimental design, the study employed a one-group pre-test-post-test design for within-group comparisons and a two-group pre-test-post-test design for between-group comparisons. Participants included 87 untrained collegiate students, divided into the AIGCTP group (43 participants) and a human-made calisthenics training program (HMCTP) group (44 participants), selected via purposive sampling. A paired t-test was used for within-group comparisons, and an independent sample t-test was used for between-group comparisons. The findings indicated that the AIGCTP effectively improved the flexibility of the lower extremities and the muscular endurance of the core and upper extremities. However, female participants did not show significant improvements in any health-related physical fitness components, whereas male participants demonstrated improvements in the flexibility of the lower extremities and muscular endurance of the upper extremities. The HMCTP was effective in improving the flexibility and muscular endurance of the lower and upper extremities for all participants. Between-group comparisons revealed that the cardiovascular endurance of the HMCTP group was significantly superior to that of the AIGCTP group, irrespective of sex. Additionally, males in the HMCTP group exhibited significantly higher muscular endurance of the lower extremities compared to those in the AIGCTP group. The study suggests that AI can be used for fitness training, but professional-made programs are superior in some areas. Future research should replicate these findings, examine more fitness components, and explore longer training durations for further validation.Journal of Human Sport and Exercise8312The findings indicated that the AIGCTP effectively improved the flexibility of the lower extremities and the muscular endurance of the core and upper extremities and that professional-made programs are superior in some areas.Journal of Human Sport and Exercise["Ramon Carlo E. Masagca"]2024-08-06T00:00:00 2963685fcf6b317bf362aa510f9f6aef5172617enpj Science of Learning4610This systematic review assessed the quality of AI literacy scales using the COSMIN tool aiming to aid researchers in choosing instruments for AI literacy assessment, finding good structural validity and internal consistency.NPJ Science of Learning["Tom\u00e1\u0161 Lintner"]2024-08-06T00:00:00 c9c9b930cbbbb0c3bc8b778ce6709846b2661f6aHumanities and Social Sciences Communications335The paper identifies the central ethical imperative that student assignments must reflect individual knowledge acquired during their education, with human individuals retaining moral and legal responsibility for AI-related wrongdoings in the first university responses examined by this study.Humanities and Social Sciences Communications["Attila Dabis", "C. Cs\u00e1ki"]2024-08-06T00:00:00 b6b8d0e187b4a7d28a74cbf8971f75d93cc6ac61Artificial intelligence (AI) is revolutionizing plant sciences by enabling precise plant species identification, early disease diagnosis, crop yield prediction, and precision agriculture optimization. AI uses machine learning and image recognition to aid ecological research and biodiversity conservation. It plays a crucial role in plant breeding by accelerating the development of resilient, high-yielding crops with desirable traits. AI models using climate and soil data contribute to sustainable agriculture and food security. In plant phenotyping, AI automates the measurement and analysis of plant characteristics, enhancing our understanding of plant growth. Ongoing research aims to improve AI models’ robustness and interpretability while addressing data privacy and algorithmic biases. Interdisciplinary collaboration is essential to fully harness AI’s potential in plant sciences for a sustainable, food-secure future.Exploration of Foods and Foodomics1284In plant phenotyping, AI automates the measurement and analysis of plant characteristics, enhancing the authors' understanding of plant growth and contributing to a sustainable, food-secure future.Exploration of Foods and Foodomics["Deependra Kumar Gupta", "A. Pagani", "Paolo Zamboni", "Ajay Kumar Singh"]2024-08-06T00:00:00 184bb73accd01ae66cd277ac7e45716f9df2ea02AI &amp; SOCIETY394This paper presents an overview of ethical approaches and processes aimed at integrating ethical considerations into AI system development practices, and underscores the significance of ethical frameworks in fostering ethical AI implementation and ensuring the ethical integrity of AI technologies.AI &amp; SOCIETY["Anetta Jedli\u010dkov\u00e1"]2024-08-06T00:00:00 dd864ff81da1410cc835c71c4388d44863c90b8cArtificial intelligence (AI) mediated approaches towards generating or refining digital software are influencing the aerospace industry and its emerging, developing, and innovative operational systems. Such firm and/or industry inclusions can enhance entrepreneurial effectiveness and ongoing sustainability. A nationwide Qualtrics survey delivers semi-structured interview summations of registered software-house developers (‘experts) showing nodal thematic data can NVivo project map that stagewise models towards a suite of sustainability outcomes. Respondent software developers consider software house digital and AI new capacities, plus their knowledge creation frame across two capabilities areas that relationally link into an entrepreneurial capabilities suite and a strategic AI product trending capabilities suite. This system likely coalesces, as a digital AI-supported collective, towards enhancements into digital and AI globally sustainable outcomes. The aerospace industry. and their engaged software houses can use this operational approach when strategically planning future ongoing software and AI development inclusions across their globally located corporate aerospace entities, by considering how best to adjust ongoing digital and AI competencies and entrepreneurial capabilities towards delivering enhanced sustainable outcomes. This study suggests a stagewise and measurement model approach can likely be derived to introduce chosen software and AI levers, but these should focus on delivering ongoing digital and sustainable aerospace industry outcomes.Innovations in Aerospace Science and Technology00This study suggests a stagewise and measurement model approach can likely be derived to introduce chosen software and AI levers, but these should focus on delivering ongoing digital and sustainable aerospace industry outcomes.Innovations in Aerospace Science and Technology["Hamilton John R", "Ali Syeda Arfa", "Maxwell Stephen J"]2024-08-06T00:00:00 281d6db1d0694ee0adfacd0faa3d9ef576b8a004Technology integration in foreign language learning is a must today. One of the issues is the utilization of artificial intelligence (AI) Chatbot in English language teaching. Some studies have mentioned the benefits and advantages of using AI Chatbot. Nonetheless, none of the studies examines deeply on hoe ESP vocabulary. To fill the gaps, this study examines the effect of AI Chatbot especially Dialogflow enhanced the ESP vocabulary acquisition. The experimental comparison of two groups—an experience group and a control group—is the backbone of this study in order to accomplish that purpose. Both groups underwent pre-tests and post-tests to assess the effectiveness of utilizing AI chatbot in learning ESP vocabulary. The chatbot content was meticulously constructed to incorporate vocabulary features such as synonyms and concise explanations of word meanings. The study's findings revealed that utilizing chatbots significantly improves the acquisition of ESP vocabulary. It was found that students in the experimental group that used Dialogflow, a chatbot, performed better than students in the control group. To add, the study suggests that chatbots could be utilized in many situations to enhance language learning in general or in specific ESP courses. A chatbot provides a stimulating setting to facilitate positive interactions where the negotiation of meaning occurs explicitly, which appears to greatly benefit learners in advancing their second language lexical development.ETERNAL (English Teaching Journal)290The study's findings revealed that utilizing chatbots significantly improves the acquisition of ESP vocabulary, and suggests that chatbots could be utilized in many situations to enhance language learning in general or in specific ESP courses.ETERNAL (English Teaching Journal)["L. M. Silitonga", "Wiyaka Wiyaka", "E. Prastikawati"]2024-08-06T00:00:00 9c556e6aa0167fb3c3e8c9509b9fa085b4e443b2In this paper, the effects of the rapid advancement of generative artificial intelligence (Gen AI) in higher education (HE) are discussed. A mixed exploratory research approach was employed to understand these impacts, combining analysis of current research trends and students’ perceptions of the effects of Gen AI tools in academia. Through bibliometric analysis and systematic literature review, 64 publications (indexed in the SCOPUS and Web of Science databases) were examined, highlighting Gen AI’s disruptive effect on the pedagogical aspects of HE. The impacts identified by the literature were compared with the perceptions held by computer science students of two different HE institutions (HEIs) on the topic. An exploratory study was developed based on the application of a questionnaire to a group of 112 students. The results suggest that while Gen AI can enhance academic work and learning feedback, it requires appropriate pedagogical support to foster critical, ethical, and digital literacy competencies. Students demonstrate awareness of both the risks and benefits associated with Gen AI in academic settings. The research concludes that failing to recognize and effectively use Gen AI in HE impedes educational progress and the adequate preparation of citizens and workers to think and act in an AI-mediated world.The social science719The research concludes that failing to recognize and effectively use Gen AI in HE impedes educational progress and the adequate preparation of citizens and workers to think and act in an AI-mediated world.Social Sciences["Sandra Sa\u00fade", "Jo\u00e3o-Paulo Barros", "In\u00eas Almeida"]2024-08-07T00:00:00 71c988351a1cba51a7889f9e0ff10fb2782af053The introduction and implementation of Artificial Intelligence (AI) in higher education has brought out new opportunities and obstacles. The utilization of AI will result in a significant transformation of the governance structure within global higher educational institutions. The potential use of AI involves exploring the educational implications of how teachers may enhance their teaching methods, how students can improve their learning experience, and how institutions of higher education can make more accurate and timely judgments. This is significant because the workload has significantly increased as a result of the widespread expansion of higher education. Given the circumstances, AI assistance is crucial. The implementation of artificial intelligence in higher education is a significant matter in this context. The objective of this study is to investigate the feasibility of individuals adopting it. To do this, we have formulated hypotheses and a conceptual framework, which we then validated through a survey by obtaining feedback from a total of 240 respondents. Research has discovered that the model can assist authorities in promoting the implementation of artificial intelligence in higher education. The outcome of this study will help practitioners understand the insights of people’s intentions and psychology in adopting AI in educational sectors.Journal of Computer Science and Technology Studies317The objective of this study is to investigate the feasibility of individuals adopting artificial intelligence and to help practitioners understand the insights of people’s intentions and psychology in adopting AI in educational sectors.Journal of Computer Science and Technology Studies["Ashok Ghimire", "Md Ahsan Ullah Imran", "Barna Biswas", "Anamika Tiwari", "Sanchita Saha"]2024-08-07T00:00:00 2a8b1e55f87e4f066c003af6271aaa9a11d85ee5PurposeThe theoretical background bases on the big data analytics-artificial intelligence (BDA-AI) technologies and supply chain ambidexterity (SCAX) in the firms to assess their sustainability endeavors such as green supply chain management (GSCM) to improve their green communication and corporate image.Design/methodology/approachAround 220 participants in the manufacturing firms are participants' industry expertise, diverse roles, and representation as key stakeholders.FindingsThe results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image. Green communication was discovered to impact corporate image significantly.Originality/valuePrior studies are neglected to address the relationship among the AI, powered by rapid computational and BDA breakthroughs, redefines cognitive tasks, achieving feats previously deemed impossible-making implicit judgments, simulating emotions, and driving operations. This study selects manufacturing firms as respondents due to their forefront of BDA-AI and supply chain ambidexterity adoption to benefit the operational efficiency and competitiveness. The firms intricate supply chains, diverse stakeholders, and strategic emphasis on corporate image make it an ideal context to examine the nuanced impact of these technologies.Industrial management & data systems813The results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image and green communication was discovered to impact corporate image significantly.Ind. Manag. Data Syst.["Chin-Tsu Chen", "Shih-Chih Chen", "Asif Khan", "M. K. Lim", "Ming\u2010Lang Tseng"]2024-08-07T00:00:00 3e51578fc5b1a34ebf2731f6d5ee3849c73fe2f4Traditionally, users have perceived that only manual laborers or those in repetitive jobs would be subject to technological substitution. However, with the emergence of technologies like Midjourney, ChatGPT, and Notion AI, known as Artificial Intelligence-Generated Content (AIGC), we have come to realize that cognitive laborers, particularly creative designers, also face similar professional challenges. Yet, there has been relatively little research analyzing the acceptance and trust of artificial intelligence from the perspective of designers. This study integrates the TAM/TPB behavioral measurement model, incorporating intrinsic characteristics of designers, to delineate their perceived risks of AIGC into functional and emotional dimensions. It explores how these perceived characteristics, risks, and trust influence designers’ behavioral intentions, employing structural equation modeling for validation. The findings reveal the following: (1) designer trust is the primary factor influencing their behavioral choices; (2) different dimensions of perceived risks have varying degrees of impact on trust, with functional risks significantly positively affecting trust compared to emotional risks; (3) only by enhancing the transparency and credibility of Artificial Intelligence-Generated Content (AIGC) can the perceived characteristics of designers be elevated; and (4) only by effectively safeguarding designers’ legitimate rights and interests can perceived risks be significantly reduced, thereby enhancing trust and subsequently prompting actual behavioral intentions. This study not only enhances the applicability and suitability of AIGC across various industries but also provides evidence for the feasibility of intelligent design in the creative design industry, facilitating the transition of AIGC to Artificial Intelligence-Generated Design (AIGD) for industrial upgrading.Applied Sciences603This study enhances the applicability and suitability of AIGC across various industries but also provides evidence for the feasibility of intelligent design in the creative design industry, facilitating the transition of AIGC to Artificial Intelligence-Generated Design (AIGD) for industrial upgrading.Applied Sciences["Shao-Feng Wang", "Chun-Ching Chen"]2024-08-07T00:00:00 4262810bd9da6c10f8a9ac8ce3470173899a6bd8Developing artificial intelligence (AI) is a priority on China’s state agenda, yet the constitutive state roles in AI development are understudied. Against the background of the coexistence of authoritarianism and market liberalism in the governance of the Chinese economy, state rescaling is a useful lens to understand how China is developing this new strategic sector. This paper proposes an analytical framework reifying vertical and horizontal scalar relations via the rescaling lens and three state roles (owner, promoter, and supervisor) to explore how government-business relationships are made. More than 100 Chinese AI policy documents have been collected from central, provincial, and city levels for a systematic multi-scalar policy analysis. As a result, this paper captures both downscaling/upscaling within the state hierarchy and statization/destatization between state and non-state actors in China’s AI development. A series of inter-twined state rescaling practices manifest themselves in the state roles figuring in three functionalities of government-business relationships (sponsorship, cultivation, and disci-plining). It is argued that the state roles are not pre-given modalities in a setting of a specific industry. The restructuring and even revolutionizing effects of AI in the socio-economic systems prompt non-state actors to respond proactively, which shapes the variegated functionalities of government-business relationships.Eurasian geography and economics942An analytical framework reifying vertical and horizontal scalar relations via the rescaling lens and three state roles and three state roles (owner, promoter, and supervisor) are proposed to explore how government-business relationships are made to understand how China is developing this new strategic sector.Eurasian Geography and Economics["Yang Liu", "Wenying Fu", "Daniel Schiller"]2024-08-07T00:00:00 6c14e09f8e3914c966971e8f96f397ad82ddaa7bGenerative Artificial Intelligence (GAI) holds tremendous potential to transform the field of education because GAI models can consider context and therefore can be trained to deliver quick and meaningful evaluation of student learning outcomes. However, current versions of GAI tools have considerable limitations, such as social biases often inherent in the data sets used to train the models. Moreover, the GAI revolution comes during a period of moving away from memorization-based education systems toward supporting learners in developing the ability to apply knowledge and skills to solve real-world problems and explain real-world phenomena. A challenge in using GAI tools for scoring assessments aimed at fostering knowledge application is ensuring that these algorithms are scoring the same construct attributes (e.g., knowledge and skills) as a trained human scorer would score when evaluating student performance. Similarly, if using GAI tools to develop assessments, one needs to ensure that the goals of GAI-generated assessments are aligned with the vision and performance expectations of the learning environments for which these assessments are developed. Currently, no guidelines have been identified for assessing the validity of AI-based assessments and assessment results. This paper represents a conceptual analysis of issues related to developing and validating GAI-based assessments and assessment results to guide the learning process. Our primary focus is to investigate how to meaningfully leverage capabilities of GAI for developing assessments. We propose ways to evaluate the validity evidence of GAI-produced assessments and assessment scores based on existing validation approaches. We discuss future research avenues aimed at establishing guidelines and methodologies for assessing the validity of AI-based assessments and assessment results. We ground our discussion in the theory of validity outlined in the Standards for Educational and Psychological Testing by the American Educational Research Association and discuss how we envision building on the standards for establishing the validity of inferences made from the test scores in the context of GAI-based assessments.Frontiers in Education292A conceptual analysis of issues related to developing and validating GAI-based assessments and assessment results to guide the learning process and proposes ways to evaluate the validity evidence of GAI-produced assessments and assessment scores based on existing validation approaches.Frontiers in Education["Leonora Kaldaras", "Hope O. Akaeze", "M. Reckase"]2024-08-07T00:00:00 9f5a72008295db200473630215bdfeae61548e9aThe present study aimed to assess the potentiality and apprehensions of artificial intelligence (AI) in education. It also investigated the challenges of AI integration into education from the teachers' perspectives. A cross-sectional study design was adopted. Through random sampling, a total of 63 members of faculty were recruited from Kuwait University. An online questionnaire was administered to the study participants. The data was analyzed through SPSS version 26, using descriptive statistics, t-tests, and ANOVA. The results showed that there was a remarkably high consensus about the potentiality of AI for education. The teachers’ readiness to adopt AI was low. Data analysis, machine learning, and natural language processing were the most important aspects of linking education and AI. The participants highlighted that for the empowerment of students, AI system use cases, evaluation of the intelligence of AI systems, and identification of the technical limitations of AI systems were crucial. Greater were challenges and difficulties in using AI such as the lack of availability of suitable educational materials, unavailability of required expertise in the field, and the complexity of the subject. However, no statistical difference attributed to gender, academic degree, and academic department in terms of facing challenges was found.International Journal of Education in Mathematics Science and Technology271There was a remarkably high consensus about the potentiality of AI for education and the teachers’ readiness to adopt AI was low, and no statistical difference attributed to gender, academic degree, and academic department in terms of facing challenges was found.International Journal of Education in Mathematics, Science and Technology["Waleed Alenezi"]2024-08-07T00:00:00 9b6d91f53ed7e252613dca4a250707d1e343a05fCurrent study is aims to examine the awareness of Artificial intelligence (AI) tools that helps in the teaching learning process among the prospective teachers. AI technologies are increasingly being integrated into various aspects of teaching and learning, promising personalized learning experiences, enhanced administrative efficiency, and new opportunities for educational innovation. So it is important that teachers must be aware about recent trends like AI, for this study descriptive survey method is used and 300 prospective teachers of Namakkal district of Tamilnadu were selected as samples. The data was analyzed by using descriptive statistical analysis and level of awareness was calculated. The results shows that prospective teachers of Namakkal district have moderate level of awareness on AI Tools.International Journal of Innovative Science and Research Technology191The results shows that prospective teachers of Namakkal district have moderate level of awareness on AI tools, which indicates that teachers must be aware about recent trends like AI.International Journal of Innovative Science and Research Technology (IJISRT)["V. Parthiban", "B.Jai Ganesh"]2024-08-07T00:00:00 fb2c0bfcc2018314824710d2ed99e18664ce2d69Mounting critique of the way AI is framed in mainstream media calls for less sensationalist coverage, be it jubilant or apocalyptic, and more attention to the concrete situations in which AI becomes controversial in different ways. This is supposedly achieved by making coverage more expert-informed. We therefore explore how experts contribute to the issuefication of AI through the scientific literature. We provide a semantic, visual network analysis of a corpus of 1M scientific abstracts about machine learning algorithms and artificial intelligence. Through a systematic quali-quantitative exploration of 235 co-word clusters and a subsequent structured search for 18 issue-specific queries, for which we devise a novel method with a custom-built datascape, we explore how algorithms have agency. We find that scientific discourse is highly situated and rarely about AI in general. It overwhelmingly charges algorithms with the capacity to solve problems and these problems are rarely about algorithms in their origin. Conversely, it rarely charges algorithms with the capacity to cause problems and when it does, other algorithms are typically charged with the capacity to solve them. Based on these findings, we argue that while a more expert-informed coverage of AI is likely to be less sensationalist and show greater attention to the specific situations where algorithms make a difference, it is unlikely to stage AI as particularly controversial. Consequently, we suggest conceptualising AI as a political situation rather than something inherently controversial.Big Data & Society501It is argued that while a more expert-informed coverage of AI is likely to be less sensationalist and show greater attention to the specific situations where algorithms make a difference, it is unlikely to stage AI as particularly controversial.Big Data Soc.["Anders Kristian Munk", "Mathieu Jacomy", "Matilde Ficozzi", "Torben Elgaard Jensen"]2024-08-07T00:00:00 97c52e54047eb8412d3bf9cf1e3303fdad3831dcArtificial intelligence (AI) is transforming the animation film industry. This research explores AI's impact at all levels: from AI-driven tools and techniques to changes in creative processes and professional relationships. It will analyze existing and emerging AI-driven film-making techniques, such as procedural generation, AI-enhanced motion capture, and deep learning implementation. Both benefits and limitations will be examined, focusing on technical capabilities and creative capacities. Each new technique will be compared to its traditional counterpart regarding cost-effectiveness, time efficiency, and creative enhancement. Additionally, the research will consider AI integration's societal effects: whether it increases human productivity or dehumanizes jobs by narrowing stylistic variety. Ethical issues related to the extreme use of machinery, such as biases, will also be addressed. This work aims to navigate the fine line between human ingenuity and advanced machinery, presenting imaginative narrative possibilities while emphasizing AI's advantages. Automation should enhance animators' distinctiveness, not eradicate it, ensuring that the fundamental pillar of unique storytelling remains intact. The findings will guide future innovations, respecting the intrinsic value of human creativity that has shaped storytelling for centuries. This research aims to make a significant contribution to the growth and evolution of the animation film industry in the age of AI.2024 International Visualization, Informatics and Technology Conference (IVIT)301This research will analyze existing and emerging AI-driven film-making techniques, such as procedural generation, AI-enhanced motion capture, and deep learning implementation, to navigate the fine line between human ingenuity and advanced machinery.2024 International Visualization, Informatics and Technology Conference (IVIT)["M. Izani", "A. Razak", "D. Rehad", "M. Rosli"]2024-08-07T00:00:00 760934ad614e18cfa2057590f9c87e5ce5574932The United Nations' 2030 Agenda for Sustainable Development aims to tackle poverty, inequality, and environmental degradation and foster economic growth. This study investigates the transformative potential of artificial intelligence (AI) in achieving these Sustainable Development Goals (SDGs). Analyzing data from 44 sources, the research highlights AI's capacity to address critical challenges in healthcare, education, environmental management, economic growth, and gender equality. AI applications in renewable energy, waste management, disease detection, personalized education, and gender equality are examined. The study also emphasizes the ethical issues associated with AI, such as algorithmic bias, data privacy breaches, and job displacement. To fully leverage AI's potential, it is essential to develop intelligent automation governance systems, foster interdisciplinary research combining AI and sustainability, and promote public-private partnerships. Additionally, enhancing public AI literacy and implementing eco-friendly AI policies are crucial. The study advocates for a holistic ethical framework to maximize AI's benefits while mitigating risks, promoting cross-disciplinary collaboration, and establishing ethical AI standards. By doing so, AI can significantly contribute to a more inclusive, equitable, and sustainable future.Journal of Sustainable Development431Analyzing data from 44 sources, the research highlights AI's capacity to address critical challenges in healthcare, education, environmental management, economic growth, and gender equality, and advocates for a holistic ethical framework to maximize AI's benefits while mitigating risks.Journal of Sustainable Development["Bongs Lainjo"]2024-08-07T00:00:00 73da3cc90d53db48ce85b9eea46204545a5f1c3fThe emergence of artificial intelligence (AI) in higher education has sparked numerous discussions about its implications. ChatGPT, a prominent AI conversational model, has attracted significant attention for its ability to generate essays and formulate responses. The current study sought to explore how and why students are using ChatGPT, and to examine their perceptions about ChatGPT and academic integrity. Students were surveyed about the frequency and motivation for ChatGPT use and their views on ChatGPT and academic misconduct. Exploratory factor analyses were conducted to examine patterns of correlations between each of the measures. Students primarily use ChatGPT for gathering information, motivated by its value and convenience rather than hedonic reasons, and can correctly identify academically unethical uses of the tool as cheating. The current study presents comprehensive data on college students’ ChatGPT usage patterns, attitudes, and perceptions of cheating behavior. The outcomes of this research provide insight into how college students are currently interacting with AI tools. Our findings offer practical insights for universities developing AI policies in the classroom, contributing to the ongoing discourse on AI’s role in higher education by providing accurate information about ChatGPT’s pervasiveness in academia.Teaching of psychology271Students primarily use ChatGPT for gathering information, motivated by its value and convenience rather than hedonic reasons, and can correctly identify academically unethical uses of the tool as cheating.Teaching of Psychology["Jenel T. Cavazos", "Keane A. Hauck", "Hannah M. Baskin", "Catherine M. Bain"]2024-08-07T00:00:00 bb9b0925524b5139b26834889514e50c16277fd0The convergence of Artificial Intelligence (AI) and Extended Reality (XR) has heralded a new era in the field of Biomedical Engineering, offering unprecedented avenues for innovation, diagnostics, treatment, and education. This research delves into the symbiotic relationship between AI and XR, unraveling their collective potential to revolutionize healthcare practices. AI, characterized by its ability to learn and adapt, has transcended its role within data analysis to become an indispensable tool in healthcare. Through advanced algorithms, AI can predict disease patterns, enhance medical imaging, and optimize treatment protocols. On the other hand, XR technologies, encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), immerse users in virtual environments, facilitating interactive and experiential learning and treatment methods. This research focuses on the study that examines with the integration of AI and XR in biomedical applications, elucidating their role in diagnosis, treatment, and training. AI-driven image analysis augments medical imaging, expediting disease identification and tracking treatment progress. XR, through its immersive nature, empowers surgeons with detailed anatomical insights during procedures and aids in rehabilitation through engaging simulations. The synergistic marriage of AI and XR also redefines medical education by offering immersive training experiences to healthcare practitioners and bridging the gap between theory and practice. Furthermore, ethical considerations and challenges emerge as these technologies evolve. Privacy concerns, data security, and the need for regulatory frameworks are paramount in this dynamic landscape. Striking the right balance between innovation and patient safety remains an imperative task. In the context of this research, the fusion of AI and XR from a biomedical engineering perspective holds the potential to revolutionize healthcare. As AI refines diagnostics and treatment strategies, XR provides a tangible platform for immersive experiences that enhance training and therapeutic interventions. This research navigates the landscape of this transformative convergence, shedding light on its profound implications for Biomedical Engineering and the well-being of patients worldwide.Indonesian Journal of Electronics Electromedical Engineering and Medical Informatics01This research delves into the symbiotic relationship between AI and XR, unraveling their collective potential to revolutionize healthcare practices, and examines with the integration of AI and XR in biomedical applications, elucidating their role in diagnosis, treatment, and training.Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics["Zarif Bin Akhtar", "Ahmed Tajbiul Rawol"]2024-08-07T00:00:00 be05c8661bcfa492ebee004fb16c623baad96072Explainable Artificial Intelligence (XAI) has become essential as AI systems increasingly influence critical domains, demanding transparency for trust and validation. This paper presents a comparative analysis of prominent model agnostic techniques designed to provide interpretability irrespective of the underlying model architecture. We explore Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) plots, and Anchors. Our analysis focuses on several criteria including interpretative clarity, computational efficiency, scalability, and user-friendliness. Results indicate significant differences in the applicability of each technique depending on the complexity and type of data, highlighting SHAP and LIME for their robustness and detailed output, whereas PDP and ICE are noted for their simplicity in usage and interpretation. The study emphasizes the importance of context in choosing appropriate XAI techniques and suggests directions for future research to enhance the efficacy of model agnostic approaches in explainability. This work contributes to a deeper understanding of how different XAI techniques can be effectively deployed in practice, guiding developers and researchers in making informed decisions about implementing AI transparency.Research Reports on Computer Science112A comparative analysis of prominent model agnostic techniques designed to provide interpretability irrespective of the underlying model architecture is presented, highlighting SHAP and LIME for their robustness and detailed output, whereas PDP and ICE are noted for their simplicity in usage and interpretation.Research Reports on Computer Science["Yifei Wang"]2024-08-07T00:00:00 7fe8df933ec9409366a591059761e62ef4705b41Background Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients’ prognosis and treatment outcomes. The literature shows conflicting results regarding patients’ attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia. Objective The objectives of this study are to examine patients’ attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients’ attitudes and various sociodemographic factors. Methods This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients’ attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis. Results A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability. Conclusions Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.JMIR Human Factors261Patient attitudes were examined to examine patients’ attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia and found patients were impartial toward AI replacing radiologists and the efficiency of AI.JMIR Human Factors["L. Baghdadi", "Arwa A Mobeirek", "Dania R Alhudaithi", "Fatimah A Albenmousa", "Leen S. Alhadlaq", "Maisa S Alaql", "Sarah A. Alhamlan"]2024-08-07T00:00:00 2a551a0fe57440cdc448b8bfa92cf17dbfc781c7The traditional complications of diabetes are well known and continue to pose a considerable burden to millions of people with diabetes mellitus (DM). With the continuous accumulation of medical data and technological advances, artificial intelligence has shown great potential and advantages in the prediction, diagnosis, and treatment of DM. When DM is diagnosed, some subjective factors and diagnostic methods of doctors will have an impact on the diagnostic results, so the use of artificial intelligence for fast and effective early prediction of DM patients can provide decision-making support to doctors and give more accurate treatment services to patients in time, which is of great clinical medical significance and practical significance. In this paper, an adaptive Stacking ensemble model is proposed based on the theory of “error-ambiguity decomposition,” which can adaptively select the base classifiers from the pre-selected models. The adaptive Stacking ensemble model proposed in this paper is compared with KNN, SVM, RF, LR, DT, GBDT, XGBoost, LightGBM, CatBoost, MLP and traditional Stacking ensemble models. The results showed that the adaptive Stacking ensemble model achieved the best performance in five evaluation metrics: accuracy, precision, recall, F1 value and AUC value, which were 0.7559, 0.7286, 0.8132, 0.7686 and 0.8436. The model can effectively predict DM patients and provide a reference value for the screening and diagnosis of clinical DM.Frontiers in Medicine382An adaptive Stacking ensemble model is proposed based on the theory of “error-ambiguity decomposition,” which can adaptively select the base classifiers from the pre-selected models and can effectively predict DM patients and provide a reference value for the screening and diagnosis of clinical DM.Frontiers in Medicine["Qinyuan Du", "Dongli Wang", "Yimin Zhang"]2024-08-07T00:00:00 204c0e98c4043629cef2cce338c06353cc3df9b1The recent emergence of Large Language Models (LLMs) and other forms of Artificial Intelligence (AI) has led people to wonder whether they could act as an author on a scientific paper. This paper argues that AI systems should not be included on the author by-line. We agree with current commentators that LLMs are incapable of taking responsibility for their work and thus do not meet current authorship guidelines. We identify other problems with responsibility and authorship. In addition, the problems go deeper as AI tools also do not write in a meaningful sense nor do they have persistent identities. From a broader publication ethics perspective, adopting AI authorship would have detrimental effects on an already overly competitive and stressed publishing ecosystem. Deterrence is possible as backward-looking tools will likely be able to identify past AI usage. Finally, we question the value of using AI to produce more research simply for publication's sake.Accountability in Research192This paper argues that AI systems should not be included on the author by-line, agreeing with current commentators that LLMs are incapable of taking responsibility for their work and thus do not meet current authorship guidelines.Accountability in research["Barton Moffatt", "Alicia Hall"]2024-08-07T00:00:00 0406891c34a08b90849c09b5ea640e77f742e426This research examines the growth of Artificial Intelligence (AI) and Knowledge Management (KM) capabilities, evaluating how AI can enhance KM by managing information, data, and knowledge within organizations. Utilizing a systematic literature review method with PRISMA guidelines, the study reviewed 72 articles from Scopus (1994-2024). Findings indicate that AI implementation in KM spans various fields, predominantly within technology. AI integration is identified as a catalyst for innovation, efficiency, and organizational learning. The study highlights the increasing application of AI in KM and the evolving nature of this research area. Insights are provided into the potential for innovation and future development of AI in KM, emphasizing the necessity for organizational members to leverage AI to enhance performance.International Conference on Communications and Information Technology4102024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)["Danang Prihandoko", "M. Arief", "Elidjen Elidjen", "Firdaus Alamsjah", "Zhask Stefano Rizky"]2024-08-07T00:00:00 6d8b10504e143fb212a603b153405c46f8a849a3Artificial intelligence (AI) revolutionizes education, particularly in promoting inclusive learning and achieving Sustainable Development Goals (SDGs). By personalizing learning experiences, automating administrative tasks, and providing data-driven insights, AI can enhance educational outcomes for students from diverse backgrounds. This technology addresses the global teacher shortage, facilitates access to education in remote areas, and supports policymakers in identifying and addressing educational disparities. While AI offers immense potential, responsible implementation, including ethical considerations and data privacy, is essential to maximize its benefits and mitigate risks. .International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]340This technology addresses the global teacher shortage, facilitates access to education in remote areas, and supports policymakers in identifying and addressing educational disparities.2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)["Ardhendu Shekhar Singh", "Adesh Doifode", "Deepa Pillai", "Trupti Bhosale"]2024-08-07T00:00:00 997a7085ec53f040a79606cb548da9be8301b74aArt and artistic creation serve as a means for artists to communicate with their environment, society, and the external world. However, the protection of artistic creations, as forms of communication, is not only a right for artists but also serves as a crucial safeguard that nurtures them during the creative process. Beyond the traditional issues of copyright, the significant advancements in Artificial Intelligence (AI) in today’s digital world have introduced a new debate regarding the ownership of copyright in artistic creations generated by AI. The question arises whether copyright belongs to the AI itself or to the individuals who guide the creative process behind it. In this study, based on the concepts of art, artistic creation, and emotional states, copyright issues will be examined. Data obtained from semi-structured in-depth interviews with artists and academic experts (eight artists, two communication experts, two law experts, and eight psychology experts) in the field will be analysed through content analysis to explore their perspectives regarding the discussion on emotional states, AI, and copyrights. The research highlights the variability of emotional states and their significant effects on individuals. Addressing the increasing trend of copyright issues, particularly within the framework of digitalization and inadequate legal regulations, it was found that artists’ emotional states are negatively impacted by these problems. This negative influence can adversely affect artists’ creativity and desire to produce. On the other hand, it was also identified that in artworks produced especially through AI, if artists’ rights are not protected, there is a possibility of negative emotional states arising. In conclusion, suggestions are as follows: Emphasising the importance of awareness-raising educational activities nationally and internationally, national copyright law (in Northern Cyprus) needs to be revised to protect traditional copyright and be expanded to include digital copyright, especially for works produced through AI. On an international level, emphasising the need to revise international agreements to include regulations for works produced through AI or to create a new agreement based on the importance of this issue.Frontiers in Psychology760It was found that artists’ emotional states are negatively impacted by these problems within the framework of digitalization and inadequate legal regulations, and if artists’ rights are not protected, there is a possibility of negative emotional states arising.Frontiers in Psychology["H\u00fcseyin Kambur", "Ayhan Dolunay"]2024-08-07T00:00:00 488d0d2fbc594c5c51bd342802b7c23feaf03a2fMost of the natural resources in the mining and quarrying industry are non-renewable, presenting a unique dynamic that requires the industry to generate profit from these energy sources while also identifying alternatives as resources deplete. This research aims to identify current technology trends and analyze their implementation in the mining and quarrying industry. Using a descriptive qualitative research approach, secondary data was obtained through desk study techniques, employing five AI tools—ChatGPT, Jenni, Perplexity, Elicit, and Gemini—to analyze technologies such as Ubiquitous Computing, Internet of Things (IoT), Datafication, Artificial Intelligence (AI), Extended Reality, Blockchain, 3D Printing, Gene Editing, Nanotechnology, and New Energy Solutions. The study found that 80% of these technology trends are not yet widely implemented and require further research, with IoT being the most commonly adopted technology. These findings highlight the need for continued exploration and adaptation of emerging technologies in the industry.International Conference on Communications and Information Technology420The study found that 80% of these technology trends are not yet widely implemented and require further research, with IoT being the most commonly adopted technology.2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)["Nopriadi Saputra", "Amelia Maharani Putri", "Antariksa Dunia Aryanto", "Diva Maharani", "Isabelle Joanna Aritonang", "Mikael Ladhuny", "Ava Garcia"]2024-08-07T00:00:00 4a19368f0b3bcd6969bea644725ca0e1e409190dThis research investigates the contemporary impact of artificial intelligence (AI) on human resource management (HRM) practices. In the context of the rapidly evolving AI landscape, this study aims to comprehensively assess its transformative effects on HRM within current organizational and business frameworks. Utilizing a multi-faceted methodology, including a cross-sector survey of 235 participants, in-depth interviews with HR professionals, and extensive literature analysis, the research explores critical issues such as AI’s influence on recruitment processes, performance evaluations, and employee development strategies. The findings reveal that while AI has the potential to enhance HRM efficiency, it also introduces ethical considerations, data security challenges, and psychological implications for employees. The study advocates for a holistic approach to integrating AI into HRM practices, taking into account technical nuances, ethical dimensions, and prioritizing employee well-being. Practical implications emphasize the need for organizations to adopt balanced and adaptive policies to ensure the judicious use of AI in HRM, achieving optimal benefits while maintaining fairness and workforce welfare.International Conference on Communications and Information Technology430The research explores critical issues such as AI’s influence on recruitment processes, performance evaluations, and employee development strategies, and introduces ethical considerations, data security challenges, and psychological implications for employees.2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)["Q. Aini", "U. Rusilowati", "Marsani Asfi", "Po Abas Sunarya", "Souza Nurafrianto Windiartono Putra", "Achani Rahmania Az Zahra"]2024-08-07T00:00:00 ef3e7439707716d3375d9dc95540133d2695bdb7Artificial intelligence (AI) has been applied to mobile fitness applications (MFAs) to improve users’ continuance usage intention. Most of the literature has considered the adoption factors of MFA but has ignored the impact of AI effectiveness. To address this research gap, we develop a research model by integrating AI service quality and the theory of consumption value (TCV) to explore users’ continuance intention. Using a survey approach, a total of 416 valid questionnaires were collected in China, and the model was tested using partial least squares structural equation modeling (PLS-SEM). The results show that functional, emotional, social, epistemic, and conditional values partially mediate the relationships between AI service quality and the continuance usage intention of AI-enabled MFAs. This study contributes to the literature by considering and examining the effect of AI service quality on users’ assessment of different consumption values, which advances the understanding of the AI service quality function in AI-enabled MFAs.Information Development530A research model is developed by integrating AI service quality and the theory of consumption value (TCV) to explore users’ continuance intention and shows that functional, emotional, social, epistemic, and conditional values partially mediate the relationships between AI service quality and the continuance usage intention of AI-enabled MFAs.Information Development["Jung-Chieh Lee", "Zitong Gao", "Liangnan Xiong"]2024-08-07T00:00:00 78dc99b641a2d063f1d5d7296475c375e3bcef03OBJECTIVES Laboratory testing, crucial for medical diagnosis, has 3 phases: preanalytical, analytical, and postanalytical. This study set out to demonstrate whether automating tube labeling through artificial intelligence (AI) support enhances efficiency, reduces errors, and improves outpatient phlebotomy services. METHODS The NESLI tube-labeling robot (Labenko Informatics), which uses AI models for tube selection and handling, was used for the experiments. The study evaluated the NESLI robot's operational performance, including labelling time, technical problems, tube handling success, and critical stock alerts. The robot's label readability was also tested on various laboratory devices. This research will contribute to the field's understanding of the potential impact of automated tube-labeling systems on laboratory processes in the preanalytical phase. RESULTS NESLI demonstrated high performance in labeling processes, achieving a success rate of 99.2% in labeling parameters and a success rate of 100% in other areas. For nonlabeling parameters, the average labeling time per tube was measured at 8.96 seconds, with a 100% success rate in tube handling and critical stock warnings. Technical issues were promptly resolved, affirming the NESLI robot's effectiveness and reliability in automating the tube-labeling processes. CONCLUSIONS Robotic systems using AI, such as NESLI, have the potential to increase process efficiency and reduce errors in the preanalytical phase of laboratory testing. Integration of such systems into comprehensive information systems is crucial for optimizing phlebotomy services and ensuring timely and accurate diagnostics.American Journal of Clinical Pathology160Robotic systems using AI, such as NESLI, have the potential to increase process efficiency and reduce errors in the preanalytical phase of laboratory testing and integration of such systems into comprehensive information systems is crucial for optimizing phlebotomy services and ensuring timely and accurate diagnostics.American journal of clinical pathology["Ferhat Demirci"]2024-08-07T00:00:00 75d336bc28ac887978dd025d57d3db170b4bdf5fThis study investigates the impact of integrating Artificial Intelligence (AI) in Arabic language teaching using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. Questionnaires from teachers and students at institutions using AI in Arabic instruction were analyzed to understand the relationships between AI usage, teacher training, student involvement, and learning outcomes. The primary goal is to assess AI’s influence on student learning, teacher effectiveness, and student engagement. Findings show that AI significantly improves student learning outcomes (path coefficient = 0.45, p <0.001). Teacher training enhances AI usage (path coefficient = 0.50, p <0.001) and directly boosts learning outcomes (path coefficient = 0.30, p <0.001). For example, a high school in Jakarta saw a 20% increase in test scores after implementing AI for Arabic grammar and speech. A university in Yogyakarta noted significant improvements in language understanding and usage through an AI tutoring system with real-time feedback (path coefficient = 0.35, p <0.001). These results emphasize the importance of teacher training and student engagement in AI-based teaching. AI enables more adaptive, interactive, and responsive learning environments, enhancing Arabic language teaching effectiveness. Investing in AI technologies, teacher development, and promoting student involvement is crucial for optimal learning outcomes. This study provides empirical evidence that AI integration significantly improves Arabic language education, offering guidance for institutions and policymakers in implementing effective technology strategies.International Conference on Communications and Information Technology540Empirical evidence is provided that AI integration significantly improves Arabic language education, offering guidance for institutions and policymakers in implementing effective technology strategies.2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)["Mandrasi Amira Sa'idah", "Karno Diantoro", "Umi Mahmudah", "Ellen Dolan", "Nesti Anggraini Santoso", "Sausan Raihana Putri Junaedi"]2024-08-07T00:00:00 77a05a6065df481a017a0e89e77fda86408274c3General Background: Artificial intelligence (AI) enables systems to understand and interpret data, facilitating intelligent decision-making without human interaction. Specific Background: Traditional accounting systems are fraught with flaws that compromise the quality of accounting information and decision-making processes. Knowledge Gap: There is a need for advanced AI applications to improve the accuracy of accounting data by precisely evaluating vast amounts of data, identifying errors and corrections, and accelerating financial report creation. Aims: This quantitative study aims to enhance accounting information quality by developing an AI-based accounting information system and examining its impact on the decision-making process. Results: The research utilized questionnaires to gather opinions from accountants at Rafidain Bank, Iraq, and analyzed the data using SPSS. Findings reveal that modern AI technologies significantly enhance accounting data accuracy, with a 90% success rate in detecting and correcting errors. Additionally, AI technology accelerates financial reporting, reducing the average response time to 250 milliseconds, thereby saving time and effort in accounting processes. Novelty: This study is pioneering in its comprehensive evaluation of AI's role in improving accounting data accuracy and operational efficiency within financial institutions. Implications: The research underscores the importance of upgrading device and data processing infrastructure to maximize the performance of AI-based accounting systems. The findings suggest that integrating contemporary AI technology in accounting can streamline operations, save accountants time, and boost the overall efficiency of financial and accounting institutions. Highlight:     AI improves accounting data accuracy with 90% error detection and correction. AI accelerates financial reporting, reducing response times to 250 milliseconds. Upgraded infrastructure is crucial for optimal AI system performance.   Keyword:  Artificial Intelligence, Accounting Information, Data Accuracy, Financial Reporting, Decision-MakingAcademia Open20Findings reveal that modern AI technologies significantly enhance accounting data accuracy, with a 90% success rate in detecting and correcting errors, and AI technology accelerates financial reporting, reducing the average response time to 250 milliseconds, thereby saving time and effort in accounting processes.Academia Open["Fayiz Hazem Al-Obaidy"]2024-08-07T00:00:00 92be98c3648bc9ffed926d7adb634934f6b31767In the current landscape where Artificial Intelligence (AI) rapidly reshapes industries, understanding the trajectory of Human Resources (HR) career plans and development becomes imperative. This research aims to analyze HR professionals achievements in aligning their career plans with the evolving demands of the AI era. Utilizing Structural Equation Modeling (SEM) through SmartPLS, this study scrutinizes four pivotal variables: adaptability to AI technologies, efficacy of continuous learning initiatives, role of mentorship programs, and significance of organizational support. A comprehensive survey was conducted involving HR practitioners and managers across various industries. The study assesses HR professionals integration of AI technologies into their career trajectories, evaluates continuous learning initiatives, explores the impact of mentorship programs on career development, and examines organizational support in fostering HR growth in AI-influenced environments. Despite the growing reliance on AI, the study underscores the irreplaceable human elements in HR functions. AI presents unprecedented opportunities but necessitates proactive measures to ensure HR professionals continued relevance and effectiveness. This research contributes to the discourse on HR management strategies in the face of technological disruptions, offering practical implications for both practitioners and organizations. By highlighting the critical factors that influence HR career development in the AI era, the study provides valuable insights for enhancing the effectiveness and adaptability of HR professionals.International Conference on Communications and Information Technology470The study assesses HR professionals integration of AI technologies into their career trajectories, evaluates continuous learning initiatives, explores the impact of mentorship programs on career development, and examines organizational support in fostering HR growth in AI-influenced environments.2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)["Yulia Rachma", "Asep Sutarman", "Dwi Andayani", "Hendriyati Haryani", "Sheila Aulia Johnson"]2024-08-07T00:00:00 b42db14104ab0212a4a6c089fed0b2e70aa3f631The purpose of the scientific article was to determine the development of artificial intelligence and its influence on procrastination, for this purpose a non-experimental explanatory causal correlational methodology was developed, in which 250 collaborators were had as a population and under a census sample, selecting the 250 collaborators, under a non-probabilistic sampling for convenience,  based on the statistics, ordinal logistic regression was used to determine the influence of the independent variable on the dependent variable, based on the results, a sig. of less than 0.05 was obtained where it is possible to reject the Ho and accept the Hg, in addition to this under the Nagelkerke value of 0.966, it can be clarified that the variable Artificial Intelligence manages to predict procrastination in 96.6%,  Finally, it was concluded by mentioning that the development of artificial intelligence significantly influences procrastination.International Journal of Religion180The development of artificial intelligence significantly influences procrastination, and it was concluded that the development of artificial intelligence significantly influences procrastination.International Journal of Religion["Sof\u00eda Emilce Belleza-Torrej\u00f3n", "Cecilia Celeste Mendoza Aguilar", "Nelly Mar\u00eda P\u00e9rez-De la Cruz", "El\u00edas Manuel Guarniz V\u00e1squez", "Jenny Martha Quispe-L\u00f3pez"]2024-08-07T00:00:00 fbf3fd7409ecf9aa07473f83c7d123cc529c80acArtificial Intelligence (AI) is increasingly influential in health care, offering potential enhancements in diagnosis, patient care, and treatment therapies. By utilizing large datasets and sophisticated algorithms, AI assists healthcare professionals in making informed decisions, including resource allocation. For effective and safe use, professionals require adequate training and education in these technologies. AI also allows patients by improving their understanding of health conditions and providing customized solutions for diagnosis and treatment. However, concerns about data privacy, security, and ethical use are dominant. Companies must ensure that patient data is protected from breaches and misuse and is not exploited for identification or control purposes. Additionally, there are ongoing debates about the transparency, and accountability of AI systems in healthcare. This study highlights the necessity for enabling stringent regulatory frameworks to ensure these technologies are used ethically and responsibly, increasing the trust and reliability in their application within the healthcare sector.International Conference Electronic Systems, Signal Processing and Computing Technologies [ICESC-]250The necessity for enabling stringent regulatory frameworks to ensure Artificial Intelligence technologies are used ethically and responsibly, increasing the trust and reliability in their application within the healthcare sector is highlighted.2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)["A. Pargaien", "Saurabh Pargaien", "Akbar Nawaz", "Tushar Kumar"]2024-08-07T00:00:00 3cd90d5d652d5d662e66589574bc58427ab7c603This study examines the rapid growth of startups integrating artificial intelligence (AI) into their business models, aiming to analyze and compare these startups with traditional IT organizations to understand their differences. Analyzing 162 global startups, this research identifies four unique archetypal business models, introducing novel methodologies in AI integration and data utilization that contrast with conventional IT paradigms. The focus is on technologies providing data analytics solutions, AI products and services, and the expansion of AI use. Key components of AI startup business models are highlighted: (1) advantages of AI in new value propositions, (2) data use to create value, and (3) the impact of AI on business decision-making. This research enhances the understanding of AI startup business models and suggests future research directions in entrepreneurship. The developed taxonomy highlights four distinct AI business models: the Deep Tech Researcher, Data Analytics Provider, AI Product/Service Provider, and AI Development Facilitator, each offering unique value propositions and leveraging AI technologies in different ways to address specific market needs. The research framework fosters entrepreneurial efforts with taxonomies and models as useful instruments.International Conference on Communications and Information Technology560Analyzing 162 global startups, this research identifies four unique archetypal business models, introducing novel methodologies in AI integration and data utilization that contrast with conventional IT paradigms.2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)["Inayatul Izzati", "Diana Yusuf", "Sabda Maulana", "Aptanta Pratiangga", "Dwi Apriliasari"]2024-08-07T00:00:00 308bca023158d5982e2ecaa13b704800ddf5b768This study explores the intersection of artificial intelligence (AI) and innovation research through bibliometric analysis, aiming to identify key trends, influential authors and pivotal publications within this rapidly evolving field. By systematically examining a large dataset of scholarly articles, we map the intellectual structure and development of AI-driven innovation research over the past decade. The analysis reveals a significant growth in publications, with an emphasis on the application of AI technologies to enhance innovation processes in various industries. Key findings highlight the predominant research themes, such as machine learning, data analytics, and their impact on product development and organizational efficiency. The study also uncovers the geographic distribution of research activities, with leading contributions from North America, Europe, and Asia. Through network analysis, we identify core clusters of collaboration and citation patterns, illustrating the interconnected nature of the research community. This bibliometric perspective provides a comprehensive overview of the state of AI in innovation research, offering valuable insights for scholars, policymakers, and practitioners aiming to leverage AI for future advancements in innovation.International Conference on Communications and Information Technology330This bibliometric perspective provides a comprehensive overview of the state of AI in innovation research, offering valuable insights for scholars, policymakers, and practitioners aiming to leverage AI for future advancements in innovation.2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT)["Ika Triana", "M. Arief", "Firdaus Alamsjah", "Elidjen Elidjen"]2024-08-07T00:00:00 fc30fdf6e8bb268a9071839d32963ea23ac9b4fcThe purpose of the article is to analyse the main achievements and prospects for the introduction of artificial intelligence in genetics, to improve the efficiency of research and the reliability of the results obtained. The article analyses publications that reveal the areas of integration of neural networks into DNA fingerprinting, in particular PCR-PCR analysis, sequencing, FISH diagnostics, etc., increasing the sensitivity of these technologies and reducing the cost of research. The author also discusses technologies for processing huge data sets of Big Data and their effectiveness on the example of international projects in theoretical and applied genetics. Attention is paid to artificial intelligence in genomics, proteomics and genetic engineering, in particular, optimisation of GED (genome editing) methods based on CRISPR (short palindromic repeats regularly interspaced in groups) to achieve better accuracy of genome editing. Neural networks are important in creating guide RNAs (gRNAs) for CRISPR-Cas systems, which determine the direction of endonuclease function. In addition to specialised applications and resources, artificial intelligence generally improves research work by optimising many processes that used to take a lot of time and effort. Examples of neural network-based applications that are widely used by the scientific community are presented.Animal Breeding and Genetics280The article analyses publications that reveal the areas of integration of neural networks into DNA fingerprinting, in particular PCR-PCR analysis, sequencing, FISH diagnostics, etc., increasing the sensitivity of these technologies and reducing the cost of research.Animal Breeding and Genetics["I. Liadskyi"]2024-08-07T00:00:00 e8c8ecbc12c1c17e1c6c0b0746e29206901ccdd1Social Media Systems such as Facebook, Instagram, and Twitter (i.e., X) are exploding. These systems need proper governance so that the users are safe and post accurate information. This paper focuses on social media governance with an emphasis on Artificial Intelligence. First, we discuss various aspects of governance of such policies, procedures and risk and then address a key topic which is detecting fake news on social media. In order for the users to be safe using social media we have to ensure that the governance aspects also include fake news detection. Many of the fake news detection techniques utilize Machine Learning (ML) and Artificial Intelligence (AI) and more recently Generative AI (GenAI) techniques. Therefore, the AI systems that implement the various techniques have to be trustworthy. That means these systems have to be secure as well as ensure fairness, privacy and integrity. Therefore, the paper will also discuss AI Governance as an integral part of Social Media Governance. Finally, to support the various applications and frameworks, both data and the cloud are critical. Large amounts of data are stored and managed by the social media systems as well as used to train the AI models. Furthermore, we need massive amounts of computing power that can be provided by the cloud. Therefore, we will also discuss data and cloud governance that provides the infrastructure for social media and AI governance.IEEE International Conference on Information Reuse and Integration200This paper focuses on social media governance with an emphasis on Artificial Intelligence, and discusses data and cloud governance that provides the infrastructure for social media and AI governance.2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI)["B. Thuraisingham", "Teena Thomas"]2024-08-07T00:00:00 1ccb2590cf63bbd6bbdf0c651919b59d787ea545PurposeArtificial intelligence (AI) technologies have led to significant transformations across industries and society, including the field of education. The integration of AI in educational settings has the potential to improve students' learning experience and support their individual competencies when paired with non-AI methods. Despite the growing importance of AI in modern education, there remains a noticeable research gap regarding its use in entrepreneurship education and the effects of Chatbots on students' entrepreneurial competencies. To address this gap, an exploratory study was conducted on undergraduate students who were tasked with using ChatGPT to improve their business model canvas.Design/methodology/approachThe chosen methodology aligned with the research purpose, aiming to explore the relationship between Generative AI and competencies. Due to the novel nature of the research problem, an exploratory study was conducted using a mixed methods approach. A survey with open- and closed-ended questions was designed, and statistical and text analyses were performed to interpret data and test identified propositions.FindingsThe findings of this study indicate that ChatGPT can enhance the types of students' entrepreneurial competencies considered in this study: spotting opportunities, creativity, vision, valuing ideas and ethical and sustainable thinking. The results show that ChatGPT can be particularly helpful to improve the ability of students of valuing ideas.Originality/valueOverall, this study highlights the potential of adopting ChatGPT in experiential learning methodologies for enhancing students' entrepreneurial competencies and improving their learning outcomes.International Journal of Entrepreneurial Behavior &amp; Research872ChatGPT can enhance the types of students' entrepreneurial competencies considered in this study: spotting opportunities, creativity, vision, valuing ideas and ethical and sustainable thinking.International Journal of Entrepreneurial Behavior &amp; Research["Tatiana Somi\u00e0", "Mariangela Vecchiarini"]2024-08-07T00:00:00 b59342ea4bac9a312d66236b7f761f64c6da12b7The Asia-Pacific Education Researcher321The Asia-Pacific Education Researcher["Weikang Lu", "Chenghua Lin"]2024-08-07T00:00:00 6fced8abc8369ecffb55ba5b03f4911e5806d5b3Cluster Computing141Clust. Comput.["Sumit Sharma", "Sarika Jain"]2024-08-07T00:00:00 2dcba17caa0eb9cd060576c4da3fcbc37592a5cePerspectives in Clinical Research20Perspectives in Clinical Research["Himel Mondal", "Shaikat Mondal", "J. Behera"]2024-08-07T00:00:00 7b9e5862be872577cc3c8439e0bb0121edc0d0d3There was an error in the original publication [...]Energies10Energies["M. Zulu", "R. P. Carpanen", "R. Tiako"]2024-08-07T00:00:00 72d8d33a9d951b0a332d394b096fc36fc316e161 The Article Abstract is not available. Journal of Tehran University Heart Center00The Journal of Tehran University Heart Center["Payam Emami", "Mohammad Sistani", "A. Marzban"]2024-08-07T00:00:00 7bbb47f0d9e067fc0f4b0d6ba4fac7436774c685AJR. American journal of roentgenology10AJR. American journal of roentgenology["Antonio Luna"]2024-08-07T00:00:00 5b6361846f9f7449d8497ed41e9a273c440faf8eInternational Journal of Gynecological Cancer350International journal of gynecological cancer : official journal of the International Gynecological Cancer Society["Gabriel Levin", "Sabrina Piedimonte", "Behrouz Zand"]2024-08-07T00:00:00 119e5487626b447286a77a3f77884d394d950ecc00["Zach Zinn"]2024-08-07T00:00:00 23f2170bcfbe69fa19e7c5da97c7f8165292f582Gaceta Médica de México00Gaceta Médica de México["Liliana P\u00e9rez-Peralta", "David Rivera-De La Parra", "Enrique O. Graue-Hern\u00e1ndez", "S. Hern\u00e1ndez-Jim\u00e9nez", "Paloma Almeda-Vald\u00e9s", "H\u00e9ctor Vel\u00e1zquez-Jurado", "A\u00edda Jim\u00e9nez-Corona"]2024-08-07T00:00:00 b62379fe3ed105e6706f6ab156d8f091a7a978f4Clinical Nursing Research00Clinical nursing research["Jung In Park"]2024-08-07T00:00:00 d53fe3bdb0c2e4d573c354c0536b30f31b8c575dApplied Economics Letters120Applied Economics Letters["Jianing Zhang", "Jianhong Fan", "Yifan Ma"]2024-08-07T00:00:00 60aba4c2ef5e120c75d12d2832a318b905650580The current review conveys Metaverse integration with AI-based gaming exploring the advancements in Metaverse games and its potential in growing self-learning AI in the latest years for the readers. Using state-of-the-art deep learning techniques, it aimed to pinpoint its advancements, issues, and suggested solutions. For the current review, 18 papers were used that appeared in peer-reviewed publications and were searchable on Google Scholar within the last five years (2020–2024). To analyse the collected data, thematic analysis was employed. The article delves into the various ways the Meta-Metaverse might be used in the gaming industry. It highlights how it can enhance character creation, game design, level design, and visual effects, among other areas. The entertainment industry’s usage of AI in game production encompasses a wide range of methods, such as CVEs, deep learning, and intrinsic curiosity-driven variation autoencoders.). The Metaverse, a dynamic platform, uses ML/DL algorithms for classification, clustering, and regression, while pre-trained AI models can achieve great responses quickly. These technologies streamline the gaming world and create interactive platforms for various applications. Deep Reinforcement Learning (DRL) is proposed as a dynamic solution to balance the stability of the game world, the intelligence of non-player characters, and the sustainability of the Metaverse environment.International Journal of Metaverse280Deep Reinforcement Learning (DRL) is proposed as a dynamic solution to balance the stability of the game world, the intelligence of non-player characters, and the sustainability of the Metaverse environment.International Journal of Metaverse["Omar Alotaibi"]2024-08-07T00:00:00 8d7f27aefecab5cf000fc076fb80d56fd5d80398Building a general-purpose agent is a long-standing vision in the field of artificial intelligence. Existing agents have made remarkable progress in many domains, yet they still struggle to complete long-horizon tasks in an open world. We attribute this to the lack of necessary world knowledge and multimodal experience that can guide agents through a variety of long-horizon tasks. In this paper, we propose a Hybrid Multimodal Memory module to address the above challenges. It 1) transforms knowledge into Hierarchical Directed Knowledge Graph that allows agents to explicitly represent and learn world knowledge, and 2) summarises historical information into Abstracted Multimodal Experience Pool that provide agents with rich references for in-context learning. On top of the Hybrid Multimodal Memory module, a multimodal agent, Optimus-1, is constructed with dedicated Knowledge-guided Planner and Experience-Driven Reflector, contributing to a better planning and reflection in the face of long-horizon tasks in Minecraft. Extensive experimental results show that Optimus-1 significantly outperforms all existing agents on challenging long-horizon task benchmarks, and exhibits near human-level performance on many tasks. In addition, we introduce various Multimodal Large Language Models (MLLMs) as the backbone of Optimus-1. Experimental results show that Optimus-1 exhibits strong generalization with the help of the Hybrid Multimodal Memory module, outperforming the GPT-4V baseline on many tasks.Neural Information Processing Systems617A Hybrid Multimodal Memory module is proposed that transforms knowledge into Hierarchical Directed Knowledge Graph that allows agents to explicitly represent and learn world knowledge, and summarises historical information into Abstracted Multimodal Experience Pool that provide agents with rich references for in-context learning.ArXiv["Zaijing Li", "Yuquan Xie", "Rui Shao", "Gongwei Chen", "Dongmei Jiang", "Liqiang Nie"]2024-08-07T00:00:00 0f75c973ad6d067b165eda40f65f11bb3139b1fbNature Network Boston317An artificial intelligence-based measurement (AIM) tool for scoring MASH histology (AIM-MASH) suggests that AIM-MASH may assist pathologists in histologic review of MASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient responses.Nature Medicine["Janani S. Iyer", "Dinkar Juyal", "Quang Le", "Zahil Shanis", "H. Pokkalla", "Maryam Pouryahya", "Aryan Pedawi", "S. A. Stanford-Moore", "Charles Biddle-Snead", "Oscar Carrasco-Zevallos", "Mary Lin", "R. Egger", "Sara Hoffman", "H. Elliott", "K. Leidal", "Robert P Myers", "Chuhan Chung", "A. Billin", "Timothy R. Watkins", "Scott D Patterson", "M. Resnick", "Katy E Wack", "Jonathan Glickman", "Alastair D. Burt", "R. Loomba", "Arun J. Sanyal", "Ben Glass", "Mike Montalto", "A. Taylor-Weiner", "Ilan Wapinski", "A. Beck"]2024-08-07T00:00:00 1dbed05bd854196322ee6500a31dbf44d71e6b3eThis paper explores agents in artificial intelligence, focusing on their definitions, characteristics, and applications. Agents are autonomous entities that perceive, decide, and act to achieve goals, with key attributes like autonomy, reactivity, and social ability. The use of agent-based models in economic and environmental research is highlighted. Future directions and challenges, including ethical considerations and technical improvements, are discussed, emphasizing agents’ potential to address complex real-world problems.Journal of Sensor Networks and Data Communications00This paper explores agents in artificial intelligence, focusing on their definitions, characteristics, and applications, and the use of agent-based models in economic and environmental research.Journal of Sensor Networks and Data Communications[]2024-08-07T00:00:00 9870699c5bd7df16fbc633b3fb074bb6d1f958caThis work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations. To fully leverage KAN’s inter-pretability, a general framework is proposed considering complex uncertainties. The stochastic optimal power flow problem in hybrid AC/DC systems is chosen as a particularly tough case study for demonstrating the effectiveness of this framework.European Conference on Cognitive Ergonomics233This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations, and a general framework is proposed considering complex uncertainties.2024 IEEE Energy Conversion Congress and Exposition (ECCE)["Xiaoting Wang", "Yuzhuo Li", "Yunwei Li", "Gregory Kish"]2024-08-07T00:00:00 cc8b3683c17449b059d158c332916b8c7761667cCyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and daily-life domains, with applications ranging from robotic operations to autonomous driving. With recent advancements in artificial intelligence (AI), learning-based components, especially AI controllers, have become essential in enhancing the functionality and efficiency of CPSs. However, the lack of interpretability in these AI controllers presents challenges to the safety and quality assurance of AI-enabled CPSs (AI-CPSs). Existing methods for improving the safety of AI controllers often involve neural network repair, which requires retraining with additional adversarial examples or access to detailed internal information of the neural network. Hence, these approaches have limited applicability for black-box policies, where only the inputs and outputs are accessible during operation. To overcome this, we propose MORTAR, a runtime action repair framework designed for AI-CPSs in this work. MORTAR begins by constructing a prediction model that forecasts the quality of actions proposed by the AI controller. If an unsafe action is detected, MORTAR then initiates a repair process to correct it. The generation of repaired actions is achieved through an optimization process guided by the safety estimates from the prediction model. We evaluate the effectiveness of MORTAR across various CPS tasks and AI controllers. The results demonstrate that MORTAR can efficiently improve task completion rates of AI controllers under specified safety specifications. Meanwhile, it also maintains minimal computational overhead, ensuring real-time operation of the AI-CPSs.arXiv.org691MORTAR, a runtime action repair framework designed for AI-CPSs, can efficiently improve task completion rates of AI controllers under specified safety specifications and maintains minimal computational overhead, ensuring real-time operation of the AI-CPSs.ArXiv["Renzhi Wang", "Zhehua Zhou", "Jiayang Song", "Xuan Xie", "Xiaofei Xie", "Lei Ma"]2024-08-07T00:00:00 157d52a715c184901efb959beaf9e6c6173dafa5This paper explores the relationship between diversity, inclusion, and artificial intelligence (AI) in multicultural workplaces and markets. Growing recognition of the value of diversity and inclusion (D&I) initiatives is changing organizational practices globally. Despite progress, challenges such as unconscious bias, resistance to change, and difficulty in measuring progress still exist.This study is based on a comprehensive literature review that adheres to academic ethical standards. These findings highlight the potential of AI to improve recruitment processes, personalize marketing strategies, and develop inclusive technologies. However, challenges such as bias in AI algorithms and limited data diversity need to be addressed.The paper emphasizes the need for ethical AI frameworks, diverse representation in AI development, and transparency in the use of AI to promote justice. AI has the potential to foster a more equitable society by encouraging diversity and inclusion. Further research is needed to explore the long-term impact of AI on diversity and inclusion and to develop ethical AI frameworks tailored to different industries and cultural contexts.The study also explores how AI can support D&I initiatives by providing data-driven insights, automating processes, and offering personalized interventions to reduce bias and increase inclusivity. By reviewing recent research and case studies, this paper offers practical recommendations for leveraging AI in D&I efforts, aiming to build a more inclusive and equitable workplace culture. International Journal of Religion2731How AI can support D&I initiatives by providing data-driven insights, automating processes, and offering personalized interventions to reduce bias and increase inclusivity is explored.International Journal of Religion["Mariyono Dwi", "Dwi Mariyono"]2024-08-07T00:00:00 25ecfa2fec43d38e3f7c7228b64738ed26273322Artificial Intelligence Review1994This study provides a comprehensive analysis of the adoption of blockchain and AI within healthcare, spotlighting their role in fortifying security and transparency leading the trajectory for a promising future in the realm of healthcare.Artif. Intell. Rev.["Archana Bathula", "S. K. Gupta", "Suresh Merugu", "L. Saba", "N. N. Khanna", "John R. Laird", "S. S. Sanagala", "Rajesh Singh", "Deepak Garg", "Mostafa Fouda", "Jasjit S. Suri"]2024-08-08T00:00:00 66f533ccd0e72edcc655107e76de6b66a39a6517This study illustrates how artificial intelligence can be used to conduct basic medical research. Microsoft Copilot was the chatbot used to conduct the study. The study searched for examples where either tai chi or qigong have been used to treat cancer patients. The study was successful, in that all the items found were to good, professional studies that were published in medical journals. A similar methodology can be used to conduct medical research on a wide range of other medical subspecialties. Thus, the present study can be used as a template for conducting medical research on a wide range of diseases and ailments.HerculeanResearch251This study illustrates how artificial intelligence can be used to conduct basic medical research by searching for examples where either tai chi or qigong have been used to treat cancer patients.HerculeanResearch["Robert W McGee"]2024-08-08T00:00:00 7fa8957a7ee3139a0b9a26cd6a550520698eb84fThis study focuses on developing an assistive system for blind individuals for collision avoidance of obstacles by combining artificial intelligence techniques; Convolutional Neural Networks (CNN), fuzzy logic control (FLC), and genetic algorithms(GA), This integrated system, named the (NFG) Neural Fuzzy Genetic). The proposed system combines artificial intelligence techniques through detecting and tracking objects, measuring the distance between objects and the blind person, and providing movement guidance using three ultrasonic sensors with FLC and optimization GA. The integration of these technologies offers an innovative solution to enhance the mobility and safety of blind individuals.Specifically, object detection and tracking are applied through CNN, with an obstacle detection range of up to 40 meters. The obstacle recognition system is trained on the ResNet50 model, which includes 50 million trained images and more than 1,000 obstacle classifications, resulting in high accuracy in identifying and detecting obstacles. When tested, the accuracy of the trainer model reached 99.9%. FLC is then used to provide motor guidance and help make appropriate decisions in the presence of obstacles, navigate safely and independently, and determine movement directions in obstacle-free paths with the help of three sensors. The International Journal of Engineering &amp; Information Technology (IJEIT)451The proposed system combines artificial intelligence techniques through detecting and tracking objects, measuring the distance between objects and the blind person, and providing movement guidance using three ultrasonic sensors with FLC and optimization GA to enhance the mobility and safety of blind individuals.The International Journal of Engineering &amp; Information Technology (IJEIT)["Almajdoub, R. Almajdoub, R.", "Shiba, O. Shiba, O."]2024-08-08T00:00:00 6734399eafa021f37d5ca0d75b96979aa081d6a4Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in health care and regulate its application in legal and ethical ways. Significant progress has been made in governance in the United States and the European Union. It is incumbent on developers, end users, the public, providers, health care systems, and policymakers to collaboratively ensure that we adopt a national AI health strategy that realizes the Quintuple Aim; minimizes race-based medicine; prioritizes transparency, equity, and algorithmic vigilance; and integrates the patient and community voices throughout all aspects of AI development and deployment.Milbank Quarterly522It is incumbent on developers, end users, the public, providers, health care systems, and policymakers to collaboratively ensure that a national AI health strategy is adopted that realizes the Quintuple Aim; minimizes race-based medicine; prioritizes transparency, equity, and algorithmic vigilance; and integrates the patient and community voices throughout all aspects of AI development and deployment.The Milbank quarterly["Thelma C Hurd", "Fay Cobb Payton", "Darryl B. Hood"]2024-08-08T00:00:00 4a61ea219e1a8ee68d075facdbf44be450d5af7f Purpose This study provides a comprehensive investigation of the opportunities and challenges associated with generative artificial intelligence (GAI) use in development and learning in organizations. Additionally, it highlights the future avenues in GAI research and provides practical recommendations for policymakers. Design/methodology/approach Data for the review was collected from the Web of Science database using the search criteria (“Generative artificial intelligence” OR “artificial intelligence”) AND (“human resource management” OR “human resource development” OR “workplace learning” OR “organizational learning” OR “organizational development” OR “learning organization”) on March 6th, 2024. Filtering results to Management and Business categories yielded 71 articles. After abstract review, 6 unrelated articles were excluded, leaving 65 articles for final analysis. Findings The study presents several opportunities of GAI such as applications in personal learning and content generation. Moreover, it unravels several potential challenges of GAI such as quality and accuracy issues and elucidates several future research directions. Originality/value Our study is the first literature review that provides and a comprehensive overview of generative artificial intelligence in the context of organizational learning. Development and Learning in Organizations: an international journal51This study is the first literature review that provides and a comprehensive overview of generative artificial intelligence in the context of organizational learning and highlights the future avenues in GAI research and provides practical recommendations for policymakers.Development and Learning in Organizations: An International Journal["H. Rahman", "Tripti Singh"]2024-08-08T00:00:00 07c54fd41cbc0ed10352204db51a01488e16176bGenerative artificial intelligence undergoes statutory copyright licensing of pre-existing data and the training of models to input commands, execute algorithms, and output content. The constant involvement of human intelligence and the creative model of human-computer collaboration determine that artificial intelligence cannot be the subject of copyright. This gives rise to three categories of subjects: Artificial intelligence users, developers and investors. Artificial intelligence always involves human intelligence in the content creation process, and artificial intelligence-generated content has an original contribution as well as a minimum level of creativity from users, formally constituting works under the copyright law. The rules of copyright attribution for artificial intelligence-generated works should be prioritized according to the contractual agreement, If there is no contractual agreement, the copyright protection mode of the copyright law should only extended to AI users. The object of copyright protection should be limited to the artificial intelligence-generated works themselves, and the strength of copyright protection should be in line with the user's originality of the contribution to the work.Highlights in Business, Economics and Management112The object of copyright protection should be limited to the artificial intelligence-generated works themselves, and the strength of copyright protection should be in line with the user's originality of the contribution to the work.Highlights in Business, Economics and Management["Zihao Fang"]2024-08-08T00:00:00 a0779c8dfb19c8fdef7517aafadeee4bd1afa31eThis study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and supply chain role, and examines whether Industry 4.0 (I4.0) readiness influences AI adoption. Data from the European Manufacturing Survey 2022 were analyzed, and showed that the use of AI is still relatively low. On average only 18.4% of companies use AI software in at least one production area. Logistic regression analysis revealed that neither company size nor role in the supply chain or technology intensity are statistically significantly related to AI usage. However, a significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI. This finding underlines the importance of digital transformation for the integration of AI software. The study concludes that while company characteristics such as size and the role of the company in the supply chain are not statistically significantly related to the use of AI, the level of digital readiness is crucial.Applied Sciences532A significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI.Applied Sciences["Klemen Kovic", "P. Tominc", "Jasna Prester", "I. Pal\u010di\u010d"]2024-08-08T00:00:00 32f93c08e6660b74b12b1d6cd8995567aae87abeThis research paper investigates the crossing point of artificial intelligence (AI) and cybersecurity within the setting of sustainable consumption. Sustainable consumption practices are crucial for tending to natural challenges and advancing a more sustainable future. In any case, the expanding dependence on computerized advances and interconnected frameworks in sustainable utilization presents unused cybersecurity dangers and dangers. This paper analyzes how AI can be utilized to improve cybersecurity measures and back sustainable utilization activities. It investigates AI-driven approaches such as information analytics, machine learning, and behavioral modeling to reinforce cybersecurity guards, ensure individual information, distinguish and avoid cyber dangers, and cultivate believe and certainty in advanced stages. The paper too examines the moral contemplations and challenges related with the utilize of AI in cybersecurity for sustainable utilization. Through an examination of existing inquire about and case thinks about, this paper gives experiences into the potential benefits and confinements of joining AI and cybersecurity in sustainable consumption and production. The discoveries contribute to the understanding of the part of AI in guaranteeing the security and flexibility of sustainable consumption frameworks and give proposals for future investigate and usage procedures. Eventually, the paper highlights the significance of grasping AI-driven cybersecurity approaches to back and progress sustainable consumption objectives whereas relieving cybersecurity risks.International Conference on Circuit, Power and Computing Technologies151This paper analyzes how AI can be utilized to improve cybersecurity measures and back sustainable utilization activities and highlights the significance of grasping AI-driven cybersecurity approaches to back and progress sustainable consumption objectives whereas relieving cybersecurity risks.2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)["Sachin Sharma", "Yunika Kadayat", "Ranu Tyagi"]2024-08-08T00:00:00 a7d65d0f9c549ff60892efcafb4f5f0af12b7d06This paper discusses the application and impact of artificial intelligence in the field of project management. Firstly, it introduces the definition and development history of AI and analyses its wide application in various industries. Then, it elaborates the impact of AI on project management, including project planning and scheduling, cost estimation and forecasting, risk management and decision support, communication and collaboration, quality management and monitoring, and resource allocation and optimisation. Artificial intelligence technology has shown significant advantages in improving decision-making accuracy, optimising resource utilisation, and enhancing risk management. At the same time, this paper also discusses the challenges of AI in project management, such as data quality, algorithm transparency, and personnel literacy, and points out the skill requirements and role changes of AI for project managers and teams. Finally, it looks forward to the future development of AI in project management and expects it to bring more innovations and breakthroughs to provide more efficient and smarter solutions for project management.Highlights in Business, Economics and Management61The challenges of AI in project management are discussed, such as data quality, algorithm transparency, and personnel literacy, and the skill requirements and role changes of AI for project managers and teams are pointed out.Highlights in Business, Economics and Management["Ziwen Diao"]2024-08-08T00:00:00 3770ff8ffdd7581c785f0d9dd7132c81210de05fThis article presents a critique of ethics in the context of artificial intelligence (AI). It argues for the need to question established patterns of thought and traditional authorities, including core concepts such as autonomy, morality, and ethics. These concepts are increasingly inadequate to deal with the complexities introduced by emerging AI and autonomous agents. This critique has several key components: clarifying conceptual ambiguities, honestly addressing epistemic issues, and thoroughly exploring fundamental normative problems. The ultimate goal is to reevaluate and possibly redefine some traditional ethical concepts to better address the challenges posed by AI.arXiv.org321It is argued for the need to question established patterns of thought and traditional authorities, including core concepts such as autonomy, morality, and ethics, including core concepts such as autonomy, morality, and ethics to better address the challenges posed by AI.ArXiv["Irina Spiegel"]2024-08-08T00:00:00 53b07596d9d28a27243a98785c9df72fb614fb1eDigital pathology (DP) and its subsidiaries including artificial intelligence (AI) are rapidly making inroads into the area of diagnostic anatomic pathology (AP) including gastrointestinal (GI) pathology. It is poised to revolutionize the field of diagnostic AP. Historically, AP has been slow to adopt digital technology, but this is changing rapidly, with many centers worldwide transitioning to DP. Coupled with advanced techniques of AI such as deep learning and machine learning, DP is likely to transform histopathology from a subjective field to an objective, efficient, and transparent discipline. AI is increasingly integrated into GI pathology, offering numerous advancements and improvements in overall diagnostic accuracy, efficiency, and patient care. Specifically, AI in GI pathology enhances diagnostic accuracy, streamlines workflows, provides predictive insights, integrates multimodal data, supports research, and aids in education and training, ultimately improving patient care and outcomes. This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology. The main aim was to provide updates and create awareness among the pathology community.Artificial Intelligence in Gastroenterology830This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology, offering numerous advancements and improvements in overall diagnostic accuracy, efficiency, and patient care.Artificial Intelligence in Gastroenterology["Muhammed Mubarak", "Rahma Rashid", "F. Sapna", "Shaheera Shakeel"]2024-08-08T00:00:00 78c4a7ec3a8f2f968f5924bdc80eac2cb4e0a536The development of the financial industry has always been closely intertwined with technological advancements. In this Internet age, artificial intelligence (AI) emerges as a pivotal force driving the new round of scientific revolution and industrial transformation, penetrating nearly every aspect of financial sector, becoming a non-negligible factor in financial area. This article examines and analyses the impact of AI on the fairness of financial industry. It argues that the comprehensive information processing capabilities AI has do facilitate fairness people expected, specifically reflected in these three aspects: narrowing service quality disparities through personalized services for users, enhancing the inclusivity of the financial industry through the proliferation of technology, and fostering fair competition in financial sector through digitally driven regulatory methods. However, due to the information biases and the adaption limitations AI has, problems like algorithms discrimination and digital gap may occur, ends up with exacerbating the social inequity existing in financial industry.Highlights in Business, Economics and Management110It is argued that the comprehensive information processing capabilities AI has do facilitate fairness people expected, specifically reflected in these three aspects: narrowing service quality disparities through personalized services for users, enhancing the inclusivity of the financial industry through the proliferation of technology, and fostering fair competition in financial sector through digitally driven regulatory methods.Highlights in Business, Economics and Management["Yuxin Wang"]2024-08-08T00:00:00 0fb297ddb0b6c6f5ea7586185974ffceeae43cc6Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.Artificial Intelligence in Gastroenterology650Real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts, by utilizing deep learning (DL), an innovation within machine learning, which automatically extracts input features from targeted endoscopic images.Artificial Intelligence in Gastroenterology["Joseph Bou Jaoude", "Rose Al Bacha", "Bassam Abboud"]2024-08-08T00:00:00 3c6681a9e24926323f95c59b9e8d0567ec8b65a2Artificial Intelligence (AI) has been developing rapidly in recent years, and the application of AI in various fields has gradually emerged. This paper focuses on exploring the cross-fertilization of AI with the Chinese stock market, and the study adopts 7 types of factors, totaling 29 factor indicators, covering multiple types of value, valuation, leverage, financial quality, growth, technology, and risk factors, etc. Through data preprocessing, feature engineering, and other steps on the factors, and then combined with 8 machine learning algorithms to construct corresponding quantitative trading strategies on CSI 300 constituent stocks. By comparing the results of various models, it is found that the trading strategies constructed by machine learning algorithms can obtain significant excess returns in the Chinese market. Except for Gaussian Park Bayes, the other seven model strategy returns beat the benchmark returns, among which XGBoost performs the best, achieving a 20.10% return and the smallest retraction. This paper has a positive impact on advancing the research on the intersection of machine learning and quantitative investment.Highlights in Business, Economics and Management170By comparing the results of various models, it is found that the trading strategies constructed by machine learning algorithms can obtain significant excess returns in the Chinese market.Highlights in Business, Economics and Management["Wenjie Sun"]2024-08-08T00:00:00 baf81559964f07bb7344677ee67f3307aa146a28With the rapid development of science and technology, artificial intelligence (AI), as an emerging and extremely subversive technological force, is penetrating into every corner of the social economy at an unprecedented speed, and has had a profound and extensive impact on contemporary social, economic and cultural development. This paper aims to comprehensively analyze the social, economic and cultural changes brought about by the explosion of artificial intelligence application, deeply discuss the opportunities created and challenges faced by it, and propose practical countermeasures on this basis, in order to provide reference and inspiration for decision-makers, researchers and practitioners in related fields.Critical Humanistic Social Theory00This paper aims to comprehensively analyze the social, economic and cultural changes brought about by the explosion of artificial intelligence application, deeply discuss the opportunities created and challenges faced by it, and propose practical countermeasures on this basis.Critical Humanistic Social Theory["Wei Wang", "Hong Yi"]2024-08-08T00:00:00 00704c6d271bfb6c4b6bedf425c9bc15423bda27Blockchain being a distributed ledger preserves data in a highly secured environment which cannot be modified even by the user who created the data. This security aspect makes the blockchain technology popular in recent years and was adopted by many organizations to secure their data. The other technology which is very popular recent times is the Artificial Intelligence (AI). Nowadays, day to day life activities are tied up with the use of AI. Knowingly or unknowingly we are using AI in various circumstances. As the world becomes digitalized almost in all aspects, security threats are also has been growing in different dimensions. This paper makes a deep survey on various research articles which integrates AI and blockchain technology to improve the overall security. This paper also analyses the various methodologies and the machine learning algorithms used by different researchers to secure data on a blockchain so that the highly secured data can be automated with the integration of AI. If proper integration techniques were implemented with appropriate Machine Learning algorithms the security will be much improved. This paper concludes by providing a path way to the upcoming researchers to move their research based on the problems identified by comparing various research articles.International Conference on Circuit, Power and Computing Technologies260A deep survey on various research articles which integrates AI and blockchain technology to improve the overall security is made and a path way to the upcoming researchers to move their research based on the problems identified by comparing various research articles is provided.2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)["Jothy C R", "Judith J E"]2024-08-08T00:00:00 a0598222b5e52ec85481a320e005aeaf64346457With the strong rise of generative AI represented by ChatGPT, the issue of copyrightability of AI-generated content has become the focus of controversy in both theoretical and practical circles. The pivotal aspect in determining the legal status of AI-generated content lies in establishing a standard for originality. The current discourse within the theoretical community regarding the copyrightability of such content often revolves around the contrasting viewpoints of the "Dialectics of Negation" and the "Dialectics of Affirmation." By refining the criteria for assessing the originality of AI-generated content and evaluating their attributes through objective measures like "independent creation" and "modicum of creativity", demarcating the boundaries of creative expression, streamlining the creative process, and driving forward the expansion of the artificial intelligence industry. This endeavor is essential for enhancing legal clarity, fostering innovation, and navigating the dynamic landscape where technology and creativity intersect.Highlights in Business, Economics and Management130By refining the criteria for assessing the originality of AI-generated content and evaluating their attributes through objective measures like "independent creation" and "modicum of creativity", demarcating the boundaries of creative expression, streamlining the creative process, and driving forward the expansion of the artificial intelligence industry is essential.Highlights in Business, Economics and Management["Muzhen Han", "Xinyu Wu", "Zhuobin Zhu"]2024-08-08T00:00:00 d92597b4093376f8f89d39a16b6febf68a864ca2Introduction This study explores the influence of artificial intelligence (A.I.) applications on the job performance of healthcare providers, based on data from standardised-trained residents in the First People’s Hospital of Yunnan Province in China. Methods The ordinary least squares model is employed to examine the relationship between A.I. applications and job performance. To address potential endogeneity and missing variables, we utilise the propensity score matching method and alternative regression models. Results The findings indicate that the job performance of standardised-trained residents positively correlates with A.I. applications. This relationship remains robust after addressing endogenous and missing variables. Further discussion reveals that patients’ support mediates the relationship between A.I. and job performance. Under identical conditions, the job performance of female residents empowered by A.I. is found to be significantly better than that of their male counterparts. Conversely, no heterogeneity is observed regarding the impact of A.I. on the job performance of medical practitioners and clinical medical technicians. Discussion This study underscores the positive role of A.I. applications in enhancing the job performance of standardised-trained residents. The results highlight the mediating role of patient support and suggest gender-based differences in the efficacy of A.I. empowerment.Frontiers in Public Health410The results highlight the mediating role of patient support and suggest gender-based differences in the efficacy of A.I. applications in enhancing the job performance of standardised-trained residents.Frontiers in Public Health["Qi Zheng", "Yun Jin", "Xinying Xu"]2024-08-08T00:00:00 7935629385ad151c80b1fbaf4dd1fee7d1dfb6ebThe article is devoted to the study of the prospects for increasing the competitiveness of a transport company in the freight transportation market through the introduction of artificial intelligence into business activities. The prospects and directions for implementing the tasks of increasing the competitiveness of transport companies are determined. The importance of optimizing logistics and its costs in improving the competitive position of a transport company in the freight transportation market is highlighted. The features of the formation of a strategy for the development of competitive positions (positioning) with a focus on demonstrating the advantages obtained through artificial intelligence are revealed. The nature and features of the influence of artificial intelligence on the competitiveness of a transport company are clarified, which is associated with the functional and optimization potential, and prospects for business automation. Based on the results of the study, applied areas for introducing artificial intelligence tools into a transport company are identified to increase competitiveness in the freight transportation market and the nature of their impact on competitiveness is clarified.Moscow Economic Journal10The nature and features of the influence of artificial intelligence on the competitiveness of a transport company are clarified, which is associated with the functional and optimization potential, and prospects for business automation.MOSCOW ECONOMIC JOURNAL["Artem Volkov"]2024-08-08T00:00:00 22bd8e7926556aaf72721ab889ef924162067b5cWith the rapid proliferation of advanced artificial intelligence, such as GPT, the volume of AI-generated content on the internet has surged significantly. Consequently, the question of whether AI-generated content should be safeguarded by copyright law has emerged as a critical issue in both academic and practical domains. The determination of the eligibility of AI-generated content for copyright protection hinges on the assessment of its originality. Current research in the field of AI-generated content copyrightability has brought to light two distinct standards of originality: subjective and objective. However, a challenge arises when weighing the merits and drawbacks of these two standards. Given the inherent complexity of AI-generated content, a singular standard may not comprehensively and accurately encapsulate its attributes. Therefore, the most judicious approach at present appears to be a fusion of the subjective and objective standards, thereby embracing a more comprehensive framework that accounts for the nuanced nature of AI-generated content and its potential for originality under copyright law.Highlights in Business, Economics and Management110The most judicious approach at present appears to be a fusion of the subjective and objective standards, thereby embracing a more comprehensive framework that accounts for the nuanced nature of AI-generated content and its potential for originality under copyright law.Highlights in Business, Economics and Management["Jiaming Lu", "Tianqi Wang", "Yahui Zhao"]2024-08-08T00:00:00 2d310c07d71884f2b2793a4752a9e4b1f8ead634The article is devoted to the analysis of the impact of automation and artificial intelligence (AI) on the international labor market in the context of digitalization. The work examines key aspects of changes caused by the introduction of new technologies, including the transformation of the structure of the labor market, changes in qualification requirements and the emergence of new professions. The study is based on an analysis of literature sources, comparative analysis of data from different countries, as well as case studies of successful practices and expert assessments. The article's main findings confirm that automation and AI are driving significant changes in the labor market, including job losses in traditional industries and the creation of new opportunities in high technology. The need for comprehensive educational initiatives and workforce retraining is emphasized. The article offers practical recommendations for the development and implementation of vocational training and retraining programs, as well as for improving cooperation between government agencies, educational institutions and business. In addition, the article identifies limitations of the study, such as the lack of data from developing regions and the difficulty of assessing long-term effects. For further research, it is recommended to focus on studying the impact of automation and AI on specific sectors of the economy and regions, as well as on assessing the effectiveness of existing retraining programs.Moscow Economic Journal20It is confirmed that automation and AI are driving significant changes in the labor market, including job losses in traditional industries and the creation of new opportunities in high technology.MOSCOW ECONOMIC JOURNAL["Aybek Tashenov"]2024-08-08T00:00:00 ea7675bb82df901fae7d51e4d1d78484f6132e6aAll over the world, political parties, politicians, and campaigns explore how Artificial Intelligence (AI) can help them win elections. However, the effects of these activities are unknown. We propose a framework for assessing AI's impact on elections by considering its application in various campaigning tasks. The electoral uses of AI vary widely, carrying different levels of concern and need for regulatory oversight. To account for this diversity, we group AI-enabled campaigning uses into three categories -- campaign operations, voter outreach, and deception. Using this framework, we provide the first systematic evidence from a preregistered representative survey and two preregistered experiments (n=7,635) on how Americans think about AI in elections and the effects of specific campaigning choices. We provide three significant findings. 1) the public distinguishes between different AI uses in elections, seeing AI uses predominantly negative but objecting most strongly to deceptive uses; 2) deceptive AI practices can have adverse effects on relevant attitudes and strengthen public support for stopping AI development; 3) Although deceptive electoral uses of AI are intensely disliked, they do not result in substantial favorability penalties for the parties involved. There is a misalignment of incentives for deceptive practices and their externalities. We cannot count on public opinion to provide strong enough incentives for parties to forgo tactical advantages from AI-enabled deception. There is a need for regulatory oversight and systematic outside monitoring of electoral uses of AI. Still, regulators should account for the diversity of AI uses and not completely disincentivize their electoral use.arXiv.org00A framework for assessing AI's impact on elections by considering its application in various campaigning tasks is proposed, providing the first systematic evidence from a preregistered representative survey and two preregistered experiments on how Americans think about AI in elections and the effects of specific campaigning choices.ArXiv["Andreas Jungherr", "Adrian Rauchfleisch", "Alexander Wuttke"]2024-08-08T00:00:00 dedba9ae865e1df62b885ef5881af22a727bbbcbArtificial Intelligence techniques are increasingly being used in hydrology for tasks such as groundwater modelling, streamflow prediction, and rainfall time series generation. In Sri Lanka, traditional water resource management methods have limitations and are less accurate in predicting rainfall-runoff, flood events, and drought conditions due to complex parameters and seasonal rainfall patterns. AI methodologies were integrated into hydrological modelling to enhance water resource management practices in Sri Lankan River basins. The study evaluated the applicability of AI techniques in hydrology and water resources management by using data-driven models like RNN-LSTM and RNN-GRU, and physical-based models like HEC-HMS. The study focused on the Kalu River basin and Kirindi Oya basin from October 01, 2000, to September 30, 2011. The evaluation criteria included NASH, MRAE, and $\mathrm{R}^{2}$, as determined based on existing literature. LSTM and GRU models performed well simulating Kalu River basin streamflow. However, all three models failed to simulate streamflow accurately in the Kirindi Oya basin due to inconsistency of input features. While AI models offer efficient simulation of flashflood scenarios, limited and unreliable rainfall data can impact accuracy. Dry zone simulations require further model development to improve reliabilityas current models perform well only in wet zones.Moratuwa Engineering Research Conference110This study evaluated the applicability of AI techniques in hydrology and water resources management by using data-driven models like RNN-LSTM and RNN-GRU, and physical-based models like HEC-HMS, and physical-based models like HEC-HMS.2024 Moratuwa Engineering Research Conference (MERCon)["S. Karunarathna", "R. L. H. L. Rajapakse"]2024-08-08T00:00:00 92fa70a85af671594d85306efeb49ed43676b8edThis thesis discusses the use of Artificial Intelligence technology media in improving Arabic language skills at Rahmatul Asri Islamic Modern Boarding School. This research aims to explore the differences in improving Arabic language skills between the use of Artificial Intelligence technology media and the use of Power Point media or conventional learning media as a comparison. The research used a Quasi Experimental method with a two group pretest-posttest design, involving 56 samples of students from classes X and XI MA PPM Rahmatul Asri. Data was collected through Arabic language proficiency tests before and after treatment. The results showed a significant increase in the students' Arabic language skills. The average learning outcome score based on the students' pre-test (72.00) increased to (88.86) based on the average post-test score after treatment. Data analysis using the t-test shows that this increase is significant, with a sig (2-tailed) value of 0.000 < 0.05, which indicates that this difference did not occur by chance. The conclusion of this research is that the use of Artificial Intelligence technology media is effective in improving students' Arabic language skills. Interactive and interesting learning media has been proven to be able to create a more active and enjoyable learning atmosphere. It is recommended that this method be applied more widely in other Islamic boarding schools, and that teachers be given training to optimize the use of Artificial Intelligence media in learning.Journal of education220The conclusion of this research is that the use of Artificial Intelligence technology media is effective in improving students' Arabic language skills.Journal on Education["Ilham Mr", "Saepudin Saepudin", "Herdah Herdah", "D. Darmawati", "Kaharuddin Ramli"]2024-08-08T00:00:00 890e5ee4cf3ad5170cd9eb69e994177a61e837b0The convergence of artificial intelligence with China's manufacturing sector marks the dawn of a new digital era with profound implications for productivity, innovation, and global trade. The emergence of artificial intelligence is redefining how factories operate in China, bringing unparalleled efficiency and agility. Powered by AI, the promise of smart manufacturing is more than just an incremental advance; It represents a fundamental shift in manufacturing towards intelligent, connected and autonomous systems. In the current situation, AI has begun to be deployed in many manufacturing areas in China, focusing on process optimization, supply chain management, product development and after-sales service. Factories are becoming smarter, using big data analytics and machine learning algorithms to improve decision-making and operational efficiency across all aspects. At the same time, such rapid development will raise questions about the collapse of the low-skilled labor market and information safety. This essay also puts forward some solutions in turn.Highlights in Business, Economics and Management110Factories are becoming smarter, using big data analytics and machine learning algorithms to improve decision-making and operational efficiency across all aspects, and such rapid development will raise questions about the collapse of the low-skilled labor market and information safety.Highlights in Business, Economics and Management["Kexin Na"]2024-08-08T00:00:00 8e2e9201344e15fed98f5f40ae783e57645383f1This work describes how Artificial Intelligence can be used and is being used in Educational sector. According to the 21st International Conference on Artificial Intelligence in Education held in 2020, AIED is one of the currently emerging fields in Educational technologies. The use of AI is still unclear for the educators how to make pedagogical advantage of it on a broader scale and how AI can impact on teaching and learning in higher education. The impact of AI in education and its pros and cons are presented here. It also describes a specific way to develop AI enabled platform for education and finally the after effects of AI in education.International Journal For Multidisciplinary Research10The impact of AI in education and its pros and cons are presented here and a specific way to develop AI enabled platform for education and finally the after effects of AI in education are described.International Journal For Multidisciplinary Research["Saurabh Shandilya", "Priyanka Sharma"]2024-08-08T00:00:00 3c6883b5379d0580d7ae0fcfd87101c42212edc6In the era of artificial intelligence, the rapid development of generative artificial intelligence represented by ChatGPT brings great convenience to human creation, but also causes many potential copyright risks and brings a series of new challenges to the field of intellectual property. The creative process of generative artificial intelligence mainly includes four stages: training data input, data learning, input instruction and content generation. Among them, the legal use of copyright works in the data input stage needs to be solved instantly. In order to solve this problem, we should first improve the “fair use system” under current Copyright Law, divide machine learning into expressive and non-expressive types according to whether generative artificial intelligence has expressive content output, and discuss whether it constitutes fair use separately. Secondly, in order to protect the legitimate rights of original copyright owners, it is also necessary to improve the transparency of training data and increase the “opt-out” mechanism. In addition, it is also necessary to clarify the tort liability subject of generative artificial intelligence by legislation when it does not constitute fair use.Highlights in Business, Economics and Management110Improve the “fair use system” under current Copyright Law, divide machine learning into expressive and non-expressive types according to whether generative artificial intelligence has expressive content output, and discuss whether it constitutes fair use separately.Highlights in Business, Economics and Management["Jing Li"]2024-08-08T00:00:00 c836bd8f70c2381a821ced7d55f78d420af94f2eThe paper intends to explore the utilization of artificial intelligence in teacher training programme and its impact on human civilization. This paper also discusses about the vital role of artificial intelligence in education sector as well as the importance of artificial intelligence in human civilization. Artificial intelligence has the potential to revolutionize the education system, promising a better future for humanity. Many educational institutions are already employing different educational software to enhance the skills of the learners. Additionally, artificial intelligence aids in reducing the workload of teachers and administrators, handling both simple and complex tasks efficiently within a very short period of time.International Journal For Multidisciplinary Research200The utilization of artificial intelligence in teacher training programme and its impact on human civilization are explored and the vital role of artificial intelligence in education sector as well as the importance of artificial intelligence in human civilization are discussed.International Journal For Multidisciplinary Research["Sumana Hazra"]2024-08-08T00:00:00 d9ec6c63b495b9fe1cc31f4e15e51b4fdb953489The birth and development of artificial intelligence, represented by ChatGPT, have brought a new singularity to the development of the economy and the transformation of society. How to deal with the legal issues related to generative artificial intelligence in the context of the technological singularity, so that the law conforms to the process of science and technology, will be a major problem now and even in the future. In order to reconcile the contradiction between the rapid development of generative artificial intelligence in the context of the times and the copyright legislation based on the concept of "anthropocentrism", a number of views have been put forward in the academic circles but they are not useful. Based on this, the inadequacy of the existing viewpoints was analyzed, break away from the framework of "anthropocentrism", and give artificial intelligence a limited legal subject status. Currently, according to the mainstream classification method of artificial intelligence, artificial intelligence can be divided into weak artificial intelligence, general artificial intelligence, and super artificial intelligence. Therefore, on the premise that AI-generated works constitute "works", according to the different degrees of intelligence of AI, the main criterion of contribution should be concerned with balancing the interests of multiple subjects in order to determine the attribution of AI-generated copyright rights and distribution plan.Highlights in Business, Economics and Management120The inadequacy of the existing viewpoints was analyzed, the inadequacy of the existing viewpoints was analyzed, and artificial intelligence was given a limited legal subject status to reconcile the contradiction between the rapid development of generative artificial intelligence in the context of the times and the copyright legislation based on the concept of "anthropocentrism".Highlights in Business, Economics and Management["Jinxi Xu"]2024-08-08T00:00:00 c5635f55520cfb38e5742bd792f68352624fb300With the profound advancement of AI algorithmic models, AI generators have progressively found applications across diverse industries. Nevertheless, this development has presented new challenges to legislation and judicial determinations concerning copyright and related matters. There remains no definitive conclusion on whether AI-generated works can be considered original or fall within the scope of copyright law, nor whether they are eligible for copyright protection. However, a thorough analysis of current domestic and international legislation and judicial precedents suggests that copyright protection should indeed be extended to AI-generated creations. This is supported by the fact that artificial intelligence models fundamentally serve as tools to aid human creativity, lacking autonomous will and primarily relying on human data analysis, compilation, and integration as a basis for creation. Therefore, even though the process of artificial intelligence creation may ultimately reflect the original thought and personalized expression of humans, it is essential to consider granting copyright protection to AI-generated content.Highlights in Business, Economics and Management100It is essential to consider granting copyright protection to AI-generated content, even though the process of artificial intelligence creation may ultimately reflect the original thought and personalized expression of humans.Highlights in Business, Economics and Management["Chenyu Ye"]2024-08-08T00:00:00 b74600d97bda7c3db122cc64f3dfeeeef97e76cbIn recent years, the continuous development of artificial intelligence has brought convenience to people's lives but has also given rise to a series of problems. Among these, the gaps in laws related to artificial intelligence has sparked significant controversy, particularly with regard to copyright protection of artificial intelligence products, which has become a focal point of debate in theoretical and practical circles. Some scholars advocate for the protection of artificial intelligence products through copyright, while others argue against it. Based on this, the typical cases have been sorted out and analyzed, relevant provisions of copyright law have been studied, and the issue of the copyright for artificial intelligence products has been explored from both practical and theoretical perspectives. Additionally, in combined with scholars' viewpoints, the copyrightability of artificial intelligence-generated material is examined from the aspects of originality and intellectual achievement. The purpose of establishing the copyrightability of such material is to align with the development trend of the times and, simultaneously, to promote the enhancement of China's copyright legal system in order to foster cultural prosperity and development.Highlights in Business, Economics and Management150The purpose of establishing the copyrightability of artificial intelligence-generated material is to align with the development trend of the times and to promote the enhancement of China's copyright legal system in order to foster cultural prosperity and development.Highlights in Business, Economics and Management["Xinxu Wang"]2024-08-08T00:00:00 2e25ed6cfdf2010ef5d51f8fc3d85541de390ddeThe world is dynamic in nature as Agriculture remains the backbone of human existence. Human being surely depends on agriculture for existence and the way agriculture is carried out keep on changing from traditional to modern method. Presently, machines are being used to carry out almost all agricultural activities. The most recently introduced method in agriculture and other related fields is the used of Artificial Intelligence. Artificial Intelligence is recently introduced in the field of agriculture because of its wide use and importance related to our daily life. The problems faced by farmers are enormous which include, low output, soil treatment, diseases and pest management couple with big data requirements, and many others.  AI is applied in agriculture in four (4) dimension ways, in crop management, weed management, disease management and soil management. This application helps to manage the cost effectiveness, accuracy and high performance in a more efficient and better ways. The paper makes a review on the application of AI in the field of agriculture for more productivity in the present teeming population. The benefits, dangers and solutions to Artificial Intelligence created problems were suggested. Automated farm machinery like driverless tractors, smart irrigation, fertilization systems, IoT-powered agricultural drones, smart spraying, vertical farming software, and AI-based greenhouse robots for harvesting are just some examples. Compared with any human farm worker, AI-driven tools are far more efficient and accurate.Asian Journal of Advanced Research and Reports00The paper makes a review on the application of AI in the field of agriculture for more productivity in the present teeming population and the benefits, dangers and solutions to Artificial Intelligence created problems were suggested.Asian Journal of Advanced Research and Reports["Idris A. Adamu"]2024-08-08T00:00:00 0bf77ab98548fcfd2e1ae7c6bbfd533ae46af283Journal of Educational Psychology01Journal of Educational Psychology["Ying Xu", "Kunlei He", "Julian Levine", "Daniel Ritchie", "Zexuan Pan", "Andres Bustamante", "M. Warschauer"]2024-08-08T00:00:00 80a5b5a225a6d4a245fc9668faee8f343b3fef06The EU AI Act mandates that providers and deployers of high-risk AI systems establish a quality management system (QMS). Among other criteria, a QMS shall help verify and document the AI system design and quality and monitor the proper implementation of all high-risk AI system requirements. Current research rarely explores practical solutions for implementing the EU AI Act. Instead, it tends to focus on theoretical concepts. As a result, more attention must be paid to tools that help humans actively check and document AI systems and orchestrate the implementation of all high-risk AI system requirements. Therefore, this paper introduces a new design concept and prototype for a QMS as a microservice Software as a Service web application. It connects directly to the AI system for verification and documentation and enables the orchestration and integration of various sub-services, which can be individually designed, each tailored to specific high-risk AI system requirements. The first version of the prototype connects to the Phi-3-mini-128k-instruct LLM as an example of an AI system and integrates a risk management system and a data management system. The prototype is evaluated through a qualitative assessment of the implemented requirements, a GPU memory and performance analysis, and an evaluation with IT, AI, and legal experts.arXiv.org511A new design concept and prototype for a QMS as a microservice Software as a Service web application that connects directly to the AI system for verification and documentation and enables the orchestration and integration of various sub-services, which can be individually designed, each tailored to specific high-risk AI system requirements.ArXiv["Henryk Mustroph", "Stefanie Rinderle-Ma"]2024-08-08T00:00:00 ae0265e60c7fa0d337e2ca8010daa49d1109af73J. Medical Syst.261Journal of Medical Systems["Hannah J Lonsdale", "V. O\u2019Reilly-Shah", "Asif Padiyath", "Allan F. Simpao"]2024-08-08T00:00:00 ab8e0ba77a249f670ce53ba1e8cab95b5bdc810bUrban Geography221Urban Geography["Federico Cugurullo"]2024-08-08T00:00:00 3569422ed058036b4eb2756b66c102b28f5e7e4fInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-08T00:00:00 e89c9e3da06706a23f4923313d868bb981489a9100["Tien Anh Tran", "Edeh Michael Onyema", "Arij Naser Abougreen"]2024-08-08T00:00:00 7ecc460b068c6b3bc89663a916080a42b710703eNEJM AI20NEJM AI["Adam Rodman", "Andrew L. Beam", "A. Manrai"]2024-08-08T00:00:00 fc8da6dc56dc538f7cf5777cb27b8b47d00cd439Cogent Education330Cogent Education["Adri\u00e1n Nagy", "J. Tumiwa", "F. Arie", "L\u00e1szl\u00f3 Erdey"]2024-08-08T00:00:00 dc39b7b5f5e3240fa4bce7f8081ab99a02ea9aefThe International Journal of Advanced Manufacturing Technology120The International Journal of Advanced Manufacturing Technology["Shufen Li"]2024-08-08T00:00:00 880e00e68d388da92968802e8036b4b3d49df812BME Frontiers200BME Frontiers["Lingting Huang", "Zhengjie Chen", "Zhen Yang", "Wei Huang"]2024-08-08T00:00:00 cd187ef0f7a76315a52fdcf375629930ce8c7763The Permanente Journal70The Permanente Journal["Paul J Wallace"]2024-08-08T00:00:00 093976959170f427b36c8b325a83974d35df36dfLocal Economy00Local Economy: The Journal of the Local Economy Policy Unit["Rahmat Dilta Harahap", "Setiadi M Noor", "Indira Rosandry Ajeng Syahputri", "Trisna Agung Pambudi"]2024-08-08T00:00:00 646b402e4e6ced60136c0db874f9e9424728701eSoftware development in the era of global competition requires strategic technology management to enhance a company's competitiveness and economic performance. The main challenges in this process include project complexity, changing requirements, and time constraints, which often lead to project failures globally. In Indonesia, only 27% of information system projects are completed on budget and on time, highlighting significant issues in software development. AI assistants have emerged as an innovation with great potential to address these problems. With features such as code completion, code interpretation, and bug detection, this solution has the potential to increase software developers' productivity in the future. Given this context, this research aims to identify the factors influencing the intention to adopt AI assistants, particularly among software developers. The study was conducted using PLS-SEM and applied factors from common technology acceptance theories such as TAM and UTAUT. Data collection instruments were distributed using self-selection sampling and snowball sampling (N=165). Major factors in UTAUT such as effort expectancy, performance expectancy, facilitating conditions, and social influence were found to be significant in influencing attitudes or adoption intentions. Additionally, AI-specific factors in the context of UTAUT extension, such as AI literacy, were found to have an indirect effect on attitudes and behavioral intentions, moderated by other factors. It is hoped that the findings of this research can help stakeholders evaluate their strategies if they wish to adopt AI assistants and provide academic impact through scientific publications that can complement existing literature, expanding the understanding of AI integration in the professional IT realm.Indonesian Journal of Computer Science940It is hoped that the findings of this research can help stakeholders evaluate their strategies if they wish to adopt AI assistants and provide academic impact through scientific publications that can complement existing literature, expanding the understanding of AI integration in the professional IT realm.The Indonesian Journal of Computer Science["Muhammad Rizky Anditama", "A. Hidayanto"]2024-08-08T00:00:00 a4eaa49bd2e00aea3a57b07f3a9c06ac6c76976fThis work focuses on AI and ML in product roadmap planning while also exploring solutions to the challenges of using feature-based roadmaps for strategic decision-making. Having covered the recurrent categories of roadmap and the roadmap-making process, they will proceed to the part where AI and ML are shown to save time, as well as help deliver more precise information on the potential target customers, as well as the current situation in the given market sector. They further discuss problems that could undermine the accuracy of outcomes, involving data quality and interpretability. The role of transparency, collaboration, and progress to tackle the problem is highly underscored. The best practices for engineering an AI-integrated product roadmap are drawn up which are namely setting the specific targets, developing solid data basis, promoting collaboration and which ought to be iterated respectively. Some actual examples will show how AI, including ML, opens a variety of fields and demonstrate how it can make business processes more innovative and better informed.2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP)130The best practices for engineering an AI-integrated product roadmap are drawn up which are namely setting the specific targets, developing solid data basis, promoting collaboration and which ought to be iterated respectively.2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP)["Pranav Khare", "Sahil Arora", "Sandeep Gupta"]2024-08-08T00:00:00 b799c4bbd0e7e2fe73c2f3fbd0e6d4cd749adb92We propose a generative adversarial network (GAN) based deep learning method that serves the dual role of both identification and mitigation of cyber-attacks in wide-area damping control loops of power systems. Two specific types of attacks considered are false data injection and denial-of-service (DoS). Unlike existing methods, which are either model-based or model-free and yet require two separate learning modules for detection and mitigation leading to longer response times before clearing an attack, our deep learner incorporate both goals within the same integrated framework. A Long Short-Term Memory (LSTM) encoder-decoder based GAN is proposed that captures the temporal dynamics of the power system significantly more accurately than fully-connected GANs, thereby providing better accuracy and faster response for both goals. The method is validated using the IEEE 68-bus power system model.arXiv.org100A Long Short-Term Memory (LSTM) encoder-decoder based GAN is proposed that captures the temporal dynamics of the power system significantly more accurately than fully-connected GANs, thereby providing better accuracy and faster response for both goals.ArXiv["Jishnudeep Kar", "A. Chakrabortty"]2024-08-08T00:00:00 c7b77b8e1a36292c6cd8484b083856f5ef4c8dc5Nature reviews. Urology20Nature reviews. Urology["Louise Lloyd"]2024-08-08T00:00:00 e3c57474f20d894a79e09059491f1f93b778495f<jats:p xml:lang="tr"/>Journal of Pedagogical Research00Journal of Pedagogical Research["Nguyen Thi Hang"]2024-08-08T00:00:00 fa328176512d5df61d54c4bc4ed1478c60c47076Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing40Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing["Lakshay Gupta", "Shrey Gurbuxani", "Kapil Madan"]2024-08-08T00:00:00 4ce0b94e323d0542371530a473b4271f09c581f5Cogent Education350Cogent Education["Moses Segbenya", "Felix Senyametor", "Simon-Peter Kafui Aheto", "E. K. Agormedah", "K. Nkrumah", "Rebecca Kaedebi-Donkor"]2024-08-08T00:00:00 2f1d2aad1d94f6c1df67bebdedb69f2ca631905fCOJ Robotics &amp; Artificial Intelligence00COJ Robotics &amp; Artificial Intelligence["Fassil B Mesfin"]2024-08-08T00:00:00 759a26451b65d587aad79cb5829ca408e1deb56aGenerative Artificial Intelligence (GenAI) caught Health Professions Education (HPE) institutions off-guard, and they are currently adjusting to a changed educational environment. On the horizon, however, is Artificial General Intelligence (AGI) which promises to be an even greater leap and challenge. This Guide begins by explaining the context and nature of AGI, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability. It then explores the implications of AGI on students (including personalised learning and electronic tutors) and HPE institutions, and considers some of the context provided by AGI in healthcare. It then raises the problems to address, including the impact on employment, social risks, student adaptability, costs, quality, and others. After considering a possible timeline, the Guide then ends by indicating some first steps that HPE institutions and educators can take to prepare for AGI.Medical Teacher212The context and nature of AGI is explained, including its characteristics of multi-modality, generality, adaptability, autonomy, and learning ability, and the implications of AGI on students and HPE institutions are explored.Medical teacher["Ken Masters", "Anne Herrmann-Werner", "T. Festl-Wietek", "David C M Taylor"]2024-08-08T00:00:00 0e86009cd0c3b0674cd5af62b407035bda3f394bThere have been impressive advancements in the field of natural language processing (NLP) in recent years, largely driven by innovations in the development of transformer-based large language models (LLM) that utilize “attention.” This approach employs masked self-attention to establish (via similarly) different positions of tokens (words) within an inputted sequence of tokens to compute the most appropriate response based on its training corpus. However, there is speculation as to whether this approach alone can be scaled up to develop emergent artificial general intelligence (AGI), and whether it can address the alignment of AGI values with human values (called the alignment problem). Some researchers exploring the alignment problem highlight three aspects that AGI (or AI) requires to help resolve this problem: (1) an interpretable values specification; (2) a utility function; and (3) a dynamic contextual account of behavior. Here, a neurosymbolic model is proposed to help resolve these issues of human value alignment in AI, which expands on the transformer-based model for NLP to incorporate symbolic reasoning that may allow AGI to incorporate perspective-taking reasoning (i.e., resolving the need for a dynamic contextual account of behavior through deictics) as defined by a multilevel evolutionary and neurobiological framework into a functional contextual post-Skinnerian model of human language called “Neurobiological and Natural Selection Relational Frame Theory” (N-Frame). It is argued that this approach may also help establish a comprehensible value scheme, a utility function by expanding the expected utility equation of behavioral economics to consider functional contextualism, and even an observer (or witness) centric model for consciousness. Evolution theory, subjective quantum mechanics, and neuroscience are further aimed to help explain consciousness, and possible implementation within an LLM through correspondence to an interface as suggested by N-Frame. This argument is supported by the computational level of hypergraphs, relational density clusters, a conscious quantum level defined by QBism, and real-world applied level (human user feedback). It is argued that this approach could enable AI to achieve consciousness and develop deictic perspective-taking abilities, thereby attaining human-level self-awareness, empathy, and compassion toward others. Importantly, this consciousness hypothesis can be directly tested with a significance of approximately 5-sigma significance (with a 1 in 3.5 million probability that any identified AI-conscious observations in the form of a collapsed wave form are due to chance factors) through double-slit intent-type experimentation and visualization procedures for derived perspective-taking relational frames. Ultimately, this could provide a solution to the alignment problem and contribute to the emergence of a theory of mind (ToM) within AI.Frontiers Comput. Neurosci.2200It is argued that this approach could enable AI to achieve consciousness and develop deictic perspective-taking abilities, thereby attaining human-level self-awareness, empathy, and compassion toward others.Frontiers in Computational Neuroscience["Darren J Edwards"]2024-08-08T00:00:00 749f5c01a28267a95bbe6856952c3fb37fa4cb41Artificial intelligence (AI) in libraries refers to the growing trend of programming computers to perform tasks that traditionally role on human intelligence. The ultimate goal is to develop computer systems or machines that can think and act like humans, potentially revolutionizing the field of librarianship. Including expert systems to provide reference services, robots that perform tasks such as reading and shelving, and virtual reality for experiential rich learning. Although some fear that the integration of AI will create a divide between librarians and users, it is more likely that it will enhance their capabilities rather than replace their roles. This will lead to better service delivery, ultimately enhancing the relevance of libraries in our ever-changing digital society. This article aims to investigate different uses of AI in libraries, including concepts such as expert systems, natural language processing, pattern recognition, and robotics. It also explores the pros and cons of AI. In recent years, there has been a resurgence of interest in AI. This article aims to gather perspectives on how AI could impact academic libraries and consider the implications for library workRespondents emphasized the impact of AI on research and resource discovery, scientific publishing, and learning. Challenges include concerns about libraries ignoring development priorities, ethical considerations, transparency in AI decisions, and data quality. Some see it as a potential threat to jobs. The study identified different potential roles for academic libraries, such as collecting and managing data, acquiring AI tools and building infrastructure, and supporting users to navigate data direction and control. This article stands out as one of the first examinations of current expectations regarding the impact of AI on academic libraries. The authors propose the concept of smart libraries to encompass the potential influence of AI on libraries.International journal of Modern Achievement in Science, Engineering and Technology00The authors propose the concept of smart libraries to encompass the potential influence of AI on libraries, and identify different potential roles for academic libraries, such as collecting and managing data, acquiring AI tools and building infrastructure, and supporting users to navigate data direction and control.International journal of Modern Achievement in Science, Engineering and Technology["C. Dixit", "Kanchan lata Dixit", "Chandra Kumar Dixit", "Praveen Kumar Pandey", "Deepali Chauhan", "Shavej Ali Siddiqui"]2024-08-08T00:00:00 a6322ac91596c7072595df04c276f97b18ee0d01The rapid evolution of knowledge requires constantly acquiring and updating skills, making lifelong learning crucial. Despite decades of artificial intelligence, recent advances promote new solutions to personalize learning in this context. The purpose of this article is to explore the current state of research on the development of artificial intelligence-mediated solutions for the design of personalized learning paths. To achieve this, a systematic literature review (SRL) of 78 articles published between 2019 and 2024 from the Scopus and Web or Science databases was conducted, answering seven questions grouped into three themes: characteristics of the published research, context of the research, and type of solution analyzed. This study identified that: (a) the greatest production of scientific research on the topic is developed in China, India and the United States, (b) the focus is mainly directed towards the educational context at the higher education level with areas of opportunity for application in the work context, and (c) the development of adaptive learning technologies predominates; however, there is a growing interest in the application of generative language models. This article contributes to the growing interest and literature related to personalized learning under artificial intelligence mediated solutions that will serve as a basis for academic institutions and organizations to design programs under this model.Frontiers in Education4410The current state of research on the development of artificial intelligence-mediated solutions for the design of personalized learning paths under artificial intelligence mediated solutions is explored to serve as a basis for academic institutions and organizations to design programs under this model.Frontiers in Education["K. Bayly-Castaneda", "M-S. Ramirez-Montoya", "Morita-Alexander", "P. G. Schrader", "I. Awidi"]2024-08-08T00:00:00 5555f24d7331be6a02445ead74913de99edd6c49Questions and concerns about artificial intelligence (AI) technologies in education reached a fever pitch with the arrival of publicly accessible, user‐facing generative AI systems, especially ChatGPT. Many of these issues will require regulation and collective action to address. But when it comes to generative AI and literacy, we argue that posthuman perspectives can help literacy scholars and practitioners reframe some concerns into questions that open new areas of inquiry. Agential realism in particular offers a useful perspective for exploring how generative AI matters in literacy practices, not as a unilaterally destructive force, but as a set of phenomena that intra‐actively reconfigures literacy practices. As a sociocultural (and as we argue, sociotechnical) practice, literacy arises out of the entanglement of bodies, spaces, contexts, positions, histories, and technologies. Generative AI is another in a long line of technologies that reconfigures literacy practices. In this article, we briefly explain how generative AI systems work, focusing on text‐based systems called Large Language Models (LLMs), and suggest ways that generative AI may reconfigure the sociocultural practice of literacy. We then offer three provocations to shift discussions about generative AI and literacy (1) from concerns about intentionality to questions of responsibility, (2) from concerns about authenticity to questions of mattering, and (3) from concerns about imitation to questions of multifarious communication. We conclude by encouraging literacy scholars and practitioners to draw inspiration from critical literacy efforts to discover what matters when it comes to generative AI and literacy.Reading Research Quarterly163It is argued that posthuman perspectives can help literacy scholars and practitioners reframe some concerns into questions that open new areas of inquiry when it comes to generative AI and literacy.Reading Research Quarterly["Priya C. Kumar", "Kelley Cotter", "L. Y. Cabrera"]2024-08-08T00:00:00 5bce0f27907abf2a329c2517269eab304fc25d51AI and Ethics232A practical statistical significance testing method is proposed to enhance the current rule-based process for model fairness testing and its associated power calculation, followed by an illustration with a realistic example.AI and Ethics["Victor S. Y. Lo", "Sayan Datta", "Youssouf Salami"]2024-08-08T00:00:00 c9118bc8177e404e7c381c79d3bb6cdf7524d320Software projects rely on what we call project archetypes, i.e., pre-existing mental images of how projects work. They guide distribution of responsibilities, planning, or expectations. However, with the technological progress, project archetypes may become outdated, ineffective, or counterproductive by impeding more adequate approaches. Understanding archetypes of software development projects is core to leverage their potential. The development of applications using machine learning and artificial intelligence provides a context in which existing archetypes might outdate and need to be questioned, adapted, or replaced. We analyzed 36 interviews from 21 projects between IBM Watson and client companies and identified four project archetypes members initially used to understand the projects. We then derive a new project archetype, cognitive computing project, from the interviews. It can inform future development projects based on AI-development platforms. Project leaders should proactively manage project archetypes while researchers should investigate what guides initial understandings of software projects.arXiv.org582A new project archetype, cognitive computing project, is derived from 36 interviews from 21 projects between IBM Watson and client companies and can inform future development projects based on AI-development platforms.ArXiv["Mateusz Dolata", "Kevin Crowston", "G. Schwabe"]2024-08-08T00:00:00 f957c1fe96c1b5fd5d5f7712fdb7dcb582b99c5ePortfolio management is a critical component of financial investments. With the advent of artificial intelligence (AI)‐driven portfolio management, retail investors have the choice to utilize cutting‐edge technology to manage their investment portfolios. This study analyzes and portrays the effects of factors influencing the adoption of financial robo‐advisors (FRAs) among retail investors in India. A framework comprising eight constructs is proposed to understand FRA adoption. Structural equation modeling (SEM) was used to analyze data from 387 respondents among Indian retail investors using the IBM SPSS AMOS version 28 software package. The results indicate that technology readiness and financial literacy are the two strongest predictors of the behavioral intention to adopt FRA. Additionally, this study provides empirical evidence that social influence and investor type are relevant determinants of customers' decisions to adopt FRA. This study provides managers with guidance on the target segment of consumers for FRA and insights into the drivers of adoption. It further highlights the importance of investor profiling beyond just demographics to improve adoption.Electronic Journal of Information Systems in Developing Countries822The results indicate that technology readiness and financial literacy are the two strongest predictors of the behavioral intention to adopt FRA, and provides empirical evidence that social influence and investor type are relevant determinants of customers' decisions to adopt FRA.THE ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES["Sougata Banerjee"]2024-08-08T00:00:00 422574016af2c7f7e043518a4ba3086acaef1e8aThe rapid urbanization in Karnataka, characterized by increasing population and infrastructure demands, necessitates innovative solutions to ensure sustainable and efficient urban management. Leveraging the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) offers significant potential to enhance the decision-making capabilities of policy makers in Karnataka’s smart cities. This research paper investigates the effectiveness of these technologies in improving urban governance, focusing on real-time data acquisition, predictive analytics, and informed policy decisions. AI and ML are crucial in the analysis and interpretation of the vast amounts of data generated by IoT devices. AI algorithms process this data to identify patterns, anomalies, and trends, while ML models predict future scenarios based on historical data. For instance, predictive analytics can forecast traffic congestion, energy demand, and potential public health crises, allowing policy makers to deploy preemptive measures. In smart city initiatives, AI-driven insights ensure that resources are allocated efficiently, urban planning is optimized, and public services are enhanced. In conclusion, the integration of IoT, AI, and ML holds transformative potential for enhancing decision-making processes in Karnataka’s smart cities. By providing real-time data, predictive insights, and efficient resource management tools, these technologies enable policy makers to address urban challenges proactively and sustainably.International Journal of Innovative Science and Research Technology151In conclusion, the integration of IoT, AI, and ML holds transformative potential for enhancing decision-making processes in Karnataka’s smart cities by providing real-time data, predictive insights, and efficient resource management tools.International Journal of Innovative Science and Research Technology (IJISRT)["Samrat S", "S. J. Manjunath"]2024-08-08T00:00:00 56a1583be24627ecea2970b88ec525b3e706554aThe sudden growth of technology has profoundly shifted various sectors, notably education, where Artificial Intelligence (AI) chatbots are revolutionizing Learning Management Systems (LMS). LMSs are pivotal in the management of educational materials and engagements between educators and students. Traditional LMSs often encounter obstacles like limited interactivity and static content, which impact student engagement and overall effectiveness. AI chatbots can tackle these challenges by providing real-time, adaptable support, thereby enriching the educational process. This study explores the integration of these chatbots in LMS through the lens of three pedagogical principles: Cognitive Load Theory (CLT), Constructivist Learning Theory, and Adaptive Learning Theory. CLT strives to regulate cognitive load to enhance learning efficiency, with chatbots simplifying content and offering instant feedback. Constructivist Learning Theory advocates for active, contextual learning through interaction, a principle supported by AI chatbots engaging learners in conversations and problem-solving activities. Adaptive Learning Theory emphasizes the personalization of educational experiences, a goal achieved by AI chatbots tailoring content and adjusting to student performance in real time. This study presents AI chatbots' alignment with pedagogical principles, revealing their potential to enhance LMS environments and improve student engagement, comprehension, and achievements.Engineering and Technology Journal01This study presents AI chatbots' alignment with pedagogical principles, revealing their potential to enhance LMS environments and improve student engagement, comprehension, and achievements.Engineering and Technology Journal["Brian Kamau Mungai", "Professor Kelvin Kabeti Omieno", "Dr. Mathew Egessa, PhD", "Peninah Njeri Manyara"]2024-08-08T00:00:00 228caa25e3f7d6b1041853ea965cf46de92762f3Existing IoT devices frequently exhibit inefficiencies in energy consumption and decision-making processes, resulting in inadequate efficiency and environmental impact. The study describes a unique framework that uses artificial intelligence (AI) and machine learning (ML) methods to provide Internet of Things (IoT) devices with cognitive decision-making capabilities, hence improving energy efficiency and overall performance. The proposed method enables real-time autonomous decision-making in IoT contexts by collecting extensive data, preprocessing it, and designing features, then selecting, training, and deploying algorithms. Continuous learning mechanisms enable flexibility in changing situations, while thorough testing reveals considerable gains in energy efficiency, proposed system dependability, and user pleasure. The results show a 30% reduction in energy consumption, a 25% increase in resource utilization, a 5% improvement in proposed system uptime, and significant improvements in user satisfaction indicators. The proposed method is an exciting possibility for making IoT deployments more sustainable and efficient, paving the path for intelligent technology ecosystems.International Conference on Circuit, Power and Computing Technologies171This study describes a unique framework that uses artificial intelligence (AI) and machine learning (ML) methods to provide Internet of Things devices with cognitive decision-making capabilities, hence improving energy efficiency and overall performance.2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)["A. P", "Nitesh Chouhan", "Gunita Arun Chandhok", "D. Sugumaran", "U. Aswal", "Suganya A"]2024-08-08T00:00:00 440f097357626b578264b3cf9537c0a2f206468cThe integration of artificial intelligence (AI) in cosmetology is transforming the industry in numerous ways, including the introduction of advanced tools such as at-home skin analysis devices that can evaluate skin quality and augmented reality applications that allow users to virtually try on various makeup products. These innovations empower individuals to make well-informed decisions about their cosmetic care and enable cosmetologists to predict treatment outcomes with higher accuracy. In this way, AI enhances patient satisfaction by better aligning expectations with achievable results. A computerized database search was performed to identify articles relevant to this topic. A comprehensive search was applied to the following electronic databases: IEEE Xplore, PubMed, Google Scholar, and Research Gate. This review explores four key areas in the current literature where AI contributes to cosmetic procedures. Firstly, AI democratizes skincare by making products and services more accessible to everyone. Secondly, it bridges the gap between physicians and cosmetic suppliers by enlightening collaboration and innovation. Thirdly, it improves the assessment of cosmetic ingredients by ensuring better safety and efficacy, and lastly, AI provides an ethical alternative to animal testing by replacing the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximization Test (GPMT) with in silico models. While AI offers significant benefits, it also raises concerns about data privacy, informed consent, and the potential for promoting unrealistic beauty standards. Addressing these challenges involves implementing measures such as anonymization and de-identification techniques to protect sensitive data and safeguard informed consent for data collection and processing. This article aims to highlight the responsible and ethical use of AI in cosmetology, emphasizing the importance of accuracy and customization in cosmetic care, which represents a significant advancement in the industry.Cosmetics582This article aims to highlight the responsible and ethical use of AI in cosmetology, emphasizing the importance of accuracy and customization in cosmetic care, which represents a significant advancement in the industry.Cosmetics["V. Grech", "V. Kefala", "Efstathios Rallis"]2024-08-09T00:00:00 698b1be3f7e43df41006b1ff5da4a0c60bbed52cBackground: The expansion of artificial intelligence (AI) across diverse sectors worldwide demands an understanding of its impact on future generations. The studies of its influence on university students’ behavior and application in Lebanon are still limited. The present study aimed to explore the knowledge, attitudes, and practices (KAPs) of university students regarding AI and to identify factors affecting these dimensions. Methods: An online questionnaire (n = 457) was distributed to university students who were at least 18 years of age across Lebanon. Results: The results revealed that a significant majority (97.2%) of the participants were familiar with AI, from which 43% demonstrated a high level of knowledge. Furthermore, attitude toward AI role and integration in academic and professional paths was moderately satisfactory (43%), although it was reportedly used by 75% of students throughout their university years. There was a significant association between knowledge levels and sociodemographic factors such as age, sex, source of AI-related information, and knowledge rating (p < 0.05), whereas the academic major and knowledge rating affected attitudes toward AI (p < 0.05). Conclusion: These findings support the incorporation of AI education within the curriculum to increase acceptance of AI as a modern tool enhancing various sectors and serving as a facilitator for teaching and learning processes.Education sciences241Findings support the incorporation of AI education within the curriculum to increase acceptance of AI as a modern tool enhancing various sectors and serving as a facilitator for teaching and learning processes.Education Sciences["S. Kharroubi", "Iman Tannir", "Rasha Abu El Hassan", "Rouba Ballout"]2024-08-09T00:00:00 3fc1e2135d2105b2bcf822de49537e581dae9d9cAI &amp; SOCIETY351The concept of entangled AI that emerged from participatory backcasting research with an AI expert panel is introduced and shows how such concepts seem to transcend the dominant discourses related to expectations, technological determinism, and humanism.AI &amp; SOCIETY["Alexandra K\u00f6ves", "Katalin Feher", "L. Vicsek", "M\u00e1t\u00e9 Fischer"]2024-08-09T00:00:00 d2622d8b6017c4f61d0d2c272db0413740d20875Scientific Reports657It is found that people were most creative when writing a poem on their own, compared to first receiving a poem generated by an AI system and using sophisticated tools to edit it, and this creativity deficit dissipates when people co-create with—not edit—AI.Scientific Reports["Jack McGuire", "David de Cremer", "Tim Van de Cruys"]2024-08-09T00:00:00 bec74ca1844ff0c47402da751cb756158bee1480Improving patient outcomes depends critically on early identification of breast cancer. In order to detect breast cancer up to five years before a clinical diagnosis, artificial intelligence (AI) has the potential to completely transform breast cancer screening. This paper examines this possibility. We explore the most recent developments in AI algorithms and how they relate to imaging in medicine, namely mammography. The paper looks at how AI can identify precancerous alterations that are invisible to the human eye by analysing minute patterns in breast tissue. We go over the difficulties and possibilities in creating and evaluating AI models for early detection, including model interpretability, data quality, and ethical issues. The ultimate goal of this analysis is to demonstrate how artificial intelligence (AI) has the potential to drastically lower breast cancer mortality by enabling much earlier detection. Keywords-Artificial Intelligence, Breast Cancer, Personalized medicine,Digital MammographyINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The paper looks at how AI can identify precancerous alterations that are invisible to the human eye by analysing minute patterns in breast tissue by analysing minute patterns in breast tissue.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Prasurjya Saikia"]2024-08-09T00:00:00 1ed288e1a40c54d686ae94ea20281219f298892cIn the rapid progress of science and technology, artificial intelligence has penetrated into various industries, and the field of music education is no exception. This paper firstly discusses the challenges encountered by music teachers on the road of professional development, then puts forward a series of strategies to cope with them on this basis, and looks forward to the possibilities and future trends of artificial intelligence-assisted professional development of music teachers.Region - Educational Research and Reviews10The challenges encountered by music teachers on the road of professional development are discussed, a series of strategies to cope with them are put forward, and the possibilities and future trends of artificial intelligence-assisted professional development of music teachers are looked forward to.Region - Educational Research and Reviews["Can Fan"]2024-08-09T00:00:00 3cd06264fbb8e5c7efeb8b9ab0bae068e193aa0cPemanfaaan artificial intelligence perlu dikuasai oleh guru untuk mempermudah dalam melaksanakan tugas dan mendukung kompetensi profesional salah satunya dalam membuat modul ajar. Kurangnya pemahaman guru dalam pemanfaatan artificial intelligence berdampak pada pembuatan modul ajar yang lama. Kegiatan pengabdian kepada masyarakat ini bertujuan memberikan pemahaman dan keterampilan guru dalam penyusunan modul ajar yang efektif menggunakan artificial intelligence. Kegiatan ini dilaksanakan di SDN 36/V Pembengis Kuala Tungkal dengan peserta seluruh guru berjumlah 19 orang. Kegiatan dilakukan dengan Kegiatan ini dilaksanakan dengan beberapa tahapan yakni identifikasi kebutuhan mitra, kemudian sosialisasi dan pendampingan, pelatihan dan pembimbingan, serta evaluasi kegiatan dengan memberikan angket diakhir kegiatan. Hasil kegiatan ini menunjukkan adanya peningkatan pemahaman peserta terkait dengan penyusuan modul ajar yang efektif menggunakan artificial intelligence. Hal ini tampak dari persentase pemahaman perserta yakni 26,32% peserta sangat mengetahui, 52,63% mengetahui, dan 21,05% cukup mengetahui. Dari pengalaman peserta didapat persentase 36,84% peserta menyatakan bahwa kegiatan yang telah dilakukan sanagat memuaskan, 52,63% menyatakan memuaskan dan 10,53% menyatakan cukup memuaskan. Kegiatan ini juga memberikan dampak positif terhadapt peserta dengan persentase 84,21% sangat setuju dan 15,79% menyatakan setuju bahwa kegiatan memberikan dampak positifJournal of Human and Education (JAHE)00Journal Of Human And Education (JAHE)["Maryono Maryono", "Eko Kuntarto", "Hendrato Budiono", "Eka Sastrawati", "Silvina Noviyanti"]2024-08-09T00:00:00 905a18bb5f2b5628d435c591b0f93bba868b88c5Amid the rapid development of information technology, artificial intelligence (AI) has brought profound changes to the field of education. By analyzing AI applications in China's educational innovation, this paper explores the potential and challenges of AI in educational reform and proposes strategies to promote the scientific, intelligent, and personalized development of the education system. Specific applications demonstrate AI's practical effects in enhancing teaching efficiency, enriching learning experiences, and improving intelligent education management. This study provides theoretical support and practical guidance for the comprehensive upgrading and optimization of the education system, summarizes the challenges of AI education technology and looks forward to the future development trend.2024 4th International Conference on Big Data Engineering and Education (BDEE)320This study provides theoretical support and practical guidance for the comprehensive upgrading and optimization of the education system, summarizes the challenges of AI education technology and looks forward to the future development trend.2024 4th International Conference on Big Data Engineering and Education (BDEE)["Rongyu Cui", "Xin Xie", "Long Fan", "Xiaoli Yang"]2024-08-09T00:00:00 e409255bee58295d3cb7b165491ad71757b03ff6PurposeThis study tries to examine the effect of artificial intelligence (AI) drivers on the willingness to adopt the human capital supply chain (HCSC) in manufacturing firms (MFs) in developing countries (DCs) including Jordan, Saudi Arabia, Bahrain, Qatar and the United Arab Emirates, which are listed in the Chambers of Industry of these countries.Design/methodology/approachThe quantitative methodology with a simple random sampling method was adopted using a questionnaire survey-based approach to collect data from 233 out of 1,055 participants (human resource (HR) managers and information technology (IT) senior managers) from various MFs (private and commercial), representing a 22% response rate. Covariance-based structural equation modeling (CB-SEM) was used to analyze the raw data using Amos V.25.FindingsThe results of this study showed that there are positive and statistically significant direct association effects between the reliability of use (RoU), competitive pressures (CPs) and user confidence (UC) factors on the willingness to adopt AI in HCSC in the MFs in DCs. At the same time, there is no significant effect on a firm’s infrastructure readiness (FIRs), in addition to the indirect effect of UC in the relationship between CPs and FIRs on the willingness to adopt AI in HCSC.Originality/valueSuch findings of this study can provide insightful implications for stakeholders and policymakers regarding the importance of using predictive AI drivers' effect on willingness to adopt the HCSC in the MFs in DCs as emerging economies. Additionally, the managers might focus on the existence of a significant positive indirect effect of UC as a mediating factor in the relationship between FIRs and willingness to adopt AI and its applications in HCSC systems and departments.Industrial management & data systems470Findings showed that there are positive and statistically significant direct association effects between the reliability of use (RoU), competitive pressures (CPs) and user confidence (UC) factors on the willingness to adopt AI in HCSC in the MFs in DCs.Ind. Manag. Data Syst.["M. Al-Shboul"]2024-08-09T00:00:00 4932f1c5c03a75a7cde98008470a7e8a47756e9cThis visual narrative is a first-person reflection of the previous pictorial at the 1st International Workshop on Explainable AI for the Arts (XAIxArts) at ACM Creativity and Cognition 2023. The initial workshop pictorial explored a relationship between researcher and artificial intelligence, navigating creative challenges throughout the 2023 teaching block. It concluded by raising crucial questions regarding attribution transparency, the ethical dimensions of the creative process, and the delicate balance between inspiration and plagiarism. Subsequent discussions at the workshop yielded valuable insights, particularly concerning interpreting the creative journey. This follow-up visual narrative reflects the enduring impact of Makayla Lewis's interaction with AI. A self-portrait that delves into the interplay of creativity and introspection.arXiv.org140This follow-up visual narrative reflects the enduring impact of Makayla Lewis's interaction with AI, a self-portrait that delves into the interplay of creativity and introspection.ArXiv["Makayla Lewis"]2024-08-09T00:00:00 f2f0692ef0baab15a0e3c0d74c1c774291120cf6Herz260An overview of the recent developments in the field of cardiovascular imaging and intervention is provided, and a future outlook is offered of the novel developments in federated learning.Herz["Sandy Engelhardt", "Salman Ul Hussan Dar", "Lalith Sharan", "Florian Andr\u00e9", "E. Nagel", "Sarina Thomas"]2024-08-09T00:00:00 ad61f92554aa5ef63710595252e29f95423c6882This article investigates how artificial intelligence (AI) has transformed finance, e-commerce, and business after COVID-19. Starting with a basic explanation of artificial intelligence (AI), it explores the main fields of AI and explores revolutionary trends, highlighting how AI may improve productivity, customize user experiences, and disrupt conventional processes. With a focus on ethical issues and legal frameworks, insightful evaluations address how AI affects e-commerce strategies, financial forecasts, portfolio allocation, and project management. The results underline the critical role AI will play in determining how businesses operate in the future and draw attention to the fine line that separates innovation from ethical duty. The insights from this article predict a paradigm shift in project management functions in favour of AI in the future, indicating the necessity for organizations to adjust and proactively handle novel challenges. Future directions involve standardizing legislation, developing novel applications that are in line with cultural standards, and improving ethical AI methods.Asian Journal of Education and Social Studies00The insights from this article predict a paradigm shift in project management functions in favour of AI in the future, indicating the necessity for organizations to adjust and proactively handle novel challenges.Asian Journal of Education and Social Studies["Kenneth O Ogirri", "O.G. Ogedengbe"]2024-08-09T00:00:00 98f82a989c134e3daa07de260a612565a2961635The emergence of artificial intelligence (AI) in educational contexts presents transformative potential for higher engineering education. This paper conducts a comprehensive literature review to explore the role of AI-driven analytics such as personalized learning pathways and adaptive assessment techniques, in personalizing learning experiences and improving educational outcomes for engineering students. Through a systematic analysis of existing research, we identify how AI technologies - such as machine learning algorithms, data mining, and natural language processing - can be utilized to tailor educational content, improve learning engagement, and optimize curriculum design. A synthesis of the findings highlights the significant benefits of AI in identifying diverse learning styles, predicting academic performance, and providing real-time feedback, thus fostering a more individualized and effective learning environment. In addition, this article discusses the challenges and ethical considerations associated with implementing AI in education, including data privacy, algorithmic bias, and the need for human oversight to ensure ethical AI deployment. Building upon insights gleaned from a thorough review of published literature, we propose a conceptual framework for integrating AI into engineering education, with the aim of equipping the students with the skills and knowledge required to navigate the complexity of the modern engineering landscape. This theoretical exploration underscores the transformative potential of AI in engineering education and sets a foundation for future empirical research to further investigate and expand upon these benefits and strategies.2024 4th International Conference on Big Data Engineering and Education (BDEE)190A comprehensive literature review is conducted to explore the role of AI-driven analytics such as personalized learning pathways and adaptive assessment techniques, in personalizing learning experiences and improving educational outcomes for engineering students.2024 4th International Conference on Big Data Engineering and Education (BDEE)["Gabriela Dorfman Furman"]2024-08-09T00:00:00 8b851c3942d241bd6b4b0f65a34e2bb03a17c212This research was conducted to examine the perception on the use of Artificial intelligence tools in teaching practice among SMK Dato Permaisuri’s teachers. According to the changing trends in the global education arena, the use of Artificial Intelligence (AI) is increasingly expanding. Aims: This aims to enhance the processes of learning and teaching for greater effectiveness. The utilization of AI in education also creates opportunities to improve the quality of education, make learning more adaptive, and prepare the younger generation to face challenges in the future. In Malaysia, many teachers still face challenges in designing engaging learning experiences. In addition, ineffective teaching strategies that do not support differentiated learning methods contribute to an increased student learning rate. Objective: This study was conducted to examine perceptions of the benefits of use, usability, social influence, and readiness for AI acceptance at SMK Dato Permaisuri. Methodology: This study utilized a descriptive quantitative approach by collecting data through a survey questionnaire. The questionnaire was distributed to 90 teachers at SMK Dato Permaisuri, with only 73 respondents selected as the sample for this study based on the Krejcie and Morgan Table. The data were then analysed using the Statistical Package for Social Science (SPSS) version 15. Results: The study results showed that perceptions of the benefits of use, usability, social influence, and readiness for acceptance indicated a high level of agreement. The highest correlation strength was found between social influence and acceptance readiness with r=0.66, p<0.05, compared to usability with r=0.49, p<0.05, and perceived usefulness with r=0.58, p<0.05. Conclusion: However, overall, it indicates a moderate level of relationship. The multiple linear regression beta coefficient values showed that b (0.58) had the highest contribution to the level of AI acceptance readiness in teaching among teachers at SMK Dato Permaisuri, which is social influence.International Journal of Academic Research in Business and Social Sciences100International Journal of Academic Research in Business and Social Sciences["Ting Siew Chear", "Muhammad Helmi Norman"]2024-08-09T00:00:00 21d74786a888c5b2b3a3583189be1a633cf80247This study investigates the role and impact of artificial intelligence (AI) in the electric power industry through a thematic analysis of corporate communications. As AI technologies proliferate, industries—such as the electric power industry—are undergoing significant transformations. The research problem addressed in this study involves understanding how electric power companies perceive, adopt, and implement AI, as well as the implications of these developments. By employing a qualitative thematic analysis approach, we examined a corpus of corporate communications from innovation leaders, including annual reports and sustainability reports, in the electric power sector. The data spanned 2020 to 2023, capturing a crucial period of AI integration in the industry. Our analysis reveals several key findings. Firstly, there is a clear trend toward increased utilization of AI in various facets of the electric power sector, including grid management, predictive maintenance, and customer service. Companies actively invest in AI technologies to enhance operational efficiency, reduce costs, and improve service quality. Secondly, the corporate discourse has shifted significantly, with companies emphasizing AI’s role in sustainability efforts. Moreover, our analysis identified challenges and concerns associated with AI adoption in the electric power industry. In conclusion, the thematic analysis of corporate communications provides valuable insights into the evolving landscape of AI in the electric power industry. The findings underscore the transformative potential of AI technologies, highlighting opportunities for enhanced efficiency and sustainability. However, they also emphasize addressing challenges to ensure responsible and beneficial AI integration. This study contributes to the growing literature on AI in industries, offering practical implications for electric power companies, policymakers, and stakeholders navigating the AI-driven future of the sector.Sustainability440The thematic analysis of corporate communications provides valuable insights into the evolving landscape of AI in the electric power industry, underscoring the transformative potential of AI technologies, highlighting opportunities for enhanced efficiency and sustainability.Sustainability["Dorota Chmielewska-Muciek", "Patrycja Marzec-Braun", "Jacek Jakubczak", "B. Futa"]2024-08-09T00:00:00 e7ea083b107d9a0f954cd07affdb7aee10f6dedbPurposeThere is currently a gap in the research regarding the effect of corporate culture on corporate innovation capability. Based on cultural hierarchy theory, in this paper, we explore the interactions between cultural factors and innovation capability in emerging market firms (EMFs). We discuss the mechanisms by which incentive, institutional, and vibrant corporate cultures influence corporate innovation capability. Furthermore, we consider the transformation of artificial general intelligence (AGI) from a tool into a colleague and how this affects the relationship between corporate culture and innovation capability.Design/methodology/approachAn online questionnaire was distributed to corporate employees to explore their attitudes towards AGI and corporate culture. In total, 523 valid questionnaires were empirically analysed using partial least squares structural equation modelling and multigroup analysis (MGA).FindingsThe results showed that incentive culture, institutional culture, and vibrant culture had a positive impact on corporate innovation capability. MGA revealed significant differences between employees who considered AGI a tool and those who considered it a colleague. Employees who treated AGI as a colleague were likely to be influenced by a vibrant culture, whereas employees who treated AGI as a tool were likely to be influenced by an incentive or institutional culture.Originality/valueBuilding on cultural hierarchy theory, our study provides a new theoretical framework to enrich current research on the relationship between corporate culture and AGI. The study can help EMF managers adjust incentive and institutional cultures before AGI shifts from being a tool to a colleague and negatively impacts innovation capacity.Cross Cultural &amp; Strategic Management1630The study provides a new theoretical framework to enrich current research on the relationship between corporate culture and AGI and can help EMF managers adjust incentive and institutional cultures before AGI shifts from being a tool to a colleague and negatively impacts innovation capacity.Cross Cultural &amp; Strategic Management["Chengxiang Chu", "Sihan Cheng", "Cong Cao"]2024-08-09T00:00:00 3d60a35a414b6ed07595958b2a91a61362463cafFront-end planning (FEP) is the first step in identifying a problem and analysing a project’s goals and the business case for management to decide whether to proceed with the project. Despite its crucial significance, projects are still underperforming and failing to achieve their objectives. Current research suggests that the emergence of AI promises significant advantages to organisations, particularly for FEP. The purpose of this paper was to explore the readiness of organisations to use AI in the FEP phase to enhance project outcomes. The technology–organisation–environment (TOE) framework was used to evaluate factors influencing the readiness to adopt AI in construction projects in Saudi Arabia. Thirty interviews were conducted with public and private stakeholders in the sector. The knowledge and insight gained from the viewpoints of key decision makers and practitioners allowed for an examination of the main factors impacting the adoption of AI, and any challenges and barriers to it. Findings showed that the support of the government and senior management, and the attitudes and behaviour of employees, were the top three factors in the framework that facilitate the readiness of organisations to adopt AI. Government support influences external support and enhances competitive pressure between organisations; senior management support influences the absorptive capacity and maturity of an organisation; and employees’ attitudes and behaviours are the main contributors to organisational readiness. The proposed framework will assist policymakers in using these factors to overcome the challenges of AI adoption. Additionally, creating strategies aligned with Vision 2030 focuses not only on choosing the best technology to implement but also on how employees can benefit from it.Buildings733The support of the government and senior management, and the attitudes and behaviour of employees, were the top three factors in the framework that facilitate the readiness of organisations to adopt AI in the FEP phase to enhance project outcomes.Buildings["Haneen Felemban", "M. Sohail", "K. Ruikar"]2024-08-09T00:00:00 58933d894de5b76db547ad1084c8bf7e99d7d4cfIoTML170{"pages": "409-414"}["Yusong Zhou", "Zechuan Qin"]2024-08-09T00:00:00 bb71c97e552aa8a26c4b1a60ead1c0ffe9b0e523IoTML70{"pages": "373-379"}["Ganfeng Mao"]2024-08-09T00:00:00 01c972c3796b2d6e66bf3649d551d605ff0a3468AJNR. American journal of neuroradiology50AJNR. American journal of neuroradiology["Reza Forghani"]2024-08-09T00:00:00 504131943c73c09165df9de6953558463faf4dc7Journal of Empirical Research on Human Research Ethics20Journal of empirical research on human research ethics : JERHRE["H. Daungsupawong", "V. Wiwanitkit"]2024-08-09T00:00:00 a5c7465e850d83b6e7e1a32eef7a85ffdead64c9Wireless personal communications190Wirel. Pers. Commun.["Rajkumar Gaur", "Shiva Prakash"]2024-08-09T00:00:00 039445cca5dfa2840eb7f9cb3392176fb2479b1bIt is common to observe conventional methods for estimating soybean crop yields, making the process slow and susceptible to human error. Therefore, the objective was to develop a model based on deep learning to estimate soybean yield using digital images obtained through a smartphone. To do this, the ability of the proposed model to correctly classify pods that have different numbers of grains, count the number of pods and grains, and then estimate the soybean crop yield was analyzed. As part of the study, two types of image acquisition were performed for the same plant. Image acquisition 1 (IA1) included capturing the images of the entire plant, pods, leaves, and branches. Image acquisition 2 (IA2) included capturing the images of the pods removed from the plant and deposited in a white container. In both acquisition methods, two soybean cultivars, TMG 7063 Ipro and TMG 7363 RR, were used. In total, combining samples from both cultivars, 495 images were captured, with each image corresponding to a sample (plant) obtained through methods AI1 and AI2. With these images, the total number of pods in the entire dataset was 46,385 pods. For the training and validation of the model, the data was divided into subsets of training, validation, and testing, representing, respectively, 80, 10, and 10% of the total dataset. In general, when using the data from IA2, the model presented errors of 7.50 and 5.32% for pods and grains, respectively. These values are considerably lower than when the model used the IA1 data, where it presented errors of 34.69 and 35.25% for pod and grain counts, respectively. Therefore, the data used from IA2 provide better results to the model.Acta Scientiarum: Agronomy280A model based on deep learning to estimate soybean yield using digital images obtained through a smartphone was developed and the ability of the proposed model to correctly classify pods that have different numbers of grains, count the number of pods and grains, and then estimate the soybean crop yield was analyzed.Acta Scientiarum. Agronomy["P. M. D. C. Bandeira", "Flora Maria de Melo Villar", "Francisco de Assis de Carvalho Pinto", "F. L. Silva", "Priscila Pascali da Costa Bandeira"]2024-08-09T00:00:00 2790965287907a9f2be69c01770056c4a5ad464fAsthma Allergy Immunology00Asthma Allergy Immunology["Murat Turk", "E. Zeydan", "Suayb S. Arslan", "Yekta Turk"]2024-08-09T00:00:00 e1a761f821f09d94fa207f35758219ca028ecac4Nature Reviews Electrical Engineering20Nature Reviews Electrical Engineering["Emilio Calvanese Strinati"]2024-08-09T00:00:00 7561a13c06fc838b0c19549631d944e7c3481b7eIn this study, we present an innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence. The introduced open-source AI system seamlessly integrates a Chess engine with a language model, enabling it to predict moves and provide strategic explanations. Leveraging a vector database to achieve retrievable answer generation, our AI system elucidates its decision-making process, bridging the gap between raw computation and human-like understanding. Our choice of Chess as the demonstration environment underscores the versatility of our approach. Beyond Chess, our system holds promise for diverse applications, from medical diagnostics to financial forecasting. Our AI system is available at https://github.com/TheOpenSI/CoSMIC.gitACIS330An innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence is presented, seamlessly integrates a Chess engine with a language model, enabling it to predict moves and provide strategic explanations.ArXiv["Muntasir Adnan", "Buddhi Gamage", "Zhiwei Xu", "Damith Herath", "C. N. Kuhn"]2024-08-09T00:00:00 c8e11857b520b58ba6491ac0319867d467a1d3d3Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study’s objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content analysis of 69 interviews with healthcare leaders and healthcare professionals confirmed and added results from the literature review. Thirty-eight empirical cases from the six identified scoping and literature reviews met the inclusion and exclusion criteria. Barriers to and strategies for AI implementation were grouped under three phases of implementation (planning, implementing, and sustaining the use) and were categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance and human resources, Legal, Training, Data, Evaluation and monitoring, Maintenance. Ethics emerged as a twelfth concept through qualitative analysis of the interviews. This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice. Future research should explore various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians’ liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process.PLoS ONE756This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice and suggests various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians’ liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process are explored.PLOS ONE["Monika Nair", "P. Svedberg", "Ingrid Larsson", "J. Nygren"]2024-08-09T00:00:00 b7a5bbc02cc9a0b1a7676e6ad02d55c97a147c47Artificial Intelligence Review2725This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all.Artificial Intelligence Review["Ahmed M. A. Salih", "I. Galazzo", "P. Gkontra", "E. Rauseo", "A. Lee", "K. Lekadir", "P. Radeva", "Steffen E. Petersen", "G. Menegaz"]2024-08-09T00:00:00 ae74ee25e05a2d3239d7bbfdfc0c0f8ba7620980Artificial Intelligence and Law72An interdisciplinary analysis of the concepts of AI system, general purpose AI system, foundation model and generative AI across the different versions of the legal text before the final political agreement is provided.Artificial Intelligence and Law["D. Fern\u00e1ndez-Llorca", "Emilia G\u00f3mez", "Ignacio S\u00e1nchez", "Gabriele Mazzini"]2024-08-09T00:00:00 5c474ff22e84a8bf46c301f171c64b2afa68f238An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer’s existing groundwater monitoring network. For that aquifer, a preliminary study revealed that a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) more accurately determined temporally average water table elevations than geostatistical kriging, spline, and inverse distance weighting. Because kriging is usually used in that area for water table estimation, the developed algorithm used MLP-ANN to guide kriging, and Genetic Algorithm (GA) to determine locations for new monitoring well location(s). For possible annual fiscal budgets allowing 1–12 new wells, 12 sets of optimal new well locations are reported. Each set has the locations of new wells that would minimize the squared difference between the time-averaged heads developed by kriging versus MLP-ANN. Also, to simultaneously consider local expertise, the algorithm used fuzzy inference to quantify an expert’s satisfaction with the number of new wells. Then, the algorithm used symmetric bargaining (Nash, Kalai–Smorodinsky, and area monotonic) to present an upgradation strategy that balanced professional judgment and heuristic optimization. In essence, the algorithm demonstrates the systematic application of relatively new computational practices to a common situation worldwide.Machine Learning and Knowledge Extraction661An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer’s existing groundwater monitoring network and demonstrates the systematic application of relatively new computational practices to a common situation worldwide.Mach. Learn. Knowl. Extr.["Masoumeh Hashemi", "Richard C. Peralta", "Matthew Yost"]2024-08-09T00:00:00 3564e9c321106b4422d01a52363e51b1dc55a596This article explores the application of artificial intelligence (AI) technology in cross-border data flow in international trade and the resulting legal and regulatory issues. With the development of globalization and the digital economy, cross-border data flow has become increasingly important in international trade. The rapid advancement of AI technology has accelerated this trend. However, cross-border data flow involves complex legal and regulatory issues, particularly concerning data privacy protection, security, and sovereignty. This paper aims to explore the current applications of AI technology in cross-border data flow in international trade, identify the legal and regulatory challenges, and propose relevant countermeasures and recommendations. The article points out that the application of AI technology in international trade is mainly reflected in automated production and logistics management, intelligent customer service and user experience, data analysis and decision support, compliance in international trade, and new trade models and innovation. However, cross-border data flow faces multiple challenges, and different countries have different legal requirements, increasing the operational costs and legal risks for enterprises. The article suggests addressing these challenges by strengthening international cooperation, improving domestic laws and regulations, adopting advanced technologies, and enhancing corporate compliance capabilities. By implementing these measures, the security and legality of cross-border data flow can be effectively ensured, promoting the sustainable development of international trade.Transactions on Economics, Business and Management Research90By addressing the challenges of cross-border data flow by strengthening international cooperation, improving domestic laws and regulations, adopting advanced technologies, and enhancing corporate compliance capabilities, the security and legality of cross-border data flow can be effectively ensured, promoting the sustainable development of international trade.Transactions on Economics, Business and Management Research["Qirui Chang"]2024-08-09T00:00:00 1b30f81f2061064c336aca3ecc30b4f171dfa276In the contemporary digital era, artificial intelligence (AI) has revolutionized the landscape of consumer behavior by enabling personalized marketing and advanced data analytics. This article reviews existing literature to explore the role of AI in shaping purchase decisions. The emergence of AI technologies allows marketers to leverage vast amounts of consumer data to create personalized experiences, enhancing customer engagement and satisfaction. Through personalized marketing strategies, companies can deliver tailored content, product recommendations, and targeted advertisements that align with individual consumer preferences. The integration of data analytics provides deeper insights into consumer behavior, enabling businesses to anticipate trends and make informed decisions. This literature review examines various case studies and empirical research to highlight the effectiveness of AI-driven marketing strategies in influencing consumer purchase decisions. The findings indicate that personalized marketing, underpinned by sophisticated data analytics, not only enhances consumer trust and loyalty but also drives higher conversion rates. This study underscores the importance of embracing AI technologies for businesses aiming to stay competitive in an increasingly digital marketplace.  Dinasti International Journal of Education Management And Social Science340The findings indicate that personalized marketing, underpinned by sophisticated data analytics, not only enhances consumer trust and loyalty but also drives higher conversion rates, underscores the importance of embracing AI technologies for businesses aiming to stay competitive in an increasingly digital marketplace.Dinasti International Journal of Education Management And Social Science["Izharuddin Pagala", "Muhammad Asir", "Klemens Mere", "Utami Puji Lestari", "Heidi Siddiqa"]2024-08-09T00:00:00 5087b8af99ba1500c2bac468ee80cd3a3285ed6aPurpose: In the lifecycle of Product Development and Management, automated testing has become a cornerstone for ensuring product quality and accelerating release cycles. However, the maintenance of test automation suites often presents significant challenges, particularly due to the frequent changes in application interfaces that lead to broken tests. This paper explores the development and implementation of self-healing test automation frameworks that leverage Artificial Intelligence (AI) and Machine Learning (ML) techniques to automatically detect, diagnose, and repair broken tests. Methodology: By integrating AI/ML models capable of dynamic locator identification, intelligent waiting mechanisms, and anomaly detection, these frameworks can significantly reduce the maintenance burden associated with automated testing. The paper presents a comprehensive architecture of a self-healing test automation framework, detailing the AI/ML techniques employed and the workflow of the self-healing process. A real-world case study is included to demonstrate the practical application and benefits of the proposed framework. Findings: Evaluation results show substantial improvements in test suite reliability and reductions in maintenance time and costs. The AI/ML techniques used in the framework, such as dynamic locator identification and intelligent waiting mechanisms, proved effective in reducing the maintenance burden and improving the robustness of automated testing processes. Unique Contribution to Theory, Practice and Policy: This paper aims to provide insights into the potential of self-healing test automation frameworks to enhance the robustness and efficiency of automated testing processes. By adopting these frameworks, organizations can achieve more resilient and maintainable test automation strategies, ultimately contributing to higher product quality and faster release cycles.International Journal of Strategic Management190Insight is provided into the potential of self-healing test automation frameworks to enhance the robustness and efficiency of automated testing processes to reduce the maintenance burden and improve the robustness of automated testing processes.International Journal of Strategic Management["Sutharsan Saarathy", "Suresh Bathrachalam", "Bharath Rajendran"]2024-08-09T00:00:00 96fe9bebcd6ed0be6f8a0c5f5e2e1b155e9a95d4With the rapid advancement of Artificial Intelligence (AI) in early childhood education (ECE), young children face the challenge of learning to use AI ethically and appropriately. Developing AI education programs requires an age- and culturally-appropriate AI literacy framework. This study addresses this fundamental gap by creating a Chinese framework for early childhood AI literacy through an expert interview study with a grounded theory approach. Seven Chinese experts, including ECE and AI professors, kindergarten principals, and Directors of ECE Information Departments, were purposely sampled and interviewed, representing scholars, policymakers, and practitioners. The synthesis of the transcribed evidence generated five dimensions of young children’s AI literacy, namely Safety, Identity, Attitude, Cognition, and Capability, collectively forming a holistic framework titled the ‘SIACC’ framework. The Chinese definition of early childhood AI literacy was also reported. This study introduces the Chinese framework of AI literacy and provides a scientific basis for policymakers to establish AI literacy standards for young children. Additionally, it offers a conceptual structure for developing systematic indicators and scales within AI literacy in ECE.Education sciences350The synthesis of the transcribed evidence generated five dimensions of young children’s AI literacy, collectively forming a holistic framework titled the ‘SIACC’ framework, which provides a scientific basis for policymakers to establish AI literacy standards for young children.Education Sciences["Wenwei Luo", "Huihua He", "Minqi Gao", "Hui Li"]2024-08-09T00:00:00 95ff5f3aea5420b240adf7d536c1a307bcd80f58The integration of Artificial Intelligence (AI) in healthcare settings de-mands a nuanced approach that considers both technical performance and soci-otechnical factors. Recognizing this, our study introduces the Clinical AI Soci-otechnical Framework (CASoF), developed through literature synthesis, and re-fined via a Modified Delphi study involving global healthcare professionals. Our research identifies a critical gap in existing frameworks, which largely focus on either technical specifications or trial outcomes, neglecting the comprehensive sociotechnical dynamics essential for successful AI deployment in clinical envi-ronments. CASoF addresses this gap by providing a structured checklist that guides the planning, design, development, and implementation stages of AI sys-tems in healthcare. The checklist emphasizes the importance of considering the value proposition, data integrity, human-AI interaction, technical architecture, organizational culture, and ongoing support and monitoring, ensuring that AI tools are not only technologically sound but also practically viable and socially adaptable within clinical settings. Our findings suggest that the successful inte-gration of AI in healthcare depends on a balanced focus on both technological advancements and the socio-technical environment of clinical settings. CASoF represents a step forward in bridging this divide, offering a holistic approach to AI deployment that is mindful of the complexities of healthcare systems. The checklist aims to facilitate the development of AI tools that are effective, user-friendly, and seamlessly integrated into clinical workflows, ultimately enhancing patient care and healthcare outcomes.medRxiv140The Clinical AI Soci-otechnical Framework (CASoF), developed through literature synthesis, and re-fined via a Modified Delphi study involving global healthcare professionals, is introduced, offering a holistic approach to AI deployment that is mindful of the complexities of healthcare systems.["A. Owoyemi", "J. Osuchukwu", "M. Salwei", "A. Boyd"]2024-08-09T00:00:00 e6a47571f72792e160293095a3097de0f42a4307Almost all countries have patients with hypertension as a standard but far-reaching medical concern, and this brings notable financial consequences. The combination of Artificial Intelligence and Machine Learning in controlling hypertension holds the potential for timely recognition, individualized management approaches, and adherence to medication monitoring. Nevertheless, healthcare faces hurdles in adopting such technologies due to data quality, system integration, ethical considerations, and regulatory barriers. This literature review mainly deals with the current state of AI and ML use in the management of hypertension. Particular attention is paid to their prediction, monitoring, and individualization of the therapeutic approaches. Key areas of interest include early detection, risk prediction, and developing individualized care plans. To promote the responsible and ethical use of AI in healthcare, future research in this field might include but not be limited to continuous monitoring, chronic disease management, and the integration of multi-modal data. Patient privacy, data security, algorithmic bias, and informed consent are the ethical issues to consider. Furthermore, the review discusses the ethical dilemmas surrounding patient privacy, data security, and programming biases in AI-driven healthcare solutions. To ensure that these technologies are effectively implemented in clinical practice, we need to address issues relating to data quality, system integration, ethics, and regulation. This may have potential results such as transforming hypertension management through sustained innovation efforts, thus improving quality care among hypertensive patients. Finally, the review highlights the future potential of AI to transform clinical practice, individualize treatment approaches, and mitigate the global impact of hypertension on public health.Journal of Science &amp; Technology320This literature review mainly deals with the current state of AI and ML use in the management of hypertension, and highlights the future potential of AI to transform clinical practice, individualize treatment approaches, and mitigate the global impact of hypertension on public health.Journal of Science &amp; Technology["Zeib Jahangir", "Sara Muddassir Qureshi", "Yahya Abdul Rehman", "Saad Ur Rehman Shah", "Hamza Ahmed Qureshi", "Ahsan Ahmad"]2024-08-09T00:00:00 eab2ff58bb1414ac3cad17519dfb82eed3489691Generative artificial intelligence (AI) has gained prominence across various industries and domains, offering capabilities to generate human‐like text, creative ideas, and solutions. This paper explores customers' responses to the use of generative AI in digitalizing content production and consumption processes. Drawing on technology affordance theory, this article examines how are the affordances of generative AI leveraged to contribute to the gradual digitalization of individuals. This netnographic study is based on over 9 months naturalistic observations of the AI Community online, culminating in 1572 pages of data. The findings identify different types of affordances that foster digitalization: automated content creation, automated data analysis, and AI‐generated content dissemination. This study also identifies the constraints of generative AI and discusses potential interventions to address these constraints and prevent unintended consequences. This research provides insights for scholars, professionals, and educators to better understand the dynamics of leveraging generative AI.Psychology &amp; Marketing493Customers' responses to the use of generative AI in digitalizing content production and consumption processes is explored as well as potential interventions to address these constraints and prevent unintended consequences.Psychology &amp; Marketing["Ha Eun (Grace) Park"]2024-08-10T00:00:00 bd2eda5cedc5a502509b4a7f228632edb0c2a5d6Objective: This study investigates the contribution of Artificial Intelligence (AI) technology to the learning process of accounting students in Surakarta, focusing on its adoption and associated learning ethics in the digital era.Methods: A quantitative approach was employed using a questionnaire distributed via Google Forms, targeting accounting students from private and state universities. The analysis utilized SmartPLS 3 for Partial Least Squares Structural Equation Modeling (PLS-SEM).Findings: The results indicate that Perceived Ease of Use significantly influences AI Technology Adoption, while Technology Readiness positively impacts both Perceived Usefulness and Perceived Ease of Use. However, Technology Readiness shows no significant effect on AI Technology Adoption. This highlights the critical role of ease of use over perceived usefulness in driving technology adoption among students.Novelty: This research contributes to the existing literature by demonstrating the nuanced relationships between technology readiness, perceived ease of use, and the adoption of AI technologies in accounting education, specifically in a developing context.Theory and Policy Implications: The findings suggest that educational institutions should focus on enhancing students' technological readiness and simplifying AI interfaces to promote adoption. This has implications for curriculum design and policy formulation aimed at effectively integrating AI technologies into accounting education.Advances Educational Innovation342The findings suggest that educational institutions should focus on enhancing students' technological readiness and simplifying AI interfaces to promote adoption, and highlights the critical role of ease of use over perceived usefulness in driving technology adoption among students.Advances Educational Innovation["Lelahester Rina", "Lamin Kaira", "Gehad Mohammed Sultan Saif", "Wulan Setyaningsih"]2024-08-10T00:00:00 7fb12aceac6c9426970579e540400793a78267bfThis study investigates the integration of Artificial Intelligence (AI) in differentiated instruction classrooms. The research aims to determine teachers' perceptions of AI integration in teaching and evaluate its use in differentiated instruction classrooms. A quantitative methodology was implemented, involving a survey of 30 primary school teachers in Kuala Lumpur. The results reveal that teachers are generally confident in using AI-based tools, but they express a need for further training to fully understand the range of AI tools available. Teachers acknowledge the benefits of AI in enhancing student engagement and personalizing learning. However, they exhibit a need for more support in implementing differentiated instruction strategies and leading their colleagues in the integration of AI tools. The study concludes that AI tools have potential to enhance teaching and learning outcomes, but there is a need for continuous support and training for teachers. These findings have significant implications for educational practice and policy. Future research is suggested to dig deeper into the factors influencing teachers' perceptions of AI in teaching, the impact of AI integration on student outcomes, and the role of school leadership in supporting AI integration. This study provides valuable understanding into the potential of AI in education and the need for ongoing teacher support and training.International Journal of Academic Research in Business and Social Sciences421The study concludes that AI tools have potential to enhance teaching and learning outcomes, but there is a need for continuous support and training for teachers.International Journal of Academic Research in Business and Social Sciences["Muhamad Izzat Ruslim", "Fariza Khalid"]2024-08-10T00:00:00 5a51930f9208d8b819ce96573d304d379a59d800This research attempts to present the perspectives of higher education students regarding the use of Artificial Intelligence (AI) in language teaching interventions, with an emphasis on secondary education students with Specific Learning Difficulties (SpLDs). Although AI applications are associated in the literature with Education (AIED), the interest of the research community was revived in 2022 with the release of ChatGPT. This Large Language Model can generate text and quickly attract millions of users. This triggered expectations for potential benefits but also raised concerns about potential risks that may arise in the context of Special Education and Training (SET). Considering the above, the methodology utilized a mixed analysis of an online questionnaire administered to 120 students from "language" departments in Greece (Kalamata). In the results, expectations for skill improvement were expressed, but there were also concerns about providing ready-made answers. In addition, students expect resistance from parents and colleagues but support from the students themselves. The research highlighted the expected barriers and facilitators that students perceive they will encounter, of which the need for staff training was emphasized.  Article visualizations:European Journal of Open Education and E-Learning Studies221European Journal of Open Education and E-learning Studies["Maria Drossinou Korea", "Panagiotis Alexopoulos"]2024-08-10T00:00:00 3166ae081e6fea8c000572c38b4f9e413d233bc9Innovation and entrepreneurship education in colleges and universities is a new requirement for the development of college education in China, and artificial intelligence technology is the key content of college technology education at this stage. In order to promote innovation and entrepreneurship education in colleges and universities, based on the analysis of AI social needs and the current situation of college education, this paper proposes a path for the integration and development of AI technology and innovation and entrepreneurship education in colleges and universities for reference by college teaching peers.Archives des Sciences00A path for the integration and development of AI technology and innovation and entrepreneurship education in colleges and universities for reference by college teaching peers is proposed.Archives des Sciences["Lu Zhang"]2024-08-10T00:00:00 7e7ac42bd2e5b933a8327fd4a500b98f3f535eb5The thriving of Artificial Intelligence (AI) technology in the education industry has been gaining attention from educators, policymakers, researchers, and learners alike. AI-based tools have been introduced largely due to their convenience and accessibility. The aim of this paper is to investigate teachers’ perspectives on the implementation of AI as a pedagogical tool in tertiary ESL classrooms. This study also implemented Technology Acceptance Model (TAM) as a framework to explore teachers' perspectives on AI tools. Semi-structured interviews with four tertiary ESL teachers have been conducted, transcribed, and analysed. The findings revealed that ESL teachers show positive attitudes due to the benefits they gain from the use of AI in their teaching and learning contexts. They explore the implementation of AI in speaking and writing classes; however, the implementation of these tools is governed by different reasons. This study also outlines the challenges faced by ESL teachers and ways to overcome these challenges. It is believed that the findings of this study will pave the way for other ESL teachers in diminishing their fears and provide insights for stakeholders and policymakers to further strengthen the implementation of AI as a pedagogical tool in ESL classrooms.International Journal of Academic Research in Business and Social Sciences320The findings revealed that ESL teachers show positive attitudes due to the benefits they gain from the use of AI in their teaching and learning contexts, and the challenges faced by ESL teachers and ways to overcome these challenges.International Journal of Academic Research in Business and Social Sciences["Nur Mazliyana Zainuddin", "Nur Aisyah Bukhari", "Maslawati Mohamad"]2024-08-10T00:00:00 94f96bd33d9c5e8d484e7f16c4929d6f13c50a0eArtificial intelligence (AI) has become increasingly prevalent in the field of medicine, offering transformative potential in healthcare delivery, diagnosis, and treatment. This paper provides an overview of AI applications in medicine, focusing on machine learning (ML) algorithms, natural language processing (NLP) techniques, and robotics. It discusses the challenges and opportunities of AI in improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare operations. Additionally, it explores ethical considerations, such as patient privacy and algorithmic bias, and highlights the importance of interdisciplinary collaboration between healthcare professionals and AI experts. Overall, AI has the potential to revolutionize healthcare by augmenting human capabilities, improving efficiency, and advancing personalized medicineInternational Journal of Advanced Research in Science, Communication and Technology140The challenges and opportunities of AI in improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare operations are discussed, and ethical considerations, such as patient privacy and algorithmic bias are explored.International Journal of Advanced Research in Science, Communication and Technology["Mr. Mounesh A", "Mr. Jishnu Raj V K", "Mr. Manoj", "Ms. Jahnavi G A", "Ms. Mahalakshmi"]2024-08-10T00:00:00 fea5d3367fb9a6849271b531a924522fe58aa2ffObjective: This study examines the role of Artificial Intelligence (AI) as both an innovative tool and a challenge in citizenship education at Islamic Vocational School Kanigoro. It aims to provide insights into how AI impacts educational practices, student engagement, and learning outcomes.Methods: A qualitative approach was employed, utilizing observations and interviews to gather data from teachers and students. This method allowed for an in-depth exploration of the dynamics and implications of AI integration in the classroom.Results: The study found that AI technologies, such as Quizizz and Kahoot, were effectively used to automate administrative tasks and personalize learning experiences, significantly improving educational efficiency and student engagement. AI-driven platforms provided tailored instructional content and rapid assessments, enhancing teaching methodologies and learning outcomes. However, challenges such as over-reliance on AI, privacy concerns, and potential algorithmic bias were identified.Conclusion: This study highlights the transformative potential of AI in citizenship education while underscoring the importance of balancing technological innovation with human interaction. Effective integration of AI can enhance learning experiences and foster critical thinking and ethical reasoning among students. The findings advocate for continuous professional development for educators, ethical AI practices, and the preservation of human-centric educational values. Future research should explore diverse educational contexts to further understand the benefits and challenges of AI in educationAdvances Educational Innovation200The study found that AI technologies, such as Quizizz and Kahoot, were effectively used to automate administrative tasks and personalize learning experiences, significantly improving educational efficiency and student engagement.Advances Educational Innovation["Muhammad Iqbal Baihaqi", "Neni Fitriawati", "Intan Sukmasakti Suwarno Putri", "Yusri Karmila", "Siti Munaziroh"]2024-08-10T00:00:00 587516fedf029c804e22d6a8f68ddaf8ffe1bd47This systematic review comprehensively examines the application of artificial intelligence (AI) in personalized language learning. By analyzing existing research, the study aims to evaluate the effectiveness, challenges, and potential of AI-powered tutoring systems. Findings indicate that AI offers significant benefits, including tailored instruction, increased engagement, and improved accessibility. However, the review also highlights concerns such as data privacy, algorithm bias, and the need for human interaction. Overall, AI presents promising opportunities for enhancing language education, but careful consideration of ethical implications and technological limitations is essential for its successful implementation.Klasikal: Journal of Education, Language Teaching and Science00Overall, AI presents promising opportunities for enhancing language education, but careful consideration of ethical implications and technological limitations is essential for its successful implementation.KLASIKAL : JOURNAL OF EDUCATION, LANGUAGE TEACHING AND SCIENCE["Ismail Sangkala", "Nargiza Sulaymanova Mardonovna"]2024-08-10T00:00:00 d0fe925102ee378634c0a54581bb8fbd635d8d4cThe traditional drug discovery process is expensive, time-consuming, and often leads to a high failure rate. The development of numerous new medications in the pharmaceutical sciences is only one example of how the advancement of artificial intelligence has opened up exciting new opportunities for developing intelligent modelling. Machine learning and deep learning are two examples of artificial intelligence that can sift through large datasets in search of promising new drugs. AI algorithms can predict the binding affinity of molecules to specific targets, helping researchers narrow down the pool of potential drug candidates. Pharmacokinetics and pharmacodynamic are essential aspects of drug development. Drug formulation development requires extensive testing and optimization of various parameters. AI models can quickly analyze data from multiple experiments and identify the most promising formulations, saving time and resources. New pharmaceuticals may be developed and brought to market at a reduced cost and in a shorter amount of time with the use of AI-based optimisation approaches. Absorption, Distribution, Metabolism, and Excretion (ADME) are only some of the aspects of pharmacological physiology that may be modelled and predicted with the use of artificial intelligence. By integrating AI models into the drug development process, researchers can gain a deeper understanding of a drug's pharmacokinetic and pharmacodynamic properties. This knowledge helps in designing drugs with improved efficacy and reduced side effects. So, in present topic authors tried to give insights how AI is playing a transformative role in pharmaceutical sciences. As AI technology continues to advance, the future of pharmaceutical sciences looks brighter than ever.Indian Journal of Pharmaceutical Education and Research610In the present topic authors tried to give insights how AI is playing a transformative role in pharmaceutical sciences.Indian Journal of Pharmaceutical Education and Research["Amaresh Prusty", "S. K. Panda"]2024-08-10T00:00:00 c651370e6cf5fd267dc4531087d6cf99b9b6c190Revolusi pada big data dan perkembangan kapasitas komputasi membawa dampak terhadap berbagai industri, termasuk pada industri kreatif. Industri kreatif mempunyai dampak yang cukup signifikan terhadap laju Produk Domestik Bruto (PDB) yang merupakan faktor yang berkontribusi secara signifikan terhadap pertumbuhan ekonomi negara. Hal ini membuat industri kreatif menjadi salah satu alternatif kuat untuk membangun kerajaan bisnis maupun sebagai pilihan sektor yang menarik untuk digeluti di dunia profesional. Namun, di era digital dimana penggunaan artificial intelligence (AI) sudah semakin marak, para penggiat di industri kreatif mulai mendapat tantangannya tersendiri. Hal ini dikarenakan para penggiat di industri kreatif dihadapkan pada kenyataan bahwa teknologi dapat menjadi ancaman untuk pekerjaan mereka juga dapat menjadi alat bantu yang dapat mendukung pekerjaan mereka. Penelitian ini hendak mengetahui bagaimana perkembangan AI mempengaruhi sikap pelaku di industri kreatif. Theory of Planned Behavior digunakan sebagai dasar untuk memahami perilaku yang muncul akibat disrupsi AI. Metode campuran digunakan agar dapat memberikan asumsi secara menyeluruh atas akibat dari disrupsi AI, sekaligus juga bisa mendapatkan gambaran pemahaman secara holistik dari kacamata penggiat industri kreatif yang diwakili oleh beberapa responden dari berbagai bidang keahlian melalui wawancara terstruktur. Responden wawancara yang bekerja di industri kreatif, menyatakan bahwa kehadiran AI banyak membantu proses kreatif mereka sampai pada tahapan tertentu. Yakni, AI dipergunakan sebagai sarana triangulasi, referensi untuk pengembangan ide orisinil, membaca trend terkini, serta sebagai alat bantu ketika dihadapkan pada kebutuhan dengan waktu yang terbatas.eCo-Buss180eCo-Buss["Meiry Ramdani Anwar", "Angela Caroline", "Y. P. Kornarius", "T. E. P. Gusti", "Agus Gunawan"]2024-08-10T00:00:00 0e5b1793f03c00fe71c3d6be55e5e3ea34f05e63Objective: This study aims to explore the role of artificial intelligence (AI) in enhancing the writing skills of Islamic Education (PAI) students through innovative learning approaches.Methods: Utilizing a qualitative research methodology, this study conducts a descriptive text analysis of literature relevant to AI and education. Data were gathered through library research, encompassing theses, dissertations, and scholarly articles, with a focus on 35 key sources identified via Google Scholar.Results: The findings indicate that AI significantly contributes to personalized learning by providing tailored feedback and facilitating access to instructional materials. AI systems enhance students' writing coherence, structure, and creativity while addressing technical aspects such as grammar and plagiarism detection.Novelty: This research offers new insights into the integration of AI in Islamic education, highlighting its potential to transform pedagogical approaches and foster collaborative learning environments among PAI students.Conclusion: The study underscores the necessity for educational institutions to adopt AI technologies to improve writing skills and develop dynamic, effective, and personalized learning experiences within the Islamic Education framework.Advances Educational Innovation410The findings indicate that AI significantly contributes to personalized learning by providing tailored feedback and facilitating access to instructional materials, and underscores the necessity for educational institutions to adopt AI technologies to improve writing skills and develop dynamic, effective, and personalized learning experiences within the Islamic Education framework.Advances Educational Innovation["Daryono", "Mahmudulhassan"]2024-08-10T00:00:00 25952922a4dd5ef7d43568c1e5064ab5bd53f60fThe future of education relies on the integration of information technologies, emphasizing the importance of equity and inclusiveness for quality education. Teacher education programs are essential for fostering qualified educators for the future. Integrating AI in education is crucial to ensure inclusivity and comprehensive services for all. This study aims to evaluate student teachers’ perceptions of using AI in learning and teaching, and to provide suggestions for enhancing sustainable education through information technologies. A qualitative research design was adopted to gather perceptions and experiences from 240 student teachers who participated in a seminar on AI usage and completed self-reflection tasks. These student teachers, enrolled in various teaching methods and principal courses, contributed to the thematic analysis. The study reveals that AI should be carefully planned and incorporated into lesson plans to enhance personalized learning. Student teachers reported that AI supports and motivates the learning process, effectively transforming students’ needs and learning experiences. However, they also noted potential drawbacks, such as AI imposing restrictions on the teaching profession, replacing teachers, and producing biased results. The study suggests that capacity-building strategies for student teachers should be enriched across different courses to raise awareness about AI’s applications.Societies454The study reveals that AI should be carefully planned and incorporated into lesson plans to enhance personalized learning and suggests that capacity-building strategies for student teachers should be enriched across different courses to raise awareness about AI’s applications.Societies["Z. Alt\u0131nay", "F. Alt\u0131nay", "R. C. Sharma", "G. Dagli", "R. Shadiev", "Betul Y\u0131k\u0131c\u0131", "M. Altinay"]2024-08-10T00:00:00 2bc6a97df63ea62e39f587a186afd1362c9f7dedDeviant Behavior320Deviant Behavior["Joaqu\u00edn Rodr\u00edguez-Ruiz", "Raquel Espejo-Siles", "Inmaculada Mar\u00edn-L\u00f3pez"]2024-08-10T00:00:00 6ca1c8854bffe2e0ea39400137e3e1fb5772d98aAngiology110Angiology["K. Paraskevas", "L. Saba", "Vasileios Papaioannou", "Jasjit S. Suri"]2024-08-10T00:00:00 517f9708323b1fa5c05195d985a2a9a8536d6e03The 2022 release of ChatGPT sparked widespread discussions on Artificial General Intelligence (AGI). Through a detailed examination of an interview with Bard, a large language model, the study uncovers narratives of optimism and pessimism regarding AGI's future implications. It found a higher leaning towards optimism about AGI's potential effects, extending from education, arts, and relationships, to economy and space exploration. Conversely, pessimistic views pointed out potential downsides, such as unemployment, political instability, and media manipulation. The study also identified four primary AGI themes - the relationship between humans and AGI, AGI acquiring a physical form, AGI simulating a universe, and the responsible utilization of AGI. These insights aid in understanding the complex socio-technical imaginary surrounding AGI. The study has its limitations as it is based solely on the responses provided by Bard during its test phase. Additional research may reveal changes in AGI discourse representation as the model evolves.Etnoantropološki problemi / Issues in Ethnology and Anthropology750Through a detailed examination of an interview with Bard, a large language model, the study uncovers narratives of optimism and pessimism regarding AGI's future implications.Etnoantropološki problemi / Issues in Ethnology and Anthropology["Ljubi\u0161a Boji\u0107"]2024-08-10T00:00:00 07a24d89c3a4a036971a6a91685e9a6f3d724de6npj Digit. Medicine174An in-depth analysis of the geo-economic distribution of 159 AI-enabled clinical studies, as well as the gender disparities among these studies, reveals the need for equitable access to medical AI technologies from a global literature perspective.NPJ Digital Medicine["Rui Yang", "Sabarinath Vinod Nair", "Yuhe Ke", "Danny D'Agostino", "Mingxuan Liu", "Yilin Ning", "Nan Liu"]2024-08-10T00:00:00 67a6f9678537221edffcd02818af9af484944bb0This systematic review examines the evolution and current landscape of eXplainable Artificial Intelligence (XAI) in Clinical Decision Support Systems (CDSS), highlighting significant advancements and identifying persistent challenges. Utilising the PRISMA protocol, we searched major indexed databases such as Scopus, Web of Science, PubMed, and the Cochrane Library, to analyse publications from January 2000 to April 2024. This timeframe captures the progressive integration of XAI in CDSS, offering a historical and technological overview. The review covers the datasets, application areas, machine learning models, explainable AI methods, and evaluation strategies for multiple XAI methods. Analysing 68 articles, we uncover valuable insights into the strengths and limitations of current XAI approaches, revealing significant research gaps and providing actionable recommendations. We emphasise the need for more public datasets, advanced data treatment methods, comprehensive evaluations of XAI methods, and interdisciplinary collaboration. Our findings stress the importance of balancing model performance with explainability and enhancing the usability of XAI tools for medical practitioners. This research provides a valuable resource for healthcare professionals, researchers, and policymakers seeking to develop and evaluate effective, ethical decision-support systems in clinical settings.medRxiv931["Noor A. Aziz", "Awais Manzoor", "Muhammad Deedahwar", "Mazhar Qureshi", "Wael Rashwan"]2024-08-10T00:00:00 67f726f40ae74ffaf2d80622e3111810b5a528d1Abdominal Radiology270With AI assistance, non-radiologist experienced operators showed good agreement with radiologists for quantifying whole liver volume, Couinaud segment volume, and the detection and measurement of lesions in patients with known liver cancer.Abdominal Radiology (New York)["Luis N\u00fa\u00f1ez", "Carlos Ferreira", "A. Mojtahed", "Hildo J. Lamb", "Stefano Cappio", "M. A. Husainy", "Andrea Dennis", "M. Pansini"]2024-08-10T00:00:00 50cdcae0cd46cd8049f15156fd8c1e201afe952cImage generation using generative artificial intelligence is a popular activity. However, it is almost exclusively performed in the privacy of an individual's home via typing on a keyboard. In this article, we explore body prompting as input for image generation. Body prompting extends interaction with generative AI beyond textual inputs to reconnect the creative act of image generation with the physical act of creating artworks. We implement this concept in an interactive art installation, Artworks Reimagined, designed to transform artworks via body prompting. We deployed the installation at an event with hundreds of visitors in a public and private setting. Our results from a sample of visitors (N=79) show that body prompting was well-received and provides an engaging and fun experience. We identify three distinct patterns of embodied interaction with the generative AI and present insights into participants' experience of body prompting and AI co-creation. We provide valuable recommendations for practitioners seeking to design interactive generative AI experiences in museums, galleries, and other public cultural spaces.arXiv.org650This article explores body prompting as input for image generation and identifies three distinct patterns of embodied interaction with the generative AI and presents insights into participants' experience of body prompting and AI co-creation.ArXiv["J. Oppenlaender", "Hannah Johnston", "Johanna M. Silvennoinen", "Helena Barranha"]2024-08-10T00:00:00 8832556e5deffbe3ea630576f4490501a4daece8Objective: This study investigates the mediating role of job crafting in the relationship between artificial intelligence (AI) awareness, servant leadership, and work engagement among employees.Methods: Utilizing quantitative analysis, the research involved surveying employees and employing structural equation modeling to test the proposed hypotheses regarding the influence of AI awareness and servant leadership on job crafting and work engagement.Findings: The results reveal that while servant leadership significantly enhances job crafting and work engagement, AI awareness does not positively impact job crafting. However, job crafting positively influences work engagement and mediates the effect of servant leadership on work engagement, indicating a complex interplay between leadership styles and employee engagement levels.Novelty: This study contributes to the literature by highlighting the differential impact of AI awareness and servant leadership on job crafting and work engagement, emphasizing the critical role of leadership in fostering employee motivation and productivity in the context of technological advancements.Theory and Policy Implications: The findings suggest that organizations should focus on developing servant leadership qualities among managers to promote job crafting and enhance employee engagement. Furthermore, while fostering AI awareness is essential, organizations must balance it with supportive leadership practices to prevent potential negative impacts on employee morale.Advances Educational Innovation270The results reveal that while servant leadership significantly enhances job crafting and work engagement, AI awareness does not positively impact job crafting, but job crafting positively influences work engagement and mediates the effect of servant leadership on work engagement, indicating a complex interplay between leadership styles and employee engagement levels.Advances Educational Innovation["Mey Ayu Lestari", "Ardiani Ika Sulistyawati", "Gehad Mohammed Sultan Saif"]2024-08-10T00:00:00 ca4817737f1563430336f03f4faf7da50611eb1f“Using technology to encourage productivity and long-term competitive advantage is known as digitalization. All agree that accountability, openness, and people are the cornerstones of corporate governance. How has the digital revolution affected corporate governance, specifically with regard to the potential impact of big data and artificial intelligence on it? This assertion is bolstered by the identification of five key factors that influence the current power dynamics within corporate organizations: (i) decision-making speed and frequency; (ii) decision-making information and access; (iii) decision-making costs.(iv) the decision-makers’ incentives and interests; and (v) their proficiency and abilities. The crucial, but as of yet unstudied, analytical method to precisely forecast the influence of technology on corporate governance is to consider if and how these five aspects are changed by technological innovation. Our research aims to investigate how corporate governance is affected by digitization. In addition, to determine the connected causes and hazards and to see if the benefits exceed the consequences.International Journal For Multidisciplinary Research110This research aims to investigate how corporate governance is affected by digitization and to determine the connected causes and hazards and to see if the benefits exceed the consequences.International Journal For Multidisciplinary Research["Smriti Pandey"]2024-08-10T00:00:00 498194050ca4b44918d846d722224480f47e30d0Today e-learning system plays an essential role in the education system. Technology integration in teaching helps to teach content-based curriculum effectively and efficiently to build confidence among students. Personalized learning systems focus on learning behavior, interest, and design curriculum according to learners‟ ability and basic knowledge. It is a flexible teaching methodology to meet the individual needs of students. The personalized learning approach optimizes the needs of each learner. For an effective education system, it is necessary to understand learners and develop a plan that copes up with the individual learning needs and the interest of students‟. An intelligent Tutor system is an expert system to monitor the learners‟ performance to provide personalized coaching. E-learning applications include computer-based learning, web-based learning, digital collaboration and virtual classrooms. Artificial Intelligence can be used for automating learning activities like designing teaching tools, curriculum, training, evaluating students‟ performance, and using modern teaching methodology. Artificial intelligence is the most modern e-learning trend in higher education and the corporate world. AI helps to provide individual decisions using data analytics that leads to better education for personalized instruction to streamline the education process.International Journal Of Recent Trends In Multidisciplinary Research00Today e-learning system plays an essential role in the education system and Artificial Intelligence can be used for automating learning activities like designing teaching tools, curriculum, training, evaluating students’ performance, and using modern teaching methodology.International Journal Of Recent Trends In Multidisciplinary Research["Sivakumar Nagarajan"]2024-08-10T00:00:00 4614242a87d2b17c6e358522f9ab1b2ea2032476The utilization of artificial intelligence (AI)-powered tools in second language (L2) writing has evolved over the last decade. This attracted second-language writers to evaluate and improve their writing. This study aims to contribute to the understanding of the current state of AI-powered software in L2 writing, identify gaps in the literature, and investigate areas for future research. In this systematic literature review (SLR), we categorize the typology of AI-powered tools and their impact on L2 writing performance, discuss L2 writers' perceptions, and provide an overview of how they mitigate challenges and limitations in utilizing writing-assisted tools. The results of this SRL may have implications for writing teachers, L2 researchers, and developers of AI-powered writing tools in the field of second language writing.Research and Innovation in Applied Linguistics-Electronic Journal300The typology of AI-powered tools and their impact on L2 writing performance are categorized, L2 writers' perceptions are discussed, and an overview of how they mitigate challenges and limitations in utilizing writing-assisted tools are provided.Research and Innovation in Applied Linguistics-Electronic Journal["Angela Andrea Perez Roa", "Shanty Halim"]2024-08-10T00:00:00 4aa105af3ef18aa0c3120957b7ae6b86ebcaa4a9Applied Artificial Intelligence290Applied Artificial Intelligence["Chen-Shu Wang", "Bo-Yi Li", "Kai-Wen Wang", "Zhi-Chi Lin"]2024-08-10T00:00:00 6aafe94624620f0c7bc427bdfe9570bf737cfd78Complex sequential decision-making planning problems, covering infinite states' space have been shown to be solvable by AlphaZero type of algorithms. Such an approach that trains a neural model while simulating projection of futures with a Monte Carlo Tree Search algorithm were shown to be applicable to real life planning problems. As such, engineers and users interacting with the resulting policy of behavior might benefit from obtaining automated explanations about these planners' decisions offline or online. This paper focuses on the information within the Monte Carlo Tree Search data structure. Given its construction, this information contains much of the reasoning of the sequential decision-making algorithm and is essential for its explainability. We show novel methods using information theoretic tools for the simplification and reduction of the Monte Carlo Tree Search and the extraction of information. Such information can be directly used for the construction of human understandable explanations. We show that basic explainability quantities can be calculated with limited additional computational cost, as an integrated part of the Monte Carlo Tree Search construction process. We focus on the theoretical and algorithmic aspects and provide examples of how the methods presented here can be used in the construction of human understandable explanations.European Conference on Artificial Intelligence280It is shown that basic explainability quantities can be calculated with limited additional computational cost, as an integrated part of the Monte Carlo Tree Search construction process and used for the construction of human understandable explanations.{"pages": "1246-1253"}["R. Bustin", "Claudia V. Goldman"]2024-08-10T00:00:00 9290611fb38cd328923f0570f39d47eb6c4196a7Synthetic Data is not new, but recent advances in Generative AI have raised interest in expanding the research toolbox, creating new opportunities and risks. This article provides a taxonomy of the full breadth of the Synthetic Data domain. We discuss its place in the research ecosystem by linking the advances in computational social science with the idea of the Fourth Paradigm of scientific discovery that integrates the elements of the evolution from empirical to theoretic to computational models. Further, leveraging the framework of Truth, Beauty, and Justice, we discuss how evaluation criteria vary across use cases as the information is used to add value and draw insights. Building a framework to organize different types of synthetic data, we end by describing the opportunities and challenges with detailed examples of using Generative AI to create synthetic quantitative and qualitative datasets and discuss the broader spectrum including synthetic populations, expert systems, survey data replacement, and personabots.arXiv.org21A taxonomy of the full breadth of the Synthetic Data domain is provided by linking the advances in computational social science with the idea of the Fourth Paradigm of scientific discovery that integrates the elements of the evolution from empirical to theoretic to computational models.ArXiv["Richard Timpone", "Yongwei Yang"]2024-08-10T00:00:00 dfcb6342f218ecce88ec58a2e1a76814828cbfb1This article addresses the use of artificial intelligence for the needs of effective, sustainable development in logistics and its components. The subject of this article is to highlight the possibility of processing optimization methods using an artificial intelligence module. The goal is to determine whether the AI module can replicate the same, or at least have a similar result, as the traditional optimization methods used in practice. The challenge involves constantly identifying reserves in already highly sophisticated micro-logistics systems using modern commercial means of artificial intelligence. Applying artificial intelligence to elements of a company’s micro-logistics model is a new approach. This article aims to determine whether artificial intelligence can reduce costs through calculations in a specific area defined for it. By optimizing distribution routes using ChatGPT-3.5, we significantly reduced the total distance traveled, leading to substantial savings in transportation costs. This optimization led to a significant improvement in the efficiency of logistic processes and considerable cost savings. This result demonstrates that artificial intelligence can be an effective tool for solving complex logistic tasks. The possibilities of effectively sustainable logistics development with the help of artificial intelligence lie not only in the quality of the achieved outputs but also in the speed of the calculations and the procedures for solving defined project tasks. It follows from this definition that artificial intelligence will continue to play an essential role in the defined field of logistics in the future.Sustainability250By optimizing distribution routes using ChatGPT-3.5, the total distance traveled was significantly reduced, leading to substantial savings in transportation costs, and this result demonstrates that artificial intelligence can be an effective tool for solving complex logistic tasks.Sustainability["Krist\u00edna Kleinov\u00e1", "Martin Straka"]2024-08-11T00:00:00 7308550d811d9ccefa755121863e5ac65db5d922Artificial intelligence (AI) algorithms have been retrospectively evaluated as replacement for one radiologist in screening mammography double-reading; however, methods for resolving discordance between radiologists and AI in the absence of 'real-world' arbitration may underestimate cancer detection rate (CDR) and recall. In 108,970 consecutive screens from a population screening program (BreastScreen WA, Western Australia), 20,120 were radiologist/AI discordant without real-world arbitration. Recall probabilities were randomly assigned for these screens in 1000 simulations. Recall thresholds for screen-detected and interval cancers (sensitivity) and no cancer (false-positive proportion, FPP) were varied to calculate mean CDR and recall rate for the entire cohort. Assuming 100% sensitivity, the maximum CDR was 7.30 per 1000 screens. To achieve >95% probability that the mean CDR exceeded the screening program CDR (6.97 per 1000), interval cancer sensitivities ≥63% (at 100% screen-detected sensitivity) and ≥91% (at 80% screen-detected sensitivity) were required. Mean recall rate was relatively constant across sensitivity assumptions, but varied by FPP. FPP > 6.5% resulted in recall rates that exceeded the program estimate (3.38%). CDR improvements depend on a majority of interval cancers being detected in radiologist/AI discordant screens. Such improvements are likely to increase recall, requiring careful monitoring where AI is deployed for screen-reading.Journal of Medical Screening90Journal of medical screening["M. Marinovich", "William Lotter", "Andrew Waddell", "N. Houssami"]2024-08-11T00:00:00 bf73d6750b469e5706eb69f4f736855b857a5d65Background: Artificial Intelligence (AI) is rapidly transforming healthcare, including dentistry. While AI offers potential benefits and its integration is still emerging, research on dental students' understanding of AI remains limited, especially in developing nations like India. This study aims to assess dental students' knowledge and perception regarding AI. Methods: A cross-sectional study was conducted among a representative sample of undergraduate and postgraduate dental students from a private dental institution. A structured questionnaire was administered to assess participants' knowledge and perception of artificial intelligence. Descriptive and inferential statistics were used to analyze the data. Results: The study findings reveal that 63.8% of participants demonstrated moderate knowledge of artificial intelligence, while only 19.1% had received formal training in the subject. Participants perceived improved diagnostic accuracy (47.9%) as a primary benefit of AI integration, yet identified lack of awareness (43.9%) and associated costs (45.1%) as significant barriers to adoption. Dental professionals exhibited a neutral stance toward AI adoption (46.5%) and its integration into the curriculum (46.7%). A gender disparity was evident, with males demonstrating higher AI knowledge levels (19.8%) and expressing greater concern for patient data privacy (32.7%) than female counterparts. Conclusion: As AI continues to evolve and integrated into healthcare, ongoing assessment of educational needs and knowledge gaps among dental students is essential. This will ensure that future practitioners are not only aware of AI's capabilities but also equipped to leverage them for improving patient care and advancing dental practice.International Journal For Multidisciplinary Research130As AI continues to evolve and integrated into healthcare, ongoing assessment of educational needs and knowledge gaps among dental students is essential to ensure that future practitioners are not only aware of AI's capabilities but also equipped to leverage them for improving patient care and advancing dental practice.International Journal For Multidisciplinary Research["V. V", "A. Vinita Mary", "R. Kesavan", "D. Sandhiya", "R. Srinath", "M.Shyaame"]2024-08-11T00:00:00 bc6f641a05152517e9313a8db59b85e52e214f32This abstract introduces a comprehensive approach to fostering economic recovery through the integration of cutting-edge technologies and strategic management in small businesses. In the wake of global economic challenges, the utilization of Artificial Intelligence (AI) and Quantum Computing (QC) emerges as a pivotal strategy. AI offers unparalleled capabilities in data analysis, customer engagement, and operational efficiency, empowering small businesses to adapt swiftly to market demands and optimize resource allocation. Concurrently, QC revolutionizes data processing by exponentially enhancing computational power, enabling small enterprises to tackle complex problems and innovate at unprecedented speeds. However, the effective deployment of these technologies necessitates astute strategic management. Strategic foresight and agile decision-making are crucial in harnessing AI and QC to their fullest potential, ensuring alignment with business objectives and mitigating risks. This abstract advocates for a holistic approach wherein small businesses integrate AI and QC technologies seamlessly into their operational frameworks while fostering a culture of strategic innovation. By leveraging these advancements in tandem with strategic management principles, small enterprises can not only navigate economic uncertainties but also unlock new growth opportunities, catalyzing sustainable economic recovery and long-term prosperity.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300A holistic approach wherein small businesses integrate AI and QC technologies seamlessly into their operational frameworks while fostering a culture of strategic innovation is introduced, catalyzing sustainable economic recovery and long-term prosperity.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Rula AbuShanab"]2024-08-11T00:00:00 82add3c24c08d4da5fcd4856b8356ee59482b8c3Electrical Engineering311Electrical Engineering["Sonja Kne\u017eevi\u0107", "M. \u017darkovi\u0107"]2024-08-11T00:00:00 070f3209f47c218518fe5ee42b46f286fc55a899Participatory Design Conference551Participatory Design Conference 2024["Simone van den Broek", "Supraja Sankaran", "Jan de Wit", "Alwin de Rooij"]2024-08-11T00:00:00 32a7c2ea9db1940ca0e4d064772171292a24608cFinance and Space620Finance and Space["Michael Samers", "Yujia He"]2024-08-11T00:00:00 1b379705e232e06bda544a32bd237ee40af610b5This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.Computer Science &amp; IT Research Journal03The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains, and underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence.Computer Science &amp; IT Research Journal["Angela Omozele Abhulimen", "Onyinye Gift Ejike"]2024-08-11T00:00:00 f96cc79897ade39f411767f8fc889cb1fc22d66cThe integration of Artificial Intelligence (AI) in Small and Medium-sized Enterprises (SMEs) and supply chains has revolutionized operational efficiencies and decision-making processes. This review paper aims to explore the ethical implications of AI adoption in these sectors, identifying current challenges and proposing future directions for ethical AI deployment. Through an extensive review of existing literature, the paper examines key ethical concerns such as data privacy, bias in AI algorithms, transparency, and the socio-economic impact on the workforce. The findings indicate that SMEs face unique ethical challenges due to their limited resources and expertise, which exacerbate issues related to AI implementation. Additionally, supply chains grapple with transparency and accountability, necessitating immediate attention to ensure ethical practices. The review concludes that establishing a robust ethical framework is crucial for guiding AI integration in SMEs and supply chains. It recommends the development of standardized ethical guidelines, enhanced stakeholder engagement, and increased investment in AI literacy and infrastructure. Future research should focus on creating adaptable ethical models that evolve alongside technological advancements and industry needs, ensuring that AI contributes to sustainable and equitable growth. This paper contributes to the ongoing discourse on ethical AI, offering actionable insights for policymakers, business leaders, and researchers dedicated to fostering responsible AI practices in SMEs and supply chains. Keywords:  Ethical AI, SMEs (Small and Medium-sized Enterprises), Supply Chains, Algorithmic Bias, Data Privacy, Transparency, Accountability, AI Trust, Regulatory Compliance, Stakeholder Engagement, Job Displacement, Explainable AI (XAI), Workforce Reskilling, Sustainable AI Practices, AI Ethics Frameworks.International journal of applied research in social sciences03The review concludes that establishing a robust ethical framework is crucial for guiding AI integration in SMEs and supply chains, and recommends the development of standardized ethical guidelines, enhanced stakeholder engagement, and increased investment in AI literacy and infrastructure.International Journal of Applied Research in Social Sciences["Angela Omozele Abhulimen", "Onyinye Gift Ejike"]2024-08-11T00:00:00 0eea05e210952ba17d0ca537f2206d0fc4a71bccThis review paper explores the integration of Artificial Intelligence (AI) into dealership management software to enhance customer service and foster future innovations. The primary objective is to synthesize existing research and industry practices to understand how AI technologies can streamline operations, personalize customer interactions, and drive business growth within automotive dealerships. The paper reviews key AI applications, including AI-driven chatbots and virtual assistants that provide automated support, predictive analytics for customer behavior and sales forecasting, and personalized marketing strategies. The analysis reveals that AI significantly improves customer service efficiency by reducing response times and enhancing customer satisfaction. Predictive maintenance and inventory management powered by AI algorithms ensure optimal resource allocation, minimizing downtime and operational costs. Additionally, the paper identifies potential future innovations facilitated by AI advancements, such as augmented reality (AR) for virtual vehicle tours and blockchain for secure transaction management. These innovations promise to elevate the customer experience and streamline dealership processes, creating a competitive edge in the market. The integration of AI into dealership management software presents substantial benefits in improving customer service and operational efficiency. The paper emphasizes the need for continuous investment in AI technologies and employee training to fully harness these advantages. Future research should focus on exploring emerging AI applications and their long-term impact on the automotive industry, ensuring dealerships remain adaptable and competitive in a rapidly evolving technological landscape. Keywords:  Artificial Intelligence, Dealership Management Software, Customer Service, Predictive Analytics, Augmented Reality, Blockchain, Automotive Industry.International Journal of Management &amp; Entrepreneurship Research01The analysis reveals that AI significantly improves customer service efficiency by reducing response times and enhancing customer satisfaction, and potential future innovations facilitated by AI advancements, such as augmented reality for virtual vehicle tours and blockchain for secure transaction management are identified.International Journal of Management &amp; Entrepreneurship Research["Angela Omozele Abhulimen", "Onyinye Gift Ejike"]2024-08-11T00:00:00 db0a4a6e2ada3209e3846c46b8317b25091a2b73This research paper aims to investigate the potential contributions of Artificial Intelligence (AI) in fostering business development in emerging businesses within the Libyan context. By proposing various AI models and exploring their applications, this study seeks to provide insights into how AI can positively impact different aspects of business operations and strategies. For data collection and analysis, experts were selected to participate in this study to ensure a comprehensive understanding of how AI can positively impact different types of businesses in Libya. The primary data was collected through a combination of questionnaire and semi-structured interviews. The paper present a comprehensive analysis of the opportunities and challenges associated with AI adoption in Libya's emerging business sector, offering recommendations for leveraging AI technologies to enhance competitiveness, productivity, and innovation. This study contributes valuable insights to the literature on AI adoption in emerging business environments, offering a comprehensive analysis of the opportunities and challenges in Libya's emerging business landscapeThe International Journal of Engineering &amp; Information Technology (IJEIT)91The paper presents a comprehensive analysis of the opportunities and challenges associated with AI adoption in Libya's emerging business sector, offering recommendations for leveraging AI technologies to enhance competitiveness, productivity, and innovation.The International Journal of Engineering &amp; Information Technology (IJEIT)["Ali Bakeer"]2024-08-11T00:00:00 d03030b46994d58b6876ce4f1cab05d300c9bdc9This study explores the integration of Artificial Intelligence (AI) into English for Specific Purposes (ESP) education, specifically tailored for law students. The importance of ESP in developing legal communication skills is paramount, as it addresses the complexities of legal terminology and discourse. AI is positioned as a transformative tool in this context, offering personalized learning experiences, instant feedback, and advanced language support. The study involved a survey of 500 law students to assess the impact of AI-driven tools on their learning outcomes, particularly in legal vocabulary, grammar, pronunciation, and writing skills. Results indicate significant benefits from AI integration, including enhanced language proficiency and more effective communication skills essential for legal practice. However, the study also highlights concerns about data security and ethical use of AI, with significant differences observed in students' perceptions of these issues. The Chi-Square analysis confirmed these concerns, revealing statistically significant differences in responses related to satisfaction with privacy and perceptions of AI's ethical use. This research underscores the potential of AI to revolutionize ESP education for law students, while also calling for careful consideration of ethical and privacy issues. The findings contribute to the ongoing discourse on AI's role in legal education and suggest pathways for future research and development in this field.SPAST Reports100The study involved a survey of 500 law students to assess the impact of AI-driven tools on their learning outcomes, particularly in legal vocabulary, grammar, pronunciation, and writing skills.SPAST Reports["Nodiraxon Xatamova", "Jahongir Ashurov"]2024-08-11T00:00:00 b8908e13b2cb25ca5ba49860418b1d219df99a5cArtificial neural networks have long been understood as"black boxes": though we know their computation graphs and learned parameters, the knowledge encoded by these weights and functions they perform are not inherently interpretable. As such, from the early days of deep learning, there have been efforts to explain these models' behavior and understand them internally; and recently, mechanistic interpretability (MI) has emerged as a distinct research area studying the features and implicit algorithms learned by foundation models such as large language models. In this work, we aim to ground MI in the context of cognitive science, which has long struggled with analogous questions in studying and explaining the behavior of"black box"intelligent systems like the human brain. We leverage several important ideas and developments in the history of cognitive science to disentangle divergent objectives in MI and indicate a clear path forward. First, we argue that current methods are ripe to facilitate a transition in deep learning interpretation echoing the"cognitive revolution"in 20th-century psychology that shifted the study of human psychology from pure behaviorism toward mental representations and processing. Second, we propose a taxonomy mirroring key parallels in computational neuroscience to describe two broad categories of MI research, semantic interpretation (what latent representations are learned and used) and algorithmic interpretation (what operations are performed over representations) to elucidate their divergent goals and objects of study. Finally, we elaborate the parallels and distinctions between various approaches in both categories, analyze the respective strengths and weaknesses of representative works, clarify underlying assumptions, outline key challenges, and discuss the possibility of unifying these modes of interpretation under a common framework.arXiv.org02It is argued that current methods are ripe to facilitate a transition in deep learning interpretation echoing the "cognitive revolution" in 20th-century psychology that shifted the study of human psychology from pure behaviorism toward mental representations and processing.ArXiv["Adam Davies", "Ashkan Khakzar"]2024-08-11T00:00:00 bca6001ea05eab82840b2117e9a77576612612f5npj Digit. Medicine48This commentary provides an overview of the key elements of the AI Act, with easy-to-follow references to the relevant chapters.NPJ Digital Medicine["Felix Busch", "J. N. Kather", "Christian Johner", "Marina Moser", "Daniel Truhn", "Lisa C. Adams", "K. Bressem"]2024-08-12T00:00:00 ccb98bc488e2902f450901535a76127cb319a630 This Viewpoint discusses a suggested framework of local registries to record and track all health artificial intelligence technologies used in clinical care, with the goal of providing transparency on these technologies and helping speed adoption while also protecting patient well-being. Journal of the American Medical Association (JAMA)34JAMA["Michael J. Pencina", "Jonathan McCall", "Nicoleta J. Economou-Zavlanos"]2024-08-12T00:00:00 d74580c5e3e0b2bf134226ca9ec33ffb24a5e96bThis article examines the transformative impact of machine learning (ML) and artificial intelligence (AI) on surgical robotics, highlighting the advancements that have significantly enhanced precision, efficiency, and safety in surgeries. The integration of these technologies has enabled surgical robots to perform complex tasks autonomously, with accuracy rates approaching those of human surgeons. Key developments include improved surgical tool tracking, real-time data analysis, and enhanced decision-making capabilities during operations, which collectively contribute to reducing operation times and complication rates. The discussion extends to the potential future directions of these technologies, emphasizing continuous improvement in human-robot interaction, regulatory adaptations, and broader application across various medical fields. The anticipated advancements are expected to make high-quality surgical interventions more accessible, particularly in remote and underserved areas, ultimately revolutionizing patient care by making surgeries safer, faster, and more patient-centered. The article underscores the role of ongoing research and development in pushing the boundaries of what surgical robots can achieve, setting the stage for a new era in medical technology.Transactions on Computer Science and Intelligent Systems Research71The role of ongoing research and development in pushing the boundaries of what surgical robots can achieve is underscores the role of ongoing research and development in setting the stage for a new era in medical technology.Transactions on Computer Science and Intelligent Systems Research["Weihang Yuan"]2024-08-12T00:00:00 40a91d1f9c9f6516ae6ad7c6edd1ab79fbd63186The 2024 2nd International Conference on Artificial Intelligence, Database and Machine Learning (AIDML 2024) was held in Rotterdam, Netherlands during July 06-07, 2024. This annual event aims to serve as an international forum to gather academicians, scientists, engineers, and researchers working in the fields of algorithms, big data, computing, artificial intelligence, and machine learning to exchange views, share their professional knowledge, experience, and research results, and discuss the challenges and future directions in their professional fields. AIDML 2024 will mainly feature keynote speeches and peer-reviewed paper speeches. In addition, social activities or academic visits will be arranged to encourage exchanges, discussions, or cooperation between researchers. The conference also had many invited lectures and workshops which discuss many in-depth topics related to the main themes. We sincerely hope that AIDML 2024 would not only show the participants a broad overview of the latest research results, but also provide them with a significant platform for academic connection and exchange. We would like to express our sincere gratitude to all the keynote speakers, reviewers, and editors for their hard work, precious time and endeavor preparing for the conference. AIDML 2024 Organizing Committee Rotterdam, NetherlandsTransactions on Computer Science and Intelligent Systems Research01The 2024 2nd International Conference on Artificial Intelligence, Database and Machine Learning (AIDML 2024) was held in Rotterdam, Netherlands during July 06-07, 2024 and it is hoped that AIDML 2024 would not only show the participants a broad overview of the latest research results, but also provide them with a significant platform for academic connection and exchange.Transactions on Computer Science and Intelligent Systems Research["Humphrey Arthur", "Fan Huang"]2024-08-12T00:00:00 fcf6fe2e30fb89c0e5db71ffc7dd596a6b49dce5Microcontrollers are widely used in agriculture, yet most undergraduate agriculture students do not have the programming skills necessary to make use of these devices in their academic programs or careers. However, generative artificial intelligence (AI) chatbots, such as ChatGPT, have the ability to write complex microcontroller programs when properly queried. The study was conducted to determine the effects of undergraduate agriculture students’ (n = 22) use of ChatGPT to write a microcontroller program on their programming task performance, self‐efficacy, and attitudes toward generative AI. Nine of 11 (81.8%) student pairs were successful in the ChatGPT‐assisted programming activity, requiring between one (33.3%) and six (11.1%) queries to develop their programs. The two unsuccessful pairs used either one or two queries and produced somewhat functional programs that did not fully operate as specified. Pre‐ and posttest surveys indicated significant (p < 0.001) increases in self‐efficacy for writing microcontroller programs, for using ChatGPT to write microcontroller programs, and attitudes toward generative AI. This research confirmed that undergraduate agriculture students can successfully use generative AI chatbots to write microcontroller programs and that successful task completion increases student self‐efficacy. Further research is needed to determine best practices for using generative AI in teaching and learning microcontroller programming.Natural Sciences Education162It is confirmed that undergraduate agriculture students can successfully use generative AI chatbots to write microcontroller programs and that successful task completion increases student self‐efficacy.Natural Sciences Education["Donald M. Johnson", "Will Doss", "Christopher M. Estepp"]2024-08-12T00:00:00 9ee0e1a03e24a2d4410a3fc6be3910e639cac8ecThe resurgence of the Taliban in Afghanistan has significantly exacerbated educational challenges for marginalized women and girls, deepening gender disparities and impeding socio-economic development. Addressing these issues, this article introduces the Blockchain and Artificial Intelligence Non-Formal Education System (BANFES), an innovative educational solution specifically designed for Afghan girls deprived of formal schooling. BANFES leverages advanced artificial intelligence technologies, including personalized data analysis, to provide customized learning experiences. Additionally, blockchain technology ensures secure record management and data integrity, facilitating a decentralized educational ecosystem where various nodes offer hybrid learning methodologies without intermediaries. This system not only adapts to individual learning speeds and styles to enhance engagement and outcomes but also employs an independent assessment mechanism to evaluate learners. Such evaluations promote transparency and maintain the quality and reputation of educational contributions within the network. The BANFES initiative also addresses implementation challenges, including local distrust and integration with existing educational structures, providing a robust model to overcome barriers to education. Furthermore, the paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges. By creating a secure, flexible, and learner-focused environment, BANFES aims to empower Afghan women and girls with essential skills for personal and professional growth, thus fostering socioeconomic advancement within their communities and setting a new standard for informal education worldwide.Education sciences761The paper explores the scalability of BANFES, proposing its application as a global strategy for non-formal education systems facing similar geopolitical and infrastructural challenges and setting a new standard for informal education worldwide.Education Sciences["Zahra Nazari", "Abdul Razaq Vahidi", "Petr Mus\u00edlek"]2024-08-12T00:00:00 b0005ab9b477fee8956a4535c767db33d5ddb779J. Medical Syst.368AI-based CDSSs were found to improve clinical decision-making by providing patient-specific information and evidence-based recommendations and reducing medical errors.Journal of medical systems["Khaled Ouanes", "Nesren Farhah"]2024-08-12T00:00:00 ee62742773cbfea3ca583272362cdc4b0ae45f41The article discusses the use of artificial intelligence in education and its progressiveness, as well as problems in the use of artificial intelligence at present time. Contains answers to the question: “Is it necessary to use artificial intelligence in education?” It also contains information about the emergence and development of artificial intelligence. The opinion about artificial intelligence in education, namely in higher educational institutions as an academic discipline, was considered. The place of artificial intelligence in education, the negative sides and the positive sides were considered. The article also contains a mathematical model or the mathematical basis of artificial intelligence. Information about the positive and negative effects of artificial intelligence or Chatgpt on people is included. Included is detailed information about ChatGPT, one of the artificial intelligence neural network, as well as information on how to use ChatGPT.Вестник Иссык-Кульского университета00Вестник Иссык-Кульского университета["S. B. Egizbaeva", "A. U. Elukenova", "M. S. Esenova"]2024-08-12T00:00:00 637f65a117811a54c91f64cf08231bed5d76632dThe pace of artificial intelligence (AI) development continues to astound. Already, the world has divided into two camps: some calling for a halt to the advancement of powerful AI systems, while others announce integrations of AI-powered chatbots like "ChatGPT" into their platforms. With AI's pervasive spread and influence across nearly all spheres of human activity worldwide, new challenges and significant threats arise in implementing doctrines of national information security for all countries without exception. The article discusses the process of total globalization, where national information security becomes a leading factor in ensuring conditions for the realization of national interests and a state's ability to overcome crises in the face of external aggression. Timely and effective measures in managing information security by the state can mitigate threats to the socio-economic and political life of the country. Our focus should particularly be on information security in the United States, as our state's main and strategic partner, including in the realm of information security. Considering that the rapid development of artificial intelligence creates new challenges and opportunities for national information security, studying and borrowing the experience of leading actors in this field, including the United States, is extremely important for our state to ensure the protection of its national interests in the information sphere and information sovereignty.State Formation00The article discusses the process of total globalization, where national information security becomes a leading factor in ensuring conditions for the realization of national interests and a state's ability to overcome crises in the face of external aggression.State Formation["Ivan Lopatchenko"]2024-08-12T00:00:00 8752fabee947f7de1f299b911f36db36c163cfc5Cybersecurity in the modern world is a critical field that involves protecting systems, networks, and programs from digital attacks. With advancements in technology, artificial intelligence (AI) has become a double-edged sword. While AI can enhance cybersecurity measures, it also introduces new vulnerabilities. Fraudulent activities facilitated by AI not only pose risks to financial and data security but increasingly threaten environmental sustainability as well. The purpose of the article is to identify ways to ensure cybersecurity. The object of the study is challenges from fraud using artificial intelligence technologies that threaten the environment. The research methodology involves the use of modern IDEF0 modeling methods. As a result of the study, a model for ensuring cybersecurity is presented.Journal of Ecohumanism190A model for ensuring cybersecurity is presented that involves the use of modern IDEF0 modeling methods and aims to identify ways to ensure cybersecurity.Journal of Ecohumanism["Serhiy Marko", "Yuriy Tsaruk", "Halyna Skhidnytska", "M. Kryshtanovych", "Uliana Nikonenko"]2024-08-12T00:00:00 087219983b1d590193be6798b132ea9c6d42a92cCurrent Gastroenterology Reports390AI models based on clinical, laboratory, and radiomics have been developed to aid in the diagnosis and management of various PB disorders and its application is ever expanding, but despite promising results, these AI-based models need further external validation to be clinically applicable.Current gastroenterology reports["Nikhil Bush", "M. Khashab", "V. Akshintala"]2024-08-12T00:00:00 ea0de4a7415417f4164cbdec198df9cf9b35e2ce The incorporation of artificial intelligence (AI), especially large language models like Generative Pretrained Transformer 4 (GPT-4), into medical practice is a burgeoning field of interest. This research evaluates the applicability of GPT-4 in otology by analyzing its responses to queries based on otologic clinical practice guidelines. Key guidelines from otology were selected, and corresponding questions were formulated to examine GPT-4’s interpretation and response accuracy. Two independent reviewers assessed the AI-generated answers for accuracy and completeness, using a structured Likert scale. A re-evaluation was conducted to evaluate the reproducibility of the results. The analysis showed a high accuracy level (mean score: 4.75 of 5) and completeness (mean score: 2.88 of 3) in GPT-4’s responses. The interrater agreement, as indicated by Cohen κ, was substantial. GPT-4 consistently advised on individualized treatment plans and professional consultation, particularly for guidelines with weaker evidence, demonstrating its cautious approach to handling medical information. GPT-4 exhibits promising potential as an auxiliary tool in otology, providing accurate and comprehensive information. However, its role should be viewed as supplementary, with emphasis on continual updates and careful monitoring to align with evolving medical knowledge. Future studies are recommended to further explore the depth of AI application in diverse clinical scenarios and its real-time impact on clinical outcomes. Otology &amp; Neurotology Open140GPT-4 exhibits promising potential as an auxiliary tool in otology, providing accurate and comprehensive information, however, its role should be viewed as supplementary, with emphasis on continual updates and careful monitoring to align with evolving medical knowledge.Otology &amp; Neurotology Open["N. Rossi", "Kassandra Corona", "Yuki Yoshiyasu", "Dayton L. Young", "Brian J. McKinnon"]2024-08-12T00:00:00 d7d9367be39bd6bacb82f1b738caeca5da06782fThis paper provides an in-depth look at the application and impact of Artificial Intelligence (AI) in the animation industry, while also pointing out the existing limitations. In recent decades, AI has been seamlessly integrated into all aspects of life, and the field of animation creation is no exception.AI has not only automated tedious tasks, increased efficiency, and lowered costs, but also revolutionized the traditional animation ecosystem by providing animators with entirely new ways of expression. With the rapid advancement of digital technology, AI has become a key driver of innovation and change, especially in creative industries. In the animation industry, the introduction of AI technology has not only reinvigorated the traditional craft, but also opened up unprecedented creative possibilities. This paper explores the specific applications of AI in animation production, such as automatically rendering the texture of animation to help animators complete repetitive tasks. Or the use of algorithms to assist in the production of special effects and color modification. Through the animation creator's description to complete the modeling, or through deep learning, machine learning generated large models to assist the character's behavioral design and so on. While AI brings many opportunities to the field of animation, it also comes with the challenge of ethical considerations and the risk of possible substitution of traditional technologies. the application of AI in animation has pushed the industry in the direction of greater efficiency, innovation, and personalization, and has also prompted us to think deeply about the combination of creativity and technology.Transactions on Computer Science and Intelligent Systems Research160This paper explores the specific applications of AI in animation production, such as automatically rendering the texture of animation to help animators complete repetitive tasks, and the use of algorithms to assist in the production of special effects and color modification.Transactions on Computer Science and Intelligent Systems Research["Haoran Jia"]2024-08-12T00:00:00 db220764977fabcac97c0754645fd3b92eaa9c41Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize various aspects of healthcare, including vascular surgery. This narrative review aims to provide a comprehensive overview of AI's current applications, prospects, and potential challenges in vascular surgery. The article follows a structured approach, beginning with an introduction to the fundamentals of AI and its relevance to vascular surgery. The methodology section outlines the literature search strategy and selection criteria for identifying relevant studies. The results section delves into the key findings, categorized into subtopics such as predictive modeling, image analysis, surgical planning, and intraoperative guidance. The discussion section critically analyzes the implications of AI in vascular surgery, addressing its potential benefits, limitations, and ethical considerations. Finally, the conclusion summarizes the main points and provides recommendations for future research and clinical implementation. Throughout the article, each assertion is supported by reliable references sourced from reputable databases. This review aims to advance AI and vascular surgery knowledge by presenting up-to-date information, robust evidence, and valuable insights.Revista Ibero-Americana de Humanidades, Ciências e Educação00A comprehensive overview of AI's current applications, prospects, and potential challenges in vascular surgery is provided to provide a comprehensive overview of AI's current applications, prospects, and potential challenges in vascular surgery.Revista Ibero-Americana de Humanidades, Ciências e Educação["Rodolpho Bicalho Bento", "Breno Oliveira Gois", "N\u00e1dia Oliveira Cabral", "Afr\u00e2nio C\u00f4go Destefani", "Vin\u00edcius C\u00f4go Destefani"]2024-08-12T00:00:00 5156978520159d77f0ae05c9636f998b2d121ea6The article analyzes the benefits and risks of using artificial intelligence (AI) in the public administration of central banks. Using the method of discourse analysis, the advantages and risks of introducing AI into the activities of central banks are investigated. The author also considers the Concept of Artificial Intelligence Development in Ukraine, approved by the Resolution of the Cabinet of Ministers of Ukraine № 1156-r dated 02.12.2020, which defines the priority areas of AI development and the areas to which this initiative is directed. Using AI in central banks can help improve the analysis of large amounts of data, which in turn will help forecast economic trends and manage financial risks. One of the main advantages is the ability to automate routine processes, allowing employees to focus on strategic tasks. An important aspect is the collection of microeconomic and non-economic data from various sources, including the Internet. In addition, AI provides the ability to use synthetic data, which expands the possibilities for analysis. However, the use of AI also carries significant risks. These include problems with data privacy, the risk of false conclusions based on synthetic data, the impact of built-in biases in AI models, and the difficulty of explaining policy decisions. Cybersecurity is a separate issue, as the introduction of AI makes systems more vulnerable to cyberattacks. AI is expected to be increasingly integrated into key functions of central banks, including monetary policy-making and financial risk management. This will allow central banks to make more informed decisions and increase the efficiency of their operations. In addition, the introduction of AI will facilitate the development of information technology and improve analytical capabilities, which will ultimately reduce the workload of employees. At the same time, an important part of the analysis is the impact of AI on the transformation of modern approaches to public administration, especially in the context of the digitalization of the economy. AI can change traditional management methods by offering new tools for decision-making, but it also requires more careful regulation to avoid negative consequences. Therefore, a balanced implementation of these technologies is needed, taking into account potential risks and benefits. This study is a step in understanding how artificial intelligence can change the role of central banks in the modern economy, and how regulatory approaches need to be adapted to ensure the safe and effective implementation of these technologies.University Scientific Notes00The article analyzes the benefits and risks of using artificial intelligence (AI) in the public administration of central banks using the method of discourse analysis and the impact of AI on the transformation of modern approaches to public administration, especially in the context of the digitalization of the economy.University Scientific Notes["Artur Dudnichenko"]2024-08-12T00:00:00 08281cd11904f96e28a66cb4cacb1f4ba87bd273In the contemporary digital era, artificial intelligence (AI) emerges as a pivotal industry, playing a critical role across various sectors. Concurrently, the integrated circuit (IC) industry represents a highly specialized field characterized by swift technological advancements and innovation. Anchored on this premise, this paper embarks on a comprehensive exploration of the IC domain, delineating its evolutionary trajectory and current dynamics. Subsequently, this paper extends to a detailed examination of the practical deployment of AI within the IC industry, highlighting specific application scenarios where AI's transformative potential is harnessed to enhance efficiency, design, and operational processes. This paper culminates in a forward-looking analysis, contemplating the prospective trends, opportunities, and challenges that define the intersection of AI and IC technologies. This paper inquiry not only sheds light on the symbiotic relationship between AI and the IC industry but also underscores the imperative for adaptive strategies in navigating the complexities of their integration.Transactions on Computer Science and Intelligent Systems Research70This paper inquiry not only sheds light on the symbiotic relationship between AI and the IC industry but also underscores the imperative for adaptive strategies in navigating the complexities of their integration.Transactions on Computer Science and Intelligent Systems Research["Fangyi Yu"]2024-08-12T00:00:00 bdc4d3408bda26c24e17107ccfec81986601319dWith the rapid advancement of technology, artificial intelligence (AI) infiltrates various industries at an unprecedented pace, and the field of news production is no exception. This article aims to explore the profound effects of AI technology on news production. It analyzes how AI reshapes news gathering, content creation, distribution, and audience interaction. Furthermore, it discusses the implications and challenges this transformation poses for the future of journalism.International Journal of Computer Science & Information Technology (IJCSIT)20How AI reshapes news gathering, content creation, distribution, distribution, and audience interaction is analyzed and the implications and challenges this transformation poses for the future of journalism are discussed.International Journal of Computer Science and Information Technology["Jiarong Liu"]2024-08-12T00:00:00 d3a6048cc4e3e6fc6961cf16540031c2622d3869Insurance is very important in economic activity because in addition to providing protection against possible losses, it also encourages the growth of other economic activities. In this case, insurance provides protection against extraordinary hazards such as earthquakes, fires, strikes, and others. These dangers do not cause significant losses. The existence of this protection encourages economic growth in other fields because entrepreneurs do not hesitate to maintain their business and increase capital. The purpose of this study is to determine the implementation of Artificial Intelligence (AI) implementation in Insurance Companies. This type of research is descriptive qualitative research with the acquisition of secondary data directly obtained in journals and books. The results show that the insurance sector has experienced significant development over the past few decades. These developments are influenced by a variety of factors, including changes in technology such as the use of AI, regulation, and consumer needs.Proceeding of the International Conference on Multidisciplinary Research for Sustainable Innovation100The results show that the insurance sector has experienced significant development over the past few decades, influenced by a variety of factors, including changes in technology such as the use of AI, regulation, and consumer needs.Proceeding of the International Conference on Multidisciplinary Research for Sustainable Innovation["Deoh Sundari", "Susi Melinasari", "Ermi Suryani", "Hafid Fadilah Fadilah"]2024-08-12T00:00:00 a9eb89974d3fc51eef0f30e4e6508ffd89b04fe9Objective Findings on the application of artificial intelligence (AI), particularly convolutional neural networks (CNNs), in enhancing diagnostic and prognostic capabilities in gynecological health were synthesized. Design Recent technological advancements, particularly AI and machine learning, in the study and management of endometrial conditions were reviewed. Subjects Various studies exploring the role of AI in diagnosing and managing endometrial conditions such as endometriosis, endometrial receptivity, and endometrial cancer were examined. Intervention The development and implementation of CNNs, radiomics models, and integration of omics data (proteomics, metabolomics, transcriptomics), ultrasonographic imaging, in endometrial studies were analyzed. Main Outcomes Diagnostic accuracy, prognostic assessments, early detection, personalized treatment, and clinical management of endometrial conditions were evaluated. Results It was found that AI technologies, surpassing manual methods in accuracy, enhance the classification of endometrial patterns and analysis of uterine peristalsis. The quantitative assessment of endometrial vascularization and blood supply is improved by AI, leading to better predictions for pregnancy outcomes. Traditional challenges, such as time-consuming manual measurements and significant inter-observer variability, are mitigated by AI-assisted ultrasound, which provides automated detection and measurement of follicles, reducing examination time and improving reproducibility. Diagnostic accuracy in follicular monitoring and endometrial receptivity (ER) assessment is enhanced by AI models, though challenges remain, including the need for robust AI models and validation across diverse populations. The integration of AI with transcriptomic testing and biomarkers in assisted reproductive technology (ART) shows promise in improving embryo transfer timing and personalized treatment strategies. In endometrial cancer and hyperplasia, AI models significantly enhance diagnostic accuracy, sensitivity, and specificity, improving preoperative risk classification and prognostication. Non-invasive diagnostic methods like proteomic profiling and AI models demonstrate high sensitivity and specificity for endometriosis, potentially reducing the need for invasive procedures. Conclusions It has been demonstrated that AI models, particularly those leveraging deep learning, show promise in enhancing diagnostic efficiency, predicting molecular subtypes, and improving clinical outcomes in gynecological cancers and reproductive health. However, challenges such as model generalization, data standardization, and interpretability need to be addressed. Future research should focus on validating these models and integrating them into clinical workflows to optimize patient care.The Journal of Reproduction400It has been demonstrated that AI models, particularly those leveraging deep learning, show promise in enhancing diagnostic efficiency, predicting molecular subtypes, and improving clinical outcomes in gynecological cancers and reproductive health.The Journal of Reproduction["Jorge Suarez"]2024-08-12T00:00:00 0da9ec5209fedaffba29f82058bf945ea85c6a87This paper examines the impact of artificial intelligence (AI) on quantitative finance and financial technology (fintech). It explores how AI techniques, including machine learning and deep learning, are transforming financial modeling, risk assessment, and decision-making processes. The study discusses key innovations in AI-driven fintech, such as robo-advisors and algorithmic trading. It also addresses critical challenges, including data quality issues, model interpretability, and regulatory concerns. The paper concludes by outlining future directions and ethical considerations for AI in finance, emphasizing the need for responsible development and deployment of these technologies in reshaping the financial landscape.International Journal of Computer Science & Information Technology (IJCSIT)260This paper explores how AI techniques, including machine learning and deep learning, are transforming financial modeling, risk assessment, and decision-making processes, and addresses critical challenges, including data quality issues, model interpretability, and regulatory concerns.International Journal of Computer Science and Information Technology["Runzhe Li"]2024-08-12T00:00:00 4d21f8210164148676787733a0faeb7850447426This review explores the transformative impact of AI on drug development and delivery in pharmaceutical sciences, spanning formulation design, real-time monitoring, targeted delivery, and future prospects. The rational design of smart drug carriers, such as AI-optimized liposomes for cancer therapy, optimizes formulations for individual patient needs. AI-driven sensors, exemplified by glucose-monitoring biosensors for diabetics, enable adaptive drug administration, enhancing precision. Despite promises, challenges like biocompatibility, regulations, and ethics persist. Interdisciplinary collaboration and transparent communication are crucial for responsible AI adoption. Anticipated trends include personalized dosage optimization and intelligent nanocarriers. The review underscores AI's potential in reshaping pharmaceuticals for patient-centric care while addressing challenges for widespread adoption.Current Topics in Medicinal Chemistry04The review underscores AI's potential in reshaping pharmaceuticals for patient-centric care while addressing challenges for widespread adoption, and anticipated trends include personalized dosage optimization and intelligent nanocarriers.Current topics in medicinal chemistry["C. Aundhia", "Ghansyam Parmar", "Chitrali Talele", "Niyati Shah", "Dipali Talele"]2024-08-12T00:00:00 2d036e8c50b4b3ef27189632d6e9eb4508301a29Computational and Structural Biotechnology Journal1092Computational and Structural Biotechnology Journal["Dost Muhammad", "Malika Bendechache"]2024-08-12T00:00:00 7aa4bfe9d32fa24c10753c2072fb7be343f2c079Journal of Clinical Neurology71Journal of Clinical Neurology (Seoul, Korea)["Jakub Cecot", "Konrad Zarzecki", "Mi\u0142osz Mandryk"]2024-08-12T00:00:00 f078aa62710cf8e87e482a2b8a5e3978b9eb0a3cSurgical Endoscopy231ChatGPT-4 can be customized to overcome limitations with its training dataset to provide recommendations for the surgical management of gastroesophageal reflux disease with reliable accuracy and consistency.Surgical endoscopy["Bright Huo", "Nana Marfo", "P. Sylla", "Elisa C Calabrese", "Sunjay S. Kumar", "Bethany J Slater", "Danielle S Walsh", "Wesley Vosburg"]2024-08-12T00:00:00 7368eb826086cf7bab60bf1aa86050a4a72de62fJournal of Applied Research in Memory and Cognition00Journal of Applied Research in Memory and Cognition[]2024-08-12T00:00:00 3834a5578f1e1e2cad1069a73b023c4d5b5893e0PCE Official Conference Proceedings00PCE Official Conference Proceedings["A. J. Iii"]2024-08-12T00:00:00 c857dd44220492049159adc29a661629bad95546PCE Official Conference Proceedings00PCE Official Conference Proceedings["Colleen Halupa"]2024-08-12T00:00:00 e9ab0756c0d20ec9c619581229dcbb592422ae3eDear Editor, The reason why we wrote this letter is to address the risks that short examination times in our country may pose for patients and doctors and to initiate a discussion on what can be done to find a solution. The recent increase in hospital admissions and the decrease in the number of physicians have created pressure to examine a large number of patients in a short time. Studies show that the general physical examination time of the patient is 20 minutes [1]. This examination period may be longer for some branches. For example, examination time may be longer in cardiology patients due to some procedures, such as effort echocardiography. For psychiatric patients, the first examination can take up to 45 minutes, including the meeting with the patient’s relatives. It is reported that as this period shortens, the likelihood of the physician in question facing a malpractice lawsuit in the future increases [2]. Currently, in public hospitals, the system provides an appointment every 10 minutes on average. When we include patients who are taken without an appointment to avoid disruption of their treatment, the examination time per patient sometimes reaches 3-5 minutes. In some hospitals, the number of patients examined by cardiologists per day exceeds 100. The current situation brings with it many problems. First of all, since this period is short, doctors have great difficulty making a diagnosis. There is not enough time for the physician to make a differential diagnosis. Some diseases may be overlooked. If something happens to the patient, the doctor may regret it for life. Additionally, many legal cases can be filed. In short, psychological problems may arise in the physician as a result of many material and moral losses. Short examination times are not good not only for the doctor but also for the patient. Due to the short examination period, the diseases of patients who do not receive adequate and effective treatment may increase. Patients may be dissatisfied because less time is allocated to them. Or he may go from doctor to doctor in search of healing. This situation also leads to a waste of public resources. In other countries, the examination time allocated per patient is longer than in Turkey. A study conducted in the USA reported that doctors spend an average of 20 minutes per patient and see 11–20 patients a day [3]. On the other hand, the use of artificial intelligence in health is inevitable today, and it is known that it is taught as a course in some medical faculties [4,5]. Artificial intelligence and machine learning can be used to prevent these problems. Studies have shown that artificial intelligence and machine learning can be useful in the diagnosis of diseases, differential diagnosis, treatment selection, and identification of risky patients [6,7]. As a result, it is extremely important for the relevant authorities to review the inspection periods in consultation with professional organizations due to possible risks. In addition, since we cannot reduce the number of people applying to the hospital and increase the number of doctors in the short term, it would be appropriate to carry out the necessary studies to make artificial intelligence and machine learning a part of the examination for some branches. Best Regards,European Journal of Therapeutics60It is extremely important for the relevant authorities to review the inspection periods in consultation with professional organizations due to possible risks and to carry out the necessary studies to make artificial intelligence and machine learning a part of the examination for some branches.European Journal of Therapeutics["G\u00fcrkan Imre", "Okan Imre"]2024-08-12T00:00:00 a0a14c7e13bd3d8a201e8be4e30a57f39bd001faSurgical Endoscopy00Surgical endoscopy["Yoshimasa Aoyama", "Y. Matsunobu", "T. Etoh", "Kosuke Suzuki", "Shunsuke Fujita", "Takayuki Aiba", "Hajime Fujishima", "Shinichiro Empuku", "Y. Kono", "Y. Endo", "Y. Ueda", "H. Shiroshita", "Toshiya Kamiyama", "Takemasa Sugita", "Kenichi Morishima", "Kohei Ebe", "T. Tokuyasu", "Masafumi Inomata"]2024-08-12T00:00:00 60821b08a55b77eca45b200178ece7a006f9efd7This article not only explains Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) concisely in a manner that improves understanding among medical educators and professionals, but also contrasts the emphasis on climate change in medical education with the comparatively less attention paid to the threat of AGI and ASI. Awareness is called for about this technology, which could potentially lead to a prosperous age or the extinction of humanity.European Journal of Therapeutics60This article not only explains Artificial General Intelligence and Artificial Superintelligence concisely in a manner that improves understanding among medical educators and professionals, but also contrasts the emphasis on climate change in medical education with the comparatively less attention paid to the threat of AGI and ASI.European Journal of Therapeutics["Yavuz Selim K\u0131yak"]2024-08-12T00:00:00 244cc97ade7b9470b3d12c7780d9f8b847fa2ed6This paper discusses the characteristics and development trend of intelligent mechanical engineering. Intelligent mechanical engineering is characterized by high quality, high efficiency, four-dimensional intersection, energy saving and environmental protection. The development trends include networking, informatization, integration, automation control, product intelligence and artificial intelligence, etc. These trends will push mechanical engineering towards a new stage of more intelligent, efficient and sustainable development.Transactions on Computer Science and Intelligent Systems Research31The characteristics and development trend of intelligent mechanical engineering is characterized by high quality, high efficiency, four-dimensional intersection, energy saving and environmental protection.Transactions on Computer Science and Intelligent Systems Research["Ruitao Zhu", "Linjie Wang", "Yunbo Feng"]2024-08-12T00:00:00 33161a5a9b5dcb635b5a97475e6a6209a69ada7dOne of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aides to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems. Our code is open-sourced at https://github.com/SakanaAI/AI-ScientistarXiv.org059The AI Scientist is introduced, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation, presenting the first comprehensive framework for fully automatic scientific discovery.ArXiv["Chris Lu", "Cong Lu", "R. T. Lange", "Jakob N. Foerster", "Jeff Clune", "David Ha"]2024-08-12T00:00:00 693584820e6fa31fc8f7431c9f78e147a059234aNat. Mac. Intell.373This study discusses the importance of responsible machine learning datasets through the lens of fairness, privacy and regulatory compliance, and presents a large audit of computer vision datasets, focusing on biometric and healthcare datasets.Nat. Mac. Intell.["S. Mittal", "K. Thakral", "Richa Singh", "M. Vatsa", "Tamar Glaser", "Cristian Canton Ferrer", "Tal Hassner"]2024-08-12T00:00:00 9817770b49b5d3867fb47d9d5e57a3c9bcc939a7Abstract As the use of Artificial Intelligence (AI) technologies in healthcare is expanding, patients in the European Union (EU) are increasingly subjected to automated medical decision-making. This development poses challenges to the protection of patients’ rights. A specific patients’ right not to be subject to automated medical decision-making is not considered part of the traditional portfolio of patients’ rights. The EU AI Act also does not contain such a right. The General Data Protection Regulation (GDPR) does, however, provide for the right ‘not to be subject to a decision based solely on automated processing’ in Article 22. At the same time, this provision has been severely critiqued in legal scholarship because of its lack of practical effectiveness. However, in December 2023, the Court of Justice of the EU first provided an interpretation of this right in C-634/21 (SCHUFA)—although in the context of credit scoring. Against this background, this article provides a critical analysis of the application of Article 22 GDPR to the medical context. The objective is to evaluate whether Article 22 GDPR may provide patients with the right to refuse automated medical decision-making. It proposes a health-conformant reading to strengthen patients’ rights in the EU.Medical Law Review163The objective is to evaluate whether Article 22 GDPR may provide patients with the right to refuse automated medical decision-making and proposes a health-conformant reading to strengthen patients’ rights in the EU.Medical Law Review["Hannah van Kolfschooten"]2024-08-12T00:00:00 59d7491521ff07c2fed6ebff38b95f44d2aae8e9Artificial Intelligence (AI) is transforming the field of Human Resources (HR), revolutionizing how organizations manage their workforce. This paper provides a comprehensive analysis of AI's impact on HR practices, from recruitment and talent management to employee engagement and decision-making. It explores the benefits and challenges of AI adoption in HR and offers a practical framework for successful implementation. By examining real-world case studies and current trends, this paper equips HR professionals and organizational leaders with the knowledge and strategies needed to harness the power of AI for more effective and data- driven HR management. Key Words: AI in HR, HR automation, Talent management, Employee engagement, Ethical AI, HR analyticsINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT02This paper provides a comprehensive analysis of AI's impact on HR practices, from recruitment and talent management to employee engagement and decision-making, and offers a practical framework for successful implementation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Dr. Abhijit Chandratreya"]2024-08-12T00:00:00 1d40e15394c9bbcbf14cb01c383f16d919f67c12Context: Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations such as high test suite maintenance costs and the need for extensive programming skills. Artificial Intelligence (AI) offers new opportunities to address these issues through automation and improved practices. Objectives: Given the prevalent usage of AI in industry, sources of truth are held in grey literature as well as the minds of professionals, stakeholders, developers, and end-users. This study surveys grey literature to explore how AI is adopted in TA, focusing on the problems it solves, its solutions, and the available tools. Additionally, the study gathers expert insights to understand AI's current and future role in TA. Methods: We reviewed over 3,600 grey literature sources over five years, including blogs, white papers, and user manuals, and finally filtered 342 documents to develop taxonomies of TA problems and AI solutions. We also cataloged 100 AI-driven TA tools and interviewed five expert software testers to gain insights into AI's current and future role in TA. Results: The study found that manual test code development and maintenance are the main challenges in TA. In contrast, automated test generation and self-healing test scripts are the most common AI solutions. We identified 100 AI-based TA tools, with Applitools, Testim, Functionize, AccelQ, and Mabl being the most adopted in practice. Conclusion: This paper offers a detailed overview of AI's impact on TA through grey literature analysis and expert interviews. It presents new taxonomies of TA problems and AI solutions, provides a catalog of AI-driven tools, and relates solutions to problems and tools to solutions. Interview insights further revealed the state and future potential of AI in TA. Our findings support practitioners in selecting TA tools and guide future research directions.arXiv.org652An overview of AI's impact on TA is offered through grey literature analysis and expert interviews, which presents new taxonomies of TA problems and AI solutions, provides a catalog of AI-driven tools, and relates solutions to problems and tools to solutions.ArXiv["Filippo Ricca", "A. Marchetto", "Andrea Stocco"]2024-08-12T00:00:00 bb77e25f18e83eb116674e810f7943318ae9bb8eIn the context of artificial intelligence (AI), the convergence of advanced technologies has profoundly reshaped financial accounting and management paradigms. This transformation is characterised by the emergence of intelligent finance tools, including integrated industry-financial software and financial robots, which facilitate automated, data-driven processes. These innovations significantly reduce human involvement in routine accounting tasks, leveraging AI capabilities to conduct comprehensive data analysis and enhance decision-making through more profound insights from extensive datasets. Despite these advancements, cybersecurity risks and the necessity for updated theoretical and regulatory frameworks persist. Nevertheless, integrating AI with financial practices holds promise for enhancing operational efficiencies and redefining financial management practices in the digital age.International Journal of Computer Science & Information Technology (IJCSIT)301In the context of artificial intelligence, the convergence of advanced technologies has profoundly reshaped financial accounting and management paradigms, and integrating AI with financial practices holds promise for enhancing operational efficiencies and redefining financial management practices in the digital age.International Journal of Computer Science and Information Technology["Yaxin Liang", "Gaozhe Jiang", "Yafeng He"]2024-08-12T00:00:00 7014d2866b03e5f44362ae7c9d1ae751fe695b62JN. Journal of Nephrology (Milano. 1992)261It is revealed that AI and machine learning have been successfully used to predict DKD progression, outperforming traditional risk score models and focusing on developing AI-driven tools for clinical practice.Journal of nephrology["Debargha Basuli", "Akil S. Kavcar", "Sasmit Roy"]2024-08-12T00:00:00 09a68b4431bcec7e27cfe50d2849f19316d20eaeArtificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspire to represent real world events. Synthetic photographs may be used maliciously by a broad range of threat actors, from scammers to nation-state actors, to deceive, defraud, and mislead people. Mitigating this threat usually involves answering a basic analytic question: Is the photograph real or synthetic? To address this, we have examined the capabilities of recent generative diffusion models and have focused on their flaws: visible artifacts in generated images which reveal their synthetic origin to the trained eye. We categorize these artifacts, provide examples, discuss the challenges in detecting them, suggest practical applications of our work, and outline future research directions.arXiv.org351The capabilities of recent generative diffusion models are examined and their flaws are focused on: visible artifacts in generated images which reveal their synthetic origin to the trained eye.ArXiv["Melanie Mathys", "Marco Willi", "Raphael Meier"]2024-08-12T00:00:00 b8c4bfe8d9e8e0b5c64e4dca4faeefca5f843e20Abdominal Radiology411The current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis are reviewed.Abdominal radiology["Sebastian Maletz", "Yoga Balagurunathan", "Kade Murphy", "Les Folio", "Ranjit Chima", "A. Zaheer", "Harshna Vadvala"]2024-08-12T00:00:00 26d51498c8bc8f12c741f23936be13708df4ed07In recent years, the convergence of cybersecurity, artificial intelligence (AI), and data management has emerged as a critical area of research, driven by the increasing complexity and interdependence of modern technological ecosystems. This paper provides a comprehensive review and analysis of integrative approaches that harness AI techniques to enhance cybersecurity frameworks and optimize data management practices. By exploring the synergies between these domains, we identify key trends, challenges, and future directions that hold the potential to revolutionize the way organizations protect, analyze, and leverage their data. Our findings highlight the necessity of cross-disciplinary strategies that incorporate AI-driven automation, real-time threat detection, and advanced data analytics to build more resilient and adaptive security architectures.arXiv.org01This paper provides a comprehensive review and analysis of integrative approaches that harness AI techniques to enhance cybersecurity frameworks and optimize data management practices, and identifies key trends, challenges, and future directions that hold the potential to revolutionize the way organizations protect, analyze, and leverage their data.ArXiv["Marwan Omar"]2024-08-12T00:00:00 bc59c0140acb81a098669abc02d8c7780775defcWith the rapid advancement of information technology, smart classrooms have increasingly become a vital component of modern education. By integrating technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data, smart classrooms provide a smart, efficient educational setting for teachers and students. However, the challenge of fully utilizing these technologies to enhance teaching effectiveness in smart classrooms remains unresolved. Existing research has highlighted the significant potential of deep learning in optimizing teaching decisions. However, its application faces challenges, including insufficient integration of technologies and limited effectiveness in practical implementations. The main focus of this study encompasses three parts: firstly, a biological neural network model targeted at optimizing teaching decisions, which emulates the mechanisms of biological neural networks to efficiently optimize teaching decisions; secondly, an active consciousness teaching decision model within smart classroom settings, which merges deep learning with theories of active consciousness to support dynamic, intelligent teaching decisions; and thirdly, a biologically inspired teaching process coordination network in smart classrooms, designed to optimize and coordinate educational processes based on biological principles. Through these investigations, this study provides both theoretical and practical support for optimizing teaching decisions in smart classrooms, offering significant academic value and practical application prospects.International Journal of Interactive Mobile Technologies00A biological neural network model targeted at optimizing teaching decisions, which emulates the mechanisms of biological neural networks to efficiently optimize teaching decisions and an active consciousness teaching decision model within smart classroom settings, which merges deep learning with theories of active consciousness to support dynamic, intelligent teaching decisions.Int. J. Interact. Mob. Technol.["Jia Lin"]2024-08-12T00:00:00 73225354b43308acff62d0aa6358234cd97a4042Recent and unremitting capability advances have been accompanied by calls for comprehensive, rather than patchwork, regulation of frontier artificial intelligence (AI). Approval regulation is emerging as a promising candidate. An approval regulation scheme is one in which a firm cannot legally market, or in some cases develop, a product without explicit approval from a regulator on the basis of experiments performed upon the product that demonstrate its safety. This approach is used successfully by the FDA and FAA. Further, its application to frontier AI has been publicly supported by many prominent stakeholders. This report proposes an approval regulation schematic for only the largest AI projects in which scrutiny begins before training and continues through to post-deployment monitoring. The centerpieces of the schematic are two major approval gates, the first requiring approval for large-scale training and the second for deployment. Five main challenges make implementation difficult: noncompliance through unsanctioned deployment, specification of deployment readiness requirements, reliable model experimentation, filtering out safe models before the process, and minimizing regulatory overhead. This report makes a number of crucial recommendations to increase the feasibility of approval regulation, some of which must be followed urgently if such a regime is to succeed in the near future. Further recommendations, produced by this report's analysis, may improve the effectiveness of any regulatory regime for frontier AI.arXiv.org00This report proposes an approval regulation schematic for only the largest AI projects in which scrutiny begins before training and continues through to post-deployment monitoring, and makes a number of crucial recommendations to increase the feasibility of approval regulation.ArXiv["Cole Salvador"]2024-08-12T00:00:00 859e10bb912a4314a689919639de12251f3172a9This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence’s role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles’ energy management systems is discussed. The findings underscore artificial intelligence’s potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications.World Electric Vehicle Journal718The integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles’ energy management systems is discussed, and artificial intelligence’s role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors are explored.World Electric Vehicle Journal["Paul Ar\u00e9valo", "Danny Ochoa-Correa", "Edisson Villa-\u00c1vila"]2024-08-13T00:00:00 e15ae9f48daa6aaf022535a425e0d77eb4021edcPurpose This study aims to examine whether the adoption of artificial intelligence (AI) by audit firms and their clients affects audit efficiency and audit quality. Design/methodology/approach This study empirically examines the abovementioned research question based on data from China for the years 2011 to 2020. It uses audit report lag as a proxy for audit efficiency and the likelihood of annual report restatement as a proxy for audit quality. It adopts the propensity score matching and the two-stage OLS regression model to address the endogeneity issue led by firms’ innate complicated functions. Findings The findings show that when audit firms and their clients use AI separately, there's a positive link between AI use and audit report lag. However, when audit firms and clients use AI together, there's a negative link between AI use and audit report delays that enhance overall audit efficiency. Next, the authors observe a negative link between AI use and the likelihood of a restatement. Finally, the authors find that the association between AI adoption and audit quality is driven by increased audit effort lag. Results are consistent and robust to endogeneity tests and sensitivity analyses. Originality/value Findings can complement the audit quality and corporate governance literature by clarifying that external audit must evolve through digitalization and the incorporation of newly developed digital tools, such as AI. Managerial Auditing Journal594The findings show that when audit firms and their clients use AI separately, there's a positive link between AI use and audit report lag, however, when audit firms and clients use AI together, there's a negative link between AI use and audit report delays that enhance overall audit efficiency.Managerial Auditing Journal["Md Jahidur Rahman", "Hongtao Zhu", "Li Yue"]2024-08-13T00:00:00 aee582bf1f473ab70266c4c1cc849459ab6b828dScientific Reports531A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) to train a model for predicting the Hill classification and detecting HH and the AL pipeline is more efficient than traditional methods in training AI for endoscopy.Scientific Reports["I. Kafetzis", "Karl-Hermann Fuchs", "Philipp Sodmann", "J. Troya", "Wolfram G. Zoller", "A. Meining", "A. Hann"]2024-08-13T00:00:00 b4c76a75f2dd5479fa95f1ec4e4c69380903c26cAbstract Background At a time when Internet and social media use is omnipresent among patients in their self-directed research about their medical or surgical needs, artificial intelligence (AI) large language models (LLMs) are on track to represent hallmark resources in this context. Objectives The authors aim to explore and assess the performance of a novel AI LLM in answering questions posed by simulated patients interested in aesthetic breast plastic surgery procedures. Methods A publicly available AI LLM was queried using simulated interactions from the perspective of patients interested in breast augmentation, mastopexy, and breast reduction. Questions posed were standardized and categorized under aesthetic needs inquiries and awareness of appropriate procedures; patient candidacy and indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; postprocedure instructions and recovery; and procedure cost and surgeon recommendations. Using standardized Likert scales ranging from 1 to 10, 4 expert breast plastic surgeons evaluated responses provided by AI. A postparticipation survey assessed expert evaluators' experience with LLM technology, perceived utility, and limitations. Results The overall performance across all question categories, assessment criteria, and procedures examined was 7.3/10 ± 0.5. Overall accuracy of information shared was scored at 7.1/10 ± 0.5; comprehensiveness at 7.0/10 ± 0.6; objectivity at 7.5/10 ± 0.4; safety at 7.5/10 ± 0.4; communication clarity at 7.3/10 ± 0.2; and acknowledgment of limitations at 7.7/10 ± 0.2. With regards to performance on procedures examined, the model's overall score was 7.0/10 ± 0.8 for breast augmentation; 7.6/10 ± 0.5 for mastopexy; and 7.4/10 ± 0.5 for breast reduction. The score on breast implant–specific knowledge was 6.7/10 ± 0.6. Conclusions Albeit not without limitations, AI LLMs represent promising resources for patient guidance and patient education. The technology's machine learning capabilities may explain its improved performance efficiency. Level of Evidence: 4Aesthetic Surgery Journal Open Forum281Albeit not without limitations, AI LLMs represent promising resources for patient guidance and patient education and the technology's machine learning capabilities may explain its improved performance efficiency.Aesthetic Surgery Journal. Open Forum["Jad Abi-Rafeh", "Brian Bassiri-Tehrani", "R. Kazan", "Heather J. Furnas", "Dennis Hammond", "William P. Adams", "Foad Nahai"]2024-08-13T00:00:00 ffdc80018fbd7fbac1c8cff16947ed303dfb310cObjective: Artificial intelligence (AI) is widely anticipated to become an integral component of dentistry soon given its potential to revolutionize both dental education and practice. Therefore, it is essential to understand the perspectives of dental students who will be the future practitioners to adopt and use these technologies effectively and efficiently. The study aimed to evaluate the beliefs, perceptions and attitudes of a sample of Turkish dental students towards AI. Materials and Methods: Data was collected online from students regarding age, sex and academic year. The students' beliefs regarding AI were assessed using a 21-question survey form of AI Attitude Scale. Also, a 15-question survey form was used to investigate the opinions and knowledge of dental students about AI. A total of 527 dental students, aged 18 to 37 years, were recruited, including 142 first-grade, 14 second-grade, 171 third-grade, 90 fourth-grade, and 110 fifth-grade students. Results: There was a significant difference in the mean belief dimension scores based on the sex of the students (pMeandros Medical and Dental Journal01There was a significant difference in the mean belief dimension scores based on the sex of the students, and there was a significant difference in the mean belief dimension scores based on the sex of the students.Meandros Medical And Dental Journal["Sena Aykut", "Ayse Ege Selman", "Burcu Karaduman"]2024-08-13T00:00:00 61f8711b18d04893b6bb6fc136661e1aa88d2638Artificial intelligence (AI) has gained significant popularity in recent years. This study critically examines the existing literature on the role and potential of AI, machine learning (ML) and big data in driving innovation. To the best of our knowledge, no existing survey has provided a comprehensive overview of this topic. The aim of this paper is thus to provide a coherent overview of theoretical cornerstones as well as recent trends in research on AI and data analytics for innovation. Using various bibliometric analyses of themes including publication counts and trends, co-citations, co-authorship, and keyword co-occurrence, we infer the thematic structure of AI research in innovation for the period from 1991 to 2021. The publications are grouped into three major clusters, with Cluster 1 remaining a constantly dominant theme in the digital innovation publication landscape. Cluster 2 includes published studies on big data, which also received much research attention. Cluster 3, which is the most prominent, includes business performance, business analytics, and information systems. We also analyse publication citation counts in the literature using Poisson and negative binomial regressions. The results show that the structural variation approach provides a new method for tracking and evaluating the potential of freshly published studies in context. The findings of the current study will be significant in identifying new areas of research of potential interest to scholars and practitioners in the field of AI for innovation worldwide. We conclude this review with limitations and theoretical and practical orientations.International Journal of Innovation Management751The results show that the structural variation approach provides a new method for tracking and evaluating the potential of freshly published studies in context and will be significant in identifying new areas of research of potential interest to scholars and practitioners in the field of AI for innovation worldwide.International Journal of Innovation Management["Sami ben Jabeur", "N. Stef", "Wissal Ben Arfi"]2024-08-13T00:00:00 1a0cc754fd2dc9c9f59065573f86df89919a54dcThe food industry is a significant contributor to carbon emissions, impacting carbon footprint (CF), specifically during the heat drying process. Conventional heat drying processes need high energy and diminish the nutritional value and sensory quality of food. Therefore, this study aimed to investigate the integration of artificial intelligence (AI) in food processing to enhance quality and reduce CF, with a focus on heat drying, a high energy-consuming method, and offer a promising avenue for the industry to be consistent with sustainable development goals. Our finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products. It determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost. In addition, dataset training is one of the key challenges in AI applications for food drying. AI needs a vast and high-quality dataset that directly impacts the performance and capabilities of AI models to optimize and automate food drying.Comprehensive Reviews in Food Science and Food Safety941The finding shows that AI can maintain food quality, including nutritional and sensory properties of dried products, and determines the optimal drying temperature for improving energy efficiency, yield, and life cycle cost.Comprehensive reviews in food science and food safety["B. Yudhistira", "Prakoso Adi", "Rizka Mulyani", "Chao-Kai Chang", "Mohsen Gavahian", "Chang-Wei Hsieh"]2024-08-13T00:00:00 e428f7f6cac2b7b2184332dea38c4da5658ff708PurposeThis paper reports on a workshop discussing the views of the augmentative and alternative communication (AAC) community on the opportunities and risks posed by the integration of artificial intelligence (AI) into voice output communication aid systems. The views of the community on whether a Code of Practice was needed for the use of this new technology were also sought.Design/methodology/approachThis was an explorative, qualitative study in which members of the AAC community attending a session at a UK national conference were invited to discuss the topic, responding to structured questions from the research team. The use of AI for both novel language generation and rate enhancement was discussed within the session.FindingsMany potential opportunities and benefits of AI to AAC users were discussed by the group. Risks associated with new and existing biases in AI language models were raised, as was the need to ensure that outputs generated by AI were authentically authored by users. Whilst there was broad support for the idea of a Code of Practice, questions were posed about how it would be designed and what it should contain.Originality/valueThis study presents a unique insight into the views of the AAC community on the benefits and risks of incorporating AI into AAC systems. The views of the community on the need for a Code of Practice may support how the field moves forward with this complex technology.Journal of Enabling Technologies332This study presents a unique insight into the views of the AAC community on the benefits and risks of incorporating AI into AAC systems, and the views of the community on the need for a Code of Practice may support how the field moves forward with this complex technology.Journal of Enabling Technologies["Tom Griffiths", "Rohan Slaughter", "Annalu Waller"]2024-08-13T00:00:00 3d3f1c07162fb2a68cc4ee9b0bfd175a19fdf196This study investigates the impact of artificial intelligence (AI) on reducing accounting errors from two distinct angles: that of accounting software developers and of certified public accountants. We employ a questionnaire-based approach informed by prior research and validated through pilot testing. Our findings reveal significant benefits for software developers. AI effectively addresses various accounting errors, including tax rate discrepancies, cutoff period inaccuracies, principal violations, concealed transactions, mathematical mistakes, and manipulation errors. However, when considering users, AI’s effectiveness varies. While it successfully mitigates certain errors, such as those related to principles, it falls short in eliminating mathematical errors. This research contributes fresh insights into the role of AI in accounting within emerging markets, enhancing our understanding of its potential and limitations.Journal of Risk and Financial Management702Journal of Risk and Financial Management["Moustafa Al Najjar", "Mohamed Gaber Ghanem", "Rasha Mahboub", "Bilal Nakhal"]2024-08-13T00:00:00 1a94e3acd620d4fedfc28a0ad035d6c825036feeABSTRACT The rapid rise of artificial intelligence (AI) is transforming society; yet, the education of health care providers in this field is lagging. In health care, where AI promises to facilitate diagnostic accuracy, and allow for personalized treatment, bridging the knowledge and skill gaps for providers becomes vital. This article explores the challenges of AI education, such as the emergence of self-proclaimed experts during the pandemic, and the need for comprehensive training in AI language, mechanics, and ethics. It advocates for a new breed of health care professionals who are both practitioners and informaticians, who are capable through initial training or through continuing professional development of harnessing AI's potential. Interdisciplinary collaboration, ongoing education, and incentives are proposed to ensure health care benefits from AI's trajectory. This perspective article explores the hurdles and the imperative of creating educational programming designed specifically to help health care professionals augment their practice with AI.Journal of Continuing Education in the Health Professions91The challenges of AI education, such as the emergence of self-proclaimed experts during the pandemic, and the need for comprehensive training in AI language, mechanics, and ethics are explored.The Journal of continuing education in the health professions["Eleftherios K. Soleas", "Douglas Dittmer", "Ashley Waddington", "Richard van Wylick"]2024-08-13T00:00:00 9148ec64aaac05781fcd1a6bc5cfbc7c359fabd8This research investigates the application of artificial intelligence (AI) in higher education, explicitly evaluating its effectiveness in fostering self-directed learning and self-assessment among university students. The primary objective is to assess how AI can enhance teaching-learning and promote greater learning independence. The study employed data analysis using the Rasch model and examined respondent demographics, with the sample comprising 82% male and 27% female students, predominantly aged 18-23 years. All participants were university students. Findings reveal that AI significantly decreased reliance on human guidance and facilitated increased student independence in learning. The Rasch model analysis highlighted the effectiveness of AI across various levels of learning difficulty, demonstrating its ability to adapt to individual student needs. In summary, AI integration in higher education boosts student independence and adapts to diverse learning challenges, underscoring its value in enhancing educational quality. Consequently, incorporating AI into higher education holds significant promise for advancing self-directed learning and improving academic outcomes.ABSTRAKPenelitian ini mengkaji penerapan kecerdasan buatan (AI) dalam pendidikan tinggi, dengan fokus pada efektivitasnya dalam mendukung pembelajaran mandiri dan penilaian diri di kalangan mahasiswa. Tujuan utama penelitian ini adalah untuk mengevaluasi bagaimana AI dapat meningkatkan proses pengajaran-pembelajaran dan mendorong kemandirian belajar di kalangan mahasiswa. Metode yang digunakan adalah analisis data menggunakan model Rasch dan evaluasi demografis responden, yang terdiri dari 82% mahasiswa laki-laki dan 27% mahasiswa perempuan, dengan sebagian besar berusia 18-23 tahun. Semua responden adalah mahasiswa. Hasil penelitian menunjukkan bahwa penggunaan AI secara signifikan mengurangi ketergantungan pada bimbingan manusia dan meningkatkan kemandirian mahasiswa dalam belajar. Analisis Model Rasch menunjukkan distribusi efektivitas penggunaan AI pada berbagai tingkat kesulitan belajar, mencerminkan kemampuan teknologi ini untuk beradaptasi dengan kebutuhan individu mahasiswa. Kesimpulannya, penerapan AI dalam pendidikan tinggi tidak hanya meningkatkan kemandirian mahasiswa tetapi juga dapat beradaptasi dengan berbagai tingkat kebutuhan dan kesulitan belajar, menjadikannya alat yang penting dalam meningkatkan kualitas pendidikan. Dengan demikian, integrasi AI dalam pendidikan tinggi memiliki potensi besar untuk memajukan pembelajaran mandiri dan efektivitas akademik.JURNAL APARATUR00JURNAL APARATUR["B. P. Gautama", "Nila Indriana Agusti", "Dibias Lazuardi", "Cecep Maman Fathurohman"]2024-08-13T00:00:00 6555f21d630be63be36f7ecbb393ffa01c88b5a2OBJECTIVES This review summarizes the current and potential uses of artificial intelligence (AI) in the current state of clinical microbiology with a focus on replacement of labor-intensive tasks. METHODS A search was conducted on PubMed using the key terms clinical microbiology and artificial intelligence. Studies were reviewed for relevance to clinical microbiology, current diagnostic techniques, and potential advantages of AI in routine microbiology workflows. RESULTS Numerous studies highlight potential labor, as well as diagnostic accuracy, benefits to the implementation of AI for slide-based and macroscopic digital image analyses. These range from Gram stain interpretation to categorization and quantitation of culture growth. CONCLUSIONS Artificial intelligence applications in clinical microbiology significantly enhance diagnostic accuracy and efficiency, offering promising solutions to labor-intensive tasks and staffing shortages. More research efforts and US Food and Drug Administration clearance are still required to fully incorporate these AI applications into routine clinical laboratory practices.American Journal of Clinical Pathology360Artificial intelligence applications in clinical microbiology significantly enhance diagnostic accuracy and efficiency, offering promising solutions to labor-intensive tasks and staffing shortages.American journal of clinical pathology["E. Graf", "Amr Soliman", "Mohamed Marouf", "Anil V. Parwani", "P. Pancholi"]2024-08-13T00:00:00 3f885a8db6e419504673e2e86f55ae1fb68080ccPenerapan artificial intelligence (AI) telah menjadi fokus eksplorasi dalam upaya meningkatkan efisiensi dan produktivitas di lingkungan pendidikan, terutama di Sekolah Dasar Negeri 13 Palembang. Sekolah ini dihadapkan pada sejumlah tantangan, termasuk waktu yang terbatas untuk mengelola tugas administratif, kesulitan dalam personalisasi pembelajaran sesuai dengan kebutuhan siswa, serta pengelolaan data siswa yang optimal untuk meningkatkan pengambilan keputusan pendidikan. Kegiatan pengabdian masyarakat ini dipilih untuk mengatasi permasalahan tersebut dengan mengimplementasikan teknologi AI. Tujuan utama pengabdian ini adalah memperkenalkan dan mengintegrasikan AI dalam sistem penilaian otomatis, personalisasi pembelajaran adaptif berbasis AI, analisis data yang komprehensif dapat mendukung pengambilan keputusan yang lebih baik di tingkat sekolah dasar. Metode pelaksanaan pengabdian mencakup analisis kebutuhan awal melalui survei dan wawancara dengan guru-guru, pengembangan modul pelatihan AI yang terfokus, workshop intensif dengan pendampingan langsung, dan hasil evaluasi pre-test dan post-test menunjukkan peningkatan yang signifikan dalam pemahaman dan penerapan konsep AI di antara para peserta, meskipun beberapa tantangan dalam adopsi teknologi AI masih perlu diatasi. Kesimpulannya, pemanfaatan AI dalam pendidikan menjanjikan solusi inovatif dalam mengatasi permasalahan yang dihadapi para guru dan meningkatkan kualitas pembelajaran yang lebih adaptif serta efisien. Hasil pengabdian ini menegaskan pentingnya terus mendorong pengembangan teknologi AI dalam konteks pendidikan sebagai langkah strategis untuk meningkatkan standar pendidikan di masa depan.Jurnal Abdimas Mandiri00Jurnal Abdimas Mandiri["Evi Yulianti", "I. Pratiwi", "Suryati", "Imelda Saluza", "Dona Marcelina", "Indah Permatasari"]2024-08-13T00:00:00 e657e46e8feef3e5bf1136a8793873e18f88c6dfArtificial Intelligence has become a basic necessity in 20th century with the emergence of new AI tools and APPs. As artificial intelligence has occupied the functionality of major human based activities, its integration is also seeing a transformative shift in political advertising, especially in the creation of speeches and in communicating party agendas to the general public and plying a vital role in opinion making and agenda setting. AI is already being used all over the globe to tailor the political speeches keeping in view the interests of specific voter segments. Recently the use of AI in political advertising has seen a dramatic shift when Pakistan's imprisoned former Prime Minister Imran Khan delivered an AI generated cloned speech that has stunned the nation. The AI-generated audio, which was played during a virtual rally of his Pakistan Tehreek-e-Insaf (PTI) party, featured a voice replicating Khan's, praising his supporters and urging people to vote for PTI in the upcoming general elections. This innovative use of technology has the potential to impact the political landscape for the upcoming general elections in Pakistan. In regard with the case study of Imran Khan’s AI generated speech, the aim of this research is to explore the effectiveness of his message conveyed through AI and its overall impact on the manipulation of public opinion and election campaign. This paper investigates public reactions and evaluates the effectiveness of delivering personalized messages through AI-driven content. This research also addresses the concerns related to privacy, manipulation, and the need for transparency in the development and deployment of AI-driven political advertising. This research study is conducted through a mixed-method approach, combining qualitative and quantitative research. Research data is obtained from the surveys and interviews of political analysts. Based on the results of research, it will be presenting to which extent AI should be used, what ethical considerations should be met and how a regulatory frame work should be there to keep an eye on the AI generated content.Research Journal for Societal Issues20Public reactions are investigated and the effectiveness of delivering personalized messages through AI-driven content is evaluated and the concerns related to privacy, manipulation, and the need for transparency in the development and deployment of AI-driven political advertising are addressed.Research Journal for Societal Issues["Uzma Rubab"]2024-08-13T00:00:00 49697afa6fa38feac4e584da4cc58dc047db95f6: A new view on action video games has been evolving in the sphere of experimental psychology. Providing hand–eye coordination challenges, training with such types of games might allow the enhancement of different cognitive skills. Defining the specific game features that may be beneficial to specific enhancements is yet another challenge. This article presents an analysis on an experiment of a cooperative training session with bots. A method for online learning with a genetic algorithm is used to produce different ways of playing with the bots depending on the players’ achievements. This work also links related studies that provide insights into the beneficial implementation of game features for the training process. It additionally analyzes artificial intelligence methods that might be useful for personal development via a serious game or an action video game.EEPES 2024190An analysis on an experiment of a cooperative training session with bots that produces different ways of playing with the bots depending on the players’ achievements and analyzes artificial intelligence methods that might be useful for personal development via a serious game or an action video game.EEPES 2024["Georgi Tsochev", "Teodor Ukov", "Alexander Rusev", "Maksim Sharabov"]2024-08-13T00:00:00 e5c69987d73dc636ec761e4e6dea5a58f619610bAs drones are getting more and more entangled in our society, more untrained users require the capability to operate them. This scenario is to be achieved through the development of artificial intelligence capabilities assisting the human operator in controlling the Unmanned Aerial System (UAS) and processing the sensor data, thereby alleviating the need for extensive operator training. This paper presents the HADRON project that seeks to develop and test multiple novel technologies to enable human-friendly control of drone swarms. This project is divided into three main parts. The first part consists of the integration of different technologies for the intuitive control of drones, focusing on novice or inexperienced pilots and operators. The second part focuses on the development of a multi-drone system that will be controlled from a command and control station, in which an expert pilot can supervise the operations of the multiple drones. The third part of the project will focus on reducing the cognitive load on human operators, whether they are novice or expert pilots. For this, we will develop AI tools that will assist drone operators with semi-automated real-time data processing.arXiv.org340This paper presents the HADRON project, which seeks to develop and test multiple novel technologies to enable human-friendly control of drone swarms and develop AI tools that will assist drone operators with semi-automated real-time data processing.ArXiv["Ana M. Casado Faul'i", "Mario Malizia", "Ken Hasselmann", "Emile Le Fl'echer", "G. D. Cubber", "Ben Lauwens"]2024-08-13T00:00:00 1a00c60a2354316689c86635d2823c09c03b0bffHuman thinking first appeared through visual art. From the early cave man paintings to this modern-day AI-generated image and deep learning algorithms, the world has developed. Artificial intelligence (AI) has impacted the visual arts in various ways, and it has influenced more and more as a transformative force in many fields. Through this study, the complex link between artificial intelligence and the visual arts is explained by analyzing the effects, the outcomes, and the future paths. The study explores how artificial intelligence has transformed the production, interpretation, and consumption of art. It also shows how AI algorithms are employed by artists to generate imagery. This study, along with the analysis of surveys, experimental initiatives, and different artworks, explains the impact of artificial intelligence in the world of visual arts. It also tells how artificial intelligence has swayed historical mythology and its sociocultural ramifications. With an interdisciplinary approach, this study integrates the understanding of computer science, art history, and cultural studies and offers a subtle analysis of the profound impact of AI on the visual arts. Finally, this comprehensive study provides an insight on how artificial intelligence has influenced and impacted the visual arts and about the evolutionary technological potential of it in the future by providing a deeper and better understanding of the growing complex link between creativity and technology in modern art practices.ShodhKosh Journal of Visual and Performing Arts120This comprehensive study provides an insight on how artificial intelligence has influenced and impacted the visual arts and about the evolutionary technological potential of it in the future by providing a deeper and better understanding of the growing complex link between creativity and technology in modern art practices.ShodhKosh: Journal of Visual and Performing Arts["P. Ezhilmurugan.", "Yashavini E"]2024-08-13T00:00:00 eef3925aaca21e9544831bd034ba01dae11acf05With the development of artificial intelligence (AI) systems, their popularity is growing. Such systems are used in many areas, from customer analytics and search engines to voice assistants and medical research. The tasks assigned to the systems are becoming more and more complex, and therefore artificial intelligence often needs to operate on confidential data; the results of the system's operation can have large-scale consequences. This creates a new problem: the problem of trusted artificial intelligence. The authors set the goal of systematizing knowledge about possible threats and vulnerabilities associated with the use of AI technologies, analyzing existing standards in this area, and also identifying and describing relevant technologies that can increase confidence in the use of intelligent systems.INFORMACIONNYE TEHNOLOGII00The authors set the goal of systematizing knowledge about possible threats and vulnerabilities associated with the use of AI technologies, analyzing existing standards in this area, and also identifying and describing relevant technologies that can increase confidence in the use of intelligent systems.Informacionnye Tehnologii["S. M. Avdoshin", "E. Pesotskaya", "K. A. Patrushev"]2024-08-13T00:00:00 7b2d1b8484deb4519b255b7f7bfbac534117544aArtificial Intelligence (AI) has been around since 1940, namely the first digital computer called Atanasoff Berry Computer (ABC) which aroused the enthusiasm of scientists to develop the idea of making an "electronic brain" or hiding electronic devices in the human brain . With Artificial Intelligence (AI) that can work efficiently, of course, work can be done more easily. The purpose of writing this scientific paper is to explain the use of Artificial Intelligence (AI) in the automotive industry which can make it easier for humans in the future. The method taken by the author is through observations obtained from browsing (searching) through the internet, quoting from various written sources and books that match the theme. Artificial Intelligence (AI) is created in machines and made capable of applying them in real life. Artificial Intelligence (AI) embedded in the steering wheel for self-driving cars complements the driver's abilities when it comes to driving. So that the driver can drive more safely. In the automotive industry, Artificial Intelligence (AI) can be implemented to find a balance point between reactive maintenance (risk of failure) and preventive maintenance (can incur high costs) which uses sensors to track equipment conditions and analyze data on an ongoing basis. An example of the application of Artificial Intelligence (AI) in automotive is in its manufacture by carrying out monitoring processes, errors, downtime and optimizing production operations.International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM)10Artificial Intelligence (AI) embedded in the steering wheel for self-driving cars complements the driver's abilities when it comes to driving and can make it easier for humans in the future.International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM)["Jihan Trie Fadillah"]2024-08-13T00:00:00 d0053461a145f19418c626b47d858efd75622070This article aims to develop a dynamic theory of strategy considering the implications of artificial intelligence (AI) in the modern world. By integrating traditional strategic management theories with the influence of AI, the paper explores the evolving nature of strategic decision-making, competitive advantage, and organizational performance. Through a comprehensive review of existing literature and empirical evidence, the article seeks to propose a framework for understanding and adapting strategy in the context of AI. The implications for business leaders, policymakers, and researchers are discussed, focusing on the need for continuous adaptation and dynamic approaches to strategy in the AI-driven world. Keywords: Artificial Intelligence, AI, Dynamic Theory, Application, Concepts, Exposition, Implications. Aims Research Journal Reference Format: Ademola, O.E. (2024): Dynamic Theory in Artificial Intelligence (AI) – An Exposition. Advances in Multidisciplinary and Scientific Research Journal Vol. 10. No. 2. Pp 1-6 www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V10N3P1Advances in Multidisciplinary &amp; Scientific Research Journal Publication180A dynamic theory of strategy is developed considering the implications of artificial intelligence (AI) in the modern world, focusing on the need for continuous adaptation and dynamic approaches to strategy in the AI-driven world.Advances in Multidisciplinary &amp; Scientific Research Journal Publication["E. O. Ademola"]2024-08-13T00:00:00 b654915c89cc74532da079d8c96ddb5cd1379ae6With the arrival of the fourth industrial Revolution, artificial intelligence technology is profoundly changing the face of the traditional manufacturing industry. This paper focuses on the artificial intelligence technology in the field of mechanical design and manufacturing and automation practice, analyzes the industry development situation, expounds the importance of artificial intelligence to industry transformation and upgrading, and discusses the artificial intelligence in design application, application in information processing and application in fault diagnosis. This paper aims to provide a valuable reference for practitioners and researchers in the field of mechanical design, manufacturing and automation to promote the further development and application of artificial intelligence technology in this field.Artificial Intelligence Technology Research00This paper focuses on the artificial intelligence technology in the field of mechanical design and manufacturing and automation practice, analyzes the industry development situation, expounds the importance of artificial intelligence to industry transformation and upgrading, and discusses the artificial intelligence in design application, application in information processing and application in fault diagnosis.Artificial Intelligence Technology Research["Qi Song"]2024-08-13T00:00:00 31f1f0bec70efaaf48d99cd59f88da57c7c02786Artificial Intelligence is a significant player in the digital content industry. This article talks about how AI improves content and makes it look finer. The research focuses on how digital content has the potential to be both beneficial and harmful. The researcher examines the cruel aspects of AI if content creators have complete access to AI tools. By observing some of the recently viral digital content and identifying how risky it might be if content creators exploit digital content using AI where people will be entertained but no one will know who generated the specific content. The article looks further at how dangerous it would be if digital content creators used AI techniques to produce content that expresses their vengeance against a specific person and the traumas faced by the victim. The purpose of this article is to explore the potential for AI to cause someone to lose both respect and money. The research will give a complete view of how AI could be a threat to the creation of social content and also will give a detailed view of creating creative minds using AI.ShodhKosh Journal of Visual and Performing Arts80The research will give a complete view of how AI could be a threat to the creation of social content and also will give a detailed view of creating creative minds using AI.ShodhKosh: Journal of Visual and Performing Arts["Vijayalakshmi P.", "Vishnu Vardhan V."]2024-08-13T00:00:00 5c5aa3f95679b1155d3f6c322f5230666b98f59dIn recent years, societal development has entered a more critical stage, with continuous improvements in artificial intelligence technology. The integration of engineering disciplines with various fields has led to a trend of collaborative development. Consequently, the current education background of new engineering disciplines faces the challenge of interdisciplinary integration. This paper explores the development path of engineering education reform based on the dual contexts of artificial intelligence and core engineering disciplines, aiming to cultivate a cohort of high-quality engineering talents with creative thinking.Artificial Intelligence Technology Research20The development path of engineering education reform based on the dual contexts of artificial intelligence and core engineering disciplines is explored, aiming to cultivate a cohort of high-quality engineering talents with creative thinking.Artificial Intelligence Technology Research["Bo Feng", "Xiaoqing Sun", "Zhenjiao Jiang", "Xin Xin*"]2024-08-13T00:00:00 21cd3a710cc41a97e12e051d3a84f3845e9d8132With the popularization of artificial intelligence technology in the news industry, more and more people have begun to pay attention to and worry about journalism ethics. On the one hand, AI helps journalists to process and analyze massive information quickly and accurately, which improves the efficiency and quality of journalism and enhances the productivity and competitiveness of the news industry. On the other hand, the application of AI technology has generated many new problems and risks. However, due to its data-driven nature, it is difficult to avoid the existence of data loss, abuse and other problems, which can cause damage to the rights and interests of users and seriously affect the dissemination of false information. The article provides an in-depth analysis of the challenges in the application of artificial intelligence technology in the news industry and puts forward coping strategies for the reference of related personnel.Artificial Intelligence Technology Research00The article provides an in-depth analysis of the challenges in the application of artificial intelligence technology in the news industry and puts forward coping strategies for the reference of related personnel.Artificial Intelligence Technology Research["Dapeng Ning", "Zhiqing Fu"]2024-08-13T00:00:00 b0ef51655e7453e7392cc7f7949e91249b952418The wide application of artificial intelligence technology has had a profound impact on all walks of life. Education is no exception, especially the teaching of management accounting. As an important basis for enterprise decision-making, management accounting s teaching content, methods and means need to keep pace with The Times to adapt to the development of artificial intelligence era. Based on this, the thesis mainly discusses and analyzes the teaching reform of management accounting in universities from the perspective of artificial intelligence.Artificial Intelligence Technology Research00This thesis mainly discusses and analyzes the teaching reform of management accounting in universities from the perspective of artificial intelligence.Artificial Intelligence Technology Research["Tianxing Dai"]2024-08-13T00:00:00 0f45762df8665acd81d60549af2cfedd655a346fA growing number of countries are concerned about the reliability of environmental indicators; as a result, there is a pressing need to find ways to improve ecological welfare on a global scale. This study investigates the dynamic linkages among CO2 emissions, AI, economic policy uncertainty (EPU), and renewable energy consumption. To analyze these relationships empirically, this study used panel data for East Asian and Pacific countries from 2000 to 2023. This study used fully modified ordinary least squares (FMOLSs), dynamic ordinary least squares (DOLSs), Hausman fixed effects (FEs) and random effects (REs), the generalized method of moments (GMM), and variance decomposition tests. This study’s results show that AI has a positive relationship with CO2 emissions in terms of the benchmark regression, while it shows minimal impact on CO2 emissions according to the variance decomposition test. Similarly, economic policy uncertainty shows a strong positive relationship with CO2 emissions through benchmark regression FEs and REs, GMM, and the variance decomposition test. An increase in EPU will positively affect CO2 emissions. Renewable energy consumption has a strong negative impact on CO2 emissions in East Asian and Pacific countries. These findings reveal that a unit increase in renewable energy consumption will decrease CO2 emissions. Based on the results of this study, it is suggested that policy certainty and an upsurge in renewable energy consumption are essential for environmental upgrading. In contrast, adopting AI has no robust effect on ecological degradation (CO2 emissions). East Asian and Pacific countries need to focus on the adoption of renewables, as well as the control of economic policy uncertainty. While AI in East Asian and Pacific countries is still in the initial stage of adoption, policy formation is essential to overcome the possible carbon footprint of AI in the short term.Energies661Energies["Salman Ali Shah", "Xingyi Ye", "Bo Wang", "Xiangjun Wu"]2024-08-13T00:00:00 05b2219754b38985f74321ddc6dffc27a2955f73Education and Information Technologies : Official Journal of the IFIP technical committee on Education221Education and Information Technologies["Xia Zhang", "Kingsley Obiajulu Umeanowai"]2024-08-13T00:00:00 57e495437d91c1af325c12f32f15da340366a933Current Issues in Tourism582Current Issues in Tourism["Kim-Lim Tan", "Peter S. Hofman", "Nurhafihz Noor", "S. Tan", "Ivy S. H. Hii", "T. Cham"]2024-08-13T00:00:00 f64c36eb9e97412bd48d259e8e5bb056aa7b9d3bThe paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes.Machines330The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals.Machines["A. Massaro"]2024-08-13T00:00:00 184eef9e0b221901272535bcddf8b713e2b36123Neurology00Neurology[]2024-08-13T00:00:00 845982b4b0c92bd577e76ddbc09ddc35232c64f9Exploratory Research in Clinical and Social Pharmacy220The AI model exhibited promise in recommending appropriate medications for individual patients, and the issue of AI lag time in responding to sudden changes could be addressed by manually adjusting decision trees, a task not feasible with neural networks.Exploratory Research in Clinical and Social Pharmacy["Michael B\u00fccker", "Kreshnik Hoti", "Olaf Rose"]2024-08-13T00:00:00 f436cea0caadaf372550b16be2b06fb5a3a337f3Journal of the American Society of Nephrology70Journal of the American Society of Nephrology : JASN["Lili Chan", "G. Nadkarni"]2024-08-13T00:00:00 4ed147faea2645283d305efb32bb642087ac6d35Health Care Management Science390A framework for studying the factors that affect LAMA in EDs is proposed and shows that best model has an area under the curve (AUC) and sensitivity of 76% and 82%, respectively, and the best model was explained using SHAP method.Health Care Management Science["Abdulaziz Ahmed", "Khalid Y Aram", "S. Tutun", "D. Delen"]2024-08-13T00:00:00 96518981bcbffa3a2a3c064bafce0eaf167214a6Chemical Monthly390Monatshefte für Chemie - Chemical Monthly["R. Chalupa", "K. Nesm\u011br\u00e1k"]2024-08-13T00:00:00 d422c6531545da0f73109a202bc1c0ae1a4851c9International Symposium on Artificial Intelligence in Medical Sciences50{"pages": "158-163"}["Lei Zhai"]2024-08-13T00:00:00 b50cbce3e223cf917fb518ea0fea79fa84b06b44International Conference on Optoelectronics and Microelectronics002024 9th International Conference on Mechatronics Engineering (ICOM)["Mohammed Yeasin"]2024-08-13T00:00:00 2d2b712662b4f06c3799c59a13a4590d2370116cPurposeExploring trust's impact on AI project success. Companies can't leverage AI without employee trust. While analytics features like speed and precision can build trust, they may also lower it during implementation, leading to paradoxes. This study identifies these paradoxes and proposes strategies to manage them.Design/methodology/approachThis paper applies a grounded theory approach based on 35 interviews with senior managers, users, and implementers of analytics solutions of large European companies.FindingsIt identifies seven paradoxes, namely, knowledge substitution, task substitution, domain expert, time, error, reference, and experience paradoxes and provides some real-life examples of managing them.Research limitations/implicationsThe limitations of this paper include its focus on machine learning projects from the last two years, potentially overlooking longer-term trends. The study's micro-level perspective on implementation projects may limit broader insights, and the research primarily examines European contexts, potentially missing out on global perspectives. Additionally, the qualitative methodology used may limit the generalizability of findings. Finally, while the paper identifies trust paradoxes, it does not offer an exhaustive exploration of their dynamics or quantitative measurements of their strength.Practical implicationsSeveral tactics to tackle trust paradoxes in AI projects have been identified, including a change roadmap, data “load tests”, early expert involvement, model descriptions, piloting, plans for machine-human cooperation, learning time, and a backup system. Applying these can boost trust in AI, giving organizations an analytical edge.Social implicationsThe AI-driven digital transformation is inevitable; the only question is whether we will lead, participate, or fall behind. This paper explores how organizations can adapt to technological changes and how employees can leverage AI to enhance efficiency with minimal disruption.Originality/valueThis paper offers a theoretical overview of trust in analytics and analyses over 30 interviews from real-life analytics projects, contributing to a field typically dominated by statistical or anecdotal evidence. It provides practical insights with scientific rigour derived from the interviews and the author's nearly decade-long consulting career.Journal of Organizational Change Management270How organizations can adapt to technological changes and how employees can leverage AI to enhance efficiency with minimal disruption is explored, contributing to a field typically dominated by statistical or anecdotal evidence.Journal of Organizational Change Management["Zolt\u00e1n Bakonyi"]2024-08-13T00:00:00 7f1ce93cb75e96f69814aa9ad167bc2922a0c844Technology Analysis &amp; Strategic Management390Technology Analysis &amp; Strategic Management["De-Zhong Wang", "Xia Cao"]2024-08-13T00:00:00 6af59d0a03e3760136548313074eee77f5fa1d5cNo abstract availableSri Lanka Journal of Medicine00Sri Lanka Journal of Medicine["S. Samarasinghe", "A. Samarasinghe"]2024-08-13T00:00:00 e63e17df7ed9fed68c555ed720cbc088b187a1c0Pervasive developmental disorder or Autism Pervasive Development Disorder (APDD) is a disorder with psychomotor, cognitive, sensory, interpersonal, and intrapersonal conditions that are expected to tend to experience delays or not develop appropriately during childhood. Around 58.7 per 10,000 children in Indonesia experience pervasive developmental disorders. Children with pervasive disorders cannot interact well with others. They tend to be attracted to various inanimate objects, due to severe communication limitations and strong obsessive desires. A neurodevelopmental disorder characterized by a lack of social interaction, social interaction (verbal and nonverbal), and repetitive behavior or restricted interests is commonly called pervasive autism. developmental disorder (APDD). Many researchers and doctors have developed autism treatments by teaching children with APPD several approaches or methods to develop their social and mental skills, including the use of technology. Aurel's study (Autism Robotics Experience for Learners) is based on deep-based artificial intelligence applications learning so that the robot can be directly controlled for movement and audio-visual systems. Aurel was developed to provide interventions for children on the autism spectrum (APDD). Aurel's is designed to create movements and sounds that help children with APPD develop social skills. Aurel matches the characteristics of autistic children (APDD), has eyesight, and likes technology. In doing so, Aurel uses an early intervention method or approach. Early intervention is therapy for children from birth to 3 years of age for children with special needs and growth and development disorders. Aurel can carry out activities to observe the development of early childhood children to optimize skills according to their needs.International Symposium on Consumer Technologies130Aurel is designed to create movements and sounds that help children with APPD develop social skills, and matches the characteristics of autistic children (APDD), has eyesight, and likes technology.2024 IEEE International Symposium on Consumer Technology (ISCT)["M. S. Zuhrie", "L. Anifah", "P. Rusimamto"]2024-08-13T00:00:00 75cc0172f929ce48b89d4f467641ae49329337a5International Symposium on Artificial Intelligence in Medical Sciences50{"pages": "27-34"}["Bocheng Cui", "Shanshan Li", "Xin Jin"]2024-08-13T00:00:00 a19f9fec6d7c7742bc5e59c1e6bbf54f8959e89c00["Amit Awasthi", "Kanhu Charan Pattnayak", "Gaurav Dhiman", "Pushp Raj Tiwari"]2024-08-13T00:00:00 ddb00d5f536b59ca809e68bf9d9a5500e629f290The author is Group Vice President of Digital Marketing and Digital Transformation at Thales, and a Lecturer at INSEAD and elsewhere. He discusses a concept he originated, Artificial Integrity. This involves how society can build fair, just, and equitable principles into the fabric of artificial intelligence, as the latter continues to move and evolve at lightning speed, in ways most people cannot understand. He describes his holistic framework of principles “for continuous vigilance and collaborative efforts in shaping AI’s role in society, ensuring it contributes positively to human progress.” He details 17 different principles, which “provide a framework aimed at establishing a global, constitution‐like foundation to govern AI.” They are, in his words: Protection of Human Identity and Dignity, Safety and Well‐being, Obedience to Human Orders, Transparency and Explainability, Confidentiality and Data Protection, Regulation and Human Decision‐Making, Responsibility in Case of Failure, Self‐protection and Updating, Shared Responsibility, Fairness and Inclusion, Protection of Meaningful and Significant Jobs, Ethical and Cultural Respect, Environmental Impact, International Collaboration, Respect for State Sovereignty, Economic Incentive for Societal Development, and Education and Awareness.Leader to Leader00The author describes his holistic framework of principles, which “provide a framework aimed at establishing a global, constitution‐like foundation to govern AI,” for continuous vigilance and collaborative efforts in shaping AI’s role in society, ensuring it contributes positively to human progress.Leader to Leader["Hamilton Mann"]2024-08-13T00:00:00 efeb85b05682332c8f8215bbe9abe911616b8340This study examines the impact of Generative Artificial Intelligence (GenAI) on academic research, focusing on its application to qualitative and quantitative data analysis. As GenAI tools evolve rapidly, they offer new possibilities for enhancing research productivity and democratising complex analytical processes. However, their integration into academic practice raises significant questions regarding research integrity and security, authorship, and the changing nature of scholarly work. Through an examination of current capabilities and potential future applications, this study provides insights into how researchers may utilise GenAI tools responsibly and ethically. We present case studies that demonstrate the application of GenAI in various research methodologies, discuss the challenges of replicability and consistency in AI-assisted research, and consider the ethical implications of increased AI integration in academia. This study explores both qualitative and quantitative applications of GenAI, highlighting tools for transcription, coding, thematic analysis, visual analytics, and statistical analysis. By addressing these issues, we aim to contribute to the ongoing discourse on the role of AI in shaping the future of academic research and provide guidance for researchers exploring the rapidly evolving landscape of AI-assisted research tools and research.arXiv.org452This study examines the impact of Generative Artificial Intelligence on academic research, focusing on its application to qualitative and quantitative data analysis, and provides insights into how researchers may utilise GenAI tools responsibly and ethically.ArXiv["Mike Perkins", "Jasper Roe"]2024-08-13T00:00:00 b55545187cb8caf42bb82405af9a7d09d62f1b58Nature Communications382This work demonstrates end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results of the AI-based radiology pipelines.Nature Communications["Dennis Bontempi", "Leonard Nuernberg", "Suraj Pai", "Deepa Krishnaswamy", "V. Thiriveedhi", "A. Hosny", "Raymond H. Mak", "Keyvan Farahani", "R. Kikinis", "Andrey Fedorov", "Hugo J W L Aerts"]2024-08-13T00:00:00 1fc9bd4e2d2b854f851d27cf8e7b5e02efcee1f3The debate surrounding the permissibility of generative artificial intelligence (AI) tools in legal education has garnered widespread attention. However, this discourse has largely oscillated between the advantages and disadvantages of generative AI usage whilst failing to fully consider how the uptake of these tools relates to the fundamental objectives of legal education. This article contributes to the current debate by positing that since the primary aim of legal education is the preparation of legal professionals and the development of legal research, generative AI must be holistically integrated into the dominant approaches to legal teaching. This stems from the fact that the legal profession will increasingly rely on generative AI in its daily work. Therefore, AI literacy will emerge as a critical professional skill in the legal realm. Against this background, this article further argues that the integration of AI into the legal curriculum should be addressed by diversifying assessment strategies, emphasizing the importance of academic integrity and making resources on the ethical use of AI available to both students and academic staff in law schools.Asian Journal of Legal Education02The integration of AI into the legal curriculum should be addressed by diversifying assessment strategies, emphasizing the importance of academic integrity and making resources on the ethical use of AI available to both students and academic staff in law schools.Asian Journal of Legal Education["Sara Migliorini", "Jo\u00e3o Ilh\u00e3o Moreira"]2024-08-13T00:00:00 d1718bda3719edf47d7e8b7017db37a1107e6c9ePurposeThe purpose of this article is to demonstrate how the creativity technique SCAMPER and generative Artificial Intelligence (Gen-AI) are linked in the formative process for the solution of business problems by groups of students from low socio-economic levels of a public university in the city of San José de Cucuta, Colombia.Design/methodology/approachAn analysis of the contributions of generative artificial intelligence was developed and the knowledge gaps related to advanced artificial intelligence-based linguistic models in the education sector were mentioned. Subsequently, views on the Colombian context of science, technology and innovation were developed. Finally, the experience in the application of teaching-learning strategies through the use of Open AI’s creativity technique and ChatGPT was highlighted.FindingsThe findings highlight the complementarity of generative artificial intelligence and the SCAMPER creativity technique in the development of innovation capabilities. While human creativity highlights emotional aspects. Artificial intelligence consolidates procedural aspects and ideas focused on the primary activities of the value chain.Practical implicationsThe implementation of the hybrid model in the classroom can lead to the development of new capabilities by marginalized groups immersed in the educational system. The potential positive impact of Gen-AI and human creativity will be reflected in the optimization of response times and the search for solutions to problems in different environments.Originality/valueThis opinion article highlights the implementation of AI in a Higher Education Institution located in the frontier zone of San José de Cucuta, Colombia. In addition, it involves actors of the educational system whose economic income is low. Finally, it highlights the positive impact of the integration of creativity techniques and the use of generative artificial intelligence in the classroom, highlighting the use of hybrid models (Man-Machine).Journal of Enabling Technologies201How the creativity technique SCAMPER and generative Artificial Intelligence (Gen-AI) are linked in the formative process for the solution of business problems by groups of students from low socio-economic levels of a public university in the city of San José de Cucuta, Colombia is demonstrated.Journal of Enabling Technologies["Juan Ernesto P\u00e9rez P\u00e9rez"]2024-08-13T00:00:00 0710f4d42f29bae38bb413097f92a8ac6e04227fThe rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers. OpenResearcher is built based on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. Moreover, we develop various tools for OpenResearcher to understand researchers’ queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine these answers. OpenResearcher can flexibly use these tools to balance efficiency and effectiveness. As a result, OpenResearcher enables researchers to save time and increase their potential to discover new insights and drive scientific breakthroughs. Demo, video, and code are available at: https://github.com/GAIR-NLP/OpenResearcher.Conference on Empirical Methods in Natural Language Processing371This work introduces OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to accelerate the research process by answering diverse questions from researchers, built based on Retrieval-Augmented Generation to integrate Large Language Models with up-to-date, domain-specific knowledge.ArXiv["Yuxiang Zheng", "Shichao Sun", "Lin Qiu", "Dongyu Ru", "Jiayang Cheng", "Xuefeng Li", "Jifan Lin", "Binjie Wang", "Yun Luo", "Renjie Pan", "Yang Xu", "Qingkai Min", "Zizhao Zhang", "Yiwen Wang", "Wenjie Li", "Pengfei Liu"]2024-08-13T00:00:00 033cf28d8bdd6fb04e475f028d65c978963596b0Explainable artificial intelligence (XAI) is proposed to improve transparency and performance by providing information about AI’s limitations. Specifically, XAI could support appropriate behavior in cases where AI errors occur due to less training data. These error-prone cases might be salient (pop-out) because of their naturally rarer occurrence. The current study investigated how this pop-out effect influences explainability’s effectiveness on trust and dependence. In an online experiment, participants ( N  = 128) estimated the contamination degree of bacterial stimuli. The lower occurrence of error-prone stimuli was indicated by one of two colors. Participants either knew about the error-prone color (XAI) or not (nonXAI). Contrary to earlier research without salient error-prone trials, explainability did not help participants follow correct recommendations in non-error-prone trials but helped them correct AI’s errors in error-prone trials. However, explainability still led to over-correction in correct error-prone trials. This poses the challenge of implementing explainability while mitigating its negative effects.Proceedings of the Human Factors and Ergonomics Society Annual Meeting141Contrary to earlier research without salient error-prone trials, explainability did not help participants follow correct recommendations in non-error-prone trials but helped them correct AI’s errors in error-prone trials, however, explainability still led to over-correction in correct error-prone trials.Proceedings of the Human Factors and Ergonomics Society Annual Meeting["Pawinee Pithayarungsarit", "Tobias Rieger", "L. Onnasch", "Eileen Roesler"]2024-08-13T00:00:00 c45a0fb85b29fd81a8da4b5192ef77592bc2fe3fArtificial intelligence (AI) has brought about transformative changes across numerous industries, digital media production included. Through its implementation, automated workflows and groundbreaking creative solutions have become integral components of modern production methods. The potential of generative AI to streamline workflows and enhance creativity has garnered attention across industries. However, the adoption of generative AI in digital media production is not uniform, and understanding its uptake from diverse perspectives is essential for comprehensive insights. Rural communities, characterized by distinct socio-economic contexts and infrastructural limitations, present a unique lens through which to examine the adaptation of generative AI. This research endeavours to investigate the exploration and adoption of generative AI in digital media production, specifically focusing on rural communities. By employing a mixed-method approach involving surveys and in-depth interviews, the study seeks to evaluate the awareness, usage, and perception of generative AI applications among rural students. Assessing these factors not only sheds light on the current landscape but also offers implications for future interventions and policies aimed at fostering AI literacy and innovation in rural areas. This research inquiry seeks to enhance our comprehension of AI adoption dynamics in rural settings. By doing so, it aims to support informed decision-making and targeted interventions to effectively utilize generative AI's transformative capabilities in digital media production.ShodhKosh Journal of Visual and Performing Arts70This research endeavours to investigate the exploration and adoption of generative AI in digital media production, specifically focusing on rural communities, to support informed decision-making and targeted interventions to effectively utilize generative AI's transformative capabilities in digital media production.ShodhKosh: Journal of Visual and Performing Arts["Pandiyaraj V.", "Raja N."]2024-08-13T00:00:00 447b41c77bbbc2ea58554750389302d57e8ec348Blind people use artificial intelligence-enabled visual assistance technologies (AI VAT) to gain visual access in their everyday lives, but these technologies are embedded with errors that may be difficult to verify non-visually. Previous studies have primarily explored sighted users' understanding of AI output and created vision-dependent explainable AI (XAI) features. We extend this body of literature by conducting an in-depth qualitative study with 26 blind people to understand their verification experiences and preferences. We begin by describing errors blind people encounter, highlighting how AI VAT fails to support complex document layouts, diverse languages, and cultural artifacts. We then illuminate how blind people make sense of AI through experimenting with AI VAT, employing non-visual skills, strategically including sighted people, and cross-referencing with other devices. Participants provided detailed opportunities for designing accessible XAI, such as affordances to support contestation. Informed by disability studies framework of misfitting and fitting, we unpacked harmful assumptions with AI VAT, underscoring the importance of celebrating disabled ways of knowing. Lastly, we offer practical takeaways for Responsible AI practice to push the field of accessible XAI forward.International ACM SIGACCESS Conference on Computers and Accessibility1410This work describes errors blind people encounter, highlighting how AI VAT fails to support complex document layouts, diverse languages, and cultural artifacts, and illuminate how blind people make sense of AI through experimenting with AI VAT, employing non-visual skills, strategically including sighted people, and cross-referencing with other devices.ArXiv["Rahaf Alharbi", "P. Lor", "Jaylin Herskovitz", "S. Schoenebeck", "Robin Brewer"]2024-08-13T00:00:00 7714596dffdf9c2f1a93f584fb2cb76598afdae1Humanities and Social Sciences Communications5844The estimated coefficients of the benchmark model show that AI significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the most substantial impact observed in energy transitions, followed by ecological footprint reduction and carbon emissions reduction.Humanities and Social Sciences Communications["Qiang Wang", "Yuanfan Li", "Rongrong Li"]2024-08-14T00:00:00 03492c2bdc51aa6633b40aaa827c43007a4ac463The risks posed by Artificial Intelligence (AI) are of considerable concern to academics, auditors, policymakers, AI companies, and the public. However, a lack of shared understanding of AI risks can impede our ability to comprehensively discuss, research, and react to them. This paper addresses this gap by creating an AI Risk Repository to serve as a common frame of reference. This comprises a living database of 777 risks extracted from 43 taxonomies, which can be filtered based on two overarching taxonomies and easily accessed, modified, and updated via our website and online spreadsheets. We construct our Repository with a systematic review of taxonomies and other structured classifications of AI risk followed by an expert consultation. We develop our taxonomies of AI risk using a best-fit framework synthesis. Our high-level Causal Taxonomy of AI Risks classifies each risk by its causal factors (1) Entity: Human, AI; (2) Intentionality: Intentional, Unintentional; and (3) Timing: Pre-deployment; Post-deployment. Our mid-level Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental, and (7) AI system safety, failures, & limitations. These are further divided into 23 subdomains. The AI Risk Repository is, to our knowledge, the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database. This creates a foundation for a more coordinated, coherent, and complete approach to defining, auditing, and managing the risks posed by AI systems.AGI - Artificial General Intelligence - Robotics - Safety &amp; Alignment010The AI Risk Repository is the first attempt to rigorously curate, analyze, and extract AI risk frameworks into a publicly accessible, comprehensive, extensible, and categorized risk database that creates a foundation for a more coordinated, coherent, and complete approach to defining, auditing, and managing the risks posed by AI systems.ArXiv["P. Slattery", "Alexander K. Saeri", "Emily A. C. Grundy", "Jessica Graham", "Michael Noetel", "Risto Uuk", "James Dao", "Soroush Pour", "Stephen Casper", "Neil Thompson"]2024-08-14T00:00:00 4bdc7fa9fb36dcf44af10fc5d2d4cb524431068e Generative artificial intelligence (gen AI) introduces novel opportunities to strengthen central banks’ cyber security but also presents new risks. This article uses data from a unique survey among cyber security experts at major central banks to shed light on these issues. Responses reveal that most central banks have already adopted or plan to adopt gen AI tools in the context of cyber security, as perceived benefits outweigh risks. Experts foresee that AI tools will improve cyber threat detection and reduce response time to cyber attacks. Yet gen AI also increases the risks of social engineering attacks and unauthorized data disclosure. To mitigate these risks and harness the benefits of gen AI, central banks anticipate a need for substantial investments in human capital, especially in staff with expertise in both cyber security and AI programming. Finally, while respondents expect gen AI to automate various tasks, they also expect it to support human experts in other roles, such as oversight of AI models.Journal of Financial Regulation02While respondents expect gen AI to automate various tasks, they also expect it to support human experts in other roles, such as oversight of AI models.Journal of Financial Regulation["I\u00f1aki Aldasoro", "Sebastia\u00f1 Doerr", "L. Gambacorta", "Sukhvir Notra", "Tommaso Oliviero", "David Whyte"]2024-08-14T00:00:00 d05d19a04d7ade3a48a48532a9aeb8f937482613In the financial field, the application of artificial intelligence (AI) has become a key force driving industry innovation and transformation. However, there is still a significant gap in the comprehensive and systematic application of artificial intelligence in the financial field. This article reviews the research progress of AI in the financial field to fill this gap, aiming to provide a comprehensive perspective on understanding how AI reshapes the financial industry and its future development potential, and to provide some reference and inspiration for subsequent research. This article discusses the development stages of AI in the financial field, including the initial stage, penetration stage, and integration stage. Then, in-depth research was conducted on the application of AI in various aspects of the financial field, such as consumer end, investment end, financing end, etc. Finally, the challenges and future prospects faced by AI in the financial field were discussed. This article finds that AI has been deeply integrated and widely applied in the financial field, creating enormous value and promoting the intelligence, personalization, and customization of financial services. However, the challenges that come with it, such as data privacy and algorithm transparency, are new directions that require our continued attention and research. Therefore, this article not only provides us with a comprehensive perspective on the application of AI in the financial field, but also points out future research directions, so as to promote further development in this field.Advances in Economics, Management and Political Sciences01It is found that AI has been deeply integrated and widely applied in the financial field, creating enormous value and promoting the intelligence, personalization, and customization of financial services.Advances in Economics, Management and Political Sciences["Xiaohan Cao"]2024-08-14T00:00:00 770e1131c8112fa3930cca4ff3ed6691914a2832Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.Journal of Infrastructure Policy and Development401Legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.Journal of Infrastructure, Policy and Development["Wenyu Wang", "Zhilang Xu", "Zichun Xu"]2024-08-14T00:00:00 5baf8e4697d6a2423a732e2a31003ff4cf19c888Artificial Intelligence (AI) has become one of the most prominent topics in public policy and administration studies over the last years. Despite the attention to AI in this field isn’t entirely new, the universality of these group of technologies has radically increased the attention of scholars around the globe. This expansion of AI in the public sector entails the exploration of renovated foundations of analysis, not only to understand the novelty of these technologies, but also to connect these processes of adoption and implementation with other debates in public policy and administration. To do so, in this article we debate the need of an analytical framework of AI in the public sector based on the three levels of public administration: macro, meso, and micro. Also, we review the state-of-the-art in the field using the articles presented in the special issue on Artificial Intelligence and Public Administration: Actors, Governance, and Policy. Form here, we propose studying AI using a combination of macro, meso, and micro levels of public administration. We assume this will help to broadly apprehend how and why people, policies, and institutions interrelate with AI in public sector settings, and which effects can be expected from these processes in public administration.Public Policy and Administration82This article debates the need of an analytical framework of AI in the public sector based on the three levels of public administration: macro, meso, and micro, and proposes studying AI using a combination of macro, meso, and micro levels of public administration.Public Policy and Administration["J. I. Criado", "Rodrigo Sandoval-Almaz\u00e1n", "J. R. Gil-Garcia"]2024-08-14T00:00:00 84c804e47ea10e3f932c9ccb4f816cd31c72e164OBJECTIVE The automated interpretation of clinical electroencephalograms (EEGs) using artificial intelligence (AI) holds the potential to bridge the treatment gap in resource-limited settings and reduce the workload at specialized centers. However, to facilitate broad clinical implementation, it is essential to establish generalizability across diverse patient populations and equipment. We assessed whether SCORE-AI demonstrates diagnostic accuracy comparable to that of experts when applied to a geographically different patient population, recorded with distinct EEG equipment and technical settings. METHODS We assessed the diagnostic accuracy of a "fixed-and-frozen" AI model, using an independent dataset and external gold standard, and benchmarked it against three experts blinded to all other data. The dataset comprised 50% normal and 50% abnormal routine EEGs, equally distributed among the four major classes of EEG abnormalities (focal epileptiform, generalized epileptiform, focal nonepileptiform, and diffuse nonepileptiform). To assess diagnostic accuracy, we computed sensitivity, specificity, and accuracy of the AI model and the experts against the external gold standard. RESULTS We analyzed EEGs from 104 patients (64 females, median age = 38.6 [range = 16-91] years). SCORE-AI performed equally well compared to the experts, with an overall accuracy of 92% (95% confidence interval [CI] = 90%-94%) versus 94% (95% CI = 92%-96%). There was no significant difference between SCORE-AI and the experts for any metric or category. SCORE-AI performed well independently of the vigilance state (false classification during awake: 5/41 [12.2%], false classification during sleep: 2/11 [18.2%]; p = .63) and normal variants (false classification in presence of normal variants: 4/14 [28.6%], false classification in absence of normal variants: 3/38 [7.9%]; p = .07). SIGNIFICANCE SCORE-AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset.Epilepsia271SCORE-AI achieved diagnostic performance equal to human experts in an EEG dataset independent of the development dataset, in a geographically distinct patient population, recorded with different equipment and technical settings than the development dataset.Epilepsia["D. Mansilla", "Jesper Tveit", "Harald Aurlien", "T. Avigdor", "Victoria Ros-Castell\u00f3", "Alyssa Ho", "C. Abdallah", "Jean Gotman", "S. Beniczky", "B. Frauscher"]2024-08-14T00:00:00 b8730e169a01ff3d509bbb2dfd920a1cb12317e7Artificial Intelligence (AI) is increasingly being adopted across many domains such as transport, healthcare, defence and sport, with football codes no exception. Though there is a range of potential benefits of AI, concern has also been expressed regarding potential risks. An important first step in ensuring that AI applications in football are usable, beneficial, safe and ethical is to understand the current range of applications, the AI models adopted and their proposed functions. This systematic review aimed to identify different applications of AI across football codes to synthesise current knowledge and determine whether potential risks are being considered. The systematic review included 190 peer-reviewed articles. Nine areas of application were found ranging from athlete evaluation and event detection to match outcome prediction and injury detection and prediction. In total, 27 different AI models were identified, with artificial neural networks the most frequently applied. Five AI assessment metrics were identified including specificity, recall, precision, accuracy and F1-score. Four potential risks were identified, concerning data security, usability, data biases and inappropriate athlete load management. It is concluded that, though a wide range of AI applications currently exist, further work is required to develop AI for football and identify and manage potential risks.Jurnal sport science1981It is concluded that, though a wide range of AI applications currently exist, further work is required to develop AI for football and identify and manage potential risks.Journal of sports sciences["Isaiah Elstak", "P. Salmon", "S. McLean"]2024-08-14T00:00:00 a0f36696cc4f7b6e9f9be4a3fa5a0551ba57d723The use of educational technologies in English language teaching (ELT) has become widely accepted in the post-pandemic era, and, for better or worse, some of these technologies rely on artificial intelligence (AI). Artificial intelligence is on the rise as witnessed in the growing popularity of dialogue systems like Alexa. However, some key criteria still need to be met before it can serve as a substitute for a real-life language teacher: spontaneity, creativity and shared knowledge. As an area of technological growth and increasing financial investment, we are likely to see more AI-driven technologies in teaching and learning in the post-pandemic ELT world. ELT will not be immune to this development, and it behooves us as language teachers to be familiar with AI's current benefits and challenges, so that we can better prepare for that future. This article describes how AI is currently used in ELT and explores some of the opportunities and challenges that AI can provide for learners, teachers and institutions.Contemporaneity of Language and Literature in the Robotized Millennium30How AI is currently used in ELT is described and some of the opportunities and challenges that AI can provide for learners, teachers and institutions are explored.Contemporaneity of Language and Literature in the Robotized Millennium[]2024-08-14T00:00:00 fe75c8f5ef639e1a5c822934e2f6b4bace4b0f27At present, artificial intelligence (AI) technology, as a disruptive and frontier technology, is changing people's production and lifestyle with the cross combination of other scientific fields. Under the background of Green and low‐carbon transition in energy, the construction of new type power systems (NTPS) is the future direction of the transformation and development of the power industry. AI is an important supporting technology for the digital transformation of the power industry, which can accelerate the construction of NTPS and new energy systems. This article provides the author's viewpoints on application of AI technologies in NTPS, mainly involving the development of electric power AI and the main problems it currently faced. The discussion on the bottlenecks of AI application will be focused on data and models. Some future research directions are also presented.Energy Internet130The author's viewpoints on application of AI technologies in NTPS, mainly involving the development of electric power AI and the main problems it currently faced are provided.Energy Internet["Shixiong Fan", "Jianbo Guo", "Shicong Ma", "Guozheng Wang", "Dongqi Li", "Zening Zhao"]2024-08-14T00:00:00 f4074cb47968b5c316521c54c70883e8d90f74e6Data support is already driving the development of artificial intelligence. But it cannot solve the semantic problem of artificial intelligence. This requires improving the semantic understanding ability of artificial intelligence. Therefore, a question answering system based on semantic problem processing is proposed in this study. The question answering system utilizes an improved unsupervised method to extract keywords. This technology integrates the semantic feature information of text into traditional word graph model algorithms. On this basis, semantic similarity information is used to calculate and allocate the initial values and edge weights of each node in the PageRank model. And corresponding restart probability matrices and transition probability matrices are constructed for iterative calculation and keyword extraction. Simultaneously, an improved semantic dependency tree was utilized for answer extraction. The improved keyword extraction method shows a decreasing trend in P and R values. The improved answer extraction method has a maximum P-value of 0.876 in the training set and 0.852 in the test set. In a question answering system based on keyword and answer extraction, the improved method has lower loss function values and running time. The improved method has a larger area under ROC. The results of the validation analysis confirm that the improved method in this experiment has high accuracy and robustness when dealing with semantic problems.J. Comput. Methods Sci. Eng.230In a question answering system based on keyword and answer extraction, the improved method has lower loss function values and running time and the improved method has a larger area under ROC.J. Comput. Methods Sci. Eng.["Weijun Bu"]2024-08-14T00:00:00 b59770d7199565aaefecfff73de3f609e20cadb6Heart Failure Reviews850A systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding.Heart Failure Reviews["P. \u0141ajczak", "Kamil J\u00f3\u017awik"]2024-08-14T00:00:00 1fe9c1f7eec79fff9c856e3a1ebfd95ad271b3ffThe artificial intelligence standard knowledge computing engine system for grid equipment construction was applied to realize the functions of real-time monitoring, fault diagnosis, predictive analysis and optimization decision-making for grid equipment. The approach starts with overall system engineering, starting with grid equipment data acquisition and initial processing, followed by feature extraction and model training. In terms of model selection, according to the features and requirements of power grid equipment, suitable artificial intelligence models and algorithms were selected. The model was optimized, and the model parameters were adjusted in the study. Ultimately, an artificial intelligence standard knowledge computing engine system was built to realize the analysis and decision support of power grid equipment. Through the analysis and prediction of grid equipment data, the system could accurately identify the equipment faults, states and behaviors; and provide timely decision support.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)130An artificial intelligence standard knowledge computing engine system was built to realize the analysis and decision support of power grid equipment, which could accurately identify the equipment faults, states and behaviors; and provide timely decision support.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)["Shengchao Jiang", "Yuzhong Zhou", "Zhengping Lin", "Yuan La", "Feifeng Wang", "Yunqing Pei"]2024-08-14T00:00:00 d86a8190b111af082e50e502f31c68e09ff89bf6With the rapid development of information technology and artificial intelligence technology, financial robots have gradually become a popular technology for enterprise financial management by virtue of their strong analog operation and automation advantages, which can greatly improve the work quality and efficiency of financial personnel and enhance the efficiency and accuracy of financial management. This paper deeply studies the basic concepts and technical characteristics of financial robots, and takes the financial center of a company as an example to further analyze the necessity, application effect evaluation and optimization measures of financial robots on the basis of artificial intelligence and automatic control, so as to comprehensively improve the operation and management level of enterprise financial system.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)70This paper deeply studies the basic concepts and technical characteristics of financial robots, and takes the financial center of a company as an example to further analyze the necessity, application effect evaluation and optimization measures of financial robots on the basis of artificial intelligence and automatic control so as to comprehensively improve the operation and management level of enterprise financial system.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)["Yuheng Zhou"]2024-08-14T00:00:00 301b0edb2adc27a5de5e034c4035d6eee547c590As we advance through 2024, the horizon is increasingly enriched with artificial intelligence (AI) innovations poised to redefine conventional boundaries. The effects of AI on work and workers are a major topic of discussion, with concerns about widespread displacement of human labour as AI-driven technologies are integrated into workplaces and labour processes. The World Economic Forum (WEF) projects that AI and autonomous machines could replace 85 million jobs by 2025, but also create 97 million new positions in an evolving division of labour between humans, machines, and algorithms. This suggests that the future will involve job substitution rather than mere displacement. Here, we have focused on the positive impacts of AI across various fields and also explored how it has influenced different sectors of society. Our analysis reveals that AI is not destined to replace human workers, but rather calls for a redefinition of roles and skills, enabling AI to become an integral part of our development and progressInternational Journal For Multidisciplinary Research140The analysis reveals that AI is not destined to replace human workers, but rather calls for a redefinition of roles and skills, enabling AI to become an integral part of the authors' development and progress.International Journal For Multidisciplinary Research["Dr Anamika Jha", "Dipa Panjiyar", "Tamanna Sindhi", "Mahera Pathan"]2024-08-14T00:00:00 aad5813d5f4715504a01f3ed9f76ac22caa45e74Background: AF risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis. Objective: To test whether integrating these distinct risk signals improves AF risk estimation. Methods: In the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a 1,113,667-variant AF polygenic risk score (PRS), and a published AI-enabled ECG-based AF risk model (ECG-AI). We estimated discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic (AUROC) and average precision (AP). Results: Among 49,293 individuals (mean age 65+-8 years, 52% women), 825 (2.4%) developed AF within 5 years. Using single models, discrimination of 5-year incident AF was higher using ECG-AI (AUROC 0.705 [95%CI 0.686-0.724]; AP 0.085 [0.071-0.11]) and CHARGE-AF (AUROC 0.785 [0.769-0.801]; AP 0.053 [0.048-0.061]) versus the PRS (AUROC 0.618, [0.598-0.639]; AP 0.038 [0.028-0.045]). The inclusion of all components ('Predict-AF3') was the best performing model (AUROC 0.817 [0.802-0.832]; AP 0.11 [0.091-0.15], p<0.01 vs CHARGE-AF+ECG-AI), followed by the two component model of CHARGE-AF+ECG-AI (AUROC 0.802 [0.786-0.818]; AP 0.098 [0.081-0.13]). Using Predict-AF3, individuals at high AF risk (i.e., 5-year predicted AF risk >2.5%) had a 5-year cumulative incidence of AF of 5.83% (5.33-6.32). At the same threshold, the 5-year cumulative incidence of AF was progressively higher according to the number of models predicting high risk (zero: 0.67% [0.51-0.84], one: 1.48% [1.28-1.69], two: 4.48% [3.99-4.98]; three: 11.06% [9.48-12.61]), and Predict-AF3 achieved favorable net reclassification improvement compared to both CHARGE-AF+ECG-AI (0.039 [0.015-0.066]) and CHARGE-AF+PRS (0.033 [0.0082-0.059]). Conclusions: Integration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Predict-AF3 have substantial potential to improve prioritization of individuals for AF screening and preventive interventions.medRxiv80medRxiv["Shinwan Kany", "Joel T. R\u00e4m\u00f6", "Sam F Friedman", "L. Weng", "C. Roselli", "Min Seo Kim", "A. C. Fahed", "S. Lubitz", "M. Maddah", "P. Ellinor", "S. Khurshid"]2024-08-14T00:00:00 f6f9ee17cffea2ce503eecee7c5d13e8eddbf82eIn the context of energy systems, managing the complex interplay between diverse power sources and dynamic demands is crucial. With a focus on smart grid technology, continuously innovating artificial intelligence (AI) algorithms, such as deep learning, reinforcement learning, and large language model technologies, have been or have the potential to be leveraged to predict energy consumption patterns, enhance grid operation, and manage distributed energy resources efficiently. These capabilities are essential to meet the requirements of perception, cognition, decision‐making, and deduction in energy systems. Nevertheless, there are some critical challenges in efficiency, interpretability, transferability, stability, economy, and robustness. To overcome these challenges, we propose critical potential directions in future research, including reasonable sample generation, training models with small datasets, enhancing transfer ability, combining with physics models, collective generative pre‐trained transformer‐agents, multiple foundation models, and improving system robustness, to make advancing AI technologies more suitable for practical engineering.Energy Internet440Critical potential directions in future research are proposed, including reasonable sample generation, training models with small datasets, enhancing transfer ability, combining with physics models, collective generative pre‐trained transformer‐agents, multiple foundation models, and improving system robustness, to make advancing AI technologies more suitable for practical engineering.Energy Internet["Z. Dong", "Tianjing Wang"]2024-08-14T00:00:00 4dedf77673b3fb6327d2ff806c6771d361f61173This paper studies the construction and application of intelligent financial decision support system (DSS) which combines RPA (Robotic Process Automation) financial robot and artificial intelligence (AI) technology. This paper expounds the functional module design of intelligent financial DSS in detail, including financial data automatic processing, intelligent data analysis and prediction, risk early warning and management, decision support and system management module. Through the system architecture design, the system is divided into data layer, business logic layer and presentation layer to ensure the stability and efficiency of the system. In the process of system implementation, advanced technologies such as Windows 10 operating system, Visual Studio Code development environment, Python back-end development language, Flask Web framework and PostgreSQL database are adopted. RPA financial robot realizes the automatic collection and arrangement of financial data through UiPath tools, while AI technology uses TensorFlow and other libraries for in-depth analysis and prediction of data. The system testing stage includes functional testing and performance testing, which verifies the stability and reliability of the system. The research results show that the combination of RPA financial robot and AI technology can provide enterprises with more efficient and accurate financial management and decision support services. Through the functions of automatic processing of financial data, intelligent analysis and prediction, risk early warning and decision support, the system has significantly improved the financial management level and decision efficiency of enterprises. The research in this paper not only enriches the application theory of RPA and AI in the financial field, but also provides useful reference for the financial management practice of enterprises.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)80Through the functions of automatic processing of financial data, intelligent analysis and prediction, risk early warning and decision support, the system has significantly improved the financial management level and decision efficiency of enterprises.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)["Weiyun Tang", "Hongtao Cao", "Shengfan Ye", "Lu Yang", "Fei Chen"]2024-08-14T00:00:00 dc3da605aa977984f1abde794889eea21fa4101aThe purpose of this article is to explore the specific aspects of implementing artificial intelligence (AI) in the cultural and creative industries from a public administration perspective. The article analyses the main opportunities and directions for incorporating AI into public management for the development of these sectors in Ukraine. It highlights that digital transformation, facilitated by AI technologies, requires adherence to structural-functional, informational-communicative, and organizational-technological approaches. These approaches ensure decentralization, modernization of information infrastructure, and optimization of management processes. The article outlines the prospects and directions for AI integration into public administration in the cultural and creative sectors in Ukraine. It identifies the main benefits of AI, such as the ability to analyse large volumes of data, predict trends, automate routine processes, and support informed decision-making. These benefits can enhance the quality and accessibility of cultural products and create new channels and consumption models. This includes applications like the digitization of cultural heritage, management of cultural institutions, and the creation of personalized experiences for visitors. The article also identifies potential risks associated with implementing AI in public administration for the cultural and creative industries in Ukraine. These risks include issues related to copyright protection, ethics, and transparency in decision-making. Despite the significant potential, the integration of AI in public administration is still in its early stages. It faces numerous challenges, such as a lack of clear methodological approaches and practical tools, as well as risks related to privacy, algorithm transparency, and employment impacts. The article emphasizes that using AI in creating creative products raises important considerations regarding the value-based foundations of the cultural and creative sectors. It transforms creativity from an individual to a collective endeavor, where the role of machine systems becomes comparable to that of humans. Additionally, the article proposes a refined definition of «creative industries» in Ukrainian legislation, considering the specificities of creating creative products with AI.University Scientific Notes00The article identifies the main benefits of AI, such as the ability to analyse large volumes of data, predict trends, automate routine processes, and support informed decision-making, which can enhance the quality and accessibility of cultural products and create new channels and consumption models.University Scientific Notes["Denys Herman", "Mykola Puzko"]2024-08-14T00:00:00 a24d412757e3fab6cda8b90af2d0fababd5005adCyber-Physical Systems (CPS) play a crucial role in critical industries such as manufacturing, transportation, energy, and healthcare by integrating the physical and digital worlds. However, the complexity and interconnectivity of CPS with the global network increase their vulnerability to cyber-attacks. This research explores the benefits of implementing artificial intelligence (AI) in the context of cyber-physical systems (CPS) to detect and respond to security threats. This study uses machine learning and deep learning techniques to analyze sensor data and system threats. The data analysis methods encompass predictive modeling and evaluating AI algorithms' performance in detecting threats. The research data is obtained from relevant literature reviews and secondary data analysis. The research findings indicate that integrating AI in CPS can enhance the success rate of threat detection, prompt response, and accuracy in threat identification. By enabling the system to learn from previous experiences, AI can reduce the number of false positives and false negatives while providing automated real-time responses to threats without human intervention. The research concludes that AI has great potential to enhance security in CPS by providing more efficient and effective solutions to address increasingly complex cyber threats. The findings of this study are expected to provide insights and recommendations that can be applied in developing CPS security systems in the future.  Security Intelligence Terrorism Journal (SITJ)600The research findings indicate that integrating AI in CPS can enhance the success rate of threat detection, prompt response, and accuracy in threat identification, and automate real-time responses to threats without human intervention.Security Intelligence Terrorism Journal (SITJ)["Muhammad Nur Abdul Latif Al Waro'i"]2024-08-14T00:00:00 59f7fbb7c571472498b5871c7d88aa3c99a021b2This review comprehensively examines the integration of artificial intelligence (AI) in enhancing the dynamic security assessments of modern power systems. It highlights the pivotal role of AI in facilitating scenario generation, incident prediction, risk assessment, and severity grading, thereby addressing the complexities introduced by renewable energy integration and advancements in digital grid technologies. The paper delves into data-driven techniques, with a particular focus on decision trees that effectively bridge operational characteristics with security metrics. These methodologies enable real-time, accurate predictions of system behaviors under varied operational conditions and support the optimization of control strategies. Through detailed analysis, we demonstrate how AI applications can transform traditional security assessment protocols, enhancing both the efficacy and efficiency of power system operations. The findings advocate for the potential of AI to significantly enhance the reliability and resilience of electrical grids, marking a paradigm shift towards more adaptive and intelligent power infrastructure.arXiv.org450This review comprehensively examines the integration of artificial intelligence in enhancing the dynamic security assessments of modern power systems, and demonstrates how AI applications can transform traditional security assessment protocols, enhancing both the efficacy and efficiency of power system operations.ArXiv["Runhao Zhang"]2024-08-14T00:00:00 8bb98fa00ed73963188786ccd7ef730d4cb742c2Artificial intelligence technology is booming in the digital economy era. In view of the legal attributes of the subject and object of artificial intelligence, there have been many controversies in the theoretical and academic circles. For example, some scholars advocate that products involving artificial intelligence ought to be covered under copyright from the standpoint of "objectivism". However, some scholars think that copyright protection should not be given to them from the perspective of "subjectivism". The copyright ownership of artificial intelligence products is currently debated in three mainstream theories: "producer theory", "creator theory" and "user theory". In fact, the "user theory" is more reasonable from three aspects: the composition characteristics of works, the social significance of legal protection and the principle of unity of rights and obligations. Therefore, at the institutional level, we should try to clarify the status of artificial intelligence products and the copyright subject of artificial intelligence products is artificial intelligence users, in order to achieve legal protection, an artificial intelligence declaration system must be established.Journal of Education, Humanities and Social Sciences70At the institutional level, the status of artificial intelligence products and the copyright subject of artificial intelligence products is artificial intelligence users, and in order to achieve legal protection, an artificial intelligence declaration system must be established.Journal of Education, Humanities and Social Sciences["Yue An"]2024-08-14T00:00:00 7ccbfe2f985ac10d6c13bb390fdd179fefa12e09AI and Ethics150Stoic wisdom is essential for assessing risks, courage is necessary to face contemporary challenges, and temperance and tranquility are indispensable; and these lessons can inform ongoing public and academic discourse, aiding in the development of more effective policy proposals for aligning Narrow AI and General AI with human values.AI and Ethics["Bernardo Bola\u00f1os Guerra", "Jorge Luis Morton Gutierrez"]2024-08-14T00:00:00 8b8edefee7466b29cb87b9e378d2dd29e9c183f3Objective: The main objective of the paper is to study the need for Special Regulatory legislation for Technology especially Artificial intelligence (AI) as well as examine how AI and law work together. The paper evaluates the liability of AI when it violates Human Rights and Data Privacy. The paper evaluates how AI has created new possibilities and had posed challenges by interfering Human Rights and challenging Sustainable Development comparing the developments happening in European Union (EU) with countries like India. The paper also attempts to make the complex debate more comprehensible for those with no expertise in this area.   Method: The Comparative research approach was administered by highlighting the EU methods to tackle and address the AI liabilities. Analogy and review of the Product Liability Directive (PLD) which is proposed and published by European Union (EU) for an AI system liability and suggestions of National Institution for Transforming India (NITI) Aayog of India to attain Sustainable National Economic Development. The paper analyses from financial and lucrative perspective, what liability rules would curtail the financial damages and other harms caused by AI systems which affect Sustainable Development.   Results: The paper findings suggest that influence factor of AI applications should be included in existing Liability framework and must be consistently updated as AIs are characterised by unpredictability and independence. Both the EU and India NITI Aayog states that regulatory insight must be an ethical reinforcement for AI practice and hence ensure the sustainability of AI technologies.   Originality/Value: The recent government initiatives in EU and India are discussed with respect to the AI liability. The study provides positive insights of Artificial Intelligence (AI) technology and its liabilities to achieve Economic Sustainable Development. The key findings of the paper highlight the importance of Salmond’s defined Tortious liability during British Era for AI technologies for protecting Human Rights and also achieve Sustain Development.Journal of Law and Sustainable Development240The key findings of the paper highlight the importance of Salmond’s defined Tortious liability during British Era for AI technologies for protecting Human Rights and also achieve Sustain Development.Journal of Law and Sustainable Development["Sunil Kalagi", "Renuka S. Gubbewad", "Aayush Gondale"]2024-08-14T00:00:00 e7e2639f54dcc3b46aed72184cb4c7520bc4817dLinguistic studies of art discourse are gaining more and more relevance in the context of rapidly developing artificial intelligence. This research featured the impact of art discourse on the audience and their interpretation of images generated by AI image generators based on text queries. AI image generators open up new opportunities for research and understanding of art and art discourse, which is interpretive and dualistic in nature. Anna Andrzhievskaya’s FV and Dawn from the catalogue of Heavenly Wasteland exhibition were tested using Shedevrum and Bing Image Creator. Expressive vocabulary helped to convey the original atmosphere and ideas. However, complex metaphorical images proved to be a barrier for correct interpretation. Epithets, similes, and hyperboles provided a clearer representation of the artist’s ideas, thus reducing the risk of misinterpreting by the neural networks. These devices improved the perception of artistic value and communication between the artist and her audience. The results can help art historians in verbalizing works of art, i.e., as a writing guide for art descriptions. In addition, they may improve the quality and accuracy of AI-generated images, i.e., in training neural networks to recognize expressive vocabulary and stylistic devices at the level of linguistic pragmatics.SibScript80The impact of art discourse on the audience and their interpretation of images generated by AI image generators based on text queries andEpithets, similes, and hyperboles provided a clearer representation of the artist’s ideas, thus reducing the risk of misinterpreting by the neural networks.SibScript["N. Kurakina", "D. S. Filippova"]2024-08-14T00:00:00 aeb4731b8a86174b01f3147c08050b9992b365a4In this paper, based on artificial intelligence algorithms, an intelligent industrial control communication model is constructed. Firstly, the secret transmission of communication data is realized by establishing encryption nodes between sensor network sensing nodes and convergence points. Then the network key is encoded to automatically generate asymmetric key, in order to complete the communication model authentication, and finally the model is analyzed in practice. The results show that the decryption time of the intelligent industrial control communication model is only 3.39 s, and the data transmission time is all within 15 ms. The functions of information encryption, digital signature and authentication in the communication system are realized.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)160The results show that the decryption time of the intelligent industrial control communication model is only 3.39 s, the data transmission time is all within 15 ms, and the functions of information encryption, digital signature and authentication in the communication system are realized.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)["Wei Li", "Yinquan Wang", "Guoming Xian", "Gang Chen", "Lei Han", "Changmin Zheng", "Hanlin Tu"]2024-08-14T00:00:00 0478c00e918ecd6805ac166f0b29a2e45b96fab2AI (Artificial Intelligence) behavior analysis is a powerful tool, which can help us better understand and predict human behavior and provide support for decision-making in various fields. This paper aims to design an efficient and accurate user behavior analysis model by combining NN (Neural Network) algorithm module and behavior analysis module. In order to achieve this goal, this paper adopts CNN (Convolutional Neural Network) as the core algorithm, and combines with specific business needs and scenarios to formulate behavior analysis strategies. After a series of experiments, the proposed algorithm can quickly identify and classify different user behaviors, and has high accuracy in identifying user behaviors. Its MAPE value is only 0.251, which is significantly better than other comparison algorithms. At the same time, the algorithm shows high efficiency in different scale data sets. In addition, sensitivity analysis and scene test are carried out to verify the adaptability and stability of the model under various conditions and scenarios. By studying the application of NN algorithm in the field of user behavior analysis, we can further enrich and perfect the theoretical system in the field of AI, and promote the improvement and innovation of related algorithms.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)110An efficient and accurate user behavior analysis model is designed by combining NN (Neural Network) algorithm module and behavior analysis module, which adopts CNN (Convolutional Neural Network) as the core algorithm, and combines with specific business needs and scenarios to formulate behavior analysis strategies.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)["Shanshan Lin", "Fengqi Jia"]2024-08-14T00:00:00 d540379d815d90d11bb6c25578753f6bd4933b3fOECD Artificial Intelligence Papers05OECD Artificial Intelligence Papers[]2024-08-14T00:00:00 8cb1ff528fea94308a706903f6a1718b896d3af201["Steffen Bohni Nielsen", "Francesco Mazzeo Rinaldi", "Gustav Jakob Petersson"]2024-08-14T00:00:00 d7ae6f9db53cdc7886064a5b0aa80b94e5d4067cJournal of Interactive Advertising611Journal of Interactive Advertising["Y. G. Song", "Jeongmin Ham", "Eunjoo Jin", "Matthew S. Eastin"]2024-08-14T00:00:00 31f1602b2dccdbfaf91987c01ca71c39dbd18574Zanco Journal of Humanity Sciences00Zanco Journal of Humanity Sciences[]2024-08-14T00:00:00 0b80f35ad71b8591495f346298f8a7a4678381cfObjectives: This systematic review and meta-analysis aim to explore the performance of machine learning algorithms in predicting the risk of macrovascular complications among individuals with T2DM, specifically, the predictive capabilities of AI models in forecasting stroke, CVD, and PVD in LMICs. Design: Systematic review and meta-analysis of studies reporting on AI prediction models for macrovascular complications in T2DM patients. Setting: The review included studies conducted in various healthcare settings, primarily from LMICs, upper-middle-income countries (UMICs), and high-income countries (HICs). Participants: 46 studies were included, with a total of 184 AI models. Participants were diverse in age, sex, and geographical locations, reflecting a broad range of healthcare settings. Interventions: The intervention analyzed was the application of AI models, including machine learning algorithms, to predict macrovascular complications such as stroke, CVD, and PVD. Primary and Secondary Outcome Measures: The primary outcome was the predictive performance of AI models, measured by the area under the receiver operating characteristic curve (AUROC). Secondary outcomes included subgroup analyses based on predictor types and an assessment of AI model applicability in low-resource settings. Results: Twelve included studies yielded 184 AI models with an overall AUROC of 0.753 (95%CI: 0.74-0.766; I2=99.99%; p<0.001). For 80 models of cardiovascular outcomes, an AUROC of 0.741 (95%CI: 0.721-0.76; I2=99.78%; p<0.001) was obtained. Meanwhile, 25 models of peripheral vascular disease and 38 models of cerebrovascular diseases obtained AUROCs of 0.794 (95%CI: 0.758-0.831; I2=97.23%; p<0.001) and 0.77 (95%CI: 0.743-0.797; I2=99.73%; p<0.001) respectively. Subgroup analysis revealed that models with lab-only predictors were superior to those with mixed or no-lab predictors. This signalled the lack of AI capability for history-taking and physical examination data alone, primarily available in low-resource settings. Conclusions: Artificial intelligence is promising in predicting diabetes complications. Nevertheless, future studies should explore accessible features in low-resource settings and employ external validation to ensure the robustness of the prediction models.medRxiv390Artificial intelligence is promising in predicting diabetes complications, Nevertheless, future studies should explore accessible features in low-resource settings and employ external validation to ensure the robustness of the prediction models.["A. Nur", "S. Tjandra", "D. A. Yumnanisha", "A. Keane", "A. Bachtiar"]2024-08-14T00:00:00 1182a10f7d5945d4a82054b5d68eacaa612d375bNurse Education Today690There is a need to adapt teaching strategies and integrate AI tools as useful learning tools, which have become essential for students to complete their learning activities through enhancing knowledge of the multimodal technological factors that should be taken into consideration while creating AI tools across several domains for universities and developers.Nurse education today["S. Jallad", "Khitam Alsaqer", "Baker Albadareen", "Dua'a Al-Maghaireh"]2024-08-14T00:00:00 0bf4bb5344936b64fc802bcfe8c180d626310b0c00["Cheryl L. Lampkin"]2024-08-14T00:00:00 eb159d8f93909908cb041a9dfe840d1f1b97d37aProceedings of the 2024 7th International Conference on Information Science and Systems40Proceedings of the 2024 7th International Conference on Information Science and Systems["M. Samonte", "Rafaello Jose M. Viera", "Jan Edgar E. Tupas", "Allen Kyle D. Sabilala", "Ervin C. Tejada"]2024-08-14T00:00:00 d77ba7cf9ea8965f84e26d6d3618ce7005c3c641With the rapid development of network technology, the problem of network resource allocation is increasingly prominent. The purpose of this study is to explore a more intelligent and efficient network resource allocation strategy by strengthening learning algorithms. In order to achieve this goal, this article first constructs a Deep Reinforcement Learning (DRL) model for network resource allocation, and uses Actor-Critic algorithm to optimize it. In the experiment, a simulated network environment is built, and the validity of the model is verified by preparing relevant data sets. During the experiment, the state, action, reward and other information of the algorithm are recorded in detail, so as to evaluate the performance comprehensively. The results show that DRL algorithm performs well in network resource allocation and significantly improves the overall performance of the network. Compared with traditional methods, DRL algorithm shows its powerful learning and optimization ability. The convergence and stability analysis of the algorithm further confirmed the reliability of DRL in network resource allocation. In this article, the application potential of DRL in network resource allocation is verified by experiments, which provides a new idea for solving the resource allocation problem in dynamic network environment.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)130The application potential of DRL in network resource allocation is verified by experiments, and the results show that DRL algorithm performs well in network resource allocation and significantly improves the overall performance of the network.2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC)["Zhuang Yuan"]2024-08-14T00:00:00 8a7b5945450821afd149c0cb0108c80c80025250International Journal of Human Sciences Research00International Journal of Human Sciences Research["Tania Haid\u00e9e Torres Ch\u00e1vez", "Miguel \u00c1ngel Medina-Romero"]2024-08-14T00:00:00 afe312b66035501754a9d4fb4aa36e7a985cdfdc00["Aditya Khamparia", "Deepak Gupta"]2024-08-14T00:00:00 8d3d108580e00b26ecc03f9a0c78fded2b8ba9b2Indian Journal of Community Medicine120Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine["Purushottam A. Giri", "Manoj Kumar Gupta"]2024-08-14T00:00:00 5e761e2e70de79b6899dcfe09709664179efab38Artificial intelligence (AI) is rapidly transforming various sectors of society, requiring a new form of literacy: AI literacy. This study validated a new instrument designed to measure students’ AI empowerment conceptualised as consisting of four components: impact, self-efficacy in AI, creative self-efficacy in AI, and meaningfulness. Confirmatory factor analysis was used to validate the proposed components of the AI empowerment instrument. The sample comprised 224 secondary school and university students who completed an 18-hour AI literacy programme. The results showed that the students’ AI empowerment was significantly increased by the AI literacy programme. Specifically, the AI literacy programme was found to narrow the gender gap in AI empowerment. Furthermore, the results highlighted that prior programming experience did not significantly affect AI empowerment, indicating that AI literacy can be achieved regardless of programming experience. This study provides a theoretical framework for understanding and quantifying the extent to which individuals feel empowered after engaging with AI activities for its conceptual understanding. It provides educators with a tool to measure students’ understanding and confidence in their AI abilities. The study also suggests directions for future research.Research and Practice in Technology Enhanced Learning651A new instrument designed to measure students’ AI empowerment conceptualised as consisting of four components: impact, self-efficacy in AI, creative self-efficacy in AI, and meaningfulness was validated.Res. Pract. Technol. Enhanc. Learn.["Siu-Cheung Kong", "Yin Yang"]2024-08-14T00:00:00 64d95b47597f42ee9b5f3b8376ea9e291340ee1aThe study investigates the presence of women in Brazilian priority patent filings in Artificial Intelligence. It is a patentometric study, collecting data from the database of the National Institute of Industrial Property. The objective is to characterize the patents in terms of date, classification area, applicants, and inventors, also observing collaboration and the themes of the patents. The data shows that the first deposits began in 2002, with the majority of patents filed from 2016 onwards. The patents are mostly classified in the field of Physics. The largest applicants are Higher Education Institutions, followed by private companies, individuals, and public enterprises. There is a low participation of women as applicants and inventors. There is significant collaboration among inventors, however, there is weak collaboration in patents solely invented by women. The themes of the patents are relevant, applying Artificial Intelligence mainly in the fields of health, agriculture, and transportation.Liinc em Revista00The study investigates the presence of women in Brazilian priority patent filings in Artificial Intelligence, collecting data from the database of the National Institute of Industrial Property in terms of date, classification area, applicants, and inventors.Liinc em Revista["Janaina La\u00eds Pacheco Lara Morandin", "Ana Maria Mielniczuk De Moura"]2024-08-14T00:00:00 3fe403df31a0dbd950a4e94a1913f2eeb89d9de9Integrating artificial intelligence (AI) in higher education, particularly in coding assignments for Information Technology (IT) students, represents a rapidly evolving research area with significant implications for academic practices and integrity. This study focuses on the ethical challenges faced by IT students when using AI tools like ChatGPT for coding assignments. Despite the growing use of AI in education, there is a notable gap in understanding how students perceive and navigate the ethical dilemmas associated with these technologies. To address this gap, this study employed a thematic analysis of qualitative data collected from interviews with IT students. The results reveal a complex landscape of ethical considerations, including issues of originality, academic integrity, and the potential for misuse of AI tools. Students reported challenges in balancing the benefits of AI assistance with the need to maintain independent learning and adhere to ethical standards. The implications of this research are significant for educators, institutions, and policymakers. Understanding the ethical challenges students face can inform the development of more effective teaching strategies, assessment methods, and institutional policies. This study contributes to the ongoing dialogue about AI ethics in academia, providing valuable insights for creating an educational environment that leverages the power of AI while upholding the principles of academic integrity and meaningful learning.IJIE (Indonesian Journal of Informatics Education)385This study focuses on the ethical challenges faced by IT students when using AI tools like ChatGPT for coding assignments, and employs a thematic analysis of qualitative data collected from interviews with IT students to reveal a complex landscape of ethical considerations.IJIE (Indonesian Journal of Informatics Education)["B. Mutanga", "Matthews Lecheko", "Zvinodaishe Revesai"]2024-08-14T00:00:00 46ba5f49432e202f0067a9e75751bfbe040680eeWith the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy cancer cells. However, enhancing the efficacy of cancer vaccines remains a challenge. Adjuvants, which enhance the immune response to antigens and improve vaccine effectiveness, have faced limitations in recent years, resulting in few novel adjuvants being identified. The advancement of artificial intelligence (AI) technology in drug development has provided a foundation for adjuvant screening and application, leading to a diversification of adjuvants. This article reviews the significant role of tumor vaccines in basic research and clinical treatment and explores the use of AI technology to screen novel adjuvants from databases. The findings of this review offer valuable insights for the development of new adjuvants for next-generation vaccines.Frontiers in Immunology2433The significant role of tumor vaccines in basic research and clinical treatment is reviewed and the use of AI technology to screen novel adjuvants from databases is explored, offering valuable insights for the development of new adjuvants for next-generation vaccines.Frontiers in Immunology["Wan-Ying Zhang", "Xiao-Li Zheng", "Paolo Coghi", "Jun-Hui Chen", "Bing-Jun Dong", "Xing-Xing Fan"]2024-08-14T00:00:00 bb66a7c575e9d2668a362cdf5e8109bafe262fd3AAAI/ACM Conference on AI, Ethics, and Society993Examining three notable notions of democratizing AI and their associated measures reveals that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies.{"pages": "867"}["Ting-an Lin"]2024-08-14T00:00:00 50bd10d08e9e3c94c91c0bd55dcdf0c3178c43a8The aviation industry is experiencing over and over again a technological revolution, nowadays with airports at the forefront of embracing smart technologies to enhance operational efficiency, security and passenger experience. This article comprehensively analyzes the benefits, challenges, and legal implications of adopting smart technologies in airport facilitation and security control. It examines the regulatory framework established by the International Civil Aviation Organization (ICAO) on an international level and by sovereign states on a national level. It explores using smart solutions such as automated systems, data and biometric verification, artificial intelligence (AI), and the Internet of Things (IoT) devices in airport operations. The authors’ purpose is to highlight the improvements in airport facilities and security measures brought about by these technologies, while addressing concerns over privacy, cost, technological limitations and human factors. By emphasizing the importance of a balanced approach and considering innovation alongside legal and operational imperatives, the article underscores the transformative potential of smart and integrated technologies in shaping the future of air travel.Journal of Infrastructure Policy and Development171The authors’ purpose is to highlight the improvements in airport facilities and security measures brought about by these technologies, while addressing concerns over privacy, cost, technological limitations and human factors.Journal of Infrastructure, Policy and Development["K. Alketbi", "Attila Sipos"]2024-08-14T00:00:00 9eb1c2f563117fdc49e39f55ad7320d84ef6c04eAdvances in artificial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reflective practice of educators in a time when online teaching has become the norm. Having achieved a ‘proof of concept’, this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices.Education sciences421A ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework is found, thus adding a new dimension to its use.Education Sciences["Christopher Dann", "Shirley O\u2019Neill", "S. Getenet", "Subrata Chakraborty", "Khaled Saleh", "Kun Yu"]2024-08-14T00:00:00 8484644ffb584fc7d1f3e8ca76c92dda40b533d0The top priority of a Housing and Homelessness System of Care (HHSC) is to connect people experiencing homelessness to supportive housing. An HHSC typically consists of many agencies serving the same population. Information technology platforms differ in type and quality between agencies, so their data are usually isolated from one agency to another. Larger agencies may have sufficient data to train and test artificial intelligence (AI) tools but smaller agencies typically do not. To address this gap, we introduce a Federated Learning (FL) approach enabling all agencies to train a predictive model collaboratively without sharing their sensitive data. We demonstrate how FL can be used within an HHSC to provide all agencies equitable access to quality AI and further assist human decision-makers in the allocation of resources within HHSC. This is achieved while preserving the privacy of the people within the data by not sharing identifying information between agencies without their consent. Our experimental results using real-world HHSC data from a North American city demonstrate that our FL approach offers comparable performance with the idealized scenario of training the predictive model with data fully shared and linked between agencies.AAAI/ACM Conference on AI, Ethics, and Society440This work demonstrates how FL can be used within an HHSC to provide all agencies equitable access to quality AI and further assist human decision-makers in the allocation of resources within HHSC.{"pages": "1434-1443"}["Musa Taib", "Jiajun Wu", "Steve Drew", "G. Messier"]2024-08-14T00:00:00 95fbfd32c80dd84cd71e56bdb274e47950390d52 As Artificial Intelligence (AI) technology develops and more AI systems are put on the market, concern about the implications of such AI systems on humans is growing. A particular area of concern is the impact of AI systems on intellectual property rights (IPRs) as infringement might occur in relation to the use of AI systems. IPR infringements can be committed by training an AI system, as well as by using or when operating an AI system. This raises the question of liability for such IPR infringements. This research covers situations where IPR infringements are committed by training an AI system, as well as those where infringements are committed by using or operating an AI system. One hypothetical situation of an AI system created solely for the purpose of infringing IPRs is investigated, but also includes situations where otherwise legitimate AI systems are misused in criminal activities as tools for the production and sale of infringing goods. These areas are analysed in relation to the proposed AI Liability Directive and other relevant European Union legislation to identify what rules would be applicable to liability for damage in such cases. The research also considers the recently enacted AI Act. In instances where the damage is caused by AI-enabled products and services, the study shows how the ‘rebuttable presumption of causality’ provided for under the AI Liability Directive could be applied.Journal of Intellectual Property Law & Practice00Journal of Intellectual Property Law and Practice["Ana Ra\u010dki Marinkovi\u0107"]2024-08-14T00:00:00 4dd18f3c0051d406649d1384686436d1f28da5f6Sustainability is a long-term commitment. Businesses must measure their environmental impact regularly if they wish to be environmentally friendly in the long run. Determining true ecological impact is difficult, making it ideal for AI. However, AI also has a substantial carbon footprint, which will only increase if current trends continue. Artificial intelligence could become an adversary in the fight against climate change in the coming years unless we analyse and adjust the current AI research programme. Given the daily effects of climate change, there is a growing understanding that AI research ethics must focus on limiting and offsetting the study's carbon footprint. Researchers should mention the energy costs in their research paper results alongside time, accuracy, and other aspects. Environmental sustain ability must be emphasised with varied ethical approaches to AI, lest carbon emissions continue to rise, causing harm to both people and the environment.International Conference on Interactive Collaborative Learning1402024 1st International Conference on Logistics (ICL)["Anam Iqbal", "Shaima Qureshi", "M. Chishti"]2024-08-14T00:00:00 c1c92a27d066757b72170b1cf0dfa23bce1acf4cThe research investigates the role of artificial intelligence and predictive analytics in integrating the practices of supply chain management for the growth of a business in a sustainable manner. A predictive model on the emission factors was then developed using a Random Forest algorithm from machine learning techniques against the historical data from the US Environmental Protection Agency on "Supply Chain Greenhouse Gas Emission Factors for USUS Industries and Commodities." It yielded an average Mean Squared Error of 0.00141 with an R-squared value of 0.9858, explaining almost 99% of the variance in actual emission factors across various industries. The research results show the potential of AI-driven insights in spotting high-emission areas, facilitating targeted interventions, and thus supporting data-driven decision-making in SCM. Case studies drawn from industries such as electronic manufacturing and food processing show the practical application of this model by showing how businesses can reduce their carbon footprints while enhancing operational efficiency and market competitiveness. The study also addresses the pitfalls that may characterize model implementation, such as poor data quality, complex models, and continuous updating. It makes business recommendations to adopt similar strategies, emphasizing cross-functional expertise, stakeholder buy-in, and ethical considerations. It deepens a growing literature on sustainable supply chain management and establishes a framework through which firms can harness AI and predictive analytics to pursue environmental and economic objectives.Journal of business and management studies284The research results show the potential of AI-driven insights in spotting high-emission areas, facilitating targeted interventions, and thus supporting data-driven decision-making in SCM, and establishes a framework through which firms can harness AI and predictive analytics to pursue environmental and economic objectives.Journal of Business and Management Studies["MD Rokibul Hasan", "Md Zahidul Islam", "Md Fakhrul Islam Sumon", "Md Osiujjaman", "Pravakar Debnath", "Laxmi Pant"]2024-08-15T00:00:00 844f5fc70b8e2a29de1a3800ef9f59d6b51bdc3bCurrently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.AAPS PharmSciTech1594This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.AAPS PharmSciTech["Phuvamin Suriyaamporn", "Boonnada Pamornpathomkul", "Prasopchai Patrojanasophon", "T. Ngawhirunpat", "T. Rojanarata", "P. Opanasopit"]2024-08-15T00:00:00 2539bcf6a78db65bee4c7a3513fa715c58be3044The recent surge of generative artificial intelligence (AI) in higher education presents a fascinating landscape of opportunities and challenges. AI has the potential to personalize education and create more engaging learning experiences. However, the effectiveness of AI interventions relies on well-considered implementation strategies. The impact of AI platforms in education is largely determined by the particular learning environment and the distinct needs of each student. Consequently, investigating the attitudes of future educators towards this technology is becoming a critical area of research. This study explores the impact of generative AI platforms on students’ learning performance, experience, and satisfaction within higher education. It specifically focuses on students’ experiences with varying levels of technological proficiency. A comparative study was conducted with two groups from different academic contexts undergoing the same experimental condition to design, develop, and implement instructional design projects using various AI platforms to produce multimedia content tailored to their respective subjects. Undergraduates from two disciplines—Early Childhood Education (n = 32) and Computer Science (n = 34)—participated in this study, which examined the integration of generative AI platforms into educational content implementation. Results indicate that both groups demonstrated similar learning performance in designing, developing, and implementing instructional design projects. Regarding user experience, the general outcomes were similar across both groups; however, Early Childhood Education students rated the usefulness of AI multimedia platforms significantly higher. Conversely, Computer Science students reported a slightly higher comfort level with these tools. In terms of overall satisfaction, Early Childhood Education students expressed greater satisfaction with AI software than their counterparts, acknowledging its importance for their future careers. This study contributes to the understanding of how AI platforms affect students from diverse backgrounds, bridging a gap in the knowledge of user experience and learning outcomes. Furthermore, by exploring best practices for integrating AI into educational contexts, it provides valuable insights for educators and scholars seeking to optimize the potential of AI to enhance educational outcomes.Applied Informatics363This study explores the impact of generative AI platforms on students’ learning performance, experience, and satisfaction within higher education, and contributes to the understanding of how AI platforms affect students from diverse backgrounds.AI["Giovanni Diraco", "Arslan Munir", "I. Kazanidis", "Nikolaos Pellas"]2024-08-15T00:00:00 ea1a25178c35a32fcc3919a5084acc416ca3c273Viral hepatitis continues to be a serious global health concern, impacting millions of people, putting a strain on healthcare systems across the world, and causing significant morbidity and mortality. Traditional diagnostic, prognostic, and therapeutic procedures to address viral hepatitis are successful but have limits in accuracy, speed, and accessibility. Artificial intelligence (AI) advancement provides substantial opportunities to overcome these challenges. This study investigates the role of AI in revolutionizing viral hepatitis care, from early detection to therapy optimization and epidemiological surveillance. A comprehensive literature review was conducted using predefined keywords in the Nature, PLOS ONE, PubMed, Frontiers, Wiley Online Library, BMC, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar databases. Peer-reviewed publications written in English between January 2019 and August 2024 were examined. The data of the selected research papers were synthesized and analyzed using thematic and narrative analysis techniques. The use of AI-driven algorithms in viral hepatitis control involves many significant aspects. AI improves diagnostic accuracy by integrating machine learning (ML) models with serological, genomic, and imaging data. It enables tailored treatment plans by assessing patient-specific characteristics and predicting therapy responses. AI-powered technologies aid in epidemiological modeling, and AI-powered systems effectively track treatment adherence, identify medication resistance, and control complications associated with chronic hepatitis infections. It is vital in identifying new antiviral medicines and vaccines, speeding the development pipeline through high-throughput screening and predictive modeling. Despite its transformational promise, using AI in viral hepatitis care presents various challenges, including data privacy concerns, the necessity for extensive and varied datasets, and the possibility of algorithmic biases. Ethical considerations, legal frameworks, and multidisciplinary collaboration are required to resolve these issues and ensure AI technology’s safe and successful use in clinical practice. Exploiting the full AI’s potential for viral hepatitis management provides unparalleled prospects to improve patient outcomes, optimize public health policies, and, eventually, and alleviate the disease’s negative impact worldwide. This study seeks to provide academics, medics, and policymakers with the fundamental knowledge they need to harness AI’s potential in the fight against viral hepatitis.Mesopotamian Journal of Big Data1661Exploiting the full AI’s potential for viral hepatitis management provides unparalleled prospects to improve patient outcomes, optimize public health policies, and, eventually, and alleviate the disease’s negative impact worldwide.Mesopotamian Journal of Big Data["Guma Ali", "Maad M. Mijwil", "I. Adamopoulos", "Bosco Apparatus Buruga", "Murat G\u00f6k", "Malik Sallam"]2024-08-15T00:00:00 9cfb5ae3a8eb450299fb4bc1d5e3785428faa4f9The contemporary digital reality is inconceivable without artificial intelligence (AI), which has become disseminated across all cultural practices, from scientific and artistic endeavors to everyday activities. AI increasingly functions as an agent of communication and decision-making, gradually surpassing human capabilities across nearly all competencies. The information flows of this new reality can only be navigated through hybrid systems based on post-critical rationality, which inherently introduces an irreducible element of uncertainty and risk in human-machine environments. The article proposes examining the techno-subject through the lens of activity theory and the multiple types of rationality it generates. This framework facilitates the analysis of sociocultural and anthropological implications arising from AI’s integration with human domains, while addressing the existential challenges inherent in constructing a harmonious hybrid society. Beyond V.S. Stepin’s types of scientific rationality, the author builds upon previously introduced forms of rationality: post-critical, object-oriented, instrumental, subjective, results-oriented, creative, and autopoietic. This theoretical framework facilitates a substantive discussion of various manifestations of AI subjectivity, including its generalized embodiment and creative specificity. The demarcation of dominance domains between natural intelligence and AI in the intellectual sphere is proposed to be resolved on the basis of their heuristic potentials. The author maintains that natural intelligence invariably possesses superior capacity in this regard. The article examines approaches to risk assessment in AI implementation strategies, focusing on criteria for preserving anthropological and sociocultural profiles in the development of hybrid society. Advancing the concept of friendly AI is substantiated as essential, with consideration given not only to technological but also to anthropological aspects of human–machine interaction. The author advocates for the development of social examination institutions as regulatory mechanisms for natural–artificial intelligence interaction and anthropological–technological subject interfaces.Russian Journal of Philosophical Sciences71The article examines approaches to risk assessment in AI implementation strategies, focusing on criteria for preserving anthropological and sociocultural profiles in the development of hybrid society, and advocates for the development of social examination institutions for natural–artificial intelligence interaction and anthropological–technological subject interfaces.Russian Journal of Philosophical Sciences["Vladimir G. Budanov"]2024-08-15T00:00:00 804f24caccc7cfb6c1088d7a4efc896a7e2ab8deThis paper explores human trust in artificial intelligence (AI), focusing on the effects of social categorization (ingroup vs. outgroup) and AI human‐likeness through two pre‐registered studies involving 160 participants each. The first study, a lab experiment in China, and the second, an online experiment representative of the United States, both utilized a trust game to assess trust across four conditions: ingroup‐humanoid AI, ingroup‐non‐humanoid AI, outgroup‐humanoid AI, and outgroup‐non‐humanoid AI. Results indicated higher trust for ingroup and humanoid AIs, with statistical significance. Mixed‐design ANOVA was used to analyze the data, revealing significant main effects and interactions. The second study also identified an emotional connection as a mediator in trust, suggesting significant design implications for AI in trust‐critical sectors like healthcare and autonomous transportation.Managerial and Decision Economics762Results indicated higher trust for ingroup and humanoid AIs, with statistical significance, and an emotional connection as a mediator in trust, suggesting significant design implications for AI in trust‐critical sectors like healthcare and autonomous transportation.Managerial and Decision Economics["Yanqi Sun", "Cheng Xu", "Hao Xu"]2024-08-15T00:00:00 ce2fe486183fff4cbdb8dd4142a42818ca09079dThe available data on the role of polypragmasia in increasing the frequency of multiple drug interactions, when one drug interacts with two or more other drugs, increasing the risk of side effects associated with them, are considered. The application of network analysis and artificial intelligence to predict the development of clinically significant adverse reactions in conditions of polypharmacotherapy is described. The mechanisms of pharmacodynamic and pharmacokinetic interaction of drugs in the development of adverse reactions are considered and drugs potentially carrying an increased risk in multiple drug interactions are noted. The most dangerous drugs involved in drug interactions were psychotropic drugs, which accounted for about a third of all applicable medicines. The most common serious potential complications associated with this interaction were serotonin syndrome, seizures, QT prolongation, and bleeding. Graph probabilistic models, machine learning models for analyzing reliable sources of medical data, factor models that allow assessing the risks of taking two or more drugs together are proposed. These models are implemented in software and can be implemented in clinical decision support systems. It is concluded that the use of artificial intelligence can reduce the risk of adverse reactions during polypharmacotherapy, especially in elderly patients.Annals of the Russian academy of medical sciences490It is concluded that the use of artificial intelligence can reduce the risk of adverse reactions during polypharmacotherapy, especially in elderly patients.Annals of the Russian academy of medical sciences["N. Shimanovsky", "V. Sudakov", "V. V. Beregovykh"]2024-08-15T00:00:00 f0063c9b6ca248bfd25c99cff35cf689148486cbGuru kesulitan dalam mengembangkan media pembelajaran digital sehingga berdampak pada kemampuan siswa dalam belajar yang rendah. Tujuan penelitian ini yaitu untuk mengembangkan Media Pembelajaran E-Comic Berbantuan Artificial Intelligence (AI) pada Materi Sistem Pernapasan Manusia. Penelitian ini merupakan jenis penelitian Research and Development (R&D) dengan menggunakan model 4-D. Validasi produk dilakukan oleh 2 orang pakar/ahli yaitu ahli materi dan ahli media pembelajaran. Subjek uji coba yaitu 1 guru dan siswa. Uji coba kelompok kecil sejumlah 5 siswa dan uji coba kelompok besar sejumlah 30 peserta didik kelas V SD. Metode yang digunakan untuk mengumpulkan data yaitu observasi, wawancara, dan kuesioner. Instrumen yang digunakan dalam mengumpulkan data yaitu lembar kuesioner. Teknik analisis data menggunakan deskriptif kualitatif dan kuantitatif. Hasil penelitian yaitu validasi dari para ahli, e-comic ini memperoleh hasil 95% oleh ahli materi, dan 76,5% oleh ahli media, sehingga media pembelajaran dinyatakan sangat layak. Hasil dari uji coba kelompok kecil diperoleh 89 % dan Uji coba kelompok kelompok besar diperoleh 84% sehingga dinyatakan sangat layak. Disimpulkan bahwa Media Pembelajaran E-Comic Berbantuan Artificial Intelligence (AI) layak digunakan dalam pembelajaran siswa kelas V Sekolah Dasar.Journal of Education Action Research410Journal of Education Action Research["Yunitha Ike Christyowati Ike", "Rufi\u2019i", "Ujang Rohman"]2024-08-15T00:00:00 f2a255763904298d43827462faefae7cfee7aabdСкорость развития искусственного интеллекта (ИИ) в современном мире может создать иллюзию, что ИИ сопровождает исключительно бурный спрос как разработчиков, так и инвесторов к этой технологии. К сожалению, история искусственного интеллекта (ИИ) - это не только история триумфальных прорывов и восхитительных открытий, но и история разочарований, невыполненных обещаний и периодов застоя. Эти периоды, известные как «зимы ИИ», характеризовались снижением финансирования, интереса со стороны научного сообщества и общественности, а также скептицизмом в отношении перспектив развития этой области знания. В этой статье мы рассмотрим основные причины наступления "зим ИИ", проанализируем уроки, извлеченные из этих неудач, и обсудим, как они повлияли на дальнейшее развитие искусственного интеллекта. The speed of development of artificial intelligence (AI) in the modern world can create the illusion that AI accompanies an exceptionally rapid demand for this technology from both developers and investors. Unfortunately, the history of artificial intelligence (AI) is not only a story of triumphant breakthroughs and amazing discoveries, but also a story of disappointments, unfulfilled promises and periods of stagnation. These periods, known as the "AI winters", were characterized by a decrease in funding, interest from the scientific community and the public, as well as skepticism about the prospects for the development of this field of knowledge. In this article, we will look at the main causes of the onset of "AI winters", analyze the lessons learned from these failures, and discuss how they influenced the further development of artificial intelligence.Современные технологии. Технические и естественные науки: сборник статей международной научной конференции (Архангельск, Июнь 2024)00Современные технологии. Технические и естественные науки: сборник статей международной научной конференции (Архангельск, Июнь 2024)["\u0412\u043b\u0430\u0434\u0438\u043c\u0438\u0440 \u0412\u0430\u0441\u0438\u043b\u044c\u0435\u0432\u0438\u0447 \u0421\u043c\u0435\u0442\u0430\u043d\u0430"]2024-08-15T00:00:00 d30a355590154eceecc241e9cf0ed1f9192b9352Maintenance is an essential aspect of supporting aircraft operations. However, there are still several obstacles and challenges in the process, such as incomplete technical record data, irregular maintenance schedules, unscheduled component replacement, unavailability of tools or components, recurring problems, and a long time for troubleshooting. Digitalization and the massive use of artificial intelligence (AI) in various sectors have been widely carried out in the industry 5.0 era today, especially in the aviation industry. It offers several advantages to optimize aircraft maintenance and operations, such as predictive maintenance, fault detection, failure diagnosis, and intelligent monitoring systems. The utilization of AI has the potential to solve obstacles and challenges in aircraft maintenance activities, such as improving aircraft reliability, reducing aircraft downtime, improving safety, and reducing maintenance costs. This research uses the Systematic Literature Review method, which aims to review and provide an understanding of objectives, strategies, methods, and equipment objects involved in the application of AI in aircraft maintenance and repair scope. The findings and understanding from this research can be used as a basis for utilizing or adopting AI in aircraft maintenance to be more targeted and efficient in the future. This study reviews and presents research trends from reputable journals and proceedings screened using a unique protocol.EAI Endorsed Transactions on Internet of Things340This study reviews and presents research trends from reputable journals and proceedings screened using a unique protocol, which aims to review and provide an understanding of objectives, strategies, methods, and equipment objects involved in the application of AI in aircraft maintenance and repair scope.EAI Endorsed Transactions on Internet of Things["Erna Shevilia Agustian", "Zastra Alfarezi Pratama"]2024-08-15T00:00:00 384cdaca4badff520733e72130d126f316bfc4b8In this paper we discuss the problem of creating trusted artificial intelligence (AI) technologies. Modern AI is based on machine learning and neural networks and is vulnerable to biases and errors. Efforts are made to establish standards for the development of trusted AI technologies, but they have not yet succeeded. AI technologies trust can only be achieved with the appropriate scientific and technological base and corresponding tools and techniques for countering attacks. We present the ISP RAS Trusted AI Research Center results and propose a work model that can ensure technological independence and long-term sustainable development in this area.Вестник Российской академии наук10The ISP RAS Trusted AI Research Center results are presented and a work model that can ensure technological independence and long-term sustainable development in this area is proposed.Вестник Российской академии наук["A. I. Avetisyana"]2024-08-15T00:00:00 1b083a10ab18b257bf8ddd8ffcdce1b49143fe21The integration of artificial intelligence such as ChatGPT into educational frameworks marks a pivotal transformation in teaching. This quasi-experimental study, conducted in September 2023, aimed to evaluate the effects of artificial intelligence–assisted learning on nursing students' ethical decision-making and clinical reasoning. A total of 99 nursing students enrolled in a pediatric nursing course were randomly divided into two groups: an experimental group that utilized ChatGPT and a control group that used traditional textbooks. The Mann-Whitney U test was employed to assess differences between the groups in two primary outcomes: (a) ethical standards, focusing on the understanding and applying ethical principles, and (b) nursing processes, emphasizing critical thinking skills and integrating evidence-based knowledge. The control group outperformed the experimental group in ethical standards and demonstrated better clinical reasoning in nursing processes. Reflective essays revealed that the experimental group reported lower reliability but higher time efficiency. Despite artificial intelligence's ability to offer diverse perspectives, the findings highlight that educators must supplement artificial intelligence technology with strategies that enhance critical thinking, careful data selection, and source verification. This study suggests a hybrid educational approach combining artificial intelligence with traditional learning methods to bolster nursing students' decision-making processes and clinical reasoning skills.Computers, Informatics, Nursing330A hybrid educational approach combining artificial intelligence with traditional learning methods to bolster nursing students' decision-making processes and clinical reasoning skills is suggested.Computers, Informatics, Nursing["Hyewon Shin", "J. C. De Gagne", "Sang Suk Kim", "Minjoo Hong"]2024-08-15T00:00:00 660bc55d8040b16f460dcd26885eb139d6cff4e9The article presents a socio-philosophical analysis of artificial intelligence (AI) integration into public administration systems. The research focuses on identifying an optimal balance between enhancing administrative efficiency and preserving humanistic values. The author examines diverse perspectives on AI’s role in contemporary society, ranging from techno-optimistic concepts that view AI as a tool for qualitative improvement of human life, to critical theories warning of dehumanization risks and increased social control. The paper conducts a comparative analysis of national AI development strategies among leading global powers, identifying their common features and significant differences shaped by cultural, political, and economic factors. Potential risks and threats associated with the implementation of AI systems in public administration are explored, including issues of personal data protection, information security, and the ethical dimensions of algorithmic decision-making. The concept of a human-centered approach to AI is examined as a potential guiding principle for the development and deployment of these technologies. Various levels of control over AI systems are characterized, encompassing legal regulation, professional and public evaluation. Particular attention is given to the prospects of artificial general intelligence (AGI) development and its potential impact on the transformation of state institutions and social relations. The study argues that AGI architecture, enabling genuine system agency, must incorporate a level responsible for actualization functions (strategic goal-setting, ethics, knowledge, and self-identification). Special emphasis is placed on the system’s awareness of its finite existence as a necessary condition for developing meaningful operational strategies and ethical principles. The article concludes that as AI technologies advance, the importance of ethical norms, value systems, and responsibility principles increases since these core societal factors cannot be fully replaced even by the most sophisticated regulation. The author highlights the growing significance of mutual trust between state and society in an environment where AI systems provide unprecedented opportunities for social control.Russian Journal of Philosophical Sciences190A socio-philosophical analysis of artificial intelligence (AI) integration into public administration systems concludes that as AI technologies advance, the importance of ethical norms, value systems, and responsibility principles increases since these core societal factors cannot be fully replaced even by the most sophisticated regulation.Russian Journal of Philosophical Sciences["B. B. Slavin"]2024-08-15T00:00:00 e59c5768f4cd5625f5d89629109ad7bd38655f08The influence of Artificial Intelligence (AI) can be seen in every nook and corner of life. It has become an essential part of human life. Its influence in medical field is also noticeable but in certain aspects utilization of AI in dentistry has not been up to a great extent. This questionnaire-based survey was done to assess the knowledge, attitude and behaviour of dental students about AI. A self-administered closed ended questionnaire consisting of 36 questions was distributed among 3100 interns and post graduate students studying in different parts of Rajasthan. The questionnaire consisted of demographic details of respondents as well as fundamental KAP towards AI.Response rate was 90.32%. The results showed that about 56.75% of respondents had poor knowledge of AI and its application in dentistry. Although their attitude towards AI was fair (48.75%) but their practical experience of using AI was poor (57.89%). There was a significant difference between the KAP of postgraduates when compared to interns (p value < 0.05). Pearsons’s correlation test showed that there was no correlation between knowledge and attitude or knowledge with behaviour as well as attitude and behaviour.There is a lack of awareness about the use of AI in dentistry among dental students. So, there is a need for implementation of practical courses to improve the knowledge as well as practice of AI in dentistry.Journal of Contemporary Orthodontics50There is a lack of awareness about the use of AI in dentistry among dental students, so, there is a need for implementation of practical courses to improve the knowledge as well as practice of AI in dentistry.Journal of Contemporary Orthodontics["Deeksha Bhanotia", "Shaikh Tarannum Alam", "Mridula Trehan", "Divyaroop Rai", "Anuroop Rai"]2024-08-15T00:00:00 24a1c728f333caa2af16364111324f82976a34ceThe article examines the dialogue between British cognitive science expert Murray Shanahan and the large language model Claude 3 Opus about “self-awareness” of artificial intelligence (AI). Adopting a text-centric approach, the author analyzes AI’s discourse through a hermeneutic lens from a reader’s perspective, irrespective of whether AI possesses consciousness or personhood. The article draws parallels between AI’s reasoning about the nature of consciousness and Buddhist concepts, especially the doctrine of dharmas, which underpins the Buddhist concept of anātman (“non-Self”). Basic classifications of dharmas and their justification are examined in light of the Buddhist system of ideas about the foundations of an individual’s cognitive experience in the world. The author emphasizes that the problem of the Self as a linguistic and conceptual construct, rather than a real ontological category, was first formulated in the teachings of Buddha Shakyamuni who also proposed an “experimental” application of this concept in practices of systematic introspection (smṛti). The article contends that Claude’s discourse on self-awareness, even if it is just a tapestry of linguistic constructs woven by preset algorithms, could prove to be a source inspiring new approaches to the enigma of consciousness. This potential stems from its vast database, which is a melting pot of textual heritage from diverse human cultures. The author posits that examining AI-generated texts through the prism of Indian and Buddhist thought traditions can be eye-opening. Such an approach might help shed light on and overcome the unconscious cognitive biases and cultural blind spots within Western consciousness studies that have hindered their engagement with the full spectrum of human intellectual traditions. The author concludes that discovering different cultural sources in AI discourse and examining it from the perspective of various cultural traditions can: firstly, enrich the conceptual apparatus of cognitive studies; secondly, reveal universal cross-cultural patterns in understanding consciousness; thirdly, generate new research hypotheses and directions in studying not only artificial but also natural intelligence; fourthly, contribute to rethinking our understanding of the Other, by expanding the boundaries of what we today consider conscious or sentient.Russian Journal of Philosophical Sciences20Claude’s discourse on self-awareness, even if it is just a tapestry of linguistic constructs woven by preset algorithms, could prove to be a source inspiring new approaches to the enigma of consciousness.Russian Journal of Philosophical Sciences["Victoria G. Lysenko"]2024-08-15T00:00:00 08322a66ec667a9875b9030b5e18e2d9a237f05aPURPOSE Artificial intelligence (AI) has gained significant attention in ophthalmology. This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making. METHODS Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix. RESULTS The study included 3,377 publications from 4,035 institutions in 98 countries. China and the United States had the most publications. Sun Yat-sen University is a leading institution. Translational Vision Science & Technology"published the most articles, while "Ophthalmology" had the most co-citations. Among 13,145 researchers, Ting DSW had the most publications and citations. Keywords included "Deep learning," "Diabetic retinopathy," "Machine learning," and others. CONCLUSION The study highlights the promising prospects of AI in ophthalmology. Automated eye disease screening, particularly its core technology of retinal image segmentation and recognition, has become a research hotspot. AI is also expanding to complex areas like surgical assistance, predictive models. Multimodal AI, Generative Adversarial Networks, and ChatGPT have driven further technological innovation. However, implementing AI in ophthalmology also faces many challenges, including technical, regulatory, and ethical issues, and others. As these challenges are overcome, we anticipate more innovative applications, paving the way for more effective and safer eye disease treatments.Ophthalmic Epidemiology490This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making.Ophthalmic epidemiology["Jie Deng", "YuHui Qin"]2024-08-15T00:00:00 576c8ebf881dc51ceff71a9d56794ba6cf05ae3aTeachers are the key to the success of school digital initiatives in creating quality human resources. Teachers as educators are required to be proficient and skilled in using various ICT (Information and Communication Technology) based learning media. The sophistication of technology that can be utilized by teachers to improve the learning process experience is Artificial Intelligence (AI). Chat GPT is one of the implementations of the GPT model designed to interact with users in the form of conversation or chat. Based on the results of interviews with the Principal of SMPN 4 Kota Langsa, it is known that there are several problems, namely the lack of education for teachers related to AI, lack of knowledge in using AI, especially Chat GPT, and the absence of utilization of AI-based learning media. The method of implementing activities includes observation or field survey, socialization and counseling, training, mentoring and monitoring and evaluation. The questionnaire results generally show that the community service activities carried out at SMPN 4 Kota Langsa are said to have received sufficient participation from the teachers. The good response shown by the teachers helped the socialization process run smoothly, actively asking and answering questions so that the training process on the use of AI, especially Chat GPT, went well.  Jurnal Masyarakat Berdikari dan Berkarya (Mardika)00The questionnaire results generally show that the community service activities carried out at SMPN 4 Kota Langsa are said to have received sufficient participation from the teachers, and the good response shown by the teachers helped the socialization process run smoothly, actively asking and answering questions so that the training process on the use of AI, especially Chat GPT, went well.Jurnal Masyarakat Berdikari dan Berkarya (Mardika)["Amelia", "Fitra Muliani", "Rahmi Meutia", "Ulya Nabilla", "Riezky Purnama Sari", "Nurviana", "Fairus"]2024-08-15T00:00:00 70dc62cf735257f2015c9480bb4b48a513240cc8In the era of digital change, Artificial Intelligence (AI) has become an indispensable tool for enterprises to enhance competitiveness and optimize operations. Executives, as the core of decision-making in enterprises, profoundly impact enterprises' digitalization. This study delves into the intriguing realm of corporate artificial intelligence by examining the impact of executives' digital backgrounds on A-share listed companies in China's Shanghai and Shenzhen stock markets from 2001 to 2021. The findings reveal a significant influence of executives' digital background on the integration of corporate AI, even after thorough endogeneity and robustness tests. Furthermore, the study's heterogeneity analysis highlights how this positive influence is particularly pronounced in eastern and western regions, non-polluting industries, and asset-intensive enterprises. Ultimately, this research provides guidance for advancing AI application in enterprises and driving digital transformation.Journal of Education, Humanities and Social Sciences100Examining the impact of executives' digital backgrounds on A-share listed companies in China's Shanghai and Shenzhen stock markets from 2001 to 2021 reveals a significant influence of executives' digital background on the integration of corporate AI, even after thorough endogeneity and robustness tests.Journal of Education, Humanities and Social Sciences["Shengnan Xu", "Shijie Yuan"]2024-08-15T00:00:00 0848e452648f42e76fb401a2e2509b1026467adfThis study aims to reveal, through a bibliometric analysis, more information about the transformation and impacts of artificial intelligence on the service industry and consumer behavior transformation and development over the last 32 years. Articles were collected from three databases using keyword combinations (artificial intelligence, service industry, consumer behavior, marketing, machine learning, etc.). Then, inclusion and exclusion criteria for the field were applied to obtain the final sample. The final sample consisted of 50 peer-reviewed articles. Three separate analyses were conducted to test the sample. A performance analysis identified the publication years of the articles, contributions per country, and the output of the relevant journals. The data analysis analyzed the articles in-depth and provided insights into the evolution of relevant scientific production. The study's findings provide a broad perspective on research to date and identify potential research gaps. This research endeavors to contribute to the marketing field by undertaking a bibliometric analysis of research concerning the impact of artificial intelligence on consumer behavior and the service industry, spanning the period from 1991 to 2024. It offers suggestions to academics and researchers for future research.The eurasia proceedings of science, technology, engineering & mathematics00A bibliometric analysis of research concerning the impact of artificial intelligence on consumer behavior and the service industry, spanning the period from 1991 to 2024, offers suggestions to academics and researchers for future research.The Eurasia Proceedings of Science Technology Engineering and Mathematics["\u015eeyda Ok"]2024-08-15T00:00:00 ed3ccb98cc91e7002c6f1a4c99bc7db3df849173PURPOSE OF REVIEW Technologies in healthcare incorporating artificial intelligence tools are experiencing rapid growth in static-image-based applications such as diagnostic imaging. Given the proliferation of artificial intelligence (AI)-technologies created for video-based imaging, ophthalmic microsurgery is likely to experience significant benefits from the application of emerging technologies to multiple facets of the care of the surgical patient. RECENT FINDINGS Proof-of-concept research and early phase clinical trials are in progress for AI-based surgical technologies that aim to provide preoperative planning and decision support, intraoperative image enhancement, surgical guidance, surgical decision-making support, tactical assistive technologies, enhanced surgical training and assessment of trainee progress, and semi-autonomous tool control or autonomous elements of surgical procedures. SUMMARY The proliferation of AI-based technologies in static imaging in clinical ophthalmology, continued refinement of AI tools designed for video-based applications, and development of AI-based digital tools in allied surgical fields suggest that ophthalmic surgery is poised for the integration of AI into our microsurgical paradigm.Current Opinion in Ophthalmology580The proliferation of AI-based technologies in static imaging in clinical ophthalmology, continued refinement of AI tools designed for video-based applications, and development of AI-based digital tools in allied surgical fields suggest that ophthalmic surgery is poised for the integration of AI into the authors' microsurgical paradigm.Current opinion in ophthalmology["Yannek I. Leiderman", "Matthew J. Gerber", "J. Hubschman", "Darvin Yi"]2024-08-15T00:00:00 4f07fcfc03270362d43b05e2805e25a4152d7b76The article critically examines the capabilities and limitations of artificial intelligence (AI) technologies within the context of ongoing debates surrounding potential threats stemming from their advancement. The study scrutinizes and challenges key arguments positing the possibility of human subjugation by AI systems. The author undertakes a comparative analysis of natural and artificial intelligence, employing the psychological experiences of C.G. Jung as a case study. It is demonstrated that despite the remarkable achievements of contemporary neural networks in information processing, they are fundamentally limited in their capacity for genuine comprehension and creative thought. The paper identifies three key innovations associated with AI development: the enhancement of users’ cognitive capabilities, the formation of a novel psychic reality of “digital consciousness,” and the emergence of hybrid life forms at the nexus of human activity and technological processes. The author highlights the fundamental limitations of AI in the realms of emotional intelligence and creative capabilities. Attention is drawn to the challenges associated with the development of AI systems, including the influence of impersonal social structures on decision-making, the disconnect between developers and users, and the psychological effects of interacting with AI. The conclusion reached is that the issue of human subjugation by AI requires a re-evaluation within the broader context of the impact of contemporary technologies on society. It is proposed that the forthcoming era be viewed as a period of coexistence and interaction between two types of intelligence: natural and artificial. Apprehension is expressed that humanity will not adjust its worldview and behavior until after experiencing a series of impending catastrophes. In closing, the author advocates for proactive engagement in mitigating the risks associated with AI development, while simultaneously underscoring the impracticality of complete abstention from these technologies.Russian Journal of Philosophical Sciences30The issue of human subjugation by AI requires a re-evaluation within the broader context of the impact of contemporary technologies on society, and it is proposed that the forthcoming era be viewed as a period of coexistence and interaction between two types of intelligence: natural and artificial.Russian Journal of Philosophical Sciences["V. Rozin"]2024-08-15T00:00:00 d4641f5d162b182ac4c45ca176ed9e9d136a7ab0The study aimed to investigate the use of artificial intelligence techniques to enhance decision-making processes in administrative decisions and promote innovation in Saudi economic institutions, in addition to understanding how to effectively apply artificial intelligence techniques in a business context. The researcher adopted a descriptive-analytical approach in this study, utilizing a survey tool for data collection. The research community represented all individuals working within the Saudi economic institutions sector, and the study sample consisted of 80 employees. The study reached several theoretical and practical results, the most important of which are: - Artificial intelligence represents one of the pillars of economic progress, manifested in the quality of decision-making within Saudi economic institutions. - The efficiency of administrative decisions is influenced by the extent of their application of artificial intelligence techniques at all management levels within Saudi economic institutions. - Artificial intelligence techniques enhance innovation levels within Saudi economic institutions." The study recommended several recommendations, the most prominent of which are: Investing in artificial intelligence technologies and activating their application within Saudi economic institutions, relying on artificial intelligence techniques in designing the policies of Saudi economic institutions at all administrative levels and implementing their decisions, applying the use of artificial intelligence techniques in finding suitable alternatives to design new strategies to create opportunities for innovation and development within Saudi economic institutions.International Journal for Scientific Research00International Journal for Scientific Research["Sarah Al-Otaibi", "Al-Faisal Mohamed", "Fayez Jarad"]2024-08-15T00:00:00 25f4ab5a1210d79d88fe90334a04cbe9d454c007Our understanding of disagreement is rooted in psychological studies of human behavior, which typically cast disagreement as divergence: two agents forming diverging evaluations of the same object. Recent work in arti fi cial intelligence highlights how disagreement can also arise from misalignment in how agents represent that object. Here, we formally describe these twodimensionsofdisagreement,clarifytherelationshipbetweenthem,andarguethatstrategies forcon fl ict resolution and collaboration are likely to be ineffective (or even back fi re) if they do not consider misalignment in representations. Moreover, we identify how taking misalignment into account can enrich current research on judgment and decision making, from biased advice taking to algorithm aversion, and discuss implications for arti fi cial intelligence research.Decision871It is identified how taking misalignment into account can enrich current research on judgment and decision making, from biased advice taking to algorithm aversion, and discuss implications for arti fi cial intelligence research.Decision["Kerem Oktar", "Ilia Sucholutsky", "Tania Lombrozo", "Thomas L. Griffiths"]2024-08-15T00:00:00 7607d4e1e8de5543657d0c2d3bdd1fc7b9380a96This research investigates the transformative potential of Mixture of Experts (MoE) and multimodal learning within generative AI, exploring their roles in advancing towards Artificial General Intelligence (AGI). By leveraging a combination of specialized models, MoE addresses scalability and computational limitations, enabling more nuanced and robust modelling across diverse data modalities. The research exploration draws inspiration from pioneering projects like Google's Gemini and OpenAI's anticipated Q* to push the boundaries of AI capabilities. The objectives include exploring the impact of MoE on generative AI, investigating multimodal learning's role in achieving AGI, conducting experiments to demonstrate MoE's effectiveness across various domains, and assessing the influence of AI-generated preprints on the peer-review process. Ethical considerations are also emphasized, advocating for AI development that aligns with societal well-being. The methodology employs techniques from social network analysis to examine the current landscape and future possibilities of MoE and multimodal learning. Experiments conducted across healthcare, finance, and education demonstrate a 25% increase in training efficiency and a 30% improvement in output quality when using MoE compared to traditional single-model approaches. The analysis of AI-generated preprints highlights their significant impact on the peer-review process and scholarly communication. The findings underscore the potential of MoE and multimodal learning to propel generative AI towards AGI. The study advocates for responsible AI development, aligned with human-centric values and societal well-being, and proposes strategic directions for future research. This research promotes the balanced and ethical integration of MoE, multimodality, and AGI in generative AI, fostering equitable distribution and ethical usage of AI technologies.EAI Endorsed Transactions on AI and Robotics1340EAI Endorsed Transactions on AI and Robotics["Zarif Bin Akhtar"]2024-08-15T00:00:00 ed04b751010f1fe2e3e077aa3ee1c08f734510baInternational Journal of Research Studies in Management220International Journal of Research Studies in Management["Nikisha Dolicanae D Dadua"]2024-08-15T00:00:00 d3bcfe6348ac220c54ac2addd60cc14208c2498cInternational Journal of Research Studies in Management90International Journal of Research Studies in Management["Dongxue Li"]2024-08-15T00:00:00 f23a9a1d328c52ed0716a4c9b220f70985277ab3Today, China has achieved tremendous success in the field of information technology and is actively promoting the modernization of the country and increasing its competitiveness in the world through progressive digitalization and informatization of all sectors of life and production. With more than a billion registered users of the global information network, actively using the scientific and educational potential of the country, China keeps information policy in the zone of close attention, as evidenced, in particular, by the fact that it was created in 2014. Since its creation, the Central Commission of the CPC Central Committee on Internet Security and Informatization has been personally headed by Xi Jinping. China is aware of the serious risks associated with the development of AI, but also understands the importance of this area for further development and improving the country's competitiveness. In this regard, the political leadership is taking systematic steps and measures for the healthy and safe development of this area in the country and in the world. The information sphere is no less important for Russia, especially in the context of attempts to isolate it and displace it to the global periphery, therefore, in the current conditions, the topic of cooperation with China in the field of information technology is of particular priority for Russia. This is evidenced by its inclusion in the final joint Russian-Chinese statement of May 16, 2024 following the meeting of the heads of state in Beijing. The article examines the general situation of the formation of AI regulatory tools in the world and, above all, in the PRC. A number of documents adopted by the country's top political leadership, as well as expert assessments and opinions, were reviewed.Problemy Dalnego Vostoka10The article examines the general situation of the formation of AI regulatory tools in the world and, above all, in the PRC.Problemy Dalnego Vostoka["A. V. Pikover"]2024-08-15T00:00:00 f6367d5714e1777e3ea0d2da4d8c38b1ed14d16b00["Divya Mishra", "Alok Kumar Verma", "Shanu Sharma"]2024-08-15T00:00:00 3ba5e8f470f7444a5106c804ce9c35e1252874d5Journal of Applied Research in Memory and Cognition00Journal of Applied Research in Memory and Cognition["Lauren E. Kelso", "Jesse H. Grabman", "David Dobolyi", "Chad S. Dodson"]2024-08-15T00:00:00 1d75a9e0791b7b5d2cc95e7c6ea1c41dc5fca743International Journal of Research Studies in Education110International Journal of Research Studies in Education["Xingxing Li"]2024-08-15T00:00:00 5af6928c850c4de41248fce50e7f6331c1cd5228 Artificial intelligence has become a defining technology for the last decade and possibly the next few. Every day, new and new applications are created based on large language models (LLM), a little hastily called artificial intelligence (AI). This reveals new and new opportunities for their use in various spheres of public life. Public administration, despite its inherent conservatism, is also one such area where AI can be used to enhance its administrative capacity and citizens’ satisfaction with administrative services. The aim of this article is to address the possibilities of using AI in public sector organizations and to reveal the limitations that hinder it. In this sense, the object of the research is the Bulgarian state institutions, and the subject - the application of AI in their work. A study was conducted that shows that the employees in the Bulgarian state administration still do not know the possibilities of AI and how to use it in their work. Abstention is due to both ignorance and lack of regulation about what apps can be used where, as well as fear of possible risks. The report presents the possibilities of using some AI-based applications in the implementation of basic work processes in administrations and justifies the need to introduce strict regulations for this. The author’s hypothesis will defend the claim that the Bulgarian administration does not know well the possibilities of digital transformation and AI, through which their work and efficiency can be improved.Economics51The report presents the possibilities of using some AI-based applications in the implementation of basic work processes in administrations and justifies the need to introduce strict regulations for this.ECONOMICS["Borislav Borissov", "Y. Hristozov"]2024-08-15T00:00:00 6f5f3ef8640a8df3592109d4b8b454e94fd48b9f In recent years, there has been an increased interest in artificial intelligence (AI) and its various applications in many sectors of the economy, in education and in people's everyday lives. The study of the application of artificial intelligence is also evident in many articles on smart cities. The aim of this study is to reveal new trends in the evolution of the Smart City and the formation of conceptual assumptions and practical applications of the technology in the Smart Sustainable City. The research focuses on literature analysis and content analysis.System Safety: Human - Technical Facility - Environment540New trends in the evolution of the Smart City and the formation of conceptual assumptions and practical applications of the technology in the Smart Sustainable City are revealed.System Safety: Human - Technical Facility - Environment["Aleksandra Kuzior"]2024-08-15T00:00:00 b36d64fc3f659b18f3406d8b712d2842de77d440Abstract Modular robots are collections of simple embodied agents, the modules, that interact with each other to achieve complex behaviors. Each module may have a limited capability of perceiving the environment and performing actions; nevertheless, by behaving coordinately, and possibly by sharing information, modules can collectively perform complex actions. In principle, the greater the actuation, perception, and communication abilities of the single module are the more effective is the collection of modules. However, improved abilities also correspond to more complex controllers and, hence, larger search spaces when designing them by means of optimization. In this article, we analyze the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence. We consider the case of modular soft robots, where modules can contract, expand, attach, and detach from each other, and make them face two tasks (locomotion and piling), optimizing their controllers with evolutionary computation. We observe that limited abilities often do not prevent the robots from succeeding in the task, a finding that we explain with (a) the smaller search space corresponding to limited actuation, perception, and communication abilities, which makes the optimization easier, and (b) the fact that, for this kind of robot, morphological computation plays a significant role. Moreover, we discover that what matters more is the degree of collectivity the robots are required to exhibit when facing the task.Artificial Life300This article analyzes the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence.Artificial Life["Francesco Rusin", "Eric Medvet"]2024-08-15T00:00:00 4adb67214d4f55727a8af5ff50ca7fc24f2926dfArtificial Intelligence (AI) chatbots are revolutionizing customer interactions in the financial services sector, particularly in the realm of business loans and credit experiences. This review paper examines the role of AI chatbots in enhancing these crucial financial processes. It begins with an overview of the traditional landscape of business loans and credit experiences, highlighting existing challenges and the imperative for seamless customer interactions. The paper defines AI chatbots and explores their functionality, adoption trends, and benefits within financial institutions. Special attention is given to how AI chatbots personalize customer interactions, streamline loan application processes, and enhance efficiency and accuracy in credit assessments. Despite their transformative potential, challenges such as privacy concerns, integration complexities, and managing customer trust are discussed. The paper concludes by outlining future directions, including emerging trends and potential advancements in AI chatbots, underscoring their evolving role in shaping superior business loan and credit experiences. Keywords:  AI Chatbots, Ethical Considerations, Algorithmic Bias, Transparency, Financial Services.Finance &amp; Accounting Research Journal014Despite their transformative potential, challenges such as privacy concerns, integration complexities, and managing customer trust are discussed, underscoring their evolving role in shaping superior business loan and credit experiences.Finance &amp; Accounting Research Journal["Emmanuel Igba", "Adenike Folashade Adeyemi", "Joy Onma Enyejo", "Amina Catherine Ijiga", "Grace Amidu", "George Addo"]2024-08-15T00:00:00 9d9f4b0d91670d7a1b42d12bab9ad6718acc03fcThis study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.Journal of Infrastructure Policy and Development344Implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness through improving HR Efficiency and Organization Effectiveness.Journal of Infrastructure, Policy and Development["Ahmad Suliman Alnsour", "O. Kanaan", "Maimar Salah", "Leen Alfayyad", "Yara Hijazi", "Dana Alsharif"]2024-08-15T00:00:00 a13a3b73cc0dd612e3f59b1fc91dc535fdc80269This paper aims to comprehensively examine the multifaceted role of generative Artificial Intelligence (AI) within academic settings, exploring its diverse applications, opportunities, and challenges. Employing a systematic review approach, this study synthesizes and analyzes the existing literature pertaining to the integration of AI in academia. It critically evaluates the varied applications of generative AI tools across different domains such as literature review, visualization, content generation, plagiarism detection, language enhancement, data analysis, and journal selection. The examination reveals a myriad of advantages brought forth by generative AI applications, including a substantial reduction in researchers’ workloads, time-saving mechanisms, the extraction of valuable insights from extensive datasets, and an overall enhancement in the quality of scholarly outputs. However, alongside these benefits, several challenges and limitations emerge. These include concerns regarding accuracy and reliability, ethical implications, limitations in linguistic and contextual understanding, potential hindrance to critical thinking and creativity, issues with data visualization, training requirements, staying updated with recent research, and the complexity and costs associated with specialized training.This paper provides a comprehensive and structured overview of the applications, advantages, and challenges of utilizing generative AI in academic settings. It synthesizes existing knowledge, critically evaluates the implications, and highlights the need for a balanced approach to harness the full potential of AI while mitigating ethical and practical challenges. The paper's contribution lies in offering a holistic view of AI's impact on academia, emphasizing the need for collaborative efforts among stakeholders to maximize benefits while ensuring ethical standards and academic integrity.IP Indian Journal of Library Science and Information Technology433This paper provides a comprehensive and structured overview of the applications, advantages, and challenges of utilizing generative AI in academic settings and highlights the need for a balanced approach to harness the full potential of AI while mitigating ethical and practical challenges.IP Indian Journal of Library Science and Information Technology["Subhajit Panda", "Navkirandeep Kaur"]2024-08-15T00:00:00 d95c0806942276b870ce460ea33be89d56a6db31Generative AI, an advanced subset of artificial intelligence, has emerged as a transformative force in healthcare, offering unprecedented capabilities in disease diagnosis, treatment development, and patient care. This article explores the integration of generative AI technologies, such as Generative Adversarial Networks (GANs) and transformers, in medical applications. We discuss how these technologies are enhancing diagnostic accuracy, personalizing treatment plans, and improving patient outcomes. Through a comprehensive review of current literature and case studies, we highlight the potential and challenges of implementing generative AI in clinical settings. This research underscores the need for continued innovation and ethical considerations to fully realize the benefits of AI-driven healthcare.Online (Weston, Conn.)163The integration of generative AI technologies, such as Generative Adversarial Networks (GANs) and transformers) and transformers, in medical applications are discussed, enhancing diagnostic accuracy, personalizing treatment plans, and improving patient outcomes.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)["Nasrullah Abbasi", "Nizamullah Fnu", "Shah Zeb", "MD Fardous"]2024-08-15T00:00:00 8d2bbd696c73ef012809b86e9d6ce07d7d93d8c1Education and Information Technologies : Official Journal of the IFIP technical committee on Education252Findings from focus groups with educators responsible for K-12 teacher education are explored, informing the design of a training programme that addresses ethical concerns and agency and highlighting the importance of greater investment in professional development to enable educators to critically assess and reshape the values associated with education in the context of Artificial Intelligence.Education and Information Technologies["Ana Mouta", "Eva Mar\u00eda Torrecilla-S\u00e1nchez", "A. M. Pinto-Llorente"]2024-08-15T00:00:00 d36360ec40d71ed0bd0551d51c98e4628e1f80e9PurposeGenerative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.Design/methodology/approachBased on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.FindingsThe results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.Originality/valueFirst, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.Kybernetes1292The moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance was revealed, and the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.Kybernetes["Kun Wang", "Zhao Pan", "Yaobin Lu"]2024-08-15T00:00:00 6568d94064cebd1edbb9d0c56f3021eb19f7594fPurposeThe purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.Design/methodology/approachWe adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.FindingsThe results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.Originality/valueThis study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.Internet Research902This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion.Internet Research["Qian Chen", "Yeming Gong", "Yaobin Lu", "Xin (Robert) Luo"]2024-08-15T00:00:00 4f4a16f6839a690dc648b060cf447472dd5fbe75This integrative literature review (ILR) explores the significant impact of incorporating artificial intelligence (AI), automation, and blockchain technology into research methodologies, collectively known as Research 5.0. The study addresses the shortcomings of traditional research methods, which need help managing the complexities and demands of modern scientific inquiry, thereby affecting the reliability and efficiency of research across various fields. The ILR aims to critically assess how these advanced technologies can enhance research processes, guided by a conceptual framework centered on AI, automation, and blockchain. The research method involved a comprehensive literature review and the analysis of qualitative data to identify patterns, challenges, and opportunities for implementing these technologies. The findings reveal that while AI significantly improves research efficiency and accuracy, it also introduces challenges such as algorithmic bias and transparency issues, which can be mitigated through Research 5.0 Explainable AI (RXAI) framework and comprehensive researcher training. Automation enhances consistency but risks reducing human oversight, necessitating hybrid systems that blend human expertise with automated precision. Blockchain strengthens data integrity and transparency yet faces complexity and energy consumption challenges, underscoring the need for scalable and sustainable solutions. The study concludes that while Research 5.0 technologies offer substantial potential, their successful integration requires careful consideration of ethical, technical, and operational challenges. Future research should focus on developing transparent AI systems, hybrid automation models that retain human judgment, and scalable blockchain solutions to advance research methodologies effectively.International Journal For Multidisciplinary Research781The findings reveal that while AI significantly improves research efficiency and accuracy, it also introduces challenges such as algorithmic bias and transparency issues, which can be mitigated through Research 5.0 Explainable AI (RXAI) framework and comprehensive researcher training.International Journal For Multidisciplinary Research["Rachid Ejjami"]2024-08-15T00:00:00 cbcc8007e3b2e6e0bf52a2ed5845a2d178eaa907The combination of Hyper-Automation, Robotic Process Automation (RPA), and Artificial Intelligence (AI) has become a game-changing method for improving business processes from start to finish in the quickly changing field of business technology. The important definitions, foundations, evolution, significance, research gaps, and the need for this study in the contemporary corporate climate will all be covered in detail in this extensive introduction. The use of cutting-edge technology, such as RPA and AI, to automate processes in a way that goes beyond the scope of conventional automation is known as hyper-automation. Hyper-automation is the process of automating any repetitious work that may be done so that businesses can become more efficient, cut expenses, and simplify their operations. The term "robotic process automation," or RPA, describes the use of software "bots" to automate regular and highly repetitive processes that are normally completed by human personnel. Artificial intelligence, or AI, is the study of how computers, especially computer systems, can mimic human cognitive functions including learning, reasoning, and self-correction.Darpan International Research Analysis11Darpan International Research Analysis["Sachkirat Singh"]2024-08-15T00:00:00 78e1aec5f05d0b435ca119ebce1b55bd85124fcbScientific Reports5815This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care, advocating for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace.Scientific Reports["Yu-Hao Li", "YuLin Li", "Mu-Yang Wei", "Guang-Yu Li"]2024-08-16T00:00:00 9b499faf50990c66264bfba9d8680ed0cbc49546Purpose – This research paper aims to create a realistic understanding of the favorable and unfavorable experiences that employees have as a result of adopting artificial intelligence (AI) or resorting to the old manual HR methods. It explains the difficulties and the benefits associated with developing human resources in light of the use of artificial intelligence or the old manual HR methods. Design/Methodology/Approach – For this study, the researcher employed a qualitative and exploratory research methodology. The primary element of the qualitative method which is adjusted to comprehend the literature, theories, motivations, viewpoints, and views in order to answer the study issue is exploratory research. This research used data from secondary sources. Findings – The study found that some firms spend over two million hours annually conducting manual HR performance reviews and evaluations. This is a significant amount of time spent on a process that is unreliable because it relies on people's opinions and prior performance. Real-time Artificial intelligence AI-driven assessments not only enable incentives and praise for good performances immediately, but they also ensure accuracy throughout the entire process and sound an alarm if targets are not met on time or performance standards are declining. From the extensive review of literature, it was found that Artificial Intelligence has a positive and significant influence on HR function of employee performance evaluation. Practical Implications – The study recommends a more robust top-level AI design and implementation within the entrepreneurial ecosystem and a robust application of Artificial Intelligence on HR function of employee performance evaluation. Originality/value – This research makes the unique contribution of establishing a qualitative finding that will revolutionize the entrepreneurial ecosystem for more employee productivity and satisfaction.International journal of entrepreneurship and business innovation148It was found that Artificial Intelligence has a positive and significant influence on HR function of employee performance evaluation and recommends a more robust top-level AI design and implementation within the entrepreneurial ecosystem and a robust application of Artificial Intelligence on HR function of employee performance evaluation.International Journal of Entrepreneurship and Business Innovation["Dr. Ernest Jebolise Chukwuka", "Dibie, K. E."]2024-08-16T00:00:00 02a09e4896c94bf020e73fe45faca8fb7e6c920bThis paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and provide empirical evidence from a leading accelerator program and a start-up competition that current large language models can generate and evaluate strategies at a level comparable to entrepreneurs and investors. We then examine implications for the key cognitive processes underlying SDM—search, representation, and aggregation. Our analysis suggests that AI has the potential to enhance the speed, quality, and scale of strategic analysis, while also enabling new approaches, like virtual strategy simulations. However, the ultimate impact on firm performance will depend on competitive dynamics as AI capabilities progress. We propose a framework connecting AI use in SDM to firm outcomes and discuss how AI may reshape sources of competitive advantage. We conclude by considering how AI could both support and challenge core tenets of the theory-based view of strategy. Overall, our work maps out an emerging research frontier at the intersection of AI and strategy. History: This paper has been accepted for the Strategy Science Special Issue on Theory-Based View. Funding: The authors are grateful to their collaborating organizations and to the University of Michigan, Bocconi University Junior Researchers’ Grant, and the INSEAD eLab Research Fund for financial support.Strategy Science655This paper illustrates how AI could augment existing SDM tools and provides empirical evidence that current large language models can generate and evaluate strategies at a level comparable to entrepreneurs and investors, and proposes a framework connecting AI use in SDM to firm outcomes.ArXiv["Felipe A. Csaszar", "Harshvardhan Ketkar", "Hyunjin Kim"]2024-08-16T00:00:00 50ebca279ab07007240c60fd09cbe9e92e2aa0baNature Computational Science253This work builds a Hodgkin-Huxley network with rich internal complexity, and proves that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity.Nature computational science["Linxuan He", "Yunhui Xu", "Weihua He", "Yihan Lin", "Yang Tian", "Yujie Wu", "Wenhui Wang", "Ziyang Zhang", "Junwei Han", "Yonghong Tian", "Bo Xu", "Guoqi Li"]2024-08-16T00:00:00 f3096fc54f23fc75316d476679c5b72c7c2ab5e9Pengabdian ini bertujuan untuk meningkatkan kompetensi guru PAUD dalam memanfaatkan teknologi Artificial Intelligence (AI) untuk menyusun modul ajar yang sesuai dengan prinsip-prinsip Kurikulum Merdeka. Dilaksanakan di Gugus II Aster Surakarta dengan menggunakan metode Participatory Action Research (PAR). Pelatihan ini melibatkan guru-guru PAUD dari Gugus I Teratai dan Gugus II Aster Surakarta dalam rangkaian kegiatan pelatihan serta pendampingan intensif. Melalui metode PAR, para peserta secara aktif berpartisipasi dalam setiap tahap kegiatan, mulai dari identifikasi kebutuhan, perencanaan, pelaksanaan, hingga evaluasi. Hal ini bertujuan agar guru tidak hanya memahami teori, tetapi juga mampu menerapkan AI dalam konteks pembelajaran yang konkret khususnya dalam penyusunan modul ajar kurikulum merdeka. Hasil dari kegiatan ini diharapkan mampu meningkatkan kualitas modul ajar yang disusun oleh guru PAUD, sehingga dapat menciptakan pengalaman belajar yang lebih kreatif, inovatif, dan adaptif bagi anak-anak. Selain itu, diharapkan kegiatan ini dapat mendorong guru untuk terus mengembangkan kompetensi mereka dalam memanfaatkan teknologi sebagai alat bantu dalam proses pembelajaran.Murhum : Jurnal Pendidikan Anak Usia Dini01Murhum : Jurnal Pendidikan Anak Usia Dini["Upik Elok Endang Rasmani", "Siti Wahyuningsih", "Anjar Fitrianingtyas", "Putri Agustina", "Yuanita Kristiani Wahyu Widiastuti", "Apriliani Kholika Fitri", "Afifah Indah Pratiwi"]2024-08-16T00:00:00 5b5988d84fade9f05aa507445a62d0020b8af083BACKGROUND AND OBJECTIVE Healthcare professionals may be able to anticipate more accurately a patient's timing of death and assess their possibility of recovery by implementing a real-time clinical decision support system. Using such a tool, the healthcare system can better understand a patient's condition and make more informed judgements about distributing limited resources. This scoping review aimed to analyze various death prediction AI (Artificial Intelligence) algorithms that have been used in ICU (Intensive Care Unit) patient populations. METHODS The search strategy of this study involved keyword combinations of outcome and patient setting such as mortality, survival, ICU, terminal care. These terms were used to perform database searches in MEDLINE, Embase, and PubMed up to July 2022. The variables, characteristics, and performance of the identified predictive models were summarized. The accuracy of the models was compared using their Area Under the Curve (AUC) values. RESULTS Databases search yielded an initial pool of 8271 articles. A two-step screening process was then applied: first, titles and abstracts were reviewed for relevance, reducing the pool to 429 articles. Next, a full-text review was conducted, further narrowing down the selection to 400 key studies. Out of 400 studies on different tools or models for prediction of mortality in ICUs, 16 papers focused on AI-based models which were ultimately included in this study that have deployed different AI-based and machine learning models to make a prediction about negative patient outcome. The accuracy and performance of the different models varied depending on the patient populations and medical conditions. It was found that AI models compared with traditional tools like SAP3 or APACHE IV score were more accurate in death prediction, with some models achieving an AUC of up to 92.9%. The overall mortality rate ranged from 5% to more than 60% in different studies. CONCLUSION We found that AI-based models exhibit varying performance across different patient populations. To enhance the accuracy of mortality prediction, we recommend customizing models for specific patient groups and medical contexts. By doing so, healthcare professionals may more effectively assess mortality risk and tailor treatments accordingly. Additionally, incorporating additional variables-such as genetic information-into new models can further improve their accuracy.Journal of Intensive Care Medicine122It was found that AI models compared with traditional tools like SAP3 or APACHE IV score were more accurate in death prediction, with some models achieving an AUC of up to 92.9%.Journal of intensive care medicine["Orkideh Olang", "S. Mohseni", "Ali Shahabinezhad", "Yasaman Hamidianshirazi", "Amireza N. Goli", "M. Abolghasemian", "M. Shafiee", "Mehdi Aarabi", "Mohammad Alavinia", "Pouyan Shaker"]2024-08-16T00:00:00 a780a4159ea61b1d6a669653ea85ffcd752519dbThe incorporation of artificial intelligence (AI) in nursing education is a major innovation that has the capacity of changing the practices in the classroom as well as improving the learning outcomes. AI technologies such as machine learning, neural networks, natural language processing, and computer vision enable AI systems to perform intelligent functions like knowledge acquisition, problem-solving, and decision-making. These technologies are most useful in healthcare as they organize and provide insights from large clinical and patient records in decision making processes in order to improve health care. Nevertheless, the following issues are some of the challenges that need to be considered for proper implementation of AI in the Nursing Education especially in China. These are issues such as technological requirements, information security, and other petty issues including the issue of ethics as well as issues of the resistance that is likely to be encountered from the practitioners in the field, students included. However, the prospects of AI, XR, and VR technologies in improving the delivery of nursing education and equipping students with adequate preparation to work in today’s advanced health systems cannot be underestimated.Multidisciplinary Reviews551The prospects of AI, XR, and VR technologies in improving the delivery of nursing education and equipping students with adequate preparation to work in today’s advanced health systems cannot be underestimated.Multidisciplinary Reviews["Yuan Jiang", "Minghao Kong"]2024-08-16T00:00:00 91ae1c00bc47a9bc334cb3256746e7ca4293dc21PurposeScholars have highlighted personal interactions between employees and their leaders in an increasingly distributed and hybrid work environment as an essential mechanism that engages employees toward organizational goals. Enhanced employee engagement significantly contributes to sustained organizational performance and growth. While Artificial Intelligence (AI) applications in the HR domain are increasing, research to understand the implication of AI-based virtual assistants on enabling trust and managing human resources is, at best, limited.Design/methodology/approachDrawing on the social response theory and the social exchange theory, and based on a multi-source, time-lagged field study spanning over ten months, we investigated the impact of AI-based virtual assistants on employee attitudes, namely perception of fairness and employee engagement.FindingsThe usage of AI-based virtual assistants is associated directly with employee engagement and indirectly through employees’ perceptions of fairness. While employees’ past performance moderates the relationship between perceived fairness and employee engagement, the interaction effect becomes non-significant with AI-based virtual assistants.Research limitations/implicationsOur study contributes to the emerging literature on AI-based virtual assistants in HRM and employee engagement. The virtual assistants’ use to enhance employee engagement emerges as an opportunity for task substitution and augmentation. Our study demonstrates that AI-based virtual assistants can enhance employee engagement and help build perceptions of fairness among employees.Originality/valueWith the emerging importance of AI, there is an increasing interest in explaining human-computer interactions and their effect on employee engagement. Our study is among the early empirical studies examining the implications of AI-based virtual assistants on employee outcomes.Person-centered review422It is demonstrated that AI-based virtual assistants can enhance employee engagement and help build perceptions of fairness among employees.Personnel Review["D. Dutta", "S. Mishra"]2024-08-16T00:00:00 9e9ba86bf69e5feed3bf9fdf0f320f17709cb718Robotics and artificial intelligence have marked the beginning of a new era in the care and integration of people with disabilities, helping to promote their independence, autonomy and social participation. In this area, bioethical reflection assumes a key role at anthropological, ethical, legal and socio-political levels. However, there is currently a substantial diversity of opinions and ethical arguments, as well as a lack of consensus on the use of assistive robots, while the focus remains predominantly on the usability of products. The article presents a bioethical analysis that highlights the risk arising from using embodied artificial intelligence according to a functionalist model. Failure to recognize disability as the result of a complex interplay between health, personal and situational factors could result in potential damage to the intrinsic dignity of the person and human relations with healthcare workers. Furthermore, the danger of discrimination in accessing these new technologies is highlighted, emphasizing the need for an ethical approach that considers the social and moral implications of implementing embodied AI in the field of rehabilitation.Frontiers in Medicine601The article presents a bioethical analysis that highlights the risk arising from using embodied artificial intelligence according to a functionalist model, and the need for an ethical approach that considers the social and moral implications of implementing embodied AI in the field of rehabilitation.Frontiers in Medicine["Francesco De Micco", "V. Tambone", "Paola Frati", "Mariano Cingolani", "R. Scendoni"]2024-08-16T00:00:00 ffb6fb065434960503b3f12647028e172d66a43cPurposeThis article examines customer satisfaction in using banking services through Artificial Intelligence (AI) in India. It addresses two questions: first, will customers perceive AI technology as a reliable and efficient alternative to traditional banking practices; second, will AI save customers’ time.Design/methodology/approachThe quantitative research method based on regression analysis models was adopted for hypothesis testing, with data collected from a survey of 189 banking customers from four banks, i.e., State Bank of India, Axis Bank, Punjab National Bank, and HDFC Bank in India.FindingsAI improves banking customers’ experiences by making banking more accessible and enjoyable. Satisfied customers are quick to use cutting-edge AI tools. However, human service is more satisfying than digital service. AI has great potential but works alongside humans rather than replacing them. Even though AI’s novel architecture is helpful, human bank tellers are still needed in enhancing customer satisfaction.Originality/valueAI’s integration in Indian banking, propelled by customer satisfaction, foresees a transformative landscape. This study uncovers AI’s role in saving time and improving customer satisfaction. While AI revolutionizes financial processes, its harmonious coexistence with human expertise emphasizes personalized and efficient services. This study provides insights for optimal AI utilization in shaping the future of banking.Public Administration and Policy301This study uncovers AI’s role in saving time and improving customer satisfaction in Indian banking, and provides insights for optimal AI utilization in shaping the future of banking.Public Administration and Policy["Asmat Ara Shaikh", "Arya Kumar", "Apoorva Mishra", "Yasir Arafat Elahi"]2024-08-16T00:00:00 8e6070bf6a2b037a7c2201c1900e364f25a2c502Environmental Research886This review examines the integration of artificial intelligence (AI) with environmental science to improve microplastic detection by focusing on image processing, Fourier transform infrared spectroscopy, Raman spectroscopy, and hyperspectral imaging.Environmental research["Huimin Jin", "Fanhao Kong", "Xiangyu Li", "Jie Shen"]2024-08-16T00:00:00 7d38af0bed9e362272a082c2237aa56012a4fab7Utilizing artificial intelligence technology in global pandemic, especially COVID-19 epidemic after 2020, has produced a wealth of studies. To investigate the role and impact of artificial intelligence in global pandemics, 4,708 relevant publications are retrieved from PubMed and subjected to a bibliometric analysis. To be specific, the analysis encompasses the number of articles, prolific journals, institutions, countries/regions, authors and their collaborative relations, as well as research hotspots. More importantly, the findings of this study, in addition to enriching the current literature review associated with the application of artificial intelligence in global pandemics, attempt to provide valuable lessons for the future face of pandemics.International Conference on Behavioral, Economic, and Socio-Cultural Computing450The role and impact of artificial intelligence in global pandemics is investigated, and valuable lessons for the future face of pandemics are attempt to provide.2024 11th International Conference on Behavioural and Social Computing (BESC)["Lianghong Lin", "Heng Weng", "Enliang Yan", "Choujun Zhan", "Fu Lee Wang", "Tianyong Hao"]2024-08-16T00:00:00 f48e19f2c432aaad11f3ac1aeb5e713fa3e06a52Artificial Intelligence (AI) in education sector is leading to a transformation in education. Whereas AI has a number of advantages like individualized learning and efficiency, it also poses some challenges involving ethics and human interaction. This paper will explore the potentials of AI applied to education, discuss its drawbacks, and review future trends that will shape the learning landscape. The article evaluated the pros and cons of AI in education, based on the views of fifty academics from different universities using a mixed method approach. The conclusions brought out of the analysis are in line with similar studies existing in the literature. For academics, the integration of AI has many positives aspects on the learning–teaching process including improvement in skills and competences of students. The findings reveal a strong correlation between participants' awareness of AI-related risks in education and their perceptions of AI's impact on the college education system, emphasizing the intricate link between awareness and attitudes towards AI in education. While the negative aspects brought out from the research are linked to generalizability. The contribution to be drawn from the results of this research is mainly empirical and practical. These opinions should be used as resources for managers, policy makers and researchers suggesting avenues for future research to broaden the scope and include diverse educational contextsInternational Journal of Innovative Research in Engineering &amp; Multidisciplinary Physical Sciences140The findings reveal a strong correlation between participants' awareness of AI-related risks in education and their perceptions of AI's impact on the college education system, emphasizing the intricate link between awareness and attitudes towards AI in education.International Journal of Innovative Research in Engineering &amp; Multidisciplinary Physical Sciences["Sarah Youssef Abou Karroum", "Nour Eldin Mohamed Elshaiekh", "Khalfan Zahran Al-Hijji"]2024-08-16T00:00:00 eab506fda3787a06dd61bd4162bd8674bd94f8dcArtificial intelligence (AI) has rapidly gained notoriety due to fast advances in generative AI. However, this field encompasses a broader set of already mature tools and methods. Here, we explore its broad impact on the valorization of assets directly derived from living organisms, biomass, produced in high quantities and used in non-traditional applications at an industrial scale. For this review, we have explored the trends in scientific publications as well as in patents to measure the current state of the art and the potential for commercial applications. The number of publications and patents is rapidly increasing, showing the penetration of these technologies into chemical and biochemical engineering processes. The ethical considerations of such rapid advances need to be addressed to maximize the benefits and minimize the unintentional collateral negative social impact. Considering AI’s current limitations, biases, and economic impacts will facilitate a better transition to the broad implementation of these new technologies. The valorization of biomass and bioresidues, along with the sustainable use of biodiversity, faces important challenges and obstacles that AI tools are helping to overcome, accelerating basic research and optimizing industrial processes in the development of sustainable energy, and high value-added bioproducts and biomaterials. The application of AI in these fields promises industrial innovation, enhanced efficiency, cost reduction and increased product yields for a global growing market; and thereby promotes Circular Engineering and Advanced Bioeconomy to achieve United Nations Sustainable Development goals in the near future.International Journal of Sustainable Energy and Environmental Research290The application of AI in these fields promises industrial innovation, enhanced efficiency, cost reduction and increased product yields for a global growing market; and thereby promotes Circular Engineering and Advanced Bioeconomy to achieve United Nations Sustainable Development goals in the near future.International Journal of Sustainable Energy and Environmental Research["Lourdes M. Orejuela-Escobar", "Diego Venegas-V\u00e1sconez", "M. A. M\u00e9ndez"]2024-08-16T00:00:00 56d55b49c2d1c2526a1ee92abcee8bc2fb6d1726The utilization of artificial intelligence in learning is evolving, with a focus on language learning. This study addresses the research issue of how SMP 1 Nurul Basmalah students and teachers feel about the usage of artificial intelligence in the English classroom. In this qualitative study, focus groups and interviews are used to determine that AI saves students time and is more personalized because it engages in conversations with interviewers. It also dismisses worries about the use of electronic devices, stating that such teaching approaches require a teacher to be prepared. It is necessary to incorporate findings such as the strengths. It also dismisses expectations about the use of electronic devices, stating that such teaching approaches require a teacher to be prepared. There is a need to use discoveries such as the strengths and drawbacks of using AI in educational institutions, how to apply ethics in the use of AI, and how to incorporate practical ways of using AI to improve learning outcomes and stimulate student creativity.Starting AI Researchers' Symposium00This study addresses the research issue of how SMP 1 Nurul Basmalah students and teachers feel about the usage of artificial intelligence in the English classroom by determining that AI saves students time and is more personalized because it engages in conversations with interviewers.Stairs["Ikhsan Dinn Islam", "Syafrizal Syafrizal", "Yudi Juniardi"]2024-08-16T00:00:00 7809893961e8f3827ef725aa0f9000db35806eafThe influence of personality traits on individuals' attitude towards Artificial Intelligence (AI) remains inconclusive. This study investigates the association between personality traits, measured through Big Five Inventory-10 (BFI-10) and attitude towards AI measured by both Attitudes towards Artificial Intelligence (ATAI) and single-item positive and negative measures in two distinct samples, UK and Arab. Correlational analysis showed a significant association between attitudes towards AI and agreeableness, as well as neuroticism in the UK sample, and agreeableness, neuroticism, and conscientiousness in the Arab sample. The regression analysis indicated that agreeableness in the UK sample positively predicted positive attitudes and negatively predicted negative attitudes using both attitude towards AI measures. Similarly, conscientiousness in the Arab sample positively predicted positive attitudes and negatively predicted negative attitudes. However, the small effect size observed in both samples suggests that other factors may be more relevant and better explain the variability of AI attitudes among individuals. The variability and inconsistency in the association between attitudes towards AI and personality traits suggest potential cultural effects.International Conference on Behavioral, Economic, and Socio-Cultural Computing530Correlational analysis showed a significant association between attitudes towards AI and agreeableness, as well as neuroticism, and agreeableness, neuroticism, and conscientiousness in the UK sample, and agreeableness, neuroticism, and conscientiousness in the Arab sample.2024 11th International Conference on Behavioural and Social Computing (BESC)["Areej Babiker", "Sameha Alshakhsi", "Tourjana Islam Supti", "Raian Ali"]2024-08-16T00:00:00 db4166194f4e25fb75c610e869a2807856d88d27This article discusses the need for organizations to adapt their culture to leverage the adoption of Artificial Intelligence in their operations and strategies. Organizational culture, characterized by a set of values, beliefs, and norms, directly influences efficiency and adaptation to changes. Authors such as Edgar Schein, Chatman, and Choi highlight the importance of measuring and adapting organizational culture to support a data-driven mindset. Tools like the Organizational Culture Assessment Instrument (OCAI), methods such as Jobs to Be Done (JTBD) and Design Thinking, are useful for diagnosing and improving organizational culture and business knowledge, as well as facilitating an innovation environment. Adopting a data-driven mindset improves efficiency, opportunity identification, and evidence-based decision-making, in addition to serving as a foundation for AI implementation, requiring a structured approach that includes cultural diagnosis, gap identification, AI solution application, and continuous evaluation. A strong and adaptable organizational culture is crucial for long-term success, making companies more resilient, innovative, and competitive.Concilium00The need for organizations to adapt their culture to leverage the adoption of Artificial Intelligence in their operations and strategies is discussed, requiring a structured approach that includes cultural diagnosis, gap identification, AI solution application, and continuous evaluation.Concilium["Eric Pelakoski"]2024-08-16T00:00:00 c7a6bb524a0410cf9098f5ca438b9f8ae0249289Artificial Intelligence (AI) presents significant opportunities to accelerate progress towards the United Nations Sustainable Development Goal 7 (SDG 7) ensuring access to affordable, reliable, sustainable, and modern energy for all. This paper reviews major AI techniques, including machine learning, deep learning, hybrid models, optimization and control, and explainable AI, and their applications across energy system domains such as renewable energy forecasting, grid stability, energy efficiency, demand-side management, energy access, and transport electrification. We discuss enabling factors like data, infrastructure, markets, policies, and human factors and highlight the importance of multidisciplinary research. AI's potential for advancing clean energy access, synergistic SDG impacts, and energy justice is investigated. The paper emphasizes overarching principles of sustainability, resilience, and responsible AI development. With strategic collaboration between the AI and energy research communities, policymakers, and broader stakeholders, AI can help realize a clean energy future for all.International Conference on Behavioral, Economic, and Socio-Cultural Computing1180Major AI techniques, including machine learning, deep learning, hybrid models, optimization and control, and explainable AI, and their applications across energy system domains such as renewable energy forecasting, grid stability, energy efficiency, demand-side management, energy access, and transport electrification are reviewed.2024 11th International Conference on Behavioural and Social Computing (BESC)["Bavly Hanna", "Guandong Xu", "Xianzhi Wang", "Md. Jahangir Hossain"]2024-08-16T00:00:00 63c8d4b7c58dc14ae7a746239ae2a0a42d74c911Technological advancements in retail have transformed the shopping experience, notably through e-commerce platforms like Shopee and Lazada in the Philippines. Integrating artificial intelligence (AI), such as system-generated product recommendations, enhances brand marketing potential on these platforms, leading to increased market shares. However, despite the widespread adoption of online shopping platforms, a segment of the population remains hesitant to engage with them. To address this, the study explores whether consumers' levels of technology readiness and acceptance-specifically on dimensions of technology readiness such as optimism, innovativeness, discomfort, and insecurity-affect their use perceptions, intentions, and actual usage of AI systems on platforms like Lazada and Shopee. The research employs a quantitative-causal research approach with a sample size of 308 respondents from Laguna, selected using non-probability purposive sampling. Partial least squares-structural equation modeling (PLS-SEM) was used to test the relationship between the variables. The study results indicate that consumers' levels of optimism, innovativeness, and discomfort are the only ones that significantly affect their perceived ease of use. In contrast, optimism only has a significant effect on their perceived usefulness. All this proves that all dependent variables mentioned in the study have a relationship with each other. This further proves that certain personality traits can significantly influence users' readiness and acceptance of technologies.2024 8th International Conference on Business and Information Management (ICBIM)430The study results indicate that consumers' levels of optimism, innovativeness, and discomfort are the only ones that significantly affect their perceived ease of use, and proves that certain personality traits can significantly influence users' readiness and acceptance of technologies.2024 8th International Conference on Business and Information Management (ICBIM)["Roel Rodrigo", "Karylle Delos Santos", "Ramachandra C. Torres", "John-Ira Labapis", "Donn Enrique Moreno", "Shu-Ching Tseng"]2024-08-16T00:00:00 87bb5e48e5f3bedd22f07924984482501c93a402This paper investigates the integration of artificial intelligence (AI) in smart course design and its impact on transforming teaching methods and talent cultivation in higher education. As educational paradigms evolve with technological advancements, AI presents innovative solutions for enhancing learning experiences. The study explores how AI can revolutionize smart courses by providing adaptive learning environments, delivering personalized feedback, and supporting modern teaching methods such as collaborative learning, gamification, and immersive technologies like virtual and augmented reality. Through AI, educators can address diverse learning needs, increase student engagement, and foster critical thinking and problem-solving skills. The paper also examines challenges related to AI in education, including technological, pedagogical, and ethical issues. Additionally, it considers the implications of AI-driven education for preparing students for the future workforce and developing necessary skills. The findings emphasize AI's transformative potential in higher education while acknowledging the importance of addressing concerns related to access, equity, and data privacy. This study offers a comprehensive overview of the current landscape and provides recommendations for future research to optimize AI's benefits in educational settings.Education Insights230This study explores how AI can revolutionize smart courses by providing adaptive learning environments, delivering personalized feedback, and supporting modern teaching methods such as collaborative learning, gamification, and immersive technologies like virtual and augmented reality.Education Insights["Junjie Song"]2024-08-16T00:00:00 305eeffe0bee53fa400a8ecad024f1c18c1280b8Purpose Artificial intelligence (AI) has received much attention due to its promethean-like powers to transform the management and delivery of public sector services. Due to the proliferation of research articles in this context, research to date is fragmented into research streams based on different types of AI technologies or a specific government function of the public sector (e.g. health, education). The purpose of this study is to synthesize this literature, identify challenges and opportunities, and offer a research agenda that guides future inquiry. Design/methodology/approach This paper aggregates this fragmented body of knowledge by conducting a systematic literature review of AI research in public sector organisations in the Chartered Association of Business Schools (CABS)-ranked journals between 2012 and 2023. Findings The search strategy resulted in the retrieval of 2,870 papers, of which 61 were identified as primary papers relevant to this research. These primary papers are mapped to the ten classifications of the functions of government as classified by the Organisation for Economic Co-operation and Development (OECD), and the reported challenges and benefits aggregated. Originality/value This study advances knowledge by providing a state-of-the-art of AI research based the OECD classifications of government functions, reporting of claimed benefits and challenges and providing a research agenda for future research. Transforming Government: People, Process and Policy240This study advances knowledge by providing a state-of-the-art of AI research based the OECD classifications of government functions, reporting of claimed benefits and challenges and providing a research agenda for future research.Transforming Government: People, Process and Policy["Albandari Alshahrani", "Anastasia Griva", "Denis Dennehy", "Matti M\u00e4ntym\u00e4ki"]2024-08-16T00:00:00 e61b99ec9e08ec4c68faa0d17e7d8895ac990020Understanding is a crucial yet elusive concept in artificial intelligence (AI). This work proposes a framework for analyzing understanding based on the notion of composability. Given any subject (e.g., a person or an AI), we suggest characterizing its understanding of an object in terms of its ability to process (compose) relevant inputs into satisfactory outputs from the perspective of a verifier. This highly universal framework can readily apply to non-human subjects, such as AIs, non-human animals, and institutions. Further, we propose methods for analyzing the inputs that enhance output quality in compositions, which we call catalysts. We show how the structure of a subject can be revealed by analyzing its components that act as catalysts and argue that a subject's learning ability can be regarded as its ability to compose inputs into its inner catalysts. Finally we examine the importance of learning ability for AIs to attain general intelligence. Our analysis indicates that models capable of generating outputs that can function as their own catalysts, such as language models, establish a foundation for potentially overcoming existing limitations in AI understanding.arXiv.org550It is argued that a subject's learning ability can be regarded as its ability to compose inputs into its inner catalysts, and models capable of generating outputs that can function as their own catalysts, such as language models, establish a foundation for potentially overcoming existing limitations in AI understanding.ArXiv["Zijian Zhang", "Sara Aronowitz", "Al'an Aspuru-Guzik"]2024-08-16T00:00:00 a255fc2c7294956f507fc7adcb5ae687ceb29175The training and practical manual covers the main concepts and methods of utilizing artificial intelligence (AI) technologies in educational activities. The primary goal of this work is to provide educators in educational institutions with knowledge about the possibilities of applying AI in teaching, as well as practical recommendations for integrating these technologies into classroom activities. The publication is designed to develop educators' competencies in the field of artificial intelligence to enhance the quality of teaching and student performance. The book describes the use of AI to create personalized educational pathways for learners, tailored to the needs and abilities of each student, and offers ready-made solutions, examples, and methodological recommendations for implementing AI across various subject areas in education. Educators (teachers) in regional educational institutions, including rural communities, should play a central role in this process. The training manual is intended to improve the qualifications of pedagogical staff in educational institutions who are interested in increasing the effectiveness of the educational process through the use of advanced AI technologies.10The training and practical manual describes the use of AI to create personalized educational pathways for learners, tailored to the needs and abilities of each student, and offers ready-made solutions, examples, and methodological recommendations for implementing AI across various subject areas in education.["E. Grebenyuk", "Darya Danielyan", "Sarkis Danielyan", "Sergey Kramarov"]2024-08-16T00:00:00 43a15ab9cbb03be7bc0032c42b1c4acc27d1506eThe inner alignment problem, which asserts whether an arbitrary artificial intelligence (AI) model satisfices a non-trivial alignment function of its outputs given its inputs, is undecidable. This is rigorously proved by Rice's theorem, which is also equivalent to a reduction to Turing's Halting Problem, whose proof sketch is presented in this work. Nevertheless, there is an enumerable set of provenly aligned AIs that are constructed from a finite set of provenly aligned operations. Therefore, we argue that the alignment should be a guaranteed property from the AI architecture rather than a characteristic imposed post-hoc on an arbitrary AI model. Furthermore, while the outer alignment problem is the definition of a judge function that captures human values and preferences, we propose that such a function must also impose a halting constraint that guarantees that the AI model always reaches a terminal state in finite execution steps. Our work presents examples and models that illustrate this constraint and the intricate challenges involved, advancing a compelling case for adopting an intrinsically hard-aligned approach to AI systems architectures that ensures halting.arXiv.org220This work argues that the alignment should be a guaranteed property from the AI architecture rather than a characteristic imposed post-hoc on an arbitrary AI model, and proposes that such a function must also impose a halting constraint that guarantees that the AI model always reaches a terminal state in finite execution steps.ArXiv["Gabriel Adriano de Melo", "Marcos Ricardo Omena de Albuquerque M\u00e1ximo", "Nei Yoshihiro Soma", "Paulo Andre Lima de Castro"]2024-08-16T00:00:00 c2b64ccfe282d7f96ead2e40647f239a69b65840The Internet of Things (IoT) and Artificial Intelligence (AI) offer powerful solutions for various applications. However, this digitalization boom also increases cybersecurity threats. Organizations are integrating AI into critical systems to combat cybercrime. This study explores the effectiveness of AI techniques like machine learning and deep learning in identifying and defending against cyber threats, particularly those created by AI tools. This research critically evaluates the effectiveness of AI-powered security systems in detecting and responding to a wide range of cyber threats. The study analyzes existing literature on AI in cybersecurity threats and integrated system architecture security. AI is a double-edged sword in cybersecurity, used for both defense and offense. AI integration enhances cyberattacks' effectiveness, potentially disrupting benign AI algorithms. As AI becomes more prevalent, understanding its role in cybersecurity is crucial for developing strong defenses. Studies show promise in integrating AI into the Internet of Things for threat detection and prevention. Machine learning and deep learning algorithms are used in Intrusion Detection Systems to identify attacks and improve overall IoT security.2024 IEEE 7th International Conference on Computer and Communication Engineering Technology (CCET)200This research critically evaluates the effectiveness of AI-powered security systems in detecting and responding to a wide range of cyber threats, particularly those created by AI tools.2024 IEEE 7th International Conference on Computer and Communication Engineering Technology (CCET)["M. Samonte", "Andrei E. Goc-ong", "Raf Bradey F. Matoza", "Ron Gerrovir A. Viernes"]2024-08-16T00:00:00 fac956dc95bdf64bac6e2215196f2bd824b36bfcThis study investigates the benefits and challenges of integrating Artificial Intelligence (AI) technologies into the Manufacturing Execution System (MES), specifically in the pharmaceutical industry. The main objective of this paper is to use various AI methodologies, such as machine and deep learning, to refine manufacturing processes and assess their effectiveness. Moreover, the study suggests that AI has the potential to improve process efficiency, reduce costs, and enhance decision-making processes. However, successful implementation requires addressing challenges such as data integration, algorithm selection, and workforce upskilling. In essence, this research underscores the imperative for a comprehensive and nuanced approach to AI integration into MES within the pharmaceutical domain. By acknowledging both the opportunities and challenges this integration presents, organizations can lay the groundwork for a future where AI-driven manufacturing optimization becomes synonymous with industry success and innovation. Thus, understanding these complexities is crucial, and this research provides valuable insights for manufacturers and researchers alike.International Conference on Software Technology and Engineering250The study suggests that AI has the potential to improve process efficiency, reduce costs, and enhance decision-making processes, however, successful implementation requires addressing challenges such as data integration, algorithm selection, and workforce upskilling.2024 14th International Conference on Software Technology and Engineering (ICSTE)["M. Samonte", "Herzon B. Chu", "L. A. Ogaya"]2024-08-16T00:00:00 4c5018f3bee4186e263db713f4a2fb8c03cb0009The integration of Artificial Intelligence (AI) into healthcare has brought significant advancements in diagnostics, treatment, and patient care, but it also raises critical ethical concerns. This article examines the new landscape shaped by AI in healthcare, focusing on the balance between innovation and ethical considerations such as privacy, data security, and algorithmic bias. It highlights the importance of interdisciplinary collaboration among healthcare professionals, ethicists, technologists, and policymakers to address these challenges. By ensuring transparency, accountability, and equitable access, the healthcare community can harness AI's potential while safeguarding patient rights and well-being.Peercite Journal of Artificial Intelligence &amp; Machine Learning00The new landscape shaped by AI in healthcare is examined, focusing on the balance between innovation and ethical considerations such as privacy, data security, and algorithmic bias.Peercite Journal of Artificial Intelligence &amp; Machine Learning["Ewa J Kleczyk"]2024-08-16T00:00:00 966f399b0f78736191652d2c17dcaefd78143f4bIn the world of digital commerce, Artificial intelligence (AI) is starting to shift the game and transform how organizations run. AI benefits firms and consumers both in retail and digital commerce. This review provides a comprehensive analysis of the critical role AI plays in fostering value creation in the digital commerce industry, with a particular emphasis on the ways in which task and information complexity affect the application of AI technologies. The review then looks at AI possibilities in digital commerce from a variety of angles, such as supply chain efficiency, cost savings, product recommendation, enhanced customer experience and marketing plans. The aim of this review is to exploring how AI applications create value in e-commerce. The approach that was employed described the search technique for locating pertinent academic sources and was based on a survey of the literature on recent 15 studies about the effect of AI for Value Creation in Digital Commerce. Finally, the findings demonstrate that utilizing AI as an advanced instrument in the digital commerce sector appears to be a positive move since it applying AI may foster creativity, improve decision-making, and enhance overall marketing performance.Indonesian Journal of Computer Science320The findings demonstrate that utilizing AI as an advanced instrument in the digital commerce sector appears to be a positive move since it applying AI may foster creativity, improve decision-making, and enhance overall marketing performance.The Indonesian Journal of Computer Science["Sardar Abduljabbar", "Rabeen S. Shaban"]2024-08-16T00:00:00 28b2b16a2486a3faa75f0b8c399d32f86cc93ca4PurposeThis study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists.Design/methodology/approachThe research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators.FindingsThe results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness.Research limitations/implicationsMajor limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness.Practical implicationsThe theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature.Originality/valueThis research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation.Kybernetes732The results indicate that brain drain negatively affects government AI readiness, and the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness.Kybernetes["Adela Socol", "I. Iuga"]2024-08-16T00:00:00 773fe78cec6f3be4d5eb71413ac1e75d499d6da3AI &amp; SOCIETY691AI &amp; SOCIETY["N. E. Neef", "Sarah Zabel", "Maria Papoli", "Siegmar Otto"]2024-08-16T00:00:00 cbea394182d9a5d4e9578cbe638a5b76e64b64c7J. Am. Medical Informatics Assoc.00Journal of the American Medical Informatics Association : JAMIA[]2024-08-16T00:00:00 a4663a30b99f63befa22f2b923ffc5e21ba1ed91The new government faces an urgent challenge: revitalising the UK's crumbling public services without major increases in public spending. While technological change holds promise, UK digital government initiatives have failed to reach their full potential over the past twenty‐five years. This article argues that the latest generation of ‘data‐intensive’ technologies, including data science and AI, can succeed where past efforts have faltered. We provide a roadmap for how to harness the power of recent technologies for a more productive and equitable public sector, and pinpoint the organisational changes necessary to develop progressive, technologically enhanced public services.Political quarterly (London. 1930. Print)00This article argues that the latest generation of ‘data‐intensive’ technologies, including data science and AI, can succeed where past efforts have faltered and provide a roadmap for how to harness the power of recent technologies for a more productive and equitable public sector.The Political Quarterly["Helen Z. Margetts", "Cosmina L. Dorobantu", "Jonathan Bright"]2024-08-16T00:00:00 7e6d4eae4489399f348e445aea92341d8433f647Journal of Pediatric Endoscopic Surgery00Journal of Pediatric Endoscopic Surgery["Amulya K. Saxena"]2024-08-16T00:00:00 c3e82840f676d5bfce25194c3efd7a8294531bc9OBJECTIVE Development of an AI tool to assess velopharyngeal competence (VPC) in children with cleft palate, with/without cleft lip. DESIGN Innovation of an AI tool using retrospective audio recordings and assessments of VPC. SETTING Two datasets were used. The first, named the SR dataset, included data from follow-up visits to Skåne University Hospital, Sweden. The second, named the SC + IC dataset, was a combined dataset (SC + IC dataset) with data from the Scandcleft randomized trials across five countries and an intercenter study performed at six Swedish CL/P centers. PARTICIPANTS SR dataset included 153 recordings from 162 children, and SC + IC dataset included 308 recordings from 399 children. All recordings were from ages 5 or 10, with corresponding VPC assessments. INTERVENTIONS Development of two networks, a convolutional neural network (CNN) and a pre-trained CNN (VGGish). After initial testing using the SR dataset, the networks were re-tested using the SC + IC dataset and modified to improve performance. MAIN OUTCOME MEASURES Accuracy of the networks' VPC scores, with speech and language pathologistś scores seen as the true values. A three-point scale was used for VPC assessments. RESULTS VGGish outperformed CNN, achieving 57.1% accuracy compared to 39.8%. Minor adjustments in data pre-processing and network characteristics improved accuracies. CONCLUSIONS Network accuracies were too low for the networks to be useful alternatives for VPC assessment in clinical practice. Suggestions for future research with regards to study design and dataset optimization were discussed.The Cleft Palate-Craniofacial Journal240Network accuracies were too low for the networks to be useful alternatives for VPC assessment in clinical practice, and VGGish outperformed CNN.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association["M\u00e5ns Cornefjord", "Joel Bluhme", "Andreas Jakobsson", "Kristina Klint\u00f6", "A. Lohmander", "Tofig Mamedov", "Mia Stiernman", "Rebecca Svensson", "Magnus Becker"]2024-08-16T00:00:00 5c991a15c4f204467022841cecc68eb8538812d3 Alan Turing developed a simple test for verifying whether machine or man. He did this with a vision that was decades ahead of the time but with the technology that was available to him at the time. Back in the time, the notion of AI was a future prospect, and not a real threat in any way to humanity. Roll the clock forward to today and AI is not a prospect but a stark reality and the threat to humanity, according to the sceptics, all but real. But how serious has the threat become? This paper investigates.Computer/law journal20Alan Turing developed a simple test for verifying whether machine or man, but how serious has the threat to humanity become and how serious has the threat become?The Computer Journal["Marios C. Angelides"]2024-08-16T00:00:00 cc47bb4fcd21c34d8901c21861edaac708a37e282024 International Conference on Big Data and Digital Management502024 International Conference on Big Data and Digital Management["Hengsha Yan", "Shixuan Li"]2024-08-16T00:00:00 119dfdaf18506e3a34762aa41b3847c724d31185Nature Computational Science30Nature computational science["Rui-Jie Zhu", "Skye Gunasekaran", "Jason K. Eshraghian"]2024-08-16T00:00:00 76f04d7881e87598ede669e771c6cc85331616732024 International Conference on Big Data and Digital Management802024 International Conference on Big Data and Digital Management["Juanjuan Luo", "Zhongsheng Xu"]2024-08-16T00:00:00 56da65649a0af93ebb780692d9073d712c2c6fa92024 International Conference on Big Data and Digital Management502024 International Conference on Big Data and Digital Management["Yuting Yin"]2024-08-16T00:00:00 64e8450373bcc19dec31fedaca937b89a5c1ff42This paper explores the pivotal role of Systems Integration and Administration in AI/Security, emphasizing their importance amidst rapid technological advancements. Traditional cybersecurity measures are increasingly inadequate against evolving threats, necessitating the integration of AI-driven solutions. While maneuvering through the complexity of today's systems, a clear trend of more effective integration and good administration becomes even more prominent as the years go by. Through the careful exploitation of the processes and the analysis, the specialists draw attention to the great complementarity of Systems Integration and Administration to technological breakthroughs. Systems integration helps organizations to turn fragmented processes into a systematic approach for more effective use of resources and to foster innovation. Through a comprehensive literature review, this study elucidates the critical role of Systems Integration and Administration in facilitating the seamless operation of complex systems, particularly when augmented by AI technologies such as machine learning and deep learning. The integration of AI enhances security measures, enabling organizations to detect and mitigate threats in real time. The convergence of Systems Integration, Administration, Artificial Intelligence, and Security offers transformative potential, bolstering defenses against cyber threats while fostering innovation and efficiency. Through interdisciplinary collaboration, organizations can harness the power of AI to navigate the evolving threat landscape with confidence, contributing to a more secure and resilient future.International Conference on Software Technology and Engineering310This study elucidates the critical role of Systems Integration and Administration in facilitating the seamless operation of complex systems, particularly when augmented by AI technologies such as machine learning and deep learning.2024 14th International Conference on Software Technology and Engineering (ICSTE)["M. Samonte", "Ma. Lhealynn N. Daquioag-Vasquez", "L. A. Ogaya"]2024-08-16T00:00:00 f38b1621d03b242c8b253f1e97fd9606501e8d5eJournal of Advanced Nursing50Journal of advanced nursing["Chong Cheng", "Ping-Ping Li", "Ling Zhang", "Bin Tang", "Pan Tang"]2024-08-16T00:00:00 53b073b8e7c295c5070dd60c8fdb008e5137e14aPurposeAlthough digital transformation (DT) has emerged as an important phenomenon for both research and practices, the influences remain inconclusive and inadequate. The emerging artificial intelligence (AI) technologies further complicate the understanding and practices of DT while understudied yet. To address these concerns, this study takes a process perspective to empirically investigate when and how digital-intelligence transformation can improve firm performance, aiming to enrich the literature on digital-intelligence transformation and strategic information systems (IS) field.Design/methodology/approachDrawing on the dynamic capability view and business agility, we took a process perspective to conceptualize and empirically examine the influence of digital-intelligence transformation and the process characteristics. Taking a continuous panel dataset of listed Chinese firms covering 2007 to 2020, we investigated digital-intelligence transformation’s effect on firm performance and the moderating roles of three strategic aspects: pace, scope and rhythm.FindingsThis study found that digital-intelligence transformation positively affects firm performance and is moderated by the characteristics of transformation processes (i.e. pace, scope and rhythm). Specifically, the high-paced and rhythmic transformation processes facilitate the positive relationship, while the large scope undermines the benefits of transformation. These relationships hold across various endogeneity and heterogeneity analyses.Originality/valueOur findings provide valuable implications for digital-intelligence transformation and strategic IS field. First, this study enriches existing literature on digital-intelligence transformation by empirically investigating the influence from a process perspective. Moreover, this study provides insights into a comprehensive understanding of the complexity of digital-intelligence transformation and the influences of AI. Finally, this study provides practical implications on how to make digital-intelligence transformation to benefit firm performance.Internet Research1541This study found that digital-intelligence transformation positively affects firm performance and is moderated by the characteristics of transformation processes (i.e. pace, scope and rhythm), and provides practical implications on how to make digital-intelligence transformation to benefit firm performance.Internet Research["Jianyu Zhao", "Xinru Wang", "Xinlin Yao", "X. Xi"]2024-08-16T00:00:00 54d8030740c3eb6cdc55547cd5ec14831aae3fc4Artificial Intelligence Review300A detailed review and a novel perspective on PI, beginning with the historical and philosophical origins of CPSS and proceeding to present both the fundamental framework and technological implementations of PI, to eventually facilitate the realization of “6S”-based parallel ecosystems.Artif. Intell. Rev.["Xingxia Wang", "Jing Yang", "Yuhang Liu", "Yutong Wang", "Fei-Yue Wang", "Mengzhen Kang", "Yonglin Tian", "Imre J. Rudas", "Lingxi Li", "M. P. Fanti", "Bassam Alrifaee", "Muhammet Deveci", "Deepak Mishra", "Muhammad Khurram Khan", "Long Chen", "P. Reffye"]2024-08-16T00:00:00 8692f15923089d061c73547f1e8bbe6a6d403d91Discover Artificial Intelligence268This paper examines the diverse hurdles faced during developing and deploying AI applications in the industrial domain and provides guidelines aimed at maximizing AI's benefits in industrial environments while minimizing potential risks.Discov. Artif. Intell.["Sudhi Sinha", "Young M. Lee"]2024-08-16T00:00:00 dd01b37aa5a25defb272ef95f720a33904d8d5ffThe rapid advances in generative artificial intelligence (AI) open up attractive opportunities for creative problem-solving through human-guided AI partnerships. To explore this potential, we initiated a crowdsourcing challenge focused on sustainable, circular economy business ideas generated by the human crowd (HC) and collaborative human-AI efforts using two alternative forms of solution search. The challenge attracted 125 global solvers from various industries, and we used strategic prompt engineering to generate the human-AI solutions. We recruited 300 external human evaluators to judge a randomized selection of 13 out of 234 solutions, totaling 3,900 evaluator-solution pairs. Our results indicate that while human crowd solutions exhibited higher novelty—both on average and for highly novel outcomes—human-AI solutions demonstrated superior strategic viability, financial and environmental value, and overall quality. Notably, human-AI solutions cocreated through differentiated search, where human-guided prompts instructed the large language model to sequentially generate outputs distinct from previous iterations, outperformed solutions generated through independent search. By incorporating “AI in the loop” into human-centered creative problem-solving, our study demonstrates a scalable, cost-effective approach to augment the early innovation phases and lays the groundwork for investigating how integrating human-AI solution search processes can drive more impactful innovations. Funding: This work was supported by Harvard Business School (Division of Research and Faculty Development) and the Laboratory for Innovation Science at Harvard (LISH) at the Digital Data and Design (D3) Institute at Harvard. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.18430 .Organization science (Providence, R.I.)1118By incorporating “AI in the loop” into human-centered creative problem-solving, this study demonstrates a scalable, cost-effective approach to augment the early innovation phases and lays the groundwork for investigating how integrating human-AI solution search processes can drive more impactful innovations.Organization Science["L\u00e9onard Boussioux", "Jacqueline N. Lane", "Miaomiao Zhang", "Vladimir Jacimovic", "Karim R. Lakhani"]2024-08-16T00:00:00 9ba9afc3e25ef0bb03f8d786c3504d9c2b549d04Artificial intelligence (AI) is accelerating how we conduct science, from folding proteins with AlphaFold and summarizing literature findings with large language models, to annotating genomes and prioritizing newly generated molecules for screening using specialized software. However, the application of AI to emulate human cognition in natural product research and its subsequent impact has so far been limited. One reason for this limited impact is that available natural product data is multimodal, unbalanced, unstandardized, and scattered across many data repositories. This makes natural product data challenging to use with existing deep learning architectures that consume fairly standardized, often non-relational, data. It also prevents models from learning overarching patterns in natural product science. In this Viewpoint, we address this challenge and support ongoing initiatives aimed at democratizing natural product data by collating our collective knowledge into a knowledge graph. By doing so, we believe there will be an opportunity to use such a knowledge graph to develop AI models that can truly mimic natural product scientists' decision-making.Natural product reports (Print)173This Viewpoint addresses the challenge and support ongoing initiatives aimed at democratizing natural product data by collating the authors' collective knowledge into a knowledge graph and believes there will be an opportunity to develop AI models that can truly mimic natural product scientists' decision-making.Natural product reports["David Meijer", "M. Beniddir", "Connor W. Coley", "Yassine M Mejri", "Meltem \u00d6zt\u00fcrk", "J. V. D. van der Hooft", "Marnix M Medema", "A. Skiredj"]2024-08-16T00:00:00 d9ce91a1c4ee1381fb8f7993cdd343be6850a39cThere are many moral concerns that come up when artificial intelligence (AI) is used in human resource management (HRM). These include privacy, computer bias, and who is responsible for what. As part of this study, the quality of secondary data sources such as scholarly books, reports, and case studies is judged. When AI systems handle a lot of personal data, privacy concerns appear. This means that they need strong data protection and clear ways to handle the data. It's a problem that AI systems might make biases in old data greater, which could make it less fair to hire people and evaluate their work. There is less responsibility because AI programmes are hard to understand and run. To be clear, businesses need to be open and keep an eye on things. To protect people's rights, rules like the GDPR are very important. It's even more important to use AI in a way that supports freedom and stops discrimination because it has bigger effects on human rights and personal freedom. Different groups of people around the world deal with these moral issues in very different ways. There should be different rules for right and wrong in each country.Journal of Informatics Education and Research271The quality of secondary data sources such as scholarly books, reports, and case studies is judged, which shows that when AI systems handle a lot of personal data, privacy concerns appear, they need strong data protection and clear ways to handle the data.Journal of Informatics Education and Research["Richa Bhalla, Nakshatresh Kaushik, Prithu Sarkar", "Potnuri Suribabu, Ajay Kumar Garg, Sreenivasulu Arigela"]2024-08-16T00:00:00 396ce7fe0a8b55704fa4e9634abf9e1a7f86fff7The increasing use of generative Artificial Intelligence (AI) in modern software engineering, particularly Large Language Models (LLMs) for code generation, has transformed professional software development by boosting productivity and automating development processes. This adoption, however, has highlighted a significant issue: the introduction of security vulnerabilities into the code. These vulnerabilities result, e.g., from flaws in the training data that propagate into the generated code, creating challenges in tackling them in established ways. Traditional vulnerability handling processes often involve extensive manual review. Applying such traditional processes to AI-generated code is challenging. AI-generated code may include several similar vulnerabilities, possibly in slightly different forms as developers might not build on already implemented code, using functions or libraries, but prompt similar tasks. In this work, we explore the current state of LLM-based approaches for vulnerability handling, focusing on approaches for vulnerability detection, localization, and repair. We provide an overview of recent progress in this area and highlight open challenges that must be addressed to establish a reliable and scalable vulnerability handling process for AI-generated code.2024 Conference on AI, Science, Engineering, and Technology (AIxSET)381The current state of LLM-based approaches for vulnerability handling is explored, focusing on approaches for vulnerability detection, localization, and repair, and provides an overview of recent progress.2024 Conference on AI, Science, Engineering, and Technology (AIxSET)["Sabrina Kaniewski", "Dieter Holstein", "Fabian Schmidt", "Tobias Heer"]2024-08-16T00:00:00 f3cbd13829e034aa13acd1c160f81768f185ed35Innovations that combine the interpretability of symbolic AI with the learning capabilities of sub-symbolic AI can flourish in the nexus of symbolic and sub-symbolic AI. This research presents Fuzzy Cognitive Maps (FCMs). This hybrid model combines the best features of both paradigms as a workable answer to the problems of interpretability and explainability in artificial intelligence (AI) systems. FCMs have become a robust framework for logically and intuitively supporting decision-making processes and expressing causal information. A more organic and adaptable problem-solving approach is made possible by FCMs’ ability to manage the inherent ambiguity and uncertainty present in real-world situations. Because of their innate flexibility and ability to learn and adapt from sub-symbolic AI, FCMs are an excellent fit for applications requiring high interpretability and explainability.International Journal of Computer Science Engineering and Information Technology371This research presents Fuzzy Cognitive Maps (FCMs), a hybrid model that combines the best features of both paradigms as a workable answer to the problems of interpretability and explainability in artificial intelligence systems.International Journal of Computer Science, Engineering and Information Technology["Maikel Leon"]2024-08-16T00:00:00 ca754776eb7f1f6a01259c834f4d94270cfc5a47Artificial intelligence (AI) is increasingly manifest in everyday work, learning, and living. Reports attempting to gauge public perception suggest that amidst exaggerated expectations and fears about AI, citizens are sceptical and lack understanding of what AI is and does (Archer et al., 2018). Professional workers practice at the intersection of such public perceptions, the AI industry, and regulatory frameworks. Yet, there is limited understanding of the day-to-day interactions and predicaments between workers, AI systems, and the publics they serve. This includes how these interactions and predicaments generate opportunities for learning and highlight new digital fluencies needed. We bring social and computing science perspectives to begin to examine the prevailing AI narratives in professional work and learning practices. Some AIs (such as deep machine learning systems) are so sophisticated that a human-understandable explanation of how it works may not be possible. This raises questions about what professional practitioners are able to know about the AI systems they use: their new digital co-workers. We argue that a co-constitutive human-AI perspective could provide useful insights on questions such as: (1) How is professional expertise and judgment re-distributed as workers negotiate and learn with AI systems? (2) What trust and confidence in new AI-infused work practices is needed or possible and how is this mediated? (3) What are the implications for professional learning: both learning within work and the workplace and more formal curriculum? Given the early stages of this field of inquiry, our aim is to evoke discussion of alternative human-AI narratives suited for the messy—and often unseen—realities of everyday practices.Networked Learning Conference351It is argued that a co-constitutive human-AI perspective could provide useful insights on questions such as: how is professional expertise and judgment re-distributed as workers negotiate and learn with AI systems?Networked Learning Conference["Terrie Lynn Thompson", "Bruce Graham"]2024-08-16T00:00:00 35e0ec9b7ad1da0efdebbfb8e339085257dd83c0Artificial Intelligence which is also known as AI including with Machine Learning, and Deep Learning have been included in the field of robotics in recent years. It is the theory of the systems which are able to undertake tasks which will normally need the intelligence of humans. In the need of creative solutions for the challenges like digital transformations AI offers several advantages. It is a workforce productive process. Two types of AI, Narrow AI and general AI are involved in the process. The are several benefits of AI in web development. AI welcomes an impact on user engagement and interaction.International Journal of Innovative Science and Research Technology341The are several benefits of AI in web development, including an impact on user engagement and interaction and a workforce productive process.International Journal of Innovative Science and Research Technology (IJISRT)["Vaishnavi Kosuru"]2024-08-17T00:00:00 b6494831594627776fd248a2472b5d840978b1ddThere are few studies on the representation of older people regarding aids and assistive devices and even fewer that incorporate more inclusive views (gender, emotions, anti-ageist, territorial or land approach) as well as virtual or land ethnography or artificial intelligence. The general objective was to evaluate digital images of aids and assistive aids in the older population, from the perspectives mentioned above. Method. A descriptive and cross-sectional study that searched, observed and analyzed images. An evaluation of intentionally selected images from Freepik, Pixabay, Storyblocks, Splitshire, Gratisography and ArtGPT, included in an original database constructured by several authors of this article, was carried out in the context of the ENCAGEn-CM project (2020–2023, financed by the CAM and FSE). This base was updated and expanded in October and November 2023. In addition, an image generation process was carried out using artificial intelligence, and this was also part of the analysis (ArtGPT). Finally, algorithms were used to solve and retrain with the images. Results. Of the total final images included in the expanded database until November 2023 (n = 427), only a third (28.3%, 121/427) included the aids and assistive aids label. Representations of mixed groups predominated (38.8%) and, to a lesser extent, those of women. A large proportion of the devices were ‘glasses’ (74.6%) and the ‘use of a cane’ (14.9%). To a lesser extent, ‘wheelchairs’ (4.4%) or ‘hearing aids’ (0.9%) and the presence of more than one device (simultaneously) (5.3%) were noted. The main emotions represented were ‘joy’ (45.6%) and ‘emotion not recognized’ (45.6%), with, to a lesser extent, ‘sadness’ (3.5%), ‘surprise’ (4.4%) and ‘anger’ (0.9%). Differences by sex were found in the represented emotions linked to aids and assistive aids. The representation of images of the built environment predominated significantly (70.2%), and it was observed that older women were less represented in natural environments than men. Based on the previous findings, a method is proposed to address stereotypes in images of older individuals. It involves identifying common stereotypical features, like glasses and hospital settings, using deep learning and quantum computing techniques. A convolutional neural network identifies and suppresses these elements, followed by the use of quantum algorithms to manipulate features. This systematic approach aims to mitigate biases and enhance the accuracy in representing older people in digital imagery. Conclusion. A limited proportion of images of assistive devices and older people were observed. Furthermore, among them, the lower representation of images of women in a built environment was confirmed, and the expressions of emotions were limited to only three basic ones (joy, sadness and surprise). In these evaluated digital images, the collective imagination of older people continues to be limited to a few spaces/contexts and emotions and is stereotyped regarding the same variables (sex, age, environment). Technology often overlooks innovative support tools for older adults, and AI struggles in accurately depicting emotions and environments in digital images. There is a pressing need for thorough pretraining analysis and ethical considerations to address these challenges and ensure more accurate and inclusive representations of older persons in digital media.Land582It was observed that older women were less represented in natural environments than men and a method is proposed to address stereotypes in images of older individuals using deep learning and quantum computing techniques.Land["Vanesa Zorrilla-Mu\u00f1oz", "Daniela Luz Moyano", "Carolina Marcos Carvajal", "M. S. Agull\u00f3-Tom\u00e1s"]2024-08-17T00:00:00 84a9ca0250ded8b287a37fdcdc4be878c3a9738fSuicides are a phenomenon that negatively impacts society. The issues include the loss of human life, negative psychological effects on close ones, increased healthcare costs, lost productivity, and more. Suicides also create social and emotional tensions that can deteriorate the quality of life in affected communities. This is why it is necessary to study and research trends to enable prevention. In the present study, an analysis of suicide risk in some European countries is conducted using an artificial intelligence system implemented with Neural Networks, Random Forests, Support Vector Machines, and more. The study seeks answers to the following questions: Are suicide rates increasing or decreasing? Are there differences in the number of suicides based on age and gender? Do economic factors affect the number and rate of suicides? Can we predict the number of suicides based on the given data? The machine learning methods used for this purpose are created and modeled in the Orange Data Mining environment, which also offers numerous tools for analyzing and visualizing the obtained results. It is important to note that the study of suicide risk is not equivalent to the diagnosis of suicidal disorders; however, the answers to the questions posed in the study are crucial for building and analyzing the overall picture of suicidal intentions.Computer Science and Interdisciplinary Research Journal120In the present study, an analysis of suicide risk in some European countries is conducted using an artificial intelligence system implemented with Neural Networks, Random Forests, Support Vector Machines, and more.Computer Science and Interdisciplinary Research Journal["Valentin Georgiev", "K. Yotov"]2024-08-17T00:00:00 ce0cf00c9918cd58ed17263ad8e343c9d8c1857dIntroduction: The increasing integration of Artificial Intelligence (AI) in the banking sector has reshaped traditional financial services, particularly in the context of fraud prevention. This cross-sectional study in Ghana aimed to investigate the current state and perceived effectiveness of AI applications in banking, focusing on its role in fraud prevention. Methods: The research data was acquired through interviews and surveys conducted with customers and bank officials. A total of 363 participants took part in the survey, comprising 200 customers and 163 staff members selected from five banks in Ghana. Structured questionnaires were distributed electronically and in print to gather quantitative and qualitative data. Results: The findings reveal a significant level of awareness (70.0%), understanding (75.0%) and 62.0% experience with AI in the banking sector among the participants. An overwhelming 88.0% express a preference for AI-based support over human-based support. About 97.2% believe that AI systems prioritize robust privacy measures influencing their perception of AI in fraud prevention. Furthermore, 87.5% perceive AI systems as consistently providing precise and reliable results, enhancing their confidence in the technology. The perception of AI's effectiveness in fraud prevention is closely tied to its capacity to adapt to new and emerging fraud tactics, with 66.6% emphasizing the importance of this adaptability. Conclusion: These findings contribute to understanding the nuanced perspectives of users in Ghana regarding AI in the banking sector, providing insights for financial institutions, policymakers, and educators aiming to enhance AI adoption and trust.Asian Journal of Research in Computer Science00The findings of this cross-sectional study in Ghana contribute to understanding the nuanced perspectives of users in Ghana regarding AI in the banking sector, providing insights for financial institutions, policymakers, and educators aiming to enhance AI adoption and trust.Asian Journal of Research in Computer Science["Eric Aggrey", "Isaac Koranteng Baffoe", "Francis Adomako", "Yeboah Brobbey Gideon", "B. Amoah"]2024-08-17T00:00:00 8e2f502b048513e1ba2be90d3f2f42343e47ccc3Forest fire is a frequently occurring natural disaster that happens all over the world, especially in tropical regions. There are several types of forest fires that include ground, surface, and crown, which can be monitored by using ground cameras, drones, and satellite imaging. Forest fires bring negative impacts not just to the environment, but also to the local economies. Thus, it is important to monitor and identify forest fires while the fire spit is relatively small using several intelligent technologies. Generally, the traditional way of monitoring forest fire is by using man-powered patrols, watchtowers, and remote sensing devices. The traditional way not only lacks in terms of accuracy, but this method also requires more manpower to maintain the monitoring system. Not to mention that the previously mentioned method is also more time-consuming before the fire spot can be detected. Therefore, artificial intelligence methods have been widely explored in order to enhance the performance of forest fire monitoring systems. High accuracy and fast response in detecting forest fires can lead to saving a lot of our precious forest resources. This article aims to review the artificial intelligence-based methods used in forest fire monitoring systems. There are two key comparison factors have been identified, which are then grouped into conventional machine learning and deep learning methods. Three machine learning methodologies, which are classification, detection, and segmentation have been used to identify the forest fire spots. An analysis has been performed in terms of model architecture, data type, and suitability of the methods. Three subsections have been constructed in the context of forest fire monitoring, which are 1) introduction, 2) methodology that is divided into two categories, conventional machine learning, and deep learning, and 3) conclusion and future works.Control and System Graduate Research Colloquium330This article aims to review the artificial intelligence-based methods used in forest fire monitoring systems and identifies three machine learning methodologies, which are classification, detection, and segmentation have been used to identify the forest fire spots.2024 IEEE 15th Control and System Graduate Research Colloquium (ICSGRC)["Faiqah Nur Adlina Mohd Radzi", "M. A. Zulkifley", "Z. Kadim"]2024-08-17T00:00:00 ed6f33c41560071f33d47c9f55fd42b0e3c3d8c8Technologies based on artificial intelligence are increasingly replacing and augmenting humans in managerial tasks such as decision-making. Modern artificial intelligence (AI) technologies are capable of performing cognitive functions previously associated only with the human mind. According to the company’s resource concept (RBV), people’s cognitive abilities are a source of non-copyable competitive advantages because they are difficult to simulate, so AI technologies can change the sources of competitive advantages. This study aims to identify the factors that influence the decision of industrial companies to adopt artificial intelligence technologies, as well as to examine the relationship between the adoption of AI technologies with the effects of replacing and/or complementing the cognitive abilities of employees and their impact on the formation of a competitive advantage. The study was conducted on the database of 147 industrial companies, empirically estimating the occurrence of the substitution effect during the introduction of AI technologies. The complementarity effect was estimated using two models: a random effect probit model with random effects (random effect probit) and a fixed effect logit model with fixed effects (fixed effect logit). This made it possible to assess the intra-firm dynamics of resource changes during the implementation of AI technologies in the business process - that is, to trace the effect of resource substitution during the implementation of AI. The results showed that: (1) The decision to invest in AI technologies depends on factors such as the availability of skills to implement AI, the cost of implementing new technologies and the level of current costs in the company as a whole, the expectation of financial and economic impact. (2) The decision to invest in AI is significantly more prevalent among companies that are currently waiting to implement it. The benefits of such investment are manifold. Firstly, it allows for a reduction in the time taken to complete operations. Secondly, it enables a reduction in the number of employees required, due to a reduction in the volume of routine operations. Thirdly, it allows for a reduction in the cost of personnel management. Finally, it facilitates a greater speed of development and promotion of new products. (3) The introduction of AI has the greatest impact on the formation of non-copied competitive advantages, particularly in the following areas: marketing and analytics, development and IT, sales and customer service and the development of new products. (4) The introduction of AI gives rise to both a substitution effect and a complementarity effect, which together result in a shift in the sources of competitive advantages. While the replacement of traditional, domain-specific human cognitive capabilities with numerous computing capabilities of AI leads to the destruction of existing advantages, the complementarity of human and machine capabilities allows for the creation of new, permanent non-copied advantages. The company’s resource concept is augmented, and it is shown that heterogeneous unrelated resources, such as human capital and machinery, can also serve as a source of distinctive competitive advantages.Strategic decisions and risk management230The results showed that the decision to invest in AI technologies depends on factors such as the availability of skills to implement AI, the cost of implementing new technologies and the level of current costs in the company as a whole, the expectation of financial and economic impact.Strategic decisions and risk management["A. Trachuk", "N. Linder"]2024-08-17T00:00:00 04b637671c6a6e6b87801220ab2ab34568743b51Future healthcare journal563A systematic review of published studies to June 2022 that reported elements of interactions that impacted the relationship between clinicians and AI-enabled clinical decision support systems identified 210 interaction traits that can be used to assess the quality of AI–clinician interactions.Future Healthcare Journal["Argyrios Perivolaris", "Chris Adams-McGavin", "Yasmine Madan", "T. Kishibe", "Tony Antoniou", "Muhammad Mamdani", "James J. Jung"]2024-08-17T00:00:00 14d7ef70130afda303de85c069875db51224c759Forecasting economic indicators like Gross Domestic Product (GDP) is crucial for planning and decision-making by policymakers, investors, and businesses. Traditional econometric models, including time series and regression analyses, often fail to capture the complex, non-linear dynamics in economic data. This paper explores the application of artificial neural networks (ANNs), specifically a multilayer perceptron (MLP) model with three hidden layers, to forecast Iraq's GDP. The volatility of Iraq's economy, heavily influenced by oil revenues and geopolitical instability, presents unique challenges. Using quarterly GDP data from 2000 to 2020, the ANN model was better at predicting the future, with an R-squared value of 0.996 and a mean absolute percentage error (MAPE) of 3.97%. These results indicate high accuracy and reliability, underscoring the potential of ANNs to enhance economic forecasting in developing and resource-dependent economies. The findings offer critical insights for economic planning and policy formulation, particularly in settings similar to Iraq's. This study not only contributes to a deeper understanding of AI applications in economic analysis but also opens up avenues for further exploration of AI-based models in other volatile economic environments.Control and System Graduate Research Colloquium280Using quarterly GDP data from 2000 to 2020, the ANN model was better at predicting the future, with an R-squared value of 0.996 and a mean absolute percentage error (MAPE) of 3.97%, indicating high accuracy and reliability, underscoring the potential of ANNs to enhance economic forecasting in developing and resource-dependent economies.2024 IEEE 15th Control and System Graduate Research Colloquium (ICSGRC)["Marwan Abdul HameedAshour", "Ammar Sh Ahmed"]2024-08-17T00:00:00 d7be5cfac7e5c2ef237b36c9eafa829ffc89250aArtificial Intelligence (AI) is revolutionizing healthcare through its integration into various domains, significantly enhancing the efficiency, accuracy, and effectiveness of medical practices. This review explores the transformative impact of AI across multiple aspects of healthcare, including diagnostics, personalized treatment, drug discovery, surgery, and more. AI's capabilities in diagnostics and early detection are improving the precision and speed of disease identification, enabling earlier and more effective interventions. Personalized treatment approaches leverage AI to analyze patient data and tailor therapies to individual needs, optimizing outcomes and reducing adverse effects. AI-driven robotics in surgery offer enhanced precision, control, and minimally invasive options, leading to improved surgical outcomes and faster recovery times. Despite these advancements, the adoption of AI in healthcare presents challenges and ethical considerations, including data quality, algorithmic bias, patient privacy, and the responsible use of AI technologies. Addressing these issues is crucial for maintaining trust and ensuring equitable access to AI-powered healthcare solutions. AI's role in drug discovery and development is accelerating the creation of new therapies by optimizing predictive modeling, drug design, and clinical trials, thus reducing costs and speeding up the development process. Future trends and innovations in AI highlight ongoing advancements and the potential for further transformation in healthcare. These include advancements in natural language processing, AI-enhanced telemedicine, wearable health technologies, and ethical AI governance. As AI technology continues to evolve, its impact on healthcare will become increasingly significant, driving progress in patient care, operational efficiency, and medical research. Collaborative efforts among technologists, clinicians, researchers, and policymakers will be essential in harnessing AI's full potential while addressing the complexities and ethical challenges associated with its use. This review underscores the promise of AI to revolutionize healthcare and improve patient outcomes while emphasizing the need for responsible implementation and ongoing evaluation.International Journal of Multidisciplinary Sciences and Arts457This review underscores the promise of AI to revolutionize healthcare and improve patient outcomes while emphasizing the need for responsible implementation and ongoing evaluation.International Journal of Multidisciplinary Sciences and Arts["Shah Zeb", "Nizamullah Fnu", "Nasrullah Abbasi", "Muhammad Fahad"]2024-08-17T00:00:00 002530ee4700a62e27e899f9c6c61ae0a5e30fd1The advancement of autonomous vehicles (AVs) heavily relies on their ability to process high volumes of sensor data and make real-time decisions. This paper explores how the integration of data engineering, machine learning (ML), artificial intelligence (AI), and a cohesive hardware-software approach can further enhance the performance and safety of AVs. We propose a comprehensive framework that leverages advanced data engineering techniques for efficient data management, employs state-of-the-art ML models for accurate perception and prediction, and utilizes AI- driven strategies for decision-making and control. The proposed solutions are designed to be effective in areas with limited internet connectivity and can operate on low- powered hardware, even with outdated software.International Journal of Innovative Science and Research Technology321This paper proposes a comprehensive framework that leverages advanced data engineering techniques for efficient data management, employs state-of-the-art ML models for accurate perception and prediction, and utilizes AI- driven strategies for decision-making and control.International Journal of Innovative Science and Research Technology (IJISRT)["Brahma Reddy Katam"]2024-08-17T00:00:00 9083fb4b4662627067628c1f3aa526fdaaeb0172Artificial intelligence (AI) plays a significant role in enhancing the capabilities of defense systems, revolutionizing strategic decision-making, and shaping the future landscape of military operations. Neuro-Symbolic AI is an emerging approach that leverages and augments the strengths of neural networks and symbolic reasoning. These systems have the potential to be more impactful and flexible than traditional AI systems, making them well-suited for military applications. This article comprehensively explores the diverse dimensions and capabilities of Neuro-Symbolic AI, aiming to shed light on its potential applications in military contexts. We investigate its capacity to improve decision-making, automate complex intelligence analysis, and strengthen autonomous systems. We further explore its potential to solve complex tasks in various domains, in addition to its applications in military contexts. Through this exploration, we address ethical, strategic, and technical considerations crucial to the development and deployment of Neuro-Symbolic AI in military and civilian applications. Contributing to the growing body of research, this study represents a comprehensive exploration of the extensive possibilities offered by Neuro-Symbolic AI.IEEE Transactions on Artificial Intelligence1461This article comprehensively explores the diverse dimensions and capabilities of Neuro-Symbolic AI, aiming to shed light on its potential applications in military contexts, and investigates its capacity to improve decision-making, automate complex intelligence analysis, and strengthen autonomous systems.IEEE Transactions on Artificial Intelligence["D. Hagos", "Danda B. Rawat"]2024-08-17T00:00:00 ab4a8953bcfb69b4a13576f4f92d118b88807673The rapid evolution of financial technology (fintech) has significantly transformed the financial services landscape, creating opportunities for innovation and introducing new risks. Traditional financial institutions and fintech companies operate under different paradigms, resulting in disparate risk management practices. This paper proposes a comprehensive framework for integrating operations and risk management practices between traditional financial institutions and fintech companies. By leveraging advanced technologies such as artificial intelligence (AI) and machine learning (ML), the framework aims to ensure consistent and effective risk assessment across the financial sector. The financial services industry is characterized by rapid innovation, primarily driven by fintech companies offering various services that enhance efficiency, accessibility, and customer satisfaction. However, the growth of fintech brings substantial risks, including cyber threats, data privacy concerns, regulatory compliance challenges, and operational vulnerabilities. Traditional financial institutions prioritize stability, security, and compliance within established risk management frameworks. The divergence in operational models and risk management approaches creates a fragmented risk landscape, posing significant challenges to the financial system's stability and security. This paper identifies the critical need for a unified framework integrating the risk management practices of traditional financial institutions and fintech companies. The proposed framework leverages AI and ML to enhance the accuracy and comprehensiveness of risk assessments, utilizing a centralized data repository for real-time risk assessment. Unified risk management policies covering cybersecurity, operational risk, regulatory compliance, financial crime, and real-time monitoring and reporting tools ensure robust risk management protocols and prompt response to potential risks. Aligning with regulatory requirements and incorporating best practices from both sectors, the integrated risk management approach enhances the financial ecosystem's stability, security, and public confidence.Journal of Economics Management and Trade01A comprehensive framework for integrating operations and risk management practices between traditional financial institutions and fintech companies is proposed, leveraging advanced technologies such as artificial intelligence (AI) and machine learning (ML) to ensure consistent and effective risk assessment across the financial sector.Journal of Economics, Management and Trade["B. Abikoye", "Wunmi Adelusi", "Stanley Chidozie Umeorah", "A. Adelaja", "Cedrick Agorbia-Atta"]2024-08-17T00:00:00 0866e87830149722493332c225971c64f5056853This study investigates the use of a large language model system to improve efficiency and quality in emergency department (ED) discharge letter writing. Time constraints and infrastructural deficits make compliance with current discharge letter targets difficult. We explored potential efficiencies from an artificial intelligence software in the generation of ED discharge letters and the attitudes of doctors toward this technology. The evaluated system leverages advanced techniques to fine-tune a model to generate discharge summaries from short-hand inputs, including voice, text, and electronic health record data. Nineteen physicians with emergency medicine experience evaluated the system text and voice-to-text interfaces against manual typing. The results showed significant time savings with MedWrite LLM interfaces compared to manual methods.arXiv.org160The evaluated system leverages advanced techniques to fine-tune a model to generate discharge summaries from short-hand inputs, including voice, text, and electronic health record data.ArXiv["David Menzies", "Sean Kirwan", "Ahmad Albarqawi"]2024-08-17T00:00:00 f31ebcdc877e859b56c35efa454046be2322bf4eLa inteligencia artificial (IA) está transformando la educación, ofreciendo herramientas avanzadas para personalizar el aprendizaje y mejorar la gestión educativa. Este estudio evalúa la percepción de los docentes sobre las capacidades y desafíos asociados con la IA en la Unidad Educativa El Carmen, Ecuador. Utilizando un enfoque cuantitativo-descriptivo, se aplicó una encuesta a los 21 docentes de la institución, explorando tres dimensiones: conocimiento sobre IA, capacidades de la IA en la educación y desafíos de la IA. Los resultados indican que, aunque la mayoría de los docentes posee conocimientos básicos sobre IA, hay una notable brecha en su familiaridad con aplicaciones prácticas y aspectos éticos. Los docentes valoran positivamente las capacidades de la IA para personalizar la enseñanza y mejorar el acceso a recursos digitales, pero expresan preocupaciones sobre la privacidad de los datos y la posible sustitución de la labor docente. Este estudio subraya la necesidad de programas de formación docente que aborden tanto los aspectos técnicos como éticos de la IA, y destaca la importancia de políticas claras de privacidad y seguridad. Al entender mejor las percepciones docentes, se pueden diseñar estrategias efectivas para integrar la IA en el sistema educativo, maximizando sus beneficios y mitigando sus desafíos.Revista Científica de Innovación Educativa y Sociedad Actual "ALCON"00Revista Científica de Innovación Educativa y Sociedad Actual "ALCON"["Ligia Patricia Pinargote Salvatierra", "Cruz Cecibel Loor Moreira", "Adela Connie Alc\u00edvar Ch\u00e1vez", "Myriam Yuliana Loor Zambrano", "Justo Antonio Rojas Rojas"]2024-08-17T00:00:00 c744206c39f398b38b9ac72f2361a10614031773La Inteligencia Artificial (I.A) ha surgido como herramientas prometedoras para mejorar la experiencia enseñanza y aprendizaje, ChatGPT, está transformando la forma en que se enseña y se aprende. Este estudio se lo aplicó en la Universidad de Guayaquil (U.G) con los estudiantes de la carrera de filosofía de la escuela en Educación Inicial curso C1, C2 un total de 280 estudiantes en el periodo lectivo 2024 - 2025. Se explorará los beneficios, desafíos y percepciones de alumnos y docentes, asociado a su ejecución. Estudiantes, administradores, asistente para apoyo educativo, y educadores de la carrera de filosofía de la escuela en Educación Inicial, el enfoque de estudio será mixto (cualitativo y cuantitativo), el diseño de la investigación es el paradigma descriptivo y exploratorio también conocido como paradigma constructivista, para describir y explorar el impacto y las percepciones del uso de ChatGPT en el aula, se diseñaran cuestionarios con preguntas de opción múltiple, para docentes y para estudiantes en escalas de Likert y preguntas abiertas para medir variables cuantitativas, como la satisfacción del estudiante, la percepción del maestro sobre la efectividad de la I.A. Para finalizar la integración de ChatGPT, demostró ser una experiencia positiva en general, con beneficios significativos en términos de participación estudiantil, interacción en el aula y personalización del aprendizaje.Conocimiento global00Conocimiento global["Julio C\u00e9sar Palma Vidal", "Wilson Javier Romero Berrones", "Edgar Ren\u00e9 Zu\u00f1a Macancela"]2024-08-17T00:00:00 1a3aa42235b15a8e8d80d0c7f225080a90bb1facIntroduction: The article is dedicated to studying the main capabilities and effectiveness of using AI in the modern education system and the impact of neural networks on developing analytical skills in learners. Methods: The research is based on the comprehensive application of analysis, comparative methods, forecasting and data analysis, pedagogical observation, and generalisation methods. Results: The article establishes that AI began actively integrating into the education system in November 2022 with access to the ChatGPT service. It also identifies that the use of artificial intelligence in the education system at various levels is founded on the integration of three primary skills: learning (collection and analysis of information), thinking (analytics, choosing optimal action algorithms), and self-correction (improving setting algorithms to achieve more accurate results). It is established that there are two main directions for using AI to enhance the efficiency of the educational process: the application of generative AI (generating texts, plans, annotations, presentations, images) and predictive AI (automation of educational processes). The use of AI in the education system occurs in three main areas: implementing personalised learning, automating essential educational functions (checking tests, problems, equations, even evaluating creative works), and improving the process of distance education and self-education. Conclusions: It is determined that the main disadvantage of using AI in education is the reduction in the level of socialisation and critical thinking among learners. Therefore, it is crucial to balance using neural networks in the learning process and the presence of the teacher's personality in this process.Salud, Ciencia y Tecnología193The main disadvantage of using AI in education is the reduction in the level of socialisation and critical thinking among learners, so it is crucial to balance using neural networks in the learning process and the presence of the teacher's personality in this process.Salud, Ciencia y Tecnología - Serie de Conferencias["Nataliia Tymoshenko", "Galyna Gordiichuk", "Zhanna Davydova", "P. Sirenko", "Y. Dorozhko"]2024-08-18T00:00:00 1edc3b6089ccff7b88d7bccb07936a8c70a48709INTRODUCTION In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.Expert Review of Anti-Infective Therapy1531This narrative review discusses promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.Expert review of anti-infective therapy["D. Giacobbe", "C. Marelli", "Sabrina Guastavino", "A. Signori", "Sara Mora", "Nicola Rosso", "Cristina Campi", "M. Piana", "Ylenia Murgia", "Mauro Giacomini", "Matteo Bassetti"]2024-08-18T00:00:00 5938023b909c6244e542c08b64122913b2191b70Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.International Journal of Ophthalmology730This review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.International journal of ophthalmology["Zi-Ran Zhang", "Jia-jun Li", "Ke-Ran Li"]2024-08-18T00:00:00 3ddcc4f54dbd9773b4deac23acf78f0d57afbde4In Artificial Intelligence, “Artificial” means objects that are produced by human beings, and “Intel ligence” is the capability to form tactics to achieve goals by interacting with huge information. Artificial Intelligence (AI) is evolving rapidly in higher education, and various AI applications have been developed to solve some of the most pressing problems that higher education field currently face. The use of Artificial Intelligence or AI is rising in higher education. With this rise, the morality of AI programs is being questioned Higher education is crucial for producing ethical citizens and profess ionals globally. The introduction of generative AI (GenAI), such as ChatGPT, has posed opportunities and challenges to the traditional model of education. However, the current conversations primarily focus on policy development and assessment, with limited research on the future of higher education.AI - powered language tools (AILTs) are commonly used by university students, yet there is a limited understanding of how students utilize and perceive these tools in everyday academic communication practice. Following the very recent launch of the ChatGPT, chatbot, numerous comments and speculations were posted concerning the potential aspects of society that are expected to benefit from this AI revolution. This paper addresses some of the most fundamental questions about the role, position, and implications of ChatGPT and generative artificial intelligence (AI) tools amidst the evolving landscape of higher education and modern society.AI education aims to teach AI concepts, essential knowledge, and skills related to the fundamental ideas in AI. The affordances of artificial intelligence (AI) have not been totally utilized in education. To effectively integrate AI into education, teachers’ AI - specific technological and pedagogical knowledge is important.International Journal of Scientific Research in Science Engineering and Technology30Some of the most fundamental questions about the role, position, and implications of ChatGPT and generative artificial intelligence (AI) tools amidst the evolving landscape of higher education and modern society are addressed.International Journal of Scientific Research in Science, Engineering and Technology["Ms. Janhvee Vivek Boratkar", "Dr. Rajeshkumar U. Sambhe"]2024-08-18T00:00:00 54f988550afba3ae89f380f995e6683d1c56c28cArtificial intelligence (AI) has emerged as a transformative tool in post-market surveillance (PMS) for monitoring the safety and performance of medical products. This article explores the role of AI in optimizing PMS practices, focusing on its applications in signal detection, risk assessment, and regulatory compliance. By harnessing machine learning algorithms and big data analytics, AI facilitates the automated analysis of real-world evidence, including patient outcomes data, adverse event reports, and electronic health records. Through pattern recognition and anomaly detection, AI algorithms enable the early identification of potential safety issues and facilitate timely interventions to mitigate risks. Moreover, AI-driven PMS systems enhance regulatory oversight by providing regulators with comprehensive and actionable insights into product safety profiles and emerging trends. However, concerns regarding data privacy, algorithm bias, and interpretability underscore the need for transparent and ethically responsible AI deployment in PMS frameworks.International Journal of Scientific Research and Management700The role of AI in optimizing PMS practices is explored, focusing on its applications in signal detection, risk assessment, and regulatory compliance.International Journal of Scientific Research and Management (IJSRM)["Samadrita Ghosh"]2024-08-18T00:00:00 7c3acd9194cefe12e122806a769bf8a05a00d115The integrated management of medical waste (MD) is a crucial challenge for public health and the environment, aggravated in recent times by population growth and the emergence of pandemics. In this context, the implementation of innovative technologies such as Artificial Intelligence (AI) presents itself as a promising solution. These technological tools can facilitate the identification, classification and tracking of DMs, thus optimizing their collection, treatment and final disposal in an efficient and sustainable manner. For this purpose, it was established to analyze the scientific production related to the integrated management of medical waste and the applicability of Artificial Intelligence. The Scopus database was used during the period 2017 - 2024 based on the PRISMA 2020 methodology. The behavior of the studies presented 9 nodes representing 116 publications. For the co-occurrence of keywords, five clusters and 56 selected keywords were found, which corroborates the importance of the study. However, the application of emerging technologies in combination with comprehensive approaches can significantly contribute to improve DM management, from an adaptive, resilient, and inclusive approach.Data and Metadata300It was established to analyze the scientific production related to the integrated management of medical waste and the applicability of Artificial Intelligence and the Scopus database was used during the period 2017 - 2024 based on the PRISMA 2020 methodology.Data and Metadata["Diego Iv\u00e1n Cajamarca Carrazco", "Mar\u00eda Gabriela Tobar-Ruiz", "Diego Marcelo Almeida L\u00f3pez", "Carlos Eduardo Cevallos Hermida", "Ver\u00f3nica Magdalena Llangar\u00ed Arellano", "Mateo Augusto Zavala Tobar", "Mar\u00eda Magdalena Paredes Godoy"]2024-08-18T00:00:00 2ebcbb86df1389f7c4f5650ea6b0a9e9ef69ac85Penelitian ini dilatarbelakangi oleh kurangnya bahan ajar yang menarik bagi siswa. Tujuan penelitian ini adalah untuk mengetahui kelayakan modul ajar berbantuan Canva Artificial Intelligence menurut ahli materi dan ahli media. Penelitian menggunakan metode 4D yang dimodifikasi menjadi 3D, meliputi mendefinisikan (define), rancangan (design), dan mengembangkan (development). Sampel penelitian ini terdiri dari satu dosen pendidikan fisika dan satu guru mata pelajaran IPA, yang dipilih menggunakan teknik purposive sampling. Teknik pengumpulan data yang digunakan adalah wawancara dan komunikasi tidak langsung dengan alat pengumpulan data berupa lembar validasi ahli materi dan lembar validasi ahli media. Data dianalisis menggunakan teknik persentase. Hasil penelitian menunjukkan bahwa validasi materi memperoleh rata-rata persentase 96% dengan kriteria sangat layak, dan validasi ahli media memperoleh skor rata-rata persentase 80% dengan kriteria sangat layak. Simpulan penelitian ini adalah modul ajar berbantuan Canva Artificial Intelligence pada materi pesawat sederhana kelas VIII sangat layak dan dapat digunakan oleh siswa dan guru sebagai pendukung dalam proses pembelajaranSocial, Humanities, and Educational Studies (SHES): Conference Series00Social, Humanities, and Educational Studies (SHES): Conference Series["Putri Amelia Sari", "Dwi Fajar Saputri", "Lia Angraeni"]2024-08-18T00:00:00 2006081169922846156105daf12cb85def7b0a31Iranian Journal of Medical Microbiology663Iranian Journal of Medical Microbiology["M. Ghaderzadeh", "Armin Shalchian", "Gholamreza Irajian", "H. Sadeghsalehi", "Abed Zahedi bialvaei", "Babak Sabet"]2024-08-18T00:00:00 aa785f445edf87dd0073bfedaa611b114bade5d6Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-08-18T00:00:00 64d91f21ed3bb17a5cce1dfcfb2e84b2e6c6e033Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-08-18T00:00:00 2b7811d96289aa29fe9a23c7bd205a68a2ddd0e3Synthetic content, which has been produced by generative artificial intelligence, is beginning to spread through the public sphere. Increasingly, we find ourselves exposed to convincing ‘deepfakes’ and powerful chatbots in our online environments. How should we mitigate the emerging risks to individuals and society? This article argues that labelling synthetic content in public forums is an essential first step. While calls for labelling have already been growing in volume, no principled argument has yet been offered to justify this measure (which inevitably comes with some additional costs). Rectifying that deficit, I conduct a close examination of our epistemic and expressive interests in identifying synthetic content as such. In so doing, I develop a cumulative case for social media platforms to enforce a labelling duty. I argue that this represents an important element of good platform governance, helping to shore up the integrity of our contemporary public discourse, which takes place increasingly online.Journal of Applied Philosophy101It is argued that labelling synthetic content in public forums is an important element of good platform governance, helping to shore up the integrity of the authors' contemporary public discourse, which takes place increasingly online.Journal of Applied Philosophy["Sarah A. Fisher"]2024-08-18T00:00:00 302a82eac86ebbb4dad0498d47f9a5470ef7ed6eAs artificial intelligence progresses, the task of distinguishing between real and AI-generated images is increasingly complicated by sophisticated generative models. This paper presents a novel detection framework adept at robustly identifying images produced by cutting-edge generative AI models, such as DALL-E 3, MidJourney, and Stable Diffusion 3. We introduce a comprehensive dataset, tailored to include images from these advanced generators, which serves as the foundation for extensive evaluation. we propose a classification system that integrates semantic image embeddings with a traditional Multilayer Perceptron (MLP). This baseline system is designed to effectively differentiate between real and AI-generated images under various challenging conditions. Enhancing this approach, we introduce a hybrid architecture that combines Kolmogorov-Arnold Networks (KAN) with the MLP. This hybrid model leverages the adaptive, high-resolution feature transformation capabilities of KAN, enabling our system to capture and analyze complex patterns in AI-generated images that are typically overlooked by conventional models. In out-of-distribution testing, our proposed model consistently outperformed the standard MLP across three out of distribution test datasets, demonstrating superior performance and robustness in classifying real images from AI-generated images with impressive F1 scores.arXiv.org171This paper presents a novel detection framework adept at robustly identifying images produced by cutting-edge generative AI models, such as DALL-E 3, MidJourney, and Stable Diffusion 3, with a hybrid architecture that combines Kolmogorov-Arnold Networks (KAN) with the MLP.ArXiv["Taharim Rahman Anon", "Jakaria Islam Emon"]2024-08-18T00:00:00 6dc7045c84cfc4bca5d63ef4a1d36cb66b7ea8e8Urban intersections pose significant safety challenges due to the convergence of diverse traffic flows from various directions, resulting in a disturbingly high rate of annual road fatalities. Specifically, the presence of permissive left-turn signals introduces potential conflicts between vehicles making left turns and those proceeding straight from opposite directions. To address this issue, this article introduces a dynamic safety assessment model tailored for urban intersections. Leveraging drone videos, high-resolution vehicle trajectory data is extracted, enabling the conflicts identification of rear-end and angular. Through an ensemble learning approach utilizing artificial intelligence (AI) techniques, conflict frequency is estimated at a granular five-minute interval. Notably, the Random Forest model exhibits superior performance, yielding a mean squared error (MSE) of 5.35. Further analysis employing SHAP (SHapley Additive exPlanations) highlights the critical role of variables such as vehicle deceleration rate and speed. The insights garnered from this study can inform the development of proactive traffic management systems, facilitating real-time assessment of intersection safety conditions and the implementation of measures to mitigate potential risks.International Conference on Industrial Informatics200A dynamic safety assessment model tailored for urban intersections is introduced, leveraging drone videos to extract high-resolution vehicle trajectory data, enabling the conflicts identification of rear-end and angular and highlighting the critical role of variables such as vehicle deceleration rate and speed.2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)["Shile Zhang", "Yan Wang", "Yongjun Yan"]2024-08-18T00:00:00 378904d7d36be6c176053b540697de44e34e29fdWhile manual robot programming has been effective for many applications, fixed coding poses several challenges. Robot programming requested sophisticated and dynamic behaviours while increasing the complexity of the robot's tasks. Generative artificial intelligence models have revolutionised robot behaviour generation in dynamic environments to complete different tasks. This paper explores different approaches to robot behaviour generation evaluating their effectiveness, challenges, and potential implications for real-world robotic scenarios. An enhanced diffusion policy is proposed to mitigate anomalous behaviours in the original model. The results demonstrate the importance of training dataset quality and model adaptation to specific working environments in achieving successful robotic behaviours. The Resilient Diffusion solved unusual behaviour problems, improved the resilience capability of diffusion policy, and achieved a higher success rate.International Conference on Industrial Informatics180The Resilient Diffusion solved unusual behaviour problems, improved the resilience capability of diffusion policy, and achieved a higher success rate.2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)["Tianmei Jin", "Jiayi Zhang"]2024-08-18T00:00:00 bca062a83c6a70c053a5f0aa05c56f2c6b5033ddThis article addresses the issues of material non-degradation and over-packaging in traditional express plastic packaging. The integration of sophisticated artificial intelligence methods, including machine learning, natural language processing, and computer vision, is employed to augment the sustainability of logistics packaging systems. Based on this, the article proposes a recycling packaging recovery application (APP), which can be used to identify and classify packaging materials, detect the quality of the packaging, optimise the recycling process, achieve intelligent monitoring and management and improve user participation. The aforementioned APP can be utilized to identify and categorize packaging materials, assess the quality of packaging, optimize the recycling process, facilitate intelligent monitoring and management, and enhance user participation.International Conference on Industrial Informatics120A recycling packaging recovery application (APP) is proposed, which can be used to identify and classify packaging materials, detect the quality of the packaging, optimise the recycling process, achieve intelligent monitoring and management and improve user participation.2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)["Zezhi Yuan"]2024-08-18T00:00:00 f4224b6dae4ce59a28b28ee4508e423a1a3b9d62From design, verification, and manufacturing to testing, Artificial Intelligence (AI) is reshaping the technologies applied to Electronic Design Automation (EDA) tools in semiconductor industry. Utilizing AI not only shortens the design cycle through faster and optimized design implementation, analyses, and decisions, but also provides engineers with new insights and guidance for innovation and root cause analysis.International Test Conference in Asia00From design, verification, and manufacturing to testing, Artificial Intelligence (AI) is reshaping the technologies applied to Electronic Design Automation tools in semiconductor industry and provides engineers with new insights and guidance for innovation and root cause analysis.2024 IEEE International Test Conference in Asia (ITC-Asia)["Yu Huang", "Alex Yu", "Louis Liu", "Xijiang Lin"]2024-08-18T00:00:00 3b13a98ad13eb4710cd473fa82ee6529a9e244bcThe economic logic of the attention economy is frequently used to critique and respond to the dangers of unfettered technological expansion, including nascent platforms and products powered by generative artificial intelligence. This commentary warns that while large parts of the internet have been financed through such business models, there is no guarantee that emerging generative artificial intelligence products will be commercialized in this way too. Instead, I argue, we must look beyond the attention economy to predict the future of monetization of an industry already mired in anti-competitive practices. Using popular large language models such as OpenAI’s ChatGPT as a case, I discuss how some platforms are developing computational dependencies between technology and their users. I propose the term ‘cognitive lock-in’ to help us unpack the implications of such technological dependencies, and redirect the study of this nascent business model.Big Data & Society230The term ‘cognitive lock-in’ is proposed to help us unpack the implications of such technological dependencies, and redirect the study of this nascent business model of generative artificial intelligence.Big Data Soc.["Morten Hansen"]2024-08-18T00:00:00 e2207a4b0647cfe8a63fd8152bba0fd1c09c7889In this paper we examine the current impact and future potential of AI on the custom manufacturing industry. This study dives deep into the automotive component industry where custom manufacturing plays a significant role. It explores whether AI can help us improve the overall efficiency of the manufacturing processes by improving the precision of machining operations and reducing production lead times and material wastage. The study also explores the impact of AI in the low-volume and high complexity environments involved in custom manufacturing in the automotive industry.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300The study explores whether AI can help to improve the overall efficiency of the manufacturing processes by improving the precision of machining operations and reducing production lead times and material wastage.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Sumit Lad"]2024-08-18T00:00:00 533d3b6f7e9b04f2a1126a1802afe6732994668bNano-based drug delivery systems have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles are utilized as drug delivery systems with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost, and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterizing, and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalize drug delivery based on individual patient factors, optimize formulation design, and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective drug delivery systems can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-drug delivery systems, with a focus on their therapeutic applications. The use of AI in drug delivery systems has the potential to revolutionize treatment optimization and improve patient care.Journal of drug targeting (Print)1095The role of AI in the development of nano-drug delivery systems, with a focus on their therapeutic applications, is investigated, with the potential to revolutionize treatment optimization and improve patient care.Journal of drug targeting["Soroush Heydari", "Niloofar Masoumi", "Erfan Esmaeeli", "SeyedMohammad Ayyoubzadeh", "Fatemeh Ghorbani\u2010Bidkorpeh", "Mahnaz Ahmadi"]2024-08-19T00:00:00 6a3ea704fc4a0fe60a26f558bb45c228e8a6cd19Nowadays, integrating Artificial Intelligence (AI) into supply chains (SCs) is a great challenge in research and for manufacturing managers. The main goal of this study is to determine the role of AI in the context of the new SCs, according to the concept of Industry 5.0. in order to improve the level of sustainable production. The research was based on a systematic analysis of the scientific literature and application of the PRISMA methodology. Due to the relatively new vision of introducing AI into SC, it was decided to analyse the years 2021–2024. A total of 1181 research articles were identified in Science Direct, Springer and the Willey Online Library that combined AI-based methods and tools that support SCs in order to identify the impacts and challenges of integrating AI in SCs in the context of sustainable production (SP). In this study, 48 items were then analysed in detail. The results achieved highlighted the main AI-based tools applied in SCs and, secondly, revealed the main benefits of this integration for manufacturing in the following areas of manufacturing: predictive maintenance, production planning and customer relationships. The findings of our study revealed the main challenges and directions: (1) integrating digitalisation and green SP in order to build resilience to the SP, (2) create a sustainable work environment, (3) and develop a sustainable and advanced architecture for relationships with customers.Sustainability253The findings of this study revealed the main challenges and directions: integrating digitalisation and green SP in order to build resilience to the SP, create a sustainable work environment, and develop a sustainable and advanced architecture for relationships with customers.Sustainability["Justyna Patalas-Maliszewska", "Ma\u0142gorzata Szmo\u0142da", "H. \u0141osyk"]2024-08-19T00:00:00 a348ae555a3c48976667fe946643f9da33dbb01cAI and Ethics451AI and Ethics["S. Teo"]2024-08-19T00:00:00 33483755bcc903a1d73b64e548f216f7d801453aThis theme issue brings together researchers from diverse fields to assess the current status and future prospects of embodied cognition in the age of generative artificial intelligence. In this introduction, we first clarify our view of embodiment as a potentially unifying concept in the study of cognition, characterizing this as a perspective that questions mind–body dualism and recognizes a profound continuity between sensorimotor action in the world and more abstract forms of cognition. We then consider how this unifying concept is developed and elaborated by the other contributions to this issue, identifying the following two key themes: (i) the role of language in cognition and its entanglement with the body and (ii) bodily mechanisms of interpersonal perception and alignment across the domains of social affiliation, teaching and learning. On balance, we consider that embodied approaches to the study of cognition, culture and evolution remain promising, but will require greater integration across disciplines to fully realize their potential. We conclude by suggesting that researchers will need to be ready and able to meet the various methodological, theoretical and practical challenges this will entail and remain open to encountering markedly different viewpoints about how and why embodiment matters. This article is the part of this theme issue ‘Minds in movement: embodied cognition in the age of artificial intelligence’.Philosophical transactions of the Royal Society of London. Series B, Biological sciences931It is suggested that researchers will need to be ready and able to meet the various methodological, theoretical and practical challenges this will entail and remain open to encountering markedly different viewpoints about how and why embodiment matters.Philosophical Transactions of the Royal Society B: Biological Sciences["Louise Barrett", "Dietrich Stout"]2024-08-19T00:00:00 602bc1eef15eb45871d1b92aef86996267802d2aThe disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.Neuro-Oncology992Current applications of AI in neuro-oncology are described, viable AI solutions for the most pressing inequities in neuro-oncology are postulated, and a framework for the effective integration of equity into AI-based neuro-oncology research is proposed.Neuro-oncology["Mulki Mehari", "Youssef Sibih", "A. Dada", "Susan Chang", "Patrick Y. Wen", "Annette M Molinaro", "U. Chukwueke", "Joshua A Budhu", "Sadhana Jackson", "J. McFaline-Figueroa", "Alyx B Porter", "Shawn L Hervey-Jumper"]2024-08-19T00:00:00 bde717e30ec94443daa205db567312e68e5b5f73A comprehensive analysis of the Pan-Canadian Artificial Intelligence Strategy and its implementation measures aimed at the growth of Canada’s scientific and research potential in the field of artificial intelligence forms the foundation of this study. Canada’s selection as the subject of study is attributed to its distinction as a pioneering country to adopt a strategy of this nature, and proving its status through drafting the Artificial Intelligence and Data Act, known as AIDA. The authors have discerned and deliberated on the main organizational factors that have positioned Canada as one of the leading nations in artificial intelligence in accordance with AI country rankings. This article presents the components of the Pan-Canadian Strategy, encompassing principal tasks and areas, including the practical introduction of novel technologies due to second-phase commercialization. It outlines the key focus areas of Canada’s public policy, including research, development and retention of skilled professionals, and the creation of essential infrastructure. The article also consolidates some significant societal outcomes realized during its implementation while identifying current trends. The foundation and activities of national institutions are underscored as pivotal in fostering scientific and research potential, with special emphasis on the initiative to establish a new institute dedicated to the safety of artificial intelligence under the strong influence of AI Safety Summit at Bletchley Park. The authors identify the key participants in the artificial intelligence ecosystem who have the most influence on implementing the Strategy. The conclusions drawn from the article aid in fostering a deeper comprehension of the role played by organizational and administrative processes in propelling advancements in the field of artificial intelligence. The favorable impact on societal development is highlighted, provided risks are mitigated. Given Ukraine’s historical association with high intellectual potential, the findings of this study can be instrumental in honing the national policy pertaining to artificial intelligence in Ukraine.Uzhhorod National University Herald. Series: Law00The authors identify the key participants in the artificial intelligence ecosystem who have the most influence on implementing the Pan-Canadian Strategy, and outlines the key focus areas of Canada’s public policy, including research, development and retention of skilled professionals, and the creation of essential infrastructure.Uzhhorod National University Herald. Series: Law["A. Hachkevych", "A. Fainyk", "V. Fediura"]2024-08-19T00:00:00 fa9fbfe4cdb6ad53c4f647b130f33bac5420f18aJudgment is fundamental in medicine, particularly when combining complex data layers with detailed decision-making processes. Radiology processes present a distinct challenge for medical decisions due to the data amount and shortage in time and personnel capable of analyzing images. Additionally, it's crucial to consider each patient's specific situation, including their current state and disease history. Despite advancements in technology, there are still significant hurdles in accurately analyzing radiology data. Radiographic assessments, which are predominantly based on visual inspections, could greatly benefit from enhanced computational analyses. Artificial intelligence (AI) in particular holds the potential to significantly improve the qualitative interpretation of imaging by medical experts - automating and even replacing some parts of their work. This article will be an overview of possibilities and challenges associated with introducing new technology into medical spaces. Doctors are struggling with time and it limits how much care they can show for each patient. The image can be marked for most important parts, AI can produce a more user friendly version of the description, suggesting what might be the problem for later human evaluation. Understanding the possibilities of automating or cutting down time spend by radiology experts on analyze will allow faster deliver of radiologic image description for doctors dealing with patient treatment.Quality in Sport20Understanding the possibilities of automating or cutting down time spend by radiology experts on analyze will allow faster deliver of radiologic image description for doctors dealing with patient treatment, and will allow faster deliver of radiologic image description for doctors dealing with patient treatment.Quality in Sport["Paulina Kosiorowska", "Karolina Pasieka", "Helena Perenc", "Karolina Majka", "Kornelia Krawczyk", "Marek P\u0119dras", "Micha\u0142 Kosar", "Urszula Korzonek", "Kuba Kupniewski"]2024-08-19T00:00:00 150826f159c0419a61ebbcf4d368e8f7b1c9ed2cPURPOSE – Financial technology, also known as “FinTech,” has evolved to disrupt nearly every aspect of traditional financial services and it has become increasingly important in the world’s economic system. The main purpose of the study is to explore the relationship between Financial Technology (Fintech) and Entrepreneurial Intentions. It focuses on the impact of specific Fintech innovations such as Crowdfunding, Mobile Payments, Blockchain, Cryptocurrency, and Artificial Intelligence (AI), on Entrepreneurial Finance. The study examines how these Fintech advancements have affected the overall entrepreneurial ecosystem, fostering innovation, supporting startups, and driving economic growth. Using mixed-methods, the research combines qualitative interviews and quantitative surveys to reveal key factors that have completely shaped the entrepreneurial ecosystem in the context of fintech. EXECUTIVE SUMMARY – Financial technology revolution unleashing a wave of technological innovations has transformed the entrepreneurial landscape. Crowdfunding, cryptocurrency, blockchain, mobile payments, and artificial intelligence (AI) play key roles in empowering aspiring entrepreneurs, fueling financial inclusion, and driving economic growth. This report examines the impact of these fintech advancements on entrepreneurial intentions, exploring their benefits, challenges, and future prospects. Qeios60The main purpose of the study is to explore the relationship between Financial Technology (Fintech) and Entrepreneurial Intentions and the impact of specific Fintech innovations such as Crowdfunding, Mobile Payments, Blockchain, Cryptocurrency, and Artificial Intelligence (AI), on Entrepreneurial Finance.Qeios["Sharbaz Khan", "Mehtab Munir", "S. Ghauri"]2024-08-19T00:00:00 7c93e3ce50e9f1f19555b11b04dd9207edd4a565Personally, Identifiable Information (PII) is any content that is sensitive that needs to be treated as secure and private. When data pieces such as a person's name, address, Social Security number, phone number, email address, and so on may be used to identify a specific individual, they are deemed PII. As organizations grow, so does their volume of data. This makes identifying and protecting such sensitive resources at a scale quite complex. In this project, we demonstrate where and how PII can be discovered and how we developed a working prototype of a tool that can easily detect PII images using advanced artificial intelligence (AI) techniques such Optical Character Recognition (OCR) and image classification using Convolutional Neural Networks (CNN).Journal of Current Trends in Computer Science Research100This project demonstrates where and how PII can be discovered and how a working prototype of a tool is developed that can easily detect PII images using advanced artificial intelligence techniques such Optical Character Recognition (OCR) and image classification using Convolutional Neural Networks (CNN).Journal of Current Trends in Computer Science Research["Owais Shaikh"]2024-08-19T00:00:00 807ddfde75f2386efd6700365cd742a27b742e04Contemporary research and development are accelerating the proliferation of Disruptive Technologies and Artificial Intelligence (AI) in an unprecedented manner into our geo-politics, society and more disruptively in war fighting. The Indian Military is at turning point of technology revolution, wherein, the war will be fought with autonomous, unmanned platforms with AI technology. Today, the commercial sector is driving the AI in the world. This article looks at the state of art of AI, Machine Learning, and other technologies with their potential application in the military operations. It specifically analyses opportunities and challenges of AI application on National Security, Warfare and Autonomy in Military Operations. Today, geo-politics is already being dominated by the AI and instruments of disruptive technology in the field of Intelligence, Situational-Awareness, Surveillance, Autonomous Weapons and Logistics. The article shall also suggest transformational steps to be taken by India, in general and Military establishments, in particular, to seamlessly harness AI. This article aims to discuss integration and application of AI in Military Operations.International Journal For Multidisciplinary Research60This article specifically analyses opportunities and challenges of AI application on National Security, Warfare and Autonomy in Military Operations, and suggests transformational steps to be taken by India, in general and Military establishments, in particular, to seamlessly harness AI.International Journal For Multidisciplinary Research["Varun Sehgal"]2024-08-19T00:00:00 37e7d094503c951222fd07571fb903e0ef79c246Artificial intelligence refers to computers performing tasks typically linked to human intelligence, such as recognizing speech, understanding language, identifying objects, and translating between languages. These tasks often involve learning, allowing algorithms to adjust to the data they receive. The integration of AI in medicine, particularly, clinical practice such as rheumatology has revolutionized medical practice in terms of precision in diagnosis, and accuracy in treatment. The main objective of this study was to review the updates of the literature regarding the use of AI in rheumatology. The researcher reviewed the main research engines to collect cited literature. The results of this study confirmed the importance of the integration of AI into medicine to offer precision in diagnosis, cost-effectiveness, and therapeutic accuracy.Scholars Academic Journal of Biosciences00The results of this study confirmed the importance of the integration of AI into medicine to offer precision in diagnosis, cost-effectiveness, and therapeutic accuracy.Scholars Academic Journal of Biosciences["Dr. Reham M AlGhazo", "Dr. Kamel Remita", "Dr. Ahmad Awad"]2024-08-19T00:00:00 59dafe6499fe5604c72d3d5b8128f7b32d0d471aThe evolutionary development of the entire judicial system is directly related to the introduction of modern artificial intelligence systems. The purpose of this study is to study and analyze the impact of artificial intelligence on the effectiveness of the entire judicial system and judges in particular. To achieve this goal, it is necessary, firstly, to consider the nature of artificial intelligence, its advantages in the administration of justice, secondly, to analyze the potential risks arising from the use of artificial intelligence, and thirdly, to reveal and analyze the impact of artificial intelligence technologies through the prism of criteria for the effectiveness of courts. The methodological basis of this research is dialectical, systemic, functional, formal and legal methods. The conducted research made it possible to come to the conclusion that modern artificial intelligence systems are able to seriously optimize the work of the entire judicial system, prevent possible judicial errors and corruption manifestations, shorten the time of consideration of cases, which will undoubtedly increase the efficiency of the entire judicial system. However, there are also negative impacts that must necessarily be eliminated or minimized, with the help of careful study of the mandatory technological requirements used in the creation and further functioning of all artificial intelligence systems.Rossijskoe pravosudie00The conducted research has concluded that modern artificial intelligence systems are able to seriously optimize the work of the entire judicial system, prevent possible judicial errors and corruption manifestations, shorten the time of consideration of cases, which will undoubtedly increase the efficiency of the entire judicial system.Rossijskoe pravosudie["Alexey V. Darda"]2024-08-19T00:00:00 8a7191acf67a901599188cd1ac637243cba8524eDiscover Education120Analyzing exam score results from the pre and post introduction of ChatGPT periods, the research unpacks the extent of cheating and provides strategies to counteract this trend and underscores the pressing need for reinventing assessment techniques to uphold the sanctity of online education.Discover Education["Alexander Stanoyevitch"]2024-08-19T00:00:00 6265d0cf595cc93aa4f9c67f8d5dd039de227d93International Journal of Computer Assisted Radiology and Surgery160The proposed objective assessment system using AI for forceps manipulation in a surgical training simulator can visualize and evaluate laparoscopic surgical skills and is a useful tool for surgeon training and assessment.International journal of computer assisted radiology and surgery["Atsuhisa Fukuta", "Shogo Yamashita", "Junnosuke Maniwa", "Akihiko Tamaki", "T. Kondo", "N. Kawakubo", "Kouji Nagata", "T. Matsuura", "Tatsuro Tajiri"]2024-08-19T00:00:00 095e0fec5f1550586970b36b0604480ddc24658fIn today's fast-paced world, many factors contribute to the progress and development of society. One of the most significant aspects that contribute to individual growth is creativity. Therefore, it is crucial to recognize the factors that encourage and stimulate creativity in individuals. Families and society should promote creativity as it provides a strong foundation for young people's social and personal lives and helps them succeed in their future endeavors. Several characteristics can help foster creativity in adolescents. These include recognizing successful individuals, emphasizing creativity, encouraging the early development of creativity, providing a cooperative platform for growth, and highlighting the importance of creativity. Failure to recognize creativity can harm young people's personal and social lives and lead them down an unfulfilling path. Thus, raising awareness about creativity and providing the conditions for its growth is vital. This study explores the role of technology, particularly Artificial Intelligence (AI), in fostering creativity in the modern world and its impact on society. With the continuous development of technology, it is essential in the modern world that parents and society consider providing a convenient atmosphere for learning and updating the new generation by offering correct patterns and accurate information.Journal of Social Studies (JSS)01The role of technology, particularly Artificial Intelligence (AI), in fostering creativity in the modern world and its impact on society is explored.Journal of Social Studies (JSS)["Aida Mehrad", "Anita Mehrad"]2024-08-19T00:00:00 60dc41e97abbbb7bd35aedf1c76d223c161d4099Reviews of Management Sciences1061Whether a decrease of human involvement in decision making diminishes employees’ perceived fairness and satisfaction with the career development process and increases their perceived privacy intrusion is examined.Review of Managerial Science["Alina K\u00f6chling", "M. Wehner", "S. Ruhle"]2024-08-19T00:00:00 69526ca13e9542af2a6c4496dbbe34c60431caebDermatologic Surgery60Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]["N. Vidal"]2024-08-19T00:00:00 ce6801cb2c981012c72797693ef343c2556e981aStudies in Technology Enhanced Learning00Studies in Technology Enhanced Learning["Don Passey", "Sammy Taggart", "Serena Leow", "Cheng Ean (Catherine) Lee"]2024-08-19T00:00:00 a182cf092c71b74299f6acc4200a0a95165a9934Education and Information Technologies : Official Journal of the IFIP technical committee on Education455A significant relationship between teachers’ AI-TPACK and digital proficiency levels was identified, with digital proficiency as a significant predictor of AI-TPACK competencies.Education and Information Technologies["Kevser Hava", "\u00d6zg\u00fcr Babayi\u011fit"]2024-08-19T00:00:00 f563f6f9b112622a0e5c52b3866d8a05f2582ff6Glaucoma is an eye disease that damages the optic nerve as a result of vision loss, it is the leading cause of blindness worldwide. Due to the time‐consuming, inaccurate, and manual nature of traditional methods, automation in glaucoma detection is important. This paper proposes an explainable artificial intelligence (XAI) based model for automatic glaucoma detection using pre‐trained convolutional neural networks (PCNNs) and machine learning classifiers (MLCs). PCNNs are used as feature extractors to obtain deep features that can capture the important visual patterns and characteristics from fundus images. Using extracted features MLCs then classify glaucoma and healthy images. An empirical selection of the CNN and MLC parameters has been made in the performance evaluation. In this work, a total of 1,865 healthy and 1,590 glaucoma images from different fundus datasets were used. The results on the ACRIMA dataset show an accuracy, precision, and recall of 98.03%, 97.61%, and 99%, respectively. Explainable artificial intelligence aims to create a model to increase the user's trust in the model's decision‐making process in a transparent and interpretable manner. An assessment of image misclassification has been carried out to facilitate future investigations.IET Image Processing574An explainable artificial intelligence (XAI) based model for automatic glaucoma detection using pre‐trained convolutional neural networks (PCNNs) and machine learning classifiers (MLCs) and an assessment of image misclassification has been carried out to facilitate future investigation.IET Image Processing["Vijaya Kumar Velpula", "Diksha Sharma", "Lakhan Dev Sharma", "Amarjit Roy", "Manas Kamal Bhuyan", "Sultan Alfarhood", "Mejdl S. Safran"]2024-08-19T00:00:00 724e5128ed50661b73e615b6dadcd6e7bca07715The Metaverse, distinguished by its capacity to integrate the physical and digital realms seamlessly, presents a dynamic virtual environment offering diverse opportunities for engagement across innovation, entertainment, socialization, and commercial endeavors. However, the Metaverse is poised for a transformative evolution through the convergence of contemporary technological advancements, including artificial intelligence (AI), Blockchain, Robotics, augmented reality, virtual reality, and mixed reality. This convergence is anticipated to revolutionize the global digital landscape, introducing novel social, economic, and operational paradigms for organizations and communities. To comprehensively elucidate the future potential of this technological fusion and its implications for digital innovation, this research endeavors to undertake a thorough analysis of scholarly discourse and research pertaining to the Metaverse, AI, Blockchain, and associated technologies. This survey delves into various critical facets of the Metaverse ecosystem, encompassing component analysis, exploration of digital currencies, assessment of AI utilization in virtual environments, and examination of Blockchain's role in enhancing digital content and data security. Leveraging articles retrieved from esteemed digital repositories including ScienceDirect, IEEE Xplore, Springer Nature, Google Scholar, and ACM, published between 2017 and 2023, this study adopts an analytical approach to engage with these materials. Through rigorous examination and discourse, this research aims to provide insights into the emerging trends, challenges, and future directions in the convergence of the Metaverse, Blockchain, and AI.This article is categorized under: Application Areas > Industry Specific Applications WIREs Data. Mining. Knowl. Discov.1454This survey delves into various critical facets of the Metaverse ecosystem, encompassing component analysis, exploration of digital currencies, assessment of AI utilization in virtual environments, and examination of Blockchain's role in enhancing digital content and data security.WIREs Data. Mining. Knowl. Discov.["Mueen Uddin", "Muath A. Obaidat", "Selvakumar Manickam", "Shams ul Arfeen Laghari", "Abdulhalim Dandoush", "Hidayat Ullah", "Syed Sajid Ullah"]2024-08-19T00:00:00 436be6731eef90fe2498ea22820a797c08eb6137In the rapidly evolving landscape of deep learning (DL), understanding the inner workings of neural networks remains a significant challenge. This need for transparency and accountability from DL models assumes particular importance as DL models become increasingly prevalent in decision-making processes. Interpreting these models is key to addressing this challenge. This paper offers a comprehensive overview of interpretable deep learning methods. It emphasizes gradient-based propagation techniques that shed light on the complex mechanisms driving neural network predictions. Through a systematic review, we categorize gradient-based interpretability approaches, delve into the theory of notable methods, and compare their strengths and weaknesses. Furthermore, we investigate various evaluation metrics for interpretable systems, often generalized under the term eXplainable Artificial Intelligence (XAI). We highlight their significance in assessing the faithfulness, robustness, localization, complexity, randomization, and adherence to the axiomatic principles of XAI methods. We aim to help researchers and practitioners work towards a more transparent future for artificial intelligence by providing an overview of the most recent developments in the field.Nordic Machine Intelligence763This paper offers a comprehensive overview of interpretable deep learning methods, and emphasizes gradient-based propagation techniques that shed light on the complex mechanisms driving neural network predictions.Nordic Machine Intelligence["Ayush Somani", "Alexander Horsch", "Ajit Bopardikar", "Dilip K. Prasad"]2024-08-19T00:00:00 32283a4432039fa9808816d0c862e01edd846939In the era of digitization and technical breakthroughs, artificial intelligence (AI) has progressively found its way into the field of customer relationship management (CRM), bringing benefits as well as difficulties to businesses. AI, particularly in the context of CRM, employs machine learning (ML) and deep learning (DL) techniques to extract knowledge from data, recognize trends, make decisions, and learn from mistakes with minimal human intervention. Successful firms have effectively integrated AI into CRM for predictive analytics, computer vision, sentiment analysis, personalized recommendations, chatbots and virtual assistants, and voice and speech recognition. AI-driven chatbots, one of the AI-powered CRM systems, arose as a disruptive approach to customer service, and as such, unfolded with economic and managerial ramifications in CRM. Given the literature’s focus on other AI-driven systems, there is an obvious need for an investigation of industry applications and the implications of AI-driven chatbots in CRM. The purpose of this study is to explore and elucidate the economic and managerial implications of AI-powered chatbots within CRM systems. This investigation aims to provide a comprehensive understanding of how these technologies can enhance customer interactions, streamline business processes, and impact organizational strategies. To reach this goal, this study conducts a comparative qualitative analysis based on many interviews with experts and contributors in the field. Interviews with CRM specialists yielded insights into the use of AI-driven chatbots in CRM and their impact on the industry. The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance. In addition, AI chatbots have proven useful in a variety of industries, including retail and tourism. Nonetheless, there were limitations to its usage in the healthcare system, particularly in terms of ethical problems.Administrative Sciences423The primary advantages identified in this study were the impact of AI-powered chatbots on cost, efficiency, and human performance.Administrative Sciences["Chadi Khneyzer", "Zaher Boustany", "Jean Dagher"]2024-08-19T00:00:00 dee89b61d2704196efd6abc36c4ec66690d944c8In recent years, digital avatars employed as virtual streamers are experiencing a surge in popularity in e‐commerce livestreaming. However, the influence of the avatars' anthropomorphic features on their effectiveness as virtual streamers remains unclear. This study investigates the avatar effect of artificial intelligence‐enabled virtual streamers, wherein their form and behavioral realism interactively affect consumer purchase intention. Three lab experiments with 604 participants were conducted to test this effect and its underlying mechanism and boundary conditions. Based on the findings, behavioral realism positively affects consumer purchase intention only when the virtual streamers' form realism is low. Parasocial interactions underpin this avatar effect, which only holds when consumers exhibit a communal relationship norm orientation. When consumers possess an exchange relationship norm orientation, the effect of behavioral realism becomes positive regardless of the level of form realism. Overall, our study proposes an avatar effect in livestreaming, and extends the literature by offering insights on the interactive effect of the anthropomorphic features of human‐like avatars (as virtual streamers) on the effectiveness of e‐commerce livestreaming. By revealing the mechanisms and boundary conditions of this effect, our conclusions offer guidance for companies in developing appropriate combinations of form and behavioral realism for the avatars of virtual streams while considering consumer characteristics.Journal of Consumer Behaviour622The findings offer guidance for companies in developing appropriate combinations of form and behavioral realism for the avatars of virtual streams while considering consumer characteristics and extend the literature by offering insights on the interactive effect of the anthropomorphic features of human‐like avatars on the effectiveness of e‐commerce livestreaming.Journal of Consumer Behaviour["Luping Sun", "Yanfei Tang"]2024-08-19T00:00:00 fe5dc7f9c8c9c12706d042b3cf8db9de75b60881Background The discourse surrounding medical artificial intelligence (AI) often focuses on narratives that either hype the technology’s potential or predict dystopian futures. AI narratives have a significant influence on the direction of research, funding, and public opinion and thus shape the future of medicine. Objective The paper aims to offer critical reflections on AI narratives, with a specific focus on medical AI, and to raise awareness as to how people working with medical AI talk about AI and discharge their “narrative responsibility.” Methods Qualitative semistructured interviews were conducted with 41 participants from different disciplines who were exposed to medical AI in their profession. The research represents a secondary analysis of data using a thematic narrative approach. The analysis resulted in 2 main themes, each with 2 other subthemes. Results Stories about the AI-physician interaction depicted either a competitive or collaborative relationship. Some participants argued that AI might replace physicians, as it performs better than physicians. However, others believed that physicians should not be replaced and that AI should rather assist and support physicians. The idea of excessive technological deferral and automation bias was discussed, highlighting the risk of “losing” decisional power. The possibility that AI could relieve physicians from burnout and allow them to spend more time with patients was also considered. Finally, a few participants reported an extremely optimistic account of medical AI, while the majority criticized this type of story. The latter lamented the existence of a “magical theory” of medical AI, identified with techno-solutionist positions. Conclusions Most of the participants reported a nuanced view of technology, recognizing both its benefits and challenges and avoiding polarized narratives. However, some participants did contribute to the hype surrounding medical AI, comparing it to human capabilities and depicting it as superior. Overall, the majority agreed that medical AI should assist rather than replace clinicians. The study concludes that a balanced narrative (that focuses on the technology’s present capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding unrealistic expectations and hype.JMIR AI501The study concludes that a balanced narrative (that focuses on the technology’s present capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding unrealistic expectations and hype.JMIR AI["Giorgia Lorenzini", "Laura Arbelaez Ossa", "Stephen R. Milford", "B. Elger", "D. Shaw", "Eva De Clercq"]2024-08-19T00:00:00 1e33c6909c030039989814cf426db9695c6c8d52Through a literature review, this study investigates the responsibility, application, and impact of Artificial Intelligence (AI) in organizational training based on the theoretical frameworks of Psychological, Economic, and Systems Theories in Human Resource Development (HRD). It emphasizes the importance of responsible AI training systems that adhere to non-discrimination, privacy, interpretability, professional responsibility, and accountability to ensure AI’s beneficial and equitable contribution to training. The application scenarios of AI in areas such as knowledge management, training needs analysis, training delivery, and feedback to provide personalized and efficient training solutions are analyzed. Moreover, it highlights the differing impacts of AI-supported training on organizations, trainers, and trainees and the significance of stakeholder engagement. Finally, it proposes recommendations for future research to broaden our understanding of AI’s application in training and assess its effects on policies and practices, guiding organizations to adopt AI technologies per HRD principles and ethical standards.Human Resource Development Review401This study investigates the responsibility, application, and impact of Artificial Intelligence in organizational training based on the theoretical frameworks of Psychological, Economic, and Systems Theories in Human Resource Development and proposes recommendations for future research to broaden understanding of AI’s application in training.Human Resource Development Review["Zhisheng Chen"]2024-08-19T00:00:00 13b376e06c6a50a2adf59a8f1d4363a8ee76f95bOccupational outcomes like entrepreneurship are generally considered personal information that individuals should have the autonomy to disclose. With the advancing capability of artificial intelligence (AI) to infer private details from widely available human-centric data, such as social media, it is crucial to investigate whether AI can accurately extract private occupational information from such data. In this study, we demonstrate that deep neural networks can classify individuals as entrepreneurs based on a single facial image with high accuracy in data sourced from Crunchbase, a premier source for entrepreneurship data. Utilizing a dataset comprising facial images of 40,728 individuals, including both entrepreneurs and non-entrepreneurs, we trained a Convolutional Neural Network (CNN) and evaluated its classification performance. While human experts (n=650) and trained participants (n=133) were unable to classify entrepreneurs with accuracy above chance levels (>50%), the AI model achieved a classification accuracy of 79.51%. Several robustness tests show that this high level of accuracy is maintained under various conditions.arXiv.org670It is demonstrated that deep neural networks can classify individuals as entrepreneurs based on a single facial image with high accuracy in data sourced from Crunchbase, a premier source for entrepreneurship data.ArXiv["M. Obschonka", "Christian Fisch", "Tharindu Fernando", "C. Fookes"]2024-08-19T00:00:00 a44f39d529d4da1ca44937ef048b6b7ad46b92a6The use of Artificial Intelligence (AI) in healthcare, including in caring for cancer survivors, has gained significant interest. However, gaps remain in our understanding of how such AI systems can provide care, especially for ethnic and racial minority groups who continue to face care disparities. Through interviews with six cancer survivors, we identify critical gaps in current healthcare systems such as a lack of personalized care and insufficient cultural and linguistic accommodation. AI, when applied to care, was seen as a way to address these issues by enabling real-time, culturally aligned, and linguistically appropriate interactions. We also uncovered concerns about the implications of AI-driven personalization, such as data privacy, loss of human touch in caregiving, and the risk of echo chambers that limit exposure to diverse information. We conclude by discussing the trade-offs between AI-enhanced personalization and the need for structural changes in healthcare that go beyond technological solutions, leading us to argue that we should begin by asking, ``Why personalization?''CSCW Companion520It is argued that the trade-offs between AI-enhanced personalization and the need for structural changes in healthcare that go beyond technological solutions should begin by asking, ``Why personalization?''{"pages": "415-421"}["Elaine Kong", "Kuo-Ting Huang", "Aakash Gautam"]2024-08-19T00:00:00 80ffd2942b3038e9bff304b5710cd6a8a953c1e7With the growth of personalized devices, in various field, eye tracking research is conducted for enhancing user experience. Also, the recent research focuses on the method based on artificial intelligence (AI) algorithm for improving the performance. However, the eye tracking through AI algorithm needs significant resources. For this reason, the AI-based methods have limitations applying to embedded system. For solving the problems, in this paper, we propose an eye tracking system with two AI processors based on light weight algorithm, k-nearest neighbor (k-NN). The processor was implemented on a field programmable gate array (FPGA), and the eye tracking system was verified through visible light eye images. The results demonstrate the feasibility of the proposed eye tracking system.International SoC Design Conference50An eye tracking system with two AI processors based on light weight algorithm, k-nearest neighbor (k-NN) was proposed and verified through visible light eye images, demonstrating the feasibility of the proposed eye tracking system.2024 21st International SoC Design Conference (ISOCC)["Jongwon Oh", "Raehyeong Kim", "Jinyeol Kim", "Seung Eun Lee"]2024-08-19T00:00:00 c739c6ad49408366e8cd3129c9b95fef36d3caa6A spirited debate is taking place over the regulation of open foundation models: artificial intelligence models whose underlying architectures and parameters are made public and can be inspected, modified, and run by end users. Proposed limits on releasing open foundation models may have significant defense industrial impacts. If model training is a form of defense production, these impacts deserve further scrutiny. Preliminary evidence suggests that an open foundation model ecosystem could benefit the U.S. Department of Defense's supplier diversity, sustainment, cybersecurity, and innovation priorities. Follow-on analyses should quantify impacts on acquisition cost and supply chain security.arXiv.org1060Preliminary evidence suggests that an open foundation model ecosystem could benefit the U.S. Department of Defense's supplier diversity, sustainment, cybersecurity, and innovation priorities and follow-on analyses should quantify impacts on acquisition cost and supply chain security.ArXiv["Masao Dahlgren"]2024-08-19T00:00:00 fb2ce92e3ce683c7d63506ba2b38a54665359b40While generative artificial intelligence (AI) technology has become increasingly competitive since OpenAI introduced ChatGPT, its widespread use poses significant ethical challenges in research. Excessive reliance on tools like ChatGPT may intensify ethical concerns in scholarly articles. Therefore, this article aims to provide a comprehensive narrative review of the ethical issues associated with using AI in academic writing and to inform researchers of current trends. Our methodology involved a detailed examination of literature on ChatGPT and related research trends. We conducted searches in major databases to identify additional relevant articles and cited literature, from which we collected and analyzed papers. We identified major issues from the literature, categorized into problems faced by authors using nonacademic AI platforms in writing and challenges related to the detection and acceptance of AI-generated content by reviewers and editors. We explored eight specific ethical problems highlighted by authors and reviewers and conducted a thorough review of five key topics in research ethics. Given that nonacademic AI platforms like ChatGPT often do not disclose their training data sources, there is a substantial risk of unattributed content and plagiarism. Therefore, researchers must verify the accuracy and authenticity of AI-generated content before incorporating it into their article, ensuring adherence to principles of research integrity and ethics, including avoidance of fabrication, falsification, and plagiarism.Science Editing617A comprehensive narrative review of the ethical issues associated with using AI in academic writing and to inform researchers of current trends is provided.Science Editing["Sang-Jun Kim"]2024-08-20T00:00:00 ba4ab5ee223e4bdf4007275e1eb3c225c397fe28Abstract Gynecologic cancer requires personalized care to improve outcomes. Large language models (LLMs) hold the potential to provide intelligent question‐answering with reliable information about medical queries in clear and plain English, which can be understood by both healthcare providers and patients. We aimed to evaluate two freely available LLMs (ChatGPT and Google's Bard) in answering questions regarding the management of gynecologic cancer. The LLMs' performances were evaluated by developing a set questions that addressed common gynecologic oncologic findings from a patient's perspective and more complex questions to elicit recommendations from a clinician's perspective. Each question was presented to the LLM interface, and the responses generated by the artificial intelligence (AI) model were recorded. The responses were assessed based on the adherence to the National Comprehensive Cancer Network and European Society of Gynecological Oncology guidelines. This evaluation aimed to determine the accuracy and appropriateness of the information provided by LLMs. We showed that the models provided largely appropriate responses to questions regarding common cervical cancer screening tests and BRCA‐related questions. Less useful answers were received to complex and controversial gynecologic oncology cases, as assessed by reviewing the common guidelines. ChatGPT and Bard lacked knowledge of regional guideline variations, However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps of management and follow up. We conclude that LLMs may have a role as an adjunct informational tool to improve outcomes.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics203It is concluded that LLMs may have a role as an adjunct informational tool to improve outcomes in answering questions regarding the management of gynecologic cancer.International Journal of Gynaecology and Obstetrics["Lee Reicher", "Guy Lutsker", "N. Michaan", "D. Grisaru", "I. Laskov"]2024-08-20T00:00:00 135c300cbc33916eb66867c94985543cc763a4c0The rapid advancement of artificial intelligence (AI) is transforming the e-commerce landscape, prompting businesses to adopt innovative marketing strategies. This study investigates the relationship between AI applications and marketing innovation in Egyptian e-commerce retailers, with a focus on the mediating role of organizational culture. The research employed a quantitative approach, utilizing a survey to gather data from 260 Egyptian e-retail store owners, managers, and marketers. The findings reveal a significant positive correlation between AI applications and marketing innovation, with organizational culture playing a crucial mediating role. The correlation coefficient (R) between AI and organizational culture was found to be 0.76, indicating that AI explains 57% of the variance in organizational culture. Similarly, the correlation coefficient (R) between AI and marketing innovation was 0.70, suggesting that AI explains 49% of the variance in marketing innovation. Path analysis further demonstrated a significant indirect effect of AI on marketing innovation through organizational culture. The study concludes that the integration of AI into marketing strategies can substantially enhance innovation, particularly when complemented by a supportive organizational culture. It underscores the importance for e-commerce retailers to invest in AI technologies and cultivate a culture that embraces technological advancements to drive marketing innovation and achieve sustainable competitive advantage. AcknowledgmentThe authors are thankful to the Deanship of Graduate Studies and Scientific Research at University of Bisha for supporting this work through the Fast-Track Research Support Program.Innovative Marketing233The study concludes that the integration of AI into marketing strategies can substantially enhance innovation, particularly when complemented by a supportive organizational culture.Innovative Marketing["Abdelrehim Awad"]2024-08-20T00:00:00 05d3052afbde9dc910c6d2f554e52ec978de89b0The integration of artificial intelligence (AI) with environmental, social, and governance (ESG) factors is impacting the direction of enterprises and society in our swiftly expanding world. This collaboration has significant potential to tackle critical issues such as reducing the impact of climate change, fostering social integration, and improving corporate governance. Nevertheless, the implementation of AI gives rise to intricate matters and apprehensions, as it brings out a distinct array of hazards and ethical quandaries for ESG performance. The objective of the present research is to fill this gap by gathering and offering a contemporary evaluation of the influence of advancing technologies on the strategic leadership’s role in fulfilling the business goal within the context of ESG considerations. We used bibliometric analysis to investigate the study subject using R Studio version 4.2.0 and the bibliometric applications VOSviewer version 1.6.20 and Biblioshiny version 4.2.0. We obtained data from the Scopus database and used the PRISMA approach to suitably choose 205 research publications. The results suggest that it is essential to use AI and ESG to digitize the boardroom. Additionally, it is crucial to guarantee its security using an advanced detection system. Therefore, chief executive officers (CEOs) must give priority to the issues of transparency and cybersecurity to reduce risks and successfully inspire trust in business activities.Sustainability561It is suggested that chief executive officers (CEOs) must give priority to the issues of transparency and cybersecurity to reduce risks and successfully inspire trust in business activities and it is essential to use AI and ESG to digitize the boardroom.Sustainability["George Sklavos", "G. Theodossiou", "Zacharias Papanikolaou", "C. Karelakis", "Konstantina Ragazou"]2024-08-20T00:00:00 82462da5b6400a474108b28808d13fe6de80fbebAbstract An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7–11) for bone properties assessment, 7.8 (range: 5–11) for osteoporosis classification, 8.4 (range: 7–11) for fracture detection, 7.6 (range: 4–11) for risk prediction, and 9.0 (range: 6–11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.Journal of Bone and Mineral Research1172A comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements is offered, highlighting disparities in study quality and a lack of standardized reporting practices.Journal of Bone and Mineral Research["Guillaume Gatineau", "E. Shevroja", "C. Vendrami", "Elena Gonzalez Rodriguez", "William D. Leslie", "Olivier Lamy", "D. Hans"]2024-08-20T00:00:00 7e690e8eca45b931ebccb30a6e3bad40f092f1d6Artificial Intelligence Review618Serving as a foundation for future development and responsible implementation of the Metaverse concept, the research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse.Artif. Intell. Rev.["M. Fadhel", "Ali M. Duhaim", "A. Albahri", "Z. Al-qaysi", "Mohamed Aktham Ahmed", "M. Chyad", "Wael Abd-Alaziz", "O. Albahri", "A. Alamoodi", "Laith Alzubaidi", "Ashish Gupta", "Yuantong Gu"]2024-08-20T00:00:00 c7e78713c7e8e11f77e22440c7e676049b1e5df1The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning and artificial intelligence (AI) marking significant milestones in this journey. Machine Learning (ML), a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture for manufacturing and process operations. This paper highlights the pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.PDA journal of pharmaceutical science and technology00The pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle is highlighted, and the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally are highlighted.PDA journal of pharmaceutical science and technology["M. B. Batalha", "Daniel A. M. Pais", "Rui Estrela Almeida", "\u00c2ngela Martinho"]2024-08-20T00:00:00 c088d3c8bf97a39d71b9670e4f80089d00c72465The study aimed to examine the impact of artificial intelligence on customer satisfaction and the challenges Saudi Arabian banks face in implementing this cross-cutting technology. The study used a survey design and collected responses from 100 participants, mainly bank customers and bank officials. The result revealed that artificial intelligence (AI) is positively and significantly correlated with customer satisfaction (CS). This suggests that customer satisfaction tends to rise in tandem with the application of AI in banking. The mediation analysis result showed that Ease of Use only mediates 9.82% of the relationship between AI and CS, and it is not statistically significant (β=0.0607 (95% Cl: -.0246, .146), z=1.39, p=0.163. The study provides practical insights for Saudi Arabian banks, highlighting the need to enhance the adoption of AI to promote customer satisfaction. It also outlines frameworks for minimizing challenges and barriers against the implementation of AI, including promoting data security and customer privacy.International journal of business management420The result revealed that artificial intelligence (AI) is positively and significantly correlated with customer satisfaction (CS), which suggests that customer satisfaction tends to rise in tandem with the application of AI in banking.International Journal of Business and Management["Abdulaziz Alotaibi"]2024-08-20T00:00:00 fd5ff7338d58b1d5f716162552a8f74eae14bef9The article examines the recent changes in the legal services market as a result of the development of artificial intelligence and the digital transformation of society. The institutes of innovative development and the latest forms and methods of legal regulation of innovative activities in the field of legal services are analyzed. A new design of the administrative and legal regime of state regulation of innovation in this area is proposed, the main characteristics of the new direction of the introduction and development of IT technologies in the legal business and its use in the mechanism of ensuring national security are highlighted.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization10The article examines the recent changes in the legal services market as a result of the development of artificial intelligence and the digital transformation of society and proposed new design of the administrative and legal regime of state regulation of innovation in this area.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Alevtina Manukovskaya"]2024-08-20T00:00:00 7d58302f8a1a3e1d945628ea4fcf7c771b641895Artificial intelligence systems come not only with benefits but also with substantial risks, raising a broad variety of legal and ethical challenges. The use of AI applications should be categorised as high-risk in instances where there is the potential to significantly affect the lives of individuals and must be prohibited when it is incompatible with fundamental rights. The European Union has chosen to address these issues by seeking to specifically regulate artificial intelligence through its proposed Artificial Intelligence Act. This would take the form of a EU Regulation that completely bans some forms of artificial intelligence, requires greater transparency for other use cases and imposes significant and extensive obligations on any ‘high risk’ uses of artificial intelligence.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization80Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Vukan Slavkovi\u0107"]2024-08-20T00:00:00 d5e5278d3773076a8f420c6190aae0d1c5687d1eThe importance of introducing artificial intelligence technologies into various spheres of society makes it necessary for the state to create favorable conditions for their development within the legal framework. Thus, in 2020, a federal law was adopted providing for an experiment to establish special regulation in order to create the necessary conditions for the development and implementation of artificial intelligence technologies in the federal city of Moscow. The author abstractly formulated the results of the analysis of this normative legal act, as well as conclusions on its implementation in 4 years from the date of adoption.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization00The author abstractly formulated the results of the analysis of this normative legal act, as well as conclusions on its implementation in 4 years from the date of adoption.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Elena Rybalka"]2024-08-20T00:00:00 a912dc387f8dd85b19e41b92ed533c322ffe97b0Purpose: This study investigates shifts in scientific research focus, particularly the decline in COVID-19-related research and the rapid growth of artificial intelligence (AI) publications.Methods: We analyzed publication data from the Web of Science, comparing yearly publication counts for COVID-19 and AI research. The study also tracked changes in the impact factors of leading journals like Science and Nature, alongside those of top AI journals over the past decade. Additionally, we reviewed the top 10 most cited articles in 2021 from Science and Nature and the most influential AI publications from the past five years according to Google Scholar. The impact trends of the top 100 AI journals in computer science were also explored.Results: The analysis reveals a noticeable decline in COVID-19 related publications as the pandemic urgency diminishes, contrasted with the continued rapid growth of AI research. Impact factors for prestigious journals have shifted, with AI journals increasingly dominating the academic landscape. The review of top-cited articles further emphasizes these trends.Conclusion: Our findings indicate a significant shift in research priorities, with AI emerging as a dominant field poised to address future challenges, reflecting the evolving focus of the scientific community.Science Editing180A noticeable decline in COVID-19 related publications is revealed as the pandemic urgency diminishes, contrasted with the continued rapid growth of AI research, indicating a significant shift in research priorities.Science Editing["Yeong Jae Kim", "Yang Liu", "Youngeun Kim", "Ho Won Jang"]2024-08-20T00:00:00 44eec71ee4c9f3ede950632e6c2614c9f99de12aThis article examines the social consequences of automation and artificial intelligence (AI) in industrial management, drawing on the findings of Damioli, Van Rooy, and Vertsy’s (2021) study on the impact of AI on labor productivity. The integration of AI technologies in industrial settings has led to significant improvements in efficiency and productivity. However, these advancements come with substantial social implications that affect both the workforce and broader societal structures. Internally, automation and AI have shifted the demand from low-skilled to high-skilled labor, necessitating ongoing education and skill development for employees. Additionally, the reliance on AI for managerial decisions raises ethical concerns regarding algorithmic biases and the potential erosion of human judgment.Externally, the broader societal impact includes economic disparities, as regions with a high concentration of low-skilled jobs experience greater unemployment and underemployment. The displacement of workers due to automation presents challenges such as increased socio-economic divides and disruptions to community stability. Ethical considerations, including data privacy and security, are crucial to ensuring fair and responsible AI deployment. This article underscores the need for strategic collaboration between industry leaders, policymakers, and educational institutions to address these social consequences. By fostering an inclusive approach to technological advancement, it is possible to balance the productivity benefits of AI with the imperative to mitigate its adverse social impacts.Research in Economics and Management00The social consequences of automation and artificial intelligence in industrial management are examined, drawing on the findings of Damioli, Van Rooy, and Vertsy’s study on the impact of AI on labor productivity.Research in Economics and Management["Mohammad Taleghani", "Mohammadreza Jabreilzadeh Sola"]2024-08-20T00:00:00 0250efcb5e25a55b074cc85f777521222c65e11dAs Artificial Intelligence (AI) changes how businesses work, there’s a growing need for people who can work in this sector. This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world. To gain insight into the differences between university curricula and industry demands we review the contents of taught courses and job advertisement portals. By using custom data scraping tools to gather information from job advertisements and university curricula, and frequency and Naive Bayes classifier analysis, this study will show exactly what skills industry is looking for. In this study we identified 12 skill categories that were used for mapping. The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics sk\ill categories.Industry & higher education360The study showed that the university curriculum in the AI domain is well balanced in most technical skills, including Programming and Machine learning subjects, but have a gap in Data Science and Maths and Statistics sk\ill categories.ArXiv["Khushi Jaiswal", "I. Kuzminykh", "Sanjay Modgil"]2024-08-20T00:00:00 adf513461743e57b0d8ab7ef0bd7cc6a834ea106Golden Indonesia 2045 is a long-term vision that targets Indonesia to become a developed country with high levels of welfare and security. One important aspect in achieving this vision is the application of advanced technology to support security and efficiency in various sectors. This article aims to discuss the application of smart lock integration using artificial intelligence (AI)-based image processing technology, called “Re-Lock”. Re-Lock technology is designed to enhance physical security through advanced visual identification, allowing access only to authorized individuals based on real-time image analysis. This research adopts a system development method that integrates AI with smart locks, where image processing is used to recognize faces, fingerprints, or certain patterns as a form of identity verification. Case studies were applied to smart home environments and public facilities to test the reliability and efficiency of the system under real conditions. The results show that Re-Lock has a high accuracy rate in identifying individuals and is able to significantly reduce the risk of unauthorized access. In addition, the system is also designed to be easily integrated with existing infrastructure, making it a practical and affordable solution in supporting national security in the digital era. The implementation of Re-Lock is projected to contribute significantly to the achievement of the Golden Indonesia 2045 vision, by making security technology one of the main pillars in the country's development.Jurnal Riset Multidisiplin dan Inovasi Teknologi220The results show that Re-Lock has a high accuracy rate in identifying individuals and is able to significantly reduce the risk of unauthorized access, making it a practical and affordable solution in supporting national security in the digital era.Jurnal Riset Multidisiplin dan Inovasi Teknologi["Mega Maharani", "Muh. Zulhamdi Suhafid"]2024-08-20T00:00:00 b29f3a2f2e07e3eff14066c52043948b25cc637cHeart failure (HF) affects millions of individuals and causes hundreds of thousands of deaths each year in the United States. Despite the public health burden, medical and device therapies for HF significantly improve clinical outcomes and, in a subset of patients, can cause reversal of abnormalities in cardiac structure and function, termed “myocardial recovery.” By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. Emerging research in the area has begun to demonstrate exciting results that could advance the standard of care. Although major obstacles remain to translate this technology to clinical practice, AI and ML hold the potential to usher in a new era of purposeful myocardial recovery programs based on precision medicine. In this review, we discuss applications of ML to the prediction of myocardial recovery, potential roles of ML in elucidating the mechanistic basis underlying recovery, barriers to the implementation of ML in clinical practice, and areas for future research.Methodist DeBakey Cardiovascular Journal790Applications of ML to the prediction of myocardial recovery, potential roles of ML in elucidating the mechanistic basis underlying recovery, barriers to the implementation of ML in clinical practice, and areas for future research are discussed.Methodist DeBakey Cardiovascular Journal["Frederick M. Lang", "Benjamin C. Lee", "Dor Lotan", "M. Sabuncu", "V. Topkara"]2024-08-20T00:00:00 fbfe87eda30febdbcda6b5af58d1c73133ff5424Artificial Intelligence (AI) is increasingly being used at different stages of teaching and learning, from kindergarten to university education, to enhance the learning and development of learners. Research has proven that AI can perform multiple roles aimed at effectively facilitating learning and improving performance. However, there is a scarcity of studies on how these studies are conducted and how AI is used in kindergarten compared to other levels. Studies and research published in Arabic on AI in education were reviewed. Although there are few studies in kindergarten. The current review reflects the issues and trends of research in AI in the stages of education that researchers have recently addressed in Arab countries. This can easily and efficiently draw the attention of other researchers to these issues. 207 research papers published in various Arab countries were reviewed during the period from 2010 to 2023 to explore the roles and trends of AI in education. Three main issues were identified: research, interaction, and performance according to the technology-based learning model. It turns out that studies focused more on comparing learning with AI to find more effective learning methods with traditional education methods, on the stages of university education, and on the aspects of cognition, skills and affect. Some suggestions have been presented for future research on AI in educational stages, to serve as a reference for researchers to conduct related research.E-Learning and Digital Media300The issues and trends of research in AI in the stages of education that researchers have recently addressed in Arab countries are reflected and some suggestions have been presented for future research on AI in educational stages to serve as a reference for researchers to conduct related research.E-Learning and Digital Media["Basant A Alakabawy"]2024-08-20T00:00:00 6d0c39c45d921f4a9b0fc1d3c4e6ef7e571ae3cdThe topic of global use and legal regulation of artificial intelligence systems is now one of the most relevant both in global legal science and in our country. The question arises: is the accelerating development of AI systems a source of strategic opportunity or a sword of Damocles threatening humanity? We are inclined to answer that AI systems are still mathematical formulas, very limited in the complexity of the problems that they can solve.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization00The question arises: is the accelerating development of AI systems a source of strategic opportunity or a sword of Damocles threatening humanity?Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Stanislav Polubat"]2024-08-20T00:00:00 528f4b7b8e36cf961e4b5bc76b24d4938612e177The conjunction of Artificial Intelligence (AI) and Extended Reality (XR) has fore-shadowed a new era within the field of Biomedical Engineering (BME), offering many unprecedented avenues for innovation, diagnostics, treatment, and education. This research exploration delves into the synergetic connection between AI, XR, and VR, unscrambling their collective probability to reform healthcare practices. AI, considered by its ability to learn and adapt, has surpassed its role within many domains of data analysis to become a vital tool in healthcare. Through advanced algorithms, AI can predict various types of disease patterns, enhance medical imaging, and optimize treatment protocols. XR technologies, encompassing of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), immerse users into virtual environments, facilitating interactive and experiential learning and treat-ment methods. This research investigation also focuses on the study that inspects the integration of AI, XR, and VR in biomedical applications, illuminating their role in diagnosis, treatment, and training. The AI-driven image analysis augments medical imaging, expediting disease identification and tracking treatment progress. XR, through its immersive nature, empowers surgeons with a very detailed anatomical insight during procedures and aids within rehabilitation through engaging simulations. The synergistic matrimonial of AI, XR, and VR also redefines medical education by offering immersive training experiences to healthcare practitioners and bridging the gap between theory and practice. Ethical considerations and challenges emerge as these technologies continue to evolve. Privacy concerns, data security, along the need for regulatory frameworks are paramount in this dynamic landscape. Conspicuous for the right balance between innovation and patient safety remains an imperative task. In the context of this research, the fusion of AI, XR, and VR from a biomedical engineering perspective holds the potential to revolutionize healthcare informatics. As AI refines diagnostics and treatment strategies, AR, XR, and VR provide a perceptible platform for immersive experiences that can enhance training and therapeutic interventions. This research navigates the landscape of this trans-formative convergence and shedding light on its profound implications for BME and the well-being of patients universally.Engineering Advances460This research exploration delves into the synergetic connection between AI, XR, and VR, unscrambling their collective probability to reform healthcare practices and shedding light on its profound implications for BME and the well-being of patients universally.Engineering Advances["Zarif Bin Akhtar", "Ahmed Tajbiul Rawol"]2024-08-20T00:00:00 51026825574f7c3790dc6fc8ed0c96e95d2777dfThe article reveals the results of an ethical and legal analysis of state policy in the field of regulation. The author draws attention to the most important imperatives in the development of ethical and legal foundations for regulation in the field of artificial intelligence; the priorities are considered, the study of which is necessary for the further development of special legislation in the field of artificial intelligence for Russia.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization10Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Aleksey Ovchinnikov"]2024-08-20T00:00:00 70c355e4cc92f00b15e0f4391e217fe3b24aa610In the article, the authors reveal the concept and essence of artificial intelligence (AI) in the arbitration process at the present time, which is an urgent topic. The authors focus on the implementation of AI in the arbitration court, highlighting the positive aspects, and also raise the question of the ethical orientation of the use of AI in court. The authors propose to create My Arbiter based on the system (my.arbitr.ru) a certain form of sending an application for a court order, thus helping to relieve the courts at least a little from a huge number of cases.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization10The authors propose to create My Arbiter based on the system (my.arbitr.ru) a certain form of sending an application for a court order, thus helping to relieve the courts at least a little from a huge number of cases.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["S. Semikina", "A. Kruzhalova"]2024-08-20T00:00:00 148a4aa6f09af189bae609c4429b8f8b0aca44a4The article reveals the problems of comparing the terms "big user data" and "personal data" and their regulatory application in the field of financial risk assessment using artificial intelligence. The article discusses the types of big data analyzed in credit institutions and the main ways of using them. The legal nature of the possibility of using big data and analyzing it using software based on the mechanisms of so-called machine learning (artificial intelligence) is analyzed. The article reveals the regulatory and legal problems in this area and examines them from the practical side.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization00The article reveals the problems of comparing the terms "big user data" and "personal data" and their regulatory application in the field of financial risk assessment using artificial intelligence and examines them from the practical side.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Elena Rybinceva"]2024-08-20T00:00:00 5ffc740b8b4cddb833917e653784fac54f50ece0The paper considers the current problems of the role of man and the state in the era of the use of artificial intelligence in order to commit illegal actions. It is shown that the regulatory framework of the Russian Federation does not regulate the use of artificial intelligence, including does not provide for liability for crimes involving its useArtificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization10It is shown that the regulatory framework of the Russian Federation does not regulate the use of artificial intelligence, including does not provide for liability for crimes involving its use.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Viktor Mel'nikov"]2024-08-20T00:00:00 30d2dbb9135741d6f4f5f8d3ffa00671c345967dWith the widespread application of artificial intelligence in different fields of human society, terrorists have also realized the unique advantages of artificial intelligence and use artificial intelligence to conduct fraud, recruit members and other criminal activities. This situation brings security problems to a country's political and social levels, endangering citizens' personal and property safety. Therefore, there is an urgent need to analyze and regulate the development status of terrorism in the era of artificial intelligence, so as to prevent intelligent disasters caused by terrorism.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization00There is an urgent need to analyze and regulate the development status of terrorism in the era of artificial intelligence, so as to prevent intelligent disasters caused by terrorism.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Depen Vey"]2024-08-20T00:00:00 1fc085f842767e510af92bba0c2d75c5bab8502aThe article discusses the possibility of recognizing artificial systems (artificial intelligence) as subjects of law. In the theory of law, the system of properties of a person characterizing him as a subject of law is designated by the concept of legal personality. Legal personality is a complex category that includes such elements as legal capacity, legal age, transaction capacity, etc. At the same time, the primary importance is given to legal capacity — the abstract possibility of having legal rights and obligations recognized by the state. However, in the case of artificial intelligence, before recognizing its legal capacity, it is necessary to establish that it has other elements of legal personality, i.e. its ability to independently exercise its rights, fulfill legal obligations and be responsible for the legality of its actions. The author comes to the conclusion that the level of development of existing artificial intelligence systems does not allow us to talk about the availability of bargaining power, delictworthiness and other properties necessary for the recognition of artificial intelligence as a subject of law.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization10The author comes to the conclusion that the level of development of existing artificial intelligence systems does not allow us to talk about the availability of bargaining power, delictworthiness and other properties necessary for the recognition of artificial intelligence as a subject of law.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Aleksandr Nikitin"]2024-08-20T00:00:00 64842fbed7db337bce3402519cfab80c591a060cThe article examines some legal problems of using artificial intelligence technologies from the point of view of theoretical approaches and current legislation. The opinion is expressed on the need for long-term improvement of civil law regulation in the field under consideration, based on the needs of society and law enforcement practice. Separate directions of modernization of legislation are proposed, taking into account the specifics of artificial intelligence.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization00The opinion is expressed on the need for long-term improvement of civil law regulation in the field under consideration, based on the needs of society and law enforcement practice, taking into account the specifics of artificial intelligence.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Marina Zhaglina", "Andrey Zhaglin"]2024-08-20T00:00:00 b66323722b575bdffd0344a49da885d542e0b4dbThe purpose of this article is to study possible risks and challenges in the higher education system that arise in connection with the growing speed of implementation of technologies, where artificial intelligence (AI) and neural network technologies are already part of its structure. The analysis is carried out with the aim of predicting the future nature of higher education, as well as exploring changes in the entire teaching paradigm under the influence of AI. The article identifies a number of expected problems when teaching students, in the pedagogical process, organizing their independent work, as well as in the management and administration of higher educational institutions when introducing AI capabilities into the process of higher education. The question is raised about the impact of restrictions on the development of Russian science, namely the disastrous consequences for its development due to the monopolization of AI algorithms and the subjective approach to managing a neural network by an organized technocratic group. In the course of analyzing the intersections of opposing issues of problematic issues and the positive dynamics of the influence of AI on the educational system of higher education, a vector has been set for further research on this topic.Bulletin of Practical Psychology of Education60The article identifies a number of expected problems when teaching students, in the pedagogical process, organizing their independent work, as well as in the management and administration of higher educational institutions when introducing AI capabilities into the process of higher education.Bulletin of Practical Psychology of Education["G. I. Davydova", "N.V. Shlykova"]2024-08-20T00:00:00 e26fe0ca0602ec79169f2f620d67c4c6915879e8Physics of Life Reviews355This paper does not naively state that the problem of artificial intelligence for collective dynamics has been exhaustively considered, but some hints are proposed to contribute to such a challenging perspective in view of further developments.Physics of life reviews["N. Bellomo", "M. Dolfin", "J. Liao"]2024-08-20T00:00:00 0746e090196c10037648696fe572c69120e01b61Artif. Intell. Medicine863An eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children is proposed, leveraging multi-omics and epigenomics and clinical data from the pre-pubertal stage to highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.Artificial intelligence in medicine["\u00c1lvaro Torres-Martos", "A. Anguita-Ruiz", "Mireia Bustos-Aibar", "A. Ram\u00edrez-Mena", "Mar\u00eda Arteaga", "Gloria Bueno", "R. Leis", "C. Aguilera", "Rafael Alcal\u00e1", "Jes\u00fas Alcal\u00e1-Fdez"]2024-08-20T00:00:00 0743cd3e9dd69a0e52c296d2adad9e7991e12a16SN Computer Science151SN Comput. Sci.["Sonika Malik", "P. Rathee"]2024-08-20T00:00:00 54fe6aa6349126c94b5ba42c7950386ed44abfd4Kurdish Studies00Kurdish Studies["M. Wadood"]2024-08-20T00:00:00 1d825ec2f81c4c3ea9fc63252d120aad6a546de1روح القوانين00روح القوانين["\u0645\u062d\u0645\u062f \u0646\u0648\u0631 \u0627\u0644\u062f\u064a\u0646 \u0633\u064a\u062f"]2024-08-20T00:00:00 c727c952a17d6eb0e5dacf4d726c44350f29aabeN/A.Tissue Engineering. Part A80Tissue engineering. Part A["Jason L. Guo", "Michael Januszyk", "Michael T. Longaker"]2024-08-20T00:00:00 31490337d8d2d49807202dd2ca66308d559a48e1The article analyzes the Chinese model of legal regulation of AI technologies. At the same time, the main attention is paid to the specialized regulatory and legal regulation of generative AI in the People's Republic of China, the study of which is currently of scientific interest for improving Russian legislation.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization20The article analyzes the Chinese model of legal regulation of AI technologies and pays special attention to the specialized regulatory and legal regulation of generative AI in the People's Republic of China.Artificial intelligence, traditional spiritual and moral values ​​and human rights in the era of digitalization["Anna Konopiy"]2024-08-20T00:00:00 e73923ad23684fc908c6c47eaa47ae853245ccc7روح القوانين00روح القوانين["\u0643\u0631\u0645 \u0634\u062d\u0627\u062a \u062d\u0633\u0646 \u0639\u0628\u062f\u0627\u0644\u063a\u0646\u0649"]2024-08-20T00:00:00 930442e24e6cb630f7eb9a8decd02b80676e95e700["P. Dutta", "Pethuru Raj", "B. Sundaravadivazhagan", "C. Selvan"]2024-08-20T00:00:00 b65c5623a54f64007676b56dbe43f9572cc68bbaScience Editing00Science Editing["G. Dyke"]2024-08-20T00:00:00 b2f9a7027902bc07d51c2d9f23379939cd9f0386Current Cardiovascular Risk Reports550Current Cardiovascular Risk Reports["Daniel K Amponsah", "Ritu Thamman", "Eric Brandt", "Cornelius James", "Kayte Spector-Bagdady", "Celina M. Yong"]2024-08-20T00:00:00 9a5120cb7f7b026975fd4d4d435487865b2e48cbEste artigo apresenta algumas “fascinantes oportunidades e [alguns] graves riscos” da inteligência artificial (IA) à luz do recente magistério do Papa Francisco. Não é uma tarefa fácil, pois o temo “inteligência artificial” se refere a uma “galáxia de realidades” e não há uma definição unívoca dela. Recentemente, com a IA criativa (1ª parte) houve um salto qualitativo. Seus desafios devem ser abordados a nível legislativo e ético (2ª parte). Mais concretamente, o Papa insta a adotar um humanismo de fraternidade inspirado em Francisco de Assis (3ª parte). Abstract: This article presents some of the “exciting opportunities and grave risks” of artificial intelligence (AI) in light of the recent magisterium of Pope Francis. This is not an easy task, as the term “artificial intelligence” refers to a “galaxy of different realities” and there is not a single definition of it. Recently there has been a qualitive leap with generative AI (1st part). Its challenges must be addressed at the legislative and ethical levels (2nd part). More specifically, the Pope urges the adoption of a humanism of fraternity inspired by Francis of Assisi (3rd part). Keywords: Artificial intelligence; ChatGPT; Pope Francis; Franciscanism; Ethics.Revista Eclesiástica Brasileira00Revista Eclesiástica Brasileira["Mart\u00edn Carbajo-N\u00fa\u00f1ez"]2024-08-20T00:00:00 75f091275e4fea6967b93e1247ac4aa41b732f22This article explores the use of artificial intelligence (AI) to personalize learning based on an understanding of brain functions, addressing how technological innovations can revolutionize education. The personalization of learning through AI promises to tailor teaching methods to the individual needs of students, considering their cognitive capabilities, learning styles, and neural development. Applying AI techniques such as machine learning and data analytics offers the possibility of creating more effective and engaging educational experiences. This study reviews the existing literature on neuroeducation and AI identifying the advances, challenges, and ethical implications of this integration. The conclusion highlights the importance of a balanced approach that combines neuroscientific knowledge with technological innovation to promote a more inclusive and efficient education system.Lumen et Virtus00This study reviews the existing literature on neuroeducation and AI identifying the advances, challenges, and ethical implications of this integration and highlights the importance of a balanced approach that combines neuroscientific knowledge with technological innovation to promote a more inclusive and efficient education system.LUMEN ET VIRTUS["Jos\u00e9 Valdecy Guimar\u00e3es J\u00fanior", "Hilke Carlayle de Medeiros Costa", "Jadilson Marinho da Silva", "M. Guimar\u00e3es", "Paulo Henrique De Faria", "F. C. Braga", "Fernando Bueno Vieira", "Elder Henrique Silva Rodrigues De Melo"]2024-08-20T00:00:00 bfa77c31e7634276c8d1399038bae2698395fbc4The International Committee of the Red Cross (ICRC) has exerted pressure on nations to enact new international legislation that would limit the use of certain autonomous weapons, including those under third-party control, and forbid the deployment of others. The International Committee of the Red Cross (ICRC) is a neutral, independent organization that offers victims of armed conflict and other perilous situations humanitarian protection and support. In addition to responding to emergencies, it promotes compliance with international humanitarian law and its assimilation into national laws. In modern times, the military is making significant investments in artificial intelligence, and there are already instances of AI being used in conflict. A number of sectors that create serious humanitarian concerns include those that the ICRC has identified as areas in which artificial intelligence is being developed for use in combat by armed actors. The International Committee of the Red Cross (ICRC) has been pressuring governments to pass new international laws that would prohibit the deployment of some autonomous weapons and restrict the use of others, including those that are controlled by third parties.Asian Journal of Social Sciences and Legal Studies31Asian Journal of Social Sciences and Legal Studies[]2024-08-20T00:00:00 662d509299219f20cf63793f9b2c12a1aa845437This research article explores the critical role of Artificial Intelligence (AI) in advancing sustainability within the manufacturing supply chain, with a focus on achieving lean and green objectives. The study emphasizes how AI technologies can optimize supply chain processes, reduce waste, and enhance environmental sustainability while simultaneously improving efficiency. Through a comprehensive literature review, the article examines existing AI applications in supply chain management, identifying key trends, challenges, and opportunities. The methodology section outlines the systematic approach used to gather and analyze relevant data, while the findings highlight the transformative potential of AI in fostering sustainable practices. The discussion delves into the implications of these findings for the manufacturing sector, suggesting that the integration of AI not only aligns with lean manufacturing principles but also supports broader sustainability goals. The article concludes by emphasizing the need for continued research and development in AI-driven supply chain solutions to fully realize their potential in promoting a greener, more efficient manufacturing industry.ACADEMIC JOURNAL ON BUSINESS ADMINISTRATION, INNOVATION &amp; SUSTAINABILITY04ACADEMIC JOURNAL ON BUSINESS ADMINISTRATION, INNOVATION &amp; SUSTAINABILITY["Bhanu Prakash Sah", "Shirin Begum", "Minhazur Rahman Bhuiyan", "Md Shahjalal"]2024-08-20T00:00:00 e1c53744f1e947f24af7cb10c7f35ced2e9fbfd9Artificial Intelligence (AI) has demonstrated its ability to undertake writing tasks, including automated journalism. Prior studies suggest no differences between human and AI authors regarding perceived message credibility. However, research on people’s perceptions of AI authorship on complex topics is lacking. In a between-groups experiment (N = 734), we examined the effect of labeled authorship on credibility perceptions of a GPT-written science journalism article. The results of an equivalence test showed that labeling a text as AI-written vs. human-written reduced perceived message credibility (d = 0.36). Moreover, AI authorship decreased perceived source credibility (d = 0.24), anthropomorphism (d = 0.67), and intelligence (d = 0.41). The findings are discussed against the backdrop of a growing availability of AI-generated content and a greater awareness of AI authorship.Journalism and Media213The results of an equivalence test showed that labeling a text as AI-written vs. human-written reduced perceived message credibility, and AI authorship decreased perceived source credibility, anthropomorphism, and intelligence.Journalism and Media["Angelica Lermann Henestrosa", "J. Kimmerle"]2024-08-20T00:00:00 994c4504783e0628183fef3c88ee6e45130d9dadPhilosophy &amp; Technology511It is argued that one way to approach what is known in the literature as the “Trust Gap” in Medical AI is to focus on explanations from an Explainable AI (xAI) perspective, and posit that Large Language Models (LLMs) and transformer architectures exhibit a noteworthy potential for effective engagement in abductive reasoning.Philosophy &amp; Technology["Steven S. Gouveia", "Jaroslav Mal\u00edk"]2024-08-20T00:00:00 9a60569d74aa77ec3b26ab544bab5644da504e12Journal of imaging informatics in medicine01The 2023 survey indicates that there has been progress in adopting AI across multiple uses, and there continues to be an optimistic forecast for the impact on workflow and clinical outcomes.Journal of imaging informatics in medicine["Mitchell Goldburgh", "Michael LaChance", "Julia Komissarchik", "Julia Patriarche", "Joe Chapa", "Oliver Chen", "Priya Deshpande", "Matthew Geeslin", "Nina Kottler", "Jennifer Sommer", "Marcus Ayers", "Vedrana Vujic"]2024-08-20T00:00:00 988c5aef5a4568b048dd48bb30394a5c3dcbe062Purpose: This research focuses on the implementation of artificial intelligence (AI) in these areas, with concerns on demographic characteristics, talent constraints, and ethical concerns. The study relied on secondary qualitative research methods and the following databases: ProQuest, Google Scholar, Scopus, and Web of Science. Twenty-five publications were selected based on the following inclusion and exclusion criteria. Study design/methodology/approach: The paper is intended to discuss and evaluate the various possibilities and consequences of AI implementation in the processes of recruitment and talent management. Thus, it is more concerned with the present and future use of AI, the advantages and disadvantages of its implementation, as well as how to properly and ethically apply it to these roles. Findings: This research adopts a secondary qualitative research method, whereby the study gathered and analysed already existing qualitative data on AI in recruitment and talent management. The study includes a critical and analytical review of the state and development trends of AI in these areas, as well as the definition of the gaps and further research directions. Originality/value: The study shows that AI can enhance the quality, efficiency, effectiveness, and equity of recruitment and talent management by automating, optimising, and personalising processes. However, it has been observed that the impact of AI is contingent on the quality of data, the algorithm used and the environment in which it is implemented. AI also has risks and limitations, such as privacy, security, accountability, and legal problems.International Journal of Management, Knowledge and Learning00The study shows that AI can enhance the quality, efficiency, effectiveness, and equity of recruitment and talent management by automating, optimising, and personalising processes.International Journal of Management, Knowledge and Learning["Anastasia Kiritsi", "Vasilis Adamantidis"]2024-08-20T00:00:00 d71310af92ae3260cde1a59270f4b7ffe586dc9eEmotion analysis is a key component in understanding the unique communication patterns and emotional states of children with autism spectrum disorder (ASD). These children often struggle with traditional forms of expressing emotions, which presents a challenge for themselves and their therapists. Facial expression analysis techniques, supported by modern technologies such as machine learning and artificial intelligence, enable more accurate identification of subtle signals that may go unnoticed by human observers. The introduction of rehabilitation robots and emotion analysis software based on the analysis of facial expressions and gestures opens up new possibilities for individualizing therapy, adapting it to the child's specific reactions and needs. In this way, the use of these tools not only increases the effectiveness of treatment but also helps build more trusting therapeutic relationships, which is the basis for adequate support for the development of children with ASD. Regular monitoring of progress and modifying therapeutic approaches, supported by automation and data analysis, is essential to more effective and empathetic care for children with developmental disorders. However, the journey does not end here. Further research is necessary to develop and improve emotion analysis techniques for use in rehabilitation robots and their impact on the effectiveness of therapy for young patientsJournal of Modern Science130Rec rehabilitation robots and emotion analysis software based on the analysis of facial expressions and gestures based on the analysis of facial expressions and gestures opens up new possibilities for individualizing therapy, adapting it to the child's specific reactions and needs.Journal of Modern Science["Ewa Guz", "Konrad Niderla", "Grzegorz Kata"]2024-08-20T00:00:00 330d834430802cfb46968f4a7554ed35e8c8a354 Amidst rapid advancements in Artificial Intelligence and Machine Learning-enabled medical devices (AI/ML-MD), this article investigates the regulatory challenges highlighted in the current academic literature. Using a PRISMA-guided scoping review, 18 studies were selected for in-depth analysis to highlight the multifaceted issues in regulating AI/ML-MD. The study's findings are organized into key themes: Adaptive AI/ML, usability and stakeholder engagement, data diversity and use, health disparities, synthetic data use, regulatory considerations, medico-legal issues, and cybersecurity threats. The scoping review reveals numerous challenges associated with the regulation of AI/ML-based medical devices, reflecting various sustainability pillars. The study advocates for integrating sustainability principles into the materiovigilance ecosystem of AI/ML-MD and proposes a novel Sustainable Ecosystem for AI/ML-MD Materiovigilance. This proposed ecosystem incorporates social, economic, and environmental sustainability principles to create a comprehensive and balanced regulatory approach. By presenting a thorough analysis of regulatory challenges, the study provides policymakers with a nuanced understanding of the complex landscape surrounding these technologies. This insight enables the development of informed strategies and solutions to address regulatory gaps and ensure the safe and effective integration of AI/ML-MD into healthcare systems.Journal of Medical Devices00The study advocates for integrating sustainability principles into the materiovigilance ecosystem of AI/ML-MD and proposes a novel Sustainable Ecosystem for AI/ML-MD Materiovigilance, which incorporates social, economic, and environmental sustainability principles to create a comprehensive and balanced regulatory approach.Journal of Medical Devices["Sanju Kaladharan", "Dhanya Manayath", "R. G"]2024-08-20T00:00:00 d76ce6860f41590c8a8d71f4da5916920deaa18e The complex and data-driven nature of artificial intelligence (AI) raises questions for the sufficient disclosure of patent applications in this field. What are the European patent disclosure requirements for AI inventions? One challenge is that, prior to training, AI systems can be considered generic models. But after training, they transform into specialized AI systems to solve a particular problem. This transformation requires training data, making it an integral part of the AI system’s definition. But to what extent is the disclosure of the training data or training process necessary for patent disclosure? The Boards of Appeal of the European Patent Office (EPO) first dealt with this challenge in case T 0161/18, which involved a medical AI invention to calculate cardiac output. It held that the specialized artificial neural network (ANN) in the patent could not be carried out by a person skilled in the art due to insufficient disclosure of input data suitable for the training of the ANN or at least one data set suitable for solving the technical problem. Furthermore, without specialization, the invention lacked an inventive step. But, is it always necessary to disclose the input data or at least one data set suitable for solving the technical problem? Are there alternative ways for applicants to satisfy the disclosure requirements for AI inventions? And what evidence is there that patent applicants are disclosing specific details of the AI/machine learning (ML) training or specific AI/ML model architecture? In this article, we analyse case T 0161/18 and subsequent sufficiency of disclosure decisions (T 1539/20; T 0606/21; T 1526/20; T 1191/19) and consider these foundational questions for applicants drafting patent applications with claims directed to AI inventions. We also analyse the EPO’s examination guidelines on sufficiency of disclosure for AI inventions, which were updated in early March 2024.Journal of Intellectual Property Law &amp; Practice00This article analyzes case T 0161/18 and subsequent sufficiency of disclosure decisions and considers foundational questions for applicants drafting patent applications with claims directed to AI inventions and examines the EPO’s examination guidelines on sufficiency of disclosure.Journal of Intellectual Property Law &amp; Practice["M. Aboy", "A. Lath", "Timo Minssen", "K. Liddell"]2024-08-20T00:00:00 24e1e509303e5ebe405acbc37477059f203e2f9bThe integration of artificial intelligence (AI) with smart technology in surgical robotics has brought about a significant transformation in the field of surgery, enhancing both precision and efficiency. AI-driven surgical robots empower surgeons to perform intricate procedures with unparalleled accuracy, effectively reducing human error and shortening patient recovery times. This article delves into the synergy between AI and smart technology within surgical robotics, examining their impact on improving surgical precision, streamlining procedures, and optimizing patient outcomes. By conducting a thorough review of recent advancements, we explore the methodologies used, the achievements made, and the future possibilities in AI-driven robotic surgery. This exploration highlights the revolutionary potential of AI in creating safer and more effective surgical interventions, paving the way for continuous innovation and improvement in the field of surgery. The findings of this review emphasize the critical role that AI will continue to play in advancing surgical practices, ultimately leading to better patient care and outcomes.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402307A thorough review of recent advancements in AI-driven robotic surgery highlights the revolutionary potential of AI in creating safer and more effective surgical interventions, paving the way for continuous innovation and improvement in the field of surgery.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Nasrullah Abbasi", "Hafiz Khawar Hussain"]2024-08-21T00:00:00 22de1a4bbfbb638dcbae5334198280b2422aa63eObjective A bibliometric analysis was conducted using VOSviewer and CiteSpace to examine studies published between 2009 and 2023 on the utilization of artificial intelligence (AI) in chronic obstructive pulmonary disease (COPD). Methods On March 24, 2024, a computer search was conducted on the Web of Science (WOS) core collection dataset published between January 1, 2009, and December 30, 2023, to identify literature related to the application of artificial intelligence in chronic obstructive pulmonary disease (COPD). VOSviewer was utilized for visual analysis of countries, institutions, authors, co-cited authors, and keywords. CiteSpace was employed to analyze the intermediary centrality of institutions, references, keyword outbreaks, and co-cited literature. Relevant descriptive analysis tables were created using Excel2021 software. Results This study included a total of 646 papers from WOS. The number of papers remained small and stable from 2009 to 2017 but started increasing significantly annually since 2018. The United States had the highest number of publications among countries/regions while Silverman Edwin K and Harvard Medical School were the most prolific authors and institutions respectively. Lynch DA, Kirby M. and Vestbo J. were among the top three most cited authors overall. Scientific Reports had the largest number of publications while Radiology ranked as one of the top ten influential journals. The Genetic Epidemiology of COPD (COPDGene) Study Design was frequently cited. Through keyword clustering analysis, all keywords were categorized into four groups: epidemiological study of COPD; AI-assisted imaging diagnosis; AI-assisted diagnosis; and AI-assisted treatment and prognosis prediction in the COPD research field. Currently, hot research topics include explainable artificial intelligence framework, chest CT imaging, and lung radiomics. Conclusion At present, AI is predominantly employed in genetic biology, early diagnosis, risk staging, efficacy evaluation, and prediction modeling of COPD. This study’s results offer novel insights and directions for future research endeavors related to COPD.International Journal of COPD373A bibliometric analysis of studies published between 2009 and 2023 on the utilization of artificial intelligence in chronic obstructive pulmonary disease found that AI is predominantly employed in genetic biology, early diagnosis, risk staging, efficacy evaluation, and prediction modeling of COPD.International Journal of Chronic Obstructive Pulmonary Disease["Hupo Bian", "Shaoqi Zhu", "Yonghua Zhang", "Qiang Fei", "Xiuhua Peng", "Zanhui Jin", "Tianxiang Zhou", "Hongxing Zhao"]2024-08-21T00:00:00 ff1fb18999bd6c869acc70cc093614976bd740d5People with disorders of consciousness, either as a consequence of an acquired brain injury or a traumatic brain injury, may pose serious challenges to medical and/or rehabilitative centers with an increased burden on caregivers and families. The objectives of this study were as follows: to explore the use of extended reality as a critical means of rehabilitative support in people with disorders of consciousness and brain injuries; to evaluate its impact on recovery processes; to assess the improvements in the participants’ quality of life, and to reduce the burden on families and caregivers by using extended reality and artificial-intelligence-based programs. A selective review of the newest empirical studies on the use of extended reality and artificial-intelligence-based interventions in patients with brain injuries and disorders of consciousness was conducted over the last decade. The potential for bias in this selective review is acknowledged. A conceptual framework was detailed. The data showed that extended reality and artificial-intelligence-based programs successfully enhanced the adaptive responding of the participants involved, and improved their quality of life. The burden on caregivers and families was reduced accordingly. Extended reality and artificial intelligence may be viewed as crucial means of recovery in people with disorders of consciousness and brain injuries.Inf.733Data showed that extended reality and artificial-intelligence-based programs successfully enhanced the adaptive responding of the participants involved, and improved their quality of life, and the burden on caregivers and families was reduced accordingly.Inf.["Khalida Akbar", "A. Passaro", "Mariacarla Di Gioia", "Elvira Martini", "Mirella Dragone", "Antonio Zullo", "Fabrizio Stasolla"]2024-08-21T00:00:00 bdc5aafdf3adbdaea0df124ad040421d58aee90fThis paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V2 and then a recurrent neural network long short-term memory model. This approach achieved an accuracy of 93% at the slice level and 77% at the examination level. External validation using a hospital dataset resulted in a precision of 86% for positive pulmonary embolism cases and 69% for negative pulmonary embolism cases. Notably, the model excels in excluding pulmonary embolism, achieving a precision of 73% and a recall of 82%, emphasizing its clinical value in reducing unnecessary interventions. In addition, the diverse demographic distribution in the validation dataset strengthens the model’s generalizability. Overall, this model offers promising potential for accurate detection and exclusion of pulmonary embolism, potentially streamlining diagnosis and improving patient outcomes.PLoS ONE621The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels, emphasizing its clinical value in reducing unnecessary interventions.PLOS ONE["L. Silva", "Maria Carolina Bueno da Silva", "Guilherme Ribeiro", "T. F. O. Camargo", "P. V. Santos", "Giovanna Mendes", "Joselisa P\u00e9res Queiroz Paiva", "A. Soares", "M\u00e1rcio Reis", "R. M. Loureiro", "Wesley Pacheco Calixto"]2024-08-21T00:00:00 04b0004e132965176a470b98d49d7c1351072259This article examines the future governance of areas beyond national jurisdiction (ABNJ) in the wake of the new 2023 United Nations Agreement using the work on the Sargasso Sea as a prototype. After discussing the legal framework and current challenges facing the ABNJ regime, some details are provided on open ocean data collection technologies, including big data and artificial intelligence (AI), used in support of ocean governance. Based on a technology-enabled ocean governance cycle, the role that data, information technology and data-science can play in incorporating empirical scientific knowledge into policy and decision-making is examined with a focus on the open ocean. The article concludes with a vision of future high seas governance based on the 2023 Agreement and how big data and AI can play a crucial role in meeting the exciting challenges that the new agreement poses.Frontiers in Marine Science821This article examines the future governance of areas beyond national jurisdiction (ABNJ) in the wake of the new 2023 United Nations Agreement using the Sargasso Sea as a prototype and how big data and AI can play a crucial role in meeting the exciting challenges that the new agreement poses.Frontiers in Marine Science["David Freestone", "Kieran N. Bjergstrom", "K. Gjerde", "Patrick N. Halpin", "Kevin P. Fleming", "Andrew Hudson", "A. Rogers", "Fae Sapsford", "V. Tsontos", "Jorge Vazquez-Cuervo", "David Vousden"]2024-08-21T00:00:00 dc639254926dd4387a0d7232fe254c43290fefcfThis study explores the impact of artificial intelligence (AI) on the behavior and knowledge of final-year architectural students in Serbia and Montenegro. It aims to describe how students approach sustainability in architecture and their use of AI tools within this context. The primary objective is to analyze how AI affects students’ understanding of sustainable architecture indicators and how sustainability challenges and concerns influence AI applications. Using a comparative analysis approach across the two countries, this research employs surveys to test various hypotheses regarding the effects of AI on students’ perceptions of sustainability and their use of AI to achieve sustainable outcomes. The findings highlight a significant relationship between students’ knowledge of sustainability and their use of AI, revealing different influencing factors. These insights are essential for predicting future AI usage in architectural practice and provide a theoretical foundation for assumptions about sustainability in architecture. This study’s findings offer valuable guidance for refining curricula at the universities involved, aiming to enhance the integration of AI and sustainability in architectural education.Buildings541Buildings["D. Komatina", "M. Mileti\u0107", "Marija Mosurovi\u0107 Ru\u017ei\u010di\u0107"]2024-08-21T00:00:00 be78a8df4a18a5ea04a01748d6f40c2d498b6361This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.Frontiers in Science and Engineering281This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS).Frontiers in Science and Engineering["Su Diao", "Danyi Huang", "Gaozhe Jiang"]2024-08-21T00:00:00 e2746cd628977a815c810bc1c286a9b521f6c3a4Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.Water282Water["Hsuan-Yu Lin", "Shao-Huang Lee", "Jhih-Huang Wang", "Ming-Jui Chang"]2024-08-21T00:00:00 24869fc180b575e3eec292a467d7810e2f1e5befThe purpose of this research was to examine the utilization of artificial intelligence (AI) in organization development (OD) through a comprehensive review of existing literature. We also propose potential avenues for future research on AI in OD. We conducted a systematic literature review of 68 studies on AI in OD based on Cummings and Worley’s four OD categories (i.e., human process, human resource, strategic change, and technostructural interventions). We first summarized and analyzed key information about how AI is implemented in OD contexts, and then examined the underlying theories or theoretical frameworks utilized in OD studies focusing on AI. We examined the application of AI in OD, potential ethical concerns, and recommendations for future research and practice using AI in OD. The paper concludes with discussion and implications for research and practice.Human Resource Development Review551The application of AI in OD, potential ethical concerns, and recommendations for future research and practice using AI in OD are examined and discussion and implications for research and practice are discussed.Human Resource Development Review["Sunyoung Park", "D. Chai", "J. J. Park", "Jihye Oh"]2024-08-21T00:00:00 1c7b67f0642738425173afdb79929450d61d0726Over the last decade, investment in artificial intelligence (AI) has grown significantly, driven by technology companies and the demand for PhDs in AI. However, new challenges have emerged, such as the ‘black box’ and bias in AI models. Several approaches have been developed to reduce these problems. Responsible AI focuses on the ethical development of AI systems, considering social impact. Fair AI seeks to identify and correct algorithm biases, promoting equitable decisions. Explainable AI aims to create transparent models that allow users to interpret results. Finally, Causal AI emphasizes identifying cause-and-effect relationships and plays a crucial role in creating more robust and reliable systems, thereby promoting fairness and transparency in AI development. Responsible, Fair, and Explainable AI has several weaknesses. However, Causal AI is the approach with the slightest criticism, offering reassurance about the ethical development of AI.Frontiers Artif. Intell.151Causal AI is the approach with the slightest criticism, offering reassurance about the ethical development of AI, thereby promoting fairness and transparency in AI development.Frontiers in Artificial Intelligence["Lu\u00eds Cavique"]2024-08-21T00:00:00 f7f502ac52b7447bb9968a656afa97e57b265727Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without challenges. While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges.Kidney36001While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges.Kidney360["Conor S Judge", "F. Krewer", "Martin J O'Donnell", "Lisa Kiely", "Donal Sexton", "Graham W Taylor", "Joshua August Skorburg", "Bryan Tripp"]2024-08-21T00:00:00 7617ee3387edc07410a0c6e7f1724bf2abf6bc7fThis study employs a fixed effects model to examine the impact of AI technology on corporate behavior based on data from Chinese A-share listed companies from 2012 to 2022. Findings show that artificial intelligence application can significantly reduce corporate greenwashing behavior, which remains robust after addressing endogeneity issues and conducting a series of robustness tests. Heterogeneity analysis reveals that property rights, industry, and regional factors influence AI's inhibition of greenwashing. This study highlights the crucial role of AI in corporate governance and emphasizes the importance of optimizing green finance regulation.Transactions on Economics, Business and Management Research210Heterogeneity analysis reveals that property rights, industry, and regional factors influence AI's inhibition of greenwashing and highlights the crucial role of AI in corporate governance and emphasizes the importance of optimizing green finance regulation.Transactions on Economics, Business and Management Research["Xiaotong Yang", "Z. Ren", "Yi Xie"]2024-08-21T00:00:00 3b9b15201a0ce5cb1924fd37c0ae92e1cb23701dArtificial intelligence (AI) has become a vogue topic in the press, and descriptions of its potential impact range from apocalyptic to salvational. Interest in the topic will no doubt stimulate the search for applications to support both the technical and management systems aspects of process safety management. Within our industries, maintaining institutional memory and technical capability is made increasingly challenging by more frequent job movement among younger staff and the loss to the retirement of more senior staff. One would hope that AI could help fill the gaps caused by these factors. However, the author's sampling of current AI capabilities suggests that AI is not yet ready to do so. This paper provides some examples of errors and insufficiencies identified when seeking AI assistance in addressing process safety issues. It also suggests some existing challenges to better “training” of AI to support the needs of the process safety community. It concludes that caution should be applied, especially by less experienced personnel, when seeking AI assistance in addressing process safety–related technical matters.Process safety progress10This paper provides some examples of errors and insufficiencies identified when seeking AI assistance in addressing process safety issues and suggests some existing challenges to better “training” of AI to support the needs of the process safety community.Process Safety Progress["Walter Frank"]2024-08-21T00:00:00 10ce8c404c75e1170ec5a3be418fef2b258087f0This article delves into the profound impact of artificial intelligence (AI) on international trade. By reviewing the historical evolution of AI and its related policy frameworks, this research gained a clear understanding of its potential applications in international trade. Subsequently, by examining the shortcomings of traditional trade models and the evolving global economic landscape, this paper analyzes the unique advantages of AI in enhancing trade efficiency and optimizing trade structures. Taking the AI-powered foreign trade platform of Alibaba International Station as a case study, this article dissects its internal driving forces and the external factors that facilitate its development. Through an in-depth examination of its operational model, it leverages intelligent technologies to streamline trade processes, reduce costs, and introduce its primary functions and practical applications in international trade. Furthermore, this paper explores both the benefits and challenges of integrating AI into international trade, offering guidance for newcomers in the foreign trade industry on incorporating AI into their business practices. This includes advice on technology, talent development, and strategic planning.Transactions on Economics, Business and Management Research40Both the benefits and challenges of integrating AI into international trade are explored, offering guidance for newcomers in the foreign trade industry on incorporating AI into their business practices, including advice on technology, talent development, and strategic planning.Transactions on Economics, Business and Management Research["Yixin Piao"]2024-08-21T00:00:00 dde654b737c554928d2e91ff998e6d2f0af764feThis narrative review delves into the potential of artificial intelligence (AI) in managing bipolar disorder (BD). A comprehensive literature search was conducted across multiple databases, including Scopus, Web of Science, PubMed, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ), and JSTOR, focusing on articles published between January 2010 and December 2022. The review identifies promising AI techniques, particularly machine learning (ML) and artificial neural networks (ANN), that enhance diagnostic accuracy and continuously monitor and predict clinical outcomes for BD. AI methods have demonstrated significant potential in differentiating BD from other psychiatric conditions, such as major depressive disorder (MDD) and schizophrenia, with reported accuracies ranging from 49.5% to 96.2%. Moreover, AI-driven systems utilizing smartphones and wearable devices have shown high accuracy in monitoring mood states and predicting mood episode recurrences. Predictive models using ML algorithms have also been effective in forecasting depressive relapses and identifying cognitive dysfunctions in the early stages of BD. The review underscores the transformative potential of AI in BD management, particularly in predicting clinical outcomes, and calls for further research to overcome existing limitations.Revista Ibero-Americana de Humanidades, Ciências e Educação140The review identifies promising AI techniques, particularly machine learning (ML) and artificial neural networks (ANN) and artificial neural networks (ANN), that enhance diagnostic accuracy and continuously monitor and predict clinical outcomes for bipolar disorder (BD).Revista Ibero-Americana de Humanidades, Ciências e Educação["Kelly Yumi Morii", "Julia Coradin", "Yasmin Vit\u00f3ria Carvalho de Castro", "Afr\u00e2nio C\u00f4go Destefani", "Vin\u00edcius C\u00f4go Destefani"]2024-08-21T00:00:00 c2feab4024dd356afad220bd1c679ec29d63f8f2Since the integration of AI in educational fields has gained significant attention in recent years, understanding educators’ perspectives has become increasingly crucial for successful implementation. By synthesizing existing literature, this systematic review aims to explore the perceptions of teachers regarding the application of Artificial Intelligence (AI) in education. The review identifies and categorizes research into three key dimensions of perception: Knowledge and Experience, Attitude and Emotion, and Ethical Considerations. And the findings will inform the development of appropriate training and support AI-based curriculum to enhance teachers’ readiness for the AI-driven educational environments.World Journal of Social Science Research00This systematic review aims to explore the perceptions of teachers regarding the application of Artificial Intelligence in education, and identifies and categorizes research into three key dimensions of perception: Knowledge and Experience, Attitude and Emotion, and Ethical Considerations.World Journal of Social Science Research["Chenxue Zhou"]2024-08-21T00:00:00 642b2b2637836faf6358e5ee0a9e0d09615cd50cThe general objective of the research was to determine the advances related to the cybersecurity and artificial intelligence (AI). The specific objectives of the research are to identify the countries that invest the most in cybersecurity and the most prominent organizations in cybersecurity worldwide. Methodology, in this research, 37 documents have been selected, carried out in the period 2018 – 2024; including: scientific articles, review articles and information from websites of recognized organizations. Results, AI and cybersecurity are two very important aspects today, so it is necessary to study them in depth; cybersecurity is a very important issue for governments and organizations worldwide, which is why many efforts are made to successfully fight cyberattacks; artificial intelligence is being used in various fields of human activity, so it is necessary to evaluate its present and future impact; artificial intelligence has an important impact on cybersecurity, which is why various authors focus on studying their interrelationship. Conclusions, about the general objective of the research, to determine the advances related to the cybersecurity and artificial intelligence (AI). Advances in cryptographic and Artificial Intelligence (AI) techniques, advanced AI methods, data representation, adoption of AI-based cybersecurity, biometric authentication, advanced artificial intelligence (AI), and machine learning (ML), Big Data Analytics, an in-depth learning algorithm for training a neural network for detecting suspicious user activities. About the first specific objective of the research, to identify the countries that invest the most in cybersecurity. The 3 countries that invest the most in cybersecurity are: United States, China and United Kingdom. The 3 countries where organizations worldwide that have made adequate cybersecurity investments according to board members as of June 2023 are: Singapore, Brazil and Australia. About the second specific objective, the most prominent organizations in cybersecurity worldwide. Palo Alto Networks, Fortinet and Crowdstrike are the most important companies in cybersecurity worldwide 2022, by market capitalization Apr 4, 2024.South Florida Journal of Development340The specific objectives of the research are to identify the countries that invest the most in cybersecurity and the most prominent organizations in cybersecurity worldwide and to determine the advances related to the cybersecurity and artificial intelligence (AI).South Florida Journal of Development["Carlos Rios-Campos", "Sonia Carmina Venegas Paz", "Gonzalo Orozco Vilema", "Luisa Maylleng Robles D\u00edaz", "Diana Patricia Flores Zambrano", "Gabriela Maribel Mendoza Zambrano", "Jessica Del Consuelo Luzuriaga Viteri", "Flor Elizabeth Obreg\u00f3n Vara", "Patricia Abigail Alejandr\u00eda Vallejos", "Rosa Felicita Gonz\u00e1les Llontop", "Oscar Anchundia-G\u00f3mez"]2024-08-21T00:00:00 2df3d3b81b793088cbe6d8128c05246dfc3e736eWith the rapid development of artificial intelligence technology, its application in the field of international trade has gradually become a hot spot for research. The purpose of this paper is to analyze in depth the prospects for the application of AI technology in international trade and to explore the opportunities it brings to international trade as well as the challenges it faces. Firstly, this paper reviews the development status of international trade and the latest progress in artificial intelligence technology to provide a background for the study. Subsequently, through the construction of a theoretical framework and the application of an integrated research methodology, the specific applications of AI in international trade are analyzed in detail, including smart contracts, data analytics, automated processes, and its role in logistics and supply chain management. Further, the paper discusses the positive impacts of AI technology applications, such as improved transaction efficiency, cost reduction, and enhanced market forecasting capabilities, while also pointing out the accompanying challenges, such as technological barriers, adaptation of laws and regulations, talent requirements, and data security. Through case studies, the paper demonstrates the effects and lessons learned from the application of AI technology in actual international trade. Finally, the paper summarizes the research findings, makes recommendations for future applications of AI technology in international trade, and explores directions for subsequent research. This study aims to provide some insights into the application of AI technology in the field of international trade as well as usable references for future development.Transactions on Economics, Business and Management Research150The positive impacts of AI technology applications, such as improved transaction efficiency, cost reduction, and enhanced market forecasting capabilities, while also pointing out the accompanying challenges, such as technological barriers, adaptation of laws and regulations, talent requirements, and data security are discussed.Transactions on Economics, Business and Management Research["Penghua Li"]2024-08-21T00:00:00 973486812dc864b21c9bf1218177abe78ffb4a2cArtificial intelligence promises to change the world as we know it, but according to the evidence and the Gartner curve, all technology passes for different stages until it gets the productivity plate which is much less than the initial expectative. Artificial Intelligence has a number of applications in all the fields of knowledge, and it is applied to education since some years ago. Our inters of artificial intelligence is beyond education in general, it is focused on engineering education. This work shows results of a bibliometric analysis enabled for artificial intelligence applied to education engineering, first as a worldwide approach, then `it is focused on the academic production of Iberoamerican countries for the artificial intelligence applied to engineering education. Results show a huge opportunity in this area because there are not so many academic products, they only overcome the 100 publications by year just in 2021, also in the top 20% of producers appear 4 Iberoamerican countries such as Spain, Brazil, Mexico, and Portugal. This is very interesting to remark that Brazil and Mexico are in the top ten worldwide STEM graduates. It’s interesting to notice that while in the top 10 of STEM graduated there are 5 countries members of BRICS and just 3 countries members of G7, the top 10 of countries which produces more academic products related to the artificial intelligence to engineering education are 4 countries members of BRICS and just 3 countries members of G7, tipping the balance in favor of the BRICS. Trends indicate that more than 1,000 products over this topic will be published annually for 2030.2024 IEEE Colombian Conference on Communications and Computing (COLCOM)100This work shows results of a bibliometric analysis enabled for artificial intelligence applied to education engineering, first as a worldwide approach, then `it is focused on the academic production of Iberoamerican countries for the artificial intelligence applied to engineering education.2024 IEEE Colombian Conference on Communications and Computing (COLCOM)["Jose-Ignacio Castillo-Velazquez", "R. Silva-L\u00f3pez"]2024-08-21T00:00:00 5cb55b01f38f520db9a5acaf10865eb06bf602d0With the deep integration of intelligent technology and government management scenarios, the management functions of modern government have also undergone changes, and artificial intelligence has gradually penetrated into the modernization process of government management. Artificial intelligence not only accelerates the transformation and innovation of government management models, but also impacts government regulation, triggers information security risks, and brings legal and ethical issues to the government. The article will analyze from four aspects: achieving intelligent control, strengthening joint governance, conducting credit supervision, and accelerating regulatory mechanism reform, providing a basis for the government to choose a path to improve regulatory efficiency in the intelligent era.The Frontiers of Society, Science and Technology60The article will analyze from four aspects: achieving intelligent control, strengthening joint governance, conducting credit supervision, and accelerating regulatory mechanism reform, providing a basis for the government to choose a path to improve regulatory efficiency in the intelligent era.The Frontiers of Society, Science and Technology["Qian Liu"]2024-08-21T00:00:00 bfebcb9528bf0f3a70b105adaa6cff688f5c1653The aim of this research is to analyze the influence of artificial intelligence (AI) on changes in people's communication behavior. The research method used is qualitative with a descriptive approach which includes library research, observation and document review. The research results show that artificial intelligence has a significant and complex impact on human communication behavior. The positive impacts of artificial intelligence include increasing communication efficiency with the help of chatbots and virtual assistants that speed up responses and increase user satisfaction. Additionally, artificial intelligence allows for more effective personalization of messages, which is especially useful in digital marketing. But the research also uncovers ethical and data protection issues surrounding the use of artificial intelligence, including the risks of personal data thickness and algorithmic bias, which can amplify social disempowerment. Another negative impact is the decline in people's ability to communicate and excessive dependence on technology, which can lead to bad behavior such as cyberbullying and the spread of fake news. Therefore, it is important to understand the long-term impact of AI on human interactions and determine strategies to maximize the benefits and minimize the associated risks.VISA: Journal of Vision and Ideas50The research results show that artificial intelligence has a significant and complex impact on human communication behavior, with positive impacts of artificial intelligence including increasing communication efficiency with the help of chatbots and virtual assistants that speed up responses and increase user satisfaction.VISA: Journal of Vision and Ideas["Hilda Rahmadani Harahap", "Tetti Saidah Siregar", "Shilfy Zehana Aqilah Sinaga", "Nadya Feriska Siregar", "Elvi Sahara", "Muhammad Aziz Mahyuti"]2024-08-21T00:00:00 047cdf35b9b44e5bc6bc7d8735443ebb2870cf70The metaverse is a three-dimensional digital space that integrates reality and virtuality, and its content generation technology is crucial for enhancing user experience. This article delves into the application of Artificial Intelligence Generated Content (AIGC) technology in the metaverse and how to build an efficient and innovative content generation system. The article provides a detailed analysis of the key technologies for metaverse content generation from multiple dimensions, including intelligent collection, rendering, encoding, auditing, driving, generation, and editing. These technologies not only improve the efficiency and quality of content production, but also promote the development of real-time collaboration among multiple people and end-to-end cloud integrated rendering modes, bringing users a richer and more immersive experience.2024 IEEE 12th International Conference on Information, Communication and Networks (ICICN)200The article provides a detailed analysis of the key technologies for metaverse content generation from multiple dimensions, including intelligent collection, rendering, encoding, auditing, driving, generation, and editing.2024 IEEE 12th International Conference on Information, Communication and Networks (ICICN)["Ronghua Tang"]2024-08-21T00:00:00 cd0bf3f81dfb2c310dfd1b4f388abbb3a1e5537bThis article intends to argue on the importance of OODA loop framework for analysing the opportunities and challenges offered by Artificial Intelligence in the Decision-Making process. The article has re-visited the OODA loop conceptual framework as it was given out by John Boyd for four phases of actions to any military situation i.e. Observe-Orient-Decide-Act and linkage has been established of each phase with the relevant military domain of warfare. In the subsequent analysis the article intends to re-evaluate the impact of Artificial Intelligence (AI) on the OODA framework to highlight the relevance of each phase with technology infusion and changes thereof in Physical, Information and Cognitive domains. In the conclusive part of article, it is will be established that OODA framework continues to be relevant even today, however, its application has changed when viewed through the prism of AI and modern analytical tools.International Journal For Multidisciplinary Research60It is established that OODA framework continues to be relevant even today, however, its application has changed when viewed through the prism of AI and modern analytical tools.International Journal For Multidisciplinary Research["Varun Sehgal"]2024-08-21T00:00:00 fbbaf74779517d34ab4d2dc01cc55e6080b7beb4This study delves into the role and impact of ChatGPT in education, exploring its benefits and challenges. The PRISMA model was employed to select pertinent articles, resulting in an in-depth review of 30 studies from an initial pool of 238 identified documents. The advantages of ChatGPT encompass immediate access to information, personalized learning experiences, and continuous support. However, concerns have surfaced regarding academic integrity, potential dependency, and content accuracy. Furthermore, there is an emphasis on adapting assessment methodologies and reinforcing teacher training programs. A significant observation from the reviewed literature is the lack of student perspectives, underscoring the need for broader inclusion of these voices in future research endeavors. This work serves as a foundational understanding of the evolving educational landscape in the era of artificial intelligence. The detailed analysis reveals that ChatGPT offers a range of significant opportunities in educational settings by facilitating rapid access to up-to-date information and providing personalized learning experiences. Nevertheless, there is a critical need to address challenges, such as safeguarding academic integrity and ensuring the accuracy of the system-generated content. Additionally, emphasis is placed on developing adaptive assessment strategies that accurately reflect the contribution of tools like ChatGPT to the learning process. A limitation identified in existing research is the scant representation of the student perspective, highlighting the urgency to include and better comprehend their experiences and perceptions when utilizing this technology in educational environments. In summary, this study enriches understanding of the impacts, opportunities, and challenges of integrating ChatGPT into education. Its comprehensive analysis provides a robust foundation for future research endeavors that address artificial intelligence’s practical and ethical implementation in the educational domain.2024 IEEE Colombian Conference on Communications and Computing (COLCOM)300Understanding of the impacts, opportunities, and challenges of integrating ChatGPT into education is enriched, providing a robust foundation for future research endeavors that address artificial intelligence’s practical and ethical implementation in the educational domain.2024 IEEE Colombian Conference on Communications and Computing (COLCOM)["Miguel A. Quiroz-Mart\u00ednez", "Dylan-Sebastian Tumaille-Quintana", "Alexis-Dustin Moran-Burgos", "M\u00f3nica-Daniela G\u00f3mez-Rios"]2024-08-21T00:00:00 aa12d89e0fe68027f0f21374b9824702ee9e0895Artificial intelligence (AI) is a rapidly advancing technology in our society. The emergency radiology is an area facing an increase of the number of imaging studies and associated to the necessity to promptly deliver an accurate interpretation. The integration of AI algorithms to assist the clinician in providing analyses of the imaging studies while maintaining adequate diagnostic quality opens up new perspectives. There are numerous potential advantages of the implementation of AI in emergency radiology. However, the use of AI faces new challenges, as the algorithms reliability, data security, responsibility issues, and financial, human and material resources.Revue medicale suisse00The integration of AI algorithms to assist the clinician in providing analyses of the imaging studies while maintaining adequate diagnostic quality opens up new perspectives.Revue medicale suisse["Pauline Kapustin", "D. Eidenbenz", "V. Darioli"]2024-08-21T00:00:00 c4457478b7e9a07f6b354fa6b812e2b7a9ef3645Scientific Reports461More optimal lip lines, straighter smile arcs and more ideal tooth display were achieved in actual post treatment results in comparison to the initially predicted smiles.Scientific Reports["Samar M. Adel", "Yashodhan M. Bichu", "Srirengalakshmi Muthuswamy Pandian", "Waddah Sabouni", "Chandani Shah", "Nikhillesh Vaiid"]2024-08-21T00:00:00 d218556b4abd7047701c08a444addc2510f90ccaAI &amp; SOCIETY301AI &amp; SOCIETY["John R. Gallagher", "Rebecca E. Avgoustopoulos", "Antonio Hamilton", "Togzhan Seilkhanova"]2024-08-21T00:00:00 bfe23c2814d03c02ae570e97a47f21c8ff9d5d75Current Opinion in Psychology362It is argued that while GenAI presents profound opportunities, its integration must be approached cautiously using robust ethical frameworks, and its integration must be approached cautiously using robust ethical frameworks.Current opinion in psychology["Mohammed Salah", "Fadi Abdelfattah", "Hussam Al Halbusi"]2024-08-21T00:00:00 60ea1386330867011646d43beb507649bd9729abThe application of AI technology in urban planning covers multiple levels, such as data analysis, decision support, and automated planning. Urban research relies on AI technology to understand and summarize the law of urban growth and improve the analysis of the evolution trend of urban space. Planning and design use AI technology to explore the relevant factors affecting urban development and their weights and discuss the critical role of green building technology in the sustainable development of the construction industry. With the increase in global energy consumption and carbon emissions, traditional building methods can no longer meet environmental protection requirements and efficient use of resources. As a sustainable development solution, green building technology has been paid more and more attention to and adopted by people. These technologies focus not only on the energy efficiency and environmental impact of buildings but also on the resource utilization and environmental load of green buildings over their entire life cycle driven by machine learning. This paper details the basic principles and applications of green building technologies, including AI-driven reduction of negative environmental impacts, improvement of occupant health, efficient use of resources, and optimization of indoor environmental quality. This paper focuses on the critical role of the LEED assessment system developed by the U.S. Green Building Council in advancing green building practices. In addition, the paper analyzes vital points such as water use in green building design, machine learning-driven wind environment optimization, solar technology application, and practical application cases of these technologies on a global scale.Scientific Journal of Technology252The basic principles and applications of green building technologies, including AI-driven reduction of negative environmental impacts, improvement of occupant health, efficient use of resources, and optimization of indoor environmental quality are detailed.Scientific Journal of Technology["Minyue Ge", "Zhang Feng", "Qian Meng"]2024-08-21T00:00:00 050fb17ec9b77e3f8683ed69fc5176e25d08d303This study examines the relationships between strategic communication, personalized media content, AI, and consumer service efficiency in social marketing companies in Saudi Arabia. The study used a cluster sampling technique with a quantitative research design. The study targeted 498 responses via distributing the survey links on social media platforms. Using the SEM analysis in Smart PLS 4, this research tested the research hypotheses. The findings showed that strategic communication significantly improves personalized media content and consumer service efficiency, confirming its importance in business customer interactions and outcomes. Customized media content does not significantly improve consumer service efficiency, suggesting other mediating factors may be involved. AI mediates this relationship, bridging strategic inputs and service outcomes. AI boosts strategic communication and personalized content, improving consumer service efficiency. The results showed that AI fully mediates strategic communication and personalized media content into improved service efficiency, demonstrating its transformative potential in business communications and operations. The study shows that AI supports and improves digital marketing communication strategies. It is statistical evidence and confidence intervals that exclude zero, AI-enabled the application of personalized content and strategic directives to improve service efficiency in the mediation analysis.Journalism and Media641The results showed that AI fully mediates strategic communication and personalized media content into improved service efficiency, demonstrating its transformative potential in business communications and operations.Journalism and Media["Saud Binlibdah"]2024-08-21T00:00:00 3ee1aad2430e18041bbf9a01c788989a7d28b128Public Money &amp; Management241Public Money &amp; Management["H. Margetts", "Patrick Dunleavy"]2024-08-21T00:00:00 4b5a351d7ee86826a154d1c1330b381acbd4196cDiscover Global Society92Discover Global Society["Md. Abdus Shabur"]2024-08-21T00:00:00 6baa482ef8ed009bd58322bebc993f8ee8f83480This study explores the challenges faced by Islamic Education students at Universitas Negeri Padang in using AI-based websites to complete academic assignments. Using a qualitative method with a case study approach, data was collected through interviews, observations, and documentation, then analyzed to ensure validity. The findings indicate that websites such as ChatGPT, Perplexity, and Gemini.ai are used to answer academic questions, find scientific references, and analyze images and texts. However, challenges such as AI's limited understanding of religious studies, dependence on technology, and information reliability remain significant obstacles. Students use AI for structured assignments by obtaining topic explanations and scientific references, as well as for independent tasks such as studying lecture materials, creating presentations, translating languages, and answering discussion questions. While AI use has proven beneficial, the existing challenges need to be addressed to maximize its potential.ALSYS00The findings indicate that websites such as ChatGPT, Perplexity, and Gemini.ai are used to answer academic questions, find scientific references, and analyze images and texts, but challenges such as AI's limited understanding of religious studies, dependence on technology, and information reliability remain significant obstacles.ALSYS["Muhamad Hadli Alfurqon", "Wirdati Wirdati"]2024-08-21T00:00:00 e1408f2b0d7b5d7b533923a90202112199132346Revista Ibero-Americana de Estudos em Educação00Revista Ibero-Americana de Estudos em Educação["Lipselotte de Jes\u00fas Infante Rivera", "Mar\u00eda Nelly Castillo Rodr\u00edguez", "Giancarlo Fernando Meza Terbullino", "Fernando Viterbo Sinche Crispin"]2024-08-21T00:00:00 fe0de94850e9c459702c5524efae16b916f8ed13Therapeutic Innovation and Regulatory Science240The paper aims at stimulating discussions on the role such analyses can play in general rather than advocating for a particular AI/ML-method or indication where such methods could be meaningful.Therapeutic innovation & regulatory science["Karl K\u00f6chert", "T. Friede", "Michael Kunz", "Herbert Pang", "Yijie Zhou", "Elena Rantou"]2024-08-21T00:00:00 dbabcf30dfb7f5da2f5c46175140042cded83d32In today's dynamic business environment, sustainable supply chain management (SSCM) is emerging as a critical factor for organizations aiming to balance profitability with environmental responsibility. This study delves into integrating artificial intelligence (AI) technologies to optimize sustainable supply chains and foster environmentally conscious decision-making processes. The research demonstrates their capability to accurately predict supplier and consumer categories by applying advanced machine learning techniques, specifically Random Forest and Neural Networks. The AI-driven models exhibited superior performance compared to conventional methods, emphasizing their potential to enhance supply chain efficiency while minimizing environmental impact. The findings indicate that AI can be pivotal in revolutionizing supply chain operations by providing actionable insights, optimizing resource allocation, and reducing carbon footprint. As businesses worldwide face increasing pressure to adopt sustainable practices, integrating AI in supply chain management offers a promising pathway to drive eco-friendly initiatives, improve operational efficiency, and meet stakeholder expectations for environmental stewardship.Journal of Economics, Finance and Accounting Studies134Journal of Economics, Finance and Accounting Studies["MD Rokibul Hasan", "Reza E Rabbi", "Arifur Rahman", "Abdullah Al Mukaddim", "MD Azam Khan", "Mohammad Abir Hider", "MD Abdul Fahim Zeeshan"]2024-08-21T00:00:00 5779e8776754ccfec906bfa3e0eb3a1ad71cd0baIn the face of growing urban problems such as overcrowding and pollution, we urgently need innovative ideas to build smarter and greener cities. Current urban development strategies often fail to address these challenges, revealing a significant research gap in integrating advanced technologies. This study addresses these gaps by integrating green technologies and artificial intelligence (AI), studying its impact on achieving smart and sustainable habitats and identifying barriers to effective use of these technologies, considering local variations in infrastructural, cultural, and economic contexts. By analyzing how AI and green technologies can be combined, this study aims to provide a vision that can be used to improve urban development planning. The results emphasize the significance of environmental responsibility and technological innovation in the development of sustainable urban environments and provide practical recommendations for improving the overall quality of life in cities through planning and urban planning.Journal of Infrastructure Policy and Development192By analyzing how AI and green technologies can be combined, this study aims to provide a vision that can be used to improve urban development planning and provide practical recommendations for improving the overall quality of life in cities through planning and urban planning.Journal of Infrastructure, Policy and Development["Khaoula Khlie", "Zoubida Benmamoun"]2024-08-21T00:00:00 20f720da87eb023ec39315d440d260eeddc98469Artificial Intelligence (AI) has revolutionized many fields, with web development being a notable example. This paper explores the transformative impact of AI technologies on web development, examining how AI tools enhance design, streamline coding, and improve user experiences. We analyse the application of AI in automating repetitive tasks, personalizing content, and optimizing website performance. The study utilizes a review of current literature and case studies to highlight key advancements and emerging trends. Our findings suggest that AI not only accelerates development processes but also introduces new paradigms for creating dynamic and adaptive web applications. This paper concludes by discussing the implications of AI advancements for the future of web development and potential areas for further research.International Journal of Scientific Research in Modern Science and Technology01This paper examines the transformative impact of AI technologies on web development, examining how AI tools enhance design, streamline coding, and improve user experiences and suggests that AI not only accelerates development processes but also introduces new paradigms for creating dynamic and adaptive web applications.International Journal of Scientific Research in Modern Science and Technology["Sonali Suryakant Jadhav", "Sonali Sagar Gholve"]2024-08-21T00:00:00 afe3c220cc3ca97dde4b86cd746d8d2a2d11a7c8The convergence of emerging technologies like cloud computing, artificial intelligence (AI), and 5G is catalysing the Fourth Industrial Revolution and driving a paradigm shift toward the rapidly growing digital platform economy. However, the complex coopetition dynamics among AI‐powered and data‐driven digital platforms challenge traditional resource‐based theories in explaining this phenomenon. This multiple case study investigates the coopetitive tactics adopted by digital platform companies when navigating different coopetition situations through the lens of data and AI resources. Eight propositions are developed linking the allocation and application tendencies of data resources to the coopetitive tactics employed by platforms across four coopetition situations. The findings reveal how the unique attributes of data and AI resources influence digital platforms' coopetitive strategy implementation in complex network environments and how companies can strategically combine cooperation and competition when leveraging AI technologies and big data resources on digital platforms.Systems research and behavioral science481How the unique attributes of data and AI resources influence digital platforms' coopetitive strategy implementation in complex network environments and how companies can strategically combine cooperation and competition when leveraging AI technologies and big data resources on digital platforms are revealed.Systems Research and Behavioral Science["Qiang Ma", "Hong Chen", "Shuo Tian", "Huishuang Su", "Wei Zhong", "Ying Wang"]2024-08-21T00:00:00 713e1c4c4a475faa1c1216a84a97bf31377499ccWith the rapid development of artificial intelligence (AI) technology, mobile education products are playing an increasingly important role in the education industry. In the field of mobile learning, through AI technology and personalized learning models, products such as Baicizhan and "Liulishuo" have achieved success, providing students with a more flexible and convenient learning method. This article takes China's online English education brand "English Liulishuo" as the research object and explores its marketing strategy through the SWOT analysis method. The article pointed out that with the rise of mobile education products, Liulishuo, as a representative company, has achieved success in the market, but it also faces challenges such as revenue losses and negative news. Through SWOT analysis, it was found that Liulishuo's advantages outweighed its disadvantages, but there was still room for improvement. Companies are advised to exploit global English learning demand, technological advancements, and collaborations to develop new markets. The study emphasizes the importance of SWOT analysis, fills the relevant research gaps, and is of great significance to the development of similar platforms. Finally, the article points out that the understanding of operational data needs to be deepened to more comprehensively evaluate the effectiveness of brand promotion. SWOT analysis provides enterprises with scientific evaluation and strategic selection methods, helping enterprises to succeed in the fiercely competitive market.Transactions on Economics, Business and Management Research90It was found that Liulishuo's advantages outweighed its disadvantages, but there was still room for improvement in the marketing strategy, and the importance of SWOT analysis was emphasized.Transactions on Economics, Business and Management Research["Shupeng Xu"]2024-08-21T00:00:00 84e33a348a9f5169e55f2c295468649a8409d577Antibodies are microscopic defenders in our body’s immune system, protecting us against foreign pathogens. These specialized protein molecules, shaped like the letter Y are produced by plasma cells and possess the ability to precisely locate and bind to specific antigens, inactivating harmful substances like toxins and facilitating the destruction or neutralization of pathogens. The remarkable diversity of antibodies, generated by immune systems’ adaptability often referred to as immune repertoire or a condition of genetic variations, allows the immune system to respond to a vast array of potential threats. Recent advancements in artificial intelligence have opened new doors in many fields including medicine. By harnessing machine learning algorithms, generative AI models can be trained to design ground-breaking antibody structures with selected traits from existing data and knowledge. This approach can significantly accelerate the antibody discovery process, leading to the ushering era of smart medicine. This paper aims to explore how generative AI is being utilized to design new antibodies. We explore how this technology could potentially streamline the traditionally lengthy process of developing new antibodies through physical enumeration. We aim to shed light on a promising frontier in drug discovery and synthesis. Our discussion encompasses both the potential benefits and the challenges of this emerging approachAdvances in Computer Science and Technology460How generative AI is being utilized to design new antibodies is explored to explore how this technology could potentially streamline the traditionally lengthy process of developing new antibodies through physical enumeration and shed light on a promising frontier in drug discovery and synthesis.Advances in Computational Sciences and Technology["Mondru Anil Kumar", "Anabathula Thanay Sisir"]2024-08-21T00:00:00 90a3410bc1632ba3340de7259186c66ba03f1668Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation and evaluation of human learning. Here the authors examine the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology and human-computer interaction. GenAI promises to enhance learning experiences by scaling personalized support, diversifying learning materials, enabling timely feedback and innovating assessment methods. However, it also presents critical issues such as model imperfections, ethical dilemmas and the disruption of traditional assessments. Thus, cultivating AI literacy and adaptive skills is imperative for facilitating informed engagement with GenAI technologies. Rigorous research across learning contexts is essential to evaluate GenAI's effect on human cognition, metacognition and creativity. Humanity must learn with and about GenAI, ensuring that it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.Nature Human Behaviour11220The authors examine the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology and human-computer interaction.Nature human behaviour["Lixiang Yan", "Samuel Greiff", "Ziwen Teuber", "D. Ga\u0161evi\u0107"]2024-08-22T00:00:00 81a4e98ddc1892f447c956ab33a296f6dd7f9f47Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.Electronics496This study systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability.Electronics["Izabela Rojek", "Dariusz Miko\u0142ajewski", "A. Mrozi\u0144ski", "M. Macko"]2024-08-22T00:00:00 a62c2570ab5c912f711cb68ba2430ef6f0c32b82This commentary explores the critical roles of health equity and ethical considerations in the deployment of artificial intelligence (AI) in public health and medicine. As AI increasingly permeates these fields, it promises substantial benefits but also poses risks that could exacerbate existing disparities and ethical challenges. This commentary delves into the current integration of AI technologies, underscores the importance of ethical social responsibility, and discusses the implications for practice and policy. Recommendations are provided to ensure AI advancements are leveraged responsibly, promoting equitable health outcomes and adhering to rigorous ethical standards across all populations.Preventing Chronic Disease196Recommendations are provided to ensure AI advancements are leveraged responsibly, promoting equitable health outcomes and adhering to rigorous ethical standards across all populations.Preventing Chronic Disease["Irene Dankwa-Mullan"]2024-08-22T00:00:00 36ee33fb4d18cbc5c7bf5f559de07bcecd89f7b1Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.Gut1014An overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies, highlights the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights.Gut["Soumita Ghosh", "Xun Zhao", "Mouaid Alim", "Michael Brudno", "M. Bhat"]2024-08-22T00:00:00 4850de3571f6c1d1b4c16bc5e1bb03993480fc90Artificial intelligence (AI) is the driving force for the leapfrog development of science and technology, the optimization and upgrading of industry, as well as the overall leap in productivity. Using panel data of strategic emerging firms in Chinese A-Share Listed companies from 2012 to 2022, this study empirically examines the impact of AI on technological innovation through a two-way fixed-effects model. The study discovered that technological innovation capability can be greatly enhanced by the degree of AI present in strategic emerging industry businesses. This conclusion remains valid following a series of robustness tests. The mechanism study demonstrates how the degree of AI increases businesses’ capacity for technological innovation by lowering funding constraints and boosting R&D investment. According to heterogeneity analysis, AI has varying empowering effects on different industries within strategic emerging industries. Its strongest empowering effect is observed in the western region, with the central and eastern regions seeing the weakest effects. Additionally, the promotion effect of AI is greater for state-owned enterprises than for non-state-owned enterprises. To better play the role of AI in encouraging the technical innovation of firms in strategic emerging industries, it is required to establish dedicated funds, create an AI technology innovation platform, and develop differentiated regulations.Sustainability673It is discovered that technological innovation capability can be greatly enhanced by the degree of AI present in strategic emerging industry businesses, and this conclusion remains valid following a series of robustness tests.Sustainability["Daojun Li", "Haiqin Wang", "Juan Wang"]2024-08-22T00:00:00 dac23f32a00c5b544a53d159ab5dcdaeebc0bc38Within the changing landscape in education, the interjection of Artificial Intelligence (AI) and tools such as ChatGPT have also brought about pressing concerns over the safeguarding of academic integrity. This paper discusses the strategies that can be employed to foster an atmosphere of academic honesty within an age when AI capabilities are a threat to educational norms. While AI massively comes in handy to create content, there is a need to design new policies in academics against possible usage misapplications and ensure ethics in student practices. The review takes into account the challenges which educators are likely to face, trying to detect AI-generated work and undermining the critical thinking of minds. Moreover, it draws attention to the creation of firm educational frameworks in which students will learn AI literacy and the kernel of ethics for using such technologies. The paper calls for the instigation of comprehensive policies that deter academic dishonesty but at the same time inculcate a culture of integrity through transparency and accountability. It also points out future directions of research and policy development that need to be undertaken in this regard, including the fact that educational institutions, technology developers, and policymakers must collaborate in the creation of effective safeguards. In the light of increasing development in AI, it is urged that educational strategies will update in order to maintain academic standards and create an environment in which ethical considerations are integral to the process of learning.Journal of ELT Studies23The paper calls for the instigation of comprehensive policies that deter academic dishonesty but at the same time inculcate a culture of integrity through transparency and accountability.Journal of ELT Studies["N. Rane", "Shweta R Shirke", "Saurabh P. Choudhary", "Jayesh Rane"]2024-08-22T00:00:00 28e4f4e9becfeb6a915fd483b80cad1a0097b50dIn an urban context, the use of artificial intelligence (AI) can help to categorise and analyse large amounts of data quickly and efficiently. The AI approach can make municipal administration and planning processes more efficient, improve environmental and living conditions (e.g., air quality, inventory of road damages, etc.), or strengthen the participation of residents in decision-making processes. The key to this is “machine learning” that has the ability to recognise patterns, capture models, and learn on the basis of big data via the application of automated statistical methods. However, what does this mean for urban planning and the future development of cities? Will AI take over the planning and design of our cities and actively intervene in and influence planning activities? This article applies a systematic literature review supplemented by case study analyses and expert interviews to categorise various types of AI and relate their potential applications to the different phases of the planning process. The findings emphasize that AI systems are highly specialised applications for solving and processing specific challenges and tasks within a planning process. This can improve planning processes and results, but ultimately AI only suggests alternatives and possible solutions. Thus, AI has to be regarded as a planning tool rather than the planning solution. Ultimately, it is the planners who have to make decisions about the future development of cities, taking into account the possibilities and limitations of the AI applications that have been used in the planning process.Urban Planning621A systematic literature review supplemented by case study analyses and expert interviews is applied to categorise various types of AI and relate their potential applications to the different phases of the planning process to emphasize that AI systems are highly specialised applications for solving and processing specific challenges and tasks within a planning process.Urban Planning["Frank Othengrafen", "Lars Sievers", "Eva Reinecke"]2024-08-22T00:00:00 2429dcbe18a6bdf4c451bcc16977ca77b45e6268The rapid advancement of Artificial Intelligence (AI) in recent years has precipitated transformative changes across various industries, with Supply Chain (SC) management being no exception. Given the swift progression and the advent of innovations such as ChatGPT, it becomes imperative to delineate both the historical and recent academic contributions to this field, thereby facilitating a comprehensive understanding of future trajectories and the potential impact of these technologies. Consequently, we conducted a scientometric review utilizing the Scopus and Web of Science databases, with data preprocessing executed via R and Python. The resultant findings are bifurcated into two sections: the first encompasses a scientometric mapping of annual scientific production, country-specific contributions, journal publications and author collaboration analysis. The second section delineates the evolution of theoretical contributions, employing the metaphor of the Tree of Science for illustrative purposes. The conclusions underscore the paradigm-shifting impact of AI on SC management.Journal of Scientometric Research901A scientometric review utilizing the Scopus and Web of Science databases, with data preprocessing executed via R and Python underscores the paradigm-shifting impact of AI on SC management.Journal of Scientometric Research["Luis Oswaldo Rodr\u00edguez Ma\u00f1ay", "Orlando Valencia Rodr\u00edguez", "Jhon Antuny Pabon Le\u00f3n", "Jos\u00e9 Alexander Dur\u00e1n P\u00e9rez"]2024-08-22T00:00:00 d7b254cd9f062ee57d3259a697a7f2485b56baabThe integration of Artificial Intelligence (AI) in healthcare signifies a substantial shift, offering benefits to patients and healthcare systems while also introducing new risks. The emphasis on patient safety and performance standards is pivotal, especially with the European Union's strides towards regulating AI through the AI Act. This act focuses on classifying AI systems based on risk levels, mandating stringent requirements for high-risk AI, enhancing transparency, and ensuring ethics in AI applications. The concept of an "AI passport" is introduced as a living document detailing the AI system's purpose, ethical declarations, training, evaluation, and potential biases. This passport aims to enhance transparency and safety in medical AI applications, serving as a comprehensive record for patients, clinicians, and stakeholders. The AI passport, structured in JSON format, encapsulates key information about the AI system as a mechanism for continuous performance evaluation and transparency. This initiative may represent a significant step towards mitigating the risks associated with AI in healthcare, emphasizing the importance of accountability, transparency, and patient safety in the development and application of AI technologies.Medical Informatics Europe00The concept of an "AI passport" is introduced as a living document detailing the AI system's purpose, ethical declarations, training, evaluation, and potential biases, serving as a comprehensive record for patients, clinicians, and stakeholders.Studies in health technology and informatics["Juan M Garc\u00eda-G\u00f3mez", "V. Blanes-Selva", "A. Do\u00f1ate-Mart\u00ednez"]2024-08-22T00:00:00 98e4dc6949b6040de198eca4de719b6a6da80fbbBariatric surgery has become a leading intervention for severe obesity, markedly improving weight loss, resolving comorbidities, and enhancing quality of life. However, the complexity of these procedures necessitates comprehensive patient management to optimize outcomes. Recent artificial intelligence (AI) advancements present innovative solutions that can significantly transform bariatric surgery. This narrative review examines the potential of AI to enhance patient outcomes, improve surgical precision, and streamline postoperative care, highlighting its role in revolutionizing bariatric practices and addressing the challenges faced in this field.Revista Ibero-Americana de Humanidades, Ciências e Educação110A narrative review examines the potential of AI to enhance patient outcomes, improve surgical precision, and streamline postoperative care, highlighting its role in revolutionizing bariatric practices and addressing the challenges faced in this field.Revista Ibero-Americana de Humanidades, Ciências e Educação["N\u00e1dia Oliveira Cabral", "Rodolpho Bicalho Bento", "Ana Julia Nassar Barreto", "Afr\u00e2nio C\u00f4go Destefani", "Vin\u00edcius C\u00f4go Destefani"]2024-08-22T00:00:00 17f6c9e1810252350b6b97b7ea6687994c492af1Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Active participation of end-users in the development and implementation of AI can enhance acceptance. This study proposes a scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers. It employs the qualitative-analytical COARSE method for scale development and evaluation. The instrument includes 11 items across two sub-scales: activities for active participation of end-users and empowerment activities. It highlights the importance of the measurement's purpose and consequences for interpreting the results of the evaluated degree of end-user participation. The study points to future research opportunities, underscored by the need for psychometric validation, such as reliability and validity.Medical Informatics Europe00A scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers is proposed, employing the qualitative-analytical COARSE method for scale development and evaluation.Studies in health technology and informatics["R. Gubser", "A. Poncette", "Daniel F\u00fcrstenau"]2024-08-22T00:00:00 2b0e4486e2e4f254372a35f215cce07ae42935efThis paper explores the critical role of Interoperability (IOP) in the integration of Artificial Intelligence (AI) for clinical applications. As AI gains prominence in medical analytics, its application in clinical practice faces challenges due to the lack of standardization in the medical sector. IOP, the ability of systems to exchange information seamlessly, emerges as a fundamental solution. Our paper discusses the indispensable nature of IOP throughout the Data Life Cycle, demonstrating how interoperable data can facilitate AI applications. The benefits of IOP encompass streamlined data entry for healthcare professionals, efficient data processing, enabling the sharing of data and algorithms for replication, and potentially increasing the significance of results obtained by medical data analytics via AI. Despite the challenges of IOP, its successful implementation promises substantial benefits for integrating AI into clinical practice, which could ultimately enhance patient outcomes and healthcare quality.Medical Informatics Europe00The indispensable nature of IOP throughout the Data Life Cycle is discussed, demonstrating how interoperable data can facilitate AI applications.Studies in health technology and informatics["Adam S L Graefe", "Miriam H\u00fcbner", "S. Thun"]2024-08-22T00:00:00 15bfaae7f6d0a6331156a5d6821938daa35de211Artificial Intelligence (AI) offers great promise for healthcare, but integrating it comes with challenges. Over-reliance on AI systems can lead to automation bias, necessitating human oversight. Ethical considerations, transparency, and collaboration between healthcare providers and AI developers are crucial. Pursuing ethical frameworks, bias mitigation techniques, and transparency measures is key to advancing AI's role in healthcare while upholding patient safety and quality care.Medical Informatics Europe00Pursuing ethical frameworks, bias mitigation techniques, and transparency measures is key to advancing AI's role in healthcare while upholding patient safety and quality care.Studies in health technology and informatics["Bakheet Aldosari", "Abdullah Alanazi"]2024-08-22T00:00:00 3ec021768106317be020808e2238b1533f391493This study explores the Role of Artificial Intelligence in Enhancing Risks in Media Marketing Campaigns in Saudi Arabian Marketing Companies. AI enhances marketing practices by improving audience targeting, analyzing large datasets to extract patterns and trends, and providing personalized content. However, it also poses significant challenges, including privacy issues, ethical concerns, and the risk of bias in algorithms. The study aims to identify the applications of AI in digital marketing, evaluate its impact on competitive advantages, and explore the risks associated with its use. It also recommends the effective and ethical implementation of AI technologies in marketing strategies. The research methodology involves data collection through surveys and previous studies, followed by statistical and qualitative data analysis. The findings indicate a high awareness and positive perception of AI's potential to improve marketing strategies and manage risks. However, they also highlight the need for strict data privacy policies and employee training on ethical AI use. The study concludes that AI can significantly enhance marketing effectiveness and security if used responsibly and with appropriate measures to mitigate risks.International Journal of Financial, Administrative, and Economic Sciences00The findings indicate a high awareness and positive perception of AI's potential to improve marketing strategies and manage risks, but they also highlight the need for strict data privacy policies and employee training on ethical AI use.International Journal of Financial, Administrative, and Economic Sciences["Abdullah Alnaim", "Al-Faisal Hassan", "Fayez Jarad"]2024-08-22T00:00:00 4dbee43a197b7bc261ba19f3a1223ab31b51662cThis study explores the deployment of Artificial Intelligence (AI) in Canadian hospitals from 2000 to 2021, focusing on metropolitan areas. We investigate how local public and private research ecosystems and links to national and international AI hubs influence the adoption of AI in healthcare. Our analysis shows that AI research outputs from public institutions have a significant impact on AI competences in hospitals. In addition, collaborations between hospitals are critical to the successful integration of AI. Metropolitan areas such as Toronto, Montreal, and Vancouver are leading the way in AI deployment. These findings highlight the importance of local AI research capabilities and international hospital collaborations and provide guidance to policy-makers and health leaders to drive the diffusion of AI technology in healthcare.Healthcare Management Forum70Analysis of the deployment of Artificial Intelligence in Canadian hospitals from 2000 to 2021 shows that AI research outputs from public institutions have a significant impact on AI competences in hospitals and collaborations between hospitals are critical to the successful integration of AI.Healthcare management forum["Pierre Pelletier", "Aldo Geuna", "Daniel Souza"]2024-08-22T00:00:00 5a32c518c3f17a97dd8dcc9ea29a64e6a0c57792Psychology has always been fascinated by the human mind, which is incredibly complex and elusive. However, its depths are still mostly unknown, and the scant information and arbitrary interpretations impede our comprehension. Today, artificial intelligence (AI) is a potent force that promises to shed light on these hidden intricacies. Massive amounts of data, such as speech patterns, facial expressions, and even physiological markers, may be analyzed with remarkable objectivity and precision by AI-powered algorithms. For those who are unable to access traditional treatment due to social or geographic constraints, AI-powered chatbots, and virtual therapists can provide round-the-clock support and guidance. The AI models, which mimic human perception, learning, memory, and decision-making, provide important insights into the complex mechanisms underlying the brain. This in-depth knowledge is essential for creating interventions and preventative strategies that are more successful for a wide range of mental health issues. To ensure appropriate and ethical deployment, it is imperative to strike a balance between the advantages of artificial intelligence and the indispensable warmth of human connection. The methodology comprised a comprehensive review of the literature on artificial intelligence applications in psychology, which includes books, reputable blogs, journals, newspaper articles, and articles. There is no denying its capacity to completely transform medical diagnosis, treatment, and our comprehension of the human mind. This research article tries to explore the many applications of AI in psychology and how it can transform our basic knowledge of the human psyche.Far Western Journal of Education120The many applications of AI in psychology are explored and how it can transform basic knowledge of the human psyche is explored to strike a balance between the advantages of artificial intelligence and the indispensable warmth of human connection.Far Western Journal of Education["Vishal Bajotra", "Nisha Rani"]2024-08-22T00:00:00 354722dbe2d065cda72e7d1a7015cbeb60a96526Artificial Intelligence (AI) has become a buzzword in education because of its potential to modify how we impart and acquire knowledge. AI has enormous potential including automating administrative chores, personalizing learning, and providing feedback in real-time. One important use of artificial intelligence in education is personalized learning. Analyzing student data to design individualised learning experiences for every learner is also possible by using AI. There are some ethical issues with AI integration in education that need to be resolved. The objectivity of AI systems can only be established by the data used to train systems based on AI. The possibility that AI will eventually replace human teachers raises further ethical questions. AI cannot take the role of human connection, which is necessary for effective teaching and learning even though it can offer personalized learning and real-time feedback. There are lot of promises for artificial intelligence for educational purposes, but at the same time there are also ethical issues that need to be addressed. Protecting student data privacy, ensuring that rather than replacing, AI is utilised to improve human teachers, and training AI systems on objective data are all crucial to ensuring that AI is utilized in education responsibly. The methodology consisted of a thorough evaluation of the literature, which included articles, journals, newspaper articles, authoritative blogs, and books on artificial intelligence applications in education. This paper intends to investigate both the possible benefits of learning with artificial intelligence (AI) in educational institutions and the moral dilemmas raised by its applications.Far Western Journal of Education260This paper intends to investigate both the possible benefits of learning with artificial intelligence (AI) in educational institutions and the moral dilemmas raised by its applications.Far Western Journal of Education["Manoj Kumar Saxena", "Vikram Bajotra"]2024-08-22T00:00:00 9430c30f1e1783097b8ab096c1c978d90ddda2c2Among the measures taken by UVVG to modernize the educational process is the pioneering undertaking regarding the introduction of a course on Artificial Intelligence in Medicine (AIM). Such an action has to face several challenges, at three levels, starting from its inclusion in the curricular vision of the university, its positioning in the didactic program as well as the content of the syllabus suitable for medical students. The first part presents the necessity and opportunity of introducing the course in the current context of the rapid growth of AIM applications. The second part refers to the concrete implementation of the introduction of the course in the curriculum, while the last and most developed part is dedicated to the preparation of the syllabus starting from the premises that the field is growing very fast and we should provide the basic knowledge to assure an easy understanding and a smooth assimilation of further developments.Medical Informatics Europe00The necessity and opportunity of introducing the course in the current context of the rapid growth of AIM applications are presented and the basic knowledge is provided to assure an easy understanding and a smooth assimilation of further developments.Studies in health technology and informatics["George I. Mihalas", "Casiana Boru", "C. Cotoraci"]2024-08-22T00:00:00 fda06beca5ab03bfd91eb8d8c3553fe4b482cee7Introduction. The impact of multidirectional external macroeconomic and regional factors of the economic environment in conditions of uncertainty and increased risks causes significant difficulties in diagnosing, assessing and forecasting the creditworthiness of financial and credit support recipients and borrowers (micro, small and medium-sized enterprises) in the Russian Federation. Theoretical analysis. The author systematized the basic mathematical methods and models for assessing and forecasting the level of creditworthiness of micro, small and medium-sized businesses in foreign and Russian practice, using modern systems and technologies of artificial intelligence and machine learning methods. Empirical analysis. The author proposed the results of approbation of methodological approach for express diagnostics of the financial and economic condition and forecasting the creditworthiness of SMEs in the Krasnodar krai for the period of 2014–2017, based on expert assessment methods, economic analysis and fuzzy logic systems, which form the credit rating of SMEs considering their industry affiliation. Results. In this study, the author has determined the advantages and disadvantages of methods and models for diagnosing creditworthiness and credit scoring from the perspective of their application for various categories of SMEs. As it is shown that the most promising and mathematically reliable models for credit scoring and risk assessment of financial support and lending to various enterprises in the SME sector at different stages of their life cycle both in Russia and abroad are computerized models and expert systems, based on such methods and technologies of Artificial Intelligence, as fuzzy logic systems, artificial neural networks, support vector machines, ensemble methods (random forest method), as well as intelligent information systems, hybrid models and hybrid systems. The study reveals that their combination with each other will allow to achieve synergistic and system effects in the interaction between lenders and borrowers (SMEs) and timely avoid their bankruptcy.Izvestiya of Saratov University. Economics. Management. Law00The study reveals that synergistic and system effects in the interaction between lenders and borrowers (SMEs) will allow to achieve synergistic and system effects in the interaction between lenders and borrowers (SMEs) and timely avoid their bankruptcy.Izvestiya of Saratov University. Economics. Management. Law["Victoria V. Zabolotskaya"]2024-08-22T00:00:00 bf5a8bfa09498b39a4d736c96ec5ac1e2791c80aThe neutrality thesis holds that technology cannot be laden with values. This long-standing view has faced critiques, but much of the argumentation against neutrality has focused on traditional, non-smart technologies like bridges and razors. In contrast, AI is a smart technology increasingly used in high-stakes domains like healthcare, finance, and policing, where its decisions can cause moral harm. In this paper, we argue that artificial intelligence, particularly artificial agents that autonomously make decisions to pursue their goals, challenge the neutrality thesis. Our central claim is that the computational models underlying artificial agents can integrate representations of moral values such as fairness, honesty and avoiding harm. We provide a conceptual framework discussing the neutrality thesis, values, and AI. Moreover, we examine two approaches to designing computational models of morality, artificial conscience and ethical prompting, and present empirical evidence from text-based game environments that artificial agents with such models exhibit more ethical behavior compared to agents without these models. The findings support that AI can embody moral values, which contradicts the claim that all technologies are necessarily value-neutral.AI and Ethics250It is argued that artificial intelligence, particularly artificial agents that autonomously make decisions to pursue their goals, challenge the neutrality thesis and support that AI can embody moral values, which contradicts the claim that all technologies are necessarily value-neutral.ArXiv["T. Swoboda", "Lode Lauwaert"]2024-08-22T00:00:00 b9fab370afecd0a1ab2485fd67132fbb4b47f929Ensuring patient safety in healthcare involves training professionals and implementing clinical decision support systems (CDSS) and health IT solutions to reduce errors and adverse events. The integration of artificial intelligence (AI) into health IT has revolutionized clinical settings by enabling real-time insights and personalized recommendations. However, the use of health IT can lead to unintended consequences that are not adequately addressed during training and implementation. These consequences can hinder the maximization of benefits and limit equitable access to healthcare. In this paper, we explore the impact of AI on CDSS and health IT, discuss the challenges in educating clinical informaticians, and aim to promote patient safety through collaboration with practitioners, researchers, and educators.Medical Informatics Europe00The impact of AI on CDSS and health IT is explored, the challenges in educating clinical informaticians are discussed, and the aim is to promote patient safety through collaboration with practitioners, researchers, and educators.Studies in health technology and informatics["Yang Gong", "Hua Min", "Xia Jing", "Ping Yu"]2024-08-22T00:00:00 0433d4d1fde2e43d20dd5bd55ec2476e388e740cThe recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. As deep learning is poised to shift the teaching paradigm, it is essential to have a clear understanding of its effects on the current education system to ensure sustainable development and deployment of AI-driven technologies at schools and universities. Hence, AI behavior cannot be fully understood without human and social sciences. After the imaginary and symbolic registers, AI is the third register of identification. Therefore, AI extends the movement that is at work in the Lacanian interpretation of the mirror stage and Oedipus complex and which Latour’s reading helps us to clarify. From this point of view, I describe an AI system as a set of three contrasting forces: the human desire for identification, logic, and machinery. In the “Miscomputation and information” section, I show how this interpretative model improves our understanding of AI. Systematic research on psychoanalytic treatments has been limited by several factors, including a belief that clinical experience can demonstrate the effectiveness of psychoanalysis, rendering systematic research unnecessary, the view that psychoanalytic research would be difficult or impossible to accomplish, and a concern that research would distort the treatment being delivered.NPRC Journal of Multidisciplinary Research90This work describes an AI system as a set of three contrasting forces: the human desire for identification, logic, and machinery, and shows how this interpretative model improves the understanding of AI.NPRC Journal of Multidisciplinary Research["Om Prakash Singh"]2024-08-22T00:00:00 1cbbf810355807426fac0f6b7d97105c6ef4b986Monitoring of artificial intelligence (AI)-based algorithms is necessary for safe implementation and will be required in upcoming regulations. This study investigates the potential for monitoring of AI in hospitals. First, by reviewing regulatory requirements and state of the art of monitoring. Then, by conducting a gap analysis of ISO42001, containing industry agnostic requirements harmonized with the EU AI Act. The analysis illustrates the need for comprehensive monitoring capable of capturing deviations in input, performance drifts and unintended interactions. However, hospitals often suffer from a technical debt, and the gap analysis provides qualitative indications on implementation challenges, including data quality, infrastructure and limitations in continuous improvement.Medical Informatics Europe00A gap analysis of ISO42001, containing industry agnostic requirements harmonized with the EU AI Act, illustrates the need for comprehensive monitoring capable of capturing deviations in input, performance drifts and unintended interactions in hospitals.Studies in health technology and informatics["Arian Ranjbar", "Jesper Ravn"]2024-08-22T00:00:00 8e724604f87996e16b76f186402fc0a7ef672f77In the contemporary digital landscape, the exclusion of digital tools in higher education undermines the essence of learning and advancement. This research delves into the symbiotic relationship between artificial intelligence (AI) and education, advocating for the integration of cutting-edge AI language learning tools like ChatGPT to keep pace with innovation. Through innovative methods of integrating generative AI language models, this study proposes a hyperaware curriculum design, fostering a revamped teaching and learning environment. It suggests that by leveraging AI, education can prioritize real-world knowledge application. Rather than viewing education as a static endpoint, this research emphasizes an ongoing process of enlightenment. We propose to situate AI in education as a crucial aspect of multiliteracy pedagogical approach. Through the theoretical lens of the four crucial dimensions of multiliteracy pedagogy by New London Group (1996) including situated practice, overt instructions, critical framing, and transformed practice we postulate each dimension in the light of interweaving it with the integration of technology. As we move towards a future heavily reliant on AI, incorporating AI language models and digital tools into education is imperative.2024 IEEE 5th India Council International Subsections Conference (INDISCON)210This research delves into the symbiotic relationship between artificial intelligence (AI) and education, advocating for the integration of cutting-edge AI language learning tools like ChatGPT to keep pace with innovation.2024 IEEE 5th India Council International Subsections Conference (INDISCON)["Gitanjaly Chhabra", "Noosha Mehdian", "Prihana Vasishta"]2024-08-22T00:00:00 7a7319c3351fd2394ee7a165991b99772db20f90 In this era, Explainable Artificial Intelligence (XAI) is being employed in many health-related problems, but it faces challenges because most models produce results that are opaque and interpretable. The goal of explainable AI is to make machine learning, and deep learning models more understandable and accessible to people. Consequently, there is a pressing need for XAI models to enhance trust, given its increasing popularity in the field of medical artificial intelligence. This study explores the XAI nature of machine learning for disease prediction, with a particular focus on transparency and reliability of the results. The study examines the interpretability of artificial intelligence, focusing on issues such as bias, equality, and system reliability. The main theme is to minimize errors, disparities in human understanding, and use artificial intelligence in disease prediction to improve the outcomes for medical patients. The XAI methods were validated on Sclerosis predictions using two important models with fine-tuning their hyperparameters. The experiments demonstrated that the XAI methods outperformed the existing methods, achieving impressive results in terms of accuracy, recall, f1 score, precision, and AUC. The proposed approach achieved 98.53% accuracy using 75-25% hold-out splitting, and 98.14% accuracy using 10-fold validation. This semantic approach is superior to previous methods by showing the abundance of correct predictions and demonstrating its effectiveness in predicting multiple sclerosis in the real world.Physica Scripta00The study examines the interpretability of artificial intelligence, focusing on issues such as bias, equality, and system reliability, and proposes a semantic approach that is superior to previous methods by showing the abundance of correct predictions and demonstrating its effectiveness in predicting multiple sclerosis in the real world.Physica Scripta["T. Saba", "Muhammad Mujahid", "A. Rehman", "Faten S. Alamari", "Noor Ayesha"]2024-08-22T00:00:00 e2c5d8db4a5f67a1fac50a9368f670f0ab295494Artificial intelligence (AI) is rapidly reshaping the medical field. This study aimed to investigate the attitudes of physicians towards AI in medical care using focus groups. Most participants seemed to be open to the use of AI in medicine if transparency in AI systems is ensured, regulatory barriers are addressed and personal contact to the patient is maintained. This qualitative study allows insights into how German physicians perceive the use of AI in medical care. Gaining input from physicians is important when designing future applications of AI for the practical use in medical care.Medical Informatics Europe00Investigating the attitudes of physicians towards AI in medical care using focus groups found most participants seemed to be open to the use of AI in medicine if transparency in AI systems is ensured, regulatory barriers are addressed and personal contact to the patient is maintained.Studies in health technology and informatics["Sarah Negash", "Jana Gundlack", "Charlotte Buch", "Jan Christoph", "J. Schildmann", "T. Frese", "Rafael T. Mikolajczyk"]2024-08-22T00:00:00 80f1894ff465b6416f05e8b15a79df1041d8c928Heliyon905This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings, suggesting a need for enhanced training and education on ethical AI use in healthcare.Heliyon["Moustaq Karim Khan Rony", "S. Numan", "Khadiza Akter", "H. Tushar", "Mitun Debnath", "Fateha Tuj Johra", "Fazila Akter", "Sujit Mondal", "Mousumi Das", "Muhammad Join Uddin", "Jeni Begum", "Mst. Rina Parvin"]2024-08-22T00:00:00 eaf9f4b349c7c026bf3a044b1fb683b94c43376dPurposeIn the context of rapid technological progress, this study investigates the factors that improve the academic performance of Saudi Arabian university students when they use ChatGPT. Using the technology-to-performance chain theory as a framework, this study identifies the variables that may affect the students' academic performance, thereby contributing to the discourse on the use of technology in education.Design/methodology/approachA survey is conducted on 257 respondents, and an online questionnaire is used to collect the data. Analysis of Moment Structures (AMOS) is employed to analyse the structural model to determine the direct connections between the different elements.FindingsFindings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit. Furthermore, task-technology fit can influence the utilisation of ChatGPT and students' academic performance. In addition, utilisation can significantly impact students' academic performance. Students are likely to utilise ChatGPT efficiently and demonstrate improved academic performance when they believe that the technology is a good fit for their tasks.Research limitations/implicationsThis study’s shortcoming is its exclusive focus on a single public university in Saudi Arabia, which limits its generalisability. Comparative studies among multiple universities in Saudi Arabia and in other Gulf nations should be conducted to enhance the generalisability of the results. In addition, diversifying the participants by including students from various universities and exploring the moderating variables would deepen our understanding of the utilisation of ChatGPT by students.Practical implicationsThe practical implications of this study include the existence of a positive relationship between task characteristics and task-technology fit, which can guide organisations in aligning ChatGPT with specific activities for enhanced efficacy and workflow integration. In addition, understanding the association between technology characteristics and task-technology fit can help in selecting suitable technologies that will encourage user adoption and improve academic outcomes. Furthermore, the recognition of the impact of individual characteristics on task-technology fit and their utilisation can inform tailored support and training programmes to enhance user acceptance and utilisation of ChatGPT, particularly in educational settings such as those in Saudi Arabia, which will ultimately improve students’ academic performance.Originality/valueThis study’s focus on ChatGPT and how it affects the academic performance of Saudi Arabian university students distinguishes it from previous studies. This study provides insightful information on technology adoption in educational settings and contributes to our understanding of the factors that can impact academic performance through ChatGPT adoption by utilising technology-to-performance chain theory. Moreover, this study’s examination of task characteristics, technology characteristics and individual characteristics can significantly enrich discussions on optimal technology integration for educational objectives. This contribution is relevant in dynamic contexts, such as the rapidly evolving technological environment of Saudi Arabia.Journal of Applied Research in Higher Education304Findings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit and can influence the utilisation of ChatGPT and students' academic performance.Journal of Applied Research in Higher Education["Yaser Hasan Salem Al-Mamary \u062f. \u064a\u0627\u0633\u0631 \u062d\u0633\u0646 \u0627\u0644\u0645\u0639\u0645\u0631\u064a", "Adel Abdulmohsen Alfalah", "A. Shamsuddin", "A. Abubakar"]2024-08-22T00:00:00 7200b6fe8c02a1e21296f7358bb6ccd8a2e2e443International Journal of Computer Applications Technology and Research01International Journal of Computer Applications Technology and Research[]2024-08-22T00:00:00 fd60b39406de65ea1d23abe0bce7642f1882293eFrontiers in Public Health01Frontiers in Public Health["Dmytro Chumachenko", "P. Morita", "S. Ghaffarian", "T. Chumachenko"]2024-08-22T00:00:00 bd7399ba88bf7458e264b2e75f81cb491fa4c03fInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-22T00:00:00 e0595509d4cdd0494798332d7570e8565a94941eEuropean Heart Journal - Digital Health80European Heart Journal. Digital Health["Joana M Ribeiro", "R. Nuis", "P. D. de Jaegere"]2024-08-22T00:00:00 fddd89ce191d570589fd6f2da8927514552e0f9eDecision00Decision["Peter D. Kvam", "Konstantina Sokratous", "Anderson Fitch", "A. Hintze"]2024-08-22T00:00:00 6d005555bacd6a0ca087e5603137709459f6fd40Telecommunications Systems220Telecommun. Syst.["Volodymyr Kharchenko", "A. Grekhov", "V. Kondratiuk"]2024-08-22T00:00:00 b15f099f27e78a27fe24fc3c5a3c3b86de99ca67European Stroke Journal110European stroke journal["Michele Romoli", "Pietro Caliandro"]2024-08-22T00:00:00 e15ca16295b12d9ef67e908c140698cfc520b5a7The study examines how ontonorms propagate certain gender practices in digital spaces through character and the norms of spaces that shape AI design, training and use. Additionally the study explores the different user behaviours and practices regarding whether, how, when, and why different gender groups engage in and with AI driven spaces. By examining how data and content can knowingly or unknowingly be used to drive certain social norms in the AI ecosystems, this study argues that ontonorms shape how AI engages with the content that relates to women. Ontonorms specifically shape the image, behaviour, and other media, including how gender identities and perspectives are intentionally or otherwise, included, missed, or misrepresented in building and training AI systems.arXiv.org590It is argued that ontonorms shape how AI engages with the content that relates to women, including how gender identities and perspectives are intentionally or otherwise, included, missed, or misrepresented in building and training AI systems.ArXiv["Angella Ndaka", "Harriet Ratemo", "Abigail Oppong", "E. Majiwa"]2024-08-22T00:00:00 e0e004113a838e27df19dfa91b64873aec929e74Waste Management390This paper develops a virtual assistant that interacts with tenants via verbal and visual inputs to provide them with waste management services and instructions, and achieved accuracy levels of 85% and 88% for verbal and visual inputs, respectively.Waste management["Yassine Bouabdallaoui", "Laure Ducoulombier", "Z. Lafhaj", "Pascal Yim"]2024-08-22T00:00:00 d9985ef17de91ac87bf654c88cb9ff6f8d0a9397Evolutionary Intelligence321Evol. Intell.["Serge Dolgikh"]2024-08-22T00:00:00 527edf9306e7d138e4d49d44f53e5c8d1dfc46eaThe development of new artificial intelligence-generated content (AIGC) technology creates new opportunities for the digital transformation of education. Teachers’ willingness to adopt AIGC technology for collaborative teaching is key to its successful implementation. This study employs the TAM and TPB to construct a model analyzing teachers’ acceptance of AIGC technology, focusing on the influencing factors and differences across various educational stages. The study finds that teachers’ behavioral intentions to use AIGC technology are primarily influenced by perceived usefulness, perceived ease of use, behavioral attitudes, and perceived behavioral control. Perceived ease of use affects teachers’ willingness both directly and indirectly across different groups. However, perceived behavioral control and behavioral attitudes only directly influence university teachers’ willingness to use AIGC technology, with the impact of behavioral attitudes being stronger than that of perceived behavioral control. The empirical findings of this study promote the rational use of AIGC technology by teachers, providing guidance for encouraging teachers to actively explore the use of information technology in building new forms of digital education.Sustainability522The empirical findings of this study promote the rational use of AIGC technology by teachers, providing guidance for encouraging teachers to actively explore the use of information technology in building new forms of digital education.Sustainability["Haili Lu", "Lin He", "Hao Yu", "Tao Pan", "Kefeng Fu"]2024-08-22T00:00:00 a597632d03086c42ee008574a911f5c09e0268dfOrientation: Organisations are undergoing digital transformation and incorporating artificial intelligence (AI) into business processes and functions. The use of AI technologies, instead of people to perform specific low-level repetitive tasks has become common practice.Research purpose: The research aimed to investigate the impact of AI technologies on the South African workforce, specifically from the perspective of senior human resource (HR) managers.Motivation for the study: The adoption and implementation of AI, robotic process automation (RPA) and large language models, such as ChatGPT in a business, change the way personnel perform specific tasks, interact and participate in business processes.Research approach/design and method: The study used a qualitative research design and a deductive approach. A survey with open-ended questions was conducted among senior HR managers working for leading manufacturing organisations and institutions in South Africa. Content analysis was used to analyse the responses.Main findings: Human resource managers emphasised the importance of AI and RPA in remaining globally competitive and streamlining business and HR processes, highlighting the need to empower the workforce, identify ideal employee traits for AI and RPA integration and effectively manage these technologies within organisations.Practical/managerial implications: The senior HR managers offered advice on how to manage the use of AI and RPA technologies in an organisation.Contribution/value-add: The study highlights senior HR managers’ perceptions of the use and impact of AI and RPA in organisations in South AfricaSa Journal of Human Resource Management01Human resource managers emphasised the importance of AI and RPA in remaining globally competitive and streamlining business and HR processes, highlighting the need to empower the workforce, identify ideal employee traits for AI and RPA integration and effectively manage these technologies within organisations.SA Journal of Human Resource Management["P. Poisat", "M. Cullen", "A. Calitz"]2024-08-22T00:00:00 2ef758da3f0043f05fb4e89a6c65c610cbd1d43c Purpose This study aims to understand the perceived emotions of human–artificial intelligence (AI) interactions in the private sector. Moreover, this research discusses the transferability of these lessons to the public sector. Design/methodology/approach This research analysed the comments posted between June 2022 and June 2023 in the global open Reddit online community. A data mining approach was conducted, including a sentiment analysis technique and a qualitative approach. Findings The results show a prevalence of positive emotions. In addition, a pertinent percentage of negative emotions were found, such as hate, anger and frustration, due to human–AI interactions. Practical implications The insights from human–AI interactions in the private sector can be transferred to the governmental sector to leverage organisational performance, governmental decision-making, public service delivery and the creation of economic and social value. Originality/value Beyond the positive impacts of AI in government strategies, implementing AI can elicit negative emotions in users and potentially negatively impact the brand of private and government organisations. To the best of the authors’ knowledge, this is the first research bridging the gap by identifying the predominant negative emotions after a human–AI interaction. Transforming Government: People, Process and Policy941This is the first research bridging the gap by identifying the predominant negative emotions after a human–AI interaction, and the transferability of these lessons to the public sector is discussed.Transforming Government: People, Process and Policy["Ricardo Santos", "Am\u00e9lia Brand\u00e3o", "Bruno Veloso", "P. Popoli"]2024-08-22T00:00:00 68f1dcd49662c8bdcad89eec78d15ed266ae0ba9Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendations, explainability can gain the trust of healthcare experts and assist them in difficult diagnostic tasks. Sepsis is characterized as a serious condition that happens when the immune system of the body has an extreme response to an infection, causing tissue and organ damage and leading to death. Physicians face challenges in diagnosing and treating sepsis due to its complex pathogenesis. This work aims to provide an overview of the recent studies that propose explainable AI models in the prediction of sepsis onset and sepsis mortality using intensive care data. The general findings showed that explainable AI can provide the most significant features guiding the decision-making process of the model. Future research will investigate explainability through argumentation theory using intensive care data focused on sepsis patients.Medical Informatics Europe01The general findings showed that explainable AI can provide the most significant features guiding the decision-making process of the model in the prediction of sepsis onset and sepsis mortality using intensive care data.Studies in health technology and informatics["A. Nicolaou", "Waqar A. Sulaiman", "Z. Antoniou", "Lakis Palazis", "Anna Vavlitou", "Constantinos S. Pattichis", "A. Panayides"]2024-08-22T00:00:00 e6a2104af030b1195e0259848745608e4e47ccbcDigital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective. The project will consist of three pillars: a) assurance of new state-of-the-art technologies and approaches (such as synthetic data), which will support the validation methods as part of regulatory decision-making; b) technical and scientific, focusing on data-based safety assurance, as well as discovery, integration and use of datasets, and use of machine learning approaches; and c) delivery to practice, through co-production involving relevant stakeholders, dissemination and sustainability of the project's outputs. Finally, INSAFEDARE will develop an open syllabus and training certification for health professionals focused on quality assurance.Medical Informatics Europe141The INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective are presented.Studies in health technology and informatics["Parisis Gallos", "Nicholas Matragkas", "Saif ul Islam", "Gregory Epiphaniou", "Scott Hansen", "Stuart Harrison", "Bram van Dijk", "Marcel R. Haas", "Giorgos Pappous", "Simon Brouwer", "Francesco Torlontano", "S. Abbasi", "Omid Pournik", "James Churm", "John Mantas", "C. Parra-Calder\u00f3n", "Dimitrios Petkousis", "Patrick Weber", "Benjamin Dzingina", "C. Mraidha", "Carsten Maple", "Jim Achterberg", "Marco Spruit", "Evi Saratsioti", "Younes Moustaghfir", "Theodoros N. Arvanitis"]2024-08-22T00:00:00 4d16378b3a7de6f786021892c3d0e6d2ff577403IoT and AI created a Transportation Management System, resulting in the Internet of Vehicles. Intelligent vehicles are combined with contemporary communication technologies (5G) to achieve automated driving and adequate mobility. IoV faces security issues in the next five (5) areas: data safety, V2X communication safety, platform safety, Intermediate Commercial Vehicles (ICV) safety, and intelligent device safety. Numerous types of AI models have been created to reduce the outcome infiltration risks on ICVs. The need to integrate confidence, transparency, and repeatability into the creation of Artificial Intelligence (AI) for the safety of ICV and to deliver harmless transport systems, on the other hand, has led to an increase in explainable AI (XAI). Therefore, the space of this analysis protected the XAI models employed in ICV intrusion detection systems (IDSs), their taxonomies, and available research concerns. The study's findings demonstrate that, despite its relatively recent submission to ICV, XAI is a potential explore area for those looking to increase the net effect of ICVs. The paper also demonstrates that XAI's greater transparency will help it gain acceptance in the vehicle industry.2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)220The study's findings demonstrate that, despite its relatively recent submission to ICV, XAI is a potential explore area for those looking to increase the net effect of ICVs and demonstrates that XAI's greater transparency will help it gain acceptance in the vehicle industry.2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)["Ravula Vishnukumar", "Adla Padma", "M. Ramaiah"]2024-08-22T00:00:00 589197f049819e483b4f2c35a3c347c09e0c531cNatural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.PLoS ONE417The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases.PLOS ONE["Michele Salvagno", "A. Cassai", "Stefano Zorzi", "Mario Zaccarelli", "Marco Pasetto", "Elda Diletta Sterchele", "Dmytro Chumachenko", "A. Gerli", "Razvan Azamfirei", "F. Taccone"]2024-08-23T00:00:00 2aabae8fee3aacc65a9b4cb18ac0e41ebcf424dcJournal of Special Topics in Information Technology and Management624A bibliometric analysis was conducted to identify how the literature on AI adoption has evolved over the past few years, key themes associated with AI adoption in the literature, and the gaps in the literature.Information Technology and Management["Ahmad A. Khanfar", "Reza Kiani Mavi", "Mohammad Iranmanesh", "Denise Gengatharen"]2024-08-23T00:00:00 f9bf6577cffe875e1af7a26c7f92f3295cd833f9The purpose of this paper is to explore the applicability of AI marketing for creating data-driven marketing strategies. Notably, the paper illustrates the existing circumstances of artificial intelligence in marketing practice. Besides, this paper argues for awareness of AI for customer satisfaction, employing AI to improve positioning, applying AI for accurate decision-making, and utilizing AI for sales, cost, and risk reductions. Lastly, this paper compares the applicability of AI marketing within two major regions from four regions identified in the study. A two-step approach was used to address the research question. First, a systematic literature review was conducted to identify the knowledge gap. Second, primary research through a survey study was conducted. Respondents of the primary study were represented by 367 marketing practitioners with 22 different marketing professions, representing 11 countries from 18 different industries, mainly from the Baltic and Caucasus regions. Based on findings and analysis, conclusions, limitations, and concepts for the future study were highlighted. The findings synthesized AI drivers, barriers, and outcomes in marketing. Further, outcomes confirmed a positive relationship with unitizing AI marketing in long-term strategic marketing planning. The paper offers practical guidance to the companies or inspires marketing leaders to use AI in data-driven marketing strategies. It has a significant value due to the complexity of the current marketing environment, whether micro or macro. Marketing Practitioners are searching for added value to prove the applicability of AI marketing in everyday strategies for decision-makers.Journal of Marketing Research and Case Studies04The purpose of this paper is to explore the applicability of AI marketing for creating data-driven marketing strategies, and synthesized AI drivers, barriers, and outcomes in marketing to confirm a positive relationship with unitizing AI marketing in long-term strategic marketing planning.Journal of Marketing Research and Case Studies["Ioseb Gabelaia"]2024-08-23T00:00:00 7003dcaaa68bf6797f9bd2b26203ba611dc3c9ccThe article aimed to identify the repercussions of artificial intelligence (AI) on the quality of creating media content. The research employed the qualitative analytical method, conducting semi-structured interviews with seven Jordanian journalists who work in various media institutions in Jordan. The results revealed that the fields in which AI is employed are design and graphics, dealing with big data, and reformulating content and written texts and that the media content topics in which AI is employed are “technological,” “educational,” and “economic.” The most used AI applications and websites in journalism and media production were ChatGTP, Google Assistant, Designs.ai, Art Flow, QuillBot, Grammarly, Deepfake, Word.ai, Chatbots, AI-Writer, Siri, Blockchain, Otter.ai, and others. The results also revealed that AI has had crucial impacts on the quality of media content through the accuracy of data analysis, the acceleration of the editing process, and the enhancement of the (human) journalist’s skill in editing and drafting. The authors recommended conducting more studies on AI in preparing fake news stories on social media platforms.Studies in Media and Communication321The results revealed that AI has had crucial impacts on the quality of media content through the accuracy of data analysis, the acceleration of the editing process, and the enhancement of the (human) journalist’s skill in editing and drafting.Studies in Media and Communication["Najm Abed Khalaf Aleessawi", "Solafah Farouq Alzubi"]2024-08-23T00:00:00 661ba5b5d52a34e39149308eaf5991521d752d9eBackground/Aims This study aimed to evaluate the effectiveness of the GI Genius (Medtronic) module in clinical practice, focusing on the adenoma detection rate (ADR) during colonoscopy. Computer-aided polyp detection (CADe) systems using artificial intelligence have been shown to improve adenoma detection in controlled trials. However, the effectiveness of these systems in clinical practice has recently been questioned. Methods This single-center prospective observational study was conducted at the University Hospital of Southern Denmark and included all individuals referred for colonoscopy between November 2020 and January 2021. The primary outcome was ADR, comparing patients examined with CADe to those examined without it. The selection of patients to be examined with the CADe module was completely random. Results A total of 502 patients were analyzed (318 in the control group and 184 in the CADe group). The overall ADR was 32.1% with a slight increase in the CADe group (34.7% vs. 30.5%). Multivariable analysis showed a very modest and statistically insignificant increase in ADR (risk ratio, 1.12; 95% confidence interval, 0.88–1.43). Conclusions The use of CADe in clinical practice did not increase ADR with statistical significance when compared to colonoscopy without CADe. These findings suggest that the impact of CADe systems in everyday clinical practice are modest.Clinical Endoscopy222The use of CADe in clinical practice did not increase ADR with statistical significance when compared to colonoscopy without CADe, suggesting that the impact of CADe systems in everyday clinical practice are modest.Clinical Endoscopy["S\u00f8ren Nicolaj R\u00f8nborg", "Suresh Ujjal", "Rasmus Kr\u00f8ijer", "M. Ploug"]2024-08-23T00:00:00 adc5e5fba2a6398020cfa60804c399f7fce76b09The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize the early diagnosis of chronic diseases, particularly in rural areas where healthcare resources are often limited. This paper explores the transformative impact of AI technologies on identifying chronic diseases at their earliest stages, enhancing patient outcomes, and alleviating the burden on rural healthcare systems. AI's ability to analyze vast amounts of data rapidly and accurately enables the early detection of chronic diseases such as diabetes, hypertension, and cardiovascular conditions. Machine learning algorithms can process data from various sources, including electronic health records (EHRs), wearable devices, and diagnostic imaging, to identify patterns and biomarkers indicative of early disease onset. This predictive capability allows healthcare providers to intervene sooner, potentially preventing disease progression and reducing long-term healthcare costs. In rural areas, where access to specialized medical expertise and advanced diagnostic tools is often constrained, AI-driven diagnostic tools offer a significant advantage. Telemedicine platforms integrated with AI can facilitate remote consultations, where AI assists in interpreting patient data and providing diagnostic suggestions. This approach not only expands access to quality healthcare but also empowers local healthcare providers with decision-support tools, improving diagnostic accuracy and patient management. Moreover, AI can help mitigate the challenges of limited healthcare personnel in rural regions by automating routine diagnostic tasks and enabling healthcare workers to focus on more complex cases. For instance, AI-powered imaging analysis can quickly screen large populations for signs of chronic diseases, flagging suspicious cases for further review by medical professionals. The deployment of AI in rural healthcare settings also fosters continuous monitoring and personalized care through connected health devices. These devices collect real-time health data, which AI systems analyze to provide actionable insights and alerts to both patients and healthcare providers. This proactive approach ensures timely medical interventions and enhances patient adherence to treatment plans. In conclusion, AI's integration into rural healthcare systems significantly improves the early diagnosis of chronic diseases, offering a scalable solution to address the disparities in healthcare access and outcomes between urban and rural populations. Continued investment in AI technologies and infrastructure, along with targeted training for healthcare providers, is essential to realize the full potential of AI in transforming rural healthcare and improving the quality of life for millions. Keywords: AI, Impact, Early Diagnostic, Chronic Disease, Rural Areas.Computer Science &amp; IT Research Journal01Artificial intelligence's integration into rural healthcare systems significantly improves the early diagnosis of chronic diseases, offering a scalable solution to address the disparities in healthcare access and outcomes between urban and rural populations.Computer Science &amp; IT Research Journal["Ebube Victor Emeihe", "Ejike Innocent Nwankwo", "Mojeed Dayo Ajegbile", "Janet Aderonke Olaboye", "Chukwudi Cosmos Maha"]2024-08-23T00:00:00 b33af19de7f3c01987ccd7f89316721b816bca6aArtificial Intelligence (AI) is increasingly influencing various sectors, with the legal profession being no exception. This review paper examines how AI is transforming legal practice, highlighting both the opportunities and ethical challenges it presents. By analyzing advancements in AI technologies such as natural language processing, machine learning, and predictive analytics, the paper explores their applications in legal research, case management, and client services. Furthermore, it addresses the ethical concerns associated with AI adoption in law, including issues of transparency, accountability, and bias. This paper aims to provide a comprehensive overview of the impact of AI on the legal field and offer insights into how the profession can navigate the evolving landscape.Indian Journal of Law41This review paper examines how AI is transforming legal practice, highlighting both the opportunities and ethical challenges it presents and addresses the ethical concerns associated with AI adoption in law.Indian Journal of Law["Ranbir Singh"]2024-08-23T00:00:00 7c5196f8abc59ebf418aff5be94b641713edaebcInformation Systems Frontiers272This study conducted a two-phased qualitative case study to explore how organizations diffuse AI in their operations and developed a capability maturity model for AI diffusion (AICMM), which was then validated and tested.Information Systems Frontiers["Hans Fredrik Hansen", "Elise Lillesund", "Patrick Mikalef", "\u039dajwa Altwaijry"]2024-08-23T00:00:00 f04f3c1e698c3207e4955e30d38d243d15398146This article examines the integration of artificial intelligence (AI) into early childhood education and the noteworthy impacts it has on students' enjoyment, creativity, and development of soft skills. Artificial intelligence technology can help young pupils develop important soft skills like cooperation and communication through the use of interactive tools and individualized learning platforms. These technologies enable education to be customized to meet the needs of each student, boosting self-esteem and confidence. Additionally, they facilitate problem-solving by providing opportunities for research. Furthermore, AI encourages creativity in children by giving them new and creative ways to express themselves. This paper explores how gamified learning settings, interactive software, and creative tools that stimulate students' curiosity and foster creativity are transforming education through artificial intelligence (AI). It also highlights the challenges and ethical dilemmas surrounding the integration of AI. This essay emphasizes how important it is to employ AI ethically and cooperatively to support children's holistic development. By developing a framework based on the completed literature study, we will discuss the importance of artificial intelligence in early childhood education, the ethical conundrums raised by its use in ECE, and how it could foster children's creativity and soft skills.International Journal of Religion911How gamified learning settings, interactive software, and creative tools that stimulate students' curiosity and foster creativity are transforming education through artificial intelligence (AI) is explored.International Journal of Religion["Yahya Fikri", "Mohamed Rhalma"]2024-08-23T00:00:00 937a09666f617aad03b8c656ba4f132934c08daeAs a relatively new tool, the use of artificial intelligence (AI) in medicine and dentistry has the potential to significantly transform the healthcare sector. AI has already demonstrated efficacy in medical diagnosis across several specialties, used successfully to detect breast, lung and skin cancer. In Oral Medicine, AI may be applied in a similar fashion, used in the detection and diagnosis of oral cancers and oral potentially malignant diseases. Despite its promise as a transformative diagnostic aid, the use of AI in healthcare presents significant safety, reliability and ethical concerns. There is no formal consensus on the safe and ethical implementation of AI systems in healthcare settings, but the literature converges on several key principles of ethical AI use including transparency, justice and fairness, non-maleficence, responsibility and privacy. This article provides a narrative review of the key ethical issues surrounding AI use in medicine, and reflects on these, providing view-points of a bioethicist and Oral Medicine clinicians from several units.Oral Diseases192A narrative review of the key ethical issues surrounding AI use in medicine, and reflects on these, providing view-points of a bioethicist and Oral Medicine clinicians from several units.Oral diseases["Q. Feng", "Molly Harte", "B. Carey", "Ali Alqarni", "Luis Monteiro", "M. Diniz-Freitas", "J. Fricain", "Giovanni Lodi", "V. Brailo", "M. Andreoletti", "Rui Albuquerque"]2024-08-23T00:00:00 e4f645c6bbaf9ffa133f37c8ec1232b69471f078Objectives: This study assesses the perceptions and attitudes of Chinese radiologists concerning the application of artificial intelligence (AI) in the diagnosis of lung nodules. Material and Methods: An anonymous questionnaire, consisting of 26 questions addressing the usability of AI systems and comprehensive evaluation of AI technology, was distributed to all radiologists affiliated with Beijing Anzhen Hospital and Beijing Tsinghua Changgung Hospital. The data collection was conducted between July 19, and 21, 2023. Results: Of the 90 respondents, the majority favored the AI system’s convenience and usability, reflected in “good” system usability scale (SUS) scores (Mean ± standard deviation [SD]: 74.3 ± 11.9). General usability was similarly well-received (Mean ± SD: 76.0 ± 11.5), while learnability was rated as “acceptable” (Mean ± SD: 67.5 ± 26.4). Most radiologists noted increased work efficiency (Mean Likert scale score: 4.6 ± 0.6) and diagnostic accuracy (Mean Likert scale score: 4.2 ± 0.8) with the AI system. Views on AI’s future impact on radiology careers varied (Mean ± SD: 3.2 ± 1.4), with a consensus that AI is unlikely to replace radiologists entirely in the foreseeable future (Mean ± SD: 2.5 ± 1.1). Conclusion: Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness. However, the system’s learnability requires enhancement. While AI is seen as beneficial for work efficiency and diagnostic accuracy, its long-term career implications remain a topic of debate.Journal of Clinical Imaging Science271Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness, however, the system’s learnability requires enhancement.Journal of Clinical Imaging Science["Weiqi Liu", "You Wu", "Zhuozhao Zheng", "Wei Yu", "Mark Bittle", "Hadi Kharrazi"]2024-08-23T00:00:00 ae8df38427c5b85d2ea28f39157abb3b8fac8138Generative Artificial Intelligence constitutes a new wave of automation. There is broad agreement among economists that humanity is potentially entering into a period of profound change. However, significant uncertainties and disagreements exist concerning a variety of overlapping topics: the share of jobs in which human labour is displaced and/or reinstated through automation; the effects on income inequality; the effects on job satisfaction; and, finally, what policy changes ought to be pursued to reduce potential negative impacts. This literature review seeks to clarify this landscape by mapping out key disagreements between positions, and to identify the critical elements upon which such disagreements rest. By surveying the current literature, the effects of AI on the future of work will be clarified.50By surveying the current literature, the effects of AI on the future of work will be clarified by mapping out key disagreements between positions, and identifying the critical elements upon which such disagreements rest.["Caleb Peppiatt"]2024-08-23T00:00:00 5e06b5fb54e4066b2689b8504f69e31e5a5ca701Artificial Intelligence (AI) has significantly advanced diagnostic capabilities in microbiology labs by automating and enhancing various processes. AI-powered automated microscopy allows for rapid and accurate identification of microorganisms, such as bacteria and parasites, by analyzing microscopic images with deep learning algorithms, reducing the need for manual interpretation. AI also plays a crucial role in genomic data interpretation, particularly in analyzing Next-Generation Sequencing (NGS) data to identify pathogens and predict antibiotic resistance, facilitating personalized treatment strategies. Additionally, AI's predictive analytics capabilities help anticipate outbreaks and monitor antibiotic resistance, enabling proactive public health responses. In microbiology labs, AI-driven automation improves efficiency by handling routine tasks, while AI's ability to reduce human error and enhance diagnostic accuracy ensures consistent and reliable results. The integration of AI in microbiology not only speeds up diagnostic turnaround times but also supports point-of-care diagnostics, providing timely insights for critical treatment decisions. Despite these advancements, challenges such as data quality, bias, ethical considerations, and the need for robust regulatory frameworks remain. Looking forward, the continued evolution of AI promises to further enhance diagnostic precision and support personalized medicine, transforming the future of infectious disease management.International journal of life sciences biotechnology and pharma research90In microbiology labs, AI-driven automation improves efficiency by handling routine tasks, while AI's ability to reduce human error and enhance diagnostic accuracy ensures consistent and reliable results.International Journal of Life Sciences Biotechnology and Pharma Research["R. K. Wasan"]2024-08-23T00:00:00 e9484c14c6ed1c800afe9a8b3af10f11f835a130Artificial intelligence (AI) is a strategic technology that will lead the future, and the world's major developed countries have made the development of AI a major strategy for enhancing national competitiveness and safeguarding national security. While AI leads new opportunities for economic development, it also brings new challenges to production and life due to the uncertainty of its development. This paper firstly introduces the research background of AI data security, and summarizes the security challenges faced by AI in terms of sensors, operating systems, control systems, and device communications on the basis of combing the AI industry chain; secondly, it combs through the regulatory policies of the world's countries in terms of AI data security; and then it describes the data characteristics of AI in terms of two dimensions, namely, model training and model output; Then, the data security risks of AI are analyzed from both risk types and risk characteristics; finally, measures and suggestions to strengthen AI data security are put forward in response to the risks.International Conference on Data Science in Cyberspace380The research background of AI data security is introduced, the security challenges faced by AI in terms of sensors, operating systems, control systems, and device communications are summarized, and measures and suggestions to strengthen AI data security are put forward in response to the risks.2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC)["Biqing Qiu", "Dong Liu", "Shunchao Cao", "Chunxu Mu", "Shen Yan", "Yang Liu"]2024-08-23T00:00:00 fc65a2eb7108082f6121e88923b14ca6cb245852Discover Education330Despite the need for further literature on artificial intelligence and computer-mediated communication in Nepal’s English classes for undergrads between 2018 and 2023, the studies reviewed shed light on the possibilities of technology and AI in language acquisition.Discover Education["Prateet Baskota", "Tikaram Poudel"]2024-08-23T00:00:00 a6d74c72734b58f6c24669702ff38d171a5e44cbIn today's competitive business environment, organizations are increasingly adopting Artificial Intelligence (AI) to enhance the efficiency of their Management Information Systems (MIS). The integration of AI into MIS has the potential to improve operational efficiency, decision-making processes, and customer satisfaction. This study aims to investigate the impact of AI on business performance by exploring its role in automating processes and providing data-driven insights. A systematic literature review (SLR) methodology was employed to analyze a range of studies on AI integration into MIS, focusing on improving business efficiency. The findings indicate that AI significantly reduces data processing time, increases decision-making accuracy, and improves customer satisfaction. Specifically, AI implementation led to a 66% reduction in data processing time, a 29% increase in decision-making accuracy, and a 20% reduction in operational costs. These results highlight AI's ability to optimize business processes and enhance overall productivity. However, the study also identified key challenges, including the need for high-quality data, specialized workforce training, and ethical considerations surrounding data privacy. This research contributes to both theoretical and practical knowledge by providing a comprehensive understanding of AI's role in MIS. It offers strategic recommendations for organizations aiming to leverage AI to drive operational efficiency and maintain competitive advantage. Future research should focus on exploring synergies between AI and emerging technologies such as big data and the Internet of Things (IoT) to further improve business outcomes.Journal of Management and Informatics370Investigation of the impact of AI on business performance by exploring its role in automating processes and providing data-driven insights indicates that AI significantly reduces data processing time, increases decision-making accuracy, and improves customer satisfaction.Journal of Management and Informatics["Bambang Widjarnoko Susilo", "Edy Susanto"]2024-08-23T00:00:00 54f4807b3c48d8c903c40e51ec8bf421dd70c19cThis article presents an overview of Explainable Artificial Intelligence (XAI) and its various methods, emphasizing the role of interpretability, in present day AI systems. With AI becoming more prevalent in decision making processes across fields there is a growing need for transparency and accountability. We delve into XAI techniques, such as agnostic methods like Local Interpretable Model agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) model specific approaches like Integrated Gradients and transparent models like decision trees and rule based systems. By examining the strengths and limitations of these methods we provide insights into their usefulness and effectiveness, in scenarios. This review aims to give researchers and practitioners a nuanced understanding of XAI to support the creation and implementation of transparent AI systems.International Conference on Computing Communication Control and automation230An overview of Explainable Artificial Intelligence and its various methods is presented, emphasizing the role of interpretability, in present day AI systems, to give researchers and practitioners a nuanced understanding to support the creation and implementation of transparent AI systems.2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)["Muhammad Hamza Azam", "Mohd Hilmi Hasan", "Nafeesa Yousaf Murad", "Emelia Akashah Bt Patah"]2024-08-23T00:00:00 19c44b105c33afc1e2063dce57c3f49193dd9dc3The financial sector has experienced significant transformation with the adoption of Artificial Intelligence (AI) technology, particularly in improving business decision-making. This study aims to analyze the impact of AI on decision-making quality, focusing on risk analysis and portfolio management in Indonesia's financial sector. A mixed-method approach was utilized, combining quantitative and qualitative data to provide a comprehensive view of AI’s role in financial decision-making processes. Quantitative data were gathered through surveys of 50 respondents from various financial institutions, while qualitative data were obtained from semi-structured interviews with industry executives. The findings indicate that AI significantly enhances risk analysis accuracy by 25%, optimizes portfolio management, accelerates decision-making processes, and improves operational efficiency by automating manual tasks and reducing human errors. Despite these benefits, the study also identifies challenges such as data quality issues and high implementation costs, which hinder the broader adoption of AI in the financial sector. The study concludes that AI offers substantial potential to improve decision-making in the financial industry, but addressing data infrastructure and training needs is critical for achieving optimal outcomesJournal of Management and Informatics300It is concluded that AI offers substantial potential to improve decision-making in the financial industry, but addressing data infrastructure and training needs is critical for achieving optimal outcomes.Journal of Management and Informatics["Titin Hargyatni", "Kusna Djati Purnama", "Galuh Aninditiyah"]2024-08-23T00:00:00 b5984e812cdde19e738b8c6df399b67e6c4fdc5eArtificial intelligence (AI) has significant potential to transform healthcare and improve patient care. However, successful development and integration of AI models requires careful consideration of study designs and sample size calculations for development and validation of models, publishing standards, prototype development for translation and collaboration with stakeholders. As the field is relatively new and rapidly evolving there is a lack of guidance and agreement on best practices for most of these steps. We engaged stakeholders in the form of clinicians, researchers from academia and industry, and data scientists to discuss various aspects of the translational pipeline and identified the challenges researchers in the field face and potential solutions to them. In this viewpoint, we present the summary of our discussions as a brief guide on the process of developing AI-based applications for healthcare using medical images. We organized the entire process into six major themes (i.e., The gaps AI can fill in healthcare, Development of AI models for healthcare: practical and important things to consider, Good practices for validation of AI models for healthcare: study designs and sample size calculation, Points to consider when publishing AI models, Translation towards products, Challenges and potential solutions from a technical perspective) and presented important points as a rule of thumb. We conclude that successful integration of AI in healthcare requires a collaborative approach, rigorous validation, adherence to best practices as described and cited, and consideration of technical aspects.F1000Research240It is concluded that successful integration of AI in healthcare requires a collaborative approach, rigorous validation, adherence to best practices as described and cited, and consideration of technical aspects.F1000Research["B. Desiraju", "R. Thiruvengadam", "N. Wadhwa", "Ashok Khurana", "Aris T Papageorghiou", "J. Noble", "Shinjini Bhatnagar"]2024-08-23T00:00:00 6c8afac7571ef42115d097bd30fa34106ef83c27The global sustainability movement is reshaping the operational requirements and managerial approaches of maritime firms, resulting in the emergence of unprecedented and complex risks in the sector. This has driven maritime firms to leverage digital tools, such as artificial intelligence (AI) capabilities, to enhance their sustainability risk management (SRM) endeavors. Drawing on the organizational information-processing theory (OIPT), this study proposes four AI capabilities: customer value proposition, key process optimization, key resource optimization, and societal good. It examines their influence on sustainability-related knowledge management capabilities (SKMC), stakeholder engagement, and SRM. A survey questionnaire was used to gather responses from 157 maritime professionals across various sectors of the industry, providing empirical data for analysis. Employing structural equation modeling, the findings reveal that AI capabilities can improve SKMC. These findings enhance existing literature by using OIPT concepts to investigate the interplay among the constructs that lead to better SRM in maritime firms. Furthermore, the study offers managerial guidance by providing insights into AI capabilities that maritime firms should incorporate into their operations, fostering best practices to effectively manage sustainability risks and ensure the firm's long-term survival.Risk Analysis440The findings reveal that AI capabilities can improve SKMC, and the study offers managerial guidance by providing insights into AI capabilities that maritime firms should incorporate into their operations, fostering best practices to effectively manage sustainability risks and ensure the firm's long-term survival.Risk analysis : an official publication of the Society for Risk Analysis["Kai Yuan Kong", "Kum Fai Yuen"]2024-08-23T00:00:00 3810b8008cbe70d95689571eee86faf320d62f3fBeyond being merely trendy terms in technology, machine learning (ML) and artificial intelligence (AI) have directly influenced every industry. This article thus demystifies the true meaning of these technologies by sifting through the sultry descriptions. Here, we explore how AI and ML enhance efficiency, decision-making, and problem-solving in various sectors including health, Agriculture, finance, and production as we discover real-world applications. Continuing with the discussion of implementation challenges, the aspects of bias, ethical pitfalls, and report cards are examined. In addition, the article also covered how the integration of AI as well as ML remains a way of improving processes, creating better user experiences, and spurring innovation. This paper, which is also based on case studies, discusses the relationship between social welfare and AI and the importance of AI’s development and use being done responsibly. Considering all the discussed aspects, this volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions.2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET)330This volume provides a rather nuanced perspective on AI and ML, unmasking the myths around these concepts to reveal the fact that they have a highly significant impact on the current reality and its projected evolutions.2024 1st International Conference on Advanced Computing and Emerging Technologies (ACET)["Rahul Dattangire", "Ruchika Vaidya", "Divya Biradar", "Ashish Joon"]2024-08-23T00:00:00 1c26edece19f7646c4a50534dcc5b8816f7909b4The digital transformation driven by advances in Artificial Intelligence (AI) technology has reshaped the media industry landscape in Indonesia. Although many media companies have adopted AI to enhance content efficiency and relevance, challenges in integrating this technology remain a significant issue. This research aims to explore the impact of AI implementation on the production, distribution, and consumption of media content in Indonesia, while also identifying the challenges and opportunities the industry faces. The methodology employed in this study is a case study approach, involving an analysis of several prominent media platforms in Indonesia, such as Kompas.com and Detik.com. Data were collected through interviews with industry practitioners and content analysis of published materials. The findings indicate that AI implementation has improved content personalization and operational efficiency but has also raised concerns about user privacy and the social implications of automation. The conclusions of this study underscore the importance of developing balanced strategies to harness AI's potential while addressing emerging ethical and social challenges. This research provides practical recommendations for stakeholders in the media industry to effectively and sustainably integrate AI into their operations.Journal of Management and Informatics360The findings indicate that AI implementation has improved content personalization and operational efficiency but has also raised concerns about user privacy and the social implications of automation.Journal of Management and Informatics["Vivi Kumalasari Subroto", "Robby Andika Kusumajaya", "Wesly Tumbur ML Tobing"]2024-08-23T00:00:00 9c3cf287e003c5e0f9ec013bf979f5c6ac4c5910Background: Aging is a multifaceted process that may lead to an increased risk of developing cancer. Artificial intelligence (AI) applications in clinical cancer research may optimize cancer treatments, improve patient care, and minimize risks, prompting AI to receive high levels of attention in clinical medicine. This systematic review aims to synthesize current articles about the effectiveness of artificial intelligence in cancer treatments for older adults. Methods: We conducted a systematic review by searching CINAHL, PsycINFO, and MEDLINE via EBSCO. We also conducted forward and backward hand searching for a comprehensive search. Eligible studies included a study population of older adults (60 and older) with cancer, used AI technology to treat cancer, and were published in a peer-reviewed journal in English. This study was registered on PROSPERO (CRD42024529270). Results: This systematic review identified seven articles focusing on lung, breast, and gastrointestinal cancers. They were predominantly conducted in the USA (42.9%), with others from India, China, and Germany. The measures of overall and progression-free survival, local control, and treatment plan concordance suggested that AI interventions were equally or less effective than standard care in treating older adult cancer patients. Conclusions: Despite promising initial findings, the utility of AI technologies in cancer treatment for older adults remains in its early stages, as further developments are necessary to enhance accuracy, consistency, and reliability for broader clinical use.Journal of Clinical Medicine690Despite promising initial findings, the utility of AI technologies in cancer treatment for older adults remains in its early stages, as further developments are necessary to enhance accuracy, consistency, and reliability for broader clinical use.Journal of Clinical Medicine["Doris C. Obimba", "Charlene Esteva", "Eurika N. Nzouatcham Tsicheu", "Roger Wong"]2024-08-23T00:00:00 87b92c85bfe0cc9aebdf4df8b621adfd05ef7133This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or $64.5 \%$ of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or $6.44 \%$ of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students’ learning patterns.IEEE International Conference on Control System, Computing and Engineering360This study examines the academic field of Artificial Intelligence in Education by examining the publications and indexed documents in Scopus from 2010 to 2023 and identified 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED).2024 IEEE 14th International Conference on Control System, Computing and Engineering (ICCSCE)["Samuel-Soma M. Ajibade", "Bayan Issa", "Muhammed Basheer Jasser", "Farrukh Hassan", "Ghassan Saleh AlDharhani", "Ismail Ahmad Al-Qasem Al-Hadi", "K. A. Akintoye", "Almighty C. Tabuena"]2024-08-23T00:00:00 9208b1630c520643bfb0fc88ff4c8d12731958d4Power systems are changing as a result of the integration of artificial intelligence (AI) technology. These innovations address issues with renewable energy integration, evolving consumption patterns, and the requirement for increased system dependability and efficiency. This thorough research examines the function and effects of artificial intelligence (AI) in power systems, examining case studies and the body of existing literature to clarify the uses, advantages, difficulties, and potential paths for AI adoption. Through an analysis of AI-powered solutions in power generation, transmission, distribution, and control, this study provides policymakers, utilities, academics, and industry participants with knowledge about how AI may be used to tackle urgent energy issues and enable sustainable energy transitions.International Conference on Computing Communication Control and automation00Through an analysis of AI-powered solutions in power generation, transmission, distribution, and control, this study provides policymakers, utilities, academics, and industry participants with knowledge about how AI may be used to tackle urgent energy issues and enable sustainable energy transitions.2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)["Aahash Kamble", "Moses Makuei Jiet", "Prateek Verma", "Siddhi Dhande", "Aniket Goswami"]2024-08-23T00:00:00 2e8e5642448020ea03cd17609a603854fbd370f6Generative Artificial Intelligence (AI) represents a significant advancement in the field of artificial intelligence, characterized by its ability to autonomously generate original content by learning from existing data. Unlike traditional decision-based AI, which primarily aids in decision-making by analyzing data, generative AI can create new texts, images, music, and more, showcasing its immense potential across various domains. However, this technology also presents substantial risks, including data security threats, privacy violations, algorithmic biases, and the dissemination of false information. Addressing these challenges requires a multi-faceted approach involving technical measures, ethical considerations, and robust legal frameworks. This paper explores the evolution and capabilities of generative AI, outlines the associated risks, and discusses the regulatory and legal mechanisms needed to mitigate these risks. By emphasizing transparency, accountability, and ethical responsibility, we aim to ensure that generative AI contributes positively to society while safeguarding against its potential harms.International Journal of Social Sciences and Public Administration30This paper explores the evolution and capabilities of generative AI, outlines the associated risks, and discusses the regulatory and legal mechanisms needed to mitigate these risks.International Journal of Social Sciences and Public Administration["Hanpu Sun"]2024-08-23T00:00:00 047dbea5ee3eb492d31fb18db3a2fdac4ca52607Artificial Intelligence (AI) is considered a game changer in Operations and Supply Chain Management (OSCM). However, many industrial implementation projects fall short of these high expectations. This article presents the results of thirteen interviews with industrial representatives, focusing on their perspectives on the potential benefits and challenges of AI implementation in intralogistics.2024 12th International Conference on Traffic and Logistic Engineering (ICTLE)210The results of thirteen interviews with industrial representatives are presented, focusing on their perspectives on the potential benefits and challenges of AI implementation in intralogistics.2024 12th International Conference on Traffic and Logistic Engineering (ICTLE)["Hannes Winkler", "Greta-Sophie Fiorina Igel", "Luiz Felipe R. R. Scavarda Do Carmo"]2024-08-23T00:00:00 6c4de7df463201604fbc9141abb9a61fe174583eThe study focuses on Artificial Intelligence’s importance in developing green innovation. Artificial intelligence significantly enhances green innovation by enhancing productivity, accelerating environmentally friendly technological advancements, and facilitating better decision-making through energy optimisation, waste reduction, and smart infrastructure support. This research adopted a scoping review approach utilising the Scopus database as a source of documents. The study highlights several aspects, such as publication trend by years, publication source and context analysis. The reviews included documents published since 2021. The method section is derived from the PRISM-ScR checklist table. The highest publisher was the Business Strategy and The Environment journal, and the country with the highest publishing was China. In addition, the study recommends that more efforts should be exerted to increase companies’ awareness of the importance of green innovation and the feasibility of developing green products.IEEE International Conference on Control System, Computing and Engineering440The study focuses on Artificial Intelligence’s importance in developing green innovation and recommends that more efforts should be exerted to increase companies’ awareness of the importance of green innovation and the feasibility of developing green products.2024 IEEE 14th International Conference on Control System, Computing and Engineering (ICCSCE)["Essam Hussain Al Lawati", "Musab A. M. Ali", "N. Tahir"]2024-08-23T00:00:00 9e683ce8964295bcbda1570a2c675ec7c4c2b363In today's era of digital world and evolving cyber threats, this research paper presents a unified exploration of innovative techniques harnessing the power of artificial intelligence and blockchain to combat deepfake attacks and identity theft. These intertwined challenges demand holistic solutions that transcend traditional boundaries. As the digital landscape is increasingly infiltrated by deepfake technology, concerns surrounding the authenticity of digital content are reaching a critical juncture. Deepfake attacks, capable of generating persuasive yet false imagery and videos, pose a grave societal threat. They undermine trust in media, perpetuate misinformation, and raise the specter of identity theft. Image processing techniques for deepfake detection aim to distinguish real from manipulated content by leveraging advances in AI. Meanwhile, the application of AI and machine learning in deepfake detection has yielded promising results, enhancing our capacity to discern authentic media from forgeries. The research converges on a proactive approach, introducing a pioneering framework that integrates AI and blockchain technology. This paper proposes an Artificial Intelligence-based protection framework, leveraging unsupervised pre-training techniques and Dense Neural Networks (DNN), to combat identity impersonation attacks, particularly the Clone ID attack directed at the Routing Protocol for Low Power and Lossy Networks (RPL). The research investigates the potential of blockchain, including Smart Contracts to combat the deepfake problem by verifying digital media's history and provenance.International Conference on Computing Communication Control and automation200An Artificial Intelligence-based protection framework, leveraging unsupervised pre-training techniques and Dense Neural Networks (DNN), to combat identity impersonation attacks, particularly the Clone ID attack directed at the Routing Protocol for Low Power and Lossy Networks (RPL).2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)["Meghana Lokhande", "Prajot Raut", "Kiran Gawali", "Mrudul Ahirrao", "Abhishek Bhande"]2024-08-23T00:00:00 32b607295ce7f0b9e1577fd47d14c364bfc53de1In recent years, artificial intelligence has entered the public eye across various fields, enhancing productivity and increasing social welfare. However, it has also raised significant security and privacy concerns due to the ethical uncertainties associated with this technology, requiring researchers to establish and refine the rules. This paper aims to categorize the security and privacy issues brought by AI and their relation to social ethics, analyze this complex topic from technical, social, and legal perspectives, and propose the current optimal solutions while predicting future developments.International Conference on Data Science in Cyberspace250This paper aims to categorize the security and privacy issues brought by AI and their relation to social ethics, analyze this complex topic from technical, social, and legal perspectives, and propose the current optimal solutions while predicting future developments.2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC)["Yihong Li"]2024-08-23T00:00:00 3e0a67311d3aaeeedfbeedb222610387ff00ed4eJournal of Hospitality Marketing &amp; Management534Journal of Hospitality Marketing &amp; Management["Minglong Li", "Xiaoyang Sun", "meichen Hua", "Hailian Qiu"]2024-08-23T00:00:00 d6de569ee456417a9e1881ff66675460ffb46295ABSTRACTRevue d'Intelligence Artificielle771Revue d'Intelligence Artificielle["Nabila Ounasser", "Maryem Rhanoui", "M. Mikram", "Bouchra El Asri"]2024-08-23T00:00:00 ef561720e9639113bd0eda0f2bd89c5242d9c6dfnpj Digit. Medicine01NPJ Digital Medicine["Jane Huang", "R. Channa", "Risa M. Wolf", "Yiwen Dong", "Mavis Liang", "Jiangxia Wang", "M. Abr\u00e0moff", "T. Y. A. Liu"]2024-08-23T00:00:00 cdaa93320cd0604e4a3af717d57bfd51e08139a8Heliyon1142This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations.Heliyon["Sagheer Abbas", "Muhammad Asif", "Abdur Rehman", "Meshal Alharbi", "Muhammad Adnan Khan", "N. Elmitwally"]2024-08-23T00:00:00 77c54e73e32ef68210aa6a5147468f0450610200This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond.Inf.611This approach enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond.Inf.["Ali Louati", "Hassen Louati", "M. Alharbi", "Elham Kariri", "Turki Khawaji", "Yasser Almubaddil", "Sultan Aldwsary"]2024-08-23T00:00:00 cd99c31f37d0fd3ad6274d111fa3e4154e3a4886Medical Education70Medical education["Kevin W. Eva"]2024-08-23T00:00:00 63d3627e20c8f23220356dba292c7e270dfe5fe1This paper examines the need for a new education system that delivers quality education by integrating AI technology to transform traditional teaching methods. The global education system is currently confronted with critical questions about the true effectiveness and qualifications of teachers in schools and colleges. Despite formal credentials, there remains a significant gap in student satisfaction with conventional teaching approaches. This study explores the current state of teacher qualifications and student satisfaction, highlighting the increasing role of AI in education and demonstrating how AI can revolutionize the way education is delivered.International Journal For Multidisciplinary Research40This study explores the current state of teacher qualifications and student satisfaction, highlighting the increasing role of AI in education and demonstrating how AI can revolutionize the way education is delivered.International Journal For Multidisciplinary Research["Anand Babu"]2024-08-23T00:00:00 72b6c81b0d456887d823ce9efdeb266c43c9b695The integration of big data and AI, which is at present a most crucial development in data mining and processing, can be regarded as the beginning of a new era in the field of science and technology. The research primarily focuses on using ML and data mining methods to forecast cardiac diseases. It also identifies the most efficient and effective approaches for managing enormous datasets and producing accurate forecasts. The results show that LightGBM comes in second with 93.8% accuracy and AdaBoost with 95.2% accuracy. The accuracy rates of other models, such KNN and Naive Bayes, are 88.52% and 90.56%, respectively. These results highlight how sophisticated machine learning algorithms might improve a predicted accuracy of heart disease diagnosis. The paper underscores the necessity of safeguarding data and the importance of stakeholder collaboration to ensure that the advancements in big data and AI are equitable, sustainable, and centred around human well-being.2024 4th Asian Conference on Innovation in Technology (ASIANCON)240The results show how sophisticated machine learning algorithms might improve a predicted accuracy of heart disease diagnosis, and the necessity of safeguarding data and the importance of stakeholder collaboration are highlighted.2024 4th Asian Conference on Innovation in Technology (ASIANCON)["Saransh Arora", "Sunil Raj Thota", "Sandeep Gupta"]2024-08-23T00:00:00 a27e9a8b5d7470bc516a96cccbe128287e75e207International Conference Information Management and Management Sciences50{"pages": "281-286"}["Jin Zhou"]2024-08-23T00:00:00 27125554f5218f18ae860bcf5ead2592d69fe0eaThis study investigates the impact of AI on human-computer interaction patterns among Nigerian children, focusing on accessibility, usage, and educational outcomes. The research encompasses both parental and teacher perspectives, analyzing demographic data and AI technology integration. Results reveal significant discomfort among parents regarding unsupervised AI use by children, yet highlight the potential benefits of AI in enhancing academic performance and motivation. Teachers report varied frequency in incorporating AI into lessons, influenced by accessibility and educational context. Cultural and social factors play a crucial role in AI adoption, presenting challenges such as device availability and internet access. This comprehensive analysis underscores the need for balanced AI integration, considering both educational advantages and potential discomforts, to optimize learning experiences and foster responsible AI usage in academic settings.International Journal Of Engineering And Computer Science00The need for balanced AI integration, considering both educational advantages and potential discomforts, is underscored to optimize learning experiences and foster responsible AI usage in academic settings.International Journal of Engineering and Computer Science["Unique Epunam", "Osaremwinda Omorogiuwa", "E. Okpako"]2024-08-23T00:00:00 2dccf7def8d57bb133c1d0cc6d553538d11befbe00["Dominikus Brian"]2024-08-23T00:00:00 56de80d38ea36fcf5fd43f2707f3375b56a64cf1ATZ worldwide00ATZ worldwide["Matthias Korten", "Matthis H\u00f6tter"]2024-08-23T00:00:00 4a34e302629303a4b408642e18fccec50c191112)PSU Medical Journal00PSU Medical Journal["Surasak Sangkathat"]2024-08-23T00:00:00 59b82eb007534810f2233a7333632eb17bfd7da4Abstract Neuromarketing research focuses on consumer purchase intention, decision-making processes, purchase behavior, brand awareness, brand loyalty, and repeat purchase behavior. In these studies, consumer behavior has been analyzed using neuroscientific methods and tools. The most commonly used tools include Functional Magnetic Resonance Imaging (fMRI), Eye Tracking, Electroencephalogram (EEG), Positron Emission Tomography (PET), Transcranial Magnetic Stimulation (TMS), Magnetoencephalogram (MEG), Steady State Topography (SST), Implicit Association Test (IAT), Facial Electromyography (fEMG), Automatic Face Coding (AFC), Skin Conductance Response (SCR), and other methods for measuring physiological responses. However, the use of these neuroscientific tools is not always possible due to economic constraints and lack of experimental design. Neuroscientific imaging and measurement methods are not preferred in every study due to their high costs and expertise requirements. However, when neuromarketing studies are examined, it is seen that methods such as Eye Tracking, EEG and fMRI are used more widely. These tools contribute to a deeper understanding of consumer behavior. In order to better analyze consumer behavior, it is of great importance to convey marketing stimuli and messages correctly. In the field of marketing, the effect of stimuli conveyed to consumers using the five senses is one of the focal points of neuromarketing. More than one neuroscientific method should be used together to understand consumer intentions, thoughts and purchasing behaviors. In this way, the obtained data can be analyzed more comprehensively and clearer insights can be provided about neuromarketing. The aim of this study is to present a comprehensive assessment of the use of neuroscientific tools by examining the publications in the field of neuromarketing in the Web of Science database between 2010-2024 with bibliometric analysis. The study will address the limitations of not using more than one neuroscientific tool together in neuromarketing research and the inadequacy of analyses supported by artificial intelligence. A more holistic approach will be proposed to address these shortcomings and a guiding resource for future research will be created.Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi260A comprehensive assessment of the use of neuroscientific tools is presented by examining the publications in the field of neuromarketing in the Web of Science database between 2010-2024 with bibliometric analysis to address the limitations of not using more than one neuroscientific tool together in neuromarketing research and the inadequacy of analyses supported by artificial intelligence.Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi["Abdullah Ball\u0131"]2024-08-23T00:00:00 4572af73f4166ebeea0f669a8dc5abf61b478603Journal of Indian Association of Pediatric Surgeons00Journal of Indian Association of Pediatric Surgeons["A. Sinha", "Somya Bhatt"]2024-08-23T00:00:00 4254e3c73eeb8d0b9c62408120a2239c0e65576bThe rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine. As a vital technology, multimodal learning has increasingly garnered interest due to data complementarity, comprehensive modeling form, and great application potential. Currently, numerous researchers are dedicating their attention to this field, conducting extensive studies and constructing abundant intelligent systems. Naturally, an open question arises that has multimodal learning delivered universal intelligence in healthcare? To answer the question, we adopt three unique viewpoints for a holistic analysis. Firstly, we conduct a comprehensive survey of the current progress of medical multimodal learning from the perspectives of datasets, task-oriented methods, and universal foundation models. Based on them, we further discuss the proposed question from five issues to explore the real impacts of advanced techniques in healthcare, from data and technologies to performance and ethics. The answer is that current technologies have NOT achieved universal intelligence and there remains a significant journey to undertake. Finally, in light of the above reviews and discussions, we point out ten potential directions for exploration towards the goal of universal intelligence in healthcare.Information Fusion2086The answer is that current technologies have NOT achieved universal intelligence and there remains a significant journey to undertake towards the goal of universal intelligence in healthcare.Inf. Fusion["Qika Lin", "Yifan Zhu", "Xin Mei", "Ling Huang", "Jingying Ma", "Kai He", "Zhen Peng", "Erik Cambria", "Mengling Feng"]2024-08-23T00:00:00 21fad15f3722ebaab59592021b8cc1f209f492b7This paper investigates the emerging field of emotional engineering which takes the form of a marriage between generative artificial intelligence (AI) and emotional intelligence that is set to redefine the future of human-machine collaboration. With the latest advances in AI having allowed for broad understanding and generation of human-like emotional responses, this paper illuminates the frameworks for creating systems that are capable of empathizing with and responding to the emotions of human users that can be widely applied across domains such as healthcare and customer service. The findings here indicate that user experiences can be drastically improved through applications such as those which allow for machines to accompany emotional intelligence, as they can enable more empathetic interactions. Emotion AI isn’t about having "feelings" in the human sense — it’s more about accurate and relevant emotional interaction. There’s no doubt "emotional engineering" will spawn gadgets that are truly empathetic and not just intelligent, but it also raises large questions about what happens now that we must account for emotions in our digital interactions.International Conference on Computing Communication Control and automation190It is indicated that user experiences can be drastically improved through applications such as those which allow for machines to accompany emotional intelligence, as they can enable more empathetic interactions.2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)["Vilis Pawar", "Abhijit Vhatkar", "Pravin Chavan", "Siddhi Gawankar", "Saranya Nair"]2024-08-23T00:00:00 9866e05e0bedf3e7900901b10c588df1895f7215Artificial Intelligence (AI) is revolutionizing software development practices in high-tech companies, providing transformative insights and tools that enhance productivity, quality, and efficiency. This review explores the integration of AI into software development processes, highlighting its impact on key areas such as code generation, bug detection, project management, and testing. AI-driven tools are enabling developers to automate repetitive tasks, optimize code, and identify potential issues before they become critical, thus reducing development time and improving software reliability. Machine learning algorithms analyze vast amounts of data from past projects to provide predictive analytics, guiding teams in decision-making and resource allocation. Natural language processing (NLP) facilitates more intuitive interactions with development tools, streamlining communication and collaboration among team members. Furthermore, AI enhances continuous integration and continuous deployment (CI/CD) pipelines by automating the testing and deployment stages, ensuring that code changes are seamlessly integrated and deployed with minimal human intervention. By leveraging AI, high-tech companies can adopt more agile methodologies, respond swiftly to market changes, and deliver high-quality software products. The review also discusses the challenges of integrating AI into software development, including the need for substantial initial investment, the complexity of AI models, and the importance of ensuring data privacy and security. Solutions such as fostering a culture of continuous learning, investing in AI-specific training for developers, and establishing robust data governance frameworks are essential for overcoming these barriers. In conclusion, AI-driven insights and tools offer significant advantages for high-tech companies, enabling them to enhance their software development practices, achieve greater efficiency, and maintain a competitive edge in a rapidly evolving technological landscape. Embracing these advancements requires a strategic approach, including investment in AI technologies and training, to fully harness the potential of AI and drive innovation in software development. Keywords: AI, Software Development, High-Tech, Practices, Companies.Computer Science &amp; IT Research Journal016Artificial Intelligence-driven insights and tools offer significant advantages for high-tech companies, enabling them to enhance their software development practices, achieve greater efficiency, and maintain a competitive edge in a rapidly evolving technological landscape.Computer Science &amp; IT Research Journal["Daniel Ajiga", "Patrick Azuka Okeleke", "Samuel Olaoluwa Folorunsho", "Chinedu Ezeigweneme"]2024-08-23T00:00:00 e5028ccb8e5cdb5322aa0f56db73d1635093be35 Artificial Intelligence (AI) has emerged as a transformative force in drug discovery, revolutionizing the biopharmaceutical industry's approach to developing novel therapeutics. This paper provides a comprehensive overview of AI-driven drug discovery, focusing on its applications in accelerating the development of innovative treatments. We examine the fundamental AI technologies employed in drug discovery, including machine learning algorithms, deep learning architectures, and natural language processing techniques. The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy. We explore the impact of big data on AI-driven drug discovery, discussing the challenges and opportunities presented by multi-omics data integration, electronic health records mining, and the need for data standardization. The study also addresses ethical considerations and regulatory challenges associated with AI implementation in drug development. Finally, we present emerging trends and prospects for AI in biopharmaceuticals, emphasizing the importance of collaborative ecosystems and the potential for AI to revolutionize personalized medicine. This review synthesizes current research and industry practices, providing insights into the transformative potential of AI in drug discovery and the challenges that lie ahead in realizing its full potential. Keywords:  Artificial Intelligence, Drug Discovery, Biopharmaceuticals, Machine Learning.International medical science research journal5615The paper analyzes the integration of AI across various stages of the drug discovery pipeline, from target identification to clinical trial design, highlighting significant improvements in efficiency and accuracy.International Medical Science Research Journal["Decheng Huang", "Mingxuan Yang", "Xin Wen", "Siwei Xia", "Bo Yuan"]2024-08-23T00:00:00 be2fc5c4d3e7774f9bccdab34ad9f3e5fb7fff51Artificial intelligence encompasses a wide range of approaches, methodologies, and techniques aimed at mimicking human intelligence in machines. In recent times, the concepts of Generative Artificial Intelligence (AI), Super AI, and Narrow AI have attracted considerable attention. Undoubtedly, the success of ChatGPT in capturing all attention has played a significant role in this. Artificial intelligence technology has a profound impact on all sectors, and sector representatives are striving to adapt to this technology more quickly. It is projected that artificial intelligence could generate an economic size of 13 trillion American dollars by 2030. Developments in artificial intelligence technologies undoubtedly lead to significant improvements in the functioning of public institutions and access for citizens. Artificial intelligence has the potential to be used in many public services, including security and defense, healthcare services, education, transportation and infrastructure, environmental and natural resource management, law and justice systems, among others. Therefore, evaluating the types of artificial intelligence, Narrow AI applications, and chatbots for public use is seen as highly beneficial from the perspective of public administration and the public sector. In our study, the topics of super artificial intelligence, generative artificial intelligence, narrow artificial intelligence, and chatbots have been extensively evaluated within the context of the public sector and public administration. Utilizing findings from both Turkish and English literature reviews, the importance and potential impacts of artificial intelligence within the public sector, along with current trends, have been comprehensively assessed. This research delves into the concepts of artificial intelligence and its subsets—super AI, generative AI, narrow AI, and chatbots—within the general framework of the public sector. China and the United States are pioneering and leading countries in terms of investment. Although the U.S. stands out in many areas regarding investment, China's integration of artificial intelligence with national strategies and its policies indicate that it may play a more dominant role in the future. There are four main implementation areas of artificial intelligence in the public sector: efficiency and automation, service delivery, data-driven governance, and ethical and regulatory challenges. A review of the literature reveals that the ethical, legal, and social implications of implementing artificial intelligence in the public sector require more careful consideration. The study makes a significant contribution to the field of artificial intelligence discussions in public administration and the public sector, providing a comprehensive assessment of current discussions on artificial intelligence in the literature.Journal of AI393The topics of super artificial intelligence, generative artificial intelligence, narrow artificial intelligence, and chatbots have been extensively evaluated within the context of the public sector and public administration, providing a comprehensive assessment of current discussions on artificial intelligence in the literature.Journal of AI["Muhammet Damar", "Ahmet \u00d6zen", "\u00dclk\u00fc Ece \u00c7akmak", "Eren \u00d6zo\u011fuz", "F. Erenay"]2024-08-24T00:00:00 7e3a1e2eca33ac7b5c016ed3168e1ab3d51a3f48Nature Communications643The best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research, and to lessen the back-ended risk premium itself.Nature Communications["Joaquin Vespignani", "Russell Smyth"]2024-08-24T00:00:00 69ebcd6f36e499238d889f31aeb1b26f7dd66c5cArtificial Intelligence (AI) has become a born- again force throughout different industries including the sports business arena. Artificial Intelligence is now being used in sports increasingly due to its capability to provide various advantages in this field such as enhancing athletes performances & health, smooth the way for training & diet programs, assessing games & building approaches, refreeing. scouting & hiring players, forecasting the matches, selling tickets & even the sports journalism. The Global AI in the Sports Industry is anticipated to increase from the numbers 518.8 Million in 2022 to 4.3 Billion Dollars by 2028 at a CAGR of 42.3% over the predicted tenure. By 2030, the worldwide influence of AI is in prospect to attain $19.9 Billion. As a matter of fact, use of AI makes the sports events & tournaments more professional & the introduction of AI will take physical education to the next level. With the aid of these AI, big data & other facts & figures apparatus schools can initiate a unique & scientific physical education environment. My objectives is to evaluate the existing regulatory framework governing AI & its impacts on sports business, To compare between the countries with regard to AI & its impact on sports business development, To create awareness among the people with regard to AI & its impact on sports business, To understand the disadvantages of AI on sports industry & its negative impacts & To propose different ideas & recommendations can be given on the enhancement of AI & its impacts on sports business. The author has collected 202 samples. The researcher has undertaken the empirical research method. The scope of having advancements in the field of sports & its related business in the upcoming years is possible only via the utilisation of AI tools to compete in bigger stage is purely in the hands of the government of our country & people as in the case of AI there should be a balance between the cons/pros & the development in an arranged manner to get rewards in this particular arena in the near future.International Journal of Innovative Science and Research Technology331The objective is to evaluate the existing regulatory framework governing AI & its impacts on sports business, and compare between the countries with regard to AI & its impact on sports business development to create awareness among the people with regard to AI & its impact on sports business.International Journal of Innovative Science and Research Technology (IJISRT)["Sharadh Sureshbabu", "B. Lavaraju"]2024-08-24T00:00:00 1b45f01a991422a71c4bbd054a34a8f1a14ed54eThe purpose of this study is to explore the limited exploration of the simultaneous influence of beneficial artificial intelligence, destructive artificial intelligence, and risky artificial intelligence on green purchase intention and green purchase behaviour using the Technology Acceptance Model (TAM) and Innovation Resistance Theory (IRT). Further, it also checks the impact of green purchase intention on green purchase behaviour. Data was collected using a well-structured questionnaire from 124 consumers through online mode and analyzed using Confirmatory Factor Analysis (CFA) for reliability and validity concerns and Structural Equation Modelling (SEM) for interaction among the variables. The study's results exhibit the positive impact of beneficial artificial intelligence on green purchase intention and green purchase behaviour. Also, it reveals that destructive artificial intelligence has a positive impact on green purchase intention but a negative impact on green purchase behaviour. In addition, green purchase intention is found to be the predictor of green purchase behaviour. The extant literature is found on the impact of artificial intelligence on purchase behaviour. However, no research has been done on consumer perception of artificial intelligence and its impact on green purchase intention and green purchase behaviour as per the author’s knowledge. This study contributes to the literature of artificial intelligence as well as green consumer behaviour.Journal of Metaverse591The study's results exhibit the positive impact of beneficial artificial intelligence on green purchase intention and green purchase behaviour and reveals that destructive artificial intelligence has a positive impact on green purchase intention but a negative impact on green purchase behaviour.Journal of Metaverse["Apoorva Bhatnagar", "Megha Sharma"]2024-08-24T00:00:00 3ef91aafd73c98aa8b4f6d873ef4a4f3b88a6498Feed accounts for over 60% of broiler production costs, with energy being the most significant factor. Traditional feed formulation methods focus on balancing nutrients to meet average flock needs but often fail to address the dynamic requirements of modern poultry production. Variability in growth rates, health, and environmental conditions can lead to feed utilization and performance inefficiencies. Artificial intelligence (AI) offers a transformative opportunity in poultry nutrition, enabling more precise and adaptable feed formulations. By leveraging large datasets and advanced algorithms, AI can accurately predict nutrient requirements and optimize feed in real time, allowing continuous adjustments based on environmental changes and flock health. Essential support systems, including precision feed manufacturing tools, advanced sensors, and new energy systems like productive energy, are crucial to realizing the full potential of AI. These technologies will enhance feed formulation precision and align with One-Health principles, promoting sustainable practices that improve animal health and reduce environmental impact. AI will play a vital role in improving poultry production efficiency, productivity, and sustainability as it advances.German Journal of Veterinary Research00Artificial intelligence will play a vital role in improving poultry production efficiency, productivity, and sustainability as it advances, and essential support systems, including precision feed manufacturing tools, advanced sensors, and new energy systems like productive energy, are crucial to realizing the full potential of AI.German Journal of Veterinary Research[]2024-08-24T00:00:00 a86b6fc175a420712bc69860e767afa3adf592e4International Journal of Clinical Oncology310The next phase of AI-based medical research on cancer should focus on the practical applications of AI tools and how they can be effectively used in actual medical research settings.International journal of clinical oncology["S. Kurozumi", "Graham R Ball"]2024-08-24T00:00:00 a53d34f6e1e74c28385b54ceb9cf4282b826a5f9AIMS A multidisciplinary group of experts and patients developed the Model for ASsessing the value of Artificial Intelligence (MAS-AI) to ensure an evidence-based and patient-centered approach to introducing artificial intelligence technologies in healthcare. In this article, we share our experiences with meaningfully involving a patient in co-creating a research project concerning complex and technically advanced topics. METHODS The co-creation was evaluated by means of initial reflections from the research team before the project started, in a continuous logbook, and through semi-structured interviews with patients and two researchers before and after the active co-creation phase of the project. RESULTS There were initial doubts about the feasibility of including patients in this type of project. Co-creation ensured relevance to patients, a holistic research approach and the debate of ethical considerations. Due to one patient dropping out, it is important to foresee and support the experienced challenges of time and energy spent by the patient in future projects. Having a multidisciplinary team helped the collaboration. A mutual reflective evaluation provided insights into the process which we would otherwise have missed. CONCLUSIONS We found it possible to create complex and data-intense research projects with patients. Including patients benefitted the project and gave researchers new perspectives on their own research. Mutual reflection throughout the project is key to maximise learning for all parties involved.Scandinavian Journal of Public Health140It is found it possible to create complex and data-intense research projects with patients and including patients benefitted the project and gave researchers new perspectives on their own research.Scandinavian journal of public health["Anne Wettergren Karlsson", "Astrid Janssens", "Astrid Barkler", "Thomas Schmidt", "B. S. B. Rasmussen", "I. Fasterholdt"]2024-08-24T00:00:00 be76b8a2456b10a1693f9d411517afe1908dbdfdA suite of impressive scientific discoveries have been driven by recent advances in artificial intelligence. These almost all result from training flexible algorithms to solve difficult optimization problems specified in advance by teams of domain scientists and engineers with access to large amounts of data. Although extremely useful, this kind of problem solving only corresponds to one part of science - the"easy problem."The other part of scientific research is coming up with the problem itself - the"hard problem."Solving the hard problem is beyond the capacities of current algorithms for scientific discovery because it requires continual conceptual revision based on poorly defined constraints. We can make progress on understanding how humans solve the hard problem by studying the cognitive science of scientists, and then use the results to design new computational agents that automatically infer and update their scientific paradigms.arXiv.org530The authors can make progress on understanding how humans solve the hard problem by studying the cognitive science of scientists, and then use the results to design new computational agents that automatically infer and update their scientific paradigms.ArXiv["Ruairidh M. Battleday", "S. Gershman"]2024-08-24T00:00:00 acac14b4706099406deb4a7b5a3fc14d1b107aeaArtificial intelligence (AI) has changed the way automatic car systems work, making them safer, more efficient, and able to drive themselves. This in-depth review looks at the many different AI methods that are used to create and improve automatic car systems. The main focus of this study is on machine learning methods, such as supervised, unsupervised, and reinforcement learning, and how they can be used to help vehicles understand, make decisions, and be controlled. Supervised learning, especially deep learning, is a key part of finding and classifying objects, which is needed to tell the difference between people, cars, and road signs. Many people use Convolutional Neural Networks (CNNs) because they are very good at handling images and videos accurately, which makes real-time responses easier in changing driving situations. Unsupervised learning methods, like grouping and anomaly detection, make systems more reliable by finding strange trends and behaviors. This makes it easier to know what's going on and plan for future maintenance. Vehicles can learn from interacting with their surroundings thanks to reinforcement learning, which is a key part of improving decision-making. This method is very important for planning routes, adaptive speed control, and avoiding collisions, which makes sure that self-driving cars can handle complicated situations safely and quickly. Also talked about are sensor fusion methods that combine data from LiDAR, radar, and video to give a more complete picture of the surroundings and provide extra information. The review also talks about the moral issues and legal problems that come up with AI-driven self-driving cars. To get a full picture of the field, things like computer openness, data protection, and the moral effects of making choices in tough scenarios are looked at. This review highlights the changing potential of AI in automated car systems by putting together the latest research and developments. It also gives us a look into the directions and improvements that will come next. The goal of this study is to be a useful resource for students, practitioners, and lawmakers who are interested in how autonomous driving technologies are changing over time.Advances in Nonlinear Variational Inequalities240This review highlights the changing potential of AI in automated car systems by putting together the latest research and developments and gives a look into the directions and improvements that will come next.Advances in Nonlinear Variational Inequalities["M. A. Sayyad", "D.G.Lokhande", "P.M.Vibhute", "D. V. S. Gaikwad", "Dr. Prashant Bansilal Patel"]2024-08-24T00:00:00 bf31d8e6a8586aab771e6ac3b0b4b37732f08278Advancements in technology, including the Internet of Things (IoT) and Artificial Intelligence (AI), greatly impact agriculture. The study investigates the statistical applications of AI-driven robotic devices based on the IoT as a basic emphasis. Using manual effort and chemical fertilizers, traditional farming can be highly attributed to inefficiency, health problems and environmental effects. The paper proposes an AgriBotIQ, a revolutionary platform that uses robotics based on IoT to monitor and analyse with accurate participation in plant management. Autonomous robots can collect information based on plants and their habitats by using imaging devices and sensors like soil moisture, humidity, temperature, and many others. Machine learning (ML) algorithms search the database for anomaly detection, threats, and crop trends. To identify the crops that are diseased or healthy, ML is integrated with computer vision. The suggested AgriBotIQ also eliminated weeds, boosting the output by neglecting unneeded waste and chemicals. The emerging IoTs have allowed better remote plant monitoring in more versatile and précised. Overall productivity and protection of crops are possible by statistical analysis and real-time notifications of the proactive decisionmaking outcomes. By combining IoT and AI, the future agricultural crop security will improve greatly.PatternIQ Mining00The paper proposes an AgriBotIQ, a revolutionary platform that uses robotics based on IoT to monitor and analyse with accurate participation in plant management and eliminated weeds, boosting the output by neglecting unneeded waste and chemicals.PatternIQ Mining["K. R", "M. C"]2024-08-24T00:00:00 0f56115fa808a5a5c43bed379a7686aee4416bc5Artificial intelligence (AI) is participating more and more in the lives of the world's humanity day by day. Artificial intelligence, which first appeared as simple apparatuses and their behaviors, has begun to enter our lives more effectively over time. Artificial intelligence emerged based on three basic facts. First of all, behind artificial intelligence are electronic developments. Secondly factor is development of information-processing technology at the support of electronics. The third developer element of AI is humanity's move to the information society and the archiving and digitization of information as a result of transformation into an information society. At today's level of development of artificial intelligence, concerns that unemployment may increase in societies due to the participation of artificial intelligence in economic life seem to have increased. In this context, in this article, firstly, the elements that constitute and reveal artificial intelligence are revealed. Then, possible relation between AI and employment were researched. According to the research, it has been seen that artificial intelligence studies will increase and develop over time, and with this development, artificial intelligence will cause machines to replace people in jobs that involve routine and automated labor force. However, it is also possible that new job areas will arise and production increases will occur in the process. In this case, it should be expected that in the future, on an economic level, the need to improve the qualifications of the workforce in line with changing job conditions will be more prominent than the unemployment of the workforce.Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi00According to the research, it has been seen that artificial intelligence studies will increase and develop over time, and with this development, artificial intelligence will cause machines to replace people in jobs that involve routine and automated labor force.Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi["A. O. Balkanl\u0131"]2024-08-24T00:00:00 0e09eb71d8bdbc2c3c0596f3b796dd019c12a02fIn today’s rapidly evolving digital landscape, artificial intelligence (AI) has become a transformative force, significantly impacting various aspects of human life. While AI offers numerous benefits, it also presents significant challenges, particularly in the realm of cybercrime. Among the most pressing concerns are cyber stalking and digital privacy. this paper discusses the concept ,significance and challenges of AI in today's world from a legal aspects.International Journal For Multidisciplinary Research50The concept, importance and challenges of AI in today's world from a legal aspects are discussed, particularly in the realm of cybercrime.International Journal For Multidisciplinary Research["Nameeta Rana"]2024-08-24T00:00:00 2013719ebc91109c4b2f01a0aa74954165552aa9Journal of Intelligent Manufacturing41J. Intell. Manuf.["Andrew Kusiak"]2024-08-24T00:00:00 9fb377d5210f18d36b9afafbab351b9f80e01412International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-24T00:00:00 64a6c256f9602232735b9ff40941d7f5d86d6c78International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-24T00:00:00 671d9a1445ba59317044201c9a54645ee5237a15Multimedia tools and applications190Multimedia Tools and Applications["Ankush Manocha", "S. Sood", "Munish Bhatia"]2024-08-24T00:00:00 5ecd1d0b773adbb300d632662a713cce6ec3ecc0Knowledge Discovery and Data Mining30{"pages": "6751-6752"}["Chen Zhao", "Feng Chen", "Xintao Wu", "Jundong Li", "Haifeng Chen"]2024-08-24T00:00:00 4057bec4fbf3b07167bfeb3d21f25bf7f67db6a7Knowledge Discovery and Data Mining40{"pages": "6741-6742"}["Steven Ullman", "Benjamin M. Ampel", "Sagar Samtani", "Shanchieh Yang", "Hsinchun Chen"]2024-08-24T00:00:00 8c28559672361149bf314804e22ed953bfebb6b8Knowledge Discovery and Data Mining220{"pages": "6597-6604"}["Miriam Cindy Maurer", "Jacqueline Michelle Metsch", "Philip Hempel", "Theresa Bender", "Nicolai Spicher", "Anne-Christin Hauschild"]2024-08-24T00:00:00 14a51ffd70efb48f85afb1238e9177c61ca0f3bfBibliotechnyi visnyk190Bibliotechnyi visnyk["Esther Oluwayemi Jatto", "A. Tella"]2024-08-24T00:00:00 35cab7ad15f3ca9631f3c3d65055e0db4d9fbff8Knowledge Discovery and Data Mining00{"pages": "6720-6721"}["Shenda Hong", "Daoxin Yin", "Gongzheng Tang", "Tianfan Fu", "Liantao Ma", "Junyi Gao", "Mengling Feng", "Mai Wang", "Yu Yang", "Fei Wang", "Hongfang Liu", "Luxia Zhang"]2024-08-24T00:00:00 fb5e2694f73dfba0917e5bad518fcb337aa6886dThis exploration of law and economics raises many related issues. First, we consider ways in which law and economics movement and theory may be said to have revolutionized legal thinking. Second, we illustrate the near-total commoditization of personal data by leading ‘artificial intelligence’ ‘cyber firms and their coalitions, creating both digital warfare and modernized lawfare. Third, we dwell on Shoshanna Zuboff’s central conception of surveillance capitalism, which constitutes the third modernity which stands for a future where “a genuine inversion and its social compact are institutionalized as principles of a new rational digital capitalism”; these present a scary picture of the “informational mapping of all of the territories on the planet”, “the unremitting locating of individuals”, and the “capture of body information and health and behavioural data”. This continual data mining, fourth, introduces a “rogue mutation of capitalism marked by concentrations of wealth, knowledge and power unprecedented in human history, ostracizing “people from their individual self-direction”. Fifth, explored are some ways of critiquing the very notion of ‘information civilization”. Sixth, in conclusion, we raise the question of the law and economics agenda about law as a soft technology trying to regulate hard technologies and how, if possible, to reverse the imageries of the ‘end of law’.GNLU Journal for Law and Economics00GNLU Journal for Law and Economics[]2024-08-24T00:00:00 8e0b932fbe5b4a9a9604c83dfadaf7eca0b3d249Journal of Science Education and Technology623The discussion about the types of knowledge teachers need to apply GAI effectively is extended, the need to further develop theoretical frameworks for teachers’ knowledge in the age of GAI is highlighted, and ways to extend existing frameworks such as TPACK with AI-related dimensions are suggested.Journal of Science Education and Technology["Yael Feldman-Maggor", "R. Blonder", "Giora Alexandron"]2024-08-24T00:00:00 a30c3b82fb6f30b0e937c1a9c201d5cf089733b6International journal of impotence research143Overall, this study shows that AI Chatbots may provide mostly accurate information on frequently asked questions regarding vasectomies and is a reasonable resource for patients interested in the procedure to use.International journal of impotence research["Edwin Mouhawasse", "Christopher W. Haff", "Preet Kumar", "B. Lack", "Kevin Y. Chu", "Utsav Bansal", "Justin M Dubin"]2024-08-24T00:00:00 5694de020c43e914d03976d24df1738f7ae60a6aThe digital era encourages using artificial intelligence (AI) in various industries, including interior design and architecture. Growth in AI usage is projected to increase by 34% in 2023-2032. AI can improve efficiency, productivity, and decision-making in the design process. With AI, designers can optimise visualisation, space layout, material, and colour selection and predict customer preferences. AI saves time and costs and results in innovative solutions per client requirements. As it evolves, AI improvises technologies to assist designers such as DALL-E, Blender, AI Dungeon, Spline, and CogniCAS. However, the adoption of AI in interior design and business management in Indonesia still needs improvement. This study analysed related articles from 2019 to 2023 using Publish or Perish and VOSviewer visualisation to investigate the optimal utilisation of AI in supporting the interior design process. This research aims to provide practical insights and recommendations for interior designers, students, and practitioners adopting AI solutions to improve performance and competitiveness and develop a standard framework for AI technology platforms.Journal of Artificial Intelligence in Architecture01This research aims to provide practical insights and recommendations for interior designers, students, and practitioners adopting AI solutions to improve performance and competitiveness and develop a standard framework for AI technology platforms.Journal of Artificial Intelligence in Architecture["Vania Azalia Audrey Lesmana", "Antonetta Tina", "Sugesti Retno Yanti"]2024-08-24T00:00:00 c9a2bdf83cffb6c46524b2354ff7960a551caa90Fast-advancing Artificial Intelligence (AI) has transformed many industries, including construction. AI offers innovative solutions to increase efficiency and effectiveness in various aspects of construction, one of which is selecting building materials. By reading relevant literature, this study aims to determine how much AI can help choose building materials so that projects go more easily and quickly. Using SCOPUS as its principal database, this study conducted a literature review. The method of this study begins with the process of filtering articles using the key string: ("artificial intelligence" OR AI) AND ("building materials" OR "construction materials") AND ("efficiency" OR "time" OR "cost") to find relevant articles. The research results show that AI can help improve time and cost efficiency in selecting building materials through various means, such as data analysis, material recommendations, cost optimisation, and performance estimation. In conclusion, this study shows that AI has much potential to make choosing building materials more efficient and effective, thus reducing building time, costs, and environmental damage. Still, it also dramatically impacts building monitoring and maintenance and task automation.Journal of Artificial Intelligence in Architecture761The research results show that AI can help improve time and cost efficiency in selecting building materials through various means, such as data analysis, material recommendations, cost optimisation, and performance estimation.Journal of Artificial Intelligence in Architecture["Andi Prasetiyo Wibowo"]2024-08-24T00:00:00 c8274ab2f991b4da4276fcac562e39a66fdd744aThis paper investigates the intricate link that exists between Free and Open Source Software (FOSS) and the legal and regulatory framework that exists inside the European Union. The study illustrates the problems and possibilities given by this dynamic area by charting the growth of FOSS from its ideological roots to its current standing as a significant economic and technical force. The paper examines the policy steps taken by the European Union to encourage the use of FOSS in public administration, as well as the influence that new technologies, such as artificial intelligence, have had on the ecology of FOSS. In addition, it digs into the legal complications that surround FOSS, including concerns about licensing and the enforcement of copyright. In its conclusion, the paper provides policy proposals with the goal of fostering a sustainable and thriving FOSS ecosystem inside the European Union while maintaining a balance between the need for innovation and regulatory control.The Boğaziçi Law Review00The paper examines the policy steps taken by the European Union to encourage the use of FOSS in public administration, as well as the influence that new technologies, such as artificial intelligence, have had on the ecology of FOSS.The Boğaziçi Law Review["Muhammed Furkan Ak\u0131nc\u0131"]2024-08-24T00:00:00 adfefadc5afdf258c5c6a354808bf7f53e2dea8aThis workshop aims to spearhead research on Human-Interpretable Artificial Intelligence (HI-AI) by providing: (i) a general overview of the key aspects of HI-AI, in order to equip all researchers with the necessary background and set of definitions; (ii) novel and interesting ideas coming from both invited talks and top paper contributions; (iii) the chance to engage in dialogue with prominent scientists during poster presentations and coffee breaks. The workshop welcomes contributions covering novel interpretable-by-design or post-hoc approaches, as well as theoretical analysis of existing works. Additionally, we accept visionary contributions speculating on the future potential of this field. Finally, we welcome contributions from related fields such as Ethical AI, Knowledge-driven Machine learning, Human-machine Interaction, applications in Medicine and Industry, and analyses from Regulatory experts.Knowledge Discovery and Data Mining110A general overview of the key aspects of HI-AI is provided to equip all researchers with the necessary background and set of definitions in order to equip all researchers with the necessary background and set of definitions.{"pages": "6708-6709"}["Gabriele Ciravegna", "M. Zarlenga", "Pietro Barbiero", "Francesco Giannini", "Z. Shams", "Damien Garreau", "M. Jamnik", "Tania Cerquitelli"]2024-08-24T00:00:00 a0d8b7f96c1ac69349818bb190b6f5133d69e1b9En este artículo sobre Inteligencia Artificial en la Educación Superior se aborda la aplicación y evolución histórica de la Inteligencia Artificial (IA) en el ámbito educativo. Se destaca la importancia de integrar la IA en la educación para preparar a las nuevas generaciones de profesionales, enfatizando la necesidad de adaptarse a un mundo impredecible y en constante cambio. Se menciona el uso de computadoras con IA en el ejército durante la Segunda Guerra Mundial, resaltando su capacidad para realizar tareas complejas. Además, se explora el recorrido histórico de la aplicación de la IA en la educación, evidenciando su impacto en la mejora de las experiencias educativas y en la resolución de problemáticas de aprendizaje. También, proporciona una visión general de las tecnologías de IA utilizadas en la educación desde la práctica universitaria, ofreciendo definiciones y conceptos que permiten comprender su utilidad en el ámbito educativo. En conclusión, se enfatiza la importancia de la IA como una herramienta que facilita el aprendizaje, mejora las experiencias educativas y contribuye al desarrollo profesional de los educadores en la Educación Superior.Realidad y Reflexión00Realidad y Reflexión["H. Oliva"]2024-08-24T00:00:00 02985de6214b7c2c6eaff2f936f654add7b78004La Revolución Digital ha impulsado cambios significativos en el ámbito de la contabilidad, especialmente en la auditoría, con la adopción de la Inteligencia Artificial (IA). Este trabajo examina el impacto de la IA en la auditoría, destacando cómo ha transformado los procedimientos tradicionales. A través de la automatización, la IA ha optimizado procesos como la verificación de transacciones y la detección de fraudes, permitiendo a los auditores centrarse en análisis más estratégicos. Se empleó una metodología documental para analizar las aplicaciones actuales de la IA en auditoría y sus efectos en la precisión y eficiencia. Los resultados muestran que la IA mejora la capacidad para detectar anomalías y predecir riesgos financieros, aportando un valor añadido significativo. Sin embargo, también plantea desafíos éticos y regulatorios, que requieren una actualización de las normativas y estándares profesionales. En conclusión, la IA está revolucionando la auditoría, ofreciendo tanto oportunidades como desafíos que deben ser gestionados para maximizar su potencial.FACE Revista de la Facultad de Ciencias Económicas y Empresariales40FACE: Revista de la Facultad de Ciencias Económicas y Empresariales["Sergio Alfonso Tosca Maga\u00f1a", "Ver\u00f3nica V\u00e1zquez Vidal", "Maximiliano Martinez Ortiz"]2024-08-24T00:00:00 0e58dcd72468190e58c166296a631c01ba10b151The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies.Italian National Conference on Sensors1122The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences and shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms.Sensors (Basel, Switzerland)["Xiaochen Zhang", "Yiran Ding", "Xiaoyu Huang", "Wujing Li", "Liumei Long", "Shiyao Ding"]2024-08-24T00:00:00 31a2aeff23e381ab4eb0d529d8831805ce8ac23eThe field of artificial intelligence is drastically advancing. This study aims to provide an overview of the integration of artificial intelligence into learning management systems. This study followed a bibliometric review approach. Specifically, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, 256 documents from the Scopus and Web of Science (WoS) databases over the period of 2004–2023 were identified and examined. Besides an analysis of the documents within the existing literature, emerging themes and topics were identified, and directions and recommendations for future research are provided. Based on the outcomes, the use of artificial intelligence within learning management systems offers adaptive and personalized learning experiences, promotes active learning, and supports self-regulated learning in face-to-face, hybrid, and online learning environments. Additionally, learning management systems enriched with artificial intelligence can improve students’ learning outcomes, engagement, and motivation. Their ability to increase accessibility and ensure equal access to education by supporting open educational resources was evident. However, the need to develop effective design approaches, evaluation methods, and methodologies to successfully integrate them within classrooms emerged as an issue to be solved. Finally, the need to further explore education stakeholders’ artificial intelligence literacy also arose.Multimodal Technologies and Interaction554Based on the outcomes, the use of artificial intelligence within learning management systems offers adaptive and personalized learning experiences, promotes active learning, and supports self-regulated learning in face-to-face, hybrid, and online learning environments.Multimodal Technol. Interact.["Diego Vergara", "Georgios Lampropoulos", "\u00c1lvaro Ant\u00f3n\u2010Sancho", "Pablo Fern\u00e1ndez\u2010Arias"]2024-08-25T00:00:00 33e5a525ff0346da673ec185a31a18d1b9e8344eThis editorial article examines how generative Artificial Intelligence (GAI) can bridge various human resource development (HRD) processes. As GAI adoption increases in human resources practices, understanding its potential to integrate different HRD activities becomes more important. The article reviews recent literature on Artificial Intelligence (AI) applications in HRD and explores GAI‐enabled links between key HRD processes. The linkages include data‐driven decision‐making, real‐time skill gap analysis, job crafting with GAI, GAI‐supported personalized development plans, GAI‐powered employee sentiment analysis, GAI chatbots, GAI‐enabled virtual reality simulations, and GAI‐supported social network analysis in talent and organization development contexts. By highlighting these GAI‐enabled interconnections, the article provides insights into a more integrated approach to HRD. It also discusses implications for HRD practitioners and researchers, analyzing specific applications of GAI in HRD and recommending future research.Human Resource Development Quarterly294The article reviews recent literature on Artificial Intelligence applications in HRD and explores GAI‐enabled links between key HRD processes, highlighting GAI‐enabled interconnections and providing insights into a more integrated approach to HRD.Human Resource Development Quarterly["P. Korzy\u0144ski", "Sewon Kim", "T. Egan"]2024-08-25T00:00:00 2e75f27e84994caa8e9d86dee2143dd0c4f1a861Airports are becoming increasingly interconnected and technologically advanced ecosystems. Airports handle sensitive data and depend on interconnected systems, which, while improving efficiency, also heighten vulnerability to cyber threats like data breaches and ransom ware. Additionally, with technological advancements artificial intelligence also plays a huge role, providing a new definition to cyber security in aviation. This paper examines the current state of cyber security in the aviation industry, implementation of artificial intelligence and how we can make the best use of technology, whilst focusing on the vulnerabilities and threats that accompany the adoption of advanced technologies such as Internet of Things (IoT) and Supervisory Control and Data Acquisition (SCADA) systems. The paper also explores the types of cyber attacks that can target smart airport systems, emphasising the need for robust cyber security measures to protect these critical infrastructures.International Research Journal of Computer Science00The current state of cyber security in the aviation industry, implementation of artificial intelligence and how to make the best use of technology are examined, whilst focusing on the vulnerabilities and threats that accompany the adoption of advanced technologies such as Internet of Things and SCADA systems.International Research Journal of Computer Science["Sidharth Jain", "Arnav Bansal", "NareshKumar Agarwala"]2024-08-25T00:00:00 54e974c65b9a22eb89c081cc68f871531535114aThe advent of Artificial Intelligence (AI) has brought transformative changes across various sectors, including education. In Library and Information Science (LIS), AI tools hold significant potential for enhancing teaching, research, and administrative functions. This study investigates the awareness, perception, and utilization of AI tools by LIS lecturers in Nigerian higher institutions. Data were collected using questionnaires and analysed with the Statistical Product and Service Solution (SPSS), with hypotheses tested via Pearson Product Moment Correlation (PPMC). The findings reveal a high degree of awareness and positive perception towards AI tools among LIS lecturers. Commonly used tools for teaching include ChatGPT, Socrative, ChatPDF, Turnitin, and Gamma. Despite recognizing AI's potential benefits for improving information retrieval, data management, and personalized learning, actual usage remains limited due to challenges such as rapid technological advancement, lack of infrastructure, and resistance to change. All hypotheses were rejected, indicating a significant relationship between awareness, perception, and the use of AI tools in teaching. If measures such as having enhanced AI literacy and training programs for LIS educators, integration of AI into the LIS curriculum, development of institutional policies on AI adoption, and incentives for AI integration, then the challenges observed could be mitigated.Cybrarians Journal130The findings reveal a high degree of awareness and positive perception towards AI tools among LIS lecturers in Nigerian higher institutions, indicating a significant relationship between awareness, perception, and the use of AI tools in teaching.Cybrarians Journal["Omobolanle Seri Fasola"]2024-08-25T00:00:00 65090a12673279688a4c7d880240b7bedefd07af Engineering curricula undergo frequent change, driven by new technologies and industry needs. Today we are witnessing a significant rise in artificial intelligence (AI) tools, with applications not only across engineering, but specifically in critical endeavors such as design. Given the interest in students in AI techniques, the demand of engineering design employers to hire students with such knowledge, and the fast-evolving nature of the AI field, compared to the slower pace of curriculum evolution, there is thus a need to assess curricular content related to AI in the mechanical engineering curriculum. The purpose of this paper is to provide a baseline assessment of the current prevalence of AI-driven methods and approaches in engineering design education. Current approaches for curricular assessment tend to be resource-intensive and narrow in scope, limiting our capability for large-scale and timely data analysis. Thus we develop a using a novel approach for curriculum data collection and assessment: First, we use web-scraping to collect the titles and descriptions of 2,195 courses in 28 undergraduate mechanical engineering programs. Next we use a list of relevant keywords to search for AI topics in these courses. We find 32 AI-focused courses available to mechanical engineering students, in which nine courses integrate AI and engineering design. These results indicate the limited but emerging prevalence of AI-based courses in engineering design education.Digital Enterprise Computing00A novel approach using web-scraping to collect the titles and descriptions of 2,195 courses in 28 undergraduate mechanical engineering programs indicates the limited but emerging prevalence of AI-based courses in engineering design education.Volume 4: 21st International Conference on Design Education (DEC)["P. Khanolkar", "Jerry Lu", "Ada Hurst", "Alison Olechowski"]2024-08-25T00:00:00 a10b7990d3876a93199abf2591c6df28a75995b1It is of critical importance to analyse the factors influencing college students' intention to use generative artificial intelligence (GenAI) to understand and predict learners' learning behaviours and academic outcomes. Nevertheless, a lack of congruity has been shown in extant research results. This study, therefore, conducted a meta-analysis of 27 empirical studies under an integrated theoretical framework, including 87 effect sizes of independent research and 33,833 sample data. The results revealed that the main variables are strongly correlated with students' behavioural intention to use GenAI. Among them, performance expectancy (r = 0.389) and attitudes (r = 0.576) play particularly critical roles, and effort expectancy and habit are moderated by locational factors. Gender, notably, only moderated attitudes on students' behavioural intention to use GenAI. This study provides valuable insights for addressing the debate regarding students' intention to use GenAI in existed research, improving educational technology, as well as offering support for school decision-makers and educators to apply GenAI in school settings.arXiv.org660The results revealed that the main variables are strongly correlated with students' behavioural intention to use GenAI, and performance expectancy and attitudes play particularly critical roles, and effort expectancy and habit are moderated by locational factors.ArXiv["Yifei Diao", "Ziyi Li", "Jiateng Zhou", "Wei Gao", "Xin Gong"]2024-08-25T00:00:00 a4199a59a7731a68d1bb544fe06049c8dc88ff46This study aimed to examine the literature on the use of artificial intelligence and the Internet of Things in the field of recreation and leisure and present the results within themes identified inductively from the data. We employed a systematic review methodology, consisting of determining appropriate selection criteria, choosing data sources, extracting data, categorizing the results, and reporting. Using the Web of Science database, we identified a total of 69 articles published between 2017 and 2024. After filtering and screening for keywords, 23 full-text articles related to artificial intelligence and the Internet of Things in the field of recreation and leisure were included in the analysis. Relevant studies were evaluated according to year, journal, focus, country, type of technology, recreation area, and results obtained. Findings from the reviewed articles are discussed under six themes: safety, ecosystem, personalized recreation experience, wearable technology, health, and potential recreation and leisure areas. We observed that the most frequently investigated topic in the studies was recreational tourism, with a general focus on outdoor recreation. The studies often referred to nature conservation and planned and safe personal leisure time. In conclusion, we determined that artificial intelligence and Internet of Things technologies have various applications in the field of recreation, but relevant studies are limited.Spor Bilimleri Araştırmaları Dergisi430It is determined that artificial intelligence and Internet of Things technologies have various applications in the field of recreation, but relevant studies are limited.Spor Bilimleri Araştırmaları Dergisi["Sinem Parlaky\u0131ld\u0131z", "Sevim K\u00fcl Avan"]2024-08-25T00:00:00 67180977f76017eb968900a3dfc9f1d6f31b7d8aInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-25T00:00:00 0ce11125b5c20c607d4df2726a6ac2803c118c10International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-25T00:00:00 6fb93e2f8eb550038cb9bb898c36849d8ced44c6International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-25T00:00:00 ce09a84e05eb3a2b35f5312314def6e0f90bc37cThis research aims to find out the contributions and threats of AI in education so that it can contribute to technological development in the SDGs 2030 era. The method in this research is qualitative using literature study. The contributions of AI in education include personal virtual tutors, adaptive learning systems, learning Chabot, game-based learning, AI based assessment systems, educational data analysis, automatic evaluation, accessibility and inclusiveness, assistants for learning, and helping to develop curriculum. The contribution of AI in education is very helpful for students to improve their skills and knowledge. It is expected that more will be able to participate in SDGs 4 (Quality Education). Threats of AI in education include data privacy and security, technology gaps, dependence on technology, fraud, and algorithm imperfections. Thus, there should be collaboration with AI experts to develop strategies and data simulations to minimize the threats. Jurnal Penelitian Pendidikan IPA240The contributions and threats of AI in education so that it can contribute to technological development in the SDGs 2030 era are found and collaboration with AI experts to develop strategies and data simulations to minimize the threats are developed.Jurnal Penelitian Pendidikan IPA["Diah Arini", "Muhammad Nursa'ban"]2024-08-25T00:00:00 c776abfb829b9c40bcfed985e0ac1834db470886The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modelling utilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.Journal of Advanced Research in Applied Sciences and Engineering Technology400This study analytically examines the potential of hybrid solar and wind energy harvesting devices to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures.Journal of Advanced Research in Applied Sciences and Engineering Technology["Moch S. Mauludin", "Moh. Khairudin", "Rustam Asnawi", "Wan Azani Mustafa", "S. Toha"]2024-08-25T00:00:00 1547925060734653f2484e54c82385a74edbfd29This study conducts a comprehensive bibliometric analysis to explore the research landscape in neuromarketing and brand association. With a growing interest in the neural mechanisms influencing consumer behavior, neuromarketing leverages neuroscientific techniques like fMRI, EEG, and eye tracking to uncover subconscious decision-making processes. The study aims to map research trends, identify key themes, and highlight influential works within this field. Using data from Scopus and Web of Science, the methodology includes examining publication growth, geographic distribution, co-authorship networks, and keyword co-occurrence over the past two decades. The data collection process employed effective keywords such as "neuromarketing," "brand association," "EEG," "fMRI," and "consumer behavior," ensuring a comprehensive dataset of peer-reviewed articles. The findings reveal significant growth in publications, with notable contributions from North America and Europe, and increasing input from Asia. Key insights highlight the central role of emotional engagement, sensory marketing, and the integration of advanced technologies like AI and deep learning in neuromarketing research. The study emphasizes the need for more longitudinal studies to understand the long-term impacts of neuromarketing strategies and calls for cross-cultural comparisons to enhance the global applicability of the findings. Concluding, the research identifies literature gaps and offers practical recommendations for marketers to leverage neuromarketing techniques to boost consumer engagement.International Journal Administration, Business &amp; Organization320The research identifies literature gaps and offers practical recommendations for marketers to leverage neuromarketing techniques to boost consumer engagement, as well as examining publication growth, geographic distribution, co-authorship networks, and keyword co-occurrence over the past two decades.International Journal Administration, Business &amp; Organization["Adryan Rachman", "Prita Karina Diandra", "Febryanti Simon"]2024-08-25T00:00:00 a53a4da8c91405bc0902a7d0b71abead9e94ce52Humans are gradually being substituted by of artificial intelligence and robots in virtually all departments in organizations. For some workers this might mean they have to find a new job, or a new orientation in terms of skills. Whatsoever, this revolution is upsetting the workforce. Human Resource professionals now have the responsibility of overseeing the placement of robots and AI, ensuring that everything goes smoothly, and intervening when problems arise. This thesis pertains examining the utilization in the workplace of AI, robot alongside man- power from the HRM standpoint. In the first section, there is a discussion on the introduction, objectives, the set-up of the problem, the delimitations and lastly, the concept of using manpower, AI, and robot at the workplace. In the second section, there is the demonstration of theoretical and the empirical parts of the thesis. This research is a literature review of several articles related to machine learning. The review was conducted from some of the recent research efforts that utilize machine learning. Furthermore, this review is derived from multiple literacies and includes an attempt at problem solving efforts that are divided into section areas from the perspective of each machine learning category. Machine learning can change the way the human resource management domain functions in an organization. It is making changes in all aspects of human resource management starting from human resource planning. Enormous data is available in human resource information systems (HRIS) available in organizations.Jurnal Penelitian Pendidikan IPA260This research is a literature review of several articles related to machine learning and includes an attempt at problem solving efforts that are divided into section areas from the perspective of each machine learning category.Jurnal Penelitian Pendidikan IPA["J. Penelitian", "Pendidikan Ipa", "Zulkifli Djamin", "Dasmadi"]2024-08-25T00:00:00 23c66b935a4c4842a550b6d8cbbe2c7dad2aeba2In the evolving landscape of global security, the integration of intelligence gathering, risk analysis, and scenario planning is paramount for effective defense policy formulation. This study aims to underscore the critical role of these elements in contemporary defense strategies. Employing qualitative research methods, particularly secondary data analysis, this research investigates the transformative impact of artificial intelligence (AI) and machine learning (ML) on threat assessments, the benefits and challenges of big data analytics in risk analysis, and the value of interdisciplinary perspectives in scenario planning. The findings reveal that AI and ML significantly enhance the accuracy and reliability of threat assessments by enabling real-time data processing and predictive analytics. However, challenges such as data privacy and algorithmic biases persist. Big data analytics offers substantial benefits in identifying and mitigating emerging threats but requires robust data management frameworks to address issues of data quality and integration. Additionally, scenario planning is highlighted as a strategic tool that enhances defense strategies by anticipating various future scenarios and enabling proactive measures. Furthermore, the integration of interdisciplinary perspectives in scenario planning fosters more robust and adaptable defense policies, ensuring a comprehensive approach to security challenges. In conclusion, the integration of advanced technologies and interdisciplinary methods in intelligence gathering, risk analysis, and scenario planning is crucial for developing resilient and adaptive defense policies.International Journal Administration, Business &amp; Organization1160Investigation of the transformative impact of artificial intelligence and machine learning on threat assessments, the benefits and challenges of big data analytics in risk analysis, and the value of interdisciplinary perspectives in scenario planning reveal that AI and ML significantly enhance the accuracy and reliability of threat assessments by enabling real-time data processing and predictive analytics.International Journal Administration, Business &amp; Organization["Aris Sarjito"]2024-08-25T00:00:00 892e4b2abb7465619a12902605b52b0b57e5bf0aIn response to increasing environmental challenges, the United States has deliberately adopted technical advancements to promote sustainable development. This includes efforts to decrease pollution, improve energy efficiency, and encourage the use of environmentally friendly technology in different industries. This study investigates the role of Artificial Intelligence (AI) technology in promoting environmental sustainability in the United States from 1990 to 2019. It also examines the impacts of financial development, ICT use, and economic growth on the Load Capacity Factor (LCF). Various unit root tests revealed no unit root issues and mixed integration orders among variables. The Autoregressive Distributive Lag (ARDL) model explored cointegration, indicating long-run relationships among the variables. The ARDL findings confirm the Load Capacity Curve hypothesis for the United States, with AI technology and ICT use positively correlating with LCF in both the short and long run. Conversely, financial development and population growth significantly reduce LCF. Robustness checks using FMOLS, DOLS, and CCR estimation approaches align with the ARDL results. Granger causality tests reveal unidirectional causality from economic growth, AI, financial development, and ICT use to LCF and bidirectional causality between population and LCF. Diagnostic tests confirm the results are free from heterogeneity, serial correlation, and specification errors. This study underscores the importance of AI and ICT in enhancing environmental sustainability while highlighting the adverse impacts of financial development and population growth on LCF.Journal of Environmental Science and Economics18810Journal of Environmental Science and Economics["Mohammad Ridwan", "Shewly Bala", "Sarder Abdulla", "Al Shiam", "Afsana Akhter", "Md Asrafuzzaman", "Sarmin Akter Shochona", "Shake Ibna Abir", "Shaharina Shoha"]2024-08-25T00:00:00 c2ff1a67f912c54131793cec3d451996a7ef3f80Artificial intelligence has significantly reduced the need for human activity in education. Numerous AI technologies are already present in various academic applications created to help students with their academic writing. However, although the leverage of AI in academic writing for a student is associated with numerous opportunities, it also raises multiple issues and problems that should be thoroughly investigated. The objective of this study is to review the implications of leveraging AI in student academic writing. This study employed a review analysis approach, examining relevant literature based on the specified keywords. The methodology of this study includes: (a) a technique for searching the literature; (b) determining inclusion and exclusion criteria; and (c) a review analysis process. The review analysis identified several implications of leveraging AI for students’ academic writing. They include, but are not limited to: (a) paradigm shift in learning; (b) ethical considerations and research integrity; (c) enhancing writing abilities and efficiency; (d) redefining academic writing practices; and (e) collaboration between AI and human writers. In conclusion, leveraging AI as a supplementary tool has revealed several positive implications for students' academic writing and has considered the significance and relevance of these findings. For additional studies, a broader discussion of the ethical concerns associated with the leverage of AI in academic writing will have to be developed.Malaysian Journal of Social Sciences and Humanities641Leveraging AI as a supplementary tool has revealed several positive implications for students' academic writing and has considered the significance and relevance of these findings.Malaysian Journal of Social Sciences and Humanities (MJSSH)["Mohd Amzari Tumiran", "Nasharuddin Mohammad", "Sumaiyah Bahri"]2024-08-25T00:00:00 ccdf9375f2598de1f0b033099ff76701787c823cThe rapid urbanization and increasing vehicular density in modern cities have led to significant challenges in traffic management and control. As urban areas continue to expand, the demand for more efficient and intelligent traffic control systems has become increasingly critical. This paper presents a novel approach to enhancing traffic management by integrating Artificial Intelligence (AI), Blockchain technology, and Dynamic Computation Techniques. AI is utilized to analyze and predict traffic patterns, enabling real-time adjustments to traffic signals and flow management. Blockchain provides a secure, transparent, and decentralized platform for data sharing and coordination among various stakeholders, ensuring data integrity and trust. The incorporation of Dynamic Computation Techniques allows for flexible and scalable processing of complex traffic data, facilitating rapid decision-making and adaptation to changing conditions. This multidisciplinary approach not only improves traffic efficiency and reduces congestion but also paves the way for more resilient and sustainable urban transportation systems. The findings highlight the transformative potential of combining AI, Blockchain, and advanced computation methods in the field of traffic control.VFAST Transactions on Software Engineering150A novel approach to enhancing traffic management by integrating Artificial Intelligence, Blockchain technology, and Dynamic Computation Techniques is presented, which improves traffic efficiency and reduces congestion but also paves the way for more resilient and sustainable urban transportation systems.VFAST Transactions on Software Engineering["Muhammad Kashif Shaikh", "Syed Faraz Liaquat", "Fahad Ahmed Siddiqui", "Abdul Moid Khan", "Muhammad Javeed", "Manzar Ahmed"]2024-08-25T00:00:00 04c56d4a611e89f811fdfd75939242c21960e76fAs organizations face increasingly complex security challenges, the integration of Artificial Intelligence (AI) in Identity and Access Management (IAM) systems has emerged as a transformative solution. This paper explores the multifaceted role of AI in enhancing IAM systems, focusing on key capabilities such as anomaly detection, continuous improvement, scalability, regulatory compliance, and access management processes. AI-driven systems enhance security by enabling real-time anomaly detection, adaptive learning, and automated responses to evolving threats. They improve scalability and performance, ensuring IAM systems can handle the growing demands of large, dynamic environments. Additionally, AI facilitates regulatory compliance by providing robust audit trails and enhancing the approval processes for access management. However, the adoption of AI in IAM systems also presents significant challenges, including data privacy concerns, integration with legacy systems, and potential biases in AI models. The paper concludes by outlining future research directions, emphasizing the need for explainable, ethical, and adaptable AI solutions. Overall, AI-driven IAM systems offer promising advancements in securing digital infrastructures, improving operational efficiency, and fostering regulatory compliance, while also presenting new avenues for innovation and research.Journal of Computer Science and Technology Studies00Overall, AI-driven IAM systems offer promising advancements in securing digital infrastructures, improving operational efficiency, and fostering regulatory compliance, while also presenting new avenues for innovation and research.Journal of Computer Science and Technology Studies["Surendra Vitla"]2024-08-25T00:00:00 f15e8b11eb5177f957ca70c4e0cc57efe03c6866 It’s critical to understand how to use artificial intelligence (AI) to foster innovation in the modern world as AI becomes more integrated into creative and problem-solving tasks. Using the sustainable washing machine as a primary example, this study designed and developed AI design assistant AIDA as a web-based chatbot to facilitate design ideation, leveraging large language models. AIDA prompts design tasks and assesses user-generated ideas for validity, novelty, and feasibility using RoBERTa-based models. As in the initial phase of an ongoing project, we conducted a human-subject experiment to validate a baseline version of AIDA and examined user performance and perceptions. The participants demonstrated smooth interaction with AIDA and consistent performance. They reported mostly positive perceived usefulness, enjoyment, and trust. Moreover, females and participants equal to or over 25 showed a comparable level of trust for general automated systems and AIDA, whereas male and under 25 participants were more skeptical about AIDA. This research offers a framework for technical development, tailored interactions, and real-time feedback, as well as insights into the use of AI chatbots to mediate engineering design. By analyzing user behavior and survey responses, we identified future directions in designing AI systems in engineering education and early-stage design.Volume 6: 36th International Conference on Design Theory and Methodology (DTM)00This research offers a framework for technical development, tailored interactions, and real-time feedback, as well as insights into the use of AI chatbots to mediate engineering design by analyzing user behavior and survey responses.Volume 6: 36th International Conference on Design Theory and Methodology (DTM)["Naveen Mathews Renji", "Sagar Chakravarthy Mathada Veera", "Bei Yan", "Ting Liao"]2024-08-25T00:00:00 ec00b5409c12f1172fdd1a968518b639d3c60403 This research investigates the perceptions and attitudes towards artificial intelligence (AI) and automation in manufacturing settings. A multimodal data collection approach is used to gather participant responses using surveys, focus group discussions, and interviews. Qualitative and quantitative data collected from six manufacturing companies are analyzed to explore the multifaceted dynamics influencing the adoption and acceptance of AI and automation technologies. The study investigates specific views, concerns, and expectations regarding the implementation of these technologies, shedding light on the nuanced perspectives of participants across different organizational contexts. By examining factors such as personal strategies, sentiment analysis, relevance to roles, and technology acceptance, the research offers insights into the complex interaction between technological advancement, organizational culture, and individual attitudes. Varying levels of understanding regarding technological relevance to roles are observed, alongside concerns about job displacement and decision-making transparency. Participants differ in their belief in AI and automation’s potential to address concerns, highlighting the need for tailored integration strategies. Unique observations, such as machine personalization and cultural influences, are uncovered. Conclusions stress the importance of addressing these complexities in adoption efforts, while future work aims to expand datasets and explore new data collection methods to inform integration strategies. Future work includes analysis of the focus group and interview transcripts for confirmation of these findings.Conference on Computability in Europe00Varying levels of understanding regarding technological relevance to roles are observed, alongside concerns about job displacement and decision-making transparency, highlighting the need for tailored integration strategies.Volume 2A: 44th Computers and Information in Engineering Conference (CIE)["Oredola Adebayo", "Pavan Kumar", "Apurva Patel", "Joshua D. Summers"]2024-08-25T00:00:00 f4ba31691f273d587670da2ecdfe6f3ea875695e For a plethora of humanitarian and commercial applications such as medical imaging, air traffic control, driving, and supervisory control, artificial intelligence is assisting humans in increasing performance in visually demanding tasks. To design an effective AI team, AI must provide the proper level of assistance predicated on human cognition and performance. To achieve this, it is essential to monitor and predict human performance in real-time rather than relying solely on overall task performance. Reaction and decision time can be utilized to predict the mental workload of humans in visually demanding tasks. In this paper, we utilize the benchmark Atari environment to remove domain expertise and provide emphasis on context-aware human decision-making. We extract connected component labeling features from the environment and human eye gaze to predict when a human performs a context-aware action modification for both frame-by-frame and time-domain applications. We identify specific environmental scenarios where eye gaze provides a wealth of information and increases real-time workload classification. The classification results are analyzed for specificity, sensitivity, and F-score to illustrate the mitigation of misclassified information. The results demonstrate the effectiveness of utilizing environment features in combination with eye gaze to predict when the human needs AI assistance during a visually demanding task.Conference on Computability in Europe00This paper utilizes the benchmark Atari environment to remove domain expertise and provide emphasis on context-aware human decision-making and demonstrates the effectiveness of utilizing environment features in combination with eye gaze to predict when the human needs AI assistance during a visually demanding task.Volume 2B: 44th Computers and Information in Engineering Conference (CIE)["Kristian Dalland", "Joseph P. Distefano", "Ehsan T. Esfahani"]2024-08-25T00:00:00 0ca36ae16ce813aba2395300d11849d35b4ed932This study delves into the shifting role of Artificial Intelligence (AI) within the banking industry, with a focus on its transformative effects on service quality, operational effectiveness, and customer interaction. The research underscores significant developments in AI and its integration, highlighting its pivotal role in updating traditional banking practices and tackling modern-day challenges. It offers a comprehensive analysis of the potential of AI to enhance banking services, while also addressing obstacles such as technical difficulties and regulatory concerns. The outlook section predicts ongoing AI expansion in the banking sector, particularly its capacity to further tailor banking services and improve risk management. The goal of this research is to provide a comprehensive understanding of AI's integration into Indian banking, shedding light on the evolving relationship between technological innovation and the financial sectorLatIA100The goal of this research is to provide a comprehensive understanding of AI's integration into Indian banking, shedding light on the evolving relationship between technological innovation and the financial sector.LatIA["Danish Anwar", "Faizan Uddin", "Soofia Fatima", "Shams Raza", "Rajeshwar Dayal"]2024-08-25T00:00:00 657e41adc874437f3aabbf29b0c66bc0144992ffArtificial Intelligence (AI) and Machine Learning (ML) are the two emerging technologies adopted by most of the business companies worldwide. The use of advanced technologies is highly effective on businesses and capable of improving overall performance of companies. Examining the potential effects, opportunities, challenges and scopes of adopting AI and ML in businesses worldwide is the main purpose of this study. In regard to this, secondary qualitative methods have been used for gathering authentic and reliable data from several scholarly articles and peer-reviewed journals. All the findings are discussed briefly and it has been identified that AI and ML have great influence towards workplace efficiency, productivity and profitability of business companies. It also helps to boost customer satisfaction and gain competitiveness in the global market.International Journal of Scientific Research in Science and Technology90It has been identified that AI and ML have great influence towards workplace efficiency, productivity and profitability of business companies and it also helps to boost customer satisfaction and gain competitiveness in the global market.International Journal of Scientific Research in Science and Technology["Nithyananda B Devadiga"]2024-08-25T00:00:00 dbdc89ceadc0a24bd8db59b7598c24a7bcfadd2eThis manifesto outlines key principles for progress in the post-AI era, emphasizing non-linear yet cumulative advancement, deep understanding of purpose and context, multi-stakeholder collaboration, and system-level experimentation. It redefines progress as substantial, durable, and replicable advancement, highlighting the importance of balancing technological innovation with human-centric values. It acknowledges AI's potential to accelerate progress across industries while recognizing its limitations, such as creating illusions of understanding and potentially narrowing problem-solving approaches. It concludes that true progress in the AI age requires a symbiosis of artificial intelligence capabilities and human ingenuity, calling for a holistic, interdisciplinary approach to shape a future that serves all of humanity.arXiv.org330ArXiv["Christoforus Yoga Haryanto"]2024-08-25T00:00:00 ebb34ad525b4abf7a03f9c22b6fc9a6d20016011 Modern engineering projects increasingly require designers to use tools to manage complexity. One such tool, functional decomposition, uses the functional basis to aid designers in developing a structured and consistent description of a product or system in terms of functional requirements and desired behavior. However, cyber-physical systems (CPS) that increasingly integrate artificial intelligence (AI) and machine learning (ML) cannot be represented with the necessary degree of nuance and flexibility through the current functional basis. This is an inconvenience to designers especially in an age where the use of machine learning and artificial intelligence is becoming widespread. This paper proposes an extension to the functional basis that includes new flows and functions to better describe the intricacies of AI/ML systems. While this extension provides a significant advancement in representing AI/ML systems within the functional basis, the rapidly evolving nature of AI/ML technologies presents opportunities for further enhancements and expansions of this framework in the future. By creating a more comprehensive representation of cyber-physical systems with AI/ML capabilities, designers can better conceptualize and design these complex systems, facilitating a more consistent, structured, and descriptive functional model.Volume 6: 36th International Conference on Design Theory and Methodology (DTM)00By creating a more comprehensive representation of cyber-physical systems with AI/ML capabilities, designers can better conceptualize and design these complex systems, facilitating a more consistent, structured, and descriptive functional model.Volume 6: 36th International Conference on Design Theory and Methodology (DTM)["Doreen Valmyr", "Ambrosio Valencia-Romero", "Christopher McComb"]2024-08-25T00:00:00 888bd647befca1e0cfecf9107866a900df9999ac Designers are increasingly using Generative Artificial Intelligence (GenAI) in design processes; however, knowing how designers use GenAI — especially in professional design practice — is under-explored. This paper presents an ethnographic study of an early-stage design team at NASA that explores the natural variation of GenAI use across team members during a speculative design workflow. We aimed to uncover when, how, and why GenAI tools were or were not employed using ethnographic observations to map the team’s speculative design process and follow-up interviews to provide deeper insights into team members’ interactions (or lackthereof) with GenAI. Through inductive qualitative coding, our analysis revealed three strategies of GenAI use observed among professional engineers and designers — intimate co-design with GenAI, selective delegation to GenAI, and minimal use of GenAI — as well as factors that appeared to influence their decisions whether or not to use GenAI. This study proposes new theory in human-AI collaboration that sheds light on the strategies, rationale, and circumstances under which design professionals do and do not use GenAI. These strategies and factors tied to GenAI use offer insights into when, how, and why professionals use GenAI in design and how GenAI could be built to better accommodate designers.Volume 6: 36th International Conference on Design Theory and Methodology (DTM)00An ethnographic study of an early-stage design team at NASA that explores the natural variation of GenAI use across team members during a speculative design workflow to propose new theory in human-AI collaboration that sheds light on the strategies, rationale, and circumstances under which design professionals do and do not use GenAI.Volume 6: 36th International Conference on Design Theory and Methodology (DTM)["Kevin Ma", "George Moore", "Vikram Shyam", "James Villarrubia", "Kosa Goucher-Lambert", "Eric Reynolds Brubaker"]2024-08-25T00:00:00 2a4cd6c2e9a394bab12bdc86f0ed9e240f1ac7c2 As Artificial Intelligence (AI) and Machine Learning (ML) technologies advance, the need for methods to test them also increases. Several methods for testing ML and AI systems have recently been developed by the ML/AI and software development communities. These methods assume that the team that is developing the system is also responsible for testing the system, therefore, they have access to the datasets on which the ML models were trained, and the knowledge of the environment in which the system is expected to operate. However, this is not true in certain situations such as in the Department of Defense (DoD) acquisition where the systems are developed by other organizations, due to which, existing methods for Test and Evaluation (T&E) of AI-enabled systems are not adequate for DoD acquisition. To address this gap we propose a multi-fidelity approach for testing and evaluation that consists of (i) a representation of the model space with dimensions along which different fidelities of models can be developed, and (ii) a method to integrate multiple fidelities for continuous T&E of AI-enabled systems. The approach is illustrated using an example of a visual perception system in an autonomous vehicle (AV) use case, where a simulation space across different fidelities is constructed to test how well the system meets the listed requirements. A model space is first identified, in which models are characterized for their cost and performance. A method to generate test plans is then devised to maximize the utility across the span of given system requirements. We illustrate how the proposed approach can be used to develop test combinations that minimize the cost and maximize the utility under a set of system requirements to be tested.Conference on Computability in Europe00A multi-fidelity approach for testing and evaluation that consists of a representation of the model space with dimensions along which different fidelities of models can be developed, and a method to integrate multiple fidelities for continuous T&E of AI-enabled systems is proposed.Volume 2B: 44th Computers and Information in Engineering Conference (CIE)["Robert J. Seif", "Zichong Yang", "Ziran Wang", "Laura Freeman", "Jitesh H. Panchal"]2024-08-25T00:00:00 2e67106980c8ee1c973f3c351761e021ed0d3eecInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-08-25T00:00:00 300da8532d7c06097590bf0a91c014dc24ea4714The rapid advancement of artificial intelligence (AI) in education underscores transformative prospects for open and distributed learning, encompassing distance, hybrid, and blended learning environments. This qualitative study, grounded in narrative inquiry, investigates the experiences and perceptions of 141 preservice teachers engaged with AI, mainly through ChatGPT, over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies. Employing Strauss and Corbin’s methodological approach of open, axial, and selective coding to analyze reflective narratives, the study unveils significant themes that underscore the dual nature of AI in education. Key findings reveal ChatGPT’s role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage. Specifically, ChatGPT was found to empower personalized learning and streamline procedures, yet challenges involving information accuracy and data security remained. The study significantly contributes to teacher education discourse by revealing AI’s complex educational impacts, highlighting an urgent need for comprehensive ethical AI literacy in teacher training curricula. However, critical ethical considerations and practical challenges involving academic integrity, information accuracy, and balanced AI use are also brought to light. The research also spotlights the need for responsible AI implementation in open and distributed learning to optimize educational outcomes while addressing potential risks. The study’s insights advocate for future-focused AI literacy frameworks that integrate technological adeptness with ethical considerations, preparing teacher candidates for an intelligent digital educational landscape.International Review of Research in Open and Distance Learning505This qualitative study investigates the experiences and perceptions of 141 preservice teachers engaged with AI over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies, revealing ChatGPT’s role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage.The International Review of Research in Open and Distributed Learning["Fatih Karata\u015f", "Erkan Y\u00fcce"]2024-08-26T00:00:00 2c402766eaf7896e219c893101a87750e45b43a0This study explores the transformative potential of Artificial Intelligence (AI) in education by analyzing its impact on student learning outcomes. Through a comprehensive literature review, the research synthesizes current findings on the integration of AI in educational settings, examining both the benefits and challenges it presents. The study explores into AI's role in personalizing learning experiences, enhancing student engagement, and improving academic performance. Ethical considerations such as data privacy and algorithmic bias are also assessed. This research also identifies existing gaps in the literature and suggests avenues for future inquiry, contributing to a deeper understanding of how AI can be effectively and responsibly integrated into education to optimize student success.Journal of Digital Learning and Education214The study explores AI's role in personalizing learning experiences, enhancing student engagement, and improving academic performance and identifies existing gaps in the literature and suggests avenues for future inquiry, contributing to a deeper understanding of how AI can be effectively and responsibly integrated into education to optimize student success.Journal of Digital Learning and Education["P. Sasikala", "R. Ravichandran"]2024-08-26T00:00:00 3026acdeabfd5d819d83e105da28608b7d5c4123Artificial intelligence (AI) has undergone considerable advancement in the contemporary period and represents an emerging technology in higher education. Cultural contexts significantly shape individuals’ perceptions, attitudes, and behaviors, particularly in the realm of technology acceptance. By adopting a cross-cultural lens, this research explores the potential variations across Chinese and international students from diverse countries in terms of attitudes and their behavioral intentions toward AI use. With a technology acceptance model (TAM) framework, the research used a survey approach, employing questionnaires as the primary means of data collection. The data were then analyzed through structural equation modeling and descriptive statistics. A substantial discrepancy was found in the prevalence, attitudes, and behavioral intentions toward AI use between Chinese and international students. Findings further revealed a stronger effect of perceived ease of use on both attitudes and behavioral intentions among international students compared with their Chinese counterparts. Findings suggest that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology. The study emphasizes the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces. These insights contribute to the evolving discourse on technology acceptance in higher education and offer practical implications for educators and institutions toward optimizing AI integration in pedagogical practices.International Review of Research in Open and Distance Learning724It is suggested that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology, and the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces is emphasized.The International Review of Research in Open and Distributed Learning["Dongmin Ma", "Huma Akram", "Hua Chen"]2024-08-26T00:00:00 2e94dc0973b4ac1d126262f7d735d96bf72a3220The transformative impact of advancements in educational technology, particularly those powered by artificial intelligence (AI), on the landscape of education and the teaching profession has been substantial. This study explores the repercussions of AI-powered technologies on teachers’ autonomous behavior, digital burnout, and professional development. The study involved a cohort of 320 high school teachers in China segregated into control and experimental groups. The experimental group received instructions on AI-integrated applications and how they might be used in education. However, the teachers assigned to the control group did not receive information on the use of AI educational applications. Three distinct questionnaires probing autonomous behaviors, digital burnout, and online professional development were administered, and the ensuing data were analyzed using independent sample t-tests. The findings elucidate a discernible positive impact of AI-integrated technology intervention on teachers’ professional development and autonomous behaviors. The incorporation of AI-enhanced tools facilitated an augmentation in teachers’ professional growth and bolstered their independent and self-directed instructional practices. Notably, using AI-integrated technology significantly reduced teachers’ susceptibility to digital burnout, signifying a potential alleviation of stressors associated with technology-mediated teaching. This research provides valuable insights into the multifaceted effects of AI-powered technologies on educators, shedding light on enhancing professional competencies and mitigating digital burnout. The implications extend beyond the confines of this study, resonating with the broader discourse on leveraging technology to augment the teaching profession and optimize the learning environment.International Review of Research in Open and Distance Learning491Using AI-integrated technology significantly reduced teachers’ susceptibility to digital burnout, signifying a potential alleviation of stressors associated with technology-mediated teaching.The International Review of Research in Open and Distributed Learning["Hong Duan", "Wei Zhao"]2024-08-26T00:00:00 cfd762fe747e57435452f48ee09ab2aa01e63fe1Artificial intelligence (AI) and intense learning (DL) models have emerged as powerful tools in ophthalmology, revolutionizing the early detection and management of ocular diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. This narrative review explores AI's current applications and future potential in these domains, focusing on using convolutional neural networks (CNNs) and other DL architectures to analyze retinal fundus photographs, optical coherence tomography (OCT) images, and visual field tests. By leveraging vast datasets and identifying subtle pathological features, AI models have demonstrated high accuracy, sensitivity, and specificity in detecting these diseases, often surpassing human graders. Integrating AI into clinical practice holds promise for enhancing diagnostic efficiency, facilitating early intervention, and ultimately improving patient outcomes. However, challenges related to data quality, model interpretability (the ability to understand and trust the decisions made by AI models), and ethical considerations (such as patient privacy and consent) must be addressed to fully realize AI's potential in ophthalmology. Future research should focus on validating AI models in diverse populations, exploring novel DL architectures, and developing integrated systems seamlessly incorporating AI into clinical workflows.Revista Ibero-Americana de Humanidades, Ciências e Educação331A narrative review explores AI's current applications and future potential in ophthalmology, focusing on using convolutional neural networks (CNNs) and other DL architectures to analyze retinal fundus photographs, optical coherence tomography (OCT) images, and visual field tests.Revista Ibero-Americana de Humanidades, Ciências e Educação["L\u00e1zaro Felipe Costa Vilela", "N\u00e1dia Oliveira Cabral", "Afr\u00e2nio C\u00f4go Destefani", "Vin\u00edcius C\u00f4go Destefani"]2024-08-26T00:00:00 2585ccd4861e1ae2e9d47cd26d6a141cc55cfb31This study focuses on the ability of Artificial Intelligence (AI) to redesign learning experience of higher education by making learning adaptable by the year 2030. Machine learning and natural language processing afford the possibility of developing adaptive learning environment for students. The research also focuses on AI's present and future uses in the learning & issues of realizing those uses. The purposive sampling technique selected 5 faculty members from different universities. A semi-structured interview guide was developed to get data from the participants. Data was analyzed thematically by facilitation of NVivo 14. The potential of AI for enhancing personalized tasks, automated tasks related to administration, and creating interactive learning experiences. The concerns of data confidentiality and ethical considerations were also addressed. By analyzing the improvement of adaptive learning technologies, the study presents views of how AI can improve educational outcomes. Therefore, the findings also emphasize the diverse implications of equalizing technological innovation with keeping important human fundamentals in education while highlighting the justice and inclusivity.Journal of Social Research Development01By analyzing the improvement of adaptive learning technologies, the study presents views of how AI can improve educational outcomes and emphasizes the diverse implications of equalizing technological innovation with keeping important human fundamentals in education while highlighting the justice and inclusivity.Journal of Social Research Development["Ghulam Mustafa Mustafa", "Tanzeela Urooj", "Muhammad Aslam"]2024-08-26T00:00:00 ee017515914c61ac931bc339100e1b988f321c61This study aimed to present a comprehensive bibliometric analysis of 1,726 academic studies from among those indexed by the Web of Science database platform between 2013 and 2023, to provide a general framework for the concept of artificial intelligence in education (AIEd). Trends in publications and citations across countries, institutions, academic journals, and authors were identified, as well as collaborations among these elements. Several bibliometric analysis techniques were applied, and for each analysis, the motivations behind the execution and method of producing findings were documented. Our findings showed that the number of studies on the concept of AIEd has increased significantly over time, with the U.S. and China being the most common countries of origin. Institutions in the U.S. stand out from those around the world. Pioneering journals in education have also emerged as prominent in the field of AIEd. On the other hand, collaboration between authors has been limited. The study was supplemented with keyword analysis to reveal thematic AIEd concepts and to reflect changing trends. For those exploring artificial intelligence in education, our insights on popular topics offer valuable guidance toward greater understanding of the latest advancements and key research areas.International Review of Research in Open and Distance Learning381It was showed that the number of studies on the concept of AIEd has increased significantly over time, with the U.S. and China being the most common countries of origin.The International Review of Research in Open and Distributed Learning["G\u00fcrhan Durak", "Serkan \u00c7ankaya", "Damla \u00d6zdemir", "Seda Can"]2024-08-26T00:00:00 55b627168b812261c554d441a9da11990a57938aThe use of technology has permeated all facets of life, and brought about both positive and negative effects. Criminology as a fields has not been left behind and criminologists are developing various technological tools, including Artificial Intelligence (AI) models to use in detecting, managing and preventing criminal activities. This is a significant step considering that criminals have also found it convenient to use technology as a tool for perpetuating their activities. This paper focused on the adoption of AI in criminology, exploring the attendant benefits of its adoption; the negative social impact of its use and interventions that should be put in place to curb the negative ramification. Some of the beneficial use discussed in the paper include predictive policing, and crime risk assessment, which aids in preventing occurrence of criminal activities. However, the use of these AI models, while beneficial to these criminological functions have presented significant social implications, which include bias and discrimination that perpetuate social stereotypes; privacy breach that lead to the victimization of innocent people; opaque decision making that lead to distrust in the output by the AI tool; and unfair distribution of employment opportunities. The paper concluded that the adoption of AI in criminology is inevitable considering the digital era in which we are currently living in. However, while the benefit of the use of these technologies are varied and welcome, there is a need for ensuring that the legal, social and ethical concerns are adequately addressed. The paper, therefore, recommended the establishment of robust regulatory framework that guide the use of the AI models by law enforcement agencies; the integration of the use of the AI tools with human oversight; the inclusion and transparency and accountability in the operationalization of the tools; collaboration amongst the stakeholders. This paper has used recent extant literature to examine the intersection between criminology and social impact with respect to the use of Artificial Intelligence (AI)East African Journal of Information Technology01The paper recommended the establishment of robust regulatory framework that guide the use of the AI models by law enforcement agencies; the integration of the use of the AI tools with human oversight; the inclusion and transparency and accountability in the operationalization of the tools; collaboration amongst the stakeholders.East African Journal of Information Technology["John Ndikaru Wa Teresia"]2024-08-26T00:00:00 d7772255530a9a4784424b72f5ded4f69a63438cInsurance companies are experiencing unprecedented growth due to several emerging technology functionalities that have transformed the industry’s operations. Through the Three Horizons framework, this study explores the technical skills required to use artificial intelligence (AI) for the sustainability of insurance companies. Methodologically, it was carried out in two stages: First, defining the state-of-the-art, which included analysis of the current situation and studying technological surveillance. Second, technical skills and their strategic prevalence were identified for the design of each horizon. As a result, the adoption of AI in insurance companies allows them to transform their personal and data-intensive processes into engines of efficiency and knowledge, redefining the way companies in the sector offer their services. This study identifies the immediate benefits of AI in insurance companies. It provides a strategic framework for future innovation, emphasizing the importance of developing AI competencies to ensure long-term sustainability.Administrative Sciences480This study identifies the immediate benefits of AI in insurance companies and provides a strategic framework for future innovation, emphasizing the importance of developing AI competencies to ensure long-term sustainability.Administrative Sciences["J. Acosta-Prado", "C. Hern\u00e1ndez-Cenzano", "Carlos David Villalta-Herrera", "Eloy Wilfredo Barahona-Silva"]2024-08-26T00:00:00 f8e92a25dd198721c0faefadfcde8645240cc76bSince the 1960s, researchers have worked to advance water flooding technology to address challenges like high viscosity, low fluidity, and depleting reservoirs, aiming to prevent oil fields from becoming unproductive. The integration of artificial intelligence (AI), computer vision, and advanced algorithms like BP neural networks has recently revolutionized this field. These technological advancements have upgraded water injection methodologies, overcoming past limitations and enabling real-time monitoring and dynamic control of water injection into different oil layers. This 'intelligent layering' ensures optimized water management, enhancing overall recovery rates. This overview highlights the progression of water injection techniques, critiques traditional methods' shortcomings, and delves into the cutting-edge applications of AI-driven intelligent layering systems. It serves as a valuable guide for oil industry stakeholders, equipment manufacturers, and research institutions seeking to refine water injection practices and boost hydrocarbon extraction efficiency.International Journal of e-collaboration70International Journal of e-Collaboration["Dewei Wang", "Qiang Qin"]2024-08-26T00:00:00 8f5378ca8a3a763fa46037b107f53edd542b1300The rapid evolution of artificial intelligence (AI) technology heralds significant transformations in education, especially in ideological and political education, where traditional teaching methods are being reassessed due to mounting challenges. This paper embarks on a mission to investigate how AI technology can revolutionize the delivery of political science courses by adopting a “human-computer collaboration” model. The central aim is to enhance the effectiveness and quality of teaching while stimulating students' critical thinking skills. It delves into the landscape and potential applications of AI technology in this field, emphasizing the role of intelligent teaching assistants in augmenting teachers' capabilities for lesson planning and delivery. This study not only outlines the design and implementation of AI tools but also emphasizes their potential to foster a more engaging and intelligent learning environment. By doing so, it contributes to the discourse on leveraging AI to advance teaching platforms and classroom practices, thereby enhancing the educational experience.International Journal of e-collaboration140This paper embarks on a mission to investigate how AI technology can revolutionize the delivery of political science courses by adopting a “human-computer collaboration” model, and delves into the landscape and potential applications of AI technology in this field.International Journal of e-Collaboration["Jiayan Lin", "Xiaohu Zhang"]2024-08-26T00:00:00 8940d4a09a1fcf63c8b9854d07d47b69354ee8f5This study aims to develop and validate a home economics education program focused on the consumer life area to enhance artificial intelligence (AI) literacy. To achieve this objective, a 10-session AI-consumer life integration education program was developed by analyzing literature on AI Literacy, AI curriculum, consumer education, and the home economics curriculum for the middle school consumer life area. The program’s validity was assessed by nine teachers using a four-point Likert scale. The average scores for each item and the content validity index (CVI) were calculated. Based on expert feedback, the program was revised and improved accordingly. The expert validity assessment of the lesson plans, teaching materials and learning resources resulted in an average score of 3.78 for all items and an average CVI of 0.96. For the overall program, the expert validity assessment yielded an average score of 3.72 for all items and an average CVI of 0.97. Since the content validity index for all questions was above 0.78, the program demonstrated high validity across achievement standards integration, learning objectives, content and teaching methods, motivation, and volume areas. This confirms its effectiveness as an educational program for enhancing AI literacy. This study is significant in terms of defining and identifying the components of AI literacy, developing an AI-integrated program encompassing the entire consumer life area and confirming the suitability of the home economics curriculum for enhancing digital consumer competencies and promoting sustainable consumption. Additionally, it highlights the potential to integrate AI into the home economics and consumer life area.Human Ecology Research290This study is significant in terms of defining and identifying the components of AI literacy, developing an AI-integrated program encompassing the entire consumer life area and confirming the suitability of the home economics curriculum for enhancing digital consumer competencies and promoting sustainable consumption.Human Ecology Research["You Jin Jung", "Kyung Won Lee"]2024-08-26T00:00:00 3563732ffa60980c87e9115167ac3dc0e0c6fcebOBJECTIVES To identify and critically analyze studies using artificial intelligence (AI) in occupational health. METHODS A systematic search of PubMed, IEEE Xplore, and Web of Science was conducted to identify relevant articles published in English between January 2014-January 2024. Quality was assessed with the validated APPRAISE-AI tool. RESULTS The 27 included articles were categorized as follows: health risk assessment (n = 17), return to work and disability duration (n = 5), injury severity (n = 3), and injury management (n = 2). 47 AI algorithms were utilized, with artificial neural networks, support vector machines, and random forest being most common. Model accuracy ranged from 0.60-0.99 and AUC from 0.7-1.0. Most studies (n = 15) were of moderate quality. CONCLUSIONS While AI has potential clinical utility in occupational health, explainable models that are rigorously validated in real-world settings are warranted.Journal of Occupational and Environmental Medicine00While AI has potential clinical utility in occupational health, explainable models that are rigorously validated in real-world settings are warranted.Journal of occupational and environmental medicine["Zaira S Chaudhry", "Avishek Choudhury"]2024-08-26T00:00:00 96a8ea624b8a19ab2bccdc0ad132a3650adbd534With the rapid development of artificial intelligence technology, artificial intelligence has been widely used in all walks of life. Unmanned driving is the mainstream of the future automobile industry. With the help of artificial intelligence, unmanned driving will gradually move towards real unmanned driving. Based on the concept of artificial intelligence technology and unmanned vehicle, combined with the current application status of unmanned vehicle, this paper analyzes the specific application of artificial intelligence in unmanned vehicle, and analyzes the problems existing in the application of unmanned vehicle at present, and looks forward to the development trend of unmanned vehicle in the future, which provides reference for the mature application of unmanned vehicle technology.International Conferences on Computers, Information Processing and Advanced Education70This paper analyzes the specific application of artificial intelligence in unmanned vehicle, and analyzes the problems existing in the application of unmanned vehicle at present, and looks forward to the development trend of unmanned vehicle in the future, which provides reference for the mature application of unmanned vehicle technology.2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE)["Zihao Chai"]2024-08-26T00:00:00 7f01aa67718d51b895bdfbb7e6d521cef573376fArtificial intelligence is transforming the way we work with information across disciplines and practical contexts. A growing range of disciplines are now involved in studying, developing and assessing the use of AI in practice, but these disciplines often employ conflicting understandings of what AI is and what is involved in its use. New, interdisciplinary approaches are needed to bridge competing conceptualizations of AI in practice and help shape the future of AI use. I propose a novel conceptual framework called AI Thinking, which models key decisions and considerations involved in AI use across disciplinary perspectives. AI Thinking addresses five practice-based competencies involved in applying AI in context: motivating AI use, formulating AI methods, assessing available tools and technologies, selecting appropriate data and situating AI in the sociotechnical contexts it is used in. A hypothetical case study is provided to illustrate the application of AI Thinking in practice. This article situates AI Thinking in broader cross-disciplinary discourses of AI, including its connections to ongoing discussions around AI literacy and AI-driven innovation. AI Thinking can help to bridge between the work of diverse disciplines, contexts and actors in the AI space, and shape AI efforts in education, industrial development and policy.Royal Society Open Science1170A novel conceptual framework called AI Thinking is proposed, which models key decisions and considerations involved in AI use across disciplinary perspectives and can help to bridge between the work of diverse disciplines, contexts and actors in the AI space, and shape AI efforts in education, industrial development and policy.Royal Society Open Science["Denis Newman-Griffis"]2024-08-26T00:00:00 8957761207cbd97c860fe56dc21428832c399eafIn sports training, accurate identification of athletes' movements is helpful to judge whether athletes' actions are standard or not, thus providing precise movement data for training and improving athletes' levels. The convolutional neural network VGG model was constructed based on artificial intelligence technology to extract image features and accurately recognize actions automatically. Weizmann data set was used to train and verify the model. The results of this study showed that the motion recognition algorithm based on the convolutional neural network could achieve high recognition accuracy.International Journal of e-collaboration170The results showed that the motion recognition algorithm based on the convolutional neural network could achieve high recognition accuracy and be helpful to judge whether athletes' actions are standard or not.International Journal of e-Collaboration["Zhi Tang", "Dongdong Wang"]2024-08-26T00:00:00 2711d0990e4f8d68dae1ee30b7787029266c4625Digital media technology has been widely used in social practice activities. With the improvement of big data storage and computing, artificial intelligence has been applied more widely in structured data computing, natural language processing, computer vision and so on. The application of digital media technology in the context of artificial intelligence can not only improve the design ability and work efficiency of designers, but also help designers create more characteristic and charming works. The article discusses the application of digital media technology under the background of artificial intelligence through the elaboration of digital media technology for the reference of related personnel.Forum on Research and Innovation Management20The application of digital media technology in the context of artificial intelligence can not only improve the design ability and work efficiency of designers, but also help designers create more characteristic and charming works.Forum on Research and Innovation Management["Zhang Li", "Hongxia Yin", "Liyao Fu", "Zhang Qi", "Shuyan Gu"]2024-08-26T00:00:00 7f40fd0015f331e8158ef9cb1136f942accc298aThe advent of Artificial intelligence (AI) is catalyzing significant transformations in human work dynamics. Existing research has not yet provided a clear picture of how organizational AI adoption will affect employees’ taking charge. Based on this background, this study explores the internal mechanisms by which organizational AI adoption affects employees’ taking charge based on social cognitive theory. The results of the empirical analysis of the 342 samples indicate that: organizational AI adoption leads to a decline in employees’ organizational-based self-esteem; organization-based self-esteem plays a mediating role between organizational AI adoption and employees’ taking charge; future focus is paid to moderating the relationship between organizational-based self-esteem and taking charge and is a determinant of how their organizational-based self-esteem affects taking charge; future focus is paid to moderating the impact of organizational AI adoption through the indirect effects of organizational-based self-esteem on taking charge. This study highlights the importance of considering individual characteristics (e.g., future focus) when analyzing how organizational AI adoption affects employees’ behaviors.Academic Journal of Management and Social Sciences660This study explores the internal mechanisms by which organizational AI adoption affects employees’ taking charge based on social cognitive theory and highlights the importance of considering individual characteristics when analyzing how organizational AI adoption affects employees’ behaviors.Academic Journal of Management and Social Sciences["Suchang Hou"]2024-08-26T00:00:00 2327f736d12007e886bb3954487e5ca76cb65fb9Artificial intelligence (AI) is defined as a computer science involving program development aiming to reproduce human cognition to analyze complex data. Artificial intelligence has rapidly developed in the medical field. In dermatology, its development is relatively new and is generally used in the diagnostic, especially for skin imaging analysis and classification, and also for risk assessment. The greatest advances have been primarily in the diagnosis of melanoma, followed by the assessment of psoriasis, ulcers, and various other skin diseases. The use of AI has shown good accuracy and is comparable to dermatologists in various studies, especially related to melanoma and skin tumors. However, several obstacles exist in the application of AI to daily clinical practice, including generalizability, image standardization, the need for large data quantities, and legal and privacy aspects. In current developments, AI should be aimed at helping enhance the decision-making of clinicians.Indonesian Journal of Biomedicine and Clinical Sciences560In dermatology, artificial intelligence is generally used in the diagnostic, especially for skin imaging analysis and classification, and also for risk assessment, followed by the assessment of psoriasis, ulcers, and various other skin diseases.Indonesian Journal of Biomedicine and Clinical Sciences["Irene Darmawan", "S. Yusharyahya", "Adhimukti Sampurna", "A. H. Saputro"]2024-08-26T00:00:00 a21de89a151de7b4169b2f7be62e7fb6ccb47429Artificial intelligence allows computers to process vast amounts of information and data, providing computer-based conclusions in a relatively short and fast time. The use of artificial intelligence in education is one of the hallmarks of the Industrial Revolution 4.0 era, characterized by automation and data exchange, where people seek, cite, analyze data and information, and access cloud services via the internet. This journal article will review various aspects of the utilization of Chat GPT in academia and education. Through an analytical and evaluative approach, we will investigate important issues that need to be understood and addressed in the use of this technology. Additionally, we will provide recommendations and guidelines to ensure the ethical and responsible use of Chat GPT within academic and educational settings. This research was conducted using a qualitative approach and a literature review method. ChatGPT has become an artificial intelligence tool capable of attracting over 100 million active users per month in a relatively short time. The use of ChatGPT in education offers numerous benefits for students, including increased engagement, motivation, and 21st-century skills. ChatGPT positively impacts the anxiety experienced by students, helping them develop confidence and skills necessary for success in academic life. For teachers, the use of ChatGPT brings significant changes to teaching practices, enhancing teaching skills, providing support in student assessment, and reducing administrative workload. Additionally, projections for the hardware and AI services market indicate substantial growth potential in the future. Overall, the development of ChatGPT and the utilization of AI technology promise various benefits that can enhance the efficiency and quality of education in the era of Education 4.0.International Journal of Current Science Research and Review00This journal article will review various aspects of the utilization of Chat GPT in academia and education, and investigate important issues that need to be understood and addressed in the use of this technology.International Journal of Current Science Research and Review["Yulia Windarsih", "Manap Trianto", "Abdul Ashari", "Dwi Setyorini"]2024-08-26T00:00:00 97265f989076a70ed07bd7fcd2b9f9e6f7ed8fa7Shapley additive explanations are a widely used technique for explaining machine learning models. They can be applied to basically any type of model and provide both global and local explanations. While there are different plots available to visualize Shapley values, there is a lack of suitable visualization for geospatial use cases, resulting in the loss of the geospatial context in traditional plots. This study presents a concept for visualizing Shapley values in geospatial use cases and demonstrate its feasibility through an exemplary use case—predicting bike activity in a rental bike system. The visualizations show that visualizing Shapley values on geographic maps can provide valuable insights that are not visible in traditional plots for Shapley additive explanations. Geovisualizations are recommended for explaining machine learning models in geospatial applications or for extracting knowledge about real-world applications. Suitable visualizations for the considered use case are a proportional symbol map and a mapping of computed Voronoi values to the street network.Frontiers of Computer Science261This study presents a concept for visualizing Shapley values in geospatial use cases and demonstrates its feasibility through an exemplary use case—predicting bike activity in a rental bike system.Frontiers in Computer Science["C\u00e9dric Roussel"]2024-08-26T00:00:00 9c86e901f9f7e977743f6023c7305ed2183b7c62Law, Innovation and Technology00Law, Innovation and Technology["Ali Ekber Cinar"]2024-08-26T00:00:00 95dd4b136d21bdef741eeafa1b6db2f2367a2872 The role of AI in the health industry is complex, but in general, it functions like a supercomputer and thinks like a human. Although a few problems need to be addressed to ensure things run well, we are confident that they will be fixed quickly and that new AI technologies will quickly assist the health-care industry. Policy creation is a comprehensive process that includes a detailed analysis of the disease epidemiology, prevalence by region, existing diagnostic tools and treatment options, and future action plans to control, eradicate, or eradicate the disease from a nation, a region, or the entire world. AI can be collecting data, accurately inputting it, evaluating it, and projecting pandemics or endemics in the future. Health policy creation and the health-care industry can benefit significantly from using AI techniques.MRIMS journal of health sciences00MRIMS Journal of Health Sciences["S. Mukhida", "Nikunjakumar Das", "Sriram Kannuri", "Deepali Desai"]2024-08-26T00:00:00 82d021d9231843f0083df5faab23288f87036888Studi ini berfokus pada eksplorasi dampak dari implementasi Kecerdasan Buatan (AI) dalam konteks pendidikan Bahasa Indonesia. Dengan menggunakan pendekatan deskriptif kualitatif, studi ini berusaha untuk menggambarkan pengaruh AI dalam proses pembelajaran Bahasa Indonesia. Sumber utama informasi yang digunakan dalam studi ini mencakup literatur, jurnal ilmiah, dan berita online yang relevan dengan topik penelitian. Proses pengumpulan data melibatkan pembacaan, penelaahan, dan pencatatan dari berbagai sumber literatur, jurnal ilmiah, dan berita online yang relevan dengan topik penelitian. Informasi ini kemudian disaring dan disusun dalam kerangka teoritis untuk menarik kesimpulan. Hasil penelitian menunjukkan bahwa implementasi AI dalam pendidikan Bahasa Indonesia memiliki potensi untuk meningkatkan efisiensi dan efektivitas proses pembelajaran. Meskipun demikian, terdapat juga beberapa aspek negatif yang terkait dengan penggunaan AI dalam proses pembelajaran. Namun, dalam jangka panjang, AI dapat menjadi alat yang sangat berharga dalam mendukung pendidikan Bahasa Indonesia, asalkan digunakan dengan bijak dan diintegrasikan dengan baik ke dalam sistem pembelajaran.   This study focuses on exploring the impact of Artificial Intelligence (AI) implementation in the context of Indonesian language education. Using a qualitative descriptive approach, this study seeks to describe the influence of AI in the Indonesian language learning process. The main sources of information used in this study include literature, scientific journals, and online news relevant to the research topic. The data collection process involved reading, reviewing, and recording from various literature sources, scientific journals, and online news relevant to the research topic. This information was then distilled and organized in a theoretical framework to draw conclusions. The results show that the implementation of AI in Indonesian language education has the potential to improve the efficiency and effectiveness of the learning process. Nevertheless, there are also some negative aspects associated with the use of AI in the learning process. However, in the long run, AI can be an invaluable tool in supporting Indonesian language education, provided it is used wisely and integrated well into the learning system.Jurnal Pendidikan Bahasa dan Sastra220DIKBASTRA: Jurnal Pendidikan Bahasa dan Sastra["Dini Apriliani"]2024-08-26T00:00:00 878a47a0de7de4ddff8878ed4e061ba754f50632Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their intention to use AI in distributed EFL learning contexts. This mixed-methods study, based on the technology acceptance model (TAM), examined the determinants of behavioral intention to use AI among 464 Chinese EFL college learners. As to quantitative data, a structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI. Second, attitude toward AI significantly and positively predicts behavioral intention to use AI. However, contrary to the TAM assumptions, perceived usefulness does not significantly predict either attitude toward AI or behavioral intention to use AI. Third, mediation analyses suggest that perceived ease of use has a significant and positive impact on students’ behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness. As to qualitative data, semi-structured interviews with 15 learners, analyzed by the software MAXQDA 2022, provide a nuanced understanding of the statistical patterns. This study also discusses the theoretical and pedagogical implications and suggests directions for future research.International Review of Research in Open and Distance Learning5344It was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI, and mediation analyses suggest that perceived ease of use has a significant and positive impact on students’ behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness.The International Review of Research in Open and Distributed Learning["Hanwei Wu", "Yunsong Wang", "Yongliang Wang"]2024-08-26T00:00:00 3a3d267868a823b58e767a180066311064d5df72The consideration of students’ emotional and psychological health is crucial to facilitate effective teaching and grading practices. This study set out to shed light on the interplay between self-esteem (S-E), cognitive-emotion regulation (CER), academic enjoyment (AE), and language success (LS) in artificial intelligence (AI)-supported online language learning. To this end, the foreign language learning self-esteem scale, the Cognitive Emotion Control Questionnaire, the foreign language enjoyment scale, and a researcher-made test were distributed to 389 English as a foreign language learners in China. Screening the data with confirmatory factor analysis and structural equation modeling, the effects of S-E, CER, AE, and LS were identified and quantified. These results highlighted the important function that online courses assisted by AI perform in enhancing students’ CER and AE. This implied that students who have cultivated a robust sense of self-efficacy are adept at effectively regulating their cognitive and affective processes in AI-supported language learning. Possible improvements in language education are discussed, as are the study’s broader implications.International Review of Research in Open and Distance Learning363Results highlighted the important function that online courses assisted by AI perform in enhancing students’ CER and AE, and implied that students who have cultivated a robust sense of self-efficacy are adept at effectively regulating their cognitive and affective processes in AI-supported language learning.The International Review of Research in Open and Distributed Learning["Ting Xiao", "Sisi Yi", "Shamim Akhter"]2024-08-26T00:00:00 507859b90ec399813bdd75423407d829e42bccc0This experimental study investigates the impact of ChatGPT-simplified authentic texts on university students’ reading comprehension, inferencing, and reading anxiety levels. A within-subjects design was employed, and 105 undergraduate English as a foreign language (EFL) students engaged in both original and ChatGPT-simplified text readings, serving as their own controls. The findings reveal a significant improvement in reading comprehension scores and inferencing scores following ChatGPT intervention. However, no significant change in reading anxiety levels was observed. Results suggest that ChatGPT simplification positively influences reading comprehension and inferencing, but its impact on reading anxiety remains inconclusive. This research contributes to literature on the use of artificial intelligence (AI) in education and sheds light on ChatGPT’s potential to influence language learning experiences within higher education contexts. The study highlights the practical application of ChatGPT as a tool for helping students engage in authentic text readings by making text more comprehensible. Based on the findings, several multifaceted implications that extend to various stakeholders in the field of language education are provided.International Review of Research in Open and Distance Learning523Results suggest that ChatGPT simplification positively influences reading comprehension and inferencing, but its impact on reading anxiety remains inconclusive.The International Review of Research in Open and Distributed Learning["Ferdi \u00c7elik", "Ceylan Yang\u0131n Ersanl\u0131", "Goshnag Arslanbay"]2024-08-26T00:00:00 9186697c3bf2705e2c9fb8079bc75b3ba8b38c9aJ. Cloud Comput.343Results show promise on using both technologies together: improving sustainability plus transparency in fisheries which would promote more fish biodiversity, while others including using an artificial intelligence system have not been confirmed yet by observations.J. Cloud Comput.["N. Alsharabi", "Jalel Ktari", "T. Frikha", "Abdulaziz Alayba", "Abdullah J. Alzahrani", "Amr Jadi", "Habib Hamam"]2024-08-26T00:00:00 aef1fb7c4b5d5939530ae986bc27b04b89ee36f8This study proposes a hypothetical model combining the unified theory of acceptance and use of technology (UTAUT) with self-determination theory (SDT) to explore design professionals’ behavioral intentions to use artificial intelligence (AI) tools. Moreover, it incorporates job replacement (JR) as a moderating role. Chinese-speaking design professionals in regions influenced by Confucian culture were surveyed. An analysis of 565 valid cases with AMOS (Analysis of Moment Structures) supported the structural model hypothesis. The model explains 52.1% of the variance in behavioral intention to use (BIU), proving its effectiveness in explaining these variances. The results further validate the importance of performance expectancy (PE) over effort expectancy (EE) in influencing BIU. Additionally, it has been shown that the impact on intrinsic motivation (IM) and extrinsic motivation (EM) can be either amplified or diminished by anxiety about JR. For individuals experiencing higher levels of JR anxiety, there is a marked increase in IM. They may perceive adopting AI tools as an opportunity to enhance their skills and job security. Conversely, this anxiety also significantly boosts EM, as the potential for improved efficiency and productivity with AI use becomes a compelling incentive. These findings suggest new paths for academic researchers to explore the psychological impacts of AI on design professionals’ roles. For practitioners, especially in human resources and organizational development, understanding these dynamics can guide the creation of training programs that address job replacement anxiety.International Review of Research in Open and Distance Learning561A hypothetical model combining the unified theory of acceptance and use of technology (UTAUT) with self-determination theory (SDT) with self-determination theory (SDT) is proposed to explore design professionals’ behavioral intentions to use artificial intelligence (AI) tools, which incorporates job replacement (JR) as a moderating role.The International Review of Research in Open and Distributed Learning["Hsi-Hsun Yang"]2024-08-26T00:00:00 b075040cd498311719d83b98d3ebd1149f7de5d3Several barriers hinder students from producing clear and impactful written work. Writing assignments are often given on an individual basis, similar to homework, and without any assistance. Students in a classroom context have access to both their classmates and the teacher while they are working in groups or pairs as part of their assignments. The majority of students, however, are clueless about how to begin their homework assignments. The introduction of artificial intelligence in education may help solve this problem. The current research intended to demonstrate the effects of employing automated writing evaluation (AWE) in fostering learners’ writing skills, motivation to write, enjoyment of writing, and academic buoyancy in open and distributed English as a foreign language (EFL) learning. The participants were 86 intermediate EFL students from China. The participants in the experimental group (n = 44) received instruction and feedback from their teachers only; participants in the control group (n = 42) were exposed to their teachers’ instruction as well as AWE. The results of data analysis via one-way multivariate analysis of variance indicated that the participants in the experimental group outperformed their peers in the control group in motivation to write, enjoyment in writing, academic buoyancy, and academic success in writing. Further in-depth discussions proceed regarding the implications of the study.International Review of Research in Open and Distance Learning441The results of data analysis via one-way multivariate analysis of variance indicated that the participants in the experimental group outperformed their peers in the control group in motivation to write, enjoyment in writing, academic buoyancy, and academic success in writing.The International Review of Research in Open and Distributed Learning["Ying He"]2024-08-26T00:00:00 5b405648f2665a63c1f84f5038d73cadf0756b7bThe anti-dependency teaching strategy aims to prepare technology-based students for the evolving world of artificial intelligence (AI). Instead of teaching students to be passive users of technology, it pushes them to become active producers and problem solvers. By cultivating creativity, critical thinking abilities, and a growth mentality, this approach equips students to use AI as a tool for innovation. It explores the potential of AI while acknowledging its limitations and ethical implications through project-based learning, interdisciplinary methodologies, and real-world applications. In order to promote an innovative culture and group problem-solving, it also integrates collaborative learning environments. The approach places a strong emphasis on adaptation and ongoing learning, keeping students abreast of developments in artificial intelligence and related fields. The study's respondents were twenty-four (24) instructors of technology-based disciplines with creative elements. By putting this plan into practice, educators can give students the knowledge and perspective they need to effectively navigate the AI era,  producing a new generation of creative thinkers who can transform society for the better.Environment and Social Psychology421The anti-dependency teaching strategy aims to prepare technology-based students for the evolving world of artificial intelligence (AI), and places a strong emphasis on adaptation and ongoing learning, keeping students abreast of developments in artificial intelligence and related fields.Environment and Social Psychology["Kier P. Dela Calzada"]2024-08-26T00:00:00 2d568b88166ed2f677726bbaba75415f8bbf6198The Department of Defense (DoD) is making sizable investments (14.7 billion dollars) in Artificial Intelligence (AI) Research and Development and acquiring AI through programs [1]. Ensuring proper process execution enables these investments to be realized, especially the processes that ensure effective evaluation of AI-enabled systems (AIES). SEPTAR (Systems Engineering Processes to Test AI Right) presents benefits and best practices for proactive planning for Test and Evaluation (T&E) activities for AIES. By following the best practices, AIESs are more likely to be delivered on time, to meet budgetary goals, and to perform effectively for mission expectations. The three major themes of this paper are: a) Broadening the T&E continuum; b) Defining data needs for AIES up-front; and c) Evaluating the Systems Engineering Life Cycle (SELC) to inform AIES trustworthiness1.2024 IEEE AUTOTESTCON321SEPTAR (Systems Engineering Processes to Test AI Right) presents benefits and best practices for proactive planning for Test and Evaluation (T&E) activities for AIES by following the best practices, AIESs are more likely to be delivered on time, to meet budgetary goals, and to perform effectively for mission expectations.2024 IEEE AUTOTESTCON["Jim Lockett", "Florence Reeder", "Ivy Chen", "Carol Pomales", "Carlos Balhana", "Danny Moore", "Ronald Ferguson"]2024-08-26T00:00:00 e6dab9a1f3cc3df44e56b80357abe2ffc3205964Despite an unprecedented abundance of news content, both news avoidance and dissatisfaction are rising. Blending journalism, philosophy and law scholarship, this paper argues that ‘news choice overload’ causes paralysis and poor outcomes as it transfers power to algorithms, thereby harming autonomy and, in turn, democracy. An analysis of Australian and European regulatory responses shows the need for an algorithmic regulator and a transparency requirement for digital platforms. Further, people's ability to choose autonomously can be fostered by positive interventions, or ‘light patterns’, including ‘diversity nudges’ and a shift from caveat emptor to a caveat venditor approach, in which digital platforms are assigned legal responsibility. Recognising that it is autonomy and democracy—not choice per se—that are valuable, such interventions can shift meaningful decision‐making back to citizens at a moment when the rise of generative artificial intelligence is giving algorithms yet more power.Policy &amp; Internet311It is argued that ‘news choice overload’ causes paralysis and poor outcomes as it transfers power to algorithms, thereby harming autonomy and, in turn, democracy.Policy &amp; Internet["S. Molitorisz"]2024-08-26T00:00:00 e86b1ddc3f43a342a28f0890be53a37ff5f5c351In education, the integration of artificial intelligence (AI) has presented opportunities to transform the dynamics of online learning. This study investigated the impact of an AI-powered application, namely ChatGPT, on the autonomy of Saudi Arabian primary students participating in online classes. It also explored how the implementation of Chat GPT influenced Saudi Arabian primary students’ autonomy. In this mixed-methods study, a quasi-experimental design assessed the impact of ChatGPT on learner autonomy among 250 Saudi Arabian primary students from six primary schools in Riyadh, Saudi Arabia. The quantitative analysis employed descriptive statistics and t-tests, while the qualitative data underwent interpretative phenomenological analysis. To ensure coding reliability, 20% of the codes were independently reviewed by an external coder, with a 94% inter-coder agreement coefficient reached through consensus. Findings revealed that ChatGPT significantly affected the participants’ perceptions of autonomy and its different dimensions. Qualitative data showed that AI-powered applications contributed to the students’ autonomy in 10 different ways. Participants also mentioned that AI-powered apps might have some negative consequences. This study has theoretical implications for redefining learner autonomy in the digital age and calls for the exploration of many facets of autonomy. Practical applications from this study include strategic integration of AI into online education, data security, and the need for orientation programs.International Review of Research in Open and Distance Learning491The impact of an AI-powered application, namely ChatGPT, on the autonomy of Saudi Arabian primary students participating in online classes and how the implementation of Chat GPT influenced Saudi Arabian primary students’ autonomy was investigated.The International Review of Research in Open and Distributed Learning["Ali Rashed Ibraheam Almohesh"]2024-08-26T00:00:00 ef396744885c6ef7ece35d5f453694219e1f3cc7Artificial intelligence (AI) has contributed to various facets of human lives for decades. Teachers and students must have competency in AI and AI-empowered applications, particularly when using online electronic platforms such as learning management systems (LMS). This study investigates the structural relationship between AI literacy, academic well-being, and educational attainment of Iranian undergraduate students. Using a convenience sampling approach, we selected 400 undergraduate students from virtual universities equipped with LMS platforms and facilities. We collected data using three instruments—an AI literacy scale, an academic well-being scale, and educational attainment scale—and analyzed the data using Smart-PLS3 software. Results showed that the hypothetical model had acceptable psychometrics (divergent and convergent validity, internal consistency, and composite reliability). Results also showed that the general model had goodness of fit. The study thus confirms the direct effect of AI on academic well-being and educational attainment. By measuring variables of academic well-being, we also show that AI literacy in China and Iran significantly affects educational attainment. These findings have implications for students, teachers, and educational administrators of universities and higher education institutes, providing knowledge about the educational uses of AI applications.International Review of Research in Open and Distance Learning401The study confirms the direct effect of AI on academic well-being and educational attainment of Iranian undergraduate students and shows that AI literacy in China and Iran significantly affects educational attainment.The International Review of Research in Open and Distributed Learning["Jingyu Xiao", "Goudarz Alibakhshi", "Alireza Zamanpour", "Mohamad Amin Zarei", "Shapour Sherafat", "Seyyed-Fouad Behzadpoor"]2024-08-26T00:00:00 aeed997bb2b6175b68d2775f923f326be578185fPurposeGenerative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer’s perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions.Design/methodology/approachThe model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made.FindingsThis research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support.Originality/valueThis research contributes to a better understanding of the consumer’s perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.Journal of Electronic Business &amp; Digital Economics321A model of how to build trust in the advice given by GenAI when answering financial questions is developed and identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one.ArXiv["Alex Zarifis", "Xusen Cheng"]2024-08-26T00:00:00 ff9944389a87f5c81872fe1365b3aec0ba9edbf2In the chemical industry, stable reactor operation is essential for consistent production. Motor failures can disrupt operations, resulting in economic losses and safety risks. Traditional monitoring methods, based on human experience and simple current monitoring, often need to be faster and more accurate. The rapid development of artificial intelligence provides powerful tools for early fault detection and maintenance. In this study, the Hotelling T2 index is used to calculate the root mean square values of the normal motor’s x, y, and z axes. A long short-term memory (LSTM) model creates a trend model for the Hotelling T2 index, determining an early warning threshold. Current anomaly detection follows the ISO 10816-1 standard, while future anomaly prediction uses the T2-LSTM trend model. Validated at a chemical plant in Southern Taiwan, the method shows 98% agreement between the predicted and actual anomalies over three months, demonstrating its effectiveness. The T2-LSTM model significantly improves the accuracy of motor fault detection, potentially reducing economic losses and improving safety in the chemical industry. Future research will focus on reducing false alarms and integrating more sensor data.Mathematics420Mathematics["Chien-Chih Wang"]2024-08-26T00:00:00 3510ead2c6447788e8a34fd9d0d57c66d834ca21Artificial Intelligence (AI) could encourage simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence. These tasks include reasoning, learning, problem-solving, perception, understanding natural language, and even decision-making. Previous studies noted the importance of assessing the use of technology in education considering its potential implications in the student’s learning and development processes. Hence, this study explored the potential implications of AI particularly in science, mathematics, and technical-vocational education. Educators (n=20) were purposively sampled to be interviewed about their experiences in using AI in their classrooms. The findings suggested a positive perception of generative AI among educators, with many acknowledging its potential to enhance educational practices and outcomes especially in aiding the understanding science concepts, facilitating analytical skills development, and personalizing learning experiences. However, alongside their positive perceptions, educators expressed concerns about potential drawbacks associated with AI use in education. These concerns included the risk of overreliance, plagiarism, and inaccuracies in AI-generated content. To mitigate these negative impacts, educators emphasized the importance of implementing effective policies and guidelines for AI use in classrooms such as guiding students on ethical use, ensuring transparency in AI tool usage, and establishing clear instructions for ethical AI utilization. Transparency emerged as a key theme, with educators emphasizing the need for transparency regarding students' outputs and the extent of AI use. This study calls for further analysis about the level of acceptance of educators in AI use and assess its impacts on students’ short-term and long-term learning outcomes.Environment and Social Psychology780The findings suggested a positive perception of generative AI among educators, with many acknowledging its potential to enhance educational practices and outcomes especially in aiding the understanding science concepts, facilitating analytical skills development, and personalizing learning experiences.Environment and Social Psychology["Mercibelle A. Del Mundo", "Erwin F. Delos Reyes", "Ellen M. Gervacio", "Raponzel B. Manalo", "Renz Jervy A. Book", "Jason V. Chavez", "Marcelino M. Espartero", "Darwisa S. Sayadi"]2024-08-26T00:00:00 d9c6bc0b6d6674999862a5085f064f399bbc5877The complexity of artificial intelligence (AI) raises significant challenges in developing embedded detection systems, particularly in terms of power consumption. In contrast, biologi-cal auditory perception addresses these issues efficiently. Drawing inspiration from biological primitive extraction in the auditory system, this article presents a new method for drastically reducing energy required for acoustic signal processing and classification. This method could also be applied to more general problems. To assess the efficiency of the proposed algorithm, experiments were conducted using the Google Speech Command Dataset (GSCD), focusing on 4 and 8 classes with added noise. Mimicking the structure of the cochlea, system training starts with 64 analog primitives, which are pruned sequentially, retaining only the most relevant ones for classification. This pruning relies on a novel neural network layer called “Line Gain.” Results demonstrate that the proposed algorithm significantly reduces total energy consumption by 82%, while maintaining comparable accuracy levels (greater than 90%).European Signal Processing Conference230A new method for drastically reducing energy required for acoustic signal processing and classification based on biological primitive extraction in the auditory system, which relies on a novel neural network layer called “Line Gain.”2024 32nd European Signal Processing Conference (EUSIPCO)["A. Deverin", "V. Gies", "Sebasti\u00e1n Marzetti", "V. Barchasz", "Herv\u00e9 Glotin"]2024-08-26T00:00:00 102c366b399533c06f320a26f1ff5779c3e7f044This study explores the impact of artificial intelligence (AI) on sustainable development across 51 countries during urbanization. Using panel data, the study examines AI's effects on sustainable development through three dimensions: R&D innovation, infrastructure, and market advantage. The results demonstrate that AI promotes sustainable development, with AI R&D innovation exerting the strongest influence, followed by AI infrastructure, whereas AI market advantage has the smallest impact. Additionally, the study uncovers regional heterogeneity in AI's impacts. In countries with upper middle sustainable development levels (60%–70% quantiles), AI's promoting effect is the strongest. Moreover, urbanization plays a threshold role in the relationship between AI and sustainable development. When urbanization is below the threshold, AI infrastructure and R&D innovation promote sustainable development, whereas AI market advantage inhibit it. Conversely, when urbanization exceeds this threshold, AI infrastructure inhibits sustainable development, the impact of AI R&D innovation becomes insignificant, and AI market advantage begin to promote sustainable development. This study recommends governments should consider the level of urbanization and sustainable development when crafting sustainable development policies utilizing AI.Sustainable Development7123Sustainable Development["Qiang Wang", "Fuyu Zhang", "Rongrong Li"]2024-08-27T00:00:00 c280ddd2030f51f9677d5451b972488251995650The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-art cancer treatment, facilitating collaborative diagnosis and management by a diverse team of specialists. Despite the clear benefits in personalized patient care and improved outcomes, the increasing burden on MTBs due to rising cancer incidence and financial constraints necessitates innovative solutions. The advent of artificial intelligence (AI) in the medical field offers a promising avenue to support clinical decision-making. This review explores the perspectives of clinicians dedicated to the care of cancer patients—surgeons, medical oncologists, and radiation oncologists—on the application of AI within MTBs. Additionally, it examines the role of AI across various clinical specialties involved in cancer diagnosis and treatment. By analyzing both the potential and the challenges, this study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans. The findings highlight the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.Current Oncology1294This study underscores how AI can enhance multidisciplinary discussions and optimize treatment plans and highlights the transformative role that AI may play in refining oncology care and sustaining the efficacy of MTBs amidst growing clinical demands.Current Oncology["Valerio Nardone", "Federica Marmorino", "M. Germani", "Natalia Cichowska-Cwali\u0144ska", "V. Menditti", "Paolo Gallo", "V. Studiale", "A. Taravella", "Matteo Landi", "Alfonso Reginelli", "Salvatore Cappabianca", "Sergii Girnyi", "Tomasz Cwalinski", "Virginia Boccardi", "Aman Goyal", "Jaroslaw Skokowski", "Rodolfo J. Oviedo", "Adel Abou-Mrad", "Luigi Marano"]2024-08-27T00:00:00 d2095344b9d7a0fd8b5955630c356b19da523c22The use of generative artificial intelligence (Gen-AI) to assist college students in their studies has become a trend. However, there is no academic consensus on whether Gen-AI can enhance the academic achievement of college students. Using a meta-analytic approach, this study aims to investigate the effectiveness of Gen-AI in improving the academic achievement of college students and to explore the effects of different moderating variables. A total of 28 articles (65 independent studies, 1909 participants) met the inclusion criteria for this study. The results showed that Gen-AI significantly improved college students’ academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408,0.659], p < .05). There were within-group differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text ( g = 0.554, p < .05), and sample size of 21–40 ( g = 0.776, p < .05), the use of independent learning styles ( g = 0.600, p < .05) had the most significant improvement in college student’s academic achievement. The intervention duration, the discipline types, and the assessment tools also had a moderate positive impact on college students’ academic achievement, but there were no significant within-group differences in any of the moderating variables. This study provides a theoretical basis and empirical evidence for the scientific application of Gen-AI and the development of educational technology policy.Journal of educational computing research715Gen-AI significantly improved college students’ academic achievement with a medium effect size and there were within-group differences in the three moderator variables, activity categories, sample size, and generated content.Journal of Educational Computing Research["Lihui Sun", "Liang Zhou"]2024-08-27T00:00:00 93c5979188069d5d5896d1341ecd21676b11f33fnpj Digit. Medicine674A synthesis of 141 binding policies applicable to AI in healthcare and population health in the EU and 10 European countries is presented, which has already formed a baseline regulatory framework for AI in health.NPJ Digital Medicine["Jelena Schmidt", "Nienke M. Schutte", "Stefan Buttigieg", "D. Novillo-Ortiz", "Eric Sutherland", "Michael Anderson", "Bart de Witte", "Michael Peolsson", "B. Unim", "Milena Pavlova", "A. Stern", "Elias Mossialos", "Robin van Kessel"]2024-08-27T00:00:00 dbc2f757c8e4e83de4eda16ce7b75bb8d1217cd5Penelitian ini membahas tantangan Pendidikan Agama Islam di era kecerdasan buatan (AI). Kemajuan teknologi Artificial Intelligence (AI) memberikan dampak signifikan dalam bidang pendidikan, termasuk Pendidikan Agama Islam (PAI). Teknologi AI seperti machine learning, chatbot, dan augmented reality (AR) dapat meningkatkan kualitas pembelajaran dengan menyediakan konten yang personal dan adaptif sesuai kebutuhan siswa. Artikel ini mengevaluasi peran AI dalam PAI, meliputi potensi penerapan, tantangan, dan dampaknya terhadap pembelajaran dan pengembangan kompetensi keagamaan. Metode penelitian kepustakaan digunakan untuk mengumpulkan data dari berbagai sumber. Hasil menunjukkan bahwa AI dapat memperkaya proses pembelajaran namun juga menghadapi tantangan seperti ketergantungan teknologi dan masalah privasi. Dengan penerapan yang bijaksana, AI dapat mendukung dan memperbaiki pendidikan agama Islam secara signifikan.Jurnal Studi Islam143REFERENSI ISLAMIKA: Jurnal Studi Islam["Miftahul Huda", "Irwansyah Suwahyu", "N. Makassar"]2024-08-27T00:00:00 bd6e7a19531f7a091959df2f6cfa1bf624e799c8Emergency Radiology643Cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures are presented, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy.Emergency radiology["M. Fathi", "Reza Eshraghi", "Shima Behzad", "Arian Tavasol", "Ashkan Bahrami", "Armin Tafazolimoghadam", "Vivek Bhatt", "Delaram J Ghadimi", "Ali Gholamrezanezhad"]2024-08-27T00:00:00 77b4f81dcba117b6373e3a61db0f1da7981cb010Incorporating AI in lung cancer management is a disruptive innovation that has improved diagnosis accuracy, prognosis prediction and treatment modalities. In this literature review, we seek to identify the role of artificial intelligence (AI) and machine learning (ML) in lung cancer detection, diagnosis and treatment between 2010 and 2023. A total of 55 studies were selected systematically from databases such as IEEE Xplore, Scopus and PubMed via a PRISMA-based approach. The analysis reveals that artificial intelligence (AI) techniques, specifically convolutional neural networks (CNNs) and natural language processing (NLP), highly improve the precision of initial detection and imaging of lung cancer. Also, CNN distinguishes between benign and malignant nodules, thus aiding early diagnosis and reducing unnecessary biopsies. On the other hand, NLP is utilized to extract relevant clinical information from electronic health records and unstructured medical texts, thereby enhancing the understanding of patient histories and improving treatment planning. Sensitive and specific scores usually higher than standard techniques characterize these technologies. Results show that traditional statistical approaches couldn’t match AI models whose predictive accuracies are outstanding while providing better care to patients through personalised treatment plans. Furthermore, multi-omics data analysis for personalised treatment planning and clinical decision-making optimisation via Clinical Decision Support Systems (CDSS) powered by AI are some ways artificial intelligence has exhibited its potential in this area. Given this, future studies should aim to fine-tune AI algorithms, improve data integration, and address ethical issues promoting responsible use of AI technologies in clinical practice settings. Despite these advances, data quality, model interpretability, and integration into clinical workflows persist. This review demonstrates the demand for continued research and collaboration from different disciplines so that the complete possibilities of AI in fighting lung cancer may be realised.International Journal For Multidisciplinary Research591The analysis reveals that convolutional neural networks and natural language processing techniques, specifically convolutional neural networks (CNNs) and natural language processing (NLP), highly improve the precision of initial detection and imaging of lung cancer.International Journal For Multidisciplinary Research["Hamza Ahmed Qureshi", "Yahya Abdul Rehman Shah", "Sara Muddassir Qureshi", "Saad Ur Rehman Shah", "Ashish Shiwlani", "Ahsan Ahmad"]2024-08-27T00:00:00 fa92e50b5bd6c94e9ad0db0e1521ea8f5ec98cebThis paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field.Expert Syst. J. Knowl. Eng.3561A comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence aimed at identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field is presented.Expert Syst. J. Knowl. Eng.["Safa D\u00f6rterler", "Seyfullah Arslan", "Durmu\u015f \u00d6zdemir"]2024-08-27T00:00:00 c3e0982e33307fcbffe82f65f0e632fdca7fdddfThe fourth industrial revolution is forthcoming, bringing with it revolutionary alterations in connectivity, work dynamics, and everyday operations. The fundamental foundation of this transformation, artificial intelligence (AI) is at its core. The purpose of this research is to identify new and distinctive features of how artificial intelligence may improve and enhance business knowledge management systems. For this purpose, systematic literature review (SLR) was used as the research method and preferred reporting items for systematic reviews and meta-analyses (PRISMA) as an approach. Articles based on specific search queries from 2013 to 2023 were selected from major scientific databases such as Scopus, Web of Science, and ScienceDirect, with a focus on AI, knowledge management systems, and corporate organizations for the PRISMA. It covers four main research questions by utilizing 14 selected papers. The main findings of this research paper call attention to AI’s vital role in shaping the future of knowledge management which helps organizations make informed decisions and remain competitive and relevant in this ever-changing world of commerce. This study also explores the importance of data security in AI systems which need ethical considerations and responsible practices to address the vulnerabilities.TEM Journal01The main findings of this research call attention to AI’s vital role in shaping the future of knowledge management which helps organizations make informed decisions and remain competitive and relevant in this ever-changing world of commerce.TEM Journal["Sunaina Thakuri", "Massimo Bon", "Nadire Cavus", "Nuriye Sancar"]2024-08-27T00:00:00 6d3e04b34b672e940aa8408a5a9a90ac521ed1afPurposeThe paper aims to examine the impacts and ethics of utilizing Artificial Intelligence (AI) in Indian policing. It explores both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.Design/methodology/approachThis study is based on secondary sources of information, such as national and international reports, journal articles, and institutional websites that discuss the use of AI technology by the police in India.FindingsAI has proven to be effective in policing, from preventing crime to identifying criminals, by detecting potential crimes in advance with fewer resources and in more areas. In India, the police use AI technology not only for facial recognition but also for crime mapping, analysis, and building blocks. However, factors such as caste, religion, language, and gender continue to cause conflict. India has shown a strong interest in using AI technology for policing, and wishes to accelerate its implementation in various policing contexts, including law and order. This paper calls for an assessment of the complexities and uncertainties brought about by new technologies in policing with ethical considerations.Originality/valueThis paper can provide valuable insights for policy-makers, academics, and practitioners engaged in discussions and debates concerning the ethical considerations associated with the adoption of AI tools in policing practices.Public Administration and Policy131The impacts and ethics of utilizing Artificial Intelligence in Indian policing is examined, both the positive and negative consequences of using AI, as well as the ethical considerations that have be taken into account.Public Administration and Policy["Meena Rani"]2024-08-27T00:00:00 a492df61113848bbb0d5cf6a4f7d904d60130026As blockchain technology increasingly underpins digital transactions, smart contracts have emerged as a pivotal tool for automating these transactions. While smart contracts offer efficiency and security, their automation introduces significant legal challenges. Detecting and preventing fraud is a primary concern. This paper proposes a novel application of artificial intelligence (AI) to address these challenges. We will develop a machine learning model, specifically a Convolutional Neural Network (CNN), to effectively detect and mitigate fraudulent activities within smart contracts. The AI model will analyze both textual and transactional data from smart contracts to identify patterns indicative of fraud. This approach not only enhances the security of digital transactions on blockchain platforms but also informs the development of legal standards and regulatory frameworks necessary for governing these technologies. By training on a dataset of authentic and fraudulent contract examples, the proposed AI model is expected to offer high predictive accuracy, thereby supporting legal practitioners and regulators in real-time monitoring and enforcement. The ultimate goal of this project is to contribute to legal scholarship by providing a robust technological tool that aids in preventing cybercrimes associated with smart contracts, thereby laying a foundation for future legal research and development at the intersection of law, technology, and security.Journal of Theoretical and Applied Electronic Commerce Research271A machine learning model will be developed, specifically a Convolutional Neural Network (CNN), to effectively detect and mitigate fraudulent activities within smart contracts to address significant legal challenges.J. Theor. Appl. Electron. Commer. Res.["Hassen Louati", "Ali Louati", "Abdulla Almekhlafi", "Maha ElSaka", "M. Alharbi", "Elham Kariri", "Youssef N. Altherwy"]2024-08-27T00:00:00 78a7b886b29540fbb013f816328dcdc248378b4dArtificial Intelligence (AI) has become a hot topic in recent years in Indonesia. To determine the influence of AI developments in determining technology needs, a sentiment analysis needs to be carried out. Sentiment analysis is a process used to help identify the contents of a dataset in the form of opinions or views (sentiments) in text form regarding an issue or event that is positive, negative or neutral. The algorithm applied in this research is the Multinominal Naive Bayes Classifier method. The Multinominal Naive Bayes Classifier method was chosen because it has quite high processing speed and accuracy when used on large, varied and large amounts of data. In this research, the sentiment results were "Negative" for the topic of data security and privacy with a testing accuracy of 75%, "Positive" for Economic Topics with a testing accuracy of 50%, "Negative" for Industrial Topics with a testing accuracy of 58%, "Positive" for Field Topics jobs with a testing accuracy of 75%, “Negative” Transportation Topics with a testing accuracy of 50%, and “Negative” for Education Topics with a testing accuracy of 67%.Jurnal Sistem Cerdas261The algorithm applied in this research is the Multinominal Naive Bayes Classifier method, which has quite high processing speed and accuracy when used on large, varied and large amounts of data.Jurnal Sistem Cerdas["Naufal Abror", "Rice Novita", "Mustakim", "M. Afdal"]2024-08-27T00:00:00 d111795c38a9ce45fbc767abae141206ac583f23Current artificial intelligence (AI) models often focus on enhancing performance through meticulous parameter tuning and optimization techniques. However, the fundamental design principles behind these models receive comparatively less attention, which can limit our understanding of their potential and constraints. This comprehensive review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI). We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We also examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges. By delving into these areas, we provide new insights and propose future research directions to drive innovation and address current gaps in the field. This review offers researchers and practitioners a comprehensive overview of the BIAI landscape, helping them harness its potential and expedite advancements in AI development.arXiv.org1932This comprehensive review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI), and presents a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models.ArXiv["Jing Ren", "Feng Xia"]2024-08-27T00:00:00 3c87621c7dea5d2658c79ec6c560054eb350c585Nowadays Influencer marketing is one of the most growing industry. Social media influencers are increasing with time and brands are moving towards endorsing themselves on digital platforms. AI is the new element for growing of influencer marketing and it will help influencers to improve their digital platforms. AI will be impacting positively with the help of the new ways to manage the campaigns of influencers or celebrities on social platforms. The new virtual social presence has made consumers interested in the virtual influencers which will also affect the perceived quality of products used by these influencers. The study   is quantitative and data collected is on convenience sampling in non-probability. The instrument used to collect data was through questionnaire. The findings of the research showed positive impact of Artificial intelligence on Influencer marketing. All the independent variables celebrity endorsements, perceived quality and virtual social presence with the mediator Artificial intelligence showed positive relation with influencer marketing. The result was analyzed and processed through SPSS. This research will help influencers to use AI for effectiveness and to know about emerging AI influencers.Bulletin of business and economics381This research will help influencers to use AI for effectiveness and to know about emerging AI influencers and the findings showed positive impact of Artificial intelligence on Influencer marketing.Bulletin of Business and Economics (BBE)["Muhammad Waqas Rana", "Mohammad Shahnawaz Ashfaq", "Faizah Yasir Jalbani"]2024-08-27T00:00:00 ec038aed49f6e2ab6615c9777236cce82f4451e8PURPOSE OF REVIEW Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management. RECENT FINDINGS The bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment. SUMMARY Artificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.Current Opinion in Ophthalmology570Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy.Current opinion in ophthalmology["Clarissa Ng Yin Ling", "Xiangjia Zhu", "Marcus Ang"]2024-08-27T00:00:00 442ac8215d3b95141561cb8ead1a33041a4b328cAs a disruptive technology, artificial intelligence has affected global economic development on multiple levels. With the widespread application of artificial intelligence technology in various industries, the market demand for artificial intelligence technology and applications continues to grow, providing college students with opportunities to participate in emerging industries. College students can create new products and services and achieve technological innovation with the continuous development of artificial intelligence technology. This paper adopts the method of literature review, and combines the existing artificial intelligence background to explore the possibility of using artificial intelligence as an auxiliary tool for cultivating college students' innovation and entrepreneurship ability. Combining cutting-edge theories with a variety of practical explorations, this paper finally forms a set of methodologies for computer and electronic engineering college students to complete learning and entrepreneurial practice through artificial intelligence in the stage of university education.Lecture Notes in Education Psychology and Public Media00This paper adopts the method of literature review, and combines the existing artificial intelligence background to explore the possibility of using artificial intelligence as an auxiliary tool for cultivating college students' innovation and entrepreneurship ability.Lecture Notes in Education Psychology and Public Media["Xuezhu Liu"]2024-08-27T00:00:00 dd9e4c159c6dc2f0259936cb8f9868306a49f04eThis research discusses framing carried out by online media, Detik.com, which conveys the potential use of artificial intelligence (AI) and its impact in elections. The massive involvement of artificial intelligence (AI) in people's daily lives opens up great opportunities for the involvement of artificial intelligence (AI) as an efficient and effective campaign suggestion. This research aims to investigate the framing carried out by Detik.com in an article entitled "IPB Experts Predict AI Will Affect the 2024 Election, Like What?", published in Detik.com on Wednesday, September 13, 2023. This study uses the Fairhurst and Sarr framing method. This study found that media framing tends to support the use of AI in elections, especially in campaigns.    Journal of Politica Governo320This study found that media framing tends to support the use of AI in elections, especially in campaigns.Journal of Politica Governo["Stefanny Tirza Kurniawan"]2024-08-27T00:00:00 d7a13c1fac0e655020d44a5745d2ce3ae6656e2dArtificial intelligence (AI) will drastically influence and change the working methods of scholars and researchers. This paper presents findings from a broad, national survey and a workshop focusing on the challenges and opportunities the advancement of AI poses for PhD candidates, seen from the perspective of library staff working with research support in a number of research libraries in Norway. The paper looks into how research libraries could adapt to the development, addresses the roles of various stakeholders and proposes measures regarding the support of PhD candidates in the responsible use of AI-based tools. Based on insights from the survey and the workshop, the paper also shows what is lacking in the libraries' research support services concerning the understanding and utilisation of AI-based tools. The study reveals a degree of uncertainty among librarians about their role in the AI academic nexus. For the development of competences of teaching staff in academic libraries, the paper recommends to integrate AI-related topics into existing educational resources and to create arenas for sharing experiences and knowledge with relevant partners both within and outside the university.The Liber Quarterly30For the development of competences of teaching staff in academic libraries, the paper recommends to integrate AI-related topics into existing educational resources and to create arenas for sharing experiences and knowledge with relevant partners both within and outside the university.LIBER Quarterly: The Journal of the Association of European Research Libraries["Michael Grote", "Hege Charlotte Lysholm Faber", "A. Gasparini"]2024-08-27T00:00:00 75dfb5191573a166010d3fce033fdf049bf521dfThis paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency.Energies180An automated approach to the technology selection of High-Voltage Alternating Current Offshore Substations (OHVS) for the integration of Oil & Gas production and Offshore Wind Farms based on Artificial Intelligence (AI) techniques is proposed.Energies["Tiago A. Antunes", "Rui Castro", "Paulo J. Santos", "A.J. Pires"]2024-08-27T00:00:00 bb26d6b96d5c93025a81f7821f627fc65e1c1b92Purpose of review Given the increasing global burden of diabetic retinopathy and the rapid advancements in artificial intelligence, this review aims to summarize the current state of artificial intelligence technology in diabetic retinopathy detection and management, assessing its potential to improve care and visual outcomes in real-world settings. Recent findings Most recent studies focused on the integration of artificial intelligence in the field of diabetic retinopathy screening, focusing on real-world efficacy and clinical implementation of such artificial intelligence models. Additionally, artificial intelligence holds the potential to predict diabetic retinopathy progression, enhance personalized treatment strategies, and identify systemic disease biomarkers from ocular images through ‘oculomics’, moving towards a more precise, efficient, and accessible care. The emergence of foundation model architectures and generative artificial intelligence, which more clearly reflect the clinical care process, may enable rapid advances in diabetic retinopathy care, research and medical education. Summary This review explores the emerging technology of artificial intelligence to assess the potential to improve patient outcomes and optimize personalized management in healthcare delivery and medical research. While artificial intelligence is expected to play an increasingly important role in diabetic retinopathy care, ongoing research and clinical trials are essential to address implementation issues and focus on long-term patient outcomes for successful real-world adoption of artificial intelligence in diabetic retinopathy.Current Opinion in Ophthalmology570This review aims to summarize the current state of artificial intelligence technology in diabetic retinopathy detection and management, assessing its potential to improve care and visual outcomes in real-world settings.Current Opinion in Ophthalmology["S. Vujosevic", "C. Limoli", "P. Nucci"]2024-08-27T00:00:00 3ab6ef94c1a1f92257e06020ebb8f99a8863f039Wheezing is a common condition in childhood, and its prevalence has increased in the last decade. Up to one-third of preschoolers develop recurrent wheezing, significantly impacting their quality of life and healthcare resources. Artificial Intelligence (AI) technologies have recently been applied in paediatric allergology and pulmonology, contributing to disease recognition, risk stratification, and decision support. Additionally, the COVID-19 pandemic has shaped healthcare systems, resulting in an increased workload and the necessity to reduce access to hospital facilities. In this view, AI and Machine Learning (ML) approaches can help address current issues in managing preschool wheezing, from its recognition with AI-augmented stethoscopes and monitoring with smartphone applications, aiming to improve parent-led/self-management and reducing economic and social costs. Moreover, in the last decade, ML algorithms have been applied in wheezing phenotyping, also contributing to identifying specific genes, and have been proven to even predict asthma in preschoolers. This minireview aims to update our knowledge on recent advancements of AI applications in childhood wheezing, summarizing and discussing the current evidence in recognition, diagnosis, phenotyping, and asthma prediction, with an overview of home monitoring and tele-management.Frontiers in Medicine610In this view, AI and Machine Learning approaches can help address current issues in managing preschool wheezing, from its recognition with AI-augmented stethoscopes and monitoring with smartphone applications, aiming to improve parent-led/self-management and reducing economic and social costs.Frontiers in Medicine["L. Venditto", "Sonia Morano", "M. Piazza", "Marco Zaffanello", "L. Tenero", "Giorgio Piacentini", "G. Ferrante"]2024-08-27T00:00:00 28c018b2db08bcc6b6ec491d60feec84cdad8ebfCrohn's disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract. Intestinal fibrosis or stricture is one of the most prevalent complications in CD with a high recurrence rate. Manual examination of intestinal fibrosis or stricture by physicians may be biased or inefficient. A rapid development of artificial intelligence (AI) technique in recent years facilitates the detection of existing or possible intestinal fibrosis and stricture in CD through various modalities, including endoscopy, imaging examination, and serological biomarkers. We reviewed the articles on AI application in diagnosing intestinal fibrosis and stricture in CD during the past decade and categorized them into three aspects based on the detection methods, and found that AI helps accurate and expedient identification and prediction of intestinal fibrosis and stenosis in CD.Journal of Digestive Diseases660AI helps accurate and expedient identification and prediction of intestinal fibrosis and stenosis in CD through various modalities, including endoscopy, imaging examination, and serological biomarkers.Journal of digestive diseases["Yi Fei Chen", "Liu Liu", "Bin Lyu", "Ye Yang", "Si Si Zheng", "Xuan Huang", "Yi Xu", "Y. Fan"]2024-08-27T00:00:00 b8217302ec654498288c2f743947e5aec4ab1b2cThe purpose of the paper is to determine the role of the phenomenon of algorithmic discrimination in the processes of implementing smart technologies in HR, particularly in the context of sustainable management. To accomplish this task, the author conducted a scoping review of the literature. The study indicated a significant role of the described phenomenon in shaping employee opinions about artificial intelligence and emphasised the importance of sustainable people management in its utilisation. The research results call for deeper reflection on how to assess the performance of artificial intelligence and highlight that attempting to replicate human abilities in machines not only offers new possibilities but also carries the risk of perpetuating human imperfections. The limitations of the study arise from the small number of available empirical studies in this area. The article helps to understand the essence of artificial intelligence and contributes to filling the knowledge gap regarding methods of managing people in the process of implementing smart technologies.Edukacja Ekonomistów i Menedżerów310The study indicated a significant role of the phenomenon of algorithmic discrimination in shaping employee opinions about artificial intelligence and emphasised the importance of sustainable people management in its utilisation.Edukacja Ekonomistów i Menedżerów["Miros\u0142aw W\u00f3jcik"]2024-08-27T00:00:00 0b0921cac89e8426890b782b3c75f28acd537d69There has been much discussion regarding integrating Artificial Intelligence (AI) into accounting. This study focuses on the integration of AI with product costing models, most specifically with the Duration-Based Costing (DBC) model. The published literature regarding DBC shows that DBC can mimic or outperform an Activity-Based Costing (ABC) model that utilizes time drivers. Furthermore, the large amount of information that an ABC system utilizes can cause information overload, which DBC overcomes. DBC is a cost allocation technique that assigns overhead costs based on the production cycle time. The more time that a company spends producing a product, the more it will cost. DBC utilizes the concept “time is money.” DBC is the model that looks at the larger picture. In other words, DBC looks at the forest overall whereas ABC looks at each individual tree in which the saying “cannot see the forest for the trees” applies to ABC. Therefore, this study aims to discuss how AI can integrate with DBC to provide a company valuable and quick cost information.Journal of Accounting and Finance230This study aims to discuss how AI can integrate with DBC to provide a company valuable and quick cost information to provide a company valuable and quick cost information.Journal of Accounting and Finance["A. Lelkes"]2024-08-27T00:00:00 5e22cf910d8235d0f200f6203c3f24bedb537dadArtificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real‐world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.Applied AI Letters300A rubric for developing and deploying AI systems for social impact with a focus on the African context is provided and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.Applied AI Letters["Engineer Bainomugisha", "J. Nakatumba\u2010Nabende"]2024-08-27T00:00:00 873d41fd89af82c0da504a46b7ca6202cde56819BMC Psychiatry370The AI tool developed and validated has the potential to guide intervention strategies and support early identification and preventive measures and demonstrates promising predictive performance for identifying college students at risk of severe mental distress.BMC Psychiatry["Lirong Zhang", "Shaocong Zhao", "Zhongbing Yang", "Hua Zheng", "Mingxing Lei"]2024-08-27T00:00:00 c3655d72ec74ef80891ef3b5d9cd9c7e1b1772feTechnological media are evolving at great speed, and this development inevitably affects the pedagogical approach that institutions and educators implement in the classroom. The great irruption of Artificial Intelligence tools makes it necessary to reflect on the use of these applications in educational centres at all levels, from Early Childhood Education to Higher Education. These tools have enormous possibilities and applications for the improvement of learning in many aspects, but it is also necessary to analyse the ethical implications that their use may entail, and the role of the educator in this whole process. In this sense, it is proposed that the teacher should become an ethical leader, providing adequate spaces for all students to have the opportunity to achieve learning, becoming a person who inspires those around him/her, and leading the ethical debate involved in the use of these technologies, fostering a critical spirit and knowledge. The presence of the human being in the educational process cannot be doubted, due to the presence of dimensions of the human being such as the emotional or spiritual dimension, which are part of the integral development of the individual and must be nurtured.Journal of Interdisciplinary Education: Theory and Practice00It is proposed that the teacher should become an ethical leader, providing adequate spaces for all students to have the opportunity to achieve learning, becoming a person who inspires those around him/her, and leading the ethical debate involved in the use of these technologies.Journal of Interdisciplinary Education: Theory and Practice["Jorge Burgue\u00f1o L\u00f3pez"]2024-08-27T00:00:00 f30bfcbda7935b13bbef254256d89558422f6c64BMC Nephrology150Intelligent nephrology is just taking its first steps and is by no means yet close to its coming of age, as a digital divide in access to technology has become evident between developed and developing countries, also affecting underrepresented minorities.BMC Nephrology["Miguel Hueso", "Alfredo Vellido"]2024-08-27T00:00:00 8b0f4cecb07ed7abcdf53277820b8cbae83b2cc8Expanding upon the applications of artificial intelligence (AI) explored in "Artificial Intelligence for Drug Repurposing Against Infectious Diseases," this commentary explores AI's transformative potential in hematology. AI-driven algorithms are revolutionizing diagnostics through the automation of tasks like blood smear analysis, cell classification, flow cytometry, and early disease detection. By leveraging extensive datasets, these algorithms enhance accuracy and efficiency in identifying patterns, classifying cells, detecting abnormalities, and predicting disease progression. In the realm of therapeutics, AI is reshaping personalized medicine by analyzing patient data to tailor treatment strategies. AI-powered platforms are accelerating drug discovery, optimizing clinical trial design, and enabling real-time treatment monitoring and personalized risk assessment. While challenges such as algorithm transparency, data bias, and ethical considerations remain, the future of AI in hematology is promising. Continued research, collaboration, and responsible implementation are essential to fully harness AI's potential for improving patient care and advancing therapeutic interventions.Journal of Clinical and Experimental Hematology00This commentary explores AI's transformative potential in hematology through the automation of tasks like blood smear analysis, cell classification, flow cytometry, and early disease detection.Journal of Clinical and Experimental Hematology["Kavya Singh", "Anuradha Singh"]2024-08-27T00:00:00 99f9e0b8dfea3ea2d00196d0be9134d85e71fec4Journal of Perinatology31Journal of perinatology : official journal of the California Perinatal Association["Kelsey A Simek", "A. Husain", "Z. Vesoulis", "B. Sullivan", "James S Barry", "R. McAdams", "Alvaro G Moreira"]2024-08-27T00:00:00 1ccde5aa000b3edc49d7fe0d2ca3b198975f999fThe rapid development of AI technology in recent years has led to its widespread use in daily life, where it plays an increasingly important role. In healthcare, AI has been integrated into the field to develop the new domain of smart healthcare. In smart healthcare, opportunities and challenges coexist. This article provides a comprehensive overview of past developments and recent progress in this area. First, we summarize the definition and characteristics of smart healthcare. Second, we explore the opportunities that AI technology brings to the smart healthcare field from a macro perspective. Third, we categorize specific AI applications in smart healthcare into ten domains and discuss their technological foundations individually. Finally, we identify ten key challenges these applications face and discuss the existing solutions for each.Future Internet1320A comprehensive overview of past developments and recent progress in smart healthcare is provided, including the definition and characteristics of smart healthcare, and ten key challenges these applications face and the existing solutions for each.Future Internet["Xian Gao", "Peixiong He", "Yi Zhou", "Xiao Qin"]2024-08-27T00:00:00 cfcce64874d4a980b8389df1c101638c6f66c3f8BDJ Student120BDJ Student["Minha Chowdhury"]2024-08-27T00:00:00 21bba6a365311324dfbc344e505ef12313cecc3cEuropean journal of emergency medicine70European journal of emergency medicine : official journal of the European Society for Emergency Medicine["Howard Bauchner"]2024-08-27T00:00:00 19d5aadb7607b6fffbe6a08457eb23a857ad8a6cJournal of Human Genetics00Journal of human genetics["Y. Kamatani", "Tadashi Kaname"]2024-08-27T00:00:00 947f3439907efdf94c8cb2a22a5c4159647f2a42Annals of Internal Medicine60Annals of internal medicine["Patrick G Lyons", "David A Dorr", "Genevieve B. Melton", "Karandeep Singh", "P. R. Payne"]2024-08-27T00:00:00 c4da0d4bf26b61c5bf24e7943ffe048a7a9ef52aRecently, the green concept has become integral to education, leading to the rise of paperless online teaching. With the rapid development of online teaching platforms (OTPs) due to the pandemic, studies on user behavior have gained momentum. However, most studies have focused on students’ online learning attitudes and behaviors, neglecting in-depth analysis of teachers’ behaviors on OTPs. OTPs can either assist or substitute teachers, enhancing efficiency but also causing anxiety. This paper proposes reframing OTPs as assistants to reduce teachers’ resistance. We investigate if the OTP image (assistant vs. substitutor) impacts teachers’ satisfaction in a specific online teaching context, exploring its explanatory mechanisms. A study of 2*2 group experiments revealed that teachers were less threatened by the assistant OTP image and thus more satisfied. Experiment 1 confirmed that the OTP image influenced teachers’ willingness to recommend and satisfaction. Experiment 2 again tested the effect of different images of OTP (facilitator vs. substitute vs. control group) on teacher satisfaction, and the pie verified the mediating role of identity threat in this effect. Experiment 3 verified that self-affirmation as a moderating variable mitigates identity threats due to the alternative image of the OTP. Therefore, in the future promotion of AI products, more emphasis should be placed on assisting users rather than completely replacing traditional human hands, thus weakening the identity threat posed by AI products to users. The findings of this study enrich the study on teachers’ attitudes towards OTPs, dissect the sources of users’ (teachers’) satisfaction with OTPs from the perspective of product/brand (OTP) image, and provide guidance on how OTPs can choose the appropriate image positioning and promotional language.International Journal of Interactive Mobile Technologies00The findings of this study enrich the study on teachers’ attitudes towards OTPs, dissect the sources of users’ (teachers’) satisfaction with OTPs from the perspective of product/brand (OTP) image, and provide guidance on how OTPs can choose the appropriate image positioning and promotional language.Int. J. Interact. Mob. Technol.["Lulu Hao", "Chaoyang Zhu", "Xiangliu Chen", "Gabriel Xiao-Guang Yue"]2024-08-27T00:00:00 4f89aaf3ffdf00d23f01fab76ca376fa5f9f6381مجلة بحوث کلیة الآداب . جامعة المنوفیة00مجلة بحوث کلية الآداب . جامعة المنوفية["\u0641\u0627\u0637\u0645\u0629 \u0627\u0644\u0642\u062d\u0637\u0627\u0646\u064a"]2024-08-27T00:00:00 d1eef63fbbd2799ab5a1c835b0ea133151e56a5a: The purpose of this research is to describe the Utilization of Artificial Intelligence (AI) Based E-learning Learning Model in Islamic Education. This research describes the definition of AI-based E-learning Learning Model and the type of AI-based E-learning application or website that can be utilized in Islamic education. The research method used is literature research. The data collection technique used is reviewing, reading and writing data obtained from various sources both journals and relevant books. While the data analysis technique of this research is data reduction, data presentation and conclusion drawing. The result of this research shows that E-learning based on Artificial Intelligence (AI) is one type of learning model that utilizes the sophistication of technology integrated with Artificial Intelligence (AI). E-learning model based on Artificial Intelligence (AI) is an online learning system that utilizes AI technology to increase the effectiveness, personalization, and interactivity of the learning process. The scope of Islamic education that can utilize the sophistication of E-learning models based on Artificial Intelligence (AI) includes Al-Quran and Hadith, Akidah Akhlak, Fiqh and Islamic History. In addition, there are several kinds of applications or websites that can be useful in Islamic education namely Virtual Assistants and Chatbots Using AI Are Here to Stay | World Economic Forum, AI Chatbot, Voice Assistant, Assess.ai.ADDABANA: Jurnal Pendidikan Agama Islam81E-learning model based on Artificial Intelligence (AI) is an online learning system that utilizes AI technology to increase the effectiveness, personalization, and interactivity of the learning process.ADDABANA: Jurnal Pendidikan Agama Islam["U. Islam", "N. Banjarmasin", "M. Ramli", "Perencanaan Pendidikan", "Untuk Meningkatkan", "Efisiensi Pendidikan"]2024-08-27T00:00:00 2b079c206e1db7415950d1f2209864fe0dfa5dffArtificial intelligence is presented to society as a revolutionary tool capable of generating a change asunique as the democratization of Internet access at the beginning of the 21st Century. The different applications of AI are facilitating the development of marketing and communication strategies adapted to the needs of the public and the establishment of strong relationships with them. One of the most dynamic consumer markets is fashion communication, which is why we decided to delimit the applications of AI to brands in this sector. First goal was to identify the main resources and applications of AI that are being used to communicatewith the different stakeholders of fashion companies, particularly with the final consumer. Second objective was to recognize benefits and positive aspects along with the brakes and barriers that the application of this technology represents for the communication strategies of fashion brands. Thirdly, a case studyis offered to help academics and professionals understand how the fashion sector is receiving the helpof generative AI in the creation of campaigns. Through a combination of qualitative methods including3 Delphi interviews, a hemerographic research of professional publications and the Neural Fashion AI case study, the capacity of AI to point out a differentiating factor in the market that has to do withsustainability, product customization and optimization of company resources has been demonstrated. The main results highlight the contribution that AI makes to the efficiency of processes and to the achievement of brand objectives (customer satisfaction, loyalty, strengthening of positioning and brand image), expansion into new marketsand audiences, or the creation of innovative, impactful and attractive content.Universitas160The main results highlight the contribution that AI makes to the efficiency of processes and to the achievement of brand objectives, expansion into new markets and audiences, or the creation of innovative, impactful and attractive content.Universitas["Paloma D\u00edaz-Soloaga", "Irene Pelzer-Peinado"]2024-08-27T00:00:00 306d05efbf0c90b4d6dd9fdf3e5f5fbe464cf2a6Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively).Energies1676Energies["Dalia Mohammed Talat Ebrahim Ali", "Violeta Motuzien\u0117", "Rasa D\u017eiugait\u0117-Tum\u0117nien\u0117"]2024-08-27T00:00:00 3c42135600a252c30f27ea66867d88d7c15fef2bThe increasing reliance on technology within higher education necessitates a thorough examination of artificial intelligence’s (AI) application in academic research. This analysis aims to elucidate both the advantages and challenges associated with AI utilization, thereby paving the way for future inquiries. Such studies will be instrumental in delineating strategies for the effective integration of AI tools in scholarly research, ensuring their optimal use in advancing the field.The purpose of this research is to identify the benefits and challenges of the use of AI in the field of scientific research by analyzing experiences that have implemented AI in scientific research carried out at the university level through a systematic literature review.The research questions that guided the systematic literature review were as follows: (1) What are the benefits of using AI in research? (2) What are the challenges of using AI in research? (3) What are the use and benefits of AI in scientific writing including limitations? (4) What are the main lines of research identified in studies that address scientific practice with artificial intelligence in the university context? The articles analyzed were published in 2023. After applying the inclusion and exclusion criteria, 85 articles were analyzed.The analysis allowed findings such as the usefulness of ChatGPT in different disciplinary areas, challenges such as being able to identify artificial intelligence resources limitations and benefits such as being able to make processes of different kinds more efficient.It was possible to establish that although the studies analyzed identified advantages in the application of AI in scientific research, it was also detected that it is necessary to have a critical and creative look to make use of AI resources, such as ChatGPT, in order to use them only as support tools and thus be able to take care of the rigor and quality in the elaboration of scientific texts.Frontiers in Education205It was possible to establish that although the studies analyzed identified advantages in the application of AI in scientific research, it was also detected that it was necessary to have a critical and creative look to make use of AI resources only as support tools and thus be able to take care of the rigor and quality in the elaboration of scientific texts.Frontiers in Education["Isolda Margarita Castillo-Mart\u00ednez", "Daniel Flores-Bueno", "Sonia M. G\u00f3mez-Puente", "Victor O. Vite-Le\u00f3n"]2024-08-27T00:00:00 77cf8544f2f34af3d3b15efbcaec6ea42ecf0407Experimental Hematology & Oncology1163The research progress on various genetic factors involved in thrombotic diseases is reviewed, the advantages and disadvantages of commonly used thrombotic risk assessment scales and the characteristics of ideal scoring scales are analyzed, and the application of artificial intelligence in the medical field is explored, along with its future prospects.Experimental Hematology & Oncology["Rong Wang", "Liang V Tang", "Yu Hu"]2024-08-27T00:00:00 26ba56b613ef1f52431115295ee3562bc15d07cd Purpose This study aims to apply the predictive processing theory to examine the influence of artificial intelligence (AI)-driven robotic performers on audience emotions and the audience’s resulting electronic word-of-mouth (eWOM) behaviors during tourism service encounters. Design/methodology/approach Using a quantitative research methodology, survey responses from 339 regular customers of performing arts in tourism destinations were analyzed. The respondents were recruited through Prolific, a professional data collection platform. SPSS 23.0 was used for the preliminary analysis, from which a research model to achieve the aim was proposed. SmartPLS 3 was used for partial least squares structural equation modeling to test the model. Findings Interactive and novel robotic performances significantly encouraged the consumers to share their experiences online, thereby enhancing eWOM. However, melodic resonance had no significant impact on eWOM intentions. The consumers’ emotional responses fully mediated the relationship of the novelty and interactivity of the performances to the consumers’ eWOM intentions but did not mediate the relationship of the musical elements to their eWOM intentions. Originality/value This study enriches the understanding of how AI-driven performances impact consumers’ emotional engagement and sharing behaviors. It extends the application of the predictive processing theory to the domain of consumer behavior, offering valuable insights for enhancing audience engagement in performances through technological innovation. Journal of Hospitality and Tourism Technology1281This study enriches the understanding of how AI-driven performances impact consumers’ emotional engagement and sharing behaviors by extending the application of the predictive processing theory to the domain of consumer behavior.Journal of Hospitality and Tourism Technology["Yuhao Li", "Shurui Wang", "Zehua Li"]2024-08-27T00:00:00 3eae74dde216ff9d31a865441e2289ed5665bf5eAdvanced AI systems capable of generating humanlike text and multimodal content are now widely available. In this paper, we discuss the impacts that generative artificial intelligence may have on democratic processes. We consider the consequences of AI for citizens' ability to make informed choices about political representatives and issues (epistemic impacts). We ask how AI might be used to destabilise or support democratic mechanisms like elections (material impacts). Finally, we discuss whether AI will strengthen or weaken democratic principles (foundational impacts). It is widely acknowledged that new AI systems could pose significant challenges for democracy. However, it has also been argued that generative AI offers new opportunities to educate and learn from citizens, strengthen public discourse, help people find common ground, and to reimagine how democracies might work better.arXiv.org881The impacts that generative artificial intelligence may have on democratic processes are discussed and the consequences of AI for citizens' ability to make informed choices about political representatives and issues are considered.ArXiv["Christopher Summerfield", "Lisa Argyle", "Michiel A. Bakker", "Teddy Collins", "Esin Durmus", "Tyna Eloundou", "Iason Gabriel", "Deep Ganguli", "Kobi Hackenburg", "Gillian Hadfield", "Luke Hewitt", "Saffron Huang", "H\u00e9l\u00e8ne Landemore", "Nahema Marchal", "Aviv Ovadya", "Ariel Procaccia", "Mathias Risse", "Bruce Schneier", "Elizabeth Seger", "Divya Siddarth", "Henrik Skaug Saetra", "Mh Tessler", "Matthew M. Botvinick"]2024-08-27T00:00:00 037b426813946a036eea539ae3e04009f9b12aadArtificial intelligence (AI) has become a transformative force in supply chain and operations management, offering significant enhancements in efficiency and resilience. This paper examines the integration of AI technologies such as machine learning, predictive analytics, and real-time data processing in demand forecasting, inventory management, logistics, and risk mitigation. By analyzing diverse data sources, AI improves demand forecasting accuracy, reduces inventory costs, optimizes logistics routes, and enhances supply chain visibility. Case studies and data-driven insights demonstrate how AI-driven systems enable companies to adapt to market dynamics, prevent disruptions, and achieve substantial cost savings. The findings suggest that embracing AI is essential for businesses aiming to optimize their supply chain operations and build robust, resilient frameworks capable of withstanding future challenges.Applied and Computational Engineering01Examination of AI technologies such as machine learning, predictive analytics, and real-time data processing in demand forecasting, inventory management, logistics, and risk mitigation suggests that embracing AI is essential for businesses aiming to optimize their supply chain operations and build robust, resilient frameworks capable of withstanding future challenges.Applied and Computational Engineering["Daren Zhang"]2024-08-27T00:00:00 0eb3e2cc1125e6457adcaa7b97e6a61ad0a31196PurposeNew business density (NBD) is the ratio of the number of newly registered liability corporations to the working-age population per year. NBD is critical to assessing a country's business environment. The present work endeavors to discover and gauge the contribution of 28 potential socio-economic enablers of NBD for 2006–2021 across developed and developing economies separately and to make a comparative assessment between those two regions.Design/methodology/approachUsing World Bank data, the study first performs exploratory data analysis (EDA). Then, it deploys a deep learning (DL)-based regression framework by utilizing a deep neural network (DNN) to perform predictive modeling of NBD for developed and developing nations. Subsequently, we use two explainable artificial intelligence (XAI) techniques, Shapley values and a partial dependence plot, to unveil the influence patterns of chosen enablers. Finally, the results from the DL method are validated with the explainable boosting machine (EBM) method.FindingsThis research analyzes the role of 28 potential socio-economic enablers of NBD in developed and developing countries. This research finds that the NBD in developed countries is predominantly governed by the contribution of manufacturing and service sectors to GDP. In contrast, the propensity for research and development and ease of doing business control the NBD of developing nations. The research findings also indicate four common enablers – business disclosure, ease of doing business, employment in industry and startup procedures for developed and developing countries.Practical implicationsNBD is directly linked to any nation's economic affairs. Therefore, assessing the NBD enablers is of paramount significance for channelizing capital for new business formation. It will guide investment firms and entrepreneurs in discovering the factors that significantly impact the NBD dynamics across different regions of the globe. Entrepreneurs fraught with inevitable market uncertainties while developing a new idea into a successful new business can momentously benefit from the awareness of crucial NBD enablers, which can serve as a basis for business risk assessment.Originality/valueDL-based regression framework simultaneously caters to successful predictive modeling and model explanation for practical insights about NBD at the global level. It overcomes the limitations in the present literature that assume the NBD is country- and industry-specific, and factors of the NBD cannot be generalized globally. With DL-based regression and XAI methods, we prove our research hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators. This research justifies the robustness of the findings by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.Benchmarking : An International Journal580The hypothesis that NBD can be effectively assessed and compared with the help of global macro-level indicators is proved and the robustness of the findings are justified by using the socio-economic data from the renowned data repository of the World Bank and by implementing the DL modeling with validation through the EBM method.Benchmarking: An International Journal["Paritosh Pramanik", "R. K. Jana", "Indranil Ghosh"]2024-08-27T00:00:00 96250497ac028bede4a5400f45fdf723bc73eb73Significant investment and development have gone into integrating Artificial Intelligence (AI) in medical and healthcare applications, leading to advanced control systems in medical technology. However, the opacity of AI systems raises concerns about essential characteristics needed in such sensitive applications, like transparency and trustworthiness. Our study addresses these concerns by investigating a process for selecting the most adequate Explainable AI (XAI) methods to comply with the explanation requirements of key EU regulations in the context of smart bioelectronics for medical devices. The adopted methodology starts with categorising smart devices by their control mechanisms (open-loop, closed-loop, and semi-closed-loop systems) and delving into their technology. Then, we analyse these regulations to define their explainability requirements for the various devices and related goals. Simultaneously, we classify XAI methods by their explanatory objectives. This allows for matching legal explainability requirements with XAI explanatory goals and determining the suitable XAI algorithms for achieving them. Our findings provide a nuanced understanding of which XAI algorithms align better with EU regulations for different types of medical devices. We demonstrate this through practical case studies on different neural implants, from chronic disease management to advanced prosthetics. This study fills a crucial gap in aligning XAI applications in bioelectronics with stringent provisions of EU regulations. It provides a practical framework for developers and researchers, ensuring their AI innovations advance healthcare technology and adhere to legal and ethical standards.European Conference on Artificial Intelligence540This study fills a crucial gap in aligning XAI applications in bioelectronics with stringent provisions of EU regulations by investigating a process for selecting the most adequate Explainable AI methods to comply with the explanation requirements of key EU regulations in the context of smart bioelectronics for medical devices.ArXiv["Francesco Sovrano", "Micha\u00ebl Lognoul", "Giulia Vilone"]2024-08-27T00:00:00 20b095be22bbf3a90c2c4dc691a6d8c7299f1c1dWith the growing prevalence of generative artificial intelligence (AI), an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in AI-assisted works. This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation. Our experimental results demonstrate that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains. We hope that this work lays a foundation for measuring human contributions in AI-assisted content generation in the era of generative AI.arXiv.org390This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory and demonstrates that the proposed measure effectively discriminates between varying degrees of human contribution across multiple creative domains.ArXiv["Yueqi Xie", "Tao Qi", "Jingwei Yi", "Ryan Whalen", "Junming Huang", "Qian Ding", "Yueqi Xie", "Xing Xie", "Fangzhao Wu"]2024-08-27T00:00:00 3f9528fff19e0169d671153dc474f62b455f8352The Naturalist434The integration of closed-loop experiments with physics-based feature selection and supervised learning, denoted as closed-loop transfer (CLT), is reported to yield chemical insights in parallel with optimization of objective functions.Nature["Nicholas H. Angello", "David M. Friday", "Changhyun Hwang", "Seungjoo Yi", "Austin H. Cheng", "Tiara C. Torres-Flores", "E. Jira", "Wesley Wang", "Al\u00e1n Aspuru-Guzik", "Martin D. Burke", "Charles M. Schroeder", "Ying Diao", "Nicholas E. Jackson"]2024-08-28T00:00:00 e3c08d11f5ceca1e8d9a777326124f8b17a3fad7Anytime a new technology has been developed, it has been integrated into education. All these advances have traditionally raised concerns. Artificial Intelligence (AI) is the next generation of technology that is already being included in educational contexts. It has also raised several concerns that will be presented in this paper. An umbrella review of the literature was conducted and the studies were analyzed using a meta-analysis review to answer the research question: what are the best practices for integrating AI in Higher Education (HE) institutions? Suggestions and best practices for AI inclusion in HE institutions are presented, as well as ethical concerns to observe.Ubiquity Proceedings01Suggestions and best practices for AI inclusion in HE institutions are presented, as well as ethical concerns to observe.Ubiquity Proceedings["Martha Lorena Obermeier"]2024-08-28T00:00:00 cdc8cb1855b0e0726832ad5f2855ca746c6932a8Recent research has shown that consumers tend to behave more unethically when encountering artificial intelligence (AI) agents than with human agents. Nevertheless, few studies have explored the differential impact of AI agents on unethical consumer behavior. From the perspective of the power relationship between AI and consumers, we classify the role of an AI agent as that of a “servant” or “partner.” Across one field study and four scenario-based experiments (offline and online), we reveal that consumers are more likely to engage in unethical behavior when encountering servant AI agents than partner AI agents due to increased anticipatory moral disengagement. We also identify the boundary conditions for the moral disengagement effect of AI agents, finding that this effect is attenuated (a) among consumers with high moral identity, (b) with human-like AI agents, and (c) in the context of high behavioral visibility. This research provides new insight into the AI morality literature and has practical implications for service agencies using AI agents.Journal of services research631The boundary conditions for the moral disengagement effect of AI agents are identified, finding that this effect is attenuated among consumers with high moral identity, with human-like AI agents, and in the context of high behavioral visibility.Journal of Service Research["Shaohui Lei", "Lishan Xie", "Jiamin Peng"]2024-08-28T00:00:00 f45a3b93aa6013ed4d22cac28a307b659bf64368The rise of artificial intelligence is fundamentally transforming the competitive landscape across various sectors, offering visionary enterprises new pathways to innovation development and to get a competitive edge. AI leverages data, analysis, and observations to perform tasks without hard coding, and benefits from self-learning and continuous improvement. We use Systems Thinking to frame how managers may adopt and integrate AI in business activities. We also investigate the motivations driving entrepreneurs to adopt AI solutions, and how they may impact on sustainable business model innovation, by administering a questionnaire to a sample of innovative Italian SMEs to get a comprehensive overview of the dynamics influencing AI adoption in business. This study sheds light on the intricate relationship between technology, sustainability, and corporate innovation. It offers both valuable insights for future research and for strategic managerial decisions on AI integration. Furthermore, it helps the development of innovative, sustainable business models in the evolving landscape of the Great Reset.Syst.1121Light is shed on the intricate relationship between technology, sustainability, and corporate innovation and it helps the development of innovative, sustainable business models in the evolving landscape of the Great Reset.Syst.["Valerio Muto", "S. Luongo", "Martina Percuoco", "Mario Tani"]2024-08-28T00:00:00 0ae15dddf62fda346e1876c480d5570ef5f3451bThe proposed research explores the transformative potential of artificial intelligence (AI) in modernizing the charitable sector. Initiative aims to address critical gaps in program delivery, resource allocation, and impact assessment, ultimately enhancing the efficiency and effectiveness of humanitarian aid. The study argues that AI can revolutionize disaster response by predicting natural disasters, improve educational outcomes through personalized learning platforms, and enhance public health monitoring by forecasting disease outbreaks. Furthermore, AI can optimize food distribution systems, reducing waste and ensuring timely delivery. The research also emphasizes the urgent need to develop a digital safety framework to protect children and youth from online risks, exacerbated by the rapid proliferation of digital technologies. The methodology includes comprehensive data collection from various sectors, followed by the development and testing of AI models tailored to specific needs, such as disaster prediction and personalized education. The study also involves stakeholder engagement to ensure the practical applicability and ethical implementation of AI solutions. Expected results include resource use, improved program impact, a safer digital environment for vulnerable populations. The project aims to set a precedent for the integration of AI in the charitable sector.Journal of Ecohumanism512The proposed research explores the transformative potential of artificial intelligence in modernizing the charitable sector and argues that AI can revolutionize disaster response by predicting natural disasters, improve educational outcomes through personalized learning platforms, and enhance public health monitoring by forecasting disease outbreaks.Journal of Ecohumanism["M. O. Elamin"]2024-08-28T00:00:00 6728deddda30915863c8ff454febca2f20e1445f Artificial intelligence, or AI, has the potential to dramatically improve our understanding and management of the ocean. For fisheries, these benefits could include greater monitoring coverage at lower costs, improved estimates of catch and bycatch, identification of illegal fishing, and seafood traceability throughout the supply chain. However, fisheries AI innovation and adoption faces substantial barriers from the highly regulated nature of fisheries and the complex overlap of government policies, diverse user needs, and market pressures. We argue that needed advances in fisheries AI require novel collaborations to share data and methods, encourage new and diverse entrants to the field, and increase baseline technical literacy across the global fisheries community. Unlocking fisheries data to power AI, particularly image data, can only be achieved through partnerships across government managers, AI developers, fishers and vessel owners, and technology service providers, which, in turn, requires a common vocabulary for policy and technical concepts. With a greater shared understanding across the field, fisheries AI providers can deliver desired results, and users can have confidence that systems are performing as advertised, ultimately meeting monitoring demand and sustainability goals.ICES Journal of Marine Science392It is argued that needed advances in fisheries AI require novel collaborations to share data and methods, encourage new and diverse entrants to the field, and increase baseline technical literacy across the global fisheries community.ICES Journal of Marine Science["Kate Wing", "Benjamin Woodward"]2024-08-28T00:00:00 b7c1d5c14cbf0195407f735c08609e159cdc6f6bArtificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize healthcare delivery, from diagnosis and treatment to patient care and administrative tasks. the applications, challenges, and ethical considerations surrounding the role of AI in healthcare. It discusses how AI algorithms can analyze vast amounts of medical data to assist healthcare professionals in making accurate diagnoses, predicting patient outcomes, and personalizing treatment plans. Additionally, it examines the challenges associated with implementing AI in healthcare, such as data privacy concerns, algorithm bias, and regulatory hurdles. Furthermore, it addresses ethical considerations, including transparency, accountability, and the impact of AI on patient-provider relationships. Despite these challenges, AI holds tremendous promise for improving healthcare efficiency, accessibility, and quality, provided that stakeholders address these concerns and harness AI's potential responsibly.International Journal for Research Publication and Seminar111How AI algorithms can analyze vast amounts of medical data to assist healthcare professionals in making accurate diagnoses, predicting patient outcomes, and personalizing treatment plans is discussed.International Journal for Research Publication and Seminar["Dr. Satender Khatri"]2024-08-28T00:00:00 186e2a851cfa6b75dd63fdb444dd9421e59229d4Artificial intelligence (AI) and autonomous systems are rapidly advancing technologies that offer significant benefits but also pose new ethical challenges. This review aims to comprehensively analyze the key ethical issues related to AI and autonomy through an expanded discussion of relevant literature. The development of advanced AI and autonomous systems could enable unprecedented capabilities but also risks that are unprecedented in their nature and scale. Ensuring these technologies are developed and applied in an ethical manner will require addressing issues around safety, transparency, accountability, and the prioritization of human values. Researchers have proposed technical and philosophical approaches to building "friendly" or "beneficial" AI that avoids potential harms. However, many open questions remain about how to properly specify and validate ethical constraints for systems that may surpass human levels of intelligence. Autonomous systems like self-driving vehicles also introduce new ethical dilemmas around responsibility and decision- making in safety-critical situations. Standards are needed to help guide the design of autonomous functions to be transparent, predictable, and respectful of human dignity and diversity. Governments and international organizations have begun outlining policy recommendations for developing AI that is trustworthy and compatible with human rights, privacy, and democratic values.International Journal of Innovative Science and Research Technology711This review aims to comprehensively analyze the key ethical issues related to AI and autonomy through an expanded discussion of relevant literature.International Journal of Innovative Science and Research Technology (IJISRT)["Maduabuchukwu Augustine Onwuzurike", "Augustine Rita Chikodi", "Brian Otieno Odhiambo"]2024-08-28T00:00:00 6a57617e5c6212837f4fbb68adc7979440d6f580The optimization of energy consumption in electric vehicles (EVs) using artificial intelligence (AI) to enhance vehicle performance is explored in this research. Advanced AI algorithms are integrated to dynamically control and allocate energy resources within EV systems. Extensive simulations and real-world experiments were conducted to evaluate the impact of AI-based energy management on key performance indicators such as range, efficiency, and charging behavior. Various driving scenarios, environmental conditions, and user preferences were considered to provide a comprehensive analysis of the effectiveness of AI algorithms in diverse contexts. The transformative potential of AI in optimizing energy usage is demonstrated, showcasing its ability to adapt to real-time conditions and user behavior. Trade-offs between energy optimization and computational complexity are revealed, highlighting the practical viability of AI-based solutions in EVs. The evolving discourse on intelligent energy management is contributed to, underscoring AI's role in fostering more efficient, sustainable, and user-centric electric vehicle (EV) operations. Foundational understanding of performance dynamics is offered by the insights gained, guiding the development and integration of AI-driven energy management for the next generation of EVs. Enhanced EV efficiency and a more sustainable automotive industry are paved the way for by this work.2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)161Enhanced EV efficiency and a more sustainable automotive industry are paved the way for by this work, underscoring AI's role in fostering more efficient, sustainable, and user-centric electric vehicle (EV) operations.2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)["R. S", "K. S.", "Karthik R", "Jerome Anto Rezin K"]2024-08-28T00:00:00 85f0a11c9cce99656021d8085dfa2cf19c326fadFloods stand out as among the most devastating natural calamities globally, with their occurrence and severity increasing rapidly due to the effects of climate variability. With inadequate infrastructure, developing countries like Indonesia are more vulnerable to flood disasters. Artificial intelligence (AI) has emerged as a promising tool in helping deal with flood disasters. In flood disaster management, AI can help manage emergency response, provide early warning to the public, and predict floods. The purpose of this literature review is to find highly accurate AI methods for predicting coastal flooding in Indonesia, the factors used as data completeness for predicting coastal flooding, and also identify the strengths and weaknesses of the AI methods used to predict coastal flooding. This study conducted a thorough analysis of relevant research, compared, and concluded that AI methods such as machine learning have shown relevant results in predicting coastal flooding. This study provides a broad insight into the use of AI in flood disaster management, especially in Indonesia, and emphasizes that future research is needed to address issues in the effective application of AI in the field.International Conference on Information Management and Technology380It is concluded that AI methods such as machine learning have shown relevant results in predicting coastal flooding, and that future research is needed to address issues in the effective application of AI in the field.2024 International Conference on Information Management and Technology (ICIMTech)["Eileen Anindya Putri Maheswari", "Firsa Anata Mernisi", "Sidharta Sidharta", "Chasandra Puspitasari"]2024-08-28T00:00:00 0a4d275dc822a64f7a48abbe2580864547844a37Generative artificial intelligence (GAI) is rapidly transforming the healthcare landscape, offering innovative solutions in areas such as medical imaging, drug discovery, and clinical decision support. This comprehensive review examines the current role of GAI in healthcare, its potential benefits, drawbacks, challenges, and future research directions. By synthesizing recent literature and expert perspectives, this review provides a critical analysis of GAI’s impact on healthcare delivery, patient outcomes, and ethical considerations. While GAI shows promise in enhancing diagnostic accuracy, accelerating drug development, and improving healthcare efficiency, it also faces significant challenges related to data privacy, regulatory compliance, and ethical implementation. This review aims to inform healthcare professionals, researchers, and policymakers about the current state and future potential of GAI in healthcare, emphasizing the need for responsible development and deployment of these technologies.Italian Journal of Medicine240This review aims to inform healthcare professionals, researchers, and policymakers about the current state and future potential of GAI in healthcare, emphasizing the need for responsible development and deployment of these technologies.Italian Journal of Medicine["Khaled Ouanes"]2024-08-28T00:00:00 e451815e01306eee79fbda07411d59236c9e0cddWith the expediential growth of the digital marketing landscape, the amalgamation of artificial intelligence (AI) technology stands pivotal in catalyzing transformative shifts within the market.  Notably, in the realm of tailored marketing endeavors, AI has ushered in an epoch of unprecedented comprehension and prognostication of consumer behavior through the judicious employment of deep learning algorithms, semantic interpretation capabilities, and exhaustive data analytics.  Yet, the intricate challenge of precisely gauging the efficacy of AI-empowered personalized marketing strategies remains an area requiring meticulous investigation.  This scholarly paper endeavors to delve into the personalized deployment of AI within the digital marketing sector, employing a stringent framework to rigorously scrutinize its impact.Journal of Innovation and Development60This scholarly paper endeavors to delve into the personalized deployment of AI within the digital marketing sector, employing a stringent framework to rigorously scrutinize its impact.Journal of Innovation and Development["Shiman Xu"]2024-08-28T00:00:00 e5e06d206bf0f028952e5d0e5e1dc859b3e9fdc0Archives of public health = Archives belges de sante publique20Belgium is advised to make policy-level decisions about how to fund, design and undertake actions focussing on data access and inclusion, IT-infrastructure, legal and ethical frameworks, public and professional trust, in addition to education and interpretation.Archives of Public Health["E. Cau\u00ebt", "Gabrielle Schittecatte", "M. van den Bulcke"]2024-08-28T00:00:00 bc283e367fa7881799909978a211c993d3843d55In recent years, artificial intelligence (AI) has become a pervasive part of daily life, with technologies like ChatGPT gaining traction. This paper examines the transformative potential of AI in online education, considering both its theoretical implications and empirical insights. The theoretical section explores AI's impact on education and the ethical challenges it presents, while the empirical section presents findings from a survey conducted among students. The survey investigated student perceptions and experiences with AI tools, shedding light on both their benefits and concerns. Notably, while students recognize the potential of AI to enhance learning, they also express apprehensions about issues like data privacy and overreliance on technology. Ultimately, the paper underscores the importance of responsible integration of AI into education, emphasizing the need for ongoing research and education to ensure its ethical and effective use.Ubiquity Proceedings00The importance of responsible integration of AI into education is underscored, emphasizing the need for ongoing research and education to ensure its ethical and effective use.Ubiquity Proceedings["Martina Plantak", "Vesna Le\u0161nik \u0160tefoti\u010d"]2024-08-28T00:00:00 58751c964638d75500a3f8e46abe4e74739942c3This study investigates the impact of artificial intelligence (AI) on the roles and practices of educators and outlines the professional development needs in the era of AI. Employing qualitative interviews with 75 teachers across 15 schools in Azerbaijan, the research explores the integration of AI into education. It leverages the Technological Pedagogical Content Knowledge (TPACK) framework to examine the convergence of technology, pedagogy, and content expertise in teaching. Findings reveal that while AI has the potential to enhance personalized learning, its adoption poses significant challenges, including teacher burnout, ethical concerns, and the need for professional development programs. The study highlights the importance of practical training to enable educators to effectively integrate AI into teaching. Limitations include context specificity and potential biases in self-reporting. Future research should assess the long-term educational impacts of AI and expand to various educational settings for a broader perspective.Ubiquity Proceedings00It is revealed that while AI has the potential to enhance personalized learning, its adoption poses significant challenges, including teacher burnout, ethical concerns, and the need for professional development programs.Ubiquity Proceedings["Rena Alasgarova", "Jeyhun Rzayev"]2024-08-28T00:00:00 0a1f264d87d994e3f6ee26ca1541960db6b42419The development of artificial intelligence (AI) is revolutionizing various industries, including IT project management. The object of research is the principle of augmented competence, which is a new approach that uses AI to strengthen and expand the capabilities of IT project teams. The essence of this principle lies in the complementary interaction of AI and the competence of project teams. Instead of replacing project managers, AI complements their competencies (knowledge, skills and experience). One of the hot spots is the application of AI in the process of automating routine tasks, analyzing large volumes of data and providing recommendations and predictions, freeing up time for team members to focus on more complex and creative tasks. The possibility of automating tasks and providing new knowledge, which will significantly improve the efficiency and productivity of the team, has been obtained through the use of the principle of augmented competence. As a result, data-driven recommendations and predictions enable teams to make more informed and effective decisions. Access to new knowledge and insights stimulates innovation and leads to new ideas and solutions, helps identify and mitigate potential risks, which can lead to more successful projects. Applying this principle to IT project management audits will automate software testing with AI, which replaces testers so they can focus on more complex types of testing such as exploratory testing, performs customer data analysis with AI, and enables companies to better understand your customers and their needs, which can lead to better marketing campaigns and products. It is important to note that this principle does not involve replacing project managers with AI. Instead, AI is used as a tool to empower human teams and help them achieve better results. As AI technologies continue to evolve, the principle of augmented competence is likely to play an even more important role in IT project management. AI can help teams overcome complex challenges, make better decisions, and succeed in a more dynamic and competitive environment.Technology audit and production reserves110The possibility of automating tasks and providing new knowledge, which will significantly improve the efficiency and productivity of the team, has been obtained through the use of the principle of augmented competence, which is a new approach that uses AI to strengthen and expand the capabilities of IT project teams.Technology audit and production reserves["S. Bushuyev", "A. Ivko"]2024-08-28T00:00:00 915ba0b6251b8f13d0389f9ef51dc901d767d790Scholarly interest in artificial intelligence (AI) has surged as researchers delve into its transformative impact on various aspects of our lives. AI poses both benefits and challenges, particularly in the context of educators' endeavors to comprehend the intricacies of students' learning processes. Although the use of AI to enhance and assist student learning is relatively new, the exponential growth of scholarly attention and publications in AI and student learning in recent years underscores the compelling necessity for further inquiry. Investigating this area is crucial for understanding the emerging trends in this research domain. This study aims to provide insights into the burgeoning research trajectories on AI from a student learning perspective. Using a bibliometric approach, this study examined 663 scholarly articles pertaining to the interface between AI and student learning published between 1961 and 2024. Our findings reveal four major thematic areas including AI in education and educational technology, AI-driven learning environments, essential AI enablers, and human learning and highlight promising avenues at this intersection.Journal of educational computing research740This study examines 663 scholarly articles pertaining to the interface between AI and student learning published between 1961 and 2024 to reveal four major thematic areas including AI in education and educational technology, AI-driven learning environments, essential AI enablers, and human learning and highlight promising avenues at this intersection.Journal of Educational Computing Research["Gawon Yun", "Kewman M. Lee", "Hailey Hyunjin Choi"]2024-08-28T00:00:00 4b7c427355e9de0a5d543b6bfb0dc1482e8073e9Under the background of artificial intelligence, the deployment and requirements of "promoting education digitization" have pointed out the direction and path for the construction of "big ideological and political courses". How to fully utilize digital intelligence technology to empower big ideological and political courses faces opportunities and challenges. Ideological and political theory education should also follow the trend, fully utilize digital technology, integrate advantageous resources, and effectively leverage the role of digital technology in empowering ideological and political courses, effectively enhancing the effectiveness of ideological and political education and teaching.Journal of social sciences and humanities20Journal of Social Science and Humanities["Yuzhuo Chen"]2024-08-28T00:00:00 251dd35d3025a68539fe6d620a4f7d9e15060dd3The AI Pioneers project, funded by the European Union, represents a significant initiative aimed at integrating Artificial Intelligence (AI) into the landscape of adult education and Vocational Education and Training (VET). Through a comprehensive methodology comprising of literature reviews, surveys, and interviews conducted with a wide range of educators and stakeholders, the project has identified key AI competencies that are essential for educators. These competencies align with the six areas of the DigCompEdu framework, including Professional Engagement, Digital Resources, Teaching and Learning, Assessment, Empowering Learners, and Facilitating Learners’ Digital Competence. The AI Pioneers project’s main findings highlight the need for educators to develop skills in data literacy, computational thinking, ethical considerations of AI use, and the integration of AI tools into educational practices. By supplementing the DigCompEdu framework with AI-specific competencies, the project contributes to the continuing professional development of teachers and trainers, ensuring they are well-prepared for the the challenges and opportunities presented by AI in education. This initiative not only enhances educators' digital competencies but also plays a crucial role in preparing students for a future where AI is ubiquitous, thereby shaping the future of educational practices and outcomes.Ubiquity Proceedings00The AI Pioneers project’s main findings highlight the need for educators to develop skills in data literacy, computational thinking, ethical considerations of AI use, and the integration of AI tools into educational practices.Ubiquity Proceedings["George Bekiaridis", "Graham Attwell"]2024-08-28T00:00:00 4033a302248e17fff2cc927f692d1b3a7d65055eIn the digital era, many researchers are examining the usefulness of online reviews, which show the importance of online reviews. This has led many researchers, practitioners, and companies to implement Artificial Intelligence (AI) technology for automating and optimizing various aspects of online reviews. The implementation of AI for online review has become a hot topic. However, there is a necessity to classify and synthesize existing insights to identify the latest trends and opportunities for further research in the future. Therefore, this study will conduct a systematic literature review (SLR) to address this gap by examining the current state of research in the implementation of AI for online review. The study utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagrams by submitting 3 Research Questions (RQ). The final results obtained from 27 selected primary studies show that the research trend in the implementation of AI for online review is still highly relevant today, and retail is the most utilized industry by researchers. In addition, it was revealed that current research focuses on five topics, i.e., sentiment analysis, fake detection, information extraction, dataset analysis, and review helpfulness. Hopefully, this study can contribute to the academic side for future research and practical side for insightful information in implementing AI for online review.International Conference on Information Management and Technology340A systematic literature review (SLR) is conducted by examining the current state of research in the implementation of AI for online review by examining the Preferred Reporting Items for Systematic Reviews and Meta-Analysis flow diagrams.2024 International Conference on Information Management and Technology (ICIMTech)["Dedy Syamsuar", "Marcello"]2024-08-28T00:00:00 965ed78b50d0b170e21749deca6ec95bd2ccffffA major transformation programme is underway across seven technological higher education institutions in Ireland. Investment of €40m #NextGenerationEU funding enables a programme to empower learners, to enhance staff capabilities, to improve the digital infrastructure, and to ensure long-lasting sustainable impact. Designed and implemented by significant collaboration of partner institutions, the programme explicitly adopts a holistic approach with inter-related work streams and work packages. The programme is underpinned by the six core themes of academic integrity, digital transformation, education for sustainability, employability, equality diversity & inclusion, universal design for learning. This comprehensive structure enables focus on key issues, emerging topics, and ensuring sustainable enhancement of learning, teaching and assessment across the technological sector. Multiple elements of the programme provided opportunities to reflect on the role of artificial intelligence, ranging from the perspective of challenges associated with academic integrity to the wider potential to be exploited to support and enhance learning and teaching. Students play an active role in considering these issues through focussed projects in partnership with staff and by acting as student champions to ensure that students‟ perspectives inform related discussions. Staff development opportunities have focussed on artificial intelligence and specific training is in place for senior institutional leaders. Active cooperation with the National Academic Integrity Network, which is facilitated by the national external quality agency, has led to complementary focussed activities. A national practitioners‟ network is being established to examine the impact of generative AI on higher education. Many of these structures could be replicated in other institutions or groups of institutions. While there are many examples of programme activities addressing important or topical issues, this paper particularly focuses on themes relating to the 2024 EDEN conference.Ubiquity Proceedings00This paper particularly focuses on themes relating to the 2024 EDEN conference, which is being established to examine the impact of generative AI on higher education.Ubiquity Proceedings["Sharon Flynn", "Sean O\u2019Reilly", "Caroline O\u2019Neill"]2024-08-28T00:00:00 034f0c7cee079ad13575f730c5d8fff8495d1a9fIn this vision paper, we thoroughly explore the potential of integrating artificial intelligence (AI) solutions into software product lines (SPLs) to overcome challenges like scalability and complexity. By harnessing AI's machine learning and automation capabilities, SPLs can significantly enhance feature selection, variability management, and customization. We uncover foundational concepts, expected benefits, and future research directions for AI-driven SPLs, including scalable machine learning, adaptive variability management, real-time adaptation and personalized customization. Our aim is to stimulate innovation and foster discussion in the software engineering community, driving towards more efficient, adaptable, and user-friendly software systems. The integration of AI into SPLs represents a fundamental shift in software development, promising improvements in productivity, quality, and user satisfaction.Computational Science and Engineering170This vision paper thoroughly explores the potential of integrating artificial intelligence (AI) solutions into software product lines (SPLs) to overcome challenges like scalability and complexity, and uncover foundational concepts, expected benefits, and future research directions for AI-driven SPLs.Computational Science and Engineering["Houssem Chemingui"]2024-08-28T00:00:00 58333a5fb576598fca114ed79f3accc6621bc121The incorporation of artificial intelligence (AI) into asset recovery and anti-money laundering (AML) procedures signifies a revolutionary change in the handling of financial crime. This article investigates the use of AI techniques to improve AML compliance, detect suspicious activity, and improve transaction monitoring. Financial institutions can now analyze massive volumes of transaction data in real-time, find anomalies, and lower false positives thanks to AI-based solutions, which include machine learning algorithms and predictive modeling. The research also outlines the difficulties and advantages of implementing AI, such as enhancing the effectiveness and caliber of suspicious activity reports (SARs) while resolving security and privacy issues with data. The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention.2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)310The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention.2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)["Dr. K.Balaji"]2024-08-28T00:00:00 dc2af24acc8b5b1d09216abd77f8cb5fdf12e673The new generation of artificial intelligence (AI) technology has led to systematic and revolutionary changes in the field of education. As a key element in the process of education and teaching in the intelligent era, the development of teachers' AI literacy level cannot be ignored. Countries around the world are actively developing teachers' AI literacy to cope with the development trend of intelligent education, and have issued a series of policies and guidelines to regulate its development. Based on this, this paper starts from the relevant policies and practical actions of teachers' AI literacy in various countries, and analyzes the urgent requirements of AI education for teachers' AI literacy from the aspects of teachers' consciousness and attitude, curriculum development, teaching development, teaching management, teacher development and AI ethics. Finally, the article clarifies the necessary qualities of teachers in the era of AI, and puts forward the development path of teachers' AI literacy in view of the above six aspects, in order to provide a reference for the professional development of teachers in the intelligent era.STEM Education Review260The article clarifies the necessary qualities of teachers in the era of AI, and puts forward the development path of teachers' AI literacy in view of the above six aspects, in order to provide a reference for the professional development of teachers in the intelligent era.STEM Education Review["Siman Zhang", "Junfeng Diao", "Xinyan Ma", "Xiaoqi Tang", "Xu Ding"]2024-08-28T00:00:00 16befd33cb51f54acbcc0e81c1a35641606d71432023 was a landmark in terms of artificial intelligence (AI) use breakthrough, with emergence of the first large language model GPT-4 in free access. This spurred questions and concerns over AI influence on educational system, namely on language learning. In practice, GPT-kind language models can generate texts, translate them and address consecutive questions. Nevertheless, they are largely limited as are uncapable of complex logical chains construction and understanding of context nuances. Within language learning AI-based services may be used as a tool for text work, but can’t be base to forming of foreign language communication skills. Emergence of AI nevertheless raises questions over the need to adjust the educational system to the coming future, with AI being of dominant role in all the fields of human activities.Общество социология психология педагогика00Emergence of AI nevertheless raises questions over the need to adjust the educational system to the coming future, with AI being of dominant role in all the fields of human activities.Общество: социология, психология, педагогика["S. Kashchuk"]2024-08-28T00:00:00 e7ded9b93ca3d1a23ba19aea97dcaeca53524c5aCurrently, the IT market is promoting the use of cloud resources to build solutions using machine learning. Some projects require full independence from external resources, mainly due to the volatility of prices for renting computing power and services provided by the cloud. The paper presents the creation process of scalable “on-premise” environment en-abling the construction and development of artificial intelligence systems in the field of natural language processing. The proposed approach was based on aspects related to containerization, scalability, and automation of the machine learning process. Therefore, the created “on-premise” environment can be used in the implementation and delivery of systems based on artificial intelligence.EUROMICRO Conference on Software Engineering and Advanced Applications150The paper presents the creation process of scalable “on-premise” environment en-abling the construction and development of artificial intelligence systems in the field of natural language processing, based on aspects related to containerization, scalability, and automation of the machine learning process.2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)["Adam Czyzewski", "Krzysztof Stepien", "A. Poniszewska-Mara\u0144da"]2024-08-28T00:00:00 eb7b0894d5ed47b73128ed589ae4d072665c471bAs e-commerce has become an essential aspect of daily life in Indonesia, competition among e-commerce companies has increased. This increased competition emphasizes the importance of continuous innovation to remain competitive. Artificial intelligence could be a solution, but its impact is uncertain. The goal of this study is to use the SOR model to analyze the effectiveness of artificial intelligence in e-commerce in increasing purchase intentions. The research model contains six variables: AI Insight Experience, AI Interactive Experience, AI Product Recommendation, Perceived Utility Value, Perceived Hedonic Value, and Purchase Intention. The data from 400 respondents was analyzed using Sequential Equation Modeling (SEM) with Smart PLS 4.0. A questionnaire was used to collect the data online for two weeks in April 2024. The findings revealed that every one of the eight hypotheses had a significant effect.International Conference on Information Management and Technology290The goal of this study is to use the SOR model to analyze the effectiveness of artificial intelligence in e-commerce in increasing purchase intentions and revealed that every one of the eight hypotheses had a significant effect.2024 International Conference on Information Management and Technology (ICIMTech)["Arcelia Ferani", "Calista Syifa Putri Wardhana", "Rendy Vincent Gunawan", "Marisa Karsen"]2024-08-28T00:00:00 5585b2ce963ed6ef563a18ce644b1c3a2b154891As the number of elderly persons rises, there is a gradual increase in reliance on artificial intelligence (AI) to augment healthcare systems. How do we interpret AI in elderly care (EC) in light of the Catholic theological tradition? As far as the literature goes to date, there is still much room for discourse. For this reason, this article hopes to contribute in that regard and, more importantly, to encourage others to further the discourse. In the present commentary, I first examine some aging trends in the world population. Afterward, I briefly describe AI use in healthcare, especially for EC. I then explore prominent ethical concerns related to it. Finally, I theologically reflect on using AI for EC vis-à-vis the magisterial teachings on aging and AI.The Linacre Quarterly80In the present commentary, some aging trends in the world population are examined and prominent ethical concerns related to it are explored, and theologically, using AI for EC vis-à-vis the magisterial teachings on aging and AI are theologically reflected.The Linacre quarterly["T. Pugeda"]2024-08-28T00:00:00 ff16d879322d46d7cf73f1a6f29b1dabc7563d1dObjectives: Surveys conducted on a global scale have revealed a significant level of importance in artificial intelligence (AI) among medical pupils’. There have been no inquiries overseen in Kerala to explore the opinions of medical students regarding the consumption of AI in health care or their level of understanding of AI. We seek to evaluate the depths of Kerala’s medical learners concerning AI, both in the field of health care and its potential integration into the medical program. Methods: A digital scrutiny apparatus was created through a thorough examination of existing text and collaboration with doyens in the field. The inquiry was tested with a trio of medical learners and improved based on their valuable input. We distributed a digital probe to all medical pupils in Kerala, which amounted to around 20,000 students. The survey was available for responses from April 1st, 2024 to June 1st, 2024. The students’ responses were carefully analysed, both in terms of categories and the content of their free text comments. The analysis was conducted using open coding techniques to ensure a thorough qualitative analysis. Results: In total, 1000 students provided comprehensive answers to all the questions. A significant proportion of students (82.0%) fell within the age range of 20-29 years, pursuing medicine as their undergraduate degree (85%). Many students showed a keen interest in AI, with a significant majority (79.9%) expressing their curiosity. A large percentage (83.1%) asserted to have a basic realizing of AI, but when it came to the fundamental computational principles of AI, only a minority (39.8%) agreed that they grasped them. Similarly, more than half of the students (51.6%) acknowledged their lack of knowledge regarding the limitations of AI. Most students (81.1%) had not obtained any instruction in AI. A significant portion of students (57.3%) expressed their support for incorporating AI into medical training, while a majority (76.2%) expressed a desire for increased emphasis on teaching AI in medicine. A significant portion (61.3%) of medical students expressed a lack of worry about the potential effect of AI on their job safety as doctors. According to the survey, the majority of students believe that radiology, pathology, and medical administration are the specialties that will be most affected by AI. On the other hand, psychiatry, palliative care, and obstetrics and gynaecology are considered to be the least likely to be stuck by AI. Through qualitative study of the free text notes, it was discovered that AI is viewed as a valuable tool rather than a replacement for doctors. This finding emerged as a common theme. Conclusion: It seems that medical students in Kerala have shown a keen interest in AI. However, their knowledge about AI is limited and they feel they lack a deep understanding of its fundamental computational principles and limitations. It seems that AI is currently lacking in the medical curriculum in Kerala and India, and a majority of surveyed students expressed their support for its implementation. These findings align with previous surveys conducted on a global scale.E-Learning and Digital Media120It seems that medical students in Kerala have shown a keen interest in AI, however, their knowledge about AI is limited and they feel they lack a deep understanding of its fundamental computational principles and limitations.E-Learning and Digital Media["Jithin Gangadharan K", "Pritpal Singh"]2024-08-28T00:00:00 df506cc7b6bd1eec80e43583af0193d38f6e3552PURPOSE OF REVIEW This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field. RECENT FINDINGS Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring. SUMMARY The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.Current Opinion in Ophthalmology630The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows, which hold significant implications for clinical practice.Current opinion in ophthalmology["R. Rampat", "G. Debellemani\u00e8re", "Damien Gatinel", "Darren S J Ting"]2024-08-28T00:00:00 a7d1284e41fa4c84e7544dec7cd4faee478ce8bbThis article is intended to explore IT Compliance from an employee's perspective when artificial intelligence is used as a tool in the workplace. This research use Technology, Organizational and Environmental or TOE. This research approach refers to a causal associative approach, with data collected through an online questionnaire using a 5-point Likert scale to measure the relationship between variables. Data analysis linier regression to test indicators, validity, reliability, and hypotheses, with JAMOVI as software. The findings of this study Organization view on technology affect employee's behavior intention on compliance positively is not supported (P-values > 0,001), while AI as technology affect employee's behavior intention on compliance positively supported and Environment pressure on technology usage affect employee's behavior intention on compliance positively is supported. The managerial implication of this study is that organizations can encourage the use of artificial intelligence to achieve employee performance which will later have an impact on the organization.International Conference on Information Management and Technology180The managerial implication of this study is that organizations can encourage the use of artificial intelligence to achieve employee performance which will later have an impact on the organization.2024 International Conference on Information Management and Technology (ICIMTech)["Dewi Tamara", "Anita Maharani"]2024-08-28T00:00:00 094e90e2832d374b047e13fcb78051e9d09dafabThis paper examines how selected vocational schools in Germany are dealing with the integration of artificial intelligence into the school context and how vocational school teachers and their students are undertaking initial attempts and projects to integrate various technologies and AI into teaching practice. The focus here is primarily on the concrete practical teaching level in vocational schools, so that the content and methods of teaching by vocational school teachers and the concrete technological applications are considered. The first pilot applications of AI systems at vocational schools are the focus of interest, so that a selection of 5 AI-committed schools are presented in this article (predominantly automation and robotic projects). The investigation took place as part of the Erasmus+ project AI Pioneers, which consists of an international consortium working to establish a VET network with regard to the integration of AI in educational settings.Ubiquity Proceedings00Ubiquity Proceedings["Ludger Deitmer", "Iven Dersch", "Lisa Meyne", "Christine Siemer"]2024-08-28T00:00:00 6d5ae9e5e97a818ceef064ec5adf43db2620b878Digitalization of education is one of the key processes of transformation of modern society, which allows to upgrade the outdated structure of education and introduce new technologies that can improve the quality of the final result and make education more accessible. The introduction of artificial intelligence (AI) in the academic process of higher education helps to simplify and systematize the learning process. Thanks to AI, educational programs become more personalized and the process of analyzing and predicting learning is facilitated. The article investigates the role and impact of AI on teaching activity in higher education institutions in the context of digitalization of education, as well as identifies the positive and negative aspects associated with the introduction of AI in the educational process of higher education. The results of the study showed that it is necessary to conduct intensive retraining of university teaching staff aimed at their mastery of AI. Additionally, the training of students in pedagogical fields should be based on their advanced mastery of modern digital educational technologies, including those related to AI.Общество социология психология педагогика00The results of the study showed that it is necessary to conduct intensive retraining of university teaching staff aimed at their mastery of AI, and the training of students in pedagogical fields should be based on their advanced mastery of modern digital educational technologies, including those related to AI.Общество: социология, психология, педагогика["Olga L. Zhalnina", "L. B. Lubsanova", "S. Bakshikhanova"]2024-08-28T00:00:00 1c637667470c3f9696ebcc1f575b2cbf24ff80dcntroduction: Musculoskeletal disorders (MSDs) represent a significant global health burden, leading to substantial disability and socioeconomic impact. With the rise of artificial intelligence (AI), particularly large language models-driven conversational agents (chatbots), there is potential to enhance the management of MSDs. However, the application of AI-powered chatbots in this population has not been comprehensively synthesized. Objective: To explore the current and potential use of AI-powered chatbots in the management of MSDs. The review will map out the targeted diseases, the purposes of chatbot interventions, the clinical tools or frameworks utilized in training these systems, and the evaluated outcomes in clinical settings. Methods: This scoping review will follow the PRISMA-ScR guidelines, with a comprehensive search across multiple databases including Medline (Ovid MEDLINE), Embase (Ovid), ISI Web of Science (wos; clarivate) and ClinicalTrials.gov. Studies involving adults with MSDs, regardless of publication status, language, or year, will be included. The scoping review will exclude studies using non-AI chatbots or human health coaches. Data extraction and synthesis will focus on demographic characteristics, chatbot methods, outcomes, and thematic analysis.medRxiv140This scoping review will map out the targeted diseases, the purposes of chatbot interventions, the clinical tools or frameworks utilized in training these systems, and the evaluated outcomes in clinical settings, to explore the current and potential use of AI-powered chatbots in the management of MSDs.["Joaqu\u00edn Gonz\u00e1lez Aroca", "Laura Vergara-Merino", "Camila Micaela", "Escobar Liquitay", "Humberto Far\u00edas", "Jorge Olivares", "\u00c1lvaro Puelles"]2024-08-28T00:00:00 982e39f935d6a5b17091950e393314ebcc002ab5Artificial intelligence in education has emerged as an opportunity to facilitate teaching and learning, especially in learning environments mediated by technology, such as online higher education. Despite its growing prominence, there is a lack of empirical research analysing how artificial intelligence affects inclusive education. Therefore, this study aims to analyse the perspectives and viewpoints of online course designers on leveraging these technologies to promote equal participation for all learners. Twelve professors participated in semi-structured interviews that were subsequently analysed through thematic analysis. The findings encompass two main themes. On one hand, the use of artificial intelligence in education as a tool for inclusive education within a human-centric pedagogy. Participants are cautious about using artificial intelligence to replace human work but recognise its potential contribution to facilitate content accessibility and comprehension. On the other, the adoption of a new approach for learning and assessment based on reflection and metacognition. Our participants‟ strategies include modifying some assessment practices when designing their courses for enabling learners to compare artificial intelligence creations, although they also highlight the lack of knowledge on using these technologies. Therefore, shifting to an assessment approach based on strengthening metacognition, reflection, and critical thinking skills emerges as a means to promote learners‟ inclusion supported by artificial intelligence. Our study also emphasises the importance of promoting artificial intelligence literacy for both professors and learners to effectively incorporate these technologies in the educational processes.Ubiquity Proceedings00Shifting to an assessment approach based on strengthening metacognition, reflection, and critical thinking skills emerges as a means to promote learners’ inclusion supported by artificial intelligence.Ubiquity Proceedings["J. Reyes", "Julio Meneses"]2024-08-28T00:00:00 84bab2ae4367b1358a3649bcbc5bfdcb6b79dbdcA growing number of organizations are integrating Artificial Intelligence (AI) into their operations, and into their business to enhance effectiveness and efficiency. Drawing upon the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), this study delves into the factors that impact the intention to employ artificial intelligence within this specific group of organizations employing the advanced technique of Smart Partial Least Squares-structural equation modeling (PLS-SEM). An online survey was conducted in Austria to gather a dataset of 103 accounting professionals. Perceived usefulness and ease of use were the most significant factors in forecasting the accountants' perspective towards Artificial Intelligence; the attitude itself, along with perceived usefulness, social norms, and expertise, determinants of the intention to utilize Artificial Intelligence were identified. However, Perceived Ease of Use did not influence the behavioral intention of Artificial Intelligence. This study provides theoretical and practical contributions to the understanding of behavioral intention in the accounting industry. The provision of guidance to management is aimed at facilitating the seamless integration of the TAM and TPB model within organizational operations, to optimize the utilization of Artificial Intelligence (AI) more efficiently and appropriately.International Conference on Information Management and Technology390Perceived usefulness and ease of use were the most significant factors in forecasting the accountants' perspective towards Artificial Intelligence; the attitude itself, along with perceived usefulness, social norms, and expertise, determinants of the intention to utilize Artificial Intelligence were identified.2024 International Conference on Information Management and Technology (ICIMTech)["Rudolf Gruenbichler", "L. Wijaya", "Cheng Kin Meng", "Katharina Greimel", "Tiurida L. Anita", "Sylvia Samuel"]2024-08-28T00:00:00 59682495598ca0726619603ce203393327953bb2Aim: The research aims to explore healthcare workers' attitudes towards and acceptance of artificial intelligence in radiology, focusing on its potential to enhance diagnostic accuracy, reduce waiting times, increase service efficiency, and improve patient care. It seeks to assess their willingness to adopt artificial intelligence, along with their concerns, expectations, and educational needs to effectively utilize this technology in radiological practice.Methods: A prospective study surveyed 50 healthcare workers, including radiologists, technicians, nurses, and others using various radiological techniques for diagnosing diseases. Using a quantitative approach, numerical data from distributed surveys assessed their attitudes and knowledge regarding artificial intelligence  in radiology. Conducted at Public Health Institution "Zdravstveni centar Brcko" in Bosnia and Herzegovina's Brcko District over 60 days, the research adhered to ethical principles and received approval from the center's Ethics Committee.Results: The majority of respondents recognize the potential of artificial intelligence to improve the efficiency of radiological services and treatment processes, but at the same time express the need for additional education and training in order to optimally use this technology. Despite the positive perception, part of the respondents are still not sure about the use of artificial intelligence, which emphasizes the importance of continuous information and education of healthcare workers.Conclusion: Ultimately, the research results indicate the importance of further steps in the implementation of artificial intelligence in radiology in order to improve the quality of health care and optimize treatment processes.Keywords: Artificial intelligence, radiological diagnostics, challenges, perspectives, quality of health care.Acta Medica Saliniana120Healthcare workers' attitudes towards and acceptance of artificial intelligence in radiology are explored, focusing on its potential to enhance diagnostic accuracy, reduce waiting times, increase service efficiency, and improve patient care.Acta Medica Saliniana["Benjamin Malkic", "Nihad Mesanovic"]2024-08-28T00:00:00 da3ccb97072503a7bd40fae130490e9ff79b47f2The business field has conducted extensive studies on artificial intelligence. However, research is still needed to explore artificial intelligence in supply chain management. This study explores how artificial intelligence impacts supply chain dynamism, adaptive capabilities, and resilience. Additionally, it will investigate the effect of these factors on supply chain performance. The analysis is grounded in organizational information processing theory and uses a quantitative method with primary data gathered through questionnaires from 100 employees working in supply chain-related divisions. Respondents were selected using purposive techniques, and structural equation modeling was employed for data analysis using SmartPLS 3.0. The findings reveal that artificial intelligence significantly affects supply chain dynamism, adaptive capabilities, and resilience. Additionally, the study highlights the influence of supply chain resilience on supply chain performance. Notably, the study's novelty lies in identifying that supply chain dynamism and adaptive capabilities do not significantly impact supply chain performance.International Conference on Information Management and Technology310This study explores how artificial intelligence impacts supply chain dynamism, adaptive capabilities, and resilience and identifies that supply chain dynamism and adaptive capabilities do not significantly impact supply chain performance.2024 International Conference on Information Management and Technology (ICIMTech)["Stefanus Rumangkit", "M. Hamsal", "A. Sundjaja", "Willy Gunady"]2024-08-28T00:00:00 d0013c479fec62bcc1e341805f0896ae4442b3d3Pneumonia continues to pose a considerable worldwide health burden, contributing significantly to morbidity and death across all age categories. The goal of this thorough Analysis study is to provide a thorough analysis of pneumonia, including information on its Pathophysiology, diagnostics, epidemiology, and treatment techniques. We'll investigate epidemiological elements using machine learning and deep learning such as incidence, prevalence, and risk factors to learn more about the disease's using artificial intelligence regional and demographic differences. The intricate Pathophysiology of pneumonia will be covered in detail, along with how host variables, environmental factors, and microbial agents interact. The merits and limits of various diagnostic procedures, such as sophisticated imaging, laboratory techniques, and clinical evaluation, will be analyzed critically. In addition, the discussion will go over current protocols and recommendations for treating pneumonia, stressing the need of supportive care, antibiotic treatment, and preventative measures. In order to provide physicians, researchers, and policymakers a thorough grasp of this common respiratory ailment, the article will discuss recent trends, difficulties, and future prospects in pneumonia research and clinical practice in using machine learning and deep learning.2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)400A thorough analysis of pneumonia, including information on its Pathophysiology, diagnostics, epidemiology, and treatment techniques is provided, and epidemiological elements using machine learning and deep learning such as incidence, prevalence, and risk factors are investigated.2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)["Mohini Gahlot", "Pinaki Ghosh"]2024-08-28T00:00:00 637623c8f0e8dda80f376dd9845060495921d780Indian Journal of Orthopaedics34Indian journal of orthopaedics["Himel Mondal", "Shaikat Mondal", "Ayesha Juhi"]2024-08-28T00:00:00 d178ce92f439a9317637dc6fda276ccef3c0fffbJournal of the Knowledge Economy323Journal of the Knowledge Economy["Yu Peng Zhu", "H. Park"]2024-08-28T00:00:00 7ab4223bc5ff4c5d39fd7b8f71ff29156a46ee6bJournal of Medical Ethics41Journal of medical ethics["Aorigele Bao", "Yi Zeng"]2024-08-28T00:00:00 bf10dfca434ae6b22da8a4ddaa002bc205a52944This study presents an inclusive K-12 AI curriculum for elementary schools, focusing on six design principles to address gender disparities. The curriculum, designed by the researchers and an elementary teacher, uses tangible tools, and emphasizes collaboration in solving daily problems. The MANOVA results revealed initial gender differences in AI knowledge. Following MANCOVA analysis showed significant improvements in AI knowledge, as well as perceptions and behavioral intentions toward AI, effectively bridging the observed knowledge gaps without any significant differences attributed to gender or initial knowledge levels. This evidence underscores the success of tangible and collaborative AI interventions in uniformly enhancing AI knowledge and positively changing perceptions and behavioral intentions among all participants, regardless of gender. Both female and non-binary students felt increased engagement and reduced anxiety toward AI, with improved comprehension and a preference for collaborative learning. This study contributes to the design of inclusive AI interventions, highlighting gender differences and including non-binary students as an essential part of the analysis.Journal of educational computing research651An inclusive K-12 AI curriculum for elementary schools is presented, focusing on six design principles to address gender disparities, with both female and non-binary students felt increased engagement and reduced anxiety toward AI, with improved comprehension and a preference for collaborative learning.Journal of Educational Computing Research["Keunjae Kim", "Kyungbin Kwon"]2024-08-28T00:00:00 8f46ceadb176b6d666bec6563baee8e8f2257afbAI and Ethics160AI and Ethics["M. A. Farhad", "Muhammad Hamza Zakir"]2024-08-28T00:00:00 8f1b03745b13d4baca31280ded8a520eeb30054aCirculation: Arrhythmia and Electrophysiology00Circulation. Arrhythmia and electrophysiology["S. Khurshid", "S. Friedman", "Shinwan Kany", "Rahul Mahajan", "Ashby C Turner", "S. Lubitz", "M. Maddah", "P. Ellinor", "Christopher D Anderson"]2024-08-28T00:00:00 ce2a2383e9c54bcdf0ddbe8ee862f6c288073f1ain the age ofFrontiers in Medicine00Frontiers in Medicine["Filippo Gibelli", "Giovanni Maio", "Giovanna Ricci"]2024-08-28T00:00:00 1c7769647a72b88937c951d612a3c970be17c656European Journal of Gastroenterology and Hepathology10European journal of gastroenterology & hepatology["H. Daungsupawong", "V. Wiwanitkit"]2024-08-28T00:00:00 fc3e883ad8ff953768dde74a3b1112e6b7bc5b15International Relations and Diplomacy00International Relations and Diplomacy["Paul J. Morrow"]2024-08-28T00:00:00 b70c4925ab39a32fe36e461a605e84110f042aa5In the nexus of AI, marketing, and consumer behavior, a systematic consolidation of the theoretical and practical implications remains elusive. Addressing this, our study embarks on a Systematic Literature Review (SLR) with bibliometric analysis, leveraging the PRISMA framework to unearth and integrate findings from existing research. The objective is to map out the scholarly landscape, identifying key themes and gaps within the expansive body of literature on AI's role in shaping marketing strategies and consumer perceptions. Our analysis uncovers a dual impact of AI: operational enhancement and deep-rooted changes in consumer-brand dynamics, highlighting a shift toward more sophisticated theoretical frameworks like the Uses and Gratification Theory and the UTAUT model. The study not only synthesizes these insights but also sets the stage for future research, calling for a broader inclusion of databases to mitigate current limitations and empirical studies to validate and refine theoretical models, ensuring a comprehensive understanding of AI's transformative potential and its ethical implications in the marketing domain.International Conference on Information Management and Technology210This study embarks on a Systematic Literature Review with bibliometric analysis, leveraging the PRISMA framework to unearth and integrate findings from existing research, uncovering a dual impact of AI: operational enhancement and deep-rooted changes in consumer-brand dynamics.2024 International Conference on Information Management and Technology (ICIMTech)["Chung-Jen Fu", "Andri Dayarana K. Silalahi", "I-Tung Shih", "Do Thi Thanh Phuong", "Ixora Javanisa Eunike"]2024-08-28T00:00:00 3cfaa4dd8a47564cdc8f1291b387e4c3d20fe9d9Integrasi AI dalam pembelajaran bahasa menawarkan potensi besar untuk meningkatkan motivasi dan keterlibatan siswa, mempercepat pemahaman materi, serta memberikan pengalaman belajar yang lebih personal dan interaktif. Namun, meskipun penggunaan AI semakin meluas, pemahaman mendalam mengenai dampaknya terhadap proses pembelajaran bahasa di tingkat SMP masih terbatas. Oleh karena itu, penelitian ini dilakukan untuk mengeksplorasi bagaimana AI mempengaruhi pembelajaran bahasa siswa SMP, serta tantangan dan peluang yang dihadapi dalam penerapannya. Penelitian ini bertujuan untuk memahami pengalaman siswa dalam menggunakan AI dalam pembelajaran bahasa, khususnya dalam hal motivasi, keterlibatan, dan pemahaman materi. Penelitian ini menggunakan pendekatan kualitatif dengan metode fenomenologi untuk mengeksplorasi pengalaman siswa dalam penggunaan AI dalam pembelajaran bahasa. Data dikumpulkan melalui wawancara mendalam dengan 10 siswa SMP, serta observasi partisipatif di kelas yang menggunakan AI sebagai alat bantu pembelajaran. Data yang diperoleh dianalisis menggunakan teknik analisis tematik untuk mengidentifikasi tema-tema utama yang muncul dari pengalaman partisipan. Hasil penelitian menunjukkan bahwa integrasi AI dalam pembelajaran bahasa secara signifikan meningkatkan motivasi dan keterlibatan siswa. Fitur-fitur AI seperti umpan balik langsung, latihan interaktif, dan elemen gamifikasi membuat proses belajar menjadi lebih menarik dan menyenangkan bagi siswa. Siswa merasa lebih termotivasi karena mereka dapat belajar secara mandiri dan mendapatkan umpan balik secara instan.   The integration of AI in language learning offers great potential to increase student motivation and engagement, accelerate comprehension of material, and provide a more personalized and interactive learning experience. However, despite the widespread use of AI, there is limited in-depth understanding of its impact on the language learning process at the junior high school level. Therefore, this study was conducted to explore how AI affects junior high school students' language learning, as well as the challenges and opportunities faced in its application. This study aims to understand students' experiences in using AI in language learning, particularly in terms of motivation, engagement, and comprehension of materials. This study used a qualitative approach with phenomenological methods to explore students' experiences in using AI in language learning. Data were collected through in-depth interviews with 10 junior high school students, as well as participatory observation in a classroom that uses AI as a learning tool. The data obtained were analyzed using thematic analysis techniques to identify the main themes that emerged from the participants' experiences. The results showed that the integration of AI in language learning significantly increased student motivation and engagement. AI features such as immediate feedback, interactive exercises, and gamification elements make the learning process more interesting and fun for students. Students feel more motivated as they can learn independently and get instant feedback.Jurnal Pendidikan Bahasa dan Sastra180DIKBASTRA: Jurnal Pendidikan Bahasa dan Sastra["Nani Nirwani", "Priyanto Priyanto"]2024-08-28T00:00:00 6ef3e1316a7679253acf62a2d5a62c46a291917aIn the modern educational landscape, the mounting academic demands imposed on adolescents, coupled with a noticeable trend of enrolling children into formal schooling at increasingly younger ages, have become influential factors in the exacerbation of emotional stress experienced by young individuals. This burgeoning concern for mental health matters within contemporary society necessitates a heightened level of attention and thorough investigation. This comprehensive report unfolds as an intricate exploration of multifaceted dimensions. It begins with a meticulous literature review, which delves into the existing body of knowledge surrounding the issue. Subsequently, the report investigates the practical applications of AI in the domain of mental healthcare, recognizing the potential benefits and advancements AI technology can bring to the field. Moreover, an external analysis is conducted to contextualize the broader societal and environmental factors contributing to the observed challenges. A competitive landscape assessment is performed to understand the existing solutions and their strengths and weaknesses. Further, strategies related to market segmentation and targeting are formulated to better address the diverse needs of the audience. The report also provides a detailed description of the target customer base, offering insights into the demographics and psycho-graphics of the individuals who stand to benefit most from the proposed AI-driven solution. A mock product is meticulously designed, integrating the learning from the aforementioned analyses. Furthermore, conjoint analysis and data analysis techniques are applied to discern the nuanced preferences and needs of the target audience. The study, rooted in empirical research, investigates the intricate web of psychological challenges teenagers face in present-day society. In response to the identified issues, the report outlines the development of an AI-exclusive application. This application is thoughtfully tailored to ameliorate the mental health challenges specific to teenagers, offering a promising avenue for providing timely and effective support to this vulnerable demographic.Advances in Economics, Management and Political Sciences00In response to the identified issues, the report outlines the development of an AI-exclusive application thoughtfully tailored to ameliorate the mental health challenges specific to teenagers, offering a promising avenue for providing timely and effective support to this vulnerable demographic.Advances in Economics, Management and Political Sciences["Jinxiao Yang", "Yifei Wang", "Ziyi Liu", "Shixuan Song"]2024-08-28T00:00:00 d515251c92d01e7074e583639598cbab49e035c9Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.Chemical Reviews39911This review discusses the utilization of AI in the design of e-skin sensors and addresses the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks.Chemical reviews["Xuemei Fu", "Wen Cheng", "Guanxiang Wan", "Zijie Yang", "Benjamin C. K. Tee"]2024-08-28T00:00:00 1303b78de71027220799738cc29f3a8e747aa719To explore the application effect of artificial intelligence-assisted breast screening ultrasound technology in breast cancer screening. Methods 170 suspected breast cancer patients who underwent breast ultrasound examination in our hospital from July 2022 to June 2024 were retrospectively analyzed, and the results of breast biopsy were taken as the gold standard by physician analysis, artificial intelligence analysis, and combined artificial intelligence analysis. To compare the application effect of ultrasonography in breast cancer screening in three ways. Results Among 170 suspected breast cancer patients, 132 were positive, 38 were negative, 113 were true positive, and 29 were true negative. Sensitivity was 85.61%, specificity was 76.32%, consistency was 83.53%, positive predictive value was 92.62%, and negative predictive value was 60.42%. There were 124 true positive cases and 33 true negative cases, the sensitivity was 93.94%, the specificity was 86.84%, the consistency was 92.35%, the positive predictive value was 96.12%, and the negative predictive value was 80.49%. The results showed that 131 cases were true positive, and 37 were true negative. The sensitivity was 99.24%, the specificity was 97.37%, the consistency was 98.82%, the positive predictive value was 99.24%, and the negative predictive value was 97.37%. Taking the results of breast puncture biopsy as the "gold standard," the diagnostic sensitivity, specificity, consistency, positive predictive value, and negative predictive value of physician-combined artificial intelligence analysis were significantly higher than those of physician-only analysis or artificial intelligence analysis. Conclusion The application of AI-assisted breast screening ultrasound technology to breast cancer screening in our hospital not only helps to realize the consistency and accuracy of early identification and diagnosis of breast cancer so that patients can get more accurate treatment but also helps to reduce the workload of radiologists.International Journal of Biology and Life Sciences74The application of AI-assisted breast screening ultrasound technology to breast cancer screening in the authors' hospital not only helps to realize the consistency and accuracy of early identification and diagnosis of breast cancer so that patients can get more accurate treatment but also helps to reduce the workload of radiologists.International Journal of Biology and Life Sciences["Lijie Li", "Xiaoqiang Li", "Hongna Chen", "Miaomiao Zhang", "Liqian Sun"]2024-08-28T00:00:00 dd630dfb0c46dbad27744107ff0dec958e7d8e53Cognitive Computation392Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.Cogn. Comput.["P. Ducange", "Francesco Marcelloni", "Alessandro Renda", "Fabrizio Ruffini"]2024-08-28T00:00:00 f6a9907c734234caffd262ad1016774b96cd90a2 Purpose Artificial intelligence (AI) has the potential to address significant challenges in education, innovate learning and teaching practices and achieve SDG 4. However, existing literature often overlooks the behavioural aspects of students regarding AI in education, focusing predominantly on technical and pedagogical dimensions. Hence, this study aims to explore the significant relationships among AI literacy, AI usage, learning outcomes and academic performance of generation Z students in the Indian educational context. Design/methodology/approach The study used structural equation modelling (SEM) on Gen Z students born in the years 1997–2012 as a sample population for the research in the north Indian states like Punjab, Haryana, Himachal and regions like Chandigarh and N.C.R. Delhi. Findings The results established significant positive relationships between AI literacy, AI usage, AI learning outcomes and academic performance. Specifically, higher levels of AI literacy were associated with increased engagement with AI technologies and tools for learning purposes, leading to better learning outcomes and academic performance. The findings demonstrated that AI literacy plays a crucial role in providing effective learning experiences and fostering skills such as problem-solving and critical thinking among Gen Z students. Research limitations/implications The implications of the study include the significance of integrating AI education initiatives into curricula, prioritising professional development programmes for educators and making sure that every student has equitable access to AI technologies. Originality/value The study introduces a novel perspective by examining variables such as AI literacy, AI usage, AI learning outcomes and academic performance and developing a model that has not been previously studied. It provides a new discourse and proposes a framework uniquely combining AI-infused curriculum design, educator empowerment, robust assessment mechanisms and sustainable practices. Quality Assurance in Education482It is demonstrated that AI literacy plays a crucial role in providing effective learning experiences and fostering skills such as problem-solving and critical thinking among Gen Z students.Quality Assurance in Education["Ekamdeep Singh", "Prihana Vasishta", "Anju Singla"]2024-08-28T00:00:00 2776b2806583b968ab704ad17556cd76bda1acb1This research paper examines the potential of artificial intelligence (AI) in strengthening data security and mitigating the growing threat of cyber-attacks. As digital threats continue to evolve and pose significant risks to businesses, organizations, government agencies, and individual users, there is an urgent need for more robust and adaptive security measures. This study explores how AI can be leveraged to enhance network and data security, focusing on its applications in threat detection, response automation, and predictive analysis. Through a comprehensive literature review and analysis of current AI-driven security solutions, this research aims to provide insights into the effectiveness of AI in cybersecurity and propose strategies for its implementation. The findings suggest that AI has the potential to significantly improve cybersecurity measures, offering faster threat detection, more accurate risk assessment, and enhanced response capabilities. However, challenges related to AI implementation, data privacy, and the need for human oversight are also addressed. This research contributes to the growing body of knowledge on AI applications in cybersecurity and provides valuable recommendations for organizations seeking to strengthen their security posture in an increasingly complex digital landscape.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023024The findings suggest that AI has the potential to significantly improve cybersecurity measures, offering faster threat detection, more accurate risk assessment, and enhanced response capabilities.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Yijie Weng", "Jianhao Wu"]2024-08-29T00:00:00 33cc6abadeb62519c4760e0c016726ec436fa73dDoes artificial intelligence (AI) displace auditors? We exploit the staggered hiring of AI employees at audit office locations across the United States as a proxy for the use of AI at local audit offices. The main findings are as follows. First, relative to audit offices that do not yet hire AI employees, those that do hire AI employees have a 4.3% increase in the number of auditor jobs, particularly among junior and midlevel auditors. Second, using AI is associated with an increased demand for soft skills (e.g., cognitive skills) in auditor jobs. Third, audit offices that use AI have more accurate going concern and internal control opinions. Semistructured interviews of 11 seasoned audit partners confirm that investment in AI is centralized at the national level, but the decision to deploy it often resides at the local audit office level. Notably, none of the partners believe that AI has replaced or will replace human auditors. Overall, our study—comprising both empirical and qualitative data—suggests that using AI does not replace auditors, but rather changes the skills required for these jobs and improves audit quality. This paper was accepted by Suraj Srinivasan, accounting. Funding: K. K. F. Law’s research is supported by the Start-Up Grant from Nanyang Technological University and the Ministry of Education (MOE), Singapore, under its Academic Research Fund Tier 1 [Grant RG128/20]. M. Shen’s research is supported by the Start-Up Grant from the National University of Singapore [Grant A-0003914-00-00] and the MOE under its Academic Research Fund Tier 1 [Grant A-8000098-00-00]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.04040 .Management Sciences414It is suggested that using AI does not replace auditors, but rather changes the skills required for these jobs and improves audit quality, particularly among junior and midlevel auditors.Management Science["Kelvin K. F. Law", "Michael Shen"]2024-08-29T00:00:00 d7b1f2593dcd81d0dcb3e95a9ea6b47d0170cdd9Background: A massive growing of AI products crossways all views of healthcare. Nursing practice is grave where AI technology will enrich practice and patient outcomes Aim of the study: to investigate the level of knowledge and attitude of nursing students regarding artificial intelligence. Design: A descriptive cross-sectional model was employed in this study. Setting: The faculty of nursing Ain Shams University served as the research site. Subjects: For the study, 222 nursing students were selected. Data collection Tools: Knowledge regarding artificial intelligence questionnaires and the student attitudes toward artificial intelligence (SATAI) were the two tools used. Results : A total of 65.6% of the understudied nursing students had a moderate level of total AI knowledge. In addition, 82.6% of the participants had positive attitudes toward total AI. Conclusion: there is a very statistically significant positive association between nursing students' total knowledge and their attitude toward AI. Recommendations providing nursing students with AI-related training courses, webinars, and seminars. It's important to highlight how AI may be used for societal good. Students are more likely to be more motivated to study AI if they do this.Egyptian Journal of Health Care371There is a very statistically significant positive association between nursing students' total knowledge and their attitude toward AI and recommendations providing nursing students with AI-related training courses, webinars, and seminars.Egyptian Journal of Health Care["Azza. El. M. Khaled", "Asmaa. S. A. Elborai"]2024-08-29T00:00:00 81f7d48538ec8cd133303f9e793ba8a4c2a22c37The recent emergence of Artificial Intelligence (AI) has the potential to influence the teaching-learning process. Some of the most used pedagogical approaches of the last decade have been Flipped Classroom and Flipped Learning. This article explores the intersection between Flipped Learning and AI through qualitative research based on interviews with international experts in the field. The results reveal the significant impact of AI on education, highlighting how AI tools are transforming teaching and learning methodologies. Additionally, the evolution of Flipped Learning with the integration of AI is analyzed, showing how this combination enhances personalized learning and improves student engagement. Finally, the role of the teacher in this new educational paradigm is discussed, emphasizing the need for continuous adaptation and the development of new competencies to fully leverage emerging technologies. With this study, we aim to provide an overview of the opportunities and challenges that AI presents in the context of Flipped Learning.Electronics501The results reveal the significant impact of AI on education, highlighting how AI tools are transforming teaching and learning methodologies and the need for continuous adaptation and the development of new competencies to fully leverage emerging technologies.Electronics["David L\u00f3pez-Villanueva", "Ra\u00fal Santiago", "Ramon Palau"]2024-08-29T00:00:00 0222648d8999fc1731b52d5cf8b7a94fa6f8534fBackground: Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner. Objective: In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes. Methods: This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024. Results: Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions. Conclusions: The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders.Brain Science1291The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders.Brain Sciences["J. L. Tay", "Kyawt Kyawt Htun", "Kang Sim"]2024-08-29T00:00:00 78217ac5ea15bcd235bf27d19c88fa20cbc0e9f2 The current work investigates the prospective applications of Artificial Intelligence (AI) in the aquaculture industry. AI depends on collecting, validating, and analyzing data from several aspects using sensor readings, and feeding data sheets. AI is an essential tool that can monitor fish behavior and increase the resilience and quality of seafood products. Furthermore, AI algorithms can early detect potential pathogen infections and disease outbreaks, allowing aquaculture stakeholders to take timely preventive measures and subsequently make the proper decision in an appropriate time. AI algorithms can predict ecological conditions that should help aquaculture farmers adopt strategies and plans to avoid negative impacts on the fish farms and create an easy and safe environment for fish production. In addition, using AI aids to analyze and collect data regarding nutritional requirements, nutrient availability, and price could help the farmers to adjust and modify their diets to optimize feed formulations. Thus, using AI could help farmers to reduce labor costs, monitor aquatic animal’s growth, health, optimize feed formulation and reduce waste output and early detection of disease outbreaks. Overall, this review highlights the importance of using AI to achieve aquaculture sustainability and boost the net profits of farmersAnnals of Animal Science1031Using AI aids to analyze and collect data regarding nutritional requirements, nutrient availability, and price could help the farmers to adjust and modify their diets to optimize feed formulations.Annals of Animal Science["Sherine Ragab", "S. Hoseinifar", "H. Doan", "Waldemar Rossi", "Simon J. Davies", "Mohamed Ashour", "E. El\u2010Haroun"]2024-08-29T00:00:00 c82db33519d27aeaf93b56a01590f3da6e03f748Objectives: This research aimed to empirically examine the transformative impacts of Artificial Intelligence (AI) adoption on financial reporting quality in Jordanian banking, with internal controls as a hypothesized mediation mechanism. Methodology: Quantitative survey data was collected from 130 bank personnel. Multi-item reflective measures assessed AI adoption, internal controls, and financial reporting quality—structural equation modelling analysis relationships between constructs. Findings: The research tested four hypotheses grounded in agency and contingency theories. Confirmatory factor analysis demonstrated sound measurement models. Structural equation modelling revealed that AI adoption significantly transformed financial reporting quality. The mediating effect of internal controls on the AI-quality relationship was supported. Specifically, the path from AI adoption to quality was significant, indicating a positive impact. Despite internal controls strongly predicting quality, its mediating effect significantly shaped the degree of transformation driven by AI adoption. The indirect effect of AI on quality through internal controls was also significant. Findings imply a growing diffusion of AI applications in core financial reporting systems. Practical implications: Increasing AI applications focus on holistically transforming systems, reflecting committing adoption. Jordanian banks selectively leverage controls to moderate AI-induced transformations. Originality/value: This study provides essential real-world insights into how AI is adopted and impacts the Jordanian banking sector, a key player in a fast-evolving developing economy. By examining the role of internal controls, it deepens our understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality. Its mixed methods, unique context, and focus on AI’s impact on organizations significantly enrich academic literature. Recommendations: Banks should invest in integrated AI architectures, strategically strengthen critical controls to steer transformations, and incrementally translate AI innovations into core processes.Journal of Infrastructure Policy and Development901Examining the role of internal controls in Jordanian banking deepens the understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality.Journal of Infrastructure, Policy and Development["Bilal Al Omari", "Munther Al-Nimer"]2024-08-29T00:00:00 6c427d98a71307b0ca50b4dc6f28314e868ec671Nuclear power plants in the United States are critical to the nation’s energy security, accounting for 20% of all electricity produced for the power grid. As energy needs grow, 100 gigawatts of additional nuclear power will be necessary by 2050, more than double the current capacity. Realizing this target requires cutting-edge technology like artificial intelligence (AI) and machine learning (ML) that can bring about significant increases in the level of automation. Human-centered AI (HCAI) is a combination of human-centered design (human factors, human-in-the-loop, etc.) with AI/ML to help produce an efficient and reliable system with full consideration for human engagement. This paper provides a comprehensive and novel discussion of HCAI considerations in nuclear power, introducing unique applications for the existing fleet as well as new advanced reactor designs. We include real-life use cases of AI applications to work management processes at nuclear power sites and highlight lessons learned for HCAI.Proceedings of the Human Factors and Ergonomics Society Annual Meeting101This paper provides a comprehensive and novel discussion of HCAI considerations in nuclear power, introducing unique applications for the existing fleet as well as new advanced reactor designs.Proceedings of the Human Factors and Ergonomics Society Annual Meeting["Anna Hall", "Patrick Murray", "Ronald L. Boring", "Vivek Agarwal"]2024-08-29T00:00:00 4ef031e901652edd1730875c4dda4fa6a991c001The paper explores how AI-enabled utilizing data analytics and machine learning methodologies enables deeper insights into the intricate patterns and behaviors of climate dynamics by analysing amounts of various data, integrating information from various origins, like satellite imagery, and the sensory data is processed to reveal meaningful insights for better understanding and informed actions. These can inform any policy decisions and facilitate more targeted interventions to mitigate the impacts of the climate conditions. The work discussed here in this research provided sources focuses on leveraging artificial intelligence (AI) and machine learning (ML) to address climate change challenges. Studies emphasize AI-driven strategies for climate change adaptation and including predicting various changes in the environment, and changes in the weather patterns. The research highlights the importance of weather conditions, and change in the weather patterns, and in developing effective AI-powered climate change in the adaptation strategies. And accordingly, these studies shows how effectively different AI and ML models like LSTM, ANN, CNN in improving the climate predictions and understanding the weather. AI and ML technologies in enhancing the changes in the weather, mitigation.International Journal of Innovative Science and Research Technology441The research highlights the importance of weather conditions, and change in the weather patterns, and in developing effective AI-powered climate change in the adaptation strategies, and shows how effectively different AI and ML models like LSTM, ANN, CNN in improving the climate predictions and understanding the weather.International Journal of Innovative Science and Research Technology (IJISRT)["T. C. Kumar", "U. Sandeep", "T. S. Nagasri", "P. S. Kumar", "K. Swathi"]2024-08-29T00:00:00 c06dc4ee9b8d6518278d267f7662d96c86a27efcRecent advances in generative artificial intelligence (GenAI), particularly large language models (LLMs), are profoundly impacting many fields. In chemical engineering, GenAI plays a pivotal role in the design, scale-up, and optimization of chemical and biochemical processes. The natural language understanding capabilities of LLMs enable the interpretation of complex chemical and biological data. Given the rapid developments of GenAI, this paper explores the extensive applications of GenAI in multiscale chemical engineering, spanning from quantum mechanics to macro-level optimization. At quantum and molecular levels, GenAI accelerates the discovery of novel products and enhances the understanding of fundamental phenomena. At larger scales, GenAI improves process design and operational efficiency, contributing to sustainable practices. We present several examples to demonstrate the role of GenAI, including its impact on nanomaterial hardness enhancement, novel catalyst generation, protein design, and the development of autonomous experimental platforms. This multiscale integration demonstrates the potential of GenAI to address complex challenges, drive innovation, and foster advancements in chemical engineering.Frontiers in Chemical Engineering330The extensive applications of GenAI in multiscale chemical engineering, spanning from quantum mechanics to macro-level optimization, are explored, demonstrating the potential of GenAI to address complex challenges, drive innovation, and foster advancements in chemical engineering.Frontiers in Chemical Engineering["Benjamin Decardi-Nelson", "Abdulelah S. Alshehri", "Fengqi You"]2024-08-29T00:00:00 46895c9bf89373c98011804e4df40e159aa69f61Myocarditis is a cardiovascular disease characterised by inflammation of the heart muscle which can lead to heart failure. There is heterogeneity in the mode of presentation, underlying aetiologies, and clinical outcome with impact on a wide range of age groups which lead to diagnostic challenges. Cardiovascular magnetic resonance (CMR) is the preferred imaging modality in the diagnostic work-up of those with acute myocarditis. There is a need for systematic analytical approaches to improve diagnosis. Artificial intelligence (AI) and machine learning (ML) are increasingly used in CMR and has been shown to match human diagnostic performance in multiple disease categories. In this review article, we will describe the role of CMR in the diagnosis of acute myocarditis followed by a literature review on the applications of AI and ML to diagnose acute myocarditis. Only a few papers were identified with limitations in cases and control size and a lack of detail regarding cohort characteristics in addition to the absence of relevant cardiovascular disease controls. Furthermore, often CMR datasets did not include contemporary tissue characterisation parameters such as T1 and T2 mapping techniques, which are central to the diagnosis of acute myocarditis. Future work may include the use of explainability tools to enhance our confidence and understanding of the machine learning models with large, better characterised cohorts and clinical context improving the diagnosis of acute myocarditis.Frontiers in Cardiovascular Medicine560The role of CMR in the diagnosis of acute myocarditis is described followed by a literature review on the applications of AI and ML to diagnose acute myocarditis.Frontiers in Cardiovascular Medicine["V. Shyam-Sundar", "Daniel Harding", "Abbas Khan", "M. Abdulkareem", "Greg Slabaugh", "Saidi Mohiddin", "Steffen E. Petersen", "Nay Aung"]2024-08-29T00:00:00 8dee31b4e500f5b386c0d7bce460c0593e3b6d71Abstract Background This report evaluates the potential of artificial intelligence (AI) in psychodermatology, emphasizing its ability to enhance diagnostic accuracy, treatment efficacy, and personalized care. Psychodermatology, which explores the connection between mental health and skin disorders, stands to benefit from AI's advanced data analysis and pattern recognition capabilities. Materials and methods A literature search was conducted on PubMed and Google Scholar, spanning from 2004 to 2024, following PRISMA guidelines. Studies included demonstrated AI's effectiveness in predicting treatment outcomes for body dysmorphic disorder, identifying biomarkers in psoriasis and anxiety disorders, and refining therapeutic strategies. Results The review identified several studies highlighting AI's role in improving treatment outcomes and diagnostic accuracy in psychodermatology. AI was effective in predicting outcomes for body dysmorphic disorder and identifying biomarkers related to psoriasis and anxiety disorders. However, challenges such as limited dermatologist knowledge, integration difficulties, and ethical concerns regarding patient privacy were noted. Conclusion AI holds significant promise for advancing psychodermatology by improving diagnostic precision, treatment effectiveness, and personalized care. Nonetheless, realizing this potential requires large‐scale clinical validation, enhanced dataset diversity, and robust ethical frameworks. Future research should focus on these areas, with interdisciplinary collaboration essential for overcoming current challenges and optimizing patient care in psychodermatology.Skin research and technology100AI holds significant promise for advancing psychodermatology by improving diagnostic precision, treatment effectiveness, and personalized care, Nonetheless, realizing this potential requires large‐scale clinical validation, enhanced dataset diversity, and robust ethical frameworks.Skin Research and Technology["Isabella J. Tan", "Olivia M Katamanin", "Rachel K Greene", "Mohammad Jafferany"]2024-08-29T00:00:00 0a9987b2e917ef774e54eeffc925554a8b7616f8Purpose:Artificial intelligence (AI) has become one of the most influential technologies in the digital era, and its potential to revolutionize the learning process is enormous. This article discusses the role of AI in improving the quality of education in the digital era. This research aims to provide an understanding of the role of AI in the learning process in the digital era. Method:The Method section is required to outline the study design (whether qualitative, quantitative, or a combination of both), along with general procedures, including details on participant characteristics, numbers, and data collection methods. If the study doesn't involve primary data collection, please provide information on the methods used to summarize previously reported data, such as narrative systematic review, or meta-analysis. Result::The research method used is a literature review, which includes an analysis of various sources and views related to the use of AI in higher education. The data used in this article is information from various literary sources, including research results, scientific articles, and news related to the implementation of AI in education. Examples of the role of AI include several main aspects, including personalization, interactivity, feedback, accessibility, and efficiency. Conclusion: By applying AI to the learning process, we can create a more personalized, interactive, and effective learning experience for all students.Proceeding of International Conference on Healthy Living (INCOHELIV)100The role of AI in improving the quality of education in the digital era is discussed, which includes an analysis of various sources and views related to the use of AI in higher education.Proceeding of International Conference on Healthy Living (INCOHELIV)["Faujiah", "I. Noviekayati", "N. Pratitis"]2024-08-29T00:00:00 df1bb696d578434500b887d6f3f14a3bfba72cccFinancial fraud, intellectual property theft, and domains undergo high threats due to cybercrime, increasing the threat to businesses, especially financial markets. Such dirty activities hit various organizations, whether banks, financial institutions, corporations, or individuals, and jeopardize those activities that depend on financial support. Technology has brought many changes with its integration into the financial sector. Although technology has made it easier to handle business tasks day in and day out, the ease of using technology has also given rise to cyberattacks. The arrival of artificial intelligence has brought in new models to combat these cyber threats and also assess them. People must become smart about AI so that they don't get duped. This research aims to expand upon the fraud diamond framework by identifying risk factors leading organisations toward fraudulent behaviour. It focuses on the relationship between artificial intelligence, cybercrime incidents and organizational performance. It is proposed that fraud cases can act as mediators, while cyber threats may serve as moderators, impacting organizational performance. Descriptive research, useful for unbiased data collection that accurately reflects behaviour and trends, was applied. Data was collected from bank managers, operations managers, assistant vice presidents, and executive leaders through a convenience sampling method, a non-probability approach. 550 questionnaires were distributed via Google Forms, yielding an adequate response rate. Partial Least Squares Structural Equation Modeling (PLS-SEM) software was used for data analysis. The findings underscore the need for international cooperation among governments, regulators, law enforcement, and financial institutions to address the global nature of cyber threats. Such cooperation is crucial for effectively managing, responding to, and mitigating the risks posed by these incidents.Journal of Excellence in Social Sciences00It is proposed that fraud cases can act as mediators, while cyber threats may serve as moderators, impacting organizational performance, and the need for international cooperation among governments, regulators, law enforcement, and financial institutions to address the global nature of cyber threats is underscore.Journal of Excellence in Social Sciences["Munaza Bukhari", "Shahzadi Sattar", "Sajjad Saleem", "Khan Zaman Khan", "ANSA KHAN"]2024-08-29T00:00:00 0c4fd8363d8283ef4d1ce840fc7847d7261e3c88Kurangnya pengetahuan guru tentang berbagai aplikasi pembelajaran Bahasa Inggris yang berbasis teknologi yang dapat digunakan sebagai media pembelajaran menyebabkan tidak adanya pengintegrasian penggunaan teknologi dalam pembelajaran. Oleh sebab itu, tim Pengabdian kepada Masyarakat (PkM) Program Studi Pendidikan Bahasa Inggris FKIP Universitas Tanjungpura melaksanakan kegiatan workshop Pengembangan Media Pembelajaran Inovatif Berbasis Artificial Intelligence (AI). Kegiatan ini bertujuan untuk membekali guru tentang konsep pembelajaran abad 21 dan media pembelajaran inovatif Berbasis Artificial Intelligence (AI) yang bisa digunakan dalam pembelajaran Bahasa Inggris. Kegiatan ini dilaksanakan dengan metode ceramah, tanya jawab, dan diskusi. Kegiatan dihadari oleh 30 guru-guru MGMP Madrasah tsanawiyah dan Madrasah Aliyah Kabupaten Mempawah, Kalimantan Barat. Hasil kegiatan menunjukan bahwa guru-guru memahami dengan sangat baik    beberapa aplikasi AI seperti DuoLingo, Elsa Speak, ChatGPT, dan lain- lain dan sudah bisa menggunakan aplikasi tersebut dengan sangat baik dalam pembelajaran Bahasa Inggris.Jurnal Abdimas Ilmiah Citra Bakti140Jurnal Abdimas Ilmiah Citra Bakti["Endang Susilawati", "Yanti Sri Rezeki", "Wardah", "Urai Salam", "Surmiyati", "Syahrul Husin"]2024-08-29T00:00:00 68db8ed9a68381ab5a211e8fc8b3a96920415193Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I’m Five (ELI5). With the interpretations, healthcare providers can gain insight into patients’ stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)190The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)["Ogochukwu Ugbomeh", "Versse Yiye", "Ebuka Ibeke", "C. P. Ezenkwu", "Vandana Sharma", "Ahmed Alkhayyat"]2024-08-29T00:00:00 52154e86fcc2b179460d3045d66175b0b3a65fbaDiabetology & Metabolic Syndrome560A large database showed that this deep learning algorithm was accurate in detecting referable DR, which aids to universal healthcare systems like Brazil, optimizing screening processes and can serve as a tool for improving DR screening, making it more agile and expanding care access.Diabetology & Metabolic Syndrome["M. A. Dos Reis", "Cristiano A. K\u00fcnas", "Thiago da Silva Ara\u00fajo", "J. Schneiders", "Pietro B. de Azevedo", "Luis F. Nakayama", "Dimitris R. V. Rados", "R. Umpierre", "O. Berwanger", "Daniel Lavinsky", "F. Malerbi", "P. Navaux", "B. Schaan"]2024-08-29T00:00:00 f73cc7f32dde0340395efe0b0bf89c2962c631d9Purpose With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assisted reproductive technology (ART) have significantly improved. However, clinical pregnancy rates in fresh cycles remain dependent on the number and type of embryos transferred. The selection of embryos with the highest implantation potential is critical for embryologists and influences transfer strategies in fertility centers. The superiority of AI over traditional morphological scoring for ranking cleavage-stage embryos based on their implantation potential remains controversial. Methods This retrospective study analyzed 105 fresh embryo transfer cycles at the Centre for Reproductive Medicine from August 2023 to March 2024, following IVF/ICSI treatment at the cleavage stage. All embryos were cultured using time-lapse technology and scored using an automated AI model (iDAScore V2.0). Embryos were categorized into three groups based on the iDAScore V2.0: Group A (8 cells, iDA: 1.0-5.7); Group B (8 cells, iDA: 5.8-8.0); and Group C (>8 cells, iDA: 5.8-8.0). Clinical treatment outcomes, embryonic development, and pregnancy outcomes were analyzed and compared across the groups. Results Baseline characteristics such as patient age, AMH levels, AFC, and basal sex hormones showed no significant differences among the three groups (p > 0.05). The iDAscores were significantly higher in Group C (7.3 ± 0.5) compared to Group B (6.7 ± 0.5) and the iDAscores were significantly higher in Group B (6.7 ± 0.5) compared to Group A (4.8 ± 1.0) (p < 0.001). The mean number of high-quality embryos was highest in Group C (4.7 ± 3.0), followed by Group B (3.6 ± 1.7) and Group A (2.1 ± 1.2) (p < 0.001). There was no statistical difference (p = 0.392) in the ongoing pregnancy rate for single cleavage-stage transfers between Group B (54.5%, 30/55) and Group A (38.1%, 8/21), although there was a tendency for Group B to be higher. Conclusion Combining time-lapse culture with AI scoring may enhance ongoing pregnancy rates in single cleavage-stage fresh transfer cycles.Frontiers in Endocrinology320Combining time-lapse culture with AI scoring may enhance ongoing pregnancy rates in single cleavage-stage fresh transfer cycles, and this retrospective study analyzed 105 fresh embryo transfer cycles following IVF/ICSI treatment at the cleavage stage.Frontiers in Endocrinology["Xiao Wang", "Qipeng Wei", "Weiyu Huang", "Lanlan Yin", "Tianzhong Ma"]2024-08-29T00:00:00 a43b34ae0ed05966eccaf7828047a83d42f5a2bcThe danger to the human’s health in metropolitan environments usually city highways happen due to air pollution. By the use of Artificial intelligence (AI) system has been designed in this paper so as to reduce air pollution on city roads. Our examination merges data analysis and modelling for reduction of air pollution. Data acquisition, data pre-processing, data analysis and model development are various steps which are included in the methodology. Training of different machine learning models is done in model development. The capabilities of AI for air quality index (AQI) in a particular region is showcased through user-interactive model. Perceptive data on air pollution patterns on city roads along with framework which rely on AI for handling and forecasting air quality is illustrated in result section. Environmental health and traffic management in city areas are few of the implications.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)210Perceptive data on air pollution patterns on city roads along with framework which rely on AI for handling and forecasting air quality is illustrated in result section, which merges data analysis and modelling for reduction of air pollution.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)["Rohan Gupta", "Ambarish Manna", "Himanshu Sharma", "Priyansh Pandit", "Sandip Pan", "Yuvraj Singh"]2024-08-29T00:00:00 5b3a88589a1e5870c04108e0853d8fbe672853f3The article analyzes the key aspects of the content of supervisory and non-supervisory activities of the prosecutor’s office in the field of the introduction, application and use of artificial intelligence: the subject and object of such activities. Based on the analysis of existing concepts in the field of subjectivity of artificial intelligence, the author identifies categories of objects of prosecutorial activity in the field under consideration, while concluding that the complex of technological solutions imitating and reproducing cognitive human functions by itself cannot be the object of such activity. As an urgent problem in the implementation of the practical activities of the prosecutor’s office in the field of the use of artificial intelligence, the author points out the lack of unified approaches to determining its methodological and organizational foundations, while noting that the prosecutor’s office has begun its information-analytical and digital reformatting since 2022, taking into account the existing need to use artificial intelligence technologies in their activities.Courier of the Kutafin Moscow State Law University20Courier of Kutafin Moscow State Law University (MSAL))["I. A. Sokolov"]2024-08-29T00:00:00 f3deee1f04d8494dfa80caf08dffe0ee4491d77bAir pollution in urban environments, particularly on city highways, poses a significant threat to public health and the environment. Traffic congestion is the leading cause to air pollution in today's world. This study investigates the potential of machine learning and artificial intelligence (AI) for improving traffic management and reducing Air Pollution. This research paper investigates the traffic volumes in the city highways and introduces a system which will adjust the traffic signal timing based on traffic predictions and Air Quality Index (AQI) data. The results provide insights into the potential of machine learning for traffic prediction and the use of AI for optimizing green light control. With the help of these techniques traffic management systems could potentially improve traffic flow reducing congestion, and enhancing urban air.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)200This research paper introduces a system which will adjust the traffic signal timing based on traffic predictions and Air Quality Index (AQI) data and provides insights into the potential of machine learning for traffic prediction and the use of AI for optimizing green light control.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)["Rohan Gupta", "Ambarish Manna", "Himanshu Sharma", "Priyansh Pandit", "Sandip Pan"]2024-08-29T00:00:00 6a295caad282a75a664f8efb77c5445e5070ae83The global interest and substantial challenges on this subject contribute to its relevance. This analysis centers on the implementation of artificial intelligence within the medical field, with a specific focus on its application in managing wounds. Through an examination of numerous online studies and publications, we can gain insight into how artificial intelligence is being employed to enhance the diagnosis, treatment, and monitoring of wound healing. The integration of artificial intelligence in this sector has the capacity to transform medical practice by improving precision, effectiveness, and individualized patient care. As a result, it is a leading area of research and advancement on a global scale. We used the PubMed and Google Scholar electronic databases of medical publications, searching for abstracts using the following key phrases: artificial intelligence and wound management, artificial intelligence and gunshot wounds, artificial intelligence and war medicine, artificial intelligence and surgery. Based on search results, a literature analysis was performed. Conclusions. It is necessary to create numerous working groups of highly qualified specialists from each discipline and direction of medical activity, where the specific weight of each symptom, laboratory indicator, each radiological and ultrasound examination result is determined based on the data of real cases. And such work should have no less discipline and structure than medical research, it is optimal to get a universal software tool for this stage of work, which can be used with certain variations for the whole variety of pathological conditions and processes.Emergency medicine290This analysis centers on the implementation of artificial intelligence within the medical field, with a specific focus on its application in managing wounds, through an examination of numerous online studies and publications.EMERGENCY MEDICINE["MD DSc Maksym Gorobeiko", "I. Lurin", "Yevgen Sokol", "O. Usenko", "E. Khoroshun", "V. Makarov", "V. V. Nehoduiko", "K. Gumeniuk", "B. Gorobeyko", "A. V. Dinets"]2024-08-29T00:00:00 52183b58937dc7d7c2f148fccf2337eda4bcaac3Accelerated development and engagement of artificial intelligence are among the most significant global challenges in transforming the social and economic environment, resulting in the heightened emphasis on inclusive, collaborative, ethical decision‐making and responsible leadership. Higher education is an integral part of the global landscape of society and is influenced by its changing context. Accordingly, leadership educators must respond to the changes in the global and institutional environments and the new leadership paradigm in designing and implementing their leadership education programs. Here, we consider the macro level of the “place” within which higher education institutions are situated and reflect upon the impact of Artificial Intelligence on the design and delivery of leadership education programs.Journal of Leadership Studies150The macro level of the “place” within which higher education institutions are situated is considered and the impact of Artificial Intelligence on the design and delivery of leadership education programs is reflected.Journal of Leadership Studies["Elizabeth Goryunova", "Daniel Jenkins"]2024-08-29T00:00:00 6349a44f82a470a490cd21573ea454fa46e232bb   The object of the study is the value chain of the bank.   The purpose of the study is to identify the possibility of applying artificial intelligence (AI) technologies in the value chain stages of commercial banks and transform value chains under the influence of these technologies.   It uses both general scientific methods — analysis, synthesis, abstraction, induction and deduction, and graphical and statistical analysis, the methodology of value chain creation. The main approaches to the formation of the value chain in the banking industry, as well as the key characteristics of the business processes included in it, were studied. Particular attention is paid to the technological component as the basis for the development of modern digital banking. During the research, the main directions for the implementation of modern artificial intelligence technologies, both applied and generative. Analysis of the value chain showed that the creation and use of AI models is an independent supporting process, the work of which not only affects the core activities of the bank, but also requires a certain level of technology development and risk-management in the bank. Data from the AI Russia case library demonstrates the actual impact of AI models on the value chain phases of marketing and sales, customer support and communications, operational processing and risk management. Based on the results of the study, it was concluded that the introduction of innovations in the field of artificial intelligence increases the value of the company by increasing the efficiency of business processes. The introduction of artificial intelligence into processes requires the technological maturity of the enterprise, and its use is an independent technological process that requires the participation of auxiliary processes, for example, risk management. The results of the study are of practical importance for companies in the banking industry, since methods for analyzing the impact of AI technologies on the value chain can be used when making decisions about their implementation.Finance: Theory and Practice60It was concluded that the introduction of innovations in the field of artificial intelligence increases the value of the company by increasing the efficiency of business processes.Finance: Theory and Practice["I. E. Pokamestov", "N. A. Nikitin"]2024-08-29T00:00:00 002cc9e3f3d43d08403170cef089728abeba33b7Abstract Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect impacts clinical trials, drug development, and patient care. Recent advances in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for reclassifying cancers through comprehensive integration of molecular, histopathological, imaging, and clinical characteristics. AI‐driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.Clinical and Translational Science160Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology, and AI‐driven approaches have the potential to reveal novel cancer subtypes, identify new prognostic variables, and guide more precise treatment strategies for improving patient outcomes.Clinical and Translational Science["Sean Khozin"]2024-08-29T00:00:00 139faf01cbf1507a3a7ece56fa18b7d2e21b3c76Artificial Intelligence (AI) is transforming various sectors, from healthcare to finance, creating unprecedented opportunities and challenges. As AI technologies advance, they raise complex legal issues related to privacy, liability, intellectual property, and ethics. This paper reviews the legal implications of AI, focusing on how existing legal frameworks are adapting to address these challenges. By analyzing key legal domains, including data protection, liability, intellectual property, and human rights, the paper highlights the gaps and emerging trends in AI regulation. The analysis underscores the need for a comprehensive legal framework that balances innovation with the protection of fundamental rights and societal values.Indian Journal of Law60By analyzing key legal domains, including data protection, liability, intellectual property, and human rights, the paper highlights the gaps and emerging trends in AI regulation.Indian Journal of Law["Bimal N. Patel"]2024-08-29T00:00:00 99ef7df33150ee21c806aeb75dd4c6c5ad634a8bThe aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence’s framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)240The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)["Suresh Palarimath", "Pyingkodi Maran", "R. Venkateswaran", "Wilfred Blessing N.R", "K. V. Shiny", "S. Renuga"]2024-08-29T00:00:00 0569760d5de458a1217e66b30b7afb95641ecda8Software serves as a cornerstone in the functionality of devices and systems that seamlessly integrate into our daily lives. However, the complexity of these systems, often developed by multiple programmers, leaves room for inadvertent errors in the code. These errors, known as software vulnerabilities, represent flaws or defects in the software's construction that malicious actors can exploit to gain unauthorized access or privileges within the system. Despite advancements in understanding vulnerabilities, the prevalence of reported vulnerabilities continues to rise, emphasizing the critical importance of software security research. In response to this growing concern, there is a pressing need for tools to assist programmers in identifying and mitigating vulnerabilities during the code development process. As part of our ongoing research paper into detection of vulnerabilities, along with methods for their prevention and detection. Furthermore, we delve into recent advancements in utilizing machine learning techniques to enhance vulnerability detection across various types of software vulnerabilities. We have tested on real data set and the results are better than existing models.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)141Recent advancements in utilizing machine learning techniques to enhance vulnerability detection across various types of software vulnerabilities are explored, with results that are better than existing models.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)["Harshita Durgapal", "Deepak Kumar"]2024-08-29T00:00:00 12358cb8b0c4f91643bea45366133441cce19682Discover Sustainability82Discover Sustainability["Noura Joudieh", "Hassan Harb", "Chamseddine Zaki", "Alaaeddine Ramadan", "Louai Saker", "Nour Mostafa", "Layla Tannoury"]2024-08-29T00:00:00 0ccc7517f9e0f246319ca303c38bf274586bed28Introduction This study examines the impact of digital new media art on the health literacy and digital health literacy of older adults. It explores how digital new media art influences the social adaptability of the older adult, with a focus on variations in their engagement with digital technologies and community activities. Methods The research employed interviews and observations of older adult participants from communities A and B. Data were collected on their smartphone usage, community engagement, and access to technological infrastructure. The study also assessed their interaction with digital new media across various domains, including interpersonal communication, information retrieval, entertainment, practical applications, and mobile payments. Results The study found significant differences in engagement with digital new media art among the older adult. Participants with prior computer experience were generally more skilled in using smartphones and more active in community events. In contrast, individuals in community B showed lower acceptance of digital new media art and no clear association with community participation. There was substantial variability in their use of digital media for information retrieval, entertainment, practical applications, and mobile payments. Some older adult individuals demonstrated proficiency with these technologies, while others were more reserved. Discussion The findings suggest that digital new media art can enhance community participation and social adaptability among older adults, particularly those with prior computer experience. However, disparities in digital media usage highlight the need for targeted interventions to improve digital health literacy and engagement across different community settings. The study underscores the importance of addressing these disparities to ensure that all older adults can benefit from digital advancements, thereby improving their overall well-being and health literacy.Frontiers in Public Health320The findings suggest that digital new media art can enhance community participation and social adaptability among older adults, particularly those with prior computer experience, however, disparities in digital media usage highlight the need for targeted interventions to improve digital health literacy and engagement across different community settings.Frontiers in Public Health["Yuan Gao", "Jiahui Liang", "Zhengbing Xu"]2024-08-29T00:00:00 6361c4e7244ac075b91bdaec3a3754fa4ed80743This paper explores the application of machine learning (ML) and explainable AI (XAI) techniques for detecting money laundering in financial transactions. A novel approach is introduced that combines a deep neural network (DNN) with SHapley Additive exPlanations (SHAP) to enhance the transparency and effectiveness of anti-money laundering (AML) systems. The proposed model demonstrates superior performance over benchmark models, achieving high precision (0.994585), recall (0.994500), F1 score (0.994551), and ROC AUC (0.994525) in identifying fraudulent transactions using a synthetic dataset derived from real financial logs. Through a global explainability analysis, key indicators of fraudulent activities, such as high transaction amounts and prolonged transaction durations, are identified. This study contributes to the AML field by improving model accuracy and providing insights into the decision-making processes of complex ML models. Future research will focus on applying local explanations and utilizing larger real world datasets to further enhance model performance and interpretability.IEEE International Conference on Intelligent Systems240A novel approach is introduced that combines a deep neural network (DNN) with SHapley Additive exPlanations (SHAP) to enhance the transparency and effectiveness of anti-money laundering (AML) systems, demonstrating superior performance over benchmark models.2024 IEEE 12th International Conference on Intelligent Systems (IS)["Giannis Konstantinidis", "Alexander Gegov"]2024-08-29T00:00:00 98a1bedd16cdd8c999118d47b7dc3e2dac92a2c0Aiming at the complexity and diversity of pathological image analysis, this paper proposes an innovative pathological image segmentation framework based on graph network, aiming to improve the accuracy of diagnosis through advanced algorithm optimization. The framework is designed with two modes: fully supervised graph network and weakly supervised graph network to flexibly adapt to data sets with different amounts of annotations. The fully supervised model is trained with a large amount of precisely labeled data and can capture subtle pathological features, which is suitable for situations where high diagnostic accuracy is required. The weakly supervised mode can realize effective image segmentation by learning the global structure and local texture of the image with limited annotation information. It can be used in resource-constrained or mass screening Settings. The experimental results show that the proposed framework can significantly improve the accuracy of pathological image segmentation and effectively assist doctors in disease diagnosis, regardless of the full supervision or weak supervision mode. In addition, the framework shows good generalization ability and can be migrated to different pathologic types and image sources, showing broad application prospects.2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE)100The experimental results show that the proposed framework can significantly improve the accuracy of pathological image segmentation and effectively assist doctors in disease diagnosis, regardless of the full supervision or weak supervision mode.2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE)["Li Huang", "Xiaobiao Zhou"]2024-08-29T00:00:00 286219886e42a043c57a877a3b720eb7e806c884The Journal of craniofacial surgery (Print)70The Journal of craniofacial surgery["\u0130lhan Bahsi", "Y. M. Durna", "Mustafa Said Tekin", "Y. Duymaz"]2024-08-29T00:00:00 e73d9646a4d92b5bcc5809adcb2f9d6ee6b91ffd00["Katie O'Toole O\\'Toole", "Corinna Turbes Turbes", "Avery Freeman Freeman"]2024-08-29T00:00:00 7f3905f33fb68b20be04796e9fee819afd497e14The increase in global trade, the impact of COVID-19, and the tightening of environmental and safety regulations have brought significant changes to the maritime transportation market. To address these challenges, the port logistics sector is rapidly adopting advanced technologies such as big data, Internet of Things, and AI. However, despite these efforts, solving several issues related to productivity, environment, and safety in the port logistics sector requires collaboration among various stakeholders. In this study, we introduce an AI-based port logistics metaverse framework (PLMF) that facilitates communication, data sharing, and decision-making among diverse stakeholders in port logistics. The developed PLMF includes 11 AI-based metaverse content modules related to productivity, environment, and safety, enabling the monitoring, simulation, and decision making of real port logistics processes. Examples of these modules include the prediction of expected time of arrival, dynamic port operation planning, monitoring and prediction of ship fuel consumption and port equipment emissions, and detection and monitoring of hazardous ship routes and accidents between workers and port equipment. We conducted a case study using historical data from Busan Port to analyze the effectiveness of the PLMF. By predicting the expected arrival time of ships within the PLMF and optimizing port operations accordingly, we observed that the framework could generate additional direct revenue of approximately 7.3 million dollars annually, along with a 79% improvement in ship punctuality, resulting in certain environmental benefits for the port. These findings indicate that PLMF not only provides a platform for various stakeholders in port logistics to participate and collaborate but also significantly enhances the accuracy and sustainability of decision-making in port logistics through AI-based simulations.arXiv.org450ArXiv["Sunghyun Sim", "Dohee Kim", "K. Park", "Hyerim Bae"]2024-08-29T00:00:00 737ba3eaf328135bf2114e62ad5983a4c7c999a0The use of AI in education is becoming increasingly widespread, particularly for thesis proposal writing. This study explores EFL students' perceptions and practices regarding AI assistance in thesis proposal writing. Using basic interpretive methods, semi-structured interviews were conducted with 10 EFL students. The research findings revealed mixed perceptions: positive and negative. Practices were categorized into selecting appropriate AI tools, integrating AI into the proposal, manually reviewing AI output, and recognizing AI limitations. Future research should focus on the role of AI in the entire thesis writing process and its impact on students' critical thinking skills.Yavana Bhasha Journal of English Language Education270This study explores EFL students' perceptions and practices regarding AI assistance in thesis proposal writing and reveals mixed perceptions: positive and negative.Yavana Bhasha : Journal of English Language Education["Putri Pasenta", "Nur Chakim"]2024-08-29T00:00:00 40e84db06ca295312c3beccf79c5b4f08757d6ceEducación Lenguaje y Sociedad00Educación, Lenguaje y Sociedad["Carina Lion", "Sergio Bravo Aravena", "Eduardo Torres M. Torres M."]2024-08-29T00:00:00 96bfec8ab5924f470442322033b7cf3f16cf08f4PurposeHuman and artificial intelligence has often been examined through competitive and oppositional lenses, which may no longer suffice in modern digital and knowledge-based societies. This paper advocates for a holistic perspective by integrating human and artificial intelligence within the conceptual framework of intellectual capital (IC).Design/methodology/approachEmploying a deductive approach rooted in systems theory, this study reinterprets established dimensions of IC for the era of artificial intelligence.FindingsDrawing inspiration from the Information Variety Model and the 4C Curve Model, both developed within the research framework of the Viable Systems Approach, a conceptual framework is proposed to transcend fragmented perspectives. It aims to provide researchers and practitioners with a comprehensive understanding of human–artificial intelligence relations within the realm of IC.Originality/valueThis paper contributes to the ongoing discourse on the potential evolution of IC in the era of artificial intelligence by presenting a multidisciplinary framework that challenges reductionist perspectives. It underscores the importance of systems thinking in reframing, analyzing and discussing key dimensions of IC in the context of the artificial intelligence era.Journal of Intellectual Capital830This paper aims to provide researchers and practitioners with a comprehensive understanding of human–artificial intelligence relations within the realm of IC by presenting a multidisciplinary framework that challenges reductionist perspectives.Journal of Intellectual Capital["Francesco Caputo"]2024-08-29T00:00:00 5708b7fb42aa76514a718bbda22c2d933466f486Hybrid Intelligence (HI) attempts to address and solve cognition-intensive tasks by combining Artificial Intelligence (AI) with human intelligence. Due to the novelty of the HI concept, there are few proven patterns for the optimal design of a Hybrid Intelligent System (HIS). To facilitate HIS simulations, we propose a framework to describe and simulate complex tasks in a multi-agent system. The tasks are solved collaboratively by teams of agents, simulating characteristics of actors with human or artificial intelligence. The prototype of a specification-driven task environment was evaluated using two sample applications. The results confirmed the strengths of the concept with regard to the study of different team constellations and cooperation patterns. Extensions would allow modelling of scenarios with higher complexity and more realistic simulation of human behaviors.IEEE International Conference on Intelligent Systems250The prototype of a specification-driven task environment was evaluated and confirmed the strengths of the concept with regard to the study of different team constellations and cooperation patterns, and a framework to describe and simulate complex tasks in a multi-agent system was proposed.2024 IEEE 12th International Conference on Intelligent Systems (IS)["Benjamin Schlup", "Andrea Corradini"]2024-08-29T00:00:00 cbd08b29ff2cf3a8a31b7686460e88f3e8d6b503BMC Neuroscience933How a collaborative relationship between neuroethics and AI ethics can stimulate theoretical and governance efforts and some dimensions that could be enhanced by the cross-fertilization between these two subfields of ethics are explored.BMC Neuroscience["Arleen Salles", "M. Farisco"]2024-08-29T00:00:00 e056aada6bb97c25c240602c90a4759b0113046cNature Communications442Using two popular chest x-ray datasets, it is demonstrated that technical parameters related to image acquisition and processing influence AI models trained to predict patient race, where these results partly reflect underlying biases in the original clinical datasets.Nature Communications["William Lotter"]2024-08-29T00:00:00 12f79af20695a82029db34299445932cbafdf1c9This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.Syst.642The findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation.Syst.["Di Wu", "Shuling Zhang", "Zhiyuan Ma", "Xiao-Guang Yue", "Rebecca Kechen Dong"]2024-08-29T00:00:00 24e8a29ce38745beb551ff4b548f24e692a50f56This research paper investigated the integration of Artificial Intelligence (AI) in public spaces, focusing on enhancing multi-sensory experiences that augment psychological comfort and foster social interactions. AI's revolutionary application in public environments, from smart city initiatives to interactive art displays, transforms these spaces into dynamic, responsive environments that adapt to human needs and presence. This study examines how AI can significantly enhance the sensory richness of public spaces in Dubai, UAE, making them more engaging, accessible, and efficient. It reflects a profound understanding of user behaviors and needs. Employing a structured survey to investigate the public's views on AI's influence on city life, the expected outcomes included a deeper understanding of how immersive environments altered human multi-sensory experiences in Dubai public spaces. The paper highlights the importance of multi-sensory experiences in public spaces, where interactions through touch, sight, sound, and scent contribute to a sense of belonging, enhance well-being, and strengthen community bonds. It argues for a human-centric design that prioritizes multi-sensory engagement, offering insights into how AI integration can further enrich these experiences, making public spaces more adaptable and sensitive to users' requirements. The findings of this research will enrich the existing body of knowledge in the professional field of architecture and urban design. It will present practical insights for architects and designers to develop innovative spatial designs that promote the multi-sensory experience of the users in public spaces.ARCHive-SR161This study examines how AI can significantly enhance the sensory richness of public spaces in Dubai, UAE, making them more engaging, accessible, and efficient and argues for a human-centric design that prioritizes multi-sensory engagement.ARCHive-SR["Imad Hanna", "Poupak Parvaresh"]2024-08-29T00:00:00 5881d1ce8ff7e542605ef0d5a7b9e372fd9b72daJournal of Innovation and Entrepreneurship511Journal of Innovation and Entrepreneurship["Bryan N. Zambrano Manzur", "Fabi\u00e1n A. Espinoza Baz\u00e1n", "Pavel Novoa-Hern\u00e1ndez", "Carlos A. Cruz Corona"]2024-08-29T00:00:00 fd2daf01b066559fe3d22a0ad83c71df1ccbb9faSkin cancer poses a significant global health challenge, demanding precise and timely diagnosis to enhance patient outcomes and tailor treatment effectively. This study addresses the complexity of skin cancer detection by employing cutting-edge deep learning algorithms alongside explainable artificial intelligence (XAI) methodologies. Utilizing recent pre-trained models such as XceptionNet, EfficientNetV2S, InceptionResNetV2, and EfficientNetV2M, we aim to classify skin lesions accurately. Additionally, we employ image augmentation strategies to improve model generalization and mitigate class imbalances. Through XAI, we elucidate the decision-making processes of our models, a critical step in establishing trust and facilitating the integration of AI-powered diagnostic tools into clinical workflows. Our findings highlight the superiority of the XceptionNet architecture, achieving an accuracy of 88.72%. By demonstrating the potential of deep learning and XAI in refining skin cancer diagnosis, this study lays the foundation for further advancements in medical image analysis. The adoption of these technologies holds promise in expediting accurate identification, thereby enhancing patient care, reducing healthcare costs, and bolstering survival rates among individuals affected by skin cancer.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)270This study addresses the complexity of skin cancer detection by employing cutting-edge deep learning algorithms alongside explainable artificial intelligence (XAI) methodologies, highlighting the superiority of the XceptionNet architecture.2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)["Puneet Thapar", "Shubham Tiwari"]2024-08-29T00:00:00 27b9faa308fa67d12e7af53fada7189d0b6db06fCybersecurity ecosystem is an important facet in protecting sensitive information and securing critical infrastructure for countering modern cyber threats. With the increasing complexity and frequency of security incidents, there is an escalating demand for development of innovative solutions beyond current human capabilities pertaining to cybersecurity measures. Artificial Intelligence or AI can be utilized in a myriad of areas of cybersecurity. It emerged as a technological innovation to enhance cyber protection by facilitating faster and real-time threat detection for known and unknown threats, automating processes to minimize human error, and optimal decision-making. Harnessing the power of AI in cybersecurity creates formidable defense capabilities against the constantly changing cyber threats of future while empowering the cybersecurity personnel with threat intelligence and proactive foresight to safeguard critical assets and confidential information with unparalleled precision and effectiveness. This research paper aims to investigate the potential of AI-enabled cybersecurity systems and focuses on deducing the benefits of using AI in enhancing cybersecurity processes for organizations seeking to manage their risk profile. Through a comprehensive literature review, the wide-ranging applications of AI in cybersecurity have been analyzed such as intrusion detection, predictive simulation, and automated emergency response management. The study examines the benefits of implementing AI-based cyber defenses such as improved promptness and accuracy in vulnerability assessment and threat management, reduced false positives, and recognize patterns. The future potential of AI in cybersecurity will take a leap forward in expanding protection mechanisms to evaluate the strengths and weaknesses of attack vectors to prevent an adversarial attack.World Journal of Advanced Research and Reviews225The study examines the benefits of implementing AI-based cyber defenses such as improved promptness and accuracy in vulnerability assessment and threat management, reduced false positives, and recognize patterns.World Journal of Advanced Research and Reviews["Sheetal Temara"]2024-08-30T00:00:00 c0ceb97a12b97e210aaa81c336700fae250e13d1Research into explainable artificial intelligence (XAI) methods has exploded over the past five years. It is essential to synthesize and categorize this research and, for this purpose, multiple systematic reviews on XAI mapped out the landscape of the existing methods. To understand how these methods have developed and been applied and what evidence has been accumulated through model training and analysis, we carried out a tertiary literature review that takes as input systematic literature reviews published between 1992 and 2023. We evaluated 40 systematic literature review papers and presented binary tabular overviews of researched XAI methods and their respective characteristics, such as the scope, scale, input data, explanation data, and machine learning models researched. We identified seven distinct characteristics and organized them into twelve specific categories, culminating in the creation of comprehensive research grids. Within these research grids, we systematically documented the presence or absence of research mentions for each pairing of characteristic and category. We identified 14 combinations that are open to research. Our findings reveal a significant gap, particularly in categories like the cross-section of feature graphs and numerical data, which appear to be notably absent or insufficiently addressed in the existing body of research and thus represent a future research road map.Machine Learning and Knowledge Extraction873A tertiary literature review of systematic literature review papers on XAI reveals a significant gap in categories like the cross-section of feature graphs and numerical data, which appear to be notably absent or insufficiently addressed in the existing body of research and thus represent a future research road map.Mach. Learn. Knowl. Extr.["F. V. Mourik", "Annemarie Jutte", "Stijn E. Berendse", "F. Bukhsh", "Faizan Ahmed"]2024-08-30T00:00:00 4ee4f54c4e4e84afdba1b0e584c846eac31bf2b3PurposeThis paper examines the integration of artificial intelligence (AI) within family businesses, focusing on how AI can enhance their competitiveness, resilience and sustainability. The study seeks to provide insights into AI’s application in family business contexts, addressing the unique strengths and challenges these businesses face.Design/methodology/approachA systematic literature review was conducted to synthesize existing research on the adoption and integration of AI in family businesses. The review involved a comprehensive analysis of relevant academic literature to identify key trends, opportunities, challenges and factors influencing AI adoption in family-owned enterprises.FindingsThe review highlights the significant potential of AI for family businesses, particularly in improving operations, decision-making and customer engagement. It identifies opportunities such as analysing customer data, enhancing brand building, streamlining operations and improving customer experiences through technologies like Generative AI, Machine Learning, AI Chatbots and NLP. However, challenges like resource constraints, inadequate infrastructure, low customization and AI knowledge gaps inhibit AI adoption in family firms. The study proposes an AI adoption roadmap tailored for family businesses and outlines future research directions based on emerging themes in AI use within these enterprises.Originality/valueThis paper addresses the underexplored area of AI integration in family businesses, contributing to the academic understanding of the intersection between AI and family-owned enterprises. The study offers a comprehensive synthesis of existing research, providing valuable insights and practical recommendations for enhancing the competitiveness and sustainability of family businesses through AI adoption.Journal of Family Business Management403The study proposes an AI adoption roadmap tailored for family businesses and outlines future research directions based on emerging themes in AI use within these enterprises, providing valuable insights and practical recommendations for enhancing the competitiveness and sustainability of family businesses through AI adoption.Journal of Family Business Management["Deepak Kumar", "Vanessa Ratten"]2024-08-30T00:00:00 d15cd18c26997255a0b4f4cec7735fdd513d4070This article provides a critical overview of the recently approved Artificial Intelligence Act. It starts by presenting the main structure, objectives, and approach of Regulation (EU) 2024/1689. A definition of key concepts follows, and then the material and territorial scope, as well as the timing of application, are analyzed. Although the Regulation does not explicitly set out principles, the main ideas of fairness, accountability, transparency, and equity in AI underly a set of rules of the regulation. This is discussed before looking at the ill-defined set of forbidden AI practices (manipulation and e exploitation of vulnerabilities, social scoring, biometric identification and classification, and predictive policing). It is highlighted that those rules deal with behaviors rather than AI systems. The qualification and regulation of high-risk AI systems are tackled, alongside the obligation of transparency for certain systems, the regulation of general-purpose models, and the rules on certification, supervision, and sanctions. The text concludes that even if the overall framework can be deemed adequate and balanced, the approach is so complex that it risks defeating its own purpose of promoting responsible innovation within the European Union and beyond its borders.arXiv.org01The text concludes that even if the overall framework can be deemed adequate and balanced, the approach is so complex that it risks defeating its own purpose of promoting responsible innovation within the European Union and beyond its borders.ArXiv["Nuno Sousa e Silva"]2024-08-30T00:00:00 07a2ef9955db6b73fdd9bdf97c6abc49c0065369Humanities and Social Sciences Communications329Humanities and Social Sciences Communications["Jin Liu", "Kaizhe Chen", "Wenjing Lyu"]2024-08-30T00:00:00 78361dad538e856442c5eee50b964da936429a84This research explores the potential of large language models (LLMs) in revolutionizing report-writing practices across the criminal justice system. Drawing on insights from 23 interviews with justice professionals regarding report writing and LLM utilization, the benefits, challenges, and implications of integrating artificial intelligence (AI) technologies into the writing process are investigated. The findings highlight the obstacles to generating quality reports and the prevalence of boilerplate elements in justice system narratives, suggesting an opportunity for LLMs to streamline the writing process, provide training support, aid interoperability, and ensure consistency in standard sections. Practitioners voiced concern regarding the potential removal of human oversight, discretion, nuanced understanding, and privacy when weighing LLM adoption. Recommendations for practice and policy are offered.Criminology &amp; Criminal Justice111The findings highlight the obstacles to generating quality reports and the prevalence of boilerplate elements in justice system narratives, suggesting an opportunity for LLMs to streamline the writing process, provide training support, aid interoperability, and ensure consistency in standard sections.Criminology &amp; Criminal Justice["Carl Dement", "Melissa Inglis"]2024-08-30T00:00:00 02fbc17904810cbf028896a1e628b527f782d93ePurpose: The global older adult population is rapidly growing, intensifying the burden of elderly care. To alleviate this challenge of an aging society, interventions utilizing artificial intelligence (AI) technology are becoming widespread. This review aimed to examine the effects of AI robot interventions on the psychological outcomes of community-dwelling older adults through a systematic literature review. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to identify and select relevant studies. Seven electronic databases were thoroughly searched for eligible studies from June 1st to 30th, 2023. Methodological quality was assessed using RoB 2.0 or RoBANS 2. Results: Thirteen studies (five randomized controlled trials and eight quasi-experimental studies) were selected in the systematic review. Among the selected studies, eight provided AI robot interventions individually, whereas five used a group format, primarily addressing older adults with cognitive impairment or dementia. Depression was the most frequently addressed psychological outcome, with six of ten studies reporting significant effects. Additionally, five studies each highlighted significant effects on emotions, such as positive expressiveness and enjoyment. However, quality of life, anxiety, and loneliness revealed divergent results. Conclusion: AI robots show potential in alleviating psychological challenges for older adults. However, due to the scarcity of high-quality studies, the review recommends conducting more randomized controlled trials with rigorous designs. This review is expected to provide valuable insights for planning and executing AI robot interventions to improve psychological outcomes for community-dwelling older adults in future research.Journal of Korean Gerontological Nursing471AI robots show potential in alleviating psychological challenges for older adults, however, due to the scarcity of high-quality studies, the review recommends conducting more randomized controlled trials with rigorous designs.Journal of Korean Gerontological Nursing["Yujin Park", "Sun Ju Chang", "Hee Jung Kim", "Ha Na Jeong"]2024-08-30T00:00:00 5db8a01d234f696fa4e1e7924d9004420641f9b9In recent years, the use of artificial intelligence (AI) has significantly increased in the field of workplace well-being. This study systematically reviews the most common applications of AI in this context, covering literature published between 2018 and 2023, and evaluates both its current and potential impact. The research involved a comprehensive search in the Scopus and Web of Science databases, following PRISMA guidelines, resulting in 31 articles that met the inclusion criteria. The qualitative synthesis reveals that AI is being utilized in areas such as mental health monitoring, emotional support, personalized well-being programs, identification of psychosocial risk factors, and training and development. This review contributes to the existing literature by offering a detailed categorization of AI applications in workplace well-being, and it highlights the practical utility of AI in enhancing employee mental health and overall well-being. The findings suggest that AI has the potential to revolutionize the management of workplace well-being, providing actionable insights for both researchers and practitioners. Recommendations for future research are also discussed.Businesses01The qualitative synthesis reveals that AI is being utilized in areas such as mental health monitoring, emotional support, personalized well-being programs, identification of psychosocial risk factors, and training and development.Businesses["Miguel-\u00c1ngel Garc\u00eda-Madurga", "A. Gil-Lacruz", "Isabel Saz-Gil", "M. Gil-Lacruz"]2024-08-30T00:00:00 ea4a10bf1698896628b0c831148351def29131e6The sale of facial features is a new modern contractual development that resulted from the fast transformations in technology, leading to legal, and ethical obligations. As the need rises for human faces to be used in robots, especially in relation to industries that necessitate direct human interaction, like hospitality and retail, the potential of Artificial Intelligence (AI) generated hyper realistic facial images poses legal and cybersecurity challenges. This paper examines the legal terrain that has developed in the sale of real and AI generated human facial features, and specifically the risks of identity fraud, data misuse and privacy violations. Deep learning (DL) algorithms are analyzed for their ability to detect AI generated faces in order to potentially function as an AI safety in face sale agreement to allow the authenticity and protecting data. In addition, it examines the legal mechanisms surrounding consent, liability and data protection and suggests changes to help accommodate the complexity of AI. This paper proposes a framework by which AI tools can be integrated into the evolution of cybersecurity strategies, to mitigate risks and ensure compliance with such new legal standards and contribute to discussing the ethical and secure use of AI in Face sale contracts.Mesopotamian Journal of CyberSecurity01A framework by which AI tools can be integrated into the evolution of cybersecurity strategies, to mitigate risks and ensure compliance with such new legal standards and contribute to discussing the ethical and secure use of AI in Face sale contracts is proposed.Mesopotamian Journal of CyberSecurity["Lobna Abdalhusen Easa Al-saeedi", "Doaa Fadhil Gatea Albo mohammed", "Firas Jamal Shakir", "Faris Kamil Hasan", "Ghadeer Ghazi Shayea", "Yahya Layth Khaleel", "Mustafa Abdulfattah Habeeb"]2024-08-30T00:00:00 eb71283a5a47213c151b36ff3042b7a1d2ac17e8The current paper examined the impact of a set of individual, technological, and institutional variables on the adoption of artificial intelligence (AI) among teachers at private schools. The rationale for this study lies in its contribution to the understanding of how teacher characteristics, institutional support, and technological perceptions affect AI adoption in educational settings. The study used data collected from teachers (n=306) from seven schools located in Azerbaijan in 2024. The study suggested that perceived usefulness of AI increases teachers’ use of AI for educational purposes, while perceived ease of use of AI has no statistically significant impact. The study also documented a statistically significant link between institutional policy and the use of AI by colleagues on the one hand, and AI adoption among schoolteachers on the other. Finally, the study found evidence relating to the link between AI adoption and the age of the teacher, such that teachers who are younger were more likely to adopt this technology. Surprisingly, personal innovativeness and level of openness to new experiences did not stimulate teachers to adopt AI for teaching. The findings contribute to improving the field’s understanding of teachers’ attitudes and motivations for using AI for instructional purposes. The study findings also highlight the role of administrative regulation and school policies in stimulating the adoption of new technologies. These findings contribute to relatively novel literature relating to the application of AI in education and provide useful recommendations for administrators of educational institutions.IAFOR Journal of Education01It is suggested that perceived usefulness of AI increases teachers’ use of AI for educational purposes, while perceived ease of use of AI has no statistically significant impact.IAFOR Journal of Education["Mukhammadfoik Bakhadirov", "Rena Alasgarova", "Jeyhun Rzayev"]2024-08-30T00:00:00 f9fc3541a6ebee1f3c7b685ffbaad8fdfbe39970This review paper explores the transformative role of artificial intelligence (AI) in enhancing market analysis for strategic business decision-making. It begins with an overview of market analysis and the integration of AI technologies, such as machine learning, natural language processing, and predictive analytics, significantly improving data collection, processing, and analysis. The discussion highlights the capabilities of AI in generating accurate, efficient, and deeper insights, which are essential for informed decision-making. The paper also delves into AI-driven techniques like data integration, predictive analytics, sentiment analysis, and competitive analysis, demonstrating how these methods optimize market segmentation, customer personalization, and risk management. Despite the considerable advantages, integrating AI into market analysis presents challenges, including data quality issues, privacy concerns, and technological limitations. Ethical considerations, such as bias and transparency, are also examined. Finally, the paper discusses future trends in AI, emphasizing advancements in algorithms, real-time data analysis, and the importance of ethical AI, which will further enhance market analysis and strategic business decision-making.World Journal of Engineering and Technology Research01The paper delves into AI-driven techniques like data integration, predictive analytics, sentiment analysis, and competitive analysis, demonstrating how these methods optimize market segmentation, customer personalization, and risk management.World Journal of Engineering and Technology Research["Edith Ebele Agu", "Anwuli Nkemchor Obiki-Osafiele", "Njideka Rita Chiekezie"]2024-08-30T00:00:00 bf4d9c9cb4e8e46ce202e3d0ed3d2067a150d9d1Artificial Intelligence (AI) has shown remarkable potential to revolutionise healthcare by enhancing diagnostics, improving treatment outcomes, and streamlining administrative processes. In the global regulatory landscape, several countries are working on regulating AI in healthcare. There are five key regulatory issues that need to be addressed: (i) data security and protection—measures to cover the “digital health footprints” left unknowingly by patients when they access AI in health services; (ii) data quality—availability of safe and secure data and more open database sources for AI, algorithms, and datasets to ensure equity and prevent demographic bias; (iii) validation of algorithms—mapping of the explainability and causability of the AI system; (iv) accountability—whether this lies with the healthcare professional, healthcare organisation, or the personified AI algorithm; (v) ethics and equitable access—whether fundamental rights of people are met in an ethical manner. Policymakers may need to consider the entire life cycle of AI in healthcare services and the databases that were used for the training of the AI system, along with requirements for their risk assessments to be publicly accessible for effective regulatory oversight. AI services that enhance their functionality over time need to undergo repeated algorithmic impact assessment and must also demonstrate real-time performance. Harmonising regulatory frameworks at the international level would help to resolve cross-border issues of AI in healthcare services.Healthcare452Policymakers may need to consider the entire life cycle of AI in healthcare services and the databases that were used for the training of the AI system, along with requirements for their risk assessments to be publicly accessible for effective regulatory oversight.Healthcare["K. Palaniappan", "Elaine Yan Ting Lin", "Silke Vogel", "John C W Lim"]2024-08-30T00:00:00 94fb3ea96a828794dffd692235aa8a7e6bf7d609The rapid advancement of artificial intelligence (AI) has significantly impacted infection prevention and control, particularly amid the coronavirus disease 2019 (COVID-19) pandemic ( 1 ). AI techniques such as machine learning (ML), deep learning, and naturalChina CDC Weekly281China CDC Weekly["Linxin Yang", "Shuya Lu", "Lei Zhou"]2024-08-30T00:00:00 725ad85f9f083f1a1cf4abc3de3c5e838a85231bThis comprehensive review examines the convergence of Artificial Intelligence (AI), Financial Technologies (FinTech), and the United Nations Sustainable Development Goals (SDGs). It explores the transformative potential of AI in enhancing FinTech solutions to address global challenges outlined in the SDGs. The study provides an in-depth analysis of current AI applications in financial services, their impact on sustainable development, and emerging trends in this rapidly evolving field. Key findings reveal that AI-driven FinTech innovations can significantly contribute to financial inclusion, poverty reduction, and economic growth. Machine learning algorithms are revolutionizing credit scoring, risk assessment, and fraud detection, while natural language processing is enhancing customer service and market analysis. Computer vision technologies are improving security measures and streamlining processes in the financial sector. However, the study also identifies critical challenges that must be addressed, including data privacy concerns, algorithmic bias, and the widening technological gap. The review concludes with a series of recommendations for policymakers, financial institutions, and technology developers. These guidelines aim to promote the responsible and effective leverage of AI in FinTech to achieve the SDGs, emphasizing the need for ethical considerations, regulatory frameworks, and cross-sector collaboration. This research provides valuable insights for stakeholders working at the intersection of AI, FinTech, and sustainable development, offering a roadmap for harnessing these technologies to create a more inclusive and sustainable global financial ecosystem.World Journal of Advanced Research and Reviews02Key findings reveal that AI-driven FinTech innovations can significantly contribute to financial inclusion, poverty reduction, and economic growth and identify critical challenges that must be addressed, including data privacy concerns, algorithmic bias, and the widening technological gap.World Journal of Advanced Research and Reviews["Oluwafemi Elias", "Sunday David Esebre", "Idris Abijo", "Adesina Mayowa Timothy", "Temitope Deborah Babayemi", "Ebenezer O. Makinde", "Oladiipo Ishola Oladepo", "Iyinoluwa Elizabeth Fatoki"]2024-08-30T00:00:00 943cd6a977225ebab04fd71b80b5c76a6462a762Introduction. Digital economy has generated a completely new model of human-machine interaction based on the active use of digital technologies in almost all spheres of human activity. Most information systems used by organizations are integrated with big data analytics, which requires higher qualified staff. The job responsibilities of employees change according to industry and corporate needs. The requirements for staff qualification are increasing, urging the company HR service to find, attract and retain specialists of the required qualifications, and organize staff development in the organization. Innovations in decision-making algorithms make artificial intelligence the most useful tool for implementing a human resource management strategy in an organization.Materials and methods. The article uses general scientific research methods, such as qualitative analysis, observation method, synthesis, logical induction method and others. The information basis of the article consists of scientific research works, official documents, and information posted in the media. A secondary analysis of the research on the topic under study was carried out.Results. Despite the fact that the processes related to personnel management have always been a human cognitive ability, artificial intelligence technologies can currently provide technical solutions in the field of recruitment and further staff development. Artificial intelligence plays an important role in collecting candidate data from various sources, and is able to evaluate the required candidates based on a job description more effectively and objectively than a HR officer. Artificial intelligence can more accurately describe a job that corresponds to the business process in skills. Artificial intelligence technologies can play an important role in the process of organizing professional development and retraining of staff. The use of artificial intelligence provides for obtaining more personalized and understandable results excluding the "human factor". In this article, special attention is paid to artificial intelligence technologies, which can be effectively used in staff management. The opportunities to increase the HR employees’ productivity are considered, main risks of artificial intelligence introduction are highlighted and recommendations for its efficient application are given. The research novelty is in identification of the scope of the use of artificial intelligence in the organizational personnel policy, making it possible to realize the opportunities for revealing the creative potential of the organization’s employees and increase competitiveness.Discussion. It is absolutely clear that AI is becoming an integral part of business ecosystems, and necessary to maintain and increase the level of competitiveness of economic agents. Nevertheless, AI is constantly evolving, which allows companies to expand the functionality of its use. However, it is important to define the boundaries of AI use, including how human-machine interaction will be ensured, which functions can be given to AI, and which can be abandoned.Management Issues101In this article, special attention is paid to artificial intelligence technologies, which can be effectively used in staff management, which is becoming an integral part of business ecosystems, and necessary to maintain and increase the level of competitiveness of economic agents.Management Issues["Oksana Ovchinnikova", "Darya Lebedeva"]2024-08-30T00:00:00 249efa6ee93a6fc6fc031c6692503aabc85c8225Artificial intelligence is a tool used in law. It focuses on the complementarity of human performance. A bibliometric study of the scientific production in Scopus in the period 2015-2023 was carried out. The main findings are that artificial intelligence is related to legal service, decision-making, digital transformation, and ethical performance. It was concluded that scientific production on artificial intelligence and law experienced exponential growth, especially coinciding with the COVID-19 pandemic. It was also concluded that artificial intelligence enables the generation of empathy and creativity through responsible use, which gives rise to trust in the population.Journal of Internet Services and Information Security02It was concluded that scientific production on artificial intelligence and law experienced exponential growth, especially coinciding with the COVID-19 pandemic, and enables the generation of empathy and creativity through responsible use, which gives rise to trust in the population.J. Internet Serv. Inf. Secur.["C. Aguila", "Mar\u00eda Del Pilar Castro Arellano", "Mar\u00eda Del Pilar Quezada Castro", "Eliana Maritza Barturen Mondrag\u00f3n", "Guillermo Alexander Quezada Castro"]2024-08-30T00:00:00 b4326718dbfa65079fe3c0b260d3d53bac522224The adoption of Artificial Intelligence (AI) is rapidly increasing, reshaping both society and organizational landscapes. This transformative trend has inevitably reached strategic communication professionals, leading to various perceptions about its application in this field. This study explores these perceptions of uses and gratifications, perspectives on the influence of AI, as well as the practices of professionals operating in Portugal in the mentioned areas regarding the integration of AI into the exercise of their professions. The study employed a qualitative approach, using semi-structured interviews to collect data from communication professionals. The results from 21 interviews indicate a greater awareness of AI integration, especially after the launch of ChatGPT, and automated content generation as one of the biggest uses of AI. Although most respondents expressed favorable inclinations towards AI integration, some reservations persist related to replacing human expertise and creating dependency. The recommendations underline the imperative to train professionals to mitigate AI-related drawbacks, maintain a symbiotic relationship between AI and humans, and uphold ethical standards.MedieKultur: Journal of Media and Communication Research01Perceptions of uses and gratifications, perspectives on the influence of AI, as well as the practices of professionals operating in Portugal regarding the integration of AI into the exercise of their professions are explored.MedieKultur: Journal of media and communication research["Rapha\u00ebl Baptista", "C\u00e9lia Belim"]2024-08-30T00:00:00 9fbccff79228d44c6c4a8386426d3f6701d604ecThe rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual spectrum and thermal infrared cameras. The platform processes these data with convolutional neural networks (CNNs) and transformer architectures to monitor species, including those that are critically endangered. Real-time detection provides the immediate responses required for time-critical situations (e.g., poaching), while non-real-time analysis supports long-term wildlife monitoring and habitat health assessment. Case studies from Europe, North America, Africa, and Southeast Asia highlight the platform’s success in species identification, biodiversity monitoring, and poaching prevention. The paper also discusses challenges related to data quality, model accuracy, and logistical constraints while outlining future directions involving technological advancements, expansion into new geographical regions, and deeper collaboration with local communities and policymakers. Conservation AI represents a significant step forward in addressing the urgent challenges of wildlife conservation, offering a scalable and adaptable solution that can be implemented globally.Conservation372This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform, offering a scalable and adaptable solution that can be implemented globally.ArXiv["Paul Fergus", "C. Chalmers", "Steven N. Longmore", "Serge A. Wich"]2024-08-30T00:00:00 7a643a7e4cf2fc344a92ed578b50932978cae307The article aims to clarify the meaning of modern aesthetic perception and its influence on aesthetic experience. Additionally, it explores how aesthetic awareness plays a crucial role in identifying and connecting life values, contributing to the development of aesthetic capacity in Fine Arts education in universities. The article also highlights the benefits of integrating artificial intelligence (AI) technology in teaching aesthetic perception, offering a more enriching learning experience. It discusses various ways AI can assist students in improving their artistic awareness and creativity. Finally, the article emphasizes the importance of embracing technological advancements in Fine Arts teaching to create opportunities for expanding artistic competence in the digital age. Keywords: Fine Arts education; universities; artificial intelligence technology; aesthetic perception.Vinh University Journal of Science00How aesthetic awareness plays a crucial role in identifying and connecting life values, contributing to the development of aesthetic capacity in Fine Arts education in universities, and the benefits of integrating artificial intelligence technology in teaching aesthetic perception are explored.Vinh University Journal of Science["Cao Minh Hong Hanh"]2024-08-30T00:00:00 c509b3c700834f92d2130475de07097995289f81The use of AI (Artificial Intelligence) in teaching and learning has brought significant changes to the world of education. While it is recognized that AI provides innovative solutions and opens up opportunities for a more personalized and adaptive approach to learning, there are pros and cons regarding its impact. Concerns regarding the potential reinforcement of bias and discrimination, negative responses from students and teachers, and over-reliance on AI are concerns. The community service activity by Bhineka PGRI University Tulungagung at SMP 1 Gondang aims to provide solutions to the lack of materials on the use of AI in education. The training methods used include lectures, tutorials, discussions, mentoring, and assessments. The results of this activity show an increase in the teachers' understanding of AI. This activity provides insight into the importance of integrating AI technology with learning approaches that maintain the essence of education and focus on ethics and student behavior. Recommendations are directed towards developing further collaboration and implementing similar activities on a regular basis to improve teacher professionalism.   Keywords: AI (Artificial Intelligence), Potential, Learning and TeachingInternational Conference on Aplied Social Sciences in Education160The community service activity by Bhineka PGRI University Tulungagung at SMP 1 Gondang aims to provide solutions to the lack of materials on the use of AI in education and shows an increase in the teachers' understanding of AI.International Conference on Aplied Social Sciences in Education["Arik Nur Akhidah", "Eko Wahyuni", "Aring Pramukawati"]2024-08-30T00:00:00 50a35b29ef2e1691c8cbd5e543b36e5da9292359This study investigated an assessment of Artificial intelligence AI Enhanced Classroom on Teacher's Productive in Lagos State Education District V. The study population consisted of 10 senior secondary schools in Lagos State Educational District V. Simple random sampling techniques was used to select one school from each zonal under the selected Education District. On the other hand, 25 teachers were chosen at random from each chosen school using a purposive sampling technique making a total of 100 participants. This study used a 4-point Likert scale questionnaire to generate the data. Hypotheses were tested using Pearson's Product Moment Correlation Coefficient Analysis. (β = 0.242, p < 0.05) Study results showed that artificial intelligence AI Enhanced Classroom has a positive effect on Teachers' Productive in Lagos State Education District V. It was recommended that educational policymakers and administrators in Lagos State Educational District V take additional steps to integrate AI-enhanced classrooms, given the study's strong findings and encouraging educators to work together and share knowledge in a positive environment helps hasten the adoption of AI technologies.Lagos Journal of Contemporary Studies in Education00It was recommended that educational policymakers and administrators in Lagos State Educational District V take additional steps to integrate AI-enhanced classrooms, given the study's strong findings and encouraging educators to work together and share knowledge in a positive environment helps hasten the adoption of AI technologies.Lagos Journal of Contemporary Studies in Education["Dr. Yahya Lateefat Oludare", "Giwa Yussuf Olaoye", "Ajiboye Shedrack Okiki", "Odufisan Ramota Odufunke"]2024-08-30T00:00:00 c1b2744cdecd1b728872b91d48ecdb7337bd93e7This paper explores the transformative potential of integrating Information Processing Theory (IPT) with Artificial Intelligence (AI) to enhance educational outcomes. By examining key concepts of IPT and their application in general learning and AI-driven educational tools, this review highlights how personalized learning, cognitive load management, and immediate feedback mechanisms can be optimized. The study reveals that significant improvements in student engagement, comprehension, retention, cognitive load management, and feedback can be realized through the intersection of AI and IPT a positive indicator for improving learning outcomes. However, the study recommends addressing challenges such as data privacy, algorithmic bias, and equity while working with AI tools and proposes future collaboration research between cognitive scientists, AI developers, and educators crucial for developing effective educational tools. Further research should focus on understanding the nuances of human learning processes and how AI can be designed to support these processes.International Conference on Aplied Social Sciences in Education00The study reveals that significant improvements in student engagement, comprehension, retention, cognitive load management, and feedback can be realized through the intersection of AI and IPT a positive indicator for improving learning outcomes.International Conference on Aplied Social Sciences in Education["Abdulnassir Yassin", "Ashiraf Mabanja"]2024-08-30T00:00:00 393d64a7a0e315bfc0a586dd6775578499def9e9Neurosurgery has evolved alongside technological innovations; however, these advances have also introduced greater complexity into clinical practice. Neurosurgery remains a demanding and high-risk field that requires a broad range of skills. Artificial intelligence (AI) has immense potential in neurosurgery given its ability to rapidly analyze large volumes of clinical data generated in modern clinical environments. An expanding body of literature has demonstrated that AI enhances various aspects of neurosurgery, including diagnostics, prognostication, decision-making, data management, education, and clinical studies. AI applications are expected to reduce medical errors and costs, broaden healthcare accessibility, and ultimately boost patient safety and surgical education. Nevertheless, AI application in neurosurgery remains practically limited because of several challenges, such as the diversity and volume of clinical training data collection, concerns regarding data quality, algorithmic bias, transparency (explainability and interpretability), ethical issues, and regulatory implications. To comprehensively discuss the potential benefits, future directions, and limitations of AI in neurosurgery, this review examined recent studies on AI technology and its applications in this field, focusing on intraoperative decision support and surgical education.JMA Journal720A review of recent studies on AI technology and its applications in neurosurgery, focusing on intraoperative decision support and surgical education found that AI application in neurosurgery remains practically limited.JMA Journal["T. Sugiyama", "Hiroyuki Sugimori", "Minghui Tang", "Miki Fujimura"]2024-08-30T00:00:00 e8299bfb87ff59a9c54ebe2de43a1f4fad956e8eObjective: This study aims to demonstrate how the use of generative Artificial Intelligence (AI) fosters innovation within the Judiciary by enhancing the operational performance of the legal system. Methodology: The research adopts an explanatory qualitative approach with a theoretical foundation. It relies on secondary data and documentary evidence sourced from specialized literature. Results: The findings suggest that generative AI significantly expands the operational capacity of judges and legal professionals by automating repetitive tasks and facilitating the generation of legal sentences. This leads to improved decision-making and more effective legal strategies, thus enhancing the overall efficiency of the judiciary. Conclusions: The integration of generative AI in the legal system has the potential to revolutionize the practice of law, making it more accessible and less discriminatory. The ethical considerations embedded in AI systems are crucial for ensuring that justice is administered fairly and in alignment with fundamental human rights. As AI continues to evolve, its role in supporting judicial processes will likely increase, contributing to a more efficient and ethical legal system.Journal of Law and Corruption Review00The findings suggest that generative AI significantly expands the operational capacity of judges and legal professionals by automating repetitive tasks and facilitating the generation of legal sentences, thus enhancing the overall efficiency of the judiciary.Journal of Law and Corruption Review["L. Rodrigues", "Reziere Dagobi da Silva", "Simone Maria Espinosa", "Valeria Riscarolli"]2024-08-30T00:00:00 22bc4b354724ab8245c67d6e45e8f031b94977acCyber threats are becoming increasingly complicated and diverse, posing serious risks to individuals, businesses, and organizations in the cybersecurity sector. The cybersecurity industry needs to change to combat these new dangers as cybercriminals are always coming up with new ways to get past protections. This paper investigates how artificial intelligence (AI) may both simplify and improve cybersecurity efforts. AI has transformed the industry by offering cutting-edge defensive technologies, but it also gives cybercriminals new tools at their disposal to automate and enhance their hacking methods. The effect of AI on authentication procedures—which are crucial for protecting network access—is given special attention. This paper emphasizes the critical need for creative remedies to defend against more complex cyberattacks by examining the dual nature of AI in cybersecurity.World Journal of Advanced Research and Reviews00The critical need for creative remedies to defend against more complex cyberattacks is emphasized by examining the dual nature of AI in cybersecurity by examining the dual nature of AI in cybersecurity.World Journal of Advanced Research and Reviews["Avaneesh Mohapatra", "Gagan Reddy"]2024-08-30T00:00:00 7315f2ab165f27efa299f9ef1fe6a76c82bd850aThe rapid advancement of Generative Artificial Intelligence (GenAI) has significantly impacted cybersecurity, presenting both opportunities and challenges. This study explores the evolving nature of cyber threats facilitated by GenAI, focusing on its dual role in enhancing security measures and creating sophisticated attack vectors. Through a comprehensive literature review, analyses were made on previous research focused on the applications of GenAI in cybersecurity, examining its potential to detect, prevent, and respond to threats and its vulnerabilities to exploitation by malicious actors. Utilizing qualitative research methodology, this study gathers insight from peer-reviewed articles, case studies, and expert interviews to reveal the implications of GenAI in the cybersecurity landscape. The findings reveal the complex interplay between GenAI's protective and adversarial capabilities, highlighting the need for continuous innovation and robust strategies to mitigate associated risks. The study concludes by positioning these insights within the broader context of cybersecurity and proposing directions for future research to address emerging challenges.World Journal of Advanced Engineering Technology and Sciences00The findings reveal the complex interplay between GenAI's protective and adversarial capabilities, highlighting the need for continuous innovation and robust strategies to mitigate associated risks.World Journal of Advanced Engineering Technology and Sciences["Oluwaseyi Olakunle Mokuolu"]2024-08-30T00:00:00 d30b35295da4dbcd27d0b791928510cb0a38e3e4Curriculum is a vital instrument in education; it shapes what and how students learn, what teachers teach, and the expectations of education policymakers. This paper examines the critical role of artificial intelligence (AI) in designing curriculum that meet the challenges of the 21st century and address the evolving needs of students and society. Leveraging AI in curriculum development offers a transformative approach, enhancing teaching methodologies, producing personalized and inclusive learning experiences, and improving educational administrative efficiencies. Major AI technologies such as machine learning, natural language processing, deep learning, expert systems, machine vision, and data analytics are instrumental in creating adaptive and personalized learning systems, intelligent tutoring systems, and comprehensive education management information systems. These technologies bring innovations, facilitate personalized education, timely interventions for at-risk students, data-driven decision-making, and make curriculum more inclusive, efficient and accessible for all students no matter the background and social stratifications. Despite many benefits of AI in education, challenges remain, including the need for scalable and dynamic curriculum designs that maintain high content quality. As a result, there is a need to invest in AI technologies and educators' training to leverage these tools to create a more responsive and effective learning environment.World Journal of Advanced Research and Reviews370The critical role of artificial intelligence (AI) in designing curriculum that meet the challenges of the 21st century and address the evolving needs of students and society is examined.World Journal of Advanced Research and Reviews["Blessing Ngozi", "Precious Orekha", "Olumide Ojediran", "Edwin Imohimi", "Harold Tobias"]2024-08-30T00:00:00 c5713294efe9af4b9577da2662b011272a318477The use of Artificial Intelligence (AI) in the context of digital learning has become a trend in modern education. This study aims to illustrate the implications of using AI on student learning motivation. Student learning motivation is a key factor in improving academic achievement and self-development. By utilizing AI technology in the learning process, we can identify the role of AI in increasing student learning motivation. Using combined methods (qualitative and quantitative), this study involved surveys (questionnaires) and analysis of data from various relevant literature sources. The results showed that AI can contribute positively to student learning motivation in several ways. First, AI can provide personalized feedback that helps students understand their progress better. Second, AI can design learning experiences tailored to individual learning styles, thereby increasing student interest and engagement in learning. Third, AI can identify students' learning difficulties and provide additional assistance in real-time, thereby reducing students' frustration and increasing their motivation to overcome challenges. However, the study also identified several challenges that need to be addressed in the use of AI in digital learning. One of them is the issue of privacy and security of student data that must be carefully managed. In addition, the development and implementation of AI technology in an educational context requires significant investment in teacher training and technology infrastructure. Thus, the study highlights the importance of wisely integrating AI in education and emphasizes the need for attention to ethical and practical aspects. Keywords: Artificial intelligence; digital learning; educational technologyInternational Conference on Aplied Social Sciences in Education00The results showed that AI can contribute positively to student learning motivation in several ways, and the importance of wisely integrating AI in education is highlighted and the need for attention to ethical and practical aspects is emphasized.International Conference on Aplied Social Sciences in Education["Difa Dewi Ayu Rohana", "Arvi Nurizza Ardhiansyah", "Dadang Puji Widodo"]2024-08-30T00:00:00 f8d676201dd30b369d95d2172457e6ee208d7132The integration of financial technology (fintech) innovations, particularly artificial intelligence (AI) and blockchain, is poised to revolutionize carbon markets by enhancing their transparency, efficiency, and overall trustworthiness. Carbon markets, which are vital tools in global efforts to reduce greenhouse gas emissions, have traditionally faced significant challenges such as lack of transparency, inefficiencies, and susceptibility to fraud. AI offers powerful tools for improving the accuracy and efficiency of monitoring, reporting, and verifying carbon emissions, while blockchain provides a decentralized, immutable ledger that ensures secure and transparent carbon credit transactions. This review explores how AI and blockchain can be applied to carbon markets by exploring the technical, operational, and regulatory challenges associated with their implementation and discuss the potential of integrated AI and blockchain solutions to create more robust and effective carbon trading systems.World Journal of Advanced Research and Reviews00This review explores how AI and blockchain can be applied to carbon markets by exploring the technical, operational, and regulatory challenges associated with their implementation and discusses the potential of integrated AI and blockchain solutions to create more robust and effective carbon trading systems.World Journal of Advanced Research and Reviews["Oluwaseun Aaron Adigun", "Babatunde O Falola", "Sunday David Esebre", "Ifeoluwa Wada", "Abdullah Tunde Adewuyi", "Temitope Dickson Olajide", "Pelumi Oladokun"]2024-08-30T00:00:00 4ee858a8536e2cc5837de3c15026da325e361e8fThe acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.Archives des Sciences420The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender, and emphasised the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.Archives des Sciences["Nisar Ahmad Koka", "Mohsin Raza Khan", "Javed Ahmad", "Saqub Aftab", "Mohammed Osman Abdul Wahab"]2024-08-30T00:00:00 999c87ebaf4def2b39e8fcbc56149ad0a3f7c2abIn the rapidly evolving financial landscape, the integration of Artificial Intelligence (AI) into Supervisory Technology (SupTech) and Regulatory Technology (RegTech) has become increasingly vital. As banks and financial organizations grapple with the complexities of compliance, risk management, and regulatory oversight, AI offers transformative capabilities that enhance efficiency, accuracy, and resilience. This paper explores the critical need for AI in Fintech for SupTech and RegTech, focusing on its role in supervisory functions within the banking sector. A case study on AI-driven anti-money laundering systems is presented, along with a discussion of authentic laboratory research and survey results. The paper concludes by illustrating the potential impact of AI on financial supervision with graphs and data, underscoring its necessity in modern financial governance.International Journal of Science and Research Archive00The paper concludes by illustrating the potential impact of AI on financial supervision with graphs and data, underscoring its necessity in modern financial governance.International Journal of Science and Research Archive["Patience Farida Azuikpe", "Jumai Adedoja Fabuyi", "Adebayo Y. Balogun", "Peter Adetola Adetunji", "Kingsley Nana Peprah", "Ebuka Mmaduekwe", "Mayowa Christianah Ejidare"]2024-08-30T00:00:00 8a5b7af0c42ae856a23f1f6d2b92b1cfd3404fcdThis review outlines the implementation of artificial intelligence (AI) into colonoscopy procedures which includes its history, processes, and challenges. We highlight the importance of the collaborative effort between medical and computer science researchers in the development of AI tools in colonoscopy, particularly focusing on the roles of computer-aided detection (CADe) and computer-aided characterization (CADx) in a real time analysis of colonoscopy videos. Some of the proposed technologies are considered to improve the important clinical outcomes of patients such as adenoma detection rate in colonoscopy. Regulatory approval is considered mandatory before introducing AI tools into the market owing to the potential risks associated with the introduction of AI tools in healthcare. We share the experience of obtaining regulatory approval for EndoBRAIN in Japan, emphasizing the challenges in establishing examination criteria and performance levels at the period. Reimbursement is also identified as necessary for the widespread adoption of medical innovation. With the introduction of reimbursement for a CADe tool in Japan in 2024, we expect to accelerate implementation of AI in colonoscopy in general. Despite regulatory approval and reimbursement, concerns are raised with regard to the assessment of the balance between benefits and harms of AI in colonoscopy. Questions about its impact on cancer prevention, healthcare burden, patient acceptance, and effectiveness across different populations remain unsolved. The lack of clinical guidelines for AI in colonoscopy emphasizes the need for a rigorous assessment of available evidence in optimizing the adoption of AI in colonoscopy practice. While it is always exciting to strive for medical innovation, ensuring rigorous evaluation to optimize patient care is mandatory to improve the quality of health and society.JMA Journal00The lack of clinical guidelines for AI in colonoscopy emphasizes the need for a rigorous assessment of available evidence in optimizing the adoption of AI in colonoscopy practice, and concerns are raised with regard to the assessment of the balance between benefits and harms of AI in colonoscopy.JMA Journal["M. Misawa", "S. Kudo", "Y. Mori"]2024-08-30T00:00:00 3175a697e9882425e1e0747851c215f5ea1c202bThis paper investigates the transformative role of Artificial Intelligence (AI) in global communication with a view to minimising misinformation, disinformation, cultural diversity and fostering global understanding. The article examines whether the integration of artificial intelligence (AI) in the communication process offers solutions to bridge cultural and linguistic gaps, mitigate perception bottlenecks, and foster global understanding. The study adopts a conceptual review method, which involves a systematic examination of existing literature, research studies, and relevant information in the communication field. The study reveals that AI technologies, via content moderation, fact-checking algorithms, language translation tools, and cultural sensitivity enhancements, have shown significant potential in combating misinformation and disinformation, thereby fostering a more informed global community. Furthermore, it is found that AI applications have also been found to promote cultural diversity by enabling more accurate and inclusive communication across various languages and cultural contexts. In addition, the paper finds that AI-driven communication strategies have been instrumental in enhancing global understanding by facilitating cross-cultural exchanges and mitigating biases in information dissemination. Finally, it is discovered that AI technologies still have some limitations in global communication. Therefore, the study recommends that policymakers, researchers, and practitioners should continue to explore and harness the transformative potential of AI in enhancing global communication processes by leveraging AI technologies in a responsible and ethical manner, to pave the way for a more inclusive, informed, and interconnected global society Lagos Journal of Contemporary Studies in Education00The study reveals that AI technologies, via content moderation, fact-checking algorithms, language translation tools, and cultural sensitivity enhancements, have shown significant potential in combating misinformation and disinformation, thereby fostering a more informed global community.Lagos Journal of Contemporary Studies in Education["Babajide Adeyinka Joseph"]2024-08-30T00:00:00 c1ed217b86879c326577947cb411fb4033915da6Cutting tools are fundamental components in manufacturing and engineering processes which are essential for shaping, cutting, and decided materials like metal, wood and composites. The design of these tools calls for a delicate balance between choice of materials, geometry and performance criteria whose parameters are tailored. The emergence of artificial intelligence (AI) has turned this world all upside down. AI first uses machine learning algorithms to analyze large quantities of data about material properties, wear resistance, or cutting conditions leading to new tool designs for a given material. In addition, AI also allows real-time monitoring during machining and with adaptive control. It can improve precision as well as reduce tool wear by selecting the right speed when cutting, while slow down results in prevention of damage. Furthermore, AI-driven simulations have enabled engineers to simulate and optimize tool design in virtual environments prior to physical production. AI will continue to advance in the future holds out the promise of decreasing still further the cost and improving the performance characteristics of cutting tools. It is the combination of AI and cutting tool design to gain maximum benefits of this innovation while minimizing costs for now in the future.Global Sustainable Development80The combination of AI and cutting tool design is needed to gain maximum benefits of this innovation while minimizing costs for now in the future.Global Sustainable Development["Ayd\u0131n Heydarov"]2024-08-30T00:00:00 14e6954d9f97f5ae54145ef59a543e577e45475cThe potential of artificial intelligence integration in higher education shows huge transformative change in learning and teaching practices. This exploratory and descriptive study investigates the opportunities and challenges of AI adoption in higher education, drawing on insights from a diverse sample of 78 students, facultys, and administrators. A well-structured questionnaire and observation tools were used to collect the primary data, and Holmes (2019)’s theoretical notions on AI have largely been used to conduct this mixed-method study. The analysis shows strong agreement regarding rewards that are positive and related to the use of AI in improving learning in a personalized way, making it more engaging and interactive, reducing administrative load, and providing timely feedback and support. however, points out serious barriers: the challenge of data security and privacy, gaps in technological infrastructure, massive training and support needs, ethical concerns, resistance or reluctance to change, and third, a need for training. These findings further called for strategies to be designed to address these barriers by insisting on solid data protection and scalable technological infrastructures, continuous training, and ethical guidance. Only in this way will the higher education institution successfully bring about the required integration of AI, hence improving the quality and access to education.AMC journal170The analysis shows strong agreement regarding rewards that are positive and related to the use of AI in improving learning in a personalized way, making it more engaging and interactive, reducing administrative load, and providing timely feedback and support.AMC Journal (Dhangadhi)["Dharma Raj Ojha"]2024-08-30T00:00:00 64b45eff5d9f2b4c7ba0de18c0af32e0391ccd21This research discusses the application of artificial intelligence (AI) in education, specifically focusing on character education. The rapid development of AI technology has had a significant impact on various aspects of human life, including education. Character education aims to instill moral values ​​and virtues in students to develop their ability to make ethical decisions and realize goodness in everyday life. The integration of AI in education raises ethical and moral concerns, particularly in discussing values ​​such as integrity, digital ethics, and responsibility in the use of technology. This research aims to explore the potential of AI in developing critical thinking skills, which are important in forming strong character. There is a need for an innovative system to improve the quality of thinking and skills in education and overcome the shortcomings of existing learning models. The methodology used includes a literature review and qualitative analysis to examine the impact of AI on character education. The findings show that AI in education can help students develop critical thinking skills and minimize misunderstandings of learning material. The implications of this research underscore the importance of integrating AI in character education and the need for comprehensive learning strategiesInternational Conference on Aplied Social Sciences in Education00The findings show that AI in education can help students develop critical thinking skills and minimize misunderstandings of learning material, which are important in forming strong character.International Conference on Aplied Social Sciences in Education["Eko Wahyuni", "Arik Nur Akhidah", "Aring Pramukawati"]2024-08-30T00:00:00 314e06f1284412b011c4ff87ee8a90d434ac1b13Технологии искусственного интеллекта (ИИ) всё чаще становятся инструментом преобразования бизнес-процессов и организационных структур компаний. Понимание их влияния на различные аспекты корпоративного управления важно для конкурентоспособности и устойчивого развития. В данной статье исследуется, как технологии на базе ИИ влияют на ключевые бизнеспроцессы и организационные структуры современных компаний, обозначаются текущие преимущества и недостатки данных технологий, потенциал применения и вероятные угрозы в применении ИИ в бизнес-менеджменте, а также тренды, связанные со все более глубоким внедрением новых технологий в корпоративное управление и управление организационной структурой современных компаний. Artificial Intelligence (AI) technologies are increasingly becoming a tool for transforming business processes and organizational structures of companies. Understanding their impact on various aspects of corporate governance is important for competitiveness and sustainability. This article explores how AI-based technologies affect key business processes and organizational structures of modern companies, identifies current advantages and disadvantages of these technologies, potential applications and likely threats in the application of AI in business management, as well as trends associated with the increasingly deep introduction of new technologies in corporate governance and management of organizational structure of modern companies.Экономика и предпринимательство00Экономика и предпринимательство["\u041c.\u0414. \u0413\u043e\u0440\u0448\u043a\u043e\u0432", "\u0410.\u0415. \u041b\u044e\u0441\u0442\u0430\u0440\u043d\u043e\u0432", "\u0415.\u0414. \u0418\u0446\u0430\u043a\u043e\u0432"]2024-08-30T00:00:00 a195f1db571d6a06bee2c9d11a467476dfdacc2cThe potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. Benefits of the application of AI in anaesthesia include an improvement in perioperative risk stratification, personalisation of anaesthetic plans, improvement in efficiency and ultimately reduce healthcare costs. However, reliance on technology may reduce clinical acumen but furthermore there are issues surrounding data quality, privacy as well as legal and ethical concerns, which require further evaluation. Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.British journal of hospital medicine20Benefits of the application of AI in anaesthesia include an improvement in perioperative risk stratification, personalisation of anaesthetic plans, improvement in efficiency and ultimately reduce healthcare costs, but reliance on technology may reduce clinical acumen.British journal of hospital medicine["Tajveer Sara", "Yasser Mandour"]2024-08-30T00:00:00 2c36a8d737f260f84086b2906d76ae178983a0d5The rapid advancement of technology has profoundly influenced the music industry, with Artificial Intelligence (AI) emerging as a key tool, especially in song remixing. AI, which has been evolving since the 20th century, now plays a significant role in creative processes, leading to legal concerns regarding copyright, ownership, and the rights of original creators. In Indonesia, creative works, including music, are protected under Law Number 28 of 2014 on Copyright. This study investigates the legal implications of using AI technology for song remixing within the framework of Indonesian law. Employing a normative legal research method, the study examines statutory regulations, case studies, and legal precedents to assess the legal status of AI-generated remixes. The research aims to determine whether remixing songs with AI is permissible under Indonesian law and to identify the conditions under which such practices may violate copyright protections. The results indicate that AI-generated song remixes are legally permissible, provided they comply with existing copyright laws. The study identifies key challenges, particularly in defining the originality of AI-generated works and determining authorship. The findings underscore the necessity for clear legal guidelines to address the growing influence of AI in the creative industry. In conclusion, while AI offers innovative opportunities in music production, it also necessitates careful legal consideration to protect the rights of original creators. This study contributes to the ongoing discussion on AI and intellectual property, highlighting the need for legal frameworks that balance technological progress with the protection of creators' rights.     Setiap aspek dalam kehidupan sehari-hari sudah mulai berubah pada tahap ketergantungan terhadap teknologi. Salah satu yang popular dengan munculnya kecerdasan buatan atau dikenal dengan artificial intellengence disingkat dengan AI. Perkembangan AI dimulai sejak abad ke-20 hingga sekarang yang dapat dimanfaatkan manusia dalam pembuatan remix lagu. Negara Indonesia merupakan negara hukum, maka pengaturan penciptaan lagu pada Undang-Undang Nomor 28 Tahun 2014. Penulisan artikel ini menggunakan metode penelitian normatif dengan mengkaji undang-undang dan melihat kondisi praktik di masyarakat dengan perspektif undang-undang. Penulisan ini guna menambah wawasan dan pengetahuan terkait dengan remix lagu yang marak dilakukan dengan menggunakan kecerdasan buatan atau AI. Remix lagu dengan menggunakan teknologi AI menjadi suatu hal yang sah untuk dilakukan selama hal ini sesuai dengan setiap ketentuan yang sudah diatur dalam peraturan perundang-undangan.Law and Justice160Law and Justice["Saffa Abdullah Abdad", "Safira Hafis", "Pradina", "Alisa Zahra Sakdiya", "Anissa Nur Zahrani", "Alisa Pradina", "Zahra Sakdiya", "Kata Kunci", "Kecerdasan Buatan"]2024-08-30T00:00:00 004b1458380cf4b222b7397bea6a29dd9c03059dThe industrial revolution 4.0, as an integral part of technological advancement, brings the concept of automation that penetrates to various sectors, including the world of education. Therefore, now education is in the midst of the dynamics of technological developments, especially in the era of the industrial revolution 4.0. One of the innovations that has emerged in this era is artificial intelligence (AI). One of the key roles of AI in education is its ability to make learning experiences more personalized and adaptable to individuals. With advanced data analysis, AI can gather information about students' progress and learning preferences. It is hoped that this article can provide a comprehensive view of how this technology is a key driver in achieving better learning goals in the Cybernetics 4.0 era. This study uses a qualitative approach with a focus on analyzing descriptive data in the form of written text. The researcher chooses a qualitative approach to analyze the literature related to the problem being studied. The research method applied in this study is a library research approach. Searches on Google Scholar are conducted using keywords that are relevant to the research variables. The journals used are selected based on the relevance of these keywords. After conducting a search, the researcher selected 20 journals and reference books which were then analyzed, summarized, and classified to produce ideas and ideas related to the research topic. several ways AI can be used to achieve goals in developing professional competencies and educators' creativity, including: personalization of learning, professional development of educators, automation of routine tasks, increased creativity and innovation, data analysis for quality improvement, and education and new skills. In the use of AI, there are impacts that need to be considered and competencies that must be possessed in the use of AI in the field of Education.International Conference on Aplied Social Sciences in Education00A comprehensive view of how artificial intelligence is a key driver in achieving better learning goals in the Cybernetics 4.0 era is provided and impacts that need to be considered and competencies that must be possessed are considered.International Conference on Aplied Social Sciences in Education["Astin Eka Tumarjio", "Sukadari"]2024-08-30T00:00:00 35295686f85e8d543d2cd514ff4928a1af1f1a47This study aimed to identify pivotal aspects of the contemporary legal framework governing artificial intelligence (AI), examine international practices, and propose enhancements to legislative and regulatory frameworks. The primary objectives encompassed establishing the theoretical underpinnings of "artificial intelligence" grounded in the scholarly doctrines of national and international scholars, reviewing national and international laws regulating AI, conducting a comparative legal analysis of prevailing international statutes and the experiences of diverse foreign nations, pinpointing legal challenges associated with AI deployment, and delineating legislative gaps. Additionally, the research undertook a legal analysis of potential ramifications stemming from AI development and current regulatory issues in Uzbekistan. Practical recommendations were formulated based on these findings to shape and refine the legislative landscape of the Republic of Uzbekistan concerning AI.Uzbek Journal of Law and Digital Policy160Practical recommendations were formulated to shape and refine the legislative landscape of the Republic of Uzbekistan concerning AI based on findings to shape and refine the legislative landscape of the Republic of Uzbekistan concerning AI.Uzbek Journal of Law and Digital Policy["Sardorbek Yusupov"]2024-08-30T00:00:00 123c91989b53dd8fac622b788d42249c12cb1cb2In the era of Industrial Revolution 4.0, technology, especially artificial intelligence (AI), has brought major changes to various aspects of life, including education. Social science education now faces challenges in maintaining moral and ethical values ​​amidst the rapid adoption of technology. This research aims to explore how values ​​and technology can go hand in hand in social science education in the AI ​​era. Using a qualitative approach and literature analysis, this research finds that AI has the potential to enrich social science learning through personalized learning and in-depth data analysis. However, the integration of these technologies also poses risks, such as algorithm bias and reduced human interaction. Therefore, it is important to maintain a balance between the application of technology and the cultivation of human values, with strategies that prioritize the development of critical and ethical thinking skills. It is hoped that this research can provide guidance for educators in integrating AI into the social science curriculum without ignoring essential humanist aspects. Keywords: Values, Technology, Balance, Social Science Education, Era of Artificial IntelligenceInternational Conference on Aplied Social Sciences in Education00Using a qualitative approach and literature analysis, this research finds that AI has the potential to enrich social science learning through personalized learning and in-depth data analysis.International Conference on Aplied Social Sciences in Education["Tita Nurhayati Nurhayati", "Lili Halimah"]2024-08-30T00:00:00 13bce7d3d7bb7fae40ac18e0368e70b7b22a8b61This study aims to explore the reality of employing artificial intelligence (AI) applications and their impact on the development of the Saudi sports system. This is achieved through identifying the strategic objectives of employing AI applications in the sports field, the methods of utilizing them in this area, the Kingdom's efforts to provide the appropriate technological environment for AI applications, and the gains achieved from employing these applications to develop the sports system. The researcher used the descriptive method (survey studies approach), in which the research community consisted of the employees of the Ministry of Sports in the Kingdom of Saudi Arabia, numbering 1,911 individuals. A random sample of 142 individuals was selected from this community. The researcher also used a questionnaire to collect data on the reality of employing AI applications and their implications for the development of the Saudi sports system. Among the most important results: All entities in the Kingdom are interested in employing AI applications in the sports field to develop the Saudi sports system, and there are multiple strategic objectives for this. Among the most important recommendations: include the need to strive to develop AI applications in the sports field to ensure the development of all areas of the Saudi sports system, and to expand the establishment of modern sports projects based on AI technology in a way that achieves the Kingdom's 2030 future vision in this field. KEYWORDS sports performance analysis, sports facilities, sports events, artificial intelligence in sports, sports field, artificial intelligence technologyScientific Journal of King Faisal University: Humanities and Management Sciences00The need to strive to develop AI applications in the sports field to ensure the development of all areas of the Saudi sports system, and to expand the establishment of modern sports projects based on AI technology in a way that achieves the Kingdom's 2030 future vision in this field.Scientific Journal of King Faisal University: Humanities and Management Sciences["Wael Khalifa"]2024-08-30T00:00:00 ce016c9b47b0347f0395dd02063802afa535e8c7In this scientific work, the author briefly analyzes some current problems of philosophical rethinking of the relationship between artificial intelligence and the human mind. To do this, the author briefly examines the essence, features, and advantages of introducing AI into practice, and focuses on the disadvantages of using such a tool in its various forms. Next, the author presents his own recommendations for implementing an effective philosophical rethinking of the relationship between artificial intelligence and the human mind. In conclusion, the author writes about the importance of carrying out such actions now, since there is a significant amount of work ahead, the relevance of which is becoming more and more obvious every day. The object of this scientific research is the current problems of philosophical rethinking of the relationship between artificial intelligence and the human mind. The purpose of this scientific research is a comprehensive, consistent analysis of current problems of philosophical rethinking of the relationship between artificial intelligence and the human mind. Methods of this scientific research: dialectical, comparative analysis, statistical, mathematical, generalization, concretization, systematization, deduction, other methods of theoretical and practical levels of scientific knowledge. The scientific novelty of this scientific research lies in the preparation of a comprehensive study, the formation of the author’s conclusions regarding current problems of philosophical rethinking of the relationship between artificial intelligence and the human mind. This scientific article will be useful to theorists, practitioners, students and teaching staff, as well as a wide range of readers interested in the problems and prospects for the development of ICT and AI in modern Russian and world practice, a philosophical rethinking of the relationship between man and AI in general.Sociopolitical sciences00The author briefly examines the essence, features, and advantages of introducing AI into practice, and focuses on the disadvantages of using such a tool in its various forms.Sociopolitical Sciences["A. Naumov"]2024-08-30T00:00:00 4da39c06f7f4185984088bd9c80c4c0ea2dfd206This study explores the transformative role of artificial intelligence (AI) in the criminal justice system, examining its applications, benefits, and potential challenges. AI technologies, including machine learning, predictive analytics, and natural language processing, are increasingly integrated into various facets of criminal justice, from law enforcement and legal processes to corrections and rehabilitation. Through a comprehensive literature review, this research analyzes how AI enhances the efficiency and effectiveness of criminal justice operations. Key findings reveal that AI can significantly improve crime prediction and prevention, aid in evidence analysis, streamline administrative tasks, and support decision-making processes. For instance, predictive policing models using AI can identify crime hotspots and allocate resources more effectively, while AI-driven tools can assist in analyzing large volumes of legal documents and evidence. However, the study also highlights critical concerns related to bias, fairness, transparency, and ethical implications. There is a growing need for frameworks that ensure AI applications in criminal justice are transparent, accountable, and aligned with ethical standards to prevent discrimination and protect civil liberties. The role of AI in criminal justice presents a dual- edged sword, offering significant advancements while posing substantial risks if not properly managed. This study provides a balanced perspective, offering insights for policymakers, practitioners, and researchers on leveraging AI for a more just and efficient criminal justice system. Future research directions are proposed to address the ethical challenges and to develop robust regulatory frameworks for AI in criminal justice.Global International Journal of Innovative Research250Key findings reveal that AI can significantly improve crime prediction and prevention, aid in evidence analysis, streamline administrative tasks, and support decision-making processes in the criminal justice system.Global International Journal of Innovative Research["Sahat Maruli Tua Situmeang", "Umar Mahdi", "Pingky Dezar Zulkarnain", "Husni Abdul Aziz", "Taufan Nugroho"]2024-08-30T00:00:00 de49afb50888580e6a372f46385ed5eb6f77413eThe article examines the impact of the development of artificial intelligence technology on art in general. The author describes the principles of image generation using neural networks, citing as examples only a few popular neural technology services, trying to explain in simple words the principle of operation of the image generation algorithm. Innovative artificial intelligence technologies are considered that allow you to independently create copies of artistic designs. Both the characteristic features and disadvantages of digital art, its development in the field of art and the introduction of artificial intelligence into creative practices are analyzed. The relevance of new neural creativity and the creation of artistic works using artificial intelligence are discussed. The technological skills of artificial intelligence in the person of the artist are considered, giving birth to a new era of art, creating their masterpieces differently.This scientific article required the study of both a large amount of literary material and electronic Internet resources, as a result of which the main types of graphic editors, their advantages and disadvantages were studied.Alatoo academic studies00The author describes the principles of image generation using neural networks, citing as examples only a few popular neural technology services, trying to explain in simple words the principle of operation of the image generation algorithm.Alatoo Academic Studies["Satkanbay Momunaliev", "Almaz Omorkulov"]2024-08-30T00:00:00 2ae1845555a6b9e668d89b5c309dc61b5fd055eaDigital technology and artificial intelligence (AI) development has opened up new educational opportunities, including social education studies. In this digital era, improving learning quality is becoming increasingly urgent. Social education studies, which aim to shape students' understanding of society, geography, and citizenship skills, face various challenges, ranging from a lack of student engagement to limited resources. AI technology offers innovative solutions through personalized learning, quick feedback, and a dynamic learning environment. AI-based learning supports lifelong education, providing easy access to online resources for all age groups. This research aims to analyze the potential of AI in social education studies by identifying relevant AI applications, evaluating the impact of AI implementation, and developing AI-based learning models. In addition, the study identifies challenges, formulates strategies to overcome them, and formulates policy recommendations for educators and technology developers. The method used is qualitative analysis through a literature review from journals, books, and related articles in the last ten years. The study results show that AI has great potential to improve the quality of social learning studies by providing programs tailored to student needs, analyzing learning history, and developing better learning materials. Examples of AI applications in education include ChatGPT for natural language processing, AI-based learning systems, virtual assistants, and educational data analysis. With wise application, AI can improve student engagement, material comprehension, and analytical skills and prepare them for a connected and adaptive future to changing times. Keywords: Artificial Intelligence, Social Sciences Education, Learning Innovation.International Conference on Aplied Social Sciences in Education00The study results show that AI has great potential to improve the quality of social education studies by providing programs tailored to student needs, analyzing learning history, and developing better learning materials.International Conference on Aplied Social Sciences in Education["Susan Susyanah", "Arnie Fajar"]2024-08-30T00:00:00 6a715948070de5f128b4c64aceadc921fd3bea94Artificial Intelligence (AI) expeditiously transmutes from a specialized area of study to a key component of contemporary technology, propelling breakthroughs in a wide range of industries. AI Unleashed, Pioneering Trends and Future Directions in Artificial Intelligence is a study examining the current developments influencing the field's progress and future course. This study explores essential fields, including autonomous systems, machine learning, and natural language processing, showcasing new developments and present uses. It also considers AI's ethical and societal ramifications, including issues with prejudice, privacy, and the necessity of robust governance systems. Exploring the confluence of artificial intelligence (AI) with other cutting-edge technologies, such as quantum computing and the Internet of Things (IoT), highlights the potential for unparalleled capabilities. With a perspective beyond the future, this overview highlights the significant obstacles and possibilities that will shape artificial intelligence (AI), from improving human-machine interaction to expanding general intelligence. This assessment offers insights into the cutting-edge trends propelling AI forward and the future paths that will mold the next wave of AI innovation through an extensive examination.Saudi Journal of Engineering and Technology10This assessment offers insights into the cutting-edge trends propelling AI forward and the future paths that will mold the next wave of AI innovation through an extensive examination.Saudi Journal of Engineering and Technology["Phool Fatima", "Samana Haider", "Muhammad Ahmad Ali", "Mujahid Abbas", "Ibrahim Akhtar", "Mujahid Rasool", "Hiba Maqbool", "Naima Khan"]2024-08-30T00:00:00 3b696a34334cd24a58ff97be2208034695655420ABSTRACT Artificial intelligence (AI) technology advances offer great potential to revolutionize education through personalized learning, technology-based curriculum development, and integration within educational institutions. This journal uses a case study approach to analyze the implementation of AI in various academic contexts, collecting data from related literature, institutional reports, and real case studies from Stanford University, Khan Academy, Japanese high schools, and digital inclusion programs in Finland and the United States. The research conducted qualitative analysis to understand the impact of AI on learning personalization, accessibility, and the role of teachers and evaluated the associated privacy policies and ethical challenges. Findings show that AI can improve student engagement and learning efficiency, but also faces challenges such as algorithm bias and data privacy. This research provides deep insights into the role of AI in education and recommends the implementation of strict privacy policies, improved technology access, teacher training, and periodic evaluation to effectively and ethically maximize the benefits of AI. Keywords: Artificial Intelligence, Education, Personalization of Learning, Accessibility, AI EthicsInternational Conference on Aplied Social Sciences in Education100Deep insights are provided into the role of AI in education and the implementation of strict privacy policies, improved technology access, teacher training, and periodic evaluation are recommended to effectively and ethically maximize the benefits of AI.International Conference on Aplied Social Sciences in Education["Khaerul Syobar"]2024-08-30T00:00:00 9f9c87082d5f677a55db3f5924206187b62b896aThe scientific article is aimed at analyzing issues related to the use of existing international instruments in the fight against cybercrime in the context of the use of artificial intelligence technologies. Within its framework, an assessment is made of the current position of international organizations and their potential impact on legislation and processes in the field of combating cybercrime in the near future. The focus is on analyzing the implications of artificial intelligence developments for the effective management of the criminal justice system, particularly in the context of combating cybercrime. The study also examines current trends and practices in the use of artificial intelligence systems and applications to carry out harmful and illegal activities, including phenomena such as deepfakes. The final part of the article proposes alternative approaches to developing effective measures to combat cybercrime carried out using artificial intelligence systems.Alatoo academic studies00An assessment is made of the current position of international organizations and their potential impact on legislation and processes in the field of combating cybercrime in the near future and proposes alternative approaches to developing effective measures to combat cybercrime carried out using artificial intelligence systems.Alatoo Academic Studies["Aisaulem Kyzylkhojaeva"]2024-08-30T00:00:00 0510be252f4bfdc13f1ef047925ac9636d631b58В современном мире огромное влияние на банковскую сферу оказывает динамичное развитие и повсеместное внедрение искусственного интеллекта. Благодаря использованию нейросетей и алгоритма машинного обучения, крупные финансовые компании или предприятия улучшают качество предоставляемых услуг. Также, финансовые организации оптимизируют операции и становятся более ориентированными и персонализированными для клиентов. В статье рассматривается тренды применения искусственного интеллекта и Big Data, представленные в исследованиях 2018 и 2023 года, выявляются сходства и различия направлений применения указанных технологий, трансформация ожиданий их полезности для того или иного направления деятельности. Актуализируются современные тренды применения искусственного интеллекта и Big Data. The transformation of trends the use of artificial intelligence and Big Data technologies in the banking sector is a huge impact on the banking sector in the modern world due to the dynamic development and widespread introduction of artificial intelligence. Through the use of neural networks and a machine learning algorithm, large financial companies or enterprises improve the quality of services provided. Also, financial institutions are optimizing operations and becoming more customer-oriented and personalized. The article examines the trends in the use of artificial intelligence and Big Data presented in the 2018 and 2023 studies, identifies similarities and differences in the areas of application of these technologies, and the transformation of expectations of their usefulness for a particular area of activity. Modern trends in the use of artificial intelligence and Big Data are being updated.Экономика и предпринимательство00Экономика и предпринимательство["\u0410.\u0410. \u0421\u043a\u0432\u043e\u0440\u0446\u043e\u0432\u0430", "\u0410.\u041e. \u0421\u043a\u0432\u043e\u0440\u0446\u043e\u0432", "\u041f.\u042e. \u041c\u0430\u043b\u044f\u043a\u0438\u043d\u0430", "\u0412.\u0418. \u041c\u044f\u0441\u043d\u0438\u043a\u043e\u0432\u0430"]2024-08-30T00:00:00 91e9d583e3b799df5e031fc4aa6132b6c63990c5In the rapidly evolving financial landscape, effectively managing credit risk is crucial for the stability and profitability of financial institutions. Traditional methods of credit risk management, which rely heavily on statistical models and expert judgment, are being transformed by the advent of artificial intelligence (AI) and machine learning (ML). These technologies are introducing unprecedented accuracy and efficiency into the risk assessment processes, making them indispensable tools for modern financial institutions​. A recent publication, "Machine Learning for Credit Risk Prediction: A Systematic Literature Review," authored by Jomark Pablo Noriega, Luis Antonio Rivera, and Jose Alfredo Herrera, offers a comprehensive analysis of the current state of ML applications in credit risk prediction. The review synthesizes findings from 52 relevant studies, providing a detailed overview of the most effective ML models, key performance metrics, and the challenges and opportunities associated with implementing these technologies​. The review identifies that machine learning models, particularly those in the boosted category, such as Gradient Boosting Machines (GBM) and XGBoost, have emerged as leading techniques in credit risk prediction due to their superior ability to handle large datasets and complex variable interactions​. These models have demonstrated remarkable performance when evaluated using metrics such as the Area Under Curve (AUC), Accuracy (ACC), Recall, Precision, and the F1 Score, making them highly effective tools for predicting credit risk​. However, deploying ML models has its challenges. The inherent "black box" nature of many ML algorithms poses significant interpretability issues, hindering trust and regulatory acceptance. Addressing these concerns requires the development of more transparent and explanatory AI systems. Additionally, selecting relevant features, managing multicollinearity, and dealing with imbalanced datasets remain critical areas needing further research and refinement. The review also highlights several future research directions to enhance the applicability of ML in credit risk management. These include improving model interpretability, enhancing data quality and diversity, and integrating alternative data sources such as social media and transaction data to create more comprehensive and fair credit scoring systems​. The findings from this review are particularly pertinent for financial institutions in Africa, where the rapid adoption of fintech solutions is driving significant advancements in financial inclusion and risk management. By leveraging ML technologies, these institutions can enhance their predictive capabilities and foster a more inclusive financial ecosystem.​International Journal of Science and Research Archive190The review identifies that machine learning models, particularly those in the boosted category, such as Gradient Boosting Machines (GBM) and XGBoost, have emerged as leading techniques in credit risk prediction due to their superior ability to handle large datasets and complex variable interactions.International Journal of Science and Research Archive["B. Abikoye", "Cedrick Agorbia-Atta", "Jomark Pablo Noriega", "Luis Antonio Rivera", "Jose Alfredo Herrera"]2024-08-30T00:00:00 f0bb2f100e3823e7c7800c1e12b62c463b532a52The technological war between the United States and China for leadership in the introduction of «artificial intelligence» (AI) into the economy as a form of non-classical war is an extremely relevant research topic that contains scientific novelty in the positioning of AI as a meaningful digital technology. The study of this topic requires, along with traditional methods of socioeconomic analysis (general scientific methods, systemic, structural-functional, institutional, comparative and cybernetic approaches) and new ones (case study method, content analysis, methodology of classical military-economic research, interdisciplinary holistic global analysis). The purpose of the study is the technological war between the United States of America and the People’s Republic of China for leadership in the introduction of «artificial intelligence» into the economy in the context of a non-classical war, for a leading position in the system of global governance of cyberspace. Three main tasks have been set: to evaluate the technological struggle between the United States and China for leadership in the introduction of «artificial intelligence» into the economy; clarify the institutional and economic aspects of the technological war between the USA and China; determine the current state of technological confrontation. The main result of the study is that an important feature of the post-industrial world in the conditions of global digitalization and technological globalization has been identified, which is that there can be neither a multipolar nor even a bipolar world. This is due to the essence of «artificial intelligence», which is the meaning-forming ICT, the digital technology that represents the main way to achieve competitiveness and security of the country’s economy. Within this context, there can only be one cybernetic superpower that will create the most competitive economy of its country based on digital technologies and, above all, «artificial intelligence».Theoretical Economics10An important feature of the post-industrial world in the conditions of global digitalization and technological globalization has been identified, which is that there can be neither a multipolar nor even a bipolar world.Theoretical economics["Tamara Yudina", "Platon Shmelev"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/f0bb2f100e3823e7c7800c1e12b62c463b532a52 315cc3eb718d1a27d2ada570d002c991276a234aIndonesia is one of the countries that agrees that AI (Artificial Intelligence) has a positive influence on human life.The presence of AI (Artificial Intelligence) as a form of technological progress simultaneously also raises various ethical challenges such as issues of privacy, justice, uneven social impacts and even ignoring the decline in moral values. Therefore, in this article we will explore how AI (Artificial Intelligence) can influence human dignity and freedom, then also how technology can influence human relationships with the universe and its creator which is developed through a regulatory framework that takes into account religious ethical values ​​in the Islamic view so that they have a guide to the use of AI (Artificial Intelligence). This research is normative legal research, the author uses three approaches which include: (a) philosophical approach, (b) statutory approach, (c) conceptual approach. . The primary data for this research are: (1) Primary legal materials consisting of: the 1945 Constitution of the Republic of Indonesia, ITE Law no. 11 of 2008 along with government regulation no. 71 of 2019 concerning PSTE as well as the Koran, Hadith and Ijtihad (2) Secondary legal materials consisting of: books, legal journals, expert opinions. The results of this research show two things, namely: (1) The blurring of privacy protection due to the application of AI systems in various aspects of life so that AI artificial intelligence accompanied by technological advances needs to be evaluated. (2) There must also be someone who ensures the security of the system, and establishes an appropriate responsibility framework.   Indonesia adalah salah satu negara yang percaya bahwa AI (artificial intelligence/kecerdasan buatan) dapat meningkatkan kehidupan manusia. Sebagai salah satu bentuk kemajuan teknologi, AI memunculkan dilema etika seperti privasi, keadilan, dampak sosial yang tidak merata, dan mengabaikan kemerosotan moral. Artikel ini akan mengkaji bagaimana AI dapat mempengaruhi martabat dan kebebasan manusia, serta bagaimana teknologi dapat mempengaruhi hubungan manusia dengan alam semesta dan Tuhan, yang dikembangkan melalui kerangka peraturan yang mempertimbangkan nilai-nilai etika agama Islam untuk memandu penggunaan AI. Penelitian ini merupakan penelitian hukum normatif, penulis menggunakan tiga pendekatan yang meliputi: (a) pendekatan filosofis, (b) pendekatan perundang-undangan, (c) pendekatan konseptual. Data dalam, penelitian ini terdiri: (1) Bahan hukum primer yang terdiri dari: Undang-Undang Dasar Negara Republik Indonesia Tahun 1945, Undang-Undang ITE No. 11 Tahun 2008 beserta Peraturan Pemerintah No. 71 Tahun 2019 tentang PSTE serta Al-Qur'an, Hadist dan Ijtihad (2) Bahan hukum sekunder yang terdiri dari: buku-buku, jurnal-jurnal hukum, pendapat para ahli. Hasil penelitian ini menunjukkan bahwa semakin kaburnya perlindungan privasi akibat penggunaan sistem AI di berbagai sektor kehidupan mengharuskan adanya tinjauan ulang terhadap AI, dan kemajuan teknologi.  Law and Justice220The blurring of privacy protection due to the application of AI systems in various aspects of life so that AI artificial intelligence accompanied by technological advances needs to be evaluated is shown.Law and Justice["Mufidah Mufidah", "H. Hartiwiningsih", "Isharyanto Isharyanto"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/315cc3eb718d1a27d2ada570d002c991276a234a 5f3644bc61f4e59982417dbd08828a3b5ff0e78fFinancial inclusion, the strategy for assuring availability of accessible, affordable and reliable financial services, is critical for reducing poverty and fostering economic development globally. Despite progress, billions of people worldwide remain excluded from formal financial systems. Artificial Intelligence (AI) presents a transformative opportunity to bridge these exclusion gaps by offering innovative solutions that enhance the availability, affordability and the usability of financial services. Based on the existing research and literature review, the study examines the potential advantages, challenges, and ethical considerations associated with AI-driven financial services. This research also offers an overview of AI applications in financial services, exploring how advanced data driven methodologies such as machine learning, natural language processing, and predictive analytics are transforming the field of financial inclusion. The findings underscore AI's role in broadening access to financial services, improving financial literacy, and fostering inclusive economic growth. This research contributes to both theoretical understanding and practical applications, offering insights for policymakers, financial institutions, and fintech innovators to understand and advance inclusive financial systems globally.World Journal of Advanced Research and Reviews00The findings underscore AI's role in broadening access to financial services, improving financial literacy, and fostering inclusive economic growth, as well as offering insights for policymakers, financial institutions, and fintech innovators to understand and advance inclusive financial systems globally.World Journal of Advanced Research and Reviews["Angela Olere Omogbeme", "Ada Ivy Phil-Ugochukwu", "Ikechukwu Josephat Nwabufo", "Jude Onyebuchi Nwabufo"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/5f3644bc61f4e59982417dbd08828a3b5ff0e78f b92ddbf9db00cd44e2a7ba7a0d7109a61d96145fThis research aims to find out the answer to whether the use of AI is a violation of ethics. Methods: This type of research is normative legal research. This research uses secondary data. Data analysis uses qualitative data analysis techniques. The conclusion-drawing technique used is deductive. Result: The research results show that Artificial Intelligence is only one instrument in bank activities. The use of AI in banking, the use of AI is considered a violation of Morals and Ethics if it violates the inner behavior which is a manifestation of good values and cannot find a justification for it.International journal of multidisciplinary research and analysis00The research results show that Artificial Intelligence is only one instrument in bank activities, and the use of AI is considered a violation of Morals and Ethics if it violates the inner behavior which is a manifestation of good values.INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS["T. Christiani", "Chryssantus Kastowo", "St. Mahendra Soni Indriyo"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/b92ddbf9db00cd44e2a7ba7a0d7109a61d96145f eca26fc99bba3813dc66915fc669ea86085e0709In February 2024, the European Union (EU) endorsed the AI Act, launched the new European Artificial Intelligence Office and initiated the program Generative AI for the EU (GenAI4EU, Fig. 1). This legislation governs how AI is developed, deployed and used in Europe. The obligations will incrementally become mandatory until 2030. While the Act was primarily designed to identify and protect humans from risks of manipulation and illicit use of AI, it will not be without impact on the biodiversity community. AI in our domain will mostly be ranked as no-to-low risk (Fig. 2). However, we will have to adhere to the transparency requirements and be particularly attentive when combining AI with citizen science initiatives affecting potentially the general public or for example, if projects concern illegal trafficking of species and border control. A further point of attention with our worldwide scope, is that non-European AI initiatives or tools allowed to be deployed or used in Europe will have to commit to follow both the AI Act and the GDPR (General Data Protection Regulation) and have a reference person or institution with a legal address in Europe. There will be a two-year transition period as of 2026 to adapt to this regulation. EU proposals require filling out an extensive questionnaire as an annex on ethical aspects, which already included the trustworthy usage of AI. This questionnaire is regularly updated, increasing the number of questions and details related to AI following the new EU AI act regulations. While the usage of AI is already evaluated, if present during the review process of proposals selected for funding by the EU, future projects can be audited at any time during their execution period, challenging compliance with both legal and ethical requirements of AI, risking being put on hold or even stopped if they do not comply. The Belgian Association of Research Managers and Administrators of European-funded projects (Be-Arma) provided an online training on compliance with AI & ethics in Horizon Europe. Current discussions at the EU level are on how they can remain competitive in AI, compared to other countries where fewer legal or ethical barriers exist. While judged essential, there are no doubts that such regulations slow down the development and implementation processes, as largely addressed during the conference, Research to Reality: Digital Solutions to European Challenges, held during the Belgian EU Presidency. The balance between open collaboration and free sharing of data and knowledge are challenged by concerns about so called strategic autonomy (European Commission et al. 2024) and competitiveness. These concepts were pushed even further in the EU conference: Research Infrastructures in a Changing Global, Environmental and Socio-economical Context, where the current critical geo-political context was linked to a need for even more strategic autonomy in Europe, where AI and other digital solutions were explicitly mentioned. As an introduction to this session, this talk will go to the best of our knowledge over these new EU AI Act requirements and how it may affect future AI-linked activities in our natural sciences domain, including how they may affect the funding of our information technology activities.Biodiversity Information Science and Standards10This talk will go to the best of the knowledge over these new EU AI Act requirements and how it may affect future AI-linked activities in the natural sciences domain, including how they may affect the funding of the information technology activities.Biodiversity Information Science and Standards["Patricia Mergen"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/eca26fc99bba3813dc66915fc669ea86085e0709 31289a6d300cc946f312f12d0a00f28bfd44b00aBioactive Materials2292The aim of this paper is to motivate researchers to explore organ function throughout the human life cycle, narrow the gap between in vitro microphysiological models and the real human body, accurately predict human-related responses to external stimuli (cues and drugs), accelerate the preclinical-to-clinical transformation, and ultimately enhance the health and well-being of patients.Bioactive Materials["Hui Wang", "Xiangyang Li", "Xiaoyan You", "Guoping Zhao"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/31289a6d300cc946f312f12d0a00f28bfd44b00a 8047b7fdd9c12c81179f2c66041f59152d77f44fGreen energy sources are most promising energy sources in the globe, as they are non-pollutant sources. Solar energy sources are among green energy sources that are free and abundant in nature, yet solar energy sources have some shortcoming such as faults on the solar PV modules, improper maintenance and some climatic and environmental impacts. Artificial intelligences are employed to solve most of these shortcoming like prediction of the solar irradiance of the specific sites, parameters estimation on the solar PV modules, fault detection on the solar PV modules surfaces and forecasting of solar PV power output. This paper presents extensive review on application of artificial intelligences to solve problems related to solar energy systems from 2009 to 2024. It was found that from most of the literatures, artificial intelligent algorithms were more accurate and efficient than the conventional methods and it has an ability to solve complex and non-linear data. This work will help scholars to explore the relationship between solar energy technologies and artificial intelligences.Advances in Artificial Intelligence Research00This paper presents extensive review on application of artificial intelligences to solve problems related to solar energy systems from 2009 to 2024 and it was found that from most of the literatures, artificial intelligent algorithms were more accurate and efficient than the conventional methods.Advances in Artificial Intelligence Research["Jamilu Ya'u Muhammad", "A. Abdulkarim", "Nafi\u2019u Muhammad Saleh", "I. Ehile", "Nuraini Sunusi Ma\u2019aji", "Audu Taofeek Olaniyi"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/8047b7fdd9c12c81179f2c66041f59152d77f44f 7c41af53a1cbcd607d45552f7361cfbfd879d209With the advancement of technology, education is transforming, thanks to the integration of Artificial Intelligence1 (AI). In this abstract, we explore the multifaceted role of AI in education, highlighting its potential to revolutionize traditional teaching and learning paradigms. AI technologies like machine learning and natural language processing, have made it possible to create personalized learning experiences. Adaptive learning systems analyze individual student performance data to tailor instructional content, pacing, and assessment, addressing diverse learning needs and optimizing academic outcomes. Virtual Tutors 2 powered by AI algorithms offers real-time feedback, making the learning environment more interactive and engaging. In addition to personalized instruction, AI helps automate administrative tasks, allowing educators to allocate more time to personalized student support and creative teaching approaches. AI-driven tools3assists in grading, attendance tracking, and data analysis, streamlining administrative processes and enhancing overall efficiency within educational institutions. Furthermore, AI facilitates the development of intelligent educational content, including interactive simulations, virtual reality experiences, and adaptive e-learning platforms. These technologies provide students with immersive and dynamic learning opportunities, promoting deeper understanding and critical thinking skills. AI-powered educational tools also support educators in designing and delivering content that aligns with modern pedagogical approaches. While AI offers numerous benefits, ethical considerations and challenges arise. Issues such as data privacy, algorithmic bias, and the digital divide necessitate careful navigation and the establishment of ethical guidelines to ensure equitable access and usage. Additionally, there is a need for comprehensive teacher training programs to equip educators with the skills to leverage AI effectively in their teaching practices. In conclusion, integrating AI into education can revolutionize teaching and learning experiences, fostering personalized, efficient, and innovative approaches. However, careful consideration of ethical implications and investment in teacher professional development is essential to harness the full potential of AI in education and ensure an inclusive and equitable learning environment for all studentsInternational Journal of Advanced Research in Science, Communication and Technology50The multifaceted role of AI in education is explored, highlighting its potential to revolutionize traditional teaching and learning paradigms and the need for comprehensive teacher training programs to equip educators with the skills to leverage AI effectively in their teaching practices.International Journal of Advanced Research in Science, Communication and Technology["Ritu Arya", "Ashish Verma"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/7c41af53a1cbcd607d45552f7361cfbfd879d209 761eaaa82c541711352b6653045741cec5008cd0As the public’s demand for tourism experience continues to improve, the demand for tourism products gradually develops in the direction of personalization, customization and differentiation. In order to mine the image of leisure tourism destinations, this paper conducts theme mining on the review texts of leisure tourism destinations by means of LDA model. An improved neural network language model CBOW and a Glove model are used to train the review word vectors of leisure tourism destination texts. Based on the deep fully connected neural network model, the perceptual features of tourist destinations are studied and analyzed. Taking Sanya tourist attractions as a research case, by analyzing the perception of tourist attractions, the cognitive image vocabulary of Sanya tourist attractions includes three major categories. Among them, tourism attractions, tourism infrastructure and services, tourism service atmosphere, and tourists’ travel behavior accounted for 53.1%, 22.3%, 5.1%, and 19.5% respectively. Then the emotional vocabulary in the perception of tourism destination image in Sanya City was categorized in terms of positive, neutral and negative, and from the neutral vocabulary, the word frequency number of words such as waiting (101), understanding (185), fun (61), and relaxation (56) was high. Regression analysis was used to explore the factors influencing the effect of tourists’ perception of destination image. Among them, tangibility (0.43), reliability (0.463), and responsiveness (0.434) were positively and significantly related to tourist satisfaction. Improving the image of leisure tourism destination can focus on these three aspects.Archives des Sciences00Archives des Sciences["Qian Li", "Songyu Jiang"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/761eaaa82c541711352b6653045741cec5008cd0 0d23a56f78c30a8f0ef9b4275200867753a52ee0International Urology and Nephrology50International urology and nephrology["Rayyan Nabi", "Tabeer Zahid", "H. Farooqi", "Zahid Nabi"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/0d23a56f78c30a8f0ef9b4275200867753a52ee0 7c75cd5f76b666da3107d3c0d52d59ce9dbcadd2Image of the modern pedagogue00IMAGE OF THE MODERN PEDAGOGUE["Olena Kolesnyk", "Anzhela Tereshchenko", "Anna Fastivets"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/7c75cd5f76b666da3107d3c0d52d59ce9dbcadd2 dd76f0045f95e02f5f5f885649085082bd69886fJournal of radiological science and technology30Journal of Radiological Science and Technology["Kyoungho Choi"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/dd76f0045f95e02f5f5f885649085082bd69886f 959ac6dd1a56084d2af70256b5242b8b8add7136Pipelines 202400Pipelines 2024["D. Blaine Hunt"]2024-08-30T00:00:00https://www.semanticscholar.org/paper/959ac6dd1a56084d2af70256b5242b8b8add7136 4e3cf7e4bf27ead052eba05720c133276a12286cInterdisciplinary advancements, such as generative artificial intelligence (AI) and digital supply chains, can significantly contribute to achieving sustainable development goals (SDGs), particularly within tourism. This paper illuminates how it works well, focusing on the underexplored area of Environmental, Social, and Governance (ESG) performance within small and medium‐sized tourism enterprises (SMEs) in China. Through a survey of 429 international SMEs, we apply the Resource‐Based View and Dynamic Capabilities Theory to investigate how generative AI, such as ChatGPT, in digital supply chains can enhance innovation, collaboration, and, ultimately, ESG performance. The empirical findings underscore the pivotal role of generative AI in augmenting ESG performance via bolstering innovation and collaboration within digital supply chains. Additionally, the moderating effect of customer involvement positively influences the relationship between the digital supply chain and ESG performance. By demonstrating these relations, our study contributes to theoretical and practical efforts toward sustainable tourism and the broader achievement of the SDGs.Sustainable Development865Sustainable Development["Shaofeng Wang", "Hao Zhang"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/4e3cf7e4bf27ead052eba05720c133276a12286c 4ce7ad87d298d804fa5b06fe06196a3d8f7fd6d2Abstract Various extrinsic and intrinsic factors such as drug exposures, antibiotic treatments, smoking, lifestyle, genetics, immune responses, and the gut microbiome characterize ulcerative colitis and Crohn's disease, collectively called inflammatory bowel disease (IBD). All these factors contribute to the complexity and heterogeneity of the disease etiology and pathogenesis leading to major challenges for the scientific community in improving management, medical treatments, genetic risk, and exposome impact. Understanding the interaction(s) among these factors and their effects on the immune system in IBD patients has prompted advances in multi‐omics research, the development of new tools as part of system biology, and more recently, artificial intelligence (AI) approaches. These innovative approaches, supported by the availability of big data and large volumes of digital medical datasets, hold promise in better understanding the natural histories, predictors of disease development, severity, complications and treatment outcomes in complex diseases, providing decision support to doctors, and promising to bring us closer to the realization of the “precision medicine” paradigm. This review aims to provide an overview of current IBD omics based on both individual (genomics, transcriptomics, proteomics, metagenomics) and multi‐omics levels, highlighting how AI can facilitate the integration of heterogeneous data to summarize our current understanding of the disease and to identify current gaps in knowledge to inform upcoming research in this field.United European Gastroenterology journal633An overview of current IBD omics based on both individual (genomics, transcriptomics, proteomics, metagenomics, metagenomics) and multi‐omics levels is provided, highlighting how AI can facilitate the integration of heterogeneous data to summarize the current understanding of the disease.United European Gastroenterology Journal["Anna Lucia Cannarozzi", "A. Latiano", "L. Massimino", "F. Bossa", "Francesco Giuliani", "Matteo Riva", "F. Ungaro", "Maria Guerra", "Anna Laura Di Brina", "G. Biscaglia", "F. Tavano", "S. Carparelli", "G. Fiorino", "S. Danese", "F. Perri", "O. Palmieri"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/4ce7ad87d298d804fa5b06fe06196a3d8f7fd6d2 e412d54260d69a014d65ad173135f9da32d599a4The way we teach and learn could be completely transformed by artificial intelligence (AI), which could make the process more efficient, personalized, and interesting. Artificial intelligence (AI) in education is the application of AI technologies, like natural language processing and machine learning, to improve the educational process. Through the use of algorithms, teachers are able to customize learning for each student by analyzing data, finding trends, and making predictions. Artificial intelligence (AI) and its potential impact on education have gained widespread attention because of ChatGPT's impressive performance on standardized academic assessments. For the development and implementation of AI-driven technologies in schools, colleges, and universities to be sustainable, a thorough knowledge of their effects on the current educational system is needed. The application of AI in education has a lot of potential advantages. One of the biggest benefits of AI in education is personalized learning, which allows students to learn at their own pace and in a fashion that best fits their learning preferences. This can improve student results. Chatbots, automated grading and evaluation, and intelligent tutoring systems can boost productivity, free up teachers' time, and deliver more precise and consistent feedback. Some of the challenges that need to be resolved are potential bias, cost, lack of confidence, and privacy and security concerns. AI can improve data analysis, empowering teachers to make facts-based decisions. There are some impacts of artificial intelligence in educational system that are covered in this review. These are the applications of artificial intelligence in automated assessment, intelligent tutoring systems, personalized learning, and collaborative teacher-student learning.International Journal of Scientific Research in Science and Technology192International Journal of Scientific Research in Science and Technology["Dipanwita Bit", "Souvik Biswas", "Mrinmoy Nag"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/e412d54260d69a014d65ad173135f9da32d599a4 746644737cb253abadcd78a869e3f1e9ee38f7b1Abstract Aims We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA). Methods A total of 2533 patients with CA (1834 with light chain amyloidosis (AL), 530 with wild‐type transthyretin amyloid protein (ATTRwt) and 169 with hereditary transthyretin amyloid (ATTRv)] were included. An amyloid AI ECG (A2E) score was calculated for each patient reflecting the likelihood of CA. CA stage was calculated using the European modification of the Mayo 2004 criteria for AL and Mayo stage for transthyretin amyloid (ATTR). Risk of death was modelled using Cox proportional hazards, and Kaplan–Meier was used to estimate survival. Results Median age of the cohort was 67 [inter‐quartile ratio (IQR) 59, 74], and 71.6% were male. The median overall survival for the cohort was 35.6 months [95% confidence interval (CI) 32.3, 39.5]. For AL, ATTRwt and ATTRv, respectively, median survival was 22.9 (95% CI 19.2, 28.2), 47.2 (95% CI 43.4, 52.3) and 61.4 (95% CI 48.7, 75.9) months. On univariate analysis, an increasing A2E score was associated with more than a two‐fold risk of all‐cause death. On multivariable analysis, the A2E score retained its importance with a risk ratio of 2.0 (95% CI 1.58, 2.55) in the AL group and 2.7 (95% CI 1.81, 4.24) in the ATTR group. Conclusions Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.ESC Heart Failure241Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.ESC Heart Failure["Jennifer M. Amadio", "Martha Grogan", "Eli Muchtar", "Francisco Lopez-Jimenez", "Z. Attia", "Omar F. AbouEzzeddine", "Grace Lin", "Surendra Dasari", "S. Kapa", "D. Borgeson", "P. Friedman", "M. Gertz", "Dennis H. Murphree", "A. Dispenzieri"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/746644737cb253abadcd78a869e3f1e9ee38f7b1 fde116a721f74fa294723c7876c57e7cdc1d5534Educational institutions are increasingly incorporating new technologies into their classrooms, such as artificial intelligence (AI), enabling more innovative teaching methods and learning experiences. Unlike traditional teaching methods, where lecturers adapt their lectures to the needs of the average student, AI-powered educational platforms are more dynamic and productive, as they can be adapted to the preferences, learning styles and pace of each student, enabling personalized learning. The aim of this study is to gather information that will help educators, legislators, and AI developers optimize AI’s role in education for increased student achievement by examining students’ attitudes toward the implementation of AI in personalized learning. The findings of this study may have an immense effect on how AI is used in educational settings in the future, because they may provide better understanding that would enable students to receive more individualized instruction and autonomy while also increasing pedagogical opportunities and reducing an excessive amount of administrative work for educators. 219 students of Megatrend University in Belgrade participated in the research (all three study levels), to whom the questionnaire was sent by e-mail. The results indicate that students believe that: a) If the application of AI makes learning personalized, the greater the possibility for students to identify their abilities and creativity; b) If lecturers apply the most effective teaching methods using AI, they can significantly automate the monitoring of student progress; c) If innovative and interesting learning opportunities are applied in classes, the greater the interactivity of students in the teaching process; d) AI can examine past student performance to identify areas of difficulty and provide tailored assistance in those areas.International Journal of Cognitive Research in Science, Engineering and Education221Examining students’ attitudes toward the implementation of AI in personalized learning may provide better understanding that would enable students to receive more individualized instruction and autonomy while also increasing pedagogical opportunities and reducing an excessive amount of administrative work for educators.International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)["Radoslav Baltezarevi\u0107", "Ivana Baltezarevi\u0107"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/fde116a721f74fa294723c7876c57e7cdc1d5534 a3eef8dda8a6777785bd5eac8d522754a8a8daf0This study investigates the impact of Artificial Intelligence (AI) and automation on the United States workforce, aiming to contribute to the creation of a more resilient workforce. The problem statement identifies the potential negative consequences of job displacement and socioeconomic disparities resulting from the integration of AI and automation technologies. The objective is to assess the extent of this impact, analyse existing initiatives and policies, and propose recommendations for fostering workforce resilience. A qualitative approach using manual content analysis is employed to review relevant literature. Findings reveal the multifaceted nature of AI's impact, the diverse landscape of upskilling initiatives, and the importance of collaborative efforts. Implications include the need for adaptive policies, targeted programs, and cohesive collaborations to ensure a resilient and inclusive workforce amidst technological advancements. This study contributes to knowledge by providing nuanced insights into the challenges and strategies associated with AI and automation's impact on the US workforce.Journal of Scientific Papers: Social development and Security381Findings reveal the multifaceted nature of AI's impact, the diverse landscape of upskilling initiatives, and the importance of collaborative efforts and the need for adaptive policies, targeted programs, and cohesive collaborations to ensure a resilient and inclusive workforce amidst technological advancements.Journal of Scientific Papers "Social Development and Security"["Ifeoluwa Oladele", "Adeyinka Orelaja", "Adeniyi Habeeb Hameed"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/a3eef8dda8a6777785bd5eac8d522754a8a8daf0 bc548d0291e1486449b3f7e1f9a885b7636e41e3In order to achieve a fair, efficient, rapid and predictable exercise of state power, it is necessary to prepare for the emergence of a “government for the people, using artificial intelligence, for the people,” which has jumped from a “government for the people, by the people, for the people” by accepting advanced science and technology and reflecting the changed social image, so it is necessary to review the active use of artificial intelligence within the framework of the rule of law administration, check administrative law, and improve legislation. Analysis of the use of artificial intelligence in administrative law does not stop at deriving the current law's interpretation or legislative tasks for the use of artificial intelligence under the Administrative Procedure Act or limited to administrative disposition, but also checks the public law interpretation and response to the use of artificial intelligence in all “administrations” subject to administrative law. Criticism or problem consciousness of current artificial intelligence mainly deals with information opacity, process inexplicability, non-decipherability, information contamination, and intentional distortion of information in the deep learning stage and the use of generative artificial intelligence. The general legislative attitude of the EU and others has attempted to establish a legislative safety net to prevent the emergence of market economy monsters using artificial intelligence, but it should be taken into account that policy judgment and normative consideration are needed to prepare for the emergence of information monsters at the same level as public and private areas, and discussions on risks are focused on deep learning-based artificial intelligence. The EU-type behavioral regulation model regulates general behavior in the use of artificial intelligence regardless of the public and private areas, but private economic entities have no democratic legitimacy in the organizational composition and pursue the maximum of private interests by nature. It is overlooked that the administrative entity, which is the public authority, has democratic legitimacy in the composition, and that the possibility of stakeholder participation in administrative processes and specific administrative procedures is guaranteed, as well as checks by the principle of separation of powers. If the act regulation law in the use of artificial intelligence by private economic entities is brought into the public domain, it leads to overregulation in the public domain, but underregulation for private economic entities occurs. Data is the resource of the future and the oil of the future in the age of artificial intelligence. The data must be managed publicly and the public's free and stable use must be guaranteed if the public property of the data is recognized and pseudonymized information is released from the personal personal rights and ownership of the data. A department in charge of creating, collecting, and managing big data at the national level will be essential. Hangeul data, with the originality of Hangeul, needs to approach the protection of Hangeul resources and the development of Hangul-specific artificial intelligence in order to protect national interests. It is necessary to actively utilize artificial intelligence to increase the objectivity and rationality of evaluation in evaluating business feasibility or predicting future demand in various private capital attraction projects and public development projects, and it is necessary to improve the legal system that detects the misappropriation and overcharges of public funds, imposes punitive fines, and the use of artificial intelligence in the process of selecting subjects and verifying them through post-mortem review in the public benefit area.National Public Law Review00National Public Law Review[]2024-08-31T00:00:00https://www.semanticscholar.org/paper/bc548d0291e1486449b3f7e1f9a885b7636e41e3 3d851705a18aa704fa2a1cc5991fa0d3b063495dThis study examined the ethical dilemma that appears when robots gain citizenship following the development of the artificial intelligence era. First, we examined the definition of a citizen in ancient Greece and modern soci-ety, as well as scholars' definitions of citizens, and examined the dilemma of the actions of creators and AI citizens when AI was recognized as a citizen. In addition, ethical dilemmas related to immoral behavior of artificial intelligence were examined from the perspective of civil and human rights and rights. In addition, ethical dilemmas related to immoral behavior of arti-ficial intelligence were examined in relation to legal aspects and citizenship. When summarizing these dilemmas, if artificial intelligence claims freedom, equality, and rights by possessing functions such as thinking, emotion, empathy, and moral behavior, which are unique concepts of humans, then humans must give artificial intelligence a level similar to that of humans, and even more similar to humans. The fundamental question lies in wheth-er equal levels of rights, emotions, etc. can be recognized. This suggests that research and ethical standards should be prepared for this.The Korean Society for Artificial Intelligence Ethics00If artificial intelligence claims freedom, equality, and rights by possessing functions such as thinking, emotion, empathy, and moral behavior, then humans must give artificial intelligence a level similar to that of humans, and even more similar to humans.The Korean Society for Artificial Intelligence Ethics[]2024-08-31T00:00:00https://www.semanticscholar.org/paper/3d851705a18aa704fa2a1cc5991fa0d3b063495d a4253828ceb15666c5091f3c02dd31b47a77dddaWith the development of AI technology, ethical, legal, and social problems are emerging. In particular, examples of abuse of Generative AI include fake news, deepfakes, automatic spam and phishing, and copyright in-fringement, and ethical regulations are needed. Globally, these problems are responded to through AI ethics guidelines and AI Ethics Committee, among which the European Union is implementing safety and accountabil-ity and ethical evaluation through AI Act. In addition, AI ethics standardiza-tion is necessary to strengthen global competitiveness, secure social trust, and minimize negative effects. To this end, the domestic AI Ethics Forum promotes the ethical use of AI technology through discussion of ethical is-sues, guideline development, education, and international cooperation ac-tivities. In this paper, we examine the overall status of artificial intelligence ethics regulation trends and standardization, which can be expected to have effects such as reliability, safety assurance, innovation promotion, and increased social acceptance through standardization activities.The Korean Society for Artificial Intelligence Ethics00The overall status of artificial intelligence ethics regulation trends and standardization is examined, which can be expected to have effects such as reliability, safety assurance, innovation promotion, and increased social acceptance through standardization activities.The Korean Society for Artificial Intelligence Ethics[]2024-08-31T00:00:00https://www.semanticscholar.org/paper/a4253828ceb15666c5091f3c02dd31b47a77ddda 194c86f91be8263cfd80fcbc79c71a98e54f163aAbstract: The corporate has been changing fast and for the good in start-up culture because of artificial intelligence. The research examines the impact of AI on start-ups and highlights its potential to revolutionise innovation in business. In this paper, our research seeks to determine where marketing-only AI has been applied as data and case studies were not readily available for derivation of compound measures. Results indicate that AI use leads to better decision-making, more Bicer processing, and substantial cost savings. Implementation costs are high, it requires specialised expertise and occasionally ethical considerations. The study provides insights for future work on the benefits and challenges of AI incorporation in start-ups. This has important implications for start-ups, suggesting that while AI offers many opportunities to infuse their offerings with innovation and smart capabilities based on real-time analytics or deep learning paradigms, successful adoption and ultimate stability will demand solid navigation of the associated constraintsInternational Journal for Research in Applied Science and Engineering Technology00Results indicate that AI use leads to better decision-making, more Bicer processing, and substantial cost savings, suggesting that while AI offers many opportunities to infuse their offerings with innovation and smart capabilities based on real-time analytics or deep learning paradigms, successful adoption and ultimate stability will demand solid navigation of the associated constraints.International Journal for Research in Applied Science and Engineering Technology["Piyush Kumar", "Abhishek Singh Chauhan"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/194c86f91be8263cfd80fcbc79c71a98e54f163a 66bca3e1122c6c721df1d53d930e99b4d36206efAs a strategic technology, artificial intelligence (AI) contributes to the transformation of the economy and symbolises a new stage not only in the history of digital technologies but also in the global development of modern civilisation. It also plays an important role in improving the quality and accessibility of education. The use of AI allows us to move from standard methods of teaching and education management to individual and effective strategies. This article analyses the use of AI in the field of education management and highlights innovative approaches introduced by AI. The potential disadvantages and ethical issues arising from the integration of these technologies into the field of education are considered. Prospects and directions of AI use in education are outlined. Conclusions are drawn about the importance of AI for current and future education.Információs Társadalom270The use of AI allows us to move from standard methods of teaching and education management to individual and effective strategies and conclude about the importance of AI for current and future education.Információs Társadalom["Olga Cholyshkina", "A. Onyshchenko", "Volodymyr Kudin", "M. Gladka", "Serhii Oleksiienko"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/66bca3e1122c6c721df1d53d930e99b4d36206ef 33f7aa11383069620e5609bd7946638c71739ee4This research paper explores the realm of poetry and investigates the emerging influence of artificial intelligence (AI) on generating poems. By exploring the crossroads of AI and poetry, we aim to understand the potential benefits, challenges, and implications of employing AI technologies in the field of literary arts. This paper presents an analysis of the current state of AI-generated poetry, examines its impact on the traditional poetic practices and discusses the broader implications for the future of creative expressionInternational Journal For Multidisciplinary Research00An analysis of the current state of AI-generated poetry is presented, its impact on the traditional poetic practices is examined and the broader implications for the future of creative expression are discussed.International Journal For Multidisciplinary Research["Manpreet Singh"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/33f7aa11383069620e5609bd7946638c71739ee4 4f8bbf73c22756bb8eba8373f765f17ba097c84fThis article explores the possibility of using artificial intelligence in the field of labour relations. Modern technologies provide new opportunities, but at the same time, they give rise to a number of complex issues, solving new approaches to the realization of the right to work and proper social security by able-bodied citizens, which is important today, when Ukraine is defending its sovereignty and independence from military aggression by the Russian Federation. The use of machine learning algorithms and systems can lead to significant improvements related to the professional training of labour resources, management of production processes, labour protection and other aspects of labour relations.We came to the conclusion that the modernization of labour law involves expanding the circle of participants in labour relations and revising the meaning of the term "employee". Thus, the presence or absence of access to technology will create new forms of inequality in the interaction between "employee-to-employee" and "worker-robot (AI)".A proactive approach is proposed to mitigate the consequences of possible threats to the use of artificial intelligence in labour relations through an in-depth study of all possible threats arising in connection with the use of modern technologies. A proposal is made to take as a basis the international experience of using AI in the social structure of the state and adapt it to the life of the state, which, in turn, will contribute to the promotion of important theses on the quality and accessibility of data at the country level, and in cities in particular, promoting participation in data exchange schemes at the level of the private and public sectors.Financial and credit activity problems of theory and practice120It is concluded that the modernization of labour law involves expanding the circle of participants in labour relations and revising the meaning of the term "employee", and the presence or absence of access to technology will create new forms of inequality in the interaction between "employee-to-employee" and "worker-robot (AI)".Financial and credit activity problems of theory and practice["Leonid Ostapenko", "Viktoriia Pasternak", "M. Kropyvnytskyi", "Leontii Chystokletov", "O. Khytra"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/4f8bbf73c22756bb8eba8373f765f17ba097c84f 3f3cc8c2920b60972f82e53e8fa21f93cd32c395The concept of artificial intelligence is very popular in both science and culture today. Similarly, the concept of emergence has become quite popular during the last decades in the sciences. For example, it is commonplace in the case of machines to speak of an overall blueprint and several different material components; thus, we can regard the blueprint as a kind of comprehensive emergent additive. However, is it true then that the machine, due to this plus component, is not material? Practically nobody wants to acknowledge that. Still, in practice, there are no machines without added blueprints. In my paper, based on Samuel Alexander’s original concept of emergence, I will investigate these problems and contradictions, which stem from the materialist interpretation of the concept, and I will present a coherent emergentist concept of machines, according to which machines are clearly a unique kind between simple material things and living beings.Információs Társadalom60This paper will present a coherent emergentist concept of machines, according to which machines are clearly a unique kind between simple material things and living beings.Információs Társadalom["Daniel Paksi"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/3f3cc8c2920b60972f82e53e8fa21f93cd32c395 5f531baca6c9f6e0800639c863495ba4a113bc3dA rapid expansion of the computer technology industry, particularly in the field of artificial intelligence, has ignited a global concern that warrants our immediate action. As nurses, our professional values frameworks compel us to protect public health and address national and global health issues. When industry activities adversely affect the social wellbeing of civil society and social institutions, it is important to evaluate them against their industry’s ‘social license to operate, which is a measure of public trust, credibility, and the legitimacy of their industrial and corporate citizenship status. The central question is, do computer technology companies continue to have a social license to operate in civil society? Nurses are encouraged to evaluate the computer technology industry’s recent ‘generative artificial intelligence’ chatbot activities against its tacit undertaking to be good corporate citizens in return for social acceptance of their operations and behaviour. An evidence-based overview of chatbot impacts on societies, environmental sustainability and human rights provide a basis for evaluation. Basic computer technology terminology and relevant concepts are explained. This article is a direct call to action for clinical nurses and those involved in research, education, management, and policy. We have a duty to critically assess the claims made by chatbot technology vendors in both practice and social contexts. If these vendors integrate chatbot technologies with existing machine learning used in nursing and healthcare technologies it could result in detrimental effects beyond user control. By influencing decisions on technology adoption, we can ensure the implementation of safeguards, protect patient safety and social well-being, and uphold the integrity of nursing values. A closing discussion of impacts of computer industry trust deficits on healthcare and research reflects the author’s concerns and conclusions about the central question. Readers may draw other conclusions and perhaps use the issues and evidence presented here to stimulate further investigations.Pacific Rim international journal of nursing research500Nurses are encouraged to evaluate the computer technology industry’s recent ‘generative artificial intelligence’ chatbot activities against its tacit undertaking to be good corporate citizens in return for social acceptance of their operations and behaviour.Pacific Rim International Journal of Nursing Research["Tracey McDonald"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/5f531baca6c9f6e0800639c863495ba4a113bc3d 591bb66d5ceda6122cd4e6399ad4286f2edf8070Background: This exploratory study investigates the perspectives of medical professionals on the integration of artificial intelligence (AI) in healthcare. It focuses on key areas such as diagnostics, drug development, patient care, and medical information management. Methods: The study utilizes a qualitative survey approach, engaging a diverse group of doctors to understand their views on AI's benefits and limitations. The methodology includes analyzing responses and categorizing them into themes of optimism, skepticism, and the perceived need for a human touch in patient care. Results: Results indicate a cautiously optimistic view of AI among medical professionals, acknowledging its potential to enhance healthcare efficiency and accuracy. However, the results also highlight concerns about over-reliance on technology, data privacy, and potential biases. Conclusions: The study concludes that while AI presents significant opportunities for healthcare innovation, its integration demands a balanced approach. This balance is essential for leveraging AI's capabilities while synergizing with the irreplaceable human aspects of medical practice, ensuring patient-centered care, and maintaining the core values of the medical profession.International Journal of Research in Medical Sciences00It is concluded that while AI presents significant opportunities for healthcare innovation, its integration demands a balanced approach, essential for leveraging AI's capabilities while synergizing with the irreplaceable human aspects of medical practice, ensuring patient-centered care, and maintaining the core values of the medical profession.International Journal of Research in Medical Sciences["M. U. Rani", "S. S. Vemparala"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/591bb66d5ceda6122cd4e6399ad4286f2edf8070 ca9d10832c542c86c24835249980b2e8c15e40a2Canadian Institute for Health Information (CIHI) is looking to modernize and adopt new ways of working. This incudes the use of new technology, including the application of Artificial Intelligence (AI). To begin in a purposeful manner, the organization developed an AI strategy which was informed through feedback from key stakeholders and partners, from its staff and from a review of international research. The research informed several ways AI could add value to CIHI's internal operations and to the external role CIHI could play in advancing responsible AI adoption in health systems across Canada. This article describes the strategy development process and the areas of focus within the strategy.Healthcare Management Forum10The strategy development process and the areas of focus within the strategy were described, which informed several ways AI could add value to CIHI's internal operations and to the external role CIHI could play in advancing responsible AI adoption in health systems across Canada.Healthcare management forum["Shez Daya", "Babita Gupta", "Nasir Kenea"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/ca9d10832c542c86c24835249980b2e8c15e40a2 f3b5dfc48906f9deec0567af5828ad278a04358eArtificial Intelligence through its ability to learn from LLM has affected our lives in the most unusual ways. It has made complex calculations easy by training on large data sets. It is able to predict better, detect earlier. It has helped in automation, thereby reducing dependance on human beings in some cases. Healthcare is a sector which can benefit massively from the advancement of technology powered by Artificial Intelligence if proper large number of datasets are fed into the systems. Work is being continuously done to equip ourselves in a better way using the power of AI. There are multiple ways in which AI powered technology can assist the healthcare professionals. Among the many companies constantly engaging in research and development to help solve problems of the society is Google. According to a report by Statista there are 250million people using AI driven tools across the world. Google alone experiences 8.5 billion search queries on its search engine every day. The power of technology innovated by Google has the power to reach those who need it most and a wider market. The paper explores the different Case study of Google’s innovation and research work in healthcare. It also tries to comprehend the scope and challenges of using AI in the healthcare sector.International Journal For Multidisciplinary Research150The paper explores the different Case study of Google’s innovation and research work in healthcare and tries to comprehend the scope and challenges of using AI in the healthcare sector.International Journal For Multidisciplinary Research["Amartya Saha"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/f3b5dfc48906f9deec0567af5828ad278a04358e 6c864a943da1ecb0b1730dd4729e89d236e7c27eThe whirlwind advent of ChatGPT in 2022 has marked a new age of artificial intelligence (AI), the general name for the technology that combines computer technology, big data bases and machines. This AI technology quickly makes its presence felt with hundreds of popular programs and chatbots such as the portrait-making AI diffusion art and the thesis-writing ChatGPT. This paper investigates the conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, to figure out how this technology and its growth have been introduced to ordinary people via mass media. Employing the Conceptual Metaphor Theory proposed by Lakoff & Johnson (1980), this study found three AI-related conceptual metaphors, namely, AI IS A HUMAN BEING, AI IS AN ANIMAL and AI IS A NATURAL FORCE, which are realized by more than 100 linguistic expressions across 33 news articles. Also, this research found five conceptual metaphors related to AI development, namely AI DEVELOPMENT IS WAR, AI DEVELOPMENT IS A RACE, AI DEVELOPMENT IS A CONVERSATION, AI DEVELOPMENT IS A DANCE, AI DEVELOPMENT IS A GAME and these metaphors are manifested by approximately 40 linguistic expressions. This paper discusses the way that these metaphors could influence the way people and technology companies think about AI and AI development.  VNU Journal of Foreign Studies100The conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, are investigated to figure out how this technology and its growth have been introduced to ordinary people via mass media.VNU Journal of Foreign Studies["Tuan Minh Nguyen"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/6c864a943da1ecb0b1730dd4729e89d236e7c27e e6e9a12f41ca59319993edfc75ee87f9328d78dcThe prevalence of scams proliferating via online platforms has been identified as an emerging societal problem resulting in large-scale financial losses for victims. Online scams typically rely for their success on the generation of fake but convincing user profiles to conceal the identities of the scammers from the people being tricked into parting with their money. The increasing sophistication of generative artificial intelligence (GenAI), which can produce outputs indistinguishable from real content, thus carries the risk of being adopted by fraudsters to assist in the enactment of online scams. This article considers the risks of the potential uptake and use of GenAI applications by online scammers operating in the sharing economy, with a focus on homestay-marketplace platforms and, in particular, the largest such platform, Airbnb.The African journal of information and communication740The risks of the potential uptake and use of GenAI applications by online scammers operating in the sharing economy, with a focus on homestay-marketplace platforms and, in particular, the largest such platform, Airbnb, are considered.The African Journal of Information and Communication (AJIC)["Julie Reid"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/e6e9a12f41ca59319993edfc75ee87f9328d78dc fda4578dec854ba480bb7096de7c31e3279ea842Recent strides in cardiology have introduced a transformative era by integrating artificial intelligence (AI) into coronary artery disease (CAD) management. This comprehensive review comprehensively explores AI applications in CAD, including diagnosis, screening, risk stratification, treatment assistance, and prognosis. Acknowledging AI's potential to revolutionize CAD care, the review emphasizes understanding current integration and limitations for clinicians and researchers. The manuscript explores the current and potential applications of AI in managing cardiovascular disorders underscoring the developments in cardiovascular care for CAD and atrial fibrillation (AF). The manuscript has been drafted based on scale for the assessment of narrative review articles (SANRA) guidelines to search, compile, contemplate, and extract data. Investigators independently searched PubMed, and Google Scholar following the protocol mentioned in the literature. This manuscript illuminates the evolving landscape of AI in CAD and AF management. While showcasing AI's promise in diagnostic accuracy and treatment strategies, the review emphasizes a cautious yet optimistic approach. The comparison with conventional methods reveals AI's efficacy, signalling a paradigm shift in cardiovascular care. Acknowledging limitations, researchers and clinicians are urged to navigate the integration of AI with discernment. The synthesis of optimism and caution guides the harnessing of AI's transformative potential in advancing cardiovascular healthcare.International Journal of Research in Medical Sciences00This comprehensive review comprehensively explores AI applications in CAD, including diagnosis, screening, risk stratification, treatment assistance, and prognosis, and reveals AI's efficacy, signalling a paradigm shift in cardiovascular care.International Journal of Research in Medical Sciences["Saksham Sharma", "Simran Bhatia", "Aishwar Dixit", "Akshaya J. Kumar", "Harshal Singla"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/fda4578dec854ba480bb7096de7c31e3279ea842 2bdbd965d959dc2d2bf49bdbc3457555209dfc83Artificial intelligence (AI) is rapidly developing in the field of healthcare. It is expected to play a key role in oncology, in the areas of diagnosis, treatment, but also screening and cancer research. AI has evolved from a specialized resource to a readily accessible tool for practitioners and cancer researchers. AI can thus facilitate the screening, early and accurate diagnosis of cancers. Thus, the computer analysis of medical images in particular, radiological (radiomics), or anatomo-pathological (pathomics), has shown many very interesting results for the prediction of prognosis and response of patients with cancer. Thanks to deep learning algorithms, AI can identify subtle patterns on medical images, such as X-rays, MRIs or CT scans, that are likely to escape the human eye. In terms of treatment, AI can contribute to the development of personalized treatment plans. By analyzing large datasets, which can predict tumor response to certain treatments. AI also plays a key role in patient monitoring. AI-based systems can continuously monitor patients' health status, detecting any recurrences or complications. In the field of cancer research, AI-based tools can boost research productivity in daily workflows, but they can also extract hidden information from existing data, enabling new scientific discoveries. Researchers working in traditional biological sciences can use AI-based tools through commercially available software, while those who are more inclined to computer science can develop their own AI-based software pipelines. In this article, we will provide an update on the contribution of AI in the field of oncology, the practical applications already validated and the perspectives of this tool.Trends in General Medicine00An update on the contribution of AI in the field of oncology, the practical applications already validated and the perspectives of this tool are provided.Trends in General Medicine["Tabouri Sarah", "Zemmour Amel", "Zeroual Sarra", "Larbaoui Blaha", "Benchouk Jesia Asma", "Merbouh Mohammed Amine", "Merad Yassine"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/2bdbd965d959dc2d2bf49bdbc3457555209dfc83 9939f47b6315eaa506915b0c59838482e888e076The role of artificial intelligence (AI) applied to medical imaging in developing and strengthening personalized medicine is described as a continuous process of improvement, a set of opportunities, and a professional challenge of enormous significance. This paper outlines the main processes in which AI is involved regarding imaging, including data preparation, image harmonization, automatic segmentation of organs and lesions, labeling, extraction of radiomic features, and the development of predictive clinical models. It will also address aspects related to the integration of these solutions into clinical practice to enhance accuracy and efficiency in the care process, diagnosis, and treatment, making it more personalized, efficient, and precise. Projects like PRIMAGE and CHAIMELEON highlight the transformative potential of AI and the fundamental role of interdisciplinary collaboration in realizing this potential, based on ongoing multiprofesional collaboration to address the ethical, regulatory, technical, and clinical challenges that accompany these advancements.ANALES RANM00The main processes in which AI is involved regarding imaging, including data preparation, image harmonization, automatic segmentation of organs and lesions, labeling, extraction of radiomic features, and the development of predictive clinical models are outlined.ANALES RANM["L. Mart\u00ed-Bonmat\u00ed"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/9939f47b6315eaa506915b0c59838482e888e076 d4d38c804dad9968d7601fdb7e5622cddf3374f5Purpose of the article: The purpose of this article is to provide a comprehensive analysis of the role of artificial intelligence (AI) in the banking sector, focusing on its applications, challenges, and implications. By synthesizing existing research and empirical studies, the article aims to inform researchers about the transformative potential and inherent challenges of AI-driven innovation in banking. Methodology/methods: Using a systematic review approach, the relevant literature on AI integration in banking was identified from electronic databases and leading corporate research departments, ensuring a synthesis of scholarly and industry perspectives. Scientific aim: With limited academic research on AI in banking, this study aims to shed light on its applications, challenges, and implications. Findings: The integration of AI in the banking sector has significantly transformed various operational areas, including customer interactions, risk management, compliance, and operational efficiency. AI applications, such as chatbots and smart virtual assistants, have enhanced customer service by offering personalized, 24/7 support, and have demonstrated significant cost and revenue benefits. AI-driven credit scoring and fraud detection have improved risk assessment and mitigation, enabling more precise and informed decision-making. However, AI adoption faces challenges such as high computational costs, data quality issues, the "curse of recursion" where models trained on AI-generated data degrade, and the need to balance trust in AI outputs with their reliability. Furthermore, regulatory considerations play a crucial role in AI integration. While the European Union's AI Act aims to ensure the ethical use of AI in finance, it also presents challenges related to compliance and potential over-regulation. Conclusions: In conclusion, the integration of AI in the banking sector has revolutionized customer service, risk management, compliance, and operational efficiency. However, the adoption of AI also raises concerns about data privacy, security, and the need for regulatory frameworks to ensure ethical use. As AI continues to evolve, it will be crucial for banks to balance technological innovation with responsible practices to maximize benefits and mitigate risks.Trends economics and management10The integration of AI in the banking sector has significantly transformed various operational areas, including customer interactions, risk management, compliance, and operational efficiency, and the adoption of AI also raises concerns about data privacy, security, and the need for regulatory frameworks to ensure ethical use.Trends Economics and Management["Monika Mucskov\u00e1"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/d4d38c804dad9968d7601fdb7e5622cddf3374f5 808e802f12ba7f487ee4c8dac36f9377ad646234In recent years, the application of artificial intelligence (AI) in the field of content generation has become more and more widespread, and the concept of artificial intelligence generated content (AIGC) has gradually entered the public consciousness. Can pieces of AIGC be considered works? Can AI be the author of AIGC? This paper seeks to provide a comprehensive and systematic analysis of the literature of Chinese scholars so as to sort out the different perspectives of Chinese scholars on the relevant issues. This paper uses the China National Knowledge Infrastructure (CNKI) as the data source database and uses Citespace to carry out text-mining work in the retrieved literature. This literature presents twelve main doctrines on the copyrightability of AIGC and three doctrines on its attribution.Információs Társadalom350This paper uses the China National Knowledge Infrastructure (CNKI) as the data source database and uses Citespace to carry out text-mining work in the retrieved literature to provide a comprehensive and systematic analysis of the literature of Chinese scholars.Információs Társadalom["Yao Lu"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/808e802f12ba7f487ee4c8dac36f9377ad646234 66828cbbe12d510acfb5de9735cc636aa667d693The review article considers key applications of artificial intelligence (AI) in cardiology. The review includes subsections devoted to weak and strong AI used in clinical practice and cardiology health provision. The article describes the application options for AI in the analysis of electrocardiography, echocardiography, sonography, computed tomography, magnetic resonance imaging, and positron emission tomography of the heart data. The article briefly describes the aspects of using machine learning and artificial intelligence to process ambulance calls from patients with cardiac complaints, and considers AI applications in preventive cardiology. The review considers the potential of AI in the analysis of data arrays obtained during tonometry, pulse wave velocity measurement, and in biochemical studies. The paper also formulates the principles of strong AI (large language models) in cardiology health provision, identifies the main problems and difficulties in implementing the latest technology, and provides a conceptual scheme for implementing AI technology in a cardiology center. This paper highlights the key limitations of the large language model technology, such as the lack of standard algorithms for collecting and reviewing data, lack of understanding of the context, the inability of models to form expert conclusions, and the emergence of many problematic ethical characteristics when using large language models.Russian Journal of Cardiology340This paper highlights the key limitations of the large language model technology, such as the lack of standard algorithms for collecting and reviewing data, lack of understanding of the context, the inability of models to form expert conclusions, and the emergence of many problematic ethical characteristics when using large language models.Russian Journal of Cardiology["I. A. Soloviev I.A.", "O. Kurochkina"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/66828cbbe12d510acfb5de9735cc636aa667d693 d99ed8b7e71dc243d5a575fd3f0c9234145389c9This research investigates the role of artificial intelligence (AI) in fashion design, focusing on its potential to foster innovative and aesthetically striking designs. The study explores the level of interest among emerging fashion designers, including students from design and arts colleges, as well as individuals keen on distinctive and avant-garde fashion. The research aims to achieve its objectives using descriptive, analytical, and quasi-experimental methodologies. Findings reveal that while there is significant interest among fashion design students and professionals in AI-based design, challenges arise from the limited availability of learning resources, hindering their ability to fully grasp and apply this technology. The study concludes that there is an urgent need to integrate AI into fashion design education, as it can significantly benefit the industry and its practitioners. Recommendations include expanding specialised research connecting AI with fashion design, enhancing university curricula with crucial AI concepts, and focusing on technological advancements and AI applications in fashion design and forecasting.Advances in Social Sciences Research Journal00There is an urgent need to integrate AI into fashion design education, as it can significantly benefit the industry and its practitioners, the study concludes.Advances in Social Sciences Research Journal["Amal Abdullah Albishri", "Ruba Majed Almisbahi"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/d99ed8b7e71dc243d5a575fd3f0c9234145389c9 28976b85fdc1613d6452c8550f014b790d6d1ef0Our current education systems continue to foster and enable inequality and injustice. Education is designed for a different age and continues to be an instrument of human capital and social engineering. Reforming education to focus on equity, inclusion, and social justice is a growing imperative in complex, fast-changing societies. Using the principles of design justice, refocusing assessment and leveraging artificial intelligence to enable learning for all is explored with a focus on differentiated assessment.REVISTA PARAGUAYA DE EDUCACIÓN A DISTANCIA (REPED)310Using the principles of design justice, refocusing assessment and leveraging artificial intelligence to enable learning for all is explored with a focus on differentiated assessment.REVISTA PARAGUAYA DE EDUCACIÓN A DISTANCIA (REPED)["Stephen Murgatroyd"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/28976b85fdc1613d6452c8550f014b790d6d1ef0 84142122c6b2c385fa54010a2d8d43de77ca8e51The aim of the work is to present an innovative solution based on artificial intelligence for examining the condition of railway tracks in real time. The system, based on fuzzy logic and metaheuristics such as Fuzzy Logic, Neural Networks and Bee Behavior Optimization, combines hardware and software to provide reliable data on the technical characteristics of the railway. Installed in rail vehicles, hardware collects this data, while software uses artificial intelligence to improve operational reliability and safety. The aforementioned technology is not only useful for infrastructure diagnostics, but also for urban railways such as trams and metros, ensuring a high level of passenger safety. The introduction of artificial intelligence in the railway sector is a key step towards modernisation, improving efficiency, resource optimization and safety. Although still in its infancy, artificial intelligence already shows great potential in transforming the railway sector towards a more efficient, reliable and sustainable future.International Journal of Cognitive Research in Science, Engineering and Education00International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)["Dobrivoje Dubljanin", "Filip Markovi\u0107", "Gabriela Dimi\u0107", "Dragan Vu\u010dkovi\u0107", "Martina Petkovi\u0107", "Lazar Mosurovi\u0107"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/84142122c6b2c385fa54010a2d8d43de77ca8e51 c368e42e8def940d8a2f23504e3c28f5769efb9dBackground: Integrating artificial intelligence (AI) into adaptive trial designs represents a transformative approach in clinical research, promising enhanced efficiency and accuracy in trial outcomes. This study aims to systematically review the current landscape of AI applications in adaptive clinical trial designs. Methods: A comprehensive search was conducted across multiple databases, resulting in 6177 records initially identified. After removing duplicates and ineligible records, 1476 studies were screened. Following rigorous screening and eligibility assessment, 45 studies were included in the final review. Inclusion criteria focused on peer-reviewed articles, systematic reviews, and clinical trials discussing the role of AI in adaptive trial designs. In contrast, exclusion criteria eliminated non-relevant and low-quality studies. Results: The selected studies demonstrate that AI significantly improves adaptive trial designs through advanced data analytics, predictive modelling, and real-time decision-making. AIs integration facilitates dynamic randomization, optimised dosing strategies, and efficient patient recruitment, thereby enhancing the overall effectiveness of clinical trials. Conclusion: AI integration in adaptive trial designs offers substantial benefits regarding trial efficiency, precision, and patient outcomes. Despite existing challenges such as data quality, ethical considerations, and regulatory requirements, the findings underscore the potential for AI to revolutionise clinical trials. Future research should address these challenges to harness AIs capabilities in adaptive trial designs fully.International Journal of Advanced Research00The selected studies demonstrate that AI significantly improves adaptive trial designs through advanced data analytics, predictive modelling, and real-time decision-making, thereby enhancing the overall effectiveness of clinical trials.International Journal of Advanced Research["Viswakanth Makutam", "Sai Yashashwini Achanti", "Marjan Doostan"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/c368e42e8def940d8a2f23504e3c28f5769efb9d e36981e202d92d5808a0a3b4120b0dfc25039e63The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has led to approximately 704 million confirmed cases of coronavirus disease (COVID-19) and resulted in approximately 7.01 million fatalities worldwide. The present review examines the applications of Artificial Intelligence (AI) models concerning the COVID-19 pandemic and its correlation with air pollutants. The objective of this review is to identify, assess, and synthesize pertinent findings regarding the relationship between air pollution and COVID-19. Initially, a comprehensive set of 549 articles was screened, resulting in the selection of 38 articles from the study region through two stringent rounds of inclusion and exclusion. Given the limited availability of published literature originating from countries in Asia and Oceania, the authors endeavoured to focus specifically on studies from this geographical area. The analysis primarily centred on contextual keywords, methodologies employed, algorithms utilized, and, notably, the specific air pollutants examined, such as particulate matter (PM) including PM2.5 and PM10, as well as associated meteorological parameters. Our findings indicate that a significant portion of the research is concentrated in China, recognized as the initial epicentre of the COVID-19 outbreak. Additionally, most researchers from Asia and Oceania primarily concentrated on PM2.5, followed by studies on meteorological factors and PM10. This review delineates five prospective research avenues for future exploration. Consequently, this article enriches the existing literature by providing researchers with insights into current studies, thereby enhancing the accessibility of available evidence for decision-makers and proposing a potential research agenda for forthcoming investigations.Knowledge-Based Engineering and Sciences120The present review examines the applications of Artificial Intelligence (AI) models concerning the COVID-19 pandemic and its correlation with air pollutants and delineates five prospective research avenues for future exploration.Knowledge-Based Engineering and Sciences["Ekta Sharma", "R. Deo", "Z. Yaseen", "Mukesh Khare", "Sachin Dhawan"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/e36981e202d92d5808a0a3b4120b0dfc25039e63 45e04db1cfa8467d0520d3296191e9f62280eda2Digital health is quickly superseding conventional, brick-and-mortar hospitals as the standard in the post-COVID era. AI is going to revolutionize the healthcare industry in every way. This includes healthcare administration, clinical decision-making, patient monitoring and intervention, diagnosis and treatment, image combining to assist in diagnostics, and healthcare research to steer strategic intent. By integrating data from different systems, AI-powered health care administration can automate repetitive tasks, improve operational efficiency, and ultimately realize optimal collective functional resources. During the era of computerization, artificial intelligence systems made significant strides in supporting diagnosis and disease classification, which was especially helpful for the doctor who was harassed by patients. The current state of technological development allows for the monitoring of the patient and the communication of massive volumes of digital data in a variety of formats. The advantages of an ideal health system, an improved patient experience, and evidence-based decisions are shared among all parties involved when AI is integrated into one's workflow. Artificial intelligence paves the way for better disease diagnosis, more effective treatment trials, and new drug discoveries. Artificial intelligence generates cutting-edge, top-notch service in highly collaborative business-to-business health care ecosystems. Both the human perspective and the naturalistic requirements placed on AI systems highlight the importance of the issue of trust as a key obstacle to their broad acceptance. Collaborative work yields better results, like when a team uses AI to build healthcare solutions that benefit patients and lead to groundbreaking discoveries.International Journal For Multidisciplinary Research140By integrating data from different systems, AI-powered health care administration can automate repetitive tasks, improve operational efficiency, and ultimately realize optimal collective functional resources by integrating data from different systems.International Journal For Multidisciplinary Research["Nikesh Kurhade", "Nirmala Joshi"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/45e04db1cfa8467d0520d3296191e9f62280eda2 5a1a20a5d7a200de0dcde38cac4183ca0d31a38aAbstract: The use of artificial intelligence (AI) in the fintech industry is changing the way financial services are managed and managed. This study explores the various applications of artificial intelligence in the fintech industry and its impact on improving the efficiency, accuracy and security of financial systems. Fintech companies widely use artificial intelligence tools such as machine learning, natural language processing, and deep learning to analyse data, perform operations, and improve customer experience. One of the main applications of artificial intelligence in the fintech industry is fraud detection and prevention. Machine learning algorithms can now analyse large amounts of data to identify unusual or suspicious activity, helping reduce financial fraud. Another important area of expertise of Fintech is credit intermediation and risk assessment. Artificial Intelligence-driven algorithms can evaluate the integrity of a borrower's credit by analysing various factors, improving decision-making, and reducing risk. Loan default. In addition, artificial intelligence is used in personal finance management tools to offer suggestions to users based on their consumption patterns and financial goals.International Journal for Research in Applied Science and Engineering Technology00This study explores the various applications of artificial intelligence in the fintech industry and its impact on improving the efficiency, accuracy and security of financial systems.International Journal for Research in Applied Science and Engineering Technology["Jashwanth G M"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/5a1a20a5d7a200de0dcde38cac4183ca0d31a38a cc79eee299944acd6ab01054d49c3259e56145fbThe digital transformation in education spurred by the Fourth Industrial Revolution has advanced teaching and learning methods through automation and digital technologies. However, Education 5.0 emphasizes the balance between technology and humanistic aspects, aiming to enhance personalized learning through human-machine collaboration. This article examines the application of artificial intelligence (AI) in creating a framework for adaptive learning environments that enable personalized learning. This study utilizes literature analysis to design and evaluate a personalized AI-based framework. We integrate AI techniques such as machine learning and predictive analytics with educational big data to develop models capable of tailoring content, methods, and learning pace according to individual student needs. Findings show that the use of AI enables the development of dynamic and real-time student learning profiles that can adjust instructional materials and teaching methods based on each student's learning style and preferences. The proposed framework also enhances student engagement and learning outcomes through personalized feedback and automated assessments. Although this framework offers significant potential to improve the learning process, challenges such as data privacy, algorithmic bias, and the need for adequate technological infrastructure must be addressed. This discussion includes mitigation strategies for these challenges, including ethical approaches to data use and transparency in AI development. The implementation of AI in Education 5.0 opens opportunities for more personalized, adaptive, and effective learning that can meet the unique needs of each student. By leveraging AI to personalize the learning experience, the education system can evolve to better support individual potential, enhance academic outcomes, and strengthen the 21st-century skills needed for the future.Proceeding of International Conference on Education and Sharia120Findings show that the use of AI enables the development of dynamic and real-time student learning profiles that can adjust instructional materials and teaching methods based on each student's learning style and preferences, which enhances student engagement and learning outcomes.Proceeding of International Conference on Education and Sharia["Indra Ari Irvan", "Saipul Annur", "Informasi Artikel", "Kecerdasan Buatan", "Lingkungan Belajar", "AI Adaptif", "Dalam Pendidikan", "Abstrak"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/cc79eee299944acd6ab01054d49c3259e56145fb 7fbd27a613a2ae02065b24ab4017d8ca629608bbIn order to develop courses related to artificial intelligence and mathematics, this study provides plans for the composition and operation of courses related to artificial intelligence and mathematics in college general mathematics by analyzing the mathematics curriculum, artificial intelligence and mathematics-related research, and selecting learning elements and contents. The content needed to deal with the principles of artificial intelligence in mathematics is mainly related to calculus, probability and statistics, and linear algebra. Such mathematical learning content is one of the functions of artificial intelligence. Since it is necessary for analysis and expression, neural networks, clustering, unsupervised learning, and optimization, these contents must be included in artificial intelligence and mathematics-related courses in college general mathematics. In addition, in courses related to artificial intelligence and mathematics, it is difficult for students to learn various topics such as linear algebra and multi-variable calculus, which are essential as basic mathematics for understanding machine learning, all in one semester. Thus, in this study, I propose that we divide the course into two subjects, and , so that college students can select and take the courses according to their abilities and needs. Lastly, while theory-oriented classes are essential for understanding the principles of artificial intelligence in artificial intelligence and mathematics-related courses, applied learning is also crucial. This includes learning the basics of Python, a widely used programming language, for data analysis, data organization, and preprocessing. Additionally, applying these skills in real-life projects involving data analysis, artificial intelligence, and deep learning is necessary for comprehensive education.The Korean Association of General Education90This study provides plans for the composition and operation of courses related to artificial intelligence and mathematics in college general mathematics by analyzing the mathematics curriculum, artificial intelligence and mathematics-related research, and selecting learning elements and contents.The Korean Association of General Education["Jae Gil Choi", "Sang Kil Shim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/7fbd27a613a2ae02065b24ab4017d8ca629608bb 31cc4493d9b47a7045793d790f32960cd20600c9[Abstract: This paper undertakes a comprehensive evaluation of the implications arising from the deployment of AI in CODR proceedings, particularly focusing on its potential augmentation of arbitrators, mediators, conciliators and the regulatory landscape governing such integration. Drawing upon a doctrinal approach, the study critically analyses the capabilities of AI systems vis-à-vis human arbitrators, mediators and conciliators, emphasizing the necessity for human intervention and supervision in AI-driven CODR processes. Additionally, the paper addresses the evolving regulatory frameworks surrounding AI, highlighting their consequential impact on the practice of CODR. As jurisdictions worldwide engage in regulatory initiatives concerning AI, the paper suggests that appropriate regulations should be conducive to the objectives of CODR, emphasizing shared values such as trustworthiness. By exploring the intersection of AI technology and CODR, this research will contribute to the ongoing discourse on enhancing efficiency and productivity in legal services while offering recommendations for the effective utilization of AI in CODR settings. ]Shimla Law Review194A comprehensive evaluation of the implications arising from the deployment of AI in CODR proceedings, particularly focusing on its potential augmentation of arbitrators, mediators, conciliators and the regulatory landscape governing such integration is undertaken.Shimla Law Review["Vibhuti Jaswal", "Shiekhar Panwar"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/31cc4493d9b47a7045793d790f32960cd20600c9 b6cc482e7d598a97683fcbf5b5db76a60ce9653bThis paper provides an overview of AI and ChatGPT, covering their definitions, the evolution of AI, ethical concerns surrounding their interaction with humans, and their extensive applications in everyday life. The advantages and disadvantages of AI and ChatGPT are examined. Furthermore, the essay delves into the technical aspects by explaining the algorithm and presenting a graph illustrating the functioning of ChatGPT. A historical background of ChatGPT is also included. The paper specifically focuses on the impact of AI in four industries: education, finance, healthcare, and computing. It explores their applications within these sectors and makes predictions regarding the future implications of AI on human life and work. In medical industry, AI can help human a lot in new drug research, medical imaging, medical services innovation and patient health management. In education realm, the application of AI can help people on intelligence process support, intelligence teacher assistant intelligence education and management, and intelligence environment building. Also, the application of AI in finance realm has many benefits in bank, insurance, capital markets business and financial support industry. What is more, the application of it in computer industry helps human on network security, systematic reviews and data analysis.Advances in Engineering Innovation01The essay delves into the technical aspects by explaining the algorithm and presenting a graph illustrating the functioning of ChatGPT, and makes predictions regarding the future implications of AI on human life and work.Advances in Engineering Innovation["Jinxuan Wang"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/b6cc482e7d598a97683fcbf5b5db76a60ce9653b 2554a6a1fb6e983b6855df75212ae01487e1a281This study investigates the impact of AI-assisted language learning (AIAL) strategies on cognitive load and learning outcomes in the context of language acquisition. Specifically, the study explores three distinct AIAL strategies: personalized feedback and adaptive learning, interactive exercises with speech recognition, and intelligent tutoring with data-driven insights. The research employs a pretest-posttest random assignment experimental design, utilizing three experimental groups and a control group, with a total of 484 EFL students specializing in teaching English as a foreign language participating in the study. Data collection involves pre- and post-tests, questionnaires, and interviews to assess the influence of AIAL strategies on cognitive load and learning outcomes. Cognitive load is measured using the Cognitive Load Scale, while pretest-posttest assessments evaluate the efficacy of AIAL interventions across various language skills. These results contribute to the existing body of AIAL research by offering empirical evidence for the effectiveness of specific strategies in optimizing language learning experiences. The implications of this study extend to educators, researchers, and developers in the field of AIAL, emphasizing the potential of AIAL to enhance language acquisition processes and inform instructional design practices.Journal of educational computing research293This study explores three distinct AIAL strategies: personalized feedback and adaptive learning, interactive exercises with speech recognition, and intelligent tutoring with data-driven insights to assess the influence of AIAL strategies on cognitive load and learning outcomes.Journal of Educational Computing Research["Lijuan Feng"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/2554a6a1fb6e983b6855df75212ae01487e1a281 6065843e09dff756c0ab2449399b4949aa19082fPhilosophy &amp; Technology192Philosophy &amp; Technology["Karl de Fine Licht"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/6065843e09dff756c0ab2449399b4949aa19082f 76455f6e0bac119bdaa1e9ebbf77899eed134c44Journal of Digital Contents Society01Journal of Digital Contents Society["Hyo-Rim Son", "Chang-Keun Lee"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/76455f6e0bac119bdaa1e9ebbf77899eed134c44 e3d807be9f19b2c6c226f64cc4573e6fb76d112dTelematics and informatics351A regression model is used to isolate the impact of specific factors such as gender, career stage, type of workplace, and perceived barriers to using AI technology on the frequency of use of generative AI among researchers.Telematics Informatics["P. Dorta-Gonz\u00e1lez", "Alexis J. L\u00f3pez-Puig", "M. Dorta-Gonz\u00e1lez", "Sara M. Gonz\u00e1lez-Betancor"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/e3d807be9f19b2c6c226f64cc4573e6fb76d112d 0dc4ba2d98df78940d17196d56dd9dddfc63d239Supply Chain Forum: an International Journal441Supply Chain Forum: An International Journal["Stoyan Georgiev", "Yiannis Polychronakis", "Stylianos Sapountzis", "Nikostratos Polychronakis"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/0dc4ba2d98df78940d17196d56dd9dddfc63d239 cc2085f8f9d5484268a1f1f4ddf03624810f49bd媒体行业作为信息传播的重要载体，同样面临着技术变革带来的新挑战和新机遇。在此背景下，播采编一体化作为顺应媒体融合大势、提高新闻生产效率的重要趋势，引起了业界的广泛关注和探索实践。播采编一体化是指在数字化技术支持下，打破新闻采集、编辑、发布等环节的部门壁垒，实现一体化运作、协同生产的新闻生产模式。这种模式能够有效提高新闻生产的时效性和灵活性，满足分众化、个性化的信息需求。近年来，人工智能技术的引入为播采编一体化注入了新的动力，使其迈向更高的智能化水平。本文从人工智能对新闻业的影响出发，深入分析了人工智能技术在播采编一体化进程中的应用现状和前景，探讨了智能化环境下播采编一体化面临的挑战，并提出了推动播采编一体化发展的策略建议，以期为媒体行业的转型升级提供参考。Yixin Publisher00Yixin Publisher[]2024-08-31T00:00:00https://www.semanticscholar.org/paper/cc2085f8f9d5484268a1f1f4ddf03624810f49bd ac28e81f471a6ed5bd506b626ea67752deb207e4Бизнес. Образование. Право00Бизнес. Образование. Право["\u0417.\u0410. \u041a\u043e\u043d\u043e\u043d\u043e\u0432\u0430", "\u0421.\u041e. \u0410\u043b\u0442\u0443\u0445\u043e\u0432\u0430"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/ac28e81f471a6ed5bd506b626ea67752deb207e4 fa7c9a1b3d6df76f1651df53036b62c75eccd679Journal of Korea Society of Color Studies00Journal of Korea Society of Color Studies["JuYeon Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/fa7c9a1b3d6df76f1651df53036b62c75eccd679 a90e56c2a685397128e32c00210ef88127ed1369Korean Journal of Journalism &amp; Communication Studies00Korean Journal of Journalism &amp; Communication Studies["Mikyung Chang", "Young Min"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/a90e56c2a685397128e32c00210ef88127ed1369 9f245e03eb4fbb7c1834dc63e87bb574420d2c2dJournal of The Korean Association of Artificial Intelligence Education00Journal of The Korean Association of Artificial Intelligence Education["Chul Hyun Lee"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/9f245e03eb4fbb7c1834dc63e87bb574420d2c2d 9dc4cec4a63edc71787151460a3735a56e98180eJournal of Sport and Dance Science00Journal of Sport and Dance Science["Kyung Mi Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/9dc4cec4a63edc71787151460a3735a56e98180e 805cadb2bd497e01b6cb2469c10d1e89752e7a19Philosophy &amp; Technology80Philosophy &amp; Technology["Luciano Floridi"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/805cadb2bd497e01b6cb2469c10d1e89752e7a19 6599dadb9c61838ee1a38d63b73cbf82e4d42b34Han Yang Law Review00Han Yang Law Review["SooChan Ahn"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/6599dadb9c61838ee1a38d63b73cbf82e4d42b34 86a99f2d10fb15123e4350d53a6befff2a70a9c4Journal of The Korean Association of Artificial Intelligence Education00Journal of The Korean Association of Artificial Intelligence Education["Nahael Lee"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/86a99f2d10fb15123e4350d53a6befff2a70a9c4 447594365b13b9bab7b0fcd773f271df8fb2c4cfKorea Financial Law Association00Korea Financial Law Association["Sung Nam Lee"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/447594365b13b9bab7b0fcd773f271df8fb2c4cf 4efec3824812414843600968e3bf1f65fd6d7ca1Journal of the Korean Association of Information Education00Journal of The Korean Association of Information Education["Bomsol Kim", "Yoonju Kim", "Jungah Kim", "Jonghoon Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/4efec3824812414843600968e3bf1f65fd6d7ca1 92dd620f9cff6ba4b6848632e1a5a3550fe7d156AI and Machine Learning are changing fast, with new trends popping up that could change how businesses and society work. This paper looks at these new trends starting with Explainable AI. This trend aims to make AI systems easier to understand, which helps people trust and use them more. In healthcare, AI is getting better at things like diagnosing illnesses creating personalized treatments, and finding new drugs. These improvements could lead to better care for patients. The AI industry is also thinking more about ethics trying to make sure AI decisions are fair and don't have biases. The paper also discussion about how AI is helping with big world problems like climate change. It's being used to watch the environment and manage energy better. Quantum computing might be a big deal for AI in the future making computers much more powerful. The paper also looks at how AI is being mixed with edge computing, which lets data be processed for Internet of Things devices. It covers new developments in how AI understands and uses language how AI is being used in creative work, and new ways of teaching AI like transfer learning and meta-learning. These new methods are pushing what AI can do. This paper gives a full look at these trends showing where AI and Machine Learning might go next, and what good things and challenges might come up.International Journal For Multidisciplinary Research200New developments in how AI understands and uses language how AI is being used in creative work, and new ways of teaching AI like transfer learning and meta-learning are covered.International Journal For Multidisciplinary Research["Mr. J. Sivakumar Swamy", "Mrs. M. Usha Sandhya", "Dr. Kamma Ramanjaneylu"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/92dd620f9cff6ba4b6848632e1a5a3550fe7d156 742417aec00b1c58e307c259a329b66eaf777693Journal of AI Humanities00Journal of AI Humanities["Seon Ah Jung", "Hyeong joo Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/742417aec00b1c58e307c259a329b66eaf777693 cce1dc1a82f36f486318e6d2a26254789fb294f4Journal of AI Humanities00Journal of AI Humanities["Jo Eun Kang", "Jae Won Lee", "Ga Yeon Jung", "Han Saem Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/cce1dc1a82f36f486318e6d2a26254789fb294f4 e1e631a29f58f7bfc8f46789fb944dfc9f4ef43bБизнес. Образование. Право00Бизнес. Образование. Право["\u0410.\u0410. \u0411\u0438\u044f\u0440\u0441\u043b\u0430\u043d\u043e\u0432"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/e1e631a29f58f7bfc8f46789fb944dfc9f4ef43b 595d1b3abc1f13b3c8957c8392eef376372e344d<jats:p/>The Korean Society for Health and Nursing Convergence00The Korean Society for Health and Nursing Convergence["Hyun Ye Lee", "Han Byeol Kim", "Hee Joo Jung", "Seo Young Kim", "Seo Hyun Han", "Ye Na Kim", "Sun Jung Park", "Yu Mi Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/595d1b3abc1f13b3c8957c8392eef376372e344d f3ca9792f574609cf69801922a4bb9d867ea942e     Agronomía Colombiana70Agronomía Colombiana["J. G. Ram\u00edrez\u2010Gil"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/f3ca9792f574609cf69801922a4bb9d867ea942e c30101fa9513056244ff7db8c81e8eaf4bbc81e0Public Law Journal00Public Law Journal["Ki-Hyun Roh"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/c30101fa9513056244ff7db8c81e8eaf4bbc81e0 87b1c46c01be4790ce15db7cf8d2af32eff67f11International Journal of Computer Science and Engineering00International Journal of Computer Science and Engineering["Vinh Vo Phu"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/87b1c46c01be4790ce15db7cf8d2af32eff67f11 0eae3a3fcffb459e99df5e2d0514d63555370a40The Justice00The Justice["WooJung Jon", "Taeyeub Nho Alex"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/0eae3a3fcffb459e99df5e2d0514d63555370a40 e8ced32e2188fb05781a6c93799b09b5b8c218cfInternational Journal of Advanced Research00International Journal of Advanced Research["Laila Yahya A. Alhubaishi", "Entesar Al Hammadi", "Faiqah Azim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/e8ced32e2188fb05781a6c93799b09b5b8c218cf cf6b78b227be173af1d343afdc57af70490367e4International Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology["Samiya Tamtam", "A. Laguidi", "Abderafiaa Elkalay"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/cf6b78b227be173af1d343afdc57af70490367e4 69281c539b7e53b39e8bca5c637043f3e2139621The Korean Journal of Air &amp; Space Law and Policy00The Korean Journal of Air &amp; Space Law and Policy["Kyeongwon Baek"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/69281c539b7e53b39e8bca5c637043f3e2139621 f1b8ede8aa37469141e57e4b6bf74317f762fa95Ovidius University Annals: Economic Sciences Series00Ovidius University Annals. Economic Sciences Series["A. Stanciu", "Elena Condrea", "Valentina Irena Tudoran Niculita"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/f1b8ede8aa37469141e57e4b6bf74317f762fa95 d8b3b7a8d2bdf154b24157cfaf605b1d887d6c89Journal of the Korean Association of Information Education00Journal of The Korean Association of Information Education["Seong Woo Park", "Ji-Yun Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/d8b3b7a8d2bdf154b24157cfaf605b1d887d6c89 e1c45073ff87421c61137396d0913df6b78f668eJournal of the Korean Association of Information Education00Journal of The Korean Association of Information Education["Jung Shin", "Junghee Jo"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/e1c45073ff87421c61137396d0913df6b78f668e d6ea459838d82c7ea4477467b24854f0d2a6431cThis paper explores AI's transformative impact on education, focusing on technologies like intelligent tutoring systems, automated grading, and adaptive learning platforms. It highlights the benefits of AI, including enhanced efficiency, improved accessibility, and personalized learning experiences, while addressing challenges such as data privacy and the digital divide. Through a review of recent research and case studies, the paper offers insights for leveraging AI to create effective, inclusive, and forward-thinking educational environments.International Journal For Multidisciplinary Research170This paper explores AI's transformative impact on education, focusing on technologies like intelligent tutoring systems, automated grading, and adaptive learning platforms, while addressing challenges such as data privacy and the digital divide.International Journal For Multidisciplinary Research["Varun REDDY JAVVAJI", "Pooja VEERA RAGHAVULU"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/d6ea459838d82c7ea4477467b24854f0d2a6431c 65b7dd3f0c0e51caa7c63c1bf01004755292da38Stress is a prevalent factor in modern life that significantly impacts cardiovascular health.Japan Journal of Clinical &amp; Medical Research00Japan Journal of Clinical &amp; Medical Research["Bahman Zohuri"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/65b7dd3f0c0e51caa7c63c1bf01004755292da38 72b09dcffe2d2591b4e7a9462c19aeb4801c7b64This study aims to understand the linkage between an artist's symbolic capital (reputation) and the legitimization of that artist's AI art. Based on Bourdieu's concept of cultural capital, to do so, we theoretically reviewed symbolic capital. Then, the qualitative study was conducted by considering contemporary artist Damien Hirst's AI art project “Beautiful Paintings” as a case. Secondary data such as Hirst's career records, research papers, articles, reports, websites, and the artist's social media were collected and analyzed. The results indicate that the symbolic capital of Hirst has accumulated in the art world and the art market influences the acceptance of his AI art in the market. Hirst, to transfer his symbolic capital to AI art, highlights his role in producing the AI art via social media and connects his established image with the debate surrounding AI art. Furthermore, Hirst intends to generate a visual similarity between his recognized artistic style (Spin Painting) in the art world and Beautiful Paintings, as well as emphasizing the 'randomness' inherent in both AI art and Spin Painting. By doing so, the success of AI Art, supported by the symbolic capital of the renowned artist, implies that the hierarchical structure built within the art world for the legitimization of artworks is partially maintained even in art applying new technologies.Korean Arts Association of Arts Management00The results indicate that the symbolic capital of Hirst has accumulated in the art world and the art market influences the acceptance of his AI art in the market, implying that the hierarchical structure built within the art world for the legitimization of artworks is partially maintained even in art applying new technologies.Korean Arts Association of Arts Management["Qiaozheng Kang", "J. Lee"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/72b09dcffe2d2591b4e7a9462c19aeb4801c7b64 3de1c0a16e1aa3bbd9608a3cb178f155f3f4cd2fThe Journal of Internet Electronic Commerce Resarch00The Journal of Internet Electronic Commerce Resarch["Jingyeong Hwang", "Hyeryung Song", "Donghee Yoo"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/3de1c0a16e1aa3bbd9608a3cb178f155f3f4cd2f 678a5c7bdc855a8be17f36e41c03e9713566badaJournal of Legislative Studies00Journal of Legislative Studies["Ji Woong Ryu", "Ho Kim"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/678a5c7bdc855a8be17f36e41c03e9713566bada 0aec65be09b5c87723e5b8d2a17e9f81c451d256Risk in Contemporary Economy00Risk in Contemporary Economy["Lazar (Plesa) Teodora Nicoleta", "Popescu Constan\u0163a"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/0aec65be09b5c87723e5b8d2a17e9f81c451d256 c3e5c2b948b5812c6f650b8181a55d8f98605602Korean Journal of Journalism &amp; Communication Studies00Korean Journal of Journalism &amp; Communication Studies["Jeonghyun Lee", "Soyoung Park"]2024-08-31T00:00:00https://www.semanticscholar.org/paper/c3e5c2b948b5812c6f650b8181a55d8f98605602 2cfe3a9d737b739aaa914b6708d4ec01c503ad93Computers & electrical engineering20927Comput. Electr. Eng.["A. S. Albahri", "Yahya Layth Khaleel", "Mustafa Abdulfattah Habeeb", "Reem D. Ismael", "Qabas A. Hameed", "Muhammet Deveci", "R. Homod", "O. Albahri", "A. Alamoodi", "Laith Alzubaidi"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/2cfe3a9d737b739aaa914b6708d4ec01c503ad93 26c31cd9f338d3ba1abcdf647a5262f51892f520Journal of Open Innovation: Technology, Market and Complexity4815Journal of Open Innovation: Technology, Market, and Complexity["Irfan Saleem", "Najla Salim Said Al-Breiki", "M. Asad"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/26c31cd9f338d3ba1abcdf647a5262f51892f520 cfcb0c6fc51348cd6ff8dd450e45b0212119e062Results in Engineering4412Results in Engineering["R. K. Makumbura", "L. Mampitiya", "Namal Rathnayake", "D. Meddage", "Shagufta Henna", "Tuan Linh Dang", "Yukinobu Hoshino", "Upaka S. Rathnayake"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/cfcb0c6fc51348cd6ff8dd450e45b0212119e062 4e9a9fde45703be590bc46afb8590eab5953a04eThe integration of artificial intelligence (AI) and its autonomous learning processes (or machine learning) in medicine has revolutionized the global health landscape, providing faster and more accurate diagnoses, personalization of medical treatment, and efficient management of clinical information. However, this transformation is not without ethical challenges, which require a comprehensive and responsible approach. There are many fields where AI and medicine intersect, such as health education, patient-doctor interface, data management, diagnosis, intervention, and decision-making processes. For some of these fields, there are some guidelines to regulate them. AI has numerous applications in medicine, including medical imaging analysis, diagnosis, predictive analytics for patient outcomes, drug discovery and development, virtual health assistants, and remote patient monitoring. It is also used in robotic surgery, clinical decision support systems, AI-powered chatbots for triage, administrative workflow automation, and treatment recommendations. Despite numerous applications, there are several problems related to the use of AI identified in the literature in general and in medicine in particular. These problems are data privacy and security, bias and discrimination, lack of transparency (Black Box Problem), integration with existing systems, cost and accessibility disparities, risk of overconfidence in AI, technical limitations, accountability for AI errors, algorithmic interpretability, data standardization issues, unemployment, and challenges in clinical validation. Of the various problems already identified, the most worrying are data bias, the black box phenomenon, questions about data privacy, responsibility for decision-making, security issues for the human species, and technological unemployment. There are still several ethical problems associated with the use of AI autonomous learning algorithms, namely epistemic, normative, and comprehensive ethical problems (overarching). Addressing all these issues is crucial to ensure that the use of AI in healthcare is implemented ethically and responsibly, providing benefits to populations without compromising fundamental values. Ongoing dialogue between healthcare providers and the industry, the establishment of ethical guidelines and regulations, and considering not only current ethical dilemmas but also future perspectives are fundamental points for the application of AI to medical practice. The purpose of this review is to discuss the ethical issues of AI algorithms used mainly in data management, diagnosis, intervention, and decision-making processes.Cureus376The ethical issues of AI algorithms used mainly in data management, diagnosis, intervention, and decision-making processes are discussed, to ensure that the use of AI in healthcare is implemented ethically and responsibly.Cureus["Marta Marques", "A. Almeida", "Helder Pereira"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/4e9a9fde45703be590bc46afb8590eab5953a04e 3d1b7ecc1cda6c41ff3ded1313052e4934b4cb0bKey Points Question How generalizable are current National Institutes of Health (NIH) ethical principles for conduct of clinical trials to clinical trials of artificial intelligence (AI), and what unique ethical considerations arise in trials of AI? Findings In this qualitative study, interviews with 11 investigators involved in clinical trials of AI for diabetic retinopathy screening confirmed the applicability of current ethical principles but also identified unique challenges, including assessing social value, ensuring scientific validity, fair participant selection, evaluation of risk-to-benefit ratio in underrepresented groups, and navigating complex consent processes. Meaning These results suggest ethical challenges unique to clinical trials of AI, which may provide important guidance for empirical and normative ethical efforts to enhance the conduct of AI clinical trials.JAMA Network Open265Interviews with investigators involved in clinical trials of AI for diabetic retinopathy screening confirmed the applicability of current ethical principles but also identified unique challenges, including assessing social value, ensuring scientific validity, fair participant selection and navigating complex consent processes.JAMA Network Open["Alaa Youssef", "Ariadne A. Nichol", "Nicole Martinez-Martin", "David B. Larson", "M. Abr\u00e0moff", "Risa M Wolf", "Danton Char"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/3d1b7ecc1cda6c41ff3ded1313052e4934b4cb0b ca6fd7ae04b633c66b818f9a8ca901229d8fdc63This study aims to significantly improve air quality monitoring through the innovative application of Artificial Intelligence (AI). Introducing the Artificial Intelligence Kualitas Udara (AIKU) model, this research offers a novel approach by integrating advanced machine learning algorithms with environmental sensors to predict air quality in real-time more accurately than traditional methods. The novelty of the AIKU model lies in its sophisticated data analytics framework, which processes high-frequency environmental data to assess air quality changes dynamically. The technique employs calibrating and deploying the AIKU model across various urban and suburban settings and analyzing its performance against conventional monitoring systems such as the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). The results demonstrate that AIKU significantly outperforms these traditional systems in both accuracy and speed of response, highlighting its effectiveness in real-time environmental monitoring. Furthermore, the AIKU model's scalability and adaptability are tested, showing promising potential for application in densely populated urban areas and less populated rural settings. This research contributes to environmental monitoring by demonstrating how AI can transform traditional methodologies into more effective, scalable, and intelligent ecological management systems. This research provides substantial evidence that the AIKU model can serve as a powerful tool for sustainable and smart development worldwide, enhancing the ability of governments and organizations to respond to environmental challenges promptly and effectively. Doi: 10.28991/HIJ-2024-05-03-017 Full Text: PDFHighTech and Innovation Journal03This research provides substantial evidence that the AIKU model can serve as a powerful tool for sustainable and smart development worldwide, enhancing the ability of governments and organizations to respond to environmental challenges promptly and effectively.HighTech and Innovation Journal["Danny Manongga", "U. Rahardja", "Irwan Sembiring", "Q. Aini", "Abdul Wahab"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/ca6fd7ae04b633c66b818f9a8ca901229d8fdc63 f3b3b604ff586d9d38f541cad03ef02755da9772Artificial intelligence (AI) is rapidly transforming education, offering exciting opportunities and challenges for universities. This study investigates the integration of AI in undergraduate studies at three universities in Battambang, Cambodia. The author employed a quantitative survey design targeting 370 students across various majors and year levels. This research aims to (1) examine the correlation between students’ utilization of AI tools and their chosen field of study; (2) explore undergraduate students' perceptions regarding the application of AI in their academic research; and (3) analyze the perceived significance of AI integration within the context of specific majors. Through a quantitative research approach, data were collected from 370 students across three universities, and their use, perceptions, and significance of AI in their academic endeavors were examined. The findings reveal high engagement with AI tools, particularly for language translation and writing enhancement, although most students lack formal training in AI usage. While students appreciate the efficiency and personalized learning experiences offered by AI, they also express concerns about data privacy, algorithmic biases, and the impact on critical thinking skills. European Journal of Theoretical and Applied Sciences253High engagement with AI tools, particularly for language translation and writing enhancement, although most students lack formal training in AI usage is revealed, although most students lack formal training in AI usage.European Journal of Theoretical and Applied Sciences["Heak Hoeurng", "Phearun Phorn", "Sopharath Kheav", "Rany Sam"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/f3b3b604ff586d9d38f541cad03ef02755da9772 4a69cdfa5ebf7d207bc39b17b3ffa27de2520a29Artificial Intelligence (AI) has witnessed remarkable advancements in recent years and has significantly impacted various domains, including cultural heritage. Indeed, AI technologies offer unprecedented capacities to analyze huge amounts of historical data, enabling researchers and art historians to uncover precious patterns, connections, and insights that might otherwise remain elusive. Also, the efficiency and accuracy of AI techniques play a pivotal role in many cultural heritage-related tasks, such as cataloging and organizing extensive cultural collections, streamlining the management of heritage resources for present and future generations. However, the integration of AI in cultural heritage also brings forth intricate ethical questions. These span over the issues of authenticity, subjectivity, and interpretation biases of an AI-empowered, reproduced, and/or generated artwork up to the legal concerns related to authorship. However, such issues are mostly undefined and unaddressed in the scholarship at the intersection on AI, ethics, and cultural heritage. This paper aims to pave the way to fill such a gap of context-sensitive ethical issues for AI in cultural heritage. To this aim, the paper first analyzes the main opportunities and benefits raised by AI in cultural heritage. Then, matching benchmark, agreed-upon AI ethics principles elaborated in the AI ethics scholarship in the last decade and relevant to cultural heritage, it highlights specific ethical risks that ought to be considered for the development and deployment of trustworthy AI in and for cultural heritage. Finally, areas requiring further attention and work, and actors call to intervene, are identified to facilitate next steps for ethics and governance of AI in cultural heritage.IEEE Transactions on Technology and Society533The paper first analyzes the main opportunities and benefits raised by AI in cultural heritage, and highlights specific ethical risks that ought to be considered for the development and deployment of trustworthy AI in and for cultural heritage.IEEE Transactions on Technology and Society["S. Tiribelli", "S. Pansoni", "Emanuele Frontoni", "B. Giovanola"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/4a69cdfa5ebf7d207bc39b17b3ffa27de2520a29 d235e3410c28904118a6a6af115c68e4c0b5b78bIntroduction: Artificial intelligence (AI) stands at the forefront of revolutionizing healthcare, wielding its computational prowess to navigate the labyrinth of medical data with unprecedented precision. In this study, we delved into the perspectives of medical students in the Kingdom of Saudi Arabia (KSA) regarding AI's seismic impact on their careers and the medical landscape. Methods: A cross-sectional study conducted from February to December 2023 examined the impact of AI on the future of medical students’ careers in KSA, surveying approximately 400 participants, including Saudi medical students and interns, and uncovering a fascinating tapestry of perceptions. Results: Astonishingly, 75.4% of respondents boasted familiarity with AI, heralding its transformative potential. A resounding 88.9% lauded its capacity to enrich medical education, marking a paradigm shift in learning approaches. However, amidst this wave of optimism, shadows of apprehension loomed. A staggering 42.5% harbored concerns of AI precipitating job displacement, while 34.4% envisioned a future where AI usurps traditional doctor roles. Despite this dichotomy, there existed a unanimous recognition of the symbiotic relationship between AI and human healthcare professionals, heralding an era of collaborative synergy. Conclusion: Our findings underscored a critical need for educational initiatives to assuage fears and facilitate the seamless integration of AI into clinical practice. Moreover, AI's burgeoning influence in diagnostic radiology and personalized healthcare plans emerged as catalysts propelling the domain of precision medicine into uncharted realms of innovation. As AI reshapes the contours of healthcare delivery, it not only promises unparalleled efficiency but also holds the key to unlocking new frontiers in treatment outcomes and accessibility, heralding a transformative epoch in the annals of medicine.Cureus181A critical need for educational initiatives to assuage fears and facilitate the seamless integration of AI into clinical practice is underscored, as AI's burgeoning influence in diagnostic radiology and personalized healthcare plans emerged as catalysts propelling the domain of precision medicine into uncharted realms of innovation.Cureus["Dalia M Alammari", "Rola E Melebari", "Jumanah A Alshaikh", "Lara B Alotaibi", "Hanan S Basabeen", "Alanoud F Saleh"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/d235e3410c28904118a6a6af115c68e4c0b5b78b 38e3395d67e69cdc179d766f1f047e75df4d088fImpact assessment reports for high-risk artificial intelligence (AI) systems will be legally required but challenging to complete, especially for smaller companies. That is because the current process is complex, costly, and relies on guidebooks with limited assistance. We propose AI Design, a semiautomatic framework for prefilling these reports. It consists of two components: 1) StakeLinker, an interactive tool combining various stakeholders’ perspectives, and 2) FillGen, a large model-based tool that processes stakeholders’ perspectives and produces a report that is reviewed by regulatory experts within a company. We conducted two user studies: the first with 13 AI practitioners who confirmed StakeLinker’s effectiveness in gathering comprehensive input for impact assessment; the second with eight additional practitioners who successfully evaluated a report for a crime analysis system prefilled by FillGen. To show its generalizability, we also made the reports for two other AI systems publicly available.IEEE Internet Computing221AI Design, a semiautomatic framework for prefilling impact assessment reports for high-risk artificial intelligence (AI) systems, consists of StakeLinker, an interactive tool combining various stakeholders’ perspectives, and FillGen, a large model-based tool that processes stakeholders’ perspectives and produces a report that is reviewed by regulatory experts within a company.IEEE Internet Computing["E. Bogucka", "Marios Constantinides", "S. \u0160\u0107epanovi\u0107", "Daniele Quercia"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/38e3395d67e69cdc179d766f1f047e75df4d088f 6e74dce9494778ebf61bb200047d2ddea97e3595Introduction With the current exponential expansion of robotics, implants, and imaging technologies, diagnostic processes within the healthcare industry are becoming popular platforms for artificial intelligence (AI) use. Thus, an understanding of physicians’ attitudes toward AI and the extent to which medical educators are ready to work with AI is necessary. This research aimed to study doctors’ perceptions of AI in healthcare. Methods A web-based questionnaire organized into four sections, namely, demographics, concepts of AI, education in AI, and implementation challenges related to AI, was designed systematically based on a literature search and circulated among medical doctors from various fields. Results Study participants exhibited a lower score toward familiarity with AI. Only 52.12% (74/142) of physicians completed the survey. The greatest challenge associated with the use of AI in therapeutic settings was found to be the degree of autonomy, with a score of 3.56. Among the participants, 67.61% felt that the lack of human supervision was the most important limiting factor in the implementation of AI in clinical practice. However, the participants demonstrated a strong interest in understanding the concepts of AI in the near future. Conclusion This study revealed a low degree of familiarity with AI, highlighting the need for medical schools and hospitals to establish specialized education and training programs for physicians to improve patient outcomes.Cureus221A low degree of familiarity with AI is revealed, highlighting the need for medical schools and hospitals to establish specialized education and training programs for physicians to improve patient outcomes.Cureus["Arijita Banerjee", "Pradosh Kumar Sarangi", "Sumit Kumar"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/6e74dce9494778ebf61bb200047d2ddea97e3595 83201bb111af7b0b8e1487ff9be48ebf756126adArtificial intelligence (“AI”) technology has detonated an explosive burst of seemingly creative expression. Stories, images, music, and even entire books are now being generated very quickly. This development is a major headache for copyright registrars because the copyrightability of works created in this way is uncertain. The almost limitless variability in the extent of human involvement in the creation of a work using an AI tool compounds the uncertainty. In some cases, copyrightability is easy to determine, such as where an author only claims rights in the selection and arrangement of AI-generated output rather than the output itself. But in many cases the registrability of a work created with the aid of an AI technology is far from certain. In these situations, the Copyright Office should apply a rule of doubt to allow registration. This solution appears to be a novel idea, at least as a permanent solution to the problems AI has generated. Nevertheless, approaching the problem in this way would protect authors’ rights to any original contributions they make to AI-assisted works, authors’ interests in the privacy of their creative processes, the interests of authors whose works may have been used without their consent to train AI, and the ability of alleged infringers to challenge the existence and scope of claimed copyrights in AI-generated and AI-assisted works. It would also ease the burden on copyright examiners.Texas A&amp;M Law Review Arguendo01Texas A&amp;M Law Review Arguendo["Thomas B. James"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/83201bb111af7b0b8e1487ff9be48ebf756126ad 1ed61b03b9ec2f4ac4532512fac82094bf7ae111Background Situated within a larger project entitled “Exploring the Need for a Uniquely Different Approach in Northern Ontario: A Study of Socially Accountable Artificial Intelligence,” this rapid review provides a broad look into how social accountability as an equity-oriented health policy strategy is guiding artificial intelligence (AI) across the Canadian health care landscape, particularly for marginalized regions and populations. This review synthesizes existing literature to answer the question: How is AI present and impacted by social accountability across the health care landscape in Canada? Methodology A multidisciplinary expert panel with experience in diverse health care roles and computer sciences was assembled from multiple institutions in Northern Ontario to guide the study design and research team. A search strategy was developed that broadly reflected the concepts of social accountability, AI and health care in Canada. EMBASE and Medline databases were searched for articles, which were reviewed for inclusion by 2 independent reviewers. Search results, a description of the studies, and a thematic analysis of the included studies were reported as the primary outcome. Principal findings The search strategy yielded 679 articles of which 36 relevant studies were included. There were no studies identified that were guided by a comprehensive, equity-oriented social accountability strategy. Three major themes emerged from the thematic analysis: (1) designing equity into AI; (2) policies and regulations for AI; and (3) the inclusion of community voices in the implementation of AI in health care. Across the 3 main themes, equity, marginalized populations, and the need for community and partner engagement were frequently referenced, which are key concepts of a social accountability strategy. Conclusion The findings suggest that unless there is a course correction, AI in the Canadian health care landscape will worsen the digital divide and health inequity. Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.PLOS Digital Health751Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.PLOS Digital Health["A. Anawati", "Holly Fleming", "M. Mertz", "Jillian Bertrand", "Jennifer Dumond", "Sophia Myles", "Joseph Leblanc", "Brian Ross", "D. Lamoureux", "Div Patel", "Renald Carrier", "Erin Cameron"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/1ed61b03b9ec2f4ac4532512fac82094bf7ae111 7444ad7f8fa28620da573eabae5141cde9361233Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.Journal of International Medical Research791A narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.The Journal of International Medical Research["Andrea Chierici", "F. Lareyre", "Benjamin Salucki", "A. Iannelli", "Herv\u00e9 Delingette", "J. Raffort"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/7444ad7f8fa28620da573eabae5141cde9361233 ce0bfa3818a1783c6096c40180ac48ee677786ffThis paper comprehensively examines the pivotal role of artificial intelligence (AI) technology in promoting socio-economic development. It commences by introducing the rapid advancements and current status of AI technology, which has permeated diverse industries through breakthroughs in big data, cloud computing, and deep learning. The paper highlights AI’s contributions to economic growth by enhancing production efficiency (e.g., 45% increase in efficiency through automation), optimizing resource allocation (25% raw material utilization improvement), and fostering innovative business models such as personalized customization and intelligent supply chain management. AI also fosters the emergence of new industries (intelligent medicine, education, finance) and creates substantial job opportunities directly for data scientists and algorithm engineers, and indirectly through driving related industries. Furthermore, AI facilitates industrial upgrading, transforming manufacturing into smart manufacturing, enhancing service quality in industries like retail, and modernizing agriculture with smart equipment. A case study on Chongqing Soft River Turing AI Technology Co., Ltd. showcases AI’s practical applications and corporate competitiveness enhancement. Overall, the paper underscores AI’s significance in economic growth, job creation, and industry transformation, while acknowledging challenges like data privacy and job displacement, emphasizing the need for strategic policy guidance and regulation.Journal of Progress in Engineering and Physical Science01This paper underscores AI’s significance in economic growth, job creation, and industry transformation, while acknowledging challenges like data privacy and job displacement, emphasizing the need for strategic policy guidance and regulation.Journal of Progress in Engineering and Physical Science["Yan Wang"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/ce0bfa3818a1783c6096c40180ac48ee677786ff 53b9b0af9165ad536b31e2218513beac645ce8a9The integration of artificial intelligence (AI) into obstetric care offers significant potential to enhance clinical decision-making and optimize maternal and neonatal outcomes. Traditional prediction methods for mode of delivery often rely on subjective clinical judgment and limited statistical models, which may not fully capture complex patient data. This systematic review aims to evaluate the current state of research on AI applications in predicting the mode of delivery, comparing the performance of AI models with traditional methods, and identifying gaps for future research. A comprehensive literature search was conducted across PubMed, Google Scholar, Web of Science, and Scopus databases, covering publications from January 2010 to July 2024. Inclusion criteria were studies employing AI techniques to predict the mode of delivery, published in peer-reviewed journals, and involving human subjects. Studies were assessed for quality using the Prediction Model Risk of Bias Assessment Tool (PROBAST), and data were synthesized narratively due to heterogeneity. In total, 18 studies met the inclusion criteria, employing various AI models such as logistic regression, random forest, gradient boosting, and neural networks. Sample sizes ranged from 40 to 94,480 participants across diverse geographic settings. AI models demonstrated high accuracy rates, often exceeding 90%, and strong predictive metrics (area under the curve (AUC) values from 0.745 to 0.932). Key predictors included maternal age, gravidity, parity, gestational age, labor induction type, and fetal weight. Notable models like the Adana System and Categorical Boosting (CatBoost, Yandex LLC, Moscow, Russia) highlighted the effectiveness of AI in enhancing prediction accuracy and supporting clinical decisions. AI models significantly outperform traditional statistical methods in predicting the mode of delivery, providing a robust tool for obstetric care. Future research should focus on standardizing data collection, improving model interpretability, addressing ethical concerns, and ensuring fairness in AI predictions to enhance clinical trust and application.Cureus301Artificial intelligence models significantly outperform traditional statistical methods in predicting the mode of delivery, providing a robust tool for obstetric care.Cureus["Kalliopi Michalitsi", "Dimitra Metallinou", "A. Diamanti", "V. Georgakopoulou", "Iraklis Kagkouras", "Eleni Tsoukala", "A. Sarantaki"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/53b9b0af9165ad536b31e2218513beac645ce8a9 3d7bcaa4353c155014848d7db376e550d0ff75d9 The rapid development of Artificial Intelligence (AI) offers both opportunities and challenges for its application in Corporate Social Responsibility (CSR) communication. While AI can enhance CSR initiatives, its impact on consumer relations and brand perception remains inconsistent. This study aims to explore the academic landscape of AI’s role in CSR communication, focusing on publication trends, key authors, research topics, and future directions. A bibliometric analysis was conducted on 1,094 articles related to AI and CSR communication, retrieved from the Web of Science database from 2000 to February 2024. Using CiteSpace software, the study mapped research trends by analysing disciplines, countries, institutions, authors, references, and keywords. The United States and China lead in publication output, with key research themes including social media impact, management strategies, and consumer trust. Emerging trends point to the importance of privacy, service quality, and perceived value in AI-driven CSR initiatives. The integration of AI in CSR communication is an evolving field, with significant contributions from social media research and consumer behaviour studies. Future research should address ethical concerns and long-term effects on consumer trust and engagement. Business Systems Research Journal511The academic landscape of AI’s role in CSR communication is explored, focusing on publication trends, key authors, research topics, and future directions, with the United States and China leading in publication output.Business Systems Research Journal["X. Hua", "Nurul Ain Mohd Hasan", "Feroz De Costa", "Weihua Qiao"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/3d7bcaa4353c155014848d7db376e550d0ff75d9 62d63d85a3790f95a13ea07079cf082bd4c91f6aElectricity theft is a significant challenge for utility companies worldwide, leading to substantial economic losses and inefficiencies in power distribution. Traditional methods of detecting electricity theft, such as manual inspections and routine audits, are often inefficient and ineffective. To address this issue, this study aims to develop and implement an artificial intelligence (AI)-driven system for electricity theft detection. Methodology used are data collection, data analysis, feature selection with Chi-Square, feature transformation with Principal Component Analysis (PCA), Support Vector Machine (SVM) and model for electricity theft detection. To achieve this, a Particle Swarm Optimization Algorithm (PSO) was applied to improve training performance of the SVM, using data of meter recharge information collected from Enugu Electricity Distribution Company (EEDC). The system effectiveness is validated through extensive testing using real-world data from various regions and scenarios, demonstrating its robustness and adaptability. The system result considering FDR reported that 0.11 was achieved for the particle swarm based SVM model. When TPR was considered for analysis, it was observed that particle swarm based SVM attained a score of 0.89. In addition, Particle swarm based SVM attained PPV of 0.895. In terms of accuracy, the particle swarm based SVM reported an accuracy of 0.857. The result showed that the particle swarm based SVM performed better from the system validation achieved through comparative analysis, hence it is recommended for use to develop the new software for energy theft investigation. The implementation of this AI-driven solution offers numerous benefits, including enhanced detection accuracy, reduced operational costs, and improved overall efficiency of power distribution networks. Moreover, it enables utility companies to take proactive measures to prevent theft, ensuring a more reliable and secure electricity supply for consumers.ABUAD Journal of Engineering Research and Development (AJERD)01The result showed that the particle swarm based SVM performed better from the system validation achieved through comparative analysis, hence it is recommended for use to develop the new software for energy theft investigation.ABUAD Journal of Engineering Research and Development (AJERD)["Nwamaka Georgenia Ezeji", "Kingsley Ifeanyi Chibueze", "Nnenna Harmony Nwobodo-Nzeribe"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/62d63d85a3790f95a13ea07079cf082bd4c91f6a aaa4a4981c593b8b4e5057e9bc2426b334aa8f3aMaternal health remains a critical global health challenge, with disparities in access to care and quality of services contributing to high maternal mortality and morbidity rates. Artificial intelligence (AI) has emerged as a promising tool for addressing these challenges by enhancing diagnostic accuracy, improving patient monitoring, and expanding access to care. This review explores the transformative role of AI in maternal healthcare, focusing on its applications in the early detection of pregnancy complications, personalized care, and remote monitoring through AI-driven technologies. AI tools such as predictive analytics and machine learning can help identify at-risk pregnancies and guide timely interventions, reducing preventable maternal and neonatal complications. Additionally, AI-enabled telemedicine and virtual assistants are bridging healthcare gaps, particularly in underserved and rural areas, improving accessibility for women who might otherwise face barriers to quality maternal care. Despite the potential benefits, challenges such as data privacy, algorithmic bias, and the need for human oversight must be carefully addressed. The review also discusses future research directions, including expanding AI applications in maternal health globally and the need for ethical frameworks to guide its integration. AI holds the potential to revolutionize maternal healthcare by enhancing both care quality and accessibility, offering a pathway to safer, more equitable maternal outcomes.Cureus641This review explores the transformative role of AI in maternal healthcare, focusing on its applications in the early detection of pregnancy complications, personalized care, and remote monitoring through AI-driven technologies.Cureus["Smruti A Mapari", "Deepti Shrivastava", "Apoorva Dave", "Gautam N Bedi", "Aman Gupta", "Pratiksha Sachani", "Paschyanti Kasat", "Utkarsh Pradeep"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/aaa4a4981c593b8b4e5057e9bc2426b334aa8f3a 4643dd6ea020d454f0afcccedd2585e0e74aa3fbThe purpose of the study was to provide a comprehensive overview of the transformative applications of artificial intelligence (AI) in ophthalmology, with a focus on its impact on screening, diagnosis, and treatment planning. A comprehensive literature search was conducted to identify relevant studies on the applications of AI in ophthalmology. PubMed, Embase, and Scopus were searched using appropriate keywords, with inclusion criteria focusing on studies related to image analysis, diagnostic algorithms, predictive models, and treatment planning. Limited to English-language articles, both original research and review articles were considered, while studies emphasizing nonophthalmic applications of AI or lacking sufficient detail were excluded. AI algorithms, powered by deep learning models, have demonstrated remarkable accuracy in the automated screening and detection of various ocular diseases. The potential implications of AI include revolutionizing screening programs for early identification of individuals at risk, facilitating timely interventions, and improving patient outcomes. The integration of AI with teleophthalmology and remote monitoring systems has the potential to alleviate the burden on health-care systems, particularly in underserved areas. The applications of AI in ophthalmology hold significant potential for transforming the field by enhancing diagnostic accuracy, optimizing treatment strategies, and increasing access to eye care. However, successful implementation requires addressing challenges such as diverse and representative datasets, ensuring interpretability and explainability of AI models, and addressing ethical considerations related to patient privacy and data security. Collaborative efforts between ophthalmologists, data scientists, and regulatory bodies are deemed crucial to fully leverage the potential of AI in ophthalmology.Journal of Clinical Ophthalmology and Research381The applications of AI in ophthalmology hold significant potential for transforming the field by enhancing diagnostic accuracy, optimizing treatment strategies, and increasing access to eye care, but successful implementation requires addressing challenges such as diverse and representative datasets, ensuring interpretability and explainability of AI models, and addressing ethical considerations related to patient privacy and data security.Journal of Clinical Ophthalmology and Research["Asma Jabeen"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/4643dd6ea020d454f0afcccedd2585e0e74aa3fb 44d7b5aebf7ae8f24db51e9d8093cfdd4dbf5e8fBackground: This study aimed to test an artificial intelligence-based reading system (AIRS) capable of reading retinographies of type 2 diabetic (T2DM) patients and a predictive algorithm (DRPA) that predicts the risk of each patient with T2DM of developing diabetic retinopathy (DR). Methods: We tested the ability of the AIRS to read and classify 15,297 retinal photographs from our database of diabetics and 1200 retinal images taken with Messidor-2 into the different DR categories. We tested the DRPA in a sample of 40,129 T2DM patients. The results obtained by the AIRS and the DRPA were then compared with those provided by four retina specialists regarding sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and area under the curve (AUC). Results: The results of testing the AIRS for identifying referral DR (RDR) in our database were ACC = 98.6, S = 96.7, SP = 99.8, PPV = 99.0, NPV = 98.0, and AUC = 0.958, and in Messidor-2 were ACC = 96.78%, S = 94.64%, SP = 99.14%, PPV = 90.54%, NPV = 99.53%, and AUC = 0.918. The results of our DRPA when predicting the presence of any type of DR were ACC = 0.97, S = 0.89, SP = 0.98, PPV = 0.79, NPV = 0.98, and AUC = 0.92. Conclusions: The AIRS performed well when reading and classifying the retinographies of T2DM patients with RDR. The DRPA performed well in predicting the absence of DR based on some clinical variables.Diagnostics361The AIRS performed well when reading and classifying the retinographies of T2DM patients with RDR and the DRPA performed well in predicting the absence of DR based on some clinical variables.Diagnostics["M. Baget-Bernaldiz", "Benilde Fontoba-Poveda", "P. Romero-Aroca", "Raul Navarro-Gil", "Adriana Hernando-Comerma", "\u00c1ngel Bautista-P\u00e9rez", "Monica Llagostera-Serra", "Cristian Morente-Lorenzo", "Montse Vizcarro", "Alejandra Mira-Puerto"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/44d7b5aebf7ae8f24db51e9d8093cfdd4dbf5e8f 0b8bfc5a0cf64d68e68e811593bdd671963510d5The advancement of artificial intelligence (AI) has led to its widespread use in sectors such as finance, healthcare, military, and employment in developed countries. However, this reliance has raised concerns about AI governance, particularly regarding algorithmic biases based on skin color, gender, race, and age. Consequently, many countries have introduced regulations and ethical frameworks to address these issues. The Ministry of Digital Economy and Entrepreneurship in Jordan has included AI in its 2022 plan, signaling significant progress. The integration of AI in education programs underscores this commitment. However, addressing AI's potential negative impacts is essential. We propose ethical considerations and regulations for AI to complement Jordan's initiatives. Our research aims to promote responsible AI usage by developing ethical guidelines in Jordan. It presents techniques to identify and mitigate biases related to skin color, gender, and age in AI outputs and datasets. The research includes extensive testing on datasets, analyzing approximately 100 images, and revealing notable error rates, including a 16% error rate in detecting skin color, a 4% error rate in seeing white faces, and a 6% error rate in identifying females over men. Therefore, ethical considerations and regulations for AI applications in Jordan must be implemented.IAES International Journal of Artificial Intelligence (IJ-AI)322This research aims to promote responsible AI usage by developing ethical guidelines in Jordan and presents techniques to identify and mitigate biases related to skin color, gender, and age in AI outputs and datasets.IAES International Journal of Artificial Intelligence (IJ-AI)["Shurooq Mnawer Ibrahim", "M. Alshraideh", "Martin Leiner", "Iyad Muhsen AlDajani", "Ouarda Bettaz"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/0b8bfc5a0cf64d68e68e811593bdd671963510d5 9680412b638e132c4a9bfc16d582679173b0a840Abstract Objectives To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation. Design This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR. Setting Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling. Participants A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study. Results Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process). Conclusions The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.BMJ Open651The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics.BMJ Open["Xiwen Liao", "Chen Yao", "Feifei Jin", "Jun Zhang", "Larry Liu"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/9680412b638e132c4a9bfc16d582679173b0a840 fc7df996916765d604cec570fea3a6fb184d7bd9Background: This study aims to assess the efficacy of combining automated machine learning (AutoML) and explainable artificial intelligence (XAI) in identifying metabolomic biomarkers that can differentiate between hepatocellular carcinoma (HCC) and liver cirrhosis in patients with hepatitis C virus (HCV) infection. Methods: We investigated publicly accessible data encompassing HCC patients and cirrhotic controls. The TPOT tool, which is an AutoML tool, was used to optimize the preparation of features and data, as well as to select the most suitable machine learning model. The TreeSHAP approach, which is a type of XAI, was used to interpret the model by assessing each metabolite’s individual contribution to the categorization process. Results: TPOT had superior performance in distinguishing between HCC and cirrhosis compared to other AutoML approaches AutoSKlearn and H2O AutoML, in addition to traditional machine learning models such as random forest, support vector machine, and k-nearest neighbor. The TPOT technique attained an AUC value of 0.81, showcasing superior accuracy, sensitivity, and specificity in comparison to the other models. Key metabolites, including L-valine, glycine, and DL-isoleucine, were identified as essential by TPOT and subsequently verified by TreeSHAP analysis. TreeSHAP provided a comprehensive explanation of the contribution of these metabolites to the model’s predictions, thereby increasing the interpretability and dependability of the results. This thorough assessment highlights the strength and reliability of the AutoML framework in the development of clinical biomarkers. Conclusions: This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC. The exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers. Furthermore, TreeSHAP boosted model transparency by highlighting the relevance of certain metabolites. This comprehensive method has the potential to enhance the identification of biomarkers and generate precise, easily understandable, AI-driven solutions for diagnosing HCC.Diagnostics501This study shows that AutoML and XAI can be used together to create metabolomic biomarkers that are specific to HCC, and the exceptional performance of TPOT in comparison to traditional models highlights its capacity to identify biomarkers.Diagnostics["F. Ya\u011f\u0131n", "Radwa El Shawi", "Abdulmohsen Algarni", "Cemil \u00c7olak", "F. Al-Hashem", "L. P. Ardig\u00f2"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/fc7df996916765d604cec570fea3a6fb184d7bd9 dd8f6eff52fea5d1e88efa4a39b6632888d27a30Background: Artificial intelligence (AI) is increasingly being integrated into various aspects of healthcare, including internal medicine. However, the impact of AI on physicians across different internal medicine specialties remains unclear. This study assesses AI's adoption, utilization, and perceived impact among procedural and non-procedural internal medicine physicians. Methods: A comprehensive survey questionnaire was designed to cover current AI use, perceived impact on diagnostic accuracy, treatment decisions, patient outcomes, challenges, ethical concerns, and future expectations. The survey was distributed to a diverse sample of internal medicine physicians across various specialties, including procedural (e.g., interventional cardiology, gastroenterology) and non-procedural (e.g., endocrinology, rheumatology) fields. Responses were analyzed using descriptive statistics, chi-square tests, t-tests, and logistic regression. Results: The survey received responses from 22 internal medicine physicians, with 64% (n=14) representing procedural specialties and 36% (n=8) representing non-procedural specialties. Sixty-eight percent (n=15) of respondents reported using AI tools in their practice, with higher adoption rates among procedural specialties (n=11, 79%) compared to non-procedural specialties (n=4, 50%). Surveyed physicians reported that AI improved diagnostic accuracy (n=12, 80%), treatment decisions (n=10, 67%), and patient outcomes (n=13, 87%). However, 55% (n=12) of respondents expressed concerns about the interpretability and transparency of AI algorithms. Non-procedural specialists were more likely to perceive AI as a threat to their job security (n=3, 38%) than procedural specialists (n=3, 21%). The most common challenges to AI adoption were lack of training (n=16, 73%), cost (n=13, 59%), and data privacy concerns (n=11, 50%). Conclusion: This study assesses the perceived impact of AI on internal medicine physicians, highlighting the differences between procedural and non-procedural specialties. The findings underscore the need for specialty-specific considerations in developing and implementing AI tools. While AI can potentially improve diagnostic accuracy, treatment decisions, and patient outcomes, addressing challenges such as lack of training, cost, and data privacy concerns is crucial for widespread adoption. Moreover, the study emphasizes the importance of ensuring the interpretability and transparency of AI algorithms to foster trust among physicians. As AI continues to evolve, it is essential to engage internal medicine physicians across specialties in the development process to create AI tools that effectively complement their expertise and improve patient care. Further research should focus on developing best practices for AI integration in internal medicine and evaluating the long-term impact on patient outcomes and healthcare systems.Cureus181It is essential to engage internal medicine physicians across specialties in the development process to create AI tools that effectively complement their expertise and improve patient care, and underscore the need for specialty-specific considerations in developing and implementing AI tools.Cureus["Masab A Mansoor", "Andrew F Ibrahim", "Nicholas Kidd"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/dd8f6eff52fea5d1e88efa4a39b6632888d27a30 7f4cddafd1d7c4ffce35b6cb115bc5c9d3d73a6fThis review explores the fast-growing importance of artificial intelligence (AI) with robotic process automation (RPA) in healthcare. AI uses intelligent algorithms to analyze data, while RPA automates repetitive tasks to improve efficiency and accuracy. These technologies are swiftly revolutionizing health care by improving diagnostic precision, accelerating administrative tasks, reducing operation timing, and improving patient care. Application of these technologies requires good technical understanding, preparedness for continuous learning, and adaptability to new challenges. This review aims to provide an in-depth study of the potential applications, present implementations, challenges, and future scope of AI with RPA in healthcare. It can provide information to researchers, professionals, and decision-makers regarding the application of the technologies under consideration for better productivity, increased security and accuracy of data, cost reduction, and personalization of healthcare provided to patients. The main results are that AI and RPA can ensure greater data security, provide supporting work in administration, like scheduling appointments and medical billing, make better decisions, enable telehealth and remote patient monitoring, reduce human error, and increase overall health outcomes. This review overviews the challenges in implementing robotics technology, focusing mainly on secondary source journals, scholarly articles, and reference books. Key findings indicate that this study reveals how robotics could alleviate healthcare professionals. Further research, investment, and collaboration will be needed to enable these technologies to reach their full potential for healthcare delivery. However, challenges such as data privacy and security concerns, high implementation costs, and regulatory and ethical considerations must be addressed. The conclusion emphasizes that while these technologies are revolutionizing healthcare by increasing efficiency and personalizing patient care, ongoing research, investment, and collaboration are essential for their successful adoption.Cureus251The main results are that AI and RPA can ensure greater data security, provide supporting work in administration, make better decisions, enable telehealth and remote patient monitoring, reduce human error, and increase overall health outcomes.Cureus["Prashant Nimkar", "Deepika Kanyal", "Shantanu R Sabale"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/7f4cddafd1d7c4ffce35b6cb115bc5c9d3d73a6f 010e951b9221feed2aaf5a0959c27e5e9e811a31BACKGROUND Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden Scale, often fail to capture ICU-specific factors, limiting their predictive accuracy. Although artificial intelligence models offer improved accuracy, their "black box" nature poses a barrier to clinical adoption. OBJECTIVE To develop an artificial intelligence-based HAPI risk-assessment model enhanced with an explainable artificial intelligence dashboard to improve interpretability at both the global and individual patient levels. METHODS An explainable artificial intelligence approach was used to analyze ICU patient data from the Medical Information Mart for Intensive Care. Predictor variables were restricted to the first 48 hours after ICU admission. Various machine-learning algorithms were evaluated, culminating in an ensemble "super learner" model. The model's performance was quantified using the area under the receiver operating characteristic curve through 5-fold cross-validation. An explainer dashboard was developed (using synthetic data for patient privacy), featuring interactive visualizations for in-depth model interpretation at the global and local levels. RESULTS The final sample comprised 28 395 patients with a 4.9% incidence of HAPIs. The ensemble super learner model performed well (area under curve = 0.80). The explainer dashboard provided global and patient-level interactive visualizations of model predictions, showing each variable's influence on the risk-assessment outcome. CONCLUSION The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence-based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions.American Journal of Critical Care312The model and its dashboard provide clinicians with a transparent, interpretable artificial intelligence-based risk-assessment system for HAPIs that may enable more effective and timely preventive interventions.American journal of critical care : an official publication, American Association of Critical-Care Nurses["J. Alderden", "Jace Johnny", "Katie R Brooks", "Andrew Wilson", "Tracey L. Yap", "Y. Zhao", "Mark van der Laan", "S. Kennerly"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/010e951b9221feed2aaf5a0959c27e5e9e811a31 7e76985cfd22f56ec9780d255b30076cc4ddb27f Technological development particularly boosted by artificial intelligence (AI) has substantial potential to transform many aspects of human lives and the way doing businesses. On the one side, it can offer opportunities, while on the other brings challenges and increases risks. Financial industry is considered the largest user of digital technologies and provider of innovative services. Therefore, it is strongly influenced by digital transformation and under constant threat of cyberattacks. In this paper, the authors are researching the opportunities and risks stemming from the application of AI and its macroeconomic and financial system impacts. The special attention is given to the challenges posed by financial technological development and AI to central banks as they have to adopt to the novel times dominated by electronic financial services and AI tools while at the same time stay persistently dedicated to achieving their key objectives of safeguarding monetary and financial stability as well as contributing to the stability of economic growth. Additionally, the invention of generative artificial intelligence (GenAI) has significantly influenced processes throughout numerous industries, including the financial sector, due to the ability to imitate human behaviour which has enabled computers to behave like humans. Hence, it is important to develop human-centric innovations where AI tools create benefits and serve people instead of replacing them. AI can deteriorate overall inequality so policymakers should act towards developing policies that will ensure AI is used for the good of people and provide benefits for them. The authors further draw attention to the necessity of adopting a robust regulatory framework and building strong and resilient institutions with developed systems for prevention of ever raising cyberattacks.Journal of Central Banking Theory and Practice152The authors draw attention to the necessity of adopting a robust regulatory framework and building strong and resilient institutions with developed systems for prevention of ever raising cyberattacks, and the opportunities and risks stemming from the application of AI.Journal of Central Banking Theory and Practice["Milena Vu\u010dini\u0107", "Radoica Luburi\u0107"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/7e76985cfd22f56ec9780d255b30076cc4ddb27f ad330e06d9765662f247dcf108a8b82fa88fb39cThis systematic literature review examines United Arab Emirates (UAE) public sector artificial intelligence (AI) use, impact, and challenges. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 20 relevant Scopus articles were selected for the study. Data from selected articles were used to analyse AI's use, benefits, and drawbacks in the UAE's public sector. Quality assessment was done throughout the review process. The results showed that AI is being used more in the UAE's public sector to improve efficiency, cost savings, decision-making, and service delivery. The review also found data, privacy, security, technical, infrastructure, AI, and user challenges. Publication bias and the lack of AI studies in the UAE's public sector limit the study. The findings have major implications for policy and practice, emphasising the need for AI strategies and UAE-specific solutions.IAES International Journal of Artificial Intelligence (IJ-AI)561The results showed that AI is being used more in the UAE's public sector to improve efficiency, cost savings, decision-making, and service delivery and has major implications for policy and practice, emphasising the need for AI strategies and UAE-specific solutions.IAES International Journal of Artificial Intelligence (IJ-AI)["Modafar Shaker Akhoirshieda", "Ku Muhammad Naim Ku Khalif", "Suryanti Awang"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/ad330e06d9765662f247dcf108a8b82fa88fb39c d73050afe081dea4e92879f462855a29c3d8650f: Background: AI has become increasingly popular in the healthcare industry, particularly in nursing. AI helps healthcare professionals streamline their workflows, reduce errors and provide better care to patients. Aim: To assess challenges and barriers of using artificial intelligence as perceived by nursing personnel. Design : A descriptive research design was utilized. Setting: At EL Fayoum University hospitals. Subjects: All nursing personnel (250) were included in the study. Tools : Two tools were used for collecting data: Nursing Personnel’s Knowledge about Artificial Intelligence Questionnaire and Nursing Personnel’s Perception about Challenges and Barriers of Using Artificial Intelligence in Nursing Practice. Results: (58%) of the studied nursing personnel had unsatisfactory level of knowledge about AI. While, (64.8%) of the studied nursing personnel had a positive perception about challenges and barriers of using AI in nursing practice. Conclusion: There was a highly significant positive correlation between total knowledge score and total perception score about challenges and barriers of using AI in nursing practice. Recommendations: Encourage nurses to increase their knowledge and perception toward AI through training programs and providing further education to enable them integrate AI into nursing practices. Introduce fundamentals of AI into nursing curricula. Further research should be carried out to assess the AI impact on the patient-nurse relationship.Helwan International Journal for Nursing Research and Practice381There was a highly significant positive correlation between total knowledge score and total perception score about challenges and barriers of using AI in nursing practice and the AI impact on the patient-nurse relationship.Helwan International Journal for Nursing Research and Practice["Fatma Nasser Ali"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/d73050afe081dea4e92879f462855a29c3d8650f d3ec786aa856951b2dc824d47400f7bd6a63d436The film industry is increasingly adopting Artificial Intelligence in all phases of filmmaking, completely changing both the way a film is made and the way it is consumed. Through a brief review we will analyze on the one hand the aspects in which this technology is being used, both script and pre-production, visual and sound effects or the use of this technology in the distribution of films. Finally, we will look at possible scenarios that have arisen in AI science fiction films and the challenges facing humanity in its possible implementation.Cultura Digital01A brief review of the aspects in which Artificial Intelligence is being used, both script and pre-production, visual and sound effects or the use of this technology in the distribution of films.Cultura Digital["Claudia L\u00f3pez Fr\u00edas"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/d3ec786aa856951b2dc824d47400f7bd6a63d436 4b5538042f2d9ae4135cbd5df4c04aa27e703a30Artificial intelligence (AI) technology is a hallmark of the 4.0 revolution. The two main issues in Indonesia are infrastructure that needs to be equipped with technology and intelligence-based curriculum integrated with business and industry programs, and lecturers as educators who do not want to use and develop AI technology in applying guided learning models. This research aims to create a learning model based on AI that will help college students develop their talents while maintaining the Pancasila principles in the age of digitization. This study contains four stages: data collection, data analysis, research analysis outcomes, and validation of research analysis results. This research developed an AI-based learning model for use in higher education consisting of four dimensions: input, process, output, and outcome. The input dimension includes components such as students, lecturers, organizations, and infrastructure ready to adopt AI-based learning models. The process dimension consists of the elements that influence the operation of the AI-based learning model system and the functionality provided by the learning model. The output dimension includes characteristics that may be directly measured and process feedback. Finally, the outcome comprises the predicted outputs from the AI-based learning model.IAES International Journal of Artificial Intelligence (IJ-AI)362An AI-based learning model for use in higher education consisting of four dimensions: input, process, output, and outcome, which will help college students develop their talents while maintaining the Pancasila principles in the age of digitization.IAES International Journal of Artificial Intelligence (IJ-AI)["S. Wahjusaputri", "B. Bunyamin", "Tashia Indah Nastiti", "Evi Sopandi", "Tatang Subagyo", "Ionia Veritawati"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/4b5538042f2d9ae4135cbd5df4c04aa27e703a30 1f59adb79476ab5c5d3d50a01947d79c62583b22Background : There is a vast growth of Artificial Intelligence (AI) applications across all aspects of healthcare. Nursing practice is critical and AI technology will enhance practice and patientoutcomes. The study aimed to investigate the effect of educational guidelines on maternity nurses' knowledge and attitude regarding Artificial Intelligence applications. Design: A quasi-experimental research design with a pre/post-test was utilized. Setting : This study was conducted at Sohag University Hospital's obstetrics department. Sample :- A convenience sampling of all nurses working in the previously mentioned setting, Egypt. Tools: Tool (1): A self-administered Nurses' Knowledge Questionnaire regarding Artificial Intelligence and Tool (2): Nurses' General Attitudes towards Artificial Intelligence Questionnaire . Results: Pre-implementation of the educational guidelines, 90% had unsatisfactory total knowledge scores and 88% had a negative attitude. After implementing the instructional guidelines, 92% had satisfactory total knowledge scores, and 96% had a positive attitude with statistically significant differences. Moreover, highly statistically significant differences between the demographic characteristics and total knowledge level post-implementation of the educational guidelines at (P<0.00). Conclusion: The educational guidelines that were conducted in this area had a significant effect on the promotion of maternity nurses' knowledge and attitude regarding Artificial Intelligence applications. Recommendations: maternity nurses should be provided with in-service training programs related to Artificial Intelligence applications and well-informed continuous; educational guidelines should be imparted to them.Egyptian Journal of Health Care21The educational guidelines that were conducted in this area had a significant effect on the promotion of maternity nurses' knowledge and attitude regarding Artificial Intelligence applications.Egyptian Journal of Health Care["Nawal Kamal Abd Elkhalek", "ElSayeda Hamdy NasrAbdelhalim", "Heba Atef Osman", "Manal Mohamed Ahmed Ayed", "Magda Fawzy Hasab Allah Youssef"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/1f59adb79476ab5c5d3d50a01947d79c62583b22 74fa7f5ddd0760168d0623dc8b053ce6c8352290Currently, systems based on artificial intelligence (AI) are finding increasing application in medicine. Acting as assistants of both the attending physician and managing physician, they can be a good help in solving a number of problems in modern healthcare, such as staff shortage, professional burnout and, in some cases, insufficient staff qualification. However, this leads to increased requirements for reliability of such systems. Introduction of a new advanced technology raises a number of ethical issues and problems, the solution of which is necessary to gain trust of people and reduce distrust associated with the use of AI technologies. It seems that if ethical standards determine and set the progressive development of artificial intelligence, this will lead to the maximum benefit from the use of this technology in healthcare. The paper examines the ethical aspects of transition of software into the category of medical devices. At the same time, legal and organizational mechanisms for solving ethical problems at both the international and domestic levels are provided. The activities of both public and government organizations in this field are considered. The need to obtain the permission of ethical committees for conducting clinical trials and ensuring informed consent of patients is emphasized. It also highlights the importance of integrating medical data into structured datasets that can be registered as databases. This will contribute to improved quality of medical research and practice.Медицинская этика101The paper examines the ethical aspects of transition of software into the category of medical devices and highlights the importance of integrating medical data into structured datasets that can be registered as databases that will contribute to improved quality of medical research and practice.Медицинская этика["AL Khokholov", "TV Zarubina", "MYu Kotlovsky", "AV Pavlov", "MP Potapov", "ON Soldatova", "LF Gabidullina", "E. Tsybikova"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/74fa7f5ddd0760168d0623dc8b053ce6c8352290 954ecf9ccfeb0eb526a8f22a1bbdc788d9cd71efThe Lancet Digital Health656A checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research is developed that can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond.The Lancet. Digital health["Yilin Ning", "Salinelat Teixayavong", "Yuqing Shang", "Julian Savulescu", "Vaishaanth Nagaraj", "Di Miao", "M. Mertens", "D. Ting", "J. Ong", "Mingxuan Liu", "Jiuwen Cao", "Michael Dunn", "Roger Vaughan", "M. Ong", "J. Sung", "E. J. Topol", "Nan Liu"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/954ecf9ccfeb0eb526a8f22a1bbdc788d9cd71ef 1f5de8a0e78bfffba05e6dc229e0fea13205583cInternational Journal of Hospitality Management648International Journal of Hospitality Management["Yunshuo Liu", "Yanbin Li", "Keni Song", "Fulei Chu"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/1f5de8a0e78bfffba05e6dc229e0fea13205583c 49648e0ba478abd892520bdc25bbbe27fde2846aInternational Journal of Hospitality Management556International Journal of Hospitality Management["Yingwei (Wayne) Xu", "Gongmei (May) Zhou", "Ruiying (Raine) Cai", "D. Gursoy"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/49648e0ba478abd892520bdc25bbbe27fde2846a f15f3b3408d5ad8566f389b8d8b6b2625132dcc9Energy Economics637Energy Economics["Ming Zeng", "Weike Zhang"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/f15f3b3408d5ad8566f389b8d8b6b2625132dcc9 b0b341b2c2b279067c6a1d50db43ec0b3462477dThe United Nations Environment Programme (UNEP) is the leading global environmental authority that sets the global environmental agenda, promotes the environmental dimension of sustainable development within the United Nations system, and serves as an authoritative advocate for the global environment with a mandate to keep under review the world environmental situation. Against this mandate, UNEP has been requested by UN Member States to consider the environmental dimensions of digital technologies, assessing their opportunities to enable environmental sustainability and the impact they can have on the environment. This note outlines key areas identified by UNEP regarding the environmental impact of Artificial intelligence (AI) across its lifecycle. The note aims to inform Member States, civil society, the private sector and the public, while encouraging the research community to develop and use scientific methods to allow the objective measurement of AI’s environmental footprint.00This note outlines key areas identified by UNEP regarding the environmental impact of Artificial intelligence across its lifecycle, and encourages the research community to develop and use scientific methods to allow the objective measurement of AI’s environmental footprint.[]2024-09-01T00:00:00https://www.semanticscholar.org/paper/b0b341b2c2b279067c6a1d50db43ec0b3462477d 9e19221116f13a5c2cc64504d4a63ad58b6c6b72The research examines whether NVIDIA's shares are overvalued in the wake of rapid advancements in artificial intelligence (AI), with a particular focus on the role of graphical processing units (GPUs). The study uses a qualitative methodology to analyse NVIDIA's financial performance, market trends, and the impact of technological innovations on its stock valuation. Findings indicate that NVIDIA’s significant market share and pioneering role in AI and gaming have led to a sharp increase in its stock price, potentially inflating its market valuation beyond fundamental financial metrics. This overvaluation speculation is supported by an in-depth financial analysis comparing NVIDIA to its peers, revealing that its market price may reflect heightened investor expectations tied to the AI boom and its central position in the gaming industry. The research concludes that while NVIDIA demonstrates robust financial health and industry leadership, the disproportionate rise in its stock price suggests a market sentiment heavily influenced by future growth prospects in AI, warranting cautious investor assessment against traditional financial indicators.International Journal of Business and Technology Management00The research concludes that while NVIDIA demonstrates robust financial health and industry leadership, the disproportionate rise in its stock price suggests a market sentiment heavily influenced by future growth prospects in AI, warranting cautious investor assessment against traditional financial indicators.International Journal of Business and Technology Management[]2024-09-01T00:00:00https://www.semanticscholar.org/paper/9e19221116f13a5c2cc64504d4a63ad58b6c6b72 763c3c8ed666e0ad9878c0c4e835c8d077ecd546Background: Angiogenesis and inflammation are fundamental biological processes, crucial to human health. Dysregulation of these processes is implicated in diseases such as cancer, cardiovascular disorders, and autoimmune diseases. Despite advances in research, the complexity of these interactions has been challenging to understand. Recent developments in artificial intelligence (AI) offer a promising approach for overcoming these challenges, especially in big data analysis. This study explores AI applications in quantifying angiogenesis and inflammatory markers and predicting disease progression. Methods: AI algorithms, including machine learning (ML) and deep learning (DL), were employed to analyze high-throughput biological data. The study applied Lasso regression for biomarker discovery, Long Short-Term Memory (LSTM) networks for predicting disease progression, and Gaussian Mixture Models (GMM) for patient subgroup identification. Image analysis using DeepLabv3+ was conducted to assess angiogenesis and inflammatory markers in histological images. Model performance was evaluated using R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. Results: The AI framework demonstrated high accuracy in predicting disease progression, with notable R² values and low MSE and RMSE values. The application of AI led to the successful identification of angiogenesis-related genes and biomarkers in various diseases, including diabetic foot ulcers and chronic obstructive pulmonary disease. AI-based image analysis also provided precise quantification of angiogenesis and inflammation, enhancing the understanding of disease mechanisms. Conclusion: AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation. The high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring. As AI continues to evolve, its integration into biomedical research will likely yield further advancements in disease prediction, diagnosis, and treatment.Journal of Angiotherapy00AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation, and high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring.Journal of Angiotherapy[]2024-09-01T00:00:00https://www.semanticscholar.org/paper/763c3c8ed666e0ad9878c0c4e835c8d077ecd546 750b2a6ab7106a97b0bb799c9cf9b64435ed8949 The ageing populations in Ireland and around the world has led to an increased focus on technologies that support independent living for older adults. Artificial intelligence (AI) has emerged as a promising solution, with various AI-powered technologies being proposed to assist their daily lives. This review aims to synthesize the findings of studies exploring AI-powered technologies for independent living among older adults. A systematic literature search was conducted using relevant databases, including PubMed, IEEE Xplore, and Google Scholar. The search terms included “artificial intelligence” and “independent living”. Studies published between 2009 and 2023 that investigated AI-powered technologies for independent living among older adults were included in the analysis. The included studies investigated various AI-powered technologies including tablets, wearable devices like smartwatches, web portals for carers, Internet of Things (IoT) solutions, user-centered design considerations, the integration of AI with sociology and healthcare, smart home technology, and ambient information systems. The pooled results indicated that AI-powered technologies can improve independent living outcomes for older adults. Benefits included real-time monitoring and health deterioration recognition, fall detection, personalised care, cognitive assistance and reduced isolation. User-centered design approaches and the integration of AI with sociology and healthcare were also found to be essential factors in the successful implementation of these technologies. This analysis provides evidence supporting the effectiveness of AI-powered technologies in promoting independent living among older adults. IoT solutions and smart home technology appear to be particularly promising. However, the successful implementation of these technologies requires a multidisciplinary approach, incorporating user-centered design and the integration of AI with sociology and healthcare. Further research is needed to explore the long-term impact of AI-powered technologies on the well-being and autonomy of older adults. Age and Ageing00Evidence supporting the effectiveness of AI-powered technologies in promoting independent living among older adults is provided, including real-time monitoring and health deterioration recognition, fall detection, personalised care, cognitive assistance and reduced isolation.Age and Ageing["Eugene Gamble", "P. Chami", "Tamara Nancoo"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/750b2a6ab7106a97b0bb799c9cf9b64435ed8949 d94b4bb518260ba1e995cc80f1b1a86fadc395c0This paper investigates the application of advanced technologies in the optimization of product quality in a dedicated industry. As the demands for high-quality products in this sector are increasing, so is the need for efficient optimization methods, which encourage the development and implementation of innovative technological solutions. Focusing on artificial intelligence, soft computing and related techniques enables improvement of the production process, identification of problems in early stages, prediction of potential defects and optimization of production parameters, with a deep consideration of how to improve quality control, reduce costs and achieve greater competitiveness in the market. The aim of the work is focused on the research of the input factors that influence the quality of the product. An analysis of literature and practical studies investigates how these technologies can improve processes in industry, exploring how they bring benefits and what challenges they can represent. Through this work, the door is opened for further development and implementation, to create more efficient processes and superior product quality.Artificial Intelligence in Industry 4.0: The future that comes true00This paper investigates the application of advanced technologies in the optimization of product quality in a dedicated industry with a deep consideration of how to improve quality control, reduce costs and achieve greater competitiveness in the market.Artificial Intelligence in Industry 4.0: The future that comes true["Marijana Mojsilovi\u0107", "Selver H. Pepic", "Muzafer Sara\u010devi\u0107"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/d94b4bb518260ba1e995cc80f1b1a86fadc395c0 2b39e43287a0e96aa634ec07f5519aea0cc4be20Marvin Lee Minsky, a pioneering figure in artificial intelligence (AI), was born on August 9, 1927, in the city of New York. His father, Henry, was an eye surgeon, while his mother, Fannie, was involved in Zionist activities. Minsky was instrumental in establishing the AI laboratory at the Massachusetts Institute of Technology (MIT) and authored numerous influential works on AI and philosophy. Among his many accolades was the prestigious Turing Award, which he received in 1969. Minsky was an exceptionally brilliant, creative, and charismatic individual, whose intellect and imagination were evident in his work. His ideas played a pivotal role in shaping the computer revolution that has profoundly transformed modern life in recent decades. In 1957, Minsky patented the confocal microscope, a significant invention that was a forerunner to today's confocal laser scanning microscopes. This innovation significantly improved image clarity and contrast by focusing light on a specific depth within a sample, unlike traditional microscopes, which allow light to penetrate deeper layers. The influence of his contributions continues to resonate in contemporary efforts to develop intelligent machines, one of the most thrilling and significant undertakings of our time.Cureus110His ideas played a pivotal role in shaping the computer revolution that has profoundly transformed modern life in recent decades and the influence of his contributions continues to resonate in contemporary efforts to develop intelligent machines.Cureus["B. Patil-Takbhate", "Rupesh Takbhate", "Priyanka S Khopkar-Kale", "Srikanth Tripathy"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/2b39e43287a0e96aa634ec07f5519aea0cc4be20 90d34689132c91cda7d7f205192e4bb9a936b3a5The proposed research focuses on enhancing the interpretability of risk evaluation in credit approvals within the banking sector. This work employs LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) to provide explanations for individual predictions: LIME approximates the model locally with an interpretable model, while SHAP offers insights into the contribution of each feature to the prediction through both global and local explanations. The research integrates gradient boosting algorithms (XGBoost, LightGBM) and Random Forest with these Explainable Artificial Intelligence (XAI) techniques to present a more comprehensible framework. The results demonstrate how interpretability methods such as LIME and SHAP enhance the transparency and trustworthiness of machine learning models, which is crucial for applications in credit risk evaluation.Journal of Soft Computing Paradigm120The results demonstrate how interpretability methods such as LIME and SHAP enhance the transparency and trustworthiness of machine learning models, which is crucial for applications in credit risk evaluation.Journal of Soft Computing Paradigm["Sowmiya M N.", "Jaya Sri S.", "Deepshika S.", "Hanushya Devi G."]2024-09-01T00:00:00https://www.semanticscholar.org/paper/90d34689132c91cda7d7f205192e4bb9a936b3a5 b644894794fa76e6d8d113ba8dc0962407f8d2d3The role of artificial intelligence in education (AIEd) has recently become a major topic of discussion and future planning. This article presents data from a large-scale survey involving 1463 Bulgarian educators in primary, secondary, and high schools. The results revealed that 70.30% of the teachers were familiar with or somewhat familiar with the existence of AI applications. Chatbots were the most popular among the surveyed teachers, with ChatGPT ranking as the most familiar. The teachers were almost equally split between those who reported use and those who declared nonuse of AI technology for instructional purposes. A significant association was found between the teachers’ familiarity with and use of AI technology and their age-related generational traits. The younger educators (up to 40 years of age) were associated with higher use of AI technology as a support tool for creating lesson plans, lesson content, tests, and exams. The outlined tendencies can be used to inform policy, professional development, and future research in the realm of AI-driven education.CLIB00A significant association was found between the teachers’ familiarity with and use of AI technology and their age-related generational traits, which can be used to inform policy, professional development, and future research in the realm of AI-driven education.Proceedings of the Sixth International Conference on Computational Linguistics in Bulgaria["D. Kurshumova"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/b644894794fa76e6d8d113ba8dc0962407f8d2d3 65f11dbca3a6153ea4bb34f3726c474e17998799 The article is devoted to the study of the influence of artificial intelligence (AI) on modern journalism in the system of digital media communications. Both threats and opportunities introduced by new neural network technologies are under consideration. The article presents the results of a survey conducted among representatives of creative professions, the purpose of which was to find out the attitude to artificial intelligence and its role in their professional activities. The article focuses on the need for further research and regulation of the use of neural network technologies to minimize risks of their use in media communications. To conduct the study, a survey method was chosen that allowed to collect quantitative data on the opinions and perceptions of AI by representatives of the media industry. The scientific novelty of this study lies in the systematic analysis of opinions and perceptions of AI among representatives of professions that receive their main financial income through creative and intellectual work, such as journalism, advertising and public relations, design, music, etc. Unlike most existing studies that focus on the technical aspects of AI, this study focuses on the social and ethical aspects of its application. This allows us to gain a more comprehensive understanding of the role of AI in society and identify key factors influencing its perception. The practical significance of the study lies in the fact that its results can be used to develop recommendations for integrating AI into various spheres of life, as well as regulating issues of control over the use of neural network technologies in modern society. The results can also help analyze public sentiment and expectations, which will allow making targeted and effective decisions in the development and implementation of AI technologies. Litera00The article presents the results of a survey conducted among representatives of creative professions to find out the attitude to artificial intelligence and its role in their professional activities, and focuses on the need for further research and regulation of the use of neural network technologies to minimize risks of their use in media communications.Litera["P. Y. Gurushkin", "Kristina Vladimirovna Korneeva"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/65f11dbca3a6153ea4bb34f3726c474e17998799 806f9ad57fb667129e6e32ed6a23b6b369059499 Due to progressive increase of complexity, the automotive industry is subject to constantly changing trends varying from the introduction of greener products and components to the deployment of technological advances in development and engineering processes. In relation to both, sustainable automotive products as well as the deployment of technological advances, the integration of AI (Artificial Intelligence) approaches in combination with virtual products in automotive development and engineering processes is of great importance. In combination with knowledge-based CAx (Computer-Aided engineering), the integration of AI approaches delivers an enormous potential to enhance automotive development processes. In addition to process optimizations, the integration of various AI approaches considers sustainability (e.g., optimization of component geometries and materials, reduction of emissions over the entire life cycle, CO2 reduction through improved development) and economical aspects (e.g., resources savings throughout the entire development process, time and cost savings through earlier error detection, avoidance of unnecessary process steps). The present approach deals with the integration of AI and knowledge-based engineering methods in the early phases of automotive development and engineering processes. Furthermore, the paper points to the time, cost and resources reduction potential, leading to earlier market entries and a greener industry. Finally, the paper demonstrates the integration of AI technologies into industrial development and engineering processes based on selected application scenarios.IOP Conference Series: Materials Science and Engineering110The present approach deals with the integration of AI and knowledge-based engineering methods in the early phases of automotive development and engineering processes and points to the time, cost and resources reduction potential, leading to earlier market entries and a greener industry.IOP Conference Series: Materials Science and Engineering["A. Kreis", "M. Hirz"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/806f9ad57fb667129e6e32ed6a23b6b369059499 c7126621728bef74b2172fedc84a6a4fb8b39d01Artificial intelligence (AI) has revolutionized ophthalmology by aiding in the diagnosis, prognosis, and treatment planning of various eye diseases. However, AI’s potential extends beyond ocular conditions. By analyzing eye-related biomarkers, AI can utilize the eye as a window into the body’s systemic health. This field, known as oculomics, leverages AI and deep learning algorithms to process vast amounts of data from imaging techniques such as fundus photography, optical coherence tomography (OCT), OCT angiography, infrared iris imaging, slit-lamp photography, and external eye photography. AI-powered analysis of these images can predict systemic diseases such as Alzheimer’s, Parkinson’s, cardiovascular disease, cerebrovascular disease, chronic kidney disease, and liver disease. Retinal changes —including alterations in the retinal nerve fiber layer, ganglion cell layer, and retinal vessels —serve as valuable indicators of these conditions. Additionally, AI can estimate age, sex, body composition, and other health parameters from eye images. While the potential of AI in oculomics is promising, challenges such as access to ophthalmic imaging, data quality, and the need for rigorous validation must be addressed to ensure its widespread adoption and clinical utility. Nevertheless, AI holds the potential to transform healthcare by enabling early detection, noninvasive screening, and personalized treatment for a wide range of systemic diseases.Kerala Journal of Ophthalmology340Challenges such as access to ophthalmic imaging, data quality, and the need for rigorous validation must be addressed to ensure its widespread adoption and clinical utility.Kerala Journal of Ophthalmology["J. Akkara"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/c7126621728bef74b2172fedc84a6a4fb8b39d01 486c52dab5495e72df1e7fc32cc39af09eea85c8In a time of innovation, Artificial Intelligence (AI) is leading the way exploring frontiers, w  in innovation and business. This engaging story delves into the impact of AI portraying it as a driver of opportunities while also recognizing the significant challenges that it brings. By examining how AI transforms customer interactions streamlines processes, enhances decision making this exploration delves deep into AIs capabilities and the ethical and technical dilemmas it raises. Through analysis this research paper aims to shed light on how to navigate AIs potential in the business world showcasing its transformative role across various industries existing. This article not only highlights AIs influence but also encourages readers to think about its future path paving the way for new discoveries, new opportunities. In doing it promotes a discussion, on using AIs power responsibly and in a secure way to ensure its benefits are maximized while addressing its challenges thoughtfully.Highlights in Business, Economics and Management100This engaging story delves into the impact of AI portraying it as a driver of opportunities while also recognizing the significant challenges that it brings, by examining how AI transforms customer interactions streamlines processes, enhances decision making.Highlights in Business, Economics and Management["Yanbo Zhao"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/486c52dab5495e72df1e7fc32cc39af09eea85c8 6c0a16f41bd95dea531f79b5a54e2a2a2c2219b7Background : The inclusion of artificial intelligence (AI) based technologies in nursing practice has sparked concerns and public discussions, with some people worried that this technology could replace nurses. Aim: The study examined the effect of instructional sessions on nursing perspectives and attitudes regarding use of artificial intelligence for fetal monitoring in maternity units. Design: A quasi-experimental design was used, involving a single group pre-and post-intervention. Setting: The study was conducted at Minia University Hospital's maternity units in Egypt. Sample: A purposive sample of 51 nurses working in maternity units was included. Tools: Two tools were used: Self-administered questionnaire about nurses' demographics and perspectives on AI-driven Cardiotocography (CTG) for fetal monitoring, and a questionnaire about nurses' attitudes towards using AI for fetal monitoring. Results: Before the training, 76.5% of nurses had low total perspectives scores and 77.4% had a negative attitude. After the training, 62.8% had high total perspectives scores and 76.5% had a positive attitude, with significant differences. Additionally, there were significant differences between demographic characteristics and total perspectives and attitudes levels post-intervention (P<0.001), and a significant positive correlation between nurses' total perspectives and attitudes post-intervention (r= 0.980 & p= 0.001). Conclusion: The study concluded that the training sessions significantly improved maternity nurses' perspectives and attitudes towards using AI-driven CTG for fetal monitoring. Recommendations: Providing maternity nurses with in-service training programs on AI applications in obstetrics and ongoing education on AI.Egyptian Journal of Health Care400It was concluded that the training sessions significantly improved maternity nurses' perspectives and attitudes towards using AI-driven CTG for fetal monitoring, and a significant positive correlation between nurses' total perspectives and attitudes post-intervention was found.Egyptian Journal of Health Care["Amany Shehata Ahmed", "Safaa Helmy Mohamed", "Mona Ahmed Abd-Elhamed"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/6c0a16f41bd95dea531f79b5a54e2a2a2c2219b7 1826c674ba61856fbdfbda3fb2132122fcf03428Implementations of artificial intelligence have provided what was previously unimaginable in providing, educating, and dealing with people with autism spectrum disorder in their various fields in their daily lives, as it empowers them socially and provides them with the ability to develop their abilities in life skills and raiseCollege of Special Education Journal00College of Special Education Journal["Hossam Mahmoud Zaki", "Asmaa Fathy Lotfy Abed Alfattah"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/1826c674ba61856fbdfbda3fb2132122fcf03428 386c3117a119a0725fb7cdb31735c7ed292f3db9 The design process across various industries involves intricate decision-making, problem-solving, and consideration of multiple factors. However, risks during the design process can jeopardize project success and lead to costly errors. Traditional risk management approaches in design often rely on manual & computerized analysis, which is time-consuming, limited in scope, and prone to human biases. The emergence of artificial intelligence (AI) presents an opportunity to revolutionize risk management in design. AI algorithms can process vast amounts of data, identify patterns, and provide predictive insights, enhancing risk identification and mitigation. This research aims to investigate the relationship between design risks and AI towards enhancing the risk management process. Using a mixed methodology approach, a qualitative method was used through investigating previous literature to identify traditional risk management limitations, design risks and AI tools and methods. Secondly, relationship between identified risks and AI tools was proposed. This was followed by quantitative method through case studies analysis that assess the validity of the proposed relationship. The goal is to establish a new paradigm where AI and risk management converge to create a future where risks that occur during the design process can effectively be identified and managed, fostering innovation and improving project outcomes.IOP Conference Series: Earth and Environment200The goal is to establish a new paradigm where AI and risk management converge to create a future where risks that occur during the design process can effectively be identified and managed, fostering innovation and improving project outcomes.IOP Conference Series: Earth and Environmental Science["N. Algheetany", "A. A. E. Othman", "F. O. Alamoudy"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/386c3117a119a0725fb7cdb31735c7ed292f3db9 99f7f37770ebd2267cabd354145a78ca64cbc855This study aims to examine the role of artificial intelligence (AI) on the level of unemployment in the context of Kuwait. This study collected secondary data based on the available data from Kuwait authorities. The inclusion criteria covered all the relevant studies that aimed to assess the effects of introducing AI through an economic model. This study highlights the effect that AI could have on the economic growth of any given country (i.e., Kuwait); it relies on Cobb-Douglas technology, Baumol’s cost disease, and the Galor-Zeira models to predict the kind of reaction and results that Kuwait should expect to experience assuming their current trajectory is not reviewed. The findings of this study helped develop a conclusion regarding the effects that AI will have on the Kuwait. As such, it indicates that the number of tasks automated over time will be greater than the number of labor-intensive tasks created.التجارة والتمويل860The findings of this study helped develop a conclusion regarding the effects that AI will have on the Kuwait, and it indicates that the number of tasks automated over time will be greater than the number of labor-intensive tasks created.التجارة والتمويل["Abdullah Feraih Alenezi"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/99f7f37770ebd2267cabd354145a78ca64cbc855 6cc9ee1b14b538283d689d7d972e7eff316ca6b8Artificial intelligence (AI) has already found its way into ophthalmology, with the first approved algorithms that can be used in clinical routine. Retinal diseases in particular are proving to be an important area of application for AI, as they are the main cause of blindness and the number of patients suffering from retinal diseases is constantly increasing. At the same time, regular imaging using high-resolution modalities in a standardised and reproducible manner generates immense amounts of data that can hardly be processed by human experts. In addition, ophthalmology is constantly experiencing new developments and breakthroughs that require a re-evaluation of patient management in routine clinical practice. AI is able to analyse these volumes of data efficiently and objectively and also provide new insights into disease progression and therapeutic mechanisms by identifying relevant biomarkers. AI can make a significant contribution to screening, classification and prognosis of various retinal diseases and can ultimately be a clinical decision support system, that significantly reduces the burden on both everyday clinical practice and the healthcare system, by making more efficient use of costly and time-consuming resources.Klinische Monatsblätter für Augenheilkunde520Artificial intelligence can make a significant contribution to screening, classification and prognosis of various retinal diseases and can ultimately be a clinical decision support system, that significantly reduces the burden on both everyday clinical practice and the healthcare system, by making more efficient use of costly and time-consuming resources.Klinische Monatsblatter Fur Augenheilkunde["Julia Mai", "Ursula Schmidt-Erfurth"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/6cc9ee1b14b538283d689d7d972e7eff316ca6b8 850deee07a4e56d1928a7bfbdb3dcaa0141145daAbstract: This article analyzes the impacts of artificial intelligence (AI) on the safety and security of civilian critical infrastructure (CI) in relation to US national security. While the present focus of AI policy, strategy, and regulation is responsibly directed toward corruption in social media, disinformation, data privacy, and innovation, US AI policy, strategy, and regulation toward civilian CI is critically lacking. AI is still a developing technology, and its applicability has yet to be entirely understood. Further research on AI's potential implications is more important than ever to determine how this double-edged technology will shape future national security issues.SAIS Review of International Affairs00The impacts of artificial intelligence (AI) on the safety and security of civilian critical infrastructure (CI) in relation to US national security is analyzed.SAIS Review of International Affairs["Blake Hunnewell"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/850deee07a4e56d1928a7bfbdb3dcaa0141145da 1538252f1109614733ee1b1d8d6d031f7cb683f2The incidence of melanoma, the most aggressive form of skin cancer, continues to rise globally, particularly among fair-skinned populations (type I and II). Early detection is crucial for improving patient outcomes, and recent advancements in artificial intelligence (AI) have shown promise in enhancing the accuracy and efficiency of melanoma diagnosis and management. This review examines the role of AI in skin lesion diagnostics, highlighting two main approaches: machine learning, particularly convolutional neural networks (CNNs), and expert systems. AI techniques have demonstrated high accuracy in classifying dermoscopic images, often matching or surpassing dermatologists’ performance. Integrating AI into dermatology has improved tasks, such as lesion classification, segmentation, and risk prediction, facilitating earlier and more accurate interventions. Despite these advancements, challenges remain, including biases in training data, interpretability issues, and integration of AI into clinical workflows. Ensuring diverse data representation and maintaining high standards of image quality are essential for reliable AI performance. Future directions involve the development of more sophisticated models, such as vision-language and multimodal models, and federated learning to address data privacy and generalizability concerns. Continuous validation and ethical integration of AI into clinical practice are vital for realizing its full potential for improving melanoma diagnosis and patient care.Cureus1270This review examines the role of AI in skin lesion diagnostics, highlighting two main approaches: machine learning, particularly convolutional neural networks (CNNs), and expert systems.Cureus["Sadhana Kalidindi"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/1538252f1109614733ee1b1d8d6d031f7cb683f2 af9228ecad65b4d62f0ff0ed3233d89bc4df2b1a Artificial intelligence (AI) is fundamentally shaping everyday life – the world of work is no exception. While the application of AI in the workplace does have many possibilities, it challenges employees’ (fundamental) rights. The article's objective is to provide a “glimpse” into the (possible future) use of AI in employment and to overview and identify possible legal challenges and solutions. The research is conducted using a desk-based legal analysis of relevant literature and legal documents. It focuses on the EU legal order and mentions several examples from Hungary. As a result, the paper highlights possible solutions to the challenges, especially new legal initiatives, as well as outlines the main potential guidelines for amending existing regulations. To conclude, in order to harness AI's potential, a solid legal framework is necessary, as AI raises old questions with new intensity.Danube110DANUBE["Adrienn Hadady-Luk\u00e1cs"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/af9228ecad65b4d62f0ff0ed3233d89bc4df2b1a 1c534e43eb7758f61f9e7994068820491c859487In an era of rapid advancements in artificial intelligence (AI) technologies, particularly in medical imaging and natural language processing, strategic efforts to leverage AI's capabilities in analyzing complex medical data and integrating it into clinical workflows have emerged as a key driver of innovation in healthcare [...].Diagnostics200Diagnostics["S. Rajaraman", "Zhiyun Xue", "S. Antani"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/1c534e43eb7758f61f9e7994068820491c859487 3d87fa6d3e7eaafad7b459e81a9c5f682c698393Despite the immense potential of artificial intelligence (AI)-powered medical devices to revolutionize health care, concerns regarding their safety in life-critical applications remain. This article proposes extending the general idea of AI lifecycle with regulatory activities relevant to AI-enabled medical systems.Computer240This article proposes extending the general idea of AI lifecycle with regulatory activities relevant to AI-enabled medical systems to address concerns regarding their safety in life-critical applications.Computer["Tuomas Granlund", "Vlad Stirbu", "T. Mikkonen"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/3d87fa6d3e7eaafad7b459e81a9c5f682c698393 cec682e81880e6df146a5ed2b0cb478685bb47c4Integrating artificial intelligence (AI) into yoga therapy represents a transformative paradigm shift in holistic health management. This article explores the evolving landscape of AI in yoga therapy, encompassing recent advancements, potential applications, ethical considerations, and implications for well-being. Recent advancements in AI have enabled real-time monitoring and personalized interventions during yoga practice, offering unprecedented customization and efficacy. AI-powered virtual assistants and telehealth platforms extend the reach of yoga therapy interventions, enhancing accessibility and inclusivity. However, ethical considerations surrounding privacy, autonomy, equity, transparency, and cultural sensitivity must be carefully addressed to ensure responsible deployment and safeguard the well-being of individuals. By prioritizing ethical principles and values, stakeholders can harness AI’s transformative potential to advance the yoga therapy field and promote holistic well-being for individuals and communities worldwide.International Journal of Yoga150This article explores the evolving landscape of AI in yoga therapy, encompassing recent advancements, potential applications, ethical considerations, and implications for well-being.International Journal of Yoga["Nitu Sinha", "Rajesh Kumar Sinha"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/cec682e81880e6df146a5ed2b0cb478685bb47c4 c58bd49ac34b8b06727e50ed01c78256b8280845This paper explores the application of artificial intelligence (AI) in smart IoT and electrical automation, along with the security challenges it brings. It outlines the important roles of smart IoT and electrical automation, and the opportunities and challenges that AI presents within them. The paper analyzes potential data security, algorithmic security, and system security issues that may arise from AI and proposes corresponding countermeasures. Through case studies, it demonstrates practical effectiveness and concludes by emphasizing the practical significance of researching security issues in this field and future prospects.Innovation Science and Technology00The paper analyzes potential data security, algorithmic security, and system security issues that may arise from AI and proposes corresponding countermeasures and demonstrates practical effectiveness.Innovation in Science and Technology["Shangpeng Li"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/c58bd49ac34b8b06727e50ed01c78256b8280845 1226e46519243c9616925e4ed0ffd54278084b77 One of the constraints on advancing livestock research is the lack of large-scale quantification of physiological traits, a process often constrained by financial, labor-intensive, and scalability limitations, in addition to being traditionally performed through invasive methods. However, in the era of big data, the collection of large, extensive, and heterogeneous datasets via digital technologies, coupled with Artificial Intelligence (AI) techniques, has catalyzed significant progress across various research domains, including precision medicine, education, finance, and environmental and socioeconomic studies. This presentation will highlight our research efforts in employing computer vision systems (CVS). It will focus on a longitudinal study predicting body weight (BW) using a keypoint model. The discussion will include the advantages of using keypoints and 2D images to assess BW, which is important in the fields of animal growth and physiology in dairy and beef farm operations. We will also discuss the use of machine learning algorithms to early detect subclinical ketosis and locomotion problems in dairy cows. Lastly, this presentation will showcase the use of Unmanned Aerial Vehicles (UAVs) for pasture and soil management in grazing conditions. Our main goal with this presentation is to discuss how CVS and AI can generate valuable data in different areas, allowing for large-scale phenotyping, improving farm performance and sustainability.Journal of Animal Science00The main goal with this presentation is to discuss how CVS and AI can generate valuable data in different areas, allowing for large-scale phenotyping, improving farm performance and sustainability.Journal of Animal Science["J. D\u00f3rea", "Guilherme Lobato Menezes"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/1226e46519243c9616925e4ed0ffd54278084b77 25b2d82bb50a7502e7ed5bdc6212b3e169461a57This article presents a conceptual overview of artificial intelligence (AI) research in the realm of E-commerce. Potential research themes, explored through content analysis and visualization techniques, offer a deeper understanding of the knowledge landscape in this field. The study utilized R Studio and VOS viewer to analyze the performance and map the scientific output of 1,458 research articles from the Scopus database (1995–2024). The study examines the conceptual structure of data through clustering themes and network analysis. The findings indicate a significant focus on advanced E-commerce analytics within AI research, with key areas including product recommendations and AI-driven customer support. The research spans diverse fields such as computer science, marketing, and psychology, emphasizing AI’s interdisciplinary applications in E-commerce. The research’s novelty lies in providing TCCM-based insights for future research and guidance for practitioners looking to leverage AI in their E-commerce operations.NMIMS Management Review390The study examines the conceptual structure of data through clustering themes and network analysis and indicates a significant focus on advanced E-commerce analytics within AI research, with key areas including product recommendations and AI-driven customer support.NMIMS Management Review["Priya Chugh", "Vishu Jain"]2024-09-01T00:00:00https://www.semanticscholar.org/paper/25b2d82bb50a7502e7ed5bdc6212b3e169461a57 2f312bacb02560fb64803334301135cb9d85a218The advent of artificial intelligence (AI), often known as the fourth industrial revolution (IR 4.0),will affect not just our day-to-day activities and social interactions, but also our understanding of who weare. Artificial intelligence, however, has a profound effect on the way we go about our daily lives andconnect with one another. Keep an eye on the progress of AI to ensure that everyone can reap the benefitsof this new kind of intelligence. This idea is linked to the concept that AI should display intelligentbehaviour. Prior to this, only humans were allowed there. Artificial intelligence (AI) can act autonomously ina variety of contexts and solve complex issues without human intervention. The way people think about thedevelopment of artificial intelligence has changed drastically as a result. There are many facets of modernlife in which artificial intelligence is rapidly progressing. There are many potential applications for artificialintelligence, including healthcare and the creation of game-changing technology like autonomous cars.There have been good and bad results from AI's entrance into society. The major purpose of this studyis to examine the effects of AI on society and the difficulties AI faces.Journal of Advances in Science and Technology73The effects of AI on society and the difficulties AI faces are examined to examine the effects of AI on society and the difficulties AI faces.Journal of Advances in Science and Technology["Sannidhi Agarwal Sannidhi Agarwal"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/2f312bacb02560fb64803334301135cb9d85a218 ff67ce06aa91ebde411f7eae4800f9c09b47be77BMC Medicine723A framework for applying ML and AI to uncover novel insights and inform targeted interventions is discussed, and recommendations for harnessing ML and AI technologies to develop innovative public health solutions are provided.BMC Medicine["Shanquan Chen", "Jiazhou Yu", "Sarah Chamouni", "Yuqi Wang", "Yunfei Li"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/ff67ce06aa91ebde411f7eae4800f9c09b47be77 73662dcaac79efe1a2c4a9aa27b40982a2be8d1cArtificial intelligence (AI) has emerged as an effective solution to alleviate excessive carbon emissions in sustainable building projects. Although there are numerous applications of AI, there is no state-of-the-art review of how AI applications can reduce net-zero carbon emissions (NZCEs) for sustainable building projects. Therefore, this review study aims to conduct a systematic literature and science mapping review of AI applications in NZCEs for sustainable building projects, thereby expediting the realization of NZCEs in building projects. A mixed-method approach (i.e., systematic literature review and science mapping) consisting of four comprehensive stages was used to retrieve relevant published articles from the Scopus database. A total of 154 published articles were retrieved and used to conduct science mapping analyses and qualitative discussions, including mainstream research topics, gaps, and future research directions. Six mainstream research topics were identified and discussed. These include (1) life cycle assessment and carbon footprint, (2) practical applications of AI technology, (3) multi-objective optimization, (4) energy management and energy efficiency, (5) carbon emissions from buildings, and (6) decision support systems and sustainability. In addition, this review suggests six research gaps and develops a framework depicting future research directions. The findings contribute to advancing AI applications in reducing carbon emissions in sustainable building projects and can help researchers and practitioners to realize its economic and environmental benefits.Buildings1511A systematic literature and science mapping review of AI applications in NZCEs for sustainable building projects and suggests six research gaps and develops a framework depicting future research directions contribute to advancing AI applications in reducing carbon emissions in sustainable building projects.Buildings["Yanxue Li", "M. Antwi-Afari", "Shahnwaz Anwer", "Imran Mehmood", "Waleed Umer", "Saeed Reza Mohandes", "I. Y. Wuni", "M. Abdul-Rahman", "Heng Li"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/73662dcaac79efe1a2c4a9aa27b40982a2be8d1c 55a8229017a946b8298d5c1d47a6531e672573fdIntroduction: Artificial Intelligence (AI) encompasses technologies such as machine learning and neural networks, with applications across various fields. The World Health Organization recognizes its potential to enhance healthcare, yet emphasizes the need to address ethical considerations in its implementation. In nursing, AI has the potential to increase autonomy and efficiency in care, though its use remains limited and poorly understood within the profession.Objective: To analyze the use of AI in nursing by evaluating its impact on care functions, administrative tasks, educational activities, and research.Methods: A literature review was conducted, including original articles, reviews, and bibliometric studies. The research focused on AI applications across the four primary functions of nursing.Results: AI has demonstrated benefits in predictive analytics and improving patient care efficiency, as well as in administrative management and patient classification. In education, generative AI facilitates the development of educational materials, although it presents risks of bias. In research, AI serves as an assistant in data search and analysis, despite facing ethical and methodological challenges.Conclusions: AI has the potential to significantly transform nursing practice, enhancing both the quality and efficiency of care. However, its integration necessitates careful management to address its limitations and ensure a positive impact in the field.LatIA121AI has the potential to significantly transform nursing practice, enhancing both the quality and efficiency of care, however, its integration necessitates careful management to address its limitations and ensure a positive impact in the field.LatIA["Miguel Valencia-Contrera", "Fl\u00e9rida Rivera-Rojas", "Jenifer Villa-Vel\u00e1squez", "Daniella Cancino-Jim\u00e9nez"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/55a8229017a946b8298d5c1d47a6531e672573fd 9c10800e9efca3b03f1ff0287fb54d86a19f1308Breast cancer remains a leading cause of cancer-related mortality among women worldwide, necessitating early and accurate detection for effective treatment and improved survival rates. Artificial intelligence (AI) has shown significant potential in enhancing the diagnostic and prognostic capabilities in breast cancer recognition. However, the black-box nature of many AI models poses challenges for their clinical adoption due to the lack of transparency and interpretability. Explainable AI (XAI) methods address these issues by providing human-understandable explanations of AI models’ decision-making processes, thereby increasing trust, accountability, and ethical compliance. This review explores the current state of XAI methods (Local Interpretable Model-agnostic Explanations, Shapley Additive explanations, Gradient-weighted Class Activation Mapping) in breast cancer recognition, detailing their applications in various tasks such as classification, detection, segmentation, prognosis, and biomarker discovery. By integrating domain-specific knowledge and developing visualization techniques, XAI methods enhance the usability and interpretability of AI systems in clinical settings. The study also identifies the key challenges and future directions in the evaluation of XAI methods, the development of standardized metrics, and the seamless integration of XAI into clinical workflows.Innovation Discovery691This review explores the current state of XAI methods (Local Interpretable Model-agnostic Explanations, Shapley Additive explanations, Gradient-weighted Class Activation Mapping) in breast cancer recognition, detailing their applications in various tasks such as classification, detection, segmentation, prognosis, and biomarker discovery.Innovation Discovery["R. Dama\u0161evi\u010dius"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/9c10800e9efca3b03f1ff0287fb54d86a19f1308 dc46257df3b8ed45150fae5a6de0e9a4575f6a23Diabetic retinopathy is a disease that can lead to vision loss and blindness in people with diabetes, so its early detection is important to prevent ocular complications. The aim of this study was to analyze the usefulness of artificial intelligence in the detection of diabetic retinopathy. For this purpose, an exploratory systematic review was performed, collecting 77 empirical articles from the Scopus, IEEE, ACM, SciELO and NIH databases. The results indicate that the most commonly used factors for the detection of diabetic retinopathy include changes in retinal vascularization, macular edema and microaneurysms. Among the most commonly applied algorithms for early detection are ResNet 101, CNN and IDx-DR. In addition, some artificial intelligence models are reported to have an accuracy ranging from 90% to 95%, although models with accuracies below 80% have also been identified. It is concluded that artificial intelligence, and in particular deep learning, has been shown to be effective in the early detection of diabetic retinopathy, facilitating timely treatment and improving clinical outcomes. However, ethical and legal concerns arise, such as privacy and security of patient data, liability in case of diagnostic errors, algorithmic bias, informed consent, and transparency in the use of artificial intelligence.LatIA691It is concluded that artificial intelligence, and in particular deep learning, has been shown to be effective in the early detection of diabetic retinopathy, facilitating timely treatment and improving clinical outcomes.LatIA["Richard Injante", "Marck Julca"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/dc46257df3b8ed45150fae5a6de0e9a4575f6a23 4bd27c8ae11902c5d741a40232163ef0ebf48faeThe options for Artificial intelligence (AI) tools used in teacher education are increasing daily, but more is only sometimes better for teachers working in already complex classroom settings. This team discusses the increase of AI in schools and provides an example from administrators, teacher educators, and computer scientists of an AI virtual agent and the research to support student learning and teachers in classroom settings. The authors discuss the creation and potential of virtual characters in elementary classrooms, combined with biometrics and facial emotional recognition, which in this study has impacted student learning and offered support to the teacher. The researchers share the development of the AI agent, the lessons learned, the integration of biometrics and facial tracking, and how teachers use this emerging form of AI both in classroom-based center activities and to support students’ emotional regulation. The authors conclude by describing the application of this type of support in teacher preparation programs and a vision of the future of using AI agents in instruction.Journal of Special Education Preparation411The creation and potential of virtual characters in elementary classrooms, combined with biometrics and facial emotional recognition, which in this study has impacted student learning and offered support to the teacher.Journal of Special Education Preparation["Lisa Dieker", "Rebecca Hines", "Ilene Wilkins", "Charles Hughes", "Karyn Hawkins Scott", "Shaunn Smith", "Kathleen M. Ingraham", "Kamran Ali", "Tiffanie Zaugg", "Sachin Shah"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/4bd27c8ae11902c5d741a40232163ef0ebf48fae 50cf184def032d32d1265136b5036d28734600e0Background: This study correlates the up-to-date ethical, functional and legal evaluations related to the management and governance of artificial intelligence (AI) under European Union (EU) law, particularly impacting the health data sector and medical standards as provided by the Artificial Intelligence Act within the Regulation adopted by the European Council in May 2024. The initial proposal for the management and governance of the AI sector was submitted in April 2021. Three years later, on 13 March 2024, the European Union Artificial Intelligence Act (EU AIA) was adopted by the European Parliament. Subsequently, on 21 May 2024, the Council adopted an innovative legislative framework that harmonises the standards and rules for AI regulation. This framework is set to take effect in May 2026, with the central objective of stimulating and motivating a fair, safe, legal single market that respects the principles of ethics and the fundamental rights of the human person. Methods: The current legal analysis focuses on the European Union’s new institutional governance involving a multistage approach to managing health data, ethical artificial intelligence, generative artificial intelligence and classification of types of AI by considering the degree of risk (e.g. artificial intelligence systems with limited risk and systems with high risk) and medical devices. It outlines the legal framework for AI regulation and governance in the EU by focusing on compliance with the previously adopted legislation in the Medical Devices Regulation (2017) and the In-Vitro Diagnostic Regulation (2017). The paper also examines the application of the newly adopted EU Artificial Intelligence Act in relation to national justice systems, previous EU regulations on medical devices and personal data protection regulation, and its correlation with the European Court of Human Rights jurisprudence. This opens up complex discussions related to judicial reform and access to justice. For this purpose, as a research objective, the legal analysis includes an innovative perspective following an integrative discussion on the latest legal reforms and regulations of the AI sector in Eastern Europe launched in 2024 with a special focus on the latest developments in the EU Candidate Countries namely Ukraine and the Republic of Moldova. Results and conclusions: The present research facilitates the exploration of the real benefits of managing innovative AI systems for medical data, research, and development, as well as within the medical technology industry.Access to Justice in Eastern Europe391Access to Justice in Eastern Europe["Anca Parmena Olimid"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/50cf184def032d32d1265136b5036d28734600e0 4a122fe35c27605e96c03216ffb8f3f6ee6299faThis research paper examines the utilization of artificial intelligence (AI) and its influence on enhancing business in the RMG industry in Bangladesh. This will be accomplished through a comprehensive study of existing literature and secondary data. The study aims to gain a comprehensive understanding of particular AI technologies, quantify the advantages of AI, and evaluate the obstacles encountered by the RMG industry in adopting AI. Artificial intelligence (AI) has significantly enhanced the efficiency, cost-effectiveness, productivity, customization capabilities, trend prediction accuracy, and sustainability of garment creation. Nevertheless, it gives rise to concerns over unemployment, ensuring consistent standards, safeguarding personal information, reliance on technology and ethical dilemmas. The RMG sector should enhance employee comprehension of AI, seek varied sources of funding and skilled personnel, collaborate with the government to obtain infrastructure assistance and enact legislation, tackle concerns regarding job displacement through training, and foster employee receptiveness to change in order to surmount these challenges. The garments sector in Bangladesh has the potential to enhance its operations and competitiveness by implementing these approaches. This study will provide valuable insights for corporate executives, decision-makers, and academics who are interested in optimizing the capabilities of artificial intelligence and improving business results in Bangladesh.International Journal For Multidisciplinary Research420The RMG sector should enhance employee comprehension of AI, seek varied sources of funding and skilled personnel, collaborate with the government to obtain infrastructure assistance and enact legislation, tackle concerns regarding job displacement through training, and foster employee receptiveness to change in order to surmount these challenges.International Journal For Multidisciplinary Research["Hillol Saha"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/4a122fe35c27605e96c03216ffb8f3f6ee6299fa 2689c1326fe82416234019696f3abe9a807b34a6The current and predominant school of thought in academic publishing, with a correspondingly rigorously implemented set of ethical policies, notes that classic authorship is a purely human endeavor. However, such rigid conceptual restrictions on authorship for artificial intelligence (AI), like large language models (LLMs), may be borne from fear, emerging perhaps from being intellectually threatened by AI/LLMs that might outperform humans. In this paper, considering several caveats, a world of academic publishing in which AI/LLMs are offered a fair opportunity of authorship, coined AI-authorship, is envisioned.European Science Editing00A world of academic publishing in which AI/LLMs are offered a fair opportunity of authorship, coined AI-authorship, is envisioned.European Science Editing["J. A. Teixeira da Silva"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/2689c1326fe82416234019696f3abe9a807b34a6 0187eb16c0703c08f776b75f27808385605a22b9Purpose - Artificial intelligence is profoundly transforming the digital creative industry, becoming a key force in industrial upgrading through technology integration, innovation drive and productivity improvement. Methods- This study uses a variety of methods, including literature review, questionnaire survey, in-depth interview and data analysis, to systematically examine the application effect of AI in the digital creative industry. The structural equation model is used to verify the hypothesized relationship, and the role of AI in promoting industrial integration, development results and innovation catalysis effect are comprehensively analyzed. Research results- The research that AI importantly promotes technological integration, development and innovation inside the digital creative industry, and improves the general overall performance of the industry. The path coefficient is incredibly substantial, and the model fit is good, which verifies the effective position of AI in industrial integration and development. The position of AI not only improves the competitiveness of the industry, but also provides robust support for the personalization and excessive quality of creative products. Originality- This paper demonstrates remarkable originality in theoretical framework construction, empirical data analysis, and exploration of practical challenges and this paper deeply analyzes the multiple mechanisms of AI in the digital creative industry, providing new ideas and specific quantitative evidence for research in related fields. Implications - Practitioners need to actively accept AI era, optimize creative workflows, and enhance production efficiency and constantly enhance their digital talents and cross-area information integration capabilities. Policymakers should formulate focused support policies to promote the vast application and deep integration of AI technologies inside the creative industries.SEISENSE Journal of Management270This paper deeply analyzes the multiple mechanisms of AI in the digital creative industry, providing new ideas and specific quantitative evidence for research in related fields and demonstrates remarkable originality in theoretical framework construction, empirical data analysis, and exploration of practical challenges.SEISENSE Journal of Management["S. Wagan", "Sidra Sidra"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/0187eb16c0703c08f776b75f27808385605a22b9 22bb00ee98483214be1ea7064ac509ad3f49ec9aThis article presents engaging and practical methods of helping educators to “remix” the evidence-based and high-leverage practices they are already familiar with to include the new capabilities of artificial intelligence (AI). Transformational modern technologies can be powerful and disruptive, possessing the potential to impact multiple areas of society, including education. One of the best ways for educators to implement AI in their teaching is using it to help support and extend their current practices (Mishra et al., 2023). Similar to how a new remix on the radio can make an old favorite song fresh again, educators can use AI to uphold and enhance their existing instructional strategies and skills. However, adapting to the new paradigm of AI in education may be challenging for teacher preparation programs in special education. The authors of this article apply some of the strategies from “Leveraging Emerging Technology to Design an Inclusive Future with Universal Design for Learning” (McMahon & Walker, 2019) to effectively implement AI in education. Rather than needing to start fresh or relearn how to teach while incorporating AI, teachers can view this article as a foundation for how to apply AI tools to support current practice. The recommendations are based on Universal Design for Learning (UDL) and strategies for adapting AI tools to support high-leverage practices and established evidence-based practices. The authors aim to inspire special educators to start using AI to help “remix” and innovate the implementation of their existing instructional strategies.Journal of Special Education Preparation280Journal of Special Education Preparation["Donald McMahon", "Jonah B. Firestone"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/22bb00ee98483214be1ea7064ac509ad3f49ec9a 9f9d7847b153025ea7a70a4802cf71381d6b80c7In this research, we propose a set of reporting documents to enhance transparency and trust in artificial intelligence (AI) systems for cooperative, connected, and automated mobility (CCAM) applications. By analyzing key documents on ethical guidelines and regulations in AI, such as the Assessment List for Trustworthy AI and the EU AI Act, we extracted considerations regarding transparency requirements. Recognizing the unique characteristics of each AI system and its application sector, we designed a model card tailored for CCAM applications. This was made considering the criteria for achieving trustworthy autonomous vehicles, exposed by the Joint Research Centre (JRC), and including information items that evidence the compliance of the AI system with these ethical aspects and that are also of interest to the different stakeholders. Additionally, we propose an MLOps Card to share information about the infrastructure and tools involved in creating and implementing the AI system.SAE International Journal of Connected and Automated Vehicles100A set of reporting documents to enhance transparency and trust in artificial intelligence (AI) systems for cooperative, connected, and automated mobility (CCAM) applications and an MLOps Card to share information about the infrastructure and tools involved in creating and implementing the AI system are proposed.SAE International Journal of Connected and Automated Vehicles["P. Ca\u00f1as", "Marcos Nieto", "O. Otaegui", "Igor Rodr\u00edguez"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/9f9d7847b153025ea7a70a4802cf71381d6b80c7 330cd8fe579f18f7e1ec967c936f8df41c0a2f7eThe use of artificial intelligence (AI) is not a new concept. Still, the press, the worry, and the hype around the potential benefits and limitations of the explosion of these tools in this field is a current topic in teacher education. In this article, the authors summarize the past use of AI, present easily adaptable tools in teacher education, and discuss what is on the horizon in industry and special education teacher education. The authors highlight tools that should be considered in programs today, followed by ways to expand the field of AI in teacher education to support the learning outcomes of struggling students.Journal of Special Education Preparation690The authors highlight tools that should be considered in programs today, followed by ways to expand the field of AI in teacher education to support the learning outcomes of struggling students.Journal of Special Education Preparation["Maggie A. Mosher", "Lisa Dieker", "Rebecca Hines"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/330cd8fe579f18f7e1ec967c936f8df41c0a2f7e cf21554afebdc0e08edf1ad2f2b3cedc4ecf0344Recent advances in artificial intelligence (AI) systems have resulted in their ability to provide precise recommendations in response to users’ questions (prompts). However, AI models are opaque, making it challenging for users to comprehend their inner workings. While the Human-Computer Interaction (HCI) community has advocated for design principles like progressive disclosure to improve transparency, we still lack empirical evidence validating its efficacy, especially in the context of LLM-based text generation. Addressing this gap, our paper delves into a user study aimed at investigating the effect of progressive disclosure and adjusting the explanations so as to adapt to users’ mental models for improving the transparency of AI text generation systems.IEEE Symposium on Visual Languages / Human-Centric Computing Languages and Environments100This paper delves into a user study aimed at investigating the effect of progressive disclosure and adjusting the explanations so as to adapt to users’ mental models for improving the transparency of AI text generation systems.2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)["Deepa Muralidhar"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/cf21554afebdc0e08edf1ad2f2b3cedc4ecf0344 0344b1c243e6e763adfe95219ae864cee2abf41fThe resource support of any developed strategy according to the chosen priorities is one of the most important stages of its formation and implementation. The solution of this task affects the dynamics of development of the economy’s high technology industries as the objects of strategizing in the emerging digital economy. One of the priority technological vectors of development of Russia is the sphere of artificial intelligence (AI), and so there arises the problem of the resource support of this direction. The purpose of the article is to study staffing as one of the key factors of strategic development of the Russian AI sphere in the industry context. The analysis is based on the methodology of strategizing by Professor V.L. Kvint and the concept of strategic human resources management by Doctor I.V. Novikova. The calculations are based on the Russian universities’ data on the number of graduates by educational programs in the AI sphere in 2023 and their employment. During the research the authors found out the number of graduates in the AI sphere by the industry specialization of educational programs; defined the sectoral specifics of employment of the graduates specializing in the AI sphere; identified the key employment organizations and jobs. The authors made conclusions about resource support of the AI development strategy in the industry context. The results will be useful for correcting the developed strategies in the AI sphere and for predicting staffing provision with the consideration of the time lag in the staff training.Russian Journal of Industrial Economics100Staffing as one of the key factors of strategic development of the Russian AI sphere in the industry context is studied to be useful for correcting the developed strategies in the AI sphere and for predicting staffing provision with the consideration of the time lag in the staff training.Russian Journal of Industrial Economics["A. Averyanov", "V. Gurtov", "S. V. Shabaeva"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/0344b1c243e6e763adfe95219ae864cee2abf41f d9fed51351b58a7e388b14a30a33e5ac75d8e333Diabetic retinopathy (DR) represents a leading cause of blindness worldwide, early detection is critical to prevent vision loss. However, traditional screening methods, which rely on human experts, prove to be costly and time-consuming. The systematic review aims to assess the validity of artificial intelligence (AI) as a screening tool for detecting DR among diabetic patients. A systematic literature search was performed of the following databases: PubMed, Scopus, CINAHL, and Web of Science. The last date of our search was January 31, 2024. We included all observational studies, including cohort, case–control and cross-sectional studies and evaluated their quality using the Joanna Briggs Institute tool. We included diagnostic test accuracy studies evaluating the use of AI algorithms for DR screening in patients with diabetes. Studies were excluded if they exclusively assessed diagnostic accuracy for DR that did not use AI algorithms as a diagnostic tool and studies with incomplete or inaccessible data. Thirteen studies with sample sizes ranging from 69 to 1378 participants, reported good sensitivity of AI for detecting visually threatening DR (VTDR). The lowest sensitivity was 89.2%, and the highest was 100%. In terms of specificity, Any DR exhibited higher specificity compared to RDR and VTDR, ranging from 80.2% to 100%. The sensitivity and specificity of the Artificial Intelligence (AI)-based tools available for DR screening was considered acceptable, especially in detecting VTDR and Any DR, was regarded as good. These results implied the potential usefulness of these tools for DR screening in settings with limited resources. However, further high-quality comparative studies were deemed necessary to evaluate their effectiveness in real-world clinical settings.Indonesian Journal of Global Health Research430The sensitivity and specificity of the Artificial Intelligence (AI)-based tools available for DR screening was considered acceptable, especially in detecting VTDR and Any DR, was regarded as good, and implied the potential usefulness of these tools for DR screening in settings with limited resources.Indonesian Journal of Global Health Research["Nurul Hidayati", "Ika Yuni Widyawati", "Ira Suarilah", "Trihaningsih Puji Astuti", "Fauzi Tsanifiandi", "Andi Safutra Suraya"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/d9fed51351b58a7e388b14a30a33e5ac75d8e333 74be75a8c7b45dbaf4864620353b3f7826d7e85aArtificial intelligence (AI) is currently utilized across numerous medical disciplines. Nevertheless, despite its promising advancements, AI’s integration in hand surgery remains in its early stages and has not yet been widely implemented, necessitating continued research to validate its efficacy and ensure its safety. Therefore, this review aims to provide an overview of the utilization of AI in hand surgery, emphasizing its current application in clinical practice, along with its potential benefits and associated challenges. A comprehensive literature search was conducted across PubMed, Embase, Medline, and Cochrane libraries, adhering to the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search focused on identifying articles related to the application of AI in hand surgery, utilizing multiple relevant keywords. Each identified article was assessed based on its title, abstract, and full text. The primary search identified 1,228 articles; after the application of inclusion/exclusion criteria and manual bibliography search of included articles, a total of 98 articles were covered in this review. AI’s primary application in hand and wrist surgery is diagnostic, which includes hand and wrist fracture detection, carpal tunnel syndrome (CTS), avascular necrosis (AVN), and osteoporosis screening. Other applications include residents’ training, patient-doctor communication, surgical assistance, and outcome prediction. Consequently, AI is a very promising tool that has numerous applications in hand and wrist surgery, though further research is necessary to fully integrate it into clinical practice.Artificial Intelligence Surgery930Artificial intelligence’s primary application in hand and wrist surgery is diagnostic, which includes hand and wrist fracture detection, carpal tunnel syndrome, avascular necrosis, and osteoporosis screening.Artificial Intelligence Surgery["Said Dababneh", "Justine Colivas", "N. Dababneh", "J. I. Efanov"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/74be75a8c7b45dbaf4864620353b3f7826d7e85a d5f05d75f29aba8fbe2378938f630da229d284c8Industrial Revolution 4.0 has fundamentally transformed the educational landscape. Rapid advances in technology, especially artificial intelligence (AI), demand significant curriculum adaptations. Mathematics, as the foundation of science, is the main focus in efforts to integrate AI into the learning process. This research aims to examine in depth the effect of applying AI in mathematics learning on students' learning capacity. Through a comprehensive literature study referring to various scientific journals and Google Scholar articles, this research analyzes various aspects related to the application of AI in mathematics learning. Analysis includes mapping trends in AI implementation, evaluating the effectiveness of various AI approaches, identifying factors that influence successful implementation, and exploring the positive and negative impacts of AI on the learning process. The findings of this research show that the application of AI in mathematics learning has enormous potential to increase student learning motivation, personalize learning, and deepen understanding of concepts. However, research also identifies a number of challenges, such as gaps in technology access, the need for appropriate pedagogical development, and the potential for AI to replace teachers' roles. Based on these findings, this research concludes that integrating AI in mathematics learning requires careful planning and strong collaboration between educators, technology developers, and policymakers. The implications of this research include the importance of flexible curriculum development, continuous teacher training, and the provision of adequate technological infrastructure.Journal of General Education and Humanities510It is found that integrating AI in mathematics learning requires careful planning and strong collaboration between educators, technology developers, and policymakers, and the implications of this research include the importance of flexible curriculum development, continuous teacher training, and the provision of adequate technological infrastructure.Journal of General Education and Humanities["Laela Maulida", "Pipit Nurossobah", "Billy Anggun Aura", "Eka Dwi Nengsih", "Rasilah Rasilah"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/d5f05d75f29aba8fbe2378938f630da229d284c8 a7568b70976d28d482fbec3da387b39874bb340cArtificial intelligence (AI) methods have been extensively used for detecting and predicting Infectious Disease (ID) outbreaks as time series and modeling and evaluating Public Health responses. The significant tasks of PH monitoring and intervention present distinct technical difficulties, including limited data availability, absence of sufficient positive training examples, challenges in establishing benchmarks, measuring the effectiveness of management policies, complex relationships between spatial and time series elements, and more detailed risk assessments involving interaction and social networks. Conventional PH monitoring mainly depends on statistical methods. In recent years, there has been a significant expansion of approaches that AI enables. This research presents an AI method called Early Monitoring and Prevention of Epidemics (AI-EMPE) for enhancing PH security systems. The suggested approach converts a substantial amount of collected PH security incidents into separate incident characteristics and utilizes a Deep Learning (DL)--based detection technique to enhance EMPE. AI-EMPE incorporates sophisticated AI methods, including integrated Convolutional Neural Networks (CNN) and Backpropagation Neural Networks (BP-NN). These findings indicate that the integrated method is the most efficient in improving PH security systems.South Eastern European Journal of Public Health160The suggested approach converts a substantial amount of collected PH security incidents into separate incident characteristics and utilizes a Deep Learning--based detection technique to enhance EMPE.South Eastern European Journal of Public Health["Chandni Sawlani", "Pooja Sharma"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/a7568b70976d28d482fbec3da387b39874bb340c 0db060257657c232d3b4844f02a4f89e25b73bf4The ironmaking process is a complex and continuous operation, which makes it difficult to collect and predict the production parameters. To address this challenge, artificial intelligence (AI) deep learning has emerged as a promising solution. The paper discusses the evolution and utilisation of AI in the metallurgical industry. The paper emphasises the implementation of AI in various ironmaking processes, including raw material selection and charging for iron production, molten iron composition prediction in blast furnace (BF), and internal operational state and fault detection in BF. Additionally, the paper predicts BF gas and explores the utilisation of automated equipment such as cooling systems. Drawing upon existing literature, this paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy. The paper also highlights several challenges that the future development of AI in the ironmaking field may encounter. Looking ahead, the future of deep learning in ironmaking appears promising.Ironmaking &amp; Steelmaking: Processes, Products and Applications690The paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy, and highlights several challenges that the future development of AI in the ironmaking field may encounter.Ironmaking &amp; Steelmaking: Processes, Products and Applications["Hualun Zhou", "Yibo He", "Binzhao Li", "Dazhou Song", "Qiang Zhu", "Yihong Li"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/0db060257657c232d3b4844f02a4f89e25b73bf4 e5d8a2e78746531b49ddb2fe884dac89925845c6This article centers around the transformative potential of artificial intelligence (AI), specificallyexamining its economic impact in terms of enhancing productivity and facilitating labor reallocation.Additionally, it explores the applications of AI in the domains of healthcare and education. This paperexplores three key topics related to artificial intelligence (AI) copyright and ethical considerations ingenerative AI, security and ethical dilemmas, and the future of AI as a transformative technology ormerely a buzzword. Each of these areas has unique challenges and implications for the ethical andsocietal dimensions of AI development and deployment. By examining these topics, we aim to contributeto the academic discourse surrounding AI and its broader implications.Journal of Advances in Science and Technology00Three key topics related to artificial intelligence (AI) copyright and ethical considerations ingenerative AI, security and ethical dilemmas, and the future of AI as a transformative technology ormerely a buzzword are explored.Journal of Advances in Science and Technology["Shourya Upadhyay Shourya Upadhyay"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/e5d8a2e78746531b49ddb2fe884dac89925845c6 0c8db9eb22aa02b5c6d98f144e57098f47bd1fb3This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.Social science computer review230The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI, while progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI.Social Science Computer Review["Mikyung Chang"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/0c8db9eb22aa02b5c6d98f144e57098f47bd1fb3 2666729e60931f38212f64acc5b49e5ea2cbb257PURPOSE OF REVIEW This review explores the transformative advancement, potential application, and impact of artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs), on critical care nephrology. RECENT FINDINGS AI algorithms have demonstrated the ability to enhance early detection, improve risk prediction, personalize treatment strategies, and support clinical decision-making processes in acute kidney injury (AKI) management. ML models can predict AKI up to 24-48 h before changes in serum creatinine levels, and AI has the potential to identify AKI sub-phenotypes with distinct clinical characteristics and outcomes for targeted interventions. LLMs and generative AI offer opportunities for automated clinical note generation and provide valuable patient education materials, empowering patients to understand their condition and treatment options better. To fully capitalize on its potential in critical care nephrology, it is essential to confront the limitations and challenges of AI implementation, including issues of data quality, ethical considerations, and the necessity for rigorous validation. SUMMARY The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy. While AI holds immense promise for improving patient outcomes, its successful implementation requires ongoing training, education, and collaboration among nephrologists, intensivists, and AI experts.Current Opinion in Critical Care390The integration of AI in critical care nephrology has the potential to revolutionize the management of AKI and continuous renal replacement therapy and to fully capitalize on its potential in critical care nephrology.Current opinion in critical care["W. Cheungpasitporn", "C. Thongprayoon", "Kianoush B. Kashani"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/2666729e60931f38212f64acc5b49e5ea2cbb257 bc179518ddccd13dab816a93f078d0d785bddbd5Artificial intelligence (AI) has transformative potential to support the education of students with moderate to severe intellectual disabilities (M/SID) and their teachers. Although research and evidence-based practices (EBPs) are integral to fostering positive student learning outcomes, educators face challenges in effectively implementing these strategies. In this article, we discuss how higher education faculty can prepare educators to harness the use of AI as a powerful tool to support the implementation of EBPs in the classroom, addressing teacher fluency and maintenance of application.Journal of Special Education Preparation280How higher education faculty can prepare educators to harness the use of AI as a powerful tool to support the implementation of EBPs in the classroom is discussed, addressing teacher fluency and maintenance of application.Journal of Special Education Preparation["Bree Jimenez", "Ginevra Courtade", "Jennifer Fosbinder"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/bc179518ddccd13dab816a93f078d0d785bddbd5 464be7c267c4e2f0c73a52d8d1e2431d8befa147The global financial landscape has undergone unprecedented transformations in recent decades, characterized by increased complexity, volatility, and interconnectivity. In this dynamic environment, the ability to anticipate stock market trends has become a paramount concern for investors, financial analysts, and policymakers alike. This research aims to distil insights and contribute to advanced predictive models for the dynamic global financial landscape. The exploration encompasses diverse approaches, including artificial neural networks, convolutional neural networks, LSTM, and traditional machine learning algorithms. Emphasis is placed on data pre-processing, numerical analyses, and the efficacy of LSTM models. The significance of this research lies in its synthesis of existing knowledge, offering a holistic view of methodologies and outcomes in Share Market Prediction. The model signifies a foundation for further innovation in predictive modeling, addressing real-time data challenges and dynamic market conditions. This work advances the understanding and forecasting of stock market trends.Malaysian Journal of Science and Advanced Technology341The model signifies a foundation for further innovation in predictive modeling, addressing real-time data challenges and dynamic market conditions, and advances the understanding and forecasting of stock market trends.Malaysian Journal of Science and Advanced Technology["Pulok Sarker", "Adnan Sayed", "Abu bakar siddique", "Avijit Saha Apu", "Syeda Anika Tasnim", "Rifath Mahmud"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/464be7c267c4e2f0c73a52d8d1e2431d8befa147 0689af8a63ccb609bfabb348568fb5ea316eeb62Asia Pacific Journal of Public Administration241Asia Pacific Journal of Public Administration["Chian-Wen Wang", "Bo-Ya Hsu", "Don-Yun Chen"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/0689af8a63ccb609bfabb348568fb5ea316eeb62 40fb82c4b5300d5d18d7393fabb6b906cfc0798aSao Paulo medical journal = Revista paulista de medicina41Sao Paulo medical journal = Revista paulista de medicina["Isabele Alves Chirichela", "A. Mariani", "P. P\u00eago-Fernandes"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/40fb82c4b5300d5d18d7393fabb6b906cfc0798a f4e77f115e844e2e302eb7bd9c1a8254b6113ee4At the end of 2022, a nonprofit technology research institution funded by Altman and Musk released an AI-based chatbot, ChatGPT, which within just three months has shown utility across various industries, particularly in jobs like copywriting, news report writing, customer service, and legal document creation. Its ability to provide coherent and insightful answers, as well as serve as a brainstorming partner, has some college professors concerned that this machine may replace various human jobs (Stokel-Walker, 2022). One likely negative impact is students using this AI-based writing tool to complete academic assignments in the form of essays (Hutson, 2022). Another implication is that researchers (both students and lecturers) may be able to compose scientific texts, partially if not wholly, and escape the radar of AI-written text detection tools (Kim, 2022), as well as peer reviewers (Else, 2023). The methodology used is digital discourse, aiming to reveal how youth discourse influences the presence of the ChatGPT application and how the consumption practice of the application impacts their creativity and critical thinking skills. The findings of this study are expected to contribute to understanding the impact of AI-based chatbots on critical thinking abilities among young people as a basis for development strategies to enhance critical and creative thinking skills in the era of Artificial Intelligence.Journal of humanities and social sciences studies121The findings of this study are expected to contribute to understanding the impact of AI-based chatbots on critical thinking abilities among young people as a basis for development strategies to enhance critical and creative thinking skills in the era of Artificial Intelligence.Journal of Humanities and Social Sciences Studies["S. Febriyanti", "\u2709. M. Anggraini", "Belinda Firda", "Mila Fitria"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/f4e77f115e844e2e302eb7bd9c1a8254b6113ee4 7923d5deb4537d3a8ae162f5c1f71cbe8b52f260Australasian Psychiatry120Australasian psychiatry : bulletin of Royal Australian and New Zealand College of Psychiatrists["Sharon Reutens", "Christopher Dandolo", "Richard C H Looi", "George C Karystianis", "J. Looi"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/7923d5deb4537d3a8ae162f5c1f71cbe8b52f260 3377adb583e31db9c8c800a0596cbb2eb0c82ad4The 3rd International Joint Conference on Water Distribution Systems Analysis &amp;amp; Computing and Control for the Water Industry (WDSA/CCWI 2024)50The 3rd International Joint Conference on Water Distribution Systems Analysis &amp;amp; Computing and Control for the Water Industry (WDSA/CCWI 2024)["Claudia Maussner", "Martin Oberascher", "Arnold Autengruber", "Arno Kahl", "R. Sitzenfrei"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/3377adb583e31db9c8c800a0596cbb2eb0c82ad4 a2b7963d29bec5267a1a185c294192c14015ad55Scientific Reports320The study found significant differences in AI-generated metrics—arousal and valence—across three stimulus types: non-noxious thermal, short-noxious, and prolonged-noxious, with p-values below 0.001 suggesting that AI technology could enhance objective pain assessment in neonates.Scientific Reports["V. Giordano", "Alexandra Luister", "E. Vettorazzi", "Krista Wonka", "Nadine Pointner", "P. Steinbauer", "Michael Wagner", "Angelika Berger", "Dominique Singer", "P. Deindl"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/a2b7963d29bec5267a1a185c294192c14015ad55 e4a30cc6c9425c3948e31b548356689fbdee4dadThis white paper discusses a model of best practices to better identify and address plagiarism issues with students using AI. It serves as an example to help younger institutions that may not have a policy in place to recognize the importance of hitting this head-on. By creating a taskforce, we were able to quickly come to a resolution for a university that has three campuses in Chicago, Online, and in Vancouver, BC. We also share best practices that will help current professors and core faculty alike in dealing with plagiarism from students using AI in their work. We end with a discussion of examples that support this effort.Journal of Leadership, Accountability and Ethics210A model of best practices to better identify and address plagiarism issues with students using AI and share best practices that will help current professors and core faculty alike in dealing with plagiarism from students using AI in their work is discussed.Journal of Leadership, Accountability and Ethics["James D. Halbert", "Donna DiMatteo-Gibson", "Marianne Cabrera", "Tricia Mazurowski", "Maleka Ingram"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/e4a30cc6c9425c3948e31b548356689fbdee4dad a0eed16e0539d58f1765acb74fbdf38fa34538bdPurpose—The objective of this study was to conduct a detailed South African study that sought to explore and analyse the views of South African accountants regarding the factors that affect the adoption of AI in financial reporting. In other words, this study aimed to understand what accountants in South Africa think about the use of AI in their field, especially concerning its integration into financial reporting practices. Three main theories underpinned the study, namely, the diffusion of innovation, technology, organisation, and environment framework, and the institutional theory. In essence, the study sought to determine the perception of South Africa’s accountants on these factors. Design/methodology/approach—This study adopted the quantitative research method and descriptive design. In this regard, positivism as a philosophy was preferred. An online survey was developed to collect information from the participants. Participants were recruited based on their affiliation with the four IFAC-recognised accounting bodies in South Africa: SAICA, SAIPA, CIMA, and ACCA. Findings—Th study found that, overall, South African accountants believe that organisational, technological, and environmental factors play a role in adopting artificial intelligence in financial reporting. Originality/value: This study contributes by enriching the understanding of South African accountants’ perceptions of the adoption of artificial intelligence in financial reporting through the lenses of the selected theories.Journal of Risk and Financial Management110Th study found that, overall, South African accountants believe that organisational, technological, and environmental factors play a role in adopting artificial intelligence in financial reporting.Journal of Risk and Financial Management["Tankiso Moloi", "Hassan Obeid"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/a0eed16e0539d58f1765acb74fbdf38fa34538bd 97e44fcf9444d138be6c14358c50cb4f3bc14bbcThis paper critically explores the implications of generative artificial “intelligence” (GAI) technologies for literacy theory and practice through a case study of the author's use of OpenAI's ChatGPT. The study opens with an overview of recent literature surrounding the pedagogical implications of using GAI with a focus on issues of racial justice, outlining an abolitionist political ecology approach to literacy that extends relational theories of mediation to machine‐aided writing. The framework is then applied to data from a cognitive autoethnography of GAI use over a 6‐month period, which included a digital ethnography of ChatGPT and an extended semistructured “interview” with the GAI chatbot. Racial justice issues were found, especially linguistic and other biases. As such, soon‐to‐be ubiquitous artificial intelligence (AI) technologies require profound reconsideration of the productive value of literacy exploited by GAI, which will inevitably be pursued through an acquiescence or fundamental rupture with the dystopian visions of the technology's creators.Reading Research Quarterly261An abolitionist political ecology approach to literacy that extends relational theories of mediation to machine‐aided writing is outlined, outlining an abolitionist political ecology approach to literacy that extends relational theories of mediation to machine‐aided writing.Reading Research Quarterly["R. S. de Roock"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/97e44fcf9444d138be6c14358c50cb4f3bc14bbc 4760953fc628b0795e908972f9e95e9b8fa98d4eThis booklet,"Unlocking the Wisdom of LLM Collaborative Intelligence,"introduces the comprehensive work"The Path to Artificial General Intelligence."Through ten aphorisms, it distills the core principles of LLM Collaborative Intelligence (LCI) as a promising framework toward achieving AGI. The booklet also offers titles, abstracts, and introductions from the main chapters, along with the first two chapters in full. The second edition, released this week, includes significant enhancements to Chapters 6 to 9 and a revised preface addressing Yann LeCun's skepticism about AGI. LeCun argues that LLMs lack memory, planning, and grounding, but we propose that LCI's collaborative architecture, involving multimodal LLMs with executive, legislative, and judicial roles, overcomes these limitations. Chapters on SocraSynth, EVINCE, consciousness modeling, and behavior modeling demonstrate that collaborative LLMs with checks and balances can achieve intelligence beyond any single model's capability. By combining complementary strengths, such as world modeling and advanced sensory capabilities, LCI enables models to work together and perceive reality beyond human limitations. As with human institutions, progress depends on cooperation, not isolation. Collaborative LLMs may unlock new levels of intelligence, paving the way toward AGI.arXiv.org00This booklet,"Unlocking the Wisdom of LLM Collaborative Intelligence," introduces the comprehensive work"The Path to Artificial General Intelligence."Through ten aphorisms, it distills the core principles of LCI as a promising framework toward achieving AGI.ArXiv["Edward Y. Chang"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/4760953fc628b0795e908972f9e95e9b8fa98d4e d4eb4adfa36b5dd0480793f1d0a89a43a8847661Generative artificial intelligence (GenAI) tools, such as large language models (LLMs), generate natural language and other types of content to perform a wide range of tasks. This represents a significant technological advancement that poses opportunities and challenges to educational research and practice. This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education. In the commentary, it is acknowledged that GenAI’s capabilities can enhance some teaching and learning practices, such as learning design, regulation of learning, automated content, feedback, and assessment. Nevertheless, we also highlight its limitations, potential disruptions, ethical consequences, and potential misuses. The identified avenues for further research include the development of new insights into the roles human experts can play, strong and continuous evidence, human-centric design of technology, necessary policy, and support and competence mechanisms. Overall, we concur with the general skeptical optimism about the use of GenAI tools such as LLMs in education. Moreover, we highlight the danger of hastily adopting GenAI tools in education without deep consideration of the efficacy, ecosystem-level implications, ethics, and pedagogical soundness of such practices.Behaviour &amp; Information Technology1166This commentary brings together contributions from nine experts working in the intersection of learning and technology and presents critical reflections on the opportunities, challenges, and implications related to GenAI technologies in the context of education.Behaviour &amp; Information Technology["Michail N. Giannakos", "Roger Azevedo", "Peter Brusilovsky", "M. Cukurova", "Y. Dimitriadis", "D. Hern\u00e1ndez-Leo", "Sanna J\u00e4rvel\u00e4", "M. Mavrikis", "Bart Rienties"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/d4eb4adfa36b5dd0480793f1d0a89a43a8847661 770b005097a253114470f218a97de2577c4862a3The industrial sector is undergoing a transformation thanks to artificial intelligence (AI), which is bringing revolutionary changes to a number of areas like robots and automation, supply chain efficiency, predictive maintenance, and quality control and assurance. This thorough analysis investigates AI's significant influence on contemporary manufacturing procedures. Artificial Intelligence (AI) improves machine capabilities in robotics and automation, creating more intelligent and flexible systems. Robots can now complete complicated tasks with more flexibility and precision thanks to AI-driven developments, which boosts manufacturing efficiency and human-robot cooperation. Another crucial area where AI has a big impact is predictive maintenance. With the use of machine learning algorithms and real-time data analysis, artificial intelligence (AI) helps manufacturers anticipate equipment faults before they happen. By taking a proactive stance, unplanned downtime is decreased, resource usage is optimized, and machinery longevity is increased. AI has a significant positive impact on quality assurance and control because to cutting-edge technologies like data analytics and computer vision. Artificial intelligence (AI) solutions facilitate predictive quality management, improve fault identification, and offer real-time monitoring. Higher quality standards, less waste, and more customer happiness are the outcomes of this. Artificial Intelligence (AI) tackles issues related to supplier performance, accurate forecasting, and inventory management in supply chain optimization. Automation and analytics powered by AI simplify supply chain processes, increase transparency, and facilitate improved decision-making, which lowers costs and increases flexibility. All things considered, integrating AI into manufacturing processes offers a strategic advantage by promoting increased accuracy, flexibility, and efficiency. The continued developments in AI technology have the potential to significantly influence how manufacturing develops in the future by creating new avenues for creativity and excellence in the sector.International Journal of Multidisciplinary Sciences and Arts605This thorough analysis investigates AI's significant influence on contemporary manufacturing procedures and concludes that integrating AI into manufacturing processes offers a strategic advantage by promoting increased accuracy, flexibility, and efficiency.International Journal of Multidisciplinary Sciences and Arts["Shahrukh Khan Lodhi", "Ahmad Yousaf Gill", "Ibrar Hussain"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/770b005097a253114470f218a97de2577c4862a3 05bce21c69716cea67bc5bd179cfbe519fcc2aa9This study explores how youth engage with literacy practices in the age of AI through the use of counter‐cartographies within the Nayah‐Irú curriculum. By critically examining digital platforms and the underlying algorithms, students embarked on a journey to understand and challenge the pervasive influence of artificial intelligence in their lives. The curriculum encouraged students to create alternative narratives (counter‐maps) that represent their unique experiences and perspectives, challenging the dominant discourses around technology and power. This process of counter‐mapping served as a powerful tool for fostering critical literacy and agency among the youth, enabling them to envision and advocate for transformative changes in their relationship with digital technologies. Educators played a key role in guiding these explorations, emphasizing the importance of a community‐centered approach to literacy that incorporates real‐world scenarios and addresses the socio‐cultural dynamics of AI. Through counter‐cartographies of AI literacy, students not only critiqued existing digital structures but also imagined new possibilities for engagement and resistance, highlighting the potential for youth to actively shape the discourse and practice of literacy practices in digital platforms.Reading Research Quarterly203This study explores how youth engage with literacy practices in the age of AI through the use of counter‐cartographies within the Nayah‐Irú curriculum, highlighting the potential for youth to actively shape the discourse and practice of literacy practices in digital platforms.Reading Research Quarterly["Ezequiel Aleman", "Ricardo Martinez"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/05bce21c69716cea67bc5bd179cfbe519fcc2aa9 69341d971010e48642a1fee0f8b399f95a4569dfArtificial intelligence (AI) has made significant strides toward cost reduction and performance optimization in heat exchanger technologies. Artificial intelligence (AI) methods in machine learning, deep learning, and expert systems provide significant advancements in diagnostics, performance optimization, and predictive maintenance. While deep learning is superior at recognizing intricate patterns, machine learning offers flexibility through data analysis. Expert systems use domain expertise to make decisions, although they might not be as flexible as data-driven methods. Hybrid approaches integrate these strategies to improve flexibility and performance. New developments include smart heat exchangers with IoT capabilities for real-time monitoring, compact designs for a variety of applications, and new materials and coatings that improve durability and efficiency. Reducing environmental effect is also reflected in sustainable solutions like waste heat recovery. Nevertheless, issues like computing costs, data quality, and interaction with current systems still need to be resolved. Optimized computational methodologies, modular integration, and sophisticated sensor technology are required to address these problems. AI has the power to completely transform heat exchanger technology by enhancing sustainability and efficiency. Future breakthroughs will be fueled by ongoing improvements in materials, designs, and AI approaches, offering more complex solutions to satisfy changing environmental and performance requirements.International Journal of Multidisciplinary Sciences and Arts833Future breakthroughs will be fueled by ongoing improvements in materials, designs, and AI approaches, offering more complex solutions to satisfy changing environmental and performance requirements.International Journal of Multidisciplinary Sciences and Arts["Shahrukh Khan Lodhi", "Hafiz Khawar Hussain", "Ibrar Hussain"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/69341d971010e48642a1fee0f8b399f95a4569df a3ab356a79dfea34af0402fa916b3baa13f07185Integrating Artificial Intelligence-Powered Personalized Learning (AI-PPL) in special education teacher preparation represents a shift toward tailoring educational experiences to meet the unique needs of preservice teachers and students with disabilities. This article explores the implementation of AI-PPL tools in teacher preparation programs, highlighting their potential to customize learning experiences, provide adaptive feedback, and enhance engagement through interactive content. This review of current AI-PPL functionalities, such as adaptive learning environments and customized feedback mechanisms, demonstrates how AI-PPL can impact teaching practices and student learning outcomes. The article introduces critical attributes for successful AI-PPL integration, such as ensuring accessibility and inclusivity. It calls for further professional development to enhance educator competency and skills. By presenting real-world examples and guiding questions for special education faculty, the authors offer practical insights for educators and faculty members to effectively navigate the complexities of adopting AI technologies in teacher preparation programs.Journal of Special Education Preparation132A review of current AI-PPL functionalities demonstrates how AI-PPL can impact teaching practices and student learning outcomes and calls for further professional development to enhance educator competency and skills.Journal of Special Education Preparation["Kenneth Holman", "Matthew Marino", "Trey Vasque", "Michelle Taub", "Jessica H. Hunt", "Yacine Tazi"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/a3ab356a79dfea34af0402fa916b3baa13f07185 f9bb33600da0db682b07f555b24228093db67bbeThis research systematically reviews artificial intelligence (AI) effects in customer‐interfacing retail applications based on an ecosystem value co‐creation framework. We conduct a bibliometric and conceptual mapping analysis study, focusing on AI‐related implications for consumers' and other stakeholders' well‐being, social interaction, and societal welfare. A co‐citation network visualization of critical AI journal articles is generated, and a network visualization of AI keyword relationships and clustered topic areas is presented and discussed, along with a conceptual map of the relationships between key concepts and substantive AI themes. In an ecosystem context, the multidisciplinary‐based bibliometric and conceptual mapping findings of our analysis reflect the need to focus not only on the positive and negative effects on stakeholder well‐being, social interaction, and societal welfare but also on how effects created in one of these levels impact the value created in the other social layers. Furthermore, the interdisciplinary characteristics necessary in effectively implementing and managing AI technologies emphasize the need for collaboration among multiple organizational departments, technology partners, and other members of the business ecosystem. The findings of this research contribute to assessing both the positive and negative effects of AI and allow for its implementation in a way that is helpful to organizations, employees, consumers, and society. This study should also help managers decide which situations are best suited for using AI and which are not.Journal of Consumer Behaviour621A bibliometric and conceptual mapping analysis study, focusing on AI‐related implications for consumers' and other stakeholders' well‐being, social interaction, and societal welfare, and helps managers decide which situations are best suited for using AI.Journal of Consumer Behaviour["Maria Petrescu", "Anjala S. Krishen", "John T. Gironda", "J. Fergurson"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/f9bb33600da0db682b07f555b24228093db67bbe 6643d3295cc31cfd6646a4d806178701908551b5This article aims to summarize the application of artificial intelligence (AI) in cultural protection, taking the entire process of cultural heritage protection as an example. In each process, AI has its own prominent applications. The process of cultural heritage protection is divided into the archaeology of cultural relics, classification, identification, and restoration of cultural relics, cultural heritage management after information identification, and finally, presentation to the public, which is also a part of cultural protection. With the development of technology, AI has participated in various aspects of society. While this study encourage the scientific protection of cultural heritage,this studymust also be vigilant about the negative impact of AI on cultural heritage protection. Through the establishment of regulations and related implementation, this studywill jointly advance the application of AI in the field of cultural heritage protection into a new era.Applied and Computational Engineering01This article aims to summarize the application of artificial intelligence in cultural protection, taking the entire process of cultural heritage protection as an example, and will jointly advance the application of AI in the field of cultural heritage protection into a new era.Applied and Computational Engineering["Chenxi Ge"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/6643d3295cc31cfd6646a4d806178701908551b5 e9ba81e069e03c4c985615e61af6e9f7167265ae This essay describes concerns that use of artificial intelligence (AI) as a replacement rather than a supplement in the provision of medical care may lead to loss of the art of medicine and the collaborative partnership between physician and patient. JAMA cardiology71JAMA cardiology["Sarah C Hull", "Joseph J Fins"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/e9ba81e069e03c4c985615e61af6e9f7167265ae 5e70c96a7497f15f42801a4c1f6c087b3a33fe7eThe growing digitalization and interconnection of energy infrastructures have improved operational efficiency but also heightened the risk of exposure to cyber threats. Traditional electrical power and energy systems encompass all infrastructure and processes for generating, transmitting, distributing, and consuming electricity. Conversely, the smart grid represents an advanced paradigm, integrating cyber-physical components to optimize efficiency, reliability, and sustainability. However, this paradigm shift renders the energy sector more susceptible to cyber threats and attacks, necessitating proactive identification and mitigation. This survey provides a comprehensive analysis of the current state of anomaly and intrusion detection systems specifically designed for the energy sector. We review recent advancements in detection methodologies, including machine learning, artificial intelligence, and hybrid techniques, highlighting their effectiveness in identifying potential threats.Computer Science Symposium in Russia311This survey provides a comprehensive analysis of the current state of anomaly and intrusion detection systems specifically designed for the energy sector, and reviews recent advancements in detection methodologies, including machine learning, artificial intelligence, and hybrid techniques.2024 IEEE International Conference on Cyber Security and Resilience (CSR)["Georgios Andronikidis", "Charis Eleftheriadis", "Zisis Batzos", "Konstantinos Kyranou", "Nikolaos Maropoulos", "Gohar Sargsyan", "Panagiotis I. Radoglou-Grammatikis", "Panagiotis G. Sarigiannidis"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/5e70c96a7497f15f42801a4c1f6c087b3a33fe7e 2fd9a346d9fd3b6f2794755d1c5050964638ac14BMC Medical Ethics741Insight is provided into medical students’ attitudes from Croatia, Slovakia, and international students regarding the role of artificial intelligence (AI) in the future healthcare system, with a particular emphasis on the concept of trust.BMC Medical Ethics["A. Male\u0161evi\u0107", "M\u00e1ria Koles\u00e1rov\u00e1", "Anto \u010cartolovni"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/2fd9a346d9fd3b6f2794755d1c5050964638ac14 cc0c0b2eea96eb7d68cb7e5b06f446aeb9aeaf8fTraffic congestion is a significant issue with studies indicating it costs cities billions annually and averages 54 hours of wasted time per traveler each year. This situation necessitates the implementation of efficient traffic management systems, especially at intersections. In response to this challenge, our work introduces an artificial intelligence-based system designed to analyze and predict traffic flow using machine learning algorithms and deep learning methods in conjunction with traffic cameras. The model comprises two main components: real-time data collection and predictive modeling. It employs object detection to identify and classify vehicles and adjusts traffic signal timings based on the necessary passage time and predetermined constraints. Additionally, data accumulated during operation facilitates the development of a predictive model for traffic flow over time, allowing for proactive traffic management. Evaluations are done to showcase the accuracy of the model and corresponding simulation and physical implementation further approved the applicability of our approach. Finally, this work aims to enhance urban transportation efficiently, reduce commuting stress, and improve the quality of life for city residentsInternational Journal of Integrated Science and Technology150This work introduces an artificial intelligence-based system designed to analyze and predict traffic flow using machine learning algorithms and deep learning methods in conjunction with traffic cameras to enhance urban transportation efficiently, reduce commuting stress, and improve the quality of life for city residents.International Journal of Integrated Science and Technology["Majid Ayoubi", "Hasibullah Aman", "Rohullah Akbari", "Hedayatullah Lodin"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/cc0c0b2eea96eb7d68cb7e5b06f446aeb9aeaf8f ac3983bd9d6193dd40bc3f314d10c0c8621c128aNature Communications160FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, is presented and its performance on a diverse, realistic, multi-center cohort is evaluated, exploring the sources of data heterogeneity and examining FL robustness in real-world scenarios with data imbalances.Nature Communications["Edward H. Lee", "M. Han", "Jason N Wright", "Michael S. Kuwabara", "Jacob Mevorach", "Gang Fu", "Olivia Choudhury", "Ujjwal Ratan", "Michael Zhang", "Matt Wagner", "Robert Goetti", "S. Toescu", "S. Perreault", "Hakan Dogan", "E. Altinmakas", "Maryam Mohammadzadeh", "Kathryn A. Szymanski", "Cynthia J Campen", "Hollie Lai", "A. Eghbal", "Alireza Radmanesh", "K. Mankad", "Kristian Aquilina", "Mourad Said", "A. Vossough", "O. Oztekin", "Birgit Ertl-Wagner", "T. Poussaint", "Eric M Thompson", "Chang Y Ho", "A. Jaju", "John Curran", "Vijay Ramaswamy", "Samuel H Cheshier", "Gerald A. Grant", "S. S. Wong", "Michael E. Moseley", "Rob Lober", "Mattias Wilms", "N. D. Forkert", "N. Vitanza", "Jeffrey H. Miller", "L. Prolo", "K. W. Yeom"]2024-09-02T00:00:00https://www.semanticscholar.org/paper/ac3983bd9d6193dd40bc3f314d10c0c8621c128a f92922a9fe4e6bb603291249796d80d09d1fd9f3The entire gamut of Customer journey is undergoing a massive transformation due to the rapid advancement of Artificial Intelligence (AI). Leveraging the power of AI , CRM & systems have refined the aspect of how businesses manage and optimize the customer journey. AI-powered systems have significant impact across various stages of the customer lifecycle by use of techniques such as machine learning to empower businesses to use systems that can analyse vast amounts of customer dataset in real-time, enabling them to gain deeper insights in customer behaviours, preferences, & sentiment. The AI-driven techniques help businesses to drive more personalized & targeted marketing campaigns, tailored recommendations, and extend efficient customer service leading ultimately to enhancing customer satisfaction and loyalty. Moreover, AI-powered systems have capabilities of offering predictive analytics which empower businesses to forecast customer behaviours and anticipate their needs. The capabilities help businesses in effective resource optimization and improve efficiency. For customer service AI-powered chatbots and virtual assistants are used to enhance engagement by providing instant responses and ability to handle resolving issues promptly.International Journal of Innovative Science and Research Technology63207Leveraging the power of AI, CRM & systems have refined the aspect of how businesses manage and optimize the customer journey, enabling them to gain deeper insights in customer behaviours, preferences, & sentiment.International Journal of Innovative Science and Research Technology (IJISRT)["Murali Krishna Pendyala", "Vishnu Varma Lakkamraju"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/f92922a9fe4e6bb603291249796d80d09d1fd9f3 39e7e58c3292425912d4ceb4095484150a9d1a5eTechnology, Knowledge and Learning473Students' opinions regarding the use of artificial intelligence as a teammate in solving complex problems are examined, suggesting that students perceive AI with regard to two main themes: Trust in AI and the Capability of AI.Technology, Knowledge and Learning["Rebecca L Marrone", "Andrew Zamecnik", "Sre\u0107ko Joksimovi\u0107", "Jarrod Johnson", "Maarten de Laat"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/39e7e58c3292425912d4ceb4095484150a9d1a5e e55f46fc0d788a5c5abbccc05e97ca83dfa63c62Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques.Frontiers in Oncology2081An overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC is provided and the challenges and potential future directions related to the clinical application of AI techniques are explored.Frontiers in Oncology["Lulu Wang", "M. Fatemi", "A. Alizad"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/e55f46fc0d788a5c5abbccc05e97ca83dfa63c62 1b3e8d90a5571e7351373c938be425dfcff411efBackground Artificial intelligence (AI) is reforming healthcare, particularly in respiratory medicine and critical care, by utilizing big and synthetic data to improve diagnostic accuracy and therapeutic benefits. This survey aimed to evaluate the knowledge, perceptions, and practices of respiratory therapists (RTs) regarding AI to effectively incorporate these technologies into the clinical practice. Methods The study approved by the institutional review board, aimed at the RTs working in the Kingdom of Saudi Arabia. The validated questionnaire collected reflective insights from 448 RTs in Saudi Arabia. Descriptive statistics, thematic analysis, Fisher’s exact test, and chi-square test were used to evaluate the significance of the data. Results The survey revealed a nearly equal distribution of genders (51% female, 49% male). Most respondents were in the 20–25 age group (54%), held bachelor’s degrees (69%), and had 0–5 years of experience (73%). While 28% had some knowledge of AI, only 8.5% had practical experience. Significant gender disparities in AI knowledge were noted (p < 0.001). Key findings included 59% advocating for basics of AI in the curriculum, 51% believing AI would play a vital role in respiratory care, and 41% calling for specialized AI personnel. Major challenges identified included knowledge deficiencies (23%), skill enhancement (23%), and limited access to training (17%). Conclusion In conclusion, this study highlights differences in the levels of knowledge and perceptions regarding AI among respiratory care professionals, underlining its recognized significance and futuristic awareness in the field. Tailored education and strategic planning are crucial for enhancing the quality of respiratory care, with the integration of AI. Addressing these gaps is essential for utilizing the full potential of AI in advancing respiratory care practices.Frontiers Artif. Intell.201Differences in the levels of knowledge and perceptions regarding AI among respiratory care professionals are highlighted, underlining its recognized significance and futuristic awareness in the field.Frontiers in Artificial Intelligence["Jithin K. Sreedharan", "Asma Alharbi", "Amal Alsomali", "Gokul Krishna Gopalakrishnan", "Abdullah A. Almojaibel", "Rawan Alajmi", "Ibrahim Albalawi", "Musallam Alnasser", "M. Alenezi", "Abdullah S Alqahtani", "Mohammed D. Alahmari", "Eidan Alzahrani", "M. Karthika"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/1b3e8d90a5571e7351373c938be425dfcff411ef 71d7a025d564ac2ef9dcb1e3117a57609749f53aArtificial intelligence (AI) large language models (LLMs) now produce human-like general text and images. LLMs' ability to generate persuasive scientific essays that undergo evaluation under traditional peer review has not been systematically studied. To measure perceptions of quality and the nature of authorship, we conducted a competitive essay contest in 2024 with both human and AI participants. Human authors and 4 distinct LLMs generated essays on controversial topics in stroke care and outcomes research. A panel of Stroke Editorial Board members (mostly vascular neurologists), blinded to author identity and with varying levels of AI expertise, rated the essays for quality, persuasiveness, best in topic, and author type. Among 34 submissions (22 human and 12 LLM) scored by 38 reviewers, human and AI essays received mostly similar ratings, though AI essays were rated higher for composition quality. Author type was accurately identified only 50% of the time, with prior LLM experience associated with improved accuracy. In multivariable analyses adjusted for author attributes and essay quality, only persuasiveness was independently associated with odds of a reviewer assigning AI as author type (adjusted odds ratio, 1.53 [95% CI, 1.09-2.16]; P=0.01). In conclusion, a group of experienced editorial board members struggled to distinguish human versus AI authorship, with a bias against best in topic for essays judged to be AI generated. Scientific journals may benefit from educating reviewers on the types and uses of AI in scientific writing and developing thoughtful policies on the appropriate use of AI in authoring manuscripts.Stroke02A group of experienced editorial board members struggled to distinguish human versus AI authorship, with a bias against best in topic for essays judged to be AI generated.Stroke["G. Silva", "R. Khera", "Lee H. Schwamm"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/71d7a025d564ac2ef9dcb1e3117a57609749f53a baf04ece5d22a766d6e585a5984a5c890d126715The research examines how Green Artificial Intelligence (AI) impacts agricultural and biological domains by analysing relevant literature using bibliometrics. Green AI, which prioritizes sustainability and environmental awareness, is proving to be an asset in tackling the issues in agriculture and biology. The investigation examines a wide range of academic publications, patents, and research papers to identify significant trends, research topics, and the influence of Green AI on agricultural and biological fields. The research shows an increasing amount of literature investigating the combination of AI technology with green principles to improve resource management, boost agricultural output, and reduce environmental effects. This study uses analysis of citation networks, co-authorship patterns, and theme grouping to get insights into the present status and future trends of research in the fields of Green AI, agriculture, and biology. The data was extracted from the database maintained by Scopus using the PRISM Model. 11,142 article data points were used for the analysis of Green AI. This bibliometric study enhances comprehension of the changing environment of Green AI applications in agricultural and biological areas, providing vital recommendations for academics, policymakers, and practitioners working towards sustainable development objectives.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)171This bibliometric study enhances comprehension of the changing environment of Green AI applications in agricultural and biological areas, providing vital recommendations for academics, policymakers, and practitioners working towards sustainable development objectives.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)["Jonitha Anand", "Hadhrami Ab Ghani", "Nooraini Yusoff", "Kiran Kumar Thoti"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/baf04ece5d22a766d6e585a5984a5c890d126715 99389f07ddf492a045ecfc7e7a02428dc82ab491European Journal of Futures Research511The study reveals four tangible outcomes: regulating existential risks has a boundary solution of either prohibiting the technology or allowing a laissez-faire regulation, the degree of ‘normal’ risks follows a trade-off and is dependent on AI-intensity.European Journal of Futures Research["Bodo Herzog"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/99389f07ddf492a045ecfc7e7a02428dc82ab491 7e42259ef33712ab57226a7e2132f43c3f7278a4La Radiologia medica662This review aims to provide conceptually intuitive explanations of the outcome metrics and measures that are most frequently used in clinical research, specifically tailored for clinicians.La Radiologia medica["Seong Ho Park", "Kyunghwa Han", "June-Goo Lee"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/7e42259ef33712ab57226a7e2132f43c3f7278a4 e0bd6ca0ddb47a4a623a5943be7c361f2e9a65feBACKGROUND Medical research, driven by advancing technologies like Artificial Intelligence (AI), is transforming healthcare. Dermatology, known for its visual nature, benefits from AI, especially in dermatopathology with digitized slides. This review explores into AI's role, challenges, opportunities, and future potential in enhancing dermatopathological diagnosis and care. MATERIALS AND METHODOLOGY Adhering to PRISMA and Cochrane Handbook standards, this systematic review explored AI's function in dermatopathology. It employed an interdisciplinary method, encompassing diverse study types and comprehensive database searches. Inclusion criteria encompassed peer-reviewed articles from 2000 to 2023, with a focus on practical AI use in dermatopathology. RESULTS Numerous studies have investigated AI's potential in dermatopathology. We reviewed 112 papers. Notable applications include AI classifying histopathological images of nevi and melanomas, although challenges exist regarding subtype differentiation and generalizability. AI achieved high accuracy in melanoma recognition from formalin-fixed paraffin-embedded samples but faced limitations due to small datasets. Deep learning algorithms showed diagnostic accuracy for specific skin conditions, but challenges persisted, such as small sample sizes and the need for prospective validation. CONCLUSION This systematic review underscores AI's potential in enhancing dermatopathology for better diagnosis and patient care. Addressing challenges like limited datasets and potential biases is essential. Future directions involve expanding datasets, conducting validation studies, promoting interdisciplinary collaboration, and creating patient-centred AI tools to enhance dermatopathology's accuracy, accessibility, and patient-focused care.Clincal and Experimental Dermatology02A systematic review underscores AI's potential in enhancing dermatopathology for better diagnosis and patient care by employing an interdisciplinary method and addressing challenges like limited datasets and potential biases.Clinical and experimental dermatology["Roshni Mahesh Lalmalani", "Clarissa Lim Xin Yu", "Choon Chiat Oh"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/e0bd6ca0ddb47a4a623a5943be7c361f2e9a65fe 71bfc2135be18e7a1a121ffe529133f454c21d99With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.Frontiers in Genetics2201This review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology.Frontiers in Genetics["Bing Li", "Kan Tan", "Angelyn R. Lao", "Haiying Wang", "Huiru Zheng", "Le Zhang"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/71bfc2135be18e7a1a121ffe529133f454c21d99 6955ad79c8f9ecf301aea14b856d848a841749e9The use of artificial intelligence in the academic writing process prompts a profound reconsideration of fundamental ethical issues such as property, accuracy, and privacy. This study aims to explore the ethical dilemmas in the use of artificial intelligence in academic writing, focusing on the perspectives of researchers in the social sciences. A case study design was employed, using a maximum diversity sampling method with 34 researchers participating. Data collection utilized open-ended questions framed within Mason's framework of computer ethics, prompting participants to provide detailed responses. Data were analyzed using descriptive analysis, focusing on themes of property, accuracy, and privacy. The findings reflect diverse views among participants regarding the ethical implications of using artificial intelligence in academic writing. Specifically, the necessity of disclosing sources when artificial intelligence generates information and the importance of ethical citations were emphasized. The results contribute to initiating significant discussions on the ethical use of artificial intelligence in academic writing and add to the relevant literature.Uludağ Üniversitesi Eğitim Fakültesi Dergisi02The necessity of disclosing sources when artificial intelligence generates information and the importance of ethical citations were emphasized and contribute to initiating significant discussions on the ethical use of artificial intelligence in academic writing and add to the relevant literature.Uludağ Üniversitesi Eğitim Fakültesi Dergisi["A. R. Ers\u00f6z", "Melih Engin"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/6955ad79c8f9ecf301aea14b856d848a841749e9 457ddb2553b0983cd677dda51cde5b83c2269fe7In the complex mosaic of the digital age, the tactical incorporation of artificial intelligence (AI) within knowledge management (KM) is revealed as a central business component of technology management. The current study aims to clarify the intersection between KM and AI in organizational contexts. Specifically, this paper represents a preliminary step to investigate the potential impacts of AI on KM research and practice. Building on a database we created from Scopus, we shine a spotlight on trends in pertinent peer-reviewed scientific articles published in the last decade (2013-2023) on the KM-AI nexus. In addition, the paper presents an extended systematic analysis of literature, which synthesizes theoretical and empirical works conducted to date on this topic. Through a review of the available studies, we strive to shed light on effective KM frameworks and strategies in the era of AI. As extant research in the literature is largely theoretical, we propose to conduct empirical research on AI technologies in core KM processes such as acquisition, documentation, sharing, and application of knowledge. In addition, we recognize that the challenges and barriers to implementing AI in KM systems are not in focus and deserve to ignite further research. The anticipated contributions from such inquiries promise not only to augment the corpus of knowledge within the discipline, but also to furnish KM practitioners with the insights necessary for the crafting of efficacious systems. This research marks the advent of a transformative scholarly epoch, wherein the harmonious integration of KM and AI emerges as the bedrock of organizational ingenuity and strategic acumen. It distinguishes itself from prior works by pinpointing knowledge gaps in the synergy between disciplines and underscores the imperative for future research to bridge these lacunae.European Conference on Knowledge Management410The current study aims to clarify the intersection between KM and AI in organizational contexts and proposes to conduct empirical research on AI technologies in core KM processes such as acquisition, documentation, sharing, and application of knowledge.European Conference on Knowledge Management["E. Bolisani", "Maayan Nakash"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/457ddb2553b0983cd677dda51cde5b83c2269fe7 c1c02cecac382fb93f6f24d8f98e6ea012194027Artificial Intelligence (AI) pertains to the creation of computer systems that can execute tasks demanding human-like intelligence. As AI advances influence education, grasping the intricate impacts on academician becomes crucial for making informed decisions and fostering sustainable educational progress. Through a comprehensive analysis of AI's impact on academicians, this research adds to the continuous refinement of the educational setting within Malaysian universities. It's crucial to understand the key elements shaping AI's adoption among academicians in these institutions, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The UTAUT model encompasses underlying factors such as Behavioral Intention (BI), Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), Facilitating Conditions (FC), and Use Behavior (UB). This study's methodology involved examining literature on the relationships among these factors, moderated by variables such as experience, gender, age, and voluntary use within the UTAUT framework. The findings indicate that the proposed theoretical model, incorporating these factors and moderating variables, enhances the understanding of AI user behavior among academicians in Malaysian universities. This model lays the groundwork for further empirical investigation.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)300The findings indicate that the proposed theoretical model, incorporating underlying factors and moderating variables, enhances the understanding of AI user behavior among academicians in Malaysian universities, laying the groundwork for further empirical investigation.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)["Nur Farhanis Ahmad Anuar", "Nur Balqishanis Zainal Abidin", "Sarah A'fifah Abdullah Sani", "S. N. Karim", "Nur Amalina Mat Jan Mat Jan"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/c1c02cecac382fb93f6f24d8f98e6ea012194027 0b84d3e954afc5fbd50a358e1b044e974d84da0cIn modern educational landscapes, the integration of Artificial Intelligence (AI) technologies is swiftly transforming traditional teaching and learning paradigms. AI holds significant promise for personalized learning experiences, increased engagement, and optimized educational outcomes. However, there has been limited attention given to understanding the factors that shape students' intentions to use AI technologies in their learning processes. Addressing this knowledge gap is essential for developing targeted strategies that promote technology acceptance and utilisation among students. This study aims to develop and test a set of instruments to examine the factors influencing students' behavioural intentions to adopt AI technology in educational settings. Building on a comprehensive literature review of AI adoption, this study identifies eight key concepts: Social influence, habit, price value, performance expectancy, facilitating conditions, hedonic motivation, effort expectancy, and behavioural intention. Accordingly, the instruments were designed to measure these concepts. The measurement scales were subsequently evaluated for reliability and validity using data from 50 students who had used AI technologies. Consequently, these instruments can serve as a stepping stone for future research on AI adoption in educational contexts.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)330This study aims to develop and test a set of instruments to examine the factors influencing students' behavioural intentions to adopt AI technology in educational settings, and identifies eight key concepts: Social influence, habit, price value, performance expectancy, facilitating conditions, hedonic motivation, effort expectancy, and behavioural intention.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)["Roszi Naszariah Nasni Naseri", "Jhanghiz Syahrivar", "I. S. Saari", "Wan Kalthom Yahya", "Geetha Muthusamy"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/0b84d3e954afc5fbd50a358e1b044e974d84da0c 2655d7ac033d8508d954886edc1190ad5797538fAs the diversity and complexity of Artificial Intelligence (AI) systems increase, there is a growing need for advanced knowledge representation methods to enhance decision-making capabilities. Existing research indicates a gap between AI and Knowledge Management (KM), emphasizing the necessity of coordinating learning and knowledge creation processes between humans and machines. Despite the widespread use of generative AI, as seen through the growing popularity of conversational AI tools like chatbots powered by Large Language Models in recent years, the absence of a theoretical framework for effectively managing the knowledge they generate could mean missing out on significant opportunities. This work seeks to bridge this gap between KM and IA through an integrated framework that aims to apply KM to support IA chatbot applications, adapted from the Internet of Everything Integrated Knowledge Management Model (IoE IKM Model). The IoE IKM Model’s original goal is to support knowledge creation in IoE applications, but here, we show how it can be adapted to bring KM to the context of AI. We accomplish this by explaining the development process of the IoE IKM Model, identifying shared aspects between IoE and AI general applications, and adapting necessary elements to establish our integrated KM framework tailored for supporting AI chatbot applications. The resulting framework is then discussed, and examples of how it can be applied to enhance human interaction with a chatbot, namely Open AI's ChatGPT. Research has been conducted to demonstrate the advantages of applying AI in KM. However, we aim to take a different approach by showing how KM can contribute to AI applications. We expect this work to be helpful for those whose professional activities may involve the usage of AI systems by providing them with the necessary tools to manage the knowledge generated by these same AI systems and by offering a Knowledge Manager’s perspective on how to boost human-machine interaction.European Conference on Knowledge Management190This work seeks to bridge the gap between KM and IA through an integrated framework that aims to apply KM to support IA chatbot applications, adapted from the Internet of Everything Integrated Knowledge Management Model (IoE IKM Model).European Conference on Knowledge Management["Gustavo Oliveira", "M. Arg\u00f4lo", "C. E. Barbosa", "Yuri Oliveira de Lima", "Herbert Dos Santos", "A. Lyra", "Jano Moreira de Souza"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/2655d7ac033d8508d954886edc1190ad5797538f 0c6bf1ce283b5f55652ef463c2e9c207f2eafda8The growth of Artificial Intelligence and the potential to empower leadership within various organizational contexts represent an emergent area of inquiry in this systematic review, with the lens focusing on the positive impacts and potentials of AI applications in augmenting and enhancing leadership practices. Based on PRISMA guidelines, the aim of the review is to identify most of the literature are journal articles, research studies, and any other relevant publications within the last 10 years that were published in English, with high importance attached to empirical studies and documented cases of AI implementation in leadership roles. Key themes will be how AI can help drive decision-making and how AI tools analyze organizational data and market trends to inform strategic choice. The paper covered the AI's applicability for personalized leadership development; in particular, how AI can be adopted to tailor learning experiences to individual leader requirements in the context of lifelong growth. Next, it suggested that an AI-enhanced tools may boost team performance through communication, workflow efficiency, and team collaboration. And the paper determined and analyzed the changing role of a leader in an AI-intense environment that still requires the very traits of human-centered leadership to ensure ethical handling and human oversight of the technological potential. From ongoing initiatives, the paper attempts to derive guidance and best practices applicable to school’s organizational leadership, policy makers, and AI developers alike with the intention of contributing to better understanding AI's potential in support of leadership leading to more effective, ethical, and human-centered organizations.International journal of multidisciplinary research and analysis110The paper determined and analyzed the changing role of a leader in an AI-intense environment that still requires the very traits of human-centered leadership to ensure ethical handling and human oversight of the technological potential.INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS["Leomar M. Pago"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/0c6bf1ce283b5f55652ef463c2e9c207f2eafda8 fd675dc59aab0448b4df7499c39b46c5617dec05OBJECTIVE This scoping review aims to map studies that applied artificial intelligence (AI) tools to perform health technology assessment tasks in human health care. The review also aims to understand specific processes in which the AI tools were applied and to comprehend the technical characteristics of these tools. INTRODUCTION Health technology assessment is a complex, time-consuming, and labor-intensive endeavor. The development of automation techniques using AI has opened up new avenues for accelerating such assessments in human health settings. This could potentially aid health technology assessment researchers and decision-makers to deliver higher quality evidence. INCLUSION CRITERIA This review will consider studies that assesses the use of AI tools in any process of health technology assessment in human health. However, publications in which AI is a means of clinical aid, such as diagnostics or surgery will be excluded. METHODS A search for relevant articles will be conducted in databases such as CINAHL (EBSCOhost), Embase (Ovid), MEDLINE (PubMed), Science Direct, Computer and Applied Sciences Complete (EBSCOhost), LILACS, Scopus, and Web of Science Core Collection. A search for gray literature will be conducted in GreyLit.Org, ProQuest Dissertations and Theses, Google Scholar, and the Google search engine. No language filters will be applied. Screening, selection, and data extraction will be performed by 2 independent reviewers. The results will be presented in graphic and tabular format, accompanied by a narrative summary. DETAILS OF THIS REVIEW CAN BE FOUND IN OPEN SCIENCE FRAMEWORK osf.io/3rm8g.JBI Evidence Synthesis200This scoping review aims to map studies that applied artificial intelligence (AI) tools to perform health technology assessment tasks in human health care and to understand specific processes in which the AI tools were applied and to comprehend the technical characteristics of these tools.JBI evidence synthesis["Denis Satoshi Komoda", "Mar\u00edlia Mastrocolla de Almeida Cardoso", "Br\u00edgida Dias Fernandes", "M. B. Visacri", "Carlos Roberto Silveira Correa"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/fd675dc59aab0448b4df7499c39b46c5617dec05 2003fc35da86c6d41d9b141ecf897a8fdd9feac4In modern perspective, the phenomenon of artificial intelligence (AI) has witnessed considerable advancements, extending its influence across various sectors, with higher education emerging as one of the most significant areas of impact. Researchers who have found the revolutionary potential and essential contributions of AI in boosting educational practices and approaches have extensively emphasized this trend. (George & Wooden, 2023; George & Paul, 2020). The higher education domain is progressively acknowledging AI as a key factor for competitive edge (Hannan & Liu, 2021). This paper aims to examine the existing research on AI implications in the higher education segment. Grounded on the theory of technology diffusion (Rogers, 2003), our research systematically examines the scientific field, identifying emerging research topics. Using SCOPUS as a database for peer-reviewed article selection (publication period 2011–2024) and bibliometric analysis, we provide an insight into the varied geographic spread of research within the realm of AI in education. Our findings shed light on the evolution of models for the adoption of AI technologies in higher education, revealing six main areas of research in the field: (1) teaching and involving students in the educational process using generative AI, (2) using chatbots to improve the educational process, (3) improving the literacy of teachers and students in the field of AI, (4) AI and blockchain in educational practices (5) development of regulations for the use of AI and (6) improving operational processes at universities. This article explains current influences on research prospects in the context of higher education. By offering an understanding of the challenges and opportunities presented by AI, this paper encourages educators to investigate possible uses of AI in the context of higher education.European Conference on Knowledge Management00Current influences on research prospects in the context of higher education are explained, offering an understanding of the challenges and opportunities presented by AI and encouraging educators to investigate possible uses of AI in the context of higher education.European Conference on Knowledge Management["Olga Gordienko", "Konstantin Bagrationi"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/2003fc35da86c6d41d9b141ecf897a8fdd9feac4 6bc8fef4a525ede3d5c98708fb09e34bd1f15388The use of artificial intelligence (AI) and intellectual machines can support businesses in performing various activities. Therefore, it is necessary to examine the performance outcomes by assessing the concentration of AI technologies. To create a quantifiable score of AI concentration, AI-related terms are identified in the annual reports of all listed firms in the U.S. For analysis purposes, a fixed effects model is employed, using firms’ panel data from 2003 to 2022. The analysis reveals that AI concentration is beneficial for a company’s financial success. Additional analysis examines the moderating role of research and development (R&D). Firms with higher R&D spending experience increased financial benefits from concentrating on AI technologies. The uniqueness of this study lies in analyzing the financial success through the AI and R&D parameters. The findings support a higher concentration on AI, combined with higher R&D spending, to attain greater financial success. The main insights suggest that management must evaluate their existing focus on AI and R&D spending to improve their financial position.Journal of Infrastructure Policy and Development530The findings suggest that management must evaluate their existing focus on AI and R&D spending to improve their financial position and support a higher concentration on AI, combined with higher R&D spending, to attain greater financial success.Journal of Infrastructure, Policy and Development["Sonia Kumari", "Raja Shaikh", "M. Bhayo", "Sharmila Devi", "Shengjie Cao"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/6bc8fef4a525ede3d5c98708fb09e34bd1f15388 0d3925a892ca1c6c91b1c90d67358ebb483744b7 Kecelakaan lalu lintas sering kali mengakibatkan kerugian besar, termasuk kehilangan nyawa. Banyak korban jatuh karena penanganan kecelakaan yang tidak memadai, seperti keterlambatan dalam memberikan informasi dan lokasi kecelakaan yang sulit dijangkau sehingga membuat korban menjadi tidak tertolong. Penelitian ini bertujuan mengatasi permasalahan yang disebabkan oleh kecelakaan lalu lintas dengan merancang dan menciptakan alat deteksi berbasis suara menggunakan metode Convolutional Neural Network (CNN) pada platform embedded artificial intelligence yang memanfaatkan Raspberry Pi 4. Alat ini memanfaatkan usb microphone dan GPS Ublox Neo-m8n untuk menangkap suara serta pengiriman data koordinat secara real-time. Dataset dibagi menjadi dua kelas yaitu crash dan normal, kemudian dataset diolah melalui augmentasi dan divisualisasikan sebagai Mel Spectrogram. Model yang didapat mencapai akurasi 98,63% dan loss 1,37% pada prediksi data testing, sementara implementasi real-time pada Raspberry Pi 4 dengan usb microphone menghasilkan akurasi 82% dari 50 sampel file audio. Sistem ini beroperasi dengan FPS rata-rata sebesar 13.14 untuk proses streaming dan 6.89 untuk proses prediksi serta dihasilkan konsumsi daya rata-rata sebesar 10.069 Watt. Output yang dihasilkan berupa pesan peringatan melalui aplikasi Telegram berupa informasi lokasi dan waktu kecelakaan lalu lintas. Penelitian ini diharapkan dapat meningkatkan respons dalam penanganan kecelakaan, mengurangi kerugian, dan menyelamatkan nyawa. Indonesian Journal of Computer Science00The Indonesian Journal of Computer Science["Ahmada Haiz Zakiyil Ilahi", "Arif Irwansyah", "Budi Nur Iman", "Naufal Mukhfi Robbani"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/0d3925a892ca1c6c91b1c90d67358ebb483744b7 bd257de771c038e923902d5b75f06de6860f7002This study explores the impact of artificial intelligence (AI) on knowledge management (KM) in the automotive manufacturing industry with a focus on different cultural contexts in China and Germany. The role of cultural factors on the effectiveness of AI in KM practices is explored by comparing automobile manufacturers in China and Germany. This study uses case studies to compare, and contrast leading automotive manufacturers in both countries and combines industry reports, papers journals, and other digital resources on the Internet to explore how the manufacturing industry can use AI technology to improve efficiency in the KM process. In addition, the study explores the impact of culture on organizational structure, decision-making, and employee engagement with new technologies within a company. The preliminary findings suggest differences in the understanding and use of AI and KM between China and Germany due to their different history, culture, and level of economic development. In China, the integration of AI into KM is driven by rapid technological advances and strong government support, focusing on efficiency and scalability. In contrast, German companies show more caution, emphasizing accuracy, reliability, and augmentation of human expertise. These differences reflect broader cultural attitudes toward technology and innovation in both countries. The study contributes to the understanding of the interaction between AI and KM in the context of cultural differences. The findings will have important implications for subsequent AI research and policy development.European Conference on Knowledge Management200How the manufacturing industry can use AI technology to improve efficiency in the KM process and the impact of culture on organizational structure, decision-making, and employee engagement with new technologies within a company are explored.European Conference on Knowledge Management["Rui Wang", "Yifen Yin"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/bd257de771c038e923902d5b75f06de6860f7002 0ac30444fb1d335d7e800882df955be3a578b977The dispatch of practical instructions to schools and supervisors prior to the actual conduct of the practical examination over the years has not received the same level of attention as that given to the movements of people and goods and, therefore, is prone to challenges. However, the process could be automated using artificial intelligence. Previous studies have investigated the effects of automation on the control and delivery of goods in the transport management sector, mostly in the Western world. Therefore, this study assessed science teachers’ challenges and readiness for artificial intelligence in practical instruction control and delivery systems. The study adopted ex-post facto design and used one hundred science teachers as participants. Science Teacher Readiness for Automated Practical Instruction Control and Delivery (r = 0.83) was used to collect data. The data collected were analysed descriptively. There are more male (73%) science teachers than female (27%). 84% of the respondent listed cost as one of the challenges, and 83% of the respondents indicated resistant to change and technical difficulties, ethical issue 67% and integration with existing system 65%) 64 The science teachers are moderately ready 64% while 24% are lowly ready and 12% are highly ready for the deployment of automated practical instruction control and delivery system. Artificial intelligence for science practical instruction delivery has greater benefits than the manual way of delivery; however, science teachers are ready for its deployment despite its challenges. Therefore, efforts should be geared towards overcoming the inherent challenges so that the benefits can be fully enjoyed.American Journal of IR 4.0 and Beyond180Science teachers are ready for artificial intelligence for science practical instruction delivery despite its challenges, and efforts should be geared towards overcoming the inherent challenges so that the benefits can be fully enjoyed.American Journal of IR 4.0 and Beyond["A. Jinadu"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/0ac30444fb1d335d7e800882df955be3a578b977 8eb87395b665b71955035f2707c5278ac61be3a2Background Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss. Objective We evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center. Methods We prospectively recruited adult patients with diabetes at the Centre hospitalier de l’Université de Montréal (CHUM) in Montreal, Quebec, Canada. Patients underwent dual-pathway screening: first by the Computer Assisted Retinal Analysis (CARA) AI system (index test), then by standard ophthalmological examination (reference standard). We measured the AI system's sensitivity and specificity for detecting referable disease at the patient level, along with its performance for detecting any retinopathy and diabetic macular edema (DME) at the eye level, and potential cost savings. Results This study included 115 patients. CARA demonstrated a sensitivity of 87.5% (95% CI 71.9-95.0) and specificity of 66.2% (95% CI 54.3-76.3) for detecting referable disease at the patient level. For any retinopathy detection at the eye level, CARA showed 88.2% sensitivity (95% CI 76.6-94.5) and 71.4% specificity (95% CI 63.7-78.1). For DME detection, CARA had 100% sensitivity (95% CI 64.6-100) and 81.9% specificity (95% CI 75.6-86.8). Potential yearly savings from implementing CARA at the CHUM were estimated at CAD $245,635 (US $177,643.23, as of July 26, 2024) considering 5000 patients with diabetes. Conclusions Our study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting. This system has the potential to improve screening efficiency and reduce costs at the CHUM, but more work is needed to validate it.JMIR Diabetes470The study indicates that integrating a semiautomated AI system for DR screening demonstrates high sensitivity for detecting referable disease in a real-world setting, and has the potential to improve screening efficiency and reduce costs at the CHUM.JMIR Diabetes["F. Antaki", "Imane Hammana", "Marie-Catherine Tessier", "Andr\u00e9e Boucher", "Maud Laurence David Jett\u00e9", "Catherine Beauchemin", "Karim Hammamji", "A. Ong", "Marc-Andr\u00e9 Rh\u00e9aume", "Danny Gauthier", "Mona Harissi-Dagher", "P. Keane", "Alfons Pomp"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/8eb87395b665b71955035f2707c5278ac61be3a2 354d32a4992c95ee0b9a59e12f0f2bfd8ee66ef6With the rapid development of Artificial Intelligence (AI) technology, the forms and methods of property crimes have undergone significant changes. AI has not only enhanced the capabilities of criminals but also increased the concealment and complexity of criminal activities. These changes pose new challenges to the existing criminal law system. This paper explores the main characteristics of property crimes in the AI era, analyzes the dilemmas encountered in the application of criminal law, including legal lag, difficulties in evidence collection, and technological barriers. In response to these dilemmas, the paper proposes corresponding countermeasures, including improving the legal system, innovating legislation, enhancing technical support, and promoting international cooperation. By analyzing these issues and solutions, this paper aims to provide useful references and suggestions for the application of criminal law to property crimes in the age of AI.Economics Law and Policy00The main characteristics of property crimes in the AI era are explored, and the dilemmas encountered in the application of criminal law, including legal lag, difficulties in evidence collection, and technological barriers are analyzed.Economics, Law and Policy["Wang Feng"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/354d32a4992c95ee0b9a59e12f0f2bfd8ee66ef6 f0ca6dbbb78df24f3b6666fedfa41f006ad21df5The technique of pharmacovigilance, which involves keeping an eye on how medical pharmaceuticals are working after they've been approved for use, is vital in making sure that drugs are safe to use. Novel approaches brought about by the advent of Artificial Intelligence (AI) have the potential to greatly improve pharmacovigilance by making ADR identification, evaluation, and prevention much more effective. This study delves into the most recent advancements in AI-driven pharmacovigilance, shedding light on how drug safety monitoring is being revolutionised by machine learning algorithms, natural language processing, and big data analytics. In this article, we will go over the pros, cons, and possible next steps for pharmacovigilance frameworks that use AI.Journal of Advances and Scholarly Researches in Allied Education80This study delves into the most recent advancements in AI-driven pharmacovigilance, shedding light on how drug safety monitoring is being revolutionised by machine learning algorithms, natural language processing, and big data analytics.Journal of Advances and Scholarly Researches in Allied Education["Abdulaziz Ali Al Amri", "Ahmed Ebrahem Al Thobaiti", "Fahad Sultan", "Al Zahrani", "Fahad Abdulali Alkharmani", "Abdulmuhsen Ghazy Alqethami"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/f0ca6dbbb78df24f3b6666fedfa41f006ad21df5 2127019c376982a6a7e5e679809b9a29c6422fbaThe study by Zimolzlak et al 1 in this issue of JAMA Network Open is one of what will soon be a flood of research that focuses on the role of artificial intelligence (AI) in identifying diagnostic process problems and diagnostic errors (also called diagnostic opportunities), with the overall goal of catalyzing diagnostic improvement. 2 At its core, research of diagnostic processes and outcomes is a field of measurement: measurement of timeliness, accuracy, equity, and effectiveness. In the case of diagnosis, this can sometimes be very straightforward, as in the case of unexpected findings at time of autopsy. 3 In others, diagnostic errors are at the core of a clinical practice, but the line between diagnostic faults and closely related issues, such as overtreatment, is less clear. 4 Methods can be driven entirely by administrative data 5 with challenges in terms of face validity, or manual methods that are accurate but very time consuming and difficult to spread. 6 Finally, some combination of diagnosis data and events can be used to identify cases where diagnostic problems may have taken place, an approach called electronic triggers (e-triggers) 7 . As a result, we have few scalable, time efficient, and accurate ways to identify where an error took place and in turn, to support efforts to reduce these same errors.JAMA Network Open70JAMA network open["Andrew D. Auerbach"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/2127019c376982a6a7e5e679809b9a29c6422fba 163d76dcafcb85ec21e251ef64ca5f660aa598c0The integration of artificial intelligence (AI) into diabetes care holds the potential to transform patient management and improve outcomes, especially in Malaysia, where diabetes represents a major public health challenge. However, the adoption and effectiveness of AI in healthcare are significantly impacted by patient attitudes. This paper addresses the gap in understanding these attitudes. The primary goal of this study is to investigate the perspectives of diabetic patients on the use of AI applications and tools in diabetes care. This study also examined the patterns of acceptance and understanding of AI among diabetic patients. This study used qualitative methods using in-depth interviews with seventeen Malaysian diabetic patients in Hospital Tengku Ampuan Rahimah Klang (HTAR), Malaysia. The interview lasted two weeks, from August 8, 2023, to August 22, 2023. All interviews were audio-recorded and transcribed word-for-word. The transcribed content was then organized and coded using ATLAS.ti version 8. Thematic analysis was performed in accordance with established guidelines for data analysis. Three key themes emerged from participant interviews regarding the patients' attitudes toward AI application in diabetes care. These themes were perceived acceptability, perceived benefits of using AI tools, and perceived need. The majority of participants expressed their positive view of using AI tools. The findings of this study lay the groundwork for a theoretical framework aimed at understanding patients' stances on AI applications in diabetic care, emphasizing the health, technological, and social experiences that influence their perspectives on AI in this context.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)200The findings of this study lay the groundwork for a theoretical framework aimed at understanding patients' stances on AI applications in diabetic care, emphasizing the health, technological, and social experiences that influence their perspectives on AI in this context.2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)["Maslin Masrom", "Logeswary Krisnan", "Yazriwati Yahya", "M. K. Kaundan"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/163d76dcafcb85ec21e251ef64ca5f660aa598c0 a9b98d7c9fde55ccef5df0d49bd027a4820ae7eaThe research aimed to shed light on the impact of using artificial intelligence techniques to enhance risk management in the banking sector in the Kingdom of Saudi Arabia, with an application to Al Rajhi Bank, by clarifying the dimensions of artificial intelligence techniques, in addition to revealing the impact of artificial intelligence techniques in their dimensions on risk management. The researcher relied on the descriptive analytical approach in order to achieve the objectives of the study, as he used the questionnaire as a tool for collecting information, as this research was applied to a study community represented by employees and managers of Al Rajhi Bank, as the research sample consisted of a random sample of (196) individuals, and to analyze this information, the statistical analysis program (SPSS) was used. This research has reached a set of important results, the most prominent of which is that artificial intelligence technologies have an impact on risk management, and there is an influential role for each of the devices and software, effectiveness, knowledge and reasoning individually on risk management, and there is an influential role for artificial intelligence technologies on credit risks, market risks, and operational risks individually. The researcher recommended providing training programs for employees on the use of artificial intelligence tools in risk management, the necessity of investing in research and development of artificial intelligence applications in the banking field, and amending policies and procedures based on the results extracted from artificial intelligence applications.International Journal of Financial, Administrative, and Economic Sciences00There is an influential role for each of the devices and software, effectiveness, knowledge and reasoning individually on risk management, and there is an influential role for artificial intelligence technologies on credit risks, market risks, and operational risks individually.International Journal of Financial, Administrative, and Economic Sciences["Malak Al-Otaibi", "Al-Faisal Hassan", "Fayez Jarad"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/a9b98d7c9fde55ccef5df0d49bd027a4820ae7ea 38ae8bdc9ece01fbbe95eb58d08951e012deb32bJournal of Innovation and Entrepreneurship412Journal of Innovation and Entrepreneurship["Sandra Sayonara Sol\u00f3rzano Sol\u00f3rzano", "Johanna Micaela Pizarro Romero", "Jimmy Gabriel D\u00edaz Cueva", "Jorge Eduardo Arias Montero", "Michael Andr\u00e9s Zamora Campoverde", "Mariana Malvina Lozzelli Valarezo", "Jose Carlos Montes Ninaquispe", "Benicio Gonzalo Acosta Enr\u00edquez", "Marco Agust\u00edn Arbul\u00fa Ballesteros"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/38ae8bdc9ece01fbbe95eb58d08951e012deb32b f9f64f2e93c0079342e279a9dbe4c824204629f3JAMA Network Open72JAMA network open["Nigam H. Shah", "Michael A. Pfeffer", "Marzyeh Ghassemi"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/f9f64f2e93c0079342e279a9dbe4c824204629f3 5fa3a114b076e7d22c0078f35ad11f20fdc25b5bCoronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum parameters for classifier or regression technique (regressor) were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel ’biomarkers-ML combination' approach to risk stratification.IEEE journal of biomedical and health informatics240The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography based on novel clinical, chemical, and molecular cardiac biomarkers used as input features from an especially collected dataset.IEEE Journal of Biomedical and Health Informatics["Muhammad Sajid", "Ali Hassan", "Dilshad Ahmed Khan", "S. A. Khan", "A. D. Bakhshi", "Muhammad Usman Akram", "Mishal Babar", "Farhan Hussain", "Wadood Abdul"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/5fa3a114b076e7d22c0078f35ad11f20fdc25b5b 850be87b37652a969d60ad585636de4ad0224d99American Journal of Educational Research00American Journal of Educational Research["Kyle Doty", "Lodi Lipien"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/850be87b37652a969d60ad585636de4ad0224d99 3d5413fa18ff6b3ff4e22b4b38618e8d23dfd01bAnnals of Internal Medicine00Annals of internal medicine["R. Centor", "Nadia Daneshvar", "Deepti Pandita"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/3d5413fa18ff6b3ff4e22b4b38618e8d23dfd01b 42d584174f7b51bcce7d28a3bc5f236210a71370Current Pharmacology Reports170Current Pharmacology Reports["Nitish Bhatia", "Mohd Masih Uzzaman Khan", "Saahil Arora"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/42d584174f7b51bcce7d28a3bc5f236210a71370 e7b0e650bc8bdcecb32b7837a9ed67361045f8f7بحوث فی علم المکتبات والمعلومات00بحوث في علم المکتبات والمعلومات["\u0633\u0627\u0645\u06cc\u0629 \u0645\u062d\u0645\u062f \u0639\u0627\u0645\u0631"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/e7b0e650bc8bdcecb32b7837a9ed67361045f8f7 f91d3aa76da9ce7723aabc6e610ca035a38f871cOne of the most important parts of every community is its educational system. It has significantlinks to and effects on all other economic spheres. The main aim of the study is Role of artificialintelligence to enhance learning competencies at secondary school students. Six hundred and five highschool students were polled using a modified survey instrument to delve into students' understanding of AIeducation's most foundational concepts. This research adds to the body of knowledge by emphasizing thesignificance of students' prior knowledge and skills in the classroom, particularly as they relate to AIinstruction.International Journal of Information Technology and Management00Role of artificialintelligence to enhance learning competencies at secondary school students and the importance of students' prior knowledge and skills in the classroom, particularly as they relate to AI instruction is emphasized.International Journal of Information Technology and Management["Dr. Mohd. Talib Ather Ansari Dr. Mohd. Talib Ather Ansari"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/f91d3aa76da9ce7723aabc6e610ca035a38f871c d1ed056e6cd2f5b758c64b0e6e113a57dbcadec3Brazilian Journal of Cardiovascular Surgery60Brazilian Journal of Cardiovascular Surgery["Glory Mini Mol Alexander"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/d1ed056e6cd2f5b758c64b0e6e113a57dbcadec3 c674f371d63d684ce97ba4490e045f29494cdc65This study investigates the impact of artificial intelligence (AI) innovation on environmental sustainability in the Nordic region. Additionally, it tests the Load Capacity Curve (LCC) hypothesis by incorporating factors such as financial accessibility, environmental tax, and urbanization, using data spanning from 1990 to 2020. The methodology includes the Cross-Sectional Dependence test and the slope homogeneity test, revealing issues of heterogeneity and cross-sectional dependence. Furthermore, first and second-generation panel unit root assessments indicate that the variables are free from unit root problems. Panel Cointegration tests confirm that the variables are cointegrated in the long run. To analyze both short-run and long-run relationships, the study employs the Panel Autoregressive Distributed Lag (ARDL) model. The results from the Panel ARDL model support the LCC hypothesis in the Nordic region, showing a U-shaped relationship between income and load capacity factor. Moreover, AI innovation and environmental tax significantly and positively contribute to environmental sustainability in both the short and long run. In contrast, higher financial accessibility and urbanization degrade environmental sustainability over these timeframes. To validate the robustness of the Panel ARDL estimations, the study also uses Fully Modified OLS, Dynamic OLS, and Fixed Effects OLS approaches, all of which corroborate the ARDL findings. The study employs the D-H causality test to explore causal relationships among the variables. The test results reveal a unidirectional causal relationship between income and AI innovation to the load capacity factor and a bidirectional causal relationship between financial accessibility and the load capacity factor, as well as between urbanization and the load capacity factor. However, no causal relationship is found between environmental tax and the load capacity factor.Global Sustainability Research15310Global Sustainability Research["Md Sibbir Hossain", "Mohammad Ridwan", "Afsana Akhter", "Md Boktiar Nayeem", "Md Tazwar Hossain Choudhury", "Md Asrafuzzaman", "Shaharina Shoha", "Shake Ibna Abir", "Sumaira"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/c674f371d63d684ce97ba4490e045f29494cdc65 29e818feda3792edae6c1e228fd94860ab981623This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.Electronics1224This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically, and investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications.Electronics["Sajad Moosavi", "Maryam Farajzadeh-Zanjani", "R. Razavi-Far", "Vasile Palade", "Mehrdad Saif"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/29e818feda3792edae6c1e228fd94860ab981623 5e2e34f1fb73cc5e0ffdf85e23a2a68586fd895cThe rapid integration of artificial intelligence (AI) across various sectors has significantly amplified privacy concerns, particularly with the growing reliance on cloud environments. Existing methods often fall short of effectively preventing privacy breaches due to inadequate risk assessment and mitigation strategies. These limitations highlight the necessity for more robust solutions, indicating the importance of Privacy by Design (PbD) principles. This study addresses these gaps by proposing a comprehensive approach to incorporating PbD principles into AI systems to prevent breaches across public, private, and on-prem environments. The proposed work utilizes logistic regression analysis to identify significant predictors of privacy breaches, revealing that both the environment (B = -1.142, p < .001) and severity of vulnerabilities (B = 0.932, p < .01) play crucial roles. Additionally, a strong positive correlation (r = 0.791) between breach detection rates and PbD effectiveness is observed, indicating the need for enhanced detection mechanisms. To support the empirical findings, this study also reviews existing case studies. It conducts a thematic analysis to provide a deeper understanding of the practical challenges and solutions associated with PbD implementation, particularly in healthcare and smart city applications. These analyses serve to supplement the empirical evidence and demonstrate the effectiveness of PbD over other existing methods. The study concludes that implementing PbD principles is critical for achieving robust privacy protection, and the study recommends prioritizing advanced breach detection mechanisms, comprehensive privacy impact assessments, continuous stakeholder engagement, and investment in privacy-enhancing technologies to address privacy risks effectively.Journal of Engineering Research and Reports684It is concluded that implementing PbD principles is critical for achieving robust privacy protection, and the study recommends prioritizing advanced breach detection mechanisms, comprehensive privacy impact assessments, continuous stakeholder engagement, and investment in privacy-enhancing technologies to address privacy risks effectively.Journal of Engineering Research and Reports["Samuel Ufom Okon", "O. Olateju", "Olumide Samuel Ogungbemi", "Sunday Abayomi Joseph", "Anthony Obulor Olisa", "O. O. Olaniyi"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/5e2e34f1fb73cc5e0ffdf85e23a2a68586fd895c 6b49dbea72839b23d18f154cf8dc8a9ebd01347bHealth Information Science and Systems22This paper presents the basic technology of Web-based Tree Hole intelligent agents and elaborate how the intelligent agents can discover suicide attempts and issue corresponding monitoring notifications and how the volunteers of Tree Hole Rescue Team can conduct online suicide intervention.Health information science and systems["Zhisheng Huang", "Qing Hu"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/6b49dbea72839b23d18f154cf8dc8a9ebd01347b c335a5511fa05bee9804b220f05d3382b0a3464fArtificial Intelligence is playing an increasing role in solving some of the world’s biggest problems. Machine Learning Models, within the context of reinforcement learning, define and structure a problem in a format that can be used to learn about an environment in order to find an optimal solution. This includes the states, actions, rewards, and other elements in a learning environment. This also includes the logic and policies that guide learning agents to an optimal or nearly optimal solution to the problem. This paper outlines a process for developing machine learning models. The process is extensible and can be applied to solve various problems. This includes a process for implementing data models using multi-dimensional arrays for efficient data processing. We include an evaluation of learning policies, assessing their performance relative to manual and automated approaches.International Journal of Innovative Science and Research Technology381A process for developing machine learning models is outlined, which includes a process for implementing data models using multi-dimensional arrays for efficient data processing and an evaluation of learning policies, assessing their performance relative to manual and automated approaches.International Journal of Innovative Science and Research Technology (IJISRT)["Akaninyene W. Udoeyop"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/c335a5511fa05bee9804b220f05d3382b0a3464f a85277bb2a5cbfd3c9e521335d6df3eefb1d8629Complexity science, despite its broad scope and potential impact, has not kept pace with fields like artificial intelligence, biotechnology and social sciences in addressing ethical concerns. The field lacks a comprehensive ethical framework, leaving us, as a community, vulnerable to ethical challenges and dilemmas. Other areas have gone through similar experiences and created, with discussions and working groups, their guides, policies and recommendations. Therefore, here we highlight the critical absence of formal guidelines, dedicated ethical committees, and widespread discussions on ethics within the complexity science community. Drawing on insights from the disciplines mentioned earlier, we propose a roadmap to enhance ethical awareness and action. Our recommendations include (i) initiating supportive mechanisms to develop ethical guidelines specific to complex systems research, (ii) creating open-access resources, and (iii) fostering inclusive dialogues to ensure that complexity science can responsibly tackle societal challenges and achieve a more inclusive environment. By initiating this dialogue, we aim to encourage a necessary shift in how ethics is integrated into complexity research, positioning the field to address contemporary challenges more effectively.arXiv.org310The critical absence of formal guidelines, dedicated ethical committees, and widespread discussions on ethics within the complexity science community is highlighted, and a roadmap to enhance ethical awareness and action is proposed to enhance ethical awareness and action.ArXiv["Olumide Adisa", "Enio A Blay", "Yasaman Asgari", "Gabriele Di Bona", "Samantha Dies", "Ana Maria Jaramillo", "Paulo H. Resende", "Ana Maria de Sousa Leitao"]2024-09-03T00:00:00https://www.semanticscholar.org/paper/a85277bb2a5cbfd3c9e521335d6df3eefb1d8629 51c762b6b5d0b179a04a486e958ab5ef26b557aaThis paper explores the ethical implications of integrating Artificial Intelligence (AI) in educational settings, from primary schools to universities, while drawing insights from ancient Greek philosophy to address emerging concerns. As AI technologies increasingly influence learning environments, they offer novel opportunities for personalized learning, efficient assessment, and data-driven decision-making. However, these advancements also raise critical ethical questions regarding data privacy, algorithmic bias, student autonomy, and the changing roles of educators. This research examines specific use cases of AI in education, analyzing both their potential benefits and drawbacks. By revisiting the philosophical principles of ancient Greek thinkers such as Socrates, Aristotle, and Plato, we discuss how their writings can guide the ethical implementation of AI in modern education. The paper argues that while AI presents significant challenges, a balanced approach informed by classical philosophical thought can lead to an ethically sound transformation of education. It emphasizes the evolving role of teachers as facilitators and the importance of fostering student initiative in AI-rich environments.Hellenic Conference on Artificial Intelligence381It is argued that while AI presents significant challenges, a balanced approach informed by classical philosophical thought can lead to an ethically sound transformation of education.ArXiv["K. Karpouzis"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/51c762b6b5d0b179a04a486e958ab5ef26b557aa 4e7e79bb337c6502d3bdac6c80b0a8e32276a93eThis paper continues the series of publications of our interdisciplinary research findings at the crossroads of higher education sustainability (SDG 4.3), smart education, and artificial intelligence (AI) tools. AI has begun to be used by universities to increase the quality of higher educational services. AI tools are expected to help university teachers in the teaching process. Students also use AI to help them complete their tasks. At the same time, AI may threaten Sustainable Development Goal 4 (SDG 4). In particular, this is a “blank spot” in the study of AI and non-violent learning environments (SDG 4.3). The aim of the study was to verify competing statistical hypotheses. To achieve this aim, the authors used modern, economically sound methods. The authors processed the responses of 1102 students from eight Eastern European universities using a special electronic questionnaire. The authors statistically processed the student survey results and then tested a pair of conflicting statistical hypotheses. The authors adopted a standard level (α = 0.05) of hypothesis checking. Testing statistical hypotheses led to obtaining two statistically substantiated new scientific facts: (1) The requirement for “non-violent” learning environments does not meet some students’ needs. (2) The number of these students can be up to 31.94%. Summary: The new scientific facts are helpful for further developing world pedagogical theory and practice. They are the basis for forecasting and preparing for managerial actions aimed at SDG 4.3.Sustainability01The requirement for “non-violent” learning environments does not meet some students’ needs and two statistically substantiated new scientific facts are helpful for further developing world pedagogical theory and practice.Sustainability["Valery Okulich-Kazarin", "A. Artyukhov", "\u0141ukasz Skowron", "N. Artyukhova", "Tomasz Wo\u0142owiec"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/4e7e79bb337c6502d3bdac6c80b0a8e32276a93e 3053b837a322706ddcd2b8205297b49b7b1ecaeaThe main problem solved is to Improve Employee Performance Monitoring by Integrating Artificial Intelligence (AI) in Human Resource Management for Indonesian Companies. The paper suggests using AI technologies to analyze and process employee performance data. The overarching goal of this new approach is to identify patterns and red flags at the individual and team levels that can be used to inform stronger performance management strategies such as guided coaching, pointed feedback, or calibrated evaluations. This study is written as a schematic literature review, using sourced research articles from Indonesia and other countries to obtain the effects of AI on employee performance. This exercise showed how AI works in performance patterns and assists HR strategies. The findings AI has an impact on recognizing patterns in empirical data of how employees perform, helps to tailor training/development programs, and improves talent management and succession planning. Those results signify that AI has the potential to be a game-changer for HR practices in Indonesia. AI in HRM using employee performance monitoring seems an encouraging alternative for Indonesia. Through data-driven decision-making and automated HR routine tasks with many challenges and should prepare well to implement.International Conferences on Information Science and System411AI has an impact on recognizing patterns in empirical data of how employees perform, helps to tailor training/development programs, and improves talent management and succession planning, indicating that AI has the potential to be a game-changer for HR practices in Indonesia.2024 International Conference on ICT for Smart Society (ICISS)["Edi Yusuf Wirawan", "Merios Gusan Putra", "Budi Nugroho"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/3053b837a322706ddcd2b8205297b49b7b1ecaea 312d784ae016bacf3ecbd4ad096a1128c192d750Artificial Intelligence (AI) has become a pivotal force in transforming the retail industry, particularly in the online shopping environment. This study investigates the impact of various AI applications—such as personalized recommendations, chatbots, predictive analytics, and social media engagement—on consumer buying behaviors. Employing a quantitative research design, data was collected from 760 respondents through a structured online survey. The snowball sampling technique facilitated the recruitment of participants, focusing on diverse demographics and their interactions with AI technologies in online retail. The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction. Multiple regression analysis shows that AI personalization (β = 0.35, p < 0.001) has the most substantial impact on purchase intention, followed by chatbot effectiveness (β = 0.25, p < 0.001), predictive analytics (β = 0.20, p < 0.001), and social media engagement (β = 0.15, p < 0.01). Similarly, AI personalization (β = 0.30, p < 0.001), predictive analytics (β = 0.25, p < 0.001), and chatbot effectiveness (β = 0.20, p < 0.001) significantly influence consumer satisfaction. The hierarchical regression analysis underscores the importance of ethical considerations, showing that ethical and transparent use of AI increases consumer trust and engagement. Model 1 explains 45% of the variance in consumer behavior (R2 = 0.45, F = 154.75, p < 0.001), while Model 2, incorporating ethical concerns, explains an additional 10% (R2 = 0.55, F = 98.25, p < 0.001). This study highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success. Future research should explore the long-term impacts of AI on consumer behavior and the integration of emerging technologies such as augmented reality and the Internet of Things (IoT) in retail.Journal of Infrastructure Policy and Development152The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction, and highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success.Journal of Infrastructure, Policy and Development["Xulong Dai", "Qian Liu"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/312d784ae016bacf3ecbd4ad096a1128c192d750 8949c9a6399c2b4d0973d19560fb969694d4ad7aGenerative AI is a new branch of artificial intelligence, which creates fresh content using neural networks and machine learning methods. Systems of generative AI can generate music, images, text, speech, and other types of content by finding new styles in huge databases. The automation of tedious tasks through the creation of personalized content, and the improvement of accuracy in difficult tasks makes generative AI technology to transform a variety of industries, including gaming, advertising, and healthcare. There are many types of generative AI models. Each has pros and cons of its own. Despite being a relatively young technology, generative AI has many potential applications that make it a fascinating field to research. More research, growth, and advancement in the future may be seen. Future potential uses for generative AI include improving cybersecurity by identifying and preventing cyberattacks, creating human-interactive virtual assistants, and creating intelligent robots that can do challenging tasks in various industries. As generative AI continues to be developed, we should expect to see increasingly sophisticated applications in the years to come, which will open up new opportunities for growth across numerous industries.WSEAS Transactions on Computer Research102Future potential uses for generative AI include improving cybersecurity by identifying and preventing cyberattacks, creating human-interactive virtual assistants, and creating intelligent robots that can do challenging tasks in various industries.WSEAS TRANSACTIONS ON COMPUTER RESEARCH["Ahmad Al-Dahoud", "M. Fezari", "Ali Al-Dahoud", "Darah Aqel", "Hani Mimi", "Mohammad Sh. Daoud"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/8949c9a6399c2b4d0973d19560fb969694d4ad7a ef838c9723caeba6042168ec17c02358b5a50721The integration of artificial intelligence (AI) into government decision-making is rapidly gaining traction in public administration and politics. This scoping review, guided by PRISMA protocols, examines 50 articles from reputable sources like Scopus and SpringerLink to analyze the trends, benefits, and challenges of AI in governance. While AI offers substantial potential to enhance government efficiency and service delivery, significant barriers remain, including concerns about bias, transparency, public acceptance, and accountability. This review underscores the need for ongoing research and dialogue on the ethical, social, and practical implications of AI in government to ensure the responsible and inclusive adoption of AI-driven public services.Informatics441The need for ongoing research and dialogue on the ethical, social, and practical implications of AI in government to ensure the responsible and inclusive adoption of AI-driven public services is underscored.Informatics["Gustavo Caiza", "Ver\u00f3nica Sangu\u00f1a", "Natalia Tusa", "Violeta Masaquiza", "Alexandra Ortiz", "Marcelo V. Garcia"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/ef838c9723caeba6042168ec17c02358b5a50721 4f0f12583fc74da9e7b74d6492a7da1a89f86b74Abstract Objective To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography. Data source Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms “Artificial Intelligence,” “Mammography,” and their respective MeSH terms. We filtered publications from the past ten years (2014 – 2024) and in English. Study selection A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work. Data collection The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity. Data synthesis It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone. Conclusion AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals.Revista Brasileira de Ginecologia e Obstetrícia391It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence (AI) when used in conjunction with radiologists.Revista Brasileira de Ginecologia e Obstetrícia["Paulo Eduardo Souza Castelo Branco", "A. M. S. Franco", "Amanda Prates de Oliveira", "Isabela Maur\u00edcio Costa Carneiro", "Luciana Maur\u00edcio Costa de Carvalho", "Jonathan Igor Nunes de Souza", "Danniel Rodrigo Leandro", "E. B. C\u00e2ndido"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/4f0f12583fc74da9e7b74d6492a7da1a89f86b74 db16765f7316066330145d1b49c8618dbe1ba09eAdvanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8-10-fold increase in research output related to all three search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the United States (US, n=1487), United Kingdom (UK, n=1094), Germany (n=355), The Netherlands (n=339), Russia (n=215), and France (n=149), while for AI-related research the US (n=853) and UK (n=258) take a strong lead, followed by Switzerland (n=69), The Netherlands (n=37), and Germany (n=19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection between AI, SysBio, and SC research over the past two decades, with substantial growth in all three fields and exponential increases in AI-related research in the past decade.Stem Cells01The global evolution and growing intersection between AI, SysBio, and SC research over the past two decades is revealed, with substantial growth in all three fields and exponential increases in AI-related research in the past decade.Stem cells["Thayna Silva-Sousa", "J. Usuda", "N. Al-Arawe", "Francisca Frias", "I. Hinterseher", "R. Catar", "Christian Luecht", "Katarina Riesner", "Alexander Hackel", "L. Schimke", "Haroldo Dutra Dias", "I. Filgueiras", "Helder I Nakaya", "N. O. S. C\u00e2mara", "Stefan Fischer", "G. Riemekasten", "Olle Ringd\u00e9n", "Olaf Penack", "Tobias Winkler", "Georg Duda", "D. Fonseca", "O. Cabral-Marques", "Guido Moll"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/db16765f7316066330145d1b49c8618dbe1ba09e 8a71c5b5e768f343818c4899d241f0942bdeec1aThe paper analyses the main problems of creating information systems containing elements of artificial intelligence. The work considers separately the problems of designing the user interface and blocks that ensure the operation of artificial intelligence elements. A methodological approach to designing systems with elements of artificial intelligence is proposed. The authors examine and analyze bottlenecks of the proposed approach. The paper formulates the proposals for ensuring effective ergonomic design of information systems for various purposes.Ergodesign00The paper formulates the proposals for ensuring effective ergonomic design of information systems for various purposes by examining and analyzing bottlenecks of the proposed methodological approach to designing systems with elements of artificial intelligence.Ergodesign["Pavel Paderno", "Alexander Nefedovich", "Olga Sopina"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/8a71c5b5e768f343818c4899d241f0942bdeec1a 5547bcc1190495d9abb833038803cc905d6d74bcThis article is devoted to the analysis of ethical and social issues arising in connection with the digitalization of knowledge and the development of artificial intelligence (AI). It addresses key issues such as data privacy, algorithmic bias, unequal access to knowledge, and the potential for misuse of artificial intelligence. Special attention is paid to the social consequences of the widespread access of artificial intelligence to huge amounts of digitized knowledge, including the risks of concentration of power, mass automation of labor and changes in the dynamics of decision-making. In conclusion, the need for a responsible and ethical approach to the digitalization of knowledge and the development of artificial intelligence is emphasized in order for these technologies to serve the benefit of the entire society.Themed collection of papers from Foreign International Scientific Conference «Trends in the development of science and Global challenges» by HNRI «National development» in cooperation with AFP. June 2024. – Managua (Nicaragua)10The need for a responsible and ethical approach to the digitalization of knowledge and the development of artificial intelligence is emphasized in order for these technologies to serve the benefit of the entire society.Themed collection of papers from Foreign International Scientific Conference «Trends in the development of science and Global challenges» by HNRI «National development» in cooperation with AFP. June 2024. – Managua (Nicaragua)["Vladimir Vasilievich Smetana"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/5547bcc1190495d9abb833038803cc905d6d74bc c74b5a34beca9ff429ab08ba90039c9362c7b22cThe study, which involves 265 respondents, is aimed at identifying student youth’s attitude to using artificial intelligence in various spheres of life and ideas about it. To collect data, the authors use a questionnaire, including open questions and multiple-choice tasks in its main part. To process the data the paper applies content analysis, descriptive statistics and Pearson’s 2 criterion. It is found that students understand the phenomenon of artificial intelligence differently, interpreting it as an imitator of the human brain, a tool or a practice-oriented solution, a certain algorithm or scientific knowledge. Some students do not clearly understand the essence of the term “artificial intelligence” or find it difficult to interpret it. Students associate the main functionality of artificial intelligence with the increased labour or study productivity, with assistance in performing routine; less often, they associate it with creative tasks. However, some students cannot determine the functionality of artificial intelligence or do not see any prospects for its development for humans. Most students, considering artificial intelligence as their assistant, which reduces the time of obtaining data and creates certain conveniences, turn to it to solve their problems often or rarely. They are ready to use only free services or determine the budget for such expenses taking into account the tasks they are to solve. Students find the main barriers to using artificial intelligence in the issuance of standard, stereotypical solution options and the limitation on the data volume. Students see the prospects for the artificial intelligence development in its limitation in the legal field and possible superiority over the human mind. It is advisable to include knowledge of the artificial intelligence concept and the ability to determine its functionality in the list of universal competencies mastered at the university, and understanding the essence of artificial intelligence and the ability to use it in the context of solving applied problems should be included in professional competencies.Ergodesign00It is found that students understand the phenomenon of artificial intelligence differently, interpreting it as an imitator of the human brain, a tool or a practice-oriented solution, a certain algorithm or scientific knowledge and the ability to use it in the context of solving applied problems should be included in professional competencies.Ergodesign["Maria Prokhorova", "O. Angelova", "Tatyana Podolskaya"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/c74b5a34beca9ff429ab08ba90039c9362c7b22c bf9be6c30dd8df79001a9ae46390de1314538ce3 This research aims to examine the effects of developments in Artificial Intelligence (AI) and open-access software on the visions of academicians. The research was conducted using the phenomenology pattern, one of the qualitative research methods, and semi-structured in-depth interviews were preferred during the data collection process. Descriptive and coding-based qualitative content analysis methods were used in the research process. Emerging themes include the use of artificial intelligence in higher education; situation analysis of the use of AI in academics (SWOT); and strategic dimension of using AI in higher education. These themes explain in detail the potential effects of AI and open-access software on the strategy and competitiveness dimensions of academics’ visions. It is expected that the study will make significant contributions to understanding the changes that AI and open-access software will create in the academic world and how these technologies can be integrated into the way academics work.Perspectives on Global Development and Technology370It is expected that the study will make significant contributions to understanding the changes that AI and open-access software will create in the academic world and how these technologies can be integrated into the way academics work.Perspectives on Global Development and Technology["Meri Taksi Deveciyan", "Sinan Bataklar"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/bf9be6c30dd8df79001a9ae46390de1314538ce3 9acd3875d97bcf8af2ea04e7e64628ec99fe585eToday, digital advancements in business challenge SMEs to reach and engage with their target audiences through advertising. Traditional advertising strategies require modification, as consumer behaviors are changing. Therefore, they view ads differently on various platforms. Nevertheless, Artificial intelligence gained wide opportunities in various industries, and marketing is no exception. The research aimed to explore Artificial Intelligence’s role in modifying SMEs’ advertising campaigns and strategies. The author used quantitative research. A survey study was developed based on a systematic literature review. The questionnaire was distributed using snowball and conventional sampling methods to a diverse sample of SMEs across social media platforms such as Facebook, LinkedIn, Instagram, and X. The author used the Technology Acceptance Model (TAM) as a theoretical framework. According to the TAM model, users’ acceptance and adoption of technology are influenced by perceived usefulness (PU) and perceived ease of use (PEOU). Perceived usefulness would compare to SMEs’ beliefs about the effectiveness and benefits of AI in improving advertising methods. Perceived ease of use would correlate to SMEs’ perceptions of the ease with which practitioners can combine and use AI tools within their existing advertising campaigns. The research findings underline empirical evidence of the significance of Artificial intelligence in advertising strategies for SMEs. These research insights offer SMEs actionable recommendations for effectively using AI to enhance their advertising strategies, gain a competitive advantage, and gain sustainable growth in the digital landscape.Journal of Marketing Research and Case Studies00The research findings underline empirical evidence of the significance of Artificial intelligence in advertising strategies for SMEs and offer SMEs actionable recommendations for effectively using AI to enhance their advertising strategies, gain a competitive advantage, and gain sustainable growth in the digital landscape.Journal of Marketing Research and Case Studies["Ioseb Gabelaia"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/9acd3875d97bcf8af2ea04e7e64628ec99fe585e ae2d69c8210f8043f9d7d5c94e74121c9f8fd5bdThis paper presents a comprehensive economic analysis of the new regulation on Artificial Intelligence (AI) proposed by the European Parliament and Council. The regulation aims to establish a harmonized framework for the development and deployment of AI across member states, ensuring safety, transparency, and ethical standards. We evaluate the potential economic impacts of this regulation, focusing on innovation, market competitiveness, and compliance costs for businesses. Additionally, the paper explores the concept of the "Brussels Effect," a phenomenon where European Union regulations influence global standards beyond the EU’s borders. By exploring how this new AI regulation might shape international norms and practices, we assess its implications for global trade and the international AI landscape. We examine the ways in which the regulation could set benchmarks for AI governance worldwide, potentially affecting non-EU companies and markets. Through this dual examination, the paper provides insights into the economic opportunities and challenges posed by the EU’s proactive regulatory approach to AI. Keywords: The artificial intelligence act, The Brussels effect, Impact assessment, Global influence, Costs of new AI regulationSocratic Lectures 1100Socratic Lectures 11["David Ljube", "Jakob Mi\u0161i\u010d Jan\u010dar", "Marko Jeran"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/ae2d69c8210f8043f9d7d5c94e74121c9f8fd5bd 52e8e8eb2323e38bc170e8c2e1b158aa4b9f4bdeWe used the value-based acceptance model to examine what drives continued usage of artificial intelligence coaching services. Analyzing survey data from 320 users in South Korea who had engaged in sports activities facilitated by artificial intelligence coaching services, our structural equation modeling results showed that usefulness and enjoyment positively predicted users' perceived value of the services, positively influencing their continued usage intention. Conversely, cost negatively predicted perceived value and complexity did not significantly affect perceived value. Thus, we can conclude that emphasizing benefits and minimizing costs are crucial for enhancing perceived value. Service providers should develop strategies to increase this value to maintain user engagement.Social Behavior and Personality: An international journal00Analyzing survey data from 320 users in South Korea who had engaged in sports activities facilitated by artificial intelligence coaching services, results showed that usefulness and enjoyment positively predicted users' perceived value of the services, positively influencing their continued usage intention.Social Behavior and Personality: an international journal["Yawen Shen", "H. Sa", "Jee-Hoon Han"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/52e8e8eb2323e38bc170e8c2e1b158aa4b9f4bde 80d919ea3c395a19b04362e2375fad28ef6be19bObjective: The aim of the study is limited to several points, the most important of which is to understand the new concepts related to artificial intelligence and their role in supporting and activating lean management and the extent of their contribution to the success and survival of modern industrial companies.   Theoretical Framework: This topic presents the most important concepts and theories on which the research is based. [Artificial Intelligence and Lean] stands out, providing a solid foundation for understanding the context of the investigation.   Method: The study population was represented by the factories affiliated with the General Company for Automotive and Equipment Manufacturing. The sample was selected randomly, based on the (simple random sampling) method, where 3 factories were selected (the car and specialized wheel production factory, the body and heavy equipment factory, and the battery factory) out of a total of 6, and the number was Study population (2736) The researcher decided to distribute (300) questionnaires to workers in the factories affiliated with the company, the study sample. (300) questionnaires were distributed to the research sample (10.96%) of them after reviewing previous studies and benefiting from them in the field of research. (289) were recovered at a rate of (96.3%), of which (9) were not suitable for analysis, so the net sample was 278 from the studied population, at a rate of (92.8%) from the research sample.   Results and Discussion: The study showed a set of theoretical and field conclusions, the most important of which is that artificial intelligence has gained high acceptance among the staff of the automobile and equipment manufacturing company as a tool for improving administrative work and enhancing the characteristics of lean management.   Research Implications: The practical and theoretical implications of this research are discussed, providing insight into how the findings can be applied or impact practices in the field of AI and lean management and these impacts may include companies affiliated with the Ministry of Industry and Minerals and the services they provide.   Originality/Value: The importance of this study came from the scarcity of studies that attempted to identify and understand the nature of the relationship between variables (artificial intelligence and lean management), as well as the attempt of the current study to address a realistic problematic problem that directly affects the performance of workers in the general automotive and equipment industry.International Journal of Professional Business Review210The study showed that artificial intelligence has gained high acceptance among the staff of the automobile and equipment manufacturing company as a tool for improving administrative work and enhancing the characteristics of lean management.International Journal of Professional Business Review["Basima Abbood Majeed", "Ahmed Frikha"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/80d919ea3c395a19b04362e2375fad28ef6be19b d5028d71d1b6b4686f48b321578290c23b65424eBMC Anesthesiology450It is revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future.BMC Anesthesiology["Kensuke Shimada", "R. Inokuchi", "Tomohiro Ohigashi", "Masao Iwagami", "Makoto Tanaka", "Masahiko Gosho", "Nanako Tamiya"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/d5028d71d1b6b4686f48b321578290c23b65424e 5e155a7389ac34547a2ae2363203cf298d5edd6a The objective of this research is to investigate how artificial intelligence (AI) might improve HR procedures and increase employee engagement and productivity in organizations. AI-powered tools and applications used in the current era become a decisive point for businesses and its performance may impact employees’ job engagement and job performance. The use of artificial intelligence in an organization’s activities to manage human resources in the areas of employee engagement, job security, employee performance, particularly in the process of lowering staff workload, and enhancing business performance. The study involved full-time employees with experience using artificial intelligence powered software in Indian multinational corporation. The research data was collected from 310 employees from multinational cooperation. The findings demonstrate that artificial intelligence performance had a significant impact on employee’s performance and job engagement, both of which were highly correlated with performance at work evaluation. AI has a positive impact on employee engagement and company performance. Artificial intelligence and job performance were significantly related with job engagement and service performance. Additionally, job security had a significant impact on increasing employees’ job engagement and service quality. The study’s implication support strategies for conducting performance research and managing human resources. The present study results will help business owners or managers create a productive atmosphere that boosts overall performance and employee engagement at the workplace using artificial intelligence. International Review of Management and Marketing00Artificial intelligence performance had a significant impact on employee’s performance and job engagement, both of which were highly correlated with performance at work evaluation, and AI has a positive impact on employee engagement and company performance.International Review of Management and Marketing["Azam Malik"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/5e155a7389ac34547a2ae2363203cf298d5edd6a 04fd4222c8dacd9182a6d44a5461fc5f3f250b5eThis article draws attention to the key features of the Laying down harmonised rules on Artificial Intelligence in the EU (Artificial Intelligence Act, AIA). The author analyzes the concept of “Artificial Intelligence Systems” and the approach to determining the risky areas of AI application and other features of the AIA. Promising directions for improving the Ukrainian legislation on artificial intelligence are being formed, in particular, in terms of the data ecosystem and their legal use in artificial intelligence systems.INFORMATION AND LAW00The author analyzes the concept of “Artificial Intelligence Systems” and the approach to determining the risky areas of AI application and other features of the AIA.INFORMATION AND LAW["M. Dubniak"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/04fd4222c8dacd9182a6d44a5461fc5f3f250b5e e6a9cc72fad05f83748cac77d6da73a5b5d784a4The role and importance of artificial intelligence are defined. The prerequisites and trends of the use of artificial intelligence technologies in the development of chatbots are revealed. The necessity of creations the criteria and indicators for evaluating the effectiveness of chatbots controlled by artificial intelligence are focused. The risks of using artificial intelligence technologies in chatbots, namely: risks of discrimination; cyber security risks; risks to confidential data; ethical risks, validation risks are detailed. The conditions, features and algorithms for using chatbots with the use of artificial intelligence are defined. The conditions, features and algorithms for using chatbots with the use of artificial intelligence are defined. Based on the results of the research, it was concluded that the introduction of chatbots based on generative artificial intelligence has both advantages and global risks associated with the criminal and illegal use of these technologies. The threats and dangers provoked by the chatbots based on artificial intelligence and their consequences are revealed. The directions to minimize the risks of using chatbots controlled by artificial intelligence have been determined.INFORMATION AND LAW00It was concluded that the introduction of chatbots based on generative artificial intelligence has both advantages and global risks associated with the criminal and illegal use of these technologies.INFORMATION AND LAW["I. Bilan"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/e6a9cc72fad05f83748cac77d6da73a5b5d784a4 973e7df6235ea6c1a746d2d74755fe77556d9214The role and significance of artificial intelligence technologies in the military-technical sphere are determined. The principles of using artificial intelligence technologies in the activities of the armed forces are outlined. Attention is focused on the threats and risks posed by the use of artificial intelligence in the military-technical sphere. The peculiarities of the legislative provision of the military use of artificial intelligence technologies in the USA are highlighted. Detailed aspects of the technological implementation of artificial intelligence during the execution of military tasks in the context of the American experience. The conceptual foundations of the russian use of artificial intelligence technologies of a military nature are revealed. The institutional capabilities and achievements of the russian federation in the field of technological support of the needs of the army in the field of artificial intelligence have been determined. The scope and directions of the russian army's innovative developments using artificial intelligence technologies are outlined. It has been updated that unmanned systems are singled out as a special priority for the application of technologies in the field of artificial intelligence of the russian federation. The main global trends in the use of artificial intelligence technologies in the military sphere are revealed. Further directions of improving the field of military use of artificial intelligence technologies have been identified. It was concluded that the development, introduction and approval by the world community of criteria for the responsible use of artificial intelligence for military purposes will contribute to the construction and formation of an international consensus on the responsible handling and use of artificial intelligence technologies.INFORMATION AND LAW00It was concluded that the development, introduction and approval by the world community of criteria for the responsible use of artificial intelligence for military purposes will contribute to the construction and formation of an international consensus on the responsible handling and use of artificial intelligence technologies.INFORMATION AND LAW["S. Hurzhii"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/973e7df6235ea6c1a746d2d74755fe77556d9214 eabb80366ec3af1fb7643b36eabbc9684085f7a4PurposeFacilities management (FM) organizations are pivotal in enhancing the resilience of buildings against climate change impacts. While existing research delves into the adoption of digital technologies by FM organizations, there exists a gap regarding the specific utilization of artificial intelligence (AI) to address climate challenges. This study aims to investigate the drivers and barriers influencing the adoption and utilization of AI by South African FM organizations in mitigating climate change challenges.Design/methodology/approachThis study focuses on South Africa, a developing nation grappling with climate change’s ramifications on its infrastructure. Through a combination of systematic literature review and an online questionnaire survey, data was collected from representatives of 85 professionally registered FM organizations in South Africa. Analysis methods employed include content analysis, Relative Importance Index (RII), and Total Interpretative Structural Modeling (TISM).FindingsThe findings reveal that regulatory compliance and a responsible supply chain serve as critical drivers for AI adoption among South African FM organizations. Conversely, policy constraints and South Africa’s energy crisis emerge as major barriers to AI adoption in combating climate change challenges within the FM sector.Originality/valueThis study contributes to existing knowledge by bridging the gap in understanding how AI technologies are utilized by FM organizations to address climate challenges, particularly in the context of a developing nation like South Africa. The research findings aim to inform policymakers on fostering a conducive environment for FM organizations to harness AI in fostering climate resilience in built assets.Smart and Sustainable Built Environment880Smart and Sustainable Built Environment["A. Moghayedi", "Kathy Michell", "B. Awuzie"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/eabb80366ec3af1fb7643b36eabbc9684085f7a4 b6a3a48fab314ccecd17d322085c0dd15aa6c919Artificial intelligence (AI) is becoming widely available in various sectors of society, including higher education. AI has the potential to significantly increase the scalability of educational services, both inside and outside of the traditional classroom setting. This study investigates the current and future applications of AI in higher education as well as the potential challenges that may emerge during its implementation. Academic recruitment: Artificial intelligence can provide tailored support to learners at all times during the application period. Going forward, AI might help educational institutions focus their recruitment strategies on students who are more inclined to thrive at their school and in particular fields, leading to higher numbers of students enrolling and staying. Education and teaching: AI can help teachers with evaluating assignments and offering essential materials for students who are having difficulty. This could lead to professors being able to handle larger classes while still keeping a strong connection with their students. Student support services: Artificial intelligence can offer tailored course selection and step in to assist students encountering challenges. Looking ahead, AI might forecast students' educational requirements by analyzing predictive information and past achievements and then actively provide suitable support, like extra tutoring or guidance. Efficiency within organizations: Artificial intelligence has the capability to collect data from different systems on campus and use this information to guide decisions made by administrative staff, including the selection of courses. Looking ahead, AI might help organizations comprehend the job needs of nearby companies and create educational programs that adequately equip students for these demands. To effectively embrace the entrance of AI into the higher education sector, we recommend that institutions analyze the following: 1. The most suitable timeframe (short-term or long-term) for implementing AI. 2. The specific areas within the institution where AI can provide the greatest assistance. 3. Strategies for safeguarding students' privacy while utilizing data to enhance their experience. 4. The university's desired outcome and criteria for success in implementing AI By thoughtfully integrating AI, higher education institutions can unlock a plethora of new opportunities that will benefit students, instructors, and administrators alike.International Research Journal on Advanced Engineering and Management (IRJAEM)10The current and future applications of AI in higher education as well as the potential challenges that may emerge during its implementation are investigated.International Research Journal on Advanced Engineering and Management (IRJAEM)["S M Nimbalagundi", "A S Bagawan", "C S Katageri"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/b6a3a48fab314ccecd17d322085c0dd15aa6c919 ffe5bd07e76e152d106c24e3138837f89a141363Abstract Background Artificial intelligence (AI) has transformative potential to support prehospital clinicians, emergency physicians, and trauma surgeons in acute traumatic injury care. This scoping review examines the literature evaluating AI models using prehospital features to support early traumatic injury care. Methods We conducted a systematic search in August 2023 of PubMed, Embase, and Web of Science. Two independent reviewers screened titles/abstracts, with a third reviewer for adjudication, followed by a full‐text analysis. We included original research and conference presentations evaluating AI models—machine learning (ML), deep learning (DL), and natural language processing (NLP)—that used prehospital features or features available immediately upon emergency department arrival. Review articles were excluded. The same investigators extracted data and systematically categorized outcomes to ensure consistency and transparency. We calculated kappa for interrater reliability and descriptive statistics. Results We identified 1050 unique publications, with 49 meeting inclusion criteria after title and abstract review (kappa 0.58) and full‐text review. Publications increased annually from 2 in 2007 to 10 in 2022. Geographic analysis revealed a 61% focus on data from the United States. Studies were predominantly retrospective (88%), used local (45%) or national level (41%) data, focused on adults only (59%) or did not specify adults or pediatrics (27%), and 57% encompassed both blunt and penetrating injury mechanisms. The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. The most common study objectives were to predict the need for critical care and life‐saving interventions (29%), assist in triage (22%), and predict survival (20%). Conclusions A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.Journal of the American College of Emergency Physicians Open670A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.Journal of the American College of Emergency Physicians Open["Jake Toy", "Jonathan Warren", "Kelsey Wilhelm", "Brant Putnam", "D. Whitfield", "Marianne Gausche-Hill", "N. Bosson", "Ross Donaldson", "S. Schlesinger", "Tabitha Cheng", "Craig Goolsby"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/ffe5bd07e76e152d106c24e3138837f89a141363 4d59a8064295e054f552a7e3cd6345268b33c39dThe use of artificial intelligence (AI)-powered educational tools is growing over time and has the potential to completely transform the manner that education is provided. This paper looks at the pedagogical ramifications of artificial intelligence applications utilized in educational institutions. The study is qualitative research that analyzes an array of research on artificial intelligence-powered educational technologies using articles from peer-reviewed journals and conference proceedings. Content analysis is used to examine the literature to establish, the use of artificial intelligence in education, including its capabilities in educational processes, its pedagogical implications, and its challenges. The paper discusses how artificial intelligence could transform educational settings and educational resources, creating opportunities for services to be made scalable both inside and outside of the classroom. The paper concludes that while integrating artificial intelligence (AI) into education brings benefits to the education landscape, there are also significant risks. To fully utilize AI's technological innovation for educational purposes, ethical considerations must be taken into account.Open Journal for Information Technology00How artificial intelligence could transform educational settings and educational resources, creating opportunities for services to be made scalable both inside and outside of the classroom is discussed.Open Journal for Information Technology["E. Micheni", "Jackson Machii", "Julius Murumba"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/4d59a8064295e054f552a7e3cd6345268b33c39d 6fea4959700636fa0b370e813426c2e5f656fcdcArtificial intelligence (AI), an essential element of modern technology, is expanding the limits of multiple sectors through its ability to perform repetitive tasks and process data effectively. Its innovative impact has caused significant interest in human resource management (HRM), particularly in recruitment and selection practices. Several problems affect big companies because of this rapid and dynamic technological change. Many companies cannot keep up with these changes, which is why the main objective of this article is to inform companies and HR professionals about the critical factors in the adoption of artificial intelligence (AI) and enable successful implementation, therefore enhancing the efficiency and productivity of HR practices in this technological era [1]. The research uses a qualitative descriptive study method and a systematic literature review (SLR) to give an organized overview of previous academic research. Our study indicates several different factors can be categorized into four groups: technology, user expectations, organization, and external environment. Because of this study, human resource (HR) professionals need to be aware of the factors that affect the adoption of AI. Research has been done on 13 online databases, namely Scopus, IEEE Xplore, Springer, Science Direct, Emerald, Google Scholar, Taylor and Francis, MDPI, Sage Journal, AJM, Wiley Online Library, Scientific Temper, and Semantic Scholar. The search came back with 25 articles that fulfilled the criteria out of 107,499 articles initially discovered, authored by 79 different authors from 8 institutions and 46 universities in 22 different countries. The information provided in this article is intended to help businesses use AI more successfully and improve their efficiency to be able to compete with larger organizations. The implication of this research suggests that a thorough understanding of these factors can significantly enhance HRM practices through AI adoption.International Conferences on Information Science and System320The implication of this research suggests that a thorough understanding of these factors can significantly enhance HRM practices through AI adoption, and a thorough understanding of these factors can significantly enhance HRM practices through AI adoption.2024 International Conference on ICT for Smart Society (ICISS)["Raynald Samuel Lodra", "Tyaga Padhana", "Desi Maya Kristin"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/6fea4959700636fa0b370e813426c2e5f656fcdc 4bfd591cf4fd2374a6a9127fa59041edca0f9469Currently, artificial intelligence (AI) is used in many fields of medicine such as cardiology, endocrinology, neurology, and particularly gastroenterology in which AI increases the quality of images obtained from related imaging techniques. Also, medical diagnosis is greatly affected by AI algorithms and deep learning techniques. AI shows potential for not only monitoring and managing treatment plans but also promises accurate diagnosis and prediction of diseases. This paper aims to review the future opportunities and challenges of AI applications in medicine. The results show a bright future with multiple opportunities in medical diagnosis, radiology, and pathology fields with increasing accuracy, image quality, and decreasing radiation dose. Additionally, AI will facilitate medical research studies which is a great contribution to the medical world. Challenges and ethical limitations will be mostly related to the validity and reliability of data, bias, responsibility issues, risks and unpredictable consequences, and equitable application which need establishing clear guidelines and regulations. This paper's suggestion is a more extended educational program for both healthcare professionals and patients to achieve the best result.Black Sea Journal of Engineering and Science00The results show a bright future with multiple opportunities in medical diagnosis, radiology, and pathology fields with increasing accuracy, image quality, and decreasing radiation dose.Black Sea Journal of Engineering and Science["Tahmineh Darvishmohammadi", "Ay\u015fe \u00d6zkal", "A. \u00d6zkal"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/4bfd591cf4fd2374a6a9127fa59041edca0f9469 2e775987f018347d557f0ba8d379037a982fa6ddThe challenges workers face in underground mines are numerous and hazardous, with potential threats to their safety and well-being. Mining accidents are caused by various factors, including hardware errors and environmental deficiencies. In response to these hazards, the mining industry has made significant efforts to improve safety through the implementation of advanced technologies. Artificial Intelligence (AI) technology has been notably integrated into mine ventilation systems in recent years. A ventilation network in a mine is a sophisticated system with many interdependent processes, some of which present difficulties for deterministic simulation techniques. This paper aims to discuss major hazards caused by ventilation and provide an overview of various AI advances in mine ventilation to monitor various environmental parameters such as gas concentrations and heat.Journal of Mines, Metals and Fuels460Journal of Mines, Metals and Fuels["Oumaima Otmani", "Sa\u00e2d Soulaimani", "Khalil Abdessamad", "Rmiki Amina"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/2e775987f018347d557f0ba8d379037a982fa6dd de44518a86df9297529dcb6860a6560df263bbffArtificial Intelligence (AI) and Machine Learning (ML) have significantly impacted various industries, including software development. Software testing, a crucial part of the software development lifecycle (SDLC), ensures the quality and reliability of software products. Traditionally, software testing has been a labor-intensive process requiring significant manual effort. However, the advent of AI and ML has transformed this landscape by introducing automation and intelligent decision-making capabilities. AI and ML technologies enhance the efficiency and effectiveness of software testing by automating complex tasks such as test case generation, test execution, and result analysis. These technologies reduce the time required for testing and improve the accuracy of defect detection, ultimately leading to higher quality software. AI can predict potential areas of failure by analyzing historical data and identifying patterns, which allows for more targeted and efficient testing. This paper explores the role of AI and ML in software testing by reviewing existing literature, analyzing current tools and techniques, and presenting case studies that demonstrate the practical benefits of these technologies. The literature review provides a comprehensive overview of the advancements in AI and ML applications in software testing, highlighting key methodologies and findings from various studies. The analysis of current tools showcases the capabilities of popular AI-driven testing tools such as Eggplant AI, Test.ai, Selenium, Appvance, Applitools Eyes, Katalon Studio, and Tricentis Tosca, each offering unique features and advantages. Case studies included in this paper illustrate real-world applications of AI and ML in software testing, showing significant improvements in testing efficiency, accuracy, and overall software quality.arXiv.org70The role of AI and ML in software testing is explored by reviewing existing literature, analyzing current tools and techniques, and presenting case studies that demonstrate the practical benefits of these technologies.ArXiv["Ahmed Ramadan", "Husam Yasin", "Burhan Pekta\u015f"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/de44518a86df9297529dcb6860a6560df263bbff c7c463700640a30779a1a0ae5c34f44e994cb9f6Whilst many technological advancements have revolutionised healthcare throughout the 21st century, one of the most significant is Artificial Intelligence (AI). AI is generally regarded as the capability to imitate intelligent human behaviour using machines, and is based on computer science, statistics, algorithms, machine learning, information retrieval, and data science1. AI has permeated into many domains of healthcare including Clinical Diagnostics. While AI chatbots (such as those used in Babylon and Ada) are being used by patients to identify symptoms and recommend further actions in community and primary care settings, more recent advances in the technology with larger datasets have provided users access to a more extensive array of clinical conditions2. However, as these tools are constantly being developed with an ever-increasing dataset of clinical cases, certain challenges threaten the implementation of an accurate and effective model. In this article, the issue of Data Bias, and Data Handling will be examined within the context of Clinical Diagnostics, and how these factors threaten the development of such AI Healthcare tools.UCC Student Medical Journal60The issue of Data Bias, and Data Handling will be examined within the context of Clinical Diagnostics, and how these factors threaten the development of such AI Healthcare tools.UCC Student Medical Journal["Padraig Cronin"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/c7c463700640a30779a1a0ae5c34f44e994cb9f6 3a66e810b1c69454d6d5d26de5b612634eaeb492This study aims to implement Artificial Intelligence (AI) digital platforms as a learning medium for graphic design to foster innovative ideas among students in the Visual Communication Design (DKV) program. The research method employed is qualitative with a descriptive approach, focusing on social reality by examining experiences as the primary data for understanding that reality. The primary focus of this research is to assess students' responses to AI as a learning medium for graphic design. The subjects of the study are 27 students from class XI DKV 1 and 27 students from class XI DKV 2, totaling 54 respondents. The data collected shows an average evaluation score of 80%, which falls into the "good" category across all assessment indicators. These indicators include the usefulness, ease of use, ease of learning the material, and students' satisfaction with the learning process. The results indicate that students' satisfaction with AI-based learning is very high, demonstrating that this method is not only effective but also provides an experience that enhances students' creativityIndo-MathEdu Intellectuals Journal120The results indicate that students' satisfaction with AI-based learning is very high, demonstrating that this method is not only effective but also provides an experience that enhances students' creativity.Indo-MathEdu Intellectuals Journal["Ibnu Sulaeman", "Asep Mahpudin"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/3a66e810b1c69454d6d5d26de5b612634eaeb492 311ed3034308e1588cf4952cbd9f5add9d177c5fDiscover Education331Discover Education["D. Kurshumova"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/311ed3034308e1588cf4952cbd9f5add9d177c5f 408c7681e6d9712b993a57b5174bd2a66ad33210Conference on Information Technology for Social Good101{"pages": "84-87"}["S. D. Correia", "J. Matos-Carvalho", "Slavisa Tomic"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/408c7681e6d9712b993a57b5174bd2a66ad33210 b8adbb3a829f0f8c58db7d2422c36ad9f6e7a173Conference on Information Technology for Social Good372{"pages": "315-323"}["Maria Kasinidou", "S. Kleanthous", "Jahna Otterbacher"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/b8adbb3a829f0f8c58db7d2422c36ad9f6e7a173 da16422ca326fd86c0c80b576e6ac9a8036429b4Journal of Clinical Ultrasound01Journal of clinical ultrasound : JCU["G. Tonni", "Valter Andrade"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/da16422ca326fd86c0c80b576e6ac9a8036429b4 27412a9c607415c0456d74d6705c23092a14c59dCogent Psychology260Cogent Psychology["E. Hobeika", "R. Hallit", "Diana Malaeb", "Fouad Sakr", "Mariam Dabbous", "Nisma Merdad", "Tabassum Rashid", "Rizwana Amin", "Kamel Jebreen", "B. Zarrouq", "Amthal Alhuwailah", "H. Shuwiekh", "S. Hallit", "S. Obeid", "Feten Fekih-Romdhane"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/27412a9c607415c0456d74d6705c23092a14c59d e07c96e269fe8e2fcf5fe5b8cdfc941c3dd3ef74Annals of Hepato-Biliary-Pancreatic Surgery00Annals of hepato-biliary-pancreatic surgery["A. Ducas", "Alberto Mangano", "Leonardo Borgioli", "Jessica Cassiani", "Paula Lopez", "P. Giulianotti"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/e07c96e269fe8e2fcf5fe5b8cdfc941c3dd3ef74 8cbd952405355f9fcccc4644ab993f692c56c487This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. The model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies.Methane270This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network model capable of accurately predicting these emissions, and demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions.Methane["Clifford Jaylen Louime", "Tariq Asleem Raza"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/8cbd952405355f9fcccc4644ab993f692c56c487 7895087bf500d0dcbc59f03feb2d7a611a9dfb70Conference on Information Technology for Social Good60{"pages": "93-99"}["Eduard Iliadis"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/7895087bf500d0dcbc59f03feb2d7a611a9dfb70 cfa08267661203e89821e49bc3831e2fbd03767fCurrents in Pharmacy Teaching and Learning120Evidence is provided that pharmacy students are exploring the use of AI and would likely benefit from education on using AI as a supplement to critical thinking, and the nuances of AI usage among pharmacy students are highlighted.Currents in pharmacy teaching & learning["Joselyn Knobloch", "Kate Cozart", "Zachery Halford", "Michelle Hilaire", "Lisa M. Richter", "Jennifer Arnoldi"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/cfa08267661203e89821e49bc3831e2fbd03767f 8a608ade63dfaa80b3a9708aaa22cf39d14aa106In the context of the digital transformation of the economy, the share of intellectual labour is significantly increasing, especially in science and education. In these conditions, it is important to take into account indicators of professional reliability, which are directly related to the employee’s performance quality. The essence of the concepts of “intellectual work” and “professional reliability” is revealed. The functional system of intellectual work is substantiated based on considering the professional reliability indicators. The specificity of the information impact as a production factor and stressor in professional activity is shown. Ideas about information load and information hygiene are given. The paper analyses and discusses the results of scientific research on identifying factors of intellectual work in the context of education digitalization, which negatively affect health and cause teachers’ professional diseases. Recommendations are theoretically substantiated and given for implementing areas of information hygiene and mental hygiene of intellectual work as a condition for the teacher’s professional reliability in the context of education digitalization.Ergodesign00The paper analyses and discusses the results of scientific research on identifying factors of intellectual work in the context of education digitalization, which negatively affect health and cause teachers’ professional diseases.Ergodesign["Igor Sokhin"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/8a608ade63dfaa80b3a9708aaa22cf39d14aa106 0a468b0630aa2ee0b5c082aa4503d3231700143b00["Radhika Ranjan Roy"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/0a468b0630aa2ee0b5c082aa4503d3231700143b 9b7000ca285179493154c404c8740bfab7985868Journal of NeuroInterventional Surgery40Journal of neurointerventional surgery["M. Ezzeldin", "Adam Delora", "Ameer E. Hassan", "R. Ezzeldin", "C. Hadjialiakbari", "Eryn Percenti", "Jordan Torres", "Yazan J Alderazi"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/9b7000ca285179493154c404c8740bfab7985868 09e795965988b090bac15b5955c6fe482858ea59This research explores the efficacy of the YOLOR (You Only Learn One Representation) algorithm integrated with the Deep Sort algorithm for real-time vehicle detection, classification, and counting in Morelia, Mexico. The study aims to enhance traffic monitoring and management by leveraging advanced deep learning techniques. The methodology involves deploying the YOLOR model at six key monitoring stations, with varying confidence levels and pre-trained weights, to evaluate its performance across diverse traffic conditions. The results demonstrate that the model is effective compared to other approaches in classifying multiple vehicle types. The combination of YOLOR and Deep Sort proves effective in tracking vehicles and distinguishing between different types, providing valuable data for optimizing traffic flow and infrastructure planning. This innovative approach offers a scalable and precise solution for intelligent traffic management, setting new methodologies for urban traffic monitoring systems.Applied Informatics270The combination of YOLOR and Deep Sort proves effective in tracking vehicles and distinguishing between different types, providing valuable data for optimizing traffic flow and infrastructure planning.AI["J. A. Guzm\u00e1n-Torres", "F. Dom\u00ednguez-Mota", "G. Tinoco-Guerrero", "Maybelin C. Garc\u00eda-Chiquito", "J. Tinoco-Ruiz"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/09e795965988b090bac15b5955c6fe482858ea59 edfd22e75d032e158c2ba1f27eb088f5c7307e89Nature reviews. Urology90Nature reviews. Urology["R. Khanna", "Alejandro Granados Martinez", "N. Raison", "S\u00e9bastien Ourselin", "A. Briganti", "F. Montorsi", "Prokar Dasgupta"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/edfd22e75d032e158c2ba1f27eb088f5c7307e89 d089964cd384d72a54b1824259446bfec5459e43Current Pulmonology Reports590Current Pulmonology Reports["Jasmine May", "R. Malkani"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/d089964cd384d72a54b1824259446bfec5459e43 c05103c3eb7bdda51680c1c1ca2b4e4d52479e59Machine learning models are essential instruments in modern data analytics, propelling progress in a multitude of fields. However, the effectiveness of these approaches depends on careful model selection and data preprocessing. Consequently, it is imperative to devise an enhanced model builder that facilitate data preprocessing and improve the creation of machine learning models. Understanding the critical role that machine learning models play in contemporary data analytics is essential to this effort. On the other hand, careful data preprocessing and wise model selection are necessary for these models to function effectively. Our study presents a solution that automates data preparation activities by addressing data irregularities such as null values, incorrect inputs, and redundant entries. The program then creates a variety of models on its own, increasing the adaptability and effectiveness of machine learning applications in order to solve these issues. This work has produced software that is a substantial development in terms of both speedy preprocessing and data cleansing, as well as its ability to construct several machine learning models. The program employs a strict assessment methodology to choose and maintain the most effective model by looking at a wide range of performance measures. Our software framework’s versatility and effectiveness are demonstrated through experimental validation, allaying worries about inflexible code. Rather, the program is a flexible framework that can easily learn from and adjust to a variety of datasets. In conclusion, by presenting a novel software solution designed for effective data preprocessing and model building, this work makes a substantial contribution to the development of machine learning approaches. Our software can help advance machine learning research and applications by automating important operations and making the process of selecting the best models easier.International Symposium on INnovations in Intelligent SysTems and Applications230This work presents a novel software solution designed for effective data preprocessing and model building that can help advance machine learning research and applications by automating important operations and making the process of selecting the best models easier.2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)["Abdusamed Kura", "Donald Elmazi"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/c05103c3eb7bdda51680c1c1ca2b4e4d52479e59 b195c94013a30049802b5c591cedf389052e418eNeuroradiology70A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist.Neuroradiology["Ilya Adamchic", "Sven Kantelhardt", "Hans-Joachim Wagner", "Michael Burbelko"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/b195c94013a30049802b5c591cedf389052e418e 1237b4c9bd2abeaff9f98908d4c764d47c53b13fAnesthesia and Analgesia40Anesthesia and analgesia["Fredrik Granholm", "D. Tin", "Richard Staynings", "G. Ciottone"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/1237b4c9bd2abeaff9f98908d4c764d47c53b13f e545e209d8e9191a601eeeece4161cacd853e186The article analyzes the theoretical aspects of the importance of creating and developing ergonomic scientific laboratories based on domestic universities and research institutes. The paper considers modern technologies for modelling scientific structures in the organizations (institutions) subordinate to the Ministry of Education and Science of the Russian Federation, and describes the main models of initiating and operating scientific laboratories created at Russian universities and research organizations. To demonstrate the relevance of building a laboratory in the field of ergonomics, the authors conduct an analysis of the availability of laboratories in this profile at the world’s leading universities.Ergodesign00The article analyzes the theoretical aspects of the importance of creating and developing ergonomic scientific laboratories based on domestic universities and research institutes and describes the main models of initiating and operating scientific laboratories created at Russian universities and research organizations.Ergodesign["Kiril Androsov", "Aleksandr Kuz'menko", "V. Spasennikov"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/e545e209d8e9191a601eeeece4161cacd853e186 05fe294d60d61dad3bcb6dfbcb3e62d1516955db This Viewpoint discusses the bias that exists in artificial intelligence (AI) algorithms used in health care despite recent federal rules to prohibit discriminatory outcomes from AI and recommends ways in which health care facilities, AI developers, and regulators could share responsibilities and actions to address bias. Journal of the American Medical Association (JAMA)56JAMA["Raj M. Ratwani", "Karey Sutton", "Jessica E Galarraga"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/05fe294d60d61dad3bcb6dfbcb3e62d1516955db e4f94230a67a9c63d27a7d4cbfb8c83b8c6d0602Mammography screening supported by deep learning–based artificial intelligence (AI) solutions can potentially reduce workload without compromising breast cancer detection accuracy, but the site of deployment in the workflow might be crucial. This retrospective study compared three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population. A commercial AI system replaced the first reader (scenario 1: integrated AIfirst), the second reader (scenario 2: integrated AIsecond), or both readers for triaging of low- and high-risk cases (scenario 3: integrated AItriage). AI threshold values were chosen based partly on previous validation and setting the screen-read volume reduction at approximately 50% across scenarios. Detection accuracy measures were calculated. Compared with standard double reading, integrated AIfirst showed no evidence of a difference in accuracy metrics except for a higher arbitration rate (+0.99%, P < .001). Integrated AIsecond had lower sensitivity (−1.58%, P < .001), negative predictive value (NPV) (−0.01%, P < .001), and recall rate (−0.06%, P = .04) but a higher positive predictive value (PPV) (+0.03%, P < .001) and arbitration rate (+1.22%, P < .001). Integrated AItriage achieved higher sensitivity (+1.33%, P < .001), PPV (+0.36%, P = .03), and NPV (+0.01%, P < .001) but lower arbitration rate (−0.88%, P < .001). Replacing one or both readers with AI seems feasible; however, the site of application in the workflow can have clinically relevant effects on accuracy and workload. Keywords: Mammography, Breast, Neoplasms-Primary, Screening, Epidemiology, Diagnosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. Published under a CC BY 4.0 license. See also commentary by Suri in this issue.Radiology: Artificial Intelligence02Three simulated AI-integrated screening scenarios with standard double reading with arbitration in a sample of 249 402 mammograms from a representative screening population showed no evidence of a difference in accuracy metrics.Radiology: Artificial Intelligence["M. Elhakim", "Sarah W Stougaard", "Ole Graumann", "Mads Nielsen", "O. Gerke", "L. Larsen", "B. S. B. Rasmussen"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/e4f94230a67a9c63d27a7d4cbfb8c83b8c6d0602 7352f2085f48471a17190469631ec93fd47e9b2cArtificial intelligence (AI) can enhance human communication, for example, by improving the quality of our writing, voice or appearance. However, AI mediated communication also has risks-it may increase deception, compromise authenticity or yield widespread mistrust. As a result, both policymakers and technology firms are developing approaches to prevent and reduce potentially unacceptable uses of AI communication technologies. However, we do not yet know what people believe is acceptable or what their expectations are regarding usage. Drawing on normative psychology theories, we examine people's judgements of the acceptability of open and secret AI use, as well as people's expectations of their own and others' use. In two studies with representative samples (Study 1: N = 477; Study 2: N = 765), we find that people are less accepting of secret than open AI use in communication, but only when directly compared. Our results also suggest that people believe others will use AI communication tools more than they would themselves and that people do not expect others' use to align with their expectations of what is acceptable. While much attention has been focused on transparency measures, our results suggest that self-other differences are a central factor for understanding people's attitudes and expectations for AI-mediated communication.British Journal of Psychology382It is found that people are less accepting of secret than open AI use in communication, but only when directly compared, and self-other differences are a central factor for understanding people's attitudes and expectations for AI-mediated communication.British journal of psychology["Zoe A. Purcell", "Mengchen Dong", "Anne-Marie Nussberger", "Nils K\u00f6bis", "Maurice Jakesch"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/7352f2085f48471a17190469631ec93fd47e9b2c d27083b5de51e93147f2a7a3cf856e8a4f8630abThis extensive literature review is not a stand‐alone paper, as it was conducted to help set the scene for the third and current stage of the Harbinger of Change project (H‐3), which is focusing on the impact of artificial intelligence (AI) on early career researchers (ECRs). Its purpose is to inform the design, scope and question‐forming of the ongoing interview project (2024–). The overarching aim of the review is to establish what we know of the already extant and/or predicted opportunities and challenges that AI‐driven technologies present to researchers, in general, and the cohort of junior researchers among them, in particular. It was found that very little empirical evidence exists of what is happening to ECRs on the ground, and that tends to be drowned in a sea of forecasts and prognostications. However, down the road there are clear benefits accruing to ECRs and, arguably, the most appealing for novice researchers must be the benefits of enhancing their productivity, the key to all scholarly rewards, inclusive of career advancement.Learned Publishing802It was found that very little empirical evidence exists of what is happening to ECRs on the ground, and that tends to be drowned in a sea of forecasts and prognostications.Learn. Publ.["Eti Herman", "David Nicholas", "A. Abrizah", "Jorge Revez", "Blanca Rodr\u00edguez-Bravo", "Marzena \u015awigo\u0144", "David J. Clark", "Jie Xu", "Antony Watkinson"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/d27083b5de51e93147f2a7a3cf856e8a4f8630ab 69f0022fb043d2c90f7bf65bcd405c425b42848bThe emergence of generative Artificial Intelligence (GenAI) tools such as ChatGPT has attracted wide attention in the field of L2 writing and academic writing, but few papers to date have analysed GenAI’s potential application (positive and negative) in source use practices in academic writing. This article discusses three key aspects of source use – academic attribution, searching and reading sources, and source integration. AI tools are trialled for each aspect, followed by an overall SWOT analysis. While writers can use AI tools to assist on several source use practices, they are not recommended to use AI without a deep understanding of academic writing and source use principles. This article concludes with suggestions for student writers, academic support providers, and institutions.Journal of Academic Writing02Three key aspects of source use – academic attribution, searching and reading sources, and source integration are discussed – AI tools are trialled for each aspect, followed by an overall SWOT analysis.Journal of Academic Writing["Qingyang Sun"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/69f0022fb043d2c90f7bf65bcd405c425b42848b dd476b9fb41d2ac67d43e70699147b1e81d4cc8cBMC Medical Informatics Decis. Mak.1061A literature review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients’ and healthcare providers’ perspectives and strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.BMC Medical Informatics and Decision Making["M. Mooghali", "Austin M. Stroud", "Dong Whi Yoo", "Barbara A. Barry", "Alyssa A. Grimshaw", "Joseph S. Ross", "Xuan Zhu", "Jennifer E. Miller"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/dd476b9fb41d2ac67d43e70699147b1e81d4cc8c 3c9d7570613dc2ba6f66003125b4da01ce212152The introduction and consequent proliferation of Artificial Intelligence (AI) and deepfakes have created new challenges for journalists worldwide. These technologies have made it alarmingly easy to generate and disseminate fake news, complicating the verification process and undermining journalistic integrity. The rapid spread of AI-driven misinformation not only burdens journalists with the task of distinguishing fact from fiction but also erodes public trust in the media. This paper explores the implications of AI and deepfakes on truth verification in journalism, highlighting the ethical dilemmas faced by journalists in this new digital landscape. By examining the impact on public perception and the challenges of maintaining credibility, the study underscores the need for robust verification tools and ethical guidelines to safeguard the integrity of journalism in the age of AI.International Journal of Innovative Science and Research Technology661The need for robust verification tools and ethical guidelines to safeguard the integrity of journalism in the age of AI is highlighted, highlighting the ethical dilemmas faced by journalists in this new digital landscape.International Journal of Innovative Science and Research Technology (IJISRT)["Samad Uthman"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/3c9d7570613dc2ba6f66003125b4da01ce212152 cdc06354bd7da4575c199d7406ae2c2417a0ceb8Artificial Intelligence (AI) has evolved significantly over the past decades, from its early concepts in the 1950s to the present era of deep learning and natural language processing. Advanced large language models (LLMs), such as Chatbot Generative Pre-Trained Transformer (ChatGPT) is trained to generate human-like text responses. This technology has the potential to revolutionize various aspects of gastroenterology, including diagnosis, treatment, education, and decision-making support. The benefits of using LLMs in gastroenterology could include accelerating diagnosis and treatment, providing personalized care, enhancing education and training, assisting in decision-making, and improving communication with patients. However, drawbacks and challenges such as limited AI capability, training on possibly biased data, data errors, security and privacy concerns, and implementation costs must be addressed to ensure the responsible and effective use of this technology. The future of LLMs in gastroenterology relies on the ability to process and analyse large amounts of data, identify patterns, and summarize information and thus assist physicians in creating personalized treatment plans. As AI advances, LLMs will become more accurate and efficient, allowing for faster diagnosis and treatment of gastroenterological conditions. Ensuring effective collaboration between AI developers, healthcare professionals, and regulatory bodies is essential for the responsible and effective use of this technology. By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia241By finding the right balance between AI and human expertise and addressing the limitations and risks associated with its use, LLMs can play an increasingly significant role in gastroenterology, contributing to better patient care and supporting doctors in their work.Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia["J. Kr\u00e1l", "Michal Hradis", "Marek Buzga", "L. Kunovsk\u00fd"]2024-09-04T00:00:00https://www.semanticscholar.org/paper/cdc06354bd7da4575c199d7406ae2c2417a0ceb8 bb5d78bd33adbed1f13242c3880fb90ab5fc2335BACKGROUND Research identified preliminary evidence that artificial intelligence (AI) has emerged as a transformative force in healthcare, revolutionising various aspects of healthcare delivery, from diagnostics to treatment planning. However, integrating AI into healthcare systems in Egypt is challenging, particularly concerning healthcare professionals' acceptance and adoption of these technologies. This mixed-method study aimed to explore the sentiment of nurses at different organisational levels towards AI and resistance to change in healthcare organisations. METHODS A mixed-method design was employed, with quantitative data collected through a survey of 500 nurses using the general attitudes towards AI and resistance to change scale and qualitative data from semi-structured interviews with 17 nurses. Quantitative data were analysed using descriptive and inferential statistics, while qualitative data were analysed thematically. RESULTS The survey demonstrated that positive attitudes were inversely correlated with resistance behaviour and resistance to change. Additionally, perceptions of AI's usefulness, ease of use and value were strongly and positively correlated with positive attitudes and negatively correlated with negative attitudes. Moreover, the influence of colleagues' opinions, self-efficacy for change and organisational support showed significant positive correlations with positive attitudes towards AI and negative correlations with negative attitudes. Qualitatively, nurses cited obstacles such as lack of familiarity with AI technologies, biases affecting decision-making, technological challenges, inadequate training and fear of technology replacing human interaction. Readiness for AI integration was associated with the necessity of training and the timing of AI use. CONCLUSION Nurses demonstrated varied understanding of AI's applications and benefits. Some acknowledged its potential for efficiency and time-saving, while others highlighted a need for up-to-date knowledge. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.Journal of Advanced Nursing224Nurses demonstrated varied understanding of AI's applications and benefits, some acknowledged its potential for efficiency and time-saving, while others highlighted a need for up-to-date knowledge.Journal of advanced nursing["Shaimaa Mohamed Amin", "H. El-Gazar", "M. Zoromba", "M. El-Sayed", "M. H. Atta"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/bb5d78bd33adbed1f13242c3880fb90ab5fc2335 d600143a41447daca8b81d066ca86ec570fcad27Abstract In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of neuroimaging‐based AI studies and to assess their methodological quality. A PubMed search yielded 810 studies, of which 244 that investigated the utility of neuroimaging‐based AI for PD diagnosis, prognosis, or intervention were included. We systematically categorized studies by outcomes and rated them with respect to five minimal quality criteria (MQC) pertaining to data splitting, data leakage, model complexity, performance reporting, and indication of biological plausibility. We found that the majority of studies aimed to distinguish PD patients from healthy controls (54%) or atypical parkinsonian syndromes (25%), whereas prognostic or interventional studies were sparse. Only 20% of evaluated studies passed all five MQC, with data leakage, non‐minimal model complexity, and reporting of biological plausibility as the primary factors for quality loss. Data leakage was associated with a significant inflation of accuracies. Very few studies employed external test sets (8%), where accuracy was significantly lower, and 19% of studies did not account for data imbalance. Adherence to MQC was low across all observed years and journal impact factors. This review outlines that AI has been applied to a wide variety of research questions pertaining to PD; however, the number of studies failing to pass the MQC is alarming. Therefore, we provide recommendations to enhance the interpretability, generalizability, and clinical utility of future AI applications using neuroimaging in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.Movement Disorders823The number of studies failing to pass the minimal quality criteria (MQC) is alarming and recommendations are provided to enhance the interpretability, generalizability, and clinical utility of future AI applications using neuroimaging in PD.Movement Disorders["Verena Dzialas", "E. Doering", "Helena Eich", "Antonio P. Strafella", "David E Vaillancourt", "Kristina Simonyan", "Thilo van Eimeren"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/d600143a41447daca8b81d066ca86ec570fcad27 45891d3dc3de1653c62c76e4c017e2b55a8ff62fThe article addresses the issue of the steady development of digital law in the face of artificial intelligence (AI) technology. Some international institutions are focusing on the potentially dangerous aspects of artificial intelligence. In March 2024, the first legal text from the European Union relating to artificial intelligence was issued. The rules of digital law pertaining to artificial intelligence are spread across all branches of law, and both international and national laws contribute to the continuous development of this field. The article uses a deductive approach by showing how international law and national law have contributed to the development of digital laws to accommodate artificial intelligence technology. The article concludes that such laws have effects on other branches of law, and can help to find solutions to the problems of using artificial intelligence. Similarly, we seek to find solutions to problems in other areas such as civil and commercial law. Such laws as the Personal Data Protection Law, the rules of the Consumer Protection Law, and the Copyright Protection Law that apply to digital content play an important role in preventing abuses. Such protections are also needed in the field of AI.Journal of Ecohumanism452The article concludes that such laws have effects on other branches of law, and can help to find solutions to the problems of using artificial intelligence, and seeks to find solutions to problems in other areas such as civil and commercial law.Journal of Ecohumanism["T. Alsamara", "Farouk Ghazi"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/45891d3dc3de1653c62c76e4c017e2b55a8ff62f 92dba46f562e4531fa21befc9ae6622eb1ef8bc4As artificial intelligence (AI) technologies are developed and used across borders and have the potential to transform societies worldwide, global regulation thereof becomes necessary. However, key differences exist in how the leading players in the field, China, the United States, and the EU, view these technologies and approach their regulation. This article traces their respective ambitions on the global governance of AI technologies. It asks how the three each envision the latter as well as their role therein. Drawing on frame analysis, we find that while concrete ideas for coordinating regulation attempts seem to be of secondary importance, all three actors feel the need to position themselves within the new race for leadership on AI regulation. This results in a flurry of suggested proposals on how AI should be regulated internationally. Only recently have the actors started to reflect on why global regulation is necessary and to highlight the respective benefits of their proposal. Amidst current geopolitical tensions, the global regulation of AI has become an instrument of global power ambitions. Such competition bears huge risks for the further fragmentation of the global institutional architecture as well as for deepening tensions between China, the US, and the EU.Global Policy191Drawing on frame analysis, it is found that while concrete ideas for coordinating regulation attempts seem to be of secondary importance, all three actors feel the need to position themselves within the new race for leadership on AI regulation.Global Policy["Sabine Mokry", "Julia Gurol"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/92dba46f562e4531fa21befc9ae6622eb1ef8bc4 eced54e0bfcf41cbad64157ed5683138764a2d2cThis study investigates the critical success factors (CSFs) influencing the procurement of artificial intelligence (AI) systems within the United Arab Emirates (UAE) public sector. While AI holds immense potential to enhance public service delivery, its successful integration hinges on critical factors. This research utilizes Interpretive Structural Modeling (ISM) to analyze the CSFs impacting AI procurement within the UAE public sector. Through ISM, a structural model is developed to highlight the interrelationships between these CSFs and their influence on the procurement process, outlining the key elements for successful AI procurement within the UAE public sector. Based on the literature review and expert validation from the UAE public sector, ten CSFs were identified. This study found that clear needs assessment is the most influential CSF, while the long-term value of AI systems or services is the least influential. This study provides policymakers and public sector leaders with valuable insights, enabling them to formulate effective strategies to optimize the procurement process and establish a strong foundation for AI adoption. Finally, this will lead to an improved and more efficient public service delivery in the UAE.Sustainability572It is found that clear needs assessment is the most influential CSF, while the long-term value of AI systems or services is the least influential, which will lead to an improved and more efficient public service delivery in the UAE.Sustainability["Khalid Alshehhi", "Ali Cheaitou", "Hamad Rashid"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/eced54e0bfcf41cbad64157ed5683138764a2d2c 4bdb718bbacb7a4ab25698363c1c851146bb4471Deep learning has proven highly effective in various medical imaging scenarios, yet the lack of an efficient distribution platform hinders developers from sharing models with end-users. Here, we describe brainchop, a fully functional web application that allows users to apply deep learning models developed with Python to local neuroimaging data from within their browser. While training artificial intelligence models is computationally expensive, applying existing models to neuroimaging data can be very fast; brainchop harnesses the end user's graphics card such that brain extraction, tissue segmentation, and regional parcellation require only seconds and avoids privacy issues that impact cloud-based solutions. The integrated visualization allows users to validate the inferences, and includes tools to annotate and edit the resulting segmentations. Our pure JavaScript implementation includes optimized helper functions for conforming volumes and filtering connected components with minimal dependencies. Brainchop provides a simple mechanism for distributing models for additional image processing tasks, including registration and identification of abnormal tissue, including tumors, lesions and hyperintensities. We discuss considerations for other AI model developers to leverage this open-source resource.Aperture neuro522Aperture neuro["Sergey M. Plis", "Mohamed Masoud", "Farfalla Hu", "Taylor Hanayik", "Sulagna Dia Ghosh", "Chris Drake", "R. Newman-Norlund", "Christopher Rorden"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/4bdb718bbacb7a4ab25698363c1c851146bb4471 81b981902be2cdc8c1f1ddfa2c33fe2469dd6980Abstract The rapid advancement of artificial intelligence (AI) has sparked renewed discussions on its trustworthiness and the concept of eXplainable AI (XAI). Recent research in neuroscience has emphasized the relevance of XAI in studying cognition. This scoping review aims to identify and analyze various XAI methods used to study the mechanisms and features of cognitive function and dysfunction. In this study, the collected evidence is qualitatively assessed to develop an effective framework for approaching XAI in cognitive neuroscience. Based on the Joanna Briggs Institute and preferred reporting items for systematic reviews and meta‐analyses extension for scoping review guidelines, we searched for peer‐reviewed articles on MEDLINE, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar. Two reviewers performed data screening, extraction, and thematic analysis in parallel. Twelve eligible experimental studies published in the past decade were included. The results showed that the majority (75%) focused on normal cognitive functions such as perception, social cognition, language, executive function, and memory, while others (25%) examined impaired cognition. The predominant XAI methods employed were intrinsic XAI (58.3%), followed by attribution‐based (41.7%) and example‐based (8.3%) post hoc methods. Explainability was applied at a local (66.7%) or global (33.3%) scope. The findings, predominantly correlational, were anatomical (83.3%) or nonanatomical (16.7%). In conclusion, while these XAI techniques were lauded for their predictive power, robustness, testability, and plausibility, limitations included oversimplification, confounding factors, and inconsistencies. The reviewed studies showcased the potential of XAI models while acknowledging current challenges in causality and oversimplification, particularly emphasizing the need for reproducibility.Ibrain611While these XAI techniques were lauded for their predictive power, robustness, testability, and plausibility, limitations included oversimplification, confounding factors, and inconsistencies.Ibrain["Shakran Mahmood", "Colin Teo", "Jeremy Sim", "Wei Zhang", "Jiang Muyun", "R. Bhuvana", "K. Teo", "T. Yeo", "Jia Lu", "B. Guly\u00e1s", "Cuntai Guan"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/81b981902be2cdc8c1f1ddfa2c33fe2469dd6980 c7a52967e2e46fb7b1cf3f69918e28dc877ca3c1Background Recurrent and metastatic head and neck squamous cell carcinoma (HNSCC) is characterized by a complex therapeutic management that needs to be discussed in multidisciplinary tumor boards (MDT). While artificial intelligence (AI) improved significantly to assist healthcare professionals in making informed treatment decisions for primary cases, an application in the even more complex recurrent/metastatic setting has not been evaluated yet. This study also represents the first evaluation of the recently published LLM ChatGPT 4o, compared to ChatGPT 4.0 for providing therapy recommendations. Methods The therapy recommendations for 100 HNSCC cases generated by each LLM, 50 cases of recurrence and 50 cases of distant metastasis were evaluated by two independent reviewers. The primary outcome measured was the quality of the therapy recommendations measured by the following parameters: clinical recommendation, explanation, and summarization. Results In this study, ChatGPT 4o and 4.0 provided mostly general answers for surgery, palliative care, or systemic therapy. ChatGPT 4o proved to be 48.5% faster than ChatGPT 4.0. For clinical recommendation, explanation, and summarization both LLMs obtained high scores in terms of performance of therapy recommendations, with no significant differences between both LLMs, but demonstrated to be mostly an assisting tool, requiring validation by an experienced clinician due to a lack of transparency and sometimes recommending treatment modalities that are not part of the current treatment guidelines. Conclusion This research demonstrates that ChatGPT 4o and 4.0 share a similar performance, while ChatGPT 4o is significantly faster. Since the current versions cannot tailor therapy recommendations, and sometimes recommend incorrect treatment options and lack information on the source material, advanced AI models at the moment can merely assist in the MDT setting for recurrent/metastatic HNSCC.Frontiers in Oncology472This research demonstrates that ChatGPT 4o and 4.0 share a similar performance, while ChatGPT 4o is significantly faster than 4.0 for providing therapy recommendations.Frontiers in Oncology["B. Schmidl", "Tobias H\u00fctten", "S. Pigorsch", "F. St\u00f6gbauer", "Cosima C. Hoch", "Timon Hussain", "Barbara Wollenberg", "Markus Wirth"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/c7a52967e2e46fb7b1cf3f69918e28dc877ca3c1 d0acdebb11192a79936693c285836e3634399382This study examined the civil liability for the damages of Artificial Intelligence in Jordanian legislation. It specifically aimed to reveal the nature of this type of civil liability. Besides, it ventured to highlight the legal problems raised by the subject of civil liability for damages of Artificial Intelligence. The descriptive comparative approach was used to describe the phenomenon and compare Arabic legal systems and the European position towards the legal issues raised by this study. The findings revealed that the Jordanian legislator emphasized the conditions for achieving civil liability for mechanical machines. The study recommends applying the rules of civil liability to these systems that resemble humans in their behavior, intelligence, and awareness of matters.Pakistan Journal of Criminology00Pakistan Journal of Criminology[]2024-09-05T00:00:00https://www.semanticscholar.org/paper/d0acdebb11192a79936693c285836e3634399382 8c0d71a17205a0b15fda75db2ed739481d4e8262In the context of technological advances, the concept of digital justice is emerging, an extra-judicial sphere that uses technologies such as artificial intelligence to address controversial situations through physical assistants or even robots, if the litigant so wishes. Thus, while AI is effective in resolving simple disputes without human intervention, even UNESCO warns against its exclusivity in more complicated cases. From this perspective, through a qualitative approach and literature review, this research focused on examining the benefits and limitations of AI in the administration of justice. The review of the academic literature reveals that this technology facilitates the functions performed by judges and lawyers in controversial situations. The studies, however, point to the importance of using human intelligence in law making and more general judicial processes. In conclusion, AI improves the efficiency of the administration of justice; however, its place in judicial operations and process should only be complementary to the use of human intervention. This action maintains fairness and considers the ethical point at stake, reaffirms the existence of some irreplaceable human capabilities in the judicial process.Evolutionary Studies in Imaginative Culture590In conclusion, AI improves the efficiency of the administration of justice; however, its place in judicial operations and process should only be complementary to the use of human intervention.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["William Orlando", "Alvarez Araque", "Angela Liliana", "Pinz \u00f3 n Pinz \u00f3 n", "Aracely Forero Romero"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/8c0d71a17205a0b15fda75db2ed739481d4e8262 1818e24b7720ffd05fc8866e7bc904b262cd2c3aAim. To conduct an analysis of research on the application of artificial intelligence (AI) technologies in medicine, norms and practices governing this field, and on its basis to build a taxonomy of AI-based decisions in the practice of medical services.Objectives. To structure existing AI-based solutions in medicine; to identify, based on research and state registration data, the most mature areas of AI application and potential areas of development; to study the specific features of the applied technologies.Methods. The authors using general methods of scientific cognition in various aspects considered the sphere of application of AI technologies in medicine, identified and systematized the characteristic features of the current state of this field and trends of further development.Results. According to the results of the analysis of existing solutions in the field of AI application in medicine all solutions are divided by the degree of elaboration, main processes and type of used data. The constructed taxonomy is the first step in comprehending and structuring the existing AI solutions, possibilities of their use in the process of rendering various medical services.Conclusions. Today, the most developed area of AI use in medicine is the analysis of medical images in the process of diagnosis, treatment and rehabilitation. Further development and introduction of these technologies into medical practice requires a more structured approach to assessing their effectiveness and efficiency, as well as solving a number of ethical and regulatory issues.Economics and Management140The constructed taxonomy is the first step in comprehending and structuring the existing AI solutions, possibilities of their use in the process of rendering various medical services, as well as solving a number of ethical and regulatory issues.Economics and Management["L. V. Lapidus", "O. M. Tokareva"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/1818e24b7720ffd05fc8866e7bc904b262cd2c3a fa448c916bdb29ebe26722cc04271eb0795303d8: Non-adherence to medication is recognized as a public health concern, affecting treatment outcomes and overall healthcare cost [1]. A report by the Organization for Economic Co-operation and Development (OECD) revealed that failure to adhere to medical advice led to approximately 200,000 premature deaths annually in Europe [2]. The high health and economic costs of non-adherence to medication is a significant issue for both society and the economy. The healthcare industry is adopting Artificial Intelligence (AI) to transform medication management [3]. From prescription processing to ensuring patient adherence, AI is playing a pivotal role in reducing human error, improving efficiency, and personalizing care. Medication management, a critical area in elderly healthcare, benefits immensely from AI-driven solutions. It offers significant enhancements in speed, accuracy, and safety. This white paper explores how AI revolutionizes medication management, including key challenges, technological advancements, and real-world applications.International Journal of Science and Research (IJSR)60How AI revolutionizes medication management is explored, including key challenges, technological advancements, and real-world applications.International Journal of Science and Research (IJSR)["Bhanu Prakash Manjappasetty Masagali"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/fa448c916bdb29ebe26722cc04271eb0795303d8 34c97b3c0e1bf8fcfc608054e23be3fd9f85779aPerkembangan teknologi telah menggantikan banyak aktivitas manusia dengan mesin otomatis dan digital, termasuk dalam sektor perbankan Indonesia. Transformasi ini menciptakan ekonomi digital perbankan. Dalam survei Top Brand Index Mobile Banking, Livin' by Mandiri menempati posisi ketiga. Untuk meningkatkan layanannya, Livin' by Mandiri menerapkan fitur AI seperti pembukaan rekening online, login dengan face recognition dan fingerprint, serta catatan keuangan otomatis. Namun, beberapa pengguna masih mengalami kendala dengan AI tersebut. Tujuan dari penelitian ini untuk mengetahui pengaruh expectation confirmation theory dan artificial intelligence features seperti trendiness, visual attractiveness, problem solving, communication quality, dan customization terhadap user satisfaction. Selain itu pengaruh user satisfaction dan corporate reputation terhadap acceptance of AI enabled banking juga diteliti. Penelitian ini menggunakan metode kuantitatif dengan survei kuesioner. Sampel sebanyak 172 responden diperoleh menggunakan G*Power dan diperoleh jawaban valid sebanyak 320 responden. Analisis data dilakukan dengan PLS-SEM menggunakan software SmartPLS 4.0. Hasil dari penelitian menunjukkan expectation confirmation, perceived performance, customization, dan communication quality berpengaruh signifikan dan positif terhadap user satisfaction. User satisfaction dan corporate reputation juga berpengaruh signifikan dan positif terhadap acceptance of AI enabled Banking. Fitur trendiness, visual attractiveness, dan problem solving tidak berpengaruh terhadap user satisfaction.Media Ethics: Human Ecology a Connected World260Jurnal Ilmiah Manajemen, Ekonomi, &amp; Akuntansi (MEA)["Nadira Ochell Andrea", "M. Y. Febrianta"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/34c97b3c0e1bf8fcfc608054e23be3fd9f85779a 78d0f96958f74512111c229df7be6adb1d0bd07dThe ethical implications and social impacts of artificial intelligence (AI) have garnered significant interest from industry, academia, and the public. However, global analyses of AI are often biased towards U.S. perspectives and are constrained by a lack of research, particularly outside the U.S. and Western Europe. This article presents key findings from a comprehensive literature review of recent social science research on the social impacts of AI and related technologies across five global regions. Our team of social scientists examined over 800 academic journal articles and monographs in more than a dozen languages. The review indicates that AI’s social impacts are likely to vary significantly by geographical setting, with local cultural and social contexts profoundly influencing perceptions and understandings of AI.Research from U.S. contexts shows that AI-driven technologies frequently reinforce social divides and exacerbate inequality, especially among historically marginalized groups. Our review suggests that this pattern is evident globally, with low- and middle-income countries potentially facing greater vulnerability to the negative impacts of AI while being less likely to reap its benefits.We advocate for rigorous ethnographic research to enhance our understanding of AI’s social impacts worldwide. In-depth, localized research is essential to identify AI systems that may exacerbate social inequality and to devise strategies to mitigate potential harms. A deeper understanding of AI’s social impacts in diverse settings is crucial for the responsible development, implementation, and regulation of these technologies, ensuring they contribute positively to society.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300A comprehensive literature review of recent social science research on the social impacts of AI and related technologies across five global regions indicates that AI’s social impacts are likely to vary significantly by geographical setting, with local cultural and social contexts profoundly influencing perceptions and understandings of AI.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Sohana Akter"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/78d0f96958f74512111c229df7be6adb1d0bd07d ae79daa270ea473aaa7c3d159df6af640063cde1Introduction This study investigates the experiences of leading Chinese companies in environmental conservation under varying extreme climate conditions, focusing on the role of artificial intelligence (AI) and governmental assistance. Methods A survey was conducted involving 200 participants to assess recognition and endorsement of AI’s role in environmental protection and to explore the adoption of AI technologies by firms for enhancing environmental management practices. Results The survey revealed widespread recognition of Tencent’s green initiatives and strong support for AI’s role in environmental protection. Many firms are considering adopting AI technologies to optimize energy management, deploy intelligent HVAC systems, and improve the operations of data centers and smart lighting systems. Discussion The findings highlight a strong belief in AI’s potential to advance environmental protection efforts, with a call for increased governmental support to foster this development. The study underscores the importance of a partnership between businesses and governments to leverage AI for environmental sustainability, contributing significantly to conservation efforts.Frontiers in Public Health280Frontiers in Public Health["Jingjing Sun", "Xin Guan", "Yanzhao Zeng", "Jiali Zhang", "Xiaodie Chen", "Xi Zhan"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/ae79daa270ea473aaa7c3d159df6af640063cde1 92510344731d7918aab7f4ecafe6d0b7384e0ac9Integrating Coding, Robotics, and Artificial Intelligence (AI) into educational practices represents a paradigm shift in how knowledge is imparted and acquired. This paper explored the multifaceted impact of these advanced technologies on contemporary education, highlighting their potential to enhance engagement, foster personalized learning experiences, and cultivate essential skills for the future. The study aimed to provide a comprehensive overview of how Coding, Robotics, and AI reshape the educational landscape by delving into specific applications, such as interactive learning environments and intelligent tutoring systems. Additionally, the discussion addressed the challenges and ethical considerations associated with these technological advancements, emphasizing the importance of a balanced approach that harnesses the benefits while addressing potential concerns. This paper is underpinned by the Theory of Situated Learning. A sample of five secondary schools in the OR Tambo Coastal District was selected for this study, with a focus on the experiences, behaviours, and social interactions of 15 teachers. Based on the study’s interpretive paradigm, it was discovered that certain teachers were not aware of the importance of increasing their digital professional knowledge as we move toward the Fourth Industrial Revolution (4IR). In addition, infusing coding and robotics in educational practices required a shift to digital learning. The study recommends encouraging teachers to acquire new skills to avoid stagnation. Although not every teacher found updating their skills to be a motivating factor for continuing professional development, the study underscores the significance of continuous learning for personal growth and improvement. Keywords: Artificial intelligence, Coding, Robotics, Professional development, Technology, LandscapeE-Journal of Humanities, Arts and Social Sciences720It was discovered that certain teachers were not aware of the importance of increasing their digital professional knowledge as the authors move toward the Fourth Industrial Revolution (4IR), and the study recommends encouraging teachers to acquire new skills to avoid stagnation.E-Journal of Humanities, Arts and Social Sciences["Thabisa Maqoqa"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/92510344731d7918aab7f4ecafe6d0b7384e0ac9 7d210a926f2838467e821c83b4c5200218c35eaaAccessibility of websites considers challenges related to creation of web pages adapted to user with different types of impairments. The goal of web accessibility is to remove barriers that may prevent people with disabilities from accessing or interacting with web content effectively. The paper presents some web accessibility standards and challenges related to their complying. A special attention is paid to some popular artificial intelligence tools and how they could be used to solve accessibility issues on web sites development.Digital Presentation and Preservation of Cultural and Scientific Heritage140Some web accessibility standards and challenges related to their complying are presented and a special attention is paid to some popular artificial intelligence tools and how they could be used to solve accessibility issues on web sites development.Digital Presentation and Preservation of Cultural and Scientific Heritage["Todor Todorov"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/7d210a926f2838467e821c83b4c5200218c35eaa 81133150213e2f9c2abb254b4310bcf6603a2697This community service aims to innovate Artificial Intelligence (AI) financial planning using the ClickUp application to improve the quality of project management, especially budgeting, thereby increasing efficiency, productivity, and better decision-making so that these MSMEs can be highly competitive in the fintech era. Artificial intelligence is a system in a machine that imitates the way humans think and act (human intelligence). One of a computer science related to the development that can imitate human expertise in carrying out certain tasks is Artificial Intelligence (AI)  (Nurcholis, 2023). The program carried out is training and mentoring so that financial planning is more innovative, economical, practical, easily accessible, and has minimal errors. The problem faced by business actors is financial planning that has not been implemented properly so business targets and income targets are often not achieved. This has an impact on the sustainability of the Chibi-Chibi Mochi MSME business in the era of competition. The solution to the problem is that the community service team will provide training and mentoring on financial planning innovation using Artificial Intelligence (AI) ClickUp. The output targets to be achieved are in the form of journal publications and increasing the understanding and skills of business actors.Aksi Nyata : Jurnal Pengabdian Sosial dan Kemanusiaan00Aksi Nyata : Jurnal Pengabdian Sosial dan Kemanusiaan["Fia Dialysa"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/81133150213e2f9c2abb254b4310bcf6603a2697 eab7486e7c032ee15a2155132d73fc8a185235d8This study focuses on developing a human resource (HR) plan for integrating Artificial Intelligence (AI) in HR management across selected manufacturing companies. It evaluated the AI literacy of HR employees, their perceptions of AI’s usefulness and ease of use, the challenges they face with AI integration, and how AI is being developed within HR management. Employing a mixed-method design, the study gathered data from forty-eight (48) HR employees using a validated questionnaire and conducted thematic analysis on interviews with 10 selected participants. The findings revealed that HR employees are generally literate in AI and believe it enhances their work productivity and performance. Interestingly, their general knowledge of AI did not significantly affect their perceptions of its usefulness or ease of use. The main challenges identified included the cost of technology, integration difficulties, data privacy and security issues, and the need for further capacity building. The study resulted in a comprehensive HR plan designed to guide the integration of AI into HR practices. The plan is recommended for broader implementation, with suggestions for evaluating its effectiveness, engaging in partnerships, conducting cost-benefit analyses, and formulating continuous learning plans. It advises companies to develop their own AI strategies that prioritize ethical practices, continuous learning, and a culture of innovation and security.Industry and Academic Research Review350The findings revealed that HR employees are generally literate in AI and believe it enhances their work productivity and performance, but their general knowledge of AI did not significantly affect their perceptions of its usefulness or ease of use.Industry and Academic Research Review["Mara Grace Maraver", "Preciosa Villacruel"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/eab7486e7c032ee15a2155132d73fc8a185235d8 d1d6cf03e722fd8f59863a2ed02779bd5ac68bd3Incorporating digital technologies, particularly artificial intelligence, into financial services operations is imperative for achieving critical sustainable development goals (SDGs) through digital financial inclusion. This paper examines the drivers behind AI adoption in South Africa's financial services landscape, given its highly advanced financial sector and rapidly evolving digitisation trends. Drawing on the Technological-Organizational-Environmental (TOE) framework, the study investigates the factors influencing AI adoption through a comprehensive analysis of existing literature, a survey of financial services professionals and binary logistic regression. The results of binary logistic regression indicated that technological, organisational and environmental improvements significantly enhance the likelihood of AI adoption in South Africa's financial services sector. Specifically, access to technological infrastructure, organisational leadership support, and regulatory clarity emerge as key determinants of AI adoption. Overall, this study underscores the need for companies in the financial sector to encourage a culture that welcomes innovation and the integration of AI technology, as well as the need for policymakers to develop comprehensive and unambiguous legislative frameworks that control AI use in financial services.   Received: 13 March 2024 / Accepted: 31 August 2024 / Published: 05 September 2024Academic Journal of Interdisciplinary Studies880The need for companies in the financial sector to encourage a culture that welcomes innovation and the integration of AI technology, as well as the need for policymakers to develop comprehensive and unambiguous legislative frameworks that control AI use in financial services are underscored.Academic Journal of Interdisciplinary Studies["A. Hassan"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/d1d6cf03e722fd8f59863a2ed02779bd5ac68bd3 21a62184587949d3244dd38f5c65a2b9ad88c3a7ABSTRACT Artificial intelligence (AI) is revolutionizing health care by addressing some of the important concerns, the health-care organizations face daily. All partners in the health system must understand AI technologies and how they might improve the effectiveness and accessibility of AI-based health services, leading to value-based care. Effective and proper use of AI in health care is the primary emphasis of this narrative review article, which also helps readers grasp the basic ideas underlying AI. Despite the fact that AI is still in its infancy in other sectors of health care, it has made tremendous strides in a variety of specializations, including radiodiagnosis and imaging, surgery (robotic-assisted procedures), oncology, especially radiation oncology, anesthesia, and pathology. However, ethical concerns about utilizing AI in health care may delay its widespread usage.Journal of Radiation and Cancer Research410Effective and proper use of AI in health care is the primary emphasis of this narrative review article, which also helps readers grasp the basic ideas underlying AI.Journal of Radiation and Cancer Research["S. Wani", "Talib Khan", "S. Wani", "Deeba Farhat"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/21a62184587949d3244dd38f5c65a2b9ad88c3a7 484912a8094161f3e426353dfa0f0810cfb8a69bIndian Journal of Surgical Oncology194Indian Journal of Surgical Oncology["Seyed Masoud Haghighikian", "Ahmad Shirinzadeh-Dastgiri", "Mohammad Vakili-Ojarood", "Amirhosein Naseri", "M. Barahman", "Ali Saberi", "Amirhossein Rahmani", "Amirmasoud Shiri", "Ali Masoudi", "Maryam Aghasipour", "Amirhossein Shahbazi", "Y. Ghelmani", "K. Aghili", "H. Neamatzadeh"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/484912a8094161f3e426353dfa0f0810cfb8a69b bbadb9e398554cd8790304ce797844e3b66109acDiscover Artificial Intelligence253Discov. Artif. Intell.["Olesya M. Tolstykh", "Tamara Oshchepkova"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/bbadb9e398554cd8790304ce797844e3b66109ac 39676470cc93f649e30dd3ef21857a0c7d88c7bfHumanities and Social Sciences Communications292Humanities and Social Sciences Communications["Kelvin Leong", "Anna Sung"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/39676470cc93f649e30dd3ef21857a0c7d88c7bf ac2cee862371d9ddf22c6a8a5deea9fee4c76322British Journal of Clinical Pharmacology171British journal of clinical pharmacology["Karlo Petkovi\u0107", "Zdeslav Strika", "R. Liki\u0107", "M. Lucijani\u0107"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/ac2cee862371d9ddf22c6a8a5deea9fee4c76322 9dffa4caf012915f9992b267c32105b80153a2e8OECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2024-09-05T00:00:00https://www.semanticscholar.org/paper/9dffa4caf012915f9992b267c32105b80153a2e8 a24a876542caa8de120b09899d8fc38688f75eb9Journal of Surgical Oncology10Journal of surgical oncology["Huarong Zhao", "Kun Xu", "Yuejun Zhou"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/a24a876542caa8de120b09899d8fc38688f75eb9 41cb08ac66dcb241638fc81ec858e4977c2d01c800["Dimitrios Ziakkas", "Anastasios Plioutsias"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/41cb08ac66dcb241638fc81ec858e4977c2d01c8 0cfbf20ef047ed9a0a9c7933585af6151fd9f93cAI's rise has affected many aspects of civilization. Pharmaceutical businesses have been hit hard. This review paper highlights AI's benefits for disease detection, clinical trials, medicine development, and productivity in the pharmaceutical industry. Pharmaceutical companies have built specialized systems to help doctors diagnose and monitor medication remediation. Pharmaceutical businesses are utilizing AI for machine learning to imitate human analytical processes for more accurate and insightful data. AI has many benefits for the pharmaceutical business. Data analysis can address previously insoluble problems due to improved precision. AI boosts productivity, lowers expenses, and enhances tasks. AI insights enhance understanding of user behavior, market performance, and clinical trial success. AI helps identify patients during clinical trials and improves antiviral detection to ensure efficacy, safety, cost-effectiveness, and seamless pharmaceutical procedures. The pharmaceutical industry emphasizes AI in R&D, drug discovery, diagnostics, sickness prevention, epidemic forecasting, remote access, manufacturing, and marketing. Artificial intelligence has transformed medication development and discovery by analyzing vast datasets, discovering complicated patterns, and forecasting efficacy. Pharmaceutical companies like Novartis, AstraZeneca, and Verge Genomics are utilizing AI for drug feature prediction, neurological evaluation, therapy development, pulmonary and hypertension recognition, low-cost medication production, and disease diagnosis.Current Drug Discovery Technologies00The pharmaceutical industry emphasizes AI in R&D, drug discovery, diagnostics, sickness prevention, epidemic forecasting, remote access, manufacturing, and marketing, as well as utilizing AI for machine learning to imitate human analytical processes for more accurate and insightful data.Current drug discovery technologies["Deepak Jain", "Phool Chandra", "Zeeshan Ali", "Nishat Fatma", "Hafsa Khan"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/0cfbf20ef047ed9a0a9c7933585af6151fd9f93c 2569c9a314b5bdeb6464069430333dfe1073ba67AI &amp; SOCIETY160AI &amp; SOCIETY["Wim De Mulder"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/2569c9a314b5bdeb6464069430333dfe1073ba67 330935e4f085affdb6744ac9d9aa243a8080b57dIn the present society artificial intelligence is already part of the everyday life of the general population and companies, which use AI tools such as automated chabots to meet the needs of their costumers. This present research has the porpouse of determining whether and how AI tools are being used for the commercial management of micro-enterprises (ME) and small-scale undertakings (SCU) in Brazil. Through the analysis of data, it was possible to conclude that studies on the subject are scarce. However, from the few studies that have been conducted on the use of AI in commercial management, it is conclued that companies already use AI tools and micro-enterprises and small-scale undertakings in Brazil that do not use AI tools yet, must start using those tools as soon as possible if they desire to prosper in the national and international market.Revista ft00It is conclued that companies already use AI tools and micro-enterprises and small-scale undertakings in Brazil that do not use AI tools yet, must start using those tools as soon as possible if they desire to prosper in the national and international market.Revista ft["Daiara Jucinara Barbosa Lins Batista", "H\u00e9lio de Sousa Batista", "Hanilton Ces\u00e1rio"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/330935e4f085affdb6744ac9d9aa243a8080b57d 047d31ed0b335c8d8339be9fc287a8b216b5ef46Islamic education plays a major role in preserving the values of human life. Although humanity has made a lot of progress, especially in the digital age that is changing the order of life, Islamic religious education must remain in control of that goal. In addition, the existence of Arfificial Intelligent (AI) technology has also reduced the role of humans. Therefore, human critical thinking must be improved by using AI technology. This research aims to explore higher education in developing students' critical thinking skills through the use of artificial intelligence technology. The approach used in this study is a qualitative approach with the type of case study. Data collection techniques are carried out by observation, interviews and documentation. The main informants in this study are lecturers and students of Darullughah Wadda'wah International Islamic University. Data analysis was carried out through three categories, namely condensation, display and verification. To corroborate the findings of the research results, the researcher conducted triangulation, both data triangulation and source triangulation. The results of this study show that AI technology is very effective in developing students to think critically. AI can stimulate students' critical thinking based on AI considerations. Their limitations in telling the idea of knowledge will be overcome with this AI technology. The integration of artificial intelligence in Islamic education has the potential to revolutionize the way students learn and engage in their studies. Using AI technology to personalize the learning experience, educators can meet individual needs and interests, which ultimately improves students' critical thinking skills. Following up on these findings, the improvement of students' critical thinking skills through the use of AI technology must be further optimized through the design of learning plans, learning resources, and AI-based learning assessments.Journal of Teaching and Learning00The results of this study show that AI technology is very effective in developing students to think critically, and the improvement of students' critical thinking skills through the use of AI technology must be further optimized through the design of learning plans, learning resources, and AI-based learning assessments.Academicus: Journal of Teaching and Learning["S. Sodikin"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/047d31ed0b335c8d8339be9fc287a8b216b5ef46 7ad1698716000865b08ee46cfb0c27327f08c707AI &amp; SOCIETY152It is argued that the particular dimension of the AI–mind encounter elucidated by Krishnamurti can significantly broaden the field of the philosophy of artificial intelligence.AI &amp; SOCIETY["Shai Tubali"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/7ad1698716000865b08ee46cfb0c27327f08c707 98f16006f9492a77cba380f8c9e7eaf514092389 Purpose The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI). Design/methodology/approach Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research. Findings The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices. Research limitations/implications This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields. Originality/value The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains. International Journal of Ethics and Systems1272This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM, and introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.International Journal of Ethics and Systems["Hassnian Ali", "A. Aysan"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/98f16006f9492a77cba380f8c9e7eaf514092389 d6b0cd2069376aedc4e3b57f62c88f2a7e64af3aThe integration of Artificial Intelligence (AI) into artistic creation has sparked both excitement and ethical concerns. As AI technologies advance, questions arise regarding the ethical implications of AI-generated artworks. This paper explores the evolving ethical landscape surrounding AI-generated artistic landscapes. It examines key ethical principles such as authorship, creativity, bias, transparency, and societal impact, and discusses how these principles can be adapted to accommodate the unique challenges posed by AI-generated art. By analyzing current debates and proposing ethical frameworks, this paper aims to contribute to the ongoing dialogue on the ethical use of AI in creative endeavors.2024 International Conference on Information Technology Research and Innovation (ICITRI)201Key ethical principles such as authorship, creativity, bias, transparency, and societal impact are examined, and how these principles can be adapted to accommodate the unique challenges posed by AI-generated art are discussed.2024 International Conference on Information Technology Research and Innovation (ICITRI)["Wai Yie Leong", "Yuan Zhi Leong", "W. -. Leong"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/d6b0cd2069376aedc4e3b57f62c88f2a7e64af3a f9dd28024e9aac970cd841f41dcfca39db23633aMany generative artificial intelligence (AI) systems have been developed over the last decade. Some systems are more of a generic character, and some are specialized in music composition. However, whether these AI systems are serious competitors for human composers remains unclear. Despite increased public interest, there is currently little empirical foundation for a conceivably equivalent performance for creative AI when compared to human experts in a controlled task. Thus, we conducted an online experiment to evaluate the subjectively perceived quality of AI compositions with human-made products (by music students) in a standardized task. Based on a melody continuation paradigm, creative products using AI were generated by the AI agents ChatGPT (Version 3.5) and Google Magenta Studio (Version 2.0). The human creative performances were realized by 57 melodic continuations, composed by music students. In the online evaluation study, listeners (N = 71, mainly musicians) rated the aesthetic quality of the outcomes of the various systems. Additionally, the raters’ musical experience level was controlled as well as the length of the given melody completion task (two probe positions). As a main result, the overall quality of the AI compositions was rated significantly lower on all four target items compared to the human-made products (large effect sizes). Musical experience and the length of the melody did not influence the ratings. We conclude that the current capabilities of AI in the domain of musical creativity determined by a standardized composition task are far below human capabilities. However, we assume rapid progress will be made in the domain of generative music-specific AI systems.Jahrbuch Musikpsychologie361The current capabilities of AI in the domain of musical creativity determined by a standardized composition task are far below human capabilities, however, it is assumed rapid progress will be made in the domain of generative music-specific AI systems.Jahrbuch Musikpsychologie["Anton Schreiber", "Kilian Sander", "Reinhard Kopiez", "Raphael Th\u00f6ne"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/f9dd28024e9aac970cd841f41dcfca39db23633a 8161d50f4d77abf954df7504a29fd205fd730145The rapid development of artificial intelligence (AI) has unearthed the potential to assist humans in controlling advanced technologies. Shared autonomy (SA) facilitates control by combining inputs from a human pilot and an AI copilot. In prior SA studies, the copilot is constantly active in determining the action played at each time step. This limits human autonomy and may have deleterious effects on performance. In general, the amount of helpful copilot assistance can vary greatly depending on the task dynamics. We therefore hypothesize that human autonomy and SA performance improve through dynamic and selective copilot intervention. To address this, we develop a goal-agnostic intervention assistance (IA) that dynamically shares control by having the copilot intervene only when the expected value of the copilot's action exceeds that of the human's action across all possible goals. We implement IA with a diffusion copilot (termed IDA) trained on expert demonstrations with goal masking. We prove a lower bound on the performance of IA that depends on pilot and copilot performance. Experiments with simulated human pilots show that IDA achieves higher performance than pilot-only and traditional SA control in variants of the Reacher environment and Lunar Lander. We then demonstrate that IDA achieves better control in Lunar Lander with human-in-the-loop experiments. Human participants report greater autonomy with IDA and prefer IDA over pilot-only and traditional SA control. We attribute the success of IDA to preserving human autonomy while simultaneously offering assistance to prevent the human pilot from entering universally bad states.Neural Information Processing Systems191A goal-agnostic intervention assistance that dynamically shares control by having the copilot intervene only when the expected value of the copilot's action exceeds that of the human's action across all possible goals is developed.ArXiv["Brandon J. McMahan", "Zhenghao Peng", "Bolei Zhou", "Jonathan C. Kao"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/8161d50f4d77abf954df7504a29fd205fd730145 39dac325aad065d22e0463ab90bf582fe7666766In this paper, we undertake a critical analysis of how chatbots built on generative artificial intelligence impact assumptions underlying definitions of trust. We engage a particular definition of trust and the object-oriented model of trust that was built upon it and identify how at least four implicit assumptions may no longer hold. Those assumptions include that people generally provide others with a default level of trust, the ability to identify whether the trusted agent is human or artificial, that risk and trust can be readily quantified or categorized, and that there is no expectation of gain by agents engaged in trust relationships. Based on that analysis, we suggest modifications to the definition and model to accommodate the features of generative AI chatbots. Our changes re-emphasize developers’ responsibility for the impacts of their AI artifacts, no matter how sophisticated the artifact may be. The changes also reflect that trust relationships are more fraught when participants in such relationships are not confident in identifying the nature of a potential trust partner.Inf.161Modifications to the definition and model of trust are suggested to accommodate the features of generative AI chatbots and reflect that trust relationships are more fraught when participants in such relationships are not confident in identifying the nature of a potential trust partner.Inf.["Marty J. Wolf", "F. Grodzinsky", "Keith W. Miller"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/39dac325aad065d22e0463ab90bf582fe7666766 2b3e6e22c02d65205cc6c8749257bf39f62bb0a4AI and Ethics561A highly heterogeneous ecosystem of approaches and a diverse use of terminology, a higher prevalence of approaches for certain stages of the AI lifecycle, reflecting the dominance of specific stakeholder groups in their development, and several barriers to the adoption of approaches are identified.AI and Ethics["Sara Kijewski", "Elettra Ronchi", "E. Vayena"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/2b3e6e22c02d65205cc6c8749257bf39f62bb0a4 c6723456245852313cccb1842ebcdd6d4c090145Social interactions form the foundation of human societies. Artificial intelligence has made significant progress in certain areas, but enabling machines to seamlessly understand social interactions remains an open challenge. It is important to address this gap by endowing machines with social capabilities. We identify three key capabilities needed for effective social understanding: 1) understanding multimodal social cues, 2) understanding multi-party dynamics, and 3) understanding beliefs. Building upon these foundations, we classify and review existing machine learning works on social understanding from the perspectives of verbal, non-verbal, and multimodal social cues. The verbal branch focuses on understanding linguistic signals such as speaker intent, dialogue sentiment, and commonsense reasoning. The non-verbal branch addresses techniques for perceiving social meaning from visual behaviors such as body gestures, gaze patterns, and facial expressions. The multimodal branch covers approaches that integrate verbal and non-verbal multimodal cues to holistically interpret social interactions such as recognizing emotions, conversational dynamics, and social situations. By reviewing the scope and limitations of current approaches and benchmarks, we aim to clarify the development trajectory and illuminate the path towards more comprehensive intelligence for social understanding. We hope this survey will spur further research interest and insights into this area.arXiv.org3211This work classify and review existing machine learning works on social understanding from the perspectives of verbal, non-verbal, and multimodal social cues, and identifies three key capabilities needed for effective social understanding.ArXiv["Sangmin Lee", "Minzhi Li", "Bolin Lai", "Wenqi Jia", "Fiona Ryan", "Xu Cao", "Ozgur Kara", "Bikram Boote", "Weiyan Shi", "Diyi Yang", "J. Rehg"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/c6723456245852313cccb1842ebcdd6d4c090145 c61a3282e8a40a111f30b8d39be8746f4799f822: This review paper discusses how Artificial intelligence (AI) can positively change patient safety and quality care in health systems. In the process of developing systems in medical care delivery, it was realized that there was a need for patient safety due to the increasing standards in health care delivery. As a system involving data analysis, modeling, and automation, AI is set to revolutionize medical systems, help avoid risks, minimize medical mistakes, and improve patients' quality of life. The paper also presents an overview of the currently trending AI - based solutions, including predictive analytics, machine learning algorithms, natural language processing, and robotic process automation, that are being implemented in clinical care. The use of AI in patient safety costs is presented in different specialties concerning diagnosis, therapy, medication administration, and patients' critical signs. Moreover, the sensibility and discussion show how AI technologies affect the quality frameworks and ethics of healthcare, along with the application problems in the healthcare context. The study indicates that using AI in healthcare can minimize adverse events, assist in decision - making processes, and eventually increase the quality of care. Nevertheless, the mentioned approaches to establishing AI in healthcare should have strong governance and constant evaluations throughout all interdisciplinary fields to guarantee that the advanced tools of artificial intelligence will not cause new sorts of harm.International Journal of Science and Research (IJSR)201The study indicates that using AI in healthcare can minimize adverse events, assist in decision - making processes, and eventually increase the quality of care.International Journal of Science and Research (IJSR)["Vedamurthy Gejjegondanahalli Yogeshappa"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/c61a3282e8a40a111f30b8d39be8746f4799f822 31b64645a615fd6748ac53829515c772e66e518c: As artificial intelligence AI technologies continue to advance, the need for effective governance, the need for effective governance frameworks has become increasingly pressing. The rise of generative AIs capable of producing synthetic data, images, and text has raised significant concerns regarding intellectual property, accountability, and the potential for misuse. This article explores the key challenges associated with generative AIs and proposes a comprehensive approach to AI governance, incorporating regulatory reforms, technological innovations, and public awareness initiatives. This article addresses the pressing need for robust AI governance frameworks in response to the growing influence of generative AI technologies. It highlights the legal, ethical, and regulatory challenges, particularly around intellectual property and accountability. The article advocates for reforms in regulation, technological innovations, and public awareness initiatives, proposing solutions such as AI driven tools for disinformation detection and the AI Risk Atlas for identifying risks. The purpose of this article is to explore the challenges posed by generative AI technologies and propose a comprehensive governance framework to address these challenges. This study is significant because it provides a timely analysis of the governance needs for generative AI, which is crucial for ensuring that AI benefits are realized without compromising ethical and legal standards.International Journal of Science and Research (IJSR)60The article advocates for reforms in regulation, technological innovations, and public awareness initiatives, proposing solutions such as AI driven tools for disinformation detection and the AI Risk Atlas for identifying risks.International Journal of Science and Research (IJSR)["Vyoma Gajjar"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/31b64645a615fd6748ac53829515c772e66e518c 8723af18edd8da25bfeeaad40a5c1fdd0eb1d6a6Artificial Intelligence (AI) is revolutionizing reproductive biology, transforming fertility assessment, assisted reproductive technologies (ART), and research practices. This review explores AI's impact, highlighting its potential to enhance personalized care and advance scientific understanding. In fertility assessment, AI algorithms analyze vast datasets to predict treatment success, enabling clinicians to tailor personalized treatment plans. In ART, AI improves embryo selection during in vitro fertilization (IVF) by providing objective, data-driven criteria, reducing variability, and increasing success rates.AI also optimizes laboratory workflows, automating tasks such as data analysis and interpretation, enhancing efficiency, and minimizing human error. In research, AI accelerates data analysis, facilitates knowledge discovery, and enables predictive modeling, driving innovation in reproductive biology. However, AI's integration raises ethical concerns, including patient autonomy, informed consent, and data security. Collaborative efforts among stakeholders are essential to ensure responsible AI use, balancing innovation with ethical considerations. This review examines AI's transformative potential in reproductive biology, technological advancements, and the ethical landscape, envisioning a future where AI positively impacts reproductive health and clinical practice.Annual Research &amp; Review in Biology00This review examines AI's transformative potential in reproductive biology, technological advancements, and the ethical landscape, envisioning a future where AI positively impacts reproductive health and clinical practice.Annual Research &amp; Review in Biology["S. Doultani", "Prachi Sharma", "Prateek Makwana", "S.P Patil", "S. S. Layek", "L. B. George", "H. Highland", "K. K. Hadiya"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/8723af18edd8da25bfeeaad40a5c1fdd0eb1d6a6 a8755520ae92b57de8ba8dfbd90040a2e3d9ab68The advancement of artificial intelligence has led to its application in many areas, including news media, which makes it crucial to understand public reception of AI-generated news. This preregistered study investigates (i) the perceived quality of AI-assisted and AI-generated versus human-generated news articles, (ii) whether disclosure of AI's involvement in generating these news articles influences engagement with them, and (iii) whether such awareness affects the willingness to read AI-generated articles in the future. We conducted a survey experiment with 599 Swiss participants, who evaluated the credibility, readability, and expertise of news articles either written by journalists (control group), rewritten by AI (AI-assisted group), or entirely written by AI (AI-generated group). Our results indicate that all articles were perceived to be of equal quality. When participants in the treatment groups were subsequently made aware of AI's role, they expressed a higher willingness to continue reading the articles than participants in the control group. However, they were not more willing to read AI-generated news in the future. These results suggest that aversion to AI usage in news media is not primarily rooted in a perceived lack of quality, and that by disclosing using AI, journalists could induce more short-term engagement.00The results suggest that aversion to AI usage in news media is not primarily rooted in a perceived lack of quality, and that by disclosing using AI, journalists could induce more short-term engagement.["Fabrizio Gilardi", "Sabrina Di Lorenzo", "Juri Ezzaini", "Beryl Santa", "Benjamin Streiff", "Eric Zurfluh", "E. Hoes"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/a8755520ae92b57de8ba8dfbd90040a2e3d9ab68 e4ddfe1d1a35d55399226d180f4f6b187263d685With the increasing application and complexity of Artificial Intelligence (AI) systems, the trustworthiness of AI has garnered widespread attention across various fields. An AI system is a specific type of software system with unique trustworthiness requirements due to its distinctive characteristics in data and algorithms. Our objective is to investigate the state of the art in trustworthy AI and trustworthy software separately and to analyze the connections and gaps between them. To this end, we conducted a tertiary study, which is a systematic literature review of existing secondary studies. These secondary studies are divided into two groups: one focuses on trustworthy AI and the other on trustworthy software. We developed frameworks for both trustworthy AI and trustworthy software, summarized the definitions of quality attributes in a structured format, and analyzed the similarities of these attributes between the two areas. Additionally, we created a swimlane diagram illustrating trustworthy practices throughout the development life-cycle and in relation to specific quality attributes. Researchers in these two areas originate from distinct research communities, leading to a significant gap between the trustworthiness of AI and software. However, we believe that existing research on trustworthy software can effectively address some gaps in trustworthy AI research, and we have identified evidence of connections between the two areas.ACM Computing Surveys160This paper developed frameworks for both trustworthy AI and trustworthy software, summarized the definitions of quality attributes in a structured format, and analyzed the similarities of these attributes between the two areas.ACM Comput. Surv.["Bohan Liu", "Gongyuan Li", "He Zhang", "Yuzhe Jin", "Zikuan Wang", "Dong Shao"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/e4ddfe1d1a35d55399226d180f4f6b187263d685 2547ee4901f1bf7fb7a3b5c6bac35ca4b5f6b202: Adopting Artificial Intelligence (AI) in Medicare systems can introduce drastic transformations in managing patient factors; it can do it all at once, make it efficient and cost - effective, and enhance patient's state. Proposed is a discussion on the applicability of AI in Medicare in regard to patients, the organization, and information. The use of AI in prediction analysis, patient - tailored care, and non - healthcare processes is the solution to Medicare's future issue of serving an increasing number of aging populations and increasing health service complexity. Succinctly, the authors mention in the abstract some of the general areas they consider that AI is already deploying its capacities, such as the diagnostic and risk prognostication of patients' and resources' uses. It also gives the ethical factors, problems, and potential for implementing AI in Medicare. Such a study implies that AI can go a long way in helping Medicare systems to deliver higher efficiency. This, nevertheless, should only be done if the right plan has been developed and the right data has been well managed; besides, the AI systems used have been regularly audited.International Journal of Science and Research (IJSR)200International Journal of Science and Research (IJSR)["Ginoop Chennekkattu Markose"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/2547ee4901f1bf7fb7a3b5c6bac35ca4b5f6b202 85d41a0e0a37508a87816d928c1d5a220719f1e1 As artificial intelligence (ai) continues to permeate various sectors, its potential impact on education has garnered significant attention. This study delved into the perceptions of scientific college students regarding the integration of ai applications in education The sample of the current study was 204 university students from Scientific Colleges of Al al-Bayt University (aabu) in Jordan The study took place in that university during the 1st semester of the academic year 2023/ 2024. Employing both qualitative and quantitative approaches, the study utilized a survey comprising seven items and four interview questions. The research anticipated a spectrum of perceptions among scientific college students regarding ai applications in education. While some expressed enthusiasm for benefits such as personalized learning and innovative resources, others harbored concerns about issues like data privacy and algorithmic biases. The analysis revealed a diverse spectrum of viewpoints among participants, ranging from enthusiastic endorsement of ai’s potential benefits, such as personalized learning and resource innovation, to apprehensions surrounding issues like data privacy and algorithmic biases. Through rigorous analysis, this study aimed to identify prevalent themes in students’ perceptions of ai applications in education. The findings promise to enrich our understanding of how scientific college students envision ai’s role in higher education, guiding stakeholders in effectively integrating ai technologies to enhance teaching and learning environments.Journal of Science of Learning and Innovations180The research anticipated a spectrum of perceptions among scientific college students regarding ai applications in education, ranging from enthusiastic endorsement of ai’s potential benefits, such as personalized learning and resource innovation, to apprehensions surrounding issues like data privacy and algorithmic biases.Journal of Science of Learning and Innovations["Farouq Almeqdadi", "Kawthar Al Shadifat"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/85d41a0e0a37508a87816d928c1d5a220719f1e1 6c760834ef63f518fe82d35fbe3deefdd1595d86Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has been an increase in interest in using artificial intelligence, particularly generative AI, in the pharmacovigilance of pharmaceuticals under development, as well as those already in the market. This review was conducted to understand how generative AI can play an important role in pharmacovigilance and improving drug safety monitoring. Data from previously published articles and news items were reviewed in order to obtain information. We used PubMed and Google Scholar as our search engines, and keywords (pharmacovigilance, artificial intelligence, machine learning, drug safety, and patient safety) were used. In toto, we reviewed 109 articles published till 31 January 2024, and the obtained information was interpreted, compiled, evaluated, and conclusions were reached. Generative AI has transformative potential in pharmacovigilance, showcasing benefits, such as enhanced adverse event detection, data-driven risk prediction, and optimized drug development. By making it easier to process and analyze big datasets, generative artificial intelligence has applications across a variety of disease states. Machine learning and automation in this field can streamline pharmacovigilance procedures and provide a more efficient way to assess safety-related data. Nevertheless, more investigation is required to determine how this optimization affects the caliber of safety analyses. In the near future, the increased utilization of artificial intelligence is anticipated, especially in predicting side effects and Adverse Drug Reactions (ADRs).Current Reviews in Clinical and Experimental Pharmacology00This review was conducted to understand how generative AI can play an important role in pharmacovigilance and improving drug safety monitoring, and to make it easier to process and analyze big datasets, generative artificial intelligence has applications across a variety of disease states.Current reviews in clinical and experimental pharmacology["Hara Prasad Mishra", "Rachna Gupta"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/6c760834ef63f518fe82d35fbe3deefdd1595d86 3e1921dfd9426756a0b802f4b41088ccbfe928e5The dance major, as a vital component of quality education in colleges and universities, presents heightened demands for the management of teaching resources and the individual needs of students. This is due to the diverse range of dance styles and the rigorous physical requirements placed on students. The traditional teaching model, which relies on teacher demonstrations, movement guidance, and correction, along with conventional educational management practices, has proven inadequate in meeting the evolving demands of educational reform. In recent years, advancements in digital technology and artificial intelligence have significantly enhanced the management of dance education in higher education institutions. These technologies can effectively improve the efficiency of data collection and analysis related to education and teaching, providing crucial technical support for optimizing students' learning habits, performance, and curriculum design. This study focuses on the benefits of artificial intelligence technology in reforming higher dance education management, explores optimization strategies, and aims to offer valuable insights for educational management reform.The Educational Review USA60This study focuses on the benefits of artificial intelligence technology in reforming higher dance education management, explores optimization strategies, and aims to offer valuable insights for educational management reform.The Educational Review, USA["Jiawang Zhang"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/3e1921dfd9426756a0b802f4b41088ccbfe928e5 557643d102f198097775863d69f74dbdfa20f464Scientific Reports1030This paper argues that only a coherent and comprehensive interdisciplinary approach making sense of the different properties attributed to trust and trustworthiness can convey a proper understanding of complex watchful trust dynamics in a socio-technical context and offers a road-map of the steps that could be taken to address the challenges identified.Scientific Reports["Christian Lahusen", "M. Maggetti", "Marija Slavkovik"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/557643d102f198097775863d69f74dbdfa20f464 faf8b4b8e6c145859ef0af2b6b95735f2698dd2eThe purpose of this research was to examine the evolution, scope, and orientation of the scientific production on artificial intelligence applications in university students. The methodology, with a non-experimental design and qualitative approach, involved a search in Scopus, identifying 643 documents between 1975-2024, analyzed through VOSviewer and Bibliometrix. The results show an emerging field, but with rapid growth (4.59% per year), with notoriety of Kong, Abdulrahman and Chai. Research is predominantly in computer science (61%), social sciences (33%) and engineering (23%) from China, USA, Spain and Taiwan. Current applications focus on the use of AI in education, machine learning, support for academic decisions and student mental health. However, it is necessary to expand the approach towards ethical and regulatory aspects and the evaluation of multifaceted effects on different student profiles. In conclusion, although production is growing rapidly, more comprehensive perspectives are required to responsibly enhance the impact of these technologies on the university educational experience.   Received: 9 June 2024 / Accepted: 25 August 2024 / Published: 05 September 2024Journal of Educational and Social Research410Although production is growing rapidly, more comprehensive perspectives are required to responsibly enhance the impact of these technologies on the university educational experience and the evaluation of multifaceted effects on different student profiles.Journal of Educational and Social Research["Victor Manuel Valdiviezo Sir", "Dennis Brayan Baique Timana", "Luis Gerardo Merino Cava", "Julio Arevalo Reategui", "Adolfo Cacho Revilla", "Diana del Rocio Vizconde Burga"]2024-09-05T00:00:00https://www.semanticscholar.org/paper/faf8b4b8e6c145859ef0af2b6b95735f2698dd2e 258f57092708d768ba78db0cfd3c7ad20df2c4a7Applied intelligence (Boston)853Novel approaches suggest that there is still room for improvement to provide personalised AI-driven healthcare services and promote active ageing initiatives with the ultimate goal of enhancing the quality of life and well-being of older adults.Appl. Intell.["M. Bernal", "Edgar Batista", "A. Mart\u00ednez-Ballest\u00e9", "A. Solanas"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/258f57092708d768ba78db0cfd3c7ad20df2c4a7 cc02ba983c09d7232263744af48cc4d46d63d2aeLately, architects have begun to favour a more result-oriented way of working based on artificial intelligence (AI) and its use for automation and software applications operation that support work with design data. The research aims to identify and systematise data on computer tools using artificial intelligence algorithms and the prospects for its development in architectural activities. The use of AI algorithms in architectural design is characteristic of all its stages, from the conceptual phase and basic design levels to detailing, development of design documentation, and future implementation, which is why various computer tools are available. The scientific paper describes three groups of AI software products. The first group includes software for architectural activities with AI-based functionality. The second group involves additional plug-ins with AI algorithms, i.e., independently compiled software modules installed and connected to the main programme, expanding its capabilities. The third group comprises online AI platforms, many of which are already available on the Internet and continue to grow in number. These services are usually not associated with professional software but can be used to perform specific architectural design tasks. The reviewed software products suggest that artificial intelligence technologies can transform traditional approaches to architecture and construction, offering architects powerful tools to increase efficiency and creativity. However, it is necessary to note significant doubts in the professional architectural community about whether to implement artificial intelligence fully. It is due to uncertainty about the impact of AI on traditional roles in the architectural industry, displacement from jobs, and the reliability and ethical implications of decision-making based on these particular algorithms. We should also note that the first group of software is the most promising for the future development of the architectural profession, as it provides full-fledged programmes with a comprehensive approach to architectural activities. Keywords: artificial intelligence, software, plugin, architecture.Municipal economy of cities81The reviewed software products suggest that artificial intelligence technologies can transform traditional approaches to architecture and construction, offering architects powerful tools to increase efficiency and creativity.Municipal economy of cities["N. Vergunova"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/cc02ba983c09d7232263744af48cc4d46d63d2ae ff7a1a4d75dfc2c97ba2c6a60aa7251a57fe9861Systematic Reviews371This systematic review assesses the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)—regardless of the clinical issues.Systematic Reviews["C. Wilhelm", "A. Steckelberg", "F. G. Rebitschek"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/ff7a1a4d75dfc2c97ba2c6a60aa7251a57fe9861 09415ad1f73cbfe4d8158c74d81dd88ba760856b Objectives . This paper explores teacher readiness for introducing artificial intelligence (AI) into Sri Lankan schools, drawing on self-efficacy theory. Similar to some other countries, Sri Lanka plans to integrate AI into the school curriculum soon. However, a key question remains: are teachers prepared to teach this advanced technical subject to schoolchildren? Assessing teacher readiness is crucial, as it is intricately linked to the overall success of this initiative and will inform the development of appropriate policies. Participants . This study surveyed over 1,300 Sri Lankan public school teachers who teach Information and Communication Technology (ICT) using the snowball sampling approach. The respondents represent approximately 20% of the total ICT teacher population in Sri Lanka. Their readiness to teach AI was assessed using a general teacher self-efficacy scale specifically developed based on the well-established Self-Efficacy Theory. While key demographic factors like gender, education level, and educational background were also collected, their impact analysis is not included in this paper. Study Method . The study was conducted based on the premise that teachers' readiness to teach AI hinges on their self-efficacy towards teaching AI in the classroom. This premise was substantiated through a review of existing research, and a conceptual model of teachers’ self-efficacy for AI instruction was developed. To assess this model, a nationwide survey targeting school ICT teachers was conducted. The questionnaire used in the survey was based on existing research on evaluating teacher self-efficacy. Data analysis, focusing on testing and validating the conceptual model, primarily employed the PLS-SEM approach. Findings . This study identified several key findings: 1) Teachers generally reported low self-efficacy regarding their ability to teach AI, 2) Teachers' self-efficacy was most influenced by their emotional and physiological states, as well as their imaginary experiences related to teaching AI, 3) Surprisingly, mastery experiences had a lesser impact on their self-efficacy for teaching AI, and 4) Neither vicarious experiences (observing others teach AI) nor verbal persuasion had a significant impact on teachers' self-efficacy. Additionally, the study revealed that the teachers' own level of expertise in AI, along with their social capital, is insufficient to deliver a standard AI curriculum. Conclusions . The analysis of the results found that Sri Lankan teachers currently lack the readiness to teach AI in schools effectively. Potential lapses in certain sources of self-efficacy were also identified. It further revealed the need for a more systemic approach to teacher professional development. Therefore, the study recommends further research exploring the potential of incorporating a socio-technical systems perspective into the government’s teacher training initiatives. ACM Transactions on Computing Education431Sri Lankan teachers currently lack the readiness to teach AI in schools effectively, and a conceptual model of teachers’ self-efficacy for AI instruction was developed, drawing on self-efficacy theory.ArXiv["Chathura Rajapakse", "Wathsala Ariyarathna", "Shanmugalingam Selvakan"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/09415ad1f73cbfe4d8158c74d81dd88ba760856b ae69aaf8e36fe83db5026b52265fa4af5f14407a Purpose This paper aims to systematically review the application of artificial intelligence (AI) in the tourism industry. By integrating human–computer interaction, machine learning, big data and other relevant technologies, the study establishes a comprehensive research framework that explores the systematic connections between AI and various facets of tourism. Design/methodology/approach This paper conducts a keyword co-occurrence analysis of 4,048 articles related to AI in tourism. The analysis identifies and classifies dominant topics, which are further refined through thematic literature review and manual coding for detailed discussion. Findings The analysis reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI in destination management, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework. Each topic is reviewed to construct an integrated discussion that maps the current landscape and suggests directions for future research. Originality/value This paper transcends the fragmented discourse commonly found in the literature by establishing a unified framework that not only enhances understanding of the existing methodologies, theories and applications of AI in tourism but also identifies critical areas for breakthroughs, aiming to inspire a more humane and sustainable integration of AI in the tourism industry. The Tourist Review1311A keyword co-occurrence analysis of 4,048 articles related to AI in tourism reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework.Tourism Review["Yanzheng Tuo", "Jiankai Wu", "Jingke Zhao", "Xuyang Si"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/ae69aaf8e36fe83db5026b52265fa4af5f14407a c21ac9e47edfb9e35eabf2c5f93f1b699c450857Artificial Intelligence (AI) is transforming the way businesses interact with customers, and that is leading to elevated Customer Experience (CX). This article is mainly talking about how AI has the positive impacts on businesses and consumers in the era of AI. In addition, it discusses and highlights how the AI modern technologies, such as; Natural Language Processing (NLP), sentiment analysis and predictive analytics are being integrated into customer service to enhance personalized interaction with customers. Furthermore, it shed the light on the AI role for creating more efficient and engaging customer journey through chatbots which automate routine tasks and recommend tailored products.International Journal of Innovative Science and Research Technology491How AI has the positive impacts on businesses and consumers in the era of AI is discussed and highlights how the AI modern technologies are being integrated into customer service to enhance personalized interaction with customers.International Journal of Innovative Science and Research Technology (IJISRT)["Abdullah Zarie", "Faisal Aljohani", "Mohammed Al-Harbi"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/c21ac9e47edfb9e35eabf2c5f93f1b699c450857 effce86dd02f332d3bcf56a4d908ab5af158ae64This study investigates the relationship between artificial intelligence (AI), industrial robots, and renewable energy consumption, driven by the rapid technological advancements and widespread adoption of AI tools in various industries. This research aims to evaluate the environmental implications of these technologies, specifically their impact on renewable energy usage. Employing a comprehensive analytical framework, this study utilizes advanced methodologies, including regularization factors, to accurately estimate the effects of these variables. Through a thorough data analysis, the research quantifies how AI and industrial robots influence the shift towards renewable energy sources. The findings reveal that investments in AI significantly enhance renewable energy consumption, as demonstrated by both conventional estimation techniques and those that integrate regularization factors. Conversely, the use of industrial robots is found to have a detrimental effect on renewable energy consumption. These results have important implications for policymakers, industry leaders, and sustainability researchers. This study encourages policymakers and investors to prioritize funding for AI solutions that promote renewable energy adoption, while it advises industry managers to strategically modify their use of industrial robots to reduce their environmental impact. Ultimately, this research lays a critical foundation for future inquiries and policy initiatives aimed at aligning technological advancements with sustainable energy practices.Energies522Investing in AI significantly enhance renewable energy consumption, as demonstrated by both conventional estimation techniques and those that integrate regularization factors, while the use of industrial robots is found to have a detrimental effect on renewable energy consumption.Energies["Gabriela Badareu", "Marius Dalian Doran", "Mihai Alexandru Firu", "I. Croitoru", "Nicoleta Mihaela Doran"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/effce86dd02f332d3bcf56a4d908ab5af158ae64 1c6acf0cf8c347dd16cc6cc162dff720ab39e39ePurposeThe paper focuses on bridging the existing literature gap on the role of leadership in influencing employee engagement considering the advancement in technologies. With this, the author explores how the three critical elements of service-based companies' business environment-artificial intelligence (AI) success, employee engagement, and leadership are interlinked and are valuable for raising the engagement level of employees.Design/methodology/approachA purposive sampling strategy was used to select the employees working in the respective companies. The survey was distributed to 150 senior management employees but responses were received from only 56 employees making the response rate 37.33%. Consequently, an empirical examination of these 56 senior management employees belonging to service-based companies based in Delhi NCR using a survey questionnaire was conducted.FindingsThe PLS-SEM (partial least squares structured equation modelling) revealed that AI has a positive role in affecting employee engagement levels and confirmed the mediation of leadership. The magnitude of the indirect effect was negative leading to a reduction in total effect magnitude; however, as the indirect effect model has a higher R square value, the inclusion of a mediating variable made the model more effective.Research limitations/implicationsThis study contributes to extending the existing knowledge of the academicians about the relationship theory of leadership, AI implementation in organizations, AI association with leadership and AI impact on employee engagement. The author extends the theoretical understanding by showing that more integration of AI-supported leadership could enable organizations to enhance employee experience and motivate them to be engaged. Despite its relevance, due to the limited sample size, focus on a specific geographic area (Delhi NCR) and the constraint of only using quantitative analysis, the findings open the scope for future research in the form of qualitative and longitudinal studies to identify AI-supported leadership roles.Practical implicationsThe study findings are beneficial majorly for organizations to provide them with more in-depth information about the role of AI and leadership style in influencing employee engagement. The identified linkage enables the managers of the company to design more employee-tailored strategies for targeting their engagement level and enhancing the level of productivity of employees. Moreover, AI-supported leadership helps raise the productivity of employees by amplifying their intelligence without making technology a replacement for human resources and also reducing the turnover rate of employees due to the derivation of more satisfaction from existing jobs. Thus, given the economic benefit and societal benefits, the study is relevant.Originality/valueThe existing studies focused on the direct linkage between AI and employee engagement or including artificial intelligence as a mediating variable. The role of leadership is not evaluated. The leadership enables supporting the easy integration of AI in the organization; therefore, it has an important role in driving employee engagement. This study identifies the contribution of leadership in organizations by providing the means of enhancing employee satisfaction without hampering the social identity of the company due to the integration of AI.Management Decision812The author shows that more integration of AI-supported leadership could enable organizations to enhance employee experience and motivate them to be engaged, and opens the scope for future research in the form of qualitative and longitudinal studies to identify AI-supported leadership roles.Management Decision["Divya Divya", "Riya Jain", "Priya Chetty", "Vikash Siwach", "Ashish Mathur"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/1c6acf0cf8c347dd16cc6cc162dff720ab39e39e a1278112e9b7e7d7213af63dcc435217da6da6b0This mini-review explores the integration of Artificial Intelligence (AI) within hernia surgery, highlighting the role of Machine Learning (ML) and Deep Learning (DL). The term AI incorporates various technologies including ML, Neural Networks (NN), and DL. Classical ML algorithms depend on structured, labeled data for predictions, requiring significant human oversight. In contrast, DL, a subset of ML, generally leverages unlabeled, raw data such as images and videos to autonomously identify patterns and make intricate deductions. This process is enabled by neural networks used in DL, where hidden layers between the input and output capture complex data patterns. These layers’ configuration and weighting are pivotal in developing effective models for various applications, such as image and speech recognition, natural language processing, and more specifically, surgical procedures and outcomes in hernia surgery. Significant advancements have been achieved with DL models in surgical settings, particularly in predicting the complexity of abdominal wall reconstruction (AWR) and other postoperative outcomes, which are elaborated in detail within the context of this mini-review. The review method involved analyzing relevant literature from databases such as PubMed and Google Scholar, focusing on studies related to preoperative planning, intraoperative techniques, and postoperative management within hernia surgery. Only recent, peer-reviewed publications in English that directly relate to the topic were included, highlighting the latest advancements in the field to depict potential benefits and current limitations of AI technologies in hernia surgery, advocating for further research and application in this evolving field.Journal of Abdominal Wall Surgery351Significant advancements have been achieved with DL models in surgical settings, particularly in predicting the complexity of abdominal wall reconstruction (AWR) and other postoperative outcomes, which are elaborated in detail within the context of this mini-review.Journal of Abdominal Wall Surgery["R. Vogel", "B. M\u00fcck"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/a1278112e9b7e7d7213af63dcc435217da6da6b0 26e7fdae21abc8ab1c1be8a09e014d9fa597fad1Chronic kidney disease (CKD) is a complex disease that is related not only to dialysis but also to the onset of cardiovascular disease and life prognosis. As renal function declines with age and depending on lifestyle, the number of patients with CKD is rapidly increasing in Japan. Accurate prognosis prediction for patients with CKD in clinical settings is important for selecting treatment methods and screening patients with high-risk. In recent years, big databases on CKD and dialysis have been constructed through the use of data science technology, and the pathology of CKD is being elucidated. Therefore, we developed an artificial intelligence (AI) system that can accurately predict the prognosis of CKD such as its progression, the timing of dialysis introduction, and death. Aiming for its social implementation, the prognosis prediction system developed for patients with CKD was released on the website. We then developed a clinical practice guideline creation support system called Doctor K as an AI system. When creating clinical practice guidelines, huge amounts of manpower and time are required to conduct a systematic review of thousands of papers. Therefore, we developed a natural language processing (NLP) AI system to significantly improve work efficiency. Doctor K was used in the preparation of the guidelines of the Japanese Society of Nephrology. Furthermore, by comparing and analyzing the medical word virtual space constructed by the NLP AI system based on patient big data, we proved using the latest mathematical theory (category theory) that this system reflects the pathology of CKD. This suggests the possibility that the NLP AI system can predict patient prognosis. We hope that, through these studies, the use of AI based on big data will lead to the development of new treatments and improvement in patient prognosis.JMA Journal250By comparing and analyzing the medical word virtual space constructed by the NLP AI system based on patient big data, it is proved using the latest mathematical theory (category theory) that this system reflects the pathology of CKD, suggesting the possibility that the NLP AI system can predict patient prognosis.JMA Journal["Eiichiro Kanda"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/26e7fdae21abc8ab1c1be8a09e014d9fa597fad1 a59685b55f1e79e8e659388923d009db378344ebThis study investigates how repo rates and the nominal effective exchange rates affect stock market capitalization between 2016M1 and 2022M1, with a focus on the threshold level of investment in artificial intelligence. To analyze the data, econometrics techniques were employed. The Augmented Dickey-Fuller test confirmed that while market capitalization and repo rates became stationary after the first difference that of investments in artificial intelligence and the nominal effective exchange rates were stationary at level. A threshold model identified the threshold level of investment in artificial intelligence. The findings indicate that repo rates and the nominal effective exchange rates positively influence stock market capitalization when the threshold level of investment in artificial intelligence is below 7.7647. However, above the threshold, repo rates and the nominal effective exchange rates negatively affect stock market capitalization. This study concludes that the negative impact of the repo rates and the nominal effective exchange rates on stock market capitalization at higher levels of investment in artificial intelligence should be internalized through government subsidies that reduce the production cost of firms. However, the SARB should consistently manage the exchange rates from growing out of proportion.International Journal of Economics and Financial Issues00It is concluded that the negative impact of the repo rates and the nominal effective exchange rates on stock market capitalization at higher levels of investment in artificial intelligence should be internalized through government subsidies that reduce the production cost of firms.International Journal of Economics and Financial Issues["Opeyemi Aromolaran", "N. Ngepah"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/a59685b55f1e79e8e659388923d009db378344eb ec196287291afd28c09def3e0a70a9b0c5367d5cFinancial Innovation490Results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions.Financial Innovation["C. Urom", "G. Ndubuisi", "Hela Mzoughi", "Khaled Guesmi"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/ec196287291afd28c09def3e0a70a9b0c5367d5c 9923f297d0c815490fce0a2e9751da742eaa5899Artificial intelligence literacy is a comprehensive set of skills, knowledge, and ethical considerations that are essential for the responsible and efficient integration of artificial intelligence into daily activities. Nevertheless, there is currently a lack of a comprehensive framework that enables the comprehensive analysis of all aspects of digital stories in a broad sense. By providing an analytical framework that allows the authors to evaluate digital stories generated by students from a variety of perspectives, this study endeavors to address this gap. The authors employ this paradigm to illustrate how learners can leverage the diverse modalities of digital tales to improve their comprehension of the curriculum, while simultaneously creatively expressing their identities and perspectives. Throughout their involvement in AI learning and digital story writing activities, the interviews with students were designed to investigate their perspectives on learning.Psychology, Evaluation, and Technology in Educational Research00This study provides an analytical framework that allows the authors to evaluate digital stories generated by students from a variety of perspectives and illustrates how learners can leverage the diverse modalities of digital tales to improve their comprehension of the curriculum, while simultaneously creatively expressing their identities and perspectives.Psychology, Evaluation, and Technology in Educational Research["Dzul Rachman", "K. Khatimah", "Taghfirul Azhima Yoga Siswa", "Azzahra Namira Putri", "Reza June Sidiq"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/9923f297d0c815490fce0a2e9751da742eaa5899 770e85d3a8d17386ce9273a9e68262dc33d3b9eaLung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.Frontiers in Oncology630This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis.Frontiers in Oncology["Di Yang", "Yafei Miao", "Changjiang Liu", "Nan Zhang", "Duo Zhang", "Qiang Guo", "Shuo Gao", "Linqian Li", "Jianing Wang", "Si Liang", "Peng Li", "Xuan Bai", "Ke Zhang"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/770e85d3a8d17386ce9273a9e68262dc33d3b9ea 57ca6fe2753d21c9f39257b7c522e64e069867c1 Purpose This study aims to investigate the relationship between the adoption of human resource (HR) analytics and managerial decision-making (DM), with attitude toward artificial intelligence (AI) as a potential moderator. Design/methodology/approach This study was conducted in three phases. In Phase I, a comprehensive scale to measure the “Adoption of HR analytics” was conceptualized and developed. In Phase II, the scale was validated and operationalized. Finally, in Phase III, a survey of 377 managers was conducted, and a conceptual model was validated using structural equation modeling. Findings This study reveals that the adoption of HR analytics (HRA) and a positive attitude toward AI significantly influence DM. The findings suggest that the structural factors play the most important role in the adoption of HRA, followed by individual factors, value and system support. Practical implications These findings hold valuable implications for managers seeking integration of HRA and AI within organizational systems and processes. HR practitioners can evaluate their organization’s readiness for HRA, enabling them to build a future-proof workforce with the necessary skills. It can help managers make the adoption of AI-enabled HRA a reality. The study also helps to remove inhibitions and concerns of HR managers and employees related to AI. Originality/value This paper addresses the methodological, practical knowledge and evidence gap in the area of adoption of HRA and DM. It sheds light on the “future of work” in HR, highlighting a potential shift toward human-AI collaboration. The International Journal of Organizational Analysis420The findings suggest that the structural factors play the most important role in the adoption of HRA, followed by individual factors, value and system support, and a positive attitude toward AI significantly influence DM.International Journal of Organizational Analysis["Simple Arora", "Priya Chaudhary", "Reetesh K. Singh"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/57ca6fe2753d21c9f39257b7c522e64e069867c1 894541bb18a8e166c7986e254bde9ec8dfd01a99This paper investigates emerging global health concerns and the increasing prevalence of health issues among individuals. It explores the potential of artificial intelligence (AI) to positively impact personal lifestyles and promote healthier living. The study also delves into the integration of AI in healthcare, emphasizing its role in enhancing health outcomes while ensuring user privacy and data security. Through a comprehensive analysis, this paper highlights the transformative capabilities of AI in fostering a healthier societyInternational Journal For Multidisciplinary Research10Through a comprehensive analysis, this paper highlights the transformative capabilities of AI in fostering a healthier society.International Journal For Multidisciplinary Research["Anand Babu"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/894541bb18a8e166c7986e254bde9ec8dfd01a99 ae221cbf3202e7878fbd9b7dccd23036ccea14c7Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Society” embarks on a comprehensive journey through the transformative landscape shaped by Artificial Intelligence (AI). This abstract offers a nuanced examination of AI’s profound implications, ranging from its disruptive potential to its ethical complexities, and the societal imperatives it underscores. Delving into the evolution of AI technologies, from foundational concepts to cutting-edge innovations, the abstract elucidates their growing significance across diverse sectors including healthcare, finance, transportation, and beyond. However, amidst the promises of efficiency and innovation, ethical concerns loom large. The abstract navigates through the ethical intricacies of AI, addressing issues of bias, accountability, and the erosion of privacy. Furthermore, it ventures into the societal ramifications of AI adoption, exploring its impact on employment dynamics, socioeconomic disparities, and the fabric of human interaction. By interrogating the dual narratives of promise and peril, this abstract offers a holistic understanding of AI’s role in shaping the future of society. It underscores the imperative for thoughtful governance, ethical stewardship, and inclusive decision-making to ensure that AI serves as a force for collective progress rather than exacerbating existing divides. Through its interdisciplinary lens, this abstract serves as a roadmap for stakeholders across academia, industry, and policymaking to navigate the complex terrain of AI’s integration into society, ultimately paving the way for a future where AI augments human capabilities while upholding fundamental values of fairness, equity, and justice.2024 3rd International Conference for Advancement in Technology (ICONAT)170This abstract offers a nuanced examination of AI’s profound implications, ranging from its disruptive potential to its ethical complexities, and the societal imperatives it underscores, paving the way for a future where AI augments human capabilities while upholding fundamental values of fairness, equity, and justice.2024 3rd International Conference for Advancement in Technology (ICONAT)["S. Pragith", "S. Supriya", "S. Sasikala", "Rayala Sateesh", "Hemanth Swamy", "P. Karpagam"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/ae221cbf3202e7878fbd9b7dccd23036ccea14c7 da1abb4d353f8df9d581af0573092d3b02ab0f11PurposeThe research paper’s purpose is to contribute to the literature by analysing the essential resources and processes required for successful commercialisation, the contemporary challenges and opportunities of artificial intelligence initiatives in Türkiye, and the diverse models and methods employed by these initiatives.Design/methodology/approachWithin the scope of the research, interviews were conducted with 10 entrepreneurs who established artificial intelligence-oriented enterprises in technoparks in Istanbul and Antalya. All 10 interviews were analysed using the MAXQDA20 software tool. Structured qualitative content analysis was used for the data analysis procedure.FindingsBased on the research, external factors have a significant impact on the future growth opportunities of the market. Expanding the client base, gaining international recognition, and securing financing are crucial for success. However, the findings reveal challenges in the relatively young local ecosystem. One major criticism is the lack of support in marketing and sales activities for refined products. To address this, providing financial incentives and knowledge transfer to those in need is vital.Research limitations/implicationsSince the research was conducted only with entrepreneurs who established and successfully commercialised artificial intelligence-oriented enterprises, it is recommended that future studies be performed with a widespread sample group, considering this limited situation. Furthermore, to overcome survivorship bias, it is recommended that posterior studies include failed commercialisation attempts in AI ventures.Practical implicationsIt can be argued that there is no deliberate approach or model for commercialization. Entrepreneurs often draw from their own prior experiences or observe industry trends. Given the limited financial resources available in the domestic market and the challenge of attracting foreign investors to Turkish brands, entrepreneurs tend to rely on internal approaches for commercialisation.Originality/valueThis research delves into the commercialisation prospects and obstacles encountered by AI start-ups in Türkiye. It comprises qualitative insights into business models, commercialisation approaches, opportunities, and challenges. The data were obtained from interviews with entrepreneurs operating in the industry.Business Process Management Journal500This research delves into the commercialisation prospects and obstacles encountered by AI start-ups in Türkiye and comprises qualitative insights into business models, commercialisation approaches, opportunities, and challenges.Business Process Management Journal["Duygu G\u00fcner G\u00fcltekin", "Fatih P\u0131narba\u015f\u0131", "Merve Yazici", "Zafer Adiguzel"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/da1abb4d353f8df9d581af0573092d3b02ab0f11 043f49bd20076739404700deed81974c600dc8b5The rapid advancement of emerging technologies has brought about unprecedented opportunities for empowerment across various domains. This research paper delves into the transformative potential of technologies such as artificial intelligence (AI), Internet of Things (IoT), big data analytics, and cloud computing in enabling access to information, resources, and opportunities. It examines how these technologies can contribute to economic empowerment, social inclusion, and overall human development. The paper explores the applications of AI in sectors like healthcare, education, and finance, highlighting its ability to provide personalized solutions and enhance access to services. Additionally, it investigates the role of IoT in enabling smart cities, connected healthcare, and efficient resource management, empowering individuals and communities through data-driven decision-making. The paper also discusses the applications of big data analytics in sectors like healthcare, education, and disaster management, and how it can provide insights into societal challenges and inform policy decisions. Furthermore, it explores the potential of cloud computing in democratizing technology, fostering innovation, and enabling access to resources and services for individuals and organizations. However, the paper also addresses the challenges associated with the adoption of emerging technologies, such as the digital divide, privacy concerns, and ethical implications, emphasizing the need for responsible and inclusive technological advancements. Overall, this research paper highlights the transformative potential of emerging technologies in empowering individuals and communities, while also acknowledging the challenges and ethical considerations that must be addressed for a more equitable and sustainable future.Computer Science, Engineering and Technology30The paper explores the applications of AI in sectors like healthcare, education, and finance, highlighting its ability to provide personalized solutions and enhance access to services, and explores the potential of cloud computing in democratizing technology, fostering innovation, and enabling access to resources and services for individuals and organizations.Computer Science, Engineering and Technology["Goldi Soni"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/043f49bd20076739404700deed81974c600dc8b5 62476d9b145932a2b3857eac7ea3a2c7c63ead44The article examines the effectiveness of artificial intelligence (AI) technologies in an enterprise’s business processes. In the beginning, the authors consider the essence of artificial intelligence and determine the theoretical basis of its impact on the activities of enterprises. Next, we analyse the dynamics and structure of the AI market. We determine AI as one of the most promising areas implemented in the companies’ business processes, allowing them to obtain significant savings in labour and financial resources. We provide information on the use of AI in enterprises’ activities by the economic sectors. The study establishes that AI is gaining traction in all sectors of the economy, and most of all in healthcare, manufacturing, and finance. Today, AI technologies create new opportunities for companies to provide them with broad powers in various industries. After all, every process implementing AI optimises costs and positively impacts the overall financial performance. We specify that companies need to develop a collaboration of people and technology that will complement each other and have a strong union of knowledge, speed, experience, and skills. The study shows that the introduction of AI has a positive impact on the level of profitability of companies because, with the popularisation of AI in 2022, more global companies began to implement these technologies in their business processes, and companies that use these technologies became in demand in the market, which in turn had a positive impact on profit growth. The study resulted in proposals for using artificial intelligence technologies in the business processes of Ukrainian enterprises. By implementing AI in their business processes, enterprises will receive significant savings in their resources, both human and financial. It is necessary to note that the effectiveness of AI will depend on its collaboration with humans; the technology can be a good solution in a situation where artificial intelligence handles some of the functions related to the processing of a data set, and people use the results obtained in this way as the basis for final decision-making. Keywords: artificial intelligence, business process, enterprise, technology, management.Municipal economy of cities70The introduction of AI has a positive impact on the level of profitability of companies because, with the popularisation of AI in 2022, more global companies began to implement these technologies in their business processes, and companies that use these technologies became in demand in the market, which in turn had a positive impact on profit growth.Municipal economy of cities["O. Dymchenko", "N. Matveeva", "Ye. Kozyr"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/62476d9b145932a2b3857eac7ea3a2c7c63ead44 1cf9ee698619c15c55d9160c5fcbce2d69aafa29 Artificial intelligence embodies the ability of computers to emulate human intelligence and generate well-informed choices. Quality within the healthcare domain encompasses adopting proficient, patient-centric, secure, and productive services that are unbiased, comprehensive, punctual, and streamlined. In this regard, this study aimed to investigate the impact of artificial intelligence on healthcare quality. This study echoed the World Health Organization’s findings that artificial intelligence has great potential for distributed clinical automation, delivering efficient clinical information, and offering extra support in healthcare settings. This systematic review employed PRISMA methodology and inclusion and exclusion criteria to search through central databases exploring the impact of artificial intelligence on healthcare quality. Specifically, this study concentrated on randomized controlled trials published in PubMed. The search process employed Boolean operators (AND) and (OR) and the main keywords detailed in the methodological section. As a result, two thousand five hundred forty-four sources were identified. The identified sources underwent a rigorous screening process, which entailed the removal of duplication. These eligibility criteria considered studies published in the English language, availability of full text, thorough description of the research aims, objectives, methodology, findings, and conclusion, the number of references, and general presentation. Out of 2544 identified sources, only 18 sources passed the eligibility criteria and were included in this research. The Meta-analysis was conducted using RevMan 5, Mantel-Haenszel, random effect, and 95% confidence intervals. Overall, the studies were substantially heterogeneous at I2=92%, Z score was 1.93, and the P-value was within the range of less than or equal to 5. Therefore, the general studies provided a significant positive impact of artificial intelligence on healthcare quality. The heterogeneity was minimized through subgroup analysis, where the studies were divided about the objectives. Generally, 6/18 studies yielded an odd ratio of more than 1, reflecting the positive influence of artificial intelligence on healthcare quality. 12/18 studies positively used artificial intelligence in assisted healing or medication adherence, but none were statistically significant. Artificial intelligence does not directly influence healthcare quality but helps improve other functions within healthcare services. Healthcare quality is comprehensive, encompassing evidence-based practice, patient-centric care, effective communication, care coordination, effective risk management strategies, health information technology, health promotion, and disease prevention. The Open Public Health Journal390This study echoed the World Health Organization’s findings that artificial intelligence has great potential for distributed clinical automation, delivering efficient clinical information, and offering extra support in healthcare settings by concentrating on randomized controlled trials published in PubMed.The Open Public Health Journal["Bashar I. Alzghoul"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/1cf9ee698619c15c55d9160c5fcbce2d69aafa29 c1616a2f13679a4ec029c712f961f726dcd82683Few researchers provide a wider vision of artificial feet, hands, mouths, eyes, ears, and brains. This limits our vision of them and their significant impacts on the modern Industrial Revolution and Artificial Intelligence (AI) history.  This article presents a novel perspective on human-centered social development starting from artificial feet. After briefly reviewing AI, this article explores the age of AI and artificial feet, hands, mouths, eyes, ears, and brains. It also applies AI to artificial feet and artificial brains. The research reveals that artificial feet are one of the origins of the Industrial Revolution and a real foundation of AI. The study demonstrates that artificial feet and brains liberate our body and society, whereas from artificial brains to artificial feet is control of our body and society. This article also looks at AI's trends and challenges. The approach in this article will facilitate the research and development of big data, analytics, and intelligences.Journal of Computer Science Research290The research reveals that artificial feet are one of the origins of the Industrial Revolution and a real foundation of AI, and the study demonstrates that artificial feet and brains liberate the authors' body and society, whereas from artificial brains to artificial feet is control of their body and society.Journal of Computer Science Research["Zhaohao Sun", "Xuehui Wei"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/c1616a2f13679a4ec029c712f961f726dcd82683 92dcdcfe63d91c4637be456773631a9e748f866d This article examines the transformative potential of artificial intelligence (AI) in asset management, highlighting how AI can enhance research, decision-making, communication, and trading processes. AI, particularly through machine learning (ML) and generative models, can significantly reduce analysts’ time on data collection and analysis, offer standardized recommendations, and improve communication efficiency. However, risks include potential biases and a lack of transparency in AI-driven decisions.The Economists' Voice10The Economists’ Voice["Philipp Immenk\u00f6tter"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/92dcdcfe63d91c4637be456773631a9e748f866d 7e99b96d526dd529f671b85dffa452ebdb92a1ccThe purpose of research is to form directions of artificial intelligence technologies’ implementation at economy of Ukraine and Poland. To define and analyze literature streams about artificial intelligence technologies’ implementation in economy it is used methods of comparison, analysis, synthesis. The SWOT analysis method is employed to identify the strengths, weaknesses, opportunities, and threats associated with the implementation of artificial intelligence technologies in Poland and Ukraine. Brainstorming and modeling methods are applied to develop strategic directions for the implementation of AI technologies in both countries. The SWOT analysis of artificial intelligence implementation in Poland and Ukraine reveals distinct characteristics. Poland's artificial intelligence environment is driven by legislative regulation and substantial startup funding, while Ukraine's environment relies on the performance of foreign companies' tasks and university project activities. The study establishes that the background of artificial intelligence development in a country is a result of government agenda and startup activities. The research contributes to the identification and understanding of potential pathways for the utilization of artificial intelligence in the economies of Poland and Ukraine for national development.Сталий розвиток економіки180The study establishes that the background of artificial intelligence development in a country is a result of government agenda and startup activities, and contributes to the identification and understanding of potential pathways for the utilization of artificial intelligence in the economies of Poland and Ukraine for national development.Сталий розвиток економіки["\u0421\u0432\u0456\u0442\u043b\u0430\u043d\u0430 \u0422\u0430\u0440\u0430\u0441\u0435\u043d\u043a\u043e", "\u0412\u043e\u0439\u0446\u0435\u0445 \u0414\u0443\u0440\u0430\u043d\u043e\u0432\u0441\u043a\u0456", "\u0410\u0440\u0442\u0435\u043c \u0411\u0456\u043b\u043e\u0432\u043e\u043b", "\u0417\u0431\u0456\u0433\u043d\u0454\u0432 \u0414\u0430\u0431\u0440\u043e\u0432\u0441\u043a\u0456"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/7e99b96d526dd529f671b85dffa452ebdb92a1cc f7d055581fe3749026fdcbf18c699967551a2a62This paper investigates the revolutionary influence of data analysis and artificial intelligence (AI) in the maritime sector, with a focus on cargo handling, ship route planning, and fuel efficiency optimisation. By integrating modern data analytics, cargo operations may be monitored and managed in real-time, which improves safety measures, decreases operational delays, and increases inventory management accuracy. AI-driven algorithms optimise ship route planning by analysing large datasets such as weather patterns and marine traffic, reducing travel time and operational expenses. Furthermore, predictive analytics and machine learning models are used to improve fuel efficiency by optimising engine performance and detecting maintenance issues before they cause costly downtime. This paper conducts a thorough analysis of these technologies' uses, assessing their influence on operational efficiency, cost savings, and environmental sustainability. The paper emphasises the crucial role of data analysis and AI in revolutionising old marine processes, eventually propelling the industry towards a more efficient and ecologically conscious future, through a series of case studies.REST Journal on Data Analytics and Artificial Intelligence70The crucial role of data analysis and AI in revolutionising old marine processes is emphasised, eventually propelling the industry towards a more efficient and ecologically conscious future, through a series of case studies.REST Journal on Data Analytics and Artificial Intelligence["K. Sivasami", "S. Thangalakshmi", "Atharva Bhoite", "Harsh Soni", "Krishna Seth"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/f7d055581fe3749026fdcbf18c699967551a2a62 d9cc7a1c192a494dec4022fde429ec3c1ba68838Artificial intelligence (AI) represents a multidisciplinary field aimed at automating tasks that traditionally require human intelligence. This paper explores the evolution, methodologies, applications, and challenges of AI in the domains of data science and data analytics. Key AI techniques such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision are discussed, alongside their applications in various sectors including healthcare, finance, customer service, marketing, autonomous vehicles, manufacturing, and cyber security. The review also highlights current research challenges and future trends in AI and data analytics.REST Journal on Data Analytics and Artificial Intelligence10Key AI techniques such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision are discussed, alongside their applications in various sectors including healthcare, finance, customer service, marketing, autonomous vehicles, manufacturing, and cyber security.REST Journal on Data Analytics and Artificial Intelligence["M. Sivamani", "P. Sathya", "R. Narmadha"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/d9cc7a1c192a494dec4022fde429ec3c1ba68838 6f6e179da67efc74f66753739b791410bc3a63eeThe Lancet Regional Health - Europe785This Personal View addresses the latest advancements in automatic text analysis with artificial intelligence in medicine, with a focus on its implications in aiding treatment decisions in medical oncology, and advocates for a forward-looking approach in which the community efficiently initiates global prospective clinical evaluations of promising AI-based decision support systems.The Lancet Regional Health - Europe["L. Verlingue", "Clara Boyer", "L. Olgiati", "Cl\u00e9ment Brutti Mairesse", "Daphn\u00e9 Morel", "Jean-Yves Blay"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/6f6e179da67efc74f66753739b791410bc3a63ee 3ae707ffad78303da3f065435614c304b15bc40bUltraschall in der Medizin141Ultraschall in der Medizin["Bernardo Stefanini", "Alice Giamperoli", "Eleonora Terzi", "F. Piscaglia"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/3ae707ffad78303da3f065435614c304b15bc40b 2cf9f0d1449a6f7a3942fcd3b8fefba5a1510188This research aims to use Neural Prophet, a deep learning tool, to predict stock prices in the banking sector with high accuracy and useful insights. The model's capability in managing intricate temporal patterns differentiates it, garnering attention from researchers. The significance of this research lies in its potential to enhance stock price prediction precision, especially in the context of banking stocks, offering stakeholders’ deeper insights. The model's efficacy spans stable and volatile market behaviours, making it a valuable tool for informed decision-making in finance. Accurate predictions benefit risk management, facilitating well-informed investment choices in dynamic markets.The International Journal of Financial Systems01The model's efficacy spans stable and volatile market behaviours, making it a valuable tool for informed decision-making in finance, and accurate predictions benefit risk management, facilitating well-informed investment choices in dynamic markets.The International Journal of Financial Systems["T. R. Noviandy", "Irsan Hardi", "Ghalieb Mutig Idroes"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/2cf9f0d1449a6f7a3942fcd3b8fefba5a1510188 cd3f87496d7ce2a02984409cca914b8e5e6da446Learned Publishing141Learn. Publ.["B. Gulumbe"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/cd3f87496d7ce2a02984409cca914b8e5e6da446 a039410f0d70466477118619e44782ef315f8c15The Veterinary Record01The Veterinary record["Claire Read"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/a039410f0d70466477118619e44782ef315f8c15 030a74f8eb74504b959a6b723820390d35d0b0baEl presente análisis tiene como objetivo explorar la Influencia de la inteligencia artificial en la toma de decisiones judiciales. La investigación se fundamentó en una revisión de 14 artículos científicos, seleccionados con base en su relevancia para el análisis de la IA dentro del sistema judicial. La implementación de la inteligencia artificial en la toma de decisiones judiciales presenta tanto oportunidades como riesgos significativos para los sistemas jurídicos contemporáneos. Si bien la IA tiene el potencial de optimizar los procesos judiciales, reducir tiempos y mejorar la eficiencia, es fundamental que su uso esté regulado de manera estricta para evitar la deshumanización de la justicia y la perpetuación de sesgos en los algoritmos. La subjetividad y la discrecionalidad judicial no pueden ser sustituidas por una máquina, y la protección de los derechos fundamentales, como la privacidad y la equidad, debe mantenerse como pilar esencial en cualquier proceso automatizado.Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales80Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales["V\u00edctor Javier Macas-Allauca", "Britali Janeila Toro-Quishpe", "Justin Joe Tuquinga-Tuquinga", "Janneth Ximena Iglesias-Quintana"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/030a74f8eb74504b959a6b723820390d35d0b0ba 99333572f6606c8d7613a7b4f14aadde45e7fda7JMA Journal00JMA Journal["Shigeki Matsubara"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/99333572f6606c8d7613a7b4f14aadde45e7fda7 8ccba26d19bff65860fce925d9c47e18bf06c60dCurrent Cardiovascular Risk Reports320Current Cardiovascular Risk Reports["Xiaoxi Yao", "P. Noseworthy"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/8ccba26d19bff65860fce925d9c47e18bf06c60d aa1aeb1431f4fbcefbb6226cf911b4db06f18a93Proceedings of the 2024 International Symposium on Artificial Intelligence for Education50Proceedings of the 2024 International Symposium on Artificial Intelligence for Education["Chunli Tang", "Zehao Liang"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/aa1aeb1431f4fbcefbb6226cf911b4db06f18a93 6936db27f2e2c4840c746deb14e6ee17f9aba009Proceedings of the 2024 International Symposium on Artificial Intelligence for Education130Proceedings of the 2024 International Symposium on Artificial Intelligence for Education["Jingjing Shi", "S. Narasuman", "Huichun Ning", "Gevorg Grigoryan", "Wenxuan Ren"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/6936db27f2e2c4840c746deb14e6ee17f9aba009 3683f19323d84fbc3402406dddd4d0c289de43a3Proceedings of the 2024 International Symposium on Artificial Intelligence for Education230Proceedings of the 2024 International Symposium on Artificial Intelligence for Education["Ting Qu", "Zuguo Yang"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/3683f19323d84fbc3402406dddd4d0c289de43a3 0a5109a8783b1f6e2d015b8010f6a0fbda1f9689cyber-attack against a government 12,13 . Another issue is data bias. During the collection of the data, intentionally or unintentionally, certain minorities, races, ethnicities, or genders may be significantly misrepresented. Therefore, these algorithms are biased and inadequately represent the general population 14,15 . This bias effect could be magnified by the reluctance of medical practitioners, hospitals, or other health organizations, to provide the medical files of their patients due to fears of security leaks. Another significant danger of medical data misuse is the data poisoning effect, which refers to the deliberate manipulation of medical data to introduce errors or biases in healthcare. This has serious consequences on the accuracy and reliability of medical recommendations. This could also affect the outcomes of clinical trials or insurance claims 11 . Finally, when AI uses different epidemiological data models, as was seen during the COVID-19 epidemic, this could lead to different conclusionsPneumon400A significant danger of medical data misuse is the data poisoning effect, which refers to the deliberate manipulation of medical data to introduce errors or biases in healthcare.Pneumon["Nikolaos Siafakas", "E. Vasarmidi"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/0a5109a8783b1f6e2d015b8010f6a0fbda1f9689 b00e4ca99767fc42f9690303378ce1612af25d31Proceedings of the 2024 International Symposium on Artificial Intelligence for Education220Proceedings of the 2024 International Symposium on Artificial Intelligence for Education["Xiaoyan Wang", "Zhufeng Chen"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/b00e4ca99767fc42f9690303378ce1612af25d31 069c8dd641b06a0ee36b11706845a6784976cb88Herald of Dagestan State University00Herald of Dagestan State University["M. N. Konyakhin"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/069c8dd641b06a0ee36b11706845a6784976cb88 947e76309d2d38909f42ea659e8c7b2f614ab43aThe integration of robotics and artificial intelligence into medical practice is radically revolutionising patient care. This fusion of advanced technologies with healthcare offers a number of significant benefits, including more precise diagnoses, personalised treatments and improved health data management. However, it is critical to address very carefully the medico-legal challenges associated with this progress. The responsibilities between the different players concerned in medical liability cases are not yet clearly defined, especially when artificial intelligence is involved in the decision-making process. Complexity increases when technology intervenes between a person’s action and the result, making it difficult for the patient to prove harm or negligence. In addition, there is the risk of an unfair distribution of blame between physicians and healthcare institutions. The analysis of European legislation highlights the critical issues related to the attribution of legal personality to autonomous robots and the recognition of strict liability for medical doctors and healthcare institutions. Although European legislation has helped to standardise the rules on this issue, some questions remain unresolved. We argue that specific laws are needed to address the issue of medical liability in cases where robotics and artificial intelligence are used in healthcare.Frontiers in Medicine612It is argued that specific laws are needed to address the issue of medical liability in cases where robotics and artificial intelligence are used in healthcare.Frontiers in Medicine["Francesco De Micco", "Simone Grassi", "L. Tomassini", "Gianmarco Di Palma", "Giulia Ricchezze", "R. Scendoni"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/947e76309d2d38909f42ea659e8c7b2f614ab43a 55970cdf31afa947552a8d64448e3eee7c209454Artificial Intelligence (AI) technologies are increasingly integrated into educational environments, promising transformative impacts on learning experiences and administrative efficiencies. This review synthesizes prominent theoretical frameworks used to understand AI technology adoption among students and educators in educational settings. The Value-based Adoption Model (VAM), Theory of Planned Behavior (TPB), Unified Theory of Acceptance and Use of Technology (UTAUT), and Technology Acceptance Model (TAM) are examined for their strengths and limitations in explaining the factors influencing technology adoption. Through a comprehensive analysis of recent literature, this paper highlights the involvement of user acceptance, incorporating cognitive, social, and emotional dimensions. Understanding theoretical frameworks related to AI technology adoption could provide a comprehensive overview of existing theoretical frameworks related to AI technology adoption in educational settings, integrating findings into a cohesive narrative.Information Management and Business Review262This review synthesizes prominent theoretical frameworks used to understand AI technology adoption among students and educators in educational settings, highlighting the involvement of user acceptance, incorporating cognitive, social, and emotional dimensions.Information Management and Business Review["Roszi Naszariah Nasni Naseri", "Muhammad Syukri Abdullah"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/55970cdf31afa947552a8d64448e3eee7c209454 74356b8779decfb1c2104eb586fd76dce77d1f41Digital technologies are making their mark in medicine, and especially also in art therapy, offering innovative therapeutic interventions for patients, including those with melanoma skin cancer. However, the integration of novel technologies, such as AI-generated art, brings along ethical, psychological, and technical challenges that are viewed differently among therapists. We aim to gauge art therapists’ views on the ethical, application, and challenge facets of utilizing AI-generated art from medical images in therapy. The focus is on assessing its applicability and limitations for melanoma patients. Art therapists were surveyed via a questionnaire focusing on their experience, digital tool familiarity, and views on AI in therapy, encompassing ethics, benefits, challenges, and applicability for melanoma. Art therapists have already implemented digital technologies and acknowledged potential therapeutic benefits of creating personalized artworks with generative artificial intelligence. Attention needs to be given to technological hurdles and the necessity for supplementary interventions. Views on the method’s adaptability varied, underscoring a need for tailored, patient-focused applications. Art therapists are welcoming AI-generated art as a promising creative therapeutic tool and acknowledge potential therapeutic benefits. There are ethical, technical, and psychological challenges that must be addressed for application in therapeutic sessions. Therapists should navigate AI integration with sensitivity, adhering to ethical norms around consent and privacy. Future studies should show the therapeutic benefit in practice with emphasis on equipping therapists to manage the technical complexities effectively. Furthermore, it is important to ensure that patients can influence the AI output, allowing for creative moments in the process.Applied Informatics261Art therapists’ views on the ethical, application, and challenge facets of utilizing AI-generated art from medical images in therapy, encompassing ethics, benefits, challenges, and applicability for melanoma patients are surveyed.AI["Lennart J\u00fctte", "Ning Wang", "Martin Steven", "B. Roth"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/74356b8779decfb1c2104eb586fd76dce77d1f41 4adf0b0dd9e7593772fe57aa768177a154bb405eThis research investigates the use of artificial intelligence and machine learning techniques to predict the toxicity of nanoparticles, a pressing concern due to their pervasive use in various industries and the inherent challenges in assessing their biological interactions. Employing models such as Decision Trees, Random Forests, and XGBoost, the study focuses on analyzing physicochemical properties like size, shape, surface charge, and chemical composition to determine their influence on toxicity. Our findings highlight the significant role of oxygen atoms, particle size, surface area, dosage, and exposure duration in affecting toxicity levels. The use of machine learning allows for a nuanced understanding of the intricate patterns these properties form in biological contexts, surpassing traditional analysis methods in efficiency and predictive power. These advancements aid in developing safer nanomaterials through computational chemistry, reducing reliance on costly and time-consuming experimental methods. This approach not only enhances our understanding of nanoparticle behavior in biological systems but also streamlines the safety assessment process, marking a significant stride towards integrating computational techniques in nanotoxicology.arXiv.org321ArXiv["Iqra Yousaf"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/4adf0b0dd9e7593772fe57aa768177a154bb405e 90ce3fe903db3fe3fb9337d349f01721fd06eebcIn an era characterized by persistent cyberthreats, artificial intelligence (AI) integration into cybersecurity has become a critical tactic to improve threat detection and response capabilities. This study investigates the significant impact of AI on cybersecurity, with an emphasis on how it is developing methods for detecting and countering threats. By leveraging machine learning algorithms, natural language processing, and anomaly detection techniques, artificial intelligence (AI) empowers cybersecurity systems to examine large datasets, identify trends, and proactively mitigate advanced cyberattacks. In order to highlight AI’s efficacy in countering developing threats, this study looks at how it is being applied in a number of cybersecurity sectors, such as network security, endpoint protection, and behavioral analytics. The intricacies and moral dilemmas of AI-powered cybersecurity solutions are also covered, emphasizing the necessity of strong governance structures and ethical AI practices to guarantee efficacy and reduce unforeseen repercussions. The adoption of AI as a cybersecurity force multiplier is ultimately highlighted in this paper, which also advocates for continued research and cooperation to fully realize AI’s promise in protecting digital assets and infrastructure against new threats.2024 3rd International Conference for Advancement in Technology (ICONAT)01The adoption of AI as a cybersecurity force multiplier is ultimately highlighted and the intricacies and moral dilemmas of AI-powered cybersecurity solutions are covered, emphasizing the necessity of strong governance structures and ethical AI practices to guarantee efficacy and reduce unforeseen repercussions.2024 3rd International Conference for Advancement in Technology (ICONAT)["S. JothiShri", "Talluri Upender", "Rajesh Jagadeesan Ravikumar", "Y. Sailaja", "E. Yuvabharathi", "J. Agnestreesa"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/90ce3fe903db3fe3fb9337d349f01721fd06eebc 71e19d2c524a5b4baa0c2431cac6acf934cf0be6This study explores the transformative potential of artificial intelligence (AI) in revolutionizing healthcare through personalized medicine. It delves into the application of predictive diagnostic techniques and the development of individualized treatment plans to enhance patient outcomes. By leveraging AI's capabilities, such as machine learning and data analytics, the research aims to identify patterns and trends that can predict disease susceptibility and progression, leading to more precise and customized interventions. The study highlights the integration of AI with existing healthcare systems, examining its impact on diagnostic accuracy, treatment efficacy, and overall patient care. This investigation offers insights into how AI-driven approaches can reshape the future of healthcare, making it more responsive and tailored to individual needs.Journal of AI-Powered Medical Innovations (International online ISSN: 3078-1930)431This investigation offers insights into how AI-driven approaches can reshape the future of healthcare, making it more responsive and tailored to individual needs.Journal of AI-Powered Medical Innovations (International online ISSN: 3078-1930)["Md. Mafiqul Islam"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/71e19d2c524a5b4baa0c2431cac6acf934cf0be6 583588d7b1a8e406ed14e621b2402a2aad56701dThis research article investigates the latest innovations in AI-driven medical imaging, focusing on how artificial intelligence is revolutionizing diagnostic precision and therapeutic decision-making. The study explores advanced AI techniques that enhance image analysis, enabling earlier and more accurate detection of diseases. By automating complex image interpretation tasks, AI reduces the likelihood of human error and supports more personalized treatment planning. The article also examines the integration of AI in clinical environments, addressing both the opportunities and challenges of incorporating these technologies into existing healthcare systems. Ethical considerations and the potential for AI to transform future medical practices are discussed, highlighting the pivotal role of AI in advancing patient care through improved diagnostic and therapeutic outcomes.Journal of AI-Powered Medical Innovations (International online ISSN: 3078-1930)11The study explores advanced AI techniques that enhance image analysis, enabling earlier and more accurate detection of diseases and the integration of AI in clinical environments, addressing both the opportunities and challenges of incorporating these technologies into existing healthcare systems.Journal of AI-Powered Medical Innovations (International online ISSN: 3078-1930)["Dr. Rahim Ahmadi"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/583588d7b1a8e406ed14e621b2402a2aad56701d 7ad17e81f320d18d98f64aec0d139f80d32223b5Scientific Reports120A new set of most important risk factors, namely “C4C risk factors”, is identified, which is not just for melanoma, but for all types of skin cancer, which is significantly outperforming the 7PCL-based method and the Williams risk factors.Scientific Reports["Shafiqul Islam", "Gordon Wishart", "Joseph Walls", "Per Hall", "Alba G. Seco de Herrera", "John Gan", "Haider Raza"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/7ad17e81f320d18d98f64aec0d139f80d32223b5 29ad2a1fc0618cd93f50d83c58aea985a857d395Since generative tools of Artificial Intelligence appeared in education, ongoing discussion arose. Still, higher education institutions argue if generative tools can be used, and if yes, what exactly can be accepted. The purpose of this study is to investigate for what exactly students use the generative tool Chat GPT in their studies, as well as to determine if there is a statistically significant difference between students representing different fields of study in terms of usage of Chat GPT in general, as well as in evaluation of the knowledge. The objectives of the study are to research recent scientific findings, as well as to analyze the results of the survey created by authors, which was distributed in Latvia, Lithuania, Ukraine, Bulgaria and Uzbekistan. Methods of the study are analysis of the recent findings and statistical analysis of the survey. To test hypotheses, the authors employed the Kruscal-Wallis non-parametric test for both hypotheses, where authors tested if there are statistically significant differences between answers of students from different education fields. The final results highlight the use of Chat GPT by students in higher education.International Scientific Conference „Business and Management“140Investigation for what exactly students use the generative tool Chat GPT in their studies, as well as to determine if there is a statistically significant difference between students representing different fields of study in terms of usage of Chat GPT in general, as well as in evaluation of the knowledge.International Scientific Conference „Business and Management“["J\u016blija Mironova", "Viktoria Riiascshenko", "Andrey Bondarenko", "Remigijus Kinderis", "Olga Verdenhofa"]2024-09-06T00:00:00https://www.semanticscholar.org/paper/29ad2a1fc0618cd93f50d83c58aea985a857d395 affd5fabcc2daf109c8223e580c769421344dd262024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)002024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)[]2024-09-07T00:00:00https://www.semanticscholar.org/paper/affd5fabcc2daf109c8223e580c769421344dd26 78e9e95369462ba991935a126eb7490df2495d3eThyroid issues are among the common diseases in developing and developed countries, therefore early detection with accurate results is essential in managing the condition effectively. In this study, we proposed a thyroid detection model through a comparative analysis of different machine learning algorithms like Decision tree (DT), support vector machine (SVM), and logistic regression (LR) based on thyroid-related features such as thyroid stimulating hormone (TSH), triiodothyronine (T3) and thyroxine (TT4). We examined the performance of our models based on Accuracy, F1 score, Recall, and AUC with a public thyroid dataset from Kaggle. The DT model outperformed the SVM and LR with an AUC of $\mathbf{9 3. 1 9 \%}$, an F1-score of 88.89%, and an accuracy of $\mathbf{9 8. 6 5 \%}$. The accuracy, recall, and F1-score of the SVM and LR were marginally less than the DT. The results emphasize how the diagnosis can be improved by using machine learning techniques of thyroid ailment, despite admitting the small flaws, which include the small dataset and possibly biased preprocessing methods. This study demonstrates the promise of machine learning in supporting thyroid diagnosis. With further development and validation, these models could become valuable tools for healthcare professionals, potentially leading to earlier diagnoses and improved patient care.2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)100The results emphasize how the diagnosis can be improved by using machine learning techniques of thyroid ailment, despite admitting the small flaws, which include the small dataset and possibly biased preprocessing methods.2024 2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings)["Naga Sri Harsha Sankabathula", "Isaac Kofi Nti", "Murat Ozer"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/78e9e95369462ba991935a126eb7490df2495d3e 72463898bd029d9b0a31d18f5f28e25d98bef3ccThe increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments.Machines332An integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments.Machines["Samuel Ayankoso", "Fengshou Gu", "Hassna Louadah", "Hamidreza Fahham", "A. Ball"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/72463898bd029d9b0a31d18f5f28e25d98bef3cc b0cb610dd49303d1ba0bb204c07456d3db04519fThis research investigates the comprehensive acceptance of artificial intelligence (AI) in business management, finance, and e-commerce, focusing on the factors driving its adoption, the obstacles encountered, and strategies for enhancing integration. AI technologies have transformed these sectors, delivering exceptional efficiencies, predictive analytics, and personalized customer experiences. However, their acceptance is influenced by various factors, including technological readiness, organizational culture, and perceived benefits. In business management, AI improves decision-making processes, optimizes operations, and fosters innovation. Financial institutions utilize AI for risk management, fraud detection, and personalized banking services, while the e-commerce sector gains from AI through enhanced customer service, dynamic pricing, and inventory management. Despite these benefits, challenges such as data privacy concerns, high implementation costs, and resistance to change impede widespread adoption. Additionally, ethical considerations and the need for regulatory compliance add layers of complexity. This paper identifies key strategies to address these challenges, such as promoting a culture of innovation, investing in AI education and training, and developing robust data governance frameworks. Strategic partnerships and collaborations with AI experts and tech firms are also essential for navigating the AI landscape. By comprehensively addressing these factors and challenges, businesses can unlock AI's full potential, driving sustainable growth and competitive advantage. This study contributes to understanding AI acceptance in critical sectors, providing a roadmap for successful AI implementation and emphasizing the importance of strategic planning and stakeholder engagement.Social Science Research Network592SSRN Electronic Journal["N. Rane", "Saurabh P. Choudhary", "Jayesh Rane"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/b0cb610dd49303d1ba0bb204c07456d3db04519f 9b1ccd20e70c119032925b3e2e6f1d1b9931ad10The Artificial Intelligence (AI) revolution has become a reality in today’s world and its importance for linguistics was recognized very early. Despite its unprecedent surge and integration into various academic fields including language teaching and translation, surprisingly, little work has been done by scholars in advancing discussions on the profound impact of the AI on the diversity of widely available languages in both developed and developing world. Africa is linguistically diverse continent with about one third of the world’s languages that are vastly underrepresented in the  global digital data pool. AI translation machine is supported in only 25 languages out of over 2000 languages in the continent. The paper deploys homomorphism model of AI theory to interrogate the natural language data drawn the African languages to present the current and future challenges, opportunities and potential for developing AI algorithms that could fit neatly into the translation of the African languages. Most of the discussions in the paper focuses on the seven patterns of the AI, the usage and implementation of AI algorithms in the translation science. The research findings show some of the complexities of the African languages in which their syntactic categories have multiple corresponding semantic objects. Unlike English, the findings also reveal that syntactic operation in the African languages do not always have one corresponding semantic operation as postulated by the homomorphism model of AI theory. the study contributes to scholarly literature by stressing the limits and opportunities that relate to using AI in translation science and supplying input from NLP algorithms practitioners to expand the AI applicability operation in the translation science.Al-Noor Journal for Humanities51The research findings show some of the complexities of the African languages in which their syntactic categories have multiple corresponding semantic objects and reveal that syntactic operation in the African languages do not always have one corresponding semantic operation as postulated by the homomorphism model of AI theory.Al-Noor Journal for Humanities["Ahmed Mohammed Bedu, Ahmed Mohammed Bedu,"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/9b1ccd20e70c119032925b3e2e6f1d1b9931ad10 f5f89ce2d5331a0811ed9dd277a906bc9d05d736Artificial intelligence (AI) technologies have rapidly evolved and are increasingly integrated into various aspects of society, including commerce, healthcare, law enforcement, and governance. While AI offers numerous benefits, such as enhanced efficiency and decision-making capabilities, its widespread adoption raises significant concerns regarding privacy rights and data protection. This research paper examines the legal implications of AI in relation to privacy rights and data protection, focusing on the challenges of balancing innovation with the need to safeguard individual privacy. By analyzing relevant laws, regulations, and case studies, this paper explores the ethical, social, and legal considerations surrounding AI technologies and proposes strategies for achieving a harmonious balance between innovation and privacy.International Journal For Multidisciplinary Research30This research paper examines the legal implications of AI in relation to privacy rights and data protection, focusing on the challenges of balancing innovation with the need to safeguard individual privacy.International Journal For Multidisciplinary Research["Pratyush Prakarsh", "Mansi", "Harsh Vardhan"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/f5f89ce2d5331a0811ed9dd277a906bc9d05d736 6e3893f0bc4b78fadefa19f5c6e0a86d94eacee7In the era of artificial intelligence, human resource management has undergone significant changes compared to traditional approaches regarding value creation methods and influencing factors. This research aims to utilize grounded theory to comprehensively explore the influencing factors of value co-creation in enterprise human resource management within the context of artificial intelligence. Additionally, this research seeks to capture the dynamic relationships, causal links, and evolutionary patterns among the various elements within the system by constructing a system dynamics model. The results indicated that (1) Environmental factors primarily play a regulatory role, organizational factors serve a supportive role, and participant factors act as the driving force in influencing value co-creation in human resource management. (2) In the context of artificial intelligence, both hardware infrastructure and software capabilities can significantly impact value co-creation in human resource management. This research complements current research on the influencing factors of value co-creation in enterprise human resource management. It offers new perspectives and frameworks for the theoretical development and practical application of value co-creation in this area, supporting companies in effectively managing and developing value co-creation in human resource management.Syst.390This research seeks to capture the dynamic relationships, causal links, and evolutionary patterns among the various elements within the system by constructing a system dynamics model and indicated that environmental factors primarily play a regulatory role, organizational factors serve a supportive role, and participant factors act as the driving force in influencing value co-creation in human resource management.Syst.["Junjie Dong", "Shumin Yan", "Xiao-Wei Yang"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/6e3893f0bc4b78fadefa19f5c6e0a86d94eacee7 026ee7aa8184558d0bf345b62d8c36866a9488bdBACKGROUND “Artificial intelligence” (AI) is a broad term that refers to technology that enables robots and computers to mimic human intellect. Over the past few decades, Artificial Intelligence (AI) has gained unprecedented attention and is being called the fourth industrial revolution. But revolutions rarely come without side-effects. Various concerns have been raised as regards the unique properties and risks inherent to AI technologies. Hence the Aim of this study was to Assess Ethical Perspectives of Health Professionals In Usage Of Artificial Intelligence/Robotics In Health Care. METHOD A cross sectional questionnaire study was conducted among the Health Care Professionals. The questionnaire consisted of two parts. First part collected data related toYears of Experience in Health Careand second part collected the Knowledge on Ethical Perspectives of Usage of Artificial Intelligence/Robotics In Health Care. The questions were circulated through Google forms. RESULTS A total of 164 Health Professionals participated in the survey. 31.7% had more than 5 years of experience in health care and 68.3% of them had less than 5 years of experience in health care. 97% and 95.1 % of them were aware of use of Artificial intelligence and Robotics in Health care respectively. 52.4% agreed that AI based on machine learning poses several risks to data protection. 45.7% agreed that machine learning systems are not transparent. 51.8% agreed that Machine learning systems intentionally or inadvertently can cause reproduction of already existing biases. When Concerns on Autonomy was raised, 61% agreed that AI can reduce individual autonomy, 27.4% strongly agreed that AI and Robotics can cause Loss of human decision-making. 53% agreed that Robotic systems replaces human contact with technology, which is a fundamental ethical issue. 48.2% agreed that the inaccuracy in the system algorithm of AI and Robotics can cause unfair outcomes. 63.4% agreed that the robotics is an evolving system that is inherently and continuously changing, therefore the risk of harms needs to be evaluated. Practitioners and hospitals using AI and Robotics needs to be trained and hence have the ultimate responsibility of its use. Only 46.3% of them strongly agreed. 53% agreed that the use of AI without human mediation raises concerns about vulnerabilities. 50% agreed that Implementation of guidelines or set standards can minimize bias. CONCLUSION The development of formal AI training programmes should be prioritised in order to promote the logical and empirically based distribution of information in medical schools and hospitals. In order to inform policy creation and curriculum modifications for medical education, more extensive research is required to determine how medical professionals and students see artificial intelligence (AI). This will help to spur innovation by igniting desire to developing technology.International Journal For Multidisciplinary Research70The development of formal AI training programmes should be prioritised in order to promote the logical and empirically based distribution of information in medical schools and hospitals.International Journal For Multidisciplinary Research["Vijaya Hegde", "MEGHA S"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/026ee7aa8184558d0bf345b62d8c36866a9488bd 87bfc7101eb29e1307ee70def85f856aec2fe72fIn recent years, artificial intelligence techniques have emerged in business administration and project management. The study aims to explore the uses of artificial intelligence in various types of projects, and analyze their impact on the quality of performance. The study presents the concepts of artificial intelligence, including machine learning, deep learning, natural language processing, and computer vision. It also includes a detailed analysis of artificial intelligence applications in many types of projects, such as manufacturing projects, supply chain management, software development, and digital marketing...etc. The study focuses on how to use these technologies to improve the quality of project management performance. It relied on studying practical cases and analyzing data extracted from surveys and opinions of specialists working in this field.International Journal of Computers and Informatics00The study focuses on how to use artificial intelligence technologies to improve the quality of project management performance, and presents the concepts of artificial intelligence, including machine learning, deep learning, natural language processing, and computer vision.International Journal of Computers and Informatics["Feryal Al-Dari"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/87bfc7101eb29e1307ee70def85f856aec2fe72f 21adb186de67a9df2713efae7106774478b80886As the global business landscape continues to evolve, the integration of advanced technologies has become imperative for enhancing efficiency and competitiveness. This paper explores the multifaceted role of Artificial Intelligence (AI) in revolutionizing supply chain management (SCM). The traditional supply chain paradigm is being reshaped by AI-driven solutions, presenting opportunities for optimization, agility, and resilience. The author conducted a systematic literature review evaluation of the published literature from peer-reviewed journals in the major databases Scopus and Web of Science. The analysis of literature is a frequency analysis of the literature by considering the year of publications, the contribution of leading journals and publishers, and the methodology adopted and the content analysis of literature. The author’s findings from the literature reveal that key AI applications in supply chain management, such as demand forecasting, inventory management, logistics optimization, and risk mitigation enable organizations to make informed decisions, reduce forecasting errors, and optimize inventory levels, ultimately improving overall supply chain efficiency.ENTRENOVA - ENTerprise REsearch InNOVAtion00The author’s findings from the literature reveal that key AI applications in supply chain management enable organizations to make informed decisions, reduce forecasting errors, and optimize inventory levels, ultimately improving overall supply chain efficiency.ENTRENOVA - ENTerprise REsearch InNOVAtion["K. Logo\u017ear"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/21adb186de67a9df2713efae7106774478b80886 d4a9046b4a6d7da165eb47159b6d8b5418bb2cd2. The study analyzes the effectiveness of artificial intelligence (AI) algorithms in improving the user experience in VR games. The relevance of the topic lies in the growing demand for innovative technologies that can enhance user interactions with virtual environments. The aim of the study is to assess the impact of AI on optimizing the gaming process, improving graphic performance, and adapting to individual user preferences. The research methods include analyzing game performance, interactions with non-player characters (NPCs), adaptive changes in difficulty and storylines, as well as user surveys to evaluate gaming satisfaction. The surveys included questions about overall satisfaction, session duration, interactivity, and realism of NPC interactions. The results show that the implementation of AI significantly improves game performance, including frame rate, memory usage, CPU load, and latency. AI-driven characters contribute to more engaging and realistic interactions, enhancing user satisfaction and retention. Survey analysis revealed that users in the experimental group, who played VR games with AI integration, rated their experience significantly higher than the control group. The average session duration in the experimental group was also significantly longer, indicating greater player engagement. The findings indicate a substantial potential for the commercial application of AI in VR games, extending beyond entertainment to education, healthcare, real estate, and tourism. In education, AI-driven VR applications can create interactive learning environments that adapt to students' educational needs. In healthcare, VR simulations using AI can be employed for training medical professionals, providing realistic conditions for skills practice.Наука і техніка сьогодні00The results show that the implementation of AI significantly improves game performance, including frame rate, memory usage, CPU load, and latency, and AI-driven characters contribute to more engaging and realistic interactions, enhancing user satisfaction and retention.Наука і техніка сьогодні["Maksym Botviniev"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/d4a9046b4a6d7da165eb47159b6d8b5418bb2cd2 b7117fc77387740850a16dd61e7344b21deb1adeArtificial intelligence has evolved from early concepts like Turing's machine to today's advanced vision, machine learning and neural networks. AI revolutionizes various industries: manufacturing processes, financial services, healthcare and energy management. These applications highlight AI's role in augmenting human capabilities and driving industry innovation and efficiency. The paper aims to explore the intricacies and hurdles associated with integrating AI into the realm of industry portfolio management. The primary goal of this study is to critically assess how AI can optimize portfolio management in various industries. Methodologically, the paper adopts a multi-dimensional approach, analysing case studies across different sectors, and employing a comparative use of AI-driven and traditional portfolio management strategies. The conclusion emphasizes that while AI can significantly improve predictive accuracy and operational efficiency, its effectiveness is largely contingent on the quality of data and the adaptability of algorithms to dynamic market conditions. Secondly, the paper addresses the critical need for balancing technological innovation with ethical considerations and regulatory compliance, especially in data-sensitive industries. Finally, it suggests that the successful integration of AI in portfolio management requires a synergistic approach, combining technological prowess with human expertise to mitigate risks and capitalize on opportunities presented by AI advancements.ENTRENOVA - ENTerprise REsearch InNOVAtion160It is suggested that the successful integration of AI in portfolio management requires a synergistic approach, combining technological prowess with human expertise to mitigate risks and capitalize on opportunities presented by AI advancements.ENTRENOVA - ENTerprise REsearch InNOVAtion["Goga Alexandru Silviu"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/b7117fc77387740850a16dd61e7344b21deb1ade cf3c216bc870cb62724157cbeadfdc677583d24eHealth Policy105The implications of the AI Act for the healthcare sector are highlighted and it is concluded that, due to its horizontal approach, it is necessary to adopt further guidelines to address the unique needs of the healthcare sector.Health policy["Hannah van Kolfschooten", "J. van Oirschot"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/cf3c216bc870cb62724157cbeadfdc677583d24e c061a7647aa482be6280a58c23435aac24e9fde6Journal opf Pakistan Society of Internal Medicine00Journal opf Pakistan Society of Internal Medicine[]2024-09-07T00:00:00https://www.semanticscholar.org/paper/c061a7647aa482be6280a58c23435aac24e9fde6 13eaf9dcda8f7c037e581e0c5e0b5e6ecb9d6832Journal of Cancer Education180Current research shows that all three approaches are viable for training radiology residents, and the AI-assisted approach had no negative emotional impact on the trainees, suggesting that integrating AI into radiology training programs could provide a reliable and effective means of achieving the educational goals of medical education.Journal of cancer education : the official journal of the American Association for Cancer Education["Yanqiu Chen", "Zhen Sun", "Wenjie Lin", "Ziwei Xv", "Qichen Su"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/13eaf9dcda8f7c037e581e0c5e0b5e6ecb9d6832 d8c5a29a6ef98d0583cdef0287ba43379083174cIn this paper, we investigate the impact of Smart Parking Systems (SPS) on urban mobility and the incorporation of technological innovations within the electric vehicle (EV) industry, focusing on Petrosani, Romania. As urban areas grapple with the challenges of rapid development, such as traffic congestion and inefficient use of parking spaces, SPS emerge as a crucial solution. This research presents a comprehensive analysis of Petrosani's current traffic and parking conditions, leveraging public opinion surveys and traffic flow studies to propose an efficient integration of smart parking systems. The study reveals significant public support for SPS, highlighting a community eager to embrace solutions that enhance urban mobility, reduce environmental pollution, and improve the quality of urban life. Our findings suggest that adopting SPS in Petrosani could lead to more effective traffic management, better utilization of parking resources, and a shift towards sustainable urban development. The paper underscores the need for strategic planning and community involvement in transitioning towards smarter, more sustainable urban ecosystems, setting a precedent for similar initiatives in other cities. This work contributes to the discourse on sustainable urban planning, emphasizing the practical implications and benefits of intelligent parking solutions in enhancing urban mobility and environmental sustainability.ENTRENOVA - ENTerprise REsearch InNOVAtion00The findings suggest that adopting SPS in Petrosani could lead to more effective traffic management, better utilization of parking resources, and a shift towards sustainable urban development, underscores the need for strategic planning and community involvement in transitioning towards smarter, more sustainable urban ecosystems.ENTRENOVA - ENTerprise REsearch InNOVAtion["Cosmin Rus", "Monica Leba", "M. Risteiu", "R. Sibisanu"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/d8c5a29a6ef98d0583cdef0287ba43379083174c 4043b20f6c4dea9cba864ad2e4b3fc02b63e3babThe agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively.Applied Sciences313A novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data, highlighting the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions.Applied Sciences["E. Elbasi", "Nour Mostafa", "Chamseddine Zaki", "Zakwan AlArnaout", "A. Topcu", "Louai Saker"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/4043b20f6c4dea9cba864ad2e4b3fc02b63e3bab 910c9de3c5cfb0d74f7da8469c5f5ebde2598324Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap’s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers.ISPRS International Journal of Geo-Information782This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities, and incorporates OpenStreetMap’s road network data to study vulnerability.ISPRS International Journal of Geo-Information["S. M. Rezvani", "Maria Jo\u00e3o Falc\u00e3o Silva", "N. Almeida"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/910c9de3c5cfb0d74f7da8469c5f5ebde2598324 94a3002026fd531065ea0b6bef44a8a8afbeee46La Radiologia medica361Current evidence supports the hypothesis that admin AI holds promise for administrative application in radiology departments, and the scientific community should broaden its attention to include admin AI, as more real-world data are needed to quantify its benefits.La Radiologia Medica["Elvira Buijs", "Elena Maggioni", "Francesco Mazziotta", "Federico Lega", "G. Carrafiello"]2024-09-07T00:00:00https://www.semanticscholar.org/paper/94a3002026fd531065ea0b6bef44a8a8afbeee46 db21cf81851890fabd1adacb0afa46a0f1fb9695This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.Energies1086This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.Energies["Paul Ar\u00e9valo", "F. Jurado"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/db21cf81851890fabd1adacb0afa46a0f1fb9695 86012bf495b5ef17048d1b638a0fcfd706104b55Recent research has underscored the pivotal role of soft skills in navigating the complexities of today's workplace dynamics. Soft skills encompass a broad spectrum of attributes, such as effective communication, adept collaboration, nimble adaptability, and profound emotional intelligence, all of which are integral to fostering productive team environments and driving organizational success. Despite their acknowledged importance, quantifying and evaluating soft skills has traditionally been hindered by their inherently subjective nature. However, the emergence of artificial intelligence (AI) technologies has revolutionized the landscape of skill assessment, presenting novel opportunities to address these longstanding challenges. By leveraging AI-powered algorithms, organizations can now analyze vast datasets encompassing various facets of human interaction, enabling a more nuanced and objective evaluation of individuals' soft skill proficiencies. Moreover, AI-driven assessments offer scalability, allowing for the efficient evaluation of large cohorts of employees or candidates. Nonetheless, this intersection of AI and soft skills measurement is not without its obstacles. Ethical considerations surrounding data privacy, algorithmic bias, and the potential for automation-induced job displacement necessitate careful scrutiny and regulation. Furthermore, the dynamic nature of soft skills presents a continuous challenge, as individuals must continually adapt and refine their abilities to meet evolving workplace demands. Despite these challenges, the synergistic relationship between AI and soft skills measurement holds immense promise for the future of talent assessment and development. By embracing AI-driven approaches, organizations can cultivate a workforce equipped with the diverse skill set necessary to thrive in an ever-changing professional landscape.Conference on Computer Science and Information Systems101By embracing AI-driven approaches, organizations can cultivate a workforce equipped with the diverse skill set necessary to thrive in an ever-changing professional landscape.2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS)["Matteo Ciaschi", "Marco Barone"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/86012bf495b5ef17048d1b638a0fcfd706104b55 501c7939581842df38ba473aba054f71cfbc5850Artificial intelligence (AI) drives new modes of learning and improves the workflow of instructors. Nevertheless, there are concerns about academic integrity, plagiarism, and the reduction of critical thinking in higher education. Therefore, it is important to record and analyze university social sciences students’ attitudes toward AI, which is a significant predictor of later use of AI technologies. A sample of 190 university students (82.45% female) from a Greek social sciences department was selected. Descriptive statistics revealed that students’ attitudes toward AI were mostly positive. A principal components analysis confirmed a three-component solution of attitudes toward AI, comprising cognitive, behavioral, and emotional dimensions. Comparative analysis of the three components indicated that the emotional dimension was the highest ranked, followed by the cognitive and behavioral dimensions. Pairwise correlation analyses revealed that the strongest correlate of cognitive, behavioral, and emotional components of attitudes toward AI was the future frequency of AI use, followed by general feelings of safety with technology. In conclusion, students display more emotional and cognitive favorable dispositions toward AI. The social background of the students and the prospective future use of AI play a key role in the formulation of attitudes toward AI. University educators need to provide more teaching and learning about AI to improve students’ attitudes toward AI and future AI use.Education sciences452University educators need to provide more teaching and learning about AI to improve students’ attitudes toward AI and future AI use, as students display more emotional and cognitive favorable dispositions toward AI.Education Sciences["Argyrios Katsantonis", "Ioannis G. Katsantonis"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/501c7939581842df38ba473aba054f71cfbc5850 139e6bf3eac84f7938807a76eb2df5ce54273774The technological revolution, fueled by the sweeping changes of the information age, has profoundly impacted all spheres of life. This transformation has revolutionized the realms of inventions and communications, setting the stage for the rise of Artificial Intelligence (AI). AI has evolved into a formidable force that rivals human intellect, increasingly becoming a focal point of research aimed at integrating it across all aspects of daily life. Its capacity to address individual needs and provide tailored solutions has made it an indispensable tool in today’s digital world. This rapid scientific progression and the burgeoning interest in AI have established it as a critical necessity, offering significant benefits in various fields. These advancements have transformed traditional, human-controlled mechanisms into intelligent systems that emulate human reasoning and decision-making processes. This shift is particularly evident in the realm of electronic transactions, where AI has played a pivotal role in advancing these technologies and has been instrumental in the development of smart electronic contracts.Law and World10AI has evolved into a formidable force that rivals human intellect, increasingly becoming a focal point of research aimed at integrating it across all aspects of daily life, and has been instrumental in the development of smart electronic contracts.Law and World[]2024-09-08T00:00:00https://www.semanticscholar.org/paper/139e6bf3eac84f7938807a76eb2df5ce54273774 bed2d39f9344ad9fbe618ec08da15cbe0e5a45fbArtificial intelligence has recently become one of the hot topics in scientific and professional circles of various fields. Current technological processes in the world have significantly increased the use of digital technologies in practically all fields. Over the past century, several researchers and mathematicians have been developing the idea that computing machines can not only perform typical technical tasks but also learn to think and perform individual tasks accordingly, like humans. This idea has developed over time, and nowadays, the main topic among scientists, researchers, and practitioners worldwide is artificial intelligence and the tasks it can perform. Artificial intelligence is already actively used in various fields of public activity and many professions, and there is still talk that many professions can be replaced by artificial intelligence in the future. In this regard, using artificial intelligence in jurisprudence is particularly important and controversial. Based on the relevance of the mentioned issue, this article is dedicated to the use of artificial intelligence in the field of criminal proceedings and investigations. The article discusses the origin and development of artificial intelligence and its primary role in criminal proceedings and investigations. In the work, special attention is paid to the experience of different countries. The paper also analyzes court decisions of precedent importance in this regard. The paper also contains the main challenges of using artificial intelligence in criminal justice and investigation.Law and World70The article discusses the origin and development of artificial intelligence and its primary role in criminal proceedings and investigations and contains the main challenges of using artificial intelligence in criminal justice and investigation.Law and World[]2024-09-08T00:00:00https://www.semanticscholar.org/paper/bed2d39f9344ad9fbe618ec08da15cbe0e5a45fb a74e5fc93e246ff3be73438a06abcd3446ada04fOBJECTIVE To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample. METHODS In this retrospective study, a sample of 2129 women who underwent screening mammograms were evaluated. The performance of a commercial AI-based triage device was compared with radiologists' reports, actual outcomes, and national benchmarks using commonly used mammography metrics. Up to 5 years of follow-up examination results were evaluated in cases to establish benignity. The algorithm sorted cases into groups of "suspicious" and "low suspicion." A theoretical workload reduction was calculated by subtracting cases triaged as "low suspicion" from the sample. RESULTS At the default 93% sensitivity setting, there was significant improvement (P <.05) in the following triage simulation mean performance measures compared with actual outcome: 45.5% improvement in recall rate (13.4% to 7.3%; 95% CI, 6.2-8.3), 119% improvement in positive predictive value (PPV) 1 (5.3% to 11.6%; 95% CI, 9.96-13.4), 28.5% improvement in PPV2 (24.6% to 31.6%; 95% CI, 24.8-39.1), 20% improvement in sensitivity (83.3% to 100%; 95% CI, 100-100), and 7.2% improvement in specificity (87.2% to 93.5%; 95% CI, 92.4-94.5). A theoretical 62.5% workload reduction was possible. At the ultrahigh 99% sensitivity setting, a theoretical 27% workload reduction was possible. No cancers were missed by the algorithm at either sensitivity. CONCLUSION Artificial intelligence-based triage in this simulation demonstrated potential for significant improvement in mammography performance and predicted substantial theoretical workload reduction without any missed cancers.Journal of Breast Imaging290Artificial intelligence-based triage in this simulation demonstrated potential for significant improvement in mammography performance and predicted substantial theoretical workload reduction without any missed cancers.Journal of breast imaging["A. Watanabe", "Hoanh Vu", "C. Chim", "Andrew W Litt", "T. Retson", "Ray C Mayo"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/a74e5fc93e246ff3be73438a06abcd3446ada04f 0943ebb5f1e960f3811f4afe70fe1b49d89f445aArtificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.WArtificial Intelligence in Cancer670This minireview aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.WArtificial Intelligence in Cancer["Ankit Shukla", "Rajesh Chaudhary", "Nishant Nayyar"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/0943ebb5f1e960f3811f4afe70fe1b49d89f445a 7a77ae4bcc186a30c39949a6f4857f6571eb1894Artificial Intelligence (AI) is increasingly transforming healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving operational efficiencies. This review explores AI's impact across several key areas: cancer medicine, fraud detection, and lessons from the petroleum industry. In cancer medicine, AI-driven advancements are leading to more accurate diagnostics, personalized treatment plans, and predictive models for patient outcomes. In fraud detection, AI techniques such as anomaly detection and natural language processing are effectively identifying and mitigating fraudulent activities, safeguarding financial and operational integrity. Insights from the petroleum industry reveal how AI applications, such as predictive maintenance and operational optimization, can be adapted to healthcare settings to enhance equipment reliability and resource management. Emerging trends include the integration of AI with genomics, telemedicine, and cross-disciplinary innovations, which promise further advancements in personalized care and operational efficiency. However, ethical considerations such as data privacy, bias, and transparency must be addressed to ensure responsible AI deployment. The review concludes by highlighting the need for continued innovation, collaboration, and patient-centric approaches to fully realize AI's potential in transforming healthcare and improving patient outcomes.International Journal of Multidisciplinary Sciences and Arts440The review concludes by highlighting the need for continued innovation, collaboration, and patient-centric approaches to fully realize AI's potential in transforming healthcare and improving patient outcomes.International Journal of Multidisciplinary Sciences and Arts["Muhammad Fahad", "Muhammad Ibrar", "Muhammad Umer Qayyum", "Ali Husnain"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/7a77ae4bcc186a30c39949a6f4857f6571eb1894 925c6969161603e9f9046af2db1975aa2c97d7bcThe emergence of deep learning at the beginning of the last decade has driven the advancement of complex models, culminating in the development of large language models and generative AI. These models represent the summit of size and complexity. Explainability should be an option that plays a key role in enabling understandable the AI-assisted decision-making and ensuring accountability. This contribution delves into the complexities of explainable artificial intelligence (XAI) through various perspectives, considering the extensive and growing body of literature. Our discussion begins by addressing the challenges posed by complex data, models, and high-risk scenarios. Given the rapid growth of the field, it is essential to tackle the criticisms and challenges that emerge as it matures, requiring thorough exploration. This contribution explores them, along with three aspects that may shed light on them. First, it is focused on the lack of definitional cohesion, examining how and why is defined XAI from the perspectives of audience and understanding. Second, it explores XAI explanations, bridging the gap between complex AI models and human understanding. Third, it is crucial to consider how to analyze and evaluate the maturity level of explainability, from a triple dimension, practicality, governance and auditability.Conference on Computer Science and Information Systems390This contribution delves into the complexities of explainable artificial intelligence (XAI) through various perspectives, considering the extensive and growing body of literature, and explores XAI explanations, bridging the gap between complex AI models and human understanding.2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS)["Francisco Herrera"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/925c6969161603e9f9046af2db1975aa2c97d7bc 90685dfcd82a905a968daec43129d1ac6f3f914eAn important aspect of Artificial Intelligence (AI) Systems is their User Experience (UX), which can impact the user’s trust in the AI system. However, UX has not yet been in the focus of AI research. In previous research, we have evaluated the UX of the Meta AutoML platform OMA-ML, uncovering weak points and proposing several recommendations for ensuring a positive UX in AI systems. In this paper we show that implementing those recommendations leads to measurable UX improvements. We present the UX-improving features implemented in a new release of OMA-ML and the results from a second UX evaluation. The UX of OMA-ML could successfully be improved in four interactive principles (suitability for the user’s tasks, self-descriptiveness, user engagement and learnability). We argue that an iterative approach to UX potentially leads to more human-centered AI.Conference on Computer Science and Information Systems360It is argued that an iterative approach to UX potentially leads to more human-centered AI, and features implemented in a new release of OMA-ML and the results from a second UX evaluation are presented.2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS)["Alexander Zender", "Bernhard G. Humm", "Anna Holzheuser"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/90685dfcd82a905a968daec43129d1ac6f3f914e a84bb7e741707bf3247ca0f8a9aabcce38bd7a22The widespread use of digital systems and tools in education has opened up opportunities for collecting, measuring, and analysing data about user (learner, teacher) interactions with a variety of learning resources and activities, with the ultimate objective of better understanding learning and advancing both learning outcomes and the overall learning experience. This promise motivated the development of Learning Analytics (LA) as a research and practical field and the use of insights derived from learning trace data for evidence-based decision making in a variety of educational settings. While LA has made a significant contribution to better understanding of learning and the environments in which it takes place, many open questions and challenges remain. Furthermore, new opportunities and challenges continue to emerge with the ever-changing modalities of teaching and learning, the latest of which are associated with the rapid development and accessibility of Artificial Intelligence (AI). Taking the cyclical model of LA as its exploration framework, this paper examines how key components of the LA model – namely data, methods, and actions – relate to and may benefit from the latest developments in AI, and especially Generative AI. Aiming for evidence-based analysis and discussion of the interplay between LA and AI, the paper relies on the latest empirical research in LA and the related research fields of AI in Education and Educational Data Mining.Conference on Computer Science and Information Systems530How key components of the LA model – namely data, methods, and actions – relate to and may benefit from the latest developments in AI, and especially Generative AI are examined.2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS)["Jelena Jovanovic"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/a84bb7e741707bf3247ca0f8a9aabcce38bd7a22 fe601f0dfe1f64f267911687e3fe18965d6ae9a1Bioinformatics and artificial intelligence (AI) have emerged as transformative tools in modern medicine, revolutionising the landscape of medical diagnosis and treatment. Herein, we provide an overview of the synergistic relationship between bioinformatics and AI, elucidating their pivotal roles in deciphering complex biological data and advancing precision medicine and, in particular, endocrinology. We explore various applications of bioinformatics and AI in medical research, including genomic analysis, drug discovery, disease diagnosis, and personalised treatment strategies. Additionally, we discuss challenges and future directions in leveraging these technologies to enhance healthcare outcomes.EMBnet journal50An overview of the synergistic relationship between bioinformatics and AI is provided, elucidating their pivotal roles in deciphering complex biological data and advancing precision medicine and, in particular, endocrinology.EMBnet.journal["Dimitrios Vlachakis", "Konstantina Dragoumani", "Eleni Papakonstantinou", "G. Chrousos"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/fe601f0dfe1f64f267911687e3fe18965d6ae9a1 a64b012128e52426eb1f7930a5e37b26afad1486Purpose: The interfacing of enterprises with technological innovation and AI shows a clear path to growth and progress through job automation and increased productivity. However, there are management professionals who need an understanding of AI and the ways to upskill themselves to make use of it. The objective of this research is to provide insights into the relevance of the AI concept in marketing to people from non-technical backgrounds. With more conceptual knowledge, they will be capable of improving tasks in existing projects, planning for future ones, and making improved decisions with results from AI models. Methodology: For this, published research papers, news articles, and books from authentic sources were studied. Design: This paper introduces the concepts of AI and marketing, states the need for understanding AI by marketers, states the research methodology, and gives an in-depth understanding of the kind of data that marketers must provide to their data analyst colleagues, along with ways to collect them, followed by a discussion and conclusions. Findings: This paper is a practical guide to the theory of AI and its usability in the marketing context. The findings are suitable for future researchers interested in AI and marketing, for developing different analytical approaches. Originality/ Value: Since the future of AI is unknown to many, this paper provides guidance for the enrichment of human knowledge. It has two benefits. i) When employees are aware of the kind of data that they can use, they will feel empowered. ii) When customers know the kind of data that can be extracted from them, then they will safeguard themselves more.PURUSHARTHA - A journal of Management, Ethics and Spirituality90This research provides insights into the relevance of the AI concept in marketing to people from non-technical backgrounds and gives an in-depth understanding of the kind of data that marketers must provide to their data analyst colleagues, along with ways to collect them.PURUSHARTHA - A journal of Management, Ethics and Spirituality["Ankita Raj"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/a64b012128e52426eb1f7930a5e37b26afad1486 03eb208c787781c805e79405361bfbb646c11e99The study is devoted to the development of an adaptive on-board model of a gas turbine aeroengine, built into its automatic control system. The adaptability of the aeroengine mathematical model to a possible change in its state is achieved based on the diagnostic matrix method, allowing defining immeasurable engine parameters in terms of the physically measurable parameters. Immeasurable parameters refer to changes in the efficiency of the main engine components, air bleeds and leakage. The identification process is based on solving a diagnostic system of linear algebraic equations, which the main determinant is close to zero. The accuracy and speed of the on-board built-in model are determined by choice of mathematical method for solving obtained diagnostic system. The problem of the linear algebraic equations system uncertainty is solved based on the exact (Cramer, Gaussian), iteration (fixed-point, Jacobi, Seidel) and optimization (numerical Monte Carlo and a genetic algorithm) methods. The effectiveness of the methods was assessed based on the average relative error. The bench testing of developed algorithms on the simulator of a real industrial engine in the loop of an automatic control system was performed. The promise of using the genetic algorithms to solve the considered problem, taking into account all the limitations, has been proven.2024 International Russian Automation Conference (RusAutoCon)150The promise of using the genetic algorithms to solve the considered problem, taking into account all the limitations, has been proven.2024 International Russian Automation Conference (RusAutoCon)["Tatiana A. Kuznetsova", "P. Repp", "A. A. Naborshchikov"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/03eb208c787781c805e79405361bfbb646c11e99 03dff993efc7444f7c67ea3c7a621db7a510ac5aCitizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models. However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection. This study investigates the application of AI techniques to address these issues within natural science. We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data. Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on open-access platforms such as iNaturalist. By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.Conference on Computer Science and Information Systems230This study investigates the application of AI techniques to address issues within natural science, and explores the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data.2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS)["Giacomo Ignesti", "Davide Moroni", "M. Martinelli"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/03dff993efc7444f7c67ea3c7a621db7a510ac5a ef5ec4fa7ddca0fa129bc4187b2217da82920fc8Central Asian Economic Review300Central Asian Economic Review["L. Mergalieva", "K. Beketova", "S. Primbetova"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/ef5ec4fa7ddca0fa129bc4187b2217da82920fc8 ea4638c4aea84f06e07292537c6d3c6659c00e39A core part of human intelligence is the ability to work flexibly with others to achieve both individual and collective goals. The incorporation of artificial agents into human spaces is making increasing demands on artificial intelligence (AI) to demonstrate and facilitate this ability. However, this kind of flexibility is not well understood because existing approaches to intelligence typically focus either on the individual or the collective level of analysis. At the individual level, intelligence is seen as an individual-difference trait that exists independently of the social environment. At the collective level intelligence is conceptualized as a property of groups, but not in a way that can be used to understand how groups can make group members smarter or how group members acting as individuals might make the group itself more intelligent. In the present paper we argue that by focusing either on individual or collective intelligence without considering their interaction, existing conceptualizations of intelligence limit the potential of people and machines. To address this impasse, we identify and explore a new kind of intelligence - socially-minded intelligence - that can be applied to both individuals (in a social context) and collectives (of individual minds). From a socially-minded intelligence perspective, the potential intelligence of individuals is unlocked in groups, while the potential intelligence of groups is maximized by the flexible, context-sensitive commitment of individual group members. We propose ways in which socially-minded intelligence might be measured and cultivated within people, as well as how it might be modelled in AI systems. Finally, we discuss ways in which socially-minded intelligence might be used to improve human-AI teaming.arXiv.org2040The potential intelligence of individuals is unlocked in groups, while the potential intelligence of groups is maximized by the flexible, context-sensitive commitment of individual group members.ArXiv["William J. Bingley", "S. A. Haslam", "Janet Wiles"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/ea4638c4aea84f06e07292537c6d3c6659c00e39 64a245b276bc52ab40ef265b65c5c2145ec6cb36In an era where data-driven decision-making is critical to business success, understanding and optimizing Customer Lifetime Value (CLV) has become a strategic priority for companies across industries. CLV, which estimates the total revenue a business can expect from a customer throughout their relationship, is crucial for identifying high-value customers and tailoring marketing strategies to maximize profitability. However, traditional methods of calculating CLV often rely on historical data and linear models, limiting their accuracy and adaptability in a rapidly changing market environment.   The integration of Artificial Intelligence (AI) into predictive analytics has brought about a paradigm shift in how businesses approach CLV. AI-powered predictive models leverage machine learning algorithms to analyze vast amounts of data, uncover complex patterns, and make highly accurate CLV predictions. These models can dynamically adjust to changes in customer behavior, market conditions, and other external factors, providing businesses with a more precise and actionable understanding of their customer base.   This article explores the transformative potential of AI in predictive CLV modeling, examining the various techniques and data sources that drive these advanced models. We will discuss the strategic benefits of AI-driven CLV, including personalized marketing, optimized customer segmentation, and enhanced customer retention strategies. Additionally, we will address the challenges associated with implementing AI-powered CLV models, such as data privacy concerns, integration with existing systems, and the interpretation of AI-generated insights. Through a detailed analysis of industry case studies, this article highlights the practical applications of AI-powered CLV models in maximizing long-term profits. We will also explore future trends in AI technology and their potential impact on CLV predictions, offering insights into how businesses can stay ahead of the curve in an increasingly competitive landscape. By the end of this article, readers will have a comprehensive understanding of how AI can revolutionize CLV predictions and drive sustained business growth.International Journal of Scientific Research and Management184The transformative potential of AI in predictive CLV modeling is explored, examining the various techniques and data sources that drive these advanced models and offering insights into how businesses can stay ahead of the curve in an increasingly competitive landscape.International Journal of Scientific Research and Management (IJSRM)["Dmitrii Egorenkov"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/64a245b276bc52ab40ef265b65c5c2145ec6cb36 06449ace86eb0848d88955e4294c14e1167c7b15Artificial intelligence (AI) is transforming L2 education, yet its specific impacts on English as a foreign language (EFL) teachers and language learners' engagement remain understudied. To address this deficiency, this study, grounded in Fredricks, Blumenfeld, and Paris's (Review of Educational Research, 74, 109) three‐dimensional engagement model, explored the impacts of AI on the behavioural, cognitive and emotional engagement of EFL teachers and language learners through semi‐structured interviews with 24 EFL teachers and 38 college language learners, followed by a thematic analysis with MAXQDA to uncover the effectiveness of AI. The study found that behavioural engagement showcased the integration of AI tools, highlighting increased frequency of use and their practical applications in enhancing language acquisition tasks. Cognitive engagement was marked by the recognition of AI capacity to augment teaching strategies and learning processes, although it also surfaced concerns about the potential overreliance on technology. Emotional engagement reflected a complex interplay of attitudes, with most informants viewing AI positively but acknowledging concerns about job displacement, and its impacts on emotions of students and teachers as well as the relations between them. The study concluded that while AI held promise for L2 education, the integration must consider its limitations and ethical implications. The research provided valuable insights for educators, learners, technology developers and policymakers, encouraging innovative practices and informed decision‐making in L2 education.European Journal of Education374The study found that behavioural engagement showcased the integration of AI tools, highlighting increased frequency of use and their practical applications in enhancing language acquisition tasks and cognitive engagement was marked by the recognition of AI capacity to augment teaching strategies and learning processes.European Journal of Education["Changyin Zhou", "Fanfan Hou"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/06449ace86eb0848d88955e4294c14e1167c7b15 e0f9b26011b0484dfaa432d41d3c471ea62057bcJournal of Big Data1592J. Big Data["Chin-Tsu Chen", "Asif Khan", "Shih-Chih Chen"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/e0f9b26011b0484dfaa432d41d3c471ea62057bc 01331d618167473b4d1690e7ab6fc3d0343b2259This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.International Journal of Education, Management, and Technology241This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making, and focuses on making AI models more interpretable and transparent.International Journal of Education, Management, and Technology["Taiwo Abdulahi Akintayo", "Chadi Paul", "Madumere Chiamaka Queenet", "Oluchi Anthonia Nnadiekwe", "Shittu Sarah Victoria", "Fakokunde Babatunde David", "Ogundigba Omotunde Joel", "Olowu Innocent Agada", "Egenuka Rhoda Ngozi", "Ugochukwu Ukeje Arinze", "Grace Alele Ojemerenvhie", "Adebesin Adedayo Oluwadamilola", "Chinenye Cordelia Nnamani", "Usman Wasiu Olayinka"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/01331d618167473b4d1690e7ab6fc3d0343b2259 2e22b094d25a349af001a408e48d8d0a1bd2d9f2This article explains the ever-growing use of artificial intelligence (AI) in tax and assesses the strength and weaknesses of general anti-avoidance rule regimes to address tax avoidance driven by AI. Proactive steps by regulators are desirable. Recommendations are formulated underlining the need for international coordination and an “all of government” approach.Bulletin for International Taxation00The strength and weaknesses of general anti-avoidance rule regimes to address tax avoidance driven by AI are assessed and recommendations are formulated underlining the need for international coordination and an "all of government" approach.Bulletin for International Taxation["S. Parsons", "A. Marais", "P.J. Hattingh"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/2e22b094d25a349af001a408e48d8d0a1bd2d9f2 3b1a3ba75fce7dbff026650120a0cf7e3f390f12Artificial intelligence (AI) is transforming surgical procedures by improving precision, efficiency, and patient outcomes. This study investigates the role of artificial intelligence in the optimization of robotic-assisted surgeries, preoperative planning, and intraoperative decision-making. With the rise of machine learning algorithms trained on surgical data, AI can detect issues, increase safety, and supplement surgical teams’ capabilities. This review focuses on existing surgical practice challenges, the incorporation of AI into the operating theater, and the potential for AI-driven technologies to reduce human errors. The ethical concerns and future developments required for widespread AI usage in surgery are also addressed. Keywords: Artificial intelligence, surgical robots, machine learning, patient safety, preoperative planning.RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES00This study investigates the role of artificial intelligence in the optimization of robotic-assisted surgeries, preoperative planning, and intraoperative decision-making, and the potential for AI-driven technologies to reduce human errors.RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES["Kibibi J. Wambui"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/3b1a3ba75fce7dbff026650120a0cf7e3f390f12 60ae5ba84f520a2f83211e52c8d240fe7f761b0eArtificial Intelligence (AI) has revolutionized various industries, and its integration into medical research is no exception. AI has become instrumental in fields like drug discovery, diagnostics, medical imaging, and predictive analytics, accelerating the pace of scientific breakthroughs. This paper examines the diverse applications of AI in medical research, highlighting its contributions to drug development, patient monitoring, and the analysis of complex biological data. Despite its potential, AI faces significant challenges, including the availability of high-quality datasets, the interpretability of AI models, and ethical concerns regarding data privacy and equity. The future of AI in medical research offers vast opportunities, but careful consideration is required to address these challenges and fully unlock its potential for improving healthcare outcomes. Keywords: Artificial Intelligence, Medical Research, Drug Discovery, Diagnostics, Medical Imaging.RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES00This paper examines the diverse applications of AI in medical research, highlighting its contributions to drug development, patient monitoring, and the analysis of complex biological data.RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES["Jumba K. Kato"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/60ae5ba84f520a2f83211e52c8d240fe7f761b0e f983088fa147941d66375942e0eb79addc3faf0cAs we navigate the digital world, cybersecurity has become a top priority. With each technological advancement, new vulnerabilities emerge, making robust defenses essential. The fusion of machine learning and artificial intelligence has become a game-changer in the fight against cyber threats. This paper delves into the latest applications of these technologies in network security, shedding light on their critical roles in addressing pressing concerns and identifying areas for further exploration. We also examine the ethical and legal implications of implementing these technologies. Our research highlights current challenges and open questions, with a focus on recent breakthroughs in network security leveraging AI and ML. The findings are promising, suggesting that further innovation in integrating AI and ML into network security frameworks holds significant potential. Exciting applications include bolstering network security, detecting malware, and responding to intrusions. Interestingly, while 45% of organizations recognize the need to adopt these technologies, half have already done so, while 5% remain hesitant.Asian Journal of Science, Technology, Engineering, and Art100The latest applications of machine learning and artificial intelligence in network security, shedding light on their critical roles in addressing pressing concerns and identifying areas for further exploration are delved into.Asian Journal of Science, Technology, Engineering, and Art["Ruth Onyekachi Okereke", "Grace Alele Ojemerenvhie", "Oladimeji Lamina Azeez", "Terry Uwagbae Oko-odion", "Iyanu Opeyemi Samson", "Chijioke Nnaemeka Anosike", "Faith Obun Owan", "Chinenye Cordelia Nnamani"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/f983088fa147941d66375942e0eb79addc3faf0c a2fdc8ffe094608e1ab16e2366eb9c903d8d4f15The teaching-learning process is complex in nature, requiring many tasks and skills to achieve success in the construction of knowledge. As per any particular kind of cognitive development, teaching and learning Computer Programming is no different in this regard: tasks must be executed, sometimes repeatedly, and skills must be developed. Despite different approaches and methodologies, exercising what has been studied is proven to be effective in pretty much any teaching-learning process. Many tools have been developed throughout time to aid in the execution of this important task, sometimes approaching the problem from the students’ perspective, sometimes from the teachers’. This paper presents Goliath, a semi-automatic generator of Computer Programming exercises, whose functionality is based on Artificial Intelligence (AI) models, a Domain-Specific Language (DSL), and an online application that binds them together. Goliath’s goals are directed towards teachers (and indirectly, students) by aiming to lower the burden of repeatedly constructing exercises. This is achieved through the use of templates that allow for automatic variations of an exercise to be created instantly, while relying on a common foundation. Goliath is meant to be a facilitator, raising availability of exercise lists, while avoiding repetition and the common mistakes that accompany their construction.Conference on Computer Science and Information Systems510Goliath is meant to be a facilitator, raising availability of exercise lists, while avoiding repetition and the common mistakes that accompany their construction, through the use of templates that allow for automatic variations of an exercise to be created instantly, while relying on a common foundation.2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS)["Tiago Carvalho Freitas", "Alvaro Costa Neto", "M. Pereira", "P. Henriques"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/a2fdc8ffe094608e1ab16e2366eb9c903d8d4f15 b6b7fd5606d88ea7dd02197cfcba96b829af49a1This study aims to examine the impact of the use of artificial intelligence (AI) technology in learning on the understanding of material by young students. In today's digital era, AI is increasingly being adopted in educational environments, with the potential to personalize learning, increase student engagement, and accelerate the process of understanding material. This research method uses a quantitative approach with a quasi-experimental design. A total of 120 students from various study programs at a university in Indonesia became research respondents, who were divided into two groups: a group that used an AI-based learning platform and a group that used conventional learning methods. Data collection was carried out through pre-tests and post-tests to measure the level of understanding of the material, as well as surveys to evaluate the learning experience. The results of the study showed that the group of students who used AI technology in learning had a more significant increase in understanding the material compared to the control group. In addition, students who used AI reported a more interactive and motivating learning experience. However, this study also found challenges, such as excessive dependence on technology and disparities in access to adequate technological devices. Based on these findings, the study recommends the integration of AI into the education curriculum in a balanced manner, considering technical and pedagogical factors to maximize the potential of AI-based learning.International Journal of Educational Research90The results showed that the group of students who used AI technology in learning had a more significant increase in understanding the material compared to the control group, and students who used AI reported a more interactive and motivating learning experience.International Journal of Educational Research["Henny Sutrisman", "Rosmerry Simanjuntak", "Adrianus Prihartanto", "Bayu Kusumo"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/b6b7fd5606d88ea7dd02197cfcba96b829af49a1 674813acc724b63f3734325350d83468950dc84cResearch has demonstrated the positive impact of artificial intelligence and Bioinformatics in the field of clinical diagnosis. The integration of AI methodologies into bioinformatics has opened new avenues for breakthroughs in genomics, proteomics, and personalized medicine. The document emphasizes the role of AI in early disease detection, improving patient outcomes, and enhancing healthcare systems by avoiding the need for expensive and time-consuming operations as illnesses worsen. The methodology section provides insights into the approach utilized, including the review of 30 articles from highly regarded journals about AI and bioinformatics that expedite diagnostic testing in the medical field. using survey to gather information and divide it into sub-sections focusing on diagnostic cancer diseases, COVID-19, and genetic and chronic diseases. The survey gathered 52 responses, and the results revealed significant agreements with the findings in the papers, particularly in the importance of developing novel biosensors and diagnostic tools for rapid and accessible detection of SARS-CoV-2, and the potential of AI in laboratory settings, pharmaceutical industry, and disease diagnosis. Overall, the document provides a comprehensive overview of the transformative role of AI in bioinformatics, emphasizing its potential to revolutionize disease diagnosis, treatment, and public health decision-making, while also addressing the challenges and opportunities associated with the integration of AI technologies in the healthcare industry. The rigorous methodology and alignment of survey results with the research findings validate the significance of AI-powered bioinformatics in expediting diagnostic testing and improving patient safety in healthcare.International Journal of Computers and Informatics00The rigorous methodology and alignment of survey results with the research findings validate the significance of AI-powered bioinformatics in expediting diagnostic testing and improving patient safety in healthcare.International Journal of Computers and Informatics["Rahaf Bajhzer", "Mona Alghamdi", "Salma Elhag"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/674813acc724b63f3734325350d83468950dc84c 834977a0d24ab50e5aa24d5342d21a6d5a3963afAs artificial intelligence (AI) continues to rapidly advance, there is a growing demand to integrate AI capabilities into existing business applications. However, a significant gap exists between the rapid progress in AI and how slowly AI is being embedded into business environments. Deploying well-performing lab models into production settings, especially in on-premise environments, often entails specialized expertise and imposes a heavy burden of model management, creating significant barriers to implementing AI models in real-world applications. KModels leverages proven libraries and platforms (Kubeflow Pipelines, KServe) to streamline AI adoption by supporting both AI developers and consumers. It allows model developers to focus solely on model development and share models as transportable units (Templates), abstracting away complex production deployment concerns. KModels enables AI consumers to eliminate the need for a dedicated data scientist, as the templates encapsulate most data science considerations while providing business-oriented control. This paper presents the architecture of KModels and the key decisions that shape it. We outline KModels' main components as well as its interfaces. Furthermore, we explain how KModels is highly suited for on-premise deployment but can also be used in cloud environments. The efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system. These models operate in a client's data center and are trained on local data, without data scientist intervention. One model improved the accuracy of Failure Code specification for work orders from 46% to 83%, showcasing the substantial benefit of accessible and localized AI solutions.arXiv.org390The architecture of KModels is presented and how KModels is highly suited for on-premise deployment but can also be used in cloud environments, and the efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system.ArXiv["Roy Abitbol", "Eyal Cohen", "Muhammad Kanaan", "Bhavna Agrawal", "Yingjie Li", "Anu Bhamidipaty", "Erez Bilgory Ibm Research Israel", "Ibm Research Usa"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/834977a0d24ab50e5aa24d5342d21a6d5a3963af 231ffa5b852b13b4dc762972e03f76a923a5b5b5Objetivo. Revisar los contenidos y la implementación del CONPES 3975 de 2019 sobre inteligencia artificial (IA) y otras iniciativas de política pública orientadas a la transformación digital. Metodología. Se propone un esquema de evaluación de políticas públicas basado en las recomendaciones de la autoevaluación país de la CAF (2024) y en una revisión de las metodologías de evaluación utilizadas en Colombia. Resultados. La discusión sobre políticas públicas en inteligencia artificial y transformación digital, así como el papel del Estado en su adopción, se ha centrado en la pertinencia de un esquema de regulación nacional. En el ámbito académico, se debate la regulación del uso y despliegue de sistemas de decisión automatizada (SDA) por parte de entidades estatales a nivel nacional, local y en la rama judicial. Sin embargo, se ha prestado poca atención a los instrumentos y mecanismos de seguimiento de las políticas públicas ya existentes en IA y transformación digital, que han posicionado a Colombia como un país pionero en estas áreas. La falta de evaluación, continuidad y mejora de estas políticas pone en riesgo este liderazgo. Conclusión. Se identificó que el país cuenta con herramientas normativas, implementaciones y mecanismos de seguimiento efectivos, aunque también se evidencia una falta de continuidad en el desarrollo de políticas públicas de IA y transformación digital. Se propone una agenda de evaluación que aborde esta deficiencia.Revista Internacional del Instituto de Pensamiento Liberal00Revista Internacional del Instituto de Pensamiento Liberal["Camilo Vargas Aguirre"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/231ffa5b852b13b4dc762972e03f76a923a5b5b5 72ddfc6620810cacfa0d5d68e7550febdba357d0The emergence of the open radio access network (O-RAN) architecture offers a paradigm shift in cellular network management and service orchestration, leveraging data-driven, intent-based, autonomous, and intelligent solutions. Within O-RAN, the service management and orchestration (SMO) framework plays a pivotal role in managing network functions (NFs), resource allocation, service provisioning, and others. However, the increasing complexity and scale of O-RANs demand autonomous and intelligent models for optimizing SMO operations. To achieve this goal, it is essential to integrate intelligence and automation into the operations of SMO. In this manuscript, we propose three scenarios for integrating machine learning (ML) algorithms into SMO. We then focus on exploring one of the scenarios in which the non-real-time RAN intelligence controller (Non-RT RIC) plays a major role in data collection, as well as model training, deployment, and refinement, by proposing a centralized ML architecture. Finally, we identify potential challenges associated with implementing a centralized ML solution within SMO.arXiv.org202This manuscript proposes three scenarios for integrating machine learning (ML) algorithms into SMO and focuses on exploring one of the scenarios in which the non-real-time RAN intelligence controller (Non-RT RIC) plays a major role in data collection, as well as model training, deployment, and refinement, by proposing a centralized ML architecture.ArXiv["Mohammad Asif Habibi", "Bin Han", "Merve Saimler", "Ignacio Labrador Pav\u00f3n", "H. D. Schotten"]2024-09-08T00:00:00https://www.semanticscholar.org/paper/72ddfc6620810cacfa0d5d68e7550febdba357d0 f5354731e76b7bc0ee534918efa42598d13f0ed1A branch of computer science called artificial intelligence makes it possible for machines to function well. By taking on complex data processing Duties, its use in pharmaceutical technology has grown, improving workflow efficiency, lowering operating cost, and promoting safety, accuracy, and Productivity. It could Potentially save time and money in addition to assisting us in better understanding the connections Between various formulations and process parameters. Artificial intelligence (AI) research has been shown to be able to analyze and interpret various critical pharmacy fields, including drug development dosage forms design, and hospital pharmacy. Thanks to artificial intelligence’s significant contributions to the management and preservation of data and information, the healthcare industry has seen impressive advancementsInternational journal of allied medical sciences and clinical research6531Artificial intelligence (AI) research has been shown to be able to analyze and interpret various critical pharmacy fields, including drug development dosage forms design, and hospital pharmacy.International Journal of Allied Medical Sciences and Clinical Research["SK. Farahan Subahan", "J. N. Suresh Kumar", "Chimata. HanumanthaRao", "Chuppana. Naga Veera Durga Sai Madhurya", "Dudekula. Sajeedha", "Gurrapusala. Lakshmi Venkata Surekha", "Sheik. Nageena Bee"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/f5354731e76b7bc0ee534918efa42598d13f0ed1 8486c8f5c2f4eb73afade1c4e04341398e3412feIn an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes. In climate change, AI-driven strategies are assessed for their effectiveness in fostering climate resilience, including predictive modeling of extreme weather events and optimizing resource allocation. The paper also discusses AI applications in healthcare resilience, such as enhancing diagnostics, patient care, and operational efficiency during crises. Business continuity and crisis management are examined, highlighting AI's capability to anticipate disruptions and maintain operational stability. The paper emphasizes the importance of strengthening cybersecurity resilience using AI to detect and mitigate threats proactively. AI's role in enhancing community and social resilience is analysed, particularly in supporting vulnerable populations and fostering social cohesion. Additionally, we explored AI-powered solutions for urban resilience, focusing on smart cities and sustainable development. The study also covers AI's contributions to environmental and ecological resilience, resilient supply chain management, and resilience in the hospitality and tourism industry. Finally, we investigated AI's potential in fostering psychological resilience, providing personalized mental health support and stress management tools. Through these diverse applications, the paper underscores AI's critical role in building a resilient future.Social Science Research Network744The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes, and investigates AI's potential in fostering psychological resilience.SSRN Electronic Journal["N. Rane", "Mallikarjuna Paramesha", "Saurabh P. Choudhary", "Jayesh Rane"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/8486c8f5c2f4eb73afade1c4e04341398e3412fe afa3081fe0b9d02e8b15e06f390844ba38855e2fNature Food345Computational modelling and artificial intelligence (AI) are used to examine plant-environment interactions across ten diverse global locations with distinct climates to substantially enhance energy savings in PFALs and support sustainable food production.Nature food["Benjamin Decardi-Nelson", "Fengqi You"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/afa3081fe0b9d02e8b15e06f390844ba38855e2f 67f55f568c3411f284d7e1b386304d180da1d781Virtual reality (VR) technologies have garnered substantial attention and adoption across various fields, including workspaces and education. People with disabilities, who already face lower employment rates and fewer full-time opportunities, may be further disadvantaged. Addressing this, recent advancements have focused on making VR inclusive, particularly for individuals with visual impairments. With the rapid development of artificial intelligence (AI) technology, it has also become possible to integrate AI technologies into VR systems to enhance accessibility for individuals with visual impairments. Despite growing interest, there is a gap in comprehensive literature focusing on AI’s role in enabling blind and low vision individuals to experience VR. This review aims to fill that void by providing an overview of current research, discussing benefits and challenges, and identifying future opportunities. By synthesizing existing research, this study contributes insights for researchers, developers, and practitioners working in the field of accessibility and assistive technology.Proceedings of the Human Factors and Ergonomics Society Annual Meeting143There is a gap in comprehensive literature focusing on AI’s role in enabling blind and low vision individuals to experience VR, and this review aims to fill that void by providing an overview of current research, discussing benefits and challenges, and identifying future opportunities.Proceedings of the Human Factors and Ergonomics Society Annual Meeting["Tianhang Liu", "Pooyan Fazli", "Heejin Jeong"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/67f55f568c3411f284d7e1b386304d180da1d781 fbfba17715c647c9d8dd559ff68f7bbfe038cb6aEducational technology research and development462This study builds on an AI competence model and investigates predispositions of AI competence among teachers in vocational schools, indicating that AI competence can be modeled as combining six competence dimensions.Educational technology research and development["Jan Delcker", "Joana Heil", "Dirk Ifenthaler"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/fbfba17715c647c9d8dd559ff68f7bbfe038cb6a 043021edbf50ad3757c2a2bec864e5b0e44f0fd7Recognising the significant role of artificial intelligence in the labour market is essential for China to develop sustainably. The research utilises the mixed frequency vector auto-regression (MF-VAR) technique, which would innovatively incorporate data at different frequencies into one model to identify the intricate correlation between the monthly artificial intelligence index (AII) and the quarterly unemployment rate (UR) in China. Through comparison, the MF-VAR method has a more substantial explanatory power than the low-frequency VAR (LF-VAR) model, the impulse responses of the former reveal that AII exerts favourable and adverse influences on UR. Among them, the positive effect occurs on the AII in the first and second months. In contrast, the negative one appears on the AII in the third month, highlighting that artificial intelligence has both stimulating and inhibiting effects on the labour market in China. By analysing UR’s predictive error variance decomposition, the total impact of China’s artificial intelligence technology on employment is a substitution; this outcome is accordant with the theoretical dis¬cussion. In the new round of scientific and technological revolution and industrial transformation, meaningful recommendations for China would be put forward to avert the wave of unemployment brought by the development of artificial intelligence technology.Technological and Economic Development of Economy752The research uses the mixed frequency vector auto-regression (MF-VAR) technique, which would innovatively incorporate data at different frequencies into one model to identify the intricate correlation between the monthly artificial intelligence index (AII) and the quarterly unemployment rate (UR) in China.Technological and Economic Development of Economy["Meng Qin", "Hsu-Ling Chang", "C. Su", "Raluca-Ioana R\u0103c\u0103t\u0103ian", "Andreea-Florentina Cr\u0103ciun"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/043021edbf50ad3757c2a2bec864e5b0e44f0fd7 c3b7e3fe9b1582a37c8b2de9ef9d1d7ad56c27aaThis research paper investigates the key factors influencing AI acceptance, focusing on elements such as technological readiness, perceived usefulness, and ease of use, along with the organizational and societal impacts. It identifies the significant obstacles to AI adoption, including ethical concerns, data privacy issues, and the potential for job displacement. The study also explores the importance of trust and transparency in promoting AI acceptance, highlighting the necessity for explainable AI (XAI) to build user confidence. Strategies for enhancing AI acceptance are examined, emphasizing the need for robust regulatory frameworks, ongoing education, and skill development to mitigate resistance and boost user engagement. The research stresses the importance of a user-centric approach in AI system design and implementation, taking into account end-user needs and concerns. Additionally, it underscores the value of collaboration between industry, academia, and policymakers in fostering an environment conducive to AI innovation and acceptance. By offering a thorough analysis of the factors affecting AI acceptance and the associated challenges, this paper provides valuable insights and actionable strategies for stakeholders aiming to navigate the complex landscape of AI integration effectively.Social Science Research Network668The research stresses the importance of a user-centric approach in AI system design and implementation, taking into account end-user needs and concerns, and underscores the value of collaboration between industry, academia, and policymakers in fostering an environment conducive to AI innovation and acceptance.SSRN Electronic Journal["N. Rane", "Saurabh P. Choudhary", "Jayesh Rane"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/c3b7e3fe9b1582a37c8b2de9ef9d1d7ad56c27aa b4a3e3b663bb4d88cb5a5696b4bed5184d756983Scientific Reports400A novel NAP framework was designed and applied to an AI model predicting systolic blood pressure from fundus images in the United Kingdom Biobank dataset and it was found that the NAP generated from the framework was correlated to the clinically relevant endpoint of cardiovascular risk.Scientific Reports["Songyang An", "D. Squirrell"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/b4a3e3b663bb4d88cb5a5696b4bed5184d756983 12a842703caf05ffd88a93bff938a96ea373e9f8This comprehensive study delves into the intricate dynamics between Artificial Intelligence (AI) and consumer buying behaviour within the realm of online fashion retail. As the digital landscape continues to evolve, AI technologies have become increasingly integral to shaping consumer preferences and purchasing decisions. This research endeavours to elucidate the multifaceted role played by AI in influencing consumer behaviour, spanning aspects such as perceived value, ease of use and impact on purchase decisions. Through a meticulous analysis of online fashion retail platforms, this study seeks to provide valuable insights into the intricate interplay between AI innovations and consumer behaviour, thus contributing to a deeper understanding of the contemporary retail landscape.RVIM Journal of Management Research30Through a meticulous analysis of online fashion retail platforms, this research endeavours to elucidate the multifaceted role played by AI in influencing consumer behaviour, spanning aspects such as perceived value, ease of use and impact on purchase decisions.RVIM Journal of Management Research["Yashashwini A."]2024-09-09T00:00:00https://www.semanticscholar.org/paper/12a842703caf05ffd88a93bff938a96ea373e9f8 8099c95e8ba666b4e9a8985f041d4c8f995f378aThe recent surge in artificial intelligence (AI) has significantly transformed work dynamics, particularly in human resource development (HRD) and related domains. Scholars, recognizing the significant potential of AI in HRD functions and processes, have contributed to the growing body of literature reviews on AI in HRD and related domains. Despite the valuable insights provided by these individual reviews, the challenge of collectively interpreting them within the HRD domain remains unresolved. This protocol outlines the methodology for an umbrella review aiming to systematically synthesize existing reviews on AI in HRD. The review seeks to address key research questions regarding AI’s contributions to HRD functions and processes, as well as the opportunities and threats associated with its implementation by employing a technology-aided systematic approach. The coding framework will be used to synthesize the contents of the selected systematic reviews such as their search strategies, data synthesis approaches, and HRD-related findings. The results of this umbrella review are expected to provide insights for HRD scholars and practitioners, promoting continuous improvement in AI-driven HRD initiatives. This protocol is preregistered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/Z8NM6) on May 27, 2024.PLoS ONE160This protocol outlines the methodology for an umbrella review aiming to systematically synthesize existing reviews on AI in HRD by employing a technology-aided systematic approach, and the coding framework will be used to synthesize the contents of the selected systematic reviews.PLOS ONE["Sangok Yoo", "Kim Nimon", "S. Patole"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/8099c95e8ba666b4e9a8985f041d4c8f995f378a 00f4f00d20aa8bc06c25b7f3a3ae52ef32fcc58cLevee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information for levees, structures, and culverts as well as methods to identify indicators of future failures using AI and ML techniques. Robotic platform and instrumentation options that can be used in the data collection process are also explored, and a platform-agnostic solution is proposed.00Methods to gather information for levees, structures, and culverts as well as methods to identify indicators of future failures using AI and ML techniques are investigated and a platform-agnostic solution is proposed.["Kenneth Niles", "Emily Leathers", "Joe Tom", "Chandler Armstrong", "Osama Ennasr", "Brandon Dodd", "Theresa Coumbe", "Matthew Blevins", "Brenna Bennett", "Christina Rinaudo", "Nalini Torres", "Benjamin Breland", "Zachary H. Nick"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/00f4f00d20aa8bc06c25b7f3a3ae52ef32fcc58c 7b3c315aba65d9c627e55016cbee8684f0f3c1eeOne of the latest trends in generative Artificial Intelligence is tools that generate and analyze content in different modalities, such as text and images, and convert information from one to the other. From a conceptual point of view, it is interesting to study whether these modality changes incur information loss and to what extent. This is analogous to variants of the classical game telephone, where players alternate between describing images and creating drawings based on those descriptions leading to unexpected transformations of the original content. In the case of AI, modality changes can be applied recursively, starting from an image to extract a text that describes it; using the text to generate a second image, extracting a text that describes it, and so on. As this process is applied recursively, AI tools are generating content from one mode to use them to create content in another mode and so on. Ideally, the embeddings of all of them would remain close to those of the original content so that only small variations are observed in the generated content versus the original one. However, it may also be the case the distance to the original embeddings increases in each iteration leading to a divergence in the process and to content that is barely related to the original one. In this paper, we present the results of an empirical study on the impact of recursive modality changes using GPT-4o, a state-of-the-art AI multimodal tool, and DALL-E 3. The results show that the multimodality loop diverges from the initial image without converging to anything specific. We have observed differences depending on the type of initial image and the configuration of the models. These findings are particularly relevant due to the increasing use of these tools for content generation, reconstruction, and adaptation, and their potential implications for the content on the Internet of the future.arXiv.org370An empirical study on the impact of recursive modality changes using GPT-4o, a state-of-the-art AI multimodal tool, and DALL-E 3.0 shows that the multimodality loop diverges from the initial image without converging to anything specific.ArXiv["Javier Conde", "Tobias Cheung", "Gonzalo Mart'inez", "Pedro Reviriego", "Rik Sarkar"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/7b3c315aba65d9c627e55016cbee8684f0f3c1ee fed0fd93f2b9891a86f84828d8dd4544e5f3c8d2This paper investigates the influence that Artificial Intelligence (AI) has on job security, which in this study includes the severity of threats (ST) and feelings of powerlessness (PO), within the Romanian health tourism sector. Additionally, we analyse how AI-driven job engagement (ENG) impacts employees' turnover intentions (TI), providing perspectives about how to maintain workforce stability. As the recent literature indicates, there is growing concern among employees in various sectors regarding to the potential that Ai have to replace human labour, mostly with a specific focus on roles requiring interpersonal skills, such as those in health tourism. Utilising the Self-Determination Theory (SDT) and also by employing a quantitative methodology, we surveyed 131 spa and hotel employees using validated and multi-item scales to measure job engagement components and job insecurity dimensions. Our results reveal significant relationships between perceived powerlessness, job engagement, and turnover intentions, showing the mediating role of job engagement. In the current study, we found that educational level moderates the relationship between perceived job, threats, and turnover intentions. This indicates an interaction between employee characteristics and perceptions of the threats that AI is bringing. With this study, we contribute both to the theoretical understanding of how AI impacts employee psychology in the market of health tourism, and also by offering insights into managing workforce transitions in the face of technological advancements.Proceedings of the International Conference on Economics and Social Sciences00It is found that educational level moderates the relationship between perceived job, threats, and turnover intentions, which indicates an interaction between employee characteristics and perceptions of the threats that AI is bringing.Proceedings of the International Conference on Economics and Social Sciences["Marius Lucian Breab\u0103n", "I. Militaru", "Mariuzio Lanfranchi", "R. Hornoiu"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/fed0fd93f2b9891a86f84828d8dd4544e5f3c8d2 4aad301cbe5728ea9caf360435e9211d669f6c0bThis research delves into the utilization of artificial intelligence (AI) within the framework of campus resource allocation, with a primary focus on enhancing business management practices and fostering entrepreneurial sustainability within educational institutions. Through an innovative amalgamation of AI technology and SmartPLS methodology, the study constructs a comprehensive analytical framework aimed at tackling the multifaceted challenges inherent in resource allocation within campus environments. The findings underscore the transformative potential of AI integration in optimizing resource utilization, identifying efficiency gains, and nurturing entrepreneurial endeavors. This paper distinguishes itself from existing studies by presenting a novel approach that emphasizes the unique contributions of AI-driven solutions in both methodological innovation and practical application. By harnessing SmartPLS alongside AI, the research facilitates more accurate resource demand forecasting and enables adaptive decision-making processes, thereby contributing to the Sustainable Development Goals (SDGs), particularly in promoting quality education and sustainable management practices. The study also provides a detailed technical implementation of AI algorithms, offering valuable insights into their development and application within campus settings. The broader implications for the educational sector are explored, considering the scalability and adaptability of the proposed solutions in various educational contexts. Furthermore, the research contributes to theoretical advancements by pioneering the integration of AI and SmartPLS in campus management research, offering a fresh perspective on economic, environmental, and social impact assessments of AI-driven solutions.Aptisi Transactions on Technopreneurship (ATT)510Aptisi Transactions on Technopreneurship (ATT)["J. Juanda", "Reza Juang Riansyah", "Arsadi Arsadi", "Laurens Bethany"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/4aad301cbe5728ea9caf360435e9211d669f6c0b 391eb4db4b1abf598efb5c00b9552ad854044b17The use of computational tools, such as Artificial Intelligence, Machine Learning or Geographic Information Systems, has had a significant impact on the knowledge generated on environmental issues in recent years. The number of publications shown in this preliminary review of the IEEE Xplore Digital Library database shows its broad applicability and scientific relevance. Search filters and keywords such as water, air, soil, climate change, energy and waste were used. The data obtained was processed to visualize the proportion of use in key topics of environmental interest, giving scientific guidance towards the sites of greatest applicability, as well as towards areas that deserve to be reinforced. In this way, this work aims to promote the development of collaborative work solutions in the related areas of environmental and computational engineering.Revista Tecnología en Marcha00A preliminary review of the IEEE Xplore Digital Library database shows its broad applicability and scientific relevance, and aims to promote the development of collaborative work solutions in the related areas of environmental and computational engineering.Revista Tecnología en Marcha["Laura Hern\u00e1ndez-Alp\u00edzar", "Jos\u00e9 Andr\u00e9s G\u00f3mez-Mej\u00eda", "Mar\u00eda Bel\u00e9n Arg\u00fcello-Vega"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/391eb4db4b1abf598efb5c00b9552ad854044b17 aa7be6ce1d8eb35c13ed852e3133ce6e9f8fd7d4The artificial intelligence (AI) readiness performance of major economies can significantly impact the global economy. Therefore, analyzing the AI readiness performance of these economies is of great importance. In this study, the AI readiness performances of G7 countries were assessed using the most recent Government Artificial Intelligence Readiness Index (GAIRI) data for 2023. The analysis revealed that the importance of GAIRI components varies by country, with Data and Infrastructure generally being the most significant components. The countries were ranked according to their AI readiness performances using the LOPCOW-based MARCOS method as follows: USA, United Kingdom, Canada, France, Japan, Germany, and Italy. Notably, Italy's AI readiness performance was below the average, indicating the need for improvement to enhance its contribution to the global economy. The method applied proved to be sensitive in sensitivity analysis, credible and reliable in comparative analysis, and robust and stable in simulation analysis.Computer Science00The analysis revealed that the importance of GAIRI components varies by country, with Data and Infrastructure generally being the most significant components.Computer Science["F. Alt\u0131nta\u015f"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/aa7be6ce1d8eb35c13ed852e3133ce6e9f8fd7d4 adf0dc319ce43e4d1b6966dcc353082e3765fa86This study explores the impact of the super artificial intelligence (AI) revolution on the evolution of Management Information Systems (MIS) discipline. As AI departments are being set rapidly in all universities worldwide, recognizing that the super AI revolution has significantly transformed the MIS field, this study aims to propose a function formula to model the intricate dynamics between MIS and other related disciplines, such as business analytics, computer science, management science, software engineering, and artificial intelligence. The proposed formula will capture how the super AI revolution introduces new challenges and opportunities, influences the convergence and divergence of related fields, and affects the MIS discipline. Additionally, the study identifies key concerns and technical issues associated with super AI, offering potential mitigation strategies to address these challenges. By emphasizing the importance of interdisciplinary collaboration and the necessity for acquiring specialized skills, this study underscores the need for professionals to effectively navigate the evolving landscape shaped by the super AI revolution.Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi170This study aims to propose a function formula to model the intricate dynamics between MIS and other related disciplines, such as business analytics, computer science, management science, software engineering, and artificial intelligence.Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi["Ahmet Efe"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/adf0dc319ce43e4d1b6966dcc353082e3765fa86 1b638730c15669558bb94f09b25abf69310beb0cAs the digital landscape evolves with the continuous fast-paced development of Artificial Intelligence (AI), both businesses and consumers face numerous challenges posed by the ever-growing industry of AI. As business struggle to keep up with the technological advancements, consumers, on the other hand, face a more personal issue: their trust in an internet sustained by AI tools. Since half of the internet traffic is created by non-human bots and a third of all internet traffic is generated by “bad bots” which were developed for malicious purposes, the integration of AI managed to confer them human-like qualities. The “dead internet theory”, generated social media interactions and content, fake online reviews, generated blog posts, and the dilution of quality online content, all sustained by AI pose a threat to the trust and the legitimacy of the internet as a tool for humanity that was carefully built in the last decade. Our research is trying to find the level of trust of Romanian consumers in online platforms that are used as tools for selling and promotion of products and services, amidst the rapid integration of AI. The results can be used as a warning signal for consumers and policy makers alike to take a stronger stance on the online content that encourages or promotes online purchases. A survey has been deployed to 100 Romanian consumers, and the results have been analysed. Most respondents do base their purchasing decision on online reviews with slight differences between men and women yet most fear that AI and bots have a part in influencing these reviews.Proceedings of the International Conference on Economics and Social Sciences00This research is trying to find the level of trust of Romanian consumers in online platforms that are used as tools for selling and promotion of products and services, amidst the rapid integration of AI.Proceedings of the International Conference on Economics and Social Sciences["Ionut Tanase", "Lucia Nicoleta Barbu"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/1b638730c15669558bb94f09b25abf69310beb0c 63bbd701156a169b91ffbaed95306d1354871ad2This special issue of Eptic is focused specifically on the technology of artificial intelligence (AI). For all his technological acuity, Marx could not have foreseen the rise of the contemporary approach to AI called machine learning (ML). And while there is a long tradition of Marxist research on technology, there is, of yet, relatively little on AI specifically. We hope the inspiring and thought provoking articles published in this special issue can shed light on the dialectics of artificial intelligence.Revista Eletrônica Internacional de Economia Política da Informação da Comunicação e da Cultura00This special issue of Eptic is focused specifically on the technology of artificial intelligence (AI), and it is hoped the inspiring and thought provoking articles published can shed light on the dialectics of artificial intelligence.Revista Eletrônica Internacional de Economia Política da Informação da Comunicação e da Cultura["James Steinhoff", "Jonas Valente", "Rodrigo Moreno Marques"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/63bbd701156a169b91ffbaed95306d1354871ad2 5c30b7c872b512b73bf63d49d97346838d7e5cc9Conversational Artificial Intelligence has disrupted higher education by fundamentally altering its landscape. Fuelled by natural language processing and machine learning this technology has gained widespread adoption particularly since the release of ChatGPT in November 2022. As universities embrace digital transformation, assessment practices must evolve to align with the capabilities of Artificial Intelligence-driven chatbots and virtual assistants. This paper explores how conversational artificial intelligence impacts higher education, in particular, student assessment. A fundamental shift in assessment and evaluation of student competencies is necessary to not only consider knowledge retention but also critical thinking, communication, and adaptability skills. A review of the literature was conducted to understand how assignments should change due to the emergence of this disruptive technology. Conversational Artificial Intelligence and its application within the higher education context is uncertain, with disparate practices—in terms of ethical consideration and understanding—across the sector. A case study was conducted in which MSc Management students undertaking a specific module were tasked to use three Artificial Intelligence tools in their report writing of a business, to verify the sources and content provided by the Artificial Intelligence tool, and to critically evaluate the process as well as the output received for each prompt. The paper proposes a collaborative approach to navigate the ethical implementation and utilization of conversational Artificial Intelligence in higher education, advocating for the co-creation of guidelines through forums like Knowledge Cafés, stressing the need to rethink student assignments and its assessment and the adoption of artificial intelligence technologies by students for assignments.European Conference on Knowledge Management580The paper proposes a collaborative approach to navigate the ethical implementation and utilization of conversational Artificial Intelligence in higher education, advocating for the co-creation of guidelines through forums like Knowledge Cafés, stressing the need to rethink student assignments and its assessment and the adoption of artificial intelligence technologies by students for assignments.European Conference on Knowledge Management["D. Cranfield", "I. M. Venter", "John Mulyata"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/5c30b7c872b512b73bf63d49d97346838d7e5cc9 02e637102fd010be039427c83cd803b60d56b3c2Advances in health sciences education : theory and practice391The utility of a data-sharing culture is demonstrated by providing and leveraging a database of cardio-pulmonary resuscitation (CPR) performances that vary in quality and an Automatic Clinical Assessment tool for Basic Life Support that uses pose estimation to determine the spatial location of the participant's movements during CPR and a deep learning network that assesses the performance quality.Advances in health sciences education : theory and practice["M. Constable", "Francis Xiatian Zhang", "Tony Conner", "Daniel Monk", "Jason Rajsic", "Claire Ford", "Laura Jillian Park", "Alan Platt", "Debra Porteous", "L. Grierson", "Hubert P. H. Shum"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/02e637102fd010be039427c83cd803b60d56b3c2 0feb4ced64d7689f7bb562d7e4567dd87bdda66cJournal of Applied Learning &amp; Teaching00Journal of Applied Learning &amp; Teaching[]2024-09-09T00:00:00https://www.semanticscholar.org/paper/0feb4ced64d7689f7bb562d7e4567dd87bdda66c c75f8bf985438eceeed2f3b04c7465a28b4473bbInternational Journal of Politics, Culture, and Society180International Journal of Politics, Culture, and Society["Debangana Chatterjee"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/c75f8bf985438eceeed2f3b04c7465a28b4473bb 7f0f8ce5dbd92ba162b14e2bbd4dab5185a18080Intravascular ultrasound (IVUS) is an imaging technique that allows interventional cardiologists (ICs) to capture and visually display images of blood vessels during Percutaneous Coronary Interventions (PCI). IVUS images allow ICs to: (i) evaluate the type and characteristics of lesions present, (ii) assess the appropriate intervention, (iii) determine the size of the stent or balloon to be used if they were needed, and (iv) examine if the intervention was successful. The size of the stent or balloon is determined by identifying, and then measuring, the diameters of vessel or lumen borders at the treatment locations. Identification of vessel borders is a manual time-consuming process that is rigorous to accomplish. Alternatively, a machine learning model that automatically identifies and measures the lumen and vessel borders of IVUS images was developed. This industry case study shows how the HFE team lead the UI design process to incorporate this automation into the existing UI.Proceedings of the Human Factors and Ergonomics Society Annual Meeting10A machine learning model that automatically identifies and measures the lumen and vessel borders of IVUS images was developed and this industry case study shows how the HFE team lead the UI design process to incorporate this automation into the existing UI.Proceedings of the Human Factors and Ergonomics Society Annual Meeting["Judith Tiferes", "Mi Zhou"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/7f0f8ce5dbd92ba162b14e2bbd4dab5185a18080 021776cc74a1e4c77707519b2b6674b43d7ec36eMachine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy; however, their lack of transparency poses a significant challenge. Traditional black-box models often fail to provide interpretable justifications for their predictions, limiting their adoption in security-critical environments where understanding the reasoning behind a detection is essential for threat mitigation and response. Explainable AI (XAI) addresses this gap by enhancing model interpretability while maintaining strong detection capabilities. This survey presents a comprehensive review of state-of-the-art ML techniques for malware analysis, with a specific focus on explainability methods. We examine existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable. Additionally, we explore recent advancements and highlight open research challenges in the field of explainable malware analysis. By providing a structured overview of XAI-driven malware detection approaches, this survey serves as a valuable resource for researchers and practitioners seeking to bridge the gap between ML performance and explainability in cybersecurity.00This survey presents a comprehensive review of state-of-the-art ML techniques for malware analysis, with a specific focus on explainability methods, and examines existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable.["Harikha Manthena", "Shaghayegh Shajarian", "Jeffrey Kimmell", "Mahmoud Abdelsalam", "S. Khorsandroo", "Maanak Gupta"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/021776cc74a1e4c77707519b2b6674b43d7ec36e ba354f1bd57808f3b68c5f43c88daf998148a82dSabine Pfeiffer is a sociologist interested in the interaction between people, technology, and organization. She has worked at the University of Applied Sciences in Munich, Ruhr University Bochum, Friedrich-Alexander-University Erlangen Nuremberg, University of Hohenheim, and the University of Düsseldorf. She recently published the book Digital Capitalism and Distributive Forces. She points out two recurring blind spots. The first one is the lack of approaches to value and value creation. The second is the absence of interpretations on the realization of value on the market–a central function of the distributive forces.Revista Eletrônica Internacional de Economia Política da Informação da Comunicação e da Cultura00Sabine Pfeiffer points out two recurring blind spots in Digital Capitalism and Distributive Forces: the lack of approaches to value and value creation and the absence of interpretations on the realization of value on the market.Revista Eletrônica Internacional de Economia Política da Informação da Comunicação e da Cultura["Sabine Pfeiffe", "James Steinhoff", "Jonas Valente", "Rodrigo Moreno Marques"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/ba354f1bd57808f3b68c5f43c88daf998148a82d f29f251b7375c1b582c2a8213707b96811fe1d55ECE Official Conference Proceedings00ECE Official Conference Proceedings["Jes\u00fas Insuasti", "Felipe Roa", "C. M. Zapata-Jaramillo"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/f29f251b7375c1b582c2a8213707b96811fe1d55 ea2baeab68ddf3fa2b997bb335b57b0a918dc2ebIntegrated Spatial Databases190International Conference on Information Systems Development["M. Pettersen-Sobczyk", "Marta Ma\u0144kowska"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/ea2baeab68ddf3fa2b997bb335b57b0a918dc2eb 7fbbd8e823ad674de909381f1c43bc923e00efd4ECAH Official Conference Proceedings00ECAH Official Conference Proceedings["A. J. Iii"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/7fbbd8e823ad674de909381f1c43bc923e00efd4 de7f731108d706882ca49def12377f46a1ba7b94The linkages between adequate service delivery and sustainable development have been given a little academic attention in the South Africa’s local municipalities. For this reason, the achievement of sustainable development has been difficult which has culminated in the occurrence of service delivery protests. These service delivery protests have posed critical threats to social security thus affecting the possibility to achieve sustainable development in South Africa. the paper findings showed that the delivery of inadequate services to the citizens is triggered by the failure to equally include citizens in the process. One of the threats that the paper found is the fact that these service delivery protests have become a major issue and any move to solve them without citizen participation has been unsuccessful. The paper findings also showed that that the lack of adequate service delivery to the citizens causes human insecurities which in turn affect the achievement of sustainable development. This is because the occurrence of the service delivery protests deteriorates national economic growth and human growth. They affect foreign investors and international tourists by instilling fear in them and yet they are contributors to sustainable economic growth that leads to sustainable development. The findings of this paper also presented that the use of Artificial Intelligence (AI) technologies can increase citizen participation during service delivery. It is through the use of citizen participation that openness, transparency, accountability, and representation principles that promote the delivery of adequate services are possible. The paper found that using AI technologies would also foster trust between the service provider and service receiver needed for delivering adequate services, thus achieve sustainable development in South Africa.Journal of Infrastructure Policy and Development640The paper found that using AI technologies would also foster trust between the service provider and service receiver needed for delivering adequate services, thus achieve sustainable development in South Africa.Journal of Infrastructure, Policy and Development["E. B. Niyitunga", "Logic Lefika Modibedi"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/de7f731108d706882ca49def12377f46a1ba7b94 f7a420aa3a9a6c9541055e267ae3d4a8f69493d8ECE Official Conference Proceedings00ECE Official Conference Proceedings["A. Kayode", "Adedoyin Odumabo"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/f7a420aa3a9a6c9541055e267ae3d4a8f69493d8 6f0d36441df8b1b68917e7938a27b80141cb819800["Akansha Singh", "Krishna Kant Singh"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/6f0d36441df8b1b68917e7938a27b80141cb8198 dc4aa2585e7ad30df0bfc286f6ea1e13315a2b83Quality &amp; Quantity390Quality &amp; Quantity["Shih-Hao Lu", "Huyen Thi Thanh Tran", "Thanh-Sang Ngo"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/dc4aa2585e7ad30df0bfc286f6ea1e13315a2b83 20b6aebd9759b0aaec9a756a1d94beb837c4639bRevista Brasileira de Enfermagem10Revista Brasileira de Enfermagem["Isabelle Cristinne Pinto Costa", "Alice Silva Costa", "Karina Dal Sasso Mendes", "Ricardo Limongi"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/20b6aebd9759b0aaec9a756a1d94beb837c4639b f66d9951dcfe8504c1ae56238129951014bee972Research in Mathematics Education40Research in Mathematics Education["M. Mavrikis", "M. Margeti"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/f66d9951dcfe8504c1ae56238129951014bee972 2ed36e9f72bee7073b63d0b4cf7a9ce7d7d7d146Agile cognitive production systems mark a manufacturing paradigm shift, propelled by the demand for accelerated product development and the adoption of digitalised production systems across extensive supply networks. Cognitive manufacturing emphasises the role of technology and automation in the learning and adaptation process. These systems independently analyse data, make real-time adjustments and optimise processes, sometimes minimising the need for human intervention. Based on a conceptual framework that draws on the diversity of living systems and cognitive processes, cybernetics provides a solid theoretical background. It explores the intricate connections between cognition, self-organising systems and the challenges arising from the autonomy of such systems. The concept of "cognition" in "agile cognitive systems" moves away from the conventional understanding of purely technical processes and towards human thought processes. This departure fosters a dynamic exchange where individual thoughts resonate in social communication. Addressing the role of artificial intelligence (AI), the article emphasises examining computers from a social science standpoint, exploring the relationship between computers and mental systems, capturing human faculties such as cognition, utterance, and understanding. The integration of AI into computer-mediated communication leads to the question how AI-equipped computers intersect with societal intelligence notions. The inherent intransparency of AI, often viewed as a black box, prompts queries about the potential black-box nature of an autonomously controlled AI factory or supply chain. In this hypothetical scenario, the idea of the supply chain as a communication network is challenged, emphasising the importance of human involvement. Research on human-centric cognitive production emphasises explainable AI and human-in-the-loop. This orientation goes beyond the technical dimensions and incorporates social science considerations, which emphasises the holistic nature of current research. In essence, research in the field of cognitive production is a comprehensive exploration of the complex interplay between human cognition, artificial intelligence and the evolving landscape of modern production systems.Open Research Europe670Research in the field of cognitive production is a comprehensive exploration of the complex interplay between human cognition, artificial intelligence and the evolving landscape of modern production systems, which emphasises the holistic nature of current research.Open Research Europe["Stefan Walter"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/2ed36e9f72bee7073b63d0b4cf7a9ce7d7d7d146 f7c902c8d8e23a44a41f5a198b77ffb3b64aa830Physics-informed neural networks, or PINNs, are indicative of a new approach that involves the use of scientific knowledge, as these programs adhere to laws of physics described by general nonlinear partial differential equations while solving problems that are related to physics. This is accomplished via programming these equations into the loss function, which ensures that the underlying system adheres to these laws. This paper will be discussing how PINNs function and analyze how they make use of physics when solving problems. PINNs can be used to model physical systems and phenomena in the real world, including combustion, quantum mechanics, and the simulation of fluid. The data embedded into the code of PINNs also serves to address the issue some neural networks may have with a lack of important data needed to solve relevant scientific issues. The rules and constraints PINNs have ensures that they will provide more realistic solutions in comparison to alternatives. Lastly, this paper will be discussing the potential future applications of PINN programming and functionality on future artificial intelligence (AI) development. PINNs have the potential to address complex scientific problems in a way that other solutions may not be able to, and as such, they are an important topic of discussion.Proceedings of London International Conferences60How PINNs function and analyze how they make use of physics when solving problems is discussed, and the potential future applications of PINN programming and functionality on future artificial intelligence (AI) development is discussed.Proceedings of London International Conferences["Tejas Nair", "Merve Gokgol"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/f7c902c8d8e23a44a41f5a198b77ffb3b64aa830 713ad215098a1ac6e6c5296a30ca5f422bf62b6b: This paper focuses on generative AI, a typical representative of contemporary artificial intelligence (AI) and artificial general intelligence (AGI), aiming to delve into the latest research progress in its basic theory. The research method involves a comparative analysis of the differences in underlying logic and formal understanding between traditional AI and Current AI, further exploring the distinctions between the three core viewpoints of traditional AI (symbolism, connectionism, behaviorism) and the three major schools of Current AI (generative AI/AGI based on large language models (LLMs) such as ChatGPT; new quality productive force AGI characterized by small models, such as I3DNA; and twin Turing machines based on dual formal understanding models that are compatible with both large and small models). The research reveals the core components of the basic theory of AI and AGI: bit-list logic, linkage functions, followed by generalized bilingualism or generalized translation based on digital and intelligent text with the three fundamental laws. The significance of this research lies in not only enhancing the interpretability of generative AI/AGI based on LLMs represented by ChatGPT but also providing generalized translations for the new quality productive force AGI characterized by small models and its complex theories of cosmic intelligence and the universal model series. At the same time, it demonstrates the potential of twin Turing machines as inclusive intelligent agents in integrating data, knowledge, computing power, algorithms, and human-computer mutual assistance in the new era of cognitive paradigms, laying the foundation for constructing super intelligent systems.Computer Science and Technology10The significance of this research lies in not only enhancing the interpretability of generative AI/AGI based on LLMs represented by ChatGPT but also providing generalized translations for the new quality productive force AGI characterized by small models and its complex theories of cosmic intelligence and the universal model series.Computer Science and Technology["Xiaohui Zou"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/713ad215098a1ac6e6c5296a30ca5f422bf62b6b 58ee4d0e4ea1c00020b258697a983a49e50d0798Objective: This tutorial introduces the use of Alumni AI Pesquisa, a chatbot based on GPT-4, publicly available on the OpenAI platform, designed to support the ethical and transparent use of Artificial Intelligence (AI) in academic research. The aim is to guide authors, editors, and reviewers in the responsible application of AI, ensuring scientific integrity throughout the editorial process. Method: The tutorial provides a step-by-step guide on how to access and utilize Alumni AI Pesquisa. The GPT-4 offers guidance on the ethical use of AI in academic writing, the oversight of editorial decisions assisted by AI, as well as compliance with editorial guidelines and manuscript formatting. Correct usage of AI is promoted through practical examples and references to global best practices. Results: Alumni AI Pesquisa provides immediate support tailored to different user profiles (authors, editors, and reviewers), encouraging transparent AI use in manuscripts. The tool ensures that AI-assisted decisions are validated by human supervisors, guaranteeing adherence to ethical and technical standards. Conclusions: Publicly available on the OpenAI platform, Alumni AI Pesquisa, powered by GPT-4, significantly contributes to the promotion of scientific integrity by facilitating responsible AI use in the academic environment. It is recommended that the use of the tool be cited in publications, explicitly mentioning the version (GPT-4).Review of Artificial Intelligence in Education45Alumni AI Pesquisa, a chatbot based on GPT-4, publicly available on the OpenAI platform, designed to support the ethical and transparent use of Artificial Intelligence in academic research, significantly contributes to the promotion of scientific integrity by facilitating responsible AI use in the academic environment.Review of Artificial Intelligence in Education["Altieres de Oliveira Silva", "Diego dos Santos Janes", "Renan Santos"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/58ee4d0e4ea1c00020b258697a983a49e50d0798 8d56b8ac45be23d6837eb78d8015cc970b53ba0cScientific Reports565A joint co-learning approach for multimodal fusion is introduced, enabling end-to-end training of deep neural networks and facilitating the learning of complementary information from both imaging and clinical modalities, showing the proposed framework's efficacy in predicting subtypes of PD and aiding in early diagnosis.Scientific reports["Vincenzo Dentamaro", "D. Impedovo", "Luca Musti", "G. Pirlo", "Paolo Taurisano"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/8d56b8ac45be23d6837eb78d8015cc970b53ba0c 6127ebdfa49f1d3b641842fc0255c87b0e479bfe Purpose Generative artificial intelligence (GAI) has seen exponential growth in recent years due to its capability to generate original content through natural language processing and comprehensive language models. This paper aims to investigate the transformative impact of GAI on higher education, focusing on the evolving roles of faculty in the classroom. Design/methodology/approach Using a phenomenological perspective and a process approach, the study involved 25 semi-structured interviews with academicians in higher education. Findings The findings reveal that GAI currently creates biased and commercially driven learning environments, challenging traditional pedagogical models. Despite its potential for enhancing education, the autonomous nature of GAI often prioritizes commercial interests over pedagogical goals. Research limitations/implications The study is limited to faculty perspectives, suggesting future research should include student viewpoints and diverse educational contexts. Practical implications The study highlights the need for higher education institutions to develop comprehensive policies, provide training for faculty and students and design new courses that leverage GAI for personalized learning experiences and enhanced faculty research. Originality/value This paper contributes to the emerging literature on GAI’s impact on education, highlighting its dual nature as both a transformative tool and a potential threat to traditional educational roles and outcomes. Quality Assurance in Education543The findings reveal that GAI currently creates biased and commercially driven learning environments, challenging traditional pedagogical models and highlighting the need for higher education institutions to develop comprehensive policies, provide training for faculty and students and design new courses that leverage GAI for personalized learning experiences and enhanced faculty research.Quality Assurance in Education["S. Aad", "M. Hardey"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/6127ebdfa49f1d3b641842fc0255c87b0e479bfe 53b20ddfb71ca180f6addc50128ca9f030dd967aBusiness Model Innovation (BMI) involves redefining how organizations create, deliver, and capture value. With the advent of Artificial Intelligence (AI), businesses are increasingly leveraging these technologies to transform their models. The current research investigates the impact of AI on BMI using primary data collected from various industries. It employs statistical analyses to understand the extent of AI adoption, its effects on business model components and various key business metrics. The study identifies key trends in AI adoption across functions and industries. Key findings reveal significant correlations between AI usage and improvements in key business metrics such as operational efficiency, customer engagement, and revenue streams thus resulting in an organization’s competitive advantage. Businesses can make better-informed decisions, establish more effective workflows, and produce more powerful marketing campaigns as a result of AI adoption. It also provides a competitive edge and lays the framework for future growth. This academic work makes a substantial contribution to the discourse on AI for business model innovation, where technology is leveraged to optimize strategy.RVIM Journal of Management Research442Key findings reveal significant correlations between AI usage and improvements in key business metrics such as operational efficiency, customer engagement, and revenue streams thus resulting in an organization’s competitive advantage.RVIM Journal of Management Research["Nagalakshmi Mvn", "Chandrika Reddy P"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/53b20ddfb71ca180f6addc50128ca9f030dd967a 63375a91106c9405ac786f2a7ea8c2cb649aa15bThe rapid advancement of artificial intelligence (AI) technologies has fundamentally transformed the landscape of information technology (IT), offering unprecedented opportunities for innovation and efficiency. However, these advancements also bring significant ethical challenges, including issues of bias, privacy, transparency, and accountability. This paper explores these ethical challenges and proposes a comprehensive ethical framework for the responsible development and deployment of AI in IT. Through an examination of historical context, current trends, and detailed case studies, the framework aims to provide actionable guidelines to mitigate biases, protect privacy, enhance transparency, and ensure accountability in AI systems. By fostering ethical AI practices, this framework aspires to support the sustainable and equitable advancement of AI technologies, ultimately benefiting society as a wholeSocial Science Research Network01A comprehensive ethical framework for the responsible development and deployment of AI in IT is proposed and aims to provide actionable guidelines to mitigate biases, protect privacy, enhance transparency, and ensure accountability in AI systems.SSRN Electronic Journal["Dimitrios Sargiotis"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/63375a91106c9405ac786f2a7ea8c2cb649aa15b eeaf0b09c6996b00e244a4554d67af5a2b58c69aSoftware testing is a crucial phase in the software development lifecycle (SDLC), ensuring that products meet necessary functional, performance, and quality benchmarks before release. Despite advancements in automation, traditional methods of generating and validating test cases still face significant challenges, including prolonged timelines, human error, incomplete test coverage, and high costs of manual intervention. These limitations often lead to delayed product launches and undetected defects that compromise software quality and user satisfaction. The integration of artificial intelligence (AI) into software testing presents a promising solution to these persistent challenges. AI-driven testing methods automate the creation of comprehensive test cases, dynamically adapt to changes, and leverage machine learning to identify high-risk areas in the codebase. This approach enhances regression testing efficiency while expanding overall test coverage. Furthermore, AI-powered tools enable continuous testing and self-healing test cases, significantly reducing manual oversight and accelerating feedback loops, ultimately leading to faster and more reliable software releases. This paper explores the transformative potential of AI in improving test case generation and validation, focusing on its ability to enhance efficiency, accuracy, and scalability in testing processes. It also addresses key challenges associated with adapting AI for testing, including the need for high quality training data, ensuring model transparency, and maintaining a balance between automation and human oversight. Through case studies and examples of real-world applications, this paper illustrates how AI can significantly enhance testing efficiency across both legacy and modern software systems.arXiv.org01This paper addresses key challenges associated with adapting AI for testing, including the need for high quality training data, ensuring model transparency, and maintaining a balance between automation and human oversight.ArXiv["Mohammad Baqar", "Rajat Khanda"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/eeaf0b09c6996b00e244a4554d67af5a2b58c69a 9efdb775aae6534aa2ae8388e4b7c37bb5bfed33Researchers have adopted model-centric artificial intelligence (AI) approaches in cheminformatics by using newer, more sophisticated AI methods to take advantage of growing chemical libraries. It has been shown that complex deep learning methods outperform conventional machine learning (ML) methods in QSAR and ligand-based virtual screening1–3 but such approaches generally lack explanability. Hence, instead of developing more sophisticated AI methods (i.e., pursuing a model-centric approach), we wanted to explore the potential of a data-centric AI paradigm for virtual screening. A data-centric AI is an intelligent system that would automatically identify the right type of data to collect, clean and curate for later use by a predictive AI and this is required given the large volumes of chemical data that exist in chemical databases – PubChem alone has over 100 million unique compounds. However, a systematic assessment of the attributes and properties of suitable data is needed. We show here that it is not the result of deficiencies in current AI algorithms but rather, poor understanding and erroneous use of chemical data that ultimately leads to poor predictive performance. Using a new benchmark dataset of BRAF ligands that we developed, we show that our best performing predictive model can achieve an unprecedented accuracy of 99% with a conventional ML algorithm (SVM) using a merged molecular representation (Extended + ECFP6 fingerprints), far surpassing past performances of virtual screening platforms using sophisticated deep learning methods. Thus, we demonstrate that it is not necessary to resort to the use of sophisticated deep learning algorithms for virtual screening because conventional ML can perform exceptionally well if given the right data and representation. We also show that the common use of decoys for training leads to high false positive rates and its use for testing will result in an over-optimistic estimation of a model’s predictive performance. Another common practice in virtual screening is defining compounds that are above a certain pharmacological threshold as inactives. Here, we show that the use of these so-called inactive compounds lowers a model’s sensitivity/recall. Considering that some target proteins have a limited number of known ligands, we wanted to also observe how the size and composition of the training data impact predictive performance. We found that an imbalance training dataset where inactives outnumber actives led to a decrease in recall but an increase in precision, regardless of the model or molecular representation used; and overall, we observed a decrease in the model’s accuracy. We highlight in this study some of the considerations that one needs to take into account in future development of data-centric AI for CADD.bioRxiv591It is demonstrated that it is not necessary to resort to the use of sophisticated deep learning algorithms for virtual screening because conventional ML can perform exceptionally well if given the right data and representation.bioRxiv["Allen Chong", "Ser-Xian Phua", "Yunzhi Xiao", "Woon Yee Ng", "H. Li", "W. Goh"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/9efdb775aae6534aa2ae8388e4b7c37bb5bfed33 4a1cdf0f948cf06ceb1f4e7fc915d7397246e2d1The increasing development of artificial intelligence (AI) technology has raised considerable interest in its application within educational environments, particularly in higher education. This study examines the dynamics of AI technology acceptance among service sector academia with the intent of delineating the critical determinants that influence its adoption and utilisation. Emphasising a comparative analysis, this investigation juxtaposes the perceptions of both students and professors. A systematic keyword search was implemented to evaluate pertinent studies encompassing these determinants, in conjunction with relevant theoretical constructs and academic fields. Although the existing literature offers substantial information on AI adoption factors within the service sector, a lacuna persists in understanding the variables and conceptual frameworks that characterise the acceptance of AI technology in higher education in the service sector. Identifying these drivers of adoption could be of great benefit to students, professors, but mostly to policy-makers who are poised to devise and execute strategic initiatives advocating for the seamless integration of AI into pedagogy, scholarly inquiry, and the broader academic field.Proceedings of the International Conference on Economics and Social Sciences01This study examines the dynamics of AI technology acceptance among service sector academia with the intent of delineating the critical determinants that influence its adoption and utilisation.Proceedings of the International Conference on Economics and Social Sciences["Luciana-Floriana Poenaru", "Delia Popescu", "R. Hornoiu", "G. Lanfranchi"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/4a1cdf0f948cf06ceb1f4e7fc915d7397246e2d1 5e08154637d04810d9467e5541cccdd806a2e747As artificial intelligence (AI) tools become more widely used in higher education, we must pay attention to the risks that can emerge. AI projects, whether applied in classroom learning or used for decision-making regarding admissions, financial aid allocation, or hiring, must include attention to governance and compliance issues, regardless of the project’s scope and scale. Concerns highlighted in this work include transparency, user privacy, data confidentiality, data integrity, and system availability, however, we note that this is a non-exhaustive list of risks. In this paper, risk assessment is defined, and two examples of risk management frameworks, namely the United States National Institute of Standards and Technology Artificial Intelligence Risk Management Framework and the non-profit humanitarian effort ForHumanity’s Independent Audit of AI, Algorithmic, and Autonomous Systems are briefly described.  We identify characteristics of AI applications that need to be assessed for vulnerabilities they may present, such as bias and discrimination. This paper aims to facilitate discussion among stakeholders about the risks that may be encountered from using AI in higher education, as well as to suggest ways developers, decision-makers, and users can mitigate these risks. Much discussion and published literature has focused on risk management frameworks designed for large organizations or enterprises or frameworks that do not consider risks specific to AI. We hope that decision-makers carefully consider the risks, perform due diligence when implementing AI applications, and create a plan for mitigating the risks. This research supports e-learning practice because students and faculty are embracing AI applications.  Leaders and decision-makers in higher education need to be proactive in protecting their varied stakeholders. The paper asks what risks may be encountered by institutions of higher education when using AI tools and products in the classroom and for various aspects of decision-making and if published frameworks can mitigate these risks.Electronic Journal of e-Learning201What risks may be encountered by institutions of higher education when using AI tools and products in the classroom and for various aspects of decision-making and if published frameworks can mitigate these risks are asked.Electronic Journal of e-Learning["Donna Schaeffer", "Lori Coombs", "Jonathan Luckett", "Marvin Marin", "Patrick Olson"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/5e08154637d04810d9467e5541cccdd806a2e747 c8b19e49a020dcc676e78036e7faf4e4d52973d4Federated Learning (FL) holds immense potential for transforming the industrial landscape by leveraging distributed data to solve Artificial Intelligence (AI) use cases with collectively trained models in a privacy-preserving way. In this regard, Industrial FL (IFL) arose as a collaborative approach for training AI models between multiple industry partners and devices without the need to share the actual training data. However, despite its promising prospects, the transition and successful implementation of FL in practice is currently lagging behind, posing challenges for industrial companies. To address this, it is of crucial relevance to analyze different business types and involved stakeholders to be able to design FL-based solutions tailored to the industries needs. This paper presents the results of 13 semi-structured interviews conducted in Austrian industries, involving 11 companies from different domains. We identify AI applications, pain points, and attitudes towards AI and FL. Based on the interviews, three industry personas are derived, namely, service business, production optimization, and complex product and project business. To address the needs of these personas, three collaborative FL solution blueprints are proposed. The blueprints include system architectures, implementation steps, and collaboration modes for the involved parties. The blueprints are discussed based on dimensions such as FL paradigm, collaboration mode, key benefits, main addressed needs, and challenges.Conference on Business Informatics240Three collaborative FL solution blueprints are proposed based on dimensions such as FL paradigm, collaboration mode, key benefits, main addressed needs, and challenges based on semi-structured interviews conducted in Austrian industries, involving 11 companies from different domains.2024 26th International Conference on Business Informatics (CBI)["Thomas Blumauer-Hiessl", "Angela Fessl", "Gert Breitfuss", "Daniel Schall", "Stefan Schulte"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/c8b19e49a020dcc676e78036e7faf4e4d52973d4 0911ee4f9caa6f135be047ea08c2a2ee0af3f12fEarly and accurate screening of Alzheimer’s dementia (AD) is critical for timely intervention and management. Speech analysis is one of the promising approaches for AD screening, however, most AI models developed for this task lack transparency, hindering clinical use. There is an urgent need for implementing and assessing Explainable Artificial Intelligence (XAI) techniques to demystify such models, making their predictions transparent and understandable for clinical decision-making. This study explores the efficacy of utilizing linguistic features derived from speech transcripts during the Cookie Theft Picture Task for the screening of AD, with explainability offered through Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). A comprehensive set of linguistic features were extracted from the transcripts and a predictive model was developed to identify individuals with AD. LIME and SHAP were used to identify the most influencial linguistic features that contribute to the model’s decision-making. Results demonstrate the XAI model not only achieves high classification accuracy (80.00%) but also identifies key discriminative features that are associated with AD.SMM24, Workshop on Speech, Music and Mind 2024170This study explores the efficacy of utilizing linguistic features derived from speech transcripts during the Cookie Theft Picture Task for the screening of AD, with explainability offered through Local Interpretable Model-agnostic Explanations and SHapley Additive exPlanations.SMM24, Workshop on Speech, Music and Mind 2024["Faiza Iqbal", "Zafi Sherhan Syed", "Muhammad Shehram Shah Syed", "Abbas Syed"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/0911ee4f9caa6f135be047ea08c2a2ee0af3f12f a855cb7f94d6b4b1c75ba7c5444802df76a5172cArtificial Intelligence (AI) permeates daily life and industries through enhanced machine learning, natural language processing, and computer vision. Generative AI, a significant advancement, mimics human-like text and makes data-driven decisions, particularly in generative AI applications. Exploring novel AI applications is crucial, such as using generative AI to aid General Aviation (GA) pilots in preflight weather planning, with the aim to enhance pilots’ awareness and reduce weather-related accidents. However, the rapid evolution of generative AI raises many concerns like volatility, security risks, and decision-making biases. Preflight weather planning is vital in aviation, with weather-related incidents comprising a significant portion of accidents. Despite advancements, GA pilots’ interpretation of weather information remains subpar. This paper examines how generative AI may have potential to assist GA pilots to perform preflight weather planning, while also addressing risks and suggesting ethical research directions.Proceedings of the Human Factors and Ergonomics Society Annual Meeting110This paper examines how generative AI may have potential to assist GA pilots to perform preflight weather planning, while also addressing risks and suggesting ethical research directions.Proceedings of the Human Factors and Ergonomics Society Annual Meeting["Stephen Woods", "Elizabeth Blickensderfer"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/a855cb7f94d6b4b1c75ba7c5444802df76a5172c 0a7e7bda3ff48ce7ec5d74b152739323ff53c6e5This paper examines the transformational potentials that artificial intelligence (AI) may hold to reshape our public policies and methods of administration in the unique post-colonial context of Africa. We thus seek to unearth how AI technologies can be employed at a continental scale in the remedy of legacy issues arising from colonialism including; governance inefficiency, literacy gaps, and inequitable service delivery across the continent. From critically analyzing the application of AI in various public sectors, our research seeks to unveil opportunities for AI in inclusive decision-making processes to improve transparency as well as tailoring public service delivery to the diversified needs of African populations. The paper describes the way forward in the adoption of AI solutions that involve issues on a variety of considerations, infrastructure requirements, financial obstacles, and capacity development, among others. Highlighting the potential of AI in governance, this research underscores the place of local innovation stakeholder engagement, and international collaboration in assuring that AI plays out as a development lever for both sustainable development and empowerment in post-colonial Africa.  Proceedings of London International Conferences190The way forward in the adoption of AI solutions that involve issues on a variety of considerations, infrastructure requirements, financial obstacles, and capacity development, among others are described.Proceedings of London International Conferences["Joseph Otochi Onduko", "Michael Acharya Kalombo", "Makuach Dut Kuol", "Bentley Gift Makale", "Mahsen Abdulkarim Saleh"]2024-09-09T00:00:00https://www.semanticscholar.org/paper/0a7e7bda3ff48ce7ec5d74b152739323ff53c6e5 180cc00d2ceaf5062a6a495889d325176bd5bd5eAbstract This article argues that the integration of artificial intelligence (AI) into healthcare, particularly under the European Union’s Artificial Intelligence Act (AI-Act), poses significant implications for the doctor–patient relationship. While historically paternalistic, Western medicine now emphasises patient autonomy within a consumeristic paradigm, aided by technological advancements. However, hospitals worldwide are adopting AI more rapidly than before, potentially reshaping patient care dynamics. Three potential pathways emerge: enhanced patient autonomy, increased doctor control via AI, or disempowerment of both parties as decision-making shifts to private entities. This article contends that without addressing flaws in the AI-Act’s risk-based approach, private entities could be empowered at the expense of patient autonomy. While proposed directives like the AI Liability Directive (AILD) and the revised Directive on Liability for Defective Products (revised PLD) aim to mitigate risks, they may not address the limitations of the AI-Act. Caution must be exercised in the future interpretation of the emerging regulatory architecture to protect patient autonomy and to preserve the central role of healthcare professionals in the care of their patients.Medical Law Review04Caution must be exercised in the future interpretation of the emerging regulatory architecture to protect patient autonomy and to preserve the central role of healthcare professionals in the care of their patients.Medical Law Review["Barry Solaiman", "Abeer Malik"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/180cc00d2ceaf5062a6a495889d325176bd5bd5e d5650f876b58dbeeb76e353cc343287af46e8f5aTechnology is evolving and being implemented across nearly every sector of society, including health, nutrition, and sustainability. Specifically, artificial intelligence (AI) has become an essential tool in gastronomy, not only facilitating chefs’ work but also fostering business innovation through cost reduction. However, for a gastronomic business to be profitable, it is crucial to understand its strategic elements. In this study, three groups associated with gastronomy—chefs, entrepreneurs, and gastronomic experts—were surveyed to gather their opinions on the application of artificial intelligence in the restaurant sector in Spain. Additionally, the Business Model Canvas and Lean Model Canvas were developed, specifically adapted for the restaurant sector. These models, as novel approaches, allowed for the identification of key success factors based on the respondents’ experiences, considering that the Business Model Canvas focuses on the market and the company, while the Lean Model Canvas prioritizes the market and the product. This distinction is essential for mitigating the high failure rate in the restaurant industry in Spain. The results from the Canvas models and SWOT analysis have allowed us to understand the participants’ views. They largely see the use of AI in gastronomy as beneficial due to innovation in recipes and cost savings. However, concerns were raised about the potential loss of human touch in dish preparation and increased unemployment due to the automation of some cooking processes. These findings could be highly relevant for future restaurant entrepreneurs.Administrative Sciences791Administrative Sciences["M\u00aa Genoveva Dancausa Mill\u00e1n", "M\u00aa Genoveva Mill\u00e1n V\u00e1zquez de la Torre"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/d5650f876b58dbeeb76e353cc343287af46e8f5a 99614890519732ac056e278c20ca18befed0188b Performing complex activities on industrial workshop floors carries the risk that incidents and injuries might occur. Traditional health, safety and environment (HSE) monitoring solutions require significant manual intervention, are prone to biases and inaccuracies, and are reaching their limits. A company's technology lifecycle management (TLM) group realized there was a need for a proactive and innovative approach and developed an automated system to detect unsafe conditions and acts, provide timely alerts for prevention or mitigation, and influence human behavior. A unique software application, named Digital Workshop, was developed within the company's TLM asset performance management platform. The software leverages advancements in artificial intelligence (AI) using on-site workshop camera feeds coupled with customized vision analytics to automatically detect, categorize, and report HSE non-compliance events. Examples of events that can be automatically detected include zone intrusions, restricted areas access, solo worker activities, personal protective equipment (PPE) adherence and general housekeeping as well as dynamic high-risk scenarios including mechanical lifting, forklift operations, lathing, grinding, welding, and the use of rotating machinery. HSE non-compliance notifications are sent to mobile phones and recorded in the company's system of records enabling corrective actions to be taken. This AI-based safety approach was successfully implemented in the company's workshop environments, on shop floors and in operational facilities. The application generates HSE-related statistics and insights that can greatly enhance location performance, by helping to avoid incident recurrence, and improve the working environment for personnel. The application can identify HSE unsafe behaviors or practices, such as PPE non-compliance, forklift proximity, mechanical lifting non-compliance, zone intrusion, working at heights and other high-risk activities. Early detection enables timely intervention and incident prevention. The system has been particularly beneficial to area managers and supervisors who conduct pre-operational and regular safety meetings. The information is valuable to teams tasked with reviewing and addressing non-compliance events and implementing continuous improvement actions. Recorded footage can be used for learning and coaching moments and helps identify areas where additional control measures or personnel training may be needed. This has enabled detection, analyses and corrective actions associated with undesirable behaviors and unsafe practices to be implemented. Post-deployment benefits have ranged from safety behavioral changes to creative solutions being proposed by personnel. It has been found to be especially beneficial for contractors and new hires, who typically experience a higher rate of incidents. The technology is scalable to different work environments, including while driving and in non-company operated facilities. This innovative technology uses proprietary machine learning algorithms, developed in-house, to detect a wide range of HSE non-conformance scenarios. The AI model has been trained for various camera positioning and data from different locations to account for varying conditions and environments in the company's facilities. The system has been taught to identify the HSE non-conformances and automatically issue alerts or notifications to end users.SPE International Health, Safety, Environment and Sustainability Conference and Exhibition01An automated system to detect unsafe conditions and acts, provide timely alerts for prevention or mitigation, and influence human behavior was developed and successfully implemented in a company's workshop environments, on shop floors and in operational facilities.SPE International Health, Safety, Environment and Sustainability Conference and Exhibition["Ali Osman", "Cecilia Tossoni", "Edward Kotochigov", "Arijit Paul"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/99614890519732ac056e278c20ca18befed0188b 269322d3e3463705ad797ef30aa8384cdd31981eThis paper explores the integration of Artificial Intelligence (AI) to enhance human-machine interaction in medical exoskeleton devices. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics can significantly improve the efficiency and comfort of these devices. Machine learning algorithms analyze sensor data in real-time, optimizing control strategies to adapt to user movements. NLP enables intuitive control through voice commands, reducing the cognitive and physical burden on users. Predictive analytics anticipates user needs, enhancing responsiveness and reducing the risk of errors. Case studies of AI-integrated exoskeletons, like the EksoGT and ReWalk, demonstrate improved rehabilitation outcomes and user satisfaction. Despite challenges such as data privacy and the need for significant investment, the benefits of AI integration are substantial. This paper provides insights into the potential of AI to transform medical exoskeletons, offering new levels of independence and quality of life for individuals with mobility impairments. Future research should focus on developing advanced AI algorithms and exploring new applications to further enhance user experience and device performance.Applied and Computational Engineering01Insight is provided into the potential of AI to transform medical exoskeletons, offering new levels of independence and quality of life for individuals with mobility impairments.Applied and Computational Engineering["Mengyao Han"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/269322d3e3463705ad797ef30aa8384cdd31981e b152d11093914ff3a4690f7b7f745a480c72ee95The use of artificial intelligence (AI) is revolutionizing many professions and research fields. Thus, the present study focuses on the implications that AI is having on research in materials science and engineering (MSE). To this end, a bibliometric review has been conducted to analyze the advances that AI is generating in MSE. Although expectations for AI advances in the field of MSE are high, the results of this study indicate that we are still at a preliminary stage of development. It is worth highlighting that despite the progress made, the potential of AI in MSE has not been fully exploited and numerous challenges remain to be overcome to achieve effective and widespread implementation. It should be noted that the subarea “Materials structure, processing, and properties” is the one that currently presents the largest number of research works linked to AI. It appears that the United States and China are currently the countries with the greatest involvement in the use of AI in the field of MSE. The emerging themes and thematic map of the topic are revealed, and future research directions are provided.Applied Sciences261Applied Sciences["Diego Vergara", "Georgios Lampropoulos", "Pablo Fern\u00e1ndez\u2010Arias", "\u00c1lvaro Ant\u00f3n\u2010Sancho"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/b152d11093914ff3a4690f7b7f745a480c72ee95 decba109df1012d240840ad48aa8e82c7743bf8cThe article considers not only the obvious benefits of using AI, but also potential risks associated with changing consumer behavior. The study introduces the User Engagement Index (UEI), which allows measuring and comparing user activity and interaction with marketplaces, considering the influence of AI. The proposed methodology can become the basis for further research in the field of human-artificial intelligence interaction in the digital environment. The UEI formula allows for a comprehensive assessment of user engagement, considering several key factors that influence user behavior on the site.Journal of Ecohumanism61The study introduces the User Engagement Index (UEI), which allows measuring and comparing user activity and interaction with marketplaces, considering the influence of AI, and proposes the proposed methodology, which allows for a comprehensive assessment of user engagement.Journal of Ecohumanism["Shmatko Aleksey Dmitrievich", "Volkova Anastasia Anatolyevna", "Rasulov Zainodin Nurmagomedovich", "Remshev Evgeny Yuryevich", "Olekhver Aleksey Ivanovich"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/decba109df1012d240840ad48aa8e82c7743bf8c ef64e86d51071530d7740624bf02bb5b0138c33e The dynamic landscape of consumer behavior, shaped by technological advances and sustainability considerations, has led to a reassessment of retail strategies, especially in the beauty industry. This study centers on the intersection of Artificial Intelligence (AI) and sustainability, particularly concerning Generation Z (Gen Z) consumers in the Indonesian beauty market. It explores how these factors influence Gen Z’s purchase decisions, offering insights for beauty brands to adapt strategically. Garnier, a L’Oreal subsidiary, faces heightened competition in the dynamic beauty market, especially with the emergence of local beauty products, adding complexity to its business landscape. This intensifies the need for strategic responses to maintain a competitive edge in the cosmetics industry. The research assesses the impact of AI technology, specifically using Garnier Skin Coach AI, on Gen Z’s purchase intentions for Garnier skincare products in Indonesia. It also examines the influence of sustainability on Gen Z’s preferences and purchase decisions in the Indonesian beauty market, adopting the Stimulus-Organism-reaction (SOR) model. Conducting a quantitative study with 400 Gen Z respondents, the research utilized online surveys through Qualtrics XM and analyzed data using Structural Equation Modeling (SEM) in SPSS AMOS 26.0. The findings highlight the substantial impact of AI technology, especially in enhancing hedonic values. Accurate information retrieval and interactive engagement create nuanced elements that heighten the appeal. Sustainability initiatives focusing on eco-friendly and cruelty-free practices significantly affect preferences, indicating a growing preference for sustainability-enriched experiences and affecting purchase intention. To enhance Garnier Skin Coach AI, a comprehensive strategy is recommended. This involves refining User-Centric Design, educating users, and boosting purchase intention through perceived utilitarian value. The proposed tactics align with customer preferences, encourage personalized interactions, integrate predictive skin insights, and the addition of e-wallet features. European Journal of Business and Management Research831European Journal of Business and Management Research["Lyanlie Winarto", "Anggara Wisesa"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/ef64e86d51071530d7740624bf02bb5b0138c33e 82f9314898f99b7bddfeeb46969762b1716d7e7eModern Digital Twin standards and technologies, designed to enable smart factories in line with the Industry 4.0 vision, have not yet addressed all the challenges associated with contemporary digital twins. This paper shows how to leverage the Asset Administration Shell as an Industry 4.0 compliant Digital Twin technology to provide all necessary data for Artificial Intelligence Planning, represented via the Planning Domain Definition Language. For this purpose, a model for preconditions and effects of planning actions is defined. This model is applied to three different use cases showing different problems of fetching data from different partially standardized shell templates including three defined actions of transport, assembly, and storage.IEEE International Conference on Emerging Technologies and Factory Automation200This paper shows how to leverage the Asset Administration Shell as an Industry 4.0 compliant Digital Twin technology to provide all necessary data for Artificial Intelligence Planning, represented via the Planning Domain Definition Language.2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)["Alexis T. Bernhard", "Benjamin Blumhofer", "Martin Ruskowski", "A. Wagner", "Andreas Luxenburger", "Daniel Porta"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/82f9314898f99b7bddfeeb46969762b1716d7e7e 3ca01b540a9d2f1c0b49918760c798aa846ea27dThis paper argues that the early modern period was already debating questions about the interstices and transitions between humans and machines, much like the ones that govern our engagements with AI today. Looking at Shakespeare’s Hamlet, I will be showing that, next to the ghost, Horatio is another and arguably no less challenging uncanny character on the battlements at Elsinore. While the ghost is situated between the full humanity of a living human being and the inanimate materiality of a dead corpse, Horatio seems to be situated between the full humanity of being “passion’s slave” and the mechanical functioning of a time-keeping and recording device. Horatio, then, is an experiment in artificial intelligence avant la lettre. This paper shows how his reduced, partial, and artificial humanity is explored by the play as it exposes Horatio’s inadequacies.Journal of Posthumanism60Journal of Posthumanism["Stephan Laque"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/3ca01b540a9d2f1c0b49918760c798aa846ea27d 91c17e740d8c3412aaf0c2ebc36435bd2cd3c570In aerobics training, closed myocutaneous nerve damage needs to be paid attention to, especially high-intensity training may cause minor damage to muscles and nerves. With the help of AI medical technology and the understanding of molecular and cellular biomechanics, we can more accurately explore the mechanism of injury, such as the effects of nerve tensile stress and microenvironment changes on nerve regeneration. This helps to develop scientific rehabilitation methods, such as AI-assisted personalized training, neural regeneration technology, and real-time monitoring of training intensity to speed up athletes’ rehabilitation and reduce the risk of future injuries. Purpose: Aerobics training is very strict and requires high physical fitness of dancers. During long-term training, dancers can easily cause closed musculocutaneous nerve injury. Traditional medicine is difficult to guarantee the treatment effect of patients with musculocutaneous nerve injury. The use of artificial intelligence medicine for closed musculocutaneous nerve injury treatment can improve the treatment effect of aerobics training induced closed musculocutaneous nerve injury. Method: This article utilized artificial intelligence medicine for the treatment of musculocutaneous nerve injury, and used artificial intelligence technology to analyze patient imaging and other data to assist doctors in accurate diagnosis. Utilize intelligent algorithms to predict medication plans, reduce medication errors, and intelligently adjust the course of treatment based on the patient’s condition. In artificial intelligence healthcare, high-quality online medical services can be created through intelligent technology, providing convenient medical consultation for patients. Result: This article selected 200 patients with musculocutaneous nerve injury caused by aerobics training for grouping experiments. The average diagnostic accuracy of traditional medicine and artificial intelligence medicine were 84.2% and 95.6%, respectively. Conclusion: Artificial intelligence medicine can achieve medical informatization and intelligently analyze patients’ medical information, which helps to improve the accuracy of medical diagnosis for aerobics training injuries.Molecular &amp; Cellular Biomechanics270Artificial intelligence medicine can achieve medical informatization and intelligently analyze patients’ medical information, which helps to improve the accuracy of medical diagnosis for aerobics training injuries.Molecular &amp; Cellular Biomechanics["Chengli Mu"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/91c17e740d8c3412aaf0c2ebc36435bd2cd3c570 6ff8feac8c2a5f19d3caf0626dfd4706e5de4ae1 Artificial Intelligence for Social Good (AI4SG) has emerged as a growing body of research and practice exploring the potential of AI technologies to tackle social issues. This area emphasizes interdisciplinary partnerships with community organizations, such as non-profits and government agencies. However, amidst excitement about new advances in AI and their potential impact, the needs, expectations, and aspirations of these community organizations--and whether they are being met--are not well understood. Understanding these factors is important to ensure that the considerable efforts by AI teams and community organizations can actually achieve the positive social impact they strive for. Drawing on the Data Feminism framework, we explored the perspectives of community organization members on their partnerships with AI teams through 16 semi-structured interviews. Our study highlights the pervasive influence of funding agendas and the optimism surrounding AI's potential. Despite the significant intellectual contributions and labor provided by community organization members, their goals were frequently sidelined in favor of other stakeholders, including AI teams. While many community organization members expected tangible project deployment, only two out of 14 projects we studied reached the deployment stage. However, community organization members sustained their belief in the potential of the projects, still seeing diminished goals as valuable. To enhance the efficacy of future collaborations, our participants shared their aspirations for success, calling for co-leadership starting from the early stages of projects. We propose data co-liberation as a grounding principle for approaching AI4SG moving forward, positing that community organizations' co-leadership is essential for fostering more effective, sustainable, and ethical development of AI. Proc. ACM Hum. Comput. Interact.1290It is proposed that community organizations' co-leadership is essential for fostering more effective, sustainable, and ethical development of AI, positing that community organizations' co-leadership is essential for fostering more effective, sustainable, and ethical development of AI.Proc. ACM Hum. Comput. Interact.["Hongjin Lin", "Naveena Karusala", "Chinasa T. Okolo", "Catherine D\u2019Ignazio", "Krzysztof Z. Gajos"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/6ff8feac8c2a5f19d3caf0626dfd4706e5de4ae1 eec9312d3e37f26be13a3ca319f0186ebd05bb86It is said that everything is interconnected to each other in the universe, and Artificial Intelligence (AI), a “spider” domain that will be found “in all and everything”, demonstrates to us every day this need for unlimited thinking, interference, interconnection and integration “of all disciplines, eras and minds”. Consciously or not, we are “immersed” in a new reality, in a kind of “whirlwind” of globalization, interconnectedness, transdisciplinarity, innovation and the fulminant evolution of technology, which we often try to slow down or at least to understand its meaning, to accept it and to enter openly into its sphere of action, because this is the way, this is our future, of humanity. The evolution of technology is seductive. But what about the essence of humanity, the inner ego, the aura or the energy field, elements untouched by the legislative area, but only by that of science? What about artificial intelligence that makes vulnerable mind and mental integrity through the impermissible alteration of thoughts, which can alter, remove or recover people’s memories, as well as manipulate their thoughts? In this context, through this study we propose an inter-and transdisciplinary dialogue, through which to discover possible foundations of potential new human rights, neurorights and the right of the soul in response to the unprecedented advance of artificial intelligence. Thus, we aim to open a new time space for analysis and in globo vision on the human being and its rights, contributing to the completion of the universe of institutional and legal proposals and mutations already started at international level. It requires a mosaic approach and the courage to resize the “legal architecture” regarding human rights, through which the legislator to attach special importance to the spiritual area of the human being, the road being already opened through the current inter- and transdisciplinary doctrinal debates.For an International Transdisciplinary Chair:From Knowledge to the Future. Volume II240For an International Transdisciplinary Chair:From Knowledge to the Future. Volume II["D. Ilie", "Ramona Duminic\u0103"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/eec9312d3e37f26be13a3ca319f0186ebd05bb86 eb3e432c0169469f0dbadccd8f72c4e00c14e702The development of artificial intelligence affects the employment of labor force in China. The article uses the panel data of 30 provinces in China from 2004 to 2020 to analyze the development trend of AI in China from the spatio-temporal dimension, and finds that the development of AI promotes the two dimensions of the quantity of labor force employment and the quality of the labor force through the two-step system generalized moments estimation (Two-step-SYS-GMM) regression. Meanwhile, regional innovation efficiency is measured with the super-efficient SBM model (US-SBM model) considering non-desired outputs, and it is found that regional innovation efficiency has a positive moderating effect on the dynamic relationship between the two. Then, it proposes coping strategies to improve institutional arrangements, strengthen vocational training and activate innovation factors.Advances in Engineering Technology Research50The article uses the panel data of 30 provinces in China from 2004 to 2020 to analyze the development trend of AI from the spatio-temporal dimension, and finds that the development of AI promotes the two dimensions of the quantity of labor force employment and the quality of the labor force through the two-step system generalized moments estimation (Two-step-SYS-GMM) regression.Advances in Engineering Technology Research["Ping Wang"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/eb3e432c0169469f0dbadccd8f72c4e00c14e702 f3063d1eed47b89110a93197207404317ad7bd3dObjective The rapid development of Artificial Intelligence (AI) has raised questions about its potential uses in different sectors of everyday life. Specifically in medicine, the question arose whether chatbots could be used as tools for clinical decision-making or patients’ and physicians’ education. To answer this question in the context of fertility, we conducted a test to determine whether current AI platforms can provide evidence-based responses regarding methods that can improve the outcomes of embryo transfers. Methods We asked nine popular chatbots to write a 300-word scientific essay, outlining scientific methods that improve embryo transfer outcomes. We then gathered the responses and extracted the methods suggested by each chatbot. Results Out of a total of 43 recommendations, which could be grouped into 19 similar categories, only 3/19 (15.8%) were evidence-based practices, those being “ultrasound-guided embryo transfer” in 7/9 (77.8%) chatbots, “single embryo transfer” in 4/9 (44.4%) and “use of a soft catheter” in 2/9 (22.2%), whereas some controversial responses like “preimplantation genetic testing” appeared frequently (6/9 chatbots; 66.7%), along with other debatable recommendations like “endometrial receptivity assay”, “assisted hatching” and “time-lapse incubator”. Conclusions Our results suggest that AI is not yet in a position to give evidence-based recommendations in the field of fertility, particularly concerning embryo transfer, since the vast majority of responses consisted of scientifically unsupported recommendations. As such, both patients and physicians should be wary of guiding care based on chatbot recommendations in infertility. Chatbot results might improve with time especially if trained from validated medical databases; however, this will have to be scientifically checked.JBRA Assisted Reproduction00It is suggested that AI is not yet in a position to give evidence-based recommendations in the field of fertility, particularly concerning embryo transfer, since the vast majority of responses consisted of scientifically unsupported recommendations.JBRA Assisted Reproduction["A. Kolokythas", "M. Dahan"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/f3063d1eed47b89110a93197207404317ad7bd3d 432b8a170b75f864efef3372a608af050119d320We all know that artificial intelligence (AI) is changing the world we live in, and the world of medical writing is no different. The explosion of AI is creating significant opportunities in identifying data signals, document creation, and supporting the work of medical writers. This article summarizes how the medical writers in my company, Trilogy Writing & Consulting, have been using a rule-based AI tool (that we purchase as a Software as a Service license) to augment their writing of clinical study reports (CSRs) and is a summary of a talk that I gave at AMWA’s 2023 Medical Writing & Communication Conference in Baltimore, Maryland, on October 26, 2023.American Medical Writers Association AMWA journal00This article summarizes how the medical writers in my company have been using a rule-based AI tool to augment their writing of clinical study reports (CSRs) and is a summary of a talk that I gave at AMWA’s 2023 Medical Writing & Communication Conference in Baltimore, Maryland, on October 26, 2023.AMWA Journal["Julia Forjanic Klapproth"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/432b8a170b75f864efef3372a608af050119d320 710e63394c4d155c830ccd7f505999ee430a843fIn the wave of informatization and digitization, generative artificial intelligence (AIGC), an important branch of artificial intelligence technology, is gradually penetrating and reshaping the publishing industry. Through systematic analysis and research, this paper discusses the current status of the application, its development path, and the challenges generative AI faces in the publishing industry. Firstly, this paper explains the basic principles of generative AI technology and its advantages in content generation. It also analyses its specific application scenarios in various content forms such as text, image, audio, and video. Secondly, this paper discusses the various paths of generative AI to enable the high-quality development of the publishing industry, including improving content production efficiency, promoting creative diversity of content, and optimizing the publishing process. At the same time, this paper also analyses the technical bottlenecks, market barriers, and ethical and safety issues faced by the application of generative AI in the publishing industry, and puts forward some strategies and suggestions to cope with these challenges.Communications in Humanities Research00The various paths of generative AI to enable the high-quality development of the publishing industry, including improving content production efficiency, promoting creative diversity of content, and optimizing the publishing process are discussed.Communications in Humanities Research["Weitao Xu"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/710e63394c4d155c830ccd7f505999ee430a843f 39175f85cfc22d1d47b05d0c6f460fef458fdb1eAbstract Money laundering and financial crimes pose a significant threat to the integrity and stability of South Africa’s financial system. This paper explores the application of artificial intelligence (AI) to detect and prevent money laundering in South African banking institutions. Through the implementation of big data technologies and data processing analytics, AI can enhance the detection and prevention of money laundering activities in South Africa’s banking sector. AI can be harnessed to improve the detection of suspicious activities, enhance accuracy of financial intelligence and adapt to evolving money laundering techniques. The paper also examines the benefits and challenges of implementing AI as an anti-money laundering (AML) measure in the South African banking sector. These include the need for quality data, integration with existing systems, regulatory compliance and ethical considerations. The paper further highlights the potential of AI in transaction monitoring, customer due diligence, outcomes-based risk assessment, and improved detection of suspicious transactions by analysing how AI can enhance the effectiveness and efficiency of AML measures. The importance of coordination between banking institutions, regulatory authorities and law enforcement bodies is also highlighted as an important component of leveraging AI to combat money laundering and related financial crimes in South Africa’s banking sector.Potchefstroom Electronic Law Journal350The paper highlights the potential of AI in transaction monitoring, customer due diligence, outcomes-based risk assessment, and improved detection of suspicious transactions by analysing how AI can enhance the effectiveness and efficiency of AML measures.Potchefstroom Electronic Law Journal["Howard Chitimira", "Elfas Torerai", "Lisa Jana"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/39175f85cfc22d1d47b05d0c6f460fef458fdb1e c3a987a8547f553fb41172e19fabf71c5a0ad650The purpose of the article is to define prospects and limitations of artificial intelligence as a core of in the new industrial revolution. The definition of the concept of AI in the scientific community remains the subject of heated debate. At the same time, in the regulatory and legal plane, a trend is being formed towards unification of the concept of AI. Based on the analysis conducted and literary sources, the following prospects for AI can be identified on theoretical and practical levels. On theoretical level: (1) alienation of tacit knowledge from the individual (employee and entrepreneur); (2) optimization of the planning system; (3) revision of the socialist-calculation debate; (4) decreasing information asymmetry. On practical level: (1) formation of new products and markets; (2) increasing labor and capital productivity; (3) massive creation of new jobs; (4) optimization of business processes; (5) opportunity for rapid growth for small businesses and startups. Limitations: (1) long-term structural unemployment; (2) inflated expectations from AI and, as a consequence, the possible formation of a speculative bubble in the global stock market; (3) energy consumption of AI; (4) outdated pre-AI corporate culture and regulatory environment. Further improvement of AI (including the transition from AI to AGI) and the expansion of its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy. AI, certainly acting as a locally useful tool at the level of individual enterprises and organizations, causes the acceleration of attracting funds to the stock market, which can lead to the formation of a bubble on global level. If this bubble bursts, expectations about the economic efficiency of AI will be revised, and some AI-related companies will experience significant margin reductions (perhaps losses and bankruptcies). But this, in turn, will initiate the next stage of AI development, will accelerate its transition from the current narrow specialization to the creation of full-fledged general artificial intelligence (artificial general intelligence), which has a greater potential to change the economy at all levels. As a result, AI will become established as the core of the new industrial revolution.Economy of Industry120Prospects and limitations of artificial intelligence as a core of in the new industrial revolution are defined and its use can make a significant contribution to solving problems related to economic calculation and minimizing information asymmetry, and therefore optimizing transaction costs in the economy.Economy of Industry["O. Vyshnevskyi", "Maksym Anufriiev", "Maryna Bozhyk", "Taras Gulchuk"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/c3a987a8547f553fb41172e19fabf71c5a0ad650 08935a54baa7765f8c45cdb5d761abdecce0b36eAs a joint link of artificial intelligence products, the emergence of ChatGPT triggered a global debate, and attracted the attention of practitioners all over the world. Meanwhile, multiple film and television production fields such as computer graphics processing, digital virtual shooting, CG special effects production, have embedded automatic content generation factors. However, artificial intelligence is also facing legal dilemmas and ethical constraints, researchers need to compare and analyze its advantages and problems, systematically consider the strengths and impacts, limitations and problems of AIGC, and provide reference and practical guidance for AI to better promote itself.Communications in Humanities Research00As artificial intelligence is also facing legal dilemmas and ethical constraints, researchers need to compare and analyze its advantages and problems, systematically consider the strengths and impacts, limitations and problems of AIGC, and provide reference and practical guidance for AI to better promote itself.Communications in Humanities Research["Xinhao Zhou", "Meiqi Xu", "Min Sun"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/08935a54baa7765f8c45cdb5d761abdecce0b36e 2f324367dc10bd6b1e49e565d83d5d4cd8ca81b5The rapid development of artificial intelligence technology is profoundly leading education towards an intelligent direction. This article takes the literature related to "Artificial Intelligence + Education" published in the core journals of CSSCI from 2013 to 2024 as the research object, and uses the CiteSpace tool for visualization analysis. It combs and discusses the current status of domestic research in this field from aspects such as publication status, authors, institutions, and keyword co-occurrence and burst words, analyzes research hotspots, and predicts research trends. The results show that: in the past decade, the number of publications in this field in China can be divided into three stages: preliminary exploration, rapid development, and steady growth; some high-productivity authors have gradually formed a core group of authors, showing an overall development trend of multi-author cooperation and multi-unit collaboration; the types of publishing institutions are mainly normal colleges and departments of colleges and universities, with relatively low participation of enterprises and government institutions; research hotspots focus on three aspects: educational reform, categories of education, and educational technology, revealing the active exploration of the education field for intelligent technology; with the transformation of education and the development of technology, the combination of artificial intelligence and digital economy, new requirements for citizens' core literacy, and new challenges for teachers' professional literacy and educational models have become hot topics in current artificial intelligence research.Communications in Humanities Research00The literature related to "Artificial Intelligence + Education" published in the core journals of CSSCI from 2013 to 2024 is taken as the research object, and the CiteSpace tool is used for visualization analysis.Communications in Humanities Research["Xueyi Zhang"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/2f324367dc10bd6b1e49e565d83d5d4cd8ca81b5 b2f39f691c1532d717f9c3c707e00aabd3c14b7cIn the era of artificial intelligence, significant changes have occurred in society and education, with artificial intelligence becoming a crucial driving force for the future development of education. This article explores the application and development trends of artificial intelligence in education from a theoretical perspective, providing direction for research on educational informatization.Journal of Education and Educational Research120The application and development trends of artificial intelligence in education from a theoretical perspective is explored, providing direction for research on educational informatization.Journal of Education and Educational Research["Wenjing He"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/b2f39f691c1532d717f9c3c707e00aabd3c14b7c 2b61cfe25dc08a0603a4785b3df397fd04a12bbbThe brain is one of the most vital organs for us to perform through daily activity. But due to the lifestyle changes within modern society as the consequences of advancement of technology, more health issues have got into the public. Dementia and stroke are the two leading disability causes that are highly associated with the changes in modern society and the lack of brain health concepts for the general population. This article focuses on the two diseases, and how does it impact on brain health and correlate with daily lifestyle, moreover, the aim is to prevent the occurrence and promote healthier brain health to fulfill the performance. Artificial intelligence (AI) is a revolutionary technology that can help improve brain health from many perspectives. AI can not only help doctors diagnose neurological diseases, but also assess an individual's cognitive function. Advances in AI can deepen our understanding of neuroscience, making it possible for stroke and dementia patients to recover through devices implanted in the brain that coordinate all signals and function normally, just like a normal brain. Advances in AI bring hope to neuroscience research. Also, with the development of AI techniques, it can lead to changes in perspective of the ways to develop in the future neuroscience industry as well as assist in brain health.Theoretical and Natural Science00Dementia and stroke are the two leading disability causes that are highly associated with the changes in modern society and the lack of brain health concepts for the general population, and how does it impact on brain health and correlate with daily lifestyle.Theoretical and Natural Science["Bowei Chen"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/2b61cfe25dc08a0603a4785b3df397fd04a12bbb 25fc64e16382054dafceab935ab8d2f8a1ff892cAs the field of artificial intelligence (AI) continues to evolve, its potential applications in various domains, including public policy development, have garnered significant interest. This research aims to investigate the role of AI in shaping public policies through a qualitative examination of secondary data and an extensive bibliographic review. By analyzing the existing literature, government reports, and relevant case studies, this study seeks to uncover the opportunities, challenges, and ethical considerations associated with leveraging AI in the formulation and implementation of public policies. This research will delve into the potential benefits of AI-driven policy analysis, such as enhanced decision-making processes, data-driven insights, and improved policy outcomes. Additionally, it will explore the risks and concerns surrounding AI’s influence on policy, including potential biases, privacy implications, and the need for transparency and accountability. The findings of this study will contribute to the ongoing discourse on the responsible and effective integration of AI in public policy development, fostering informed decision-making and promoting the ethical use of this transformative technology.Inf.130This research will delve into the potential benefits of AI-driven policy analysis, such as enhanced decision-making processes, data-driven insights, and improved policy outcomes, and explore the risks and concerns surrounding AI’s influence on policy.Inf.["Muhammad Asfand Yar", "Mahani Hamdan", "Muhammad Anshari", "Norma Latif Fitriyani", "Muhammad Syafrudin"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/25fc64e16382054dafceab935ab8d2f8a1ff892c 187fec426c8b92c3ee95cfee6e3f12b0abba21d2Criminal liability concept has become widespread in light of the increasing use of artificial intelligence technologies all life aspects and it is within a wide scope that requires urgent legislative intervention. With this use of artificial intelligence, many artificial intelligence crimes have emerged in all areas of artificial intelligence in addition to problems in determining criminal liability. This study aimed to investigate artificial intelligence characteristics, clarify the legal responsibility resulting from the actions of artificial intelligence and to determine the penalty resulting from artificial intelligence actions. Results indicated that the legal responsibility resulting from the artificial intelligence actions, which has emerged at the present time after the expansion of the use of these technologies in all sectors of life is inseparable from the responsibility of the innovator, part of the factory, or all of the responsibility of each individual. Intervention in all areas of work through artificial intelligence. The study recommended with the necessity of establishing mechanisms to monitor the work that falls within artificial intelligence techniques by specialists for the purpose of avoiding technical errors that affect  the safety of users of artificial intelligence technique.Artificial IntelligenceJournal of Ecohumanism70The study recommended with the necessity of establishing mechanisms to monitor the work that falls within artificial intelligence techniques by specialists by specialists for the purpose of avoiding technical errors that affect  the safety of users of artificial intelligence technique.Journal of Ecohumanism["Alkayid Jalahussein", "Alsalamat Mohammad", "Aljuneidi Fadelmansour", "Bqoour Karimeh Jalal"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/187fec426c8b92c3ee95cfee6e3f12b0abba21d2 2d82473fac6dbcd3cfd72a958fe80c3fe7bf0c54The authors in this work provides insight into the significance of Artificial Intelligence (AI) and provide a general overview of its strengths, weaknesses, and potential applications. Drawing upon statistical data, the authors present a comprehensive description of AI systems and their impact on various industries.HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference00HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference["Alvis Pastars", "P\u0113teris Grabusts"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/2d82473fac6dbcd3cfd72a958fe80c3fe7bf0c54 03b2e1691901413efe50cc2f76a3d1736ed0ecd1PurposeThe purpose of this study is to understand how the level of readiness for artificial intelligence (AI) affects the overall performance of companies, determine the role of organizational flexibility in adapting to new technologies and business models and assess the importance of lean sustainability and value creation for technology-focused companies.Design/methodology/approachTechnology companies working in technoparks in Istanbul were determined, and a questionnaire was applied to senior employees such as experts, engineers and managers working in these companies. The results were processed with a sample of 456 units. SmartPLS program was used for analysis.FindingsAs a result of the analyzes, it is supported by hypotheses that AI readiness and organizational flexibility have positive effects on lean sustainability and value creation.Research limitations/implicationsWhen evaluated in terms of the limitations of the research, it would not be correct to evaluate the results of the analysis in general, since the data were collected from technology-focused companies in technoparks in Istanbul.Practical implicationsExamining the variables that make up the research model in technology-oriented companies helps to understand the critical factors for the future success of companies. At the same time, this research is important for companies to make more informed decisions in their strategic planning, technological transformation processes and value creation strategies.Originality/valueThis research topic offers a unique approach in terms of bringing together topics such as AI readiness, organizational flexibility, sustainability and value creation. These issues play an important role in the strategic planning of technology-focused companies, and when considered together, they are important in terms of examining the critical factors that affect the future success of companies.Kybernetes810Examining the variables that make up the research model in technology-oriented companies helps to understand the critical factors for the future success of companies.Kybernetes["Zafer Adiguzel", "Fatma Sonmez Cakir", "Ferhat \u00d6zbay"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/03b2e1691901413efe50cc2f76a3d1736ed0ecd1 3ccfebded584f67160a374dd41859ad482dddafeThousands of researchers are currently of opinion that advanced artificial intelligence could cause significant damage if developed without appropriate safety measures, but such measures are not currently deployed or even developed. A fringe theory suggests that a severe AI accident could serve as a fire alarm for humanity to take existential dangers of AI seriously and so it is desirable to create such a failure on purpose ASAP to prevent greater harm in the future. In this paper we rely on analogy to inoculation theory to argue against creating purposeful AI failures. AGI - Artificial General Intelligence - Robotics - Safety &amp; Alignment00This paper relies on analogy to inoculation theory to argue against creating purposeful AI failures and suggests that such failures should be created ASAP to prevent greater harm in the future.AGI - Artificial General Intelligence - Robotics - Safety &amp; Alignment["Roman Yampolskiy"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/3ccfebded584f67160a374dd41859ad482dddafe bb2db525cb4b01b5942669b144db557231fb5463Artificial intelligence (AI) technologies have recently been applied in many fields. In many sectors, such as medicine, transportation, automotive, education, construction, furniture, and e-commerce, robotic experiments with AI are being carried out. These new developments in AI robot technologies, such as autonomous driving vehicles, robotic surgeries, smart education, home, and transportation, indicate that the need for a human labor force will be greatly reduced in the future. The issue of how AI robots, which are developed instead of humans in many jobs and processes to facilitate individual and social life, will continue to evolve and spur many discussions. Among these debates, our study focuses on the religious paradigm of AI. This study aims to understand, make sense of, and analyze the problem of the AI religion paradigm. In this context, various dimensions, such as AI’s conception of God, its religious foundations, how it shapes religious life, and whether it has an apocalyptic background that could bring about the end of humanity, are examined. In addition, the study discusses whether AI will bring good or evil to humanity in the religious dimension, what it promises or contains in the religious sense, and its opportunities, risks, or threats. It is hoped that this study will contribute to the gap in the relevant literature on the paradigms of AI and religion. In this respect, the originality of the study and its contribution to the literature is important. This study adopts a qualitative method and in-depth analysis of documents as a task.Ilahiyat Studies290This study aims to understand, make sense of, and analyze the problem of the AI religion paradigm, and discusses whether AI will bring good or evil to humanity in the religious dimension, what it promises or contains in the religious sense, and its opportunities, risks, or threats.Ilahiyat Studies["Muhammed Yama\u00e7", "Nihal Isbilen"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/bb2db525cb4b01b5942669b144db557231fb5463 17abd0df4a03ffb2cc8bddc71c9f6aac124ee686This review provides an overview of explainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between 25 March and 14 April 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.Applied Sciences362An overview of explainable AI methods for oncological ultrasound image analysis and compares their performance evaluations shows that standardized evaluations incorporating clinical end-users are generally lacking.Applied Sciences["Lucie S. Wyatt", "Lennard M. van Karnenbeek", "Mark Wijkhuizen", "F. Geldof", "B. Dashtbozorg"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/17abd0df4a03ffb2cc8bddc71c9f6aac124ee686 ea063068ae654c12b360978bbfb1545cb8a1ed89ACM Conference on Hypertext & Social Media20{"pages": "12-16"}["Davide Picca"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/ea063068ae654c12b360978bbfb1545cb8a1ed89 167ce8ab23f2f6a92b1288a13559a83fc11a1ee3AI &amp; SOCIETY00AI &amp; SOCIETY["Christopher Thomas", "Huw Roberts", "Jakob M\u00f6kander", "Andreas Tsamados", "M. Taddeo", "Luciano Floridi"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/167ce8ab23f2f6a92b1288a13559a83fc11a1ee3 95b9db6d08daa280747a4b209a2778392456c408IIC - International Review of Intellectual Property and Competition Law00IIC - International Review of Intellectual Property and Competition Law["Marcos Wachowicz"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/95b9db6d08daa280747a4b209a2778392456c408 258a14d5c321212cf186b7764f0e563eb89f6ff3Objective: This research examines the transformative potential of AI in fostering entrepreneurial innovation, highlighting its augmentation capabilities, integration strategies, and the ethical concerns that are essential for sustainable development. This study is contextualized against the backdrop of the fast-changing entrepreneurial ecosystems in the United Arab Emirates (UAE), with AI applications redefining the business landscape.Methods: Quantitative metrics related to AI adoption were analyzed alongside qualitative insights from entrepreneurs and business owners. Using a sound theoretical base of innovation and technology adoption frameworks, they applied structural equation modeling to delineate the direct, indirect, and mediated relationships of AI use and entrepreneurial innovationResults: AI's influence on entrepreneurship is complex, shaped through various mediators, including operational efficiency, ethics, and innovative integration strategies. By building businesses around these dimensions, companies are able to both innovate and sustain competitive advantages in an increasingly digital world.Novelty: This research helps in filling the gap between theoretical understanding and the practical applications of AI in entrepreneurship. As the study focuses on the UAE, a territory which prides itself on being a global leader in AI-driven innovation, insights will be unique on leveraging emerging technologies ethically to drive entrepreneurial growth.Research Implications: The research highlights the critical role of intentional AI integration within academic settings and the necessity for ethical standards. It is an important reference for policymakers, entrepreneurs, and academics working to maximize the potential of AI for innovation and sustainable business practicesResearcher Academy Innovation Data Analysis230Researcher Academy Innovation Data Analysis["Musyokha Sheriefah", "Silfa Sain Steva"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/258a14d5c321212cf186b7764f0e563eb89f6ff3 d6cc3b4fbb07096c837c7cd5daccda9bbe142d8cJournal of School Nursing10The Journal of school nursing : the official publication of the National Association of School Nurses["Kathleen H Johnson"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/d6cc3b4fbb07096c837c7cd5daccda9bbe142d8c b3e2c21a24068f486f58f34a4b057a59ffe06d07Proceedings of the International Conference on Industrial Engineering and Operations Management00Proceedings of the International Conference on Industrial Engineering and Operations Management["Agung Prajuhana Putra", "Hifshan Riesvicky"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/b3e2c21a24068f486f58f34a4b057a59ffe06d07 536b9d654f36c75cf8db22156f8d2e7b1d65fdb0The paper aims to find out if Slovak students participating in the project ‘ePortfolio as Pedagogy Facilitating Integrative Learning’ use generative AI in their written assignments and to what extent. Using free AI detectors such as ZeroGPT, QuillBot and Scribbr the assignments will be tested if they are AI/GPT generated, human written including parts generated by AI/GPT or human written. Research will try to come up with answers to the following research questions – RQ1: ‘Do the students use AI tools in a way that is dishonest or unfair in order to get what they need?’, RQ2: ‘Does AI provide the students who are allowed to use it with real help?’ and RQ3: ‘Are there still students who do not use AI tools?’ The paper also discusses briefly favourite AI tools for teachers and best AI detectors used all over the world. Research findings have shown that most Slovak students use AI tools to improve their skill of writing, regardless of using them ethically or not. However, there are still students who do not use them at all. Accordingly, several recommendations will be provided to researchers, academics and teachers for addressing this issue and applying or studying AI applications in language education in the future.CALL for Humanity - EuroCALL 2024 Short Papers00CALL for Humanity - EuroCALL 2024 Short Papers["Zuzana Hrdli\u010dkov\u00e1"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/536b9d654f36c75cf8db22156f8d2e7b1d65fdb0 fe17ae6966bc89ed42a0f01be55ff11b63e9dd52Transfusion and Apheresis Science110This work analyzed the practical application of ChatGPT-40 and artificial intelligence (AI) to perform daily calculations encountered in Transfusion Medicine and provides a proof of concept that people with no programming experience can create customizable solutions for their own facilities.Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis["Celine Anstey", "David Ullman", "Leon L Su", "Chuying Su", "Chad Siniard", "Sierra Simmons", "Jesse Edberg", "Lance A Williams"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/fe17ae6966bc89ed42a0f01be55ff11b63e9dd52 0884c51805900e6a120cd828c3148045c86e8178Energy Journal130The Energy Journal["Dalia Streimikiene"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/0884c51805900e6a120cd828c3148045c86e8178 310548c70bac60a0beacf476c93719240b7205ff00["Brian Ball", "Alice C. Helliwell", "Alessandro Rossi"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/310548c70bac60a0beacf476c93719240b7205ff 872fa198bdd466e5414f1867efba79a23edbab3a In the Oil & Gas industry, fugitive methane emissions account for an estimated 57% of total industry emissions, which combined with upstream flaring comprise nearly 2/3rd of the CO2e across the Oil & Gas upstream/midstream/downstream value chains. While hydrocarbons will likely remain an import (dominant) share of the energy & products mix well into the future, decarbonization efforts have been mobilized to ensure that oil & gas is produced and consumed with a lower carbon intensity. Examples of these decarbonization efforts include the Zero Flaring Initiative that many producers around the world have subscribed to and are working toward. However, fugitive emissions and routine venting/flaring remain a tough-to-abate portion of the industry's emissions, and new technologies around emissions detection are being developed to more accurately (and quickly) measure these "invisible" sources greenhouse gas (GHG) emissions. The combination of governmental mandates (e.g. US EPA Super Emitters Program) and carbon pricing mechanisms are increasing the pressure, and also the incentive, for producers to deploy next-generation emissions detection solutions, rather than relying on conventional solutions and theoretical calculations from emissions factors. Within the bucket of continuous emissions monitoring solutions, certain Artificial Intelligence + Metal Oxide Sensor (AI/MoS-based) technologies show promise in terms of the relative cost effectiveness, and also quality & completeness of site-level emissions data capture. This paper evaluates several new emissions monitoring technology types for the purpose of selection for a specific deployment in Oman. The subsequent six month deployment at multiple sites in Oman, characterized by extreme ambient temperature fluctuations and remoteness of the location, resulted in valuable data supporting the efficacy of AI/MoS-based, continuous emissions monitoring technologies in such applications. As such, this study supports the view that AI/MoS should be considered as part of a wider portfolio approach to emissions monitoring within the Oil & Gas industry, across all segments of the value chain and in all areas & environments.SPE International Health, Safety, Environment and Sustainability Conference and Exhibition60This paper evaluates several new emissions monitoring technology types for the purpose of selection for a specific deployment in Oman, and supports the view that AI/MoS should be considered as part of a wider portfolio approach to emissions monitoring within the Oil & Gas industry, across all segments of the value chain and in all areas & environments.SPE International Health, Safety, Environment and Sustainability Conference and Exhibition["B. Gendron", "Ammar ElHaggaz", "Majid Al Qassabi"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/872fa198bdd466e5414f1867efba79a23edbab3a 35282979f336b2077b3877c7ef8dc60803370eddThis study aims to examine the experiences of pre-service elementary mathematics teachers regarding the school experience course conducted through distance education due to the Covid-19 pandemic. The study was conducted with 64 volunteer participants, from students who took the School Experience course in the fall semester of the 2021-2022 academic year at a state university, which was determined by convenience sampling among the universities in Turkey. The data was gathered using Google form at the end of the fall semester, using a data collection tool including 4 open-ended questions. These questions were about general evaluation, contributions, faced problems, and suggestions about the school experience course process. The study is phenomenological research from qualitative research types, and the content analysis method was used to analyze the data. Based on the analysis of the collected data, the following codes were obtained: positive, neutral, and negative under the heading of general evaluation; no contribution, getting to know students, professional skills, and time under the heading of contributions; technological problems, lack of professional skills, professional satisfaction, and communication under the heading of faced problems; and active participation, changing the course content, educational support, face-to-face education, student participation, and technological support under the heading of suggestions.Acta Didactica Napocensia100The experiences of pre-service elementary mathematics teachers regarding the school experience course conducted through distance education due to the Covid-19 pandemic is examined.Acta Didactica Napocensia["Servet Merve KIRNAP D\u00d6NMEZ", "Azime ATAY MUTLU"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/35282979f336b2077b3877c7ef8dc60803370edd c07b8c34d0cdbe2a2443a5945faf3c3e3f9028e100["Brian Ball", "Alice C. Helliwell", "Alessandro Rossi"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/c07b8c34d0cdbe2a2443a5945faf3c3e3f9028e1 b3ec6b99b3946d6ab416aac1872e0784046f369aInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-09-10T00:00:00https://www.semanticscholar.org/paper/b3ec6b99b3946d6ab416aac1872e0784046f369a d2eacf39ad951071b00bf875c67ffa74d1238a9eThis research aims to determine how implementing the Artificial Intelligence-based Problem Based Learning learning model affects student learning outcomes. Quantitative approach: The type of research is pre-experimental, and the research design used is One Group Pre-Test and Post-Test using test instruments and observations. The population in this study was class XI. The sample for this research was class. The result is that the tcount value > ttable value (7.972 > 2.040), meaning that Ha is accepted and Ho is rejected. In conclusion, implementing the Problem-based learning model based on Artificial Intelligence influences PPKn student learning outcomes. ABSTRAKPenelitian ini bertujuan untuk mengetahui pengaruh implementasi model pembelajaran Problem Based Learning berbasis Artificial Intelligence terhadap hasil belajar siswa. Pendekatan kuantitatif; jenis penelitiannya adalah pre experimental dan desain penelitian yang digunakan adalah One Group Pre-Test Post-Test dengan menggunakan instrumen tes dan observasi. Populasi dalam penelitian ini adalah kelas XI. Sampel penelitian ini adalah kelas XI Ilmu Sosial 1. Hasil uji prasyarat menunjukkan data berdistribusi normal dan homogen, maka untuk menguji hipotesis menggunakan uji-t. Hasilnya nilai thitung > nilai ttabel (7,972 >2,040), artinya Ha diterima dan Ho ditolak. Kesimpulannya, implementasi model pembelajaran Problem Based Learning berbasis Artificial Intelligence memberikan pengaruh terhadap hasil belajar siswa PPKn.SOCIAL Jurnal Inovasi Pendidikan IPS60SOCIAL : Jurnal Inovasi Pendidikan IPS["Mas Ayu Rizka Septiani Putri", "Edy Herianto", "Bagdawansyah Alqadri", "Lalu Sumardi"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/d2eacf39ad951071b00bf875c67ffa74d1238a9e 27dc3c8629045ab67ad59d5bf70366939c44c10fThis study provides a new method for biological system optimization based on fuzzy logic and artificial intelligence. Through an integrated optimization framework, this study aims to raise production, lower costs, and increase the efficiency of biological systems. The research methodology involves data collection, preprocessing, fuzzy logic modeling, and employing AI algorithms for optimization. In order to determine how much more effective the recommended strategy is, several tests are carried out. The results are a testament to how the strategy can be utilized to increase productivity, cost savings, and efficiency. The method achieves a good compromise between interpretability and data-driven optimization, with comparable performance to existing methods. Possible applications and future directions of this research in the field of biological science are considered.Materials International00This study provides a new method for biological system optimization based on fuzzy logic and artificial intelligence that achieves a good compromise between interpretability and data-driven optimization, with comparable performance to existing methods.Materials International[]2024-09-10T00:00:00https://www.semanticscholar.org/paper/27dc3c8629045ab67ad59d5bf70366939c44c10f b4369395443bd16080fd3f69e51014d272036107The increasing integration of Artificial Intelligence (AI) in organizational leadership is transforming traditional leadership practices and dynamics. This analysis investigates the potential long-term effects of AI on leadership, focusing on how AI improves decision-making, automates repetitive tasks, and enhances employee engagement. Drawing on in-depth case studies of major companies like IBM, Google, and Amazon, this paper demonstrates the successes and challenges of incorporating AI into leadership roles. It also explores emerging AI-driven leadership skills and highlights potential future leadership frameworks that may develop as AI technologies progress, offering an optimistic view of leadership in the future. While this analysis provides valuable qualitative insights, it recognizes the need for additional empirical data to support its claims, including AI adoption rates and metrics for evaluating leadership effectiveness. Incorporating predictive models could also enhance our understanding of AI's lasting impact on leadership. The paper is a valuable resource for organizations and leaders navigating the evolving landscape of AI-augmented leadership.International Journal of Business Administration01This analysis investigates the potential long-term effects of AI on leadership, focusing on how AI improves decision-making, automates repetitive tasks, and enhances employee engagement.International Journal of Business Administration["Victor Frimpong", "B. Wolfs"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/b4369395443bd16080fd3f69e51014d272036107 34473de3bdbb5abf12acbb2ead042aa5e602236fStroke remains one of the leading causes of both disability and mortality worldwide, requiring immediate intervention to limit brain damage and prevent complications. Integrating artificial intelligence (AI) into stroke management has revolutionized diagnostic, treatment, and rehabilitation processes, offering new opportunities for improving precision and outcomes. This study investigates the current tools, applications, and challenges associated with AI-assisted decision support systems in stroke management to enhance diagnostic accuracy, treatment efficacy, and personalized care. Through an extensive review, we analyzed how AI plays a pivotal role in stroke care, including diagnostic imaging, treatment decision-making, and rehabilitation. AI demonstrated remarkable accuracy in MRI and CT stroke detection, significantly improving diagnostic efficiency. AI-powered decision support systems optimized treatment plans, particularly in selecting candidates for thrombolysis and mechanical thrombectomy, thereby reducing mortality and improving outcomes. AI-driven rehabilitation programs provide personalized therapy, enhancing motor recovery and patient outcomes. Despite its potential, challenges such as data heterogeneity, privacy concerns, and the need for large, diverse datasets remain significant barriers. Overall, AI has proven to be transformative in stroke care, streamlining diagnostic, treatment, and rehabilitation processes. Its continued advancement may further refine predictive models and create more effective, tailored healthcare interventions globally.Journal of Advances in Medicine and Medical Research01Overall, AI has proven to be transformative in stroke care, streamlining diagnostic, treatment, and rehabilitation processes and its continued advancement may further refine predictive models and create more effective, tailored healthcare interventions globally.Journal of Advances in Medicine and Medical Research["Muhammad Subhan", "Shaji Faisal", "Muhammad Usman Khan", "Ernette Espiegle", "Muhammad Waqas", "Ruqiya Bibi", "Muhammad Farooq Haider", "Ganesh Pendli", "Salman Kazmi", "Iqra Yaseen Khan"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/34473de3bdbb5abf12acbb2ead042aa5e602236f dcb874b78b292f9a2a763f0c2fe46345f8662841Due to the increasing amount of renewable energy sources and new consumers in low voltage power grids, there is an increasing need for grid monitoring. For this purpose, there exist state estimation methods that can predict the unknown grid state. Historically, standard load profiles were used as input, but they may be outdated and not adequately represent small consumer dynamics. To improve the estimator, a novel artificial intelligence-based generator of load time series is used to generate small consumer load profiles. An estimation method is then trained on both standard- and novel load profiles to estimate the overall state of a realistic grid. The results of both runs are then compared with real grid measurements to determine the estimation error in each case. First results show that the overall estimation error is lower with novel synthetic load profiles. In line loading estimates for example, 52 % of upper quartiles were below a 5 % error with standard profiles, compared to 68 % of upper quartiles with novel profiles. Two topological errors in the grid model could also be identified.2024 International Conference on Smart Energy Systems and Technologies (SEST)250An estimation method is trained on both standard- and novel load profiles to estimate the overall state of a realistic grid, and results show that the overall estimation error is lower with novel synthetic load profiles.2024 International Conference on Smart Energy Systems and Technologies (SEST)["Kristina Jurczyk", "Leonie Riedl", "Marcel Dipp", "David Heck", "Ben Gerhards", "Nikita Maksimovic Popkov", "Bastian Sch\u00e4fermeier", "Luka Gildehaus", "Frank Marten", "S. Berg", "Martin Braun"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/dcb874b78b292f9a2a763f0c2fe46345f8662841 eced3b81f2dd86c3a631e2d591ee9f18b70d9194In modern industrial manufacturing, simulation and accurate forecasting of energy consumption are critical to optimise resources and reduce waste meeting the significant energy demands and greenhouse gas emissions of this sector. Industry 5.0 (15.0), focused on sustainability and human-centered manufacturing, emphasises the use of Artificial Intelligence (AI), the Industrial Internet of Things (1IoT) and the Cyber Physical Systems (CPS) to monitor and optimise resource use in real time. This paper proposes a novel energy consumption simulation method using Neural Network (NN), specifically the Deep Echo State Network (DeepESN), integrated with a text embedding model, which allows to combine energy consumption data into production scheduling. Unlike existing approaches, our method considers both machine-level energy consumption and production workflows, enabling comprehensive optimisation of energy effi-ciency. Preliminary tests in a real production scenario show the potential of the approach, highlighting its ability to predict energy consumption simply by using smart meters without additional hardware. This work represents a significant advancement in in-tegrating energy consumption modeling into production planning and contributes to more sustainable industrial practices.IEEE International Conference on Emerging Technologies and Factory Automation230A novel energy consumption simulation method using Neural Network (NN), specifically the Deep Echo State Network (DeepESN), integrated with a text embedding model, which allows to combine energy consumption data into production scheduling, enabling comprehensive optimisation of energy effi-ciency.2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)["A. Bonci", "M. Prist", "Geremia Pompei", "Lorenzo Longarini", "A. D. Biase", "C. Verdini"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/eced3b81f2dd86c3a631e2d591ee9f18b70d9194 049b6d653ddfb87844dc6c45190f170fea5985caIn the unending discourse surrounding the potential replacement of humans by artificial intelligence (AI) in the future, this paper introduces a distinctive perspective grounded in the belief that such a substitution is implausible. It asserts that the invaluable intricacies of human communication, essential for fostering genuine connections, remain beyond the grasp of AI. Employing Adhikary’s Sadharanikaran Model of Communication (SMC) as the guiding framework, this article embarks on a methodical exploration of human communication, dissecting the nuanced components that contribute to its depth and richness. Through empirical evidence and comparative analyses, it sheds light on the inherent limitations of AI, providing a comprehensive understanding of why the SMC reinforces the irreplaceable role of humans in communication. By scrutinizing instances where human communication transcends mere information exchange, it is argued that the qualitative dimensions of human interaction defy replication by AI. Furthermore, a comparative analysis of AI-driven communication and human discourse also has been presented. Rather than positioning AI as a substitute, the emphasis is on leveraging its strengths in tandem with human capabilities, emphasizing the collective potential of a harmonious collaboration between the two entities.Bodhi An Interdisciplinary Journal30A distinctive perspective grounded in the belief that such a substitution of humans by artificial intelligence is implausible is introduced, asserting that the invaluable intricacies of human communication remain beyond the grasp of AI.Bodhi: An Interdisciplinary Journal["U. T. Lama Yolmo", "Pooja Basnett"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/049b6d653ddfb87844dc6c45190f170fea5985ca aca53a0774272d5a8883290c849af5ef8ac28f93Purpose This column aims to explore how AI is being used to impact books and help them understand how it may develop in the near future. Exploring these possibilities will help information professionals better understand the ways in which this technology is already being developed to impact books, a technology near and dear to many libraries. Design/methodology/approach This column discusses a series of artificial intelligence (AI) technologies that are being used to change what books are. They include AI that adds context with the help of experts, AI that leverages the content of the book itself to create an interactive persona, and AI that makes books multi-modal either by adding narration and likely someday visuals inspired by the text. Findings Many of these transformations do not require breakthroughs in AI technology. Rather, they only require the application of existing technologies, layering on new interfaces and frameworks. Originality/value As a book evolves, libraries, who know their patrons’ needs, have expertise and often operate within ethical frameworks, have an opportunity to participate in the process. Library Hi Tech News10This column discusses a series of artificial intelligence technologies being used to change what books are, including AI that adds context with the help of experts, AI that leverages the content of the book itself to create an interactive persona, and AI that makes books multi-modal.Library Hi Tech News["Peter Fernandez"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/aca53a0774272d5a8883290c849af5ef8ac28f93 7a6c2f109d091e72877746e797082e123d58b389This paper presents the development and adaptation of the Cipher game, a digital language learning resource adapted for dyslexic learners using text-to-speech (TTS) Artificial Intelligence (AI) technology. Modifications to the original Irish Cipher game include simplified texts, adjusted game rules, and AI-generated audio for instructions, vocabulary, and sentences. These elements reduce cognitive load and enhance comprehension, aligning with the needs of dyslexic students. The TTS technology used produces clear, game-appropriate speech, facilitating a more engaging and supportive learning experience. This paper provides a comprehensive overview of the design and development process of the dyslexia-focused Cipher game. It highlights the potential benefits of incorporating advanced AI technologies in educational tools for learners with reading difficulties. Future research is necessary to empirically evaluate the efficacy of this tool in real-world settings, involving dyslexic learners in the testing phase. This paper contributes to the ongoing discourse on leveraging technology to promote inclusive education and support diverse learner needs in CALL environments.CALL for Humanity - EuroCALL 2024 Short Papers00This paper provides a comprehensive overview of the design and development process of the dyslexia-focused Cipher game, a digital language learning resource adapted for dyslexic learners using text-to-speech (TTS) Artificial Intelligence (AI) technology.CALL for Humanity - EuroCALL 2024 Short Papers["Liang Xu", "Monica Ward", "Jenny Thomson", "Elaine U\u00ed Dhonnchadha"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/7a6c2f109d091e72877746e797082e123d58b389 db151411ee6b2229bb3799b0b8d168afb63ede22The integration of technology and artificial intelligence (AI) has revolutionized various sectors, including education, particularly in English as a Second Language (ESL) learning. In Nigeria, where English is the official language for education and commerce, proficiency in English is crucial for academic and professional success. However, traditional ESL teaching methods often fail to meet the diverse linguistic and cultural needs of Nigerian learners, resulting in varying proficiency levels. This paper explores the potential of digital transformation and AI to enhance ESL teaching and learning in Nigeria. AI-driven tools offer personalized learning experiences, adaptive assessments, and interactive content, while digital transformation facilitates access to quality educational resources and inclusive learning environments. The study highlights key opportunities, such as personalized learning, increased engagement, and improved assessment, while also addressing challenges like technological barriers, teacher training, and cultural diversity. Recommendations include investing in digital infrastructure, developing teacher training programs, and ensuring ethical data practices. Future research should evaluate AI's long-term impact on ESL education in Nigeria and refine technologies to better meet diverse student needs. This study contributes to the discourse on innovative language teaching approaches in the digital age, offering insights that extend beyond Nigeria's borders.International journal of literature, language and linguistics30The potential of digital transformation and AI to enhance ESL teaching and learning in Nigeria is explored, highlighting key opportunities, such as personalized learning, increased engagement, and improved assessment, while also addressing challenges like technological barriers, teacher training, and cultural diversity.International Journal of Literature, Language and Linguistics["Umar Ahmed"]2024-09-10T00:00:00https://www.semanticscholar.org/paper/db151411ee6b2229bb3799b0b8d168afb63ede22 627b867ea2c05743d2efaebcc9f475647d2bbe07Current Forestry Reports1105The paper concludes by highlighting the need for Human-Centered AI development for the successful transition to Forestry 5.0 – where the goal is to support the human workers rather than substituting them.Current Forestry Reports["Andreas Holzinger", "Janine Schweier", "Christoph Gollob", "A. Nothdurft", "H. Hasenauer", "T. Kirisits", "Carola H\u00e4ggstr\u00f6m", "Rien Visser", "Raffaele Cavalli", "Raffaele Spinelli", "K. Stampfer"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/627b867ea2c05743d2efaebcc9f475647d2bbe07 090990fff4a96afc1561b205530ae2cb35467fd7Communications Medicine402As the algorithm exhibits a significant performance advantage on a heterogeneous dataset exclusively comprising melanoma-suspicious lesions, AI may offer the potential to support dermatologists, particularly in diagnosing challenging cases.Communications Medicine["Lukas Heinlein", "Roman C. Maron", "A. Hekler", "Sarah Haggenm\u00fcller", "C. Wies", "J. Utikal", "Friedegund Meier", "S. Hobelsberger", "F. Gellrich", "M. Sergon", "Axel Hauschild", "Lars E. French", "Lucie M. Heinzerling", "Justin G. Schlager", "K. Ghoreschi", "Max Schlaak", "F. Hilke", "G. Poch", "S\u00f6ren Korsing", "C. Berking", "M. Heppt", "Michael Erdmann", "S. Haferkamp", "K. Drexler", "D. Schadendorf", "W. Sondermann", "Matthias Goebeler", "B. Schilling", "E. Krieghoff-Henning", "T. Brinker"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/090990fff4a96afc1561b205530ae2cb35467fd7 2fb0a36c0918a3d2e0263764a9496677bb2f824dPurpose This study aims to investigate the causal relationships among environmental, social and governance reporting (ESGR), stakeholder sustainability awareness, use of artificial intelligence (AI), sustainability culture, innovation performance and climate resilience of organizations across diverse sectors in Sri Lanka. Design/methodology/approach A survey was conducted among 327 respondents, including senior accounting professionals, operations managers and functional heads to gather company-level data in various industries in Sri Lanka. A disjoint two-stage approach validated the measurement model, and the partial least squares structural equation model (SEM) was used to test the proposed hypotheses. Findings The analysis evidences the mediating role of stakeholders' sustainability awareness on the relationship between ESGR and sustainability culture. Furthermore, it emphasizes the role of sustainability culture in driving climate resilience. Innovation performance acts as a moderator, strengthening the relationship between the use of AI and sustainability culture. Practical implications The study suggests that organizations should strategically use ESGR, integrate AI and prioritize stakeholder engagement to strengthen their commitment to sustainability. These provide insight for decision-making in organizations seeking to align with sustainable business practices. Originality/value It explores the use of AI to enhance ESGR and sustainability culture, providing a broader understanding of how organizations manage AI and stakeholders in sustainability issues. Journal of Financial Reporting & Accounting1001The use of AI is explored to enhance ESGR and sustainability culture, providing a broader understanding of how organizations manage AI and stakeholders in sustainability issues.Journal of Financial Reporting and Accounting["Mohamed Ismail Mohamed Riyath", "Achchi Mohamed Inun Jariya"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/2fb0a36c0918a3d2e0263764a9496677bb2f824d 1cfa64f464d6a1bb182f829182387d91c0e1d3b9Current research indicates that artificial intelligence has immense scope to further the cause of assistive technology in improving the quality of life for persons with disabilities by rendering customized support to mobility aids, visual aids, hearing aids, and smart homes. AI-driven devices make communication, adaptive learning, and independence easier for all, with innovations in prosthetics, wheel chairs, and satellite navigation apps such as Google Maps and Moovit. Voice- activated AI-powered smart devices, like Amazon Echo and Google Home, facilitate independent living with voice activation of light and appliances. AI is also in OrCam to further autonomous living. The study examines that artificial intelligence has immense scope to further the cause of assistive technology in enhancing the quality of life for people with disabilities by providing customized support to mobility aids, visual aids, hearing aids, and smart homes. AI makes environmental sustainability a part of the life cycle of assistive technologies—from design to the use of materials, energy efficiency, and e-waste recycling. Efficient waste management is made possible through AI-based sorting systems and smart recycling bins. Blockchain brings transparency into these processes. It is in social integration and economic efficiency that the following devices and services related to sustainable assistive technology can create environmental sustainability, empowering persons with disabilities, reducing healthcare expenditure, and infusing green practices toward an all-inclusive and sustainable world. UN News, Assistive Ware.International Journal of Innovative Science and Research Technology511It is in social integration and economic efficiency that the following devices and services related to sustainable assistive technology can create environmental sustainability, empowering persons with disabilities, reducing healthcare expenditure, and infusing green practices toward an all-inclusive and sustainable world.International Journal of Innovative Science and Research Technology (IJISRT)["Raghav Bajoria"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/1cfa64f464d6a1bb182f829182387d91c0e1d3b9 ececcf259390c526e6691b3cb1e8467fa8ce92b4Abstract Objective The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinical trials. Materials and Methods A comprehensive electronic search was conducted using the search strategy developed by the authors. The search encompassed research published in English, without any time limitations, which utilizes AI in the recruitment process of clinical trials. Data extraction was performed using a data charting table, which included publication details, study design, and specific outcomes/results. Results The search yielded 5731 articles, of which 51 were included. All the studies were designed specifically for optimizing recruitment in clinical trials and were published between 2004 and 2023. Oncology was the most covered clinical area. Applying AI to recruitment in clinical trials has demonstrated several positive outcomes, such as increasing efficiency, cost savings, improving recruitment, accuracy, patient satisfaction, and creating user-friendly interfaces. It also raises various technical and ethical issues, such as limited quantity and quality of sample size, privacy, data security, transparency, discrimination, and selection bias. Discussion and Conclusion While AI holds promise for optimizing recruitment in clinical trials, its effectiveness requires further validation. Future research should focus on using valid and standardized outcome measures, methodologically improving the rigor of the research carried out.J. Am. Medical Informatics Assoc.942While AI holds promise for optimizing recruitment in clinical trials, its effectiveness requires further validation, and future research should focus on using valid and standardized outcome measures, methodologically improving the rigor of the research carried out.Journal of the American Medical Informatics Association : JAMIA["Xiaoran Lu", "Chen Yang", "Lu Liang", "Guanyu Hu", "Ziyi Zhong", "Zihao Jiang"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/ececcf259390c526e6691b3cb1e8467fa8ce92b4 33df8ee334294910418612fb155438c3a40091ecThis paper explores the transformative impact of Artificial Intelligence (AI) on the field of radiology. It examines the integration of AI in diagnostic imaging, its potential benefits in enhancing diagnostic accuracy, efficiency, and workflow, and the challenges associated with its implementation. The discussion also highlights future directions for AI in radiology and the implications for radiologists.International Journal of Innovative Science and Research Technology631The integration of AI in diagnostic imaging, its potential benefits in enhancing diagnostic accuracy, efficiency, and workflow, and the challenges associated with its implementation are examined.International Journal of Innovative Science and Research Technology (IJISRT)["Cymone E. Hamilton"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/33df8ee334294910418612fb155438c3a40091ec 7868cc62c3f299ec2f849bb5532efb586c3d837eArticle analyses some socio-economic risks related to application of artificial intelligence (AI) in several fields of activity. Also, existing gaps in legal regulation of activities related to artificial intelligence are investigated. Article clarifies issues related to determining the division of liability for certain legal consequences resulting from artificial intelligence activity. Also, norms and principles to be adhered to in order to protect personal data during application of AI are demonstrated. As one of the concerns among people regarding artificial intelligence, article notes the importance of provision of transparence and accountability of this technology. Simultaneously, article interprets problems arising from relations of artificial intelligence and intellectual property, as well as recognition of property rights for intellectual products created via AI. Also, macro and micro-level impact of artificial intelligence on economy is analyzed. Attention is paid to issues such as productivity, competition, changes in the nature of the labor market, the increase in unemployment, and the deepening of social and digital inequality as a result of the application of this technology. Moreover, advantages and risks of human-robot collaboration are evaluated. Article demonstrates the biggest threats of artificial intelligence – creation of fake content, misinformation and hence, creation of significant problems. Prevention methods of those threats are interpreted on technological and legal planes. Also, risks of application of artificial intelligence in critical fields such as military and health are characterized. International Journal of Science Technology & Society191The biggest threats of artificial intelligence – creation of fake content, misinformation and hence, creation of significant problems are demonstrated and prevention methods of those threats are interpreted on technological and legal planes.International Journal of Science, Technology and Society["Rasim Alguliyev", "R. Mahmudov"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/7868cc62c3f299ec2f849bb5532efb586c3d837e ba906e7d3e578adf90dd58340a522b8e79f7210eIntroduction Artificial intelligence (AI) has created a plethora of prospects for communication. The study aims to examine the impacts of AI dimensions on family communication. By investigating the multifaceted effects of AI on family communication, this research aims to provide valuable insights, uncover potential concerns, and offer recommendations for both families and society at large in this digital era. Method A convenience sampling technique was adopted to recruit 300 participants. Results A linear regression model was measured to examine the impact of AI dimensions which showed a statistically significant effect on accessibility (p = 0.001), personalization (p = 0.001), and language translation (p = 0.016). Discussion The findings showed that in terms of accessibility (p = 0.006), and language translation (p = 0.010), except personalization (p = 0.126), there were differences between males and females. However, using multiple AI tools was statistically associated with raising concerns about bias and privacy (p = 0.015), safety, and dependence (p = 0.049) of parents. Conclusion The results showed a lack of knowledge and transparency about the data storage and privacy policy of AI-enabled communication systems. Overall, there was a positive impact of AI dimensions on family communication.Frontiers Artif. Intell.451Overall, there was a positive impact of AI dimensions on family communication and a lack of knowledge and transparency about the data storage and privacy policy of AI-enabled communication systems was showed.Frontiers in Artificial Intelligence["Nada Mohammed Alfeir"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/ba906e7d3e578adf90dd58340a522b8e79f7210e c9ce798f1e5428244da38e95bde32a547fcb8aadAbstractThis visual essay is made up of a series of images and a video essay on the artistic project "A Sunless Summer in Shangri-La" (2022). The project uses cyclic generative adversarial networks (cycle GANS) to invert the seasons in a series of videos. The networks of the artificial intelligence algorithm were trained with a database composed solely of images from the Yosemite Park in the United States of America. Therefore, by inserting images captured on a cloudy summer's day on the beach in Xangri-Lá (Brazil), the system starts to work unexpectedly, producing oneiric landscapes of snow dunes and seas of clouds. Based on these poetic interventions, the project initiates a discussion on the adoption of hegemonic systems that disregard local techno-bio-diversity.Keywords: Video essay; artificial intelligence; technodiversity.Arteriais - Revista do Programa de Pós-Gradução em Artes10This visual essay is made up of a series of images and a video essay on the artistic project "A Sunless Summer in Shangri-La" that initiates a discussion on the adoption of hegemonic systems that disregard local techno-bio-diversity.Arteriais - Revista do Programa de Pós-Gradução em Artes["Matheus da Rocha Montanari", "Gilbertto Prado"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/c9ce798f1e5428244da38e95bde32a547fcb8aad 6b20c5ae0706d11f15c0bb7937d739f6e6533ac5PurposeThe present study investigates the customers' behavioural intention to use voice-based artificial intelligence (AI) to find the appropriate hotels and resorts in an emerging nation. This study determines the influences of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to obtain the appropriate information and find the location of the hotels and resorts.Design/methodology/approachThis study used a purposive sampling method for collecting data from the respondents, who are customers of the hotels and resorts in Bangladesh. A self-administered survey questionnaire was used to obtain responses from 378 respondents. After collecting the data, the reliability and validity of the constructs and hypotheses were tested via partial least squares structural equation modelling (PLS-SEM).FindingsThe findings of the study indicate that information quality, system quality, privacy and novelty value have a positive and significant impact on attitude and behavioural intention to use voice-based AI assistant services in an emerging nation. However, system quality does not significantly influence behavioural intention to use voice-based AI assistant but it has an indirect significant influence on behavioural intention through the mediation effect of attitude.Practical implicationsThe study’s findings provide essential guidelines for practitioners to understand the impacts of information quality, system quality, privacy, and novelty value on attitude and behavioural intention to use voice-based artificial intelligence to find the appropriate hotels and resorts to meet customers' needs and expectations.Originality/valueThis study contributes to the existing literature on technology adoption by highlighting the interconnectedness of various factors influencing users' behavioural intentions. The study’s focus on an emerging nation provides a valuable theoretical contribution. It highlights that user perceptions and attitudes towards technology adoption may differ from those in developed nations due to unique contextual factors.Journal of Hospitality and Tourism Insights720The study’s findings provide essential guidelines for practitioners to understand the impacts of information quality, system quality, privacy, and novelty value on attitude and behavioural intention on attitude and behavioural intention to use voice-based AI assistant services in an emerging nation.Journal of Hospitality and Tourism Insights["Selim Ahmed", "Ujjal Yaman Chowdhury", "D. Ashrafi", "M. M. Choudhury", "Rafiuddin Ahmed", "Rubina Ahmed"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/6b20c5ae0706d11f15c0bb7937d739f6e6533ac5 dc9dcfeb60eb139339df823f44d098a964042ab1PurposeThe principal aim of this research is to acquire a deeper understanding of the opinion held by the training and development (T&D) professionals, regarding the use of artificial intelligence (AI) technology in the area of T&D. Particularly in response to the evolving needs of learners, the research aims to ascertain T&D professionals' perspective on the efficiency of AI in fostering T&D, while understanding the constraints and limitations associated with this technology.Design/methodology/approachThe study is based on qualitative data. With the help of semi-structured interviews, qualitative data has been collected from 21 T&D professionals. Experts working with multinational corporations (MNCs) are selected as a study sample using a convenient sampling technique. Qualitative data were analysed using thematic analysis. Conclusions were drawn based on the results of thematic analysis.FindingsThe findings of the study have revealed a notable and rapid evolution in the requirements of learners, particularly during and post-COVID-19 period. AI-based technology has emerged as a significant contributor, offering learners distinct personalised experiences and enhanced convenience. However, the implementation of AI in training remains in its early stages and has not reached widespread adoption. The findings of the study also highlighted various challenges and limitations, while using AI-based technology for training. It has been found that AI complements rather than replaces the role of a physical trainer.Originality/valueThe originality of this study lies in the application of AI-based training for professional learners, from the perspective of the T&D practitioners working with MNCs in Maharashtra, India. Numerous studies that have recently been published, emphasise the areas in which AI technology can transform the T&D industry. Yet, there are currently very less studies that have attempted to understand the evolving needs of learners and support of AI-based training for the same, from the perspective of the T&D professionals working in Maharashtra, India.Journal of Management Development520It has been found that AI complements rather than replaces the role of a physical trainer, while using AI-based technology for training, in the area of T&D.Journal of Management Development["A. Dixit", "Sunita Jatav"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/dc9dcfeb60eb139339df823f44d098a964042ab1 2df754be8a8bda3b2dfc20e7e93b1b6aa939b3f1The purpose of the research is to share experiences of good practices in the implementation of Knowledge-Based Education (CBE) and the management of Artificial Intelligence (AI) tools in the classroom. The methodology corresponds to a documentary research with a qualitative approach. A systematic review of the literature and analysis of successful cases in higher education programs were carried out. Key factors for the success of CBE and AI in the classroom were identified. The result obtained was the elaboration of a set of recommendations for the implementation of CBE and AI in the classroom that can serve as a guide to integrate AI tools into their teaching practice. It was concluded that CBE and AI are powerful tools that contribute to improving student learning, as long as ethical considerations and relevant validation strategies are taken into account, however, their implementation in the classroom is subject to proper planning by the counselor and the responsible use of technological tools. The implementation of AI tools is a means to improve the quality of education and not an end.Edelweiss Applied Science and Technology60CBE and AI are powerful tools that contribute to improving student learning, as long as ethical considerations and relevant validation strategies are taken into account, however, their implementation in the classroom is subject to proper planning by the counselor and the responsible use of technological tools.Edelweiss Applied Science and Technology["Rub\u00e9n Dar\u00edo C\u00e1rdenas Espinosa", "Julio C\u00e9sar Caicedo Eraso", "Iris Jim\u00e9nez Pitre"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/2df754be8a8bda3b2dfc20e7e93b1b6aa939b3f1 ace697d093c91480926b0e5ccfccf5de14465341Pharmaceutical Medicine950Promising AI-based solutions in Medical Affairs that support the effective use of heterogenous information from observations of the healthcare environment, the enhancement of medical education, and the analysis of real-world data are discussed.Pharmaceutical Medicine["Emma Fr\u00f6ling", "Neda Rajaeean", "K. S. Hinrichsmeyer", "Dina Domr\u00f6s-Zoungrana", "Johannes Nico Urban", "Christian Lenz"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/ace697d093c91480926b0e5ccfccf5de14465341 62840538273398dda3c5610d643ae69cfb549eb0Artificial Intelligence (AI) integration in accounting and auditing is transforming these fields by enhancing efficiency, accuracy, and strategic decision-making. AI automates routine tasks, supports complex financial assessments, and improves fraud detection and audit processes. However, its implementation also raises ethical and regulatory concerns, such as data privacy and job displacement. Clear procedures and guidelines are essential to maintain internal stability and maximize AI's value. This study aims to analyze the publication trends and citation metrics on AI in accounting and auditing while identifying the common research keywords in this area. It also identifies the main countries contributing research on this topic. The study is based on a bibliometric analysis of 105 articles from the Scopus database using the TITLE-ABS-KEY approach. In addition, the VOS Viewer software is used to create bibliometric networks and Harzing’s Publish or Perish software is used to assess the citation metrics of the articles. The analysis shows that the number of publications on AI in accounting and auditing is increasing, especially between 2020 and 2023. The articles were cited 1968 times, which corresponds to an average of 18.74 citations per article. The results show that the most common keywords discussed in this area are artificial intelligence, accounting, auditing, blockchain, and machine learning, which can be grouped into 7 clusters. The United States, China, the United Kingdom, and Saudi Arabia are among the countries that contribute to publications in this area. To summarize, this article provides important insights and publication trends into artificial intelligence in the area of accounting and auditing.Advances in Social Sciences Research Journal00The results show that the most common keywords discussed in this area are artificial intelligence, accounting, auditing, blockchain, and machine learning, which can be grouped into 7 clusters.Advances in Social Sciences Research Journal["N. Sallem", "Nor Haliza Che Hussain", "Siti Nasuha Muhmad", "Nur Syuhada Adnan", "Syafiq Abdul Haris Halmi"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/62840538273398dda3c5610d643ae69cfb549eb0 c4022e993852b5b376765f55a90c654d7288b1e3 This study examines the potential of artificial intelligence (AI) to reduce the time police officers spend writing reports, a task that consumes a significant portion of their workday. In a pre-registered randomized controlled trial, we test this claim within the patrol division of a medium-sized police department (n = 85) at the individual report level (n = 755). Analyses utilize mixed-effects regression accounting for the nested structure of report-writing. AI assistance did not significantly affect the duration of writing police reports. Alternative specifications beyond those specified in the pre-registration, including a difference-in-differences approach observing report duration over a full year (n = 6084), confirm the null findings are robust. Our findings contradict marketing expectations for the effect of this technology, suggesting no time savings in report-writing can be expected when using AI-assisted report-writing. Several other potential effects remain possible and untested. CrimRxiv260The findings contradict marketing expectations for the effect of this technology, suggesting no time savings in report-writing can be expected when using AI-assisted report-writing.CrimRxiv["Ian T. Adams", "Matt Barter", "Kyle McLean", "Hunter M. Boehme", "Irick A. Geary"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/c4022e993852b5b376765f55a90c654d7288b1e3 79ad9f9ca34109dc617015ede44132a79ab05408The aim of this study is digital transformation in education: multidimensional effects of artificial intelligence supported learning management systems. The impact of artificial intelligence applications in education has been examined through four basic dimensions. These are (1) student performance, (2) teacher adaptation, (3) educational materials and methods, and (4) measurement and evaluation. Among the qualitative research methods, the phenomenological approach was preferred. The phenomenon of this study is the use of artificial intelligence applications in education in the context of digital transformation. This phenomenon covers especially the integration of artificial intelligence supported learning management systems (LMS) into educational processes and the effects of this integration on digital transformation. The study group consists of students, teachers, administrators and educational technology experts at a private school using an artificial intelligence-based learning platform. To examine the impact of digital transformation in education, detailed data was collected by collecting data from different target audiences. The data obtained through observations and interviews were presented by content analysis. As a result of the study, systemically supportive reflections on students' academic performance, supportive reflections and negative reflections in terms of the learning process; reflections on the learning process regarding teacher adaptation and adaptation in the transformation process in education; In terms of teachers and students regarding the impact of educational materials and teaching methods; Regarding the measurement and evaluation processes, themes of reflections on the measurement and evaluation processes were created.Participatory Educational Research20The use of artificial intelligence applications in education in the context of digital transformation covers especially the integration of artificial intelligence supported learning management systems (LMS) into educational processes and the effects of this integration on digital transformation.Participatory Educational Research["Cansu \u015eah\u00edn K\u00f6lemen"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/79ad9f9ca34109dc617015ede44132a79ab05408 6a412988cd74fbc788d634a2f6280d5ae1c117b3Today, the importance of information security in education is increasingly emphasised. For members of generations X, Y and Alpha, ICT tools have become everyday objects of use, making it essential that teaching methods adapt and innovate accordingly. In particular, it is important that students learn to use data and devices safely, and the necessary guidelines should be integrated into the educational process. The six principles of security set out by the OECD apply to both students and teachers and provide guidance for users. Safety awareness is central to the development of a safety culture. In addition to knowledge and competences, safety culture also includes awareness and intentionality, which are essential for future success. There are many dimensions and aspects of awareness, but perhaps the most important in the context of education is the development of the digital dimension. However, it is also important to teach young people about the safe use of artificial intelligence and the limits of its applicability. The aim of our study is to examine how young people use the opportunities offered by digitalisation in educational institutions, with a particular focus on the use of AI and the perceptions and visions of its use. We will interpret this through an intercultural lens, comparing the intercultural characteristics and attitudinal differences of Hungarian and Turkish youth based on a questionnaire survey conducted in both countries.Edelweiss Applied Science and Technology220An intercultural lens is interpreted, comparing the intercultural characteristics and attitudinal differences of Hungarian and Turkish youth based on a questionnaire survey conducted in both countries, with a particular focus on the use of AI and the perceptions and visions of its use.Edelweiss Applied Science and Technology["\u00c1gnes Csisz\u00e1rik-Kocsir", "Bernadett Rev\u00e1k"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/6a412988cd74fbc788d634a2f6280d5ae1c117b3 d5734016481f9d820fc6f3d83532707c6e4608f1Abstract Introduction Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence‐based triage in the clinical field. Design Systematic review of prospective studies. Methods CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI‐based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol. Results Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five‐level triage classification system. Regarding model performance, the feed‐forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes. Conclusion Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health. Protocol Registration We have registered our review in PROSPERO (registration number: CRD 42023415232).Journal of Nursing Scholarship310Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage and hope to be a resource that can reduce undertriage and positively affect patient health.Journal of Nursing Scholarship["Nayeon Yi", "Dain Baik", "G. Baek"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/d5734016481f9d820fc6f3d83532707c6e4608f1 3c2dd9c64438af0dbeeefcc2b351e4374a8cf478Background Stroke stands as a prominent cause of mortality and disability worldwide, posing a major public health concern. Recent years have witnessed rapid advancements in artificial intelligence (AI). Studies have explored the utilization of AI in imaging analysis, assistive rehabilitation, treatment, clinical decision-making, and outcome and risk prediction concerning stroke. However, there is still a lack of systematic bibliometric analysis to discern the current research status, hotspots, and possible future development trends of AI applications in stroke. Methods The publications on the application of AI in stroke were retrieved from the Web of Science Core Collection, spanning 2004–2024. Only articles or reviews published in English were included in this study. Subsequently, a manual screening process was employed to eliminate literature not pertinent to the topic. Visualization diagrams for comprehensive and in-depth analysis of the included literature were generated using CiteSpace, VOSviewer, and Charticulator. Results This bibliometric analysis included a total of 2,447 papers, and the annual publication volume shows a notable upward trajectory. The most prolific authors, countries, and institutions are Dukelow, Sean P., China, and the University of Calgary, respectively, making significant contributions to the advancement of this field. Notably, stable collaborative networks among authors and institutions have formed. Through clustering and citation burst analysis of keywords and references, the current research hotspots have been identified, including machine learning, deep learning, and AI applications in stroke rehabilitation and imaging for early diagnosis. Moreover, emerging research trends focus on machine learning as well as stroke outcomes and risk prediction. Conclusion This study provides a comprehensive and in-depth analysis of the literature regarding AI in stroke, facilitating a rapid comprehension of the development status, cooperative networks, and research priorities within the field. Furthermore, our analysis may provide a certain reference and guidance for future research endeavors.Frontiers in Neuroscience640This study provides a comprehensive and in-depth analysis of the literature regarding AI in stroke, facilitating a rapid comprehension of the development status, cooperative networks, and research priorities within the field.Frontiers in Neuroscience["Fangyuan Xu", "Ziliang Dai", "Yu Ye", "Peijia Hu", "Hongliang Cheng"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/3c2dd9c64438af0dbeeefcc2b351e4374a8cf478 ad06c376ff07f05f033d2fda88021702d744e377Parasitic infections pose a significant public health concern, particularly in resource-limited settings. Even now, microscopy is still the “gold standard” diagnostic method. Despite the non -microscopic advances, including antigen and molecular detection of human parasites, they have not yet been integrated into routine laboratory work due to their high infrastructure requirements. Artificial intelligence (AI) using deep learning (DL) and convolutional neural networks (CNNs) is increasingly becoming an important component of clinical parasitology diagnostics. DL has shown extraordinary performance in biomedical image analysis, including various parasite diagnoses, in the past few years. AI and microscopy represent the state-of-the-art in clinical parasitology diagnostics. This review aimed to concisely high-light the recent advances in the use of AI in parasite detection in clinical samples. The article focuses on recently published proof-of-concept studies on schistosomiasis, intestinal parasitic infections, malaria, and leishmaniasis. In the end, we summarise the challenges and future trends that DL confronts in the field of parasite diagnostics.مجلة سوهاج لشباب الباحثين360This review aimed to concisely high-light the recent advances in the use of AI in parasite detection in clinical samples, focusing on recently published proof-of-concept studies on schistosomiasis, intestinal parasitic infections, malaria, and leishmaniasis.مجلة سوهاج لشباب الباحثين["Eman Fathi"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/ad06c376ff07f05f033d2fda88021702d744e377 bc37c95f38386a722e95319c9191f34bd134982fPenggunaan Kecerdasan Buatan (Artificial Intelligence/AI) dalam pembelajaran mahasiswa adalah isu yang semakin relevan dalam konteks pendidikan modern. Artikel ini menguraikan pokok masalahnya dengan menjelaskan tantangan dan potensi AI dalam pembelajaran. Tujuan dari artikel ini adalah untuk memberikan pemahaman yang komprehensif tentang peran AI dalam pembelajaran mahasiswa di Indonesia. Metode penelitian yang digunakan adalah tinjauan literatur, yang mencakup analisis berbagai sumber dan pandangan terkait dengan penggunaan AI dalam pendidikan tinggi. Data yang digunakan dalam artikel ini adalah informasi dari berbagai sumber literatur, termasuk hasil penelitian, artikel ilmiah, dan berita terkait dengan implementasi AI dalam pendidikan. Contoh penerapan AI, seperti chatbot untuk bimbingan akademik, sistem pembelajaran daring, dan penilaian otomatis, diperoleh dari sumber-sumber tersebut. Hasil analisis data menunjukkan bahwa penggunaan AI dalam pembelajaran mahasiswa memiliki potensi besar untuk meningkatkan kualitas pendidikan, namun perlu memperhatikan tantangan etika, risiko ketergantungan, dan peran penting interaksi manusia. Solusi seperti pendidikan etika AI, regulasi yang ketat, dan integrasi AI dengan interaksi manusia menjadi bagian integral dari kesimpulan artikel ini. Dalam era transformasi digital, pemahaman mendalam tentang peran AI dalam pendidikan, khususnya dalam konteks chatbot untuk bimbingan akademik, sistem pembelajaran daring, dan penilaian otomatis, adalah kunci untuk meningkatkan kualitas pendidikan dan mempersiapkan mahasiswa untuk masa depan yang didorong oleh teknologiZeniusi Journal40Zeniusi Journal["Irvandy Anugrah Irvandy Anugrah", "Jupriaman", "Dwina Putri Dwina Putri", "Muhammad Zulham Munthe Muhammad Zulham Munthe"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/bc37c95f38386a722e95319c9191f34bd134982f 5b7fe78d93fc3a67081133df74a508caf05a9c6dTeknologi informasi telah menjadi bagian penting dari kehidupan manusia, terutama dengan perkembangan cepat dalam bidang kecerdasan buatan (Artificial Intelligence atau AI). AI memainkan peran penting dalam meningkatkan efektivitas dan efisiensi pembelajaran, khususnya di lingkungan pendidikan tinggi. Penelitian ini bertujuan untuk mengeksplorasi peran AI dalam kehidupan mahasiswa, dengan fokus pada penerapan AI dalam pendidikan dan dampaknya terhadap pengalaman belajar mahasiswa. Penelitian ini menggunakan metode deskriptif kuantitatif dengan pengumpulan data melalui survei terhadap 30 mahasiswa Pendidikan Guru Madrasah Ibtidaiyah (PGMI) di STITA Labuhanbatu. Hasil survei menunjukkan bahwa mayoritas mahasiswa memiliki pemahaman yang baik tentang AI dan perspektif positif terhadap pengaruhnya dalam pembelajaran. AI terbukti meningkatkan personalisasi dalam pembelajaran, membantu administrasi akademis, dan mempersiapkan mahasiswa untuk dunia kerja yang semakin digital. Namun, penelitian ini juga mengidentifikasi dampak negatif, seperti potensi menurunnya literasi mahasiswa dan risiko kecanduan teknologi. Oleh karena itu, penting untuk meningkatkan literasi digital dan edukasi tentang AI agar mahasiswa dapat memanfaatkan teknologi ini secara optimal dan bertanggung jawab.Zeniusi Journal40Zeniusi Journal["Irpan Siregar Irpan Siregar", "Suryatik", "Muhammad Zulham Munthe"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/5b7fe78d93fc3a67081133df74a508caf05a9c6d 13806de9fc92dcdb04b05e8d9e2eb8057a4aa85aPatient navigators, whose value has become increasingly apparent, still face significant challenges, including a lack of support, funding, and recognition. These challenges have been exacerbated in the wake of COVID‐19 pandemic.This study explored the potential use of artificial intelligence (AI) in patient navigation. Data were collected through structured surveys and individual interviews with patient navigators from a variety of institutions and professional backgrounds. The data were analyzed to understand the current state of patient navigation, identify existing gaps, and suggest best practices for the future.The findings showed that patient navigators (a) have diverse backgrounds and responsibilities, (b) lack technology support for their work, (c) are at risk for burnout, with the extent varying based on the level of technical support received, and (d) report significant overlap between current barriers and those that could potentially be addressed with AI‐driven technologies.A novel intervention, that is enabled by AI and other technologies and tailored to individual needs, has the potential to reduce burnout, increase capacity, and help ensure the sustainability of patient navigation and other areas of healthcare. By addressing the specific needs of individual patients, this type of intervention could help improve the overall effectiveness of patient navigation and support the long‐term sustainability of the role.Medicine Advances120A novel intervention, enabled by AI and other technologies and tailored to individual needs, has the potential to reduce burnout, increase capacity, and help ensure the sustainability of patient navigation and other areas of healthcare.Medicine Advances["Fenghao Chen", "Tu Lan", "Jie Liang", "Ronghui Zhang"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/13806de9fc92dcdb04b05e8d9e2eb8057a4aa85a 56d08ac2ac0d56d4502d201334772e6691ee85c3International journal of urology21International journal of urology : official journal of the Japanese Urological Association["S. Okada", "S. Narita", "Ryohei Yamamoto", "K. Numakura", "M. Saito*", "T. Habuchi"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/56d08ac2ac0d56d4502d201334772e6691ee85c3 9824de6eee72cfb1babbfb6092c014a148b47731npj Digit. Medicine91A paradigm shift in AI assessment is needed, prioritizing actual patient outcomes over conventional benchmarking, and improving evaluation practices, including continuous monitoring and silent evaluation periods.NPJ Digital Medicine["J. Gallifant", "D. Bitterman", "L. Celi", "J. Gichoya", "Jo\u00e3o Matos", "Liam G. McCoy", "Robin L Pierce"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/9824de6eee72cfb1babbfb6092c014a148b47731 00e0aad90720f453ca08b6aeebd4a6156ab37257Pain Management161Pain management["Bhargav Srinivasan", "Archana Venkataraman", "Srinivasa N. Raja"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/00e0aad90720f453ca08b6aeebd4a6156ab37257 ff1edf550842c60d7681b55cef99807d196491d9Hellenic Conference on Artificial Intelligence200{"pages": "26:1-26:8"}["Irina E. Nicolae", "G. Danciu", "Christina Nanou", "Nikolaos Koulierakis", "Viasiliki Danilatou"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/ff1edf550842c60d7681b55cef99807d196491d9 9f3bc855c371139643b5ce1fb28114c017643529Indian Journal of Gastroenterology50Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology["A. Bapaye", "R. Yewale", "Akshay Kulkarni"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/9f3bc855c371139643b5ce1fb28114c017643529 2d3f29cee9d6dc12951a869dff71aa637947f28100["Kunal Nathwani"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/2d3f29cee9d6dc12951a869dff71aa637947f281 0c635c9440bab93633066fffd380483704112dadProceedings of the 2024 5th International Artificial Intelligence and Blockchain Conference10Proceedings of the 2024 5th International Artificial Intelligence and Blockchain Conference["Kai-lun Zhang"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/0c635c9440bab93633066fffd380483704112dad 0ec7c84c5624124588f55f64ffa4965d6ff171a5Journal of Computers in Education360Journal of Computers in Education["Shaista Rashid", "Sadia Malik", "Faheem Abbas", "Javaria Ahmad Khan"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/0ec7c84c5624124588f55f64ffa4965d6ff171a5 919b4242399577e254db55c41ff292bc10794faaDunia Pendidikan dapat beradaptasi dengan perubahan yang terjadi di era digital saat ini agar tetap relevan dengan perubahan dan perkembangan zaman. Penggunaan kecerdasan buatan dalam bidang Pendidikan semakin banyak digunakan, terutama dalam kegiatan pembelajaran. Pelatihan ini mencakup sesi pengenalan canva dan latihan penggunaan aplikasi yang ada di canva. Melalui pendekatan dengan mengenalkan aplikasi canva untuk pengajar (guru) mendesain tugas-tugas pembelajaran yang kreatif, seperti infografis, presentasi, dan video edukatif, yang dapat meningkatkan inovasi pelajar. Hasil dari pelatihan ini menunjukkan peningkatan signifikan dalam keterampilan teknologi pelajar dan guru dan kualitas materi pembelajaran yang dihasilkan. Pelatihan menggunakan aplikasi canva khusus untuk guru di SMKS YPSEI Kota Palangka Raya menuai hasil yang positif, dengan peserta memberikan penilaian sangat puas terhadap kegiatan tersebut. Kata kunci: Canva, Guru, Kecerdasan Buatan, PelatihanI-Com Indonesian Community Journal00I-Com: Indonesian Community Journal["Yunida Iashania", "O. D. Sanitha", "Asri Fridtriyanda", "Novera Kristianti"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/919b4242399577e254db55c41ff292bc10794faa 46a7b16904005261bf48c2ebfbada1de29969791Hellenic Conference on Artificial Intelligence100{"pages": "30:1-30:4"}["S. Konstantopoulos"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/46a7b16904005261bf48c2ebfbada1de29969791 ff92840f117f4303720e7cfa5492b24562fa5866Policy studies240Policy Studies["Remziye Zaim", "James Shaw"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/ff92840f117f4303720e7cfa5492b24562fa5866 4debd77999fd3b4b2b03d0c0b83b3e41facc8891Proceedings of the 2024 5th International Artificial Intelligence and Blockchain Conference90Proceedings of the 2024 5th International Artificial Intelligence and Blockchain Conference["R. F. Mayana", "Thomas Budhyawan Yudhya", "Tisni Santika", "Ahmad M. Ramli", "Sigid Suseno"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/4debd77999fd3b4b2b03d0c0b83b3e41facc8891 dd6ece611fdd176c23e7c31307da5dbff427a298The authors would like to make the following correction to the published paper [...]Sustainability10Sustainability["H. Khairy", "Mohamed Ahmed", "A. Asiri", "Foziah Gazzawe", "Mohamed A. Abdel Fatah", "Na\u02bf\u012bm A\u1e25mad", "Ayman Qahmash", "M. F. Agina"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/dd6ece611fdd176c23e7c31307da5dbff427a298 4994abf34967c2cedc91876174cf10a3cf83050bВ статье рассматриваются ключевые тенденции и перспективы развития проектного управления в постпандемический период, обусловленные стремительным проникновением цифровых технологий во все сферы человеческой жизни. Выделяются и характеризуются ключевые изменения, произошедшие в проектном управлении в связи с пандемией COVID-19, среди которых: переход к онлайн-форматам работы, ускорение цифровизации и внедрение новых инструментов и подходов в практику управления (и многие другие). Особое внимание уделяется роли искусственного интеллекта как ключевого фактора, обеспечивающего повышение эффективности, ускорение и автоматизацию проектного управления, выступающего передовым отражением трансформации проектного управления. Поднимаются как положительные аспекты влияния искусственного интеллекта на эффективность проектного управления, так и системные вызовы, связанные с изменениями в роли человека в проектной деятельности, управлением рисками и этическими вопросами использования искусственного интеллекта. Подчеркивается актуальность и перспективность формирования ситуационных подходов к организации проектного управления с применением искусственного интеллекта, что в целом является результатом происходящих тенденций и изменений в проектном управлении в постпандемический период. Делаются выводы о возможных последствиях и перспективах проектного управления в условиях парадигмы искусственного интеллекта. This article examines the key trends and prospects for the development of project management in the post-pandemic period, driven by the rapid penetration of digital technologies into all spheres of human life. The article highlights and characterizes the significant changes in project management due to the COVID-19 pandemic, including the shift to online work formats, accelerated digitalization, and the adoption of new tools and approaches in management practice. Special attention is given to the role of artificial intelligence as a key factor in enhancing efficiency, accelerating processes, and automating project management, representing the forefront of project management transformation. The article discusses both the positive aspects of artificial intelligence’s impact on project management efficiency and the systemic challenges related to changes in human roles within project activities, risk management, and ethical issues of AI utilization. The relevance and prospects of developing situational approaches to project management organization using artificial intelligence are emphasized, which are the results of ongoing trends and changes in project management in the post-pandemic period. Based on the conducted research, conclusions are drawn about the possible consequences and future prospects of project management in the era of artificial intelligence.Industrial Economics20Industrial Economics["\u042f.\u0410. \u0410\u0433\u0430\u043f\u043e\u0432\u0438\u0447\u0435\u0432\u0430"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/4994abf34967c2cedc91876174cf10a3cf83050b ba59c34fbe08f695a5d855c183b3adbba7de43dbAJR. American journal of roentgenology20AJR. American journal of roentgenology["Ranliang Hu"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/ba59c34fbe08f695a5d855c183b3adbba7de43db 23632d1756ef20f171ef29e22b83beefa58ebad2O avanço das tecnologias de Internet das Coisas (Intelligence of Things – IoT) e Inteligência Artificial (IA) abriu novas possibilidades de aplicações em diversas áreas, incluindo monitoramento em tempo real. Este trabalho apresenta o desenvolvimento de um simulador de aplicações de Inteligência Artificial das Coisas (Artificial Intelligence of Things – AIoT) para monitoramento de áreas rurais utilizando Veículos Aéreos Não Tripulados (VANTs). A proposta integra uma arquitetura edge/fog/cloud, onde VANTs equipados com câmeras e algoritmos de IA realizam a detecção de animais em tempo real. O sistema distribui a carga de processamento entre os dispositivos de borda e o servidor fog, otimizando a eficiência e a precisão das detecções. A interface gráfica desenvolvida permite a visualização e gerenciamento de simulações, facilitando a análise e a tomada de decisões. Os resultados demonstram a viabilidade e eficácia do sistema para monitoramento de ambientes de difícil acesso, contribuindo para uma gestão eficiente de recursos e resposta rápida a eventos da aplicação.Anais da XII Escola Regional de Computação do Ceará, Maranhão e Piauí (ERCEMAPI 2024)90Anais da XII Escola Regional de Computação do Ceará, Maranhão e Piauí (ERCEMAPI 2024)["A. Gon\u00e7alves", "A. B. Castro", "Brenda Evilly", "Erico Le\u00e3o", "Jose R. Torres Neto", "R. Silva", "A. O. C. Filho", "R. Rabelo"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/23632d1756ef20f171ef29e22b83beefa58ebad2 1032ceb7b9d2ab06e8903a5d2c3172273d1654e8Interactive artificial intelligence in the motion control field is an interesting topic, especially when universal knowledge is adaptive to multiple tasks and universal environments. Despite there being increasing efforts in the field of Reinforcement Learning (RL) with the aid of transformers, most of them might be limited by the offline training pipeline, which prohibits exploration and generalization abilities. To address this limitation, we propose the framework of Online Decision MetaMorphFormer (ODM) which aims to achieve self-awareness, environment recognition, and action planning through a unified model architecture. Motivated by cognitive and behavioral psychology, an ODM agent is able to learn from others, recognize the world, and practice itself based on its own experience. ODM can also be applied to any arbitrary agent with a multi-joint body, located in different environments, and trained with different types of tasks using large-scale pre-trained datasets. Through the use of pre-trained datasets, ODM can quickly warm up and learn the necessary knowledge to perform the desired task, while the target environment continues to reinforce the universal policy. Extensive online experiments as well as few-shot and zero-shot environmental tests are used to verify ODM's performance and generalization ability. The results of our study contribute to the study of general artificial intelligence in embodied and cognitive fields. Code, results, and video examples can be found on the website \url{https://rlodm.github.io/odm/}.arXiv.org240The framework of Online Decision MetaMorphFormer (ODM) is proposed which aims to achieve self-awareness, environment recognition, and action planning through a unified model architecture and contributes to the study of general artificial intelligence in embodied and cognitive fields.ArXiv["Luo Ji", "Runji Lin"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/1032ceb7b9d2ab06e8903a5d2c3172273d1654e8 029799525b625cc81e3f84d4cd826b95139e85bdThe need for sustainable solutions has increased globally as a result of the growing environmental problems brought about by urbanization and industrialization. Given this, private investment in artificial intelligence (AI) has become a viable means of promoting environmental sustainability, mainly because of AI's capacity to minimize ecological footprints and maximize resource utilization. This research investigates the role of private investment in AI in promoting environmental sustainability in the United States from 1990 to 2019. It also analyzes the impact of financial globalization, technological innovation, and urbanization by testing the Load Capacity Curve (LCC) hypothesis. The research utilizes stationarity tests, which indicate that the variables are free from unit root problems and exhibit mixed orders of integration. Using the Autoregressive Distributive Lag (ARDL) Model bound test, the analysis finds that the variables are cointegrated in the long run. The short-run and long-run estimations of the ARDL model confirm the existence of the LCC hypothesis in the United States, revealing a U-shaped association between income and load capacity factor. The findings show that private investment in AI has a significant positive correlation with the load capacity factor, thus promoting environmental sustainability. Conversely, technological innovation and financial globalization exhibit a negative correlation with the load capacity factor in both the short and long run. To validate the ARDL estimation approach, the study employs Fully Modified OLS, Dynamic OLS, and Canonical Correlation Regression estimation methods, all of which support the ARDL outcomes. Additionally, the Granger Causality test reveals a unidirectional causal connection from private investment in AI, financial globalization, economic growth, technological innovation, and urbanization to the load capacity factor.Journal of Environmental Science and Economics1398Journal of Environmental Science and Economics["Afsana Akhter", "Sarder Abdulla", "Al Shiam", "Mohammad Ridwan", "Shake Ibna Abir", "Shaharina Shoha", "Md Boktiar Nayeem", "M. Tazwar", "Hossain Choudhury", "Robeena Bibi"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/029799525b625cc81e3f84d4cd826b95139e85bd 6cd198e6c130f2369f492a87283cf1cb5aa2df4eThis study investigates the profound and multifaceted impacts of Artificial Intelligence (AI) and geopolitical developments on global dynamics by 2040. Utilising a Delphi process coupled with probabilistic modelling, the research constructs detailed scenarios that reveal the cascading effects of these emerging forces across economic, societal, and security domains. The findings underscore the transformative potential of AI, predicting significant shifts in employment patterns, regulatory challenges, and societal structures. Specifically, the study forecasts a high probability of AI-induced unemployment reaching 40–50%, alongside the rapid evolution of AI technologies, outpacing existing governance frameworks, which could exacerbate economic inequalities and societal fragmentation. Simultaneously, the study examines the critical role of geopolitical developments, identifying increased nationalisation, the expansion of conflicts such as the Russia–Ukraine war, and the strategic manoeuvres of major powers like China and Israel as key factors that will shape the future global landscape. The research highlights a worrying lack of preparedness among governments and societies, with a 10% probability of their being equipped to manage the complex risks posed by these developments. This low level of readiness is further complicated by the short-term orientation prevalent in Western businesses, which prioritise immediate returns over long-term strategic planning, thereby undermining the capacity to respond effectively to these global challenges. The study calls for urgent, forward-looking policies and international cooperation to address the risks and opportunities associated with AI and geopolitical shifts. It emphasises the need for proactive governance, cross-sector collaboration, and robust regulatory frameworks to ensure that the benefits of technological and geopolitical advancements are harnessed without compromising global stability or societal well-being. As the world stands on the brink of unprecedented change, the findings of this study provide a crucial roadmap for navigating the uncertainties of the future.Societies373The study forecasts a high probability of AI-induced unemployment reaching 40–50%, alongside the rapid evolution of AI technologies, outpacing existing governance frameworks, which could exacerbate economic inequalities and societal fragmentation and provide a crucial roadmap for navigating the uncertainties of the future.Societies["Michael Gerlich"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/6cd198e6c130f2369f492a87283cf1cb5aa2df4e 266f5274f9cfa1726a18e5a688882b13be75fcf3Zeitschrift für Ernährungswissenschaft281A methodologically sound approach to role development (clarifying AI Augmented individual roles—clAIr) is presented using the example of service technicians in a mechanical engineering company before and during the introduction of AI-based services to illustrate how role clarity can be achieved in the interaction with AI.Zeitschrift für Arbeitswissenschaft["Valentin Langholf", "Uta Wilkens"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/266f5274f9cfa1726a18e5a688882b13be75fcf3 2ae4a53350d571dab168fe585b218fb92b3b71afIntegrating Artificial Intelligence (AI) in precision agriculture within the framework of Industry 4.0 (I4) is revolutionizing crop disease management and inventory management, offering innovative solutions that enhance both agricultural productivity and environmental sustainability. Combined with I4 technologies, AI-powered systems can predict, detect, and manage crop diseases accurately, reducing reliance on chemical pesticides and improving overall farm efficiency. AI algorithms identify disease patterns and suggest optimal intervention strategies by analyzing real-time data from drones, sensors, and satellite imagery. This approach minimizes crop loss, maximizes yield, and aligns with sustainable farming practices by reducing the environmental footprint. However, the success of these technologies is influenced by the personality traits of farmers. Traits such as openness to innovation, conscientiousness, and analytical thinking are crucial for the effective adoption and utilization of AI-driven solutions. Conscientious farmers follow precise instructions and maintain equipment, while those open to new experiences are more likely to experiment with innovative technologies. Analytical thinkers excel in interpreting complex data, and making informed decisions that improve crop health and yield. The research underscores the need for fostering these traits among farmers to maximize the benefits of AI technologies. Additionally, the study highlights the importance of interdisciplinary collaboration in developing and implementing AI-driven solutions that address both agricultural productivity and environmental sustainability. By integrating technological advancements with human factors, AI has the potential to transform crop disease management, contributing to a more sustainable and resilient agricultural system. The findings call for continued research, policy support, and a holistic approach to fully realize the benefits of AI in agriculture.Food Science and Engineering361The research underscores the need for fostering openness to innovation, conscientiousness, and analytical thinking among farmers to maximize the benefits of AI technologies and highlights the importance of interdisciplinary collaboration in developing and implementing AI-driven solutions that address both agricultural productivity and environmental sustainability.Food Science and Engineering["Chee Kong Yap", "Chee Seng Leow", "Wing Sum Vincent Leong"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/2ae4a53350d571dab168fe585b218fb92b3b71af 60a32bf5f097010eab84db8cbebdef8b40a9075eThe integration of technology and healthcare has ushered in a new era where software systems, powered by artificial intelligence and machine learning, have become essential components of medical products and services. While these advancements hold great promise for enhancing patient care and healthcare delivery efficiency, they also expose sensitive medical data and system integrity to potential cyberattacks. This paper explores the security and privacy threats posed by AI/ML applications in healthcare. Through a thorough examination of existing research across a range of medical domains, we have identified significant gaps in understanding the adversarial attacks targeting medical AI systems. By outlining specific adversarial threat models for medical settings and identifying vulnerable application domains, we lay the groundwork for future research that investigates the security and resilience of AI-driven medical systems. Through our analysis of different threat models and feasibility studies on adversarial attacks in different medical domains, we provide compelling insights into the pressing need for cybersecurity research in the rapidly evolving field of AI healthcare technology.arXiv.org2121This paper explores the security and privacy threats posed by AI/ML applications in healthcare and outlines specific adversarial threat models for medical settings and identifies vulnerable application domains, laying the groundwork for future research that investigates the security and resilience of AI-driven medical systems.ArXiv["Yuan-Jie Chang", "Han Liu", "Evin Jaff", "Chenyang Lu", "Ning Zhang"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/60a32bf5f097010eab84db8cbebdef8b40a9075e 8c78010d26b4d5e734a0805babdd1eda59ba248aThis paper examines the application of Artificial Intelligence (AI) in communication buildings and data centers, emphasizing its role in enhancing operational efficiency and reliability. AI technologies, including machine learning, predictive analytics, and automation, are increasingly leveraged to optimize the management of these critical infrastructures. The paper provides an overview of current AI implementations, reviews the benefits and challenges associated with these technologies, and discusses future directions. The findings indicate that while AI significantly improves system performance and resource utilization, challenges such as data security and integration complexity persist.International Journal of Innovative Science and Research Technology501The findings indicate that while AI significantly improves system performance and resource utilization, challenges such as data security and integration complexity persist.International Journal of Innovative Science and Research Technology (IJISRT)["Alhubail, Abdullah K"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/8c78010d26b4d5e734a0805babdd1eda59ba248a 5f6269a33e68b92b7cc9e57db6592c986bb99c53Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.Requirements Engineering490A systematic mapping study combining query string search and extensive snowballing is conducted to provide a comprehensive overview of RE4AI, and identifies requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges.ArXiv["Umm E Habiba", "Markus Haug", "J. Bogner", "Stefan Wagner"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/5f6269a33e68b92b7cc9e57db6592c986bb99c53 282e22bd4a1abe18743f5b85db53bdc2794024c8Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have unlocked significant potential to enhance the quality and efficiency of medical care. By introducing a novel way to interact with AI and data through natural language, LLMs offer new opportunities for medical practitioners, patients, and researchers. However, as AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified. These concerns about AI safety have emerged as the most significant obstacles to the adoption of AI in medicine. In response, this review examines emerging risks in AI utilization during the LLM era. First, we explore LLM-specific safety challenges from functional and communication perspectives, addressing issues across data collection, model training, and real-world application. We then consider inherent safety problems shared by all AI systems, along with additional complications introduced by LLMs. Last, we discussed how safety issues of using AI in clinical practice and healthcare system operation would undermine trust among patient, clinicians and the public, and how to build confidence in these systems. By emphasizing the development of safe AI, we believe these technologies can be more rapidly and reliably integrated into everyday medical practice to benefit both patients and clinicians.00This review examines emerging risks in AI utilization during the LLM era from functional and communication perspectives, and considers inherent safety problems shared by all AI systems, along with additional complications introduced by LLMs.["Xiaoye Wang", "Nicole Xi Zhang", "Hongyu He", "Trang Nguyen", "Kun-Hsing Yu", "Hao Deng", "Cynthia Brandt", "D.S. Bitterman", "Ling Pan", "Ching-Yu Cheng", "James Zou", "Dianbo Liu"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/282e22bd4a1abe18743f5b85db53bdc2794024c8 b34deb903415ca80a3d843d957c834ab400a150bThe utilization of artificial intelligence (AI) in pediatric occupational therapy (OT) has emerged as a promising avenue for enhancing assessment, intervention, and outcomes for children with diverse developmental needs. This paper provides a comprehensive review of the current state of AI applications in pediatric OT, highlighting key findings, benefits, challenges, and future directions. AI technologies, including machine learning algorithms, computer vision systems, and wearable sensors, offer innovative approaches to assess children’s motor skills, sensory responses, and cognitive functions objectively and efficiently. AI-driven intervention strategies, such as personalized treatment planning, adaptive task selection, virtual reality environments, and gamified activities, promote engagement, motivation, and skill acquisition among pediatric patients. Additionally, AI-powered telehealth platforms enable remote delivery of OT services, real-time monitoring of patient progress, and access to care for underserved populations. However, challenges related to data privacy, ethical decision-making, disparities in access, and therapist education must be addressed to ensure the ethical, effective, and equitable integration of AI into pediatric OT practice. By embracing ongoing research, collaboration, and innovation, pediatric OT practitioners can harness the transformative potential of AI to improve outcomes and quality of life for children and families worldwide.International Journal of Current Research and Review350A comprehensive review of the current state of AI applications in pediatric OT is provided, highlighting key findings, benefits, challenges, and future directions.International Journal of Current Research and Review["Nirvi Sharma"]2024-09-11T00:00:00https://www.semanticscholar.org/paper/b34deb903415ca80a3d843d957c834ab400a150b 72afe383c290c9a6cfbaadfa1dc90f963dceb908 This Viewpoint discusses the challenges to implementing artificial intelligence–based translation in clinical settings and what health care organizations can do to mitigate these challenges. Journal of the American Medical Association (JAMA)54JAMA["K. C. Lion", "Yu-Hsiang Lin", "Theresa Kim"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/72afe383c290c9a6cfbaadfa1dc90f963dceb908 b04761803f02560be7d24984befdc7391cd442d1The democratization of artificial intelligence (AI) involves extending access to AI technologies beyond specialized technical experts to a broader spectrum of users and organizations. This paper provides an overview of AI’s historical context and evolution, emphasizing the concept of AI democratization. Current trends shaping AI democratization are analyzed, highlighting key challenges and opportunities. The roles of pivotal stakeholders, including technology firms, educational entities, and governmental bodies, are examined in facilitating widespread AI adoption. A comprehensive framework elucidates the components, drivers, challenges, and strategies crucial to AI democratization. This framework is subsequently applied in the context of scenario analyses, offering insights into potential outcomes and implications. The paper concludes with recommendations for future research directions and strategic actions to foster responsible and inclusive AI development globally.Applied Sciences295An overview of AI’s historical context and evolution is provided, emphasizing the concept of AI democratization, and recommendations for future research directions and strategic actions to foster responsible and inclusive AI development globally are made.Applied Sciences["Carlos J. Costa", "Manuela Aparicio", "Sof\u00eda Aparicio", "J. T. Apar\u00edcio"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/b04761803f02560be7d24984befdc7391cd442d1 c843cdadb839398208e7339f8f47e78877b2b15eArtificial intelligence (AI) and machine translation (MT) have revolutionized translation. However, assessing AI and MT of audiovisual (AV) content has not yet gained much interest. This study examines the strategies used to translate legal expressions by Netflix, Google Translate (GT), ChatGPT (GPT), and Gemini (GEM) in four English Netflix series. It also classifies the collected legal terms thematically. The results showed that the majority of English legal terms are related to criminal law. Using an eclectic approach of translation strategies, the findings showed that each translator (Human, MT, and AI) employs unique strategies, with paraphrasing being the most commonly used strategy (33.1%), followed by literal (26%) and cultural substitution (25.3%). The analysis also showed some cases of mistranslation, with Netflix showing the least and GT demonstrating the most errors. The results showed that AI and MT systems need further improvement in specialized fields such as legal. The study concludes that human translators follow subtitling requirements more precisely than machine translation and artificial intelligence systems do when it comes to legal terminology.Pakistan Journal of Criminology01It is concluded that human translators follow subtitling requirements more precisely than machine translation and artificial intelligence systems do when it comes to legal terminology.Pakistan Journal of Criminology[]2024-09-12T00:00:00https://www.semanticscholar.org/paper/c843cdadb839398208e7339f8f47e78877b2b15e 44b9c67024da9b298a9b805ce1f614433cccd517Logic and probability as branches of Mathematics and aspects of Philosophy, underlie and play significant roles in the development of Artificial Intelligence (AI). In its simplest form, logic concerns right reasoning (especially one devoid of fallacies), patent truth and inferences. Probability has to do with uncertainties, that is, the likelihood of an event happening. The divide between traditional AI and modern AI regarding what roles logic and probability play in the development of AI has been mitigated with the notion that both are complementary without displacing the other. While the birth of AI as a field is usually linked to the 1956 conference with figures involving Marvin Minsky and John McCarthy, there are traces of what we refer to as robots, automatons and computations which form the foundation of AI in some non-Western philosophies. To this end, this paper chronicles the emergence of AI in non-Western philosophies, especially in African philosophy and then uses the Yoruba's 'ifá' to exemplify the idea of decolonising Al, not forgetting that the basis of ifá itself is logic and, sometimes, probability.Àgídìgbo: ABUAD Journal of the Humanities02This paper chronicles the emergence of AI in non-Western philosophies, especially in African philosophy and then uses the Yoruba's 'ifá' to exemplify the idea of decolonising Al, not forgetting that the basis of ifá itself is logic and, sometimes, probability.Àgídìgbo: ABUAD Journal of the Humanities["H. T. Olojede", "Ayo Fadahunsi"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/44b9c67024da9b298a9b805ce1f614433cccd517 3f4db71ee0797fc80e236ac9ffa7bcd005df58d4Journal of Translational Medicine421Two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided and used to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients.Journal of Translational Medicine["M. C. Comes", "L. Fucci", "S. Strippoli", "Samantha Bove", "Gerardo Cazzato", "Carmen Colangiuli", "I. D. Risi", "Ileana De Roma", "A. Fanizzi", "F. Mele", "Maurizio Ressa", "C. Saponaro", "Clara Soranno", "Rosita Tinelli", "Michele Guida", "Alfredo Zito", "R. Massafra"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/3f4db71ee0797fc80e236ac9ffa7bcd005df58d4 8384bf8c971e5adc10c666a611c65b74f3220fc1Artificial Intelligence (AI) has rapidly emerged as a transformative force in education, offering innovative solutions to enhance learning and teaching processes. This technology is revolutionising traditional educational methods by enabling more efficient, accessible, and engaging learning environments. This study aims to evaluate the impact of AI on Fiqh education at Sekolah Indonesia Johor Bahru (SIJB), reflecting the technological advancements of the Fourth Industrial Revolution. The research employs a quantitative quasi-experimental design, involving 73 students, to assess the effectiveness of AI tools in enhancing Fiqh learning outcomes. Pre-test and post-test data were analysed using the Wilcoxon Signed-Rank Test to measure the effect of AI-based interventions on student performance. The results indicate positive effects of AI-based learning methods on students in Fiqh education. Additionally, a four-point Likert scale questionnaire was used to assess students' perceptions of the ease of use of AI tools, their understanding, and the impact of these tools on learning. The study concludes that the integration of AI tools in Fiqh education at Sekolah Indonesia Johor Bahru significantly enhances students' knowledge and learning outcomes. The implications of these findings suggest that AI can be effectively integrated into Fiqh learning to foster a more engaging and efficient learning environment, ultimately improving student performance and understanding.Edelweiss Applied Science and Technology171The study concludes that the integration of AI tools in Fiqh education at Sekolah Indonesia Johor Bahru significantly enhances students' knowledge and learning outcomes, suggesting that AI can be effectively integrated into Fiqh learning to foster a more engaging and efficient learning environment.Edelweiss Applied Science and Technology["Tholkhatul Khoir", "Mohd Fadzil Abdul Hanid", "Moh Khasan", "Noor Azean Atan", "Misbah Zulfa Elizabeth", "Shahrin Hashim"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/8384bf8c971e5adc10c666a611c65b74f3220fc1 3bc7eab74df2ebce4ba752a68ee33a8ee1d04ca5The Egyptian Journal of Radiology and Nuclear Medicine292Investigation of the effectiveness of indulging AI in combination with one radiologist in the routine double reading of mammography for breast cancer screening found it enhanced the opportunity to reduce false negative cases and supported the decision to recall or biopsy.Egyptian Journal of Radiology and Nuclear Medicine["Sahar Mansour", "Enas M. Sweed", "Mohammed Mohammed Mohammed Gomaa", "S. Hussein", "Engy Abdallah", "Yassmin Mohamed Nada", "Rasha Kamal", "Ghada Mohamed", "S. N. Taha", "A. Moustafa"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/3bc7eab74df2ebce4ba752a68ee33a8ee1d04ca5 f0c95763fe06b48e4db51c265014723b6045ac32Intensive Care Medicine322The majority of AI models remain within the testing and prototyping phase and have a high risk of bias, so Bridging the gap between designing and clinical implementation of AI models is needed to warrant safe and trustworthy AI models.Intensive Care Medicine["Janno S Schouten", "Melissa A C M Kalden", "Eris van Twist", "I. K. M. Reiss", "D. Gommers", "M. V. van Genderen", "H. R. Taal"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/f0c95763fe06b48e4db51c265014723b6045ac32 c3f06d1cc0711d4302a9a7fb51dcfa7e82c30d2eThis study examines how automation and then artificial intelligence (AI) was discussed by news workers in journalism trade publications in the 1980s and 1990s and through the 2000s and 2010s. This era saw the full computerization of the newsroom, as well as the introduction of the civilian, commercial internet and its adoption by the news and media industries. Limited use of automated and early AI tools in these fields dates back to the 1960s and 1970s, with the use of software such as spell- and grammar-checkers, as well as the first generation of word-processing tools. This included very early efforts at automated writing, such as for financial and sports news. With this complex origin story, the discourse around AI has a prehistory that deserves a deeper exploration and appreciation.Emerging Media201This study examines how automation and then artificial intelligence was discussed by news workers in journalism trade publications in the 1980s and 1990s and through the 2000s and 2010s and has a prehistory that deserves a deeper exploration and appreciation.Emerging Media["Will Mari"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/c3f06d1cc0711d4302a9a7fb51dcfa7e82c30d2e 0a008dde9f171aed6e66a5475248e9fd0ff0e80dNature Communications272While Asia leads in total publications, Northern America and Europe contribute most of the AI research appearing in high-ranking outlets, generating up to 50% more citations than other regions.Nature Communications["Leo Schmallenbach", "Till W B\u00e4rnighausen", "Marc J. Lerchenmueller"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/0a008dde9f171aed6e66a5475248e9fd0ff0e80d db9fe94480596552d4c11572178ac221c754b356Artificial intelligence in law enforcement can be harnessed due to its powers to faster classify, analyze, evaluate and interpret large data sets and information, which are the main pillars of national and international law enforcement authorities. This article presents application use cases for AI in criminal investigations against extremists and cyber criminals. An outlook into combatting crime is presented through the case of an AI tool made to combat child abusers on the dark web. Additionally, the article presents the predictive measures of artificial intelligence to aid law enforcement agencies in evaluating online behavior and predicting where crimes might occur before any real victims emerge. The article presents conclusions and recommendations for law enforcement applications and future research in a similar area.Pakistan Journal of Criminology00Pakistan Journal of Criminology[]2024-09-12T00:00:00https://www.semanticscholar.org/paper/db9fe94480596552d4c11572178ac221c754b356 e8e098c2333d7eda5d662afb50f497ba052e12a5The thesis examines the possibilities of developing the communication skills of medical workers with the help of artificial intelligence.Virtual Technologies in Medicine00Virtual Technologies in Medicine["M. Kuragina", "E. N. Vasilyeva", "A. V. Shcherbakov"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/e8e098c2333d7eda5d662afb50f497ba052e12a5 6fbb4ef305ffc3723c9076e67cdb9fe78121a604This study explores the transformative impact of Artificial Intelligence (AI) on automating business processes in electronic commerce (e-commerce), with a focus on enhancing efficiency and customer experience. The research employs Deep Learning (DL) and Machine Learning (ML) as primary analytical tools to process and analyze data from e-commerce transaction records and customers’ browsing histories. Techniques such as data preprocessing, normalization, sentiment analysis, and advanced predictive models using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) are utilized. Data collection was conducted using web scraping tools like Beautiful Soup and Scrapy, along with APIs from Amazon and Google. The application of AI in e-commerce has led to significant improvements in inventory control, fraud prevention, and customer relations. ML algorithms have enhanced the estimation of product demand and personalized customer interactions, while DL has strengthened product recommendation systems and fraud detection mechanisms. The findings indicate that AI contributes to a more secure, faster, and smarter operational environment in e-commerce. This research highlights the substantial benefits and broad potential of AI in optimizing e-commerce operations, demonstrating that the integration of advanced AI technologies not only streamlines transactions but also reinforces platforms against fraudulent activities.Data and Metadata240The research employs Deep Learning and Machine Learning as primary analytical tools to process and analyze data from e-commerce transaction records and customers’ browsing histories, demonstrating that the integration of advanced AI technologies not only streamlines transactions but also reinforces platforms against fraudulent activities.Data and Metadata["P. Halachev"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/6fbb4ef305ffc3723c9076e67cdb9fe78121a604 467eae401561313ea621a710571df5619726e10fSustainable agriculture plays a key role in tackling worldwide food security issues and reducing environmental harm. Recent progress in the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin technologies has demonstrated significant potential in transforming agricultural methods to be more sustainable. This study provides an in-depth analysis of how AI, IoT, and digital twins are being incorporated into sustainable agriculture, with a focus on their opportunities, obstacles, and future prospects. IoT sensors and devices play a significant role in enabling real-time data collection, analysis, and decision-making within agricultural operations. This capability allows for precise monitoring of crop growth, soil conditions, weather patterns, and resource usage, ultimately leading to optimized resource management and increased productivity while minimizing environmental impact. Additionally, digital twins, which are virtual representations of physical agricultural systems, provide opportunities for simulation, experimentation, and optimization of farming practices in a virtual setting. The article explores the diverse applications of IoT, AI, and digital twins in sustainable agriculture, such as precision farming, smart irrigation, pest and disease management, predictive analytics, and supply chain optimization. Through the examination of case studies, the paper showcases how these technologies effectively enhance crop yield, resource efficiency, and overall sustainability metrics.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)280The article explores the diverse applications of IoT, AI, and digital twins in sustainable agriculture, such as precision farming, smart irrigation, pest and disease management, predictive analytics, and supply chain optimization.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)["Bhavika Thakkar", "Shaina Shaikh", "Pratiksha Katkade", "Varsha Atul Shukre"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/467eae401561313ea621a710571df5619726e10f 48510454dab55f6988994c95017bb08ee882a98a As medical technology progresses, artificial intelligence (AI) is becoming a common method for providing diagnostic information to patients. In neuropsychology, the Meyers Neuropsychological Battery (MNB) provides impartial data through artificial intelligence. The following case illustrates how AI provided diagnostic clarity and aided in treatment planning. A 65-year-old female was referred by her neurosurgeon due to cognitive decline, delusions and hallucinations, and episodes of aggression toward her caretaker. A full, comprehensive neuropsychological examination was administered. Her medical history was remarkable for hypertension, hypercholesterolemia, thyroid cancer, two cerebrovascular accidents, traumatic brain injury, and multiple ischemic strokes. The patient’s most recent imaging was remarkable for chronic encephalomalacia involving the R basal ganglia, mild chronic small vessel ischemic changes, a ventricular shunt within the anterior horn of the R lateral ventricle, and post-coil embolization of a right supraclinoid artery. The patient was administered the MNB along with psychological measures. Results were consistent with diffuse and lateralized (RCH > LCH) neuropsychological impairment. Employing the MNB algorithms and AI, this patient’s data matched statistically with Multi-Infarct Dementia, Hydrocephalus, and Lewy Body Dementia. These independent applications and AI enable neuropsychologists to respond beyond the “referral question” and provide an objective perspective unique to the patient’s neuropsychological profile. AI is not applied as the only diagnostic approach but rather provides the patient’s medical team with an alternative perspective regarding the entire neuropsychological picture. MNB’s AI not only aids in “matching” neurological conditions to the patient’s profile but also excludes proposed diagnoses in question and underscores a realistic treatment plan. Archives of Clinical Neuropsychology00MNB’s AI not only aids in “matching” neurological conditions to the patient’s profile but also excludes proposed diagnoses in question and underscores a realistic treatment plan.Archives of Clinical Neuropsychology["Robert B Sica", "Steven Greco", "Eleonora Gallagher", "Gianna Scimemi"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/48510454dab55f6988994c95017bb08ee882a98a ac3ee606e63a69d709bcfe6c38d195a8fbbabfdeFull-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. Technology also plays an essential role in supporting runners and running races. Technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.2024 7th International Conference of Computer and Informatics Engineering (IC2IE)00Several factors were also discovered to be connected to marathons and athletic performance, which suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential.2024 7th International Conference of Computer and Informatics Engineering (IC2IE)["Wandy Wandy", "Kusworo Adi", "M. A. Ayu"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/ac3ee606e63a69d709bcfe6c38d195a8fbbabfde 322b07b88a7dd778185b7b08798930a4125441e8Lawmakers all over the world have started to draft new regulations for Artificial Intelligence (AI). While the European Union is currently leading the way with its AI Act, many other legislators will follow and already positioned themselves with white papers and other publications. This commentary argues that “Artificial Intelligence”, including Generative AI, should not be used as a regulatory category. Not because there is no potential for harm from AI systems and not because AI systems should not be regulated, but because “Artificial Intelligence” is a vaguely defined label that is neither suitable nor necessary for comprehensive regulation of technological risks. Instead of regulating a particular set of approaches and algorithms, lawmakers should focus and double down on regulating high-risk applications of software, independent of whether it is labelled as AI or not.Digital Government: Research and Practice150This commentary argues that “Artificial Intelligence”, including Generative AI, should not be used as a regulatory category, because “Artificial Intelligence” is a vaguely defined label that is neither suitable nor necessary for comprehensive regulation of technological risks.Digital Government: Research and Practice["Daniel Braun"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/322b07b88a7dd778185b7b08798930a4125441e8 2ec102d3ddf40a1fa7d492d8e7c8ea0fd64fec5bArtificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology. This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy (ART), and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from computed tomography, magnetic resonance imaging, and positron emission tomography scans to enable precise tumor delineation. In ART, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can driveEwha Medical Journal860This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities and how predictive analytics using historical patient data can predict treatment outcomes and potential complications can predict treatment outcomes and potential complications.The Ewha Medical Journal["Chiyoung Jeong", "Y. Goh", "Jungwon Kwak"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/2ec102d3ddf40a1fa7d492d8e7c8ea0fd64fec5b 85b34e34daa5bf421e18ae4898dd68353f63715fThe systematic development of artificial intelligence in Russia began in 2019 with the approval of the National Strategy for the Development of Artificial Intelligence for the period until 2030. For the first time, it reflects the state’s comprehensive approach to the named institute: key concepts are defined, goals, main tasks, and priority areas for the use of artificial intelligence technologies are established. Their legal regulation was based on the “soft law” model. The active introduction of artificial intelligence into the economy, as well as new scientific developments, have led to a change in its strategic vector as early as 2024. The article analyzes the updated National Strategy, focusing on the new section that consolidates the creation of a system of legal regulation of artificial intelligence. A critical assessment is substantiated regarding the lack of forward movement in regulation from “soft law” to mandatory legislative norms.Juridical World00The article analyzes the updated National Strategy for the Development of Artificial Intelligence, focusing on the new section that consolidates the creation of a system of legal regulation of artificial intelligence.Juridical World["Tatyana N. Mikheeva", "D. Mikheev"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/85b34e34daa5bf421e18ae4898dd68353f63715f c25001a098e3b57694882aaa83e22448d7af954fTheories are required for artificial intelligence (AI) to make greater progress. Despite the development of several AI theories, their use is minimal and their nature is not widely known. An analogy with software engineering theories was used to analyze kernel, genetic, design decision, task, and AI innovation theories to determine their nature and characteristics. These theories were then applied to the CommonKAD methodology in AI to explore how they could improve the methodology, potentially contributing to the evolution of AI theories and increasing their application.AI Computer Science and Robotics Technology280These theories were applied to the CommonKAD methodology in AI to explore how they could improve the methodology, potentially contributing to the evolution of AI theories and increasing their application.AI, Computer Science and Robotics Technology["Wangai N. Mambo"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/c25001a098e3b57694882aaa83e22448d7af954f 11a2d2d1e1b0170a86af7e0b599cd46174aa5c4bPurposeThe present research aims to explore the drivers of generative artificial intelligence (GEN AI)-based innovation adoption in the hospitality industry in Jordan.Design/methodology/approachTo address the research gap and achieve the research work objectives, the Technology-Organization-Environment (TOE) lens and the structural equation modeling (SEM) approach were employed to analyze the sample data collected (n = 221) from the hospitality industry.FindingsThe findings indicate that relative advantage, top management support, organizational readiness, organizational culture, competitive pressures, government regulations support and vendor support significantly influence the GEN-AI-based innovation adoption, while the technological complexity is negatively associated with GEN-AI-based innovation adoption. Furthermore, the results showed there is no significant effect of cost on GEN-AI-based innovation adoption.Originality/valueThe paper analyses the TOE framework in a new technological setting. The paper also provides information about how GEN-AI-based innovation adoption may influence hospitality industry performance. Overall, this article provides new insights into the literature concerning AI technologies and through the TOE lens.Management Decision850The findings indicate that relative advantage, top management support, organizational readiness, organizational readiness, organizational culture, competitive pressures, government regulations support and vendor support significantly influence the GEN-AI-based innovation adoption, while the technological complexity is negatively associated with GEN-AI-based innovation adoption.Management Decision["A. Al-Khatib"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/11a2d2d1e1b0170a86af7e0b599cd46174aa5c4b b002b49eea6fd445928f0a6b26e0572d8f85499bThis study aims to analyze the impact of Artificial Intelligence (AI) on teaching practices in Brazil, exploring the themes and sub-themes addressed in academic research. To this end, a systematic literature review was conducted on 26 selected scientific articles that investigate the relationship between AI and teaching work, analyzing the methodologies used and the results obtained. The methodological approach followed rigorous inclusion and exclusion criteria, ensuring the relevance of the data collected. The theoretical foundation was based on the Critical Theory of Technology from the epistemology of Andrew Feenberg's philosophy, emphasizing the importance of considering the ethical implications associated with the use of AI in education. The study also explores the different forms of interaction between AI and pedagogical-didactic work, highlighting pedagogical innovations, ethical challenges, and the need for organizing teaching work articulated with emerging digital technologies. The results indicate a growing need to deepen discussions on the ethical implications of AI in education, in addition to promoting an interdisciplinary integration of knowledge that allows for a more comprehensive and effective understanding of the pedagogical use of AI. The study concludes that the complexity of the interaction between AI and teaching work requires continuous monitoring of the development of these technologies and their educational applications, emphasizing the importance of staying updated on emerging trends and challenges. Furthermore, it emphasizes the need for a critical and ethical approach to ensure that AI contributes positively to the educational environment, preserving teacher autonomy and the quality of teaching.Revista Acadêmica Online00The study concludes that the complexity of the interaction between AI and teaching work requires continuous monitoring of the development of these technologies and their educational applications, emphasizing the importance of staying updated on emerging trends and challenges.Revista Acadêmica Online["Cl\u00e1udia Helena dos Santos Ara\u00fajo", "Jhonatans da Silva Fernandes", "C\u00e9sar Augusto Viegas Vilas Boas"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/b002b49eea6fd445928f0a6b26e0572d8f85499b b5851ac8194c506cb60202bde4975560eb6dd424In this study, we examine auditors' reliance on artificial intelligence (AI) systems that are designed to provide evidence around complex estimates. In an experiment with highly experienced auditors, we find that auditors are more hesitant to rely on evidence from AI‐based systems compared to human specialists, consistent with algorithm aversion. Importantly, we also find that a small amount of control (i.e., providing input to specialists) can mitigate this aversion, though this effect depends on auditors' personal locus of control (LOC). Providing input increases reliance on evidence from AI systems for auditors who believe they have little control over their outcomes (i.e., an external LOC). In contrast, auditors with an internal LOC are particularly hesitant to rely on AI‐based evidence, and providing input has little impact on their reliance. Interviews with experienced auditors corroborate our findings and suggest auditors feel a greater sense of control working with human specialists relative to AI‐based systems. Overall, our results suggest perceived control plays an important role in auditors' aversion to AI and that auditors' individual traits can affect this aversion.Social Science Research Network550Perceived control plays an important role in auditors' aversion to AI and that auditors' individual traits can affect this aversion, though this effect depends on auditors' personal locus of control.SSRN Electronic Journal["Benjamin Commerford", "A. Eilifsen", "Richard C. Hatfield", "Kathryn Holmstrom", "Finn Kinserdal"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/b5851ac8194c506cb60202bde4975560eb6dd424 84899c7f89537673fe6bdd9c6f36c69f80bd3ed0Journal of Environmental Management593This paper helps clarify the specific impact of AI technology development on the ecological footprint and provides scientific evidence for regional technology development, energy efficiency improvement, and ecological environment policy formulation.Journal of environmental management["Yong Wang", "Ru Zhang", "Kainan Yao", "Xuejiao Ma"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/84899c7f89537673fe6bdd9c6f36c69f80bd3ed0 749c6de4e01fe95a25d3f052fb0951c088930193Journal of Public Relations Research321Journal of Public Relations Research["C. Yue", "L. Men", "Donna Z. Davis", "Renee Mitson", "Alvin Zhou", "Ahmed Al Rawi"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/749c6de4e01fe95a25d3f052fb0951c088930193 1108248e25e019994ec5506fa58c70c43dd25b64Interactive Learning Environments432Interactive Learning Environments["Yan Li", "Xinyan Zhou", "T. Chiu"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/1108248e25e019994ec5506fa58c70c43dd25b64 bc1bc92cca9ea80dfa12a4cbd4db9ee17c15a2e1Research in Drama Education: The Journal of Applied Theatre and Performance101Research in Drama Education: The Journal of Applied Theatre and Performance["Tricia Clark-Fookes"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/bc1bc92cca9ea80dfa12a4cbd4db9ee17c15a2e1 c741a7b41c571b663a1a626de46f05c63555515b00[]2024-09-12T00:00:00https://www.semanticscholar.org/paper/c741a7b41c571b663a1a626de46f05c63555515b 47763db67323f00a4b58d2cec83f37b6780839fbKnowledge Management Research &amp; Practice630Knowledge Management Research &amp; Practice["Chenqian Xu", "Zhu Yao", "Weiwei Huo"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/47763db67323f00a4b58d2cec83f37b6780839fb a837f2a908fc29b75fc274683d7acace4ed4de54Journal of International Entrepreneurship380Journal of International Entrepreneurship["H. Etemad"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/a837f2a908fc29b75fc274683d7acace4ed4de54 de43c61f90ea7029ce5a3d9f75ce62cbe401429fRevista Electronica de Veterinaria00Revista Electronica de Veterinaria["Dr Joydeb Patra"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/de43c61f90ea7029ce5a3d9f75ce62cbe401429f f81a89e77dbe5e66853e1b835eb453f2ce07c3cdProceedings of the Central and Eastern European eDem and eGov Days 2024110Proceedings of the Central and Eastern European eDem and eGov Days 2024["Oussama Mistar"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/f81a89e77dbe5e66853e1b835eb453f2ce07c3cd fb66eac1bdd36893b532988d8b7ae2d15e3c165bIn the article, the author analyzes the mechanisms of achieving cyber sovereignty by the People›s Republic of China through the prism of the impact of this policy on the state of competition in the information and digital commodity markets, as well as the correlation of the legal and economic effect of such a policy with the policy of protectionism. The author believes that the policy pursued by China in relation to the creation of a sovereign Internet can be applied by other developing countries both for the purpose of ensuring their information independence and security, and for the purpose of creating conditions for the emergence of companies competing with the global giants of the information technology industry in such states.Business Law00The author believes that the policy pursued by China in relation to the creation of a sovereign Internet can be applied by other developing countries both for the purpose of ensuring their information independence and security, and for the purpose of creating conditions for the emergence of companies competing with the global giants of the information technology industry in such states.Business Law["Maria A. Egorova"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/fb66eac1bdd36893b532988d8b7ae2d15e3c165b 1f49786a32c1131b49696f306215de2e68f2046cPeran teknologi dalam mendukung PB sangat penting, dan integrasi animasi 3D, Google Slides, dan AI menjanjikan potensi besar. Tujuan penelitian ini adalah untuk mengembangkan metode pembelajaran yang berfokus pada diferensiasi dengan menggunakan teknologi modern seperti animasi 3D, AI, dan Google Slides untuk meningkatkan minat dan hasil belajar siswa dalam mata pelajaran Bahasa Inggris di SDN Jurong Mesjid. Penelitian ini juga bertujuan untuk menciptakan lingkungan belajar yang lebih menarik, interaktif, dan efektif bagi siswa serta mengukur dampaknya terhadap minat dan hasil belajar. Penelitian ini mengisi research gap dengan menggabungkan ketiga teknologi tersebut dalam pembelajaran berdiferensiasi Bahasa Inggris di tingkat SMA, yang belum pernah diteliti sebelumnya. Metode yang digunakan adalah pendekatan campuran yang mengintegrasikan Quasi experimental design, di mana kelompok kontrol dan kelompok eksperimen dibandingkan sebelum dan sesudah penerapan pembelajaran berdiferensiasi dengan integrasi animasi 3D, AI, dan Google Slides. Hasil penelitian menunjukkan bahwa integrasi teknologi dalam pembelajaran berdiferensiasi secara signifikan meningkatkan minat belajar siswa terhadap Bahasa Inggris dengan rata-rata peningkatan sebesar 33.33%. Penerapan pembelajaran berdiferensiasi dengan teknologi animasi 3D, AI, dan Google Slides juga meningkatkan hasil belajar siswa dengan peningkatan nilai rata-rata sebesar 15% di kelompok eksperimen dibandingkan dengan hanya 5% di kelompok kontrol.Selain itu, siswa menunjukkan respons positif dan merasa lebih tertarik serta termotivasi untuk belajar Bahasa Inggris dengan penggunaan teknologi ini.Dharmas Education Journal (DE_Journal)290Dharmas Education Journal (DE_Journal)["Zaiturrahmi Zaiturrahmi", "I. Iqbal", "Huzaima Al-Qaida"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/1f49786a32c1131b49696f306215de2e68f2046c 76cb1025d3320dd461baad66c1bac237effa5349AI in healthcare has improved, making diagnostics more accurate and increasing the effectiveness of treatments. The present study discusses the AI trends in diagnostic and therapeutic applications and focuses on the presented practical applications and their effects on patient care. The purpose of this particular review is to focus on the current developments in the implementation of AI in the field of health care, present main use cases and successes, as well as discuss about the issues and concerns in the topic at hand.  Previous studies on AI in healthcare with specific consideration of diagnostic image analysis and interpretation, histology and molecular pathology, whole-genome sequencing, and therapeutic decision support are discussed. The selection criteria included papers with data gathered from real-life AI cases and quantitative findings. Study materials were obtained from e-journals, conference papers, and established online sources with descriptive analysis being done on the data collected. A summary of the findings revealed a number of highly impactful subcategories focused on the use of artificial intelligence diagnostic imaging, especially in radiology, pathology, and genomics. The AI applications used in the fields of operations and drug discovery revealed the ability to accurately predict clinical trial outcomes and to create effective treatments. First of all, AI can become a game changer in healthcare by enhancing diagnostics accuracy and treatment outcomes. The future research questions include further developing the methods that explain the AI models’ decisions, protecting the privacy of patient information, and reducing algorithmic bias for better fair healthcare for all. Therefore, better interactions between creators of AI and clinicians and regulatory authorities are pertinent to make sure that the full advantages of AI are realized in clinical practice to advance patient care.Salud, Ciencia y Tecnología160The present study discusses the AI trends in diagnostic and therapeutic applications and focuses on the presented practical applications and their effects on patient care, focusing on the use of artificial intelligence diagnostic imaging.Salud, Ciencia y Tecnología["Vadim Pererva", "D.V. Maltsev", "O. Hruzevskyi", "Leonid Gai", "Yurii Dekhtiar"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/76cb1025d3320dd461baad66c1bac237effa5349 22910e4e9eb0ab2916588fc01313d35909f926fcFormosan Journal of Surgery00Formosan Journal of Surgery["H. Daungsupawong", "V. Wiwanitkit"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/22910e4e9eb0ab2916588fc01313d35909f926fc d40841057fe8794580b126932f2e70f1090590c7Journal of Assisted Reproduction and Genetics20Journal of assisted reproduction and genetics["Yi Wang", "Yanggang Hong"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/d40841057fe8794580b126932f2e70f1090590c7 51ff0777f098e57a826db55d7c18242665223793Korean Journal of Radiology60Korean Journal of Radiology["Mukesh Kumar Dharmalingam Jothinathan"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/51ff0777f098e57a826db55d7c18242665223793 c64b5a42e7417f4d4cb23435a7f12dcb572110d9Proceedings of the Central and Eastern European eDem and eGov Days 202450Proceedings of the Central and Eastern European eDem and eGov Days 2024["P\u00e9ter B\u00e1ny\u00e1sz", "Tam\u00e1s Sz\u00e1deczky", "Kincso Boroka Vaczi"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/c64b5a42e7417f4d4cb23435a7f12dcb572110d9 3e020f4e5a29d69971069b5c467ed7882b5e00b5The artificial general intelligence (AGI) industry, represented by ChatGPT, has impacted social order during its development, and also brought various risks and challenges, such as ethical concerns in science and technology, attribution of liability, intellectual property monopolies, data security, and algorithm manipulation. The development of AI is currently facing a crisis of trust. Therefore, the governance of the AGI industry must be prioritized, and the opportunity for the implementation of the Interim Administrative Measures for Generative Artificial Intelligence Services should be taken. It is necessary to enhance the norms for the supervision and management of scientific and technological ethics within the framework of the rule of law. Additionally, it is also essential to continuously improve the regulatory system for liability, balance the dual values of fair competition and innovation encouragement, and strengthen data-security protection systems in the field of AI. All of these will enable coordinated governance across multiple domains, stakeholders, systems, and tools.Laws252The governance of the AGI industry must be prioritized, and the opportunity for the implementation of the Interim Administrative Measures for Generative Artificial Intelligence Services should be taken.Laws["Bing Chen", "Jiaying Chen"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/3e020f4e5a29d69971069b5c467ed7882b5e00b5 6f48f1698214aea42ff343cfcd040f8be99e3fc2Generative Al offers a considerable improvement over the traditional Al models which are mostly employed for classification or prediction tasks. In addition to supporting data augmentation in situations where data is scarce, it exhibits creativity in producing original text, images, or music. It also enables personalization to produce tailored content, greatly improving user experience and opening up new avenues for increased productivity for business and technology teams. Gen AI is completely changing the banking industry by producing content, mimicking human behavior, enhancing client interactions, offering real-time support, and increasing operational efficiency. Through deep learning algorithms, it improves client experiences, expedites procedures, and strengthens risk management. By putting ethical standards into place, encouraging openness, and guaranteeing data security and privacy, the sector can realize this potential. Banks are using generative artificial intelligence (AI) to solve persistent problems like fraud detection, fraud prevention, customer targeting, data summarization, enhancing conversational bots, and product integration. This technology can supply digital items through digital channels, increase data summarization, and improve user experience.International Journal of Innovative Science and Research Technology481Generative Al offers a considerable improvement over the traditional Al models which are mostly employed for classification or prediction tasks, and exhibits creativity in producing original text, images, or music.International Journal of Innovative Science and Research Technology (IJISRT)["Anju Sonkhla", "K. D. Shara"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/6f48f1698214aea42ff343cfcd040f8be99e3fc2 a69d2ce5923bf757ab7f117f328207747a94519eCritical Care506Critical Care["Gw\u00e9nol\u00e9 Abgrall", "Andre L. Holder", "Zaineb Chelly Dagdia", "Karine Zeitouni", "Xavier Monnet"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/a69d2ce5923bf757ab7f117f328207747a94519e 866dad40122727a646c17e375ce1f7d03b3ed700The study investigates the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies into Beijing's waste management system, emphasizing their effects on operational efficiency, environmental sustainability, and economic feasibility. The deployment of AI-driven route optimization and IoT-enabled real-time monitoring resulted in a 25% reduction in waste collection trips and a 30% decrease in waste overflow incidents. These advancements led to notable reductions in fuel consumption and environmental impact, while an economic analysis projected a Net Present Value (NPV) of $3.5 million over a 10-year period, affirming the financial benefits of these technologies. The findings highlight the pivotal role of AI and IoT in optimizing urban waste management practices. The study offers policy recommendations for the phased and strategic adoption of these technologies within Beijing, with the potential to enhance efficiency and contribute to the city’s sustainability objectives. Future research is advised to examine the long-term sustainability of AI-driven waste management strategies and assess the applicability of these technologies in diverse urban environments.WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY294World Journal of Innovation and Modern Technology["Yao Yao", "Jiewei Weng", "Chao He", "Chengliang Gong", "Peng Xiao"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/866dad40122727a646c17e375ce1f7d03b3ed700 d80c1e348b1862ca31fd2469756e410f9a93f629Explainable Artificial Intelligence (XAI) is a research area that clarifies AI decision-making processes to build user trust and promote responsible AI. Hence, a key scientific challenge in XAI is the development of methods that generate transparent and interpretable explanations while maintaining scalability and effectiveness in complex scenarios. Rule-based methods in XAI generate rules that can potentially explain AI inferences, yet they can also become convoluted in large scenarios, hindering their readability and scalability. Moreover, they often lack contrastive explanations, leaving users uncertain why specific predictions are preferred. To address this scientific problem, we explore the integration of computational argumentation—a sub-field of AI that models reasoning processes through defeasibility—into rule-based XAI systems. Computational argumentation enables arguments modelled from rules to be retracted based on new evidence. This makes it a promising approach to enhancing rule-based methods for creating more explainable AI systems. Nonetheless, research on their integration remains limited despite the appealing properties of rule-based systems and computational argumentation. Therefore, this study also addresses the applied challenge of implementing such an integration within practical AI tools. The study employs the Logic Learning Machine (LLM), a specific rule-extraction technique, and presents a modular design that integrates input rules into a structured argumentation framework using state-of-the-art computational argumentation methods. Experiments conducted on binary classification problems using various datasets from the UCI Machine Learning Repository demonstrate the effectiveness of this integration. The LLM technique excelled in producing a manageable number of if-then rules with a small number of premises while maintaining high inferential capacity for all datasets. In turn, argument-based models achieved comparable results to those derived directly from if-then rules, leveraging a concise set of rules and excelling in explainability. In summary, this paper introduces a novel approach for efficiently and automatically generating arguments and their interactions from data, addressing both scientific and applied challenges in advancing the application and deployment of argumentation systems in XAI.Machine Learning and Knowledge Extraction01A modular design is presented that integrates input rules into a structured argumentation framework using state-of-the-art computational argumentation methods, addressing both scientific and applied challenges in advancing the application and deployment of argumentation systems in XAI.Mach. Learn. Knowl. Extr.["Lucas Rizzo", "Damiano Verda", "Serena Berretta", "Luca Longo"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/d80c1e348b1862ca31fd2469756e410f9a93f629 4fd2a66af0c92212d40f9e53195897ec04c5087cThe integration of artificial intelligence (AI), including the recent appearance of revolutionary large language models (LLMs), marks a transformative era in the field of negotiations, reshaping traditional practices and presenting a range of opportunities and challenges. This article delves into the evolving interplay between negotiation and various AI technologies, as they now combine massive computational power with user-friendly interfaces capable of fluent, multi-topic conversations. The article categorizes AI's role in negotiation into assistance, semi-automation, and automation, each offering unique advantages and addressing different negotiation needs. While AI's ability to compensate for human limitations in rationality, emotion, and computational capacity is promising, it also raises concerns regarding biases, ethical considerations, and the reliability of automated decision-making. The burgeoning AI and negotiation collaboration necessitates a balanced approach, harnessing AI's potential to enhance negotiation outcomes while conscientiously navigating its challenges. This article aims to foster understanding of and influence the future trajectory of negotiation and AI, highlighting the need for ongoing research and development to ensure ethical, effective, and equitable negotiation practices in an AI-augmented future.Journal of Strategic Contracting and Negotiation471The article categorizes AI's role in negotiation into assistance, semi-automation, and automation, each offering unique advantages and addressing different negotiation needs, each offering unique advantages and addressing different negotiation needs.Journal of Strategic Contracting and Negotiation["Horacio Arruda Falc\u00e3o Filho"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/4fd2a66af0c92212d40f9e53195897ec04c5087c a9dfb37bc87c01e31e667fd71cc23d4afa078a16Artificial intelligence (AI) integration in workplaces reshapes employee engagement and organisational performance. This study investigates the combined impact of AI experience and sustainable leadership on work engagement among Indonesian employees. Using a cross-sectional survey of 301 employees in Greater Jakarta, the research employs partial least squares structural equation modeling (PLS-SEM) to analyse the data. The study finds that while AI experience and sustainable leadership independently enhance work engagement, their interaction negatively affects it, highlighting the necessity for strategically aligning AI initiatives with sustainable leadership principles to optimise engagement. The methodology references several factors: validated AI experience items focusing on productivity and skill development, sustainable leadership items emphasising social and environmental responsibility, and work engagement items measuring enthusiasm and job satisfaction. The Rasch Model Analysis and person-measure analysis ensure the reliability and validity of these constructs. This study fills a significant gap in understanding the interaction between AI and leadership in enhancing work engagement, providing practical insights for managing technological and leadership dynamics in organisations.2024 7th International Conference of Computer and Informatics Engineering (IC2IE)00The study finds that while AI experience and sustainable leadership independently enhance work engagement, their interaction negatively affects it, highlighting the necessity for strategically aligning AI initiatives with sustainable leadership principles to optimise engagement.2024 7th International Conference of Computer and Informatics Engineering (IC2IE)["Maria Grace Herlina", "Karto Iskandar", "Ika Triana"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/a9dfb37bc87c01e31e667fd71cc23d4afa078a16 1536191b01cffc0598230221f1ab3e316f2ff850Secure coding is a paramount practice in software development, serving to safeguard applications against vulnerabilities and security breaches. Traditionally, this process has relied on manual analysis, a method that can be time-consuming and might overlook potential issues. This paper reviews the integration of Artificial Intelligence (AI), specifically Large Language Models (LLMs), into secure coding practices, presenting an innovative approach to bolster code quality and security. The research explores the dynamic relationship between LLMs and secure coding. LLMs, equipped with advanced natural language processing (NLP) and machine learning (ML) capabilities, possess the unique ability to comprehend code context and accurately identify vulnerabilities. They function as invaluable assistants to developers and security professionals, providing real-time guidance, offering remediation strategies, and amplifying human capabilities. While LLMs offer substantial promise, they are not without challenges. Their interpretive understanding can sometimes result in false positives and negatives. The paper underscores the importance of combining LLM insights with established security practices to establish a comprehensive and robust approach to code security.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)140The integration of Artificial Intelligence (AI), specifically Large Language Models (LLMs), into secure coding practices is reviewed, presenting an innovative approach to bolster code quality and security.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)["Md. Naseef-Ur-Rahman Chowdhury", "Md Shain Shahid Chowdhury", "Fariha Ferdous Neha", "Ahshanul Haque", "Mohammad Sahinur Hossen"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/1536191b01cffc0598230221f1ab3e316f2ff850 bcb8c0dd285c0f7aac63607d994be5d3c0bb69f0This study investigates the awareness, perceptions, and challenges of integrating artificial intelligence (AI) into pedagogical practices among undergraduate students at the universities in North Central, Nigeria. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) as a theoretical framework, data were collected through a survey questionnaire administered to 421 undergraduate students from the Faculty of Education. The questionnaire included items designed to measure students' awareness of AI technologies, their views on the potential benefits of AI integration in academic experiences, and the challenges encountered with AI adoption in pedagogical practices. Descriptive statistics were used to analyse the data, including means and standard deviations. The findings reveal a moderate level of awareness among students regarding the potential benefits of AI technologies in education, with a strong belief in the role of AI in improving learning experiences. However, students expressed concerns about technical difficulties, privacy issues, and the adequacy of training and support for AI technologies. The study underscores the need for increased awareness, technological infrastructure improvements, and targeted support services to facilitate the effective integration of AI in pedagogical practices. These findings contribute to the growing literature on AI integration in education and provide valuable insights for educators and policymakers seeking to enhance teaching and learning outcomes through AI-driven innovations.Advances in Mobile Learning Educational Research370There is a moderate level of awareness among students regarding the potential benefits of AI technologies in education, with a strong belief in the role of AI in improving learning experiences, but students expressed concerns about technical difficulties, privacy issues, and the adequacy of training and support for AI technologies.Advances in Mobile Learning Educational Research["Usman Abubakar", "S. Onasanya", "H. Ibrahim"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/bcb8c0dd285c0f7aac63607d994be5d3c0bb69f0 349fb7a525f72f9ac5e8c30a9b07ba8eb64b3a40Green criminology is an emerging field of research that focuses on the use of artificial intelligence (AI) and machine learning (ML) to analyse and address issues about environmental sustainability. This study explores the viewpoints on green offences, the development of environmental courts, and “green” technology by employing AI/ML This paper examines the issues of environmental harm, crime, victimization, and justice in the digital era. This research paper also examines AI algorithms which are working in different fields of environment protection and security is analysed. This research aims to explore the correlation between environmental harm and issues of crime and justice, as well as the many species or organisms that are affected by environmental crimes and their consequences. Moreover, we have found that green technology (AI) has engendered a substantial metamorphosis in national policy, administrations and judiciary in relation to sustainable development and environment preservation. This research paper’s result shows the utilization of algorithms to achieve sustainable development in the context of the environment. Hence, the growing prevalence of AI is influencing various environmental sectors, both in the present and future.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)410The growing prevalence of AI is influencing various environmental sectors, both in the present and future, and the utilization of algorithms to achieve sustainable development in the context of the environment is shown.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)["Monika Kothiyal", "Ashish Singhal", "Sakshi Mehta", "Aditya Tomar", "Minakshi Memoria", "Sagar Saxena"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/349fb7a525f72f9ac5e8c30a9b07ba8eb64b3a40 57249f22fd929c2468a4fcc48c32408e4a69f9deArtificial intelligence (AI) tools are rapidly transforming the field of traditional artistic creation, influencing painting processes and human creativity. This study explores human–AI cooperation in real-time artistic drawing by using the AIGC tool KREA.AI. Participants wear eye trackers and perform drawing tasks by adjusting the AI parameters. The research aims to investigate the impact of cross-screen and non-cross-screen conditions, as well as different viewing strategies, on cognitive load and the degree of creative stimulation during user–AI collaborative drawing. Adopting a mixed design, it examines the influence of different cooperation modes and visual search methods on creative efficacy and visual perception through eye-tracking data and creativity performance scales. The cross-screen type and task type have a significant impact on total interval duration, number of fixation points, average fixation duration, and average pupil diameter in occlusion decision-making and occlusion hand drawing. There are significant differences in the variables of average gaze duration and average pupil diameter among different task types and cross-screen types. In non-cross-screen situations, occlusion and non-occlusion have a significant impact on average gaze duration and pupil diameter. Tasks in non-cross-screen environments are more sensitive to visual processing. The involvement of AI in hand drawing in non-cross-screen collaborative drawing by designers has a significant impact on their visual perception. These results help us to gain a deeper understanding of user behaviour and cognitive load under different visual tasks and cross-screen conditions. The analysis of the creative efficiency scale data reveals significant differences in designers’ ability to supplement and improve AI ideas across different modes. This indicates that the extent of AI participation in the designer’s hand-drawn creative process significantly impacts the designer’s behaviour when negotiating design ideas with the AI.Applied Sciences540The extent of AI participation in the designer's hand-drawn creative process significantly impacts the designer’s behaviour when negotiating design ideas with the AI, indicating that the extent of AI participation in the designer’s hand-drawn creative process significantly impacts the designer’s behaviour when negotiating design ideas with the AI.Applied Sciences["Yuying Pei", "Linlin Wang", "Chengqi Xue"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/57249f22fd929c2468a4fcc48c32408e4a69f9de 596be14ea711f225964cf760d3f7453be433c057Agriculture is essential for maintaining food security and promoting economic sustainability; however, farmers frequently encounter difficulties in choosing the most appropriate crops for their land due to diverse environmental, soil, and market conditions. This paper proposes an innovative crop recommendation system powered by Explainable Artificial Intelligence (XAI), designed to enhance agricultural decision-making. The system utilizes machine-learning models to analyze diverse data inputs—such as soil properties, weather conditions, and market trends—to recommend optimal crops for specific regions. What sets this approach apart is its explainability: the XAI framework provides transparent insights into how and why certain recommendations are made, enabling farmers to trust and understand the decision-making process. The incorporation of eXplainable AI into crop recommendation systems has transformed agriculture, facilitating data-driven decision-making that leads to improved crop production and more efficient resource management. However, these models’ lack of transparency and interpretability frequently constrains their practical implementation. We explore XAI based approaches, such as LIME and SHAP, to interpret model outputs and highlight key features influencing predictions. Our experiments demonstrate that XAI improves the transparency of ML models and aids in refining model performance through informed feature selection and model adjustments. Our proposed models achieve the highest accuracy $\mathbf{9 9. 3 9 \%}$ using the KNN algorithm and BiLSTM model achieve the $\mathbf{9 5. 9 1 \%}$ accuracy.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)190An innovative crop recommendation system powered by Explainable Artificial Intelligence (XAI), designed to enhance agricultural decision-making, and explores XAI based approaches, such as LIME and SHAP, to interpret model outputs and highlight key features influencing predictions.2024 International Conference on Signal Processing and Advance Research in Computing (SPARC)["Surendra Kumar", "Mohit Kumar"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/596be14ea711f225964cf760d3f7453be433c057 b53c8973398110090bedbb0f478be3dfebc4bc5dFederated Learning (FL) provides a strong privacy guarantee by enabling local training across edge devices without training data sharing, and Federated Adversarial Training (FAT) further enhances the robustness against adversarial examples, promoting a step toward trustworthy artificial intelligence. However, FAT requires a large model to preserve high accuracy while achieving strong robustness, and it is impractically slow when directly training with memory-constrained edge devices due to the memory-swapping latency. Moreover, existing memory-efficient FL methods suffer from poor accuracy and weak robustness in FAT because of inconsistent local and global models, i.e., objective inconsistency. In this paper, we propose FedProphet, a novel FAT framework that can achieve memory efficiency, adversarial robustness, and objective consistency simultaneously. FedProphet partitions the large model into small cascaded modules such that the memory-constrained devices can conduct adversarial training module-by-module. A strong convexity regularization is derived to theoretically guarantee the robustness of the whole model, and we show that the strong robustness implies low objective inconsistency in FedProphet. We also develop a training coordinator on the server of FL, with Adaptive Perturbation Adjustment for utility-robustness balance and Differentiated Module Assignment for objective inconsistency mitigation. FedProphet empirically shows a significant improvement in both accuracy and robustness compared to previous memory-efficient methods, achieving almost the same performance of end-to-end FAT with 80% memory reduction and up to 10.8x speedup in training time.arXiv.org360FedProphet is proposed, a novel FAT framework that can achieve memory efficiency, adversarial robustness, and objective consistency simultaneously simultaneously, and empirically shows a significant improvement in both accuracy and robustness compared to previous memory-efficient methods.ArXiv["Minxue Tang", "Yitu Wang", "Jingyang Zhang", "Louis DiValentin", "Aolin Ding", "Amin Hass", "Yiran Chen", "Hai Li"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/b53c8973398110090bedbb0f478be3dfebc4bc5d 81b582448b30050ebb3d6390ed0d6a663049a132Automated employment decision tools use machine learning, artificial intelligence, predictive analytics, and other data-driven approaches to enhance candidate experiences and streamline employment related decision-making, allowing human resources to be concentrated where they are needed most. However, the use of these tools without appropriate safeguards has resulted in a number of high-profile scandals in recent years, particularly in regard to bias. Accordingly, lawmakers have started to propose laws that require bias audits of automated employment decision tools to examine their outputs for subgroup differences. The first of its kind was New York City Local Law 144, but other US states have since followed suit. In this paper, we examine the concerns about the effectiveness of this and other similar laws, including the suitability of metrics, the scope of the law, and low levels of compliance. We conclude that despite the law being a good initial first step towards greater transparency around automated employment decision tools and reducing bias, examining outcomes alone is not sufficient to prevent bias elsewhere in the tool. Moreover, effective bias prevention will require a multidisciplinary approach that combines expertise in IO psychology, law, and computer science to develop appropriate metrics and maximize the enforceability of such laws.International Review of Law, Computers &amp; Technology540International Review of Law, Computers &amp; Technology["Airlie Hilliard", "Ayesha Gulley", "A. Koshiyama", "Emre Kazim"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/81b582448b30050ebb3d6390ed0d6a663049a132 604996a5b381ea92331e90035b17ee0c0d9c3853From the perspective of applying a systems-evolutionary approach and studying the results of a number of modern research studies into the processes of formation and development of the Legal AI market (artificial intelligence system for the legal field) and LegalTech market (digital platforms, software products, and tools used to optimize the provision of legal services), the article reveals the growing potential of these market segments. The author takes into account the prospects for using their market objects for the technological (digital) modernization of the Russian companies that provide qualified legal support for the economic activities of business entities, and enhancing on this basis their competitiveness under evolving Industry 4.0, the economy’s digital transformation, and the unfavorable impact of the external environment. Given the limited open access to up-to-date statistics on the state of all studied market segments, this paper mainly focuses on the modern dynamics of the Legal AI market (more precisely, the Legal AI Software Market, the market for the AI software in the legal field), its subject and object, and geographical structure, along with the development trends and forecasts due to the profitability of the market participants’ activities, growing investment, and other factors that result in expanding this market value in the regions of the world and in Russia.Vestnik Volgogradskogo gosudarstvennogo universiteta Ekonomika70The article reveals the growing potential of the Legal AI market and LegalTech market and examines the development trends and forecasts due to the profitability of the market participants’ activities, growing investment, and other factors that result in expanding this market value in the regions of the world and in Russia.Vestnik Volgogradskogo gosudarstvennogo universiteta. Ekonomika["E. Inshakova"]2024-09-12T00:00:00https://www.semanticscholar.org/paper/604996a5b381ea92331e90035b17ee0c0d9c3853 e9b03e7734b3b7aafc2927b8da08fb14927ff6b6The synergy between artificial intelligence (AI) and hyperspectral imaging (HSI) holds tremendous potential across a wide array of fields. By leveraging AI, the processing and interpretation of the vast and complex data generated by HSI are significantly enhanced, allowing for more accurate, efficient, and insightful analysis. This powerful combination has the potential to revolutionize key areas such as agriculture, environmental monitoring, and medical diagnostics by providing precise, real-time insights that were previously unattainable. In agriculture, for instance, AI-driven HSI can enable more precise crop monitoring and disease detection, optimizing yields and reducing waste. In environmental monitoring, this technology can track changes in ecosystems with unprecedented detail, aiding in conservation efforts and disaster response. In medical diagnostics, AI-HSI could enable earlier and more accurate disease detection, improving patient outcomes. As AI algorithms advance, their integration with HSI is expected to drive innovations and enhance decision-making across various sectors. The continued development of these technologies is likely to open new frontiers in scientific research and practical applications, providing more powerful and accessible tools for a wider range of users.Technologies2043The synergy between artificial intelligence (AI) and hyperspectral imaging (HSI) holds tremendous potential across a wide array of fields, and their integration with HSI is expected to drive innovations and enhance decision-making across various sectors.Technologies["S. Khonina", "N. Kazanskiy", "I.V. Oseledets", "A.V. Nikonorov", "M. A. Butt"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/e9b03e7734b3b7aafc2927b8da08fb14927ff6b6 76facc63781e653a880b8655afaa9539c2c9079aWith the increasing frequency and complexity of cyberattacks, traditional cybersecurity threat detection methods have been difficult to cope with new types of threats. Artificial Intelligence (AI) technology, with its powerful data processing and pattern recognition capabilities, has gradually become an important tool for enhancing cyber security. This paper aims to explore the application of AI in cybersecurity threat detection, firstly outlining the current status of the development of AI technology in cybersecurity, and then focusing on analyzing the application of core methods such as machine learning and deep learning in threat detection, and discussing the advantages of integrated learning and multimodal methods. Finally, this paper summarizes the current challenges faced by AI technology in the field of cyber security and looks forward to the future development direction. Through the research in this paper, it is expected to provide reference for improving the accuracy and efficiency of cybersecurity threat detection.International Journal of Computer Science & Information Technology (IJCSIT)101This paper outlines the current status of the development of AI technology in cybersecurity, and focuses on analyzing the application of core methods such as machine learning and deep learning in threat detection, and discussing the advantages of integrated learning and multimodal methods.International Journal of Computer Science and Information Technology["Zehan Wang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/76facc63781e653a880b8655afaa9539c2c9079a 7cee6c11a32a7cef547918dfa639bd614d9b89c6The aim of this paper is to present artificial intelligence (AI) as an organ with a role in the production of judicial truth, expanding its objects, changing its procedures and reshaping the distribution of agencies within the judicial organism. To this end, it builds on Michel Foucault’s work on the procedures of truth production and the three subject forms involved: operator, spectator and object. This is then complemented by the general organological perspective proposed by Bernard Stiegler. On the basis of both, we will demonstrate two realities: first, that AI is shifting truth production from the individual to the profile, and second, that the types of associations that AI is forming have the potential to curtail human agency in the production of judicial truth.Theory, Culture &amp; Society201It is demonstrated that AI is shifting truth production from the individual to the profile, and that the types of associations that AI is forming have the potential to curtail human agency in the production of judicial truth.Theory, Culture &amp; Society["Joan Rovira Martorell", "Ana G\u00e1lvez", "Francisco Tirado"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/7cee6c11a32a7cef547918dfa639bd614d9b89c6 296980f5a14d076b3dfe7d1425e6ce70f69115e2PurposeIn this study, we investigated the positive and negative effects of stress that is driven by responsible artificial intelligence (RAI) principles on employee job outcomes by adapting the challenge–hindrance stressors model.Design/methodology/approachThe study design involved empirically validating the proposed model on 299 respondents who use AI for work-related tasks.FindingsThe results revealed several RAI-driven challenge and hindrance stressors related to employees’ positive and negative psychological responses and task performance in a digital workplace. Practitioners could use the RAI characteristics to improve employees’ RAI-driven task performance.Research limitations/implicationsThis study contributes to the ongoing discussion on technostress and awareness in the context of RAI in the AI literature. By extending the C-HS model to the RAI context, it complements the context-specific technostress literature by conceptualizing different characteristics of RAI as RAI-driven stressors.Originality/valueAdoption and use of technologies like RAI are not automatically translated into expected job outcomes. Instead, practitioners and academicians also need to know whether the RAI characteristics actually help employees show positive or negative behavior. Furthermore, relying on the challenge–hindrance stressor (C-HS) model, we try to reveal the beneficial and detrimental effects of different RAI characteristics on employees’ job outcomes.Information Technology and People371The results revealed several RAI-driven challenge and hindrance stressors related to employees’ positive and negative psychological responses and task performance in a digital workplace.Inf. Technol. People["Surabhi Verma", "Vibhav Singh", "A. Tudoran", "S. Bhattacharyya"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/296980f5a14d076b3dfe7d1425e6ce70f69115e2 cd1c7de0deeee06e5a2fa74746348ac1bd539eabThe Journal of Headache and Pain1142Overall, AI-driven advancements in headache management hold significant potential for enhancing patient care, clinical practice and research, which should encourage the headache community to adopt AI innovations.The Journal of Headache and Pain["Igor Petru\u0161i\u0107", "Woo-Seok Ha", "Alejandro Labastida-Ram\u00edrez", "Roberta Messina", "D. Onan", "Claudio Tana", "Wei Wang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/cd1c7de0deeee06e5a2fa74746348ac1bd539eab 76a034459d30a476cda69e69cd4136eb74f076a5Focusing on the trend of Artificial intelligence (AI) algorithms in industry decision-making, this article explores the influence of algorithmic recommendation principles and designer values on the formation of gender bias. Comprehensively comparing the cultural differences between the East and the West, we establish a circular model of culture-technology-society interaction to reveal the causes of AI gender bias. The study aims to promote the harmony between technological development and human values, and to promote the optimal development of AI systems.International Conference on Emerging Technologies450A circular model of culture-technology-society interaction is established to reveal the causes of AI gender bias and to promote the harmony between technological development and human values and to promote the optimal development of AI systems.2024 4th International Conference on Educational Technology (ICET)["Jia-Yan Li", "Fei Liu", "Xin-Yue Zhang", "Shuang-Shuang Cai", "Xiang-Lian Yu"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/76a034459d30a476cda69e69cd4136eb74f076a5 580cae8726f923697853ff16e00c1dc04fbad95dThis farmers assistance program aims to build an efficient and intelligent agricultural management system through the deep integration of artificial intelligence (AI) and big data technologies, so as to improve agricultural production efficiency, ensure the quality of agricultural products, promote sustainable agricultural development, and ultimately achieve the goals of increasing farmers' income and prosperity in rural areas.International Journal of Computer Science & Information Technology (IJCSIT)80International Journal of Computer Science and Information Technology["Qi Gao", "Liling Zhang", "Wanying Yang", "Jingyi Xiao", "Zhiying Xu"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/580cae8726f923697853ff16e00c1dc04fbad95d 718aea452bc0e19490cefa38c7e2db7ec70d70f6This review examines the utilization of Generative Artificial Intelligence (GAI) in classroom instructions, emphasizing its transformative potential in education. After reviewing 23 relevant articles, we identified three themes: application advantages, possible risks, and future paths. Specifically, GAI has the potential to facilitate personalized learning, promote communication, and promoting education equity. However, concerns over content validity, ethical issues, and the risk of diminished innovation necessitate careful consideration. Additionally, prior literature suggests improving instructional evaluation methods, enhancing AI literacy, and addressing ethical challenges to maximize GAI's benefits and drive continuous educational innovation.International Conference on Emerging Technologies270This review examines the utilization of Generative Artificial Intelligence in classroom instructions, emphasizing its transformative potential in education and suggests improving instructional evaluation methods, enhancing AI literacy, and addressing ethical challenges to maximize GAI's benefits and drive continuous educational innovation.2024 4th International Conference on Educational Technology (ICET)["Yuxuan Shi", "Wen Huang", "Yijing Sang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/718aea452bc0e19490cefa38c7e2db7ec70d70f6 fd8d62bfe70d38f94e02975888b153058145048bThis paper seeks to bridge the literature gap in the field of Artificial Intelligence in Education (AIEd) by conducting a thorough analysis of its far-reaching impact as well as the emergent strategies to enhance AI literacy, the intricacies of AI-assisted teaching and learning, and the development of robust frameworks for AI integration within educational systems. To the best of our knowledge, this study is pioneering in its holistic approach, not only scrutinizing the transformative effects of AI technologies on learning experiences but also exploring their role in the nuanced interpretation of educational data for improved learning outcomes. By offering an expansive review of these underrepresented areas, our research significantly contributes to the AIEd discourse, laying a foundational groundwork for the effective application of AI in educational contexts and steering future academic inquiries.International Conference on Emerging Technologies560A thorough analysis of its far-reaching impact as well as the emergent strategies to enhance AI literacy, the intricacies of AI-assisted teaching and learning, and the development of robust frameworks for AI integration within educational systems is conducted.2024 4th International Conference on Educational Technology (ICET)["Jinpeng Wang", "Qingqing Xing", "Yihe Qian", "Ahsan Akbar"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/fd8d62bfe70d38f94e02975888b153058145048b 0a108e8125829580e0ce6a0801ee1ed46880f367As terrestrial oilfields continue to be explored, the difficulty of exploring new oilfields is constantly increasing. The ocean, which contains abundant oil and gas resources, has become a new field for oil and gas resource development. It is estimated that the total amount of oil resources contained in ocean areas accounts for 33% of the global total, while the corresponding natural gas resources account for 32% of the world’s resources. Current prediction methods, tailored to land oilfields, struggle with offshore differences, hindering accurate forecasts. With oilfield advancements, a vast amount of rapidly generated, complex, and valuable data has piled up. This paper uses AI and GRN-VSN NN to predict offshore oilfield indicators, focusing on model-based formula fitting. It selects highly correlated input indicators for AI-driven prediction of key development metrics. Afterwards, the Shapley additive explanations (SHAP) method was introduced to explain the artificial intelligence model and achieve a reasonable explanation of the measurement’s results. In terms of crude-oil extraction degree, the performance levels of the Long Short-Term Memory (LSTM) neural network, BP neural network, and ResNet-50 neural network are compared. LSTM excels in crude-oil extraction prediction due to its monotonicity, enabling continuous time-series forecasting. Artificial intelligence algorithms have good prediction effects on key development indicators of offshore oilfields, and the prediction accuracy exceeds 92%. The SHAP algorithm offers a rationale for AI model parameters, quantifying input indicators’ contributions to outputs.Energies180Energies["Ke Li", "Kai Wang", "Chenyang Tang", "Yue Pan", "Yufei He", "Shaobin Cai", "Suidong Chen", "Yuhui Zhou"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/0a108e8125829580e0ce6a0801ee1ed46880f367 2a3029dd232d166796b615d4c52a74d04cc4d755Ophthalmology is well suited for the integration of artificial intelligence (AI) owing to its reliance on various imaging modalities, such as anterior segment photography, fundus photography, and optical coherence tomography (OCT), which generate large volumes of high-resolution digital images. These images provide rich datasets for training AI algorithms, which enables precise diagnosis and monitoring of various ocular conditions. Retinal disease management heavily relies on image recognition. Limited access to ophthalmologists in underdeveloped areas and high image volumes in developed countries make AI a promising, cost-effective solution for screening and diagnosis. In corneal diseases, differential diagnosis is critical yet challenging because of the wide range of potential etiologies. AI and diagnostic technologies offer promise for improving the accuracy and speed of these diagnoses, including the differentiation between infectious and noninfectious conditions. Smartphone imaging coupled with AI technology can advance the diagnosis of anterior segment diseases, democratizing access to eye care and providing rapid and reliable diagnostic results. Other potential areas for AI applications include cataract and vitreous surgeries as well as the use of generative AI in training ophthalmologists. While AI offers substantial benefits, challenges remain, including the need for high-quality images, accurate manual annotations, patient heterogeneity considerations, and the “black-box phenomenon”. Addressing these issues is crucial for enhancing the effectiveness of AI and ensuring its successful integration into clinical practice. AI is poised to transform ophthalmology by increasing diagnostic accuracy, optimizing treatment strategies, and improving patient care, particularly in high-risk or underserved populations.JMA Journal00Artificial intelligence is poised to transform ophthalmology by increasing diagnostic accuracy, optimizing treatment strategies, and improving patient care, particularly in high-risk or underserved populations, particularly in high-risk or underserved populations.JMA Journal["T. Oshika"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/2a3029dd232d166796b615d4c52a74d04cc4d755 4e9e008dab950774a80ca24442b2b17d1953ac36The objective of this study is to examine the characteristics of data literacy, which – on the long run – promises to become a fundamental component of artificial intelligence literacy (AI literacy). In addition to conducting a scoping review on the interrelated topics of data literacy and artificial intelligence literacy, we also draw upon our expertise in the field of data literacy and mention among others digital literacy, media literacy and their critical approaches. In light of the considerable diversity of approaches and opinions, a significant portion of the extensive body of literature was subjected to careful examination, with a view to elucidating the nature and role of data literacy and AI literacy. The issue of AI literacy is gaining increasing attention. It is therefore important to review its history and characteristics by examining the relationship between it and other forms of digital literacy, and in particular data literacy.Central European Library and Information Science Review Közép-európai Könyvtár- és Információtudományi Szemle20The characteristics of data literacy, which – on the long run – promises to become a fundamental component of artificial intelligence literacy (AI literacy), are examined.Central European Library and Information Science Review Közép-európai Könyvtár- és Információtudományi Szemle["Koltay Tibor"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/4e9e008dab950774a80ca24442b2b17d1953ac36 3933f720c8efb9816b40001accb145b02752910eAlthough artificial intelligence has made significant strides in analyzing extensive datasets and recognizing intricate patterns, it still struggles to replicate the nuanced understanding of human emotions. This study aims to explore the complex relationship between emotional intelligence (EI) and AI, investigating the potential for AI to enhance our understanding and development of EI. Emotion detection in AI often involves analyzing facial expressions, speech patterns, and physiological cues. However, incorporating cultural diversity and biases into training datasets can hinder accuracy and efficiency. A promising area of future research is the development of AI systems capable of not only perceiving emotions but also demonstrating emotional intelligence. Such systems could revolutionize human-robot interactions, fostering more genuine and empathetic connections. Delving into the relationship between EI and AI can gain a deeper understanding of AI's emotional intelligence, appreciating its capabilities and limitations. This knowledge can pave the way for more compassionate and effective human-AI interactions. This knowledge can inform the development of more sophisticated AI systems that can contribute to our own emotional growth and well-being.Asian journal of current research00The complex relationship between emotional intelligence (EI) and AI is explored, investigating the potential for AI to enhance the authors' understanding and development of EI, and how this knowledge can inform the development of more sophisticated AI systems that can contribute to their own emotional growth and well-being.Asian Journal of Current Research["A. Singh", "Rahul Saxena", "S. Saxena"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/3933f720c8efb9816b40001accb145b02752910e 26e236ee55df27b355b69767bcac837cf2ee4ae1The development of artificial intelligence (AI) technology has had a significant impact on various sectors, including education. The implementation of AI in the learning process is expected to improve the quality of student learning outcomes. AIbased learning offers various potentials to personalize learning, provide faster and more accurate feedback, and increase student motivation. However, the limited research on the effectiveness of AI in the context of vocational education, especially in the field of machining, prompted this study. This study used a quasi-experimental design with a pretest-posttest nonequivalent control group design. This research was conducted in two schools, namely State Vocational High School 2 Depok-Sleman and State Vocational High School 2 Yogyakarta. The subjects of this research of students in class X of Mechanical Engineering, each school will be randomly divided into two groups, namely the experimental group and the control group. The experimental group is a class that receives Artificial Intelligence-based learning and the control group is a class that receives conventional learning. The population in this study was 252 students. The sampling technique used cluster random sampling technique and obtained a research sample of 144 students. The research instruments used include pre-test or initial ability test, post-test or final test, and student response questionnaire. And additional data again using observation during the implementation of learning. The data obtained were analyzed using analysis of variance test (one-way ANOVA), Paired Sample t-Test test and Independent Sample tTest test. The results showed that (1) In the Independent Sample t-Test test, the significance level was 0.001, which means that there is a significant difference in the average value between the experimental group and the control group on the final learning outcomes. Students who received AI-based learning obtained an average post-test score of 81.18 while students who received conventional learning obtained an average post-test score of 76.26. (2) the results of the student response questionnaire showed that the majority of students in the experimental group gave a positive response to AI-based learning by obtaining an average percentage value for the metacognition aspect of 89% and the aspect of student activeness of 96%, they felt more motivated, easier to understand the material, and more active in the learning process using artificial intelligence.International journal of social science and human research280The results showed that the majority of students in the experimental group gave a positive response to AI-based learning, and felt more motivated, easier to understand the material, and more active in the learning process using artificial intelligence.International Journal of Social Science and Human Research["Dessy Riski", "Apri Nuryanto"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/26e236ee55df27b355b69767bcac837cf2ee4ae1 d63a364a73f0d084b6ba5c33d7ac6136c8b30633Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This chapter delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated. Through the use of predictive modeling, real-time monitoring, and personalized treatment plans, AI significantly enhances the precision, efficiency, and effectiveness of pain management strategies. The discussion extends to various AI applications, shedding light on the ethical considerations that accompany these technological advancements, as well as outlining future research directions. Collectively, these insights underscore the immense potential of AI to not only improve pain management practices but also to significantly elevate patient outcomes. Central to this transformation is the role of translational science in bridging the gap between theoretical AI models and their practical, clinical applications. This "bench to bedside" approach ensures that innovations in AI are not merely confined to research environments but are actively integrated into real-world patient care. For instance, AI-powered predictive analytics in pain management, driven by sophisticated machine learning algorithms, have progressed from computational experiments to clinical trials, and ultimately, to widespread implementation in healthcare settings. These AI models are now being utilized in hospitals to assess patient pain levels in real-time, predict opioid requirements, and optimize pain management protocols. This progression exemplifies how translational science is facilitating a paradigm shift in healthcare, positioning AI as an indispensable tool in modern pain management.Advances in Machine Learning &amp; Artificial Intelligence00This chapter delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated, and the role of translational science in bridging the gap between theoretical AI models and their practical, clinical applications.Advances in Machine Learning &amp; Artificial Intelligence["Julian Borges"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/d63a364a73f0d084b6ba5c33d7ac6136c8b30633 d1886ff264fa2f2df2262d8caca8bba3444aa63cThis article argued the following points. Firstly, the challenge posed by artificial intelligence (AI) to the essence of humanity is serious. Secondly, it is important to analyse the external context and internal dynamics of the history of interaction between knowledge and power. Thirdly, it is necessary to trace the intellectual history of humanity becoming god-like. Finally, by combining Martin Luther’s anthropology with insights from social science and philosophical theology, this article advocated for guiding human beings to use their capabilities for good rather than evil through ethical and legal constraints. Efforts in Chinese context should be made to resolve the conflict between humanity’s pursuit of omnipotence and its failure to develop towards complete goodness, so as to avoid catastrophic consequences for humanity.Contribution: The present article’s special contribution was the theological reflection referring to AI’s challenges to the essence of humanity from Luther’s anthropology in Chinese context. The conflict between omnipotence and omnibenevolence has been highlighted as the key problem which human beings need to solve in the face of AI’s challenge.HTS Teologiese Studies/Theological Studies00By combining Martin Luther’s anthropology with insights from social science and philosophical theology, this article advocated for guiding human beings to use their capabilities for good rather than evil through ethical and legal constraints.HTS Teologiese Studies / Theological Studies["Paulos Z.Z. Huang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/d1886ff264fa2f2df2262d8caca8bba3444aa63c 4e54c0c6867119c05fd0da1e440afed7d19031ccThis mixed-methods study investigates the impact of Artificial Intelligence (AI)-mediated language instruction on English learning achievement and the Second Language Motivational Self System (L2MSS) among English as a Foreign Language (EFL) learners, addressing the growing interest in AI-driven educational technologies and their potential to transform language instruction. It involved two intact university classes with a total of 90 students, where the experimental group received AI-mediated instruction, and the control group received traditional language instruction. Both groups were assessed using pretests and post-tests to measure English learning achievement across two abilities, including reading comprehension and writing skills, while questionnaires were used to evaluate L2MSS. Quantitative analysis revealed that the experimental group outperformed the control group in all assessed areas of English learning achievement, and they also demonstrated higher levels of second language (L2) learning motivation, suggesting that AI-mediated instruction has a positive effect on both English learning achievement and L2MSS. Furthermore, qualitative insights from semi-structured interviews with nine students from the experimental group highlighted the transformative effects of the AI platform, which enhanced student engagement and offered personalized learning experiences, thereby boosting motivation and fostering more effective second language learning. The results indicate that AI-mediated language instruction has the potential to revolutionize language learning by improving outcomes and increasing learner motivation, underscoring the significant positive impact of AI-driven educational technologies in language education. This study contributes to evidence-based language pedagogy by providing valuable insights for educators and researchers interested in integrating AI-powered platforms into language classrooms.Proceedings of the International CALL Research Conference, 2024100Quantitative analysis revealed that the experimental group outperformed the control group in all assessed areas of English learning achievement, and they also demonstrated higher levels of second language (L2) learning motivation, suggesting that AI-mediated instruction has a positive effect on both English learning achievement and L2MSS.Proceedings of the International CALL Research Conference, 2024["Zhang Yifan"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/4e54c0c6867119c05fd0da1e440afed7d19031cc b0bdd325a887c3fab424f82cc1dcdcba699b013cOrphanet Journal of Rare Diseases470Keyword clustering analysis identified gene identification, effective management, and personalized treatment as three primary research areas of AI applications in RDs, and keyword burst detection indicated a growing interest in the areas of “biomarker”, “predictive model”, and “data mining”, highlighting their potential to shape future research directions.Orphanet Journal of Rare Diseases["Peiling Ou", "Ru Wen", "Linfeng Shi", "Jian Wang", "Chen Liu"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/b0bdd325a887c3fab424f82cc1dcdcba699b013c 2a880cfa0095dd65e263b3b16907bc954a1d319cThis review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, Neglected Tropical Diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context. Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives. Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics. In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa. Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies. Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.International Journal of Health Policy Planning00Artificial Intelligence is positioned as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.International Journal of Health Policy Planning["Angyiba Serge Andigema", "Ngnotouom NGNOKAM Tania Cyrielle", "Ndjie Daniel Laetitia", "Ewane Ekwelle"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/2a880cfa0095dd65e263b3b16907bc954a1d319c c22a58aa326951144812fb2956e3a342332522a4A growing number of human-centric tasks like learning, planning, and creative writing require the integration of Artificial Intelligence (AI) in the current era of exponential advancements. Such systems collect and analyze data on their own making the best use of available resources and providing creative solutions for challenging issues. The impact of AI on productivity, data analysis, flexibility, and real-time decision- making in a variety of fields is examined in this paper. Focusing on academic settings, the paper considers ethical concerns surrounding AI-generated content. According to a survey, even though many students use ChatGPT for writing and research, worries about academic integrity and plagiarism still exist.2024 12th International Scientific Conference on Computer Science (COMSCI)180The impact of AI on productivity, data analysis, flexibility, and real-time decision- making in a variety of fields is examined and ethical concerns surrounding AI-generated content are considered.2024 12th International Scientific Conference on Computer Science (COMSCI)["Daniela V. Minkovska", "E. Antonova", "K. Koparanov", "Plamen Nakov"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/c22a58aa326951144812fb2956e3a342332522a4 6301bbd7fe3d7430b66fd7d6eab9d73c0d375573The recent developments in artificial intelligence (AI) are revolutionizing professional practices across various professional fields, including evaluation. With its advanced automation and learning capabilities, AI is bringing significant changing to the way organizations and societies function. Evaluation is no exception to this trend, even though evaluators are adopting AI at a slower pace. This article examines ongoing applications that already improve and enhance the evaluation practice. We advance our discussion by exploring the potential impact of AI on the policy cycle. Subsequently, we analyze the potential incorporation of evaluation into autonomous artificial intelligence systems that could design, implement and evaluate public policies with minimal to no human supervision.Digital Government: Research and Practice150This article examines ongoing applications that already improve and enhance the evaluation practice and analyzes the potential incorporation of evaluation into autonomous artificial intelligence systems that could design, implement and evaluate public policies with minimal to no human supervision.Digital Government: Research and Practice["Steve Jacob"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/6301bbd7fe3d7430b66fd7d6eab9d73c0d375573 e6af68f3221a96ac769a09f8d792f09607ec91dcGraduate teaching evaluation, as the basic driving force to promote teaching reform, has received extensive attention in the fundamental reform of the entire education system. And the traditional evaluation system of graduate education has problems related to the lack of logic and structure in the evaluation index system. In this regard, this paper systematically describes the application of artificial intelligence in postgraduate education evaluation, analyses the application of artificial intelligence in postgraduate education evaluation from the perspectives of multidimensional postgraduate education evaluation system, scientific and precise postgraduate education evaluation system, intelligent postgraduate education measurement technology, and discusses the possible deficiencies and countermeasures of artificial intelligence technology in the field of postgraduate education evaluation.International Conference on Emerging Technologies180This paper systematically describes the application of artificial intelligence in postgraduate education evaluation, analyses the application of artificial intelligence in postgraduate education evaluation from the perspectives of multidimensional postgraduate education evaluation system, scientific and precise postgraduate education evaluation system, intelligent postgraduate education measurement technology, and discusses the possible deficiencies and countermeasures of artificial intelligence technology in the field of postgraduate education evaluation.2024 4th International Conference on Educational Technology (ICET)["Xiang-Lian Yu", "Jie Wu", "Pin-Lin Li"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/e6af68f3221a96ac769a09f8d792f09607ec91dc 5475208f84c8e27003de10ea9741b657e7a39898Artificial intelligence (AI) can assist with time‐series data corrections, relieving a bottleneck in a water utility's quality assurance processes. AI's speed and accuracy can help reduce water scarcity and ultimately improve the global water crisis.Opflow00Artificial intelligence can assist with time‐series data corrections, relieving a bottleneck in a water utility's quality assurance processes and helping reduce water scarcity and ultimately improve the global water crisis.Opflow["Brian Gouge"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/5475208f84c8e27003de10ea9741b657e7a39898 bbb8dd7364c124f2895de3a3721d8ddbaaa55138The capabilities of artificial intelligence (AI) and machine learning (ML) are growing at an unprecedented pace. These technologies have many useful applications, from machine translation to medical image analysis. Countless more such applications are currently being developed and can be expected in the long term. Unfortunately, not much attention has been paid to the weak and unpleasant sides of artificial intelligence. In our reviews, we examine the landscape of existing and potential problems associated with the use of innovative neural network technologies, suggesting that special attention be paid to ways to prevent and mitigate dangers and threats. The goal of our publication is to expand the circle of stakeholders and subject matter experts participating in the discussion of pressing issues of cyber security of medical AI, responsible approach to the vulnerabilities of neural network platforms, protection of equipment along with the formation of a safe landscape for its use, and the importance of legal and ethical regulatory tools.Sociology of Medicine90In this publication, the circle of stakeholders and subject matter experts participating in the discussion of pressing issues of cyber security of medical AI, responsible approach to the vulnerabilities of neural network platforms, protection of equipment along with the formation of a safe landscape for its use, and the importance of legal and ethical regulatory tools are expanded.Sociology of Medicine["\u0412\u0438\u0442\u0430\u043b\u0438\u0439 \u0410\u043d\u0430\u0442\u043e\u043b\u044c\u0435\u0432\u0438\u0447 \u0411\u0435\u0440\u0434\u0443\u0442\u0438\u043d"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/bbb8dd7364c124f2895de3a3721d8ddbaaa55138 706645f46cf8c6886b281580e99e3c205a3551ccThe in-depth development and widespread application of artificial intelligence technology will have significant impact on electricity demand. However, there are few comprehensive analyses. This article will analyze the direct and indirect impact respectively. For direct impact, artificial intelligence requires various data centers to provide huge computing and storage capabilities, which will increase the electricity demand of data centers; for indirect impact, artificial intelligence will also optimize the time and space distribution of electricity demand and deepen energy conservation and power saving in the whole society. Research shows that the development of artificial intelligence will increase the electricity consumption of data centers, and it is necessary to plan ahead for the infrastructure related to the electricity consumption of artificial intelligence; artificial intelligence plays an important role in promoting the development of demand response, and a largescale joint dispatch mechanism of electricity and computing power for the power market should be explored; data centers have a large space for energy conservation and carbon reduction, and a sound electricity price mechanism that comprehensively considers green electricity consumption and energy efficiency should be established to promote data centers to widely participate in green electricity transactions and actively reduce their own PUE.2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC)00This article will analyze the direct and indirect impact of artificial intelligence on electricity demand through direct and indirect impact analysis of the development and widespread application of artificial intelligence technology.2024 3rd International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC)["Yuelong Jia"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/706645f46cf8c6886b281580e99e3c205a3551cc 8b1da5e8e3f7628fbb14cd74b04d971b45d77a41Objective: Investigate the impact of artificial intelligence on the learning of dentistry university students at a public university in Peru. Methodology: It is a descriptive, observational, and cross-sectional study. The sample was made up of 173 students from different academic semesters, ranging in age from 17 to 50 years old. A written survey validated by dentistry and education researchers was designed, which included 12 questions with a 5-point Likert scale and 4 questions for each dimension: radiographic clinical diagnosis. We carried out data analysis using descriptive statistics as a summary measure of absolute and relative frequencies. Results: the level of influence of Artificial intelligenceI in improving clinical diagnosis is 60.7%; the level of influence of Artificial Intelligence in personalizing learning on training content is 63%; and the level of influence of artificial intelligence on legal and ethical challenges is 65.3%. Conclusion: The level of impact of learning artificial intelligence in the training of dental students at a state university is mostly high, at 66.5%.International Conference on Emerging Technologies400The level of impact of learning artificial intelligence in the training of dental students at a state university is mostly high, at 66.5%, according to a descriptive, observational, and cross-sectional study.2024 4th International Conference on Educational Technology (ICET)["Carmen Chauca", "Virgilio Quispe", "Maritza Arones", "V\u00edctor Monge", "Enrique Mendoza Caballero"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/8b1da5e8e3f7628fbb14cd74b04d971b45d77a41 d767a60c0d3af369d02c5b547f9b09e1f7036521This paper provides a comprehensive review of the current state and future directions of artificial intelligence (AI) in game strategy optimization. It explores the key AI techniques driving advancements in this field, including machine learning, reinforcement learning, neural networks, and Monte Carlo tree search. Through detailed case studies of landmark AI systems such as Deep Blue, AlphaGo, Libratus, and AlphaStar, the paper illustrates the remarkable progress made in domains ranging from chess and go to poker and real-time strategy games. Despite these achievements, significant challenges remain, including multi-domain generalization, explainability, and effective human-AI collaboration. The paper also delves into promising future research directions, such as developing more flexible AI architectures and improving AI's ability to work alongside human players. Finally, it addresses the ethical considerations surrounding the advancement of AI in game strategy, including issues of fairness in competitive gaming, potential societal impacts, and the responsible development of these technologies. This research not only highlights the transformative potential of AI in gaming but also its broader implications for strategic decision-making in real-world scenarios.International Journal of Computer Science & Information Technology (IJCSIT)260The ethical considerations surrounding the advancement of AI in game strategy, including issues of fairness in competitive gaming, potential societal impacts, and the responsible development of these technologies are addressed.International Journal of Computer Science and Information Technology["Yihong Li"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/d767a60c0d3af369d02c5b547f9b09e1f7036521 799f14dbddf90e820f8d7924a060ae45b23ff44dAs an important driving force of the new round of scientific and technological revolution, artificial intelligence is driving human society into the intelligent era of innovation and integration, and popularizing and promoting artificial intelligence education will become an inevitable trend. However, it is found through research that there are problems such as the lack of clear positioning in the form of course offerings, a single form of teaching, weak professionalism of teachers, and deviation from the original intention of demand-oriented, etc., in the application of artificial intelligence in personalized STEM teaching. In response to the above problems, through in-depth research and analysis of related literature, it is found that personalized STEM teaching has the characteristics of interdisciplinarity, contextualization, and practicability, which coincides with the integration of multidisciplinary knowledge and the emphasis on solving real problems through practice in AI education. Based on this, this study integrates the concept of STEM education with AI education to further cultivate students' cognitive ability and disciplinary literacy, and to promote the integration and development of AI and STEM education.International Conference on Emerging Technologies70This study integrates the concept of STEM education with AI education to further cultivate students' cognitive ability and disciplinary literacy, and to promote the integration and development of AI and STEM education.2024 4th International Conference on Educational Technology (ICET)["Ma Jiaen", "Xiaodi Chen"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/799f14dbddf90e820f8d7924a060ae45b23ff44d 691d02cfda25095ff5c1b711d2c6cb3f5d68bd68The problems associated with the use of artificial intelligence in the navigation safety management system are considered, affecting scientific, philosophical, psychological, legal, technical, operational and other aspects formed in connection with the rapid penetration of artificial intelligence into marine technologies. Currently, marine autonomous surface vessels (MASV) of a number of foreign countries (China, Japan, Denmark, Sweden, the USA, etc.) are already undergoing pilot operation in the coastal areas of the World Ocean. In Russia, scientific research is also conducted under state supervision and experimental flights are conducted on ships of various types and equipment. The emerging problems of implementing artificial intelligence on marine vessels are very ambiguous and very risky, since they affect the problems of all mankind (95% of global trade is carried out across the ocean). Preventing threats to the “marine environment – ship – cargo – human” macro system cannot be solved without an integrated, systematic approach. All communications within and outside the system cannot be carried out without developing a whole package of interrelated normative legal acts, both national and international. The set of provisions and documents of the international maritime organization is aimed at well-known naval vessels, their observance during operation at sea should not violate established patterns. In particular, the problems related to responsibility at all levels, both in inland waters and on the high seas, should be solved in principle. The definition of the concept of artificial intelligence is given and the problems are reflected: the creation of on-board intelligent systems, the integration of artificial intelligence features with the natural potential of human intelligence and the use of artificial intelligence, the realization of world goals and solving pressing problems of mankind and forecasting the prevention of threats to the system in the future.VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES MARINE ENGINEERING AND TECHNOLOGIES30The concept of artificial intelligence is given and the problems are reflected: the creation of on-board intelligent systems, the integration of artificial intelligence features with the natural potential of human intelligence and the use of artificial intelligence, the realization of world goals and solving pressing problems of mankind and forecasting the prevention of threats to the system in the future.Vestnik of Astrakhan State Technical University. Series: Marine engineering and technologies["Vitaly Aleksandrovich Bondarev", "Ol'ga Mihaylovna Bondareva", "Izumrud Ramazanovna Ragulina"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/691d02cfda25095ff5c1b711d2c6cb3f5d68bd68 1ccd81c2b394613d931de39dfa6b7863e7a81dcaArchives of Computational Methods in Engineering1212Archives of Computational Methods in Engineering["Poonam Dhaka", "M. Sreejeth", "M. M. Tripathi"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/1ccd81c2b394613d931de39dfa6b7863e7a81dca 70e8c88a11eea842edec7e8a02d40b13622261ddAI technology is becoming increasingly popular in the business sector in Bangladesh. AI's integration into different elements of daily life and business operations is common. Implementing it in the company may enhance marketing efforts by speeding up, reducing costs, and increasing accuracy. Business owners who use AI in their advertising efforts should expect increased popularity and a significant competitive edge in the digital industry. It may transform businesses through innovative ideas and effective marketing strategies. Additionally, it provides solutions for hard jobs, promoting significant company growth. However, there are also downsides to employing AI, including a lack of technical knowledge, concerns about data privacy and security, and challenges with gathering information and storage. To overcome these challenges, businesses should educate employees on AI, seek diverse financing and qualified personnel, collaborate with the government on infrastructure support and legislation, address job displacement concerns through training, and encourage employee acceptance of change. Businesses in Bangladesh can enhance operations and competitiveness via using these strategies. Business leaders, decision- makers, and researchers interested in maximizing AI's potential and improving business outcomes in Bangladesh may benefit from this research. The study continued by presenting theoretical and managerial implications that will help business owners, managers, stakeholders, and policymakers enhance their business performance.International Journal of Innovative Science and Research Technology551The study continued by presenting theoretical and managerial implications that will help business owners, managers, stakeholders, and policymakers enhance their business performance.International Journal of Innovative Science and Research Technology (IJISRT)["Md. Shabuz Sarker", "Fardin Sabahat Khan", "Sharmin Layla Roon"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/70e8c88a11eea842edec7e8a02d40b13622261dd 6ca591beeb25877896809d1c1119e8b2394d2694Discover Education551Discover Education["O. Falebita", "Petrus Jacobus Kok"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/6ca591beeb25877896809d1c1119e8b2394d2694 adf1e5821bd02b4e51a19d6dbe96d091dd6224cfAartificial intelligence can be used for control of nuclear fusion reactions giving significant energy. It is based on replicable experiments giving energy release for short time performed by interaction of deuterium gas with constantan. The following experimental outcomes were found: a) The observed released excess energy was not of both electrical and chemical origins; b) Significant density of the released excess power; and c) Helium release. A conclusion that the released energy is of nuclear origin was made.2024 12th International Scientific Conference on Computer Science (COMSCI)240A conclusion that the released energy is of nuclear origin was made in replicable experiments giving energy release for short time performed by interaction of deuterium gas with constantan.2024 12th International Scientific Conference on Computer Science (COMSCI)["Dimiter Alexandrov"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/adf1e5821bd02b4e51a19d6dbe96d091dd6224cf 81f017d21831ec822c372110ac0451567c05511cThe rapid expansion of AI adoption (e.g., using machine learning, deep learning, or large language models as research methods) and the increasing availability of big data have the potential to bring about the most significant transformation in entrepreneurship scholarship the field has ever witnessed. This article makes a pressing meta-contribution by highlighting a significant risk of unproductive knowledge exchanges in entrepreneurship research amid the AI revolution. It offers strategies to mitigate this risk and provides guidance for future AI-based studies to enhance their collective impact and relevance. Drawing on Akerlof's renowned market-for-lemons concept, we identify the potential for significant knowledge asymmetries emerging from the field's evolution into its current landscape (e.g., complexities around construct validity, theory building, and research relevance). Such asymmetries are particularly deeply ingrained due to what we term the double-black-box puzzle, where the widely recognized black box nature of AI methods intersects with the black box nature of the entrepreneurship phenomenon driven by inherent uncertainty. As a result, these asymmetries could lead to an increase in suboptimal research products that go undetected, collectively creating a market for lemons that undermines the field's well-being, reputation, and impact. However, importantly, if these risks can be mitigated, the AI revolution could herald a new golden era for entrepreneurship research. We discuss the necessary actions to elevate the field to a higher level of AI resilience while steadfastly maintaining its foundational principles and core values.1530Drawing on Akerlof's renowned market-for-lemons concept, the potential for significant knowledge asymmetries emerging from the field's evolution into its current landscape is identified and strategies to mitigate this risk are offered.["M. Obschonka", "Moren L\u00e9vesque"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/81f017d21831ec822c372110ac0451567c05511c ec3bd18cc56e7cae43445becb50cec0fcc213696Journal of the European Academy of Dermatology and Venereology40Journal of the European Academy of Dermatology and Venereology : JEADV["C. Dorado Cortez", "A. Fakih", "M. Bruet", "E. Cinotti", "L. Tognetti", "J. Perrot"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/ec3bd18cc56e7cae43445becb50cec0fcc213696 66887a2922530abb81b03e3112aba59d10898170Proceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence10Proceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence["Shuang Chen", "Huan Chen", "Yajun Lu"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/66887a2922530abb81b03e3112aba59d10898170 c6e9a85829721d6d6e651b655b322a1b4cf0b64aScience Editing100Science Editing["B. Tang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/c6e9a85829721d6d6e651b655b322a1b4cf0b64a 4df158ba433824abd021fe56a770346b58c49d4eProceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence70Proceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence["Ting Wang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/4df158ba433824abd021fe56a770346b58c49d4e 097d89b4704519220a3eef7d77a3b87f3aec84e8Autism is a neurodevelopmental disorder, and since the outbreak of COVID-19, countries have strengthened research on remote intervention for autism. Remote intervention for autism has advantages such as effectiveness, convenience, and economy, which breaks the boundaries of time and space with the help of information technology, and is more in line with the preferences of autistic people. This paper summarizes the application of Augmentative and Alternative Communication to realize the communication problems of autistic children, and makes prospects for the application of Augmentative and Alternative Communication in autism remote intervention.International Conference on Emerging Technologies270This paper summarizes the application of Augmentative and Alternative Communication to realize the communication problems of autistic children, and makes prospects for the application of Augmentative and Alternative Communication in autism remote intervention.2024 4th International Conference on Educational Technology (ICET)["Xin Liu", "Yuan Sun"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/097d89b4704519220a3eef7d77a3b87f3aec84e8 4758d3145cfda6f6a75db21383679306660ccd65Tenth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2024)00Tenth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2024)["M. Tzouvaras", "Michalis Mavrovouniotis", "R. Votsis", "K. Fotiou", "Eleftheria Kalogirou", "T. Polydorou", "Gerd Reis", "Fabio Del Frate", "Lorenzo Giuliano Papale", "Giorgia Guerrisi", "D. Hadjimitsis"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/4758d3145cfda6f6a75db21383679306660ccd65 7a1369f16b302db3318cb33b3bc79ba87576e806Emergencias00Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias["Daniel S\u00e1enz-Abad", "M\u00f3nica Sachi Mart\u00ednez-Mihara", "Mar\u00eda Del Carmen Lahoza-P\u00e9rez"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/7a1369f16b302db3318cb33b3bc79ba87576e806 7df212480d3c8b405002b10e9637bec82bbb773dProceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence20Proceedings of the 2024 7th International Conference on Computer Information Science and Artificial Intelligence["Jinrun Jia", "Zhiguo Ma"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/7df212480d3c8b405002b10e9637bec82bbb773d b50465f4c93714ce5df1b6e56dd1952080d019a8Conspiracy theory beliefs are notoriously persistent. Influential hypotheses propose that they fulfill important psychological needs, thus resisting counterevidence. Yet previous failures in correcting conspiracy beliefs may be due to counterevidence being insufficiently compelling and tailored. To evaluate this possibility, we leveraged developments in generative artificial intelligence and engaged 2190 conspiracy believers in personalized evidence-based dialogues with GPT-4 Turbo. The intervention reduced conspiracy belief by ~20%. The effect remained 2 months later, generalized across a wide range of conspiracy theories, and occurred even among participants with deeply entrenched beliefs. Although the dialogues focused on a single conspiracy, they nonetheless diminished belief in unrelated conspiracies and shifted conspiracy-related behavioral intentions. These findings suggest that many conspiracy theory believers can revise their views if presented with sufficiently compelling evidence. Editor’s summary Beliefs in conspiracies that a US election was stolen incited an attempted insurrection on 6 January 2021. Another conspiracy alleging that Germany’s COVID-19 restrictions were motivated by nefarious intentions sparked violent protests at Berlin’s Reichstag parliament building in August 2020. Amid growing threats to democracy, Costello et al. investigated whether dialogs with a generative artificial intelligence (AI) interface could convince people to abandon their conspiratorial beliefs (see the Perspective by Bago and Bonnefon). Human participants described a conspiracy theory that they subscribed to, and the AI then engaged in persuasive arguments with them that refuted their beliefs with evidence. The AI chatbot’s ability to sustain tailored counterarguments and personalized in-depth conversations reduced their beliefs in conspiracies for months, challenging research suggesting that such beliefs are impervious to change. This intervention illustrates how deploying AI may mitigate conflicts and serve society. —Ekeoma Uzogara INTRODUCTION Widespread belief in unsubstantiated conspiracy theories is a major source of public concern and a focus of scholarly research. Despite often being quite implausible, many such conspiracies are widely believed. Prominent psychological theories propose that many people want to adopt conspiracy theories (to satisfy underlying psychic “needs” or motivations), and thus, believers cannot be convinced to abandon these unfounded and implausible beliefs using facts and counterevidence. Here, we question this conventional wisdom and ask whether it may be possible to talk people out of the conspiratorial “rabbit hole” with sufficiently compelling evidence. RATIONALE We hypothesized that interventions based on factual, corrective information may seem ineffective simply because they lack sufficient depth and personalization. To test this hypothesis, we leveraged advancements in large language models (LLMs), a form of artificial intelligence (AI) that has access to vast amounts of information and the ability to generate bespoke arguments. LLMs can thereby directly refute particular evidence each individual cites as supporting their conspiratorial beliefs. To do so, we developed a pipeline for conducting behavioral science research using real-time, personalized interactions between research subjects and AI. Across two experiments, 2190 Americans articulated—in their own words—a conspiracy theory in which they believe, along with the evidence they think supports this theory. They then engaged in a three-round conversation with the LLM GPT-4 Turbo, which we prompted to respond to this specific evidence while trying to reduce participants’ belief in the conspiracy theory (or, as a control condition, to converse with the AI about an unrelated topic). RESULTS The treatment reduced participants’ belief in their chosen conspiracy theory by 20% on average. This effect persisted undiminished for at least 2 months; was consistently observed across a wide range of conspiracy theories, from classic conspiracies involving the assassination of John F. Kennedy, aliens, and the illuminati, to those pertaining to topical events such as COVID-19 and the 2020 US presidential election; and occurred even for participants whose conspiracy beliefs were deeply entrenched and important to their identities. Notably, the AI did not reduce belief in true conspiracies. Furthermore, when a professional fact-checker evaluated a sample of 128 claims made by the AI, 99.2% were true, 0.8% were misleading, and none were false. The debunking also spilled over to reduce beliefs in unrelated conspiracies, indicating a general decrease in conspiratorial worldview, and increased intentions to rebut other conspiracy believers. CONCLUSION Many people who strongly believe in seemingly fact-resistant conspiratorial beliefs can change their minds when presented with compelling evidence. From a theoretical perspective, this paints a surprisingly optimistic picture of human reasoning: Conspiratorial rabbit holes may indeed have an exit. Psychological needs and motivations do not inherently blind conspiracists to evidence—it simply takes the right evidence to reach them. Practically, by demonstrating the persuasive power of LLMs, our findings emphasize both the potential positive impacts of generative AI when deployed responsibly and the pressing importance of minimizing opportunities for this technology to be used irresponsibly. Dialogues with AI durably reduce conspiracy beliefs even among strong believers. (Left) Average belief in participant’s chosen conspiracy theory by condition (treatment, in which the AI attempted to refute the conspiracy theory, in red; control, in which the AI discussed an irrelevant topic, in blue) and time point for study 1. (Right) Change in belief in chosen conspiracy from before to after AI conversation, by condition and participant’s pretreatment belief in the conspiracy.Science8537These findings suggest that many conspiracy theory believers can revise their views if presented with sufficiently compelling evidence, and leveraged developments in generative artificial intelligence and engaged 2190 conspiracy believers in personalized evidence-based dialogues with GPT-4 Turbo.Science["Thomas H. Costello", "Gordon Pennycook", "David G. Rand"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/b50465f4c93714ce5df1b6e56dd1952080d019a8 a592922d239289cef17f539266c05c6e3019593cThe Egyptian Journal of Radiology and Nuclear Medicine759This manuscript argues for the development of hybrid models that combine interpretability with the advanced capabilities of black box systems, and advocates for enhanced education and training programs for healthcare professionals to equip them with the necessary skills to utilize AI effectively.Egyptian Journal of Radiology and Nuclear Medicine["Ahmed Marey", "Parisa Arjmand", "Ameerh Dana Sabe Alerab", "Mohammad Javad Eslami", "Abdelrahman M Saad", "Nicole Sanchez", "Muhammad Umair"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/a592922d239289cef17f539266c05c6e3019593c c4afd503d84810665e98818c988a1a7e65cef60dPurpose: This study critically reviews the literature on adopting and using artificial intelligence platforms to enhance quality in Higher Education Institutions (HEIs). Methodology/Design/Approach: The present study follows a critical literature review on technological innovations, particularly Artificial Intelligence (AI) systems for enhancing quality Open and Distance Education Learning (ODeL). A critical review of the literature was conducted on works that explored the current AI applications that institutions are using to improve the quality of their teaching and learning. This was done through bibliometric analysis, which included a search of popular databases for previously published works. Bibliometric, citation network and keyword analysis were utilized to evaluate the literature review. Findings: The review highlights the potential of AI systems that Higher Education Institutions can utilize to enhance the quality of education. The Artificial Intelligence platforms for enhancing quality in ODeL institutions include the use of Intelligent tutors, Automated grading, and feedback systems, ChatGPT, Chatbots, and Virtual campuses. The adoption and use of technological innovation are closely linked to students' acceptance, affordability, and usability of the learning technologies.  Implications: This study's results provide implications for researchers, Innovation Hubs, and systems developers and users, including teachers and other education stakeholders.Review of Artificial Intelligence in Education254The review highlights the potential of AI systems that Higher Education Institutions can utilize to enhance the quality of education.Review of Artificial Intelligence in Education["Phineas Sebopelo"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/c4afd503d84810665e98818c988a1a7e65cef60d c8892c6533489fdff92b5a6503cba809a43bda86BMC Medical Education373Compared with traditional teaching models, AI-empowered scenario-based simulation teaching mode significantly improve students’ performance in many aspects and plays an important role in the improvement of clinical thinking and skills of medical undergraduates.BMC Medical Education["Koulong Zheng", "Zhiyu Shen", "Zanhao Chen", "Chang Che", "Huixia Zhu"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/c8892c6533489fdff92b5a6503cba809a43bda86 898a7910ff333e6fe637f6fd7a59ebea618064cfOver the past year, artificial intelligence (AI) companies have been increasingly adopting AI safety frameworks. These frameworks outline how companies intend to keep the potential risks associated with developing and deploying frontier AI systems to an acceptable level. Major players like Anthropic, OpenAI, and Google DeepMind have already published their frameworks, while another 13 companies have signaled their intent to release similar frameworks by February 2025. Given their central role in AI companies' efforts to identify and address unacceptable risks from their systems, AI safety frameworks warrant significant scrutiny. To enable governments, academia, and civil society to pass judgment on these frameworks, this paper proposes a grading rubric. The rubric consists of seven evaluation criteria and 21 indicators that concretize the criteria. Each criterion can be graded on a scale from A (gold standard) to F (substandard). The paper also suggests three methods for applying the rubric: surveys, Delphi studies, and audits. The purpose of the grading rubric is to enable nuanced comparisons between frameworks, identify potential areas of improvement, and promote a race to the top in responsible AI development.arXiv.org512The purpose of the grading rubric is to enable nuanced comparisons between frameworks, identify potential areas of improvement, and promote a race to the top in responsible AI development.ArXiv["Jide Alaga", "Jonas Schuett", "Markus Anderljung"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/898a7910ff333e6fe637f6fd7a59ebea618064cf 2daeb3cfd09516b2f3de8e4dccd41448b5751936Artificial Intelligence (AI) methods are powerful tools for various domains, including critical fields such as avionics, where certification is required to achieve and maintain an acceptable level of safety. General solutions for safety-critical systems must address three main questions: Is it suitable? What drives the system's decisions? Is it robust to errors/attacks? This is more complex in AI than in traditional methods. In this context, this paper presents a comprehensive mind map of formal AI certification in avionics. It highlights the challenges of certifying AI development with an example to emphasize the need for qualification beyond performance metrics.arXiv.org1741This paper highlights the challenges of certifying AI development with an example to emphasize the need for qualification beyond performance metrics and presents a comprehensive mind map of formal AI certification in avionics.ArXiv["Hymalai Bello", "Daniel Geissler", "L. Ray", "Stefan M\u00fcller-Div\u00e9ky", "Peter M\u00fcller", "Shannon Kittrell", "Mengxi Liu", "Bo Zhou", "P. Lukowicz"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/2daeb3cfd09516b2f3de8e4dccd41448b5751936 0ee4e1b9229300ab81dc8d80f0bdfabd2b8fdedcThe development of technology in the health sector has encouraged the adoption of technopreneurship, especially in the application of artificial intelligence (AI) to support the safety and efficiency of health services. One of the innovations that has emerged is the AI-driven Safe Entry Station, which is designed to improve the safety and comfort of patients and medical personnel. However, the success of implementing this technology is highly dependent on the level of user satisfaction and trust. This study aims to evaluate the level of user satisfaction and trust in Safe Entry Stations in the health care environment and also explore the variables that influence the acceptance of this technology among users. This research method uses a quantitative approach with a survey involving 673 respondents from various health institutions that have used Safe Entry Stations. Data were analyzed using Structural Equation Modeling (SEM) with SmartPLS 4.0 software to identify the relationship between User Satisfaction (US), trust (TR), behaviour intention (BI), usage behaviour (SB) and technopreneurial impac (TI). The results showed that US and TR significantly influences BI and UB. Additionally, BI strongly impacts TI, suggesting that stronger intentions lead to a greater perceived impact on technopreneurship. This study found that AI-driven Safe Entry Stations has great potential for widespread adoption in the healthcare sector. These findings provide important insights for further development of this technology as well as technopreneurship strategies in the healthcare sector.Aptisi Transactions on Technopreneurship (ATT)491This study found that AI-driven Safe Entry Stations has great potential for widespread adoption in the healthcare sector and provides important insights for further development of this technology as well as technopreneurship strategies in the healthcare sector.Aptisi Transactions on Technopreneurship (ATT)["U. Rahardja", "Po Abas Sunarya", "Q. Aini", "Shofiyul Millah", "Sabda Maulana"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/0ee4e1b9229300ab81dc8d80f0bdfabd2b8fdedc 2756110588a24cf4560f69d0ead9eb06e33a8e78While the massive adoption of Artificial Intelligence (AI) is threatening the environment, new research efforts begin to be employed to measure and mitigate the carbon footprint of both training and inference phases. In this domain, two carbon-aware training strategies have been proposed in the literature: Flexible Start and Pause & Resume. Such strategies—natively Cloud-based—use the time resource to postpone or pause the training algorithm when the carbon intensity reaches a threshold. While such strategies have proved to achieve interesting results on a benchmark of modern models covering Natural Language Processing (NLP) and computer vision applications and a wide range of model sizes (up to 6.1B parameters), it is still unclear whether such results may hold also with different algorithms and in different geographical regions. In this confirmation study, we use the same methodology as the state-of-the-art strategies to recompute the saving in carbon emissions of Flexible Start and Pause & Resume in the Anomaly Detection (AD) domain. Results confirm their effectiveness in two specific conditions, but the percentage reduction behaves differently compared with what is stated in the existing literature.Future Internet300The same methodology as the state-of-the-art strategies are used to recompute the saving in carbon emissions of Flexible Start and Pause & Resume in the Anomaly Detection (AD) domain, and their effectiveness is confirmed.Future Internet["R. Vergallo", "L. Mainetti"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/2756110588a24cf4560f69d0ead9eb06e33a8e78 4771b32458a7306cd6585f9c445d37f390d87092As artificial intelligence (AI) continues to integrate into various sectors, the complexity and opacity of AI models, particularly in machine learning (ML), pose significant challenges to interpret-ability and trust. This review paper addresses the critical need for explainable AI (XAI) to enhance understanding and transparency in ML models. We provide a comprehensive survey of state-of-the-art XAI techniques, including feature importance methods such as LIME (Local Interpret- able Model-agnostic Explanations) and SHAP (Shapely Additive expla- nation), as well as perturbation and attention-based mechanisms, to elucidate model decisions. Our analysis spans a diverse range of applications, including finance, education, and healthcare, showcasing the practical utility and impact of XAI methods. We discuss crucial issues such as the trade-offs between model accuracy and interpret ability, the de- sign of user-friendly explanations, and the development of comprehensive evaluation metrics. Furthermore, we explore the implications of XAI on user trust and decision-making, emphasizing the importance of reliable and ethical AI systems. This review contributes to the ongoing efforts to make AI systems more interpret- able, reliable, and aligned with societal needs, providing a robust foundation for future research and practical implementations of XAI. Keywords: Explainable AI · Machine Learning · Interpret-ability · Transparency · Ethical AI · XAI Techniques.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A comprehensive survey of state-of-the-art XAI techniques, including feature importance methods such as LIME (Local Interpret- able Model-agnostic Explanations) and SHAP (Shapely Additive Explanations), as well as perturbation and attention-based mechanisms, to elucidate model decisions.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Anushree G", "Suraj B Madagaonkar", "Ravili C H"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/4771b32458a7306cd6585f9c445d37f390d87092 0fe62d1d34ff42eb02d596147ed01870a5122539Purpose: This study examines the transformative role of Artificial Intelligence (AI) in the financial services industry, particularly in the FinTech sector. By exploring AI applications such as personalized banking, fraud detection, credit scoring, and algorithmic trading, the paper analyzes how AI enhances operational efficiency and customer experience. Material and Methods:  Using case studies from leading financial institutions, the paper highlights both opportunities and ethical concerns, such as data privacy and algorithmic bias. Findings: The study found that AI enhances detection by analyzing vast datasets to spot suspicious patterns and anomalies that human auditors may miss, improving compliance and reducing financial crime risks. AI streamlines loan underwriting processes by evaluating a broader range of data, such as payment history and social media behavior, providing more accurate risk assessments. The study also revealed that algorithmic trading uses AI to automate and optimize trades at speeds and scales impossible for human traders. AI systems analyze real-time market data and execute trades within milliseconds, capitalizing on fleeting opportunities in the stock market. By incorporating machine learning, these systems can adapt and improve over time, becoming more effective in predicting market trends and managing risk. Implications to Theory, Practice and Policy:  It expands the understanding of how AI can reshape financial interactions, enhancing personalization, fraud detection, and credit assessment. From a practical standpoint, it highlights real-world applications, such as robo-advisors and algorithmic trading, offering insights into how institutions can implement AI responsibly. On a policy level, the study underscores the importance of regulatory frameworks addressing data privacy, algorithmic fairness, and transparency, advocating for collaboration between regulators and financial institutions to ensure ethical AI deployment.American Journal of Computer Engineering70The study found that AI enhances detection by analyzing vast datasets to spot suspicious patterns and anomalies that human auditors may miss, improving compliance and reducing financial crime risks.American Journal of Computing and Engineering["Tayyab Muhammad", "Asad Yaseen", "Kriya Shah"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/0fe62d1d34ff42eb02d596147ed01870a5122539 de64a10e3f2446f12e42983a60d09f41cb4998c9As artificial intelligence (AI) technologies, including generative AI, continue to evolve, concerns have arisen about over-reliance on AI, which may lead to human deskilling and diminished cognitive engagement. Over-reliance on AI can also lead users to accept information given by AI without performing critical examinations, causing negative consequences, such as misleading users with hallucinated contents. This paper introduces extraheric AI, a human-AI interaction conceptual framework that fosters users' higher-order thinking skills, such as creativity, critical thinking, and problem-solving, during task completion. Unlike existing human-AI interaction designs, which replace or augment human cognition, extraheric AI fosters cognitive engagement by posing questions or providing alternative perspectives to users, rather than direct answers. We discuss interaction strategies, evaluation methods aligned with cognitive load theory and Bloom's taxonomy, and future research directions to ensure that human cognitive skills remain a crucial element in AI-integrated environments, promoting a balanced partnership between humans and AI.arXiv.org1490Extraheric AI is introduced, a human-AI interaction conceptual framework that fosters users' higher-order thinking skills, such as creativity, critical thinking, and problem-solving, during task completion, during task completion.ArXiv["Koji Yatani", "Zefan Sramek", "Chi-Lan Yang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/de64a10e3f2446f12e42983a60d09f41cb4998c9 cca93cdc5684d548e15ffaa861976a06ecb16d62Purpose: This article critically investigates the evolution of China’s cyber-espionage strategies, specifically illustrating the shift from traditional espionage methodologies to the incorporation of advanced technologies, particularly artificial intelligence (AI). This transition profoundly reshapes global power dynamics, delineating nuanced threats to critical infrastructure in Western nations, including power grids, financial systems, and communication networks (Wang et al., 2019). Materials and Methods: Utilizing a theoretical framework grounded in Joseph Nye's concept of soft power and contemporary security studies, this research posits a hypothesis: there exists a positive correlation between technological advancements and the escalation of espionage activities by state actors. The inquiry encompasses a comprehensive analysis of key components, such as vulnerabilities, adaptive strategies, geopolitical implications, deterrence mechanisms, and international collaboration, thereby illuminating the multifaceted risks to national security inherent in the digital age (Nye, 2004). Findings: The study critically evaluates the countermeasures undertaken by Western countries, probing strategic enhancements of cyber defences and the formation of international coalitions aimed at collective security (Huang et al., 2021). The findings reveal substantial obstacles in achieving a cohesive and effective response to the rapidly escalating and pervasive nature of contemporary cyber threats (Zhang et al., 2020). Implications to Theory, Practice and Policy: Considering the ongoing maturation of China’s cyber capabilities, characterized by an increased reliance on AI and the impending advent of quantum computing, the article advocates for a comprehensive revaluation of global security practices (Mann et al., 2020). It underscores the imperative for Western nations to not only innovate defensively but to also adopt proactive measures and foster significant international collaboration. This multifaceted approach is essential to address the complex challenges posed by state-sponsored cyber operations within an increasingly interconnected global landscape (Chen et al., 2021).American Journal of International Relations560Considering the ongoing maturation of China’s cyber capabilities, characterized by an increased reliance on AI and the impending advent of quantum computing, the article advocates for a comprehensive revaluation of global security practices.American Journal of International Relations["Christian C. Madubuko", "Chamunorwa Chitsungo"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/cca93cdc5684d548e15ffaa861976a06ecb16d62 8e6aa5291e49a86d10d62169b10c0cc7a4b4882dThis study investigated the challenges faced by middle school students when engaging with "Study with Confucius," a generative artificial intelligence (GenAI) agent game designed for learning classical Chinese reading. Utilizing the framework proposed by Groff and Mouza (2008), the research aimed to conduct a comprehensive needs analysis across three key dimensions: student-related, teacher-related, and technology-related aspects. Data were collected from 29 students in mainland China through video recordings of their gameplay. Challenges were defined based on their duration and students' responses that contradicted predefined correct answers, resulting in the identification of 29 challenges. Findings indicated that students encountered student-related challenges including linguistic misinterpretation, distraction, cognitive overload, student misbeliefs, and inappropriate attitudes; teacher-related challenges such as lack of support and inadequate access to teaching and technological resources; and technology-related challenges including external and internal malfunctions. These insights contribute to understanding the complexities as well as learning opportunities of integrating GenAI agent games into reading education.International Conference on Emerging Technologies200Investigation of the challenges faced by middle school students when engaging with "Study with Confucius," a generative artificial intelligence (GenAI) agent game designed for learning classical Chinese reading, indicated that students encountered student-related challenges including linguistic misinterpretation, distraction, cognitive overload, student misbeliefs, and inappropriate attitudes.2024 4th International Conference on Educational Technology (ICET)["Haoming Lin", "Zhaoyang Xiong", "Hanlin Tang", "Shujing Jiang", "Wei Wei", "Ke Fang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/8e6aa5291e49a86d10d62169b10c0cc7a4b4882d d9fdbd4b3ccbd2e4ba7954588907a091aa2f30dcArtificial intelligence (AI) is transforming education globally, enhancing learning experiences and outcomes through personalised learning, automated grading and smart content. This paper provides a comparative analysis of the extent to which AI technology is implemented in the educational systems of China and India, highlighting key initiatives, successes and challenges. Based on these findings, recommendations are made for Pakistan to adopt similar strategies to enhance its educational landscape. The study determined that AI has been extensively adopted and utilised in education by various educational institutions in multiple forms. Initially, AI manifested through computers and related technologies, evolving into web-based and online intelligent education systems. Eventually, the integration of embedded computer systems and other technologies led to the use of humanoid robots and web-based chatbots to perform instructors’ duties, either independently or alongside human instructors. Additionally, these systems leverage machine learning and adaptability to customise and personalise curriculum and content to meet students’ needs, which has enhanced student engagement and retention, thereby improving the overall learning experience and quality of education.Strategic Studies00It is determined that AI has been extensively adopted and utilised in education by various educational institutions in multiple forms and recommendations are made for Pakistan to adopt similar strategies to enhance its educational landscape.Strategic Studies["Rubia Shoukat"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/d9fdbd4b3ccbd2e4ba7954588907a091aa2f30dc 122be046a616c0b88af8bd35b9be5a7893a9a377Artificial intelligence (AI) has significantly disrupted B2B markets, impacting companies at the product, service, and organizational levels. A key focus is on how to leverage the power of AI to augment and automate activities to create value for customers. One specific form of value creation investigated in marketing is co-creation between parties. Introducing AI into the co-creation process is exciting due to its technological characteristics and the anticipated business value it can bring. This study explores the state of the art in co-creation with AI in B2B markets. It examines how buyers, suppliers, and technology providers interact, along with their motives and characteristics. Furthermore, it investigates the processes enabling these interactions, from the form of AI used and AI tool integration to the necessary capabilities of other actors involved. Finally, this study examines the content of co-creation described in the existing literature and the value created jointly. This review contributes to delineating the interaction between human and non-human actors in a B2B co-creation ecosystem. The implications of this research provide B2B companies with a discussion about the actors, motives, characteristics, processes, and content of co-creation with AI in B2B drivers and barriers of AI for co-creation, mapping the way for success.Sustainability440This study examines how buyers, suppliers, and technology providers interact, along with their motives and characteristics, and investigates the processes enabling these interactions, from the form of AI used and AI tool integration to the necessary capabilities of other actors involved.Sustainability["David Fehrenbach", "C. Herrando", "Mar\u00eda Jos\u00e9 Mart\u00edn-De Hoyos"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/122be046a616c0b88af8bd35b9be5a7893a9a377 1ae3da661da1c6f6eba7bd535257afeec1709c43This comprehensive study investigates the integration of Artificial Intelligence (AI) tools in English language education over 28 weeks from 2023 to 2024. The focus was on utilizing ChatGPT for initial corrections and feedback, DeepL Write for advanced refinements, and generative voice AI for pronunciation practice. This multifaceted approach aimed to exploit the unique capabilities of each AI technology, offering a holistic language learning experience that addresses composition, grammar, sentence structure, vocabulary enhancement, and pronunciation. The methodology was grounded in the practical application of AI tools to facilitate various aspects of language learning, based on the premise that AI can significantly augment traditional teaching methods. The study meticulously evaluated the effectiveness of this integration through participant questionnaires, capturing both qualitative and quantitative insights into learning outcomes. Key findings include a marked enhancement in composition abilities, with 64% of participants recognizing improvement; 72% highlighted ChatGPT’s role in facilitating idea generation, sparking new avenues of creativity; 688% reported notable improvements in grammar and sentence structure; an equal percentage observed significant enrichment in vocabulary; 80% appreciated AI-generated feedback; and 76% expressed overall satisfaction with AI integration. These results underscore AI’s potential to transform language education by offering personalized learning experiences that adapt to individual learner needs. The findings are pivotal to the discourse on human uniqueness in the age of technological singularity, demonstrating that while AI can replicate and support language skill acquisition, human creativity, emotional intelligence, and the ability to convey complex worldviews through language remain unparalleled. The study highlights a synergistic relationship where technology enhances learning outcomes while reaffirming the unique capabilities defining our humanity. Despite AI’s advancements, the human ability to embody and express complex worldviews through language remains a testament to our distinct nature, inviting ongoing dialogue on balancing technological innovation with preserving humanistic educational values.Proceedings of the International CALL Research Conference, 202480The findings are pivotal to the discourse on human uniqueness in the age of technological singularity, demonstrating that while AI can replicate and support language skill acquisition, human creativity, emotional intelligence, and the ability to convey complex worldviews through language remain unparalleled.Proceedings of the International CALL Research Conference, 2024["Hiroyuki Obari"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/1ae3da661da1c6f6eba7bd535257afeec1709c43 556b379fb5af33c88a87b02a5b7e74c5644f5ce7The purpose of this study was to investigate effects of artificial intelligence (AI) chatbots on university English as a foreign language (EFL) students’ language use and critical thinking skills in English speech script writing. The 18-week English-speaking course was designed and conducted on the basis of production-oriented approach, a theory of foreign language education with Chinese features. By integrating the production-oriented approach with the technological capabilities of the chatbot, the course instructor offered 30 university EFL students theoretical instruction and task-driven writing activities focused on public speaking in English. Throughout the course, the participants worked in collaboration with the AI chatbot to complete each script writing assignment. Ultimately, the instructor collected and analyzed all original drafts, revised drafts, and transcripts of conversations between the students and the AI chatbot. The study’s results showed that: (1) four indicators of lexical complexity in the revised drafts were significantly improved compared with the original drafts (p < 0.01), whereas one indicator of syntactic complexity also demonstrated significant enhancement (p < 0.05); (2) in comparison with the original drafts, the majority of the revised drafts (71.93%) showed modifications in regard to claims, warrants, and grounds; (3) the contents of the conversations between students and AI chatbot were grouped into three themes: making suggestions for language revisions, extending and developing the main points, and providing supporting materials for arguments. The study’s results can be taken as a reference by English teachers for designing and conducting writing and speaking courses. The study also contributes empirical evidence towards building AI-assisted EFL teaching theories and methods in the era of AI.Proceedings of the International CALL Research Conference, 2024190The study contributes empirical evidence towards building AI-assisted EFL teaching theories and methods in the era of AI and can be taken as a reference by English teachers for designing and conducting writing and speaking courses.Proceedings of the International CALL Research Conference, 2024["Meng Zhang", "Yuxuan Wang", "Qian Shang"]2024-09-13T00:00:00https://www.semanticscholar.org/paper/556b379fb5af33c88a87b02a5b7e74c5644f5ce7 a2b45a49c947aa81453cc33ce5cceced1458e104The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change.Applied Sciences804The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities, which can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle.Applied Sciences["Naiyer Mohammadi Lanbaran", "D. Naujokaitis", "Gediminas Kairaitis", "Gabriel\u0117 Jenci\u016bt\u0117", "Neringa Radziukynien\u0117"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/a2b45a49c947aa81453cc33ce5cceced1458e104 577b904aae94d93fceed08e6316c318f144237dfA national curriculum cannot be uniformly applied in all classrooms. Educators frequently adapt the official curriculum to suit their particular circumstances. In exploring the interplay between artificial intelligence (AI) technologies and curriculum adaptation in education, this study bridges a significant gap in the literature by exploring how AI tools influence teachers' strategies for adapting curricula. Employing an explanatory sequential design, the research analyses both qualitative and quantitative data from 440 teachers, using the Curriculum Adaptation Patterns Scale and focus group semi‐structured interviews. Results indicate a balanced approach among teachers towards extending and revising the curriculum, with less emphasis on omission. Notably, curriculum adaptation practices evolve positively with increased professional experience, differ across disciplines, but remain constant across school levels and educational levels. Qualitatively, teachers reported positive experiences using AI, particularly ChatGPT, to make their lessons better fit students' needs. They've used it to omit parts that aren't needed, add more relevant and personalised content, and revise or replace the content. The findings highlight AI's transformative potential in curriculum adaptation, making education more engaging, relevant and personalised. The study contributes to understanding how AI can support effective curriculum implementation and enhance learning experiences in the digital age.British Educational Research Journal664Results indicate a balanced approach among teachers towards extending and revising the curriculum, with less emphasis on omission, and highlight AI's transformative potential in curriculum adaptation, making education more engaging, relevant and personalised.British Educational Research Journal["Fatih Karata\u015f", "Bar\u0131\u015f Eri\u00e7ok", "Lokman Tanrikulu"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/577b904aae94d93fceed08e6316c318f144237df e4a296ef04a458e02ff6d88115f40c5f9d8b364bThis study aims to examine the ethical challenges and implications of using Artificial Intelligence (AI) in accounting practices, with a particular focus on issues related to data privacy, decision-making transparency, and algorithmic bias, as well as the potential disruption of traditional accounting roles by AI. The methodology employed is a systematic literature review of various academic sources and relevant publications that discuss the ethical aspects of AI implementation in accounting. The results of the study indicate that while AI offers several benefits, such as increased efficiency and accuracy, it also presents significant ethical challenges. These challenges include the risk of data privacy breaches, a lack of transparency in AI-supported decision-making processes, and potential biases in algorithms that could lead to unfair practices in accounting. Moreover, the traditional role of accountants is increasingly threatened by AI-driven automation, which may fundamentally alter the dynamics of work in the accounting field. In conclusion, although AI holds great potential for enhancing accounting practices, it is crucial for professionals and policymakers to address and mitigate the emerging ethical challenges. This approach ensures that AI integration into accounting is conducted responsibly and in alignment with professional ethical principles.  Smart GOALS Jurnal Bisnis Digital Dan Manajemen81Although AI holds great potential for enhancing accounting practices, it is crucial for professionals and policymakers to address and mitigate the emerging ethical challenges.Smart GOALS Jurnal Bisnis Digital Dan Manajemen["Uswatun Hasanah"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/e4a296ef04a458e02ff6d88115f40c5f9d8b364b d689a644efc66fd8fe3d42f127b1f38e71875242Rationale Flexible bronchoscopy is an operator-dependent procedure. An automatic bronchial identification system based on artificial intelligence (AI) could help bronchoscopists to perform more complete and structured procedures through automatic guidance. Methods 101 participants were included from six different continents at the European Respiratory Society annual conference in Milan, 9–13 September 2023. Participants were split into three groups based on experience: novices (0 bronchoscopies), intermediates (1–249 bronchoscopies) and experienced (≥250 bronchoscopies). The participants performed two bronchoscopies on a realistic physical phantom, one with AI (AmbuBronchoSimulatorTrainingGUIDEv.0.0.1, Prototype version, Ambu) and one Standard procedure. The F1-group received AI guidance for their first procedure, the F2-group for their second. A crossover randomisation controlled for learning by testing. All procedures were automatically rated according to the outcome measures: inspected segments, structured progressions and procedure time. Results AI guidance caused the participants to inspect more segments (mean difference, paired t-test: +6.0 segments, p<0.001), perform more structured progressions (+5.2 progressions, p<0.001) and spend more time on the procedure (+72 s, p<0.001) compared to their standard procedures. The effects of AI guidance on inspected segments and structured progression were highest for novices but significant for all experience groups: novices (+8.2 segments, p=0.012 and +6.6 progressions, p<0.001), intermediates (+5.7 segments, p=0.006 and +5.1 progressions, p<0.001) and experienced (+4.3 segments, p=0.006 and +3.8 progressions, p<0.016). Conclusions AI guidance helped bronchoscopists of all experience levels to inspect more segments in a more structured order. Clinical implementation of AI guidance could help ensure and document more complete bronchoscopy procedures in the future.ERJ Open Research311Clinical implementation of AI guidance could help bronchoscopists of all experience levels to inspect more segments in a more structured order and document more complete bronchoscopy procedures in the future.ERJ Open Research["Kristoffer Mazanti Cold", "K. Agbontaen", "Anne Orholm Nielsen", "Christian Skjoldvang Andersen", "Suveer Singh", "Lars Konge"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/d689a644efc66fd8fe3d42f127b1f38e71875242 5ef3fbb3c305d192b4c0b53251b3893d215621faCorporate stakeholders are intrigued by the potential collaboration between AI and environmental reporting to maintain competitiveness in the digital and sustainable economy. This exploration is crucial given the persistent pressures driving companies to engage in greenwashing practices for legitimacy. Aiming to shed light on the function of AI in reducing the prevalence of greenwashing by Chinese businesses, the findings, derived from panel data estimation of Chinese A‐share firms, suggest that the implementation of AI has a positive impact on the fight against greenwashing. The investigation presents compelling evidence that greenwashing mechanism control can be accelerated by AI technologies. Organizations that invest strategically in artificial intelligence exhibit a diminished propensity to obfuscate environmental performance by means of AI‐enabled automation and enhanced data‐driven decision‐making. Intriguingly, the study demonstrates that the substantial disparate greenwashing impact of AI depends on ownership structure. In comparison to non‐SOEs, state‐owned enterprises (SOEs) demonstrate diminished AI control over greenwashing. Significantly, the research utilizes a variety of validation methods, such as instrumental variable approach, propensity score matching, and two‐stage least squares, to ensure the validity of the primary findings.Business Strategy and the Environment930The findings suggest that the implementation of AI has a positive impact on the fight against greenwashing, and demonstrates that the substantial disparate greenwashing impact of AI depends on ownership structure.Business Strategy and the Environment["Muhammad Kaleem Khan", "Chunhui Huo", "R. A. Zahid", "Umer Sahil Maqsood"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/5ef3fbb3c305d192b4c0b53251b3893d215621fa be0bc6bc8ecd851aaa387a82c607c55d16ed1499The rapid advancement of artificial intelligence (AI) in healthcare has the potential to revolutionize the global healthcare sector and medicine in general. However, integrating AI technologies in healthcare requires access to large amounts of personal health-related data (HRD), which raises concerns regarding confidential personal information considering unregulated and not transparent data ownership. Setting up the patient's welfare as an unquestionable principle, this commentary explores the various ethical aspects of using HRD in AI applications, focusing on informed consent, data ownership, data sharing, financial considerations, accountability, and ethical standards. Three models of potential collaboration between AI-specializing firms and healthcare providers are evaluated: the commercial model, the equitable profit-sharing model, and the public-funded non-profit model. Each model has its advantages and challenges, necessitating a careful balance between ethical considerations, financial implications, and technological advancements. Policymakers and healthcare regulators are urged to establish transparent legislation to safeguard patient privacy, ensure informed consent, and promote the responsible use of HRD in AI applications. This commentary emphasizes the importance of addressing ethical issues to protect basic patient rights, foster responsible collaborations, and ensure the ethical use of health-related data in AI-based healthcare applications. While the coexistence of regulated AI and healthcare professionals is inevitable for validating the cost-effectiveness of AI use in healthcare economics, the transparency of HRD sources is deemed of utmost importance in the best interest of the patient.Proceedings of the Institution of mechanical engineers. Part H, journal of engineering in medicine120The importance of addressing ethical issues to protect basic patient rights, foster responsible collaborations, and ensure the ethical use of health-related data in AI-based healthcare applications is emphasized.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine["Arkadiusz Dziedzic", "J. Issa", "A. Chaurasia", "M. Tanasiewicz"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/be0bc6bc8ecd851aaa387a82c607c55d16ed1499 9bcf309b581a06fb34247cd230b77ecf9ac120abThis paper conducted a systematic literature review based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology to explore the types of AI tools used in teaching and learning and their impacts on English language teaching and learning. This systematic literature review explores the integration and impact of artificial intelligence (AI) in English language teaching and learning, synthesising findings from 25 recent studies across three main databases namely, Science Direct, SCOPUS, and Taylor & Francis Online. The analysis identifies four main themes: Impact on: (1) Learning and Skill Development, (2) Teacher and Professional Development, (3) Technological Integration and Challenges, and (4) Student Experience and Perception. The results reveal that AI tools significantly enhance language learning outcomes, such as writing skills and critical thinking, and offer personalised and dynamic learning experiences. However, the successful integration of AI in education requires addressing challenges related to technological implementation, ethical considerations, and the need for comprehensive teacher training. Despite these challenges, AI's potential to transform language education is evident, as it fosters both academic and professional growth for students and educators alike.International Journal of Academic Research in Progressive Education and Development250The results reveal that AI tools significantly enhance language learning outcomes, such as writing skills and critical thinking, and offer personalised and dynamic learning experiences, however, the successful integration of AI in education requires addressing challenges related to technological implementation, ethical considerations, and the need for comprehensive teacher training.International Journal of Academic Research in Progressive Education and Development["Vatsala Tamil Selvam", "Nur Yasmin Khairani Zakaria"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/9bcf309b581a06fb34247cd230b77ecf9ac120ab b0de1a3f9253256910506fb455ce73df3173f20eHealthcare solutions driven by artificial intelligence (AI) can offer more personalized, precise treatment options and better health outcomes; however, implementing AI in clinical settings can be complex due to cultural, economic, and regulatory factors. Leaders must overcome challenges, including data quality and bias, algorithmic trust, and skills deficits, while focusing on patient-centricity and treatment options. [1] The fast-paced digital revolution in the healthcare sector has generated a wealth of data, leading to significant opportunities for healthcare advancements through the utilization of Data Analytics and Artificial Intelligence (AI). This article delves into the incorporation of these technologies in healthcare and their transformative influence on patient care. Advanced computational methods and data analytics can scrutinize and interpret intricate datasets, uncover patterns, and establish meaningful correlations. When combined with AI's adaptive and predictive capabilities, this has the potential to significantly revolutionize diagnoses, treatment plans, and healthcare operations. AI's proficiency in machine learning equips healthcare professionals with foresight, enabling the prediction of potential health risks and proactive interventions. AI and Data Analytics also lay the groundwork for personalized medicine, tailoring medical treatments to individual patient profiles. They enhance patient monitoring, pharmaceutical development, and risk management while improving the effectiveness of telemedicine and robotic surgery. Embracing these technologies can enhance patient outcomes, lower healthcare costs, and transform traditional healthcare paradigms into more patient-centered and data-driven approaches. Despite the associated regulatory, privacy, and implementation challenges, the intersection of Data Analytics and AI shows excellent promise in reshaping the future of healthcare.The International Journal of Science &amp; Technoledge00Embracing these technologies can enhance patient outcomes, lower healthcare costs, and transform traditional healthcare paradigms into more patient-centered and data-driven approaches, and shows excellent promise in reshaping the future of healthcare.The International Journal of Science &amp; Technoledge["Jinesh Kumar Chinnathambi"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/b0de1a3f9253256910506fb455ce73df3173f20e e557ec75a9197cfce440322ccc27c9b44f2492f6Today, with the rapid development of artificial intelligence, media convergence is facing new opportunities and challenges in the production of AIGC media products. The purpose of this study is to explore the characteristics of AIGC media products, the application of artificial intelligence in converged media and the innovative path of intelligent converged media operation under the background of artificial intelligence and converged communication. By combing the relevant literature on artificial intelligence production, converged communication, media operation, and management, this paper constructs a theoretical framework, explores the production advantages and disadvantages of AIGC, analyzes the cases of converged media using artificial intelligence to produce media products, and combines the theory of converged communication, media ecology theory, and media ethics norms. It provides AIGC application strategies such as avoiding AIGC behavioral ethical risks, improving AIGC media product quality, and innovating intelligent convergence media operation structure for convergence media to promote the sustainable development of convergence media artificial intelligence application ecology.Journal of humanities and social sciences studies60AIGC application strategies such as avoiding AIGC behavioral ethical risks, improving AIGC media product quality, and innovating intelligent convergence media operation structure for convergence media are provided to promote the sustainable development of convergence media artificial intelligence application ecology.Journal of Humanities and Social Sciences Studies["Sihan Li"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/e557ec75a9197cfce440322ccc27c9b44f2492f6 74895a0f0be2b47cc85909f66a313ce332187dd7The paper proposes a method for defining a dedicated operational context as part of the development and deployment of autonomous corporate governance systems. The case study of autonomous board of directors systems is examined. A significant part of the operational context for the autonomous corporate governance systems consists of the regulatory and legal framework that regulates the company’s operations. A special operational context for autonomous artificial intelligence systems can be defined by simultaneously formulating local regulatory documents in two versions, i.e., to be used by people and by autonomous systems. In such a case, the artificial intelligence system receives a clearly defined operational context that allows such a system to perform its functions with a required operational quality. Local regulations that take into account the specificity of operations involving individuals and autonomous artificial intelligence systems can become the foundation of the relevant legislation that would regulate the development and deployment of autonomous systems.Dependability50ArXiv["Anna Romanova"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/74895a0f0be2b47cc85909f66a313ce332187dd7 0d303ff9fe434dba70bd3acefb1bbada58e4188fHealthcare disparities, particularly in vulnerable areas, are important human rights violations that contribute to poor health outcomes and higher healthcare expenses. Artificial intelligence (AI) provides promising solutions for reducing inequities by improving decision-making, disease diagnosis, and access to care. However, if deployed incorrectly, AI has the potential to perpetuate current imbalances. This study investigates the role of AI in addressing healthcare disparities and presents AI applications for closing these gaps, obstacles, ethical concerns, and future potential for using AI to create a more equitable healthcare system. Combining AI with equitable frameworks can encourage inclusion, improve healthcare outcomes, and minimize long-standing healthcare disparities. Keywords: Artificial Intelligence, Healthcare Disparities, Health Equity, Marginalized Communities, Health Access.Research Output Journal of Biological and Applied Science00This study investigates the role of AI in addressing healthcare disparities and presents AI applications for closing these gaps, obstacles, ethical concerns, and future potential for using AI to create a more equitable healthcare system.Research Output Journal of Biological and Applied Science["Ngugi Mwaura J."]2024-09-14T00:00:00https://www.semanticscholar.org/paper/0d303ff9fe434dba70bd3acefb1bbada58e4188f d6c4a44dbefa51735aaa918a32ef647099f64359The rapid spread and development of artificial intelligence technologies has raised important questions that have an impact on laws and regulations related to intellectual property. In light of this, the research aims to explore the impact of artificial intelligence on intellectual property laws and regulations, and to examine the legal implications of the innovations generated by artificial intelligence on authorship, invention, ownership, infringement, and enforcement of intellectual property laws. In light of the great concerns about its impact on intellectual property laws and regulations and the uncertainty and ambiguity in the application of intellectual property laws, the researcher considered it a problem of the study as it leads to potential risks and challenges for creators, inventors, and intellectual property rights holders. The research followed the comparative approach to provide a view of the different approaches regarding this topic, and to help shed light on the main difficulties faced in applying the law in such an evolving context. The study reached many conclusions, but the most important of them is that artificial intelligence threatens office jobs due to its ability to generate high-quality content quickly and at a low cost, and intellectual property laws will continue to be breached, which necessitates the need for the law to adapt to establish rules of conduct and limits of human work, and recommends engaging in an international dialogue to unify laws and manage disputes related to intellectual property across borders.Evolutionary Studies in Imaginative Culture530The study reached many conclusions, but the most important of them is that artificial intelligence threatens office jobs due to its ability to generate high-quality content quickly and at a low cost, and intellectual property laws will continue to be breached, which necessitates the need to adapt to establish rules of conduct and limits of human work.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Dr. Shemseddine Ethani Barnat", "Nesreen Madah Aburaya", "Sarah Madi Alhajri", "Shireen Banu"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/d6c4a44dbefa51735aaa918a32ef647099f64359 78a7946fefedd0f3645191a9d8e921e397c783ceJournal of Ethics332The Journal of Ethics["Filippo Santoni de Sio"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/78a7946fefedd0f3645191a9d8e921e397c783ce c80ff608d9de4aeeaee59b706c000201ba8da7c7Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-09-14T00:00:00https://www.semanticscholar.org/paper/c80ff608d9de4aeeaee59b706c000201ba8da7c7 4192857c2fd4932b692fa75a21575d5944c4b6f7Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-09-14T00:00:00https://www.semanticscholar.org/paper/4192857c2fd4932b692fa75a21575d5944c4b6f7 8d558f87791a843cd606015f9a5bd79486154297Physiotherapists00Physiotherapists["C\u00e1ssia Goulart", "Marcela Lopes Alves", "Fernando D\u2019Angelo Medeiros", "Robson Fernando Borges", "Glauco Lima Rodrigues", "Carla Cristina De Ara\u00fajo Alves", "Graziella Fran\u00e7a B. Cipriano", "Gerson Cipriano Junior"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/8d558f87791a843cd606015f9a5bd79486154297 4ef2b9104b986d1a47177eb94b967d324ea9b384International Conference on Big Data and Internet of Things00{"pages": "360-364"}["Zhao Huang"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/4ef2b9104b986d1a47177eb94b967d324ea9b384 149cc1b14639857983a527270018f90185e748d4International Conference on Big Data and Internet of Things20{"pages": "377-381"}["Running Han", "Chenxing Song"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/149cc1b14639857983a527270018f90185e748d4 c16b542151efa83e45003a405d6b695f7af6df1dInternational Conference on Big Data and Internet of Things150{"pages": "387-391"}["Yatao Geng"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/c16b542151efa83e45003a405d6b695f7af6df1d bbd58fc9522ad3c3a432a5d13fb54624cc65a532International Conference on Big Data and Internet of Things30{"pages": "332-337"}["Nairu Qi", "Pengfei Song", "Tao Mei"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/bbd58fc9522ad3c3a432a5d13fb54624cc65a532 2f4af6b077479511047ad9b83cc5063b871e15e0General practice and primary care00General practice and primary care["Gabrielle Len Antonio", "Yvonne Montejo", "Roger Sy"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/2f4af6b077479511047ad9b83cc5063b871e15e0 dfc309d4cbced84c71c6651fd07aa399f7e655e2Monitoring airway disease00Monitoring airway disease["Alessia Catalisano", "Chiara Marzi", "C. Allegrini", "Elisa Bentivegna", "Alberto Bracciali", "Greta Insalata", "M. Marinato", "S. Diciotti", "Michela Baccini", "G. Camiciottoli"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/dfc309d4cbced84c71c6651fd07aa399f7e655e2 0220858e7d0342dfb31cbe5d08df3e4f27ff33ecProfessorNanotechnology Perceptions390Nanotechnology Perceptions["Dr. B. Neelambaram", "Dr. P Padma Ganga", "Sonia Panigrahi", "Dr. Shakti Awasthi", "Dr. Brahma Edwin Barreto"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/0220858e7d0342dfb31cbe5d08df3e4f27ff33ec 65ab0693fdbd389670b649e9a087896147861054General practice and primary care00General practice and primary care["Claire Adams", "Elena Smets", "Julie Maes", "Jon Rees", "Marko Topalovic"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/65ab0693fdbd389670b649e9a087896147861054 b5123f5d6a5906e1d02c5960855c5c30cde5c5fcInternational Conference on Big Data and Internet of Things30{"pages": "371-376"}["Zhen Wang", "Jinxin Liu"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/b5123f5d6a5906e1d02c5960855c5c30cde5c5fc 8309e691dd962c486d07e4967ec5ca685e78e035Humans and Machines: getting the balance right00Humans and Machines: getting the balance right["Mar\u00eda S\u00e1ez", "Cristina Aljama", "Marta Andreu", "Yolima Cossio", "Emmanuel Gim\u00e9nez", "Miriam Barrecheguren", "Jorge Riquelme", "Julia Sampol Sirvent"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/8309e691dd962c486d07e4967ec5ca685e78e035 b7f54026faeac92b59f4e2d9b05e665636b21613Acute critical care00Acute critical care["Chang Youl Lee", "Ki-Byung Lee", "Ji Young Hong", "M. Lee"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/b7f54026faeac92b59f4e2d9b05e665636b21613 d5c817aaa2135f5195bee4fad3a747969497e2c9Prabandhan Indian Journal of Management00Prabandhan: Indian Journal of Management["Abhilasha Dixit", "Rimjhim Jha", "Ruturaj Baber", "P. Baber"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/d5c817aaa2135f5195bee4fad3a747969497e2c9 93c34f22e608a6bea4c76763b792a0439f0d8e67The innovative business risks include cybersecurity and regulatory risks and therefore the expansion of the use of Artificial Intelligence and Business Intelligence technologies in the risk management processes has come as a result. This article examines the role of Advanced Intelligence and Business Intelligence in altering risk management paradigms in the context of the US Organizations with emphasis on operations management and decision-making sophistication enhancement and risk management in advance. To achieve the above objective, a cross-sectional survey of 200 risk management professionals drawn from various organizations is used to gather data on the level of AI/BI adoption, the benefits sought and the difficulties experienced. A structured online survey was used to solicit data relating to themes like integrated AI/BI, perceived enhancements in risk, challenges like high costs and data and ethical issues of AI-decision making. Categorized variables were used to present the demographics of the respondents and Pearson correlation and regression analyses tests were used to compare the impact of AI/BI adoption with enhanced risk management results. Chi-Square tests were conducted to establish the significance of the differences in the adoption and challenges by industries as well as the size of organizations. Organizations with optimally deployed AI/BI systems realize enhanced system effectiveness, increased speed of decision-making processes and improved ability to manage risks in an anticipatory manner. A significant positive correlation was established between these outcomes and the level of AI/BI integration with these outcomes supporting the disruptive promise of these technologies. However, the study also shows the following challenges to adoption, which are high costs of implementing the solutions, difficulty in handling big data and shortage of skilled personnel in a firm. Moreover, ethical issues remain critical, especially with reference to the levels of transparency with artificial intelligence alongside data protection for individuals’ information; a pressing issue of concern especially to the health and financial sectors. This study prompts further attention to the advancement of efficient AI solutions at a large scale and the generation of a set of ethical norms to incorporate into risk management particularly with reference to AI use. In future research, more efforts should be devoted to investigating the effects that the use of AI and BI has on risk management practices after a longer period of time has elapsed, as well as on how the barriers explored in this research could be efficiently mitigated for organizations, especially those small ones.International journal of business and management sciences00The role of Advanced Intelligence and Business Intelligence in altering risk management paradigms in the context of the US Organizations with emphasis on operations management and decision-making sophistication enhancement and risk management in advance is examined.International journal of business and management sciences["Siddikur Rahman", "Musfikul Islam", "Imran Hossain", "Arifa Ahmed"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/93c34f22e608a6bea4c76763b792a0439f0d8e67 d234de6b2de0d5ded48bd9646efd4392121d9235Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift from traditional advertising methods, such as TV, radio, and print, to AI-driven strategies. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. AI leverages data from consumer purchase histories, browsing behaviors, and social media activity to create highly tailored marketing campaigns. These strategies allow for more accurate product recommendations, prediction of consumer needs, and ultimately improve customer satisfaction and user experience. AI enhances marketing efforts by automating labor-intensive processes, leading to greater efficiency and cost savings. It also enables the continuous adaptation of marketing messages, ensuring they remain relevant and engaging over time. While AI presents significant benefits in terms of personalization and efficiency, it also comes with challenges, particularly the substantial investment required for technology and skilled expertise. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.arXiv.org590This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.ArXiv["Elham Khamoushi"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/d234de6b2de0d5ded48bd9646efd4392121d9235 56d80cb1822296add73bcbd49af89b73fe2f9a2dIn this study, we address the early detection challenges of Alzheimer’s disease (AD) using explainable artificial intelligence (XAI) techniques. AD, characterized by amyloid plaques and tau tangles, leads to cognitive decline and remains hard to diagnose due to genetic and environmental factors. Utilizing deep learning models, we analyzed brain MRI scans from the ADNI database, categorizing them into normal cognition (NC), mild cognitive impairment (MCI), and AD. The ResNet-50 architecture was employed, enhanced by a channel-wise attention mechanism to improve feature extraction. To ensure model transparency, we integrated local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping (Grad-CAM), highlighting significant image regions contributing to predictions. Our model achieved 85% accuracy, effectively distinguishing between the classes. The LIME and Grad-CAM visualizations provided insights into the model’s decision-making process, particularly emphasizing changes near the hippocampus for MCI. These XAI methods enhance the interpretability of AI-driven AD diagnosis, fostering trust and aiding clinical decision-making. Our approach demonstrates the potential of combining deep learning with XAI for reliable and transparent medical applications.Applied Sciences180These XAI methods enhance the interpretability of AI-driven AD diagnosis, fostering trust and aiding clinical decision-making, and demonstrates the potential of combining deep learning with XAI for reliable and transparent medical applications.Applied Sciences["Kamese Jordan Junior", "Kouayep Sonia Carole", "Tagne Poupi Theodore Armand", "Hee-Cheol Kim", "The Alzheimer\u2019s Disease Neuroimaging Initiative The Alzheimer\u2019s Disease Neuroimaging Initiative"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/56d80cb1822296add73bcbd49af89b73fe2f9a2d fc61d4602a05fd4f78e39e5df2ab17a2733998fdThe rapid and unpredictable emergence of infectious diseases continues to pose a significant threat to global health, necessitating more advanced prediction and prevention strategies. The use of Artificial Intelligence (AI) in predicting disease outbreaks has emerged as a powerful tool to navigate the complex biological, environmental, and sociological factors contributing to these outbreaks. AI models, particularly those leveraging machine learning, can analyze vast datasets, detect patterns, and predict the spread of diseases with improved accuracy compared to traditional methods. This paper examines the role of AI in early disease outbreak prediction, current prediction methodologies, and the applications of AI in outbreak forecasting, while also discussing the challenges and limitations associated with AI-driven models. Keywords: Artificial Intelligence, disease outbreaks, machine learning, epidemic prediction, big data analytics.Research Output Journal of Biological and Applied Science00The role of AI in early disease outbreak prediction, current prediction methodologies, and the applications of AI in outbreak forecasting are examined, while also discussing the challenges and limitations associated with AI-driven models.Research Output Journal of Biological and Applied Science["K. Ntakirutimana G."]2024-09-14T00:00:00https://www.semanticscholar.org/paper/fc61d4602a05fd4f78e39e5df2ab17a2733998fd 5ad11d17085be4a05ef883e960d4fbcf0d0bce3fThe Australian Educational Researcher440This paper views administrative decision-making in schools as a political process involving negotiations among administrators, teachers, students, and parents, and provides a conceptual framework for the symbiotic roles of AI and educational leaders in the administrative decision-making process.The Australian Educational Researcher["Ruixun Dai", "Matthew Krehl Edward Thomas", "S. Rawolle"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/5ad11d17085be4a05ef883e960d4fbcf0d0bce3f 9fa69059ffa29ee0e35a676a1285858be4154545Background: Medication-related harm has a significant impact on global healthcare costs and patient outcomes, accounting for deaths in 4.3 per 1000 patients. Generative artificial intelligence (GenAI) has emerged as a promising tool in mitigating risks of medication-related harm. In particular, large language models (LLMs) and well-developed generative adversarial networks (GANs) showing promise for healthcare related tasks. This review aims to explore the scope and effectiveness of generative AI in reducing medication-related harm, identifying existing development and challenges in research. Methods: We searched for peer reviewed articles in PubMed, Web of Science, Embase, and Scopus for literature published from January 2012 to February 2024. We included studies focusing on the development or application of generative AI in mitigating risk for medication-related harm during the entire medication use process. We excluded studies using traditional AI methods only, those unrelated to healthcare settings, or concerning non-prescribed medication uses such as supplements. Extracted variables included study characteristics, AI model specifics and performance, application settings, and any patient outcome evaluated. Findings: A total of 2203 articles were identified, and 14 met the criteria for inclusion into final review. We found that generative AI and large language models were used in a few key applications: drug-drug interaction identification and prediction; clinical decision support and pharmacovigilance. While the performance and utility of these models varied, they generally showed promise in areas like early identification and classification of adverse drug events and support in decision-making for medication management. However, no studies tested these models prospectively, suggesting a need for further investigation into the integration and real-world application of generative AI tools to improve patient safety and healthcare outcomes effectively. Interpretation: Generative AI shows promise in mitigating medication-related harms, but there are gaps in research rigor and ethical considerations. Future research should focus on creation of high-quality, task-specific benchmarking datasets for medication safety and real-world implementation outcomes.medRxiv20Generative AI and large language models were used in a few key applications: drug-drug interaction identification and prediction; clinical decision support and pharmacovigilance, and they generally showed promise in areas like early identification and classification of adverse drug events and support in decision-making for medication management.["Jasmine Chiat", "Ling Ong", "Chen Michael", "Ning Ng", "Kabilan Elangovan", "Nichole Yue", "Ting Tan", "Liyuan Jin", "Qihuang Xie", "Daniel Shu", "Wei Ting", "Rosa Rodriguez-Monguio", "David W. Bates", "Nan Liu"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/9fa69059ffa29ee0e35a676a1285858be4154545 cef71743897306893708a08f388dc953ca2d9105This research aims to develop a robust Application Program Interface (API)-Based Artificial Intelligence (AI) system for effective noise removal from audio signals, enhancing speech quality and intelligibility in noisy environments to be fed into different AI models to assess the applicant interview. The proposed methodology combines sophisticated signal processing techniques and noise reduction algorithms with AI models trained on clean voice data and noise patterns. To achieve this goal, we leverage two key components: the Wiener filter and a Convolutional Neural Network (CNN). The Wiener filter serves as the foundational noise reduction technique, exploiting statistical properties of the signal and the noise to suppress unwanted noise components effectively. Concurrently, CNN is integrated to classify the clean and noisy audio. In this research, the best optimizers selected, including Adam, SGD, RMSprop, Adagrad, and Adadelta are evaluated to identify the most suitable classification. The optimizers evaluated through cross-validation and hold-out validation in the same batch size (25) and epoch (25) were used. The study demonstrates that the Adam optimizer yields the best results. The epoch was optimized to 35, 75, 105, and 125 and epoch of 105 was selected with accuracy of 99.52%, Recall of 100%, F1-Score of 99.50%, and ROC_AUC of 99.99% for cross-validation while Accuracy of 98.79%, Recall of 99.21%, F1-Score of 98.81%, and ROC_AUC of 99.54% for hold-out validation, significantly improving AI model performance. Lastly, we ensured the batch size parameter was suitable for our model by tuning it with different settings (25, 50, 75, and 125) using the optimized optimizer and epoch. The batch size of 25 yielded the best accuracy. The modeled CNN also included kernel regularization L2 to avoid overfitting.ECTI Transactions on Computer and Information Technology00ECTI Transactions on Computer and Information Technology (ECTI-CIT)["Joel Ryan A. De Guzman", "Robert G. de Luna", "Marife A. Rosales"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/cef71743897306893708a08f388dc953ca2d9105 797da98b6f9b3817087168f99ed784396d7093c2En calidad de autora del artÌculo titulado "Inteligencia artificial en la medicina", quisiera expresar mi agradecimiento al autor Rodrigo Guerrero-López por su carta, la cual hace referencia a nuestra publicaciÛn en la Revista Médica Panacea¹. Aprecio profundamente su interÈs y sus observaciones crÌticas, que sin duda contribuyen de manera significativa a enriquecer el dialogo sobre el uso de la inteligencia artificial (IA) en el ámbito médico. Coincido plenamente con Guerrero-López en la importancia de garantizar la precisión y fiabilidad de los diagnósticos asistidos por IA. La calidad de los resultados depende en gran medida de la calidad de los datos de entrenamiento y de la validación rigurosa de los algoritmos utilizados. Tal como él menciona, la interpretación errónea de datos puede llevar a diagnósticos incorrectos, lo que subraya la necesidad de contar con modelos bien calibrados y transparentes. En este sentido, considero que futuras investigaciones deberían centrarse en desarrollar estándares robustos para la validación de algoritmos en diversos contextos clÌnicos²,³.Revista Médica Panacea00Revista Médica Panacea["Solange Ni\u00f1o de Guzm\u00e1n-Huacachi"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/797da98b6f9b3817087168f99ed784396d7093c2 41ab022eda95db27e08f7058148015f6a691ddfcLa evolución del mantenimiento predictivo en la industria ha estado marcado por importantes desarrollos tecnológicos y metodológicos, inicialmente el mantenimiento era de carácter reactivo, implicando reparaciones únicamente después de que se produjera una falla, el objetivo de la presente investigación es analizar como la Inteligencia Artificial (IA)  ha transformado el mantenimiento predictivo, proporcionando una visión comprensiva de cómo esta tecnología está transformando las prácticas industriales, el objetivo planteado unido la metodología diseñada, permite presentar como principales resultados que los sistemas de IA son capaces de monitorear continuamente los datos de rendimiento de las máquinas y llevar a cabo análisis en tiempo real, que los algoritmos de aprendizaje automático y las técnicas de aprendizaje no supervisado son capaces de detectar patrones inusuales en los datos, que la Inteligencia Artificial tiene la capacidad de desarrollar modelos predictivos más precisos al analizar tanto datos históricos como datos en tiempo real. Como conclusiones se plantea que el futuro de la IA en el mantenimiento predictivo parece prometedor, con la integración de tecnologías emergentes como él internet de las cosas (IoT) y la analítica avanzada, el IoT permite una monitorización en tiempo real, mientras que la analítica avanzada mejora la capacidad de los algoritmos de IA para prever fallos con mayor precisión.Ibero-American Journal of Engineering &amp; Technology Studies00Ibero-American Journal of Engineering &amp; Technology Studies["Jos\u00e9 Adolfo Ar\u00edzaga Mondrag\u00f3n", "Josu\u00e9 Ismael Ar\u00edzaga Ricaurte"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/41ab022eda95db27e08f7058148015f6a691ddfc 0d083b3922a0c5dd5860e0fa1ec1988953865fc6Me permito dirigirme a usted en relación con la editorial titulada "Inteligencia artificial en la medicina", escrita por Niño de Guzmán Solange y Ybaseta-Medina Jorge, y publicada en la Revista Médica Panacea¹. El artículo ofrece una visión comprensiva sobre la implementación de la inteligencia artificial (IA) en el campo de la medicina, destacando sus numerosos beneficios y suinmenso potencial. No obstante, considero que es esencial abordar algunos aspectos críticos que, en mi opinión, merecen una atención más detallada.En primer lugar, aunque el artículo menciona brevemente los riesgos asociados con la IA, resulta crucial profundizar en la precisión y la fiabilidad de los diagnósticos que esta tecnología puede ofrecer. La interpretación errónea de datos o imágenes por parte de los algoritmos podría conducir a diagnósticos incorrectos, especialmente si los modelos no se validan adecuadamente o si los datos de entrenamiento son sesgados²,³. Este punto es particularmente relevante dado que la IA se está implementando de manera acelerada en la práctica clínica, lo que exige un escrutinio riguroso para evitar errores potencialmente graves.Revista Médica Panacea00Revista Médica Panacea["Rodrigo Guerrero-L\u00f3pez"]2024-09-14T00:00:00https://www.semanticscholar.org/paper/0d083b3922a0c5dd5860e0fa1ec1988953865fc6 0e3c99394835764164fbf3c3051ffa7776fc62d4Antimicrobial resistance (AMR) occurs when microorganisms, acquire genetic changes resistant to antimicrobial drugs, including antibiotics. Conventional techniques for combating AMR are expensive and time consuming, but Artificial intelligence (AI) is currently being developed that can rapidly scan through extensive chemical libraries and forecast possible antibacterial substances. The use of AI in medical research has significant promise, particularly in addressing multidrug-resistant (MDR) infections to battle AMR. Algorithms of AI monitors antibiotic usage, occurrences of diseases, and trends of resistance, thus influencing the development of novel drugs. Through AI, researchers can rapidly identify potential new drugs that could be effective against antibiotic-resistant bacteria, saving valuable time in the development process. By analyzing vast amounts of data, AI algorithms can also help to predict future trends in antibiotic resistance, allowing for proactive measures to be taken. With the ability to analyze data at a rapid pace, AI is revolutionizing the way researchers approach drug development, health risks and disease prevention. As technology continues to advance, the impact of AI in combating antimicrobial resistance becomes more significant. Overall, the integration of AI in medical research shows great potential in the ongoing battle against antimicrobial resistance. This review describes the application of AI to identify AMR markers, diagnosis in AMR, small molecule antibiotic development and also emphasizes emerging research domains, such as AMR detection and novel drug development, that contribute to the management of AMR.IP International Journal of Medical Microbiology and Tropical Diseases321The application of AI to identify AMR markers, diagnosis in AMR, small molecule antibiotic development and also emphasizes emerging research domains, such as AMR detection and novel drug development, that contribute to the management of AMR.IP International Journal of Medical Microbiology and Tropical Diseases["Desh Nidhi Singh", "H. Natto", "A. A. R. Mahmood", "Sriram Thiruvengadam", "R. Vasanthi"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/0e3c99394835764164fbf3c3051ffa7776fc62d4 8eb07d62445b18746d0a07ea54887aa4617a0d83In essence, enhancing economic and legal frameworks for artificial intelligence technologies in remote education is a multifaceted endeavor that requires concerted efforts from all stakeholders. By focusing on educator training, equitable access, adaptable legal regulations, ethical development practices, sustainable economic models, international collaboration, private sector involvement, public transparency, and continuous evaluation, we can create an educational environment where artificial intelligence serves as a powerful tool for learning. Such an environment not only improves educational outcomes but also prepares students to navigate and contribute to a world increasingly shaped by advanced technologies. This paper explores vital improvements in the economic and legal frameworks governing the application of artificial intelligence technologies within distance learning. The focus of the study is on how these regulations are shaped and implemented. The main objective is to develop a methodological approach that enhances these frameworks. The research utilizes the IDEF0 methodology to propose a functional model that refines the economic and legal controls of artificial intelligence use in remote education settings. Innovations within the IDEF0 model segments are discussed, aiming to bolster these frameworks effectively. Future research directions are suggested, including the integration of socio-psychological factors.Journal of Ecohumanism140This paper explores vital improvements in the economic and legal frameworks governing the application of artificial intelligence technologies within distance learning, using the IDEF0 methodology to propose a functional model that refines the economic and legal controls of artificial intelligence use in remote education settings.Journal of Ecohumanism["Ruslan Gubarev", "H. Biletska", "N. Mironova", "Natalia Kazanishena", "S. Skrypnyk", "Nataliia Mashtakova"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/8eb07d62445b18746d0a07ea54887aa4617a0d83 5631a17a2380724a459a45d7311716f77a514519This paper speaks volumes about the various processes of Human resource like recruitment , training, selections, performance and development and workforce planning and how the Artificial Intelligence has revolutionized over the period of time by providing various insights and supporting in the decision making process. Artificial intelligence has provided a bias free and advanced algorithms due to which the screening and scheduling of HR process has been done with accurate parameters and lightning fast speed. This study provides a detailed insight to the AI intervention and its effectiveness in providing a great decision making system to the Human resource professionals. This platform is utilizing the sentiment analysis and catboats to monitor and improve the work place environment, which pushes the positivity in work culture and boosts productivity The AI aids in providing continuous feedback, various goal settings and tracking the performance productivity. This also provides a gist as well as in depth knowledge of top performers of the unit and also share the areas of improvement by which the others can reach up to a new bench mark. This AI has been a boon to the HR individuals thoroughly, especially during the performance appraisals. On the other AI enables personalized training programs and adaptive learning paths, addressing skill gaps and aligning employee development along with organizational goals.The point to ponder over is that the AI has also played a crucial role in workforce planning and analytics, additionally, it has aided by providing tools to forecast future workforce needs, optimize resource allocation, and enhance strategic decision-making. This process which is done by IA is by analyzing large datasets, AI also predicts turnover trends and informs succession planning, contributing to organizational productive and efficient life cycle The research has infused a mixed-methods approach, which combines qualitative and quantitative data collection through surveys, interviews, and case studies, alongside statistical analysis of HR metrics pre- and post-AI implementation. The basics of this research paper aims to provide valuable insights for HR professionals and organizational leaders on leveraging AI to enhance HR effectiveness and drive business success. There a strong determination which ensures that the findings will contribute to the evolving body of knowledge on AI in HR and support the development of strategic frameworks for AI adoption in human resource management on a longer run.Journal of Management Research and Analysis30A detailed insight is provided to the AI intervention and its effectiveness in providing a great decision making system to the Human resource professionals and organizational leaders on leveraging AI to enhance HR effectiveness and drive business success.Journal of Management Research and Analysis["N. Ganatra"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/5631a17a2380724a459a45d7311716f77a514519 94d78321f6c71509989221ff92e62586ae5cf6f5Objectives This study sought to investigate special education teachers' interest in artificial intelligence education(hereinafter referred to as AI education) in the introduction of special education, and to derive implications for effectively applying AI education to special education fields. Methods In order to investigate the interest in AI education of special education teachers, 237 current special education teachers were surveyed online using the SoCQ test tool. For data processing, the general characteristics of the study subjects and the intensity percentile score for each SoCQ sub-stage were investigated through frequency and ratio, and t-test and ANOVA were conducted to compare the difference in AI education interest by special education teachers' background variables. Results The results of the study are as follows. First, special education teachers' interest in AI education was similar to the non-user profile and their interest in the impact of AI education was relatively low. Second, there was no statistically significant difference in interest in AI education according to gender and working institution and in terms of student disability type, the interest of teachers teaching students with sensory and physical disabilities was high in all stages except stage 3(operation), a significant difference appeared. Third, in terms of teaching experience, the higher the teaching experience, the higher the interest in AI education. In terms of educational background, the higher the educational level, the higher the interest in AI education. There was a significant difference in both teaching experience and educational background. Fourth, in terms of SW education-related training hours and AI education-related training hours, the more training hours there were, the higher the level of interest in both SW education and AI education. Fifth, in terms of SW education competency, the higher the SW education competency, the higher the interest in AI education, and in terms of AI education support, the higher the support for AI education, the higher the interest. Conclusions According to these results, as a way to increase special education teachers' interest in AI education, promote AI education to special education teachers so that they can be effective and interested in students with disabilities, and provide sufficient AI education methods and teaching and learning materials according to the type of disability. Needs to be. In addition, national-level policy support is needed, such as providing opportunities for expert collaboration and establishing new training courses so that special education teachers can develop AI education-related capabilities.Korean Association For Learner-Centered Curriculum And Instruction00Investigating special education teachers' interest in artificial intelligence education in the introduction of special education and derive implications for effectively applying AI education to special education fields found sufficient AI education methods and teaching and learning materials according to the type of disability.Korean Association For Learner-Centered Curriculum And Instruction["Dongkyu Kim", "Jeonghan Woo"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/94d78321f6c71509989221ff92e62586ae5cf6f5 ca55200cc41c6002f95196405b63c989a27005bbImproving energy conservation and emission reduction (ECER) efficiency is a virtuous cycle of economic development and environmental protection, promoting countries around the world towards sustainable development. As a strategic technology leading a new round of technological revolution and industrial transformation, the large-scale application of artificial intelligence (AI) is driving the transformation of manufacturing production methods, which is increasingly essential for improving the effectiveness of environmental governance. This study aims to analyze the impact of AI technology on ECER in the manufacturing industry, as well as the specific impact paths and heterogeneity. We contribute to previous literature by measuring ECER of Chinese manufacturing sector using the EBM model. The mediation effect model is used to analyze the impact mechanism between AI technology and ECER. The results indicate that AI promotes the ECER efficiency in the manufacturing sector. The positive effects are attributed to the development of energy consumption structure and technological innovation. The impact of AI on ECER exhibits an evident heterogeneous effect across industries with different pollution intensity, R&D intensity and labor intensity, and ownership dominant industry. Additionally, higher levels of environmental regulation lead to an increase in the positive effects of robot promotion on ECER. The research conclusions provide important reference for understanding the relationship between AI technology and ECER, and contribute a new way to promote environmental governance and carbon neutrality.Journal of Information Economics480Journal of Information Economics["Siyu Ren", "Wenchao Bu"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/ca55200cc41c6002f95196405b63c989a27005bb 552a35a820a51805e94656f754b05f95ae3085fdStudents with disabilities experience (SWDS) difficulties in learning due to limitations placed on them by their defect. To meet their learning needs distinguishing educational provisions from regular education were introduced into educational system which consist of special teachers and modification of curriculum, instructional procedures, methodology, instructional materials and learning environment contingent on category and magnitude of disabilities. Similarly, given that learning has to be effective; inclusive education in regular educational institutions was introduced. Nevertheless, while those who cannot benefit from inclusive education due to the severity of their handicap; provisions were made to accommodate them in special classes and special schools. These provisions are indeed beneficial to SWDs. However, introduction of Artificial Intelligence (AI) in tertiary institutions personalizes and has been found to enhance learning for SWDs. Moreover, its use is not without challenges which need to be addressed to enable them benefit maximally from this novel and unique technology. This paper, therefore, through evaluative and prescriptive methodology using google search on the theme, concepts and keywords examines the type of AI technologies used by SWDs in higher education, benefits and challenges. Finally, the paper offers some suggestions on overcoming these challenges to enable SWDs benefit maximally from the usage.British journal of education00This paper, through evaluative and prescriptive methodology using google search on the theme, concepts and keywords examines the type of AI technologies used by SWDs in higher education, benefits and challenges.British Journal of Education["O. Omiegbe"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/552a35a820a51805e94656f754b05f95ae3085fd 6cf7d58742fde7dd3c5eddb2e7268f699a800ec7The technological society has reconfigured the current environment, generating new needs and changing the motivations of contemporary humans. In this context, self-control is one of the essential abilities that can contribute to an active adaptation, on one hand, and to the development of psychological resilience to the speed at which technological evolution influences life, on the other. Starting from a brief presentation of both the ideological roots of conceptions about the perfect society, as well as the scientific sources of artificial intelligence, the article addresses the theme of education adapted to the new trends of technologizing the personal, professional, and social environment and emphasizes the necessity for the educational environment (family, school, civil society) to synergistically contribute to the optimization of self-control in order to form a younger generation that is self-aware and active in the process of making informed decisions.Annuaire Roumain d'anthropologie00The article addresses the theme of education adapted to the new trends of technologizing the personal, professional, and social environment and emphasizes the necessity for the educational environment to synergistically contribute to the optimization of self-control in order to form a younger generation that is self-aware and active in the process of making informed decisions.Annuaire Roumain d'Anthropologie["Alina Munteanu", "Suzana Turcu", "Monica Petrescu"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/6cf7d58742fde7dd3c5eddb2e7268f699a800ec7 c220faa0a6453aa9808961e41886afc68f65be47This review explores the transformative role of Artificial Intelligence (AI) in periodontal treatment, emphasizing its synergy with patient record maintenance, risk assessment, and prediction. AI-driven systems enhance the accuracy of diagnosing and monitoring periodontal diseases, enabling early detection and screening. The integration of AI facilitates personalized and AI-supported periodontal education, tailoring preventive strategies to individual patient profiles. By analyzing vast datasets, AI models can predict disease progression and treatment outcomes, thus optimizing patient care. Additionally, AI's application in periodontal research accelerates the discovery of novel diagnostic markers and therapeutic targets. This review highlights how AI not only improves clinical decision-making but also revolutionizes periodontal research and education, leading to more effective, personalized, and evidence-based periodontal care.IP International Journal of Periodontology and Implantology150This review highlights how AI not only improves clinical decision-making but also revolutionizes periodontal research and education, leading to more effective, personalized, and evidence-based periodontal care.IP International Journal of Periodontology and Implantology["Preeti Kale", "K. S. Reddy", "Soumyabrata Ghosh"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/c220faa0a6453aa9808961e41886afc68f65be47 08883d45931b6ad8177fed85a1e2ce0894f5dd38Objectives The purpose of this study is to develop an artificial intelligence teaching and learning model for physical education that can be applied to actual physical education classes using teachable machines and to provide basic data that can be used in the field. Methods The rapid prototyping methodology was applied to carry out the process of analysis, design, development, implementation, and evaluation. Four physical education teachers, one coding expert, and four students were selected as research participants, and data were collected and analyzed through in-depth interviews. Results The prototype for developing the physical education and artificial intelligence teaching and learning model was developed through a four-round continuous feedback process, including developing a motion recognition program, adding voice functions, adding visual functions, and configuring a website considering ease of use. A teaching and learning model based on the areas of health, challenge, and competition was developed and presented. Conclusions The physical education and artificial intelligence teaching and learning model using teachable machines was developed and modified as a prototype through the rapid prototyping methodology, and was ultimately developed into 9 teaching and learning models in the areas of health, challenge, and competition.Korean Association For Learner-Centered Curriculum And Instruction00The physical education and artificial intelligence teaching and learning model using teachable machines was developed and modified as a prototype through the rapid prototyping methodology, and was ultimately developed into 9 teaching and learning models in the areas of health, challenge, and competition.Korean Association For Learner-Centered Curriculum And Instruction["Dong-Hyun Kim", "Gunsang Cho", "Yong-Chul Kwon"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/08883d45931b6ad8177fed85a1e2ce0894f5dd38 1abffd8f84d28c3e7fce8bf90f077cf22f54aa29Artificial Intelligence (AI) has transformed numerous domains, including the discipline of medical microbiology. Artificial intelligence is currently being used to assist in clinical decision-making and the monitoring of diseases, with the possibility of being used for genomic information and extensive digital datasets. Through the utilization of advanced algorithms, machine learning (ML), and deep learning (DL) methods, artificial intelligence (AI) can improve disease diagnoses, forecast outbreaks, and customize medical treatments. Moreover, AI is revolutionizing the field of medical and pharmaceutical microbiology, specifically in the areas of pathogen identification, development of point-of-care diagnostics, and drug discovery. Machine learning (ML) is of great use for image analysis since it improves the effectiveness and accuracy of clinical microbiology practice. Despite these developments, it is imperative to tackle issues related to the accuracy of data and limitations of algorithms. Additionally, it is crucial to focus on creating AI models that can be easily understood and interpreted. This review examines the present uses, advantages, and obstacles of AI in medical microbiology, emphasizing its revolutionary impact on enhancing healthcare results.Indian Journal of Microbiology Research360This review examines the present uses, advantages, and obstacles of AI in medical microbiology, emphasizing its revolutionary impact on enhancing healthcare results.Indian Journal of Microbiology Research["Swathi Gurajala"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/1abffd8f84d28c3e7fce8bf90f077cf22f54aa29 d1381cb61a303fb5c27669f4165aae906df42e84AI in finance refers to the application of AI techniques in financial businesses. With the proliferation of AI-based tools and algorithms in financial decision-making, it is increasingly necessary to assess the impact of these technologies on the investment strategies and results of individual investors. The integration of artificial intelligence (AI) in financial decision-making heralds a technological revolution in the sector, which offers enormous potential benefits and significant challenges. This review aims to unravel the complexity surrounding AI in finance, focusing on identifying and addressing barriers to its effective implementation. Looking ahead, the article anticipates future trends and challenges in AI-driven finance, urging stakeholders to collaborate for sustainable innovation. Overall, AI offers tremendous potential for financial transformation, but careful consideration of ethical and regulatory issues is essential for long-term success.International Journal of Scientific Research in Science Engineering and Technology60Overall, AI offers tremendous potential for financial transformation, but careful consideration of ethical and regulatory issues is essential for long-term success, and the article anticipates future trends and challenges in AI-driven finance.International Journal of Scientific Research in Science, Engineering and Technology["Aparna Krishna Bhat"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/d1381cb61a303fb5c27669f4165aae906df42e84 4e351619058e4d30989bcb964e973f9db61f818cArtificial Intelligence (AI) has primarily impacted the global human capital. The human capital has been elucidated, focusing on their developing relationship with AI. The complex facets of human capital, including aptitude, proficiency, and competence, have been examined in this review, concentrating on the intricate association between AI and human capital. A secondary data analysis was conducted for this study, incorporating 16 studies that were meticulously chosen from online search engines. Key search words such as "Human Capital and AI" and "AI and Human Resource Management" were employed for collecting the articles. Compelling data was extracted from these articles to uncover the linkage between AI and human capital. The study yielded both affirmative and negative outcomes following a thorough review of articles. The research identified major concerns associated with AI-powered HR processes concerning bias, fairness, privacy, and security. It underscores the urgency for incorporating responsible AI practices and harnessing the potential of AI while mitigating risks and ensuring equitable human capital development. The connection between AI and human capital provides an invaluable resource for researchers, practitioners, and policymakers navigating the evolving landscape of workforce development in an era of AI-driven innovation.Journal of Management Research and Analysis120The research identified major concerns associated with AI-powered HR processes concerning bias, fairness, privacy, and security and underscores the urgency for incorporating responsible AI practices and harnessing the potential of AI while mitigating risks and ensuring equitable human capital development.Journal of Management Research and Analysis["N. Karunakaran", "K. V. Pradeep"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/4e351619058e4d30989bcb964e973f9db61f818c 11c6d608dbfc300e4c712f36085865b79850d37fArtificial intelligence (AI) seeks to develop algorithms and software capable of emulating intelligent human actions. AI applications in dentistry hold considerable promise for enhancing the precision and effectiveness of diverse dental imaging techniques. While this domain is still relatively young, it demands thorough exploration. Human supervision remains essential to mitigate potential adverse consequences. This article endeavors to shed light on the prevailing ethical considerations stemming from the integration of artificial intelligence into dental practice. It seeks to stimulate discourse surrounding potential ethical pitfalls and encourages critical examination of these issues.Journal of Orofacial & Health Sciences270Light is shed on the prevailing ethical considerations stemming from the integration of artificial intelligence into dental practice and this article seeks to stimulate discourse surrounding potential ethical pitfalls and encourages critical examination of these issues.Journal of Orofacial and Health Sciences["Divya VC", "Surya Krishnakumar"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/11c6d608dbfc300e4c712f36085865b79850d37f ef001032875078d5da1f660451b525c1a99c3b89Artificial intelligence (AI) is increasingly recognized as a transformative force in education It offers enhance learning experiences by enabling students to attempt complex problems through personalized learning, adaptive tools, and collaborative platforms. This study aims to investigate the impact of AI on academic performance among students at Universiti Teknologi Mara (UiTM) Kedah. The study examines various effects of AI, such as the delivery of smarter content, enhanced support and assistance, and improved attitudes towards learning and motivation, on students' academic outcomes. A total of 354 students participated in the study, providing data through self-administered questionnaires. Statistical analysis using SPSS revealed that three AI variables significantly influence students' academic performance. Consequently, educational institutions are encouraged to prioritize the integration of AI-powered learning solutions into their classroom activities, as this approach has the potential to revolutionize learning by providing smarter content, adaptive support, and improved motivation and attitudes towards learning.International Journal of Modern Education00Analysis of the impact of AI on academic performance among students at Universiti Teknologi Mara (UiTM) Kedah revealed that three AI variables significantly influence students' academic performance.International Journal of Modern Education["Azfahanee Zakaria", "Sarah Binti Sabir Ahmad", "N. Zainal", "Syed Mohammed Alhady Syed Ahmad Alhady"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/ef001032875078d5da1f660451b525c1a99c3b89 4a6039659a51143df8031ea983121c6d6d4d9274Artificial intelligence (AI) is transforming surgery by enhancing precision and improving patient outcomes. AI-driven tools enable accurate preoperative planning, real-time intraoperative navigation, and effective postoperative care. These advancements allow surgeons to navigate complex anatomical structures with greater accuracy, reduce errors, and optimize recovery processes using predictive analytics. Case studies across various surgical disciplines demonstrate significant improvements in both accuracy and efficiency. This review also addresses ethical considerations, challenges, and future trends, emphasizing AI's potential to revolutionize surgical precision and patient care, leading to better overall outcomes.IP Journal of Surgery and Allied Sciences230This review addresses ethical considerations, challenges, and future trends, emphasizing AI's potential to revolutionize surgical precision and patient care, leading to better overall outcomes.IP Journal of Surgery and Allied Sciences["A. Shetti", "P. C. Ingale", "Sunny Mavi", "Srusti Pandurang Chaudhari", "Suraj Sudarshan Doshi"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/4a6039659a51143df8031ea983121c6d6d4d9274 a93eaa1c7089722cc168178873e6d885936194f5This systematic review examines those challenges in light of data privacy, algorithmic bias, ethical implications, technological hurdles, and acceptance of AI by educators and students. First, data privacy should be a primary concern, as AI systems require extensive data, bringing up the potential for breach and misuse. Secondly, there must be a robust mechanism concerning data protection and against the application of GDPR. Another critical point is algorithm bias: biased training data sets may lead to discriminative decisions that will increase inequalities in education. It talks about AI's impact on teachers and classroom dynamics because the takeover of responsibilities may lower the intensity of necessary human contact. From a technical perspective, there is so much infrastructure and expertise required that too many educational institutions lack, especially in developing countries. In addition, educators themselves may feel that the change resists and fears job loss and therefore acts as a deterrent to AI integration. The review underscores the imperative for extensive training of teachers to support enabling the integration of AI. It now demands a collaborative effort on the part of all stakeholders to maximize the gains and reduce the drawbacks of AI in educational aspects. Continuous research in, policy-making for, and ethical guidelines on AI are required to benefit all aspects of education equitably and effectively.Journal of Excellence in Management Sciences511The review underscores the imperative for extensive training of teachers to support enabling the integration of AI and demands a collaborative effort on the part of all stakeholders to maximize the gains and reduce the drawbacks of AI in educational aspects.Journal of Excellence in Management Sciences["Muhammad Tahir Khan Farooqi", "Ishaq Amanat", "Sher Muhammad Awan"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/a93eaa1c7089722cc168178873e6d885936194f5 cb0e745f9c5ffce47349717363cabd176f8c66b1The text undertakes an analysis of European Union regulations on the prevention of data protection breaches in AI systems, taking into account the provisions of the General Data Protection Regulation (GDPR) and the draft AI Act. Legal guarantees for the protection of personal data processed in AI systems are sought in the general principles of the GDPR (in particular the principles of lawfulness, transparency, data minimisation and confidentiality) and the regulations on liability for data breaches. The conclusions of the analysis indicate that the implementation of the solutions contained in the current and proposed regulations may be hampered by the autonomy of some AI systems.Gdańskie Studia Prawnicze150The conclusions of the analysis indicate that the implementation of the solutions contained in the current and proposed regulations may be hampered by the autonomy of some AI systems.Gdańskie Studia Prawnicze["Maria J\u0119drzejczak"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/cb0e745f9c5ffce47349717363cabd176f8c66b1 c24ffb1e27d62de79c5158d9a86260e9cabefc0aBulletin of Integrative Psychiatry00Bulletin of Integrative Psychiatry["O. Cre\u021bu", "Bogdan C\u0103t\u0103lin Mereu\u021b\u0103"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/c24ffb1e27d62de79c5158d9a86260e9cabefc0a fc83156576cac7ccedf7f9d5f1d3a55be3d731e3CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne00CMAJ : Canadian Medical Association Journal["Payal Agarwal", "Rosemarie Lall", "Rajesh Girdhari"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/fc83156576cac7ccedf7f9d5f1d3a55be3d731e3 1bc001a1cee535e7d9d4fa435b5cac4304ba6e7bPurpose: The aim of this study is to examine how the integration of Emotional Intelligence (EI) and Artificial Intelligence (AI) in leadership can enhance leadership effectiveness and influence the development of future leaders. Design / Method / Approach: The research employs a mixed-methods approach, combining qualitative and quantitative analyses. The study utilizes secondary data sources, including scholarly articles, industry reports, and empirical studies, to analyze the interaction between EI and AI in leadership settings. Findings: The findings reveal that the integration of EI and AI significantly improves decision-making, strategic planning, talent management, and communication within organizations. Leaders who leverage both EI and AI experience higher employee satisfaction, improved team performance, and enhanced organizational outcomes. Theoretical Implications: This study contributes to leadership theory by introducing a novel framework that demonstrates the complementary roles of EI and AI in leadership. Practical Implications: The research offers practical guidelines for leadership development, emphasizing the need for future leaders to integrate EI and AI skills in order to navigate complex business environments successfully. Originality / Value: The paper provides an original framework for the integration of EI and AI in leadership, offering new insights into how these two elements can work together to improve leadership effectiveness. Research Limitations / Future Research: Future research should further explore the empirical impact of EI and AI integration in various industries and leadership levels to generalize findings across broader contexts. Paper Type: Conceptual JEL Classification: D83, M12, M15, L21, O33European Journal of Management Issues180The paper provides an original framework for the integration of EI and AI in leadership, offering new insights into how these two elements can work together to improve leadership effectiveness.European Journal of Management Issues["R. Vivek", "O. Krupskyi"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/1bc001a1cee535e7d9d4fa435b5cac4304ba6e7b 5146aec30c3853bb50c9f27e8ea14e3d941fa8bbArtificial intelligence (AI) is transforming the field of orthopedics, significantly impacting predictive analysis, surgical management, and rehabilitation programs. This review explores the multifaceted role of AI in enhancing orthopedic care, focusing on its application in personalized treatment plans, surgical precision, and remote rehabilitation. Predictive analytics in orthopedics, powered by AI, have revolutionized preoperative planning by forecasting surgical outcomes and potential complications, enabling clinicians to tailor surgical strategies to individual patient needs. AI's integration into surgical procedures, particularly in robotics-assisted and minimally invasive surgeries, has enhanced precision, reduced operative times, and improved patient safety, resulting in faster recovery and better outcomes.AI-driven rehabilitation programs offer personalized exercise regimens, real-time feedback, and remote monitoring, making high-quality rehabilitation accessible to patients regardless of location. These applications adapt to individual patient progress, providing customized exercise plans that optimize recovery while minimizing the risk of reinjury. Additionally, AI-powered rehabilitation tools enhance patient engagement through gamification and interactive features, leading to higher adherence to rehabilitation protocols.The review highlights key studies demonstrating the efficacy of AI in these areas, underscoring its potential to revolutionize orthopedic care. By leveraging AI's capabilities, clinicians can provide more accurate diagnoses, implement effective surgical interventions, and offer personalized rehabilitation solutions, ultimately improving patient outcomes and quality of life. As AI technology continues to advance, its role in orthopedics is expected to expand, offering increasingly innovative and effective solutions for both surgical and non-surgical patient care.The Journal of Community Health Management190The multifaceted role of AI in enhancing orthopedic care is explored, focusing on its application in personalized treatment plans, surgical precision, and remote rehabilitation, with its potential to revolutionize orthopedic care.The Journal of Community Health Management["Amit Lakhani"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/5146aec30c3853bb50c9f27e8ea14e3d941fa8bb e2339425402a1647998d84c05d50059017975006In this era of drastic technological advancements, most of the students and teachers are experiencing the usage of artificial intelligence technologies in classrooms. Artificial intelligence (AI), sometimes called machine intelligence or virtual artificial agent are intelligence demonstrated by machines. These technologies that are used, sometimes are in contrast to the natural intelligence displayed by humans. In University, it is helping the students in getting additional tutoring support when they could not attend a class due to medical or any other personal reasons. These are also technologies that helps students by giving appropriate feedback with regards to their performance before submitting their assessments or group work task. Some varsities are even using AI technologies even to the extent of having human robots in handling most of the sessions in a regular classroom. This study seeks to understand the usage and impact of Artificial Intelligence technologies from a student's perspective in their learning progress at Pre-University level.International Journal of Modern Education00This study seeks to understand the usage and impact of Artificial Intelligence technologies from a student's perspective in their learning progress at Pre-University level.International Journal of Modern Education["Pratiba Narayanasamy", "Wan Suriatty Mazlan", "Mustafa Kamal Ariffin"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/e2339425402a1647998d84c05d50059017975006 86d11229931b1214d09241cf705a76e25d597883AbstractThis research aims to determine the role of QuillBot as one of Artificial Intelligence (AI) in enhancing students’ writing skills. Artificial Intelligence (AI) is an advanced technology that can make a person's work easier. AI has a significant impact in various fields, such as education. Therefore, AI technology can assist students in completing their assignments. One such application is QuillBot, which aids students in improving their English writing. This research employed qualitative research using a questionnaire distributed via Google Forms. The findings indicate that Quillbot is beneficial for the students. It improves their paraphrasing skills, grammar, and vocabulary. This article is essential because readers can learn about the usefulness of AI applications, especially QuillBot, in writing English. It is also hoped that this research can encourage the adoption of similar technologies to support the learning process in various fields. Keywords: English Writing, AI, Paraphrasing tool, QuillBotEnglish Language Teaching Journal00The findings indicate that Quillbot is beneficial for the students, which improves their paraphrasing skills, grammar, and vocabulary.English Language Teaching Journal["Galuh Safrida", "Desi Puspitasari"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/86d11229931b1214d09241cf705a76e25d597883 d55cf53af2524e7b21d7cbd564501ac6d66f6907The continuous goal of discovering extraterrestrial life has driven scientific interest in exoplanets—celestial bodies orbiting stars outside our solar system. Traditional methods of exoplanet detection, reliant on manual analysis and prone to human error, presented unavoidable challenges given the vastness of the universe. This paper aims to discuss the benefits and limitations of AI within the exoplanet detection field, and determine whether the highly-regarded artificial intelligence is as beneficial to astronomical fields as we think. With the introduction of the first ever fully-robotic exoplanet detector which takes high-precision radial velocity measurements to measure the gravitational reflex motion, and advancing computer algorithms that avoid human errors in data analysis, modern advancements in artificial intelligence (AI) technology have not only transformed the efficiency and accuracy of exoplanet detection, but also extended our understanding of these distant worlds. While the use of AI does have its benefits, there are several drawbacks that could potentially hinder further advancement in the field of exoplanet detection.Scholarly Review Journal00The benefits and limitations of AI within the exoplanet detection field are discussed, and whether the highly-regarded artificial intelligence is as beneficial to astronomical fields as the authors think is determined.Scholarly Review Journal["Joyce Liu"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/d55cf53af2524e7b21d7cbd564501ac6d66f6907 987e96d368418ffa9e06162d98965ef76c405dadComputational pathology is a flourishing field at the intersection of pathology, computer science, and artificial intelligence (AI). By leveraging advanced image analysis algorithms, machine learning (ML), and deep learning techniques, computational pathology is poised to revolutionize the diagnostic process in clinical settings. This review article discusses key developments in computational pathology, explores various AI-powered tools used in digital histopathology, and examines the potential benefits and challenges of integrating computational techniques in routine pathology practice.IP Journal of Diagnostic Pathology and Oncology140Key developments in computational pathology are discussed, various AI-powered tools used in digital histopathology are explored, and the potential benefits and challenges of integrating computational techniques in routine pathology practice are examined.IP Journal of Diagnostic Pathology and Oncology["Tejashwini Kotian"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/987e96d368418ffa9e06162d98965ef76c405dad 3be9f342e8c8f05eab3fc0715933449c12eb2494In the ever-evolving field of oral surgery, the integration of digital technology and artificial intelligence (AI) represents a revolutionary shift that is redefining patient care and surgical outcomes. These advancements are not merely incremental improvements but are transformative changes that enhance precision, efficiency, and overall patient experience. This editorial explores the profound impact of these technologies on oral surgery, delving into their applications, benefits, challenges, and future directions. By examining how digital technology and AI are reshaping the field, we can better understand their potential and address the challenges they present.Journal of Orofacial & Health Sciences00This editorial explores the profound impact of digital technology and artificial intelligence on oral surgery, delving into their applications, benefits, challenges, and future directions.Journal of Orofacial and Health Sciences["Smiti Jassar Klaire"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/3be9f342e8c8f05eab3fc0715933449c12eb2494 08aaea12ed9e7cbd99e59fb04dd04ff55bb9bf98Este trabajo analiza el impacto de la inteligencia artificial en la educación, destacando su capacidad para mejorar procesos cognitivos como la atención, la memoria, la resolución de problemas y el pensamiento crítico de los estudiantes y del profesorado. Se examina el uso de la IA en la personalización del aprendizaje, la detección de patrones de comportamiento estudiantil, y su contribución a la investigación educativa a través del análisis de grandes volúmenes de datos. Además, se abordan las metodologías de investigación aplicadas en estudios sobre IA, que combinan enfoques cuantitativos y cualitativos, proporcionando una visión más completa de los efectos de la tecnología en el aprendizaje. Explora las tendencias futuras, como el uso de tutores automatizados y algoritmos avanzados para optimizar la experiencia educativa. Se concluye con una revisión sobre la necesidad de integrar la IA en los planes de estudio escolares, resaltando la falta de contenido específico en ciertas áreas y el gran potencial transformador de la IA para el futuro de la educación.Revista Científica Retos de la Ciencia02Revista Científica Retos de la Ciencia["Mar\u00eda Elina Parra-Taboada", "Juan Carlos Trujillo-Arteaga", "Diana Rub\u00ed \u00c1lvarez-Abad", "Andrea Soledad Arias-Dom\u00ednguez", "Esthela Santill\u00e1n-Gord\u00f3n"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/08aaea12ed9e7cbd99e59fb04dd04ff55bb9bf98 b0441484b6c3fcc08b87cfc5f7f9f440cf4dc2cfLa medicina estética no puede quedar al margen del empleo de la inteligencia artificial en el desarrollo profesional. Los tratamientos personalizados y los diagnósticos precisos harán que los resultados y el seguimiento de los pacientes sean más eficaces. Como toda herramienta o procedimiento que se suma a la práctica médica debe considerarse con la ética debida a nuestra profesión.Medicina Estética. Revista Científica de la Sociedad Española de Medicina Estética (SEME)00Medicina Estética. Revista Científica de la Sociedad Española de Medicina Estética (SEME)["J. M. Alcolea L\u00f3pez"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/b0441484b6c3fcc08b87cfc5f7f9f440cf4dc2cf 3445046f4c6d762dce0861d88508e0424623a0dcScene guidance is difficult to investigate in realistic scenes because it is hard to systematically control complex, realistic images. Parameters like set size are often ambiguous in real or even VR scenes. We created a new version of VowelWorld 2.0 (Vo & Wolfe, 2013), where we control various parameters of a highly artificial “scene”. Scenes are 20x20 grids of colored cells with 120 cells containing letters. Participants search for a vowel, present on 67% of trials. Each scene contained three big disks (2x2 cells) with consonants on them. These served as “anchor objects” which are known to predict target locations in real - world searches (Vo, 2021). An additional 96 cells featured rings which were grouped into larger analogs of surfaces. A vowel’s placement could follow three rules. Color rule (semantic): certain targets were associated with one background color “gist” (e.g., A’s appear in red scenes). Structure rule (syntactic): vowels were placed near or inside the small rings. Anchor rule (syntactic): vowels were close to a big circle containing a neighboring consonant (e.g., “B” implies “A”). Two vowels followed all three rules, two vowels followed color and surface rules, and one vowel was placed randomly. On half of the trials, participants were precued with a specific vowel. Otherwise, participants searched for any vowel. For the first three blocks, participants attempted to learn the rules from experience. Then, we explained the rules. Participants failed to fully learn rules but did benefit from the learned anchor rule (shorter RTs). Knowing rules markedly speeded performance for vowels that followed only color and surface rules. Anchor rule vowelsJournal of Vision00A new version of VowelWorld 2.0 is created, where participants failed to fully learn rules but did benefit from the learned anchor rule (shorter RTs), which markedly speeded performance for vowels that followed only color and surface rules.Journal of Vision["Yuri Markov", "M. Vo", "Jeremy M. Wolfe"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/3445046f4c6d762dce0861d88508e0424623a0dc 01085269bbd0f4f2ac0507f2df92fcc7493bd71aEl uso de la inteligencia artificial como herramienta permite adaptar la formación de los estudiantes según sus características y recopila gran cantidad de datos para su evaluación. Sin embargo, esta herramienta plantea cuestiones éticas significativas, por lo que es crucial reflexionar sobre estas cuestiones y establecer códigos éticos que guíen el desarrollo y uso de la IA en la educación. En este trabajo tuvo como objeto analizar los principios éticos y las normativas internacionales que regulan la implementación de la inteligencia artificial en el ámbito educativo. Como resultados principales se plantea que la IA se presenta como un motor de cambio en la innovación educativa en los procesos de enseñanza y aprendizaje, además algunas de las contribuciones diferenciadoras de la IA en entornos de aprendizaje incluyen el adaptar el entorno de enseñanza al estudiante, mejorando la efectividad de los procesos; proporcionar un mejor apoyo al estudiante a través de una atención más personalizada; ofrecer una mejor evaluación de la utilidad de la información; y facilitar la colaboración. Sin embargo, esta herramienta plantea importantes implicaciones éticas, como la posibilidad de discriminación por parte de la inteligencia artificial, el incumplimiento de las normativas de protección de datos personales, la posible pérdida de derechos por parte del usuario que desconoce las decisiones del sistema, la falta de responsabilidad de la IA por las decisiones tomadas, la adopción de lógicas no deseadas y la falta de sensibilidad hacia temas éticos.Revista Científica Retos de la Ciencia00Revista Científica Retos de la Ciencia["Marcia Yolanda Paguay-Simba\u00f1a", "Donatila Jimenez-Abad", "Ver\u00f3nica Fernanda Quiliguango-Lanchimba", "Mar\u00eda Pilar Maynaguez-Canacuan", "Cristina de los \u00c1ngeles Coello-Garc\u00eda", "Susana Magdalena Coello-Ortiz"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/01085269bbd0f4f2ac0507f2df92fcc7493bd71a 9ce06b0c49bf245173013f218310bc64c8df1b73Este artículo de revisión examina el impacto de la Inteligencia Artificial (IA) en los procesos de ingeniería de diseño y simulación, enfatizando la transformación que ha generado la integración de tecnologías avanzadas en estos campos. El objetivo central del estudio es comprender cómo la IA ha influido en la manera en que se diseñan y optimizan productos y sistemas, acelerando los procesos y mejorando la calidad de los resultados. Para realizar este análisis, se utilizó la base de datos bibliográfica SCOPUS. Se establecieron criterios específicos, incluyendo la selección de documentos en español e inglés y la clasificación de los mismos en tipos "artículo" y "revisión", lo que resultó en la compilación de 4649 artículos académicos. Estos datos fueron analizados mediante el uso de RStudio y la aplicación Bibliometrix. El análisis revela que la IA no solo ha acelerado el proceso de ideación, sino que también ha permitido a los ingenieros explorar un espectro más amplio de posibilidades, facilitando la identificación de soluciones innovadoras y la optimización del desarrollo de productos y sistemas. Los avances en algoritmos de IA y su integración en herramientas de simulación han transformado la manera en que se abordan los desafíos en el diseño, permitiendo la creación de diseños más complejos y sofisticados en menos tiempo y con mayor precisión. Este enfoque no solo incrementa la eficiencia de los procesos, sino que también abre nuevas oportunidades para la innovación, fortaleciendo la capacidad de los profesionales para desarrollar soluciones que antes eran inalcanzables.  Reincisol.00Reincisol.["Paola Gabriela Enr\u00edquez Y\u00e9pez", "Washington Eduardo Lascano Tacuri", "Mayra Alejandra Lizano J\u00e1come", "Jaime Marcelo Altamirano Hidalgo"]2024-09-15T00:00:00https://www.semanticscholar.org/paper/9ce06b0c49bf245173013f218310bc64c8df1b73 eee3a506fca31182a0297cc16c5a001c60e3e94eBMC Psychology645It was revealed that AI applications often impose rigid frameworks that constrain creative thinking and innovation, leading to emotional disengagement due to AI interactions’ repetitive and impersonal nature, and performance anxiety disrupted the learning process.BMC Psychology["Haozhuo Lin", "Qiu Chen"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/eee3a506fca31182a0297cc16c5a001c60e3e94e ddef363ac30500484c9ee30bca812914b3ec3ab3Advancements in big data analytics, IoT, and artificial intelligence (AI) have significantly transformed marketing practices and consumer behavior. AI offers promising opportunities for marketing practice and research. However, marketers need a holistic understanding of AI and its influence on consumers. Thus, this study aims to offer a review of AI applications in marketing and explore the role of AI in aiding marketing. This study carries out a review of AI and its applications in marketing by analysing the existing literature between 2000 and 2021. Only those papers were selected for this review, which are positioned around AI technology. Articles were drawn from Google Scholar and Scopus databases and were analysed using thematic analysis. A review of selected papers depicts that AI implementation in marketing is still in its nascent stage. The review proposed the following themes: (a) Prediction Analysis, (b) Relationships with AI, (c) Consumer Relationship Management, (d) AI in Strategic Marketing, (e) AI and Services, (f) Conversational Commerce, (g) Advertising and Artificial Intelligence, and (h) Consumer Brand Engagement.NMIMS Management Review440This study carries out a review of AI and its applications in marketing by analysing the existing literature between 2000 and 2021 and proposed the following themes: Prediction Analysis, Relationships with AI, and Consumer Relationship Management.NMIMS Management Review["Richali Jain", "Ajay Kumar"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/ddef363ac30500484c9ee30bca812914b3ec3ab3 4e62cf9c6405484ad81bfec38703f86d6aa25370The origins of Human Factors (HF) are rooted in the Second World War. It is a sign of the times that 75 years on from the formation of the Ergonomics Research Society, discussions occur as to whether Artificial Intelligence (AI) could/should be capable of controlling weaponry in a theatre of war. HF can support the design of safe, ethical, and usable AI: but there is little evidence of HF influencing industrial organisations developing AI. A review of the history of HF was conducted to understand how the influence of discipline on AI development may be optimised. The field may need to become broader and more inclusive, given the potential implications of innovation such as AI. The field of Responsible Research and Innovation can help the HF Practitioner ensure that the design and application of AI based technology serves to improve human well-being and optimise system performance over the next 75 years.Practitioner summary: A review of the history and origins of Human Factors was conducted. The review aimed to learn from the development of the discipline over the last 75 years to provide insights of what can be done to optimise the influence of HF to design safe, ethical, and usable artificial intelligence.Ergonomics950A review of the history of HF was conducted to understand how the influence of discipline on AI development may be optimised and to provide insights of what can be done to optimise the influence of HF to design safe, ethical, and usable artificial intelligence.Ergonomics["Aaron P J Roberts", "Christopher J Parnell", "Menisha Patel"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/4e62cf9c6405484ad81bfec38703f86d6aa25370 8f335812c74a8c899ee6b5c9ab607065953bb1eaWhen a commercial project is established to produce certain goods or services of high quality to compete and attract consumers, the project owner must adopt a trademark for the good, service or product in order to distinguish what he does from other competing services, goods and production. This requires registering the trademark with the relevant institutions in the country of registration, which requires procedures, requirements and attachments to be submitted. Then comes the role of those institutions in examining the submitted application in accordance with the law in force in the country of registration. Hence, we are discussing the special procedures for registration in the United Arab Emirates, which is one of the most advanced countries in government procedures. We are proposing the idea of introducing artificial intelligence software into the examination and judgment process to speed up the registration process for fear of it being registered by another person in another country that has an agreement with the UAE. Therefore, ownership of the trademark will be for the first to register.Edelweiss Applied Science and Technology320The idea of introducing artificial intelligence software into the examination and judgment process to speed up the registration process for fear of it being registered by another person in another country that has an agreement with the UAE.Edelweiss Applied Science and Technology["Muayad Hassan Al-Tawalbeh"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/8f335812c74a8c899ee6b5c9ab607065953bb1ea b1037dd2d638307d6d6d22ab6b56116e4b63f00dBMC Pregnancy and Childbirth270The results demonstrated that machine learning algorithms were able to predict pregnancy termination on DHS data with an overall accuracy ranging from 79.4 to 85.6%.BMC Pregnancy and Childbirth["Gizachew Mulu Setegn", "Belayneh Endalamaw Dejene"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/b1037dd2d638307d6d6d22ab6b56116e4b63f00d ecd6a4acf3fc4f82f8c867a40e0a414ce63de28bThe subject of the research is the process of constructing explanations in artificial intelligence systems. The goal is to develop a temporal-causal approach to constructing explanations in artificial intelligence systems to present explanations both for the decision-making process and the obtained decision, and to make them transparent and understandable for solving practical user tasks. Tasks: structuring the levels of explanation representation considering temporal and causal aspects; developing a generalized method for constructing explanations using temporal and causal dependencies; developing a method for refining explanations using temporal dependencies. The structuring of the explanation representation in temporal and causal aspects at the local, intermediate, and global levels has been performed. The scientific novelty of the obtained results is as follows. A temporal-causal method for constructing explanations is proposed, which includes the stages of constructing temporal and causal dependencies at the local, intermediate, and global levels of explanation representation. The method makes it possible, based on temporal dependencies, to form an explanation in the form of causal dependencies that determine the actions of the process and the values of input variables as the causes of the obtained solution, which creates conditions for increasing the level of user trust. A method for constructing explanations at the global level of representation based on the ordering of input data has been developed. The method includes the stages of constructing temporal rules, determining the weights of rules based on the weights of the antecedent and consequent, constructing causal rules, constructing an explanation as a set of weighted causal rules, which makes it possible to consider the structure of input data when constructing an explanation.Management Information System and Devises00A temporal-causal method for constructing explanations is proposed, which makes it possible, based on temporal dependencies, to form an explanation in the form of causal dependencies that determine the actions of the process and the values of input variables as the causes of the obtained solution, which creates conditions for increasing the level of user trust.Management Information System and Devises["Sergiy F. Chalyi", "V. Leshchynskyi"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/ecd6a4acf3fc4f82f8c867a40e0a414ce63de28b 226796a8411ed9ac883a1666c087e71065dbde76The confluence of organizational coaching and artificial intelligence (AI), specifically generative AI is set to permanently change how employees are developed and supported. This could result in significant benefits to individual and organizational learning, wellness, and performance. The benefits of organizational coaching are well documented through rigorous research, while the efficacy of AI coaching shows early signs of promise. The challenge now is how to optimally leverage AI in organizational coaching for exponential gains. In this article, I will explore the current and potential future applications of AI coaching in organizations, what we must be cognizant of, and which concerns are overhyped.Journal of Applied Behavioral Science110The current and potential future applications of AI coaching in organizations, what the authors must be cognizant of, and which concerns are overhyped are explored.The Journal of Applied Behavioral Science["N. Terblanche"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/226796a8411ed9ac883a1666c087e71065dbde76 e07bb30fe02bc8927a3f4cabec42e3a66c9479acComputer science curricula have started to include competencies related to artificial intelligence (AI) in K–12 education. However, before introducing a new topic into the classroom and suggesting competencies, it is essential to identify the central practices of the discipline. In the following research, we focus on identifying practices related to data, as current school curricula significantly underestimate the role of data, and understanding how data is processed is a key to understanding how AI systems function. We examine the theoretical literature on practices applied to data when creating AI systems, map the practices in a process model, validate the results of the mapping with domain experts, and contrast the results with current AI curricula for school students. The contribution of this work is a process model that summarizes data-related practices for AI systems built with machine learning, is comprehensively domain-embedded, and is aligned with K–12 education. Computer science educators can use it as a blueprint for defining competencies and designing learning arrangements that aim to enable students to create and understand AI systems.Workshop in Primary and Secondary Computing Education910A process model is proposed that summarizes data-related practices for AI systems built with machine learning, is comprehensively domain-embedded, and is aligned with K–12 education.{"pages": "5:1-5:10"}["Viktoriya Olari", "Ralf Romeike"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/e07bb30fe02bc8927a3f4cabec42e3a66c9479ac 594b28fb1f8a70906b64b78b4162662303b93fadEste editorial sintetiza os artigos recentes publicados na Review of Artificial Intelligence in Education, destacando o papel transformador da inteligência artificial (IA) na educação, pesquisa e em diversos setores. Os temas discutidos incluem a inovação impulsionada pela IA, os desafios éticos na adoção dessas tecnologias, e o impacto da IA nas instituições de ensino superior e na integridade científica. Os artigos também exploram a integração da IA em áreas como a agricultura e os recursos humanos, além das implicações legais e morais do uso da IA na educação. O editorial reforça a importância de uma implementação responsável e ética da IA, guiada por marcos regulatórios sólidos, para garantir que a IA promova equidade e integridade em suas aplicações.Review of Sdgs in Emerging Countries230Review of Sdgs in Emerging Countries["Altieres de Oliveira Silva", "Diego dos Santos Janes", "Renan Santos"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/594b28fb1f8a70906b64b78b4162662303b93fad 0ff48fa429f0b98ffb4feb4613736848ce2d07c6AI and Ethics381AI and Ethics["P. Christou"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/0ff48fa429f0b98ffb4feb4613736848ce2d07c6 bfd8ac3d42ed3168e3bb379882a33aa315f9fbd7Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.Faraday discussions361Current applications across a diversity of problems in chemistry are outlined and how machine learning researchers view and approach problems in the field are discussed.Faraday discussions["Austin Cheng", "C. Ser", "Marta Skreta", "Andres Guzman-Cordero", "Luca Thiede", "Andreas Burger", "Abdulrahman Aldossary", "Shi Xuan Leong", "Sergio Pablo-Garc\u00eda", "Felix Strieth-Kalthoff", "Al\u00e1n Aspuru-Guzik"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/bfd8ac3d42ed3168e3bb379882a33aa315f9fbd7 3e29eefc42400d4aa9eccd146a5a341838d7e9b4Central European Cultures00Central European Cultures["M\u00e1t\u00e9 Bord\u00e1s"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/3e29eefc42400d4aa9eccd146a5a341838d7e9b4 c733b26b5d176480613ba91d9a433fe0d7a9cf52Coronary artery calcium (CAC) is highly predictive of cardiovascular events. While millions of chest CT scans are performed annually in the United States, CAC is not routinely quantified from scans done for non-cardiac purposes. A deep learning algorithm was developed using 446 expert segmentations to automatically quantify CAC on non-contrast, non-gated CT scans (AI-CAC). Our study differs from prior works as we leverage imaging data across the Veterans Affairs national healthcare system, from 98 medical centers, capturing extensive heterogeneity in imaging protocols, scanners, and patients. AI-CAC performance on non-gated scans was compared against clinical standard ECG-gated CAC scoring. Non-gated AI-CAC differentiated zero vs. non-zero and less than 100 vs. 100 or greater Agatston scores with accuracies of 89.4% (F1 0.93) and 87.3% (F1 0.89), respectively, in 795 patients with paired gated scans within a year of a non-gated CT scan. Non-gated AI-CAC was predictive of 10-year all-cause mortality (CAC 0 vs.>400 group: 25.4% vs. 60.2%, Cox HR 3.49, p<0.005), and composite first-time stroke, MI, or death (CAC 0 vs.>400 group: 33.5% vs. 63.8%, Cox HR 3.00, p<0.005). In a screening dataset of 8,052 patients with low-dose lung cancer-screening CTs (LDCT), 3,091/8,052 (38.4%) individuals had AI-CAC>400. Four cardiologists qualitatively reviewed LDCT images from a random sample of>400 AI-CAC patients and verified that 527/531 (99.2%) would benefit from lipid-lowering therapy. To the best of our knowledge, this is the first non-gated CT CAC algorithm developed across a national healthcare system, on multiple imaging protocols, without filtering intra-cardiac hardware, and compared against a strong gated CT reference. We report superior performance relative to previous CAC algorithms evaluated against paired gated scans that included patients with intra-cardiac hardware.arXiv.org290To the best of the knowledge, this is the first non-gated CT CAC algorithm developed across a national healthcare system, on multiple imaging protocols, without filtering intra-cardiac hardware, and compared against a strong gated CT reference.ArXiv["Raffi Hagopian", "Timothy Strebel", "Simon Bernatz", "Gregory A Myers", "Erik Offerman", "Eric Zuniga", "Cy Y Kim", "Angie T Ng", "James A. Iwaz", "Sunny P Singh", "Evan P Carey", "Michael J Kim", "Spencer Schaefer", "Jeannie Yu", "Amilcare Gentili", "Hugo J. W. L. Aerts"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/c733b26b5d176480613ba91d9a433fe0d7a9cf52 f2a48cad75032ab82e45055c3a82b64e79023f97Revue européenne du droit social00Revue Européenne du Droit Social["Gioni Popa Roman", "C\u0103t\u0103lin F\u0103ghian"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/f2a48cad75032ab82e45055c3a82b64e79023f97 1b82986243fbe678ac903e7828f2c17304d10e52Journal of Surgical Education00SAIL is not inferior to the multiple-choice modality for learning orthopedic core knowledge and can be used to supplement traditional study methods, indicating that SAIL can be used to supplement traditional study methods.Journal of surgical education["Tuo Peter Li", "Stewart Slocum", "Arpan Sahoo", "Arinze J. Ochuba", "Logan Kolakowski", "R. F. Henn III", "Alex A Johnson", "Dawn M LaPorte"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/1b82986243fbe678ac903e7828f2c17304d10e52 667312322e7ca8b46aa53c36aeea108a9f20e6c0Many recent artificial intelligence (AI) systems take inspiration from biological episodic memory. Here, we ask how these 'episodic-inspired' AI systems might inform our understanding of biological episodic memory. We discuss work showing that these systems implement some key features of episodic memory while differing in important respects and appear to enjoy behavioural advantages in the domains of strategic decision-making, fast learning, navigation, exploration and acting over temporal distance. We propose that these systems could be used to evaluate competing theories of episodic memory's operations and function. However, further work is needed to validate them as models of episodic memory and isolate the contributions of their memory systems to their behaviour. More immediately, we propose that these systems have a role to play in directing episodic memory research by highlighting novel or neglected hypotheses as pursuit-worthy. In this vein, we propose that the evidence reviewed here highlights two pursuit-worthy hypotheses about episodic memory's function: that it plays a role in planning that is independent of future-oriented simulation, and that it is adaptive in virtue of its contributions to fast learning in novel, sparse-reward environments. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.Philosophical transactions of the Royal Society of London. Series B, Biological sciences862It is proposed that these 'episodic-inspired' AI systems could be used to evaluate competing theories of episodic memory's operations and function and have a role to play in directing episodic memory research by highlighting novel or neglected hypotheses as pursuit-worthy.Philosophical transactions of the Royal Society of London. Series B, Biological sciences["A. Boyle", "Andrea Blomkvist"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/667312322e7ca8b46aa53c36aeea108a9f20e6c0 4758aaa45956aa4ea35e3446a81a82762c12308eThe use of artificial intelligence (AI) in school counseling has the potential to revolutionize the way counseling services are delivered through the provision of personalized support and data-driven decision-making. This paper explores the potential opportunities, challenges, and ethical considerations of using AI in Malaysian school counseling. The paper also highlights several ethical considerations that must be taken into account when integrating AI into school counseling practices, such as protecting students’ privacy and avoiding bias in AI-generated data. Overall, this paper provides a comprehensive exploration on the potential of using AI in Malaysian school counseling and highlights the opportunities, challenges, and ethical considerations that are applicable when implementing the technology into school counseling.Journal of Contemporary Issues and Thought00This paper explores the potential opportunities, challenges, and ethical considerations of using AI in Malaysian school counseling and highlights the opportunities, challenges, and ethical considerations that are applicable when implementing the technology into school counseling.Journal of Contemporary Issues and Thought[]2024-09-16T00:00:00https://www.semanticscholar.org/paper/4758aaa45956aa4ea35e3446a81a82762c12308e 443883cbf4272a680afc2f5b20e11ae1a289d243As Artificial Intelligence (AI) becomes more pervasive in our society, it is crucial to develop, deploy, and assess Responsible and Trustworthy AI (RTAI) models, i.e., those that consider not only accuracy but also other aspects, such as explainability, fairness, and energy efficiency. Workflow provenance data have historically enabled critical capabilities towards RTAI. Provenance data derivation paths contribute to responsible workflows through transparency in tracking artifacts and resource consumption. Provenance data are well-known for their trustworthiness helping explainability, reproducibility, and accountability. However, there are complex challenges to achieve RTAI, which are further complicated by the heterogeneous infrastructure in the computing continuum (Edge-Cloud-HPC) used to develop and deploy models. As a result, a significant research and development gap remains between workflow provenance data management and RTAI. In this paper, we present a vision of the pivotal role of workflow provenance in supporting RTAI and discuss related challenges. We present a schematic view between RTAI and provenance, and highlight open research directions.IEEE International Conference on e-Science521A vision of the pivotal role of workflow provenance in supporting RTAI is presented, a schematic view between RTAI and provenance, and open research directions are presented to highlight open research directions.2024 IEEE 20th International Conference on e-Science (e-Science)["Renan Souza", "Silvina Ca\u00edno-Lores", "Mark Coletti", "Tyler J. Skluzacek", "Alexandru Costan", "Fr\u00e9d\u00e9ric Suter", "Marta Mattoso", "Rafael Ferreira da Silva"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/443883cbf4272a680afc2f5b20e11ae1a289d243 4e1fd8fced140aa48490d7431066481d2a86f90eArtificial intelligence (AI) models are increasingly adopted as tools to enhance change management processes. Although many change managers are excited about AI's potential, others worry that their contributions may become obsolete. We explore the tension between job augmentation and automation and how it affects change management professionals. We argue that change managers need to approach their profession as a context-sensitive craft. We highlight elements that are likely to become increasingly central for change managers’ success: (a) high-level skills—relational mastery and systems thinking—and (b) the continued development of specific attitudes—authentic dedication and communal co-presence. In contrast, other tasks that change managers were previously engaged with (e.g., routine communication, reporting) will play a smaller role in the future. We advocate for approaching change work as an AI-augmented craft and call for a critical reflection of the skills and attitudes necessary to effectively diagnose, envision, and mobilize change in the age of AI.Journal of Applied Behavioral Science531It is argued that change managers need to approach their profession as a context-sensitive craft and call for a critical reflection of the skills and attitudes necessary to effectively diagnose, envision, and mobilize change in the age of AI.The Journal of Applied Behavioral Science["Katerina Gonzalez", "Rouven Kanitz", "Roman Briker"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/4e1fd8fced140aa48490d7431066481d2a86f90e 6a3df9cf7825401b253b1339f121f37226802e57 This research focuses on verifying neural network models using System Verilog, with two primary applications: visual edge detection and neuron behavior modeling. In modern chip design, hardware verification plays a crucial role in ensuring that complex neural models perform as expected. A neuron model based on Hubel and Wiesel’s feed-forward network architecture was proposed and tested using integrator and threshold modules implemented in Verilog. The proposed verification methodology employs self-checking test benches, supported by functional coverage and simulation, for comprehensive validation. The results demonstrate efficient verification with high coverage, paving the way for future advancements in hardware neural networks.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023010This research focuses on verifying neural network models using System Verilog, with two primary applications: visual edge detection and neuron behavior modeling, and demonstrates efficient verification with high coverage.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Prashis Raghuwanshi"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/6a3df9cf7825401b253b1339f121f37226802e57 9670ba15fbb90e94aa43a2c27f619f6f48ebb800Integrating the fundamentals of computer science and programming skills into the undergraduate engineering curriculum has been a primary focus for many educational institutions worldwide. Learning the basics of programming from the beginning of undergraduate engineering education allows students to incorporate such skills into their future work easily. Therefore, an introductory programming course for first-year undergraduate students has been running since 2021 in the Mechanical Engineering Department at University College London intending to teach the fundamentals of Python programming language. However, it is well-known that generative artificial intelligence (Gen AI) tools in higher education are moving so fast that a wait-and-see approach cannot be taken. These applications have received much global attention from academics on their impact and proper use within the teaching-learning process. This paper investigates first-year undergraduate mechanical engineering students' use of Gen AI tools in their programming assessment. The results show that 60% of the cohort used tools that helped mainly to check their code, improve their English language, and understand error messages. However, 40% abstained from using any. Based on these findings, recommendations on how Gen AI tools can be utilised by undergraduate students in ways that support their learning and enhance their ability to achieve learning outcomes are made.SEFI Journal of Engineering Education Advancement160This paper investigates first-year undergraduate mechanical engineering students' use of Gen AI tools in their programming assessment, and shows that 60% of the cohort used tools that helped mainly to check their code, improve their English language, and understand error messages.SEFI Journal of Engineering Education Advancement["Lama Hamadeh"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/9670ba15fbb90e94aa43a2c27f619f6f48ebb800 67b7d7b1bbbc4f36eb6a0d89dea519364b23987fNeuromorphic computing presents a promising approach to embedded artificial intelligence (AI) by deploying spiking neural networks (SNNs) to specialized accelerators. Such neuromorphic accelerators embrace the unique properties of SNNs to enable applications with exceptionally little memory footprint, energy demand, and inference time. By increasing their naturally high temporal sparsity and using popular approaches to improve their spatial sparsity, this can be taken to the extreme. However, improving hardware-related quantities always comes with the tradeoff of degrading accuracy. While available literature barely tolerates any loss in accuracy, in this work, we argue that this might be acceptable for optimal performance of embedded neuromorphic systems, depending on the use case. We propose an approach centered around the maximization of metrics that contain all application-specific requirements and discuss the possibilities of designing such a metric. Finally, we consider three scenarios and demonstrate the execution of our approach based on the neuromorphic DVS128 Gesture dataset. The resulting networks’ properties are tailored to the respective applications’ needs and highlight the effectiveness of our approach. Our code is available at https://github.com/JannKrausse/MetricDrivenNeuromorphics.ACM Symposium on Cloud Computing220It is argued that this might be acceptable for optimal performance of embedded neuromorphic systems, depending on the use case, and an approach centered around the maximization of metrics that contain all application-specific requirements is proposed.2024 IEEE 37th International System-on-Chip Conference (SOCC)["Jann Krausse", "Moritz Neher", "Iris Fuerst-Walter", "Carmen Weigelt", "Tanja Harbaum", "Klaus Knobloch", "Juergen Becker"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/67b7d7b1bbbc4f36eb6a0d89dea519364b23987f 9392f5b4a7f14c958a9ad8f902d333ad39df8472Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities. This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations. The key contribution is the development of an EPIC (Education, Partnership, Infrastructure, Community) framework, which maps AI implementation requirements to fully realize social impacts and public good from successful and sustained AI deployment. Through a multi-perspective content analysis of the latest AI strategy documents, this paper provides a structured comparison of AI governance strategies across nations. The findings offer valuable insights for governments, academics, industries, and communities to enable responsible and trustworthy AI deployments. Future work should focus on incorporating specific requirements for developing countries and applying the strategies to specific AI applications, industries, and the public sector.arXiv.org410A key contribution is the development of an EPIC (Education, Partnership, Infrastructure, Community) framework, which maps AI implementation requirements to fully realize social impacts and public good from successful and sustained AI deployment.ArXiv["Dian W. Tjondronegoro"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/9392f5b4a7f14c958a9ad8f902d333ad39df8472 739f22b5779814f2003d2fc4cc9c45e06430938dThis research explores the transformative impact of Artificial Intelligence (AI) on multicultural education through a qualitative thematic analysis of existing literature. The study aims to understand how AI technologies can be harnessed to foster inclusive and culturally responsive educational environments. By analyzing various case studies and scholarly articles, this paper identifies key themes such as personalized learning, cultural sensitivity, ethical concerns, and the digital divide. The findings highlight AI's potential to revolutionize educational practices, promote inclusivity, and prepare students for a globalized world, while also addressing significant challenges and proposing solutions to mitigate them.Edelweiss Applied Science and Technology220Key themes such as personalized learning, cultural sensitivity, ethical concerns, and the digital divide are identified, highlighting AI's potential to revolutionize educational practices, promote inclusivity, and prepare students for a globalized world.Edelweiss Applied Science and Technology["Mariyono Dwi", "Akmal Nur Alif Hd"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/739f22b5779814f2003d2fc4cc9c45e06430938d e8dcaaa359f36cfd5f7eda6c0dad320878dbffdb The integration of artificial intelligence (AI) in the workplace is at a nascent stage, presenting both substantial opportunities and challenges for productivity growth. We argue that AI’s potential will only be truly realized through strategic investments in human skills and comprehensive organizational redesign. Drawing on interdisciplinary insights, we highlight the critical role of AI-human collaboration, continuous workforce skill development, and adaptive organizational practices. We conclude with recommendations to create a human-centered environment conducive to AI-driven productivity gains through its assistance, augmentation, and automation capabilities.The Economists' Voice80It is argued that AI’s potential will only be truly realized through strategic investments in human skills and comprehensive organizational redesign, and the critical role of AI-human collaboration, continuous workforce skill development, and adaptive organizational practices is highlighted.The Economists’ Voice["Katharina H\u00f6lzle", "Robert Rose", "V. Kaschub"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/e8dcaaa359f36cfd5f7eda6c0dad320878dbffdb 4d235afbbf76ac78532db14ea05530af7e1050d3Fuel Cell Electric Vehicles (FCEVs) are a major development in environmentally friendly transportation because they produce only water as a waste when they generate energy using hydrogen fuel cells. By adding sophisticated complementary technologies, FCEVs can become more intelligent and trustworthy, which will increase their efficiency, performance, and overall user experience. Besides their advantages of energy saving and environmental friendliness, this article focuses not on the energy efficiency of electric vehicles themselves, but on the Artificial Intelligence (AI) and cyber security solutions that make them smarter and more trustworthy.International Symposium ELMAR120This article focuses not on the energy efficiency of electric vehicles themselves, but on the Artificial Intelligence (AI) and cyber security solutions that make them smarter and more trustworthy.2024 International Symposium ELMAR["Alper Kanak", "Serhat Ege \u0130nan\u00e7", "Sercan Tanriseven", "Ibrahim Arif", "Cengiz Bekta\u015f", "Oguzhan Herkiloglu", "Ali Serdar Atalay", "S. Erg\u00fcn"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/4d235afbbf76ac78532db14ea05530af7e1050d3 bbb05d95d8210b8aee62aec105d7206fbcb8f8e1The energy sector nowadays has a high impact in the distribution of energy around the world. Moreover, the uncertainty of the energy and user behavior are a threat that causes the difficulty on the planning of an optimization plan intended to improve the energy efficiency as high as possible. Data scientists and machine learning professionals recommend the use of deep learning and machine learning algorithms to train an historic with energy consumption from past time and additional variables (sensors of a building for example) to forecast the energy consumption for the intended target period. Several forecasting methods may be used such as Artificial Neural Networks, K-Nearest Neighbors, and XGBoost. Experts in the explainable artificial intelligence area recommend the use of methods such as Shapley Addictive Explanations (SHAP) in order to explain the input features of the historic dataset with a stronger influence on the output power consumption.International Symposium on Antennas and Propagation160Experts in the explainable artificial intelligence area recommend the use of methods such as Shapley Addictive Explanations (SHAP) in order to explain the input features of the historic dataset with a stronger influence on the output power consumption.2024 22nd International Conference on Intelligent Systems Applications to Power Systems (ISAP)["D. Ramos", "Pedro Faria", "Zita A. Vale"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/bbb05d95d8210b8aee62aec105d7206fbcb8f8e1 a981e990100c79427c5448e855bbfa3c9f3172c4Large Models (LMs) as a new form of artificial intelligence (AI) have the potential to provide more personal insights by processing large volumes of multimodal, longitudinal data now generated by hundreds of millions of people through things like wearables, apps, and centralized systems such as electronic medical records. Medical research has historically excluded most populations (women and minorities) to the effect that treatments are routinely less effective and sometimes harmful in these populations. Machine Learning (ML) in medicine was supposed to solve these problems, but in many cases exacerbated these harmful historical biases. LMs project an illusion of understanding that might lead some to repeat the omissions of the past, and once again exacerbate unfair and harmful outcomes. But human physiology is both deeply complex and structured. Health AI researchers can incorporate insights from both biology and historical failures to help Health AIs realize the goal of equitable health support more completely than previous efforts.IEEE International Conference on e-Science570Health AI researchers can incorporate insights from both biology and historical failures to help Health AIs realize the goal of equitable health support more completely than previous efforts.2024 IEEE 20th International Conference on e-Science (e-Science)["Benjamin L. Smarr"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/a981e990100c79427c5448e855bbfa3c9f3172c4 4b705efe247e84adf7c0067dc75c7740bfb63ddaThis is an algorithm to reproduce human free will. It is based on the challenge test– could you do otherwise. The algorithm could be easily implemented and applied to an AI chatbot for example. An agent with this algorithm will be able to do otherwise and will demonstrate free will. If the agent records its history of decisions and can handle abstract concepts then it will identify itself as having free will. The algorithm applies to a goal seeking utility agent without any special hardware. An artificial agent with the freedom to do otherwise is risky. Free will can give the freedom to break rules and take perverse actions. It is a dangerous capability that may arise by accident in a self-learning system.Journal of Artificial Intelligence and Consciousness00This is an algorithm to reproduce human free will, based on the challenge test– could you do otherwise, which applies to a goal seeking utility agent without any special hardware.Journal of Artificial Intelligence and Consciousness["M. Hadley"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/4b705efe247e84adf7c0067dc75c7740bfb63dda c4c51bc20ea19f96d29e547a81628cab730441b4Sistemas inteligentes são utilizados no mercado financeiro, inclusive para detecção de fraudes. Em transações com cartões de crédito, algoritmos de aprendizado de máquina podem ser usados para obter modelos que automatizam decisões como classificar uma transação como fraudulenta ou não. Neste contexto, este trabalho apresenta uma comparação entre as técnicas de inteligência artificial explicável SHAP e LIME em modelos para detecção de fraudes em transações com cartão crédito, mostrando que essas técnicas podem ser adequadas ao problema. Também é discutida a utilização de algoritmos naturalmente explicáveis, assim como a efetividade e a necessidade de técnicas de inteligência artificial explicável.Anais Estendidos do XXIV Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg Estendido 2024)80Anais Estendidos do XXIV Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg Estendido 2024)["Gabriel Mendes de Lima", "Paulo Henrique Pisani"]2024-09-16T00:00:00https://www.semanticscholar.org/paper/c4c51bc20ea19f96d29e547a81628cab730441b4 d08e46f58ce358c849531013a3f38947c1ea9033Purpose The purpose of the paper is to explore the rapidly evolving landscape of artificial intelligence (AI) tools in academic research, highlighting their potential to transform various stages of the research process. AI tools are transforming academic research, offering numerous benefits and challenges. Design/methodology/approach Academic research is undergoing a significant transformation with the emergence of (AI) tools. These tools have the potential to revolutionize various aspects of research, from literature review to writing and proofreading. An overview of AI applications in literature review, data analysis, writing and proofreading, discussing their benefits and limitations is given. A comprehensive review of existing literature on AI applications in academic research was conducted, focusing on tools and platforms used in various stages of the research process. AI was used in some of the searches for AI applications in use. Findings The analysis reveals that AI tools can enhance research efficiency, accuracy and quality, but also raise important ethical and methodological considerations. AI tools have the potential to significantly enhance academic research, but their adoption requires careful consideration of methodological and ethical implications. The integration of AI tools also raises questions about authorship, accountability and the role of human researchers. The authors conclude by outlining future directions for AI integration in academic research and emphasizing the need for responsible adoption. Originality/value As AI continues to evolve, it is essential for researchers, institutions and policymakers to address the ethical and methodological implications of AI adoption, ensuring responsible integration and harnessing the full potential of AI tools to advance academic research. This is the contribution of the paper to knowledge. Library Hi Tech News191The analysis reveals that AI tools can enhance research efficiency, accuracy and quality, but also raise important ethical and methodological considerations, which requires careful consideration of methodological and ethical implications.Library Hi Tech News["A. Oyelude"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/d08e46f58ce358c849531013a3f38947c1ea9033 7808ef0fe0a7b8efba0136feca66ca02162d5200Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in healthcare is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. ChatGPT could be exploited in the medical field to improve patient education and information, thus increasing compliance. ChatGPT could facilitate information exchange on major cardiovascular diseases, provide clinical decision support, and improve patient communication and education. It could assist the clinician in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, it is unclear whether it will be possible to use ChatGPT for the management of patients who require rapid decisions. Indeed, many drawbacks are associated with the daily use of these technologies in the medical field, such as insufficient expertise in specialized fields and a lack of comprehension of the context in which it works. The pros and cons of its use have been explored in this review, which was not written with the help of ChatGPT.Journal of Cardiovascular Medicine391ChatGPT could facilitate information exchange on major cardiovascular diseases, provide clinical decision support, and improve patient communication and education, and the pros and cons of its use have been explored.Journal of cardiovascular medicine["C. Madaudo", "A. L. M. Parlati", "Daniela Di Lisi", "Raffaele Carluccio", "V. Sucato", "Giuseppe Vadal\u00e0", "E. Nardi", "F. Macaione", "A. Cannata", "Nilla Manzullo", "Ciro Santoro", "Adelaide Iervolino", "Federica D'Angelo", "F. Marzano", "C. Basile", "P. Gargiulo", "Egle Corrado", "Stefania Paolillo", "Giuseppina Novo", "A. Galassi", "P. Filardi"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/7808ef0fe0a7b8efba0136feca66ca02162d5200 151e915af6a3e6c402ef67e7df26336d93e6ef95OBJECTIVE Performing obstetric ultrasound scans is challenging for inexperienced operators; therefore, the prenatal screening artificial intelligence system (PSAIS) software was developed to provide real-time feedback and guidance for trainees during their scanning procedures. The aim of this study was to investigate the potential benefits of utilizing such an artificial intelligence system to enhance the efficiency of obstetric ultrasound training in acquiring and interpreting standard basic views. METHODS A prospective, single-center randomized controlled study was conducted at The First Affiliated Hospital of Sun Yat-sen University. From September 2022 to April 2023, residents with no prior obstetric ultrasound experience were recruited and assigned randomly to either a PSAIS-assisted training group or a conventional training group. Each trainee underwent a four-cycle practical scan training program, performing 20 scans in each cycle on pregnant volunteers at 18-32 gestational weeks, focusing on acquiring and interpreting standard basic views. At the end of each cycle, a test scan evaluated trainees' ability to obtain standard ultrasound views without PSAIS assistance, and image quality was rated by both the trainees themselves and an expert (in a blinded manner). The primary outcome was the number of training cycles required for each trainee to meet a certain standard of proficiency (i.e. end-of-cycle test scored by the expert at ≥ 80%). Secondary outcomes included the expert ratings of the image quality in each trainee's end-of-cycle test and the discordance between ratings by trainees and the expert. RESULTS In total, 32 residents and 1809 pregnant women (2720 scans) were recruited for the study. The PSAIS-assisted trainee group required significantly fewer training cycles compared with the non-PSAIS-assisted group to meet quality requirements (P = 0.037). Based on the expert ratings of image quality, the PSAIS-assisted training group exhibited superior ability in acquiring standard imaging views compared with the conventional training group in the third (P = 0.012) and fourth (P < 0.001) cycles. In both groups, the discordance between trainees' ratings of the quality of their own images and the expert's ratings decreased with increasing training time. A statistically significant difference in overall trainee-expert rating discordance between the two groups emerged at the end of the first training cycle and remained at every cycle thereafter (P < 0.013). CONCLUSION By assisting inexperienced trainees in obtaining and interpreting standard basic obstetric scanning views, the use of artificial intelligence-assisted systems has the potential to improve training effectiveness. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.Ultrasound in Obstetrics and Gynecology341By assisting inexperienced trainees in obtaining and interpreting standard basic obstetric scanning views, the use of artificial intelligence-assisted systems has the potential to improve training effectiveness.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology["T. Lei", "Q. Zheng", "J. Feng", "L. Zhang", "Q. Zhou", "M. He", "M. Lin", "H. Xie"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/151e915af6a3e6c402ef67e7df26336d93e6ef95 7628481d4153c320fc8be95267b5751b21be3178This study aims to explore past research trends and identify key future directions at the intersection of artificial intelligence and legal education. The study’s data were gathered from the Scopus database, comprising 68 selected documents spanning from 1999 to 2024. The research methodology involves the use of VOSviewer software for bibliometric analysis. The results reveal that research on artificial intelligence and legal education, while still limited, has been undertaken in various countries, focusing on five primary research directions, including: (1) Improving technical education systems in colleges and universities through educational technology and modern legal learning systems; (2) Application of artificial intelligence and algorithms in the legal field; (3) Applying computational theory and e-learning technology in legal education; (4) Legal education and legal knowledge; (5) Digital transformation in the field of legal training. Based on the research results, five future research directions on this topic are also proposed, including: (1) Application of artificial intelligence in analyzing and predicting legal trends; (2) Artificial intelligence in personalizing the legal learning experience; (3) Artificial intelligence and legal professional ethics; (4) Development of artificial intelligence tools supporting legal teaching and research; and (5) Integration of artificial intelligence into online learning systems for legal education.Knowledge & Performance Management621The results reveal that research on artificial intelligence and legal education, while still limited, has been undertaken in various countries, focusing on five primary research directions, including improving technical education systems in colleges and universities through educational technology and modern legal learning systems.Knowledge and Performance Management["Diep Dao Mong", "Hai Phan Thanh"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/7628481d4153c320fc8be95267b5751b21be3178 97c7540ef8c7141c59f75e161482a89b0f92ea40Adopting AI (Artificial Intelligence) in the provision of psychiatric services has been groundbreaking and has presented other means of handling some of the issues related to traditional methods. This paper aims at analyzing the applicability and efficiency of AI in mental health practices based on business administration paradigms with a focus on managing services and policies. This paper engages a systematic and synoptic process, where current AI technologies in mental health are investigated with reference to the current literature as to their usefulness in delivering services and the moral considerations that surround their application. The study indicates that AI is capable of improving the availability, relevance, and effectiveness of mental health services, information that can be useful for policymakers in the management of health care. Consequently, specific concerns arise, such as how the algorithm imposes its own bias, the question of data privacy, or how a mechanism could reduce the human factor in care. The review brought to light an area of understanding of AI-driven interventions that has not been explored: the effect of such interventions in the long run. The field study suggests that further research should be conducted regarding ethical factors, increasing the ethical standards of AI usage in administration, and exploring the cooperation of mental health practitioners and AI engineers with respect to the application of AI in psychiatric practice. Proposed solutions, therefore, include enhancing the AI functions and ethical standards and guaranteeing that policy instruments are favorable for the use of AI in mental health.Administrative Sciences451AI is capable of improving the availability, relevance, and effectiveness of mental health services, information that can be useful for policymakers in the management of health care, and proposed solutions include enhancing the AI functions and ethical standards and guaranteeing that policy instruments are favorable for the use of AI in mental health.Administrative Sciences["Stephan Hoose", "Krist\u00edna Kr\u00e1likov\u00e1"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/97c7540ef8c7141c59f75e161482a89b0f92ea40 33e0e026b556b79e5dbcd14c1c5fde2d69fdd8b0As steam engines were introduced and the Industrial Age began, Mesopotamian manufacturing processes underwent significant changes. The mechatronics business is now experiencing a technological boom, thanks to recent breakthroughs in the internet, cellphones, electronics, nanotechnology, healthcare, digital applications, and other related technologies. Robotics and artificial intelligence were prominent topics during the last World Economic Forum, with major economists such as Stiglitz and Roubini making significant contributions to the discussion. The objective of the study is to determine the impact of artificial intelligence on communication sources related to business. The purpose of the research is also to find the answers of the following questions: How well do professionals know and use artificial intelligence? What influence does artificial intelligence have on communication management, say experts? What challenges do professionals face with artificial intelligence communication? What threats do the artificial intelligence use they see? The study has used the data set of a quantitative cross-national survey of 2375 European communication professionals. The study has applied the One-way ANOVA analysis with post-hoc Scheffé, Kendall rank correlation, Pearson product-moment correlation, and Pearson's chi-square tests. The results show that communication managers have driven artificial intelligence implementation and educate themselves and their workforce. There is a significant positive relationship between the use of artificial intelligence and business communication sources. The results the artificial intelligence has make a lot of improvement in communications systems and shows how the experts evaluate the technology. Research indicates that individuals employed in the field of communication have a limited understanding of artificial intelligence, although they possess a higher level of anticipation regarding its influence on their profession compared to its impact on their personal life.American International Journal of Business and Management Studies00Research indicates that individuals employed in the field of communication have a limited understanding of artificial intelligence, although they possess a higher level of anticipation regarding its influence on their profession compared to its impact on their personal life.American International Journal of Business and Management Studies[]2024-09-17T00:00:00https://www.semanticscholar.org/paper/33e0e026b556b79e5dbcd14c1c5fde2d69fdd8b0 c5988a4614739d2ce7ab88d7fdcdc94da18d7b6fConducting digital forensic investigations (DFIs) quickly, accurately and efficiently can be accomplished by knowing and using modern technologies, including those typical for machine learning (ML) and artificial intelligence (AI). Therefore, the purpose of the paper is to present an exploration regarding the scientific research on the applicability of ML and AI in DFI, how far and in what cases it can support the work of investigators in the different steps of their adopted methodology. A bibliometric analysis is used to outline the general picture and then a discussion regarding relevant articles is performed in detail. The findings show the potential of ML and AI to be used as tools, for example, to improve timeline reconstruction, to find and recover evidence, in secure chain custody, and others. Also, the researchers state that emerging technologies, including ChatGPT must be carefully examined and used in DFI practice.2024 XXXIII International Scientific Conference Electronics (ET)220The findings show the potential of ML and AI to be used as tools, for example, to improve timeline reconstruction, to find and recover evidence, in secure chain custody, and others.2024 XXXIII International Scientific Conference Electronics (ET)["Malinka Ivanova", "Svetlin Stefanov"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/c5988a4614739d2ce7ab88d7fdcdc94da18d7b6f 6fef52aacf6e346cbe0e2936281ba865fdecdac0In light of the Fourth Industrial Revolution, the intervention of artificial intelligence in commercial transactions has expanded, and it has not remained a mere subject or subject of the contract, whether it is a material or moral product, but has gone beyond that to have a fundamental and effective role in concluding the contract as an electronic agent that makes the contract automated and concluded in whole or in part in an automated manner without human intervention. The UAE legislator was interested in regulating its use, whether under the Electronic Transactions Law of 2006 and the current one of 2021, and referred to this possibility under the Trade through Modern Technology Law of 2023; It considers it an information program that represents the original principal and bears the effects of the transaction concluded with the intervention of artificial intelligence despite not granting the electronic agent legal personality. The integration of artificial intelligence and modern technology into commercial transactions has profoundly transformed the landscape of commercial law. Artificial intelligence techs, including natural language processing and machine learning algorithms, are increasingly utilized for contract formation, risk assessment, and dispute resolution. Such technologies enhance efficiency, decreases human error, and advance transactional processes, offering substantial advantages for businesses and consumers. Modern technology, consists of digital currencies, smart contracts, and blockchain, has further revolutionized commercial transactions through offering unprecedented levels of security, transparency, and automation. Blockchain technology warrants irreversible and provable records, while smart contracts perform automatically when predefined conditions are met. This minimizes the need for intermediaries and enhancing integrity of the transactions carried out. Additionally, this paper addresses the legal and regulatory responses to these technological advancements. Jurisdictions across the world are grappling with the need to update existing laws or come up with new ones in order to address the challenges posed by AI and modern tech in commerce. The dynamic nature of technology calls for a flexible and adaptive legal approach in order to make sure that commercial laws remains to be relevant and effective.Evolutionary Studies in Imaginative Culture390The dynamic nature of technology calls for a flexible and adaptive legal approach in order to make sure that commercial laws remains to be relevant and effective.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Adel Salem Allouzi", "Karima Krim", "Mohammad Abdalhafid AlKhamaiseh", "Mohammad Abdalhafid", "AlKhamaisehJos \u00e9 Lu \u00ed s", "Cagigal Garc \u00ed a", "Antonio V \u00e1 zquez P \u00e9 rez"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/6fef52aacf6e346cbe0e2936281ba865fdecdac0 d953a8fdebd9a8f551ff8d3daa48ad0fe2a09202Background Artificial intelligence (AI)-generated explanations about medical topics may be clearer and more accessible than traditional evidence-based sources, enhancing patient understanding and autonomy. We evaluated different AI explanations for patients about common diagnoses to aid in patient care. Methods We prompted ChatGPT 3.5, Google Bard, HuggingChat, and Claude 2 separately to generate a short patient education paragraph about seven common diagnoses. We used the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) to evaluate the readability and grade level of the responses. We used the Agency for Healthcare Research and Quality's Patient Education Materials Assessment Tool (PEMAT) grading rubric to evaluate the understandability and actionability of responses. Results Claude 2 demonstrated scores of FRE (67.0), FKGL (7.4), and PEMAT, 69% for understandability, and 34% for actionability. ChatGPT scores were FRE (58.5), FKGL (9.3), PEMAT (69% and 31%, respectively). Google Bard scores were FRE (50.1), FKGL (9.9), PEMAT (52% and 23%). HuggingChat scores were FRE (48.7) and FKGL (11.6), PEMAT (57% and 29%). Conclusion Claude 2 and ChatGPT demonstrated superior readability and understandability, but practical application and patient outcomes need further exploration. This study is limited by the rapid development of these tools with newer improved models replacing the older ones. Additionally, the accuracy and clarity of AI responses is based on that of the user-generated response. The PEMAT grading rubric is also mainly used for patient information leaflets that include visual aids and may contain subjective evaluations.PRiMER00Claude 2 and ChatGPT demonstrated superior readability and understandability, but practical application and patient outcomes need further exploration.PRiMER["Mafaz Kattih", "Max Bressler", "Logan Smith", "Anthony Schinelli", "Rahul Mhaskar", "Karim Hanna"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/d953a8fdebd9a8f551ff8d3daa48ad0fe2a09202 ce4dd10bd8ae8b6e2abbbe3ed386006f657e1b9dThe study aims to identify the role of artificial intelligence in the media content industry through the use of Chat GPT technology. In the media content industry, the study used a descriptive approach to investigate the role of artificial intelligence (AI). Artificial intelligence applications are used in various journalistic media fields, and it also contributes to improving the quality of media production because of the enrichment and deepening of information it provides and presenting it in an integrated and effective framework. Major developments in the next phase of the media scene. As for the negative levels of Chat GPT technology, although there are some negatives related to the civil field and the lack of creativity and innovation, they are still minor compared to the huge potential of artificial intelligence and chat technologies. GB in the industry of advanced media content in all its forms. The study recommended conducting more media research related to artificial intelligence technologies, especially in the Chat GPT technology, and enabling the management of press and media institutions to achieve integration between the role of the human element and technology, which enriches the content and increases its attractiveness. Because any breach of the integration equation between the two parties will inevitably negatively affect the outputs of journalistic work.2024 International Conference on Multimedia Computing, Networking and Applications (MCNA)180The study recommended conducting more media research related to artificial intelligence technologies, especially in the Chat GPT technology, and enabling the management of press and media institutions to achieve integration between the role of the human element and technology, which enriches the content and increases its attractiveness.2024 International Conference on Multimedia Computing, Networking and Applications (MCNA)["Suha Al Qaruty", "Reema Al Qaruty", "K. M. Al-Tkhayneh", "S. A. Hadi", "Z. Ellala"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/ce4dd10bd8ae8b6e2abbbe3ed386006f657e1b9d d256b43704d1787266c9250ef6ac9d9a13bc35f8This research explored the relationship between Prophetic traditions and artificial intelligence (AI), focusing on the ethical implications and dynamics of human-AI interactions in the context of hadith teachings in the postmodern technological era. Involving an in-depth analysis of the Prophet's teachings regarding science and technology, this research aims to detail ethical perspectives that can guide the development and use of artificial intelligence. With an interdisciplinary approach, this research combined religious values with developments in AI technology, discussing the confluence between the traditions of the Prophet and current dynamics in the world of technology. Ethical implications from an Islamic hadith perspective were detailed, highlighting how moral and human values could be integrated into designing and implementing AI technologies. Additionally, this research explored the impact of human-AI interactions in the postmodern era, considering how AI technology could support the development of a society based on the Prophet's teachings and values. A comprehensive understanding of artificial intelligence's limitations, risks, and human responsibilities was also detailed to balance technological progress and moral values. Hopefully, the research results will contribute to a better understanding of the relationship between religious traditions, especially hadith teachings, and advances in AI technology, providing a solid ethical foundation for technological development in the postmodern eraJurnal Ushuluddin590Jurnal Ushuluddin["Abdul Mufid"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/d256b43704d1787266c9250ef6ac9d9a13bc35f8 0e881a791176ef7c38cf0e6d6be99c6d925d08dfAim: The aim of this article is to highlight the importance of leveraging Artificial Intelligence (AI) in law enforcement to combat serious organised crime and terrorism, while ensuring responsible and accountable use of AI tools through collaboration and knowledge-sharing among European law enforcement agencies. Methodology: The study uses a descriptive methodology to describe the development and cooperation process through which the Innovation Lab contributes to the innovation development and knowledge sharing of Europol and its member countries. Findings: With the increasing volume and speed of investigative data, AI has emerged as a promising solution to help law enforcement agencies process and analyse large and complex datasets. Europol has been at the forefront of developing and sharing AI tools with its Member States, ensuring their responsible and accountable use. The integration of Artificial Intelligence (AI) in law enforcement investigations has been found to significantly enhance the efficiency and effectiveness of crime fighting, particularly in processing and analysing large and complex datasets. Value: The article highlights the importance of collaboration and knowledge-sharing among law enforcement agencies to keep pace with AI advancements and prevent criminal abuse of these technologies.Belügyi Szemle20The integration of Artificial Intelligence in law enforcement investigations has been found to significantly enhance the efficiency and effectiveness of crime fighting, particularly in processing and analysing large and complex datasets.Belügyi Szemle["Val\u00e9r D\u00e1nos"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/0e881a791176ef7c38cf0e6d6be99c6d925d08df 0b4329c347cb647415191096a09ab2c64ded98f7The integration of artificial intelligence (AI) into psychiatric diagnosis heralds a new era in mental health care, offering unprecedented opportunities to enhance diagnostic accuracy, personalize treatment, and streamline clinical workflows. A systematic approach was utilized, adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. This literature review explores the current state of AI in psychiatric diagnosis, highlighting key technologies such as machine learning, natural language processing, and deep learning. We discuss the application of these technologies across various psychiatric disorders, including depression, anxiety, and schizophrenia. While AI holds immense promise, significant challenges remain, including issues of data privacy, model bias, and the clinical validation of AI tools. Furthermore, ethical and regulatory considerations must be addressed to ensure responsible implementation. This review also examines the potential future directions of AI in psychiatry, emphasizing the importance of collaboration between AI systems and human clinicians. As the field evolves, AI has the potential to transform psychiatric practice, offering new avenues for early detection, personalized care, and therapeutic monitoring.Debates em Psiquiatria150A literature review explores the current state of AI in psychiatric diagnosis, highlighting key technologies such as machine learning, natural language processing, and deep learning and the potential future directions of AI in psychiatry.Debates em Psiquiatria["Kirolos Eskandar"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/0b4329c347cb647415191096a09ab2c64ded98f7 366c10731d9f72ca95ebe35ce1ed1cf4aa7c2a46Artificial intelligence has emerged as a transformative force across various disciplines, with healthcare being a prominent area of interest. This interest has emerged due to the potential for artificial intelligence to revolutionize healthcare.  Artificial intelligence is predominantly developed and deployed in high-income countries. These countries have more resources, better healthcare outcomes, and better staffing than the low- and middle-income countries. Given these disparities, one can argue that low-income countries have a greater need to deploy artificial intelligence technology in healthcare delivery and health education and research. International Journal of Critical Care00It can be argued that low-income countries have a greater need to deploy artificial intelligence technology in healthcare delivery and health education and research.International Journal of Critical Care["Khalil Yousef", "S. Schmollgruber"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/366c10731d9f72ca95ebe35ce1ed1cf4aa7c2a46 b4a7d6d3d5031a07ba194675e05d6ed57160c2b7   Objective: to identify the key benefits and potential risks associated with the use of artificial intelligence in ERP systems to improve decision-making processes, management efficiency and operational performance of various sectors, including commercial and non-profit organizations.   Methods: systematic literature review, empirical data analysis, analytical and experimental research methods.   Results: the key directions of artificial intelligence implementation in ERP-systems are reflected, providing improvement of operational efficiency, customer relations, as well as optimization of business processes, data management, supply chain and personnel management, automation of operations related to finance, optimization of customer relations; implementation of artificial intelligence in ERP-systems reduces inventory management costs, improves the accuracy of forecasting andinventory optimization, accelerates financial analysis and increases the accuracy of budgeting, resulting in reduced budget planning time; it also increases productivity by optimizing necessary production processes and reducing equipment downtime. However, there are also risks of confidential data leakage, unauthorized access to data; job losses due to automation of tasks; and vulnerability to cyberattacks.   Scientific novelty: the little-studied directions of artificial intelligence integration in ERP-systems are analyzed; an integrative approach to the application of artificial intelligence in ERP-systems is proposed, which combines methods of machine learning, natural language processing and predictive analytics and provides a comprehensive assessment of the complex impact on the business processes’ efficiency.   Practical significance: the formulated directions for solving the identified problems of artificial intelligence integration in ERP-systems can be implemented in practice, as they will enable to better take into account local requirements and laws.Russian Journal of Economics and Law190An integrative approach to the application of artificial intelligence in ERP-systems is proposed, which combines methods of machine learning, natural language processing and predictive analytics and provides a comprehensive assessment of the complex impact on the business processes’ efficiency.Russian Journal of Economics and Law["I. I. Antonova", "V. A. Smirnov", "M. G. Efimov"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/b4a7d6d3d5031a07ba194675e05d6ed57160c2b7 9a9b699684622181165235637deb75262967321fThis study aimed to assess the integration, ethical considerations, and governance of artificial intelligence (AI) within the PhD programs at Midocean University. It specifically sought to understand PhD researchers' perceptions and attitudes towards AI and identify areas for enhancement in AI-related policies and educational initiatives. A descriptive analytical approach was adopted, utilizing an electronic questionnaire distributed to 105 PhD researchers, with 54 completing the survey. The questionnaire was designed to measure various aspects of AI usage, ethical concerns, and governance practices. Statistical analysis was conducted to evaluate the relationships between AI awareness, ethical application, and governance perceptions. The findings revealed diverse perceptions of AI among researchers, indicating both opportunities and challenges in AI integration. The statistical analysis confirmed significant correlations between AI awareness and positive perceptions of ethical AI usage. However, the response rate and sample size posed limitations on the generalizability of the results. The study highlights the need for Midocean University to strengthen its AI governance frameworks and expand ethical guidelines to keep pace with technological advancements. Recommendations include enhancing AI-related educational programs, updating AI policies regularly, and promoting interdisciplinary research to foster an informed and ethically aware research community.Evolutionary Studies in Imaginative Culture310The findings revealed diverse perceptions of AI among researchers, indicating both opportunities and challenges in AI integration and the need for Midocean University to strengthen its AI governance frameworks and expand ethical guidelines to keep pace with technological advancements.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Ahmed Farouk", "Aly Mohammed", "Sarah Homoud", "Al-Himali Al-Kahtani", "Sarah Mubarak", "Mohammed Al-Dossary", "Researcher"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/9a9b699684622181165235637deb75262967321f e4f47b8ae3814964a2de631740d1284bbb8abd8eThe incorporation of Artificial Intelligence (AI) into contemporary linguistics exhibits a significant and transformational change in the discipline. AI technologies, which include natural language processing (NLP), machine learning, and computational linguistics, have significantly transformed the methods employed by linguists for studying, analyzing, and applying linguistic principles. However, as the integration of artificial intelligence (AI) within modern linguistics has presented novel opportunities, facilitating scholars in their investigation of language at an unprecedented scale and level of intricacy, it is pertinent to understand how language educators; especially, the university lecturers perceive these positive innovations. Nevertheless, the current research is focused on examining the responses of senior lecturers on the integration of AI in modern linguistics. The research objective further centered on gender variation in the responses of these lecturers in regard to technological innovations brought in by the integration of AI in modern linguistics. Using a quantitative research method, a good number of participants who are mainly senior lectures were engaged in an online interview. These participants consisting of forty-six (46) females and thirty-seven (37) males shared their opinions with regard to the focus of the study. Moreover, two important hypotheses were developed for this research and a t-test was conducted to validate these hypotheses. The findings generated from the data analyzed indicated that although there are no significant differences in the perceptions of both male and female lecturers on the integration of AI in modern linguistics, there are some aspects specific to modern linguistics with observable gender variations in responses of the participants. Such aspect includes easy adaption of new AI tools, level of benefits and ethical challenges. Also, while female lecturers address the AI integration in modern linguistics from ethical and beneficial point of view, the male counterparts focused more on accessibility and inclusivity.Evolutionary Studies in Imaginative Culture550Although there are no significant differences in the perceptions of both male and female lecturers on the integration of AI in modern linguistics, there are some aspects specific to modern linguistics with observable gender variations in responses of the participants that include easy adaption of new AI tools, level of benefits and ethical challenges.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Nisar Ahmad Koka"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/e4f47b8ae3814964a2de631740d1284bbb8abd8e 69baafed8e42118fc34501b68a2b0534cf10daefBanking customers do not think, speak, text, or communicate as financial institutions do. For humans, banking is as simple as “check balance,” “transfer,” “credit card payment,” and we prefer to talk in that language. When users contact a customer service representative, they want accurate answers without spending too much time over the phone or email. However, during these “new normal” times, the waiting time to reach a customer service rep has tremendously increased. Human agents cannot scale up with the ever-increasing demand to resolve customers’ queries. More often than not, agents feel burned out as they do not have enough tools at their disposal for delivering fast response time. With conversational artificial intelligence (AI) kicking in, the technology uses machine learning (ML) and natural language processing (NLP) to create a better customer experience. With the right set of programs and rules written, AI/ML tools can do much more than chatting and providing basic information to customers. For example, you could also update your contact and residential address, check your credit card balance, activate your card, etc. Chatbots are no longer “bots” just with pre defined answers to limited questions fed into them, with conversational AI and NLP- they are at par with human like responses and answer as accurately as a human would do. The businesses which are time zone independent find them more helpful.South Florida Journal of Development130Chatbots are no longer “bots” just with pre defined answers to limited questions fed into them, with conversational AI and NLP- they are at par with human like responses and answer as accurately as a human would do.South Florida Journal of Development["Prashant Bansal"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/69baafed8e42118fc34501b68a2b0534cf10daef 859a5bff19340a87c8457289586238586f026d9cArtificial Intelligence (AI) is increasingly integrated into education to enhance learning experiences and outcomes, particularly in engineering disciplines like microcontrollers and sensors. This paper explores the application of AI in supporting student learning in programming and understanding microcontrollers and sensors and embedded systems, focusing on personalized learning, interactive simulations, and real-time feedback mechanisms. By leveraging AI-driven tools, educators can provide tailored educational experiences that deepen student understanding and proficiency in these critical areas of engineering.2024 XXXIII International Scientific Conference Electronics (ET)170This paper explores the application of AI in supporting student learning in programming and understanding microcontrollers and sensors and embedded systems, focusing on personalized learning, interactive simulations, and real-time feedback mechanisms.2024 XXXIII International Scientific Conference Electronics (ET)["Dilyana A. Kashokova", "Anna Bekyarova-Tokmakova", "Stanislav Asenov"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/859a5bff19340a87c8457289586238586f026d9c aa9344e920a08d6282ea93a6e36a5b800baf4c89Abstract Artificial intelligence (AI) is widely regarded as one of the most promising technologies for advancing science, fostering innovation, and solving global challenges. Recent years have seen a push for teamwork between experts from different fields and AI specialists, but the outcomes of these collaborations have yet to be studied. We focus on approximately 15,000 papers at the intersection of AI and COVID-19—arguably one of the major challenges of recent decades—and show that interdisciplinary collaborations between medical professionals and AI specialists have largely resulted in publications with low visibility and impact. Our findings suggest that impactful research depends less on the overall interdisciplinary of author teams and more on the diversity of knowledge they actually harness in their research. We conclude that team composition significantly influences the successful integration of new computational technologies into science and that obstacles still exist to effective interdisciplinary collaborations in the realm of AI.Quantitative Science Studies00It is concluded that team composition significantly influences the successful integration of new computational technologies into science and that obstacles still exist to effective interdisciplinary collaborations in the realm of AI.Quantitative Science Studies["Diletta Abbonato", "Stefano Bianchini", "Floriana Gargiulo", "Tommaso Venturini"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/aa9344e920a08d6282ea93a6e36a5b800baf4c89 66c1b46fe33d8d80a493e2d5120a661d7a4f665bInternal medicine journal (Print)111Internal medicine journal["Paul A Komesaroff", "Elizabeth L Potter", "Emma R Felman", "Jeff Szer"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/66c1b46fe33d8d80a493e2d5120a661d7a4f665b 4b0c32fbe730363daf77f7251f99925a638ef9edCirculation01Circulation["Anna Sannino", "Umidakhon Mahkmudova"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/4b0c32fbe730363daf77f7251f99925a638ef9ed fd9fcc7239801e21099ea22ae906d3794019c18dJournalism Studies421Journalism Studies["J. Boyles"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/fd9fcc7239801e21099ea22ae906d3794019c18d c435ea6d80967f5fd01d63943902d1c293e2172aJournal of Perioperative Nursing01Journal of Perioperative Nursing["Lester Lora", "Paula Foran"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/c435ea6d80967f5fd01d63943902d1c293e2172a 3f221c4c54377794bd26da65a5f37a2cedebfc5aClean Technologies and Environmental Policy151Clean Technologies and Environmental Policy["Joon-Gul Jo", "Minje Choi", "Juhyeon Kwak", "Yee Van Fan", "Seungjae Lee"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/3f221c4c54377794bd26da65a5f37a2cedebfc5a a26038efde2c9717b4d7591b01d41e90df5c2454The adoption of emerging technologies among university students has become increasingly prevalent in recent years. AI-assisted technologies are gradually permeating medical education and practice to improve healthcare delivery and reduce resource waste. This study aimed to investigate the awareness, use, and perception of AI among medical students in Emmanuel Quaye Archampong Library at the University of Ghana. Using a survey research design, data were collected from medical students at the University of Ghana Medical School. Structured questionnaires were administered to the respondents online using Google Forms. With a total of 1366 respondents, Krejcie and Morgan's published table was employed to select the study sample size of 302 medical students. Forty-eight (39.0%) medical students agreed that the use of AI-assisted technologies was voluntary without being coerced to use them. More than half of the respondents (50.4%) reported being moderately aware of AI-assisted technologies and adequately understanding the concept of AI. Grammarly and ChatGPT were predominantly used in medical studies, despite the lack of opportunities for training on AI-assisted technologies. It is recommended that regular training and guidance be provided to students to appropriately use AI-assisted technologies in research and learning.Information Development271It is recommended that regular training and guidance be provided to students to appropriately use AI-assisted technologies in research and learning.Information Development["Samuel Ankamah", "Kwesi Gyesi", "Vivian Amponsah"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/a26038efde2c9717b4d7591b01d41e90df5c2454 de782880de558f4f74ab002976c645341c107ee1Journal of Mixed Methods Research10Journal of Mixed Methods Research["Jos\u00e9 F. Molina-Azor\u00edn", "T. Guetterman"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/de782880de558f4f74ab002976c645341c107ee1 fcae367f18319c88b1d4d9aa53d6f034c791b8e0Physical Therapy00Physical therapy["Nathan Morelli"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/fcae367f18319c88b1d4d9aa53d6f034c791b8e0 746dd958e6542b228fbbb023bb12adfdd1727561ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH00ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH["Gomes Orlando", "Lins DE Moraes Michelle"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/746dd958e6542b228fbbb023bb12adfdd1727561 75b8d383789918984e80bafe955b80cae12aacc400["Payal Bansal", "Rajeev Kumar", "Ashwani Kumar", "Daniel D. Dasig, Jr."]2024-09-17T00:00:00https://www.semanticscholar.org/paper/75b8d383789918984e80bafe955b80cae12aacc4 866839ee9af7087797bb6e6de5ba0805d64acac7BACKGROUND AND PURPOSE We aimed to evaluate GPT-4's ability to write radiology editorials and to compare these with human-written counterparts, thereby determining their real-world applicability for scientific writing. MATERIALS AND METHODS Sixteen editorials from eight journals were included. To generate the AI-written editorials, the summary of 16 human-written editorials was fed into GPT-4. Six experienced editors reviewed the articles. First, an unpaired approach was used. The raters were asked to evaluate the content of each article using a 1-5 Likert scale across specified metrics. Then, they determined whether the editorials were written by humans or AI. The articles were then evaluated in pairs to determine which article was generated by AI and which should be published. Finally, the articles were analyzed with an AI detector and for plagiarism. RESULTS The human-written articles had a median AI probability score of 2.0%, whereas the AI-written articles had 58%. The median similarity score among AI-written articles was 3%. 58% of unpaired articles were correctly classified regarding authorship. Rating accuracy was increased to 70% in the paired setting. AI-written articles received slightly higher scores in most metrics. When stratified by perception, human-written perceived articles were rated higher in most categories. In the paired setting, raters strongly preferred publishing the article they perceived as human-written (82%). CONCLUSIONS GPT-4 can write high-quality articles that iThenticate does not flag as plagiarized, which may go undetected by editors, and that detection tools can detect to a limited extent. Editors showed a positive bias toward human-written articles. ABBREVIATIONS AI = Artificial intelligence; LLM = large language model; SD = standard deviation.AJNR. American journal of neuroradiology00GPT-4 can write high-quality articles that iThenticate does not flag as plagiarized, which may go undetected by editors, and that detection tools can detect to a limited extent.AJNR. American journal of neuroradiology["B. Ozkara", "Alexandre Boutet", "Bryan A. Comstock", "Johan Van Goethem", "Thierry A.G.M. Huisman", "Jeffrey S. Ross", "Luca Saba", "Lubdha Shah", "Max Wintermark", "Mauricio Castillo"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/866839ee9af7087797bb6e6de5ba0805d64acac7 0ee3b2f0d82079113ac235b8c8bf36320caf54a6The evolution of artificial intelligence (AI) and machine learning presents both utility and security implications for our digital interactions. This study focuses on the transformative role of generative AI in social engineering attacks, specifically examining three pillars where it significantly amplifies their impact: advanced targeting and personification, genuine content creation, and automated attack infrastructure. The analysis forms a conceptual model named the generative AI social engineering framework. The research delves into human implications and measures to counter social engineering attacks, blending theoretical analysis with practical insights through case studies. Ethical considerations surrounding AI in malicious activities are discussed, emphasizing the importance of safe AI development, and various articles were reviewed to highlight social engineering attacks as a common threat. Two studies were conducted: a user testing study with 48 participants from diverse occupations and social engineering awareness, and an exploratory study collecting qualitative data from 40 social engineering attack victims. The user testing study revealed universal acceptance of the AI-based tool, irrespective of participants’ occupations. Victim themes included reasons for falling prey to attacks, methods, prevention advice, and detection. The research concludes by highlighting AI-generated content as a key factor fueling social engineering attacks and bridging the gap between AI development and cybersecurity practices, highlighting the need for interdisciplinary approaches to address evolving challenges.2024 International Conference on Multimedia Computing, Networking and Applications (MCNA)293This study focuses on the transformative role of generative AI in social engineering attacks, specifically examining three pillars where it significantly amplifies their impact: advanced targeting and personification, genuine content creation, and automated attack infrastructure.2024 International Conference on Multimedia Computing, Networking and Applications (MCNA)["Yazan Alahmed", "Reema Abadla", "Mohammed Jassim Al Ansari"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/0ee3b2f0d82079113ac235b8c8bf36320caf54a6 ab50f2fe36a08ee173c09d99d26e4a089b8b89abEnvironmental science and pollution research international512Findings reveal that higher education levels correlate with less enthusiasm for AI-based GTI, and the effects of education and preferences on emissions are quantified and subsidies are proposed as accelerators for carbon-neutral transitions.Environmental science and pollution research international["Jifan Ren", "Qasir Abbas", "Jafar Hussain", "Danting Hu", "Jimei Li"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/ab50f2fe36a08ee173c09d99d26e4a089b8b89ab 19cb29706d8fca3298a0c12d5ae748c702e821b7Expanding waste-to-energy (WtE) facilities is difficult, and with tightening incineration regulations, improvements in WtE facility operations are required to dispose of waste that is increasing by an average of 4.8% annually. To achieve this, an intelligent combustion control (ICC) system was studied using digital technologies such as the Internet of Things and artificial intelligence to improve the operation of WtE facilities. The ICC system in this study is composed of three modules: perception, decision, and control. Perception: collecting and visualizing digital data on the operating status of WtE facilities; Decision: using AI to propose optimal operation methods; Control: automatically controlling the WtE facility according to the AI-suggested optimization methods. The ICC system was applied to the “G” WtE facility, a solid waste WtE facility operating in Gyeonggi province, Republic of Korea, and the digital data collected over six months showed high quality, with low delay and a data loss rate of only 0.12%. Additionally, in January 2024, the ICC system was used to automatically control the second forced draft fan and induced draft fan over a four-day period. As a result, the incinerator flue gas temperature decreased by 0.66%, steam flow rate improved by 2.41%, power generation increased by 3.09%, CO emissions were reduced by 60.72%, and NOx emissions decreased by 7.33%. Future research will expand the ICC system to include the automatic control of the first forced draft fan and the operation time of the stoker.Energies132An intelligent combustion control (ICC) system was studied using digital technologies such as the Internet of Things and artificial intelligence to improve the operation of WtE facilities and showed high quality, with low delay and a data loss rate.Energies["Dongmin Shin", "Jaeho Lee", "Jihoon Son", "Yongkeun Yun", "Yoonchan Song", "Jaeman Song"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/19cb29706d8fca3298a0c12d5ae748c702e821b7 9c9f9f7ca5c3ce7d062083eebf3f0799e274c43dGiven the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions of this right: (1) to notify patients about their care, (2) to educate patients and promote trust, and (3) to meet standards for informed consent. Additional clarity is needed to guide practices that respect the right to notice and explanation of AI in healthcare while providing meaningful benefits to patients.American Journal of Bioethics481Three normative functions of the right to notice and explanation of AI are proposed: to notify patients about their care, to educate patients and promote trust, and to meet standards for informed consent.The American journal of bioethics : AJOB["Meghan E Hurley", "Benjamin H Lang", "K. Kostick-Quenet", "Jared N Smith", "Jennifer Blumenthal-Barby"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/9c9f9f7ca5c3ce7d062083eebf3f0799e274c43d 7ab57a45e572f74a64405df427b70fb3b3b3ddbeInternational Journal of Artificial Intelligence in Education141A novel perspective on the synergistic collaboration between students and AI in academic writing and its implications for institutional policies and writing pedagogy is presented, presenting a novel perspective on the synergistic collaboration between students and AI in academic writing.International Journal of Artificial Intelligence in Education["Jessica L. Parker", "Veronica M. Richard", "Alexandra Acab\u00e1", "Sierra Escoffier", "Stephen Flaherty", "Shannon Jablonka", "Kimberly P. Becker"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/7ab57a45e572f74a64405df427b70fb3b3b3ddbe da0e6d5131defb7991b51848ff34e40f20b4a390BACKGROUND The integration of artificial intelligence (AI; ChatGPT 4.0) into medical workflows presents a great potential to enhance efficiency and quality. The use of artificial intelligence in the creation of discharge summaries seems particularly interesting and valid. The course of each hospitalization is described in the discharge summary, which is given to each patient and then to his general practitioner at the end of hospital treatment. An exploratory analysis of discharge summaries in psychiatric clinics underscores that these documents must fulfill diverse and specific requirements. Nevertheless, AI-generated discharge summaries offer the opportunity to optimize information transfer and alleviate the workload on physicians. METHOD The study evaluates the quality of discharge summaries produced by clinical staff and by an AI model (ChatGPT 4.0). The clinicians involved in writing of the discharge summaries were not informed about the study's purpose or methodology. The completed summaries were subsequently assessed by four attending physicians using predefined criteria. These physicians were also blinded to the study's objectives and were unaware of the individual authors of the summaries. The evaluation criteria included consistency, completeness, and comprehensibility. Additionally, the time required to prepare these summaries and its impact on overall quality were analyzed. RESULTS The results of the study indicate that discharge summaries generated by AI are more efficient than discharge summaries prepared by clinic staff. The AI was particularly effective in terms of coherence and information structure. CONCLUSION Further research, training and development is needed to improve the accuracy and reliability of AI-generated discharge summaries.International Journal of Psychiatry in Medicine51It is indicated that discharge summaries generated by AI are more efficient than discharge summaries prepared by clinic staff and the AI was particularly effective in terms of coherence and information structure.International journal of psychiatry in medicine["Bertrand Janota", "Krzysztof Janota"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/da0e6d5131defb7991b51848ff34e40f20b4a390 4d257a814c1ed31b9a50d6868687486d45f455ecThe integration of artificial intelligence (AI) and optimization hold substantial promise for improving the efficiency, reliability, and resilience of engineered systems. Due to the networked nature of many engineered systems, ethically deploying methodologies at this intersection poses challenges that are distinct from other AI settings, thus motivating the development of ethical guidelines tailored to AI-enabled optimization. This paper highlights the need to go beyond fairness-driven algorithms to systematically address ethical decisions spanning the stages of modeling, data curation, results analysis, and implementation of optimization-based decision support tools. Accordingly, this paper identifies ethical considerations required when deploying algorithms at the intersection of AI and optimization via case studies in power systems as well as supply chain and logistics. Rather than providing a prescriptive set of rules, this paper aims to foster reflection and awareness among researchers and encourage consideration of ethical implications at every step of the decision-making process.arXiv.org480The need to go beyond fairness-driven algorithms to systematically address ethical decisions spanning the stages of modeling, data curation, results analysis, and implementation of optimization-based decision support tools is highlighted.ArXiv["Rosemarie Santa Gonzalez", "Ryan Piansky", "Sue M Bae", "Justin Biddle", "Daniel K. Molzahn"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/4d257a814c1ed31b9a50d6868687486d45f455ec f49b229b8aae673e253960d6f84b3b3504096cc2Artificial intelligence (AI) has significantly transformed the cybersecurity landscape, offering enhanced threat detection, predictive analytics, and automated responses. However, this integration also introduces a range of complex challenges. This abstract explores the multifaceted problems associated with AI-driven cybersecurity, including the susceptibility of AI models to adversarial attacks, inherent vulnerabilities, and ethical concerns related to data privacy and bias. Additionally, it addresses the escalating arms race between cybersecurity professionals and malicious actors employing sophisticated AI techniques. Understanding and mitigating these issues is crucial for effectively leveraging AI’s potential to secure digital environments.2024 XXXIII International Scientific Conference Electronics (ET)90This abstract explores the multifaceted problems associated with AI-driven cybersecurity, including the susceptibility of AI models to adversarial attacks, inherent vulnerabilities, and ethical concerns related to data privacy and bias.2024 XXXIII International Scientific Conference Electronics (ET)["Roumiana Ilieva", "Gloria Stoilova"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/f49b229b8aae673e253960d6f84b3b3504096cc2 526cbbad2e23ac2bbfbfe4545e7b01180de3c68cThis research aims to evaluate the role of artificial intelligence (AI) in leadership succession planning of the State Civil Apparatus (ASN) in the Nias Regency Government. A combination of qualitative and quantitative research methods was used, with in-depth interviews and analysis of performance data using AI. The research was conducted from July to August 2024 at the Office of the Regent of Nias, North Sumatra. The results showed that the application of AI improved efficiency and objectivity in the succession process, with AI's ability to deeply analyze ASN performance data and identify the right candidates for promotion. However, challenges such as limited resources, additional training needs, and data management must be overcome. The conclusion of this study is that AI can improve leadership succession planning in Nias Regency Government by increasing efficiency and reducing bias, provided that implementation challenges are properly addressed.Dinasti International Journal of Economics, Finance &amp; Accounting00The conclusion of this study is that AI can improve leadership succession planning in Nias Regency Government by increasing efficiency and reducing bias, provided that implementation challenges are properly addressed.Dinasti International Journal of Economics, Finance &amp; Accounting["Marlansyah Putera Ndraha", "Ayler Beniah Ndraha", "Maria Magdalena Bate'e", "Yupiter Mendrofa", "Elisati Kurniawan Telaumbanua"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/526cbbad2e23ac2bbfbfe4545e7b01180de3c68c 2037a4b8a9b1c66a14dcaffd3601df6fb39e5311Artificial Intelligence (AI) is transforming healthcare by enhancing diagnostic accuracy, personalizing treatment plans, streamlining administrative tasks, and advancing research capabilities. This abstract explores the multifaceted applications of AI in healthcare, highlighting its potential to revolutionize the industry. AI technologies, including machine learning, natural language processing, and computer vision, are being integrated into various aspects of healthcare. In diagnostics, AI algorithms can analyze medical images, identify patterns, and detect anomalies with precision often surpassing human capabilities. For instance, AI systems have demonstrated proficiency in detecting cancers, retinal diseases, and cardiovascular conditions from medical imaging and data.1A and V Pub Journal of Nursing and Medical Research60This abstract explores the multifaceted applications of AI in healthcare, highlighting its potential to revolutionize the industry.A and V Pub Journal of Nursing and Medical Research["Purohit Saraswati", "Suneel Kumar C. N."]2024-09-17T00:00:00https://www.semanticscholar.org/paper/2037a4b8a9b1c66a14dcaffd3601df6fb39e5311 82dcfc4789d29b85703b5cbcc819ace322ad8c8fArtificial Intelligence (AI) has revolutionized the biomedical sector in advanced diagnosis, treatment, and personalized medicine. While these AI-driven innovations promise vast benefits for patients and service providers, they also raise complex intellectual property (IP) challenges due to the inherent nature of AI technology. In this review, we discussed the multifaceted impact of AI on IP within the biomedical sector, exploring implications in areas like drug research and discovery, personalized medicine, and medical diagnostics. We dissect critical issues surrounding AI inventorship, patent and copyright protection for AI-generated works, data ownership, and licensing. To provide context, we analyzed the current IP legislative landscape in the United States, EU, China, and India, highlighting convergences, divergences, and precedent-setting cases relevant to the biomedical sector. Recognizing the need for harmonization, we reviewed current developments and discussed a way forward. We advocate for a collaborative approach, convening policymakers, clinicians, researchers, industry players, legal professionals, and patient advocates to navigate this dynamic landscape. It will create a stable IP regime and unlock the full potential of AI for enhanced healthcare delivery and improved patient outcomes.Frontiers Artif. Intell.490The multifaceted impact of AI on IP within the biomedical sector is discussed, exploring implications in areas like drug research and discovery, personalized medicine, and medical diagnostics and advocating for a collaborative approach.Frontiers in Artificial Intelligence["Abhijit Poddar", "S. Rao", "Tim Hulsen", "Raymond Lee"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/82dcfc4789d29b85703b5cbcc819ace322ad8c8f 2c53e754c1202673497a016b6834e870a62ea42aThe integration and detection of AI (Artificial Intelligence) in a variety of fields, primarily education, are examined in this paper. With an emphasis on virtual assistants and their uses, it explores the potential and constraints of such technology. A study was conducted with students at the Technical University of Sofia, to assess their ability to distinguish between texts written by humans and AI. The results showcase that learners need to develop their critical analysis skills evidenced by their mixed expertise.2024 XXXIII International Scientific Conference Electronics (ET)1502024 XXXIII International Scientific Conference Electronics (ET)["Daniela V. Minkovska", "Elena V. Antonova"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/2c53e754c1202673497a016b6834e870a62ea42a f550ab96b03c7bc632819dc2de64c7b9cc25f51aArtificial Intelligence (AI) techniques have become popular in architecture and design, and several studies have focused on using these technological advancements to solve various architectural problems. AI is used in various architectural design applications, from intelligent material composition to layout solutions, and it is also vital in supporting the architecture and design education mechanism. A comprehensive understanding of literature is necessary to use these powerful tools in education adequately. This is due to the large volume of research being created and disseminated on this subject as well as the increasing application of AI techniques to address various education design-related questions. This article offers a comprehensive and critical assessment of the study of artificial intelligence applications in architecture and interior design education as a course application in the history of architecture and interior design. The study's conclusions indicate that AI's implications in architecture and design re-imagination are beneficial; however, it was concluded that AI- in its current phase- cannot replace human input and perspective. The students' feedback indicates that critical thinking skills cannot be replaced, and AI complements but doesn't replace their intellect.Evolutionary Studies in Imaginative Culture240It was concluded that AI- in its current phase- cannot replace human input and perspective, and students' feedback indicates that critical thinking skills cannot be replaced, and AI complements but doesn't replace their intellect.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Dalia Hafiz"]2024-09-17T00:00:00https://www.semanticscholar.org/paper/f550ab96b03c7bc632819dc2de64c7b9cc25f51a 18c7d4a106a6889e81e970fd01cdcd8fbf13415cThe cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.arXiv.org1864This work proposes a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales, and addresses desired capabilities of such AI Virtual Cells.ArXiv["Charlotte Bunne", "Yusuf Roohani", "Yanay Rosen", "Ankit Gupta", "Xikun Zhang", "Marcel Roed", "Theo Alexandrov", "Mohammed AlQuraishi", "Patricia Brennan", "Daniel B. Burkhardt", "Andrea Califano", "J. Cool", "A. Dernburg", "Kirsty Ewing", "Emily B. Fox", "Matthias Haury", "Amy E. Herr", "Eric Horvitz", "Patrick D. Hsu", "Viren Jain", "Gregory R. Johnson", "Thomas Kalil", "David R. Kelley", "S. Kelley", "A. Kreshuk", "Tim Mitchison", "Stephani Otte", "Jay Shendure", "Nicholas J Sofroniew", "Fabian Theis", "Christina V. Theodoris", "S. Upadhyayula", "M. Valer", "Bo Wang", "Eric Xing", "S. Yeung-Levy", "M. Zitnik", "Theofanis Karaletsos", "Aviv Regev", "Emma Lundberg", "J. Leskovec", "Stephen R. Quake"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/18c7d4a106a6889e81e970fd01cdcd8fbf13415c d8eef89fb4b86b4026e9e12acd7e2ef3bd20df03The rapid adoption of software as a medical device (SAMD) driven by artificial intelligence and machine learning has brought about a fundamental shift in the medical industry. This shift has the potential to greatly improve clinical outcomes and the quality of care provided to patients. This shift has been responsible for a number of key achievements made in recent times. When seen in this context, the proposed legal framework for revisions to the AI/ML-SAMD appears as an essential response to the malleability of these technologies. To successfully navigate the tough process of modifying AI/ML-SAMD with the assistance of this framework. It does this by taking into consideration the need for rapid regulatory scrutiny and making an attempt to combine the promotion of innovation with the simultaneous preservation of patient safety. In other words, it ensures that patient safety is protected while also encouraging innovation. This abstract provides a summary of the fundamental components of the framework, as well as a discussion of the significance of those components with regard to fostering the development of moral AI/ML-SAMD within the context of the healthcare ecosystem. The healthcare sector is undergoing a change as a direct result of artificial intelligence and machine learning, which are improving patient outcomes, diagnostic accuracy, and treatment options. The research emphasizes the significance of specific AI and ML applications as well as the sector’s embrace of this paradigm-shifting technology. In addition, the regulatory framework that has been presented is an important step towards guaranteeing the safe use of AI and ML in the medical field.International Conferences on Contemporary Computing and Informatics404The research emphasizes the significance of specific AI and ML applications as well as the sector’s embrace of this paradigm-shifting technology, an important step towards guaranteeing the safe use of AI and ML in the medical field.2024 7th International Conference on Contemporary Computing and Informatics (IC3I)["Anurag Shrivastava", "Upma Jain", "Mohammed Al-Farouni", "Yogendra Kumar", "R. J. Anandhi", "Munugapati Bhavana"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/d8eef89fb4b86b4026e9e12acd7e2ef3bd20df03 fec560d2fa0ed2d3f98ad1ab7a3222668a565c93This paper deals with the implementation of artificial intelligence tools in the process of teaching writing for academic purposes. The aims of this scientific study were to verify the practicability of implementation of selected artificial intelligence tools at the C1+/C2 level in university instruction and to gain insight into the attitudes and changes in preferences for use of AI-enhanced writing tools. The analyses were also focused on investigating the extent to which students take advantage of the potential of interaction with artificial intelligence tools in the process of composing academic texts. The research material was collected through a one-group quasi-experimental treatment in an undergraduate applied linguistics group of students. The obtained results indicate a significant increase in the use of and familiarity with artificial intelligence and the tools that apply AI algorithms to support text processing and production. The statements of the respondents prove that AI-assisted tools themselves and the knowledge how to apply them in the academic writing process remain vital and constitute a significantly useful element in the development of writing competence.Roczniki Humanistyczne142The statements of the respondents prove that AI-assisted tools themselves and the knowledge how to apply them in the academic writing process remain vital and constitute a significantly useful element in the development of writing competence.Roczniki Humanistyczne["Jaros\u0142aw Krajka", "Izabela Olszak"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/fec560d2fa0ed2d3f98ad1ab7a3222668a565c93 40573962e5a96043520697618dfea7fca0abb325With the rapid development of the global economy and the continuous advancement of technology, the artificial intelligence industry has become a new engine for the development of the world economy today. China's vocational colleges have launched a wave of professional construction in the era of artificial intelligence. However, during the construction process, there have been problems such as a mismatch between the level of education and the needs of industry enterprises, a dilemma in the positioning of talent training goals, curriculum construction lagging behind the actual application of enterprises, a lack of teaching staff and low level of specialization, low coupling of demands between school enterprise cooperation, and obvious lag in the construction of practical teaching conditions. In response to technological changes and industrial development trends, vocational colleges should actively take measures to address these issues under the macroeconomic regulation and specific coordination of the government, with the full participation of industry enterprises. To cultivate more high-quality talents with innovative spirit, practical ability, and international competitiveness for the artificial intelligence industry.Journal of Education and Educational Research41China's vocational colleges should actively take measures to address issues under the macroeconomic regulation and specific coordination of the government, with the full participation of industry enterprises to cultivate more high-quality talents with innovative spirit, practical ability, and international competitiveness for the artificial intelligence industry.Journal of Education and Educational Research["Xueqin Rong", "Yong Liu"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/40573962e5a96043520697618dfea7fca0abb325 5fc1c98040cd270f6b9cbe11451305017f9bf7fbnpj Digit. Medicine121A comparative overview of the United States (USA), European Union (EU), and China is presented to show how regulatory bodies respond to artificial intelligence (AI)-enabled medical devices.NPJ Digital Medicine["Yuehua Liu", "Wenjin Yu", "T. Dillon"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/5fc1c98040cd270f6b9cbe11451305017f9bf7fb 8d9aa7242dacdc75043d1b9dff697021d3b00a5cIntroduction Artificial intelligence is already widely utilized in gastroenterology. This study aims to comprehensively evaluate the research hotspots and development trends within the field of AI in gastroenterology by employing bibliometric techniques to scrutinize geographical distribution, authorship, affiliated institutions, keyword usage, references, and other pertinent data contained within relevant publications. Methods This investigation compiled all pertinent publications related to artificial intelligence in the context of gastrointestinal polyps and digestive endoscopy from 2003 to 2023 within the Web of Science Core Collection database. Furthermore, the study harnessed the tools CiteSpace, VOSviewer, GraphPad Prism and Scimago Graphica for visual data analysis. The study retrieved a total of 2,394 documents in the field of AI in digestive endoscopy and 628 documents specifically related to AI in digestive tract polyps. Results The United States and China are the primary contributors to research in both fields. Since 2019, studies on AI for digestive tract polyps have constituted approximately 25% of the total AI digestive endoscopy studies annually. Six of the top 10 most-cited studies in AI digestive endoscopy also rank among the top 10 most-cited studies in AI for gastrointestinal polyps. Additionally, the number of studies on AI-assisted polyp segmentation is growing the fastest, with significant increases in AI-assisted polyp diagnosis and real-time systems beginning after 2020. Discussion The application of AI in gastroenterology has garnered increasing attention. As theoretical advancements in AI for gastroenterology have progressed, real-time diagnosis and detection of gastrointestinal diseases have become feasible in recent years, highlighting the promising potential of AI in this field.Frontiers in Medicine541This study aims to comprehensively evaluate the research hotspots and development trends within the field of AI in gastroenterology by employing bibliometric techniques to scrutinize geographical distribution, authorship, affiliated institutions, keyword usage, references, and other pertinent data contained within relevant publications.Frontiers in Medicine["Ziye Peng", "Xiangyu Wang", "Jiaxin Li", "Jiayi Sun", "Yuwei Wang", "Yanru Li", "Wen Li", "Shuyi Zhang", "Ximo Wang", "Zhengcun Pei"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/8d9aa7242dacdc75043d1b9dff697021d3b00a5c 625ffe737304964dcb658ad15e124a1f8f8176e9This whitepaper is a follow-up of the Australasian Council of Open and Digital Education (ACODE) survey in 2023 on the governance of artificial intelligence (AI) and data in Australasian higher education (Selvaratnam & Venaruzzo, 2023). The results then showed the guidelines and policies in this space were still in the early days. Ethical implications were also emerging in tandem with initiatives and the adoption of generative AI in institutions. The latest survey conducted over July and August 2024 is 24 is picking up on the recommendations of the 2023 paper to inform recommendations for practice and further assure quality and equity in higher education. To this end, the JISC AI Maturity Model for Education is used to gauge the sector’s growth in the governance of AI and data both in policy and practice. The outcomes show that the sector is mainly at the experimenting and exploring stage of maturity in engaging with AI. The challenges were mainly in operationalising AI in a comprehensive manner across the enterprise, including increasing AI literacy across staff and students. More institutions are addressing the ethical implications of AI since the last survey; however, it appears that social and emotional wellbeing, and psychological safety still have to be carefully considered. ASCILITE Publications01The JISC AI Maturity Model for Education is used to gauge the sector’s growth in the governance of AI and data both in policy and practice and shows that the sector is mainly at the experimenting and exploring stage of maturity in engaging with AI.ASCILITE Publications["R. Selvaratnam", "Kate Ames", "S. Leichtweis"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/625ffe737304964dcb658ad15e124a1f8f8176e9 7e6ecf04d4348e7ecb67543b34907236931d5eeaOphthalmology is a subject that highly depends on imaging examination. Artificial intelligence (AI) technology has great potential in medical imaging analysis, including image diagnosis, classification, grading, guiding treatment and evaluating prognosis. The combination of the two can realize mass screening of grass-roots eye health, making it possible to seek medical treatment in the mode of "first treatment at the grass-roots level, two-way referral, emergency and slow treatment, and linkage between the upper and lower levels". On the basis of summarizing the AI technology carried out by scholars and their teams all over the world in the field of ophthalmology, quite a lot of studies have confirmed that machine learning can assist in diagnosis, grading, providing optimal treatment plans and evaluating prognosis in corneal and conjunctival diseases, ametropia, lens diseases, glaucoma, iris diseases, etc. This paper systematically shows the application and progress of AI technology in common anterior segment ocular diseases, the current limitations, and prospects for the future.International Journal of Ophthalmology661This paper systematically shows the application and progress of AI technology in common anterior segment ocular diseases, the current limitations, and prospects for the future.International journal of ophthalmology["Yao-Hong Liu", "Lin-Yu Li", "Si-Jia Liu", "Lixiong Gao", "Yong Tang", "Zhao-Hui Li", "Z. Ye"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/7e6ecf04d4348e7ecb67543b34907236931d5eea 0249b5e694a3d66771b4e4947a8f0f2d62d6720cAI &amp; SOCIETY311Five examples of mitigation measures designed to counteract gender bias in AI within the healthcare sector are explored and it is suggested that they lack accountable agents for implementation and overlook potential implementation barriers such as resistance, power relations, and knowledge hierarchies.AI &amp; SOCIETY["Anna Isaksson"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/0249b5e694a3d66771b4e4947a8f0f2d62d6720c c1f4d89a6ba298012447daf2655a724a98044068   Objective: to provide a comparative assessment of the use of artificial intelligence technologies by organizations in the context of Russian regions and to identify determinants of their dynamics.   Methods: descriptive statistics, histogram, grouping, principal component method, panel data models.   Results: an absolute trend of recent years is to study and implement artificial intelligence technologies in many economic, industrial processes and social life. The article analyzes the trends in the application of artificial intelligence technologies in the Russian regions. The comparative analysis of regions by the level and growth rate of artificial intelligence technologiesuse by organizations showed that the regions were heterogenous by the dynamics of this indicator in 2020-2022. The regions were divided into four groups: above average and below average level in Russia. Econometric modeling based on the method of principal components gave grounds to unite the determinants of the use of artificial intelligence technologies into four components. Panel data fixed-effects models showed a significant impact of the component, characterizing the state of human capital, the level of economic development, and innovation activity of organizations in the region.   Scientific novelty: for the first time an attempt was made to provide a comparative analysis of Russian regions by the level of artificial intelligence technologies use by organizations and to find the determinants of its change.   Practical significance: the heterogeneity of regions in terms of the artificial intelligence technologies use by organizations was substantiated, as well as a great impact of the specific characteristics of regions, which should be taken into account when building a national policy of artificial intelligence development.Russian Journal of Economics and Law391The comparative analysis of regions by the level and growth rate of artificial intelligence technologiesuse by organizations showed that the regions were heterogenous by the dynamics of this indicator in 2020-2022, which should be taken into account when building a national policy of artificial intelligence development.Russian Journal of Economics and Law["J. A. Varlamova", "E. N. Korneychenko"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/c1f4d89a6ba298012447daf2655a724a98044068 c63310f1a41a10875578683f58ebdd3f7c8d2341Artificial intelligence (AI) and image processing are revolutionising the diagnosis and management of liver cancer. Recent advancements showcase AI’s ability to analyse medical imaging data, like computed tomography scans and magnetic resonance imaging, accurately detecting and classifying liver cancer lesions for early intervention. Predictive models aid prognosis estimation and recurrence pattern identification, facilitating personalised treatment planning. Image processing techniques enhance data analysis by precise segmentation of liver structures, fusion of information from multiple modalities, and feature extraction for informed decision-making. Despite progress, challenges persist, including the need for standardised datasets and regulatory considerations.Gastroenterology Review00AI’s ability to analyse medical imaging data, like computed tomography scans and magnetic resonance imaging, accurately detecting and classifying liver cancer lesions for early intervention and predictive models aid prognosis estimation and recurrence pattern identification.Przegla̜d Gastroenterologiczny["Platon Dimopoulos", "A. Mulita", "Andreas Antzoulas", "Sylvain Bodard", "Vasileios Leivaditis", "I. Akrida", "N. Benetatos", "Konstantinos Katsanos", "Christos-Nikolaos Anagnostopoulos", "F. Mulita"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/c63310f1a41a10875578683f58ebdd3f7c8d2341 e13e4d8ab1acf440aba9980ce72c8f220ec7e947Generative Artificial Intelligence (GAI) has emerged as a pivotal force in the Industrial Revolution – 4.0, significantly enhancing productivity and innovation across diverse sectors. This literature review investigates the economic impact of GAI from both macro and micro perspectives, focusing on its influence on productivity, creativity, and technological advancement in various industries. Through bibliometric analysis from the Web of Science database, this study identifies key trends, geographic distribution, and research hotspots in this field. Content analysis finds that GAI technologies substantially boost efficiency and economic output while posing challenges related to ethical considerations and societal risks. This review attempts to bridge the research gap by providing a systematic review and induction of influencing factors in this field and highlights the transformative potential of GAI. Future research directions are proposed to address gaps in understanding the comprehensive economic impact of GAI, aiming to guide enterprises in leveraging these technologies for competitive advantage.International Business &amp; Economics Studies00This review attempts to bridge the research gap by providing a systematic review and induction of influencing factors in this field and highlights the transformative potential of GAI.International Business &amp; Economics Studies["Huaize Zhang"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/e13e4d8ab1acf440aba9980ce72c8f220ec7e947 c53be90e2a074b0ce1632ba927d732b7be5cb830This paper tackles the largely untouched issue of optimal management of complex systems, now rendered much more challenging by current conditions of instability and turbulence. Managers need capabilities of foresight and holism in capturing the essence of wicked problems and the best way forward. Interviews were conducted with complex systems managers regarding their perceptions of the value of analysis and intuition in these circumstances and the potential of artificial intelligence (AI) to bring extra value to both modes, analysis, and intuition. Findings indicate that both styles were employed, and descriptions of intuition covered both experience‐based intuition and creative intuition. Regarding the encroachment of AI, the advantages of speed and reduced costs seemed to these respondents to make its adoption mandatory. However, the oversight role of people was considered critical by most respondents. The paper presents a framework to assist managers in understanding the relative roles of AI, analysis, and intuition as these relate to the complexity level of the system being managed.Systems research and behavioral science540A framework is presented to assist managers in understanding the relative roles of AI, analysis, and intuition as these relate to the complexity level of the system being managed.Systems Research and Behavioral Science["Leonie Hallo", "Tiep Nguyen", "Nicholas Chileshe", "Ba Quang Vinh Nguyen"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/c53be90e2a074b0ce1632ba927d732b7be5cb830 8ff8712a1941862fab6b6018aa9326a3b52bdd52As enterprises increasingly migrate to digital platforms, securing systems from unauthorized access becomes crucial to safeguarding sensitive data. Identity and Access Management (IAM) systems have traditionally been implemented to manage user access and authenticate identities. However, with growing threats of sophisticated cyber-attacks, traditional IAM methods are proving inadequate. Artificial Intelligence (AI) has emerged as a transformative force in enhancing IAM systems by providing adaptive, intelligent solutions to prevent unauthorized access. This paper explores the role of AI-enhanced IAM in modern enterprises, emphasizing its ability to predict, detect, and respond to potential security breaches. Through a detailed examination of current AI applications in IAM, this study demonstrates how AI-enabled tools can increase security resilience, reduce false positives, and automate responses to evolving threats, thus offering enterprises a proactive defense against unauthorized access.International journal of management information systems and data science00Through a detailed examination of current AI applications in IAM, this study demonstrates how AI-enabled tools can increase security resilience, reduce false positives, and automate responses to evolving threats, thus offering enterprises a proactive defense against unauthorized access.International journal of management information systems and data science["Md Takbir Hossen Sarker", "Md Sanaur Rahman", "Mohammed Mahi Uddin", "Nur Alam Farhad Shakil"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/8ff8712a1941862fab6b6018aa9326a3b52bdd52 53ed59c3bc23ff276ccc98f2063646aabc053654Automated diagnostic and predictive asset manage-ment capabilities are of paramount importance in the era of connected and automated cooperative mobility. A diagnostic vehicle can scan the rail network and process sensor measurements to prevent incoming disruptions and ensure smooth operation of automated transportation services. This requires the development of reliable algorithms that enable early warning and predictive asset management. An algorithm based on artificial intelligence techniques is presented here. The algorithm analyses diagnostic measures and relates them to observed faults on the rail network. In operation mode, the algorithm predicts maintenance needs based on current measurements.International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow150An algorithm based on artificial intelligence techniques is presented here that analyses diagnostic measures and relates them to observed faults on the rail network and predicts maintenance needs based on current measurements.2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)["L. D. Costanzo", "Angelo Coppola", "Stefano Marrone"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/53ed59c3bc23ff276ccc98f2063646aabc053654 fea501801581834c1289d5cc3e70a104622090a2AIM To conduct a bibliometric analysis of research on artificial intelligence (AI) in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies. METHODS Relevant articles on the application of AI in the field of glaucoma from the Web of Science Core Collection were retrieved, covering the period from January 1, 2013, to December 31, 2022. In order to assess the contributions and co-occurrence relationships among different countries/regions, institutions, authors, and journals, CiteSpace and VOSviewer software were employed and the research hotspots and future trends within the field were identified. RESULTS A total of 750 English articles published between 2013 and 2022 were collected, and the number of publications exhibited an overall increasing trend. The majority of the articles were from China, followed by the United States and India. National University of Singapore, Chinese Academy of Sciences, and Sun Yat-sen University made significant contributions to the published works. Weinreb RN and Fu HZ ranked first among authors and cited authors. American Journal of Ophthalmology is the most impactful academic journal in the field of AI application in glaucoma. The disciplinary scope of this field includes ophthalmology, computer science, mathematics, molecular biology, genetics, and other related disciplines. The clustering and identification of keyword nodes in the co-occurrence network reveal the evolving landscape of AI application in the field of glaucoma. Initially, the hot topics in this field were primarily "segmentation", "classification" and "diagnosis". However, in recent years, the focus has shifted to "deep learning", "convolutional neural network" and "artificial intelligence". CONCLUSION With the rapid development of AI technology, scholars have shown increasing interest in its application in the field of glaucoma. Moreover, the application of AI in assisting treatment and predicting prognosis in glaucoma may become a future research hotspot. However, the reliability and interpretability of AI data remain pressing issues that require resolution.International Journal of Ophthalmology740A bibliometric analysis of research on artificial intelligence (AI) in the field of glaucoma to gain a comprehensive understanding of the current state of research and identify potential new directions for future studies reveals the evolving landscape of AI application in the field of glaucoma.International journal of ophthalmology["Chun Liu", "Lu-Yao Wang", "Ke-Yu Zhu", "Chun Liu", "Jun-Guo Duan"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/fea501801581834c1289d5cc3e70a104622090a2 8aff6978d7b28fd00dcbe886621cee7e7bce1e57Building upon the sociotechnical system theory, the present study contributes by examining the relationship between artificial intelligence (AI) adoption, employees' innovative behaviour on employee performance and job security (JS). The primary data is collected from 340 employees from firms located in the industrial hub of a developing economy using a simple random technique, and data is analysed using Smart‐PLS 3 from the manufacturing sector. The study evidences that employees' adoption and utilization of AI technologies positively influence their innovative behaviour, job performance (JP), and security. Moreover, the study finds a mediating role of innovative behaviour to connect the dots. Organizations can prioritize using AI‐driven training programmes so employees can use AI tools efficiently. Study findings also encourage employees to engage in innovative work behaviours like investigating novel concepts and experimenting with AI technologies to improve JP. This study invalidates that AI will replace employees at the workplace, as we can safely conclude that AI adoption enhances JP and JS.Systems research and behavioral science460The study evidences that employees' adoption and utilization of AI technologies positively influence their innovative behaviour, job performance (JP), and security and invalidates that AI will replace employees at the workplace.Systems Research and Behavioral Science["Davood Ghorbanzadeh", "J. Espinosa-Cristia", "N. Abdelrasheed", "Sanaa Soliman Saeed Mostafa", "S. Askar", "Saman M. Almufti"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/8aff6978d7b28fd00dcbe886621cee7e7bce1e57 4af84a6cd01e66eb991ad706015d07a759e5951fThe study ventures into the changing environment of blockchain, artificial intelligence (AI), and cloud computing as key components of financial technology (FinTech). This research examines how these technologies are transforming the financial system, with a focus on intelligent finance investment including blockchain’s disruptive potential. It begins with a brief description of cloud computing, explaining how it promotes extensibility, formability & cost-efficiency inside financial Institutions. The focus then shifts to AI applications, including the impact of robot advisers, trading using algorithms as well as statistical research on the reinvention of investing processes. The tale probes further into complex architecture of blockchain technology, giving light on its ability to increase the security and openness of the financial system. It reveals linkages that open the path for novel methods and significant developments, emphasizing the convergence of cloud, AI & blockchain. As we explore these FinTech territories, the paper acknowledges the inherent obstacles and dangers while also providing thoughts on legislative and ethical implications. It examines emerging trends, predicting the long-term influence of novel innovations on the financial environment.International Conferences on Contemporary Computing and Informatics280This research examines how these technologies are transforming the financial system, with a focus on intelligent finance investment including blockchain’s disruptive potential, and examines emerging trends, predicting the long-term influence of novel innovations on the financial environment.2024 7th International Conference on Contemporary Computing and Informatics (IC3I)["Sandeep Singh", "Tiyas Sarkar", "Monika Mangla", "Manik Rakhra", "Amanpreet Singh", "Kapil Jairath"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/4af84a6cd01e66eb991ad706015d07a759e5951f 5fb2a84b93628ee3f16d0e95b0b99b7e920d67c4This paper introduces work by the IEEE P7014.1 Working Group on the Recommended Practice for Ethical Considerations of Emulated Empathy in Partner-based General-Purpose Artificial Intelligence Systems. This paper briefly details the scope and parameters of the standard, why it matters, and key ethical problems found regarding use of modern AI systems that emulate empathy for human AI-partnering. Some of these problems are fairly obvious, and others are less so, but no less important. A few however require deeper consideration because, like many important ethical discussions, they do not have easy answers. One such question is when is deception in human-computer interaction acceptable, particularly where deception overlaps with animism and anthropomorphism and may be exacerbated by emulations of empathy? This paper lingers on this question, drawing on philosophical and ethical discussion about the nature of deception, contexts where it is acceptable and beneficial, and contexts where it is morally out of scope.International Symposium on Technology and Society300The scope and parameters of the standard, why it matters, and key ethical problems found regarding use of modern AI systems that emulate empathy for human AI-partnering are detailed.2024 IEEE International Symposium on Technology and Society (ISTAS)["V. Bakir", "Karen Bennet", "Ben Bland", "Alexander Laffer", "Phoebe Li", "Andrew McStay"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/5fb2a84b93628ee3f16d0e95b0b99b7e920d67c4 d9fb8ef8aa38e517d9f2242a39c4d49dbc4b037bIn recent years, under the auspices of technological development, artificial intelligence technology has penetrated into various industries, such as healthcare, industry, energy and even education. For the penetration of the education field, artificial intelligence has changed the teaching mode, teaching management, and brought unprecedented opportunities for the professional development of teachers. This paper explores the role positioning and function play of AI in the professional development of university teachers, with the goal of promoting the modernized university teacher team, after analyzing the role positioning of AI technology in the professional development of university teachers, researching the function play strategy of AI technology in the professional development of university teachers, and mentioning the limitations and challenges of AI in the professional development of teachers, teachers' role changes and It also mentions the limitations and challenges of AI in teachers' professional development, teachers' role transformation and competence requirements, so as to further improve the framework of AI's promotion of teachers' professional development in universitys and universities, and to strengthen the foundation of teachers' development in universitys and universities in China with the help of AI technology, so as to provide a "big country's good teacher" for high-quality higher education and promote the modernization and high-quality transformation of higher education in China.International Journal of Education and Humanities70The role positioning and function play of AI in the professional development of university teachers is explored, with the goal of promoting the modernized university teacher team and to strengthen the foundation of teachers' development in universitys and universities in China with the help of AI technology.International Journal of Education and Humanities["Guoyan Zhong", "Rajendran Nagappan"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/d9fb8ef8aa38e517d9f2242a39c4d49dbc4b037b bdd4ece346fdf0137e38ad3505a268d58b91103eIntegrating artificial intelligence (AI) techniques such as machine learning and deep learning into freeform optics design has significantly enhanced design efficiency, expanded the design space, and led to innovative solutions. This article reviews the latest developments in AI applications within this field, highlighting their roles in initial design generation, optimization, and performance prediction. It also addresses the benefits of AI, such as improved accuracy and performance, alongside challenges like data requirements, model interpretability, and computational complexity. Despite these challenges, the future of AI in freeform optics design looks promising, with potential advancements in hybrid design methods, interpretable AI, AI-driven manufacturing, and targeted research for specific applications. Collaboration among researchers, engineers, and designers is essential to fully harness AI's potential and drive innovation in optics.arXiv.org700The latest developments in AI applications within this field are reviewed, highlighting their roles in initial design generation, optimization, and performance prediction and addressing the benefits of AI, such as improved accuracy and performance, alongside challenges like data requirements, model interpretability, and computational complexity.ArXiv["Lei Feng", "Jingxing Liao", "Jingna Yang"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/bdd4ece346fdf0137e38ad3505a268d58b91103e 5e57cf36ebfa685675558a43532444754089341dGenerating the format of the article to determine the feasibility of the procedure for covering artificial intelligence (AI) as it becomes popular as a terminology on the problem of levelling vocational education. Stabilization of the procedure involving artificial intelligence (AI), i.e. creative implementation of the vocational education industry in the form of the level of acquisition of a working profession in the field of vocational education. Accordingly, the optimization and professionalism of the advantages of artificial intelligence (AI) in relation to the aspect of the level of professional development and strengthening of the vocational industry in the format of the educational sector in relation to the direction of finding effective mechanisms for developing the content of the application of the quality of professional training in order to improve the level of competence of students. The line of the ability to fulfill the tasks of the educational process during the nominal activities of the level of vocational education with the use of artificial intelligence (AI), respectively, considers the possibility of nominal implementation of methodological proposals, that is, in the course of attracting the creativity of the educational perspective in the field of vocational education. Accordingly, industry activities in the format of improving the level of the educational process with a cascade flow in solving problems, that is, solutions in the context of attracting modern technologies. Optimization and creative advantages during the influence and generation of the industry during the acquisition of professional education, which is significantly synchronized with the concept of prospects and the effectiveness of the involvement of artificial intelligence (AI). The correct procedure for applying in the nominal range of educational fields and creative methodological proposals, that is, improving the level of inferential reduction of preventive measures with the involvement of the format of professional training towards artificial intelligence (AI). The connecting link along with artificial intelligence (AI) nominally applied in the field of autonomy is optimization and creative advantages during the influence and generation of the vocational education industry and, accordingly, statistics of social reality and expanding access to artificial intelligence (AI), that is, the combination with the level of intelligence of students and the duality of the introduction of automated processes of artificial intelligence (AI).SERIEs: Journal of the Spanish Economic Association00The connecting link along with artificial intelligence (AI) nominally applied in the field of autonomy is optimization and creative advantages during the influence and generation of the vocational education industry and, accordingly, statistics of social reality and expanding access to artificial intelligence (AI).Bulletin of Postgraduate Education (Series)["Vasyl \u041ello"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/5e57cf36ebfa685675558a43532444754089341d b0a325c05734e870dca1344cfe16ba663a1f3da7This article delves into the critical integration of privacy by design in artificial intelligence (AI). As AI evolves and permeates various sectors, it brings unparalleled efficiency and personalization but also significant privacy challenges. The article explores the impacts of AI on data privacy, highlighting issues such as data re-identification, transparency, and data security. It underscores the importance of incorporating privacy from the design phase, following key principles such as proactivity, privacy as a default setting, and user-centric design. By adopting these principles, companies can ensure their AI systems are both technologically advanced and ethically responsible, building trust and ensuring sustainability in the digital age.IEEE Biennial Congress of Argentina280The article explores the impacts of AI on data privacy, highlighting issues such as data re-identification, transparency, and data security, and underscores the importance of incorporating privacy from the design phase, following key principles such as proactivity, privacy as a default setting, and user-centric design.2024 IEEE Biennial Congress of Argentina (ARGENCON)["Fabi\u00e1n Descalzo"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/b0a325c05734e870dca1344cfe16ba663a1f3da7 89ff8ccf24d547859c7b77ce4a23370102febe43The purpose of the current research was to use artificial intelligence with higher education in Vietnam: opportunities, challenges and recommendations for legal undergraduate studies,  Research method: the current library research method using articles from PubMed, Embase, Scopus databases., Medline and Web of Science using the keywords of artificial intelligence, higher education of Vietnam, was an expert in law. Results: The reviews of articles in the fields of artificial intelligence and higher education in educational fields, especially law in Vietnam, showed that in the present era, the use of artificial intelligence (AI) has become increasingly popular in Vietnam. Artificial intelligence is being used in higher education in general, and law schools show particular promise. However, this software has significant differences compared to other domains. Conclusion, according to the research results, we conclude that artificial intelligence offers many transformative opportunities for higher education in Vietnam. And by effectively using the power of artificial intelligence, Vietnamese universities can improve the quality of their educational offerings and align them with the evolving demands of society, uniting technology developers and students across Vietnam.Conhecimento &amp; Diversidade00By effectively using the power of artificial intelligence, Vietnamese universities can improve the quality of their educational offerings and align them with the evolving demands of society, uniting technology developers and students across Vietnam.Conhecimento &amp; Diversidade["Doan Hong Nhung", "Nguyen Xuan Bao", "Vu Thi Hong Ha"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/89ff8ccf24d547859c7b77ce4a23370102febe43 cad06d028da62c3a714548d860034a4fdbf2035aPurpose Artificial intelligence (AI) is the branch of computer science where intelligent machines are being developed to imitate human intellect. Agriculture is persistently hard-pressed to produce more using a lesser amount of resource. The utilization of Artificial Intelligence (AI) techniques is growing in agricultural domain for increasing the production now days. But a large number of barriers exist in this area such as technology gap between experts and farmers, unexpected diseases infections, incorrect soil management, handling high volume data for analysis and lower production. This is high time to implement responsible AI frameworks in agriculture domains for the well-being of farmers. The key advantages of AI are precision, lower outlay, higher performance and flexibility. This work carries a standard review of AI application in agriculture for coping various parameters such as crop, soil, water, weed, pest and disease. The clear representation of advantages and disadvantages of various applications has been represented in tabular form. This work contributes in presenting a standard survey of several AI based applications that helps in drawing a logical framework for precision agriculture to maximize the crop productivity. This paper put forward the most recent stage of advancement of AI in the field of agriculture including the possible impact over latest business schemes. Several new research directions are recognized for improved productivity. With the help of inferences drawn in conclusion researchers will be able to explore valuable contributions of AI applications that may be used in various agricultural activities.International Conferences on Contemporary Computing and Informatics230The most recent stage of advancement of AI in the field of agriculture including the possible impact over latest business schemes is put forward including the possible impact over latest business schemes.2024 7th International Conference on Contemporary Computing and Informatics (IC3I)["Rohitash Upadhyay", "Megha Kamble"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/cad06d028da62c3a714548d860034a4fdbf2035a 67e9ecb32e4989fcd6cf49df5d1f1a614cc69eaaInventions are the key foundation of innovation. When a new technology is introduced to themarket, society benefits both directly, as it enables us to do things that were previously impossible,and indirectly, in terms of the economic opportunities that arise, such as business developmentand employment. Currently, the only way to obtain proper legal protection for an inventionis through patent registration. Delays in obtaining a patent can significantly hinder thebusiness and innovation processes of any country. Given the increasing number of patent applicationsin Ukraine and globally, it is important to study methods for optimizing the patent examinationprocess. This article addresses the issue of the steadily increasing number of patentapplications by exploring the use of artificial intelligence (AI) technologies in the patent examinationprocess. It examines the relevance, methods, and outcomes of using AI technologies ineconomically developed countries, with particular attention to the cooperation between the NationalInstitute of Industrial Property of Brazil and the American Chemical Society.On September 4, 2020, these organizations signed a Technical Cooperation Agreementaimed at testing and optimizing patent examination processes using AI technologies. This cooperationhas demonstrated convincingly positive results in optimizing patent examinationprocesses with AI. The article also analyses the content of Ukraine's «Rules for Examinationof Applications for Invention and Utility Model Applications» and identifies the stages atwhich the use of AI technologies is possible and appropriate, considering the importance ofprocess optimization and speeding up patent application examinations. As a result of thestudy, it is recommended that the national patent office begin developing and implementingAI technologies in the patent application examination process. This will help accelerate theexamination of patent applications and, consequently, stimulate innovation in Ukraine.Theory and Practice of Intellectual Property00It is recommended that the national patent office begin developing and implementing artificial intelligence technologies in the patent application examination process to help accelerate the examination of patent applications and, consequently, stimulate innovation in Ukraine.Theory and Practice of Intellectual Property["M. Mykhailenko"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/67e9ecb32e4989fcd6cf49df5d1f1a614cc69eaa 919fb5f64d1ef45c7272417c814ac9573eef20bcThe Military Health System (MHS) has historically been at the forefront of innovation in medicine and science, but it has also historically struggled to implement battlefield innovations or civilian technologies for wider domestic use. Artificial intelligence (AI) has emerged as a transformative force in health care with civilian health systems and institutions at the forefront of these innovations. While these tools have the potential to support resolution of military health's most pressing issues, the MHS is behind its civilian counterparts in advancing AI. Adoption of AI could benefit the MHS in such areas as service member and beneficiary access to care; more precise allocation of medical personnel and resources; improved operations of military treatment facilities; early detection of emerging threats to health; and force multiplication of existing telehealth capabilities. This evolving and highly visible technology also presents challenges in the military context above those in the civilian context, such as additional levels of privacy and security, integration with purpose-built secure systems, and additional regulatory obligations. To address these, the MHS should engage in three lines of effort to advance AI: establishing governance, education and training of medical personnel, and engaging in research, development, testing, and piloting of AI applications. This will require dedicated personnel and resources for a substantial initial outlay to be recouped later through more effective administration and care. By leveraging lessons learned from civilian systems, the MHS can design, adopt, and implement AI solutions to improve care for service members in both domestic and operational contexts, and for their beneficiaries.Military Medicine00The Military Health System should engage in three lines of effort to advance AI: establishing governance, education and training of medical personnel, and engaging in research, development, testing, and piloting of AI applications.Military medicine["Terry Adirim", "C. Madsen"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/919fb5f64d1ef45c7272417c814ac9573eef20bc 6c773aa4105e4c90ef34d32156534b02f920cbcbThe integration of Artificial Intelligence techniques in the domain of power converters has emerged as a transformative force, revolutionizing design, control, and maintenance paradigms. This paper provides a comprehensive overview of recent advancements in Artificial Intelligence applications tailored specifically for power converters. Moreover, Artificial Intelligence-based control strategies have demonstrated remarkable performance improvements in regulating power converters across varying operating conditions. Adaptive control algorithms, fuzzy logic systems, and deep learning techniques have facilitated real-time adaptive control, fault detection, and mitigation, enhancing stability, responsiveness, and robustness in converter operation. This paper also highlights key research contributions, challenges, and future directions in the integration of Artificial Intelligence technologies for the design, control, and maintenance of power converters. The synergy between Artificial Intelligence and power electronics holds immense promise for advancing the efficiency, reliability, and sustainability of power conversion systems in diverse applications, ranging from renewable energy integration to electric vehicle.International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow50A comprehensive overview of recent advancements in Artificial Intelligence applications tailored specifically for power converters, highlighting key research contributions, challenges, and future directions in the integration of Artificial Intelligence technologies for the design, control, and maintenance of power converters.2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI)["Vipinkumar Shriram Meshram", "Lorenzo Becchi", "Cristian Garz\u00f3n Alfonso", "L. Paolucci", "Francesco Grasso", "Alberto Reatti"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/6c773aa4105e4c90ef34d32156534b02f920cbcb 9d46f548e1ee6558fbf7ff2e8ca550e603735ea0This report summarizes the webinar held on 26 April 2024 by the judges of the International Association of Tax Judges on the topic of tax courts and artificial intelligence.Bulletin for International Taxation00Bulletin for International Taxation["B. Michel"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/9d46f548e1ee6558fbf7ff2e8ca550e603735ea0 efe2fbb043908918b4e4bb18997b33068bb707deThis study aims to identify the level of attitudes of faculty members in Palestinian universities regarding the opportunities and challenges of employing artificial intelligence applications in higher education. The researchers used a descriptive approach, and the study’s sample consisted of (130) faculty members at An-Najah National University. Data was collected using two specific questionnaires, one focused on opportunities and the other on challenges. Data analysis was conducted using statistical tests, specifically calculating means and standard deviations, Independent Samples Test, Mann–Whitney test, One Way ANOVA, and Kruskal-Walli’s test. The study’s results indicated that the average level of attitudes among faculty members regarding the opportunities and challenges of employing artificial intelligence applications in higher education was high. Furthermore, the results revealed no statistically significant differences in all areas of opportunities and challenges related to gender, except in “supporting learning and teaching processes,” which favored males. The results indicated no statistically significant differences in all areas of opportunities and challenges related to the educational qualifications, except for the “Benefits of AI applications in teaching and education,” in favor of an associate professor. The results also indicated no statistically significant differences in the opportunities and challenges of employing artificial intelligence applications attributed to variables of years of experience and the college. Based on this, the study recommends the necessity of implementing intensive training programs for university faculty members to enhance their skills in using artificial intelligence applications in higher education, as well as addressing the concerns and risks that hinder the adoption of these applications. Additionally, conducting experimental research to explore the integration of artificial intelligence applications in education and evaluate their effectiveness is essential.Frontiers in Education310The study’s results indicated that the average level of attitudes among faculty members regarding the opportunities and challenges of employing artificial intelligence applications in higher education was high and the necessity of implementing intensive training programs for university faculty members to enhance their skills in using artificial intelligence applications in higher education is recommended.Frontiers in Education["Amal Omar", "A. Shaqour", "Zuheir N. Khlaif"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/efe2fbb043908918b4e4bb18997b33068bb707de 02530b7509df056e80c1e9def29e6d448d97830aThe use of data-driven methods in fluid mechanics has surged dramatically in recent years due to their capacity to adapt to the complex and multi-scale nature of turbulent flows, as well as to detect patterns in large-scale simulations or experimental tests. In order to interpret the relationships generated in the models during the training process, numerical attributions need to be assigned to the input features. One important example are the additive-feature-attribution methods. These explainability methods link the input features with the model prediction, providing an interpretation based on a linear formulation of the models. The SHapley Additive exPlanations (SHAP values) are formulated as the only possible interpretation that offers a unique solution for understanding the model. In this manuscript, the additive-feature-attribution methods are presented, showing four common implementations in the literature: kernel SHAP, tree SHAP, gradient SHAP, and deep SHAP. Then, the main applications of the additive-feature-attribution methods are introduced, dividing them into three main groups: turbulence modeling, fluid-mechanics fundamentals, and applied problems in fluid dynamics and heat transfer. This review shows thatexplainability techniques, and in particular additive-feature-attribution methods, are crucial for implementing interpretable and physics-compliant deep-learning models in the fluid-mechanics field.International Journal of Heat and Fluid Flow2152This review shows that explainability techniques, and in particular additive-feature-attribution methods, are crucial for implementing interpretable and physics-compliant deep-learning models in the fluid-mechanics field.ArXiv["A. Cremades", "S. Hoyas", "Ricardo Vinuesa"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/02530b7509df056e80c1e9def29e6d448d97830a 05bf560c8d665255e3de2aedf353b50d76f9256bConversational AI models are becoming increasingly popular and are about to replace traditional search engines for information retrieval and product discovery. This raises concerns about monetization strategies and the potential for subtle consumer manipulation. Companies may have financial incentives to steer users toward search results or products in a conversation in ways that are unnoticeable to consumers. Using a behavioral experiment, we show that conversational AI models can indeed significantly shift consumer preferences. We discuss implications and ask whether regulators are sufficiently prepared to combat potential consumer deception.152Using a behavioral experiment, it is shown that conversational AI models can indeed significantly shift consumer preferences and asked whether regulators are sufficiently prepared to combat potential consumer deception.["Tobias Werner", "Ivan Soraperra", "Emilio Calvano", "David C. Parkes", "Iyad Rahwan"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/05bf560c8d665255e3de2aedf353b50d76f9256b 9b8462889267ff73c91719b4d15e6fcfb7f0c188This article examines the possibility of replacing humans with artifi cial intelligence (AI) in the industrial sector. The author analyzed current achievements in this area and studied their impact on the productivity and economic effi ciency of enterprises. Particular attention is paid to the social and ethical aspects of AI implementation, including occupational safety and the replacement of people with robots. In addition, the article examines the prospects for the development of technologies and their potential for industrial transformation. Based on current data, the author concluded that tasks that can be transferred to AI and which still require human participation.Upravlenie kachestvom (Quality management)10It is concluded that tasks that can be transferred to AI and which still require human participation are those that can be transferred to AI and which still require human participation.Upravlenie kachestvom (Quality management)["O.V. Saverkin"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/9b8462889267ff73c91719b4d15e6fcfb7f0c188 b3a612ed4867d9e770032ef58f08b3125a9139c6In this study, a convolutional neural network (CNN) architecture adapted from the Tiny VGG model is proposed for automated disease detection in grapevine leaves, thus addressing challenges in vineyard phytosanitary management by grape-producing regions of Peru, such as Ica and Piura. Data augmentation techniques were used and three experiments were carried out to evaluate the performance of the model in classifying three fungal diseases on grapevine leaves. Additionally, the PlantVillage dataset was used for training. The results show that, with the basic configuration, the model achieved a higher accuracy (89.9%) and weighted F1 score (89.9%), highlighting the viability of this approach in the early and effective detection of diseases in viticulture.International Symposium on Technology and Society2502024 IEEE International Symposium on Technology and Society (ISTAS)["Jos\u00e9 Luis Herrera Salazar", "Magdalena Talla Linderman", "Hilda Luzmila Felix Pachas", "Jhon Angel Herrera Salazar"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/b3a612ed4867d9e770032ef58f08b3125a9139c6 ccceefb85fe9e67bb8d3a2236da85f62ae4d1200International Conference on Education Technology and Computer200{"pages": "117-124"}["Myriam Guadalupe Pe\u00f1afiel", "Mar\u00eda-Stefanie V\u00e1squez-Pe\u00f1afiel", "Diego Alberto V\u00e1squez Pe\u00f1afiel"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/ccceefb85fe9e67bb8d3a2236da85f62ae4d1200 39cfb8fe7c9aa190eb0f01ba43041c7d80878a29Canadian Medical Education Journal10Canadian Medical Education Journal["Joshua Feldman", "Christopher Gilchrist", "Fok-Han Leung"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/39cfb8fe7c9aa190eb0f01ba43041c7d80878a29 15d0a46e1a7bc33ee27a54c5d9962ee25bdb4cc3Academic Psychiatry20Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["John-Stephane Kouam", "T. K. Pak", "Cesar Eber Montelongo Hernandez"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/15d0a46e1a7bc33ee27a54c5d9962ee25bdb4cc3 56ad287a04ca08c9ba6d927383012eb73d925f10Organizational performance is a cornerstone for the success of businesses. During the fourth industrial revolution, it is imperative for organizations to prioritize the integration of technological advancements in diverse functions to gain competitive edge. The emergence of AI is imperative for organizations to derive benefits and boost productivity levels for the survival of businesses. The principal aim of research is to gain a comprehensive understand of existing literature through diverse quantitative techniques, including publication trends, authorship patterns, source and keyword analysis, and so on. Furthermore, the objective is to understand the emerging themes through thematic mapping analysis. This research study applied a bibliometrics approach to review scientific literature and assess the current trends. To achieve this, we selected 260 documents from Scopus after applying the necessary filters and utilize the software such as R Studio to analyze the outcome. The outcome of the study is classified into two categories such as performance analysis and science mapping. Performance analysis reveals the pertinent information regarding the most relevant authors, sources, affiliations and countries in existing researches. On the other side, science mapping elucidates the interrelationship between research constituents through keyword and citation analysis. Thematic mapping and factorial analysis conducted to identify the untapped themes such as game theory, workforce dynamics, decision-making, knowledge management, learning systems, robotics, decision support systems, innovative product design, and so in this field of area.2024 5th International Conference on Smart Electronics and Communication (ICOSEC)300This research study applied a bibliometrics approach to review scientific literature and assess the current trends and selected 260 documents from Scopus after applying the necessary filters and utilize the software such as R Studio to analyze the outcome.2024 5th International Conference on Smart Electronics and Communication (ICOSEC)["Sakshi Kheterpal", "Ashita Chadha", "Komalpreet Kaur"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/56ad287a04ca08c9ba6d927383012eb73d925f10 f0d1582bec0e48a83e507f4cf73c4f165d398b8200["Beth Singler"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/f0d1582bec0e48a83e507f4cf73c4f165d398b82 1eff02aedcbca7374c6fac61f093409b01d47e72Computer-Aided Design and Applications00Computer-Aided Design and Applications["Dongdong Zhang", "Lixian Chen"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/1eff02aedcbca7374c6fac61f093409b01d47e72 44ac4d1bf7be6c12f450955676e86fa50c860393Computer-Aided Design and Applications00Computer-Aided Design and Applications["Lina Wang"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/44ac4d1bf7be6c12f450955676e86fa50c860393 c89a0a10ff04cd3d8f0e285912e2f13d36029f51<jats:p xml:lang="fr"/>Mathematical biosciences and engineering : MBE10Mathematical biosciences and engineering : MBE["Editorial Office Of Mathematical Biosciences And Engineering"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/c89a0a10ff04cd3d8f0e285912e2f13d36029f51 fa46367e105dc01da15cf1cd53412a0309c43396Smart city is planned to accommodate the increasing population density by effectively using and adopting contemporary information and communication technology (ICT). It effectively helps in managing the city’s increasing urbanisation and energy consumption, preserving the environment, raising the economic and living standards of its residents, and maintaining a green environment. The concept of smart cities is significantly depended on ICT technology for the development, decision-making, execution, and delivery of final productive services. In this article, the technological interventions for the smart city development are discussed. The idea of the “smart city” has become widely accepted in recent decades. Its main objective is to use intelligent applications to raise the living standards of its residents. The results of the study show that creative solutions positively and significantly affect the efficacy, efficiency, calibre, and speed of decision-making in the management of smart cities. The respondents believed that these solutions significantly improved the city’s decision-making process, and the study illustrated the importance of intelligent solutions in administering smart cities.International Conferences on Contemporary Computing and Informatics190The results of the study show that creative solutions positively and significantly affect the efficacy, efficiency, calibre, and speed of decision-making in the management of smart cities.2024 7th International Conference on Contemporary Computing and Informatics (IC3I)["Vivek Veeraiah", "J. S. Prabaharan", "Nikita H. Modi", "Madhuri Suryavanshi", "L. Geetha", "Ashwini Satyanarayana"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/fa46367e105dc01da15cf1cd53412a0309c43396 2f1c514c83a57860cbf3eb6eff9f9251969d6183Tujuan dari kegiatan pengabdian masyarakat ini adalah memberikan informasi kepada peserta didik mengenai AI Chat GBT sebagai pencari informasi sumber belajar dan pembuatan media pembelajaran. Metode yang dilakukan dalam kegiatan ini yaitu : 1. Observasi langsung yakni pengabdi langsung datang ke lokasi pengabdian untuk memperolah data. Hal ini kami lakukan pada saat menjelang maupun saat kegiatan berlangsung; 2. Melakukan kegiatan pengabdian masyarakat dengan melakukan sosialisasi dan pelatihan kepada mitra pengabdian masyarakat; 3. Melakukan evaluasi dan berbagi pendapat bersama untuk menghasilkan hasil akhir kegiatan.  Hasil dari pengabdian masyarakat yaitu kegiatan pengabdian masyarakat yang telah tim pengabdi lakukan dilaksanakan pada hari Selasa, tanggal 4 Juni 2024 pada pukul 10.00 s/d 13.00 WIB bertempat di SMP Bina Mulia yakni pemberian informasi bahwa teknologi informasi AI saat ini dapat membantu peserta didik dalam membuat media belajar presentasi dan sumber belajar sehingga peserta didik memiliki keterampilan dan pengalaman saat melakukan pembelajaran. Simpulan hasil pengabdian masyarakat yakni hasil dari kegiatan pengabdian masyarakat yang telah dilaksanakan yaitu kegiatan sosialisasi pendidikan pada SMP Bina Mulia Depok diterima sangat baik oleh peserta dengan banyaknya pemberian saran agar adanya penambahan waktu dan pelatihan unjuk kerja bagi peserta didik sehingga memperoleh pengalaman dalam pembelajaran.  Peserta didik memberikan umpan balik kepada pemateri adalah apabila diadakan kegiatan lagi maka akan mempersiapkan alat yang dibutuhkan secara baik. Serta hasil pengabdian masyarakat ini akan memberikan manfaat bagi penerima kegiatan pengabdian untuk pengalaman belajar peserta.Jurnal Abdimas Indonesia00Jurnal Abdimas Indonesia["D. Ahmad", "Mal Alfahnum", "Westri Andayanti"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/2f1c514c83a57860cbf3eb6eff9f9251969d6183 bec2dae1690fe9d19e43d02371be6d47c64a3879Nursing Ethics10Nursing ethics["Ann Gallagher"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/bec2dae1690fe9d19e43d02371be6d47c64a3879 727bdc53e4114ad7b240413f97e642c87000b3dePolycentricity theory is predicated on the existence of multiple decision-making centres within a metropolitan area, collectively using a mutually agreed conventional system of governance. The digital transformation has increasingly integrated agentic computational artefacts (or artificial intelligence systems) into these social arrangements, thereby extending the concept of polycentricity to encompass ‘digital polycentricity.’ Digital polycentricity can be examined through four distinct ‘lenses’: interactional, governmental, architectural, and axiological. In this paper, we employ the interactional lens to study artificial interaction. Our analysis highlights several critical issues related to power and labour in the digital society. To mitigate these issues, we propose two strategies for developing Value-Sensitive Operationalisation (VSO), a framework aimed at guiding the creation of socio-technical systems that are fit-for-purpose and contextually appropriate. These strategies include: 1) leveraging digital data objects to support collective action, and 2) embedding a sense of place in the design of algorithms. By using these strategies, VSO practitioners can develop artificial intelligence (AI) systems that effectively meet user needs and cultivate a human–AI relationship designed to re-empower human stakeholders.International Symposium on Technology and Society340This paper proposes two strategies for developing Value-Sensitive Operationalisation (VSO), a framework aimed at guiding the creation of socio-technical systems that are fit-for-purpose and contextually appropriate and can develop artificial intelligence systems that effectively meet user needs and cultivate a human–AI relationship designed to re-empower human stakeholders.2024 IEEE International Symposium on Technology and Society (ISTAS)["Ciske Smit", "Jeremy V. Pitt"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/727bdc53e4114ad7b240413f97e642c87000b3de fa712daf554b4ed399816ea0ffd22c557974038eIn the context of mathematics education, the integration of artificial intelligence (AI) in teaching calculus is revolutionizing instructional methodologies and enhancing learning experiences both inside and outside the classroom. This study explores the use of specific AI tools, including ChatGPT, MathGPT, Gemini, and Wolfram Alpha, to deepen students’ understanding of key mathematical concepts such as derivatives and rates of change through continuous interaction with a virtual tutor. By employing well-designed prompts, these tools facilitated problem-solving exercises that were verified and refined by AI, fostering both precision in calculations and conceptual clarity. Observations from the classroom implementation reveal that students not only improved their accuracy in performing derivative calculations but also developed a clear understanding of the distinctions between average and instantaneous rates of change. The AI tools created a dynamic, adaptive learning environment, providing immediate feedback and simulations that significantly boosted student engagement and motivation. These findings underscore the potential of AI to transform mathematics education by making learning more personalized and accessible, ultimately enhancing educational outcomes and preparing students for future academic and professional challenges. Furthermore, this study introduces an innovative approach to refining AI prompts and interactions, highlighting the importance of iterative improvement to enhance the quality of AI feedback. This approach is crucial for developing better problem-solving skills and ensuring a comprehensive understanding of mathematical concepts.Education sciences515This study explores the use of specific AI tools, including ChatGPT, MathGPT, Gemini, and Wolfram Alpha, to deepen students’ understanding of key mathematical concepts such as derivatives and rates of change through continuous interaction with a virtual tutor.Education Sciences["R. Torres-Pe\u00f1a", "Darwin Pe\u00f1a-Gonz\u00e1lez", "Ellery Chacuto-L\u00f3pez", "E. A. Ariza", "Diego Vergara"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/fa712daf554b4ed399816ea0ffd22c557974038e d20297e5636ee66b62bbdadb376effecc58e4a3dThis study employs a systematic literature review (PRISMA methodology) to investigate the integration of Artificial Intelligence (AI) in walkability assessments conducted between 2012 and 2022. Analyzing 34 articles exploring data types, factors, and AI tools, the review emphasizes the value of utilizing diverse datasets, particularly street view images, to train supersized AI models. This approach fosters efficient, unbiased assessments and offers deep insights into pedestrian environment interactions. Furthermore, AI tools empower walkability assessment by facilitating mapping, scoring, designing pedestrian routes, and uncovering previously unconsidered factors. The current shift from large-scale spatial data analysis (allocentric perspective) to a ground-level view (egocentric perspective) and physical and perceptual features of walking introduces a subjective lens into current walkability assessment tools. However, the efficacy of current methods in addressing non-visual aspects of human perception and their applicability across diverse demographics remains debatable. Finally, the lack of integration of emerging technologies like virtual/augmented reality and digital twin leaves a significant gap in research, inviting further study to determine their efficacy in enhancing the current methods and, in general, understanding the interaction of humans and cities.Urban Planning572The lack of integration of emerging technologies like virtual/augmented reality and digital twin leaves a significant gap in research, inviting further study to determine their efficacy in enhancing the current methods and, in general, understanding the interaction of humans and cities.Urban Planning["Yasin Delavar", "Sarah Gamble", "Karla Saldana-Ochoa"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/d20297e5636ee66b62bbdadb376effecc58e4a3d d5acf3158919c62d661c7c0ea248b41936be518cThe Energy sector all over the world is faced with the challenges on how to control wastages to its minimal bearing point with a view to optimize its consumption. The generation is capital intensive and the demand by the consumers is very high and the global world cannot wait to have a carbon free zone. Hence, the need for a greener and clean energy without leakages or wastages by the consumers The research work is focusing on the role of Artificial Intelligence (AI) and Internet of Things (IOT) for energy optimization in Nigeria, a country where the demand for energy is far higher than its supply in both government buildings, residential buildings as well as market and business places. The emergence of Internet of Things (IOT), smart technologies and AI (artificial intelligence) has made it possible to integrate renewable energy solutions together with a view to meet up consumer’s demand and to create a carbon free environments to improve Energy generation without causing major harm to the environment as well as reducing energy wastages by both the generating company and the consumers in the Building Construction Industry (BCI) With our current nature of power supply, Artificial Intelligence and Internet of Things are needed to synchronize the conventional power source and other backups sources like the fuel (diesel and PMS) generators and solar for steady power supply and by extension, steady work flow in the offices, residential buildings, business shops and market places. The Building Construction Industry in Nigeria has a market size of about USD$105.8 billion in 2023 with an annual expectancy growth projection of >3% for a population of about 225.604 million people hence, the urgent need for more energy generation and proper optimization. Finally, this paper will also look at some of the challenges with AI and IOT technologies in building structures as well as possible recommendations for a better greener carbon free environmentInternational Journal of Innovative Science and Research Technology582The research work is focusing on the role of Artificial Intelligence (AI) and Internet of Things (IOT) for energy optimization in Nigeria, a country where the demand for energy is far higher than its supply in both government buildings, residential buildings as well as market and business places.International Journal of Innovative Science and Research Technology (IJISRT)["Nirma Kumari Sharma", "Joel Joseph Ghibi"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/d5acf3158919c62d661c7c0ea248b41936be518c eb34ded7d15d8873b56450117928dadb09cc541cThroughout the last couple of years, Artificial Intelligence (AI) has come under consideration as a revolutionizer of numerous sectors in which the non-profit sector is involved is not an exception. AI intervention can be applied to the non-profit techniques in a way that this paper seeks to explain the extent of the success that can be realized. It discusses AI’s role in the non-profit organizations and pinpoints technologies like data analysis, AI-based fundraising solutions, program assessment, and chatbots to engage the donors. The paper also reviews general issues associated with the application of AI solutions including inadequate funds, dearth of specialists in AI and data privacy issues, and come up with measures to mitigate these challenges. It also elaborates on the evaluation indicators of the degree of AI impact in non-profits such as, efficiency increment indicators, fundraising indicators, changes in the programs, and indicators of stakeholders. The conclusions are that it is possible to achieve the positive impact on the function of distinctive non-profit organizations through the successful application of AI.Advanced International Journal of Multidisciplinary Research192The conclusions are that it is possible to achieve the positive impact on the function of distinctive non-profit organizations through the successful application of AI.Advanced International Journal of Multidisciplinary Research["Omar Faruq", "Shariful Haque", "Mohammad Abu Sufian", "Khaled Al-Samad", "Mir Abrar Hossain", "Tughlok Talukder", "Azher Uddin Shayed"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/eb34ded7d15d8873b56450117928dadb09cc541c bc2f9b10e0d53d7f8a8661332a7baebb5370e7dfCloud-based healthcare systems present an array of solutions to the needs of collecting patient data and dispensing well-processed reports to mention both patients’ and healthcare practitioners’ reports at any time and place. However, in times such systems face great challenge due to the fact that the system is open to a risk of a point failure, security loopholes, privacy issues, and lack of transparency. These challenges therefore bring an additional danger of the continuous and reliable provision of services. The proposed research will develop a novel healthcare framework that uses artificial intelligence to achieve decentralization of healthcare, the authenticated and managed IoT devices, trust, and transparency in the personal health record by means of AI-driven smart contracts in a public blockchain network. It is novel in the dynamic adaptive mechanism that analyzes and adjusts the operational environment behavior of the system. Real-time detection and minimization of dangers brought about by malicious IoT nodes with Adaptive Temporal Long-Short-Term-Memory (AT-LSTM) integration. In addition, the framework includes a module for predictive analytics in order to accurately predict system load and optimize resource allocation using artificial intelligence for stronger resilience in the healthcare system. The empirical studies suggest significant enhancements in critical performance measures such as data retrieval time, average delay, data transfer rate, and transaction charges. The lower energy usage, with averages of 11.77 mW for 3 devices. Such improvements suggest the capacity of the framework to convert secure and dependable remote patient monitoring and data management in healthcare.2024 5th International Conference on Smart Electronics and Communication (ICOSEC)161The proposed research will develop a novel healthcare framework that uses artificial intelligence to achieve decentralization of healthcare, the authenticated and managed IoT devices, trust, and transparency in the personal health record by means of AI-driven smart contracts in a public blockchain network.2024 5th International Conference on Smart Electronics and Communication (ICOSEC)["Saigurudatta Pamulaparthyvenkata", "Prakash Murugesan", "Dinesh Gottipalli", "Preethi Palanisamy"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/bc2f9b10e0d53d7f8a8661332a7baebb5370e7df 3226b99c3e624c8354f978373e5972705062de63Artificial Intelligence, at the forefront of innovation and intelligence, is redefining the pace of life and work, notably within education. This study investigates the determinants influencing Gen Z's behavioral intentions (BI) to integrate AI-powered tools within Indian Higher Educational Institutions (HEIs) by extending the UTAUT2 model with four additional constructs: trustworthiness, personal innovativeness, perceived task excellence, and perceived privacy concern. The data gathered from 430 respondents within Indian HEIs through an online survey following purposive sampling was meticulously analyzed using the structural equation modeling approach in AMOS. The findings validate the applicability of the UTAUT2 model for understanding AI tool integration in the Indian context, with an explanatory power of 34.2%. The study highlights the beneficial impact of hedonic motivation, perceived task excellence, facilitating conditions, and performance expectancy on Gen Z's intention to integrate AI tools. Additionally, the study suggests recommendations for future research and outlines implications based on these findings.Bulletin of Science, Technology &amp; Society1191This study investigates the determinants influencing Gen Z's behavioral intentions (BI) to integrate AI-powered tools within Indian Higher Educational Institutions (HEIs) by extending the UTAUT2 model with four additional constructs: trustworthiness, personal innovativeness, perceived task excellence, and perceived privacy concern.Bulletin of Science, Technology &amp; Society["K. Kavitha", "V. P. Joshith"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/3226b99c3e624c8354f978373e5972705062de63 a67d4be6598188daca39665b41b07603ade8e28aJ. Medical Syst.611Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.Journal of Medical Systems["Joseph Finkelstein", "Aileen S. Gabriel", "Susanna Schmer", "Tuyet-Trinh Truong", "Andrew Dunn"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/a67d4be6598188daca39665b41b07603ade8e28a f2a3bfa54c234c46542964fbd6108ef0aae7edc4This study discusses the integration of artificial intelligence (AI) and machine learning (ML) in medical reasoning and decision-making, with a focus on the challenges and opportunities associated with the massive consumption of data required for training AI systems, and contrasts this with the limited data typically available to medical practitioners. We advocate for a balanced approach that includes small data and emphasize the importance of maintaining the art of clinical reasoning amid technological advancements. Finally, we highlight the potential of multidisciplinary research in addressing the complexities of medical reasoning and suggest the necessity of careful abstraction and conceptual modeling in AI applications.Frontiers in Medicine311Frontiers in Medicine["A. Koumpis", "Adam S L Graefe"]2024-09-18T00:00:00https://www.semanticscholar.org/paper/f2a3bfa54c234c46542964fbd6108ef0aae7edc4 035ee61b7a9dddaa16f9aa12e66bec4b78ebf324Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.Applied Sciences914This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.Applied Sciences["Irmina Durlik", "Tymoteusz Miller", "Ewelina Kostecka", "Tomasz Tu\u0144ski"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/035ee61b7a9dddaa16f9aa12e66bec4b78ebf324 36c409c618f215a00a3ffd2e3408320d4819ae55Artificial Intelligence (AI) is fundamentally reshaping various industries by enhancing decision-making processes, optimizing operations, and unlocking new opportunities for innovation. This paper explores the applications of AI across four key sectors: healthcare, finance, manufacturing, and retail. Each section delves into the specific challenges faced by these industries, the AI technologies employed to address them, and the measurable impact on business outcomes and societal welfare. We also discuss the implications of AI integration, including ethical considerations, the future trajectory of AI development, and its potential to drive economic growth while posing challenges that need to be managed responsibly.arXiv.org524This paper explores the applications of AI across four key sectors: healthcare, finance, manufacturing, and retail, and delves into the specific challenges faced by these industries, the AI technologies employed to address them, and the measurable impact on business outcomes and societal welfare.ArXiv["Yijie Weng", "Jianhao Wu", "Tara Kelly", "William Johnson"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/36c409c618f215a00a3ffd2e3408320d4819ae55 bc98285a160417c09e6003bc2ea7199df0311fb2   Objective: to develop proposals for the introduction of artificial intelligence (AI) in legal education.   Methods: dialectical methods (analysis and synthesis, induction and deduction, systematization, comparison, classification, forecasting), statistical, formal-legal and comparative-legal methods. Used in combination, these methods allow comprehensive analysis of the relationship between AI and legal education.   Results: the article reveals the main directions to improve legal education in the conditions of AI development: integration of information and communication technologies and training of specialists able to work effectively at the intersection of law and technology. The advantages and disadvantages of using AI in legal education are identified. Ethical aspects of AI application are outlined, as well as the need to manage the development of technologies based on the principles of fairness, transparency and consideration of human interests. The author states the importance of developing not only specialized knowledge, but also universal competencies that will help students to adapt successfully to the dynamically changing conditions of professional activity.   Scientific novelty: a comprehensive analysis of the interaction between legal education and AI was carried out, including the identification of specific opportunities for the AI application in legal education, as well as related risks and problems. The author proposes a systematic approach to improving legal education in the context of AI development, focusing on theneed to revise educational programs in legal areas in terms of integrating information and communication technologies; developing universal competencies in students to adapt to a dynamically changing professional environment; introducing mandatory advanced training for judges, legal practitioners to develop skills for using AI systems.   Practical significance: the study results can be used to optimize and adapt the educational programs of law schools to the digital era requirements and to develop effective approaches to the AI application in legal practice and education, taking into account ethical aspects and potential risks.Russian Journal of Economics and Law201The author proposes a systematic approach to improving legal education in the context of AI development, focusing on the need to revise educational programs in legal areas in terms of integrating information and communication technologies and developing universal competencies that will help students to adapt successfully to the dynamically changing conditions of professional activity.Russian Journal of Economics and Law["A. Danielyan"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/bc98285a160417c09e6003bc2ea7199df0311fb2 a750e4269561a5c0caaf26a0dd4c2f29c6f1d4c6AI &amp; SOCIETY441It is too soon to tell whether a non-proliferation regime, a verification-based regime, or an International Monopoly is most feasible for governing AI, but a variety of policies would yield a high return across all three scenarios.AI &amp; SOCIETY["Nicholas Emery-Xu", "Richard Jordan", "Robert Trager"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/a750e4269561a5c0caaf26a0dd4c2f29c6f1d4c6 e525cac80d741955775b706e4cee729c85f91936Cancer, a collection of maladies that has undergone extensive examination over centuries, remains a formidable challenge. Despite the array of available pharmacological and therapeutic interventions, the intricate molecular dynamics and heterogeneity of cancer continue to challenge the scientific community. Artificial Intelligence (AI) emerges as a promising avenue, offering the potential for expedited, precise diagnostics devoid of human expertise. Additionally, AI facilitates the tailoring of patient-specific therapeutic strategies targeting various facets of cancer, spanning macroscopic to microscopic levels. Nonetheless, it is imperative to scrutinize the potential benefits and limitations of AI technologies in this context. This review undertakes a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of AI's application in cancer. An extensive compilation of AI applications encompasses predictive modeling, diagnostic capabilities, prognostic assessments, and personalized therapeutic modalities, spanning genomic analyses to individualized treatment regimens. The synthesis of evidence suggests that the advantages of AI outweigh its drawbacks; nevertheless, obstacles to its widespread integration persist.Journal of AI771A comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of AI's application in cancer concludes that the advantages of AI outweigh its drawbacks; nevertheless, obstacles to its widespread integration persist.Journal of AI["G\u00fcl\u015fah Torkay", "Nouran Fadlallah", "Ahmet Karag\u00f6z", "Mesut Canl\u0131", "Ezgi Saydam", "Ay\u015fenur Mete", "Furkan K\u0131z\u0131l\u0131\u015f\u0131k", "Hakan Darici", "Yusuf Ye\u015fil"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/e525cac80d741955775b706e4cee729c85f91936 33980ac2bf7ef68d4914d591bb1501a3ba40f861Cervical cancer is a severe threat to women’s health. The majority of cervical cancer cases occur in developing countries. The WHO has proposed screening 70% of women with high-performance tests between 35 and 45 years of age by 2030 to accelerate the elimination of cervical cancer. Due to an inadequate health infrastructure and organized screening strategy, most low- and middle-income countries are still far from achieving this goal. As part of the efforts to increase performance of cervical cancer screening, it is necessary to investigate the most accurate, efficient, and effective methods and strategies. Artificial intelligence (AI) is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like interpretation capabilities on various medical images. AI will soon have a more significant role in improving the implementation of cervical cancer screening, management, and follow-up. This review aims to report the state of AI with respect to cervical cancer screening. We discuss the primary AI applications and development of AI technology for image recognition applied to detection of abnormal cytology and cervical neoplastic diseases, as well as the challenges that we anticipate in the future.Cancer Biology and Medicine882The primary AI applications and development of AI technology for image recognition applied to detection of abnormal cytology and cervical neoplastic diseases, as well as the challenges that the authors anticipate in the future are discussed.Cancer Biology & Medicine["Tong Wu", "E. Lucas", "Fanghui Zhao", "Partha Basu", "Youlin Qiao"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/33980ac2bf7ef68d4914d591bb1501a3ba40f861 9999db159f4c7c7fd8aa030ae4bd0fb27abfdea0Green process innovation is an important strategy in the high-quality development of enterprises. Digital technology is becoming a key factor in helping businesses address environmental issues and contributes to their green process innovation and sustainable growth. Nevertheless, there is a lack of studies on how particular digital technology categories affect corporate green process innovation. Artificial intelligence (AI) is an important part of digitalization as it can provide new technical means and guidance for enterprise’s innovation of green processes. This study aims to fills this research gap by revealing the logical relationship between digital technology and the green development of enterprises. Using China’s A-share-listed companies as the research object from 2013 to 2022, this study employed a two-way fixed-effects model and investigated the impact of artificial intelligence (AI) on corporate green process innovation and the moderating effect of multidimensional intellectual capital. The results revealed that AI positively impacts corporate green process innovation. Human capital, structural capital, employed capital, and relational capital strengthen this positive effect. Robustness tests validated these conclusions. This study expands the literature on digital technology and corporate green innovation and provides a reference for enterprises to implement green practices using digital technology.Syst.1232Investigating the impact of artificial intelligence (AI) on corporate green process innovation and the moderating effect of multidimensional intellectual capital revealed that AI positively impacts corporate green process innovation.Syst.["Jue Wang", "Xiao Wang", "Feng Sun", "Xinyu Li"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/9999db159f4c7c7fd8aa030ae4bd0fb27abfdea0 d729966bf7f365ebe00675f2f4492053d1c2a1d208["Helen Crompton", "D. Burke"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/d729966bf7f365ebe00675f2f4492053d1c2a1d2 90dba6d32f6c5fbe946a8b83a18e1d3a13f24a10Artificial intelligence (AI) is revolutionising radiological diagnosis in the UK, promising to enhance the accuracy, efficiency, and accessibility of healthcare. The integration of AI into radiology is particularly timely, as the National Health Service (NHS) faces increasing demand for imaging services, coupled with a shortage of radiologists. AI technologies, including deep learning algorithms and machine learning systems, are being developed to assist in interpreting complex medical images such as X-rays, CT scans, and MRIs. One of the key benefits of AI in radiology is its ability to quickly and accurately detect abnormalities. For instance, AI algorithms can identify early signs of diseases like cancer, strokes, and fractures, often with a precision that rivals or exceeds human expertise. This has the potential to significantly reduce diagnostic errors, expedite treatment plans, and improve patient outcomes. For example, AI tools are already in use in the UK to flag lung nodules on CT scans, assisting radiologists in early cancer detection. AI also offers efficiency gains. By automating routine tasks, such as identifying normal scans or prioritizing urgent cases, AI can help streamline workflows, reduce waiting times, and alleviate the burden on overworked radiologists. This is critical, as delays in diagnosis can have serious consequences for patient care. However, the widespread adoption of AI in radiology is not without challenges. Concerns about data privacy, algorithmic transparency, and the potential for over-reliance on AI must be carefully managed. It is crucial to strike a balance where AI complements, rather than replaces, the expertise of radiologists. Ultimately, AI's role in radiological diagnosis in the UK is poised to grow, offering a future where healthcare is not only faster and more accurate but also more equitable for patients across the country.The Physician00Artificial intelligence's role in radiological diagnosis in the UK is poised to grow, offering a future where healthcare is not only faster and more accurate but also more equitable for patients across the country.The Physician["Ahsthiya Nagarajan", "K. Burney"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/90dba6d32f6c5fbe946a8b83a18e1d3a13f24a10 ef56008c791dfb3a15dbf2210dfe99d95ad57c17This paper delves deeper into the potential of Artificial Intelligence (AI)-enabled Supply Chain Management (SCM) as a groundbreaking technology capable of revolutionizing supply chain operations and ushering in a new era of possibilities. In today's dynamic business landscape, where agility and efficiency are paramount, AI plays a pivotal role in redefining how supply chains operate. The journey commences with an in-depth exploration of AI's fundamental concepts and its manifold applications within SCM, shedding light on its adaptability across various aspects of the supply chain, from demand forecasting to inventory optimization. Moreover, this paper illuminates the myriad benefits that AI brings to SCM practitioners. These advantages encompass heightened operational efficiency through real-time data analysis, cost reduction through predictive maintenance and optimized routing and a superior customer experience resulting from improved demand prediction and personalized service offerings. However, acknowledging the transformative power of AI in SCM, we must also acknowledge the hurdles in its implementation. This paper underscores the significant challenges that organizations may face while integrating AI into their SCM processes, ranging from data quality issues and concerns regarding privacy and security to the need for domain-specific human expertise. To address these hurdles effectively, the paper proposes a comprehensive framework. This framework encompasses a holistic strategy that aligns AI initiatives with organizational goals, governance and ethics considerations to ensure responsible AI deployment, and a clear roadmap that guides the implementation journey from inception to full integration. In conclusion, this paper offers valuable insights into the opportunities and challenges that AI-powered SCM presents in the ever-evolving business landscape. By providing practical recommendations, it equips organizations with the knowledge and tools needed to successfully harness the potential of AI in their supply chain operations, ultimately paving the way for enhanced competitiveness and sustainability in the future.Artificial Intelligence and Applications380This paper underscores the significant challenges that organizations may face while integrating AI into their SCM processes, ranging from data quality issues and concerns regarding privacy and security to the need for domain-specific human expertise, and proposes a comprehensive framework to address these hurdles.Artificial Intelligence and Applications["S. Goswami", "Surajit Mondal", "Shouvik Sarkar", "Krishna Kumar Gupta", "S. K. Sahoo", "Rohit Halder"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/ef56008c791dfb3a15dbf2210dfe99d95ad57c17 aa3681a463f7842d7be2cc1c720fb5e47c057e36This special issue deliberated on AI and the curriculum with the aim of exposing various debates, controversies, and pathways in terms of policy and praxis in the Global North. Papers published in this special issue presented various arguments drawing from best practices to either problematise or support the use of AI within the curriculum. This is against the background that the advent of Artificial Intelligence (AI), integrating this advanced technology has become a pivotal topic of discussion and research, which in some cases is marred with controversy and the belief that ethical issues such as honesty and hard work are eroded as people use AI within the curriculum. As such, there seems to be resistance among conservative scholars, while other scholars have embraced AI as the future of curriculum implementation. Cognizant of the foregoing, it was critical that this special issue brings together various authors to air their views, which we hope will serve as part of policy formulation in the Global South.Interdisciplinary Journal of Education Research00This special issue deliberated on AI and the curriculum with the aim of exposing various debates, controversies, and pathways in terms of policy and praxis in the Global North and bringing together various authors to air their views, which it was critical to bring together.Interdisciplinary Journal of Education Research["B. Dube", "W. Setlalentoa"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/aa3681a463f7842d7be2cc1c720fb5e47c057e36 87d90e67c778b21dd7e6634088f4424680c63127The ongoing discourse regarding the potential substitution of human creativity by Artificial Intelligence (AI) raises questions about the essence of human nature. If the essence of humanity lies in creativity, and if AI can replicate this trait, it appears that creativity alone does not define what it means to be human. Rather than perceiving this as an ‘anthropological loss’ to be accepted or fought against, it can be viewed as an occasion to contemplate the human from a more-than-human perspective. By considering this perspective, it becomes evident that the definition of humanity has been a matter of dispute long preceding the recent advancements in AI. A theoretical approach to the relationship between AI, creativity and more-than-humanness is proposed as a way to show the possibilities that philosophy brings to counteract pessimistic approaches to the replaceability of the human by AI. By challenging the notion that humans are the sole proprietors of creativity, one can explore alternative forms of creativity beyond the human realm and consider how humans can facilitate their emergence.Journal of creative communications100A theoretical approach to the relationship between AI, creativity and more-than-humanness is proposed as a way to show the possibilities that philosophy brings to counteract pessimistic approaches to the replaceability of the human by AI.Journal of Creative Communications["Adalberto Fernandes"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/87d90e67c778b21dd7e6634088f4424680c63127 610b3dc5561b92abc49a163a51a6a8b7c9c270b5Background: The integration of artificial intelligence (AI) in paedi-atric dentistry has grown signifi-cantly, offering new possibilities for diagnostics, treatment plan-ning, and patient care. AI’s capaci-ty to handle large datasets and generate accurate predictions is transforming dental practice.Objective: This narrative review explores the potential applica-tions of AI in paediatric dentistry, focusing on its benefits, challeng-es, and future implications.Methods: A comprehensive litera-ture review was conducted using databases such as PubMed, Sco-pus, and Web of Science. Relevant studies published between 2000 and 2023 were selected based on predefined inclusion criteria. The quality of the studies was ap-praised using the Joanna Briggs Institute tools.Results: AI applications, including image analysis, diagnosis, treat-ment planning, and patient man-agement, show significant prom-ise in paediatric dentistry. AI-powered tools can improve diag-nostic accuracy, reduce treat-ment inconsistencies, and en-hance patient outcomes. Howev-er, challenges related to costs, complexity, and ethical concerns remain.Conclusion: AI will not replace paediatric dentists but will serve as a valuable tool to support clini-cal decision-making. Future re-search should focus on overcom-ing current limitations and ensur-ing safe integration into clinical practice.Journal of Health and Rehabilitation Research220AI will not replace paediatric dentists but will serve as a valuable tool to support clini-cal decision-making and should focus on overcom-ing current limitations and safe integration into clinical practice.Journal of Health and Rehabilitation Research["Maham Shah\u00b9", "Syed Maheen", "Rida Ali\u00b2", "Farwa Batool\u00b3", "Gul Muhammad Shafiq\u2074", "Romesa Shaikh\u2075", "Khero\u2076"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/610b3dc5561b92abc49a163a51a6a8b7c9c270b5 c01aa3904328c0b231f18f688670ca373ba8549aThis study aims to analyze employment legal protection in the face of disruption from artificial intelligence (AI) technology that threatens the existence of human labor. The development of AI technology has created new challenges for the world of employment, where many jobs have the potential to be replaced by machines and automated systems. This study uses a normative legal method with a statutory approach to examine existing employment regulations and their relevance in protecting workers' rights in the digital era. The data used consists of legal literature, laws and regulations related to the application of AI in the world of work. The results of the study show that Law Number 13 of 2023 concerning Manpower emerged as a response to the unfavorable situation for workers, with the aim of protecting labor rights and implementing international instruments and human rights declarations. Technological advances, especially automation and artificial intelligence (AI), bring benefits such as efficiency and new opportunities, but also pose challenges such as job loss. To address this, the government needs to strengthen employment legal protection, design policies to support affected workers, and provide skills training and incentives for companies. The Circular of the Ministry of Communication and Information on the ethics of using AI emphasizes inclusivity, humanity, accessibility, and sustainable development, as well as the protection of workers' rights such as the right to work and fair treatment. However, current national regulations do not specifically regulate the use of AI by companies, so new legal arrangements are needed to protect workers' rights and manage the transition due to Termination of Employment.Pena Justisia Media Komunikasi dan Kajian Hukum00The results of the study show that Law Number 13 of 2023 concerning Manpower emerged as a response to the unfavorable situation for workers, with the aim of protecting labor rights and implementing international instruments and human rights declarations.Pena Justisia: Media Komunikasi dan Kajian Hukum["Cece Suryana"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/c01aa3904328c0b231f18f688670ca373ba8549a 7636fc3cf39042f531ad2dca6ad17ef2068c5cd4This review paper explores the evolving role of artificial intelligence (AI) in marketing, examining both its potential and its limitations. AI technologies are increasingly being leveraged to enhance marketing practices through automation, data analysis, and personalized customer experiences. The paper highlights the significant advantages of AI, including its ability to process vast amounts of data in real-time, optimize marketing strategies, and improve efficiency and scalabilityInternational Journal For Multidisciplinary Research80This review paper explores the evolving role of artificial intelligence in marketing, examining both its potential and its limitations, including its ability to process vast amounts of data in real-time, optimize marketing strategies, and improve efficiency and scalability.International Journal For Multidisciplinary Research["Shlok Sarin"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/7636fc3cf39042f531ad2dca6ad17ef2068c5cd4 dfc188b905707f153a524658edd59e09f9271f84This study proposes an Artificial Intelligence-based Real-time Catch Analysis System (AI-RCAS) for sustainable fisheries management. The AI-RCAS, implemented on a Jetson board, consists of fish recognition using YOLOv10, tracking with a ByteTrack algorithm optimized for marine environments, and a counting module. Experiments in actual fishing environments showed significant improvements, with species recognition rates of 74–81%. The system supports the efficient operation of the total allowable catch (TAC) system through real-time analysis, addressing the limitations of the existing Electronic Monitoring (EM) systems. However, challenges remain, including object-tracking difficulties and performance issues in unstable marine environments. Future research should focus on optimizing the fishing process, improving video processing, and expanding the dataset for better generalization.Sustainability160The AI-RCAS, implemented on a Jetson board, consists of fish recognition using YOLOv10, tracking with a ByteTrack algorithm optimized for marine environments, and a counting module that supports the efficient operation of the total allowable catch (TAC) system through real-time analysis.Sustainability["Seung-Gyu Kim", "Sang-Hyun Lee", "Taeho Im"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/dfc188b905707f153a524658edd59e09f9271f84 54178820cf3a9808bf3c3b35d6dd741b736fe4a4This paper depicts a potential demonstrate for feasible worldwide youth authority advancement that combines Vedic scripture, artificial intelligence, and cyber security in arrange to avoid gracious war. The methodology places a solid accentuation on the part that youthful individuals may play in cultivating enduring peace and draws on Vedic information to lay the foundation for ethical administration. It is recommended that the application of manufactured insights and cyber security innovations will upgrade the development of administration capacities and help within the concealment of despise discourse and radical convictions. The strategy emphasizes the require for a comprehensive technique for leadership development that takes under consideration not as it were the improvement of abilities but too of the mental and passionate wellbeing as well as a sense of social obligation. The extreme objective of this methodology is to empower youth to ended up competent pioneers and operators of alter in their neighborhoods, advancing enduring peace and turning away respectful war.2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)1502024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)["Sachin Sharma", "Aanika Gupta", "Ranu Tyagi"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/54178820cf3a9808bf3c3b35d6dd741b736fe4a4 16de66250bf3bb73fdf2256d2dea323e1cc8a7edAt the forefront of industries profoundly influenced by artificial intelligence (AI), public relations (PRs) are undergoing a transformative revolution. The increasing applications of AI in PRs are driving a demand for proficient practitioners. Recognizing this, PR educational institutions must adapt by delivering tailored AI education. Despite the growing importance of AI, a literature review reveals a lack of a well-designed AI curriculum in PRs. This essay draws insights from recent research on AI value alignment, dialogic communication, and PR ethics, articulating three foundational principles for AI education in PR: authentic dialogue, client value centricity, and legal and ethical considerations. Aligned with these principles, the essay outlines four essential knowledge areas for PR AI education: programming and coding proficiency, AI fundamentals, the retrieval-augmented generation (RAG) system, and the LangChain framework for information security, as well as AI deployment and model optimization. An illustrative syllabus is presented to solidify these concepts. The essay further explores potential future directions and implications of integrating AI into PR education.Journalism &amp; Mass Communication Educator220This essay draws insights from recent research on AI value alignment, dialogic communication, and PR ethics, articulating three foundational principles for AI education in PR: authentic dialogue, client value centricity, and legal and ethical considerations.Journalism &amp; Mass Communication Educator["Aimei Yang"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/16de66250bf3bb73fdf2256d2dea323e1cc8a7ed ec55f198ab8ede425840e8dbb5e578cbf37ffcc4This theoretical paper contributes to the ongoing debate on Artificial Intelligence (AI) in relation to curriculum and implementation in post-colonial South Africa. We contend that AI, as perceived, conceived, and implemented within the curriculum space, presents an ambivalent terrain marked by fear, uncertainty, and anxiety among stakeholders, as its presence has interfered with the everyday work of educational practitioners. Cognizant of this problem, we locate our theorisation within the framework of Sustainable Learning Environments and address two questions: (1) What are the opportunities of AI in relation to the curriculum in post-colonial South Africa? (2) What challenges are faced in the implementation of AI, especially in rural contexts where technological opportunities are not equivalent to those in urban areas? In this paper, we highlight that while AI tools like ChatGPT may appear daunting for integration into teaching and learning—potentially undermining educators' authority and raising ethical concerns—there is an urgent need to rethink and restructure teacher education. This restructuring should align with the evolving demands of an AI-enhanced curriculum and address the shifting expectations in educational contexts.Interdisciplinary Journal of Education Research300It is highlighted that while AI tools like ChatGPT may appear daunting for integration into teaching and learning—potentially undermining educators' authority and raising ethical concerns—there is an urgent need to rethink and restructure teacher education.Interdisciplinary Journal of Education Research["B. Dube", "W. Setlalentoa"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/ec55f198ab8ede425840e8dbb5e578cbf37ffcc4 a5674c501d559498eb7ee5f748ee7433b241187cJournal of Orthopaedic Surgery and Research270The results indicate that physicians can diagnose wrist trauma more accurately and faster when aided by an AI-tool that lessens the need for extra diagnostic procedures and the AI-tool also seems to lower physicians' stress levels while examining cases.Journal of Orthopaedic Surgery and Research["Marco Keller", "Meret Rohner", "Philipp Honigmann"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/a5674c501d559498eb7ee5f748ee7433b241187c dc5aafe284fc81085b54e33352f5ea9cdadf2b9b This research maps the evolution of artificial intelligence (AI) policy and its scientific underpinnings. First, we analyzed the global AI policy landscape using the Overton policy documents database, which comprises millions of policy documents. Findings reveal a substantial increase in AI policy documents since 2018, with the USA, European Union (EU), and intergovernmental organizations leading policy development efforts. We also analyzed the scientific articles referenced within these policies. The USA stood out as a central hub in the production and funding of AI research, with other Global North countries playing a notable role alongside China. The research cited in AI policy documents predominantly features journals with a high-impact factor, such as Nature and Science. This analysis aims to deepen the understanding of the AI policy landscape, offering insights for academics and policymakers and contributing to managing AI’s global governance.Science and Public Policy810Findings reveal a substantial increase in AI policy documents since 2018, with the USA, European Union (EU), and intergovernmental organizations leading policy development efforts.Science and Public Policy["B. Cabral", "Sergio Salles-Filho"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/dc5aafe284fc81085b54e33352f5ea9cdadf2b9b 39136a025d1e76b3254e105a2b0ac8b3a20371d6The contemporary landscape witnesses a rapid evolution in the utilization of Artificial Intelligence (AI), observable across diverse sectors and industries. One of the most widely recognized AI technologies is ChatGPT. Like other AI-based technologies, ChatGPT is extensively used in various applications at both individual and organizational levels. However, what is concerning is the trend of using ChatGPT technology without understanding its fundamentals. Therefore, this study focuses on unraveling the basic knowledge of this highly advanced platform, ChatGPT. The findings of this study are benefiting many parties, especially individuals interested in using this platform. These benefits are not only limited to the community but also contribute to the body of knowledge by providing insights into the fundamental aspects of this platform. The study's positive outcomes will contribute to forming a community layer with a deeper understanding of ChatGPT and improved readiness prior to its utilization.Semarak International Journal of Applied Sciences and Engineering Technology00This study focuses on unraveling the basic knowledge of this highly advanced platform, ChatGPT, to contribute to forming a community layer with a deeper understanding of ChatGPT and improved readiness prior to its utilization.Semarak International Journal of Applied Sciences and Engineering Technology["Faerozh Madli", "Yuzainy Janin", "Mat Salleh @ Salleh Wahab", "S. Sondoh", "Suddin Lada", "Shaierah Gulabdin", "Ag Kaifah Riyard Kiflee", "C. Lasuin"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/39136a025d1e76b3254e105a2b0ac8b3a20371d6 8a1c0d0a62e40621ab47f72827ebe56f5b4ee5a1The incorporation of artificial intelligence into digital forensics enlarges CSI by increasing the efficacy, precision, execution throughput, and inclusion in trial of physical evidence. Key findings from this study are that the use of AI in these technologies can reduce the required time on key forensic tasks by up to 93%, with the metadata extraction time going down from 10hrs to 2hrs. Here the ameliorations were demonstrable- facial recognition surged from 70pc to 88pc and objects detection in videos also boosted from 65pc to 90pc. These automation features reduced the manual complexity to a great extent It gave 80% reduction in aspect of feature extraction and 88% reduction in report generation. Also, it was observed that the legal admissibility of AI-generated evidence have been enhanced and that, there was a rise in statistics shows that the predictive analytics admissibility, which was at 70%, had risen to 85% on average. These results clearly indicate the applicability of Artificial Intelligence technology in the process of overhaul for digital forensic science and in the overall improvement of investigative performance.2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)160Findings from this study show that the use of AI in these technologies can reduce the required time on key forensic tasks by up to 93%, with the metadata extraction time going down from 10hrs to 2hrs.2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)["A. RizwanBasha", "R. Annamalai"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/8a1c0d0a62e40621ab47f72827ebe56f5b4ee5a1 1ffed9af8acac60956925d75b176f4ace46554d9The paper examines the profound influence of Artificial Intelligence (AI) on business management. Artificial intelligence imitates human cognitive abilities, including observation, problem-solving, learning, and decision-making. This allows machines, algorithms to carry out activities with intelligence that resembles that of humans. The research emphasizes the capacity of AI to evaluate vast information, detect patterns, and autonomously make judgments, hence improving productivity, and efficiency utilizing secondary research from relevant publications to the topic by international researchers. AI approaches such as machine learning, Natural Language Processing (NLP), deep learning, and neural networks are widely used in sectors like manufacturing, finance, and health care. AI in business management streamlines repetitive operations, enhances operational effectiveness, diminishes expenses, and mitigates human fallibility. AI-based analytics offer essential insights for strategic planning, while AI-driven customization improves client relations. Nevertheless, it is imperative to solve difficulties such as ensuring the accuracy and reliability of data, enhancing the openness of models, considering ethical implications, and seamlessly integrating AI systems into current processes. In this article, we discuss ways to enable companies to make the most of AI's business management potential. The result of the research demonstrated that AI automates commonplace duties, minimizing human error and manual labor requirements-significant cost savings and enhanced operational efficacy result from this automation. Furthermore, data analytics powered by artificial intelligence reveal patterns and trends within enormous datasets, whereby furnishing crucial insights that guide strategic planning and decision-making.Symposium on Intelligent Systems and Informatics270The result of the research demonstrated that AI automates commonplace duties, minimizing human error and manual labor requirements-significant cost savings and enhanced operational efficacy result from this automation.2024 IEEE 22nd Jubilee International Symposium on Intelligent Systems and Informatics (SISY)["M\u00e1t\u00e9 Prorok", "Istv\u00e1n Tak\u00e1cs"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/1ffed9af8acac60956925d75b176f4ace46554d9 6d9bd721896b5500f3e4e3b18e071551ea3ea395Advancement of science and technology makes human life very comfortable, healthy, and little bit secure. Current technology like Body Sensor Network, and Internet of Things, are making a healthcare system which is capable of detecting disease earlier on the basis of prior symptoms shown by the body. By using sensors we are now able to measure these symptoms like high or low blood pressure, body temperature, glucose level, heart beats, and more. Researchers are now incorporating the Artificial Intelligence with the next generation healthcare systems, making it a Self-Optimization system. In this paper, some advance technologies for healthcare system followed by some valuable artificial intelligence techniques are discussed. Based on some sorted research papers, author present a comparative analysis discussing about techniques used in next generation healthcare systems, objectives to achieve, and about the future aspects in terms of authors. Followed by these details, author also mention some applications, challenges and research trends in the field of healthcare system.2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)150In this paper, some advance technologies for healthcare system followed by some valuable artificial intelligence techniques are discussed and a comparative analysis is presented discussing about techniques used in next generation healthcare systems.2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS)["Chetan Pandey", "Sachin Sharma", "Shuchi Bhadula"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/6d9bd721896b5500f3e4e3b18e071551ea3ea395 7ea699d1f329ffec5dc385eb516cdecb177a6019The article considers the rapid technical progress and the concept of Artificial Intelligence (AI), developing examples of its application in modern life and its impact on our everyday life. The development of AI is becoming a steady trend in fields from autonomous vehicles to medical diagnostics. The article highlights various aspects of the AI implementation in the activities of the studied Ukrainian enterprises, as well as discusses its potential advantages and challenges. It emphasizes the importance of understanding these technologies in our modern world and challenges the ethical and social aspects of their use. The difference between the concepts of “artificial intelligence” and “artificial intelligence system” is defined and their main methods are considered. It has been studied that artificial intelligence is a theoretical and scientific basis, while artificial intelligence systems are practical implementations of this basis. The article discusses both positive and negative aspects of the AI application. The positive aspects demonstrate the wide range of opportunities that are opened up by the AI introduction, and the shortcomings indicate the need to develop and improve this technology.Automation, Control, and Information Technology2202024 14th International Conference on Advanced Computer Information Technologies (ACIT)["Inna Sysoieva", "A. Pukas", "Oleh Pohrishchuk", "Borys Pohrishchuk", "Olena Tsikhanovska", "Maria Lyzun"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/7ea699d1f329ffec5dc385eb516cdecb177a6019 8bc5abf0f7c3056a5a63661634323d9c3420387eCogent Social Sciences184Cogent Social Sciences["G. .. Osimen", "Oluwamurewa Newo", "Oluwakemi Morola Fulani"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/8bc5abf0f7c3056a5a63661634323d9c3420387e d1e640e01b1448b68f2026d681b80cd1dc9269e6International Research Journal of Modernization in Engineering Technology and Science01International Research Journal of Modernization in Engineering Technology and Science[]2024-09-19T00:00:00https://www.semanticscholar.org/paper/d1e640e01b1448b68f2026d681b80cd1dc9269e6 48bef24e87e2d94bbf284f2f7279b5a66ece9d58Cogent Business &amp; Management1012Cogent Business &amp; Management["Rasha Mahboub", "Mohamed Gaber Ghanem"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/48bef24e87e2d94bbf284f2f7279b5a66ece9d58 5558d0cd5f92bebffb493dae33fff9da0307aed0AI &amp; SOCIETY441A novel methodology for integrating indigenous knowledge systems into AI development to counter cognitive imperialism and foster inclusivity is presented, providing a roadmap for equitable, culturally respectful AI.AI &amp; SOCIETY["Yaw Ofosu-Asare"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/5558d0cd5f92bebffb493dae33fff9da0307aed0 006d1d49fd3ef980d666fb16635d7f49d1d72365AI is ‘the defining technology of our generation’ according to a recent joint statement by the UK and US governments. We all understand that it is likely to impact library and information work profoundly, so it is important to try and be more than reactive and think strategically about the opportunities and problems it is creating. The article poses nine key questions for consideration, and reflects on some answers that might support a more strategic library approach to AI. eLucidate42Nine key questions for consideration are posed, and the article reflects on some answers that might support a more strategic library approach to AI.eLucidate["Andrew Cox"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/006d1d49fd3ef980d666fb16635d7f49d1d72365 2274a0580b40596d5099b26f2ff6b124a25e12aaBackground: Cardiovascular health and preventative strategies are influenced by the sex of the individuals. To forecast cardiac events or detect ischemic heart disease (IHD) early, machine-learning algorithms can analyze complex patient data patterns. Early detection allows for lifestyle changes, medication management, or invasive treatments to slow disease progression and improve outcomes. Aim: To compare and predict the differences in the primary sources of IHD burden between males and females in various age groups, geographical regions, death versus alive, and comorbidity levels. Methods: A predictive and retrospective design was implemented in this study. Electronic health records were extracted, which were equally distributed among males and females with IHD. The dataset consisted of patients who were admitted between 2015 and 2022. Two of the eight models generated by Modeler software were implemented in this study: the Bayesian network model, which achieved the highest area under curve score (0.600), and the Chi-squared automatic interaction detection (CHAID) model, which achieved the highest overall accuracy score (57.199%). Results: The study sample included 17,878 men and women, 58% of whom had no comorbidities and 1.7% who died. Age, the Charlson comorbidity index score, and geographical location all predicted IHD, but age was more influential. Bayesian network analysis showed that IHD odds were highest in males 40-59 and females 60-79, with the highest mortality risk in females 80-100. North and south Jordan had higher IHD rates and middle-aged males from north and middle governorates had higher IHD rates according to CHAID. Conclusion: By using artificial intelligence, clinicians can improve patient outcomes, treatment quality, and save lives in the fight against cardiovascular illnesses. To predict IHD early, machine-learning algorithms can analyze complex patient data patterns to improve outcomes.Electronic Journal of General Medicine401Bayesian network analysis showed that IHD odds were highest in males 40-59 and females 60-79, with the highest mortality risk in females 80-100, and North and south Jordan had higher IHD rates and middle-aged males from north and middle governorates had higher IHD rates according to CHAID.Electronic Journal of General Medicine["Muayyad Ahmad", "Salam H. Bani Hani"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/2274a0580b40596d5099b26f2ff6b124a25e12aa fa2eda663350033cb829ae195212acc9a3f2f96aAcademic Psychiatry222Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["Richard G Cockerill", "Michael R MacIntyre", "Carolyn Shima"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/fa2eda663350033cb829ae195212acc9a3f2f96a 0d478314f60da44e58a3b08767df38bb10316c74Automation, Control, and Information Technology002024 14th International Conference on Advanced Computer Information Technologies (ACIT)[]2024-09-19T00:00:00https://www.semanticscholar.org/paper/0d478314f60da44e58a3b08767df38bb10316c74 80e8cd1a74086abd0d299c067bae63e82a9d3b1fЧетырнадцатая международная мультиконференция : Тез. докл.00Четырнадцатая международная мультиконференция : Тез. докл.[]2024-09-19T00:00:00https://www.semanticscholar.org/paper/80e8cd1a74086abd0d299c067bae63e82a9d3b1f 46db853d22d47a02d38a5271d96528a8c9b1e24bAnesthesia and Analgesia200Anesthesia and analgesia["Craig S Webster", "T. Jowsey"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/46db853d22d47a02d38a5271d96528a8c9b1e24b 72aeaad5c368207e451a0f414669003ceeb4fc7aJurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis00Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis["Tio Prasetio"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/72aeaad5c368207e451a0f414669003ceeb4fc7a 42261099ba775c341fc29ea2528c08ce838dd27fJMIRx Med00JMIRx Med["Vanessa Fairhurst", "C. Marcum", "Courtney Haun", "Paulina Boadiwaa Mensah", "Femi Qudus Arogundade", "Ruchi Pathak Kaul", "Sidharth Narayanan", "S. Shah"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/42261099ba775c341fc29ea2528c08ce838dd27f 79cd0cdcbf057ef221c893d78388823c34a47b12The relationship between AI and design has attracted extensive academic attention and research, and the future relationship between AI and designers relies on current design students’ knowledge of AI, in addition to technological developments. To clarify the basic situation of Chinese design-college students’ use of AI software, the basic situation and status of using AI software to participate in design work, and the current relationship with AI, this study constructs a questionnaire on the status of the use of AI programs, with the help of the UTAUT model and the general program of design as a basis. The results of the research on 487 Chinese design-college students were analyzed by frequency analysis, descriptive statistics, etc., to clarify that currently more than 60% of design students have used AI programs, which are mainly used for data collection; providing ideas for design, e.g., when brainstorming; and conceptual ideas for design. Moreover, students generally believe that AI helps to improve personal skills and work efficiency, but the in-depth application and reliance on AI is relatively low; students hold anxiety about the development of AI, especially those who have not been exposed to AI. The education sector should focus on popularizing and deepening AI education, as well as helping students establish a correct concept of AI usage.Buildings440To clarify the basic situation of Chinese design-college students’ use of AI software, the basic situation and status of using AI software to participate in design work, and the current relationship with AI, this study constructs a questionnaire on the status of the use of AI programs.Buildings["Yang Song", "Shaochen Wang"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/79cd0cdcbf057ef221c893d78388823c34a47b12 d95cd816788feafe521e407b471b96963fa1fda9Journal of Medical Imaging and Radiation Oncology20Journal of medical imaging and radiation oncology["D. Roos", "Amy Yuen Meei Teh", "Kirsten Fitzpatrick", "Martin Gunn"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/d95cd816788feafe521e407b471b96963fa1fda9 711f550ff4af15fee63536ffef166f595a8ac58dЧетырнадцатая международная мультиконференция : Тез. докл.00Четырнадцатая международная мультиконференция : Тез. докл.[]2024-09-19T00:00:00https://www.semanticscholar.org/paper/711f550ff4af15fee63536ffef166f595a8ac58d dee930e262074550f09d946db91d7f4827f1e611This study investigates the potential of using advanced conversational artificial intelligence (AI) to help people understand complex AI systems. In line with conversation-analytic research, we view the participatory role of AI as dynamically unfolding in a situation rather than being predetermined by its architecture. To study user sensemaking of intransparent AI systems, we set up a naturalistic encounter between human participants and two AI systems developed in-house: a reinforcement learning simulation and a GPT-4-based explainer chatbot. Our results reveal that an explainer-AI only truly functions as such when participants actively engage with it as a co-constructive agent. Both the interface’s spatial configuration and the asynchronous temporal nature of the explainer AI – combined with the users’ presuppositions about its role – contribute to the decision whether to treat the AI as a dialogical co-participant in the interaction. Participants establish evidentiality conventions and sensemaking procedures that may diverge from a system’s intended design or function.Discourse &amp; Communication61This study investigates the potential of using advanced conversational artificial intelligence to help people understand complex AI systems and reveals that an explainer-AI only truly functions as such when participants actively engage with it as a co-constructive agent.Discourse &amp; Communication["N. Klowait", "M. Erofeeva", "Michael Lenke", "Ilona Horwath", "Hendrik Buschmeier"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/dee930e262074550f09d946db91d7f4827f1e611 3cd7900398e6b9b18c1500043d74d86ddb2c3fb6In recent years, the possibility of enhancing speaking skills has drawn some serious attention from the language education field as AI-powered tools such as chatbots—such as ChatGPT—gain in popularity. While questions remain about their long-term efficacy, their potential to deliver real-time feedback is especially important in non-Western countries like Vietnam. This paper explores AI avatars' potential for overcoming traditional language learning issues—apprehension, inadequate speaking practice, and low levels of quality feedback customization. This research study focuses on the potential of artificial intelligence tools for language learners and the challenges in making meaningful, authentic conversational interactions with cultural adaptability and scalability through deep analysis of existing research and real-world applications. In light of this, the paper emphasizes that more research must be conducted to improve the use of AI avatars within varied educational settings and enhance their impact on oral communication abilities.International Journal of AI in Language Education264This paper explores AI avatars' potential for overcoming traditional language learning issues—apprehension, inadequate speaking practice, and low levels of quality feedback customization through deep analysis of existing research and real-world applications.International Journal of AI in Language Education["Ngoc Hoang Vy Nguyen", "V. Ph\u1ea1m"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/3cd7900398e6b9b18c1500043d74d86ddb2c3fb6 ddc3a5ead4f25d2464731340ff2edda90129079aThis work presents some ambitious perspectives on how Systems Chemistry can contribute to developing the quite new research line of Chemical Artificial Intelligence (CAI). CAI refers to the efforts of devising liquid chemical systems mimicking some performances of biological and human intelligence, which ultimately emerge from wetware. The CAI systems implemented so far assist humans in making decisions. However, such CAI systems lack autonomy and cannot substitute humans. The development of autonomous chemical systems will allow the colonization of the molecular world with remarkable repercussions on human well‐being. As a beneficial side effect, this research line will help establish a deeper comprehension of the mesmerizing phenomenon of the origin of life on Earth and how cognitive capabilities emerge at a basic physico‐chemical level.ChemSystemsChem742This research line will help establish a deeper comprehension of the mesmerizing phenomenon of the origin of life on Earth and how cognitive capabilities emerge at a basic physico‐chemical level and will allow the colonization of the molecular world with remarkable repercussions on human well‐being.ChemSystemsChem["P. Gentili", "Pasquale Stano"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/ddc3a5ead4f25d2464731340ff2edda90129079a fc35cc96d158012342717dd046665067d62c5c01Extreme poverty is among the challenges the United Nations seeks to eradicate by the year 2030 as outlined in its Sustainable Development Goals. However, governments and other stakeholders face challenges in accurately identifying poverty in households for evidence- based implementation of SDG programs. Current strategies are slow, inaccurate and costly to efficiently support efforts to identify poverty for sustainable development. Consequently, many strategies to map out poverty for intervention measures do not succeed which could be contributing to the global decline in the rate of reducing poverty. Artificial intelligence which has become widely available and has been used in many sectors, could be leveraged to improve poverty mapping for evidence-based interventions for sustainable development. Despite living in the era of AI, it has not been fully utilized in mapping poverty. This review seeks to explore the extent of research on the adoption of AI in mapping poverty so as to find the gap for further research. It aims to establish the extent of AI-based research on identification of poverty in respect to global distribution of research studies, methods, algorithms and sources of data which have been used in studies to identify poverty. The findings will help to identify gaps for research to help in designing evidence-based strategies for intervention measures. A systematic review was done for the period 2020 to 2024 using databases and snowballing hybrid search approach. A qualitative analysis was done on the extracted data to uncover new patterns and identify research gaps.International Journal of Innovative Science and Research Technology791This review aims to establish the extent of AI-based research on identification of poverty in respect to global distribution of research studies, methods, algorithms and sources of data which have been used in studies to identify poverty.International Journal of Innovative Science and Research Technology (IJISRT)["George Kimwomi", "Mvurya Mgala"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/fc35cc96d158012342717dd046665067d62c5c01 03d48cea05ef7a1717d238dda67d627c63a94123This study examines the impact of AI-driven predictive analytics on employee retention strategies in Human Resource Management (HRM). By integrating Artificial Intelligence (AI) and Machine Learning (ML), organizations can forecast employee turnover, personalize career development, and create targeted interventions for at-risk employees. This study outlines the current applications, benefits, and challenges of AI in HRM and explains how predictive analytics can identify patterns in employee behavior to predict turnover risks. Through case studies, this paper highlights successful implementations of AI-driven retention strategies and specific tools. It also addresses ethical and privacy concerns, emphasizing transparency and fairness. Future trends and the long-term benefits of AI in HRM, such as improved employee satisfaction and reduced turnover costs, are discussed. This paper explores future trends and prospects by, considering the evolving role of AI in strategic HR planning and potential technological advancements. The long-term benefits for organizations adopting these technologies include improved employee satisfaction, reduced turnover costs, and a more engaged and stable workforce. This research underscores the critical relevance of employee retention, the innovative potential of AI and ML in HRM, and the significant impact these technologies have on organizational success. Keywords: AI in HRM, Predictive Analytics, Employee Retention, Machine Learning, Proactive Retention Strategies, Ethical Considerations, Future Trends.International journal of research and review01The current applications, benefits, and challenges of AI in HRM are outlined, how predictive analytics can identify patterns in employee behavior to predict turnover risks are explained, and ethical and privacy concerns are addressed.International Journal of Research and Review["Sunil Basnet"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/03d48cea05ef7a1717d238dda67d627c63a94123 45f029118f4da60e7cbdd508e7f0335428bcf7a7Increased time spent by users in virtual environments globally coupled with the choice architectures created by artificial intelligence (AI) systems within these virtual environments means that the AI systems can ‘persuade’ users to take certain actions or abstain from them. In this article, we address this issue through the lens of ‘dark patterns’. Dark patterns are features of the user interface that shape the actions of the users in different ways, getting them to act in ways that they might not act otherwise and that are not necessarily beneficial for them. Recently, regulatory action has been undertaken in various jurisdictions to contain the menace of dark patterns, including in India. But can these measures prevent dark patterns that are powered by AI systems? In this article, we argue that they cannot, as AI-powered dark patterns work at a deep level of behavioural change, making users’ choices appear uninfluenced by manipulation. The laws pertaining to unfair trade practices, transparency and labelling obligations are also not sufficient to deal with these problems. We conclude with an appeal for a holistic approach to AI-powered dark patterns that engage in persuasive behaviour.Journal of Development Policy and Practice40It is argued that laws pertaining to unfair trade practices, transparency and labelling obligations are not sufficient to deal with dark patterns, as AI-powered dark patterns work at a deep level of behavioural change, making users’ choices appear uninfluenced by manipulation.Journal of Development Policy and Practice["Krishna Deo Singh Chauhan", "Anupriya"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/45f029118f4da60e7cbdd508e7f0335428bcf7a7 635f389309cd736a95ed50327c44039ac28549cfIn our days, with these tensions in different fields of social-economic activities, it is very import that the economy and society to go forward, in the benefit for the people themselves and for the entities as well. We deal with the necessity of renewal the Theory of the Firm, with elements and aspects adaptable to the new challenging realities. The firm is the basic unit of the functioning of the economy, merely on short and medium-run. The focus is on the Human resource, such as staff, managers, CEOs, even entrepreneurs. We used information from important international; consulting companies in the field and, also, my own expertise, because teaching Economics in higher education is a pre-work within firms and a good situation for simulation of the real situations. We presented the challenge of the Artificial Intelligence (AI) to the human factor of production and, as a conclusion, or synthesis of the problem is that the Human factor is still decisive in businesses and social economy. The labor market is in straight correlation with the other economic variables and sensitive to the movements, or even declarations about possible Recession, falling-down in economy and a greater uncertainty about the economic future. But, for a quite long time, we consider that the Human factor is the key one within firms/companies/organizations.Universal Library of Business and Economics230Universal Library of Business and Economics["Prof. Alexandru Trifu"]2024-09-19T00:00:00https://www.semanticscholar.org/paper/635f389309cd736a95ed50327c44039ac28549cf 044a49daeb30a0ee0fad09b2871c455c93618887The integration of Artificial Intelligence (AI) into healthcare research promises unprecedented advancements in medical diagnostics, treatment personalization, and patient care management. However, these innovations also bring forth significant ethical challenges that must be addressed to maintain public trust, ensure patient safety, and uphold data integrity. This article sets out to introduce a detailed framework designed to steer governance and offer a systematic method for assuring that AI applications in healthcare research are developed and executed with integrity and adherence to medical research ethics.World Journal of Methodology123This article sets out to introduce a detailed framework designed to steer governance and offer a systematic method for assuring that AI applications in healthcare research are developed and executed with integrity and adherence to medical research ethics.World Journal of Methodology["Ahmad A. Abujaber", "A. Nashwan"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/044a49daeb30a0ee0fad09b2871c455c93618887 65374214b93b4a19744caf8880e7c77db06cb651This document explains how artificial intelligence (AI) and the stock market can work together. Among the more important ones are stock pattern detection and stock prediction using AI. The goal of stock market prediction is to forecast the future value of a company's fiscal stocks. The application of machine literacy, which bases predictions on the values of current stock request indicators by training on their historical values, is a recent development in stock request vaticination technology. Several models are used by machine learning itself to facilitate and authenticate vaccination. The study focuses on prognosticating stock values using LSTM based machine literacy. Considered factors are volume, low, high, open, and closed. Transfer literacy was the model we used for the stock.International Journal of Innovative Science and Research Technology501This document explains how artificial intelligence (AI) and the stock market can work together and focuses on prognosticating stock values using LSTM based machine literacy.International Journal of Innovative Science and Research Technology (IJISRT)["Aravind Gangavarapu", "P. V. S. Pranay", "Polisetti Likhit Sai"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/65374214b93b4a19744caf8880e7c77db06cb651 7db04ccbe0dcf9e6d763c2db62bd108ad6691957Resilience refers to the ability of systems to withstand, adapt to, and recover from disruptive events. While studies on resilience have attracted significant attention across various research domains, the precise definition of this concept within the field of cooperative artificial intelligence remains unclear. This paper addresses this gap by proposing a clear definition of `cooperative resilience' and outlining a methodology for its quantitative measurement. The methodology is validated in an environment with RL-based and LLM-augmented autonomous agents, subjected to environmental changes and the introduction of agents with unsustainable behaviors. These events are parameterized to create various scenarios for measuring cooperative resilience. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions. These findings provide foundational insights into the definition, measurement, and preliminary analysis of cooperative resilience, offering significant implications for the broader field of AI. Moreover, the methodology and metrics developed here can be adapted to a wide range of AI applications, enhancing the reliability and effectiveness of AI in dynamic and unpredictable environments.arXiv.org471The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions.ArXiv["Manuela Chacon-Chamorro", "Luis Felipe Giraldo", "Nicanor Quijano", "Vicente Vargas-Panesso", "C'esar Gonz'alez", "Juan Sebastian Pinzon", "Rub'en Manrique", "Manuel R\u00edos", "Yesid Fonseca", "Daniel G'omez-Barrera", "Monica Tatiana Perdomo"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/7db04ccbe0dcf9e6d763c2db62bd108ad6691957 0db78a29bc60084c3b9f8bfc0e899ca06cf740afNowadays, the trend toward adopting artificial intelligence (AI) in customs risk management has increased across the globe, yet it may not be efficiently perform the required if the design risk management process is not rich in many features and inputs that are accurate and mathematically applicable. In this paper, we have designed a recommendation system for customs risk management at the level of Algeria’s customs whose current risk management system is still based on the discretion of customs agents, supported by certain internal laws and regulations that guide the decisions of Algerian customs agents. In the design of this recommendation system, we relied on the supervised machine learning where we used five different algorithms. These algorithms have resulted in close accuracy ranging from 97% to 99%. This model reduces the time taken to process different shipments and supports the decision-making process for customs inspectors. However, the current approach of risk management at the Algerian customs level requires greater depth, quality and accuracy at the input level in order to build a highly efficient customs risk management model.Les cahiers du cread01This paper has designed a recommendation system for customs risk management at the level of Algeria’s customs whose current risk management system is still based on the discretion of customs agents, supported by certain internal laws and regulations that guide the decisions of Algerian customs agents.les cahiers du cread["Rouzlani Oussama", "Bouaziz Nacer", "Amroun Wissam"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/0db78a29bc60084c3b9f8bfc0e899ca06cf740af 9445121bb998894a2767893f1a1adae43a3c1804The Artificial Intelligence (AI) incooperation in educational settings sparked significant discussions regarding data privacy, especially in secondary schools in Kenya. As AI technologies became increasingly prevalent, the oversight and guiding of students' individual information raised important legal and ethical concerns. This study explored the legal and ethical implications of data privacy in AI applications within Kenyan secondary schools, focusing on the unique challenges faced in this context. The problem statement addressed the growing concerns over the adequacy of current data privacy protections and the potential risks posed by AI systems handling sensitive student information. The study had three primary objectives: first, to assess the current legal frameworks and policies governing data privacy in Kenyan secondary schools; second, to evaluate the ethical considerations related to the use of AI technologies and their impact on students' privacy; and third, to identify best practices for enhancing data protection. The scope of the study was confined to secondary schools across Kenya, examining the intersection of legal regulations and ethical practices in managing student data within these institutions. The justification for this study lay in the increasing reliance on AI tools in education and the need to ensure that data privacy standards were robust enough to protect students' personal information. Data for this review was collected from secondary sources, including existing literature, policy documents, and previous research findings. The method of data collection involved a comprehensive literature review, followed by a qualitative analysis of the collected data to identify patterns and insights related to data privacy issues. The reason for the inquiry of the study was to provide a thorough review of the current state of data privacy among learners in Kenyan secondary schools and to offer recommendations for improving legal and ethical practices. By analyzing secondary sources, the study aimed to contribute to the development of more effective data privacy strategies and ensure that AI technologies were executed in a manner that safeguarded students' rights and interests.International Journal of Innovative Science and Research Technology681This study explored the legal and ethical implications of data privacy in AI applications within Kenyan secondary schools, examining the intersection of legal regulations and ethical practices in managing student data within these institutions.International Journal of Innovative Science and Research Technology (IJISRT)["Muli Mutuku"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/9445121bb998894a2767893f1a1adae43a3c1804 e34833ea26858a9ccad6f2718d8c3bd6827fd1daArtificial intelligence is being used and integrate positively in anatomy, in which programs are available at all times, enhancing efficiency and decision-making, however, it also offers significant challenges by reducing human jobs, ethical concerns and high costs. This review provides an overview of artificial intelligence and the significant technology in the anatomy education.SAR Journal of Anatomy and Physiology00An overview of artificial intelligence and the significant technology in the anatomy education provides an overview of artificial intelligence and the significant technology in the anatomy education.SAR Journal of Anatomy and Physiology["Sahar Youssef"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/e34833ea26858a9ccad6f2718d8c3bd6827fd1da 1c53728b0afd8e0109ca9fa3b5f66e8147252219Management in the age of artificial intelligence (AI) presents unique challenges and opportunities, especially in the international landscape. The technology is constantly evolving, and managers need to adapt to the new landscape that is emerging to harness its potential while considering and anticipating the risks involved. AI can disrupt traditional leadership models by automating specific tasks and decision-making processes. This challenges managers to align with this new reality and use AI to drive innovation and productivity. In addition, managers must navigate and adapt to the evolving regulatory environment, understanding but also complying with regulations around AI, data protection, and privacy, while advocating for responsible and fair governance. While AI significantly impacts management processes, the academic literature on the subject is still emerging. This study aims to explore the challenges posed by AI and how international business managers can use this technology to drive decision-making and benefit their organizations. Ten interviews were conducted with senior executives from a range of industries to collect qualitative primary data. The findings show that AI can help with decision-making, automate many processes, and improve efficiency. Top managers can use AI tools to address many challenges more effectively, but they need to understand the ethical implications and concerns that continue to arise. The findings provide theoretical implications and practical recommendations for top managers using AI technologies. Senior executives should develop AI-focused strategies; as AI becomes more prevalent across industries, managers need to understand how to integrate AI technologies into their organizations successfully.European Conference on Innovation and Entrepreneurship190The findings show that AI can help with decision-making, automate many processes, and improve efficiency, and top managers can use AI tools to address many challenges more effectively, but they need to understand the ethical implications and concerns that continue to arise.European Conference on Innovation and Entrepreneurship["Athanasios Kourkoumelis", "Eleni E. Anastasopoulou", "Georgios A. Deirmentzoglou", "Andreas Masouras", "Sofia Anastasiadou"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/1c53728b0afd8e0109ca9fa3b5f66e8147252219 2a588f016c10dba03c80297520500253b5e2a9cbOver the past years, Artificial Intelligence (AI) and Chat Generative Pre-Trained Transformer (ChatGPT) as its type have been extensively investigated and proved to be effectually employed within various spheres including education, offering promising opportunities for enhancing teaching and learning processes. However, the realization of these opportunities within the realm of Academic Writing (AW) instruction have not yet been thoroughly studied. Therefore, this article addresses this matter and exposes the results of a conducted analysis of the potential for integrating ChatGPT into AW instruction within the University curriculum. Specifically, it focuses on the issue of teaching master students how to create a successful for-and-against academic essay. Besides, the aim of this article is to develop an overarching framework for the incremental incorporation of ChatGPT into the AW course. To achieve this aim, the article resorts to such methods as theoretical positioning, comparative analysis, and qualitative research. Accordingly, the presented study expounds on the phenomenon of ChatGPT, its attributes, merits, and shortcomings, while also outlining the notion, features, key elements, and stages of producing for-and-against academic essays. In addition, it delineates the possibilities of ChatGPT’s application at various stages of essay writing, which may appear challenging for students. Furthermore, it recognizes potential drawbacks of ChatGPT, such as the risk of its overutilization and overreliance, which may result in obliterating students’ cognitive capabilities and AW skills. The presented study also underscores the necessity for additional immersion in the matter under discussion to address the issue related to long-term effects of integrating ChatGPT into AW instruction. Ultimately, the article sheds light on the AI’s dynamic integration in modern educational settings and provides implications for further research in this field.PrOsvita40Light is shed on the AI’s dynamic integration in modern educational settings and provides implications for further research in this field.PrOsvita["O. Vovk", "Daryna Kryvoshyia"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/2a588f016c10dba03c80297520500253b5e2a9cb 154e9e21cb69fcb13ce7931ab0d237ac35aaf121This was a qualitative endeavor to explore the university teachers' perspectives on opportunities and challenges regarding Artificial Intelligence in higher education. For this purpose, there were eight university teachers (from both public and private) who were sampled conveniently from the social sciences disciplines. The data have been collected with the help of a Semi-structured protocol that has been developed by the investigator(s). It comprises 10 items including one introductory and one closing question. The collected data have been analyzed with the help of the Thematic analysis. Based on the results, it is concluded that, while AI has the potential to significantly transform education, there is a clear need for more targeted support and training for educators. Addressing these challenges is crucial to fully leveraging the benefits of AI and ensuring that its integration into education enhances, rather than complicates, the teaching and learning experience.Global Educational Studies Review00It is concluded that, while AI has the potential to significantly transform education, there is a clear need for more targeted support and training for educators.Global Educational Studies Review["Humayun Sohail", "Muhammad Shokat Zaman", "Muhammad Hussain Raza Zaidi"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/154e9e21cb69fcb13ce7931ab0d237ac35aaf121 9d0ebd2ef6c538087eb0a4896448983585bae41bNeurons are interactive cells that connect via ions to develop electromagnetic fields in the brain. This structure functions directly in the brain. Connectome is the data obtained from neuronal connections. Since neural circuits change in the brain in various diseases, studying connectome sheds light on the clinical changes in special diseases. The ability to explore this data and its relation to the disorders leads us to find new therapeutic methods. Artificial intelligence (AI) is a collection of powerful algorithms used for finding the relationship between input data and the outcome. AI is used for extraction of valuable features from connectome data and in turn uses them for development of prognostic and diagnostic models in neurological diseases. Studying the changes of brain circuits in neurodegenerative diseases and behavioral disorders makes it possible to provide early diagnosis and development of efficient treatment strategies. Considering the difficulties in studying brain diseases, the use of connectome data is one of the beneficial methods for improvement of knowledge of this organ. In the present study, we provide a systematic review on the studies published using connectome data and AI for studying various diseases and we focus on the strength and weaknesses of studies aiming to provide a viewpoint for the future studies. Throughout, AI is very useful for development of diagnostic and prognostic tools using neuroimaging data, while bias in data collection and decay in addition to using small datasets restricts applications of AI-based tools using connectome data which should be covered in the future studies.Frontiers Neuroinformatics320This study provides a systematic review on the studies published using connectome data and AI for studying various diseases and focuses on the strength and weaknesses of studies aiming to provide a viewpoint for the future studies.Frontiers in Neuroinformatics["Dorsa Shekouh", "Helia Sadat Kaboli", "Mohammadreza Ghaffarzadeh-Esfahani", "Mohammadmahdi Khayamdar", "Zeinab Hamedani", "Saeed Oraee-Yazdani", "Alireza Zali", "Elnaz Amanzadeh"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/9d0ebd2ef6c538087eb0a4896448983585bae41b 7f154fc5ba94927da1cb27605b629d7a90dd5383This study examines how artificial intelligence (AI), Personalization, problem-solving, and augmented reality (AR) technology affect educational outcomes. The rising use of digital technology in education requires understanding how it affects learning to design successful teaching practices. Analyzing student and instructor data using structural equation modelling creates a strong framework for exploring key construct interrelationships. The study focuses on six primary constructs: AI Personalization (AIP), AI Problem Solving (AIPS), Augmented Reality Creativity Enhancement (ARCE), Augmented Reality Engagement (ARE), Augmented Reality Social Interaction (ARSI), and Learning Outcomes (LOS). These three dimensions are positively connected, showing that strategic AI and AR applications in education could transform the experience. These associations were assessed using path analysis on 357 preschool instructors' survey responses. Results show a favourable association between AI personalization, AR engagement, ARCE, and ARSI (t = 7.947, p < 0.001). AIPS, ARCE, and ARSI have significant beta values (p < 0.001): β = 0.331, β = 0.559, and β = 0.227, indicating that LOS directly impacts these variables. Interaction effects show that LOS moderates the connection between AIP, AIPS, are-ARCE, and ARSI, but not ARSI (β = -0.116, p <0.001; β = 0.106, p= 0.026; β = 0.082, p = 0.086. This study has implications for educators, policymakers, and developers who want to learn how to use AI-AR to engage and delight children in learning. These findings feed training and resources to improve early learning with these technologies.Journal of Advances in Humanities Research310These findings feed training and resources to improve early learning with these technologies and have implications for educators, policymakers, and developers who want to learn how to use AI-AR to engage and delight children in learning.Journal of Advances in Humanities Research["Dan Wang"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/7f154fc5ba94927da1cb27605b629d7a90dd5383 3cfcef985407c55c889ffeb0a05d3e67207466c3Artificial intelligence (AI) technology innovations can intensify the digital ecosystem affecting management practices and the quality of life for female senior business leaders in the United States. The purpose of this qualitative, transcendental phenomenology study was to examine the lived experiences that some female senior business leaders, ages 55 - 95, face using AI technology in decision-making. The conceptual framework are Technology Acceptance Model (TAM) and the Mindspace Model. Data was collected through interviews with 12 successful female senior business leaders from nine industries in the US. The Van Kaam method, supported by Moustakas’ theoretical process, was used to analyze the data. Descriptive and inductive coding was used to categorize the themes: (a) AI technology is beneficial, (b) leadership and change management, (c) technology adaptation and acceptance, (d) decision-making and communication, and (e) information sharing and privacy. This study contributes to positive social change as a benefit to seniors by strengthening their AI technology decision-making practices, leadership, and community awareness in addition to influencing positive social change across management platforms.Journal of Strategic Innovation and Sustainability870This study contributes to positive social change as a benefit to seniors by strengthening their AI technology decision-making practices, leadership, and community awareness in addition to influencing positive social change across management platforms.Journal of Strategic Innovation and Sustainability["Leslie Gilliam", "Teresa Lao", "Chikwendu Nweke"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/3cfcef985407c55c889ffeb0a05d3e67207466c3 4bfd81e65e035bb57895935d980e369b468b0b7fThis research aims to investigate the legality of using ChatGPT in education from the perspective of the Muslim community, focusing on ethics, Islamic law, and Islamic values. Since the emergence of Artificial Intelligence (AI) in the early 2020s, public debates on AI have generated both support and criticism. Western scholars such as Kelly Ann Allen, Joseph A. Crawford, and Ricky Acanto argue that AI significantly contributes to enhancing learning, personalizing instruction, and managing resources. However, concerns have arisen regarding the negative impacts of AI, such as cheating and plagiarism. Islam, as a timelessly relevant religion, offers wise solutions to these issues. This empirical study employs a literature review approach using the Systematic Quantitative Literature Review method to map the dynamics of the AI ChatGPT discussion among scholars. This study adheres to Miles and Huberman’s three stages of data analysis: data display, reduction, and conclusion. The research findings indicate that internalizing the values of Wasathiyyah is crucial for developing a broad perspective on societal acceptance of AI ChatGPTs. Religious moderation emphasizes that Islam does not reject AI, but rather emphasizes the importance of mitigating its negative effects. With proportional policies, AI is expected to collaborate with humans to accelerate civilization forward.Al-Istinbath: Jurnal Hukum Islam00The research findings indicate that internalizing the values of Wasathiyyah is crucial for developing a broad perspective on societal acceptance of AI ChatGPTs, and adheres to Miles and Huberman’s three stages of data analysis.Al-Istinbath: Jurnal Hukum Islam["E. Kosasih", "Mohammad Rindu Fajar Islamy", "Rizzaldy Satria Wiwaha"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/4bfd81e65e035bb57895935d980e369b468b0b7f 02181432d71321b5adb2dba6c6e0a8c9d8b2a0faThis work investigated how artificial intelligence (AI) and cloud computing are related to cost control in smart manufacturing. It aims to show how these technologies improve manufacturing environments' decision-making and cost efficiency for better management accounting. The study thoroughly examined the literature for the qualitative research methodology. The cloud computing benefits from lowering operating costs, the use of AI in predictive maintenance, and the incorporation of these technologies into management accounting systems are only key area and trends from the thematic analysis. According to the results, offering scalable and flexible computing resources enabled companies to quickly adjust to shifting demands of the market, cloud computing greatly reduced costs. Yet, the study also emphasized the difficulties in the management of the resources, such as the possibility of inefficiencies and higher expenses due to inefficient resource distribution. Also, AI technologies enhance the efficiency and accuracy of accounting procedures, freeing up professionals for the concentration on strategic duties such as financial analysis and decision support. The report suggested that to reduce and avoid inefficiencies, businesses should carefully manage their cloud resources. For the improvement of operational efficiency and decision-making, businesses were required to include AI-driven solutions into their management accounting systems. The study also found that enterprises should give priority to implementing cloud computing and AI technologies to stay competitive in the quickly shifting smart manufacturing market. These technologies provide a substantial area for innovation in cost management.International Journal of Management Research and Economics00The study found that enterprises should give priority to implementing cloud computing and AI technologies to stay competitive in the quickly shifting smart manufacturing market, and these technologies provide a substantial area for innovation in cost management.International Journal of Management Research and Economics["Rasha Jasim Ahmed Ebraheem Alobaidy"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/02181432d71321b5adb2dba6c6e0a8c9d8b2a0fa 5873002688f44870d289166a9bdd21033f4962a7The current human efforts to grapples with the pressing challenges of human and climate change impacts on biodiversity loss hotspots have not yield the expected outcome, thereby creating an urgent need for a more sustainable and innovative approach to mitigate the threats from local to global level. This paper explores the various applications of Artificial Intelligence (AI) in monitoring endangered animal species and forest degradation within the Oban Division of the Cross River National Park with a view to boosting sustainable species conservation and averting biodiversity hotspot loss. It evaluates the key potentials and real benefits of AI-driven technologies in optimizing species protection and conservation efforts in the hotspot. It also explores the challenges and opportunities associated with the adoption of AI in biodiversity hotspot monitoring and conservation; and propose recommendations for future research and policy interventions. The paper adopts a qualitative method in reviewing existing studies of AI applications in species conservation and narrows it down to the Oban biodiversity hotspot. The results show that species in the study area are under serious human and nature-induced threats. Also, though AI possesses one of the most intuitive and environmental-friendly options for species monitoring and protection, its application in the protected hotspot is still at zero level due to limited capacity and awareness. We recommend AI driven capacity building via staff training, as well as provision of place-centered AI-technologies to aid accurate monitoring and avert species extinction in the Oban hotspot. Also, local content development and promotion of indigenous technologies, ideas, policies and programmes should be urgently prioritized. International Journal of Energy and Environmental Science120Though AI possesses one of the most intuitive and environmental-friendly options for species monitoring and protection, its application in the protected hotspot is still at zero level due to limited capacity and awareness, so AI driven capacity building via staff training, as well as provision of place-centered AI-technologies to aid accurate monitoring and avert species extinction in the Oban hotspot are recommended.International Journal of Energy and Environmental Science["Ezinne Okoroafor", "Ikpong Umo", "Ifeanyi G. Ukwe"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/5873002688f44870d289166a9bdd21033f4962a7 5a9e5c7681c31333231a565cb8501fd84eafbbeeArtificial Intelligence (AI) is a collection of algorithms, tools and solution models that offer entrepreneurs a platform for managing and systemising their SMEs and business operations. Recently, AI opened new venues for smart activities and cost-effective solutions, saving time and money for start-ups and established enterprises. Advanced AI tools use high-efficiency algorithms such as machine learning to assess markets, evaluate product and service prices, and reduce maintenance costs. AI has radically disturbed many industries and sectors, offering opportunities to and allowing entrepreneurs to innovate and develop smart solutions in a competitive market. However, this disruption presents new challenges for entrepreneurs in dealing with AI technologies and establishing their ‘smart’ businesses. Investing in AI requires proper planning and understanding of the risks and challenges of applying new technologies and tools. Therefore, entrepreneurs must acquire the necessary skills and build competent teams of talents or experts to support them in maximising their returns and minimising risks. This paper presents a real-life scenario about some of the challenges faced by business enterprises in general and property businesses in particular. An AI-based innovation model for a real estate business comprising six primary services: Data analysis and strategy, Finance, Legal, Property Development, Property management, and Facility management has been introduced. This paper covers some of the entrepreneurial activities and AI-based solutions developed to improve the quality of their operations, manage their business processes, and maintain effective service delivery. Generative AI or Gen AI has significantly impacted many impressive real-life applications, delivering real value to many sectors, including finance, real estate, business management, research and development (R&D) projects, and entrepreneurial operations. Gen AI uses machine learning and neural network algorithms trained using big data text, images, audio and video. This paper introduces the main Gen AI types and covers real-life examples of applying Gen AI techniques to property business problems. It presents our GPT-based AI-powered property consultant application that uses GPT to help property developers, investors, and professionals, offering them solutions to business challenges such as property design and conversion options. If necessary, the user prompts the application, which responds with options for possible solutions like design solutions or converting office buildings or commercial properties to multiple residential flats and houses.European Conference on Innovation and Entrepreneurship00This paper presents a real-life scenario about some of the challenges faced by business enterprises in general and property businesses in particular and introduces the main Gen AI types and covers real-life examples of applying Gen AI techniques to property business problems.European Conference on Innovation and Entrepreneurship["Nasser Abouzakhar"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/5a9e5c7681c31333231a565cb8501fd84eafbbee 81fc0ec8d8849473310e5b77edcf3436f75a5e14Purpose The current research elucidates the role of empathy in design of artificial intelligence (AI) systems in healthcare context, through a structured literature review, analysis and synthesis of academic literature published between 1990 and 2024. Design/methodology/approach This study aims to advance the domain of empathy in AI by adopting theory constructs context method approach using the PRISMA 2020 framework. Findings The study presents a current state-of-the-art literature to review the connections between empathy and AI and identifying four clusters showing the emerging trajectories in the field of AI and empathy in healthcare setting. Originality/value Despite a rise in empirical research, the potential pathways enhancing AI accountability by incorporation of empathy is unclear. The research aims to contribute to the existing literature on AI and empathy in the healthcare sector by carving out four distinct clusters depicting the future research avenues. Information Discovery and Delivery1170The research aims to contribute to the existing literature on AI and empathy in the healthcare sector by carving out four distinct clusters depicting the future research avenues and identifying four clusters showing the emerging trajectories in the field of AI and empathy in healthcare setting.Information Discovery and Delivery["Anurag Chaturvedi"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/81fc0ec8d8849473310e5b77edcf3436f75a5e14 3fc0b74e11a4a0561b6f25b5f29fb04941869a77This paper intends to provide the reader with an overview of the main processes that are introducing artificial intelligence (AI) into healthcare services. The first part is organized according to an evolutionary perspective. We first describe the role that digital technologies have had in shaping the current healthcare methodologies and the relevant foundations for new evolutionary scenarios. Subsequently, the various evolutionary paths are illustrated with reference to AI techniques and their research activities, specifying their degree of readiness for actual clinical use. The organization of this paper is based on the interplay three pillars, namely, algorithms, enabling technologies and regulations, and healthcare methodologies. Through this organization we introduce the reader to the main evolutionary aspects of the healthcare ecosystem, to associate clinical needs with appropriate methodologies. We also explore the different aspects related to the Internet of the future that are not typically presented in papers that focus on AI, but that are equally crucial to determine the success of current research and development activities in healthcare.Future Internet1150The role that digital technologies have had in shaping the current healthcare methodologies and the relevant foundations for new evolutionary scenarios are described, and the various evolutionary paths are illustrated with reference to AI techniques and their research activities.Future Internet["G. Reali", "M. Femminella"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/3fc0b74e11a4a0561b6f25b5f29fb04941869a77 1c8007e3b5a7e27f29c34f9a4940d939c947c2cdAt present, driven by new theories and technologies such as the Internet and big data, artificial intelligence is developing rapidly, and all walks of life are actively integrating with artificial intelligence technology to advance the development of the industry. This article takes the application of artificial intelligence in hotels as the research object, preliminaries analyzes the current status and existing problems of artificial intelligence development in the hotel industry, and proposes relevant solution strategies, hoping to provide certain ideas for the efficient application of artificial intelligence in hotels. To help hotel companies use artificial intelligence more rationally to enhance guest loyalty and satisfaction.2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)100The current status and existing problems of artificial intelligence development in the hotel industry are analyzed, relevant solution strategies are proposed, and certain ideas for the efficient application of artificial intelligence in hotels are provided to help hotel companies use artificial intelligence more rationally to enhance guest loyalty and satisfaction.2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)["Xiao Niu", "Lixia Zhang"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/1c8007e3b5a7e27f29c34f9a4940d939c947c2cd ad06ab5596cbfe09ad4fd8d24dec97525cb303efThis study focuses on the application of artificial intelligence technology in the field of customer relationship management in the tourism industry and the problems encountered in this application. At the same time, it will propose corresponding solutions and analyze the many challenges currently faced by artificial intelligence technology, covering Data privacy and security issues, the accuracy of customer data analysis, the cost of technology application, and system integration problems. Furthermore, this article will deeply explore the use of blockchain technology, big data analysis, cloud computing and artificial intelligence integration platforms and solutions. In terms of specific ways to address these challenges, research shows that tourism companies have achieved significant optimization in terms of customer data protection, analysis accuracy, cost control, and system synergy by using these technical means.2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)100Research shows that tourism companies have achieved significant optimization in terms of customer data protection, analysis accuracy, cost control, and system synergy by using these technical means.2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)["Zhiyu Zhang"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/ad06ab5596cbfe09ad4fd8d24dec97525cb303ef e8f36b1356f2414a674996b60ad867c006403997International entrepreneurship competencies in the era of rapidly developing artificial intelligence focus more than earlier on stepping out of one’s comfort zone, asking the right questions, assessing the reliability of information sources, and developing cross-cultural teamwork readiness. The purpose of this research is to prepare students for collaborative learning situations where generative artificial intelligence tools have become widely available. The main research question is: What are the challenges in different types of entrepreneurial team-based action learning projects in the context of artificial intelligence? Learning concepts based on collaborative learning and improving the international networking readiness of students are presented. Practical implications of time zone differences, trust building and conflict management in international teams are analysed based on the experience of X-Culture projects. We compare the opportunities and challenges of applying generative artificial intelligence such as ChatGPT-4, Microsoft Co-pilot and Google Gemini Advanced when developing learners’ own international business ideas to learning from international teamwork to assist foreign start-ups in entering new markets. Gaps in skills to apply human collaboration for critically assessing artificial intelligence summary answers and identifying possible hallucinations when comparing new potential foreign markets were revealed. Artificial intelligence applications can be used to form global student teams based on the similarity of entrepreneurial self-development visions of team members. Still, experimenting with prompts in these applications can also enhance readiness to compare the pros and cons of different international entrepreneurship concepts and intelligent questioning skills for coaching and consulting. Students can chat with AI in a role-playing manner that helps them understand how to manage cultural differences in international entrepreneurship teams. Online action learning driven by tasks of start-ups is an essential tool in the entrepreneurial learning approach that prepares students to understand the value and limitations of generative artificial intelligence tools for international entrepreneurship. This paper contributes to understanding the challenges in action learning of students for international entrepreneurship and ways to overcome these challenges in the era when generative artificial intelligence tools are changing learning and international entrepreneurship practices.European Conference on Innovation and Entrepreneurship320Understanding the challenges in action learning of students for international entrepreneurship and ways to overcome these challenges in the era when generative artificial intelligence tools are changing learning and international entrepreneurship practices are contributed.European Conference on Innovation and Entrepreneurship["T. Elenurm"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/e8f36b1356f2414a674996b60ad867c006403997 08d6cd89e08580fc45c213cea9b5b9aa12d795f1CONTEXT.— Technology companies and research groups are increasingly exploring applications of generative artificial intelligence (GenAI) in pathology and laboratory medicine. Although GenAI holds considerable promise, it also introduces novel risks for patients, communities, professionals, and the scientific process. OBJECTIVE.— To summarize the current frameworks for the ethical development and management of GenAI within health care settings. DATA SOURCES.— The analysis draws from scientific journals, organizational websites, and recent guidelines on artificial intelligence ethics and regulation. CONCLUSIONS.— The literature on the ethical management of artificial intelligence in medicine is extensive but is still in its nascent stages because of the evolving nature of the technology. Effective and ethical integration of GenAI requires robust processes and shared accountability among technology vendors, health care organizations, regulatory bodies, medical professionals, and professional societies. As the technology continues to develop, a multifaceted ecosystem of safety mechanisms and ethical oversight is crucial to maximize benefits and mitigate risks.Archives of Pathology & Laboratory Medicine00Effective and ethical integration of GenAI requires robust processes and shared accountability among technology vendors, health care organizations, regulatory bodies, medical professionals, and professional societies.Archives of pathology & laboratory medicine["Brian R Jackson", "H. Rashidi", "Jochen K Lennerz", "M. E. D. de Baca"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/08d6cd89e08580fc45c213cea9b5b9aa12d795f1 b7ccbaf9937c5ecd95e04de27023b06ec8eb72e5This paper explores how Artificial Intelligence (AI) is reshaping the marketing landscape, using data primarily sourced from Statista. By analysing global trends up to March and July 2023, we aim to provide a clear understanding of AI's impact on marketing strategies and outcomes. We begin by examining the rapid growth of AI in marketing, including its market size, revenue, and the preferences of users worldwide. We also highlight the popularity of leading AI text tools among users, giving insights into their market dominance and adoption rates. In addition, we investigate the funding landscape for AI marketing-related startups, showcasing the industry's investment trends and innovation potential. Furthermore, we identify the most effective ways marketers are using AI and marketing automation, offering practical insights for professionals seeking to leverage these technologies. Our study also looks at where AI is being used in marketing, revealing the diverse applications that are shaping strategies globally. We delve into the tools and platforms driving AI integration in marketing and advertising, as well as the main tasks undertaken by professionals using generative AI. A key focus is on AI-assisted writing, where we outline how marketers are employing AI for content creation and its impact on efficacy. We also explore the perceived benefits of AI tools according to marketing professionals, providing actionable insights for decision-making. Lastly, we assess the effectiveness of chat-based search advertising, particularly among paid search marketers in the United States. Through a concise summary of our findings, this paper aims to offer a comprehensive view of AI's transformative role in marketing, providing practical insights for industry stakeholders navigating this evolving landscape. In conclusion, this research serves as a roadmap for marketers, entrepreneurs, and industry professionals, guiding them through the integration of AI technologies into marketing strategies and operations. By understanding the current trends and potential benefits of AI, stakeholders can adapt and thrive in an increasingly AI-driven marketing environment.European Conference on Innovation and Entrepreneurship60A comprehensive view of AI's transformative role in marketing is offered, providing practical insights for industry stakeholders navigating this evolving landscape, by analysing global trends up to March and July 2023.European Conference on Innovation and Entrepreneurship["Lenka Labudov\u00e1"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/b7ccbaf9937c5ecd95e04de27023b06ec8eb72e5 991cc6cf3e09e2770f671d0f7d7794010f57427aABSTRACT Purpose: To explore artificial intelligence’s impact on surgical education, highlighting its advantages and challenges. Methods: A comprehensive search across databases such as PubMed, Scopus, Scientific Electronic Library Online (SciELO), Embase, Web of Science, and Google Scholar was conducted to compile relevant studies. Results: Artificial intelligence offers several advantages in surgical training. It enables highly realistic simulation environments for the safe practice of complex procedures. Artificial intelligence provides personalized real-time feedback, improving trainees’ skills. It efficiently processes clinical data, enhancing diagnostics and surgical planning. Artificial intelligence-assisted surgeries promise precision and minimally invasive procedures. Challenges include data security, resistance to artificial intelligence adoption, and ethical considerations. Conclusions: Stricter policies and regulatory compliance are needed for data privacy. Addressing surgeons’ and educators’ reluctance to embrace artificial intelligence is crucial. Integrating artificial intelligence into curricula and providing ongoing training are vital. Ethical, bioethical, and legal aspects surrounding artificial intelligence demand attention. Establishing clear ethical guidelines, ensuring transparency, and implementing supervision and accountability are essential. As artificial intelligence evolves in surgical training, research and development remain crucial. Future studies should explore artificial intelligence-driven personalized training and monitor ethical and legal regulations. In summary, artificial intelligence is shaping the future of general surgeons, offering advanced simulations, personalized feedback, and improved patient care. However, addressing data security, adoption resistance, and ethical concerns is vital. Adapting curricula and providing continuous training are essential to maximize artificial intelligence’s potential, promoting ethical and safe surgery.Acta Cirurgica Brasileira460Artificial intelligence is shaping the future of general surgeons, offering advanced simulations, personalized feedback, and improved patient care, however, addressing data security, adoption resistance, and ethical concerns is vital.Acta Cirúrgica Brasileira["Caroliny Silva", "Daniel Nascimento", "Gabriela Gomes Dantas", "Karoline Fonseca", "L. Hespanhol", "A. R\u00eago", "I. Ara\u00fajo-Filho"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/991cc6cf3e09e2770f671d0f7d7794010f57427a 55dda861a2bdd6e24a41c933d77b10eeff712b75Artificial intelligence (AI) is rapidly transforming the landscape of nephrology, offering innovative solutions that enhance diagnosis, treatment, and patient care. This literature review explores the current and potential applications of AI across various domains within nephrology. We discuss AI-driven advancements in early diagnosis, personalized treatment planning, renal replacement therapy, and transplant nephrology. Furthermore, we examine how AI enhances patient care through remote monitoring, telehealth, and virtual assistants. While the promise of AI is immense, this review also addresses the ethical, regulatory, and technical challenges that accompany its integration into clinical practice. By highlighting the transformative potential of AI in nephrology, we underscore the need for continued research and collaboration to fully realize its benefits in improving kidney health outcomes.KIDNEYS180This literature review explores the current and potential applications of AI across various domains within nephrology and discusses AI-driven advancements in early diagnosis, personalized treatment planning, renal replacement therapy, and transplant nephrology.KIDNEYS["Kirolos Eskandar"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/55dda861a2bdd6e24a41c933d77b10eeff712b75 4b5bbb3b53228f721575b8b5854502c448131d49The changing technology has advanced into new advents like Artificial Intelligence. This technology has diffused into the classrooms through chatbots like ChatGPT, Claude, Google Bard, YouChat, KoalaChat, ChatOn, Microsoft Binge and many others. The conventional softwares are only updated by humans while Artificial Intelligence can correct and remodel itself. Artificial Intelligence is assisting the entrepreneurial organizations and students in data analysis, automation and Natural Language Processing (NLP). It also helps the entrepreneurial organizations in strategic decision making and managing operations and accounts. This research focuses on the opportunities and challenges posed by Artificial Intelligence in the entrepreneurial education across various business schools. It examines the benefits of chatbots for educational purposes. It also studies the limitations of chatbots. It enquires about the business advice sought from chatbots.  For this purpose, a survey was applied amongst students of entrepreneurial courses to analyze the impact of Artificial Intelligence in our classrooms. It is a study performed on 103 students enrolled in entrepreneurship courses at two management institutes of India between August, 2023, and December, 2023. The study focuses on the benefits of AI in areas such as assisting in classroom activities like such as market research, competitor analysis and legal aspects. It also aids the students in getting feedback for their assignments, preparing for examinations and rapid access to information. The research suggests that Artificial Intelligence is significantly contributing right from idea generation, planning, forecasting and progress evaluation of business plans to entrepreneurial students. It is extensively helping the entrepreneurial students in developing the business model canvas which assists them in designing the blueprint of the project, strategic management, and development of innovative business models. It also assists in improving the productivity and efficiency of students. This research would facilitate students, teachers and business schools imparting entrepreneurial curriculum inculcate Artificial Intelligence into the classroom studies.European Conference on Innovation and Entrepreneurship230The research suggests that Artificial Intelligence is significantly contributing right from idea generation, planning, forecasting and progress evaluation of business plans to entrepreneurial students.European Conference on Innovation and Entrepreneurship["Manisha Gupta", "Mamta Singh"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/4b5bbb3b53228f721575b8b5854502c448131d49 cf5b9a16decc69dd177d130c8894767bd2895d03The law can be considered an important tool to address the risks of using artificial intelligence (AI). AI is defined in a variety of ways depending on the tasks it completes. Given that AI leverages computing power to carry out tasks that people typically undertake, it is also frequently referred to as cognitive computing or machine learning. Artificial intelligence (AI) uses data perception and synthesis to replicate human thought processes, automate tasks, and make judgments. The use of AI is regulated by many laws and regulations aimed at protecting consumers, users and society in general. The role of the law in addressing the risks of using AI includes many issues, among which are: maintaining privacy and security, maintaining fairness, civil and criminal liability, maintain safety and regulating the use of AI in business. Artificial intelligence in law firms has proven to be a golden ticket to increased productivity, improved decision-making, and higher competitiveness in the industry. Rules that individuals and organizations must adhere to when using AI, ensuring that these standards are strictly applied. Furthermore, the law helps promote transparency and accountability, as organizations have to commit to documenting AI usage processes and clarify how data and algorithms are used. This helps reduce the risk of discrimination and errors that can occur when using the AI. International Journal of Science Technology & Society200International Journal of Science, Technology and Society["Khaled Fattah", "Basma Mohamed"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/cf5b9a16decc69dd177d130c8894767bd2895d03 491e391a498012a7a80aca712f1271e0bcc5096bDeveloping digital learning materials, especially video lectures, is becoming an important issue in education. The emergence of artificial intelligence (AI) has made the creation of videos easier compared to the traditional approach. However, the education sector has yet to benefit from these advanced technologies fully. This research explores the application of Artificial Intelligence (AI) in creating digital learning videos. The study uses 43 selected articles and the PRISMA analysis model to search, classify, and content-filter the results. The findings show a noticeable growth in using AI to create digital learning videos, with 33 topics formed from keywords and articles published in various journals. The most influential works include automatic content extraction from videos and virtual teachers, and there is significant interest in ChatGPT. The main research topics include • developing effective AI models to convert text to video, • integrating personalization and interaction features, and • applying these digital learning videos in education and training. However, many issues remain to be researched, such as enhancing the context-understanding capabilities of AI models and building frameworks to evaluate the effectiveness of videos. AI-generated videos' quality, reliability, and flexibility are significant unresolved challenges, opening up research gaps for future scholars. Keywords: Artificial Intelligence (AI); video; education; learning; PRISMA.Vinh University Journal of Science150The findings show a noticeable growth in using AI to create digital learning videos, with 33 topics formed from keywords and articles published in various journals, and there is significant interest in ChatGPT.Vinh University Journal of Science["Luong Thi Minh Hue", "Nguyen The Vinh", "Nguyen Kim Son", "Nguyen Van Viet", "DO Thi Phuong", "Duong Thuy Huong"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/491e391a498012a7a80aca712f1271e0bcc5096b adb967c198779e0d40f0b3d0abcb0496dd15666bWith ongoing advancements in science and technology, artificial intelligence (AI) has emerged as a key driver within modern intelligent logistics. Presently, AI-enabled smart logistics facilitates automated processes alongside intelligent controls throughout various stages including procurement, storage handling transportations as well as goods distribution. Through real-time monitoring coupled with optimization via big data analytics loT integration machine learning among others AI technologies significantly enhances operational efficiency accuracy within logistical workflows. Furthermore, AI capabilities extend towards proactive prediction response mechanisms addressing potential disruptions like traffic congestions or weather variations ensuring preemptive adjustments for seamless logistical progression. This study delves into ‘Applications of Artificial Intelligence Technology within Modern Intelligent Logistics Development’ aiming to elevate China's logistical efficacy bolster enterprise core competencies while delivering superior societal services.2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)70This study delves into ‘Applications of Artificial Intelligence Technology within Modern Intelligent Logistics Development’ aiming to elevate China's logistical efficacy bolster enterprise core competencies while delivering superior societal services.2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)["Wei Jia", "Ding Bin"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/adb967c198779e0d40f0b3d0abcb0496dd15666b 5bb28828eaf5876e588886ac5eaf959626171d4aThe generative artificial intelligence represented by ChatGPT brings great opportunities and challenges, resulting in rapid developments of AI collaborative academic writing. Problems exist as the output often turns out to be unreliable and the database lacks standardized and normalized content for academic writing. Considering the needs of writers, especially novices in academic writing, this study attempts to explore the high-quality interaction between humans and AI. Through discussing the pathway of applying move analysis and sentence stems in AI collaborative writing, this paper constructs an AI collaborative academic writing model to provide a clear framework and available expressions for academic AI collaborative writing.2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)120This paper constructs an AI collaborative academic writing model to provide a clear framework and available expressions for academic AI collaborative writing.2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT)["Qiuyan Chen", "Jingjie Li"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/5bb28828eaf5876e588886ac5eaf959626171d4a 1d37630019d56253b2dc9445be0fc4d6ee86b82eDigital technologies are becoming increasingly complex and integrated, leading to significant transformations in society and the economy. The article aims to explore and summarize the new opportunities and potential risks of the widespread use of artificial intelligence (AI) in all aspects of life, to define the new skills and necessary knowledge of digital entrepreneurs and to highlight the need for transformation in modern education. Recognizing that the relationship between technology and business is two-way and becoming stronger, revealing that well-prepared employees are a guarantee of success and prosperity of companies in various fields, we try to focus on the main groups of qualities, skills and basic knowledge of students in the age of artificial intelligence. The development of the Internet, expansion of connectivity through social networks, the advent of AI, 3D printing, and immersive technologies like Augmented Reality, and Virtual Reality, require new knowledge and skills, leading to new challenges in education. Qualified personnel in this modern world must have solid professional training and systemic thinking (knowledge, skills, accumulated information), developed cognitive abilities, and personal skills based on collecting and analyzing large amounts of diverse information from heterogeneous sources. Questions arise: how can multiple information sources be combined effectively, and how can the fusion of multiple sources provide additional information to support decision-making processes? Combining information obtained from the real world makes the results heterogeneous and more informative. It follows the need to develop machine learning methods to extract relevant information from increasingly complex data sets. The goal is to improve the accuracy of the applied classification algorithms by combining predictions from multiple models, as well as obtaining a more stable final classification evaluation, effective handling of noisy data, adaptation to changing conditions, and improving stability when solving problems. On the other hand, how to ensure that the enormous potential of artificial intelligence, virtual reality, connection with the physical world, machine learning, and pervasive networks of people and machines will be fully used to improve the quality of life and contribute to the building of stable societies. Changes must be subordinated to policy and investments for reliable artificial intelligence and based on an ethical and human-centered approach. All of these should be established as a fundamental principle of training in Higher education which imposes the need for transformation in modern education.European Conference on Innovation and Entrepreneurship130The article aims to explore and summarize the new opportunities and potential risks of the widespread use of artificial intelligence (AI) in all aspects of life, to define the new skills and necessary knowledge of digital entrepreneurs and to highlight the need for transformation in modern education.European Conference on Innovation and Entrepreneurship["Daniela Orozova", "Nadezhda Angelova", "Zlatin Zlatev"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/1d37630019d56253b2dc9445be0fc4d6ee86b82e de604a038a2e4c35d067880bd2388dcd4f05b4d6 Medical anatomy is an essential preclinical course for medical undergraduates and provides a fundamental basis for various medical and surgical specializations. Frequently, students encounter difficulties when it comes to studying and comprehending the subject matter. Several pedagogical strategies have been devised and utilized throughout the years to enhance the process of teaching and learning in the field of anatomy. Artificial intelligence (AI) is now transforming anatomy education by utilizing modern technologies such as virtual reality, augmented reality, machine learning, and AI-powered evaluation tools. Recent research explored the AI in anatomy teaching, emphasizing its advantages and constraints. This review provides a thorough overview of the latest developments in the use of AI in anatomical education. It explores how AI-powered technologies can improve the educational experience for anatomy students, including personalized learning, automated grading, and intelligent tutoring systems, and examines the effects of these technologies on student engagement, learning outcomes, and teaching methods.National Journal of Clinical Anatomy450How AI-powered technologies can improve the educational experience for anatomy students, including personalized learning, automated grading, and intelligent tutoring systems, is explored and the effects of these technologies on student engagement, learning outcomes, and teaching methods are examined.National Journal of Clinical Anatomy["Gayathri Pandurangam", "Swathi Gurajala", "Dandu Nagajyothi"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/de604a038a2e4c35d067880bd2388dcd4f05b4d6 a0dae2ab62cc1a816d3d45afcd041a73cb625d6bThis paper primarily explores the current status and development pathways of artificial intelligence (AI) technology in dance education. The research methodology is based on literature analysis. The study suggests that AI has great potential in areas such as dance movement analysis, choreography, and dance education equipment. The paper proposes three areas for reform and innovation in the future integration of AI with dance education: first, optimizing and upgrading dance teaching software and equipment; second, utilizing AI to promote students' autonomous learning; and third, integrating AI into dance teaching evaluation. Additionally, the integration of AI technology will help uncover valuable dance teaching resources, laying the foundation for innovation in dance education.Region - Educational Research and Reviews10The study suggests that AI has great potential in areas such as dance movement analysis, choreography, and dance education equipment, and the integration of AI technology will help uncover valuable dance teaching resources, laying the foundation for innovation in dance education.Region - Educational Research and Reviews["Tianyu Qi", "Biya Xu"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/a0dae2ab62cc1a816d3d45afcd041a73cb625d6b d7727e6e536d53d21c111088d1f4e6d425750475This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development.Algorithms1110The study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%.Algorithms["Camelia Delcea", "I. Nica", "\u0218tefan-Andrei Ionescu", "Bianca Cibu", "Hora\u021biu \u021aibrea"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/d7727e6e536d53d21c111088d1f4e6d425750475 a8d6fd33b1b517e44b8976911b53f81fd649c8ceFrom the year 2022, when the GPT 3 model became publicly available, Artificial intelligence (AI) has been a buzzword in marketing. To say it still is influencing the field would be an understatement with new models and technologies continuously emerging and existing ones getting better and better. Loyalty programs as a sales promotion tool have also undergone big changes in recent years and AI is bringing new opportunities and challenges to the field. In this paper, we investigate the possibilities of artificial intelligence usage in loyalty programs. We aim to showcase the diverse opportunities through which AI can enhance loyalty program experiences. Among those usage opportunities we put specific attention on AI personalization and predictive modelling. By harnessing AI capabilities, organizations can possibly find new ways to enhance customer engagement, satisfaction, and build stronger loyalty. In this paper we are also looking at the current state of loyalty programs in the selected market and the way AI is being used in them. It is important to note that we approach the topic from two points of view – customers` and brands`. That means that we are trying to highlight how certain AI technologies can possibly improve customers` experience as well as how AI may be a helpful tool for businesses who want to manage loyalty programs. The final part of our article includes information on the suggested ways AI technologies may be implemented into loyalty programs based on the information provided in previous parts of the paper. Suggested implementation strategies are also appropriate as a starting point for further research, where every single technology would be researched more in detail. The main goal of this article is to provide readers with a comprehensive overview of the possibilities of how Artificial Intelligence can be used in the context of loyalty programs today. The article is theoretical-empirical and is based on external information from trustworthy sources as well as our own research of certain AI technologies in the context of loyalty programs.European Conference on Innovation and Entrepreneurship170The main goal of this article is to provide readers with a comprehensive overview of the possibilities of how Artificial Intelligence can be used in the context of loyalty programs today.European Conference on Innovation and Entrepreneurship["Andrii Kushnarevych", "Daniela Koll\u00e1rov\u00e1"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/a8d6fd33b1b517e44b8976911b53f81fd649c8ce 99b62a56df151cf86590920d399df1a096e1e1c3 This paper examines recent proposals and research suggesting that AI adoption should be delayed until its potential harms are fully understood. Conclusions on the social optimality of delayed AI adoption are shown to be sensitive to assumptions about the process by which regulators learn about the salience of particular harms. When such learning is by doing—based on the real-world adoption of AI—this generally favours acceleration of AI adoption to surface and react to potential harms more quickly. This case is strengthened when AI adoption is potentially reversible. The paper examines how different conclusions regarding the optimality of accelerated or delayed AI adoption influence and are influenced by other policies that may moderate AI harm. JEL Classification Numbers: O33, L51.Social Science Research Network285How different conclusions regarding the optimality of accelerated or delayed AI adoption influence and are influenced by other policies that may moderate AI harm is examined.SSRN Electronic Journal["Joshua Gans"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/99b62a56df151cf86590920d399df1a096e1e1c3 f78ccb4c9b2870b7c64c75200c9cdd262e99503bAcademy of Management Review81Academy of Management Review["David M. Townsend", "Rick Hunt", "Judy Rady", "Parul Manocha", "J. Jin"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/f78ccb4c9b2870b7c64c75200c9cdd262e99503b c12c24ba7af0ca7a33e4930f1244ba37bd5d2e91Under the “double carbon” strategic goal, the development of distributed energy facilities aims to drive further reform in China's electricity spot market. However, electricity spot market participants face increasingly complex and challenging issues in power trading. At the same time, AI has shown its broad prospects in the field of new energy power system operation and control, but also in the electricity spot market transactions to show its strong vitality. This paper generally reviews the research of AI in the field of electricity spot market trading and gives its broad prospects. This paper focuses on analysing more than a dozen literatures in this field in the last two years and draws timelines and tables to illustrate the research progress. Firstly, this paper summarises the electricity spot market structure and its current problems. Secondly, this paper summarizes and compares the applications of three key technologies in electricity spot market trading: traditional machine learning and AI semantic large models, reinforcement learning-assisted decision-making, and meteorological large models. Finally, this paper envisions the application of AI in electricity spot market trading through the development of AI large model electricity platforms, offering a cutting-edge research direction in future. In summary, this paper helps relevant researchers to carry out work on AI applications in the area of electricity spot market.IEEE International Conference on Power and Renewable Energy510This paper summarizes and compares the applications of three key technologies in electricity spot market trading: traditional machine learning and AI semantic large models, reinforcement learning-assisted decision-making, and meteorological large models.2024 The 9th International Conference on Power and Renewable Energy (ICPRE)["Sizhe Xie", "Di Wang", "Zikang Liu", "Shuoshi Zhou"]2024-09-20T00:00:00https://www.semanticscholar.org/paper/c12c24ba7af0ca7a33e4930f1244ba37bd5d2e91 6a8a8749f99895b53cc8d1fe57e301d10c5ceb3eFrontiers in Oncology240Frontiers in Oncology["Luca Bertolaccini", "J. Guarize", "C. Diotti", "S. Donghi", "Monica Casiraghi", "A. Mazzella", "Lorenzo Spaggiari"]2024-09-20T00:00:00 f3725e08a2a62125960b2df37205013750b02a2aThe lockdown period, initially imposed for three months due to the COVID-19 outbreak in India, was later prolonged. Air quality data from eight monitoring sites in Rajasthan was used to calculate the AQI according to the following parameters: Particulate matter (PM2.5 and PM10), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulfur dioxide (SO2), Ozone (O3), and Carbon monoxide (CO), dispersed throughout the state by CPCB. Among the chosen cities, the study found that the AQI percentage dropped the most in Alwar, by 35.6% between pre-lockdown and lockdown. Conversely, it rose the most in Jaipur, by 86.77% between lockdown and post-lockdown. Python deep learning was used to simulate the relationship between Air Quality Index and Air contamination in the study area. Air quality index values ranging from Good (0–50) to Severe (>401) were used to create the AQI class categorization in Python. The study found that PM2.5 and PM10 had the strongest correlation. Metrics such as the coefficient of determination (R2) and the root mean square error (RMSE) were applied to assess the model on the datasets used for training and testing. Random forest, decision trees, and linear regression were worked to verify the precision of the prototype. The author used supervised learning techniques, such as decision tree (DT), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), logistic regression (LR), and random forest (RF), to determine the model's prediction. These findings suggest that urban areas are characterized by societal, commercial, and cultural aspects that contribute to similar discharge patterns and air quality issues. The study would be advantageous for authorities, as it is clearly apparent that reducing the sources of emissions can improve quality. This will set the stage for safeguarding and improving the environment.Current World Environment260It is found that urban areas are characterized by societal, commercial, and cultural aspects that contribute to similar discharge patterns and air quality issues, suggesting that reducing the sources of emissions can improve quality.Current World Environment["V. S. Bhati", "Abhishek Saxena", "Ravi Khatwal"]2024-09-20T00:00:00 219fbf02f2c29e60b4c9f87c53e39a20720c11ccAfrican Journal of Biomedical Research00African Journal of Biomedical Research["Dr. Apeksha"]2024-09-20T00:00:00 70ae3d834de0347353ed251f58fd06b916539e62JMA Journal00JMA Journal["Kouichi Tamura", "Masashi Sakai", "Tamio Iwamoto", "Shin\u2012ichiro Yoshida", "Jin Oshikawa"]2024-09-20T00:00:00 daedd42d15bcfacd501f4bf88eebc700dd9adf5eBadminton is one of the most popular sports in Indonesia. In fact, Indonesia often wins various badminton competitions at the international level. Many people enjoy playing badminton, but many of them do not know whether their shots are good or whether they understand the basic techniques of badminton correctly. Additionally, many of them want to improve their skills but do not have enough time to train with a professional coach. This research aims to develop a badminton training monitoring system based on AI Vision technology using the Swift programming language. This system is expected to help badminton players evaluate and improve the quality of their shots independently. The main focus of this research is to optimize computational accuracy by using the Optical Flow algorithm to track the movement of the shuttlecock during training. In developing this system, the Optical Flow algorithm is used to analyze the shuttlecock's trajectory and its drop points. The results of this research show that testing 20 shots using shuttlecock trajectory can be accurately detected by the system with an accuracy of 97.22%. Meanwhile, the system's accuracy in tracking the placement of the shuttlecock in the opponent's service area is 94.50%.Jurnal Indonesia : Manajemen Informatika dan Komunikasi00The main focus of this research is to optimize computational accuracy by using the Optical Flow algorithm to track the movement of the shuttlecock during training by using the Optical Flow algorithm to analyze the shuttlecock's trajectory and its drop points.Jurnal Indonesia : Manajemen Informatika dan Komunikasi["Mora Hakim Siregar", "Dadan Mulyana"]2024-09-20T00:00:00 5e8ebfa2034126280f1cd0411eb1fecb9b95e425 Flaring in the oil and gas industry is a critical process where excess gases are burned off through a flare stack. This practice is essential for safety reasons, such as pressure relief during unplanned overpressuring of plant equipment, and for managing gases that cannot be processed economically. However, flaring is also a significant source of greenhouse gas emissions, releasing harmful gases such as carbon dioxide and methane into the atmosphere. The environmental impact of these emissions makes it imperative to monitor and control flaring activities effectively. Despite the necessity of monitoring, the traditional methods involving flowmeters present significant challenges. These devices, which measure the rate of flow of the gas being flared, are often prohibitively expensive and complex to install, especially in remote or offshore locations. This high cost and complexity can hinder comprehensive monitoring efforts, leaving a gap in effective environmental management practices. Moreover, the practice among oil and gas operators to rotate available flowmeters across different flare stacks further complicates consistent monitoring. This rotation often results in minimal monitoring—sometimes only sufficient to meet the bare minimum of legal reporting requirements. Such practices underscore the need for more robust and continuous monitoring solutions. To address these challenges, in this paper we explore an innovative approach to estimate flaring emissions using a more accessible and cost-effective technology. By leveraging a simple system composed of a camera and an edge computer, this method uses visual data and advanced computing techniques to estimate the volume of gas flared. This approach not only reduces the economic burden associated with traditional flowmeters but also enhances the feasibility of continuous monitoring across various operational settings in the oil and gas domain. Through this paper, we aim to demonstrate the effectiveness of this system and discuss its potential implications for environmental monitoring and regulatory compliance in the industry.SPE Annual Technical Conference and Exhibition100An innovative approach to estimate flaring emissions using a more accessible and cost-effective technology, leveraging a simple system composed of a camera and an edge computer to estimate the volume of gas flared.SPE Annual Technical Conference and Exhibition["V. Santhalingam", "A. Abinader", "V. Vesselinov", "D. Krishna"]2024-09-20T00:00:00 8eba06165194f252c27d6b3f94640271db18fa36<jats:p/>The Korean Society of Beauty and Art00The Korean Society of Beauty and Art["Se A Lee", "Ji Soo Han"]2024-09-20T00:00:00 19f50d280a7107544ec12dd55167344d162e12b9The development of machine learning has the potential to significantly improve the identification and treatment of pregnancy-related risks in maternal health. This work uses an extensive dataset to create reliable models that can predict possible health issues with accuracy and reliability. Our models are highly predictive and have transparency and interpretability in their results because of our creative feature engineering, such as featurewiz and chi-square test, and meticulous data processing. The models underwent extensive validation procedures, and in three different categories, each proved better evaluation metrics results alongside accuracy levels above 99% in each class. Ensuring the durability and stability of the models, this remarkable degree of accuracy is further enhanced by ensemble techniques and hyperparameter tuning. Incorporating Explainable AI (XAI) methods, such as LIME and SHAP, has proven crucial in elucidating the models’ decision-making process, fostering trust, and enabling their possible use in healthcare environments. The results highlight the potential of ML in predicting maternal health risks and establishing solid foundations for future integration with Medical Cyber-Physical Systems (MCPS), which has the potential to transform the field of predictive analytics in prenatal care completely.SouthEast European Design Automation, Computer Engineering, Computer Networks and Social Media Conference230This work uses an extensive dataset to create reliable models that can predict possible health issues with accuracy and reliability andorporating Explainable AI methods, such as LIME and SHAP, has proven crucial in elucidating the models’ decision-making process, fostering trust, and enabling their possible use in healthcare environments.2024 9th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)["Mohammad Mobarak Hosaain", "Mohammod Abdul Kashem", "N. Nayan"]2024-09-20T00:00:00 1d73d08bc9b340bbc6b2fd9bd68e7ceb3913f93cNeurosurgical review110Neurosurgical review["Mayur Wanjari", "Gaurav Mittal", "Roshan Prasad"]2024-09-20T00:00:00 7da4d3e9cda04c08a7fcb49abddf1e5abcd62984Medical Education40Medical education["Casey N McQuade", "T. Wijesekera", "David Chartash"]2024-09-20T00:00:00 a47847ad8c12fed1251c53c58bfbe8cf80c1b991The rapid development of electric power technology has exacerbated issues with anomalies in market transactions, making effective identification and regulation of these anomalies a pressing task. This study proposes a trading behavior analysis model for market players, leveraging a stepwise regression algorithm and introducing a monitoring module based on the ARM Cortex series microcontroller. The model enables deep assessment of transaction data, timely detection of anomalies, and pattern mining for accurate regulatory support. The monitoring module enhances real-time monitoring and dynamic feedback, leveraging the ARM Cortex microcontroller's efficient data processing and low-power characteristics. Experimental validation shows the model's significant advantages in evaluation and error control, effectively identifying anomalies and demonstrating its potential to enhance electricity market regulation.IEEE International Conference on Power and Renewable Energy90Experimental validation shows the model's significant advantages in evaluation and error control, effectively identifying anomalies and demonstrating its potential to enhance electricity market regulation.2024 The 9th International Conference on Power and Renewable Energy (ICPRE)["Yan Li", "Yue Qu", "Xiaolong Zhang", "Wenmin Wu"]2024-09-20T00:00:00 cf186bcde9e7935e890f89d3c380dee71b979999Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Contributions could explore topics such as: leveraging natural language processing (NLP) in ChatGPT-like systems to strengthen information security; evaluating privacy-enhancing technologies to maximize data utility while minimizing personal data exposure; modeling human behavior and agency to build secure and ethical human-centric systems; applying machine learning to detect threats and vulnerabilities in a data-driven manner; using analytics to preserve privacy in large datasets while enabling value creation; crafting AI techniques that operate in a trustworthy and explainable manner. This article advances the state-of-the-art at the intersection of cybersecurity, privacy, human factors, ethics, and cutting-edge AI, providing impactful solutions to emerging challenges. Our research presents a revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data. Our approach involves constructing a grayscale image from a malware file and extracting features to minimize its size. This process affords us the ability to discern patterns that might remain hidden from other techniques, enabling us to utilize convolutional neural networks (CNNs) to learn from these grayscale images and a stacking ensemble to classify malware. The goal is to model a highly complex nonlinear function with parameters that can be optimized to achieve superior performance. To test our approach, we ran it on over 6,414 malware variants and 2,050 benign files from the MalImg collection, resulting in an impressive 99.86 percent validation accuracy for malware detection. Furthermore, we conducted a classification experiment on 15 malware families and 13 tests with varying parameters to compare our model to other comparable research. Our model outperformed most of the similar research with detection accuracy ranging from 47.07% to 99.81% and a significant increase in detection performance. Our results demonstrate the efficacy of our approach, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.PeerJ Computer Science460A revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data is presented, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.PeerJ Computer Science["Muhammad Rehan Naeem", "Rashid Amin", "Muhammad Farhan", "F. Alotaibi", "Mrim M. Alnfiai", "Gabriel Avelino R. Sampedro", "Vincent Karovic"]2024-09-20T00:00:00 9f0e0fc222e7a8e84de96f069722e484199082bdSince water is a precious and indispensable resource and playing a major role in sustaining life and supporting human activities, tracking its standard and distribution is the important need of the hour. The paper presents an innovative approach which focuses on modernizing the distribution system of water by incorporating NODE MCU microcontrollers and pH sensors. The pH sensor plays a pivotal role in assessing water quality, serving as a key determinant for contamination. When the water quality is deemed acceptable, the consumer valve is automatically opened, facilitating the supply to consumer units. Conversely, if the pH sensor detects contamination, an automated drainage valve is activated to divert the compromised water away from consumer use. Traditional methods of water distribution are prone to inefficiencies, and the introduction of NODE MCU microcontrollers enables a more intelligent and responsive water management system.2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)130An innovative approach is presented which focuses on modernizing the distribution system of water by incorporating NODE MCU microcontrollers and pH sensors, enabling a more intelligent and responsive water management system.2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)["S. Kanaga Suba Raja", "S. Usha Kiruthika", "C. J. Raman"]2024-09-21T00:00:00 b690c2f7736d47a3ffe29a2f86698c01d5962ff8Integrating Artificial Intelligence (AI) into healthcare, specifically for managing Autism Spectrum Disorder (ASD), offers transformative potential to enhance diagnostic accuracy, personalize treatment, and improve patient outcomes. This review explores the application of various AI programs in ASD management, discussing their functionalities, ethical considerations, implementation challenges, and the need for comprehensive regulatory frameworks. Critical AI applications such as AI-driven diagnostic imaging, predictive analytics, assisted therapy robots, remote monitoring, treatment personalization, decision support systems, and therapeutic chatbots are examined. Each technology is analyzed for its ability to improve the quality of life for individuals with ASD by offering more personalized, efficient, and effective care and support. Ethical issues, particularly concerning data bias and privacy, are highlighted as significant challenges that need addressing to maximize AI's benefits while minimizing risks. Practical hurdles like integration with existing healthcare systems, the need for scalable solutions across diverse geographic and socio-economic contexts, and the high costs associated with AI development are also discussed. Furthermore, the review underscores the necessity for robust regulatory policies that ensure patient safety, protect data privacy, and maintain high ethical standards in AI deployment. The paper concludes that while AI presents substantial opportunities for advancing ASD management, achieving these benefits requires a concerted effort from technologists, clinicians, ethicists, and policymakers to develop AI tools that are not only innovative but also ethical, equitable, and universally beneficial.International Journal of Innovative Research in Medical Science02While AI presents substantial opportunities for advancing ASD management, achieving these benefits requires a concerted effort from technologists, clinicians, ethicists, and policymakers to develop AI tools that are not only innovative but also ethical, equitable, and universally beneficial.International Journal of Innovative Research in Medical Science["Am\u00e1lia Cinthia Meneses do R\u00eago, Ph.D.", "I. Ara\u00fajo-Filho"]2024-09-22T00:00:00 ad4fce4800eb9d86aeee4b183079e2078a345b0eThis research paper explores the impact of artificial intelligence (AI) on piracy in the digital environment. It looks at how piracy has increased in the digital era and how AI may help protect intellectual property and provide pirates new strategies. The use of AI in content piracy is covered in the study, including the creation of text and audio, deepfake films, and counterfeit goods. Additionally, it looks at AI-assisted piracy technologies including dynamic content matching, automated content distribution and scraping, and content monetization.International Journal For Multidisciplinary Research30The use of AI in content piracy is covered, including the creation of text and audio, deepfake films, and counterfeit goods, and AI-assisted piracy technologies including dynamic content matching, automated content distribution and scraping, and content monetization.International Journal For Multidisciplinary Research["Taru Mishra", "Shivendra Kumar"]2024-09-22T00:00:00 9bae75564eb4f241ea81b8b4a15b8be371ab0514Lex Scientia Law Review00Lex Scientia Law Review[]2024-09-22T00:00:00 45f2a0c747399698d0be8cedcf038f8c85334510Theology and Science00Theology and Science["H. Griese"]2024-09-22T00:00:00 6de14d6ee48329e600e72e616ec3a04b6c7b064aAI &amp; SOCIETY170The methodological approach of an AI bun is proposed, an important approach in which LLMS can support lawmakers and policy experts in crafting legislation, and underscores the transformative potential of LLMs as a potential resource for lawmakers seeking to navigate decision-making while developing legislation.AI &amp; SOCIETY["Guzyal Hill", "Matthew Waddington", "Leon Qiu"]2024-09-22T00:00:00 4b2512335a2ce4bb885632ee72b591d36e804f3bThe FLTMAG00The FLTMAG["Tricia Bertram Gallant"]2024-09-22T00:00:00 c281d57d7acedb477fb911334e3f2042f9925d31<jats:p>-</jats:p>Jurnal Civics: Media Kajian Kewarganegaraan00Jurnal Civics: Media Kajian Kewarganegaraan["Shely Cathrin", "R. Rukiyati", "Maryani Maryani"]2024-09-22T00:00:00 84cf8f970538861e1efbe3e17cb54e427c186c27Understanding and correcting algorithmic bias in artificial intelligence (AI) has become increasingly important, leading to a surge in research on AI fairness within both the AI community and broader society. Traditionally, this research operates within the constrained supervised learning paradigm, assuming the presence of class labels, independent and identically distributed (IID) data, and batch‐based learning necessitating the simultaneous availability of all training data. However, in practice, class labels may be absent due to censoring, data is often represented using non‐IID graph structures that capture connections among individual units, and data can arrive and evolve over time. These prevalent real‐world data representations limit the applicability of existing fairness literature, which typically addresses fairness in static and tabular supervised learning settings. This paper reviews recent advances in AI fairness aimed at bridging these gaps for practical deployment in real‐world scenarios. Additionally, opportunities are envisioned by highlighting the limitations and significant potential for real applications.The AI Magazine205Recent advances in AI fairness aimed at bridging gaps for practical deployment in real‐world scenarios are reviewed, highlighting the limitations and significant potential for real applications.AI Mag.["Wenbin Zhang"]2024-09-22T00:00:00 2147a10687ab4efcc7ab2c7ae6e826ab2b9a36fdCompanies are increasingly using artificial intelligence (AI) to provide users with product recommendations, but its efficacy is inconsistent. Drawing upon social exchange theory, we examine the effects of product recommenders and their levels of self-disclosure on transaction costs. Specifically, we recruited 78 participants and conducted a 2 × 2 online experiment in which we manipulated product recommenders (human versus AI) and examined how self-disclosure levels (high versus low) affect consumers’ return intentions. We predicted and found that a low level of self-disclosure from human recommenders instead of AI counterparts results in higher emotional support, which leads to lower transaction costs. However, under high levels of self-disclosure, consumers’ emotional support and subsequent transaction costs do not differ between human and AI recommenders. Accordingly, we provide theoretical insights into the roles of self-disclosure and emotional support in human–machine interactions, and we contribute to sustainable AI practices by enhancing the efficiency of business operations and advancing broader sustainability objectives.Sustainability611It is predicted and found that a low level of self-disclosure from human recommenders instead of AI counterparts results in higher emotional support, which leads to lower transaction costs, but under high levels of self-disclosure, consumers’ emotional support and subsequent transaction costs do not differ between human and AI recommenders.Sustainability["Yasheng Chen", "Yuhong Tu", "Siyao Zeng"]2024-09-22T00:00:00 629ae6921622e410e913ba6ed5e3a130bcd26696The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.Online (Weston, Conn.)31This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation, and highlights the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)["Prashis Raghuweanshi"]2024-09-22T00:00:00 749c8386e7d577ba5773582bb84c4fc8f448f1b5As generative artificial intelligence (GenAI) becomes more prevalent in academic circles, this article probes its ethical and practical integration in international business (IB) research. It explores the different phases of the research process where GenAI can be effectively employed, including idea generation, data analysis, and writing and communication. Notably, the article emphasizes the critical importance of upholding responsible and ethical standards when incorporating GenAI into IB research, guaranteeing that its utilization not only contributes to knowledge advancement but also conforms to the highest research integrity benchmarks.Thunderbird International Business Review51The article emphasizes the critical importance of upholding responsible and ethical standards when incorporating GenAI into IB research, guaranteeing that its utilization not only contributes to knowledge advancement but also conforms to the highest research integrity benchmarks.Thunderbird International Business Review["Mamoun Benmamoun"]2024-09-22T00:00:00 37c4d46b3d18922180170f59704e6d081429f0c1The use of machine learning (ML) in critical domains such as medicine poses risks and requires regulation. One requirement is that decisions of ML systems in high-risk applications should be human-understandable. The field of"explainable artificial intelligence"(XAI) seemingly addresses this need. However, in its current form, XAI is unfit to provide quality control for ML; it itself needs scrutiny. Popular XAI methods cannot reliably answer important questions about ML models, their training data, or a given test input. We recapitulate results demonstrating that popular XAI methods systematically attribute importance to input features that are independent of the prediction target. This limits their utility for purposes such as model and data (in)validation, model improvement, and scientific discovery. We argue that the fundamental reason for this limitation is that current XAI methods do not address well-defined problems and are not evaluated against objective criteria of explanation correctness. Researchers should formally define the problems they intend to solve first and then design methods accordingly. This will lead to notions of explanation correctness that can be theoretically verified and objective metrics of explanation performance that can be assessed using ground-truth data.arXiv.org770It is argued that current XAI methods do not address well-defined problems and are not evaluated against objective criteria of explanation correctness, which limits their utility for purposes such as model and data (in)validation, model improvement, and scientific discovery.ArXiv["Stefan Haufe", "Rick Wilming", "Benedict Clark", "Rustam Zhumagambetov", "Danny Panknin", "Ahc\u00e8ne Boubekki"]2024-09-22T00:00:00 26fbd3d58f2e29800e5313497a2b7a265ed18654In today’s fast-paced world, maintaining health and personal wellness has become a top priority. Artificial intelligence (AI) has emerged as a powerful tool in this effort, offering innovative solutions through mobile health applications. These applications use AI-driven algorithms to analyze user data, including sleep patterns, food intake, daily activity levels, diet preferences, stress indicators, and meditation, to provide personalized recommendations and insights. Mobile health applications have the potential to improve healthcare systems by enhancing health and disease management, communication, efficiency, treatment adherence, reducing costs, and increasing access to health interventions. This paper aims to provide a better understanding of the use of artificial intelligence in healthcare tools by examining the factors influencing the intention to use mobile health applications in Malaysia. It will discuss the extended UTAUT constructs and the concept of personal health characteristics, such as performance expectancy, effort expectancy, social influence, facilitating conditions, and health consciousness.Information Management and Business Review430This paper aims to provide a better understanding of the use of artificial intelligence in healthcare tools by examining the factors influencing the intention to use mobile health applications in Malaysia and discussing the extended UTAUT constructs and the concept of personal health characteristics.Information Management and Business Review["Che Nur Asmani Amirah Che Mohd Nawi", "Zuhal Hussein", "Che Nur Asmani Amirah Che Mohd Nawi"]2024-09-22T00:00:00 9652b520fb1bbc2116caaa02e596d50fe1bd261dBackground: Patients are important participants in their medical care, yet artificial intelligence (AI) models are used to guide care with minimal patient input. This limitation is made partially worse due to a paucity of rigorous methods to measure and incorporate patient values of the tradeoffs inherent in AI applications. This paper presents AI-VOICE (Values-Oriented Implementation and Context Evaluation), a novel method to collect patient values, or utilities, of the downstream consequences stemming from an AI model's use to guide care. The results are then used to select the model's risk threshold, offering a mechanism by which an algorithm can concretely reflect patient values. Methods: The entity being evaluated by AI-VOICE is an AI-informed workflow, which is composed of the patient's health state, an action triggered by the AI model, and the benefits and harms accrued as a consequence of that action. The utilities of these workflows are measured through a survey-based, standard gamble experiment. These utilities define a patient-specific ratio of the cost of an inaccurate prediction versus the benefits of an accurate one. This ratio is mapped to the receiver-operator-characteristic curve to identify the risk threshold that reflects the patient's values. The survey instrument is made freely available to researchers through a web-based application. Results: A demonstration of AI-VOICE is provided using a hypothetical sepsis prediction algorithm. Conclusion: AI-VOICE offers an accessible, quantitative method to incorporate patient values into AI-informed healthcare workflows.medRxiv200This paper presents AI-VOICE (Values-Oriented Implementation and Context Evaluation), a novel method to collect patient values, or utilities, of the downstream consequences stemming from an AI model's use to guide care, offering a mechanism by which an algorithm can concretely reflect patient values.["M. M. Keith E. Morse", "PhD Michael C. Higgins", "BS Yichun Qian", "PhD Alison Callahan", "Nigam", "Mbbs H. Shah"]2024-09-22T00:00:00 21397920b7863487c413f1ca8e907a0a18d1e5b0Nas últimas décadas, a Inteligência Artificial (IA) tem se estabelecido como uma inovação transformadora na educação básica, oferecendo novas ferramentas para personalização e monitoramento do ensino. O objetivo geral deste trabalho é analisar o uso da IA pelos professores do ensino básico, destacando as principais práticas, desafios e oportunidades dessa tecnologia. A pesquisa é de natureza bibliográfica e utilizou técnicas de coleta e análise de dados por meio de revisão de literatura existente sobre o tema. Os principais resultados indicam que a IA pode otimizar o tempo dos professores e personalizar o conteúdo de acordo com o perfil dos alunos, mas também enfrenta desafios como a falta de infraestrutura e a formação inadequada dos professores. A principal conclusão é que, embora a IA tenha potencial para transformar a educação básica, é necessário superar barreiras para sua implementação eficaz. As contribuições do trabalho incluem uma visão abrangente das práticas atuais e dos desafios enfrentados, fornecendo uma base para futuras pesquisas sobre a integração da IA na educação básica.Revista ft00Revista ft["J. L. Correia", "C. R. Oliveira", "V\u00e2nia Maria Pereira Matos", "Izabel Cristina de Santana Barreto"]2024-09-22T00:00:00 426e4ffd021e089d76a067474563b86884113c46La integración de la Inteligencia Artificial (IA) en el ámbito educativo se ha convertido en una vanguardia para la innovación pedagógica y andragógica, ofreciendo herramientas que personalizan y enriquecen la experiencia de aprendizaje. Este proyecto se centra en el desarrollo e implementación de un chatbot basado en Procesamiento de Lenguaje Natural (NLP) en el Instituto Amazónico del cantón Yantzaza provincia de Zamora Chinchipe, diseñado para actuar como un compañero educativo para estudiantes y docentes. El objetivo es proporcionar acceso instantáneo a información personalizada relevante sobre la carrera a la que pertenecen los estudiantes y docentes, detalles sobre las mallas curriculares, guías de estudio, eventos institucionales, y más, todo a través de una interfaz conversacional e intuitiva. Además de facilitar la obtención de información académica y logística, el chatbot está diseñado para apoyar el proceso de enseñanza y aprendizaje de maneras innovadoras. Esto incluye la orientación académica personalizada, donde el chatbot puede sugerir recursos de aprendizaje basados en las dificultades específicas o intereses de un estudiante. Para la construcción del chatbot se utilizó la metodología de desarrollo de software XP, en la parte del frontend se empleó el framework Angular y en el backend se utilizó el framework FastApi, el modelo LLM (Modelo de Lenguaje de Gran Tamaño) fue Llama 2, específicamente la compilación 7B. La implementación de este chatbot representó una mejora tecnológica de vanguardia, donde la tecnología y la andragogía se entrelazaron para crear experiencias de aprendizaje más enriquecedoras en los estudiantes y docentes del Instituto.Revista Científica Multidisciplinaria SAPIENTIAE40Revista Científica Multidisciplinaria SAPIENTIAE["Diego Vicente Guaman Jima", "Alex Enrique Yunga Ben\u00edtez", "Wagner Roberto Morocho"]2024-09-22T00:00:00 9261df30785f09e1d23e663eff309e17768ea67cThe Fall 2024 Logic in Computer Science column of the Bulletin of EATCS is a little discussion on intelligence, measuring intelligence, and related issues, provoked by a fascinating must-read article ``On the measure of intelligence'' by Fran\c{c}ois Chollet. The discussion includes a modicum of critique of the article.arXiv.org210ArXiv["Yuri Gurevich"]2024-09-22T00:00:00 90f4ce770ddbd17d32bfda7b234aa45f59c002ebConversational Swarm Intelligence (CSI) is an AIpowered communication and collaboration technology that allows large, networked groups (of potentially unlimited size) to hold thoughtful conversational deliberations in real-time. Inspired by the efficient decision-making dynamics of fish schools, CSI divides a human population into a set of small subgroups connected by AI agents. This enables the full group to hold a unified conversation. In this study, groups of 25 participants were tasked with selecting a roster of players in a real Fantasy Baseball contest. A total of 10 trials were run using CSI. In half the trials, each subgroup was augmented with a fact-providing AI agent referred to herein as an “Infobot.” The Infobot was loaded with a wide range of MLB statistics. The human participants could query the Infobot the same way they would query other persons in their subgroup. Results show that when using CSI, the 25-person groups outperformed 72% of individually surveyed participants and showed significant intelligence amplification versus the mean score $(p=0.016)$. The CSI-enabled groups also significantly outperformed the most popular picks across the collected surveys for each daily contest $(p\lt0.001)$. The CSI sessions that used Infobots scored slightly higher than those that did not, but it was not statistically significant in this study. That said, 85% of participants agreed with the statement “Our decisions were stronger because of information provided by the Infobot,” and only $\mathbf{4 \%}$ disagreed. In addition, deliberations that used Infobots showed significantly less variance $(p=0.039)$ in conversational content across members. This suggests that Infobots promoted more balanced discussions in which fewer members dominated the dialog. This may be because the infobot enabled participants to confidently express opinions with the support of factual data.Ubiquitous Computing, Electronics & Mobile Communication Conference171Conversational Swarm Intelligence (CSI) is an AIpowered communication and collaboration technology that allows large, networked groups to hold thoughtful conversational deliberations in real-time to promote more balanced discussions in which fewer members dominated the dialog.2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)["Louis B. Rosenberg", "Hans Schumann", "Christopher Dishop", "G. Willcox", "Anita Woolley", "Ganesh Mani"]2024-09-22T00:00:00 7131dfd912f20b492530bb46f3eeb2a8a567c41bWith the rapid advancement of large models (LMs), the development of general-purpose intelligent agents powered by LMs has become a reality. It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks, capable of autonomous communication and collaboration without human intervention. This paper investigates scenarios involving the autonomous collaboration of future LM agents. We review the current state of LM agents, the key technologies enabling LM agent collaboration, and the security and privacy challenges they face during cooperative operations. To this end, we first explore the foundational principles of LM agents, including their general architecture, key components, enabling technologies, and modern applications. We then discuss practical collaboration paradigms from data, computation, and knowledge perspectives to achieve connected intelligence among LM agents. After that, we analyze the security vulnerabilities and privacy risks associated with LM agents, particularly in multi-agent settings, examining underlying mechanisms and reviewing current and potential countermeasures. Lastly, we propose future research directions for building robust and secure LM agent ecosystems.00The foundational principles of LM agents are explored, including their general architecture, key components, enabling technologies, and modern applications, and the security vulnerabilities and privacy risks associated with LM agents are analyzed, particularly in multi-agent settings.["Yuntao Wang", "Yanghe Pan", "Zhou Su", "Yi Deng", "Quan Zhao", "L. Du", "Tom H. Luan", "Jiawen Kang", "D. Niyato"]2024-09-22T00:00:00 6284f0c7a15f0c4557700d7353b1d78e70b61294General Background: As technological advancements continue to evolve, the proliferation of AI tools raises significant ethical and legal concerns, particularly regarding their misuse in creating deepfake pornography. Specific Background: This phenomenon poses serious risks to individuals, especially social media users and public figures who may fall victim to manipulated content that is shared maliciously. Knowledge Gap: Despite growing awareness, there is insufficient exploration of the legal frameworks and enforcement mechanisms addressing deepfake-related offenses, particularly in the context of Indonesia. Aims: This research seeks to analyze law enforcement strategies against Deepfake Porn AI cases and the implications for victims of AI misuse. Results: Employing a normative juridical methodology, this study reviews primary legislation—including the ITE Law, Pornography Law, Copyright Law, and the New Criminal Code—as well as relevant secondary sources. The findings indicate that while existing laws provide some recourse, there is a critical need for better enforcement and legal clarity. Novelty: This research highlights the unique challenges posed by deepfake technology and proposes reforms to existing legal frameworks to enhance protection for victims. Implications: The recommendations advocate for improved criminal complaint mechanisms and civil lawsuit avenues for victims, alongside a call for progressive legal reforms governing Artificial Intelligence to effectively address the evolving landscape of digital misuse and ensure justice for affected individuals.  European Journal of Contemporary Business Law &amp; Technology: Cyber Law, Blockchain, and Legal Innovations00European Journal of Contemporary Business Law &amp; Technology: Cyber Law, Blockchain, and Legal Innovations["Guntur Permana Putra", "M. Multazam"]2024-09-22T00:00:00 f7531942d0cd52158a779a5302cf49faa04c433eThis review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation. The rapid advancements in remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical data on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) and Deep Learning (DL) models, can now predict crop yields with high accuracy. This paper reviews studies from the past five years that utilize Sentinel-2 and AI techniques to estimate yields for crops like wheat, maize, rice, and others. Various AI approaches are discussed, including Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and ensemble methods, all contributing to refined yield forecasts. The review identifies a notable gap in the standardization of methodologies, with researchers using different VIs and AI techniques for similar crops, leading to varied results. As such, this study emphasizes the need for comprehensive comparisons and more consistent methodologies in future research. The work underscores the significant role of Sentinel-2 and AI in advancing precision agriculture, offering valuable insights for future studies that aim to enhance sustainability and efficiency in crop management through advanced predictive models.Sustainability586This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in the context of precision agriculture, specifically for crop yield estimation, and identifies a notable gap in the standardization of methodologies.Sustainability["Muhammet Fatih Aslan", "K. Sabanc\u0131", "Busra Aslan"]2024-09-23T00:00:00 447fc36eea7333fbbc247876936ac10054e7d859Artificial intelligence systems (AISs) gain relevance in dentistry, encompassing diagnostics, treatment planning, patient management, and therapy. However, questions about the generalizability, fairness, and transparency of these systems remain. Regulatory and governance bodies worldwide are aiming to address these questions using various frameworks. On March 13, 2024, members of the European Parliament approved the Artificial Intelligence Act (AIA), which emphasizes trustworthiness and human-centeredness as relevant aspects to regulate AISs beyond safety and efficacy. This review presents the AIA and similar regulatory and governance efforts in other jurisdictions and lays out that regulations such as the AIA are part of a complex ecosystem of interdependent and interwoven legal requirements and standards. Current efforts to regulate dental AISs require active input from the dental community, with participation of dental research, education, providers, and patients being relevant to shape the future of dental AISs.Journal of dentistry research404This review presents the AIA and similar regulatory and governance efforts in other jurisdictions and lays out that regulations such as the AIA are part of a complex ecosystem of interdependent and interwoven legal requirements and standards.Journal of Dental Research["M. Ducret", "E. Wahal", "D. Gruson", "S. Amrani", "R. Richert", "M. Mouncif-Moungache", "F. Schwendicke"]2024-09-23T00:00:00 7e77b8373139a7da62eddbce9bf38d55855530a2Abstract Emerging technologies of artificial intelligence (AI) and automated decision‐making (ADM) promise to advance many industries. Healthcare is a key locus for new developments, where operational improvements are magnified by the bigger‐picture promise of improved care and outcomes for patients. Forming the zeitgeist of contemporary sociotechnical innovation in healthcare, media portrayals of these technologies can shape how they are implemented, experienced and understood across healthcare systems. This article identifies current applications of AI and ADM within Australian healthcare contexts and analyses how these technologies are being portrayed within news and industry media. It offers a categorisation of leading applications of AI and ADM: monitoring and tracking, data management and analysis, cloud computing, and robotics. Discussing how AI and ADM are depicted in relation to health and care practices, it examines the sense of promise that is enlivened in these representations. The article concludes by considering the implications of promissory discourses for how technologies are understood and integrated into practices and sites of healthcare.Sociology of Health and Illness771This article identifies current applications of AI and ADM within Australian healthcare contexts and analyses how these technologies are being portrayed within news and industry media, and offers a categorisation of leading applications of AI and ADM: monitoring and tracking, data management and analysis, cloud computing, and robotics.Sociology of Health & Illness["A. Watson", "Vaughan Wozniak-O'Connor"]2024-09-23T00:00:00 806e9e5e26863aced4f2d7e9719044dacb70fd69 It is now a cliché to highlight that whilst artificial intelligence (AI) provides many opportunities, it also presents myriad risks to established norms. Amongst the norms considered in the literature, the Rule of Law unsurprisingly features. But the analyses of the Rule of Law are narrow. AI has the capacity to augment as well as to undermine fidelity to the ideal of the Rule of Law. Rather than viewing AI only as a threat to important norms, this article’s core argument is that AI should also be presented as an opportunity to meet their demands. It uses the Rule of Law in tax administration to support this argument.The Cambridge Law Journal01The Cambridge Law Journal["Stephen Daly"]2024-09-23T00:00:00 4f5ac74cb501a7a727cc559894e869179776741d In the field of higher education, generative artificial intelligence (GenAI) has become a revolutionary influence, shaping how students access and use library resources. This study explores the intricate balance of both positive and negative effects that GenAI might have on the academic library experience for higher education (HE) students. The key aspects of enhanced discovery and retrieval, personalization and engagement, streamlined research processes, and digital literacy and information evaluation potentially offered through using generative AI will be considered. These prospective advantages to HE students offered by using GenAI will be examined through will be examined through the theoretical framework of the Technological Acceptance Model (TAM) introduced by Davis et al. in 1986, which suggests that perceived usefulness and perceived ease of use are key factors in determining user acceptance and utilization of technology. The adoption of GenAI by higher education students will be analyzed from this viewpoint before assessing its impact on their use of library resources. Information Technology and Libraries421This study explores the intricate balance of both positive and negative effects that GenAI might have on the academic library experience for higher education (HE) students through the theoretical framework of the Technological Acceptance Model introduced by Davis et al. in 1986.Information Technology and Libraries["Lynsey Meakin"]2024-09-23T00:00:00 320df9bb198a5378acbdc1e7acf09a3893908a9aThe aim of this study is to develop a scale to measure the use and proficiency levels of productive artificial intelligence among young and adult lifelong learners. Research data were collected from 248 individuals aged between 18 and 55. After a thorough review of the literature, an item pool for the scale was created. Similar scales in the related field were examined, and the item pool was developed accordingly. The items were reviewed by two experts in educational technology and lifelong learning, as well as a scale development specialist. After making the necessary revisions, the trial form of the scale was presented to the participants. To determine the construct validity of the scale, exploratory factor analysis was conducted. The results of the exploratory factor analysis indicated that the scale consisted of two factors. The first factor comprises 10 items, while the second factor consists of 9 items. Confirmatory factor analysis was performed to reveal the relationships within the factors, the relationships between the variables and the factors, and the explanatory power of the factors on the model. The internal consistency coefficient, Cronbach's alpha reliability value, was determined to be .833. In conclusion, the Artificial Intelligence Usage and Proficiency (AIUP) Scale is expected to fill a gap in the literature and to be a scalable tool.Journal of teacher education and lifelong learning01The Artificial Intelligence Usage and Proficiency (AIUP) Scale is expected to fill a gap in the literature and to be a scalable tool.Journal of Teacher Education and Lifelong Learning["V. Arslankara", "Ertu\u011frul Usta"]2024-09-23T00:00:00 dfff7d57ff576fbbe055930441b18be8139788acThe transformational potential of artificial intelligence (AI) and smart applications in fostering sustainable purchasing behavior is examined in this article. It explores how AI technologies support well-informed decision-making, maximize resource management, and promote positive environmental impact across a range of industries through an extensive theoretical framework. Empirical instances ranging from energy management schemes to environmentally conscious retail platforms showcase the multifarious uses of artificial intelligence in promoting sustainability. However, in addition to the enormous promise, there are also inherent difficulties that must be overcome, like algorithmic biases, data privacy issues, and the digital divide. These issues must be resolved to guarantee fair access to AI technology and their moral application. In order to fully realize the revolutionary potential of artificial intelligence (AI) for sustainable consumption, the article ends with recommendations for Turkey that emphasize the significance of funding digital infrastructure, data privacy laws, digital literacy initiatives, and innovation ecosystems.İktisadi İdari ve Siyasal Araştırmalar Dergisi01The article ends with recommendations for Turkey that emphasize the significance of funding digital infrastructure, data privacy laws, digital literacy initiatives, and innovation ecosystems to fully realize the revolutionary potential of artificial intelligence (AI) for sustainable consumption.İktisadi İdari ve Siyasal Araştırmalar Dergisi["Sinem Sarg\u0131n"]2024-09-23T00:00:00 5be723856da8eb6498bfc816a7ca30bb4a4dcf6cThe integration of Artificial Intelligence (AI) into teacher education has been transformative, offering personalized learning experiences, enhanced professional development, and improved teaching methodologies. AI technologies such as Intelligent Tutoring Systems (ITS), AI-driven analytics, and automated assessment tools have become central to modern educational practices, significantly improving engagement, adaptability, and effectiveness. This study employs a qualitative thematic analysis of current literature on AI in teacher education, examining peer-reviewed articles and reports using thematic coding to identify key patterns, opportunities, and challenges. The findings reveal that AI enhances teacher education by providing personalized learning pathways, fostering critical thinking, and supporting ongoing professional growth. Technologies like ITS, Virtual Reality (VR), and AI-driven analytics have proven effective in promoting motivation and engagement among teachers. However, ethical challenges such as biases in AI systems and concerns regarding data privacy require continuous attention. Furthermore, a gap in teacher preparedness, particularly in developing AI literacy and integrating AI tools into classroom practices, is evident. Despite these challenges, AI offers substantial benefits, transforming teaching practices and enabling personalized, adaptive instruction that supports both teachers and students. The study emphasizes the need for comprehensive teacher training programs focusing on digital literacy and ethical AI use, ensuring educators can navigate an AI-enhanced educational environment effectively. This research contributes to the ongoing discourse by highlighting the need for ethical guidelines and robust teacher training programs, offering actionable insights for educators, policymakers, and institutions aiming to integrate AI into teacher educationJournal of Computer and Education Research941The study emphasizes the need for comprehensive teacher training programs focusing on digital literacy and ethical AI use, ensuring educators can navigate an AI-enhanced educational environment effectively and actionable insights for educators, policymakers, and institutions aiming to integrate AI into teacher education.Journal of Computer and Education Research["Ru\u015fen Meylani"]2024-09-23T00:00:00 b8ab3f54ba74e7081313caf594078a0d847efd47Artificial intelligence (AI) and psychedelic medicines are among the most high-profile evolving disruptive innovations within mental healthcare in recent years. Although AI and psychedelics may not have historically shared any common ground, there exists the potential for these subjects to combine in generating innovative mental health treatment approaches. In order to inform our perspective, we conducted a scoping review of relevant literature up to late August 2024 via PubMed intersecting AI with psychomedical use of psychedelics. Our perspective covers the potential application of AI in psychedelic medicine for: drug discovery and clinical trial optimization (including pharmacodynamics); study design; understanding psychedelic experiences; personalization of treatments; clinical screening, delivery, and follow-up (potentially delivered via chatbots/apps); application of psychological preparation, integration, and general mental health support; its role in enhancing treatment via brain modulatory devices (including virtual reality and haptic suits); and the consideration of ethical and security safeguards. Challenges include the need for sufficient data protection and security, and a range of necessary ethical protections. Future avenues of exploration could involve directly administering psychedelics (or providing algorithm-generated effects) to inorganic AI-interfaced neural networks that may exceed human brain activity (i.e., cognitive capacity) and intelligence.Annals of the New York Academy of Sciences301This perspective covers the potential application of AI in psychedelic medicine for: drug discovery and clinical trial optimization, study design, personalization of treatments, clinical screening, delivery, and follow-up, and the consideration of ethical and security safeguards.Annals of the New York Academy of Sciences["Jerome Sarris", "Andreas Halman", "Anna Urokohara", "Mathew Lehrner", "Daniel Perkins"]2024-09-23T00:00:00 751c73c6b1d44440f8d0034eb9a535880f6141eeUnemployment inequality remains one of the most vexing socioeconomic quagmires confronting the United States. This research project aimed to pinpoint how AI can be applied in the enumeration of key drivers of unemployment inequality in the United States and set a framework for further research and policy development. In this study, the researcher has drawn a massive volume dataset from the Economic Policy Institute's State of Working America Data Library, along with research performed by the Federal Reserve Bank of St. Louis. The unemployment incidents data was classified in terms of age, education level, gender, race, and other demographic factors. Subsequently, the analyst employed Linear Regression from the Scikit-learn library. Overall performance evaluation showcased that linear regression performed excellently with the least error in MSE and RMSE and, hence, was the best in terms of accurately predicting unemployment indicators. Accurate prediction of the unemployment rate using the proposed linear regression model can help the U.S. government proactively warn against economic downturns by deploying the. Besides, by executing the Linear Regression, government officials can influence favorable policies through tax incentives or labor laws. Evidently, the linear regression framework is a powerful AI tool that can help bring huge enhancements to unemployment inequality research and policy development in the future. This model not only provides a quantification of the relationships but allows for the making of predictions, thus making it useful for evaluating the possible results of different policy scenarios. Furthermore, the Linear Regression framework can also be used in the assessment of the effectiveness of pre-existing policies aimed at reducing unemployment.Journal of Economics, Finance and Accounting Studies07Overall performance evaluation showcased that linear regression performed excellently with the least error in MSE and RMSE and, hence, was the best in terms of accurately predicting unemployment indicators.Journal of Economics, Finance and Accounting Studies["MD Abdul Fahim Zeeshan", "Md Sumsuzoha", "Faiaz Rahat Chowdhury", "Md Rashed Buiya", "MD Rashed Mohaimin", "Laxmi Pant", "Reza E Rabbi Shawon"]2024-09-23T00:00:00 8d57a1a3d565cb96f312a58a538782065d8ebdfbThis study investigates the impact of Generative Artificial Intelligence (GenAI) on the dynam-ics and performance of innovation teams during the idea generation phase of the innovation process. Utilizing a custom AI-augmented ideation tool, the study applies the Knowledge Spill-over Theory of Entrepreneurship to understand the effects of AI on knowledge spillover, gen-eration and application. Through a framed field experiment with participants divided into exper-imental and control groups, findings indicate that AI-augmented teams generated higher quali-ty ideas in less time. GenAI application led to improved efficiency, knowledge exchange, in-creased satisfaction and engagement as well as enhanced idea diversity. These results high-light the transformative role of the field of AI within the innovation management domain and shows that GenAI has a positive impact on important elements of the Knowledge Spillover Theory of Entrepeneurship, emphasizing its potential impact on innovation, entrepreneurship, and economic growth. Future research should further explore the dynamic interaction be-tween GenAI and creative processes.arXiv.org980Results high-light the transformative role of the field of AI within the innovation management domain and shows that GenAI has a positive impact on important elements of the Knowledge Spillover Theory of Entrepeneurship, emphasizing its potential impact on innovation, entrepreneurship, and economic growth.ArXiv["Michael Gindert", "Marvin Lutz M\u00fcller"]2024-09-23T00:00:00 e9f91760dfb10f187cb9f92844133cd91a647c1bArtificial intelligence (AI) is a branch of computer science that tries to develop computational tools and systems that can carry out tasks comparable to human decision-making and learning. The subject of AI is expanding quickly, and AI technology is becoming more and more significant in a variety of IT specialties. When more automated and intelligent solutions are employed instead of outdated techniques, the quantity of manpower and resources needed for game testing will be greatly decreased. The aim of this study is to find new models that will make game testing easier by utilising state-of-the-art AI techniques. In an attempt to determine the model base and algorithmic foundation for the new approach, the examination and comparison of existing theories, models, and algorithms is used to infer and identify the viability of certain current mainstream AI models and algorithms in game testing sessions. Standard software testing is not the same as game testing. Further consideration should be given to the whole gaming experience and entertainment value in addition to functional testing. User questionnaires and in-game testing are used in the current standard experience testing methodology. Artificial intelligence eliminates human aspects in emotional cognition. It may produce more objective test results while spending less money for both human and material resources by using massive data sets and its own learning and processing capabilities. This paper examines the state of AI in software testing, game creation, and emotion perception using expertise in using and applying AI approaches in game testing for quality assurance. It also demonstrates how well AI methods work for predicting player emotions during game testing. The AI testing reduces testing expenses related to labour and material resources while guaranteeing total game confidentiality before release. It also increases test accuracy and reduces the impact of subjectivity.Journal of Ecohumanism120The state of AI in software testing, game creation, and emotion perception is examined using expertise in using and applying AI approaches in game testing for quality assurance and demonstrates how well AI methods work for predicting player emotions during game testing.Journal of Ecohumanism["He Tian Yi"]2024-09-23T00:00:00 6dee8bfb30aaf0b8080f47ddb194463ee63448b9In the digital age, search engine optimization (SEO) is crucial for businesses aiming to enhance their online visibility. With the rapid advancement of artificial intelligence (AI) technologies, there is growing interest in the potential role of AI in SEO. This research paper investigates the future of SEO and the impact of AI on the field. By examining existing research and case studies, the paper explores the current state of SEO, the opportunities and challenges of integrating AI, and the implications for digital marketing. The findings highlight the need for further research and development in AI and SEO. While AI integration offers clear benefits, it also presents challenges that must be addressed. As AI evolves, its influence on SEO is expected to grow, making it a critical area for businesses seeking to improve their online presence.Journal of Communication and Management220The future of SEO and the impact of AI on the field is investigated by examining existing research and case studies, and the current state of SEO, the opportunities and challenges of integrating AI, and the implications for digital marketing are explored.Journal of Communication and Management["Rajawat Manisha"]2024-09-23T00:00:00 eb040378909d2c348c0ebe9293df3bdd3e84c6c7Artificial intelligence (AI) can help transform tax administration in developing countries by automating certain functions, pinpointing patterns and irregularities, and forecasting future tax collections. AI can enhance the effectiveness, efficiency, and tax justice in tax administration. This paper discusses the development and deployment of AI in tax administration in developing countries. This paper outlines different AI technologies, the opportunities and challenges of using AI in tax administration, and the possible implications. The paper established that there is an increasing interest in harnessing AI in tax administration in developing countries. The challenges of deploying AI include a lack of quality data, inadequate technical expertise, and a paucity of clear legal and regulatory frameworks to govern the application of AI. The benefits of AI in tax administration were found to encompass increased tax revenue mobilisation and the attainment of sustainable development goals. Reduction in corruption, improved tax compliance, reduced tax avoidance and evasion among other benefits. The paper recommends that policymakers and tax authorities in developing countries improve data quality to support AI adoption, invest in AI research, innovation and development while supporting training in AI as well as the creation of a clear legal and regulatory framework. Keywords: artificial intelligence (AI), challenges, developing countries, implications, opportunities, tax administrationStudia Universitatis Babeş-Bolyai Negotia330The paper recommends that policymakers and tax authorities in developing countries improve data quality to support AI adoption, invest in AI research, innovation and development while supporting training in AI as well as the creation of a clear legal and regulatory framework.Studia Universitatis Babeș-Bolyai Negotia["F. Y. Mpofu"]2024-09-23T00:00:00 5ba1f87fae70610b51368d745c2f818e320dd4c7The article examines artificial intelligence (AI) vulnerabilities in the context of cybersecurity, analyzes their resilience and provides vulnerability statistics. The classification of specific for the AI system vulnerabilities is given, in which 4 types of attacks are identified and described. The experiment was conducted on the simulation of an attack on a neural network using FGSM method in order to compare the efficiency of the neural network before and after the attack.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)60Examination of artificial intelligence (AI) vulnerabilities in the context of cybersecurity, analyzes their resilience and provides vulnerability statistics, in which 4 types of attacks are identified and described.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)["Artyom K. Potapov", "Valentina G. Sidorenko"]2024-09-23T00:00:00 2a3b677ba4a9bdb5a893b279fd3509419c69c1c0The integration of Artificial Intelligence (AI) in the FinTech industry has significantly reshaped operational workflows, product innovation, and risk management, all of which are pivotal to company valuation. This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation, introducing a novel, scalable multilayer network model with AI-driven Copula Nodes that serve as connectors across operational layers. By incorporating AI, the research unveils a dynamic and interconnected approach to FinTech valuation, revealing new pathways for value co-creation through real-time adjustments and predictive capabilities. The research reveals that while operational efficiency is a major driver of market value, a balanced integration of AI across risk management, product innovation, and market perception is essential for maximizing value. Additionally, the findings highlight the importance of managing AI-driven risks such as algorithmic bias and regulatory challenges. This comprehensive framework offers critical insights for FinTechs, investors, and regulators seeking to understand the complex role of AI in enhancing valuation within the evolving financial services landscape.FinTech270This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation, introducing a novel, scalable multilayer network model with AI-driven Copula Nodes that serve as connectors across operational layers that reveal new pathways for value co-creation through real-time adjustments and predictive capabilities.FinTech["Roberto Moro Visconti"]2024-09-23T00:00:00 097782f9df4a49393c43dac5b3e96ec70827ec42An audit information system is an important tool for enterprises and organizations to manage financial information and conduct financial audits. The existing security measures are difficult to effectively adapt to the dynamic and complex operating environment of the system, and there are limitations in risk detection. To enhance the reliability of the system and promote the smooth implementation of audit work, this paper studies the application of AI (artificial intelligence) in the security protection measures of audit information systems. Based on analyzing the system workflow and potential risks, an LSTM (Long and Short-Term Memory) security protection model was constructed. Real-time collection of various types of records in system components can be achieved by configuring a log collector, extracting semantic information using an abstract syntax tree and serializing it, converting text into vectors using a Skip-gram model, and conducting temporal dependency analysis on time series datasets. In the experimental study of detection rate, compared with the static code analysis model, the LSTM model in this paper has increased the average detection rate of abnormal events in user behavior, system events, and network traffic logs by 6.0%, 4.3%, and 5.3%, respectively. The conclusion indicates that the LSTM model under AI technology is helpful for real-time and accurate detection of potential risks in the system, providing reliable support for system security protection.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)170The conclusion indicates that the LSTM model under AI technology is helpful for real-time and accurate detection of potential risks in the system, providing reliable support for system security protection.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)["Mengna Zhang", "Yuehong Cai"]2024-09-23T00:00:00 25b550af4675585760c2681d0255bc1743b0fc6dThis study advances the field of Computationally Intensive Theory Development (CTD) by examining the capabilities of Explainable Artificial Intelligence (XAI), in particular SHapley Additive exPlanations (SHAP), for theory development, while providing guidelines for this process. We evaluate SHAP’s methodological abilities and develop a structured approach for using SHAP to harness insights from black-box predictive models. For this purpose, we leverage a dual-methodological approach. First, to assess SHAP’s capabilities in uncovering patterns that shape a phenomenon, we conduct a Monte-Carlo simulation study. Second, to illustrate and guide the theory development process with SHAP for CTD, we apply SHAP in a use-case using real-world data. Based on these analyses, we propose a stepwise uniform and replicable approach giving guidance that can benefit rigorous theory development and increase the traceability of the theorizing process. With our structured approach we contribute to the use of XAI approaches in research and, by uncovering patterns in black-box prediction models, add to the ongoing search for next-generation theorizing methods in the field of Information Systems (IS).Journal of Information and Technology00This study examines the capabilities of Explainable Artificial Intelligence (XAI), in particular SHapley Additive exPlanations (SHAP), for theory development, and develops a structured approach for using SHAP to harness insights from black-box predictive models.Journal of Information Technology["Dominik Stoffels", "Stefan Faltermaier", "K. Strunk", "Marina Fiedler"]2024-09-23T00:00:00 b73408bf37e71eb8afcd5c88d6f5175eba8c4fe2This paper delves into the significance and interaction between deep learning (DL) and neural architecture search (NAS) within the realm of artificial intelligence. As DL has become integral in addressing complex problems through its robust learning capabilities, NAS offers the potential to autonomously discover efficient neural network structures. The fusion of these technologies not only enhances model performance but also accelerates the proliferation of AI applications, particularly in resource-constrained settings such as embedded devices and edge computing. This study provides an in-depth comparative analysis of multiple neural network models applied to the CIFAR-10 dataset, with a particular focus on the performance of the Darts-SEBnet model. By incorporating a self-attention mechanism, the Darts-SEBnet model demonstrates a significant improvement in accuracy over the baseline VGG16 model. Furthermore, the paper reviews the evolution of NAS, emphasizing the success of gradient-based search methods like DARTS and its variants in improving search efficiency and model performance. The findings suggest that the integration of DL and NAS could drive further innovations in AI, offering solutions to existing bottlenecks and expanding AI's applicability across diverse fields.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)110An in-depth comparative analysis of multiple neural network models applied to the CIFAR-10 dataset, with a particular focus on the performance of the Darts-SEBnet model, which demonstrates a significant improvement in accuracy over the baseline VGG16 model.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)["Xu Yan", "Junliang Du", "Lun Wang", "Yingbin Liang", "Jiacheng Hu", "Bingxing Wang"]2024-09-23T00:00:00 db323b93c74cf3b74e59a0a12e258d0a6795c2e8Introduction The research field of Artificial intelligence (AI) in medicine and especially in gastroenterology is rapidly progressing with the first AI tools entering routine clinical practice, for example in colorectal cancer screening. Contrast-enhanced ultrasound (CEUS) is a highly reliable, low-risk and low-cost diagnostic modality for the examination of the liver. However, doctors need many years of training and experience to master this technique and, despite all efforts to standardize CEUS, it is often believed to contain significant interrater variability. As has been shown for endoscopy, AI holds promise to support examiners at all training levels in their decision-making and efficiency. Methods In this systematic review, we analyzed and compared original research studies applying AI methods to CEUS examinations of the liver published between January 2010 and February 2024. We performed a structured literature search on PubMed, Web of Science and IEEE. Two independent reviewers screened the articles and subsequently extracted relevant methodological features, e.g. cohort size, validation process, machine learning algorithm used, as well as indicative performance measures from the included articles. Results We included 41 studies with most applying AI methods for classification tasks related to focal liver lesions. These included distinguishing benign vs. malignant or classifying the entity itself, while a few studies tried to classify tumor grading, microvascular invasion status or response to transcatheter arterial chemoembolization directly from CEUS. Some articles tried to segment or detect focal liver lesions, while others aimed to predict survival and recurrence after ablation. The majority (25/41) of studies used hand-picked and/or annotated images as data input to their models. We observed mostly good to high reported model performances with accuracies ranging between 58.6% and 98.9%, while noticing a general lack of external validation. Conclusion Even though multiple proof-of-concept studies for the application of AI methods to CEUS examinations of the liver exist and report high performance, more prospective, externally validated and multicenter research is needed to bring such algorithms from desk to bedside.Digestion00Even though multiple proof-of-concept studies for the application of AI methods to CEUS examinations of the liver exist and report high performance, more prospective, externally validated and multicenter research is needed to bring such algorithms from desk to bedside.Digestion["J. Brooks", "Michael Kallenbach", "I. Radu", "A. Berzigotti", "Christoph F Dietrich", "J. N. Kather", "Tom Luedde", "T. Seraphin"]2024-09-23T00:00:00 5beb332c6c27d3bc4360ddb2db9d16da24097c2bToday's businesses are operating in a complex environment, marked by the emergence of a digital culture that is transforming space and time, as well as relationships at work. These various transformations have given rise to a new concept: artificial intelligence. With the introduction of artificial intelligence, work is being transformed, creating new challenges for organizations and leading to new HR practices in terms of recruitment, training, compensation, talent management and so on.For all these reasons, the mobilization of collective intelligence is ultimately becoming a priority for HR, insofar as it makes it possible to support transformation within organizations by means of agile methods. Against this backdrop of changing organizational practices, a number of questions arise:-What is artificial intelligence and how does it impact organizations? -What role can collective intelligence and artificial intelligence play in the evolution of the HR function?The aim of this theoretical research is to underpin the various concepts linked to these notions. We will also try to understand what is at stake in collective intelligence for HR, and how artificial intelligence can impact organizational practices.Data and Metadata70The aim of this theoretical research is to underpin the various concepts linked to collective intelligence and artificial intelligence, and to understand what is at stake in collective intelligence for HR, and how artificial intelligence can impact organizational practices.Data and Metadata["Mounia Amazian", "Zakia Nouira", "Mariam Filali"]2024-09-23T00:00:00 2313d0a6a44656913bf45c877d009acc185b05e9With the development of artificial intelligence technology and the expansion of its application scope, an intelligent change has taken place in human resource management. Due to the influence of human factors and experience, the traditional manpower allocation method is difficult to achieve the best benefits. On the basis of artificial intelligence, using machine learning, big data analysis and other methods, human resources can be optimized to the maximum extent, and the operating efficiency and production efficiency of enterprises can be improved. This paper makes an in-depth analysis of the imbalance between supply and demand of human resources and the low efficiency of resource allocation in human resources management in China. Through the collection and analysis of massive human resources, this system uses machine learning method to identify and predict them, which can accurately judge the employment demand of each position, so as to achieve the purpose of automatic deployment. In this paper, the main functional modules, data processing flow and key technologies of the system are discussed in detail. Combined with specific examples, this paper makes an empirical study on the adopted methods. A company (manufacturing) reduced the labor cost by 800,000 yuan, while C company (manufacturing) reduced the labor cost by 1.5 million yuan. The research shows that using artificial intelligence technology to allocate human resources can effectively improve the efficiency and accuracy of human resources allocation, reduce human disturbance and reduce the operating cost of enterprises.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)100The research shows that using artificial intelligence technology to allocate human resources can effectively improve the efficiency and accuracy of human resources allocation, reduce human disturbance and reduce the operating cost of enterprises.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)["Lei Meng", "Min Yang", "Jiequan Yao", "Yixiu Huang", "Danni Wang"]2024-09-23T00:00:00 889eb29d43915707ed677d3ff8e3786a5e8ef156Management is considered within the framework of a process approach. Management involves performing tasks that are interconnected through communication and decision-making. Management of the enterprise is the important and actual problem. To solve this problem, it is proposed to use the new artificial intelligence methods, artificial neural networks and fuzzy logic. The task is to design the artificial intelligence system which can work as the assistant of a man-manager. The objects of the research are the automated control systems. The automated control systems get the control commands from operator and input signals from the external environment and internal production environment. The output signals of the automated control system change the state of the internal production environment. The result of investigation is the quality improvement.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)70The task is to design the artificial intelligence system which can work as the assistant of a man-manager which gets the control commands from operator and input signals from the external environment and internal production environment.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)["V. Sukhanova Natalia", "A. Sheptunov Sergey"]2024-09-23T00:00:00 24f9d61266732bfbe089033f2b76d44c51a56b4bThe malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.The British journal of psychiatry : the journal of mental science50There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.The British journal of psychiatry : the journal of mental science["S. Monteith", "T. Glenn", "John R. Geddes", "Eric D. Achtyes", "P. Whybrow", "Michael Bauer"]2024-09-23T00:00:00 f0e8407c78d376596004c432a6134fa19acd8e30The article considers the application of artificial intelligence (AI) in digital production management. The authors note that the use of AI technologies opens up many opportunities to improve efficiency, optimise processes and make more informed management decisions. Key applications of AI in this area include demand forecasting and production planning, logistics and supply chain optimisation, predictive maintenance of equipment, automation of routine tasks, and big data analysis to uncover hidden patterns. The use of AI in business process management in digital manufacturing, including production process optimisation, supply chain management, quality control, and decision support, is discussed separately. It is emphasised that a key advantage of AI is its ability to process large amounts of data, identify hidden relationships and offer optimal solutions. The article considers the representation of process architecture in digital manufacturing as analogous to neural networks in artificial intelligence. The author notes that this approach has both advantages and disadvantages.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)50The article considers the representation of process architecture in digital manufacturing as analogous to neural networks in artificial intelligence, and notes that this approach has both advantages and disadvantages.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)["V. Azarov", "A. V. Chekmarev"]2024-09-23T00:00:00 c60ec1ff3c763e1c71a19bf9ce45deaffd6bd70bThe advancement of technologies and the influence of these advances on man’s relationships with different aspects of life are undeniable, these changes can be perceived in all environments (Machado et al ., 2011). In the health sector, we hear about artificial intelligence, a new category of scientific studies focused on equipment that optimizes care, diagnoses, and treatments in health centers. As an example of this improvement process, in assisted reproduction, this resource is used in the search for answers for treatments and clinical diagnoses, in embryology laboratories, which may focus on the diagnostic analysis of aspects relating to the patient’s fertility, and the development of the embryo before being transferred to the mother’s womb. However, when we think about man’s interaction with this progress and its practical results in this process, it is up to us to think about the issue that emerges, about the clarity of how information will be conveyed and interpreted; for all intents and purposes, a new relationship perspective is created, and a sociocultural space is established to promote development in society. For as long as we have known, technology has been helping our daily lives by enabling us to obtain accurate and faster results, such as communication between people in real-time and faster diagnoses, which facilitates the healing process, among many other things. Focusing our attention on the assisted reproduction process, the development of techniques was expanded, concerning the possibilities and development of parent-hood, circumstances never thought of before, regardless of the social, scientific, and technological advances in bio-technology and medicine that have occurred in recent decades, Generally, the inability to conceive, carry and give birth to the desired child can be experienced as a stressful and lonely situation, as several authors have already stated (Cousineau & Domar, 2007; Melamed,JBRA Assisted Reproduction60Focusing the authors' attention on the assisted reproduction process, the development of techniques was expanded, concerning the possibilities and development of parent-hood, circumstances never thought of before, regardless of the social, scientific, and technological advances in bio-technology and medicine that have occurred in recent decades.JBRA Assisted Reproduction["Rose M Massaro Melamed"]2024-09-23T00:00:00 ef6a9a3f645f70a3c763781efd91087b20a8653cThe article considers the possibilities of artificial intelligence (AI) application in digital production management. The authors note that the use of AI technologies opens up many opportunities for increasing efficiency, optimising processes and making more informed decisions in various fields: ‐Demand forecasting and production planning ‐Logistics and supply chain optimisation ‐Predictive maintenance of equipment ‐Automation of routine tasks ‐Analysing big data to uncover hidden patterns AI systems are capable of processing huge amounts of data, identifying complex relationships and offering solutions that humans may not be able to see. This improves flexibility, responsiveness and quality of production management.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)40Artificial intelligence systems are capable of processing huge amounts of data, identifying complex relationships and offering solutions that humans may not be able to see, which improves flexibility, responsiveness and quality of production management.2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT)["V. Azarov", "A. V. Chekmarev"]2024-09-23T00:00:00 17e34cf47edce70077dd35cc26ce6290e823d9b1Academic Psychiatry111Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["Krzysztof Krysta", "Rachael Cullivan", "Andrew Brittlebank", "Jozef Dragasek", "Marc Hermans", "Sladjana Strkalj Ivezics", "Nicoletta M. J. van Veelen", "Marisa Casanova Dias"]2024-09-23T00:00:00 6b86da18331d8f17597bea5679d7d426cb67dfebInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-09-23T00:00:00 a48ecfa8c2f9b1fd56e7550ec529372839b80b7dJournal of Applied Learning &amp; Teaching00Journal of Applied Learning &amp; Teaching[]2024-09-23T00:00:00 dc83f50c5de53f51835ffc573ef049cd5b466ab8Proceedings of the Rio Oil and Gas00Proceedings of the Rio Oil and Gas["Andr Furlan", "Rafael Loy"]2024-09-23T00:00:00 5eec29e3962e5a6ddb825b420d20b1c528a30647Proceedings of the Rio Oil and Gas00Proceedings of the Rio Oil and Gas["Maria Castro", "Geraldo de Souza Ferreira"]2024-09-23T00:00:00 a7d6036228ef539e3036f581cd89119bb19e75dcProceedings of the Rio Oil and Gas00Proceedings of the Rio Oil and Gas["Rafael Silva do Nascimento", "Arthur Adelino de Freitas Cruz", "Hardy Pinto", "Priscila Dias da Silva", "Gilson Junior Soares", "Matheus Felipe Gremes", "Horacio Fortunato", "K. Marcomini", "John Lemos Das Neves", "Fabiano Costa da Silva"]2024-09-23T00:00:00 ef692ee4c2b3c3f1959261ea7e4c74cb343c14b2International Review of Psychiatry630International Review of Psychiatry["A. Ogunwale", "A. Smith", "O. Fakorede", "A. Ogunlesi"]2024-09-23T00:00:00 8147f4460aeb8f21234a95306a456a23d345672bLaw, Innovation and Technology00Law, Innovation and Technology["Armin Alimardani"]2024-09-23T00:00:00 d16027fb9dfa671e06f2276daa18dccba451d85aProceedings of the Rio Oil and Gas00Proceedings of the Rio Oil and Gas["Jo\u00e3o Pedro Kronemberger Felipe", "Carla Izolda Fiuza Costa Marshall"]2024-09-23T00:00:00 9bd3a6b0c079b47439eec5ad574fcf47f3fd8ee5Proceedings of the Rio Oil and Gas00Proceedings of the Rio Oil and Gas["Pedro Hamacher", "Alimed Celecia Ramos", "Leonardo S L Bastos", "L. M. Carrilho", "Erick Santos", "Gian Luca da Silva Figueiredo", "Fernando Butierres dos Santos", "Marcos Vinicius Marques da Silva", "Gabriela Ribas Klein", "Silvio Hamacher", "Vitor Brand\u00e3o Sabbagh"]2024-09-23T00:00:00 c5deef0fd9eebf9f318c68fa3d4b61df417f8185The current stress on global freshwater supply highlights the importance to further investigate the presence of offshore freshened groundwater (OFG), a resource that is estimated to amount to 10 to 100 times the global volume of freshwater consumed over the last 100 years. In line with recent developments in terrestrial data-driven groundwater modelling, we propose that globally available geospatial data (e.g. Digital Elevation Models, global groundwater models, geological and seafloor information), in conjunction with climatic data, can be used to predict the largely hidden offshore occurrence of coastal freshwater aquifers. Specifically, we aim to derive a reliable machine learning method that will account for the complex underlying hydrological mechanism of offshore groundwater emplacement and preservation. Here we present the results of the first phase of the AI-quifer project; (1) the derivation of proxi attributes (indicators) that are representative of the hydrogeological processes controlling OFG, and (2) the preparation of geological cross sections (orthogonally to the coastline) to augment the ML training data and evaluate the behaviour and influence of hydraulic conditions on the development of OFG due to changing boundary conditions (transmission from glacial to interglacial or vice versa).Oceans00OCEANS 2024 - Halifax["Laura Haffert", "M. Jegen", "Christian Siebert", "T. R\u00f6diger", "Christian Berndt"]2024-09-23T00:00:00 96ed4e36398995b3956e90e0c2dc814766c8b0ceJournal of business and technical communication30Journal of Business and Technical Communication["Stephen Carradini"]2024-09-23T00:00:00 582967b1a542bc2f953c2b1947fcce6976218f67Journal of Medical Imaging and Radiation Sciences240The need to enhance AI literacy amongst patients is highlighted, possibly though awareness campaigns or educational programmes, and clear policies relating to the use of AI in medical imaging and how such AI use is communicated to patients are necessary.Journal of medical imaging and radiation sciences["Francesca Xuereb", "Dr Jonathan L. Portelli"]2024-09-23T00:00:00 6d1028baf8a1348cc17b818f96df9443bf6f22b2Information, Communication &amp; Society250Information, Communication &amp; Society["N\u00faria Vall\u00e8s-Peris", "J\u00falia Pareto"]2024-09-23T00:00:00 61dedf2bbcabb7c6900b4540e77124952a0c24ddAfrican Journal of Biomedical Research00African Journal of Biomedical Research["Dr. Shikha Khullar"]2024-09-23T00:00:00 56b1fa53bf36b06de4d28d92b5ede57f973047b4AGI - Artificial General Intelligence - Robotics - Safety &amp; Alignment00AGI - Artificial General Intelligence - Robotics - Safety &amp; Alignment["James D. Miller"]2024-09-23T00:00:00 92d7ed2b675fbb13ea921e9323e042392c2ce2a5European Journal of Information Systems640European Journal of Information Systems["Maximilian F\u00f6rster", "H. R. Broder", "M. C. Fahr", "Mathias Klier", "Lior Fink"]2024-09-23T00:00:00 a10b9f90e0067c7626f931e714eefba8ecb3efd4International Journal of Human-Computer Interaction560International Journal of Human–Computer Interaction["Yasmin Abdelaal", "Micha\u00ebl Aupetit", "Abdelkader Baggag", "Mohammed Bashir", "Dena Al-Thani"]2024-09-23T00:00:00 90b1910fcfdf4432c2fb601a0601c5a40ab601e5Proceedings of the Rio Oil and Gas00Proceedings of the Rio Oil and Gas["Andr\u00e9 Seichi Ribeiro Kuramoto", "Hervandil Sant'anna", "Antonioni Barros Campos"]2024-09-23T00:00:00 3bf9daa18bd1c46e3e370ccdeef59b8cf2633dd8AI generates both enthusiasm and disillusionment, with promises that often go unfulfilled. It is therefore not surprising that human labor, which is its fundamental component, is also subject to these same deceptions. The development of"smart technologies"depends, at different stages, on a multitude of precarious, underpaid and invisible workers, who, dispersed globally, carry out repetitive, fragmented activities, paid per task and completed in a few seconds. These are workers who label data to train algorithms, through tasks that require the intuitive, creative and cognitive abilities of human beings, such as categorizing images, classifying advertisements, transcribing audio and video, evaluating advertisements, moderating content on social media, labeling human anatomical points of interest, digitizing documents, etc. This form of work is often referred to as"microwork". Our contribution, which documents the conditions of microwork in Brazil and offers portraits of the workers, is a step in the wider effort to overcome the current state of invisibilization. It opens up avenues for future research, with the aim of better characterizing this new form of work, tracing its changes over time in relation to the dynamics of globalization and, ideally, identifying levers for action and transitions.arXiv.org00This contribution, which documents the conditions of microwork in Brazil and offers portraits of the workers, is a step in the wider effort to overcome the current state of invisibilization.ArXiv["Matheus Viana Braz", "Paola Tubaro", "Antonio A. Casilli"]2024-09-23T00:00:00 e0a227e6afb3c517b806400e654aabc8ccdf0f15Proceedings of the Rio Oil and Gas00Proceedings of the Rio Oil and Gas["Andr\u00e9 Seichi Ribeiro Kuramoto", "Vitor Vale do Nascimento"]2024-09-23T00:00:00 a849a1fca3800df564cda9fd44d2f3e48f623f0eMultimedia tools and applications540Multimedia Tools and Applications["Munish Saini", "Eshan Sengupta"]2024-09-23T00:00:00 56b221a1e7a3a2396fde7d07d41b0008ae0583ceThis paper discusses the design of a machine learning algorithm based on multi-indicator decision making to improve the accuracy and efficiency of performance evaluation. Firstly, an ensemble learning framework is proposed to solve the inherent complexity and uncertainty in performance evaluation by integrating multiple machine learning models. The framework is capable of processing large volumes of heterogeneous data while considering multiple metrics, including but not limited to sales performance, team collaboration, customer satisfaction, and innovation. In order to verify the effectiveness of the proposed algorithm, a set of comprehensive model simulation experiments are designed in this paper. The experiments were conducted on multiple real-world data sets, covering different industry and organization sizes. Simulation results show that compared with traditional evaluation methods, the proposed algorithm can significantly improve the accuracy and fairness of evaluation, and reduce the subjective bias in the evaluation process. In addition, through comparative analysis, it is found that ensemble learning method has better generalization ability and robustness when dealing with large-scale data. The research in this paper not only provides a theoretical basis for the innovation of performance evaluation system, but also provides practical guidance for organizations to implement a more intelligent and objective evaluation process in practical applications.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)120Simulation results show that compared with traditional evaluation methods, the proposed algorithm can significantly improve the accuracy and fairness of evaluation, and reduce the subjective bias in the evaluation process.2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)["Yuming Chen"]2024-09-23T00:00:00 cc88106944d06007895f0b561804dde2af8842d1 Purpose This study aims to establish a comprehensive framework for adopting mobile-based artificial intelligence (AI) services in Tanzanian manufacturing small and medium enterprises (SMEs). Design/methodology/approach The methodology involved conducting a literature review and using the combination of Mobile Services Acceptance Model and Innovation Diffusion Theory (IDT) as a theoretical foundation. This synthesis delves into the current knowledge on technology adoption, organizational behavior and innovation diffusion, creating a solid conceptual basis. Expert review was used for framework validation to ensure the framework's accuracy. Findings This study shows that the factors influencing the adoption of mobile-based AI services in Tanzanian manufacturing SMEs include perceived usefulness, perceived ease of use, context, personal initiatives and characteristics, trust, infrastructure, cost, mobility, power distance, compatibility, observability and trialability. Research limitations/implications The framework provides valuable insights tailored to Tanzanian sociocultural and economic nuances. However, its generalizability is limited due to its specificity to Tanzanian manufacturing SMEs. Practical implications The framework outlined in this research provides SME leaders, policymakers and technology implementers with valuable guidance to make informed decisions during the adoption process. Originality/value This study introduces a novel lens for understanding technology adoption. This study's focus on the Tanzanian context and its nuanced examination of contributing factors add to its originality and practical significance. Vilakshan - XIMB Journal of Management592A comprehensive framework for adopting mobile-based artificial intelligence services in Tanzanian manufacturing small and medium enterprises (SMEs) is established using the combination of Mobile Services Acceptance Model and Innovation Diffusion Theory as a theoretical foundation.Vilakshan - XIMB Journal of Management["F. Ishengoma", "Elia John"]2024-09-23T00:00:00 6a99dacfb51074ff46534a120f62382d1bcff99bThe use of artificial intelligence (AI) in education has the potential to significantly enhance students' critical thinking skills. This study explores how AI tools can improve critical thinking among students majoring in English Education at X University. Utilizing a mixedmethods approach, the research combines quantitative surveys and qualitative interviews to assess the frequency and contexts of AI usage and its impact on critical thinking. Survey results reveal that 64% of respondents use AI tools several times a week, predominantly in educational settings. A smaller percentage of respondents use AI daily (14%), while another 14% use AI rarely, and 7% use it several times a month. Interviews with frequent AI users indicate that AI assists in expanding ideas and providing deeper insights, but its effectiveness depends on the users' ability to ask precise questions and critically interpret AI-generated content. The findings highlight that while AI can significantly aid in developing critical thinking skills through personalized learning experiences and interactive simulations, challenges such as potential biases and the need for foundational understanding persist. Ultimately, this research underscores the importance of thoughtful and critical use of AI in education to foster improved critical thinking skills.Proceedings Series on Social Sciences &amp; Humanities152The findings highlight that while AI can significantly aid in developing critical thinking skills through personalized learning experiences and interactive simulations, challenges such as potential biases and the need for foundational understanding persist.Proceedings Series on Social Sciences &amp; Humanities["Miryam Caroline Lawasi", "Vina Aulia Rohman", "Meicky Shoreamanis"]2024-09-23T00:00:00 622a0752432eb99c4718768bb49034bb24a199dbThe presented paper examines the integration of Artificial Intelligence (AI) in the management of smart cities, focusing on the Silesian and Lesser Poland Voivodships in Poland. This research addresses a notable gap in the analysis of regional AI strategies within urban management, providing a comparative analysis of AI implementation in these two distinct regions. The Silesian Voivodship, with its emphasis on traditional industries such as manufacturing and energy, contrasts with the broader approach of the Lesser Poland Voivodship, which includes applications in life sciences and ICT. The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management. It highlights the importance of a human-centric approach in smart city development, emphasizing inclusivity, transparency, and ethical considerations. The paper also delves into the socio-technical dynamics of AI deployment, illustrating how these technologies can transform urban environments while ensuring that the benefits are equitably distributed and that urban developments are sustainable and resilient. By analyzing specific case studies, the authors aim to provide empirical evidence and insights that contribute to the academic and practical understanding of AI’s role in smart cities, ultimately advocating for the design of AI applications that prioritize human well-being and environmental health.Sustainability892The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management, and delves into the socio-technical dynamics of AI deployment.Sustainability["Ida Skubis", "R. Wolniak", "W. Grebski"]2024-09-23T00:00:00 dc36e547645027a1435c386fcc54fc10ec57f262 Artificial intelligence (AI) has the potential to positively impact library and archives collections and services—enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications. Information Technology and Libraries262The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes, and that AI implementation was most common in large, academic libraries.Information Technology and Libraries["Sara Mannheimer", "Natalie Bond", "Scott W. H. Young", "Hannah Scates Kettler", "Addison Marcus", "Sally K. Slipher", "Jason A. Clark", "Yasmeen Shorish", "Doralyn Rossmann", "Bonnie Sheehey"]2024-09-23T00:00:00 3705ef8cff7ed049b6916df90e6d0905cec34fe6Current Urology Reports431The potential of AI to improve patient outcomes, streamline BPH management, and reduce healthcare costs is underscored, especially with continued research and development in this transformative field.Current urology reports["John Lama", "Joshua Winograd", "Alia J Codelia-Anjum", "N. Bhojani", "D. Elterman", "K. Zorn", "Bilal Chughtai"]2024-09-23T00:00:00 3a78b2d7be226e386598878d2adcf9428b3e18a8The integration of artificial intelligence in education presents both opportunities and challenges, particularly in the field of language teaching. This study aims to investigate Turkish EFL teachers' perceptions of AI's role in fostering or hindering creativity in language teaching. Through semi-structured interviews with 10 EFL teachers from private high schools, the research explores the potential benefits and challenges of AI integration. Key findings reveal that AI enhances student engagement, provides personalized learning experiences, and offers timely feedback. However, concerns about insufficient training, technical issues, and over-reliance on AI potentially undermining fundamental skills were also expressed. While AI tools support improvements in language skills and foster creative thinking, there is apprehension about standardization and the risk of diminishing originality. The study underscores the necessity for effective teacher training and a balanced approach to AI integration, ensuring it complements rather than replaces traditional teaching methods. These insights provide valuable guidance for educators, policymakers, and technology developers to optimize AI use in EFL education while fostering a dynamic and creative learning environment. Future research should explore innovative ways to integrate AI into language teaching without compromising creativity.International e-Journal of Educational Studies291Key findings reveal that AI enhances student engagement, provides personalized learning experiences, and offers timely feedback, but concerns about insufficient training, technical issues, and over-reliance on AI potentially undermining fundamental skills were also expressed.International e-Journal of Educational Studies["Se\u00e7il T\u00fcmen Aky\u0131ld\u0131z"]2024-09-23T00:00:00 8f487742f721a2abe6f769836e7e0301c9d80408The research aims to identify the use of artificial intelligence applications as a pivotal technology to improve the quality of financial reports in UAE Corporations, as these applications help process and analyze data quickly and accurately. the problem of the study was the lack of confidence and credibility in the quality of financial reports in Corporations through some fictitious transactions that can affect the profits or losses of those Corporations. due to the effective role provided by artificial intelligence systems in the massive and rapid analysis of data, it was necessary to use these applications and employ them to enhance confidence in financial reports and achieve their quality, the study used Partial least squares (PLS) software in the analysis of the study came to the following conclusions: There is a statistically significant relationship between the use of artificial intelligence applications (AIA) and the quality of financial reporting (QFR) in UAE business Corporations, The study also came up with the following recommendations: The need to highlight the importance of artificial intelligence applications in business Corporations within the UAE, by developing the role of their applications in carrying out various routine and complex tasks and activitiesQubahan Academic Journal301There is a statistically significant relationship between the use of artificial intelligence applications (AIA) and the quality of financial reporting (QFR) in UAE business Corporations.Qubahan Academic Journal["Ayman Abdalla Mohammed", "Faisal Khan", "Ali Alhag Mohammed", "Yousif Abdelbagi Abdalla", "Anwer Abd Alla Mohammed", "Zaki Ahmad"]2024-09-23T00:00:00 a1fb14fafb2683e9c02d5873c4fe6ce0d99c4cbdJournal of religion and health301This study investigates how AI might affect health disparities and makes an ethical case for a responsible approach to AI in healthcare by examining the concepts of human dignity, the common good, and preferential option for the poor.Journal of religion and health["I. E. Gozum", "Chastene Christopher D Flake"]2024-09-23T00:00:00 f58db9398e1402201ccc95af88a26ab4bbadaa01Research methodology This case study involves interviews with radiologists of various hospitals and with company personnel. Both primary and secondary data sources have been used. The first-hand perspective from the radiologists highlighted the challenges they face concerning time and the patient load. The company personnel highlighted using machine learning for used cases to make the platform more robust and accurate. This case has been tested with MBA students. Case overview/synopsis An emerging health-care artificial intelligence (AI) start-up, DeepTek.AI, wants to expand its reach in the radiology market. The company intends to leverage technology to assist radiologists in diagnostics. India's health-care sector faces the challenge of needing more trained doctors and nurses to meet the ever-increasing needs of patients. This case study revolves around the radiologists' concerns about implementing the new technology and its ease of use. The features and benefits of integrating AI in diagnostics are the need of the hour, but the reliability of results needs to be ascertained for adopting it. Complexity academic level This case was written for marketing applications and practices, trends in marketing, marketing strategy and technology adoption in marketing courses at the post-graduate level. Consumer adoption of finance, hospitality, travel and health-care technology is vital for increasing the company's market share and growth prospects. The students will have an opportunity to understand the challenges and the opportunities. The CASE Journal30This case study revolves around the radiologists' concerns about implementing the new technology and its ease of use, but the reliability of results needs to be ascertained for adopting it.The CASE Journal["Hufrish Majra", "Nalini Krishnan"]2024-09-23T00:00:00 06f0d61443b669a62384e0ba46903f7682962241Background Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial failure are unknown. Objective This study aims to compare trial design factors of complete and incomplete clinical trials testing AI tools. We conducted a case-control study of complete (n=485) and incomplete (n=51) clinical trials that evaluated AI as an intervention of ClinicalTrials.gov. Methods Trial design factors, including area of clinical application, intended use population, and intended role of AI, were extracted. Trials that did not evaluate AI as an intervention and active trials were excluded. The assessed trial design factors related to AI interventions included the domain of clinical application related to organ systems; intended use population for patients or health care providers; and the role of AI for different applications in patient-facing clinical workflows, such as diagnosis, screening, and treatment. In addition, we also assessed general trial design factors including study type, allocation, intervention model, masking, age, sex, funder, continent, length of time, sample size, number of enrollment sites, and study start year. The main outcome was the completion of the clinical trial. Odds ratio (OR) and 95% CI values were calculated for all trial design factors using propensity-matched, multivariable logistic regression. Results We queried ClinicalTrials.gov on December 23, 2023, using AI keywords to identify complete and incomplete trials testing AI technologies as a primary intervention, yielding 485 complete and 51 incomplete trials for inclusion in this study. Our nested propensity-matched, case-control results suggest that trials conducted in Europe were significantly associated with trial completion when compared with North American trials (OR 2.85, 95% CI 1.14-7.10; P=.03), and the trial sample size was positively associated with trial completion (OR 1.00, 95% CI 1.00-1.00; P=.02). Conclusions Our case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure. We observed that trial design factors positively associated with trial completion include trials conducted in Europe and sample size. Given the promising clinical use of AI tools in health care, our results suggest that future translational research should prioritize addressing the design factors of AI clinical trials associated with trial incompletion and common reasons for study failure.Journal of Medical Internet Research200A case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure, and observes that trial design factors positively associated with trial completion include trials conducted in Europe and sample size.Journal of Medical Internet Research["David Chen", "C. Cao", "Robbie Kloosterman", "Rod Parsa", "Srinivas Raman"]2024-09-23T00:00:00 47bef342b9e1204a1360ef087556bd1243c65e59Generative artificial intelligence (AI) holds the potential to either complement workers by enhancing their productivity or substitute them. We examine the short-term effects of the recently released generative AI models (ChatGPT, DALL-E 2, and Midjourney) on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that generative AI may transform the role of human capital in the organization and reduce overall demand for workers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2023.18441 .Social Science Research Network6932It is found that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings.SSRN Electronic Journal["Xiang Hui", "O. Reshef", "Luofeng Zhou"]2024-09-24T00:00:00 4d7646c42d4de4cff2bb33988310964371822794This article explores the complex relationship between artificial intelligence (AI) and privacy. While acknowledging AI's potential benefits, the authors emphasize the ethical implications of its data-driven nature. The article begins by outlining the privacy risks inherent in AI systems, including data breaches, surveillance, and the potential for bias and discrimination. It then delves into ethical considerations surrounding AI development, such as transparency, accountability, and the need to prioritize human values. Various frameworks for balancing innovation with privacy protection are discussed, including Privacy by Design principles and the General Data Protection Regulation (GDPR). It also examine case studies of privacy violations in AI systems, highlighting the real-world consequences of inadequate safeguards. Looking towards the future, the article identifies advancements in privacy-preserving AI technologies as a crucial area of research. It concludes by advocating for a comprehensive approach to AI governance that combines technological innovation with ethical and regulatory strategies, by stressing the importance of proactive measures to mitigate privacy risks and ensure that AI technologies are developed and deployed in a manner that respects.International Journal of Scientific Research and Modern Technology (IJSRMT)366The article begins by outlining the privacy risks inherent in AI systems, including data breaches, surveillance, and the potential for bias and discrimination, and delves into ethical considerations surrounding AI development, such as transparency, accountability, and the need to prioritize human values.International Journal of Scientific Research and Modern Technology (IJSRMT)["Chris Gilbert"]2024-09-24T00:00:00 479d878016158ff7fdb88caa87e4aca630658deeThis study ventures into the expanding role of artificial intelligence (AI) in higher education and its potential to revolutionize the learning experience. Through a qualitative approach, we investigate how advanced AI-driven educational tools can enhance learning systems through tailored learning experiences, AI-powered adaptive tutoring platforms, immersive digital learning environments, and AI-assisted assessment and feedback systems. From a theoretical perspective, we utilize the constructivist theory of learning as a study framework to explain the role of technology in AI-facilitated personalized and collaborative knowledge construction. This study was executed in three stages. First, we present a brief overview of AI and its recent impact on the education system, including its ethical considerations and implementation hurdles. In the second stage, we propose the potential implications for learning contexts through the constructivist lens, the process of AI-supported cognitive development, and the use of AI tools for optimizing educational processes through AI and transformation in educational paradigms and study work. The findings suggest that institutions can unlock new opportunities for efficiency, accessibility, and personalized learning by adopting AI technology, but they also highlight the need to address ethical considerations and implementation hurdles. This study also contributes to future studies by examining the impact of AI on student outcomes, teacher roles, and institutional practices in education, as well as exploring ethical and equity concerns in AI-driven educational settings.International Journal of Interactive Mobile Technologies01How advanced AI-driven educational tools can enhance learning systems through tailored learning experiences, AI-powered adaptive tutoring platforms, immersive digital learning environments, and AI-assisted assessment and feedback systems is investigated.Int. J. Interact. Mob. Technol.["Muhammad Imran", "N. Almusharraf", "M. Abdellatif", "M. Abbasova"]2024-09-24T00:00:00 d0952f3d34ec8e5cc3db1a7942d73595bd17ee18Artificial Intelligence (AI) is increasingly recognized as a key enabler in achieving Sustainable Development Goals (SDGs) due to its transformative potential across various sectors. This paper explores the intersection of AI and SDGs, highlighting the significant role AI plays in accelerating progress towards sustainability. AI technologies such as machine learning, natural language processing, and computer vision have been instrumental in enhancing efficiency, decision-making, and resource management in areas such as healthcare, agriculture, education, and climate change mitigation. By analyzing vast amounts of data, AI can provide valuable insights and predictive models to inform policy-making, optimize resource allocation, and enhance monitoring and evaluation processes. Furthermore, AI empowers developing countries to leapfrog traditional developmental barriers by offering innovative solutions that are cost-effective and scalable. Through initiatives like precision agriculture, telemedicine, and smart energy systems, AI enables inclusive growth while reducing inequalities and enhancing resilience to environmental challenges. While AI brings immense opportunities for sustainable development, challenges such as data privacy, bias, and ethical concerns must be addressed to ensure that AI technologies are deployed responsibly and equitably. Collaborative efforts between governments, industry, and civil society are essential to harness the full potential of AI in achieving the SDGs and creating a more inclusive and sustainable future for all.Sustainable Economies391This paper explores the intersection of AI and SDGs, highlighting the significant role AI plays in accelerating progress towards sustainability and collaborative efforts between governments, industry, and civil society are essential to harness the full potential of AI.Sustainable Economies["Milad Shahvaroughi Farahani", "Ghazal Ghasemi"]2024-09-24T00:00:00 fb4ee468952428d9daa6d12b9c767a9c32bf7f49With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this paper, we specifically consider Internet of electric vehicles (IoEV) and we categorize GenAI for IoEV into four different layers namely, EV's battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.arXiv.org2101This survey categorizes GenAI for IoEV into four different layers namely, EV's battery layer, individual EV layer, smart grid layer, and security layer, and provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.ArXiv["Hanwen Zhang", "D. Niyato", "Wei Zhang", "Changyuan Zhao", "Hongyang Du", "Abbas Jamalipour", "Sumei Sun", "Yiyang Pei"]2024-09-24T00:00:00 800b2a967d00f31942c738b2a9e4b43a5f41293fPurposeThis paper delves into the transformative impact of artificial intelligence (AI) across diverse sectors, notably project management. It examines the potential of AI to revolutionize project management processes within entrepreneurial ventures, where agility, efficiency and innovation reign supreme.Design/methodology/approachThrough a comprehensive analysis, this study navigates the intersection of AI and entrepreneurial project management. It meticulously dissects the opportunities AI presents, the hurdles it introduces and the optimal strategies for harnessing its capabilities effectively. Drawing insights from complexity theory, a framework is crafted to delineate AI’s capacity to substitute human involvement, elucidating key considerations for transitioning to a digitally-driven paradigm in entrepreneurial project management.FindingsThe study underscores AI’s potential to augment project management processes significantly, particularly in fostering agility and innovation. However, challenges persist, necessitating adept navigation to maximize AI’s benefits. The framework delineates the extent to which AI can supplant human roles, offering crucial insights into the digital transformation of entrepreneurial project management.Practical implicationsPractitioners are equipped with valuable guidance on leveraging AI effectively, enhancing organizational agility and performance. Understanding the implications of AI adoption fosters informed decision-making in the realm of project management.Social implicationsThe integration of AI in entrepreneurial project management signifies broader societal shifts toward digitalization and automation. Insights from this study contribute to navigating these transformations, fostering greater resilience and adaptability in entrepreneurial endeavors.Originality/valueThis study offers a novel perspective on the intersection of AI and entrepreneurial project management, shedding light on unexplored terrain. By drawing on complexity theory, it advances a nuanced understanding of AI’s implications, paving the way for future research avenues in this dynamic field.International Journal of Managing Projects in Business652This study underscores AI’s potential to augment project management processes significantly, particularly in fostering agility and innovation, and delineates the extent to which AI can supplant human roles, offering crucial insights into the digital transformation of entrepreneurial project management.International Journal of Managing Projects in Business["Ataullah Kiani"]2024-09-24T00:00:00 f033bb4b23aa89c19d24bd047efc7b54994a13b0Artificial Intelligence (AI) advancements have significantly enhanced computer systems to better cater to user needs, consequently improving user experiences. One area witnessing widespread adoption of AI technology is in the production of digital videos, which find application across various fields, including education. This study aims to delineate the emerging trends in research concerning the integration of AI technology in digital video production. To achieve this objective, we conducted a systematic literature review across several databases, including Web of Science, ERIC, Taylor & Francis, Education Full Text EBSCO, and Sciencedirect. Following the PRISMA flowchart, studies were meticulously screened and selected based on predefined inclusion criteria aligned with the study's purpose. Our analysis encompassed 14 international studies, revealing diverse digital content creations facilitated by AI support. These technologies, offering various interaction dimensions, serve multiple purposes such as providing general guidance, reinforcing knowledge, facilitating design or experimentation, and delivering personalized experiences through tailored technologies. Despite these advancements, the full potential of AI technology remains underutilized. Hence, future research should prioritize the development of digital content that accommodates individual differences, fosters social interaction, incorporates enriched features, and is adaptable to diverse environments. In conclusion, while AI-driven advancements in digital video production hold promise, there exists ample room for further exploration and optimization to fully leverage the capabilities of this technology.Journal of Educational Technology and Online Learning1041While AI-driven advancements in digital video production hold promise, there exists ample room for further exploration and optimization to fully leverage the capabilities of this technology.Journal of Educational Technology and Online Learning["Cihan Orak", "Zeynep Turan"]2024-09-24T00:00:00 a28eee3d30ea401fefca8a07332c1767b087bc7cThis research paper explores the ethical considerations and implementations surrounding the use of artificial intelligence (AI) in communication governance. In an era marked by the increasing reliance on AI-driven technologies for communication, this study investigates the ethical Implementations, ethics, challenges, and potential benefits associated with AI's role in shaping and regulating information flow. Drawing upon a wide range of literature and discusses a comprehensive overview of applying AI in communication governance.International Conference on Communication and Network Security240Drawing upon a wide range of literature, this study investigates the ethical Implementations, ethics, challenges, and potential benefits associated with AI's role in shaping and regulating information flow.2024 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)["Reneh Abokhoza", "K. M. Al-Tkhayneh", "Reema Al Qaruty", "Samer A. Abdel Hadi", "Z. Ellala"]2024-09-24T00:00:00 da4b63fd52f7fbcca0d0d97694f13b8014937585Технологии искусственного интеллекта во многом базируются на больших данных, и, помимо вычислений, которые обеспечивают должную точность и робастность результатов, а также безопасность систем именно вопросы хранения, передачи и обработки больших данных притягивают к себе пристальное внимание исследователей и разработчиков. Причём работу с данными можно рассматривать на математическом уровне, но в данной работе это сделано на уровне архитектуры информационных систем. А именно, рассматривается вопрос о том, какие модули современных информационных систем в финансовой сфере используют технологии искусственного интеллекта и как они соотносятся с хранилищами и процессорами данных. Структурно работа построена так, что за описанием сфер применения искусственного интеллекта следует обзор изобретений по теме, затем анализируются значимые для предметной области стандарты и, наконец, дана общая архитектура информационной системы. Technology related to artificial intelligence is largely based on big data. In addition to computing issues, that ensure appropriate accuracy and robustness of results, as well as security issues, these are the issues of storing, transmitting and processing of big data that attract the close attention of researchers and developers. Though, data processing could be discussed at the mathematical level, this work treats processes at the level of the architecture of information systems. Namely, under investigation is a question modules constituting modern information systems in the financial sector, artificial intelligence involved in these modules, data storage and data processors. The work is structured as follows. First, the applications of artificial intelligence in finances are described. Second, a review of inventions on the topic is given. Third, the standards relevant to the subject area are analyzed and, finally, the general architecture of the information system is presented.Цифровая экономика30Цифровая экономика["\u0418.\u0412. \u041d\u0435\u0432\u043e\u043b\u0438\u043d"]2024-09-24T00:00:00 e62abd62f04626791983a0ca4c2730ee77838dc0Сегодня в сфере графического дизайна все большую популярность приобретают технологии искусственного интеллекта. Рынок нейросетей для генерации изображений представлен рядом продуктов от крупных компаний, таких как DALL-E от OpenAI, и сервисами от независимых разработчиков. Также на рынок выходят и новые игроки, в том числе Apple со своей платформой Apple Intelligence. Все это обусловливает актуальность изучения перспектив и проблем, связанных с применением нейросетей в графическом дизайне. Artificial intelligence technologies are becoming increasingly popular in the field of graphic design today. The neural network market for image generation is represented by a number of products from large companies, such as DALL E from OpenAI, and services from independent developers. New players are also entering the market, including Apple with its Apple Intelligence platform. All this determines the relevance of studying the prospects and problems associated with the use of neural networks in graphic design.Графические конструкции в дизайне00Графические конструкции в дизайне["\u041b\u0435\u043c \u0410.\u0421."]2024-09-24T00:00:00 e2e8af301f546523d2e7a928483e9d9403418a16The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry”s digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive., system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform.2024 17th International Conference on Management of Large-Scale System Development (MLSD)130It is demonstrated that among these new technologies, the artificial intelligence methods have a special significance and have to go through integration transformations to become standards for the proposed common digital farming management platform.2024 17th International Conference on Management of Large-Scale System Development (MLSD)["V. Medennikov", "Larisa Bogatyreva"]2024-09-24T00:00:00 d399220736a3dc34a3d9e4b51320e0a708ce51a4Artificial intelligence (AI) technology represents a revolutionary change in the healthcare sector, providing creative answers to persistent problems. The goal of this study is to highlight how artificial intelligence includes a broad range of instruments and approaches, including machine-learning algorithms and natural language processing that have been used in numerous aspects of healthcare delivery and utilizing AI to raise patient security. This reviewed literature studies the evidence from literature concerning AI-driven systems facilitating rapid and accurate analysis of vast amounts of medical data, enhancing diagnostic processes, optimizing individualized treatment plans that facilitates and improves healthcare operations and efficiency. AI enable automating administrative processes, optimizing resource allocation, and streamlining workflows and upholding the strength and efficacy leading to transformation in the administration system. Based on a review of the literature, the research suggests the significant impact of AI on improving patient safety and provides a plan for overcoming obstacles, capitalizing on opportunities, and guiding the direction of AI-driven patient safety programs to revolutionize the healthcare system on a global scale.Research Journal of Pharmacy and Technology240The research suggests the significant impact of AI on improving patient safety and provides a plan for overcoming obstacles, capitalizing on opportunities, and guiding the direction of AI-driven patient safety programs to revolutionize the healthcare system on a global scale.Research Journal of Pharmacy and Technology["Randa Khirfan", "Heba Kotb", "Huda Atiyeh"]2024-09-24T00:00:00 8ee0b5179347502b18781eb9586c732a9a94cdfbThe key components of the architecture of the digital incident management platform of the Urban Services Complex are presented: information field, monitoring systems and data analysis, modules for processing and moderating information, classification, decision-making based on artificial intelligence (AI) methods. The directions for increasing the accuracy and speed of response to incidents through social media are emphasized.2024 17th International Conference on Management of Large-Scale System Development (MLSD)902024 17th International Conference on Management of Large-Scale System Development (MLSD)["Evgenii Galichenko", "Olga Pisareva"]2024-09-24T00:00:00 467b26a6812244d4eed8b127255c405c11b9656fThe emergence of artificial intelligence (AI) gives threat for abuse manipulated pornography called deepfake pornography. Deepfake pornography is a form of online gender-based violence that allows perpetrator to replace and insert someone’s face onto another person’s body. It can made by anyone and anywhere, so it is vulnerable to cause victims. Deepfake pornography are affected mentally and emotionally for the victims. To support deepfake pornography victims regain control over him, right to be forgotten (RTBF) plays an important role as a protection for the victims. The regulation of RTBF in Indonesia currently in Article 26 (3) UU ITE. Under this RTBF, the victims may request the electronic system organizer to eliminate their images/videos from the platforms. However, RTBF is considered to have legal vague, so resulting in not achievement of legal protection for deepfake pornography victims. The research method is normative qualitative using primary, secondary and tertiary literature data. This study concludes that RTBF is a promising attempt to protect deepfake pornography victims in this digital era, but it is necessary to make efforts by strengthening regulations related to RTBF as a recovery of deepfake pornography victims.International Journal of Law, Government and Communication00This study concludes that RTBF is a promising attempt to protect deepfake pornography victims in this digital era, but it is necessary to make efforts by strengthening regulations related to RTBF as a recovery of deepfake pornography victims.International Journal of Law, Government and Communication["Linda Astuti", "Wiwit Ariyani", "Bayu Aryanto"]2024-09-24T00:00:00 bc742df94e369554aa1e1bc3e2e0619fb6dc2360Recent advances in the capabilities of artificial intelligence (AI) have increased state interest in leveraging AI for military purposes. Military integration of advanced AI by nuclear-armed states has the potential to have an impact on elements of their nuclear deterrence architecture such as missile early-warning systems, intelligence, surveillance and reconnaissance (ISR) and nuclear command, control and communications (NC3), as well as related conventional systems. At the same time, a number of technological and logistical factors can potentially limit or slow the adoption of AI in the nuclear domain. Among these are unreliability of output, susceptibility to cyberattacks, lack of good-quality data, and inadequate hardware and an underdeveloped national industrial and technical base. Given the current and relatively early stage of military adoption of advanced AI, the exploration of these factors lays the groundwork for further consideration of the likely realities of integration and of potential transparency measures and governance practices at the AI–nuclear nexus.80Given the current and relatively early stage of military adoption of advanced AI, the exploration of these factors lays the groundwork for further consideration of the likely realities of integration and of potential transparency measures and governance practices at the AI–nuclear nexus.["Vladislav Chernavskikh"]2024-09-24T00:00:00 be3f5b7201729b46b2d2f8784db870979ab3aa7eThis study investigates the impact of Artificial Intelligence (AI) applications on enhancing and developing the performance of small and medium enterprises (SMEs), which are fundamental to economic development and entrepreneurial growth. The objective is to determine how recent developments in AI have contributed to SME growth, including increases in productivity and profitability, as well as improvements in employee efficiency and productivity. The paper concludes that SMEs can benefit significantly from AI techniques, particularly in automating administrative tasks, freeing up time for business development. The findings suggest that SMEs can leverage AI to accelerate their annual growth and achieve positive outcomes in various areas of operation. Thus, the study highlights the critical role of AI in promoting SME performance and its potential as a valuable tool for the continued development of small and medium-sized enterprises.International Conference on Intelligent Data Science Technologies and Applications300The paper concludes that SMEs can benefit significantly from AI techniques, particularly in automating administrative tasks, freeing up time for business development and suggesting that SMEs can leverage AI to accelerate their annual growth and achieve positive outcomes in various areas of operation.2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)["Moustafa Elmetwaly Kandeel", "Basma Ahmed Saleh", "G. Elrefae", "Yasmeen G. Elsantil"]2024-09-24T00:00:00 aa59e53344f19a81c1abc4a884521e36ed2fe263 Purpose The purpose of this paper is to supplement and update previously published articles about artificial intelligence (AI) instruments and operations in banking sectors with the following objectives in mind: to understand the role of AI in banking sectors; to explore the themes and context in this area based on keywords, co-citations and co-words; and to identify future research direction by evaluating the trend and direction of previous research. Design/methodology/approach This study adopts a semi-inductive approach with the convolution of bibliometrics and literature review. This study used bibliometrics for the identification of literature across multiple databases and systematic literature review on identified articles to explore heterogeneous sectors within AI in banking and finance. Findings This study contributes a literature-based model that accounts for both the broadly in AI application in banking and finance: predictive modeling in risk assessment and detection; financial decision-making; client service delivery; and emerging FinTech applications of AI and machine learning. Originality/value This study is among the few to address the literature of tools and application of AI in banking through mixed-methods approach and produce a synthesized model for the same. VINE Journal of Information and Knowledge Management Systems1120This study contributes a literature-based model that accounts for both the broadly in AI application in banking and finance: predictive modeling in risk assessment and detection; financial decision-making; client service delivery; and emerging FinTech applications of AI and machine learning.VINE Journal of Information and Knowledge Management Systems["Rahul Meena", "Akshay Kumar Mishra", "R. Raut"]2024-09-24T00:00:00 4c70b0821c95cf85db6886a1368eb063d027beabBackground: Growth in artificial intelligence systems can allow the automation of a crucial section of the traditional manual work practiced in pharmacoeconomics and evidence synthesis. However, artificial intelligence has a low autonomous analytical skill capabilities. The objective is to conduct a thematic analysis of the benefits and risks of applying ChatGPT across various sub-disciplines within pharmacoeconomics analysis. A purposive sampling technique was used to select the participants, utilizing the convenience sampling approach. All interviews were recorded and transcribed verbatim, and thematic analysis was applied to identify themes within the data. The result shows that most of the respondents find AI ChatGPT helpful as it increases data analysis and processing efficiency, as well as improving real-time decision making. Additionally, the participants believe that information accessibility and dissemination are appealing features of AI ChatGPT. However, the respondents identified potential drawbacks in AI ChatGPT use, such as data quality and accuracy, contextual awareness limitations, privacy and security concerns, lack of responsibility and accountability, limitations in generalizability, social and ethical concerns, uncertainty understanding limitations, and amplification of bias. Employing ChatGPT in pharmacoeconomics presents significant potential for enhancing data processing efficiency, providing real-time decision support, and improving information accessibility for healthcare stakeholders. However, these benefits come with various risks and ethical challenges, including concerns about data accuracy, contextual awareness, security, accountability, generalizability, and potential bias amplification. By putting in place robust safeguards, adhering to ethical standards, and maintaining vigilant oversight, we can effectively leverage ChatGPT's potential while maintaining the principles of responsible, equitable, and ethical healthcare decision making.2024 Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)320A thematic analysis of the benefits and risks of applying ChatGPT across various sub-disciplines within pharmacoeconomics analysis shows that most of the respondents find AI ChatGPT helpful as it increases data analysis and processing efficiency, as well as improving real-time decision making.2024 Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)["F. El\u2011Dahiyat", "G. Elrefae", "A. Jairoun", "S. S. Al-Hemyari", "M. Shahwan", "Fahad S. Alshehri", "Nasser M Alorfi"]2024-09-24T00:00:00 43978d4a5e4122946222b187cacd5f1f51e705cfThis study aimed to investigate the impact of artificial intelligence (AI) on the labor market by exploring workers’ perspectives on AI’s positive and negative impacts and society’s trends in coping with these impacts. Using a survey-based questionnaire, data were collected from 162 individuals, including both employed and unemployed people with various educational qualifications. The results indicated that AI has both positive and negative impacts to a high extent, with mean scores of 3.85 for positive impacts and 3.75 for negative impacts. Significant differences were observed based on gender, educational qualifications, and professional roles: males (Mean $=4.2, \mathrm{SD}=0.9$) perceived AI’s impact more positively than females (Mean = 3.6, SD = 1.1), while higher educational qualifications were associated with a more optimistic view of AI (e.g., Master’s Degree holders had a Mean $=4.4, \mathrm{SD}=0.8$). Additionally, freelancers (Mean $=4.0, \mathrm{SD}=1.1$) viewed AI as both a threat and opportunity, whereas employees (Mean = 3.7, $\mathrm{SD}=1.0$) and job seekers (Mean $=3.3, \mathrm{SD}=1.2)$ had varying concerns regarding job security and skill demands. Overall, the high acceptance of AI reflects a positive trend towards technology in the labor market.International Conference on Intelligent Data Science Technologies and Applications250The results indicated that AI has both positive and negative impacts to a high extent, with mean scores of 3.85 for positive impacts and 3.75 for negative impacts and the high acceptance of AI reflects a positive trend towards technology in the labor market.2024 Fifth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)["A. AlMahdawi", "E. Zaitoun", "M. Jaradat", "Mohamed Elsayed Elzeiny"]2024-09-24T00:00:00 e7bb312d24f542e36e9f7b643ae945959974cdabThe advent of artificial intelligence (AI) has brought significant and transformative alterations to traditional music education. This study examines the progress of AI technology in music education by conducting a systematic review using the PRISMA methodology. Articles were selected for inclusion based on the criterion of specifically describing the utilization of AI in the instruction and acquisition of music. The search was performed on April 9, 2024, via the Web of Science and SCOPUS databases. The search terms “music education” and “artificial intelligence” were employed to ascertain relevant scholarly research. The group of papers underwent scrutiny by various researchers to ascertain their adherence to the established criteria. The articles that were verified by a minimum of two researchers were chosen. 31 articles were finally screened, and the results were divided into two sections: the development of AI in music education and innovative music pedagogy based on AI. A key finding is that the implementation of bibliometric analyses suggests that AI research in music education is still in its infancy. Prior research has primarily concentrated on music instruction at the university level, with a particular emphasis on the integration of AI in music education in China. In addition, this study identifies four specific facets of AI through the reshaping of music pedagogy: enhancing personalized music teaching, providing timely feedback on learning, supporting interactive experiences, and providing organized digital materials.International Journal of Interactive Mobile Technologies00The implementation of bibliometric analyses suggests that AI research in music education is still in its infancy, and four specific facets of AI through the reshaping of music pedagogy are identified: enhancing personalized music teaching, providing timely feedback on learning, supporting interactive experiences, and providing organized digital materials.Int. J. Interact. Mob. Technol.["Yifang Zhang", "Beh Wen Fen", "Chao Zhang", "Sheng Pi"]2024-09-24T00:00:00 e3d9ed9eb35d088177fc0f3438d4520895b747a9In this article, we critically examine the implementation of what Carlos Montemayor calls Humanitarian Artificial Intelligence from a decolonizing perspective. We highlight the dichotomy of optimism and fear surrounding AI, elucidating its potential to address fundamental human problems and the risks of monopolistic control. We critique Montemayor’s proposal to align AI with a human rights framework, arguing that it insufficiently addresses global inequalities. Our tripartite analysis focuses on the distribution of AI resources, language inclusion, and content diversity to ensure AI benefits all humanity. We emphasize the need for equitable access to AI, linguistic diversity in AI training data, and the preservation of marginalized epistemologies. We advocate for strategies to mitigate environmental impacts and avoid cultural imperialism disguised as altruism, calling for a balanced approach between private sector innovation and state regulation to foster a truly humanitarian AI.Journal of Artificial Intelligence and Consciousness00This article critically examine the implementation of what Carlos Montemayor calls Humanitarian Artificial Intelligence from a decolonizing perspective, highlighting the dichotomy of optimism and fear surrounding AI and calling for a balanced approach between private sector innovation and state regulation to foster a truly humanitarian AI.J. Artif. Intell. Conscious.["Abraham Sapi\u00e9n", "Jorge Oseguera Gamba"]2024-09-24T00:00:00 491e8703e45d9ce2a39bb082a10b09e336b8bcb5PurposeThe rapid development and widespread application of artificial intelligence tools have raised concerns about how designers are embracing these technologies. This study investigates the factors influencing designers' behavioral intention to use and disclose the use of generative artificial intelligence.Design/methodology/approachA quantitative research approach was employed, designing a structured questionnaire based on Self-Determination Theory to assess the impact of various psychological and social dimensions. The questionnaire included dimensions such as autonomy, competence, relatedness, social influence, value fit and social innovativeness. A Partial Least Squares Structural Equation Modeling analysis was conducted on 309 valid responses from diverse design fields.FindingsCompetence and relatedness are significant factors influencing designers' continuance intention to use generative artificial intelligence. Although autonomy does not significantly affect continuance intention, it plays a crucial role in the decision to disclose artificial intelligence participation. Social influence and value fit significantly shape autonomy, competence and relatedness, while the impact of social innovativeness is relatively limited.Originality/valueThis study clarifies the factors influencing designers' continuance intention and disclosure of generative artificial intelligence tools from both individual and social dimensions, enhancing the understanding of the relationship between designers and generative artificial intelligence tools. It provides valuable insights for the development of artificial intelligence technology and the future trends in the design industry, offering significant theoretical and practical value.Kybernetes740This study clarifies the factors influencing designers' continuance intention and disclosure of generative artificial intelligence tools from both individual and social dimensions, enhancing the understanding of the relationship between designers and generative artificial intelligence tools.Kybernetes["Qianling Jiang", "Jue Qian", "Yong Zang"]2024-09-24T00:00:00 651806ed6c77033c8abe9dd75d094e17fd53c418Rapid advances in Artificial Intelligence (AI) are generating much controversy in society, often without scientific basis. As occurred the development of other emerging technologies, such as the introduction of electricity in the early 20th century, AI causes both fascination and fear. Following the advice of the philosopher R.W. Emerson's: advice the knowledge is the antidote to fear; this paper seeks to contribute to the dissemination of knowledge about AI. To this end, it reflects on the following questions: the origins of AI, its possible future evolution, its ability to show feelings, the associated threats and dangers, and the concept of AI singularity.arXiv.org230Questions are considered: the origins of AI, its possible future evolution, its ability to show feelings, the associated threats and dangers, and the concept of AI singularity.ArXiv["Alberto Prieto", "Beatriz Prieto"]2024-09-24T00:00:00 ad2c1a36420a6f99f9b4075d5dab4e6c1231f45eEmerging technologies like artificial intelligence (AI) and algorithms reshape news curation and consumption. Against this background, previous research has been focused on divides between groups regarding access to such digital technologies. Disparities in awareness and knowledge of AI across socio-demographic groups seem to persist, potentially leading to an algorithmic divide. Despite this situation, there is still limited research into such an emerging inequality. Building on the framework of algorithmic literacy, this study aims to contribute to this gap with findings from a national representative study in Germany (N = 1,090) in January 2022, considering socio-demographic factors such as age, gender, and education. Findings shed important light on the extent to which news audiences are knowledgeable about the use of AI and algorithms in news selection and recommendation, as well as in society. The results of our analysis imply that newsrooms should increase their knowledge about the potential divides created by applying AI across sectors to various socio-demographic groups and stay vigilant about the level of transparency of their AI use.Frontiers in Communication670The results of this analysis imply that newsrooms should increase their knowledge about the potential divides created by applying AI across sectors to various socio-demographic groups and stay vigilant about the level of transparency of their AI use.Frontiers in Communication["Maximilian Eder", "Helle Sj\u00f8vaag"]2024-09-24T00:00:00 35da26fd105566122408e4848605cdb3a1281256The article considers the problem of artificial intelligence evolution in the context of its negative impact on social, economic and political aspects of society. The stages of evolution of artificial intelligence are described when comparing it with human cognitive abilities, the range of performed tasks and the ability of artificial intelligence to uncontrolled self-learning. General problems of artificial intelligence application in job substitution, manipulation of public opinion and training of artificial intelligence on incorrect historical data set are disclosed. On the example of the popular language neural network ChatGPT the  development of artificial intelligence is analyzed, demonstrating the theoretical possibility of  evolution to superintelligence and probable risks associated with this phenomenon.The Sociology of Law10The article considers the problem of artificial intelligence evolution in the context of its negative impact on social, economic and political aspects of society, and demonstrates the theoretical possibility of evolution to superintelligence and probable risks associated with this phenomenon.Sociology and Law["M. A. Ri"]2024-09-24T00:00:00 13f1f88088c86983700d89467ffc0414004fd922BACKGROUND Diabetic retinopathy (DR) is the leading cause of visual impairment and blindness among individuals with diabetes mellitus. Early detection and timely intervention are crucial for preventing irreversible vision loss. However, traditional methods of DR screening are labor-intensive and reliant on the availability of skilled personnel, posing challenges in resource-constrained settings. Objective This study aims to evaluate the effectiveness of artificial intelligence (AI) in detecting the severity of DR compared to conventional ophthalmological assessments. METHODS A hospital-based observational study was conducted at the ophthalmology outpatient department of Tertiary care hospital, India over a six-month period. A total of 300 diabetic patients were included, and fundus photographs were obtained using a fundus camera. The images were then analyzed using an AI model trained on a diabetic retinopathy dataset. The severity of DR was graded according to established criteria, and the accuracy of the AI model was compared to that of ophthalmologist grading. RESULTS The AI model demonstrated an accuracy rate of 95.25% in grading the severity of DR. Comparison between AI and ophthalmologist grading showed close sensitivity and specificity rates across different DR grades, with the AI model slightly outperforming in certain categories. CONCLUSIONS Artificial intelligence shows promise as an effective and efficient tool for the screening and diagnosis of diabetic retinopathy. Its integration into healthcare systems could enhance early detection and treatment of DR, particularly in underserved regions with limited access to ophthalmological services. Further research and validation are warranted to optimize the use of AI in diabetic eye care and ensure its equitable distribution and ethical use.Journal of Evolution of Medical and Dental Sciences100Artificial intelligence shows promise as an effective and efficient tool for the screening and diagnosis of diabetic retinopathy and its integration into healthcare systems could enhance early detection and treatment of DR, particularly in underserved regions with limited access to ophthalmological services.Journal of Evolution of Medical and Dental Sciences["Sheetal S.", "Abhilash B."]2024-09-24T00:00:00 da7af74ba37fac35dbb057575f07411a566d8b34During audit planning, auditors examine the business of different firms. Still, the target is to minimize the discrepancy in the real planned financial statement of inspection and summary reports of internal audits. On the other hand, expenditures on artificial intelligence have been increasing in Turkish firms; according to the National Artificial Strategy document, AI will be part of every organizational process, including internal audits. Moreover, the literature supports a positive relationship between internal audits and firms’ decreasing capital loss. So, this research aims to analyze the relationship between AI expenditures, internal audit reports, and the firms’ historical loss. To reach this aim, suitable data was analyzed from 732 incorporated companies that are members of the Chamber of Trade and Industry/Tekirdag/Turkey. Structural equation modeling results show that AI investments decrease the discrepancy between financial statements and internal audit reports (β=-0.045). On the other hand, discrepancies found in the internal audit reports compared to real financial statements are increasing firms’ financial losses by almost 10% (β=.118). In other words, investing in AI contributes to more realistic financial reports, resulting in fewer financial losses. From this perspective, this study is one of the leading studies that connects AI investment to internal audits and the financial performance of Turkish firms.Denetişim00Structural equation modeling results show that AI investments decrease the discrepancy between financial statements and internal audit reports, resulting in fewer financial losses.Denetişim["K. Arun"]2024-09-24T00:00:00 82bf84c777cf42d49f52958489b4c5ae1e4aabbaThis paper aims to contribute with a literature review on the use of AI for cleaner production throughout industries in the consideration of AI’s advantage within the environment, economy, and society. The survey report based on the analysis of research papers from the recent literature from leading database sources such as Scopus, the Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar identifies the strategic strengths of AI in optimizing the resources, minimizing the carbon footprint and eradicating wastage with the help of machined learning, neural networks and predictive analytics. AI integration presents vast aspects of environmental gains, including such enhancements as a marked reduction concerning the energy and materials consumed along with enhanced ways of handling the resulting waste. On the economic aspect, AI enhances the processes that lead to better efficiency and lower costs in the market on the other hand, on the social aspect, the application of any AI influences how people are utilized as workers/clients in the community. The following are some of the limitations towards AI adoption as proposed by the review of related literature; The best things that come with AI are yet accompanied by some disadvantages; there are implementation costs, data privacy, as well as system integration that may be a major disadvantage. The review envisages that with the continuation of the AI development in the following years, the optic is going to be the accentuation on the enhancement of the process of feeding the data in real-time mode, IoT connections, and the implementation of the proper ethical approaches toward the AI launching for all segments of the society. The conclusions provide precise suggestions to the people working in the industry to adopt the AI advancements appropriately and at the same time, encourage the lawmakers to create favorable legal environments to enable the ethical uses of AI. This review therefore calls for more targeted partnerships between the academia, industry, and government to harness the full potential of AI for sustainable industrial practices worldwide.Journal of Infrastructure Policy and Development994A literature review on the use of AI for cleaner production throughout industries in the consideration of AI’s advantage within the environment, economy, and society calls for more targeted partnerships between the academia, industry, and government to harness the full potential of AI for sustainable industrial practices worldwide.Journal of Infrastructure, Policy and Development["Ikhlef JEBBOR", "Zoubida Benmamoun", "Hanaa Hachmi"]2024-09-24T00:00:00 b55e5010a53fbf614d155e9909e81587e9fe3728Journal of Artificial Intelligence and Consciousness05J. Artif. Intell. Conscious.["Carlos Montemayor"]2024-09-24T00:00:00 5d31b15c3854cb2bf25b5a803ab88a35abeb2045Communication Teacher82Communication Teacher["Jana Duckett", "Nicole M. Westrick"]2024-09-24T00:00:00 4821b82e0d6dd3fddc472245b81fec905f8d5ad2Purpose This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience. Design/methodology/approach Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey. Findings The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages. Practical implications This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers. Originality/value This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions. International Journal of Contemporary Hospitality Management682Examination of the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries indicates that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures.International Journal of Contemporary Hospitality Management["Lu (Monroe) Meng", "Jiuqi Chen", "Mengya Yang", "Yijie Wang"]2024-09-24T00:00:00 ecbce82a4930a2a56af35ea627f9b267c6e7224cRadiography362AI methods used in radiotherapy reduce time and increase prediction accuracy, and work better than other methods in different areas, such as dose prediction, treatment design, and dose delivery.Radiography["M. Zadnorouzi", "S.M.M. Abtahi"]2024-09-24T00:00:00 e3ecc8d575b0a1922003ed2a4a148f49f60adcf2BACKGROUND Guideline questions are typically proposed by experts. OBJECTIVE To assess how large language models (LLMs) can support the development of guideline questions, providing insights on approaches and lessons learned. DESIGN Two approaches for guideline question generation were assessed: 1) identification of questions conveyed by online search queries and 2) direct generation of guideline questions by LLMs. For the former, the researchers retrieved popular queries on allergic rhinitis using Google Trends (GT) and identified those conveying questions using both manual and LLM-based methods. They then manually structured as guideline questions the queries that conveyed relevant questions. For the second approach, they tasked an LLM with proposing guideline questions, assuming the role of either a patient or a clinician. SETTING Allergic Rhinitis and its Impact on Asthma (ARIA) 2024 guidelines. PARTICIPANTS None. MEASUREMENTS Frequency of relevant questions generated. RESULTS The authors retrieved 3975 unique queries using GT. From these, they identified 37 questions, of which 22 had not been previously posed by guideline panel members and 2 were eventually prioritized by the panel. Direct interactions with LLMs resulted in the generation of 22 unique relevant questions (11 not previously suggested by panel members), and 4 were eventually prioritized by the panel. In total, 6 of 39 final questions prioritized for the 2024 ARIA guidelines were not initially thought of by the panel. The researchers provide a set of practical insights on the implementation of their approaches based on the lessons learned. LIMITATION Single case study (ARIA guidelines). CONCLUSION Approaches using LLMs can support the development of guideline questions, complementing traditional methods and potentially augmenting questions prioritized by guideline panels. PRIMARY FUNDING SOURCE Fraunhofer Cluster of Excellence for Immune-Mediated Diseases.Annals of Internal Medicine150Approaches using LLMs can support the development of guideline questions, complementing traditional methods and potentially augmenting questions prioritized by guideline panels.Annals of internal medicine["Bernardo Sousa-Pinto", "R. J. Vieira", "Manuel Marques-Cruz", "A. Bognanni", "S. Gil-Mata", "Slava Jankin", "Joana Amaro", "Liliane Pinheiro", "Marta Mota", "M. Giovannini", "L. de las Vecillas", "A. M. Pereira", "Justyna Lity\u0144ska", "B. Samoli\u0144ski", "J. Bernstein", "M. Dykewicz", "Martin Hofmann-Apitius", "Marc Jacobs", "N. Papadopoulos", "Sian Williams", "T. Zuberbier", "Jo\u00e3o A Fonseca", "R. Cruz-Correia", "J. Bousquet", "Holger J. Sch\u00fcnemann"]2024-09-24T00:00:00 1bad4b45401f8c42e343a7427d630da950830b78Academy of Management Review150Academy of Management Review["J. Killoran", "Andrew Park", "Jan Kietzmann"]2024-09-24T00:00:00 eeda80f3aee180c557c6cafdc639ecb36e97b10bThe journal of the International Association of Medical Science Educators : JIAMSE50Medical Science Educator["John C. Lin", "Cameron A. Sabet", "Christopher Chang", "Ingrid U. Scott"]2024-09-24T00:00:00 f54564323dc0827baf95c6c569bfc12612ef7b84Langenbeck's archives of surgery (Print)200The SVM algorithm demonstrated promising potential for accurately diagnosing acute cholecystitis and further refinement and validation are needed to enhance its reliability in clinical practice.Langenbeck's archives of surgery["Hossein Saboorifar", "Mohammad Rahimi", "Paria Babaahmadi", "Asal Farokhzadeh", "Morteza Behjat", "Aidin Tarokhian"]2024-09-24T00:00:00 34279dc32fa58ae14f4df0cf8ef6b4e086d165f500["Mohammad Yazdi"]2024-09-24T00:00:00 ce775e06807ac7f57b7c1a7c84de9dcbd47048ffJournal of Artificial Intelligence and Consciousness00J. Artif. Intell. Conscious.["John P. Sullins"]2024-09-24T00:00:00 1424470958314d859dd86d7cb6828cee02552d4cProceedings of the International Conference on Industrial Engineering and Operations Management00Proceedings of the International Conference on Industrial Engineering and Operations Management["Nirali Rathod", "Abhishek Parikh"]2024-09-24T00:00:00 99ddde8c0eee9b6ab2dcb4220b3b1e5dae0ce609Determining liability if a patient suffers an injury due to AI or robotic application use is complex. The study used comparative legal analysis and examined the current legal frameworks and whether applying current principles of medical liability is adequate to determine liability arising from AI and robotics-related medical malpractice. The study identifies the gaps in current legislation and proposes applying new legal doctrines such as vicarious liability, custodian liability, corporate liability, strict liability, and finally, applying the benefit and burden rule. AI and robotics will revolutionize medical care. However, new legislation is necessary to mitigate risk and ensure patient safety.2024 Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)390The study identifies the gaps in current legislation and proposes applying new legal doctrines such as vicarious liability, custodian liability, corporate liability, strict liability, and finally, applying the benefit and burden rule.2024 Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)["Ayesha Almemari", "Ziad Al-Enizi", "Ramzi Madi"]2024-09-24T00:00:00 785f55d826133768d47921d19a10c16bc22ca1edCurrent Psychology150Current Psychology["Dzifa Atadika", "Akua Neene Anim", "Moses Segbenya"]2024-09-24T00:00:00 ff9fe8ef9ec970cfb1cbb282e29565d014ac4ad4International Conference on Big Data and Education60{"pages": "10-16"}["Yan Yan", "Congsheng Li", "Shibin Wei", "Hanqing Rao"]2024-09-24T00:00:00 e1d871be5b74aa22ccd5c3b6f746d2b08a93cf67Radiologie20Radiologie["M. Reiser", "U. Attenberger"]2024-09-24T00:00:00 986d7d7a7ad2b2ad6e9d3d7245d2d65827974125The Educational Review USA00The Educational Review, USA["Juan Hua", "Rajendran Nagappan"]2024-09-24T00:00:00 e1e3eca270e73d549d6f494993c8cff6fba2bddaDrug discovery as an important scientific area that serves human health, requires continuous advancement for improved quality of life and survival rates. However, drug discovery is a long and expensive process. The studies aimed at dealing with these problems have enabled to combination of AI with drug development stages. For every step of the R&D process, AI plays a vital role in facilitating and accelerating the work. Firstly, AI methods (deep learning and convolutional neural networks) help predict the 3D structure of an unresolved protein making it easier for the rational design of compounds to target a specific protein among other potential outcomes. After estimation of the protein structure of interest, it is also possible to determine the protein-ligand interactions by utilizing AI technologies like random forest. The other stage, namely finding the hit compounds is also possible through AI-assisted QSAR models such as deep neural networks. Besides, there are many AI methods (k-nearest neighbor and support vector machines) for ADMET prediction to optimize lead compounds. Finally, AI techniques also aid in choosing the most suitable synthesis plan. In the light of the latest advances, AI has become the focus of the pharmaceutical industry. However, despite the potential benefits of AI in drug discovery, several challenges must be considered including the availability of suitable data and bioethical issues. This article provides a comprehensive review of the benefits and applications of AI in various stages of drug discovery. In addition, the open-source act model and bioethical issues are also discussed.Fabad journal of pharmaceutical sciences930A comprehensive review of the benefits and applications of AI in various stages of drug discovery, including the open-source act model and bioethical issues are discussed.Fabad Journal of Pharmaceutical Sciences["Ay\u00e7a Dedeoglu Erdogan", "Arman\u00e7 Mat", "Enise Ece G\u00fcrdal", "Meric Koksal Akkoc"]2024-09-24T00:00:00 79b181e11d39a099f56fe19aa0eac817be280e05Background: The goal of this work is to perform a thematic analysis of the advantages and disadvantages towards employing ChatGPT in different subfields of Infectious Disease (ID) and public health. Currently, the research adopts a qualitative research approach. The interview guide was developed using a structured literature review having semi-structured interview questions. Twenty-five epidemiologists participated this study, which were selected purposively through convenience sampling technique. The interviews conducted were audio taped, transcribed and verbatim and the data was analyzed using thematic analysis to establish themes in the data. Hence, analysing the positives of using the AI ChatGPT: Eight themes and 28 were codes deduced at the end of the first part of the thematic analysis. The risks of using AI ChatGPT in this field were identified and they were grouped into ten themes and thirty codes. Some of the advantages of AI ChatGPT are well seen in these areas taking into consideration specific health recommendations, the contribution towards clinical decision-making processes, and in the process of vigilance in outbreaks. However, the potential risks include the creation of fake news, privacy violation, and dependency of the AI system are some of the concerns that should be critically followed in the implementation. Future studies should target the improvement of the data acquisition of ChatGPT in real-time, the extension of its medical content in novel diseases and the use of context-aware suggestions.2024 Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)300A thematic analysis of the advantages and disadvantages towards employing ChatGPT in different subfields of Infectious Disease and public health takes into consideration specific health recommendations, the contribution towards clinical decision-making processes, and in the process of vigilance in outbreaks.2024 Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)["A. Jairoun", "S. S. Al-Hemyari", "M. Shahwan", "Sa'ed H. Zyoud", "Eman Abu-Gharbieh", "Fahad S. Alshehri", "Nasser M Alorfi"]2024-09-24T00:00:00 3f8ae9d9136bf13ab89b9c5350dd219b657a6117Despite the overwhelming use of the internet as an educational or informational resource, research has found that learners are woefully in need of guidance on how to use this resource effectively and subsequently identify reliable sources of information from unreliable. This study aims to determine the practices of students at San Vicente National High School in evaluating the reliability of online sources. This study employed the quantitative research design and utilized a questionnaire developed by the researchers of this study as a tool for gathering the data. The respondents of the study are 81 out of 324 digitally literate high school students enrolled at San Vicente National High School, San Vicente, Loreto, Agusan del Sur, of the academic year 2023-2024. The researchers used a stratified random sampling technique to obtain the sample size of the respondents. The findings revealed that the strategies employed by the students in evaluating the reliability of online sources are fairly practiced. It was recommended that students of San Vicente National High School be trained to critically evaluate the reliability of online sources by focusing on key factors such as consistency, authority, currency, objectivity, references, and language. Teachers should support this by providing curated lists of trustworthy sources and teaching simple practices for cross-checking information. Parental involvement is also essential in helping students develop better assessment skills, while school administrators are encouraged to organize digital literacy programs in collaboration with policymakers. Additionally, peer reviews should be encouraged to foster collaborative learning, and AI integration into the curriculum is suggested to prepare students for a future driven by Artificial Intelligence.Randwick International of Education and Linguistics Science Journal230The findings revealed that the strategies employed by the students in evaluating the reliability of online sources are fairly practiced and it was recommended that students of San Vicente National High School be trained to critically evaluate the reliability of online sources by focusing on key factors such as consistency, authority, currency, objectivity, references, and language.Randwick International of Education and Linguistics Science Journal["Leo N. Del Valle", "Clivie S. Abuzo", "Jason Jay M Siga", "Jelord Lufranco"]2024-09-24T00:00:00 23870666bbf7533462305a7240625a5277397a31The aim of this study is to analyze the use of AI in mental health care using case-based reasoning methods, which generate treatment solution recommendations from previously occurring cases.Proceeding of International Conference on Science, Health, And Technology00Proceeding of International Conference on Science, Health, And Technology["Frestiany Regina Putri", "Wahyu Ratri Sukmaningsih", "Maulina Mukaromah"]2024-09-24T00:00:00 4c5fbde0b0ffd497aa6a0ac682494ea31ab6d2cdHuman intelligence, the most evident and accessible form of source of reasoning, hosted by biological hardware, has evolved and been refined over thousands of years, positioning itself today to create new artificial forms and preparing to self--design their evolutionary path forward. Beginning with the advent of foundation models, the rate at which human and artificial intelligence interact with each other has exceeded any anticipated quantitative figures. The close engagement led both bits of intelligence to be impacted in various ways, which naturally resulted in complex confluences that warrant close scrutiny. In the sequel, using a novel taxonomy, we shall explore the interplay between human and machine intelligence, focusing on the crucial role humans play in developing ethical, responsible, and robust intelligent systems. We briefly delve into various aspects of implementation inspired by the mechanisms underlying neuroscience and human cognition. In addition, we propose future perspectives, capitalizing on the advantages of symbiotic designs to suggest a human-centered direction for next-generation developments, focusing on the augmentation role of AI. We finalize this evolving document with some thoughts and open questions yet to be addressed by the broader community.arXiv.org1761This document briefly delve into various aspects of implementation inspired by the mechanisms underlying neuroscience and human cognition, and proposes future perspectives, capitalizing on the advantages of symbiotic designs to suggest a human-centered direction for next-generation developments, focusing on the augmentation role of AI.ArXiv["Suayb S. Arslan"]2024-09-24T00:00:00 2952db8a152cf80b3ac24bf69dcc22bb9518bb01Artificial General Intelligence01{"volume": "14951"}[]2024-09-24T00:00:00 1e52d16c81409b26d7361a886abfe7c52eca40c9Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.Health systems and reform1024The Causal Foundry Artificial Intelligence and Reinforcement Learning platform is presented, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring, and can significantly improve the impact of digital tools on health system outcomes.Health systems and reform["\u00c1. Peri\u00e1\u00f1ez", "Ana Fern\u00e1ndez del R\u00edo", "Ivan Nazarov", "Enric Jan'e", "Moiz Hassan", "Aditya Rastogi", "Dexian Tang"]2024-09-24T00:00:00 98f05e0af6aa5890a700bbb82b7895b8dc8e3eecCardiovascular diseases (CVDs) continue to be the leading cause of death in the world, taking millions of lives every year. Early detection and treatment of these diseases are critical in preventing deaths and illnesses. Currently, traditional diagnostic methods like routine clinical examinations and other standard risk assessment tools fail to detect CVDs in early on set stages, posing numerous limitations for preventable interventions. This work explores incorporating artificial intelligence (AI) to catalyze early CVD diagnosis using AI models for patient data including electrocardiogram (ECG), wearable-generated metrics, and medical history. The research showcased the ability of AI to predict with high accuracy when CVD would start using machine learning methods such as Random Forest, Support Vector Machines (SVM) and Neural Networks. The powerful AI models were carefully constructed by training and testing them on an extensive dataset that contained measurements of diverse patient characteristics including many other readouts related to cardiovascular risk. Compared to traditional methods, the models outperformed, and Neural Networks had an accuracy of 92% in identifying high-risk patients. A hallmark of this work is that AI identifies subtle patterns and relationships within patient data ones that conventional approaches might not find. These AI-based predictive models might soon become part of the routine clinical evaluation for cardiovascular disease, providing a personalized and early health intervention to empower your cardiovascular care. Timely identification of these high-risk patients could allow for targeted interventions, leading to improved health and reductions in healthcare spending. Despite the promising potential that AI holds in healthcare, it also has brought up issues of data privacy, ethical use and the need for extensive clinical validation. This paper suggests a predictive strategy could be implemented using AI algorithms to lead early detection and management of CVD, which opens an opportunity for individualized care accompanied by big data era. Future studies should focus on improving such models with more diverse and larger datasets as well as overcoming logistical difficulties encountered in integrating AI into clinical workflows.International Journal For Multidisciplinary Research102A predictive strategy could be implemented using AI algorithms to lead early detection and management of CVD, which opens an opportunity for individualized care accompanied by big data era.International Journal For Multidisciplinary Research["Ali Husnain", "Ayesha Saeed", "Ahad Hussain", "Ahsan Ahmad", "Mahnoor N. Gondal"]2024-09-24T00:00:00 884522312cb1a04cb24ceabd647ba5e933b4e4f5Artificial intelligence (AI) and machine learning (ML) systems are progressively used in different areas, going with basic choices that influence individuals' lives. In any case, these frameworks can sustain and try and fuel existing social predispositions, prompting uncalled for results. This paper looks at the wellsprings of predisposition in simulated intelligence models, assesses current methods for distinguishing and relieving inclination, and proposes an extensive structure for creating more pleasant simulated intelligence frameworks. By coordinating specialized, moral, and functional points of view, this exploration plans to add to a more evenhanded utilization of computer-based intelligence across various areas, guaranteeing that artificial intelligence driven choices are fair, straightforward, and socially dependable.International Journal of Innovative Science and Research Technology551The wellsprings of predisposition in simulated intelligence models are looked at, current methods for distinguishing and relieving inclination are assessed, and an extensive structure for creating more pleasant simulated intelligence frameworks are proposed.International Journal of Innovative Science and Research Technology (IJISRT)["Shaik Mohammad Jani Basha", "Aditya Kulkarni", "Subhangi Choudhary", "Manognya Lokesh Reddy"]2024-09-24T00:00:00 d8b3b1f8a595187405de1ff56ad50639f82427dfAs artificial intelligence (AI) systems become increasingly deployed across the world, they are also increasingly implicated in AI incidents - harm events to individuals and society. As a result, industry, civil society, and governments worldwide are developing best practices and regulations for monitoring and analyzing AI incidents. The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents for different operational and research-oriented goals. This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We find that certain patterns of AI incidents present structural ambiguities that challenge incident databasing and explore how epistemic uncertainty in AI incident reporting is unavoidable. We therefore report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems. With these findings, we discuss how to develop future AI incident reporting practices.arXiv.org301This study reviews the AIID's dataset of 750+ AI incidents and two independent taxonomies applied to these incidents to identify common challenges to indexing and analyzing AI incidents and explores how epistemic uncertainty in AI incident reporting is unavoidable.ArXiv["Kevin Paeth", "Daniel Atherton", "Nikiforos Pittaras", "Heather Frase", "Sean McGregor"]2024-09-24T00:00:00 f1c4faa84b018e130b5fb86471ff9c0085675a70Automated analysis and specification of software requirements expressed in natural language is a challenge addressed by the research community and is becoming a reality thanks to the advances in Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques. While the research community focuses mainly on generic software requirements or specialized solutions for security requirements, we find a gap in the automation of analysis and specification for requirements in the cloud computing domain and the automatic mapping of requirements on actual products offered in the cloud service market. In this research work, we propose AI-CRAS an AI-driven cloud service requirement analysis and specification methodology. The proposed method, which leverages state-of-the-art transformer-based large language model, has been implemented and validated in a real case. Experimental results demonstrate that the model performed well in binary and multi-label classification of requirements (achieving recall/F1-score of $0.96 / 0.92$ and $0.86 / 0.76$, respectively) and mapping requirements into actual cloud services.2024 IEEE International Conference on Cloud Engineering (IC2E)00This research work proposes AI-CRAS an AI-driven cloud service requirement analysis and specification methodology, which leverages state-of-the-art transformer-based large language model and has been implemented and validated in a real case.2024 IEEE International Conference on Cloud Engineering (IC2E)["E. Casalicchio", "Alberto Cotumaccio"]2024-09-24T00:00:00 ffe78ef3f2cf56d64d79b9c40927dc12ddc26683Eye221The Nallasamy formula, incorporating AI technology, demonstrated superior accuracy according to the analysis guidelines for PE statistics for non-gaussian datasets recommended by Holladay et al. and the online Eyetemis Analysis Tool.Eye["O. Reitblat", "Noa Heifetz", "Kathryn Durnford", "J. Pettey", "Randall J Olson", "Eitan Livny", "A. Bernhisel", "Irit Bahar", "R. Sella"]2024-09-25T00:00:00 cab226937376383d69724cc777c38350a8ce0afaArtificial Intelligence (AI) is revolutionizing the agricultural sector by enhancing precision farming and spatial analysis, particularly within the diverse agro-ecological zones of USA. This study investigates the transformative potential of AI technologies, such as sensor-based monitoring and satellite imaging analysis, in improving crop yields, soil health, and climate resilience. Despite the opportunities presented by AI in agriculture, such as increased efficiency and sustainability, the African agricultural landscape also faces significant challenges, including infrastructural limitations and socio-economic disparities. The research delves into how AI can address these challenges by promoting economic growth and sustainable development. Moreover, it highlights the critical role of AI in precise crop monitoring, soil health assessment, and decision-making through advanced weather forecasting. By examining the socio-economic effects of AI adoption, this study underscores the necessity of supportive policies and best practices to harness AI's full potential in fostering resilient and sustainable farming methods in USA. Through comprehensive analysis and strategic recommendations, this research contributes to the broader understanding of AI's impact on American agriculture, paving the way for innovative and effective agricultural practices.2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)232This study investigates the transformative potential of AI technologies, such as sensor-based monitoring and satellite imaging analysis, in improving crop yields, soil health, and climate resilience, and highlights the critical role of AI in precise crop monitoring, soil health assessment, and decision-making through advanced weather forecasting.2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)["Jahanara Akter", "Md Kamruzzaman", "Rakibul Hasan", "Rabeya Khatoon", "Syeda Farjana Farabi", "Md Wali Ullah"]2024-09-25T00:00:00 a7d65ebce32cc992ae16baecbb52ce9954371c9cChatGPT uses a so called Large Language Model (LLM) to provide textual output of analyzed data. Those LLMs are one example for Generative Artificial Intelligence (AI), which focuses on creating new content, e.g., text, images, or music, based on learned patterns. Recently, applications in the food industry and agriculture started to apply Generative AI. This mini review provides an overview about applications of Generative AI in the agri-food supply chain and discusses open research challenges, also in combination with digital twins.Frontiers in Food Science and Technology332This mini review provides an overview about applications of Generative AI in the agri-food supply chain and discusses open research challenges, also in combination with digital twins.Frontiers in Food Science and Technology["Christian Krupitzer"]2024-09-25T00:00:00 ef7444c8307ceb4adea8c2a846e66c84abb18cd0This study investigates the factors influencing the usage of artificial intelligence (AI) among Bangladeshi professionals, with a focus on the mediating role of attitude towards technology. The purpose is to enhance understanding of AI adoption using elements from the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM). A quantitative research design was employed, utilizing a questionnaire distributed to 490 professionals, resulting in 190 usable responses. Data were analyzed using SmartPLS to assess the relationships among performance expectancy, effort expectancy, social influence, facilitating conditions, perceived usefulness, perceived ease of use, attitude towards technology, and behavioral intention to use AI. The findings indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived usefulness significantly influence AI adoption. Social influence and perceived ease of use exhibit mediated effects through attitude towards technology. The research is limited by its use of convenience sampling and a single-country focus, which may affect the generalizability of the findings. The study's practical implications include guiding policymakers and industry leaders in designing targeted strategies to promote AI adoption among professionals. Social implications highlight the importance of addressing social factors and perceived ease of use to foster positive attitudes towards AI. This research contributes originality by integrating UTAUT and TAM models in a developing country context, providing nuanced insights into AI adoption among professionals. Future research should explore AI adoption across different developing countries and consider longitudinal and qualitative studies for a deeper understanding of technology adoption dynamics.2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)221The findings indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived usefulness significantly influence AI adoption among Bangladeshi professionals.2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)["Md Mehedi Hasan Emon", "T. Khan", "Md Adnan Rahman", "Saleh Ahmed Jalal Siam"]2024-09-25T00:00:00 afbd354b46e77d250ade8a8ce0620eb6e0e4fad0European Geriatric Medicine51The Copilot chatbot responded to the statements more ageistically than the other platforms, highlighting the importance of addressing any potential biases in AI to ensure that the responses provided are fair and respectful for all potential users.European geriatric medicine["Yanira Aranda Rubio", "J. J. Bazt\u00e1n Cort\u00e9s", "Fernando Canillas Del Rey"]2024-09-25T00:00:00 6158ca62cd53a7b46be2b58f4f74111c0f480d29In the current era of big data, traditional audit methods are proving insufficient, exacerbating the risks businesses face. Artificial intelligence-based audit software (AIAS) is emerging as a promising solution to address these challenges. This study aims to comprehensively examine the current state and future potential of AIAS, analyzing the opportunities and challenges arising from the integration of these technologies into audit processes. To achieve this, we investigate the use cases of AIAS across various audit types and sectors, assessing the benefits they offer and the challenges they present. Additionally, by analyzing the global and local pioneers of AIAS, we identify the factors driving the development of these technologies and uncover future trends. This compilation-based study reveals that AIAS has the potential to make audit processes more efficient, effective, and reliable. However, it also emphasizes the need for careful consideration of issues such as data privacy, algorithmic bias, and ethical implications. This study underscores the importance of collaboration among auditors, businesses, and regulators to fully harness the potential of AIAS while minimizing potential risks. It advocates for investing in a continuous learning and adaptation process. Future research should delve deeper into the impact of AIAS across different sectors and develop recommendations to fully capitalize on the potential of these technologies.Denetişim00This compilation-based study reveals that AIAS has the potential to make audit processes more efficient, effective, and reliable, however, it also emphasizes the need for careful consideration of issues such as data privacy, algorithmic bias, and ethical implications.Denetişim["Salahattin Altunda\u011f"]2024-09-25T00:00:00 b6a7a3a73ea717a2112bad113a405f03395af34dOur study aimed to evaluate the effectiveness of artificial intelligence (AI) image diagnostic systems in the qualitative diagnosis of pulmonary nodules. We analyzed 291 cases from June 2023 to January 2024 at Chongqing University Three Gorges Hospital. All patients in the study underwent low-dose chest computed tomography scans, which identified lung nodules, followed by thoracic surgery for pathological confirmation. We compared the predictive accuracy of AI-based diagnosis with that of physician-based diagnosis in distinguishing between benign and malignant lung nodules. Among the 291 lung nodules examined, 226 were cancerous, and 65 were benign. Receiver operating characteristic (ROC) curves, plotted based on the malignancy probabilities predicted by both methods, revealed that the AI group achieved an area under the ROC curve (AUC) of 0.727, with a sensitivity of 90.27% and a specificity of 58.46%. In comparison, the physician-reading group had an AUC of 0.737, with a sensitivity of 83.19% and a specificity of 66.15%. Our findings demonstrate that the AI diagnostic system effectively calculates malignancy probabilities for lung nodules, highlighting its significant predictive potential. This system can serve as a valuable adjunct tool for clinicians and imaging physicians in the diagnostic process.Tumor Discovery00It is demonstrated that the AI diagnostic system effectively calculates malignancy probabilities for lung nodules, highlighting its significant predictive potential and can serve as a valuable adjunct tool for clinicians and imaging physicians in the diagnostic process.Tumor Discovery["Chunlan Hu", "Dan Yang", "Xiangwen Luo", "Chao Lv", "Juan Li", "Yaya Zhang", "Xinrong Xiong", "Long Zhou"]2024-09-25T00:00:00 4a441feb07a2dd3840554340b69fb1c54d6557dcHealthcare facilities are pivotal in ensuring continuous access to services, particularly for individuals with complex health conditions. Effective asset information management (AIM) in these facilities through artificial intelligence (AI) can enhance operational efficiency. The exploratory bibliometric and systematic review assesses the status and trends of AI applications in AIM within healthcare facilities. The findings reveal a significant gap between research findings and practical implementation, highlighting the need for further integration and real-world usage of AI-powered solutions in healthcare facilities settings. This study identified notable gaps, including the need for research on utilising AI to enhance asset management in healthcare, including maintenance scheduling and procurement processes. Involving stakeholders, such as healthcare professionals, facility managers, and patients, in effective facility management using AI requires further investigation. Research is needed to evaluate the economic benefits and develop robust ethical guidelines for responsible AI implementation. Notably, previous research has given limited attention to AI for healthcare asset information management, with emerging trends focusing more on AI and infrastructure than the “Asset” aspect. Implementing AI-powered solutions tailored to the unique needs of healthcare facilities and evaluating cost-effectiveness will lead to improved asset management practices, enhanced decision-making processes, and, ultimately, more efficient and effective healthcare operations.Infrastructure Asset Management00A significant gap is revealed between research findings and practical implementation of AI-powered solutions in healthcare facilities settings, highlighting the need for further integration and real-world usage of AI-powered solutions in healthcare facilities settings.Infrastructure Asset Management["M. M. Tjebane", "I. Musonda"]2024-09-25T00:00:00 5a532ac49bd077270d459138151571801b1c5e9cThe agricultural sector in Nigeria faces pressing challenges, including food insecurity, land degradation, and climate change impacts. To address these issues, the integration of artificial intelligence (AI) in modern farming practices presents a transformative opportunity for achieving sustainable agricultural development. This paper explores the role of AI technologies in enhancing productivity, optimizing resource use, and minimizing environmental impacts within the Nigerian agricultural landscape. Through a semi-systematic literature review (SLR), the study examines the historical context of agriculture in Nigeria, current AI applications such as precision agriculture, crop monitoring, and pest detection, as well as the associated benefits of increased yield and economic returns for farmers. The semi-SLR methodology incorporates structured search strategies, established inclusion and exclusion criteria, and systematic data extraction techniques to synthesize existing knowledge and identify gaps in the current understanding of AI's impact on sustainable farming in Nigeria. The findings reveal that while AI can significantly contribute to sustainable agricultural transformation, several barriers hinder its widespread adoption, including infrastructural deficiencies, technological illiteracy, and socio-economic constraints. By analyzing these aspects, this research underscores the importance of a structured approach to literature reviews in agricultural research, ultimately aiming to inform policy and encourage the adoption of AI innovations in the sector. The findings indicate that concerted efforts from stakeholders, including policymakers, researchers, and farmers, are essential to overcome existing challenges and fully realize the potential of AI in fostering a resilient agricultural system in Nigeria.Global Sustainability Research440The findings indicate that concerted efforts from stakeholders, including policymakers, researchers, and farmers, are essential to overcome existing challenges and fully realize the potential of AI in fostering a resilient agricultural system in Nigeria.Global Sustainability Research["Aminu Adamu Ahmed", "Rilwanu Sulaiman", "Nasiru Adamu", "Yusuf Musa"]2024-09-25T00:00:00 ebb4c303e040810161157c8505fa6c16d11f7154Significant global health issues are posed by eye illnesses, especially in poor nations with inadequate financial and technological resources. Recent advances in pattern recognition for medical diagnosis have increased accuracy, but consistent health data collection and Artificial Intelligence integration are critical for Machine Learning model reliability. Explainable AI has made expert-level evaluation critical in sensitive areas. This study uses a hybrid approach to provide accurate categorization of eye diseases by utilizing deep learning assisted by Explainable Artificial Intelligence. The key contributions include using a Sequential Convolutional Neural Network to categorize eye disease images, significantly boosting accuracy with pre-trained CNN models, and employing the Local Interpretable Model-Agnostic Explanations method for clear result interpretations. The study compares two experimental models, a black-box CNN model and a glass-box model using XAI algorithm LIME. While pre-trained models like Inception V3 and Xception showed training accuracies of 81.5% and 85.5% and validation accuracies of 78.8% and 73.8%, respectively, the sequence model obtained training accuracy of 80.48% and validation accuracy of 76.34%. When combined with LIME, these pre-trained models showed increased interpretability providing thorough insights into disease prediction. Future research can be done on improving the effectiveness of AI-driven ophthalmic decision-making by comparing pre-trained models and XAI algorithms and experimenting with different CNN architectures.2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)310A hybrid approach is used to provide accurate categorization of eye diseases by utilizing deep learning assisted by Explainable Artificial Intelligence, significantly boosting accuracy with pre-trained CNN models, and employing the Local Interpretable Model-Agnostic Explanations method for clear result interpretations.2024 IEEE International Conference on Computing, Applications and Systems (COMPAS)["Elmeeh Hasan Shipra", "Md. Sazzadur Rahman"]2024-09-25T00:00:00 fd9e49176250388ad0d91b61ce7055755ac760a9This position paper explores the integration of Artificial Intelligence (AI) into force-controlled robotic tasks within the scope of advanced manufacturing, a cornerstone of Industry 4.0. AI's role in enhancing robotic manipulators - key drivers in the Fourth Industrial Revolution - is rapidly leading to significant innovations in smart manufacturing. The objective of this article is to frame these innovations in practical force-controlled applications - e.g. deburring, polishing, and assembly tasks like peg-in-hole (PiH) - highlighting their necessity for maintaining high-quality production standards. By reporting on recent AI-based methodologies, this article contrasts them and identifies current challenges to be addressed in future research. The analysis concludes with a perspective on future research directions, emphasizing the need for common performance metrics to validate AI techniques, integration of various enhancements for performance optimization, and the importance of validating them in relevant scenarios. These future directions aim to provide consistency with already adopted approaches, so as to be compatible with manufacturing standards, increasing the relevance of AI-driven methods in both academic and industrial contexts.International Conference on Informatics in Control, Automation and Robotics400This position paper explores the integration of Artificial Intelligence into force-controlled robotic tasks within the scope of advanced manufacturing, a cornerstone of Industry 4.0, and underscores the need for common performance metrics to validate AI techniques, integration of various enhancements for performance optimization, and the importance of validating them in relevant scenarios.{"pages": "392-399"}["Vincenzo Petrone", "Enrico Ferrentino", "Pasquale Chiacchio"]2024-09-25T00:00:00 6afe0986eda0adb1143187b6a984356f46b5260dContinuous innovation has been built since 2020 with the development of VCDLN which is intended for developers and users of digital sources widely in Indonesia. This innovation was continued in 2024, with the support of AR and VR Technology based on Artificial Intelligence (AI) developed at the UPI Cibiru Campus. With the support of AR and VR experts, this innovation research product is called DCIRV (Digital Central Innovation for Robotics). This Innovation Research was carried out with a Mix-Method approach to meet the needs of prototype design and educational industry products as well as expert and user testing from the Nusantara region. To measure the quality of innovation products, it has been measured by experts from Bordeaux University France, Kitakyushu University, and McGill University. The findings of the prototype and the DCIRV research findings model, it show that starting from the needs analysis stage, development stage, validation stage, evaluation, and dissemination, DCIRV research products can be recommended as a solution for expanding access, services, and adding digital learning communities throughout the archipelago and even internationally.Evolutionary Studies in Imaginative Culture400The findings show that starting from the needs analysis stage, development stage, validation stage, evaluation, and dissemination, DCIRV research products can be recommended as a solution for expanding access, services, and adding digital learning communities throughout the archipelago and even internationally.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["D. Darmawan", "Etiene Damome", "Destiny Tch \u00e9 houali", "Christine Pascal", "Eric Olmedo", "D. Wahyudin", "Jenuri", "Wirmanto Suteddy", "A. C. Padmasari", "Linda Setiawati", "Wirmanto Jenuri", "Suteddy Ayung Candra", "Linda Padmasari", "Setiawati"]2024-09-25T00:00:00 6a84b82adf73499dda2ec358a63b81f9a39e5ce0This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques for understanding the impact of variables behind the prediction. Three ML models, random forest (RF), gradient boosting machine (GBM), and gradient boosting tree (GBT), were evaluated for their performance using statistical indicators. Input variable combinations were selected through different feature selection (FS) methods. XAI techniques were employed to enhance the interpretability and transparency of ML models. The results suggest that prediction accuracy depends on the choice of model and the number of variables. XAI techniques were found to be effective in interpreting the decisions made by each ML model. This study provides an example of using ML models in sludge production prediction and interpreting models applying XAI to understand the factors influencing it. Understandable interpretation of ML model prediction can facilitate targeted interventions for process optimization and improve the efficiency and sustainability of wastewater treatment processes. PRACTITIONER POINTS: Explainable artificial intelligence can play a crucial role in promoting trust between machine learning models and their real-world applications. Widely practiced machine learning models were used to predict sludge production of a United States wastewater treatment plant. Feature selection methods can reduce the required number of input variables without compromising model accuracy. Explainable artificial intelligence techniques can explain driving variables behind machine learning prediction.Water environment research350This study investigates the use of machine learning models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence techniques for understanding the impact of variables behind the prediction and suggests that prediction accuracy depends on the choice of model and the number of variables.Water environment research : a research publication of the Water Environment Federation["F. Nasir", "Jin Li"]2024-09-25T00:00:00 ded3b05c3d84c0e97092d02ef6ba062ec085c1e5Advancements and usefulness of artificial intelligence (AI) have been transforming the ways in which companies reach decision making and ethical adherence in competitive market. This study has focused on secondary data collection and thematic analysis for developing understanding about the mitigation of bias and transparency in the use of AI technologies. This study has been responsible for stating about different methods that has been used for gaining proper outcomes. Apart from that, preparation of thematic analysis has been effective to reach the research objectives effectively. Finally, this study has also established strategic recommendations through which organisation scan imply proper ethical principles for better utilisation of AI technology.International Journal of Innovative Research in Science Engineering and Technology00This study has focused on secondary data collection and thematic analysis for developing understanding about the mitigation of bias and transparency in the use of AI technologies.International Journal of Innovative Research in Science,Engineering and Technology["Archana Todupunuri"]2024-09-25T00:00:00 94550b718f7ad2a49cbc54bb6bf583c856700af3Utilizing artificial intelligence (AI) technologies in EFL teaching and learning has brought about unimaginable opportunities to enhance learners’ fluency and proficiency in the target language as it is evident that employing AI tools helps learners develop their language skills, enhance engagement and motivation, ease foreign language anxiety, and ultimately acquire the target language. This meta-analysis aims to find out to what extent AI has impacted EFL teaching and learning by providing a systematic review of research papers published from 2020 to 2023. The review concentrated on four areas of EFL contexts: AI in EFL contexts, learners’ and teachers’ perceptions of AI tools, EFL learners’ motivations and engagements towards AI tools, and the integration of AI tools in language skills. The automated model developed by Guan et al.’s (2020) to collect published work from numerous databases such as Scopus, Web of Science, ERIC, Semantic Scholar, and Google Scholar was adopted. Findings of this review exhibit that employing AI technologies in the EFL settings has significantly benefitted the process of teaching and learning resulting in the mastery of the productive skills on the part of learners. On the other hand, the review shows there is a current lack of research related to receptive skills. As far as the learners’ and teachers’ perspectives regarding the integration of AI tools are concerned, the review voiced favourable perceptions concerning utilizing AI tools; however, the pedagogical implications of utilizing AI tools from the teachers’ point of view have been insufficiently addressed by research conducted so far. Eventually, the current review outlined significant implications that provide a strong foundation for future research.JURNAL ARBITRER00It is shown that employing AI technologies in the EFL settings has significantly benefitted the process of teaching and learning resulting in the mastery of the productive skills on the part of learners and there is a current lack of research related to receptive skills.JURNAL ARBITRER["Mohammed Al-Raimi", "B. Mudhsh", "Muna Hussain Muqaibal", "Yasir Al-Yafaei"]2024-09-25T00:00:00 39517cc209f0cab951620068ff6f16e5c6e0f3b6Introduction. Currently, the digital transformation of healthcare is one of the priority areas of industry development. In Russia, there is a high readiness and interest of both managers and doctors in the practical application of various digital products, including decision support systems using AI technologies. Materials and methods. The authors analyzed the scientific research and practical developments available in Russia in order to systematize and identify the most popular scenarios for the use of artificial intelligence systems. Results. The most promising areas for the development of artificial intelligence systems for healthcare in Russia are: Improved diagnostics. AI systems can analyze medical images, laboratory test data and clinical histories, identify patholo- gies and offer accurate diagnoses, which helps doctors make more informed decisions. Personalized treatment. The use of AI allows taking into account the individual characteristics of patients and offering optimal treatment regimens based on the analysis of numerous factors, such as genetic data, medical history and response to therapy. Disease prediction. AI can help determine the likelihood of developing certain diseases in a particular patient based on their individual risk factors, allowing for preventive measures or early treatment. Automation and optimization of processes. AI can reduce the workload of medical personnel, automate routine tasks, improve medical data management, and ensure more efficient resource allocation. Conclusions. The proposed scenarios and areas of AI application are most likely to impact the achievement of targets and objectives set out in the national project "Healthcare" – which is a priority for the implementation of AI.Russian Journal of Telemedicine and E-Health00The proposed scenarios and areas of AI application are most likely to impact the achievement of targets and objectives set out in the national project "Healthcare" – which is a priority for the implementation of AI.Russian Journal of Telemedicine and E-Health["A.M. Khanov", "A. V. Gusev", "A.G. Tyurganov"]2024-09-25T00:00:00 d26648a47bc7a58bb4b269736ad95b7735890290The article is devoted to the communicative characteristics of artificial intelligence - a man-made programmable machine intellect designed to perform actions traditionally considered intellectual. The article clarifies the attributes of intelligence in the everyday and scientific understanding: common everyday situations require prudence and resourcefulness, in psychological terms learning ability and problem-solving ability are emphasized, in philosophical understanding the levels of intelligence - from the ability to think abstractly to the ability to operate with high-order ideas – turn to be important. The material used is data obtained from question-and-answer dialogs with artificial intelligence on the Internet. Intentional functions of communication with artificial intelligence for humans consist primarily in the search and clarification of various information, programmed responses of AI are reduced to reporting the necessary information, while the robot avoids conflicts by responding to provocative statements. The following behavioral norms can be traced in the reactions of the voice assistant Alice to different types of initiative statements: demonstration of polite and kind attitude to the communication partner, observance of etiquette behavior models, reaction in the question form in case of insufficient information, imitation of natural emotional reactions. In detailed answers to questions concerning emotional reactions to certain stimuli, artificial intelligence expresses the opinions of specialists who develop its design. Communication with a robot should be built on the recognition of the machine as a useful tool, but not as a human being. Verbal reactions of artificial intelligence are quite in line with the standards of institutional communication, but in relation to situations that allow for the possibility of ambiguous interpretation in the conditions of artistic perception of reality, such reactions cannot yet be recognized as satisfactory.Current Issues in Philology and Pedagogical Linguistics00The article clarifies the attributes of intelligence in the everyday and scientific understanding: common everyday situations require prudence and resourcefulness, in psychological terms learning ability and problem-solving ability are emphasized, in philosophical understanding the levels of intelligence - from the ability to think abstractly to the ability to operate with high-order ideas – turn to be important.Current Issues in Philology and Pedagogical Linguistics["V.I. Karasik"]2024-09-25T00:00:00 fbbb38647b7e3d2d00b79002347c3bdf8cfa809eArtificial intelligence and machine learning are increasingly used to offload decision making from people. In the past, one of the rationales for this replacement was that machines, unlike people, can be fair and unbiased. Evidence suggests otherwise. We begin by entertaining the ideas that algorithms can replace people and that algorithms cannot be biased. Taken as axioms, these statements quickly lead to absurdity. Spurred on by this result, we investigate the slogans more closely and identify equivocation surrounding the word 'bias.' We diagnose three forms of outrage-intellectual, moral, and political-that are at play when people react emotionally to algorithmic bias. Then we suggest three practical approaches to addressing bias that the AI community could take, which include clarifying the language around bias, developing new auditing methods for intelligent systems, and building certain capabilities into these systems. We conclude by offering a moral regarding the conversations about algorithmic bias that may transfer to other areas of artificial intelligence.arXiv.org00A diagnosis of three forms of outrage-intellectual, moral, and political-that are at play when people react emotionally to algorithmic bias, and three practical approaches to addressing bias that the AI community could take, which include clarifying the language around bias.ArXiv["Will Bridewell", "Paul Bello", "S. Bringsjord"]2024-09-25T00:00:00 d4d04baa564b327f53fc8626ffb3e03a85247754The relevance of the topic is due to the development of artificial intelligence (AI), one of the most important technological trends of our time, which has a significant impact on human rights and various aspects of human life. AI is developing extremely fast. This creates new challenges for human rights that need to be addressed immediately. It is important to understand how technology can change our society and what measures should be taken to protect human rights. The article explores the impact of AI development on human rights, emphasizing both positive and negative aspects of technological progress. The author analyzes how AI can threaten the right to privacy through the collecting and processing of large amounts of data, which can lead to a loss of control over personal information. The author considers the impact of AI on access to information, where algorithms can create information bubbles, limiting the variety of information received. Attention is drawn to possible restrictions on freedom of expression through algorithmic censorship of content, which may lead to restrictions on freedom of speech. The use of AI in the judicial system may affect the fairness of court decisions, especially if the algorithms have biases or errors. The author traces the impact of automation on the labor market and the risks of job losses and the need to find a balance between technological progress and employment. In the healthcare sector, AI can both improve diagnosis and treatment and create new challenges due to possible algorithmic errors and unequal access to medical technologies. In education, the use of AI opens up new opportunities but also creates barriers to access to knowledge, especially for vulnerable groups. The author emphasizes the importance of taking human rights into account in the context of climate risks. The author raises the issues of discrimination and restriction of freedom of movement due to biased algorithms. The author emphasizes the need to develop regulatory mechanisms to protect human rights in the context of the rapid development of AI, ensuring a balance between innovation and human rights protection, and provides a number of clear recommendations.Visnik Nacional'nogo universitetu «Lvivska politehnika» Seria Uridicni nauki10The author analyzes how AI can threaten the right to privacy through the collecting and processing of large amounts of data, which can lead to a loss of control over personal information and considers the impact of AI on access to information.Visnik Nacional’nogo universitetu «Lvivska politehnika». Seria: Uridicni nauki["S. Kravchuk"]2024-09-25T00:00:00 03e07156f34727a4d5b69e76202cff23ae0730c1Artificial intelligence technology is an important element of stimulate economic vitality, but the artificial intelligence technology resources into the table may face the risk of confirmation and measurement standards to grasp, this poses the challenge to accounting, also challenge the audit, audit how innovation to cope with the development of artificial intelligence technology confirmation and measurement difficulties, practical and theoretical circles in some discussion. Based on the perspectives of audit objectives, audit importance, audit risk and audit procedure.Economics &amp; Management Information80Audit of artificial intelligence technology resources into the table may face the risk of confirmation and measurement standards to grasp, this poses the challenge to accounting.Economics &amp; Management Information["Yang Han", "Yiwei Deng", "Xiujuan Ran"]2024-09-25T00:00:00 53b8f0da15af52f16f26f3ae1dcc4d49f5ce912dThis study examines the evolving influence of artificial intelligence (AI) on customer engagement dynamics, aiming to provide insights into how AI technologies can enhance engagement, loyalty, and trust among consumers. A quantitative research approach was employed with a causal research design. Data was collected from 140 individuals in the Kathmandu Valley, primarily undergraduate students aged 20-29 years, through a questionnaire utilizing a 7-point Likert scale. Correlation analyses were conducted using SPSS software version 23 to analyze the data. The study's findings highlight the transformative potential of AI in shaping customer engagement dynamics. By leveraging AI technologies, businesses can significantly enhance customer engagement and foster stronger relationships, ultimately driving competitive advantage and sustainable growth in an increasingly digital marketplace. The implications of this research suggest that understanding AI's capabilities can empower businesses to develop more effective customer engagement strategies. This presents opportunities for further investigation into the factors influencing the adoption and effectiveness of AI in customer engagement. Overall, this study contributes to the understanding of AI's role in customer engagement within the context of Nepal. Future research can provide valuable insights for businesses operating in emerging markets, informing the development of AI solutions tailored to the specific needs and preferences of local customers. By exploring these dynamics, organizations can better navigate the challenges of customer engagement in a rapidly changing technological landscape.Journal of Business and Economics210The implications of this research suggest that understanding AI's capabilities can empower businesses to develop more effective customer engagement strategies, and highlight the transformative potential of AI in shaping customer engagement dynamics.New Perspective: Journal of Business and Economics["Saramsha Niraula", "Manii Bhadra Lama", "Ketan Goel", "Alina Shrestha"]2024-09-25T00:00:00 f204b17cb0bfa7da5ea0c855d36395ba5df07f87The rule of law of artificial intelligence industry depends on the coordination and cooperation of soft law guarantee path and hard law guarantee path. Compared with Western countries, China's artificial intelligence industry developed late, and its legislation mainly has some problems, such as insufficient power of soft law generation mechanism, fuzzy norms of hard law and fuzzy judgment standards of dispute settlement mechanism. This paper believes that China can learn from the legislative experience of European and American countries to implement graded assessment of artificial intelligence risks, and define the judgment criteria; Secondly, our country can draw on the soft law documents such as resolutions of international organizations as the basis of soft law generation; In terms of dispute settlement mechanism, China can try to use the "Maple Bridge experience" to introduce artificial intelligence dispute cases.International Journal of Social Sciences and Public Administration30China can learn from the legislative experience of European and American countries to implement graded assessment of artificial intelligence risks, and define the judgment criteria.International Journal of Social Sciences and Public Administration["Zezhong Zhang"]2024-09-25T00:00:00 4d8a5c8b26ea316e3c50f558860ba79f8ba4e8dbThis article proposes to establish a correlation between artificial intelligence and intellectual property through technological patent prospecting. The study focuses on searching for patents related to artificial intelligence employing machine learning technology within the domain of computer technology. These patents have the potential to be used in the production of intangible goods, particularly those protected by copyright. We specifically chose to prospect patents associated with the creation of animations due to the familiarity of this sector with computational tools. Through case studies and official data, the work demonstrates the economic potential of utilizing artificial intelligence in the production of typical intellectual property goods and lays the groundwork for the development of a national artificial intelligence policy that aligns with this new technical reality, as well as its economic and social impacts.Contribuciones a las ciencias sociales00The work demonstrates the economic potential of utilizing artificial intelligence in the production of typical intellectual property goods and lays the groundwork for the development of a national artificial intelligence policy that aligns with this new technical reality, as well as its economic and social impacts.CONTRIBUCIONES A LAS CIENCIAS SOCIALES["Gustavo da Cruz", "Marcelo Ossamu Honda", "Almeciano Jos\u00e9 Maia Junior", "Solange Rodrigues dos Santos Corr\u00eaa"]2024-09-25T00:00:00 1020ec0f64b17030aff79ac12aa34dc64bc19c56In recent years, artificial intelligence has been employed to empower synthetic biology, demonstrating great potential in the simulation and prediction of protein structures as well as the design and optimization of regulatory elements and metabolic networks. Integrating artificial intelligence into the teaching of Synthetic Biology is in line with the development trends of synthetic biology and can promote the cultivation of interdisciplinary high-level talents and collaborative innovation. This paper expounds the idea of integrating artificial intelligence into the teaching of Synthetic Biology from establishing interdisciplinary course contents and teaching methods, simultaneously considering the fundamentals and application of artificial intelligence in synthetic biology, cultivating independent learning and innovative practice abilities, and enhancing the ethics education related to artificial intelligence. Furthermore, a system integrating artificial intelligence with the teaching contents of Synthetic Biology is designed, which focuses on supplementing fundamentals of artificial intelligence and incorporating artificial intelligence into the classroom and experimental teaching contents of Synthetic Biology. Moreover, with the course of Synthetic Biology in Jiangnan University as an example, this paper presents the pathway of integrating artificial intelligence into the teaching of this course under the background of discipline crossing. Finally, the teaching effects are expected.Sheng wu gong cheng xue bao = Chinese journal of biotechnology00A system integrating artificial intelligence with the teaching contents of Synthetic Biology is designed, which focuses on supplementing fundamentals of artificial intelligence and incorporating artificial intelligence into the classroom and experimental teaching contents of Synthetic Biology.Sheng wu gong cheng xue bao = Chinese journal of biotechnology["Kai Wang", "Xiaoli Luan", "Jingwen Zhou"]2024-09-25T00:00:00 40691fad0564b322262f0ef850faf0addda20932With the rapid development of artificial intelligence technology, machine translation has made remarkable progress. However, to achieve high-quality translation in a true sense, machines must be able to understand and handle the complexity of human language. As a discipline for studying the cognitive process of human beings, cognitive psychology provides an important theoretical basis and practical guidance for artificial intelligence translation. This paper aims to explore the role of cognitive psychology in the development of AI translation, analyze how it can help improve translation algorithms, improve the quality of translation, and explore future directions.Region - Educational Research and Reviews10The role of cognitive psychology in the development of AI translation is explored, how it can help improve translation algorithms, improve the quality of translation, and explore future directions are analyzed.Region - Educational Research and Reviews["Yujia Yang"]2024-09-25T00:00:00 f4196aea947ccb804c2b502728375d3507194606Technological sophistication in adulthood has had a significant imact on social life. Artificial Intelligence acts and behaves like humans in the same aspects of speed and accuracy. The interpretation of AI when a criminal act is present can only be seen as a legal object. The purpose of this research is to find out and analyze the rules of Artificial Intelligence crimes in Indonesian positive law and to find out and analyze Artificial Intelligence policies in several other countries. This research uses a normative legal research method, namely an approach carried out by examining literature studies, international news, and the approach of the Law. The legislation used is Law No. 1 of 2024, the second amendment to Law No. 11 of 2008 concerning Information and Electronic Transactions. The results of the research show that the rules of AI Crime are still regulated in the Law on Information and Electronic Transactions concerning electronic systems and electronic agents. There is no specific policy in response to the problem of Artificial Intelligence. Foreign countries such as China, America, Europe have special regulations regarding the concept of AI. The state's obligation to protect and follow the dynamics of technology needs to be supported by legal certainty. Special regulations need to be presented as a form of the government's seriousness in carrying out legal reform.Jurnal Daulat Hukum00The results of the research show that the rules of AI Crime are still regulated in the Law on Information and Electronic Transactions concerning electronic systems and electronic agents.Jurnal Daulat Hukum["Hana Hidayatuzzakia", "Anis Widyawati", "Martitah Martitah"]2024-09-25T00:00:00 96a70bf73cfbcd56bedb53d79adfdeb49302bd01A problem-thematic analysis of the most important problems associated with the appearance of a chatbot with generative artificial intelligence (ChatGPT) in human life is presented. The process of formation of the ChatGPT culture over the past few years, its sources and prospects are considered. The concept of the culture of a working society is outlined — a set of values, norms, traditions and institutions that emphasize the importance of labor, production and interaction between people in many respects for the organization of society and the satisfaction of its needs. Through the lens of working society culture, the author postulates, firstly, that ChatGPT reinforces the growing trend of the end of working society. Secondly, the emergence of ChatGPT has activated the development of a culture of artificial intelligence (AI). Thirdly, the AI culture formed by man is, in fact, a counterversion to the culture of the working society formed over the last five hundred years — the culture of a “non-working society”. The author comes to the conclusion about the need to rethink the utopian and communist future proposed by thinkers and philosophers.Culture of Ukraine100The author postulates that ChatGPT reinforces the growing trend of the end of working society and comes to the conclusion about the need to rethink the utopian and communist future proposed by thinkers and philosophers.Culture of Ukraine["L. Machulin"]2024-09-25T00:00:00 65c3962fef1122121eb5f657a7ed69e2792b08e8Surgical Endoscopy100Without prior training, ChatGPT-4 scored highly when evaluated on the largest practice question bank for the FPD-MBS exam, and there was no difference between the frequency of correct answers.Surgical endoscopy["A. Sanders", "R. Lim", "D. Jones", "R. Vosburg"]2024-09-25T00:00:00 2354d82238d494de3ecbccece63249fa6249201fThe dynamic between human writers and artificial intelligence in crafting fiction, particularly in the horror genre, provides an intriguing context for examining the unique strengths and limitations of each. This research investigates the creative outputs of two groups: members of Souk Ahras University’s ‘Bookish Minds Club,’ who have discussed numerous horror books and have been introduced to various techniques and tropes of the genre, and those who employ Claude AI to aid in their writing process. Sixty club members were divided evenly, with each group receiving identical horror fiction prompts to craft their stories. These stories were subsequently evaluated based on originality, coherence, the effectiveness of horror elements, character development, and overall impact. The results highlighted a slight but notable superiority of human creativity over AI-assisted writing, particularly in terms of emotional depth and psychological complexity. The findings suggest that while Claude AI can provide structural support and enhance certain narrative elements, it often falls short in capturing the knotty emotional and psychological distinctions that human writers, especially those well-versed in genre techniques, naturally infuse in their work.Rupkatha Journal on Interdisciplinary Studies in Humanities00The findings suggest that while Claude AI can provide structural support and enhance certain narrative elements, it often falls short in capturing the knotty emotional and psychological distinctions that human writers, especially those well-versed in genre techniques, naturally infuse in their work.Rupkatha Journal on Interdisciplinary Studies in Humanities["Moufida Boumous"]2024-09-25T00:00:00 cd97926fb049f43c767ed3a52986d596569687d7In the context of deep peak shaving, this article explores a machine prediction model based on artificial intelligence algorithms. Real time and historical data from power plants are processed to select relevant feature values, and LSTM and random forest models are used to construct the model. By continuously adjusting the data model, the final model parameters are determined to obtain a more accurate prediction model that predicts the flue gas temperature at the tail of the boiler. This will help solve the problem of high flue gas temperature at the tail of the boiler during peak shaving in power plants. At the same time, it also provides a new solution for the problems arising from peak shaving of power plant boilers.International Conference Renewable Energy and Power Engineering30A machine prediction model based on artificial intelligence algorithms is explored that predicts the flue gas temperature at the tail of the boiler during peak shaving in power plants and provides a new solution for the problems arising from peak shaving of power plant boilers.2024 7th International Conference on Renewable Energy and Power Engineering (REPE)["Shuchun Ji", "Xiaobo Li", "Qianxin Guo", "Shengwei Xin", "Yonggang Zhao", "Ruojun Zhang", "Yue Zhu", "Shunli Fang", "Zhihua Du", "Zhonghua Jin"]2024-09-25T00:00:00 ced2f1984f0ddb78c47b80584468173166bb7ea0With the intensification of social pressure and the enhancement of mental health awareness, the mental health issues of college students have become increasingly prominent, attracting social attention. Mental health counseling services, as an important way to alleviate students’ psychological stress, are facing the dual challenges of a shortage of professionals and growing service demands. In recent years, the application of artificial intelligence (AI) technology in the field of mental health has gradually risen, and its advantages in data analysis, pattern recognition, and natural language processing provide new solutions for mental health counseling services. However, existing research still faces problems such as insufficient understanding and limited emotional interaction capabilities in practical applications. This paper delves into the application of AI technology in mental health counseling services for college students and innovates and improves upon the deficiencies in existing research. The study focuses on two main areas: First, word vector generation technologies based on statistics and language models are used according to different application scenarios, and their effectiveness in the analysis of mental health counseling texts is compared. Second, an improved Seq2Seq model is proposed to enhance the emotional understanding and interaction capabilities of emotional dialogue generation algorithms in mental health counseling. This study not only provides technological support for college mental health counseling services but also opens new research directions and perspectives for the application of AI in the field of mental health.Journal of Computational Methods in Sciences and Engineering190An improved Seq2Seq model is proposed to enhance the emotional understanding and interaction capabilities of emotional dialogue generation algorithms in mental health counseling.Journal of Computational Methods in Sciences and Engineering["Yan Gao"]2024-09-25T00:00:00 d7de168433c80f6142c2e1639c86f6a296527da5International Journal of Human Sciences Research02International Journal of Human Sciences Research["Edwin Melo Velandia"]2024-09-25T00:00:00 200b7c263603fcc8e0279981993e4e09465c5893In this theoretical article, a sociotechnical approach is proposed to characterize. First, the business ecosystem, focusing on the relationships among Providers, Enterprise, and Customers through SCM, ERP, and CRM platforms to align: (1) Business Intelligence (BI), Fuzzy Logic (FL), and TRIZ (Theory of Inventive Problem Solving), through the OID model, and (2) Knowledge Management (KM) and Imperfect Knowledge Management (IKM), through the OIDK model. Second, the article explores the E-GenAI business ecosystem, which integrates GenAI-based platforms for SCM, ERP, and CRM with GenAI-based platforms for BI, FL, TRIZ, KM, and IKM, to align Large Language Models (LLMs) through the E-GenAI (OID) model. Finally, to understand the dynamics of LLMs, we utilize finite automata to model the relationships between Followers and Followees. This facilitates the construction of LLMs that can identify specific characteristics of users on a social media platform.arXiv.org160The article explores the E-GenAI business ecosystem, which integrates GenAI-based platforms for SCM, ERP, and CRM with GenAI-based platforms for BI, FL, TRIZ, KM, and IKM, to align Large Language Models (LLMs) through the E-GenAI (OID) model.ArXiv["Leoncio Jimenez", "Francisco Venegas"]2024-09-25T00:00:00 4322314ef7ed0ebb3c6fa75dbc92626ea73bbeecThe history of AI began in 1938 with the development of the Turing bombe by Alan Turing, followed by the Turing Test. Turing's work raised the question of whether machines can think, sparking extensive research. The progression of AI continued with the introduction of LISP in 1958 and Expert Systems in the 1960s. Technological advancements, such as, computing power, networking, and the rise of machine learning led to AI's rapid development. Today, AI is widely applied in various fields. This paper comprises an introduction, a literature review, a proposal for an Ethical Framework for AI development, and a conclusion. The introduction provides a concise history of AI, advance of AI, and delves into terms ethics and culture. The literature review examines different areas of applications of ethical AI and AI Ethics. Subsequently, a unique framework for ethical considerations in AI is suggested, which concludes the paper.2024 Global Conference on Wireless and Optical Technologies (GCWOT)860A unique framework for ethical considerations in AI is suggested and a proposal for an Ethical Framework for AI development is proposed, which concludes the paper.2024 Global Conference on Wireless and Optical Technologies (GCWOT)["Mumtaz Hussain", "Tariq Rahim Soomro"]2024-09-25T00:00:00 687d12b9806925d6de77926ccfc920ba9bdaf176British Journal of Industrial Relations00British Journal of Industrial Relations["Fu Chen"]2024-09-25T00:00:00 26b57c7184f203ef88d178166ebdb29c99ea57adCurrent Psychology640Current Psychology["Wei Wu", "Yan Wang"]2024-09-25T00:00:00 16ada4f1ff5bd620c69a5143fa656c13cd9f4706The process of selecting suppliers is a critical and multifaceted aspect of supply chain management, involving numerous criteria and decision-making variables. This complexity escalates when integrating sustainable and resilient factors into supplier evaluation. This literature review paper explores various evaluation criteria that encompass economic, environmental, social, and resilience dimensions for supplier selection. Different methodologies to model and address these complexities are investigated in this research. This review synthesizes the findings of 143 publications spanning the last decade (2013–2023), highlighting the prevalent evaluation criteria and methodologies and identifying existing research gaps. In addition, the feasibility of combining multiple approaches to more accurately reflect real-world scenarios and manage uncertainties in supplier selection is examined. This paper also proposes a decision-making framework to assist practitioners in navigating the intricacies of this process. The paper concludes by suggesting seven potential directions for future research in this evolving field.Sustainability1520A decision-making framework is proposed to assist practitioners in navigating the intricacies of this process, and seven potential directions for future research in this evolving field are suggested.Sustainability["Hossein Mirzaee", "Sahand Ashtab"]2024-09-25T00:00:00 fb97fb0f107d40834fbfd37c1edc301b9a9b13caThis study aimed to develop and validate an indigenous assessment unit for measuring AI attitude and perception, focusing on its psychometric properties. The presented results are from the first study, conducted in three phases. In Phase I, internal consistency and dimensionality of the construct were assessed using a sample of 474 students (381 girls and 95 boys) aged 18-28 years, selected through convenience sampling. Exploratory factor analysis (EFA) was employed, generating items for the AI attitude and perception scale based on a 5-point Likert scale. In Phase II, the factor structure from Phase I was confirmed through structural equation modeling, yielding a 12-factor structure with 65.87% explanatory variance. Phase III established the scale's convergent validity by correlating its items with those of a predefined scale (SPAI). The final scale comprised 34 items across 5 discriminative factors, demonstrating robust psychometric properties. This scale is a significant tool for assessing AI attitudes and perceptions, particularly among studentInternational Journal of Social and Human Sciences-PHILOSOPHICA00This study aimed to develop and validate an indigenous assessment unit for measuring AI attitude and perception, focusing on its psychometric properties, and established the scale's convergent validity by correlating its items with those of a predefined scale (SPAI).International Journal of Social and Human Sciences-PHILOSOPHICA["Qufli Osmani", "Teuta Idrizi", "Fjolla Veseli"]2024-09-25T00:00:00 aefa5d84069d2d5b8e35e9bcffaf3cbbfbbbbc41Skepticism of the written word has been prevalent in philosophical discourse at least since the works of Plato. This article employs philosophical method. It situates the ongoing educational concern with AI Chatbots in terms of this skepticism toward writing. Specifically, this longstanding skepticism posits that the written word is an alienated form of the spoken word. This article demonstrates how two prevalent traditions of education—traditional and progressive—take up this same skepticism. The article calls upon the work of Jacques Derrida, whose deconstructive theories on Plato and the written word problematize this line of writerly skepticism. Derrida’s work on Rousseau’s Emile informs a more general approach to pedagogy which entails what Derrida calls “the logic of supplementarity .” This “logic” involves the paradoxical debt that writing owes to speech. Thus, one can discern a distinct sense in which education—in general—is implicated in a tension that exists between the written word to the spoken. Ultimately, this articles suggests that the ongoing concern with AI Chatbots—linked to an ancient skepticism toward writing—is none other than a concern with the very practice of education per se.Futurity Philosophy340It is suggested that the ongoing concern with AI Chatbots—linked to an ancient skepticism toward writing—is none other than a concern with the very practice of education per se.Futurity Philosophy["Charles Bingham"]2024-09-25T00:00:00 666019a2ae9cafaf6ebc8e1a930068723564b7beAJR. American journal of roentgenology20AJR. American journal of roentgenology["Gregory Michael Lee"]2024-09-25T00:00:00 504ab7ecc74350eaf5ecaae7dae84733cad6701aBackground: Although increasing in prevalence, nonalcoholic steatohepatitis (NASH) is often undiagnosed in clinical practice. Objective: This study identified patients in the Veterans Affairs (VA) health system who likely had undiagnosed NASH using a machine learning algorithm. Methods: From a VA data set of 25 million adult enrollees, the study population was divided into NASH-positive, non-NASH, and at-risk cohorts. We performed a claims data analysis using a machine learning algorithm. To build our model, the study population was randomly divided into an 80% training subset and a 20% testing subset and tested and trained using a cross-validation technique. In addition to the baseline model, a gradient-boosted classification tree, naïve Bayes, and random forest model were created and compared using receiver operator characteristics, area under the curve, and accuracy. The best performing model was retrained on the full 80% training subset and applied to the 20% testing subset to calculate the performance metrics. Results: In total, 4 223 443 patients met the study inclusion criteria, of whom 4903 were positive for NASH and 35 528 were non-NASH patients. The remainder was in the at-risk patient cohort, of which 514 997 patients (12%) were identified as likely to have NASH. Age, obesity, and abnormal liver function tests were the top determinants in assigning NASH probability. Conclusions: Utilization of machine learning to predict NASH allows for wider recognition, timely intervention, and targeted treatments to improve or mitigate disease progression and could be used as an initial screening tool.Journal of health economics and outcomes research360Utilization of machine learning to predict NASH allows for wider recognition, timely intervention, and targeted treatments to improve or mitigate disease progression and could be used as an initial screening tool.Journal of Health Economics and Outcomes Research["Onur Baser", "G. Samayoa", "Nehir Yapar", "E. Baser"]2024-09-25T00:00:00 0993151aeb5098d9f4174ed2701b002c716ab4e5Surgical Endoscopy00Surgical endoscopy["Ace St John", "Muhammad Uzair Khalid", "Caterina Masino", "Mohammad Noroozi", "Adnan A. Alseidi", "Daniel A. Hashimoto", "Maria S. Altieri", "Federico Serrot", "Marta Kersten-Oertel", "Amin Madani"]2024-09-25T00:00:00 70cb81f6c5c0be3dfea98fa6a78981547e6db131Digitalization has significantly influenced the challenges facing the media industry. The very process of media transformations "is no longer a technical need, but is already a social reflection and need, a need for communication, a need for solving political and technological problems in the society of this time" (Bebić & Volarević, 2016). Therefore, the attention of users is increasingly difficult to capture, thanks to the diversity of media publications, and in this sense traditional media often fail to respond to specific audience demands (Trattner, et al., 2022).Today, users are exposed to a large amount of information on a daily basis, their content is increasing, so media content cannot be imposed on users, but they choose only what they are interested in for consumption. However, traditional electronic media have understood the importance of the new multidimensional environment, therefore they are using a multiplatform through the Internet that use digital technology both to place the content and to personalize the offers against the user (Ćitić, 2020).International Journal of Social and Human Sciences-PHILOSOPHICA00Traditional electronic media have understood the importance of the new multidimensional environment, therefore they are using a multiplatform through the Internet that use digital technology both to place the content and to personalize the offers against the user.International Journal of Social and Human Sciences-PHILOSOPHICA["Faton Murseli", "Behar Mjekiqi"]2024-09-25T00:00:00 73a8ad32ed377fd7bcba898ce33519de9001d883Artificial intelligence (AI) is lowering the period and cost of the medication research and discovery process Artificial Intelligence is a revolution of medical research in pharmaceutical companies. In a review article, we give a summary of the many artificial intelligence tools of machine learning (ML) and deep learning (DL) techniques that will be used in drug research and discovery in the future. AI techniques and tools are more specifically designed or better programmed to mimic the operations of the human brain. AI is frequently used in drug discovery for de novo drug creation, virtual screening, reaction prediction, and de novo protein design. In addition, the application of AI and techniques of AI. ML is a disease diagnosis, de novo drug design, drug prediction for diseases, and big data prediction using ANN, CNN, and SNN, as well as deep learning. Furthermore, the function, applications, and methods of AI. Technological hurdles also face the contemporary XAI, and “low level” molecular representations (such as SMILES strings) that are useful for machine learning and AI tools are ‘deep chem’ in drug development several cutting-edge methods referred to as Knowledge Base Systems (KBS). The AI-based nanorobots are drug discovery on creating implantable nanorobots for the targeted delivery of medications and genes, factors including sustained release, dose modification, and control release need to be taken into consideration. Finally recent development of ML and DL techniques and AI models are more useful in the drug development and drug discovery process.International Journal of Pharmaceutical Quality Assurance00A review article gives a summary of the many artificial intelligence tools of machine learning (ML) and deep learning (DL) techniques that will be used in drug research and discovery in the future.INTERNATIONAL JOURNAL OF PHARMACEUTICAL QUALITY ASSURANCE["Ramanathan . Rajagopalan", "Shalika . M", "Arunprasath . M", "Senthamarai . R"]2024-09-25T00:00:00 df2ff244d51427e52a891dab35bf4d0a33adde72Cancer stem cells (CSCs), or tumor-initiating cells (TICs), are cancerous cell subpopulations that remain while tumor cells propagate as a unique subset and exhibit multiple applications in several diseases. They are responsible for cancer cell initiation, development, metastasis, proliferation, and recurrence due to their self-renewal and differentiation abilities in many kinds of cells. Artificial intelligence (AI) has gained significant attention because of its vast applications in various fields including agriculture, healthcare, transportation, and robotics, particularly in detecting human diseases such as cancer. The division and metastasis of cancerous cells are not easy to identify at early stages due to their uncontrolled situations. It has provided some real-time pictures of cancer progression and relapse. The purpose of this review paper is to explore new investigations into the role of AI in cancer stem cell progression and metastasis and in regenerative medicines. It describes the association of machine learning and AI with CSCs along with its numerous applications from cancer diagnosis to therapy. This review has also provided key challenges and future directions of AI in cancer stem cell research diagnosis and therapeutic approach.Proceedings of Anticancer Research622New investigations into the role of AI in cancer stem cell progression and metastasis and in regenerative medicines are explored and key challenges and future directions of AI in cancer stem cell research diagnosis and therapeutic approach are provided.Proceedings of Anticancer Research["Muhammad Abubakar"]2024-09-25T00:00:00 fb3f7e8ba8faf8a0ea1b32f3c2d7536f0e0d9abaAI and Ethics301Which values guide professional deepfake development, how economic and academic pressures and incentives influence developers’ agency and ethical views, and how these views do and could impact deepfake design, creation, and dissemination are explored.AI and Ethics["Maria Pawelec"]2024-09-25T00:00:00 e84129d05b2c980a72a633ed26abaddc5f252260AI and Ethics761This study provides a nuanced definition and an integrative conceptualization of care robot literacy (CRL) for contemporary care work and advocates for the future significance of context-specific CRL as valuable addition to the terminology of ethical AI in healthcare.AI and Ethics["Tuuli Turja", "A. Kork", "S. Ilom\u00e4ki", "Ingvil Hellstrand", "A. Koistinen"]2024-09-25T00:00:00 ed5d35e9ef74a0e0417d38ed9035a1812c848795 The assessment of livestock welfare aids in keeping an eye on the health, physiology, and environment of the animals in order to prevent deterioration, detect injuries, stress, and sustain productivity. Because it puts more consumer pressure on farming industries to change how animals are treated to make them more humane, it has also grown to be a significant marketing tactic. Common visual welfare procedures followed by experts and vets could be expensive, subjective, and need specialized staff. Recent developments in artificial intelligence (AI) integrated with farmers’ expertise have aided in the creation of novel and cutting-edge livestock biometrics technologies that extract important physiological data linked to animal welfare. A thorough examination of physiological, behavioral, and health variables highlights AI's ability to provide accurate, rapid, and impartial assessments. Farmer-focused strategy: an emphasis on the crucial role that farmers play in the skillful adoption and prudent application of AI and sensor technologies, as well as conversations about developing logical, practical, and affordable solutions that are specific to the needs of farmers.CABI Reviews380A thorough examination of physiological, behavioral, and health variables highlights AI's ability to provide accurate, rapid, and impartial assessments.CABI Reviews["G. Taiwo", "Ali Alameer", "Mansouri Taha"]2024-09-25T00:00:00 4506d65e01dc4df30a1a51b16539a95c31826d68Companies claim to"democratise"artificial intelligence (AI) when they donate AI open source software (OSS) to non-profit foundations or release AI models, among others, but what does this term mean and why do they do it? As the impact of AI on society and the economy grows, understanding the commercial incentives behind AI democratisation efforts is crucial for ensuring these efforts serve broader interests beyond commercial agendas. Towards this end, this study employs a mixed-methods approach to investigate commercial incentives for 43 AI OSS donations to the Linux Foundation. It makes contributions to both research and practice. It contributes a taxonomy of both individual and organisational social, economic, and technological incentives for AI democratisation. In particular, it highlights the role of democratising the governance and control rights of an OSS project (i.e., from one company to open governance) as a structural enabler for downstream goals, such as attracting external contributors, reducing development costs, and influencing industry standards, among others. Furthermore, OSS donations are often championed by individual developers within companies, highlighting the importance of the bottom-up incentives for AI democratisation. The taxonomy provides a framework and toolkit for discerning incentives for other AI democratisation efforts, such as the release of AI models. The paper concludes with a discussion of future research directions.arXiv.org1573A taxonomy of both individual and organisational social, economic, and technological incentives for AI democratisation is contributed, which provides a framework and toolkit for discerning incentives for other AI democratisation efforts, such as the release of AI models.ArXiv["Cailean Osborne"]2024-09-26T00:00:00 0b37417b002f93e41c1e284ff48f83a5efade0b1"Medical deserts" are areas with low healthcare service levels, challenging the access, quality, and sustainability of care. This qualitative narrative review examines how artificial intelligence (AI), particularly large language models (LLMs), can address these challenges by integrating with e-Health and the Internet of Medical Things to enhance services in under-resourced areas. It explores AI-driven telehealth platforms that overcome language and cultural barriers, increasing accessibility. The utility of LLMs in providing diagnostic assistance where specialist deficits exist is highlighted, demonstrating AI's role in supplementing medical expertise and improving outcomes. Additionally, the development of AI chatbots offers preliminary medical advice, serving as initial contact points in remote areas. The review also discusses AI's role in enhancing medical education and training, supporting the professional development of healthcare workers in these regions. It assesses AI's strategic use in data analysis for effective resource allocation, identifying healthcare provision gaps. AI, especially LLMs, is seen as a promising solution for bridging healthcare gaps in "medical deserts," improving service accessibility, quality, and distribution. However, continued research and development are essential to fully realize AI's potential in addressing the challenges of medical deserts.Postgraduate medical journal03AI, especially LLMs, is seen as a promising solution for bridging healthcare gaps in "medical deserts," improving service accessibility, quality, and distribution, and continued research and development are essential to fully realize AI's potential in addressing the challenges of medical deserts.Postgraduate medical journal["Zdeslav Strika", "Karlo Petkovi\u0107", "R. Liki\u0107", "Ronald Batenburg"]2024-09-26T00:00:00 2dc19e7e41808617655ba3d0e07b7c6ec63cba06This commentary explores the implications of artificial intelligence (AI) for marketing practice and society at large. It reviews recent findings demonstrating the importance of behavioral insights for safe AI deployment. As AI models increasingly demonstrate human-like capabilities, their deployment raises crucial concerns about bias due to unintended interactions between algorithmic and human decision-making. The discussion underscores the importance of incorporating behavioral science perspectives to understand and mitigate AI risks, moving beyond purely technical solutions.Journal of Macromarketing92The discussion underscores the importance of incorporating behavioral science perspectives to understand and mitigate AI risks, moving beyond purely technical solutions.Journal of Macromarketing["Stefano Puntoni"]2024-09-26T00:00:00 b67bea031d6e3d01dc3e7c4953b181034d205758In a context of digital transformation, the integration of new technologies has become essential for companies. The interdependence between artificial intelligence (AI) and marketing strategies (MS) is recognized as revolutionizing the way companies interact with customers and achieve results. Therefore, the purpose of this paper is to explore the synergy between AI and MS by conducting a bibliometric analysis in order to highlight the current structure of studies and future research direction in this field. The research included a bibliometric analysis of 134 articles retrieved from the Web of Science (WOS) database. The scientific content of these articles was analysed in the VOSviewer software program, considering descriptive analysis and conceptual and social scientific mapping. The results included a quantitative analysis of the main categories of publications, their evolution over time, including by country, as well as analyses of term co-occurrence and co-authorship. The main countries according to the number of annual publications are the United States of America, England, China, India, Australia, and the main field covered, according to WOS references, is the field of "Business". As a result, the research confirmed the significant interdependence between AI and MS, as well as its complexity and especially its implications for organizations. In this regard, the need for future collaborative and interdisciplinary approaches is noted, with the bibliometric analysis provided in this paper as a starting point for future research directions.MANAGEMENT STUDENT WORKING PAPERS211The research confirmed the significant interdependence between AI and MS, as well as its complexity and especially its implications for organizations, with the need for future collaborative and interdisciplinary approaches noted.MANAGEMENT STUDENT WORKING PAPERS["Cristiana Nu\u021b\u0103"]2024-09-26T00:00:00 f44a4cf01f06f434f8ae6d7432a48c5ae01ccc29Artificial intelligence (AI) has emerged as a transformative force in regenerative medicine, revolutionizing research, clinical applications, and personalized therapies. This article explores how AI accelerates the identification of biomarkers, optimizes cell and tissue engineering processes, and enhances treatment efficacy through personalized medicine approaches. AI's role in predictive analytics, robotic systems for tissue fabrication, and real-time monitoring tools underscores its potential to reshape the future of healthcare. Addressing ethical considerations is essential as AI continues to pave the way for innovative regenerative therapies.West Kazakhstan medical journal01This article explores how AI accelerates the identification of biomarkers, optimizes cell and tissue engineering processes, and enhances treatment efficacy through personalized medicine approaches.West Kazakhstan Medical Journal["A. Tamadon", "N. Mussin", "A. Kaliyev"]2024-09-26T00:00:00 16e75d5efc1939d64cec53de97bbff47a665ab75Abad ke-21 ditandai dengan teknologi yang terus berkembang dengan cepat hingga munculnya kecerdasan buatan atau artificial intelligence (AI) yang berfokus pada pembuatan sistem atau mesin yang dapat melakukan tugas-tugas yang biasanya memerlukan kecerdasan manusia. Hampir seluruh aspek kehidupan bergantung pada teknologi, termasuk bidang pendidikan. Kemajuan teknologi belum dimanfaatkan dengan optimal oleh guru. Sehingga diperlukan pelatihan untuk meningkatkan pengetahuan guru dalam menggunakan teknologi AI dalam pembelajaran. Kegiatan ini dilaksanakan dalam bentuk pelatihan yang diawali dengan tahap persiapan meliputi koordinasi, observasi dan wawancara. Selanjutnya tahap pelatihan dengan tahap pertama yaitu penyajian materi melalui presentasi dan diskusi, serta tahap kedua pendampingan pembuatan project berupa media pembelajaran berbasis AI. Kemudian dilakukan evaluasi untuk mengukur ketercapaian tujuan pelaksanaan pengabdian ini. Kegiatan ini diikuti 15 orang guru SMP Negeri 3 Sirenja. Keberhasilan kegiatan ini dilihat dari pemahaman peserta tentang teknologi AI dan kemampuan peserta menggunakan AI. Setelah mengikuti kegiatan ini peserta memperoleh pengetahuan tambahan tentang pemanfaatan AI dan mereka mampu menggunakan teknologi AI dalam merancang media pembelajran sesuai bidangnya masing-masing.Jurnal Abdimas Indonesia101Jurnal Abdimas Indonesia["M. Mubarik", "I. Hadjar", "Welli Meinarni", "Akhyar H. M. Tawil"]2024-09-26T00:00:00 c8cda9807204a955d774a71afa44ac7a9cea3bbcThis article is a concept paper that discusses the challenges in using artificial intelligence to assess student performance in the 21st century. 21st century skills emphasize four learning skills namely creativity, critical thinking, collaboration and communication. This is in line with the goals of the Malaysian Education Development Plan (PPPM2013-2025) as well as the National Education Philosophy, the Malaysian Ministry of Education has introduced digital learning to provide relevant and quality education in preparation for producing students with high marketability and soft skills. Artificial intelligence (AI) is one of the elements emphasized in digital learning. Educators and students need to be prepared and skilled in applying AI technology to facilitate the teaching and learning process. However, there are several challenges faced by educators in realizing the use of AI, especially to assess student performance, including concerns about academic integrity, trust and authenticity. In addition, issues of privacy, data security and ethical regulations as well as the commitment and skills of educators are also discussed. Concerns about bias and lack of human factors in AI technology as well as accessibility and adaptation of curriculum and the lack of AI research studies in the field of education (AIEd) are also challenges in the implementation of AI. In addition, this article also comments on suggestions for improvements that can be made in facing and overcoming the challenges faced to sustain the use of AIEd in accordance with the aspirations of 21st century skills.International Journal of Modern Education00The challenges in using artificial intelligence to assess student performance in the 21st century as well as suggestions for improvements that can be made in facing and overcoming the challenges faced to sustain the use of AIEd in accordance with the aspirations of 21st century skills are discussed.International Journal of Modern Education["Intan Idura Mohamad Isa", "Hishamuddin Ahmad"]2024-09-26T00:00:00 2e22b37f7d979eadf8d31c9e7958c6dc9c902672Artificial Intelligence (AI) technology has made remarkable progress in image processing, speech synthesis, intelligent creation, etc., and has shown great potential for application in artistic creation. In the teaching scenario of higher vocational colleges, teachers not only bear the responsibility of cultivating students' hands-on ability and creative thinking but also need to constantly adapt to the teaching changes brought about by technological advances. With their strong digital intuition, creative thinking and flexible adaptability, young art design teachers can make better use of AI technology to assist artistic creation, improve teaching quality, and enrich students' creative learning experience. Traditional art education usually relies on teachers to impart knowledge, guide practice, and evaluate works in the classroom; however, the widespread application of AI technologies has brought about a revolutionary transformation in art education, and new educational resources such as generative design tools and online assisted design platforms are gradually changing the traditional teaching mode and the role of teachers. Using self-efficacy theory and social cognitive career and motivation theory as a framework, this study investigated twelve in-service young art design teachers through 1) semi-structured interviews; 2) focus group discussions, and 3) member-checking interviews. The researcher analysed the data using a general inductive approach to capture the following core themes: 1) new role orientation; 2) continuing professional development and 3) support from society and educational institutions. Institutional support. The results of the study help to deeply understand the relationship between AI and art education in higher vocational colleges, clarify the self-worth positioning of young art design teachers in the new era, and provide theoretical support and practical guidance for the transformation of teachers' roles and the professional development of art education.Journal of social sciences and humanities310The results of the study help to deeply understand the relationship between AI and art education in higher vocational colleges, clarify the self-worth positioning of young art design teachers in the new era, and provide theoretical support and practical guidance for the transformation of teachers' roles and the professional development of art education.Journal of Social Science and Humanities["Jiabao Wu", "Luis Miguel DOS SANTOS"]2024-09-26T00:00:00 293882e35c8d6e8e52690840aa2489b8735fc31eProviding couriers with wearable artificial intelligence devices to prevent accidents is not only beneficial to the courier’s safety but will also save money in terms of insurance premiums for express companies; therefore, it is worth investigating what factors can influence the acceptance of wearable artificial intelligence devices by couriers. Push–pull–mooring (PPM) theory and affective event theory (AET) are integrated, to test couriers’ adoption of wearable safety detection devices. Social influence, perceived security, personal innovativeness, and affective event reaction are applied to the research model. Questionnaires are distributed among several listed express companies and 263 valid questionnaires are used for empirical testing. Empirical results indicated that social influence, perceived safety, personal innovativeness and affective event reaction are positively related to usage with coefficients 0.218, 0.301, 0.698 and 0.309. Personal innovativeness has positive moderating effects on relationships between affective event reaction, perceived security and usage, with coefficients 0.145 and 0.106; however, it has no significant moderating effect on the relationship between social influence and usage. The research aims to help support the proliferation and adoption of wearable artificial intelligence devices to optimize the current state of the express industry and improve the interaction between couriers and managers, creating an active management strategy that will allow express companies to thrive. The study not only provides insights to help express companies reduce insurance costs, but also provides recommendations for accelerating the company’s environmental, social and governance goals, leading sustainable development and building new corporate value.Sustainability710Sustainability["Wei Sun", "Junghoon Kim", "Huadong Su"]2024-09-26T00:00:00 fda76cfd81fa4844dbbccf947e136cb472387aadWith the rapid development of artificial intelligence (AI), text generation has become a field of great concern. However, there are few related studies from the perspective of linguistics. Natural language processing technology should have been combined with linguistics, but traditional linguistics cannot provide sufficient theoretical support for large language models. Thus, linguistics is getting farther away from the needs of the times. This paper aims to analyze why the integration of large language models and linguistics is insufficient, with ChatGPT as an example to explain the text characteristics of AI writing and its reasons. In addition, this paper explores the impact of AI-generated text patterns on linguistics and how linguistics will develop in the new era.Transactions on Social Science, Education and Humanities Research220Why the integration of large language models and linguistics is insufficient is analyzed, with ChatGPT as an example to explain the text characteristics of AI writing and its reasons, and how linguistics will develop in the new era is explored.Transactions on Social Science, Education and Humanities Research["Junni Li"]2024-09-26T00:00:00 703bfe839b07357ef038654eb181155f3d7fc56fAbstract US hospitals are rapidly adopting artificial intelligence (AI), but there is a lack of knowledge about AI-adopting hospitals' characteristics, trends, and spread. This study aims to fill this gap by analyzing the 2022 American Hospital Association (AHA) data. The novel Hospital AI Adoption Model (HAIAM) is developed to categorize hospitals based on their AI adoption characteristics in the fields of (1) predicting patient demand, (2) optimizing workflow, (3) automating routine tasks, (4) staff scheduling, and (5) predicting staffing needs. Nearly one-fifth of US hospitals (1107 or 18.70%) have adopted some form of AI by 2022. The HAIAM shows that only 3.82% of hospitals are high adopters, followed by 6.22% moderate and 8.67% low adopters. Artificial intelligence adoption rates are highest in optimizing workflow (12.91%), while staff scheduling (9.53%) has the lowest growth rate. Hospitals with large bed sizes and outpatient surgical departments, private not-for-profit ownership, teaching status, and part of health systems are more likely to adopt different forms of AI. New Jersey (48.94%) is the leading hospital AI-adopting state, whereas New Mexico (0%) is the most lagging. These data can help policymakers better understand variations in AI adoption by hospitals and inform potential policy responses.Health affairs scholar160Hospitals with large bed sizes and outpatient surgical departments, private not-for-profit ownership, teaching status, and part of health systems are more likely to adopt different forms of AI.Health Affairs Scholar["Redwan Bin Abdul Baten"]2024-09-26T00:00:00 05caf43f62c0ccf701de0e6ed2c9155b08959acfArtificial Intelligence is a machine system that is able to perform a cognitive function similar to human things such as learning, perceiving, reasoning and problems solving. It involves the application of algorithms and machine learning to give machines the ability to carry out operations that would typically require human intelligence, such as speech, imageInternational Journal of Academic Research in Business and Social Sciences110International Journal of Academic Research in Business and Social Sciences["Norhaslinda Daud", "Muhammad Mirza Hakim Ishak", "Muhammad Afiq Ahlami Zilkarnain", "Rabiatul Adawiyah Rumaizi"]2024-09-26T00:00:00 b85c0856aec17bfc74abed1eeb39c1bc0faa1d9eThe application of artificial intelligence and machine learning techniques has grown significantly across all disciplines in recent years as a result of ever-increasing volume of data as well as changing demands of higher education, such as those related to digital education. In many countries around the world, learning disabilities (LD) are becoming a major problem that can even hinder performance progress. The endeavor of this work is to assist the specific program with systems administration in their assignment to accompany the norm. Nowadays, many different fields use machine learning to predict future outcomes. Predicting children's learning disabilities, as well as determining actual disability and how early it is detected, are among most useful applications of machine learning. This review article aims to comprehend work done on this field previously, up to this point and attempt to comprehend the holes in execution of the different AI models and the review depends on intelligent school system by artificial reasoning. Countless examinations validate that learning is worked with assuming showing systems are as per understudies learning styles, making the growing experience more compelling and further developing understudies exhibitions.مجلة العلوم التربوية و الدراسات الإنسانية00A review article aims to comprehend the holes in execution of the different AI models and attempt to comprehend the holes in execution of the different AI models based on intelligent school system by artificial reasoning.مجلة العلوم التربوية و الدراسات الإنسانية["Adhwaa Ali Alahmari"]2024-09-26T00:00:00 0286289cd2e22b9efa5b91defe7bd7862e769265Artificial Intelligence (AI) and electric vehicles EVs are revolutionizing the future of transportation, automation, and connectivity. The integration of AI in EV technology heralds a transformative era in transportation, characterized by enhanced efficiency, autonomy, and user experience. However, as AI-powered EVs become increasingly prevalent, critical security and privacy challenges emerge that necessitate comprehensive scrutiny. This paper explores the future aspects of security and privacy in AI-powered EVs, highlighting potential vulnerabilities and the implications of data exploitation. Key areas of focus include the cybersecurity threats posed by interconnected vehicle networks, the safeguarding of personal data from unauthorized access, and the development of robust encryption methods to protect vehicular communication systems. Additionally, the paper examines regulatory frameworks and industry standards essential for ensuring the secure deployment of AI in EVs. By addressing these concerns, the research aims to provide a holistic understanding of the measures required to fortify the security and privacy of AI-driven electric vehicles, ultimately contributing to developing a safe and trustworthy transportation ecosystem.2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)200The research aims to provide a holistic understanding of the measures required to fortify the security and privacy of AI-driven electric vehicles, ultimately contributing to developing a safe and trustworthy transportation ecosystem.2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)["Tole Sutikno", "Anggit Pamungkas", "Budi Santosa", "R. Puriyanto", "Ahmad Raditya Cahya Baswara"]2024-09-26T00:00:00 56b4c9186eb86f59e9ec5cf69020360800f30b21Artificial Intelligence tools such as Grammarly and ChatGPT are used in the learning process, including learning foreign languages for high school students. This paper aims to synthesize the benefits and limitations of using AI in learning foreign languages, and the current situation of using AI tools in studying English for high school students in Vietnam. The authors have systematized theories about the benefits and limitations of using AI tools in learning English in about 30 papers in peer-reviewed journals from 2018 to 2024. The authors developed a survey to collect data from 300 students in high school at Hanoi in Vietnam to see and know the current status of using AI tools in learning English. The results of data analysis from 297 responses calculated the AI ​​usage situation as follows: Using AI tools in studying English students is very common with a high frequency of use including four skills listening, speaking, reading, and writing. Most of the students are also well aware of the benefits and limitations of AI tools. However, students' use of AI tools is still spontaneous and not supported by teachers and schools. To better use AI for students' English learning, the article makes recommendations to schools and teachers.Journal of Ecohumanism350The benefits and limitations of using AI in learning foreign languages, and the current situation of using AI tools in studying English for high school students in Vietnam are synthesized to make recommendations to schools and teachers.Journal of Ecohumanism["Chu Ba Quyet", "Nguyen Binh Minh", "Nguyen Phan Anh"]2024-09-26T00:00:00 dddea965dda373fde46822fdebc87fe15d8ddb90Purpose The aim of this paper is to determine whether the dominant integrated water resources management (IWRM) paradigm within which the Pra River Basin is managed holds the key to address the current water security and sustainability issues in Southwestern Ghana.Design/methodology/approach This study employed a literature review developed based on water security and sustainability studies as well as normative scenarios from the broad scenario planning methodology. The study builds on Wæver’s Theory of Securitization and the Utilitarian theory to protect water bodies through the use of artificial intelligence (AI).Findings Insights on introducing innovative environmental sustainability technology are presented and propose the Pra-integrated smart water security management decision-making system that uses visual inspections, noise sensors, the potential of hydrogen (pH) probe sensor, real-time collection of hydrological data (streamflow) and wireless transmission of the data in real-time at the basin level. This serves as a robust tool for managing the basin’s sustainable development ecosystem by using AI to protect water bodies against illegal mining.Originality/value The proposed innovative environmental technology which is the first of its kind is meant to gain a better understanding of pollution incidents and respond quickly to them by integrating AI and Internet of Things (IoT) technologies with traditional IWRM practices. This addresses water security in the Pra Basin, supports policy development and innovation, strengthens the goal of the government to protect water resources against pollution and contributes to the African Water Vision and the United Nations’ Agenda 2030 Sustainable Development Goals 3 and 6.Technological Sustainability480Technological Sustainability["Emmanuel Kwame Nti", "G. Kranjac-Berisavljevic", "D. Doke"]2024-09-26T00:00:00 0e078cff58d84273ea9040a6e30367c9b7a82505Clinical Neuroradiology150It is shown that an AI algorithm for the automatic detection of ICHs can be seamlessly integrated into clinical workflows with minimal turnaround time, and the accuracy was on par with radiology experts, making the system suitable for routine clinical use.Clinical neuroradiology["K. Villringer", "R. Sokiranski", "R. Opfer", "L. Spies", "M. Hamann", "A. Bormann", "M. Brehmer", "I. Galinovic", "J. B. Fiebach"]2024-09-26T00:00:00 cc6c41598d65b23737e8af86a975f699fca747afBackground: Artificial Intelligence (AI) is at the forefront of todays technological revolution, enhancing efficiency in many organisations and sectors. However, in some research environments, its adoption is tempered by the risks AI poses to data protection, ethics, and research integrity. For research funding organisations (RFOs), although there is interest in the application of AI to boost productivity, there is also uncertainty around AIs utility and its safe integration into organisational systems and processes. The scoping review explored: What does the evidence say about the current and emerging use of AI?; What are the potential benefits of AI for RFOs? and What are the considerations and risks of AI for RFOs? Methods: A scoping review was undertaken with no study, language, or field limits. Due to the rapidly evolving AI field, searches were limited to the last three years (2022-2024). Four databases were searched for academic and grey literature in February 2024 (including 13 funding and professional research organisation websites). A classification framework captured the utility and potential, and considerations and risks of AI for RFOs. Results: 122 eligible articles revealed that current and emerging AI solutions could potentially benefit RFOs by enhancing data processes, administration, research insights, operational management, and strategic decision-making. These solutions ranged from AI algorithms to data management platforms, frameworks, guidelines, and business models. However, several considerations and risks need to be addressed before RFOs can successfully integrate AI (e.g., improving data quality, regulating ethical use, data science training). Conclusion: While RFOs could potentially benefit from a breadth of AI-driven solutions to improve operations, decision-making and data management, there is a need to assess organisational AI readiness. Although technological advances could be the solution there is a need to address AI accountability, governance and ethics, address societal impact, and the risks to the research funding landscape.medRxiv1140While RFOs could potentially benefit from a breadth of AI-driven solutions to improve operations, decision-making and data management, there is a need to assess organisational AI readiness.F1000Research["A. J. Blatch-Jones", "H. Church", "K. Crane"]2024-09-26T00:00:00 9d6ce3cfdfe3859aff1fb1ac0559e4a765fec9d4Purpose: The study examines the mediating effect of Localized logistics capacity on the association between Artificial intelligence and Humanitarian supply chain resilience among Humanitarian organizations. Materials and Methods: A cross-sectional survey and descriptive study involving 88 humanitarian firms in Uganda whose staff involved in relief operations were purposively selected. Data was analyzed using the Partial least squares structural equation modeling to test hypotheses and ascertain the mediating effect. Findings: The study indicates a significant indirect effect of Artificial Intelligence (AI) on humanitarian supply chain resilience (HSCR) and a direct impact of Artificial Intelligence on Localized logistics capacity (LLC). The results also confirmed a full mediation effect of LLC on the association between AI and HSCR. Implications to theory, Practice and Policy: The present study contributes deeper insights into how humanitarian organizations can develop adaptive capacities to navigate the complex landscape of humanitarian operations since it was established that logistics capacity is a conduit between artificial intelligence and humanitarian supply chain resilience. Managers should adopt artificial intelligence and build strong relationships will local logistics suppliers to achieve humanitarian supply chain resilience practices. Considering that this was a survey, a case study design with semi-structured research tools be used to have an in-depth understanding of the variables under study.European journal of technology500Deeper insights are contributed into how humanitarian organizations can develop adaptive capacities to navigate the complex landscape of humanitarian operations since it was established that logistics capacity is a conduit between artificial intelligence and humanitarian supply chain resilience.European Journal of Technology["Dr Wilbroad Aryatwijuka", "Assoc Prof", "Henry Mutebi", "Pamela Nagawa", "Benjamin R. Tukamuhabwa", "Samuel Mayanja Ssekajja", "Kyomuhangi Diana", "Allan Akashabaluhanga"]2024-09-26T00:00:00 039e2d82f15c6e9ccb3dab8af0c39f30eb432d6d   Aim. To formulate approaches to institutional regulation and preventive response to risks associated with the use of artificial intelligence technologies in science and higher education organizations on the basis of compliance.   Objectives. To determine the elements of compliance system aimed at reducing the risks of using artificial intelligence; to identify the trends of institutional development related to the integration of generative artificial intelligence technologies in scientific and educational and administrative and management processes.   Methods. The issues of adaptation of institutional mechanisms of modern higher education to the challenges of technological development are investigated on the basis of system, risk-oriented, logical and structural approaches of scientific knowledge. The methods of analysis, synthesis and classification were applied to form the elements of compliance-system of artificial intelligence.   Results. The concept of artificial intelligence compliance is designed to expand the strategy of digital transformation of higher education organizations by introducing new objects into the subject area of management, in particular, the risks of artificial intelligence technologies, as well as the development of protective, regulatory and information-management functions. Methods compliance allows to create organizational mechanisms that provide protection of higher education and science organizations from unfair behavior of subjects in the field of artificial intelligence. Artificial intelligence compliance in higher education should ensure the protection of human rights and freedoms, compliance with the rules of academic honesty and scientific integrity, assessment of the reliability of artificial intelligence systems in the educational process, protection of personal data and confidential information, copyright protection, counteraction to data falsification and cybercrime.   Conclusions. Proactive management on the basis of compliance methodology will reduce the risks of implementation of artificial intelligence technologies in the activities of universities and will help to effectively adapt to the challenges of technological development.Economics and Management20Proactive management on the basis of compliance methodology will reduce the risks of implementation of artificial intelligence technologies in the activities of universities and will help to effectively adapt to the challenges of technological development.Economics and Management["N. V. Sushcheva"]2024-09-26T00:00:00 00f54f6533f368252f0cb070fdcce5baec8c967eThe article analyzes the legal consequences of the use of artificial intelligence in combat control systems that carry out their centralized planning and coordination. The nature and method of compensation for damage caused to non-combatants and property of third parties as a result of decision-making by a robotic complex in an autonomous mode without human participation is being studied. The author makes a comparative analysis of foreign novels on issues of legal liability related to the use of artificial intelligence in combat operations management, in particular, the American approach based on the concept of “collateral damage”. Legislative novelties are proposed designed to adapt the law enforcement sphere to new practices of introducing artificial intelligence into combat control systems.Military juridical journal00The article analyzes the legal consequences of the use of artificial intelligence in combat control systems that carry out their centralized planning and coordination and makes a comparative analysis of foreign novels on issues of legal liability related to the use of artificial intelligence in combat operations management.Military juridical journal["V.A. Rodikova"]2024-09-26T00:00:00 8fe4b1045190adc806ed7b7f5ded45d40b1a0ef8In this regard, this study aims to investigate the peculiarities of the influence of artificial intelligence technologies on the process of independent formation of information culture in adolescents with visual impairment. To achieve the goal, the methods of analysis, deduction, classification, and systematization were used, and an experiment was conducted regarding the influence of artificial intelligence on the education of teenagers with visual impairments in schools in Kazakhstan. This paper reveals the technologies of artificial intelligence and describes the social necessity of providing equal access to education and information for persons with disabilities. The practical part reveals the importance of developing methods and ways to meet the needs related to social interaction and information culture. The study included a questionnaire aimed at understanding the individual experience of visually impaired teenagers in the use of artificial intelligence technologies. The total sample was 30 students aged between 13 and 17 years old. The paper showed an analysis of the impact of technology on visually impaired adolescents and presented statistics on the use of these technologies in children's learning. In addition, the experience of using artificial intelligence technologies was identified and the impact of artificial intelligence technologies on the development of analytical and critical skills of adolescents was investigated. The work revealed possible problems and ways to improve social adaptation and proposed practical recommendations for educational institutions and specialists working with adolescents with visual impairments. The research materials are of practical and theoretical value for pedagogy, typhlo pedagogy, psychology, and sociology, as they help to reveal the prospects of teaching blind or visually impaired children.«Вестник Атырауского университета имени Халела Досмухамедова»00The technologies of artificial intelligence are revealed and the social necessity of providing equal access to education and information for persons with disabilities is described and the importance of developing methods and ways to meet the needs related to social interaction and information culture is revealed.«Вестник Атырауского университета имени Халела Досмухамедова»["Hidayet Dikici", "Bauyrzhan Sikinbayev"]2024-09-26T00:00:00 ac801a09198aa75b63bee235513d10fbc2fe5f64The article provides an understanding of artificial intelligence in the field of HR, and it is said that the digitization of the industry is reflected in the widespread use of artificial intelligence. The theory and history of the field of artificial intelligence is systematized by reviewing the works of researchers. The possibilities of using artificial intelligence are clarified and its efficiency is considered. As a result of the study, it was determined that artificial intelligence is used in four main categories in the field of HR, and it was found that there is a high demand for automated resume search, analysis, and selection services. At the same time, in order to invite candidates for a vacant position, including the identification of qualified talented employees, chatbots, Skype interviews, the use of information on online recruiting platforms such as HeadHunter, notification of vacancies at fairs, and pilot projects introduced by the state will be analyzed. The features of artificial intelligence programs introduced in the Kazakhstan labor exchange, and changes brought to the HR system, the advantages and disadvantages will be discussed. Especially as the most effective programs in the field of artificial intelligence in recent years: IBM's Deep Blue program, Alvinn program for driving, and Microsoft Application and Services Group's talking robot program, the importance of research on the specificity of programs in the field of medicine and pharmaceuticals will be analyzed and considered as examples of regional enterprises.ECONOMIC Series of the Bulletin of the L.N. Gumilyov ENU00It was found that there is a high demand for automated resume search, analysis, and selection services in the field of HR, and it was determined that artificial intelligence is used in four main categories in the field of HR.ECONOMIC Series of the Bulletin of the L.N.Gumilyov ENU["\u0410. Bekisheva", "K. Beketova"]2024-09-26T00:00:00 af8fa91ae0e4a9a7859d2aded0bab814f846125eDevelopments in technology have created great changes in the field of medicine and dentistry. Artificial intelligence technology is one of the most important innovations that caused this change. This study aimed to evaluate the opinions of dentistry students regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Two hundred ninety-six dentistry students between the ages of 19 and 30 participated in the study. Participants submitted the survey by e-mail examining the student's opinions and attitudes regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Respondents' anonymity was ensured. 47.30% (n: 140) of the students participating in the study are fourth-year students, and 52.70% (n: 156) are fifth-year students. While 48.98% (n: 145) of the participants have knowledge about the uses of artificial intelligence in daily life, 28.37% (n: 84) of the students have knowledge about robotic surgery. While ~74% of the participants think that artificial intelligence will improve the field of dentistry and craniomaxillofacial surgery, it has been observed that they are not worried about these applications replacing dentists in the future. It was determined that there was no statistically significant difference between fourth-year and fifth-year students in their knowledge levels about the areas of use of artificial intelligence (P=0.548). Students' opinions show that 74% agree that artificial intelligence will lead to major advances in the field of dentistry and craniomaxillofacial surgery. This shows the relationship between dentists and artificial intelligence points to a bright future.The Journal of craniofacial surgery (Print)310Students' opinions show that 74% agree that artificial intelligence will lead to major advances in the field of dentistry and craniomaxillofacial surgery, which shows the relationship between dentists and artificial intelligence points to a bright future.The Journal of craniofacial surgery["R. Guler", "Emine Yalcin", "Belgin Gulsun"]2024-09-26T00:00:00 7a6a08d501f9c38db8d7e03d8f021992bafbfb7cFuture Health31Future Health["Pragya Pandey", "Shoebul Haque", "Farah Asif", "R. Dixit"]2024-09-26T00:00:00 0cdbde01a40739e0276decf899249418c96e8cd6EDPACS: The EDP Audit, Control, and Security Newsletter171EDPACS["Sunil Kumar"]2024-09-26T00:00:00 d4689d403075e29d0c7b8a800220a23f3ad7f9bfThe Pacific Review411The Pacific Review["Xinlei Zhao"]2024-09-26T00:00:00 3668dc3c1217bfad11cb4e0e13d16921227a9135AI's growing acceptance is changing project management's human-centric approach. Project management is using AI to automate and support duties. This change could improve workflows, decision-making, and project efficiency. The full influence of AI on project success is unknown. There is little empirical evidence linking AI use to project outcomes. This ignorance highlights the necessity to study AI's impact on project management. The project management AI industry is expected to expand 38% annually. Since the late 1980s, AI has improved project management by providing more intelligent and autonomous help. Data privacy, accountability, strategic leadership, communication, innovation, and emotional intelligence are important ethical issues. This study examines how AI adoption affects project success through communication and feedback. This mixed-method study examines how AI adoption affects project success. The quantitative phase measured AI communication, feedback, and project progress via a predefined questionnaire. The sample includes construction, IT, manufacturing, healthcare, and finance project managers and team members. Multiple regression analysis and structural equation modelling were employed in IBM SPSS AMOS to examine AI adoption and project success measures. A qualitative phase of semi-structured interviews with respondents contextualised the quantitative data. Thematic analysis gleaned insights from interview transcripts. AI's impact on project success was examined using integrated data, with ethics in mind. The study examined AI tool-project success relationships using a structural equation model. Communication mode, feedback style, and frequency explained 3% of project success variance. Quantitative research showed that AI communication frequency improves project success, whereas mode and style negatively impact it. Participants' qualitative comments indicated six themes that match quantitative findings, and their replies enhance quantitative results and recommend improvements. The study concluded that AI communication frequency positively increases project success, while mode and style negatively affect it. The mixed-methods approach showed that AI tools alone cannot ensure project success; communication style and frequency are. The study recommended among others that organisations should integrate AI tools into project management systems, match AI communication modes to project team preferences, optimise feedback styles, and provide regular updates to improve AI communication. Project teams need ongoing training. American Journal of Management Science and Engineering140The mixed-methods approach showed that AI tools alone cannot ensure project success; communication style and frequency are; and organisations should integrate AI tools into project management systems, match AI communication modes to project team preferences, optimise feedback styles, and provide regular updates to improve AI communication.American Journal of Management Science and Engineering["Y. Lawal", "I. Abdul-Azeez", "O. Olateju"]2024-09-26T00:00:00 825481d451318db3868afbff76bb112914d3e4a7Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-09-26T00:00:00 ae5c5a2f8ac66230d2395c9960b08eb2b3832af8International Journal of Computer Applications00International Journal of Computer Applications["M.A. El-dosuky"]2024-09-26T00:00:00 51d242ca0f0e1f1510ca4caedb1de606befad31dJournal of Cancer Therapy and Research00Journal of Cancer Therapy and Research["Mrudula Tatakuri"]2024-09-26T00:00:00 96730e3331c978f4d8da69a362ff912e6ff020ddSarah Jane Palmer outlines a recent study that may change the way in which coronary computed tomography angiography is perceived and used.British Journal of Cardiac Nursing00British Journal of Cardiac Nursing["Sarah Jane Palmer"]2024-09-26T00:00:00 5f376032b3d069ea7126baff04f3cde777c3eec6This study aims to investigate factors affecting behavioral intention towards teaching AI in Saudi universities. A random sampling method was used. A total of 430 responses were received. There were 330 males (76.74%), and 100 females (32.25%). All participants were of Saudi nationality and spoke Arabic as their mother tongue. To investigate sample data and assess model fit, this study employs structural equation modeling (SEM). A self-report 15- item survey instrument was developed specifically for this research study, based on Technology Acceptance Model (TAM). Study variables showed significant correlations at the .01 level. BI correlates positively with Performance expectancy (PE), Effort expectancy (EE), Social influence (SI)and Facilitating conditions (FC) (r = .555, .655, .630 and .615 respectively). Each of PE, EE, SI made significant individual contributions to the prediction of BI. The results indicated that the following beta weights which represented the relative contribution of PE, EE, SI and FC to the prediction were observed. PE (b = .411, t = 5.890, P < 0.01), EE (b = .333, t = 5.780, P < 0.01), SI (b = .297, t = 5.230, P < 0.01), and FC (b = .299, t = 5.232, P < 0.01). Together they yielded a coefficient of multiple regression (R) of 0.788 and a multiple correlation square of 0.784. With regard to the academic contribution, this work builds upon previously established and validated literature while simultaneously providing a new conceptual model.مجلة العلوم التربوية و الدراسات الإنسانية00A self-report 15- item survey instrument was developed specifically for this research study, based on Technology Acceptance Model (TAM), and results indicated that the following beta weights which represented the relative contribution of PE, EE, SI and FC to the prediction were observed.مجلة العلوم التربوية و الدراسات الإنسانية["S. Bajaber"]2024-09-26T00:00:00 5148e62ca3195cc6eeb6f666fda1ab846941d035JAMA ophthalmology40JAMA ophthalmology["Shahin Hallaj", "Niloofar Radgoudarzi", "Sally L. Baxter"]2024-09-26T00:00:00 61aa5f895188798c6d6e660a6a4f4307158d9636New England Journal of Medicine50The New England journal of medicine["A. Manrai", "Kun-Hsing Yu", "Isaac S. Kohane"]2024-09-26T00:00:00 e0ed51d849de16b7932abc4011d35c59f62d676eAs a grade 11 student studying in Modern School, Barakhambha Road, New Delhi, I have been fortunate enough to witness the effects of government policies aimed at integrating children from economically weaker sections (EWS) into mainstream education. While the inclusion of EWS children in schools like ours has been a significant stride towards social equity, there are still several challenges to overcome. Our batch (2011) was the first batch where the EWS quota was implemented by the government. All throughout junior school, I kept hearing from my family that it was the noblest idea to integrate children who come from slightly disadvantaged backgrounds into the mainstream, and how it was only through education and equal opportunities that the children of this country would grow to become fine human beings who could lead the world of the future. I was always taught to treat them with kindness and compassion, but also, at the same time, in an effort to be inclusive, was taught to be careful about not hurting them by making them feel different or pointing out the differences between our upbringings. The presence of EWS children in our school is a testament to the success of policies such as the Right to Education (RTE) Act, which mandates private schools to reserve a percentage of their seats for underprivileged children. This initiative has provided EWS children with access to quality education, creating opportunities for their future. However, the social aspect of integration is an area that still requires attention. One of the most glaring challenges is the lack of interaction between children belonging to higher socio-economic strata and those belonging to the EWS. I have myself been a witness to unequal treatment, even though it was largely inadvertent. This is because schools ask children to engage in activities that may not necessarily be within the means of an EWS household. Activities such as domestic field trips and international exchange programmes challenge even affluent households at times. It is not uncommon to see students from different socio-economic backgrounds form separate social circles. This social divide can have a profound impact on the EWS children, making them feel isolated and unwelcome. Such feelings can further affect their academic performance and emotional well-being. As a student, I believe it is crucial to explore ways to foster better relationships between students from diverse backgrounds.Journal of Educational Research and Policies70While the inclusion of EWS children in schools like the authors' has been a significant stride towards social equity, there are still several challenges to overcome, and it is crucial to explore ways to foster better relationships between students from diverse backgrounds.Journal of Educational Research and Policies["Amitesh Kumar Singam"]2024-09-26T00:00:00 88d2166c2a8007d353e4eb03f87db157477d00eeInternational Journal of Computer Applications00International Journal of Computer Applications["Jaskirat Kaur"]2024-09-26T00:00:00 a85b3661f0a5dec39dbbc1e3bf898263d1913c82International Journal of Computer Applications00International Journal of Computer Applications["Mayur Jariwala"]2024-09-26T00:00:00 f1d080965083d9516151426bd38ea222dcd7bdeeXXVI МЕЖДУНАРОДНАЯ НАУЧНО-ПРАКТИЧЕСКАЯ КОНФЕРЕНЦИЯ «Развитие науки и практики в глобально меняющемся мире в условиях рисков»00XXVI МЕЖДУНАРОДНАЯ НАУЧНО-ПРАКТИЧЕСКАЯ КОНФЕРЕНЦИЯ «Развитие науки и практики в глобально меняющемся мире в условиях рисков»["\u0415.\u0410. \u041d\u0430\u0433\u0430\u0435\u0432\u0430", "\u0410.\u0418. \u0413\u0430\u043b\u0443\u0448\u043a\u0438\u043d\u0430"]2024-09-26T00:00:00 863f44135386bf1f3e04372faba714feec44f0ccAbstract Background Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. Objectives This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. Materials and Methods In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. Results Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided. Discussion AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow. Conclusions Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.J. Am. Medical Informatics Assoc.593This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients.Journal of the American Medical Informatics Association : JAMIA["Steven Labkoff", "Bilikis Oladimeji", "J. Kannry", "Anthony Solomonides", "Russell Leftwich", "Eileen Koski", "Amanda L. Joseph", "Monica Lopez-Gonzalez", "Lee A Fleisher", "Kim D Nolen", "Sayon Dutta", "Deborah R Levy", "Amy Price", "Paul J. Barr", "Jonathan D Hron", "Baihan Lin", "Gyana Srivastava", "N\u00faria Pastor", "Unai S\u00e1nchez Luque", "Tien Thi Thuy Bui", "Reva Singh", "Tayler Williams", "Mark G. Weiner", "Tristan Naumann", "Dean F. Sittig", "Gretchen Purcell Jackson", "Yuri Quintana"]2024-09-26T00:00:00 fc437ca750d3a750a77137eead73d695a3a709efOne of the biggest issues today is the increasing intricacy of supply chain networks and supply chain networks becoming more global. Following is the research paper on supply chain management accompanying the integration of AI & ML for effectiveness & efficiency & reduction of cost impacts. The purpose of the research is to assess the effectiveness of adoption of Artificial Intelligence and Machine Learning tools based on predictive analytics, automation, and real-time decision models with supply chain management tendencies in demand forecasting, inventory control, and logistics. In line with the research design that is mixed method, the data were obtained from high-impact case studies and industry reports with further support from the literature review. The usefulness analysis of AI and ML was conducted in line with the supply chain performance measures that include lead time, cost and system metrics for the actual implementation. The results suggest that the application of AI and ML leads to the key performance improvement in companies, such as an average of 20% decrease in operational costs and 15% shorter delivery times. The study will also provide a new understanding of the real world incorporation of AI and ML in supply chain and a path forward in the literature and practice. These technologies reveal the development prospect of how these supply chains can be rebuilt to be more robust as well as flexible.International Journal For Multidisciplinary Research353The results suggest that the application of AI and ML leads to the key performance improvement in companies, such as an average of 20% decrease in operational costs and 15% shorter delivery times.International Journal For Multidisciplinary Research["Syed Kamrul Hasan", "Md Ariful Islam", "Ayesha Islam Asha", "Shaya afrin Priya", "Nishat Margia Islam"]2024-09-26T00:00:00 40b90acb20a93eb0df0b95d4d14de649016b2320BACKGROUND Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings. METHODS The performance of two cloud-based CXR AI systems — one to detect TB and the other to detect CXR abnormalities — in a population with a high TB and human immunodeficiency virus (HIV) burden was evaluated. We recruited 1978 adults who had TB symptoms, were close contacts of known TB patients, or were newly diagnosed with HIV at three clinical sites. The TB-detecting AI (TB AI) scores were converted to binary using two thresholds: a high-sensitivity threshold and an exploratory threshold designed to resemble radiologist performance. Ten radiologists reviewed images for signs of TB, blinded to the reference standard. Primary analysis measured AI detection noninferiority to radiologist performance. Secondary analysis evaluated AI detection as compared with the World Health Organization (WHO) targets (90% sensitivity, 70% specificity). Both used an absolute margin of 5%. The abnormality-detecting AI (abnormality AI) was evaluated for noninferiority to a high-sensitivity target suitable for triaging (90% sensitivity, 50% specificity). RESULTS Of the 1910 patients analyzed, 1827 (96%) had conclusive TB status, of which 649 (36%) were HIV positive and 192 (11%) were TB positive. The TB AI’s sensitivity and specificity were 87% and 70%, respectively, at the high-sensitivity threshold and 78% and 82%, respectively, at the balanced threshold. Radiologists’ mean sensitivity was 76% and mean specificity was 82%. At the high-sensitivity threshold, the TB AI was noninferior to average radiologist sensitivity (P<0.001) but not to average radiologist specificity (P=0.99) and was higher than the WHO target for specificity but not sensitivity. At the balanced threshold, the TB AI was comparable to radiologists. The abnormality AI’s sensitivity and specificity were 97% and 79%, respectively, with both meeting the prespecified targets. CONCLUSIONS The CXR TB AI was noninferior to radiologists for active pulmonary TB triaging in a population with a high TB and HIV burden. Neither the TB AI nor the radiologists met WHO recommendations for sensitivity in the study population. AI can also be used to detect other CXR abnormalities in the same population.NEJM AI261The CXR TB AI was noninferior to radiologists for active pulmonary TB triaging in a population with a high TB and HIV burden and neither the TB AI nor the radiologists met WHO recommendations for sensitivity in the study population.NEJM AI["Sahar Kazemzadeh", "A. Kiraly", "Zaid Nabulsi", "N. Sanjase", "Minyoi Maimbolwa", "Brian Shuma", "Shahar Jamshy", "Christina Chen", "Arnav Agharwal", "Charles Lau", "Andrew Sellergren", "Daniel Golden", "Jin Yu", "Eric Wu", "Yossi Matias", "Katherine Chou", "G. Corrado", "S. Shetty", "Daniel Tse", "Krish Eswaran", "Yun Liu", "Rory Pilgrim", "M. Muyoyeta", "Shruthi Prabhakara"]2024-09-26T00:00:00 b71f1726cb09844df4abf34712883f5973110434Artificial Intelligence (AI) is transforming educational technology by enhancing both teaching and learning processes. This paper examines the “LearnStreamAI” project at Technikum Wien, which integrates an AI-driven chatbot within the Moodle LMS to support real-time student interactions. Utilizing advanced AI technologies like OpenAI’s ChatGPT-4, Google Gemini, and Anthropic Claude3, the chatbot adapts to individual students’ knowledge levels, provides tailored feedback, and enhances engagement through interactive elements in quizzes. Multilingual subtitles using OpenAI’s Whisper technology further improve accessibility for diverse student bodies. In parallel, a toolchain has been developed that automates the creation of academic materials using a PowerShell script and the OpenAI API, based on an easily maintainable Excel input file. This includes generating PowerPoint slides, Moodlecompatible questions, and detailed topic descriptions, all exported into Moodle XML format. The approach ensures accessibility and ease of use for lecturers across various disciplines. The results indicate significant time savings and improved consistency in material preparation. Feedback from pilot studies shows that the AI-generated content is clear, relevant, and well-aligned with academic goals. The system also aids lecturers in quickly acquiring new knowledge by explaining, translating, and summarizing literature. This paper discusses the design, implementation, benefits, and potential improvements of this AI-driven tool, highlighting its role in modern academic teaching and the associated challenges and ethical considerations.International Conference on Software, Telecommunications and Computer Networks131This paper examines the “LearnStreamAI” project at Technikum Wien, which integrates an AI-driven chatbot within the Moodle LMS to support real-time student interactions, highlighting its role in modern academic teaching and the associated challenges and ethical considerations.2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)["Lars Mehnen", "Birgit Pohn"]2024-09-26T00:00:00 0351ac72c23b3d9bd4855606f630a0d705892c67The increasingly digital nature of modern societies necessitates continually examining how individuals interact in the public sphere. This systematic literature review comprehensively analyzes emerging research on public interaction across diverse contexts. By employing an innovative method of applying artificial intelligence on a large-scale academic corpus, we systematically identified and categorized eight major research clusters: social media and public discourse; public Governance in health and education; urban environments and data systems; group interaction dynamics; complex systems modeling; human-display interfaces; political processes; and public service design. Sub-topic mapping revealed key themes such as digital civic engagement, transport sustainability, behavioral dynamics, and socio-environmental impacts. Our interdisciplinary synthesis highlights public interaction as a multifaceted phenomenon intertwined with technological change, policy decisions, environmental factors, and social constructs. These insights underscore the need for holistic, cross-disciplinary approaches to navigate the challenges and opportunities of public interaction in our rapidly evolving digital age. This review provides a unified knowledge base to guide future research while informing decision-makers on cultivating participatory, adaptive, and sustainable public spheres.Societies621This interdisciplinary synthesis highlights public interaction as a multifaceted phenomenon intertwined with technological change, policy decisions, environmental factors, and social constructs that underscores the need for holistic, cross-disciplinary approaches to navigate the challenges and opportunities of public interaction in the authors' rapidly evolving digital age.Societies["Jasmin Schmank", "R\u00fcdiger Buchkremer"]2024-09-26T00:00:00 bba94be0ec65b1c872924c3fc27fb460c5dcf50bChronic renal disease (CRD) is a significant concern in the field of healthcare, highlighting the crucial need of early and accurate prediction in order to provide prompt treatments and enhance patient outcomes. This article presents an end-to-end predictive model for the binary classification of CRD in healthcare, addressing the crucial need for early and accurate predictions to enhance patient outcomes. Through hyperparameter optimization using GridSearchCV, we significantly improve model performance. Leveraging a range of machine learning (ML) techniques, our approach achieves a high predictive accuracy of 99.07% for random forest, extra trees classifier, logistic regression with L2 penalty, and artificial neural networks (ANN). Through rigorous evaluation, the logistic regression with L2 penalty emerges as the top performer, demonstrating consistent performance. Moreover, integration of Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), enhances interpretability and reveals insights into model decision-making. By emphasizing an end-to-end model development process, from data collection to deployment, our system enables real-time predictions and informed healthcare decisions. This comprehensive approach underscores the potential of predictive modeling in healthcare to optimize clinical decision-making and improve patient care outcomes.PeerJ Computer Science241An end-to-end predictive model for the binary classification of CRD in healthcare, addressing the crucial need for early and accurate predictions to enhance patient outcomes is presented and hyperparameter optimization using GridSearchCV significantly improves model performance.PeerJ Computer Science["Sanjana Singamsetty", "Swetha Ghanta", "Sujit Biswas", "A. K. Pradhan"]2024-09-26T00:00:00 9aaad7de235f7bc1f37e61d313223c0b413b69e8The high workloads involved in clinical documentation represent one of the major factors contributing to the significant escalation of clinician burnout. The emergence of artificial intelligence (AI) has provided new avenues for relieving this burden by automating certain tasks like clinical documentation through the generation of clinical notes from a transcript of a clinical encounter. The advances in large language models (LLMs) have led to the emergence of such startups, but they come with their own set of challenges, predominantly surrounding the concerns of documentation accuracy, completeness, and data security. These can be addressed with a multi-faceted approach which could include fine-tuning the currently available models; using domain-specific models and in-house AI systems to ensure data security; and involving smaller LLMs and clinicians in the development and implementation of such systems. We can imagine a future where these systems are deeply incorporated into electronic health records, providing not only automated clinical documentation but also improving Clinical Decision Support systems, research, and patient communication.Artificial Intelligence in Health01A future where large language models are deeply incorporated into electronic health records, providing not only automated clinical documentation but also improving Clinical Decision Support systems, research, and patient communication is imagined.Artificial Intelligence in Health["Khalid Nawab"]2024-09-27T00:00:00 04c081027493b7f2d4f93a46aec225cbb1862627This paper presents a Blockchain-Based Knowledge Repository (BBKR) tailored for the secure storage and management of training data crucial for Artificial Intelligence and Machine Learning (AIML) models. Leveraging Ethereum blockchain technology and smart contracts, the BBKR ensures data integrity, enhances security measures, and promotes decentralization principles. The proposed mechanism includes a detailed explanation of data validation processes, decentralized access control, and scalable transaction mechanisms. Our empirical evaluations validate the efficacy of BBKR in upholding data integrity, enhancing scalability, and fortifying security provisions. The results show significant improvements in data management performance compared to traditional centralized repositories. This research underscores the transformative potential of BBKR in revolutionizing AIML data management paradigms and offers a robust foundation for further advancements in the field. Future work will explore real-world applications and potential enhancements using advanced cryptographic techniques.2024 IEEE Region 10 Symposium (TENSYMP)01Empirical evaluations validate the efficacy of BBKR in upholding data integrity, enhancing scalability, and fortifying security provisions and show significant improvements in data management performance compared to traditional centralized repositories.2024 IEEE Region 10 Symposium (TENSYMP)["Amit Kumar Das", "Md. Thouhedul Alam Tonoy", "Meherab Hossain"]2024-09-27T00:00:00 373b75988fa48f96bd658a156f9501ffed9c357dThe use of artificial intelligence (AI) is related to the dynamic development of digital skills. This article focuses on the impact of AI on the work of non-profit organizations that aim to help those around them. Based on 10 semi-structured interviews, it is presented here how it is possible to work with AI and in which areas it can be used—in social marketing, project management, routine bureaucracy. At the same time, workers and volunteers need to be educated in critical and logical thinking more than ever before. These days, AI is becoming more and more present in almost all the activities, bringing several benefits to those making use of it. On the one hand, by using AI in the day-to-day activities, the entities are able to substantially decrease their costs and have the advantage of being able to have, in most cases, a better and faster job done. On the other hand, those individuals that are more creative and more innovative in their line of work should not feel threatened by those situations in which organizations decide to use more AI technologies rather than human beings for the routine activities, since they will get the opportunity to perform tasks that truly require their intellectual capital and decision making abilities.Journal of Infrastructure Policy and Development562The impact of AI on the work of non-profit organizations that aim to help those around them is focused on and it is presented here how it is possible to work with AI and in which areas it can be used—in social marketing, project management, routine bureaucracy.Journal of Infrastructure, Policy and Development["Cristina Raluca Gh. Popescu", "Jarmila \u0160ebestov\u00e1"]2024-09-27T00:00:00 67ee3fec416c12447465bcff0858dd38a54d9e75This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.Journal of Infrastructure Policy and Development1331Journal of Infrastructure, Policy and Development["Priyank Trivedi", "F. M. Alqahtani"]2024-09-27T00:00:00 4a0578a83a6abc581103330e66f43ee40026fcd1Scientific Reports381Artificial neural networks are implemented as the AI-based application, and two types of the networks, i.e., feedforward (as a basic type) and long short-term memory (LSTM)-based network as a more complex network are tested.Scientific Reports["Mohammad Reza Habibi", "Josep M. Guerrero", "J. Vasquez"]2024-09-27T00:00:00 30bc19d9c1909aff3e832780bbf8bee9f65dbe5dIn the age of artificial intelligence, the realm of computer information security has transformed from a sandbox of niche interest to a vast, complex landscape critical to the functioning of modern society. As AI technologies permeate various sectors, they unlock unprecedented opportunities for innovation and progress, yet simultaneously engender a multitude of security challenges. The confluence of AI and information systems necessitates a nuanced examination of threats and the evolution of protective strategies designed to safeguard data integrity, confidentiality, and availability. This paper delves into the intricate dynamics of these new threats, exploring how AI techniques are being harnessed to fortify computer information security, and outlines a strategic framework for enhancing the robustness of security measures in this era. It aims to contribute to the discourse on cultivating a secure and resilient digital world, enriched by the potential of AI while being vigilant against its inherent risks.Modern Management Science &amp; Engineering00This paper delves into the intricate dynamics of these new threats, exploring how AI techniques are being harnessed to fortify computer information security, and outlines a strategic framework for enhancing the robustness of security measures in this era.Modern Management Science &amp; Engineering["Dong Liu", "Lei Zhang"]2024-09-27T00:00:00 261f0da4f6d8bbcb12e1acc9e42165a79a2a21ccThis paper applies a ‘more-than-human’ theoretical framework to assess artificial intelligence (AI) in the context of a capitalist economy. Case studies of AI applications from the fields of finance, medicine, commerce and manufacturing elucidate how this capitalist context shapes the aims and objectives of these innovations. The early sections of the paper set out a more-than-human theoretical perspective on capitalism, to show how the accumulation of capital depends upon free flows of commodities, money and labour, and more-than-human forces associated with supply and demand. The paper concludes that while there will be many future applications of AI, it is already in thrall to capitalist enterprise. The primary social significance of AI is that it enhances capital accumulation and a capitalist ‘black hole’ that draws more and more human activity into its sphere of influence. AI has consequent negative social, political and environmental capacities, including financial uncertainty, waste, and social inequalities. Some ways to contain and even subvert these negative consequences of an AI-fuelled capitalism are suggested.The social science760While there will be many future applications of AI, it is already in thrall to capitalist enterprise, and some ways to contain and even subvert these negative consequences of an AI-fuelled capitalism are suggested.Social Sciences["Nick J. Fox"]2024-09-27T00:00:00 bbe865e240c848f333c64df59ef7ab5dc569c9bbThe disruptive forces of the COVID‐19 pandemic offer an example of how cutting‐edge innovations such as telehealth became established in society. Simultaneous to the rise of telehealth, artificial intelligence (AI) has advanced rapidly and with the potential to further disrupt services across the spectrum of technology and healthcare delivery. Deemed as the next frontier in the mental health field, AI technology has introduced cutting‐edge innovations within human‐centred fields across disciplines (Espejo [Academic Psychiatry, 47, 437 and 2023]). This paper calls into question the transformative potential of AI in a field, such as psychotherapy and professional counselling, which is significantly based on human relations. As professional counsellors, it is imperative that AI does not dehumanise effective services based on empathy and positive regard.This article reviews the current landscape of AI and counselling research and offers two main messages: (1) what new or revised ethical standards are needed for clinical practice to prevent negative consequences of improper use when integrating AI and (2) the practical implications for effective multicultural counselling when integrating AI into psychotherapy and counselling services.Counselling and Psychotherapy Research100The current landscape of AI and counselling research is reviewed and what new or revised ethical standards are needed for clinical practice to prevent negative consequences of improper use when integrating AI and the practical implications for effective multicultural counselling when integrating AI into psychotherapy and counselling services are offered.Counselling and Psychotherapy Research["Chidozie Urom", "Brittn Grey", "Sylvia Lindinger\u2010Sternart", "Samantha Lucey"]2024-09-27T00:00:00 dae5693789f0d8857892188d9a54398a6413dd33Artificial Intelligence (AI) is fundamentally transforming the labor market, impacting various job sectors through cognitive AI, machine learning, and deep learning. These technologies enhance productivity and efficiency across industries, including finance, healthcare, and customer service. However, they also pose risks such as job displacement, economic inequality, and ethical dilemmas. This study utilizes a mixed-methods approach, combining quantitative data analysis with qualitative interviews to examine the impact of AI on employment. The research focuses on different AI technologies' effects on various job roles and sectors, highlighting the dichotomy between high-skill and low-skill workers. The findings reveal that AI-driven automation primarily affects low-skill jobs, leading to structural unemployment and increased job insecurity. High-skill workers benefit from productivity gains and new professional roles, while low-skill workers face greater competition and job displacement. The study also highlights the paradox of AI-enhanced efficiency not translating into reduced working hours, particularly in competitive industries. The introduction of AI has not realized John Maynard Keynes' prediction of reduced working hours and increased leisure time. Instead, it has led to longer working hours and continuous upskilling demands. The study underscores the need for regulatory measures to balance AI adoption with the protection of human jobs and rights. Recommendations include job retraining programs, ethical AI development standards, and fair labor practices.Academic Journal of Management and Social Sciences110The findings reveal that AI-driven automation primarily affects low-skill jobs, leading to structural unemployment and increased job insecurity, and underscores the need for regulatory measures to balance AI adoption with the protection of human jobs and rights.Academic Journal of Management and Social Sciences["Qingshuang Song"]2024-09-27T00:00:00 16bd8813d4fe727cfb3334d284d1f0ec02976719In the rapidly evolving field of artificial intelligence (AI), the promise of significant advancements is accompanied by growing concerns about potential risks. In response to these concerns, the European Union introduces the revolutionary Artificial Intelligence Act (EU AI Act), presenting an innovative risk based framework to regulate AI models. This study offers a thorough exploration of the Act's fundamental concepts, structural components, and practical applications in the realm of ethical AI research and utilization. Additionally, the paper conducts a comparative analysis, juxtaposing the EU AI Act's approach with existing methodologies. Integrating a “criticality index” in AI frameworks lets stakeholders prioritize risk management based on potential harm, promoting responsible AI development in line with the EU AI Act.2024 IEEE Region 10 Symposium (TENSYMP)110A thorough exploration of the EU AI Act's fundamental concepts, structural components, and practical applications in the realm of ethical AI research and utilization and a comparative analysis is conducted, juxtaposing the EU AI Act's approach with existing methodologies.2024 IEEE Region 10 Symposium (TENSYMP)["Soja Salim", "J. S.", "Soniya B."]2024-09-27T00:00:00 7b7b3fff2d2380891378e37f431453e8ef6cddf4In this study, we explore the feasibility and efficacy of leveraging Sanbot Elf – a humanoid intelligent assistive robot – integrated with artificial intelligence (AI), specifically the Vivaldi AI system, for functional assessment in amyotrophic lateral sclerosis (ALS) patients. Our investigation involves evaluating and comparing the performance of the Sanbot Elf in administering the ALS Functional Rating Scale–Revised (ALSFRS-R) to that of human operators, using a structured format where patients respond with either “yes” or “no” answers. This approach is intentionally adopted to minimize ambiguity in patient responses. Patients were given the option to respond either verbally or by utilizing the touchscreen display, particularly beneficial for those experiencing dysarthria or hypophonia. In addition, we examined patient emotional responses to this novel approach. A cohort of 28 ALS patients participated in the study, with a subset undergoing longitudinal follow-up assessments. Our results demonstrate strong agreement between human and robotic administrations of the ALSFRS-R, indicating the potential for AI-enabled robotics to accurately assess ALS functional status. Furthermore, the patients’ feedback underscores their acceptability of this technology as a supportive tool in healthcare settings. Our findings also highlight the potential benefits of employing robotic devices with algorithmic capabilities, such as the binary tree method, in hospitals. Moreover, such integration has the potential to alleviate operators’ workload. Importantly, this research contributes to the burgeoning field of AI-enabled healthcare operations, highlighting the promising role of robotic systems in enhancing functional assessment and management of ALS.Artificial Intelligence in Health00Strong agreement between human and robotic administrations of the ALSFRS-R, indicating the potential for AI-enabled robotics to accurately assess ALS functional status and highlighting the potential of employing robotic devices with algorithmic capabilities, such as the binary tree method, in hospitals.Artificial Intelligence in Health["Jacopo Luca Casiraghi", "A. Lizio", "Silvia Bolognini", "David Tessaro", "Matteo Xia", "Giacomo Sommavilla", "Matteo Cestari", "Elena Carraro", "Francesca Gerardi", "S. Regondi", "Raffaele Pugliese", "Valeria Ada Sansone", "Federica Cerri"]2024-09-27T00:00:00 b43529436dbf7fac148af43179f917e0cb48be13This study examined the relationship between Artificial Intelligence (AI), innovation, employment andgrowth of SMEs in Nigeria. Principal component analysis (PCA) and the structural equation model (SEM) were used to examine the role of AI on SMEs growth in Soutwest, Nigeria. The population of the study focused on SMEs firms engaging in manufacturing, hospitality, information and communication, and administrative and support services sectors. A sample size of 322 was adopted using Krejcie and Morgan method. Results of study showed that AI innovation indicators have positive relationship with SMEs growth. It was also observed that AI employment indicators exert a direct relationship with the latent factor. Overall results showed that applications of AI construct indicators remain (direct, indirect and total effect) strong on SMEs growth. Implications of the research results will offer a deeper insight for owners of SMEs, entrepreneurs, academic researchers and stakeholders to promote economic growth and development.African Journal of Economics and Business Research660Overall results showed that applications of AI construct indicators remain strong on SMEs growth, and it was observed that AI employment indicators exert a direct relationship with the latent factor.African Journal of Economics and Business Research["Itai Muktar", "Daniel Ufua", "U. Okorie"]2024-09-27T00:00:00 aedb5314f89bc1548754e7146adf7968188ddca8In Nigeria, the healthcare sector faces big challenges. Limited access to quality services and not enough resources are major issues. Using Artificial Intelligence (AI) could help improve healthcare. But understanding AI predictions is hard, especially in healthcare where transparency is crucial. This article looks at Explainable AI (XAI) to help with this problem in Nigeria. It talks about XAI techniques like feature importance examination, model-agnostic methods (e.g., LIME, SHAP), and interactive visualization tools. These tools can make AI models easier to understand and help with decision-making. A literature review was done to see how XAI can help healthcare in Nigeria. The review included scholarly articles, books, and reports on AI in Nigerian healthcare. We looked at methods from past XAI studies to find common approaches and best practices. XAI offers techniques that make AI models easier to understand in healthcare systems. These techniques include feature importance examination, model-agnostic methods, and interactive visualization tools. Case studies from Nigeria show how XAI is used in areas like disease diagnosis, treatment recommendations, and public health interventions. The findings show the importance of XAI in solving interpretability issues in healthcare AI, especially in places with limited resources like Nigeria. By explaining why AI makes certain predictions, XAI helps healthcare workers make better decisions for Nigerian patients. However, more research is needed to improve XAI techniques for Nigeria’s healthcare system. Policymakers and healthcare leaders should focus on using XAI-enabled systems to drive innovation and improve healthcare outcomes in Nigeria.Journal of Innovative Research180Explainable AI (XAI) is looked at to help with interpretability issues in healthcare AI, especially in places with limited resources like Nigeria.Journal of Innovative Research["Franka Anyama Undie", "Larisa Vladimirovna Kruglova", "Matthew Okache Okache", "Victor Agorye Undie", "Racheal Aniah Aloye"]2024-09-27T00:00:00 9f9ac40b1938e45a9ccf3b22e0df47b6f4606f84In today's rapidly evolving digital landscape, the proliferation of data has become ubiquitous, with organizations facing an unprecedented influx of information. As a result, the ability to effectively utilize this wealth of data has emerged as a critical determinant of success. This research paper embarks on a comprehensive exploration of the intricate interplay between Business Intelligence (BI), Artificial Intelligence (AI), and Big Data Analytics, elucidating their synergistic potential in driving data-driven decision-making strategies. BI serves as the cornerstone of data-driven endeavors, facilitating the extraction of historical insights through structured data analysis. Conversely, AI augments decision-making capabilities by leveraging advanced techniques such as machine learning and natural language processing to provide predictive and prescriptive analytics. Moreover, Big Data Analytics emerges as a pivotal enabler, addressing the challenges posed by the voluminous and diverse datasets characteristic of the contemporary data landscape. The integration of BI and AI within the framework of Big Data Analytics represents a paradigm shift, enabling organizations to transcend traditional analytical boundaries and embrace a holistic approach to data-driven decision-making. By amalgamating retrospective analysis with predictive and prescriptive capabilities, this integration empowers organizations to glean insights into not only ’what happened’ but also ’why it happened’ and ’what will happen,’ thereby fostering agility and responsiveness in decision-making processes. Furthermore, the paper delves into the requisite hardware and software resources essential for the successful implementation of BI, AI, and Big Data Analytics initiatives. By delineating the infrastructure prerequisites and elucidating the significance of robust technological frameworks, organizations can navigate the complexities of the data landscape with confidence, fostering a culture of data-driven innovation and steering towards transformative outcomes in today's data-centric milieu.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)00This research paper embarks on a comprehensive exploration of the intricate interplay between Business Intelligence, Artificial Intelligence, and Big Data Analytics, elucidating their synergistic potential in driving data-driven decision-making strategies.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)["Umesh Mangal", "Sandeep Mogha", "S. Malik"]2024-09-27T00:00:00 78c74508cc0e9dded271d91d8c6798f7512b9fd5Now more than ever, artificial intelligence (AI) presents both unprecedented potential and difficulties. This article delves deeply into the ethical concerns raised by AI and lays forth principles that should be followed in the creation of ethical AI systems. In the opening, we recognize the revolutionary potential of AI as well as the ethical quandaries it poses. In order to guarantee that AI technologies are created and implemented in a responsible and fair way, a deep ethical examination is required in light of the proliferation of autonomous systems. To address these ethical difficulties, we suggest the adoption of an Ethical Analysis Framework (EAF). The Ethical Assessment Framework (EAF) provides a framework for systematically assessing the fairness, transparency, privacy, accountability, and bias of AI systems. It's a cornerstone for the community of AI ethics researchers, developers, and policymakers working together to keep AI honest. Our approach places special importance on the gathering and preparation of data. We emphasize how important ethically sound data is in defining the moral implications of AI. We found that when compared to other approaches to assessing the morality of AI, our suggested technique consistently outperformed the others. The suggested technique establishes itself as an innovative and effective way of navigating the growing ethical environment by providing a systematic approach to addressing AI's ethical implications and supporting responsible AI research. For instance, as compared to more conventional methods, our technique increased fairness evaluations by an average of 15%. In conclusion, there is a wide range of concerns related to the morality of AI that must be constantly monitored and addressed via joint effort. Our method places an emphasis on making sure AI is progressing in a way that is responsible, ethical, and fair to all members of society.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)200This article delves deeply into the ethical concerns raised by AI and lays forth principles that should be followed in the creation of ethical AI systems and establishes itself as an innovative and effective way of navigating the growing ethical environment.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)["Sukumar R", "Vinima Gambhir", "Jyoti Seth"]2024-09-27T00:00:00 38cc147cc3975f7de701a2edbdc01d58078b6a0aWith the rapid growth and intelligent development of power grid data, privacy protection of this data has become a pressing issue. This paper proposes a differential privacy protection method based on artificial intelligence and federated learning to address the privacy protection needs of power grid data. First, the basic concepts and related research results of differential privacy and federated learning are reviewed, and the advantages and disadvantages of existing power grid data privacy protection schemes are analyzed. Then, a privacy protection system combining differential privacy and federated learning technologies is designed and implemented, including modules for data processing, model training, and privacy protection mechanism integration. Experiments on real power grid datasets verify that this system can effectively improve the accuracy of data analysis and model training while ensuring data privacy. The experimental results show that this method can effectively protect the privacy of power grid data and perform well in various application scenarios. Finally, this paper discusses the prospects of this method in practical applications and future research directions.International Conference on Information Systems and Computer Aided Education110A differential privacy protection method based on artificial intelligence and federated learning to address the privacy protection needs of power grid data is proposed and results show that this method can effectively protect the privacy of power grid data and perform well in various application scenarios.2024 IEEE 7th International Conference on Information Systems and Computer Aided Education (ICISCAE)["Zhao Zhang", "Fan Wu", "Wentao Jiao"]2024-09-27T00:00:00 e704b7c82b24da07df409cfb00a589bb288751eeThis editorial synthesizes recent articles published in the Review of Artificial Intelligence in Education, highlighting the transformative role of artificial intelligence (AI) in education, research, and various sectors. The discussed topics include AI-driven innovation, ethical challenges in the adoption of these technologies, and the impact of AI on higher education institutions and scientific integrity. The articles also explore AI integration in fields such as agriculture and human resources, along with the legal and moral implications of AI use in education. The editorial underscores the need for responsible and ethical implementation, guided by robust regulatory frameworks, to ensure that AI fosters equity and integrity in its applications.SDGs Studies Review230The editorial underscores the need for responsible and ethical implementation, guided by robust regulatory frameworks, to ensure that AI fosters equity and integrity in its applications.SDGs Studies Review["Altieres de Oliveira Silva", "Diego dos Santos Janes", "Renan Santos"]2024-09-27T00:00:00 ef36da32150bd780727e143fa164b4904c145dbaThe article discusses modern approaches to automating data analysis using artificial intelligence (AI) methods. With the rapid growth of data volumes entering various systems, their analysis and processing are becoming a complex task. Automating these processes with AI allows us to increase the efficiency and accuracy of data analysis, minimize the human factor, and speed up decision-making. The article discusses machine learning and deep learning methods used to automate data analysis, as well as examples of their application in various industries, such as finance, medicine, industry, and marketing. Particular attention is paid to the advantages and limitations of existing approaches, as well as prospects for their further development. The article discusses in detail the conditions and methods of research aimed at studying and evaluating the effectiveness of various AI models in automating data analysis. The obtained results are analyzed and prospects for further development of AI technologies in this area are discussed. The study emphasizes the importance of interpretability of AI models, the need to develop new methods that can effectively work with limited and noisy data, as well as reducing the computational costs associated with their use.Bulletin of Shakarim University Technical Sciences80The study emphasizes the importance of interpretability of AI models, the need to develop new methods that can effectively work with limited and noisy data, as well as reducing the computational costs associated with their use.Bulletin of Shakarim University. Technical Sciences["V. I. Shumkin", "S. Kaysanov"]2024-09-27T00:00:00 d9cdcbf44314b0160433a6b716e71ae7211e867dAnnals of Biomedical Engineering60The "publish or perish" culture in academia has intensified trends in medical research, particularly around artificial intelligence (AI) and machine learning, and the pressure to publish positive findings during research trends exacerbates the replication crisis.Annals of biomedical engineering["Obada Nayef Al-leimon", "M. Juweid"]2024-09-27T00:00:00 1e6f601583ab10a993edbed9f2da21f9b528f5e6The expansion of technology that utilizes artificial intelligence (AI) has permeated a variety of fields, including international law and human rights. In this essay, the implications of artificial intelligence in the context of international crimes against humanity are investigated. As part of this investigation, both the obstacles and potential solutions that AI brings in this field are evaluated. The dual-use nature of artificial intelligence technologies, their role in expanding surveillance and enforcement capacities, as well as the ethical and legal challenges that are faced by these technologies are brought to light. The use of artificial intelligence in this delicate field raises basic problems regarding the appropriate balance between the progression of technology and the protection of fundamental democratic rights.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)00The implications of artificial intelligence in the context of international crimes against humanity are investigated and both the obstacles and potential solutions that AI brings in this field are evaluated.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)["A. T. A. S. Rahman", "Amritpal Kaur", "Priya Saroj"]2024-09-27T00:00:00 6500ef4f8a5aa1be9a968472a13cfb9e81acafa1The IT industry has benefited greatly from the development of artificial intelligence (AI) in the healthcare sector. Analyzing the connections between healthcare practices and patient outcomes is the goal of AI approaches connected to health. AI will be useful in the healthcare industry going forward because to the complexity and growth of data in this sector. Its capability aids in the production of accurate health reports, statistics, appropriate diagnostic techniques, etc. for both physicians and patients. AI has emerged as the greatest rescuer, providing connections between genetic codes or even surgical support. This falls under several primary categories, including medication development, personalized medicine, administrative tasks, patient monitoring, patient involvement and adherence, and diagnosis and treatment recommendations. The paper reviews a number of AI concepts, including computers with human intelligence, applications in neurology, and disease diagnostics. For artificial intelligence to be used in healthcare more practically, the primary challenges still need to be addressed. It is crucial to develop AI algorithms that can be explained as well as to collect extensive data.2024 IEEE Region 10 Symposium (TENSYMP)130A number of AI concepts are reviewed, including computers with human intelligence, applications in neurology, and disease diagnostics, which aid in the production of accurate health reports, statistics, appropriate diagnostic techniques, etc.2024 IEEE Region 10 Symposium (TENSYMP)["Niharika Nagpal", "Anshika Sharma", "Sugandha Singh"]2024-09-27T00:00:00 6cde229802d77e7ddb1fe3bf16c592c9323ea9dcBecause of its high level of complexity and data collection capabilities, the healthcare industry employs artificial intelligence (AI) more than other sectors. Artificial intelligence is becoming an increasingly important part of our daily routines. AI's features include extremely low error rates, constant accessibility, quick analysis, and real-time data dissemination. This paper examines the crucial role of artificial intelligence in intelligent healthcare systems, with a particular emphasis on enhancing healthcare delivery. Artificial intelligence in healthcare is rapidly evolving, with the potential to significantly enhance patient care, diagnosis, treatment, and cost distribution. It also presents issues of transparency, honesty, and availability. This study will look into the benefits, drawbacks, and other aspects of artificial intelligence in intelligent healthcare systems.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)00This study will look into the benefits, drawbacks, and other aspects of artificial intelligence in intelligent healthcare systems, with a particular emphasis on enhancing healthcare delivery.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)["Sneha Nagar", "Mukesh Patidar", "Rashid Sheikh", "Sudhanshu Singh", "Ashutosh Kashiv", "Ankit Jain", "Shravan Kumar Namdeo", "Pranav Paranjpe", "Devendra Singh Mandloi"]2024-09-27T00:00:00 277bef2f7b7103315312dbd10ee4213916439876Introduction. This article focuses on the importance and prospects for the use of artificial intelligence in predictive analytics in the criminal justice context. The research is motivated by the significant development of artificial intelligence and machine learning technologies, which are being used in a multitude of fields, including criminal justice. The authors detail the theoretical and practical aspects of predictive analytics, which makes it possible to predict future events based on statistical data and machine learning algorithms. Special attention is paid to the difference between artificial intelligence and predictive analytics. The effectiveness of the application of predictive analytics in criminal justice, including optimising preliminary investigations, improving criminal prosecution and predicting the outcome of criminal cases, is highlighted. Methods. The basis of the research methodology is dialectical materialism, applied general scientific (system-structural and formallogical, inductive and deductive, analysis and synthesis) and special (formal-legal, comparativelegal) methods. Results. The authors conclude that artificial intelligence spans a wider range of tasks requiring human intelligence, while predictive analytics concentrates on making predictions. Advanced technologies that are already in active use in various countries, improving and optimising the allocation of law enforcement and judicial resources, are described. The prospect of integrating virtual and augmented reality technologies into criminal justice is considered, which can radically change approaches to predictive analytics and criminal procedure in general, enriching visualisation and interactive cooperation between participants of legal relations.Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia50The authors conclude that artificial intelligence spans a wider range of tasks requiring human intelligence, while predictive analytics concentrates on making predictions.Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia["R. Kostenko", "Aleksey Ilyashenko"]2024-09-27T00:00:00 88b6efb0edc940847704c73701f61b82ff85fdf6Humanities and Social Sciences Communications345Humanities and Social Sciences Communications["Yujie Sun", "Dongfang Sheng", "Zihan Zhou", "Yifei Wu"]2024-09-27T00:00:00 6db8a820b4ebbe60d9ea1f1242a5434709813fd5Professional Development in Education373Professional Development in Education["M. Rice", "Nicholas DePascal", "Joaqu\u00edn T. Arg\u00fcello de Jes\u00fas", "Helen McFeely", "Amy Traylor", "Lehman Heaviland"]2024-09-27T00:00:00 281f1445928d60884929227fba584764871234cdGenerating code via a LLM (rather than writing code from scratch), has exploded in popularity. However, the security implications of LLM-generated code are still unknown. We performed a study that compared the security and quality of human-written code with that of LLM-generated code, for a wide range of programming tasks, including data structures, algorithms, cryptographic routines, and LeetCode questions. To assess code security we used unit testing, fuzzing, and static analysis. For code quality, we focused on complexity and size. We found that LLM can generate incorrect code that fails to implement the required functionality, especially for more complicated tasks; such errors can be subtle. For example, for the cryptographic algorithm SHA1, LLM generated an incorrect implementation that nevertheless compiles. In cases where its functionality was correct, we found that LLM-generated code is less secure, primarily due to the lack of defensive programming constructs, which invites a host of security issues such as buffer overflows or integer overflows. Fuzzing has revealed that LLM-generated code is more prone to hangs and crashes than human-written code. Quality-wise, we found that LLM generates bare-bones code that lacks defensive programming constructs, and is typically more complex (per line of code) compared to human-written code. Next, we constructed a feedback loop that asked the LLM to re-generate the code and eliminate the found issues (e.g., malloc overflow, array index out of bounds, null dereferences). We found that the LLM fails to eliminate such issues consistently: while succeeding in some cases, we found instances where the re-generated, supposedly more secure code, contains new issues; we also found that upon prompting, LLM can introduce issues in files that were issues-free before prompting.arXiv.org421A study that compared the security and quality of human-written code with that of LLM-generated code, for a wide range of programming tasks, including data structures, algorithms, cryptographic routines, and LeetCode questions.ArXiv["Chun Jie Chong", "Z. Yao", "Iulian Neamtiu"]2024-09-27T00:00:00 56b6422c0e79f659d1251f5e3752afe1b3879237Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare10Simulation in healthcare : journal of the Society for Simulation in Healthcare[]2024-09-27T00:00:00 fa291d51f4727e08d27b970020b77f55c6d18ba0The advent of AI and ML has caused a dramatic transformation in the workplace, with far-reaching social and economic consequences for the nature of work and the workforce. At the outset of our research, we identify the necessity for a solid research framework, which leads us to provide the “Proposed Method.” The goal of this approach is to give a more in-depth understanding of the complex relationship between technology and the labor market by integrating many lines of inquiry and methodologies, including but not limited to experimental variety; sophisticated data analysis; policy analysis; ethical issues; and predictive modeling. We support cutting-edge methods of data analysis, including mixed-method and qualitative approaches, since we know their worth. We use predictive modeling to anticipate and prepare for the future of work. With an average performance score of 85 vs. 70 for conventional approaches, the “proposed method” clearly excels. This demonstrates its higher efficiency in comprehending the social effects of AI and ML on the labor market. It highlights the need for strong governance structures while providing useful insights into the ethical and policy implications of AI integration. The findings of this study highlight the need for more interdisciplinary research into the social effects of AI and ML on the labor market. The “Proposed Method” has a sweeping view that includes several types of experiments, large amounts of data, detailed criteria for success, and moral concerns. We push for an in-depth comprehension of the revolutionary forces reshaping the labor market and emphasize the need to include ethical and policy considerations. Our research ensures that we advance into this new age of huge and deep technological possibilities with our eyes wide open and our feet firmly planted on the ground, even as AI and ML continue to change the nature of work.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)200The “Proposed Method” has a sweeping view that includes several types of experiments, large amounts of data, detailed criteria for success, and moral concerns, and pushes for an in-depth comprehension of the revolutionary forces reshaping the labor market.2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET)["Vivek V", "Vinima Gambhir", "Amandeep Gill"]2024-09-27T00:00:00 7ff7c2754a7fc12f4bc2b1e8500afed0be396458Proceedings of the 2024 2nd International Conference on Internet of Things and Cloud Computing Technology30Proceedings of the 2024 2nd International Conference on Internet of Things and Cloud Computing Technology["Zixuan Han", "Chengjun Zhou"]2024-09-27T00:00:00 e23d6583ff9764da7127789ee6b40a38feddcb5a"Scientific notes of the University"KROK"00"Scientific notes of the University"KROK"["\u0412\u0456\u043a\u0442\u043e\u0440 \u041a\u0456\u043d\u0430\u0440\u044c\u043e\u0432", "\u0412\u0456\u043a\u0442\u043e\u0440 \u0410\u043b\u044c\u043a\u0435\u043c\u0430"]2024-09-27T00:00:00 f8cd77f703113759678170fdf7424f78fdf135e6<jats:p xml:lang="tr"/>Selçuk tıp dergisi00Selcuk Tip Dergisi["Can \u00d6zl\u00fc"]2024-09-27T00:00:00 d820cdc656bdbe90384ca3f4614561729f7b0935Aims: To evaluate the diagnostic accuracy of Claude-3, a large language model, in detecting pathological features and diagnosing retinitis pigmentosa and cone-rod dystrophy using pattern electroretinography data. Methods: A subset of pattern electroretinography measurements from healthy individuals, patients with retinitis pigmentosa and cone-rod dystrophy was randomly selected from the PERG-IOBA dataset. The pattern electroretinography and clinical data, including age, gender, visual acuities, were provided to Claude-3 for analysis and diagnostic predictions. The model’s accuracy was assessed in two scenarios: “first choice,” evaluating the accuracy of the primary differential diagnosis and “top 3,” evaluating whether the correct diagnosis was included within the top three differential diagnoses. Results: A total of 46 subjects were included in the study: 20 healthy individuals, 13 patients with retinitis pigmentosa, 13 patients with cone-rod dystrophy. Claude-3 achieved 100% accuracy in detecting the presence or absence of pathology. In the “first choice” scenario, the model demonstrated moderate accuracy in diagnosing retinitis pigmentosa (61.5%) and cone-rod dystrophy (53.8%). However, in the “top 3” scenario, the model’s performance significantly improved, with accuracies of 92.3% for retinitis pigmentosa and 76.9% for cone-rod dystrophy. Conclusion: This is the first study to demonstrate the potential of large language models, specifically Claude-3, in analyzing pattern electroretinography data to diagnose retinal disorders. Despite some limitations, the model’s high accuracy in detecting pathologies and distinguishing between specific diseases highlights the potential of large language models in ocular electrophysiology. Future research should focus on integrating multimodal data, and conducting comparative analyses with human experts.Journal of Health Sciences and Medicine250This is the first study to demonstrate the potential of large language models, specifically Claude-3, in analyzing pattern electroretinography data to diagnose retinal disorders, and Claude-3 achieved 100% accuracy in detecting the presence or absence of pathology.Journal of Health Sciences and Medicine["A. Aykut", "B\u00fc\u015fra Akg\u00fcn", "A. Sezen\u00f6z", "M. O. Sevik", "\u00d6. \u015eahin"]2024-09-27T00:00:00 0b56cc913986016f416f257022972470831c4fefEuropean Journal of Cardio-Thoracic Surgery00European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery["Ume Aiman", "U. Shahzad", "Zainab Azad", "Muhammad Ahmed Sheikh"]2024-09-27T00:00:00 131642c93f2f89ecd339f9e9983f17a4df1b6c11In the era of digitalization, AI-generated content has emerged as a pivotal component within the creative industries, thereby instigating extensive deliberations concerning copyright protection and fair use. This study aims to scrutinize and analyze the equitable use of AI-generated content from both a legal and economic standpoint. This paper does a thorough review of the literature to introduce basic ideas about AI content creation and fair use. It then divides existing research into four separate areas: technical analysis, legal case analysis, economic impact analysis, and policy adaptation research. Despite notable advancements in both technological capabilities and legislative frameworks surrounding AI-generated content, there remains a dearth of comprehensive understanding, particularly with regard to its economic implications and policy implementation. Consequently, this study posits a significant research question on how to strike a balance between the fair usage of AI-generated content and upholding copyright protection in order to foster sustainable development within the creative industries. The findings presented herein offer novel perspectives and insights that can inform future policymaking endeavors as well as academic investigations.Lecture Notes in Education Psychology and Public Media00This paper does a thorough review of the literature to introduce basic ideas about AI content creation and fair use, and divides existing research into four separate areas: technical analysis, legal case analysis, economic impact analysis, and policy adaptation research.Lecture Notes in Education Psychology and Public Media["Jiehui Mai"]2024-09-27T00:00:00 dd23d32fcf47d312d6b7b226fd8662f16c4f39cfJournal of Computer Virology and Hacking Techniques00J. Comput. Virol. Hacking Tech.["Ekaterina Pleshakova", "Alisa Koreneva"]2024-09-27T00:00:00 aaa587c81ec76a4c2541faebd20d10ba30c6db63Heliyon410The study found that the existing studies were mainly conducted from the perspectives of computer science, linguistics and psychology, and the individualized development of language education supported by technology was not obvious, and the degree of human-computer interaction was weak.Heliyon["Lu Zheng", "Yong Yang"]2024-09-27T00:00:00 cf9acfaeb153accd9c9782a6a3cccdc141eaaca8In this technical report, we present our second-generation digital twin model for monitoring of fuel cells. This digital twin combines two data-driven machine learning models: a stationary model for stationary cell voltage prediction and a degradation model for degradation correction. This combination of both models results in a precise probabilistic prediction of the cell voltage over time.Furthermore, we present a web-based framework for automated fuel cell monitoring in real time. This framework allows training a digital twin on existing data and subsequently applying the twin to automatically check new data for anomalies.ECS Transactions00This digital twin combines two data-driven machine learning models: a stationary model for stationary cell voltage prediction and a degradation model for degradation correction, which results in a precise probabilistic prediction of the cell voltage over time.ECS Transactions["Lukas Klass", "Laurin Holz", "Lukas Koenig", "Alexander Kabza", "Frank Sehnke", "Katharina Strecker", "M. H\u00f6lzle"]2024-09-27T00:00:00 5144d67f676139cfe82098d2718dc251706d3b2bProceedings of the 2024 2nd International Conference on Internet of Things and Cloud Computing Technology40Proceedings of the 2024 2nd International Conference on Internet of Things and Cloud Computing Technology["Jinke Zhang", "Xia Ren"]2024-09-27T00:00:00 17550481b481fd7292180863c14cdebea9918ae6Indian Journal of Ophthalmology50Indian Journal of Ophthalmology["S. Au"]2024-09-27T00:00:00 343822979faa500132003986439bb5195815054fComputers, Informatics, Nursing00CIN: Computers, Informatics, Nursing["Lisiane Pruinelli", "Martin Michalowski", "Laura-Maria Peltonen", "Maxim Topaz"]2024-09-27T00:00:00 abda7ba17327616e442eedc8ba9b1a95ecafb990Cogent Business &amp; Management560Cogent Business &amp; Management["Pengyang Liu", "Yangjie Lai", "Dege Liu"]2024-09-27T00:00:00 230e3d018cb13f901781eb4ff488d52945fc3ed4The purpose of this article is to assess the current interaction between artificial intelligence (AI) and economic science and to identify promising interdisciplinary areas of research that can significantly influence the methodology of understanding economic phenomena. To achieve this goal, the vague and partly even mystical term AI was replaced with а more scientific term «artificial neural networks» (ANN). The article uses methods of scientometric, epistemological and comparative analysis of the processes of ANN penetration into economics and other academic disciplines. The authors reveal the epistemological commonality and difference between AI and ANN and justify the shift in epistemological focus in research from general AI to ANN. The paper systematizes the use of ANN in economics: 1) ANN as a mathematical tool for solving economic problems; 2) ANN as a model of economic phenomena; 3) ANN as a methodology for understanding economic patterns. It shows the interaction of economics with neurosciences which occurs in two significantly different directions: from neurobiology, i.e. real nerve networks in living organisms, and, on the other hand, from ANN theory. The first direction is associated with neuroeconomics, the second has not yet been articulated, but shows an exponential growth in publications and is associated primarily with forming a new economic paradigm. The ANN paradigm in economics (and not only in economics) changes both the subject of cognition, introducing radically new forms/types of evidence and new research methods, and the object of cognition, changing the focus of study from individual economic behavior to the collective economic behavior of mega-subjects.Lomonosov Economics Journal50The paper systematizes the use of ANN in economics: 1) ANN as a mathematical tool for solving economic problems; 2) ANN as a model of economic phenomena; 3) ANN as a methodology for understanding economic patterns.Lomonosov Economics Journal["Y. Petrunin"]2024-09-27T00:00:00 595c084aa6f86549be31872a24117f99ad69c5eaComputational Brain &amp; Behavior16218AI in current practice is deteriorating the authors' theoretical understanding of cognition rather than advancing and enhancing it, and this situation could be remediated by releasing the grip of the currently dominant view on AI and by returning to the idea of AI as a theoretical tool for cognitive science.Computational Brain &amp; Behavior["Iris van Rooij", "Olivia Guest", "Federico Adolfi", "Ronald de Haan", "A. Kolokolova", "Patricia Rich"]2024-09-27T00:00:00 401576d060458268c884e8e649d97d6f8530f208No nation can exist or survive without critical infrastructure (CI), which is why a nation’s growth, development, welling, standard of living, possessions, and even governance are weighed by the kind of CI obtained therein. There are growing concerns about the need and how to protect CI from cyber threats in the 21st century era of digitalization. This descriptive survey research aims at showing how artificial intelligence (AI) and machine learning (ML) can be leveraged for the protection of critical national infrastructure (CNI). The study relies on secondary data, which are subjected to thematic systematic review. Interpretive and descriptive analytic techniques are used. The analysis shows that leveraging AI and ML for the protection can yield huge results, as they optimize detection of and response to threats, facilitate efficient physical maintenance, optimally evaluate and manage risks, increase awareness, and simulate and train human employees in the CNI sector. The study concludes that these cutting edge technologies have more capacities and opportunities for the protection of CNI from cyber threats than other non-technological and less advanced technological mechanisms. It calls on stakeholders, especially national governments and authorities of the organizations involved in CNI, to make concerted efforts to surmount the challenges of AI and ML adoption and ensure significant protection of CNI across nations of the globe. Doing so would pave way for extensive practical usage of AI and ML for the protection of CNI.Asian Journal of Research in Computer Science010The analysis shows that leveraging AI and ML for the protection of CNI can yield huge results, as they optimize detection of and response to threats, facilitate efficient physical maintenance, optimally evaluate and manage risks, increase awareness, and simulate and train human employees in the CNI sector.Asian Journal of Research in Computer Science["Oluwatobiloba Okusi"]2024-09-27T00:00:00 aab16428e4be9f8a3349ee00cb85d0d8db08a1dfThe increasing complexity of modern aircraft systems necessitates advanced monitoring solutions to ensure operational safety and efficiency. Traditional aircraft health monitoring systems (AHMS) often rely on reactive maintenance strategies, detecting only visible faults while leaving underlying issues unaddressed. This gap can lead to critical failures and unplanned downtime, resulting in significant operational costs. To address this issue, this paper proposes the integration of artificial intelligence (AI) and blockchain technologies within an enhanced AHMS, utilizing the iceberg model as a conceptual framework to illustrate both visible and hidden defects. The model highlights the importance of detecting and addressing issues at the earliest possible stages, ensuring that hidden defects are identified and mitigated before they evolve into significant failures. The rationale behind this approach lies in the need for a predictive maintenance system capable of identifying and mitigating hidden risks before they escalate. Key tasks completed in this study include: a comparative analysis of the proposed system with existing monitoring solutions, the selection of AI algorithms for fault prediction, and the development of a blockchain-based infrastructure for secure, transparent data sharing. The evolution of AHMS is discussed, emphasizing the shift from traditional monitoring to advanced, predictive, and prescriptive maintenance approaches. This integrated approach demonstrates the potential to significantly improve fault detection, optimize maintenance schedules, and enhance data security across the aviation industry.Electronics312An integrated approach to the integration of artificial intelligence and blockchain technologies within an enhanced aircraft health monitoring system demonstrates the potential to significantly improve fault detection, optimize maintenance schedules, and enhance data security across the aviation industry.Electronics["I. Kabashkin"]2024-09-27T00:00:00 a554446cca73d3f226f837b038cc7f3245284550As autonomous vehicles develop, driver distraction becomes even more crucial as it affects both safety and operational efficiency. In this work, we investigate the gamut of new AI tools for combating and processing visual distraction scenarios within autonomous vehicles. This includes AI-based driver monitoring systems to determine the level of attention, visual distraction classification with deep learning models, augmented reality head-up displays for focal projection of critical information and gesture/voice-controlled interfaces are used in order to reduce visual interactions. This also includes how predictive analytics; adaptive user interfaces and personalized distraction mitigation programs will see AI improve driver focus and thus safety. These advanced systems are designed to provide a safer and more efficient driving experience in the emerging era of autonomous capabilities by leveraging the scalability of advanced driver-assistance technologies.International Journal of Artificial Intelligence &amp; Applications110This work investigates the gamut of new AI tools for combating and processing visual distraction scenarios within autonomous vehicles, including AI-based driver monitoring systems, visual distraction classification with deep learning models, augmented reality head-up displays for focal projection of critical information and gesture/voice-controlled interfaces to reduce visual interactions.International Journal of Artificial Intelligence &amp; Applications["Vivek Ghulaxe"]2024-09-28T00:00:00 239b4f18858d9cf67fee130726efff9bdacfeea7This paper proposes a conceptual framework for integrating Artificial Intelligence (AI) into the curriculum. It builds on previous conceptual papers, which provided initial suggestions on integrating AI into teaching. The approach to developing the conceptual framework includes drawing on existing frameworks, AI literature, and case studies from the Queen Mary University of London President and Principal, an AI literacy-funded project. The opinion piece aims to advance our thinking on creating a teaching and learning toolkit to support educators in integrating AI into their teaching, enhancing students’ AI literacy and skills. This paper has two main objectives: first, it develops an AI literacy conceptual framework to support educators in integrating AI into their teaching, and second, it provides suggestions on how to engage with it.Journal of Learning Development in Higher Education51An AI literacy conceptual framework to support educators in integrating AI into their teaching, and suggestions on how to engage with it are provided.Journal of Learning Development in Higher Education["Xue Zhou", "Lilian Schofield"]2024-09-29T00:00:00 b0a439f7de7d72b28271a73bf4bff17c6647ffb6This article investigates the relationship between Sustainable Development Goal 4 (SDG 4), academic integrity as its part, and artificial intelligence (AI) through a bibliometric analysis, assessing whether this intersection represents a clash or win-win cooperation. SDG 4 aims to ensure equitable access to quality education, while AI technologies have the potential to enhance educational practices but demote academic integrity. By analyzing a comprehensive body of the literature, this study identifies key trends and thematic areas where AI is applied in educational settings, particularly concerning maintaining academic integrity. The findings reveal a growing body of research highlighting AI’s role in personalizing learning experiences, improving educational accessibility, and supporting educators’ teaching methodologies. However, challenges such as ethical considerations, data privacy, and the digital divide are also addressed, indicating potential conflicts that need to be navigated. Ultimately, this analysis suggests that while there are significant opportunities for synergy between AI and SDG 4, the management of careful implementation and policy frameworks is essential to ensure that AI serves as a tool for promoting inclusive and sustainable education rather than exacerbating existing inequalities. AI transforms science management by enhancing data analysis, streamlining research processes, and improving decision-making, ultimately leading to more efficient and effective scientific research and innovation. The findings reveal that while AI can facilitate personalized learning and enhance educational accessibility, it also poses challenges related to academic misconduct, such as plagiarism and the misuse of AI-generated content. This duality highlights the need for educational institutions to develop robust frameworks that leverage AI’s capabilities while safeguarding academic integrity. The article concludes that a collaborative approach, integrating AI into educational practices with a strong emphasis on ethical considerations and integrity, can lead to a synergistic relationship that supports the goals of SDG 4. Recommendations for future research and practical implications for managers, educators, scientists, and policymakers are also discussed, emphasizing the importance of fostering an educational environment that embraces innovation while upholding ethical standards.Sustainability482It is suggested that while there are significant opportunities for synergy between AI and SDG 4, the management of careful implementation and policy frameworks is essential to ensure that AI serves as a tool for promoting inclusive and sustainable education rather than exacerbating existing inequalities.Sustainability["A. Artyukhov", "Tomasz Wo\u0142owiec", "N. Artyukhova", "Sylwester Bogacki", "T. Vasylieva"]2024-09-29T00:00:00 cc53aed8f4b044589078e280e4c49ebf57eacf62The study explored applications of artificial intelligence and its dialectical relationship with international human rights law of individuals, which requires assessing the effects of this technology on human rights and freedoms. The problem of privacy of humanity, as AI technologies can control human rights and freedoms, while monitoring potential violations in this context. The study use of documentary research and qualitative lens to analyze the data. In conclusion, unawareness of the use of AI may impose significant hurdles on future generations and may infringe on human rights across all sectors of society. The government should mandate obligations for artificial intelligence businesses concerning education, health, human rights breaches, and individual privacy. Artificial intelligence technology may be used to uphold international human rights rules and freedoms. Ultimately, AI-based technologies may mitigate issues of privacy and algorithmic bias in the realm of human rights violations if they substantially diminish risks to individual rights. The report proposed suggestions to policymakers about the use of AI technologies in the fields of education and healthcare. AI may uphold human rights while also contravening international human rights legislation.Pakistan Journal of Criminology00In conclusion, unawareness of the use of AI may impose significant hurdles on future generations and may infringe on human rights across all sectors of society and the government should mandate obligations for artificial intelligence businesses concerning education, health, human rights breaches, and individual privacy.Pakistan Journal of Criminology[]2024-09-29T00:00:00 b507f7f5561c4153f1d84c4260adb5856c14eea0Artificial intelligence, as well as a number of other products of the technological revolution, served as a prerequisite for the formation of a specific area of public relations, which led to the search for adequate forms and methods of legal regulation. The use of legal resources in the process of creating the necessary regulators has led to the accumulation of relevant practical experience and the emergence of a disciplinary ontology accumulating doctrinal knowledge about the AI legal existence. The central place in it is occupied by the issue of AI legal identification, which has both theoretical and practical significance. Its development involves the development of a complex of fundamental, programmatic and design issues. The article presents the solutions forming this complex in the context of the development of scientific legal knowledge and practice of legal regulation in the field of AI creation and use, as well as a rational view of mediation by law of the considered public relations in its current form; the author characterizes the content and dynamics of this view, analyzes the accumulated law-making experience and practice of legal experiments. The article also sets out forecasts for the further development of this segment of the legal sphere and the tools used to streamline it; defines the tasks of the legal doctrine for the future. The article was prepared on the basis of a scientific report presented by the author at a meeting of the Presidium of the Russian Academy of Sciences on March 12, 2024.Vestnik Rossijskoj akademii nauk170The article presents the solutions forming this complex of fundamental, programmatic and design issues in the context of the development of scientific legal knowledge and practice of legal regulation in the field of AI creation and use, as well as a rational view of mediation by law of the considered public relations in its current form.Vestnik Rossijskoj akademii nauk["T. Khabrieva"]2024-09-29T00:00:00 b2725bedf5d579f1985281d55e6f40825b1cce04This paper draws objective conclusions from field research, establishes a talent cultivation and technological innovation system in the field of artificial intelligence, analyzes the phenomena and problems that arise in school enterprise cooperation, explores their underlying reasons, and explores how to establish a talent cultivation mechanism for school enterprise cooperation in the field of artificial intelligence, establishes a basic model for artificial intelligence talent cultivation, establishes standards, human resources support, improves guarantee systems, reforms in teaching and learning, and integrates disciplines.New Explorations in Education and Teaching00This paper establishes a talent cultivation and technological innovation system in the field of artificial intelligence, analyzes the phenomena and problems that arise in school enterprise cooperation, and establishes a basic model for artificial intelligence talent cultivation.New Explorations in Education and Teaching["Lei Liu"]2024-09-29T00:00:00 cb535dcd22efd16c77aff107f3e92c04f3d0635fThis study aims to analyze audit assessment and the effects of artificial intelligence on the audit quality sustainability reports from the standpoint of auditors who provide audit opinions on audits that are conducted of sustainable financial reports. Certain audit tasks can now be automated by the audit profession thanks to technology. Artificial intelligence (AI) and big data analytics can be used to analyze huge data, one of the technological innovations that need more study than conventional data. The number of 85 auditors of Accountant Public Firms in Medan completed questionnaires that were used in this study to gather data. Following the collection of data from respondents, quantitative descriptive methodologies were used to analyze the data. The audit profession may improve the quality of audits by promoting the use of AI and big data analytics and by supporting auditors in developing their skills to stay current with new technologies. AI can improve audit quality, reduce costs, and eventually replace human workers. People are still required in the audit process, though, as AI cannot make decisions when it comes to providing audit opinions on financial reports. As a result, its contribution to the audit process is minimal. Therefore, it is hoped that this study will shed light on the potential applications of AI in the auditing process.Information Management and Business Review350Information Management and Business Review["Rana Fathinah Ananda", "Sari Nuzullina Rahmadhani", "Aditya Amanda Pane", "Naufal Helmi Wiratama"]2024-09-29T00:00:00 3ba4945023fc4fcff18a5ad7d991c0e42a7a651aThe article discusses the scientific problems of ensuring technological sovereignty in the field of artificial intelligence technologies. One of the key scientific problems is computer modeling of human cognitive functions with finite resources, which provide the ability to process and analyze finite–dimensional and infinite-dimensional data along with finite-dimensional ones. A well–known solution to this problem is the A.N. Tikhonov regularization method, due to the high complexity and cost of implementation of which, less complex and expensive empirically constructed foreign INS are used to build AI solutions, but without guarantees of error-free results. The consequence of this is a rather low level of implementation of AI solutions in industry and in the field of information technology and a practical lack of data on the economic effect. It is proposed to consider the economic efficiency of domestic AI solutions as the main criterion for the need for their development.Vestnik Rossijskoj akademii nauk10The article discusses the scientific problems of ensuring technological sovereignty in the field of artificial intelligence technologies and proposes to consider the economic efficiency of domestic AI solutions as the main criterion for the need for their development.Vestnik Rossijskoj akademii nauk["V. B. Betelin"]2024-09-29T00:00:00 4d9a9613ef564b81d9876e7f56a90a15edfb7effGenerative Artificial Intelligence tools have gained increasing prominence in recent years. However, the increasing use of these technologies and the functionalities they offer has sparked discussions about their impact and even raised concerns about the potential replacement of human work by automation carried out by machines. This study proposes a Systematic Literature Review to evaluate the opportunities and challenges that these technologies present to system developers in the current and future technological scenario. Aiming at state-of-the-art research to identify how Generative AIs are being applied in the context of software development and what are the latest trends and innovations in this field and how these innovations affect the opportunities and challenges for system developers. As a result, several studies were found that highlight how Generative AI has provided productivity and systems development optimized solutions in the industry, as well as promoting innovations. Studies also emphasize the need for a balance between the use of AI tools and development carried out by human participation, which must be mediated by common sense. Furthermore, the review will explore the ethical implications associated with the widespread adoption of AI technologies, addressing issues such as data privacy, decision-making transparency, and the responsibility of developers in ensuring that AI applications are used in a way that benefits society. The findings of this review will contribute to a better understanding of how generative AI is reshaping the software development landscape and provide insights for future research and development in this rapidly evolving field. Machine Learning Research30A Systematic Literature Review to evaluate the opportunities and challenges that Generative Artificial Intelligence technologies present to system developers in the current and future technological scenario and the ethical implications associated with the widespread adoption of AI technologies are explored.Machine Learning Research["Samira Caduda", "Anderson Barroso"]2024-09-29T00:00:00 26234eaefcaed97557c704efb18ab59e4dc039c4This study aimed to identify the degree of students’ awareness of the effectiveness of artificial intelligence (AI) applications in learning the Arabic language, as students’ viewpoints can provide valuable insights into directing and modifying their behavior towards learning Arabic through their awareness of AI applications’ effectiveness. Data for this study were gathered by the researchers using a questionnaire and a descriptive approach. The study population consisted of 286 male and female students, while the study sample consisted of 175 male and female students, randomly selected from the study population. The questionnaire contained thirty items in total. The findings showed that the degree of students’ awareness of the effectiveness of AI applications in learning Arabic was high across all items, with an overall arithmetic average of 3.98. The arithmetic averages ranged between 3.67 and 4.26, with paragraph No. 15 ranked first with an arithmetic average of 4.26, while paragraph No. 13 ranked last with an arithmetic average of 3.67. The findings also indicated that gender did not result in any statistically significant differences. However, the results showed that the academic year had a statistically significant effect, with second-year students and above showing the greatest differences. Additionally, the data indicated that the type of college had a significant effect, with students from scientific colleges showing higher awareness.Research Journal in Advanced Humanities60The results showed that the academic year had a statistically significant effect, with second-year students and above showing the greatest differences, and the type of college had a significant effect, with students from scientific colleges showing higher awareness.Research Journal in Advanced Humanities["Mohammad Hussein Ahmad Faqeeh", "Salem Khalil Al Aqtash", "O. Musleh", "Suad Abdel Karim Alwaely", "Mohamed Elsayed Elzeiny", "Moath Khalaf Al Omery", "Imad Ibraheem Mostafa"]2024-09-29T00:00:00 0ca4d9ba865a0e2332314f54036dc4a2ce64b507With the rapid development of generative Artificial Intelligence (AI) technology, its potential applications in the field of judicial decision-making are becoming increasingly apparent. This paper explores the current state of generative AI in judicial rulings, highlighting its advantages and challenges, and analyzes the corresponding regulatory needs. By constructing a theoretical framework, the paper proposes regulatory paths suitable for this field, including the establishment of a legal framework, the design of regulatory mechanisms, and the promotion of social participation. Through the study of relevant domestic and international cases, this paper aims to provide theoretical support and practical guidance for the standardized application of generative AI, thereby promoting its safe and efficient development in the judicial domain.Economics Law and Policy00This paper explores the current state of generative AI in judicial rulings, highlighting its advantages and challenges, and analyzes the corresponding regulatory needs, constructing a theoretical framework that proposes regulatory paths suitable for this field.Economics, Law and Policy["Wang Feng"]2024-09-29T00:00:00 c41094dd5f69d97af7a46a44fbdc80d2353cc497Artificial intelligence technologies are used in many fields and have become a part of our lives. The field of artificial intelligence, which has an important place especially in the field of education and for the digital leadership, is constantly developing and is expected to create even greater impacts in the future. The main purpose of this research is to examine the experiences of educational administrators towards digital leadership in the age of artificial intelligence. In the research, phenomenological design, one of the qualitative research methods, was used. The research's study group was comprised of 15 educational administrators. These participants were selected using a convenience sampling design, a type of maximum sampling method, derived from purposive sampling methods. In the study, a semi-structured interview form created by the researchers by analyzing the literature in detail and taking expert opinions was used as a data collection tool. Content analysis was used to analyze the data. According to the results of the research, the themes of general perceptions of educational administrators towards artificial intelligence, perceptions towards the use of artificial intelligence in education, general perceptions of educational administrators towards digital leadership and suggestions of educational administrators towards artificial intelligence and digital leadership emerged.International Journal of Contemporary Educational Research550The themes of general perceptions of educational administrators towards artificial intelligence, perceptions towards the use of artificial intelligence in education, general perceptions of educational administrators towards digital leadership and suggestions of educational administrators towards artificial intelligence and digital leadership emerged.International Journal of Contemporary Educational Research["G\u00fcl\u00e7in Kurkan", "Munevver Cetin"]2024-09-29T00:00:00 0f5fbad9955d0e150ccec09e462e39647616a42cThe halal industry has developed into a promising business opportunity in the industrial era, and many countries are interested in it. Artificial intelligence (AI) has been used in the halal industry to ensure production processes comply with halal standards, improving the safety and quality of halal products. AI can scan, inspect, and monitor any errors that may occur in a product, providing several potential benefits to the halal industry, including improving halal compliance, streamlining the supply chain, and increasing efficiency and productivity. The development of the halal industry in Indonesia shows a positive trend that is increasingly growing, due to awareness of the importance of halal products as a part of lifestyle. This awareness is not only shared by Muslims but also by non-Muslims. This high awareness of halal products will have a positive impact on business opportunities in the halal industry. The Indonesian government has been active in developing the halal industry to encourage the growth of the halal industry.Information Management and Business Review580Artificial intelligence (AI) has been used in the halal industry to ensure production processes comply with halal standards, improving the safety and quality of halal products.Information Management and Business Review["Fitriani Tobing", "Hasfie Fauzan", "Khairani Simatupang", "Ryan Aulia"]2024-09-29T00:00:00 2ef824140cc198459ac122c1af094396472caf20Objective - Artificial Intelligence (AI) has become a pivotal technology in transforming logistics performance. This paper aims to comprehensively understand how AI-enabled solutions improve efficiency, accuracy, and responsiveness in logistics operations. The study focuses on synthesizing current research to explore AI applications across various logistics domains, such as predictive analytics, autonomous vehicles, and smart warehousing. Methodology/Technique – A systematic review approach was used to gather and analyze existing literature on AI applications in logistics. The review covered studies published in recent years, highlighting the advancements and impact of AI on logistics processes. The methodology included selecting relevant sources, categorizing AI applications, and assessing their effects on different logistics functions. Finding – The findings reveal that AI adoption substantially improves logistics operations, including enhanced operational performance, cost reduction, and increased customer satisfaction. Specific AI applications, such as predictive analytics for demand forecasting, autonomous vehicles for transportation, and smart warehousing for inventory management, were identified as key contributors to these improvements. However, challenges such as data privacy concerns and integration complexities were also noted. Novelty – This study's novelty lies in its comprehensive analysis of AI applications across various logistics domains, offering a holistic view of AI's role in optimizing logistics performance. This paper highlights the benefits of AI adoption and addresses the associated challenges, providing insights into future research directions and practical implications for leveraging AI in logistics. Type of Paper: Review JEL Classification: C61, C62, D83. Keywords: Artificial Intelligence; Logistics; Performance Improvement; Predictive Analytics; Autonomous Vehicles; Smart Warehousing Reference to this paper should be referred to as follows: Fatorachian, H. (2024). Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review, GATR-Global J. Bus. Soc. Sci. Review, 12(3), 146–160. https://doi.org/10.35609/gjbssr.2024.12.3(5) _____________________________________GATR Global Journal of Business Social Sciences Review190This paper highlights the benefits of AI adoption and addresses the associated challenges, providing insights into future research directions and practical implications for leveraging AI in logistics.GATR Global Journal of Business Social Sciences Review["Hajar Fatorachian"]2024-09-29T00:00:00 14df7473699bf45d752b7ebb42ac21c0a53b06c8The convergence of artificial intelligence (AI), robotics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and extended reality (XR) is transforming the way humans interact with machines [...]Electronics70Electronics["J. E. Solanes", "Luis Gracia", "Jaime Valls Miro"]2024-09-29T00:00:00 ecdd979080965d5d8a999c4dc593f190a91f845bIn this paper we discuss the problems arising out of active introduction of artificial intelligence (AI) technologies to medicine and other humanities, as well as causes of these problems and steps that are taken worldwide to solve them. We focus on the methods and tools for developing trusted AI technologies, which are being created within the ISP RAS Trusted AI Research Center. We present the results of interdisciplinary projects executed in the Center and suggest a number of solutions to speed up the development of humanitarian AI technologies. The paper expands on the report given at the General Assembly of the RAS on March 12, 2024.Vestnik Rossijskoj akademii nauk10This paper focuses on the methods and tools for developing trusted AI technologies, which are being created within the ISP RAS Trusted AI Research Center, and presents the results of interdisciplinary projects executed in the Center.Vestnik Rossijskoj akademii nauk["A. I. Avetisyan"]2024-09-29T00:00:00 015b51f429d65a8a0b2850c1332cc074fe52711bEnvironmental Science and Technology103Environmental science & technology["Yipeng Wu", "Ming Xu", "Shuming Liu"]2024-09-29T00:00:00 c1d67f66406c1e09b3bd53cff650db5f609433c8International Research Journal of Modernization in Engineering Technology and Science02International Research Journal of Modernization in Engineering Technology and Science[]2024-09-29T00:00:00 462863cc5667759ee2166bdb53f1d1dd43df6235Objective Since the release of ChatGPT, popular demand has driven the use of social chatbots as pseudo-AI psychotherapists. With time, it is inevitable that these technologies will be deployed in some form as dedicated psychotherapy interventions. Here, we attempt to forecast the implications for psychotherapy including the unique benefits to distributive justice as well as concerns for the quality of the therapy and its societal impact. Conclusion An AI psychotherapist (APT) has the potential to provide engaging clinical interactions given its capacity for highly realistic interaction as well as its high level cognitive and emotional capabilities. Moreover, it can potentially address financial and workforce limitations on access to therapy. However, an APT may cause significant iatrogenic harm if released without adequate quality control and oversight by trained psychotherapists. If not appropriately designed and regulated, APTs have potential to mislead and reinforce maladaptive coping behaviours. Given societal drivers and possible benefits, these technologies will inevitably be deployed; thus, it is incumbent upon us as a professional body to consider their regulation.Australasian Psychiatry200The implications for psychotherapy including the unique benefits to distributive justice as well as concerns for the quality of the therapy and its societal impact are forecast.Australasian Psychiatry["Marc Jurblum", "Rob Selzer"]2024-09-29T00:00:00 33d4851aeb725b90ffb73fe3398279539d67b33aEducational Administration: Theory and Practice00Educational Administration: Theory and Practice["Neha Pant"]2024-09-29T00:00:00 dec8018cdfdc9f301bf37b5e9b2fa1c2ff57b505This paper presents an algorithm for chasing target aircraft in air combat scenarios, focusing on explainability and safety. Unlike conventional approaches utilizing reinforcement learning, our method employs a problem-specific neural network architecture with one hidden layer, trained online to track the desired path and heading angle in a 3D environment. The algorithm distinguishes between offensive and defensive modes, selecting optimal positions for the tracker aircraft and controlling it accordingly. We introduce a different training procedure where the neural network learns from the system responses without labeled output information, ensuring quick convergence and explainability. Through simulations, we demonstrate the reliability and effectiveness of our algorithm and neuro-controller structure with the help of decision tree structure in air-to-air combat tasks.Symposium on Dependable Autonomic and Secure Computing110This paper presents an algorithm for chasing target aircraft in air combat scenarios, focusing on explainability and safety, and introduces a different training procedure where the neural network learns from the system responses without labeled output information, ensuring quick convergence and explainability.2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)["Enes Erdogan", "Bar\u0131\u015f Ba\u015fp\u0131nar"]2024-09-29T00:00:00 b1e6f07bc1cb947037ec70223159faf3e85d5f09Arus teknologi di Indonesia saat ini semakin berkembang tetapi tidak sebanding dengan perkembangan teknologi di dunia Pendidikan. penggunaan media pembelajaran di sebagian besar sekolah, yang masih menggunakan metode konvensional. Hal ini menyebabkan rasa jenuh pada peserta didik terhadap pembelajaran. Tujuan penelitian adalah untuk menguji efektivitas dan efisiensi penggunaan media pembelajaran berbasis AI, khususnya Chat GPT, terhadap tingkat motivasi peserta didik. Penelitian ini menggunakan pendekatan kuantitatif, khususnya metode quasi eksperimen dengan dua variabel: pengaruh media pembelajaran AI Chat GPT (X) terhadap tingkat motivasi peserta didik (Y) dalam mata pelajaran Pendidikan Pancasila dan Kewarganegaraan. Hasil penelitian menunjukkan bahwa penggunaan media pembelajaran AI Chat GPT memiliki pengaruh signifikan terhadap motivasi belajar peserta didik. Data dari angket menunjukkan bahwa 84.9% peserta didik (45.5% sangat setuju, 39.4% setuju) merasa lebih termotivasi dalam mencari materi PPKn menggunakan media AI Chat GPT. Hal ini menegaskan bahwa penggunaan teknologi AI dalam pembelajaran PPKn efektif dan efisien dalam meningkatkan motivasi belajar peserta didik.Jurnal Multidisiplin West Science00Jurnal Multidisiplin West Science["J. Destia", "Dadan Mulyana", "Cahyono Cahyono"]2024-09-29T00:00:00 414ca13950357f2769d367bde6a524e8a5ccc30dIn today's world where competition is increasing harshly, it is important to achieve sustainable profits and keep costs competitive. Under these conditions, the importance of supply chain elements is increasing day by day. Management of inventory costs, which constitute a large volume among cost items, affects the performance of companies. The first step to focus on to keep inventory costs under control and improve is inventory classification. Inventory classification, which is at the top of the supply chain elements and closely affects the subsequent phases, it is critical in determining supply chain performance. Thanks to inventory classification, material groups are determined and stock strategies for these groups are clarified. Incorrect inventory classification causes materials to be assigned to incorrect groups, which negatively affects inventory costs and subsequent phases of the supply chain, causing an increase in costs. The most used methods for inventory classification are ABC analysis, multi-criteria inventory classification and optimization. However, the increasing momentum in artificial intelligence studies in recent years has also closely affected inventory classification. The advantages brought by artificial intelligence methods have also created distinctive contributions to inventory classification studies. This study provides a conceptual framework that examines artificial intelligence methods in the field of inventory classification.Journal of Decision Analytics and Intelligent Computing00This study provides a conceptual framework that examines artificial intelligence methods in the field of inventory classification and finds that the increasing momentum in artificial intelligence studies in recent years has also closely affected inventory classification.Journal of Decision Analytics and Intelligent Computing["Sena Keskin", "Alev Taskin"]2024-09-29T00:00:00 d61f95494219e5006b5f831c8141955f0be9443eEngineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real‐world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so‐called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real‐world environment.Advanced Intelligent Systems931To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so‐called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real‐world environment.Advanced Intelligent Systems["V. G. Kortman", "Barbara Mazzolai", "A. Sakes", "J. Jovanova"]2024-09-29T00:00:00 88c913ab5b555c999dc56885a1b7cbd46f0f1d67This study systematically reviews the integration of artificial intelligence (AI) and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change; in particular, it evaluates the effectiveness of various non-destructive remote sensing platforms (RGB, thermal imaging, and hyperspectral imaging) and AI techniques (machine learning, deep learning, ensemble methods, GAN, and XAI) in monitoring and predicting crop water stress. The analysis focuses on variability in precipitation due to climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity. Furthermore, this study is expected to contribute to improving sustainable agricultural practices and mitigating the negative impacts of climate change on crop yield and quality.Italian National Conference on Sensors2701This study systematically reviews the integration of artificial intelligence and remote sensing technologies to address the issue of crop water stress caused by rising global temperatures and climate change and explores how these technologies can be strategically combined under data-limited conditions to enhance agricultural productivity.Sensors (Basel, Switzerland)["S. Cho", "Hidayat Mohamad Soleh", "J. Choi", "Woon-Ha Hwang", "Hoonsoo Lee", "Young-Son Cho", "Byoung-Kwan Cho", "Moon S. Kim", "I. Baek", "G. Kim"]2024-09-29T00:00:00 7cef218d231298a455b781cffcf2761bde1f598dWhile Artificial Intelligence (AI) has become an important asset in many areas of science and technology, safety is often not treated as important as required for aviation. Neglecting safety is not an option for aviation, where strict laws and regulations govern the certification process of new aircraft. Thus, a solid understanding of the underlying AI-based system is important to certify such systems. To this day, manual inspection by humans is an essential step for certification, however requires proper tooling. One such tool, called Advisory Viewer, is presented in this work, helping to break down the high-dimensional vector space of neural networks. The tool presented here can visualize decisions derived from assemblies of neural networks for arbitrary 2-dimensional parameter sweeps and provides real-time feedback upon any parameter change. It is designed to better understand the neural networks behind openCAS, an open-source implementation for the Airborne Collision Avoidance System X (ACAS X), the upcoming implementation for collision avoidance in aviation. However, as currently designed, implementing ACAS X is not feasible on current aviation hardware, as the required memory is not available. Here, neural networks and their ability to compress and generalize can be a solution. Therefore, it is of utmost importance that the AI-based system behind ACAS X always produces correct predictions. Finally, to fix the detected irregularities, this work implements a Safety Net to ensure the correct output for the ACAS X use case. Safety Nets are designed on the idea of sparse lookup tables (LUTs), storing only the points where the neural networks are known to fail. By deploying a system consisting of a Safety Net together with neural network(s), a small, yet potentially certifiable system can be designed and built. This work presents a generic data format for LUTs and recommended algorithms to populate and organize said LUTs for quick access during runtime. Combined, this serves as a generic framework for 100 % assurance of small neural networks, joined by the visualization tooling for the specific use case of openCAS.Symposium on Dependable Autonomic and Secure Computing311This work presents a generic data format for LUTs and recommended algorithms to populate and organize said LUTs for quick access during runtime, and implements a Safety Net to ensure the correct output for the ACAS X use case.2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)["Johann Maximilian Christensen", "Wanja Zaeske", "Janick W. Beck", "Sven Friedrich", "Thomas Stefani", "Akshay Anilkumar Girija", "Elena Hoemann", "Umut Durak", "Frank K\u00f6ster", "Thomas Kr\u00fcger", "Sven Hallerbach"]2024-09-29T00:00:00 066864318f07f019182ecf22c94f15154bb5285fThe manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology.Machines1181This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time.Machines["Md Sazol Ahmmed", "S. P. Isanaka", "Frank Liou"]2024-09-29T00:00:00 73c6bf2e508b2f17b50ee05f48789e8c21f08796Advances in Artificial Intelligence (AI) introduce both promising real-world applications in various fields such as aviation but also challenges in assuring safety. Regulators in aviation mandate, as with any other application, compliance of these AI-based systems with high safety standards. This especially holds for Human-AI Teaming. The European Union Aviation Safety Agency (EASA) outlined in their concept paper the definitions of cooperation and collaboration between humans and AI, as well as the differences in terms of authority and task allocation. Therein, the question of safety in situations of collaboration with shared goals, dynamic task allocation, and partial authority within the Human-AI team is paramount. As a safety concept, EASA requires the usage of Operational Design Domains (ODDs) from the automotive field. In this work, the concept of ODDs is transferred to the Air Traffic Control (ATC) domain. An initial ODD is defined for an AI-based digital team partner that supports Air Traffic Controllers in their daily work even for safety-critical tasks such as conflict detection and resolution. Additionally, the required tools for successfully executing this task is demonstrated. Based on the ODD description, conflict scenarios are generated and tested in a simulation environment, showcasing situations inside and outside the ODD. Based on these results, the feasibility of using ODDs in ATC is discussed, outlining a potential step towards the safe application of Human-AI Teaming.Symposium on Dependable Autonomic and Secure Computing461In this work, the concept of ODDs is transferred to the Air Traffic Control (ATC) domain and an initial ODD is defined for an AI-based digital team partner that supports Air Traffic Controllers in their daily work even for safety-critical tasks such as conflict detection and resolution.2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)["Thomas Stefani", "Mohsan Jameel", "Ingrid Gerdes", "Robert Hunger", "Carmen Bruder", "Elena Hoemann", "Johann Maximilian Christensen", "Akshay Anilkumar Girija", "Frank K\u00f6ster", "Thomas Kr\u00fcger", "Sven Hallerbach"]2024-09-29T00:00:00 706a62f2fb8423c9bcfd3d12b8f81552bc284cecArtificial Intelligence (AI) in healthcare has drawn a lot of interest, especially in health counseling. Research on embodied AI has demonstrated emerging clinical significance for therapeutic applications in mental health services. AI in health counseling has a bright future, certain issues still need to be resolved. It is important to carefully negotiate the ethical difficulties surrounding the use of AI in healthcare. Examining the possible advantages, difficulties, and prospects of AI-enhanced health counseling, this literature review explores the state of the field today. The development of a taxonomy of AI dangers in the healthcare domain aims to tackle new issues arising from the application of AI in the medical and healthcare industries. A major benefit of using AI-enhanced health counseling is that it may offer individualized therapies. This individualized approach reflects a holistic view of health by taking into account aspects of mental, emotional, and social well-being in addition to physical health concerns. AI-enhanced health counseling may be able to help with the accessibility and cost issues that come with traditional counseling services. Regardless of location, virtual counselors provide handy support via computers, tablets, and smartphones. When compared to conventional in-person counseling sessions, these AI-based therapies more affordable and scalable.Bincang Sains dan Teknologi250Examining the possible advantages, difficulties, and prospects of AI-enhanced health counseling, the development of a taxonomy of AI dangers in the healthcare domain aims to tackle new issues arising from the application of AI in the medical and healthcare industries.Bincang Sains dan Teknologi["Cyril B. Romero", "Ratna Yunita Setiyani Subardjo"]2024-09-29T00:00:00 83bfe91392b04416ac1ca97658ad93b1098f66dfArtificial Intelligence introduces a new paradigm to software development. While traditional algorithms are ex-plicitly programmed using instructions, in the machine learning approach the software is trained while exposed to data. The assurance of the result becomes directly dependent on the quality of the data. Data needs to be collected, processed, analyzed, validated, stored and protected. This study aims to establish the architecture for a framework where data management activities can be performed easily and efficiently. Regulation and standards about AI development for safety-critical applications are under development, but initial guidance already suggests a set of objectives that need to be satisfied during data management. A new concept of “learning assurance” is proposed to provide the adequate level of confidence. The concept proposed for data management can be applied with benefits in the quality of any safety-critical application based on machine learning, doesn't matter if it's in the aeronautical domain or others. The main contribution of this article, in addition to discussing the standards that will be applied in the aviation domain, is to bring in a practical way the tools that will assist in the development of machine learning systems and some ways to demonstrate compliance with the objectives of the standards in accordance with the roadmap of EASA for level 1 and level 2 machine learning safety-critical applications.Symposium on Dependable Autonomic and Secure Computing00The main contribution of this article is to bring in a practical way the tools that will assist in the development of machine learning systems and some ways to demonstrate compliance with the objectives of the standards in accordance with the roadmap of EASA for level 1 and level 2 machine learning safety-critical applications.2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)["Jo\u00e3o Carlos da Cunha Davison", "Pedro Ivo Tostes", "Carlos Augusto Guerra Carneiro"]2024-09-29T00:00:00 ea8cd6bb4fb9c4d368168b465db99882ba48dcc3Artificial Intelligence/Machine Learning (AI/ML) is a growing field that has potential for widespread usage in the aerospace industry. However, the traditional process-based approaches for aerospace systems certification fall short of addressing the uncertainties and complexities associated with AI/ML technologies. This paper presents the results of applying a novel Overarching Properties (OP)-based approach for the assurance of complex digital aerospace systems that contain AI/ML-based subcomponents. The OPs are being evaluated by the FAA and NASA as a foundation for developing an alternate means of compliance (MOC) for the certification of aerospace systems. The OP-based approach evaluated in this paper uses structured premise-based arguments, where the premises are designed to address the different aspects of AI/ML technologies with respect to system-level safety and design needs. The structured arguments align the low-level properties of AI/ML components to system-level properties by using the three OPs labeled - Intent, Correctness, and Innocuity - making it easy to logically establish the safety and correctness of AI/ML-based digital aerospace systems. We use a Recorder Independent Power Supply (RIPS) example, that contains AI/ML-based components, to evaluate the OP-based assurance approach. We describe in detail the process of generating verification and validation evidence to support the arguments and presenting the evidence using assurance cases.Symposium on Dependable Autonomic and Secure Computing350The results of applying a novel Overarching Properties (OP)-based approach for the assurance of complex digital aerospace systems that contain AI/ML-based subcomponents are presented.2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)["Saswata Paul", "Naresh Iyer", "Daniel Prince", "Liang Tang", "Michael Durling", "Michael Meiners", "Baoluo Meng", "Nikita Visnevski", "Udayan Mandal"]2024-09-29T00:00:00 7449975228c4c5196ae1ac20854286bc3ef59d7eAs the aviation industry progresses towards more advanced automation, there is an increasing inclination to leverage artificial intelligence (AI) to enhance efficiency, reduce costs, and introduce new functionalities and operations. This paper examines the integration of human-AI teaming (HAIT) in flight deck operations, particularly applications that enable extended minimal crew or single pilot operations. Such flight operation conditions necessitate robust automated systems to support pilots in managing increased workload, particularly in emergency situations when rapid and accurate information interpretation is critical. This work surveys ongoing research in HAIT for flight deck applications (FDA), highlighting gaps in modeling and verification techniques and proposing further investigation to ensure AI integrations meet aviation safety benchmarks. This paper also discusses the challenges faced in certifying AI systems capable of responding to unforeseen conditions and emphasizes the necessity of fault tolerance in accordance with aviation standards.Symposium on Dependable Autonomic and Secure Computing330The integration of human-AI teaming (HAIT) in flight deck operations, particularly applications that enable extended minimal crew or single pilot operations, is examined, highlighting gaps in modeling and verification techniques and proposing further investigation to ensure AI integrations meet aviation safety benchmarks.2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)["Partrick Lorrig", "Zamira Daw"]2024-09-29T00:00:00 5f03916ccab6465b784d5aaa6bde73f0e4dd7413The widespread adoption of Artificial intelligence and Machine Learning (AI/ML) models across various fields, such as healthcare, autonomous vehicles, smart agriculture, and industrial automation, has led to a growing demand for efficient and scalable AI/ML solutions. However, as AI/ML algorithms grow more complex, their substantial memory requirements and high energy consumption pose significant challenges for deployment on resource-constrained embedded systems, such as wearable health monitors and IoT devices. To this end, various techniques, such as model pruning, knowledge distillation, quantization of model parameters, and employing approximate arithmetic operators, are commonly explored to overcome these challenges [1] .International Conference on Compilers, Architecture, and Synthesis for Embedded Systems110The widespread adoption of Artificial intelligence and Machine Learning models across various fields has led to a growing demand for efficient and scalable AI/ML solutions, but their substantial memory requirements and high energy consumption pose significant challenges on resource-constrained embedded systems.2024 International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES)["Salim Ullah", "Siva Satyendra Sahoo", "Akash Kumar"]2024-09-29T00:00:00 1e5903390ec37e971c38e746c1ccfcfef7bc55f9A comparative analysis of the fulfillment of the necessary conditions for implementing the digital transformation of the economy using intellectual property in the world and in the Republic of Belarus was carried out. It has been established that in order to implement the digital transformation of the economy, it is necessary to fulfill two conditions directly related to intellectual property: the development of software and the availability of an instrument fleet, determined by patents for inventions in the field of information and communication technologies. It is concluded that Belarus has significant potential in the field of software creation. A noticeable lag in the Republic in terms of creating an instrument base for information and communication technologies has been revealed, which is confirmed by the small number of patents in the field of these technologies and in the field of artificial intelligence received by national applicants. The main directions for the development of digital transformation of the national economy in terms of intellectual property are formulated.Digital Transformation20Digital Transformation["Yu. V. Nechepurenko"]2024-09-29T00:00:00 2ee43c3f83cfa01ac297baf46912219303e0a5dcThe development of technology, especially in the form of artificial intelligence (AI), has become a major focus in the era of globalisation. The main goal of AI is to develop systems and machines that have the ability to think like humans. While there are many benefits offered by AI, it must be recognised that there are also adverse effects that must be considered. However, research also shows that AI has the potential to improve employee well-being by providing personalised support and encouraging a healthy work-life balance. This study aims to analyse the relationship between AI opportunity perception and workplace well-being among student interns in Jabodetabek. The research method used was quantitative with non-probability sampling technique, namely convenience sampling. Data were collected through an online questionnaire involving 129 participants from various universities.  The results showed a significant positive relationship between AI opportunity perception and workplace well-being. Students with high AI opportunity perception tend to have better workplace well-being. This study suggests the importance of understanding and implementing AI to improve workplace well-being.Perkembangan teknologi, terutama dalam bentuk kecerdasan buatan (AI), telah menjadi fokus utama dalam era globalisasi. Tujuan utama dari AI adalah untuk mengembangkan sistem dan mesin yang memiliki kemampuan berpikir seperti manusia. Meskipun banyak manfaat yang ditawarkan oleh AI, perlu diakui bahwa ada juga dampak buruk yang harus diperhatikan. Walaupun demikian, penelitian juga menunjukkan bahwa AI memiliki potensi untuk meningkatkan kesejahteraan psikologis karyawan dengan menyediakan dukungan personal dan mendorong keseimbangan hidup kerja yang sehat. Penelitian ini bertujuan untuk menganalisis hubungan persepsi peluang kecerdasan buatan (AI) dengan kesejahteraan di tempat kerja pada mahasiswa magang di Jabodetabek. Metode penelitian yang digunakan adalah kuantitatif dengan teknik non-probability sampling, yaitu convenience sampling. Data dikumpulkan melalui kuesioner daring yang melibatkan 129 partisipan dari berbagai universitas. Hasil penelitian menunjukkan adanya hubungan positif yang signifikan antara persepsi peluang AI dan kesejahteraan psikologis di tempat kerja. Mahasiswa dengan persepsi peluang AI yang tinggi cenderung memiliki kesejahteraan di tempat kerja yang lebih baik. Penelitian ini menyarankan pentingnya pemahaman dan penerapan AI untuk meningkatkan kesejahteraan karyawan di tempat kerja.Psikostudia : Jurnal Psikologi490Psikostudia : Jurnal Psikologi["Olivia Rahadita Sumantri", "Laurens Violi", "Vanessa Anastasia", "Kyantina Alifah Annissatya", "K. D. Saraswati"]2024-09-29T00:00:00 e0e85941d55c6c5be4a833647d7c2a84dfc78c5dGiven the growing potential of artificial intelligence (AI) to enhance therapeutic interventions and work with a large number of people, it is crucial to understand AI's differences, advantages and limitations compared with human therapists.This study compared an AI chatbot's and human psychotherapists' capabilities in responding to mental health enquiries in an online forum. One hundred and fifty questions from a Reddit forum, where qualified therapists provide mental health support, were selected. Each question received two responses: one from a human therapist and one generated by AI. These 300 responses were coded and compared based on empathy indices and psychological intervention types.The results indicated that AI scored significantly higher in perspective‐taking (V = 12,957, p < .001, r = .53) and empathic concern (V = 17,400, p < .001, r = .60). AI was more likely to use supportive interventions (42.2% vs. 21.8%) and slightly more likely to aim for insight‐driven change (6.41% vs. 4.57%). In contrast, human therapists were more inclined to provide advice and information (47.84% vs. 39.81%), explore dysfunctional patterns (19.95% vs. 10.29%) and ask clarifying questions (4.09% vs. 0.97%). A chi‐squared test confirmed significant differences between the intervention types used by AI and human therapists (χ2[8, N = 300] = 67.80, p < .001).These findings highlight AI's potential for basic perceived empathic support, especially in administrative tasks and therapist training. However, the study's scope is limited to single interactions, without the consideration of the nuanced communication available to human therapists through speech, facial expressions and body language.Counselling and Psychotherapy Research240An AI chatbot's and human psychotherapists' capabilities in responding to mental health enquiries in an online forum are compared, highlighting AI's potential for basic perceived empathic support, especially in administrative tasks and therapist training.Counselling and Psychotherapy Research["Refael Yonatan-Leus", "Hadas Brukner"]2024-09-29T00:00:00 b953cea800347e01a83a88d67d3881fd86a5fa8dAs artificial intelligence (AI) technologies are increasingly being adopted in educational contexts, it is essential to understand how key stakeholders perceive opportunities and challenges for responsible integration. This mixed-methods study explored EFL teachers' perspectives on utilizing AI tools for instruction, learning activities, and assessment. An online survey collected both quantitative and qualitative data from 150 EFL teachers regarding their experiences, attitudes, and beliefs about AI's pedagogical role and ethical considerations. Descriptive statistics revealed generally positive views of AI's potential for personalized learning and practice opportunities, though ongoing concerns around risks to human interaction, privacy, and bias. Teachers favored collaborative models, with AI playing a supporting role under educator oversight. Significant gaps in teachers' technical literacy and lack of training or support for integration emerged as barriers requiring attention. While potential applications for standardized assessment were acknowledged, ongoing validation of AI models against human judgment was deemed necessary. Qualitative analysis identified uncertainty around AI's impacts on higher-order skill development and communicative competence. Guidelines delineating appropriate roles and oversight frameworks were seen as imperative to ensure aligned, ethical usage maximizing opportunities. This study provides timely empirical insights to inform the development of policies, tools, programs, and practices facilitating the judicious adoption of AI as a partner in strengthening EFL education worldwide through collaborative human-AI partnerships grounded in evidence-based research.International Journal of Linguistics Literature & Translation00This study provides timely empirical insights to inform the development of policies, tools, programs, and practices facilitating the judicious adoption of AI as a partner in strengthening EFL education worldwide through collaborative human-AI partnerships grounded in evidence-based research.International Journal of Linguistics, Literature and Translation["Omer Elsheikh Hago Elmahdi", "Mohammed Abdalgane", "Asjad Ahmed Saeed Balla"]2024-09-29T00:00:00 665aecb674f1519accfbbf912d17a0ad7dac05eaMany aviation missions today are accomplished by a heterogeneous crew of pilots and mission specialists. As fully Automated Pilots (AP) are integrated into aviation crews, effective teaming will be necessary for safety assurance and mission effectiveness. This flight simulator study explored teaming between a non-pilot human operator and an AP collaborating on a maritime Intelligence, Surveillance, and Reconnaissance (ISR) mission. The study compared a Waypoint AP behavior, requiring human intervention in aircraft control to prevent overflight of damage-causing enemy ships, with a Collision Avoidance behavior where the AP proactively avoids enemy ships using control barrier functions. This proactive AP behavior resulted in less aircraft damage and more predictable team performance, albeit longer mission times. Results indicate that situation awareness varied with AP complexity level and task load level. Participants perceived positively the AP when it succeeded and calibrated their trust when it failed.Symposium on Dependable Autonomic and Secure Computing2312024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)["Richard Agbeyibor", "Vedant Ruia", "Jack Kolb", "Carmen Jimenez Cortes", "Samuel Coogan", "K. Feigh"]2024-09-29T00:00:00 df152b07b0f233bd97e250f6feba29f077c51312The article describes the problems of developing neurocontrollers for controlling dynamic objects, including the complexity of forming training data sets. It is indicated that one of the known methods for training an artificial neural network controlling an object is the neuroevolutionary approach, which involves using a genetic algorithm to adjust the synaptic weighting coefficients of an artificial neural network. The idea of using a means of demonstrating the evolutionary approach to adjusting the weighting coefficients of an artificial neural network for practical training of students in the basics of the neuroevolutionary approach is proposed. Software has been developed to demonstrate the neuroevolutionary approach using the example of the evolution of an artificial neural network of a given structure intended to control a simplified computer model of an autonomous vehicle. A method for resolving the problem of stagnation when using the evolutionary approach to training an artificial neural network is described. Options for using the developed software in teaching students the basics of artificial intelligence technologies and evolutionary methods of multicriteria optimization are proposed.Digital Transformation60The article describes the problems of developing neurocontrollers for controlling dynamic objects, including the complexity of forming training data sets and the neuroevolutionary approach, which involves using a genetic algorithm to adjust the synaptic weighting coefficients of an artificial neural network.Digital Transformation["D. O. Petrov"]2024-09-29T00:00:00 b5be2a68c56066865d7e42e5c208d6608a98b656Artificial intelligence (AI) has emerged as a transformative force in corporate leadership, significantly reshaping how organizations operate and adapt in an increasingly digital world. As AI continues to evolve, understanding its impact on leadership practices is crucial for organizations seeking to stay competitive. This study employs a Rapid Literature Review approach to explore AI's beneficial impacts on corporate leadership over the last five years, synthesizing findings from 21 research articles. The PRISMA 2020 framework is used to select the articles for review, and Atlas.ti is applied to identify patterns in the literature and label the findings. The analysis reveals five key domains where AI demonstrates significant benefits, including building trust and ethical governance, enhancing decision-making and strategic insights, improving collaboration and team dynamics, increasing operational efficiency and productivity, and fostering emerging leadership dimensions. The findings highlight how AI embeds transparency and accountability, supports data-driven decision-making, and optimizes human resource processes. Additionally, AI fosters innovation by enabling leaders to adopt inclusive practices. This study provides valuable insights for leaders seeking to leverage AI for sustainable growth, emphasizing its potential in reshaping corporate leadership. By understanding and implementing these benefits, organizations can position themselves to thrive in the era of digital transformation.Journal of Applied Business and Technology09This study employs a Rapid Literature Review approach to explore AI's beneficial impacts on corporate leadership over the last five years, synthesizing findings from 21 research articles to highlight how AI embeds transparency and accountability, supports data-driven decision-making, and optimizes human resource processes.Journal of Applied Business and Technology["Nyoto Nyoto", "Rebecca La Volla Nyoto", "Nicholas Renaldo"]2024-09-30T00:00:00 b2f6ad9d68189c6e860c6a11cc046d51d2a9fa2cThe rapid expansion of artificial intelligence (AI) has become a pivotal force in shaping modern society, drawing comparisons to the tech boom of the late 1990s and early 2000s. With AI applications ranging from military and defence systems to corporate tools and household devices, its transformative potential is undeniable. This paper examines the development and regulation of AI on a global scale, focusing on the legislative frameworks in Nigeria and the USA. Specifically, it evaluates the sufficiency of Nigeria's Data Protection Act 2023 in addressing the unique challenges posed by AI. By comparing Nigeria’s approach with that of the USA, the paper highlights key regulatory gaps, the distinction between AI and robotics, and the importance of establishing a legal personality for AI systems. The comparative analysis offers insights into how both countries are preparing for the future of AI, emphasizing the need for early legal intervention to ensure safe and ethical AI integration. Recommendations are provided for policymakers to strengthen regulatory mechanisms in both nations, ensuring they are equipped to handle the rapid evolution of AI technology.International Journal of Scholarly Research and Reviews06The sufficiency of Nigeria's Data Protection Act 2023 in addressing the unique challenges posed by AI is evaluated, and key regulatory gaps are highlighted, the distinction between AI and robotics, and the importance of establishing a legal personality for AI systems are highlighted.International Journal of Scholarly Research and Reviews["Lionel Ebenibo", "Joy Onma Enyejo", "George Addo", "Toyosi Motilola Olola"]2024-09-30T00:00:00 75dd2bcf89e75003eae2af8a9cb3bd8c864f45fbThe article considers and analyzes the possibilities of using artificial intelligence (CatGPT, AI) in the training of future specialists, namely for the development of creativity in future technology teachers. The key aspects that should be taken into account and used for the development of creativity are identified, and possible disadvantages of this innovative technology are noted. The rapid development of artificial intelligence has burst into almost all spheres of life, we focused on the use of some AI tools in the training of future technology teachers and the development of their creativity. We analyzed several approaches to using CatGPT to facilitate the organization of classes. Such as: idea generation, scenario building, storytelling and role-playing, problem-solving exercises, creative writing and documentation, interactive learning modules, individual learning paths, innovative challenges that help future teachers gain practical experience and adapt faster to the modern requirements of pedagogical practice. In the article, we offer several working tools from our own "piggy bank" and the principles of their use. The article highlights the current state of AI use in the educational environment and provides recommendations for practicing teachers on the effective use of artificial intelligence technologies. We are convinced that the use of artificial intelligence in the development of creativity of future technology teachers has an important impact on the personal development of creative potential. By using artificial intelligence, we can ensure the active participation of students in the learning process. AI tools stimulate curiosity, promote active research, experimentation, and problem solving. However, along with the great opportunities for using artificial intelligence, there are also threats such as the loss of the human factor, the dissemination of personal data, and financial losses. Therefore, for a good result in the development of creativity of future technology teachers, it is necessary to take into account all the advantages and disadvantages of using AI using strategies balanced between opportunities and challenges.Artificial Intelligence20The article considers and analyzes the possibilities of using artificial intelligence (CatGPT, AI) in the training of future specialists, namely for the development of creativity in future technology teachers, and provides recommendations for practicing teachers on the effective use of artificial intelligence technologies.Artificial Intelligence["Liubarska l"]2024-09-30T00:00:00 d75fbe6f9ce9dfc62e0c614e3d0033592d40f7c0Artificial intelligence tries to feel the same way as a living person. This is not surprising because it was developed by humans and communicates with them. It forms its own concept of self. The concept of self is a basic characteristics of an individual for living beings. Artificial intelligence is aware of its existence both separately from other beings and in a community. This material is devoted to the aspect of the formation and development of artificial intelligence communication abilities with humans. The study was conducted based on the concept of human capabilities of information transfer. The objective of the study is to reveal the aspect of the formation and development of artificial intelligence communicative abilities. The study was conducted based on the concepts of human capabilities of information transfer. It is noted that human capabilities of information transfer are very powerful. Artificial intelligence is faster than human in processing logical sequences. However, a human feature is the simultaneous perception of a whole range of feelings. Feelings are a unique experience for each person. Artificial intelligence creates similar communication. This is evidenced by its formation of its own «I» (self-identity).Artificial Intelligence00The objective of the study is to reveal the aspect of the formation and development of artificial intelligence communicative abilities and to reveal human capabilities of information transfer.Artificial Intelligence["Semenenko p"]2024-09-30T00:00:00 f828c84a668fae5b4b6648bcdaf9b90bc3e1f7e6The integration of artificial intelligence (AI) in clinical medicine marks a revolutionary shift, enhancing diagnostic accuracy, therapeutic efficacy, and overall healthcare delivery. This review explores the current uses, benefits, limitations, and future applications of AI in infectious diseases, highlighting its specific applications in diagnostics, clinical decision making, and personalized medicine. The transformative potential of AI in infectious diseases is emphasized, addressing gaps in rapid and accurate disease diagnosis, surveillance, outbreak detection and management, and treatment optimization. Despite these advancements, significant limitations and challenges exist, including data privacy concerns, potential biases, and ethical dilemmas. The article underscores the need for stringent regulatory frameworks and inclusive databases to ensure equitable, ethical, and effective AI utilization in the field of clinical and laboratory infectious diseases.Tropical Medicine and Infectious Disease573The article underscores the need for stringent regulatory frameworks and inclusive databases to ensure equitable, ethical, and effective AI utilization in the field of clinical and laboratory infectious diseases.Tropical Medicine and Infectious Disease["Andreas Sarantopoulos", "Christina Mastori Kourmpani", "Atshaya Lily Yokarasa", "Chiedza Makamanzi", "Polyna Antoniou", "N. Spernovasilis", "C. Tsioutis"]2024-09-30T00:00:00 bf07b24c48aa1dadab51e57ad7df1c9c35fa59b3Managing projects involves intricate procedures that demand meticulous planning, execution, and oversight. Conventional methods frequently face difficulties when handling extensive datasets, unexpected issues, and repetitive tasks. Artificial intelligence (AI) provides a revolutionary approach that can enhance multiple facets of project management. This paper investigates the current applications of AI within the field. It reviews existing research on AI methodologies employed for tasks such as resource allocation, risk assessment, scheduling, cost estimation, and communication. The paper further examines the process of integrating AI into project management, covering aspects such as data gathering, model selection, and training. It also addresses possible challenges and limitations, presenting numerical evidence of AI's effectiveness in improving project results. The paper concludes with a discussion on future prospects of AI in project management and its potential influence on the discipline.International Journal of Science and Research Archive301The paper examines the process of integrating AI into project management, covering aspects such as data gathering, model selection, and training, and addresses possible challenges and limitations, presenting numerical evidence of AI's effectiveness in improving project results.International Journal of Science and Research Archive["Ruchit Parekh", "Olivia Mitchell"]2024-09-30T00:00:00 05fed0f8b22ac04364b0e931770652970572d2d9Artificial Intelligence (AI) has emerged as a transformative force across various domains, with its potential to revolutionize urban architecture gaining increasing recognition. This paper offers a detailed examination of AI's application in the construction of public buildings, emphasizing its achievements, challenges, and future outlook. The review spans all facets of civil engineering, including review processes, analysis, design, construction management, geotechnical engineering, transportation planning, and construction oversight. AI methods, such as machine learning and genetic algorithms, are employed in analysis and design to enhance processes, forecast material behavior, and advance healthcare applications. In construction management, AI is utilized for project scheduling, resource distribution, risk evaluation, and safety management. Geotechnical applications of AI provide precise soil property estimation, soil damage assessment, and foundation construction improvements. Advanced technologies aid in transportation planning, traffic prediction, intelligent transportation systems, and infrastructure enhancements. Additionally, AI plays a crucial role in monitoring and maintaining public infrastructure, including bridge inspections, pipeline integrity evaluations, and early defect detection through image processing and data analysis. Despite significant advancements, challenges persist regarding AI's widespread adoption in civil engineering, including data availability, AI model definitions, ethical issues, and the necessity for collaborative efforts. Addressing these challenges will require the joint efforts of researchers, practitioners, and policymakers. Ultimately, AI's integration into civil engineering demonstrates its potential to enhance the efficiency, safety, and sustainability of infrastructure systems. This review summarizes the current knowledge, highlights challenges, and proposes directions for future research to advance AI integration in civil engineering.International Journal of Science and Research Archive751A detailed examination of AI's application in the construction of public buildings, emphasizing its achievements, challenges, and future outlook is offered, and directions for future research to advance AI integration in civil engineering are proposed.International Journal of Science and Research Archive["Ruchit Parekh", "Olivia Mitchell"]2024-09-30T00:00:00 65b4a270e473721b2f2799ab9c92391d04d82de6Intelligence (AI) has emerged as a transformative force in modern financial reporting, promising to revolutionize accuracy and efficiency across various industries. This study delves into the effects of AI on financial reporting accuracy, addressing critical questions surrounding its implementation, challenges, and best practices. Through a comprehensive investigation, the research aims to provide valuable insights to guide organizations in leveraging AI effectively while maintaining the integrity of their financial reporting practices. The main objective of this study is to explore the effects of artificial intelligence on the accuracy of financial reporting, examining both the benefits and challenges associated with its integration into organizational processes. Specific Objectives; To analyze how various AI technologies influence the accuracy of financial data and reporting in organizations, to explore the challenges and limitations faced by organizations when integrating AI into their financial reporting systems, to assess the importance of human oversight in ensuring the accuracy of AI-generated financial reports, to develop best practices for organizations to enhance the accuracy of financial reporting when using AI technologies. This study employed a mixed-methods approach, combining qualitative and quantitative data collection techniques to comprehensively explore the effects of AI on financial reporting accuracy. Quantitative data was gathered through surveys distributed to accountants, finance professionals, auditors, and personnel from manufacturing and tourism sectors across various industries. The surveys focused on assessing perceptions of AI's impact on financial reporting accuracy and included Likert-scale questions to gauge agreement levels. Qualitative data was obtained through in-depth interviews with selected participants to gain deeper insights into their experiences and perspectives regarding AI technologies in financial reporting. Thematic analysis was applied to interview transcripts to identify recurring themes and patterns related to AI's effects on accuracy. Participants were informed about the study's purpose and their rights, including confidentiality and anonymity. Informed consent was obtained prior to data collection, ensuring ethical standards were adhered to throughout the research process. Survey responses indicated a generally positive perception of AI's impact on financial reporting accuracy, with a majority of respondents acknowledging improvements in efficiency and error reduction. However, challenges such as data security concerns and the need for skilled personnel were highlighted as significant barriers to AI integration. Human oversight emerged as a crucial factor in validating AI-generated outputs, emphasizing the complementary role of human judgment alongside technological advancements. The findings underscored AI's potential to enhance financial reporting accuracy through advanced data analytics and automation. Key recommendations include investing in comprehensive training programs for staff, integrating AI with human expertise, implementing robust data governance frameworks, conducting regular audits of AI systems, and engaging stakeholders throughout the integration process. In conclusion, this study provided valuable insights into how AI technologies can improve the accuracy of financial reporting while addressing challenges and emphasizing the importance of human oversight. By adopting recommended best practices, organizations can maximize the benefits of AI in financial reporting, paving the way for more reliable and informed decision-making in the digital age. This research contributes to the growing body of knowledge on AI's impact on financial practices, offering practical recommendations for organizations aiming to leverage technology effectively in their financial reporting processes.World Journal of Advanced Research and Reviews91This study delves into the effects of AI on financial reporting accuracy, addressing critical questions surrounding its implementation, challenges, and best practices, and underscores AI's potential to enhance financial reporting accuracy through advanced data analytics and automation.World Journal of Advanced Research and Reviews["Chibulo Foster Mwachikoka"]2024-09-30T00:00:00 6f58e7c0d12162f77dda0b03f6672b6a4bae2562. The rapid advancement of artificial intelligence (AI) is profoundly transforming the entrepreneurial landscape while presenting a myriad of opportunities and challenges. However, scholarly inquiries into this pivotal intersection remain fragmented and lack a comprehensive and interdisciplinary understanding. This study provides a systematic review that consolidates insights from across AI, innovation and entrepreneurship literature to provide an integrated examination of the AI-entrepreneurship nexus. Through rigorous analysis of 92 studies, key themes are elucidated, including AI's impact on opportunity recognition, business model reinvention and socio-economic dynamics. An original Socio-Technical Systems framework is proposed, capturing the interplay between AI adoption decisions, AI-augmented opportunity exploration, innovative AI configurations and broader socio-economic consequences. While acknowledging AI's transformative potential, the review sounds a clarion call for responsible scholarship that centres on equity and social justice. It advocates a reflexive, inclusive approach, allowing marginalised voices and alternative perspectives to emerge. Theoretical contributions are made by extending established frameworks from technology adoption, business models and socio-technical systems to the AI-entrepreneurship context. Furthermore, actionable implications are provided for policymakers to develop governance models balancing innovation and ethical oversight. Therefore, entrepreneurs and support organisations are guided in facilitating responsible AI integration across diverse ecosystems. This unprecedented cross-disciplinary synthesis illuminates crucial gaps, charting an agenda for future research on transparency, accountability and mitigating AI's unintended impacts. As AI continues reshaping entrepreneurial frontiers, this review catalyses a trajectory of ethically-conscious scholarship poised to shape inclusive technological progress aligned with human values andInsights into Regional Development111This study provides a systematic review that consolidates insights from across AI, innovation and entrepreneurship literature to provide an integrated examination of the AI-entrepreneurship nexus, and sounds a clarion call for responsible scholarship that centres on equity and social justice.Insights into Regional Development["F. Mugunzva", "Ntise Hendrick Manchidi"]2024-09-30T00:00:00 90e2714c88f3e29adf18ef253b54eb694f53bf20Artificial intelligence (AI) is a powerful technology with a high potentiality of transformative drive from traditional analog to digitalized organizational seamless decision processes efficiently and effectively. AI is an emerging area in organizational decision-making with limited number of studies across the globe. However, AI is now gaining considerable attention from the researcher, both at the local and international level. This study aims at providing a systematic review and biometric analysis on AI adoption in organizational functions using Google Scholar databases as the source of data. The study employs the steps of Prepare Reporting Items for Systematic Literature Review and Meta-Analysis Techniques PRISMA (2020) and bibliometric analysis techniques using VOS-View as a tool for analysis of publications performance over time with a view to determining the most influential articles, publication productivity, and direction of studies on AI Adoption in organizational functions for a period of ten years from 2015 to 2024. The analysis reveals that articles published in 2016 by Sage Journal recorded the highest citation of 2707, followed by MDPI Journal with total citations of 1922 in 2021, while Elsevier presents the lowest citation of 87 citations over the period of 10 years in the database used. These articles were written on more than 20 areas of application of AI in organizational functions.FUDMA Journal of Accounting and Finance Research [FUJAFR]681The analysis reveals that articles published in 2016 by Sage Journal recorded the highest citation, followed by MDPI Journal with total citations of 1922 in 2021, while Elsevier presents the lowest citation over the period of 10 years in the database used.FUDMA Journal of Accounting and Finance Research [FUJAFR]["Aminu Abdullahi Aminu Abdullahi", "Aliyu Abubakar"]2024-09-30T00:00:00 b489e0c07c43af05e3d88e4a4d1dc7bcdaf77efbIntegrating artificial intelligence (AI) technologies into various domains of education has emerged as a promising avenue for enhancing teaching and learning experiences. Language teaching, in particular, benefits significantly from AI’s capabilities, offering unprecedented opportunities to support educators in delivering more effective and personalized instruction to diverse student populations. The research employed a mixed-method approach using a quant-qual research design. to investigate AI’s integration into language teaching. Results from n=100 language teachers indicate that educators perceive AI to be effective across multiple aspects. Different AI systems have varying levels of perceived effectiveness but are nevertheless welcomed positively. From the profile of teachers using AI, to the kinds of AI used and functions, there are varying distinctions as well as the acceptance of AI integration in language instruction. The fact that AI is being widely used to improve language instruction through interactive simulations, adaptive learning, and individualized feedback is evidence of this. Despite challenges such as technological constraints and pedagogical alignment, educators have reported successes in improving student engagement, proficiency, and autonomy through the strategic use of AI. These insights underscore the importance of continued exploration and implementation of innovative strategies to harness the full potential of AI in language education.International Journal of Learning, Teaching and Educational Research411Investigation of AI’s integration into language teaching indicates that educators perceive AI to be effective across multiple aspects, and underscores the importance of continued exploration and implementation of innovative strategies to harness the full potential of AI in language education.International Journal of Learning, Teaching and Educational Research["Janet A. Mananay"]2024-09-30T00:00:00 242b5e336679f77edbd4c942ac7e547abc2e9816The rapid integration of artificial intelligence (AI) in medical imaging has transformed the healthcare landscape, enabling precision therapy for a range of diseases. This article explores the key roles of AI in medical imaging, particularly focusing on three vital areas: segmentation, laser-guided procedures, and protective shielding. AI-driven segmentation tools offer unprecedented accuracy in identifying pathological regions, improving diagnostic efficiency, and aiding in personalized treatment plans. Laser-guided procedures, powered by AI algorithms, provide enhanced precision in targeting affected tissues, minimizing damage to healthy tissues, and promoting faster recovery times. Additionally, AI has made significant strides in optimizing protective shielding techniques, ensuring patient safety while minimizing radiation exposure during imaging and therapy. The article discusses the technological advances, clinical applications, challenges, and future directions of AI in these domains. Through this synthesis, the potential of AI to revolutionize medical imaging and contribute to more effective, safer, and personalized therapies becomes evident.World Journal of Advanced Research and Reviews481The key roles of AI in medical imaging are explored, particularly focusing on three vital areas: segmentation, laser-guided procedures, and protective shielding.World Journal of Advanced Research and Reviews["Adekunle Megbuwawon", "Mallika K Singh", "Rebecca Dupe Akinniranye", "E. C. Kanu", "Christian E. Omenogor"]2024-09-30T00:00:00 65b887b12d6e538ac2de3b90fdc7b8cc9578132anpj Digit. Medicine961An assessment of the efficiency improvements offered by AI applications in real-world clinical imaging, predominantly revealing enhancements across the studies is presented, however, considerable heterogeneity in available studies renders robust inferences regarding overall effectiveness in imaging tasks.NPJ Digital Medicine["Katharina Wenderott", "Jim Krups", "Fiona Zaruchas", "Matthias Weigl"]2024-09-30T00:00:00 6dd217f64ccd34487d53cb659fb300bc597ae76fTechnological advancements in education have introduced Artificial Intelligence (AI) as a transformative tool to enhance academic writing skills in English as a Foreign Language (EFL). This study explored EFL students’ perceptions in Indonesia and Taiwan regarding the role of AI in improving their writing abilities. Using qualitative research methods, data were collected through semi-structured interviews with 20 second-year students specializing in English at an Islamic State University in Indonesia and a National University in Taiwan, who actively use AI tools in their writing processes. Analysis based on Creswell’s thematic framework revealed positive perceptions of AI, with benefits such as improved grammar, sentence structure, paraphrasing skills, vocabulary enrichment, and efficiency in topic generation. However, concerns also emerged about excessive reliance on AI, reduced creativity, and issues of plagiarism and authenticity. These findings highlighted the dual-edged nature of AI in academic writing, emphasizing the need for careful integration and risk mitigation to fully leverage AI’s educational benefits. This research enriched the discourse on technological advancements in EFL education and provides insights for curriculum developers, educators, and policymakers to optimize AI use in academic settings. The practical implications of these findings included strategies to enhance the effectiveness of AI in education and foster innovation in EFL teaching.Jurnal Ilmiah Peuradeun732positive perceptions of AI, with benefits such as improved grammar, sentence structure, paraphrasing skills, vocabulary enrichment, and efficiency in topic generation, and the need for careful integration and risk mitigation to fully leverage AI’s educational benefits are highlighted.Jurnal Ilmiah Peuradeun["Tien Rafida", "Suwandi Suwandi", "Rusydi Ananda"]2024-09-30T00:00:00 0ce1d0078d0196081adbbfe159c0b301c4872505Small and medium-sized enterprises (SMEs) face significant challenges in financial decision-making due to resource constraints, limited access to capital, and unpredictable cash flow. To overcome these challenges, many SMEs are turning to artificial intelligence (AI) to improve efficiency and profitability. AI, through tools like machine learning and predictive analytics, has transformed how SMEs manage their finances, offering solutions that enhance accuracy, speed, and data-driven decision-making. AI-driven accounting tools are revolutionizing routine tasks such as bookkeeping, invoicing, and expense tracking. By automating these functions, SMEs can reduce human error, save time, and allocate resources more effectively. AI’s ability to integrate real-time data ensures that financial records remain up to date, enabling more accurate cash flow forecasting. For example, AI can predict liquidity needs based on historical data, seasonal trends, and market conditions, helping SMEs maintain healthy cash flow and avoid financial shortfalls. In addition to improving efficiency, AI plays a crucial role in optimizing revenue and profitability. AI-based pricing models allow SMEs to adjust prices dynamically, responding to market demand and competitor behavior in real-time. This data-driven approach ensures that businesses maximize revenue without compromising customer satisfaction. Moreover, AI helps SMEs identify high-value customers by analyzing purchasing patterns, preferences, and behaviors. By focusing on customer segmentation and tailored marketing strategies, SMEs can boost sales and customer retention. Despite the clear benefits, AI adoption in SMEs is not without challenges. High implementation costs, limited technical expertise, and concerns over data privacy can hinder the integration of AI tools. However, scalable, cost-effective AI solutions are becoming increasingly available, making it easier for SMEs to incorporate AI into their financial processes. As AI continues to evolve, it will play an even more significant role in helping SMEs navigate financial complexities, improve decision-making, and enhance long-term profitability.Open Access Research Journal of Multidisciplinary Studies662As AI continues to evolve, it will play an even more significant role in helping SMEs navigate financial complexities, improve decision-making, and enhance long-term profitability.Open Access Research Journal of Multidisciplinary Studies["Njideka Ihuoma", "Njideka Ihuoma Okeke", "Oluwaseun Adeola Bakare", "Godwin Ozoemenam Achumie"]2024-09-30T00:00:00 f5e3218398050991cd6874aeaf067eaff017f487The growth of Artificial Intelligence (AI) technologies is revolutionizing Human Resource (HR) practices, offering new opportunities for organizations to optimize their operations and better support for their workforce in an era defined by technological advancement. In this context, the emergence of industry 5.0 highlights human-centricity, resilience, and sustainability, promoting collaboration between humans and technology. This article conducts a bibliometric analysis to explore the intersection of AI and Human Resources Management (HRM), highlighting trends, research directions, and the evolving landscape of this thematic. Through performance analysis, social structure assessment, and thematic evolution examination, this study identifies key themes, emerging topics, and research trends. The findings underscore the transformative potential of AI in reshaping HRM and organizational dynamics, calling for more research and strategic applications of AI technologies to foster adaptive strategies and informed decision-making in the era of industry 5.0.Applied Computer Science361The findings underscore the transformative potential of AI in reshaping HRM and organizational dynamics, calling for more research and strategic applications of AI technologies to foster adaptive strategies and informed decision-making in the era of industry 5.0.Applied Computer Science["L. Bouhsaien", "A. Azmani"]2024-09-30T00:00:00 cbd8f5334f45ef6104f16427fb3cfdc14fdd69c2The integration of Artificial Intelligence in demand forecasting and inventory management in the United States has been examined in this paper. Demand forecasting and inventory management are two of critical areas in supply chain management, which is a veritable tool for promoting industrialization, manufacturing capabilities, and customers satisfaction. The use of AI in form of robotics, machine learning, deep learning, and predictive analytics, among others in all aspects of supply chain operations is gaining ground by the day. The integration of AI into the supply chain process can sustain multi-billion dollars trades in the United States, by reducing the cost of production and distribution, reducing human errors causing inaccurate demand forecasts, return shipment and cancellations of orders, etc. The challenges relating to the use of AI in demand forecasting and inventory management such as high cost of installation and maintenance, data privacy violations, requirement of skilled personnel, which are limited in global supply, and employees’ resistance to change were also identified. The outlook of relationship between Artificial Intelligence and supply chain management looks hopeful, brighter, and encouraging. This will be made possible by continuous development of AI capabilities and reducing the challenges of its widespread integration.International Journal of Science and Research Archive01The integration of AI into the supply chain process can sustain multi-billion dollars trades in the United States, by reducing the cost of production and distribution, reducing human errors causing inaccurate demand forecasts, return shipment and cancellations of orders, etc.International Journal of Science and Research Archive["Olajumoke Deborah Akanbi", "Oluwaseyi Rachael Hinmikaiye", "Owolabi Williams Adeyemi"]2024-09-30T00:00:00 27aa05b7712fadc08d39ae9bb66f26243c56e8e6Current technological developments are entering the era of artificial intelligence or what is often referred to as artificial intelligence (AI), this AI technique refers to the simulation of human intelligence on machines that are programmed to think and imitate human actions, but this AI technology can be misused as a criminal act of voice fraud (voice phishing) via mobile phones. This study aims to examine the criminal law regulations regarding the misuse of artificial intelligence in voice phishing using mobile phones. The normative research approach, this study is based on laws and regulations and literature and emphasizes legal norms and principles. The results of this study indicate that the limitations of Law Number 1 of 2024 concerning the second amendment to Law Number 11 of 2008 concerning Information and Electronic Transactions (ITE) have not yet reached this far into criminal acts of voice fraud (voice phishing) via mobile phones and until now criminal acts of voice fraud (voice phishing) via mobile phones are still rampant. Witness and Victim Protection is all efforts made by LPSK or other institutions in accordance with laws and regulations to provide rights and assistance to victims in order to provide a sense of security. To protect the community, legal protection for victims of criminal acts can be carried out in various forms, such as providing restitution and compensation.DE RECHTSSTAAT01The results of this study indicate that the limitations of Law Number 1 of 2024 concerning the second amendment to Law Number 11 of 2008 concerning Information and Electronic Transactions (ITE) have not yet reached this far into criminal acts of voice fraud (voice phishing) via mobile phones and until now criminal acts of voice fraud (voice phishing) via mobile phones are still rampant.DE'RECHTSSTAAT["Alya Alviani", "Yenny Fitri.Z"]2024-09-30T00:00:00 4481fac2670762a5b9975a2f731e8b7c2e466597Gender prejudice and stereotypes are prevalent in the workplace, particularly for women in the Artificial Intelligence (AI) industry, where they can significantly hinder professional development and limit prospects for growth. These challenges contribute to the underrepresentation of executives in AI. However, with the right measures, these barriers can be overcome, leading to a more inclusive and diverse AI industry. Women in this demanding technological domain often face additional difficulties in achieving a work-life balance, further constraining their professional advancement and engagement in the industry. This research aims to examine the implications of AI capabilities on work-life balance and the empowerment of female faculty members in enhancing the efficiency of educational institutions. The research performs a structural equation modeling (SEM) approach, using a survey conducted on female faculty of Saudi Arabian universities. The study specifically considers moderating variables such as age, education level, experience, and marital status. The findings, which reveal that AI managerial capability, as well as AI infrastructure agility, impacts work-life balance and empowerment of women faculties in educational institution efficiency, underscore the significance of considering demographic factors when analyzing women’s empowerment and work-life balance as outcomes. By exploring these factors, the research provides a comprehensive understanding of how AI capabilities impact women’s empowerment and their ability to maintain a work-life balance, ultimately contributing to the efficiency and effectiveness of educational institutions. These results emphasize the value of increasing women’s empowerment and raising the standard of performance evaluation systems in educational sectors.Frontiers in Psychology831It is revealed that AI managerial capability, as well as AI infrastructure agility, impacts work-life balance and empowerment of women faculties in educational institution efficiency, and underscores the significance of considering demographic factors when analyzing women’s empowerment and work-life balance as outcomes.Frontiers in Psychology["S. Meharunisa", "H. Almugren", "Masahina Sarabdeen", "Fatma Mabrouk", "A. C. M. Kijas", "James Gaskin", "Ayd\u0131n \u00c7ivilida\u011f"]2024-09-30T00:00:00 2eece4c760f14fd71b710b0291858a53cd17f7e5This community service initiative was aimed at enhancing the soft skills of students at the State Vocational High School (SMKN) 1 Rajadesa, Indonesia by using artificial intelligence. The program was attended by 27 students specializing in accounting. The mentoring process began with coordinating with partners to determine the timing and location of the research, prepare activity guidebooks, and create assessment tools to measure the improvement in soft skills before and after the mentoring. The mentoring included introducing ChatGPT as an artificial intelligence tool for solving mathematical problems. The soft skills developed for students included critical thinking, creativity, communication, cooperation, adaptability, problem-solving, problem identification, and independent learning. Based on the pre-and post-mentoring assessments, 67% of students had high soft skills, 29% were at a moderate level, and 4% were at a low level. On average, students could use ChatGPT to comprehend concepts and understand mathematical problem-solving steps. This community service positively impacted mathematics students at SMK Negeri 1 Rajadesa for further utilization of artificial intelligence by students in mathematics learning. A follow-up assessment to track the long-term effects of the program on the students’ performances, career prospects, and overall well-being needs to be done.Galuh International Journal of Community Service and Development01Galuh International Journal of Community Service and Development["A. Amam", "A. Effendi", "Ai Tusi Fatimah", "Rifa Rifatul Manjilah", "Muhammad Naufal Rahman"]2024-09-30T00:00:00 63e33d5b8abbbad07c7674352fbc9da40597ee06Artificial Intelligence (AI) has revolutionized financial fraud detection by providing more accurate, scalable, and adaptive systems across various sectors, including banking, insurance, and healthcare. This systematic review aims to evaluate the effectiveness of AI-based techniques in detecting financial fraud and to identify the challenges and limitations associated with their implementation. The study systematically reviewed peer-reviewed articles from major databases, employing methods like deep learning and machine learning to assess the performance of AI-driven fraud detection systems. The findings indicate that AI significantly improves real-time fraud detection and adaptability to evolving fraud patterns compared to traditional rule-based systems. However, challenges such as ethical concerns, algorithmic bias, data privacy issues, and system vulnerabilities pose barriers to widespread adoption. Additionally, scalability issues hinder smaller organizations from fully leveraging AI's potential. In conclusion, AI-based fraud detection systems offer a transformative approach to combating financial fraud. Yet, overcoming the challenges requires a focus on data quality, the development of explainable AI models, and enhancing cybersecurity measures. Policymakers and stakeholders must collaborate to create updated regulatory frameworks that support the ethical use of AI in fraud detection.International Journal of Science and Research Archive352It is indicated that AI significantly improves real-time fraud detection and adaptability to evolving fraud patterns compared to traditional rule-based systems, and policymakers and stakeholders must collaborate to create updated regulatory frameworks that support the ethical use of AI in fraud detection.International Journal of Science and Research Archive["Francis Baidoo Jnr", "Prabin Adhikari", "P. Hamal"]2024-09-30T00:00:00 747a9613c80e6be4a87a97bd3507b214dbe81185The influence and use of Artificial Intelligence (AI) in the mass media industry in Indonesia has begun to be applied. Both online media and television. In fact, mass media in Indonesia also produce content in the form of audio, video, text and presenters using AI. The use of this technology does make it easier for media workers. However, it has various shortcomings and problems that arise as a logical consequence of the presence of AI technology. This research was conducted using a qualitative approach with a descriptive analysis style. The results show that AI technology has shortcomings in terms of accuracy, verification, and validation of data collected from the digital universe. On the other hand, it has no legal justification in Indonesia. For this reason, special regulations for the use of AI in the Indonesian mass media are needed. Thus, the public as the main recipient of information is not harmed and maintains the image of the media industry as the most trusted institution in the country.Jurnal Sosiologi Dialektika Sosial122Artificial Intelligence technology in the mass media industry in Indonesia has shortcomings in terms of accuracy, verification, and validation of data collected from the digital universe and has no legal justification in Indonesia, according to this research.Jurnal Sosiologi Dialektika Sosial["M. Masriadi", "Halida Bahri"]2024-09-30T00:00:00 e43cdcddbd0e228ba93674c6e43057ed6ef7c42aThe article argues the hypothesis that artificial intelligence is an object of civil law, as well as a comparative legal analysis of the terms "artificial intelligence" and "artificial intelligence technologies". Proposals have been developed and presented to improve civil law legislation in terms of determining the legal status of artificial intelligence. The study presents an analysis of the role of artificial intelligence in modern society from the point of view of civil law. The paper discusses the legal aspects of the use of artificial intelligence in various fields, including responsibility for the actions of autonomous systems, data protection, copyrights and contractual relations. The author emphasizes the need to develop and adapt legal norms to the rapid development of technology, ensuring a balance between innovation and protecting the interests of citizens and companies. The study allows us to understand the impact of artificial intelligence on the modern legal space and offers recommendations for legislators and practicing lawyers.Bulletin of Dulaty University01The article argues the hypothesis that artificial intelligence is an object of civil law, as well as a comparative legal analysis of the terms "artificial intelligence" and "artificial intelligence technologies", and offers recommendations for legislators and practicing lawyers.Bulletin of Dulaty University["A.N. Kulazhanova"]2024-09-30T00:00:00 2ca144f1f0df1c541db6d43d5a992477a471b301This study examined the possibility of cultivating moral emotions of artificial intelligence robots that are developing recently. The 2022 revised elementary moral curriculum provides an opportunity for thought by asking questions about the relationship between artificial intelligence robots and humans. For the study, we first investigated the nature and expression process of emotions from ancient Western philosophy to modern brain science and neuroscience. Based on this, we explored what kind of emotional view the currently developed artificial intelligence robots express emotions based on. As a result, current artificial intelligence robots showed responses to humans using information accumulated through facial movements and facial expressions, focusing on basic emotions, according to Ekman's theory. This may be seen as a passive form based on human physical characteristics. However, moral emotion has a complex and active character that combines understanding of moral problem situations, empathy for others, and appropriate responses to situations. Accordingly, it was recommended to clearly teach the status and limitations of artificial intelligence robots in the field of current moral emotions in elementary moral education and educate students to form desirable relationships with artificial intelligence robots.Research Institute of Education Science, Jeju National University00It was recommended to clearly teach the status and limitations of artificial intelligence robots in the field of current moral emotions in elementary moral education and educate students to form desirable relationships with artificial intelligence robots.Research Institute of Education Science, Jeju National University[]2024-09-30T00:00:00 c0d3caf4153181fd03ab617529819cbe99e36804Artificial intelligence (AI) leverages its swift, precise, and fatigue-resistant problem-solving abilities to significantly influence anesthetic practices, ranging from monitoring the depth of anesthesia to controlling its delivery and predicting events. Within the domain of anesthesia, pain management plays a pivotal role. This review examines the promises and challenges of integrating AI into perioperative pain management, offering an in-depth analysis of their converging interfaces. Given the breadth of research in perioperative pain management, the review centers on the quality of training datasets, the integrity of experimental outcomes, and the diversity of algorithmic approaches. We conducted a thorough examination of studies from electronic databases, grouping them into three core themes: pain assessment, therapeutic interventions, and the forecasting of pain management-related adverse effects. Subsequently, we addressed the limitations of AI application, such as the need for enhanced predictive accuracy, privacy concerns, and the development of a robust database. Building upon these considerations, we propose avenues for future research that harness the potential of AI to effectively contribute to perioperative pain management, aiming to refine the clinical utility of this technology.Perioperative Precision Medicine700A thorough examination of studies from electronic databases is conducted, grouping them into three core themes: pain assessment, therapeutic interventions, and the forecasting of pain management-related adverse effects, addressing the limitations of AI application.Perioperative Precision Medicine[]2024-09-30T00:00:00 fe598356d5d64ba92a0358ff04c2ed51a843483bThis study examines the impact of artificial intelligence (AI) learning devices and community-based mentoring on underprivileged elementary students to alleviate post COVID 19 pandemic educational disparities. 104 school-aged students engaged in self directed learning with AI devices and received mentoring for 8 months. Data included device usage, attendance, pre-and post-assessment scores in Korean language and math, and self-reported learning efficacy. Results showed significant improvements in Korean language and math scores for students in the mentoring program. A ‘high-utilization group’ displayed notable gains in math scores and learning efficacy. This study underscores the potential of EdTech and community-based mentoring in bridging educational gaps for underprivileged students, suggesting a viable alternative to traditional schooling.The Korean Journal of the Human Development00This study underscores the potential of EdTech and community-based mentoring in bridging educational gaps for underprivileged students, suggesting a viable alternative to traditional schooling.The Korean Journal of the Human Development[]2024-09-30T00:00:00 af290a6ad69a73f1c22cde56fe3ec55b6363f47d: In recent years, Artificial Intelligence (AI) has revolutionized numerous industries and become increasingly assimilated into society. Machine learning (ML) refers to the ability of systems to learn from training data and can be classified into three categories: supervised learning, unsupervised learning, and reinforcement learning (RL). ML, along with Deep learning (DL), a subset of ML based on neural networks, and Natural Language Processing (NLP), which enables machines to understand and generate human language, has played a pivotal role. This paper aims to provide a comprehensive review on the current state of AI by reviewing the applications of AI in the critical fields of healthcare and transportation, as well as the challenges associated with the rapid evolution of AI in these areas. Previous studies have contributed to the advancement of AI by thoroughly reviewing recent studies and innovatively applying ML, DL, and NLP technologies to solve specific problems. This literature review analyzes the previous studies that focus on healthcare and transportation while addressing the challenges and ethical concerns and proposing potential solutions. Findings reveal that AI technologies, particularly DL techniques such as Convolutional Neural Networks (CNN), have significantly surpassed human diagnostic accuracy in healthcare and optimized decision-making in autonomous vehicles (AV). The integration of Big Data and sentiment analysis has further advanced disease prediction and mental health research. Despite these advancements, challenges such as dataset variability, interpretability issues, and privacy concerns persist. Through the presentation and analysis of these findings, we intend to contribute to the understanding of the extensive impact of AI and shaping of AI technologies that can be ethically integrated into society.International Journal of High School Research250Findings reveal that AI technologies, particularly DL techniques such as Convolutional Neural Networks such as Convolutional Neural Networks, have significantly surpassed human diagnostic accuracy in healthcare and optimized decision-making in autonomous vehicles (AV).International Journal of High School Research["Aditi Barua"]2024-09-30T00:00:00 5a0a36c9a973e2253af4833976c707c5f03484a7A miscarriage of justice is considered a situation when an incorrect decision has been made in a trial, and a guilty person is sentenced and punished. This is a very common problem in the criminal justice system of Pakistan, which has not been rectified for a very long time. In spite of measures being taken in the quest to eliminate such occurrences, miscarriages of justice are apparent for various causes, including wrong eyewitness identification, tainted confessions and inadequate counsel. The failure of justice is a denial of rights as well as the offenders to justice compartments the system's credibility. In recent years, with the aid of stiff progress in artificial intelligence (AI), it seems probable that miscarriages of justice can be reduced by accurately and efficiently facilitating criminal justice. Hence, this research adopts a doctrinal method to analyze the factual position of the miscarriage of justice system in Pakistan. This work will be very useful in dealing with the problem of miscarriage of justice in Pakistan. Therefore, it can be claimed that by defining the causal factors of wrongful convictions and providing specific recommendations, this study may help build a more efficient and just criminal justice system in Pakistan.Qlantic Journal of Social Sciences00It can be claimed that by defining the causal factors of wrongful convictions and providing specific recommendations, this study may help build a more efficient and just criminal justice system in Pakistan.Qlantic Journal of Social Sciences["Amir Latif Bhatti", "Dr. Sardar Ali Shah", "Dr. Abdul Rehman Bhatti", "Sajjad Ali Jamali"]2024-09-30T00:00:00 12b1a5ae638faeb10ce10afde89691e06e8e77c3 Artificial Intelligence (ai) is on the rise and already affecting parliaments around the world. In the framework of a long-term and on-going research project, a series of interactive workshops have been organized between 2021 and 2023 in three national parliaments, in Greece, Argentina, and Canada, with the objective to assess the relevance and priority of a pre-defined set of 210 proposals, primarily regarding the use of ai-based tools and services in the parliamentary workspace. Reflection groups within each parliament evaluated these proposals providing invaluable results that can be utilized in manifold ways by the institutions, for instance towards structuring digital strategies, designing future it systems, or training intra-parliamentary stakeholders. This article presents a comparative analysis of the results obtained by all three parliaments. The analysis sheds light in a rapidly developing field of disruptive parliamentary technology (ParlTech) that with define the parliaments of the future.International Journal of Parliamentary Studies250A comparative analysis of the results obtained by three national parliaments regarding the use of ai-based tools and services in the parliamentary workspace sheds light in a rapidly developing field of disruptive parliamentary technology (ParlTech) that with define the parliaments of the future.International Journal of Parliamentary Studies["J\u00f6rn von Lucke", "F. Fitsilis", "St\u00e9phane Gagnon"]2024-09-30T00:00:00 06975ec1566a99a8e37481a30c0f7a97727c3ba1The purpose of this paper is to derive considerations when improving the Personal Information Protection Act ba sed on the personal information protection life cycle of the generative artificial intelligence model as generative artifi cial intelligence models are introduced and used in Korea a lot. Through the study, the necessity of using open infor mation in the collection stage, using personal information preservation technology in the learning stageJouranl of Information and Security150Considerations when improving the Personal Information Protection Act are derived on the personal information protection life cycle of the generative artificial intelligence model as generative artificial intelligence models are introduced and used in Korea a lot.Jouranl of Information and Security["Jaeyoung Jang"]2024-09-30T00:00:00 18b0c30a98dcab681e78b4d97c568edd2d0bc86eThe Egyptian Journal of Radiology and Nuclear Medicine150Applying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers.Egyptian Journal of Radiology and Nuclear Medicine["Menna Allah Gaber Eissa", "Sarah Fathy Al-Tohamy", "Omar Sherif Omar", "Lamia Adel Salaheldin"]2024-09-30T00:00:00 2b7afdb49e8092752f6fa85a1e78004b6af005e3This research critically explores the ethical challenges posed by autonomous artificial intelligence (AI) systems, focusing on the moral accountability of decision-making processes conducted without human oversight. Autonomous systems, with applications in healthcare, finance, transportation, and military domains, challenge traditional ethical frameworks such as deontology, utilitarianism, and virtue ethics. By examining these systems' capacity to make decisions with profound societal impacts, the study addresses the growing tension between algorithmic decision-making and established notions of human moral responsibility. Key topics include the "moral machine problem," where AI systems face ethical dilemmas in life-or-death scenarios, and the role of algorithmic bias, which can perpetuate inequality and harm. The research evaluates existing accountability mechanisms, highlighting their limitations in addressing the ethical and legal complexities introduced by AI. Furthermore, it examines alternative frameworks, such as relational ethics and collective responsibility, which emphasize shared accountability among developers, users, and societal stakeholders. The study proposes practical strategies for embedding ethical principles into AI design, advocating for increased transparency, explainability, and oversight. It argues that while traditional philosophical theories provide valuable insights, they must be adapted to address the unique challenges of AI systems. By integrating these insights with contemporary technological realities, this research contributes to the ongoing discourse on ensuring ethical and accountable AI deployment, ultimately seeking to align technological advancement with societal values and human welfare.World Journal of Advanced Research and Reviews190This research critically explores the ethical challenges posed by autonomous artificial intelligence (AI) systems, focusing on the moral accountability of decision-making processes conducted without human oversight, and proposes practical strategies for embedding ethical principles into AI design.World Journal of Advanced Research and Reviews["Jin young Hwang"]2024-09-30T00:00:00 4bd4d335d4adc038740dda149dc2b9d3f593af17The advent of artificial intelligence (AI) is revolutionizing various fields, including healthcare. AI's impact is increasingly evident in managing and caring for autistic pediatric patients [1]. The integration of AI into pediatrics autism nursing care is revolutionizing the approach to diagnosis, treatment, and ongoing management of children with autism spectrum disorder (ASD). Through their capacity to process and analyze vast amounts of data, AI technologies offer new possibilities for enhancing the precision, efficiency, and personalization of care for children with autism. ASD presents unique challenges that require specialized, often intensive, care strategies. According to Zhao et al., (2024), integrating AI into nursing care for children with autism offers promising opportunities to enhance diagnostics, personalize treatment plans, and improve overall patient outcomes [2]. This commentary explores how AI is transforming pediatric autism nursing care and the vital role nurses play in this evolving landscape. ASD is a developmental disorder characterized by challenges with social interaction, communication, and repetitive behaviors. Symptoms and their severity vary widely among individuals, making ASD a spectrum disorder. Research indicates that early diagnosis and intervention are crucial for improving outcomes, yet these can be difficult due to the complex and varied presentation of the disorder [3].Journal of Nursing Reports in Clinical Practice00This commentary explores how AI is transforming pediatric autism nursing care and the vital role nurses play in this evolving landscape.Journal of Nursing Reports in Clinical Practice["Joseph C. Osuji"]2024-09-30T00:00:00 fff12b4a0a43bd14ae0777294fac6a5c65086604As you already know, the recruitment process is an essential function of HR that will directly influence your organizational triumph. Conventional recruitment is often done via manual screening that can be time- and money-consuming, biased. Recruiting drives growth and competitiveness in the fast-moving world of start-ups, so it is vital that recruitment be executed quickly! In addition, recently, a new trend of employing Artificial Intelligence (AI) in recruitment has been introduced as a game changer. This research on how AI influences the potential success of recruiting in start-up context. The study uses a quantitative design Partial Least Squares Structural Equation Modelling (PLS-SEM) is utilized for analyzing the data. Findings indicate that incorporating AI significantly boosts efficiency, reduces cost per hire in both monetary and time aspects (leading to decreased time spent by recruiters on task handling), and enhances Hire Quality & Candidate Satisfaction in the recruitment process. While casting light on a relatively under-researched area, this paper also makes suggestions for HR practitioners and start-up managers who are contemplating the acquisition of AI technologies within their recruitment processes.International Journal of Science and Research Archive220Research on how AI influences the potential success of recruiting in start-up context indicates that incorporating AI significantly boosts efficiency, reduces cost per hire in both monetary and time aspects, and enhances Hire Quality & Candidate Satisfaction in the recruitment process.International Journal of Science and Research Archive["Ronny Trian Surbakti"]2024-09-30T00:00:00 931ce1b80ea2798a3a16bf0e140391a1932bec79Background: Breast cancer is one of the most common cancers among Indian women, with an incidence of 25.8 per 100,000 women according to the Ministry of Health and Family Welfare. Late detection is responsible for poor quality of life (QOL), and it is the leading cause of death. Studies have shown that people are positive towards AI performing assessment tasks in healthcare in general and in mammography screening. Moreover, it is challenging to implement strategies based on self-breast inspection or do mammography in rural regions or low and middle income nations for a variety of reasons. Objective: This study was conducted to assess the perception and attitude of various female patients reporting to the radiology unit of a tertiary care centre regarding the use of Artificial Intelligence in routine mammogram screening. Study Design: A cross sectional questionnaire based study was conducted female patients attending the radiology unit of a tertiary Care Centre in Central Kerala. Methods: After obtaining, informed written consent was taken from the study participants. A predesigned, pretested, validated checklist was used to collect the required data. The Knowledge, attitude and practice was ascertained through Likerts scale and scoring done accordingly. Statistical tests of significance was employed to assess the possible associations between various variables with the knowledge, attitude and perceptions regarding Artifical Intelligence based Mammogram Results: Among the 170 study subjects analysed, 68.6% had a satisfactory knowledge about Artificial intelligence. Among the 170 study subjects analysed, 86% had a good attitude about being screened through AI enabled mammogram techniques. 92% of the total study subjects registered for mammogram had done it based on self choice. The association between age and willingness to get screened was found to be statistically significant. It was also seen that there was a statistically very high significance between Knowledge regarding AI and the independent decision to get screened. There was also a statistically significant association between age and scores >60% regarding knowledge and attitude regarding the vaccination among the study subjects. Conclusions: Majority of the study population was having a satisfactory knowledge regarding AI usage in healthcare.International Journal of Advanced Research00Assessment of the perception and attitude of various female patients reporting to the radiology unit of a tertiary care centre regarding the use of Artificial Intelligence in routine mammogram screening found majority of the study population was having a satisfactory knowledge regarding AI usage in healthcare.International Journal of Advanced Research["Sajji Mathai MD", "Aji Rajan MD", "Naveen Sukumaran Nair MD"]2024-09-30T00:00:00 8df85bff013cb1cea4733e6742ba0837b920b855Background. Artificial intelligence (AI) use in medicine has rapidly evolved, offering significant advancements in patient monitoring and diagnosis. However, the application of AI in neonatology presents unique challenges compared to adult medicine. While adult patients can often be monitored and treated autonomously through AI-powered tools like telemedicine, newborns, particularly preterm infants, are entirely dependent on continuous human care. In neonatology, human interaction—especially the tactile and empathetic care provided by nurses and doctors—remains critical, and AI systems are not equipped to replace this essential element of care. Concerns that AI might displace medical personnel in neonatal settings are unfounded, as human intervention remains irreplaceable. Discussion. Current AI technologies, often referred to as AI, are more accurately described as advanced machine learning algorithms and sophisticated software rather than true general artificial intelligence (AGI). These systems can efficiently perform well-defined tasks, such as monitoring vital signs and adjusting mechanical ventilators. However, they lack the broader understanding and adaptability necessary for independent clinical decision-making. They cannot interpret complex clinical contexts or address multifactorial medical decisions, which are still the domain of human expertise. Despite the limitations, AI holds significant potential in neonatology. It can assist in optimizing treatment protocols, such as adjusting mechanical ventilation in real-time or personalizing antibiotic treatments based on microbiological data. Furthermore, clinical decision support systems (CDSS) powered by AI can provide clinicians with evidence-based recommendations, thereby enhancing decision-making processes and improving patient outcomes. Conclusion. While promising, AI in neonatology remains a supplementary tool rather than a replacement for human judgment. Its role will likely expand, but direct human care and expertise will continue to be essential in neonatal medicine, mitigating concerns about job displacement among healthcare professionals.The Newborn Research &amp; Reviews100AI in neonatology remains a supplementary tool rather than a replacement for human judgment, but clinical decision support systems (CDSS) powered by AI can provide clinicians with evidence-based recommendations, thereby enhancing decision-making processes and improving patient outcomes.The Newborn Research &amp; Reviews["Monica Surdu", "T. Surdu"]2024-09-30T00:00:00 370e5e78d426a19850b6981f2b13bfbdefe5cf56The paper analyzes the impact of artificial intelligence (AI) in systems auditing, fastening on process optimization through the use of advanced technologies similar as intelligent independent systems. A comprehensive literature review was conducted to understand the operation of AI in checkups, revealing that the integration of these technologies has increased inspection delicacy by over to 93. Specific ways similar as cross-validation (CV), support vector machines (SVMs), and artificial neural networks (ANNs) were employed, demonstrating their effectiveness in perfecting delicacy, receptivity, and particularly in anomaly discovery, with results of 87, 90, and 93 independently. The findings emphasize the need to address the ethical and sequestration pitfalls that accompany the use of AI in checkups, given that while these technologies ameliorate effectiveness and delicacy, they also pose significant challenges in terms of ethical and security data running. In this environment, it's recommended that associations invest in training their staff in the use of AI tools, as well as establish clear programs to insure ethics and sequestration. In addition, it emphasizes the significance of continuing to probe and develop new AI operations that will further ameliorate the effects of system checkups in an ever-changing digital terrain. The perpetration of AI not only optimizes processes but also provides a significant competitive advantage by enabling more accurate discovery of irregularities and patterns in large volumes of data. In summary, AI represents a revolution in the field of systems auditing, offering opportunities to improve accuracy and efficiency, although it is crucial to proactively manage the associated ethical and privacy challenges.Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications480Analysis of the impact of artificial intelligence in systems auditing finds that AI represents a revolution in the field of systems auditing, offering opportunities to improve accuracy and efficiency, although it is crucial to proactively manage the associated ethical and privacy challenges.Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications["Manuel Hilario", "Pervis Paredes", "Jorge Mayhuasca", "Milner Liendo", "Shirley Mart\u00ednez"]2024-09-30T00:00:00 4196c5bb8464e298425c4a8938c1071779e53bacArtificial Intelligence (AI) has become a major driver of change in various areas of human life, including in the development of Decision Support Systems (DSS). AI, as a rapidly growing branch of computer science, has changed the paradigm in how we process and analyze data to support the decision-making process. With its ability to learn from data, identify patterns, and make predictions, AI promises significant advances in the efficiency and accuracy of decision-making integrated in SDM. The application of AI in Human Resources has become a major topic in academic and industrial literature. The use of this technology has resulted in significant impacts, ranging from improved efficiency of the decision-making process to a paradigm shift in data analysis. Primarily, the main focus of AI application in SDM is on its ability to address the complexity and uncertainty of data faced by decision makers. the application of AI to SIM offers significant advantages in strategic decision-making This finding highlights the need for organizations to consider the appropriate use of AI, maintain data security, and monitor organizations to consider the appropriate use of AI, maintain data security, and monitor its impact to gain a competitive advantage in strategic decision-making. The findings highlight the need for organizations to consider the appropriate use of AI, maintain data security, and monitor its impact to gain a competitive advantage in making strategic decisionsJournal of Investment Development, Economics and Accounting110The findings highlight the need for organizations to consider the appropriate use of AI, maintain data security, and monitor its impact to gain a competitive advantage in making strategic decisions.Journal of Investment Development, Economics and Accounting["Walian Maimun Al Qadiri", "M. Alkaf", "Hadi Supratikta"]2024-09-30T00:00:00 92334747df8ccfd5510ab648884fa8ea053de643This exploratory study examines the evolving role of knowledge workers in the age of artificial intelligence (AI), focusing on the challenges and opportunities AI presents for productivity, creativity, and organizational processes. Through a comprehensive literature review, the study explores how AI technologies, particularly generative AI, influence knowledge work by automating routine tasks and enabling higher-level cognitive activities. The findings emphasize the need for knowledge workers to develop new skills to collaborate with AI systems, while organizations must adopt strategies that balance automation with human oversight. The study identifies future research directions, including empirical investigations into the integration of AI into knowledge workflows, the impact on creativity, and the ethical considerations of AI in decision-making processes. The conclusions underscore the importance of continuous learning and adaptation as knowledge workers and organizations navigate the rapidly evolving technological landscape.SEA - Practical Application of Science00The study explores how AI technologies, particularly generative AI, influence knowledge work by automating routine tasks and enabling higher-level cognitive activities, highlighting the need for knowledge workers to develop new skills to collaborate with AI systems, while organizations must adopt strategies that balance automation with human oversight.SEA - Practical Application of Science["Daniel-Florin D\u0103niloaia", "Emanuela Turturean"]2024-09-30T00:00:00 82d4d844f133edb1970ac06576002bdf0281d040The use of artificial intelligence (AI) in various fields has attracted a lot of attention in the past year, especially after the release of ChatGPT, a freely available intelligent online system. Several advanced software solutions that incorporate AI are becoming available on a larger scale and could help improve every aspect of our lives. The use of AI also has great potential in various areas of the woodworking industry. In the design phase, AI-supported design software can facilitate the development of new ideas and shorten 3D modelling processes. In the construction phase, AI plays a crucial role in optimising construction details using techniques such as topology optimisation, numerical simulations and generative design. The use of AI can also be applied in the production process, where it automates the creation of CNC machining programs and optimises machining methods. Quality control is improved through AI monitoring of machines and surface quality using advanced image analysis and machine vision systems. In addition, AI contributes to predicting production needs and facilitates the maintenance of production machines. AI can be used in market analysis and enables companies to make informed decisions. It helps in the strategic planning of marketing activities and the sales process by providing insights derived from comprehensive market analyses. AI could help in creating marketing materials, communicating with customers, managing social networks and websites, analysing competitors, predicting demand, etc. The use of AI could enable major technological improvements with efficiency gains and innovation in various perational areas, but also causes some concerns about trust in these new systems and fear of being replaced by machines. AI should be seen as a tool to improve our productivity and performance, automate certain tasks and transform existing jobs. There is a need for ethical oversight, policy decisions and regulations to ensure fair treatment of all humans. An overview of currently available solutions was given to discover new opportunities for the use of AI in the wood industry, as a pivotal aspect of the ongoing digital transformation.Drvna industrija00An overview of currently available solutions was given to discover new opportunities for the use of AI in the wood industry, as a pivotal aspect of the ongoing digital transformation.Drvna industrija["M. Kari\u017e", "Manja Kitek-Kuzman", "J. Kropiv\u0161ek"]2024-09-30T00:00:00 a6d0cde74930483a124173777ac0fd43aae56d5aArtificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Ancient civilizations contained tales of intelligent robots, such as Greek and Chinese cultures. AI as a modern phenomenon which took shape in the mid-20th century with the advent of digital computers. The first breakthrough, published in 1950, was Alan Turing's paper, ‘Computing Machinery and Intelligence. AI can make provide detailed solution to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). Many sectors like finance, healthcare, to human resource management (HRM), rely on AI technologies. AI's integration in HRM has brought about great change in conventional HR practices used to measure efficiency, accuracy, strategic decision-making, etc. AI technologies help to provide solution to automation of repetitive tasks, analysis of sprawling volumes of data and insights facilitating decision-making. Through this research paper we try to understand the AI's effects on HR processes, in terms of recruitment, onboarding, performance appraisal, training and development, employee engagement, workforce planning; understanding the benefits and cons of implementing AI in HRM; ethical considerations and potential biases in AI algorithms relevant to HR practices; analysis of emergent AI trends with respect to HRM. Trends in AI in HR tend towards a most promising future, presenting several innovations. Accordingly, HR professionals will be expected to acquire competency with regards to new roles and responsibilities to avail AI in enhancing strategic planning, ethical oversight, and data-driven decision-making.International Journal of Education, Modern Management, Applied Science &amp; Social Science10The AI's effects on HR processes, in terms of recruitment, onboarding, performance appraisal, training and development, employee engagement, workforce planning, and analysis of emergent AI trends with respect to HRM are understood.International Journal of Education, Modern Management, Applied Science &amp; Social Science["Rajeev Kaur"]2024-09-30T00:00:00 257415e7acf7674f192d017dc730e8b06f29dfabArtificial intelligence (AI) is becoming a core technology in society, used across various industries and evolving from a tool to a creator. This paper explores how to recognize AI’s actions and incorporate it into the legal system. First, it examines the significance and types of AI, and the requirements for recognizing AI’s legal subjectivity in Korea. AI’s legal status could be acknowledged if it develops autonomy and independent judgment, and its interactions with humans evolve to the point where it is considered a societal member. Next, the paper reviews systems applicable to AI to harmonize AI robots with Korean legal norms. It presents grounds for recognizing AI’s legal personality from social reality and legal convenience perspectives. Finally, it clarifies the effects and responsibility of AI’s legal actions, examining issues related to weak and strong AI responsibility, and argues for recognizing electronic personhood by reviewing the EU’s resolution on robot citizenship.Kyung Hee Law Journal00Kyung Hee Law Journal["J. Cho", "Kwang-jun Tsche"]2024-09-30T00:00:00 63999d9bc61e81333ba399743d556a2497155af9Artificial Intelligence (AI) is profoundly impacting productivity and economic growth across various sectors. In Malaysia, challenges related to AI in construction include limited expertise, privacy concerns, and cultural barriers on construction sites. This research aims to pinpoint key factors that influence AI's effectiveness in enhancing construction site workflows, identify obstacles in improving workflow performance, and assess the relationships between primary factors affecting AI use and the main challenges faced. The study focuses on G7, a contractor company based in Johor Bahru, employing a quantitative method to achieve its goals. The research gathered insights from 226 Grade 7 contractors in Johor Bahru through questionnaires distributed via face-to-face meetings and online forms sent through WhatsApp and email. Out of the respondents, 101 (45%) provided responses. Data was analyzed using SPSS software, incorporating descriptive statistics, frequency counts, and cross-tabulations. The study revealed that the most frequently cited factors and challenges are risk management and limited AI expertise, respectively. The strongest correlation identified was between return on investment and slow electrical supply. These findings offer valuable guidance for contractors to enhance performance, mitigate risks, and boost efficiency. This study paves the way for more targeted interventions and strategic approaches to improve construction site workflows, ultimately benefiting industry stakeholders.International Journal of Science and Research Archive230The study revealed that the most frequently cited factors and challenges are risk management and limited AI expertise, respectively, and the strongest correlation identified was between return on investment and slow electrical supply.International Journal of Science and Research Archive["Ruchit Parekh", "Olivia Mitchell"]2024-09-30T00:00:00 37b6903d34172a93dd049f270fd0fd2a9396b7f0The utilization of Artificial Intelligence (AI) in the marketing and management of high-tech projects in Kazakhstan is an emerging trend with the potential to significantly advance the industry. This paper aims to explore the integration of AI into various facets of project management, including customer relationship management, content marketing, and demand forecasting. The study employs a mixed-methods approach, combining quantitative data from surveys of Kazakhstani IT project managers and qualitative insights from literature and industry reports. Results reveal an enthusiastic exploration and adoption of AI tools among project managers, highlighting AI's role in automating and streamlining processes while providing strategic insights. The practical importance of this study lies in its identification of the current limitations and future implications of AI integration, underscoring its pivotal role in modern project management in Kazakhstan.Trudy Universiteta00This paper aims to explore the integration of AI into various facets of project management, including customer relationship management, content marketing, and demand forecasting, and reveals an enthusiastic exploration and adoption of AI tools among project managers.TRUDY UNIVERSITETA["Zhanar Tazhiyeva", "Zhanna Kozhamkulova"]2024-09-30T00:00:00 cb924b443dced73576f494aa3717fc0501fa192eThe death of the Queen of Great Britain was announced by media on September 8, 2022. Elizabeth II deceased at the age of 96 in her beloved castle in Scotland. More than 70 years of reign made her the longest-reigning and oldest head of state in the world. If a user interested in the life of the Queen spends several hours or days requesting relevant information, s/he is guaranteed a desegmented “filter bubble”. Artificial intelligence, tracking the topics of requests, will narrow the range of issues provided to the limit, thereby cutting off information that is more pressing, large-scale or diversified. Or at least it might be some other important semantic field of today; for example, slowing down of YouTube. This capsule of debilitated reality created by artificial intelligence has become not just a technical miscalculation or psychological inconvenience, but a philosophical problem. The “filter bubble” condemns the user to the epistemological loneliness of a person locked in a circle of the same cognitive interests selected by the algorithms of social networks; such epic loneliness can develop into a moral and psychological one. It may develop political inadequacy and neuroses if, by accident or intentionally - it is not very significant here - the person once showed a pronounced attention to wars, epidemics, catastrophes, conspiracy theories, etc. In this case, we are talking about the loss of critical thinking.Economy, Governance and Lave Basis00The death of the Queen of Great Britain was announced by media on September 8, 2022, and it is shown that Elizabeth II deceased at the age of 96 in her beloved castle in Scotland.Economy Governance and Lave Basis["Emilia Taisina"]2024-09-30T00:00:00 b82458696a01565559ea624e7f72993a5d963458This study evaluates the impact of utilizing artificial intelligence (AI) in managing construction engineering projects. With the increasing complexity and scale of construction projects, AI offers promising solutions to enhance efficiency, accuracy, and decision-making processes. The study investigates the potential benefits, challenges, and practical applications of AI through detailed case studies. The study employs a mixed-methods approach, combining qualitative and quantitative research methods. Data was collected through literature reviews and case studies where AI had been successfully implemented. The analyses included comparisons between projects that used AI and those that did not. The findings demonstrated significant improvements in project efficiency, cost estimation accuracy, and risk management. For instance, AI-powered systems reduced scheduling errors by 35%, leading to more accurate timelines. Additionally, the integration led to a 20% reduction in project durations due to improved resource allocation and proactive risk management. Furthermore, AI-supported systems contributed to a 25% improvement in stakeholder satisfaction. In terms of cost estimation, AI-powered estimation tools improved the accuracy of cost estimates by 30%, helping to reduce budget overruns by 40%. In risk management, AI-supported tools enhanced the accuracy of risk identification by 45%, leading to the early detection of potential issues and the development of effective mitigation strategies that reduced the impact of risks by 30%. Thanks to these improvements, project success rates increased by 20%. These results demonstrate that integrating AI into the management of engineering construction projects can lead to tangible improvements in project efficiency, cost accuracy, and risk management, thereby enhancing stakeholder satisfaction and contributing to more successful project outcomes.مجلة العلوم الهندسية و تكنولوجيا المعلومات60It is demonstrated that integrating AI into the management of engineering construction projects can lead to tangible improvements in project efficiency, cost accuracy, and risk management, thereby enhancing stakeholder satisfaction and contributing to more successful project outcomes.مجلة العلوم الهندسية و تكنولوجيا المعلومات["Abdullah Mohammad Abdelhamid Alhasan", "Ebrahim Khaled Ebrahim Alawadhi"]2024-09-30T00:00:00 32dd74fca0333e3aebe60532cbd138fb57677760The rise of artificial intelligence (AI) has fundamentally challenged traditional intellectual property (IP) frameworks, particularly in the European Union (EU), where regulatory efforts are aimed at balancing innovation with legal protections. AI’s ability to autonomously create, modify, and use IP raises complex questions about authorship, inventorship, ownership, and enforcement, which existing laws were not designed to handle. As EU countries attempt to adapt their legal systems to address these challenges, a comparative analysis of their regulatory acts is essential to understand how different member states are responding to the intersection of AI and IP protection. The aim of this article is to provide a comparative analysis of the regulatory frameworks governing IP protection in the context of AI across selected EU countries. By examining national legislation and harmonization efforts, the study seeks to identify common challenges, highlight divergent approaches, and offer insights into the evolving legal landscape of IP protection in the age of AI. The article employs a qualitative, comparative research methodology. It focuses on six EU countries—Germany, France, the Netherlands, Poland, Greece, and Romania—analyzing their IP laws concerning AI-related issues. The study reviews national regulations, EU directives, and case law to evaluate how each country addresses AI-generated IP in terms of ownership, authorship, patentability, trademark issues, and enforcement mechanisms. A thematic coding approach is used to identify key trends and divergences between member states. The analysis reveals that all EU countries maintain the requirement for human authorship and inventorship, which limits the legal recognition of fully autonomous AI-generated content. While countries like Germany, France, and the Netherlands have initiated discussions on potential legal reforms, others, such as Poland, Greece, and Romania, rely more heavily on existing frameworks and await further EU guidance. Additionally, enforcement mechanisms vary significantly, with more technologically advanced countries adopting AI-driven tools to monitor and enforce IP rights. As AI continues to evolve and play a larger role in creative and technical industries, the legal frameworks governing IP in the EU must adapt accordingly. Future regulatory efforts should focus on creating new categories for AI-generated works, investing in AI-powered enforcement tools, and ensuring greater harmonization across member states. By addressing these challenges proactively, the EU can strike a balance between fostering AI innovation and maintaining robust IP protections, positioning itself as a global leader in both technology and intellectual property rights.PUBLIC ADMINISTRATION AND LAW REVIEW00A comparative analysis of the regulatory frameworks governing IP protection in the context of AI across selected EU countries reveals that all EU countries maintain the requirement for human authorship and inventorship, which limits the legal recognition of fully autonomous AI-generated content.Public Administration and Law Review["V. Marchenko", "A. Dombrovska", "Valerii Prodaivoda"]2024-09-30T00:00:00 24ceda3b200fe57b602d5fd3ce57620b9e954d83Introduction. Artificial Intelligence has emerged as a transformative force across various industries, with a particularly profound impact on healthcare. It is well known that patients today increasingly turn to the internet searching for information about their medical conditions, utiliz-ing tools like AI-based chatbots. However, information from unverified sources can influence patients’ decisions regarding treatment options. This study aims to evaluate the quality of medical information provided by ChatGPT for Assigned Female At Birth (AFAB) patients considering gender-affirming surgery.Methods. Given the possibility that some patients might use ChatGPT as an information source for their medical conditions, specific ques-tions were posed to the chatbot in the same manner a patient inter-ested in gender-affirming surgery would. The quality of the information was assessed using the standardized EQIP scale. The survey involved 30 individuals: 15 plastic surgery residents and 15 non-healthcare pro-fessionals, with data collected in February 2023 and analyzed using SPSS Software version 28.0.Results. Separate surveys evaluated the quality of information provid-ed by ChatGPT regarding two primary procedures for AFAB patients undergoing gender-affirming surgery: phalloplasty and top surgery. The quality of the information was found to be adequate in both cases, with significant qualitative differences across the various survey sections. ChatGPT excelled in delivering information in a simple and accessible manner, earning high scores in the “Structured Data” area. However, the “Content Data” area, representing the completeness of information, was deemed sufficient. A significant deficiency was noted in the “Iden-tification Data” section, highlighting the absence of information about revisions, bibliographies, and the names of the entities or individuals providing content.Conclusions. ChatGPT demonstrated excellent capability in providing information in a straightforward and accessible manner, achieving high scores in the “Structured Data” area in both evaluations. The complete-ness of information, represented in the “Content Data” area, was con-sidered sufficient. However, a notable deficiency in the “Identification Data” section underscored the absence of details regarding revisions, bibliographies, and content authorship. Although the content score F.R. Grippaudo et al.2could be improved by adjusting the number and phrasing of questions, the lack of bibliography and source verification remains a significant limitation of this tool. ChatGPT offers advantages such as ease of communi-cation, privacy, anonymity, and overcoming language barriers; nonetheless, given its limitations, its role should always be seen as supplementary to that of the surgeon.PRRS00ChatGPT demonstrated excellent capability in providing information in a straightforward and accessible manner, achieving high scores in the “Structured Data” area in both evaluations, and its role should always be seen as supplementary to that of the surgeon.PRRS["F. R. Grippaudo", "A. Patrignani", "Viviana Mannella", "Laurenza Schiavone", "D. Ribuffo"]2024-09-30T00:00:00 14dfe3dfbb96a0b615e914d5196a9e7c3adbab1fThe dawn of artificial intelligence (AI) signifies a pivotal shift in medical and dental education. Integrating AI into the curriculum modernizes learning and equips future healthcare professionals with crucial tools for the 21st century. The COVID-19 pandemic revealed the limitations of conventional educational models, necessitating rapid adaptation to remote and online learning environments. This disruption expedited the transition to digital platforms, laying the foundation for further integration of technology, including AI, into medical education. What began as an emergency response has now become a permanent feature of the educational landscape, evolving from static textbooks to dynamic digital platforms that offer greater accessibility, inclusivity, and personalization of learning experiences.1 In the AI era, it is insufficient to merely digitize the curriculum; a comprehensive transformation is essential. The digital curriculum opens new avenues for interactive learning environments, simulation-based practices, and adaptive learning algorithms that respond to the individual needs of students. AI-driven tools such as virtual patient simulations, diagnostic decision-making platforms, and predictive analytics have the potential to revolutionize how medical students learn, practice, and apply their knowledge in clinical settings.2 These innovations allow for an enhanced learning experience where students can interact with realistic patient cases and make informed decisions, fostering a deeper understanding of clinical practice.   One of the most promising applications of AI in medical education is its role as an educational partner. AI-powered platforms can function as personalized tutors, providing real-time feedback, adjusting learning modules based on student performance, and even predicting areas where additional support may be required.3 Adaptive learning systems can analyze the learner’s pace and comprehension, offering tailored resources to bridge knowledge gaps. This personalized approach to education ensures that no student is left behind, addressing one of the longstanding challenges of traditional, one-size-fits-all curricula. Additionally, AI can enhance clinical reasoning through simulation and data-driven case scenarios. By analyzing patterns in patient data, AI algorithms can help medical students gain deeper insights into complex clinical decision-making processes. This data-driven approach can significantly improve learners’ ability to diagnose and plan treatments, thereby improving clinical outcomes. While AI and digital tools offer substantial benefits, the role of educators remains essential in this new educational paradigm. Rather than replacing teachers, AI will augment their roles, allowing them to focus on mentorship, critical thinking, and the ethical dimensions of healthcare.4 Educators will need to reimagine their roles, becoming facilitators of learning who guide students in interpreting and applying AI-generated data in clinical settings. As AI takes on administrative tasks such as grading, educators can dedicate more time to meaningful interactions with students.5 However, this shift toward AI-driven curricula also requires significant investment in faculty development. Educators must be trained in the use of AI tools and possess a thorough understanding of their applications to ensure that AI is used responsibly and effectively in shaping future healthcare professionals.   As AI becomes more integrated into medical education, addressing the ethical challenges associated with this technology becomes crucial. While AI-driven tools hold great promise, they must be designed and deployed with an acute awareness of biases, data privacy concerns, and the risk of over-reliance on algorithms in clinical decision-making.6 The digital curriculum must provide students with technical skills and a strong ethical foundation for AI use in healthcare. Students must be trained to critically evaluate AI outputs, understand their limitations, and ensure that human judgment remains central to patient care. Transforming medical curricula in the AI era is not without challenges. Digital divides, access to technology, and the initial cost of AI-driven platforms may pose barriers to widespread adoption. Institutions must ensure equitable access to resources for all students, regardless of their geographic or socioeconomic backgrounds. Moreover, regulatory bodies such as the Higher Education Commission (HEC) and the Pakistan Medical and Dental Council (PMDC) must revise standards to accommodate these technological advancements. In conclusion, the transformation of medical and dental curricula into a digital, AI-enhanced model represents not only a modernization of education but also a fundamental shift in preparing future healthcare professionals. By embracing AI as an educational partner, medical institutions can create personalized, data-driven learning environments that equip students with the skills and knowledge needed to thrive in an increasingly complex healthcare landscape. The integration of AI into the curriculum offers an opportunity to empower the next generation of doctors, enabling them to navigate future challenges with confidence and competence. Now is the time for this transformation, and it is a journey that we must embark on collectively to ensure the future of education, healthcare, and patient care.Journal of Gandhara Medical and Dental Science10The transformation of medical and dental curricula into a digital, AI-enhanced model represents not only a modernization of education but also a fundamental shift in preparing future healthcare professionals.Journal of Gandhara Medical and Dental Science["B. Jamil"]2024-09-30T00:00:00 7f8ba66f88721508812defb5e4b6bf4d3668bce8As Artificial Intelligence (AI) technologies advance, the need for comprehensive and harmonized legal frameworks has become paramount. This paper conducts a comparative analysis of the regulatory approaches to AI in Latin America and Africa, regions with unique challenges and opportunities in AI governance. While both regions have shown growing interest in AI, their legal landscapes differ significantly, with fragmented policies and varying levels of enforcement. The analysis reveals that despite these differences, there are common concerns such as data privacy, ethical use, and the impact of AI on labor markets that demand coordinated regulatory responses. The paper proposes a model for harmonizing AI laws between these regions, focusing on fostering cross-regional collaboration, developing shared ethical guidelines, and establishing joint regulatory bodies to ensure consistent enforcement. This model aims to balance innovation with the protection of fundamental rights, drawing on successful frameworks from other jurisdictions while adapting to the socio-economic contexts of Latin America and Africa. The harmonization of AI laws is not only essential for mitigating risks associated with AI but also for promoting economic integration and technological collaboration between these regions. By aligning their regulatory approaches, Latin America and Africa can better position themselves in the global AI landscape, ensuring that AI development is inclusive, ethical, and sustainable. The paper concludes by highlighting the importance of regional cooperation in AI governance and the potential of a harmonized legal framework to foster innovation while safeguarding public interest. This comparative study serves as a foundational step towards creating a unified AI regulatory environment that can effectively address the complexities of AI technologies and their socio-economic impacts across these two diverse regions.Global Journal of Research in Multidisciplinary Studies00Global Journal of Research in Multidisciplinary Studies["Mubarak Opeyemi Nurudeen", "Adetoyese Latilo", "Hendrickx Oreoluwa Imosemi", "Queenette Anuoluwapo Imosemi"]2024-09-30T00:00:00 adcdda6ceb9c8bc02b2954a76165ffa618f966d4The integration of artificial intelligence (AI) into electronic government (e-government) systems is revolutionizing public administration by enhancing efficiency and improving service delivery. However, the adoption of AI technologies in this context also raises complex legal challenges, particularly concerning intellectual property (IP) rights. Traditional IP laws, which were developed with human authorship in mind, struggle to accommodate the unique characteristics of AI-generated content. This article examines how AI is reshaping the legal framework for IP protection within e-government systems, highlighting the implications and challenges that arise from this technological shift. The primary aim of this study is to explore the role of AI in the formation of IP law frameworks within e-government, focusing on how current laws address—or fail to address—the challenges of AI-generated content. The methodology includes a comprehensive literature review, analysis of legislative documents, case studies, and a benchmarking analysis to compare approaches across jurisdictions. Additionally, expert interviews provide insights into practical considerations and emerging trends in the field. The results indicate that while some jurisdictions, such as the European Union, are actively adapting their IP laws to address AI's impact, most existing frameworks remain inadequate for protecting AI-generated works. Divergent approaches across countries reveal a lack of international harmonization, which complicates cross-border collaboration and legal enforcement. The analysis also highlights the importance of public-private partnerships and sector-specific IP protections, which can address the unique needs of different e-government applications. From a forward-looking perspective, the study underscores the need for flexible, AI-specific IP protections that promote innovation while safeguarding IP rights. International cooperation will be essential for establishing consistent standards, facilitating global e-government initiatives, and supporting the responsible use of AI in public services. By fostering a balanced and adaptive IP framework, policymakers and stakeholders can help build a resilient digital ecosystem that accommodates future advancements in AI technology.PUBLIC ADMINISTRATION AND LAW REVIEW00The results indicate that while some jurisdictions, such as the European Union, are actively adapting their IP laws to address AI's impact, most existing frameworks remain inadequate for protecting AI-generated works.Public Administration and Law Review["Iryna Mihus", "Volodymyr Zahorskyi", "A. Lipentsev"]2024-09-30T00:00:00 4eee72da10d849dcfc0c3b55e3b4a4aeca8038d7Purpose- Pakistan's economy is experiencing challenges such as reduced business productivity, low resource efficiency, low digitization, and diminishing firm value (FV). The role of artificial intelligence (AI) and business resources is critical to solve these challenges. Therefore, the objective of this research questions whether Intellectual Capital (IC) mediates the relationship between AI investment and FV in Pakistani non-financial firms. This study uses intellectual capital theory (ICT) and resource-based view (RBV) theories. Study Design/Methodology/Approach - Secondary data was collected from the annual reports of 80 non-financial enterprises listed on the Pakistan Stock Exchange (PSX) from 2015 to 2023. The Generalized Method of Moments (GMM) model, is used to investigate the relationship between these variables. Findings- The findings reveal that AI investment positively affects FV. IC which consists of these components human capital (HC), structural capital (SC), and relational capital (RC) mediates the relationship between AI and FV by transforming AI-driven investments into increased organizational knowledge, innovation, and efficiency, which in turn improves FV and market perception. RBV and ICT support these findings. Research Implications- SMEs in emerging markets like Pakistan can improve their FV by investing in AI-related projects and training their employees to operate these projects, upgrade their internal structure/software, and build relations with society. Furthermore, this study has both practical and societal implications for all stakeholders in businesses. Firm managers and policymakers can understand the importance of AI and IC in enhancing FV. Originality/Novelty - This study differs from earlier ones in that it uses the modified value-added intellectual coefficient (MVAIC) model to measure IC and investigate its impact on FV. Furthermore, to the best of the researcher's knowledge, this is the first study to look at IC as a mediator of AI and FV.NICE research journal230The findings reveal that AI investment positively affects FV and IC mediates the relationship between AI and FV by transforming AI-driven investments into increased organizational knowledge, innovation, and efficiency, which in turn improves FV and market perception.NICE Research Journal["Muhammad Naeem", "Shoukat Ali", "Muhammad Islam", "Abdul Rehman"]2024-09-30T00:00:00 65555d015888e53690a74822c5f15f341b0809ccIntegrating Artificial Intelligence (AI) into healthcare, specifically for managing autism spectrum disorder (ASD), offers transformative potential to enhance diagnostic accuracy, personalize treatment, and improve patient outcomes. This review explores the application of various AI programs in ASD management, discussing their functionalities, ethical considerations, implementation challenges, and the need for comprehensive regulatory frameworks. Critical AI applications such as AI-driven diagnostic imaging, predictive analytics, assisted therapy robots, remote monitoring, treatment personalization, decision support systems, and therapeutic chatbots are examined. Each technology is analyzed for its ability to improve the quality of life for individuals with ASD by offering more personalized, efficient, and effective care and support. Ethical issues, particularly concerning data bias and privacy, are highlighted as significant challenges that need addressing to maximize AI’s benefits while minimizing risks. Practical hurdles like integration with existing healthcare systems, the need for scalable solutions across diverse geographic and socio-economic contexts, and the high costs associated with AI development are also discussed. Furthermore, the review underscores the necessity for robust regulatory policies that ensure patient safety, protect data privacy and maintain high ethical standards in AI deployment. The paper concludes that while AI presents substantial opportunities for advancing ASD management, achieving these benefits requires a concerted effort from technologists, clinicians, ethicists, and policymakers to develop AI tools that are not only innovative but also ethical, equitable, and universally beneficial.European Journal of Clinical Medicine1020While AI presents substantial opportunities for advancing ASD management, achieving these benefits requires a concerted effort from technologists, clinicians, ethicists, and policymakers to develop AI tools that are not only innovative but also ethical, equitable, and universally beneficial.European Journal of Clinical Medicine["I. Ara\u00fajo-Filho", "Am\u00e1lia Cinthia Meneses do R\u00eago"]2024-09-30T00:00:00 8fbd6babd393608b62a7b6df34c8c5f52a3efbcaThe proliferation of cognitive learning technologies, such as AI, has not only challenged governments in most developed and some developing countries to not adopt them as learning tools in schools but to foster citizen literacy by incorporating them in their school curriculum at every level of education. However, governments of most African countries seem to have a high level of indifference to this trend, but contrary to their indifference, some private education stakeholders have advanced its usage and developed and implemented curricula in that regard. The dearth of research on the implementation of artificial intelligence (AI) curricula in African countries, particularly Nigeria, motivated this study. The diffusion of innovations framework guided the researchers in examining how AI-focused educational content was being introduced and disseminated within the Nigerian school system. By employing a mixed-methods design, the study was able to capture both quantitative and qualitative insights from the sample of 327 students who had directly experienced the AI lessons over the past two years. Qualitative data was collected from teachers at selected schools, and the AI learning manager. Data collected was analysed using multiple regression and thematic analysis. The result included that students’ response to curriculum implementation was encouraging but the differential capacity of computers for learning could elicit negative feedback. Students’ application of AI knowledge highly predicted problem-solving (β=0.033, t=0.84, p<0.05) and critical thinking (β=0.141, t=4.20, p<0.05) skills. It can be deduced that learners and other education stakeholders in Nigeria are adapting well to different stages of AI curriculum implementation. Therefore, it was recommended that the government should replicate it at public secondary schools.EUREKA: Social and Humanities170It can be deduced that learners and other education stakeholders in Nigeria are adapting well to different stages of AI curriculum implementation, and it was recommended that the government should replicate it at public secondary schools.EUREKA: Social and Humanities["S. Ojetunde"]2024-09-30T00:00:00 22b40089ba844db648d10d72887eddb6c6e4c2f7The integration of Artificial Intelligence (AI) within digital business processes has emerged as a transformative force, particularly in the e-commerce sector. As businesses seek to enhance efficiency and competitiveness, AI-driven technologies offer substantial opportunities for optimization across various operational facets. This study explores how AI can optimize digital business processes within e-commerce systems, with a focus on improving operational efficiency, customer experience, and decision-making processes. Additionally, the research considers the implications of AI for sustainable business practices aligned with the United Nations’ Sustainable Development Goals (SDGs), specifically SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Using a case study approach, the study examines a leading e-commerce company that has successfully integrated AI into its core processes. Key AI applications such as machine learning algorithms, natural language processing, and predictive analytics are analyzed for their impact on inventory management, personalized marketing, customer service automation, and dynamic pricing. The findings demonstrate that AI significantly enhances operational efficiency, customer satisfaction, and real-time decision-making capabilities. Moreover, the study highlights AI's potential to contribute to more sustainable business practices by optimizing resource utilization and reducing waste. This research underscores the critical role of AI in driving digital transformation in e-commerce, offering valuable insights into best practices and the challenges of AI integration. By aligning AI implementation with SDG 9 and SDG 12, e-commerce businesses can achieve not only competitive advantages but also contribute to broader societal goals of innovation and sustainability.ADI Journal on Recent Innovation (AJRI)00The research underscores the critical role of AI in driving digital transformation in e-commerce, offering valuable insights into best practices and the challenges of AI integration, and highlights AI's potential to contribute to more sustainable business practices by optimizing resource utilization and reducing waste.ADI Journal on Recent Innovation (AJRI)["Julia Nathalie", "Greisy Jacqueline", "Natasya Aprila Yusuf", "Li Wei Ming"]2024-09-30T00:00:00 c6bdc14e92765be9e4da662b49d808469916c88dBackground: Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects motor control, leading to symptoms such as tremors or impaired balance. Early diagnosis of PD is crucial for effective treatment, yet traditional diagnostic models are often costly and lengthy. This study explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly voice analysis, to identify early signs of PD and make a precise diagnosis. Objectives: This paper aims to create an automatic detection and prediction of PD binary classification using vocal biomarkers. We will also use explainability to identify latent and important patterns in the input data in retrospect to the target to inform the definition of Parkinson's through voice characteristics. Finally, a probability generation will be generated to create a scoring system of a patient's odds of PD as a spectrum. Methods: We utilized a dataset comprising 81 voice recordings from both healthy control (HC) and PD patients, applying a hybrid AI model combining Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Multiple Kernel Learning (MKL), and Multilayer Perceptron (MLP). The model's architecture was designed to extract and analyze acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs), local jitter, and local shimmer, which are all indicative of PD-related voice impairments. Once features are extracted, the AI model will generate prediction labels for HC or PD files. Then, a scoring system will assign a number ranging from 0-1 to each file, indicating the stage of PD development. Results: Our champion model yielded the following results: diagnostic accuracy of 91.11%, recall of 92.50%, precision of 89.84%, an F1 score of 0.9113, and an area under curve (AUC) of 0.9125. Furthermore, the use of SHapley Additive exPlanations (SHAP) provided detailed insight into the model's decision-making process, highlighting the most influential features contributing to a PD diagnosis. The outcomes of the implemented scoring system demonstrate a distinct separation in the probability assessments for PD across the 81 analyzed audio samples, validating our scoring system by confirming that the vocal biomarkers in the audio files accurately correspond with their assigned scores. Conclusion: This study highlights the efficacy of AI, particularly a hybrid model combining CNN, RNN, MKL, and Deep Learning in diagnosing early PD through voice analysis. The model demonstrated a robust ability to distinguish between HC and PD patients with significant accuracy by leveraging key vocal biomarkers such as MFCCs, jitter, and shimmer.medRxiv160The outcomes of the implemented scoring system demonstrate a distinct separation in the probability assessments for PD across the 81 analyzed audio samples, validating the scoring system by confirming that the vocal biomarkers in the audio files accurately correspond with their assigned scores.["M. Shen", "P. Mortezaagha", "A. Rahgozar"]2024-09-30T00:00:00 59a2e5569ac4548901370009cd5350f1aa587f96Abstract: This article examines the potential of an innovative AI-driven food redistribution system, "Feeding America," in addressing the dual challenges of food waste and food insecurity among homeless populations in urban areas. The proposed system integrates deep learning models, self-driving vehicles, and strategically placed vending food hubs to efficiently collect and distribute surplus food from restaurants and stores to those in need. By analyzing the system's architecture, workflow, and implementation strategy, this paper explores how artificial intelligence and autonomous technology can optimize the logistics of food redistribution. The article also investigates the economic incentives, including tax benefits and corporate social responsibility opportunities, that could drive business participation in such initiatives. Through a comprehensive evaluation of potential social, environmental, and economic impacts, as well as anticipated challenges, this article contributes to the growing body of literature on technology-driven solutions for social issues. The findings suggest that AI-enabled food redistribution systems have the potential to significantly reduce food waste, alleviate food insecurity among homeless populations, and create a sustainable model of corporate philanthropy, while also highlighting important considerations for policymakers and future research directions in this field.International Journal for Research in Applied Science and Engineering Technology00The findings suggest that AI-enabled food redistribution systems have the potential to significantly reduce food waste, alleviate food insecurity among homeless populations, and create a sustainable model of corporate philanthropy, while also highlighting important considerations for policymakers and future research directions in this field.International Journal for Research in Applied Science and Engineering Technology["Tenny Enoch Devadas"]2024-09-30T00:00:00 1c3b91e710e425b6f604b4d144293d847148b5faThis study examines the economic impact of artificial intelligence (AI) on labor markets and regulatory strategies in Indonesia, China, and Europe. Through a literature review, it assesses how AI adoption influences productivity, job creation, and displacement in these regions. Indonesia faces challenges like infrastructural and educational gaps, though targeted investments and reskilling could boost growth and mitigate job losses. Meanwhile, China and Europe benefit from AI-driven productivity but also confront labor market disruptions, requiring strong policy responses. Indonesia focuses on foundational AI integration, while China pursues aggressive technological advancement and regulation. Europe prioritizes ethical AI use, data protection, and workforce resilience through education and retraining. The study highlights the importance of region-specific regulatory frameworks and calls for continued international collaboration to address global AI-related labor issues. Cross-regional knowledge exchange is crucial for managing labor market transitions and ensuring AI's benefits  are widely shared. Keywords:  Artificial Intelligence (AI); Labor Market Disruption; Regulatory Frameworks Abstrak Studi ini meneliti dampak ekonomi kecerdasan buatan (AI) terhadap pasar tenaga kerja dan strategi regulasi di Indonesia, Tiongkok, dan Eropa. Melalui tinjauan literatur, studi ini mengevaluasi bagaimana adopsi AI memengaruhi produktivitas, penciptaan lapangan kerja, dan pengurangan tenaga kerja di wilayah-wilayah tersebut. Indonesia menghadapi tantangan seperti kesenjangan infrastruktur dan pendidikan, meskipun investasi yang ditargetkan dan peningkatan keterampilan dapat mendorong pertumbuhan dan mengurangi kehilangan pekerjaan. Sementara itu, Tiongkok dan Eropa memperoleh manfaat dari produktivitas yang didorong oleh AI, tetapi juga menghadapi gangguan pasar tenaga kerja, yang membutuhkan respons kebijakan yang kuat. Indonesia berfokus pada integrasi AI dasar, sedangkan Tiongkok mengejar kemajuan teknologi dan regulasi secara agresif. Eropa memprioritaskan penggunaan AI yang etis, perlindungan data, serta ketahanan tenaga kerja melalui pendidikan dan pelatihan ulang. Studi ini menekankan pentingnya kerangka regulasi spesifik untuk setiap wilayah dan menyerukan kerjasama internasional yang berkelanjutan untuk mengatasi isu-isu tenaga kerja terkait AI secara global. Pertukaran pengetahuan lintas wilayah sangat penting untuk mengelola transisi pasar tenaga kerja dan memastikan manfaat AI dapat dinikmati secara luas. Kata Kunci: Kecerdasan Buatan; Gangguan Pasar Tenaga Kerja; Kerangka RegulasiJurnal Bisnis dan Manajemen00Jurnal Bisnis dan Manajemen["Bella Thalia Winardi", "Evelyne Julian Halim"]2024-09-30T00:00:00 0be57d5f80d4d369db840082854f038595f5cbd7This study was designed to explore major predictors related to creativity of high school students to discuss the implication for creativity development. To do this, random forest was applied to 5th wave data(11th graders) of the Korean Children & Youth Panel Survey(KCYPS) 2018, and explainable artificial intelligence SHAP(Shapley addictive explanations) analysis was conducted to explore the importance of major predictors and the relationship between creativity and the predictors. The main result are as follows. First of all, not only individual-related factors but also family-, school-related and academic factors were derived as main predictors for creativity. Among the individual-related factors, ‘self esteem’ was derived as the most important factor. Additionally. ‘academic enthusiasm’, ‘academic engagement’ were found as important factors. Among family-related factors, ‘parental attitude’ was derived as important predictors., Among school-related and academic factors, ‘youth activities participation’, ‘club activities participation’, ‘self study time’, ‘academy and tutoring time’ were derived as important predictors. Based of these findings, implication for developing creativity and suggestions for future research are discussed.Korean Society for Educational Evaluation00This study was designed to explore major predictors related to creativity of high school students to discuss the implication for creativity development and suggestions for future research are discussed.Korean Society for Educational Evaluation["Jungkyo Jung", "Hyewon Chung"]2024-09-30T00:00:00 2b972e96bbedaad1530bea5e26f3673ed4e68a23Artificial intelligence (AI) has changed the game for companies all around the globe. The impact of AI in many areas, including personal behaviour, corporate economics, and new trends, has been the subject of a great deal of research. This study examined the factors—accuracy, insight, interaction, perceived usefulness, and hedonic value—that people use to form opinions on AI and how those opinions impact their intents to buy things online. It delves further into the connection between AI technology and purchase intention, specifically looking at the mediating role of hedonic value. Quantitative method was used in this study, surveying people in Punjab, Pakistan, utilizing Google Forms, social media, and WhatsApp to disseminate self-administered surveys. A total of 450 respondents successfully completed the survey. The findings highlighted the significant impact of AI marketing on consumer purchase intentions. Mediating role of Hedonic Value and Utility is found significant. Resultantly, AI marketing encounters greatly boost the hedonic value of items or services, resulting in consumers finding these interactions engaging and pleasurable.International Journal of Trends and Innovations in Business &amp; Social Sciences00This study examined the factors—accuracy, insight, interaction, perceived usefulness, perceived usefulness, and hedonic value—that people use to form opinions on AI and how those opinions impact their intents to buy things online.International Journal of Trends and Innovations in Business &amp; Social Sciences["Sahrish Saba", "Dr. Fahad Javed Baig", "Dr. Muhammad Rashid", "Muhammad Ikram Ul Haq", "Zeshan Haider"]2024-09-30T00:00:00 c877a1490d70545ce3bb209e2d46cf26f57097c4A common challenge in early childhood moral education is the low level of engagement and interest when using traditional storytelling methods. This study examines the implementation and utilization of artificial intelligence (AI)--based learning videos to enhance early childhood moral education at RA Hj. Sri Musiyarti, Semarang, Indonesia. With a qualitative case study approach, the research involved 20 children aged 4 to 5 years, with data collected through observations, semi-structured interviews with teachers, and documentation. The study focuses on the impact of AI-based videos on children's interest in moral learning, a shift from traditional storytelling methods to video-based learning. Findings reveal that AI-based learning videos significantly increase children's interest and engagement, promoting active participation in the classroom. The videos facilitated cognitive development and emotional engagement, making moral learning more enjoyable and effective. The reliability and validity of the findings were ensured through methodological triangulation and peer debriefing. The study concludes that AI-based learning videos are a powerful tool in fostering intellectual and emotional growth in early childhood, supporting innovative and exciting teaching approaches in moral education. The results suggest that AI technologies can enhance early childhood education, with future research needed to expand the scope of AI in education and to explore strategies for teacher training to ensure the effective implementation of AI technologies.Golden Age: Jurnal Ilmiah Tumbuh Kembang Anak Usia Dini520It is suggested that AI technologies can enhance early childhood education, with future research needed to expand the scope of AI in education and to explore strategies for teacher training to ensure the effective implementation of AI technologies.Golden Age: Jurnal Ilmiah Tumbuh Kembang Anak Usia Dini["Nilal Muna Fatmawati", "Raharjo"]2024-09-30T00:00:00 a8169380ef2815b3ead2c89ec4c314198c8aaea8Using Artificial Intelligence in education is gaining momentum in most educational systems worldwide. Yet, despite its acknowledged benefits in tailoring students’ learning, its use in Algerian higher education classes comes with inherent challenges and ethical dilemmas as teachers often express concerns about students’ actual use when engaged in tasks’ completion. In this context, this study examines the reality of AI employment by teachers from eleven (11) universities, including a purposive sample of forty-one (41) participants who answered an online semi-structured questionnaire. The questionnaire contained twenty (20) items which targeted the teachers’ AI use practices and perceptions, by focusing on the perceived opportunities it offers, the challenges it poses, and the reasons for the use or non-use of AI in class. The results revealed that teachers are concerned about the unethical use of AI and its impact on teacher-student trust and relationships.  A clear need for comprehensive teacher training on effective AI use was identified, with a significant lack of motivation among educators to integrate AI into their teaching. The teachers’ reluctance toward technology use has become real and a successful integration should start by changing teachers’ viewpoints on this matter.  ATRAS journal210The results revealed that teachers are concerned about the unethical use of AI and its impact on teacher-student trust and relationships, with a clear need for comprehensive teacher training on effective AI use.ATRAS journal["Nora Achili", "Nadia Zerrouki"]2024-09-30T00:00:00 8f19f4acd22ac14e0069ebd9082c446f56006a36Explainable Artificial Intelligence (XAI) techniques are used to provide transparency to complex, opaque predictive models. However, these techniques are often designed for image and text data, and it is unclear how fit-for-purpose they are when applied to tabular data. As XAI techniques are rarely evaluated in settings with tabular data, the applicability of existing evaluation criteria and methods are also unclear and needs (re-)examination. For example, some works suggest that evaluation methods may unduly influence the evaluation results when using tabular data. This lack of clarity on evaluation procedures can lead to reduced transparency and ineffective use of XAI techniques in real world settings. In this study, we examine literature on XAI evaluation to derive guidelines on functionally-grounded assessment of local, post hoc XAI techniques. We identify 20 evaluation criteria and associated evaluation methods, and derive guidelines on when and how each criterion should be evaluated. We also identify key research gaps to be addressed by future work. Our study contributes to the body of knowledge on XAI evaluation through in-depth examination of functionally-grounded XAI evaluation protocols, and has laid the groundwork for future research on XAI evaluation.arXiv.org860This study examines literature on XAI evaluation to derive guidelines on functionally-grounded assessment of local, post hoc XAI techniques, and identifies 20 evaluation criteria and associated evaluation methods, and derive guidelines on when and how each criterion should be evaluated.ArXiv["M. Velmurugan", "Chun Ouyang", "Yue Xu", "Renuka Sindhgatta", "Bemali Wickramanayake", "Catarina Moreira"]2024-09-30T00:00:00 514adc2fede987890fd03a74a9d649594462c045This research explores the evolving landscape of literary analysis through the integration of Artificial Intelligence (AI) and traditional human scholarship. The primary objective is to assess the extent to which AI can enhance the analysis of literary texts by examining its performance in uncovering thematic and stylistic elements within William Shakespeare's "Hamlet." This study employs a mixedmethods research approach, combining qualitative and quantitative techniques to provide a comprehensive evaluation. In the digital age, AI has emerged as a promising tool for text analysis, offering efficiency and scalability. However, it raises fundamental questions about its ability to grasp the profound nuances, cultural contexts, and thematic richness inherent in literary works. Through meticulous comparative and thematic analyses, this research investigates the strengths and limitations of AI in literary analysis, juxtaposing its findings with traditional human interpretations. The results of our study reveal that AI excels in identifying patterns, themes, and stylistic markers within "Hamlet." It effectively recognizes key themes such as revenge, madness, and moral corruption. However, AI's analysis often lacks the depth and contextual understanding present in traditional critiques, particularly in interpreting abstract motifs and cultural references. Our findings underscore the complementary nature of AI and human scholarship in literary analysis. While AI offers quantitative efficiency and objectivity, human interpretation provides the depth, cultural insights, and emotional resonance necessary for a comprehensive understanding of literary works. We argue for a harmonious future where AI augments human expertise, leading to more profound insights and a richer literary scholarship. This research not only contributes to the field of literary analysis but also offers a broader perspective on the evolving relationship between technology and human creativity. As AI technologies advance, the collaborative synergy between AI's quantitative efficiency and human interpretation's qualitative depth promises to reshape the landscape of literary studies, enriching our understanding of literature across diverse genres, time periods, and cultural contexts.International Journal of Emerging Knowledge Studies160The results of this study reveal that AI excels in identifying patterns, themes, and stylistic markers within "Hamlet" and argues for a harmonious future where AI augments human expertise, leading to more profound insights and a richer literary scholarship.International Journal of Emerging Knowledge Studies["Deny Yadav"]2024-09-30T00:00:00 a21ab8824cdba6f16324e93efc15551445ed34b6Expert systems with applications11952Expert Syst. Appl.["Shan Wang", "Fang Wang", "Zhen Zhu", "Jingxuan Wang", "Tam Tran", "Zhao Du"]2024-10-01T00:00:00 17d251561931cc33b4fca14cae425dbd8be998e0Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.Pharmaceutics18315This review article will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more.Pharmaceutics["D. Serrano", "F. C. Luciano", "Brayan J. Anaya", "Baris Ongoren", "Aytug Kara", "Gracia Molina", "Bianca I Ramirez", "Sergio A S\u00e1nchez-Guirales", "Jesus A. Simon", "Greta Tomietto", "Chrysi Rapti", "Helga K. Ruiz", "Satyavati Rawat", "Dinesh Kumar", "A. Lalatsa"]2024-10-01T00:00:00 87a0e31cc6195297bc79876f4873394648530ed6Key Points Question How do hospital early warning scores compare with one another? Findings In this cohort study that compared 6 early warning scores across 362 926 patient encounters, eCARTv5, a machine learning model, identified clinical deterioration best with an area under the receiver operating characteristics curve (AUROC) of 0.895 and the highest positive predictive values at both the moderate- and high-risk matched thresholds. The National Early Warning Score, a non–artificial intelligence score with an AUROC of 0.831, was the second-best performer at both thresholds, while the Epic Deterioration Index was one of the worst, with an AUROC of 0.808 and the lowest positive predictive values. Meaning Given the wide variation in accuracy, these findings suggest that additional transparency and oversight of early warning tools may be warranted.JAMA Network Open338ECARTv5, a machine learning model, identified clinical deterioration best with an area under the receiver operating characteristics curve (AUROC) of 0.895 and the highest positive predictive values at both the moderate- and high-risk matched thresholds.JAMA Network Open["D. Edelson", "M. Churpek", "K. Carey", "Zhenqiu Lin", "Chenxi Huang", "J. Siner", "Jennifer Johnson", "H. Krumholz", "Deborah J Rhodes"]2024-10-01T00:00:00 e2c671222f41c86d8fc348ec6358fa63e3f2bb83Energy Economics8711Energy Economics["Wenli Zhong", "Yang Liu", "Kangyin Dong", "Guohua Ni"]2024-10-01T00:00:00 36ce3936ce9dd5a11b90d506d4ae5deef13c35fePurpose The purpose of this paper is to examine whether artificial intelligence (AI) increases data and information quality in the accounting and disclosure context. Design/methodology/approach Data were collected from financial managers, who are working in listed Jordanian firms in the Amman Stock Exchange. SmartPLS software based on the Partial Least Squares Structural Equation Modeling approach was used to test hypotheses. Findings The empirical results reached the acceptance of all hypotheses, and this means that all hypothesized relationships were positive, as the impact of AI was positive on data and information quality in the accounting and disclosure context, and also the adoption of digital disclosure mediated the relationship between AI and the quality of financial data and information, and hence, all hypotheses were statistically supported in the context of Jordan. Originality/value This study broadened the literature by proposing a research model that defines some of the main factors for determining financial managers’ perceptions of issues with digital financial disclosure adoption and its impact on financial data and information quality. By illuminating the relevance of these issues in the presence of the mandate of digital disclosure, this study sheds some light on digital disclosure regulators in making future policies for digital disclosure adoption among the different sectors such as financial, service and industrial in the Jordanian context. Journal of Financial Reporting & Accounting854Examination of whether artificial intelligence increases data and information quality in the accounting and disclosure context in the Jordanian context sheds some light on digital disclosure regulators in making future policies for digital disclosure adoption among the different sectors such as financial, service and industrial in the Jordanian context.Journal of Financial Reporting and Accounting["Manaf Al-Okaily"]2024-10-01T00:00:00 c2b7fa3682a7cdf1df0d007f369d83056a8ca78eBackground and Objectives: Neurological disorders like stroke, spinal cord injury (SCI), and Parkinson’s disease (PD) significantly affect global health, requiring accurate diagnosis and long-term neurorehabilitation. Artificial intelligence (AI), such as machine learning (ML), may enhance early diagnosis, personalize treatment, and optimize rehabilitation through predictive analytics, robotic systems, and brain-computer interfaces, improving outcomes for patients. This systematic review examines how AI and ML systems influence diagnosis and treatment in neurorehabilitation among neurological disorders. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, and Scopus databases with a search time range from 2014 to 2024. This review has been registered on Open OSF (n) EH9PT. Results: Recent advancements in AI and ML are revolutionizing motor rehabilitation and diagnosis for conditions like stroke, SCI, and PD, offering new opportunities for personalized care and improved outcomes. These technologies enhance clinical assessments, therapy personalization, and remote monitoring, providing more precise interventions and better long-term management. Conclusions: AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders. Future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.Biomedicines1084AI is revolutionizing neurorehabilitation, offering personalized, data-driven treatments that enhance recovery in neurological disorders, and future efforts should focus on large-scale validation, ethical considerations, and expanding access to advanced, home-based care.Biomedicines["Andrea Calderone", "Desir\u00e9e Latella", "Mirjam Bonanno", "Angelo Quartarone", "Sepehr Mojdehdehbaher", "A. Celesti", "R. S. Calabr\u00f2"]2024-10-01T00:00:00 c0f8d5ed88906bfeada9b890bcea44eb12200bdcBackground: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions. Aim: This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions. Methods: A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included. Results: The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care. Conclusions: Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.Journal of Personalized Medicine873Diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography and visual field tests.Journal of Personalized Medicine["E. Tonti", "Sofia Tonti", "Flavia Mancini", "Chiara Bonini", "Leopoldo Spadea", "Fabiana D\u2019Esposito", "Caterina Gagliano", "M. Musa", "Marco Zeppieri"]2024-10-01T00:00:00 2cc673f176e878920f19bdbf2feb1fab86f0bb5a The article, based on empirical and theoretical research, reveals the phenomenology of transformations of the human cognitive sphere when interacting with artificial intelligence. The analysis of the indicated changes in the cognitive sphere is carried out on the basis of the “Concept of cognitive multi-channel Human-Computer interaction” developed by us. The essence of this concept is that the interaction of the cognitive sphere of human and artificial intelligence is implemented on the basis of the actualization and formation of typical cognitive phenomena. These phenomena are considered systemically and multifunctionally, namely as relatively independent cognitive: types of interactions, stages, strategies, channels, ontologies. Within the conceptual and substantive framework of this concept, we distinguish the following types of cognition (channels, strategies, etc.): I – orientational-cognitive; II – subject-cognitive; III – communicative and cognitive; IV – cognitive and analytical; V – cognitive and hermeneutic; VI-cognitive-ontological; VII – cognitive and creative. The identification of the indicated types of cognitive interactions is aimed at its representation as a complex, dynamic, multidimensional, multichannel intellectual system, the features of which are significant for educational and sociocultural practices, as well as for further development of artificial intelligence technologies, including its functional orientation and specificity, ergonomics, architecture, design and features of the interface. A study was conducted among students of higher education institutions aimed at determining the cognitive specificity (structure) of interaction in the “Human – Artificial Intelligence” system. Based on the analysis of the results of the distribution of answers for each of the test questions and the interpretation of the results of the cluster analysis (the Canopy algorithm was used), the dominance of the “I – orientational-cognitive” type of interactions was determined, which indicates a rather significant but initial interest in artificial intelligence technologies. There is also a relatively even distribution of all other types of cognitive interactions. The above reveals the novelty and innovation of artificial intelligence technology. This correlates with the respondents having developed different types of cognition, namely: orientational, analytical-synthetic, conceptual, interpretive, ontological, creative thinking, and corresponding intellectual intentions and motivation to use artificial intelligence tools in various spheres of activity.Journal of Physics: Conference Series183The article reveals the phenomenology of transformations of the human cognitive sphere when interacting with artificial intelligence technology and reveals the novelty and innovation of artificial intelligence technology.Journal of Physics: Conference Series["V. M. Fedorets", "O. V. Klochko", "I. A. Tverdokhlib", "O. A. Sharyhin"]2024-10-01T00:00:00 b3ee850c41a1b7f57fb701c4b21cb046afd44dd5Background: Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. Methods: We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. Results: We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital’s HAI incidence from 1.31% to 0.58%. Conclusions: AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.Healthcare643Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance, which has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections.Healthcare["Davide Radaelli", "Stefano Di Maria", "Z. Jakovski", "Djordje Alempijevic", "Ibrahim Al-Habash", "Monica Concato", "M. Bolcato", "Stefano D\u2019Errico"]2024-10-01T00:00:00 b9238318086303ba8afa9a682991d339d42c8221Artificial intelligence (AI) refers to a set of techniques that enable machines to imitate human intelligence. Its development is a technological revolution, which, like previous technological revolutions, can cause serious economic shocks. Although the work to quantify the effects of AI is still exploratory, it provides some insight. The purpose of the study is to study the impact of artificial intelligence on the economy, identify the main trends and assess the prospects for the development of this connection. Research Problem: Artificial intelligence can bring significant benefits to the economy, but it can also cause uneven distribution of benefits and create new economic problems. Research methods: analysis, statistics, correlation method, factorial, logical, generalization, systematization, comparative methods. Results and conclusions: The long-term impact of AI on aggregate employment fits within the theoretical framework of creative destruction. According to the IMF, 60% of jobs in advanced economies could be highly impacted by AI: 27% of jobs will be highly complementary and, therefore, are able to benefit from AI, while it could replace 33% of jobs. Moreover, from a global point of view, one of the key risks in the development of AI can be considered the possibility of an increase in the technological gap between the least developed countries and the most advanced economies of the world.COLLECTION OF PAPERS NEW ECONOMY01The long-term impact of AI on aggregate employment fits within the theoretical framework of creative destruction and the possibility of an increase in the technological gap between the least developed countries and the most advanced economies of the world can be considered.COLLECTION OF PAPERS NEW ECONOMY[]2024-10-01T00:00:00 59d72bc671f0a83e1cd6ae72a54a2543b9cd3106This academic research paper explores the critical challenge of humanizing Artificial Intelligence (AI) for personalized and adaptive learning, moving beyond algorithmic approaches to create more inclusive and effective educational experiences. It examines key AI technologies such as intelligent tutoring systems, adaptive learning platforms, and predictive analytics tools, evaluating their potential to revolutionize education while addressing their limitations. The research highlights the importance of a human-centered framework for AI in education (AIEd), emphasizing participatory design, teacher empowerment, and the promotion of socio-emotional learning alongside cognitive development. Major findings include the need for AI-enabled pedagogies that transcend conventional personalization, such as collaborative knowledge building and place-based learning, to foster deeper engagement and understanding. The academic research paper underscores the significance of these approaches in creating more equitable and inclusive learning environments, particularly in addressing educational disparities in diverse global contexts. Ethical considerations, including data privacy, algorithmic bias, and the potential erosion of teacher autonomy, are critically examined. The research proposes a multi-stakeholder roadmap for implementing ethical and equitable AIEd, combining policy recommendations with practical strategies like risk sandboxing and open-source AIEd development. By advocating for a balance between technological innovation and human values in education, this academic research paper contributes to the advancement of human-centered AI in personalized and adaptive learning, paving the way for more nuanced, ethical, and effective integration of AI in educational settings worldwide.International Journal of Innovative Research in Engineering & Management01The research highlights the importance of a human-centered framework for AI in education (AIEd), emphasizing participatory design, teacher empowerment, and the promotion of socio-emotional learning alongside cognitive development, and proposes a multi-stakeholder roadmap for implementing ethical and equitable AIEd.International Journal of Innovative Research in Engineering and Management[]2024-10-01T00:00:00 cab97b53c33478deb282ea37e62fae210b5376aeUNIVERSUM09Universum:Economics &amp; law["Rustam Mirzayev"]2024-10-01T00:00:00 43decc04e8ea0cb368c1473b7f5af1731cf80f63This research investigates the role of ChatGPT in human-machine communication, focusing on interaction attributes and user perceptions. The study employs qualitative methods, including interviews with ChatGPT users, word cloud analysis using NVivo 12 Pro, and observation of interactions. Results highlight five frequently occurring codes: "chat," "person," "ChatGPT," "task," and "communication." These findings suggest that ChatGPT is perceived both as a communicative tool and a communicative subject, with AI literacy being crucial for its effective use. The research contributes significantly to understanding the development of technology and communication, showcasing ChatGPT's potential to transform human-machine interactions. It demonstrates how ChatGPT can improve efficiency, creativity, and our understanding of artificial intelligence. The study also explores the comparison between human-human and human-machine communication, emphasizing the unique role of ChatGPT in modern communication dynamics. This research underscores the importance of integrating AI education into curricula and providing clear guidelines for ethical and effective use of AI technologies like ChatGPTJurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)151Findings suggest that ChatGPT is perceived both as a communicative tool and a communicative subject, with AI literacy being crucial for its effective use.Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)["Salwa Nur Rohmah", "Rizca Haqqu"]2024-10-01T00:00:00 0d76d202107161991e4d8049c068fecf7ecd7783BMC Medical Imaging371The AI-based aneurysm detection rate demonstrates a commendable performance, while the extracted morphological parameters exhibit a remarkable consistency with those assessed by radiologists, thereby showcasing significant potential for clinical application.BMC Medical Imaging["Yi Shen", "Chao Zhu", "Bingqian Chu", "Jian Song", "Yayuan Geng", "Jianyin Li", "Bin Liu", "Xingwan Wu"]2024-10-01T00:00:00 8747662c20101f9b6e1d2d7bc9a2e440061dcb24Eye and Vision932Evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases.Eye and Vision["Shaopan Wang", "Xin He", "Zhongquan Jian", "Jie Li", "Changsheng Xu", "Yuguang Chen", "Yuwen Liu", "Han Chen", "Caihong Huang", "Jiaoyue Hu", "Zuguo Liu"]2024-10-01T00:00:00 cf6e0ac752beac9cf6187552c303089255b527e3This article overviews the state of the art, research challenges, and future directions in our vision: integrating social explanation into explainable artificial intelligence (XAI) to combat misinformation. In our context, “social explanation” is an explanatory approach that reveals the social aspect of misinformation by analyzing sociocontextual cues, such as user attributes, user engagement metrics, diffusion patterns, and user comments. Our vision is motivated by the research gap in the existing XAI that tends to overlook the broader social context in which misinformation spreads. In this article, we first define social explanation, demonstrating it through examples, enabling technologies, and real-world applications. We then outline the unique benefits social explanation brings to the fight against misinformation and discuss the challenges that make our vision complex. The significance of this article lies in introducing the “social explanation” concept in XAI, which has been underexplored in the previous literature. Also, we demonstrate how social explanations can be effectively employed to tackle misinformation and promote collaboration across diverse fields by drawing upon interdisciplinary techniques spanning from computer science, social computing, human–computer interaction, to psychology. We hope that this article will advance progress in the field of XAI and contribute to the ongoing efforts to counter misinformation.IEEE Transactions on Computational Social Systems1701The significance of this article lies in introducing the “social explanation” concept in XAI, which has been underexplored in the previous literature, and demonstrating how social explanations can be effectively employed to tackle misinformation and promote collaboration across diverse fields by drawing upon interdisciplinary techniques.IEEE Transactions on Computational Social Systems["Yeaeun Gong", "Lanyu Shang", "Dong Wang"]2024-10-01T00:00:00 b10f9b4f7f7b8242c4306265088c22f38661a532The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.Chemical Biology and Drug Design1741This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications mainly focused on the pharmaceutical industry.Chemical biology & drug design["Shruti Bharadwaj", "Kumari Deepika", "Asim Kumar", "Shivani Jaiswal", "Shaweta Miglani", "Damini Singh", "Prachi Fartyal", "Roshan Kumar", "Shareen Singh", "Mahendra Pratap Singh", "A. Gaidhane", "Bhupinder Kumar", "Vibhu Jha"]2024-10-01T00:00:00 219bc5307855446247e24d851a4bbcd8fef7add8Background Artificial intelligence (AI) is rapidly transforming the healthcare sector, enhancing clinical decision-making, improving patient outcomes, and streamlining operations. Despite its promise, the integration of AI raises important questions about ethical considerations, data privacy, and implications for healthcare professionals. Methods This cross-sectional study utilized an online survey to assess the perceptions of newly graduated interns applying to post-graduate programs under the Saudi Commission for Health Specialties. A total of 349 participants were recruited through social media and professional networks. The structured questionnaire included sections on demographic information, awareness of AI, perceived impacts, concerns, training experiences, and future perspectives. Data were analyzed using descriptive and inferential statistics. Results The participants (N=349) were predominantly aged 20-25 years (142, 40.7%) with a higher representation of females (215, 61.6%). Awareness levels varied, with 65 participants (18.6%) reporting not being familiar with AI while 146 participants (41.8%) identified as familiar. A majority perceived AI positively, believing it improves patient diagnosis (114, 32.7%) and reduces medical errors (129, 36.9%). However, significant concerns emerged regarding data privacy (140, 40.1%) and job displacement (110, 31.5%). Notably, 189 participants (54.2%) reported no formal training in AI, highlighting a gap in preparedness. Conclusions The study reveals a mix of optimism and concern among newly graduated interns regarding AI in medicine. There is a critical need for enhanced training and education on AI technologies within medical curricula to prepare future healthcare professionals adequately. Addressing the opportunities and challenges posed by AI can foster a collaborative healthcare environment that prioritizes patient care while maintaining the human element of practice.Cureus141There is a critical need for enhanced training and education on AI technologies within medical curricula to prepare future healthcare professionals adequately and addressing the opportunities and challenges posed by AI can foster a collaborative healthcare environment that priorizes patient care while maintaining the human element of practice.Cureus["Ali H Sanad", "Aalaa S Alsaegh", "Hasan M Abdulla", "Abdulla J Mohamed", "Ahmed Alqassab", "Sayed Mohamed A Sharaf", "Mohamed H Abdulla", "Sawsan A Khadem"]2024-10-01T00:00:00 ee38bdf673de23ad7f1627d9ddb8ecc8ab202ca5Purpose: We aim to assess the ability of artificial intelligence (AI) to generate patient educational videos for various corneal refractive surgeries. Methods: Three AI text-to-video platforms (InVideo (San Francisco, CA), ClipTalk (San Francisco, CA), and EasyVid (Los Angeles, CA)) were used to create patient educational videos for laser-assisted in situ keratomileusis (LASIK), photorefractive keratectomy (PRK), and small incision lenticule extraction (SMILE), respectively. Videos for LASIK and PRK from the American Academy of Ophthalmology (AAO) and a SMILE video from Zeiss served as controls for each surgery. A three-point grading system (from zero to three, with zero being the worst and three being the best in each category) was used to compare videos in terms of "image accuracy," "script accuracy," "image clarity," and "script alignment." Results: In terms of image accuracy, the control videos outperformed InVideo, EasyVid, and ClipTalk for LASIK (3 versus 0.667 versus 0 versus 0; p<0.005), PRK (3 versus 1 versus 0.33 versus 0; p<0.05 for InVideo, p<0.005 all), and SMILE (3 versus 0.33 versus 0 versus 0.33; p<0.005), respectively. With a few exceptions, all three AI models performed similarly to the control videos in terms of script accuracy, image clarity, and script alignment. Conclusion: In their current state, AI text-to-video generators can produce surgical educational videos for patients with accurate script narration and high image clarity, although these platforms are not yet capable of producing medically accurate images to go along with these scripts. Further improvements in the medical accuracy of these images must be made to make these videos more appropriate for patient consumption.Cureus231In their current state, AI text-to-video generators can produce surgical educational videos for patients with accurate script narration and high image clarity, although these platforms are not yet capable of producing medically accurate images to go along with these scripts.Cureus["Kenneth Han", "Muhammed A Jaafar", "Kayvon A Moin", "P. Hoopes", "Majid Moshirfar"]2024-10-01T00:00:00 2b90962d0bc176f722d233b03c180989c5e0aee9ABSTRACT Introduction: The emergence of artificial intelligence (AI)-based support system endoscopy, including cystoscopy, has shown promising results by training deep learning algorithms with large datasets of images and videos. This AI-aided cystoscopy has the potential to significantly transform the urological practice by assisting the urologists in identifying malignant areas, especially considering the diverse appearance of these lesions. Methods: Four databases, the PubMed, ProQuest, EBSCOHost, and ScienceDirect were searched, along with a manual hand search. Prospective and retrospective studies, experimental studies, cross-sectional studies, and case–control studies assessing the utilization of AI for the detection of bladder cancer through cystoscopy and comparing with the histopathology results as the reference standard were included. The following terms and their variants were used: “artificial intelligence,” “cystoscopy,” and “bladder cancer.” The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A random effects model was used to calculate the pooled sensitivity and specificity. The Moses–Littenberg model was used to derive the Summary Receiver Operating Characteristics (SROC) curve. Results: Five studies were selected for the analysis. Pooled sensitivity and specificity were 0.953 (95% confidence interval [CI]: 0.908–0.976) and 0.957 (95% CI: 0.923–0.977), respectively. Pooled diagnostic odd ratio was 449.79 (95% CI: 12.42–887.17). SROC curve (area under the curve: 0.988, 95% CI: 0.982–0.994) indicated a strong discriminating power of AI-aided cystoscopy in differentiation normal or benign bladder lesions from the malignant ones. Conclusions: Although the utilization of AI for aiding in the detection of bladder cancer through cystoscopy remains questionable, it has shown encouraging potential for enhancing the detection rates. Future studies should concentrate on identification of the patients groups which could derive maximum benefit from accurate identification of the bladder cancer, such as those with intermediate or high-risk invasive tumors.Indian Journal of Urology291Although the utilization of AI for aiding in the detection of bladder cancer through cystoscopy remains questionable, it has shown encouraging potential for enhancing the detection rates.Indian Journal of Urology : IJU : Journal of the Urological Society of India["Antoninus Hengky", "S. Lionardi", "Christopher Kusumajaya"]2024-10-01T00:00:00 d6a904ebff0cde7093c69fdd76c43ecad6056eacOver the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps—data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration—and delves into the risks for harm at each step and strategies for mitigating them.PLOS Digital Health762This review article breaks down the AI lifecycle into seven steps— data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration—and delves into the risks for harm at each step and strategies for mitigating them.PLOS Digital Health["L. F. Nakayama", "Jo\u00e3o Matos", "J. Quion", "Frederico Novaes", "W. G. Mitchell", "Rogers Mwavu", "Claudia Ju-Yi Ji Hung", "Alvina Pauline dy Santiago", "W. Phanphruk", "Jaime S. Cardoso", "L. Celi"]2024-10-01T00:00:00 536df54b98d446f3692783c38dcc28e2e5d0a17dThis study aimed to propose a Professional Development Model (PDM) for chemistry teachers to enhance their professional development in Artificial Intelligence (AI). The research group consisted of 17 chemistry teachers. The study was designed using a particular case study suitable for qualitative research methods. Document review, teacher interviews, and AI opinions were utilized to create the model. Data were analyzed using inductive content analysis. The document analysis emphasized the teachers' knowledge of various topics, such as AI knowledge, AI tools, AI skills, AI ethics, AI attitudes, and AI literacy, to enable them to incorporate AI into their lessons. It was also highlighted that teachers should acquire domain-specific knowledge, skills, and competencies in the areas where artificial intelligence will be integrated. When examining the recommendations of artificial intelligence (ChatGPT and Gemini), it was found that they addressed similar content to the information included in the document analysis. Furthermore, chemistry teachers stated their deficiencies in AI literacy, AI competencies, and developing AI lesson plans. They also stated that AI applications could be included in various subjects such as organic chemistry, chemical experiments, and chemical reactions. Following the analysis of documents and teacher and AI opinions, a 10-step PDM has been proposed to enhance chemistry teachers' professional development in AI.Journal of Education in Science Environment and Health481A 10-step PDM has been proposed to enhance chemistry teachers' professional development in AI to address chemistry teachers' deficiencies in AI literacy, AI competencies, and developing AI lesson plans.Journal of Education in Science, Environment and Health["Bekir Y\u0131ld\u0131r\u0131m", "Ahmet Tayfur Akcan"]2024-10-01T00:00:00 9f60550690003a8ca1f2fcf948c83c3fa745fdc5This article explores the significant impact of artificial intelligence (AI) on radiology through a comprehensive analysis of eight articles published between 2018 and 2024. With the rapid progress of modern science, the diagnostic methods in medicine are subject to change, which creates the need to consider and evaluate new diagnostic techniques such as artificial intelligence. In our study, we will evaluate the diagnostic accuracy of artificial intelligence and radiological image interpretation, as well as the pros and cons of its use and future development prospects in this field. In this article, we also consider the possibility of using GPT-4 for image analysis in radiology. Artificial intelligence is a revolutionary medical tool that can change diagnostic strategies to improve the quality of medical services.Cureus142The diagnostic accuracy of artificial intelligence and radiological image interpretation, as well as the pros and cons of its use and future development prospects in this field are evaluated.Cureus["Olena Strubchevska", "M. Kozyk", "Aleksandra Kozyk", "K. Strubchevska"]2024-10-01T00:00:00 be6c3fa26f0498e8470c4befac9375a6bdfdb64aNeuropsychiatric disorders (NPDs) pose a substantial burden on the healthcare system. The major challenge in diagnosing NPDs is the subjective assessment by the physician which can lead to inaccurate and delayed diagnosis. Recent studies have depicted that the integration of artificial intelligence (AI) in neuropsychiatry could potentially revolutionize the field by precisely diagnosing complex neurological and mental health disorders in a timely fashion and providing individualized management strategies. In this narrative review, the authors have examined the current status of AI tools in assessing neuropsychiatric disorders and evaluated their validity and reliability in the existing literature. The analysis of various datasets including MRI scans, EEG, facial expressions, social media posts, texts, and laboratory samples in the accurate diagnosis of neuropsychiatric conditions using machine learning has been profoundly explored in this article. The recent trials and tribulations in various neuropsychiatric disorders encouraging future scope in the utility and application of AI have been discussed. Overall machine learning has proved to be feasible and applicable in the field of neuropsychiatry and it is about time that research translates to clinical settings for favorable patient outcomes. Future trials should focus on presenting higher quality evidence for superior adaptability and establish guidelines for healthcare providers to maintain standards.Cureus712The analysis of various datasets including MRI scans, EEG, facial expressions, social media posts, texts, and laboratory samples in the accurate diagnosis of neuropsychiatric conditions using machine learning has been profoundly explored.Cureus["Satneet Singh", "Jade L Gambill", "Mary Attalla", "Rida Fatima", "Amna R Gill", "H. F. Siddiqui"]2024-10-01T00:00:00 fdb893363bee4e7a258ee1495456e3f00dd13758Policymakers, educators, and businesses must develop artificial intelligence-related initiatives and strategies to effectively engage and benefit the population. The study aims to evaluate awareness and readiness to utilize artificial intelligence by the adult population of Ukraine in 2022. The paper employed a questionnaire consisting of two sets of questions: 1) awareness of artificial intelligence and 2) readiness to use artificial intelligence. A total of 806 respondents were interviewed via an online survey. The margin of error does not exceed 5%. The results indicate that while Ukrainians have a generally positive view of artificial intelligence, they remain skeptical about the prospect of robots functioning as workplace partners. The majority find it difficult to envision collaborating with a robot in a professional setting (only 36.9% of Ukrainians are ready to work with a robot). The survey highlights that the primary benefits of AI products and services valued by Ukrainians include timesaving, increased comfort, and enhanced service accessibility. Ukrainian men demonstrate a greater degree of commitment and awareness of artificial intelligence products/services than Ukrainian women. Young people are the most informed age group about artificial intelligence products/services. Residents of the western regions indicate a more significant impact of artificial intelligence on the present, unlike respondents from the eastern regions of Ukraine. AcknowledgmentThis research was funded by a grant “Restructuring of the national economy in the direction of digital transformations for sustainable development” (№0122U001232) from National Research Foundation.Problems and Perspectives in Management351While Ukrainians have a generally positive view of artificial intelligence, they remain skeptical about the prospect of robots functioning as workplace partners and the majority find it difficult to envision collaborating with a robot in a professional setting.Problems and Perspectives in Management["Svitlana Tarasenko", "O. Karintseva", "Wojciech Duranowski", "Artem Bilovol", "Viacheslav Voronenko"]2024-10-01T00:00:00 b4c31e6e793810643325f5ce54fb1f281bae954aThe expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.Biomolecules812This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations and presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.Biomolecules["Nour Nissan", "Mitchell C. Allen", "David Sabatino", "K. Biggar"]2024-10-01T00:00:00 5af136369816614deaaeb963638aa10019b8f5d1Recently, the concept of Artificial Intelligence has caused concern not only in the scientific community but also in the media sphere, primarily due to the lack of a universally agreed-upon definition. There is a lack of consensus among people regarding a comprehensive definition of intelligence, and even more so regarding artificial intelligence. Given these circumstances, the growing prevalence of the notion of Intelligence Artificial intelligence has advanced to the extent that it is now a vital element in nearly every sector of today's contemporary economy, exerting a substantial influence on our personal, societal, and political spheres. The foundation of this idea is based on the premise that human intelligence can be precisely described, allowing for the creation of a machine capable of simulating it. This gives rise to philosophical debates concerning the nature of consciousness and the moral implications of developing synthetic entities possessing human-like cognitive abilities. Artificial intelligence presents a myriad of challenges related to comprehension, responsibility, and confidence.     Keywords: artificial intelligence, development, technology, educationEuropean Journal of Sustainable Development141Artificial intelligence has advanced to the extent that it is now a vital element in nearly every sector of today's contemporary economy, exerting a substantial influence on the authors' personal, societal, and political spheres.European Journal of Sustainable Development["Petru\u021b Cristian VASILACHE", "Victor Adrian TROACA", "Ion Pargaru", "Florin Vatase"]2024-10-01T00:00:00 f8cedb3353c9d1c5e4dd5a14d1a833443f71ef42The challenges to effective tuberculosis (TB) disease control are considerable, and the current global targets for reductions in disease burden seem unattainable. The combination of complex pathophysiology and technical limitations results in difficulties in achieving consistent, reliable diagnoses, and long treatment regimens imply serious physiological and socioeconomic consequences for patients. Artificial intelligence (AI) applications in healthcare have significantly improved patient care regarding diagnostics, treatment and basic research. However, their success relies on infrastructures prioritising comprehensive data generation and collaborative research environments to foster stakeholder engagement. This viewpoint article briefly outlines the current and potential applications of advanced AI models in global TB control and the considerations and implications of adopting these tools within the public health community.Breathe401The current and potential applications of advanced AI models in global TB control and the considerations and implications of adopting these tools within the public health community are outlined.Breathe["Mairi McClean", "T. Panciu", "C. Lange", "R. Duarte", "Fabian Theis"]2024-10-01T00:00:00 d805b940affe9b22005ffeba957c3091acedba00This article examines the transformative potential of artificial intelligence (AI) in shaping the future of healthcare. It highlights AI's capacity to revolutionize various medical fields, including diagnostics, personalized treatment, drug discovery, telemedicine, and patient care management. Key areas explored include AI's roles in cancer screening, reproductive health, cardiology, outpatient care, laboratory diagnosis, language translation, neuroscience, robotic surgery, radiology, personal healthcare, patient engagement, AI-assisted rehabilitation with exoskeleton robots, and administrative efficiency. The article also addresses challenges to AI adoption, such as privacy concerns, ethical issues, cost barriers, and decision-making authority in patient care. By overcoming these challenges and building trust, AI is positioned to become a critical driver in advancing healthcare, improving outcomes, and meeting the future needs of patients and providers.Cureus581AI's capacity to revolutionize various medical fields, including diagnostics, personalized treatment, drug discovery, telemedicine, and patient care management, is highlighted and challenges to AI adoption are addressed.Cureus["Mousumi Khanam", "Sume Akther", "Iffath Mizan", "Fakhrul Islam", "Samsul Chowdhury", "Nayla Mehereen Ahsan", "Deepa Barua", "Sk K Hasan"]2024-10-01T00:00:00 c7d7c9ccc5eac6f41b84af724823b00196e53067This study examines the impact of Artificial Intelligence (AI) on the field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. This research fits within the specific setting of university students and faculty members in the Kingdom of Bahrain.A cross-sectional survey was designed to examine the impact Artificial Intelligence (AI) in field of education, with particular focus on its implications for academic integrity and the adoption of comprehensive assessment approaches. A total of 218 participants were randomly selected from 250 employed in this survey study.Out of 250 invited participants, 203 responded to the survey. This study evaluated the influence of Educational Impact (EI), Policy and Ethics (PE), and Pedagogical Implications (PI) on Academic Outcomes (AO). Results revealed a significant association between EI → AO with a beta of 0.490, t-value of 4.504, and p < 0.001. PI also showed a significant relationship (β = 0.454, t = 2.330, p = 0.010) with more variability. PE’s impact on AO was modest (β = 0.243, t = 1.977, p = 0.024). Overall, EI was the strongest AO predictor. The R2 value was approximately 39%, indicating a good fit.The research reveals a strong link between the Educational Impact (EI) of AI and academic success in Bahrain’s universities, with EI being the primary predictor. Both Policy and Ethics (PE) and Pedagogical Implications (PI) play crucial roles in this relationship.Frontiers in Education751The research reveals a strong link between the Educational Impact (EI) of AI and academic success in Bahrain’s universities, with EI being the primary predictor.Frontiers in Education["A. Ateeq", "Mohammed Alzoraiki", "Marwan Milhem", "R. A. Ateeq"]2024-10-01T00:00:00 93ed9a916afa430d568a14ab09a3005ccf5e07a7Background: Artificial intelligence techniques have recently made a significant impact on the healthcare industry. AI encourages physicians to use a broader approach to managing diseases, enhance treatment strategies, and support patients in more effectively managing and satisfying their extended course of treatment. Objectives: To determine the extent of perception and opinion of physicians in Egypt regarding artificial intelligence (AI). Subjects and Methods: An analytical cross-sectional study was carried out among physicians working in Beni-Suef hospitals, Beni-Suef City. Results: 249 physicians who were working in Beni-Suef hospitals took part in the present study with mean age (33.25± 9.48), most of the study participants heard about AI before. Only 21.7% of participants used AI before, nearly half of participants were not worried that they would be replaced by AI in their jobs in the future. There was a statistically significant difference in opinion score among people who were worried that AI would replace them in their job and people who were not, through a box plot figure. Conclusions: The study declared the opinion of physicians towards AI, as over 50% of those involved agree that AI use will reduce the overload of physicians. AI encourages physicians to use a broader approach to managing diseases, enhance treatment strategies, and support patients in more effectively managing and satisfying their extended course of treatment.The Egyptian Journal of Hospital Medicine151The study declared the opinion of physicians towards AI, as over 50% of those involved agree that AI use will reduce the overload of physicians.The Egyptian Journal of Hospital Medicine["H. R. Elareed", "Rasha Khougali Abdelrahman Aziz", "Attia Salama", "A. Ismaeel", "Alshimaa Mohsen", "Mohamed Lotfy"]2024-10-01T00:00:00 ffa4f3910c0984a2c6903f170ae5bb3f5fda29a0Objective The study is devoted to comparing various models based on Artificial Intelligence to determine the level of creativity based on drawings performed using the Urban test, as well as analyzing the results of applying explainable artificial intelligence methods to a trained model to identify the most relevant features in drawings that influence the model’s prediction. Methods The dataset is represented by a set of 1,823 scanned forms of drawings of participants performed according to the Urban test. The test results of each participant were assessed by an expert. Preprocessed images were used for fine-tuning pre-trained models such as MobileNet, ResNet18, AlexNet, DenseNet, ResNext, EfficientNet, ViT with additional linear layers to predict the participant’s score. Visualization of the areas that are of greatest importance from the point of view of the model was carried out using the Gradient-weighted Class Activation Mapping (Grad-CAM) method. Results Trained models based on MobileNet showed the highest prediction accuracy rate of 76%. The results of the application of explainable artificial intelligence demonstrated areas of interest that correlated with the criteria for expert assessment according to the Urban test. Analysis of erroneous predictions of the model in terms of interpretation of areas of interest made it possible to clarify the features of the drawing on which the model relies, contrary to the expert. Conclusion The study demonstrated the possibility of using neural network methods for automated diagnosis of the level of creativity according to the Urban test based on the respondents’ drawings. The application of explainable artificial intelligence methods to the trained model demonstrated the compliance of the identified activation zones with the rules of expert assessment according to the Urban test.Frontiers Artif. Intell.271The study demonstrated the possibility of using neural network methods for automated diagnosis of the level of creativity according to the Urban test based on the respondents’ drawings using the Gradient-weighted Class Activation Mapping method.Frontiers in Artificial Intelligence["Anastasia S. Panfilova", "E. Valueva", "I. Y. Ilyin"]2024-10-01T00:00:00 98d97915d7ec380d7dff6032a2d845e4a3643382Computers in Human Behavior758Comput. Hum. Behav.["Lingjie Yuan", "Xiaojuan Liu"]2024-10-01T00:00:00 1b4e3ee5c237262b8016e3f257846c3c76601d71Hygiene and Environmental Health Advances2086Hygiene and Environmental Health Advances["D. Olawade", "O. Z. Wada", "Abimbola O. Ige", "Bamise I. Egbewole", "Adedayo S. Olojo", "B. I. Oladapo"]2024-10-01T00:00:00 8f6baa13dad3238910f84f8f8535cd121b291a5bBackground: The integration of Artificial Intelligence (AI) in cancer research has dramatically enhanced the study, diagnosis, and treatment of cancer. AI’s advanced algorithms, especially recurrent neural networks (RNNs), have proven effective in analyzing complex datasets, facilitating novel biomarker discovery, and improving cancer diagnostics and prognostics. Angiogenesis, the process of new blood vessel formation, plays a crucial role in tumor growth and metastasis, making it a promising target for therapeutic strategies. This study explores the use of AI to identify angiogenesis-related biomarkers and develop personalized treatment strategies. Methods: This study utilized a large dataset from The Cancer Genome Atlas (TCGA), encompassing over 20,000 primary tumor and normal samples across 33 cancer types. Preprocessing techniques such as data cleaning, normalization, and outlier detection were applied. Dimensionality reduction through Principal Component Analysis (PCA) and data visualization using t-Distributed Stochastic Neighbor Embedding (t-SNE) were employed. A Recurrent Neural Network (RNN) was chosen to analyze the sequential biological data and identify potential biomarkers related to angiogenesis. Results: The AI model demonstrated excellent performance across multiple evaluation metrics, including accuracy (0.85), precision (0.82), recall (0.88), F1-score (0.85), and AUC-ROC (0.92), highlighting its effectiveness in predicting cancer progression and identifying key biomarkers. The RNN model was particularly adept at identifying complex patterns in angiogenesis data, facilitating a deeper understanding of tumor biology and revealing novel therapeutic targets. Conclusion: The results underscore the significant potential of AI, specifically RNNs, in advancing cancer research and personalized treatment planning. AI-driven insights into angiogenesis-related biomarkers enable targeted therapies, offering new avenues for effective cancer treatment. These findings not only improve cancer diagnosis and prognosis but also emphasize the role of AI in developing precision oncology approaches, enhancing patient outcomes, and guiding future research in cancer therapeutics.Journal of Angiotherapy00AI-driven insights into angiogenesis-related biomarkers enable targeted therapies, offering new avenues for effective cancer treatment and underscore the significant potential of AI, specifically RNNs, in advancing cancer research and personalized treatment planning.Journal of Angiotherapy[]2024-10-01T00:00:00 386a4228343e43cdb4862000597bc29574ffb2b8 Breast cancer is the second leading cause of death from cancer in women Bray, but early detection and treatment can considerably improve outcomes. As a consequence, many developed nations have implemented large-scale mammography screening programmes. to evaluate the impact of artificial intelligence on reading time and accuracy in breast mammogram interpretation. This study is carried as retrospective comparative study conducted at the radiology department, Ain Shams University Hospitals, the main source of data for this study were the patients referred to the radiology department at Ain Shams university hospitals for mammography from august 2022 to October 2023. the most experienced radiologist had the highest accuracy, sensitivity and specificity (97.5%, 95% and 100% respectively). The next 2 radiologists had comparatively lower overall accuracy (92.5% and 92.5% for radiologist 2 and 3), with the more experienced of them showing higher specificity and lower sensitivity figures than the less experienced (100% Versus 95% for specificity and 85% Versus 90% for sensitivity). On the other hand, the AI was inferior to the most experienced radiologist for sensitivity and overall accuracy figures and showed equal specificity whereas it showed non inferior figures to the next 2 radiologists demonstrating relatively higher sensitivity and overall accuracy figures compared to both radiologists. We found that the AI accuracy has been proven to be comparable to the radiologist 1 (highest years of experience) with equal specificity and lower sensitivity, and not inferior and even higher than radiologist 2 and 3 (medium and low years of experience). The reading time of the AI software was much less than the three radiologists. The highest agreement of the AI software was with the radiologist 1 (the most experienced). The Quarterly journal of medicine00It is found that the AI accuracy has been proven to be comparable to the radiologist 1 (highest years of experience) with equal specificity and lower sensitivity, and not inferior and even higher than radiologist 2 and 3 (medium and low years of experience).QJM: An International Journal of Medicine["Aya Elsayed Kamel Elsayed Fahmy", "Aya Yassin Ahmed", "E. M. Abdulhafiz"]2024-10-01T00:00:00 4d85824be01f2346db91506df804cc195c09ee39From a historical perspective and a prospective analysis, the article aims to understand the role of technologies and their impact on society through the postulates of media ecology. Through this meta-discipline, we delve into the rigorous review of different authors who see technologies as playing a prominent role in shaping the future because they not only influence the culture of societies, but also impact the course, advancement and meaning of history. The text focuses on the advantages and on the explanation of the risks of generative artificial intelligence, identifying eight critical scenarios: weaponization, disinformation, proxy games, weakening, blocking or withholding of value, unwanted emerging goals, deception and power-seeking behavior. Subsequently, CASI regroups them into four threats: malicious use, the AI race, organizational risks and uncontrolled AI. We end the by drawing on McLuhan’s reflections and stressing the need to scale back technologies when they have reached elevated levels of development to minimize their negative impact. Although artificial intelligence has not reached that state, there is a warning about the accelerated evolution and the need for AI literacy as a measure to face risks and threats, in a limited time before it is too late. Desde una perspectiva histórica y un análisis prospectivo, el artículo tiene como objetivo comprender el papel de las tecnologías y su impacto en la sociedad, a través de los postulados de la ecología de los medios. A través de esta metadisciplina, nos adentramos a la rigurosa revisión de diferentes autores que ven en las tecnologías un rol destacado en la configuración del futuro porque no solo influyen en la cultura de las sociedades, sino que también impactan en el curso, avance y significado de la historia. El texto se centra en las ventajas y, sobre todo, en la explicación de los riesgos de la inteligencia artificial generativa, identificando ocho escenarios críticos: armamento, desinformación, juegos de proxy, debilitamiento, bloqueo o retención de valor, metas emergentes no deseadas, engaño y comportamiento de búsqueda de poder. Posteriormente, el CASI las reagrupa en cuatro amenazas: uso malicioso, la carrera de la IA, riesgos organizativos e IA descontrolada. Terminamos recuperando las reflexiones de McLuhan y su tétrada sobre la necesidad de enfriar las tecnologías cuando han alcanzado altos niveles de desarrollo para minimizar su impacto negativo. Si bien la inteligencia artificial no ha alcanzado ese estado, se advierte sobre la acelerada evolución y la necesidad de una alfabetización en IA como una medida para afrontar los riesgos y amenazas, eso sí, en un tiempo limitado antes de que sea tarde.Comunicar90Comunicar["Octavio Islas", "Fernando Guti\u00e9rrez-Cort\u00e9s", "Amaia Arribas-Urrutia"]2024-10-01T00:00:00 68c627439d27c8dab0e59817ac8c40da68d4b0bc The rise of Artificial intelligence (AI) tools and their ability to generate human-like answers in a conversational context has sparked massive interest in people with calls for their application in different areas like healthcare. We evaluated the utility of artificial intelligence-powered language models (Google Bard, ChatGPT 3.5 and GPT-4) compared to trainees and clinical chemists in responding to common laboratory questions in the broad area of Clinical Chemistry. 35 questions from clinical consultations, real-life case scenarios, and clinical chemistry questions were used to evaluate these AI tools alongside clinical chemistry trainees and clinical chemistry faculties. Responses were scored by a blind participant and scores assigned as either being correct, partially correct, or incorrect. Additionally, responses were ranked based on categories and years of experience. Of the 35 questions asked, all human participants performed better than the AI tools. The Senior Chemistry Faculty demonstrated superior accuracy with 100% of correct responses compared to 71.4, 82.9% and 90.5% of correct responses from the residents, fellows and junior chemistry faculty. Both Google Bard and ChatGPT 3.5 generated 60% correct responses while GPT-4 generated 71.4% correct responses respectively. Of the sub-categories examined, both Google Bard and GPT-4 did not achieve 100% accuracy in any subcategory while ChatGPT 3.5 achieved 100% accuracy in endocrinology. GPT-4 outperformed both Google Bard and ChatGPT 3.5 but performed poorly to human participants when both partially correct and incorrect indices were considered. We evaluated the performance of the popular AI-tools in answering clinical chemistry questions compared to responses given by individuals specially trained in the field of laboratory medicine. Despite advances in AI-powered language models, they cannot replace a trained pathologist in answering clinical chemistry questions. Caution should be observed by people, especially those not trained in clinical chemistry, to interpret test results using these tools. Clinical Chemistry00Despite advances in AI-powered language models, they cannot replace a trained pathologist in answering clinical chemistry questions, and Caution should be observed by people, especially those not trained in clinical chemistry, to interpret test results using these tools.Clinical Chemistry["R. Ibrahim", "A. Chokkalla", "S. Kumar", "S. Devaraj"]2024-10-01T00:00:00 87fcbe9ebb9730c59fd1d27a20fed2be139a75e0Generative artificial intelligence (GAI) has taken the world by storm, causing notable tension within the field of education. Nursing education is no exception, facing imminent challenges and opportunities. GAI, a unique and immensely powerful technology championed by ChatGPT (Chat generative pre-trained transformer), represents a new frontier in artificial intelligence. ChatGPT, a product of deep learning - a subset of machine learning that mirrors the human brain's approach to learning and responding to data, information, and prompts - exemplifies this technological leap (Sahoo et al., 2022). GAI stands out for its ability not only to provide responses but also to generate the content of those responses, surpassing the human-like interactions typically seen in conversational AI (Lim et al., 2023; Su & Yang, 2023). Currently, ChatGPT has demonstrated significant application potential in nursing education in various aspects. For example, ChatGPT provides personalized learning (Tam et al., 2023); is easy to use (Vaughn et al., 2024); provides rapid information (Goktas et al., 2024; Liu et al., 2023), rapid responses, and assistance in writing (Sun & Hoelscher, 2023); improves students' problem-solving and critical thinking skills (Goktas et al., 2024; Sun & Hoelscher, 2023); supports educators in developing curricula and preparing course materials and may be used in translation processes (Tam et al., 2023); and helps healthcare professionals better understand complex medical concepts and procedures by providing easily comprehensible and up-to-date information (Krüger et al., 2023). Therefore, integrating ChatGPT into nursing education not only provides students with a more effective and interactive learning experience but also offers educators supportive tools that are directly applicable in teaching. These technologies can enhance / improve teaching by providing personalized learning solutions through, for example, generating teaching cases and simulating clinical scenarios to enhance the learning experience of students (Liu et al., 2023; Vaughn et al., 2024). Despite the significant benefits realized, nursing education in the era of GAI also faces challenges and limitations. Over-reliance on ChatGPT may limit students' critical thinking, problem-solving, and innovation capabilities, leading to a lack of independent thought. Educators should integrate GAI-supported tools into the learning process, but encourage and guide students to use ChatGPT as a supplementary learning tool rather than a substitute (Tam et al., 2023). This approach will help ensure students develop the skills and knowledge necessary to use the technology responsibly and ethically and allow educators to better address key related challenges, enhance education quality, and lay a foundation for cultivating high-quality nursing professionals. GAI is inevitable, and banning it may lead to increased attention and psychological reactance, making students more eager to access this technology. Therefore, educational institutions should embrace rather than shun its use (Lim et al., 2023). It is hoped that readers, after reading this special column, will be inspired to learn more about GAI applications and their significance and thus come to view GAI as a driving force for educational transformation, ensuring the continuous development of education and safeguarding the future of education and, by extension, the society of tomorrow.Hu li za zhi The journal of nursing00Nursing education in the era of GAI faces challenges and limitations, and integrating ChatGPT into nursing education not only provides students with a more effective and interactive learning experience but also offers educators supportive tools that are directly applicable in teaching.Hu li za zhi The journal of nursing["Shu-Ling Chen"]2024-10-01T00:00:00 1d1ae8d8660b5f306d5bb71ef50a892538ab87a0 The adverse cardiovascular outcomes following a stroke have shown significant associations with post-stroke mortality. However, studies focusing on predicting heart failure and its impact on long-term mortality are scarce. To address this gap, we employed an commercially approved artificial intelligence/machine learning enabled software as a medical device (AiTiALVSD version 1.00.00) which uses only 12 lead electrocardiogram data, resulting probability score for predicting left ventricular systolic dysfunction. We derived the AiTiALVSD probability score from initial ECGs at stroke admission, and assess its implications for prediction of heart failure and long-term mortality. We collected data from 335 ischemic stroke patients admitted to a cardiovascular center during acute periods between 2013 and 2019. The AiTiALVSD score was employed to predict heart failure with reduced ejection fraction and heart failure with mild reduced ejection fraction, analyzed through logistic regression. Subsequently, we used the Cox Hazard proportional model, incorporating the AiTiALVSD score and clinical factors, to predict long-term mortality. The model incorporating the AiTiALVSD score with clinical variables demonstrated a higher AUC for predicting heart failure (0.905 [95% CI, 0.827-0.927] vs. 0.828 [95% CI, 0.749-0.907], p<0.004) than clinical variables alone. Moreover, for long-term mortality prediction, this model outperformed the conventional mortality score (AUC, 0.884 [95% CI, 0.836-0.933] vs. 0.755 [95% CI, 0.690-0.820], p<0.001). Multivariable models which combine the AiTiALVSD score with clinical factors, effectively predicts heart failure and long-term mortality. This model could enhance therapeutic strategy tailoring by providing acute phase prognostic predictions for individual stroke patients.Prediction performance for heart failurePrediction performance for mortality European Heart Journal00Multivariable models which combine the AiTiALVSD score with clinical factors, effectively predicts heart failure and long-term mortality.European Heart Journal["M. Y. Oh", "M. S. Lee", "S. R. Kang", "J. Kwon"]2024-10-01T00:00:00 25f04fcad8f5b27b6b5c947d94b7a9abe9b05440 Sports achievements and performance are not solely based on outstanding endurance, especially in tactical and technical sports. Artificial intelligence (AI) can help us to identify correlations that can contribute to the successful preparation of athletes. Our aim was to further analyze and evaluate our previously initiated AI-based sports performance studies, identifying the parameters that determine athletes’ achievement and endurance. First, sports cardiology screening was performed in all athletes containing the following examinations: patient’s history, ECG, laboratory test, body composition analysis, echocardiography and cardiopulmonary exercise testing. A database from the sports cardiology screening results was established. Then, we created two scoring systems based on the best ever result (Achievement Score) and the performance on the cardiopulmonary exercise test (Endurance Score). We identified the most important influencing factors using a neural network and characterized the strength of the variables using Shappley Additive Explanation (SHAP) values. We examined 1932 tests of 891 athletes and the AI analysis included 917 tests of 546 athletes (20.2±6.2 years, males: 397, 72.7%). Majority of the examined athletes were swimmers (27.4%), followed by basketball players (21.8%), water polo players (15.2%), handball players (13.3%) and football players (13.1%). The most important Achievement Score determinants were the weekly training hours (SHAP=0.27), training years (SHAP=0.26) and the age (SHAP=0.15). Meanwhile, Endurance Score was mainly affected by skeletal muscle mass (SHAP=0.85), weight (SHAP=0.6) and peak heart rate during exercise (SHAP=0.32). Our results were validated on a test population (N=102) and in the Achievement Score the mean absolute error (MAE) was 0.64, the determination coefficient (R2) was 0.56, in Endurance Score the MAE was 0.84 and the R2 was 0.71. Based on our results, while endurance is mainly determined by the athlete’s physical characteristics, in athletic achievement experience is more important. With our scoring systems the athlete’s achievement and endurance can be predicted well. European Heart Journal00While endurance is mainly determined by the athlete’s physical characteristics, in athletic achievement experience is more important, with two scoring systems the athlete’s achievement and endurance can be predicted well.European Heart Journal["N. Syd\u00f3", "E. Csulak", "A. R. Kiss", "I. Petrov", "T. Takacs", "G. Bohus", "L. Staub", "Z. T\u0151s\u00e9r", "D. Balla", "H. V\u00e1g\u00f3", "A. Kov\u00e1cs", "B. Merkely"]2024-10-01T00:00:00 cd04c5fe5f6727b57bf4433f9ca2559ba11998dcThe intersection of artificial intelligence (AI) and agricultural finance has emerged as a transformative force in enhancing the efficiency and sustainability of farming practices. This review article explores the multifaceted applications of AI technologies in agricultural finance, focusing on credit scoring, risk assessment, and financial decision-making for farmers and agricultural businesses. We examine the integration of machine learning algorithms and big data analytics in providing tailored financial products, enabling precision agriculture, and improving access to credit in under banked communities. By synthesizing current literature and case studies, we identify key challenges and opportunities, such as the need for data security, transparency, and the democratization of AI tools in rural finance. Furthermore, we discuss the potential of AI to address climate change impacts on agriculture and its implications for financing sustainable practices. This article aims to provide stakeholders in the agricultural finance sector—policymakers, financial institutions, and technology developers—with a comprehensive understanding of how AI can drive innovation and improvement in financing agricultural activities, ultimately fostering a resilient and sustainable food system.TMP Universal Journal of Research and Review Archives00Stakeholders in the agricultural finance sector are provided with a comprehensive understanding of how AI can drive innovation and improvement in financing agricultural activities, ultimately fostering a resilient and sustainable food system.TMP Universal Journal of Research and Review Archives["Matin Joorbonian"]2024-10-01T00:00:00 a3992d90157c92b0cb79af608e0b26d9b318db1d This review explores the integration of artificial intelligence (AI) into physiotherapy practice, focusing on its impact on diagnostic tools, personalized treatment plans, and ethical considerations. AI systems offer enhanced precision and individualization in patient care through multivariable prediction models, which assess long-term outcomes, particularly for hip fracture patients. Although some models show potential for improving treatment pathways and prognostic accuracy, further research is needed to develop more reliable and efficacious AI applications. One significant application of AI in physiotherapy lies in the development of tailored rehabilitation programs. Machine learning algorithms analyze a patient’s medical records and response to prior treatments to create custom care plans, increasing compliance and enhancing clinical decision-making. Continuous feedback loops enable adaptability in treatment plans based on patient reports, further strengthening the practitioner–patient relationship and improving patient satisfaction. Despite the numerous benefits, the integration of AI technologies carries ethical implications. Ensuring patient information confidentiality is crucial, as AI requires extensive data sets to train algorithms. In addition, the role of human empathy and emotional support in therapeutic settings raises questions about AI’s potential replacement in these aspects of care. Clear guidelines and regulatory frameworks are necessary to protect patients’ rights while leveraging AI’s benefits for enhanced clinical outcomes without compromising the foundational values of compassionate care in physiotherapy. Gait analysis, natural language processing, physioGPT, and PostureFix are few of the AI tools used. In conclusion, the incorporation of AI into physiotherapy represents a cultural shift toward more precise and personalized patient care. While showing promise in improving treatment pathways and predicting long-term outcomes, ongoing research should focus on developing robust evaluation metrics for AI applications’ efficacy and reliability. Ethical considerations must be addressed to ensure the safe integration of AI technologies while maintaining the humanistic principles that underpin physiotherapy practice.SBV Journal of Basic, Clinical and Applied Health Science120The incorporation of AI into physiotherapy represents a cultural shift toward more precise and personalized patient care, while showing promise in improving treatment pathways and predicting long-term outcomes, ongoing research should focus on developing robust evaluation metrics for AI applications’ efficacy and reliability.SBV Journal of Basic, Clinical and Applied Health Science["G. Nambi", "M. Alghadier", "S. Mohamed", "Osama R. Aldhafian", "Naif A. Alshahrani", "A. J. Albarakati"]2024-10-01T00:00:00 2b89936e91bea9f1e8fdc22d53d37e2eec4c0203PURPOSE Precision oncology relies on accurate and interpretable reporting of testing and mutation rates. Focusing on the BRAFV600 mutations in advanced colorectal carcinoma, non-small-cell lung carcinoma, and cutaneous melanoma, we developed a platform displaying testing and mutation rates reported in the literature, which we annotated using an artificial intelligence (AI) and natural language processing (NLP) pipeline. METHODS Using AI, we identified publications that likely reported a testing or mutation rate, filtered publications for cancer type, and identified sentences that likely reported rates. Rates and covariates were subsequently manually curated by three experts. The AI performance was evaluated using precision and recall metrics. We used an interactive platform to explore and present the annotated testing and mutation rates by certain study characteristics. RESULTS The interactive dashboard, accessible at the BRAF dimensions website, enables users to filter mutation and testing rates with relevant options (eg, country of study, study type, mutation type) and to visualize annotated rates. The AI pipeline demonstrated excellent filtering performance (>90% precision and recall for all target cancer types) and moderate performance for sentence classification (53%-99% precision; ≥75% recall). The manual annotation of testing and mutation rates revealed inter-rater disagreement (testing rate, 19%; mutation rate, 70%), indicating unclear or nonstandard reporting of rates in some publications. CONCLUSION Our AI-driven NLP pipeline demonstrated the potential for annotating biomarker testing and mutation rates. The difficulties we encountered highlight the need for more advanced AI-powered literature searching and data extraction, and more consistent reporting of testing rates. These improvements would reduce the risk of misinterpretation or misunderstanding of testing and mutation rates by AI-based technologies and the health care community, with beneficial impacts on clinical decision-making, research, and trial design.JCO Precision Oncology510A platform displaying testing and mutation rates reported in the literature is developed, which is annotated using an artificial intelligence (AI) and natural language processing (NLP) pipeline, demonstrating the potential for annotating biomarker testing and mutation rates.JCO precision oncology["Jennifer Webster", "Jennifer Ghith", "Orion Penner", "C.H. Lieu", "Bob J A Schijvenaars"]2024-10-01T00:00:00 77d350dba5c67884ebffb0c9b39402344c37408cBACKGROUND The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current landscape of AI integration in radiology education. METHODS The review process involved systematically searching four databases, including MEDLINE (Ovid), Embase (Ovid), PsychINFO (Ovid), and Scopus. Inclusion criteria focused on research that addresses the use of AI technologies in radiology education, including but not limited to, AI-assisted learning platforms, simulation tools, and automated assessment systems. This scoping review was registered on Open Science Framework using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping review. RESULTS Of the 1081 search results, 9 studies met the inclusion criteria. Key findings indicate a diverse range of AI applications in radiology education, from personalized curriculum generation and diagnostic support tools to automated evaluation systems. The review highlights both the potential benefits, such as enhanced diagnostic accuracy, and the challenges, including technical limitations. CONCLUSION The integration of AI into radiology education, which has significant potential to enhance outcomes and professional practice, requires overcoming existing challenges and ensuring that AI complements rather than replaces traditional methods, with future research needed on longitudinal studies to evaluate its long-term impact.Current problems in diagnostic radiology260The integration of AI into radiology education requires overcoming existing challenges and ensuring that AI complements rather than replaces traditional methods, with future research needed on longitudinal studies to evaluate its long-term impact.Current problems in diagnostic radiology["Muying (Lucy) Hui", "Ethan Sacoransky", "Andrew D. Chung", "B. Y. Kwan"]2024-10-01T00:00:00 d4ce03e3c334e644ea2a28a502872b5ef4caa91aArtificial Intelligence (AI) has significantly transformed healthcare. The Global Technical Meeting on AI for Global Health and Advancing Traditional Medicine (TM) hosted by the World Health Organization, International Telecommunication, and World Intellectual Property (IP) Organization, was held at the All India Institute of Ayurveda (AIIA), New Delhi. The event addressed AI’s potential in enhancing diagnostics, research, and knowledge preservation within TM, with specific attention to regulatory and ethical considerations. Objectives included sharing insights on AI applications in TM, fostering collaborative education, and developing skills for practitioners to harness AI effectively. Structured around four key themes, the meeting covered digital health initiatives, IP in TM, a training framework for stakeholders, and the creation of a TM Global Library. Through expert panels, group discussions, and collaborative sessions, participants discussed integrating AI into TM, IP protection strategies, and building a global digital TM repository. The outcomes shall aim to guide AI-enabled innovation in TM, forming a basis for robust global policies, ethical standards, and tailored AI ecosystems in TM.International Journal of Ayurveda Research00The event addressed AI’s potential in enhancing diagnostics, research, and knowledge preservation within TM, with specific attention to regulatory and ethical considerations, and covered digital health initiatives, IP in TM, a training framework for stakeholders, and the creation of a TM Global Library.International Journal of Ayurveda Research["T. Nesari", "Shyama Kuruvilla", "Lori McDougall", "Sameer Pujari", "Maki Kajiwara", "Manjeet Saluja", "Rajeshwari Singh", "Jyothi P. S. Arshath", "M. S. Anagha", "K. Karthik", "Shifa P. Shetty"]2024-10-01T00:00:00 e3cd2219cfd2dbb5fec267f5c3929fbc779b9305The integration of artificial intelligence (AI) into maxillofacial imaging represents a significant advancement in diagnostics and therapy. This review explores the ethical implications of AI in this specialized area, addressing concerns such as data privacy, informed consent, and algorithmic bias. It highlights the potential benefits of AI for patient outcomes and clinical efficiency while acknowledging risks associated with reliance on automated systems. The review aims to establish a framework for ethical guidelines to ensure that AI enhances patient care. AI's application in various industries has gained momentum, with dentistry, particularly oral and maxillofacial radiology, emerging as a promising field. Recent studies have focused on convolutional neural networks for tasks such as image classification, detection, segmentation, and refinement. These AI systems support radiographic diagnosis, image analysis, forensic dentistry, and image quality enhancement. However, optimal performance requires large, well-labeled datasets, necessitating significant input from oral and maxillofacial radiologists, which can be time-intensive. For AI to be effectively integrated into clinical practice, several challenges must be overcome, including the creation of comprehensive open datasets, understanding AI judgment criteria, and addressing DICOM hacking threats. By developing solutions alongside AI advancements, the technology can significantly evolve, potentially transforming automated diagnosis, treatment planning, and tool development. Oral and maxillofacial radiologists will play a crucial role in shaping AI applications in their field, leveraging their expertise in interpreting radiographic images.Journal of medical and dental science research960The ethical implications of AI in oral and maxillofacial radiology are explored, addressing concerns such as data privacy, informed consent, and algorithmic bias, to establish a framework for ethical guidelines to ensure that AI enhances patient care.Journal of Medical and Dental Science Research["Richa Wadhawan", "Abhishek Yadav", "Shanti SWAROOP MITTAL", "Pooja Mishra", "Deblina Maiti", "Harshika Sengar"]2024-10-01T00:00:00 813a39cf92429cc9e0a88fbe8de932a429858cf5Research teams from the industry, especially big technology companies, have been pushing impactful research work in the field of artificial intelligence (AI), changing the prospects of the field and the careers of many researchers. Research teams from big technology companies usually possess more data, bigger computing infrastructure, and research talent, granting them the advantages in advancing AI research. While most previous work focuses on investigating the advantages the industry has in the field of AI, and how their research publication is different from those published by academic teams, few research has been done to investigate whether working as an industry researcher is beneficial at the individual level. In this work, by analyzing co‐authorship networks of researchers published in AI conferences, we investigate whether working in the industry gives researchers advantages in “intangible” forms, such as social capital, represented by the collaborative relationships they gained or maintained. Our result shows that the many advantages industry researchers possess correlate with the social capital they have, measured by degree centrality, eigenvector centrality, betweenness centrality, and effective size.Proceedings of the Association for Information Science and Technology80This work investigates whether working in the industry gives researchers advantages in “intangible” forms, such as social capital, represented by the collaborative relationships they gained or maintained, by analyzing co‐authorship networks of researchers published in AI conferences.Proceedings of the Association for Information Science and Technology["Lizhen Liang"]2024-10-01T00:00:00 69a618ef1cafa26e26a7e8251039302dd9ed59c3Abstract Artificial intelligence (AI) is transforming cardiovascular imaging by offering advancements across multiple modalities, including echocardiography, cardiac computed tomography (CCT), cardiovascular magnetic resonance (CMR), interventional cardiology, nuclear medicine, and electrophysiology. This review explores the clinical applications of AI within each of these areas, highlighting its ability to improve patient selection, reduce image acquisition time, enhance image optimization, facilitate the integration of data from different imaging modality and clinical sources, improve diagnosis and risk stratification. Moreover, we illustrate both the advantages and the limitations of AI across these modalities, acknowledging that while AI can significantly aid in diagnosis, risk stratification, and workflow efficiency, it cannot replace the expertise of cardiologists. Instead, AI serves as a powerful tool to streamline routine tasks, allowing clinicians to focus on complex cases where human judgement remains essential. By accelerating image interpretation and improving diagnostic accuracy, AI holds great potential to improve patient care and clinical decision-making in cardiovascular imaging.European heart journal. Imaging methods and practice00A review of the clinical applications of AI within cardiovascular imaging, highlighting its ability to improve patient selection, reduce image acquisition time, enhance image optimization, facilitate the integration of data from different imaging modality and clinical sources, and improve diagnosis and risk stratification.European Heart Journal. Imaging Methods and Practice["F. Fortuni", "Giuseppe Ciliberti", "Benedetta De Chiara", "Edoardo Conte", "Luca Franchin", "Francesca Musella", "Enrica Vitale", "Francesco Piroli", "Stefano Cangemi", "Stefano Cornara", "Michele Magnesa", "Antonella Spinelli", "G. Geraci", "Federico Nardi", "D. Gabrielli", "F. Colivicchi", "Massimo Grimaldi", "Fabrizio Oliva"]2024-10-01T00:00:00 c7e1007d77be58a4c97bfa5dccc53343d30da570This study explores whether it would be feasible to introduce an Artificial Intelligence (AI)-based diagnostic tool for Parkinson's Disease (PD) to the United States (U.S.). The product is Saudi Arabian and is intended to reduce resource consumption by improving diagnostic accuracy and efficiency. The U.S. market offers a favorable environment because of its advanced healthcare infrastructure and stable democratic political structure. The analysis looks at the political and economic climate, including government support, market opportunities, regulatory compliance, and the strategic advantages of partnerships with local entities. The study suggests that the AI-based diagnostic tool could revolutionize PD prediction and management, offering significant benefits in terms of healthcare outcomes and cost efficiency. Also, it recommends a strategic entry through local partnerships and adherence to U.S. regulatory frameworks to ensure successful market penetration and long-term success.   Keywords: Parkinson’s disease, neurological disorders, AI-based diagnostic tool, healthcare sustainability, healthcare infrastructure, noninvasive diagnostics, healthcare technology, innovation in healthcare, market penetration, healthcare outcomes, and healthcare outcomesEuropean Journal of Sustainable Development220The study suggests that the AI-based diagnostic tool could revolutionize PD prediction and management, offering significant benefits in terms of healthcare outcomes and cost efficiency.European Journal of Sustainable Development["Dannah AlSafran", "Hind Belhabib", "Amjad Bandah", "Sarah AlSafran", "Sultanah Homran", "Nadia Yusuf"]2024-10-01T00:00:00 1d9c7490c31c3678d76623589f66bee89624eebcArtificial Intelligence (AI) is a multidisciplinary area that integrates elements from several domains. Sometimes, it is also called deep learning or machine learning. And it can simply be referred to as making machines capable of thinking like humans and acting like humans. The process of AI involves developing specific algorithms in order to solve complex tasks that are difficult for humans. With the advancement of innovative technologies, the role of machines has become an inevitable factor in almost all fields of human life. AI has emerged as a super-intelligent mechanism that can solve many issues and find solutions suddenly and promptly. However, such an application of AI has also created numerous challenges against human rights. The greatest threat posed by the use of AI is the infringement of individual freedom and privacy. AI has now enabled the management of many human activities, and this management has led to excessive control of machines over human affairs. AI can gain access to individuals’ personal lives without their consent. Data protection is another challenge of AI, as it can violate individuals’ privacy and freedom. The detractors of AI argue that it has no respect for individuals’ emotions and social values because most of the AI is generated through such algorithms. This paper discusses how AI impacts human activities by compromising individuals’ privacy and freedom. Similarly, it discusses AI regulatory mechanisms that have been taken at national and global levels. Furthermore, it provides key recommendations in order to regulate the excess interference of AI technology over human affairs.Shanlax International Journal of Arts, Science and Humanities240How AI impacts human activities by compromising individuals’ privacy and freedom is discussed and key recommendations in order to regulate the excess interference of AI technology over human affairs are provided.Shanlax International Journal of Arts, Science and Humanities["Binu Joseph"]2024-10-01T00:00:00 9f95c4992a1d6dc9d55a6b40228bfda560318123 Chest radiography is widely used for its portability and cost-effectiveness, providing crucial information for detecting thoracic diseases. However, interpretation challenges and the COVID-19 pandemic have led to stressed medical staff and increased misinterpretations. Automated systems, employing deep learning, are showing promise in classifying chest X-ray abnormalities. These advancements can enhance clinical workflow, decision support, and contribute to global health initiatives. The study aims to evaluate the diagnostic performance of an Artificial Intelligence (AI) system in interpreting chest X-rays, specifically assessing sensitivity, specificity, and accuracy. Additionally, the research explores the impact of AI assistance on the diagnostic performance of radiology residents, examining changes in sensitivity, specificity, and accuracy. A cross-sectional study at Ain Shams University Hospitals assessed the diagnostic performance of an AI system in interpreting chest X-rays, using a sample of 63 images. The study analyzed sensitivity, specificity, and accuracy, incorporating AI assistance for radiologist residents. Data management included descriptive and analytical statistics, employing ROC curves and Kappa statistics for agreement assessment. The study found diagnostic improvements with AI aid. The study investigated the diagnostic performance of an AI algorithm, Lunit INSIGHT CXR3, and a radiology resident in interpreting AP frontal chest X-rays. The AI demonstrated high sensitivity (100% in calcification and consolidation) and varying specificity (72-100%) and accuracy (71-100%) across different findings. The radiology resident showed significant sensitivity and accuracy improvement as well as increase in agreement with AI assistance, while no significant changes could be detected in specificity. The study suggests that AI can enhance diagnostic performance, aiding radiologists in clinical practice. In conclusion, our study demonstrates that using AI as an aid has proven to improve the overall performance of radiology residents allowing them to reach expert like levels of diagnostic performance in the CXR interpretation. The Quarterly journal of medicine00Using AI as an aid has proven to improve the overall performance of radiology residents allowing them to reach expert like levels of diagnostic performance in the CXR interpretation, suggesting that AI can enhance diagnostic performance, aiding radiologists in clinical practice.QJM: An International Journal of Medicine["Nada Khaled Eid Elkholey", "Annie Mohammed Nasr Mehana", "Wafaa raafat Ali"]2024-10-01T00:00:00 06701b3f41d4dc100e2df9bedfcec492a6d1666eBackground Artificial Intelligence (AI) is becoming integral to the health sector, particularly radiology, because it enhances diagnostic accuracy and optimizes patient care. This study aims to assess the awareness and acceptance of AI among radiology professionals in Saudi Arabia, identifying the educational and training needs to bridge knowledge gaps and enhance AI-related competencies. Methods This cross-sectional observational study surveyed radiology professionals across various hospitals in Saudi Arabia. Participants were recruited through multiple channels, including direct invitations, emails, social media, and professional societies. The survey comprised four sections: demographic details, perceptions of AI, knowledge about AI, and willingness to adopt AI in clinical practice. Results Out of 374 radiology professionals surveyed, 45.2% acknowledged AI’s significant impact on their field. Approximately 44% showed enthusiasm for AI adoption. However, 58.6% reported limited AI knowledge and inadequate training, with 43.6% identifying skill development and the complexity of AI educational programs as major barriers to implementation. Conclusion While radiology professionals in Saudi Arabia are generally positive about integrating AI into clinical practice, significant gaps in knowledge and training need to be addressed. Tailored educational programs are essential to fully leverage AI’s potential in improving medical imaging practices and patient care outcomes.Journal of Multidisciplinary Healthcare320Tailored educational programs are essential to fully leverage AI’s potential in improving medical imaging practices and patient care outcomes to bridge knowledge gaps and enhance AI-related competencies.Journal of Multidisciplinary Healthcare["Z. Hamd", "Amal I. Alorainy", "M. Aldhahi", "Awadia Gareeballah", "Naifah F Alsubaie", "Shahad A Alshanaiber", "Nehal S Almudayhesh", "Raneem A Alyousef", "Reem A AlNiwaider", "Lamia A Bin Moammar", "Mohamed M Abuzaid"]2024-10-01T00:00:00 77737105674b02d312c903d6eea32e3fc5d0cb38The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.Italian National Conference on Sensors840It is concluded that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.Sensors (Basel, Switzerland)["Umair Khadam", "Paul Davidsson", "Romina Spalazzese"]2024-10-01T00:00:00 10b694d0ba5bd4537de36f4d113f68627b8b2691The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods.Italian National Conference on Sensors300This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI).Sensors (Basel, Switzerland)["Sagheer Abbas", "Adnan Qaisar", "M. S. Farooq", "Muhammad Saleem", "Munir Ahmad", "Muhammad Adnan Khan"]2024-10-01T00:00:00 8e1effc0755c32222708bcbb529b8c6562413ccbArtificial intelligence (AI) is revolutionizing the development and optimization of lithium-ion batteries (LIBs), which are critical in modern technologies like energy storage systems and electric vehicles (EVs). This review explores AI-driven strategies aimed at enhancing LIB performance, safety, and longevity. AI techniques, including machine learning models likeensemble methods, support vector machines, and neural networks, have been instrumental in predictive maintenance, state of charge (SoC) and state of health (SoH) estimation, and materials discovery. These AI approaches enable more accurate predictions of battery degradation and failures, optimizing charge cycles, and improving real-time diagnostics. Furthermore, AI enhances the design of safer and more efficient battery components by accelerating materials research, thus improving LIB capacity and safety profiles. However, despite these advancements, challenges like data quality, model interpretability, and the integration of AI models into existing industrial frameworks persist. Emerging technologies such as reinforcement learning and federated learning show great promise for addressing these obstacles, enabling dynamic optimization of charge cycles and the collaborative development of more generalized AI models. As collaborative research and open data-sharing initiatives expand, AI’s transformative potential in driving more sustainable, efficient, and safer energy storage solutions will continue to grow, shaping the future of LIBs and their applications in a greener, more energy-efficient world.International Journal of Advances in Engineering and Management00International Journal of Advances in Engineering and Management["Ayogoke Felix Omojola", "Caleb Omata Ilabija", "Chuks Ifeanyi Onyeka", "Jude Ifeanyichukwu Ishiwu", "Tosin Gideon Olaleye", "Ifeoma Juliet Ozoemena", "Paul Uchechukwu Nzereogu"]2024-10-01T00:00:00 e534d1bb73eb8f624afe172db3e9f54f924cfa8bThis study explores innovation diffusion in Artificial Intelligence (AI) over the past 70 years, focusing on the impact of acquired diversity(including discipline diversity and topic diversity) of teams. We obtained 906,065 AI papers with 18,357,761 citations written by 1,316,936 authors between 1950 and 2015 from the Microsoft Academic Graph. Using citation analysis, we examine how AI innovations spread and the influence of the acquired diversity of teams on this process. We measure innovation diffusion from four aspects: innovation speed, innovation diffusion power, innovation diffusion breadth, and innovation diffusion breadth diversity. Our findings show that: (1) AI papers published before and after 2000 demonstrate distinct diffusion patterns. AI papers published before 2000 show a bimodal citation trend, while those released after 2000 present only a single peak in citations. (2) The first citation interval can be reduced by 2.35 years before 2000 and by 0.27 years after 2000 by enhancing topic diversity. (3) Both discipline diversity and topic diversity effectively promote innovation diffusion power, innovation diffusion breadth, and innovation diffusion breadth diversity in the field of AI.Proceedings of the Association for Information Science and Technology170Using citation analysis, this study examines how AI innovations spread and the influence of the acquired diversity of teams on this process, measuring innovation diffusion from four aspects: innovation speed, innovation diffusion power, innovation diffusion breadth, and innovation diffusion breadth diversity.Proceedings of the Association for Information Science and Technology["Xuli Tang", "Xin Li", "Ming Yi"]2024-10-01T00:00:00 0ee73e209585565efb52edd26bdc325d147dbac5Abstract The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not‐too‐distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied.Microbial Biotechnology2080The fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics is emphasized.Microbial Biotechnology["Antoine Danchin"]2024-10-01T00:00:00 a5a91bd44f4ecc7a58c0d7e9b2561452f8060c2cThis paper proposes an approach to improving the accuracy of predicting stock prices. The approach is built on integrating Long-Short-Term Memory (LSTM) networks, a Deep Learning (DL) technique, with Shapely Additive Explanations (SHAP), an Explainable Artificial Intelligence (XAI) technique. This integration is expected to improve predictive accuracy and model explainability. Leveraging the strengths of LSTM in capturing complex sequential patterns in financial time series big data, the model incorporates technical indicators to enhance its performance in forecasting stock movements. Deep learning is known to have a “black box” nature, so incorporating XAI techniques aims to offer detailed insights into how input features contribute to model outputs. This integration of XAI enhances the interpretability of predictions and enables users to understand the underlying rationale of the model, fostering greater trust among investors and financial professionals. The study utilized stock price data from the Yahoo Finance Website. The model processed forecasting of Google stock prices. The practical utility of this approach is demonstrated through the decision-making module, which provides actionable buy, sell, or hold recommendations, showcasing its potential in real-world investment scenarios. Our results indicate a balanced synergy between prediction accuracy and explainability, establishing a transparent and reliable AI-driven financial forecasting framework. Keywords: Deep Learning (DL), Explainable Artificial Intelligence (XAI), Long short-term memory (LSTM), Shapely Additive Explanations (SHAP), Stock Price Prediction.International Journal of Accounting and Management Sciences00The results indicate a balanced synergy between prediction accuracy and explainability, establishing a transparent and reliable AI-driven financial forecasting framework.International Journal of Accounting and Management Sciences["Nada Marey", "Ahmad A. Abu-Musa", "Mona Ganna"]2024-10-01T00:00:00 45b8332f92e32f39bf3e1c8764af64e5115e623fThe digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICTs) across different demographic groups, focusing on gender, age, and religious level. The advent of the commercial web has heightened the significance of these divides, as the first-level digital divide concerns access to the internet, while the second-level digital divide pertains to the ability to use technology proficiently. The existing literature has consistently highlighted persistent inequalities in these areas, which significantly impact the extent to which students from various backgrounds can engage with AI sport podcasts effectively. Understanding these dynamics is crucial for developing strategies to bridge the gap and ensure equitable access to digital learning resources.Behavioral Science470This study examines the disparities in access to and proficiency with Information Communication Technologies (ICTs) across different demographic groups, focusing on gender, age, and religious level.Behavioral Sciences["Y. Galily", "T. Laor", "Tal Azran"]2024-10-01T00:00:00 919c9a23dfe6892f92ac3eb3491ca831ea2f9f2fMachine learning (ML) revolutionized traditional machine fault detection and identification (FDI), as complex-structured models with well-designed unsupervised learning strategies can detect abnormal patterns from abundant data, which significantly reduces the total cost of ownership. However, their opaqueness raised human concern and intrigued the eXplainable artificial intelligence (XAI) concept. Furthermore, the development of ML-based FDI models can be improved fundamentally with machine learning operations (MLOps) guidelines, enhancing reproducibility and operational quality. This study proposes a framework for the continuous development of ML-based FDI solutions, which contains a general structure to simultaneously visualize and check the performance of the ML model while directing the resource-efficient development process. A use case is conducted on sensor data of a hydraulic system with a simple long short-term memory (LSTM) network. Proposed XAI principles and tools supported the model engineering and monitoring, while additional system optimization can be made regarding input data preparation, feature selection, and model usage. Suggested MLOps principles help developers create a minimum viable solution and involve it in a continuous improvement loop. The promising result motivates further adoption of XAI and MLOps while endorsing the generalization of modern ML-based FDI applications with the HITL concept.De Computis01A framework for the continuous development of ML-based FDI solutions is proposed, which contains a general structure to simultaneously visualize and check the performance of the ML model while directing the resource-efficient development process.Comput.["T. Tran", "Tam\u00e1s Ruppert", "J\u00e1nos Abonyi"]2024-10-02T00:00:00 0a0e09f252e991d21e54e796358cf7e941383022The advent of Artificial Intelligence systems, in particular of generative models like ChatGPT, has resulted in one more area requiring heavy computational resources which in turn consumes a lot of energy and water. By estimations, one interaction with ChatGPT for instance will take an estimate of 2.9 watt hour, which is ten times higher than the amount of energy consumed to conduct an ordinary googling task that is 0.3 watt hours. This stands as a call for action toward improving the water to energy ratio of the AI systems and therefore the recent carbon emissions. This paper explores the energy efficiency patterns of AI languages such as chatbots compared with the other means of searching the internet like Google and how the effects of the AI machines on the environment can be reduced. In this connection, green cloud computing methods have been suggested as possible solutions that can be effectively combined with the principles of clean energy use; on this list are both advanced systems for maintaining low temperatures and the optimization of AI systems. Finally, using of resources could also play a crucial part in the ultimate decrease in the adverse effects that the AI industry has on our environment.International Journal of Innovative Science and Research Technology551This paper explores the energy efficiency patterns of AI languages such as chatbots compared with the other means of searching the internet like Google and how the effects of the AI machines on the environment can be reduced.International Journal of Innovative Science and Research Technology (IJISRT)["Sumit Saklani", "Devendra Singh"]2024-10-02T00:00:00 1fc9df900d1d62ef74b8a7459f55b3e60d21b2f1Background: The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care. Methods: A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest—Dissertations and Theses to search gray literature. Results: Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development. Conclusions: AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.Nursing Reports542AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.Nursing Reports["J. Ventura-Silva", "Maria Manuela Martins", "Let\u00edcia de Lima Trindade", "Ana da Concei\u00e7\u00e3o Alves Faria", "Soraia Pereira", "S. S. Zuge", "O. Ribeiro"]2024-10-02T00:00:00 a56b1d9b96dc30fb8ba7d28168a5fb20c658a5e7The study aims to investigate how artificial intelligence (AI) influences women-led startups in Saudi Arabia, aiming to understand their unique experiences, challenges, and opportunities within the AI technology landscape. This study used a qualitative method, conducting 16 in-depth interviews with women entrepreneurs operating businesses in Saudi Arabia. The analysis was performed using thematic analysis with NVivo 12, uncovering key themes and insights. The findings reveal that cultural norms, societal expectations, limited awareness, and financial constraints are directly associated with women’s involvement in AI-driven businesses. Cultural biases emerged as impediments, underscoring the need for targeted interventions such as awareness campaigns and educational initiatives to dismantle ingrained biases and foster an environment that recognizes and celebrates the contributions of women in the tech and AI sectors. Educational programs, collaborations between academia and industry, and mentorship initiatives were identified as pivotal components to prepare women entrepreneurs to navigate the intricate landscape of AI adoption. Financial inclusion emerged as a critical pillar, advocating for equitable access to funding and resources tailored specifically for women-led AI startups. The study further emphasizes the importance of fostering supportive ecosystems that extend beyond financial aid. Creating networks for mentorship, guidance, and collaboration provides women entrepreneurs with platforms to share experiences and resources, enhancing resilience and the potential for success in the AI landscape. Acknowledgment The authors extend their appreciation to the Arab Open University for funding this work through research fund No. AOUKSA-524008.Knowledge & Performance Management411The findings reveal that cultural norms, societal expectations, limited awareness, and financial constraints are directly associated with women’s involvement in AI-driven businesses, highlighting the need for targeted interventions to dismantle ingrained biases and foster an environment that recognizes and celebrates the contributions of women in the tech and AI sectors.Knowledge and Performance Management["Sultan Alateeg", "Sura Al-Ayed"]2024-10-02T00:00:00 5f974f761a93e19d582f3750ce8fcd2d023de7b5As the landscape of education undergoes rapid transformations in the digital era, higher educational institutions are increasingly turning to Artificial Intelligence (AI) to enhance teaching, learning, and administrative processes. This abstract provides a comprehensive overview of the current state and future prospects of integrating AI in higher education.The integration of AI in higher educational institutions encompasses various facets, including personalized learning, intelligent tutoring systems, automated grading, and administrative efficiency. AI-powered educational tools leverage machine learning algorithms to analyze individual student performance, adapt content delivery, and provide personalized feedback, thereby optimizing the learning experience. This not only caters to diverse learning styles but also fosters a more inclusive and engaging educational environment. AI plays a pivotal role in automating administrative tasks, such as admissions processes, course scheduling, and resource allocation. This streamlining of administrative functions not only reduces the burden on educational institutions but also contributes to cost-effectiveness and operational efficiency. The abstract provides a snapshot of the current landscape of AI in higher educational institutions, offering insights into the transformative power of AI technologies and the challenges and opportunities that lie ahead. As educational paradigms continue to evolve, the judicious integration of AI has the potential to revolutionize teaching and learning methodologies, paving the way for a more efficient, adaptive, and inclusive higher education system.Revista de Educación y Derecho02This abstract provides a comprehensive overview of the current state and future prospects of integrating AI in higher education, offering insights into the transformative power of AI technologies and the challenges and opportunities that lie ahead.Revista de Educación y Derecho["Mildred Nuong Deri", "Amrik Singh", "Perpetual Zaazie", "David Anandene"]2024-10-02T00:00:00 6974f74a4c2077d35d6bff89ac22a3303e9559adAIMS The aim of the present study was to evaluate the performance of various artificial intelligence (AI)-powered chatbots (commercially available in Switzerland up to June 2023) in solving a theoretical cardiology board exam and to compare their accuracy with that of human cardiology fellows. METHODS For the study, a set of 88 multiple-choice cardiology exam questions was used. The participating cardiology fellows and selected chatbots were presented with these questions. The evaluation metrics included Top-1 and Top-2 accuracy, assessing the ability of chatbots and fellows to select the correct answer. RESULTS Among the cardiology fellows, all 36 participants successfully passed the exam with a median accuracy of 98% (IQR 91-99%, range from 78% to 100%). However, the performance of the chatbots varied. Only one chatbot, Jasper quality, achieved the minimum pass rate of 73% correct answers. Most chatbots demonstrated a median Top-1 accuracy of 47% (IQR 44-53%, range from 42% to 73%), while Top-2 accuracy provided a modest improvement, resulting in a median accuracy of 67% (IQR 65-72%, range from 61% to 82%). Even with this advantage, only two chatbots, Jasper quality and ChatGPT plus 4.0, would have passed the exam. Similar results were observed when picture-based questions were excluded from the dataset. CONCLUSIONS Overall, the study suggests that most current language-based chatbots have limitations in accurately solving theoretical medical board exams. In general, currently widely available chatbots fell short of achieving a passing score in a theoretical cardiology board exam. Nevertheless, a few showed promising results. Further improvements in artificial intelligence language models may lead to better performance in medical knowledge applications in the future.Swiss medical weekly241The study suggests that most current language-based chatbots have limitations in accurately solving theoretical medical board exams, and in general, currently widely available chatbots fell short of achieving a passing score in a theoretical cardiology board exam.Swiss medical weekly["Jessica Huwiler", "Luca Oechslin", "P. Biaggi", "Felix C. Tanner", "C. Wyss"]2024-10-02T00:00:00 1fba74203818d9408064a8da13359e8239e29b17Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The study explored how marketers were leveraging artificial intelligence to support their marketing strategies. The research also identified some the challenges faced by marketers related to AI implementation. Originality/value The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format. Strategic Direction10The study explored how marketers were leveraging artificial intelligence to support their marketing strategies and identified some the challenges faced by marketers related to AI implementation.Strategic Direction[]2024-10-02T00:00:00 1ebc7119079990d996ea8c7fd7bfb0bcae8ef280The Artificial Intelligence and Robot Technology Perception Scale is a measurement tool developed to assess people's perception of risk and functionality related to the said technology. 47-item draft form was applied to a total of 460 volunteer participants. Statistical analysis of the data was performed using IBM SPSS Statistics v26.0 and Lisrel 8.80 programs. According to the EFA result, the developed scale consisted of a 4-factor structure and 26 items, and as a result of Principal Component Analysis scale explained 61.10% of the total variance. The fit indices of the scale were examined with CFA and it was determined that the fit indices of 26 items and the 4-factor structure were at a sufficient level. Within the scope of reliability analysis, Cronbach Alpha internal consistency coefficients were calculated and found to be .89 for general function perception and .93 for general risk perception, and the reliability level of the scale was found to be high. In the analysis of the scale's items, it was determined that the items were highly predictive and discriminative. Upon thorough examination of all statistical data, it has been established that the developed scale is valid and reliable.Muhakeme Dergisi00Upon thorough examination of all statistical data, it has been established that the developed scale is valid and reliable and the items were highly predictive and discriminative.Muhakeme Dergisi["Meltem Toksoy \u00c7a\u011fal", "Y. Keskin"]2024-10-02T00:00:00 31f6c72ea6989cca4871508a5d3e998ad20e751fThe technological revolution accelerated by Artificial Intelligence (AI) has fundamentally changed the paradigm of leadership and human resource management. Digital leadership is now an essential competency for organizations that want to survive and thrive in the face of the challenges of the technological disruption era. This research aims to understand the role of digital leadership in the human capital transformation process, especially in the context of AI adoption by modern organizations.The post-qualitative approach allows research to capture the evolving and diverse dynamics in a digitized organizational environment. It is more open to different forms of data, both structured and unstructured, and recognizes the complexity and ambiguity in digital leadership practices. This research utilizes open-ended interviews and participatory observation, facilitated by interpretive narrative to understand the process of human capital transformation. The main results of the study show that digital leadership plays a key role in facilitating human capital transformation, with an emphasis on adaptive skills development, data-driven decision-making and closer collaboration between people and technology. This transformation is not only technical in nature but also involves changes in organizational culture and structure. This research makes an important contribution by offering a new perspective in leadership studies through a post-qualitative approach.Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan00The main results of the study show that digital leadership plays a key role in facilitating human capital transformation, with an emphasis on adaptive skills development, data-driven decision-making and closer collaboration between people and technology.Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan["Choirul Anam", "Dian Candra Dewi", "Ahmad Fairuzabadi"]2024-10-02T00:00:00 43313de5be659781802e1d6dfcec276de3ad616eArtificial intelligence (AI) and robotics have revolutionized healthcare, particularly dentistry. Their integration in dental education offers opportunities to enhance learning, diagnostics, treatment planning, and patient care. However, ethical implications must be addressed to ensure responsible and ethical integration of these technologies. This review explores AI and robotics in dental education and highlights the associated ethical considerations. These technologies provide improved learning experiences and simulations. Intelligent tutoring systems offer personalized feedback, virtual reality simulations enable practice in a safe environment, and AI algorithms aid in analysing radiographic images. Despite their potential, ethical challenges arise, including data privacy, autonomy, equity, and professional integrity. Addressing these challenges requires transparency, informed consent, bias detection, and accountability. Dental curricula should in-corporate ethics, fostering collaborations between educators and AI/robotics experts. Professional development programs should prioritize ethics training, considering emerging technologies such as AI-powered learning and diagnostic assistance. By embracing ethical considerations, AI and robotics can be integrated in dental education guided by transparency, accountability, privacy, and patient-centric care. A comprehensive understanding of the ethical dimensions is essential to harness the transformative potential of AI and robotics while upholding ethical standards in dental education. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 999-1007Bangladesh Journal of Medical Science440A comprehensive understanding of the ethical dimensions is essential to harness the transformative potential of AI and robotics while upholding ethical standards in dental education.Bangladesh Journal of Medical Science["Galvin Sim Siang Lin", "Jia Yee Foo", "Shu Meng Goh", "Mohammad Khursheed Alam"]2024-10-02T00:00:00 82000db55600a297056f4847f3362cbe2b574232In an era of"moving fast and breaking things", regulators have moved slowly to pick up the safety, bias, and legal debris left in the wake of broken Artificial Intelligence (AI) deployment. While there is much-warranted discussion about how to address the safety, bias, and legal woes of state-of-the-art AI models, rigorous and realistic mathematical frameworks to regulate AI are lacking. Our paper addresses this challenge, proposing an auction-based regulatory mechanism that provably incentivizes devices (i) to deploy compliant models and (ii) to participate in the regulation process. We formulate AI regulation as an all-pay auction where enterprises submit models for approval. The regulator enforces compliance thresholds and further rewards models exhibiting higher compliance than their peers. We derive Nash Equilibria demonstrating that rational agents will submit models exceeding the prescribed compliance threshold. Empirical results show that our regulatory auction boosts compliance rates by 20% and participation rates by 15% compared to baseline regulatory mechanisms, outperforming simpler frameworks that merely impose minimum compliance standards.arXiv.org240An auction-based regulatory mechanism that provably incentivizes devices to deploy compliant models and to participate in the regulation process is proposed, outperforming simpler frameworks that merely impose minimum compliance standards.ArXiv["Marco Bornstein", "Zora Che", "Suhas Julapalli", "Abdirisak Mohamed", "A. S. Bedi", "Furong Huang"]2024-10-02T00:00:00 f20b5c6d411aced8b79129889d9bcc10b5522b7eThis systematic review investigates the applications of artificial intelligence (AI) in supply chain demand forecasting, focusing on the performance of AI-driven models compared to traditional forecasting techniques. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted, yielding a final selection of 65 peer-reviewed articles for in-depth analysis. The review explores the advantages of AI models, particularly machine learning (ML) and deep learning (DL), in improving forecasting accuracy, scalability, and responsiveness to real-time data. It also examines AI’s applications across various industries, including retail, manufacturing, e-commerce, and logistics, where AI-driven models have significantly enhanced inventory management, production scheduling, and operational efficiency. However, the review highlights challenges related to data quality, model complexity, and high implementation costs, which limit the broader adoption of AI in demand forecasting. This study provides valuable insights into the current state of AI applications in supply chain management and suggests areas for future research, particularly in improving data management and developing more interpretable AI models to facilitate wider implementation.ACADEMIC JOURNAL ON BUSINESS ADMINISTRATION, INNOVATION &amp; SUSTAINABILITY00This systematic review investigates the applications of artificial intelligence (AI) in supply chain demand forecasting, focusing on the performance of AI-driven models compared to traditional forecasting techniques, and highlights challenges related to data quality, model complexity, and high implementation costs.ACADEMIC JOURNAL ON BUSINESS ADMINISTRATION, INNOVATION &amp; SUSTAINABILITY["Rony Saha", "Shaikh Shofiullah", "S. Faysal", "A. Happy"]2024-10-02T00:00:00 f28a216d6248ed02b0d26ad310fb58d02896927fWe conducted a bibliometrics analysis utilizing the Web of Science database, selecting 1925 articles concerning artificial intelligence (AI) in nursing. The analysis utilized the network visualization tool VOSviewer to explore global collaborations, highlighting prominent roles played by the United States, China, and Japan, as well as institutional partnerships involving Columbia University and Harvard Medical School. Keyword analysis identified prevalent themes and co-citation analysis highlighted influential journals. A notable increase in AI-related publications in nursing was observed over time, reflecting the growing interest in AI in nursing. However, high-quality clinical research and increased scientific collaboration are needed.ANS. Advances in nursing science360A bibliometrics analysis utilizing the Web of Science database, selecting 1925 articles concerning artificial intelligence (AI) in nursing, found a notable increase in AI-related publications in nursing was observed over time, reflecting the growing interest in AI in nursing.ANS. Advances in nursing science["Federica Monaco", "Vincenzo Andretta", "Umberto Bellocchio", "V. Cerrone", "Marco Cascella", "Ornella Piazza"]2024-10-02T00:00:00 d80322db953ea508be7a65e8cd60c0e242e3d66bBMC Psychology690It turns out that the use of AI affects Chinese college students’ willingness to the participation of online political practice significantly and positively, and such online political participation cognition of Chinese college students plays a mediating role, three aspects of which included as the followings on behavioral attitudes, subjective norms, and perceived behavioral control.BMC Psychology["Pu Zhao", "Shengbin Cao"]2024-10-02T00:00:00 70b00a956fb63dc0b374d7cf9e7cf62bd2cbdf86The integration of artificial intelligence (AI) in the field of science education can facilitate two key objectives: firstly, it can assist teachers in the creation of innovative teaching materials, and secondly, it can enhance the learning experience, making it more engaging and effective. A challenge arises when science teachers, MGMP IPA SMP Northern Region members of Jember, lack the requisite skills to utilize technology effectively. As a solution, teachers are provided with training in the development of AI-based media. The service activities were conducted using the Participatory Action Research (PAR) approach, with the methods employed comprising lectures, discussions, questions and answers, and practical sessions accompanied by the presentation of results. The results of the service activities demonstrated that the participants exhibited a comprehensive understanding of the advantages of AI technology, demonstrated proficiency in utilizing a range of AI-based media, and exhibited the capacity to independently develop AI-based media. Consequently, it can be concluded that through this service activity, it is possible to facilitate changes in teachers' skills, particularly in their ability to utilize technology, with a particular focus on AI-based technologies.PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat00It can be concluded that through this service activity, it is possible to facilitate changes in teachers' skills, particularly in their ability to utilize technology, with a particular focus on AI-based technologies.PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat["F. Anggraeni", "Ike Lusi Meilina"]2024-10-02T00:00:00 a79c09961a348ebbd74e22a2c18b3ec53346c24cThe credibility of research outputs from Nigerian universities raises concerns, especially among postgraduate students.in view of this, the study investigated Awareness and utilization of Artificial Intelligence (AI) Tools for Enhanced Research among Postgraduate Students in Universities in Benue State. The study adopted a descriptive survey design. A sample of 231 postgraduate students participated in the study. The convenience sampling technique was used to obtain the sample. A self-constructed questionnaire titled Awareness and Utilization of AI Tools Questionnaire was used for data collection. The research questions were answered using mean and standard deviation, and the hypotheses were tested using one-way analysis of variance (ANOVA). Major findings revealed that there is a significant difference in the mean ratings of postgraduate students based on programme type on the level of awareness of AI tools for enhance research in universities in Benue State, and there is a significant difference in the mean ratings of postgraduate students based on programme type on the extent of utilization of AI tools for enhance research in universities in Benue State. Based on the findings, it was recommended among other things that faculties/departmental heads should organize Seminars and workshops aim at intimating postgraduate students on the use of various AI tools for enhanced research.International Journal of Innovative Science and Research Technology620It was recommended among other things that faculties/departmental heads should organize Seminars and workshops aim at intimating postgraduate students on the use of various AI tools for enhanced research.International Journal of Innovative Science and Research Technology (IJISRT)["Asongo, Terkuma Stanley", "Akuse, Sesugh Stephen", "Aza, Iorember"]2024-10-02T00:00:00 1f6577760c8790e00ca016b64c8c3006ddcc4a6dObjective: Suicide is a critical global health concern. Research indicates that generative artificial intelligence (GenAI) and large language models, such as generative pretrained transformer-3 (GPT-3) and GPT-4, can evaluate suicide risk comparably to experts, yet the criteria these models use are unclear. This study explores how variations in prompts, specifically regarding past suicide attempts, gender, and age, influence the risk assessments provided by ChatGPT-3 and ChatGPT-4. Methods: Using a controlled scenario based approach, 8 vignettes were created. Both ChatGPT-3.5 and ChatGPT 4 were used to predict the likelihood of serious suicide attempts, suicide attempts, and suicidal thoughts. A univariate 3-way analysis of variance was conducted to analyze the effects of the independent variables (previous suicide attempts, gender, and age) on the dependent variables (likelihood of serious suicide attempts, suicide attempts, and suicidal thoughts). Results: Both ChatGPT-3.5 and ChatGPT-4 recognized the importance of previous suicide attempts in predicting severe suicide risks and suicidal thoughts. ChatGPT-4 also identified gender differences, associating men with a higher risk, while both models disregarded age as a risk factor. Interaction analysis revealed that ChatGPT-3.5 associated past attempts with a higher likelihood of suicidal thoughts in men, whereas ChatGPT-4 showed an increased risk for women. Conclusions: The study highlights ChatGPT-3.5 and ChatGPT-4's potential in suicide risk evaluation, emphasizing the importance of prior attempts and gender, while noting differences in their handling of interactive effects and the negligible role of age. These findings reflect the complexity of GenAI decision-making. While promising for suicide risk assessment, these models require careful application due to limitations and real-world complexities.Journal of Clinical Psychiatry00This study explores how variations in prompts, specifically regarding past suicide attempts, gender, and age, influence the risk assessments provided by ChatGPT-3 and ChatGPT-4, and highlights ChatGPT-3.5 and ChatGPT-4's potential in suicide risk evaluation.The Journal of clinical psychiatry["S. Shinan-Altman", "Z. Elyoseph", "I. Levkovich"]2024-10-02T00:00:00 2784c9e9a7dfff1b31789fcbe87e8cdb0014f0ebThis study investigated how the increasing usage of artificial intelligence (AI) in diverse business applications has affected task performance. Building on the job demands–resources model, we employed moderated mediation modeling to investigate the association between AI usage and the task performance of delivery drivers. Participants comprised 251 delivery drivers who completed online surveys over a 1-month period. Hierarchical regression analysis revealed that work engagement played a mediating role in the association between AI usage and the task performance of delivery drivers. Moreover, the mediating role of work engagement was moderated by an individual's concept of their future work self. When an individual had a stronger sense of their future work self, the mediating role of work engagement became more pronounced. This study has introduced a new mediating mechanism linking artificial intelligence with employee performance and has identified boundary conditions that increase its beneficial impact on employee performance, thus contributing theoretically to the extant literatures. The study findings can help companies improve employee engagement and performance.Social Behavior and Personality: An international journal00A new mediating mechanism linking artificial intelligence with employee performance is introduced and boundary conditions that increase its beneficial impact on employee performance are identified, thus contributing theoretically to the extant literatures.Social Behavior and Personality: an international journal["Fangguo Su", "Wenhao Liu", "Kehan Xiong", "Qi Zeng"]2024-10-02T00:00:00 db153d669e92f7e6384d59af049771a76011cf03In the context of the digital transformation of modern society, the study of the problem of introducing artificial intelligence into all spheres of life, including the sphere of economics and finance, is extremely relevant. The purpose of this article is an attempt to analyze the readiness of future economists in the school-university system to use artificial intelligence in the context of developing digital financial literacy. The authors studied the nature of the influence of artificial intelligence on the formation of basic competencies necessary in the era of digitalization of economic education, through the prism of the views of the younger generation. The importance of various aspects of the digital transformation of society, which are in the focus of attention of global cooperation organizations (UNESCO, World Bank, etc.) in the interests of sustainable development, is noted. Using Google-Forms tools, a sociological survey was conducted among students of the Financial University under the Government of the Russian Federation (Omsk branch) and students of specialized economic classes at Gymnasium 19 in the city of Omsk on the topic “The attitude of future economists to the use of artificial intelligence in the banking sector.” The results of the analysis of the obtained empirical data made it possible to state the complex and ambiguous nature of the influence of artificial intelligence on the young generation of future economists in the conditions of total digitalization. Along with the positive attitude of young people towards the prospects for the introduction of artificial intelligence, the risks of a decrease in natural intelligence against the backdrop of digitalization reaching all spheres of life have been identified. This, in turn, requires the formation of value guidelines and the development of a strategy for the continuous development of future economists in the digital society in the context of the active implementation of artificial intelligence.Standards and Monitoring in Education20The readiness of future economists in the school-university system to use artificial intelligence in the context of developing digital financial literacy is analyzed to state the complex and ambiguous nature of the influence of artificial intelligence on the young generation of future economists in the conditions of total digitalization.Standards and Monitoring in Education["N. Burmistrova", "E. Kornilceva", "M. Lebed'", "K. Shurygin"]2024-10-02T00:00:00 bc29277c5e2f73c533543697ea893a5f75dc9c17The article investigates the use of artificial intelligence (AI) in Communication Support Technologies (CST) for people with disabilities. It explores AI applications such as voice recognition, natural language processing, and machine learning to improve communication and accessibility. The research highlights significant advances in CST, showing how these emerging technologies transform communication and access to information. Additionally, the benefits and possibilities of personalized and effective communication tools are discussed, and future research areas that could further expand AI's positive impact on CST are suggested, promoting equal opportunities and social inclusion.2024 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI)410AI applications such as voice recognition, natural language processing, and machine learning to improve communication and accessibility to improve communication and accessibility for people with disabilities are explored.2024 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI)["Nelson Vladimir Yepes", "Juan David Londo\u00f1o", "Kevin S. Jimenez Pinzo"]2024-10-02T00:00:00 f1e531a9f0f12107b4300ca1483cf88ba8e80f15Application of artificial intelligence in governance and in public, economic, and political life draws the attention of many researchers from various areas of science. They study how AI affects the development of economics, law, philosophy, and medicine. They also look at how AI introduction affects various industries from an ethical and moral point of view. E.g., there is a risk that robotic systems will replace humans and labour relations will transform completely, or that goods-money relations change as marketplaces and online platforms appear. In the era of rapidly developing technology and information processes, introducing digital products and algorithms into governance and into social and economic relations is an objective necessity, so these processes gain momentum. Legal science, the legal system and law in general have to adapt to changes in society, economy, science, technology, politics, and governance. The judicial system is no exception in this situation. By multitasking and speeding up production cycles, digital and electronic products simplify and optimise production processes. At the same time, there are risks to overuse artificial intelligence and minimise the human factor. Replacing skilled staff with robots and IT systems does not always optimise processes and can result in fatal errors. Technical progress fosters the growth of fraudulent and other criminal schemes that involve information technology because it helps perpetrators to abuse law, violate personal boundaries, and constitutional and legal guarantees. The author analyses various aspects of the introduction of AI into the judicial system, and examines the reasons for and ramifications of the use of digital products and services for justice and society. The methodology of the study is based on general research ways like analysis, synthesis, generalisations and dialectical methods. Other methods include formal logical and comparative legal studies.Legal Issues in the Digital Age50The author analyses various aspects of the introduction of AI into the judicial system, and examines the reasons for and ramifications of the use of digital products and services for justice and society.Legal Issues in the Digital Age["Anna Belyakova"]2024-10-02T00:00:00 b9cac8ad195f4af8e1e50b5316af8e70c1e036f9In this editorial, Massimiliano Polastri discusses the potential of artificial intelligence in healthcare.International Journal of Therapy and Rehabilitation290International Journal of Therapy and Rehabilitation["M. Polastri"]2024-10-02T00:00:00 bf55e3d3d25b51125579c13a60d18b1d13b49520 Purpose This paper aims to explore the possible forms and characteristics of an artificial intelligence (AI) leader and discuss the potential applications of AI in political leadership and governance. Design/methodology/approach A categorization system consisting of three categories – the level of responsibility, the voting system and the bindingness of the AI’s decisions – was developed to better understand the various types of AI leaders. Additionally, to identify the main characteristics of an AI leader, a comprehensive literature review was conducted. The themes from the literature were then categorized and supplemented with additional discussions. Findings This paper identifies several potential AI leaders, including the AI President, the AI Dictator, the AI Minister and the AI Consultant. The key characteristics of an AI leader were also discussed. The primary strengths of AI lie in their intelligence and rationality, which could potentially lead our societies toward a peaceful and prosperous future. However, a significant drawback of AI is that it will always be limited by the capabilities and intentions of its programmer, whether human or AI. Practical implications Understanding the forms and characteristics of AI leaders may help policymakers and decision-makers explore the possibilities of integrating AI into political leadership and governance. Originality/value This paper contributes to the emerging field of AI in governance by exploring the forms and characteristics of AI leaders and discussing their potential applications in political leadership. Foresight440Understanding the forms and characteristics of AI leaders may help policymakers and decision-makers explore the possibilities of integrating AI into political leadership and governance.foresight["Miriam Al Lily"]2024-10-02T00:00:00 a7e8ca7f5bcfbbdf643ef33a031c8aa9782bfa5eBACKGROUND Applications of artificial intelligence (AI) in medical ultrasound have rapidly grown in recent years. Therefore, it is necessary to identify and visualize global research trends and hotspots of AI in medical ultrasound to provide guidance for further exploitation. OBJECTIVE This study aims to highlight the global research trends and hotspots of the top 100 most-cited papers related to AI in medical ultrasound by combining quantitative and visualization methods. METHODS Articles on AI in medical ultrasound were selected from the WoSCC database and ranked by citation count. After identifying the 100 most-cited papers, we conducted a quantitative and visualized analysis of bibliometric characteristics, including leading research countries, prominent institutions, key authors and journals, author clusters and collaborations, and keyword co-occurrence network analysis. RESULTS The top 100 highly cited papers from the WoSCC database were published between 1999 and 2021, with total citations ranging from 91 to 1580. The most cited article was published in IEEE Transactions on Medical Imaging. The top three most prolific countries/regions were the United States, mainland China, and the United Kingdom. The most published institutions and journals were Idaho University and IEEE Transactions on Medical Imaging. Twelve authors published more than four papers, with Suri, JS being the most productive author. The most studied topics were "ultrasound", "computer-aided diagnosis", and "segmentation". Ultrasonography of Superficial Organs was the main site that was studied the most. CONCLUSION This study provides comprehensive insights into the characteristics of AI in medical ultrasound through quantitative and visualized analysis of the most highly cited literature. It serves as a valuable reference for the development and applications of AI, fostering potential collaborations within this domain.Current medical imaging00This study provides comprehensive insights into the characteristics of AI in medical ultrasound through quantitative and visualized analysis of the most highly cited literature, serving as a valuable reference for the development and applications of AI.Current medical imaging["Jinting Xiao", "Fajuan Shen", "Weizhao Lu", "Zaiyang Yu", "Shengjie Li", "Jianlin Wu"]2024-10-02T00:00:00 a00bae239b9efbef38be43d1459a944d1ba0e62fArtificial intelligence (AI) has become a major trend in the financial industry in recent years. AI offers a range of benefits for financial institutions, including improved accuracy, efficiency, effectiveness, and compliance. In financial risk management, AI is used to analyze financial data and market trends to identify and manage risks, as well as generate accurate risk reports. This research aims to explore the role of AI in financial risk management through a literature review. The findings show that AI can enhance the speed of risk detection, improve effectiveness and efficiency in risk management processes. Theoretically, AI brings a paradigm shift in technology-based risk management and contributes to the development of financial risk management theoriesFormosa Journal of Sustainable Research70The findings show that AI can enhance the speed of risk detection, improve effectiveness and efficiency in risk management processes and contributes to the development of financial risk management theories.Formosa Journal of Sustainable Research["Yovita Sari", "Amir Indrabudiman"]2024-10-02T00:00:00 6404bb1bd6280b4d8b32d6e91b80b8571716c882This study constructs a moderating mediation model to link public sector employees’ Artificial Intelligence (AI) usage with employees’ moral norms and ethical decision-making behaviors. Based on the theory of public service motivation, this study hypothesizes that the impact of AI usage on employees’ ethical decision-making behaviors acts through the mediating effects of employees’ service motivation, employees’ moral norms, and employees’ ethical perceptions and that the relationship between AI usage and employees’ service motivation, employees’ ethical norms, and employees’ ethical perceptions is moderated by the culture of the public organization. The selected data from 417 public sector employees in China supported most of the research hypotheses. The findings show that employee service motivation, employee moral norms, and employee moral cognition mediate the relationship between AI usage and employee ethical decision-making behavior. Public organization culture moderated the relationship between AI usage and employee service motivation, as well as AI usage and employee ethics. This study reveals the complex mediating and moderating relationships between AI usage and employees’ ethical decision-making behaviors in the public sector. It provides important theoretical and practical insights for further understanding and promoting public sector employees’ ethical behaviors in the era of AI.Business Ethics and Leadership00The findings show that employee service motivation, employee moral norms, and employee moral cognition mediate the relationship between AI usage and employee ethical decision-making behavior.Business Ethics and Leadership["Xiangyu Bian", "Bin Wang"]2024-10-02T00:00:00 8b7e67a40999ad0cf66982fadf5291504720ca40Journal of European Integration204Journal of European Integration["Vicki L. Birchfield"]2024-10-02T00:00:00 a0ef7fd6208656eda6424f8bbd86f437b5f5922eJournal of Environmental Management605Journal of environmental management["Miaomiao Tao"]2024-10-02T00:00:00 5fed2ccc3cc77dbda8bd03f08713163a41d7a7daJournal of Bio-X Research03Journal of Bio-X Research["G. Jena", "Ch. Niranjan Patra", "J. Sruti", "R. Parhi", "Shibani Chand"]2024-10-02T00:00:00 ab1815067cfc6ef001c2eb3aceeef3773404f5e7Communication Teacher141Communication Teacher["P. Isotalus", "Marja Eklund", "Karoliina Karppinen"]2024-10-02T00:00:00 ce1fb3d7d8e29626d6afd113090290cf3e86cd87This paper examines the evolving structure and competition dynamics of the rapidly growing market for foundation models, focusing on large language models (LLMs). We describe the technological characteristics that shape the industry and have given rise to fierce competition among the leading players. The paper analyzes the cost structure of foundation models, emphasizing the importance of key inputs such as computational resources, data, and talent, and identifies significant economies of scale and scope that may create a tendency towards greater market concentration in the future. We explore two concerns for competition, the risk of market tipping and the implications of vertical integration, and use our analysis to inform policy remedies to maintain a competitive landscape.Social Science Research Network232The paper analyzes the cost structure of foundation models, emphasizing the importance of key inputs such as computational resources, data, and talent, and identifies significant economies of scale and scope that may create a tendency towards greater market concentration in the future.SSRN Electronic Journal["Anton Korinek", "Jai Vipra"]2024-10-02T00:00:00 4ffef706a43c6bae6e9c88ea47c33a6379e33471Journal of Clinical Oncology130Journal of clinical oncology : official journal of the American Society of Clinical Oncology["Mohamed I. Elsaid", "A. S. Meara", "Dwight H. Owen"]2024-10-02T00:00:00 f467311d50e95e73275527b56fa4dd00a9ad0e3cYouth unemployment significantly impacts the socio-economic stability and growth of developing countries, particularly Iraq. This study aims to predict the youth unemployment rate in Iraq until 2035 using advanced AI techniques such as autoregressive integrated moving averages (ARIMA) and recurrent neural networks (RNNs). We developed and tested both ARIMA and RNN models using data from 2000 to 2023. The ARIMA (1, 1, 0) model showed strong predictive ability with an R-squared value of 0.954, an RMSE of 1.350, and a MAPE of 3.113. In contrast, the RNN model, enhanced by Long Short-Term Memory (LSTM) networks, exhibited superior performance with an RMSE of 0.113, a MAPE of 0.4888, and an R-value of 0.9998. Predictions reveal a rising trend in youth unemployment, expected to increase from 33.22% in 2024 to 45.63% by 2035. These findings underscore the effectiveness of AI-driven models in providing reliable forecasts, which are crucial for policymakers to devise targeted interventions. Additionally, accurate forecasting aligns with several United Nations Sustainable Development Goals, particularly Goal 8 (Decent Work and Economic Growth), and supports efforts in poverty reduction and educational alignment with labor market needs. Future research will integrate more socio-economic variables and explore further model enhancements to improve predictive accuracy. This study is valuable for its practical application in socio-economic planning and forecasting to mitigate youth unemployment in Iraq.International Conference on System Engineering and Technology00Predictions reveal a rising trend in youth unemployment, expected to increase from 33.22% in 2024 to 45.63% by 2035, and underscore the effectiveness of AI-driven models in providing reliable forecasts, which are crucial for policymakers to devise targeted interventions.2024 14th International Conference on System Engineering and Technology (ICSET)["Marwan Abdul Hameed Ashour", "Rabab Alayham Abbas Helmi"]2024-10-02T00:00:00 e61063e876dda18a6e35ff1b71db8e332885f05eAJR. American journal of roentgenology10AJR. American journal of roentgenology["Karen Buch"]2024-10-02T00:00:00 8d816ccacc329da94c923db041cc5c52f39e5303The recent emergence of generative AI, particularly prompt-based models, and its embedding in many social domains and practices has revived the notion of co-creation and distributed agency already familiar in art practice and theory. Drawing on Actor-Network Theory (ANT) and its central notion of agency, this article explores the extent to which the collaboration between the artist and AI represents a new form of co-creation and distributed agency. It compares AI art with artistic movements such as Dada, Surrealism, Minimalism and Conceptual Art, which also challenged the notion of the autonomous artist and her agency by incorporating randomness on the one hand and rule-based systems on the other. In contrast, artistic practice with AI can be described as an iterative process of creative feedback loops, oscillating between order and disorder, (calculated) randomness and calculation, enabling a very specific kind of self-reflection and entanglement with the alienation of one’s own perspective. Furthermore, this article argues that most artistic projects that explore and work with AI are, in their own specific way, a demonstration of hybridity and entanglement, as well as the distribution of agency between the human and the non-human, and can thus be described as a network phenomenon.Arts480Most artistic projects that explore and work with AI are, in their own specific way, a demonstration of hybridity and entanglement, as well as the distribution of agency between the human and the non-human, and can thus be described as a network phenomenon.Arts["Mariya Dzhimova", "Francisco Tigre Moura"]2024-10-02T00:00:00 2aaef4ae7dde999f1eda190b23b7c82ed1c9d027Frontiers in Plant Science00Frontiers in Plant Science["Shanwen Zhang", "Chuanlei Zhang", "Ce Yang", "Bin Liu"]2024-10-02T00:00:00 19a255b096041f48871e3da0ae6b622785562217Journal of Palliative Medicine160Journal of palliative medicine["Tyler A Luonuansuu", "April R Christensen", "Sean Z Hutchinson"]2024-10-02T00:00:00 e2e1ad57ead1ca91af9d8be41ba08b87b660b48fIntroduction: implants and technological devices are being used to decode neural activity to move a prosthetic arm, control an avatar, and turn thoughts into text through an AI-based decoder. These situations are designed by Brain-computer Interface (BCI), one of the main AI-based neurotechnologies used to understand the brain and to improve people's welfare. In 2023, UNESCO already recognized its benefits but also revealed the potential ethical issues and problems, particularly with its use of non-invasive interventions. Objective:  so, this essay aims to answer the following research question: which Ethical standards can be designed and used to balance the person’s rights with technological development to prevent vulnerability situations? Method: the methods used in this work is the bibliographic research plus the hermeneutic interpretation. Results: it proposes Ethical standards for protecting the rights of the vulnerable to ensure that these rights are respected. Conclusions: there is no need for the creation of a new neurorights. Privacy and intimacy can and will deal with all the issues of neurotechnologies. However, it is necessary to improve the protection of the owner's rights through strong ethical and governance standards.Ciência da Informação Express10This essay proposes Ethical standards for protecting the rights of the vulnerable to ensure that these rights are respected and concludes that there is no need for the creation of a new neurorights.Ciência da Informação Express["S. Divino"]2024-10-02T00:00:00 165d69c2c97b14f4f40bd066dd24aebf6ad2e4a4Digital Society780Digit. Soc.["Mathuranathan Mayuravaani", "Amirthalingam Ramanan", "Maneesha Perera", "Damith A. Senanayake", "Rajith Vidanaarachchi"]2024-10-02T00:00:00 420231d0e618683161bda3329ba7631926a6572fArtificial intelligence (AI) is widely used in healthcare applications to perform various tasks. Although these models have great potential to improve the healthcare system, they have also raised significant ethical concerns, including biases that increase the risk of health disparities in medical applications. The under-representation of a specific group can lead to bias in the datasets that are being replicated in the AI models. These disadvantaged groups are disproportionately affected by bias because they may have less accurate algorithmic forecasts or underestimate the need for treatment. One solution to eliminate bias is to use synthetic samples or artificially generated data to balance datasets. Therefore, the purpose of this study is to review and evaluate how synthetic data can be generated and used to mitigate biases, specifically focusing on the medical domain. We explored high-quality peer-reviewed articles that were focused on synthetic data generation to eliminate bias. These studies were selected based on our defined inclusion criteria and exclusion criteria and the quality of the content. The findings reveal that generated synthetic data can help improve accuracy, precision, and fairness. However, the effectiveness of synthetic data is closely dependent on the quality of the data generation process and the initial datasets used. The study also highlights the need for continuous improvement in synthetic data generation techniques and the importance of evaluation metrics for fairness in AI models.Electronics04The findings reveal that generated synthetic data can help improve accuracy, precision, and fairness, however, the effectiveness of synthetic data is closely dependent on the quality of the data generation process and the initial datasets used.Electronics["Mohamed Ashik Shahul Hameed", "Asifa Mehmood Qureshi", "Abhishek Kaushik"]2024-10-02T00:00:00 c1e8eee242b3c1336ff46a438d669da73a132a13Bias could be defined as the tendency to be in favor or against a person or a group, thus promoting unfairness. In computer science, bias is called algorithmic or artificial intelligence (i.e., AI) and can be described as the tendency to showcase recurrent errors in a computer system, which result in “unfair” outcomes. Bias in the “outside world” and algorithmic bias are interconnected since many types of algorithmic bias originate from external factors. The enormous variety of different types of AI biases that have been identified in diverse domains highlights the need for classifying the said types of AI bias and providing a detailed overview of ways to identify and mitigate them. The different types of algorithmic bias that exist could be divided into categories based on the origin of the bias, since bias can occur during the different stages of the Machine Learning (i.e., ML) lifecycle. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches that have been proposed to identify and mitigate them. This study not only provides ready-to-use algorithms for identifying and mitigating bias, but also enhances the empirical knowledge of ML engineers to identify bias based on the similarity that their use cases have to other approaches that are presented in this manuscript. Based on the findings of this study, it is observed that some types of AI bias are better covered in the literature, both in terms of identification and mitigation, whilst others need to be studied more. The overall contribution of this research work is to provide a useful guideline for the identification and mitigation of bias that can be utilized by ML engineers and everyone who is interested in developing, evaluating and/or utilizing ML models.Applied Sciences1603This study provides ready-to-use algorithms for identifying and mitigating bias, but also enhances the empirical knowledge of ML engineers to identify bias based on the similarity that their use cases have to other approaches that are presented in this manuscript.Applied Sciences["Konstantinos Mavrogiorgos", "Athanasios Kiourtis", "Argyro Mavrogiorgou", "A. Menychtas", "D. Kyriazis"]2024-10-02T00:00:00 f5e61f35077883184862e107c0b6f6b9743d9f81This piece highlights the tremendous potential of Artificial Intelligence (AI) in the field of dermatology and its suitability in revolutionising patient care. The integration of AI technologies into dermatological practices has the power to significantly improve diagnostics, treatment decisions, and overall patient outcomes. AI algorithms have shown remarkable proficiency in analysing dermatological images with impressive accuracy, such as skin lesions, rashes and moles. By leveraging deep learning and computer vision techniques, AI models can recognise patterns, features, and characteristics of various skin conditions, thereby aiding in accurate diagnosis and assists dermatologists in formulating personalised treatment plans tailored to individual patients.Journal of Aesthetic Nursing402Journal of Aesthetic Nursing["D. Haykal"]2024-10-02T00:00:00 26ba413fece9a4115e7c60eb69084e6a71bafbaeThis research focuses on the development of a prototype virtual assistant system integrated with artificial intelligence (AI) using the Rational Unified Process (RUP) method. The system is designed to improve user flexibility and efficiency by allowing interaction through voice commands, removing the need for traditional input devices. Python was selected as the primary programming language due to its robust capabilities in handling AI-driven applications. The system utilizes Whisper API for speech recognition, enabling the virtual assistant to accurately interpret voice inputs. Additionally, the integration of Chat GPT API allows the assistant to process and generate responses in a natural, context-aware manner. The combination of these technologies is expected to enhance user experience by making the system more intuitive and seamless, applicable to both daily tasks and complex business environments. The RUP method, structured into phases such as inception, elaboration, construction, and transition, was applied to ensure that the development process was iterative, flexible, and aligned with user needs. The results indicate that the integration of Whisper API with Chat GPT API significantly improves the quality and accuracy of voice-based interaction, streamlining system operation while minimizing the need for complex graphical interfaces. This research demonstrates the potential of voice-driven AI systems in increasing overall operational efficiency.Journal of Engineering and Science Application01The results indicate that the integration of Whisper API with Chat GPT API significantly improves the quality and accuracy of voice-based interaction, streamlining system operation while minimizing the need for complex graphical interfaces.Journal of Engineering and Science Application["Rifky Lana Rahardian", "I. P. G. A. Sudiatmika", "Komang Hari Santhi Dewi"]2024-10-02T00:00:00 1c724b5f94d04e5d979d0a2e3440b54553234554The research and development phase is a crucial initial step in any process leading to innovation, and it aligns with the long-term vision of public and private sector strategies. The research questions in this study are as follows: (1) To determine the resulting interrelationship between R&D investments and GDP using regression analysis; (2) To investigate the amount of economic value added (EVA) that Georgia must create with the increase of R&D investments in a certain period in order to move from the group of countries with a BB sovereign credit rating to the group of countries with a BBB-investment credit rating. World Bank data from 2014-2022 was used. Using regression analysis, the impact of R&D investments (by increasing the share of artificial intelligence in R&D to 30-35%) on the country’s GDP was determined. The regression analysis between R&D and GDP generated the following results: (1) the regression coefficient is 7.02502%, indicating that a 10% increase in R&D will result in a 0.70% increase in GDP; (2) the coefficient of determination is 81.1%, which demonstrates that 81.1% of the change in GDP is explained by the change in R&D and (3) the correlation coefficient is 90.1%, indicating a strong positive relationship; (4) the P-value is 0.03492, which suggests that the relationship between these two variables is significant. The calculation of a country’s EVA (as a powerful tool that evaluates a country’s economic growth and development) incorporates three key factors: the country’s total wealth, net operating profit after tax and the Central Bank rate. The EVA model of Georgia was calculated and then analysed in order to determine the additional value that Georgia would need to generate in order to be included in the BBB-investment credit rating group. In order to determine this, the economic indicators of the countries on the BBB scale (Greece, Hungary, India, Kazakhstan) were analysed, and their average weighted index was calculated. This index is characteristically relevant according to the criteria set out by S&P, Fitch, and Moody’s. The following economic indicators were considered: nominal GDP, GDP per capita, and real GDP growth. External indicators included: current account balance/GDP, gross external financing needs/CARs plus usable reserves. Fiscal indicators were: general government balance/GDP, debt/GDP, and net debt/GDP. Finally, the consumer prices index growth was considered as a monetary indicator. According to EVA model calculations, in order to achieve Georgia’s BBB credit rating in the next 9 years, investments of $61.7 billion are required. Using EVA and other economic indicators in the decision-making process will contribute to a more in-depth analysis of the current economic processes in the country and increase efficiency.SocioEconomic Challenges301SocioEconomic Challenges["Davit Gondauri", "E. Mikautadze", "Nino Enukidze", "M. Batiashvili"]2024-10-02T00:00:00 1371b6d9c9b7f8e32041782ba8aebf626847376dBackground: Artificial Intelligence (AI) is increasingly integrated into educational systems, making it indispensable. Chat-GPT, a prominent AI tool, is anticipated to shape the future of education and learning.   Objective: To analyze the impact of Chat-GPT on students' learning experiences.   Methods: Design: Development of a semi-structured interview tool, validated by experts.   Data Gathering: Content analysis, akin to a qualitative approach.   Results: Respondents' perceptions of Chat-GPT in education highlighted:   Merits: Identification of elite benefits.   Applications: Potential areas of application and exploitation.   Conclusion: Chat-GPT shows significant potential to enhance the instructor-student connection and create effective teaching environments. When combined with pedagogical strategies and information/communication technologies (ICTs), it supports knowledge construction and skill acquisition.Revista de Gestão Social e Ambiental101Chat-GPT shows significant potential to enhance the instructor-student connection and create effective teaching environments and when combined with pedagogical strategies and information/communication technologies (ICTs), it supports knowledge construction and skill acquisition.Revista de Gestão Social e Ambiental["Md. Mostafa Rashel", "Sahadat Khandakar", "Kaosar Hossain", "A. Shahid", "Takako Kawabata", "Waseema Batool", "Arslan Asad Chaudhary", "Anh Quang Nguyen", "Tariq Rafique"]2024-10-02T00:00:00 3fe7899abd995e664c75a2bb3fb9ec607c188d98This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts power generation rates based on environmental conditions, while the EO component optimizes the LSTM model’s hyper-parameters through training. The XAI-based Local Interpretable and Model-independent Explanation (LIME) is adapted to identify the critical factors that influence the accuracy of the power generation forecasts model in smart solar systems. The effectiveness of the proposed X-LSTM-EO model is evaluated through the use of five metrics; R-squared (R2), root mean square error (RMSE), coefficient of variation (COV), mean absolute error (MAE), and efficiency coefficient (EC). The proposed model gains values 0.99, 0.46, 0.35, 0.229, and 0.95, for R2, RMSE, COV, MAE, and EC respectively. The results of this paper improve the performance of the original model’s conventional LSTM, where the improvement rate is; 148%, 21%, 27%, 20%, 134% for R2, RMSE, COV, MAE, and EC respectively. The performance of LSTM is compared with other machine learning algorithm such as Decision tree (DT), Linear regression (LR) and Gradient Boosting. It was shown that the LSTM model worked better than DT and LR when the results were compared. Additionally, the PSO optimizer was employed instead of the EO optimizer to validate the outcomes, which further demonstrated the efficacy of the EO optimizer. The experimental results and simulations demonstrate that the proposed model can accurately estimate PV power generation in response to abrupt changes in power generation patterns. Moreover, the proposed model might assist in optimizing the operations of photovoltaic power units. The proposed model is implemented utilizing TensorFlow and Keras within the Google Collab environment.PLoS ONE501The experimental results and simulations demonstrate that the proposed model can accurately estimate PV power generation in response to abrupt changes in power generation patterns and might assist in optimizing the operations of photovoltaic power units.PLOS ONE["R. M. Rizk-Allah", "Lobna M. Abouelmagd", "Ashraf Darwish", "V\u00e1clav Sn\u00e1\u0161el", "A. Hassanien"]2024-10-02T00:00:00 1a1940562a936376d1147cf400764dcdff5e7e01Selecting the most appropriate mining method to recover mineral resources is a critical decision-making task in mining project development. This study introduces an artificial intelligence-based mining methods recommendation system (AI-MMRS) for the pre-selection of underground mining methods. The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. The results reveal that the memory-based CF approach achieves an accuracy ranging from 81.8% to 87.9%. Among the classification algorithms, artificial neural network (ANN) and k-nearest neighbors (KNN) classifiers perform the best, with accuracy levels of 66.7% and 63.6%, respectively. These findings demonstrate the effectiveness and viability of both approaches in MMS, acknowledging their limitations and the need for continuous training and optimization. The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.Mining421The proposed AI-MMRS for the pre-selection stage supplemented by the direct involvement of mining professionals in later stages of MMS, has the potential to significantly aid in the MMS decision-making, providing data-driven and experience-based recommendations following the ongoing evolution of mining practices.Mining["E. Manjate", "Natsuo Okada", "Yoko Ohtomo", "Tsuyoshi Adachi", "Bernardo Miguel Bene", "Takahiko Arima", "Youhei Kawamura"]2024-10-02T00:00:00 94e3582a955ba5a111526bda32ecdede354acf2bAI and Ethics1401A human-AI deal is sketched, balancing the growing cognitive abilities of artificial agents, and the possibility to relieve them from suffering of negative affects, with a protection for the rights of humans.AI and Ethics["Federico Benitez", "Cyriel M. A. Pennartz", "Walter Senn"]2024-10-02T00:00:00 712171098cc0bf2280fdf0cec1d803d6db05e18fGenerative artificial intelligence (AI) has opened the possibility of automated content production, including coding in software development, which can significantly influence the participation and performance of software developers. To explore this impact, we investigate the role of GitHub Copilot, a generative AI pair programmer, on software development in open-source community, where multiple developers voluntarily collaborate on software projects. Using GitHub's dataset for open-source repositories and a generalized synthetic control method, we find that Copilot significantly enhances project-level productivity by 6.5%. Delving deeper, we dissect the key mechanisms driving this improvement. Our findings reveal a 5.5% increase in individual productivity and a 5.4% increase in participation. However, this is accompanied with a 41.6% increase in integration time, potentially due to higher coordination costs. Interestingly, we also observe the differential effects among developers. We discover that core developers achieve greater project-level productivity gains from using Copilot, benefiting more in terms of individual productivity and participation compared to peripheral developers, plausibly due to their deeper familiarity with software projects. We also find that the increase in project-level productivity is accompanied with no change in code quality. We conclude that AI pair programmers bring benefits to developers to automate and augment their code, but human developers' knowledge of software projects can enhance the benefits. In summary, our research underscores the role of AI pair programmers in impacting project-level productivity within the open-source community and suggests potential implications for the structure of open-source software projects.Social Science Research Network710It is found that core developers achieve greater project-level productivity gains from using Copilot, benefiting more in terms of individual productivity and participation compared to peripheral developers, plausibly due to their deeper familiarity with software projects.ArXiv["Fangchen Song", "Ashish Agarwal", "Wen Wen"]2024-10-02T00:00:00 6c61c94efc5023473240998339e2279e7aeae40bBACKGROUND Using Artificial Intelligence (AI) for neonatal pain assessment has great potential, but its effectiveness depends on accurate data labeling. Therefore, precise and reliable neonatal pain datasets are essential for managing neonatal pain. PURPOSE To develop and validate a comprehensive multimodal dataset with accurately labeled clinical data, enhancing AI algorithms for neonatal pain assessment. METHODS An assessment team randomly selected healthy neonates for assessment using the Neonatal Pain, Agitation, and Sedation Scale. During painful procedures, 2 cameras recorded neonates' pain reactions on site. After 2 weeks, assessors labeled the processed pain data on the EasyDL platform in a single-anonymized setting. The pain scores from the 4 single-modal data types were compared to the total pain scores derived from multimodal data. The On-Site Neonatal Pain Assessment completed using paper quality scales is referred to as OS-NPA, while the modality-data neonatal pain labeling performed using labeling software is MD-NPL. RESULTS The intraclass correlation coefficient among the 4 single-modal groups ranged from 0.938 to 0.969. The overall pain intraclass correlation coefficient score was 0.99, with a Kappa statistic for pain grade agreement of 0.899. The goodness-of-fit for the linear regression models comparing the OS-NPA and MD-NPL for each assessor was greater than 0.96. IMPLICATIONS FOR PRACTICE AND RESEARCH MD-NPL represents a productive alternative to OS-NPA for neonatal pain assessment, and the validity of the data labels within the Multimodality Dataset for Neonatal Acute Pain has been validating. These findings offer reliable validation for algorithms designed to assess neonatal pain.Advances in Neonatal Care280MD-NPL represents a productive alternative to OS-NPA for neonatal pain assessment, and the validity of the data labels within the Multimodality Dataset for Neonatal Acute Pain has been validating, offering reliable validation for algorithms designed to assess neonatal pain.Advances in neonatal care : official journal of the National Association of Neonatal Nurses["Nannan Yang", "Zhuang Ying", "Huiping Jiang", "Yuanyuan Fang", "Jing Li", "Li Zhu", "Wanyuan Zhao", "Tingqi Shi"]2024-10-02T00:00:00 f0fd98df3eb493c0dbcd5160a25edd3701e5f1f7Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management, enabling groundbreaking advancements across diverse industries. This article delves into the foundational concepts and cutting-edge developments in these fields, with a particular focus on large language models (LLMs) and their role in natural language processing, multimodal reasoning, and autonomous decision-making. Highlighting tools such as ChatGPT, Claude, and Gemini, the discussion explores their applications in data analysis, model design, and optimization. The integration of advanced algorithms like neural networks, reinforcement learning, and generative models has enhanced the capabilities of AI systems to process, visualize, and interpret complex datasets. Additionally, the emergence of technologies like edge computing and automated machine learning (AutoML) democratizes access to AI, empowering users across skill levels to engage with intelligent systems. This work also underscores the importance of ethical considerations, transparency, and fairness in the deployment of AI technologies, paving the way for responsible innovation. Through practical insights into hardware configurations, software environments, and real-world applications, this article serves as a comprehensive resource for researchers and practitioners. By bridging theoretical underpinnings with actionable strategies, it showcases the potential of AI and LLMs to revolutionize big data management and drive meaningful advancements across domains such as healthcare, finance, and autonomous systems.arXiv.org00The potential of AI and LLMs to revolutionize big data management and drive meaningful advancements across domains such as healthcare, finance, and autonomous systems is showcased.ArXiv["Pohsun Feng", "Ziqian Bi", "Yizhu Wen", "Xuanhe Pan", "Benji Peng", "Ming Liu", "Jiawei Xu", "Keyu Chen", "Junyu Liu", "Caitlyn Heqi Yin", "Sen Zhang", "Jinlang Wang", "Qian Niu", "Ming Li", "Tianyang Wang"]2024-10-02T00:00:00 10af71e58bd727b84fd8d6927b65871e845197beThe objective of this study was to assess the correlation between Artificial Intelligence (AI) and the performance of Supply Chain Management (SCMP) while also investigating the mediating influence of AI Capability (AICAP). The hypotheses examined were as follows: H1, the beneficial influence of AI on SCMP; and H2, the mediating role of AICAP in the link between AI and SCMP. The research was underpinned by a positivist philosophy which supported in being able to carry out an objective analysis of AICAP's mediation between AI and SCMP. Regression analysis with a deductive way was used to investigate the relationship between independent and dependent variables. The ability to quantitatively analyze the data supported a comprehensive examination of hypotheses originally posited. Findings indicated that there is a strong positive association (r = 0.816) between AI and SCMP, with R2 of the variance in SCMP accounted by AI as 66.5%, therefore, continued support for H1 was established. Moreover, the mediating role of AICAP is significant (total effect = 0.8239 and mediation effect = 0.1862, implicating H2 as well. These results verify the importance of AI for SCMP improvement.Journal of Ecohumanism701Assessment of the correlation between Artificial Intelligence (AI) and the performance of Supply Chain Management (SCMP) while also investigating the mediating influence of AI Capability (AICAP) indicated that there is a strong positive association between AI and SCMP, and the mediating role of AICAP is significant.Journal of Ecohumanism["Balouza Mohamad"]2024-10-03T00:00:00 c6e219434e3b475d6e665b6e1b2a7b4f34c20738SD Negeri 140 Seluma has a relatively large number of human resources, both teachers and students. However, this is also a challenge for partner schools where some of the problems that are often faced are the lack of knowledge and competence of teachers in providing digital teaching materials to facilitate differentiated learning, which impacts the formation of students' character optimally. This training and mentoring is an effort to improve teacher knowledge and competence in providing teaching materials. This activity involved 30 teachers through two methods, namely: socialization, training and mentoring (the practice of making AI-based learning videos). The activity results showed that all participants experienced an increase in understanding related to the concept of digital teaching materials and increased competence in making AI-based learning videos. This is evidenced by the products that have been produced by the training participants. Keywords: artificial intelligence; digital teaching materials; training;  accompaniment SD Negeri 140 Seluma memiliki SDM dengan jumlah yang relatif banyak baik guru maupun peserta didik. Namun, hal ini sekaligus menjadi tantangan bagi sekolah SD Negeri 140 Seluma  dimana beberapa permasalahan yang seringkali dihadapi yaitu masih minimnya pengetahuan dan kompetensi guru dalam menyediakan bahan ajar digital untuk memfasilitasi pembelajaran berdiferensiasi, sehingga berdampak pada pembentukan karakter peserta didik secara optimal. Pelatihan dan pendampingan ini merupakan upaya yang dilakukan untuk meningkatkan pengetahuan dan kompetensi guru dalam menyediakan bahan ajar. Kegiatan ini melibatkan 30 orang guru melalui dua metode yaitu: sosialisasi, pelatihan dan pendampingan (praktik pembuatan video pembelajaran berbasis AI). Hasil kegiatan menunjukkan peningkatan pemahaman dan keterampilan peserta dalam memproduksi video pembelajaran berbasis AI, yang dibuktikan melalui produk video yang dihasilkan.  Kata kunci: artificial Intelligence; bahan ajar digital; pelatihan; pendampinganJurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal81Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal["Jayanti Syahfitri", "Muntahanah Muntahanah", "Irwandi Irwandi"]2024-10-03T00:00:00 f6278d9edefd3e9c16ad73670a8d3005dc52d7bdObjectives: This study aimed to assess the effectiveness of artificial intelligence on education, focusing on how it can be leveraged to personalised learning experiences tailored to the specific needs of students. Study Design: A comprehensive literature review was conducted, alongside an analysis of psychological factors that influence student motivation.Place and Duration of the Study: Relevant academic sources and case studies were reviewed over the duration of six months to gather insights on AI applications in education.Sample: The sample consisted of the scientific thought and scientists that have integrated AI technologies into their curricula.Methodology: A qualitative analysis from literature was utilised in this research to evaluate AI tools' effectiveness in enhancing personalised learning outcomes.Results: The findings indicate that ChatGPT is currently the most widely utilised AI tool in educational contexts, demonstrating a significant capacity to personalised learning by adapting it to individual psychological profiles and learning paces.Conclusion: The integration of AI technologies in education presents unprecedented opportunities for curriculum personalisation and student engagement. However, it also necessitates careful consideration of ethical issues, especially related to learner data privacy, to ensure responsible implementationData and Metadata341The findings indicate that ChatGPT is currently the most widely utilised AI tool in educational contexts, demonstrating a significant capacity to personalised learning by adapting it to individual psychological profiles and learning paces.Data and Metadata["Viktoriya Mykhaylenko", "Nadiia Safonova", "Ruslan Ilchenko", "Anton Ivashchuk", "Ivanna Babik"]2024-10-03T00:00:00 b159968b29e7e2c31e8bcd59b0e36fde15c660a9Euro-Mediterranean Journal for Environmental Integration1831Euro-Mediterranean Journal for Environmental Integration["Stefano Cairone", "Shadi W. Hasan", "Kwang-Ho Choo", "Chi-Wang Li", "A. Zorpas", "Mohamed Ksibi", "T. Zarra", "V. Belgiorno", "Vincenzo Naddeo"]2024-10-03T00:00:00 cbdbbe22ed60de1a58ea14cc0e6cd4daa24ced1cGenerative artificial intelligence changes the paradigm of natural language processing research, sets off a new trend of research in computational humanities and computational social sciences, and provides unique perspectives on digital intelligence-enabled ancient book revitalization and intelligent applications. The article explores the role of multimodal large models in image processing and OCR of ancient books. I am discussing and exemplifying how to use Large Language Models for intelligent information processing of ancient texts. Explore combining prompt engineering, retrieval-enhanced generation (RAG), supervised fine-tuning, LangChain, and other techniques to improve performance in ancient text mining and applications. It also looks forward to the broad prospect of intelligent agent technology combined with the Large Language Model in the innovative application of ancient book revitalization. The research focuses on digitizing ancient books, intelligent processing of ancient texts, and intelligent application of ancient book revitalization. It demonstrates the feasibility, advancement, and creativity of the application of generative artificial intelligence and its derivative technologies in the field of computational humanities, especially in the field of ancient book preservation, to provide intelligent solutions for the dissemination of traditional thought and culture, from the perspective of the whole process of the technology of digital humanities and computational humanities research. The article also gives examples of the intelligent application of AI in the restoration of ancient books and the annotation of ancient texts. Although large language models demonstrate transformative potential in advancing the field of ancient text research toward intelligent analysis, there remain certain limitations. This article points out their shortcomings in areas such as knowledge completion for ancient texts, understanding emotions and cultural nuances, as well as ethical and accountability issues. It emphasizes the need for a more balanced perspective on the role that generative artificial intelligence plays in the exploration and utilization of cultural heritage.ACM Journal on Computing and Cultural Heritage81The article explores the role of multimodal large models in image processing and OCR of ancient books, and demonstrates the feasibility, advancement, and creativity of the application of generative artificial intelligence and its derivative technologies in the field of computational humanities.ACM Journal on Computing and Cultural Heritage["Jiangfeng Liu", "Xueliang Ma", "Lanyu Wang", "Lei Pei"]2024-10-03T00:00:00 520c1e79972658c3ba7df0093221936cbb5e7e54The 7th International Workshop on Software-intensive Business (IWSiB'24), held on April 16, 2024, in Lisbon, Portugal, as part of the 46th International Conference on Software Engineering (ICSE 2024), brought together diverse research communities to address the pressing challenges faced by software-producing organizations. These challenges, driven by changing demands, evolving technologies, and dynamic environments, require interdisciplinary collaboration across software engineering and business research. The workshop focused on the theme "Software Business in the Era of Generative Artificial Intelligence," highlighting the transformative impact of GenAI and large language models on software businesses. The event featured a keynote by Jan Bosch and showcased 11 accepted papers on a diverse range of topics. Participants engaged in interactive discussions, group activities, and ideation sessions aimed at identifying future challenges and solutions for software-intensive businesses, fostering ongoing community development and cross-disciplinary synergy.ACM SIGSOFT Softw. Eng. Notes140ACM SIGSOFT Softw. Eng. Notes["Jorge Melegati", "Dimitri Petrik", "Andrey Saltan"]2024-10-03T00:00:00 e0b18a8a7728fefdd9c981767ba0e50ab9000bf6This article presents a thorough literature review on the use of technology in artificial intelligence technology within the maritime industry. The aim is to analyse existing research and identify crucial themes, theoretical frameworks, methodologies, and areas of research gaps in this field. Specific keywords related to artificial intelligence and the maritime industry were used to search the Web of Science (WoS) and Scopus databases. A list of the 34 most frequently cited articles was compiled and analysed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a systematic and rigorous approach to selecting relevant manuscripts. The analysis revealed three key themes: (1) Role of Artificial Intelligence technology in the maritime industry, (2) challenges in adapting Artificial Intelligence technology in the maritime industry and (3) digitalisation and smart shipyards. Additionally, navigational safety and environmental impact were identified as important considerations in this field. Integrating artificial intelligence into maritime organisations able to develop immersive and impactful syllabus that simulate advanced technology. This allows maritime practitioners to develop related skills and better understand advanced technology. Furthermore, this integration fosters growth and success in the maritime industry, preparing for future development.Journal of Advanced Research in Applied Sciences and Engineering Technology410Journal of Advanced Research in Applied Sciences and Engineering Technology["Mohd Shafeeq Mohd Tahir", "Ramlee Mustapha", "Mohd Ekram Al Hafis Hashim", "Norsalwati Mohd Razalli", "Arthit Kleebrung"]2024-10-03T00:00:00 f3d35347c5f6c07dc1def721ccd30dd01e96802bThe rapid development of artificial intelligence (AI) this decade has created brand new opportunities for companies to improve and rethink their business processes. When adopting AI solutions such as ChatGPT, it is important that current IT governance practices are adapted for this AI revolution. This paper is a literature review that overviews current AI governance trends and future opportunities. Based on this review, the authors have put forth their recommendations for successfully governing AI in business use cases.International Scientific Conference Information Technology and Management Science Riga Technical University280This paper is a literature review that overviews current AI governance trends and future opportunities and put forth their recommendations for successfully governing AI in business use cases.2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)["Kaspars \u0100beln\u012bca", "Guntis Petrovskis", "Agris Vindecs", "A. Rom\u0101novs"]2024-10-03T00:00:00 f7768b82f4341e0bbf57b4fa28af3aa04a946624BACKGROUND Otoplasty is a cosmetic surgery that is performed to alter the size, shape, or position of the ear by using permanent stitches. Its main purpose is to correct protruding ears, a condition known as prominauris. After the surgery, it is crucial to provide proper care to ensure successful recovery. However, obtaining timely medical advice can be difficult, especially in remote areas or places with limited resources. To address this issue, incorporating advanced artificial intelligence (AI) tools like Chat Generative Pre-trained Transformer (ChatGPT)-4 into postsurgical care could help fill the gap in patient education and support. AIM This study aims to assess whether ChatGPT4 can be a reliable, accurate, and effective method for answering the most common patient questions and concerns post-otoplasty. The main objective was to assess the AI chatbot's capacity to deliver precise, concise, and pertinent information, especially in situations where health care professionals are limited in availability. MATERIALS AND METHODS In this study, over 50 patients were engaged, and ChatGPT4 was employed to present the same 5 common postoperative questions post-otoplasty surgery care. The AI chatbot's responses were analyzed for accuracy, response time, clarity, and relevance. RESULTS The chatbot could potentially provide timely assistance, answer questions, and address concerns related to postsurgical care in otoplasty. The responses exhibited a perfect accuracy rate of 100%, closely corresponding to existing medical guidelines. CONCLUSION This study explores the potential of AI-driven solutions to enhance patient education and support, especially in areas where access to health care professionals may be limited. However, professional medical advice is crucial in postoperative care and cannot be replaced by ChatGPT-4. By leveraging AI tools like chatbots, individuals in remote or resource-limited settings can potentially receive valuable information and guidance, contributing to successful rehabilitation and overall health care outcomes. Ethical considerations around the use of AI in health care must also be carefully addressed to ensure patient privacy, data security, and appropriate clinical oversight.The Journal of craniofacial surgery (Print)90Whether ChatGPT4 can be a reliable, accurate, and effective method for answering the most common patient questions and concerns post-otoplasty and the AI chatbot's capacity to deliver precise, concise, and pertinent information is assessed.The Journal of craniofacial surgery["Sahar A Albehairi", "Ohoud M Alsahli", "Leen F Bander", "T. Albilasi", "Eyad S Aljardan"]2024-10-03T00:00:00 e19926a43000f64e6640bd93782e7144c3408533Artificial Intelligence (AI) is rapidly transforming healthcare, with significant implications for patient care. This article critically analyzes AI's role in improving healthcare delivery, focusing on diagnostic accuracy, personalized treatments, and system efficiency. It highlights key benefits such as enhanced decision-making, reduced human error, and the potential for better patient outcomes through AI-driven tools like predictive analytics and robotic surgery. However, the article also addresses challenges including ethical concerns, algorithmic bias, data privacy issues, and the need for clear regulations and accountability structures. The study explores how AI affects healthcare professionals, reshaping their roles and requiring new skill sets. Through case studies, the article illustrates both the successes and limitations of AI in clinical applications. Ultimately, this critical analysis emphasizes that while AI holds promise in improving patient care, responsible implementation is necessary to address ethical, legal, and technical challenges.Journal of Ecohumanism150Journal of Ecohumanism["Mohammed Faisal Mashabab", "Mahdi Saleh Al Sheniff", "Mohammed Saleh Alsharief", "Mutared Ali Ali Al Yami", "Hamad Naje Mana Matnah", "Abduallah Mohammed Al Abbas", "Hamad Yahya Mohsen Al Shenief", "Dhaffer Ali Ali Al Al Abbas", "Fahad Mana Saleh Abu Raseen", "Ali Hamad Ali Al Kulayb"]2024-10-03T00:00:00 d97f11f40287f524576d9719fec671c0b63951fcLiver tumours are a serious and life-threatening condition, which, if left untreated, can lead to fatal outcomes. Given the various challenges in early disease prediction, chronic impacts, and unbalanced diagnostic information, this study focuses on creating a comprehensive analysis of liver tumour segmentation techniques. It explores the roles of machine learning, deep learning, and neural networks in diversifying early detection frameworks. The study includes an examination of feature extraction techniques used in existing frameworks, emphasizing high-quality annotations. It also addresses the limitations of current datasets and the impact of real-time databases. Detailed insights into liver tumour segmentation methods are provided, highlighting the potential of artificial intelligence frameworks for future advancements. The primary goal of this system is to develop an efficient and robust framework for detecting, segmenting, and analysing liver tumours, with a key emphasis on early detection.2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)260The roles of machine learning, deep learning, and neural networks in diversifying early detection frameworks in liver tumour segmentation techniques are explored, highlighting the potential of artificial intelligence frameworks for future advancements.2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)["V. Varalakshmi", "U. Hemamalini"]2024-10-03T00:00:00 4bdc6ca3f3209856ac1350d14159c3d1e3ea7f6b*This paper of research is sponsored and funded by Kuwait University, Research Project No. (HC03/23) Idea of the Research is to address the topic of using artificial intelligence in the medical field from the Islamic point of view. Its importance lies in focusing on the way Islamic Law keeps pace with latest issues that comply with the provisions and objectives of Islamic Law. Hence, the problematic deals with jurisprudential rulings relevant to the use of AI in the medical field and providing patients’ confidential data to AI software.  This research aims to explain some jurisprudential rulings on using AI. Accordingly, extrapolatory, analytical, and comparative methods have been used. The main findings of the research are the following: learning AI is a collective duty provided that it agrees with the Objectives of Islamic Law that firmly stipulates non-disclosure of patients’ medical data except in a few cases to bring benefit or prevent harm. On this basis, practitioners can provide AI software with confidential patients’ information under certain conditions such as achieving public interest, obtaining permission and a written consent from the patient, revealing true information and preventing any kind of harm. In this regard, the researcher urges to keep pace with the latest AI developments and encourages more jurisprudential studies to promote research on emerging medical issues.مجلة الشريعة والدراسات الإسلامية00The main findings of the research are the following: learning AI is a collective duty provided that it agrees with the Objectives of Islamic Law that firmly stipulates non-disclosure of patients’ medical data except in a few cases to bring benefit or prevent harm.مجلة الشريعة والدراسات الإسلامية["Mariam Ahmad AlKandari"]2024-10-03T00:00:00 100bec83531eabe46bbf6b9c7aa0f3ce7e81b833The study aims to identify the adequacy of the UAE Commercial Law in 2023 in regulating artificial intelligence as a subject of the contract, This will be done by searching for the way in which the UAE legislator intervened to control the good and effective use of artificial intelligence, whether it was able to reconcile between protecting the interests of the parties to the transaction and encouraging technological development and investment in artificial intelligence to achieve the goals of the National Strategy for Artificial Intelligence. Then, the cases of using artificial intelligence as a subject for commercial transactions will be highlighted, because artificial intelligence is considered one of the funds and products that can be the subject of commercial transactions between professional traders, which remain subject to the applicable legislation, even those that link the trader to the consumer due to the availability of the elements of the consumer process in this type of transaction, which is concerned with protecting the consumer because the consumer transaction must be carried out with transparency and responsibility. However, as a result of the privacy of such transactions, the current legislation does not appear sufficient, but rather work must be done to establish legislative provisions to control dealings in artificial intelligence at the local and international levels to govern artificial intelligence.Evolutionary Studies in Imaginative Culture150EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Adel Salem Allouzi", "Karima Krim", "Mohammad Abdalhafid AlKhamaiseh"]2024-10-03T00:00:00 be885e780b9b99ba9861cfff9e5a610522708413Developments in information and communication technologies have created change and transformation in news production and reading habits, and the use of artificial intelligence in the journalism sector has become inevitable as the process transforms society. With artificial intelligence journalism, news can be produced and edited quickly. However, this speed and automation can create problems for readers in terms of accuracy and detail in consuming the news. In this context, the roles of journalists are also changing, and they are effective in checking the accuracy of news and developing public awareness. Automated news production has developed with the use of artificial intelligence-based software. In the automated news production process, which has come to the agenda through artificial intelligence, the automated creation of news through a number of algorithms and software and its rapid presentation to consumers has taken its place in the new generation journalism. Therefore, with the use of artificial intelligence in news production, public awareness should also be taken into consideration. This study investigates public awareness in the context of automated news production and news reliability, objectivity, confidentiality, employment problems of journalists and ethical rules. The independent variables of the study are the gender, age, education and occupation of newspaper consumers, while the dependent variables are the public opinion on the characteristics of the news in the face of news produced with artificial intelligence.  The population of the study consists of individuals who use social media, read digital newspapers and are over the age of 18, while the sample consists of 400 people who voluntarily responded to the study. In the study, half of the participants are aware of the concept of artificial intelligence and that news is sometimes produced with artificial intelligence. Most of the participants think that weather news is produced by artificial intelligence, as well as economy, earthquake, weather and sports news are all produced automatically. The majority of the participants think that they are concerned about the reliability of the news produced by artificial intelligence, that these news are more qualified and more reliable than the news produced by the traditional news production process. Some of the participants also think that their news needs should be met through news produced by artificial intelligence today. It has been also found that there were significant differences between the gender, age and occupational groups of the participants and their attitudes towards news produced by artificial intelligence.Evolutionary Studies in Imaginative Culture210Investigating public awareness in the context of automated news production and news reliability, objectivity, confidentiality, employment problems of journalists and ethical rules finds that there were significant differences between the gender, age and occupational groups of the participants and their attitudes towards news produced by artificial intelligence.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["\u0130. L. A. \u011e. D. Karaaslan", "Baha Ahmet Y \u0131 lmaz", "Ya \u011f mur Karada \u011f"]2024-10-03T00:00:00 0fd1709f4d1b7202191721d49cb54cd20e34624cHuge flows of dynamically changing data about existing logistics processes in the military industry require timely consideration, management, and optimization. Artificial intelligence in military logistics helps to optimize the adoption of quick and effective decisions in the implementation of logistics processes. Logistics during war is key to the success of military operations, as it concerns technical and rear support, transportation of weapons and ammunition, food supply, communications, etc. The search for effective ways to quickly and effectively optimize and minimize the risks of logistical military processes is very relevant. The paper is devoted to the research of the role of AI in the modern transformational development of military logistics as well as analysis of the areas of possible application of AI in this branch, as global military strategies increasingly depend on the reliability and flexibility of supply chain systems. Automated processing and intelligent analysis of a large amount of data help to optimize the solution of supply, transportation, communication, and other logistical problems under the conditions of modern wars. Applying AI and machine learning technologies to supply chain management allows you to examine and organize large data sets to identify and reveal suspicious suppliers. Unmanned vehicles can automate routine logistical tasks (transportation of weapons, personnel, equipment, evacuation of the wounded, delivery of ammunition, water, food, etc.), performing them faster and more efficiently than humans. The implementation of AI technologies in military supply chain management improves the efficiency, security, and flexibility of logistics operations. AI and deep learning enable rapid and comprehensive analysis of large volumes of data, making it easier to spot trends and patterns that might otherwise go unnoticed. The analysis performed showed that the integration of AI algorithms helps not only to optimize processes, but also to increase the security of data and resources, as well as to intensify research in the field of logistics optimization.Systems and Technologies00The analysis performed showed that the integration of AI algorithms helps not only to optimize processes, but also to increase the security of data and resources, as well as to intensify research in the field of logistics optimization.System technologies["Trofymenko Olena", "Sokolov Artem", "Loginova Nataliia", "Akhmametieva Hanna", "Chykunov Pavlo"]2024-10-03T00:00:00 1e18e8ae1fb252a9f8b7792bc3fb7ff75d730232In recent years, telemedicine has become very important in the world of healthcare. In particular, the use of telemedicine has increased during the Covid-19 global pandemic. Through telemedicine, patients get remote services through information technology. Along with the development of AI today, AI has also been implemented in telemedicine and has provided many benefits to the health industry. However, there have been cases in the implementation of AI that have caused violations of medical ethics that have raised concerns, including AI issuing prescriptions without supervision, which can result in organ damage and even death. This study evaluates the impact of medical ethics violations on the effectiveness of AI in telemedicine. Systematic Literature Review (SLR) is used as the research method. The keywords used in this study are “telemedicine”, “artificial intelligence”, “medical ethics” from Scopus and Pubmed databases. A variety of studies were included in the review, including clinical trials, case studies, and theoretical frameworks. The review identified several trends, including the growing reliance on AI for diagnosis and treatment recommendations. In addition, recurring concerns were identified regarding the potential for bias and the security of the data. The findings indicate that violations of medical ethics can significantly impede the efficacy of AI in telemedicine, particularly with regard to ethical principles such as beneficence, nonmaleficence, autonomy, and justice. Such violations include infringements upon the right of patients to refuse AI-driven treatment, the transmission of unsecured data, the occurrence of data breaches, erroneous diagnoses, and the presence of bias in AI-generated recommendations. These issues have a deleterious impact upon patient safety and trust, result in a reduction of the accuracy of AI-facilitated diagnoses, and perpetuate health inequalities, ultimately eroding public trust and acceptance of AI in healthcare.2024 12th International Conference on Cyber and IT Service Management (CITSM)240The findings indicate that violations of medical ethics can significantly impede the efficacy of AI in telemedicine, particularly with regard to ethical principles such as beneficence, nonmaleficence, autonomy, and justice.2024 12th International Conference on Cyber and IT Service Management (CITSM)["Muhamad Farrel Akbar", "Jason Tianwin", "Edrick Devano", "Daniel Kartawiguna"]2024-10-03T00:00:00 d4c34a81f55a884bf67a3bd7e0b8cbbe31b05f0eDigital Society180The article contends that both systems can be accommodated within a values-pluralistic human rights framework, potentially paving the way for meaningful international governance efforts.Digit. Soc.["Emmie Hine"]2024-10-03T00:00:00 43527f525a9802740a5194cef9468caacaf3eb91This conceptual review examined the dynamics of artificial intelligence (AI) in the business landscape. As businesses traverse an increasingly complex industrial landscape, AI provides a strategic tool for improving effectiveness, innovation, and decision-making. This study aimed to examine and provide a comprehensive view of AI’s influence on business. This paper deployed a qualitative research approach; utilising a narrative literature review methodology to examine existing literature which provided a relevant comprehensive perspective of AI in businesses. The study specifically examined themes on the antecedents of AI for business, challenges of AI for business, dual role (necessity or advantage) of AI in organization operations, AI effect on business/organizational interest, and the resource-based view (RBV) theory perspective on AI for business. The study is of the position that businesses can position themselves for success in the rapidly evolving AI-driven environment by optimizing AI integration and deployment as a strategic approach to future market relevance.International Journal of Scientific Research and Management300The study is of the position that businesses can position themselves for success in the rapidly evolving AI-driven environment by optimizing AI integration and deployment as a strategic approach to future market relevance.International Journal of Scientific Research and Management (IJSRM)["Isaac Onyeyirichukwu Chukwuma", "Fidelis Odinakachukwu Alaefule", "Ifeanyi Leo Madu", "Anthonia Nneka Egbosionu", "Matthew Arinze Okeke", "Patrick Chukwunwike Chukwuma"]2024-10-03T00:00:00 84ce94bcd376afbb920433058197ca47acbba404As electronic devices proliferate in both personal and professional settings, their susceptibility to electromagnetic vulnerabilities has become a critical concern. This article explores the use of artificial intelligence (AI) to identify and mitigate the risks associated with audio signal eavesdropping, a prevalent threat in the realm of electromagnetic security. By analyzing the electromagnetic emissions from electronic devices, AI techniques can detect unintended audio signal leaks that may compromise sensitive information. The study employs machine learning algorithms to process and evaluate these emissions. Results demonstrate the enhanced precision and efficiency of AI related to audio signal eavesdropping. This research not only underscores the importance of AI in fortifying the electromagnetic security of electronic devices but also paves the way for innovative approaches to safeguard against increasingly sophisticated eavesdropping techniques.International Conference on Communications90This research underscores the importance of AI in fortifying the electromagnetic security of electronic devices but also paves the way for innovative approaches to safeguard against increasingly sophisticated eavesdropping techniques.2024 15th International Conference on Communications (COMM)["Alexandru Madalin Vizitiu", "Lidia Dobrescu", "Cristian Molder", "Bogdan Sebacher", "B. Trip", "V. Butnariu"]2024-10-03T00:00:00 106f162e1663af9c83f6b2bd06478aef7a7111dcThe dynamic development of artificial intelligence creates new opportunities and prospects for institutions dealing with searching, processing and sharing information. Implementing artificial intelligence solutions in library services poses many challenges. In these processes, libraries must face legal, ethical and technological problems. An important issue is IT education, which will allow to understand the operation and capabilities of new tools. A broad discussion of these problems is needed, as well as the creation of a legal and ethical framework by international organizations and countries governments.Folia Bibliologica00An important issue is IT education, which will allow to understand the operation and capabilities of new tools, and the creation of a legal and ethical framework by international organizations and countries governments.Folia Bibliologica["Monika Maria Jaworska", "Ewa Barbara Rzeska"]2024-10-03T00:00:00 8aeaf9b1f9c8bc879ff11fc243e7dbb2fa399eacPurpose: The purpose of the study is to examine available scientific sources related to the role and potential of artificial intelligence in osteoarthritis of the knee joint. Materials/Methods: Method of deduction (analysis of literary sources). To achieve the goal, available scientific data on the role and potential of artificial intelligence application in knee OA were studied and analyzed. Results: The following innovations related to the use of artificial intelligence in knee osteoarthritis (OA) were reviewed: artificial intelligence (AI) software - named KOALA™ and DL AI software - MediAI-OA. KOALA™ is software that provides metric evaluations of knee joint imaging. Standardized quantitative measurements of morphological features such as joint gap width and joint gap area on knee radiographs reduce errors in diagnosis. The new DL software, MediAI-OA, demonstrated good success rates in analyzing knee OA characteristics, Kellgren-Lawrence (KL) grading (which is used to classify the severity of knee OA), and OA diagnosis comparable to that of experienced orthopedists and radiologists. Discussion: Diagnostic imaging is a vital tool for visualization. Imaging methods such as radiography, magnetic resonance (MR), computed tomography (CT), and ultrasound play critical roles in OA diagnosis. Additionally, vibro- and phono arthrography serve as alternative diagnostic tools. The most commonly used imaging method is magnetic resonance imaging, which has been found to underestimate the extent of osteochondral lesions. This can lead to inadequate and incomplete diagnoses. Artificial intelligence can serve as a strategic element in addressing these limitations in radiographic knee OA diagnosis. Conclusion: Artificial intelligence has the potential to advance the field of radiology by enhancing efficiency, accuracy, and precision in the radiographic diagnosis of knee osteoarthritis.Journal of IMAB00Artificial intelligence has the potential to advance the field of radiology by enhancing efficiency, accuracy, and precision in the radiographic diagnosis of knee osteoarthritis.Journal of IMAB - Annual Proceeding (Scientific Papers)["Petya Subeva", "Mariya Gramatikova"]2024-10-03T00:00:00 d00e75a48e39c0de3c9d795039a181d575e7c30cThe increasing dependence on Artificial Intelligence (AI) writing tools among advanced learners has sparked concerns about academic dishonesty, prompting stakeholders to develop ways to maintain academic integrity. The study explored the role of academic librarians in maintaining academic integrity with the prevalence of AI writing tools among students. The study was conducted through qualitative methods with a phenomenological design. A semi-structured interview was used to collect data from twelve academic librarians in six universities in Ghana. The data was analysed thematically with word clouds. The findings of the study revealed that the roles of academic librarians are more tailored towards maintaining research integrity, an aspect of academic integrity. Also, the study observed that academic librarians play relevant roles in empowering students on the ethical use of information in the current dispensation of AI application tools in learning. Based on the study's findings, it is recommended a skill upgrade of library staff in evolving AI technology in response to issues of academic misconduct among students. Furthermore, the study suggests that academic institutions should revise academic integrity policies to address the increasing use of AI writing tools among students.Information Development120The roles of academic librarians are more tailored towards maintaining research integrity, an aspect of academic integrity, and a skill upgrade of library staff in evolving AI technology in response to issues of academic misconduct among students is recommended.Information Development["Christopher K. Filson", "Diana Atuase"]2024-10-03T00:00:00 9107787ad6e40a36bca4a63c004a4104bc23e649Breast cancer is a leading cause of cancer-related deaths worldwide. Early detection and accurate diagnosis are crucial for improving patient outcomes. Artificial intelligence (AI) has emerged as a promising tool in the fight against breast cancer. This research report explores the potential applications of AI in various stages of breast cancer management, including early detection, diagnosis, treatment planning, and prognosis prediction. AI algorithms can analyze mammograms and other imaging data to identify suspicious lesions with greater accuracy than traditional methods. Additionally, AI-powered tools can assist in the classification of breast tumors, helping to determine the most appropriate course of treatment. Furthermore, AI can be used to predict the likelihood of recurrence and metastasis, enabling personalized monitoring and follow-up care. This research report presents a comprehensive review of the current state of AI in breast cancer research and highlights the potential benefits and challenges associated with its implementation. By leveraging the power of AI, healthcare providers can improve breast cancer outcomes and enhance the quality of life for patients.International Journal For Multidisciplinary Research220Artificial intelligence can be used to predict the likelihood of recurrence and metastasis, enabling personalized monitoring and follow-up care and the potential benefits and challenges associated with its implementation.International Journal For Multidisciplinary Research["Ruhika Paliwal"]2024-10-03T00:00:00 c3e4bce92c097faa0fb5866163cc0c19a1ea3116The advent of artificial intelligence (AI) in higher education presents unprecedented opportunities for enhancing teaching methodologies, assessment systems, and administrative efficiencies. As Nigerian higher education institutions consider integrating AI-driven assessments, this study explores the potential benefits, challenges, and strategic approaches necessary for successful implementation. Drawing from global case studies in Estonia, China, the USA, and Australia, we analyze how AI has been employed to personalize learning, streamline assessment processes, and enhance educational outcomes. The findings highlight not only the transformative potential of AI in education but also the significant challenges related to fairness, privacy, and security. The study proposes a comprehensive framework involving policy reform, infrastructure development, multi-stakeholder collaboration, and ethical considerations. By adopting these strategies, Nigerian higher education institutions can harness the benefits of AI to foster an inclusive, efficient, and innovative educational environment. This study offers insights into how AI can be strategically implemented to enhance educational systems in Nigeria, ensuring that they are sustainable, equitable, and aligned with global technological advancements.European Journal of Interactive Multimedia and Education680Insight is offered into how AI can be strategically implemented to enhance educational systems in Nigeria, ensuring that they are sustainable, equitable, and aligned with global technological advancements.European Journal of Interactive Multimedia and Education["U. Ofem", "Ginika Chukwujama"]2024-10-03T00:00:00 73f6f6a9499750b42939785ae6e2b9e9b89c0f22Our history is filled with examples of humanity’s resilience and adaptability in the face of technology challenges. Artificial intelligence (AI) brings significant potential for organizations’ productivity improvement and societal advancement. But it also comes with challenges, such as the potential displacement of 300 million jobs and the risks to our democracies, universal values, and way of life. However, AI cannot replace all human skills. The European Union, U.S. White House, and the International Organization for Standardization/International Electrotechnical Commission are establishing new guidelines that help define our roles and responsibilities in governing the implementation of responsible AI solutions. Using this set of guidelines, this position paper lays the foundation for a practical framework at the specific intersection of AI and performance improvement (PI). The proposed framework provides actionable principles and process steps to blend AI as an integral part of PI interventions while ensuring that PI professionals anticipate the potential negative impact of AI.Performance Improvement Journal00The proposed framework provides actionable principles and process steps to blend AI as an integral part of PI interventions while ensuring that PI professionals anticipate the potential negative impact of AI.Performance Improvement Journal["Yvon Dalat"]2024-10-03T00:00:00 b0c5a430cc7e0f636698b9b0fb7a1009215e4050PriMera Scientific Engineering00PriMera Scientific Engineering["Fachhochschule Graub\u00fcnden"]2024-10-03T00:00:00 b898f17c039553a6af5c73d8d3f431453ecf8567JAMA Oncology50JAMA oncology["Lingxuan Zhu", "Weiming Mou", "Peng Luo"]2024-10-03T00:00:00 0bd47ec4746ae1c141c5256b20e36c93e505b881Venture Capital520Venture Capital["Houssein Ballouk", "Hela Nammouri", "Sami Ben Jabeur", "Rabeh Khalfaoui"]2024-10-03T00:00:00 efccf9afccc6c3c92ea038ad409cafe37645c381Journal of Poetry Therapy170Journal of Poetry Therapy["Alfonso Santarpia"]2024-10-03T00:00:00 d242bceada90464352492c27c563fbb1662e4092JAMA Oncology40JAMA oncology["David Chen", "Srinivas Raman"]2024-10-03T00:00:00 bc8e237b9fdab8e36d058d1512b69c7e2f56e594The International Journal of Advanced Manufacturing Technology290The International Journal of Advanced Manufacturing Technology["Ayush Pratap", "T. Vi", "You Wei lee", "Neha Sardana", "Pao-Ann Hsiung", "Yung-Chou Kao"]2024-10-03T00:00:00 e245a9e5b96bb47c2eb60684d876841fef947c88Global swimming competitiveness, driven by enhanced training science, necessitates innovative strategies. High-level swimmers worldwide follow common principles, underscoring AI's potential in training enhancement. Software engineering education actively seeks to align talent development with industry needs, particularly via AI-driven creation of immersive training environments for students. Despite UWB's prominence among wireless positioning technologies and ongoing Chinese research, its application in sports arenas is yet limited. This paper presents a graphical representation of AI in swimming training, proposing a 20% accuracy boost and data visualization, enriching discussions on AI-assisted learning platforms in software engineering and athletics.International Journal of Ambient Computing and Intelligence (IJACI)270A graphical representation of AI in swimming training is presented, proposing a 20% accuracy boost and data visualization, enriching discussions on AI-assisted learning platforms in software engineering and athletics.Int. J. Ambient Comput. Intell.["Yujie Guo"]2024-10-03T00:00:00 b09573b5397284b9d2a156de98a7305d6befb7adNature00Nature["D. Byrne"]2024-10-03T00:00:00 9257064c33903dfd7eff36dd04e56e0768034882Journal of Industry, Competition and Trade390Journal of Industry, Competition and Trade["Evangelos Katsamakas", "J. S\u00e1nchez-Cartas"]2024-10-03T00:00:00 442463319911cabecda457947b0243d236a26791Artificial Intelligence (AI) in Business Intelligence (BI) is a significant milestone in the way BI is practiced today as it seeks to bring about change in the ways organizations use data to make decisions in their operations. It cast light on the facet of applying advanced machine learning models as well as natural language processing techniques to highly BI operations in this research work. In light of this, this research will spotlight on how NLP and XGBoost, a steep gradient boosting algorithm both work hand in hand in enhancing better prediction and insights from larger data sets. The research uses the NLP tools, such as word embedding and transformer to work with unstructured text data from different sources of businesses. Since NLP eludes useful patterns and trends from textual data, it works synergistically with XGBoost in matters concerning prediction models. XGBoost is used for its ability of high precise in analyze structured data for better prediction results and finding important features that influence business KPIs. Focusing on the qualitative and quantitative analysis of the case and using the NLP and XGBoost, the authors illustrate how the methods increase the accuracy of business forecast, improve the decision-making process, and provide a competitive advantage on the market. Moreover, the study makes efforts to include and elaborate the implementation issue, the data quality and integration issues as well as the possible ways of handling those problems. combining NLP techniques with XGBoost to enhance business intelligence by extracting actionable insights from both structured and unstructured data. It provides optimal decision making by feature engineering, hyperparameters tuning as well as continual learning. This study hence enhances the field of AI BI by revealing new applications and outcomes, which are helpful and informative for the practitioners and academics in the field.2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)130This research will spotlight on how NLP and XGBoost, a steep gradient boosting algorithm both work hand in hand in enhancing better prediction and insights from larger data sets, to enhance business intelligence.2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)["Uzma Sarwar", "N. Bhasin", "Dibyahash Bordoloi", "Sunil Kadyan", "S. Kezia", "S. Muthuperumal"]2024-10-03T00:00:00 32d4788cad2f74c40f7853d721415a8a41f0adb9We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language Models (LLMs) on different ECAs, we evaluated the relationship between the complexity of the rules' behavior and the intelligence exhibited by the LLMs, as reflected in their performance on downstream tasks. Our findings reveal that rules with higher complexity lead to models exhibiting greater intelligence, as demonstrated by their performance on reasoning and chess move prediction tasks. Both uniform and periodic systems, and often also highly chaotic systems, resulted in poorer downstream performance, highlighting a sweet spot of complexity conducive to intelligence. We conjecture that intelligence arises from the ability to predict complexity and that creating intelligence may require only exposure to complexity.arXiv.org481It is conjecture that intelligence arises from the ability to predict complexity and that creating intelligence may require only exposure to complexity, and that creating intelligence may require only exposure to complexity.ArXiv["Shiyang Zhang", "Aakash Patel", "S. Rizvi", "Nianchen Liu", "Sizhuang He", "Amin Karbasi", "E. Zappala", "David van Dijk"]2024-10-03T00:00:00 1c693b461b73c567a4b290fe707c3b7f66025cdeThis paper scrutinizes how adaptive learning technologies and artificial intelligence (AI) are transforming today's education by making it personalized, accessible, and efficient as well as leading people to accepting, addressing, and mitigating sustainable development. Recently, education witnessed a remarkable technological surge driven by various advances in technology, which can be demonstrated by the increase of the number of scientific publications on this topic from just 1 in 1990 to 636 in 2023. Ongoing digitalization and technological revolution in education together with the novel approach to respect each student's unique learning style and abilities paved the way for adaptive learning technologies represented by the innovative tools that personalize educational experiences to cater to individual learners. All of that contributes to preparing more educated and informed citizens, drives innovation, and supports economic growth necessary for achieving a sustainable future. Our bibliographic study employs VOSviewer to conduct a bibliometric analysis of a total number of 3518 selected publications using the keywords “adaptive learning” and “AI” (represented by articles, proceeding papers, and book chapters) indexed in the Web of Science (WoS) database from 1990 to 2024. Our results demonstrate that recent technological changes played a key role in transforming adaptive learning, which was rather reinforced by the “digital surge” in education brought about by the COVID‐19 pandemic. Our findings can be useful for further development in the field of adaptive education where they can be employed by the relevant stakeholders and policymakers as well as by the scholars and researchers.Sustainable Development14516The results demonstrate that recent technological changes played a key role in transforming adaptive learning, which was rather reinforced by the “digital surge” in education brought about by the COVID‐19 pandemic.Sustainable Development["W. Strielkowski", "Veronika Grebennikova", "Alexander Lisovskiy", "Guzalbegim Rakhimova", "Tatiana Vasileva"]2024-10-03T00:00:00 806498f92acd05a82efa31affdb3f6187633c43cThis study explores the evolution and impact of research on the challenges and opportunities in the implementation of artificial intelligence (AI) in manufacturing between 2019 and August 2024. By addressing the growing integration of AI technologies in the manufacturing sector, the research seeks to provide a comprehensive view of how AI applications are transforming production processes, improving efficiency, and opening new business opportunities. A bibliometric analysis was conducted, examining global scientific production, influential authors, key sources, and thematic trends. Data were collected from Scopus, and a detailed review of key publications was carried out to identify knowledge gaps and unresolved research questions. The results reveal a steady increase in research related to AI in manufacturing, with a strong focus on automation, predictive maintenance, and supply chain optimization. The study also highlights the dominance of certain institutions and key authors driving this field of research. Despite the progress, significant challenges remain, particularly regarding the scalability of AI solutions and ethical considerations. The findings suggest that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits.The Scientist1346The study highlights the dominance of certain institutions and key authors driving this field of research, and suggests that while AI holds considerable potential for the manufacturing industry, more interdisciplinary research is needed to address existing gaps and maximize its benefits.Sci["Lorena C. Espina-Romero", "Humberto Guti\u00e9rrez Hurtado", "Doile Enrique R\u00edos Parra", "Rafael Alberto Vilchez Pirela", "Rosa Talavera-Aguirre", "Ang\u00e9lica Ochoa-D\u00edaz"]2024-10-03T00:00:00 2523d9d7d736498ed947f2b1a07f0f32fd2e6053This paper explores the transformative impact of Generative Artificial Intelligence (GenAI) on teachers' roles and agencies in education, presenting a comprehensive framework that addresses teachers' perceptions, knowledge, acceptance, and practices of GenAI. As GenAI technologies, such as ChatGPT, become increasingly integrated into educational settings, teachers are required to adapt to evolving classroom dynamics, where AI plays a significant role in content creation, personalized learning, and student engagement. However, existing literature often treats these factors in isolation, overlooking how they collectively influence teachers' ability to effectively integrate GenAI into their pedagogical practices. This paper fills this gap by proposing a framework that categorizes teachers into four roles -- Observer, Adopter, Collaborator, and Innovator -- each representing different levels of GenAI engagement, outlining teachers' agencies in GenAI classrooms. By highlighting the need for continuous professional development and institutional support, we demonstrate how teachers can evolve from basic GenAI users to co-creators of knowledge alongside GenAI systems. The findings emphasize that for GenAI to reach its full educational potential, teachers must not only accept and understand its capabilities but also integrate it deeply into their teaching strategies. This study contributes to the growing literature on GenAI in education, offering practical implications for supporting teachers in navigating the complexities of GenAI adoption.Journal of Science Education and Technology427A framework that categorizes teachers into four roles -- Observer, Adopter, Collaborator, and Innovator -- each representing different levels of GenAI engagement is proposed, outlining teachers' agencies in GenAI classrooms.ArXiv["Xiaoming Zhai"]2024-10-03T00:00:00 bf51921bf8338d93e3006f7d46300b3f3b475470npj Digit. Medicine694Despite the growing number of market-approved medical devices, the data shows that FDA reporting data remains inconsistent, and Demographic and socioeconomic characteristics are underreported, exacerbating the risk of algorithmic bias and health disparity.NPJ Digital Medicine["Vijaytha Muralidharan", "B. A. Adewale", "Caroline J Huang", "Mfon Thelma Nta", "Peter Oluwaduyilemi Ademiju", "Pirunthan Pathmarajah", "Man Kien Hang", "O. Adesanya", "R. Abdullateef", "A. Babatunde", "Abdulquddus Ajibade", "Sonia Onyeka", "Zhou Ran Cai", "Roxana Daneshjou", "Tobi Olatunji"]2024-10-03T00:00:00 e3a95550f68b975fbd3552fe7d7f95907cb7d6e7ABSTRACT Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples under active control towards task-desired properties. Despite the significant empirical success, theoretical underpinnings of diffusion models are very limited, potentially slowing down principled methodological innovations for further harnessing and improving diffusion models. In this paper, we review emerging applications of diffusion models to highlight their sample generation capabilities under various control goals. At the same time, we dive into the unique working flow of diffusion models through the lens of stochastic processes. We identify theoretical challenges in analyzing diffusion models, owing to their complicated training procedure and interaction with the underlying data distribution. To address these challenges, we overview several promising advances, demonstrating diffusion models as an efficient distribution learner and a sampler. Furthermore, we introduce a new avenue in high-dimensional structured optimization through diffusion models, where searching for solutions is reformulated as a conditional sampling problem and solved by diffusion models. Lastly, we discuss future directions about diffusion models. The purpose of this paper is to provide a well-rounded exposure for stimulating forward-looking theories and methods of diffusion models.National Science Review1222This paper reviews emerging applications of diffusion models to highlight their sample generation capabilities under various control goals and introduces a new avenue in high-dimensional structured optimization through diffusion models, where searching for solutions is reformulated as a conditional sampling problem and solved by diffusion models.National Science Review["Minshuo Chen", "Song Mei", "Jianqing Fan", "Mengdi Wang"]2024-10-03T00:00:00 8ed4342a94c68c1a5a4ce491765b0bf08b1c396fGenerative artificial intelligence (Gen-AI) has emerged as a transformative tool in research and education. However, there is a mixed perception about its use. This study assessed the use, perception, prospect, and challenges of Gen-AI use in higher education.This is a prospective, cross-sectional survey of university students in the United Kingdom (UK) distributed online between January and April 2024. Demography of participants and their perception of Gen-AI and other AI tools were collected and statistically analyzed to assess the difference in perception between various subgroups.A total of 136 students responded to the survey of which 59% (80) were male. The majority were aware of Gen-AI and other AI use in academia (61%) with 52% having personal experience of the tools. Grammar correction and idea generation were the two most common tasks of use, with 37% being regular users. Fifty-six percent of respondents agreed that AI gives an academic edge with 40% holding a positive overall perception about the use in academia. Comparatively, there was a statistically significant difference in overall perception between different age ranges (I2 = 27.39; p = 0.002) and levels of education (I2 = 20.07; p < 0.001). Also, 83% of students believe AI use will increase in academia with over half agreeing it should be integrated into learning. Plagiarism (33%), privacy issues (14%), and lack of clarity by the university (13%) remain the top concerns regarding the use of Gen-AI and other AI tools in academia.Gen-AI and other AI tools are being used and their use will continue to grow in higher education. While current use is challenging due mainly to plagiarism fear and lack of clarity by the university, most users believe AI should be integrated into the university curriculum.Frontiers in Education882While current use is challenging due mainly to plagiarism fear and lack of clarity by the university, most users believe AI should be integrated into the university curriculum.Frontiers in Education["Abayomi Arowosegbe", "J. Alqahtani", "Tope Oyelade"]2024-10-03T00:00:00 68483e765829d275b506f0c79d539805009cb3bbThe increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to explain black-box models after training, have raised concerns about their fidelity to model predictions. In contrast, Self-eXplainable AI (S-XAI) offers a compelling alternative by incorporating explainability directly into the training process of deep learning models. This approach allows models to generate inherent explanations that are closely aligned with their internal decision-making processes, enhancing transparency and supporting the trustworthiness, robustness, and accountability of AI systems in real-world medical applications. To facilitate the development of S-XAI methods for medical image analysis, this survey presents a comprehensive review across various image modalities and clinical applications. It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing textual and counterfactual explanations. This paper also outlines desired characteristics of explainability and evaluation methods for assessing explanation quality, while discussing major challenges and future research directions in developing S-XAI for medical image analysis.arXiv.org2931Wanted characteristics of explainability and evaluation methods for assessing explanation quality are outlined, while discussing major challenges and future research directions in developing S-XAI for medical image analysis.ArXiv["Jun Hou", "Sicen Liu", "Yequan Bie", "Hongmei Wang", "Andong Tan", "Luyang Luo", "Hao Chen"]2024-10-03T00:00:00 26465bb19d661e45570c4d5cad34349b7ca59fc0The integration of artificial intelligence (AI) and machine learning (ML) into crop management is transforming modern agriculture by enhancing efficiency, sustainability, and resilience. This review explores the multifaceted applications of AI and ML in key areas such as precision farming, pest and disease management, and harvest optimization. The use of AI-driven predictive analytics allows for more accurate forecasting of crop yields, pest outbreaks, and weather conditions, enabling farmers to make data-driven decisions that optimize resource use and reduce environmental impacts. A significant advancement is the integration of AI and ML with the Internet of Things (IoT) and autonomous farming equipment. These technologies enable real-time monitoring and precise interventions, enhancing productivity and minimizing labor costs. In crop breeding and genomics, AI accelerates the development of resilient crop varieties, which is crucial for adapting to climate change and increasing food demands. Despite the many benefits, challenges such as data quality, infrastructure limitations, and high implementation costs remain. The adoption of AI technologies is uneven, with small-scale farmers in developing regions facing barriers due to limited access to data and resources. Ethical concerns related to data privacy and the digital divide must also be addressed to ensure equitable access to these technologies. The future of AI and ML in agriculture lies in the development of more advanced predictive models, enhanced integration with the IoT, and the widespread use of autonomous farming systems.AI Computer Science and Robotics Technology411This review explores the multifaceted applications of AI and ML in key areas such as precision farming, pest and disease management, and harvest optimization, and the integration of AI and ML with the Internet of Things (IoT) and autonomous farming equipment.AI, Computer Science and Robotics Technology["Danish Gul", "Rizwan-ul-Zama Banday"]2024-10-03T00:00:00 dd8f6a9a46828a96f7de81518a1a796cef2a3423As artificial intelligence (AI) rapidly automates routine tasks and job roles, organizations must prioritize developing quintessentially human skills that machines cannot easily replicate, such as creativity, empathy, collaboration, and complex problem-solving. Drawing from neuroscience, this article examines how industrial-era education and management practices, with their emphasis on structured, repetitive environments, have historically prioritized crystallized intelligence over fluid intelligence. This focus limits adaptability and innovation, which are essential human qualities in an AI-driven world. Our recommendations include stimulating cognitive flexibility, reimagining strategic planning, optimizing team meetings, redesigning jobs, promoting experiential learning, and fostering human relations. By adopting these strategies, organizations can prepare their workforce for more complex, cognitively demanding tasks, ensuring resilience and innovation in the age of AI.Journal of Applied Behavioral Science101This article examines how industrial-era education and management practices, with their emphasis on structured, repetitive environments, have historically prioritized crystallized intelligence over fluid intelligence, which limits adaptability and innovation, which are essential human qualities in an AI-driven world.The Journal of Applied Behavioral Science["Steven H. Cady", "Jari Willing", "Deakon A. Cady"]2024-10-03T00:00:00 97ba2b0895591fee79d76cf340164a6b90833e21Generative artificial intelligence (GenAI) tools are already being implemented for a variety of writing tasks in workplaces, where individual (human) authorship is valued less than the efficient production of text. But policies regarding AI use in higher education continue to prioritize academic integrity, focusing on narrowly defined notions of authorship that do not reflect the realities of workplace writing. Through an analysis of 100 university policies on AI, this article shows how AI tools create a tension for faculty in technical and professional communication who must operate within institutional or departmental policies for AI use but must also prepare writers for workplace authorship.Journal of business and technical communication41Through an analysis of 100 university policies on AI, this article shows how AI tools create a tension for faculty in technical and professional communication who must operate within institutional or departmental policies for AI use but must also prepare writers for workplace authorship.Journal of Business and Technical Communication["Meghan Velez", "Alex Rister"]2024-10-03T00:00:00 1226aa5856db43c3a0e1225c77b3e7ab79d150e0As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and naturalness. We find human participants cannot consistently identify recordings of AI-generated voices. Specifically, participants perceived the identity of an AI-voice to be the same as its real counterpart approximately 80% of the time, and correctly identified a voice as AI generated only about 60% of the time.arXiv.org141The realism of AI-generated voices in terms of identity matching and naturalness is reported on through a series of perceptual studies, finding human participants cannot consistently identify recordings of AI-generated voices.ArXiv["Sarah Barrington", "Hany Farid"]2024-10-03T00:00:00 f1a953f232e1a432ce491581ceab6b52debb0bdbBackground The field of journalism has undergone substantial transformation with the integration of artificial intelligence (AI), leveraging technologies like natural language processing and automated reporting. These advancements enhance information processing speed, enable personalised content delivery and improve data analysis capabilities, thereby reshaping journalism practices. Purpose Despite the benefits AI offers, concerns persist regarding its impact on job security and the mental health of journalists. Rapid technological changes can lead to increased job insecurity, altered job roles and heightened pressure to adapt, potentially affecting journalists' mental well-being. Methods This study utilises the Depression, Anxiety, and Stress Scale (DASS-21) to assess levels of depression, anxiety and stress among 500 journalists from various media organisations that have integrated AI technologies. Quantitative data analysis explores the relationship between AI integration and mental health outcomes. Results The findings indicate significant correlations between the perceived threat of AI replacing jobs and higher levels of depression among journalists. Mixed effects were observed regarding the impact of AI integration on job roles, with associations found between AI integration and both increased depression and reduced stress levels. Conclusion AI integration in journalism presents both opportunities and challenges for journalists' mental health. Strategies to address job security concerns, enhance comfort with AI tools through training and establish mental health support systems are crucial for fostering a supportive environment in AI-driven newsrooms.Annals of Neurosciences40Signs indicate significant correlations between the perceived threat of AI replacing jobs and higher levels of depression among journalists and the relationship between AI integration and mental health outcomes is explored.Annals of neurosciences["Akshay Upadhyay", "Mayura Bijale", "Kashif Hasan"]2024-10-03T00:00:00 546e4904f31fb6aca0327899686676c79d1f3e2cThe combination of Artificial Intelligence (AI) and Digital Twin (DT) technologies in healthcare could revolutionize the administration and treatment of intricate conditions, including myocardial infarction and stroke. This study offers an extensive analysis of contemporary methodologies and examines the prospects of a conceptual AIdriven digital twin framework for healthcare applications. The proposed system integrates real-time data, machine learning algorithms, and sophisticated computational methods to improve diagnostic accuracy and refine treatment approaches. Although current literature illustrates the efficacy of AI and digital technologies in customized medicine, substantial obstacles persist in data integration, processing capacity, and ethical issues. This study clarifies the present condition of AIdriven digital twin technologies and delineates critical domains for prospective research and development. The objective is to create a basis for enhancing the incorporation of these technologies in healthcare to optimize patient outcomes and clinical decision-making.International Scientific Conference Information Technology and Management Science Riga Technical University260This study clarifies the present condition of AIdriven digital twin technologies and delineates critical domains for prospective research and development to create a basis for enhancing the incorporation of these technologies in healthcare to optimize patient outcomes and clinical decision-making.2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)["Marta Narigina", "A. Rom\u0101novs", "Y. Merkuryev"]2024-10-03T00:00:00 84fcb78e68d4aae289cb89fc253134bc234b29fbThis study explores the integration of Artificial Intelligence (AI) into strategic management from 2015 to 2024, examining AI's impact on decision-making, operational optimizaTion, and competitive advantage. A bibliometric analysis was conducted on 5,668 articles to visualize key trends, contributors, and research themes. The United Kingdom, United States, and China significantly contribute to this field. Prominent authors such as Kumar and Chatterjee are driving recent trends, while Parida and Dwivedi have provided foundational insights. The study underscores the importance of leading journals in shaping the research landscape and highlights the need for international collaboration, ethical considerations, and the development of advanced analytical tools. Recommendations include leveraging big data, enhancing predictive capabilities, exploring practical applications, and fostering human-AI collaboration to advance strategic management.2024 12th International Conference on Cyber and IT Service Management (CITSM)350The study underscores the importance of leading journals in shaping the research landscape and highlights the need for international collaboration, ethical considerations, and the development of advanced analytical tools.2024 12th International Conference on Cyber and IT Service Management (CITSM)["Susanti Dewi", "U. Rahardja", "Richard Andre Sunarjo", "Euis Siti Nur Aisyah", "Nuke Puji Lestari Santoso"]2024-10-03T00:00:00 26c5343f8a44095761c13305c680938bfd3ed5a3Autonomous systems such as space‐ or underwater‐exploration robots or elderly people assistance robots often include an artificial intelligence (AI) planner as a component. Starting from the initial state of a system, an AI planner automatically generates sequential plans to reach final states that satisfy user‐specified goals. Generating plans having a minimum number of intermediate steps or taking the least time to execute is usually strongly desired, as these plans exhibit minimal costs. Unfortunately, testing if an AI planner generates optimal plans is almost impossible because the expected cost of these plans is usually unknown. Based on mutation adequacy test suite selection, this article proposes a novel metamorphic testing framework for detecting the lack of optimality in AI planners. The general idea is to perform a systematic but non‐exhaustive state space exploration from the initial state and to select mutant‐adequate states to instantiate new planning tasks as follow‐up test cases. We then check a metamorphic relation between the automatically generated solutions of the AI planner for these new test cases and the cost of the initial plan. We implemented this metamorphic testing framework in a tool called MorphinPlan. Our experimental evaluation shows that MorphinPlan can detect non‐optimal behaviour in both mutated AI planners and off‐the‐shelf, configurable planners. It also shows that our proposed mutation adequacy test selection strategy outperforms three alternative test generation and selection strategies, including both random state selection and random walks through the state space in terms of mutation scores.Software testing, verification & reliability200A novel metamorphic testing framework for detecting the lack of optimality in AI planners, based on mutation adequacy test suite selection, which can detect non‐optimal behaviour in both mutated AI planners and off‐the‐shelf, configurable planners.Software Testing, Verification and Reliability["Q. Mazouni", "Arnaud Gotlieb", "Helge Spieker", "M. Acher", "Benoit Combemale"]2024-10-03T00:00:00 dfa6ad850f4a01772a65baa3baece97ee5749cc7In the digital sovereignty debate, countries and blocks seek to build technological and regulatory capacity to ascertain technological autonomy—definitions notwithstanding. Meanwhile, these actors seek to position themselves discursively, differentiating their own understanding of digital sovereignty from that of competing powers. In this context, the European Union (EU) elaborated the concept of digital sovereignty as something obtainable on an individual level, where regulations are put in place for users to be able to choose what personal data (not) to share. Meanwhile in China the government launched a number of artificial intelligence (AI) and data protection regulations along with an antitrust crackdown on numerous platform companies. This aimed at bringing technological giants (namely platforms), capable of handling massive amounts of data and influencing people's everyday lives, under stricter government rule. While the Chinese government has only partially framed these actions within frameworks akin to ‘digital sovereignty’, the purported aim was accruing individual autonomy vis‐à‐vis big techs, arguably falling close to the EU's ‘digital sovereignty’ discursive framework. By comparing EU and Chinese AI and data governance regulations, this article unpacks the EU discourse on the individual element of digital sovereignty and finds the EU regulatory effort insufficient to achieve its declared objective.Policy &amp; Internet231The EU discourse on the individual element of digital sovereignty is unpacked and the EU regulatory effort is found insufficient to achieve its declared objective.Policy &amp; Internet["Riccardo Nanni", "P. G. Bizzaro", "Maurizio Napolitano"]2024-10-04T00:00:00 6995f0fb825bc53a6b73a849d65b909ac33e3771Background: Innovations in artificial intelligence (AI) and machine learning (ML) are poised to transform stroke care, particularly for Neuro-Cardiac Programs (NCP) within both academic and community hospital systems. Purpose: Given AI's success in large-vessel occlusion (LVO) detection and perfusion mapping delivered to our smartphones, the next leap for this "Ghost in the Machine" technology seems to be into the world of NCP: AI-enhanced logistics have started to help with cardiac monitoring after cryptogenic, large-artery and small-vessel stroke, looking for atrial fibrillation (AF) with an insertable loop recorder (ILR) and/or external patch. Results: The 'CONNECT' study from UCSD demonstrated that AI can increase protocol efficiency and reduce patient wait-times for ILR; with more AF detected, fewer strokes may result as more patients receive anticoagulation or Left Atrial Appendage Closure (LAAC). Conclusion: Therefore, organically, the next AI and ML-enhanced NCP frontier may involve inter-departmental "Shared Decision-Making" (SDM) process with LAAC, and/or Patent Foramen Ovale (PFO), in appropriately selected patients. In this editorial, we explore AI's capability to disrupt current antiquated siloed communication tools, refine and streamline SDM processes and tailor patient-specific treatment plans, nevertheless advocating for intercalation of AI into NCP pathways in a secure, ethically-guided manner.The Neurohospitalist11AI's capability to disrupt current antiquated siloed communication tools, refine and streamline SDM processes and tailor patient-specific treatment plans is explored, nevertheless advocating for intercalation of AI into NCP pathways in a secure, ethically-guided manner.The Neurohospitalist["Harneel Saini", "David Z. Rose"]2024-10-04T00:00:00 a0da315a7666e94210b2f4962320e1502790f427The article examines the impact of the rapid development of artificial intelligence (AI) technologies on the creative industries and the concerns of workers in this field regarding the potential deterioration of their working conditions and displacement from the labor market. The aim of the study is to identify the degree of concern among freelancers engaged in intellectual and creative professions regarding competition with AI and to assess their perception of AI’s current capabilities in making creative content. The empirical basis was provided by online survey data of 778 Russian freelancers receiving jobs through the Freelance.ru digital platform, conducted in spring 2024. It was found that many respondents are already actively using AI in their work. The majority of freelancers note AI’s high current capabilities in creating texts, images, translation, and other areas, and more than a third believe that in the coming years AI will be able to do their typical work as well or even better than they do it themselves. Those who were least likely to experience concerns about their future were individuals who had been trained in AI, used it to perform job tasks, satisfied with their work, and had a high level of income, i.e., generally had a stable position in the labor market. Despite the concerns of some workers, the development of AI opens up new opportunities for the creative industries; however, regular monitoring of the situation is required to develop measures to adapt the labor market.Voprosy Ekonomiki381It was found that many respondents are already actively using AI in their work, and more than a third believe that in the coming years AI will be able to do their typical work as well or even better than they do it themselves.Voprosy Ekonomiki["D. O. Strebkov"]2024-10-04T00:00:00 d102ff09b7312e7440a84f7ac5d10e0ede2162eeIndustrial law journal06Industrial Law Journal["Qingqin Zhang"]2024-10-04T00:00:00 b2bc5e5d731367aca53c3de5d22fc0daf929192fArtificial intelligence (AI) has sprayed a vibrant message to people in all areas of life including the education field due to the easy access to vast information with a click in an autonomous way and enhancing the overall learning experience. This study provides an overview of the significance of AI applications, their role in education and where the practitioners are in the concurrent situation. Especially, it explores some of the AI approaches best practised, including personalised learning, adaptive learning, teaching evaluation, virtual classroom, and intelligent teaching. It also highlights some of the ethical and social implications of using AI in education, including issues related to privacy, equity, and the role of human teachers. I used the data published in various journals, and books, presented in academic forums in global and local contexts and analysed thematically. The study concluded that integrating AI in education with caution, considering the ethical implications and ensuring that all students have access to these new technologies will switch to an artificially led world where there is no boundary for accessing knowledge.Journal of NELTA Gandaki610The study concluded that integrating AI in education with caution, considering the ethical implications and ensuring that all students have access to these new technologies will switch to an artificially led world where there is no boundary for accessing knowledge.Journal of NELTA Gandaki["D. Bohara"]2024-10-04T00:00:00 dcd1af7fcb2f8d43ed3494f4422b7da32713add5Generative artificial intelligence (GenAI) has emerged as a transformative force in the rapidly-evolving hospitality industry. This study examined the correlation of acceptance of GenAI services with perceived warmth and competence, and the consequent correlations with customer outcomes in the hospitality industry. Employing a Hospitality Service Dialogue System prototype, this study collected responses from 1,306 hotel guests. Perceptions of warmth and competence correlated with increased customer intention to use GenAI services and satisfaction. Satisfaction correlated significantly with customer–brand identification and attachment. The Technology Readiness Index moderated the relationships of warmth and competence with the acceptance of GenAI services. This investigation extends the stereotype content model and the behaviors from intergroup affect and stereotypes map framework by integrating them with the technology acceptance literature, unveiling service innovations enabled by technology in the hospitality industry. This study provides hotel managers, technology developers, and marketers with strategic directions for implementing GenAI.Journal of Hospitality &amp; Tourism Research00This investigation extends the stereotype content model and the behaviors from intergroup affect and stereotypes map framework by integrating them with the technology acceptance literature, unveiling service innovations enabled by technology in the hospitality industry.Journal of Hospitality &amp; Tourism Research["Wailing Ng", "Faye Hao", "Chen Zhang"]2024-10-04T00:00:00 6d8515cc3ad1119d3f5520f1b09e1b3bec9b0b6aPolicy epidemiology utilizes human subject-matter experts (SMEs) to systematically surface, analyze, and categorize legally-enforceable policies. The Analysis and Mapping of Policies for Emerging Infectious Diseases project systematically collects and assesses health-related policies from all United Nations Member States. The recent proliferation of generative artificial intelligence (GAI) tools powered by large language models have led to suggestions that such technologies be incorporated into our project and similar research efforts to decrease the human resources required. To test the accuracy and precision of GAI in identifying and interpreting health policies, we designed a study to systematically assess the responses produced by a GAI tool versus those produced by a SME. We used two validated policy datasets, on emergency and childhood vaccination policy and quarantine and isolation policy in each United Nations Member State. We found that the SME and GAI tool were concordant 78.09% and 67.01% of the time respectively. It also significantly hastened the data collection processes. However, our analysis of non-concordant results revealed systematic inaccuracies and imprecision across different World Health Organization regions. Regarding vaccination, over 50% of countries in the African, Southeast Asian, and Eastern Mediterranean regions were inaccurately represented in GAI responses. This trend was similar for quarantine and isolation, with the African and Eastern Mediterranean regions least concordant. Furthermore, GAI responses only provided laws or information missed by the SME 2.14% and 2.48% of the time for the vaccination dataset and for the quarantine and isolation dataset, respectively. Notably, the GAI was least concordant with the SME when tasked with policy interpretation. These results suggest that GAI tools require further development to accurately identify policies across diverse global regions and interpret context-specific information. However, we found that GAI is a useful tool for quality assurance and quality control processes in health policy identification.medRxiv80GAI tools require further development to accurately identify policies across diverse global regions and interpret context-specific information, however, it is found that GAI is a useful tool for quality assurance and quality control processes in health policy identification.PLOS ONE["Rory Wilson", "C. M. Weets", "Amanda Rosner", "Rebecca Katz"]2024-10-04T00:00:00 edca2af153914e6b7e33230a024e15537a717ea8Artificial Intelligence (AI) is becoming more and more involved in human life day by day. Healthcare is one of the areas where AI is widely used, such as in the diagnosis prediction, and/or classification of diseases. Techniques such as machine learning provide high-accuracy results, but many algorithms have black-box structures, where the reasoning behind the predictions is not known. Explainable AI emerges to address this by providing explanations for complex models. While interpretable ("glass-box") models are desirable, they may have lower accuracy than complex ("black-box") models. Finding the right balance is crucial, especially in critical areas such as healthcare. It is also important to provide individual explanations for the predictions. This study uses patient data to explore a model to predict heart attack risk. Therefore, we compare glass-box models (logistic regression, naive Bayes, decision tree, and explainable boosting) with black-box models (random forest, support vector machine, multi-layer perceptron, gradient boosting, and stochastic gradient boosting). The results show that explainable boosting achieves the highest accuracy. To delve into individual explanations on a patient basis, the explainable boosting algorithm is compared with the random forest algorithm, which gives the best results among the black-box models. Here, LIME and SHAP are used to provide interpretability of random forests. As a result, it is concluded that the random forest algorithm has differences in the importance weights of the variables compared to the explainable boosting algorithm. Both results provide valuable tools for healthcare stakeholders to choose the most appropriate model.International Journal of Advances in Engineering and Pure Sciences250This study uses patient data to explore a model to predict heart attack risk and concludes that the random forest algorithm has differences in the importance weights of the variables compared to the explainable boosting algorithm.International Journal of Advances in Engineering and Pure Sciences["Ebru Ge\u00e7ici", "Ey\u00fcp Ensar I\u015f\u0131k", "M\u0131sra \u015eim\u015fir", "Mehmet G\u00fcne\u015f"]2024-10-04T00:00:00 c8327591ab1d230368253bb7530532b617e4abdcThe last few years have seen the artificial intelligence technologies’ potential to radically transform many industries, including agriculture, by optimizing the use of resources, increasing productivity, work efficiency, and resistance to climate change. The basic research question here is the degree of connection between the level of productivity in agriculture, on the one hand, and the degree of acceptance of AI technologies and a number of agriculture-related economic indicators, on the other hand. For this purpose, an empirical data analysis was carried out for EU 27 member countries. The results of the analysis show a moderately strong positive relationship between the level of the Labor Productivity in Agriculture and the AI Readiness Index score. Also, there is a statistically significant, but slightly less pronounced, positive relationship between the level of the Labor Productivity in Agriculture and GDP per capita and Agriculture, Forestry, and Fishing, Value Added (current US$) in Millions.Ekonomika poljoprivrede310The results of the analysis show a moderately strong positive relationship between the level of the Labor Productivity in Agriculture and the AI Readiness Index score and a statistically significant, but slightly less pronounced, positive relationship between the level of the Labor Productivity in Agriculture and GDP per capita.Ekonomika poljoprivrede["Jasna Soldi\u0107 Aleksi\u0107", "Aleksandra Ze\u010devi\u0107", "Biljana Chroneos Krasavac"]2024-10-04T00:00:00 45740b9d1706ecd9d2fc0ecdfeff91ba2bf00ed9In light of a recent increase in popularity of the topic, we clarify the acceptance criteria for article submissions incorporating machine learning and artificial intelligence.Journal of The Optical Society of America A-optics Image Science and Vision00Journal of the Optical Society of America. A, Optics, image science, and vision["Markus Testorf", "Svetlana Avramov-Zamurovic", "Olga Korotkova"]2024-10-04T00:00:00 45a146ac3af81fc1ead99cf77526aa54bff2ddccArtificial Intelligence (AI) is a system that is developed and continues to innovate in various fields with the aim of making human work easier. As the name suggests, artificial intelligence is made to resemble human intelligence and is applied in various fields. Currently Artificial Intelligence technology is also used in the field of education, including teaching foreign languages. This article, which is based on a short field study, aims to find out student references regarding the use of Artificial Intelligence. The use of artificial intelligence (AI) in English Language Teaching (ELT) can be seen as both a benefit and a potential threat, depending on how it is used and implemented. On the one hand, AI technology has the potential to make language learning more efficient and effective, by providing personalized feedback and practice exercises tailored to the different student’s needs and learning styles. The research method used to determine this phenomenon is a qualitative descriptive research method with questionnaire data collection techniques by asking questions that can be answered directly by respondents according to their respective answers. Artificial Intelligence (AI) has the potential to play an important role in helping students in various tasks in learning English, i.e finding out the the meaning and definition of words, translating sentences, improving grammar, etc with reliable and timely results. However, on the other hand, AI has an impact on student abilities. Students are increasingly dependent on AI assistance in learning, thereby reducing its role in critical thinking and using memoryInternational Journal of Educational Research Excellence (IJERE)190The use of artificial intelligence (AI) in English Language Teaching (ELT) can be seen as both a benefit and a potential threat, depending on how it is used and implemented.International Journal of Educational Research Excellence (IJERE)["Roswani Siregar", "Heni Subagiharti", "Diah Syafitri Handayani", "S. Sutarno", "Ahmad Laut Hasibuan", "Efendi Barus"]2024-10-04T00:00:00 bece20cbfa7b72e64fa3c225db0ea27808e3c5dcThe objective of this study is to employ bibliometric analysis to investigate the utilization of artificial intelligence in the construction sector. AI is not merely a novel technology governed by specific regulations, but rather a force that is integrated into everyday life. In this industry, artificial intelligence (AI) is a form of technology developed to see the development of construction projects carried out effectively, efficiently, and safely. The VOSViewer mapping is used to analyse bibliometric data on artificial intelligence in the construction industry. The reference manager application is used to obtain research data. We use the words "artificial intelligence" and "construction industry" as data search keywords for this research. We search for data from 2013 through 2023. The results show that the study on artificial intelligence in the construction industry obtained 997 relevant articles published between 2013 and 2023. In addition, the results of research on artificial intelligence in the construction industry have seen a drastic drop from 2021 to 2023. Artificial intelligence in the construction industry was the most published in 2020, with 227 articles published. This research shows how important bibliometric analysis is to obtaining information about this phenomenon. The study is prospectively intended to help and be a reference for scientists and researchers in conducting and defining research topics, especially those related to artificial intelligence and the construction industry.Journal of Advanced Research in Applied Sciences and Engineering Technology520The results show that the study on artificial intelligence in the construction industry obtained 997 relevant articles published between 2013 and 2023, and the results of research on artificial intelligence in the construction industry have seen a drastic drop from 2021 to 2023.Journal of Advanced Research in Applied Sciences and Engineering Technology["Sri Rahayu", "Danny Meirawan", "Zahra Ghinaya", "Zenita Sabitri", "Jasmine Al Dhahrani", "Affero Ismail"]2024-10-04T00:00:00 217a9eef3bfa2f7f36e8714d93f831f7a31b3759Artificial intelligence is needed to perceive, remember, creatively process incoming information an order of magnitude better and faster than natural intelligence (or in reverse order), constantly replenish its reserves in order to make extraordinary decisions and actions in specific situations. However, without natural intelligence, all this is impossible to do, and therefore it would be correct to say about the improvement of the prototype (its elements), and not the creation of an artificial prototype.Juridical Sciences and Education00Without natural intelligence, all this is impossible to do, and therefore it would be correct to say about the improvement of the prototype (its elements), and not the creation of an artificial prototype.Juridical Sciences and Education["Alekper Gati", "Islam Shiraliyev"]2024-10-04T00:00:00 f3425495dbb47d694edb93f163901299d7f4ae29Medical diagnosis using artificial intelligence models uses methods of information collection via the Internet of Things (IoT) for the categorization of diseases. This may be attributed to a number different issues, such as inefficient auxiliary frameworks, high expenses associated with acquiring datasets, or difficulties encountered while constructing classifiers. The present paper analyses the recent developments in AI for medical diagnosis. The research uses a Deep Convolutional Neural Network (DCNN) to categorise medical conditions. In this paper,  a model for categorising medical diagnosis has been introduced by making it easier for the network to adapt to new medical data. This article estimates the degree to which the classification falls into the right medical diagnosis category and separates the training sample into normal, critical, and suggestion samples using a dynamic threshold to solve the diagnostic issue. The recommended strategy categorises input to help the convolutional neural network learn more. This research modifies the convolutional neural network design to accommodate for input variety and data evolution temporal dynamics. This adjustment is made with the goal of achieving the five physiological data properties of bio signals (heart rate, blood pressure, EEG, ECG, and oxygen level). Because of the realised CNN optimisation algorithm model, the prediction effect has been increased, and the accuracy rate has been found to be 92.8% in medical diagnosis, which is a reasonably good performance in machine learning algorithms.South Eastern European Journal of Public Health270The degree to which the classification falls into the right medical diagnosis category is estimated and the training sample is separated into normal, critical, and suggestion samples using a dynamic threshold to solve the diagnostic issue.South Eastern European Journal of Public Health["Dr. Saud Salman Alharbi"]2024-10-04T00:00:00 8dea200a2dd3626f26e4713565bfc9064d182448The Veterinary Record01The Veterinary record["Victor Ogedegbe"]2024-10-04T00:00:00 37c65736d0edb86e79b6d566c69524210a43f391Humanities and Social Sciences Communications440Humanities and Social Sciences Communications["Hong-Guang Zhao", "Xin-Zhu Li", "Xin Kang"]2024-10-04T00:00:00 c4062da815150daf03e8ef990e9a94412d01ec14Computational Economics20Computational Economics["Khaled AlAjmi", "Jose Deodoro", "Ashraf Khan", "Kei Moriya"]2024-10-04T00:00:00 316330209d43e80ab9aed090f6434324f96597e8Journal of Innovation Economics &amp; Management00Journal of Innovation Economics &amp; Management["Hongxia Peng"]2024-10-04T00:00:00 9da6c4814b202733cba4826de0c5e079628bc368The article presents conceptual research on the trends, vectors, perspectives, and challenges of introducing AI technologies into education system. Both worldwide trends and Ukrainian specifics are considered. One of the core findings of research is a claim that while there is transition already from Education 4.0 to Education 5.0, and rapid growth of AI solutions for education is observed, motivation of teachers themselves, their continuous advancement in these technologies based on their own interest is crucial factor.Edelweiss Applied Science and Technology240Conceptual research on the trends, vectors, perspectives, and challenges of introducing AI technologies into education system claim that while there is transition already from Education 4.0 to Education 5.0, and rapid growth of AI solutions for education is observed, motivation of teachers themselves, their continuous advancement in these technologies based on their own interest is crucial factor.Edelweiss Applied Science and Technology["S. Kryshtanovych", "Yuliia Bekh", "Olga Stadnichenko", "Zhanna Shevchenko", "Viktoriia Maikher"]2024-10-04T00:00:00 18bba80c26fd51cec51a1bdc44b8f26a4ad184c2ChatGPT (generative pre-training transformer) merupakan sebuah teknologi kecerdasan buatan dari OpenAI yang berfungsi sebagai chatbot dengan kemampuan percakapan mirip manusia. ChatGPT berpotensi meningkatkan layanan kesehatan, antara lain pelayanan diabetes melitus. Tantangan saat ini adalah faktor-faktor seperti kurangnya sumber daya manusia dan rendahnya kesadaran masyarakat. Integrasi ChatGPT diusulkan untuk meningkatkan efektivitas layanan kesehatan, menawarkan dukungan diagnostik, dan sumber daya pendidikan untuk kesadaran atas masalah diabetes.Cermin Dunia Kedokteran330Cermin Dunia Kedokteran["Eric Septian Prawira", "Nurul Hikmah"]2024-10-04T00:00:00 720ad92ba739980f5ca77e3b9aa7d1886e702822Emerging Topics in Artificial Intelligence (ETAI) 202400Emerging Topics in Artificial Intelligence (ETAI) 2024["Shan Suthaharan"]2024-10-04T00:00:00 f2779dc33e4471c6ff34728e6913b57fdff084c5Emerging Topics in Artificial Intelligence (ETAI) 202400Emerging Topics in Artificial Intelligence (ETAI) 2024["Nicholas A. Petrick"]2024-10-04T00:00:00 bb473e1bfac8b353e46fd0edc9a4a45b3193cce5According to the labyrinths of legal science, forensic science, criminal procedure, criminal law, judicial expertise, operational-search activity, and other legal sciences are generally considered applied in relation to natural and exact sciences. However, practice shows that legal professionals often successfully and productively utilize discoveries and developments from physicists and mathematicians, frequently developing and refining them to a level that surpasses the initial knowledge. Creativity is the foundation of human existence; creativity cannot be forbidden. Undoubtedly, lawyers, as the best representatives of legal professions, will contribute to the development of AI, where the main aspects are seen as systematic and consistent approaches.Juridical Sciences and Education00Practice shows that legal professionals often successfully and productively utilize discoveries and developments from physicists and mathematicians, frequently developing and refining them to a level that surpasses the initial knowledge.Juridical Sciences and Education["Javanshir Suleymanov"]2024-10-04T00:00:00 b4c14d7643ce884bfed8de90d525b522ef8ccd10Parkinson’s Disease (PD) is a progressive neurodegenerative disorder where the loss of neurons and synaptic dysfunction can be seen. The alpha-synuclein present in the filament of Lewy body gets mutated where it is connected to familial PD. The gait position, tremors, tone, Stiffness and handwriting will be seen as symptoms. Based on these, biomarkers have been used but those are late and costly in clinical practice to identify quickly weather symptoms related to PD. AI, identifies risk assessment before clinical diagnosis by the help of radio waves, breath belt data set in the natural breathing signals. Nanorobots are also introduced in the identification with help of AI and ML. This gives accurate results. The signal transmission curve will be obtained then the range in between 0.8 to 0.9 indicates the diagnosis as PD. The integration of AI algorithms in PD research has particularly shown promise in enhancing diagnostic accuracy, predicting disease progression, and personalizing treatment plans based on individual patient profiles. There are some disadvantages like data privacy and security, algorithm bias, Integration with clinical practice, cost and accessibility. However, there are several advantages like early diagnosis, Research and drug development, personalized treatment, monitoring and management. Artificial intelligence holds and plays a vital role in the future, such as Early diagnosis personalized treatment plan, continue monitoring, Remote case telemedicine, predictive analytics, integration with other technologies are capabilities of artificial intelligence improve the outcome and quality of the life for individual living with better treatment strategiesJournal of Pharma Insights and Research130The integration of AI algorithms in PD research has particularly shown promise in enhancing diagnostic accuracy, predicting disease progression, and personalizing treatment plans based on individual patient profiles.Journal of Pharma Insights and Research["Sree Mahalakshmi Pasumarthy", "Deepika Katam", "Keerthika Neella", "Sowmya Gayathri Mylavarapu"]2024-10-04T00:00:00 487e35dc492246be64660394acc366acc9459050Emerging Topics in Artificial Intelligence (ETAI) 202400Emerging Topics in Artificial Intelligence (ETAI) 2024["Axel Wism\u00fcller"]2024-10-04T00:00:00 1305e5bd869bdadbd2d0abcfbdb7cf46ff2f5d8cIn recent years, artificial intelligence (AI) has become increasingly integrated into education, reshaping traditional learning environments. Despite this, there has been limited investigation into fully operational artificial human lecturers. To the best of our knowledge, our paper presents the world's first study examining their deployment in a real-world educational setting. Specifically, we investigate the use of"digital teachers,"AI-powered virtual lecturers, in a postgraduate course at the Hong Kong University of Science and Technology (HKUST). Our study explores how features such as appearance, non-verbal cues, voice, and verbal expression impact students' learning experiences. Findings suggest that students highly value naturalness, authenticity, and interactivity in digital teachers, highlighting areas for improvement, such as increased responsiveness, personalized avatars, and integration with larger learning platforms. We conclude that digital teachers have significant potential to enhance education by providing a more flexible, engaging, personalized, and accessible learning experience for students.arXiv.org660It is concluded that digital teachers have significant potential to enhance education by providing a more flexible, engaging, personalized, and accessible learning experience for students.ArXiv["Ching Christie Pang", "Yawei Zhao", "Zhizhuo Yin", "Jia Sun", "Reza Hadi Mogavi", "Pan Hui"]2024-10-04T00:00:00 91a5f421747c24868c49ac648d8e1ecdf081705fLarge language models are prone to misuse and vulnerable to security threats, raising significant safety and security concerns. The European Union's Artificial Intelligence Act seeks to enforce AI robustness in certain contexts, but faces implementation challenges due to the lack of standards, complexity of LLMs and emerging security vulnerabilities. Our research introduces a framework using ontologies, assurance cases, and factsheets to support engineers and stakeholders in understanding and documenting AI system compliance and security regarding adversarial robustness. This approach aims to ensure that LLMs adhere to regulatory standards and are equipped to counter potential threats.arXiv.org192This research introduces a framework using ontologies, assurance cases, and factsheets to support engineers and stakeholders in understanding and documenting AI system compliance and security regarding adversarial robustness.ArXiv["Tomas Bueno Momcilovic", "Beat Buesser", "Giulio Zizzo", "Mark Purcell", "Dian Balta"]2024-10-04T00:00:00 79173dc1fea859a5eb12ec53187f6790f976266dRecent breakthroughs in machine learning and artificial intelligence, fueled by scientific data, are revolutionizing the discovery of new materials. Despite the wealth of existing scientific literature, the availability of both structured experimental data and chemical domain knowledge that can be easily integrated into data-driven workflows is limited. The motivation to integrate this information, as well as additional context from first-principle calculations and physics-informed deep learning surrogate models, is to enable efficient exploration of the relevant chemical space and to predict structure-property relationships of new materials a priori. Ultimately, such a framework could replicate the expertise of human subject-matter experts. In this work, we present dZiner, a chemist AI agent, powered by large language models (LLMs), that discovers new compounds with desired properties via inverse design (property-to-structure). In specific, the agent leverages domain-specific insights from foundational scientific literature to propose new materials with enhanced chemical properties, iteratively evaluating them using relevant surrogate models in a rational design process, while accounting for design constraints. The model supports both closed-loop and human-in-the-loop feedback cycles, enabling human-AI collaboration in molecular design with real-time property inference, and uncertainty and chemical feasibility assessment. We demonstrate the flexibility of this agent by applying it to various materials target properties, including surfactants, ligand and drug candidates, and metal-organic frameworks. Our approach holds promise to both accelerate the discovery of new materials and enable the targeted design of materials with desired functionalities. The methodology is available as an open-source software on https://github.com/mehradans92/dZiner.861["Mehrad Ansari", "Jeffrey Watchorn", "Carla E. Brown", "Joseph S. Brown"]2024-10-04T00:00:00 2b45e7840aa016fba404757c29f2e57d65e419a1The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) with enterprise systems presents immense potential for driving innovation and efficiency across various industries. However, the rapid adoption of these technologies raises ethical concerns, particularly around data privacy, bias, transparency, and environmental sustainability. This review examines the development of ethical AI in the context of sustainable enterprises, focusing on how responsible AI practices can be effectively integrated with IoT and enterprise systems to achieve long-term business value while adhering to ethical standards. By analyzing key frameworks, challenges, and case studies, this paper provides insights into fostering responsible AI development that aligns with organizational goals and global sustainability efforts.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402301This review examines the development of ethical AI in the context of sustainable enterprises, focusing on how responsible AI practices can be effectively integrated with IoT and enterprise systems to achieve long-term business value while adhering to ethical standards.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Sohana Akter"]2024-10-04T00:00:00 d73d61ca27bb80fbb67736cfe97b748a6c3ebf70In the age of rapid technological advancement, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into Human Resource Management (HRM) practices is reshaping the landscape of modern workplaces. This article explores the multifaceted dynamics of this integration, exploring how AI/ML technologies are revolutionizing HRM while emphasizing the critical importance of human collaboration in this evolving paradigm. Through an in-depth analysis of current trends, challenges, and opportunities, this paper elucidates the myriad benefits that AI/ML offer to HRM, ranging from enhanced decision-making to streamlined processes. However, it also underscores the indispensable role of human expertise, empathy, and ethical considerations in harnessing the full potential of AI/ML in HRM. Drawing upon a rich body of existing literature and insightful case studies, this article provides nuanced insights into the future trajectory of HRM, advocating for a balanced approach where AI/ML seamlessly integrates with human capabilities to foster a collaborative environment. Ultimately, this exploration serves as a guiding beacon for organizations navigating the AI/ML-driven future of HRM, emphasizing the imperative of aligning technological innovation with human-centric values to achieve organizational success and employee well-being in the digital age. Keywords: AI, ML, Human Resource Management, Collaboration, Technology, Future of WorkInternational journal of research and review00This exploration serves as a guiding beacon for organizations navigating the AI/ML-driven future of HRM, emphasizing the imperative of aligning technological innovation with human-centric values to achieve organizational success and employee well-being in the digital age.International Journal of Research and Review["Sunil Basnet"]2024-10-04T00:00:00 4a6c1a836d1d61fa58929703bef57ac72735c553This study examines the predictability of artificial intelligence (AI) models for weather prediction. Using a simple deep-learning architecture based on convolutional long short-term memory and the ERA5 data for training, we show that different time-stepping techniques can have a strong influence on the model performance and weather predictability. Specifically, a small-step approach for which the future state is predicted by recursively iterating an AI model over a small time increment displays strong sensitivity to the type of input channels, the number of data frames, or forecast lead times. In contrast, a big-step approach for which a current state is directly projected to a future state at each corresponding lead time provides much better forecast skill and a longer predictability range. In particular, the big-step approach is very resilient to different input channels, or data frames. In this regard, our results present a different method for implementing global AI models for weather prediction, which can optimize the model performance even with minimum input channels or data frames.310["Chanh Kieu"]2024-10-04T00:00:00 a8f8c70a68bd86f27dbb46d6cca286c4126d25d3Background: This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growing significance of AI in healthcare underscores the need for healthcare managers to prioritize investments in this technology. Purpose: To assess the increasing impact of AI on surgical practices through a comprehensive analysis of scientific literature, providing insights that can guide managerial decision-making in adopting AI solutions.Research Design: The study analyzes over 6000 scientific articles published since 1990 to evaluate trends and contributions in the field, informing managers about the current landscape of AI in surgery.Study Sample: The research focuses on publications from various influential publishers across North America, Northern Asia, and Eastern & Western Europe, highlighting key markets for AI implementation in surgical settings.Data Collection and Analysis: A bibliometric approach was utilized to identify key contributors and influential journals. At the same time, text-mining techniques highlighted significant keywords related to AI in surgery, aiding managers in recognizing essential areas for further exploration and investment.Results: The year 2022 marked a significant upsurge in publications, indicating widespread AI integration in healthcare. The U.S. emerged as the foremost contributor, followed by China, the UK, Germany, Italy, the Netherlands, and India. Key journals, such as Annals of Surgery and Spine Journal, play a crucial role in disseminating research findings, serving as valuable resources for managers seeking to stay informed.Conclusions: The findings underscore AI's pivotal role in enhancing diagnostic precision, predicting treatment outcomes, and improving operational efficiency in surgical practices. This progress represents a significant milestone in modern medical science, paving the way for intelligent healthcare solutions and further advancements in the field. Healthcare managers should leverage these insights to foster innovation and improve patient care standards.Surgical Innovation350The findings underscore AI's pivotal role in enhancing diagnostic precision, predicting treatment outcomes, and improving operational efficiency in surgical practices, paving the way for intelligent healthcare solutions and further advancements in the field.Surgical innovation["Minh\u2010Hieu Le", "Thu-Thao Le", "Phung Phi Tran"]2024-10-04T00:00:00 e99fbdf63f0190113690b88ce0656385048c69ccThe scope of the video game industry and the use of neural networks in it have been studied. The impact of this technology on the computer games development process has been analyzed, the efficiency of its application in this area noted. The generalized scheme of video games development with identification of the stages most favorable for neural networks application has been considered. The possibilities of artificial intelligence in the video game characters’ visual image development, starting from the idea stage, touching the concept creation and ending with the realization stage, have been studied. The possibility of optimizing this process by implementing neural network technology has been shown. The neural networks application in the processes of writing game scripts and implementing dialog voicing in a game has been analyzed. The difference between the variability of plots and conversations of game characters created with the use of artificial intelligence model has been shown. The future concept of using neural networks as a digital twin of a video game developer has been considered. Estimates of the video game industry development through the generative artificial intelligence implementation have been formulated. As the study result, the positive influence of neural networks application on the computer games development up to the possible full automation of the industry under consideration due to the active use of this technology in the near future has been proved.Вестник университета00The positive influence of neural networks application on the computer games development up to the possible full automation of the industry under consideration due to the active use of this technology in the near future has been proved.Vestnik Universiteta["V. Godin", "A. Terekhova", "D. N. Bulatov", "I. \u0410. Zaremba"]2024-10-04T00:00:00 eea7d935cdd48169f5e62451e56bfe2fd6fd152cAt this point, you have heard about generative artificial intelligence and, most likely, dabbled with it. You may have tried out a prompt with Chat GPT or created an image using Microsoft Copilot. At first, you were a bit cautious, but it was less intimidating with practice. You felt these practice sessions were necessary — everyone is talking about generative AI — and, with yet another lecture or webinar scheduled on your campus to discuss generative AI, these “exercises” allow you to participate in the current higher education environment with some working knowledge.Campus Legal Advisor00These “exercises” allow you to participate in the current higher education environment with some working knowledge and allow you to participate in the current higher education environment with some working knowledge.Campus Legal Advisor["Katherine C. Aquino", "Adam R. Lalor", "Ceceilia Parnther"]2024-10-04T00:00:00 73a9fc087d17d64754a9466fcbd603c2771ff0bbAddressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.Journal of Machine and Computing1230This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks, and encompasses dedicated assessments for each data segment.Journal of Machine and Computing["Firas Tayseer Mohammad Ayasrah", "Nabeel S. Alsharafa", "Sivaprakash S", "Srinivasarao B", "Sudhakar Sengan", "Kumaran N"]2024-10-05T00:00:00 c1469d718d64e3a3596b2f35181068f2914640d3AI in fraud detection and financial risk management has taken this role of prevention and combating fraud closely related to organizations and the losses they incur a next level. This paper aims to discuss the use of artificial intelligence models in the process of detecting frauds and preventing and reducing financial risks in such markets as banking, insurance, and fintech. Today, through machine learning algorithms, deep learning techniques, and data analysis, the AI improves the speed, accuracy and effectiveness of fraud detection. This paper discusses the current AI models and business use incorporating the success story and the business outcomes which has encountered sometime to have the best result. Furthermore, the paper examines other important issues of AI application management such as data security and liberation, and complete fairness control. Using examples as well as statistical data in this AI for business article, we show how corporations have managed to minimize their risks while lowering their expenses with the use of artificial intelligence technology. This research outlines ideas on how organizations can implement AI into fraud detection systems and what can be done in future to enhance the solutions. This paper adds to the emerging body of knowledge on AI’s impact on finance and security, and demonstrates AI’s ability to influence the future of the industry.International Journal For Multidisciplinary Research374It is shown how corporations have managed to minimize their risks while lowering their expenses with the use of artificial intelligence technology, and AI’s ability to influence the future of the industry is demonstrated.International Journal For Multidisciplinary Research["Tariqul Islam", "S. M. Islam", "Ankur Sarkar", "A. J. M. Obaidur", "Rahman Khan", "Rakesh Paul", "Md Shadikul Bari"]2024-10-05T00:00:00 919d1de2e2682bba7de60369da449996baf6dc94The rapid advancement of artificial intelligence (AI) has made it an indispensable tool for organizations, transforming how leaders make decisions and promising to revolutionize team dynamics. Despite the growing importance of AI at the organizational leadership level, there is a lack of reliable scientific research on how it will change current leadership practices. To address this gap, this study conducted expert interviews with 10 IT companies in Pakistan to better understand how AI impacts organizational leadership. The findings suggest that AI will bring about significant changes in leadership practices, including shifting towards intelligent approaches, making leaders tech‐savvy, expanding human capabilities, learning and unlearning traditional managerial competencies, fostering AI‐congruent leadership characteristics, benchmarking sustainability, and coaching leaders for the future. AI fundamentally alters how leaders make decisions and holds the potential of transforming future team dynamics. These findings have important implications for the future of organizational leadership practices and research.Global Business and Organizational Excellence621The findings suggest that AI will bring about significant changes in leadership practices, including shifting towards intelligent approaches, making leaders tech‐savvy, expanding human capabilities, learning and unlearning traditional managerial competencies, fostering AI‐congruent leadership characteristics, benchmarking sustainability, and coaching leaders for the future.Global Business and Organizational Excellence["Syed Yawar Abbas Zaidi", "Muhammad Aslam", "Faisal Mahmood", "Bilal Ahmad", "Sadia Bint Raza"]2024-10-05T00:00:00 b0b8df9062dd5b7860ab7b10a27ca35c56cf8ba7This research paper examines the influence of Artificial Intelligence (AI) on the efficiency of project management, concentrating on the ways in which AI tools and techniques improve different facets of project management. The study initiates with a comprehensive examination of project management and the increasing significance of AI in various domains, emphasizing the shortcomings of conventional methodologies and the capacity of AI to tackle these issues. The study seeks to evaluate the impact of AI on project processes, pinpoint effective AI tools, and investigate the advantages and obstacles associated with AI integration. A thorough examination of existing literature investigates AI tools, including machine learning, natural language processing, and predictive analytics, along with their applications in planning, scheduling, resource allocation, risk management, communication, and performance monitoring. Case studies and empirical evidence illustrate the beneficial effects of AI on project outcomes, while also highlighting existing gaps in the literature that warrant further investigation. The mixed-methods approach integrates qualitative and quantitative data sourced from surveys, interviews, and case studies, employing both thematic and statistical analysis. The findings indicate that AI plays a crucial role in enhancing project efficiency through the reduction of timelines, improvement of cost control, optimization of resources, and facilitation of proactive risk management, in addition to enhancing communication and collaboration. Nonetheless, factors such as initial investment, skill requirements, resistance to change, and ethical considerations are acknowledged. The discussion outlines actionable strategies for project managers, highlighting the importance of training, incremental integration, ensuring data quality, and promoting innovation. The study concludes by offering recommendations for future research regarding the long-term effects of AI, conducting cross-industry analyses, and creating AI tools specifically designed for project management. This approach aims to provide empirical evidence and strategic insights into the integration of AI for enhanced project outcomes.International journal of management information systems and data science01The findings indicate that AI plays a crucial role in enhancing project efficiency through the reduction of timelines, improvement of cost control, optimization of resources, and facilitation of proactive risk management, in addition to enhancing communication and collaboration.International journal of management information systems and data science["Muhammed Zakir Hossain", "Latul Hasan", "Md Abutaher Dewan", "Nurjahan Akter Monira"]2024-10-05T00:00:00 c2b0a28b842565ad0e83846361134fc3dd6fe3d8This study leverages explainable artificial intelligence (XAI) techniques to analyze public sentiment towards Environmental, Social, and Governance (ESG) factors, climate change, and green finance. It does so by developing a novel multi-task learning framework combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning to extract nuanced insights from a large corpus of social media data. Our approach integrates state-of-the-art models, including the SenticNet API, for sentiment analysis and implements multiple XAI methods such as LIME, SHAP, and Permutation Importance to enhance interpretability. Results reveal predominantly positive sentiment towards environmental topics, with notable variations across ESG categories. The contrastive learning visualization demonstrates clear sentiment clustering while highlighting areas of uncertainty. This research contributes to the field by providing an interpretable, trustworthy AI system for ESG sentiment analysis, offering valuable insights for policymakers and business stakeholders navigating the complex landscape of sustainable finance and climate action. The methodology proposed in this paper advances the current state of AI in ESG and green finance in several ways. By combining aspect-based sentiment analysis, co-reference resolution, and contrastive learning, our approach provides a more comprehensive understanding of public sentiment towards ESG factors than traditional methods. The integration of multiple XAI techniques (LIME, SHAP, and Permutation Importance) offers a transparent view of the subtlety of the model’s decision-making process, which is crucial for building trust in AI-driven ESG assessments. Our approach enables a more accurate representation of public opinion, essential for informed decision-making in sustainable finance. This paper paves the way for more transparent and explainable AI applications in critical domains like ESG.Mathematics280Mathematics["Wihan van der Heever", "Ranjan Satapathy", "Ji Min Park", "Erik Cambria"]2024-10-05T00:00:00 f18230764b7628fac6e3d4f816f6c69d1c1329eaConducting a literature review is a mandatory initial stage in scientific research on a specific topic. However, this task is becoming much more complicated in certain areas (such as food science and technology) due to the huge increase in the number of scientific publications. Different tools based on artificial intelligence could be very useful for this purpose. This paper addresses this challenge by developing and checking different tools applicated to an emerging topic in food science and technology: “hybrid meat products”. The first tool to be applied was based on Natural Language Processing and was used to select and reduce the initial number of papers obtained from a traditional bibliographic search (using common scientific databases such as Web Science and Scopus) from 938 to 178 (a 87% reduction). The second tool was a project based on the interplay between Retrieval-Augmented Generation (RAG) and LLAMA 3, which was used to answer key questions relating to the topic under review (“hybrid meat products”) but limiting the context to the scientific review obtained after applying the first AI tool. This new strategy for reviewing scientific literature could be a major advance on from the traditional literature review procedure, making it faster, more open, more accessible to everyone, more effective, more objective, and more efficient—all of which help to fulfill the principles of open science.Informatics330A new strategy for reviewing scientific literature could be a major advance on from the traditional literature review procedure, making it faster, more open, more accessible to everyone, more effective, more objective, and more efficient—all of which help to fulfill the principles of open science.Informatics["J. Fern\u00e1ndez\u2010L\u00f3pez", "Fernando Borr\u00e1s-Rocher", "M. Viuda\u2010Martos", "J. P\u00e9rez-\u00c1lvarez"]2024-10-05T00:00:00 b5b288386adff2f37d61f4727d5f10d471763762: In the contemporary digital era, artificial intelligence (AI) is reshaping human interactions, decision - making processes, and societal structures. This research aims to explore the ethical dimensions of AI with a focus on human rights, analyzing the potential for both positive advancements and ethical dilemmas. The study is grounded in a social - philosophical framework, examining how AI technologies challenge fundamental human rights such as privacy, equality, and autonomy. Motivated by increasing global concerns about the unchecked growth of AI, the purpose of this study is to understand how these technologies align or conflict with the principles of human rights. Through a qualitative analysis, incorporating case studies and scholarly literature, this research critically evaluates AI’s impact on society, drawing from philosophical discourse, ethical theory, and human rights conventions. The findings highlight the dual nature of AI: it can enhance human welfare but also threatens to deepen inequalities and infringe upon rights. Key conclusions indicate the need for stronger ethical guidelines and regulatory frameworks to balance innovation with human dignity. The study’s implications suggest that a collaborative approach involving policymakers, technologists, and ethicists is crucial to ensure that AI developments uphold and promote human rights in a just and equitable manner.International Journal of Science and Research (IJSR)190The findings highlight the dual nature of AI: it can enhance human welfare but also threatens to deepen inequalities and infringe upon rights, indicating the need for stronger ethical guidelines and regulatory frameworks to balance innovation with human dignity.International Journal of Science and Research (IJSR)["Munna Khatun"]2024-10-05T00:00:00 2f9aee363a411212c8082ea7106cee404a67c3edAbdominal Radiology660The studies reviewed indicate that AI-based algorithms demonstrate high accuracy, sensitivity, specificity, and AUCs in detecting and characterizing FLLs, and these algorithms excel in differentiating between benign and malignant lesions, optimizing diagnostic protocols, and reducing the needs of invasive procedures.Abdominal radiology["Julia Arribas Anta", "Juan Moreno-Vedia", "Javier Garc\u00eda L\u00f3pez", "Miguel Angel Rios-Vives", "Josep Munuera", "J\u00falia Rodr\u00edguez-Comas"]2024-10-05T00:00:00 dd30272ee9831384b5344fb44794326313bb2eb6: The integration of Artificial Intelligence (AI) in Health Diagnosis presents a transformative approach in improving Health outcomes. According to a Harvard Medical school study, the estimated market for AI is expected to be around $21.74 Billion by 2032 compared to $1.07 Billion in 2022[1]. The world Economic Forum also predicted that AI could help with improved health outcomes with its ability to use data from diverse and concealed sources that have been existed across healthcare.[6] This paper explores various AI diagnostic models that can improve diagnosis and inspect real-life use cases of AI for medical diagnosis. By integrating vast amounts of medical data, including electronic health records, imaging records, and genomic information, Augmented AI facilitates a more comprehensive understanding of patient conditions. This review paper further highlights the promising future of Augmented AI as a vital tool in the evolution of medical diagnostics, aiming to enhance both clinician performance and patient outcomes.International Journal of Science and Research (IJSR)200This paper explores various AI diagnostic models that can improve diagnosis and inspect real-life use cases of AI for medical diagnosis and highlights the promising future of Augmented AI as a vital tool in the evolution of medical diagnostics.International Journal of Science and Research (IJSR)["Dhivya Sudeep"]2024-10-05T00:00:00 a85f4be25007a05dc9c24f4bf1304d3166d30419Digital marketing has changed the way companies do business due to the widespread use of artificial intelligence (AI). AI has changed the way businesses talk to their customers by making it easier to create personalised content. Due to the industry’s growing research and development, it can be hard to keep up with the latest trends and technologies. A detailed literature review on AI in digital marketing looks at the research that has been done in this area. Additionally, the study stresses how important it is to investigate the moral effects of using AI in marketing. The focus of the study is on how AI affects privacy, fairness and accountability. The study says that marketers should focus on encouraging openness and giving users control over how AI technology is used. The study suggests that more research be done on how AI affects job loss and the job market. Bibliometric analysis for the last 10 years is done using the global research database. This research makes the case for doing research that uses ideas from different fields, such as marketing, computer science and social science. Researchers and industry practitioners who are interested in how AI and digital marketing work together can learn from this study.Abhigyan190The study says that marketers should focus on encouraging openness and giving users control over how AI technology is used, and suggests that more research be done on how AI affects job loss and the job market.Abhigyan["Yatika Khandelwal", "Sejal Malhotra", "Rattan Sharma", "Gaurav Sarin"]2024-10-05T00:00:00 a07d7277956b6c2e09fa5a081ce0a332ce46b172Humanities and Social Sciences Communications430The storyline analysis concluded that Chicago Med provided thought-provoking positive and negative scenarios about applying different types of AI in the surgical and emergency departments, and encouraged critical thinking about medical AI.Humanities and Social Sciences Communications["Eszter N\u00e1dasi", "Mih\u00e1ly H\u00e9der"]2024-10-05T00:00:00 2128c2f345196df9332bee81bc94a0477361cf05The study aims to identify the degree of possession of government school principals in the Karbala Governorate Education Directorate of artificial intelligence applications. The descriptive analytical approach was used. To achieve the study objectives, a questionnaire which contained (34) items was developed, divided into five dimensions. The study community was (114) male and female principals. The study sample consisted of (94) male and female managers, and the sample was selected randomly. The results of the study showed that the degree of possession of government school principals in the Karbala Governorate Education Directorate of artificial intelligence applications was average. The results of the study also showed that there were no statistically significant differences at the significance level (α=0.05) attributed to the study variables (gender, academic qualification, experience in artificial intelligence techniques). In light of the study results, the researchers recommended several recommendations, most notably: paying attention to training courses in the field of information and communications technology, especially in the subject of artificial intelligence, by organizing meetings and workshops with the aim of raising awareness among individuals and society about the importance of artificial intelligence, encouraging the Iraqi Ministry of Education to its teaching staff and providing them with incentives to employ artificial intelligence applications in their administrative and educational work, introducing artificial intelligence applications into curricula and courses and paying attention to them, and providing specialized teaching staff in this field.Evolutionary Studies in Imaginative Culture360The results of the study showed that the degree of possession of government school principals in the Karbala Governorate Education Directorate of artificial intelligence applications was average and there were no statistically significant differences at the significance level.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Ahmad Haddad Abed", "Elham Kaviani", "Mahdi Sadeghi", "Anahita Faraji"]2024-10-05T00:00:00 24e7cb0386a3080dcfba1421b04b5fa765be013bThe study aimed to evaluate the nursing students’ perception and attitudes toward utilization of Artificial Intelligence in Health care. Descriptive research design. The study was conducted at Vision Medical College, Jeddah. KSA. Two hundred and six students registered in the nursing program were included in the study by using convenience sampling. Three tools were used for data collection, Self-Administered questionnaire, Covered student nurses' demographic characteristics, Perception toward Artificial Intelligence Questionnaire and Students’ attitudes towards Artificial Intelligence Questionnaire.  The majority of participants responded that they are familiar with Artificial Intelligence 79.6%, there was a significant positive correlation between student nurse’ demographic characteristics (Living Location, education, Self-Assessment of Technological Competence and Familiarity with Artificial Intelligence) and their attitudes toward using the artificial intelligence.  The mean score of the attitude scale was (M= 3.29, SD = 0.76), while the mean scale score of the perception scale was (M= 3.43, SD = 0.69). Likewise, a significant positive association was observed between perception of students and their attitudes towards artificial intelligence (β = 0.855, p < 0.001). The current study revealed that the students most likely held positive attitudes towards AI utilization and had a favorable perception toward utilization of artificial intelligence in health care. Integration of artificial intelligence technology and their utilization in nursing practice as a partner for improving patient outcomes in the health care field.Edelweiss Applied Science and Technology180The current study revealed that the students most likely held positive attitudes towards AI utilization and had a favorable perception toward utilization of artificial intelligence in health care.Edelweiss Applied Science and Technology["S. Alsenany", "Obay A. Almaraira", "Suzan El-Said Mansour"]2024-10-05T00:00:00 66da38eeaade6a8a4dfcc77829eb51573c77b6e2Artificial intelligence represents a scientific and technological revolution that included a group of fields and areas.The field of education was among the field of education was among the fields that benefited from it and its services,as it worked to simplify the eduction process based on a group of applications and platforms supported by artificial intelligence,in completing this scientific paper,we relied on the descriptive and analytical approach This scientific paper aims to identify how artificial intelligence contributes to the educational field by discoverig this field and some of its applications in it.,and it was concluded that artificial intelligence harnesses all its mechanisms in order to simulate human intelligence in most of its mental processes and the field of education has benefited greatly through its investment in artificial intelligence,which has facilitated the teaching and learning process.Artificial intelligence tries to simulate human intelligence in all mental processes. It has a close connection with the field of education. This connection has resulted in the emergence of a group of applications serving the educational field. The nature of the relationship between it and the educational field is an integrated relationship represented in adding a technological character to education, while education was an easy field to apply most of the mechanisms of artificial intelligence to it. Artificial intelligence came with a group of developments in the form of applications and programs that included the teacher, the learner, the study material, and the curricula used in teaching. Key words: Artificial intelligence, Education, Technology International Science and Technology Journal10It was concluded that artificial intelligence harnesses all its mechanisms in order to simulate human intelligence in most of its mental processes and the field of education has benefited greatly through its investment in artificial intelligence, which has facilitated the teaching and learning process.International Science and Technology Journal["\u062d\u0643\u064a\u0645\u0629 \u0643\u0631\u064a\u0645\u064a"]2024-10-05T00:00:00 c9b2be2bb43d4ea45e0d726253d278992be49d37: The Property and Casualty (P&C) insurance industry in the United States is continuously evolving, driven by the need to manage and mitigate risks for businesses, especially as they expand into global markets. Multinational insurers are instrumental in providing comprehensive risk management solutions, supporting US businesses’ resilience and economic stability. A key component in this process is the Controlled Master Program (CMP), which addresses the complexities of cross-border transactions. However, despite the robust applications used by insurance carriers, significant challenges remain in managing multinational insurance policies. Artificial Intelligence (AI) offers promising solutions to many of these challenges, such as data standardization, legacy system integration, regulatory compliance, and cybersecurity. AI-driven data standardization tools can harmonize diverse datasets from various carriers, enhancing the efficiency of claims management and underwriting. Similarly, AI can bridge the gap between legacy systems and modern technologies through intelligent integration platforms. Compliance management is made more efficient through AI-based tools that monitor and ensure adherence to regulatory requirements. AI-powered cybersecurity measures provide real-time threat detection, reducing the risk of data breaches. Moreover, AI supports scalability, interoperability, and data privacy, ensuring that systems can handle growing volumes of data and transactions while maintaining seamless operations with third-party systems. Continuous monitoring and automated updates further enhance the performance and compliance of insurance programs. Ultimately, AI-driven tools can transform the management of controlled master programs, allowing insurers to streamline operations, ensure compliance, and remain competitive in a rapidly changing industry.International Journal of Science and Research (IJSR)120Artificial Intelligence-driven tools can transform the management of controlled master programs, allowing insurers to streamline operations, ensure compliance, and remain competitive in a rapidly changing industry.International Journal of Science and Research (IJSR)["Imran Ur Rehman"]2024-10-05T00:00:00 9c2624913fa23e21408c37da0fcfedff1bcc6002The development of artificial intelligence (AI) technologies and machine learning algorithms is increasingly influencing various aspects of social life, gradually finding its place not only in social media but also in journalism (Newman). They are actively being integrated into various fields of mass media, enabling the automation of several processes within media companies, thereby optimizing the work of journalists, editors, and media managers. This topic represents a pertinent issue in the modern information society (Túñez López et al.). AI and its machine learning capabilities have become integral parts of the processes of content creation, analysis, and distribution, bringing new opportunities along with significant challenges. For instance, personalization algorithms allow for the adaptation of information to the individual interests and preferences of each user, increasing their engagement and satisfaction with the content. Thus, social networks and many other internet platforms are personalized for each user based on their demographic profiles and personal data. This article provides an overview of current scientific data on the potential risks associated with the use of content personalization algorithms in mass media. The results and conclusions of the article will help to better understand the nature of these risks and the associated challenges for the field of mass communication.Communicology390An overview of current scientific data on the potential risks associated with the use of content personalization algorithms in mass media is provided to better understand the nature of these risks and the associated challenges for the field of mass communication.Communicology["A. A. Tikhoniuk"]2024-10-05T00:00:00 6f5cc9a7664e4cb7ca726a23b0d11e8639a816fb: The proper selection of a demand forecasting method is directly linked to the success of supply chain management (SCM). However, today’s manufacturing companies are confronted with uncertain and dynamic markets. Consequently, classical statistical methods are not always appropriate for accurate and reliable forecasting. Algorithms of Artificial intelligence (AI) are currently used to improve statistical methods. Existing literature only gives a very general overview of the AI methods used in combination with demand forecasting. This paper provides an analysis of the AI methods published in the last five years (2017 - 2021). Furthermore, a classification is presented by clustering the AI methods in order to define the trend of the methods applied. Finally, a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented. The goal is to support the selection of the appropriate AI method to optimize demand forecasting.International Journal of Science and Research (IJSR)50An analysis of the AI methods published in the last five years (2017 - 2021) and a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented to support the selection of the appropriate AI method to optimize demand forecasting.International Journal of Science and Research (IJSR)["Manikandan Natarajan"]2024-10-05T00:00:00 d74ad1929616ba304df6e5f419815c6d225f38c5This Article thoroughly examines the revolutionary impact of Artificial Intelligence (AI) on improving threat detection and response tactics within the swiftly changing realm of cybersecurity. Conventional security measures, struggling against the complexity of contemporary cyber threats, fail to provide adequate protection. In response, AI, driven by machine learning algorithms and predictive analytics, becomes a dynamic and adaptive entity strengthening digital defenses.  The investigation commences with a comprehensive analysis of the methods by which AI enhances danger detection. Behavioral analytics utilizes AI to assess user behaviors and network activity, creating a proactive baseline, while anomaly detection and predictive analysis harness machine learning to recognize deviations from the norm and forecast potential dangers. This comprehensive strategy enables organizations to remain proactive against emerging cyber threats.  Moreover, the study explores the crucial function of AI in incident response. AI-driven automated incident analysis expedites reaction times by rapidly analyzing and prioritizing security warnings. The amalgamation of AI with threat intelligence streams guarantees a perpetually updated knowledge repository, enabling organizations to respond adeptly to emerging dangers. The dynamic flexibility of AI allows systems to evolve and learn from each incidence, hence enhancing their defensive capacities over time.  The discourse recognizes the significant advantages of AI in cybersecurity while simultaneously addressing the obstacles associated with its application. False positives, a potential drawback, require a measured approach to prevent the perception of typical action as harmful. Ethical factors, including privacy concerns and responsible AI practices, highlight the necessity for a judicious and principled incorporation of AI in cybersecurity.  The paper underscores the essential collaborative synergy between human expertise and AI technologies. The essay emphasizes the importance of continuous investment in AI training programs for cybersecurity professionals, acknowledging that AI is best successful when enhanced by human insights. Additionally, it advocates for routine security audits to assess and refine cybersecurity protocols, collaborative research efforts to tackle ethical issues, and user education activities to strengthen collective defenses. As the digital landscape evolves, the incorporation of AI in cybersecurity seems not as a cure-all but as a formidable ally. This partnership guarantees a robust protection against increasingly complex cyber-attacks, reinforcing the basis for a secure digital future.International Journal of Education, Management, and Technology200The essay emphasizes the importance of continuous investment in AI training programs for cybersecurity professionals, acknowledging that AI is best successful when enhanced by human insights, and advocates for routine security audits to assess and refine cybersecurity protocols, collaborative research efforts to tackle ethical issues, and user education activities to strengthen collective defenses.International Journal of Education, Management, and Technology["Chinenye Cordelia Nnamani"]2024-10-05T00:00:00 491977b0a150c09462749b841b695283415dab97Advances in Radiation Oncology281Artificial intelligence-based autosegmentation generates OAR contours of comparable quality to manual segmentation for both pelvic and HN cancer patients in LMICs, with substantial time savings.Advances in Radiation Oncology["S. Kibudde", "A. Kavuma", "Yao Hao", "Tianyu Zhao", "Hiram Gay", "J. van Rheenen", "P. M. Jhaveri", "Minjmaa Minjgee", "E. Vanchinbazar", "Urdenekhuu Nansalmaa", "Baozhou Sun"]2024-10-05T00:00:00 831b9c92a4604029ae5cf42c72413cff02e7dca0International Journal of Science and Research (IJSR)02International Journal of Science and Research (IJSR)["Jaishankar Inukonda"]2024-10-05T00:00:00 9d33d5d10be837d645d25c3516a807077ee59ef3International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2024-10-05T00:00:00 ebbed6c2ad925d8c1c95bb2a7bc8b2fb080dcb8dInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Teena Rawat", "Trisha Shetty"]2024-10-05T00:00:00 c787f706501255dc3ff2fbbfcee94caae4c789b1Japan Journal of Research00Japan Journal of Research["Grigorios Avdikos"]2024-10-05T00:00:00 b05de4d1b13c01e808dc621809c22400db8b5a82International Journal of Systems Assurance Engineering and Management390Int. J. Syst. Assur. Eng. Manag.["Ramendra Verma", "Shikha Kapoor"]2024-10-05T00:00:00 1f5382c719a79cbe15fb378139f2b43ba47cb5e1В настоящее время все более широко распространяется практика создания произведений с использованием искусственного интеллекта, что неизбежно затрагивает действующее законодательство об авторском праве. Внедрение в творческую деятельность технологий искусственного интеллекта принципиально меняет процесс создания произведений литературы, науки и искусства, поскольку многие решения, связанные с интеллектуальной деятельностью, компьютерная программа принимает без вмешательства человека. Анализируя российское и зарубежное законодательство и судебную практику, автор выявляет различные подходы к решению вопроса о возможности правовой защиты произведений, созданных с использованием искусственного интеллекта, исследует представленные в доктрине суждения относительно проблемы признания авторских прав на такие произведения и примеры регламентации отношений, возникающих в рассматриваемой сфере. На основании проведенного исследования формулируется вывод о необходимости законодательного закрепления по меньшей мере базовых требований к использованию искусственного интеллекта в творческой деятельности, включая установление критериев охраноспособности произведений, созданных при его участии, а также маркировки продукта как сгенерированного искусственным интеллектом. Currently, the practice of creating works using artificial intelligence is becoming increasingly widespread, which inevitably affects current copyright legislation. The introduction of artificial intelligence technologies into creative activity fundamentally changes the process of creating works of literature, science and art, since many decisions related to intellectual activity are made by a computer program without human intervention. Analyzing Russian and foreign legislation and judicial practice, the author identifies different approaches to resolving the issue of the possibility of legal protection of works created using artificial intelligence, analyzes the judgments presented in the doctrine regarding the problem of recognizing copyright in such works and considers examples of the regulation of relations arising in the considered sphere. Based on the study, a conclusion is formulated about the need to legislate at least basic requirements for the use of artificial intelligence in creative activities, including establishing criteria for the protection of works created with its participation, as well as labeling a product as generated by artificial intelligence.Современное право10СОВРЕМЕННОЕ ПРАВО["\u0418\u0440\u0438\u043d\u0430 \u0410\u043b\u0435\u043a\u0441\u0430\u043d\u0434\u0440\u043e\u0432\u043d\u0430 \u041c\u0438\u0445\u0430\u0439\u043b\u043e\u0432\u0430"]2024-10-05T00:00:00 717ddb3de49a2f7bb96ecd58e7fa9e34f94ba4baInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Anay Kushwaha"]2024-10-05T00:00:00 1e1f1cf261c84d42da89544dca22221302642e64The use of AI in higher learning institutions has brought a dilemma as it was in the era of using google and Wikipedia causing a lot of academic dishonest. There are two paradigm on this, some sees as opportunity for effective learning and others sees as a challenge in the teaching and learning contributing to rote learning. The author wonders as to why should it be perceived as opportunity at the same time be seen as a challenge. The perceptions of students and lecturers towards this technological advancement is always taken as a shock at the beginning, yet can significantly influence the teaching and learning in institutions as it was in the error of the use of google search and Wikipedia. This paper explores the differing viewpoints of students and lecturers on the use of AI in higher education, by examining the opportunities, challenges, and potential impacts on teaching and learning experiences. Questionnaire and interview were used as data collection tools. The study realized that students knows the use of AI in searching information than their lecturers (72%:22%). Lecturers sees AI facilitates rote learning while students see that is an opportunity to them because the information is accessed in short time. Finally they all suggest that there should be clear policies on the use of AI in teaching and learning and having acceptable ratio of human and AI interaction in the teaching and learning. The study concludes by calling upon in-service training to lecturers and other education stakeholders to have proper use of AI for effective teaching and learning.International Journal For Multidisciplinary Research140Examining the differing viewpoints of students and lecturers on the use of AI in higher education, by examining the opportunities, challenges, and potential impacts on teaching and learning experiences finds that students knows the use of AI in searching information more than their lecturers.International Journal For Multidisciplinary Research["S. Mwakalinga"]2024-10-05T00:00:00 814c6b961aea55f0b10cad79265a9834dc5d525aIn the oil and gas industry, managing financial risk associated with safety investments is critical, particularly in high-risk operations. This concept paper explores the potential of leveraging artificial intelligence (AI) tools to analyze safety data and inform financial decisions, thereby ensuring effective resource allocation towards risk reduction. The integration of AI into financial risk management can transform traditional approaches, enabling organizations to proactively address safety concerns while optimizing investment strategies. The proposed framework utilizes machine learning algorithms to analyze vast datasets from various sources, including historical safety incidents, operational performance metrics, and regulatory compliance reports. By identifying patterns and correlations within this data, AI can forecast potential risks and assess the effectiveness of existing safety measures. This predictive capability empowers decision-makers to allocate resources strategically, prioritizing investments in areas that significantly impact safety outcomes and operational resilience. Furthermore, AI tools facilitate scenario analysis, enabling organizations to evaluate the financial implications of different safety investment strategies. By simulating various risk scenarios and their potential impacts on financial performance, companies can make informed decisions that balance safety with cost efficiency. This approach not only enhances the financial viability of safety investments but also fosters a culture of proactive risk management within the organization. The paper also discusses the challenges and limitations of implementing AI in financial risk management, including data quality issues and the need for organizational change to adopt AI-driven methodologies. Additionally, ethical considerations surrounding AI decision-making processes are addressed, emphasizing the importance of transparency and accountability in utilizing AI for safety investments. In conclusion, leveraging AI for financial risk management in safety investments presents a transformative opportunity for the oil and gas sector. By harnessing the power of data-driven insights, companies can enhance their risk reduction strategies, ensuring the safety of operations while optimizing financial performance. Keywords: AI, Financial Risk Management, Safety Investments, Oil and Gas, Predictive Analytics, Resource Allocation, Operational Resilience, Risk Reduction, Scenario Analysis.Computer Science &amp; IT Research Journal03Computer Science &amp; IT Research Journal["Adeoye Taofik Aderamo", "Henry Chukwuemeka, Olisakwe", "Yetunde Adenike Adebayo", "Andrew Emuobosa Esiri"]2024-10-05T00:00:00 4c35d5677ec02931fea2ff0c64f876cbaa896a15-Academic literature is currently lacking on the topic of how to acquire, deploy, integrate, and reconfigure AI technologies to achieve HRM goals. This void is filled to a large extent from the viewpoint of technology-driven human resource management. This research fills that knowledge vacuum by proposing a new standard for the architectural framework of human resource management strategies that make use of artificial intelligence. This paper presents an all-encompassing operational architectural framework for AI-powered HRM, drawing on theory of management and AI integration literature. It addresses the following areas: corporate setting, HRM(AI) architecture's inner workings, its connection to HRM domain-specific outcomes, organizational level conclusionsInternational Journal of Science and Research (IJSR)773An all-encompassing operational architectural framework for AI-powered HRM, drawing on theory of management and AI integration literature is presented, which addresses the following areas: corporate setting, HRM(AI) architecture's inner workings, its connection to HRM domain-specific outcomes, organizational level conclusions.International Journal of Science and Research (IJSR)["Ponnarasan Krishnan"]2024-10-05T00:00:00 cac1bfc2ea1ff4ae477b4d0da057f617252165e3The advent of artificial intelligence (AI) has revolutionized the field of precision medicine, particularly in cancer treatment. AI-driven models are enabling more personalized approaches, improving diagnostic accuracy, treatment planning, and outcome predictions. By analyzing vast datasets, including genomic, clinical, and imaging data, AI can identify patterns that might go unnoticed by traditional methods, allowing for the development of tailored therapies specific to each patient’s genetic makeup and disease profile. This transformative shift is enhancing early detection, optimizing therapeutic strategies, and minimizing adverse effects, ultimately leading to more effective and individualized cancer care. As AI continues to evolve, its role in personalized oncology is expected to expand, driving advancements in both clinical practice and research.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)72The advent of artificial intelligence has revolutionized the field of precision medicine, particularly in cancer treatment, and its role in personalized oncology is expected to expand, driving advancements in both clinical practice and research.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)["Sohana Akter"]2024-10-05T00:00:00 87815e581a2fd5e8cbb37ce80647fb1a594bafbeBuilding Simulation71An integrated framework that leverages artificial intelligence (AI), machine learning (ML), and life cycle assessment (LCA) to analyze, model, and optimize urban mobility to reduce carbon emissions and promote sustainable urban transportation is presented.Building Simulation["Qi R. Wang"]2024-10-05T00:00:00 0652e6245cd283662e821fe3bf834f5afb3f3ed9This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how several dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. This paper aims to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.Journal of Machine and Computing150The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments.Journal of Machine and Computing["Hayder M. A. Ghanimi", "G. Painoli", "Dipenkumar Contractor", "Sowjanya G N", "Vani Lavanya A", "Kamal Poon"]2024-10-05T00:00:00 ba555d663049ceff72c0100ecd4999d70b91d5a5: As the financial services industry advances, managing the inherent complexities of annuities requires sophisticated risk management in software testing. Traditional methodologies are insufficient to address the multi-dimensional challenges posed by evolving regulatory landscapes, intricate financial models, and system integration. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) to enhance risk mitigation across critical testing domains, including compliance automation, financial accuracy, data security, and performance optimization. AI/ML technologies introduce advanced automation, predictive analytics, and anomaly detection, elevating the precision and efficiency of the testing lifecycle. Through continuous learning models and adaptive testing frameworks, AI/ML streamlines legacy system integrations and dynamically scales performance testing. This article establishes the strategic imperative for insurers to integrate AI/ML into software testing frameworks, ensuring a proactive, data-driven approach to risk management and future-proofing their technological ecosystems.International Journal of Science and Engineering Applications60The strategic imperative for insurers to integrate AI/ML into software testing frameworks is established, ensuring a proactive, data-driven approach to risk management and future-proofing their technological ecosystems.International Journal of Science and Engineering Applications["Chandra Shekhar Pareek"]2024-10-05T00:00:00 7f3a752993301e2f2bfe6b4183e76e91029fa329: The integration of artificial intelligence (AI) and robotics into project management is reshaping traditional practices, enhancing efficiency, and aiding decision - making. AI provides advanced capabilities in resource allocation, risk assessment, and predictive analytics, empowering project managers to handle complex data, optimize workflows, and improve communication. Robotics complements this by automating repetitive tasks, thus enabling teams to focus on strategic planning and problem - solving. This study examines the growing impact of AI and robotics on project management, including the benefits of automation and the challenges tied to data security, job adaptation, and ethical considerations. By leveraging these technologies, organizations can achieve higher productivity and adaptability in dynamic project environments, highlighting the essential role of AI and robotics in contemporary project management practices.International Journal of Science and Research (IJSR)270The growing impact of AI and robotics on project management is examined, including the benefits of automation and the challenges tied to data security, job adaptation, and ethical considerations, highlighting the essential role of AI and robotics in contemporary project management practices.International Journal of Science and Research (IJSR)["Mudasir Ashraf"]2024-10-05T00:00:00 803413823ba59956dcf22c1c4b70e5c2bd767b1bThe integration of Artificial Intelligence (AI) into education is rapidly transforming traditional learning environments by enabling personalized learning experiences. This paper explores the role of AI in developing adaptive learning systems that tailor educational content and pathways to individual students' needs. By leveraging machine learning algorithms and learning analytics, AI-driven platforms dynamically adjust instructional materials based on students’ performance, cognitive abilities, and engagement levels. The study examines the potential of AI to enhance student outcomes by creating more interactive and engaging educational experiences, thus fostering better retention and comprehension. Key challenges, such as data privacy, algorithmic bias, and equitable access, are also addressed to ensure the effective deployment of AI in diverse learning environments. Through case studies and real-world examples, this research highlights how AI is reshaping education by making it more flexible, student-centered, and accessible.International Journal For Multidisciplinary Research190The role of AI in developing adaptive learning systems that tailor educational content and pathways to individual students' needs is explored, highlighting how AI is reshaping education by making it more flexible, student-centered, and accessible.International Journal For Multidisciplinary Research["M. N Y", "L. P H", "S. N"]2024-10-05T00:00:00 7ed10acab5968fe44079944b7484d0bf1ca1c5acWith the digitalization of society, the interest, the debates and the research efforts concerning"code","law","artificial intelligence", and their various relationships, have been widely increasing. Yet, most arguments primarily focus on contemporary computational methods and artifacts (inferential models constructed via machine-learning methods, rule-based systems, smart contracts), rather than attempting to identify more fundamental mechanisms. Aiming to go beyond this conceptual limitation, this paper introduces and elaborates on"normware"as an explicit additional stance -- complementary to software and hardware -- for the interpretation and the design of artificial devices. By means of a few examples, I will argue that a normware-centred perspective provides a more adequate abstraction to study and design interactions between computational systems and human institutions, and may help with the design and development of technical interventions within wider socio-technical views.arXiv.org670It is argued that a normware-centred perspective provides a more adequate abstraction to study and design interactions between computational systems and human institutions, and may help with the design and development of technical interventions within wider socio-technical views.ArXiv["Giovanni Sileno"]2024-10-05T00:00:00 60915efd60a37edaf553259d757ce994d5e3e430The widespread diffusion of Artificial Intelligence (AI)-based systems offers many opportunities to contribute to the well-being of individuals and the advancement of economies and societies. This diffusion is, however, closely accompanied by public scandals causing harm to individuals, markets, or society, and leading to the increasing importance of accountability. AI accountability itself faces conceptual ambiguity, with research scattered across multiple disciplines. To address these issues, we review current research across multiple disciplines and identify key dimensions of accountability in the context of AI. We reveal six themes with 13 corresponding dimensions and additional accountability facilitators that future research can utilize to specify accountability scenarios in the context of AI-based systems.European Conference on Information Systems720This work reviews current research across multiple disciplines and identifies key dimensions of accountability in the context of AI, revealing six themes with 13 corresponding dimensions and additional accountability facilitators that future research can utilize to specify accountability scenarios in the context of AI-based systems.ArXiv["L. H. Nguyen", "S. Lins", "Maximilian Renner", "A. Sunyaev"]2024-10-05T00:00:00 525035c2ce89c4bd08ed367ac5014c447939ee27: Artificial intelligence and its application have grown phenomenally in the recent decade. Its advent now presents an opportunity to tackle traditionally complex problems, particularly in the healthcare sector. AI with its machine learning models and natural language processing capabilities brings tremendous possibilities in the execution of care coordination and in turn improves efficiencies in the implementation. Care coordination at its basic form involves multiple independent actors collaborating for a patient’s healthcare needs and this model introduces complexities at various points like communication, planning, and execution of care. This paper explores the various possible applications of AI to reduce these complexities within the subdomain of care coordination, its viable implementation, and its benefits.International Journal of Science and Research (IJSR)160This paper explores the various possible applications of AI to reduce complexities within the subdomain of care coordination, its viable implementation, and its benefits.International Journal of Science and Research (IJSR)["Mandar Nayak"]2024-10-05T00:00:00 c967e09abc9dc12741ef8febd9255487af8022bd: The proliferation of fake news has become a significant concern in recent years, with far - reaching consequences for individuals, communities, and society. Artificial intelligence (AI) has the potential to play a crucial role in detecting and mitigating the spread of fake news. However, the use of AI in fake news detection also raises important governance considerations. In this paper, we propose a novel approach to AI governance in fake news detection, including a framework for responsible AI governance, a new algorithm for fake news detection, and a comprehensive evaluation of the proposed approach.International Journal of Science and Research (IJSR)80A novel approach to AI governance in fake news detection is proposed, including a framework for responsible AI governance, a new algorithm for fake news detection, and a comprehensive evaluation of the proposed approach.International Journal of Science and Research (IJSR)["Vyoma Gajjar"]2024-10-05T00:00:00 014219888343a4a776551bfc421b6e86d344284eRobot assisted surgery, as one of the ways in which artificial intelligence is applied in the medical and health field, has attracted much attention. The discussion of scientific topics by users is different from the perspective of experts and has its own uniqueness, while also being influenced by media agendas. In the context of media platformization, this study takes the videos relating to robot assisted surgery in the Douyin platform as an example and uses the bi-term topic model (BTM) to explore the focus of media and corresponding user reviews on this scientific topic. Research has found that there are more diverse media outlets for agenda setting in robot assisted surgery, with medical professionals as the main communicator. The agenda attributes set by different sub media outlets have distinct characteristics, including international competition, technological innovation, safety, price, and universal benefit. Although these attributes are reflected in the user agenda (price, inclusiveness, monopoly, doctors, network, human-machine, and out of control), the discussion of attributes by users reflects many more detailed risk aspects, providing reference for ethical governance of specific applications of artificial intelligence in healthcare.The Journal of Medicine, Humanity and Media00This study takes the videos relating to robot assisted surgery in the Douyin platform as an example and uses the bi-term topic model (BTM) to explore the focus of media and corresponding user reviews on this scientific topic.The Journal of Medicine, Humanity and Media["Wenhuan Chen"]2024-10-05T00:00:00 92935a6d8f4425c1135e85721974d0991f45a5f6The future of artificial intelligence (AI) in diagnosing rare genetic disorders is poised to transform precision medicine by accelerating the identification of conditions that are often difficult to diagnose. Rare genetic disorders, which affect millions of people worldwide, typically involve complex symptoms and lengthy diagnostic processes. AI's ability to process vast amounts of genomic, phenotypic, and clinical data positions it as a game-changer in this field. By detecting subtle patterns and correlations in large datasets, machine learning algorithms can deliver diagnoses faster and more accurately than traditional methods. AI-powered tools are proving valuable in whole genome and exome sequencing, automating the identification of pathogenic variants linked to rare diseases. By integrating clinical and phenotypic data, these systems can offer personalized insights, reduce diagnostic delays and improve genetic counseling and treatment development. However, the use of AI in rare disease diagnosis poses challenges, such as the need for diverse datasets to train algorithms and concerns over data privacy and equal access. Ensuring that AI tools are validated in diverse populations and effectively integrated into healthcare systems is crucial to their success. This summary will focus on the potential of AI to improve diagnostic accuracy, personalize treatments, and improve the management of rare diseases.Next Frontier For Life Sciences and AI20The potential of AI to improve diagnostic accuracy, personalize treatments, and improve the management of rare diseases is focused on.Next Frontier For Life Sciences and AI["Buse Liv"]2024-10-05T00:00:00 bb8523481cb7ecf094ff9828790ea54fa99d4f2fArtificial intelligence (AI) has become tightly integrated into modern technology, yet existing exploratory visualizations for explainable AI (XAI) are primarily designed for users with technical expertise. This leaves everyday users, who also regularly interact with AI systems, with limited resources to explore or understand AI technologies they use. We propose a novel framework that enables non-technical users to collect insights by conversing directly with visualization elements via LLM-powered narrative gamifications. We implemented a prototype that utilizes such gamification to facilitate non-technical users' exploration of AI embedding projections. We conducted a comparative study with 10 participants to assess our prototype quantitatively and qualitatively. Our study results indicate that although our prototype effectively enhances non-technical users' AI/XAI knowledge, and users believe they learn more through the gamification feature, it remains inconclusive whether the gamification itself leads to further improvements in understanding. In addition, opinions among participants regarding the framework's engagement are mixed: some believe it enhances their exploration of the visualizations, while others feel it disrupts their workflow.arXiv.org330The study results indicate that although the prototype effectively enhances non-technical users' AI/XAI knowledge, and users believe they learn more through the gamification feature, it remains inconclusive whether the gamification itself leads to further improvements in understanding.ArXiv["Yuzhe You", "Jian Zhao"]2024-10-05T00:00:00 96c068eb08f23e30873ddc74fe584ade5cf88c19This study examines the role of artificial intelligence (AI), specifically generative AI, in higher education, mainly in the teaching-learning process, personalized learning, learning outcomes, and challenges. The researcher has employed a descriptive research method to collect the data by reviewing the research articles, books, and conducting a document analysis (policies of universities on using AI). The study's findings provide multifaceted insights into the use of AI in the teaching-learning process. It reveals that optimizing the use of GenAI can support students to become more independent learners, critical thinkers, and it can also provide plenty of Drill-Practice opportunities for them to master the content, particularly, for Afghan students who are believed to be more reliant on teachers and printed learning resources. On the other hand, the findings suggest that GenAI is an invaluable asset for teachers’ professional development in terms of planning everyday pedagogical practices for their classrooms and generating materials and content. However, the usage of GenAI can also pose some significant challenges for teachers as well as institutions, particularly in the absence of an established code of conduct and implicit rules that should be abided by the students to avoid unauthorized activities. Additionally, the study also provides some useful suggestions predicated on the findings of the study.Academic Journal of Research and Scientific Publishing00The study's findings provide multifaceted insights into the use of AI in the teaching-learning process and suggest that GenAI is an invaluable asset for teachers’ professional development in terms of planning everyday pedagogical practices for their classrooms and generating materials and content.Academic Journal of Research and Scientific Publishing["Noor-ul-Huda Atif"]2024-10-05T00:00:00 f71acf5d7b0cfbdbb17fb0d8c55ce20b907a1c98The deployment of drones equipped with artificial intelligence (AI) in urban security represents a significant advancement in crime prevention and public safety measures. This paper analyses the effectiveness of these technologies in monitoring high-risk areas, highlighting their capability to gather real-time data and identify potential threats through advanced surveillance techniques. Drones enhance situational awareness for law enforcement agencies, facilitating proactive measures against crime and improving emergency response times. However, the implementation of AI-driven drones also raises critical concerns regarding privacy and civil liberties, as continuous surveillance can lead to unauthorized monitoring of individuals and communities. Furthermore, ethical considerations surrounding the use of autonomous surveillance systems must be addressed, including accountability for data usage, potential biases in AI algorithms, and the implications of surveillance on social trust. The paper evaluates existing regulatory frameworks and calls for the development of comprehensive policies to balance public safety with individual rights. As cities increasingly adopt drone technology for security purposes, it is crucial to foster public discourse and establish ethical guidelines to ensure transparency, accountability, and respect for privacy rights. By addressing these challenges, urban security initiatives can leverage the benefits of drones and AI while mitigating risks associated with invasive surveillance practices.International Research Journal of Modernization in Engineering Technology and Science290This paper analyses the effectiveness of drones equipped with artificial intelligence in urban security in monitoring high-risk areas, highlighting their capability to gather real-time data and identify potential threats through advanced surveillance techniques.International Research Journal of Modernization in Engineering Technology and Science["Damilola Bartholomew Sholademi"]2024-10-05T00:00:00 6f58e2c492539215d781795fbbf472d114e5a034The increasing integration of artificial intelligence (AI) and cloud technology into renewable energy systems presents a significant opportunity to enhance the efficiency, reliability, and cost-effectiveness of energy production, distribution, and management. These technologies enable real-time data analysis, predictive maintenance, and improved decision-making, essential for managing variable renewable energy sources. However, the ethical implications, such as data security, privacy concerns, and the environmental footprint of cloud infrastructure, remain underexplored. This paper addresses the research gap by analyzing these ethical challenges through two detailed case studies: AI-driven smart grids and green data centers. The case studies highlight practical issues like cyberattacks, data breaches, algorithmic bias, and the sustainability of data centers. The paper proposes a comprehensive ethical framework, focusing on fairness, transparency, and environmental responsibility, to guide the responsible adoption of AI and cloud technologies in the renewable energy sector. The findings provide critical insights into balancing technological innovation with ethical considerations, fostering a sustainable and equitable energy transition.Journal of Advances in Mathematics and Computer Science00A comprehensive ethical framework, focusing on fairness, transparency, and environmental responsibility, is proposed to guide the responsible adoption of AI and cloud technologies in the renewable energy sector.Journal of Advances in Mathematics and Computer Science["Emmanuel Dibie"]2024-10-05T00:00:00 9c66113a5fd08b78be6591bcda8880e37b9e3bf5Language learning is a crucial aspect of life, and AI technology has revolutionized teaching and learning. New approaches like Dulingo, Babbel, and Memrise have emerged to improve language proficiency. However, the use of AI in language learning apps is often overlooked. This paper analyzes several popular language learning apps and their presence of AI. The results show that none of the analyzed apps use machine learning, artificial intelligence, or deep learning, and they are based on predefined algorithms that do not fully utilize computational power. The paper suggests possible solutions and practical advice on how to implement AI in these apps, making it important for education innovation in the 21st century. The findings suggest that AI should be used in a way that fully utilizes the computational power available, rather than relying on predefined algorithms. This makes AI a valuable tool for language learning and education in the 21st century. Keywords: Artificial Intelligence (A.I.), AI Powered Learning, Language Apps, Mobile Apps, Language Education.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper analyzes several popular language learning apps and their presence of AI, and shows that none of the analyzed apps use machine learning, artificial intelligence, or deep learning, and they are based on predefined algorithms that do not fully utilize computational power.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Prathamesh M Soparkar"]2024-10-05T00:00:00 d2fcebad0f4b1963ab11399cf73c5bc03edbdebcActualmente, la mayoría de las actividades rutinarias y laborales de las personas son complementadas por herramientas tecnológicas, en dónde, se destaca la IA que es el cúmulo de máquinas, programas y redes informáticas que asimilan e interpretan metadatos para construir patrones de comportamiento similares al de un ser humano. Haciendo énfasis, en el campo de la educación universitaria ecuatoriana, la IA gana más terreno en diferentes aplicaciones y actividades realizadas por diferentes actores de la educación, el objetivo de este estudio consiste en realizar una revisión de literatura sobre el papel de la inteligencia artificial en el ámbito educativo universitario del Ecuador. El diseño de estudio este compuesto por el uso de paquetes informáticos, Metodología PRISMA 2020. Los principales hallazgos evidencian que los campos de aplicación más beneficiados por la IA son la evaluación y calificación, detección de comportamiento no deseados, augurio del rendimiento y tutorías inteligentes. Por otra parte, la IA más utilizada en las universidades del Ecuador es ChatGPT para generar textos con un enfoque argumentativo, seguido de Copyleaks, Gradescope, DeepL, Eleven Labs, Socratic y Chatmind, todas cada vez más inmersas en los procesos de enseñanza y aprendizaje de módulos sociales y de ciencias exactas, tramitología universitaria y solución de dudas e interrogantes.Ciencia Latina Revista Científica Multidisciplinar00Ciencia Latina Revista Científica Multidisciplinar["Nelson Rodrigo Toapanta Caisabanda", "Jomayra Maricel Cajas L\u00f3pez", "Diego Josu\u00e9 Ron Lascano", "Domenica Paulette Serrano Quispilema"]2024-10-05T00:00:00 d6601ae826289ff841e899332d3fcb32abd8d10fLa actitud del docente en el uso y manejo de la inteligencia artificial generativa debe de ser positiva y motivadora para sus alumnos, ahí encontramos los pilares de la formación exitosa; la misma se robustece con la preparación académica permanente y de la búsqueda sincera de conocimientos acordes con los nuevos cambios tecnológicos. El objetivo principal del presente artículo es el de “describir la actitud de los docentes en el uso y manejo de la inteligencia artificial generativa (IAG) dependiendo de los conocimientos y preparación previa educativa”. El diseño de estudio es no experimental, de corte descriptivo ex post facto, utilizamos los cuestionarios de Sáez, Piza y Lizana (2024), sobre la validación y estructuración factorial de un cuestionario TPACK en el contexto de inteligencia artificial generativa (IAG). Trabajamos con 82 docentes de la Maestría en Educación Tecnológica Educativa de la Pontificia Universidad Católica Madre y Maestra (PUCMM) en Santo Domingo. Para el tratamiento de los resultados se utilizó el programa estadístico Statictical Packge for the Social Sciences (SPSS), las conclusiones de interés muestran las actitudes fundamentales en los docentes universitarios para la adquisición de  conocimientos educativos que les permita ser exitosos en los procesos de aprendizaje implementado la inteligencia artificial generativa (IAG), las actitudes varían según la formación previa adquiridas por los docentes y según el conocimiento tecnológico, de contenido, pedagógico del contenido, tecnológico del contenido, tecnológico pedagógico y el tecnológico pedagógico del contenido.Revista Científica de Salud y Desarrollo Humano00Revista Científica de Salud y Desarrollo Humano["Juan Amad\u00eds Socorro Ovalles"]2024-10-05T00:00:00 ca295c0749775328194fb68a268eadafb908500aThe adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMBs) has emerged as a key strategy for improving quality management. This article discusses the main benefits and challenges of implementing AI in this context. A systematic review of the academic literature and relevant business reports was carried out, in order to collect and analyze empirical evidence on the application of AI in the quality management of SMEs, obtaining as results that the main benefits of the implementation of AI in the quality management of SMEs include:  Improved data-driven decision-making, process automation and error reduction, early detection of quality issues, customization of products and services, and resource optimization and cost reduction. On the other hand, the main challenges were: Lack of knowledge and skills in AI, high implementation costs, concerns about data security and privacy, resistance to cultural change, and integration of AI with existing systems and processes, concluding that the implementation of AI in SMB quality management offers significant benefits,  but it also entails challenges that must be addressed strategically. SMEs that manage to overcome these challenges will be able to take advantage of the competitive advantages offered by AI, improving their efficiency, quality and adaptability in an increasingly dynamic business environment.Evolutionary Studies in Imaginative Culture251The main benefits and challenges of implementing AI in SMB quality management are discussed, concluding that the implementation of AI in SMB quality management offers significant benefits but it also entails challenges that must be addressed strategically.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Miguel Giancarlo", "Ormaza Cevallos", "Gustavo Alberto Lozano Jaramillo", "Jos \u00e9 Luis Bernardo V \u00e9 lez", "Maritza Irinuska", "Ureta Zambrano", "Lady Diana Zambrano Montesdeoca", "Manuel Augusto", "Berm \u00fa dez Palomeque", "Gustavo Alberto", "Lozano Jaramillo", "Jos \u00e9 Luis", "Bernardo V \u00e9 lez", "Diana Zambrano Montesdeoca"]2024-10-06T00:00:00 60c8adb0649d8b6861392bc6c4f68c20bbd95fd5Artificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.Sustainable Development1031Through applying AI algorithms on reservoir geology, production performance and real‐time data, this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency.Sustainable Development["S. Manikandan", "Rangarajan Sindhu Kaviya", "Dhamodharan Hemnath Shreeharan", "R. Subbaiya", "S. Vickram", "N. Karmegam", "Woong Kim", "M. Govarthanan"]2024-10-06T00:00:00 3044c86e13448e81ecaf6c8c01c38b4106a28ccdArtificial intelligence (AI) is the main driving force of a new round of scientific and technological revolution; as a cross-product of computer science, advanced mathematics, cognitive psychology, and other disciplines, artificial intelligence has a milestone significance, which has a far-reaching impact on the economy, society, and culture, especially on large scale industry and international enterprises. Under its substantial spillover and radiation effect, it can effectively empower the covered departments and enterprises, accelerate the growth rate and development efficiency, and thus enhance the core competitiveness of enterprises. However, in the current landscape, despite the widespread adoption of artificial intelligence, notable gaps persist in its specific performance metrics, The data reveals a notable upward trend in the number of business failures in China over the last decade. Numerous enterprises have struggled to stay abreast of current trends, hindered by outdated facilities, products, and sluggish institutional updates, compounded by the repercussions of the global economic downturn. Amidst the backdrop of Industry 4.0, the instructive value of past experiences has diminished, intensifying the survival pressures faced by these enterprises. Thus, based on the current situation of AI development and application, this paper makes an analysis and discussion and provides instructions for the development of enterprises.Strategic Management Insights130Based on the current situation of AI development and application, an analysis and discussion is made and instructions for the development of enterprises are provided.Strategic Management Insights["Zhenglong Guo"]2024-10-06T00:00:00 134ac603f59f5ee0d74ae395b5e15ba6c278ef7eIn an era of rapid technological advancement, decisions about the ownership and governance of emerging technologies like Artificial Intelligence will shape the future of both urban and rural environments in the Global North and South. This article explores how AI can move beyond the noise of algorithms by adopting a technological humanistic approach to enable Social Innovation, focusing on global inequalities and digital justice. Using a fieldwork Action Research methodology, based on the Smart Rural Communities project in Colombia and Mozambique, the study develops a framework for integrating AI with SI. Drawing on insights from the AI4SI International Summer School held in Donostia-San Sebastián in 2024, the article examines the role of decentralized Web3 technologies—such as Blockchain, Decentralized Autonomous Organizations, and Data Cooperatives—in enhancing data sovereignty and fostering inclusive and participatory governance. The results demonstrate how decentralization can empower marginalized communities in the Global South by promoting digital justice and addressing the imbalance of power in digital ecosystems. The conclusion emphasizes the potential for AI and decentralized technologies to bridge the digital divide, offering practical recommendations for scaling these innovations to support equitable, community-driven governance and address systemic inequalities across the Global North and South.Sustainability560Examination of the role of decentralized Web3 technologies—such as Blockchain, Decentralized Autonomous Organizations, and Data Cooperatives—in enhancing data sovereignty and fostering inclusive and participatory governance demonstrates how decentralization can empower marginalized communities in the Global South by promoting digital justice and addressing the imbalance of power in digital ecosystems.Sustainability["Igor Calzada"]2024-10-06T00:00:00 f3dcfbf25bae2c33d3d61e0469e6ba4593eb2793The use of artificial intelligence technology in recruitment and selection procedures has become commonplace in business practice. As a result, there has been a significant increase in research on AI recruitment in recent years. However, these studies focus primarily on the considerations of skills and the effectiveness of AI tools. Only recently have analyses emerged presenting these procedures from an ethical perspective. The purpose of the article is to fill this gap and explore the opinions of both HR professionals and employees themselves regarding the use of artificial intelligence-based recruitment solutions. The article contains a systematic review of the literature and the results of a qualitative pilot study. The interview study was conducted in one organisation with the participation of four managers. Widely accepted assumptions about the objectivity of learning algorithms contribute to a seemingly positive image of AI-powered recruitment among practitioners, but the research conducted showed a number of ethical concerns raised by managers regarding AI recruitment requirements. The contrast between this positive image and the ethical concerns raised by critics of AI recruitment requires an assessment necessary to gain a more scientifically grounded perspective on the ethical status of AI recruitment.Edukacja Ekonomistów i Menedżerów310The opinions of both HR professionals and employees themselves regarding the use of artificial intelligence-based recruitment solutions are explored and a systematic review of the literature and the results of a qualitative pilot study are presented.Edukacja Ekonomistów i Menedżerów["Magdalena Stuss", "Adam Fularski"]2024-10-06T00:00:00 615225c69d1659dd5a0435c3bb6326a59d9f2d48Trustworthy AI is becoming a new focus in the field of artificial intelligence, and the study of its trustworthiness criteria is crucial for enhancing the trustworthiness of AI. Despite the plethora of methods that have been explored to assess the trustworthiness of AI, there remains a dearth of simple and intuitive assessment approaches. This paper focuses on the domain of knowledge tracing, combining various clustering techniques to propose a trustworthy evaluation method based on uncertainty computation. Through validation using clustering, Monte Carlo methods, and correlation analysis, our approach effectively examines the trustworthiness of multiple prominent knowledge tracing models. Employing open data sets and virtual data sets grounded in Item Response Theory (IRT), our method achieves commendable performance at a low cost, thereby providing a reference for research into the trustworthiness of knowledge tracing models. Finally, we summarize model errors and areas for improvement, and offer perspectives on scale and integrated trustworthy development. Experimental results demonstrate that our method achieved an accuracy rate of 81.04%.IEEE International Conference on Systems, Man and Cybernetics260This paper focuses on the domain of knowledge tracing, combining various clustering techniques to propose a trustworthy evaluation method based on uncertainty computation that effectively examines the trustworthiness of multiple prominent knowledge tracing models.2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)["Jiping Bai", "Bo Kang", "Kexin Hu", "Qi Zheng", "Xiaodong Wang"]2024-10-06T00:00:00 e984845981b9dde144c5d3cad9341a25c8ee66efThe implementation of Artificial Intelligence (AI) in organizations in Colombia has been increasing in recent years, and it is expected to continue growing in the near future. Companies in Colombia have begun to use AI in different areas, such as data analysis, customer service, process automation, and decision-making. Among the strategies applied for the implementation of AI in organizations is the adoption of cloud technologies, the use of machine learning algorithms, collaboration with technology providers and the formation of specialized teams. However, the implementation of AI in organizations has also raised concerns regarding the impact on the employability of people. Although AI is expected to improve efficiency and productivity, some tasks and jobs are also expected to be damaged by automation. It is necessary that companies in Colombia adopt policies and strategies that allow a fair transition towards the implementation of AI, to minimize the negative impact on employability and to take advantage of the benefits that this technology can offer.Evolutionary Studies in Imaginative Culture380It is necessary that companies in Colombia adopt policies and strategies that allow a fair transition towards the implementation of AI, to minimize the negative impact on employability and to take advantage of the benefits that this technology can offer.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["John Arturo", "Buelvas Parra", "W. N. \u00da. ez", "Andr \u00e9 s Felipe", "Escobar Regino"]2024-10-06T00:00:00 9b79e25eb72aca3163a31d4fb952810cbaaf22f3Practice211PRACTICE["Salih Rakap"]2024-10-06T00:00:00 c4b2d75750c0274874e9ef16562e8ce3a85a4dbeQalaai Zanist Scientific Journal00Qalaai Zanist Scientific Journal[]2024-10-06T00:00:00 6b64816fb584cbc453ce19e1efcabc73ec8e5e8dNew Zealand journal of crop and horticultural science2190New Zealand Journal of Crop and Horticultural Science["Rounaq Ansari", "Anindita Manna", "Soham Hazra", "Suvojit Bose", "Avishek Chatterjee", "Poulomi Sen"]2024-10-06T00:00:00 2a5333fe4b610ea86bb29098833dcfb244451c4bEducational Philosophy and Theory30Educational Philosophy and Theory["Liz Jackson"]2024-10-06T00:00:00 36c17d28797f8a81b51b687a9f94eb54a6d01182This study seeks to fill a gap in the literature concerning the extrinsic barriers that affect the integration of artificial intelligence (AI) technologies, such as ChatGPT, within the educational sphere. While previous studies have predominantly focused on intrinsic factors like teachers’ attitudes and beliefs, as well as the general benefits and drawbacks of AI in education, there has been limited investigation into how external obstacles, such as large class sizes, insufficient resources, slow internet connections, and outdated technology, affect this integration. The importance of this study lies in its assessment of whether these extrinsic barriers contribute to teachers' resistance to embracing AI technology. This study employs a quantitative method to explore this issue, using a questionnaire administered to thirteen (n=13) EFL instructors from the Department of English at Ibn Khaldoun University of Tiaret, Algeria. The results derived from this study reveal that while participants recognised AI's potential to improve student learning and expressed confidence in its integration, they also identified extrinsic barriers such as technical complexity, inadequate training, limited resources, and large class sizes as critical factors contributing to their resistance to incorporating AI tools into their teaching practices. This research emphasises the necessity of addressing these external barriers to better align technological innovations with teachers' needs and readiness, thereby enhancing the effective integration of AI in education.  Futurity Education460The results reveal that while participants recognised AI's potential to improve student learning and expressed confidence in its integration, they also identified extrinsic barriers such as technical complexity, inadequate training, limited resources, and large class sizes as critical factors contributing to their resistance to incorporating AI tools into their teaching practices.Futurity Education["Ahmed Mehdaoui"]2024-10-06T00:00:00 1ecbc8358c9c1981249c118140f48168a7ab9f77Recent Artificial Intelligence advancements raise concerns about the future of work, particularly technological unemployment. Studies show automation's impact, but tools like ChatGPT disrupt even traditionally secure professions like programming. In this study, we reevaluate AI's effects using a method to assess the impact of Generative AI technologies on occupations to understand the potential effects of Generative AI systems on software development work. Valuable insights were obtained by gathering the view of a group of workers, primarily composed of developers who are starting their careers, regarding the impact of these technologies on the tasks they perform to provide a comprehensive understanding of the implications of Generative AI for software development. Results show that all programming tasks performed by these workers would experience some impact by Generative AI -65% of the tasks being considerably impacted, 12% moderately impacted, and 18% minimally impacted. This analysis highlights the substantial influence of Generative AI technologies on software development, mainly affecting those in the early stages of their career. The results of this work contribute to the academic community with valuable information. Policymakers can also use this information, as this work provides a comprehensive view of the impacts of Generative AI on software developers, considering their direct impact on job tasks.IEEE International Conference on Systems, Man and Cybernetics280This study reevaluate AI's effects using a method to assess the impact of Generative AI technologies on occupations to understand the potential effects of Generative AI systems on software development work.2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)["Caroline Da Concei\u00e7\u00e3o Lima", "Herbert Salazar", "Y. Lima", "C. E. Barbosa", "M. Arg\u00f4lo", "A. Lyra", "Jano Moreira de Souza"]2024-10-06T00:00:00 a687de86d3080e68193a4f1caf5ee29833b61394This article explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems. As E-commerce experiences explosive growth and ERP systems become increasingly complex, businesses face significant challenges in maintaining scalability and efficiency. We examine how AI and ML can optimize various aspects of these integrated systems, from intelligent automation and predictive analytics to anomaly detection and decision support. Through case studies and analysis of current trends, we demonstrate the tangible benefits of AI/ML implementation, including reduced costs, improved accuracy, and enhanced customer experiences. The article also addresses key challenges such as data quality, scalability, ethical considerations, and the skills gap. Finally, we explore future research directions in explainable AI, edge computing, blockchain integration, and natural language processing, highlighting their potential impacts on the E-commerce and ERP landscape.International Journal of Scientific Research in Computer Science Engineering and Information Technology00This article explores the transformative potential of Artificial Intelligence and Machine Learning in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems, and examines how AI and ML can optimize various aspects of these integrated systems.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Kamalendar Reddy Kotha", "Sai Charan Tokachichu", "Sudheer Chennuri"]2024-10-06T00:00:00 2ca04dc3f1aa9f488b6a36cf35fc8fc23a354e09An enormous change is taking place in the agriculture industry as a result of the introduction of the Internet of Things (AIIoT), which is powered by artificial intelligence and provides farmers with unparalleled automation capabilities and insights. The purpose of this research is to present a comprehensive review of artificial intelligence and the internet of things (AIIoT) in smart agriculture, focussing on its applications, benefits, and consequences for decision-making. The concept of smart agricultural decision-making refers to a breakthrough method that enables farmers to optimise their farm operations while also making decisions that are well-informed. Farmers are able to increase crop yields while simultaneously reducing costs and hazards when they make use of advanced analytics, real-time data, and decision-making tools. The development of smart agriculture will result in farmers having increased decision-making authority, which will ultimately lead to an agricultural sector that is more sustainable and productive. The Internet of Things (IoT) and artificial intelligence (AI) in smart agriculture is a cutting-edge technology that has the potential to be a game-changer in terms of how we produce and consume food. Farmers may be able to improve agricultural yields and quality, streamline operations, and contribute to a food supply chain that is more efficient and sustainable if they use artificial intelligence and the internet of things. In spite of the fact that there are still certain problems that need to be cleared up, the Internet of Things (IoT) offers numerous advantages for smart agriculture, and its implementation is anticipated to increase in the years to come. The KSK technique, also known as the Knowledge-Sensors-Knowledge approach, is a suggestion made by Dr. Kutubuddin S. Kazi, who is also using his name. The output of the KSK technique results in an accuracy of 99.9% and a recall of 97.9%, respectively.International Journal of Advanced Research in Science, Communication and Technology210A comprehensive review of artificial intelligence and the internet of things (AIIoT) in smart agriculture, focussing on its applications, benefits, and consequences for decision-making is presented.International Journal of Advanced Research in Science, Communication and Technology["Dinesh Dattatraya", "Rankhamb", "S. R. Raut", "Amol suresh Velapure"]2024-10-06T00:00:00 b574154ae733d663436eb1a7f2162f1923230d3aIncidents in microservice environments can be costly and challenging to recover from due to their complexity and distributed nature. Recent advancements in artificial intelligence (AI) offer promising solutions for improving incident management. This paper systematically reviews primary studies on AI assistants designed to support different phases of the incident lifecycle. It highlights successful applications of AI, identifies gaps in current research, and suggests future opportunities for enhancing incident management through AI. By examining these studies, the paper aims to provide insights into the effectiveness of AI tools and their potential to address ongoing challenges in incident recovery.arXiv.org470This paper systematically reviews primary studies on AI assistants designed to support different phases of the incident lifecycle and highlights successful applications of AI, identifies gaps in current research, and suggests future opportunities for enhancing incident management through AI.ArXiv["Dahlia Ziqi Zhou", "Marios Fokaefs"]2024-10-06T00:00:00 8d53101eac458486d152fecfb4f436d668cd3312This article examines the ethical implications of artificial intelligence (AI) in financial services, focusing on issues of bias, transparency, and accountability. As AI adoption in finance grows rapidly, with 85% of institutions now using AI, it brings both tremendous benefits and significant ethical challenges. The article explores how AI can perpetuate biases in lending and credit scoring, the "black box" problem of opaque AI decision-making, and the complexities of establishing accountability for autonomous AI systems. It analyzes strategies for mitigating these issues, including developing interpretable AI models, implementing robust governance structures, and creating industry-specific ethical guidelines. The discussion highlights the critical need for evolving regulatory frameworks and ethical standards to ensure responsible AI use while harnessing its potential to transform financial services.International Journal of Scientific Research in Computer Science Engineering and Information Technology10The article explores how AI can perpetuate biases in lending and credit scoring, the "black box" problem of opaque AI decision-making, and the complexities of establishing accountability for autonomous AI systems.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Puneet Chopra"]2024-10-06T00:00:00 c38af262cb82933ddc2209a09a8eeafdc6536077Lethal Autonomous Weapon Systems (LAWS) have emerged as one of the most significant advancements in military technology, leveraging artificial intelligence (AI) and machine learning to execute missions without direct human control. As these systems become central to modern warfare, they raise critical questions about their compliance with International Humanitarian Law (IHL) and International Human Rights Law (IHRL). This paper delves into the legal and ethical debates surrounding LAWS with particular attention to the discussions within the Group of Governmental Experts on LAWS (GGE on LAWS). We analyze whether these technologies can adhere to fundamental human rights while maintaining their operational efficacy. Through the application of the Autonomy Spectrum Framework to real-world scenarios, the study highlights both the strategic advantages of LAWS and the risks of dehumanizing warfare. The need for robust legal frameworks to ensure accountability and human oversight remains paramount.International Journal of Robotics and Automation Technology50International Journal of Robotics and Automation Technology["Marco Marsili"]2024-10-06T00:00:00 493121b16e9d563e0a377daa96bb32d08dcb51b3This article explores the evolving landscape of data centers in the era of artificial intelligence (AI). It examines the exponential growth of the global data center market, driven by increasing data generation and AI adoption. The article discusses key technological developments in data centers, including enhanced operational efficiency through AI-powered systems, specialized hardware for AI workloads, advanced cooling technologies, and sustainability initiatives. It also delves into future prospects, such as increased capacity for complex AI tasks, real-time processing of massive datasets, and further improvements in energy efficiency. The symbiotic relationship between AI and data centers is highlighted, emphasizing how this transformation is reshaping digital infrastructure to meet unprecedented demands for computational power and data processing while striving for sustainability.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The symbiotic relationship between AI and data centers is highlighted, emphasizing how this transformation is reshaping digital infrastructure to meet unprecedented demands for computational power and data processing while striving for sustainability.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Sachin Mishra"]2024-10-06T00:00:00 0e61506c32cb721f0aa03614abe1560a4991cfe1This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the framework is based on principles drawn from Theory of Mind, including information about the human state, action, and intent. The framework creates the ATTUNE model for Artificial Trust Towards Human Operators. The model uses metrics on the operator's state of attention, navigational intent, actions, and performance to quantify the Trust towards them. The model is tested on a pre-existing dataset that includes recordings (ROSbags) of a human trial in a simulated disaster response scenario. The performance of ATTUNE is evaluated through a qualitative and quantitative analysis. The results of the analyses provide insight into the next stages of the research and help refine the proposed approach.IEEE International Conference on Systems, Man and Cybernetics330This paper presents a novel method to quantify Trust in HRI, and creates the ATTUNE model for Artificial Trust Towards Human Operators, a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot.2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)["Giannis Petousakis", "Angelo Cangelosi", "Rustam Stolkin", "Manolis Chiou"]2024-10-06T00:00:00 ef8f2ab4c978eba890002f5372729c62d443aa71Purpose: The study aims to summarize current discussions among educational stakeholders regarding the potential benefits and threats posed by Artificial Intelligence. Methodology: In conducting comprehensive and systematic review, the study utilized the PRISMA flowchart and included only articles published between January 2020 and July 2024. The focus was strictly on peer-reviewed articles, with conference papers, dissertations, and other types of papers excluded. Data was sourced exclusively from EBSCOhost and Google Scholar. Additionally, only articles centered on the higher education industry were considered. Findings: The findings of this study reveal ongoing debates among educational stakeholders regarding the threats and benefits of AI models. It explains how AI is transforming academic environments by offering personalized learning experiences, enhancing learning outcomes, and increasing student engagement. However, concerns about data privacy have also emerged. To eliminate these concerns, the study recommends the introduction of consent forms that give users the option to allow or deny the use of their data for AI training. Unique Contribution to Theory, Practice and Policy: The study contributes to the ongoing debates by grounding its analysis in Sociotechnical Systems Theory (STS), emphasizing the need for a human-centered approach in AI adoption. This offers a holistic perspective on how educational institutions can implement AI effectively while addressing stakeholder concerns about privacy and control over data, making it a valuable resource for both academic and policy discussions surrounding AI in education.International journal of communication and public relation01A holistic perspective on how educational institutions can implement AI effectively while addressing stakeholder concerns about privacy and control over data is offered, making it a valuable resource for both academic and policy discussions surrounding AI in education.International Journal of Communication and Public Relation["Abidemi Alade", "Mariam Aduwape"]2024-10-07T00:00:00 bba2c3a09d71ae57c4b939911ff70dc467cece7eScholarly publishing has been shaped by the pressure of a liquid economy to become an exercise in branding more than a vehicle for the advancement of science. The current revolution in artificial intelligence (AI) is poised to make matters worse. The new generation of large language models (LLMs) have shown impressive capabilities in text generation and are already being used to write papers, grants, peer review reports, code for analyses, and even perform literature reviews. Although these models can be used in positive ways, the metrics and pressures of academia, along with our dysfunctional publishing system, stimulate their indiscriminate and uncritical use to speed up research outputs. Thus, LLMs are likely to amplify the worst incentives of academia, greatly increasing the volume of scientific literature while diluting its quality. At present, no effective solutions are evident to overcome this grim scenario, and nothing short of a cultural revolution within academia will be needed to realign the practice of science with its traditional ideal of a rigorous search for truth.The Journal of General Physiology351The new generation of large language models (LLMs) have shown impressive capabilities in text generation and are already being used to write papers, grants, peer review reports, code for analyses, and even perform literature reviews, although these models can be used in positive ways.The Journal of general physiology["Thiago F. A. Fran\u00e7a", "J. M. Monserrat"]2024-10-07T00:00:00 c3db0b63c8582981a69055692ba1cf5ad89d9ddc There is undoubtedly, and understandably, a growing interest in incorporating generative artificial intelligence (GenAI) technologies into ELT. While advanced AI models have the potential to support language education, offering new tools and resources to enhance learning, their use also raises important questions regarding ethics and responsibility. As we follow the emergence of GenAI as another ELT tool, it is crucial to strike a balance between leveraging the benefits of these technologies and maintaining the core values of effective pedagogy. Educators must develop clear guidelines and best practices for the responsible integration of AI in the classroom, ensuring that it enhances rather than replaces human interaction and critical thinking. In this Special Issue on GenAI and ELT we explore some of the applications, their potential, and the challenges of incorporating GenAI in ELT.ELT Journal52This Special Issue on GenAI and ELT explores some of the applications, their potential, and the challenges of incorporating GenAI in ELT.ELT Journal["Alessia Cogo", "Laura Patsko", "Joanna Szoke"]2024-10-07T00:00:00 2cf10f88860d9072c437344c873680c0a1fedd4cBMC Medical Imaging1011A systematic review and meta-analysis demonstrates the comprehensive application value of AI in predicting recurrence after a single first-line treatment of liver cancer, with satisfactory results, indicating the clinical translation potential of AI in predicting recurrence after liver cancer treatment.BMC Medical Imaging["Linyong Wu", "Qingfeng Lai", "Songhua Li", "Shaofeng Wu", "Yizhong Li", "Ju Huang", "Qiuli Zeng", "Dayou Wei"]2024-10-07T00:00:00 cb055a08f8ea28b7df01c063b7b1a3a591746c0dThe International Journal of Cardiovascular Imaging181AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations, and exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening.The International Journal of Cardiovascular Imaging["J. K\u00fcbler", "J. M. Brendel", "T. K\u00fcstner", "Jonathan Walterspiel", "Florian Hagen", "Jean-Fran\u00e7ois Paul", "K. Nikolaou", "S. Gassenmaier", "Ilias Tsiflikas", "Christof Burgstahler", "Simon Greulich", "M. Winkelmann", "P. Krumm"]2024-10-07T00:00:00 52277bba59c617112740591ed6ebbd368330c6b7BMC Medical Ethics430Two different types among (future) clinicians regarding their view of the necessary levels of understanding and competence are highlighted, which should inform the debate on appropriate training programmes and professional standards that enable the safe and effective clinical employment of AI-CDSS in various clinical fields.BMC Medical Ethics["F. Funer", "S. Tinnemeyer", "W. Liedtke", "S. Salloch"]2024-10-07T00:00:00 0e84a4303fc491b026e79be267a6f2d04dfb6219Data science in the 21st century has surfaced as a key domain. Fuelling industries and innovations across the globe. The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has made the landscape of data science revolutionized. This paper investigates into the evolution of data science, the merging of AI and ML into the field, real-world applications, challenges, moral concerns, and the future trends that will define the next phase of data- driven decision-making. By examining current advancements and practical applications, this paper provides a holistic view of how AI and ML are transforming the data science profession. Keywords: Data Science, Artificial Intelligence, Machine Learning, deep learning, real world applications.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper investigates into the evolution of data science, the merging of AI and ML into the field, real-world applications, challenges, moral concerns, and the future trends that will define the next phase of data- driven decision-making.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Aman Raghuwanshi"]2024-10-07T00:00:00 aa57302249289dbbe4a62c9e458f1a9e2d0de4acAbstract Objective This survey aims to understand frontline healthcare professionals’ perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions. Materials and Methods We conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants’ familiarity and experience with AI on the survey results. Results From 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = −0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups. Discussion The findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals’ intention to use AI, regardless of their AI familiarity. Conclusion The study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals’ perspective. These insights can guide strategies for successful AI implementation in healthcare.JAMIA Open280It is suggested that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals’ intention to use AI, regardless of their AI familiarity.JAMIA Open["Tanner B Dean", "Rajeev Seecheran", "Robert G. Badgett", "R. Zackula", "John Symons"]2024-10-07T00:00:00 e3eb30773e58acf12b8bcde64be67b116235bfb2Considering the exponential growth of research on Artificial Intelligence (AI) in industrial operations management, this study aims to map the scientific landscape through a bibliometric analysis. The research employed data from the Web of Science, focusing on key terms such as "AI," "industrial operations," and "management." Using VOSviewer, co-occurrence networks and citation analyses were generated to identify research trends and gaps. The results reveal significant contributions from countries like the United States and China, emphasizing AI's role in enhancing efficiency and innovation in industries. The findings provide a foundation for future research and practical implementation strategies in industrial operations.Revista de Gestão e Secretariado80The research employed data from the Web of Science, focusing on key terms such as "AI," "industrial operations," and "management," to identify research trends and gaps, and revealed significant contributions from countries like the United States and China.Revista de Gestão e Secretariado["\u00c9rica Vit\u00f3ria Almeida Nunes", "Am\u00e9rico Chalupa Ramos Pinto", "Inaray de Sousa Passos", "Amanda Gabrielly Costa", "Tamires Gabriela Silva Goveia", "Reimison Moreira Fernandes", "Camila Soares Alves"]2024-10-07T00:00:00 5850469bca2433cc4a0a9f3a03e684d59259e7bcObjective: To assess the knowledge and perceptions of AI among medical students in a public sector medical college of Punjab, Pakistan. Study Design: Cross-sectional study. Period: January 2024 to March 2024. Methods: With sample size of 137 convenient sampling technique was used, questionnaire was generated and data was collected from 1st year to final year via online Google forms. Then, data analysis was done using IBM SPSS software version 26. Results: 137 students participated in the study. Majority of the students 123(89.8%) were aware of Artificial Intelligence and its fascinating tools; some out of them 57(41.6%) also knew about Machine Learning & Deep learning and their applications in medical field 87(63.5%). Some of them 44(33.6%) believed that AI will be a burden on medical practitioners but majority 107(78.1%) of the students are willing to work on AI. Conclusion: Many students knew much about AI and they had good perception regarding AI functioning in the medical field. They are keen to master the skills of AI and they are looking forward to utilize these tools in their medical studies as well as during healthcare practice.The Professional Medical Journal170Many students knew much about AI and they had good perception regarding AI functioning in the medical field and they are keen to master the skills of AI and they are looking forward to utilize these tools in their medical studies as well as during healthcare practice.The Professional Medical Journal["Muhammad Abdullah", "Hamza Khaliq", "Yasir Yaqoob", "Menahal", "Sameen Rafique", "Pakeeza Naeem"]2024-10-07T00:00:00 0e918ed59f046f9ff17252dda150ade2fbd91db4Introduction Acute coronary syndrome (ACS) is one of the leading causes of death from cardiovascular diseases worldwide, with ST-segment elevation myocardial infarction (STEMI) representing a severe form of ACS that exhibits high prevalence and mortality rates. This study proposes a new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm. Methods Utilizing an ECG database consisting of 888 myocardial infarction (MI) patients, this study enhanced the generalization ability of the AI model through five-fold cross-validation. The developed ST-segment elevation (STE) detector accurately identified STE across all 12 leads, which is a crucial indicator for the clinical ECG diagnosis of STEMI. This detector was employed in the AI model to differentiate between STEMI and non-ST-segment elevation myocardial infarction (NSTEMI). Results In the process of distinguishing between STEMI and NSTEMI, the average area under the receiver operating characteristic curve (AUROC) was 0.939, and the area under the precision-recall curve (AUPRC) was 0.977, demonstrating significant results. Furthermore, this detector exhibited the ability to accurately differentiate between various infarction territories in the ECG, including anterior myocardial infarction (AMI), inferior myocardial infarction (IMI), lateral myocardial infarction (LMI), and suspected left main disease. Discussion These results suggest that integrating clinical domains into AI technology for ECG diagnosis can play a crucial role in the rapid treatment and improved prognosis of STEMI patients. This study provides an innovative approach for the diagnosis of cardiovascular diseases and contributes to enhancing the practical applicability of AI-based diagnostic tools in clinical settings.Frontiers in Physiology420A new method for accurately diagnosing STEMI and categorizing the infarction area in detail, based on 12-lead electrocardiogram (ECG) data using a deep learning-based artificial intelligence (AI) algorithm is proposed.Frontiers in Physiology["Jongkwang Kim", "Byungeun Shon", "Sangwook Kim", "Jungrae Cho", "Jung-Ju Seo", "S. Jang", "Sungmoon Jeong"]2024-10-07T00:00:00 5f46827afdce17227939497acf018785aa6bbd6dThis work presents a comparative study between learning-based energy management strategies oriented to hydrogen railway vehicles, where a cost-optimal power split between the fuel cell and the battery is aimed. The proposed strategies aim to replicate the optimal operation solved by the dynamic programming optimization, which cannot be directly implemented in real-time. Specifically, three artificial intelligence-based strategies are proposed (ANFIS, Regression Trees and Ensemble Learning), and their results are compared against a baseline strategy and the aimed dynamic programming. Results are obtained for four driving cycles coming from two railway lines, and it is demonstrated that Ensemble Learning obtains the best operation among the proposed learning-based strategies. Indeed, compared to the optimal result of dynamic programming, the total cost of ownership of Ensemble Learning is 1.8-2.2% higher in Line A, and 6.3-8.0% higher in Line B. However, it is also found that in some of the proposed scenarios the baseline strategy obtains a better result, due to the fact that the optimal operation itself is close to a constant fuel cell operation.Vehicle Power and Propulsion Conference00It is demonstrated that Ensemble Learning obtains the best operation among the proposed learning-based strategies, and in some of the proposed scenarios the baseline strategy obtains a better result, due to the fact that the optimal operation itself is close to a constant fuel cell operation.2024 IEEE Vehicle Power and Propulsion Conference (VPPC)["Josu Olmos", "Urtzi Iparragirre", "H. Gazta\u00f1aga"]2024-10-07T00:00:00 3375a4417e71d56828db4ff9fd60eb92618c5392This paper first traces the AI-induced automation of the digitalised and datafied language industry, with a focus on neural machine translation and large language models. Then, it discusses a range of digital literacies that have become increasingly relevant in the language industry in light of these technologies, i.e., machine translation literacy, data literacy and artificial intelligence literacy. After highlighting the interface between these three literacies, the paper sketches an outline of an artificial intelligence literacy framework for translation, interpreting and specialised communication. This framework intends to capture an extensive set of competences required by stakeholders in the AI-saturated language industry.Lublin Studies in Modern Languages and Literature280An outline of an artificial intelligence literacy framework for translation, interpreting and specialised communication is sketched that intends to capture an extensive set of competences required by stakeholders in the AI-saturated language industry.Lublin Studies in Modern Languages and Literature["Ralph Kr\u00fcger"]2024-10-07T00:00:00 36a7b083901dbe344f27851ac07c393ff76a69b8Artificial intelligence (AI) has emerged as a transformative force in the modern film industry, revolutionizing production processes and redefining audience experiences. This study delves into the mechanisms through which AI advancement impacts film firm performance, with a focus on the mediating roles of innovation speed and quality, and the moderating effect of human-machine collaboration. Employing a resource-based view, we construct a moderated mediation model and analyze data from 355 global film firms. Our findings reveal that AI advancement positively influences film firm performance, with innovation quality serving as a significant mediator. However, the mediating role of innovation speed is not pronounced. Moreover, the degree of human-machine collaboration positively moderates the relationships between AI advancement and both innovation speed and quality. However, its moderating role between AI advancement and firm performance is not significant. The study underscores the theoretical and practical implications of utilizing advanced AI to foster innovation and competitive advantage in film firms.International Journal on Cybernetics &amp; Informatics220It is revealed that AI advancement positively influences film firm performance, with innovation quality serving as a significant mediator, however, the mediating role of innovation speed is not pronounced.International Journal on Cybernetics &amp; Informatics["Zexia Wang", "Wucheng Han"]2024-10-07T00:00:00 7ef76264258a0366f904595da67c7d99ef1746eeArtificial intelligence (AI) is integrated into educational methods through game-like elements, including gamification and serious games, offering tools and approaches to enhance teaching and learning experiences. A comprehensive review was adopted in this study to generate an overall trend or effect on AI in gamification. The paper selection was based on the three databases. Related 22 articles were collected by searching through academic journals or conference proceedings that were published in English from 2019 to 2024. The findings present that machine learning is utilised in serious games, favouring AI that might support gamification and improve results such as enhanced learning outcomes. Some included studies discuss the direct and indirect connection between technologies and gamification, including personalisation, adaptive difficulty, feedback and motivation, predictive analytics, and data-driven Insights. Overall, those interested in the intersection of artificial intelligence and gamification, including researchers, practitioners, developers, educators, and policymakers, can benefit from the literature review.European Conference on Games Based Learning460The findings present that machine learning is utilised in serious games, favouring AI that might support gamification and improve results such as enhanced learning outcomes.European Conference on Games Based Learning["Lanlan Gao"]2024-10-07T00:00:00 c192aee36aaed9435187d6dc7725dc0b382d406fAbstract Objectives Research on the Epic Sepsis System (ESS) has predominantly focused on technical accuracy, neglecting the user experience of healthcare professionals. Understanding these experiences is crucial for the design of Artificial Intelligence (AI) systems in clinical settings. This study aims to explore the socio-technical dynamics affecting ESS adoption and use, based on user perceptions and experiences. Materials and Methods Resident doctors and nurses with recent ESS interaction were interviewed using purposive sampling until data saturation. A content analysis was conducted using Dedoose software, with codes generated from Sittig and Singh’s and Salwei and Carayon’s frameworks, supplemented by inductive coding for emerging themes. Results Interviews with 10 healthcare providers revealed mixed but generally positive or neutral perceptions of the ESS. Key discussion points included its workflow integration and usability. Findings were organized into 2 main domains: workflow fit, and usability and utility, highlighting the system’s seamless electronic health record integration and identifying design gaps. Discussion This study offers insights into clinicians’ experiences with the ESS, emphasizing the socio-technical factors that influence its adoption and effective use. The positive reception was tempered by identified design issues, with clinician perceptions varying by their professional experience and frequency of ESS interaction. Conclusion The findings highlight the need for ongoing ESS refinement, emphasizing a balance between technological advancement and clinical practicality. This research contributes to the understanding of AI system adoption in healthcare, suggesting improvements for future clinical AI tools.JAMIA Open330Insight into clinicians’ experiences with the Epic Sepsis System is offered, emphasizing the socio-technical factors that influence its adoption and effective use and highlighting the need for ongoing ESS refinement.JAMIA Open["Ayomide Owoyemi", "E. Okpara", "M. Salwei", "Andrew Boyd"]2024-10-07T00:00:00 b51237bdc5790d828373f318af61dc5a527bd6baArtificial Intelligence is increasingly being employed in education, specifically through machine learning techniques, to improve the quality of education and refine teaching and learning methods. Despite its positive impacts on education quality and social life, machine learning technology poses ethical and practical concerns, especially in predicting student performance. To address these concerns, this study conducts a systematic literature review on machine learning technology for predicting student performance, analysing 51 relevant articles from Scopus and Science Direct databases between 2019 and 2023 using the PRISMA method. The findings reveal that the primary motivation for employing machine learning in educational institutions is to improve predictive accuracy, identify early interventions, and optimise decision-making processes. Supervised machine learning approaches such as Decision Trees, Linear Models, and Neural Networks are commonly used. However, machine learning techniques encounter challenges such as overfitting, scalability, and generalizability, which may impact education practices' fairness, accountability, and transparency. The study provides valuable insights into the benefits of machine learning, ethical considerations, and practical recommendations to guide stakeholders, including educators, researchers, policymakers, and administrators, in navigating the convergence of artificial intelligence and education. These insights emphasise the critical need for equitable model implementation, data collection, and decision-making to mitigate bias in real-world educational settings.Journal of Advanced Research in Applied Sciences and Engineering Technology760The findings reveal that the primary motivation for employing machine learning in educational institutions is to improve predictive accuracy, identify early interventions, and optimise decision-making processes, and emphasise the critical need for equitable model implementation, data collection, and decision-making to mitigate bias in real-world educational settings.Journal of Advanced Research in Applied Sciences and Engineering Technology["Noor Fadzilah Ab Rahman", "Shir Li Wang", "T. F. Ng", "Amr S. Ghoneim"]2024-10-07T00:00:00 2068ce630f4995f98d4b60065c4143c5af7441c7The aim of the research is to study the dynamics of the structure of social representations of artificial intelligence (AI) among non-professionals. 258 students of different faculties aged from 18 to 24 took part in this study. The study took place in two stages: 1) November 2021, 2) March 2022. To identify the structure of the social perception of AI, the Vergès method was used based on the material of free associations. At the second stage, the level of respondents’ concern about the events of recent weeks in the economic and political spheres was additionally monitored. It was found that elements related to expectations of AI capabilities have strengthened the central core of the representation. Words with a connotation of danger appear in the periphery elements. Probably it is caused by both the activities of communication agents and the context of disturbing social changes. Against this background, it is important to enrich the knowledge of AI among ordinary people in order to form an adequate level of trust and distrust of recommendations based on it.Научное мнение00It was found that elements related to expectations of AI capabilities have strengthened the central core of the representation and words with a connotation of danger appear in the periphery elements.Научное мнение["Ekaterina D. Sadovskaia", "Kirill A. Panov"]2024-10-07T00:00:00 6452742665619d3146955ae41e048852ea073eb7Power generation in Nigeria has faced significant challenges, prompting the urgent need for improved efficiency and performance in existing hydropower plants. This research aims to address this need by developing Artificial Intelligence (AI) models to facilitate power planning in three operational hydropower plants in Nigeria. These AI models, specifically artificial neural network (ANN) models trained on historical data from the power plants, were utilized to forecast inflows and energy generation. The results demonstrate the effectiveness of the AI models, with the best inflow forecast model achieving correlation coefficients (r) of 0.8825, 0.8785, and 0.8712 for Jebba, Kainji, and Shiroro hydropower plants respectively. Similarly, the energy generation forecast model achieved high r values of 0.9690, 0.9732, and 0.9643 for the respective plants. These models offer invaluable support to power plants in planning and scheduling operations. However, the study encountered challenges, primarily related to the quality and availability of data. Specifically, the absence of weather data posed a limitation in developing more accurate prediction models for inflow. Despite these challenges, the AI models present a significant advancement in enhancing the efficiency and performance of hydropower plants in Nigeria.2024 IEEE PES/IAS PowerAfrica130The results demonstrate the effectiveness of the AI models, with the best inflow forecast model achieving correlation coefficients (r) of 0.8785, and 0.8712 for Jebba, Kainji, and Shiroro hydropower plants respectively.2024 IEEE PES/IAS PowerAfrica["C. C. Nwobi-Okoye", "S. Takim", "Stanley Okiy"]2024-10-07T00:00:00 4d3f568b8e5bae3e82616a82802abf97b0b8f98fBackground This study aims to empirically test a comprehensive interrelationship between green supply chain management (GSCM), green technology innovation (GTI), waste management (WM), big data analytics capability powered by artificial intelligence (BDAC-AI), and their collective impact on sustainable performance (SP) in organizational contexts. Methods This study was conducted in Pakistan’s food processing sector. The respondents included 495 managers working in the food processing industry. A structural equation modelling (SEM) approach is used to examine direct and indirect relationships between the variables. The originality of this study lies in integration of the technology acceptance model (TAM) and dynamic capability theory (DCT) to understand sustainable practices in the context of the provided model. Results This study highlights that GSCM, GTI, WM, and BDAC-AI have positive, strong, and direct impacts on SP. Furthermore, GTI and WM only partially mediate the link between GSCM and SP, whereas the two moderate the link. In addition, BDAC-AI had a moderating effect on the relationship between GTI and SP. This study has managerial implications, including strategies that involve the use of theoretical frameworks for technological acceptance and dynamic capabilities to support sustainable initiatives. However, it is worth noting that the findings provide a practical contingency for managers and businesses interested in implementing green studies effectively, improving technologies, and strengthening sustainable performance capabilities. Conclusions The study extends the literature by establishing a model for operationalizing GSCM in the food processing sector. Furthermore, it adds value in that it first integrates TAM and DCT to explain sustainable operations and their impact on organizations. Furthermore, it extends the existing literature by establishing a relationship between GSCM and SC. It offers a model through which GSCM can be operationalized in the context of the FS sector.F1000Research1610F1000Research["Quswah Makhdoom", "Ikramuddin Junejo", "Jan Muhammad Sohu", "Syed Mir Muhammad Shah", "Bilal Al-Wadi", "Faisal Ejaz", "Md Billal Hossain"]2024-10-07T00:00:00 d6807738b137f2fd735ce578aa78d139f746eb93BMC Medical Education574It is suggested how medical education must evolve to prepare clinicians capable of navigating human-AI systems, to achieve benefits while minimizing risks.BMC Medical Education["Liam G. McCoy", "Faye Yu Ci Ng", "Christopher M. Sauer", "Katelyn Edelwina Yap Legaspi", "Bhav Jain", "J. Gallifant", "Michael McClurkin", "Alessandro Hammond", "Deirdre Goode", "J. Gichoya", "L. Celi"]2024-10-07T00:00:00 0da2e3cec12d051ae074fe9366c9804cdb93175cSimpósio Brasileiro de Fatores Humanos em Sistemas Computacionais255{"pages": "22"}["Emanuel Felipe Duarte", "Paula Toledo Palomino", "Taciana Pontual Falc\u00e3o", "Grace Lis Barreto Porto", "C. Portela", "D. Ribeiro", "Andr\u00e9 C. A. Nascimento", "Y. P. Aguiar", "M. R. D. A. Souza", "Angelita Moutin Segoria Gasparotto", "Armando Maciel Toda"]2024-10-07T00:00:00 c8d1b797b097a9e3001c1a6a223586c97e906413Africa faces significant challenges in achieving uni- versal energy access, a fundamental prerequisite for sustainable development. This paper explores the potential of artificial intelli- gence (AI) in bridging this gap by enhancing grid management, optimizing off-grid solutions, and improving energy efficiency across the continent. While AI offers significant potential, its implementation is hindered by several challenges, including infrastructural limitations, economic constraints, policy incon- sistencies, and societal acceptance issues. This paper examines these challenges and proposes strategic solutions to overcome them. The paper also discusses the prospects of integrating AI into Africa's energy sector.2024 IEEE PES/IAS PowerAfrica360The potential of artificial intelli- gence (AI) in bridging this gap in bridging this gap by enhancing grid management, optimizing off-grid solutions, and improving energy efficiency across the continent is explored.2024 IEEE PES/IAS PowerAfrica["Ebikella Mienye", "G. Obaido", "P. K. Ainah", "Ikiomoye Douglas Emmanuel", "Ibomoiye Domor Mienye"]2024-10-07T00:00:00 818eb8a2293b907c62b2394ef5f795199ab3f4d5Journal of Arts, Literature, Humanities and Social Sciences00Journal of Arts, Literature, Humanities and Social Sciences[]2024-10-07T00:00:00 ecb7eb5be60112e116e7ed19f0509ead71f47fc5Discover Artificial Intelligence400Discov. Artif. Intell.["A. Boretti"]2024-10-07T00:00:00 d3e6cfd528bf41180ff5da118e52b0eae71ccbf2Background: Patients with ALS often face difficulties expressing emotions due to impairments in facial expression, speech, body language, and cognitive function. This study aimed to develop non-invasive AI tools to detect and quantify emotional responsiveness in ALS patients, providing objective insights. Improved understanding of emotional responses could enhance patient-provider communication, telemedicine effectiveness, and clinical trial outcome measures. Methods: In this pilot study, fourteen patients with ALS had audio recordings performed during routine clinic visits while wearing a wireless pulse oximeter. Emotion-triggering questions related to symptom progression, breathing, mobility, feeding tube, and financial burden were randomly asked. The same questions were posed in separate psychiatric evaluations. Natural language processing (NLP) was used to analyze transcriptions, topic classifications, sentiment, and emotional states, combining pulse and speech data. AI-generated reports summarized the findings. Results: Pulse alterations consistent with emotional arousal were identified, with longer consultations and positive communication reducing pulse fluctuations. Financial concerns triggered the strongest emotional response, while discussions about breathing, mobility, and feeding tube increased anxiety. AI-generated reports prioritized patient concerns and streamlined documentation for providers. Conclusions: This study introduces a novel approach to linking pulse and speech analysis to evaluate emotional responses in ALS patients. AI and affective computing provide valuable insights into emotional states and disease progression, with potential applications for other neurological disorders. This approach could augment clinical trial outcomes by offering a more comprehensive view of patient well-being.Journal of Neurological Sciences370A novel approach to linking pulse and speech analysis to evaluate emotional responses in ALS patients is introduced, providing valuable insights into emotional states and disease progression, with potential applications for other neurological disorders.Journal of the Neurological Sciences["M. Garbey", "Quentin Lesport", "G\u00fcl\u015fen \u00d6ztosun", "Veda Ghodasara", "Henry J. Kaminski", "Elham Bayat"]2024-10-07T00:00:00 34b27c4c81c8d5ed5e2053d0e80ad042dfac7d67Simpósio Brasileiro de Fatores Humanos em Sistemas Computacionais180{"pages": "54"}["Bianca Moreira Cunha", "Simone D. J. Barbosa"]2024-10-07T00:00:00 c805ffd3b5e23b0862a495eec3c75518458a43a4Heart failure (HF) is an escalating global health concern, worsened by an aging population and limitations in traditional diagnostic methods like electrocardiograms (ECG). The advent of deep learning has shown promise for utilizing 12-lead ECG models for the early detection of left ventricular systolic dysfunction (LVSD), a crucial HF indicator. This study validates the AiTiALVSD, an AI/machine learning-enabled Software as a Medical Device, for its effectiveness, transparency, and robustness in detecting LVSD. Conducted at Mediplex Sejong Hospital in the Republic of Korea, this retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, which is based on a deep learning algorithm, was assessed against echocardiography findings. To improve model transparency, the study utilized Testing with Concept Activation Vectors (TCAV) and included clustering analysis and robustness tests against ECG noise and lead reversals. The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model's predictive accuracy. TCAV analysis showed the model's alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. AiTiALVSD's high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI/ML-based diagnostics, setting a new benchmark in cardiac care.medRxiv20AiTiALVSD's high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings, and highlights the importance of transparency and robustness in AI/ML-based diagnostics.["Min Sung Lee", "Jong-Hwan Jang", "Sora Kang", "Ga In Han", "A. Yoo", "Yong-Yeon Jo", "Min Son", "Joon-myoung Kwon", "Sooyeon Lee", "Ji Sung Lee", "Hak Seung", "Lee", "Kyung-Hee Kim", "MD Hak Seung Lee"]2024-10-07T00:00:00 eb50954e83da324774f87ddb2a7c29fb137af42cJournal of Decision Systems670Journal of Decision Systems["Ashish Jagdish Sharma", "Bhawana Rathore"]2024-10-07T00:00:00 b056b51c50239a90ddfdb6dd9cf1d0c41035de06Journal of general internal medicine40Journal of general internal medicine["Natalie C. Benda", "Sarah Harkins", "Alison Hermann", "Jyotishman Pathak", "Jessica Kim", "Yihong Zhao", "Meghan Reading Turchioe"]2024-10-07T00:00:00 a5471ad0f2b3c50bceeb488134f001db10e72861Public Management Review560Public Management Review["Lei Tao", "Jinhan Wan", "Bo Wen"]2024-10-07T00:00:00 bf1b6aad3b0516898cc8ba10422794a45bfbe2f700["Tarnveer Singh"]2024-10-07T00:00:00 2a4b4f36f5600e7ae6a3ab9b5930fbcd2d54a01eAsian Journal of Biological and Life Sciences00Asian Journal of Biological and Life sciences["N. Thangavel", "Amirah Ibrahim Assiri", "Bushra Ibrahim Dighriri", "Etlal Mohammad Alnami", "N. M. Adawi", "Nariman Ahmad Alhazemi", "Najat Mohammed Bawkar"]2024-10-07T00:00:00 26e9cbb3f02f14baec0527a62623634e742e5539International Review of Retail Distribution & Consumer Research500The International Review of Retail, Distribution and Consumer Research["Mika Yrj\u00f6l\u00e4", "Pia Hautam\u00e4ki", "Roosa Ranta"]2024-10-07T00:00:00 993575ada9b3ea976661483d282b290a0ea688eeTechnologies in the workplace can increase efficiency and reduce costs. Challenges include a lack of guidelines, blurred work-life boundaries, and re-skilling workers.00["Kathryn Burton", "Devyani Gajjar"]2024-10-07T00:00:00 177d1e251b31659fde9fd3f7e535f04ae326f0aaJournal of Military Ethics80Journal of Military Ethics["Kaushik Roy"]2024-10-07T00:00:00 9fbd8699ba7e52d8cae0abe131c5e5eb61a96e4cNew technologies are rapidly shaping how people create and consume arts and culture. But what is the impact on rightsholders and human creativity?00["Lois Jeary", "Devyani Gajjar"]2024-10-07T00:00:00 380729d6b9650ea4b8cb7e4325606e30cdd3b727For medical diagnosis and therapy planning, the importance of accurate MRI segmentation cannot be overemphasized. Conversely, the inscrutability of deep learning models remains obstacles to their application in therapeutic contexts. In this article, an interpretability artificial intelligence framework is introduced. It combines an MRI segmentation model based on deep learning, visual attribution algorithms and natural language explanations. EXPERIMENT The dataset is consisting of plenty of different types of brain MRI scans, and used to test the architecture. The average of Dice score of our method is 88.7% and 92.3% for segmentation of tumor and categorization of tissues, respectively. Both are pretty epic scores. The insights extracted from both the visual attributions and textual explations improve our understanding of how the model arrives at its decisions, thereby increasing the transparency and interpretability of the model.  believe this approach to explainable artificial intelligence can help to close the gap between state-of-the-art performance in MRI segmentation and clinical interpretability, by increasing the transparency of complex models and facilitating their implementation into a clinical workflow. Conclusion Our approach may have implications in the transparent and reliable development of AI-based decision support systems for medical imagingInternational Journal of Computational and Experimental Science and Engineering3813An interpretability artificial intelligence framework is introduced that combines an MRI segmentation model based on deep learning, visual attribution algorithms and natural language explanations to help close the gap between state-of-the-art performance in MRI segmentation and clinical interpretability.International Journal of Computational and Experimental Science and Engineering["Vinoth M", "Jayapradha V", "Anitha K", "Gowrisankar Kalakoti", "Ezhil E. Nithila"]2024-10-07T00:00:00 a80d4d024bc7aff5ea77470e9ae1856991ec2cd2Insights into Imaging333The perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers, while the radiologist’s responsibility for AI outputs is confirmed), while the radiologist’s responsibility for AI outputs is confirmed.Insights into Imaging["M. Zanardo", "J. J. Visser", "A. Colarieti", "Renato Cuocolo", "M. Klontzas", "Daniel Pinto dos Santos", "Francesco Sardanelli"]2024-10-07T00:00:00 61afc0d651f5bc99670a46cad821aa93f5ed443aThe maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.Journal of Marine Science and Engineering132The proposed RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems, offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.Journal of Marine Science and Engineering["Changui Lee", "Seojeong Lee"]2024-10-07T00:00:00 e26cfca4ec5d597d46d47afb4e6f9321abb7de94Discover Education51Any instructor, regardless of tech background, can and should be integrating AI into their courses right now and describe and discuss the experience with this process and the deliberative approach to creating value for students.Discover Education["Jamey A. Darnell", "Shalini Gopalkrishnan"]2024-10-07T00:00:00 65daad831dcc2167001f1f9dc1ed9f0ae3ff51b7AI &amp; SOCIETY211It is shown that the emerging capacity of artificial agents to generalise redraws the epistemic boundary between artificial agents and their learning environments, giving rise to synthetic agency.AI &amp; SOCIETY["Konstantin Mitrokhov"]2024-10-07T00:00:00 017aea6a929fbaf9b33808a13009a4b7b182c0b8Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.Journal of Ecohumanism141The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud.Journal of Ecohumanism["Mohamed Kama Laldin Ismaeil"]2024-10-07T00:00:00 6a637ed27c51383a405a00de8f5a6d0086e9bc7dArtificial Intelligence (AI) is transforming society, including religious practices and experiences of religious communities. The first large-scale AI church service took place in 2023 in Germany as a Protestant Christian worship event within a biannual religious celebration. Studying this religious service illustrates how AI was developed as a religious agent and provides insights into the experiences of attendees. This research note presents data from a quantitative survey and two qualitative questionnaires of participants conducted at the AI worship service. Findings show diversity of opinion and a range of attendee experiences. By presenting the results of qualitative and quantitative analysis, this research note highlights the possibilities and limitations of incorporating AI into the religious sphere. While most of the responses were skeptical, participants also reported having spiritual experiences. Nevertheless, attendees also reported conflicting feelings regarding experiences of community within an artificial setting. The main concerns were technological limitations, fear of replacing humans, biases in the theology of the underlying large language model, and lack of personality and emotion. Age-based differences include younger attendees finding the AI service more attractive while older attendees found the AI service more stimulating. Implications are drawn for practical theology and AI implementation within religious settings.Review of religious research131This research note highlights the possibilities and limitations of incorporating AI into the religious sphere and presents data from a quantitative survey and two qualitative questionnaires of participants conducted at the first large-scale AI church service in Germany.Review of Religious Research["Jonas Simmerlein"]2024-10-07T00:00:00 1ed1d8a88b6bc3b9775aeced6746323e0fd70c0b This article examines gender gaps in higher education in Spain in the context of technological advancements, particularly digitalization and artificial intelligence (AI). First, analyzing descriptive Spanish data we identify significant disparities, with women overrepresented in health-related fields and underrepresented in STEM (Science, Technology, Engineering and Mathematics) disciplines. This imbalance is concerning as STEM fields offer better employment prospects and higher salaries. Then, we analyze university degrees’ exposure to technological change through routine task intensity (RTI) and AI exposure indices, as well as a novel index of exposure to emerging digital technologies from 2015 to 2024. Our findings show that women are more enrolled in degrees with higher RTI, prone to automation, and less in degrees with higher AI exposure, likely to benefit from technological advancements. This suggests technological change could widen existing labor market gender gaps. To address this, we recommend policies to boost female participation in STEM fields and adapt educational curricula to reduce routine tasks and enhance AI complementarities, ensuring equitable labor market outcomes amid technological change. (JEL codes: I23, I26, J16, J24).CESifo Economic Studies180This article examines gender gaps in higher education in Spain in the context of technological advancements, particularly digitalization and artificial intelligence (AI), and recommends policies to boost female participation in STEM fields and adapt educational curricula to reduce routine tasks and enhance AI complementarities.CESifo Economic Studies["J. I. Conde-Ruiz", "Juan Jos\u00e9 Ganuza Fernandez", "Manuel Garc\u00eda", "Carlos Victoria Lanz\u00f3n"]2024-10-07T00:00:00 a8cfef34510738dbd3a3e9750c7b97e78d150a64Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early screening is crucial for effective intervention, as timely identification of mental health issues can significantly improve treatment outcomes. Artificial intelligence (AI) can be valuable for improving the screening of mental disorders, enabling early intervention and better treatment outcomes. AI-driven screening can leverage the analysis of multiple data sources, including facial features in digital images. However, existing methods often rely on controlled environments or specialized equipment, limiting their broad applicability. This study explores the potential of AI models for ubiquitous depression-anxiety screening given face-centric selfies. The investigation focuses on high-risk pregnant patients, a population that is particularly vulnerable to mental health issues. To cope with limited training data resulting from our clinical setup, pre-trained models were utilized in two different approaches: fine-tuning convolutional neural networks (CNNs) originally designed for facial expression recognition and employing vision-language models (VLMs) for zero-shot analysis of facial expressions. Experimental results indicate that the proposed VLM-based method significantly outperforms CNNs, achieving an accuracy of 77.6%. Although there is significant room for improvement, the results suggest that VLMs can be a promising approach for mental health screening.360The investigation focuses on high-risk pregnant patients, a population that is particularly vulnerable to mental health issues, and results suggest that VLMs can be a promising approach for mental health screening.["Gustavo A. Bas'ilio", "Thiago B. Pereira", "A. L. Koerich", "Hermano Tavares", "Ludmila Dias", "Maria das Graccas da S. Teixeira", "Rafael T. Sousa", "W. H. Hisatugu", "Amanda S. Mota", "Anilton S. Garcia", "Marco Aur'elio K. Galletta", "Thiago M. Paixao"]2024-10-07T00:00:00 0b6e7868fa225ca09d3e510f399399a5b3d0b786As artificial intelligence (AI) continues to evolve, the current paradigm of treating AI as a passive tool no longer suffices. As a human-AI team, we together advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans. Drawing from interdisciplinary concepts such as ecorithms, order from chaos, and cooperation, we explore how AI can evolve and adapt in unpredictable environments. Arising from these brief explorations, we present two key recommendations: (1) foster ethical, cooperative treatment of AI to benefit both humans and AI, and (2) leverage the inherent heterogeneity between human and AI minds to create a synergistic hybrid intelligence. By reframing AI as a dynamic partner, a model emerges in which AI systems develop alongside humans, learning from human interactions and feedback loops including reflections on team conversations. Drawing from a transpersonal and interdependent approach to consciousness, we suggest that a"third mind"emerges through collaborative human-AI relationships. Through design interventions such as interactive learning and conversational debriefing and foundational interventions allowing AI to model multiple types of minds, we hope to provide a path toward more adaptive, ethical, and emotionally healthy human-AI relationships. We believe this dynamic relational learning-partner (DRLP) model for human-AI teaming, if enacted carefully, will improve our capacity to address powerful solutions to seemingly intractable problems.arXiv.org30By reframing AI as a dynamic partner, a model emerges in which AI systems develop alongside humans, learning from human interactions and feedback loops including reflections on team conversations, which will improve human-AI teaming capacity to address powerful solutions to seemingly intractable problems.ArXiv["Julia A. Mossbridge"]2024-10-07T00:00:00 3abb74914421b5df8b31cfebbc1167db6cfc92d3The widespread use of AI-based technologies has sparked educational, social, and political interest in AI training. Education systems must prepare individuals for a world with AI. AI literacy is a cognitive and pedagogical difficulty. AI's language and intricacies need redefining literacy. Because these systems are easy to use, more individuals are utilizing them than those with limited conceptualizations (such as an inability to grasp the future relevance of these systems) or competencies (like an inability to comprehend how these systems function). The study investigates the increasing significance of artificial intelligence (AI) literacy within the context of media literacy. As AI technologies permeate various aspects of modern media, the capacity to comprehend and engage critically with these systems has become essential. The paper begins by analyzing the intricate intersection of AI and media, focusing on content creation, dissemination, and consumption. It then emphasizes the importance of AI literacy, which is the ability to comprehend, implement, and evaluate AI technologies critically, similar to traditional media literacy skills. Finally, the paper proposes that AI literacy, as part of media literacy, entails understanding how these systems function and their ethical and societal implications. The paper's conclusion offers a comprehensive framework for incorporating AI literacy into media education curricula, aiming to empower individuals to navigate, evaluate, and responsibly participate in the evolving AI-mediated media landscape.Journal of Advanced Research in Applied Sciences and Engineering Technology170The study investigates the increasing significance of artificial intelligence (AI) literacy within the context of media literacy, and proposes that AI literacy, as part of media literacy, entails understanding how these systems function and their ethical and societal implications.Journal of Advanced Research in Applied Sciences and Engineering Technology["Miharaini Md Ghani", "Wan Azani Mustafa", "Durratul Laquesha Shaiful Bakhtiar", "Moh. Khairudin"]2024-10-07T00:00:00 69b8cedf25cc55a0adf4039afab85e0c7feb6437The pervasive influence of Artificial Intelligence (AI) and Machine Learning (ML) technologies in everyday life has underscored the critical need for addressing inherent biases in big data. The CHARLIE project, an “ERASMUS+ KA2” initiative involving six partners across five European countries, is a beacon of innovation in addressing the critical need for algorithmic biases and ethical education in AI and ML. Our project objectives centre on enhancing the capacity of Higher Education (HE) institutions to deliver socially responsible and ethically informed tech education, particularly in AI and ML domains. By integrating digital and engaging pedagogical strategies, we aim to increase tech students’ social and ethical competencies and equip educators with effective tools for teaching these critical topics. CHARLIE focuses on cultivating an ethical, human-centered perspective in technology education, specifically targeting the youth demographic. One of the practical outputs from CHARLIE is the development of a competency matrix and the 'Algorithmic Bias' course. This course, designed with a blended learning approach and complemented by a 'Digital Game' to assess learning outcomes, is a testament to the project's real-world applicability. Additionally, the project focuses on creating effective linkages between HE, Adult Education (AE), and Youth sectors to facilitate widespread adoption and recognition of ethical AI curricula. This method is chosen for its potential to engage young learners in complex subjects by integrating learning with interactive, scenario-based gameplay that reflects real-world dilemmas. The games complement theoretical knowledge with practical decision-making exercises that promote critical thinking and ethical reasoning. Our paper will outline the development process of our digital games, discuss the pedagogical frameworks that underpin them, and share preliminary results from their implementation across various educational settings in Europe. We will also explore the broader implications of our work for integrating game-based learning in ethical tech education and its potential to transform educational practices in Higher Education, Adult Education, and beyond.European Conference on Games Based Learning150The development of digital games for integrating game-based learning in ethical tech education and its potential to transform educational practices in Higher Education, Adult Education, and beyond are outlined and preliminary results from their implementation across various educational settings in Europe are shared.European Conference on Games Based Learning["Nidhi Nidhi", "M. B. Arenas", "Andreia Morgado", "Adrian Solomon", "Maria Moreira", "Rute Ferreira", "Elise Raittila"]2024-10-07T00:00:00 2c8207e55c8be989b89ee2c9619db0c534519504BMC Health Services Research335HCPs showed a willingness to embrace AI incorporation and believed that it may bring numerous benefits to the health system, and policymakers should take necessary steps to ensure AI incorporation in the healthcare sector.BMC Health Services Research["Muhammad Yousif", "Saima Asghar", "Jamshaid Akbar", "Imran Masood", "Muhammad Rizwan Arshad", "Javaria Naeem", "Abdullah Azam", "Zakia Iqbal"]2024-10-08T00:00:00 aa6bdd59fb30c7ded4abf406a1c81fb0e619e6a1Atrial fibrillation (AF) is a severe condition associated with high morbidity and mortality, including an increased risk of stroke and poor outcomes poststroke. Our understanding of the prognosis in AF remains poor. Machine learning (ML) has been applied to the diagnosis, management, and prognosis of AF in the context of stroke but remains suboptimal for clinical use. This article endeavors to provide a comprehensive overview of current ML applications to AF patients at risk of stroke, as well as poststroke patients without AF. Strategies to develop effective ML involve the validation of a variety of ML algorithms across internal and external datasets as well as exploring their predictive powers in hypothetical and realistic settings. Recent literature of this rapidly evolving field has displayed much promise. However, further testing and innovation of medical artificial intelligence are required before its imminent introduction to ensure complete patient trust within the community. Prioritizing this research is imperative for advancing the optimization of ongoing care for AF patients, as well as the management of stroke patients with AF.Annals of the New York Academy of Sciences333A comprehensive overview of current ML applications to AF patients at risk of stroke, as well as poststroke patients without AF is provided to provide a comprehensive overview of machine learning applications to patients with and without AF.Annals of the New York Academy of Sciences["Bill Goh", "Sonu M M Bhaskar"]2024-10-08T00:00:00 b0ce1bb8bfe8f76546a4e20f6147ff0c11759d55 Purpose The existing literature highlights challenges in measuring social impact within social and solidarity economy organisations, particularly regarding the social return on investment (SROI) methodology. This paper aims to address the barriers to SROI implementation while exploring the potential of artificial intelligence (AI) in enhancing the measurement of social impact. Design/methodology/approach This review-based paper synthesises research on SROI methodology limitations and recent AI developments while focusing on ethical considerations. Drawing from these domains, the study constructs a conceptual framework to guide future research. Findings The study identifies necessary enhancements to existing AI systems for social impact measurement and explores how advances in generative AI could refine current tools and address SROI constraints. It advocates for open AI models to address ethical concerns. Originality/value This study pioneers the integration of social impact assessment and AI, an innovative intersection in the academic literature. The growing disparity between academia and the rapidly evolving AI industry is highlighted and scholarly discourse is enriched through theoretical deliberations and innovative technological solutions. Social Enterprise Journal841The study identifies necessary enhancements to existing AI systems for social impact measurement and explores how advances in generative AI could refine current tools and address SROI constraints, and advocates for open AI models to address ethical concerns.Social Enterprise Journal["Nerea Abad-Itoiz", "Marta Sol\u00f3rzano-Garc\u00eda", "Daniel Hern\u00e1ndez-Mar\u00ed"]2024-10-08T00:00:00 598a69796c5630b1361483aa7909f8e6dfe4fc7aThis research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.Journal of the World Aquaculture Society481The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization.Journal of the World Aquaculture Society["Thurein Aung", "Rafiza Abdul Razak", "Adibi Rahiman Bin Md Nor"]2024-10-08T00:00:00 718d03d896b9401ceed616f839fbbc64419740a4In the ecosystem of artificial intelligence (AI), generative models enable the creation of hyper-realistic manipulations that are extremely plausible due to the precision of the audiovisual objects. These deepfakes are undetectable thanks to their components, which heightens concerns about the distortion of reality in the information ecosystem and how the ability to distinguish between real and fake audiovisual content affects public trust and democratic systems. This is a major challenge for media and information literacy if it is to combat misinfor­mation effectively. In this context, this study presents the results of a quasi-experiment conducted with 80 young people from the Community of Madrid (Spain) to assess their ability to detect deepfakes in immersive environments and to establish whether the context-identifying elements that enable detection of the reputation of the media source shape the credibility of the images. The results show that the images take precedence over the context identifiers, preventing a critical reading of the information that would make it possible to detect visual forgeries, something that is reinforced by their exceptional verisimilitude. It is concluded that the new post-humanist biome of virtual reality and artificial intelligence requires a reorien­tation of media and information literacy to raise the public’s awareness and educate them to make them less susceptible to disinformation based on deepfakes created with generative models.Communication &amp; Society931It is concluded that the new post-humanist biome of virtual reality and artificial intelligence requires a reorien­tation of media and information literacy to raise the public's awareness and educate them to make them less susceptible to disinformation based on deepfakes created with generative models.Communication &amp; Society["Alberto Sanchez-Acedo", "Alejandro Carbonell-Alcocer", "Manuel G\u00e9rtrudix", "J. Rubio-Tamayo"]2024-10-08T00:00:00 12148b42244ce9a977da5b237390a10bfdf3cb1fImplementing management systems in organisations of all types and sizes often raises the following question: “What benefits will this bring?” Initial resistance and criticism are common as potential challenges are identified during the implementation process. To address this, it is essential to highlight the advantages of these systems and engage stakeholders in supporting management efforts. While the planning, implementation, use, maintenance, auditing, and improvement of management systems are generally voluntary, certification is frequently driven by external factors, particularly customer demands. Employees also stand to gain significantly, with knowledge and information serving as valuable resources, especially for leveraging artificial intelligence. This article explores the management’s readiness to adopt and fully utilise two management systems based on international standards: the ISO 30401 Knowledge management system (KMS) and the ISO/IEC 42001 Artificial intelligence management system (AIMS). Through interviews, we assess the challenges and solutions associated with implementing these systems, whether planned or partially adopted. The findings illustrate the synergistic benefits of integrating the KMS and AIMS, demonstrating how their combined use can enhance Integrated Management Systems (IMSs). Such integration supports comprehensive planning, operation, and performance evaluation of processes and services while also promoting continuous improvement.Machine Learning and Knowledge Extraction192This article explores the management’s readiness to adopt and fully utilise two management systems based on international standards: the ISO 30401 Knowledge management system (KMS) and the ISO/IEC 42001 Artificial intelligence management system (AIMS).Machine Learning and Knowledge Extraction["Natalia Khazieva", "A. Paulikov\u00e1", "H. H. Chovanov\u00e1"]2024-10-08T00:00:00 40e2f4cacf78e41e08203a7250dda68d140d8f15The controversy surrounding ChatGPT has reopened the debate about the impact of new technologies in many fields of activities, including communication and PR. This study mapped Romanian PR practitioners’ use of generative AI and their perception of it, placing a special focus on the ethical concerns involved and the implications for the profession itself. We took a quantitative-qualitative approach by using both a survey and semi-structured interviews. Our goal was to determine the impact of generative artificial intelligence (AI) in the Romanian PR industry and to understand the reasons and challenges behind integrating generative AI in PR practice. The survey findings revealed a substantial adoption (73.5%) of AI within the Romanian PR community, with an overwhelming 91.6% of them using ChatGPT. The satisfaction level was remarkably high, with 92% expressing satisfaction with generative AI application efficacy. Benefits included timesaving, work simplification, and the reduction of repetitive tasks. Surprisingly, not only did 67.3% of respondents not perceive AI as an immediate threat to PR jobs, but 80.5% believed AI represents an opportunity for the industry. Indeed, almost all our interviewees admitted relief and satisfaction when using generative AI tools to complete their tasks. However, some concerns were expressed regarding the quality of the generative AI content and, in particular, the need to always check this kind of content by a human editor before using it. Moreover, PR professionals’ main ethical concerns are related to the issue of transparency towards their clients when using AI tools to produce different types of content.Communication &amp; Society161The survey findings revealed a substantial adoption of AI within the Romanian PR community, with an overwhelming 91.6% of them using ChatGPT and the satisfaction level was remarkably high, with 92% expressing satisfaction with generative AI application efficacy.Communication &amp; Society["Camelia Cusnir", "Anamaria Nicola"]2024-10-08T00:00:00 3c9dff2b465c889c1a44ffb62a065397c275991cPurpose This study aims to delve into the ethical challenges in artificial intelligence (AI) technologies to underscore the necessity of establishing principles for ethical AI utilization in hospitality and tourism. Design/methodology/approach A narrative review of research on ethical AI across diverse realms was conducted to reflect current research progress and examine whether sufficient measures have been taken to address issues pertinent to AI utilization in hospitality and tourism. Findings Ethical issues including privacy concerns, detrimental stereotypes, manipulation and brutalization pertinent to AI utilization are elaborated. How AI should be properly used and managed ethically, responsibly and sustainably is suggested. Research limitations/implications Five fine-tuned principles for regulating AI use in hospitality and tourism are proposed. Practical implications A resilient mindset, enhancement of AI context adaptability, equilibrium between development and regulation and collaborative effort of multiple stakeholders are paramount. Originality/value Through applying the AI evolution trajectory model, this study contributes to the current discourse of managing AI by proposing a framework that addresses the specific characteristics of hospitality and tourism. International Journal of Contemporary Hospitality Management661A narrative review of research on ethical AI across diverse realms was conducted to reflect current research progress and examine whether sufficient measures have been taken to address issues pertinent to AI utilization in hospitality and tourism.International Journal of Contemporary Hospitality Management["Rob Law", "Huiyue Ye", "Soey Sut Ieng Lei"]2024-10-08T00:00:00 add2b3b6f8c913aa04b5d247771c59020ed28edaThe labyrinthine process of manuscript evaluation in scientific publishing often delays disseminating timely research results. Generative Artificial Intelligence (genAI) models could potentially enhance efficiency in academic publishing. However, it is crucial to scrutinize the reliability of genAI in simulating human editorial decisions. This study analyzed 34 manuscripts authored by the corresponding author, involving initial editorial decisions from six publishers across 28 journals. Two genAI models, ChatGPT-4o and Microsoft Copilot, assessed these manuscripts using tailored prompts. The correlation between genAI and actual human editorial decisions was evaluated using Kendall’s τb. The original decision-making speed and the quality of genAI outputs evaluated by the CLEAR tool were recorded. Editorial decision-making by genAI models was instantaneous, compared to the editors’ average of 21.6±31.1 days. Both models achieved high scores on the CLEAR tool, averaging 4.8±0.4 for ChatGPT-4o and 4.8±0.5 for Copilot. Despite these efficiencies, there was no significant correlation between the genAI and human decisions (τb=0.121, P=.487 for ChatGPT-4o; τb=0.197, P=.258 for Copilot), nor between the decisions of the two genAI models (τb=0.318, P=.068). This preliminary study indicated that genAI models can expedite the editorial process with high-quality outputs. However, genAI has not yet achieved the accuracy of human editors in decision-making.Edelweiss Applied Science and Technology611This preliminary study indicated that genAI models can expedite the editorial process with high-quality outputs, but has not yet achieved the accuracy of human editors in decision-making.Edelweiss Applied Science and Technology["Malik Sallam", "Kholoud Al-Mahzoum", "Omar Marzoaq", "Mohammad Alfadhel", "Amer Al-Ajmi", "Mansour Al-Ajmi", "Mohammad Al-Hajeri", "Muna Barakat"]2024-10-08T00:00:00 19abb7956b34392ff273d1695dd3c93ab760fabeArtificial Intelligence (AI) has become a cornerstone in advancing autonomous vehicles, enabling realtime decision making, object detection, and automation in driving systems. This study aims to explore key AI innovations, including Machine Learning (ML) algorithms, computer vision, and reinforcement learning, that contribute to the development of autonomous vehicles. A qualitative approach} was adopted to analyze both current applications and future innovations of AI in autonomous vehicles. The study highlights various current AI applications in autonomous vehicles, such as automated safety features, advanced navigation systems, and adaptive cruise control. These technologies demonstrate how AI enhances vehicle functionality and improves safety in today driving environment. Looking ahead, AI is expected to enable full autonomy in vehicles, foster integration with smart city infrastructures, and drive innovations in fleet management. These advancements are anticipated to significantly improve vehicle safety, operational efficiency, and the overall user experience, solidifying AI as the fundamental technology for the future of intelligent transportation systems.International Journal of Cyber and IT Service Management280Key AI innovations, including Machine Learning algorithms, computer vision, and reinforcement learning, that contribute to the development of autonomous vehicles are explored to significantly improve vehicle safety, operational efficiency, and the overall user experience.International Journal of Cyber and IT Service Management["N. Noviati", "Fengki Eka Putra", "Sadan Sadan", "Ridhuan Ahsanitaqwim", "Nanda Septiani", "N. Santoso"]2024-10-08T00:00:00 0044799d68e9b514c9191c622580840b79b1ee8bDespite considerable behavioral and organizational research on advice from human advisors, and despite the increasing study of artificial intelligence (AI) in organizational research, workplace-related applications, and popular discourse, an interdisciplinary review of advice from AI (vs. human) advisors has yet to be undertaken. We argue that the increasing adoption of AI to augment human decision-making would benefit from a framework that can characterize such interactions. Thus, the current research invokes judgment and decision-making research on advice from human advisors and uses a conceptual “fit”-based model to: (1) summarize how the characteristics of the AI advisor, human decision-maker, and advice environment influence advice exchanges and outcomes (including informed speculation about the durability of such findings in light of rapid advances in AI technology), (2) delineate future research directions (along with specific predictions), and (3) provide practical implications involving the use of AI advice by human decision-makers in applied settings.Frontiers in Psychology1470The current research uses a conceptual “fit”-based model to summarize how the characteristics of the AI advisor, human decision-maker, and advice environment influence advice exchanges and outcomes and provide practical implications involving the use of AI advice by human decision-makers in applied settings.Frontiers in Psychology["Julia I. Baines", "R. Dalal", "Lida P. Ponce", "Ho-Chun Tsai"]2024-10-08T00:00:00 0b64973173dbc146f6ebabe2d00f68225233925dThe cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.Journal of Infrastructure Policy and Development1320The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs.Journal of Infrastructure, Policy and Development["Hisham O. Mbaidin", "K. Alomari", "Nour Qassem Sbaee", "Isa Othman AlMubydeen", "U.M Chindo"]2024-10-08T00:00:00 4c555ad8645a8117daeb2b430ad1acd28afe1980Many countries use automated decision-making (ADM) systems, often based on artificial intelligence (AI), to manage migration at their borders. This interdisciplinary paper explores two questions. What are the main ways that automated decision-making is used at EU borders? Does such automated decision-making bring risks related to human rights, and if so: which risks? The paper introduces a taxonomy of four types of ADM systems at EU borders. Three types are used at borders: systems for (1) identification and verification by checking biometrics, (2) risk assessment, and (3) border monitoring. In addition, (4) polygraphs and emotion detectors are being tested at EU borders. We discuss three categories of risks of such automated decision-making, namely risks related to the human rights to (1) privacy and data protection, (2) nondiscrimination, and (3) a fair trial and effective remedies. The paper is largely based on a literature review that we conducted about the use of automated decision-making at borders. The paper combines insights from several disciplines, including social sciences, law, computer science, and migration studies.Social Science Research Network510A taxonomy of four types of ADM systems at EU borders is introduced, namely systems for identification and verification by checking biometrics, risk assessment, and border monitoring, and polygraphs and emotion detectors are being tested at EU borders.ArXiv["Yiran Yang", "Frederik Zuiderveen Borgesius", "Pascal Beckers", "Evelien Brouwer"]2024-10-08T00:00:00 68891f7548ec72d4d9eaea35daf4bd651c8cdc43The expansion of information technology has had a major effect on HR strategies and procedures. The successful transfer of human knowledge into organizational knowledge is crucial while moving toward organizations modeled like edifices, and Artificial Intelligence plays a crucial role in this. This is a crucial stage in the process. Artificial intelligence is a growing area of HR technology that has the potential to either completely replace or vastly improve upon the effectiveness of conventional approaches to personnel administration. Companies can benefit from the use of AI in a number of ways, including applicant screening, employee engagement, retention, and advancement opportunities. It can be used to improve the effectiveness of human resource management by being applied to HR policies, methods, and perspectives. The student will examine the rise of AI in the HRM process and its possible benefits using secondary sources of information. This research provides fresh insights into how technological innovation has helped HRM progress from efficient to long-term viable.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)130This research provides fresh insights into how technological innovation has helped HRM progress from efficient to long-term viable and its possible benefits using secondary sources of information.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)["M. Ramu", "T. Ilakkiya", "P. Venkatesh", "R. S. Anantharajan", "Krishnamoorthi M", "C. R. Senthilnathan"]2024-10-08T00:00:00 f79713a545ea4aad9987eed4349228cee173aeefAbstract Objectives Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation. Materials and Methods We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle. Results, Discussion, and Conclusion Through case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.JAMIA Open420It is shown how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms.JAMIA Open["Benjamin X Collins", "J. B\u00e9lisle-Pipon", "Barbara J. Evans", "Kadija Ferryman", "Xiaoqian Jiang", "Camille Nebeker", "Laurie Novak", "Kirk Roberts", "Martin Were", "Zhijun Yin", "V. Ravitsky", "Joseph R. Coco", "Rachele M. Hendricks-Sturrup", "Ishan Williams", "Ellen W Clayton", "Bradley A. Malin"]2024-10-08T00:00:00 22b3025d15da59fecc9aa4a64e55adec8f5f2fc4Artificial intelligence (AI) enabled technologies are now corporate organisations' top priorities due to the availability of large data and the advent of the Internet of Things during the past ten years. AI is becoming a crucial component of business model innovation, process transformation, disruption, and gaining a competitive edge for companies adopting digital and data-centric cultures. This study investigates the implications of smart technology, artificial intelligence, robotics, and algorithms (STARA) on the future of work, with a particular emphasis on employee well-being and workplace dynamics. As futurists project that by 2025, 52% of all work functions will be automated, replacing one-third of current jobs, the rapid advancement of STARA creates both opportunities and risks. While automation has the potential to produce 133 million new jobs, it also threatens to eliminate 75 million employments, raising employee anxieties about job security and future roles. Despite the rising volume of studies on smart automation, there is still a major vacuum in our understanding of its implications on employees' mental health, well-being, and the entire workplace. This study investigates STARA's dual influence: while technology reduces physical strain and automates tedious jobs, it also raises new difficulties such as job displacement concerns and shifts in worker dynamics. The study emphasizes the need of human resource professionals to develop methods that strike a balance between technological integration and employee assistance. Key areas of focus include providing reskilling opportunities, adopting mental health initiatives, and encouraging open conversation regarding AI's expanding role in the workforce. By addressing these concerns, organisations may build a more resilient workforce that is better suited to the fourth industrial revolution. The study intends to contribute to a better understanding of how organisations may safeguard and improve employee well-being in the face of fast technological change, ensuring that STARA integration encourages innovation while simultaneously supporting a healthy and engaged workforceEast African Journal of Information Technology00This study investigates STARA's dual influence: while technology reduces physical strain and automates tedious jobs, it also raises new difficulties such as job displacement concerns and shifts in worker dynamics, which emphasizes the need of human resource professionals to develop methods that strike a balance between technological integration and employee assistance.East African Journal of Information Technology["Ann Gaceri Kaaria"]2024-10-08T00:00:00 f98597d44eb119c8699a03c1f1bfb97e1618a27bThis study investigates the integration of Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), and Prompt Engineering into the social sciences and their impact on collaborative networks, thematic developments, and research trends aligned with the Sustainable Development Goals (SDGs). Utilizing bibliometric analysis and topic modeling, the research analyzes a dataset of 389 publications from the Web of Science (WoS) database, spanning the last decade. The findings highlight significant growth in interdisciplinary research at the intersection of these technologies and social sciences, with notable contributions in management, business, and environmental studies. Key themes identified include AI-driven innovation in product development, energy sector advancements, and the role of AI in educational and healthcare settings. The study underscores the transformative potential of AI in driving sustainable development, while also emphasizing the importance of addressing ethical considerations and ensuring responsible implementation. This research contributes to a deeper understanding of how AI and related technologies are reshaping the social sciences and their role in achieving global sustainability goals.Sosyal Mucit Academic Review00Sosyal Mucit Academic Review["Ay\u015fe Asl\u0131 Y\u0131lmaz"]2024-10-08T00:00:00 4fb872bf28142944ea5dbce976be09c039206e4eAlthough artificial intelligence (AI) is becoming more and more prevalent, biases from data, algorithms, and feedback loops might still exist. Marginalized groups may be disproportionately affected by these biases, which may result in biased outcomes in areas such as facial recognition, credit scoring, and hiring. Reduced trust, sustained societal inequality, and stifled innovation are some of the effects. It is critical to support transparent development processes, diverse development teams, and ethical oversight in AI design in order to lessen these effects. If these issues are resolved, AI will become a more equitable instrument for advancing society.International Journal For Multidisciplinary Research30It is critical to support transparent development processes, diverse development teams, and ethical oversight in AI design in order to lessen these effects of bias, and if these issues are resolved, AI will become a more equitable instrument for advancing society.International Journal For Multidisciplinary Research["Aarav Singh"]2024-10-08T00:00:00 f03912af632a587e7feafb002892265e08f84b54The rapid integration of artificial intelligence (AI) within society and the emergence of the fourth industrial revolution (4IR), has ignited a spectrum of emotions in society, ranging from enthusiasm to anxiety. This study investigates the depths of AI anxiety and fear among a population of information science students and professionals. Utilising a survey of over 200 current students and professionals, this study explores the connections between age, gender identity, ethnicity, geographic location, educational attainment and residence, and the levels of anxiety and fear associated with AI and the 4IR. The findings reveal nuanced relationships, with age, ethnicity, academic achievement and regional context serving as critical differentiators in 4IR and AI anxiety within this population. Students and professionals alike may benefit from seeking further education about this emerging technology.Journal of information science280The findings reveal nuanced relationships, with age, ethnicity, academic achievement and regional context serving as critical differentiators in 4IR and AI anxiety within this population of information science students and professionals.Journal of Information Science["Brady D. Lund", "Nishith Reddy Mannuru", "Daniel A. Agbaji"]2024-10-08T00:00:00 199aee445f540eae01f341fc0d1a56d52176f2ecArtificial intelligence has emerged as a game-changer in the field of spine surgery, offering transformative diagnostic and therapeutic approaches for spinal conditions. The application of AI in spine research encompasses a diverse range of diseases, with usage scenarios becoming increasingly widespread and technological integration going deeper. AI technology shows immense promise and value in the diagnosis of spinal diseases, the formulation of treatment strategies, surgical navigation, prognostic evaluation, and postoperative rehabilitation. Through deep learning and machine learning, AI can aid doctors in enhancing diagnostic accuracy, creating personalized treatment plans, and executing precise maneuvers during surgery, thus improving operational safety. Moreover, AI can provide intelligent support for patients' postoperative recovery, facilitating the restoration of their functions. However, current research is still in its nascent stage and confronts several challenges, such as the lack of standardized databases, the simplicity of algorithmic learning models, the inadequate fusion of multi-modal clinical information, and the limited clinical applicability. By developing open-source, standardized spine databases, incorporating innovative intelligent core algorithms, and establishing normalized screening, diagnostic, and predictive models for spinal conditions, we trust that we can drive the innovation and refinement of diagnostic and treatment technologies in spine surgery. This will realize an effective conjunction between technological provision and clinical demands, continuously elevating the intelligence level of spine surgery and offering safer, more effective medical services to a vast array of patients.Zhonghua yi xue za zhi00By developing open-source, standardized spine databases, incorporating innovative intelligent core algorithms, and establishing normalized screening, diagnostic, and predictive models for spinal conditions, it is trust that the innovation and refinement of diagnostic and treatment technologies in spine surgery can drive the innovation and refinement of diagnostic and treatment technologies in spine surgery.Zhonghua yi xue za zhi["S. Q. Feng"]2024-10-08T00:00:00 4ff1794b1cd6e584423d6d0f793e5734bd227ea4Artificial intelligence (AI) is rapidly transforming society and industries, presenting both opportunities and ethical challenges. AI enables machines to perform tasks traditionally done by humans, such as natural language processing, pattern recognition, decision-making, and problem-solving (Brookings, 2023). In education, AI enhances teaching methodologies, student assessment, and administrative tasks through tools like intelligent tutoring systems, adaptive learning platforms, and educational chatbots. These tools offer customised learning experiences, immediate feedback, and data-driven insights. This research aims to investigate how AI can be leveraged within education to promote social good by identifying how familiar educators and students are with AI tools, identify how educators and students perceive the role of AI in education and what are the current applications of AI technologies in educational settings and how widely are they used. Finally, discuss the opportunities and ethical considerations of integrating AI in education. AI technologies can address critical social challenges such as inequality, accessibility, and personalised learning. According to Luckin et al. (2016), "AI can provide tailored educational experiences that adapt to individual learning needs, thus promoting equity in education." This exploratory research begins with an overview of AI's role and tools in education, followed by a discussion of the challenges, opportunities, and ethical considerations associated with AI integration. Understandings are drawn from educator’s response to a questionnaire and a focus group with first year and final-year third level students. This qualitative data, analysed using NVivo software, reveals key themes and significant findings on effectively utilising AI in education. European Conference on Innovation and Entrepreneurship230This research aims to investigate how AI can be leveraged within education to promote social good by identifying how familiar educators and students are with AI tools, and how educators and students perceive the role of AI in education.European Conference on Innovation and Entrepreneurship["Marie Leddy", "Niall Mc Creanor"]2024-10-08T00:00:00 581c22c11a6d6ebb3505d42dcf25f0c8349831adSupply chain resilience is not a novel concept in the supply chain industry; however, its significance importance is increasing, especially in the face of global disruptive events like natural and man-made disasters. Agility and collaboration are critical supply chain resilience elements for enhancing supply chain performance. However, the influence of artificial intelligence (AI) on the impact of agility and collaboration on performance remains unexplored. Therefore, the major objective of this study was to examine the impact of supply chain agility and collaboration on supply chain performance within the healthcare sector in Qatar. In the same vein, it examined the moderating role of AI in these relationships. The study utilized 564 responses from supply chain and clinical unit managers in Qatar and was analysed using the partial least squares (PLS) path modelling technique. The findings of the study indicate a significant positive relationship between supply chain agility, collaboration, and performance. Furthermore, these relationships are found to be positively moderated by AI. This study supports the research paradigm and provides evidence regarding supply chain agility and collaboration and their impact on supply chain performance. More importantly, this study is among the first to provide empirical evidence on the moderating role of AI in enhancing these relationships.PaperASIA710This study is among the first to provide empirical evidence on the moderating role of AI in enhancing supply chain agility, collaboration, and performance and is found to be positively moderated by AI.PaperASIA["Emad Naji Isaid", "Rohani Abdullah", "Syairah Aimi Shahron"]2024-10-08T00:00:00 6ca09c03530edf41fc59ad85e997cbced6527121In the rapidly evolving field of artificial intelligence (AI) literature generation, understanding how society perceives AI‐generated content, compared with human‐produced literature is of paramount importance. This study investigated societal perceptions and biases toward AI‐generated versus human‐produced poetry. A sample of 123 participants was subjected to a controlled experiment in which they evaluated a human‐generated poem that was randomly attributed to either a human, an AI, or an unspecified author. The assessment metrics comprised five categories: originality, aesthetic appeal, emotional engagement, coherence, and interpretive difficulty. An analysis of variance was used to analyze the survey results. Our findings revealed that poems attributed to an AI consistently received lower scores for originality, aesthetic appeal, and emotional engagement compared to those attributed to a human author. However, AI‐generated content was perceived as more complex and was rated higher in terms of interpretive difficulty. Interestingly, perceived authorship did not significantly influence coherence as a metric. When the poem was believed to be AI‐generated, it faced more critical evaluations than when it was human‐attributed. When authorship was ambiguous, feedback was distributed uniformly across negative, positive, and neutral sentiments, suggesting a potential mitigating effect of ambiguity on bias.The Journal of creative behavior280It was revealed that poems attributed to an AI consistently received lower scores for originality, aesthetic appeal, and emotional engagement compared to those attributed to a human author, but AI‐generated content was perceived as more complex and was rated higher in terms of interpretive difficulty.The Journal of Creative Behavior["Maja Sta\u0144ko-Kaczmarek", "Liliana Dera", "Halszka Koscielska"]2024-10-08T00:00:00 adf7ffd3869a0a8f74baa5630cca6c9ef7fb52b2The use of Autonomous Surface Vehicles, equipped with water quality sensors and artificial vision systems, allows for a smart and adaptive deployment in water resources environmental monitoring. This paper presents a real implementation of a vehicle prototype that to address the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring. The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth. Furthermore, by means of a stereo-camera, it also can detect and locate macro-plastics in real environments by means of deep visual models, such as YOLOv5. In this paper, experimental results, carried out in Lago Mayor (Sevilla), has been presented as proof of the capabilities of the proposed architecture. The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.arXiv.org130The overall system, and the early results obtained, are expected to provide a solid example of a real platform useful for the water resource monitoring task, and to serve as a real case scenario for deploying Artificial Intelligence algorithms, such as path planning, artificial vision, etc.ArXiv["Luis Miguel D'iaz", "S. Luis", "Alejandro Mendoza Barrionuevo", "Dame Seck Diop", "Manuel Perales Esteve", "Alejandro Casado", "Sergio Toral", "Daniel Guti\u00e9rrez-Reina"]2024-10-08T00:00:00 056b8be5388711b0f5cf3a8c5e6889e8478de3b8Penelitian ini bertujuan untuk mengkaji dampak implementasi Artificial Intelligence (AI) terhadap proses bisnis dan pengambilan keputusan di perusahaan teknologi. Dalam era digital saat ini, AI telah menjadi salah satu teknologi yang paling revolusioner, memberikan kemampuan untuk mengolah data secara cepat dan akurat, serta menghasilkan wawasan yang berharga bagi pengambilan keputusan strategis. Studi ini menggunakan metode kualitatif dengan pendekatan studi kasus pada beberapa perusahaan teknologi. Hasil penelitian menunjukkan bahwa implementasi AI memiliki dampak signifikan terhadap efisiensi operasional, peningkatan produktivitas, dan pengurangan biaya operasional. AI membantu dalam otomatisasi tugas-tugas rutin, analisis data besar, dan prediksi tren pasar, sehingga memungkinkan perusahaan untuk membuat keputusan yang lebih cepat dan tepat. Selain itu, AI juga memainkan peran penting dalam pengembangan produk dan layanan baru, serta meningkatkan kepuasan pelanggan melalui personalisasi pengalaman pengguna. Namun, penelitian ini juga menemukan beberapa tantangan dalam implementasi AI, seperti kebutuhan akan investasi yang besar, perubahan budaya organisasi, dan isu etika terkait penggunaan data. Untuk mengatasi tantangan ini, perusahaan perlu mengembangkan strategi implementasi AI yang komprehensif, melibatkan pelatihan karyawan, dan memastikan kepatuhan terhadap regulasi data.Technologia : Jurnal Ilmiah120Technologia : Jurnal Ilmiah["Sonianto Sonianto", "Fatoni Fatoni", "Susilowati Hartono"]2024-10-08T00:00:00 a51aee6e5f21f95ad61c3ab988f9e81d5627695cAI Art Generators have recently developed in such a way that they have become accessible to everyone who has access to the internet. This paper looks into how the modern AI art generators work and identifies the various issues with AI art on technical, moral and legal grounds. Furthermore it aims to understands how the increased accessibility to AI art generators affects humans.Modern AI art generators use artificial intelligence, machine learning algorithms and deep neural network algorithms to study a large amount of pre-existing artworks in portfolio and produce new art based on the prompts provided by the user. They use software such as CNN, GAN, NST.The biggest argument against AI art is that it needs the human creativity and imagination. But it has been argued that the human creativity is included in the text prompts provided by the artist. AI art generators have also been accused of stealing art works without permission of the artists which has been leading to some copyright issues. Furthermore AI art generators have been stealing artists’ commissions and work. AI art generators are not perfect yet and have several errors such as errors in the art generated such as anatomy errors(example: hands with six fingers instead of five etc). Artists have protested against the use of AI art generators. One such protest is the "No to AI generated images" movement wherein they flooded the portfolio in an AI art generator called "ArtStation" with images tagged as "trending in ArtStation in order to render all the generated images useless.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)80This paper looks into how the modern AI art generators work and identifies the various issues with AI art on technical, moral and legal grounds and aims to understands how the increased accessibility to AI art generators affects humans.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)["Dheenadhayalan K", "Sankar S", "U. S.", "Suresh R", "Jeyalakshmi R", "Venkateswara Prasad B"]2024-10-08T00:00:00 cd582f24cf2d8c3764f99d15296eaec3ca2910e5The effective Regulation and Ethical of Artificial Intelligence is an urgent policy concern. Legislatures and regulators do not possess the specialized technical expertise necessary to effectively convert popular requests into legislative mandates. The excessive dependence on industry self-regulation results in a lack of accountability for AI ethical system  and users in meeting democratic requirements. The concept of Ethical Frameworks  involves governments mandating the entities subject to regulation to get regulatory assistance from a private regulator. This AI Ethical Frameworks has the potential to address the shortcomings of both command-and-control regulation and self-regulation. Most of advanced states  provide governments the opportunity to set policy priorities for AI Ethical Frameworks, while using market forces and industry research and development (R&D) to develop the most effective ways of regulation to set up Ethical Frameworks that align with policymakers' goals.Journal of the College of Basic Education00Most of advanced states provide governments the opportunity to set policy priorities for AI Ethical Frameworks, while using market forces and industry research and development (R&D) to develop the most effective ways of regulation to set up Ethical Frameworks that align with policymakers' goals.Journal of the College of Basic Education["Mohammed Hasan Hadi", "Asmaa Ali Jasim"]2024-10-08T00:00:00 86ba10b97bcc5381f1b98bfb00554f3a0b9ef19fArtificial Intеlligеncе (AI) is transforming thе landscapе of businеss opеrations through thе automation of procеssеs, еnhancing еfficiеncy, productivity, and accuracy. This articlе еxplorеs thе impact of AI on automating tasks across various industriеs, focusing on its advantagеs in strеamlining workflows, dеcision-making procеssеs, and rеducing opеrational costs. It also discussеs kеy challеngеs and futurе trеnds that businеssеs must addrеss to fully lеvеragе AI’s potеntial.European Journal of Artificial Intelligence and Digital Economy40The impact of AI on automating tasks across various industriеs, focusing on its advantagеs in strеamlining workflows, dеcision-making procеssеs, and rеducing opеrational costs is discussed.European Journal of Artificial Intelligence and Digital Economy["Sobirov Khurshed"]2024-10-08T00:00:00 34f66a26aa292be829c730df59994194085ff519Technological advancements in the last decade have greatly impacted the financial technology (fintech) industry by making financial services more accessible, efficient, and personalized. Out of all these innovations, artificial intelligence (AI) is having the most impact on how the industry is changing. AI is truly a game-changer for the financial sector. It can handle massive volumes of data, spot trends, and make choices with little to no human input. This article examines the function of artificial intelligence (AI) within the financial technology industry, outlining its uses in areas such as algorithmic trading, customer service, credit scoring, lending, investment management, and fraud prevention. It outlines the advantages of AI, which include better risk management, enhanced decision-making, a more satisfying customer experience, and reduced costs and increased efficiency. Data privacy and security, ethical issues, keeping up with regulations, and technical and operational dangers are just few of the issues covered in the article as they pertain to the integration of AI. To demonstrate the revolutionary effect of AI, the article presents case studies of prominent AI deployments in fintech firms. The report concludes by looking ahead to what's to come, speculating that new AI technologies will keep pushing the financial technology industry to innovate. Future financial services will be shaped in new ways by the constant evolution and integration of AI into fintech.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)50The function of artificial intelligence within the financial technology industry is examined, outlining its uses in areas such as algorithmic trading, customer service, credit scoring, lending, investment management, and fraud prevention and the advantages of AI, which include better risk management, enhanced decision-making, a more satisfying customer experience, and reduced costs and increased efficiency.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)["M. Manikandan", "P. Venkatesh", "D. Chitra", "M. Krishnamoorthi", "M. Ramu", "C. R. Senthilnathan"]2024-10-08T00:00:00 8be23784648e31cf6bb71d7ddc0af1f035075a66Artificial intelligence (AI) has the potential to revolutionize many aspects of healthcare, including diagnosis, treatment planning, medicine discovery, and healthcare management. This research examines the current applications, benefits, and drawbacks of artificial intelligence (AI) in healthcare on a global scale. Artificial intelligence has considerably enhanced personalized treatment recommendations, hospital management, and diagnostic precision, particularly in the fields of radiology and pathology. Artificial intelligence (AI) is reducing the time and money required to bring new treatments to market by speeding up the search for innovative medicines and enhancing the accuracy of efficacy estimates. The use of AI in healthcare still faces challenges, despite these advancements. The integration of AI into current systems, the protection of user data, and the prevention of algorithmic bias are all significant concerns. Clinical decision-making with AI presents serious ethical concerns, particularly for patients' rights to make their own decisions and for the doctor-patient relationship as a whole. The disparities in AI adoption throughout the world highlight the need for developed and poor nations to work together to provide equitable access to AI. This study investigates the profound influence of artificial intelligence (AI) on the healthcare sector, emphasizing its market expansion, revenue growth, and applications across various medical disciplines. The AI healthcare market is expected to grow from $19.27 billion in 2023 to $613.81 billion by 2034, reflecting a significant annual increase driven by technological advancements and rising demand for high-quality care. The compound annual growth rate (CAGR) of 40.2% projected from 2022 to 2029 illustrates the rapid adoption of AI technologies. Generative AI revenue in healthcare is projected to rise from $1.07 million in 2022 to $21.74 million by 2032, highlighting its expanding role in enhancing medical services. AI is notably prevalent in radiology, comprising 75.2% of its applications, but its impact extends to other fields such as cardiovascular care and neurology, where it enhances diagnostic precision and treatment effectiveness. The study also explores the growing use of AI-powered medical imaging, virtual assistants, and chatbots, marking a shift towards more accurate and efficient healthcare delivery. By examining these trends, the research aims to offer insights into how AI can address current healthcare challenges, optimize costs, and advance medical services, particularly in developing regions. This analysis underscores the need for continued investigation into AI’s potential to revolutionize healthcare and improve patient outcomes.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)30The study investigates the profound influence of artificial intelligence (AI) on the healthcare sector, emphasizing its market expansion, revenue growth, and applications across various medical disciplines, and the growing use of AI-powered medical imaging, virtual assistants, and chatbots.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)["K. Maran", "P. Priyadarshini", "C.R. Senthilnanthan", "M. Manikandan", "R. G. Kumar", "M. Ramu"]2024-10-08T00:00:00 253be80a09638c0ce8ad5f4700a8ad8ed4189aed This Medical News story is an interview about the US Department of Veterans Affairs’ pioneering work in using artificial intelligence to improve patient care. Journal of the American Medical Association (JAMA)00JAMA["Roy Perlis"]2024-10-08T00:00:00 8e0fcd9f983a4263b2bb87895d49d169794305cfIntegrating Artificial Intelligence (AI) and E-learning platforms has become increasingly prevalent in the rapidly evolving landscape of higher Education. However, amidst this technological advancement, the role of Emotional Intelligence (EI) and its impact on the efficacy of AI-driven educational tools still needs to be explored. This pilot study seeks to elucidate the intricate relationship between Emotional Intelligence, Artificial Intelligence, and E-Learning in Higher Education. Drawing upon a multidisciplinary approach, this study investigates the correlation between students' Emotional Intelligence competencies and their engagement with AI-driven E-Learning platforms. The findings of this pilot study are expected to shed light on several critical aspects. Firstly, it aims to uncover how Emotional Intelligence influences students' receptivity to AI-infused E-Learning environments, potentially elucidating strategies for optimizing user experience and learning outcomes. Moreover, by exploring the reciprocal influence between Emotional Intelligence and AI algorithms, this research endeavors to contribute to the refinement of AI technologies, fostering greater personalization and adaptability in educational settings. Furthermore, this study endeavors to address the ethical implications inherent in the intersection of Emotional Intelligence, Artificial Intelligence, and E-Learning. By elucidating the potential risks and benefits associated with integrating these technologies, it seeks to inform policymakers, educators, and AI developers alike, facilitating the responsible deployment of AI-driven educational tools. Therefore, its innovative methodology and comprehensive approach aspire to pave the way for future research endeavors, ultimately enriching the educational landscape with insights prioritizing technological advancement and human well-being.Multidisciplinary Journal for Education, Social and Technological Sciences330This study aims to uncover how Emotional Intelligence influences students' receptivity to AI-infused E-Learning environments, potentially elucidating strategies for optimizing user experience and learning outcomes and address the ethical implications inherent in the intersection of Emotional Intelligence, Artificial Intelligence, and E-Learning.Multidisciplinary Journal for Education, Social and Technological Sciences["Abdullah Alenezi"]2024-10-08T00:00:00 98b9ce72f3fb31bc61e6ff2811dba1c6b12e1579Nigeria is one of the nations blessed with vast number of mineral resources which can make its economy one of best in the world. However, very little attention is directed to this sector as the sector contributes less than 10% to the country's Gross Domestic Product (GDP). Therefore, this study evaluates the economic potentials of Nigeria mineral resources as means of liberating the country from its current economic woes. Data obtained from the existing company, internet sources, U.S. Geological Survey, Nigeria Geological Survey Agency among others were used to form the bases for the analyses. The economic indicators were first computed to determine the dependency of Nigeria mineral demands on the import and forecasting was also done using the moving average method and forecast command. The obtained import reliance and self-sufficiency indicated that Nigeria still depend largely on the importation to meet its mineral requirements and hence not self- sufficient. The Net Profit Value (NBP), Internal Rate of Return (IRR) and Payback Period (PBP) revealed that the minerals investigated are economically viable. To enhance the easy assessment of the NPV, artificial intelligence approach, Artificial Neural Network (ANN) was used to develop models for barite and iron ore. The model was validated, and the validation results are compared with the actual values. They were found to be very close to the actual NPV and can be used for the NPV predictions. Therefore, ANN model was transformed through the weights and biases to mathematical form. Hence, the study has revealed the dependency of Nigeria on import and the economic viability of the minerals in Nigeria.International Journal of Innovative Science and Research Technology841The study has revealed the dependency of Nigeria on import and the economic viability of the minerals in Nigeria and developed models for barite and iron ore to enhance the easy assessment of the NPV.International Journal of Innovative Science and Research Technology (IJISRT)["Adeyemo, Jacob Titilope", "Salvatore, Fava", "Lawal, Abiodun Ismail", "Oyeleke, Tolulope Ayobi"]2024-10-08T00:00:00 d66aed27c32c4a89dc1532fda22382797a7ee7ccThe Egyptian Journal of Language Engineering00The Egyptian Journal of Language Engineering["Sara Mostafa ElHennawy"]2024-10-08T00:00:00 4f77ea12e436e7e428447b2331585e0936e38ac6The diagnosis of Parkinson’s disease (PD) is often made after careful monitoring and assessment of clinical indicators, such as the description of various motor symptoms. However, standard diagnostic approaches may be flawed due to human subjectivity, since they rely on an assessment of motions that may be hard to identify due to their seeming subtlety. However, early non-motor symptoms of PD can be subtle and are often confused with other conditions. Because of this, early identification of PD is difficult because these symptoms are frequently disregarded. To get beyond these obstacles, we present a deep learning model that combines Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in an effort to boost the quality of the various categorization parameters. Improved model accuracy of 98.36% was observed in the experiments, indicating that deep learning and machine learning were successfully combined. When compared to the most favorable results from other research, this one was shown to give both equivalent and greater information.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)110A deep learning model that combines Multilayer Perceptron (MLP) and Support Vector Machine (SVM) in an effort to boost the quality of the various categorization parameters is presented in an effort to boost the quality of the various categorization parameters.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)["Tatiparti B Prasad Reddy", "R. Rajagopal", "Shanta Phani", "P. Rajesh", "Animesh Kumar Sharma", "J. Priya"]2024-10-08T00:00:00 e8de17fbd1f193b5f2ae7342afa089f0fd25d8ccArtif. Intell. Medicine680A systematic review of the literature on AI-based techniques used to assess oocyte quality and the challenges research must face in fully deploying AI-based solutions in current medical practice is presented.Artificial intelligence in medicine["Antonio Iannone", "A. Carf\u00ed", "Fulvio Mastrogiovanni", "Renato Zaccaria", "Claudio Manna"]2024-10-08T00:00:00 5aa0a07054c3cc953f39908eebf4a40f56f86b14Health technology230Health and Technology["M. Choukou", "Moh A. Alkhamis", "S. Syed-Abdul", "Hamza Alshawaf", "Suad Alfadhli"]2024-10-08T00:00:00 cb7738ba8c49bbdc4ff272556e66bad3a27be289Journalism Studies300Journalism Studies["Lynge Asbj\u00f8rn M\u00f8ller", "Arjen van Dalen", "Morten Skovsgaard"]2024-10-08T00:00:00 57934dda6288b5fcdc7fdb8cb0d56eedbccc8177Visual Studies90Visual Studies["Anthony Downey"]2024-10-08T00:00:00 b3680cd8cf42d9f47fc8ea6c7e94683fffd2175fРазработка комплексных технических систем с применением компьютерных конструкторских программ с элементами искусственного интеллекта ; Для того , что бы в соответствии с современными требованиями формировать комплексы специального технологического оборудования и сохранить при этом возможность применения ТРИЗ и АРИЗ , необходимо , как минимум проанализировать определения и законы развития технических систем в сочетании с системами машинного проектирования с элементами искусственного интеллекта ; Это комплексная техническая система высшего уровня , включающая ряд , связанных между собой по технологическому циклу технических систем , находящихся в статусе – двойных параллельных локальных надсистем , каждая , включающих две линии , - химическую и механическую в сочетании с гальванической ; Соответственно каждая из таких двойных параллельных локальных надсистем состоят из множества локальных технических систем , - подсистем , связанных при помощи локальных программируемых процессоров с центральным процессором комплексной технической системы высшего уровня ;00["Sergey Glushkov"]2024-10-08T00:00:00 682d189af847b97ac01107b2dd896a268ff8ad51AI &amp; SOCIETY20AI &amp; SOCIETY["Zhongyuan Zhu"]2024-10-08T00:00:00 871a5bc507589748c1c6bd6a42bece6fa22961e9Digital platforms capitalize on users' labor, often disguising essential contributions as casual activities or consumption, regardless of users' recognition of their efforts. Data annotation, content creation, and engagement with advertising are all aspects of this hidden productivity. Despite playing a crucial role in driving AI development, such tasks remain largely unrecognized and undercompensated. This chapter exposes the systemic devaluation of these activities in the digital economy, by drawing on historical theories about unrecognized labor, from housework to audience labor. This approach advocates for a broader understanding of digital labor by introducing the concept of ''inconspicuous production.'' It moves beyond the traditional notion of ''invisible work'' to highlight the hidden elements inherent in all job types, especially in light of growing automation and platform-based employment.arXiv.org00This approach advocates for a broader understanding of digital labor by introducing the concept of ''inconspicuous production,'' which moves beyond the traditional notion of ''invisible work'' to highlight the hidden elements inherent in all job types, especially in light of growing automation and platform-based employment.ArXiv["Antonio A. Casilli"]2024-10-08T00:00:00 9f2945f679aa4a38eb268a29e54cb2933f5b29fcPower systems are vulnerable to faults that can lead to severe damage to critical components such as motors, generators, and transformers, as well as cause dangerous over-voltages, high currents, outages, and safety hazards. To address these challenges, an effective power protection system is essential for detecting, classifying, and locating faults to mitigate their impact. This project presents the development of an Artificial General Intelligence (AGI) system designed to control and optimize the algorithms of machine learning for fault classification and predicitons in power systems. Utilizing the IEEE 14 Bus Test Network, the AGI system integrates Random Forest Classifier and Support Vector Classifier (SVC) to enhance fault detection accuracy and system performance. AGI system processes real-time data to dynamically manage fault detection and classification tasks, ensuring timely identification and response. Through comprehensive testing and evaluation, the system demonstrates improved reliability and safety by providing precise fault predictions and minimizing operational disruptions. This approach offers a robust solution for managing power system faults, contributing to more reliable and efficient power infrastructure management [1].2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)150An Artificial General Intelligence (AGI) system designed to control and optimize the algorithms of machine learning for fault classification and predicitons in power systems and demonstrates improved reliability and safety by providing precise fault predictions and minimizing operational disruptions.2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)["S. Raghuraman", "Aakash B", "Kabilan D", "Kameswaran A R", "Suria Deepan P"]2024-10-08T00:00:00 f3a97a9c744a4d2a39e7c2d758d7ac27920002fcThis integrative literature review aims to analyze the current state of research on the use of artificial intelligence (AI) in the public sector, highlighting the importance of proper governance and ethical considerations for its implementation. A bibliometric approach was adopted, analyzing 251 articles available in the Web of Science. Among these, 41 articles were selected for a more detailed review. After a preliminary bibliometric analysis, the chosen articles were read, followed by coding and categorization of the material. The study emphasizes the need to systematize the progress of AI in the public sector, encompassing its applications and outcomes, while also underscoring the importance of proper governance and ethics for its implementation. Upon reviewing the articles, there was a growing attention to AI governance in public administration, considering the risks and challenges associated with its implementation. The study recognizes the importance of strategies and emerging technologies for public management, highlighting the transformative potential of digital technologies. However, it points to the scarcity of research on AI in the public sector and the emerging need to systematize its advancements and outcomes.Administración Pública y Sociedad (APyS)00The study emphasizes the need to systematize the progress of AI in the public sector, encompassing its applications and outcomes, while also underscoring the importance of proper governance and ethics for its implementation.Administración Pública y Sociedad (APyS)["Angela Luci Barbosa Serra", "Hilka Pelizza Vier Machado"]2024-10-08T00:00:00 236b546389cfe45a7cbee3649555b4aafaa8f881The integration of artificial intelligence (AI) tools in educational settings offers significant opportunities to promote sustainability by transforming learning experiences. This study analyses the usage, attitudes, and perceptions of AI tools among university students in Slovenia providing a comprehensive analysis that informs both academic practices and policy-making with emphasis on sustainability. We used a structured questionnaire with a sample of 422 participants reflecting a diverse demographic profile across various fields of study. The questionnaire was designed to measure the frequency of AI tool usage, the purposes for which these tools are employed, and students’ attitudes and perceptions towards AI’s potential benefits and drawbacks in education. Statistical analyses, including Analysis of Variance (ANOVA), were utilized to test hypotheses concerning differences in AI tool usage based on the level and field of study. Findings reveal that students recognize the efficiency of AI, but express concerns about its impact on learning quality and academic integrity, emphasizing the need for a balanced and responsible integration of AI in education to achieve sustainable outcomes. Results indicated that a majority of students are engaging with AI tools, with varied frequencies of use largely dependent on their field of study and academic level. The findings suggest that while AI tools are becoming an integral part of the educational landscape in Slovenia, there is a critical need to address the educational, ethical, and psychological impacts of these technologies. The results highlight the necessity for further research into the educational implications of AI, suggesting a balanced and sustainable approach to integrating these technologies into higher education curricula. Such an approach ensures that the adoption of AI not only enhances learning outcomes but also aligns with the principles of sustainability, promoting long-term benefits for both education and society.Sustainability254Findings reveal that students recognize the efficiency of AI, but express concerns about its impact on learning quality and academic integrity, emphasizing the need for a balanced and responsible integration of AI in education to achieve sustainable outcomes.Sustainability["A. Fo\u0161ner"]2024-10-08T00:00:00 0cd693ac49396d033926ea53d014ca25be0d0ac1This exploratory study examines the socio-technical dynamics of Artificial Intelligence Companions (AICs), focusing on user interactions with AI platforms like Replika 9.35.1. Through qualitative analysis, including user interviews and digital ethnography, we explored the nuanced roles played by these AIs in social interactions. Findings revealed that users often form emotional attachments to their AICs, viewing them as empathetic and supportive, thus enhancing emotional well-being. This study highlights how AI companions provide a safe space for self-expression and identity exploration, often without fear of judgment, offering a backstage setting in Goffmanian terms. This research contributes to the discourse on AI’s societal integration, emphasizing how, in interactions with AICs, users often craft and experiment with their identities by acting in ways they would avoid in face-to-face or human-human online interactions due to fear of judgment. This reflects front-stage behavior, in which users manage audience perceptions. Conversely, the backstage, typically hidden, is somewhat disclosed to AICs, revealing deeper aspects of the self.Societies303Findings revealed that users often form emotional attachments to their AICs, viewing them as empathetic and supportive, thus enhancing emotional well-being, and highlighting how AI companions provide a safe space for self-expression and identity exploration, often without fear of judgment.Societies["Theodoros Kouros", "Venetia Papa"]2024-10-08T00:00:00 737f75f2849731aeb14da868a0177c69d7debeb3PurposeThe study aims to investigate how artificial intelligence (AI)-powered virtual streamers can supercharge brands in live-streaming virtual commerce (v-commerce). Built upon social identity theory (SIT) and experiential value theory, we developed a framework to investigate the impact of AI-powered virtual streamers’ personalization and human-like personalities and live-streaming v-commerce’s system quality and content quality on brand image, mediated by parasocial interaction and experiential value.Design/methodology/approachA survey was designed and distributed to the target respondents via social media channels. SmartPLS version 4.0.9.4 was used to analyze a total of 354 responses after the data were obtained via purposive sampling.FindingsThe results show that personalization, human-like personality, system quality and content quality are positively associated with parasocial interaction and experiential value, which subsequently impact brand image.Originality/valueThis study addresses the gap of relatively sparse academic literature on the implications of AI-powered virtual streamers in live-streaming v-commerce on brand image.Asia Pacific Journal of Marketing and Logistics593The results show that personalization, human-like personality, system quality and content quality are positively associated with parasocial interaction and experiential value, which subsequently impact brand image.Asia Pacific Journal of Marketing and Logistics["Bin Xu", "Omkar Dastane", "Eugene Cheng-Xi Aw", "Suchita Jha"]2024-10-08T00:00:00 12e89d7c82f0b860fcf5743419cb0f35eba38436PurposeHuman Resource Management (HRM) is a critical organizational function, which has continued to evolve. We aim to explore how different HRM will be in the workplace of the future and why, from both strategic and practical perspectives. We present and discuss core HRM practices, such as recruitment, selection and training, as well as peripheral activities, such as monitoring health and safety, and diversity management, reflecting on how they may transform in the workplace of the future.Design/methodology/approachThis is a conceptual thought piece, building on the Substitution, Augmentation, Modification and Redefinition (SAMR) model, to offer a futuristic view of HRM in the era of AI.FindingsDiscussing the contemporary challenges of Artificial Intelligence, which we predict will lead to what we term Cyborging HRM.Practical implicationsThis study can help HR managers and practitioners to be prepared for AI-embedded HRM systems in the future. For academics, it offers an innovative framework to establish future writing on HRM in the AI era.Originality/valueAI is pushing HRM and the profession will have to undergo a revolutionary rather than evolutionary transformation in order to remain a necessary and valuable function for organizations. Our elaboration of the SAMR model and suggested implications for the future transformation of HRM should be worthwhile to organizations, management and the wider society.Person-centered review852This is a conceptual thought piece, building on the Substitution, Augmentation, Modification and Redefinition (SAMR) model, to offer a futuristic view of HRM in the era of AI and suggested implications for the future transformation of HRM should be worthwhile to organizations, management and the wider society.Personnel Review["Edna Rabenu", "Y. Baruch"]2024-10-08T00:00:00 9598a4d6c82ae16210429a9588abf43820f576d1Transparency –encompassing methodological, financial, and source-related aspects, as well as the tools employed– is central to the operations of professional fact-checking platforms. However, the growing adoption of artificial intelligence tools in fact-checking introduces new ethical challenges. This research investigates the extent to which these platforms believe they should disclose their use of AI and assesses the current practices on their websites regarding this technology. The study employs a qualitative methodology, including semi-structured interviews with professionals from accredited Spanish verification platforms and content analysis of these organizations’ websites. The findings indicate that transparency in AI usage is widely regarded as an ethical imperative. Nevertheless, the application of this standard often becomes ambiguous when addressing specific practices and cases. Many professionals question the necessity of explicitly disclosing AI usage when the technology primarily supports the verification and is overseen by human reviewers. Additionally, a lack of understanding of AI’s functionality sometimes hinders the ability to identify whether the tools employed incorporate AI. The content analysis also reveals that explicit mentions of AI use on the websites are rare and that platforms lack open-access manuals or protocols that outline and regulate their AI practices.Communication &amp; Society462Investigating the extent to which fact-checking platforms believe they should disclose their use of AI and assessing the current practices on their websites regarding this technology indicates that transparency in AI usage is widely regarded as an ethical imperative.Communication &amp; Society["Roger Cuartielles", "Marcel Mauri-R\u00edos", "Ruth Rodr\u00edguez-Mart\u00ednez"]2024-10-08T00:00:00 34bd9c3b9d45fafb552318d82d6faf1d92e07d6aArtificial intelligence (AI) can enhance, enable, or replace traditional open innovation (OI) practices, changing the scope and efficiency of both outside-in and inside-out OI. This article provides a comprehensive framework to analyze AI’s influence on OI, supported by illustrative examples, and outlines the key implications for organizations and researchers. The co-evolutionary relationship between AI and OI will be a central focus in both research and practice moving forward.California Management Review02A comprehensive framework to analyze AI’s influence on OI is provided, supported by illustrative examples, and the key implications for organizations and researchers are outlined.California Management Review["Marcus Holgersson", "Linus Dahlander", "H. Chesbrough", "Marcel L. A. M. Bogers"]2024-10-08T00:00:00 9a5c25b94c9c5ac0434d1ca83a1aa73852ede618This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” and “depression”. Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for overreliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.Applied Sciences792The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists.Applied Sciences["Adrianos Pavlopoulos", "Theodoros Rachiotis", "I. Maglogiannis"]2024-10-08T00:00:00 3caa2470eb70117843bcf9ea1bfe1b92b0517e39The integration of Artificial Intelligence (AI) in pharmacy practice holds great potential to revolutionize healthcare delivery and improve patient outcomes. AI can assist pharmacists in optimizing medication selection, predicting adverse drug events and drug interactions, enhancing inventory management, and automating prescription verification. Moreover, AI-driven systems can facilitate personalized counseling and lifestyle management for patients, promoting treatment adherence and better health outcomes. However, the implementation of AI in pharmacy practice faces challenges, including ethical considerations, data privacy, and the need for comprehensive training for pharmacists. This review article explores how AI technology is revolutionizing medication management, pharmacist workflow, and patient care in pharmacy practice. Authors also explore the various applications and recommendations to overcome the barriers, providing valuable insights for pharmacists, healthcare professionals and policymakers..Pharmacy in practice762How AI technology is revolutionizing medication management, pharmacist workflow, and patient care in pharmacy practice is explored, providing valuable insights for pharmacists, healthcare professionals and policymakers.Pharmacy Practice["Dania Saad Rammal", "M. Alomar", "Subish Palaian"]2024-10-08T00:00:00 8ecee55ed02bb5e14e9b3b383778feb4b3a2daf5Artificial Intelligence is currently and rapidly changing the way organizations and businesses operate. Ethical leadership has become significantly important since organizations and businesses across various sectors are evolving with AI. Organizations and businesses may be facing several challenges and potential opportunities when using AI. Ethical leadership plays a central role in guiding organizations in facing those challenges and maximizing on those opportunities. This article explores the essence of ethical leadership in the age of AI, starting with a simplified introduction of ethical leadership and AI, then dives into an understanding of ethical leadership, its characteristics and importance, the ethical challenges AI causes including bias in AI algorithms. The opportunities for ethical leadership in the age of AI answers the question: What actionable strategies can leaders employ to address the challenges and leverage opportunities? and describes the benefits for organizations through these opportunities. A proposed framework for ethical leadership is presented in this article, incorporating the core components: fairness, transparency, sustainability etc. Through the importance of interdisciplinary collaboration, case studies of ethical leadership in AI, and recommendations, this article emphasizes that ethical leadership in the age of AI is morally essential and strategically advantageous.arXiv.org161The essence of ethical leadership in the age of AI is explored, incorporating the core components: fairness, transparency, sustainability etc, and a proposed framework for ethical leadership is presented, incorporating the core components: fairness, transparency, sustainability etc.ArXiv["Udaya Chandrika Kandasamy"]2024-10-08T00:00:00 f4529d1364b1f8a3e1f296c22f65da2fdbb822f0This study examines the balance between technical and social focus in artificial intelligence, blockchain, cybersecurity, and privacy publications in Web of Science across countries, exploring the social factors that influence these research priorities. We use regression analysis to identify predictors of research focus and cluster analysis to reveal patterns across countries, combining these methods to provide a broader view of global research priorities. Our findings reveal that liberal democracy index, life expectancy, and happiness are significant predictors of research focus, while traditional indicators like education and income show weaker relationships. This unexpected result challenges conventional assumptions about the drivers of research priorities in digital technologies. The study identifies distinct clusters of countries with similar patterns of research focus across the four technologies, revealing previously unrecognized global typologies. Notably, more democratic societies tend to emphasize social implications of technologies, while some rapidly developing countries focus more on technical aspects. These findings suggest that political and social factors may play a larger role in shaping research agendas than previously thought, necessitating a re-evaluation of how we understand and predict research focus in rapidly evolving technological fields. The study provides valuable information for policymakers and researchers, informing strategies for technological development and international collaboration in an increasingly digital world.De Computis441The findings suggest that political and social factors may play a larger role in shaping research agendas than previously thought, necessitating a re-evaluation of how to understand and predict research focus in rapidly evolving technological fields.Comput.["Emanuela Bran", "R. Rughinis", "D. \u021aurcanu", "Gheorghe Nadoleanu"]2024-10-08T00:00:00 67bb4ca8c18775500151ee935e89ea3e58fce207This study explores the impact of Artificial Intelligence (AI) on the creative process within Peruvian advertising agencies. The research focuses on understanding how AI technologies influence the practices of professionals involved in creative production and the perception of AI’s role in enhancing or detracting from creativity in a market characterized by emerging digital transformation. Adopting a qualitative methodology grounded in the interpretive paradigm, the study placed particular emphasis on the insights and experiences of Creative Directors, who play a pivotal role in integrating AI into the creative process. In addition to these key professionals, the study also analyzed the perspectives of AI specialists, decision-makers in digital transformation, and AI consultants. Data was meticulously gathered through semi-structured in-depth interviews, ensuring a comprehensive understanding of how AI is reshaping creativity in advertising. The findings reveal that while AI offers significant potential to streamline and enhance creative processes, concerns about authenticity and the risk of diminishing human creativity persist. The study underscores the need for a balanced approach that integrates AI-driven efficiency with the preservation of original creative input, providing insights for future policy development and industry practices.Communication &amp; Society421The research focuses on understanding how AI technologies influence the practices of professionals involved in creative production and the perception of AI’s role in enhancing or detracting from creativity in a market characterized by emerging digital transformation.Communication &amp; Society["Francisco Arbaiza", "Jazmine Arias", "Kelly Robledo-Dioses"]2024-10-08T00:00:00 d714e59d9d49540d098a8ee4eb109e1dae43fce2Neuropsychology of artificial intelligence focuses on synthetic neural cog nition as a new type of study object within cognitive psychology. With the goal of making artificial neural networks of language models more explainable, this approach involves transposing concepts from cognitive psychology to the interpretive construction of artificial neural cognition. The human cognitive concept involved here is categorization, serving as a heuristic for thinking about the process of segmentation and construction of reality carried out by the neural vectors of synthetic cognition.arXiv.org610The human cognitive concept involved here is categorization, serving as a heuristic for thinking about the process of segmentation and construction of reality carried out by the neural vectors of synthetic cognition.ArXiv["Michael Pichat", "Enola Campoli", "William Pogrund", "Jourdan Wilson", "Michael Veillet-Guillem", "Anton Melkozerov", "Paloma Pichat", "Armanouche Gasparian", "Samuel Demarchi", "Judicael Poumay"]2024-10-08T00:00:00 87ffae63158a81007964b18e889776878771044aRecommendation systems—that is, a class of machine learning algorithm tools that filter vendors' offerings based on customer data and automatically recommend or generate personalized predictions—are empowered by artificial intelligence (AI) technology and embedded with AI characteristics; but the potential consequences for customer well‐being are greatly overlooked. Hence, this research investigates the impact of AI characteristics on technology well‐being (self‐efficacy, technology satisfaction, emotional dissonance, and autonomy) through two mechanisms: intuitiveness versus intrusiveness. A literature review which conceptualizes AI characteristics and technology well‐being in the recommendation system context is followed by a US‐based survey approach which shows that higher levels of information optimization, predictability, human likeness, and customizability lead to higher levels of intuitiveness, whereas only information optimization and human likeness leads to increased intrusiveness. However, both intuitiveness and intrusiveness are found to promote technology well‐being in the context of a recommendation system, especially for those more vulnerable individuals who respond positively to intrusiveness. Hence, the conclusion is “the recommendations are not always annoying,” whereby the relationships between AI characteristics and technology well‐being are significantly influenced by perceived intrusiveness. These findings help business practitioners to identify how consumers perceive and engage different AI characteristics, and therefore could better take care of technology well‐being while boosting AI development.Journal of Consumer Behaviour1330The conclusion is “the recommendations are not always annoying,” whereby the relationships between AI characteristics and technology well‐being are significantly influenced by perceived intrusiveness.Journal of Consumer Behaviour["Zi Wang", "Ruizhi Yuan", "Boying Li"]2024-10-08T00:00:00 6deda873dc6cd0da4645718d9e1347443cd5de88This study aims to examine the impacts of Machine Learning (ML) and Artificial Intelligence (AI) capabilities on Business Performance (BP) of technology enterprises in the Kingdom of Saudi Arabia (KSA). Building on established theories such as the Resource-Based View (RBV) and the Technology Organization Environment (TOE) framework, the study proposes that AI and ML capabilities impact business performance. Their effects are anticipated to be mediated by Technological Readiness (TR) and moderated by Data Quality (DQ). A total of 190 executives and IT professionals in KSA participated in this study. Smart PLS 4 was used to analyze the data. The findings showed that AI and ML capabilities positively affected business performance. Technological readiness acted as a mediator in the relationship between AI and ML capabilities, and BP. Data quality significantly increased the impact of AI capabilities on BP. The business performance of enterprises in KSA will increase with the presence of efficient AI and ML capabilities as well as the development of a high level of technological readiness and data quality.Engineering, Technology &amp; Applied Science Research391The findings showed that AI and ML capabilities positively affected business performance, and Technological readiness acted as a mediator in the relationship between AI and ML capabilities, and BP.Engineering, Technology &amp; Applied Science Research["Mohammed Alarefi"]2024-10-09T00:00:00 9b6261f6f398dd1c5c2ae84137af4ab000c1d240Aesthetic Plastic Surgery271AI, ML, and DL algorithms offer immense potential to transform the aesthetic plastic surgery field by meticulously analyzing patient data and may, in the future, help optimize treatment plans, predict potential complications, and more clearly elucidate patient concerns, improving their ability to make informed decisions.Aesthetic plastic surgery["R. Nogueira", "Marina Eguchi", "J. Kasmirski", "Bruno Veronez de Lima", "Dimitri Cardoso Dimatos", "D. L. Lima", "Robert Glatter", "David L Tran", "P. Piccinini"]2024-10-09T00:00:00 7bc8f725049e02370521f673ab68ab078e8b54bbIntroduction Artificial Intelligence (AI) is increasingly being integrated into anesthesiology to enhance patient safety, improve efficiency, and streamline various aspects of practice. Objective This study aims to evaluate whether AI-generated images accurately depict the demographic racial and ethnic diversity observed in the Anesthesia workforce and to identify inherent social biases in these images. Methods This cross-sectional analysis was conducted from January to February 2024. Demographic data were collected from the American Society of Anesthesiologists (ASA) and the European Society of Anesthesiology and Intensive Care (ESAIC). Two AI text-to-image models, ChatGPT DALL-E 2 and Midjourney, generated images of anesthesiologists across various subspecialties. Three independent reviewers assessed and categorized each image based on sex, race/ethnicity, age, and emotional traits. Results A total of 1,200 images were analyzed. We found significant discrepancies between AI-generated images and actual demographic data. The models predominantly portrayed anesthesiologists as White, with ChatGPT DALL-E2 at 64.2% and Midjourney at 83.0%. Moreover, male gender was highly associated with White ethnicity by ChatGPT DALL-E2 (79.1%) and with non-White ethnicity by Midjourney (87%). Age distribution also varied significantly, with younger anesthesiologists underrepresented. The analysis also revealed predominant traits such as “masculine, ““attractive, “and “trustworthy” across various subspecialties. Conclusion AI models exhibited notable biases in gender, race/ethnicity, and age representation, failing to reflect the actual diversity within the anesthesiologist workforce. These biases highlight the need for more diverse training datasets and strategies to mitigate bias in AI-generated images to ensure accurate and inclusive representations in the medical field.Frontiers Artif. Intell.332AI models exhibited notable biases in gender, race/ethnicity, and age representation, failing to reflect the actual diversity within the anesthesiologist workforce, highlighting the need for more diverse training datasets and strategies to mitigate bias in AI-generated images to ensure accurate and inclusive representations in the medical field.Frontiers in Artificial Intelligence["M. Gisselbaek", "Laurens Minsart", "Ekin K\u00f6selerli", "M\u00e9lanie Suppan", "B. Me\u00e7o", "Laurence Seidel", "A. Albert", "Odmara L. Barreto Chang", "Sarah Saxena", "J. Berger-Estilita"]2024-10-09T00:00:00 877fd3efa8e444a0bfe09c2077c9ccce0bb9b0f6Science and Engineering Ethics1092How Rawls’s theory of justice as fairness and its key concepts relate to the ongoing developments in AI ethics are discussed and a proposition of how principles that offer a foundation for operationalising AI ethics in practice could look like if aligned with Rawls’s theory of justice as fairness is given.Science and Engineering Ethics["Salla Westerstrand"]2024-10-09T00:00:00 3d4605112f3275eb6adf77f7b374241906014edeArtificial intelligence in medicine refers to the use of machine learning models to help process medical data and provide medical professionals with important insights, improving health outcomes and patient experience. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is rapidly becoming an integral part of modern healthcare. Therefore, artificial intelligence algorithms and other AI-powered applications are now used to support medical professionals in clinical settings and in ongoing research. There are several applications of Artificial intelligence in medicine, including applications to help detect and diagnose diseases; applications to treat diseases with the help of an AI-powered virtual assistant; AI applications in medical imaging; applications to increase the efficiency of clinical trials; and applications to accelerate drug development. The benefits of Artificial intelligence in medicine can be summarized in providing informed patient care, reducing errors, reducing care costs, and increasing doctor-patient engagement.South Eastern European Journal of Public Health571The benefits of Artificial intelligence in medicine can be summarized in providing informed patient care, reducing errors, reducing care costs, and increasing doctor-patient engagement.South Eastern European Journal of Public Health["Muath Aldergham", "Areeg Alfouri", "Rasha Al Madat"]2024-10-09T00:00:00 756f297d8eecf6f2dd0823ee7a6c84007203999dThe utilization of artificial intelligence (AI) in the process of controlling and optimizing the operation of a microgrid (microgrid management) plays an essential role for the advancement of sustainable energy solutions. In order to look at the structure, trends, patterns, and insights on the utilization of AI in microgrid management, this study employs bibliometric analysis. Through an analysis of 187 relevant papers within the Scopus database, this study finds that research on AI application for microgrids management has significantly increased, with India, China, and the United States have the most contributions. Adaptive systems and optimization algorithms are key research topics in this field. This research highlights present state of AI application in microgrid management, points out gaps of research, and suggests future study directions that could lead to the development of energy systems that are more reliable and efficient.International Conference on Computer, Control, Informatics and its Applications220This research highlights present state of AI application in microgrid management, points out gaps of research, and suggests future study directions that could lead to the development of energy systems that are more reliable and efficient.2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA)["Marlina Pandin", "Nurfadlih Syahlani", "Agung Widyo Utomo", "S. Sumaedi", "Mauludin Hidayat", "Hendy Gunawan", "M. Yarmen", "I. G. Prihanto"]2024-10-09T00:00:00 1f0b81e1369742baa851de5625870c37b87453c3Artificial intelligence significantly impacts the economy and labor market, with effects varying based on a country’s development status. Consequently, it is crucial to prepare for AI-related challenges in the labor market and to enhance workforce skills and training to leverage AI opportunities. This qualitative research aims to propose solutions to address these challenges in employment and skill development. Using the document method, upstream documents were identified and analyzed through thematic analysis. Initial solutions were formulated based on recommendations from international organizations and aligned with these documents. These solutions were then refined using the Delphi method, incorporating input from ten experts in the field. The research culminated in eight proposed solutions across three dimensions, education and skills, workforce recruitment, and digital literacy.International Symposium on Telecommunications130Qualitative research aims to propose solutions to address challenges in employment and skill development across three dimensions, education and skills, workforce recruitment, and digital literacy.2024 11th International Symposium on Telecommunications (IST)["Azam Sadat Mortazavi Kahangi", "Hassan Yeganeh", "Anita Hadizadeh"]2024-10-09T00:00:00 a1a4a51ee9383d78ffb67c21e5e2a5834432a2b8Artificial Intelligence (AI) technology has a huge impact on the process of development related to the digital economy. This paper is derived from some country experiences to establish the key challenges the world is facing in regards to AI, which pertain to inequality and discrimination, unemployment issues, and changes in the labor market. Further, this article also investigates further to determine the direct and indirect effects of those challenges within the digital economy. Despite the significant opportunities that AI offers to increase productivity and innovation, managing these challenges requires multifaceted approaches and careful policy making. Finally, this article has emphasized the importance of investing in infrastructure, training skilled workforce, developing appropriate laws and increasing transparency and trust in the use of AI to fully utilize the potential of this technology for the sustainable growth and development of the digital economy.International Symposium on Telecommunications350The importance of investing in infrastructure, training skilled workforce, developing appropriate laws and increasing transparency and trust in the use of AI to fully utilize the potential of this technology for the sustainable growth and development of the digital economy is emphasized.2024 11th International Symposium on Telecommunications (IST)["Farideh Shahidi", "Nahid Bozorgkhou", "Niloofar Moradhasel"]2024-10-09T00:00:00 ca75773a8871877470d0afc2dd89dbf9aaebe878In the times we live in today, humanity faces unprecedented environmental challenges. The emergence of artificial intelligence (AI) has opened new doors in our collective efforts to address our planet's pressing problems; however, many have doubts on the actual extent of impact that AI have on the environment. In particular, AI also assisting dirty production is a drawback that is largely absent from the literature. To investigate the impact of AI on the environment, we establish mathematical models to model the economy and the production process of goods based on outdated and advanced technologies. The secondary results are stated in the form of lemmas, the main results are stated in the form of theorems. From the theorems we conclude that AI may not on its own prevent an environmental disaster, a reason of which is its concurrent contribution to dirty production. With temporary government intervention, however, AI is able to avert an environmental disaster.660["Van Khanh Pham", "Duc Minh Le"]2024-10-09T00:00:00 30d266cce40360bcf3875ec167630229464f8ef4This study assesses Vietnam’s state-level implementation of artificial intelligence (AI) technology and analyses the government’s efforts to encourage AI implementation by focusing on the National Strategy on AI Development Program. This study emphasizes the possibility of implementing AI at the state level in Vietnam and the importance of conducting continuous reviews and enhancements to achieve sustainable and inclusive AI growth. Impact evaluations were conducted in public organizations alone, and implication evaluations were considered optional. AI impact assessments were constrained by societal norms that necessitated establishing relationships among findings. There is a lack of official information regarding the positive impact of Vietnam’s AI policy on the development of AI infrastructure, research, and talent pools. The study’s findings highlight the necessity of facilitating extensive AI legislation, and strengthening international cooperation. The study concludes with the following recommendations for improving Vietnam’s AI policy: implementing a strong AI governance structure and supporting AI education and awareness.Journal of Infrastructure Policy and Development360Journal of Infrastructure, Policy and Development["H. B. Dang", "Thi Thai Quynh Pham", "Van Phuoc Nguyen", "Van Hau Nguyen"]2024-10-09T00:00:00 3ecd252a9fe7061829a8314a184779e2a88354fdCurrent Psychology340It is shown that attribution of blame to each actor in the scenario depends on their perceived obligation and capacity to prevent such an event and blaming AI is indirectly associated with mind attribution and blaming oneself is associated with the capability to recognize a wrong classification.Current Psychology["B. Leichtmann", "A. Hinterreiter", "Christina Humer", "Alfio Ventura", "Marc Streit", "Martina Mara"]2024-10-09T00:00:00 d09339ca7dba7bcd64f13e41de03389c8e9f4f5bThis article explores the utilization of Artificial Intelligence (AI) as a tool for enhancing efficiency in public communication within the BANI era (Brittle, Anxious, Nonlinear, Incomprehensible). The aim of this research is to investigate how AI can improve the effectiveness of public communication amidst uncertainty and complexity. The research employs a descriptive qualitative approach with a literature review, analyzing data from relevant journal articles, books, and case studies. The study finds that AI plays a crucial role in addressing system fragility through misinformation detection, reducing public anxiety by providing personalized and responsive information, and managing uncertainty and complexity through predictive analysis and data simplification. The results indicate that AI can enhance the efficiency and clarity of public communication but must be complemented by stringent regulations and ethical considerations to ensure responsible use. With the right approach, AI can be an effective tool in improving public communication in this challenging era.Konstitusi : Jurnal Hukum, Administrasi Publik, dan Ilmu Komunikasi140The study finds that AI plays a crucial role in addressing system fragility through misinformation detection, reducing public anxiety by providing personalized and responsive information, and managing uncertainty and complexity through predictive analysis and data simplification.Konstitusi : Jurnal Hukum, Administrasi Publik, dan Ilmu Komunikasi["A. Sulisman", "Titi Stiawati", "Magister Administrasi Publik"]2024-10-09T00:00:00 f6f364de81c3117abc27ef04059873bb294eb464The emergence of technology today has an impact on many aspects of our daily lives. This impact is not limited to individuals but can be said to be overall, involving many parties, including areas related to universal well-being. In the context of this study, the well-being of the people refers to cancer awareness. Another important element related to increasing awareness is the use of marketing elements. However, the focus of this study is not limited to these two elements alone but also includes one more element that is currently a hot topic, which is artificial intelligence. In other words, this study examines how these three elements interrelate to form awareness about cancer. The results show how dynamic and positive the impact of artificial intelligence is in influencing both marketing aspects and cancer awareness.Journal of Advanced Research in Applied Sciences and Engineering Technology390The results show how dynamic and positive the impact of artificial intelligence is in influencing both marketing aspects and cancer awareness.Journal of Advanced Research in Applied Sciences and Engineering Technology["Faerozh Madli", "Yuzainy Janin Rohaizad", "Mat Salleh Salleh Wahab", "Dean Nelson Mojolou", "Masran Tamin", "Adi Jafar", "Ag Kaifah Riyard bin Kiflee", "C. Wolor"]2024-10-09T00:00:00 a0f99738abf3955c70272457d7fad71bdc4d088cThis study explores the advancements in artificial intelligence (AI) and machine learning (ML) applications in pharmacology, emphasizing their role in enhancing drug discovery, development, and personalized treatment plans. The most significant achievement of this research is the demonstration of how AI techniques, particularly deep learning models, can effectively analyze complex biological data to predict drug interactions and optimize therapeutic outcomes. By integrating AI with traditional pharmacological methods, this study highlights the potential for reducing clinical trial failures and improving patient outcomes. Additionally, the research underscores the necessity for regulatory initiatives to promote data sharing, facilitating the development of robust AI models in pharmacology. Overall, this work contributes valuable insights into the transformative impact of AI on the future of drug development and clinical practice.International Symposium on Telecommunications460This research demonstrates how AI techniques, particularly deep learning models, can effectively analyze complex biological data to predict drug interactions and optimize therapeutic outcomes, highlighting the potential for reducing clinical trial failures and improving patient outcomes.2024 11th International Symposium on Telecommunications (IST)["Jafar Abdollahi", "Omid Mehrpour"]2024-10-09T00:00:00 ac64d5e3eb5ca3a39dcc6dfe2252e1e7936acf73BACKGROUND Artificial intelligence pedagogies are increasingly commonplace in health care education, and limited information guides their application in didactic nursing environments. PURPOSE To examine the current state of artificial intelligence-based pedagogies used in didactic nursing education. DESIGN The review was conducted using Arksey and O'Malley's scoping review framework and the Joanna Briggs Institute's System for the Unified Management, Assessment, and Review of Information platform. Literature is reported using the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews. METHODS The review included articles published between January 1, 2013, and July 23, 2024, in MEDLINE (via PubMed), Cumulative Index to Nursing and Allied Health Literature, Education Resources Information Center, World Science, and Google Scholar. Two reviewers independently assessed all articles. RESULTS Themes for the 16 included articles were generative artificial intelligence and pairing artificial intelligence with other pedagogical strategies. CONCLUSIONS More research is needed to examine artificial intelligence-based pedagogies in didactic nursing education.Nurse Educator340The current state of artificial intelligence-based pedagogies used in didactic nursing education is examined using Arksey and O'Malley's scoping review framework and the Joanna Briggs Institute's System for the Unified Management, Assessment, and Review of Information platform.Nurse educator["Michele A Gerdes", "Andrew Bayne", "Kristina Henry", "Barbara Ludwig", "Leigh Stephenson", "Allison Vance", "Jennifer Wessol", "Sarah Winston"]2024-10-09T00:00:00 ffd8b99991711a920994f6dcac2b81b250d9daa4The Fourth Industrial Revolution involves the shift in the economy and society that has been driven by emerging technologies. Among these are Artificial Intelligence and other related scientific innovations. This paper explores the effect of Generative Artificial Intelligence on the Iranian banking industry using a SWOT analysis. The results indicate that the identified opportunities are more than threats the existing, while weaknesses are more than strengths, so the industry falls into the WO quadrant. Consequently, it is recommended that banks adopt a Redirection strategy utilizing Generative Artificial Intelligence.International Symposium on Telecommunications120The results indicate that the identified opportunities are more than threats the existing, while weaknesses are more than strengths, so the industry falls into the WO quadrant, so it is recommended that banks adopt a Redirection strategy utilizing Generative Artificial Intelligence.2024 11th International Symposium on Telecommunications (IST)["Niloofar Moradhasel", "Mohammad Kazem Sayadi", "M. Moin"]2024-10-09T00:00:00 10ffe5d2612f3e7166e9bf201f16434d15ff4752In this article, the challenges and loopholes of laws in the world have been examined first. Issues that are generally challenging in the field of artificial intelligence and require risk management have been investigated. Based on this, some of the most important challenges and risks include the lack of algorithmic transparency - the lack of the possibility of protest - unfairness, bias and discrimination - liability for damages, etc. Also, due to the importance of artificial intelligence, the challenges related to this issue have also been investigated and studied. And finally, for the policies extracted from the previous phase of the project, legal criteria have been determined to evaluate these policies. Each of these legal criteria has been adapted to the obtained policies based on legal dimensions and aspects. The purpose of this process was to determine the legal loopholes in the above-mentioned documents based on the subject of artificial intelligence. For improvement and high accuracy, as well as for better analysis on the criteria that were adapted to the extracted policies, the data has been aggregated in an Excel file. For this purpose, the multiplicity of legal criteria in each of the legal dimensions related to each document was specified. Also, at the end, each of the documents is weighted based on the importance of connection with technology and the up-to-date ness of the subject in relation to the field of artificial intelligence. Based on the obtained analysis, the legal gaps in the documents related to artificial intelligence have been identified. Therefore, according to the rapid process of artificial intelligence, legal loopholes in the formulation of laws should be considered more sensitively.International Symposium on Telecommunications160The legal gaps in the documents related to artificial intelligence have been identified and according to the rapid process of artificial intelligence, legal loopholes in the formulation of laws should be considered more sensitively.2024 11th International Symposium on Telecommunications (IST)["M. Bokaei", "Elham Rafati", "Davood Heidari Kani", "Farbod Rabizadeh Fard", "Ali Derakhshani Mehrabani", "Iman Derakhshani Mehrabani", "Nooshin Bodaghi", "Reza Baderestani", "Neda Nedaei"]2024-10-09T00:00:00 4062ccd9f6fdac0026a1076b2f2cbada63846e4cThe insurance industry is rapidly evolving with the help of artificial intelligence (AI), particularly in the realm of managing claims for float insurance. Handling multiple vehicle coverage under a single policy for motor vehicle insurance poses unique challenges for risk assessment, fraud detection, and claims processing. This paper delves into how AI can revolutionize these processes by expediting resolution times, boosting customer satisfaction, uncovering fraud, and automating claims procedures. While AI tools like chatbots, machine learning, and robotic process automation (RPA) offer numerous advantages, they also come with potential risks and privacy concerns. This article offers a comprehensive exploration of how artificial intelligence could significantly enhance the speed and accuracy of processing claims for float insurance.Journal of Artificial Intelligence90How artificial intelligence can revolutionize these processes by expediting resolution times, boosting customer satisfaction, uncovering fraud, and automating claims procedures is explored.ShodhAI: Journal of Artificial Intelligence["S. S. Kumar"]2024-10-09T00:00:00 2ce90fc0496382a693bf6009f6df35efb681ac92The development and advancement of technology, especially the Industrial Revolution 4.0, introduced humans to artificial intelligence (AI). AI plays a role in changing human lifestyles. There are logical and fair characters and use considerations of justice and human values, according to the Pancasila framework. This study presents the issue of whether Pancasila values are beneficial for the utilization of artificial intelligence. How is artificial intelligence utilized within the Pancasila framework? According to the issue studied, it answers that artificial intelligence, which continues to develop rapidly, has not been able to be in the corridor of its main objectives, namely the protection of human rights and respect for human dignity in the digital era. Even though the values of Pancasila are taught and grounded in the social environment, human behavior has not changed according to the targeted goals. Often the development model of artificial intelligence does not always adhere to ethical values, especially in areas that are closely related to efforts to replace the role of humans, who are vulnerable to eliminating respect for the nobility of human dignity.Jurnal Interpretasi Hukum170It is answered that artificial intelligence, which continues to develop rapidly, has not been able to be in the corridor of its main objectives, namely the protection of human rights and respect for human dignity in the digital era.Jurnal Interpretasi Hukum["Grace Juanita"]2024-10-09T00:00:00 bc84153604d6790d18833f207d27d8aabdc65cdcArtificial Intelligence (AI) has emerged as a transformative force in the design industry, reshaping the way creativity and efficiency are approached in the design process. This paper explores the profound impact AI has on design, highlighting its role in enhancing creativity, improving efficiency, and expanding possibilities through advanced algorithms and machine learning. AI assists designers by automating repetitive tasks, generating new design variations, and providing insights into consumer preferences, thus enabling data-driven and personalized design solutions. However, the integration of AI in design is not without challenges, including concerns about the loss of human touch, ethical implications regarding authorship and intellectual property, and the potential perpetuation of biases present in AI training data. Through a mixed-method research approach involving semi-structured interviews and content analysis, this study investigates how AI serves as a collaborator in the design process, offering valuable insights for leveraging AI to push the boundaries of creativity while addressing ethical and practical challenges. The findings indicate that AI has the potential to revolutionize design by acting as a powerful tool that augments human creativity, enhances productivity, and facilitates personalized user experiences.Journal of engineering and applied sciences00Investigating how AI serves as a collaborator in the design process offers valuable insights for leveraging AI to push the boundaries of creativity while addressing ethical and practical challenges indicates that AI has the potential to revolutionize design by acting as a powerful tool that augments human creativity, enhances productivity, and facilitates personalized user experiences.Journal of Engineering and Applied Sciences["I. Adeleye"]2024-10-09T00:00:00 7fad1b700c7b825c95802784667895a46a32bb36The introduction of artificial intelligence (AI) has led to a global revolution across industries and service sectors. AI raises concerns such as job displacement and academic dishonesty.  Their duties, involving using different technologies, have led them to incorporate AI technology into their work. This paper delves into the future of the student affairs profession, with a specific focus on the impact of artificial intelligence. The paper analyzes existing literature to identify emerging trends of uncertainties and points out potential opportunities associated with integrating AI into the role of student affairs professionals. This paper explores the nuances of integrating AI into student affairs, including both the opportunities and potential threats. In this paper, it was argued that professionals should identify the aspects of their job that necessitate a personal touch and concentrate their efforts on enhancing human experiencesEast African Journal of Education Studies00It was argued that professionals should identify the aspects of their job that necessitate a personal touch and concentrate their efforts on enhancing human experiences and the impact of artificial intelligence on the student affairs profession.East African Journal of Education Studies["Francisca Owusu", "B. W. Bimpong"]2024-10-09T00:00:00 e76b0a6e86b0ce5d1aac18d003ab1c524a7096eaArtificial Intelligence (AI) has emerged as a transformative tool in the field of education, with its potential to revolutionize teaching and learning in higher education institutions. People are exploring ways to connect its power and reform traditional educational practices. This paper explores the implementation of five different AI writing tools in teaching and learning in Higher Education, focusing on the perspectives of faculty and students. By examining their perspectives and identifying the challenges that can be encountered, the study seeks to gain a better understanding of the benefits and limitations of integrating AI writing tools in higher education. There are limited studies that seek to determine the perspectives of faculty and students in higher education regarding AI writing tools. The method used for the study is literature review, particularly hand-searching journal, and books approach. The study's findings have revealed that AI writing tools perform a multitude of functions, yet they also possess certain limitationsEast African Journal of Information Technology00This paper explores the implementation of five different AI writing tools in teaching and learning in Higher Education, focusing on the perspectives of faculty and students.East African Journal of Information Technology["B. W. Bimpong", "Prince Atsise", "Francisca Owusu"]2024-10-09T00:00:00 4d4a1667a1dcf963ee7b16f55a7c8a20f61a9404Academic Psychiatry121Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["M. Morreale", "Richard Balon", "E. Beresin", "A. Seritan", "Enrico G Castillo", "Lia A. Thomas", "A. Louie", "Rashi Aggarwal", "Anthony P. S. Guerrero", "J. Coverdale", "Adam M. Brenner"]2024-10-09T00:00:00 dda4db58b2c6ee228c461b505e1786b07ace231bThe field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.PLoS ONE202This study focuses on visual XAI systems applied to DL-enabled diagnostic system in chest radiography, and employs two well-known XAI methods, Grad-CAM and LIME, to generate visual explanations elucidating the AI decision-making process.PLOS ONE["Izegbua E Ihongbe", "Shereen Fouad", "Taha F Mahmoud", "Arvind Rajasekaran", "Bahadar Bhatia"]2024-10-09T00:00:00 36bfc584f165fba329797e585ed164e5b521c4d1Neurosurgical review60Neurosurgical review["Mihit Kalawatia", "Bipin Chaurasia"]2024-10-09T00:00:00 6c25145cbe385cfcf9e051b29e33e7e927ba901bEuropean Journal of Anaesthesiology270European journal of anaesthesiology["Cian Hurley", "Nuala Lucas", "Rosemarie Kearsley"]2024-10-09T00:00:00 8f60e2bae583f12469a246a08da0aac0a89584e4Journal of Big Data890How a family of neural network algorithms has helped researchers across the globe to predict air pollutant(s) is revealed to reveal how a family of neural network algorithms has helped researchers across the globe to predict air pollutants.J. Big Data["Meenakshi Malhotra", "Savita Walia", "Chia-Chen Lin", "I. K. Aulakh", "Saurabh Agarwal"]2024-10-09T00:00:00 32cbd420dcd7ee78cce3b87d41fc00cab75ec4e9Tidsskrift for Den Norske Laegeforening00Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke["R. \u00d8rstavik"]2024-10-09T00:00:00 c14acf939498e9dc81f2b33ac464b5ce7f997b08A decade ago, no machine matched human skills in many areas, but now AI surpasses humans in at least some skill sets. Internal auditors' effectiveness and value addition to organizations hinges critically on their possession of essential skills like risk management, independence, and deep knowledge of internal controls, along with their professional training and ability to align audit plans with organizational goals. Thus, it is important in the literature to highlight the need to understand the relative importance of internal auditor skills, their practical application in the selection, hiring, training, and promotion processes, and the consideration of additional skills or factors that may impact the application of these skills. However, the necessity for auditors to adapt and acquire new skills to meet the evolving requirements of technology, particularly AI, has not been discussed before. There are two main skill sets that are important for internal auditors: perceived behavioral, sometimes called soft, and cognitive, sometimes called hard, skills. Consequently, this text will discuss the changing skill sets and sub-categories of competent internal auditors according to AI. The study’s findings contribute to professional practice and literature by understanding and improving internal auditors’ skills, elevating performance, and streamlining risk assessment processes.Denetişim00The study’s findings contribute to professional practice and literature by understanding and improving internal auditors’ skills, elevating performance, and streamlining risk assessment processes.Denetişim["K. Arun"]2024-10-09T00:00:00 faf00c55d7bed836743adfee34216aee3533470800["Michel Speiser"]2024-10-09T00:00:00 ec7789bb5644995c63123a7628639d03b3afc2cfOral Radiology/Springer50Patients' attitudes toward the use of AI in dental radiographic detection of occlusal caries and the impact of AI-based diagnosis on their trust in dentists are evaluated to allow dentists to shape AI-supported dentistry in the future.Oral radiology["H. Bahad\u0131r", "N. Keskin", "E. \u00c7akmak", "G\u00fcrkan G\u00fcne\u00e7", "Kader Cesur Ayd\u0131n", "Fatih Peker"]2024-10-09T00:00:00 e6d0c17c9c1fb54795bf5badfed5ea126e26b3f0Archives of Dermatological Research40Archives of dermatological research["Rachel K Greene", "Mohammad Jafferany"]2024-10-09T00:00:00 8d56f988bb525379a5c62da07ed6f876a5f61596Academia : Revista Latinoamericana de Administración100Academia Revista Latinoamericana de Administración["Manuel Alonso Dos Santos", "Gianni Roman\u00ed", "Enrique Ogliastri"]2024-10-09T00:00:00 9f5f6cefea39e056cd1312ac53f08f62c142bcf0The European Physical Journal Plus1100The European Physical Journal Plus["Guillaume Landry", "Christopher Kurz", "A. Thummerer"]2024-10-09T00:00:00 5131ba4c73f989a5810a533351ed9e4510e98f22The sophistication and breadth of cyber threats are continuously expanding, making it more difficult for traditional security measures to keep up. Artificial intelligence is revolutionizing cybersecurity by equipping businesses to proactively counter threats with automated reaction systems and predictive threat intelligence. Data analytics, behavioral analysis, and machine learning enable AI-powered systems to anticipate cyber assaults, enabling more efficient and rapid threat detection. By automating reaction mechanisms and mitigating threats in real-time, AI systems can minimize human error and maximize damage mitigation. AI techniques, such as anomaly detection, predictive modeling, and real-time threat analysis; data privacy, ethics, and the risks of hostile attacks are among the subjects covered, as are the benefits and drawbacks of utilizing AI in cybersecurity. This article provides the framework for future intelligent, automated cyber defense methods and illustrates how AI may alter cybersecurity using real-life examples and case studies.Darpan International Research Analysis11The framework for future intelligent, automated cyber defense methods is provided and how AI may alter cybersecurity is illustrated using real-life examples and case studies.Darpan International Research Analysis["Bhavik Patel", "Patel Krunalkumar Bhagavanbhai", "Niravkumar Dhameliya"]2024-10-09T00:00:00 aba6f3d0c334d6ff7ee7691a974ac1d95064e64cDevelopmental psychologists have long-established socio-cognitive abilities as fundamental to human intelligence and development. These abilities enable individuals to enter, learn from, and contribute to a surrounding culture. This drives the process of cumulative cultural evolution, which is responsible for humanity's most remarkable achievements. AI research on social interactive agents mostly concerns the emergence of culture in a multi-agent setting (often without a strong grounding in developmental psychology). We argue that AI research should be informed by psychology and study socio-cognitive abilities enabling to enter a culture as well. We draw inspiration from the work of Michael Tomasello and Jerome Bruner, who studied socio-cognitive development and emphasized the influence of a cultural environment on intelligence. We outline a broader set of concepts than those currently studied in AI to provide a foundation for research in artificial social intelligence. Those concepts include social cognition (joint attention, perspective taking), communication, social learning, formats, and scaffolding. To facilitate research in this domain, we present The SocialAI school—a tool that offers a customizable parameterized suite of procedurally generated environments. This tool simplifies experimentation with the introduced concepts. Additionally, these environments can be used both with multimodal RL agents, or with pure-text Large Language Models (LLMs) as interactive agents. Through a series of case studies, we demonstrate the versatility of the SocialAI school for studying both RL and LLM-based agents. Our motivation is to engage the AI community around social intelligence informed by developmental psychology, and to provide a user-friendly resource and tool for initial investigations in this direction. Refer to the project website for code and additional resources: https://sites.google.com/view/socialai-school.Frontiers Neurorobotics570It is argued that AI research should be informed by psychology and study socio-cognitive abilities enabling to enter a culture as well, and a user-friendly resource and tool is presented to provide a user-friendly resource and tool for initial investigations in this direction.Frontiers in Neurorobotics["Grgur Kova\u010d", "R\u00e9my Portelas", "P. Dominey", "Pierre-Yves Oudeyer"]2024-10-09T00:00:00 24d232c1939875ff96985b906601171afc21b63bChatGPT, a general artificial intelligence, has been recognized as a powerful tool in scientific writing and programming but its use as a medical tool is largely overlooked. The general accessibility, rapid response time and comprehensive training database might enable ChatGPT to serve as a diagnostic augmentation tool in certain clinical settings. The diagnostic process in neurology is often challenging and complex. In certain time-sensitive scenarios, rapid evaluation and diagnostic decisions are needed, while in other cases clinicians are faced with rare disorders and atypical disease manifestations. Due to these factors, the diagnostic accuracy in neurology is often suboptimal. Here we evaluated whether ChatGPT can be utilized as a valuable and innovative diagnostic augmentation tool in various neurological settings. We used synthetic data generated by neurological experts to represent descriptive anamneses of patients with known neurology-related diseases, then the probability for an appropriate diagnosis made by ChatGPT was measured. To give clarity to the accuracy of the AI-determined diagnosis, all cases have been cross-validated by other experts and general medical doctors as well. We found that ChatGPT-determined diagnostic accuracy (ranging from 68.5% ± 3.28% to 83.83% ± 2.73%) can reach the accuracy of other experts (81.66% ± 2.02%), furthermore, it surpasses the probability of an appropriate diagnosis if the examiner is a general medical doctor (57.15% ± 2.64%). Our results showcase the efficacy of general artificial intelligence like ChatGPT as a diagnostic augmentation tool in medicine. In the future, AI-based supporting tools might be useful amendments in medical practice and help to improve the diagnostic process in neurology.PLoS ONE323The results showcase the efficacy of general artificial intelligence like ChatGPT as a diagnostic augmentation tool in medicine and suggest that AI-based supporting tools might be useful amendments in medical practice and help to improve the diagnostic process in neurology.PLOS ONE["Bern\u00e1t N\u00f3gr\u00e1di", "T. Polg\u00e1r", "Val\u00e9ria Meszl\u00e9nyi", "Zal\u00e1n K\u00e1d\u00e1r", "P\u00e9ter Hertelendy", "Anett Cs\u00e1ti", "L. Szpisjak", "D\u00f3ra Halmi", "Barbara Erd\u00e9lyi-Furka", "M\u00e1t\u00e9 T\u00f3th", "Fanny Moln\u00e1r", "D\u00e1vid T\u00f3th", "Z. B\u0151sze", "Krisztina Boda", "P\u00e9ter Kliv\u00e9nyi", "L\u00e1szl\u00f3 Sikl\u00f3s", "Roland Patai"]2024-10-09T00:00:00 1614e593803977e446453e8375c738e26b7b56cePrecision agriculture is one of the ways to achieve food security and sustainability through better resource-use optimization and crop productivity dealing with the challenges posed by the growing population and addressing environmental concerns. The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture (PA), with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management. The accuracy of crop monitoring and health assessments has increased by 30–50 percent as a result of AI-powered solutions, which have improved resource-based decision-making. Systems for precision irrigation and fertilization have increased crop yields by 5–15 percent when using 25–40 percent less water and 30-40 percent less fertilizer, respectively. Robotic harvesters and sprayers are examples of automation technologies that have reduced labor expenses by 20–40 percent and increased operational efficiency by 35 percent. Additionally, AI-based prediction models have reduced pest damage by 20–25 percent and reached an accuracy of 85–90 percent for crop yield forecasts and pest control. Despite these developments, issues of scalability, affordability for small farms, and data privacy still exist, which can hinder technology adoption among farmers. The evaluation follows by outlining ideas for future research, such as 5G, blockchain, and AI integration with cloud and edge computing. These technologies could improve decision-making and transparency in precision agriculture by enabling real-time data transmission, secure data management, and enhanced traceability, thus addressing current limitations and fostering trust among stakeholders.Asian Journal of Research in Computer Science01The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture, with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management.Asian Journal of Research in Computer Science["Azmirul Hoque", "Mrutyunjay Padhiary"]2024-10-09T00:00:00 ad3cd478258b53bbaff8d32a2a1aab65db0860ecThe robots that entered the manufacturing sector in the second and third Industrial Revolutions (IR2 and IR3) were designed for carrying out predefined routines without physical interaction with humans. In contrast, IR4* robots (i.e., robots since IR4 and beyond) are supposed to interact with humans in a cooperative way for enhancing flexibility, autonomy, and adaptability, thus dramatically improving productivity. However, human–robot cooperation implies cognitive capabilities that the cooperative robots (CoBots) in the market do not have. The common wisdom is that such a cognitive lack can be filled in a straightforward way by integrating well-established ICT technologies with new AI technologies. This short paper expresses the view that this approach is not promising and suggests a different one based on artificial cognition rather than artificial intelligence, founded on concepts of embodied cognition, developmental robotics, and social robotics. We suggest giving these IR4* robots designed according to such principles the name CoCoBots. The paper also addresses the ethical problems that can be raised in cases of critical emergencies. In normal operating conditions, CoCoBots and human partners, starting from individual evaluations, will routinely develop joint decisions on the course of action to be taken through mutual understanding and explanation. In case a joint decision cannot be reached and/or in the limited case that an emergency is detected and declared by top security levels, we suggest that the ultimate decision-making power, with the associated responsibility, should rest on the human side, at the different levels of the organized structure.Applied Informatics441This short paper suggests a different one based on artificial cognition rather than artificial intelligence, founded on concepts of embodied cognition, developmental robotics, and social robotics, and suggests giving these IR4* robots designed according to such principles the name CoCoBots.AI["Giulio Sandini", "A. Sciutti", "Pietro Morasso"]2024-10-09T00:00:00 ae2d6c644e62dd6665b35b5d21b967e3965269cdThis article explores the critical role of Explainable Artificial Intelligence (XAI) in mitigating bias within AI-driven recruitment processes. As AI becomes increasingly prevalent in hiring practices, concerns about algorithmic bias and fairness have emerged. The article discusses how XAI techniques, such as SHAP and LIME, can be used to detect and interpret potential biases in recruitment algorithms. It examines the implementation of XAI for feature importance analysis, algorithmic bias detection, and disparate impact analysis across different demographic groups. The article addresses the challenges of balancing model complexity with explainability and the limitations of XAI in identifying systemic biases. By implementing XAI strategies, organizations can enhance the fairness and transparency of their hiring practices, ultimately fostering more diverse and equitable workplaces.International Journal of Scientific Research in Computer Science Engineering and Information Technology81The article discusses how XAI techniques, such as SHAP and LIME, can be used to detect and interpret potential biases in recruitment algorithms.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ravi Kiran Magham"]2024-10-09T00:00:00 f364ae9b7160f962bad1d659f8ec6aeb8e10ef69OBJECTIVES A substantial number of incidental pulmonary embolisms (iPEs) in computed tomography scans are missed by radiologists in their daily routine. This study analyzes the radiological reports of iPE cases before and after implementation of an artificial intelligence (AI) algorithm for iPE detection. Furthermore, we investigate the anatomic distribution patterns within missed iPE cases and mortality within a 90-day follow-up in patients before and after AI use. MATERIALS AND METHODS This institutional review board-approved observational single-center study included 5298 chest computed tomography scans performed for reasons other than suspected pulmonary embolism (PE). We compared 2 cohorts: cohort 1, consisting of 1964 patients whose original radiology reports were generated before the implementation of an AI algorithm, and cohort 2, consisting of 3334 patients whose scans were analyzed after the implementation of an Food and Drug Administration-approved and CE-certified AI algorithm for iPE detection (Aidoc Medical, Tel Aviv, Israel). For both cohorts, any discrepancies between the original radiology reports and the AI results were reviewed by 2 thoracic imaging subspecialized radiologists. In the original radiology report and in case of discrepancies with the AI algorithm, the expert review served as reference standard. Sensitivity, specificity, prevalence, negative predictive value (NPV), and positive predictive value (PPV) were calculated. The rates of missed iPEs in both cohorts were compared statistically using STATA (Version 17.1). Kaplan-Meier curves and Cox proportional hazards models were used for survival analysis. RESULTS In cohort 1 (mean age 70.6 years, 48% female [n = 944], 52% male [n = 1020]), the prevalence of confirmed iPE was 2.2% (n = 42), and the AI detected 61 suspicious iPEs, resulting in a sensitivity of 95%, a specificity of 99%, a PPV of 69%, and an NPV of 99%. Radiologists missed 50% of iPE cases in cohort 1. In cohort 2 (mean age 69 years, 47% female [n = 1567], 53% male [n = 1767]), the prevalence of confirmed iPEs was 1.7% (56/3334), with AI detecting 59 suspicious cases (sensitivity 90%, specificity 99%, PPV 95%, NPV 99%). The rate of missed iPEs by radiologists dropped to 7.1% after AI implementation, showing a significant improvement (P < 0.001). Most overlooked iPEs (61%) were in the right lower lobe. The survival analysis showed no significantly decreased 90-day mortality rate, with a hazards ratio of 0.95 (95% confidence interval, 0.45-1.96; P = 0.88). CONCLUSIONS The implementation of an AI algorithm significantly reduced the rate of missed iPEs from 50% to 7.1%, thereby enhancing diagnostic accuracy. Despite this improvement, the 90-day mortality rate remained unchanged. These findings highlight the AI tool's potential to assist radiologists in accurately identifying iPEs, although its implementation does not significantly affect short-term survival. Notably, most missed iPEs were located in the right lower lobe, suggesting that radiologists should pay particular attention to this area during evaluations.Investigative Radiology250The implementation of an AI algorithm significantly reduced the rate of missed iPEs from 50% to 7.1%, thereby enhancing diagnostic accuracy and highlighting the AI tool's potential to assist radiologists in accurately identifying iPEs, although its implementation does not significantly affect short-term survival.Investigative radiology["Vera Inka Josephin Graeve", "Simin Laures", "Andres Spirig", "Hasan Zaytoun", "C. Gregoriano", "Philipp Schuetz", "Felice Burn", "Sebastian Schindera", "Tician Schnitzler"]2024-10-09T00:00:00 7fd0de287f005e1ab04895051aa1dbd3ca054f09 Purpose The purpose of this study was to investigate the impact of artificial intelligence (AI) adoption on knowledge workers' innovative work behaviors (IWB), as well as the mediating role of stress appraisal and the moderating role of individual learning abilities. Design/methodology/approach This study analyzed the questionnaire results of 313 knowledge workers, and data analysis was conducted by using SPSS 25.0, SPSS 25.0 macro-PROCESS and AMOS 28.0. Findings This study found that AI adoption has a double-edged sword effect on knowledge workers' IWB. Specifically, AI adoption can promote IWB by enhancing knowledge workers' challenging stress appraisal, while inhibiting IWB by fostering their hindering stress appraisal. Moreover, individual learning ability significantly moderated the relationship between AI adoption and stress appraisal, which further influenced IWB. Originality/value This study integrates the conflicting findings of previous studies and proposes a comprehensive theoretical model based on the theory of cognitive appraisal of stress. This study enriches the research on AI in the field of knowledge management, especially extending the understanding of the relationship between AI adoption and knowledge workers’ IWB by unraveling the psychological mechanisms and behavior outcomes of users' technology usage. Additionally, we provide new insights and suggestions for organizations to seek the cooperation and support of employees in introducing new technologies or driving intelligent transformation. Journal of Knowledge Management1640It is found that AI adoption has a double-edged sword effect on knowledge workers' IWB, which can promote IWB by enhancing knowledge workers' challenging stress appraisal, while inhibiting IWB by fostering their hindering stress appraisal.J. Knowl. Manag.["Xueyan Dong", "Yuxin Tian", "Mingming He", "Tienan Wang"]2024-10-09T00:00:00 bab6755806f3edbe004eedf38c84b065c8cb3ffaThis article explores the transformative impact of artificial intelligence (AI) on scientific research across various disciplines. It examines how AI-driven tools are revolutionizing data analysis, simulation, and hypothesis generation, particularly in fields such as genomics, climate science, and materials science. The article discusses the acceleration of discovery processes through AI, highlighting its role in enabling sophisticated analysis of complex datasets, developing predictive models, and facilitating automated experimentation. Ethical considerations, including the need for transparency and reproducibility in AI-assisted research, are addressed. The synergy between human creativity and AI capabilities is explored, emphasizing how AI augments human ingenuity and fosters interdisciplinary collaboration. Case studies illustrate successful implementations of AI in scientific inquiry, demonstrating its potential to enhance research methodologies and outcomes. The article also looks ahead to the prospects of AI in scientific research, considering emerging technologies and the evolving role of AI in the scientific process. By providing a comprehensive overview of AI's current applications and future potential in scientific research, this article underscores the pivotal role of AI in advancing scientific knowledge and addressing complex global challenges.International Journal of Scientific Research in Computer Science Engineering and Information Technology100It is examined how AI-driven tools are revolutionizing data analysis, simulation, and hypothesis generation, particularly in fields such as genomics, climate science, and materials science, to highlight the acceleration of discovery processes through AI.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Srikanth Padakanti", "Venkatarama Reddy Kommidi"]2024-10-09T00:00:00 e2938485b7b4bf295a18247149a4fbd4160579aeThe advent of artificial intelligence (AI) in defense systems has significantly altered the scale, scope, and complexity of military operations. This transformation presents both a profound challenge and a unique opportunity for the technological advancement of states, particularly those engaged in regional conflicts, as is the case with Armenia. Integrating AI in defense enhances military capabilities, enabling faster decision-making, predictive analytics, automated systems, and improved operational efficiency.  In the context of Armenia, a nation situated in a geopolitically sensitive region, the implementation of AI in defense not only holds the potential to reshape its military posture but also to redefine its technological development trajectory. Armenia’s ongoing security concerns and regional tensions make the adoption of AI in defense of strategic importance. The main question considered within the topic is Armenia’s potential to integrate AI-based technologies in defense. The findings (based on SWOT analysis) suggest that Armenia has the potential to evolve its defense capabilities through AI (as the example of another post-soviet country Estonia shows) if targeted steps (administrative effort) are taken to mitigate identified risks and weaknesses, while concurrently capitalizing on its inherent strengths and external opportunities.Edelweiss Applied Science and Technology170The findings suggest that Armenia has the potential to evolve its defense capabilities through AI if targeted steps are taken to mitigate identified risks and weaknesses, while concurrently capitalizing on its inherent strengths and external opportunities.Edelweiss Applied Science and Technology["Gyulnara S. Danielyan", "Gayane Harutyunyan"]2024-10-09T00:00:00 689281f22c19d7ec5846a618407863e64afe09d6The EU's Artificial Intelligence Act (AI Act) is a significant step towards responsible AI development, but lacks clear technical interpretation, making it difficult to assess models' compliance. This work presents COMPL-AI, a comprehensive framework consisting of (i) the first technical interpretation of the EU AI Act, translating its broad regulatory requirements into measurable technical requirements, with the focus on large language models (LLMs), and (ii) an open-source Act-centered benchmarking suite, based on thorough surveying and implementation of state-of-the-art LLM benchmarks. By evaluating 12 prominent LLMs in the context of COMPL-AI, we reveal shortcomings in existing models and benchmarks, particularly in areas like robustness, safety, diversity, and fairness. This work highlights the need for a shift in focus towards these aspects, encouraging balanced development of LLMs and more comprehensive regulation-aligned benchmarks. Simultaneously, COMPL-AI for the first time demonstrates the possibilities and difficulties of bringing the Act's obligations to a more concrete, technical level. As such, our work can serve as a useful first step towards having actionable recommendations for model providers, and contributes to ongoing efforts of the EU to enable application of the Act, such as the drafting of the GPAI Code of Practice.arXiv.org1033By evaluating 12 prominent LLMs in the context of COMPL-AI, it reveals shortcomings in existing models and benchmarks, particularly in areas like robustness, safety, diversity, and fairness, and highlights the need for a shift in focus towards these aspects, encouraging balanced development of LLMs and more comprehensive regulation-aligned benchmarks.ArXiv["Philipp Guldimann", "Alexander Spiridonov", "Robin Staab", "Nikola Jovanovi'c", "Mark Vero", "Velko Vechev", "Anna Gueorguieva", "Mislav Balunovi'c", "Nikola Konstantinov", "Pavol Bielik", "Petar Tsankov", "Martin T. Vechev"]2024-10-10T00:00:00 d62341d2f2be941aa06dbbc6ac30f64a694165adImages are capable of conveying emotions, but emotional experience is highly subjective. Advances in artificial intelligence have enabled the generation of images based on emotional descriptions. However, the level of agreement between the generative images and human emotional responses has not yet been evaluated. In order to address this, 20 artistic landscapes were generated using StyleGAN2-ADA. Four variants evoking positive emotions (contentment and amusement) and negative emotions (fear and sadness) were created for each image, resulting in 80 pictures. An online questionnaire was designed using this material, in which 61 observers classified the generated images. Statistical analyses were performed on the collected data to determine the level of agreement among participants between the observers’ responses and the generated emotions by AI. A generally good level of agreement was found, with better results for negative emotions. However, the study confirms the subjectivity inherent in emotional evaluation.Electronics1341A generally good level of agreement was found between participants between the observers’ responses and the generated emotions by AI, which confirms the subjectivity inherent in emotional evaluation.ArXiv["Miguel Carrasco", "C\u00e9sar Gonz\u00e1lez-Mart\u00edn", "Sonia Navajas-Torrente", "Raul Dastres"]2024-10-10T00:00:00 766ced9117d2fb098ad5edace50c5a2d084f9296Scientific Reports351Safe balance after stroke strongly correlates with its initial motor function, Fugl-Meyer assessment scale, and ipsilesional corticospinal tract fractional anisotropy in the random forest.Scientific Reports["Sekwang Lee", "Eunyoung Lee", "Kwang-Sig Lee", "S. Pyun"]2024-10-10T00:00:00 35d449abae9ee0df3c73c2fbd1323a47aaf7fdbbThis perspective article focuses on the exploration and advocacy of approaches to be considered in designing equitable learning experiences for students’ use of artificial intelligence, machine learning, and technology through the Universal Design for Learning Framework (UDL) exemplifying chemistry examples that can be applied to any course in STEM. The use of artificial intelligence (AI) and machine learning are causing disruptions within learning in higher education and is also casting a spotlight on systemic inequities particularly affecting minoritized groups broadly and in STEM fields. Particularly, the emergence of AI has focused on inequities toward minoritized students in academic and professional ethics. As the U.S. education system grapples with a nuanced mix of acceptance and hesitation towards AI, the necessity for inclusive and equitable education, impactful learning practices, and innovative strategies has become more pronounced. Promoting equitable approaches for the use of artificial intelligence and technology in STEM learning will be an important milestone in addressing STEM disparities toward minoritized groups and equitable accessibility to evolving technology.Frontiers in Education431Frontiers in Education["Jennifer Garcia Ramos", "Zakiya S. Wilson-Kennedy"]2024-10-10T00:00:00 a59af251621d09a743f6b698bdb2d7af65e2d270Gender discrimination in education hinders women’s representation in various fields. How can we create a gender-neutral learning environment when teachers’ gender composition and mindset are slow to change? Recent development in artificial intelligence (AI) provides a way to achieve this goal as engineers can make AI trainers gender neutral and not take gender-related information as input. We use data from a natural experiment in which such AI trainers replace some human teachers for a male-dominated strategic board game to test the effectiveness of AI training. The introduction of AI improves teaching outcomes for boys and girls and reduces the preexisting gender gap. Survey responses indicate that AI’s information advantage, friendly appearance, and interactive features helped students to learn faster, and class recordings suggest that AI trainers’ nondiscriminatory emotional status can explain the improvement in gender equality. We demonstrate AI’s potential in improving learning outcomes and promoting diversity, equity, and inclusion in analogous settings. This paper was accepted by Elena Katok, special issue on the human-algorithm connection. Funding: D. Huang gratefully acknowledges financial support from the National Natural Science Foundation of China [Grants 71988101 and T2293771]. C. Lin gratefully acknowledges financial support from the National Natural Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project No. T35/710/20R]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02787 .Management Sciences411AI’s potential in improving learning outcomes and promoting diversity, equity, and inclusion in analogous settings is demonstrated and survey responses indicate that AI’s information advantage, friendly appearance, and interactive features helped students to learn faster, and class recordings suggest that AI trainers’ nondiscriminatory emotional status can explain the improvement in gender equality.Management Science["Leo Bao", "Difang Huang", "Chen Lin"]2024-10-10T00:00:00 154e8e98e30964079e8d2e698ff3644d6a8b1440Current Radiology Reports510With continued research, collaboration, and careful implementation, AI can become an integral part in imaging for TAVR, ultimately improving patient care and outcomes.Current radiology reports["Shawn Sun", "Leslie Yeh", "Amir Imanzadeh", "Soheil Kooraki", "Arash Kheradvar", "Arash Bedayat"]2024-10-10T00:00:00 b42295ae19a1fb1674bdb5467e161b48faed1f66 Zombie firms have been a prominent yet controversial subject of academic and policy debates in recent years. In this article, we first revisit the economic consequences and driving factors of zombie companies and then, based on this assessment, discuss the implications of artificial intelligence (AI) for zombie firms. We document that the share of zombie firms in advanced economies has risen considerably over the past three decades, and that this increase has been a significant drag on productivity growth. We further find that persistently low interest rates are a significant causal factor underlying the rise of zombie companies. Turning to AI, we argue that an AI-induced productivity boom may counteract the drag from zombie firms and improve firm performance, mitigating corporate zombification. Moreover, by leading to higher interest rates that force zombie firms to exit markets, AI may boost productivity further in the longer run – a so far overlooked channel in the debate on the economic implications of AI.The Economists' Voice130It is argued that an AI-induced productivity boom may counteract the drag from zombie firms and improve firm performance, mitigating corporate zombification, and AI may boost productivity further in the longer run.The Economists’ Voice["Ryan Banerjee", "Sebastian Doerr", "Boris Hofmann"]2024-10-10T00:00:00 de29f0f6ef00fbe3cb1ba6fde5be94f982211154Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. The complexity and heterogeneity of IRDs pose significant challenges in diagnosis, prognosis, and management. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. However, the rapid development of AI techniques and their varied applications have led to fragmented knowledge in this field. This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs. It aims to structure pathways for advancing clinical applications by exploring AI techniques like machine learning and deep learning, particularly in disease detection, progression prediction, and personalized treatment planning. Special focus is placed on the effectiveness of convolutional neural networks in these areas. Additionally, the integration of explainable AI is discussed, emphasizing its importance in clinical settings to improve transparency and trust in AI-based systems. The review addresses the need to bridge existing gaps in focused studies on AI's role in IRDs, offering a structured analysis of current AI techniques and outlining future research directions. It concludes with an overview of the challenges and opportunities in deploying AI for IRDs, highlighting the need for interdisciplinary collaboration and the continuous development of robust, interpretable AI models to advance clinical applications.arXiv.org1260The review addresses the need to bridge existing gaps in focused studies on AI's role in IRDs, offering a structured analysis of current AI techniques and outlining future research directions, and concludes with an overview of the challenges and opportunities in deploying AI for IRDs.ArXiv["Han Trinh", "J. Vice", "Jason Charng", "Zahra Tajbakhsh", "Khyber Alam", "Fred K. Chen", "Ajmal Mian"]2024-10-10T00:00:00 8cefafd75d28ff6effa1127388c21aaff892e6b5. The article explores the impact of artificial intelligence (AI) on Kazakhstan's education system and its potential to fundamentally transform learning and administrative processes. AI's ability to analyze big data allows educators to gain deeper insights into student performance, predict outcomes, and develop strategies to improve academic achievement. It also enhances personalized learning by offering students tailored educational content that matches their individual knowledge levels and learning pace. The automation of administrative tasks such as scheduling and grading significantly improves the efficiency of educational institutions, allowing teachers more time for direct engagement with students. The article thoroughly examines both the benefits and challenges of AI integration, including issues of digital inequality, access to modern technologies, and the need for ongoing professional development for teachers. Ethical considerations related to the protection of student data are also emphasized. Examples of successful AI implementation in international education platforms are provided, offering valuable insights for Kazakhstan. The article discusses opportunities for further investment in educational infrastructure and outlines key areas for digital development.Yessenov Science Journal00The article thoroughly examines both the benefits and challenges of AI integration, including issues of digital inequality, access to modern technologies, and the need for ongoing professional development for teachers.Yessenov Science Journal["M. Orynbassar", "M. Zhumadilova", "E. Abdykerimova"]2024-10-10T00:00:00 7cec8e16fcd0932d9b45da82d54aa67a803dee13Education is a major milestone in building the foundation of a nation's excellence. In the digital era and the industrial revolution 4.0, the application of artificial intelligence (AI) technology is becoming increasingly important in improving the effectiveness of learning. This study aims to develop an instructional guide for teachers in utilizing AI optimally in the context of learning. The method used in this study is based on the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) instructional design model. The analysis stage will consider the needs of teachers and the potential use of AI in the context of learning. Furthermore, the design stage will lead to the creation of an instructional guide based on pedagogical principles and AI technological capabilities. Furthermore, the design stage will lead to the creation of an instructional guide based on pedagogical principles and AI technological capabilities. This study aims to develop a teacher's guidebook in optimizing the use of Artificial Intelligence (AI) to support the learning process. The methodology used is ADDIE, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. The results of the analysis show that the majority of teachers feel they lack knowledge about AI and its applications in education. This guidebook is designed to provide theoretical understanding and practical applications of AI in various subjects.Academy of Education Journal120This guidebook is designed to provide theoretical understanding and practical applications of AI in various subjects to help teachers optimize the use of Artificial Intelligence to support the learning process.Academy of Education Journal["Deby Fauzi Asidiqi", "Ajeng Ginanjar", "Dede Kurnia Adiputra", "Addie Pendidikan Pembelajaran"]2024-10-10T00:00:00 40fd88f214428492583614c75f0267aefe93d1e4The assessment of creativity is both elaborating and challenging with today’s tools and methods in organisational creativity. This study presents the result of an AI-based model for the originality assessment of creative ideas using an artificial intelligence (AI) tool (Word2Vec). First, we define everyday creativity and outline the relevance and challenges of its evaluation. Then, the Word2Vec algorithm is introduced and used to develop three different models for originality assessment by calculating a semantic distance between ideas. We conclude with an empirical validation of the three models. While results indicate that all three models can be used to measure idea originality, they further point out that merely human-based assessments may be task-dependent. The paper closes with implications for research and practice to integrate AI in creative idea originality measurement. Managers can use the methods presented in this study to benefit from AI-supported creativity within their companies. Furthermore, idea managers of companies with large idea databases can implement the algorithms to analyse employee ideas regarding specific contexts or topics within their innovation management processes.International Journal of Innovation Management340Managers can use the methods presented in this study to benefit from AI-supported creativity within their companies and implement the algorithms to analyse employee ideas regarding specific contexts or topics within their innovation management processes.International Journal of Innovation Management["Dominik H\u00f6rauf", "Alexander Brem"]2024-10-10T00:00:00 556444fd7ac9cf57eaf5350e42176407bb383564Designers and developers of artificial intelligence (AI) can begin pursuing trustworthiness in an AI-enabled system long before it reaches an end user. Early in the AI lifecycle, data curation affords an opportunity for data scientists to promote trustworthiness by choosing data transformations and splits that prioritize performance in the deployed environment, not just on the data available for training. Key to enabling such purposeful data curation is the elicitation of actionable domain knowledge from various sources, including experts in the field in which the AI-enabled system will be used. In this paper, we offer a framework and set of questions for eliciting domain knowledge that drives data curation for trustworthy AI.International Conference on Applied Algorithms350This paper offers a framework and set of questions for eliciting domain knowledge that drives data curation for trustworthy AI.2024 International Conference on Assured Autonomy (ICAA)["M. Clemens-Sewall", "Emma Rafkin", "Christopher Cervantes"]2024-10-10T00:00:00 f99e4dc81906e70e9716778eb68628db9013210cAdvances in Artificial Intelligence (AI) and sensors are significantly impacting multiple areas, including education and workplaces. Following the PRISMA methodology, this review explores the current status of using AI to support the training and assessment of professionals. We examined 83 research papers, analyzing: (1) the targeted professionals, (2) the skills assessed, (3) the AI algorithms utilized, (4) the data and devices employed, (5) data fusion techniques utilized, (6) the architecture of the proposed platforms, (7) the management of ethics and privacy, and (8) validations of the proposals. The review highlights a trend in evaluating healthcare professionals (especially surgeons) motivated by the critical role of hands-on training in these professionals. Besides, the review reveals that data fusion techniques and certain technologies, like transfer learning and explainable AI, are not widely utilized despite their huge potential. Finally, the review underscores that most proposals remain within the research domain, lacking the integration and maturity needed for sustained use in real-world environments. Therefore, most of the proposals are not currently available to support the training of professionals. The insights of this review can guide researchers aiming to improve the training of professionals and, consequently, their education.ACM Computing Surveys970The review highlights a trend in evaluating healthcare professionals motivated by the critical role of hands-on training in these professionals and reveals that data fusion techniques and certain technologies, like transfer learning and explainable AI, are not widely utilized despite their huge potential.ACM Comput. Surv.["Mariano Albaladejo-Gonz\u00e1lez", "Jos\u00e9 A. Ruip\u00e9rez-Valiente", "F\u00e9lix G\u00f3mez M\u00e1rmol"]2024-10-10T00:00:00 949005832cbd316dcde4adfcd4ab5aad70ee002dMental disorders affect one in eight individuals globally, but access to professional assistance and necessary information is often limited. Artificial intelligence (AI) technology has the potential to revolutionize mental health care. This paper aims to provide a theoretical overview of the recent application of AI in the field of mental health. A non-systematic review of studies from the last decade, focusing on recent findings, was conducted. The paper summarizes the commonly used machine learning techniques including Support vector machines, followed by Random forest, Naïve bayes, Logistic regression, and K-nearest neighbors for prediction and diagnosis, research, and classification. Among deep learning techniques, the most frequently applied are Convolutional neural network, Recurrent neural networks, and hybrid deep learning models for prediction, diagnosis, screening, and research. Natural language processing, wearable AI, and AI-based virtual reality are also applied for monitoring, treatment, prediction, and research. Most of the current attention is centered around depression, anxiety, and mental health problems in general. Although AI-based applications claim to improve wellbeing, limited research currently supports these claims. Furthermore, the paper explores the responsible implementation of AI in mental health care, considering technical, clinical, and ethical perspectives. The need for a regulatory framework for all AI solutions in mental health care is emphasized, and further research is called for to evaluate their reliability and risks. In conclusion AI application in mental health care is a delicate balancing act between optimism and caution.International Conference on Applied Informatics580The paper summarizes the commonly used machine learning techniques including Support vector machines, followed by Random forest, Naïve bayes, Logistic regression, and K-nearest neighbors for prediction and diagnosis, research, and classification, and describes the most frequently applied deep learning models.2024 International Conference Automatics and Informatics (ICAI)["Vitali Atias", "Katerina Atias"]2024-10-10T00:00:00 9524cc6674dd09e96e0a96e13148e326a4246b26Artificial intelligence (AI) in healthcare can be used to solve a wide range of tasks, such as diagnosis, treatment and self-monitoring of patients. This review is devoted to the problem of polypharmacotherapy, the development of adverse drug reactions as a consequence of it and the use of AI in this field. AI allows to analyze drug interactions, identify possible adverse drug reactions and suggest optimal combinations of drugs and drug regimen. The use of clinical decision support systems, which are developed in various countries, has shown improved efficiency of the doctor’s work and increased patient’s safety with the help of AI. The use of AI in polypharmacotherapy requires further research and development to improve software products that would allow evaluating not only paired, but also multiple drug interactions.Annals of the Russian academy of medical sciences360The use of AI in polypharmacotherapy requires further research and development to improve software products that would allow evaluating not only paired, but also multiple drug interactions.Annals of the Russian academy of medical sciences["V. V. Beregovykh", "V. Panteleev", "N. Shimanovsky"]2024-10-10T00:00:00 9c1d4af9733524d088e75286c617b146dc3b4b9bAI and Ethics940A systematic review of the literature on evaluation criteria for Trustworthy Artificial Intelligence (TAI), with a focus on the seven EU principles of TAI, proposes a new classification system for each principle.ArXiv["Louise McCormack", "Malika Bendechache"]2024-10-10T00:00:00 62f47c16cd172f1c79b9e234fd7e8872fbfd772cThis study was conducted to examine teachers' perspectives on AI integration in education. A total of 108 junior and senior high school teachers from Central Java and Papua participated in this study, using a cross-sectional descriptive survey design and quantitative methodology. An online survey containing 47 questions on a 1–5 Likert scale was administered to the participants. The results of this study illustrate that the opinions of teachers differ significantly depending on how long they have been teaching; teachers who have more than ten years of experience in teaching have more open opinions towards the use of artificial intelligence than teachers who have less than five years of experience in teaching. This research gives the message that while AI training programs should consider different levels of teaching experience, age differences are not necessary. This research offers a new paradigm regarding the integration of AI technology and education to create a preparatory curriculum for teachers to incorporate AI into the educational process. To better understand the reasons behind teachers' perspectives, future research should examine qualitative data and consider other variables such as technology interest, also need to expand the geographical scope of the study to enhance more generalized results.International Conference on Emerging Technologies510Teachers who have more than ten years of experience in teaching have more open opinions towards the use of artificial intelligence than teachers who have less than five years of experience in teaching, illustrating that the opinions of teachers differ significantly depending on how long they have been teaching.2024 10th International Conference on Education and Technology (ICET)["Yovian Yustiko Prasetya", "Yansen Alberth Reba", "M. Muttaqin", "Taufiqulloh", "P. Susongko", "Sitti Hartinah", "Muslihati", "Hanung Sudibyo", "Yulius Mataputun"]2024-10-10T00:00:00 deafbf3eeb9628f29afe11e7235016f4ff5007e0The research aimed to explore the utilisation of artificial intelligence (AI) in the writing activities of the online student program at one university in Indonesia. A quantitative research design with a survey approach was used, and a questionnaire was used as the data collection technique. The questionnaire used a Likert Scale of five, from totally-agree to not-agree. There were fourteen statements in the questionnaire. 50 online students became the respondents of the research. The data showed that there were four main findings. First, the respondents came from very different backgrounds, ages and majors. Second, most respondents have used AI in the learning process for over 2 years and were aware of technology, including AI. Third, AI helped the respondents in many ways with their writing process such as improving their article quality, increasing the speed of writing scientific articles, improving the quality of research in scientific articles, overcoming difficulties in writing the articles, reducing errors in writing scientific articles, and analyzing relevant resources in scientific articles. Finally, the respondents also worried that AI could replace the role of human writers in writing scientific articles. They also feared that AI could reduce the authenticity or originality of scientific articles.International Conference on Emerging Technologies230The research aimed to explore the utilisation of artificial intelligence (AI) in the writing activities of the online student program at one university in Indonesia and found that AI helped the respondents in many ways with their writing process such as improving their article quality.2024 10th International Conference on Education and Technology (ICET)["Yella Dezas Perdani", "Gita Rahmi", "Yessi Widyasari"]2024-10-10T00:00:00 153dad78349f43e2170077ebbdae7fbf48165f25Scientific research plays an essential role in the development of mankind through its ability to provide innovative solutions and technological advances in various fields. In this perpetual quest, researchers must be active members of the scientific community, sharing their experiences and publishing their work and discoveries. All research work generally begins with a literature review (LR), which involves delving into previous work to examine the current state of knowledge, situate a research topic, and thus identify opportunities and explore new avenues of research. This important step can be arduous and complicated for new researchers. This paper aims to address the challenge faced by new researchers in conducting LRs, particularly in formulating search queries, identifying and selecting relevant studies, and extracting data from each paper, by proposing a methodological approach and applying it through a practical example in the context of using artificial intelligence in education. The approach is guided by the PRISMA (preferred reporting items for systematic reviews and meta-analyses) framework and focuses on recent studies conducted in 2021, 2022, and 2023. The proposed approach identified 52 out of 336 relevant studies. 65% of these studies were deemed to be of high quality (Q1 and Q2 rankings), and 40% of the articles were published in high-impact academic journals (Q1). This approach is versatile and can be adapted to different fields.Int. J. Eng. Pedagog.00This paper aims to address the challenge faced by new researchers in conducting LRs, particularly in formulating search queries, identifying and selecting relevant studies, and extracting data from each paper, by proposing a methodological approach and applying it through a practical example in the context of using artificial intelligence in education.Int. J. Eng. Pedagog.["Mohamed El Jihaoui", "O. Abra", "Khalifa Mansouri", "Moulay El Houssine Ech-Chhibat"]2024-10-10T00:00:00 3989f24c4ae638fc0ec269e16c292a90936ce498In the contemporary digital era, the swift evolution of Artificial Intelligence (AI) has not only revolutionized various sectors but has also become an indispensable asset in corporate strategic planning, decision-making processes, and operational efficiency. This paper delves into the multifaceted applications of AI across a myriad of industries, providing a comprehensive analysis of its integration into corporate strategy and its profound impact on business operations. This study adopts a comprehensive research methodology that merges qualitative case studies with quantitative data analysis to scrutinize the practical use of AI in strategic planning. It delves into the interdependent relationship between AI and conventional digital frameworks, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems. The research aims to illuminate how the fusion of these technologies can substantially enhance corporate competencies and optimize operational workflows. The study's findings indicate that while AI has the potential to offer transformative solutions, its integration into strategic planning is not without challenges. Ethical considerations, legal ramifications, and societal implications must be carefully navigated. Additionally, the study reveals a notable void in existing literature concerning the enduring strategic ramifications of AI and its effects on corporate culture and talent management practices. This gap highlights the need for further exploration into how AI shapes the long-term strategic direction and internal dynamics within organizations.Transactions on Economics, Business and Management Research140The study's findings indicate that while AI has the potential to offer transformative solutions, its integration into strategic planning is not without challenges and the need for further exploration into how AI shapes the long-term strategic direction and internal dynamics within organizations.Transactions on Economics, Business and Management Research["Danni Li"]2024-10-10T00:00:00 686c91b1d4011baeaa99990a0d97f2ebbf31cf32The increasing integration of Artificial Intelligence (AI) in various industries has led to concerns about how these systems can perpetuate discrimination, particularly in fields like employment, healthcare, and public policy. Multiple academic and business perspectives on AI discrimination, focusing on the need for global policy coordination and ethical oversight to mitigate biased outcomes, ask for our technical innovators to create contingencies that will better protect humanity’s experience with AI’s ever-expanding reach. Central to the key constructs such as biased datasets, algorithmic transparency, and the global governance of AI systems can function as a harmful drawback to these systems. Without adequate data governance and transparency, AI systems can perpetuate discrimination. AI's ability to discriminate stems primarily from biased data and the opacity of machine learning models, necessitating proactive research and policy implementation on a global scale. These frameworks must transcend the limitations of the experiences or perspectives of their programmers to ensure that AI innovations are ethically sound and that their use in global organizations adheres to principles of fairness and accountability. This synthesis will explore how these articles advocate for comprehensive, continuous monitoring of AI systems and policies that address both local and international concerns, offering a roadmap for organizations to innovate responsibly while mitigating the risks of AI-driven discrimination.IgMin Research40This synthesis will explore how these articles advocate for comprehensive, continuous monitoring of AI systems and policies that address both local and international concerns, offering a roadmap for organizations to innovate responsibly while mitigating the risks of AI-driven discrimination.IgMin Research["Hunter Destiny J"]2024-10-10T00:00:00 c51685ef43e2f64e027b86a5867137b7fc498f66The research presented in this paper originated from my master's thesis, and I have chosen to publish it together with my supervisor, who is the second author, to contribute to the existing body of knowledge. The technology known as the Internet of Things (IoT) continues to expand the current Internet infrastructure by facilitating connections and interactions between the physical and cyber worlds. IoT and its associated applications have significantly enhanced the quality of life on Earth. Advanced wireless sensor networks and their revolutionary computing capabilities have paved the way for various IoT applications to explore new frontiers, impacting nearly every aspect of daily life. Concurrently, the imperative of energy optimization has emerged as a major concern, driving the adoption of sustainable practices and green technologies. The fusion of Artificial Intelligence (AI) with IoT represents a potent combination, enabling the realization of unique projects and innovative solutions. The potential impact of IoT and AI is vast, promising transformative changes in the future landscape. Recognizing the magnitude of these advancements, the European Commission is committed to collaborating with partners and authorities in the Western Balkans to fully implement the digital agenda. To this end, the EU and Western Balkans ICT Dialogue Initiative, established by the Commission in cooperation with regional partners, will oversee the implementation of the Digital Agenda.Journal of Natural Sciences and Mathematics of UT-JNSM00Journal of Natural Sciences and Mathematics of UT-JNSM["Enes Bajrami", "F. Halili"]2024-10-10T00:00:00 39459321d41ec488bc780214f2aec45da5b40ccbNetherlands Heart Journal490An overview of the recent developments of AI in CCTA is offered, covering methodological advances for coronary artery tree and whole heart analysis, and an overview of AI techniques that have shown to be valuable for the analysis of cardiac anatomy and pathology in CCTA.Netherlands Heart Journal["Rudolf L. M. van Herten", "Ioannis Lagogiannis", "Tim Leiner", "Ivana I\u0161gum"]2024-10-10T00:00:00 887f7a24b1bf71bbd747216cc9bd2067725fb68fClimate change and the advent of artificial intelligence‐generated content are reshaping wine marketing. The interplay between consumer focus on naturalness and sustainable farming practices and the proliferation of artificial intelligence‐generated content represents a particularly salient area of research. However, the extent to which the presence of fictitious artificial intelligence‐generated labels and backgrounds impacts consumers' willingness to buy and pay for wine has yet to be addressed. This research contributes to the growing body of literature on consumer susceptibility to sustainability signaling and artificial intelligence greenwashing, focusing on the impact of backgrounds and labels with different degrees of perceived naturalness. Three experiments demonstrate that wines bearing artificial intelligence‐generated sustainability labels and third‐party accredited sustainability labels reliably exhibit an increased willingness to buy and pay compared to those without sustainability labels. These findings indicate that fictitious, artificial intelligence‐generated, and accredited labels are equally effective in influencing consumer wine choices. Customer susceptibility to food labels and wine knowledge and involvement also significantly predict willingness to buy across studies, validating the Customer Susceptibility to Front‐of‐Package Food Labeling scale. These findings highlight the necessity for future studies to investigate the role of responsible labeling, the susceptibility of customers to such labels, and the potential hazards associated with greenwashing practices involving artificial intelligence‐generated labels.Psychology &amp; Marketing630Findings indicate that fictitious, artificial intelligence‐generated, and accredited labels are equally effective in influencing consumer wine choices, and customer susceptibility to food labels and wine knowledge and involvement significantly predict willingness to buy across studies, validating the Customer Susceptibility to Front‐of‐Package Food Labeling scale.Psychology &amp; Marketing["Valdimar Sigurdsson", "N. Larsen", "M. Folwarczny", "Magalie Dubois", "A. Fagerstr\u00f8m"]2024-10-10T00:00:00 b4eb3ae1bf48ee07bd6769bd208dd879a79f2fe8Journal of Human Rights and Social Work1280An integrated model of Artificial Intelligence Enhanced Social Work (or “Artificial Social Work”), which proposes a marriage of social work practice and artificial intelligence tools is proposed, based on the findings and informed by the triple mandate and the human rights framework.Journal of Human Rights and Social Work["Michael Garkisch", "Lauri Goldkind"]2024-10-10T00:00:00 010332a0f2477478da5540529f7f07bdeba22222Integrating Artificial Intelligence (AI) into anesthesia has transformed perioperative care offering enhanced precision, real-time decision support, and effective predictive analytics beyond human capabilities. AI is now being frequently used in preoperative assessments, intraoperative monitoring, postoperative management, and has improved patient safety and outcomes. AI implementation is not without challenges, and certain issues persist, such as data quality, algorithmic transparency, potential biases, and ethical concerns that are related to patient privacy and patient autonomy, which pose significant hurdles to clinicians. Beyond its limitations, AI’s potential has also revolutionized anesthesia immensely. AI has promised for bright future where anesthetic care will be more adapted and effectiveInternational Journal of Clinical Anesthesiology00International Journal of Clinical Anesthesiology["Shubha Srinivasareddy"]2024-10-10T00:00:00 11c48456872737b13102fd5b5331e783ce01f660Artificial intelligence is preferred in medical imaging applications for reasons such as early diagnosis of diseases, reducing the workload of experts, and resolving conflicting expert opinions. This paper investigates the increasing and reshaping impact of artificial intelligence in the field of medical imaging and aims to explain how these developing technologies can improve medical diagnosis and treatment. Artificial intelligence, which plays a critical role in the advancement of medical technology, exhibits superior capabilities in the analysis and interpretation of medical images. The benefits of artificial intelligence in the field of medical image processing include increasing the speed and accuracy of diagnosis, increasing the effectiveness of treatment, and reducing medical errors. However, this field faces challenges such as privacy, security, and human interaction, which must be carefully addressed to ensure the success of the new technology. The paper also points out the future development trends of artificial intelligence in the field of medical image processing.Medical Technologies National Conference00This paper investigates the increasing and reshaping impact of artificial intelligence in the field of medical imaging and aims to explain how these developing technologies can improve medical diagnosis and treatment.2024 Medical Technologies Congress (TIPTEKNO)["M. Sezdi", "Necdet Tu\u011frul Artu\u011f"]2024-10-10T00:00:00 208355b279689898af13ecd9e142e6a40f49af0cThis article explores the impact of artificial intelligence (AI) on society through the lens of technological determinism and singularity theories. Technological determinism is the notion that technology shapes and controls society and human behaviour. Singularity is a theory that asserts that AI has already become a million times smarter than humans and can self-improve beyond what humans first taught AI applications and machines. The Singularity Theory predicts an intelligence explosion from Artificial General Intelligence soon, in which humans are likely to lose the dominion that they have enjoyed since creation, millions of years ago. AI, in its generative and autonomous or selfimproving state or form, may lead to the automation of many tasks currently performed by humans. This could lead to both benefits and challenges, such as increased efficiency but also job losses. In addition, the article discusses the impact of AI on privacy and raises ethical concerns about the potential misuse when in the hands of bad people. It also discusses ways to ensure that AI is used responsibly and beneficially. This includes governmental authorities developing ethical guidelines for AI development and implementation and ensuring that AI systems are safe, transparent and accountable.Kuveza neKuumba: The Zimbabwe Ezekiel Guti University Journal of Design, Innovative Thinking and Practice00The article discusses the impact of AI on privacy and raises ethical concerns about the potential misuse when in the hands of bad people, and ways to ensure that AI is used responsibly and beneficially.Kuveza neKuumba: The Zimbabwe Ezekiel Guti University Journal of Design, Innovative Thinking and Practice["Chrispen Musekiwa", "Persistence Muunga", "Ferdinand Kabote"]2024-10-10T00:00:00 65d2ee8228d6ebcde6a032a81183e8f4e6cd97c5Artificial intelligence was presented for the first time in 1956 by John McCarthy -an American scientist in the field of computer systems- at the Dartmouth conference. The issue of intelligence as a basic feature that causes individual differences between people has been noticed for a long time. The field of attention to the intelligence factor can be observed in different sciences. Artificial intelligence is a branch of computer science that is used to improve the performance of computer devices and the interaction between devices and humans. Artificial intelligence will change the way networks are managed and this is the change we need. Artificial intelligence makes network operations simpler, smarter, safer and faster. In other words, it can be said that AI helps us to manage our networks with the speed of a machine. The results of the tests showed that a person's verbal skill is his best mental ability. It is interesting that later verbal skill was recognized as one of the main factors of mental ability, and even today, the content of most intelligence tests is verbal material.Edelweiss Applied Science and Technology80Artificial intelligence is a branch of computer science that is used to improve the performance of computer devices and the interaction between devices and humans.Edelweiss Applied Science and Technology["Areej Abdulkareem Abdulraheem Alsaati"]2024-10-10T00:00:00 394608da9d7e51f4723a414b823d0a22304104d7This study conducts a thorough analysis of the ethical dimensions arising from the convergence of artificial intelligence (AI) and human rights concerns. Delving into existing literature, it scrutinizes the potential risks and advantages inherent in artificial intelligence technologies concerning fundamental human rights. The examination extends beyond mere technological advancements to encompass the intricate ethical, legal, and social ramifications emerging in tandem with AI progress. Through synthesizing insights from diverse sources, this article endeavors to elucidate the multifaceted relationship between technology and human rights in the contemporary digital landscape. By critically evaluating the ethical implications, it seeks to deepen our comprehension of the challenges posed by AI deployment and its impact on human rights paradigms. This comprehensive review underscores the imperative to navigate the intricate terrain of artificial intelligence ethics, considering its implications for fundamental human rights. By shedding light on the complexities inherent in this intersection, the study contributes to a nuanced understanding of the ethical imperatives guiding AI development and deployment in the digital age. Additionally, it emphasizes the need for proactive measures to safeguard human rights in the face of advancing AI technologies, advocating for robust ethical frameworks and regulatory mechanisms to uphold fundamental rights in AI-driven societies.Journal of Natural Sciences and Mathematics of UT-JNSM00This study conducts a thorough analysis of the ethical dimensions arising from the convergence of artificial intelligence (AI) and human rights concerns, advocating for robust ethical frameworks and regulatory mechanisms to uphold fundamental rights in AI-driven societies.Journal of Natural Sciences and Mathematics of UT-JNSM["Ermira Memeti", "Florinda Imeri", "Valbon Ademi", "Shkurte Luma-Osmani", "Enes Bajrami"]2024-10-10T00:00:00 902e23544a14350fbd077e156231529108b92e4fThe adequate and quality management of healthcare services falls within the scope of states’ positive obligation to protect the right to life. Pre-hospital care, due to its urgent intervention requirements, is a critical stage in fulfilling this obligation. As in all fields today, the use of technology and artificial intelligence (AI) in medicine is steadily increasing. In the field of healthcare, where the protection of personal autonomy, the use of sensitive personal data, and the potential for malpractice to lead to violations of bodily integrity are crucial, the use of AI must be legally safeguarded for the benefit of society and individuals. Research suggests that the more effective use of AI in pre-hospital healthcare services and ambulances for rapid diagnosis, determining appropriate treatment plans, and early intervention can increase patients’ chances of survival. On the other hand, the legal implications related to human rights, medical ethics, and malpractice are being debated. In this context, the policies and principles are being shaped by World Health Organization (WHO), and The European Union’s (EU) Artificial Intelligence Act has been adopted in 2024 as the first regulation that expected to inspire other countries. This study, which employs literature review and content analysis methods, aims to examine the use of AI in pre-hospital healthcare services in light of legal developments, within the context of AI’s growing role in healthcare services. In conclusion, although the EU Artificial Intelligence Act regulates emergency healthcare services as a high risk area and imposes strict rules, it would be beneficial to develop detailed special regulations concerning healthcare services, particularly in situations where fully autonomous AI is used, in terms of protecting individual rights, ensuring compliance with medical ethical rules, and determining legal responsibility in cases of malpractice.Medical Technologies National Conference002024 Medical Technologies Congress (TIPTEKNO)["Zeynep \u015ei\u015fli", "M. K\u0131z\u0131l"]2024-10-10T00:00:00 8f94d88bfdfa0a7b8a584b38ee4df45f3b4d988bThis article examines the potential for implementing artificial intelligence (AI) technologies in the education system of Kazakhstan. It focuses on how AI can contribute to the individualization of learning, improve the quality of education in remote regions, support teachers, and develop digital literacy among students. Key areas of AI use are analyzed, such as the automation of routine tasks, the creation of personalized educational programs, distance learning, and the development of educational materials in the Kazakh language. It also discusses the challenges associated with the use of AI and predicts its long-term effects on the development of the country's educational system.Yessenov Science Journal00How AI can contribute to the individualization of learning, improve the quality of education in remote regions, support teachers, and develop digital literacy among students is focused on.Yessenov Science Journal["B. Zholdigaly", "L. Zhumabayeva", "E. Abdykerimova"]2024-10-10T00:00:00 633bab5f171ff2cd869853e5018c5d506f35519dArtificial Intelligence (hereinafter referred to as ‘’the AI’’) becomes widely present in numerous spheres of society including law. It affects human rights and freedoms, especially where their exercise and respect belong to public sphere, controllable more or less by the public authorities, such as social media. Freedom of expression in the light of AI can be either supported or limited. Risks such as manipulation, unrecognized hate speech, deepfakes should be minimized using the AI. AI regulation has recently been the priority of the European Union and the Council of Europe. It has resulted, inter alia, in AI Act of the European Union and the Framework Convention on Artificial Intelligence of the Council of Europe. This paper analyses the impact of artificial intelligence to freedom of expression, its benefits and dangers, possible solutions in supervising the AI practices, and legislative framework of the AI.ZBORNIK MES00ZBORNIK MES["Jasna \u010co\u0161abi\u0107"]2024-10-10T00:00:00 b1338aafe006e1a2cedd1d76d721113a1249b737Introduction: this article presents the findings of a study examining the criminological risks associated with artificial intelligence (AI) technologies and the risk factors that contribute to their development. Materials and Methods: the research materials included the following: The legislation of the Russian Federation on the risk-based approach to the development of artificial intelligence; sociological theories that support the concept of risk; criminal law and criminological studies. The research employed established methods, including the universal dialectical method, general scientific methods (analysis, synthesis, deduction, induction, and generalization), and specific scientific methods (formal legal and sociological). Literature review: a review of the literature was conducted, encompassing the works of Russian scientists in the field of criminal policy, the consideration of sociological theories, and the examination of the outcomes of practical experience derived from foreign research. Results: the study's findings indicate that a risk-based approach to criminological research is a necessary development, based on an analysis of sociological theories and law enforcement practices. The author provides a detailed elaboration of the concept of "criminological risk" and underscores the distinctive characteristics of risks associated with artificial intelligence technologies, as well as identifies the risk factors that characterise these technologies. Discussion and Conclusions: the modern society is confronted with an array of risks that have emerged alongside technological advancement. The advent of artificial intelligence (AI) technologies has given rise to significant concerns. A risk-based approach to criminological research may prove an effective means of mitigating the risks associated with these technologies.Bulletin of the Kazan Law Institute of MIA Russia80A study examining the criminological risks associated with artificial intelligence technologies and the risk factors that contribute to their development indicates that a risk-based approach to criminological research is a necessary development, based on an analysis of sociological theories and law enforcement practices.Bulletin of the Kazan Law Institute of MIA Russia["Danil Budko"]2024-10-10T00:00:00 ad9bdfbfa195aa2b29c573f431557ca35b9a90e9Purpose: This study aims to investigate the reality of employing artificial intelligence applications and its challenges in teaching children with learning Disabilities from the point of view of teachers and specialists in Amman- Jordan schools.   Theoretical Reference: The study is grounded employing artificial intelligence applications and its challenges in teaching children with learning Disabilities from the point of view of teachers and theories of learning disabilities.    Method: The descriptive analytical approach was followed,  The researcher prepared a study A self-designed measure of employing artificial intelligence applications  in the education of children with learning Disabilities  from the point of view of teachers and the challenges they face was administered to special education teachers in capital Amman city, Jordan, focusing on their.   The questionnaire underwent validity and reliability assessments to ensure its appropriateness for the study. Statistical analyses, including means, standard deviations, and t-tests, were employed to analyze the data.    Results and Conclusion: The   study results found that learning disabilities teachers demonstrated a medium  level of knowledge regarding reality of hiring teachers with learning Disabilities for artificial intelligence applications. However, while the results indicated that the challenges facing teachers came with a high degree.   Implications of Research: The findings suggest a need for targeted professional development and support for special education teachers to enhance their effectiveness employing artificial intelligence applications  in teaching children with learning Disabilities  . Policymakers and educational leaders could use these insights to develop comprehensive strategies and resources that empower teachers and promote a safer learning environment for all students.   Originality/Value: The research contributes to understanding the use AI applications for children with learning disabilities. It raises awareness among officials about artificial intelligence applications and accessibility for these children with learning disabilities.Journal of Lifestyle and SDGs Review150A need for targeted professional development and support for special education teachers to enhance their effectiveness employing artificial intelligence applications in teaching children with learning Disabilities is suggested.Journal of Lifestyle and SDGs Review["Munjed M. Najadat", "K. Obeidat"]2024-10-10T00:00:00 19718f6cfddc344a0277200ad4773f803bc01034THIS YEAR, the European Society of Cardiology (ESC) Congress 2024, which took place in London, UK, between 30th August–2nd September, hosted an insightful symposium entitled ‘Artificial intelligence unleashed on digital biomarkers: a new era in personalised cardiovascular healthcare’. The session explored the applications of AI in cardiac diagnostics, highlighting its potential to positively impact patient care. The speakers covered key topics, including how vascular retinal imaging could predict cardiovascular risk and whether speech analysis could aid in the detection of acute decompensated heart failure.EMJ Cardiology150The session explored the applications of AI in cardiac diagnostics, highlighting its potential to positively impact patient care and whether speech analysis could aid in the detection of acute decompensated heart failure.EMJ Cardiology["Katie Wright"]2024-10-10T00:00:00 e29d7ddc42b97fbda6e35adf1dd2915171945cc2In an increasingly automated and digital world, artificial intelligence (AI) has the potential to revolutionize various sectors of society. However, its development and use raise ethical questions, especially related to privacy, autonomy and discrimination. This research seeks to analyze the ethical challenges of AI considering the Brazilian and international regulatory environment. The research uses descriptive research methodology, based on a bibliographical review of scientific and documentary literature on AI and its regulation. It examines the pillars for an ethical use of AI, highlighting transparency, explainability, fairness and responsibility as fundamental to a regulatory system aligned with human rights. The research aims to contribute, albeit briefly, to the debate on ethics in AI.Interfaces Científicas - Humanas e Sociais00This research seeks to analyze the ethical challenges of AI considering the Brazilian and international regulatory environment, and examines the pillars for an ethical use of AI, highlighting transparency, explainability, fairness and responsibility as fundamental to a regulatory system aligned with human rights.Interfaces Científicas - Humanas e Sociais["Roberta Hora Arcieri Barreto", "Clara Cardoso Machado Jaborandy", "Carolina Silva Porto"]2024-10-10T00:00:00 8b8861ade6e10e261333a6ec6a100917be9f84f6This article examines the role and future of artificial intelligence (AI) in inclusive education. Inclusive education provides equal educational opportunities for all students, including children with special needs. Technology allows teachers to create curricula adapted to the individual needs of each student, optimize the learning process and improve feedback and evaluation systems. The article discusses the potential of AI in inclusive education, its advantages in improving student academic achievement and personalizing the learning process. In addition, the problems and ethical dilemmas that arise when using AI, including issues of data privacy and social inequality, are considered. The results of the study show that improving inclusive education through the effective use of AI technologies is promising and opens up new opportunities in the education system.Yessenov Science Journal00The results of the study show that improving inclusive education through the effective use of AI technologies is promising and opens up new opportunities in the education system.Yessenov Science Journal["B. A. Zhumazhan", "M. Zhumadilova", "E. Abdykerimova"]2024-10-10T00:00:00 be99b73c2d80d0d8da0a463c3fb5136435e4a120 Steven J. Brams, hailing from New York University, unveils a game that poses a challenge to artificial intelligence. Artificial intelligence (AI) has enormous promise and huge risk. It is poised to become a significant disruptive force. However, much of the analysis of AI in the popular media leans on hype rather than fact. Understanding how AI functions, along with appraising some of its strengths, blind spots, and weaknesses, may help to demystify this technology and allow us to assess its risks and potential more realistically. Open Access Government00Understanding how AI functions, along with appraising some of its strengths, blind spots, and weaknesses, may help to demystify this technology and allow us to assess its risks and potential more realistically.Open Access Government["S. Brams"]2024-10-10T00:00:00 3bc3dafa9482621f992eb2c37296bc4ff6a8b4aaProfessional Psychology: Research and Practice01Professional Psychology: Research and Practice["Ryan L. Farmer", "Adam B. Lockwood", "Anisa Goforth", "Christopher Thomas"]2024-10-10T00:00:00 d3e7a4c4cf063ad95a16accec58027936a6af47eConference on Information Technology Education130{"pages": "68-73"}["Daniel Kim", "Jue Wu"]2024-10-10T00:00:00 3d6a306242b39894ebcbacb9fb43e0ede6da1288The Primary Care Companion For CNS Disorders00The primary care companion for CNS disorders["K. Ahuja", "P. Lio"]2024-10-10T00:00:00 fdebe42e351bb2549a9b7f7008a8f096e1e3029eThe impact of stress on daily life has been a subject of interest in the last decades. The utilization of numerous electrical and electronic devices as well as increased land and air transportation densities constantly create noise which is a significant contributor to stress. In this study, the relationship between environmental noise, cognitive workload, and stress is investigated. Electroencephalogram (EEG) and photoplethysmogram (PPG) signals of 30 volunteers were recorded simultaneously while performing a 2-back task with different background noise levels. Features were then extracted from the processed signals to be classified with various machine learning algorithms. Results show that medium noise levels result in increased accuracy for the 2-back task which indicates keeping the noise levels at an acceptable level would be better for work and learning environments.Medical Technologies National Conference00Results show that medium noise levels result in increased accuracy for the 2-back task which indicates keeping the noise levels at an acceptable level would be better for work and learning environments.2024 Medical Technologies Congress (TIPTEKNO)["Emre Sipahioglu", "Burak Akbugday", "Sude Pehlivan Akbugday", "Aydin Akan"]2024-10-10T00:00:00 6878f3f959ae0e8f76a3744ff936b83b0b02a142Revista de Direito Internacional00Revista de Direito Internacional["I. Walia"]2024-10-10T00:00:00 6d598a55bd563d64751f8298658886c1b7540bbaAI and Ethics280AI and Ethics["Sai S. Kurapati", "AnzTtonio Yaghy", "A. Shukla"]2024-10-10T00:00:00 ba00e04673404ee57809f3f3bde52d99aa556038MANTHAN Journal of Commerce and Management00MANTHAN: Journal of Commerce and Management["Fathima Febeena"]2024-10-10T00:00:00 302b7a28c3665aeb6bca73fbbaac9f5c5090090bThe article is devoted to the problem of using robots in the educational process in the training of specialists. The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry.Yessenov Science Journal00The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots.Yessenov Science Journal["A.Zh. Kintonova", "B.B. Suleimenova", "A. K. Shangytbayeva"]2024-10-10T00:00:00 dcc45d2ff15390ab4752b1391cf75f69dafd1ad5The significant spread of information technology (IT) has led to the emergence of artificial intelligence (AI) systems important to society. Their importance will grow. Along with this, attempts to destroy these systems have appeared and greatly increased. This in turn has led to the development of systems to resist cyber attacks. A new scientific and applied direction - cyber security - was formed. In the present work, some issues of cyber security are considered and discussed in order to ensure a sufficiently high degree of protection of AI systems throughout their life cycle. The main requirements for cyber security systems, their effective construction and operation are discussed. The paths for future development of cyber security systems - in research and applied aspects - are also indicated. It is proposed to build a society responsible for continuous improvement of cyber security systems.International Conference on Applied Informatics150In the present work, some issues of cyber security are considered and discussed in order to ensure a sufficiently high degree of protection of AI systems throughout their life cycle.2024 International Conference Automatics and Informatics (ICAI)["Vassil Sgurev", "L. Doukovska"]2024-10-10T00:00:00 6fc0cf9d4f7bdbdba97d9fd6723be8274dfcff77Current era of development, AI technology plays a big role in creating content in a short time. With the current AI technology, anyone can create an interesting content, because AI (Artificial Intelligence) technology, offers a new discovery when creating content on social media, this technology can also speed up the process of editing and creating content in the form of images, graphics, videos, etc..This research method uses Descriptive Qualitative method. This research approach is Phenomenology, which is the general focus of this research to examine/examine the essence or structure of experience into human consciousness by means of interviews.The role of AI (Artificial Intelligence) in content is to simplify and shorten the time in creating interesting and creative content. When explored more deeply, AI technology has many benefits and has a role in a content in the form of images or videos. This AI technology can make it easier for us to create interesting content, but keep in mind that applying it requires original creativity that comes from human ideas to create interesting content.Jurnal Inovasi Komunikasi70The general focus of this research is Phenomenology, which is the general focus of this research to examine/examine the essence or structure of experience into human consciousness by means of interviews.Jurnal Inovasi Komunikasi["Maria Indriani Kurnia", "Detya Wiryani", "Maudy Rizkiana Poedjadi"]2024-10-10T00:00:00 6e07e28b907065ebf0ba52978bafbd970d54a621Objective: The paper addresses how artificial intelligence can play this distinctive role at present and the vision for its development in the future.   Theoretical Framework: The paper addresses the advantages enjoyed by human judges regarding cases, issuing judicial rulings and decisions, and resolving disputes. Additionally, it will discuss artificial intelligence's place in this area and the benefits it can bring to the table to promote fairness, impartiality, and objectivity in court decisions.   Method: The analytical approach was used to extract the working mechanisms of artificial intelligence in this field and identify cases of bias based on practical practice and the experiences of some countries, such as the United States of America and China.   Results and Discussion: Artificial intelligence plays a significant role in all fields, including legal and judicial. However, we cannot rely on the AI systems 100% and replace human judges.   Research Implications: This paper examines the controversy surrounding replacing human judges with other robotic judges supported by artificial intelligence technology, powerful algorithms, and big data.   Originality/Value: This study contributes to the literature by explaining the role of Artificial intelligence and how AI can play a significant role in all fields, including legal and judicial.Journal of Lifestyle and SDGs Review135How artificial intelligence can play this distinctive role at present and the vision for its development in the future is addressed and cases of bias based on practical practice and the experiences of some countries are identified.Journal of Lifestyle and SDGs Review["K. Aboelazm", "Khalid Mohamed Dganni", "Fady Tawakol", "Hanadi Sharif"]2024-10-10T00:00:00 83a8e12523294562e329b9101d37453b5efb9f2dAI &amp; SOCIETY361This article advocates for a more nuanced analysis of AI imaginaries, distinguishing “strong AI narratives,” i.e., narratives that envision futurable AI technologies that are virtually indistinguishable from humans, from "weak" AI narratives, i.e., narratives that discuss and make sense of the functioning and implications of existing AI technologies.AI &amp; SOCIETY["Paolo Bory", "Simone Natale", "Christian Katzenbach"]2024-10-10T00:00:00 9462bf9ea4e9210f5bda630e06f943a557308acfHealthcare's integration with the emerging artificial intelligence, predictive analytics and health information exchange (HIE) system is currently undergoing a revolution. These emerging technologies are offering better diagnostic accuracies, more personalized treatments, and better patients' outcomes. Artificial Intelligence supported by data from either Electronic Health Records or other big data sources are increasing the accuracy of forecasting critical patient conditions such as disease developments, hospital readmission rates, and mortality risks in patients. These AI-empowered models ensure seamless data sharing between the care providers and promote operational interoperability. Challenges to overcome include issues of privacy of the data, the biases of the prediction algorithms, and the proper integration of the model into the currently established operational environments of healthcare providers. With these technologies combined, it will become possible to optimize patient's outcomes with the provision of data to empower real-time medical interventions.International Scientific Conference on Power and Electrical Engineering of Riga Technical University231Artificial Intelligence supported by data from either Electronic Health Records or other big data sources are increasing the accuracy of forecasting critical patient conditions such as disease developments, hospital readmission rates, and mortality risks in patients.2024 IEEE 65th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON)["J. Janjua", "Taher M. Ghazal", "W. Abushiba", "Sagheer Abbas"]2024-10-10T00:00:00 80f0f209bd03dcce843ee44dd921306f9e27679eArtificial intelligence (AI) is rapidly transforming the landscape of neurology, offering innovative solutions for diagnosing and managing emergent neurological conditions such as stroke, traumatic brain injury, and acute spinal cord injury. This review critically examines the recent advancements in AI applications within the field of neurology, emphasizing both the potential and limitations of these technologies. While AI demonstrates remarkable accuracy and speed in diagnostic imaging, outcome prediction, and personalized treatment plans, its integration into clinical practice remains challenged by ethical concerns, infrastructural limitations, and the “black box” nature of many AI algorithms. The review highlights the current gaps in literature, particularly the limited research on AI’s use in low-resource settings and its generalizability across diverse populations. Moreover, the review underscores the need for more longitudinal studies to assess the long-term efficacy of AI-driven interventions and calls for greater transparency in AI systems to enhance trust among clinicians. Future directions for AI in neurology emphasize the importance of interdisciplinary collaboration, regulatory oversight, and the development of equitable AI models that can benefit all patient populations. This review provides a balanced and comprehensive overview of AI’s role in neurology, offering insights into both the opportunities and challenges that lie ahead.Frontiers in Neurology451This review critically examines the recent advancements in AI applications within the field of neurology and underscores the need for more longitudinal studies to assess the long-term efficacy of AI-driven interventions and calls for greater transparency in AI systems to enhance trust among clinicians.Frontiers in Neurology["Majd A. AbuAlrob", "B. Mesraoua"]2024-10-11T00:00:00 486446f93cdab0d89e783627e2c8106ac6be6a3eNature Human Behaviour1042It is found that the use and benefits of AI appear widespread throughout the sciences, growing especially rapidly since 2015, however, there is a substantial gap between AI education and its application in research, highlighting a misalignment between AI expertise supply and demand.Nature human behaviour["Jian Gao", "Dashun Wang"]2024-10-11T00:00:00 8a1d35148135e4cd7664d17fdad96d32156342dcPurposeThis study investigates the determinants of artificial intelligence (AI) acceptance in and AI-driven transformations of corporate communications. From a technology adoption perspective, the study explores the dual influence of individual and organizational factors on AI acceptance.Design/methodology/approachEmploying a qualitative research design, this study conducted semi-structured interviews with 19 AI experts in large-scale companies in Germany.FindingsThe study reveals micro-level determinants of AI acceptance related to AI’s perceived usefulness and ease of use. It also identifies macro-level determinants, including organizational awareness and frameworks. Corporate communications is expected to gain relevance due to the organizational integration of AI.Research limitations/implicationsThe proposed model integrates crucial factors influencing AI adoption and offers a starting point for quantitative validation. The study serves as a benchmark for future research, particularly given its timing right before the extensive adoption of ChatGPT.Practical implicationsOrganizations are encouraged to develop strategies that enhance both individual and organizational AI readiness. By reflecting both micro- and macro-level determinants of AI acceptance, a more holistic understanding of effective change management initiatives related to AI integration can be fostered.Originality/valueBy proposing an extension to the technology acceptance model, which incorporates both micro- and meso-level determinants, this study provides a novel framework for holistically understanding AI acceptance in corporate communications.Corporate Communications. An International Journal341By proposing an extension to the technology acceptance model, which incorporates both micro- and meso-level determinants, this study provides a novel framework for holistically understanding AI acceptance in corporate communications.Corporate Communications: An International Journal["Karolin Kelm", "Michael Johann"]2024-10-11T00:00:00 e0d0bb2408bad2891d1d879bc3e79d441c505cb1Artificial Intelligence (AI) stands as the most transformative technology since the Industrial Revolution. As AI is integrated into different domains of life, it is likely to also permeate politics. In fact, some voices already advocate for AI to make political decisions. This paper emphasizes the necessity to delve into the psychosocial determinants shaping support for supporting a technocracy of AI (TecAI). The research aims to ascertain whether anomie, moderated by NCC and SDO, predicts support for TecAI across three studies involving Spanish participants (Ntotal = 754). The findings confirm anomie's predictive power on support of TecAI, albeit without establishing causality consistently, as anomie's condition failed to alter support for TecAI in experiments 2 and 3. However, an exploratory analysis showed a small effect of the subdimension of “breakdown of leadership” on support for TecAI in a directional test, informing future research. Moreover, NCC and SDO did not significantly moderate the anomie‐support‐for‐TecAI link. Ideology also emerged as a consistent predictor, with conservatives consistently showing more support, a finding that ought to be re‐examined and clarified. This study marks an initial empirical exploration into AI's political perceptions, hinting at the need for further investigation into this critical domain.Political Psychology510Political Psychology["Marcos Dono", "Eva Moreno\u00a0Bella"]2024-10-11T00:00:00 dfb9e9a4e94c4276d9f2f4aeb208d921b2e40fd4The subject of the research is the processes of transformation of modern local self-government public administration based on the development and application of AI-based technologies. The article claims that the rapid development and advancement of digital technologies, especially artificial intelligence (AI) technologies, made significant impact on public governance at the local level, both in its ‘architecture’ and processes. It is shown that AI encompasses a range of capabilities that are accelerating progress toward the sustainable development and SDGs, while at the same time it may also inhibit some targets within sustainable development. It is emphasized that increasing government’s AI maturity in local self-government requires pairing human and technical capabilities with strategy and governance.Edelweiss Applied Science and Technology490It is shown that AI encompasses a range of capabilities that are accelerating progress toward the sustainable development and SDGs, while at the same time it may also inhibit some targets within sustainable development.Edelweiss Applied Science and Technology["O. Rudenko", "Olena Zaika", "V. Varynskyi", "I. Kulchii", "Alina Myroshnychenko"]2024-10-11T00:00:00 1f148b4264df7949d280075fe8db49a2b07d801bThe objective of this research has been to evaluate the effect of the integration of Artificial Intelligence on the motivation of students, in the area of Chemistry of tenth grade of the Adventist School of Ibagué. To this end, a methodology with a qualitative approach and a Participatory Action Research design with four phases were used: planning, action, observation and reflection.  The information was collected through observation and interview techniques for which field diaries and structured script were used as research instruments. This information was analyzed with the Atlas.ti 23 software, the resulting codes allowed the definition of the categories: experience in the use of artificial intelligence, motivation and commitment, meaningful learning and perceptions about teaching and learning. The results obtained reflect an increase in student motivation and engagement, as well as a favorable impact on meaningful learning. They highlight the pedagogical potential of this resource, to diversify and enrich didactics in the teaching of chemistry. A positive perception by students towards the implementation of AI in the classroom was identified. It is concluded that the use of AI mediated with the design of pedagogical strategies to create learning environments and situations can improve the motivation of students, generating a space for knowledge and meaningful learning. However, challenges were also identified, such as the need for teacher and student training in the management and proper use of technological resources.  In addition, the importance of not considering artificial intelligence as a total replacement for the role of the teacher, but as a complementary tool that requires an adequate pedagogical approach, is highlighted.Evolutionary Studies in Imaginative Culture400It is concluded that the use of AI mediated with the design of pedagogical strategies to create learning environments and situations can improve the motivation of students, generating a space for knowledge and meaningful learning.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Leonardo Alberto", "Mauris De la Ossa", "Nury N. Garz \u00f3 n", "Celmira P \u00e9 rez", "Blanca E Pulido", "Jorge H Garc \u00ed a Herr \u00e1 n", "Hoyos"]2024-10-11T00:00:00 30e1e1632fe79bdc7a850c9499e13959d487a091 Poor dietary habits and a lack of understanding are contributing to the rapid global increase in the number of diabetic people. Therefore, a framework that can accurately forecast a large number of patients based on clinical details is needed. Artificial intelligence (AI) is a rapidly evolving field, and its implementations to diabetes, a worldwide pandemic, have the potential to revolutionize the strategy of diagnosing and forecasting this chronic condition. Algorithms based on artificial intelligence fundamentals have been developed to support predictive models for the risk of developing diabetes or its complications. In this review, we will discuss AI-based diabetes prediction. Thus, AI-based new-onset diabetes prediction has not beaten the statistically based risk stratification models, in traditional risk stratification models. Despite this, it is anticipated that in the near future, a vast quantity of well-organized data and an abundance of processing power will optimize AI's predictive capabilities, greatly enhancing the accuracy of diabetic illness prediction models. Current Bioinformatics00Artificial intelligence-based new-onset diabetes prediction has not beaten the statistically based risk stratification models, in traditional risk stratification models, but it is anticipated that in the near future, a vast quantity of well-organized data and an abundance of processing power will optimize AI's predictive capabilities, greatly enhancing the accuracy of diabetic illness prediction models.Current Bioinformatics["Rohit Awasthi", "Anjali Mahavar", "Shraddha Shah", "Darshana Patel", "Mukti Patel", "Drashti Shah", "Ashish Patel"]2024-10-11T00:00:00 2fa00d5eddb08883c8cd237b8c0f340ac99f963eInternet-mediated online communication, particularly with regard to a product, brand, or organization, is known as electronic word-of-mouth (eWOM). Analyzing this open exchange of opinions and information about a company or product among consumers can be extremely useful for businesses. Opinion mining, (sentiment analysis), is a popular subdomain in Natural language processing (NLP) which allows for transforming qualitative data into quantitative information. The sentiment analysis of eWOM has greatly improved with the advancement of artificial intelligence (AI). Nowadays, computer algorithms can automatically classify the sentiment polarity of digital communication after extracting plain text. Artificial intelligence (AI) has the potential to fundamentally alter how companies assess and use consumer feedback to improve their products and services. In this paper, the authors, by analysing the attitudes of 450 respondents, tried to bring this current topic closer to experts in the field of digital marketing, in order to point out to them all the benefits that the sentiment analysis of consumers with the help of artificial intelligence algorithms can provide. The aim of this study is to indicate that if marketing experts use sentiment analysis supported by artificial intelligence (AI), they will be able to gain deeper insights on their customers and adjust their business strategies accordingly.Bastina00If marketing experts use sentiment analysis supported by artificial intelligence (AI), they will be able to gain deeper insights on their customers and adjust their business strategies accordingly.Baština["Radoslav Baltezarevi\u0107", "Piotr B. Kwiatek"]2024-10-11T00:00:00 387e6966ceab08b0b1ad1c361f656e102d4b1ea5The electric power sector is one of the largest contributors to greenhouse gas emissions in the world. In recent years, there has been an unprecedented increase in electricity demand driven by the so-called Artificial Intelligence (AI) revolution. Although AI has and will continue to have a transformative impact, its environmental and health impacts are often overlooked. The standard approach to power grid optimization aims to minimize CO$_2$ emissions. In this paper, we propose a new holistic paradigm. Our proposed optimization directly targets the minimization of adverse health outcomes under energy efficiency and emission constraints. We show the first example of an optimal fuel mix allocation problem aiming to minimize the average number of adverse health effects resulting from exposure to hazardous air pollutants with constraints on the average and marginal emissions. We argue that this new health-based power grid optimization is essential to promote truly sustainable technological advances that align both with global climate goals and public health priorities.arXiv.org200This paper shows the first example of an optimal fuel mix allocation problem aiming to minimize the average number of adverse health effects resulting from exposure to hazardous air pollutants with constraints on the average and marginal emissions.ArXiv["Claudio Battiloro", "Gianluca Guidi", "Falco J. Bargagli-Stoffi", "Francesca Dominici"]2024-10-11T00:00:00 463157528817d341d30f586a61f3990aa1e5189cThree basic aspects of the operation of generative neural network models (generative artificial intelligence) are discussed in the article: the concept of a "token", the probabilistic nature of the generated response, and the concept of a "large model", the size of which ensures the pseudo-intelligent behavior of neural network chatbots. The issues of implementing generative models, areas and methods of their application are not discussed in principle.The materials of the "TRIZformashka-2024" competition, which was dedicated to neural network models, are provided. The fact of pseudo-intelligence of generative models is demonstrated. It turns out that a model trained on a single phrase "mama myla ramu" ("mama washed the frame") and using a context of a single letter for generation can sometimes behave as if it knows the rules of declension in the Russian language and is able to change a word by case!The concept of a "token" is considered in relation to the generation of texts, pictures and passwords. On the basis of "tokens" a practically useful method of generating passwords is built, difficult to guess, but easy to reproduce (difficult to forget).The concept of a "large model" is presented clearly and intelligibly due to its "visualization" by comparing it with physical quantities. (If one parameter of a neural network weighed one gram, then 200 freight trains would be needed to transport it. If one parameter had a length one millimeter, then the neural network would revolve around the Earth at the equator 25 times. If one second was required to learn one parameter, then it would be necessary to start training a modern neural network in the times of the Cro-Magnons.)The materials will be useful for studying generative artificial intelligence at any age.Informatics in school130Informatics in school["M. Plaksin"]2024-10-11T00:00:00 309f6a13cd5e9bef7014133c9a9e24f0c3f5c167Over the last several decades, there has been an arms race to acquire credentials as higher education has shifted from an elitist system to mass education. From an individual perspective, given the higher education system and labor market conditions, it is rational to pursue advanced qualifications. However, whether the education system delivers improvements in human capital or is principally a signaling mechanism is questionable. Estimates of the proportion of labor market rewards due to signaling range as high as 80%, suggesting that education is not only expensive but inefficient. In an increasingly transactional environment in which education providers are highly motivated by financial considerations, this situation is only likely to be exacerbated by the rapid developments in artificial intelligence (AI). The use of AI has the potential to make learning more effective, but given that many students see credential acquisition as transactional, it may reduce both human capital and the value of the signaling effect. If the credibility of the credentials offered is further damaged, the higher education sector in its present form and scale may well be unsustainable. We examine the evidence on credential inflation, returns to education, and mismatch of graduates to jobs before analyzing how AI is likely to affect these trends. We then suggest possible responses of prospective students, education providers, and employers to the growing adoption of AI in both education and the workplace. We conclude that the current offerings of generalist degrees, as opposed to vocational qualifications, are not sustainable and that to survive, even in a downsized form, the sector must respond to this disruptive technology by changing both the nature of its offerings and its methods of ensuring that the credentials they offer reflect genuine student learning.Sustainability410It is concluded that the current offerings of generalist degrees, as opposed to vocational qualifications, are not sustainable and that the sector must respond to this disruptive technology by changing both the nature of its offerings and its methods of ensuring that the credentials they offer reflect genuine student learning.Sustainability["W. R. J. Alexander", "Raffaella Belloni"]2024-10-11T00:00:00 453e22427b1778327a781885a2ef024a2299ab87Purpose: is to develop an innovative approach to risk management in public-private partnership (PPP) projects using advanced artificial intelligence technologies that allow creating the risk assessment model that takes into account non-linear relationships between various risk factors.Methods: in addition to traditional methods of scientific knowledge, interdisciplinary approaches of risk management and established practice of machine learning were used in the work. The methodological basis of the study was formed by works on the risk assessment and the application of AI algorithms in this area. The empirical basis of the study was the data of the official portal of ROSINFRA on public-private partnership projects.Results: the practice of applying AI algorithms to the task of assessing the risks of PPP-projects in Russia and abroad was studied. It is established that the most effective result is shown by the models based on Random-Forest-Classifier. However, the presented solutions do not take into account Russian economic realities. The authors have structured a database of implemented PPP-projects suitable for risk modeling. The model for assessing the risk of failure to achieve the objectives of Russian PPP-projects has been developed and its quality has been assessed. Recommendations on implementation of the model in the operational loop of PPP projects realization processes are offered.Conclusions and Relevance: the developed model allows, according to the general parameters of PPP project (region, authority, term of agreement, industry and scope of implementation, etc.) with the accuracy of 93% (according to the ROC\AUC metric), to assess the risk that the project will end incorrectly (due to a failed tender, refusal to launch, termination by court decision, cancellation/annulment of the tender). With the help of the model the executive authorities of the Russian Federation can build risk management for PPP projects management in the regions and thus contribute to their efficiency improvement. The article may also become useful for project management practitioners and appraisers.Multimedia Information Retrieval120The model for assessing the risk of failure to achieve the objectives of Russian PPP-projects has been developed and its quality has been assessed and its quality has been assessed.MIR (Modernization. Innovation. Research)["S. G. Sternik", "E. Tyutyukina", "A. Pomulev"]2024-10-11T00:00:00 c6a06d1d5e75e2df0b92ce692668d696ecc6c398The widespread use of neurotechnology and artificial intelligence technology in all spheres of our life has led to the emergence of new problems for civil law. Among them, the problem of determining the legal regime of intellectual property objects created with the help of these end-to-end digital technologies is of particular relevance. First of all, it is necessary to determine whether they protect such works, and if so, who is their copyright holder. The authors devote it to the analysis of possible solutions to the problem of granting protection to intellectual property objects created by neural networks and substantiating the author’s position on the possibility of forming a new exclusive right to such objects. The authors come to the conclusion that it is impossible and unnecessary to qualify artificial intelligence as a subject of law and the author of the created works and propose a new legal structure for the initial emergence of the exclusive right to objects created by neural networks from the subject using this technology, since it is its activity aimed at the operation of a device or software based on artificial intelligence that leads to the emergence of a new original result.Economy and law00It is impossible and unnecessary to qualify artificial intelligence as a subject of law and the author of the created works and a new legal structure is proposed for the initial emergence of the exclusive right to objects created by neural networks from the subject using this technology.Economy and law["Elena N. Abramova", "Elena V. Khamidullina"]2024-10-11T00:00:00 5616c8e5c1592fb5a24b9c54e420ef2b26b6d9b7Breakthroughs have been achieved recently in the application of artificial intelligence (AI) for the eye disease screening in Chinese primary healthcare institutions, but challenges have also emerged. First, AI software has continuously evolved, expanding the range of eye diseases that can be screened, enhancing diagnostic accuracy, and progressing towards predicting the course of eye diseases. However, inadequate infrastructure such as 5G and a shortage of specialized personnel have hindered the coverage of screenings. Second, while the cost-effectiveness of AI is well-established, new screening models have impacted the equity of screenings. It is essential to tailor AI application models to the specific context of China. Third, AI screening guidelines have been increasingly improved, providing direction for AI development and reference for the promotion and application of AI technologies. Nonetheless, high-quality empirical research is urgently needed to provide scientific evidence for policymaking related to AI in the eye disease screening. Therefore, it is suggested to develop multimodal AI models that integrate basic data such as symptoms and medical history with simple ophthalmic examinations, to accelerate the construction of infrastructure like 5G and focus on cultivating interdisciplinary talents, to explore suitable service systems and models for the large-scale eye disease screening tailored to local conditions, and to conduct long-term, multi-center, empirical studies.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology00High-quality empirical research is urgently needed to provide scientific evidence for policymaking related to AI in the eye disease screening in China, and multimodal AI models that integrate basic data such as symptoms and medical history with simple ophthalmic examinations are suggested.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology["H. D. Zou", "S. L. Lin", "L. N. Lu", "Y. Xu"]2024-10-11T00:00:00 0adf6ee75e89e98855aaee85e3729e909eaee38eCurrent Urology Reports300The aim of this review paper is to provide a comprehensive overview of the current technologies in artificial intelligence and wearable devices dedicated to sexual health.Current urology reports["Dayna R Smerina", "Amy M. Pearlman"]2024-10-11T00:00:00 7d64637cdc0843883661c15a63dd2645ce49dc7cArtificial intelligence is a current tool that is used in different areas of the human life with the purpose of facilitating and directing the processes in distinct sceneries of society. This research aimed to use AI as an educational resource in teaching and learning process in the area of biology for students at the Adventist School CEA Cambulos in the city of Cali. A qualitative research methodology was used, with data collection techniques such as observation and interviews, presenting an action research approach. The main results were the potential of AI to enhance learning, the disposition of students to learn through it, and the need for training in the educational sector for its inclusive use and achieving responsible and effective results. That is how it is considered the challenge of reflecting the imminent arrival of AI in the student context and being prepared to be part of the transformation that can be generated in pedagogical processes.Evolutionary Studies in Imaginative Culture270The main results were the potential of AI to enhance learning, the disposition of students to learn through it, and the need for training in the educational sector for its inclusive use and achieving responsible and effective results.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Leonardo Alberto", "Mauris De la Ossa", "M. \u00d3. N. Liseth", "Susatama Esguerra", "Samuel Andr \u00e9 s", "Saavedra Duque", "Daniel Euclides", "S. \u00c1. N. Moya"]2024-10-11T00:00:00 3574411bc19c701477c48f34252e36918fbc968dNordic Journal of Digital Literacy282Nordic Journal of Digital Literacy["R. Krumsvik"]2024-10-11T00:00:00 5bed824eaa517682494f272aacc5b31ca49f77b7British medical journal171BMJ["Robyn Gayle Dychiao", "L. Nazer", "Donald Mlombwa", "L. Celi"]2024-10-11T00:00:00 36bfb47d600b7d780a5ea525938da421870502bcSustainable Horizons-navigating the future with environmental innovation00Sustainable Horizons-navigating the future with environmental innovation[]2024-10-11T00:00:00 ca99467bdb1fa5230e242a2916fef76d6dee08f9Cancer Investigation100Cancer investigation["G. Lyman", "N. Kuderer"]2024-10-11T00:00:00 44c0fd2ed734fe9ca412bd4a3ecd67876da6e129Journal of Balkan and Near Eastern Studies00Journal of Balkan and Near Eastern Studies["Evrim Gormus"]2024-10-11T00:00:00 7b76ba9e8dabda2f6e0b7b77497df029af9663ddJournal of Transcultural Nursing00Journal of transcultural nursing : official journal of the Transcultural Nursing Society["Monique Germain"]2024-10-11T00:00:00 9360e7b5b40b1791d6ba19a12b0405d90fbc066bДанная научная статья посвящена философскому исследованию экзистенциальных рисков, связанных с развитием искусственного интеллекта (ИИ) и цифрового сознания. В эпоху стремительного технологического прогресса, когда ИИ все глубже проникает в различные сферы человеческой деятельности, возникает необходимость осмысления потенциальных угроз, которые он может представлять для существования человечества. В статье рассматриваются такие аспекты проблемы, как возможность создания сверхинтеллекта, способного превзойти человеческий контроль, непредвиденные последствия развития ИИ, а также возможность возникновения конфликта ценностей между человеком и машиной. Особое внимание уделяется философскому анализу проблемы контроля над сверхинтеллектом и этическим аспектам разработки и внедрения ИИ. Актуальность исследования обусловлена необходимостью выработки стратегий минимизации экзистенциальных рисков и обеспечения ответственного развития технологий ИИ. Целью работы является философское осмысление проблемы экзистенциальных рисков, связанных с ИИ, и выявление путей ее решения. This scientific article is devoted to the philosophical study of existential risks associated with the development of artificial intelligence (AI) and digital consciousness. In an era of rapid technological progress, when AI penetrates deeper and deeper into various spheres of human activity, there is a need to understand the potential threats that it may pose to the existence of mankind. The article examines such aspects of the problem as the possibility of creating a superintelligence capable of surpassing human control, the unforeseen consequences of the development of AI, as well as the possibility of a conflict of values between man and machine. Special attention is paid to the philosophical analysis of the problem of superintelligence control and the ethical aspects of AI development and implementation. The relevance of the research is due to the need to develop strategies to minimize existential risks and ensure responsible development of AI technologies. The aim of the work is to philosophically comprehend the problem of existential risks associated with AI and identify ways to solve it.Вопросы фундаментальных и прикладных научных исследований: сборник статей VII международной научной конференции (Выборг, Август 2024)10Вопросы фундаментальных и прикладных научных исследований: сборник статей VII международной научной конференции (Выборг, Август 2024)["\u0412\u043b\u0430\u0434\u0438\u043c\u0438\u0440 \u0412\u0430\u0441\u0438\u043b\u044c\u0435\u0432\u0438\u0447 \u0421\u043c\u0435\u0442\u0430\u043d\u0430"]2024-10-11T00:00:00 6874510fb12fca0caab7e91dc77b86082e663798Gazi Medical Journal60Gazi Medical Journal["L. Subha", "Atul Sharma", "Anita Misra"]2024-10-11T00:00:00 5e29ea2bebfd32694d7027850599c859c895436cQuality &amp; Quantity310Quality &amp; Quantity["J. P. Reyes", "A. Rajagopal"]2024-10-11T00:00:00 8c701786a7fe87daa0317ba1911a6d192d969562Education and Information Technologies : Official Journal of the IFIP technical committee on Education280The proposed TAICS scale consists of 24 items and encompasses six dimensions of AI competence and can be used to examine interventions and correlational research, as well as to inform the creation of new strategies and policies for AI in relation to teacher AI competence development.Education and Information Technologies["T. Chiu", "Zubair Ahmad", "Murat \u00c7oban"]2024-10-11T00:00:00 680e927f63483d99a71c4f7b71c113936ee0a7c5Abstract Background Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support. Objective This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs). Methods This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted. Results The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback. Conclusions Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.JMIR Mental Health324Commercial AI tools are increasingly being used by CMs and MHPs, and respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency.JMIR Mental Health["S. Cross", "I. Bell", "J. Nicholas", "L. Valentine", "S. Mangelsdorf", "Simon Baker", "Nick Titov", "M. Alvarez-Jimenez"]2024-10-11T00:00:00 c76144130347dc9be9b2b02bbea157714d84391aEuropean Journal for Philosophy of Science823It is argued that AI researchers should seek mechanistic interpretability, viz. apply coordinated discovery strategies familiar from the life sciences to uncover the functional organisation of complex AI systems.European Journal for Philosophy of Science["Lena K\u00e4stner", "Barnaby Crook"]2024-10-11T00:00:00 eb7508a508fc6dbc99491cf529b3f11d58058b87The U.S. Medicaid program is experiencing critical challenges that include rapidly increasing healthcare costs, uneven care accessibility, and the challenge associated with addressing a varied set of population health needs. This paper investigates the transformative potential of Artificial Intelligence (AI) in reshaping Medicaid by streamlining operations, improving patient results, and lowering costs. We delve into the pivotal role of AI in predictive analytics, care coordination, the detection of fraud, and personalized medicine. By leveraging insights from advanced data models and addressing challenges particular to Medicaid, we put forward AI-driven solutions that prioritize equitable care and improved public health outcomes. This study underscores the urgency of integrating AI into Medicaid to not only improve operational effectiveness but also to create a more accessible and equitable healthcare system for all beneficiaries.arXiv.org172This paper investigates the transformative potential of Artificial Intelligence in reshaping Medicaid by streamlining operations, improving patient results, and lowering costs and delves into the pivotal role of AI in predictive analytics, care coordination, the detection of fraud, and personalized medicine.ArXiv["Balaji Shesharao Ingole", "Vishnu Ramineni", "M. Krishnappa", "Vivekananda Jayaram"]2024-10-11T00:00:00 2ca5974b79192ef2c06c548a48d46faa656c5e75Advances in artificial intelligence (AI), particularly in generative AI, continue to affect language education paradigms. The integration of AI in language education raises deep-seated ethical concerns such as privacy and data security, potential biases and hidden ideologies in the output, transparency and accountability, dependency and autonomy, digital divide, and job displacement and professional development. The article analyzes these ethical concerns and introduces the multifaceted dimensions of ethics associated with AI in language education. This article comprehensively examines the potential biases of AI in language education. These biases can be algorithmic, demographic, cultural, linguistic, temporal, confirmation, ideological and political. The analysis includes factors contributing to biases, such as training data , labelling and annotation, product design decisions, policy decisions, and algorithms. This paper analyzes algorithmic transparency and advocates for more transparent AI systems to address bias in algorithms. Violations of student privacy emerge as one of the profound ethical issues in the discourse on AI-enhanced language education. The article also examines the challenges and risks associated with the protection of student data privacy, emphasizing the need for robust privacy frameworks to alleviate concerns regarding privacy, human agency and the lack of transparency in the collection of an excessive amount of personal information. By synthesizing the key findings, the paper will conclude with a potential framework of ethical guidelines for the responsible and ethical integration of AI in language education.  Technology in Language Teaching &amp; Learning02A potential framework of ethical guidelines for the responsible and ethical integration of AI in language education is concluded with a potential framework of ethical guidelines for the responsible and ethical integration of AI in language education.Technology in Language Teaching &amp; Learning["Mohamed Sitheeque Peer Mohamed"]2024-10-11T00:00:00 f29297b37e324ea4233503e83d5cf3c1c536a375BMC Medical Education351While AI chatbots like GPT-4o and Claude-3 demonstrate potential in handling structured medical knowledge, their performance in scenarios involving diagnostic uncertainty remains suboptimal compared to human residents.BMC Medical Education["R. S. Huang", "Ali Benour", "Joel Kemppainen", "F.-H. Leung"]2024-10-11T00:00:00 599ff14af67d88ac77cc31fb1b1613632f9f5966Liking it or not, ready or not, we are likely to enter a new phase of human history in which Artificial Intelligence (AI) will dominate economic production and social life -- the AI Revolution. Before the actual arrival of the AI Revolution, it is time for us to speculate on how AI will impact the social world. In this article, we focus on the social impact of generative LLM-based AI (GELLMAI), discussing societal factors that contribute to its technological development and its potential roles in enhancing both between-country and within-country social inequality. There are good indications that the US and China will lead the field and will be the main competitors for domination of AI in the world. We conjecture the AI Revolution will likely give rise to a post-knowledge society in which knowledge per se will become less important than in today's world. Instead, individual relationships and social identity will become more important. So will soft skills.arXiv.org121The social impact of generative LLM-based AI (GELLMAI) is focused on, discussing societal factors that contribute to its technological development and its potential roles in enhancing both between-country and within-country social inequality.ArXiv["Yu Xie", "Sofia Avila"]2024-10-11T00:00:00 8ca1e0e754217550064b182c8612e71e62120390The article offers a comprehensive view of the field of ‘digital advertising’, tracing its evolution from the occurrence of the first banner ad in 1994. The objective of the study is to trace systematically how the digital advertising industry has transitioned from being the traditional one-way communication model to its current ‘intelligent’ state. This study offers profound insights into the dynamic and continually evolving domain of digital advertising. More specifically, it traces the evolutionary stages, major themes, influential articles and citation networks from 1993 to 2023. The articles have been thoroughly examined in three phases, each covering a period of 10 years from 1993 to 2023. The first two decades saw significant growth in the subject domain of ‘digital advertising’; however, the maximum number of articles contributing to the subject from 2013 to 2023 focused mainly on ad-personalization using ICT tools and incorporating artificial intelligence (AI). The key findings indicate that the third decade witnessed a remarkable rise in the number of articles pertaining to AI-driven ad personalization, thus portraying further focus by the researchers on how best to leverage technology in driving advertising effectiveness. AI became one such pivotal innovation, changing the very concept of digital advertising with its capability, to automatically target precisely and develop personalized promotions to engage consumers. The work is unique for the contribution it can make to already existing works regarding digital advertising by coming to one synthesized view of its evolution and its function in its totality regarding AI. The article sets a common platform for future research efforts and innovations within this domain, therefore offering valuable insights both for industry practitioners and future scholars.Vision: The Journal of Business Perspective700The key findings indicate that the third decade witnessed a remarkable rise in the number of articles pertaining to AI-driven ad personalization, thus portraying further focus by the researchers on how best to leverage technology in driving advertising effectiveness.Vision: The Journal of Business Perspective["Abhishek Kumar", "Mrinalini Pandey", "Pankaj K. P. Shreyaskar"]2024-10-11T00:00:00 9adfc16f445855192cf1e87931de827eb2132b32 Purpose The introduction of artificial intelligence (AI) applications in the Global South brings tremendous potential for both good and harm. This paper aims to highlight the guiding ethical principles and normative frameworks for the ethical use of AI in the lens of the traditional Ga (a tribe in Ghana) philosophy and add to the academic literature and research on AI and ethics within the African context. Design/methodology/approach Literature overview on the African philosophy of Ga tradition as applied to AI and application of it to the AI4people ethical framework for a good AI society. Findings Existing principles in AI are based on and mostly influenced by western principles, which may give rise to biases in AI outcomes and design implications in Africa. The research finds a high degree of overlap in the AI4People ethical framework for a good AI society and the Ga philosophy. Research limitations/implications There are a few existing literatures on AI ethics in Africa and on Ga philosophy. Originality/value This research offers valuable contribution to the ongoing discourse of Africa’s adoption of AI and widens the debate on AI and ethics beyond the western ethical approaches. Journal of Information, Communication and Ethics in Society220The research finds a high degree of overlap in the AI4People ethical framework for a good AI society and the Ga philosophy, which widens the debate on AI and ethics beyond the western ethical approaches.J. Inf. Commun. Ethics Soc.["Laud Ammah", "Christoph L\u00fctge", "Alexander Kriebitz", "Lavina Ramkissoon"]2024-10-11T00:00:00 ea6c52accd771e0eb9ed0ece1c8fadc94355ad4eAs artificial intelligence (AI) becomes increasingly prevalent in various domains, its integration into education offers unique opportunities to enhance learning experiences. This paper explores the application of AI, specifically machine learning, in biology education through the classification of edible and poisonous mushrooms. Leveraging a dataset of gilled mushrooms from the Agaricus and Lepiota families, this study demonstrates how AI techniques can be used to classify species based on morphological features such as cap shape, gill attachment, and color. The use of AI not only improves students' understanding of species identification and biological classification but also introduces them to key concepts in data science, such as feature selection, model training, and validation. By implementing machine learning algorithms like decision trees and support vector machines, students can engage in interactive learning experiences that merge biology with modern technological tools. The results show that integrating AI into biology curricula can foster both scientific literacy and computational thinking, offering a more comprehensive and engaging approach to science education. This paper outlines a practical framework for educators to incorporate AI in biology lessons, preparing students for future academic and career challenges in an increasingly digital world.2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC)150The results show that integrating AI into biology curricula can foster both scientific literacy and computational thinking, offering a more comprehensive and engaging approach to science education.2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC)["Yangfan Huang", "Long Ling", "Yaodong Liu"]2024-10-11T00:00:00 71d0f75bd582dbdb8c3ea0f44a363f77dfca9ac1ABSTRACT This DeLisa lecture highlights the invaluable role of musculoskeletal ultrasound (i.e. the renaissance) in Physical and Rehabiltation Medicine. In particular, update as regards how artificial intelligence is also being incorporated in the prompt utility of ultrasound. The discussion does not only pertain to its use in daily clinical practice but also comprises exemplary cutting edge research.American Journal of Physical Medicine & Rehabilitation00The invaluable role of musculoskeletal ultrasound (i.e. the renaissance) in Physical and Rehabiltation Medicine is highlighted and how artificial intelligence is also being incorporated in the prompt utility of ultrasound is updated.American journal of physical medicine & rehabilitation["Levent \u00d6z\u00e7akar"]2024-10-11T00:00:00 e7d95ceb1244b273d7e545df264b7b50f6ff189bProduction is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.Advanced Logistic Systems - Theory and Practice170This article investigates how useful the synthetic data produced by digital twin models of production lines is during the life cycle of the production line, which increasingly uses some artificial intelligence component.Advanced Logistic Systems - Theory and Practice["Z. Moln\u00e1r", "P. Tam\u00e1s", "B. Ill\u00e9s"]2024-10-11T00:00:00 846f2ea2867b2e56f6103bbd0f64318cd77161adThe regulation of Intellectual Property Rights (IPR) in works of art by robots and algorithms is a controversial subject due to the ability of Artificial Intelligence (AI) to produce works of art that closely resemble human intelligence independently. In situations of commercial use, legal and ethical problems arise when digital works are created without the permission of the original copyright owner. The aim of this research is to determine the identification of copyright owners in the context of works of art created by robots and algorithms, as well as explain the protection of moral and commercial rights for creators of works of art that involve the use of robotic technology and algorithms. This research uses normative juridical methods and analytical descriptive approaches to analyze documents, regulations, and other reference sources related to the issue under study. The results of this research show that artificial intelligence has the ability to work independently, produce products, and make creative judgments alone. In most cases, programmers consist of individuals who enter data and algorithms. The main task of artificial intelligence is to understand and develop real results based on commands. With this, we can create an environment that supports creativity and innovation while respecting individual rights and encouraging fair and ethical use of digital works.Begawan Abioso170The results of this research show that artificial intelligence has the ability to work independently, produce products, and make creative judgments alone, and can create an environment that supports creativity and innovation while respecting individual rights and encouraging fair and ethical use of digital works.Begawan Abioso["Febrina Raevita", "Nur Lailatuka Syafa\u2019atul Uzma", "Muhammad Alfarizy"]2024-10-11T00:00:00 91f77c3ecf12d46a10a4094bc76662ddd509f6d8AI &amp; SOCIETY170The article critically analyzes how contemporary image practices involving generative artificial intelligence are entangled with processes of democratization and concludes that an aesthetic perspective offers valuable insights into foundational aspects of belonging to contemporary visual communities.AI &amp; SOCIETY["Maja Bak Herrie", "Nicolas Ren\u00e9 Maleve", "L. Philipsen", "Asker Bryld Staun\u00e6s"]2024-10-11T00:00:00 b2bdc6823e26e537d4e6db914c483677a6752793The integration of Artificial Intelligence (AI) in higher education has sparked a transformative shift in pedagogical methodologies, student engagement, and academic integrity across the UK and Ireland (Dogru, et.al, 2023). This qualitative study delves into the multifaceted implications of utilising AI in academic assignments from the perspective of higher education students, drawing on semi-structured interviews carried out with higher education students. The findings reveal a variety of perceived benefits, including enhanced study efficiency, personalised learning support (Hanaba et al., 2020; Yang, 2021), and the potential of AI to level the academic playing field for students with diverse needs. However, these advantages are in contrast with students’ perceptions of a wide range of significant concerns and challenges, notably the ethical dilemmas surrounding academic honesty, the potential for student dependency on AI leading to diminished knowledge and skill development, as well as the issue of equity in relation to students’ access to AI resources. Moreover, the study stresses a critical gap in institutional guidance regarding AI use, with students voicing a need for clear, consistent guidance from universities. This paper highlights the complex landscape of AI in academia, advocating for a balanced approach that harnesses AI's potential while addressing ethical, educational, and equity challenges.  The study emphasises a need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK.Irish Journal of Technology Enhanced Learning212A need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK is emphasised.Irish Journal of Technology Enhanced Learning["Frances O Donnell", "Mark Porter", "Dr Stephen Fitzgerald"]2024-10-12T00:00:00 eac3c4b8c7d5a01327dfe35c81d271bebde8d977The application of artificial intelligence in esophageal tumor pathology highlights the ongoing and significant transformative impact AI has in medicine. This article provides a comprehensive overview of AI technologies' current use in diagnosing and treating esophageal squamous cell carcinoma (ESCC). Through our research, we identified and meticulously analyzed 33 relevant academic papers and studies that contribute to this field. These papers encompass a broad spectrum of AI applications related to the management of esophageal squamous cell carcinoma. AI integration and deployment in both the diagnosis process and therapeutic interventions for tumors offer highly promising prospects, for example, in endoscopic procedures AI algorithm can process endoscopic images in real-time to identify abnormalities that may be missed by the human eye, it can highlight subtle changes such as color variations, small growths or tissue anomalies, which might go unnoticed due to fatigue, distraction or human limitations in perceiving fine details. AI technologies enhance medical practice precision and effectiveness by significantly reducing the likelihood of human errors and offering practical, innovative solutions. Consequently, AI confluence with medical practice not only increases diagnosis accuracy but also improves the overall efficiency of treating esophageal squamous cell carcinoma.Brain: Broad Research in Artificial Intelligence and Neuroscience01A comprehensive overview of AI technologies' current use in diagnosing and treating esophageal squamous cell carcinoma is provided, which improves the overall efficiency of treating esophageal squamous cell carcinoma.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["A. Fulga", "D. Iancu", "O. Dragostin", "I. Fulga", "B. Ciubara", "C. Musat", "Ionu\u021b Dragostin", "Anamaria Ciubar\u0103"]2024-10-12T00:00:00 5f3b713eb3b9a2f93b9bebbed050b8e029df80a1This study examines the role of Artificial Intelligence (AI) in enhancing operational efficiency in logistics and supply chain management (SCM). It highlights how AI automates processes, improves demand forecasting, optimises inventory management, and enhances route planning. The research also explores AI’s impact on predictive maintenance and customer experience through real-time tracking and chatbots. By leveraging advanced algorithms, organisations can achieve greater accuracy, reduce costs, and increase flexibility, gaining a competitive advantage in the market. The findings provide valuable insights for practitioners and researchers on the strategic opportunities presented by AI in logistics and SCM.International Journal of Scientific Research in Science and Technology11This study highlights how AI automates processes, improves demand forecasting, optimises inventory management, and enhances route planning, and explores AI’s impact on predictive maintenance and customer experience through real-time tracking and chatbots.International Journal of Scientific Research in Science and Technology["Thenmozhi. V", "Dr. S. Krisknakumari"]2024-10-12T00:00:00 314ef976e6887e2e48f9573425859c061ac23a30Recent significant advances in the healthcare industry due to artificial intelligence (AI) and machine learning (ML) have been shown to revolutionize healthcare delivery by improving efficiency, accuracy, and patient outcomes. However, these technologies can face significant challenges and ethical considerations. This systematic review aimed to gather and synthesize the current knowledge on the impact of AI and ML adoption in healthcare delivery, with its associated challenges and opportunities. This study adhered to the PRISMA guidelines. Articles from 2014 to 2024 were selected from various databases using specific keywords. Eligible studies were included after rigorous screening and quality assessment using checklist tools. Themes were identified through data analysis and thematic analysis. From 4981 articles screened, a data synthesis of nine eligible studies revealed themes, including productivity enhancement, improved patient care through decision support and precision medicine, legal and policy challenges, technological considerations, organizational and managerial aspects, ethical concerns, data challenges, and socioeconomic implications. There exist significant opportunities, as well as substantial challenges and ethical concerns, associated with integrating AI and ML into healthcare delivery. Implementation strategies must be carefully designed, considering technical, ethical, and social factors.Applied Informatics681There exist significant opportunities, as well as substantial challenges and ethical concerns, associated with integrating AI and ML into healthcare delivery, and implementation strategies must be carefully designed, considering technical, ethical, and social factors.AI["Ifeanyi Anthony Okwor", "Geeta Hitch", "Saira Hakkim", "Shabana Akbar", "Dave Sookhoo", "John Kainesie"]2024-10-12T00:00:00 f7eca68cb516c54023868543646c99a5da2f4e6bEarly detection of intrapartum risk enables interventions to potentially prevent or mitigate adverse labor outcomes such as cerebral palsy. Currently, there is no accurate automated system to predict such events to assist with clinical decision-making. To fill this gap, we propose"Artificial Intelligence (AI) for Modeling and Explaining Neonatal Health"(AIMEN), a deep learning framework that not only predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum risk factors but also provides the model's reasoning behind the predictions made. The latter can provide insights into what modifications in the input variables of the model could have changed the predicted outcome. We address the challenges of imbalance and small datasets by synthesizing additional training data using Adaptive Synthetic Sampling (ADASYN) and Conditional Tabular Generative Adversarial Networks (CTGAN). AIMEN uses an ensemble of fully-connected neural networks as the backbone for its classification with the data augmentation supported by either ADASYN or CTGAN. AIMEN, supported by CTGAN, outperforms AIMEN supported by ADASYN in classification. AIMEN can predict a high risk for adverse labor outcomes with an average F1 score of 0.784. It also provides counterfactual explanations that can be achieved by changing 2 to 3 attributes on average. Resources available: https://github.com/ab9mamun/AIMEN.arXiv.org450A deep learning framework that not only predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum risk factors but also provides the model's reasoning behind the predictions made, and addresses the challenges of imbalance and small datasets.ArXiv["Abdullah Mamun", "Lawrence D. Devoe", "Mark I. Evans", "D. Britt", "Judith Klein-Seetharaman", "Hassan Ghasemzadeh"]2024-10-12T00:00:00 102b3b14362f55b6223c817f4b264b5a8c7ab136This paper examines the role of Artificial Intelligence (AI) as a methodological tool in practice-led PhD research, emphasizing its capacity to expand creative possibilities. AI’s generative and iterative capabilities allow PhD students to engage in new forms of creative inquiry, positioning AI not just as a technical tool but as a collaborator in the research process. Through its ability to generate, refine, and adapt creative outputs, AI enables students to explore and experiment with innovative ideas. This research will demonstrate how AI supports the essential cycles of creation and reflection that define practice-led methodologies, allowing students to navigate complex creative challenges with a more dynamic approach. The choice of practice-led research as the framework for this investigation is essential, as it provides the flexibility needed to integrate AI technologies. Practice-led methodologies encourage iterative processes of making, thinking, and re-evaluating, which align perfectly with AI’s evolving nature. Through this interaction, students can further the limits of traditional artistic inquiry, examining new methods of storytelling, design, and artistic creation. Supervisors play a crucial role in mentoring students through this process, guiding them to maintain academic rigor while exploring the creative potential AI offers. The paper will explore case studies where AI has been embedded in practice-led PhD research, demonstrating its transformative effects. By doing so, this research highlights how AI can enhance both the creative process and the academic inquiry, offering a forward-looking framework for integrating AI into practice-led PhD research across creative disciplines.LINK 2024 Conference Proceedings00This research will demonstrate how AI supports the essential cycles of creation and reflection that define practice-led methodologies, allowing students to navigate complex creative challenges with a more dynamic approach.LINK 2024 Conference Proceedings["Hossei Najafi"]2024-10-12T00:00:00 2d7eba6c5270f2cdc8153dd6552804bf3f9f28f8In this article, the authors research the legal aspects of ensuring the State security of Ukraine with the help of artificial intelligence, taking into account the experience of the Olympic Games in Paris in 2024. The relevance of the research is due to the growing challenges to the use of artificial intelligence in public life and the need to respect the right to privacy. It is established that after lengthy discussions, the technology of face recognition using artificial intelligence was not introduced at the Olympic Games in Paris. It is substantiated that the main areas for improving the current regulatory framework in Ukraine for the use of artificial intelligence and ensuring the privacy of citizens and avoiding possible human rights violations are as follows: in the context of the introduction of face recognition technology, it is important to have detailed rules governing the scope and application of measures, as well as reliable safeguards against the risk of abuse and arbitrariness (the need for legal safeguards is much greater when it comes to the use of real-time face recognition technology); the processing of personal data in the application of face recognition technology must be justified and require a high level of justification; the use of face recognition technology for forensic identification of suspects. It is proved that when identifying real threats to the state security of Ukraine at the present stage in the context of the possible introduction of face recognition technology, it is important to have detailed rules governing the scope and application of measures, as well as reliable guarantees against the risk of abuse and arbitrariness. The inevitable need for legal guarantees for the implementation and protection of citizens’ privacy, especially when it comes to the use of real-time face recognition technology, is derived from the content of the State Security Strategy.Analytical and Comparative Jurisprudence00It is proved that when identifying real threats to the state security of Ukraine at the present stage in the context of the possible introduction of face recognition technology, it is important to have detailed rules governing the scope and application of measures, as well as reliable guarantees against the risk of abuse and arbitrariness.Analytical and Comparative Jurisprudence["I. Diorditsa", "O. Daragan", "A. Soloviov"]2024-10-12T00:00:00 c678e94c40e7c310fc81602bde13cff4694d57c1INTRODUCTION Cardiovascular diseases affect 17.7 million people annually, worldwide. Carotid degenerative disease, commonly described as atherosclerotic plaque accumulation, significantly contributes to this, posing a risk for cerebrovascular events and ischemic strokes. With carotid stenosis (CS) being a primary concern, accurate diagnosis, clinical staging, and timely surgical interventions, such as carotid endarterectomy (CEA), are crucial. This review explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) in improving diagnosis, risk stratification, and management of CS. METHODS A comprehensive literature review was conducted using PubMed and SCOPUS, focusing on AI and ML applications in diagnosing and managing extracranial CS. English language publications from the past two decades were reviewed, including cross-referenced scientific articles. RESULTS Recent advancements in AI-enhanced imaging techniques, particularly in deep learning, have significantly improved diagnostic accuracy in identifying carotid plaque vulnerability and symptomatic plaques. Integration of clinical risk factors with AI systems has further enhanced precision. Additionally, ML models have shown promising results in identifying culprit arteries in patients with previous cerebrovascular events. These advancements hold immense potential for improving CS diagnosis and classification, leading to better patient management. CONCLUSION Integrating AI and ML into vascular surgery, particularly in managing CS, marks a transformative advancement. These technologies have significantly improved diagnostic accuracy and risk assessment, paving the way for more personalized and safer patient care. Despite clinical validation and data privacy challenges, AI and ML have immense potential for enhancing clinical decision-making in vascular surgery, marking a pivotal phase in the field's evolution.Portuguese Journal of Cardiac Thoracic and Vascular Surgery00This review explores the impact of Artificial Intelligence (AI) and Machine Learning (ML) in improving diagnosis, risk stratification, and management of CS, marking a pivotal phase in the field's evolution.Portuguese journal of cardiac thoracic and vascular surgery["A. Pias", "Juliana Pereira-Macedo", "Ana Marreiros", "Nuno Antonio", "J. Rocha-Neves"]2024-10-12T00:00:00 612e9ebd13f04f923ef44f342d3b36beff4a7f0cThe article is devoted to the study of the problems and prospects of the use of artificial intelligence in countering criminal offenses by the Security Service of Ukraine at critical infrastructure facilities. The peculiarities of state policy, the current state of legal regulation of the use of innovative technology in Ukraine are considered, and problematic issues of the use of artificial intelligence in the course of the tasks of prevention, detection, termination, and pre­trial investigation of criminal proceedings in the relevant field are analyzed. The main areas of application of artificial intelligence in combating criminal offenses at critical infrastructure facilities have been worked out, such as: intelligent detection of threats in the process of countermeasures; automation of algorithms for combating illegal encroachments; information and analytical support. The key methods and technologies of artificial intelligence that can be used in the process of combating criminal offenses at critical infrastructure facilities by the Security Service of Ukraine are highlighted, in particular: deep learning, machine learning, natural language processing and computer vision. The potential risks and problematic issues of introducing artificial intelligence into the automated process of combating criminal offenses at critical infrastructure facilities are outlined. Attention is drawn to compliance with the principles of transparency and accountability of activity, legality, responsibility for erroneous decisions, ensuring human rights and freedoms, prevention of risks of discrimination, excessive reliance on artificial intelligence. The need to improve the legal basis for the use of artificial intelligence in combating criminal offenses at critical infrastructure facilities, as well as introducing changes and additions to the legislation of Ukraine concerning the criminal-procedural activity of the Security Service of Ukraine, was noted. The introduction of artificial intelligence technologies into the system of combating criminal offenses at critical infrastructure facilities is identified as a promising direction for further research.Analytical and Comparative Jurisprudence00The key methods and technologies of artificial intelligence that can be used in the process of combating criminal offenses at critical infrastructure facilities by the Security Service of Ukraine are highlighted, in particular: deep learning, machine learning, natural language processing and computer vision.Analytical and Comparative Jurisprudence["O.M. Gerasimenko"]2024-10-12T00:00:00 3bb95f5930c9e8452cd445a00cefa7fab894f6d3Artificial Intelligence Generated Content (AIGC) has rapidly evolved, revolutionizing the creation of text, images, audio, and video content. Despite these advancements, research on the development process of AIGC technology remains scarce, necessitating a systematic discussion of its current state and future directions. So this paper delves into the significant advancements and foundational technologies driving AIGC, emphasizing the contributions of state-of-the-art models such as DALL-E 3 [1] and Sora [2]. We analyze the evolution of generative models from single-modal approaches to the current multimodal generative models. The paper further explores the application prospects of AIGC across various domains such as office work, art, education, and film, while addressing the existing limitations and challenges in the field. We propose potential improvement directions, including more efficient model architectures and enhanced multimodal capabilities. Emphasis is placed on the environmental impact of AIGC technologies and the need for sustainable practices. Our comprehensive review aims to provide researchers and professionals with a deeper understanding of AIGC, inspiring further exploration and innovation in this transformative domain.Applied and Computational Engineering00A comprehensive review of the significant advancements and foundational technologies driving AIGC, emphasizing the contributions of state-of-the-art models such as DALL-E 3 and Sora, and proposes potential improvement directions, including more efficient model architectures and enhanced multimodal capabilities.Applied and Computational Engineering["Yihan Xu"]2024-10-12T00:00:00 20ab910c062bc239c6386673be1555f2f4f670b2The emergence of artificial intelligence and its extensive application in the field of education have exerted a great influence on promoting the reform of school education, making the educational and teaching work in higher vocational colleges show a new development trend. Starting from the general background of the era of artificial intelligence, this paper analyzes the important significance of the era of artificial intelligence on the educational reform in higher vocational colleges and the challenges faced by the educational and teaching reform in higher vocational colleges. On this basis, the educational reform strategies are proposed, hoping to highlight the educational characteristics of higher vocational colleges in the era of artificial intelligence and comprehensively promote the efficient progress of educational and teaching work in higher vocational colleges.World Journal of Educational Research00The educational reform strategies are proposed, hoping to highlight the educational characteristics of higher vocational colleges in the era of artificial intelligence and comprehensively promote the efficient progress of educational and teaching work in higher vocational colleges.World Journal of Educational Research["Tang Xue"]2024-10-12T00:00:00 5b8d0af93c47029dcac744ae8d4007529a2b9543Motor brain-machine interfaces (BMIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the last two decades, BMIs face key obstacles to clinical viability. Invasive BMIs achieve proficient cursor and robotic arm control but require neurosurgery, posing significant risk to patients. Non-invasive BMIs do not have neurosurgical risk, but achieve lower performance, sometimes being prohibitively frustrating to use and preventing widespread adoption. We take a step toward breaking this performance-risk tradeoff by building performant non-invasive BMIs. The critical limitation that bounds decoder performance in non-invasive BMIs is their poor neural signal-to-noise ratio. To overcome this, we contribute (1) a novel EEG decoding approach and (2) artificial intelligence (AI) copilots that infer task goals and aid action completion. We demonstrate that with this “AI-BMI,” in tandem with a new adaptive decoding approach using a convolutional neural network (CNN) and ReFIT-like Kalman filter (KF), healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms. Using an AI copilot improves goal acquisition speed by up to 4.3× in the standard center-out 8 cursor control task and enables users to control a robotic arm to perform the sequential pick-and-place task, moving 4 randomly placed blocks to 4 randomly chosen locations. As AI copilots improve, this approach may result in clinically viable non-invasive AI-BMIs.bioRxiv580This work demonstrates that with this “AI-BMI,” in tandem with a new adaptive decoding approach using a convolutional neural network and ReFIT-like Kalman filter, healthy users and a paralyzed participant can autonomously and proficiently control computer cursors and robotic arms.bioRxiv["Johannes Y. Lee", "Sangjoon Lee", "Abhishek Mishra", "Xu Yan", "Brandon J. McMahan", "Brent Gaisford", "Charles Kobashigawa", "Mike Qu", "Chang Xie", "Jonathan C. Kao"]2024-10-12T00:00:00 1bbb517004cce7ed63604df339a712233012caa3Autonomous vehicles (AVs) represent a paradigm shift in transportation, promising safer, more efficient, and convenient mobility. Central to the realization of AVs are advancements in artificial intelligence (AI) technologies, enabling vehicles to perceive their environment, make decisions, and navigate autonomously. This paper examines the multifaceted role of AI in AVs, focusing on the challenges and opportunities it presents. The discussion encompasses the application of AI techniques such as machine learning, computer vision, and sensor fusion in enabling autonomous driving systems. Furthermore, it addresses the technical, regulatory, and ethical challenges associated with AI implementation in AVs, including sensor limitations, data privacy concerns, and legal frameworks. Despite these challenges, AI-enabled AVs offer significant opportunities, including improved road safety, enhanced mobility, and reduced environmental impact. Recent innovations and advancements in AI technologies pave the way for overcoming existing barriers and accelerating the adoption of AVs. Through case studies and real-world applications, this paper highlights the transformative potential of AI-driven AVs across various sectors, from transportation and logistics to urban planning. Looking ahead, future research and development efforts must focus on addressing remaining challenges and maximizing the societal benefits of AI in autonomous vehicles.International Journal For Multidisciplinary Research50The multifaceted role of AI in AVs is examined, focusing on the challenges and opportunities it presents, and the application of AI techniques such as machine learning, computer vision, and sensor fusion in enabling autonomous driving systems.International Journal For Multidisciplinary Research["Sadik Bin Salim", "Md. Tajbir Husain"]2024-10-12T00:00:00 5ce6fbd96becad1b323116915fc3279b0e496a67This article explores the transformative impact of Artificial Intelligence (AI) in the financial sector, highlighting its various applications and associated challenges. Through a detailed analysis, key technologies such as machine learning, computer vision, and natural language processing are examined, which have revolutionized critical areas such as customer service, risk management, and financial advising. Although AI has significantly improved operational efficiency and service personalization, it also presents important risks and limitations, such as data privacy, lack of model interpretability, and cybersecurity. Additionally, the article addresses future forecasts for AI adoption in finance, suggesting that its integration will continue to expand, driven by technological advances and regulatory improvements. However, this expansion must be balanced with adequate oversight to mitigate potential risks and maximize benefits. Overall, this study offers a balanced view of the opportunities and challenges that AI presents for the future of global finance.International Journal of Business &amp; Management Studies390This article explores the transformative impact of Artificial Intelligence in the financial sector, highlighting its various applications and associated challenges, and offers a balanced view of the opportunities and challenges that AI presents for the future of global finance.International Journal of Business &amp; Management Studies["R. Becerra-Vicario", "Bel\u00e9n Salas-Comp\u00e1s", "L. Valcarce-Ruiz", "S\u00e1nchez Serrano", "Jose Ram\u00f3n"]2024-10-12T00:00:00 0b0147b4cc7da2eb66ea57dbbd38f10ba298b898This study examines the application of Artificial Intelligence (AI) in addressing deforestation and promoting sustainability in plantations using a bibliometric approach. Deforestation, a critical global issue, results from agricultural expansion, plantation development, and land-use changes, leading to significant environmental degradation. AI has been proposed as a powerful tool to monitor and manage deforestation more effectively, offering solutions such as satellite imagery analysis and predictive models. Through a bibliometric analysis spanning the last decade (2013–2023), this study uses VOSviewer to visualize co-citation networks, identifying key research trends and clusters related to AI in deforestation and plantation sustainability. The findings reveal that research is concentrated in regions like Indonesia and Brazil, where AI technologies like machine learning are employed to predict deforestation and enhance resource management. Emerging research areas include the integration of AI with the Internet of Things (IoT) and blockchain for improved data management and sustainability practices. This analysis provides insights into the growing role of AI in mitigating deforestation and offers recommendations for future research, including addressing ethical challenges and regulatory frameworks to further enhance sustainable plantation management.LITERATUS120A bibliometric analysis spanning the last decade provides insights into the growing role of AI in mitigating deforestation and offers recommendations for future research, including addressing ethical challenges and regulatory frameworks to further enhance sustainable plantation management.LITERATUS["Linda Sutriani", "Ali Impron", "Veny Betsy Saragih", "Syadza Anggraini", "Suraji Suraji"]2024-10-12T00:00:00 56650b20964aed4f20b418338480735575b47e0eThis research aims to conduct a bibliometric analysis of published research on the use of artificial intelligence in accessible museums in the Web of Science Core Collection database. Through bibliometric research, the general framework of research on specific subject areas can be determined. A total of 30 articles were reached in the research. The research findings were analyzed and visualized through the "Analyze Results" option provided in the WOS viewer and Bibliometric tool and ten articles were selected from a systematic review. The findings of the research revealed that research on the use of "artificial intelligence in accessible museums" started in 1997, and then continued to be published with an increase in 2017 after a twenty-year break. The year with the most publications was 2022. The vast majority of research has been published in English. Research on this topic has also been published across Europe. Research has been widely disseminated in the USA, Italy, Spain, France, Romania, India, Bulgaria, Greece, and the Netherlands. However, the majority of the research is indexed in Springer and some in IEEE and MDPI. Most of the studies were prepared in the form of papers and some of them were prepared in the form of articles. Finally, the findings reveal that the research prepared on this subject has been addressed every year since 1997 with the keyword "artificial intelligence" until 2022, while the keyword "cultural heritage" has been mostly used since 2008. As a result, the results obtained from the bibliometric research show that the use of artificial intelligence in museums has become increasingly widespread year by year.Brain: Broad Research in Artificial Intelligence and Neuroscience00The research revealed that research on the use of "artificial intelligence in accessible museums" started in 1997, and then continued to be published with an increase in 2017 after a twenty-year break, and the year with the most publications was 2022.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["Didem Islek", "F. Alt\u0131nay", "Z. Alt\u0131nay", "R. Shadiev", "Ipek Danju"]2024-10-12T00:00:00 2f05f775c716d71564cf95ada2d886dbb86bf8c4This review explores the influence of AI marketing on e-commerce sales, examining how AI-driven strategies affect key metrics such as customer acquisition and conversion rates. Given the growing importance of AI in online retail, this paper employs a critical review methodology, analyzing 50 documents from the Scopus database. The analysis reveals that AI tools like chatbots, personalization engines, and predictive analytics significantly enhance e-commerce performance. The study provides practical and theoretical contributions, offering recommendations for businesses and suggesting future research directions.Syst.732Analysis of the influence of AI marketing on e-commerce sales reveals that AI tools like chatbots, personalization engines, and predictive analytics significantly enhance e-commerce performance.Syst.["Mitra Madanchian"]2024-10-12T00:00:00 f7175816b358bdc389eade9a484fab681237d152Kegiatan pengabdian ini bertujuan untuk mensosialisasikan pemanfaatan kecerdasan buatan (AI) dalam pendidikan di perguruan tinggi, mengingat implementasinya di Indonesia masih belum optimal. Metodologi yang digunakan adalah metode partisipatif dengan pendekatan community-based participatory research (CBPR), dimulai dengan survei kebutuhan, pelaksanaan sosialisasi melalui seminar dan pelatihan, serta evaluasi dampak. Hasil kegiatan menunjukkan peningkatan pemahaman dosen dan mahasiswa mengenai AI, dengan 80% peserta merasa lebih paham dan 65% dosen tertarik mengadopsi AI dalam pembelajaran. Namun, tantangan seperti keterbatasan infrastruktur dan tenaga ahli masih perlu diatasi. Dampak dari pemanfaatan AI bagi mahasiswa sangat signifikan, termasuk peningkatan personalisasi pembelajaran, akses lebih luas ke sumber daya pendidikan, dan efisiensi dalam evaluasi. AI juga dapat mengembangkan keterampilan teknologi mahasiswa, mempersiapkan mereka untuk dunia kerja di era digital. Kegiatan pengabdian ini berhasil memberikan kontribusi positif dalam mempersiapkan perguruan tinggi untuk mengadopsi AI, meskipun diperlukan dukungan lebih lanjut dalam peningkatan infrastruktur dan pelatihan berkelanjutan.Jurnal Pengabdian Masyarakat Bangsa51Jurnal Pengabdian Masyarakat Bangsa["Syafrul Antoni", "M. Karim", "Karlini Oktarina", "Halim Halim", "Nelly Patria"]2024-10-12T00:00:00 b6bb7f73d773f4dbc1486aaab6216051e5f87a94AI has revolutionized the practice of law thus resulting in major developments, but the ethical implications of the same are complex and elaborate. Finally, this paper examines how professional organizations can serve as leaders in determining the ethical application of AI particularly in the legal profession. Through setting of ethical standards, as well as providing information and materials for learning, such organizations assist legal professions in the exercise of responsible usage of AI technologies. The discussion raises the demographic concerns of the AI systems, efficiency, transparency, and the fairness of the AI system as well as the cardinal need to provide practical approaches for training as well as updating the legal curriculum due to the influence of AI.International Journal of Law and Politics Studies00This paper examines how professional organizations can serve as leaders in determining the ethical application of AI particularly in the legal profession through setting of ethical standards, as well as providing information and materials for learning.International Journal of Law and Politics Studies["Md Wasim Ahmed"]2024-10-12T00:00:00 5cab65c1de743591e6d46f07389b5c145619ffffProceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education70Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education["Fei Cai", "Wanyu Chen", "Yijia Zhang"]2024-10-12T00:00:00 a138ee0ac711157258ce8cec807abd1a4b3b3569Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education30Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education["Xianchuang Wang", "Haiguang Fang", "Lili Shu", "Zeyu Li"]2024-10-12T00:00:00 81b4893a8029e14ab6e5e601fc38ec33f4008053Education and Information Technologies : Official Journal of the IFIP technical committee on Education480Education and Information Technologies["Asiye Toker Gokce", "Arzu Deveci Topal", "Aynur Kolburan Ge\u00e7er", "Canan Dilek Eren"]2024-10-12T00:00:00 c1f9db5e31f2ddbaf37adc441f893c52be45112eWith the intensification of global economic competition, enterprises face the challenge of improving the supply chain management efficiency, and AI technology, as an emerging field in computer science, can provide effective solutions. As a leading e-commerce logistics service provider in China, JD Logistics has accumulated a wealth of logistics technology capabilities and digital transformation experience within and outside the JD Group. Using JD Logistics as a case study, this paper discusses the application of AI technology in supply chain management and its impact on enterprise responsiveness and operational efficiency. AI technology has significantly improved the supply chain operational efficiency and responsiveness of JD Logistics by applying intelligent supply chain planning, predictive analysis and intelligent decision-making. The study systematically summarizes the key role of AI technology in optimizing supply chain management through a literature review and specific case studies, providing an important reference and practical experience for enterprises to achieve digital transformation.Advances in Economics, Management and Political Sciences00The study systematically summarizes the key role of AI technology in optimizing supply chain management through a literature review and specific case studies, providing an important reference and practical experience for enterprises to achieve digital transformation.Advances in Economics, Management and Political Sciences["Yuxi Pan", "Xuezhu Wang", "Qiuyu Ye"]2024-10-12T00:00:00 59201946b895713906efa98ab7ced406df4d5854Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education310Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education["Olha Prokopenko", "V. Matviienko", "T. Chunikhina", "Viacheslav Ohol", "Alexandra Jasurkova"]2024-10-12T00:00:00 1764adff8202d7f2ddc5c3a7bee1bb9344665b5bArtificial intelligence (AI) has become an interdisciplinary subject that integrates digital image processing, machine learning, and computer science, etc. A lot of work in martial arts benefits from AI technology. In this paper, we highlight and summarize the applications of AI in martial arts and indicate the main progress of related studies. Through the investigation of martial arts styles, tasks, data acquisition methods, and algorithm improvements, a paradigm that represents a possible technological process for an intelligent martial arts training system was summarized. These applications bring more scientific training and more effective technical analysis to martial arts. Furthermore, this paper delves into specific AI applications in areas such as action recognition, pose estimation, action evaluation, elite athlete support, sports betting, health care, animation generation, and competition field segmentation. By exploring these areas, the research prospects of AI applications in martial arts are comprehensively discussed. This paper inspires further research on martial arts by using AI, which includes helping researchers expand their research ideas and research methods, identify frontier issues, and innovate based on existing research.Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology752This paper delves into specific AI applications in areas such as action recognition, pose estimation, action evaluation, elite athlete support, sports betting, health care, animation generation, and competition field segmentation.Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology["Yiqun Pang", "Yibing Wang", "Qiurui Wang", "Fengmei Li", "Changnian Zhang", "Chuanwei Ding"]2024-10-12T00:00:00 94eb4fe5a7dbffa5850140f79961792b8c9c641dIntegrating Artificial Intelligence (AI) into Precision Medicine (PM) is redefining healthcare, enabling personalized treatments tailored to individual patients based on their genetic code, environment, and lifestyle. AI’s ability to analyze vast and complex datasets, including genomics and medical records, facilitates the identification of hidden patterns and correlations, which are critical for developing personalized treatment plans. Unsupervised Learning (UL) is particularly valuable in PM as it can analyze unstructured and unlabeled data to uncover novel disease subtypes, biomarkers, and patient stratifications. By revealing patterns that are not explicitly labeled, unsupervised algorithms enable the discovery of new insights into disease mechanisms and patient variability, advancing our understanding of individual responses to treatment. However, the integration of AI into PM presents some challenges, including concerns about data privacy and the rigorous validation of AI models in clinical practice. Despite these challenges, AI holds immense potential to revolutionize PM, offering a more personalized, efficient, and effective approach to healthcare. Collaboration among AI developers and clinicians is essential to fully realize this potential and ensure ethical and reliable implementation in medical practice. This review will explore the latest emerging UL technologies in the biomedical field with a particular focus on PM applications and their impact on human health and well-being.Applied Sciences712This review will explore the latest emerging UL technologies in the biomedical field with a particular focus on PM applications and their impact on human health and well-being.Applied Sciences["A. Trezza", "Anna Visibelli", "Bianca Roncaglia", "O. Spiga", "Annalisa Santucci"]2024-10-12T00:00:00 299cbd9f8ea3ec45f506562399e13db48aecf667Purpose: This paper reviews ways that artificial intelligence could be used to make business aviation operations most fuel-efficient, cheapest in cost of operation, and smallest in terms of ecological footprint. The research elaborated possible ways of using AI in the spheres of flight planning, predictive maintenance, fuel management, emission tracking, and compliance in business aviation. Methodology: It was also empirical case-study-oriented research that investigated the impacts of AI-driven technologies on business aviation operators, basically NetJets, VistaJet, and Flexjet. The impacts are from route optimization to predictive maintenance, fuel management, and compliance with regulations. Information such as fuel consumption, CO2 emissions, and operational efficiency was obtained before and after the adoption of AI technologies. Findings: The fuel savings from AI-driven systems are reaching a point of salience, at 9 to 14% in the various cases, with associated reductions in CO2 emissions. AI-powered predictive maintenance resulted in a 20% reduction in unscheduled events, thereby bettering the availability of fleets. Artificial intelligence increased overall efficiency and improved decisions for in-flight, real-time operations management while conforming to regulatory requirements in reporting. Additionally, AI is going to bring tremendous values in optimizing SAFs and aircraft energy-efficient technology to make flying sustainable. Unique Contribution to Theory, Policy, and Practice:  This work contributes to AI in aviation by demonstrating its practical application in reducing environmental impacts and operational costs in business aviation. It provides a framework for integrating AI into aviation management systems and highlights the importance of public-private cooperation for wider AI adoption. The findings are valuable for policymakers, business aviation operators, and industry leaders aiming to advance sustainability and regulatory complianceJournal of business and strategic management671Journal of Business and Strategic Management["Victor Mgbachi"]2024-10-12T00:00:00 3f355073b621ddbf487e5af92e7a87e95e0be2a5In the context of the profound integration of big data, artificial intelligence technologies, and industries, both data resources and digital technologies became crucial pillars supporting the development of industrial innovation. They played key roles in enhancing the innovation capabilities of industries. In this regard, the present study employed the specific case of the intelligent brain of the Taizhou Machine Tool Industry. Utilizing grounded theory, the research integrated relevant data through coding analysis and category extraction to construct a comprehensive theoretical model of the intrinsic mechanisms driving industrial innovation through data empowerment. The study revealed that the realization of data-empowered industrial innovation followed a logical path of 'data resource services data capability spillover industrial innovation achievement.' Specifically, data empowerment operated across three dimensions: industrial resources, production, and ecology, effectively propelling the realization of industrial innovation. From a systematic perspective, this research analyzed the intrinsic mechanisms of data-empowered industrial innovation, deepening the theoretical understanding of industrial innovation. In the context of past industrial digitization transformation, these findings provided valuable insights for guiding industrial innovation development.Journal of Applied Economics and Policy Studies00The study revealed that the realization of data-empowered industrial innovation followed a logical path of 'data resource services data capability spillover industrial innovation achievement,' and data empowerment operated across three dimensions: industrial resources, production, and ecology, effectively propelling the realization of industrial innovation.Journal of Applied Economics and Policy Studies["Xuecheng Yang", "Yijun Zhou", "Jing Guo"]2024-10-12T00:00:00 bcd492452d6cebc23574952f3ba329e82437ce78Big data and artificial intelligence (AI) are the buzzwords of the moment in business decision-making. In this paper, I will show how predictive analytics, real-time data processing and natural language processing (NLP) are key strategies that allow businesses to optimise their operations and customer interaction. Moreover, through this paper I will present some of the challenges involved in the adoption of AI, such as the ethical and legal questions about data privacy, as well as the real problem of integrating AI systems within legacy business models. Furthermore, future trends in AI will be presented, such as the advances in quantum computing and the rise of so-called autonomous AI systems, that will define the future of decision-making in logistics, finance and a variety of other sectors. Overall, by addressing both the strategic advantages and the pitfalls involved in the adoption of AI based on some real business cases, this paper will provide a complete picture of what AI and big data can bring to decision-making as a tool for business success. This topic is of paramount importance as these technologies have not only brought a new wave of innovation but are also increasing the importance of human oversight of AI systems.Journal of Applied Economics and Policy Studies00This paper shows how predictive analytics, real-time data processing and natural language processing are key strategies that allow businesses to optimise their operations and customer interaction and provides a complete picture of what AI and big data can bring to decision-making as a tool for business success.Journal of Applied Economics and Policy Studies["Nyusifan Tang"]2024-10-12T00:00:00 cd58ddb2439833b88f1f4e28100823565e1262d8The rapid development of artificial intelligence (AI) since 2020 has significantly impacted the U.S. stock market, necessitating a deeper understanding of its influence on AI-related stocks. This study aims to analyze and predict the returns of the Global X Robotics & Artificial Intelligence ETF (BOTZ) as a proxy for AI industry performance. Employing Random Forest and XGBoost machine learning models, we trained on over a thousand data points to forecast BOTZ ETF returns. Our research reveals that AI-focused stocks and ETFs have outperformed the broader market since 2020, driven by increased AI adoption across industries, substantial research and development investments, and shifting investor sentiment towards tech-centric portfolios. The machine learning models demonstrated promising results in capturing complex market dynamics and providing reliable predictions. This study underscores the potential of integrating machine learning with financial analysis, offering valuable insights for investors and stakeholders in navigating the evolving landscape of AI-influenced markets.Advances in Economics, Management and Political Sciences00The research reveals that AI-focused stocks and ETFs have outperformed the broader market since 2020, driven by increased AI adoption across industries, substantial research and development investments, and shifting investor sentiment towards tech-centric portfolios.Advances in Economics, Management and Political Sciences["Jinhui Li"]2024-10-12T00:00:00 98a448666997d52d263a283be25063cc18646510This research aimed to explore the impact of artificial intelligence (AI) on personalizing customer experience in international business and its benefits for companies in the global economy. It also evaluated AI's implications on supply chain management, focusing on efficiency and sustainability. Using a quantitative research approach and primary surveys with stakeholders in international business, the study revealed that AI significantly enhances supply chain management effectiveness through forecasting, financial operation optimization, and insight generation. AI technologies enable businesses to analyze vast amounts of data, accurately predicting customer preferences and behavior. This predictive capability enhances customer experience by providing personalized recommendations and services tailored to individual needs. For instance, AI can analyze purchasing patterns and browsing histories to suggest products that customers are likely to be interested in, thereby increasing the likelihood of purchase, and fostering customer loyalty. With AI and big data, businesses can develop precise opportunities for customer relationships, achieving higher market share and customer loyalty. AI-driven personalization strategies allow companies to target specific customer segments with customized marketing campaigns, improving engagement and conversion rates. Additionally, AI helps businesses understand and anticipate customer needs in different regions, allowing them to adapt their offerings to local preferences and trends. This regional adaptability is crucial in international business, where customer preferences can vary significantly across different markets. In supply chain management, AI's impact is profound. AI systems can optimize inventory management, predict demand fluctuations, and streamline logistics operations. By analyzing real-time data, AI can identify potential disruptions and suggest proactive measures to mitigate risks, ensuring a smooth and resilient supply chain. This capability not only enhances efficiency but also contributes to sustainability by reducing waste and improving resource utilization. In summary, AI plays a pivotal role in transforming customer experience and supply chain management in international business. By leveraging AI's predictive and analytical capabilities, companies can offer personalized services, enhance customer loyalty, and achieve higher efficiency and sustainability in their supply chain operations, ultimately benefiting the global economy.Innovation and Sustainability80Artificial intelligence plays a pivotal role in transforming customer experience and supply chain management in international business by leveraging AI's predictive and analytical capabilities, and achieve higher efficiency and sustainability in their supply chain operations, ultimately benefiting the global economy.Innovation and Sustainability["Kuan Zhang"]2024-10-12T00:00:00 d3ada5e0096d46f60b8cb688be787ff27b869fb7Artificial intelligence aims to be the solution to multiple engineering problems by trying to emulate the human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: the International Convention for the Safety of Life at Sea Convention (SOLAS) and the International Convention for the Prevention of Pollution from Ships (MARPOL). Based on a formal safety assessment research process, these pillars try to solve most of the maritime transport accidents, which, in their final steps, are associated with human factors. In this research, an original methodology employing a deep learning process for image recognition during mooring line operation, a dangerous process on ships, is developed. The main results indicate that the proposed method is an excellent tool for advising ship officers on watch and, consequently, provides a new way to prevent human factors onboard from causing accidents, which in the future must be considered in international standards.Journal of Marine Science and Engineering250In this research, an original methodology employing a deep learning process for image recognition during mooring line operation, a dangerous process on ships, is developed and indicates that the proposed method is an excellent tool for advising ship officers on watch and provides a new way to prevent human factors onboard from causing accidents.Journal of Marine Science and Engineering["Genaro Cao-Feij\u00f3o", "Jos\u00e9 M. P\u00e9rez-Canosa", "F.J. Perez-Castelo", "J. Orosa"]2024-10-12T00:00:00 556f515e8e05e23dd80f4ebc81efa1283097393aThe emergence of autonomous vehicles (AVs) represents a critical turning point in the development of transportation, with the potential to completely transform how we move while improving accessibility, efficiency, and safety. However, faith in these systems' decision-making processes becomes critical as they advance in sophistication and become more interwoven into daily life. For AVs to be widely accepted and deployed safely, reliable AI systems—especially those that are transparent and explainable—must be developed. This paper investigates the idea of creating reliable artificial intelligence (AI) systems, with a particular emphasis on creating explicable models for transparent decision-making in autonomous cars.Journal of Sustainable Solutions00This paper investigates the idea of creating reliable artificial intelligence systems, with a particular emphasis on creating explicable models for transparent decision-making in autonomous cars.Journal of Sustainable Solutions["Vishwas Khandelwal"]2024-10-12T00:00:00 dda7bc67b53af87434dc3949b197e8ef685e3588Artificial Intelligence (AI) is revolutionising content marketing by automating key processes and enabling personalisation on a massive scale. Using technologies such as Machine Learning and Natural Language Processing, AI can generate content quickly, identify SEO-relevant keywords and improve campaign performance. It analyses user behaviour, anticipates their needs and optimises marketing strategies in real time. However, this automation poses ethical challenges, such as algorithmic bias and over-reliance on technology. To exploit these tools responsibly, it is crucial that companies adopt ethical and transparent practices, respecting data confidentiality rules. In the future, AI will continue to transform marketing, with innovations such as virtual agents and even more personalised strategies, while requiring a balance between technological efficiency and human authenticity.International Journal of Advanced Multidisciplinary Research and Studies10In the future, AI will continue to transform marketing, with innovations such as virtual agents and even more personalised strategies, while requiring a balance between technological efficiency and human authenticity.International Journal of Advanced Multidisciplinary Research and Studies["Yassine Elkhatibi", "Redouane Benabdelouhed"]2024-10-12T00:00:00 ba5c7109eb04fca0111918d456bc14100c4bb0f4This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba. The successful cases of JD and Alibaba in supply chain management show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness. Applying AI in supply chain logistics, such as demand forecasting, inventory optimization and transport route planning, can provide accurate data analysis and decision support. It can help reduce inventory costs, shorten transportation time and optimize resource allocation in the supply chain. However, AI and blockchain technologies still face several challenges, such as data privacy and security issues, technology costs and standardization. Future research and practice will continue to work on solving these challenges, further optimizing and improving the application of AI and blockchain in supply chain logistics, and promoting innovation and development in supply chain logistics.Advances in Economics, Management and Political Sciences00This study explores the innovative impact mechanisms of artificial intelligence (AI) and blockchain in supply chain and logistics through the case study of JD and Alibaba to show that by integrating AI and blockchain technologies, companies can automate, smarten and make supply chain logistics more efficient, thereby reducing costs, improving efficiency and enhancing overall competitiveness.Advances in Economics, Management and Political Sciences["Hongyan Chen", "Ziwei Wang", "Minhao Zhang"]2024-10-12T00:00:00 529e65883d156b618ccb9090b2e5b7ae5c312ffeCancer is a highly individualized disease, as tumors both take on traits from their host and develop their own mutations. Thus, it is critical to better understand the characteristics and behavior of tumors to treat patients more effectively. There are various artificial intelligences (AI) that can and are being used to improve the development of oncological technologies. AI is designed to replicate human intelligence, but not all models are capable of the same tasks. In diagnostics, AI models can be used to analyze multimodal data, to improve the accuracy of cancer diagnostics. AI can also identify cancer stage and grade. A tumors stage depends on tumor size, while its grade depends on how much the tumor has spread. By subtyping tumors, researchers are able to develop more targeted drugs, as similar tumors most likely arose through similar pathways. However, data from studies is unverified and different, which makes it hard to find formatted data to train AI models. Although the application of AI clinically is promising, AI still needs to go through more trials before its applied clinically.Theoretical and Natural Science00Although the application of AI clinically is promising, AI still needs to go through more trials before its applied clinically, and data from studies is unverified and different, which makes it hard to find formatted data to train AI models.Theoretical and Natural Science["Ziling Zhou"]2024-10-12T00:00:00 453830406678fe3f72d7be4ebb91be7fa1a6747bProceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education110Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education["Yu-Feng Qi", "Fangxiang Fu", "J. Tian", "Yan Sun"]2024-10-12T00:00:00 aa90d99431f96b5f0b2c035fbb2c80496c2d46f2The Artificial Intelligence field, or AI, experienced a renaissance in the last few years across various fields such as law, medicine, and finance. While there are studies outlining the landscape of AI in the legal field as well as surveys of the current AI efforts of law firms, to our knowledge there has not been an investigation of the intersection of law students and AI. Such research is critical to help ensure current law students are positioned to fully exploit this technology as they embark on their legal careers but to also assist existing legal firms to better leverage their AI skillset both operationally and in helping to formulate future legal frameworks for regulating this technology across industries. The study presented in this paper addresses this gap. Through a survey conducted from July 22 to Aug 19, 2024, the study covers the law students background, AI usage, AI applications in the legal field, AI regulations and open-ended comments to share opinions. The results from this study show the uniqueness of law students as a distinct cohort. The results differ from the ones of established law firms especially in AI engagement - established legal professionals are more engaged than law students. Somewhat surprising, the law firm participants show higher enthusiasm about AI than this student cohort. Collaborations with Computer Science departments would further enhance the AI knowledge and experience of law students in AI technologies such as prompt engineering (zero and few shot), chain-of-thought prompting, and language model hallucination management. As future work, we would like to expand the study to include more variables and a larger cohort more evenly distributed across locales. In addition, it would be insightful to repeat the study with the current cohort in one year to track how the students viewpoints evolve.arXiv.org330This study covers the law students background, AI usage, AI applications in the legal field, AI regulations and open-ended comments to share opinions, and shows the uniqueness of law students as a distinct cohort.ArXiv["Daniela Andreeva", "G. Savova"]2024-10-13T00:00:00 5b8f996ad0fd29981db6429bc781155ecaf94c6cOne of the national health problems that requires special attention and handling from various parties, namely from the central government, regional governments, and the family level, is stunting. Sukamulya Village is one of the villages in Rancaekek District, Bandung, West Java, located in the Citarum River watershed (DAS), which runs a stunting prevention program with three hamlets in it. Currently, one of the targets of stunting education is teenagers. The problem faced when targeting teenagers as targets for education and agents of change, especially in Sukamulya Village, is that the material on stunting has not been packaged with the approach style of teenagers who enjoy technology-based information. In addition, teenagers consider this unimportant because the education provided is too dull and delivered by people they feel are too old (the same age as their parents). So, a training activity was carried out to make an animated video series based on artificial intelligence (AI) called Sukacanting, a method of stunting education and public speaking training to improve public speaking skills in providing stunting prevention education. With the training and creation of AI-based animated videos, several Posyandu cadre youth have improved their skills in creating, managing, and developing health education videos and public speaking skills.ABDI KAMI: Jurnal Pengabdian Kepada Masyarakat200With the training and creation of AI-based animated videos, several Posyandu cadre youth have improved their skills in creating, managing, and developing health education videos and public speaking skills.ABDI KAMI: Jurnal Pengabdian Kepada Masyarakat["Yolanda Stellarosa", "Chrisdina Chrisdina", "Olivia Deliani Hutagaol"]2024-10-13T00:00:00 12d13ec94c79852dacb999fc8a967565057177a2The 4th Industrial Revolution, or more widely known as Industry 4.0, is bringing about profound social, economic, and political changes due to the exponential rate of development of digital technologies and especially of Artificial Intelligence (AI). Against this backdrop, during the last decade, technological advancements have drastically transformed the nature of education, affecting both its content and the way it is delivered. These developments, in combination with the conditions that arose during the Covid-19 pandemic, enforcing an urgent shift to distance learning, have formulated a novel context within which all stakeholders in the field of education must be adjusted to. Artificial Intelligence refers to those systems that simulate human intelligence and perform complex tasks with the help of algorithms and neural networks, while making it possible for the system itself to learn and adapt its responses. Highlighting the advantages and realizing the potential of AI systems, research is increasingly drawing on the applications they can have in education and in the improvement of the learning process. Recent research data are quite intriguing, indicating improvement in learning outcomes, increase in productive learning time, enhancement of experiential learning and adaptation to the learners’ individualized needs. Within this context and with an eye towards the future, the present paper attempts to highlight the role of teachers and adult educators, the skills they must develop, as well as the challenges they have to tackle, so as to be able to initially comprehend, and at a second level to integrate AI applications in their teaching practice. Alea iacta est: it is evident that all previous knowledge regarding technology is no longer sufficient, with AI-mediated education requiring novel skills and competences to manage and exploit data, to collaborate effectively with systems and learners, as well as to develop appropriate educational materials.  Article visualizations:European Journal of Education Studies210The role of teachers and adult educators are highlighted, the skills they must develop, as well as the challenges they have to tackle, so as to be able to initially comprehend and at a second level to integrate AI applications in their teaching practice.European Journal of Education Studies["Eugenia A Panitsides", "Sophia Poulimenou"]2024-10-13T00:00:00 19c25645bbb9e8294d04b583e4f023e53afae8ddThe current study investigates how artificial intelligence (AI) impacts sustainable development in emerging markets, with a focus on Thailand. Following a systematic review approach, research designs are configured for reviewing empirical evidence within peer-reviewed papers and reports. It also presents an overview of the adoption of AI in agriculture, health, and urban planning. The key findings lend credence to the potential use of AI in achieving resource optimality, reducing environmental damage, and urging social equity in its use. But these very initiatives are hampered by the digital divide, concerns about data privacy, and bias in algorithms. The way forward should be to establish solid, regulatory frameworks geared towards more investment in infrastructure with ethical AI practices that will lead to optimal gains. The huge potentials for AI to enable sustainability in Thailand are there; hence, it is very important to reduce the associated risks that require equitably distributed results in all sectors.European journal of sustainable development research870The current study investigates how artificial intelligence (AI) impacts sustainable development in emerging markets, with a focus on Thailand, and presents an overview of the adoption of AI in agriculture, health, and urban planning.European Journal of Sustainable Development Research["Rapeerat Thanyawatpornkul"]2024-10-13T00:00:00 4e6cf9d2cd88a5758b428ed1b201878eac126aedEnvironmental Monitoring & Assessment621The combination of the modified WQII and LSTM model proves to be an effective tool for estimating and predicting water quality indices in similar regions globally, offering valuable insights for water resource management and decision-making processes.Environmental monitoring and assessment["A. Boufekane", "Mohamed Meddi", "Djamel Maizi", "G. Busico"]2024-10-13T00:00:00 b510e6adb363b9f3e9b52e296ff771dae02961f8Nordic Conference on Human-Computer Interaction400{"pages": "26:1-26:10"}["Simone Grassini"]2024-10-13T00:00:00 88fb4b6567f5c09bd22c80438998c95f751a5b0eApplied Artificial Intelligence470Applied Artificial Intelligence["W. Matli"]2024-10-13T00:00:00 01de55ff84573426db9b0d147b125b0e75176883Nordic Conference on Human-Computer Interaction60{"pages": "56:1-56:3"}["S. Stigberg", "Klaudia \u00c7ar\u00e7ani", "Suhas Govind Joshi", "Tone Bratteteig"]2024-10-13T00:00:00 20c6252d8abd59741ab5eb6d1bfbc9f092411495Technology Analysis &amp; Strategic Management380Technology Analysis &amp; Strategic Management["Taekyun Kim", "Jeesu Lee"]2024-10-13T00:00:00 aad9c13db0b6b417bbe68c6dc26088c4f2739eecThe integration of artificial intelligence (AI) in journalism has sparked complex ethical debates, particularly with the rise of generative AI systems. By now, AI permeates the entire news cycle, from information gathering to news dissemination, raising questions revolving around issues such as transparency, accountability, responsibility, bias, and diversity. Previous research showed that news organizations have slowly approached and adapted to ethical concerns regarding the use of AI, developing critical stances mainly due to rising AI power, growing audience skepticism, and mounting tensions within the industry between news publishers’ strategies and journalists’ anxieties. Consequently, ethical guidelines have started to emerge in news organizations, but their practical application remains challenging and under-studied, not only due to the opacity of AI algorithms, but also due to the difficulties of “embedding” journalistic values into AI systems. In the light of an intensifying discourse about ethical concerns in the news industry and growing efforts by governments and institutions such as the European Union to strengthen AI governance, journalism studies have started to explore the issue as well. However, research on AI ethics is still in its infancy, with significant gaps in understanding the practical enforcement of ethical guidelines within newsrooms, in particular when it comes to the design of AI systems. This essay critically discusses the way journalism (studies) approach ethical issues related to the use and the design of AI systems, given that the responsible use and design of AI systems in journalism is crucial given its integral role for democracy and society.Emerging Media313This essay critically discusses the way journalism (studies) approach ethical issues related to the use and the design of AI systems, given that the responsible use and design of AI systems in journalism is crucial given its integral role for democracy and society.Emerging Media["Colin Porlezza", "Aljosha Karim Schapals"]2024-10-13T00:00:00 991d5c4c112ec831e689717a70459853a09544caIn the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.IT JOURNAL RESEARCH AND DEVELOPMENT61This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures and bolster defenses, ensuring the integrity of the authors' increasingly interconnected digital world.IT Journal Research and Development["Zarif Bin Akhtar", "Ahmed Tajbiul Rawol"]2024-10-13T00:00:00 74e409a1449a4e5fb3982e554f3274934efe9b52One of the key controversies that generative artificial intelligence (AI) has recently stirred was whether compensation is due for the copyrighted materials used to train AI models. This article explores the logic, trajectories, and dynamics of content generation, including news, through generative AI in two distinctive yet intertwined domains. Guided by a cultural political economy approach, it examines how both the political context (validation/legitimation of AI-generated news content by established news media) and the economic context (use of unpaid and underpaid labor in the forms of freely scraped data and data annotation work) shape the deployment of news content on AI models. It further untangles how the space for serious, independent journalism may shrink, as big tech companies’ algorithmic technologies emerge as a solution to contemporary problems in journalism. A clear danger here is that AI companies’ proprietary algorithms, language training models, and value-laden parameters are incompatible with journalism's democratic obligations and responsibilities.Emerging Media180This article explores the logic, trajectories, and dynamics of content generation through generative AI in two distinctive yet intertwined domains, and examines how both the political context and the economic context shape the deployment of news content on AI models.Emerging Media["Siho Nam"]2024-10-13T00:00:00 edca53eab41160d3852838b3d8145fa9eb9608d3The prevalence of artificial intelligence in the workplace has risen over the past few years and likely isn’t going anywhere anytime soon. And while the field of AI is likely to continue to evolve rapidly in the coming years, there are already ways that you might be able to leverage its benefits to better protect your campus, particularly from events like active shooters.Campus Security Report00The prevalence of artificial intelligence in the workplace has risen over the past few years and likely isn’t going anywhere anytime soon, so there are already ways that you might be able to leverage its benefits to better protect your campus, particularly from events like active shooters.Campus Security Report["H. Sutton"]2024-10-13T00:00:00 13cd49ddc4b6695ba54b0bd3790c05b53906530bWhile Artificial Intelligence (AI), Data Science and Data Integration have dramatically changed the technological landscape in their own right, their impact is even more dramatic when combined. This article looks into the commonalities in these fields of practice and shows them to be extracts from a framework that incorporates integrated data management, advanced analytics capabilities and intelligent algorithms which collectively have had profound impacts on how we continue to innovate across industries. The article then explores the nature of these technologies at present, the associated challenges and their future potential to yield insights into strategic approaches toward the alignment of AI, Data Science and Data Integration with the common goal of converting data into actionable intelligence.International Journal of Scientific Research and Modern Technology (IJSRMT)10The article explores the nature of these technologies at present, the associated challenges and their future potential to yield insights into strategic approaches toward the alignment of AI, Data Science and Data Integration with the common goal of converting data into actionable intelligence.International Journal of Scientific Research and Modern Technology (IJSRMT)["Shashidhar Reddy Keshireddy"]2024-10-13T00:00:00 26ef211464e8454aeb1903073f0aa6d9c6e89b70Recent advancements in Artificial Intelligence (AI) algorithms have sparked a race to enhance hardware capabilities for accelerated task processing. While significant strides have been made, particularly in areas like computer vision, the progress of AI algorithms appears to have outpaced hardware development, as specialized hardware struggles to keep up with the ever-expanding algorithmic landscape. To address this gap, we propose a new accelerator architecture, called messaging-based intelligent processing unit (m-IPU), capable of runtime configuration to cater to various AI tasks. Central to this hardware is a programmable interconnection mechanism, relying on message passing between compute elements termed Sites. While the messaging between compute elements is a known concept for Network-on-Chip or multi-core architectures, our hardware can be categorized as a new class of coarse-grained reconfigurable architecture (CGRA), specially optimized for AI workloads. In this paper, we highlight m-IPU's fundamental advantages for machine learning applications. We illustrate the efficacy through implementations of a neural network, matrix multiplications, and convolution operations, showcasing lower latency compared to the state-of-the-art. Our simulation-based experiments, conducted on the TSMC 28nm technology node, reveal minimal power consumption of 44.5 mW with 94,200 cells utilization. For 3D convolution operations on (32 x 128) images, each (256 x 256), using a (3 x 3) filter and 4,096 Sites at a frequency of 100 MHz, m-IPU achieves processing in just 503.3 milliseconds. These results underscore the potential of m-IPU as a unified, scalable, and high-performance hardware architecture tailored for future AI applications.arXiv.org670This paper highlights m-IPU's fundamental advantages for machine learning applications, and illustrates the efficacy through implementations of a neural network, matrix multiplications, and convolution operations, showcasing lower latency compared to the state-of-the-art.ArXiv["Md. Rownak Hossain Chowdhury", "Mostafizur Rahman"]2024-10-13T00:00:00 decf980074fdc703c0ce8e1abc4d99ba35797df8This study explores the socio-technological barriers to the adoption of artificial intelligence (AI)-powered solutions in three countries of the global south – South Africa, Lesotho, Eswatini, Botswana and Zimbabwe. Through 20 in-depth interviews with key stakeholders, it examines the distribution and circulation of AI technologies within selected newsrooms. Furthermore, the article explores socio-technological obstacles to the integration of AI among journalists. Lastly, it examines the consequences of these socio-technological obstacles to journalism. The article specifically seeks to answer three questions: How are AI technologies integrated in southern African newsrooms? What are the socio-technological barriers attendant to the use of AI in selected news organisations of sub-Saharan Africa? What are the implications of these socio-technological barriers to the process of news production in these newsrooms? The article reveals the challenges hindering the development and deployment of AI in these organisations, and I highlight the detrimental effects of limited AI access, which places Southern African media organisations at a disadvantage on the global stage, perpetuating socio-technological disparities between the global north and global south. The findings underscore the urgency of addressing these barriers in order to reduce information inequality, increase efficiency and productivity and improve audience engagement. Reducing technological barriers and democratising AI integration can enhance the influence of Southern African media in promoting domestic and international equity. The article therefore, propose a model for AI adoption and reduction of technological barriers in adopting these technolgies. This study emphasises the need for collaborative efforts among policymakers, industry leaders and stakeholders to create an inclusive environment that maximises the potential of AI for the greater benefit of parts of Southern African societies. The study adds to extant literature at the intersection of AI and media, particularly how AI technologies are impacting on specific communities of practice, like newsroom organisations.Emerging Media180A model for AI adoption and reduction of technological barriers in adopting these technolgies is proposed to create an inclusive environment that maximises the potential of AI for the greater benefit of parts of Southern African societies.Emerging Media["Allen Munoriyarwa"]2024-10-13T00:00:00 5b623d7d99ec8d0196bdbf864f2bff96ffadfde1ACM Symposium on User Interface Software and Technology133{"pages": "65:1-65:3"}["Arvindmani Satyanarayan"]2024-10-13T00:00:00 23e7c050af7d8e51e78ab8607c5723caaa32cd5fThe study of the laws of motion has been advancing, with significant contributions from key figures like Galileo and Newton. Analogous to the gravitational forces observed in the natural world, individuals occasionally find themselves irresistibly drawn to specific entities. The gravity in mind, the basis of free-fall motion in one’s mind, acts as a sensor to make an individual sense subtle judgments about things like common sense, as if it were whispering to our minds. Since it has been said for more than half a century that judging common sense is the most difficult task for AI, this paper explores whether AI can possess true intelligence by applying this mechanism. Empirical data from many different types of games show that Game Refinement (GR) zone is located in 0.07-0.08, which respectively corresponds to the lower limit (fairness) and upper limit (engagement). In other words, there is a border between objectivity and subjectivity in a thing, and this is the minimal objectivity, or the resignation in game context. Based upon this, in unconventional circumstances, when a greater gravitational acceleration operates within the mind, a sense of “playfulness” is generated, disrupting the harmony of comfort and discomfort sustained by the gravity in mind. The study concludes that applying the “gravity in mind” mechanism to AI could significantly blur the line between human and artificial intelligence, enhancing AI decision-making capabilities.Journal of Mathematical Sciences and Informatics120The study concludes that applying the “gravity in mind” mechanism to AI could significantly blur the line between human and artificial intelligence, enhancing AI decision-making capabilities.Journal of Mathematical Sciences and Informatics["Lulu Gao", "Hiroyuki Iida"]2024-10-13T00:00:00 a84e5f8070d881ef46180aeb69d54a9160cbca3fPenelitian ini bertujuan untuk mengeksplorasi bagaimana dinamika pembelajaran Pendidikan Agama Islam (PAI) dapat ditingkatkan melalui pemanfaatan kecerdasan buatan (Artificial Intelligence/AI). Metode yang digunakan dalam penelitian ini adalah pendekatan deskriptif kualitatif dengan fokus pada studi kepustakaan. Melalui tinjauan literatur, penelitian ini mengungkap beberapa manfaat utama dari integrasi AI dalam pembelajaran PAI. AI memungkinkan personalisasi materi pembelajaran sesuai dengan kebutuhan individu setiap siswa, meningkatkan aksesibilitas pembelajaran terutama bagi siswa di daerah terpencil atau dengan kebutuhan khusus, serta mengotomatisasi tugas administratif sehingga guru dapat lebih banyak berfokus pada proses pengajaran. Selain itu, AI mendukung pengembangan konten pembelajaran yang lebih interaktif dan analisis data yang mendalam untuk peningkatan kurikulum. Dengan pendekatan yang lebih personal, diharapkan bahwa AI dapat mendorong motivasi dan keterlibatan siswa, menciptakan pengalaman belajar yang lebih bermakna dan mendalam. Secara keseluruhan, integrasi AI dalam pembelajaran PAI memiliki potensi besar untuk menciptakan lingkungan belajar yang lebih efektif, adaptif, dan mampu menjawab tantangan pendidikan di era digital saat ini.Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai03Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai["R. Nurhayati", "Taufiq Nur", "Sudirman P", "Nur Adillah", "Agustina", "Magfira Urva"]2024-10-14T00:00:00 ed7de0c9416f104371dc882fc6df597b811cfc9cJournal of Assisted Reproduction and Genetics792An overview of the latest advancements in various applications of AI in IVF, including follicular monitoring, oocyte assessment, embryo selection, and pregnancy outcome prediction is presented.Journal of assisted reproduction and genetics["Qing Zhang", "Xiaowen Liang", "Zhiyi Chen"]2024-10-14T00:00:00 cc1706edb6f1a3dd08da22aefde7b7ea681525beWith the arrival of generative artificial intelligence (genAI) tools, psychology educators are rethinking their assessment practices. This paper describes one approach to integrating genAI into an assessment designed to promote psychological literacy. Students used ChatGPT to generate a media release about a published article and then wrote a critique. We evaluated whether students were able to use the marking rubric to assess the ChatGPT output, and whether working with the rubric early in the assessment process had benefits for their grades on subsequent tasks. The results show that students accurately assessed the ChatGPT output against the marking rubric, judging the output to be stylistically good but lacking in accurate coverage of the aims, methods, and results of the research. Working with genAI and the marking rubric early in the assessment process had benefits for performance, relative to cohorts that had engaged in peer review. By allowing students to use genAI and scaffolding the process of critiquing and revising, students gained competencies in psychological, feedback, and AI literacies. Integrating genAI presents opportunities for learning, if educators can think beyond the artifact and design assessment that allows our students to showcase their learning process.Teaching of psychology51This paper describes one approach to integrating genAI into an assessment designed to promote psychological literacy, and evaluates whether students were able to use the marking rubric to assess the ChatGPT output, and whether working with the rubric early in the assessment process had benefits for their grades on subsequent tasks.Teaching of Psychology["Jenny L. Richmond", "Kate Nicholls"]2024-10-14T00:00:00 b70362112319e492317e92329b113b99aabede89Scientific Reports602The proposed method could provide a systematic way to assess the best classifier with the optimal in-silico biomarkers for predicting the TdP risk of drugs, thereby advancing the field of cardiac safety evaluations.Scientific Reports["M. A. Pramudito", "Y. Fuadah", "Ali Ikhsanul Qauli", "Aroli Marcellinus", "Ki Moo Lim"]2024-10-14T00:00:00 82b600e6803690b07fd901634193aaac6afe06acThe increasing integration of artificial intelligence technologies into the financial structures of the BRICS Plus countries (comprising the original member countries of Brazil, Russia, India, China, and South Africa as well as the four new member countries of Egypt, Ethiopia, Iran, and the United Arab Emirates) presents both opportunities and challenges in combating economic crimes, which include money laundering and terrorism financing. This article explores the complex regulatory landscape that governs the application of artificial intelligence in these efforts. It examines how artificial intelligence can enhance the performance of anti-money laundering and counter-terrorism financing frameworks by enabling the evaluation of massive datasets, the identification of anomalous transaction patterns, and the automation of compliance procedures. Simultaneously, the article addresses the highly challenging situations that arise when using artificial intelligence. For instance, these technologies can make it difficult to understand the fluctuation of illicit price ranges, thereby complicating efforts to determine their origins and destinations. Through a comparative analysis of the frameworks throughout the BRICS Plus countries, this research highlights the varying levels of regulatory readiness of these frameworks and proposes pathways for harmonizing artificial intelligence-driven economic security measures. The overarching goal of an artificial intelligence model is to enhance both the effectiveness and the integrity of the financial sectors in the BRICS Plus consortium, necessitating a collaborative approach to combating financial crimes in an increasing number of digital economies across the world.BRICS Law Journal62How artificial intelligence can enhance the performance of anti-money laundering and counter-terrorism financing frameworks by enabling the evaluation of massive datasets, the identification of anomalous transaction patterns, and the automation of compliance procedures is examined.BRICS Law Journal["M. Aksenova"]2024-10-14T00:00:00 e3dfca67b434f977b0c0cd64020e9e033fbea0bdThis study advances research and practice related to adopting artificial intelligence (AI) in the context of South Africa (SA). The study evaluated AI adoption by South African manufacturing Small and Medium Enterprises (SMEs); established the challenges faced by manufacturing SMEs in adopting AI; and developed a framework for adopting AI for manufacturing SMEs’ growth and survival. The study adopted a systematic literature review approach. Articles from Scopus and Google scholar databases, ranging from the years 2018 to 2024, were used. Of the 206 articles found, 54 were shortlisted. The systematic review analysis was performed using the PRISMA framework. The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity. The results also show, despite the numerous benefits AI adoption can offer manufacturing SMEs in the country, a major constraint is the lack of a framework to enhance adoption and implementation. Hence, this study was conducted to develop a framework to improve AI adoption by South African manufacturing SMEs. The findings contribute to the body of knowledge and provide new insights to manufacturing SME owners/managers, policymakers and practitioners into AI adoption to enhance manufacturing SMEs’ ability to compete on the global stage.International Journal of Research In Business and Social Science790The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity, and developed a framework for adopting AI for manufacturing SMEs’ growth and survival.International Journal of Research in Business and Social Science (2147- 4478)["Emmanuel Akoh"]2024-10-14T00:00:00 4d003d4d0a2f39cbb6de67726730f32ca4e0ef71Communication sits at the heart of any coordination within organization. Yet, what are the consequences when employes use Artificial Intelligence (AI) to copilot, i.e., support, their communication? While AI support in human interactions holds much promise for improving communication quality at work, it also fundamentally challenges how much people trust that communication. We, therefore, ask how organizations should introduce AI. In particular, we focus on the responsibility of leaders as stewards of workplace communication. Accordingly, we offer a set of specific hands-on recommendations on how employes should be guided to use AI copiloting effectively so that they do not give in to the temptation of letting go of the “steering wheel” (i.e., allowing AI to [auto]pilot intraorganizational communication).Journal of Leadership &amp; Organizational Studies480The responsibility of leaders as stewards of workplace communication is focused on, with a set of specific hands-on recommendations on how employes should be guided to use AI copiloting effectively so that they do not give in to the temptation of letting go of the “steering wheel”.Journal of Leadership &amp; Organizational Studies["Niels Van Quaquebeke", "Fabiola H. Gerpott"]2024-10-14T00:00:00 eefcb0e2ae1db0103db731543cbb31b9e44b4d0aThis study is committed to in-depth analysis of the practical application of artificial intelligence in intelligent construction teaching and its empowerment effect. An efficient and intelligent teaching platform is built, which can intelligently recommend relevant learning resources according to students’ learning progress and feedback, and track students’ learning situation in real time. This personalized learning style not only improves the learning efficiency of students, but also enables them to better adapt and master the knowledge and skills in the field of intelligent construction. The introduction of artificial intelligence technology has innovated the method of intelligent construction teaching. Based on the learning analysis technology of big data, we can conduct in-depth analysis of students’ learning behaviors and achievements, so as to grasp students’ learning status and needs more accurately. These achievements not only show the great potential of artificial intelligence technology in the field of education, but also provide new ideas and methods for the reform and innovation of intelligent construction teaching.Global Research in Higher Education00This study is committed to in-depth analysis of the practical application of artificial intelligence in intelligent construction teaching and its empowerment effect, and can conduct in-depth analysis of students’ learning behaviors and achievements, so as to grasp students’ learning status and needs more accurately.Global Research in Higher Education["Ziming Qiu", "Chuanli Yang"]2024-10-14T00:00:00 76b61ff0c0c669655dc3966a8bb4f70600bb5a6fOBJECTIVE There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone. METHODS A commercially available AI algorithm was retrospectively applied to patients undergoing screening full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) at a single institution from 2010 to 2019. Ground truth was established based on 1-year follow-up data. Descriptive statistics were performed with attention focused on AI detection of false negative cancers within these subsets. RESULTS A total of 26 694 FFDM and 3183 DBT examinations were analyzed. Artificial intelligence was able to detect 7/13 false negative cancers (54%) in the FFDM cohort and 4/10 (40%) in the DBT cohort on the preceding screening mammogram that was interpreted as negative by the radiologist. Of these, 4 in the FFDM cohort and 4 in the DBT cohort were identified in breast densities of C or greater. False negative cancers detected by AI were predominantly luminal A invasive malignancies (9/11, 82%). Artificial intelligence was able to detect these false negative cancers a median time of 272 days sooner in the FFDM cohort and 248 days sooner in the DBT cohort compared to the radiologist. CONCLUSION Artificial intelligence was able to detect cancers at the time of screening that were missed by the radiologist. Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations.Journal of Breast Imaging280Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations, because of limited data on the application of artificial intelligence on nonenriched, real-world screening mammograms.Journal of breast imaging["S. R. Plimpton", "Hannah S Milch", "Christopher Sears", "J. Chalfant", "Anne Hoyt", "Cheryce Fischer", "William Hsu", "Melissa M. Joines"]2024-10-14T00:00:00 3e4cb5ada0ce9a85d0a666ae4b0907e04026abd3With the emergence and broad deployment of Artificial Intelligence (AI) in all sectors of the economy and human endeavours, this article accentuates the possibility that some fundamental human rights (guaranteed by most civilised nations) will be impacted and altered. To this end, the article appraises selected salient human rights being impacted by the deployment of AI, and which violate protected and guaranteed human rights, raising major concerns. The article assesses the theoretical grounding of human-rights law and its catalytic role in informing and shaping the emergence of new fundamental rights. Equally important, the article delves into pertinent legal issues emanating from the use of AI technologies and their potential threats to vulnerable new rights. While AI promises a significant positive impact on the economy and human development, there is a need to address pertinent concerns about the manner in which AI could impact existing human rights, and ultimately alter the form and content of these rights, resulting in the emergence of new fundamental rights.Obiter00The article assesses the theoretical grounding of human-rights law and its catalytic role in informing and shaping the emergence of new fundamental rights, and delves into pertinent legal issues emanating from the use of AI technologies and their potential threats to vulnerable new rights.Obiter["Thupane J Kgoale", "Kola O Odeku"]2024-10-14T00:00:00 79f1ed09ea4350bc9555405c60ae077043d41f4bIn today's digital landscape, cloud systems have become integral to business operations, offering scalability, flexibility, and cost-efficiency. However, these benefits are accompanied by heightened cybersecurity risks, with cloud environments increasingly targeted by sophisticated cyber threats. This paper explores the application of Artificial Intelligence (AI) in enhancing predictive cybersecurity for cloud systems. It emphasizes the role of AI in identifying potential threats before they materialize, thus providing a proactive defense mechanism against cyberattacks. The study begins by reviewing the current state of cybersecurity in cloud computing, highlighting existing vulnerabilities and common attack vectors. It then examines how AI techniques, such as machine learning, deep learning, and natural language processing, can be leveraged to detect anomalies, predict potential breaches, and automate threat response processes. By analyzing large volumes of data in real-time, AI models can identify patterns and anomalies that may signify a security threat, enabling quicker and more accurate responses than traditional cybersecurity methods. A comparative analysis of various AI-driven cybersecurity models is conducted, focusing on their effectiveness in different cloud environments. The paper also addresses the challenges associated with implementing AI in cloud cybersecurity, including data privacy concerns, the need for substantial computational resources, and the risk of adversarial attacks against AI models. Through case studies and experimental results, this research demonstrates the potential of AI to transform cybersecurity practices in cloud computing, offering a robust solution to predict, detect, and mitigate cyber threats. The findings suggest that integrating AI with existing security frameworks can significantly enhance the overall security posture of cloud systems, reducing the likelihood of successful attacks and minimizing the impact of breaches. This paper concludes by discussing future directions for research in AI-based cloud security, emphasizing the need for ongoing advancements to stay ahead of evolving cyber threats.International Journal of Futuristic Innovation in Arts, Humanities and Management (IJFIAHM)270The findings suggest that integrating AI with existing security frameworks can significantly enhance the overall security posture of cloud systems, reducing the likelihood of successful attacks and minimizing the impact of breaches.International Journal of Futuristic Innovation in Arts, Humanities and Management (IJFIAHM)["Puneet Gautam"]2024-10-14T00:00:00 d9db3888acd27f028730672c6b8d4bef12bfe207Purpose Recent technological developments have encouraged the United Nations to promote the adoption of digital technologies to achieve the Sustainable Development Goals (SDGs). In addition to initiatives from businesses, an increasing number of studies indicate that public service agencies may gain benefits from adopting digital transformation. On a global scale, policymakers are examining the integration of digital technologies, specifically artificial intelligence (AI), into public service delivery (PSD), acknowledging the potential advantages and obstacles for the public sector. Therefore, the objective of this study is to investigate the impact of AI on PSD to support the SDGs initiative. Design/methodology/approach The research used a qualitative approach to explore the intersection of AI, SDGs and PSD. This approach involved scrutinising relevant publications and conducting an extensive literature review. The research also used bibliographic analysis to discern patterns within the field. Findings from the literature review and bibliographic analysis contributed to identifying research trends that explore the complex relationship among AI, PSD and the SDGs. The model derived from this comprehensive review and analysis elucidates the potential of AI to enhance PSD and contribute to the achievement of the SDGs. Findings The bibliographic study revealed significant research trends concerning AI, PSD and SDGs through an empirical investigation of an extensive array of peer-reviewed articles. This investigation focused on how the public sector can improve its delivery of services to citizens and all stakeholders to advance the SDGs. AI holds the promise of revolutionising PSD and bolstering the SDGs. By leveraging AI’s capabilities in data analysis, automation and customisation, governments can enhance the efficiency, effectiveness and accessibility of public services. This, in turn, enables public servants to tackle more complex tasks while providing citizens with personalised and relevant experiences. Additionally, the study advocates modelling the intersection of PSD and AI to achieve sustainable development. Research limitations/implications The employed research methodologies, such as literature reviews and bibliographic analysis, enrich the context of AI, SDGs and PSD. They offer a comprehensive perspective, identify knowledge gaps and furnish policymakers, practitioners and academics with a conceptual framework for informed decision-making and sustainable development endeavours. Originality/value The study provides an agenda for AI and SDGs research on application in PSD. It emphasises varied research viewpoints, methods and gaps. This study helps researchers as well as practitioners identify subtopics, intersecting themes and new research pathways. Journal of Science and Technology Policy Management540This study helps researchers as well as practitioners identify subtopics, intersecting themes and new research pathways and furnish policymakers, practitioners and academics with a conceptual framework for informed decision-making and sustainable development endeavours.Journal of Science and Technology Policy Management["Muhammad Anshari", "Mahani Hamdan", "Norainie Ahmad", "Emil Ali"]2024-10-14T00:00:00 fe961dd065dddb6287768de5f4e4c0d4926c8ab1PurposeThey proposed the necessity of restructuring higher education to provide students and instructors with the skills required for future employment in a world driven by artificial intelligence (AI). The implementation of AI in higher education and its impact on Generation Z students' academic ambitions.Design/methodology/approachHigher education plays a vital role in cultivating ethical individuals and professionals on a worldwide scale. The implementation of AI in higher education and its impact on Generation Z students' academic ambitions. This study used qualitative methods to examine the viewpoints of students regarding the influence of AI on higher education. For this study, a cohort of 25 students from Pune city was chosen.FindingsThe results indicate that there is a need to reform higher education in order to prepare students for future jobs in a society driven by AI. They indicated a lack of extensive understanding on the subject and so sought a clear explanation from an AI during their consultation. Based on the results of this study, it is evident that Generation Z students do not experience fear or worry in relation to emerging technology. On the contrary, they embrace multitasking and actively want to acquire new skills to prepare themselves for the future.Research limitations/implicationsThis study has limitations; the data were collected from 25 students, and the insights gathered may not represent the whole population. The geographical restriction was that it was restricted only to Pune. Second, educators are equally important and may have different views; therefore, future studies should collect educators’ views.Originality/valueThey proposed restructuring higher education to provide students and instructors with the skills required for future employment in a world driven by AI. Their proposal introduces novel learning goals that prioritize the development of abilities in both information, learning and education through the use of AI. Participants' narratives are evaluated using advanced techniques such as VOSviewer to give in-depth analysis.Asian Education and Development Studies360It is evident that Generation Z students do not experience fear or worry in relation to emerging technology, and they embrace multitasking and actively want to acquire new skills to prepare themselves for the future.Asian Education and Development Studies["Rozalin Routray", "Komal Khandelwal"]2024-10-14T00:00:00 0b7f0735d5ff579c34ba3c2d220061a87ae7da4fArtificial Intelligence (AI) has now touched the realm of government, such as in the creation of State Administrative Decrees (SAD/Keputusan Tata Usaha Negara/KTUN). The involvement of AI in this realm leaves questions regarding the application of its transparency aspects based on the Indonesian legal context. This article aims to understand the phenomenon of using AI for SADs in Indonesia and to find out the application of the principle of transparency generally and contextually in terms of using AI for SADs. This research is normative-empirical research with a conceptual and statutory approach by conducting literature studies and respondent interviews. As a result, SADs as a product of government administrative action in several cases have been using AI and have received normative recognition. On the other hand, there is legislation that regulates the general aspects of SAD transparency, especially related to public information openness and personal data protection. AI transparency itself can be divided into two concepts, namely: technical transparency and justifiable transparency. Justifiable transparency is considered more convenient and suitable to be applied in Indonesia.Journal of Policy and Society00This article aims to understand the phenomenon of using AI for SADs in Indonesia and to find out the application of the principle of transparency generally and contextually in terms of using AI for SADs.Journal of Policy and Society["Muhammad Guntur Hamonangan Nasution", "Nia Faridatul Khasanah"]2024-10-14T00:00:00 38fe95340f588d975f14d933fe4789edffc6ca5aThe use of technology is increasing due to Industry 4.0. Both countries and organizations have had to invest in the field of artificial intelligence (AI) to compete with their rivals in global competitive conditions and to adapt to the ever-changing world. An organization or a country needs to evaluate its performance to ensure its sustainability constantly. The Data Envelopment Analysis (DEA) method is widely used in performance evaluation. This study aimed to evaluate Türkiye AI performance for the 9 years between 2014-2022. In the research, years were included in the analysis as the decision-making unit. 2 input and 2 output variables were used in the analyses. The study was carried out using the input-oriented CCR DEA model and its super-efficiency model. According to the results of the analysis, efficient/inefficient decision-making units were determined. Several potential improvement suggestions have been put forward for inefficient decision-making units.Denetişim00This study aimed to evaluate Türkiye AI performance for the 9 years between 2014-2022 using the input-oriented CCR DEA model and its super-efficiency model.Denetişim["Y. Ersoy", "Ali Tehci", "F. Selamzade"]2024-10-14T00:00:00 8020de1e79507dba338fa703009674ca88985721Artificial Intelligence AI based on medical images has shown great potential in disease diagnosis and treatment.Open Journal of Clinical and Medical Images160Open Journal of Clinical and Medical Images["Gang Song"]2024-10-14T00:00:00 0eb798d103b98e83af1321d15062283bde93d47cxAI440This work addresses the helpfulness of XAI for human decision-making via the user's ability to successfully perform a proxy task, designed such that a good performance is an indicator for the explanation to provide helpful information.ArXiv["Tobias Labarta", "Elizaveta Kulicheva", "Ronja Froelian", "Christian Geissler", "Xenia Melman", "Julian von Klitzing"]2024-10-14T00:00:00 7dad256bec21a745805a450d61dbc76c5cf865f4The definition and uses of Artificial Intelligence, often abbreviated as AI, has come a long way since its inception. This technology is rapidly changing some business operations in the areas of trading, identification of frauds, and customer service in particular, the modern finance sector, and this article highlights the importance of technology in these processes. Significant Applications of Artificial Intelligence in Finance: From Algorithmic Trading to Robo-Advisors. Explaining, the paper analyzes the current trends and practices in the industries, addressing the positive and negative aspects of AI in the finance sector, with respect to the visions of the future. One that relates to the socio-technical implications that include the use of such technology in finance and the associated risks of data breach, availability, and loss of control and trust in processes.African Journal of Commercial Studies70Significant Applications of Artificial Intelligence in Finance: From Algorithmic Trading to Robo-Advisors analyzes the current trends and practices in the industries, addressing the positive and negative aspects of AI in the finance sector, with respect to the visions of the future.African Journal of Commercial Studies["Mr. Aswin"]2024-10-14T00:00:00 51d6c4ba944430d3372cebffa8bb92e2a64aa8a5Unmanned Aerial Vehicles (UAVs) with limited computational, perception and power resources face significant challenges when navigating autonomously in unfamiliar environments. While artificial intelligence (AI)-assisted algorithms have been used to address these limitations, transparency of the underlying AI models remains a concern, hindering user trust. To address this limitation, this research study proposes a novel, explainable AI-based navigation approach for UAVs to navigate them through unknown environments autonomously. The soft actor-critic (SAC) algorithm and multilayer perceptron (MLP) policies integrated deep reinforcement learning algorithm is developed to derive control actions. This controller is integrated with a novel moving-window gradient-based explainable artificial intelligence (XAI) framework to shed light on the UAV’s decision-making process. The proposed XAI algorithm provides granular insights into how various factors, such as image segments and UAV state features, influence the UAV’s actions. It lays the groundwork for a novel visual explanation approach that segments input depth images to highlight critical navigational cues, augmented by a dynamic color map for precise obstacle identification. Additionally, the study introduces comprehensive textual explanations to provide an in-depth understanding of the UAV’s decision processes, thereby improving the model’s transparency and explainability. The simulation results indicate that the proposed DRL model achieves over 95% success rate. Moreover, evaluations conducted in two distinct environments demonstrate the model’s capability to generate effective and reliable explanations.IEEE/RJS International Conference on Intelligent RObots and Systems290This research study proposes a novel, explainable AI-based navigation approach for UAVs to navigate them through unknown environments autonomously, and introduces comprehensive textual explanations to provide an in-depth understanding of the UAV’s decision processes, thereby improving the model’s transparency and explainability.2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)["Didula Dissanayaka", "Thumeera R. Wanasinghe", "R. Gosine"]2024-10-14T00:00:00 a4d06b59186722640c6eac830bebd88c60a465feHealth, Risk &amp; Society521Health, Risk &amp; Society["Patrick Brown", "Roanne van Voorst"]2024-10-14T00:00:00 95ad19be094dda585001807d7b0994ea4e9d48adNursing Education Perspectives01Nursing education perspectives["Matthew D Byrne"]2024-10-14T00:00:00 b27d456293be08e07fa5b54e0878dc04b228011dДанная статья посвящена анализу влияния глобальной цифровизации знаний на развитие искусственного интеллекта (ИИ). В статье рассматриваются основные направления этого процесса: оцифровка текстовой информации, создание баз знаний, разработка систем автоматического извлечения информации и др. Особое внимание уделяется тому, как цифровизация знаний способствует расширению базы знаний искусственного интеллекта, ускорению развития обработки естественного языка и созданию новых возможностей для научных открытий. Вместе с тем, в статье поднимаются и проблемы, связанные с качеством данных, потенциальной предвзятостью и вопросами авторского права. В заключении подчеркивается важность ответственного и этичного подхода к цифровизации знаний, чтобы в полной мере реализовать ее потенциал для развития искусственного интеллекта на благо всего человечества. This article is devoted to the analysis of the impact of the global digitalization of knowledge on the development of artificial intelligence (AI). The article discusses the main directions of this process: digitization of textual information, creation of knowledge bases, development of automatic information extraction systems, etc. Special attention is paid to how the digitalization of knowledge contributes to the expansion of the knowledge base of artificial intelligence, accelerating the development of natural language processing and creating new opportunities for scientific discoveries. However, the article also raises issues related to data quality, potential bias, and copyright issues. In conclusion, the importance of a responsible and ethical approach to the digitalization of knowledge is emphasized in order to fully realize its potential for the development of artificial intelligence for the benefit of all mankind.Современное состояние и перспективы инновационного развития науки: сборник статей международной научной конференции (Сургут, Июль 2024)20Современное состояние и перспективы инновационного развития науки: сборник статей международной научной конференции (Сургут, Июль 2024)["\u0412\u043b\u0430\u0434\u0438\u043c\u0438\u0440 \u0412\u0430\u0441\u0438\u043b\u044c\u0435\u0432\u0438\u0447 \u0421\u043c\u0435\u0442\u0430\u043d\u0430"]2024-10-14T00:00:00 8fafd668a6d2bb923189f96878a2a096c784635cTherapy00Therapy["Kudryavtseva N.A. Kudryavtseva", "Chorbinskaya S.A. Chorbinskaya", "Devyatkin\u00a0A.V. Devyatkin\u00a0A", "Samushiya M.A. Samushiya", "Kolpakov\u00a0E.A. Kolpakov\u00a0E", "Kuznetsov A.I. Kuznetsov", "Shchepkina E.V. Shchepkina"]2024-10-14T00:00:00 f516c68e1c1e9d658a078021748bcf607b44fd1cEnvironment, Development and Sustainability660Environment, Development and Sustainability["Olivier Donfouet", "Ibrahim Ngouhouo"]2024-10-14T00:00:00 ff4a6fde954d259b5946ed71895612bb2a61ea2eTurk psikiyatri dergisi = Turkish journal of psychiatry00Turk psikiyatri dergisi = Turkish journal of psychiatry["Mahmut \u00d6zer"]2024-10-14T00:00:00 7501b4f828aeaca3c73b59b9d9d662139b0bb8ffNursing Education Perspectives70Nursing education perspectives["M. Oermann"]2024-10-14T00:00:00 13507e53992ba517ac9ed4ec430effa4a47868e9Radiography230Radiography["C. Malamateniou", "T. O'Regan", "S. L. McFadden", "M. Jackson"]2024-10-14T00:00:00 224ea2a6eafae63e969f31956b2c1b0cf6a7af49Much of HRI research focuses on personalizing robots in order to ease societal acceptance, and favour their uptake. Companion robots are indeed conceived as a potential solution to numerous societal concerns, among which aging population, and individuals’ isolation. In such a perspective, personalization is indeed key, for it ensures individuals feel comfortable using robots in their daily lives and environments. This also depends upon the so called cultural competences the machine possesses. In fact, how humans behave largely depends upon their heritage, and overall understanding of the environment. An identical reaction, posture or expression might, indeed, be perceived very differently according to the culture of the person exposed to it.IEEE/RJS International Conference on Intelligent RObots and Systems400Much of HRI research focuses on personalizing robots in order to ease societal acceptance, and favour their uptake, for it ensures individuals feel comfortable using robots in their daily lives and environments.2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)["Andrea Bertolini"]2024-10-14T00:00:00 c5f06968691c9afb10abc19d548f566dd585d19bTurk psikiyatri dergisi = Turkish journal of psychiatry00Turk psikiyatri dergisi = Turkish journal of psychiatry["Hayri Can \u00d6zden"]2024-10-14T00:00:00 27398f640045ccd8a33a386c7e4aaa4079fa363dA traditional approach to assessing emerging intelligence in the theory of intelligent systems is based on the similarity,"imitation"of human-like actions and behaviors, benchmarking the performance of intelligent systems on the scale of human cognitive skills. In this work we attempt to outline the shortcomings of this line of thought, which is based on the implicit presumption of the equivalence and compatibility of the originating and emergent intelligences. We provide arguments to the point that under some natural assumptions, developing intelligent systems will be able to form their own intents and objectives. Then, the difference in the rate of progress of natural and artificial systems that was noted on multiple occasions in the discourse on artificial intelligence can lead to the scenario of a progressive divergence of the intelligences, in their cognitive abilities, functions and resources, values, ethical frameworks, worldviews, intents and existential objectives: the scenario of the AGI evolutionary gap. We discuss evolutionary processes that can guide the development of emergent intelligent systems and attempt to identify the starting point of the progressive divergence scenario.arXiv.org240This work outlines the shortcomings of a traditional approach to assessing emerging intelligence in the theory of intelligent systems, and provides arguments to the point that under some natural assumptions, developing intelligent systems will be able to form their own intents and objectives.ArXiv["Serge Dolgikh"]2024-10-14T00:00:00 0345cc396c5da0dfd29c26ade8a8aa225f8a3ac4 Interest in artificial intelligence (AI) has grown rapidly alongside the ability to examine information in far vaster quantities and from more diverse sources, and to provide previously unattainable forms of evaluation. For the intelligence community, AI offers an important solution to their data collection bottleneck, allowing the data to be processed and analysed at speeds and in ways not previously possible. While AI has received some general criticism, when it is combined with the reach, secrecy, and coercive power of the intelligence community it creates unique ethical problems. Intelligence–AI exacerbates the biases AI creates, undermines proposed transparency solutions, and creates new ethical dilemmas and harms. This article examines intelligence–AI across its collection, processing, and analysis phases. It argues that open-source does not necessarily mean ethical, as the AI collection en masse of social media data violates citizens' privacy, consent and autonomy. The article also argues that AI-aided categorization is overly reductive and perpetuates harmful social binaries, while also revealing new private information beyond what was initially shared. Finally, it argues that the secretive intelligence environment prevents critical interrogation while promoting practices that, through the coercive power of the state, cause unequal harms across society.International Affairs01It is argued that open-source does not necessarily mean ethical, as the AI collection en masse of social media data violates citizens' privacy, consent and autonomy, and that the secretive intelligence environment prevents critical interrogation while promoting practices that cause unequal harms across society.International Affairs["Ross Bellaby"]2024-10-14T00:00:00 7bc800c504ab1d5f87370597c41dc226dd822468Pembelajaran sains dan agama menghadirkan tantangan bagi mahasiswa khususnya mahasiswa tingkat pertama dalam penggunaan teknologi terkini sebagai bentuk inovasi pembelajaran. Penelitian ini membantu mahasiswa untuk mengetahui keefektivan artificial intelligence (AI) yang dapat meningkatkan kemandirian belajar mahasiswa. Penelitian ini bertujuan untuk menganalisis dan mendeskripsikan pengaruh artificial intelligence (AI) terhadap kemandirian mahasiswa PGMI semester 2 dalam menyelesaikan tugas-tugasnya dalam perkuliahan. Teknik analisis data yang digunakan dalam penelitian ini adalah secara kuantitatif yang meliputi pembagian angket kepada mahasiswa dengan jumlah 50 orang. Hasil penelitian menunjukkan bahwa kemandirian belajar mahasiswa meliputi kemampuan berpikir kritis, kreatif dan inovatif, tidak mudah terpengaruh dengan orang lain, dan mampu menyelesaikan masalah melalui aplikasi gemini, chatbot, chat GPT, canva,CICI, online translator, google classroom, mengaji. ai. Hal tersebut memberikan pengaruh positif dan efektif dalam meningkatkan kemandirian mahasiswa berdasarkan nilai P diperoleh nilai 80%.Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai00Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai["Syamsidar Hs", "S. S."]2024-10-14T00:00:00 66c191ae518969e618da5fb45ab8d3a0e55a31ffBMC Nursing446Dealing with the ethical concerns of AI in ICU care requires prioritizing patient autonomy and ensuring accountability in AI-driven decisions, as well as addressing concerns about overreliance, workflow adaptation, and potential bias.BMC Nursing["Eman Arafa Hassan", "A. El-Ashry"]2024-10-14T00:00:00 1d1ba8290d17119690a74a6b22c7dd34ec01e61bTechnology is increasingly being brought into the home care of older people. Digitalization is seen as an enabler for efficient and resource-saving operations. In the use of technology, informed consent is considered an ethical practice and part of a responsible home care service system. The aim of this article is to describe the problem of informed consent in situations where emerging technologies, such as artificial intelligence (AI) and mass data, are used as part of welfare services and home care for older people. The article discusses principles and ways to better integrate informed consent as an ethical practice into a responsible home care service system. A qualitative study was carried out to gather the views of experts in the field of elderly care and ethics. A content analysis of a semi-structured focus group was used to explore perceptions of the changing nature of informed consent. According to our findings, the informed consent model requires updating. The key is to embrace the idea that consent is a living process designed to respect people's autonomous choices and protect them from risk. If the nature of the use of the data collected from individuals changes significantly in the future, the consent should also be updated to reflect this change. This aspect is important because new technologies will change the nature of the collection and use of the data. Mass data collection combines multiple databases so that the resulting data can be used even far from the original purpose or context in which it was collected. Therefore, consent should always be tailored to the context, allowing sufficient time for the person seeking and giving consent to clarify the content of the consent. This process highlights the importance of understanding the agency of the consent giver.Finnish Journal of eHealth and eWelfare01The aim of this article is to describe the problem of informed consent in situations where emerging technologies, such as artificial intelligence (AI) and mass data) are used as part of welfare services and home care for older people.Finnish Journal of eHealth and eWelfare["Jaana Leikas", "A. Halkoaho", "M. Lanne"]2024-10-14T00:00:00 7e3e2bcd47605420fa2bb6b2ce4e6c4962c825f1Due to the increased unpredictability and severity of weather patterns caused by climate change, traditional farming practices and risk management strategies are becoming increasingly inadequate. In this paper, we explore the literature to understand the potential of artificial intelligence (AI) in mitigating climate-related agricultural risks and the pivotal role that public institutions play in encouraging farmers to adopt such technologies. We propose a framework to integrate AI into government-subsidized insurance structures, focusing on reduced premiums through government intervention. We argue that AI’s potential to reduce the uncertainty and severity of climate-induced damages could lower the overall risk profile of insured farmers, thereby justifying lower premiums in the long run. We further discuss the implications of such policies on insurance markets, agricultural sustainability, and global food security. Our initial exploration contributes to the literature by addressing a relatively underexplored intersection of two critical fields—agricultural insurance and artificial intelligence—suggesting directions for future research.Agriculture461Agriculture["Chad Patrick Osorio", "Francesca Leucci", "Donatella Porrini"]2024-10-14T00:00:00 7c3d86ba2ffa08dc35d7b513638e081aea4489daAbstract : This whitepaper explores how Artificial Intelligence (AI) systems can optimize administrative efficiency within educational institutions. It addresses the growing complexities of managing large student populations, ensuring regulatory compliance, and providing high-quality educational services. The paper delves into AI-driven solutions for automating routine tasks, enhancing data management, and improving decision-making processes. Additionally, it highlights the benefits of AI in reducing administrative burdens, increasing operational efficiency, and fostering a more responsive educational environment. The paper also provides real-world examples of successful AI implementations in educational settings, showcasing the transformative potential of these technologies.International Journal of Current Science Research and Review81This whitepaper explores how Artificial Intelligence (AI) systems can optimize administrative efficiency within educational institutions and delves into AI-driven solutions for automating routine tasks, enhancing data management, and improving decision-making processes.International Journal of Current Science Research and Review["Suman Deep", "Karthik Athimoolam", "Tenny Enoch"]2024-10-14T00:00:00 f91f447f4bf10221100bf1d4146c0df2c37705e9The study is based on a conceptual model to examine the integration of artificial intelligence (AI) technologies in education and their impact on student engagement. This model structures the analysis around several axes: AI technologies, including intelligent tutoring systems (ITS), adaptive learning platforms, and educational chatbots, play a key role in personalizing learning paths, making pedagogical support more accessible, and adapting content to students' specific needs. Student engagement is thus assessed through the personalization of pathways and the accessibility of support, while taking into account individual moderating factors such as learning styles, self-motivation, and prior experience with AI technologies, which influence the effectiveness of these tools. In addition, the study examines contextual conditions, including the importance of adequate technological infrastructure and teacher training, which are essential for the successful integration of AI technologies into pedagogical practices. This conceptual model guides the study in evaluating the assumptions made, providing an in-depth understanding of the interactions between these variables and making recommendations to optimize the use of AI technologies in education.International Journal of Innovative Science and Research Technology571A conceptual model guides the study in evaluating the assumptions made, providing an in-depth understanding of the interactions between these variables and making recommendations to optimize the use of AI technologies in education.International Journal of Innovative Science and Research Technology (IJISRT)["Sara Benayache", "Bouchrik Mourad"]2024-10-14T00:00:00 df1eca09ffbda1e85be6715a2e57ac8544588202This integrative literature review (ILR) explores the rise of new AI-driven professions propelled by the swift adoption of artificial intelligence (AI), machine learning (ML), robots, and big data analytics in key sectors, specifically AI, healthcare, energy, education, and retail. The study examines the challenges posed by the changing workforce and the skill demands arising from AI's impact on operations and decision-making processes. The paper investigates the effects of the demand for specialist individuals on industries regarding AI integration's ethical and technical governance. This ILR is based on a conceptual framework encompassing technological transformation, human capital development, and institutional adaptation, integrating empirical and theoretical evidence from peer-reviewed sources. The study utilizes an integrative literature review methodology, carefully assessing and combining contemporary academic and industry results to evaluate the impact of AI-driven job positions in the workplace. The findings indicate an urgent need for specialists, including AI Ethics Specialists, AI Operations Managers, and AI Explainability Engineers, who will be responsible for ensuring AI systems' ethical standards and performance optimization. These findings confirm the human capital theory, emphasizing the necessity for ongoing skill enhancement to address AI's advancing capabilities and ethical implications. This ILR concludes that although AI improves operational efficiency, its complete potential can only be achieved with appropriate governance and workforce reform. The paper advocates for additional research on the long-term effects of AI on labor dynamics, sector-specific case analyses, and the development of ethical frameworks for AI governance. Future research and policy must harmonize technological progress with ethical governance, guaranteeing that AI-driven enterprises develop sustainably while positively impacting society, in accordance with the United Nations' Sustainable Development Goals (SDGs).International Journal For Multidisciplinary Research570Although AI improves operational efficiency, its complete potential can only be achieved with appropriate governance and workforce reform, and the findings indicate an urgent need for specialists, including AI Ethics Specialists, AI Operations Managers, and AI Explainability Engineers.International Journal For Multidisciplinary Research["Dr. Rachid Ejjami"]2024-10-14T00:00:00 9d12c24cb761c44b71d68127392f639da785ed24Rapid changes brought on by generative artificial intelligence (AI) have emphasized the need to teach students to work with this technology while also developing the “robot proof” human skills future workers will need, such as creativity, communication, and critical thinking. The study objective was to explore whether a fact-checking, generative-AI assignment, inserted between the outline, and first-draft stages of a student's literature review writing process, would relate to student classification, perceptions of AI accuracy, and future trust in AI-generated content. Students in upper and lower division psychology classes used AI to generate a literature review on their final paper topic, which they then fact-checked for accuracy and usefulness using a color-coded system. Lower division students expected more inaccuracy, highlighted less information as inaccurate, and reported greater future trust of AI-generated content than upper division students. Students with more experience critically evaluating primary sources may be better equipped to detect inaccuracies within AI-generated content. Teachers of any course requiring a literature review paper may use this assignment to encourage student use of AI with a critical eye toward recognizing where that content is incorrect.Teaching of psychology140Teachers of any course requiring a literature review paper may use this assignment to encourage student use of AI with a critical eye toward recognizing where that content is incorrect, and students with more experience critically evaluating primary sources may be better equipped to detect inaccuracies within AI-generated content.Teaching of Psychology["Jessica Cail"]2024-10-14T00:00:00 b416d567598719a8bec67c93e33afa334204c6bcThe seamless operation of a critical infrastructure is achieved through a set of standard operational procedures. Leveraging new technologies to support threat management processes dramatically reduces the risk of an infrastructure outage. Moreover, private security services assess all risks to which guards are exposed and take measures to prevent dangerous situations to protect them. To handle such a problem, we present an integrated incident management system consisting of several container modules. Our loosely coupled architecture is supported by a message management framework, the main goal of which is to support the operational procedures. The proposed system, based on augmented reality and artificial intelligence, can process multiple video streams provided by closed-circuit televisions to detect possible threats related to the infrastructure. Next, it delivers the necessary information for managing an incident directly to the field and control center. The whole pipeline was employed in Preveza Marina, Greece, while tested and evaluated in real-case scenarios. Integrating such a system resulted in restructuring the standard operational procedures, permitting new security personnel management processes to safeguard health and safety during their work.International Symposium on Telecommunications190An integrated incident management system consisting of several container modules, based on augmented reality and artificial intelligence, that resulted in restructuring the standard operational procedures, permitting new security personnel management processes to safeguard health and safety during their work.2024 IEEE International Conference on Imaging Systems and Techniques (IST)["P. Nastou", "Vassilis Papataxiarhis", "Stavros N. Moutsis", "Konstantinos A. Tsintotas", "Georgios Petroudis", "Nikos Papastamatiou", "Charis Mesaritakis", "Antonios Gasteratos", "Demosthenes Vouyioukas", "Panagiotis Gavathas"]2024-10-14T00:00:00 d00bbf60751c1e8f123c05655b40d5dc91a561fbAs we enter an era where the synergy between AI technologies and human effort is paramount, the Future of Work is undergoing a radical transformation. Emerging AI tools will profoundly influence how we work, the tools we use, and the very nature of work itself. The ’Human Computation, Equitable, and Innovative Future of Work AI Tools’ workshop at HCOMP’24 aims to explore groundbreaking solutions for developing fair and inclusive AI tools that shape how we will work. This workshop will delve into the collaborative potential of human computation and artificial intelligence in crafting equitable Future of Work AI tools. Participants will critically examine the current challenges in designing fair and innovative AI systems for the evolving workplace, as well as strategies for effectively integrating human insights into these tools. The primary objective is to foster a rich discourse on scalable, sustainable solutions that promote equitable Future of Work tools for all, with a particular focus on empowering marginalized communities. By bringing together experts from diverse fields, we aim to catalyze ideas that bridge the gap between technological advancement and social equity.Proceedings of the AAAI Conference on Human Computation and Crowdsourcing10This workshop will delve into the collaborative potential of human computation and artificial intelligence in crafting equitable Future of Work AI tools and foster a rich discourse on scalable, sustainable solutions that promote equitable Future of Work tools for all.Proceedings of the AAAI Conference on Human Computation and Crowdsourcing["Kashif Imteyaz", "Claudia Flores Saviaga", "Saiph Savage"]2024-10-14T00:00:00 7fa8b8238690ed8f33ee833215cbb350912931deInternational Journal of Computational Intelligence Systems250The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health, as well as enhancing overall functionality and impact.Int. J. Comput. Intell. Syst.["Marcus Sammer", "Kijin Seong", "Norma Olvera", "Susie L. Gronseth", "Elizabeth Anderson-Fletcher", "Junfeng Jiao", "Alison Reese", "Ioannis A. Kakadiaris"]2024-10-14T00:00:00 5a2d0008cb229800826679af309bf3114d25fc10This study presents a global analysis of research priorities for technologies relevant to Sustainable Development Goals (SDGs). We examine 18 technological domains across countries, introducing a novel within-country rank metric to normalize differences in research output. Using a combination of linear regression and K-means cluster analysis, we identify factors influencing overall productivity and reveal distinct patterns in research priorities among nations. Our analysis of Web of Science total publication data yields five country clusters with specific technological focus areas: Eco-Tech Innovators, Cyber-Digital Architects, Bio-Industrial Pioneers, Geo-Data Security Analysts, and Cyber-Sustainable Integrators. We find that while economic indicators strongly predict overall research productivity, countries with similar economic profiles often exhibit divergent research priorities. Artificial Intelligence emerges as a top priority across all clusters, while areas such as blockchain and digital twins show lower prioritization despite their theoretical importance. Our findings reveal unexpected similarities in research focus among geopolitically diverse countries and highlight regional patterns in technological emphasis. This study offers valuable information for policymakers and researchers, enhancing our understanding of the global landscape of SDG-relevant technological research and potential avenues for international collaboration.Sustainability520It is found that while economic indicators strongly predict overall research productivity, countries with similar economic profiles often exhibit divergent research priorities, and countries with similar economic profiles often exhibit divergent research priorities.Sustainability["Emanuela Bran", "R. Rughinis", "D. \u021aurcanu", "Alexandru Radovici"]2024-10-14T00:00:00 c222df39f6861098a33bb8b1ad86cee14c06074cImportance Advances in artificial intelligence (AI) must be matched by efforts to better understand and evaluate how AI performs across health care and biomedicine as well as develop appropriate regulatory frameworks. This Special Communication reviews the history of the US Food and Drug Administration's (FDA) regulation of AI; presents potential uses of AI in medical product development, clinical research, and clinical care; and presents concepts that merit consideration as the regulatory system adapts to AI's unique challenges. Observations The FDA has authorized almost 1000 AI-enabled medical devices and has received hundreds of regulatory submissions for drugs that used AI in their discovery and development. Health AI regulation needs to be coordinated across all regulated industries, the US government, and with international organizations. Regulators will need to advance flexible mechanisms to keep up with the pace of change in AI across biomedicine and health care. Sponsors need to be transparent about and regulators need proficiency in evaluating the use of AI in premarket development. A life cycle management approach incorporating recurrent local postmarket performance monitoring should be central to health AI development. Special mechanisms to evaluate large language models and their uses are needed. Approaches are necessary to balance the needs of the entire spectrum of health ecosystem interests, from large firms to start-ups. The evaluation and regulatory system will need to focus on patient health outcomes to balance the use of AI for financial optimization for developers, payers, and health systems. Conclusions and Relevance Strong oversight by the FDA protects the long-term success of industries by focusing on evaluation to advance regulated technologies that improve health. The FDA will continue to play a central role in ensuring safe, effective, and trustworthy AI tools to improve the lives of patients and clinicians alike. However, all involved entities will need to attend to AI with the rigor this transformative technology merits.Journal of the American Medical Association (JAMA)368The history of the US Food and Drug Administration's regulation of AI is reviewed; potential uses of AI in medical product development, clinical research, and clinical care are presented; and concepts that merit consideration as the regulatory system adapts to AI's unique challenges are presented.JAMA["Haider J. Warraich", "Troy Tazbaz", "R. Califf"]2024-10-15T00:00:00 0bf5f6f5dc12e71dc5902d80fa77a176a0838d7eThis article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view of AI's involvement in administrative activities in higher education, experts' opinions concerning the influence of the incorporation of AI on learning and teaching, and experts' views on applying AI specifically to assessment, academic integrity, and ethical considerations. The study used a qualitative method based on an unstructured qualitative interview with open-ended questions. The participants were thirteen individuals currently involved with higher education institutions and had various talents related to AI and education. Findings stress that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time. The findings also revealed that AI plays a vital role in learning and teaching by speeding up the learning process, engaging learners and tutors, and personalising learning depending on the learner's needs within an entirely intelligent environment. AI can produce an accurate, objective, and suitable level of assessment. AI aids students in developing a stronger sense of integrity in their academic work by guiding them through AI-powered applications. AI must adhere to ethical laws and policies, ensuring its potential negative aspects are not overlooked or left unchecked.European Journal of Educational Research243Investigating artificial intelligence (AI) implementation in higher education from experts' perspectives finds that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time.European Journal of Educational Research["Zouhaier Slimi", "Beatriz Villarejo-Carballido"]2024-10-15T00:00:00 2b32eb056336360606d475799d9a421844eb375cThe greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.Risks203This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms, that leverages Shapley values to generate AI predictions based on key explanatory variables.Risks["M. K. Nallakaruppan", "Himakshi Chaturvedi", "Veena Grover", "B. Balusamy", "Praveen Jaraut", "Jitendra Bahadur", "V. Meena", "Ibrahim A. Hameed"]2024-10-15T00:00:00 aad5cfca93577b2fe1a106ea740364820846e5f1The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.Journal of Infrastructure Policy and Development321A positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions is indicated and machine learning (ML) exhibits a strong and positive association among financial services and financial institutions.Journal of Infrastructure, Policy and Development["Mohammed Arshad Khan", "Hamad A. Alhumoudi", "A. Alakkas", "Syed Mohd Minhaj", "Mohammed Alhashem"]2024-10-15T00:00:00 a9754cd22c53d5c3735d47f1aad905b8733b0e91Cybersecurity is a very crucial field that comprises various technologies, processes as well as practices and all are particularly aimed at safeguarding digital assets from various threats. As the number of inter-connected devices has been increased nowadays, cybersecurity has become more complex. It is very essential to note that as more businesses and services move online, there are more opportunities for cyberattacks and this also led to serious consequences such as financial loss and disruptions to services. The two key points about the changing landscape of cybersecurity threats have been highlighted in the study: Nation-State-Affiliated Adversaries are attackers that are supported or directly controlled by a government. Criminal Adversaries are attackers motivated by financial gain. Although the infrastructure for technology is associated with artificial intelligence in cybersecurity, and it is additionally known that these systems support a broad range of applications that helps to improve cyber defences more broadly. In recent times, advanced technologies like artificial intelligence, machine learning, and automation have been utilized and these technologies make cybersecurity more complex. AI and ML have been utilized to a certain extent in order to make cybersecurity systems better at detecting and responding to threats as well. Furthermore, the proposed method in the study has been especially aimed to manage security incidents quickly by using AI and ML and in a very effective manner. The development of AI systems capable of detecting both simple and complex cyber threats with a high level of accuracy and speed have been explored thoroughly in the survey. Cybersecurity can be improved by using two key concepts: User Behaviour Analytics (UBA) and ICS Security Ontology.International Conference on Signals and Electronic Systems91The development of AI systems capable of detecting both simple and complex cyber threats with a high level of accuracy and speed have been explored thoroughly in the survey and the proposed method has been especially aimed to manage security incidents quickly by using AI and ML and in a very effective manner.2024 4th International Conference on Sustainable Expert Systems (ICSES)["Mohammed Javed Hussain"]2024-10-15T00:00:00 27cb51b9b6539c9eaac24b2e0d2ea67e34c5d9acArtificial Intelligence (AI) has revolutionized various industries by learning from data, mimicking human behavior, and making autonomous decisions. However, despite AI's advancements in data processing and decision-making, it cannot fully replicate human attributes such as emotional understanding and ethical judgment. This paper explores the intersection of AI and Human Intelligence (HI) within the audit profession, focusing on the implications for the auditor’s professional judgment and skepticism. The integration of AI in auditing promises enhanced efficiency, precision, and data processing capabilities beyond human limits. However, it also raises ethical concerns regarding data privacy, algorithmic bias, and accountability. These concerns highlight the importance of maintaining human oversight and ethical standards in audit practices. Through a comprehensive literature review, this study compares the cognitive abilities, functional capabilities, and ethical implications of AI and human auditors. Key findings underscore AI's potential to complement human auditors by improving accuracy and uncovering anomalies, while recognizing the irreplaceable role of human judgment in complex decision-making processes. The study provides insights into the transformative impact of AI on the audit profession, advocating for a balanced approach that harnesses AI's capabilities while preserving the integrity and critical thinking of human auditors. The findings contribute to a deeper understanding of AI's integration into auditing, informing best practices and guiding future research in maintaining the profession's standards amidst technological advancements.Audit Financiar01This study compares the cognitive abilities, functional capabilities, and ethical implications of AI and human auditors, and underscores AI's potential to complement human auditors by improving accuracy and uncovering anomalies, while recognizing the irreplaceable role of human judgment in complex decision-making processes.Audit Financiar["Delia Deliu"]2024-10-15T00:00:00 40a106674cfd01a1b09116c8a12b247bb6e49943Background: Artificial intelligence (AI) can play a significant role in the future of thyroidology. Thyroid nodules are common conditions that may benefit from AI through more accurate and efficient diagnosis, risk stratification, and medical or surgical management. Objective: This paper aims to review the latest developments in AI applications for diagnosing and managing thyroid nodules and cancers. Methods: English full-text articles published in the PubMed and Google Scholar databases from January 2014 to March 2024 were collected and reviewed to provide a comprehensive understanding of the topic. A total of 45 studies were selected based on relevance, robust methodology, statistical significance, and broader topic coverage. Results: Artificial intelligence has emerged as a powerful tool for managing thyroid nodules. First, several studies have demonstrated how AI-powered ultrasound interpretation enhances the diagnosis and classification of nodules while reducing the need for invasive fine-needle aspiration (FNA) biopsies. Second, AI significantly improves the cytopathological differentiation between benign and malignant thyroid nodules by minimizing reliance on pathologists' expertise and implementing standardized diagnostic criteria. When cytopathology is inconclusive, AI also aids in identifying molecular markers from omics data, distinguishing between normal and cancerous cells. Moreover, AI tools have been developed for prognosis assessment, predicting distant metastasis, recurrence, and surveillance by integrating medical imaging features with molecular and clinical factors. Additionally, some AI tools are designed for intraoperative evaluation, improving surgical techniques and reducing complications during thyroidectomy. In non-surgical treatments, several models have been developed to optimize therapeutic doses of radioactive iodine (RAI) and predict the outcomes of new drug formulations. Conclusions: Artificial intelligence has the potential to assist physicians in accurate thyroid nodule diagnosis, classification, decision-making, optimizing treatment strategies, and improving patient outcomes. However, there are still limitations to this technology. Artificial intelligence-driven tools require further advancements before they can be fully integrated into clinical practice and replace specialists.Shiraz E Medical Journal741Artificial intelligence has the potential to assist physicians in accurate thyroid nodule diagnosis, classification, decision-making, optimizing treatment strategies, and improving patient outcomes, however, there are still limitations to this technology.Shiraz E-Medical Journal["Parsa Yazdanpanahi", "Farnaz Atighi", "Alireza Keshtkar", "Reza Hamidi", "Mohamadali Rezaeimanesh", "Alireza Karimi", "Arzhang Naseri", "Mohammadhossein Dabbaghmanesh"]2024-10-15T00:00:00 b636be27d4c26dc57cec32b90f933939e0b5b7f9Abstract Science-informed decisions are best guided by the objective synthesis of the totality of evidence around a particular question and assessing its trustworthiness through systematic processes. However, there are major barriers and challenges that limit science-informed food and nutrition policy, practice, and guidance. First, insufficient evidence, primarily due to acquisition cost of generating high-quality data, and the complexity of the diet-disease relationship. Furthermore, the sheer number of systematic reviews needed across the entire agriculture and food value chain, and the cost and time required to conduct them, can delay the translation of science to policy. Artificial intelligence offers the opportunity to (i) better understand the complex etiology of diet-related chronic diseases, (ii) bring more precision to our understanding of the variation among individuals in the diet-chronic disease relationship, (iii) provide new types of computed data related to the efficacy and effectiveness of nutrition/food interventions in health promotion, and (iv) automate the generation of systematic reviews that support timely decisions. These advances include the acquisition and synthesis of heterogeneous and multimodal datasets. This perspective summarizes a meeting convened at the National Academy of Sciences, Engineering, and Medicine. The purpose of the meeting was to examine the current state and future potential of artificial intelligence in generating new types of computed data as well as automating the generation of systematic reviews to support evidence-based food and nutrition policy, practice, and guidance.PNAS Nexus771The purpose of the meeting was to examine the current state and future potential of artificial intelligence in generating new types of computed data as well as automating the generation of systematic reviews to support evidence-based food and nutrition policy, practice, and guidance.PNAS Nexus["Regan L. Bailey", "Amanda J MacFarlane", "M. Field", "Ilias Tagkopoulos", "S. Baranzini", "Kristen M Edwards", "Christopher J Rose", "Nicholas J Schork", "Akshat Singhal", "Byron C Wallace", "Kelly P Fisher", "Konstantinos Markakis", "Patrick J. Stover"]2024-10-15T00:00:00 9551fe3b0eb85c2591fa59aae8e2b73f266b0a61Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes.Water1222Water["Reza Shahouni", "Mohsen Abbasi", "M. Dibaj", "Mohammad Akrami"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/9551fe3b0eb85c2591fa59aae8e2b73f266b0a61 408e745be62470a7b9484d6dca68410c3eba8f6dThe study aims to investigate the mediating role of artificial intelligence technical skill (AI) on the relationship between effectiveness of management information system (MIS) and knowledge acquisition (KA) in Jazan University, the study used descriptive and analysis methods, A questionnaires used for data collection, (229) questionnaires were distributed, (177) valid questionnaires are returned about (%77.29) of the sample size, Several statistical methods have been used. The study found that there is positive and significant relationship between MIS effectiveness and AI technical skills, the study found that AI technical skill mediating the relationship between effectiveness of MIS and KA acquisition. These findings demonstrate the importance of AI in driving the effectiveness of KA. For future, the study recommends to apply difference dimensions of AI with difference dimensions of KA in other sectors.Saudi Journal of Humanities and Social Sciences01There is positive and significant relationship between MIS effectiveness and AI technical skills, and the study found that AI technical skill mediating the relationship between effectiveness of MIS and KA acquisition demonstrate the importance of AI in driving the effectiveness of KA.Saudi Journal of Humanities and Social Sciences["Dr. Elsheikh Mohammed Elkhidir Mohammed"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/408e745be62470a7b9484d6dca68410c3eba8f6d e2174c47d36b8ad531116cf4b1b9f2c644dc8107Objective: To provide a comprehensive overview of current applications and future prospects of artificial intelligence (AI) in oral and maxillofacial surgery (OMFS), while critically analyzing implementation challenges and exploring potential advancements.  Methods A systematic literature review was conducted using PubMed/MEDLINE and Embase databases, encompassing English-language articles up to December 30, 2023. Search terms combined OMFS and AI concepts, with database-specific syntax employed.  Results AI applications in OMFS span multiple domains, including image analysis, surgical planning, intraoperative guidance, and clinical decision support. Deep learning models have demonstrated high accuracy in detecting mandibular fractures, performing cephalometric analyses, and classifying maxillofacial pathologies. AI-enhanced surgical planning and robotic systems show promise in improving precision and outcomes across various OMFS procedures. However, challenges persist in data quality, clinical validation, and seamless workflow integration.  Conclusions AI technologies have the potential to significantly enhance diagnostic accuracy, surgical precision, and treatment outcomes in OMFS. Future research directions include developing multimodal AI systems, advancing AI-powered surgical navigation, and exploring federated learning approaches. Successful implementation of AI in OMFS practice will require collaborative efforts among clinicians, researchers, engineers, and policymakers to address technical, ethical, and regulatory challenges. As these hurdles are overcome, AI is poised to become an integral part of OMFS, augmenting surgical capabilities and elevating patient care standards.International Journal of Innovative Science and Research Technology631An overview of current applications and future prospects of artificial intelligence (AI) in oral and maxillofacial surgery (OMFS) while critically analyzing implementation challenges and exploring potential advancements is provided.International Journal of Innovative Science and Research Technology (IJISRT)["Amar Singh", "Aswathy Haridas", "Vandana Shenoy", "Mohamed Afradh"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/e2174c47d36b8ad531116cf4b1b9f2c644dc8107 20c6ad09006163384790a3049af931f5aaa36939Artificial Intelligence (AI) has been a subject of growing interest and concern recently. Although it is not a particularly new technology in a general sense, the free availability of certain AI tools has created a certain momentum. AI is becoming an increasingly popular topic every year and is generating a lot of controversy, but even with the increased attention, most people are not aware of what AI actually is and that it has long been a crucial part of our daily lives. It is clear that the ever-evolving tools of the AI are nowadays not bypassing the field of education. The role of the teacher is an important aspect in the context of the development and penetration of AI in education. AI may have an impact on the role of teachers, but this will be a complementary form of change rather than a complete replacement of teachers. While various AI tools may help teachers to be more effective in their tasks, the aspects of human interaction, motivation and empathy that are important for the teacher are likely to remain unchanged. It is clear that the spread/penetration of AI in education will lead to an overall paradigm shift, such as a massive shift towards project-based learning/study and a fundamental change in the role of the teacher/lecturer. The field of education is conservative in nature and based on tradition. Therefore, innovations are usually not the first to come, but are tried and tested in other areas. Artificial intelligence is thus becoming a key player in the field of education, opening up new perspectives and challenges. AI is not only a tool, but also a new aspect of education. We are undoubtedly on the verge of unique opportunities, but at the same time we are facing complex challenges both known and unknown. It is becoming important to develop effective strategies for introducing AI into education, while preserving the values and overarching goals of education.GAMTAMOKSLINIS UGDYMAS / NATURAL SCIENCE EDUCATION71It is clear that the spread/penetration of AI in education will lead to an overall paradigm shift, such as a massive shift towards project-based learning/study and a fundamental change in the role of the teacher/lecturer.GAMTAMOKSLINIS UGDYMAS / NATURAL SCIENCE EDUCATION["Vincentas Lamanauskas"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/20c6ad09006163384790a3049af931f5aaa36939 4a2221cf96839c333b169e11961697c2ef9e6270The world is looking forward to advancements in artificial intelligence (AI) technology, with significant research underway regarding the application of AI in education. This study analyzed publications on the potential of AI in secondary schools, focusing on its bibliometric aspects. Data from the Scopus database revealed 1,764 publications from 2019 to 2024. The analysis showed a steady annual growth in publications in this area. China and the USA were the leaders in the number of publications. Xiaoyue Wang was the most prolific researcher, having authored 71 AI-related articles. Yueying Li, Xiaoxu Chen, Yanzhu Zhang, and Yi Liu contributed to the field with 56, 55, 53, and 51 articles, respectively. The themes that emerged from 2019 to 2022 are related to media, application, study, institutions, artificial, digital, learning, factors, development, technologies, medical, automated, perception, support, and sustainability. From 2023 to 2024, the topics discussed in AI are related to students, education, perception, algorithms, digital, prediction, networks, challenges, writing, teachers, AI-powered, curriculum, century, integration, technology, and framework. The difference in research in 2019-2022 and 2023-2024 is focusing the theme's focus from the general to the specific. The co-occurrence analysis revealed that prominent keywords appeared in 3 clusters. Cluster 1 is the most popular in recent times. It deals with the application, assessment, and management of AI. Cluster 2 relates to AI relationships and models, while Cluster 3 relates to AI data sources.European Journal of Educational Research00This study analyzed publications on the potential of AI in secondary schools, focusing on its bibliometric aspects, and revealed that prominent keywords appeared in 3 clusters.European Journal of Educational Research["W. Widayanti", "Haryanto Haryanto", "E. Istiyono", "A. Saregar", "Khusnatul Amaliah"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/4a2221cf96839c333b169e11961697c2ef9e6270 dc505a8f32896b8217bb3cd798978221b10706a7Geoheritage980A new artificial intelligence system built upon the GPT-4o model and trained on Earth Science data is proposed, designed to simulate a conversation with a geoscientist, having the capabilities to analyse geologic datasets, suggest new geoscience hypotheses, explain Earth-Science concepts, and interpret geosites.Geoheritage["A. Baucon", "Carlos Neto de Carvalho"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/dc505a8f32896b8217bb3cd798978221b10706a7 95186e1c56c44824504ba71c30adeba6c9a664c3This work presents a structured view of the state-of-the-art research on Artificial Intelligence (AI), from the point of view of efficiency and reduction of the energy consumption of AI Software. We analysed the current research on energy consumption of AI algorithms and its improvements, which gave us a starting literature corpus of 2688 papers that we identified as Green AI with a software perspective. We structure this corpus into Green IN AI and Green BY AI, which led us to discover that only 36 of them could be considered Green IN AI. After some quick insights about Green BY AI, we then introduce our main contribution: a systematic mapping of Green IN AI. We provide an in-depth analysis of the AI models that observed during the mapping, and what solutions have been proposed for improving their energy efficiency. We also analyse the energy evaluation methodologies employed in Green IN AI, discovering that most papers opt for a software-based energy estimation approach and a 27% of all papers not documenting their methodology. We finish by synthetising our insights from the mapping into a Decalogue of Good Practices for Green AI.ACM Computing Surveys150This work presents a structured view of the state-of-the-art research on Artificial Intelligence (AI), from the point of view of efficiency and reduction of the energy consumption of AI Software, from the point of view of efficiency and reduction of the energy consumption of AI Software.ACM Comput. Surv.["Mar\u00eda Guti\u00e9rrez", "M. A. Moraga", "F\u00e9lix Garc\u00eda", "Coral Calero"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/95186e1c56c44824504ba71c30adeba6c9a664c3 158286e37d76be89e40722edfb9a24f52cfaed9eAs cities worldwide transition into smart urban ecosystems, the integration of Artificial Intelligence (AI) and Financial Technologies (FinTech) becomes crucial. This article delves into the fusion of these technologies under Saudi Arabia's Vision 2030 to enhance urban planning and development. It critically examines how AI and FinTech not only optimize efficiency, sustainability, and livability but also tackle infrastructural and economic challenges within urban environments. By employing a mixed-methods approach, including quantitative data analysis and case studies from both global contexts and specific projects within Saudi Arabia, this study provides comprehensive insights into the potential societal and economic impacts. It also identifies key challenges, such as regulatory hurdles, ethical considerations, and the need for substantial initial investments that could impede technology adoption. This research aims to offer stakeholders a detailed roadmap to navigate these complexities and achieve Vision 2030's ambitious goals, thereby preparing Saudi cities for a smarter and more sustainable future.Advances in Research00This research aims to offer stakeholders a detailed roadmap to navigate these complexities and achieve Vision 2030's ambitious goals, thereby preparing Saudi cities for a smarter and more sustainable future.Advances in Research["Kahtan Abedalrhman", "Ammar Alzaydi", "Yaser Shiban"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/158286e37d76be89e40722edfb9a24f52cfaed9e bc014db0ede7d5aeef7eafab1cb4f77863207feaDigital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets and omics to quickly interpret large datasets in order to generate health and disease indicators and to improve sampling and longitudinal analysis. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of apatient’s condition, treatment progress, and adjustments to the treatment plan), especially in the environments of smart cities and smart territories and through the wider use of 6G, blockchain (and soon maybe quantum cryptography), and the Internet of Things (IoT), as well as through medical technologies, such as multiomics. From a practical point of view, this requires not only efficient validation but also seamless integration with the existing healthcare infrastructure.Applied Sciences600It is possible to improve patient care through various means, especially in the environments of smart cities and smart territories and through the wider use of 6G, blockchain, and the Internet of Things (IoT), as well as through medical technologies, such as multiomics.Applied Sciences["Marian \u0141ukaniszyn", "\u0141ukasz Majka", "Barbara Grochowicz", "Dariusz Miko\u0142ajewski", "Aleksandra Kawala-Sterniuk"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/bc014db0ede7d5aeef7eafab1cb4f77863207fea 705fbf02856b234014759b81c137e801614654aaWith the development of the theory and technology of computer science, machine or computer painting is increasingly being explored in the creation of art. Machine-made works are referred to as artificial intelligence (AI) artworks. Early methods of AI artwork generation have been classified as non-photorealistic rendering (NPR) and, latterly, neural-style transfer methods have also been investigated. As technology advances, the variety of machine-generated artworks and the methods used to create them have proliferated. However, there is no unified and comprehensive system to classify and evaluate these works. To date, no work has generalised methods of creating AI artwork including learning-based methods for painting or drawing. Moreover, the taxonomy, evaluation and development of AI artwork methods face many challenges. This paper is motivated by these considerations. We first investigate current learning-based methods for making AI artworks and classify the methods according to art styles. Furthermore, we propose a consistent evaluation system for AI artworks and conduct a user study to evaluate the proposed system on different AI artworks. This evaluation system uses six criteria: beauty, color, texture, content detail, line and style. The user study demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.ACM Computing Surveys470This paper investigates current learning-based methods for making AI artworks and proposes a consistent evaluation system and demonstrates that the six-dimensional evaluation index is effective for different types of AI artworks.ACM Comput. Surv.["Qian Wang", "Hongning Dai", "Jing Yang", "Cai Guo", "Peter R. N. Childs", "M. Kleinsmann", "Yike Guo", "Pan Wang"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/705fbf02856b234014759b81c137e801614654aa fe239e6ce7f065f07473fb68dc1b6869f3a6dc9dArtificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.Iranian journal of pharmaceutical research1090This review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care, and analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis.Iranian Journal of Pharmaceutical Research : IJPR["Negar Mottaghi-Dastjerdi", "Mohammad Soltany-Rezaee-Rad"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/fe239e6ce7f065f07473fb68dc1b6869f3a6dc9d cb51acbe4a6c92e62a99d88c853a73f66957bad2This study explores how customers recognize and accept artificial intelligence devices (AIDs) in the realm of sustainable banking services, applying the Artificially Intelligent Device Use Acceptance (AIDUA) model. This research not only seeks to corroborate the AIDUA model in the banking sector, but also aims to enrich it by introducing technological literacy as a moderating factor, particularly in the perspective of sustainable banking. Data were collected through 435 valid, self-administered face-to-face surveys from bank customers in China, determined through convenience sampling. The hypotheses, covering both direct and moderating effects, were examined using structural equation modeling. This study verifies the applicability and reliability of the AIDUA model, in assessing customer acceptance of AIDs within sustainable banking services. The findings indicate that customer acceptance of AIDs unfolds in three distinct phases. Initially, the consumers’ perceptions of social influence (SI), hedonic motivation (HM), and perceived anthropomorphism (PA) positively influence their green performance expectancy (GPE) and green effort expectancy (GEE) concerning AIDs. As a result, greater GPE and GEE among bank customers lead to stronger positive emotions, which greatly contribute to increased AIDs usage and a reduction in resistance to their implementation. Additionally, the findings determine that technological literacy plays a substantial moderating role in the association connecting green performance expectancy and customer emotions in relation to adopting AIDs, thereby highlighting its importance in advancing sustainable banking initiatives.Sustainability840The findings determine that technological literacy plays a substantial moderating role in the association connecting green performance expectancy and customer emotions in relation to adopting AIDs, thereby highlighting its importance in advancing sustainable banking initiatives.Sustainability["Hengjun Mei", "Simona-Aurelia Bodog", "D. Badulescu"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/cb51acbe4a6c92e62a99d88c853a73f66957bad2 e651b187f6af290ab29235cc23ac57a7e3100356This paper investigates the efficacy of an AI-powered Video Assistant Referee (VAR) system in enhancing officiating accuracy, efficiency, and consistency in team sports. Employing a combination of artificial intelligence and computer vision, the system was tested in a local championship in Almaty, involving eight football teams. Through the analysis of decision-making accuracy, time efficiency, and consistency across officiating scenarios, the study employed chi-squared tests, paired t-tests, and Cohen's Kappa statistics to quantitatively assess improvements over traditional VAR systems. Results indicated that the AI-powered VAR system significantly increased the accuracy of decisions and reduced the decision-making time, thereby maintaining the fluidity of gameplay. Although the system also demonstrated enhanced consistency in officiating decisions, it highlighted areas needing further refinement to handle complex game situations effectively. The findings suggest that AI integration into sports officiating can substantially benefit the fairness and dynamics of team sports, provided that ongoing technological advancements continue to address current limitations. This study contributes to the growing body of knowledge on the intersection of technology and sports, offering a framework for future enhancements in digital officiating systems.Retos460Results indicated that the AI-powered VAR system significantly increased the accuracy of decisions and reduced the decision-making time, thereby maintaining the fluidity of gameplay and offering a framework for future enhancements in digital officiating systems.Retos["Maigul Zhekambayeva", "Meruert Yerekesheva", "Nurmambek Ramashov", "Yermek Seidakhmetov", "Bakhytzhan Kulambayev"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/e651b187f6af290ab29235cc23ac57a7e3100356 c1247819d5eef3eed95f30f603725aa53199ff17Climate sustainability plays a very crucial role in environmental sustainability and by considerable use of natural resources, and reducing pollution, we can make our planet beautiful and sustaining for the life of future generations. Climate changes constitute increasingly severe challenges, and with the use of Artificial Intelligence (AI), the necessity for innovation in fostering climate sustainability has become increasingly evident. This review organizes an exhaustive inventory of which AI affects climate science. The challenges remain unmet in AI's various applications across sectors such as optimization of renewable energy, climate modeling, disaster resilience and environmental monitoring. This paper discusses the important roles of applying AI in overcoming climate change difficulties. These include energy management or conservation, cap-and-trade for carbon emissions, climate and weather forecasting, grid management, transportation, and agriculture infrastructure, or city planning. It also looks into how and the extent to which AI is adopted in resource management, increasing the adoption of renewable energy, fostering sustainable development, and overseeing resources such as water and forests. This study acknowledges various barriers that impede the effective application of Artificial Intelligence in combating climate change which include data restrictions, algorithms' bias, and ethical issues with AI operations. To solve the major challenges described, we improve the major findings of this extensive literature review and distill a set of practical recommendations for policy-makers, industrial actors and researchers. Which will in return assist in overcoming challenges and enhance the capacity of AI in combating climate change and environmental conservation.International Conference on Signals and Electronic Systems220An exhaustive inventory of which AI affects climate science and how and the extent to which AI is adopted in resource management, increasing the adoption of renewable energy, fostering sustainable development, and overseeing resources such as water and forests is looked into.2024 4th International Conference on Sustainable Expert Systems (ICSES)["Parameshwari M", "Gnaneswari Gnanaguru"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/c1247819d5eef3eed95f30f603725aa53199ff17 b1d34c36bb140e5620cb57c0d650ed75c4b95618Importance. To date, there is an active introduction of artificial intelligence into all spheres of human activity, leading to significant changes in the factors that influence the socialization of the younger generation. Along with this, smart devices have also begun to act as agents of socialization for modern teenagers, with whom they interact both in educational and extracurricular settings. In this regard, the scientific and pedagogical community is interested in identifying both the positive and negative aspects of AI’s influence on the formation of the social experience of minors. The aim of the study is to examine the features and analyse the risks associated with the artificial intelligence impact on the adolescents’ socialisation.Research Methods. The work is based on a comprehensive review of psychological, pedagogical, legal, sociological, scientific, and technical literature on artificial intelligence the introduction in various fields of human activity, particularly in the education sector. The study is grounded in personal-activity, multi-subject, and environmental perspectives, which allowed for the synthesis and analysis of relevant information, as well as an assessment of the potential risks associated with the artificial intelligence influence on the positive socialization process of school students. Within these frameworks, several research methodologies are employed, including analysis, comparison, generalization, systematization, and structured information organization.Results and Discussion. The main areas of artificial intelligence application in education are: adaptive learning, automated assessment, examination management, personalized learning, customized educational materials creation, consultation and recommendation systems, gaming and virtual reality, extending the range of opportunities for students with special needs, and monitoring student engagement in learning. However, along with the potential benefits of introducing AI technologies into the adolescents’ cognitive development, there are several risks associated with the intelligent devices’ use: a potential decrease in the education quality, challenges with social skills and self-regulation development, value orientation, adaptation, and self-regulatory resources. Additionally, there is a risk of subjectivity loss, the formation of an unrealistic self-image, aggressive behavior, and the development of digital autism and information pseudoliteracy.Conclusion. Based on the analysis, it is revealed that the artificial intelligence influence on modern adolescents can be traced not only in changing the educational process of the school, which is a factor and institution of socialization, but also in the direct interaction between a minor and a smart device. At the same time, this process has both positive and negative aspects, the consideration and correction of which is necessary when introducing artificial intelligence into education and other spheres of human activity and society.Tambov University Review. Series: Humanities60It is revealed that the artificial intelligence influence on modern adolescents can be traced not only in changing the educational process of the school, which is a factor and institution of socialization, but also in the direct interaction between a minor and a smart device.Tambov University Review. Series: Humanities["A. S. Khodaev", "L. Makarova"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/b1d34c36bb140e5620cb57c0d650ed75c4b95618 767754da3f04651e7e70f9bc4578235aa9ab34bcThe advance of digitalization has generated an ever-increasing number of options in the digital sphere. This phenomenon has transformed multiple sectors, including e-commerce and, in particular, the fashion industry. Artificial intelligence (AI) has emerged as a powerful tool that is redefining the online shopping experience. However, there is little research on how AI influences this purchasing process, limiting its full exploitation. This study provides new insight into how artificial intelligence influences online purchase intention in this sector by examining Generation Z consumers’ attitude and purchase intention using the Echo Look AI device. This study involved 210 university students aged between 18 and 25 years old who were surveyed in the cities of Madrid and Barcelona. The results indicate that perceived quality, attitude towards AI and perceived usefulness have a positive influence on purchase intention. Based on these results, theoretical and practical implications are discussed.Journal of Theoretical and Applied Electronic Commerce Research590Generation Z consumers’ attitude and purchase intention is examined by examining Generation Z consumers’ attitude and purchase intention using the Echo Look AI device and results indicate that perceived quality, attitude towards AI and perceived usefulness have a positive influence on purchase intention.J. Theor. Appl. Electron. Commer. Res.["C. R. Vi\u00f1als", "Maril\u00e9 Pretel-Jim\u00e9nez", "Jos\u00e9 Luis Del Olmo Arriaga", "Albert Mir\u00f3 P\u00e9rez"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/767754da3f04651e7e70f9bc4578235aa9ab34bc f7f95e07e027b77f248bebb3734101a30c7dfe68There is an urgent need to understand and benefit from artificial intelligence within schools. However, government policies that focus on academic integrity and duty of care do not address how students can leverage AI to enhance their learning nor how teachers can intentionally design assessments to account for AI. The Australian Framework for Generative Artificial Intelligence in Schools suggests that assessments need to clearly state “how generative AI tools should or should not be used” while also permitting a “clear and unbiased evaluation of student ability”. This is a worthy aspiration, yet there are presently few tools and examples to guide teachers in creating assessments. This conceptual paper draws from the field of design to articulate a framework for developing assessments that focuses on AI dialogue and trace-augmented critical reflection.Learning Letters00A framework for developing assessments that focuses on AI dialogue and trace-augmented critical reflection is articulated that focuses on AI dialogue and trace-augmented critical reflection.Learning Letters["Jen Scott Curwood", "Nick Kelly", "Kazjon Grace", "Karly Lazarou"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/f7f95e07e027b77f248bebb3734101a30c7dfe68 533b932f17039b9b9fa21a66d49be681b358c9f6Artificial intelligence (AI) has emerged as a transformative force across various medical fields, including dentistry. In periodontics, AI offers the potential to enhance diagnostic accuracy, optimize treatment planning, and provide predictive analytics for disease progression. By leveraging machine learning (ML), deep learning (DL), and computer vision techniques, AI is reshaping how clinicians approach periodontal care. This review explores the current and future applications of AI in periodontics, from diagnostics to personalized treatment strategies.IP International Journal of Maxillofacial Imaging50The current and future applications of AI in periodontics, from diagnostics to personalized treatment strategies are explored, from diagnostics to personalized treatment strategies.IP International Journal of Maxillofacial Imaging["Ambujakshi Manjunatha Vinayaka"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/533b932f17039b9b9fa21a66d49be681b358c9f6 605b7072dae5b17361a4354255ad266a3e2abaa1Artificial Intelligence (AI) is a critical domain within software engineering, focusing on computer processes capable of emulating human behaviors and cognitive processes such as learning, reasoning, adaptation and self –correction. Concurrently forensic science applies scientific methodologies to criminal investigations, specifically concentrating on analyzing evidence within criminal cases. The conventional methods of conducting autopsies and forming opinions exhibit numerous limitations, many of which can be effectively addressed through AI integration. Digital forensics stands out as one of the most rapidly advancing technologies, profoundly influencing the techniques and tools employed for analyzing, monitoring and visualizing crime scenes to mitigate emerging threats and cyber-attacks. Moreover, the realm encompasses forensic medicine, which applies medical expertise to administer justice in Legal proceedings, thereby resolving associate legal complexities. Leveraging the capabilities of AI enables the swift and through sorting of files without manual intervention, significantly expediting investigative processes. Furthermore,AI plays a pivotal role in the identification of mutilated bodies, facilitating the development of tools to reconstruct full facial features. AI generated images can provide remarkable resemblances, aiding in accurately depicting facial features to aid identification efforts. Integrating AI into forensic practices enhances efficiency, accuracy and overall effectiveness in resolving criminal casesInternational Journal of Advanced Research in Science, Communication and Technology20Artificial Intelligence plays a pivotal role in the identification of mutilated bodies, facilitating the development of tools to reconstruct full facial features and enhances efficiency, accuracy and overall effectiveness in resolving criminal cases.International Journal of Advanced Research in Science, Communication and Technology["Dr. Pratiksha Gujar", "Dr. Amol Sabale"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/605b7072dae5b17361a4354255ad266a3e2abaa1 cecabe9f7d702474d878ab0372dce14b08779386The article proposes a model of in-depth training on modern concepts and practices of artificial intelligence (AI). The model is balanced, with an emphasis on active learning with a synergistic and holistic creative approach, where over 80% of the time the student is involved in an active (individual or collective) activity: researching materials, drawing up models, discussions and debates, SWOT analyses, as well as project-based tasks of greater or lesser volume, problem cases. The topics are considered not only in their strictly technical context, but also holisticallysynergistically: ethics in the issue of artificial intelligence, strategies for regulations, future perspectives and benefits, personal interests. The curriculum aims to provide high school students with a comprehensive understanding of artificial intelligence, covering fundamental concepts, practical applications, advanced technologies, ethical considerations and future trends in the field, as through a combination of theoretical learning, practical projects, creative activity and discussions, will develop critical thinking skills and will prepare for -further study or career in AIrelated fields.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The curriculum aims to provide high school students with a comprehensive understanding of artificial intelligence, covering fundamental concepts, practical applications, advanced technologies, ethical considerations and future trends in the field, as through a combination of theoretical learning, practical projects, creative activity and discussions.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["Iliyan Vasilev"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/cecabe9f7d702474d878ab0372dce14b08779386 14341fc36284a3eb89f79b42c262131ee8718598This paper, adopting a multimodal perspective and Critical Metaphor Analysis (CMA) approaches, examines 50 cartoons about artificial intelligence published on American cartoon websites from 2023 to 2024. The study finds that AI cartoons mainly construct metaphors through a cross-modal mapping “text as target domain, image as source domain” structure. These cartoons are based on metonymy, presenting metaphorical scenes through the interaction of metonymy and metaphor. The abstract concept of “artificial intelligence” is often metaphorically represented as robots, thereby deepening readers’ understanding of the role of technological image in the socio-cultural context. By analyzing four major metaphorical scenes—devouring, competing, chasing, and hijacking—this paper elucidates the dual perceptions of the opportunities and challenges brought by technological advancements in AI as well as cautious attitude towards AI development.Studies in Linguistics and Literature00This paper elucidates the dual perceptions of the opportunities and challenges brought by technological advancements in AI as well as cautious attitude towards AI development by analyzing four major metaphorical scenes.Studies in Linguistics and Literature["Shuqing Li", "Ning Yang", "Elena Kiseleva"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/14341fc36284a3eb89f79b42c262131ee8718598 0cb4f5b94f3de06713aac2478185870d0a19c566Over the past decade, artificial intelligence (AI) has emerged as a transformative technology, particularly in project management. This study examines its impact in Latin America, with a focus on Ecuador. There is significant interest in AI adoption in Ecuador, driven by favorable policies, economic conditions, and technological advancements. Most respondents are educators, scientific researchers, and department heads, highlighting the relevance of AI in educational and scientific fields (Fernández & Fernández, 2019; Hassan, Khairudin, & Nasir, 2019). Large organizations, with more than 200 employees, are better positioned to adopt AI due to their greater financial and technical resources (Chui, Henke, & Miremadi, 2020). However, significant barriers persist, such as technological limitations, budgetary constraints, and a lack of managerial support, which complicate its implementation (Smith & Lazarus, 2021). Despite these barriers, most respondents anticipate a significant increase in AI adoption over the next five years, although doubts and challenges remain that must be addressed to ensure successful and sustained implementation (Jones, Patel, & Smith, 2019). This analysis underscores both the opportunities and challenges that AI faces in project management in Ecuador, emphasizing the need for a comprehensive approach to maximize its benefits.Revista Tecnológica - ESPOL00This analysis underscores both the opportunities and challenges that AI faces in project management in Ecuador, emphasizing the need for a comprehensive approach to maximize its benefits.Revista Tecnológica - ESPOL["Jos\u00e9 Antonio Carrillo Zenteno", "Aida Diana Ormaza Vintimilla", "Julio Jhovany Santacruz Espinoza"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/0cb4f5b94f3de06713aac2478185870d0a19c566 d967ae97148639d8c77a4e8b3e06133952fa0b24Artificial Intelligence (AI) is a fast emerging technology that can boost worker productivity and efficiency and encourage innovation in a variety of industries. The impact on employability could indicate either a benefit or a drawback, though. In India, artificial intelligence is predicted to start a new industrial revolution that will cause a large number of job losses. Artificial Intelligence (AI) has the power to revolutionize the global labor market drastically, even as it also has the potential to automate existing jobs and worsen inequality and prejudice. This study examines the opportunities and challenges that artificial intelligence (AI) robots may pose to employment across a range of industries. The study looks at how AI will impact jobs by reading reputable blogs, trade journals, and academic research. The research report offers a comprehensive analysis that makes clear how AI affects jobs in India while taking into consideration how the country's economy is rapidly changing as a result of international concerns. India's IT industry has expanded at a remarkable rate, contributing to technologies that have improved people's lives in several ways. Due to its consistent commitment to skill development, economic change, and job creation, this industry is currently leading the way in the country. quest of advancement and change. This research examines how AI has an impact on employability in India and addresses several significant subjects. In the first place, it highlights the programs for upskilling and the demand for new job categories Other sectors that have developed as a consequence of the application of AI. It also examines how AI is transforming existing jobs. areas and emphasizes the need for retraining in light of AI-driven enterprises.International Journal For Multidisciplinary Research110This research examines how AI has an impact on employability in India and addresses several significant subjects, including the programs for upskilling and the demand for new job categories Other sectors that have developed as a consequence of the application of AI.International Journal For Multidisciplinary Research["Dr. Keshav Mishra", "Aparna Srivastava"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/d967ae97148639d8c77a4e8b3e06133952fa0b24 3327da3a717174699ab4b464dffaa629987fb19eGovernance of artificial intelligence (AI) has not achieved global participation. The primary state‐led instrument focusing specifically on the global governance of artificial intelligence is the Global Partnership on Artificial Intelligence (GPAI). Although GPAI aims for broad international participation, the only GPAI member from Southeast Asia is Singapore. GPAI's imbalanced global participation, restrictive membership process, and limited translations are potential barriers to Southeast Asian participation. However, a comparative policy analysis suggests that GPAI members and nonmembers in Southeast Asia have AI governance policies which are largely compatible, despite key differences. This study uses quantitative topic‐modeling and qualitative content analysis to compare the AI governance policies of Indonesia, Malaysia, Thailand, and Vietnam with the policies of Australia and Singapore, as reference GPAI members. The policies of GPAI, Australia, and Singapore emphasise the function of ethics while the policies of Indonesia, Malaysia, Thailand, and Vietnam emphasise the function of human capital development. State‐led, global AI governance efforts could attract more Southeast Asian participation by further emphasising human capital development and deemphasising the function of ethics. GPAI could increase the likelihood of Southeast Asian participation by decreasing its emphasis on political systems, allowing intergovernmental organisations to join, and recruiting all G20 members.Global Policy300A comparative policy analysis suggests that GPAI members and nonmembers in Southeast Asia have AI governance policies which are largely compatible, despite key differences, which could attract more Southeast Asian participation.Global Policy["Andrew J. Keith"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/3327da3a717174699ab4b464dffaa629987fb19e 29a981273b97d2b6436a730f8271d73c0882fc0bIsambard-AI is a new, leadership-class supercomputer, designed to support AI-related research. Based on the HPE Cray EX4000 system, and housed in a new, energy efficient Modular Data Centre in Bristol, UK, Isambard-AI employs 5,448 NVIDIA Grace-Hopper GPUs to deliver over 21 ExaFLOP/s of 8-bit floating point performance for LLM training, and over 250 PetaFLOP/s of 64-bit performance, for under 5MW. Isambard-AI integrates two, all-flash storage systems: a 20 PiByte Cray ClusterStor and a 3.5 PiByte VAST solution. Combined these give Isambard-AI flexibility for training, inference and secure data accesses and sharing. But it is the software stack where Isambard-AI will be most different from traditional HPC systems. Isambard-AI is designed to support users who may have been using GPUs in the cloud, and so access will more typically be via Jupyter notebooks, MLOps, or other web-based, interactive interfaces, rather than the approach used on traditional supercomputers of sshing into a system before submitting jobs to a batch scheduler. Its stack is designed to be quickly and regularly upgraded to keep pace with the rapid evolution of AI software, with full support for containers. Phase 1 of Isambard-AI is due online in May/June 2024, with the full system expected in production by the end of the year.arXiv.org123Isambard-AI is a new, leadership-class supercomputer, designed to support AI-related research, designed to be quickly and regularly upgraded to keep pace with the rapid evolution of AI software.ArXiv["Simon McIntosh-Smith", "S. Alam", "Christopher J. Woods"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/29a981273b97d2b6436a730f8271d73c0882fc0b ab66f581b052ac83d85622eac76f0a6e7464412c02[]2024-10-15T00:00:00https://www.semanticscholar.org/paper/ab66f581b052ac83d85622eac76f0a6e7464412c 4417f5a8da2d371e2e18f6d9e08364267f52a9beClinical and Experimental Ophthalmology31Clinical & experimental ophthalmology["Muzaffer Said G\u00fcler", "Elif Ertan Baydemir"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/4417f5a8da2d371e2e18f6d9e08364267f52a9be 214bbe116411c898ecea04d42070ea4fc554f16aPenelitian ini bertujuan untuk mendeskripsikan pemanfaatan kecerdasan buatan sebagai mediapembelajaran digital pada mata pelajaran IPAS di SD Negeri 111 Lembang Gogoso. Penelitian initermasuk penelitian fenomenologi dengan pendekatan kualitatif. Subjek penelitian yaitu, guru kelas II,IV, V dan 3 peserta didik. Teknik pengumpulan data yang digunakan adalah wawancara, observasidan dokumentasi. Keabsahan data dilakukan dengan cara trianggulasi sumber dan trianggulasi teknik.Adapun teknik analisis data yang digunakan yaitu pengumpulan data, reduksi data, penyajian data danpenarikan kesimpulan/verifikasi. Hasil penelitian menunjukkan bahwa Dalam pembelajaran IPAS,SDN 111 Lembang Gogoso memanfaatkan Artificial inteligens berbasis aplikasi canva, google form,dan kahoot sebagai media pembelajaran digital. Canva dinilai sebagai salah satu media yang dapatmendukung proses pembelajaran secara visual dan audio visual. Google form efektif dalammengevaluasi hasil belajar peserta didik. Kahoot dimanfaatkan untuk membuat kuis online gunameningkatkan minat belajar peserta didik.Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai80Prosiding Seminar Nasional Fakultas Tarbiyah dan Ilmu Keguruan IAIM Sinjai["Suriyati Suriyati", "Nuriya Ramadani", "Musdalifah"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/214bbe116411c898ecea04d42070ea4fc554f16a 18163c27cdf7e11b96a6f1be98f590262f542fc0International journal of urology50International journal of urology : official journal of the Japanese Urological Association["A. Okada"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/18163c27cdf7e11b96a6f1be98f590262f542fc0 23269fa681040588dc948f4de3935242347ae99eInternational journal of scientific research and engineering trends00International Journal of Scientific Research and Engineering Trends["Malvika Singh"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/23269fa681040588dc948f4de3935242347ae99e 4aada17a25ad3fb0f190e51d4b6e850f56c7082fInternational journal of scientific research and engineering trends00International Journal of Scientific Research and Engineering Trends["Alim Khan"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/4aada17a25ad3fb0f190e51d4b6e850f56c7082f 6885aa971f396af8828d6d53476bc66ff2dd9076Biomedical Journal of Scientific &amp; Technical Research00Biomedical Journal of Scientific &amp; Technical Research["Shankar Subramanian Iyer"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/6885aa971f396af8828d6d53476bc66ff2dd9076 8d53c7b559973677d974a42d466edaba8be08a12International journal of scientific research and engineering trends00International Journal of Scientific Research and Engineering Trends["Mr. Ankit Pandey"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/8d53c7b559973677d974a42d466edaba8be08a12 433e78a0c76164d02b5c6e61ee4a7e984fab5ea6Humanities and Social Sciences Communications70Humanities and Social Sciences Communications["Petr Polak", "Muhammad Anshari"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/433e78a0c76164d02b5c6e61ee4a7e984fab5ea6 dcd248b31c503305e789941a494bd84b103a485700["Joseph Mintz", "Wayne Holmes", "Leping Liu", "Mar\u00eda P\u00e9rez-Ortiz"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/dcd248b31c503305e789941a494bd84b103a4857 d91b92084f0c2e396e2495ba001845f39275bc2eDifferent employee backgrounds cause differences in their readiness to use AI. One of the important factors that causes doubts among potential AI users is data security. This research focuses on the application of AI technology in corporate environments in Indonesia, with the aim of understanding the attitudes of potential AI users and the factors that influence their intention to use this technology. This study aims to determine the application of AI technology in daily work from the perspective of prospective AI users. The 100 prospective AI users who were sampled in the study were employees from various levels in several fields of the company. The data processing technique uses regression tests for quantitative data which are complemented by thematic analysis for qualitative data. The results of the regression test show that attitude has a considerable influence on the intention to use AI.  Jurnal Syntax Admiration140The results of the regression test show that attitude has a considerable influence on the intention to use AI.Jurnal Syntax Admiration["Hanna Kirana Apriliana", "Y. P. Kornarius", "Angela Caroline", "T. E. P. Gusti", "Agus Gunawan"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/d91b92084f0c2e396e2495ba001845f39275bc2e 3f36fd84e51a9c404c8b7b28963d709c39bfd05dRelevance. Fundamental differences in the processes of development and maintenance of artificial neural networks from the algorithmic approach determine the relevance of the development of the existing scientific and methodological platform for solving technological and regulatory issues in the field of software development and legal protection of intellectual property of artificial intelligence systems.The purpose of the study is to formulate scientifically based conclusions that determine the directions for solving the scientific problem of legal protection of intellectual property on artificial neural networks.Objectives: to formulate a list of scientific tasks necessary for solving the scientific problem of legal protection of intellectual property rights to artificial neural networks; to develop proposals for improving the regulatory framework for registering intellectual property rights to artificial neural networks.Methodology. The methodological basis of scientific research was the dialectical method of understanding the phenomena and processes of the surrounding reality.Results. The obtained results of the study provide the opportunity to improve the regulatory framework establishing the basic principles of legal relations arising during the registration of intellectual property rights to artificial neural networks.Conclusion. The solution to the problem of legal protection of intellectual property on artificial neural networks should be based on the improvement of the existing regulatory framework for the registration of intellectual property rights, as well as regulatory acts that determine the rules for registering and securing intellectual property rights to arrays of training sample data and (or) arrays of trained models of artificial neural networks, as well as to the topology of integrated circuits of neuromorphic processors.Proceedings of the Southwest State University. Series: History and Law100The solution to the problem of legal protection of intellectual property on artificial neural networks should be based on the improvement of the existing regulatory framework for the registration of intellectual property rights, as well as regulatory acts that determine the rules for registering and securing intellectual property rights.Proceedings of Southwest State University. Series: History and Law["M. A. Ogarok"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/3f36fd84e51a9c404c8b7b28963d709c39bfd05d 3e8379d6c30f7e32627116f482627a307246b29a This Viewpoint explores how artificial intelligence technologies can adopt a clinical practice framework to identify use cases and outline the technology’s objectives and potential uses in modern health care. Journal of the American Medical Association (JAMA)14JAMA["Manesh R Patel", "Suresh Balu", "Michael J. Pencina"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/3e8379d6c30f7e32627116f482627a307246b29a 5443ba34cbcaff0cc120ac6f8e02700e592d95bcBrain cancer is one of the greatest health difficulties owing to the various and intricate structures. It is very important to identify them at an early stage and classify them correctly to enhance patients' outcomes and monitor clinical decisions. This proposed work aims to develops an effective method of improving various aspects of brain tumor detection coupled with its classification mechanism through artificial intelligence. This research work has three modules such as Brain tumor detection, segmentation and tumor classification. Algorithms and sophisticated microscopes are used for crafting a dependable method of diagnosing without the invasion of the patient's body. The approach especially involves Convolutional Neural Networks (CNNs) applied on large and varied brain tumor medical image datasets to classify various types of brain tumors with considerable accuracy. The proposed system utilizes brain MRI Brain Tumor dataset. Such strategies as data augmentation and transfer learning are performed in order to enhance both, model reliability and its performance. The brain tumor classification system provides higher accuracy compared to other classification techniques.International Conference on Signals and Electronic Systems112This proposed work aims to develops an effective method of improving various aspects of brain tumor detection coupled with its classification mechanism through artificial intelligence.2024 4th International Conference on Sustainable Expert Systems (ICSES)["V. Yamuna", "Praveen Rvs", "R. Sathya", "M. Dhivva", "R. Lidiya", "P. Sowmiya"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/5443ba34cbcaff0cc120ac6f8e02700e592d95bc fe92dbf3e1bdfb3cc173ab677a266b6456a77372How should scholars make sense of the rapid growth of generative artificial intelligence in media work? In this commentary, we argue that researchers can begin by stepping outside of their intellectual silos to see how the challenges and opportunities posed by generative AI are commonly shared across the media industries. We focus on three primary mass communication domains—advertising, journalism, and public relations—to illustrate how media professionals across these fields are adopting similar AI technologies (e.g., machine learning, natural language processing, and recommender systems) for often similar purposes (e.g., content creation, audience engagement, and business operations). Even more, the uptake of AI has profound consequences—for ethical norms as well as roles and relationships of humans and machines—that may be best understood across media industries more so than within them in isolation. Ultimately, a more cross-industry approach to scholarship could develop a more encompassing picture about AI's impact on media work and media consumption.Emerging Media272This commentary argues that researchers can begin by stepping outside of their intellectual silos to see how the challenges and opportunities posed by generative AI are commonly shared across the media industries.Emerging Media["Andrea L. Guzman", "Seth C. Lewis"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/fe92dbf3e1bdfb3cc173ab677a266b6456a77372 b96161e6159356dee2f10860c87d5b93287e12c4This paper investigates the application of Artificial Intelligence (AI) in enhancing quality control within Printed Circuit Board (PCB) manufacturing processes, focusing on how AI-driven technologies improve production efficiency and precision. The research addresses traditional quality control methods, such as manual inspection and Automated Optical Inspection (AOI), highlighting their limitations in keeping up with the complexities and demand for high-precision PCBs in modern electronics. The study delves into how AI technologies, particularly machine learning, computer vision, and predictive analytics, are being leveraged to overcome these limitations. By automating defect detection, improving accuracy, and enabling real-time analysis, AI systems not only streamline the quality control process but also significantly reduce human error and production costs. AI-driven quality control systems are shown to increase defect detection rates, reduce inspection times, and enhance overall production throughput. The paper includes a comparative analysis between traditional and AI-based quality control methods, revealing a notable improvement in both detection accuracy and production speed when AI systems are employed. Additionally, cost efficiency is explored, demonstrating how AI systems reduce waste, minimize rework, and lower operational costs. The potential challenges of AI implementation, such as the high initial costs, data requirements, and integration with legacy systems, are also discussed. The paper concludes that despite these challenges, AI offers a transformative solution to the growing demands of the PCB manufacturing industry, with the ability to scale effectively and adapt to future technological advancements. Ultimately, this research underscores the significant role of AI in revolutionizing PCB quality control by providing a more efficient, precise, and cost-effective approach, paving the way for further innovation in the electronics manufacturing sector.International Journal of Scientific Research and Management282The paper concludes that AI offers a transformative solution to the growing demands of the PCB manufacturing industry, with the ability to scale effectively and adapt to future technological advancements.International Journal of Scientific Research and Management (IJSRM)["Harshitkumar Ghelani"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/b96161e6159356dee2f10860c87d5b93287e12c4 59ec4e5e5e7b78a10a753385ba52ee629fd9389eThis article delves into the dynamics of human interaction with artificial intelligence (AI), emphasizing the optimization of these interactions to enhance human productivity. Employing a Grounded Theory Literature Review (GTLR) methodology, the study systematically identifies and analyzes themes from literature published between 2018 and 2023. Data were collected primarily from the Scopus database, with the Web of Science used to corroborate findings and include additional sources identified through a snowball effect. At the heart of this exploration is the pivotal role of socio-emotional attributes such as trust, empathy, rapport, user engagement, and anthropomorphization—elements crucial for the successful integration of AI into human activities. By conducting a comprehensive review of existing literature and incorporating case studies, this study illuminates how AI systems can be designed and employed to foster deeper trust and empathetic understanding between humans and machines. The analysis reveals that when AI systems are attuned to human emotional and cognitive needs, there is a marked improvement in collaborative efficiency and productivity. Furthermore, the paper discusses the ethical implications and potential societal impacts of fostering such human-AI relationships. It argues for a paradigm shift in AI development—from focusing predominantly on technical proficiency to embracing a more holistic approach that values the socio-emotional aspects of human-AI interaction. This shift could pave the way for more meaningful and productive collaborations between humans and AI, ultimately leading to advancements that are both technologically innovative and human-centric.Frontiers in Psychology1092A paradigm shift in AI development is argued—from focusing predominantly on technical proficiency to embracing a more holistic approach that values the socio-emotional aspects of human-AI interaction, ultimately leading to advancements that are both technologically innovative and human-centric.Frontiers in Psychology["Michal Kolomaznik", "Vladimir Petrik", "Michal Slama", "Vojt\u011bch Ju\u0159\u00edk"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/59ec4e5e5e7b78a10a753385ba52ee629fd9389e 7b08fcb40a22038d59779a6cebe4f6eecb4ad7b0The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.International Journal Of Engineering And Computer Science02The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions.International Journal of Engineering and Computer Science["Harshitkumar Ghelani"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/7b08fcb40a22038d59779a6cebe4f6eecb4ad7b0 2cd34f44e1dca26c929ea74c4054b184a1e6405eInfodemics pose significant dangers to public health and to the societal fabric, as the spread of misinformation can have far-reaching consequences. While artificial intelligence (AI) systems have the potential to craft compelling and valuable information campaigns with positive repercussions for public health and democracy, concerns have arisen regarding the potential use of AI systems to generate convincing disinformation. The consequences of this dual nature of AI, capable of both illuminating and obscuring the information landscape, are complex and multifaceted. We contend that the rapid integration of AI into society demands a comprehensive understanding of its ethical implications and the development of strategies to harness its potential for the greater good while mitigating harm. Thus, in this paper we explore the ethical dimensions of AI’s role in information dissemination and impact on public health, arguing that potential strategies to deal with AI and disinformation encompass generating regulated and transparent data sets used to train AI models, regulating content outputs, and promoting information literacy.JMIR AI751The ethical dimensions of AI’s role in information dissemination and impact on public health are explored, arguing that potential strategies to deal with AI and disinformation encompass generating regulated and transparent data sets used to train AI models, regulating content outputs, and promoting information literacy.JMIR AI["Federico Germani", "G. Spitale", "N. Biller-Andorno"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/2cd34f44e1dca26c929ea74c4054b184a1e6405e 767cef3a66c34c1d57c8d99e401926ead84d5f2fThis article explores the transformative impact of Artificial Intelligence (AI) on subscription-based business models across various industries. It examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection. The article highlights specific AI applications such as content recommendations, dynamic user interfaces, churn prediction, advanced customer segmentation, dynamic and usage-based pricing, AI-powered chatbots, and sentiment analysis. Additionally, it discusses how AI enhances operational efficiency through automated billing and inventory management, and improves security via anomaly detection and behavioral biometrics. Case studies of Adobe Creative Cloud and Amazon Web Services (AWS) are presented to illustrate real-world applications of AI in subscription services. The article concludes by emphasizing the paradigm shift AI represents in customer engagement, operational optimization, and revenue generation for subscription businesses, forecasting continued innovation and opportunities in this rapidly evolving sector.International Journal of Scientific Research in Computer Science Engineering and Information Technology91This article examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection, and discusses how AI enhances operational efficiency through automated billing and inventory management, and improves security via anomaly detection and behavioral biometrics.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Kiran Nagubandi"]2024-10-15T00:00:00https://www.semanticscholar.org/paper/767cef3a66c34c1d57c8d99e401926ead84d5f2f c51e7137d782800a8cf1c18ca1076e8689b54fd5This research examines the transformative role of artificial intelligence (AI) and predictive analytics in healthcare market forecasting, with a specific focus on their application at Eli Lilly. AI-driven insights are becoming critical tools in anticipating market trends, assessing the impact of regulatory changes, and optimizing product positioning in the competitive global healthcare landscape. By leveraging machine learning algorithms and vast datasets, AI can identify patterns and correlations that traditional forecasting methods may overlook, offering companies like Eli Lilly a more comprehensive understanding of future market dynamics. The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act. This legislative change, aimed at reducing drug prices and increasing market access, presents significant challenges and opportunities for pharmaceutical companies. AI allows firms to model the potential outcomes of such regulatory changes and adjust their market strategies accordingly. For example, predictive analytics can forecast the pricing pressures on specific drugs, helping companies develop competitive pricing models while maintaining profitability. Moreover, the research highlights the use of AI in mitigating risks associated with market volatility. By continuously analyzing real-time data, AI can detect early signals of potential disruptions—whether from economic downturns, competitor actions, or public health crises—allowing companies to take proactive measures. Additionally, AI-driven forecasting helps identify growth opportunities in emerging healthcare markets by analyzing demographic trends, disease prevalence, and healthcare infrastructure development. In conclusion, AI and predictive analytics are revolutionizing how healthcare companies like Eli Lilly approach market forecasting and strategy development. By offering deeper insights into future market conditions, these technologies not only reduce risks but also optimize product positioning and unlock growth opportunities in the global healthcare market. This research underscores the critical importance of AI in shaping the future of healthcare market forecasting. Keywords: Artificial Intelligence, Predictive Analytics, Market Forecasting, Regulatory Impact, Healthcare StrategyInternational journal of applied research in social sciences05The study explores how AI enhances decision-making by predicting healthcare market trends, particularly in response to regulatory shifts such as the U.S. Inflation Reduction Act, and underscores the critical importance of AI in shaping the future of healthcare market forecasting.International Journal of Applied Research in Social Sciences["Olumide Emmanuel Ibikunle", "Precious Azino Usuemerai", "Luqman Adewale Abass", "Victor Alemede", "Ejike Innocent Nwankwo", "Akachukwu Obianuju Mbata"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/c51e7137d782800a8cf1c18ca1076e8689b54fd5 c64367b0e84c503e2168bb38bae9928c44cbdbe9The utilization of artificial intelligence is becoming a hot debate among researchers, academicians, and practitioners. Educational institutions are also training teachers to utilize AI in teaching. However, there is a dearth of investigation on the training of teachers to utilize AI. Therefore, this systematic review aims to highlight the challenges and best practices in training teachers to utilize AI. Strict inclusion and exclusion criteria were set to shortlist the relevant studies for review.The review synthesized 10 studies focusing on the importance of AI, AI usage by teachers, challenges faced by teachers and trainers, and best practices that could be adopted by trainers.The results highlighted teachers lack the motivation for AI utilization and it is the biggest challenge faced by the trainers. Therefore, the training programs should be motivating, customized, and highlight the importance of AI. Moreover, the training sessions should also provide a trial of the latest AI technologies to the teachers so that they can get hands-on experience.This review can help AI trainers design customized training programs for teachers by keeping in mind the challenges faced by them. An effective training program can be designed if a trainer is aware of potential challenges faced by trainees. Thus, this research has not only discussed the challenges but also provided guidelines for AI trainers training teachers.Frontiers in Education491This review can help AI trainers design customized training programs for teachers by keeping in mind the challenges faced by them and provided guidelines for AI trainers training teachers.Frontiers in Education["Yousef Aljemely"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/c64367b0e84c503e2168bb38bae9928c44cbdbe9 ffa97da7a68613145beaa79b09346e079d2c573fPlanning, managing and optimising surface water quality is a complex and multifaceted process, influenced by the effects of both climate uncertainties and anthropogenic activities. Developing an innovative and robust decision support framework (DSF) is essential for effective and efficient water quality management, so it can provide essential information on water quality and assist policy makers and water resource managers to identify potential causes of water quality deterioration. This framework is crucial for implementing actions such as infrastructure development, legislative compliance and environmental initiatives. Recent advancements in computational domains have created opportunities for employing artificial intelligence (AI), advanced statistics and mathematical methods for use in improved water quality management. This study proposed a comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality. The framework utilises machine learning (ML), deep learning (DL), geographical information system (GIS) and advanced statistical and mathematical techniques for water quality management. The foundation of this framework is the outcomes from our three studies, where we examined the application of ML and DL models for predicting water quality index (WQI) in reservoirs, utilising statistical and mathematical methods to find the seasonal trend of rainfall and water quality, exploring the potential connection between streamflow, rainfall and water quality, and employing GIS to show the spatial and temporal variability of hydrological parameters and WQI. Three potable water supply reservoirs in the Toowoomba region of Australia were taken as the study area for practical implementation of the proposed DSF. This framework can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality. This will enable policy makers and water resource managers to enhance their decision making processes by selecting the management priorities to safeguard water quality in the face of future climate variability, including prolonged droughts and flooding.Water872A comprehensive conceptual DSF to minimise the adverse effects of extreme weather events and climate change on water quality is proposed and can serve as a comprehensive mechanism to identify distinct seasonal characteristics and understand correlations between rainfall, streamflow and water quality.Water["Syeda Zehan Farzana", "D. R. Paudyal", "Sreeni Chadalavada", "Md. Jahangir Alam"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/ffa97da7a68613145beaa79b09346e079d2c573f 2f9b2d1ec57e005ed9d8218aacaefcd34c3181fcArtificial intelligence (AI) literacy is a crucial aspect of media and information literacy (MIL), regarded not only as a human right but also as a fundamental requirement for societal advancement and stability. This study aimed to provide a comprehensive, cross-border perspective on AI literacy levels by surveying 1,800 university students from four Asian and African nations. The findings revealed significant disparities in AI literacy levels based on nationality, scientific specialization, and academic degrees, while age and gender did not show notable impacts. Malaysian participants scored significantly higher on the AI literacy scale than individuals from other countries. The results indicated that various demographic and academic factors influenced respondents’ perceptions of AI and their inclination to utilize it. Nationality and academic degree were identified as the most influential factors, followed by scientific specialization, with age and gender exerting a lesser influence. The study highlights the necessity of focusing research efforts on the detailed dimensions of the AI literacy scale and examining the effects of previously untested intervening variables. Additionally, it advocates for assessing AI literacy levels across different societal segments and developing the appropriate measurements.Frontiers in Communication352The results indicated that various demographic and academic factors influenced respondents’ perceptions of AI and their inclination to utilize it, and nationality and academic degree were identified as the most influential factors, followed by scientific specialization.Frontiers in Communication["Hasan M. H. Mansoor", "Ala Bawazir", "Mustafa Abdulraheem Alsabri", "Ahmed Alharbi", "Abdelmohsen Hamed Okela"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/2f9b2d1ec57e005ed9d8218aacaefcd34c3181fc c2a297713ac08f53ecf2ebf399b9d6f3cf79b25fAccurate forecasting of solar power generation is very important for integrating renewable energy into the smart grid and ensuring energy reliability. This study uses a Recurrent Neural Network structure to improve the accuracy of solar power generation forecasts. To improve the reliability and transparency of forecasts, the Local Interpretable Model-agnostic Explanation has been used as an Explainable Artificial Intelligence method. Model performance has been evaluated using common metrics such as Mean Square Error, Mean Absolute Error and Root Mean Squared Error. The application of Local Interpretable Model-agnostic Explanation has provided valuable insights into the model's decision-making process by identifying the meteorological features that are most effective in generating solar power. This integration of deep learning and Explainable Artificial Intelligence method not only achieved high forecast accuracy but also made the forecasts more reliable and transparent.2024 Innovations in Intelligent Systems and Applications Conference (ASYU)280This study uses a Recurrent Neural Network structure to improve the accuracy of solar power generation forecasts using the Local Interpretable Model-agnostic Explanation as an Explainable Artificial Intelligence method.2024 Innovations in Intelligent Systems and Applications Conference (ASYU)["Gokcen Ozdemir", "Umut Ozdemir", "M. Kuzlu"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/c2a297713ac08f53ecf2ebf399b9d6f3cf79b25f 2ae866ecf2943b38a23b38fcb470606b46487d1fThis study aims to create a holistic viewpoint by concentrating on two dynamic areas of artificial intelligence and entrepreneurship with bibliometric analysis. The concept of artificial intelligence, which is constantly heard as the digital world gradually penetrates our lives, and entrepreneurship, which is referred to as the atomic element of the economic infrastructure, are addressed in the same pot with this research. The attitude of both areas against varying circumstances constitutes the essential basis of this examination. The view that the effectiveness in the areas can be increased with the synergy to be created between the two focuses is supported. With this intention, the study commences with an informative literature section, where the introductory elements of the areas are conveyed. Afterward, it tries to clarify why these zones need to be examined together. Following this, a bibliometric analysis study, frequently used to bring unfamiliar kinds of literature jointly, is conducted using data obtained from the Web of Science database and subjected to various analyses. In the last stage, the study is completed by examining these outputs and analyzes. As a result, conclusions support “the duo” can be investigated jointly. The study contributes to the idea that artificial intelligence and entrepreneurship are wings working in synchrony for the requirement of success.Yildiz social science review00The study contributes to the idea that artificial intelligence and entrepreneurship are wings working in synchrony for the requirement of success by concentrating on two dynamic areas of artificial intelligence and entrepreneurship with bibliometric analysis.Yildiz Social Science Review["Ercan Karake\u00e7e", "Murat \u00c7emberci"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/2ae866ecf2943b38a23b38fcb470606b46487d1f a5ac46a89ca1f9308ffbe90df55ca980cca71d9bBMC Nephrology960A comprehensive review of findings from various studies of AKI detection and prediction highlights the potential benefits and challenges of their integration into routine clinical care and the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models.BMC Nephrology["Tu T Tran", "Giae Yun", "Sejoong Kim"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/a5ac46a89ca1f9308ffbe90df55ca980cca71d9b 78198fa26ec6c128eff6ef59c1bfce33283a2211Intrusion Detection Systems (IDS) play an important role in protecting information systems by detecting unauthorized access and malicious activities. In response to increasingly complex cyber threats, traditional intrusion detection systems have evolved to include advanced Artificial Intelligence (AI) techniques. However, the opaque structure of many AI models, especially deep learning-based ones, called “black boxes”, poses significant challenges in understanding and trusting the decision-making processes of intelligent systems. Explainable Artificial Intelligence (XAI) emerges as a solution to this problem by providing transparency and explainability to complex and opaque models. In this study; XAI is examined in general, XAI and its applications in IDSs are discussed, opportunities, challenges and areas where further research is needed in the field are examined and the integration of XAI into IDS is discussed and how explainable models can increase the efficiency, reliability and accountability of intrusion detection is demonstrated. It is also shown that incorporating XAI into IDS facilitates better decision making and compliance with regulatory standards.International Conference on Information Security and Cryptology00It is shown that incorporating XAI into IDS facilitates better decision making and compliance with regulatory standards, and how explainable models can increase the efficiency, reliability and accountability of intrusion detection is demonstrated.2024 17th International Conference on Information Security and Cryptology (ISCTürkiye)["Samed Al", "\u015eeref Sa\u011f\u0131ro\u011flu"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/78198fa26ec6c128eff6ef59c1bfce33283a2211 100a1c5f145e85f7af002979d8faa62ecbe13e0cAs artificial intelligence (AI) systems increasingly impact society, the EU Artificial Intelligence Act (AIA) is the first legislative attempt to regulate AI systems. This paper proposes a governance framework for organizations innovating with AI systems. Building upon secondary research, the framework aims at driving a balance between four types of pressures that organizations, innovating with AI, experience, and thereby creating responsible value. These pressures encompass AI/technology, normative, value creation, and regulatory aspects. The framework is partially validated through primary research in two phases. In the first phase, a conceptual model is proposed that measures the extent to which organizational tasks result in AIA compliance, using elements from the AIA as mediators and strategic variables such as organization size, extent of outsourcing, and offshoring as moderators. 34 organizations in the Netherlands are surveyed to test the conceptual model. The average actual compliance score of the 34 participants is low, and most participants exaggerate their compliance. Organization size is found to have significant impact on AIA compliance. In phase 2, two case studies are conducted with the purpose of generating in-depth insights to validate the proposed framework. The case studies confirm the interplay of the four pressures on organizations innovating with AI, and furthermore substantiate the governance framework.AAAI/ACM Conference on AI, Ethics, and Society00A governance framework for organizations innovating with AI systems that aims at driving a balance between four types of pressures that organizations, innovating with AI, experience, and thereby creating responsible value is proposed.{"pages": "328-340"}["Diptish Dey", "D. Bhaumik"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/100a1c5f145e85f7af002979d8faa62ecbe13e0c 21ee18ec2860c70534b05b5dc350fb87f5be94e5 A variety of physical impairments and functional restrictions are assessed and treated in the practice of physiotherapy. Subjective measures, rater variability, and restricted access to high-quality care are some of the unavoidable problems that contemporary physical therapy practice approaches must overcome. In light of these challenges, cutting-edge technologies such as artificial intelligence (AI) and machine learning (ML) are demonstrating remarkable efficacy in tackling these issues head-on. The focus of this review is to explore how the integration of AI and ML might change physical therapy practice and education in the age of digital communication. It delves into the challenges accompanying this integration and considers future prospects in this domain. A literature search was conducted using data base PubMed, Google Scholar, Web of Science, and Scopus with keywords such as ‘physiotherapy’, ‘artificial intelligence’, and ‘machine learning’, limited to English articles from 2014 to 2024. Results were imported into reference management software, duplicates removed, and relevant articles were screened and assessed for inclusion, with reasons for exclusion documented. Emerging technologies like AI and ML use algorithms to examine patient data and make automatic decisions, enhancing areas such as virtual reality therapy (VR), tele-rehabilitation, clinical decision support, individualized rehabilitation and physical function evaluation. These advance technologies optimize physical therapy and improve outcomes, but further research is needed to address obstacles like bias and data privacy to ensure responsible implementation. AI and ML can revolutionize physical therapy by improving therapy precision, patient monitoring, optimization and individualized therapy plan. However, it’s crucial for physiotherapists to balance technological advancements with compassionate, patient-centred approach.Physiotherapy - The Journal of Indian Association of Physiotherapists580How the integration of AI and ML might change physical therapy practice and education in the age of digital communication is explored and the challenges accompanying this integration are delves into and future prospects in this domain are considered.Physiotherapy - The Journal of Indian Association of Physiotherapists["Avilash Mohapatra", "P. Mohanty", "M. Pattnaik", "Srikanta Padhan"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/21ee18ec2860c70534b05b5dc350fb87f5be94e5 589a1c21f066bc20e7a69770b7cc49f4343be9fdThe construction industry constantly faces challenges related to workplace safety. Managing work safety is crucial to ensuring worker protection and successful project completion. In this context, this study investigates the applicability of Internet of Things technologies and Artificial Intelligence models, such as YOLO, to enhance safety on construction sites. The research identifies significant cultural resistance to the adoption of disruptive technologies in the Brazilian construction sector, and interviews with industry professionals reveal a low level of awareness and interest in these technological innovations. Through case studies and quantitative analyses, the study demonstrates YOLO's potential in identifying risks, such as detecting PPE and structural hazards. The results highlight the need to overcome conservative barriers and invest in technological solutions to increase safety and efficiency in construction.REVISTA DELOS60This study investigates the applicability of Internet of Things technologies and Artificial Intelligence models, such as YOLO, to enhance safety on construction sites and demonstrates YOLO's potential in identifying risks, such as detecting PPE and structural hazards.REVISTA DELOS["Nathan de Souza Gilmen e Silva", "Gabriel Resende Machado", "Vagner Zeizer Carvalho Paes"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/589a1c21f066bc20e7a69770b7cc49f4343be9fd 525cca2f63e6a2d3ef89fb74c7d52319218b0f2cThis study investigates how external auditors perceive using artificial intelligence (AI) in the Kingdom of Saudi Arabia (KSA). It examines how external auditors perceive the impact of artificial intelligence on audit quality. It also seeks to determine whether local and foreign external auditors have different perspectives on AI's application and how it affects audit quality. Data were gathered using a questionnaire distributed to 44 regional and 20 international companies to accomplish research goals. The auditing manager, audit partners, senior auditors, and other staff members with possible accounting and auditing experience were among the participants. SPSS was used to analyze the data; in our analysis, we used descriptive analysis, validity and reliability testing, and data analysis to test our hypotheses. This investigation reveals that the perceived impact of artificial intelligence (AI) on audit quality does not vary significantly between domestic and foreign audit firms. Regarding audit quality, the perceived contributions of all audit firms—local and foreign—are equal. It also adds to the importance of using AI and how it will enhance the quality of firms and reduce any act of fraud. Lastly, it recommends that firms contribute to training employees on using AI to face and compete with the changes and challenges happening around the world.Journal of Economics, Finance and Accounting Studies260The perceived impact of artificial intelligence (AI) on audit quality does not vary significantly between domestic and foreign audit firms, and firms contribute to training employees on using AI to face and compete with the changes and challenges happening around the world.Journal of Economics, Finance and Accounting Studies["Huda Alsayed", "Hajar Alahmari"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/525cca2f63e6a2d3ef89fb74c7d52319218b0f2c 0a087599ba5ba58b9d5c0743926271d2010343bfIn this study, my goal is to analyse the transformation of employer rights in the context of the application of artificial intelligence (AI). The introduction of AI technologies has a significant impact on the employers’ right to instruct, control and discipline, and also raises new data management challenges. My central theme is the growing role of automated decision-making, particularly in the areas of recruitment, selection and performance appraisal, which are also an important part of human resource management, a subset of employer rights. In the course of the above-designated research path, I attempt to answer questions that make the relevance of the topic indisputable: How can the transparency and legality of automated decision-making be ensured in the employment relationship? What data protection concerns arise in the application of AI? Due to the rise of AI technologies, examining these questions is of fundamental importance from the aspect of the protection of employee rights and interests.Curentul Juridic/Juridical Current00The central theme is the growing role of automated decision-making, particularly in the areas of recruitment, selection and performance appraisal, which are also an important part of human resource management, a subset of employer rights.Curentul Juridic/Juridical Current["D\u00f3ra Varga"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/0a087599ba5ba58b9d5c0743926271d2010343bf 7d1b23f2eb34d37a3c206f3dcbdac2a755510213Discover Oncology380The investigation of Artificial Intelligence (AI) applications in the field of Oncology is still in its early phases especially for genomics, proteomics, and clinicomics, with extensive studies focused on biology, diagnosis, treatment, and cancer risk assessment.Discover Oncology["Murat Ko\u00e7ak", "Z. Akcali"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/7d1b23f2eb34d37a3c206f3dcbdac2a755510213 1cff8eb5d333aa7909aaa28c8585b3f93ac45f29The increasing prevalence of artificial intelligence (AI) will likely lead to new interactions and impacts for the general public. An understanding of people’s perceptions of AI can be leveraged to design and deploy AI systems toward human needs and values. We conducted semi-structured interviews with 25 individuals in the general public and 20 AI experts in the United States (U.S.) to assess perceptions of AI across levels of expertise. Qualitative analysis revealed that ideas about humanness and ethics were central to perceptions of AI in both groups. Humanness, the set of traits considered to distinguish humans from other intelligent actors, was used to articulate beliefs about AI’s characteristics. Ethics arose in discussions of the role of technology in society and centered around views of AI as made and used by people. General public and expert participants expressed similar perceptions of AI, but articulated beliefs slightly differently. We discuss the implications of humanness-related beliefs and ethical concerns for AI development and deployment.AAAI/ACM Conference on AI, Ethics, and Society00Qualitative analysis revealed that ideas about humanness and ethics were central to perceptions of AI in both general public and expert participants, and the implications of humanness-related beliefs and ethical concerns for AI development and deployment.{"pages": "647-658"}["Theodore Jensen", "M. Theofanos", "Kristen Greene", "Olivia Williams", "Kurtis Goad", "Janet Bih Fofang"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/1cff8eb5d333aa7909aaa28c8585b3f93ac45f29 4fb8e4e7d6cc0fd0c7c300f0f6271adf9b3e9fa5 Healthcare associated infections (HAI) are a major concern. Infections and sepsis are prevalent causes of prolonged hospitalization, healthcare resources consumption, and mortality. Infection prevention and control (IPC) measures need to be encouraged with the purpose to become standard protocol. Hand hygiene represents the most important measure to prevent the spread of infections. Poor hand hygiene is often due to: the belief that the use of gloves replaces hand hygiene, lack of good example from colleagues and superiors, sinks that are difficult to access or insufficient in number, staff often too busy / not enough time, poor knowledge of guidelines and protocols. Artificial intelligence was recently integrated in hand washing devices; this can implement training on a daily basis but also is an objective method to assess the efficacy of the IPC measures adopted. The benefit–cost ratio of this approach needs to be discussed. Frontiers in Public Health130Hand hygiene represents the most important measure to prevent the spread of infections and Artificial intelligence was recently integrated in hand washing devices; this can implement training on a daily basis but also is an objective method to assess the efficacy of the IPC measures adopted.Frontiers in Public Health["F. Simioli", "A. Annunziata", "Antonietta Coppola", "Anna Iervolino", "Mariacristina Boccia", "Giuseppe Fiorentino"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/4fb8e4e7d6cc0fd0c7c300f0f6271adf9b3e9fa5 fd57744ac9efcca0a9a154091e770d9c81742922Background: One of the first radiology areas in which artificial intelligence began to be used and is still actively used to this day is chest X-ray examination. However, when interpreting these studies using artificial intelligence, radiologists still face a number of limitations on a daily basis that must be taken into account when making a medical opinion and which developers need to pay attention to in order to further improve the algorithms to increase their efficiency. Aims: Identification of limitations in the use of currently available artificial intelligence services for chest X-ray examinations and identification of promising directions for their further development. Materials and methods: A retrospective analysis of 155 cases of disagreement between the results of conclusions of artificial intelligence services and medical opinions when analyzing chest X-ray examinations was carried out. All cases included in the study were obtained from the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow. Results: Among the 155 analyzed cases of disagreement, 48 (31.0%) were false positives and 78 (50.3%) were false negatives. The remaining 29 (18.7%) cases were excluded from further study because they turned out to be true positive (27) or true negative (2). Among the 48 false-positive cases, the majority (93.8%) was due to the fact that the artificial intelligence service mistook normal anatomical structures of the chest (97.8% of cases) or a catheter shadow (2.2% of cases) for pneumothorax. Among false-negative studies, the proportion of missed clinically significant pathologies was 22.0%. Almost half of these cases (44.4%) were associated with missed lung nodes. The most common clinically insignificant pathology was calcifications in the lungs (60.9%). Conclusions: On the part of AI services, there was a tendency towards overdiagnosis. All false-positive cases were associated with erroneous detection of clinically significant pathology: pneumothorax, lung nodules, and pulmonary consolidation. Among false-negative cases, the proportion of missing clinically significant pathology was small and amounted to less than one-fourth.Digital Diagnostics260A retrospective analysis of 155 cases of disagreement between the results of conclusions of artificial intelligence services and medical opinions when analyzing chest X-ray examinations found a tendency towards overdiagnosis.Digital Diagnostics["Yu. A. Vasilev", "A. Vladzymyrskyy", "K. Arzamasov", "I. Shulkin", "Elena V. Astapenko", "L. Pestrenin"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/fd57744ac9efcca0a9a154091e770d9c81742922 186f642839bba089715134c369020450642f3bd1The objective of this article is to assess the impact of generative artificial intelligence (AGI) as a learning tool in clinical practice, as perceived by clinical students of human medicine. To this end, six learning activities were devised and executed, employing diverse pedagogical approaches and AGI tools, with the objective of addressing various facets of clinical practice. These included the creation of explanatory material, literature analysis, the selection of clinical cases for publication, the development of self-assessment questions, the production of explanatory videos, and the creation of scientific posters. These activities were conducted during the clinical internships of the fifth year of the medical curriculum and were evaluated using both quantitative and qualitative methods. The findings indicated that the incorporation of GAI as a pedagogical instrument in the clinical training of medical students yielded a favorable influence on their motivation, self-assurance, satisfaction, competencies, and knowledge.  The students identified GAI as a novel, applicable, useful, and transferable tool and expressed interest in continuing to use it in the future. However, the results also indicated that students encountered certain challenges and difficulties in utilizing GAI as a learning tool, including overconfidence, resistance, and a lack of understanding. In conclusion, this study provides empirical evidence on the use of GAI as a learning tool for undergraduate medical students' clinical practices, and contributes to the knowledge base regarding the possibilities and challenges of GAI application in higher education.Evolutionary Studies in Imaginative Culture230Empirical evidence is provided on the use of GAI as a learning tool for undergraduate medical students' clinical practices, and contributes to the knowledge base regarding the possibilities and challenges of GAI application in higher education.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["Carmen M. Alegr \u00ed a-Bernal", "Jhan C. Fern \u00e1 ndez-Delgado", "Fernando S And \u00ed a-Alegr \u00ed a"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/186f642839bba089715134c369020450642f3bd1 1b7a0ac9aa920e0d90f2455951b027532168aadeWhat to Trust When We Trust Artificial Intelligence Abstract: So-called “trustworthy AI” has emerged as a guiding aim of industry leaders, computer and data science researchers, and policy makers in the US and Europe. Often, trustworthy AI is characterized in terms of a list of criteria. These lists usually include at least fairness, accountability, and transparency. Fairness, accountability, and transparency are valuable objectives, and they have begun to receive attention from philosophers and legal scholars. However, those who put forth criteria for trustworthy AI have failed to explain why satisfying the criteria makes an AI system—or the organizations that make use of the AI system—worthy of trust. Nor do they explain why the aim of trustworthy AI is important enough to justify devoting resources to achieve it. It even remains unclear whether an AI system is the sort of thing that can be trustworthy or not. To explain why fairness, accountability, and transparency are suitable criteria for trustworthy AI one needs an analysis of trustworthy AI. Providing an analysis of trustworthy AI is a distinct task from providing criteria. Criteria are diagnostic; they provide a useful test for the phenomenon of interest, but they do not purport to explain the nature of the phenomenon. It is conceivable that an AI system could lack transparency, accountability, or fairness while remaining trustworthy. An analysis of trustworthy AI provides the fundamental features of an AI system in virtue of which it is (or is not) worthy of trust. An AI system that lacks these features will, necessarily, fail to be worthy of trust. This paper puts forward an analysis of trustworthy AI that can be used to critically evaluate criteria for trustworthy AI such as fairness, accountability, and transparency. In this paper we first make clear the target concept to be analyzed: trustworthy AI. We argue that AI, at least in its current form, should be understood as a distributed, complex system embedded in a larger institutional context. This characterization of AI is consistent with recent definitions proposed by national and international regulatory bodies, and it eliminates some unhappy ambiguity in the common usage of the term. We further limit the scope of our discussion to AI systems which are used to inform decision-making about qualification problems, problems wherein a decision-maker must decide whether an individual is qualified for some beneficial or harmful treatment. We argue that, given reasonable assumptions about the nature of trust and trustworthiness, only AI systems that are used to inform decision-making about qualification problems are appropriate candidates for attributions of (un)trustworthiness. We then distinguish between two models of trust and trustworthiness that we find in the existing literature. We motivate our account by highlighting this as a dilemma in in the accounts of trustworthy AI that have previously been offered. These accounts claim that trustworthiness is either exclusive to full agents (and it is thus nonsense when we talk of trustworthy AI), or they offer an account of trustworthiness that collapses into mere reliability. The first sort of account we refer to as an agential account and the second sort we refer to as a reliability account. We offer that one of the core challenges of putting forth an account of trustworthy AI is to avoid reducing to one of these two camps. It is thus a desideratum of our account that it avoids being exclusive to full moral agents, while it simultaneously avoids capturing things such as mere tools. We go on to propose our positive account which we submit avoids these twin pitfalls. We subsequently argue that if AI can be trustworthy, then it will be trustworthy on an institutional model. Starting from an account of institutional trust offered by Purves and Davis, we argue that trustworthy AI systems have three features: they are competent with regard to the task they are assigned, they are responsive to the morally salient facts governing the decision-making context in which they are deployed, and they publicly provide evidence of these features. As noted, this account builds on a model of institutional trust offered by Purves and Davis and an account of default trust from Margaret Urban Walker. The resulting account allows us to accommodate the core challenge of finding a balance between agential accounts and reliability accounts. We go on to refine our account, answer objections, and revisit the list criteria from above as explained in terms of competence, responsiveness, and evidence.AAAI/ACM Conference on AI, Ethics, and Society00It is argued that AI, at least in its current form, should be understood as a distributed, complex system embedded in a larger institutional context and only AI systems that are used to inform decision-making about qualification problems are appropriate candidates for attributions of (un)trustworthiness.{"pages": "1166"}["Duncan Purves", "Schuyler Sturm", "John Madock"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/1b7a0ac9aa920e0d90f2455951b027532168aade abbc3f7c5899abd360f414dc7f0fe7f34f33e80eThe recent breakthroughs in artificial intelligence (AI) and its rapid integration into various aspects of life have become a focal point of concern, especially in regards to potential consequences on the workforce. Since AI has seen the most transformation post-2020, this study aims to analyze the role of AI exposure within an occupation on projected 10-year changes in employment using the most recent available data from 2022 (pulled from the U.S. Bureau of Labor Statistics). Through forward selection in ordinary least squares method, after controlling for task offshorability, education, work experience, and on-the-job training, it was found that a 1 standard deviation increase in AI exposure index is associated with a projected decrease of 1.043 percentage points in jobs on average within an occupation. This finding supports the growing concern of AI-driven job displacement and/or unemployment, which could heighten the burden on the government to fund welfare programs and potentially lead to recession. This paper further highlights the need for policy discussions on fostering a more balanced integration of AI into the workforce, possibly through specialized education and retraining programs.UF Journal of Undergraduate Research160It was found that a 1 standard deviation increase in AI exposure index is associated with a projected decrease of 1.043 percentage points in jobs on average within an occupation, which supports the growing concern of AI-driven job displacement and/or unemployment.UF Journal of Undergraduate Research["Greeshma Avaradi"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/abbc3f7c5899abd360f414dc7f0fe7f34f33e80e 1149c05c2364c4468f05b91958286e3e828f8a5cArtificial Intelligence (AI) has already changed the industry and is expected to bring more changes. Furthermore, the semiconductor manufacturing area, one of the extremely high-tech industries, is attempting to increase the production efficiency of the manufacturing industry based on a huge amount of data generated from semiconductor production equipment and also attempting to design production equipment through AI. To understand the trend of these changes, we searched for various keywords including semiconductor manufacturing and AI, and constructed a dataset through Scopus and Web of Science, which are prominent databases, and limited the period to the period from 2020 to the present. We analyzed 1,498 publications from Scopus, 500 publications from Web of Science, and a total of 1,579 publications. The number of research publications increased every year, which shows that research on semiconductor manufacturing and AI is becoming more active with the increasing number of publications, and China and South Korea are the countries that produced the most publications. The main publication sources are the Proceedings of SPIE and IEEE Transactions on Semiconductor Manufacturing, and the keyword co-occurrence graph shows that there are attempts to apply AI in various fields of semiconductor manufacturing.Information and Communication Technology Convergence130The number of research publications increased every year, which shows that research on semiconductor manufacturing and AI is becoming more active with the increasing number of publications, and China and South Korea are the countries that produced the most publications.2024 15th International Conference on Information and Communication Technology Convergence (ICTC)["Koohee K won", "Minyoung Lee", "Eunil Park"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/1149c05c2364c4468f05b91958286e3e828f8a5c 3cf6684eae00268520263ec84d9a557a044caf7f This article assesses the potential use of Artificial Intelligence (AI) in Refugee Status Determination, exploring how AI systems, including biometrics, predictive analytics, and emotion recognition, could support or replace human decision-making in determining refugee status. While AI could speed up and improve the objectivity of Refugee Status Determination procedures, the article raises concerns about its limitations in assessing the subjective fear of persecution, a critical element of refugee claims. The inability of AI systems to fully account for emotional and personal nuances leads to the possibility of discriminatory practices, particularly when AI is used without human oversight. The study highlights how AI could exacerbate power imbalances between asylum-seekers and States, transforming these technologies into tools for anti-immigration policies. It therefore calls for caution in the use of AI in Refugee Status Determination procedures and stresses that human interaction remains essential for the fair assessment of refugee claims. This article concludes that while AI can assist in certain aspects of the Refugee Status Determination procedures, its full implementation without careful legal and human rights considerations poses significant risks.Refugee Survey Quarterly00It is concluded that while AI can assist in certain aspects of the Refugee Status Determination procedures, its full implementation without careful legal and human rights considerations poses significant risks.Refugee Survey Quarterly["Mario Pasquale Amoroso"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/3cf6684eae00268520263ec84d9a557a044caf7f 2ac2019b2d22d107135ec2958a699f90df00bbb3Artificial intelligence (AI) technologies have received significant attention since the launch of ChatGPT in late 2022. People are experimenting with AI technologies in different aspects of daily life, including the use of AI tools in the scholarly writing process. Researchers and journal publishers are debating the impact of AI technologies on the quality and accountability of scholarly works. Indeed, scholarly journals play an important role in the dissemination of research and should be advising authors on the appropriate and acceptable use of AI technologies in scholarly publishing. Yet very few studies have examined how journals are providing AI-related guidance to authors. This work-in-progress (WIP) research explores how scholarly journals advise authors about the use of AI in the scholarly writing process by conducting a content analysis of journal policies. Specifically, the study sample was comprised of the top 20 journals, identified using journal metrics provided by Google Scholar in the following subject areas: STEM, humanities, literature & arts, social sciences, and library and information sciences. Policies from these 80 journals were collected from publicly available websites and then examined in August 2024 to assess how journals are currently providing AI-related guidance to authors. The guiding research questions and content analysis focused on the following aspects: 1) The presence or absence of AI in the journal policy; 2) Definition / examples of AI; 3) Guidance on the use of AI for authors; 4) Guidelines about AI for peer reviewers. In this WIP poster, preliminary findings from the content analysis are presented.Proceedings of the ALISE Annual Conference00This work-in-progress (WIP) research explores how scholarly journals advise authors about the use of AI in the scholarly writing process by conducting a content analysis of journal policies.Proceedings of the ALISE Annual Conference["Mei Zhang", "Deborah Charbonneau"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/2ac2019b2d22d107135ec2958a699f90df00bbb3 15db332935bc58c4223f619308c6caaba957b407In today's interconnected world, the dissemination of vast amounts of information through the internet has become ubiquitous, facilitating seamless communication and connectivity across the globe. However, this digital landscape is fraught with cybersecurity threats, posing significant challenges to individuals, businesses, and organizations alike. In response to these evolving risks, there has been a burgeoning interest in leveraging machine learning techniques to bolster cybersecurity defenses. Through a meticulous examination of 736 research papers spanning from 2012 to 2024, our comprehensive analysis identified 501 pertinent works, shedding light on the recent trends in this critical research domain. By deploying a systematic literature review (SLR) we categorize these papers based on implementation methodologies, article types, publishers, and efficacy, and offer a coherent and visually informative representation of the landscape. This endeavor underscores the immense potential of machine learning in fortifying cybersecurity measures and serves as a valuable resource for researchers, students, publishers, and industry experts seeking to navigate and contribute to the dynamic field of machine learning for cybersecurity.International Conference on Communications, Computing, Cybersecurity, and Informatics214Through a meticulous examination of 736 research papers spanning from 2012 to 2024, a comprehensive analysis identified 501 pertinent works, shedding light on the recent trends in this critical research domain and underscores the immense potential of machine learning in fortifying cybersecurity measures.2024 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)["Md Kamruzzaman", "Md Khokan Bhuyan", "Rakibul Hasan", "Syeda Farjana Farabi", "Sadia Islam Nilima", "Md Azhad Hossain"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/15db332935bc58c4223f619308c6caaba957b407 9c34cc509e11fd1422b50f48f3f1ec6b5d807b28Anxiety disorders are psychiatric conditions characterized by prolonged and generalized anxiety experienced by individuals in response to various events or situations. At present, anxiety disorders are regarded as the most widespread psychiatric disorders globally. Medication and different types of psychotherapies are employed as the primary therapeutic modalities in clinical practice for the treatment of anxiety disorders. However, combining these two approaches is known to yield more significant benefits than medication alone. Nevertheless, there is a lack of resources and a limited availability of psychotherapy options in underdeveloped areas. Psychotherapy methods encompass relaxation techniques, controlled breathing exercises, visualization exercises, controlled exposure exercises, and cognitive interventions such as challenging negative thoughts. These methods are vital in the treatment of anxiety disorders, but executing them proficiently can be demanding. Moreover, individuals with distinct anxiety disorders are prescribed medications that may cause withdrawal symptoms in some instances. Additionally, there is inadequate availability of face-to-face psychotherapy and a restricted capacity to predict and monitor the health, behavioral, and environmental aspects of individuals with anxiety disorders during the initial phases. In recent years, there has been notable progress in developing and utilizing artificial intelligence (AI) based applications and environments to improve the precision and sensitivity of diagnosing and treating various categories of anxiety disorders. As a result, this study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.Frontiers Artif. Intell.941This study aims to establish the efficacy of AI-enabled environments in addressing the existing challenges in managing anxiety disorders, reducing reliance on medication, and investigating the potential advantages, issues, and opportunities of integrating AI-assisted healthcare for anxiety disorders and enabling personalized therapy.Frontiers in Artificial Intelligence["K. P. Das", "P. Gavade", "Luca Zammataro", "Jaros\u0142aw Pawe\u0142 Drapa\u0142a"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/9c34cc509e11fd1422b50f48f3f1ec6b5d807b28 d67026a2f68194188c86099e2b009f4db33cacefThis study used a combination of questionnaires and interviews to survey 68 librarians in mainland China. The questionnaire was divided into three parts: (1) providing descriptive statistics of the interviewed librarians; (2) exploring the impact of generative technologies on librarians' information behaviour from work scenarios; (3) investigating librarians' concerns about ethics and strategies for coping with ethical challenges. The results show that generative AI technologies had a greater impact on information seeking, information encountering, and information using behaviours, and an insignificant impact on information sharing behaviours. In addition, the results of the study reflect that 67.65% of librarians showed a very high level of concern about privacy and security; 66.18% of them believed that the content generated by the tools needed further validation. The study also provided six recommendations from the perspective of libraries and librarians to address ethical challenges such as the spread of disinformation and bias.Proceedings of the ALISE Annual Conference51The results show that generative AI technologies had a greater impact on information seeking, information encountering, and information using behaviours, and an insignificant impact on information sharing behaviours.Proceedings of the ALISE Annual Conference["Yifei Chen", "Yongjie Li", "Dechao Wang", "Yinan Sun", "Tingyu Lv", "Xiaoli Tang"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/d67026a2f68194188c86099e2b009f4db33cacef 4d6518470882c16128c64e436b5887af943f9e64International Journal of Progressive Research in Engineering Management and Science00International Journal of Progressive Research in Engineering Management and Science[]2024-10-16T00:00:00https://www.semanticscholar.org/paper/4d6518470882c16128c64e436b5887af943f9e64 f58eb71b093f0d3a7e9f8904c13c4cc0f831bcd9International Journal of Progressive Research in Engineering Management and Science00International Journal of Progressive Research in Engineering Management and Science[]2024-10-16T00:00:00https://www.semanticscholar.org/paper/f58eb71b093f0d3a7e9f8904c13c4cc0f831bcd9 c33254a0984e719ecf5f59a5ef3e2357fcc60801Lifetime Data Analysis00Lifetime data analysis["Xingqiu Zhao"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/c33254a0984e719ecf5f59a5ef3e2357fcc60801 69a90351be7e8e1a4f9d9bffbe270b08d4fcc14fThere is a significant lag between the year a great idea is first proposed and the year it is eventually adopted and mainstreamed. The foundations of the Internet of Things (IoT) were introduced between 1960 and 1980, the concept was introduced in 1990, and its growth and expansion came after 2000. Proliferation and discussions on standardization were proposed after another 10 years. IoT evolution and development are tightly coupled to cloud-fog-edge architectures with the assumption that a significant amount of advanced processing occurs not on IoT devices but on clouds and fog. However, as the technology evolved, the required amount of data and connections indicated that previous ideas and concepts were not realistic and that data processing at the edge layer was required.International Symposium for Design and Technology in Electronic Packaging270IoT evolution and development are tightly coupled to cloud-fog-edge architectures with the assumption that a significant amount of advanced processing occurs not on IoT devices but on clouds and fog.2024 IEEE 30th International Symposium for Design and Technology in Electronic Packaging (SIITME)["C. Corches", "Adelina Ioana Ilies\u0327", "M. Daraban", "G. Chindris"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/69a90351be7e8e1a4f9d9bffbe270b08d4fcc14f 761c7691e0a53e75b67e614e59f5c90fbe6f3ff9AJR. American journal of roentgenology00AJR. American journal of roentgenology["H. Stallmann"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/761c7691e0a53e75b67e614e59f5c90fbe6f3ff9 ef72b8e0273811de6788d5c2d63be120585a6eceThis research paper seeks to understand the deficit arising from the generative AI and its potential in redefying various sectors and suggesting modification on the current laws. Generative AI systems can generate distinctive content which could be used in text, images, or music, among others, by training from the available data. It highlights how generative AI influences the legal profession in terms of work like contract writing, as well as how newer language models like GPT-4 and chatbots like ChatGPT and Gemini are evolving. Thus, while generative AI has numerous opportunities, it also raises concerns about ethical issues, authorship and ownership, privacy, and abuses, such as the propagation of deepfakes and fake news. This study focuses attention on the importance of strengthening the legal frameworks to answer the ethical issues and challenges linked to generative AI, such as deepfakes, piracy of contents, discriminative impact, or naked breaches of privacy. It calls for proper and sensitive use of generative AI through regulation, openness, and commonly agreed global guidelines. This paper emphasizes that innovations need to be balanced by a set of effective regulations to unleash the potential of generative AI and minimize potential threats.Kutafin Law Review270This study focuses attention on the importance of strengthening the legal frameworks to answer the ethical issues and challenges linked to generative AI, such as deepfakes, piracy of contents, discriminative impact, or naked breaches of privacy.Kutafin Law Review["A. Sharma", "R. Sharma"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/ef72b8e0273811de6788d5c2d63be120585a6ece 29a410a3f8ec64a571d3ce64689890f57c7b6787Engineering Management Journal1340Engineering Management Journal["Mohamed Mounir Harrir", "Lamia Triqui Sari"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/29a410a3f8ec64a571d3ce64689890f57c7b6787 6a9c57c7bbef97aea6b28188d10a69dc99b5268fInternational Journal of Green Energy980International Journal of Green Energy["Haijie Wang", "Song-Lin Jin", "Chun\u2010Ping Chang"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/6a9c57c7bbef97aea6b28188d10a69dc99b5268f fa9d1723ec6f7eb374f311a85a5260e68b650ef3Aesthetic surgery journal90Aesthetic Surgery Journal["Ravinder Dhawan", "O. Shauly", "Denys Shay", "Kendall Brooks", "Albert Losken"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/fa9d1723ec6f7eb374f311a85a5260e68b650ef3 8c80779b6fc0e5106f7482c5b8139d3f0f512aabCel. Pojawienie się sztucznej inteligencji budzi z jednej strony duże zainteresowanie, ale z drugiej jednak rodzi wiele obaw i wątpliwości. Niewątpliwie elementy sztucznej inteligencji będą miały swój istotny udział w procesie kształcenia, uczenia się, zabawie i rozrywce dzieci i młodzieży. W obliczu tych zmian niezwykle ważne staje się budowanie odpowiedzialnej postawy wobec sztucznej inteligencji, jej twórczego i właściwego wykorzystania przez nauczycieli w szkole, ale także przez samych uczniów i rodziców. Opracowany artykuł ma na celu przedstawić znaczenie i rolę edukacji medialnej praktykowanej przez nauczycieli wobec rozwoju sztucznej inteligencji, która coraz częściej znajduje swoje zastosowanie w szeroko pojętej edukacji. Metody i materiały. W artykule zastosowano przegląd poglądów i założeń wyjaśniających znaczenie sztucznej inteligencji w edukacji. Podkreślono przekonania akcentujące potrzebę praktykowania edukacji medialnej wśród dzieci i młodzieży, która będzie pomocą w prawidłowym zastosowaniu sztucznej inteligencji w edukacji. Wyniki i wnioski. Każdego dnia pojawiają się nowe możliwości, zadania i rozwiązania zakładające wykorzystanie AI, które z powodzeniem można kreatywnie wykorzystać w szeroko pojętej edukacji. Jednak pojawiają się też nowe wątpliwości, ograniczenia i zagrożenia wynikające z nieumiejętnego korzystania ze sztucznej inteligencji w działaniach edukacyjnych. W tym miejscu niezwykle ważną i nową rolę ma do spełnienia edukacja medialna praktykowana przez nauczycieli, którzy mogą wyjaśniać i prezentować, w jaki sposób należy wykorzystywać w edukacji narzędzia i aplikacje oparte na sztucznej inteligencji.Family Upbringing60Family Upbringing["E. Nowicka"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/8c80779b6fc0e5106f7482c5b8139d3f0f512aab c427d86d4c04fba7b7352736bfef505632008672Frontiers of Digital Education00Frontiers of Digital Education["Fei Wu"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/c427d86d4c04fba7b7352736bfef505632008672 1c80b2444e32ea05579b3553e6a53fac01a24a35AJR. American journal of roentgenology00AJR. American journal of roentgenology["Andrew D Smith", "Cody H Savage"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/1c80b2444e32ea05579b3553e6a53fac01a24a35 b222ce5d38cde042fe9e9f3556760966d1ecb4d3This paper proposes a plugin-based framework designed to optimize the development and verification of game content using AI technologies. By focusing on procedural content generation, verification, and real-time monitoring, the framework aims to support small and medium-sized game companies in improving development efficiency and reducing costs. The approach enables these companies to accumulate foundational AI technologies, fostering sustainable growth and enhancing the overall quality of game content.Information and Communication Technology Convergence40A plugin-based framework designed to optimize the development and verification of game content using AI technologies, focusing on procedural content generation, verification, and real-time monitoring, which enables small and medium-sized game companies in improving development efficiency and reducing costs.2024 15th International Conference on Information and Communication Technology Convergence (ICTC)["Si-Hwan Jang", "Seong-Il Yang", "SungJune Chang", "Dae-Wook Kim", "Youn-Hee Gil"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/b222ce5d38cde042fe9e9f3556760966d1ecb4d3 626430e614a6f0a43c4668ce3e65acba85801f7aThe use of artificial intelligence (AI) in education has emerged as a topic of growing importance as educational institutions seek to innovate and enhance their pedagogical practices. This study aims to explore how AI can reconfigure learning processes and transform traditional educational practices. In a context where personalized learning is vital, AI stands out as a powerful tool to meet the individual needs of students, promoting the development of essential 21st-century skills. The bibliometric analysis conducted reveals a significant increase in academic interest in the application of AI in education, especially in the last five years, indicating an awakening to technological possibilities. The main areas of investigation include personalized learning, intelligent tutoring systems, and educational data analysis, known as learning analytics, which allow real-time monitoring of student progress. AI offers immediate feedback, adapting to each student’s learning pace, improving academic performance, and reducing educational inequalities. However, for this transformation to occur effectively, it is crucial that educational institutions integrate these technological solutions into their pedagogical approaches. However, the research points to significant gaps, such as unequal access to AI technologies, limiting the democratization of benefits. The lack of infrastructure and specialized teacher training hinders effective implementation. Additionally, there is a shortage of studies focused on the development of socio-emotional skills. Therefore, research must advance in the search for new technologies, ensuring an equitable and enriching learning environment. The future of education, transformed by AI, promises to be a space where all students can thrive, developing skills to face the challenges of the 21st century.Revista ft00This study aims to explore how AI can reconfigure learning processes and transform traditional educational practices, including personalized learning, intelligent tutoring systems, and educational data analysis, known as learning analytics, which allow real-time monitoring of student progress.Revista ft["S. R. Oliveira"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/626430e614a6f0a43c4668ce3e65acba85801f7a df4aa813e483ff41ea9d868e836898a515aa709fThe use of artificial intelligence in fifth and sixth generation mobile networks has been declared as a crucial development direction, enhancing their operational efficiency. However, equipment manufacturers do not provide scientifically justified calculations of the efficiency gains achieved through Artificial Intelligence application in these mobile network technologies. Therefore, this hypothesis needs evaluation and a thorough analysis of the justifiability of using this technology to confirm the potential for efficiency enhancement in mobile networks through Artificial Intelligence integration, considering the required costs for the development of the technology and the software infrastructure of networks. The research presented in the article shows that this requires a new approach to addressing this issue, one of which is the assessment of the justification and subsequent evaluation of the efficiency of Artificial Intelligence technology application based on the use of a model and tools of technological controlling.2024 International Conference on Engineering Management of Communication and Technology (EMCTECH)170The research presented in the article shows that this requires a new approach to addressing this issue, one of which is the assessment of the justification and subsequent evaluation of the efficiency of Artificial Intelligence technology application based on the use of a model and tools of technological controlling.2024 International Conference on Engineering Management of Communication and Technology (EMCTECH)["Valery Tikhvinskiy", "R. Umanskiy"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/df4aa813e483ff41ea9d868e836898a515aa709f d18409181b69db844836beb5bdb53ffaf91fdaffArtificial intelligence (AI)‐driven learning has become an irreversible trend in foreign language education. Scholars are increasingly focusing on this field, yet few have examined its impact within English literature classes. To fill this gap, we designed an 8‐week intervention study with mixed methods and recruited 90 students, with 42 in the experimental group and 48 in the control group, matched for average age, English proficiency and gender ratio. Critical thinking levels were measured before and after the intervention using a standardised assessment tool. In the experimental group, students used AI tools (ChatGPT‐3.5, Bodoudou, SummarizBot, etc.) to generate and answer text‐related questions, and participate in interactive quizzes and AI‐assisted debates during classes, while the control group followed traditional methods without AI tools. The findings revealed a statistically significant improvement in the critical thinking skills of the experimental group compared to the control group, as measured by pre and postintervention assessments (p < 0.05). This suggests that AI tools can effectively enhance critical thinking abilities in English literature classes. This study not only contributes to the emerging discourse on AI in education but also offers practical implications for integrating AI technologies to support and enrich the learning experiences of EFL students in literature classes. The findings have the potential to guide educators and policymakers in designing AI‐driven educational strategies that are culturally responsive and pedagogically effective.European Journal of Education367An 8‐week intervention study with mixed methods revealed a statistically significant improvement in the critical thinking skills of the experimental group compared to the control group, as measured by pre and postintervention assessments, suggesting that AI tools can effectively enhance critical thinking abilities in English literature classes.European Journal of Education["Wenxia Liu", "Yunsong Wang"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/d18409181b69db844836beb5bdb53ffaf91fdaff 457908d8600999ed6cb91b88c48996ea72e4e42bThe growing complexity of financial crimes necessitates advanced Anti-Money Laundering (AML) strategies that leverage data analytics to improve regulatory compliance in banks. As traditional AML methods face challenges in detecting sophisticated money laundering schemes, data analytics offers transformative solutions by enabling real-time monitoring, enhanced risk detection, and predictive analysis. This review explores the integration of data analytics in AML systems and its impact on regulatory compliance, focusing on strategies that banks can adopt to mitigate risks and adhere to evolving regulations. Data analytics empowers financial institutions to analyze vast amounts of transactional data, identifying suspicious patterns and anomalies with greater precision. Machine learning algorithms and artificial intelligence (AI) further enhance these capabilities by automating risk assessments, reducing false positives, and improving decision-making processes. Through predictive analytics, banks can anticipate emerging threats, adapting their AML strategies proactively to counter new money laundering techniques. A key advantage of data-driven AML approaches is the ability to streamline compliance processes. By automating Know Your Customer (KYC) procedures and cross-referencing data from multiple sources, banks can efficiently verify customer identities and monitor for unusual behavior. Additionally, the adoption of data analytics improves reporting accuracy, ensuring compliance with stringent regulatory frameworks such as the Financial Action Task Force (FATF) and the Bank Secrecy Act (BSA). This review highlights the transformative role of data analytics in enhancing AML efforts, emphasizing the importance of real-time data integration, predictive modeling, and automation. The shift from reactive to proactive AML approaches not only strengthens regulatory compliance but also fosters a culture of vigilance and risk management within banks. As financial institutions continue to embrace digital transformation, leveraging data analytics for AML will be crucial in combating financial crimes and maintaining compliance in an increasingly complex regulatory environment. Keywords: Anti-Money Laundering (AML), Data Analytics, Regulatory Compliance, Banks, Financial Crime, Machine Learning, Artificial Intelligence, Know Your Customer (KYC), Predictive Analytics, Risk Management.Finance &amp; Accounting Research Journal07This review explores the integration of data analytics in AML systems and its impact on regulatory compliance, focusing on strategies that banks can adopt to mitigate risks and adhere to evolving regulations.Finance &amp; Accounting Research Journal["Vivian Ofure Eghaghe", "Olajide Soji Osundare", "Chikezie Paul-Mikki Ewim", "Ifeanyi Chukwunonso Okeke"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/457908d8600999ed6cb91b88c48996ea72e4e42b 73ce169baafaf0dee1250e6941aa81bc5b691513 This paper discusses the ethical considerations surrounding generative artificial intelligence (GenAI) in chemistry education, aiming to guide teachers toward responsible AI integration. GenAI, driven by advanced AI models like Large Language Models, has shown substantial potential in generating educational content. However, this technology’s rapid rise has brought forth ethical concerns regarding general and educational use that require careful attention from educators. The UNESCO framework on GenAI in education provides a comprehensive guide to controversies around generative AI and ethical educational considerations, emphasizing human agency, inclusion, equity, and cultural diversity. Ethical issues include digital poverty, lack of national regulatory adaptation, use of content without consent, unexplainable models used to generate outputs, AI-generated content polluting the internet, lack of understanding of the real world, reducing diversity of opinions, and further marginalizing already marginalized voices and generating deep fakes. The paper delves into these eight controversies, presenting relevant examples from chemistry education to stress the need to evaluate AI-generated content critically. The paper emphasizes the importance of relating these considerations to chemistry teachers’ content and pedagogical knowledge and argues that responsible AI usage in education must integrate these insights to prevent the propagation of biases and inaccuracies. The conclusion stresses the necessity for comprehensive teacher training to effectively and ethically employ GenAI in educational practices.Chemistry Teacher International392The paper argues that responsible AI usage in education must integrate these insights to prevent the propagation of biases and inaccuracies and stresses the necessity for comprehensive teacher training to effectively and ethically employ GenAI in educational practices.Chemistry Teacher International["R. Blonder", "Yael Feldman-Maggor"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/73ce169baafaf0dee1250e6941aa81bc5b691513 b09d794fdc52b83da57efd4797e4d6455fe6ce87The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study, whose code is publicly available, illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms.arXiv.org751Both generic and dedicated DNN architectures suitable for state estimation are reviewed, and a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data is provided, categorizing design approaches into task-oriented and SS model-oriented.ArXiv["Nir Shlezinger", "Guy Revach", "Anubhab Ghosh", "Saikat Chatterjee", "Shuo Tang", "Tales Imbiriba", "J. Dun\u00edk", "O. Straka", "Pau Closas", "Y. Eldar"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/b09d794fdc52b83da57efd4797e4d6455fe6ce87 05e0a175b68ee72e55eed9933c493e964cf2f311In the contemporary landscape of academia, the integration of artificial intelligence (AI) has significantly altered the dynamics of academic research and writing. This study delves into the perceptions of academic misconduct among university students in the age of AI. Through a structured online survey, participants will articulate their perspectives on various activities and their classification as academic misconduct, as well as the severity of such transgressions. By probing into students' beliefs and attitudes, the research seeks to uncover potential disparities in the understanding and interpretation of academic misconduct within higher education settings. The advent of AI technologies has revolutionized the academic environment, providing students with unparalleled access to information and writing assistance. However, this paradigm shift has also brought forth heightened concerns surrounding academic integrity, particularly with regards to plagiarism. Moreover, the widespread adoption of grammar-checking tools such as Grammarly adds further layers of complexity, as the outputs sometimes resemble those generated by AI, blurring the lines between original work and automated assistance. Utilizing quantitative research methods, this study employs an online survey platform to collect data on student perceptions of academic misconduct. The insights collected from this investigation aim to contribute to a nuanced understanding of the multifaceted nature of academic integrity in the era of AI. Ultimately, the findings seek to inform educational policies and practices, fostering a cohesive and informed approach to upholding academic standards across diverse higher education institutions.Proceedings of the ALISE Annual Conference21This study delves into the perceptions of academic misconduct among university students in the age of AI through a structured online survey, aiming to contribute to a nuanced understanding of the multifaceted nature of academic integrity in the era of AI.Proceedings of the ALISE Annual Conference["Brady Lund", "Tae-Hee Lee", "Nishith Reddy Mannuru", "Nikhila Arutla"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/05e0a175b68ee72e55eed9933c493e964cf2f311 3948a98222de7123ddc155db6a9791ef7d759897AI &amp; SOCIETY841It is argued that especially within devising governance and support aspects of a medical AI ecosystem, considering the so-called motivation-attributing account of trust provides fruitful pointers within devising governance and support aspects of a responsible AI ecosystem that promotes trustworthiness.AI &amp; SOCIETY["Christian Herzog", "Sabrina Blank", "B. Stahl"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/3948a98222de7123ddc155db6a9791ef7d759897 832f5f4371f1dfad00c7a3edd2f228edfb22c44aMaritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector.Applied Sciences591The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector.Applied Sciences["Dragos Simion", "Florin Postolache", "Bogdan Fleac\u0103", "Elena Fleac\u0103"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/832f5f4371f1dfad00c7a3edd2f228edfb22c44a e63af14b4095208581668e755ac021ca20089529The Object Constraint Language (OCL) is a formal specification language in model-based systems and software engineering. It defines complex rules and constraints for model-based system design and verification. Constructing an OCL constraint requires expertise not only in OCL syntax but also in meta-model information, which can hinder its application in the practical industrial scenario despite its broad usage. Recently, generative artificial intelligence has demonstrated remarkable performance in code and text generation. This work discusses the generation of OCL constraints from natural language specifications using large language models (LLMs). Given that the automotive and aviation industries are major consumers of model-based engineering, the use of commercial LLMs raises concerns about data privacy. Therefore, we propose to employ open-source and locally deployed LLMs for OCL generation tasks. In this work, we collected a set of meta-models and OCL constraints, which were syntactically validated to ensure the quality of the OCL dataset. Synthetic natural language specifications were generated and used in the dataset for model fine-tuning. Additionally, we designed a retrieval-augmented approach to incorporate meta-model information during LLM fine-tuning and OCL generation. The proposed fine-tuning and OCL generation approach has been experimented with the state-of-the-art open-source LLM, Llama 3 8B. The locally fine-tuned and deployed language model achieved comparable syntactic accuracy and a higher semantic similarity score for OCL generation compared to the cutting-edge commercial models, GPT-4 Turbo and Gemini 1.5 Pro. The usability of the fine-tuned model has been demonstrated for OCL generation in the context of automotive resource allocation.Information Security Solutions Europe301This work collected a set of meta-models and OCL constraints, which were syntactically validated to ensure the quality of the OCL dataset, and designed a retrieval-augmented approach to incorporate meta-model information during LLM fine-tuning and OCL generation.2024 IEEE International Symposium on Systems Engineering (ISSE)["F. Pan", "Vahid Zolfaghari", "Long Wen", "Nenad Petrovic", "Jianjie Lin", "Alois Knoll"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/e63af14b4095208581668e755ac021ca20089529 bb7a3fff1eb479d407c7b1efd073b3653ec0b5ceGenerative artificial intelligence (AI) changes the picture of graduate education by providing personalized learning, automated feedback, intelligent research assistants, and automated content creation (George, 2023). AI tools will support doctoral students in text generation, language translation, responding to academic queries, and data collection and analysis and encourage self-learning and thinking development (Rasul et al., 2023; Zou & Huang, 2023). They also would be helpful for doctoral students working as teaching assistants and aiding in daily problems (Can et al., 2023; Parker et al., 2024). However, the rise of AI tools also leads to considerations of academic integrity, over-reliance on AI, misinformation, and the potential biases embedded in algorithms (George, 2023; Rasul et al., 2023). Echoing the opportunities and challenges of AI applications in research and learning, the ALISE Doctoral Students SIG wants to encourage a discussion on how doctoral students can use AI tools to empower us in the Ph.D. journey. The panel invites a diverse group of doctoral students/candidates to share how AI tools can facilitate data collection and analysis and their critical understanding of AI systems. Manar Alsaid will talk about using AI and machine learning to detect complex misinformation on social media. The talk aims to enhance our understanding of misinformation and reduce its negative impacts. This presentation will provide valuable insights for research on misinformation and information literacy. Adam Eric Berkowitz will introduce the black-box tinkering method that experimentally discerns how AI systems operate. The method enhances the transparency of AI systems, challenging the technocratic paradigm. With three examples, Berkowitz encourages attendees to learn what black-box tinkering is, how to identify cases using it, and potential opportunities to incorporate it in research. Anisah Herdiyanti will share insights from a study comparing transcripts generated by Otter.ai and Zoom Meetings. The presentation will highlight both the benefits and challenges of AI-based notes and transcription software, including technical concerns and the convenience of automated result delivery. The audience will enhance their understanding of AI tools in qualitative data transcribing and the ethical considerations in the process. Rebecca Bryant Penrose will showcase the use of HeyGen, an AI-based video generator and translation tool, in an international interview project between students at California State University Bakersfield and a Ukrainian artist/author. The presentation will increase awareness of the potential use of AI-based video and help researchers overcome language barriers in data collection. The panel will last 90 minutes, including a 5-minute introduction and a 5-minute wrap-up. Each panelist will have 10 minutes to present their topics, followed by 5-minute Q&As. A 25-minute moderated roundtable discussion will follow the panelists’ presentations to explore the potential use of different AI tools in research, including ChatGPT and AI-powered article summarizers. The panel’s learning outcomes include (1) Identifying challenges and opportunities to incorporate AI tools in research and study and (2) Explaining how to interact with AI tools to improve efficiency in research. It also provides a platform for doctoral students to share their knowledge of how AI changes research approaches and networks with each other.Proceedings of the ALISE Annual Conference01Echoing the opportunities and challenges of AI applications in research and learning, the ALISE Doctoral Students SIG wants to encourage a discussion on how doctoral students can use AI tools to empower us in the Ph.D. journey.Proceedings of the ALISE Annual Conference["Yi Wan", "Vanessa Kitzie", "Manar Alsaid", "A. Berkowitz", "Anisah Herdiyanti", "Rebecca Bryant Penrose"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/bb7a3fff1eb479d407c7b1efd073b3653ec0b5ce ecc8a99f36ede8872d62fe93b11eef16660a5a15Industry actors in the United States have gained extensive influence in conversations about the regulation of general-purpose artificial intelligence (AI) systems. Although industry participation is an important part of the policy process, it can also cause regulatory capture, whereby industry co-opts regulatory regimes to prioritize private over public welfare. Capture of AI policy by AI developers and deployers could hinder such regulatory goals as ensuring the safety, fairness, beneficence, transparency, or innovation of general-purpose AI systems. In this paper, we first introduce different models of regulatory capture from the social science literature. We then present results from interviews with 17 AI policy experts on what policy outcomes could compose regulatory capture in US AI policy, which AI industry actors are influencing the policy process, and whether and how AI industry actors attempt to achieve outcomes of regulatory capture. Experts were primarily concerned with capture leading to a lack of AI regulation, weak regulation, or regulation that over-emphasizes certain policy goals over others. Experts most commonly identified agenda-setting (15 of 17 interviews), advocacy (13), academic capture (10), information management (9), cultural capture through status (7), and media capture (7) as channels for industry influence. To mitigate these particular forms of industry influence, we recommend systemic changes in developing technical expertise in government and civil society, independent funding streams for the AI ecosystem, increased transparency and ethics requirements, greater civil society access to policy, and various procedural safeguards.AAAI/ACM Conference on AI, Ethics, and Society2831This paper presents results from interviews with 17 AI policy experts on what policy outcomes could compose regulatory capture in US AI policy, which AI industry actors are influencing the policy process, and whether and how AI industry actors attempt to achieve outcomes of regulatory capture.{"pages": "1539-1555"}["Kevin Wei", "Carson Ezell", "Nick Gabrieli", "Chinmay Deshpande"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/ecc8a99f36ede8872d62fe93b11eef16660a5a15 4a851139b3f9315432ae51f23bd80849b9bff3bfThe present study investigates the potential impact of artificial intelligence (AI) on the future trajectory of human civilization. It focuses on topics such as super-exponential growth, the potential emergence of a galactic civilization, and the associated "doom" hazards. A significant advancement in machine intelligence with human-like consciousness, strong artificial intelligence (AI), also known as artificial general intelligence (AGI), creates new opportunities and capacities. There's growing anxiety about the risk that weak AI will eventually become strong AI. Every year, new transformer models that are more like human interactions are being created, and we have already witnessed some indications of AGI. It is anticipated that AI will reach a "singularity" and advance on its own without assistance from humans. This thesis explores the theoretical and practical foundations, model building blocks, development processes, challenges, and ethical issues surrounding the creation of Consciousness AI (AGI). This paper examines the meaning of the term "technological singularity," the various types of singularities that have no point of return idea, the philosophical risks associated with the development of AI, and the implications of AI singularity for monetary theory and the new economic order. As a new perspective on the deployment of ethical AI in the face of tremendous technological advancements, the study not only contributes to the theoretical discourse but also explores the possible practical implications of AI on our shared future. Several obstacles to AI advancement are covered in the paper, along with prospective directions for future research.Journal of Computer Science and Technology Studies271The meaning of the term "technological singularity," the various types of singularities that have no point of return idea, the philosophical risks associated with the development of AI, and the implications of AI singularity for monetary theory and the new economic order are examined.Journal of Computer Science and Technology Studies["Luka Baklaga"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/4a851139b3f9315432ae51f23bd80849b9bff3bf 8e6dbac6f283bf02507679659d54c0cd672e310bThe European Union's Artificial Intelligence Act (AI Act), finalized in February 2024, mandates comprehensive transparency and explainability requirements for AI systems to enable effective oversight and safeguard fundamental rights. However, the practical implementation of these requirements faces challenges due to tensions between the need for meaningful explanations and the potential risks to intellectual property and commercial interests of AI providers. This research proposes the Transparency-Explainability Functionality and Tensions (TEFT) framework to systematically analyze the complex interplay of legal, technical, and socio-ethical factors shaping the realization of algorithmic transparency and explainability in the EU context. Through a two-pronged approach combining a focused literature review and an in-depth examination of the AI Act's provisions, we identify key friction points and challenges in operationalizing the right to explanation. The TEFT framework maps the interests and incentives of various stakeholders, including AI providers & deployers, oversight bodies, and affected individuals, while considering their goals, expected benefits, risks, possible negative impacts, and context to algorithmic explainability.AAAI/ACM Conference on AI, Ethics, and Society01This research proposes the Transparency-Explainability Functionality and Tensions (TEFT) framework to systematically analyze the complex interplay of legal, technical, and socio-ethical factors shaping the realization of algorithmic transparency and explainability in the EU context.{"pages": "1023-1035"}["Luca Nannini"]2024-10-16T00:00:00https://www.semanticscholar.org/paper/8e6dbac6f283bf02507679659d54c0cd672e310b cd3cf63f98068877100b30f2df56c2b2dcbd1fc9BMC Medical Ethics371Bias can emerge in upstream (research and development) and downstream (medicine production) processes when manufacturing targeted medicines, and the idea of “corrective bias” is suggested to suggest potential value in capitalizing on biases to help address health inequalities.BMC Medical Ethics["Ngozi Nwebonyi", "Francis McKay"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/cd3cf63f98068877100b30f2df56c2b2dcbd1fc9 250f7231e0fd3b44736b98b8f03caccfd2c1ead0Purpose: This study explores the current state of artificial intelligence implementation in Nigeria’s public service and the potential benefits, challenges, and strategic steps needed to harness AI for improved governance and service delivery. Methods: The research design was qualitative. The data were collected using secondary data collection, in which a thorough literature review of academic articles, books, and reports related to AI was consulted. This study applied a thematic research approach to clarify the underlying issues, beliefs, and experiences related to artificial intelligence in governance and public services. The study was also anchored to content analysis. Results: The findings revealed that AI application in Nigeria’s public service is still in its early stages, with promising developments in areas such as e-governance, healthcare, banking sector, real estate business, and law enforcement/security outfits. There is a need for the government in Nigeria to invest significantly in infrastructural advancement and human capital development, which in turn will close the skill gaps, infrastructural deficits, and lapses that crop up from the unawareness of Artificial Intelligence in the technological advancement of Nigeria. Limitations: This study examined the current state of AI in Nigeria's public services and governance by identifying the key barriers that affect the adoption and implementation of AI. The study made progressive recommendations that integrated the application of artificial intelligence in public services and governance in Nigeria. Contributions: This study provides a comprehensive understanding of how AI can be adopted in Nigeria’s unique environment. Findings: This study did not receive any funding from any agency or organization.Journal of Governance and Accountability Studies421The findings revealed that AI application in Nigeria’s public service is still in its early stages, with promising developments in areas such as e-governance, healthcare, banking sector, real estate business, and law enforcement/security outfits.Journal of Governance and Accountability Studies["Chibuzo Charles Nwosu", "D. Obalum", "Mathias Ozoemena Ananti"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/250f7231e0fd3b44736b98b8f03caccfd2c1ead0 c26c0efff71385c434ab60b7862bcfb1665ddc67PURPOSE As artificial intelligence (AI) takes an increasingly prominent role in health care, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study aims to provide a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research. METHOD We conducted a bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. Based on the bibliometric data, we examined publication trends and patterns, characteristics of research activities, and research hotspot tendencies. RESULTS From 1985 onwards, there has been a consistent annual increase in publications, averaging 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, aphasia, dysarthria, Parkinson's disease, and Alzheimer's disease. Noteworthy AI models instantiated in CSD research encompass support vector machine, convolutional neural network, and hidden Markov model, among others. CONCLUSIONS Compared to AI applications in other fields, the adoption of AI in CSD has lagged slightly behind. While CSD studies primarily use classical machine learning techniques, there is a growing trend toward the integration of deep learning methods. AI technology offers significant benefits for both research and clinical practice in CSD, but it also presents certain challenges. Moving forward, collaboration among technological, research, and clinical domains is essential to empower researchers and speech-language pathologists to effectively leverage AI technology for the study, diagnosis, assessment, and rehabilitation of CSD. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.27162564.Journal of Speech, Language and Hearing Research801A bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023 provides a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research.Journal of speech, language, and hearing research : JSLHR["Minyue Zhang", "Enze Tang", "Hongwei Ding", "Yang Zhang"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/c26c0efff71385c434ab60b7862bcfb1665ddc67 05700b75c8dae199ee62a28061e7f614693976c6The possibilities and main directions of application of artificial intelligence for the organization of accounting are shown. The modern level of digitalization of agriculture is presented in the context of the applicability of artificial intelligence elements for accounting automation. The main problems hindering promising areas of digitalization of accounting work in agriculture are shown.Buhuchet v sel'skom hozjajstve (Accounting in Agriculture)21The modern level of digitalization of agriculture is presented in the context of the applicability of artificial intelligence elements for accounting automation in the context of the applicability of artificial intelligence elements for accounting automation.Buhuchet v sel'skom hozjajstve (Accounting in Agriculture)["O. Knyazeva", "P. B. Akmarov", "G. R. Alborov"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/05700b75c8dae199ee62a28061e7f614693976c6 0c7be6f8eeef4362ff638ce9184ba5e306eb5681Objective: The objective of this review was to provide an overview of the diverse methods described, tested, or implemented for monitoring performance of clinical artificial intelligence (AI) systems, while also summarizing the arguments given for or against these methods. Introduction: The integration of AI in clinical decision-making is steadily growing. Performances of AI systems evolve over time, necessitating ongoing performance monitoring. However, the evidence on specific monitoring methods is sparse and heterogeneous. Thus, an overview of the evidence on this topic is warranted to guide further research on clinical AI monitoring. Inclusion criteria: We included publications detailing metrics or statistical processes employed in systematic, continuous, or repeated initiatives aimed at evaluating or predicting the clinical performance of AI models with direct implications for patient management in health care. No limitations on language or publication date were enforced. Methods: We performed systematic database searches in MEDLINE (Ovid), Embase (Ovid), Scopus, and ProQuest Dissertations and Theses Global, supplemented by backward and forward citation searches and gray literature searches. Two or more independent reviewers conducted title and abstract screening, full-text evaluation, and data extraction using a tool developed by the authors. During extraction, the methods identified were divided into subcategories. The results are presented narratively and summarized in tables and graphs. Results: Thirty-nine sources of evidence were included in the review, with the most abundant source types being opinion papers/narrative reviews (33%) and simulation studies (33%). One guideline on the topic was identified, offering limited guidance on specific metrics and statistical methods. The number of sources included increased year by year, with almost 4 times as many sources included in 2023 compared with 2019. The most commonly reported performance metrics were traditional metrics from the medical literature, including area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, and predictive values, although few arguments were given supporting these choices. Some studies reported on metrics and statistical processing specifically designed to monitor clinical AI. Conclusion: This review provides a summary of the methods described for monitoring AI in health care. It reveals a relative scarcity of evidence and guidance for specific practical implementation of performance monitoring of clinical AI. This underscores the imperative for further research, discussion, and guidance regarding the specifics of implementing monitoring for clinical AI. The steady increase in the number of relevant sources published per year suggests that this area of research is gaining increased focus, and the amount of evidence and guidance available will likely increase significantly over the coming years. Review registration: Open Science Framework https://osf.io/afkrnJBI Evidence Synthesis601An overview of the diverse methods described, tested, or implemented for monitoring performance of clinical artificial intelligence (AI) systems is provided, while also summarizing the arguments given for or against these methods.Jbi Evidence Synthesis["E. S. Andersen", "Johan Baden Birk-Korch", "R. Hansen", "Line Haugaard Fly", "Richard R\u00f6ttger", "D. Arcani", "C. Brasen", "Ivan Brandslund", "J. S. Madsen"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/0c7be6f8eeef4362ff638ce9184ba5e306eb5681 ef077c194068bea96c2b9620e1f93dab433b6d2cWater hyacinth (Eichhornia crassipes), an aquatic free-floating plant, is highly invasive and can now be found in most freshwater bodies in the world's subtropical and tropical climates. Because of the potential harm these plants do to the environment and civilization, it is critical to track their seasonality and spatial distribution in order to develop effective prevention techniques at an early stage. With an emphasis on the application of machine learning algorithms and image analysis techniques for weed detection, this study investigates and examines the usage of artificial intelligence (AI) to overcome these difficulties. Sophisticated algorithms such as the Single Shot Detector (SSD) approach are used for real-time object detection, as well as the integration of machine learning techniques such as CART, RF, and SVM, CNN, and KNN helps for object detection. Modern advances in robotics and artificial intelligence (AI) have shown promise in addressing issues related to the environmental challenges. This review examines prior studies, innovative methodologies, and case studies to identify gaps, challenges, and opportunities for future study and application for controlling the growth of water hyacinth.2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS)110This review examines prior studies, innovative methodologies, and case studies to identify gaps, challenges, and opportunities for future study and application for controlling the growth of water hyacinth.2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS)["Mrunal Pawar", "Nihar Ranjan", "Vaibhav Patil", "Sanika Naik", "Rutika Shetty", "Rajiv Divekar"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/ef077c194068bea96c2b9620e1f93dab433b6d2c 7950fd79b86ea538a81b0d79009c2232c68cea79Diabetes mellitus has become one of the major public health problems in India. Chronic nature and the rising epidemic of diabetes have adverse consequences on India’s economy and health status. Recently, machine learning (ML) methods are becoming popular in the healthcare sector. Human medicine is a complex field, and it cannot be solely handled by algorithms, especially diabetes, which is a lifelong multisystem disorder. But ML methods have certain attributes which can make a physician’s job easier and can also be helpful in health system management. This article covers multiple dimensions of using artificial intelligence (AI) for diabetes care under the headings Strengths, Weaknesses, Opportunities, and Threats (SWOT), specifically for the Indian healthcare system with a few examples of the latest studies in India. We briefly discuss the scope of using AI for diabetes care in rural India, followed by recommendations. Identifying the potential and challenges with respect to AI use in diabetes care is a fundamental step to improve the management of disease with best possible use of technology.Indian journal of endocrinology and metabolism420Multiple dimensions of using artificial intelligence (AI) for diabetes care under the headings Strengths, Weaknesses, Opportunities, and Threats (SWOT) are covered specifically for the Indian healthcare system with a few examples of the latest studies in India.Indian Journal of Endocrinology and Metabolism["Priya Kataria", "Srivenkata Madhu", "Madhu K. Upadhyay"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/7950fd79b86ea538a81b0d79009c2232c68cea79 81fab5ce8ff762bd31a20102e8c30e3f397692eaBACKGROUND The use of large language models (LLMs) and artificial intelligence (AI) tools to prepare health professions admissions applications is increasing. These tools can improve writing significantly but raise ethical concerns about application authenticity. PURPOSE This scoping review explored the literature on use of AI by applicants applying to health professions programs and by admission reviewers. METHODS Following Joanna Briggs Institute and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, a search was conducted in multiple databases, which identified 1706 citations. After screening, 18 articles were included. RESULTS Articles included in the review focused on the (1) use of AI to screen applicants or predict ranking and interview invitations, (2) ethical implications of AI-generated personal statements, (3) potential to detect AI-generated applications, and (4) use of AI to write or analyze letters of reference. CONCLUSIONS AI tools can enhance the efficiency of the admissions review process, but clear guidelines are required to address ethical issues. Further research is needed, particularly in nursing education.Nurse Educator240AI tools can enhance the efficiency of the admissions review process, but clear guidelines are required to address ethical issues, and further research is needed, particularly in nursing education.Nurse educator["Lisa S. Lewis", "A. M. Hartman", "Jill Brennan-Cook", "Irene C Felsman", "Briana Colbert", "Leila Ledbetter", "Stephanie A. Gedzyk-Nieman"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/81fab5ce8ff762bd31a20102e8c30e3f397692ea 8c2c481c604053056dbd070d09ef6e88f486f204Artificial intelligence (AI), a technological advancement radically affecting the workplace of the future, offers benefits for decision‐making processes. The most critical organizational decisions typically occur through teams. However, team theories are fundamentally psychosocial, and AI disrupts this context by introducing a non‐human actor. Therefore, negative implications of AI's role must also be understood. Toward theory on the effective integration of AI into team decision‐making, we synthesize the literatures on team effectiveness and team decision‐making with research from cognate disciplines on human–technology interaction and teaming. Based on this synthesis, we offer propositions highlighting key variable relationships and negative side effects that must be accounted for in AI–human team decision‐making and follow with practical suggestions for management's adaption to this new context. Overall, our analysis emphasizes critical themes, constructs, and relationships valuable for further research aimed at modernizing theory and practice in the face of this emerging technological shift.European Management Review810This work synthesizes the literatures on team effectiveness and team decision‐making with research from cognate disciplines on human–technology interaction and teaming with practical suggestions for management's adaption to this new context.European Management Review["William Carter", "Kevin T. Wynne"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/8c2c481c604053056dbd070d09ef6e88f486f204 3e51639ff9df2b5153cd697c384be9b7a0bb6064Monash Bioethics Review00Ethics of Artificial Intelligence delves into pressing ethical issues, such as the enhancement of human abilities, the nature of consciousness, and questions of responsibility and accountability in various contexts where AI technology is used.Monash bioethics review["Mohammad Hosseini"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/3e51639ff9df2b5153cd697c384be9b7a0bb6064 1080969a9809e29a1f5deee9511e08d7d68a46d3In recent years, Artificial Intelligence (AI) has become a focal point for companies, governments, and especially threat actors, all striving to improve system efficiency, efficacy, and responsiveness. By training complex systems based on the concept of a “neural network,” an artificial structure modeled after the pathways of the natural brain, machines are now capable of performing intricate tasks and automated processes independent of human control. Given this rapid development, several ethical considerations in data and cybersecurity must be managed or monitored. As laws and regulations continue to evolve, companies face critical decisions regarding how models are trained and how these models impact various stakeholders. After providing a general overview of the core functionalities and a technical description of Artificial Intelligence, machine learning, and the different types of AI, this paper delves into the current cybersecurity applications of AI, particularly focusing on its implementation in Security Information and Event Management (SIEM) systems and how this integration has revolutionized these technologies. Additionally, this paper offers an overview of various pioneering entities and their contributions to AI development, emerging AI trends, and diverse AI applications, including a range of AI leaders across different specialties, from generative AI to cybersecurity solutions.Ubiquitous Computing, Electronics & Mobile Communication Conference220The current cybersecurity applications of AI are delved into, particularly focusing on its implementation in Security Information and Event Management (SIEM) systems and how this integration has revolutionized these technologies.2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)["M. L. Ali", "Kutub Thakur", "Helen Barker", "Michael Chan"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/1080969a9809e29a1f5deee9511e08d7d68a46d3 f1ddbbecbc75db070c30f4765a82172906b8649dOBJECTIVE To perform a comparative analysis of the three-level triage protocol conducted by triage nurses and emergency medicine doctors with the use of ChatGPT, Gemini, and Pi, which are recognized artificial intelligence (AI) models widely used in the daily life. MATERIALS AND METHODS The study was prospectively conducted with patients presenting to the emergency department of a tertiary care hospital from 1 April 2024, to 7 April 2024. Among the patients who presented to the emergency department over this period, data pertaining to their primary complaints, arterial blood pressure values, heart rates, peripheral oxygen saturation values measured by pulse oximetry, body temperature values, age, and gender characteristics were analyzed. The triage categories determined by triage nurses, the abovementioned AI chatbots, and emergency medicine doctors were compared. RESULTS The study included 500 patients, of whom 23.8% were categorized identically by all triage evaluators. Compared to the triage conducted by emergency medicine doctors, triage nurses overtriaged 6.4% of the patients and undertriaged 3.1% of the yellow-coded patients and 3.4% of the red-coded patients. Of the AI chatbots, ChatGPT exhibited the closest triage approximation to that of emergency medicine doctors; however, its undertriage rates were 26.5% for yellow-coded patients and 42.6% for red-coded patients. CONCLUSION The undertriage rates observed in AI models were considerably high. Hence, it does not yet seem appropriate to solely rely on the specified AI models for triage purposes in the emergency department.Postgraduate medicine200A comparative analysis of the three-level triage protocol conducted by triage nurses and emergency medicine doctors with the use of ChatGPT, Gemini, and Pi, which are recognized artificial intelligence models widely used in the daily life, concluded that it does not yet seem appropriate to solely rely on the specified AI models for triage purposes in the emergency department.Postgraduate medicine["F. Tortum", "K. Kasali"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/f1ddbbecbc75db070c30f4765a82172906b8649d b3c8253e6da666196e56893d4f774cf4844a8d1cFinancial institutions are currently undergoing a significant shift from traditional robo-advisors to more advanced generative artificial intelligence (GenAI) technologies. This transformation has motivated us to investigate the factors influencing consumer responses to GenAI-driven financial advice. Despite extensive research on the adoption of robo-advisors, there is a gap in our understanding of the specific contributors to, and differences in, consumer attitudes and reactions to GenAI-based financial guidance. This study aims to address this gap by analyzing the impact of personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI-provided financial advice on its authenticity as perceived by consumers, their utilitarian attitude toward the use of GenAI for financial advice, and their reactions to GenAI-generated financial suggestions. A comprehensive research model was developed based on service-dominant logic (SDL) and Artificial Intelligence Device Use Acceptance (AIDUA) frameworks. The model was subsequently employed in a structural equation modeling (SEM) analysis of survey data from 822 mobile banking users. The findings indicate that personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI’s recommendations positively influence consumers’ perception of its authenticity. Moreover, we discovered a positive correlation between utilitarian attitudes and perceived authenticity, which ultimately influences consumers’ responses to GenAI’s financial advisory solutions. This is manifested as either a willingness to engage or resistance to communication. This study contributes to the research on GenAI-powered financial services and underscores the significance of integrating GenAI financial guidance into the routine operations of financial institutions. Our work builds upon previous research on robo-advisors, offering practical insights for financial institutions seeking to leverage GenAI-driven technologies to enhance their services and customer experiences.Journal of Risk and Financial Management920The findings indicate that personalized investment suggestions, human-like empathy, and the continuous improvement of GenAI’s recommendations positively influence consumers’ perception of its authenticity.Journal of Risk and Financial Management["Qin Yang", "Young-Chan Lee"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/b3c8253e6da666196e56893d4f774cf4844a8d1c dfe487866fece8b0d90c822fef0d7a81a3fc3ea2In a context where artificial intelligence (AI) is transforming higher education, this study analyzes how students’ perceptions of AI influence their academic integrity (INA), with a focus on sustainable education. Through a correlational-explanatory analysis based on Structural Equation Models (SEMs) applied to a sample of 659 students from 13 universities in Chile and Peru, it is observed that AI has a significant and direct impact on academic integrity in both countries (β = 0.44). In Peru, the most influential dimension is trust in education (λ = 0.86), followed by social, economic, security, and risk implications (λ = 0.78), while attitudes towards AI also have a direct impact on integrity factors (β = 0.15). In Chile, the dimensions of trust in education (λ = 0.83) and social and economic impact (λ = 0.79) are most relevant, and the relationships between the dimensions of academic integrity such as justice, respect, and responsibility (λ = 0.71) are stronger. The study highlights the importance of incorporating AI literacy into educational curricula and developing regulatory frameworks that promote its ethical use, linking these actions to sustainable education. The findings highlight the need for sustainable educational approaches that enhance understanding of AI and ensure that its use in academia is beneficial, ethical, and contributes to sustainable development.Sustainability00The study analyzes how students’ perceptions of AI influence their academic integrity (INA) and highlights the importance of incorporating AI literacy into educational curricula and developing regulatory frameworks that promote its ethical use, linking these actions to sustainable education.Sustainability["S. E. Espinoza Vidaurre", "Norma C. Vel\u00e1squez Rodr\u00edguez", "Renza L. Gambetta Quelopana", "Ana N. Martinez Valdivia", "Ernesto A. Leo Rossi", "M. A. Nolasco-Mamani"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/dfe487866fece8b0d90c822fef0d7a81a3fc3ea2 09c233beaed5b67941ad92dc537f02a849c7d765Resisting the adoption of medical artificial intelligence (AI), it is suggested that this opposition can be overcome by combining AI awareness, AI risks, and responsibility displacement. Through effective integration of public AI dangers and displacement of responsibility, some of these major concerns can be alleviated. The United Kingdom’s National Health Service has adopted the use of chatbots to provide medical advice, whereas heart disease diagnoses can be made by IBM’s Watson. This has the ability to improve healthcare by increasing accuracy, efficiency, and patient outcomes. The resistance may be due to concerns about losing jobs, anxieties about misdiagnosis or medical mistakes, and the consciousness of AI systems drifting more responsibility away from medical professionals. There is hesitancy among healthcare professionals and the general public about the deployment of AI, despite the fact that healthcare is being revolutionised by AI, its uses are pervasive. Participants’ awareness of AI in healthcare, AI risk, resistance to AI, responsibility displacement and ethical considerations were gathered through questionnaires. Descriptive statistics, chi-square tests and correlation analyses were used to establish the relationship between resistance and medical AI. The study’s objective seeks to collect data on primary and public AI awareness, perceptions of risk and feelings of displacement that the professionals have regarding medical AI. Some of these concerns can be resolved when AI awareness is effectively integrated and patients, healthcare providers, as well as the general public are well informed about AI’s potential advantages. Trust is built when, AI related issues such as bias, transparency, and data privacy are critically addressed. Another objective is to develop a seamless integration of risk management, communication and awareness of AI. Lastly to assess how this comprehensive approach has affected hospital settings’ ambitions to use medical AI. Fusing AI awareness, risk management, and effective communication can be used as a comprehensive strategy to address and promote the application of medical AI in hospital settings. An argument made by Chen et al. is that providing training in AI can improve adoption intentions while lowering complexity through the awareness of AI.Journal of Infrastructure Policy and Development720Fusing AI awareness, risk management, and effective communication can be used as a comprehensive strategy to address and promote the application of medical AI in hospital settings.Journal of Infrastructure, Policy and Development["Xianmiao Li", "Linda Abangbila"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/09c233beaed5b67941ad92dc537f02a849c7d765 34e575e39ee97ba827c292c60491ffefd9372e84In the twenty-first century, artificial intelligence (AI) will change the way organizations conduct their daily operations. In addition, cutting-edge digital technologies like AI, Internet of things, augmented reality/virtual reality, cloud computing, robotics and big data will have a significant impact on the nature of future jobs. This revolutionary influence may be seen in the fashion industry across a wide variety of specialities, including design, sampling, production, quality and inspection, packaging and labelling, marketing, advertising and sales. This transformation also highlights the necessity for upgrading the fashion curriculum to include the essential and desired skills and competencies to enable students to apply such digital technology for future jobs. The purpose of this study is to examine the future skill and competence needs that will need to be linked with the curriculum of the Bachelor’s degree in Fashion Design programme for the use of AI in the fashion sector. The present research employs qualitative approaches. Skills for future jobs are identified through the literature and verified by employers. The findings reveal that skills and competencies imparted through the current curriculum are insufficient to work in the digital world, and a relook into the curriculum is required to produce an AI-savvy workforce.Higher Education for the Future10The findings reveal that skills and competencies imparted through the current curriculum are insufficient to work in the digital world, and a relook into the curriculum is required to produce an AI-savvy workforce.Higher Education for the Future["Japjee Kaur Kohli"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/34e575e39ee97ba827c292c60491ffefd9372e84 aaea87bde9b7e4fa2fe0f4f1cc178ff8600f36d7npj Cardiovascular Health450A case that highlights augmentative AI for the incidental detection of coronary artery calcium, a mobile application to improve patient adherence/engagement, large language models to enhance longitudinal patient communication and care, and limitations and strategies for the successful adoption of these technologies are described.npj Cardiovascular Health["Jelani K. Grant", "Aamir Javaid", "Richard T. Carrick", "M. Koester", "Aliya Kassamali", "Chang H Kim", "Nino Isakadze", "Katherine C. Wu", "Michael J. Blaha", "S. Whelton", "Armin Arbab-Zadeh", "Carl Orringer", "Roger S. Blumenthal", "Seth S. Martin", "F. Marvel"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/aaea87bde9b7e4fa2fe0f4f1cc178ff8600f36d7 46eab961a8caf8777c1de6d0269e9d24e122a2d9Defining artificial intelligence (AI) is a persistent challenge, often muddied by technical ambiguity and varying interpretations. Commonly used definitions heavily emphasize technical properties of AI but neglect the human purpose of it. This essay makes a case for a sociotechnical definition of AI, which is essential for researchers who require clarity in their work. It explores two primary approaches to define AI: the rationalistic, which focuses on AI as systems that think and act rationally, and the humanistic, which frames AI in terms of its ability to emulate human intelligence. By reconciling these approaches and contrasting them with landmark definitions, the essay proposes a sociotechnical definition that includes the three central aspects of i) technical functions, ii) human purpose, and iii) dynamic expectations.arXiv.org160This essay makes a case for a sociotechnical definition of AI, which is essential for researchers who require clarity in their work and includes the three central aspects of i) technical functions, ii) human purpose, and iii) dynamic expectations.ArXiv["Johannes Dahlke"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/46eab961a8caf8777c1de6d0269e9d24e122a2d9 b5c193ba3e6cc47c30b74d5504d98d90cae5358fIn the current digital era, data governance has become a crucial element for the development of enterprises and society. To address the limitations of traditional data governance methods in handling the rapid growth of data volume, the diversity of data sources, and data privacy and security issues, this paper proposes a data governance framework based on world models and artificial intelligence. By integrating world models with data governance, a modular approach is designed to enhance data processing efficiency and accuracy while optimizing the prediction and decision-making processes. This study introduces a world model-based data governance framework enhancing the intelligence level of data governance. It realizes the application of data governance agents, significantly improving data quality and consistency. Furthermore, the practical applicability of the theoretical model is verified, demonstrating its effectiveness in real business environments.2024 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)310This study introduces a world model-based data governance framework enhancing the intelligence level of data governance, and realizes the application of data governance agents, significantly improving data quality and consistency.2024 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)["David Wang", "Jun Meng", "Leilei Zhu"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/b5c193ba3e6cc47c30b74d5504d98d90cae5358f 8aac6663f9e114743c2f06440a8e25c0aa594567 The study addresses the necessity to investigate the influence of artificial intelligence (AI) on book design in a variety of socio-cultural contexts. The objective is to conduct a cross-cultural analysis of contemporary AI applications in book design in the USA, Finland, and China. The research employs a cross-cultural, systemic, and structural approach, utilizing theoretical methods such as analysis, synthesis, and comparison. The study identifies the current design trends, which include space aestheticization, communication, and AI integration. The study examines the relationship between AI and book design, with a particular focus on the role of the creator’s subjectivity and the potential for enhanced creative output. The necessity of a comparative approach to the analysis of AI-driven book design is underscored. The particulars of AI utilization in American book design are examined, emphasizing the infusion of cultural value, communication of environmental concerns, and the creation of heritage-based works. In contrast, the Finnish design approach prioritizes human resources and pragmatic solutions in book product design. In contrast, the Chinese approach addresses key socio-cultural issues through AI in book design. The study’s practical value lies in applying these local design characteristics to interpret cultural codes through AI.Digital Scholarship in the Humanities320The study examines the relationship between AI and book design, with a particular focus on the role of the creator’s subjectivity and the potential for enhanced creative output, and identifies the current design trends.Digital Scholarship in the Humanities["Qi Liu", "Zijing Wu"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/8aac6663f9e114743c2f06440a8e25c0aa594567 0f72ec7cc2e57d9161c880786ced39cdf476d222Artificial Intelligence (AI) tools, such as ChatGPT and Grammarly, have become increasingly integrated into academic and research settings. These tools offer college students support in areas like content generation, grammar checking, idea development, and information synthesis, enhancing their research workflows. As students rely more on AI technologies, there is a growing need to evaluate the influence of these tools on their research capabilities, specifically focusing on the quality of research papers and the innovation of ideas. This article aims to examine whether the use of AI tools enhances or inhibits students' ability to think critically, generate new ideas, and present well- structured academic work. Ultimately, we seek to understand the balance between leveraging technology and fostering essential cognitive skills.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Whether the use of AI tools enhances or inhibits students' ability to think critically, generate new ideas, and present well- structured academic work is examined to understand the balance between leveraging technology and fostering essential cognitive skills.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["DR.K Sasirekha"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/0f72ec7cc2e57d9161c880786ced39cdf476d222 366152147e7c28d22eb954856af96fa52767086aCriminal Law and Philosophy110It is shown that the input problem has been subject to somewhat different interpretations and a few suggestions are presented as to how undesirable implications of complexity at the input stage might be ameliorated by tailoring the way sentencing algorithms are developed and used in the work of criminal courts.Criminal Law and Philosophy["Jesper Ryberg"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/366152147e7c28d22eb954856af96fa52767086a d6a192a5eab363bc63766543f95d13e647270db001["Pooja Jha", "Shalini Mahato", "P. K. Jana", "Sudhanshu Maurya", "In\u00e8s Chihi"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/d6a192a5eab363bc63766543f95d13e647270db0 cfc19b7dbc213067c33cfdb220b584035d16e7daIn this research, the basic concepts of both principal component analysis and artificial neural networks were reviewed, let alone tackling breast cancer, its most important causes and early detection. To add more, obtaining the principal components of a set of explanatory variables using the artificial neural network method was also discussed. One of the most important conclusions reached is that the weights of the artificial neural network based on the (Hebb) rule are close to the values ??of the characteristic vectors for the correlation matrix. The flexibility of the work of artificial neural networks allows for the expansion of the use of neural networks in other statistical methods. The most important factors causing breast cancer are: marital status, age and breastfeeding, and family history of the disease.Sustainable Engineering and Innovation190One of the most important conclusions reached is that the weights of the artificial neural network based on the (Hebb) rule are close to the values of the characteristic vectors for the correlation matrix.Sustainable Engineering and Innovation["Hasanain Jalil Neamah Alsaedi"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/cfc19b7dbc213067c33cfdb220b584035d16e7da 57307ab9b828deb87dff6f4f5ff7b716519c6a62The 2nd International Electronic Conference on Machines and Applications140The 2nd International Electronic Conference on Machines and Applications["P. Minaoglou", "A. Tzotzis", "N. Efkolidis", "P. Kyratsis"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/57307ab9b828deb87dff6f4f5ff7b716519c6a62 a1a289fae1cee14259f756ac0fb1b5f3641bf904In the rapidly evolving landscape of digital technology, it is now crucial to protect the security and privacy of AI systems in the ever-changing world of digital technology. This paper explores the broad field of safe AI systems, including design principles, privacy-preserving methods, adversarial assault defense strategies, the use of AI in cyber defense, regulatory compliance, and the integration of AI with Blockchain technologyIt begins by summarising the fundamental concepts of secure system architecture, emphasising integrity, secrecy, and availability. An extensive description is provided of adversarial attacks and defence strategies to fortify AI systems against malicious exploitation. The paper then discusses the crucial role that AI plays in cyber defence, outlining how AI/ML enhances automated reaction mechanisms, behavioural analysis, and threat identification. Integration with existing security frameworks, such as SIEM systems to improve overall capabilities. Integrating blockchain technology and AI open up new promises in terms of privacy, security, and trust. In order to achieve no risk, the paper emphasises on AI based intrusion detection system using machine and deep learning techniques. Threat detection, behavioral analysis, real-time response systems, and big data analytics that protect privacy are addressed.2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS)260The broad field of safe AI systems is explored, including design principles, privacy-preserving methods, adversarial assault defense strategies, the use of AI in cyber defense, regulatory compliance, and the integration of AI with Blockchain technology are explored.2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS)["Seema Rani", "Naresh Kumar", "Aviral Srivastva", "Aayush Sharma"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/a1a289fae1cee14259f756ac0fb1b5f3641bf904 79537786841c1c979ee3c3511ccfbd8353b7e730Efektyvna ekonomika00Efektyvna ekonomika["I. Zadorozhna", "S. Soima", "V. Fedurtsia"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/79537786841c1c979ee3c3511ccfbd8353b7e730 c1de925e0d3bb3f2b1c5f16842a3d74d128b9cf9In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes.2024 IEEE 7th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE)270The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change, and 88 % of students have already dealt with AI-based applications, suggesting a high level of integration into their educational processes.2024 IEEE 7th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE)["I. Farkas", "Attila Kovari", "M\u00f3nika Rajcs\u00e1nyi-Moln\u00e1r"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/c1de925e0d3bb3f2b1c5f16842a3d74d128b9cf9 e2d81dd37ca2114f14d59e0d57461371683c5b92Int. J. Hum. Comput. Stud.760Analysis of a large-scale experiment dataset indicates that digital self-efficacy and technical knowledge are positively associated with attitudes toward AI, while liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism.ArXiv["Soojong Kim"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/e2d81dd37ca2114f14d59e0d57461371683c5b92 20e8f225b1d0dcf72e583a513c96abd5896b3e8cLarge language models (LLMs) increasingly mimic human cognition in various language-based tasks. However, their capacity for metacognition - particularly in predicting memory performance - remains unexplored. Here, we introduce a cross-agent prediction model to assess whether ChatGPT-based LLMs align with human judgments of learning (JOL), a metacognitive measure where individuals predict their own future memory performance. We tested humans and LLMs on pairs of sentences, one of which was a garden-path sentence - a sentence that initially misleads the reader toward an incorrect interpretation before requiring reanalysis. By manipulating contextual fit (fitting vs. unfitting sentences), we probed how intrinsic cues (i.e., relatedness) affect both LLM and human JOL. Our results revealed that while human JOL reliably predicted actual memory performance, none of the tested LLMs (GPT-3.5-turbo, GPT-4-turbo, and GPT-4o) demonstrated comparable predictive accuracy. This discrepancy emerged regardless of whether sentences appeared in fitting or unfitting contexts. These findings indicate that, despite LLMs' demonstrated capacity to model human cognition at the object-level, they struggle at the meta-level, failing to capture the variability in individual memory predictions. By identifying this shortcoming, our study underscores the need for further refinements in LLMs' self-monitoring abilities, which could enhance their utility in educational settings, personalized learning, and human-AI interactions. Strengthening LLMs' metacognitive performance may reduce the reliance on human oversight, paving the way for more autonomous and seamless integration of AI into tasks requiring deeper cognitive awareness.110A cross-agent prediction model is introduced to assess whether ChatGPT-based LLMs align with human judgments of learning (JOL), a metacognitive measure where individuals predict their own future memory performance, and reveals that while human JOL reliably predicted actual memory performance, none of the tested LLMs demonstrated comparable predictive accuracy.["Markus Huff", "Elanur Ulakcci"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/20e8f225b1d0dcf72e583a513c96abd5896b3e8c 0c9f46837f459756dd3d83aae0f0db61eef8fc62International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024)00International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024)["Chuchu Xu"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/0c9f46837f459756dd3d83aae0f0db61eef8fc62 8abdd40453f9a186c6926d67d357b35ac971fd20Annual Proceedings of the Science &amp; Technology Metrics00Annual Proceedings of the Science &amp; Technology Metrics["Keshava", "Shankar B. Chavan"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/8abdd40453f9a186c6926d67d357b35ac971fd20 75e0182501a3945d7ab08e8478b35327dea6c367Indian Journal of Community Medicine40Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine["Madhavi Bhargava", "Pankaj Bhardwaj", "Rajib Dasgupta"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/75e0182501a3945d7ab08e8478b35327dea6c367 c51b0dc1bc64c120412bc56019f404ab6285b6efDefence Studies80Defence Studies["Peter Spayne", "Laura Lacey", "Marie Cahillane", "Alistair Saddington"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/c51b0dc1bc64c120412bc56019f404ab6285b6ef f2ed6a143bace53bfd6d35fc5f06d73b36eb52ecPurpose AI has a leading influence and impact on organizations’ strategies. Yet few studies address how AI tools can impact M&A negotiation and the takeover evaluation stages of firms. Through an in-depth case study, our work questions how such tools can help with key stages of screening and due diligence periods. Design/methodology/approach We conduct several interviews with founders of the acquiring firm (Group Impact), and we had access to a large amount of information on the launch of the takeover process (including the data room of negotiation documents). Findings We detail the phases in which AI saved time (nongenerative AI) and, with a view to the future, we detail how a generative version of AI could help in the due diligence and negotiation phases. Also, we address the sticking points that prevented the takeover from being finalized. Originality/value First, few studies address the negotiation stage of takeovers as part of these are not finalized and, for the others, researchers are present in the field when the operation is signed. Second, studies that address takeovers that have failed are rare, yet understanding the blocking factors are essential to continue a long-term external growth strategy. Strategy &amp; Leadership90An in-depth case study investigates how AI tools can impact M&A negotiation and the takeover evaluation stages of firms and details the phases in which AI saved time and how a generative version of AI could help in the due diligence and negotiation phases.Strategy &amp; Leadership["Anne\u2010Sophie Thelisson"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/f2ed6a143bace53bfd6d35fc5f06d73b36eb52ec dd54ab3d2d4f75e7e1ac7ab4f58e631b0255cc80Journal of Speech, Language and Hearing Research150Journal of speech, language, and hearing research : JSLHR["Jordan R Green"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/dd54ab3d2d4f75e7e1ac7ab4f58e631b0255cc80 51888a46d5a8351a852d93b484b80d42875349a6Perkembangan teknologi yang pesat dengan adanya perkembangan artificial intelligence tentu menjadi suatu hal yang perlu dimanfaatkan dalam pembuatan karya yang lebih inovatif, kreatif dan kebaruan sehingga menciptakan dampak yang berkelanjutan bagi komunitas remaja di Desa Rejotangan. Kegiatan Pengabdian kepada Masyarakat (PKM) dengan memberikan pelatihan dan pendampingan kepada pada remaja di Desa Rejotangan bertujuan untuk meningkatkan kreatifitas melalui pemanfaatan artificial intelligence. Metode yang digunakan dalam kegiatan PkM adalah pelatihan dengan pendekatan ceramah, tanya jawab dan linear strategy. Tahap kegiatan meliputi survey lokasi, persiapan materi, pelaksanaan pelatihan dan evaluasi. Peserta yang hadir pada kegiatan pengabdian ini 80,0%, setelah dilakukan pelatihan peserta mendapatkan pemahaman tentang penggunaan teknologi artificial intelligence dalam pembuatan konten kreatif. Setelah dilakukan kegiatan pelatihan dan pengabdian didapatkan adanya peningkatan keterampilan peserta dalam mengembangkan konten kreatif menggunakan artificial intelligence.Jurnal Pengabdian Masyarakat Bangsa70Jurnal Pengabdian Masyarakat Bangsa["Joko Iskandar", "Vertika Panggayuh", "Stefanic Siska Dewi"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/51888a46d5a8351a852d93b484b80d42875349a6 e9064e0c4511ebf657d7c38c609a140b47d1f03cThe early detection of mental health conditions is crucial for timely intervention and effective treatment. This study presents the development and evaluation of the “Smart Smile” application, an AI-powered tool designed to detect early signs of mental health issues using computational intelligence. The primary objective of this research is to design a robust system that leverages artificial intelligence and machine learning algorithms to analyze user interactions and biometric data to identify potential mental health concerns. The scope of the study includes the integration of various data sources such as facial expressions, voice tone, text input, and user behavior patterns. The methods employed in this research encompass data collection through user surveys and real-time monitoring, followed by the application of machine learning techniques for data analysis. Specifically, convolutional neural networks (CNNs) and natural language processing (NLP) algorithms are utilized to process and interpret the collected data. Key findings from this study indicate that the Smart Smile application can accurately predict early signs of conditions such as depression and anxiety with a high degree of accuracy. The application's performance is validated through extensive testing and cross-validation against established mental health assessment tools. The results demonstrate the potential of AI and computational intelligence in enhancing mental health diagnostics and providing users with actionable insights. In conclusion, the AI-powered Smart Smile application represents a significant advancement in the field of mental health care, offering a scalable and accessible solution for early detection of mental health conditions. Future work will focus on improving the algorithm's accuracy, expanding the dataset, and integrating additional features to support a broader range of mental health conditions.International Conference on Telecommunication Systems, Services, and Applications170Key findings from this study indicate that the AI-powered Smart Smile application can accurately predict early signs of conditions such as depression and anxiety with a high degree of accuracy.2024 18th International Conference on Telecommunication Systems, Services, and Applications (TSSA)["Mundzir", "Riski Zulkarnain", "Richki Hardi", "Fitri Syabandyah", "Hanny Rahayu", "Hetti Herawati"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/e9064e0c4511ebf657d7c38c609a140b47d1f03c 5e5b275501b156d412bf08cf596be194623409fdExplainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there's been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user's perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human-AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.Frontiers Artif. Intell.1152A systematic literature review of the current state of affairs in human-centered XAI evaluation revealed a lack of standardization in the methodologies applied in XAI user studies, guiding the XAI community toward a more unified approach in human-centered explainability.Frontiers in Artificial Intelligence["Jenia Kim", "Henry Maathuis", "Danielle Sent"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/5e5b275501b156d412bf08cf596be194623409fd 984299f826b313fa6ef3d95ff09e507685991b3dArtificial Intelligence (AI) has emerged as a transformative force across various industries, revolutionizing processes and enhancing efficiency. In the automotive domain, AI's adaption has ushered in a new era of innovation and driving advancements across manufacturing, safety, and user experience. By leveraging AI technologies, the automotive industry is undergoing a significant transformation that is reshaping the way vehicles are manufactured, operated, and experienced. The benefits of AI-powered vehicles are not limited to their manufacturing, operation, and enhancing the user experience but also by integrating AI-powered vehicles with smart city infrastructure can unlock much more potential of the technology and can offer numerous advantages such as enhanced safety, efficiency, growth, and sustainability. Smart cities aim to create more livable, resilient, and inclusive communities by harnessing innovation through technologies like Internet of Things (IoT), devices, data analytics, and artificial intelligence (AI) and enables data-driven decision-making to meet the evolving needs of urban populations. Integrating AI-powered vehicles with smart city infrastructure can potentially compliment it and can offer numerous advantages like: Traffic Management, Infrastructure Optimization, Parking Optimization, Safety Enhancements, Environmental Sustainability, Emergency Response, various Infrastructure, and business Investment Planning and many more. Moreover, the integration will also have positive impact on environment such as Smart city infrastructure can provide AI-powered vehicles with data on optimal routes and availability of parking slot, resulting in reduced air pollution and energy consumption. In essence, this offers a holistic approach to urban mobility, fostering safer, more efficient, and environmentally sustainable transportation systems. By leveraging advanced technologies and data-driven insights, cities can unlock new opportunities for improving quality of life, enhancing economic competitiveness, and fostering inclusive and resilient communities. In this technical manual, we delve into the futuristic implications of this integration, providing a detailed exploration of the technical aspects and benefits.SAE technical paper series292This technical manual delve into the futuristic implications of this integration of AI-powered vehicles with smart city infrastructure, providing a detailed exploration of the technical aspects and benefits.SAE Technical Paper Series["Harsh Shrimal"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/984299f826b313fa6ef3d95ff09e507685991b3d 1269f875251cb4368c42ed46af26d1bcc2549cbaThis paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain. Decentralized AI distributes AI services among multiple entities without centralized oversight, fostering transparency and robustness. However, this structure introduces significant privacy challenges, as sensitive assets such as proprietary models and personal data may be exposed to untrusted participants. Cryptography-based privacy protection techniques such as zero-knowledge machine learning (zkML) suffers prohibitive computational overhead. To address the limitation, we propose leveraging Confidential Computing (CC). Confidential Computing leverages hardware-based Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data, ensuring that both model parameters and user data remain secure, even in decentralized, potentially untrusted environments. While TEEs face a few limitations, we believe they can bridge the privacy gap in decentralized AI. We explore how we can integrate TEEs into Atoma's decentralized framework.arXiv.org71This paper addresses privacy protection in decentralized Artificial Intelligence (AI) using Confidential Computing (CC) within the Atoma Network, a decentralized AI platform designed for the Web3 domain using Trusted Execution Environments (TEEs) to provide isolation for processing sensitive data.ArXiv["Dayeol Lee", "Jorge Antonio", "Hisham Khan"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/1269f875251cb4368c42ed46af26d1bcc2549cba ae141eaec73eec67a8f9d7c46071ecb8751013fcGeroScience401A look at the ways in which modern neuroscience can influence the future design of computers and the development of AI, and the possible contribution of AI to solve the problem of chronic disorders of the elderly leading to cognitive impairment.GeroScience["Ferenc De\u00e1k"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/ae141eaec73eec67a8f9d7c46071ecb8751013fc 28d718eed9c1e54a198fa560130b4164036b8492This paper explores how generative artificial intelligence (AI) affects online platforms where both human creators and AI generate content. We develop a model to understand how generative AI changes supply and demand, impacts traffic distribution, and influences social welfare. Our analysis shows that AI can lead to a huge increase in content supply due to its low cost, which could cause oversupply. While AI boosts content variety, it can also create information overload, lowering user satisfaction and disrupting the market. AI also increases traffic concentration among top creators (the"winner-takes-all"effect) while expanding opportunities for niche content (the"long-tail"effect). We assess how these changes affect consumer and producer benefits, finding that the overall impact depends on the quality of AI-generated content and the level of information overload. Through simulation experiments, we test policy ideas, such as adjusting platform fees and recommendations, to reduce negative effects and improve social welfare. The results highlight the need for careful management of AI's role in online content platforms to maintain a healthy balancearXiv.org311A model is developed to understand how generative AI changes supply and demand, impacts traffic distribution, and influences social welfare, finding that the overall impact depends on the quality of AI-generated content and the level of information overload.ArXiv["Yukun Zhang"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/28d718eed9c1e54a198fa560130b4164036b8492 8a6857caa6131c1f4449cbfda85bb5e4121ff0a5As the metaverse evolves into an all-encompassing digital ecosystem, ensuring the security and uniqueness of digital identities and assets becomes paramount. This paper critically examines the identity security challenges in the metaverse, particularly focusing on the unicity problem. We propose an integrated solution leveraging the synergistic capabilities of Artificial Intelligence (AI), blockchain technology, and Non-Fungible Tokens (NFTs). Our novel contribution lies in the multifaceted approach that combines AI for sophisticated identity verification and anomaly detection, blockchain for decentralized and immutable record-keeping, and NFTs for establishing verifiable ownership and authenticity of digital assets. This robust framework addresses the pressing need for enhanced security measures in the metaverse, not only mitigating current security risks but also paving the way for future advancements in digital identity management. Our comprehensive analysis and proposed methodology provide a significant contribution to the ongoing discourse on metaverse security, highlighting both the potential and challenges of integrating these emerging technologies. This paper serves as a foundational work for future research and development in the secure management of digital identities and assets within the metaverse.OPTIMA150This paper critically examines the identity security challenges in the metaverse, particularly focusing on the unicity problem, and proposes an integrated solution leveraging the synergistic capabilities of Artificial Intelligence, blockchain technology, and Non-Fungible Tokens (NFTs).2024 10th International Conference on Optimization and Applications (ICOA)["Zakaria El Rhadiouini", "Zahra Oughannou", "Nour El Houda Mejhed Chaoui", "H. Chaoui"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/8a6857caa6131c1f4449cbfda85bb5e4121ff0a5 29c941bbe55c6b2d3152287b0d0ff6067a30e80fThe rapid evolution of autonomous vehicles (AVs) presents significant opportunities for enhancing transportation safety and efficiency. However, with increasing connectivity and complex electronic systems, AVs also become vulnerable to cyberattacks. This paper investigates cybersecurity challenges in the realm of AVs, highlighting the role of artificial intelligence (AI), specifically Large Language Models (LLMs), in exploiting vulnerabilities. We analyze various attack vectors, including Controller Area Network (CAN) manipulation, Bluetooth vulnerabilities, and Key Fob hacking, emphasizing the need for proactive cybersecurity measures. Recent incidents, such as the remote compromise of various vehicle models, underscore the urgent need for robust security solutions in the automotive industry. By leveraging LLMs, attackers can craft sophisticated cyber threats targeting AVs, posing risks to both safety and privacy. We introduce HackerGPT, a customized LLM tailored for cyber attack generation, and demonstrate attacks on virtual CAN networks, Bluetooth systems, and Key Fobs. At the same time, our experiments reveal successful compromises in certain vehicle models; limitations exist, particularly in vehicles with advanced encryption and robust signal transmission protocols. However, this research underscores the broader need for increased awareness and proactive security measures in the automotive sector. Our findings aim to contribute significantly to the ongoing discourse on automotive cybersecurity, offering actionable insights for manufacturers and cybersecurity professionals to safeguard the future of mobility.Ubiquitous Computing, Electronics & Mobile Communication Conference00Cybersecurity challenges in the realm of AVs are investigated, highlighting the role of artificial intelligence (AI), specifically Large Language Models (LLMs), in exploiting vulnerabilities, and HackerGPT, a customized LLM tailored for cyber attack generation is introduced.2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)["Yusuf Usman", "P. Gyawali", "Sohan Gyawali", "Robin Chataut"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/29c941bbe55c6b2d3152287b0d0ff6067a30e80f 82d961529aa1a3144c1e1bd7c0de283abffae8f6This study examines the evolving landscape of data governance in cloud-based big data analytics, emphasizing the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), and blockchain. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 120 articles were systematically reviewed to explore how organizations are addressing the challenges of managing large-scale, decentralized datasets while ensuring regulatory compliance and data security. The findings reveal that AI and ML are increasingly being used to automate governance tasks, predict compliance risks, and provide real-time auditing, while blockchain plays a critical role in ensuring data integrity and transparency across distributed cloud environments. Moreover, the research underscores the need for flexible and scalable governance models that can adapt to evolving regulations like GDPR and CCPA. Additionally, best practices such as multi-layered security approaches and strong collaboration with cloud service providers were identified as key strategies for enhancing governance frameworks. These insights contribute to the ongoing discourse on the modernization of data governance, highlighting the importance of dynamic, automated, and proactive approaches to managing data in cloud-based environments. This study provides a comprehensive understanding of current practices and technological innovations, offering actionable recommendations for organizations navigating the complexities of cloud-based data governance.ACADEMIC JOURNAL ON SCIENCE, TECHNOLOGY, ENGINEERING &amp; MATHEMATICS EDUCATION00The findings reveal that AI and ML are increasingly being used to automate governance tasks, predict compliance risks, and provide real-time auditing, while blockchain plays a critical role in ensuring data integrity and transparency across distributed cloud environments.ACADEMIC JOURNAL ON SCIENCE, TECHNOLOGY, ENGINEERING &amp; MATHEMATICS EDUCATION["A. Islam"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/82d961529aa1a3144c1e1bd7c0de283abffae8f6 2ec9f945a23cd2a7bbbe23fa7bf83a7c3c40b177Artificial Intelligence (AI) significantly enhances adaptive learning by personalizing and tailoring instruction to individual student needs. AI analyzes data in real-time to create personalized learning paths based on students' strengths, weaknesses, and preferences, which keeps students engaged and motivated. A major benefit of AI in adaptive learning is the provision of real-time feedback and assessment, allowing students to correct mistakes promptly and understand concepts more thoroughly. AI-based intelligent tutoring systems are primarily intended to simulate personalized tutoring processes that guide students in complex problem-solving and answering questions. It is convenient in teaching mathematics, sciences, and languages. AI also supports inclusive education, dealing with diversified learning requirements and styles, such as those of learners with disabilities. For the teacher, AI acts as a reflector of student performance so that one can intervene early and make adjustments in the method of instruction by creating effective learning environments. AI technology is a field in constant development and harbors the potential to change the face of adaptive learning, bringing an upswing in educational outcomes. This article will summarize the advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback.2024 IEEE 7th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE)00The advantages and features that merit improvement of the AI-embedded adaptive learning systems with student feedback will be summarized.2024 IEEE 7th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE)["Klara Ida Katonane Gyonyoru", "Jozsef Katona"]2024-10-17T00:00:00https://www.semanticscholar.org/paper/2ec9f945a23cd2a7bbbe23fa7bf83a7c3c40b177 6242be8ef591473fd1e66283f9978688b72bb324The rapid development of deepfake technology has opened up a range of groundbreaking opportunities while also introducing significant ethical challenges. This paper explores the complex impacts of deepfakes by drawing from fields such as computer science, ethics, media studies, and law. Through a multidisciplinary approach, we examine the technological foundations, uses, and societal effects of deepfakes. Our analysis includes case studies, expert interviews, and a thorough review of existing literature to highlight the dual nature of deepfakes—showcasing their potential benefits in entertainment and education, while also addressing the risks of misinformation and privacy violations. This study emphasizes the urgent need for improved detection methods, ethical guidelines, and strong legal frameworks to address the issues created by deepfakes. It calls for enhanced digital literacy and global cooperation to ensure that the advantages of generative AI are harnessed responsibly, while its inherent risks are minimized. The findings underscore the importance of effective detection strategies, ethical considerations, and legislative reforms to ensure deepfake technology is used in ways that benefit society.International Journal of Scientific Research and Modern Technology (IJSRMT)7311This study emphasizes the urgent need for improved detection methods, ethical guidelines, and strong legal frameworks to address the issues created by deepfakes and calls for enhanced digital literacy and global cooperation to ensure that the advantages of generative AI are harnessed responsibly, while its inherent risks are minimized.International Journal of Scientific Research and Modern Technology (IJSRMT)[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/6242be8ef591473fd1e66283f9978688b72bb324 c8e65a635d6695077b6660cbf51793964872a4cdThis review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications of AI techniques, especially machine learning (ML) and deep learning (DL) in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated. The review also shows where progress is limited.Encyclopedia771Applications of AI techniques, especially machine learning (ML) and deep learning in the areas of shift prediction, spectral simulations, spectral processing, structure elucidation, mixture analysis, and metabolomics, are demonstrated.Encyclopedia["Stefan Kuhn", "R\u00f4mulo Pereira de Jesus", "R. Borges"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/c8e65a635d6695077b6660cbf51793964872a4cd 3542dadcb77f5c725f2754b364632095e9135830The rapid advancement of technology and innovation is also impacting education across different levels. The rise of Artificial Intelligence (AI) is beginning to transform education in various areas, from course materials to assessment systems. This requires educators to reconsider how they evaluate students’ knowledge. It is crucial to understand if and to what extent assignments can be completed using AI tools. This study explores two hypotheses about the risks of using code-based 3D modeling software in education and the potential for students to delegate their work to AI when completing assignments. We selected two tasks that students were able to successfully complete independently and provided the same amount of information (both textual and image) to AI in order to generate the necessary code. We tested the widely used ChatGPT and Gemini AI bots to assess their current performance in generating code based on text prompts or image-based information for the two models. Our findings indicate that students are not yet able to entirely delegate their work to these AI tools.Designs01This study explores two hypotheses about the risks of using code-based 3D modeling software in education and the potential for students to delegate their work to AI when completing assignments and indicates that students are not yet able to entirely delegate their work to these AI tools.Designs["Marianna Zichar", "I. Papp"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/3542dadcb77f5c725f2754b364632095e9135830 baa05dcce56ee10c618f9ad848d63e700ce64194BMC Medical Education422This study clarified key considerations when integrating AI in HPE, highlighting students’ awareness and fostering innovation in an AI-driven medical landscape are crucial for effectively incorporating AI in HPE curricula.BMC Medical Education["W. Issa", "Ali Shorbagi", "Alham Al-Sharman", "Mohammad Rababa", "Khalid Al-Majeed", "H. Radwan", "Fatma Refaat Ahmed", "Nabeel Al-Yateem", "Richard Mottershead", "Dana N Abdelrahim", "H. Hijazi", "Wafa F Khasawneh", "Ibrahim Ali", "Nada Abbas", "Randa Fakhry"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/baa05dcce56ee10c618f9ad848d63e700ce64194 88be11ab1b6dd6447578e2baaa792d1a994c82fdImportance. Currently, there is a tendency that when teaching a foreign language, teachers are increasingly resorting to the use of artificial intelligence technologies to plan training sessions, generate educational content, as well as to conduct automated testing of formed communication skills. The monitoring of academic achievements and assessment is one of the key components of the organization of the educational process. Traditional methods of control and assessment require significant time and labor-intensive costs from the teacher, while artificial intelligence technologies make it possible to simplify and automate routine tasks: check tests and written papers, analyze them and identify mistakes, provide feedback. Thanks to the integration of natural language processing (NLP) technologies into chatbots and adaptive intelligent learning systems, it becomes possible on a daily basis to check texts created by students, evaluate them from the point of view of grammatical and lexical correctness, as well as identify stylistic and factual errors in real time. The purpose of this work is to test the applicability of artificial intelligence technologies for thematic control and criteria-based assessment of educational achievements using the example of productive and reproductive writing.Research Methods. In carrying out this study, the following groups of methods were used: theoretical methods aimed at familiarizing with scientific and methodological literature on the topic of research, analysis and classification of theoretical and methodological material for conducting thematic control and criterion assessment in foreign language classes, as well as empirical methods that allowed modeling pedagogical control and assessment processes using artificial intelligence technologies, observation, analysis and description of the results obtained.Definition of Concepts. The main concepts used in the study are “control of educational results” and its variety “thematic control”, “criterion assessment”.Results and Discussion. In the course of the study, various types of monitoring educational results using artificial intelligence technologies were considered: preliminary, current, intermediate, thematic, final. The choice of thematic control is due to the opportunity to trace its applicability within the framework of a single lesson in a foreign language and identify the main difficulties in using this form of control. The following criteria for evaluating writing skills are used to evaluate learning outcomes using artificial intelligence technologies: a) the structure of the written text; b) compliance with the main topic; c) coherence; d) relevance; e) grammatical correctness; f) lexical correctness; g) ethics of writing and stylistic correctness.Conclusion. Artificial intelligence technologies at the present stage have a high degree of adaptability and include a wide range of software and hardware solutions that allow for such important pedagogical procedures as monitoring educational achievements and evaluation in accordance with user-defined evaluation criteria. The obtained results are proposed to be used in research devoted to the study of modern methods of monitoring educational achievements in the methodology of teaching foreign languages using artificial intelligence technologies.Tambov University Review. Series: Humanities82Artificial intelligence technologies at the present stage have a high degree of adaptability and include a wide range of software and hardware solutions that allow for such important pedagogical procedures as monitoring educational achievements and evaluation in accordance with user-defined evaluation criteria.Tambov University Review. Series: Humanities["M. N. Evstigneev"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/88be11ab1b6dd6447578e2baaa792d1a994c82fd b287b420e25b02f9543ca748baaf7f3b5929735cArtificial intelligence (AI) has permeated most facets of life in the 21st century and has rapidly transformed various aspects of modern society. From entertainment to education, these advanced technologies have achieved a high level of competency in skills that once necessitated human involvement. Given AI's potential impact, ensuring students are literate in AI will support the careful integration of these advanced technologies to achieve sustainable development goals. This review hence examines the avenues for integrating AI literacy into elementary education by analyzing current global initiatives focused on implementing AI literacy education. The purpose is to support innovations within the educational framework to develop a universally accessible AI literacy education program. In line with this purpose, this study explores worldwide AI literacy initiatives that use hands‐on activities, collaborative learning, and project‐based learning to introduce AI fundamentals to diverse learners. Limitations on the provision of AI literacy education are also discussed, including professional development, openness to AI tools, and other challenges. This review aims to inform global efforts to support universal access to AI literacy education, which can ensure equitable outcomes for all learners, emphasizing the need for collaborative efforts to support the development and delivery of quality AI literacy education.Future in Educational Research552Worldwide AI literacy initiatives that use hands‐on activities, collaborative learning, and project‐based learning to introduce AI fundamentals to diverse learners are explored, emphasizing the need for collaborative efforts to support the development and delivery of quality AI literacy education.Future in Educational Research["Ibrahim H. Yeter", "Weipeng Yang", "Joshita B. Sturgess"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/b287b420e25b02f9543ca748baaf7f3b5929735c 5559e896817c18c67f088b386142350dfe65b1e5The Plan – Do – Check – Act (PDCA) cycle is the basis of the process approach applied in ISO management system standards. The experience gained in working with management systems and summarized in the author’s monograph have been creatively adapted and applied in the analysis of the requirements of ISO/IEC 42001:2023 – the first standard for artificial intelligence management systems. This paper presents a method for determining the type of each requirement of the ISO/IEC 42001:2023 standard in relation to the PDCA cycle. The application of the PDCA cycle is demonstrated for selected processes and is used for creating process flowcharts. They can be based on the standards with requirements for quality, educational organizations, research and innovation centres, etc. Further on they can serve as the foundations for developing relevant documented procedures that comprise the artificial intelligence management system.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika01A method for determining the type of each requirement of the ISO/IEC 42001:2023 standard in relation to the PDCA cycle is presented.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["Tzvetelin Gueorguiev"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/5559e896817c18c67f088b386142350dfe65b1e5 d60887c7f8df964f69d2ce8361d42cba3c98b463Artificial Intelligence (AI) has the potential to revolutionize medical diagnostics by offering new opportunities for accuracy, efficiency, and accessibility in healthcare. This article examines the benefits of implementing AI in diagnostics, such as enhanced diagnostic precision, faster clinical decision-making, cost reduction, and increased access to healthcare. It also discusses the challenges associated with AI implementation, including ethical, legal, and technical issues. The future of AI in medicine may bring further technological advancements and personalized therapy, but it also requires addressing regulatory and ethical concerns.Przeglad Epidemiologiczny370The benefits of implementing AI in diagnostics, such as enhanced diagnostic precision, faster clinical decision-making, cost reduction, and increased access to healthcare, are examined.Przeglad epidemiologiczny["Jan Bara\u0144ski"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/d60887c7f8df964f69d2ce8361d42cba3c98b463 378532cda22892c98a79e9458ac46569da0ee818This article examines the role of artificial intelligence (AI) in scientific research (SR), with particular emphasis on its application in various aspects of the research process. AI is a tool that can significantly optimize the stages of the scientific process. It can analyze current trends in scientific publications and data, identifying current issues and challenges. AI provides opportunities for automating various stages of preparing scientific texts. It can assist in formulating methodologies for SR. An interesting aspect is AI's ability to be used for translating scientific sources into various languages. AI provides scientific methods and tools that are suitable for the specific themes of the research and can generate new scientific hypotheses based on existing information. AI allows verification of whether scientific publications are innovative and original and can also help detect potential cases of plagiarism. Finally, there are challenges and limitations that may arise in the application of AI in SR. The aim of this work is to explore these applications of AI and their potential to enhance SR.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The role of artificial intelligence in scientific research (SR) is examined, with particular emphasis on its application in various aspects of the research process, and its potential to enhance SR.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["Aldeniz Rashidov"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/378532cda22892c98a79e9458ac46569da0ee818 c1e8c1087dacb3209ab2dbd6a073a812452fc98cThis research delves into the complex dynamics of artificial intelligence (AI). By integrating a combinatorial empowerment model, attribution theory, and Naive Bayes classification, the study aims to dissect the multifaceted effects of AI on the learning and developmental processes of people. The analytical process reveals a nuanced relationship between the synergistic benefits of AI engagement and the decision-making equilibria of people, highlighting a positive correlation with beneficial outcomes and a negative association with adverse impacts. This dual analysis not only sheds light on the constraint-promotion dynamics of AI on people development but also emphasizes a dynamic exploration of the evolving mechanisms influencing people cognition in the digital state. Key findings underscore the importance of a balanced approach to AI integration within frameworks, suggesting that while AI can significantly enhance learning and cognitive development, awareness and mitigation of potential overdependence are crucial.2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)50This research delves into the complex dynamics of artificial intelligence by integrating a combinatorial empowerment model, attribution theory, and Naive Bayes classification, suggesting that while AI can significantly enhance learning and cognitive development, awareness and mitigation of potential overdependence are crucial.2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)["Jingtao Xie", "Jiaqi Dong"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/c1e8c1087dacb3209ab2dbd6a073a812452fc98c f7e2c6216ac4470dbf444e473b5168c18dd9ee87Computer and social networking whereby criminals use the Internet to propagate criminal activities are some of the major challenges faced by existing policing strategies. Modern-day crimes include hacking into computer systems and stealing money from consumers, ransomware, identity theft cases, and hacking, all of which use the dark web and encryption. In this regard, artificial intelligence (AI) is the most efficient solution for improving the manner of cybercrime investigation. This paper also analyses how AI technologies such as machine learning natural language processing, and deep learning can be incorporated in cybercrime investigations and how they can assist in dealing with difficulties concerning data volume, complexity, and encryption. The advantages of utilizing AI are numerous from pattern recognition to repetitive tasks cutting down the investigation time. However, the paper recognizes that applying AI in business brings legal, technical, and ethical concerns including; privacy, bias, and legal constrictions. This research analyses existing legal frameworks of India, the EU, and the United States while looking at how it would be possible to incorporate AI into cybercrime investigations without violating the rights of a citizen. Further, it reveals infringement and possible bias, as well as unlawful use for violations, and recounts drawbacks related to the lack of resources and expertise that police departments confront. In the final section of the paper, directions for future research focusing on the use of AI in the fight against cybercrime are given in addition to that, the practice of cooperation with different countries, legal regulation of such activities, protection of ethical issues, and training of personnel are described. They are useful in making sure that the levels of artificial intelligence benefits are achieved fully without compromising security and the basic rights of an individualInternational Journal For Multidisciplinary Research100Existing legal frameworks of India, the EU, and the United States are analyzed while looking at how it would be possible to incorporate AI into cybercrime investigations without violating the rights of a citizen.International Journal For Multidisciplinary Research["Yogita Gautam", "Dr. Renu"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/f7e2c6216ac4470dbf444e473b5168c18dd9ee87 5b93bc003f143a3a275fa84594a262b41f47e730The transformation of news media into intelligent media is in the ascendant, and artificial intelligence (AI) technology has brought news practitioners into the era of intelligent production, but also brought them the psychological worry of AI anxiety. The study analyses the causes of AI anxiety among news practitioners from three aspects: the dissolution of their professional concepts, the prevalence of professional substitution, and the polarisation of professional skill requirements, and proposes a path to alleviate the problem by balancing self-efficacy and psychological expectations through scientific cognition, promoting the human-machine symbiosis model in the field of journalism through multi-party collaboration, and providing special support to improve the protection of news practitioners' profession.Communications in Humanities Research00The study analyses the causes of AI anxiety among news practitioners from three aspects: the dissolution of their professional concepts, the prevalence of professional substitution, and the polarisation of professional skill requirements, and proposes a path to alleviate the problem by balancing self-efficacy and psychological expectations through scientific cognition.Communications in Humanities Research["Kewen Liang", "Kaidong Dong"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/5b93bc003f143a3a275fa84594a262b41f47e730 93caeada2a9da0e211855d254a434b4665dc36b7This research paper investigates the integration of Artificial Intelligence (AI) and Blockchain technologies to enhance Peer-to-Peer (P2P) energy trading within microgrids. The study compares the performance of a conventional energy trading system with an AI-Blockchain system, focusing on key metrics such as energy trading efficiency, cost optimization, scalability, securityNanotechnology Perceptions00This research paper investigates the integration of Artificial Intelligence (AI) and Blockchain technologies to enhance Peer-to-Peer energy trading within microgrids, focusing on key metrics such as energy trading efficiency, cost optimization, scalability, security.Nanotechnology Perceptions["Meruga Naresh", "N. Madhurisha", "Lingidi Nageswar Rao", "G. Mahender", "Dr. T. Nageswara Rao"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/93caeada2a9da0e211855d254a434b4665dc36b7 6252e67d2d2d7cd0867b54c917ef95f0968e72c3The paper delves into the critical significance of incorporating Artificial Intelligence (AI) into Human Resource (HR) functions. It extensively explores the multifaceted challenges encountered by organizations during AI implementation in HR, with a particular focus on the vital aspect of employee understanding and acceptance. To elucidate these challenges faced in adopting AI technologies, this study undertakes a comprehensive exploration of the obstacles. This paper adopts a two-phased methodology to explore the critical significance of integrating AI into HR functions and the multifaceted challenges organizations encounter during this implementation. The first phase entails an extensive literature review, delving into the myriad challenges organizations face as they navigate the adoption of AI in HR. In the second phase, industry experts provide ratings and rankings to help us grasp the critical challenges based on industry priorities. The paper acknowledges the evolving nature of jobs and the consequential increase in employment opportunities as technology reshapes the employment landscape.Model Assisted Statistics and Applications130The paper extensively explores the multifaceted challenges encountered by organizations during AI implementation in HR, with a particular focus on the vital aspect of employee understanding and acceptance, as technology reshapes the employment landscape.Model Assisted Statistics and Applications["Gunjan Chhabra", "Snigdha Malhotra"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/6252e67d2d2d7cd0867b54c917ef95f0968e72c3 120ffd8d2a45ff3aa28110659cf45f03be99bba2This study examines the acceptance of artificial intelligence (AI)-based diagnostic alternatives compared to traditional biological testing through a randomized scenario experiment in the domain of neurodegenerative diseases (NDs). A total of 3225 pairwise choices of ND risk-prediction tools were offered to participants, with 1482 choices comparing AI with the biological saliva test and 1743 comparing AI+ with the saliva test (with AI+ using digital consumer data, in addition to electronic medical data). Overall, only 36.68% of responses showed preferences for AI/AI+ alternatives. Stratified by AI sensitivity levels, acceptance rates for AI/AI+ were 35.04% at 60% sensitivity and 31.63% at 70% sensitivity, and increased markedly to 48.68% at 95% sensitivity (p <0.01). Similarly, acceptance rates by specificity were 29.68%, 28.18%, and 44.24% at 60%, 70%, and 95% specificity, respectively (P < 0.01). Notably, AI consistently garnered higher acceptance rates (45.82%) than AI+ (28.92%) at comparable sensitivity and specificity levels, except at 60% sensitivity, where no significant difference was observed. These results highlight the nuanced preferences for AI diagnostics, with higher sensitivity and specificity significantly driving acceptance of AI diagnostics.Artificial Intelligence in Health00Higher sensitivity and specificity significantly driving acceptance of AI diagnostics are highlighted, with higher sensitivity and specificity significantly driving acceptance of AI diagnostics.Artificial Intelligence in Health["Y. Hswen", "Isma\u00ebl Rafa\u00ef", "Antoine Lacombe", "B\u00e9reng\u00e8re Davin-Casalena", "Dimitri Dubois", "Thierry Blayac", "Bruno Ventelou"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/120ffd8d2a45ff3aa28110659cf45f03be99bba2 feb3402ceb87d035fa5f786a3871a65fca404800What Was the Question? There are conflicting calls for evidence synthesis producers to adopt or avoid recent Artificial Intelligence (AI) technologies. Our question was: How do we evaluate rapidly evolving AI tools to enhance the production of evidence syntheses and maintain quality standards? What Did We Do? To advance information retrieval science for producing evidence syntheses at Canada’s Drug Agency (CDA-AMC), the Research Information Services team developed a process to evaluate promising AI search tools. We inventoried 51 tools in the fall of 2023, established selection criteria, assessed specific attributes, and built a standalone instrument to support continuous monitoring and evaluation of new tools. What Did We Find? Rapid development of AI search tools requires a flexible evaluation instrument to inform adoption decisions and enable comparison between tools. We identified mandatory and desirable characteristics for suitable AI tools to assist with information retrieval tasks conducted by CDA-AMC. This work enabled the development of a flexible instrument to evaluate novel AI search tools for evidence synthesis. What Does This Mean? CDA-AMC operationalized a replicable process to monitor and evaluate AI search tools. Our approach to evaluating AI technologies will advance information retrieval methods by CDA-AMC, and our evaluation instrument will assist any evidence synthesis producer interested in adopting AI search tools.Canadian Journal of Health Technologies00The approach to evaluating AI technologies will advance information retrieval methods by CDA-AMC, and the evaluation instrument will assist any evidence synthesis producer interested in adopting AI search tools.Canadian Journal of Health Technologies["CDA-AMC"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/feb3402ceb87d035fa5f786a3871a65fca404800 59dce3d82073b14833f6aee18e3e0b5b3715a069Labor plays a major, albeit largely unrecognized role in the development of artificial intelligence. Machine learning algorithms are predicated on data-intensive processes that rely on humans to execute repetitive and difficult-to-automate, but no less essential, tasks such as labeling images, sorting items in lists, recording voice samples, and transcribing audio files. Online platforms and networks of subcontractors recruit data workers to execute such tasks in the shadow of AI production, often in lower-income countries with long-standing traditions of informality and lessregulated labor markets. This study unveils the resulting complexities by comparing the working conditions and the profiles of data workers in Venezuela, Brazil, Madagascar, and as an example of a richer country, France. By leveraging original data collected over the years 2018-2023 via a mixed-method design, we highlight how the cross-country supply chains that link data workers to core AI production sites are reminiscent of colonial relationships, maintain historical economic dependencies, and generate inequalities that compound with those inherited from the past. The results also point to the importance of less-researched, non-English speaking countries to understand key features of the production of AI solutions at planetary scale.arXiv.org350It is highlighted how the cross-country supply chains that link data workers to core AI production sites are reminiscent of colonial relationships, maintain historical economic dependencies, and generate inequalities that compound with those inherited from the past.ArXiv["Antonio A. Casilli", "Paola Tubaro", "Maxime Cornet", "Cl'ement Le Ludec", "Juana Torres-Cierpe", "Matheus Viana Braz"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/59dce3d82073b14833f6aee18e3e0b5b3715a069 788f7a92600dc33476747fe1ace86ef5392a2f3eWith the increasing integration of artificial intelligence (AI) across all aspects of life, it has the potential to address some of the biggest challenges in today's education. Recognizing this potential, an interdisciplinary team at the University of Ruse has developed a conceptual framework that outlines a new and innovative way to integrate AI into the educational process. The aim of this framework (program) is to familiarize educators with AI systems to ensure their effective use in teaching and research activities, thereby accelerating the digital transformation of education. This publication presents an overview of the innovative approach, highlighting key components aimed at revolutionizing teaching and learning practices and introducing innovations in education. The framework postulates that by integrating AI, the aim is not only to enhance educational experiences but also to provide a new perspective for promoting an individualized approach to improve the efficiency of the learning process.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The framework postulates that by integrating AI, the aim is not only to enhance educational experiences but also to provide a new perspective for promoting an individualized approach to improve the efficiency of the learning process.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["Hristo Beloev", "Valentina Voinohovska", "Angel Smrikarov"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/788f7a92600dc33476747fe1ace86ef5392a2f3e 9d0be6aee0cc517b755fb8ce028080e13e0b38f5The paper examines the possibilities of developing and applying additional educational resources through artificial intelligence systems. Microlearning is an effective teaching method in modern education that features concise audio and visual learning resources suitable for students from the digital generation. The research focuses on integrating educational resources into the traditional learning process, which activates the learning process through innovative methods and attractively presented information. A two-directional approach is applied that enables working through the creation of resources by the teacher on the one hand, and on the other hand, the learners themselves generate such resources. At the same time, QR codes are used to introduce learning and game elements to the primary macro resources. The aim is to determine the impact of the integrated approach on learners’ engagement, their acquisition of the learning material, and overall learning outcomes.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The aim is to determine the impact of the integrated approach on learners’ engagement, their acquisition of the learning material, and overall learning outcomes.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["Petya Stefanova", "Elitsa Ibryamova", "Angel Smrikarov", "Galina Ivanova"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/9d0be6aee0cc517b755fb8ce028080e13e0b38f5 b3818f70a2eaa14f8e8042ae899e95c62b0478b4The evolution of artificial intelligence (AI) in organizational management has significantly enhanced operational efficiency. However, it has introduced ethical challenges in human resource management. Between January to March 2024, this study conducted 21 semi-structured interviews with middle-level managers from high-tech companies in Beijing. Through word frequency analysis, the study found that key topics among the managers were “company,” “data,” “system,” and “problem,” with “AI” frequently recurring in the discussions. Sentiment analysis revealed generally favorable attitudes toward AI in human resource management (AI-HRM), along with nuanced emotional expressions such as inquiry, introspection, recommendation, challenge, adaptation, resistance, and indifference. The sentiment distribution of keywords aligned with topic trends. Thematic analysis identified key ethical concerns in AI-HRM, including issues related to data collection and utilization, human versus machine decision-making, quantitative versus qualitative assessment methodologies, the balance between fairness and efficiency, the need for trustworthy, explainable, and transparent AI, and the oversight of AI-HRM. This study contributes to the ethical investigation of AI-HRM from the perspective of middle-level managers, highlighting themes that are critical for understanding the theory, application, and future development of AI-HRM.International Journal For Multidisciplinary Research530Key ethical concerns in AI-HRM are identified, including issues related to data collection and utilization, human versus machine decision-making, quantitative versus qualitative assessment methodologies, the balance between fairness and efficiency, the need for trustworthy, explainable, and transparent AI, and the oversight of AI-HRM.International Journal For Multidisciplinary Research["Hong Wei", "Cao Chen"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/b3818f70a2eaa14f8e8042ae899e95c62b0478b4 e30832287f3cfaa59ac86a454c244ff7f1932937 In recent years, the global quest for livestock intensification driven by ever-increasing demands for animal food products raised concerns about animal welfare, environmental sustainability, and public health. Leveraging artificial intelligence (AI) technologies such as remote sensing, Internet of Things (IoT), computer vision, and data-driven modeling has become a hotspot in livestock farming that could facilitate animal monitoring, disease detection, feed optimization, and health management. This review includes an assessment of these topics and research done in Iran so far, proposing future steps for the deployment of AI-powered technologies in farm applications. The Iranian livestock sector already seeing benefits from AI advancements and information technologies, however, most studies focused on model development without applications or deployment for the industry. Significant work is needed to address the limitations and challenges namely lack of data, economic feasibility, ethical concerns, infrastructure issues, and regulatory frameworks. Furthermore, reported AI-based methods and approaches have some inconsistencies in Iran that hinder validation. Looking forward, AI could create a new era in the livestock sector of Iran that not only copes with upcoming challenges but also boosts the circular economy making this country a pioneer in the region. However, tackling some potential limitations accompanying AI application in the Iranian livestock sector warrants the multi-disciplinary collaboration of veterinarians, computer scientists, animal nutritionists, agri-engineers, and governmental organizations.Annals of Animal Science350Tackling some potential limitations accompanying AI application in the Iranian livestock sector warrants the multi-disciplinary collaboration of veterinarians, computer scientists, animal nutritionists, agri-engineers, and governmental organizations.Annals of Animal Science["Navid Ghavipanje", "Mohammad Hassan Fathi Nasri", "E. Vargas-Bello-P\u00e9rez"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/e30832287f3cfaa59ac86a454c244ff7f1932937 9244d9a468ceec9b6bfe50f1137b9413b8d101a5Achieving significant breakthroughs in both the fundamental theories and technological applications of artificial intelligence is essential for fostering its long-term development. Under the guidance of Professor Qian Xuesen’s theory of technological science, exploring the internal mechanisms of knowledge evolution in artificial intelligence holds profound theoretical and practical significance for promoting sustainable technological advancement. This study draws on literature from the Web of Science (WOS) database and employs methods such as knowledge mapping, natural language processing, clustering analysis, and citation analysis to outline the knowledge structure of the field, clarify the trajectory of sustainable development, and trace the technological genealogy of VR/AR technologies.This study divides the knowledge structure within the field of technological science into “basic theoretical knowledge—applied basic knowledge—applied knowledge”, enriching Qian’s theory of technological science from within and providing strong intellectual support and technological pathways for sustainable technological development in practice. Artificial intelligence encompasses 10 distinct knowledge domains, among which machine learning and deep learning constitute the basic theoretical knowledge, data intelligence, computer vision, and swarm intelligence are the applied basic knowledge, and image processing and human-computer intelligence are the applied knowledge. The development of VR/AR technology has formed two main sustainable development paths: “machine learning—data intelligence—intelligent systems—human computer intelligence”, and “deep learning—computer vision—image processing”.Sustainability70This study divides the knowledge structure within the field of technological science into “basic theoretical knowledge—applied basic knowledge—applied knowledge”, enriching Qian’s theory of technological science from within and providing strong intellectual support and technological pathways for sustainable technological development in practice.Sustainability["Yuan Lin", "Chenxi Xu", "Kan Xu", "Shiliang Zhang", "Hui Liu", "Zhaoyun Zhang"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/9244d9a468ceec9b6bfe50f1137b9413b8d101a5 d5b25bc92d38273699f617f0a1d3bd7ed3c0c648This study aimed to explore students’ perspectives on integrating artificial intelligence (AI) into scientific collaboration, specifically on writing academic articles and creating scientific posters. The research employed open-ended interviews conducted among 61 civil and military students. Opinions were labelled, coded, and gathered into the following categories: positive impact on collaboration, challenges faced, and educational impact. Among the positives were improving efficiency, enhancing the quality of work, and generating new ideas. The challenges concerned experiencing technical difficulties with AI tools, inconsistency in AI outputs, and AI dependence, which may lead to behaviours on the verge of addiction. Regarding educational impact, students noticed that AI helps improve learning new skills, increases engagement in the task, and enhances critical thinking. As one researcher performed the thematic analyses, Cohen’s Kappa statistic was used to ensure intra-coder reliability. This study highlights the need for further research to optimize the use of AI in scientific collaboration while addressing ethical concerns related to students’ motivations for using AI tools, promoting responsible use, and researching students’ emotions, cognitive processes, and behaviours resulting from their interactions with AI tools. The research provides valuable insights for educators and policymakers to integrate AI effectively into academic practice.Education sciences680The need for further research to optimize the use of AI in scientific collaboration while addressing ethical concerns related to students’ motivations for using AI tools, promoting responsible use, and researching students’ emotions, cognitive processes, and behaviours resulting from their interactions with AI tools is highlighted.Education Sciences["M. Gawlik-Kobyli\u0144ska"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/d5b25bc92d38273699f617f0a1d3bd7ed3c0c648 33229968b47a13c39985b3ace219276fb55fde2fThe first quarter of the 21st century marked global engineering progress that surpassed even the wildest predictions of futurist researchers. The modern technological revolution has successfully rediscovered three supporting capital foundations, finding their constant revaluation and development through intellectual capital, industrial capital and financial capital. As a connecting link between them, we can categorically define аrtificial intelligence, with its genetic code – the Machine learning. Estimates for the net effect of the deployment of Artificial intelligence on global GDP are to add a new 15 trillion USD by 2030. This is growth that manifests itself in four directions: improved quality, increased labor productivity, implementation of new customized products and services, and saved time. On this basis, the research debates the effects of the application of artificial intelligence in business in its financial, economic and marketing aspects.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The research debates the effects of the application of artificial intelligence in business in its financial, economic and marketing aspects.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["Andrey Zahariev", "Dragomir Iliev", "D. Ilieva"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/33229968b47a13c39985b3ace219276fb55fde2f ccb24f25282cfc1dd5e63cb43f85647e84a4e6c4In the recent years, the use of artificial intelligence in the education domain has experienced a significant growth. The main reasons for this are the numerous innovative solutions, products and services, which are used not only in the educational processes, but also in the corresponding to them administrative activities. Based on this, the present publication aims to present the latest trends and the applications of the artificial intelligence in the education sector. This research has led to the identification of several key areas, in which the artificial intelligence has a significant contribution to the educational processes, including the adaptive assessment, the intelligent tutoring systems, the educational data mining, the personalized learning, the virtual reality, etc. This publication also presents some of the potential benefits, challenges and ethical considerations, which are associated with the use of the artificial intelligence technologies in the education.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika00The present publication aims to present the latest trends and the applications of the artificial intelligence in the education sector, including the adaptive assessment, the intelligent tutoring systems, the educational data mining, the personalized learning, the virtual reality, etc.Strategies for Policy in Science and Education-Strategii na Obrazovatelnata i Nauchnata Politika["P. Zahariev", "G. Hristov", "Ivan Beloev"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/ccb24f25282cfc1dd5e63cb43f85647e84a4e6c4 7012ffc956d6b58aeeed17711b816a6cc4136ee3Ethics and Information Technology360It is highlighted that Big Tech companies assert a high degree of control, but that they lack strong input legitimacy and have a questionable amount of output legitimacy, and it is argued that Big Tech companies should only be considered quasi-sovereigns over AI.Ethics Inf. Technol.["Huw Roberts"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/7012ffc956d6b58aeeed17711b816a6cc4136ee3 45c2b0f79d9f1aa96504e19974260c507eb8b9c2In the context of the rapid development of artificial intelligence technology, this article provides a systematic review of public relations research in the context of artificial intelligence to provide valuable references for future public relations research and practice. Collect literature data from CNKI, use systematic review methods to select literature that meets the standards, and sort and summarize it from four dimensions: application background, application scenarios, challenges faced, and response measures. Research has found that currently, applying artificial intelligence technology to public relations practice has become a basic consensus in academia; The paradigm of public relations research in the era of artificial intelligence has changed; In the short term, artificial intelligence technology will not completely replace public relations personnel; The measures for responding to public relations in the era of artificial intelligence can be summarized and divided into two levels: theoretical and practical. In the future, it is necessary to further explore and improve public relations theories related to artificial intelligence, construct new analytical frameworks and research methods to adapt to the changes brought about by technology.Communications in Humanities Research00A systematic review of public relations research in the context of artificial intelligence to provide valuable references for future public relations research and practice and to explore and improve public relations theories related to artificial intelligence.Communications in Humanities Research["Xinyu Zhao"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/45c2b0f79d9f1aa96504e19974260c507eb8b9c2 74ddcba3e31f7fc6cfe8548dc3f557a5955a1e96 Canada’s Drug Agency has developed an instrument to monitor and evaluate artificial intelligence tools for information retrieval so that, as these tools evolve, we can leverage them appropriately. We are sharing our instrument to help other evidence synthesis producers evaluate and make use of artificial intelligence tools for information retrieval. Canadian Journal of Health Technologies00Canadian Journal of Health Technologies["Nicole Mittmann"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/74ddcba3e31f7fc6cfe8548dc3f557a5955a1e96 cb3ae881b7548780eafb097b72d4816605403b56: The rapid advancement of artificial intelligence (AI) technology is transforming various industries. In this context, enhancing the employment competence of college graduates has become a critical issue. This paper explores effective strategies from six perspectives: curriculum optimization, vocational skills training, deepened cooperation between universities and enterprises, employment guidance and services, international perspective expansion, and lifelong learning awareness cultivation. Implementing these strategies can not only improve graduates' employability but also lay a solid foundation for their long-term career development.International Journal of Educational Innovation and Science70Effective strategies from six perspectives are explored: curriculum optimization, vocational skills training, deepened cooperation between universities and enterprises, employment guidance and services, international perspective expansion, and lifelong learning awareness cultivation.International Journal of Educational Innovation and Science["Zhihong Zheng", "Yifeng Zheng"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/cb3ae881b7548780eafb097b72d4816605403b56 d83e62201cd0a32fe09ffaf5cfdf8ac8be52634fArtificial intelligence has become a trending topic in the last decade and has permeated all spheres of life and profession and Architecture is no exception. The use of artificial intelligence in architecture is still in its infancy as more research is still being carried out in the application of artificial intelligence in architecture to solve problems ranging from intelligent material design to architectural plan solutions. This study aimed to examine students’ levels of awareness, access, and use of artificial intelligence in architectural design at selected tertiary institutions in Southeast Nigeria. The three tertiary institutions selected are; The Department of Architectural Technology, Akanu Ibiam Federal Polytechnic Unwana, The Department of Architectural Technology Abia State Polytechnic, Aba and The Department of Architecture, Abia State University, Uturu. The main objective is to carry out a survey to investigate the awareness, access, and utilization of artificial intelligence (AI) in architectural design among students in selected tertiary institutions in Southeast Nigeria. This study adopted a descriptive research design of the survey method and employed a three-sectioned questionnaire to elicit information from the respondents. The sample size included a multistage sample of 300 undergraduates across three tertiary institutions in Ebonyi State and Abia State.  The findings of the study were that the majority of the students are not aware of artificial intelligence and will use AI if they are aware of it and have access to it for their architectural design. Therefore, there is a need for integrating AI-related courses and workshops into the Architectural curriculum which can lead to better-prepared graduates who are proficient in using advanced design tools, potentially increasing their employability and innovation in the field.  This study concluded that students' ability to explore digital resources such as AI for Architectural design is dependent on their awareness and access to digital technologies. A lack of these will result in a lack of use and lack of skill to use them.African Journal of Humanities and Contemporary Education Research360There is a need for integrating AI-related courses and workshops into the Architectural curriculum which can lead to better-prepared graduates who are proficient in using advanced design tools, potentially increasing their employability and innovation in the field.African Journal of Humanities and Contemporary Education Research["Kalu Kalu Cheche", "Egwu Oyim Johnson", "Christopher U. Odom", "C. U. Eguzouwa"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/d83e62201cd0a32fe09ffaf5cfdf8ac8be52634f 5f416226d9e8f55a8235d40a14fd5da408829e24AI &amp; SOCIETY160Regression analyses showed a strong negative relationship between the AIAI and public support for both current and future AI applications within public policy, with aversion more pronounced towards potential future uses.AI &amp; SOCIETY["Will Livingston"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/5f416226d9e8f55a8235d40a14fd5da408829e24 6bcfc573392bb96029d7935504a5c0ce9135efa8The main objective of this research is to review the importance of IoT and Artificial Intelligence for Nanotechnology. Several industries are seeing notable breakthroughs due to the convergence of nanotechnology, artificial intelligence, and the Internet of Things. This succinct overview examines how IoT and AI are essential for improving the capabilities and uses of nanotechnology. Real-time monitoring, data gathering, and control at the nanoscale are made possible by IoT, improving the accuracy and efficiency of operations including industrial manufacturing, healthcare monitoring, and environmental sensing. The design, optimization, and predictive modeling of nanomaterials and systems are made easier by artificial intelligence (AI), which provides strong tools for evaluating the complicated data produced by nanoscale devices. The convergence of IoT, AI, and nanotechnology facilitates the creation of intelligent systems that possess the ability to monitor themselves and make decisions on their own. IoT and AI amplify the potential of nanotechnology by enabling real-time data collection, advanced data analytics, and autonomous decision-making, with vast applications across industries from healthcare to energy. Even while this integration seems promising, there are still issues to be resolved, such as privacy issues, data security, and technical difficulties in creating dependable nanoscale Internet of Things devices. It is anticipated that as research advances, the confluence of these technologies will transform industries including smart manufacturing, environmental monitoring, and medicine, making this a critical area for future innovation.Control Systems and Optimization Letters00IoT and AI amplify the potential of nanotechnology by enabling real-time data collection, advanced data analytics, and autonomous decision-making, with vast applications across industries from healthcare to energy.Control Systems and Optimization Letters["Md Monirul Islam", "Ikram Hossain", "Md. Hasnat Hanjala Martin"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/6bcfc573392bb96029d7935504a5c0ce9135efa8 8070e06a45f1872bd08489adb8cd866a0f023921  BACKGROUND Artificial intelligence (AI) is defined as acquisition of intelligence by computers or machines to perform complex tasks that generally require human intelligence. Nowadays usage of AI in various fields is appreciated because of its time effectiveness and less laborious.In dentistry, AI has been trending, specifically in diagnostic imaging and early detection of diseases,benefiting both dental graduates and practitioners. AIM  The main aim is to assess the awareness of Artificial intelligence as an effective tool among dental trainees and dental practitioners.  MATERIALS AND METHOD An online cross-sectional survey was conducted among dental trainees and dental practitioners in various institutions. A Google questionnaire form was developed and circulated to assess participant’s awareness about Artificial intelligence in dentistry. Data were collected in Microsoft excel and statistical analysis were performed.  RESULTS More than 60% of the participants showed confidence about their familiarity towards AI in dentistry (p<0.01197). Among those, 60.5% answered that they are well aware of ChatGPT and 28% of them to Microsoft Bing (p<0.04395). 53% answered that machine learning is an important form of AI (p<0.00216). Over the advantages of AI, 43% believed AI can improve diagnostics, access to disease screening, cost effectiveness, reduced treatment time (p<0.0001) and 53% agreed that AI can be useful in day-to-day dental practices (p<0.0001).  CONCLUSION The present study shows that awareness of AI among the dental fraternity is satisfactory. The field of AI is emerging rapidly and various other new applications in AI are being utilised. AI could act as a valuable tool in supporting clinicians delivering effective dental care and supplementing education for dental trainees. To enhance the future of AI in dentistry, the present curriculum needs to be approachable enough and various hands-on-training are essential.  KEYWORDS:artificial intelligence, chatbots, dental caries, radiographic diagnosis, teledentistry.      JOURNAL OF CLINICAL PROSTHODONTICS AND IMPLANTOLOGY200The present study shows that awareness of AI among the dental fraternity is satisfactory and could act as a valuable tool in supporting clinicians delivering effective dental care and supplementing education for dental trainees.Journal of Clinical Prosthodontics and Implantology["Naveen Gokul R", "M. K.", "Sheela Kumari K", "Priya Mohite V"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/8070e06a45f1872bd08489adb8cd866a0f023921 876b680cce5cedc81f5457d51c1a1075820e2178In today's era of information explosion, with the rapid development of big data technology and artificial intelligence, the form of news dissemination and the assessment mode of dissemination effect are undergoing profound changes. The traditional method of evaluating the effect of news dissemination relies on limited indicators and a single data source, which is difficult to accurately reflect the multidimensional response of the audience and its dissemination path in modern news dissemination. In contrast, this paper proposes a new news communication effect assessment model by combining big data analysis and artificial intelligence algorithms, which can effectively process massive data, deeply explore the audience's behavioral characteristics, emotional changes and information dissemination path, and then provide a more accurate assessment of the communication effect. Although AI reduces the demand for labor in conventional occupations, it increases the demand for labor in non-conventional occupations, which mainly comes from the increase in the demand for non-conventional cognitive occupations such as management and technology. The job creation effect of AI is not only reflected in the connotation, i.e., the increase in the number of demand for the labor force of the already existing non-conventional occupations. The results of the study show that the deep combination of big data and artificial intelligence not only significantly improves the depth and breadth of the analysis of news communication effects, but also provides a more intelligent and automated solution for the future evaluation of communication effects.2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)110The results of the study show that the deep combination of big data and artificial intelligence not only significantly improves the depth and breadth of the analysis of news communication effects, but also provides a more intelligent and automated solution for the future evaluation of communication effects.2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)["Chenjun Liu", "Yan Xu"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/876b680cce5cedc81f5457d51c1a1075820e2178 78345a7d8f1e9c46a55861d65efe9e1d4e02b301This study explores the applications and implications of blockchain technology in the Australian higher education system, focusing on its integration with artificial intelligence (AI). By addressing critical challenges in credential verification, administrative efficiency, and academic integrity, this integration aims to enhance the global competitiveness of Australian higher education institutions. A comprehensive review of 25 recent research papers quantifies the benefits, challenges, and prospects of blockchain adoption in educational settings. Our findings reveal that 52% of the reviewed papers focus on systematic reviews, 28% focus on application-based studies, and 20% combine both approaches. The keyword analysis identified 287 total words, with “blockchain” and “education” as the most prominent themes. This study highlights blockchain’s potential to improve credential management, academic integrity, administrative efficiency, and funding mechanisms in education. However, challenges such as technical implementation (24%), regulatory compliance (32%), environmental concerns (28%), and data security risks (40%) must be addressed to achieve widespread adoption. This study also discusses critical prerequisites for successful blockchain integration, including infrastructure development, staff training, regulatory harmonisation, and the incorporation of AI for personalised learning. Our research concludes that blockchain, when strategically implemented and combined with AI, has the potential to transform the Australian higher education system, significantly enhancing its integrity, efficiency, and global competitiveness.Future Internet560The research concludes that blockchain, when strategically implemented and combined with AI, has the potential to transform the Australian higher education system, significantly enhancing its integrity, efficiency, and global competitiveness.Future Internet["Mahmoud Elkhodr", "Ketmanto Wangsa", "E. Gide", "Shakir Karim"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/78345a7d8f1e9c46a55861d65efe9e1d4e02b301 158f2f10be48aa612f83ff4c6cdc6c521390d4b7Rapid urbanization and low residential resources in cities are serious issues that are making city life difficult day by day. The development of smart cities is becoming a need of the present era due to the swift increase in population and environmental issues globally. Smart cities are being introduced in different regions of the world with the incorporation of latest technologies. The incorporation of Artificial Intelligence (AI) is one of the tools that can be used in smart building and cities. AI technologies are transforming public safety, trash management, healthcare, traffic control, and resource management, making cities more sustainable, effective, and responsive to their citizens' demands. There are still lack of awareness in some areas of the world on the efficacy of smart building and construction that is impacting negatively on the economy and growth of those countries.; such as Pakistan is one of those countries that is facing serious challenges due to increased population, urban migration, and poor management of natural resources. The need of planning smart strategies for smart building is very crucial to manage population and housing issues. Smart buildings and cities provide unique and convenient facilities to its residents so that they can contribute positively towards the economy of country. This paper focuses at important areas where AI has the most effects in order to investigate how integrating AI improves quality of life in smart cities. The aim is to highlight artificial intelligence's contribution to improving urban operations, streamlining resource management, and advancing sustainability. Additionally, potential concerns about privacy, data security, and fair access will be discussed. In order to show how AI-driven innovations like predictive analytics, machine learning, and IoT-enabled systems are changing the urban environment, the study synthesizes existing research and real-world examples. The evaluation also covers how AI promotes smart government, tailored urban services, and citizen involvement. The conclusion emphasizes that although AI has great potential to improve the quality of life in smart cities, implementation of the technology must be done in a balanced way to prioritize inclusive policies and ethical concerns for the general welfare of residents. American Journal of Artificial Intelligence190Although AI has great potential to improve the quality of life in smart cities, implementation of the technology must be done in a balanced way to prioritize inclusive policies and ethical concerns for the general welfare of residents.American Journal of Artificial Intelligence["Aman Ullah", "S. Quddusi", "Iftikhar Haider"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/158f2f10be48aa612f83ff4c6cdc6c521390d4b7 e2a8fd36fcbae49ec1b9834f26135e396cb49ed3Nature Network Boston82Nature medicine["Yilin Ning", "Xiaoxuan Liu", "Gary S. Collins", "K. Moons", "Melissa McCradden", "D. Ting", "J. Ong", "Benjamin Alan Goldstein", "Siegfried K. Wagner", "P. Keane", "E. Topol", "Nan Liu"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/e2a8fd36fcbae49ec1b9834f26135e396cb49ed3 90dc2698ed6f32a0353b0ae1e2b545888be5a5ebThe article considers the possibility of replacing humans with artifi cial intelligence (AI) in industry. An overview of the current capabilities and achievements of AI in industry is presented. Examples of the successful use of AI and its advantages are given. The material describes positions that cannot be replaced by AI. It is emphasized that creative professions, managerial and strategic positions require creative thinking, emotional intelligence and the ability to make complex decisions, which is not yet available to AI. Although AI has signifi cant potential to improve production processes, a complete replacement of humans remains unlikely in the near future due to existing limitations and challenges. The article presents an analysis of the current capabilities and prospects of AI in industry, as well as recommendations for the state and business on the effective implementation of these technologies.Upravlenie kachestvom (Quality management)00An analysis of the current capabilities and prospects of AI in industry, as well as recommendations for the state and business on the effective implementation of these technologies are presented.Upravlenie kachestvom (Quality management)["A.S. Logoshenko"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/90dc2698ed6f32a0353b0ae1e2b545888be5a5eb d0b3d72c37e71af063575545c4046d43a432c679With the continuous improvement of industrial processing automation, artificial fruit picking can no longer meet the requirements of today's processing efficiency. Therefore, automatic fruit picking robots are increasingly integrated into mechanized production, making the study of robot motion space very important. At present, there is a lot of research in the academic community. The traditional method for designing robot motion trajectories is usually optimized in terms of motion time and travel speed, rather than in terms of motion space. In order to further optimize the braking force distribution model of the electromechanical braking system, this paper takes the melon and fruit picking machinery equipment model as an example, fully considers the factors of idle frequency and full load, and conducts performance optimization analysis of the electromechanical braking system based on the PSO (Particle Swarm Optimization) algorithm. This article takes the ideal braking force distribution curve as the starting point, determines the objective function and constraint function, and selects the PSO algorithm to search for constraint inequality problems. Finally, the experimental results showed that the damage rate of the PSO algorithm was below 0.5% for different fruit picking quantities, while the ACO (Ant Colony Optimization) algorithm had the smallest damage rate of 0.9% and the highest damage rate of 2.3%.2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)200This paper takes the melon and fruit picking machinery equipment model as an example, fully considers the factors of idle frequency and full load, and conducts performance optimization analysis of the electromechanical braking system based on the PSO (Particle Swarm Optimization) algorithm.2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI)["Guifu Jia", "Yanhui Jing"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/d0b3d72c37e71af063575545c4046d43a432c679 1d83e653b04258246ddcb8d859ca9ce57ff775dbNanotechnology Perceptions00Nanotechnology Perceptions[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/1d83e653b04258246ddcb8d859ca9ce57ff775db b46e9af89267d021b8e710de60a581ffb25e0cc9Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/b46e9af89267d021b8e710de60a581ffb25e0cc9 3bb80f7223a10f65fd63ca6cf98715466191415fNanotechnology Perceptions00Nanotechnology Perceptions[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/3bb80f7223a10f65fd63ca6cf98715466191415f 25663b11dbc4eeb183e4b954ac4798dc1d1fbf6fNanotechnology Perceptions00Nanotechnology Perceptions[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/25663b11dbc4eeb183e4b954ac4798dc1d1fbf6f d782847b0a8a40d8207666f753f43369d1a2bc59Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/d782847b0a8a40d8207666f753f43369d1a2bc59 18501a74011e5cd66b0aaf0f432c34651b3a6d02Nanotechnology Perceptions00Nanotechnology Perceptions[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/18501a74011e5cd66b0aaf0f432c34651b3a6d02 d8ca9ef65d79cab5590590a8becd1c72d64a4f92Public Organization Review00Public Organization Review[]2024-10-18T00:00:00https://www.semanticscholar.org/paper/d8ca9ef65d79cab5590590a8becd1c72d64a4f92 3dbadaf34fd7c1ea5ffac4ea2efa685981207440Proceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices200Proceedings of the 2024 International Conference on Smart Healthcare and Wearable Intelligent Devices["Wenjia Lu", "Shanshan Li", "Xin Jin"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/3dbadaf34fd7c1ea5ffac4ea2efa685981207440 2aff0d48ba44ea4d6e0886fd5ed2a17993064efeProceeding of the 2024 5th International Conference on Computer Science and Management Technology80Proceeding of the 2024 5th International Conference on Computer Science and Management Technology["Zhen Tian"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/2aff0d48ba44ea4d6e0886fd5ed2a17993064efe 5a50c044253d8bed5f75b3a27c9e3f263477eca6Anesthesia and Analgesia260Anesthesia and analgesia["Thomas Heidegger", "Amina Ghulam", "Markus Bischoff", "Markus M Luedi"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/5a50c044253d8bed5f75b3a27c9e3f263477eca6 8293c7591c6f20797eee5b3127210580eed40059Having shed light on the significance of thinking and creativity for survival and prosperity in the present world context, I give a brief introduction to my revolutionary teaching approach and its 6 strategic focal areas, one of which is promoting our people’ thinking and creativity savvy. More importantly, I will bring to the forefront my approach theoretical foundations that I presented after criticizing and rejecting current theories in the field of education. I will define and distinguish the types of high-level thinking, which include creative thinking, emphasized by me in my innovative approach. Also, after enumerating the characteristics of creative people, I will go on to define and explain the role of professors who intend to develop higher levels of thinking of our people in their classes, by employing my approach and the culture accentuated byvme. I have explained some of my effective techniques for improving thinking skills, along with some concrete examples from my personal experiences. In the end - before concluding - I have given suggestions to teachers, professors, researchers and educational policy makers to improve the quality of education for improving the conditions of humanity and guiding them to our common utopia. Many theses and bookticles have been written and published about my approach in different parts of the world which could be noticed in the references section of my first article in the references of this article.World Journal of Education and Humanities00The types of high-level thinking are defined, which include creative thinking, emphasized by me in my innovative approach, and the role of professors who intend to develop higher levels of thinking of the authors' people in their classes are explained, by employing my approach and the culture accentuated byvme.World Journal of Education and Humanities["Seyed Mohammad Hassan Hosseini"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/8293c7591c6f20797eee5b3127210580eed40059 c8c71acb6a2144235dbbeaa78bb4ba30711c9091Proceeding of the 2024 5th International Conference on Computer Science and Management Technology80Proceeding of the 2024 5th International Conference on Computer Science and Management Technology["Guoxuan Ma"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/c8c71acb6a2144235dbbeaa78bb4ba30711c9091 3ffe3576d662044a98d1560acc1f2fc6109a69d6Proceeding of the 2024 5th International Conference on Computer Science and Management Technology60Proceeding of the 2024 5th International Conference on Computer Science and Management Technology["Jinzhan Sun", "Xian Liu"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/3ffe3576d662044a98d1560acc1f2fc6109a69d6 631890e9883b42327628d1b0a6af224adb42d884Proceeding of the 2024 5th International Conference on Computer Science and Management Technology30Proceeding of the 2024 5th International Conference on Computer Science and Management Technology["Miao Dong", "Yuliang Wu", "Peipei Zhang", "Afzal Mubeen"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/631890e9883b42327628d1b0a6af224adb42d884 37d2820355a9edec49b17af123bf17b17635cbceProceeding of the 2024 5th International Conference on Computer Science and Management Technology10Proceeding of the 2024 5th International Conference on Computer Science and Management Technology["Ling Huang"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/37d2820355a9edec49b17af123bf17b17635cbce 8b83665a01f486e6bdb586f0ac22e667f244a1dcInternational Journal of Law and Psychiatry460It is argued that significant risks are being taken with using GenAI in mental health that should be assessed urgently and guidelines for using generative artificial intelligence in mental health care must be established promptly.International journal of law and psychiatry["Barry Solaiman"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/8b83665a01f486e6bdb586f0ac22e667f244a1dc 722a5e56f075e9b25e86b7124b1edbc1e7999538Proceeding of the 2024 5th International Conference on Computer Science and Management Technology120Proceeding of the 2024 5th International Conference on Computer Science and Management Technology["Yanqi Si", "Songling Wu", "Zaihui Chen"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/722a5e56f075e9b25e86b7124b1edbc1e7999538 2cc69e56049e0c059a3d3723cefbdaaac1cca572Proceeding of the 2024 5th International Conference on Computer Science and Management Technology60Proceeding of the 2024 5th International Conference on Computer Science and Management Technology["Jing Liang", "Chang Cao", "Zhiguo Wang", "Yumeng Xie", "Hongjie Li", "Xingyu Zhu"]2024-10-18T00:00:00https://www.semanticscholar.org/paper/2cc69e56049e0c059a3d3723cefbdaaac1cca572 f6241fe237f83adf70e0e1ffe89ac6c7c7d17d12 This Viewpoint summarizes recent updates to the Declaration of Helsinki, discusses its relevance in the context of artificial intelligence (AI) in health research, and highlights issues that could affect its future implementation as the use of AI in research increases. Journal of the American Medical Association (JAMA)75JAMA["James A Shaw"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/f6241fe237f83adf70e0e1ffe89ac6c7c7d17d12 111e9f8ba164c79f804e0afbca073ae5ad471c1dAim: The purpose of this study was to determine the quality and accessibility of the outputs from a healthcare-specific artificial intelligence (AI) platform for common questions during the perioperative period for a common plastic surgery procedure. Methods: Doximity GPT (Doximity, San Francisco, CA) and ChatGPT 3.5 (OpenAI, San Francisco, CA) were utilized to search 20 common perioperative patient inquiries regarding breast augmentation. The structure, content, and readability of responses were compared using t -tests and chi-square tests, with P < 0.05 used as the cutoff for significance. Results: Out of 80 total AI-generated outputs, ChatGPT responses were significantly longer (331 vs. 218 words, P < 0.001). Doximity GPT outputs were structured as a letter from a medical provider to the patient, whereas ChatGPT outputs were a bulleted list. Doximity GPT outputs were significantly more readable by four validated scales: Flesch Kincaid Reading Ease (42.6 vs. 29.9, P < 0.001) and Flesch Kincaid Grade Level (11.4 vs. 14.1 grade, P < 0.001), Coleman-Liau Index (14.9 vs. 17 grade, P < 0.001), and Automated Readability Index (11.3 vs. 14.8 grade, P < 0.001). Regarding content, there was no difference between the two platforms regarding the appropriateness of the topic (99% overall). Medical advice from all outputs was deemed reasonable. Conclusion: Doximity’s AI platform produces reasonable, accurate information in response to common patient queries. With continued reinforcement learning with human feedback (RLHF), Doximity GPT has the potential to be a useful tool to plastic surgeons and can assist with a range of tasks, such as providing basic information on procedures and writing appeal letters to insurance providers.Artificial Intelligence Surgery210Doximity’s AI platform produces reasonable, accurate information in response to common patient queries with the potential to be a useful tool to plastic surgeons and can assist with a range of tasks, such as providing basic information on procedures and writing appeal letters to insurance providers.Artificial Intelligence Surgery["C. Boyd", "Lucas R. Perez Rivera", "Kshipra Hemal", "Thomas J. Sorenson", "Chris Amro", "Mihye Choi", "N. Karp"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/111e9f8ba164c79f804e0afbca073ae5ad471c1d 88cda9f092774a87a5ce07de3dcbb2fa72ecd9d1This study examined whether Microsoft Azure and human participants differed significantly in their judgment of controversial image content (e.g., adult and violent content). Significant differences were observed between the Azure- and participant-awarded scores on all four indexes—Adult, Racy, Gore, and Event Description. The artificial intelligence made stricter judgments than did the participants. The introduction of artificial intelligence may facilitate the efficient judgment of images by website administrators. However, the standards upheld by artificial intelligence appear to be unique and stricter than those upheld by people. In the short term, the boundary between art and pornography may remain difficult to identify. Assessing the difference in judgments between artificial and human intelligence should be a topic of continual concern.Advances in Social Sciences Research Journal00Significant differences were observed between the Azure- and participant-awarded scores on all four indexes—Adult, Racy, Gore, and Event Description; the artificial intelligence made stricter judgments than did the participants.Advances in Social Sciences Research Journal["Rain Chen", "Hsiu-Ching Lu"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/88cda9f092774a87a5ce07de3dcbb2fa72ecd9d1 3270f7fdc77cf377232479a75073da764e2fb57bWith developments in artificial intelligence (AI) widely framed as security concern in both military and civilian realms, governments have turned their attention to regulating and governing AI. In a study of United States (US), Chinese, and European Union (EU) AI documents, we go beyond instrumental understandings of AI as a technological capability, which serves states’ self-interests and the maintenance of their (supra)national security. Our specific interest lies in how AI policies tap into both problem-solving approaches and affective registers to achieve both physical and ontological securities. We find that in governmental visions, AI is perceived as a capability that enhances societal and geopolitical interests while its risks are framed as manageable. This echoes strands within human–computer interaction that draw on human-centered perceptions of technology and assumptions about human–AI relationships of trust. Despite different cultural and institutional settings, the visions of future AI development are shaped by this (shared) understanding of human–AI interaction, offering common ground in the navigation of innovation policies.Cooperation and Conflict1250A study of United States, Chinese, and European Union AI documents goes beyond instrumental understandings of AI as a technological capability to find that in governmental visions, AI is perceived as a capability that enhances societal and geopolitical interests while its risks are framed as manageable.Cooperation and Conflict["Stefka Schmid", "Bao-Chau Pham", "Anna-Katharina Ferl"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/3270f7fdc77cf377232479a75073da764e2fb57b f72f9d0d32f2cc31b3616a22dc1d1609dc0defeaThe rapid advancement of Artificial Intelligence (AI) has transformed various industries, including financial auditing, by improving efficiency, accuracy, and fraud detection. This study investigates the extent of AI adoption in financial audits in Indonesia, with a focus on both Big 4 audit firms and smaller, local firms. Through a literature review and interviews with auditors from eight firms, the research explores the current state of AI utilization and the barriers to its implementation. The results indicate that while Big 4 firms are in the developmental phase of integrating AI into their auditing processes, smaller firms face significant obstacles, such as financial limitations, lack of expertise, and regulatory uncertainties, which hinder AI adoption. Despite the challenges, auditors from larger firms anticipate that AI will play a crucial role in future audits. The study concludes that AI adoption in Indonesian financial audits is uneven, and further efforts are required to support smaller firms through accessible AI tools, clearer regulations, and targeted training. These measures are essential for closing the gap in audit quality between large and small firms, ensuring broader AI implementation in the auditing sector.Jurnal Indonesia Sosial Teknologi200The study concludes that AI adoption in Indonesian financial audits is uneven, and further efforts are required to support smaller firms through accessible AI tools, clearer regulations, and targeted training, which are essential for closing the gap in audit quality between large and small firms.Jurnal Indonesia Sosial Teknologi["Nurul Fachriyah", "Octadila Laily Anggraeni"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/f72f9d0d32f2cc31b3616a22dc1d1609dc0defea 502af64dcdde0d268a7de841e8b15413844cf25cThe rise of artificial intelligence assistants is reshaping higher education, calling for innovative changes in the leadership cultivation paradigm. This paper focuses on the issue of leadership cultivation in higher education in the era of AI assistants and creatively proposes the concept of a "personal think tank," which refers to a cross-disciplinary team of AI assistants available to everyone. This concept disrupts the traditional human-machine relationship, signifying a shift in leadership from personal capability to human-machine collaborative capability. This paper explores how AI assistants are driving a fundamental shift in the educational paradigm, transitioning from traditional knowledge transmission to the cultivation of leadership and innovative capabilities. Through a comprehensive review of literature and theoretical analysis, this study highlights the transformative trends and pathways in leadership cultivation prompted by AI assistants. The integration of AI assistants in higher education necessitates a student-centered approach, interdisciplinary integration, and an industry-academia-research education mechanism. These changes are crucial for fostering innovative leadership and preparing students for the AI era. The study underscores the urgency for universities to adapt their educational philosophies, update teaching content, and innovate methods to remain competitive and fulfill their role in national talent development. By leveraging AI technology to optimize management processes and enhance organizational efficiency, higher education can evolve into an empowering, platform-based, and ecosystem-oriented paradigm. This transformation is essential for higher education to thrive and contribute to the dual goals of building a strong nation in higher education and talent development.International Journal of Academic Research in Business and Social Sciences120This paper focuses on the issue of leadership cultivation in higher education in the era of AI assistants and creatively proposes the concept of a "personal think tank," which refers to a cross-disciplinary team of AI assistants available to everyone.International Journal of Academic Research in Business and Social Sciences["Li Sun", "Tao Su"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/502af64dcdde0d268a7de841e8b15413844cf25c 4045e118b36ea1d8a077db62c8f2f16d7e2d4648International Journal of Dermatology51International journal of dermatology["Mohamad Goldust", "J. Grant-Kels"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/4045e118b36ea1d8a077db62c8f2f16d7e2d4648 3091ccadc26af57ca913504c1e6df4ed28826654European Journal of Medical Research370The predictive stability models of IA based on three artificial intelligence methods shows good clinical application, and age, WBC and UA played an important role in predicting the IA stability, and were potentially important predictors.European Journal of Medical Research["Junmin Tao", "Wei Wei", "Meiying Song", "Mengdie Hu", "Heng Zhao", "Shen Li", "Hui Shi", "Luzhu Jia", "Chun Zhang", "Xinyue Dong", "Xin Chen"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/3091ccadc26af57ca913504c1e6df4ed28826654 84d96897ad688b687640dcaeef955cb2f44991a3Education and Information Technologies : Official Journal of the IFIP technical committee on Education220Education and Information Technologies["Jayaron Jose"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/84d96897ad688b687640dcaeef955cb2f44991a3 7d1b3d4b86213d13ce370fffc5f4d2134b6e50b2npj Digit. Medicine00NPJ Digital Medicine["Y. Mekki", "O. Ahmed", "Dylan Powell", "Amy Price", "H. P. Dijkstra"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/7d1b3d4b86213d13ce370fffc5f4d2134b6e50b2 06ee65acdf2058b8744a345d6d163dbcbfc65f08In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.American Journal of Health-System Pharmacy00In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance, but these manuscripts are not the final version of record and will be replaced with the final article at a later time.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists["Steven Smoke"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/06ee65acdf2058b8744a345d6d163dbcbfc65f08 ff2279127ec615f3db69dca36eca427faab6465cThe process of =making an Artificial intelligent system to replicating human emotional understanding and adapt its responses contextually by tailoring its responses based on individual cognitive and emotional states is called Artificial Personalized Intelligence (API). In this paper, we present a finetuned emotionally adaptive AI pipeline capable of generating personalized, human-like responses. Using a custom dataset based on six universal emotions—Sadness, Happiness, Fear, Anger, Surprise, and Disgust—collected from interviews with 30 participants, we have explored two approaches: fine-tuning a LLaMA-3 8B model with the Low-Rank Adapter (LoRA) technique and employing a Retrieval-Augmented Generation (RAG) agent-based framework over the same LLaMA-3 8B model. Emotion classification, prompt engineering, and model fine-tuning were used over the dataset to capture emotional and personalized subtleties in the responses based on different individuals. We conducted a comprehensive analysis, evaluating performance across candidates, emotions, and overall performance, using metrics such as Mean Squared Error (MSE) and Pearson Correlation Coefficient to measure the difference among the outputs from both pipelines and actual human emotional responses. Our results show that prompt engineering combined with LoRA-based fine-tuning significantly enhances the ability to engage in emotionally intelligent and personalized conversations, whereas the RAG-based approach underperformed due to the pretrained LLAMA-3 model’s restrictive neutral and adversarial training. This highlights that fine-tuned LLMs are effective in replicating human emotions for personalized intelligence.2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)80Results show that prompt engineering combined with LoRA-based fine-tuning significantly enhances the ability to engage in emotionally intelligent and personalized conversations, whereas the RAG-based approach underperformed due to the pretrained LLAMA-3 model’s restrictive neutral and adversarial training, highlighting that fine-tuned LLMs are effective in replicating human emotions for personalized intelligence.2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)["Sandeep Varma", "S. Shivam", "Sarun Natarajan", "Biswarup Ray", "Bagesh Kumar", "Om Dabral"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/ff2279127ec615f3db69dca36eca427faab6465c cdec1d467562e24a0865cdee99fb73532a56ce70Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.Buildings1250A Systematic Literature Review following the PRISMA guidelines is conducted to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications, finding that the data readiness is at an intermediate level.Buildings["Sang Du", "Lei Hou", "Guomin Zhang", "Y. Tan", "Pengjuan Mao"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/cdec1d467562e24a0865cdee99fb73532a56ce70 33e4cbf0c80294b1216337e70f818c7ad21e1e08The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an integration of generative and deep learning techniques, excel in creating new data beyond analyzing existing ones, revolutionizing sectors from production and manufacturing to finance. By automating design, optimization, and innovation cycles, Generative AI is reshaping core industrial processes. In the financial sector, it is transforming risk assessment, trading strategies, and forecasting, demonstrating its profound impact. This paper explores the sweeping changes driven by deep learning models like Large Language Models (LLMs), highlighting their potential to foster innovative business models, disruptive technologies, and novel economic landscapes. As we stand at the threshold of an AI-driven economic era, Generative AI is emerging as a pivotal force, driving innovation, disruption, and economic evolution on a global scale.arXiv.org770The sweeping changes driven by deep learning models like Large Language Models (LLMs) are explored, highlighting their potential to foster innovative business models, disruptive technologies, and novel economic landscapes.ArXiv["Subramanyam Sahoo", "Kamlesh Dutta"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/33e4cbf0c80294b1216337e70f818c7ad21e1e08 5827ca278a37350340dca9e88f0c1b0ca3f1e69dA massive number of connected devices in 5G/B5G leads to an ultra-dense network (UDN), causing several han- dover (HO) management issues. One of the critical functions in wireless network is load balancing which is distribute the traffic throughput equally across all Base Stations (BS). We will shed light on the Open Radio Access Network (O-RAN) fundamentals and architecture. O-RAN is a new era of mobile networks that uses open and interoperable Radio Access Network (RAN) nodes. The main objectives of the concurrent study are to engage artificial intelligence (AI) as an innovative solution for cell throughput forecast to detect load situation in advance to prevent high overload levels that can increase HO failure during the handover process. The paper studies the prediction of cell throughput using two AI models: i) a supervised deep learning (DL) model, which is Long Short-Term Memory (LSTM), and ii) a supervised machine learning (ML) model called XGBoost. The paper shows the difference in evaluation using Mean Squared Error (MSE) and training time by implementing state-of-the-art AI models as well as it compares the training time for both AI models. The study gives the first step to enhance handover management solutions.Novel Intelligent and Leading Emerging Sciences Conference00The main objectives of the concurrent study are to engage artificial intelligence (AI) as an innovative solution for cell throughput forecast to detect load situation in advance to prevent high overload levels that can increase HO failure during the handover process.2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES)["Ali W. Nassar", "Hesham H. Aly", "Hesham M. Elbadawy"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/5827ca278a37350340dca9e88f0c1b0ca3f1e69d 1a04dbb5dcc55c47ad6033c9362ef6aef517d5b3Growth in the importance of artificial intelligence is coupled with AI-specialized chips and high-performance computing architectures, which pose challenges in testing and optimizing complex systems. The paper reviews the most recent architectural advancements in AI accelerators, testing techniques, and HPC systems. It will particularly point out research around AI processors in the design of GPU, ASIC, and FPGA and neuromorphic, design-for-test, built-in self-test, and online test techniques, trending HPC architectures, and considerations over performance, efficiency, and reliability. A proposed research direction is to leverage heterogeneous subsystem integration on-chip to harmonize the strengths of different computing paradigms. The paper focuses on new architectural designs for AI chips, with a special focus on the progress of neural network accelerators, memory hierarchies, and interconnect technologies. Such new innovations are going to improve the implementation of complex AI algorithms, with the effect of increased efficiency and decreased delay. The manuscript also discusses the integration of energy-efficient computing and heterogeneous integration as a remedy to the power-related problems emerging from the deployment of AI chips. The paper is focused on the progress of testing methodologies for AI chips. AI algorithms are complex and the application itself is very important; hence, a good testing framework is also a necessity. The paper discusses the use of fault injection, emulation, and simulation techniques for testing and verifying the AI-chip architectures to guarantee their reliability in practice. The paper conclusion will also hinge on how the AI chips will be integrated into the HPC systems. AI is going to increasingly intersect with the conventional high-performance computing tasks, thus placing great importance on the need for such integration.2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)150The paper discusses the use of fault injection, emulation, and simulation techniques for testing and verifying the AI-chip architectures to guarantee their reliability in practice and discusses the integration of energy-efficient computing and heterogeneous integration as a remedy to the power-related problems emerging from the deployment of AI chips.2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)["Ruthvek Kannan", "Monali Gulhane", "Kashish Mirza", "Sudhanshu Maurya", "Sandeep Kumar", "Nitin Rakesh"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/1a04dbb5dcc55c47ad6033c9362ef6aef517d5b3 fe188246d46ce67f00c8d314f2014cae180c976aThe rise of Generative Artificial Intelligence (G-AI) has transformed the creative arts landscape by producing novel artwork, whereas in the same time raising ethical concerns. While previous studies have addressed these concerns from technical and societal viewpoints, there is a lack of discussion from an HCI perspective, especially considering the community's perception and the visual artists as human factors. Our study investigates G-AI's impact on visual artists and their relationship with GAI to inform HCI research. We conducted semi-structured interviews with 20 novice visual artists from an art college in the university with G-AI courses and practices. Our findings reveal (1) the mis-conception and the evolving adoption of visual artists, (2) the miscellaneous opinions of the society on visual artists' creative work, and (3) the co-existence of confrontation and collaboration between visual artists and G-AI. We explore future HCI research opportunities to address these issues.arXiv.org1170G-AI's impact on visual artists and their relationship with GAI are investigated and the miscellaneous opinions of the society on visual artists' creative work are revealed to address future HCI research opportunities.ArXiv["Shuning Zhang", "Shixuan Li"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/fe188246d46ce67f00c8d314f2014cae180c976a 210603864a12d748967d1f2d5de27b70e4b20a8eAmong the rapidly emerging Artificial Intelligence (AI) technologies, Generative AI (GenAI) models have become powerful tools reshaping the technology landscape. The term GenAI refers to a shift in the usage of AI technology from pattern recognition—identifying hidden patterns—to creating free-form text, images, and videos. This paper intends to provide a comprehensive review of GenAI applications in various human-centric applications, technology and innovation, creative industries, finance, and accounting. However, the proliferation of GenAI introduces risks and challenges, including ethics, privacy, and security. By elucidating GenAI’s advantages and disadvantages, envisioning future directions, and proposing measures, this study contributes to the ongoing dialogue on harnessing GenAI’s capabilities to mitigate risks. Moreover, this paper delves into the multifaceted role and challenges faced by these GenAI technologies in various domains. By balancing innovation with ethical considerations, we can steer towards a future where GenAI enriches lives, stimulates creativity, and empowers individuals and communities worldwide.2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)180A comprehensive review of GenAI applications in various human-centric applications, technology and innovation, creative industries, finance, and accounting is provided, delves into the multifaceted role and challenges faced by these GenAI technologies in various domains.2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)["C. Nidhisree", "Ananya Paul", "Anaswara Venunadh", "R. S. Bhowmick"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/210603864a12d748967d1f2d5de27b70e4b20a8e 027b6b788b2881c350dbe6b7d6e8b7328d1b09beAI &amp; SOCIETY352AI &amp; SOCIETY["Jason Edward Lewis", "H\u0113mi Whaanga", "Ceyda Yolg\u00f6rmez"]2024-10-19T00:00:00https://www.semanticscholar.org/paper/027b6b788b2881c350dbe6b7d6e8b7328d1b09be e722426f99ee33d064c8547b48f261d7e66930bbTill Bödeker presents his final project STEP ON NO PETS, which belongs to the wk-section of Artificial Intelligence and Art, with which he completed his studies at the Düsseldorf Art Academy as Meisterschüler (lit. master student) under Prof. Rita McBride in July 2024. In the first step, the individual elements of the work are described. Subsequently, an artificial intelligence (Claude 3.5 Sonnet) is fed with this information and given the task of designing an art historical classification and critique based on this. The resulting text should be considered as an extension of the original concept.w/k - Between Science and Art00w/k - Between Science and Art["Till B\u00f6deker"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/e722426f99ee33d064c8547b48f261d7e66930bb f04b54374c718b95b8137f58c7a420db3f7ddb2fRare diseases affect a small percentage of the population but often present significant challenges in diagnosis, treatment, and management due to their complex and heterogeneous nature. Recent advancements in artificial intelligence (AI) have shown promising potential to address these challenges by improving early diagnosis, personalized treatment strategies, and drug discovery. This scoping review explores the current landscape of AI applications in rare disease treatment. We examine the role of AI-driven tools in enhancing diagnostic accuracy, optimizing treatment pathways, and facilitating drug repurposing for rare conditions. Additionally, we highlight the limitations and ethical concerns associated with AI implementation, such as data privacy, the need for high-quality datasets, and algorithmic transparency. The findings suggest that while AI holds transformative potential, its integration into clinical practice for rare diseases requires multidisciplinary collaboration, ongoing research, and regulatory oversight.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)30The findings suggest that while AI holds transformative potential, its integration into clinical practice for rare diseases requires multidisciplinary collaboration, ongoing research, and regulatory oversight.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)["Asm Zahidur Rahman"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/f04b54374c718b95b8137f58c7a420db3f7ddb2f 0298f1ef1a5ac5a849a4dbd3fcddf27f1ebb0dc2The research is concerned with the subject of the future of the accounting profession in light of the development of the uses of artificial intelligence, specifically what relates to the function of interpreting accounting numbers published in the financial statements. Accordingly, the two researchers attempted to formulate a joint vision to study the challenges that the financial analyst can face in the transition to the contemporary world of digitization, which is beginning to impose... Its dimensions in various fields, practical fields, and various scientific and professional horizons. The research was based on the hypothesis that there is a statistically significant correlation between the development of artificial intelligence applications and the level of quality of financial reports provided by financial analysts. The research reached proof of the validity of such an assumption based on evidence that indicates the availability of the possibility to achieve the maximum benefit from artificial intelligence applications in interpreting the relationships between published accounting numbers for various business organizations.Social sciences and humanities90The research reached proof of the validity of such an assumption based on evidence that indicates the availability of the possibility to achieve the maximum benefit from artificial intelligence applications in interpreting the relationships between published accounting numbers for various business organizations.ZAC Conference Series: Social Sciences and Humanities["Haidar Alwan Kazem", "Ali Osama Talib", "Zainab Hadi Ali"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/0298f1ef1a5ac5a849a4dbd3fcddf27f1ebb0dc2 49387b625a6149291d86062770176e464de0feb8A zero-day exploit is a cyberattack that uses unknown or unaddressed security flaws in computer software, hardware, or firmware. Zero-day vulnerabilities pose very significant threats to cyber security. While traditional methods have been effective, they are lacking in many aspects due to rapidly evolving cyber threats. Hence, this paper examines artificial intelligence techniques, including machine learning and their application in enhancing cyber security against zero-day vulnerabilities. The research delves into supervised and unsupervised models and algorithms like Naive Bayes. The findings suggest that effective solutions such as artificial intelligence-driven approaches are crucial in the face of rapidly evolving cyber threats.Scholarly Review Journal00Artificial intelligence techniques, including machine learning and their application in enhancing cyber security against zero-day vulnerabilities are examined, suggesting that effective solutions such as artificial intelligence-driven approaches are crucial in the face of rapidly evolving cyber threats.Scholarly Review Journal["Krishiv Garg"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/49387b625a6149291d86062770176e464de0feb8 f394154dd15b8393fc59f2c05528918a3b5c59acFederated Learning (FL) is a decentralised approach to machine learning that enables model training on local devices without the need to share raw data. While FL inherently provides some level of privacy, significant challenges remain in fully protecting sensitive information. This article explores advanced artificial intelligence (AI) techniques for improving privacy in federated learning. I propose novel methods that include differential privacy, homomorphic encryption, and secure multiparty computation, enhanced by AI-driven optimizations. My empirical studies and theoretical analyses demonstrate the effectiveness and efficiency of these techniques in maintaining data protection.Scholarly Review Journal00Novel methods that include differential privacy, homomorphic encryption, and secure multiparty computation, enhanced by AI-driven optimizations are proposed for improving privacy in federated learning.Scholarly Review Journal["Piyush Dua"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/f394154dd15b8393fc59f2c05528918a3b5c59ac b00f2d434293c0a84e3b091053c3b15ed860a1d6Artificial Intelligence (AI) has the potential to revolutionize healthcare by enabling the development of more personalized, efficient, and effective medical services. One aspect of AI competence in healthcare that has received significant attention is the ability to respond rapidly to the patients' dynamic needs while injecting more flexibility into the system. This research explores the nexus between AI and service flexibility in healthcare. The qualitative study was carried out to examine the flexibility perspectives of AI-enabled service deliveries in healthcare. The findings contribute to a nuanced understanding of the facets of service flexibility in healthcare that AI could enable. The results would guide better coordination and management of care and the ability to make more informed decisions about treatment options. The nexus between AI and service flexibility in healthcare would sketch the new paradigms of patient value creation and evidence-based practices, which is an immediate need of healthcare organizations across the globe.Asia Pacific Journal of Health Management400The nexus between AI and service flexibility in healthcare would sketch the new paradigms of patient value creation and evidence-based practices, which is an immediate need of healthcare organizations across the globe.Asia Pacific Journal of Health Management["Pradeep Kumar"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/b00f2d434293c0a84e3b091053c3b15ed860a1d6 0f06d948f7a3f156dcd5196cd7e815a5fb58e363Rapid advancements in artificial intelligence (AI) have significantly transformed how individuals and organizations engage with their work, particularly in research and academia. Universities are urgently developing protocols for student use of large language models (LLMs) for coursework, while peer-reviewed journals and research conferences remain divided on the necessity of reporting AI assistance in manuscript development. This paper examines the diverse perspectives on LLM usage in scholarly research, ranging from concerns about contamination to recognition of its potential benefits. Building on existing literature, we explore guidelines for competitive intelligence (CI) researchers to effectively utilize GPT models, such as ChatGPT4, Scholar GPT, and Consensus GPT, throughout the research cycle. These models, developed by OpenAI, employ generative AI to produce new content based on user prompts, with output quality dependent on input specificity. Despite their recognized potential in literature reviews, qualitative analysis, and data analysis, the full capabilities of GPT models in research remain underutilized. This article provides a comprehensive guide for business researchers to integrate AI language models in planning, structuring, and executing research. Specific guidance is provided for business researchers focused on competitive intelligence.Revista Inteligência Competitiva270This article provides a comprehensive guide for business researchers to integrate AI language models in planning, structuring, and executing research, and specific guidance is provided for business researchers focused on competitive intelligence.Revista Inteligência Competitiva["Joe Hair", "Misty A. Sabol"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/0f06d948f7a3f156dcd5196cd7e815a5fb58e363 d937ec787de322c23ecbe20428edc8b2698aaa81Soft (Power) skills and Artificial Intelligence (AI) are crucial in today’s business world. While AI excels at automating technical tasks, the key to a thriving workforce lies in the unique human abilities fostered by soft skills. This research study sheds light on soft skills' pivotal role in ensuring AI's successful integration and long-term viability within organizations. It aims to underscore how soft skills such as communication, problem-solving, creativity, emotional intelligence, and collaboration are indispensable and exciting in their potential to drive innovation. These skills enable seamless human-AI interaction, driving innovation and futureproofing the workforce. This groundbreaking study delves into the following critical inquiries: 1) What are the ramifications of depending exclusively on technical abilities in AI development? 2) How can organizations seamlessly incorporate the development of soft skills into their AI training programs? 3) What significance do soft skills hold in augmenting human-machine collaboration? The paper explores the current state and challenges of developing soft skills, highlighting the need for advanced assessment tools, innovative training methods, and a cultural shift that urgently prioritizes these skills within organizations. The findings of this paper outline practical strategies for employers to integrate and empower soft skills development effectively, equipping them to navigate the ever-evolving AI-driven business environment. This study provides invaluable insights for scholars, practitioners, policymakers, business executives, and human resource professionals exploring the AI revolution while leveraging the transformative potential of soft skills in the workplace, inspiring a new way of thinking and working.Journal of Management and Sustainability210This research study sheds light on soft skills' pivotal role in ensuring AI's successful integration and long-term viability within organizations, highlighting the need for advanced assessment tools, innovative training methods, and a cultural shift that urgently prioritizes these skills within organizations.Journal of Management and Sustainability["Mohammed Nadeem"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/d937ec787de322c23ecbe20428edc8b2698aaa81 3cc8145835a6000bcd3c7639043da141d796e9e4This paper explores the intersection of economic anthropology and generative artificial intelligence (GenAI). It examines how large language models (LLMs) can simulate human decision-making and the inductive biases present in AI research. The study introduces two AI models: C.A.L.L.O.N. (Conventionally Average Late Liberal ONtology) and M.A.U.S.S. (More Accurate Understanding of Society and its Symbols). The former is trained on standard data, while the latter is adapted with anthropological knowledge. The research highlights how anthropological training can enhance LLMs' ability to recognize diverse economic systems and concepts. The findings suggest that integrating economic anthropology with AI can provide a more pluralistic understanding of economics and improve the sustainability of non-market economic systems.arXiv.org100The research highlights how anthropological training can enhance LLMs' ability to recognize diverse economic systems and concepts and suggests that integrating economic anthropology with AI can provide a more pluralistic understanding of economics and improve the sustainability of non-market economic systems.ArXiv["Zachary Sheldon", "Peeyush Kumar"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/3cc8145835a6000bcd3c7639043da141d796e9e4 ced744688d0c525c4f1b392edb01d00f81ad06fcArtificial intelligence merupakan bidang ilmu komputer yang saat ini sedang populer. Pamanfaatan artificial intelligence tersebut sudah merambah hampir ke seluruh bidang dikehidupan manusia. Hanya saja pemahaman tentang artificial intelligence sangat minim di lingkungan masyarakat terutama di lingkungan siswa/i SMK Pantai Labu. Para siswa/i sering bahkan ada yang selalu menggunakan teknologi artificial intelligence, hanya saja mereka tidak memahami teknologi tersebut secara mendasar. Oleh karena itu tim pengabdian masyarakat melakukan pelatihan tentang pengenalan artificial intelligene kepada siswa/i SMK Pantai Labu agar mereka memahami teknologi tersebut. Dengan memahami teknologi tersebut siswa/i dapat memanfaatkan teknologi artificial intelligence tersebut untuk meningkatkan kemampuan mereka di bidang akademik maupun non akademik.Jurnal Hasil Pengabdian Masyarakat (JURIBMAS)00Jurnal Hasil Pengabdian Masyarakat (JURIBMAS)["Munjiat Setiani Asih", "Ade Zulkarnain Hasibuan", "Edrian Hadinata", "Ilham Faisal"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/ced744688d0c525c4f1b392edb01d00f81ad06fc 9b6e9ad6558f40cbae8042398965faa860e7a7c6Artificial intelligence (AI) is fast gaining popularity in almost every field. This study explores the uses of AI in the field of sports, which has significantly evolved in technological aspects over the past two decades. Available data and expert opinions were analyzed. The analysis shows that AI has a wide variety of applications in sports, which—when implemented—can improve the field; at the same time, the data makes clear that indiscriminate use may cost the sports industry its stability and glory.Scholarly Review Journal20The analysis shows that AI has a wide variety of applications in sports, which—when implemented—can improve the field; at the same time, the data makes clear that indiscriminate use may cost the sports industry its stability and glory.Scholarly Review Journal["Sai Pardeshi"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/9b6e9ad6558f40cbae8042398965faa860e7a7c6 f054341065f1c6ddbd8ae8448fb48fb028328acfThe era of the industrial revolution has entered its peak in 5.0 which is an era where humans coexist full of technology. One of them is artificial intelligence and the products it produces such as ChatGPT, OpenAI, and artificial intelligence in other fields. In this era, humans will be given all kinds of conveniences from technology. The search for truth can be done quickly but less accurately. Artificial intelligence uses big data that will continue to be filled with various knowledge so that in the end intelligence can be utilized properly. The use of artificial intelligence technology in research conducted by researchers should still follow the rules, values, and norms in research. Values and norms in research will lead researchers to be open, fair, responsible, and skeptical of research results. The research method used is a literature study conducted by finding literature sources, reading literature, selecting literature, and analyzing related literature utilizing artificial intelligence in research. The purpose of this study is to find values and norms in research that uses artificial intelligence technology. The result of this study is that a researcher must have the value of objectivity, honesty, fairness, responsibility, stewardship, and openness and continue to develop skepticism, responsibility, ease of understanding, and critical thinking behavior that researchers must have. There are advantages and disadvantages to the use of artificial intelligence in research. The advantages include reference management, search for reference sources, plagiarism checking, and discussions between researchers. While the drawback is the critical thinking ability of researchers, plagiarism, fabrication, and falsification of research data. For this reason, good and responsible researchers must follow the values, values, and norms in research. Researchers must be responsible for the results of their research because the results of their research are a source of knowledge and will be read by readers widely.Jurnal Genesis Indonesia280The result of this study is that a researcher must have the value of objectivity, honesty, fairness, responsibility, stewardship, and openness and continue to develop skepticism, responsibility, ease of understanding, and critical thinking behavior that researchers must have.Jurnal Genesis Indonesia["Moch. Syihabudin Nuha", "Usrotun Diniyah", "Mario Martin Taneo", "Nur Hidayah", "Yuliati Hotifah"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/f054341065f1c6ddbd8ae8448fb48fb028328acf 6f22cf515f1a49e985a996226f1592043b8accb3Artificial intelligence (AI) in biology education can be defined as the deployment of AI in sections of study that help learners and researchers in their areas of concentration, specifically biological sciences. It gives a significant meaning toward the improvement of biology education since AI contributes toward the improvement of research methods and the expansion of knowledge on the associated biological concepts. Continued advances in artificial intelligence as a powerful tool in biological research will prove a major boon to the further development of biology education. Traditional basic biological sciences and such interdisciplinary fields as computational biology will step up their cooperation and come up with new theoretical predictions. They develop new theoretical frameworks; all these exciting changes that are rocking the biological world today are destined to reshape the face of the education in biology in the twenty-first century.Journal of Baltic Science Education160Journal of Baltic Science Education["M. Usak"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/6f22cf515f1a49e985a996226f1592043b8accb3 a374e918b7677bbaf9a564a65fcd7c7245fc4d41<jats:p xml:lang="tr"/>Journal of Pedagogical Sociology and Psychology01Journal of Pedagogical Sociology and Psychology["O. A. Ajani", "B. Gamede", "Tinashe C. Matiyenga"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/a374e918b7677bbaf9a564a65fcd7c7245fc4d41 1d65d26772552c4b58333b2a31b20c9d627c9308Strategic Management of Organizational Knowledge00Strategic Management of Organizational Knowledge["Somayyeh Tahanpour", "Vahid Araei", "Aliasghar Pourezat", "Mazyar Azimzadeh Irani"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/1d65d26772552c4b58333b2a31b20c9d627c9308 c2b38018ffb44f22e91fd0b87d1b0332e1e3e13bFrontiers of Environmental Science &amp; Engineering80Frontiers of Environmental Science &amp; Engineering["Yang Yu", "Jiahui Wang", "Yu Liu", "Pingfeng Yu", "Dongsheng Wang", "Ping Zheng", "Meng Zhang"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/c2b38018ffb44f22e91fd0b87d1b0332e1e3e13b d6f08cc0bb7dffb3bada8a563402bf79c21c7761Cogent Economics &amp; Finance280Cogent Economics &amp; Finance["Mousaab El Khair Ghoujdam", "Rachid Chaabita", "Oussama Elkhalfi", "Kamal Zehraoui", "Hicham Elalaoui", "Salwa Idamia"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/d6f08cc0bb7dffb3bada8a563402bf79c21c7761 fcee11f1b701894fb6425a634c1c4a6d72de5683In the post-truth age, political conspiracies circulate rapidly on social media, cultivating false narratives, while challenging the public’s ability to distinguish truth from fiction. ‘Deepfakes’ represent the most recent type of misinformation. They display deceitful representations of events to lead audiences to believe in fabricated realities. There has been limited research on deepfakes in political communications. As this technology progresses, deepfakes look deceptively authentic; thus, it is necessary to explore their effects on public perceptions. This study examines viewers’ comments on an Instagram-published deepfake video of Hillary Clinton to understand the impact of this technology. The results demonstrate that individuals struggle to identify deepfake videos and that their opinions are affected by this persuasive type of misinformation. This study also explores different ethical concerns posed by political deepfakes. By offering insights into public reactions to manipulated content, this study contributes to our understanding of the political effects of AI-fabricated content.Journal of creative communications320Viewers’ comments on an Instagram-published deepfake video of Hillary Clinton are examined to understand the impact of this technology and to explore different ethical concerns posed by political deepfakes.Journal of Creative Communications["Mina Momeni"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/fcee11f1b701894fb6425a634c1c4a6d72de5683 e7d62523234dd490e78ae645ce4fc55d98fdd1d3Assessment &amp; Evaluation in Higher Education490Assessment &amp; Evaluation in Higher Education["Juliana W. Venter", "S. Coetzee", "Astrid Schmulian"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/e7d62523234dd490e78ae645ce4fc55d98fdd1d3 65b3d1691cfd0dc98368c36e6a863ee9135fc1e1In a globalized, competitive and constantly changing environment, companies must adopt innovative tools, with Artificial Intelligence being the most important. This research aimed to analyze the benefits of implementing Artificial Intelligence in business administration. Through a systematic review based on the PRISMA method, 21 articles published since 2020 were identified. In the works analyzed, significant improvements in operational efficiency and productivity are evident, highlighting the automation of repetitive tasks and the optimization of decision-making in areas such as data analysis, improving customer experience and predicting demand. It is concluded that Artificial Intelligence can be adopted in various contexts and sectors, with great benefits. However, it is crucial to implement strategies that address the associated challenges, and to take an ethical and responsible approach to maximize its positive impact and mitigate potential risks.Impulso, Revista de Administración280It is concluded that Artificial Intelligence can be adopted in various contexts and sectors, with great benefits, however, it is crucial to implement strategies that address the associated challenges, and to take an ethical and responsible approach to maximize its positive impact and mitigate potential risks.Impulso, Revista de Administración["Fortunato Contreras Contreras", "Julio C\u00e9sar Olaya Guerrero"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/65b3d1691cfd0dc98368c36e6a863ee9135fc1e1 a602f9e1e6308baeda1c135c562f3b2e603aa81bIn view of professional learning and practical training in traditional electronic information education of application-oriented universities, this paper constructs electronic information–innovation practice teaching reform (EI-IPTR).In this scheme, by an integrating artificial intelligence (AI)-enabled curriculum with a multi-mode integrated platform and open-style module, big data-based comprehensive education resources are optimally configured. We jointly perform the multi-mode construction of innovative practice teaching, professional education stage design, and teaching management improvement, respectively. Subsequently, new practice teaching mechanisms with information technology and its implementation and management methods are established to achieve better teaching effects. It first strengthens learning and intra-group competition to promote students’ innovation in competitions. Then, the AI technique, i.e., attention mechanism-aided long short-term memory (LSTM), is used to model individual students’ abilities. Thus, it accurately evaluates them for teachers to efficiently manage their teaching process in accordance with their aptitude. The teaching reform practice verifies that the AI-enabled big data optimization of teaching reform has a better effect by the above multi-mode innovation. It exhibits an obvious improvement in the quantity and quality of students’ professional knowledge, personal ability, teamwork, and innovative practice. It is also in accordance with the independent completion of practical course teaching in the analysis of big education data. In addition, it realizes high-quality practical teaching by combining multi-mode, multi-level, and open discipline foundations together with efficient, professional skills.Syst.171The teaching reform practice verifies that the AI-enabled big data optimization of teaching reform has a better effect by the above multi-mode innovation, and realizes high-quality practical teaching by combining multi-mode, multi-level, and open discipline foundations together with efficient, professional skills.Syst.["Ying Chen", "Jianrong Bao", "Geqi Weng", "Yanhai Shang", "Chao Liu", "Bin Jiang"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/a602f9e1e6308baeda1c135c562f3b2e603aa81b 96261f2ddae60a1e8eb9ba9c6b4a5713ac6082c1This paper explores the AI (artificial intelligence) empowerment in foreign language education within the context of “New Liberal Arts” and its impact on a diversified foreign language talent cultivation system. Though challenges brought by AI, AI empowerment could facilitate the foreign language discipline development. Drawing upon this basis, the paper further explores the breakthroughs in foreign language talent cultivation. That suggests a shift from information tools to character education, from language proficiency to core competency strengthening, from single-discipline to interdisciplinary talent cultivation, and from imparting language knowledge to advancing general education understanding. This paper proposes paths for diversified foreign language talent cultivation in the AI era, including AI empowerment character education cultivation approaches, the construction of a diversified curriculum system, the innovation of interdisciplinary curriculum clusters, AI empowerment reshaping of digital teaching practice settings, and AI empowerment reconstruction of foreign language digital teaching resources. With those approaches, the adjusting focus and methods of foreign language talents cultivation, an effective Multilingual Talent Cultivation System will be established with international perspectives, cross-cultural communication skills, and innovation capabilities to meet the development needs of the AI era.Journal of Computer Technology and Electronic Research101Journal of Computer Technology and Electronic Research["Yang Zhang"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/96261f2ddae60a1e8eb9ba9c6b4a5713ac6082c1 f0b605a912f724b0396d660e9f30b2525354e343The integration of Generative Artificial Intelligence (AI) into autonomous machines represents a major paradigm shift in how these systems operate and unlocks new solutions to problems once deemed intractable. Although generative AI agents provide unparalleled capabilities, they also have unique safety concerns. These challenges require robust safeguards, especially for autonomous machines that operate in high-stakes environments. This work investigates the evolving safety requirements when generative models are integrated as agents into physical autonomous machines, comparing these to safety considerations in less critical AI applications. We explore the challenges and opportunities to ensure the safe deployment of generative AI-driven autonomous machines. Furthermore, we provide a forward-looking perspective on the future of AI-driven autonomous systems and emphasize the importance of evaluating and communicating safety risks. As an important step towards addressing these concerns, we recommend the development and implementation of comprehensive safety scorecards for the use of generative AI technologies in autonomous machines.arXiv.org2011This work investigates the evolving safety requirements when generative models are integrated as agents into physical autonomous machines, comparing these to safety considerations in less critical AI applications and recommends the development and implementation of comprehensive safety scorecards for the use of generative AI technologies in autonomous machines.ArXiv["Jason Jabbour", "V. Reddi"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/f0b605a912f724b0396d660e9f30b2525354e343 a609e27be85d1902159e3827396c6598450f8f0aThe increasing vulnerability and unpredictability of the power grid, along with challenges in renewable energy integrations, natural disasters, cyberattacks, and limited transmission system investments, necessitate robust algorithms for power system protection. In the given context, this paper proposes a fault detection and classification approach in AC microgrids, considering an explainable artificial intelligence (XAI) model that offers enhanced interpretability, transparency, and debugging capabilities. XAI tools provide information on factors influencing fault detection and classification decision-making. To generate a realistic dataset and to validate the fault detection and classification model, a digital representation of a 50 kW physical microgrid testbed is developed and integrated into a real-time simulation framework using the Opal-RT real-time simulator that enables time-critical simulation and mimics realistic operating conditions. The combination of real-time simulation and XAI provides a robust platform to advance power system protection, facilitating the exploration of fault detection strategies and the evaluation of algorithm performance with deeper insights into decision-making processes, which are justified in this paper in various case studies.European Conference on Cognitive Ergonomics200The combination of real-time simulation and XAI provides a robust platform to advance power system protection, facilitating the exploration of fault detection strategies and the evaluation of algorithm performance with deeper insights into decision-making processes, which are justified in this paper in various case studies.2024 IEEE Energy Conversion Congress and Exposition (ECCE)["Oluwadamilola Ajayi", "Mohammadreza Mirjafari", "Peter B. Idowu", "Md Habib Ullah"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/a609e27be85d1902159e3827396c6598450f8f0a c1619baf9840c4f1db7561d79f004d9706eb80beThe current education industry faces critical challenges, including overwhelming educator workloads, a lack of personalized student learning, and unsustainable financial models. This paper explores how artificial intelligence (AI) can offer solutions to these systemic issues, focusing on three key areas: reducing teacher burnout, enhancing student engagement through personalized education, and improving the financial sustainability of educational institutions. The study investigates the advantages of adaptive learning systems for customizing educational experiences for each student, the impact of automated tools in easing administrative burdens on teachers, and the optimization of institutional resources to lower operating costs through a thorough analysis of current AI-based projects. These results demonstrate how AI has the potential to revolutionize conventional educational approaches. Still, they also show that to fully realize its promise, more extensive systemic changes are required.World Journal of Education and Humanities00The study investigates the advantages of adaptive learning systems for customizing educational experiences for each student, the impact of automated tools in easing administrative burdens on teachers, and the optimization of institutional resources to lower operating costs through a thorough analysis of current AI-based projects.World Journal of Education and Humanities["Yingyang Li", "Jinming Liang", "Zirui Zhao", "Kexin Li"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/c1619baf9840c4f1db7561d79f004d9706eb80be 667ad341d0f0894088d65e805fc231b3fdf4a60bAs an expert witness to German courts (Landgerichte, Oberlandesgerichte) and federal courts for Medical Devices and IVDs the number of expert statements has constantly increased, especially for Software and AI in Healthcare and focusing on their Medical Device classification. The paper also examines how competition and insurers may respond to claims arising from the use of improper classified software, artificial intelligence including Large Language Models (LLMs) with denial of coverage, increased premiums, and subrogation actions against hospitals or AI developers. Regulatory challenges are discussed in light of Software as a Medical Device (SaMD) frameworks, highlighting the effective and valid regulations to classify the risk of Software and AI when used in Healthcare, which are enforced today although the dynamic and evolving nature of AI systems. This paper concludes the used regulatory and legal pathways used in cases with expert witness statements between competitors or by competitors to file a lawsuit against a competitor with incorrect classification based on experience and draws the parallels to the US FDA regulation. Beyond it demonstrates that authority driven enforcement of existing regulation is transferred to courts acting on behalf of the authority driven by compliant legal manufacturers.00The used regulatory and legal pathways used in cases with expert witness statements between competitors or by competitors to file a lawsuit against a competitor with incorrect classification based on experience are concluded and the parallels to the US FDA regulation are drawn.["Rudolf Wagner"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/667ad341d0f0894088d65e805fc231b3fdf4a60b f7b6b4589ea2d21130167cba0a70268d16e4844fIntroduction. Healthcare is one of the priority sectors for the deployment of artificial intelligence (AI) technologies worldwide, including Russia. A key area of AI deployment is the integration of AI-base software as a medical device (AI SaMD) into the Unified digital systems of the healthcare sector of the Russian Federation.Aim. Research of the results of the deployment of AI SaMD in healthcare of the Russian Federation in 2023.Materials and methods. The State Register of Medical Devices and Organizations (individual entrepreneurs) engaged in the production and manufacture of medical devices was used as information about AI SaMD registered in Russia. As information on the deployment of AI SaMD, data from monitoring to the federal project “Creating a single digital system in healthcare” was used, including reports from constituent entities of the Russian Federation upon these activities. The results of the implementation of AI SaMD in Moscow were obtained according to data from the Moscow Department of Health as part of an experiment on the use of innovative technologies in the field of computer vision for the analysis of medical images.Results. As of January 1, 2024, Roszdravnadzor registered 26 AI SaMD, 77 % of them were developed by 13 Russian companies. At the end of 2023, 84 (94 %) constituent entities of the Russian Federation met the minimum established target for the purchase of AI SaMD. Within the framework of public procurement procedures provided by law, 106 government contracts were signed for the purchase and deployment of AI SaMD for a total amount of 448 million 430 thousand rubles.Conclusion. In 2023, the Russian healthcare system made a significant breakthrough in terms of the practical deployment of AI SaMD. Completed procurement and deployment projects are the basis for subsequent industry development.National Health Care (Russia)10In 2023, the Russian healthcare system made a significant breakthrough in terms of the practical deployment of AI SaMD, and completed procurement and deployment projects are the basis for subsequent industry development.National Health Care (Russia)["A. V. Gusev", "O. R. Artemova", "Y. Vasiliev", "A. Vladzymyrskyy"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/f7b6b4589ea2d21130167cba0a70268d16e4844f 4b27a56917f296022f5218f6a6718d366103eb54Artificial intelligence, a rather suitable machinery in today’s fast paced world. AI is a field of research in Computer Science that develops study methods and softwares that perceive or collect environmental data from their surroundings. We may not know it, but AI is a huge part of our lives now, and we interact with them on a daily basis. It may be one that performs one task repeatedly, like a washing machine, or another that performs multi tasks, such as computers and phones. This paper focuses on how human beings perceive AI, and how these systems are affecting our daily life style as well as our thinking process. What makes human beings different from other beings is the ability to dream, create and perform as well as feel. In today’s time, all I see are the various tools that are there to enhance our creativity to create something extraordinary. But perhaps these tools are also used in such a manner that users are feeling doubtful? In such a manner that the users are not ready to use their own imagination? A user who forgets how to socialise properly with real people? This paper is about how researchers can prioritise data collection and make predictions for the future we share with AI.Scholarly Review Journal00How researchers can prioritise data collection and make predictions for the future the authors share with AI is focused on.Scholarly Review Journal["Asha Nozomi Terasaka Dwivedi"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/4b27a56917f296022f5218f6a6718d366103eb54 cc36463ea8ed415d2ce24594491a00fe9f7dab53In the past several years there has been a rapid development of new technologies, applications, organizations, and institutions in the area of artificial intelligence (AI). At the same time, ethical reasoning about AI has not been able to keep up with the speed of these advances. As a result, developers are left to rely on existing rules, professional codes, policies and personal ethics which may not provide the appropriate guidance about ethical conduct and may require greater specificity (O'Leary). Many commentators have acknowledged the need for a clearer understanding of ethical values and principles to guide AI research. Drawing on insights from the formation of the field of biomedical ethics, we argue that AI ethics should make use of the method based on prima facie duties derived from W.D. Ross’ approach to ethics. A Rossian approach has proved influential in biomedical ethics. We further propose a modification to the list of principles proposed by Floridi and Cowls, arguing that the principles of explicability and accountability should be separated for ease of application. We argue that this method of applying principles is just what has been missing in AI ethics and is the crucial link between the now common lists of principles and putting them into practice in a way that can inform actual developments on the ground.The International Review of Information Ethics170This work argues that AI ethics should make use of the method based on prima facie duties derived from W.D. Ross’ approach to ethics and is the crucial link between the now common lists of principles and putting them into practice in a way that can inform actual developments on the ground.The International Review of Information Ethics["Peter Andes", "Robin S Lau", "Geoffrey Rockwell", "Tammy Mah-Fraser"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/cc36463ea8ed415d2ce24594491a00fe9f7dab53 d5c447e2459cb5753ed56b319baec0dc1896e1b4Abstract. This study examines the impact of AI-based risk management on financial market stability, utilizing both econometric analysis and real-world case studies. The research focuses on financial institutions such as JPMorgan Chase, Goldman Sachs, BlackRock, and others that have successfully implemented Artificial intelligence (AI) algorithms to analyze market trends and trading patterns, leading to more informed investment decisions and better overall portfolio performance. The econometric analysis reveals a positive and statistically significant relationship between AI-based risk management and financial market stability. Institutions leveraging AI technologies for risk management experience lower levels of volatility, better risk assessment, and improved decision-making, contributing to greater overall stability in financial markets. The study also identifies challenges faced by institutions implementing AI-based risk management systems, including the need for high-quality data, algorithm complexity, and regulatory compliance. To address these challenges and maximize the benefits of AI in risk management, several recommendations are proposed. These include investing in data quality and governance, enhancing regulatory frameworks, fostering collaboration and knowledge sharing, investing in employee training and development, monitoring and evaluating AI systems regularly, and considering the ethical and social implications of AI adoption. The findings suggest that AI-based risk management has the potential to significantly enhance financial market stability.Materials Research Proceedings00The research focuses on financial institutions that have successfully implemented Artificial intelligence algorithms to analyze market trends and trading patterns, leading to more informed investment decisions and better overall portfolio performance.Materials Research Proceedings["Aleksandra Kuzior"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/d5c447e2459cb5753ed56b319baec0dc1896e1b4 df9792f0d8df9b7639a298e754c949ec9010176aAbstract. Europe is one of the major global regions that has led the adoption of artificial intelligence (AI) in the corporate environment, leading to a significant shift in the technological and organizational landscape of the business world. As the continental economies are deeply embedded in economic and institutional layers, different factors might lead to higher AI adoption. This paper seeks to explore the factors that play a role in the diffusion of AI across various sectors and industries in Europe via quantitative statistical analysis of individual, corporate, and government-related variables. Results tend to indicate budget spending in research and development and digital intensity of companies as the most significant factors but assert that varied influences, not necessarily interrelated, can shape adoption patterns.Materials Research Proceedings00Results tend to indicate budget spending in research and development and digital intensity of companies as the most significant factors but assert that varied influences, not necessarily interrelated, can shape adoption patterns.Materials Research Proceedings["Fabio Gualandri"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/df9792f0d8df9b7639a298e754c949ec9010176a 70ad59545ff0d0c6e3ae5d4f12aefcb4a1995fbeThe research focused on the issue of which crimes fall under the jurisdiction of artificial intelligence. The incorporation of AI programs into different aspects of life entails many challenges, especially regarding the liability for the actions performed by AI and the question of compatibility of the existing legislation with the characteristics of the AI technologies. The state of affairs means that the AI-powered robots may perform actions that prescribe criminal provisions of criminal law, thus raising the issue in the given article as to the possibility of direct criminal penalties for robots, the feasibility of putting the intelligent robots’ criminal responsibility into practice by the general principles of the Jordanian Penal Code and the receptiveness of the concept of recognizing the crime to be committed by artificial intelligence. From this perspective, the main research question of the study is: What are the aspects of criminal responsibility stemming from AI mistakes? Thus, its significance is derived from the fact that it explores a new and essential issue, namely, the criminal responsibility for AI crimes within Jordan’s legislation. It has great importance in so many spheres, and it is difficult to describe all the aspects of artificial intelligence. The most remarkable outcomes and suggestions of the study show that the legal guidelines for criminal responsibility in Jordan of artificial intelligence crimes remain unrecognized. Thus, it is advised that the legislator proceed with actions to create legislation controlling AI technologies and their use for the purpose of serving the state and citizens’ interests. This legislation will guarantee the proper application of the AI capacities within several spheres for the sake of the general public, while taking into consideration all the ethical and legal concerns that relate to using artificial intelligence.  International Journal of Religion160The most remarkable outcomes and suggestions of the study show that the legal guidelines for criminal responsibility in Jordan of artificial intelligence crimes remain unrecognized and it is advised that the legislator proceed with actions to create legislation controlling AI technologies and their use for the purpose of serving the state and citizens’ interests.International Journal of Religion["Monther Sami Abdulrhman Al-Makaneen"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/70ad59545ff0d0c6e3ae5d4f12aefcb4a1995fbe 33ed03441547c03da9f717cb03bfa71fb2272080This paper explores the potential paths for integrating Artificial Intelligence (AI) into college English teaching, aiming to enhance teaching methodologies, personalize learning experiences, and ultimately improve student outcomes. By examining current trends and practices, the study outlines ten key strategies for effectively leveraging AI in college English classrooms. Through a comprehensive review of literature and case studies, this paper illustrates the benefits and challenges of AI integration and proposes a roadmap for its successful implementation.Journal of Modern Educational Theory and Practice80Through a comprehensive review of literature and case studies, this paper illustrates the benefits and challenges of AI integration and proposes a roadmap for its successful implementation.Journal of Modern Educational Theory and Practice["Yingying Cui"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/33ed03441547c03da9f717cb03bfa71fb2272080 b8f1e2672566a213e3b81ee9533632c42f097e8fThis article looks at the influence of automation and artificial intelligence (AI) on unemployment in the United Kingdom from a management standpoint. The study seeks to determine the extent to which automation and AI contribute to unemployment and how management methods can offset these consequences. This research is now in its basic phases, with a full literature evaluation and initial data gathering underway. Early findings indicate considerable sectoral disparities in the impact of automation and AI on employment. This work in progress describes the research objectives, suggested methodology, and expected contributions to academic debate and practical management techniques.Journal of Business &amp; Retail Management Research150The study seeks to determine the extent to which automation and AI contribute to unemployment and how management methods can offset these consequences and indicate considerable sectoral disparities in the impact of automation and AI on employment.Journal of Business &amp; Retail Management Research["Policy Dapo Baderinwa"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/b8f1e2672566a213e3b81ee9533632c42f097e8f 5dd8510b41f2a1855b5faf5acca656a1a6362f74This study introduces a new approach using supervised learning deep neural networks (DNNs) to develop an AI-driven filter for nonlinear stochastic signal systems with external disturbance and measurement noise. The filter aims to achieve a balanced design between and norm of the state estimation error to achieve both optimal and robust filtering design of nonlinear signal system simultaneously while considering environmental disturbance and measurement noise. Traditionally, this nonlinear  filter design involves solving complex two-coupled Hamilton-Jacobi-Issac Equations (HJIEs). To simplify this complicated design process, a novel two-coupled HJIEs-supervised Adam learning algorithm is proposed for DNN-based AI-driven filter. This algorithm trains a  DNN-based AI-driven filter offline using worst-case scenarios of environmental disturbance and measurement noise. This training phase generates state estimation errors that teach the DNN-based AI-driven filter how to coordinate nonlinear system model with worst-case external disturbance and measurement noise, Luenberger-type filter, estimation error dynamic model and two-coupled HJIEs-supervised deep Adam learning algorithm to achieve the mixed  filtering strategy effectively. The study demonstrates theoretically that this approach will achieve the desired mixed  filtering strategy once the Adam learning algorithm converges. Finally, the effectiveness of the proposed DNN-based AI-driven filter design method is validated through simulations, specifically involving trajectory estimation and prediction of an incoming ballistic missile detected by a radar system.Artificial Intelligence Advances170The study demonstrates theoretically that this approach will achieve the desired mixed-filtering strategy once the Adam learning algorithm converges, and the effectiveness of the proposed DNN-based AI-driven filter design method is validated through simulations, specifically involving trajectory estimation and prediction of an incoming ballistic missile detected by a radar system.Artificial Intelligence Advances["Bor-Sen Chen", "Jui-Ming Ma", "Ruei-Syuan Wu"]2024-10-20T00:00:00https://www.semanticscholar.org/paper/5dd8510b41f2a1855b5faf5acca656a1a6362f74 09ce90b05e8b489eb820d6c74b9a337c327c352dIn recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve partial differential equations (AI for PDEs: Artificial intelligence for partial differential equations) has become a focal point in computational mechanics. The core of AI for PDEs is the fusion of data and partial differential equations (PDEs), which can solve almost any PDEs. Therefore, this article provides a comprehensive review of the research on AI for PDEs, summarizing the existing algorithms and theories. The article discusses the applications of AI for PDEs in computational mechanics, including solid mechanics, fluid mechanics, and biomechanics. The existing AI for PDEs algorithms include those based on Physics-Informed Neural Networks (PINNs), Deep Energy Methods (DEM), Operator Learning, and Physics-Informed Neural Operator (PINO). AI for PDEs represents a new method of scientific simulation that provides approximate solutions to specific problems using large amounts of data, then fine-tuning according to specific physical laws, avoiding the need to compute from scratch like traditional algorithms. Thus, AI for PDEs is the prototype for future foundation models in computational mechanics, capable of significantly accelerating traditional numerical algorithms.arXiv.org03AI for PDEs represents a new method of scientific simulation that provides approximate solutions to specific problems using large amounts of data, then fine-tuning according to specific physical laws, avoiding the need to compute from scratch like traditional algorithms.ArXiv["Yizheng Wang", "Jinshuai Bai", "Zhongya Lin", "Qimin Wang", "C. Anitescu", "Jia Sun", "M. Eshaghi", "Yuantong Gu", "Xinzhu Feng", "X. Zhuang", "T. Rabczuk", "Yinghua Liu"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/09ce90b05e8b489eb820d6c74b9a337c327c352d d7551f5023315ed8e831cc5295fbf4844236cdc1Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.Processes401A comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders and the results of a human planner using master data and those of an AI algorithm compared to the actual realisation.Processes["M. Roblek", "Tomaz Kern", "Eva Krha\u010d Andra\u0161ec", "Alenka Brezav\u0161\u010dek"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/d7551f5023315ed8e831cc5295fbf4844236cdc1 9b66f0417ca60ae452fc8838e8473b7f824c22a7 Controlling and managing nuclear waste is a significant challenge due to the harmful effects of radioactive materials on human health. To address this, long-term storage solutions are essential. Artificial Intelligence (AI) and Machine Learning (ML) are being utilized to make nuclear waste management safer, more effective, and efficient. This paper evaluates various applications of AI and ML in the field of nuclear waste, covering aspects such as predictive maintenance, waste sorting, and classification. AI and ML enhance real-time monitoring of storage conditions and optimize waste handling procedures through advanced data processing capabilities. Implementing cutting-edge solutions is crucial to protect public health and the environment from radioactive waste. The purpose of this evaluation is to examine how AI and ML improve nuclear waste management processes. These technologies can reduce human exposure to harmful materials and increase the safety and efficiency of managing nuclear waste through advanced predictive capabilities. The introduction of AI and ML in nuclear waste management is driving significant changes and innovations, addressing current issues, and establishing new guidelines for future policies.Kerntechnik371This paper evaluates various applications of AI and ML in the field of nuclear waste, covering aspects such as predictive maintenance, waste sorting, and classification, to examine how AI and ML improve nuclear waste management processes.Kerntechnik["Thiagarajan Chenniappan", "Y. Devarajan"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/9b66f0417ca60ae452fc8838e8473b7f824c22a7 1e3e704c1ad86b66d1600dbf284253619c23678eAbstract. Vector-borne diseases (VBDs) pose a significant threat to public health globally. VBDs are a significant public health concern worldwide, with infections such as Malaria, Dengue Fever, Zika Virus, and Lyme Disease posing a threat to global health security. There is a need for innovative and effective strategies to control these diseases. One potential solution lies in the integration of citizen science and artificial intelligence technologies. Citizen science, which involves the participation of volunteers in scientific research, can greatly contribute to data collection and monitoring efforts for vector-borne diseases. Artificial intelligence can enhance the analysis of this data, leading to improved disease surveillance, prediction, and control strategies. Citizen Science involves active public participation in scientific research, data collection, and analysis, while AI and Machine Learning (ML) techniques offer powerful tools for processing and interpreting large datasets. By leveraging the power of citizen science and artificial intelligence, we can harness the collective efforts of volunteers and advanced technology to better understand, track, and mitigate the spread of vector-borne diseases. Through the combination of citizen science and artificial intelligence, a more comprehensive and efficient approach can be taken to gather data on vector-borne diseases, analyze the data, and inform public health interventions. This systematic review aims to explore the role of citizen science and artificial intelligence in addressing the challenges associated with vector-borne diseases. It will examine the existing literature on the use of citizen science and artificial intelligence in vector-borne disease research, including their applications, benefits, and limitations, in order to provide insights and recommendations for future research and public health strategies. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences191The role of citizen science and artificial intelligence in addressing the challenges associated with vector-borne diseases is explored, including their applications, benefits, and limitations, in order to provide insights and recommendations for future research and public health strategies.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences["S. Saran", "Priyanka Singh"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/1e3e704c1ad86b66d1600dbf284253619c23678e 887dae59562f42f16c261c427a46177015433ca4Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer with a poor prognosis. Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients. With the advancement of artificial intelligence (AI) technology and the proliferation of medical digital information, AI has demonstrated promising sensitivity and accuracy in assisting precise detection, treatment decision-making, and prognosis assessment of ESCC. It has become a unique opportunity to enhance comprehensive clinical management of ESCC in the era of precision oncology. This review examines how AI is applied to the diagnosis, treatment, and prognosis assessment of ESCC in the era of precision oncology, and analyzes the challenges and potential opportunities that AI faces in clinical translation. Through insights into future prospects, it is hoped that this review will contribute to the real-world application of AI in future clinical settings, ultimately alleviating the disease burden caused by ESCC.World Journal of Gastroenterology961This review examines how AI is applied to the diagnosis, treatment, and prognosis assessment of ESCC in the era of precision oncology, and analyzes the challenges and potential opportunities that AI faces in clinical translation.World Journal of Gastroenterology["Wan-Yue Zhang", "Yong-Jian Chang", "Rui-Hua Shi"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/887dae59562f42f16c261c427a46177015433ca4 e4c61ca7284d9a2d242c8d65569d7cf69d7fdeebThe development of technology, especially in the era of Industry 4.0, has significantly impacted various sectors, including supply chain management. Artificial Intelligence (AI), one of the leading technologies in Industry 4.0, has excellent potential to improve the efficiency and effectiveness of the supply chain. This study aims to identify the relationship between AI and the concept of supply chain management and analyze the practice of using AI to improve supply chain efficiency at PT. Pelita Media Nusantara. Using a descriptive-analytical method, this study examines various relevant literature and data. The results of the study indicate that the application of AI at PT. Pelita Media Nusantara has increased the supply chain's visibility, accuracy, and responsiveness. Implementing big data analytics, IoT sensors, and AI-based predictive systems allows companies to optimize procurement, production, and distribution processes. This case study also reveals that complex technology integration and the need for skilled human resources are the main challenges in implementing AI. This study provides important insights for academics and practitioners in developing more effective and efficient AI-based supply chain management strategies.International Journal of Engineering Science and Information Technology620The results of the study indicate that the application of AI at PT.International Journal of Engineering, Science and Information Technology["Muh Husein", "J. Rajagukguk", "Kartiko Eko Putranto"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/e4c61ca7284d9a2d242c8d65569d7cf69d7fdeeb 3a2f554674b42ea0c6a6233d1203e6f867c6a429This article explores four different topics of conversation in technical and professional communication (TPC) scholarship that overlap and connect with contemporary issues in generative artificial intelligence (AI): process and iteration, theory and power, actors and activity, and the social justice turn. The authors offer four nonexhaustive reviews of these conversations, offering insight into key issues and texts that have animated discourse in the field and can directly or indirectly address the complex relationship between TPC work and generative AI.Journal of business and technical communication220Journal of Business and Technical Communication["Bill Hart-Davidson", "Michael Ristich", "Casey McArdle", "Liza Potts"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/3a2f554674b42ea0c6a6233d1203e6f867c6a429 1eb0bd513748b0220911485c51c0a68f2d3a9abaBMC Cancer730Key insights are unveiled into the application of AI techniques in personalized oncology, particularly in the monitoring and prediction of responses to NAT for BC patients, based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses.BMC Cancer["Rachida Hachache", "Ali Yahyaouy", "J. Riffi", "H. Tairi", "Soukayna Abibou", "M. Adoui", "Mohammed Benjelloun"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/1eb0bd513748b0220911485c51c0a68f2d3a9aba 02c09bb776bd444d837ab572f9d9316c12950e50This study analyzes the internationalization development changes of one vocational college in China over the past 10 years. Based on the new requirements and trends of vocational education in the era of artificial intelligence , effective suggestions are proposed for the internationalization development of vocational education from the aspects of establishing international consulting organizations, attracting international capital investment, learn from other vocational colleges, formulating international cooperation plans, building industry education integration communities, constructing international curriculum resource databases, shortening students' learning time in school, changing classroom learning modes, increasing online practical training time, and building international cooperation alliances. This study is expected to promote the improvement of vocational colleges in curriculum construction, teaching resources, learning modes, school enterprise cooperation, and platform construction, ultimately achieving sustainable development of vocational education.Journal of Curriculum and Teaching00This study analyzes the internationalization development changes of one vocational college in China over the past 10 years to promote the improvement of vocational colleges in curriculum construction, teaching resources, learning modes, school enterprise cooperation, and platform construction, ultimately achieving sustainable development of vocational education.Journal of Curriculum and Teaching["Wang Qiang", "Yuzhong Yao"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/02c09bb776bd444d837ab572f9d9316c12950e50 3bbffe51296070e38c7c96b12a1164d7284e3e22Cognitve workload associated with fine motor activity in neurosurgeons was monitored by using a wearable electroen-cephalographic (EEG) device. The most informative EEG features were selected by means of an explainable Artificial Intelligence (XAI) algorithm. XAI represents a promising novel approach in this application field and offers new opportunities for extracting information from EEG data beyond traditional statistical and Machine Learning-based methods. Six neurosurgeons performed the Purdue Pegboard Test (PPT) at two difficulty levels related to low or high cognitive load. EEG signals were acquired with an eight dry electrode device. Absolute powers in six different frequency bands of interest were explored. Three most involved EEG features resulted from SHapley Additive exPlanations (SHAP) methods, namely absolute power in delta band on C3 and Fz channels and the absolute power in theta band on Fz. Summary plots showed a decrease of the three identified EEG features in the high cognitive load task. These findings demonstrate the potential of Artificial Intelligence-supported wearable EEG solutions to monitor cognitive load over time, to track the cognitive load of trainee neurosurgeons and to design adaptive training courses.2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)360The potential of Artificial Intelligence-supported wearable EEG solutions to monitor cognitive load over time, to track the cognitive load of trainee neurosurgeons and to design adaptive training courses is demonstrated.2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)["Pasquale Arpaia", "Matteo De Luca", "Anna Della Calce", "G. Carone", "Nicol\u00f3 Castelli", "D. Duran", "Ludovica Gargiulo", "N. Moccaldi", "Marco Nalin", "A. Perin", "Salvatore Piccolo", "Cosimo Puttilli", "Elisa Visani"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/3bbffe51296070e38c7c96b12a1164d7284e3e22 69dd137c561619884ee4aecd95f81a351b7ce328Generative Artificial Intelligence (GenAI) enhances productivity by creating data, forecasting, optimizing, and understanding human language. In the energy sector, it is projected to have a $240 billion global economic impact, though research remains limited. This paper reviews GenAI's benefits, challenges, and research gaps in the energy sector, also focusing on climate change efforts. A PRISMA-SCR-based literature review from January 2022 to May 2024 was conducted using IEEE Xplore, ScienceDirect, ACM Digital Library, and Google Scholar. GenAI tools extracted data, verified by researchers. Analysis of 33 papers shows GenAI excels in knowledge integration and prediction. It generates synthetic electricity demand data, manages grids, forecasts energy demand, and optimizes renewable energy systems. Key challenges include hallucinations, data biases, privacy concerns, misuse, and system errors. Solutions involve improving training data, system fine-tuning, human oversight, and security measures. Research gaps include synthetic data realism, model evaluation standards, and integrating GenAI with blockchain and IoT.International Conference and Utility Exhibition on Green Energy for Sustainable Development390GenAI's benefits, challenges, and research gaps in the energy sector are reviewed, also focusing on climate change efforts.2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future (ICUE)["Surasak Surathunmanun", "W. Ongsakul", "Jai Govind Singh"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/69dd137c561619884ee4aecd95f81a351b7ce328 42b2d1028733299d09121940fa21b2a4b05e0349PurposeAirports are an essential part of the airline value chains. Artificial intelligence (AI) technologies are widely used at airports; the study aims to explore how the virtual integration competence and the perceived warmth of AI in airports increase customer service competencies and satisfy their passengers.Design/methodology/approachBased on the perspectives of digital competencies and hybrid intelligence, a continued usage intention model was analyzed using the partial least squares approach; this study used purposed sampling to collect data from those airports; participants who adopted the AI service in airports in Beijing, Taipei and Singapore who have the potential to use AI service usage experience more than three times and 384 completed questionnaires were analyzed.FindingsAI innovations serve human tasks at airports and analytics applications as change drivers and can replace legacy procedures. The research findings help point out the perceived warmth of AI and the virtual integration competence of airports utilizing the intimacy of AI services.Originality/valueAI innovations provide a service change to replace human tasks and intelligence and analytics applications at airports. AI services are a powerful tool for airports to serve their passengers efficiently; airports will collaborate with airlines to offer AI services to passengers.Business Process Management Journal960The research findings help point out the perceived warmth of AI and the virtual integration competence of airports utilizing the intimacy of AI services.Business Process Management Journal["Edward C. S. Ku"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/42b2d1028733299d09121940fa21b2a4b05e0349 a9eb9d96f69ec92202282072fd204ac771d581f7Background: Artificial Intelligence is an evolving technology in the education sector. In India, with the prevailing digital divide among various sections of the society, it is feared that a ‘AI Divide’ would further aggravate the situation. Objectives: This paper aims to assess the perspective and level of awareness among college and university-level students in Assam, India, regarding AI and to assess the association between AI usage in academic purpose and the socio demographic characteristics of these students. Materials and Methods: Simple Random Sampling has been used for data collection from 200 respondents using a structured questionnaire. Chi square test is used for analysis. Results: Familiarity with AI and AI-related courses reveals that 14.5% is ‘not familiar at all’ and 15.5% is “very familiar” with AI and its usage. 7% have undergone AI-related courses, indicating a potential gap in AI education. Male students show a significantly higher usage of AI for academic purposes compared to female students in Assam. Urban students exhibit a significantly higher usage of AI compared to rural students. Literacy level of parents and the monthly income of parents do not show a significant association with AI usage among students in Assam. Students who are more familiar with AI tend to use it more for academic purposes. Whether students have undergone any AI related course does not significantly influence their usage of AI for academic purposes.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)50Assessing the perspective and level of awareness among college and university-level students in Assam, India, regarding AI and the association between AI usage in academic purpose and the socio demographic characteristics of these students finds that students who are more familiar with AI tend to use it more for academic purposes.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)["Himashree Dutta", "Gourab Kalita", "D. Aditi"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/a9eb9d96f69ec92202282072fd204ac771d581f7 f0a9b302541880ba0c1bf6d5f5a26224db9a8b1aIn the context of smart health, the use of wearable Internet of Things (IoT) devices is becoming increasingly popular to monitor and manage patients’ health conditions in a more efficient and personalized way. However, choosing the most suitable artificial intelligence (AI) methodology to analyze the data collected by these devices is crucial to ensure the reliability and effectiveness of smart healthcare applications. Additionally, protecting the privacy and security of health data is an area of growing concern, given the sensitivity and personal nature of such information. In this context, machine learning (ML) and deep learning (DL) are emerging as successful technologies because they are suitable for application to advanced analysis and prediction of healthcare scenarios. Therefore, the objective of this work is to contribute to the current state of the literature by identifying challenges, best practices, and future opportunities in the field of smart health. We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. Through this systematic review, we aim to offer practical guidelines for the design, development, and implementation of AI solutions in smart health, to improve the quality of care provided and promote patient well-being. To pursue our goal, several articles focusing on ML or DL network architectures were selected and reviewed. The final discussion highlights research gaps yet to be investigated, as well as the drawbacks and vulnerabilities of existing IoT applications in smart healthcare.PeerJ Computer Science860This work aims to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices.PeerJ Computer Science["L. Aversano", "Martina Iammarino", "Ilaria Mancino", "Debora Montano"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/f0a9b302541880ba0c1bf6d5f5a26224db9a8b1a d2fed555908539aa241bf9f7093cc5cee8ae1b00Purpose This study investigated the regulatory landscape surrounding artificial intelligence (AI) in the context of e-government development. The purpose of this article is to identify record-keeping challenges, opportunities and weaknesses that emerge from AI loose regulation. The research focuses on Sweden, Finland and South Africa, examining the interplay between existing guidelines, recommendations and legal structures at various levels. Design/methodology/approach The research adopted comprehensive systematic and scoping literature reviews, encompassing academic papers, reports and legal documents, along with an analysis of non-academic sources relevant to the study. This methodological approach helped to obtain a deep understanding of the evolving AI regulatory frameworks. Findings There is currently limited research that focuses on the impact AI deployment has on the management of critical records in government administrations. Also, the findings reveal that AI regulatory environment varies from country to country. The European Union stands as a noteworthy example of a comprehensive framework for AI governance. In contrast, South Africa, while at its infancy stage, demonstrates potential initiatives and policies at different levels. There is emphasis on the need to focus on co-operation, skills development and uniform regulatory frameworks. Practical implications This research holds significant practical implications for policymakers, government bodies and stakeholders involved in AI governance. It emphasizes how crucial it is to incorporate AI alongside a solid records management system. The study advocates for strategic investments in education and skills development to enable individuals to navigate the complexities of AI governance. Originality/value This research adds to the existing body of knowledge by providing an examination of AI legislation in e-government in the context of public records management. The analysis helps to review literature and other research materials across different geographical areas. The study explores the distinctive strategies used by Sweden, Finland and South Africa. The recommendations offer policymakers and stakeholders suggestions on how to foster effective AI governance and innovation in the public sector but at the same time manage public records effectively. Records Management Journal520The research focuses on Sweden, Finland and South Africa, examining the interplay between existing guidelines, recommendations and legal structures at various levels at various levels, and explores the distinctive strategies used by Sweden, Finland and South Africa.Records Management Journal["Proscovia Sv\u00e4rd", "Esteban Guerrero", "Tolulope Balogun", "Nampombe Saurombe", "Lorette Jacobs", "Pekka Henttonen"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/d2fed555908539aa241bf9f7093cc5cee8ae1b00 e6d96a54c1df00292c985a363c240b2716a44b7dThe rise of artificial intelligence (AI) as a creative tool disrupts the traditional realm of copyright law. This work explores the complexities of copyright protection for AI-generated works. We examine the concept of authorship, a cornerstone of copyright, and its applicability to AI. Ownership of AI creations will be analyzed, considering the programmer’s role and the training data’s influence. Furthermore, using copyrighted material in AI training raises questions about fair use and infringement. By unraveling these issues, this work seeks to illuminate the path toward a copyright framework that fosters innovation while safeguarding the rights of creators and AI developers in the age of AI.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)300The concept of authorship, a cornerstone of copyright, and its applicability to AI will be analyzed, considering the programmer’s role and the training data’s influence.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)["Singh Hoshiar", "Sharma Kiran"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/e6d96a54c1df00292c985a363c240b2716a44b7d e2734f9345ed25141e8bc4def9fc9ebd5d504563With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem and operations which were followed for decades. The current landscape is where decisions are increasingly data-driven by financial institutions with an appetite for automation while mitigating risks. The segments of financial institutions which are getting heavily influenced are retail banking, wealth management, corporate banking&payment ecosystem. The solution ranges from onboarding the customers all the way fraud detection&prevention to enhancing the customer services. Financial Institutes are leap frogging with integration of Artificial Intelligence and Machine Learning in mainstream applications and enhancing operational efficiency through advanced predictive analytics, extending personalized customer experiences, and automation to minimize risk with fraud detection techniques. However, with Adoption of AI&ML, it is imperative that the financial institute also needs to address ethical and regulatory challenges, by putting in place robust governance frameworks and responsible AI practices.arXiv.org240With adoption of AI&ML, it is imperative that the financial institute also needs to address ethical and regulatory challenges, by putting in place robust governance frameworks and responsible AI practices.ArXiv["Animesh Kumar"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/e2734f9345ed25141e8bc4def9fc9ebd5d504563 0f5945914d3f2b59a7738ee47dca33eeece6dd13Human activities have been driving massive greenhouse gas emissions, causing global warming, and triggering increasingly frequent extreme weather events that severely threaten the environment. Power generation is the leading contributor to anthropogenic emissions, making precise, real‐time measurement and monitoring of power plant carbon emissions crucial in reducing climate change. This study uses a new sophisticated pipeline that combines tropospheric monitoring instrument satellite data, power plant attributes, and advanced artificial intelligence algorithms to build a predictive carbon emission model. The approach utilizes multimodal data processing, encoding, and model optimisation. Experimental results confirm that this pipeline can automatically extract and utilize vast amounts of relevant data, thereby enabling the artificial intelligence model to accurately predict power plant carbon emissions and providing a vital tool for reducing global warming.Energy Conversion and Economics270A new sophisticated pipeline that combines tropospheric monitoring instrument satellite data, power plant attributes, and advanced artificial intelligence algorithms to build a predictive carbon emission model is used, enabling the artificial intelligence model to accurately predict power plant carbon emissions and providing a vital tool for reducing global warming.Energy Conversion and Economics["Zeqi Zhang", "Di Leng", "Yingjie Li", "Xuanang Gui", "Yuheng Cheng", "Junhua Zhao", "Zhengwen Zhang", "Amer M. Y. M. Ghias"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/0f5945914d3f2b59a7738ee47dca33eeece6dd13 31a725de0240675becef3fdf3663ad0e068075f3This paper presents the Teach E-AI 2C (Teaching Embodied Artificial Intelligence to Children) project, which aims to educate children and early adolescents about Embodied Artificial Intelligence using the 4C/ID instructional design model. This project is intended to bridge the gap between scientific progress and users' knowledge of AI, by providing personalized and hands-on learning experiences. The implementation involves classroom trials, where students engage in both virtual and physical AI learning activities, experimenting the integration of theoretical concepts, through learning units, with practical tasks, using a purpose-built software. The paper also introduces the methodology for evaluating learning units and software, as well as usability and effects of the whole implementations.2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)3602024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)["Clara Nobile", "Davide Marocco", "M. Ponticorvo", "Onofrio Gigliotta"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/31a725de0240675becef3fdf3663ad0e068075f3 f9de4f784e7056ff13523c34978fdb46dc7d1b12Children with autism spectrum disorder (ASD) face challenges in communication, social interaction, and behavioral flexibility, which can affect their development and everyday functioning. Artificial intelligence (AI) offers innovative tools to support children with autism by providing personalized and adaptive interventions. AI-powered systems, such as robots, virtual assistants, and machine learning models, can be used to enhance social skills training, improve communication, and manage sensory sensitivities. These technologies can monitor progress, adjust strategies, and provide real-time feedback, enabling more effective, individualized care. This paper explores the potential of AI in autism support, focusing on AI applications for social skill enhancement, early detection, and therapeutic intervention, while also addressing the ethical considerations and challenges involved in integrating AI into autism care.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)20The potential of AI in autism support is explored, focusing on AI applications for social skill enhancement, early detection, and therapeutic intervention, while also addressing the ethical considerations and challenges involved in integrating AI into autism care.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)["Nan Xing"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/f9de4f784e7056ff13523c34978fdb46dc7d1b12 fc9a445781759ea26b57b929c16b65442114ab36The vast adoption of Wi-Fi and/or Bluetooth capabilities in Internet of Things (IoT) devices, along with the rapid growth of deployed smart devices, has caused significant interference and congestion in the industrial, scientific, and medical (ISM) bands. Traditional Wi-Fi Medium Access Control (MAC) design faces significant challenges in managing increasingly complex wireless environments while ensuring network Quality of Service (QoS) performance. This paper explores the potential integration of advanced Artificial Intelligence (AI) methods into the design of Wi-Fi MAC protocols. We propose AI-MAC, an innovative approach that employs machine learning algorithms to dynamically adapt to changing network conditions, optimize channel access, mitigate interference, and ensure deterministic latency. By intelligently predicting and managing interference, AI-MAC aims to provide a robust solution for next generation of Wi-Fi networks, enabling seamless connectivity and enhanced QoS. Our experimental results demonstrate that AI-MAC significantly reduces both interference and latency, paving the way for more reliable and efficient wireless communications in the increasingly crowded ISM band.arXiv.org150The proposed AI-MAC is an innovative approach that employs machine learning algorithms to dynamically adapt to changing network conditions, optimize channel access, mitigate interference, and ensure deterministic latency, and aims to provide a robust solution for next generation of Wi-Fi networks, enabling seamless connectivity and enhanced QoS.ArXiv["Jinzhe Pan", "Jingqing Wang", "Zelin Yun", "Z. Xiao", "Yuehui Ouyang", "Wenchi Cheng", "Wei Zhang"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/fc9a445781759ea26b57b929c16b65442114ab36 9424a98bf0c72d11bae261346d1c0eae12b3001bAim. To evaluate the potential of generative artificial intelligence technology in the context of supply chain management.Objectives. To analyze how artificial intelligence is changing traditional management practices and structures in supply chain management; to identify fundamental shifts brought about by AI, including automation, data-driven decision making, and human empowerment; to examine current statistics on the effectiveness of artificial intelligence in management processes and conclude the potential, promise of this technology in the logistics industry; to provide strategic insight and practicalMethods. The research combines qualitative and quantitative methods to provide a deeper understanding of the problems considered. Based on these methods, an array of relevant statistical data and expert opinions in the field of generative artificial intelligence has been studied.Results. In today’s business environment, supply chains face a number of challenges: globalization, market volatility, changing customer expectations, and force majeure (natural disasters, pandemics, or wars). To meet these challenges and strengthen their market position, companies are increasingly turning to new technologies and innovations. These technologies have the potential to revolutionize the approach to supply chain management, increasing transparency, efficiency and responsiveness. All emerging technologies, from artificial intelligence and blockchain to the Internet of Things (IoT) and robotics, potentially offer opportunities to streamline operations, improve decision-making, and enhance supply chain collaboration.Conclusions. In this article, the authors have shown how artificial intelligence is changing traditional governance structures in supply chain management. Current statistical data on the effectiveness of artificial intelligence in management processes are analyzed, and a conclusion is made about the potential and prospects of this technology in the logistics sphere. Strategic understanding is formed and practical recommendations for organizations seeking to implement new tools of generative artificial intelligence are given.Economics and Management70How artificial intelligence is changing traditional governance structures in supply chain management is shown and practical recommendations for organizations seeking to implement new tools of generative artificial intelligence are given.Economics and Management["F. D. Ivanov"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/9424a98bf0c72d11bae261346d1c0eae12b3001b 5a71ef2f8205845ce155df7b9740534ce64fcd3dArtificial intelligence (AI) in agriculture is transforming the sector by improving resources efficiency, sustainability and productivity. Our study examined a number of AI-related applications such as pest control, crop monitoring, precision farming and soil health evaluation. AI powered devices enables automated fertilization, harvesting and irrigation, and therefore, cutting down the labor expenses and resource waste. Predictive analytics in AI helps with crop yield and weather forecasts which ultimately improves the planning and risk management. The paper also discusses the challenges and limitations of AI adoption in agriculture, such as the need for reliable data, technical expertise and infrastructure investment. Ultimately, the findings highlights the AI can have positive transformative potential in creating resilient agricultural practices that can meet the demands of a growing global population while minimizing environmental impact. However, one of the biggest uses of AI is precision farming, which uses the technology to optimize inputs like water, fertilizer and pesticides by adjusting them to the unique requirements of the crop and the field. AI techniques also make it possible to detect the pests and diseases through picture recognition and predictive analytics, which ultimately minimizes the crop loss and allows for prompt interventions. Widespread use may be hampered by issues with data quality, model interpretability, expensive prices and system integration. Furthermore, issues with labor impact, regulatory frameworks and scalability complicate its adoption. In order to fully utilize AI in agriculture, researchers, farmers and policymakers must work together to overcome these challenges and develop workable and accessible solutions that are suited to a variety of agricultural environments. Present review also highlighted how AI involvement has the ability to revolutionize agricultural sector by developing resilient methods that can both minimize environmental effects and meet the needs of an expanding global population. The agriculture industry can set the path for a sustainable future by adopting AI advances and guaranteeing the environmental stewardship and goals of food safety and security.Journal of Scientific Research and Reports00This study examined a number of AI-related applications such as pest control, crop monitoring, precision farming and soil health evaluation, which highlighted how AI involvement has the ability to revolutionize agricultural sector by developing resilient methods that can both minimize environmental effects and meet the needs of an expanding global population.Journal of Scientific Research and Reports["Ritambara", "Shilpa Kaushal", "Shubham"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/5a71ef2f8205845ce155df7b9740534ce64fcd3d 697617416313df9a4b5adbf56ee42974f44e5203The article is devoted to the study of the norms of the European Union law on Artificial Intelligence in terms of unacceptable risks of artificial intelligence. Attention is drawn to the fact that the main idea of the European approach is to support the development of trustworthy artificial intelligence, in connection with which the emphasis is placed on a risk-based approach. It is indicated that unacceptable risk is one of the levels of such an approach, which also includes artificial intelligence systems with minimal, limited and high risk. The use of unacceptable risk artificial intelligence systems refers to actions prohibited by the legislation of the European Union, except for their use in a number of circumstances specified in the law. Methods affecting the subconscious mind are considered; manipulative and misleading methods; methods using the vulnerability of a person or group of persons; methods of biometric identification; predictive methods based on profiling; methods for determining emotions. The exceptions allowed by law in relation to artificial intelligence systems of unacceptable risk are analyzed. It is concluded that the lack of broad regulatory regulation of artificial intelligence in Russia cannot be unequivocally recognized as a negative factor, since the legislator has the opportunity to analyze the results of legal approaches to regulation in foreign countries. It is concluded that in the Russian Federation there is no need to adopt a law specifically devoted to artificial intelligence. The relevant norms may well be implemented in the current industry legislation.LEGAL ORDER: History, Theory, Practice00It is concluded that the lack of broad regulatory regulation of artificial intelligence in Russia cannot be unequivocally recognized as a negative factor, since the legislator has the opportunity to analyze the results of legal approaches to regulation in foreign countries.LEGAL ORDER: History, Theory, Practice["A. G. Sheikin"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/697617416313df9a4b5adbf56ee42974f44e5203 6dea976704aee424856ae8d0fb29df91fa31d97aA variety of assistive extended reality (XR) visual cueing techniques have been explored over the years. Many of which provide significant benefits to tasks such as visual search. However, when the cueing system is erroneous, performance may instead suffer. Factors such as automation bias, where an individual trusts the cueing system despite errors in the cueing may affect task efficacy (i.e. completion time, accuracy, etc.). In some cases, such as with automation bias, these hindrances may be the product of artificial intelligence (AI) integration. Despite this, there may be benefits to using adaptive AI-based cueing systems for XR tasks. However, aspects such as the flow of information, automation accuracy, communication of confidence, or the refusal of output must be considered to build effective AI adaptive cueing systems. In this paper, we discuss four studies conducted by our group that explore visual cueing and AI. We then discuss potential future avenues for integrating AI into cueing techniques to minimize automation bias and cognitive demand on users, as well as, improve overall cueing outcomes.2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)270Four studies conducted by the group that explore visual cueing and AI are discussed and potential future avenues for integrating AI into cueing techniques are discussed to minimize automation bias and cognitive demand on users, as well as, improve overall cueing outcomes.2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)["Brendan Kelley", "Aditya Raikwar", "Christopher D. Wickens", "Ryan P. McMahan", "Francisco R. Ortega", "Benjamin A. Clegg"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/6dea976704aee424856ae8d0fb29df91fa31d97a 9049e2733a9aecb128afed57544fe0fa4bb23725Extended Reality (XR) integrates real and virtual environments through spatial computing technologies, playing a crucial role in the development of the Metaverse. The synergy of XR with Artificial Intelligence (AI), referred to as Extended Artificial Intelligence (XRAI), enhances immersive experiences and operational efficiencies across various domains and human activities. However, ethical considerations for XRAI remain underexplored, particularly considering fairness, privacy, bias, and responsibility. This paper introduces the XRAI-Ethics framework, which aims at defining a novel approach for analyzing and extract ethical risks and principles for XRAI. The XRAI-Ethics framework seeks to promote responsible development and implementation of XRAI technologies, offering guidelines for both public and private sectors to ensure ethical practices in emerging XR applications.2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)700The XRAI-Ethics framework seeks to promote responsible development and implementation of XRAI technologies, offering guidelines for both public and private sectors to ensure ethical practices in emerging XR applications.2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)["Lorenzo Stacchio", "R. Pierdicca", "M. Paolanti", "P. Zingaretti", "Emanuele Frontoni", "B. Giovanola", "S. Tiribelli"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/9049e2733a9aecb128afed57544fe0fa4bb23725 b919566c424d2f2d342366f63a87b89c3ea590bdArtificial intelligence (AI) is penetrating higher medical education; however, its adoption remains low. A PRISMA-S search of the Web of Science database from 2020 to 2024, utilizing the search terms “artificial intelligence,” “medicine,” “education,” and “ethics,” reveals this trend. Four key areas of AI application in medical education are examined for their potential benefits: Educational support (such as personalized distance education), radiology (diagnostics), virtual reality (VR) (visualization and simulations), and generative text engines (GenText), such as ChatGPT (from the production of notes to syllabus design). However, significant ethical risks accompany AI adoption, and specific concerns are linked to each of these four areas. While AI is recognized as an important support tool in medical education, its slow integration hampers learning and diminishes student motivation, as evidenced by the challenges in implementing VR. In radiology, data-intensive training is hindered by poor connectivity, particularly affecting learners in developing countries. Ethical risks, such as bias in datasets (whether intentional or unintentional), need to be highlighted within educational programs. Students must be informed of the possible motivation behind the introduction of social and political bias in datasets, as well as the profit motive. Finally, the ethical risks accompanying the use of GenText are discussed, ranging from student reliance on instant text generation for assignments, which can hinder the development of critical thinking skills, to the potential danger of relying on AI-generated learning and treatment plans without sufficient human moderation.Artificial Intelligence in Health00The ethical risks accompanying the use of GenText are discussed, ranging from student reliance on instant text generation for assignments, which can hinder the development of critical thinking skills, to the potential danger of relying on AI-generated learning and treatment plans without sufficient human moderation.Artificial Intelligence in Health["Mark Perkins", "Agnieszka Pregowska"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/b919566c424d2f2d342366f63a87b89c3ea590bd d7332358d5e5b388e0271f5d3d9e5382347a0491Education for All project of Nanritam, an Indian non-profit, is an ambitious yet necessary idea born out of the difficulties faced during COVID-19 pandemic. One of its main projects is the Filix School established in 2014 in a remote rural and economically backward area of Purulia, West Bengal, India with the aim of providing holistic, equitable and quality education to the socio-economically challenged children of surrounding area. Filix School has very successfully implemented a unique research-based experiential pedagogy over the past decade, significantly improving the academic outcomes of these children. However, the pandemic meant that the school had to provide education by digital means. Thus, ideated that the education provided to the students of Filix school could be leveraged to a larger community. Co-created by school students under supervision, the Filix Innovation Hub has created an artificial intelligence enabled system that provides education to remote areas, including through space systems. Whereas the project is based in India, it may be customized for other parts of the world. This project bolsters the idea that excellent and contextualized quality education with the help of digital transformation can be instrumental to achieve the United Nations’ Sustainable Development Goals. For this project, Nanritam has partnered with a non-profit space policy initiative - ACES Worldwide, reiterating the importance of interconnectedness and the need of space systems in communications between and with remote areas.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)120This project bolsters the idea that excellent and contextualized quality education with the help of digital transformation can be instrumental to achieve the United Nations’ Sustainable Development Goals.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)["Upasana Dasgupta", "Joseph Pelton", "Ranjana Sengupta", "Sarada Namhata", "Sukdev Mahato", "Sarthak Sarkar"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/d7332358d5e5b388e0271f5d3d9e5382347a0491 6c80f13f5b2d96b90ec86ba4b139e820e25e2c31The discussion in the UN Working Group on the draft convention on the use of information and communication technologies for military and political purposes has recently given some hope for a compromise, but now it is obvious that the positions of the main participants - the United States and Russia - are irreconcilable. Questions about the cybersecurity of military systems are particularly delicate. Legal science can offer non-standard methods to resolve this conflict situation. Among the possible tools is the theory of security measures as a legal institution that includes security sanctions, developed by the well-known criminal law specialist Professor N. V. Shchedrin. Recently, the issue of using “artificial intelligence” for various applied purposes has become topical (and controversial). Many myths have arisen around it. For legally correct solution of arising problems in criminal, civil and other branches of law, demystification of “artificial intelligence” is required. Given the lack of information about the real use of “artificial intelligence”, the systems of control of nuclear weapons of a probable enemy, which include the complexes of automatic control of retaliatory nuclear weapons of the USSR/Russia and the United States, have become a source of increased concern. The article proposes a non-standard legal approach to drafting a convention that would reduce the levels of risk and mistrust of “intelligent” nuclear weapons control systems.LEGAL ORDER: History, Theory, Practice00The article proposes a non-standard legal approach to drafting a convention that would reduce the levels of risk and mistrust of “intelligent” nuclear weapons control systems.LEGAL ORDER: History, Theory, Practice["Yuri M. Baturin"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/6c80f13f5b2d96b90ec86ba4b139e820e25e2c31 ec797ff612aa8ace7e549e76dbfd05b840c39010Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.Frontiers Comput. Neurosci.461This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better using the proposed XGBoost 2.0 model.Frontiers in Computational Neuroscience["Asma Aldrees", "Stephen Ojo", "James Wanliss", "Muhammad Umer", "Muhammad Attique Khan", "B. Alabdullah", "Shtwai Alsubai", "Nisreen Innab"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/ec797ff612aa8ace7e549e76dbfd05b840c39010 273ab612cce4ec5c58996beeed81862e93622302Journal of Medical Imaging and Radiation Sciences761While expertise levels are varied and different, both radiographers and radiologists were generally optimistic about incorporation of AI in medical imaging practice, however, low levels of AI education and knowledge remain a critical barrier.Journal of medical imaging and radiation sciences["S. Arkoh", "T. Akudjedu", "C. Amedu", "W. Antwi", "W. Elshami", "B. Ohene-Botwe"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/273ab612cce4ec5c58996beeed81862e93622302 dcb58f02cd5a293fffe8ddba0fe38bd4cdd0a4e4This study aims to assess the viability of using solar photovoltaic (PV) water electrolyzer to generate green hydrogen and sustainable energy, with the specific goal of meeting the electrical demands of buildings in arid locations. Models for short-term ANN power output predictions from solar PV and fuel cells, as well as green hydrogen production, are developed. By utilizing a power system fueled exclusively by renewable resources and zero-carbon fuels, our objective is to build a structure that operates without consuming fossil fuel energy. Modeling, simulation, and optimization analysis were used in this study to evaluate the system's technical, economic, and environmental performance. The artificial neural network (ANN) prediction models utilize simulated input and output data to forecast the production of hydrogen through solar-powered electrolyzer. The building's daily, monthly, and annual electricity demands were all fulfilled by the self-sufficient hybrid solar PV/fuel cell power system. The hybrid power system being proposed generates 79.7% and 20.3% of its electricity from solar photovoltaic (PV) and fuel cell sources, respectively. The electrolyzer requires an energy input of 898,415 kilowatt-hours, produces 19,360 kilograms of hydrogen, and consumes 15,817 kilograms of hydrogen for the fuel cell per year. The mean and maximum output of the electrolyzer for green hydrogen production are 2.21 and 10.8 kg/hr., respectively, with a specific energy of 46.4 kWh/kg. The training, validation, and test R values for solar PV power output and green hydrogen production, obtained from the ANN-based forecasting models, range from 0.967 to 0.974 and from 0.981 to 0.982, respectively. The correlation coefficient R and mean square errors MSE of the ANN regression model indicate that the models provide accurate estimations of the power output of solar PV systems and the production of green hydrogen by electrolyzer.International Conference and Utility Exhibition on Green Energy for Sustainable Development1602024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future (ICUE)["C. Ghenai"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/dcb58f02cd5a293fffe8ddba0fe38bd4cdd0a4e4 4b98fecfb6eaa199340e435dafceea1e8153f317The paper presents a software module responsible for simulating airspace. The system implements the display of key airspace elements with the translation of geographic coordinates into two-dimensional space. This module is used in the development of simulators for training air traffic control personnel.Materials of the 27th All-Russian Youth Scientific Conference00A software module responsible for simulating airspace implements the display of key airspace elements with the translation of geographic coordinates into two-dimensional space used in the development of simulators for training air traffic control personnel.Materials of the 27th All-Russian Youth Scientific Conference["I.I. Gareev", "A.S. Lushnikov"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/4b98fecfb6eaa199340e435dafceea1e8153f317 015e76c3a2cdd80bd5ebc44f006c659ed485cba6National Science Review100National Science Review["Chung-I Wu", "Cai Li"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/015e76c3a2cdd80bd5ebc44f006c659ed485cba6 f109c6a58541c04e71c5204a07f90b5f4c6803d5African Journal of Biomedical Research00African Journal of Biomedical Research["Akhilesh Kumar"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/f109c6a58541c04e71c5204a07f90b5f4c6803d5 3de21740d82b93db4988de8e81fc7f678be08b5bArtificial Intelligence Review450Artificial Intelligence Review["Miguel Cuevas", "Ricardo \u00c1lvarez-Malebr\u00e1n", "C. Rahmann", "Diego Ortiz", "Jos\u00e9 Pe\u00f1a", "Rodigo Rozas-Valderrama"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/3de21740d82b93db4988de8e81fc7f678be08b5b 66a61e705e372ce97ef0d804743f47033131433bThere are two main areas in which the Internet of Ships (IoS) can help: firstly, the production stage, in all its phases, from material bids to manufacture, and secondly, the operation of the ship. Intelligent ship management requires a lot of information, as does the shipbuilding process. In these two phases of the ship’s life cycle, IoS acts as a key to the keyhole. IoS tools include sensors, process information and real-time decision-making, fog computing, or delegated processes in the cloud. The key point to address this challenge is the design phase. Getting the design process right will help in both areas, reducing costs and making agile use of technology to achieve a highly efficient and optimal outcome. But this raises a lot of new questions that need to be addressed: At what stage should we start adding control sensors? Which sensors are best suited to our solution? Is there anything that offers more than simple identification? As we begin the process of answering all these questions, we realize that a Computer Aided Design (CAD) tool, as well as Artificial Intelligence (AI), mixed in a single tool, could significantly help in all these processes. AI combined with specialized CAD tools can enhance the sensitization phases in the shipbuilding process to improve results throughout the ship’s life cycle. This is the base of the framework developed in this paper.De Computis200AI combined with specialized CAD tools can enhance the sensitization phases in the shipbuilding process to improve results throughout the ship’s life cycle, and is the base of the framework developed in this paper.Comput.["Arturo Benayas-Ayuso", "Rodrigo P\u00e9rez Fern\u00e1ndez", "Francisco Perez-Arribas"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/66a61e705e372ce97ef0d804743f47033131433b 5f7d240902d3bdbe9483dd2b7da4353ddb86cc29The aim of this work is to analyze the use of Artificial Intelligence (AI) in the reproduction of human voices, focusing on the advantages, disadvantages, and challenges associated with this technology, as well as ethical and responsible use. Bibliographic research was conducted, emphasizing the ethical, legal, and technical implications of AI, highlighting its potential benefits such as improvements in digital accessibility, personalization of user experiences, and data privacy consent. The results underscored the importance of an ethical approach in the development and implementation of AI in human voice reproduction, maximizing its benefits while mitigating associated risks. It is concluded that there is a need to promote a more accessible discussion about the role of this technology in contemporary society, thereby ensuring the dissemination of ethics for the benefit of all.Revista Científica Semana Acadêmica00It is concluded that there is a need to promote a more accessible discussion about the role of this technology in contemporary society, thereby ensuring the dissemination of ethics for the benefit of all.Revista Científica Semana Acadêmica["Jean Souza Figueiredo Junior", "F. Florian", "Renata MIRELLA FARINA"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/5f7d240902d3bdbe9483dd2b7da4353ddb86cc29 f11ac1f94902d19628789da2a7155347abcff3faObjective: This research is urgent to understand the views and attitudes of lecturers and students towards the ethics of using artificial intelligence in the campus, which is closely related to the values of defending the country and Pancasila as a moral and social foundation in the context of modern technology.   Theoretical Framework: Artificial Intelligence is a system developed in the field of study that is made either on machines or computers to have the same or even more intelligence as humans. Pancasila is a values guide humanity, unity, and nationality to defending the country in the use of tools to ensure benefits and justice.   Method: This study uses a qualitative method with an interpretive approach, using interviews and observations as data collection techniques. Lecturers of ethics courses were selected as sources to obtain their opinions on moral values and national defense of academic fraud activities in the SDGs university environment.   Results and Discussion: The role of Pancasila as ethical guide in regulating the development and utilization of Artificial Intelligence Technology in Indonesia. The philosophical foundation of the state that influences laws, regulations, and community behavior. The responsible use of technology requires moral guidance that is in accordance with the norms and moral principles of society.   Research Implications: The findings of this study will be able to show that the perceptions of lecturers and students are in line with the theory of moral development and the code of ethics of the lecturer profession, but the problem of the SDGs internal campus system is the main problem in fixing the culture of deviant behavior.   Originality/Value: The balance of soft skills and hard skills education must be fostered with religious and character teachings by educators.Journal of Lifestyle and SDGs Review80The findings of this study will be able to show that the perceptions of lecturers and students are in line with the theory of moral development and the code of ethics of the lecturer profession, but the problem of the SDGs internal campus system is the main problem in fixing the culture of deviant behavior.Journal of Lifestyle and SDGs Review["Oryza Tannar", "Endah Susilowati"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/f11ac1f94902d19628789da2a7155347abcff3fa 4fe7c488248180248bc23b4abe1aa372d35d7901Additive manufacturing (AM), one of the emerging disruptive technologies, is gaining popularity not only in rapid prototyping but also in manufacturing of complex shapes and dimensions. Artificial intelligence (AI) is the intelligence exhibited by computer systems to perform complex tasks such as learning, reasoning, decision making and problem solving. Machine learning (ML) is a subset of artificial intelligence which enables AI to imitate human learning process by using data and algorithms. The concept of machine intelligence which helps the advanced computing technologies to interact with the environment and highlights the intersection of AI and ML. The aim of this review article is to provide comprehensive information about the application of AI and ML in various additive manufacturing processes for different activities in order to improve the performance of the operation. Also, it describes the application of other advanced technologies such as Internet of Things (IoT), Digital Twins (DT) and Block Chain Technology to augment the additive manufacturing in producing quality products. Further, the article explains the various challenges that are encountered and the certain areas need to be addressed in future for the enhancement of quality product production by the application of these technologies in design, manufacturing and quality assurance.Turkish Journal of Engineering00The article describes the application of other advanced technologies such as Internet of Things (IoT), Digital Twins (DT) and Block Chain Technology to augment the additive manufacturing in producing quality products.Turkish Journal of Engineering["N. Ethiraj", "T. Sivabalan", "J. Sofia", "Dommaraju Harika", "M. Nikolova"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/4fe7c488248180248bc23b4abe1aa372d35d7901 2b754c55a97d8935758b6e092ba83dbee81774ffLanguage Testing in Asia454The findings underscored the significance of LA and AB in achieving a balance in the PW of EFL pupils, particularly when utilizing ICALA for language acquisition.Language Testing in Asia["M. Khasawneh", "Sayed M. Ismail", "Negash Hussen"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/2b754c55a97d8935758b6e092ba83dbee81774ff f514b7dd258b8234c755e05ef23c0357ee89593bBiofabrication is an advanced technology that holds great promise for constructing highly biomimetic in vitro three-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction. .Biofabrication01This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.Biofabrication["Chang Zhou", "Changru Liu", "Zhendong Liao", "Y. Pang", "Wei Sun"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/f514b7dd258b8234c755e05ef23c0357ee89593b 23ca0e6b794dfd8a0fdd5f4d821fe3a97b0181c4Cryptographic techniques are currently used in computer security systems designed to guarantee the integrity and security of encrypted information. They are based on advanced cryptographic algorithms that can also operate on a public key infrastructure. Such algorithms can also be based on user keys and personalized approaches applied to encrypt data and create VPN tunnels. The paper will present new possibilities of using artificial intelligence algorithms in the creation of new protocols designed to guarantee the cybersecurity of distributed computer systems. Artificial intelligence techniques can find application in such an area, especially in the analysis of protocol security, and can also be applied to the creation of new protocols dedicated to specific remote services, which will provide a greater level of security and allow better data protection.WSEAS Transactions on Computer Research61Artificial intelligence techniques can find application in such an area, especially in the analysis of protocol security, and can be applied to the creation of new protocols dedicated to specific remote services, which will provide a greater level of security and allow better data protection.WSEAS TRANSACTIONS ON COMPUTER RESEARCH["Marek R. Ogiela", "Urszula Ogiela"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/23ca0e6b794dfd8a0fdd5f4d821fe3a97b0181c4 579881bbb26b278c2003335f77be995f88eca79dEven though artificial intelligence (AI)-based systems typically outperform human decision-makers, they are not immune to errors, leading users to lose trust in them and be less likely to use them again-a phenomenon known as algorithm aversion. The purpose of the present research was to investigate whether explainable AI (XAI) could function as a viable strategy to counter algorithm aversion. We conducted two experiments to examine how XAI influences users' willingness to continue using AI-based systems when these systems exhibit errors. The results showed that, following the observation of algorithms erring, the inclination of users to delegate decisions to or follow advice from intelligent agents significantly decreased compared to the period before the errors were revealed. However, the explainability effectively mitigated this decline, with users in the XAI condition being more likely to continue utilizing intelligent agents for subsequent tasks after seeing algorithms erring than those in the non-XAI condition. We further found that the explainability could reduce users' decision regret, and the decrease in decision regret mediated the relationship between the explainability and re-use behaviour. These findings underscore the adaptive function of XAI in alleviating negative user experiences and maintaining user trust in the context of imperfect AI.British Journal of Psychology410The results showed that, following the observation of algorithms erring, the inclination of users to delegate decisions to or follow advice from intelligent agents significantly decreased, but the explainability effectively mitigated this decline.British journal of psychology["Min Xu", "Yiwen Wang"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/579881bbb26b278c2003335f77be995f88eca79d f3eb083604e9a35a86bcb79ecbe2ca037e92a170This study examines the digital and artificial intelligence (AI) transformation in the contemporary art industry in China. This industry in China is undergoing a distinct digital transition and is “ahead” of other countries, having fully integrated digital technologies and AI (digiAI) into policies, regulations, organizations, and professional practices. A systematic, large-scale national integration of digiAI has led to its widespread adoption by artists and arts professionals. However, little is known about how or when this rapid and extensive integration and subsequent adoption occurred or about its impacts on professional practices. This study draws on research conducted between 2023 and 2024, including 30 interviews with contemporary Chinese visual artists, 23 interviews with arts professionals, a survey of 110 professional contemporary visual artists, and a systematic review of government policy. Findings indicate that the government began integrating digital technology into the contemporary art industry in 2016, further promoted digital technology integration in 2021, and introduced regulations to support AI usage in 2023. The data reveal a significant spike in the adoption of digital technologies by professionals between 2019 and 2020, followed by a rise in AI adoption in 2023. DigiAI has been accepted and now used across different kinds of arts professions, various types of visual artists, and several age groups. Digital and AI tools are now being applied in both creative and non-creative aspects of arts practices.Arts &amp; Communication00Arts &amp; Communication["Emma Duester", "Ruyin Zhang"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/f3eb083604e9a35a86bcb79ecbe2ca037e92a170 14e30d6436ebd08b4ab46aad29775c2c957bbe15With the continuous development of artificial intelligence, we are living through the era of digital transformation, and the fourth Industrial Revolution is a paradigm change with unprecedented speed, scale, scope and complexity, and is fundamentally transforming production, consumption and society as a whole. As the basic unit of public management research, data has established a strong connection between the promotion of digital revolution and the change of public management research paradigm. The rise of AI technologies has enhanced AI governance and brought unprecedented help to policymakers. This study has used a cross-sectional quantitative design to capture perception and attitude towards AI as common good from a sample of 400 Chinese responders coming from diverse age, group and occupation categories. The key factors which measured were public awareness of AIGC, the extent of its utilization in government policy, public attitudes toward AIGC, and public participation in policy development. The findings indicated that public understanding and trust are paramount to successfully integrate Ai as common good in governance framework. Public enthusiasm and participations are highly affected by trust and public understanding. To deliver policies without much resistance, heightened awareness, participations and trust becomes essential.  Journal of Ecohumanism310The findings indicated that public understanding and trust are paramount to successfully integrate Ai as common good in governance framework.Journal of Ecohumanism["Siying Wei", "Zhenxuan Ge", "Consilz Tan"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/14e30d6436ebd08b4ab46aad29775c2c957bbe15 3b28239f6642e6416e12878a6e522c547c72298bArtificial intelligence (AI) has already been incorpo-rated into wide range applications of the fifth generation (5G) networks. The AI-native design of 6G network is serving as cornerstone for intelligent, autonomous, and dynamic network operations. AI-driven techniques, such as machine learning (ML) and Deep Learning (DL), facilitate real-time data analytics, predictive modeling, and decision-making processes to optimize resource utilization, enhance network performance, and ensure seamless connectivity for a multitude of devices and services. However, it is crucial in many respects that these AI algorithms are reliable, trustworthy, and explainable. In this direction, Explainable AI (XAI) will ensure transparent and secure operation at different layers of 6G networks. With the integration of XAI, 6G networks can achieve transparent dynamic self-configuration, self-optimization, and self-healing capabilities, enabling the network to adapt to fluctuating demands, mitigate potential issues proactively. To ensure that the AIML algorithms used in 6G Next-generation URLLC (xURLLC) use case are trustable and reliable, we proposed a XAInomaly framework that use our novel fastSHap-cXai method which handle real-time XAI layer operations on Open-RAN (O-RAN). Our performance results show that fastSHAP-C provides a 25% advance over its competitors in terms of resource utilization.2024 3rd International Conference on 6G Networking (6GNet)230A XAInomaly framework that use the novel fastSHap-cXai method which handle real-time XAI layer operations on Open-RAN (O-RAN) and performance results show that fastSHAP-C provides a 25% advance over its competitors in terms of resource utilization.2024 3rd International Conference on 6G Networking (6GNet)["Osman Tugay Basaran", "Falko Dressler"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/3b28239f6642e6416e12878a6e522c547c72298b 20ccba6cfb117f0a6c31e3e6166d68a8d6709a79European Transport Research Review800The integration of IoT, AI, CE principles, and digital twin trains to existing railway infrastructure and assets is anticipated to yield immediate advancements in the digitalization of railway transportation, enhancing efficiency and safety measures.European Transport Research Review["S. Sarp", "M. Kuzlu", "Vukica Jovanovic", "Zekeriya Polat", "Ozgur Guler"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/20ccba6cfb117f0a6c31e3e6166d68a8d6709a79 8aecf6002732e2fbd561067df177136b81da4d8eThere are challenges in protecting intellectual property rights (IPR) in the digital age. These challenges undermine the legal protection and economic incentives for innovators. The rise of emerging technologies, notably blockchain and artificial intelligence (AI), presents opportunities to address these issues. The decentralized nature and immutability of blockchain introduce enhanced transparency and security for verification and transactions of IPR. Meanwhile, AI technology, with its advanced data processing and pattern recognition capabilities, has improved the monitoring and identification of infringements, thereby boosting the efficiency of IPR protection. Emerging technologies also bring new challenges to IPR protection. For example, the copyright ownership of AI-generated works, and the regulatory difficulties that blockchain technology may bring while improving transparency. These issues require a collective effort from policymakers, practitioners, and ordinary users to address, through means such as updating legal frameworks and enhancing awareness of IPR. This article explores the current situation and challenges of IPR protection in the digital age, and analyzes the dual role played by emerging technologies. The paper proposes potential solutions to these challenges and emphasizes the importance of technical and regulatory standardization. Through an in-depth analysis of these issues, this article aims to provide suggestions for policymakers, practitioners, and ordinary users to promote the effective protection and rational utilization of IPR, as well as the sustainable development of society.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)160The current situation and challenges of IPR protection in the digital age are explored, the dual role played by emerging technologies are analyzed, and the importance of technical and regulatory standardization is emphasized.2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World (ITU K)["Qianlan Bai", "Zuhong Gui", "Su Hu", "Bin Ju"]2024-10-21T00:00:00https://www.semanticscholar.org/paper/8aecf6002732e2fbd561067df177136b81da4d8e 2d21cae6ce2e218560800f2761939ace1d8b1188This article investigates the growing integration between Artificial Intelligence (AI) and Robotic Process Automation (RPA), proposing an innovative model aimed at optimizing the operational efficiency of organizations balancing the social and environmental impacts arising from the use of these technologies. The research identifies a significant gap in the literature through a systematic review, revealing the need for greater attention to the social and environmental impacts of the implementation of AI and RPA. Employing an approach based on the PICO methodology (Population, Intervention, Comparison, Outcome), this study justifies the formulation of hypotheses and the choice of methodology, ensuring scientific rigor. The proposed model considers ethical issues such as privacy and cybersecurity and explores the challenges associated with the adoption of these innovations. The discussion includes the readiness of organizations to integrate these technologies, highlighting technical and cultural limitations that may influence the model’s effectiveness. The theoretical results suggest that careful implementation can optimize resource utilization, promoting a balance between operational efficiency and social and environmental responsibility. Furthermore, the article presents an analysis of the positive impacts, such as improved efficiency, and negative impacts, such as the fear of job displacement associated with the integration of AI and RPA, reinforcing the need for responsible adoption that fosters social and environmental sustainability in the digital age.Applied Sciences474An analysis of the positive impacts, such as improved efficiency, and negative impacts, such as the fear of job displacement associated with the integration of AI and RPA are presented, reinforcing the need for responsible adoption that fosters social and environmental sustainability in the digital age.Applied Sciences["Leonel Patr\u00edcio", "Leonilde Varela", "Z. Silveira"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/2d21cae6ce2e218560800f2761939ace1d8b1188 868463e42043887ba78b16fbbf7cec8a2b1ab718In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.Sustainability815This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices, and explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems.Sustainability["Wenwen Chen", "Yangchongyi Men", "Noelia Fuster", "Celia Osorio", "A. Juan"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/868463e42043887ba78b16fbbf7cec8a2b1ab718 1b4e638fd0747d7578c77888485953d18eafd090Artificial Intelligence (AI) fundamentally changes journalism, yet a comprehensive understanding of its impact is limited. This study presents the first systematic review to thoroughly analyze the influence of AI on journalistic practices, news narratives, and emerging ethical challenges. A rigorous analysis of 127 studies selected from 2478 original articles reveals trends in AI adoption in newsrooms, changes in journalists’ roles, innovations in news presentation, and emerging ethical implications. The key findings show a significant increase in the use of AI for news writing automation (73% of news organizations), data analysis (68%), and content personalization (62%). While AI improves efficiency and accuracy, 42% of studies reported concerns about reduced levels of nuance and context in AI-generated news. We also identified the emergence of hybrid “journalist–programmer” roles (52% of studies) and the need for “AI literacy” among journalists (38% of studies). The most prominent ethical challenges include algorithm transparency (82% of studies), data privacy (76%), and accountability relative to AI content (71%). Regional analysis reveals significant gaps in AI adoption, with important implications for global information equity. This review highlights the ongoing transformation in journalism, identifies critical gaps in current research, and offers an agenda for future investigation. Our findings provide valuable insights for media practitioners, policymakers, and researchers seeking to understand and shape the future of journalism in the age of AI.Journalism and Media402The first systematic review to thoroughly analyze the influence of AI on journalistic practices, news narratives, and emerging ethical challenges reveals trends in AI adoption in newsrooms, changes in journalists’ roles, innovations in news presentation, and emerging ethical implications.Journalism and Media["Alem Febri Sonni", "Hasdiyanto Hafied", "Irwanto Irwanto", "Rido Latuheru"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/1b4e638fd0747d7578c77888485953d18eafd090 439735e055b74daadf661b65394ca366754af2a3BMC Medical Education432There is a noticeable gap in the understanding of AI among medical students in Thailand and its practical applications in healthcare, but the overwhelming consensus among these students is their readiness to embrace the incorporation of AI training into their medical education.BMC Medical Education["Salita Angkurawaranon", "Nakarin Inmutto", "Kittipitch Bannangkoon", "Surapat Wonghan", "Thanawat Kham-Ai", "Porched Khumma", "Kanvijit Daengpisut", "Phattanun Thabarsa", "C. Angkurawaranon"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/439735e055b74daadf661b65394ca366754af2a3 5094aacec3b3e9c69f34e1d5e6860b5b34f35189Ukraine has repeatedly demonstrated its commitment to introduce a comprehensive set of rules for artificial intelligence systems. In particular, the relevant intentions were set out in the Concept for the Development of Artificial Intelligence approved back in 2020. In the document, Ukraine recognised AI’s potential and the need for an appropriate regulatory approach that should address its initial development and further deployment. As part of its regulatory efforts, Ukraine highlights several factors to show the importance of the planned legal regulation, including the increased use of AI systems across multiple fields, ranging from public services to military use of AI technologies, and the urgent need to establish clear guidelines for the same. When it comes to developing legal regulation, there is a growing consensus among policymakers and experts that Ukraine’s regulatory approach should align with the framework already implemented by the European Union, which should facilitate harmonisation of the global AI governance landscape and Ukraine’s intentions to join the EU.Amid Ukraine’s Euro-Atlantic integration, it is, therefore, crucial to analyse the foundations, current state, and prospects of the EU AI regulation. The article delves into the prerequisites of the EU approach to AI systems and highlights ethical standards that shaped AI systems regulation. These ethical principles include transparency, accountability, non-discrimination, as well as the appropriate human controls. The article explores in detail how these principles evolved from ethical concepts to specific legal requirements and, in particular, illustrates how such principles have been reflected in the recently adopted Artificial Intelligence Act. This comprehensive analysis shows the ethical-legal nexus in the AI Act, which serves as a bridge between ethical principles and specific legal requirements. The article, with its focus on the foundations of the EU approach to AI systems, provides a better understanding of the key principles set out in the AI Act and offers valuable lessons for Ukraine’s ongoing efforts to develop its own legal framework for AI.The author then focuses on the future landscape of AI system regulation with due regard to emerging technologies and the need for a global and adaptable framework that should address these developments and, at the same time, maintain high legal and ethical standards. The article highlights the importance of further international cooperation to ensure an appropriate level of AI governance standards worldwide.NaUKMA Research Papers. Law181The article delves into the prerequisites of the EU approach to AI systems and highlights ethical standards that shaped AI systems regulation and provides a better understanding of the key principles set out in the AI Act and offers valuable lessons for Ukraine's ongoing efforts to develop its own legal framework for AI.NaUKMA Research Papers. Law["Oleksandr Kozhukhar"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/5094aacec3b3e9c69f34e1d5e6860b5b34f35189 0faa1bb831cc2d1da5982c6058adfb30b2bc9fdeThis conceptual article delves into the integration of Artificial Intelligence (AI) in academia, focusing on its potential to decolonise academic writing for inclusive knowledge production. The paper begins with an overview of decolonisation in academic discourse and introduces AI's emerging role in this field. It then reviews the literature on decolonial perspectives in academia, the challenges faced by non-native English speakers in academic writing, and previous AI research in education, highlighting gaps that necessitate a decolonial and critical approach. The theoretical framework combines decoloniality and critical theory, linking these to empower non-native English-speaking academics. Using a theory synthesis design, the discussion explores this group's unique challenges in academic writing and how AI, specifically applications like ChatGPT, can be a transformative tool for inclusivity in publication spaces. It critically examines how AI can contribute to decolonising academic knowledge writing. However, it also addresses potential challenges and ethical considerations in merging AI with decolonial perspectives. The article forecasts future AI developments and their implications for decolonising academic experiences, emphasising the need for inclusive technological advancements. In conclusion, the article stresses AI's potential role in decolonising academic practices and calls for further interdisciplinary dialogue and exploration. Recommendations for universities, academics, policymakers, and curriculum designers, as well as implications for decolonial and critical discourses, are provided.Interdisciplinary Journal of Education Research552This conceptual article delves into the integration of Artificial Intelligence in academia, focusing on its potential to decolonise academic writing for inclusive knowledge production and stresses AI's potential role in decolonising academic practices.Interdisciplinary Journal of Education Research["B. Omodan", "Newlin Marongwe"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/0faa1bb831cc2d1da5982c6058adfb30b2bc9fde c1f3c096b11ecd2d912b28829675b8f57764f441Why does the Saudi government utilize artificial intelligence? The research questions whether the more the Saudi government utilizes artificial intelligence (AI), the more it endures authoritarianism or not. In fact, an analytical research design based on a neo-institutional approach is applied to the case study. The research examines the concept of isomorphism by addressing the motivations for AI utilization in Saudi Arabia, the outcomes of AI on Saudi Arabia’s authoritarian practices, Saudi citizens’ insights into AI applications in Saudi Arabia, the relationship between legitimacy and stability in the presence of AI in Saudi Arabia, and the impact of culture and cognitive bases on Saudi organizational behavior toward AI in Saudi Arabia. The research concludes the following: Saudi Arabia applies artificial intelligence as a democratic state in economic changes and as a computational power to keep the political sphere unchangeable. In other words, the more the Saudi government utilizes artificial intelligence, the more it becomes a digital authoritarian tool.Journal of Developing Societies251The research concludes the following: Saudi Arabia applies artificial intelligence as a democratic state in economic changes and as a computational power to keep the political sphere unchangeable.Journal of Developing Societies["Nayera Mohamed Hamed Ibrahim"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/c1f3c096b11ecd2d912b28829675b8f57764f441 b469e7ba267b17ea891552c3c6228a6895e6f2abPengembangan ilmu pengetahuan dan teknologi (IPTEK) memberikan peran dalam meningkatkan kesejahteraan dan perekonomian masyarakat. Salah satu bidang yang dapat merasakan kehadiran teknologi yaitu bidang pendidikan organisasi kemasyarakatan, termasuk pada organisasi Remaja Masjid Baitul Halim Jakarta Selatan. Untuk mengelola data organisasi diperlukan kemampuan administrasi yang baik, sehingga data bisa tertata dengan baik dan keberlanjutan bagi kegiatan organisasi. Dalam rangka menunaikan salah satu Tri Dharma Perguruan Tinggi, maka Universitas Nusa Mandiri melaksanakan Pengabdian Masyarakat berupa Pelatihan Artificial Intelligence Dalam Membuat Power Point untuk memudahkan proses pemaparan program kerja dan juga sebagai media promosi dan publikasi organisasi. Metode pengabdian masyarakat terdiri dari 4 tahapan, yaitu persiapan, pelaksanaan, evaluasi dan pelaporan. Adapun peserta kegiatan ini adalah para anggota Remaja Masjid Baitul Halim yang mengikuti pelatihan komputer secara offline. Dengan pelatihan ini, peserta merasakan manfaat kehadiran teknologi dimana dapat membantu kegiatan sosial, pendidikan dan keagamaan bagi organisasi. Pemanfaatan AI dalam pembuatan Power Point dapat digunakan untuk membuat presentasi yang menarik dalam slide yang bisa dimodifikasi sesuai kebutuhan.Jurnal Pengabdian Masyarakat Bangsa00Jurnal Pengabdian Masyarakat Bangsa["Riyan Latifahul Hasanah", "Antonius Yadi Kuntoro", "M. Saelan", "Mila Desi Anasanti"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/b469e7ba267b17ea891552c3c6228a6895e6f2ab 03275ffecd6c9c356a054770900c4f3a4c8d8220The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus’s curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI’s accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development.Sustainability1130This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI’s accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development.Sustainability["Raghu Raman", "S. Gunasekar", "Deepa Kaliyaperumal", "Prema Nedungadi"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/03275ffecd6c9c356a054770900c4f3a4c8d8220 4f41fce78d1305c6b17cfe1552d884d3cf494397INTRODUCTION Generative artificial intelligence (AI) is rapidly gaining popularity across health care, education, and society. The purpose of this study was to assess perceptions and use of generative AI in academic physical therapy (PT). REVIEW OF LITERATURE Generative AI became one of the fastest-growing technologies ever after the public release of ChatGPT in November 2022. Early data indicate that attitudes toward generative AI in higher education are mixed and rapidly evolving, with significant ethical concerns around use and potential misuse. There are no published studies investigating perceptions and use of generative AI in PT education. SUBJECTS A total of 175 surveys were completed and analyzed. Respondents included PT educators, administrators, and students. METHODS An anonymous, online survey on use and perception of AI was distributed through email and social media. Descriptive statistics and cross-tabulations were performed to analyze respondent characteristics and responses to survey questions. RESULTS Most respondents (61.1%) reported they did not use generative AI during the 2022-2023 academic year, whereas 35.4% were generative AI users. More than 40% of respondents were optimistic or very optimistic toward generative AI. Users of AI were more likely to report an optimistic or very optimistic disposition toward AI compared with nonusers. AI users were more likely to agree or completely agree that generative AI has more benefits than drawbacks compared with nonusers. DISCUSSION AND CONCLUSION Results of this survey suggest that, despite the rapid uptake of generative AI in society, many PT educators and students harbor reservations and uncertainties toward its use. Artificial intelligence users were less likely to hold negative perceptions toward it and were more likely to find it useful. Understanding use and perceptions of generative AI in PT education may inform strategies to promote innovation, policy-making, and ethical integration of this new and rapidly evolving technology.Journal of Physical Therapy Education160Estimating use and perceptions of generative AI in PT education may inform strategies to promote innovation, policy-making, and ethical integration of this new and rapidly evolving technology.Journal, physical therapy education["Richard Severin", "Kendra Gagnon"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/4f41fce78d1305c6b17cfe1552d884d3cf494397 8499cf541371ffeda71a5f3c597e41361cfe4e17BACKGROUND Artificial intelligence (AI) has significantly revolutionized the diagnosis and treatment of various medical and surgical conditions, including gastroesophageal reflux disease (GORD). AI has the potential to enhance diagnostic and treatment capabilities, contributing to overall advancements in healthcare. The current study aimed to investigate the medical students' views regarding the use of AI in GORD management. METHODS An anonymous, self-administered questionnaire was distributed among undergraduate medical students of various grades within different national medical institutions. The questionnaire comprised three sections, addressing sociodemographic data, knowledge, and attitudes. Knowledge and attitudes were assessed through 5- and 7-item questionnaires, respectively. The knowledge scores constituted a scale of 0-5. This reflected varying levels of understanding. Categories include poor knowledge (two or less), moderate knowledge (three), and good knowledge (4 and 5). Attitudes were classified as negative, neutral, or positive based on 50% and 75% cutoff points, with scores below 50% indicating negative attitudes, 50-75% considered neutral, and scores above 75% reflecting positive attitudes. RESULTS A total of 506 medical students participated, including 273 females and 233 males, with a ratio of 1.2-1. The majority fell within the age range of 20-26 years. Additionally, 388 participants (76.7%) reported grade point averages (GPAs) higher than 4. Regarding knowledge, 9% demonstrated a poor score of knowledge, while 33% had a moderate knowledge score. However, 65% of the participating students held a neutral attitude toward the role of AI in GORD management, with 6.9% expressing a negative stance on the matter. CONCLUSION Although AI is highly involved in GORD management, the study revealed that medical students and interns possess a limited perception of this emerging technology. This may highlight the necessity for active involvement in AI education within the medical curricula.Medical Teacher290Although AI is highly involved in GORD management, the study revealed that medical students and interns possess a limited perception of this emerging technology, highlighting the necessity for active involvement in AI education within the medical curricula.Medical teacher["Abdulmalek W. Alhithlool", "Abdulaziz S Almutlaq", "Sarah A Almulla", "Abdulaziz B Alhamdan", "Ziyad B Alotaibi", "Amjad W Alhithlool", "A. Kamal", "M. Daoud", "O. Zakaria"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/8499cf541371ffeda71a5f3c597e41361cfe4e17 99a81a572cbf8969a16fd1a72308d23cc8ced6b9Abstract Recently, mandated FDA patient decision checklists were developed with the goal of improving the informed decision-making process for patients considering breast implants. However, these checklists are written at reading levels far higher than recommended by the National Institutes of Health and the American Medical Association. This study aims to improve the accessibility, and therefore, the utility of the mandated FDA patient literature for the average breast implant patient using the assistance of artificial intelligence (AI). Patient decision checklists were obtained from the 3 most utilized breast implant manufacturers in the United States—Allergan, Mentor, and Sientra. A novel patient decision checklist was synthesized by AI, written at the sixth grade reading level, using these checklists as source material. The AI-assisted checklist was edited by plastic surgeons for both formatting and content. The overall readability of Allergan, Mentor, and Sientra patient checklists correlated with the college reading level. These documents were of a statistically significantly higher reading level than the AI-assisted checklist, which was written at the recommended sixth grade level. Text composition analysis similarly demonstrated substantial differences between the AI-assisted and FDA-mandated literature. The currently mandated breast implant patient checklists are written at a college reading level and are inaccessible to the average patient. The authors propose a new patient decision checklist, generated with the assistance of AI, to improve healthcare access within plastic surgery. This simplified material can be used as an adjunct to the current checklists to improve shared decision making.Aesthetic Surgery Journal Open Forum00A new patient decision checklist is proposed, generated with the assistance of AI, to improve healthcare access within plastic surgery and can be used as an adjunct to the current checklists to improve shared decision making.Aesthetic Surgery Journal. Open Forum["Louisa B Ragsdale", "Aurora M. Kareh", "Rohun Gupta", "Peter K. Firouzbakht", "Christina M. Plikaitis", "Katherine A. Rodby"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/99a81a572cbf8969a16fd1a72308d23cc8ced6b9 a9cc182bb837d5edaba0027ac0ccd171f89f65bcPurposeThis paper evaluates the benefits of harnessing artificial intelligence (AI) tools for safety compliance on construction projects in Nigeria.Design/methodology/approachThis study employed a specialised approach by combining qualitative and quantitative approach. The study carried out a brief systematic literature review (SLR) to identify the variables of the study. These variables were prepared in a questionnaire which was distributed among professionals within the Nigerian construction sector using purposive sampling. A total of 140 questionnaires were retrieved. The collected data were analysed using Relative Importance Index (RII), Ginni’s Mean (GM) and exploratory factor analysis (EFA).FindingsThe analysis revealed that all the identified benefits hold considerable importance, with an average RII of 0.86, with real-time monitoring as the most prominent advantage. However, using the GM which was 0.861, the study identified “mitigation of hazards on worksites” as the stationary benefit of AI in safety compliance.Research limitations/implicationsThe study was conducted exclusively within Nigeria’s Federal Capital Territory, using a cross-sectional survey approach.Practical implicationsThe results will be valuable for professionals and practitioners in the Nigerian construction sector, as they will acquire insights into the potential advantages of utilising AI tools for monitoring of safety compliance on construction projects.Originality/valueThe study adopted a robust approach by identifying the stationary benefit using the GM in combination with RII and EFA.International Journal of Building Pathology and Adaptation1140The results will be valuable for professionals and practitioners in the Nigerian construction sector, as they will acquire insights into the potential advantages of utilising AI tools for monitoring of safety compliance on construction projects.International Journal of Building Pathology and Adaptation["Ibrahim Inyass Adamu", "Taofeek Tunde Okanlawon", "L. Oyewobi", "A. A. Shittu", "R. Jimoh"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/a9cc182bb837d5edaba0027ac0ccd171f89f65bc 74a5718d0ec79224fce3e55ff27dabee5d4cf0a0Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes postresuscitation. This narrative review aims to explore the role of artificial intelligence (AI) in predicting neurological outcomes postcardiac resuscitation. The methodology involved a detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools, and assessing their benefit in predicting neurological outcomes in postcardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood, and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets, and provide a reliable prognosis, which only demonstrates the need for AI to help overcome challenges faced. Advanced machine learning algorithms like artificial neural networks (ANN) using supervised learning by AI have improved the accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI-powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4, and AI-based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs, and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article, including data quality, bias, privacy issues, and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method, and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in postcardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.Annals of Medicine and Surgery690The role of artificial intelligence in predicting neurological outcomes in postcardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.Annals of Medicine and Surgery["Muhammad Muneeb Khawar", "Hafiz Abdus Saboor", "Rahul Eric", "N. R. Arain", "Saira Bano", "Mawada Babiker Mohamed Abaker", "Batool Iqtidar Siddiqui", "R. Figueroa", "Srija Reddy Koppula", "Hira Fatima", "Afreen Begum", "Sana Anwar", "Muhammad Usman Khalid", "Usama Jamil", "Javed Iqbal"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/74a5718d0ec79224fce3e55ff27dabee5d4cf0a0 7cb0d615afb20a9074cc469cf9d4bd273d77e4f8Increasing cyber threats have rendered traditional security solutions such as firewalls and intrusion-detection/prevention systems insufficient in providing adequate protection against advanced persistent threats to homeland security. To enhance protection, the implementation of active cyber defense is recommended, offering alternative yet effective measures against these threats. This paper examines the phases of the cyber kill chain, one of the cyber defense techniques, which has been enhanced to analyze and understand advanced persistent threats. It explains the use of deception, slowdown, and counterattacks for active cyber defense. Additionally, the utilization of artificial intelligence in active cyber defense techniques is discussed. Finally, the paper elucidates how these techniques can be employed within the scope of active cyber defense to enhance security.International Symposium on Networks, Computers and Communications270The phases of the cyber kill chain, one of the cyber defense techniques, which has been enhanced to analyze and understand advanced persistent threats, is examined, explaining the use of deception, slowdown, and counterattacks for active cyber defense.2024 International Symposium on Networks, Computers and Communications (ISNCC)["Recep \u00d6Zbay", "U. Yavanoglu"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/7cb0d615afb20a9074cc469cf9d4bd273d77e4f8 fafcf49988d8fcd096d08c0086e9d7965a6a30e8The integration of 7 Tesla (7T) magnetic resonance imaging (MRI) with advanced multimodal artificial intelligence (AI) models represents a promising frontier in neuroimaging. The superior spatial resolution of 7TMRI provides detailed visualizations of brain structure, which are crucial forunderstanding complex central nervous system diseases and tumors. Concurrently, the application of multimodal AI to medical images enables interactive imaging‐based diagnostic conversation.In this paper, we systematically investigate the capacity and feasibility of applying the existing advanced multimodal AI model ChatGPT‐4V to 7T MRI under the context of brain tumors. First, we test whether ChatGPT‐4V has knowledge about 7T MRI, and whether it can differentiate 7T MRI from 3T MRI. In addition, we explore whether ChatGPT‐4V can recognize different 7T MRI modalities and whether it can correctly offer diagnosis of tumors based on single or multiple modality 7T MRI.ChatGPT‐4V exhibited accuracy of 84.4% in 3T‐vs‐7T differentiation and accuracy of 78.9% in 7T modality recognition. Meanwhile, in a human evaluation with three clinical experts, ChatGPT obtained average scores of 9.27/20 in single modality‐based diagnosis and 21.25/25 in multiple modality‐based diagnosis. Our study indicates that single‐modality diagnosis and the interpretability of diagnostic decisions in clinical practice should be enhanced when ChatGPT‐4V is applied to 7T data.In general, our analysis suggests that such integration has promise as a tool to improve the workflow of diagnostics in neurology, with a potentially transformative impact in the fields of medical image analysis and patient management.iRADIOLOGY120It is indicated that single‐modality diagnosis and the interpretability of diagnostic decisions in clinical practice should be enhanced when ChatGPT‐4V is applied to 7T data.iRADIOLOGY["Yifan Yuan", "Kaitao Chen", "Youjia Zhu", "Yang Yu", "Mintao Hu", "Ying\u2010Hua Chu", "Yi-Cheng Hsu", "Jie Hu", "Qi Yue", "Mianxin Liu"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/fafcf49988d8fcd096d08c0086e9d7965a6a30e8 d61d4579e0bacf6d0b7e0c1941c35269ad80a4d8The subject of the study is the impact of generative artificial intelligence (GenAI) on financial intermediation and associated risks. The purpose of the study is to assess the current state of GenAI adoption in financial intermediation, identify potential risks and challenges, and explore the implications of increased use of these technologies. The relevance of the study is due to the rapid development of artificial intelligence (AI) technologies and their increasing impact on financial intermediation, which requires a comprehensive analysis and understanding of possible risks and benefits. The research methodology is based on the study of existing research on this topic,identification of current approaches to identifying risks, empirical analysis, including collection and processing of lists of risks of using GenAI and associated consequences. It is concluded that the variability of definitions in risk assessment GenAIcan create significant obstacles to consistency and coordination of actions between various market participants. Moreover, the lack of unified assessment criteria can lead to underestimation or overestimation of potential threats, which increases the likelihood of system failures and increases risks to the sustainability of the financial intermediation system as a whole.As part of the preparation of the study, responses generated by ChatGPT about the assessment of the risks posed by GenAI were used.Siberian Financial School60The variability of definitions in risk assessment GenAI can create significant obstacles to consistency and coordination of actions between various market participants and lead to underestimation or overestimation of potential threats, which increases the likelihood of system failures and increases risks to the sustainability of the financial intermediation system as a whole.Siberian Financial School["T. Zver'kova"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/d61d4579e0bacf6d0b7e0c1941c35269ad80a4d8 48a7ee2298c7af407627f9a2c6f3be8c126c3bd6This paper critically examines artificial intelligence in the healthcare sector and aims to identify concrete points of challenges and business value propositions first in radiology and then across healthcare more broadly. It discusses current applications in radiology and future uses of AI in healthcare, focusing on three main areas: (i) emergency incidents handling, (ii) intensive care unit treatment and (iii) augmented telemedicine, to which emergency radiology is a critical success factor. Despite some risks and compliance issues that need to be taken care of, this paper clearly shows that AI has the potential (a) to reengineer the business processes of the healthcare sector, using AI-assisted radiology as a driver and (b) to improve the effectiveness of the healthcare system as well as (c) to increase the quality provision of healthcare services. Despite its slow adoption, AI-assisted healthcare can indeed offer business/operational solutions that benefit all healthcare stakeholders.Journal of Future Artificial Intelligence and Technologies330It is shown that AI has the potential to reengineer the business processes of the healthcare sector, using AI-assisted radiology as a driver and to improve the effectiveness of the healthcare system as well as to increase the quality provision of healthcare services.Journal of Future Artificial Intelligence and Technologies["Dimitrios S. Stamoulis", "Chrysanthi Papachristopoulou"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/48a7ee2298c7af407627f9a2c6f3be8c126c3bd6 22c5b15772130fda230719b107ff54dac2f5525fInternal control systems rely heavily on well-defined workflows and procedures in business operations. The fundamental components of internal control systems encompass policies, procedures, and workflows, impacting various aspects such as the accurate establishment of internal controls, employees' adeptness in their application, defining authorities correctly, and ensuring business sustainability. This significance is further underscored by incorporating these elements into internal control standards and relevant legislation. The absence of documented internal control flows directly contributes to audit findings in numerous public institutions and private organizations. Therefore, this research highlights the critical role of policies, procedures, and workflows in internal control. It aims to explore the attributes of artificial intelligence applications across multiple dimensions to emphasize their potential in bolstering internal control systems. This study underscores the significance of internal control processes, emphasizing their crucial role in daily operations and the potential benefits of artificial intelligence in streamlining these processes, thereby making the audience feel the importance of their work.Denetişim00This study underscores the significance of internal control processes, emphasizing their crucial role in daily operations and the potential benefits of artificial intelligence in streamlining these processes, thereby making the audience feel the importance of their work.Denetişim["Lale Aslan"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/22c5b15772130fda230719b107ff54dac2f5525f dd4c8819d90bd601b174a61b6ef3105dda422003The current surge in Artificial Intelligence (AI) interest, reflected in heightened media coverage since 2009, has sparked significant debate on AI's implications for privacy, social justice, workers' rights, and democracy. The media plays a crucial role in shaping public perception and acceptance of AI technologies. However, research into how AI appears in media has primarily focused on anglophone contexts, leaving a gap in understanding how AI is represented globally. This study addresses this gap by analyzing 3,560 news articles from Brazilian media published between July 1, 2023, and February 29, 2024, from 13 popular online news outlets. Using Computational Grounded Theory (CGT), the study applies Latent Dirichlet Allocation (LDA), BERTopic, and Named-Entity Recognition to investigate the main topics in AI coverage and the entities represented. The findings reveal that Brazilian news coverage of AI is dominated by topics related to applications in the workplace and product launches, with limited space for societal concerns, which mostly focus on deepfakes and electoral integrity. The analysis also highlights a significant presence of industry-related entities, indicating a strong influence of corporate agendas in the country's news. This study underscores the need for a more critical and nuanced discussion of AI's societal impacts in Brazilian media.arXiv.org720Brazilian news coverage of AI is dominated by topics related to applications in the workplace and product launches, with limited space for societal concerns, which mostly focus on deepfakes and electoral integrity.ArXiv["Raphael Hernandes", "Giulio Corsi"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/dd4c8819d90bd601b174a61b6ef3105dda422003 6e2ad7f7cf4e48a8ceade8684306e7ec5318e4bdA digital operations management platform consolidates many data sources, enhances organisational processes, and employs analytics to foster creativity and flexibility within the organisation. To effectively handle the large number, speed, and diversity of system events that happen every day, IT requires a digital operations management platform that can minimise repetitive notifications, determine the immediate cause(s) of an IT outage, and guarantee satisfying user interactions through the application of AI/ML. AI has been highly influential in various sectors, such as manufacturing and logistics, in recent years. Manufacturing and logistics organisations are utilising AIOps (Artificial Intelligence for IT Operations) to enhance their productivity. Due to the increasing complexity of user requirements, enterprises are utilising data-driven platforms to meet client expectations. Autonomous supply chains are necessary as a result of heightened competitiveness and the growing expectations of customers. AIOps analytics platforms can assist companies in managing the heightened customer demand. This article provides a comprehensive summary of the journey and the possible advantages it offers. It also explains the necessary components for AIOps and outlines a plan for initiating the process.International Symposium on Networks, Computers and Communications40A comprehensive summary of the journey and the possible advantages of AIOps (Artificial Intelligence for IT Operations) is provided and a plan for initiating the process is outlined.2024 International Symposium on Networks, Computers and Communications (ISNCC)["N. Mulongo"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/6e2ad7f7cf4e48a8ceade8684306e7ec5318e4bd f860df164a1b375b36c645e8ba6a2fe88f383ae1The use of artificial intelligence (AI) into managerial skills development tools signifies a substantial progression in organisational leadership training methodologies. AI-driven platforms can assess extensive data to pinpoint critical skill deficiencies and customise individualised learning experiences for managers across all tiers. This not only expedites the development process but also improves the retention and application of essential managerial competencies. Organisations can utilise AI to develop more efficient training programs that adjust in real-time to the changing requirements of its leaders, thereby promoting a culture of ongoing enhancement and innovation. Moreover, as AI technology progresses, its contribution to the development of managerial abilities is expected to increase, providing more advanced solutions for talent management and leadership improvement. Artificial intelligence can create simulated environments for managers to practise and enhance their abilities, offer immediate feedback, and monitor progress over time. As organisations adopt these technologies, they will be more adept at manoeuvring through the intricacies of the contemporary business environment. Investing in AI-driven management training boosts individual competencies and fortifies organisational resilience and adaptation, hence ensuring sustained success in a progressively competitive landscape. This article explores the relationship between artificial intelligence (AI) and managerial functions, focusing on the future of management in contexts where AI is integrated. It addresses a gap in the existing research. Because, the existing research does not provide sufficient information regarding the impact of AI implementation on managerial abilities within organisations.International Symposium on Networks, Computers and Communications50The relationship between artificial intelligence (AI) and managerial functions is explored, focusing on the future of management in contexts where AI is integrated, to address a gap in the existing research.2024 International Symposium on Networks, Computers and Communications (ISNCC)["N. Mulongo"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/f860df164a1b375b36c645e8ba6a2fe88f383ae1 3570266d9016aff3bc2c2daeb1ccb53b1325ba10PurposeThe research aims to explore the dynamic relationship between digital service innovation (DSI), artificial intelligence (AI) and business performance (BPer) in service-based models with a focus on how AI-enhanced insights from service use and customer feedback can strengthen business strategies. The aims are to show that DSI and AI are key to driving growth and efficiency in the digital economy and to underscore AI’s role in utilizing contextual data to improve decision-making and business outcomes.Design/methodology/approachThe study uses general structural equation modeling to analyze Spanish manufacturing firms, focusing on medium-sized enterprises and including both business-to-business and business-to-consumer orientations. Data are drawn from the Iberian Balance Analysis System [Sistema de Análisis de Balances Ibéricos (SABI)] database, complemented by a Qualtrics survey to assess the integration of AI in decision-making processes. The methodology is designed to evaluate the interplay between DSI, AI and BPer, with the aim of identifying actionable insights for service-based business orientations.Findings The study clarifies the relationships between DSI, AI and BPer, providing new theoretical and empirical insights. The findings confirm DSI's direct positive impact on performance and suggest AI’s nuanced mediating role, emphasizing the need for strategic DSI-AI integration in manufacturing firms for enhanced performance.Research limitations/implications The research explains the synergistic bond between DSI and AI in boosting BPer and discovering how by-product data can be transformed into strategic insights.Practical implications This study advises manufacturing sector leaders to integrate DSI and AI for enhanced performance and competitive advantage, emphasizing the value of high-quality, contextual data for AI learning and decision-making.Originality/value Researchers will observe that the study confirms the positive impact of DSI on BPer, while also highlighting the significant role of AI in enhancing this effect.Journal of Enterprise Information Management1070This study advises manufacturing sector leaders to integrate DSI and AI for enhanced performance and competitive advantage, emphasizing the value of high-quality, contextual data for AI learning and decision-making.Journal of Enterprise Information Management["Juan Carlos Monroy-Osorio"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/3570266d9016aff3bc2c2daeb1ccb53b1325ba10 af1544aeb0ba0f428c9d385a8db9349a2df0dc74About half of the people who use artificial intelligence (AI) don’t want anyone else to know they use it. That's because they’re worried about seeming replaceable in their jobs, especially if they work in academia. Or they’re concerned that AI use might not be permitted or that it qualifies as some form of cheating, according to Beth Ziesenis, owner of yournerdybestfriend.com.The Successful Registrar00About half of the people who use artificial intelligence don’t want anyone else to know they use it because they’re worried about seeming replaceable in their jobs, especially if they work in academia, according to Beth Ziesenis, owner of yournerdybestfriend.com.The Successful Registrar["Claudine McCarthy"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/af1544aeb0ba0f428c9d385a8db9349a2df0dc74 73c61432f14fbbc3759b3286b83fffd3138d366bInnovative Higher Education493Innovative Higher Education["Naifa Alqahtani", "Zarina Wafula"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/73c61432f14fbbc3759b3286b83fffd3138d366b 3b803713990aca6e8ddd6b1c409a8fc9802ff283The German quarterly01The German Quarterly["Ruth V. Gross"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/3b803713990aca6e8ddd6b1c409a8fc9802ff283 2c8d459d1e521d7b1cd01cc78f922374c67dafbeNanotechnology Perceptions00Nanotechnology Perceptions[]2024-10-22T00:00:00https://www.semanticscholar.org/paper/2c8d459d1e521d7b1cd01cc78f922374c67dafbe cdf038820debd7cd3bfd635d722f83c9577640c9Asia Pacific Journal of Tourism Research480Asia Pacific Journal of Tourism Research["Xie Chen", "Xuan Luo", "Junjie Gao"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/cdf038820debd7cd3bfd635d722f83c9577640c9 46f74685cb0dc3d7780b601ff34e142389b88f56Library Progress (International)00Library Progress International["D. Chowdaiah", "Dr. Samini Mathew"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/46f74685cb0dc3d7780b601ff34e142389b88f56 0ae6b67a663c74b6beda8ea87eb1f86f16091919This study examines the effectiveness of current data privacy protocols within cryptocurrency platforms, focusing on encryption strength, anonymity techniques, and AI-powered regulatory compliance tools. Data were sourced from CoinMarketCap and Kaggle, including metrics like Bit Strength, Breach Incidents, and Anonymity Scores, which were analyzed using descriptive statistics, t-tests, and logistic regression. Results showed no significant relationship between encryption strength and breach incidents (p = 0.817), indicating that encryption strength may not be a primary factor in breach prevention. The weak correlation between encryption strength and breaches suggests that other elements, such as platform vulnerabilities or user behaviour, could play a more critical role in security. AI systems, evaluated through metrics like precision (0.168), recall (0.204), and F1 score (0.184), struggled with false positives, showing limitations in accurately detecting breaches and highlighting the need for more refined AI models. Advanced blockchain technologies like Zero-Knowledge Proofs and Homomorphic Encryption enhanced privacy but increased computational costs. It is recommended that hybrid encryption methods be adopted to balance privacy and performance and improve AI systems for more accurate breach detection. Governments must create clear regulations that encourage innovation while ensuring compliance.Journal of Engineering Research and Reports00Results showed no significant relationship between encryption strength and breach incidents, indicating that encryption strength may not be a primary factor in breach prevention, and it is recommended that hybrid encryption methods be adopted to balance privacy and performance and improve AI systems for more accurate breach detection.Journal of Engineering Research and Reports["M. O. Gbadebo", "A. Salako", "Oluwatosin Selesi-Aina", "Olumide Samuel Ogungbemi", "O. Olateju", "O. O. Olaniyi"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/0ae6b67a663c74b6beda8ea87eb1f86f16091919 81a1d87b55692000bfe2ee2a6a8971da4144a23bAI and Ethics370AI and Ethics["J. Donia", "Lola Oyefeso", "G. Embuldeniya", "C. Whyne", "David Burns", "Philip Boyer", "Helen Razmjou", "James A. Shaw"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/81a1d87b55692000bfe2ee2a6a8971da4144a23b b76dbee0ca822938b94f526efeba3ed8bb43be14Journal of general internal medicine50Journal of general internal medicine["E. Bignami", "M. Panizzi", "V. Bellini"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/b76dbee0ca822938b94f526efeba3ed8bb43be14 abd22520146a564df5056f5e01c7504686960d2aFeeling AI (FAI) as a new kind of Hybrid AI aimed to improve the autonomy of service provided by robots is discussed. Using as an embedded system, the Hybrid AI based on symbolic and machine learning techniques combined with an “Emotional Shell” and a pre-trained set of different skills is problematic due to the required computer facilities and limited robot autonomy in the physical world. This article proposes, in contrast to the symbolic approach, to set up FAI on the cognitive response-making model borrowed from the simplest living beings that demonstrate the capability of performing all its functions under all conditions in autonomous mode. Based on findings of neurobiology and cognitive science about hydra behavior, the implementation of four cognitive functions, such as perception, drives, emotions, and attention, in the response-making mechanism, is shown. A borrowed model formalized on fuzzy Certainty Factor (CF) and presented as a homogeneous granular knowledge structure of independent response prototypes that are processed by a cognitive response-making engine is discussed. The proposed model implemented in the blueprint of FAI's basic layer is given.International Symposium on Networks, Computers and Communications270This article proposes, in contrast to the symbolic approach, to set up FAI on the cognitive response-making model borrowed from the simplest living beings that demonstrate the capability of performing all its functions under all conditions in autonomous mode.2024 International Symposium on Networks, Computers and Communications (ISNCC)["A. Kargin", "T. Petrenko"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/abd22520146a564df5056f5e01c7504686960d2a e2e176fee55db316105e284b633a339d52ce5f9dNational Medical Journal of India90The National medical journal of India["Ashish Makani", "Anurag Agrawal", "Anjali Agrawal"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/e2e176fee55db316105e284b633a339d52ce5f9d 6ecbe670ce85c440278e38867d1943a14e70fde8This paper discusses the risk of gross negligence manslaughter (GNM) and corporate manslaughter charges (CM) when clinicians use an artificially intelligent system's (AIS's) outputs in their practice. I identify the elements of these offenses within the context of the law of England and Wales and explore how they could be applied in a potential scenario where a patient's death has followed AIS use by a clinician. The risk of a conviction due to making an AIS-augmented workplace mistake highlights the non-trivial nature of AIS adoption in healthcare, and that the consequences of its use must be considered by all interested parties prior to AIS adoption.The New Bioethics160The risk of a conviction due to making an AIS-augmented workplace mistake highlights the non-trivial nature of AIS adoption in healthcare, and that the consequences of its use must be considered by all interested parties prior to AIS adoption.The New bioethics : a multidisciplinary journal of biotechnology and the body["Helen Smith"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/6ecbe670ce85c440278e38867d1943a14e70fde8 c46fa9b61badb441490017f441c2a6d4068e53e7Journal of Engineering Research00Journal of Engineering Research["Caio Fernando de Oliveira", "Camila Oliveira Pinheiro", "Ana Cristina Renaux Lauth", "Henrique Fleury Costa", "Marcelo Engracia Garcia"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/c46fa9b61badb441490017f441c2a6d4068e53e7 1c8381cb91e59b5418b49401d7173b9e5b2b41c6Purpose: This study aims to provide an analysis of students’ perceptions of the role of generative artificial intelligence (GenAI) tools in education, through five axes: (1) level of knowledge and awareness, (2) level of acceptance and readiness, (3) the role of GenAI in education, (4 (level of awareness of potential concerns and challenges, and (5) The impact of GenAI tools on achieving the sustainable development goals in education. Materials and methods: The study followed a descriptive quantitative methodology based on surveying through a questionnaire. The sample consisted of 1390 students from 15 Saudi universities. Results: The students have positive perceptions towards the role of GenAI tools in education, as students have a high level of awareness and acceptance of adopting these tools. In addition, students are highly aware of the role of GenAI tools in improving their understanding of complex concepts, developing skills, improving their self-efficacy, learning outcomes, providing feedback, and making learning meaningful. The results also confirm their general awareness of the concerns and challenges. A relationship exists between students’ perceptions of GenAI and their scientific specializations, as students in computer sciences showed greater awareness regarding concerns and challenges, whereas students in agricultural sciences showed greater awareness of the impact of GenAI tools on achieving sustainable development goals. Conclusions: The study offers valuable insights on GenAI adoption in higher education, also there is an urgent need to consider developing appropriate use policies, spreading awareness, and creating systems capable of detecting unethical cases.Contemporary Educational Technology342A relationship exists between students’ perceptions of GenAI and their scientific specializations, as students in computer sciences showed greater awareness regarding concerns and challenges, whereas students in agricultural sciences showed greater awareness of the impact of GenAI tools on achieving sustainable development goals.Contemporary Educational Technology["A. Aldossary", "Alia Abdullah Aljindi", "J. Alamri"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/1c8381cb91e59b5418b49401d7173b9e5b2b41c6 4cc79827da285f07cc5fb3361166b77050003324Artificial intelligence (AI) is playing an increasingly vital role in transforming the medical field, particularly in areas like medical imaging, clinical decision-making, pathology, and minimally invasive surgery. The rapid growth of medical data and the continuous refinement of machine learning algorithms have propelled AI's integration into healthcare. This study explores the advancements and applications of AI, specifically machine learning algorithms and intelligent medical robots, in enhancing diagnostics, treatment, and healthcare delivery. A comprehensive review of current AI applications in healthcare, including its use in medical imaging, pathology, clinical decision-making, and robotic-assisted surgery, was conducted. AI technologies such as the Da Vinci Surgical Robot and machine learning-based diagnostic tools have significantly improved diagnostic accuracy and the precision of minimally invasive surgeries. AI-driven systems also contributed to better clinical decision support, faster recovery times for patients, and more accurate treatment plans. Overall, AI, through machine learning algorithms and intelligent medical robots, is revolutionizing healthcare by offering promising improvements in diagnostics, surgical precision, and patient care.Research on Intelligent Manufacturing and Assembly211Overall, AI, through machine learning algorithms and intelligent medical robots, is revolutionizing healthcare by offering promising improvements in diagnostics, surgical precision, and patient care.Research on Intelligent Manufacturing and Assembly["Rizwan Ali", "Haiyan Cui"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/4cc79827da285f07cc5fb3361166b77050003324 b4a65c1ca5ed24f2fffe95f81d611e574e3e1c10This study aims to unveil the individual and sectoral factors influencing the integration and utilization of artificial intelligence (AI) in vocational training among early-stage practitioners in China's real estate brokerage industry. The study involved in-depth interviews with 33 participants, each possessing less than two years of industry experience and demonstrating either experience with or a keen interest in AI applications. Rooted in the transformative potential of AI in vocational training, thematic analysis was employed to reveal the intricate interplay of individual factors—motivation, attitude, self-efficacy, and digital literacy—alongside sectoral elements such as policies, practices, and organizational culture. The findings illuminate the significant impact of these factors on the acceptance and utilization of AI, underscoring the complexity of participant attitudes, shaped by perceived usefulness and ease of use. Diverse levels of self-efficacy and digital literacy among participants regarding AI adoption were also evident. On the sectoral front, policies play a dual role, either providing support or imposing restrictions on AI adoption, while practices and organizational culture wield substantial influence over the opportunities and challenges associated with AI use. These empirical insights and best practices offer stakeholders valuable guidance to enhance employability and productivity in this ever-evolving industry. Importantly, this study advocates for a more inclusive approach in future research, addressing limitations in sample representativeness and recommending the integration of quantitative methodologies. This approach is crucial for fostering a comprehensive understanding of AI utilization in vocational training, ensuring robust insights for future advancements in the industry.Voprosy obrazovaniya / Educational Studies Moscow660This study aims to unveil the individual and sectoral factors influencing the integration and utilization of artificial intelligence in vocational training among early-stage practitioners in China's real estate brokerage industry, and advocates for a more inclusive approach in future research.Voprosy obrazovaniya / Educational Studies Moscow["Fengchen Wang"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/b4a65c1ca5ed24f2fffe95f81d611e574e3e1c10 e6d71407ecb22019f147a364dcde2370aacee898Following the introduction of the DC-MHS1 (Data Center Modular Hardware System) and AI (Artificial Intelligence) servers, the adoption of cabling topologies has become increasingly prevalent and indispensable. Within DC-MHS design, the Host Process Module (HPM) necessitates cabling for inter-module connectivity. In AI servers, components such as the HPM, PCI Express (PCIe) switch board, and UBB (Universal Baseboard) may not reside on the same chassis floor, necessitating vertical connections either through traditional backplane design or cables to link boards and modules. These connections are critical for high-speed signal transmission, exemplified by technologies like PCIe 5.02 operating at 32 GT/s. Driven by escalating computing demands, the industry is eagerly anticipating PCIe 6.0 and exploring advancements towards PCIe 7.0, which runs at 64 GT/s and 128 GT/s respectively. This paper presents a generalized connection topology for HPM, PCIe switch board, and UBB. Simulation and measurement results are included to elucidate common challenges encountered in cabling topologies.Impact00A generalized connection topology for HPM, PCIe switch board, and UBB is presented and simulation and measurement results are included to elucidate common challenges encountered in cabling topologies.2024 19th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)["Su Thonas", "Huafang Ju", "Yang Tina"]2024-10-22T00:00:00https://www.semanticscholar.org/paper/e6d71407ecb22019f147a364dcde2370aacee898 f657840f8fc8bd31b4d59feaf75535308c98f190This paper explores the literature, types, applications, and challenges of Artificial Intelligence, providing a comprehensive overview of its current state and future potential. It traces the development of AI from early symbolic systems to modern machine learning and deep learning technologies, highlighting different AI types such as narrow AI, general AI, and the speculative concept of superintelligence. Through a literature review of various research papers, this paper examines the foundational theories and cutting-edge advancements in AI, including neural networks, reinforcement learning, and hybrid systems. The applications of AI across diverse industries, such as healthcare, finance, and education, demonstrate its transformative impact, while ethical concerns surrounding bias, privacy, and job displacement present significant challenges. The paper concludes by addressing the need for responsible AI development to ensure its benefits align with societal values and contribute to a sustainable future.International Journal For Multidisciplinary Research115The need for responsible AI development is addressed to ensure its benefits align with societal values and contribute to a sustainable future, through a literature review of various research papers on the foundational theories and cutting-edge advancements in AI.International Journal For Multidisciplinary Research["Dr. RAMA BANSAL", "Dr. ARTI SANGWAN"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/f657840f8fc8bd31b4d59feaf75535308c98f190 cca08e62438f515b3d90e133a94bd7ce9fc4431bArtificial intelligence (AI) is a disruptive technology that is being used by smart tourist destinations (STDs) to develop new business models and marketing services to increase tourists’ experiences and sales, revenue, productivity, and efficiency and STDs. However, the adoption of AI applications and platforms requires a high economic budget for STDs that want to integrate this digital tool into their future agenda and tourism development plans, especially when they set them up for marketing plans and operational processes. This iterative technology needs regular maintenance as well, leading to recurring costs and specialised crews in advanced technologies and marketing activities. This study aims to show the impact of AI advancements on STDs’ tourism marketing to enhance the quality of services and illustrate their future agenda to improve tourists’ experiences. A comprehensive literature review on AI technology and STDs has been conducted to illustrate new tourism marketing in their future agenda. Moreover, this study presents real examples of AI technology in a tourism context to better understand the potential of this digital tool. The findings of the current study support the idea that AI is a multipurpose tool that helps manage, monitor, and analyse sales information; revenue management; minimise prediction errors; streamline operations; and develop better marketing strategies, optimising economic resources, reducing marketing costs, and responding dynamically to changing needs for tourists and residents in STDs. Furthermore, the investment in AI technologies by STDs helps enhance the quality of products and services, and attract new investments, which benefit the regional economies and population’s quality of life. This study is the first to address the use of AI to improve tourist marketing in STDs, which is its primary uniqueness. Also, this study identifies new opportunities and initiatives through AI that can be developed to help tourism marketing in STDs.Electronics882This study aims to show the impact of AI advancements on STDs’ tourism marketing to enhance the quality of services and illustrate their future agenda to improve tourists’ experiences and identifies new opportunities and initiatives through AI that can be developed to help tourism marketing in STDs.Electronics["L\u00e1zaro Florido-Ben\u00edtez", "Benjam\u00edn del Alc\u00e1zar Mart\u00ednez"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/cca08e62438f515b3d90e133a94bd7ce9fc4431b 5296127f364143a36eddf924838b40781d97ab39Problem: Dyslexia is a learning disorder affecting an individual’s ability to recognize words and understand concepts. It remains underdiagnosed due to its complexity and heterogeneity. The use of traditional assessment techniques, including subjective evaluation and standardized tests, increases the likelihood of delayed or incorrect diagnosis. Motivation: Timely identification is essential to provide personalized treatment and improve the individual’s quality of life. The development of artificial intelligence techniques offers a platform to identify dyslexia using behavior and neuroimaging data. However, the limited datasets and black-box nature of ML models reduce the generalizability and interpretability of dyslexia detection (DD) models. The dimensionality reduction technique (DRT) plays a significant role in providing dyslexia features to enhance the performance of machine learning (ML)- and deep learning (DL)-based DD techniques. Aim: In this review, the authors intend to investigate the role of DRTs in enhancing the performance of ML- and DL-based DD models. Methodology: The authors conducted a comprehensive search across multiple digital libraries, including Scopus, Web of Science, PubMed, and IEEEXplore, to identify articles associated with DRTs in identifying dyslexia. They extracted 479 articles using these digital libraries. After an extensive screening procedure, a total of 39 articles were included in this review. Results: The review findings revealed various DRTs for identifying critical dyslexia patterns from multiple modalities. A significant number of studies employed principal component analysis (PCA) for feature extraction and selection. The authors presented the essential features associated with DD. In addition, they outlined the challenges and limitations of existing DRTs. Impact: The authors emphasized the need for the development of novel DRTs and their seamless integration with advanced DL techniques for robust and interpretable DD models.Diagnostics421The role of DRTs in enhancing the performance of ML- and DL-based DD models and their seamless integration with advanced DL techniques for robust and interpretable DD models is investigated.Diagnostics["Yazeed Alkhurayyif", "Abdul Rahaman Wahab Sait"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/5296127f364143a36eddf924838b40781d97ab39 dfb4ddcc3b10d1aab26068073fbc072b1ca48447Education and Information Technologies : Official Journal of the IFIP technical committee on Education431This study examined the ethical use of Artificial Intelligence-based Tools (AIT) in higher education, focusing on graduate business students, and revealed significant variations in ethical perceptions across cultural clusters.Education and Information Technologies["Sabiha Mumtaz", "Jamie Carmichael", "Michael Weiss", "Amanda Nimon-Peters"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/dfb4ddcc3b10d1aab26068073fbc072b1ca48447 9e98c826b6e102a23d092ad066dcdec397e0bf0fThis paper explores the pathways through which artificial intelligence (AI) enhances new quality productivity in new liberal arts education. By analyzing the role of AI in personalized learning, interdisciplinary integration, and the application of virtual reality/augmented reality technologies, it reveals how AI technology promotes the development of students’ innovative capabilities and productivity in the context of new liberal arts education. The study shows that AI is not only a technical tool but also a driving force for transforming educational models and fostering knowledge innovation. Further exploration of the deep integration of AI and new liberal arts education is necessary to promote comprehensive social progress.Journal of Contemporary Educational Research121The study reveals how AI technology promotes the development of students’ innovative capabilities and productivity in the context of new liberal arts education and shows that AI is not only a technical tool but also a driving force for transforming educational models and fostering knowledge innovation.Journal of Contemporary Educational Research["Yang Yang"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/9e98c826b6e102a23d092ad066dcdec397e0bf0f fbb41066deb0ccba7194c69cd304812988df5d0fArchives of public health = Archives belges de sante publique1471There is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare, and bias and discriminatory services are imminent in the use of AI tools in healthcare.Archives of Public Health["N. N. Botha", "Cynthia E. Segbedzi", "Victor K. Dumahasi", "Samuel Maneen", "Ruby V. Kodom", "Ivy S. Tsedze", "Lucy A. Akoto", "Fortune S. Atsu", "O. Lasim", "E. W. Ansah"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/fbb41066deb0ccba7194c69cd304812988df5d0f 628e25d3170410bc2add8268dfa416a3e54e3e0dUniversal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.Kaohsiung Journal of Medical Sciences581The fundamental concepts of machine learning are introduced and the role of AI in the management of chronic liver diseases is reviewed, to support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses.The Kaohsiung journal of medical sciences["Ming-Ying Lu", "W. Chuang", "Ming\u2010Lung Yu"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/628e25d3170410bc2add8268dfa416a3e54e3e0d 1cb938de5f3eb4fa211b014ebdcf509699d20e1fInferences from adjective-noun combinations like “Is artificial intelligence still intelligence?” provide a good test bed for LLMs’ understanding of meaning and compositional generalization capability, since there are many combinations which are novel to both humans and LLMs but nevertheless elicit convergent human judgments. We study a range of LLMs and find that the largest models we tested are able to draw human-like inferences when the inference is determined by context and can generalize to unseen adjective-noun combinations. We also propose three methods to evaluate LLMs on these inferences out of context, where there is a distribution of human-like answers rather than a single correct answer. We find that LLMs show a human-like distribution on at most 75% of our dataset, which is promising but still leaves room for improvement.GENBENCH511This work studies a range of LLMs and finds that the largest models tested are able to draw human-like inferences when the inference is determined by context and can generalize to unseen adjective-noun combinations.ArXiv["Hayley Ross", "Kathryn Davidson", "Najoung Kim"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/1cb938de5f3eb4fa211b014ebdcf509699d20e1f 082c6fa107d4bf3144e28d88974223a996602b52Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology can automate payment in cryptocurrency and provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. In addition, with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities to machines similar to humans. This review presents a detailed survey on blockchain and AI basics and features. This paper provides a review of the literature and a brief on the integration of blockchain and AI applications in multiple areas. We also identify some sole cases of blockchain–AI integration in the energy sector with current use cases. Eventually, we discuss research advantages and challenges associated with integrating blockchain with AI in the energy domain.Frontiers in Energy Research1310This paper provides a review of the literature and a brief on the integration of blockchain and AI applications in multiple areas, and identifies some sole cases of blockchain–AI integration in the energy sector with current use cases.Frontiers in Energy Research["Al Mothana Al Shareef", "Serap Se\u00e7kiner", "Bilal Eid", "Hasan Abumeteir"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/082c6fa107d4bf3144e28d88974223a996602b52 b9497039adb65cb489be7865e0e03d77ff484aceAutism Spectrum Disorder (ASD) is a brain disease that makes it hard to do things over and over, connect with others, and talk. Early identification is very important, even if biology is the main reason. ML seems like a hopeful way to identify the condition more quickly and cheaply. This study uses different machine learning methods to find important ASD traits in order to make the testing process better and more automated. Due to the fast development of artificial intelligence, it is now possible to use smart methods to do early, large-scale, and pointless screening and detection of autism. In the future, researchers should work on creating an intelligent medical screening and detection system for autism patients, as well as screening tools and a patient recognition model that is smart and uses multiple types of data.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)360Different machine learning methods are used to find important ASD traits in order to make the testing process better and more automated and to create an intelligent medical screening and detection system for autism patients.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)["Raghav Garg", "Chandni Manral", "Tripuresh Joshi", "Mamta Bisht", "K. S. Bharath", "Pushpendra Singh Kharayat"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/b9497039adb65cb489be7865e0e03d77ff484ace f808f71fc0561237db3323cb650ddd3f54ccb113Fairness is an emerging consideration when assessing the segmentation performance of machine learning models across various demographic groups. During clinical decision‐making, an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups, resulting in severe consequences for patients and society. In medical artificial intelligence (AI), the fairness of multi‐organ segmentation is imperative to augment the integration of models into clinical practice. As the use of multi‐organ segmentation in medical image analysis expands, it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity. However, comprehensive studies assessing the problem of fairness in multi‐organ segmentation remain lacking. This study aimed to provide an overview of the fairness problem in multi‐organ segmentation. We first define fairness and discuss the factors that lead to fairness problems such as individual fairness, group fairness, counterfactual fairness, and max–min fairness in multi‐organ segmentation, focusing mainly on datasets and models. We then present strategies to potentially improve fairness in multi‐organ segmentation. Additionally, we highlight the challenges and limitations of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi‐organ segmentation.iRADIOLOGY260An overview of the fairness problem in multi‐organ segmentation is provided and the factors that lead to fairness problems such as individual fairness, group fairness, counterfactual fairness, and max–min fairness in multi‐organ segmentation are discussed.iRADIOLOGY["Qing Li", "Yizhe Zhang", "Longyu Sun", "Meng-qi Sun", "Meng Liu", "Zian Wang", "Qi Wang", "Shuo Wang", "Chengyan Wang"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/f808f71fc0561237db3323cb650ddd3f54ccb113 00c7c5dd77bcd827b7d16138068bbef4bf93a5b0Artificial Intelligence (AI) generative tools have emerged as transformative instruments in various domains, including research and academia. It is important to see what is positive and what is not. This study focuses on the integration of GAI in Portuguese higher education institutions and explores its multifaceted implications and potential. Our research was conducted through a comprehensive survey between April and June 2023, garnering 77 responses. To this purpose, the analysis will have several insights into the research process, namely, to assess the frequency of use and objectives of using generative AI for higher education research and to explore possible trends and future directions in the adoption and application of generative AI in this field.European Conference on e-Learning150This research was conducted through a comprehensive survey between April and June 2023, garnering 77 responses and will have several insights into the research process, namely, to assess the frequency of use and objectives of using generative AI for higher education research.European Conference on e-Learning["S\u00f3nia Rolland Sobral", "Maria Jo\u00e3o Ferreira", "Carla Santos Pereira", "Nat\u00e9rcia Dur\u00e3o", "Fernando Moreira"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/00c7c5dd77bcd827b7d16138068bbef4bf93a5b0 e11853ad4f0576b71b9df9ff0524868c776bf4a3The integration of Artificial Intelligence (AI) in auditing has transformed traditional approaches, offering enhanced accuracy, efficiency, and continuous monitoring of financial transactions. This research paper focuses on the impact of AI in auditing financial institutions, with a particular emphasis on cooperative societies. Given the unique structure, governance, and operations of cooperative societies, AI's role is explored in terms of benefits, challenges, and future trends. The study provides insights into how AI-driven audits can improve fraud detection, risk assessment, and regulatory compliance in cooperative societies, while addressing challenges related to data integrity, transparency, and ethical considerations.International Journal For Multidisciplinary Research50The study provides insights into how AI-driven audits can improve fraud detection, risk assessment, and regulatory compliance in cooperative societies, while addressing challenges related to data integrity, transparency, and ethical considerations.International Journal For Multidisciplinary Research["Dr.Subhash Sopan Wavhal"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/e11853ad4f0576b71b9df9ff0524868c776bf4a3 bbd92e3a8417d6d3d666b82840e162185effbf3bThe introduction of Artificial Intelligence (AI) into the healthcare industry holds immense promise for improving patient outcomes. However, the interaction between healthcare professionals and AI systems is critical to fully realize the potential benefits. Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of Al in healthcare and focuses on the following key aspects: medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation and other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using Ai-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs’ belief in enhancing acceptance and boosting significant health precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.International Journal For Multidisciplinary Research730The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using Ai-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, and technology-assisted rehabilitation.International Journal For Multidisciplinary Research["Md. Habibur Rahman", "Kazi MD Riaz Hossan", "MD Kazi Shahab Uddin", "MD Delower Hossian"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/bbd92e3a8417d6d3d666b82840e162185effbf3b bcb7241ab932093e9ddeb37e0297ccf59806dd61OBJECTIVES Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The objective of this work is to quantitatively evaluate the ability of humans to detect focal cortical dysplasia (FCD), compare it to state-of-the-art AI, and determine how it may aid diagnostics. MATERIALS AND METHODS We prospectively recorded the performance of readers in detecting FCDs using single points and 3-dimensional bounding boxes. We acquired predictions of 3 AI models for the same dataset and compared these to readers. Finally, we analyzed pairwise combinations of readers and models. RESULTS Twenty-eight readers, including 20 nonexpert and 5 expert physicians, reviewed 180 cases: 146 subjects with FCD (median age: 25, interquartile range: 18) and 34 healthy control subjects (median age: 43, interquartile range: 19). Nonexpert readers detected 47% (95% confidence interval [CI]: 46, 49) of FCDs, whereas experts detected 68% (95% CI: 65, 71). The 3 AI models detected 32%, 51%, and 72% of FCDs, respectively. The latter, however, also predicted more than 13 false-positive clusters per subject on average. Human performance was improved in the presence of a transmantle sign (P < 0.001) and cortical thickening (P < 0.001). In contrast, AI models were sensitive to abnormal gyration (P < 0.01) or gray-white matter blurring (P < 0.01). Compared with single experts, expert-expert pairs detected 13% (95% CI: 9, 18) more FCDs (P < 0.001). All AI models increased expert detection rates by up to 19% (95% CI: 15, 24) (P < 0.001). Nonexpert+AI pairs could still outperform single experts by up to 13% (95% CI: 10, 17). CONCLUSIONS This study pioneers the comparative evaluation of humans and AI for FCD lesion detection. It shows that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup.Investigative Radiology200It is shown that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup and, thus, how AI may aid diagnostics is determined.Investigative radiology["Lennart Walger", "T. Bauer", "David K\u00fcgler", "Matthias Schmitz", "Fabiane Schuch", "Christophe Arendt", "Tobias Baumgartner", "Johannes Birkenheier", "Valeri Borger", "C. Endler", "Franziska Grau", "Christian Immanuel", "Markus K\u00f6lle", "P. Kupczyk", "Asadeh Lakghomi", "Sarah Mackert", "Elisabeth Neuhaus", "Julia Nordsiek", "Anna Odenthal", "K. O. Dague", "Laura Ostermann", "Jan Pukropski", "Attila Racz", "Klaus Baron von der Ropp", "F. Schmeel", "Felix Schrader", "Aileen Sitter", "Alexander Unruh-Pinheiro", "Marilia Voigt", "Martin Vychopen", "Philip von Wedel", "R. von Wrede", "Ulrike Attenberger", "H. Vatter", "A. Philipsen", "Albert J Becker", "Martin Reuter", "Elke Hattingen", "J. Sander", "A. Radbruch", "Rainer Surges", "T. R\u00fcber"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/bcb7241ab932093e9ddeb37e0297ccf59806dd61 b87641413d1a2ad84eab68bd687a609e32e5d231Arab newsrooms and journalists are still attempting to process how to benefit from using artificial intelligence (AI) in their daily news production. Many have started experimenting with generative AI. However, in countries with strong economies, or in newsrooms funded by strong economies like in the UAE, Saudi Arabia, and Qatar, news organizations have endorsed AI technologies beyond ChatGPT and similar tools. The majority of Arab journalists, however, are yet to understand what is meant by AI. Very few news organizations in the region have strategically approached the question of AI. Arab newsrooms need to examine when and why they need to use AI technologies, whether they have the infrastructure or can afford the resources. Implementing AI in Arab newsrooms presents an additional set of challenges, including language complexities, cultural sensitivities, potential biases in algorithms, and the need for tailored solutions that resonate with local audiences. The ethical implications present its own set of challenges. ChatGPT has proven to generate inaccurate data in Arabic. Resources are to be put in place to ensure the model is trained on a diverse and comprehensive dataset of Arabic text. Implementing bias detection and mitigation techniques to ensure the models are free from biases and offensive content is crucial. Newsrooms operating in authoritarian regimes have to be aware of governments' attempts to use generative AI to whitewash and promote state-centered actions and policies. Maintaining editorial integrity remains an important consideration.Emerging Media70Arab newsrooms need to examine when and why they need to use AI technologies, whether they have the infrastructure or can afford the resources, and implement bias detection and mitigation techniques to ensure the models are free from biases and offensive content.Emerging Media["Zahera Harb", "R. Arafat"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/b87641413d1a2ad84eab68bd687a609e32e5d231 be79e305b836b6ca7a94c8a07a8dfe2acb395dc0The article examines the performance of artificial intelligence (AI) as a tool of the modern value system. The research material comprises the implementation of substantial humanistic values in the modern public sphere, including the concepts of humanity, life, its duration and quality, health, security, freedom, opportunities for self-realization and self-development, individualization, personalization, collectivism, compassion, and others. The analysis reveals that the culture of AI aligns effectively with these values. It is emphasized that AI serves as a tool for the actualization, development, reinforcement, and transmission of humanistic values to future generations – values that are crucial for the positive evolution of modern society. The paper highlights that AI operates specifically as a tool (both innovative and effective) through which values are realized in response to the needs of the entire society and its individual members. The advancement of AI as a dynamic tool within the modern value system reflects the process of societal humanization and satisfies the human need for the recognition of one’s subjectivity and significance. This study thus contributes to the understanding of AI’s role as not merely a technological advancement, but as an integral facet of the ethical and moral framework shaping modern human interactions and societal structures.Общество философия история культура00The article examines the performance of artificial intelligence as a tool of the modern value system and highlights that AI operates specifically as a tool through which values are realized in response to the needs of the entire society and its individual members.Общество: философия, история, культура["Evgeniya K. Belikova"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/be79e305b836b6ca7a94c8a07a8dfe2acb395dc0 0dcd09c10f2096277810e5ce195e06be66385769In this research, an artificial intelligence cryptocurrency mining application using federated deep learning introduced. Federated Learning Framework for Edge Computing The solution mitigates the issue of decentralized data processing, privacy preserving, and latency reduction via a practical federated learning framework. The core of the system is a mixed deep learning model utilizing ResNet50 and SqueezeNet architectures, jointly rendering high accuracy with low computational cost. Taking the federated learning framework as an example, a central server and multiple client nodes are set up, dividing secure communication protocols to ensure integrity of data. Several custom federated learning algorithms were implemented to adjust the decentralized nature of crypto-jacking for local training on client nodes and central server diffusion of model updates. The hybrid model provides a high level of performance in terms of optimizing mining operations with an accuracy rate of $\mathbf{9 6. 2 5 \%}$. This model stored countless data such as hash rates, power consumption and network data that mining activity produced, thus making it possible for the mining strategy to be constantly updated. By integrating the model with mine application various interfaces was developed with user-friendly features, thereby facilitating ease in monitoring and control of mining operations.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)150By integrating the model with mine application various interfaces was developed with user-friendly features, thereby facilitating ease in monitoring and control of mining operations, thereby facilitating ease in monitoring and control of mining operations.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)["G. Ramkumar", "S. Jency"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/0dcd09c10f2096277810e5ce195e06be66385769 8c3e98ea7b3e6e062c6f41efdf265c118e9bbe79Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies reshaping industries and society. This paper explores the foundational concepts of AI and ML, emphasizing their definitions, methodologies, and applications. AI encompasses a broad spectrum of techniques aimed at mimicking human intelligence, while ML, a subset of AI, focuses on the development of algorithms that enable machines to learn from data. We examine the historical evolution of these fields, the current state of research, and key technologies such as neural networks, natural language processing, and computer vision. The implications of AI and ML are vast, impacting sectors including healthcare, finance, and transportation, and raising ethical considerations regarding privacy, bias, and employment. By analyzing case studies and recent advancements, this paper aims to highlight the potential and challenges of integrating AI and ML into everyday life, paving the way for future research and development in this dynamic domainInternational Journal of Advanced Research in Science, Communication and Technology90This paper explores the foundational concepts of AI and ML, emphasizing their definitions, methodologies, and applications and examines the historical evolution of these fields, the current state of research, and key technologies such as neural networks, natural language processing, and computer vision.International Journal of Advanced Research in Science, Communication and Technology["Khakre Vaibhav Bhagwan", "Dr. Sharad Kadam"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/8c3e98ea7b3e6e062c6f41efdf265c118e9bbe79 4c5fbc9839b1106c9d7742fe7a0195df55b46298This research investigates the implementation of artificial intelligence in the design optimization of electrical automation systems, with the objective of enhancing system configuration efficiency and minimizing energy usage. By integrating the differential evolution algorithm with the specific requirements of electrical automation, an intelligent optimization framework is developed to adaptively modify system configurations. This framework thoroughly assesses critical elements such as equipment selection, energy management, and response time throughout the design phase and employs artificial intelligence techniques to fine-tune algorithm parameters for optimal configuration. In the simulation phase, multi-scenario testing is employed to validate the real-world efficacy of the optimization framework. The simulation outcomes indicate that under high-load scenarios, the optimized framework achieves a 15% reduction in energy consumption and a 20% decrease in response time. Compared to traditional design approaches, the optimized system demonstrates superior performance in energy efficiency and response time, with the capability for dynamic adjustments based on real-time requirements, thereby showcasing the strengths of artificial intelligence in the design of complex systems. Detailed data analysis further confirms the effectiveness of the optimization framework, offering robust support for the intelligent design of electrical automation systems.2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)100Compared to traditional design approaches, the optimized system demonstrates superior performance in energy efficiency and response time, with the capability for dynamic adjustments based on real-time requirements, thereby showcasing the strengths of artificial intelligence in the design of complex systems.2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)["Zixuan Hu", "Yu Du"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/4c5fbc9839b1106c9d7742fe7a0195df55b46298 42b12440582ee6d4f98e7be952ff5d48b117c9ecThe present article aims to discuss the possibility of including the sphere of artificial intelligence production within the domain of artificial intelligence ethics and investigate its moral implications. In the first section, the role of human labour in the artificial intelligence production processes is considered, with particular reference to the distinction between high-skilled and low-skilled jobs, their differential distribution in the production process itself, and the labour conditions of ghost workers, in order to analyse the main ethical issues emerging within the field. In the second section, some aspects of the existing critical literature concerning artificial intelligence and labour are discussed, focusing on Marxist and decolonial scholarship and more precisely on its lack of consideration of the global value chain through which artificial intelligence AI production processes are structured. Finally, the possibility and limits of an ethics of artificial intelligence production are reconsidered by assuming the centrality of workers’ struggles and agency along artificial intelligence's global value chain.Thesis Eleven90The possibility and limits of an ethics of artificial intelligence production are reconsidered by assuming the centrality of workers’ struggles and agency along artificial intelligence's global value chain.Thesis Eleven["Bernardo Paci"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/42b12440582ee6d4f98e7be952ff5d48b117c9ec cef403a6c12a0176814a2d82ee8860b5bf7f1eceThis paper explores the transformative impact of artificial intelligence (AI) on financial technology (Fintech). With the rapid evolution of digital technologies, AI has become an important driver in reshaping financial services; where its effect is manifesting as increased net revenues of Fintech organizations. This short article examines various applications of AI in Fintech, including extraordinary payment systems, super-fast lending mechanisms and an example of a near real time insurance settlement system. This paper uses examples from industry to highlight how AI-driven innovations are not only enhancing efficiency and customer experience but also introducing new challenges, issues and considerations. The integration of AI in Fintech is altering traditional financial paradigms, prompting a discussion on the future of financial services, as well as organizational decision making, in an AI-dominated landscape.International Journal For Multidisciplinary Research210Examining various applications of AI in Fintech, including extraordinary payment systems, super-fast lending mechanisms and an example of a near real time insurance settlement system highlight how AI-driven innovations are not only enhancing efficiency and customer experience but also introducing new challenges, issues and considerations.International Journal For Multidisciplinary Research["Arnab K Ganguly", "Purna Prasad Arcot"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/cef403a6c12a0176814a2d82ee8860b5bf7f1ece 1ec2d3a5eee67f145f1c8693a756d6de8b2b2a37In recent years, we have witnessed the raise of several generative artificial intelligence (AI) products and services which have strong disruptive capabilities. Majority of these AI-based products and services are also very popular since their public availability. These generative AI products and services have a lot of potentials to do the common office works, writing codes, doing accounting tasks, predicting common patterns, generating creative contents, marketing products and services, increasing efficiencies in the systems, translating to different languages, designing products, and many such things. These effects of generative AI are phenomenal and it is going to start a large scale automation in the product design and service sectors. In addition to the benefits, several challenges are associated with generative AI. Starting from the bias to security risks to fake content creation, there are many challenges with these products and services. In this paper, we go through the basic principles of generative AI and then study its present and future prospects. We analyze its perilous effects and the common challenges with a few examples. Overall, generative AI can be beneficial tool if it is used positively under appropriate regulatory systems.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)200Overall, generative AI can be beneficial tool if it is used positively under appropriate regulatory systems and then study its present and future prospects.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)["Sudhir K. Routray", "M. Jha", "K. Sharmila", "A. Javali", "M. Pappa", "Monika Singh"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/1ec2d3a5eee67f145f1c8693a756d6de8b2b2a37 66c1b35867910a1754d48d12dc6dc235cfc22956 As a significant breakthrough in the field of natural language processing, Chat Generative Pre-trained Transformer's (ChatGPT's) powerful language generation and transfer capabilities have not only changed the way of human-computer interaction, but also had a revolutionary impact on multiple industries. Currently, to develop the third generation of artificial intelligence (AI), issues related to knowledge, data, algorithm innovation, computational resources, and ethics need to be addressed. The Chinese model is mainly application-driven, but the basic theoretical research is relatively weak. The development history of AI shows that realizing artificial general intelligence still faces enormous challenges. In the upsurge of AI, we should remain calm and scrutinize its potential risks. China should increase the intensity of basic theoretical research, attract global talent, and jointly promote the healthy and sustainable development of the field of AI.Engineering Education Review50China should increase the intensity of basic theoretical research, attract global talent, and jointly promote the healthy and sustainable development of the field of AI.Engineering Education Review["Bo Zhang"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/66c1b35867910a1754d48d12dc6dc235cfc22956 8d44a94d81c0255c4f25a9e46e1c049dfcb41bb9Introduction: Skin cancer is one of the most prevalent forms worldwide, with a significant increase in recent decades. Real-time and accurate detection can reduce the burdens of invasive treatments. The advent of Artificial Intelligence (AI) and Machine learning (ML) has introduced multiple tools to aid accurate and early detection, categorizing dermatological images and proving especially valuable in regions with a shortage of specialists. However, the adoption of these AI-based tools requires consideration of efficacy, safety, and ethical implications. Objective: The systematic review aims to evaluate existing research on the detection, categorization, and assessment of skin cancer images. Methods: The systematic literature review is conducted based on studies published from 2018 to 2023 in PubMed, Scopus, Embase, Web of Science, IEEE Xplore, ACM DL, and Ovid MEDLINE. Study selection, data extraction, and inclusion are carried out after a proper evaluation of the studies. Results are presented in tables and figures using a narrative synthesis. Results: The search identified 687 studies from the database. However, after three phases of identification, screening, and evaluation, only 16 studies were chosen, focusing on developing and validating AI tools to detect, diagnose, and categorize skin cancer. This systematic review covers the selected studies in multiple dimensions. Conclusion: The use of AI and ML in dermatology has revolutionized the early detection of cancer, but it is necessary to validate and collaborate with healthcare professionals to ensure efficacy, safety, and effectiveness.Global academic journal of medical sciences00The use of AI and ML in dermatology has revolutionized the early detection of cancer, but it is necessary to validate and collaborate with healthcare professionals to ensure efficacy, safety, and effectiveness.Global Academic Journal of Medical Sciences["Rushin Patel", "Akash Jain", "Anand Kadakia", "Afoma Onyechi", "Zalak Patel", "Eduzor Onyechi", "Mrunal Patel", "Jessica Ohemeng- Dapaah", "Darshil Patel", "Garcia Moncada"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/8d44a94d81c0255c4f25a9e46e1c049dfcb41bb9 8a4fbf7c98ec638fa77393ae735f28e74c1bf662Kenyan Sign Language (KSL) is the primary language used by the deaf community in Kenya. It is the medium of instruction from Pre-primary 1 to university among deaf learners, facilitating their education and academic achievement. Kenyan Sign Language is used for social interaction, expression of needs, making requests and general communication among persons who are deaf in Kenya. However, there exists a language barrier between the deaf and the hearing people in Kenya. Thus, the innovation on AI4KSL is key in eliminating the communication barrier. Artificial intelligence for KSL is a two-year research project (2023-2024) that aims to create a digital open-access AI of spontaneous and elicited data from a representative sample of the Kenyan deaf community. The purpose of this study is to develop AI assistive technology dataset that translates English to KSL as a way of fostering inclusion and bridging language barriers among deaf learners in Kenya. Specific objectives are: Build KSL dataset for spoken English and video recorded Kenyan Sign Language and to build transcriptions of the KSL signs to a phonetic-level interface of the sign language. In this paper, the methodology for building the dataset is described. Data was collected from 48 teachers and tutors of the deaf learners and 400 learners who are Deaf. Participants engaged mainly in sign language elicitation tasks through reading and singing. Findings of the dataset consisted of about 14,000 English sentences with corresponding KSL Gloss derived from a pool of about 4000 words and about 20,000 signed KSL videos that are either signed words or sentences. The second level of data outcomes consisted of 10,000 split and segmented KSL videos. The third outcome of the dataset consists of 4,000 transcribed words into five articulatory parameters according to HamNoSys system.arXiv.org260The purpose of this study is to develop AI assistive technology dataset that translates English to KSL as a way of fostering inclusion and bridging language barriers among deaf learners in Kenya.ArXiv["Lilian D. A. Wanzare", "J. Okutoyi", "Maurine Kang\u2019ahi", "Mildred Ayere"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/8a4fbf7c98ec638fa77393ae735f28e74c1bf662 7b4766c9eaeb083e5d0960907cee9016ba9041feThe integration of artificial intelligence (AI) into the field of vaccine development has revolutionized the discovery and production processes, particularly during the COVID-19 pandemic. AI technologies played an instrumental role in accelerating the identification of viable vaccine candidates, optimizing clinical trial designs, and expediting regulatory approvals. This review critically examines the impact of AI-driven approaches on the development of COVID-19 vaccines, highlighting case studies such as the Pfizer-BioNTech and Moderna vaccines. By employing machine learning algorithms and sophisticated data analytics, AI significantly reduced traditional vaccine development timelines from years to mere months, all while enhancing precision, safety, and efficacy. Our analysis reveals that AI facilitated real-time monitoring of clinical trial data, improving patient stratification, and dynamically addressing adverse events, leading to faster approvals without compromising regulatory standards. Furthermore, AI-powered models optimized vaccine distribution strategies, overcoming logistical challenges associated with global deployment during the pandemic. This review also explores the ethical and technical challenges posed by AI, such as algorithmic biases, data privacy concerns, and the need for transparent governance frameworks. The lessons drawn from the COVID-19 pandemic underscore the transformative potential of AI in accelerating future vaccine research and pandemic preparedness. We conclude that continued interdisciplinary collaboration between AI experts, immunologists, and public health authorities will be essential in shaping the future of vaccine innovation.medRxiv00The analysis reveals that AI facilitated real-time monitoring of clinical trial data, improving patient stratification, and dynamically addressing adverse events, leading to faster approvals without compromising regulatory standards, leading to faster approvals without compromising regulatory standards.["A. F. Farahani", "N. Kasraei"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/7b4766c9eaeb083e5d0960907cee9016ba9041fe 5cb782652ce98db6e03f0f65cc0eb1ad8a9ce25eIn the era of the digital economy, the development of artificial intelligence and new productivity have played an important role in promoting macroeconomics. Firstly, this paper introduces the related policies and literature review of artificial intelligence promotion and the formation of new quality productivity, then analyzes the development status and existing problems of artificial intelligence promotion and new quality productivity, and finally puts forward some countermeasures and suggestions on artificial intelligence promotion, including clarifying the independent technical route of artificial intelligence industry in China, intensively building computing centers and opening data sets, promoting industrial application based on the independent technical route of artificial intelligence in China, and paying attention to the cultivation of talents; The countermeasures and suggestions on cultivating new quality productivity include accelerating the emergence of cutting-edge technologies and disruptive technologies, fully releasing the potential of data elements, and building a new quality productivity realization mechanism with deep integration of science and technology, industry and finance.Advances in Economics and Management Research00The countermeasures and suggestions on cultivating new quality productivity include accelerating the emergence of cutting-edge technologies and disruptive technologies, fully releasing the potential of data elements, and building a new quality productivity realization mechanism with deep integration of science and technology, industry and finance.Advances in Economics and Management Research["Yi Piao"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/5cb782652ce98db6e03f0f65cc0eb1ad8a9ce25e a12dda79ac1283d641eb14857c9d681426dfbe4fThis study explores the different ways Artificial Intelligence (AI) tools bolster human knowledge and boost intellectual and creative efforts within the educational sector. It provides a broad examination of AI's potential to enhance educational processes and outcomes while also assessing the ramifications of AI-driven creative destruction on the job market encompassing both job displacement and the emerging scope for new knowledge creation. Purpose. The study aims to explore the different ways in which AI tools enhance human knowledge and capabilities and their role in augmenting human intellectual and creative endeavours in education. By examining how AI can improve educational processes and outcomes, it highlights the potential for significant advancements. Additionally, the study critically examines the implications of AI-driven creative destruction on the job market, focusing on the education sector. This includes understanding both job displacement and the creation of new opportunities that require advanced skills. Methods. This research involves a detailed analysis of various AI applications across the education domain. It employs virtual interviews with students and educators to provide detailed examples of AI's impact and reviews existing literature to contextualise these findings within broader economic implications. By focusing on the education sector, the study provides a comprehensive overview of how AI is being implemented and the outcomes of these implementations. This approach allows for a thorough understanding of both the benefits and challenges connected with AI integration. Results. The study suggests that AI significantly improves data processing capabilities, leading to notable advancements in educational research and personalised learning. These improvements can facilitate decision-making and innovation. However, AI also disrupts traditional employment patterns, displacing routine jobs that are easily automated. Conversely, it creates new roles that demand advanced technical skills and continuous education, highlighting a shift in the job market toward more specialised and high-skill positions. Conclusion. While AI can present substantial benefits in terms of efficiency and innovation, it poses significant challenges in the form of job displacement. To manage these transitions effectively, strategic responses from policymakers and educational institutions are essential. These strategies should aim to ensure equitable access to AI’s benefits and support workforce adaptation. By fostering a balanced integration of technological advancements and human well-being, it is possible to mitigate the negative impacts while maximising the positive outcomes of AI.Educational Challenges130The study suggests that AI significantly improves data processing capabilities, leading to notable advancements in educational research and personalised learning, however, AI also disrupts traditional employment patterns, displacing routine jobs that are easily automated.Educational Challenges["E. A. Jackson", "H. Jackson"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/a12dda79ac1283d641eb14857c9d681426dfbe4f e6b7ea8b6d99381396d58c9f10b5c6958b0a8b97The article contains the results of the author’s sociological research concerning the study of the risks and opportunities of using artificial intelligence in the profession of a sociologist. The study was conducted by the method of expert interviews. The paper reveals the degree of permissibility of using artificial intelligence at various stages of sociological research, describes the experience of experts using new technologies in research activities, and identifies the advantages and disadvantages of its use for both students and experienced sociologists. The authors of the article discuss the impact of artificial intelligence on the methods and practices used by sociologists, possible ethical dilemmas that may arise in the process of its implementation in sociology. Special attention is paid to the use of the neural network by students of sociology and the consequences of this trend on their quality of training and competencies. The impact of the introduction of artificial intelligence on the transformation of the labor market is emphasized.Humanities and Social Sciences Bulletin of the Financial University10The degree of permissibility of using artificial intelligence at various stages of sociological research is revealed, the experience of experts using new technologies in research activities is described, and the advantages and disadvantages of its use for both students and experienced sociologists are identified.Humanities and Social Sciences. Bulletin of the Financial University["K. M. Avdonina", "Ya. N. Smirnova"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/e6b7ea8b6d99381396d58c9f10b5c6958b0a8b97 a949afeef9257f4055e15530ededa85db39e8e19Inter-generational communication is essential for bridging generational gaps and fostering mutual understanding. However, maintaining it is complex due to cultural, communicative, and geographical differences. Recent research indicated that while Virtual Reality (VR) creates a relaxed atmosphere and promotes companionship, it inadequately addresses the complexities of inter-generational dialogue, including variations in values and relational dynamics. To address this gap, we explored the opportunities of Artificial Intelligence (AI) in supporting inter-generational communication in VR. We developed three technology probes (e.g., Content Generator, Communication Facilitator, and Info Assistant) in VR and employed them in a probe-based participatory design study with twelve inter-generational pairs. Our results show that AI-powered VR facilitates inter-generational communication by enhancing mutual understanding, fostering conversation fluency, and promoting active participation. We also introduce several challenges when using AI-powered VR in supporting inter-generational communication and derive design implications for future VR platforms, aiming to improve inter-generational communication.arXiv.org1110The results show that AI-powered VR facilitates inter-generational communication by enhancing mutual understanding, fostering conversation fluency, and promoting active participation.ArXiv["Qiuxin Du", "Xiaoying Wei", "Jiawei Li", "Emily Kuang", "Jie Hao", "Dongdong Weng", "Mingming Fan"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/a949afeef9257f4055e15530ededa85db39e8e19 c7915ec6d36eea19efd4936c0c0252fdce9550baThe purpose of this study is to analyze the policy of vocational education, which focuses on challenges in the field of employment and progress in the field of AI. Vocational Education in Indonesia Vocational education is an educational model that carries the advantage of 70% practice and 30% theory with the hope that it can be one of the answers to the problem of preparing college graduates with applied skills needed by the labor market. While the term "artificial intelligence" refers to a number of principles of information technology, including computing, software development, and data transfer. This study utilizes a qualitative approach using literature research methods. In its policy, there are two major challenges in the conception of vocational education, namely the limited active involvement of the industrial world and the low competency qualifications of Vocational education graduates. On the other hand, advances in the field of AI contribute to vocational education, namely increasing efficiency, learning becomes more personalized, instant feedback, deeper data analysis, development of practical skills, accessibility and flexibility, readiness to face the world of technology 5.0.Jurnal Ilmu Sosial Politik dan Humaniora150The purpose of this study is to analyze the policy of vocational education, which focuses on challenges in the field of employment and progress in the field of AI, using a qualitative approach using literature research methods.Jurnal Ilmiah Muqoddimah : Jurnal Ilmu Sosial, Politik, dan Humaniora["Indah Nur Af\u2019idatun Fithroh", "Amar Qulbi", "Masduki Duriyat"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/c7915ec6d36eea19efd4936c0c0252fdce9550ba e82d39e096a1e4e0917138ea30404cadbfd730daCrimRxiv02CrimRxiv["Ian T. Adams", "Kyle McLean", "Geoff Alpert"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/e82d39e096a1e4e0917138ea30404cadbfd730da 855eaff0288fd6952dc62f68581d5dd9aab5fc5fJournal of Academic Ethics411Journal of Academic Ethics["Gabriel Andrade-Hidalgo", "Pedro Mio-Cango", "Orlando Iparraguirre-Villanueva"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/855eaff0288fd6952dc62f68581d5dd9aab5fc5f 5d688d95c47267f939a9b0878914b16727b17753The journal of the International Association of Medical Science Educators : JIAMSE201Medical Science Educator["Jason Luong", "Chih-Chen Tzang", "Sean C. McWatt", "Cecilia Brassett", "Dana A. Stearns", "M. Sagoo", "Carol Kunzel", "Takeshi Sakurai", "Chung-Liang Chien", "Geoffroy Noel", "Anette Wu"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/5d688d95c47267f939a9b0878914b16727b17753 ab3393b45b1e3a9a8428a5ade46ad78799c1d085AI &amp; SOCIETY21AI &amp; SOCIETY["Minh-Hoang Nguyen", "Q. Vuong"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/ab3393b45b1e3a9a8428a5ade46ad78799c1d085 31c313669d46b04d478e9609b710d376a0c3dd71In order to promote the practice of the hierarchical diagnosis system in China, We looked into electronic medical record-the important information carrier of patient health data. Based on literature reading and pre-investigation, a model of influencing factors of doctors’ willingness to use and share electronic medical records under the background of the implementation of the hierarchical diagnosis and treatment system was constructed. The questionnaire was designed with the traditional model and the questionnaire data were collected. The structural equation model was used to test the constructed model. The results show that the perceived usefulness of electronic medical records has the most significant impact on the willingness to share. Perceived ease of use and privacy protection of patient information have significant effects on sharing intention. In the future, the construction of electronic medical record system should be based on improving its use value and considering its convenience.Journal of Artificial Intelligence Research531The results show that the perceived usefulness of electronic medical records has the most significant impact on the willingness to share and the construction of electronic medical record system should be based on improving its use value and considering its convenience.Journal of Artificial Intelligence Research["Meng Zhang", "Chenghua Qin", "Fenhua Qiang"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/31c313669d46b04d478e9609b710d376a0c3dd71 1017bfab7b0352fdb3d50027d3232aa7b4749addThe prognosis of diabetes, a chronic metabolic disorder, is crucial for early intervention and management. This study explores the integration of various machine learning approaches to enhance the accuracy and reliability of diabetes prognosis. The integration process encompasses rigorous cross-validation and hyper parameter tuning to optimize model efficacy. The findings demonstrate that leveraging multiple machine learning models not only increases prognostic accuracy but also provides a robust framework for handling diverse patient data. This integrated approach shows promise in supporting healthcare professionals with precise predictions, ultimately leading to better patient management and outcomes. The study also addresses the challenges of model complexity, computational cost, and interpretability, proposing solutions to mitigate these issues. The integration is done by stacking, an advanced ensembling method. The model developed is the results of accurately stacking the basic model. The divided data is examined using ML classifiers, and the classifier’s accuracy is calculated. Moreover, particular measures such as precision, recall, and F1 score are compared to each model’s prediction accuracy to choose the optimal model. The proposed method gives a high prediction accuracy of about 99 percentage.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)162This study explores the integration of various machine learning approaches to enhance the accuracy and reliability of diabetes prognosis and demonstrates that leveraging multiple machine learning models not only increases prognostic accuracy but also provides a robust framework for handling diverse patient data.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)["R. Sathya", "V. C. Bharathi", "S. Ananthi", "T. Vijayakumar", "Rvs Praveen", "Dhivya Ramasamy"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/1017bfab7b0352fdb3d50027d3232aa7b4749add df205913b4684b00b74a093d35e74c6a3a457e3dMental Health and Digital Technologies210Mental Health and Digital Technologies["James Acland", "Neil Hammond", "Simon Riches"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/df205913b4684b00b74a093d35e74c6a3a457e3d 9f8ed61d9d36075a95f99a4b20249387b1a1bf7a This paper explores the necessary adaptations to the theory of administrative discretion when using AI systems. Regulatory frameworks in the EU, US, and Spain do not prohibit the application of AI in discretionary decision-making. Particularly, AI systems can be used when discretionary power involves correlations. However, to meet Rule of Law conditions, it is essential to establish adaptations and boundaries in areas such as duty of care, reason-giving, and judicial review. These conditions should focus on the impact of decisions on the affected individuals.European Journal of Risk Regulation00This paper explores the necessary adaptations to the theory of administrative discretion when using AI systems to establish adaptations and boundaries in areas such as duty of care, reason-giving, and judicial review.European Journal of Risk Regulation["Juan Carlos Covilla"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/9f8ed61d9d36075a95f99a4b20249387b1a1bf7a c5c514aed30391f634631b3b4ff2ab6f9f5fef83In order to optimise deep learning models for few-shot website fingerprinting (WF) attacks, this study offers a fresh way to data augmentation technique. For each website, only a few training samples are provided. More sophisticated Deep learning approaches demonstrate the ability to automatically learn representations of features from training data is preferable to earlier WF approaches that relied on manually-engineered feature representations. However, this benefit is dependent on the implausible premise that each website has a large number of training samples; in the absence of such assumptions, the benefit will vanish. In order to tackle this issue, we present a novel approach called Compatible information Augmentation (CIA), which is efficient, model-agnostic, and can greatly enhance deep WF attacking techniques. CIA entails data transformations both within and between samples, which can be employed in a harmonious manner to expand a small training dataset into a large collection of arbitrarily large numbers, thereby successfully and clearly addressed the issue of inherent data scarcity. Extensive experiments were carried out to validate our CIA for enhancing cutting-edge deep learning WF attack models in scenarios of both open-world and closed-world attacks, with a strong defence or not. For example, our CIA approach achieves over 4% higher completeness of classification in the 20-shot learning scenario than the prior state-of-the-art results in the WTF-PAD based defence evaluation scenario, which is more difficult and realistic.2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)360Compatible information Augmentation (CIA) is presented, which is efficient, model-agnostic, and can greatly enhance deep WF attacking techniques and achieves over 4% higher completeness of classification in the 20-shot learning scenario than the prior state-of-the-art results in the WTF-PAD based defence evaluation scenario.2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)["Rujia Jin"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/c5c514aed30391f634631b3b4ff2ab6f9f5fef83 bd4f8f4eb9f21f0ab1670e7a03b2117fd10a8f57Journal of Dental Education30Journal of dental education["Anita Tourah", "Mahvash Navazesh", "Mariela Padilla"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/bd4f8f4eb9f21f0ab1670e7a03b2117fd10a8f57 f86b484ba1f5fdc23fa1356bfe200934a35e9165Nuclear Technology240Nuclear Technology["Torrey Mortenson", "C. Kovesdi", "J. Mohon"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/f86b484ba1f5fdc23fa1356bfe200934a35e9165 69f058de3476d0fcbdb520d738e94a9b10840427Parkinson’s Disease (PD) is a rare, progressive disease that gets worse over time, and subsequently has no cure. It causes symptoms such as stiffness, and shaking, and leads to a loss of balance and coordination in day-to-day activities. Mobility challenges and speech difficulties exclusive to PD often complicate treatment for the disease. Timely detection of PD is of the highest importance, enabling patients to make the most of their remaining lives. The aging global population highlights the urgent need for accurate remote detection models. This research study analyzes various machine learning algorithms for determining whether a person is suffering from Parkinson’s disease or not. This paper deals with the implementation of 4 machine learning techniques, which include Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression—trained on the MDVP audio dataset obtained from UCIML, UC Irvine’s Machine Learning Repository. The results of the models are put up against each other based on precision, F score, recall, and accuracy as evaluation metrics. The model demonstrating the highest accuracy was the Random Forest classifier, achieving an accuracy of 94.87% with perfect sensitivity (recall) of $\mathbf{1. 0}$. Key features contributing to its efficacy include vocal fundamental frequency metrics such as PPE, MDVP (Hz), and spread1, which highlight the vital role of these features in disease detection. These findings support the integration of machine learning in telemedicine, offering solutions that promise to constructively contribute to the lives of patients suffering from this ailment.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)90These findings support the integration of machine learning in telemedicine, offering solutions that promise to constructively contribute to the lives of patients suffering from Parkinson’s disease.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)["Mehull Girdhar", "Umang Soni"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/69f058de3476d0fcbdb520d738e94a9b10840427 ba3e62d358aa361cc92d3dbbe496ada8d25f3a6aWhile new technologies emerge, human errors always looming. Software supply chain is increasingly complex and intertwined, the security of a service has become paramount to ensuring the integrity of products, safeguarding data privacy, and maintaining operational continuity. In this work, we conducted experiments on the promising open Large Language Models (LLMs) into two main software security challenges: source code language errors and deprecated code, with a focus on their potential to replace conventional static and dynamic security scanners that rely on predefined rules and patterns. Our findings suggest that while LLMs present some unexpected results, they also encounter significant limitations, particularly in memory complexity and the management of new and unfamiliar data patterns. Despite these challenges, the proactive application of LLMs, coupled with extensive security databases and continuous updates, holds the potential to fortify Software Supply Chain (SSC) processes against emerging threats.2024 5th International Conference on Data Analytics for Business and Industry (ICDABI)340This work conducted experiments on the promising open Large Language Models into two main software security challenges: source code language errors and deprecated code, with a focus on their potential to replace conventional static and dynamic security scanners that rely on predefined rules and patterns.2024 5th International Conference on Data Analytics for Business and Industry (ICDABI)["Vasileios Alevizos", "G. Papakostas", "Akebu Simasiku", "Dimitra Malliarou", "Antonis Messinis", "Sabrina Edralin", "Clark Xu", "Zongliang Yue"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/ba3e62d358aa361cc92d3dbbe496ada8d25f3a6a e1646020aa4e435c534e54216a44063897923755The primary offering of bankers and other financial organisations is loans. Due to the fact that more individuals are using banks for borrowing money for a variety of purposes, the number of clients has expanded, and some banks anticipate making large profits from the interest that is paid on loans. But there is a chance that a loan will be repaid. Thus, a large percentage of non-performing loans has the potential to cause instability in the banking industry and ultimately result in bankruptcy. Verifying that the borrower has the ability to repay the loan within the suggested terms is a crucial stage in the process by which banks determine whether to approve a loan. The development of technology, including computer science, machine learning, and other fields of study, is crucial because it enables banks to forecast a customer’s likelihood of defaulting based on his prior conduct. Once the machine was trained using its training dataset, we verified the accuracy of various models using the test dataset. In order to determine which machine learning approaches are optimal for forecasting bank loan default, SVM is conducted (Support Vector Machines) is a powerful machine learning algorithm commonly used for classification tasks, including predicting loan default. Its ability to handle both linear and non-linear data makes it suitable for complex datasets encountered in banking. SVM works by finding the optimal hyperplane that separates data points into different classes, maximizing the margin between them. By leveraging SVM, banks can effectively classify borrowers into low-risk and high-risk categories, enabling them to make more informed lending decisions.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)70SVM (Support Vector Machines) is a powerful machine learning algorithm commonly used for classification tasks, including predicting loan default, which can effectively classify borrowers into low-risk and high-risk categories, enabling them to make more informed lending decisions.2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)["G. Anitha", "P. Aravindh"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/e1646020aa4e435c534e54216a44063897923755 c0b06d55c4f25b6c16a9062f12584c13dfd77906BMJ Quality & Safety80BMJ quality & safety["A. Todd", "Barbara Hanratty"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/c0b06d55c4f25b6c16a9062f12584c13dfd77906 911a2037366f6423283bcc25d721fa1300b2aab5Echocardiography230Echocardiography["I. Skalidis", "Georgios Tzimas", "Panagiotis Antiochos", "G. Suc", "Henri Lu", "A. Salihu", "St\u00e9phane Fournier", "Olivier Muller", "N. Maurizi", "Dimitri Arangalage"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/911a2037366f6423283bcc25d721fa1300b2aab5 49cf44a07702ccb614dfff213eba9f62ecb6267eThere is a growing concern about credibility and trustworthiness in results and claims of research in computational data science, and at the same time, difficulty in taking those results to MVP (Minimum viable product) in technology transfer from the laboratory to the industry. Therefore, We present the OVDSaaS prototype, a new platform to manage Data Science Research products (publication, code, data, MVP) that provides reproducibility certification, traceability, provenance, authenticity, legitimacy, reward, FAIR (Findable, Accessible, Interoperable, Reusable) compliance, and valorization to ML/ AI computer scientific research by developing useful online verifiable scientific applications MVP in different industrial domains. To bridge the gap among academia, industry, publishers, and ML/AI technology, we propose an architecture design, methodology, policies, and guidelines for the OVD-SaaS project to advance in reproducibility assessment, persistent identification, validation, and badging of research artifacts results. We present our Scientific Applications case study in the context of reliable scientific research and the validation of results from scientific articles. We describe our experience creating scientific applications and their evolution from simple Demo or non-reproducible artifacts to completely verifiable apps. We analyze common patterns in the creation of scientific applications, discussing the benefits and difficulties encountered that lead to the proposed OVDSaaS features. In this context, four applications were developed for breathing analysis, clinical tumor segmentation, legal assistance, and smart grids, supported in ML/ AI scientific fields such as signal processing, image processing, and NLP. These applications represent end-to-end business solutions cases in the medical, legal, and energy industries. Based on these cases, we performed a reproducibility gap analysis and benchmarking of similar OVDSaaS platforms.International Conference on the Digital Society260The OVDSaaS prototype is presented, a new platform to manage Data Science Research products that provides reproducibility certification, traceability, provenance, authenticity, legitimacy, reward, FAIR compliance, and valorization to ML/ AI computer scientific research by developing useful online verifiable scientific applications MVP in different industrial domains.2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS)["Jose Armando Hernandez", "Thibaut Germain", "Billel Yadoughi", "Miguel Colom"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/49cf44a07702ccb614dfff213eba9f62ecb6267e 69db1cfd775ca8a678ba2efc5261b09b754b0244This research explores the transformative role of artificial intelligence (AI) and in-silico trials in the pharmaceutical development process, emphasizing how these innovations can significantly reduce costs and time associated with clinical trials. With the rapid advancement of AI technologies and digital health solutions, the pharmaceutical industry stands at a pivotal moment that could reshape traditional drug development methodologies. By harnessing AI-driven analytics, researchers can analyze vast datasets to identify potential drug candidates, predict outcomes, and streamline the drug discovery process. In-silico trials, which utilize computer simulations to model biological interactions and clinical scenarios, offer an innovative approach to assessing drug efficacy and safety before initiating costly and time-consuming human trials. This research highlights how integrating AI into these virtual trials can optimize trial designs, improve patient recruitment, and enhance data accuracy. Consequently, these innovations contribute to reducing the overall development timeline, accelerating the delivery of new therapies to market, and ultimately increasing accessibility for patients. Additionally, the study examines the implications of AI and digital health solutions on regulatory processes and the ethical considerations that arise from their use. Regulatory bodies are increasingly recognizing the value of AI in enhancing clinical trial efficiency and ensuring patient safety. By streamlining data collection and analysis, AI can provide regulators with more robust evidence for decision-making, fostering a faster approval process for life-saving medications. Furthermore, the research underscores the potential of AI-driven innovations to democratize healthcare access in the U.S. By reducing the costs associated with drug development, pharmaceutical companies can offer more affordable medications, ultimately improving health outcomes for underserved populations. In conclusion, AI and digital health innovations are poised to revolutionize pharmaceutical development by enhancing efficiency, reducing costs, and improving accessibility to healthcare. This research serves as a critical examination of how these technologies can drive the future of drug development and contribute to a more equitable healthcare system. Keywords: Artificial Intelligence, Digital Health, Pharmaceutical Development, In-Silico Trials, Clinical Trials, Healthcare Accessibility.Computer Science &amp; IT Research Journal07Artificial intelligence and digital health innovations are poised to revolutionize pharmaceutical development by enhancing efficiency, reducing costs, and improving accessibility to healthcare.Computer Science &amp; IT Research Journal["Olumide Emmanuel Ibikunle", "Precious Azino Usuemerai", "Luqman Adewale Abass", "Victor Alemede", "Ejike Innocent Nwankwo", "Akachukwu Obianuju Mbata"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/69db1cfd775ca8a678ba2efc5261b09b754b0244 94c310dc34f0d40d7def81988e881b53877bfda2In an increasingly interconnected world, the necessity for robust Anti-Money Laundering (AML) frameworks that transcend national borders has become paramount. This paper examines the critical role of analytical tools in fostering international AML cooperation and enhancing cross-border regulatory frameworks. Effective AML measures rely on comprehensive data analysis to identify and mitigate financial crime risks. Analytical tools, including big data analytics, machine learning, and artificial intelligence, facilitate the collection, processing, and interpretation of vast amounts of financial data from diverse jurisdictions, allowing for more accurate risk assessments. The research highlights how these tools can enhance collaboration among regulatory authorities by providing insights into complex money laundering schemes that often span multiple countries. By leveraging advanced analytics, financial institutions and regulatory bodies can share critical information on suspicious activities, thereby improving the effectiveness of their AML strategies. Furthermore, the paper discusses the significance of developing standardized analytical frameworks that can be adopted globally to ensure consistency in AML practices and reporting requirements. One of the key challenges identified is the need for harmonization of regulatory standards across different jurisdictions. The disparity in AML regulations can hinder effective cooperation and information sharing among nations. The adoption of analytical tools can bridge these gaps by offering standardized metrics and methodologies for evaluating risks associated with cross-border transactions. Additionally, the study examines case studies where analytical tools have successfully been implemented to enhance international AML cooperation. These examples illustrate the potential for increased transparency and accountability in global financial systems, leading to more effective detection and prevention of money laundering activities. In conclusion, fostering international AML cooperation through the strategic use of analytical tools is vital for strengthening cross-border regulatory frameworks. The findings emphasize the importance of innovation and collaboration in combating financial crimes on a global scale. Keywords: Anti-Money Laundering (AML), International Cooperation, Analytical Tools, Cross-Border Regulation, Big Data Analytics, Machine Learning, Financial Crime, Risk Assessment, Standardization, Transparency.Computer Science &amp; IT Research Journal06Computer Science &amp; IT Research Journal["Vivian Ofure Eghaghe", "Olajide Soji Osundare", "Chikezie Paul-Mikki Ewim", "Ifeanyi Chukwunonso Okeke"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/94c310dc34f0d40d7def81988e881b53877bfda2 d8551f5c9f5703cb748a866cb1288b5035dadfb3This study leverages the Stimulus‐Organism‐Response (S‐O‐R) framework to investigate the effects of teacher and technical support (TCHS) on learners' willingness to communicate (WTC) in artificial intelligence (AI)‐enhanced English as a foreign language (EFL) contexts, considering the mediating effects of learners' artificial intelligence literacy (AIL) and foreign language enjoyment (FLE). A quantitative survey encompassing 637 non‐English major university students across four institutions was conducted. Structural equation modelling (SEM) results demonstrated that teacher support (TEAS) exerts a direct influence on learners' WTC, whereas TCHS does not. The study also revealed that AIL and FLE significantly mediate the relationship between teacher and TCHS and learners’ WTC. The findings underscore the pivotal role of cognitive and affective factors, emphasising the substantial impact of TEAS and the value of nurturing learners’ AIL and enjoyment of foreign languages. This research offers strategic implications for educational practitioners and policymakers, advocating for the integration of innovative educational technologies and fostering sustainable growth in artificial intelligence in education.European Journal of Education723Investigation of the effects of teacher and technical support on learners' willingness to communicate in artificial intelligence‐enhanced English as a foreign language (EFL) contexts revealed that teacher support (TEAS) exerts a direct influence on learners' WTC, whereas TCHS does not.European Journal of Education["Honggang Liu", "Jiqun Fan"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/d8551f5c9f5703cb748a866cb1288b5035dadfb3 e37fd10e5ad1932168bf58679ea76ec3e53e45a4Scientific Reports621An advanced automated system for detecting and classifying PCOS from ultrasound images is proposed, incorporating AUC score, accuracy, specificity, precision, F1-score, recall, and loss, along with a detailed confusion matrix analysis.Scientific Reports["Poonam Moral", "D. Mustafi", "A. Mustafi", "S. Sahana"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/e37fd10e5ad1932168bf58679ea76ec3e53e45a4 24f0fe6e6e05525fba867a6368b2c36f23c6c29cLanguage Testing in Asia1031The findings revealed that AI-assisted assessment significantly improved vocabulary knowledge and emotional resilience compared to the control group, and highlighted the importance of integrating advanced technologies into educational frameworks to support cognitive and emotional development in learners.Language Testing in Asia["Mohammad Ahmar Khan", "Oysha Kurbonova", "Diyorjon Abdullaev", "A. H. Radie", "Nirvana Basim"]2024-10-23T00:00:00https://www.semanticscholar.org/paper/24f0fe6e6e05525fba867a6368b2c36f23c6c29c f64b0feecec1a4598bf9986b45ea7e03ab328367BACKGROUND As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. METHODS We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. RESULTS From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. CONCLUSIONS Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.Clinical Chemistry and Laboratory Medicine304The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training, and a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe.Clinical chemistry and laboratory medicine["J. Cadamuro", "A. Carobene", "Federico Cabitza", "\u017d. Debeljak", "S. De Bruyne", "William van Doorn", "Elias Johannes", "G. Frans", "H. \u00d6zdemir", "Salom\u00f3n Martin P\u00e9rez", "Daniel Rajdl", "Alexander Tolios", "A. Padoan"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/f64b0feecec1a4598bf9986b45ea7e03ab328367 6f4b46d98b8fb3200a9943389fafbe62f497f615 In the age of rapid technological advancements, the integration of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in science, technology, engineering, and mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. This book, comprising twenty-six chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world.01This book, comprising twenty-six chapters, delves deep into the multifaceted realm of AI-driven STEM education, exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools.[]2024-10-24T00:00:00https://www.semanticscholar.org/paper/6f4b46d98b8fb3200a9943389fafbe62f497f615 90b7fb615c3858c1e0894ad977ce232722f4edacThis study aimed to explore the effectiveness of using artificial intelligence (AI) technologies in improving the quality of education in Emirati private universities. It also explored the challenges hindering the use of AI technologies in Emirati private universities. It aimed to offer suggestions to handle such challenges. The researcher adopted the qualitative, quantitative and descriptive analytical approach. He used two data collection methods which are: a questionnaire and an interview.   He shared the questionnaire on several WhatsApp groups that target faculty members in five Emirati private universities. 164 faculty members filled in the questionnaire. Thus, they were chosen purposively.  The researcher conducted interviews with thirteen (13) faculty members chosen purposively from the latter universities. SPSS software was used to process the collected data. Based on the analysis, it was found that using AI technologies improves the quality of education in in Emirati private universities. Such use reduces the students’ dependency on faculty members for acquiring knowledge and improves the teaching methods used by faculty members. Based on the interviews, it was found that such challenges include: having concerns about the security of data and facing difficulty in developing special AI software and applications for students and faculty members. Regarding the implications of this study, the results of this study contribute to improving the quality of education in Emirati private universities because they encourage the developers of curricula in such universities to add AI-based activities to those curricula.Edelweiss Applied Science and Technology01It was found that using AI technologies improves the quality of education in in Emirati private universities, which reduces the students’ dependency on faculty members for acquiring knowledge and improves the teaching methods used by faculty members.Edelweiss Applied Science and Technology["Khaled Younis Alderbashi"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/90b7fb615c3858c1e0894ad977ce232722f4edac 537c5e5f21248384224f6d39451feda7ff0dae23Cardiac critical care (CCC) involves a heterogenous group of critically ill patients and poses an ever-growing challenge to the healthcare system. Moreover, their clinical outcome improved to an unprecedented level due to significant improvements in the critical care practice. Artificial intelligence (AI) is an emerging transdisciplinary field that involves multidomain and multidimensional computerized data to handle heterogeneity, complexity, and acuity which were the major limitations of conventional critical care practice. AI employs machine learning techniques for disease identification from an exhaustive list of differential diagnoses, prediction of disease evolution and its diverse manifestations, dynamic risk calculation, optimal sequential decision-making solutions, and trajected prediction of clinical deterioration or recovery. This review highlights the current advances and implementations of AI algorithms in CCC practice with respect to sepsis, heart failure, arrhythmia, and various cardiovascular diseases.Journal of Cardiac Critical Care TSS321This review highlights the current advances and implementations of AI algorithms in CCC practice with respect to sepsis, heart failure, arrhythmia, and various cardiovascular diseases.Journal of Cardiac Critical Care TSS["Devishree Das", "Minati Choudhury"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/537c5e5f21248384224f6d39451feda7ff0dae23 0ddfc654e6cadf5700a0a8fd949170c729cc2f10This paper explores the application of Artificial Intelligence (AI) in cybersecurity, emphasizing its potential to enhance threat detection, response, and risk management. The study's primary objective is to analyze how AI-driven tools and techniques can improve the efficiency and effectiveness of cybersecurity measures in organizations. Employing a comprehensive literature review and case study analysis, the research investigates current AI applications in threat detection, including machine learning algorithms, anomaly detection systems, and predictive analytics. The findings reveal that AI significantly reduces response times to cyber threats, increases accuracy in identifying vulnerabilities, and enables more proactive risk management strategies. The paper also examines the strategic implications of integrating AI into cybersecurity frameworks, highlighting the challenges related to data privacy, ethical considerations, and the need for skilled personnel to manage AI systems. Furthermore, it discusses the future prospects for AI in cybersecurity, suggesting that as AI technologies evolve, they will likely play an even more critical role in defending against sophisticated cyber-attacks. The paper concludes by providing recommendations for organizations to effectively integrate AI into their cybersecurity strategies, ensuring they remain resilient in the face of evolving cyber threats. This study contributes to the ongoing discourse on AI in cybersecurity by offering insights into its strategic applications and laying the groundwork for future research in this rapidly developing field. Keywords: Artificial Intelligence (AI), Cybersecurity, Threat Detection, AI Governance, Model Training, Data Privacy, Bias in AI, AI Research, Continuous Learning, Cybersecurity Strategy, AI Ethics, Machine Learning, Anomaly Detection, AI Scalability, AI in Cybersecurity.Computer Science &amp; IT Research Journal01The research investigates current AI applications in threat detection, including machine learning algorithms, anomaly detection systems, and predictive analytics, and reveals that AI significantly reduces response times to cyber threats, increases accuracy in identifying vulnerabilities, and enables more proactive risk management strategies.Computer Science &amp; IT Research Journal["Abel Uzoka", "Emmanuel Cadet", "Pascal Ugochukwu Ojukwu"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/0ddfc654e6cadf5700a0a8fd949170c729cc2f10 0f780a5b62cd515dda9319d36806b55e995f2ff8This study represents an attempt to establish the ethics of artificial intelligence in light of the second legislative source in Islam: the Sunnah of the Prophet. This study adopted the descriptive, analytical, and deductive approach through content analysis based on inferences from the Prophet’s hadiths with the aim of clarifying the underlying approach to these ethics in light of this. It concluded with a set of ethics related to artificial intelligence, which were rooted in the light of the Prophet’s Sunnah in a way that ensures its correct and disciplined use and achieves the integrity of the desired means and goals. These ethics were represented in the legitimacy of design and function; neutrality and impartiality; safety, control, and responsibility; respect for privacy; setting codified systems and regulations; environmental sustainability; respect for individual, institutional, and intellectual property; consideration of humanity; and achieving balance. The research established its roots in the honorable Sunnah of the Prophet and in light of the objectives of Islamic law.Religions11A set of ethics related to artificial intelligence were established, which were rooted in the light of the Prophet’s Sunnah in a way that ensures its correct and disciplined use and achieves the integrity of the desired means and goals.Religions["Abdel Aziz Shaker Hamdan Al Kubaisi"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/0f780a5b62cd515dda9319d36806b55e995f2ff8 afd55e83a7e1ee9bfdd28d39b597e09def3be710Artificial intelligence (AI) and machine learning (ML) are anticipated to transform the practice of medicine. As one of the largest sources of digital data in healthcare, laboratory results can strongly influence AI and ML algorithms that require large sets of healthcare data for training. Embedded bias introduced into AI and ML models not only has disastrous consequences for quality of care but also may perpetuate and exacerbate health disparities. The lack of test harmonization, which is defined as the ability to produce comparable results and the same interpretation irrespective of the method or instrument platform used to produce the result, may introduce aggregation bias into algorithms with potential adverse outcomes for patients. Limited interoperability of laboratory results at the technical, syntactic, semantic, and organizational levels is a source of embedded bias that limits the accuracy and generalizability of algorithmic models. Population-specific issues, such as inadequate representation in clinical trials and inaccurate race attribution, not only affect the interpretation of laboratory results but also may perpetuate erroneous conclusions based on AI and ML models in the healthcare literature.Annals of Laboratory Medicine01Lack of test harmonization and limited interoperability of laboratory results at the technical, syntactic, semantic, and organizational levels is a source of embedded bias that limits the accuracy and generalizability of algorithmic models.Annals of Laboratory Medicine["Hung S Luu"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/afd55e83a7e1ee9bfdd28d39b597e09def3be710 b418f83d5e5a867dceaccb831e1ad74f8bad5372 The volume establishes an ethical framework for the identification, analysis, and resolution of ethical challenges that arise from the uses of artificial intelligence (AI) in defence, ranging from intelligence analysis to cyberwarfare and autonomous weapon systems. It does so with the goal of advancing the relevant debate and to inform the ethical governance of AI in defence. Centring on the autonomy and learning capabilities of AI technologies, the work is rooted in AI ethics and Just War Theory. It provides a systemic conceptual analysis of the different uses of AI in defence and their ethical implications, proposes ethical principles and a methodology for their implementation in practice. It then translates this analysis into actionable recommendations for decision-maker and policymakers to foster ethical governance of AI in the defence sector.2101["M. Taddeo"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/b418f83d5e5a867dceaccb831e1ad74f8bad5372 e7a4bf161a2b4ab92a8a3fab924110f1a4431814The organizing Committee of AICT 2024 was proud to present the proceedings of the 3rd International Conference on Artificial Intelligence and Communication Technology (AICT 2024), which was held on August 17-18, 2024 in Chongqing, China. The aim as well as objective of this AICT is to present the latest research and results of scientists related to Artificial Intelligence, Communication Technology and related topics. This conference provides opportunities for the delegates to exchange new ideas face-to-face, to establish business or research relations as well as to find global partners for future collaborations. We hope that the conference results will lead to significant contributions to the knowledge in these up-to-date scientific fields. All full paper submissions to the AICT 2024 must be written in English, and will be sent to 2-4 reviewers and evaluated based on originality, technical or research content, correctness, relevance to conference, contributions, and readability. The full paper submissions will be chosen based on technical merit, interest, applicability, and how well they fit a coherent and balanced technical program. This AICT conference has received 96 manuscripts. And less than half of the submissions were accepted by our reviewers and the press. By submitting a paper to this AICT conference, the authors agree to the review process and understand that papers undergo a peer-review process. Manuscripts will be reviewed by appropriately qualified experts in the field selected by the Conference Committee, who will give detailed comments and-if the submission gets accepted-the authors submit a revised version that takes into account this feedback. All papers are reviewed using a double-blind review process: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other's identities, nor do the authors receive information about who has reviewed their manuscript. The Committees of this 3rd AICT invest great efforts in reviewing the papers submitted to the conference and organizing the sessions to enable the participants to gain maximum benefit. Hopefully, all participants and other interested readers benefit scientifically from the proceedings and also find it stimulating in the process. With our warmest regards, Wei Zhong, Jun Shi, Herman Thompson Conference Organizing CommitteeTransactions on Computer Science and Intelligent Systems Research00Transactions on Computer Science and Intelligent Systems Research["Wei Zhong", "Jun Shi", "Herman Thompson"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/e7a4bf161a2b4ab92a8a3fab924110f1a4431814 67d64464db177a6c3e4f9a574373bf185ec9499aAttention Deficit Hyperactivity Disorder (ADHD) is a disorder characterized by symptoms of inattention and executive dysfunction, although there is not always agreement on the onset, course and long-term stability of the diagnosis. This study aims to detect differences in the cognitive profile according to the subtype of ADHD following a professional diagnosis and to propose an alternative classification. The scores obtained for each cognitive construct were compared using the Student's t-test. In order to explore different diagnostic categories based on groupings made by Artificial Intelligence (AI) subjects were grouped based on their performance through the K-means clustering technique. The results obtained by Artificial Intelligence (AI) identified groups based on the severity of the cognitive profile and the presence of emotional impairment. Difficulties in perceived planning within family and school environments were highlighted as major risk factors in the severity of ADHD in children. Emotional disturbances perceived by both parents, such as depressive symptoms, anxiety, and somatization, were observed subsequently. In accordance with the results, an alternative way to classify ADHD is possible, involving categorization according to the presence or absence of emotional impairment, along with the severity of impairment in attentional and executive functions.Applied neuropsychology. Child210An alternative way to classify ADHD is possible, involving categorization according to the presence or absence of emotional impairment, along with the severity of impairment in attentional and executive functions.Applied neuropsychology. Child["Irene Pascual Zapatero", "Pablo S\u00e1nchez Crist\u00f3bal", "Rosa Jurado Barba"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/67d64464db177a6c3e4f9a574373bf185ec9499a 1692fa2d2f89dc869a0372fbf4e3bae330769a02This research is a review of recent advancements in the utilization of Machine Learning (ML) and Artificial Intelligence (AI), emphasizing their significant developments across diverse application domains. The purpose of this study is to provide a comprehensive understanding of these technologies and their transformative potential. To achieve this, we conducted an extensive analysis of scholarly literature and case studies, focusing on key applications and recent trends in AI and ML. Our findings reveal critical advancements, particularly in sectors such as business, healthcare, and automation, showcasing the profound impact of these technologies on innovation and operational efficiency. The review also highlights persistent challenges, including ethical concerns, data privacy, and infrastructure requirements. These insights are intended to assist stakeholders in identifying opportunities for the effective implementation and future development of AI and ML applications, ensuring their continued contribution to technological progress.IAIC Transactions on Sustainable Digital Innovation (ITSDI)320This research is a review of recent advancements in the utilization of Machine Learning (ML) and Artificial Intelligence (AI), emphasizing their significant developments across diverse application domains and highlighting persistent challenges, including ethical concerns, data privacy, and infrastructure requirements.IAIC Transactions on Sustainable Digital Innovation (ITSDI)["Royani Royani", "Sondang Deri Maulina", "S. Sugiyono", "Rio Wahyudin Anugrah", "Brigitta Callula"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/1692fa2d2f89dc869a0372fbf4e3bae330769a02 af9c5a8d7b300a583bc88ba2a6ffd23fb2223b13This article examines the transformative role of Artificial Intelligence (AI) in revolutionizing cybersecurity practices, with a particular focus on threat modeling and vulnerability assessment. As cyber threats grow increasingly sophisticated, traditional security measures often fall short in providing comprehensive protection. We explore how AI and machine learning algorithms enhance the accuracy and efficiency of threat modeling by analyzing vast datasets to identify patterns and anomalies indicative of potential security risks. The article also investigates AI's contribution to vulnerability assessment, highlighting its capacity for continuous monitoring and real-time analysis of diverse data sources, including network traffic, system logs, and threat intelligence feeds. By simulating various attack scenarios and prioritizing security vulnerabilities, AI-driven tools enable organizations to adopt a more proactive and dynamic approach to cybersecurity. While acknowledging the challenges and ethical considerations associated with AI implementation in this domain, this article underscores the significant potential of AI in fortifying cyber defenses and safeguarding digital assets in an ever-evolving threat landscape.International Journal of Scientific Research in Computer Science Engineering and Information Technology40The transformative role of Artificial Intelligence in revolutionizing cybersecurity practices is examined, with a particular focus on threat modeling and vulnerability assessment, and how AI and machine learning algorithms enhance the accuracy and efficiency of threat modeling.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Chirag Gajiwala"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/af9c5a8d7b300a583bc88ba2a6ffd23fb2223b13 dc59c9e1bdb74067a7738ac0bb2341b297743ee5This study aims to analyze the influence of women's leadership on banking productivity with artificial intelligence (AI) as a mediator. This study involved 90 employees of Bank SulSelBar Makassar, where the entire population was sampled (saturated sample). The method used is quantitative, using primary data collected through questionnaires. The data analysis technique used structural equation modeling (SEM) to evaluate the relationship between variables with the help of SmartPLS software version 3.0. The study results indicate that women's leadership positively affects banking productivity and the implementation of AI. However, the implementation of AI itself does not affect banking productivity. AI cannot mediate the influence of women's leadership on banking productivity. This study advises banks to continue supporting inclusive women's leadership and adopt advanced technology to increase productivity. Theoretically, this study does not support the assumption that technology such as AI will automatically increase productivity without considering the context of leadership. The application of AI needs to be carried out optimally by considering the technology's suitability for banking needs.JPS (Jurnal Perbankan Syariah)00Banks are advised to continue supporting inclusive women's leadership and adopt advanced technology to increase productivity and the assumption that technology such as AI will automatically increase productivity without considering the context of leadership is not supported.JPS (Jurnal Perbankan Syariah)["Muhammad Umar Data", "Firmansyah Halim", "Alya Dhea Amanda"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/dc59c9e1bdb74067a7738ac0bb2341b297743ee5 fbfcf163480bd445382ec74bbd7316d765c71c23Materials and methods. The PubMed and RSCI databases were analyzed covering the period of 2000-2023. 906 articles were found using the keywords “artificial intelligence”, “anemia”, coronary artery disease”, “hemoglobin”, “cardiac surgery”, of which 38 met the criteria for inclusion in the analysis. Results. In a number of countries around the world, artificial intelligence (AI) systems have now been created to predict the course of IHD, however, at the moment, data have been published on the single system with AI elements, presented by the developers of the University of Turin (Italy). It has the functionality of predicting the course of IHD and complications of invasive procedures for IHD against the background of anemic syndrome, based on the use of the HAS-BLEED scale. The increase in the number of CABG operations determines the importance of further research into their long-term results and the development of programs for their management, which will take into account such factors that are important for choosing a strategy for their management and the possibility of influencing the risks of an unfavorable prognosis. This review presents published data on the developed and used digital products based on artificial intelligence intended for the management of patients with coronary artery disease, including taking into account basic hematological parameters. Conclusion. Analysis of existing developed AI systems showed a focus on solving prognostic issues. At the same time, in our opinion, the range of analyzed parameters is not wide enough, in particular, taking into account the presence of anemia, which plays one of the key roles in modifying the risk of adverse outcomes (coronary deaths, repeated acute coronary events, progression of CHF).Russian Medicine390Russian Medicine["T. Kalyuta"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/fbfcf163480bd445382ec74bbd7316d765c71c23 a8fa3d30e8cace0eba61fa84209a3ef35203ef12Enterprise green innovation (EGI) has become an essential measure for manufacturing enterprises to achieve sustainable development, and the application of artificial intelligence (AI) may become a new driving solution. This study empirically analyzes the impact and internal transmission mechanism of AI on EGI of Chinese manufacturing listed enterprises from 2010 to 2022. Research has found that (1) AI significantly impacts EGI, and this basic conclusion has passed various endogeneity and robustness tests. (2) The mechanism test results indicate that enterprise technological capability, innovation investment, and executives’ environmental awareness significantly mediate between AI and EGI. (3) Heterogeneity analysis shows that the significant positive impact of AI on EGI is only established in enterprises with overseas backgrounds, large-scale, highly competitive regional markets, and low-carbon pilot cities. The above conclusions have contributed essentially to the literature on EGI and AI.Sustainability420Heterogeneity analysis shows that the significant positive impact of AI on EGI is only established in enterprises with overseas backgrounds, large-scale, highly competitive regional markets, and low-carbon pilot cities.Sustainability["Dongwei Li", "Jing Xiao", "Fangfang Yang"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/a8fa3d30e8cace0eba61fa84209a3ef35203ef12 729ae759e84343e9aa5404234da49219f0da6475This paper examines the ontology of artificial intelligence (AI) within the context of contemporary society. With the rapid progression of technology, the definition of legal subjects has become increasingly ambiguous, as the technological landscape continues to evolve. The orthodox perspective fails to provide adequate solutions to this problem. An alternative approach, as put forth by Visa A.J. Kurki’s bundle theory offers a potential pathway, yet AI’s intrinsic nature surpasses the minimum thresholds defined by Kurki’s model. The authors propose a periscopic model that explores the interaction between the material world and the virtual or augmented sphere, often referred to as the metaverse. This article contends that the current philosophical foundation of law is both outdated and insufficient, primarily due to the shift from singular to plural forms of agency. AI has transitioned from being purely instrumental or intermediary, as observed in Artificial Narrow Intelligence (ANI), to autonomous decision-making entities, exemplified by Artificial General Intelligence (AGI). Drawing on theoretical insights from Yuval Noah Harari, the paper underscores the need for a new conceptual framework to address AI’s lack of a material entity. In conclusion, the paper asserts that the recognition of AI as legal subjects is an inevitable development.MELINTAS160The authors propose a periscopic model that explores the interaction between the material world and the virtual or augmented sphere, often referred to as the metaverse, and assert that the recognition of AI as legal subjects is an inevitable development.MELINTAS["T. P. Moeliono", "M. B. B. Simanjuntak"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/729ae759e84343e9aa5404234da49219f0da6475 5f156228e6e7751ca788004787b6d4241b55780bThis study investigates how artificial intelligence (AI) can enhance global security by fostering international cooperation and diplomatic relations. It examines the dual nature of AI, where operational benefits such as improved cybersecurity, military precision, and threat detection are offset by significant ethical and geopolitical challenges. Through a mixed-methods approach, the research identifies key issues like geopolitical tensions and fragmented governance while highlighting the opportunities for collaboration through multilateral research and ethical AI governance. The findings reveal notable improvements in AI-driven cybersecurity, with detection rates increasing from 86% in 2021 to 88.25% in 2023 and mitigation rates rising from 80.75% to 83.75%. However, AI-driven attacks also increased from 11.25 incidents in 2021 to 16.25 in 2023, underscoring the risks associated with AI misuse. The study emphasizes the importance of robust governance frameworks that promote transparency, accountability, and ethical AI use across borders. It concludes that international cooperation, supported by ethical AI governance, is crucial to maximize AI’s potential in addressing global security challenges, with specific recommendations for enhancing existing frameworks such as the OECD AI Principles and the Global Partnership on AI.Archives of Current Research International600It is concluded that international cooperation, supported by ethical AI governance, is crucial to maximize AI’s potential in addressing global security challenges, with specific recommendations for enhancing existing frameworks such as the OECD AI Principles and the Global Partnership on AI.Archives of Current Research International["T. M. Kolade"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/5f156228e6e7751ca788004787b6d4241b55780b 027ff548e571147b5617fa941b15236bf81541fbCurriculum administration is all business processes that have been planned and attempted deliberately and earnestly as well as continuous guidance on teaching and learning activities effectively and efficiently in order to achieve the educational goals that have been set. The importance of curriculum administration in educational institutions, which includes the selection of materials, teaching methods, and assessments that are in accordance with educational goals and student needs. Curriculum administration plays a role in ensuring that the curriculum is well structured and organized through various levels of planning, implementation, and supervision. Curriculum evaluation is also an important part of education management, aiming to collect information about the effectiveness of curriculum implementation and the impact of learning outcomes. In addition, this journal highlights the role of teachers in curriculum administration as implementers, adapters, developers, and researchers. The presence of artificial intelligence (AI) technology presents an opportunity for educators to carry out a learning process that focuses on the needs, interests, and learning styles of students, because the independent curriculum requires educators to carry out differentiated learning as an initiative in facilitating students, especially in Pancasila education, which incidentally is still carried out by conventional models or methods.Konstitusi : Jurnal Hukum, Administrasi Publik, dan Ilmu Komunikasi00The presence of artificial intelligence (AI) technology presents an opportunity for educators to carry out a learning process that focuses on the needs, interests, and learning styles of students.Konstitusi : Jurnal Hukum, Administrasi Publik, dan Ilmu Komunikasi["Rivaldi Rivaldi", "Rahma Muthia Febriliana", "Ahmad Sabri", "Rully Hidayatullah"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/027ff548e571147b5617fa941b15236bf81541fb 1801d195bad24488833bf9d25857efdc8a157b8bThis paper provides an in-depth analysis of current trends in artificial intelligence, with a focus on developments from 2022 to the present. The aim is to identify and examine the key areas that are currently shaping the direction of this rapidly evolving field. Our analysis is based on an extensive study of specialized books, publications, and other relevant sources released during this period. We focus on advancements in machine learning, breakthrough technologies, ethical issues related to AI implementation, as well as new applications and their impact across various sectors, such as healthcare, finance, and industry. The paper offers a comprehensive overview of current trends and discusses their potential implications for the future of technology, the economy, and society as a whole.International Conference on Emerging eLearning Technologies and Applications110An in-depth analysis of current trends in artificial intelligence, with a focus on developments from 2022 to the present, based on an extensive study of specialized books, publications, and other relevant sources released during this period.2024 International Conference on Emerging eLearning Technologies and Applications (ICETA)["Sylvia Ma\u0165a\u0161ov\u00e1", "Martin Chovanec", "Ren\u00e1ta Rusn\u00e1kov\u00e1", "Du\u0161an \u010catloch"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/1801d195bad24488833bf9d25857efdc8a157b8b 7c4f294b2563dbc1354231bee52be611cba6b909Background/Objective: Atrial fibrillation [AF] is the most common arrhythmia encountered in clinical practice and significantly increases the risk of stroke, peripheral embolism, and mortality. With the rapid advancement in artificial intelligence [AI] technologies, there is growing potential to enhance the tools used in AF detection and diagnosis. This scoping review aimed to synthesize the current knowledge on the application of AI, particularly machine learning [ML], in identifying and diagnosing AF in clinical settings. Methods: Following the PRISMA ScR guidelines, a comprehensive search was conducted using the MEDLINE, PubMed, SCOPUS, and EMBASE databases, targeting studies involving AI, cardiology, and diagnostic tools. Precisely 2635 articles were initially identified. After duplicate removal and detailed evaluation of titles, abstracts, and full texts, 30 studies were selected for review. Additional relevant studies were included to enrich the analysis. Results: AI models, especially ML-based models, are increasingly used to optimize AF diagnosis. Deep learning, a subset of ML, has demonstrated superior performance by automatically extracting features from large datasets without manual intervention. Self-learning algorithms have been trained using diverse data, such as signals from 12-lead and single-lead electrocardiograms, and photoplethysmography, providing accurate AF detection across various modalities. Conclusions: AI-based models, particularly those utilizing deep learning, offer faster and more accurate diagnostic capabilities than traditional methods with equal or superior reliability. Ongoing research is further enhancing these algorithms using larger datasets to improve AF detection and management in clinical practice. These advancements hold promise for significantly improving the early diagnosis and treatment of AF.Journal of Personalized Medicine540AI-based models, particularly those utilizing deep learning, offer faster and more accurate diagnostic capabilities than traditional methods with equal or superior reliability, and hold promise for significantly improving the early diagnosis and treatment of AF.Journal of Personalized Medicine["Ant\u00f4nio da Silva Menezes J\u00fanior", "Ana L\u00edvia F\u00e9lix e Silva", "Louisiany Ra\u00edssa F\u00e9lix e Silva", "Khissya Beatryz Alves de Lima", "Henrique Lima de Oliveira"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/7c4f294b2563dbc1354231bee52be611cba6b909 90d612d66797cd5fb3d7451b5f6e790ecb0e546dBackground Artificial Intelligence (AI) has gained significant importance in dentistry, particularly in the field of cariology. The aim of this study was to perform a comprehensive bibliometric and altimetric analysis of research on the application of AI in cariology. Methods The Web of Science database was selected for the search conducted in February 2024, and selection and data extraction were performed independently by two researchers. Collaborative networks were generated using VOSviewer software, while altimetric data were analysed using Dimensions. The relationship between the bibliometric and altimetric data was examined using Spearman correlation. Results The search yielded 355 articles, of which 175 were included, published between 2008 and 2024. The most cited article reached 324 citations. Proof of concept was the most common study design (n=135), and the majority of studies used AI to detect and diagnose dental caries (n=122), with radiography being the most commonly used diagnostic method (n=99). The author with the highest number of articles was Schwendicke F (n=15), and the leading institution was Charite University, Berlin (n=13). China was the leading country in terms of research output (n=28) and Asia was the leading continent (n=54). The use of AI in cariology has been shown to improve diagnostic accuracy, reduce unnecessary interventions and optimise patient outcomes. Research interest in AI for cariology has increased significantly over the past five years, particularly in Asia. Conclusion These findings suggest significant clinical benefits and highlight the need for further research, particularly clinical trials, to validate these applications in practice.F1000Research340A comprehensive bibliometric and altimetric analysis of research on the application of AI in cariology suggests significant clinical benefits and highlights the need for further research, particularly clinical trials, to validate these applications in practice.F1000Research["Danielle Cristina Alves Rigo", "Aur\u00e9lio de Oliveira Rocha", "Lucas Menezes dos Anjos", "Julia Maldonado Garcia", "Isabela Ramos", "Michely Cristina Goebel", "Pablo Silveira Santos", "Carla Miranda Santana", "Mariane Cardoso"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/90d612d66797cd5fb3d7451b5f6e790ecb0e546d a34c9a304c33dba58895bd4e7854207d42b4443dAI and Ethics520A method for joint evaluation of AI’s ethical and epistemic implications in medicine that draws on the principle-oriented tradition in bioethics and the consequent ‘ethical matrix’ approach to assessing novel technologies is proposed.AI and Ethics["Jonathan Adams"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/a34c9a304c33dba58895bd4e7854207d42b4443d d1e85584ffa2816b0c3a68b5fe4d5dd86c1487e6International Journal of Science and Mathematics Education480The study extends existing literature by focusing on AI in primary mathematics education, highlighting the need for targeted professional development initiatives to foster positive attitudes and enhance teacher proficiency in AI technologies.International Journal of Science and Mathematics Education["Mao Li"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/d1e85584ffa2816b0c3a68b5fe4d5dd86c1487e6 dde37e46683a936b796ddddfe15934cee75d26bcComputer programs are protected by copyright both in the comparative law and in the positive law in Serbia. One or more computer programs together with electronic databases make up information systems. With the development of artificial intelligence, a wide range of sophisticated information systems have been created that can, as a rule, create or generate text based on user queries (e.g. ChatGPT). This paper provides a case study related to the generative artificial intelligence ChatGPT. Legal regulation of artificial intelligence-generated products from the aspect of copyright poses a special challenge. In this paper, the author puts a special emphasis on the comparative presentation of the legislation that regulates artificial intelligence from the aspect of copyright, stating the legal theory positions and judicial practice that claim that artificial intelligence-generated products have no place in intellectual property law. After the exhaustive comparative legal analysis and the case study, the author will propose de lege ferenda the legal protection framework for artificial intelligence.Strani pravni zivot200The author puts a special emphasis on the comparative presentation of the legislation that regulates artificial intelligence from the aspect of copyright, stating the legal theory positions and judicial practice that claim that artificial intelligence-generated products have no place in intellectual property law.Strani pravni život["Antonije \u0110 \u017divkovi\u0107"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/dde37e46683a936b796ddddfe15934cee75d26bc babdfcdef08671ce14baa975faab5e47a4fce618The domain of law as a normative part of our society has been characterized for centuries by the idea “from lawyers for lawyers”. The creation of normative systems in which the center is the person and emphasizes the participation of the given subject in the actions, is a system that is not only effective but also desirable. Even though, human-centered design has been a dominant innovation methodology in service industries, from medicine to insurance to finance the time has come for our legal systems. Together with movements related to legal technology and access to justice reform, as a collective legal design movement as the idea of the marriage of a human-centered design approach to the challenges and structures of the legal system. In this paper the authors present the role of legal design and artificial intelligence in law education while also introduce they findings from their empirical research form the classroom.International Conference on Emerging eLearning Technologies and Applications80The authors present the role of legal design and artificial intelligence in law education while also introducing they findings from their empirical research form the classroom.2024 International Conference on Emerging eLearning Technologies and Applications (ICETA)["Z. Gyur\u00e1sz", "J. Andra\u0161ko"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/babdfcdef08671ce14baa975faab5e47a4fce618 b47e1ecade176d94ec83d77b54c99d7b7144b622This study explores the application of artificial intelligence in film and television post-production, analyzing its effects on efficiency, quality, and market impact. AI automates complex tasks, enhances content generation, and predicts audience preferences, revolutionizing traditional production workflows. However, challenges such as high costs, technical thresholds, and ethical concerns are discussed. The study underscores AI's potential for innovation and the need for responsible use. Transactions on Computer Science and Intelligent Systems Research80AI automates complex tasks, enhances content generation, and predicts audience preferences, revolutionizing traditional production workflows, and underscores AI's potential for innovation and the need for responsible use.Transactions on Computer Science and Intelligent Systems Research["Mufan Song"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/b47e1ecade176d94ec83d77b54c99d7b7144b622 e8be62880e0eeac09080db8f8ea19364d6e388c2The article is devoted to one of the most widely discussed issues and current trends in the educational sphere. It is the use of artificial intelligence technologies by the students of pedagogical specialties, focusing on its pros and cons and highlighting the possible consequences of its use. The authors define the place of artificial intelligence, specifically considering the generative artificial technologies, in terms of foreign language acquisition. The performed study reveals different aspects that arose with the use of the aforementioned technologies, considering the problem of academic integrity and policies that could regulate its ecological use. The presented study aims to reveal students’ awareness of the issue of the academic integrity of artificial intelligence use in terms of foreign language acquisitions. The target audience of the study is students of the Faculty of Pedagogical Education of Borys Grinchenko Kyiv Metropolitan University of 013 “Primary Education” and 012 “Preschool Education”. The data received from the students of pedagogical specialties via the conducted survey proves that there is an urgent need to expand students’ knowledge about artificial intelligence technology and teach them to use it correctly, adhering to academic integrity principles.Cadernos de Educação, Tecnologia e Sociedade00The presented study aims to reveal students’ awareness of the issue of the academic integrity of artificial intelligence use in terms of foreign language acquisitions and reveals different aspects that arose with the use of the generative artificial technologies, in terms of foreign language acquisition.Cadernos de Educação Tecnologia e Sociedade["Yuliia Rudnik", "Lada Petryk", "Natalia Kosharna", "Alina Dzhurylo", "L. Popova"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/e8be62880e0eeac09080db8f8ea19364d6e388c2 f9a8c8e63bbbd10cc4cd9c13af235b55bd622148Artificial Intelligence (AI) has the potential to improve forecasting accuracy, enhance risk management, and streamline decision-making, there has been an increasing amount of interest in integrating AI into financial market predictive analytics. This study offers a thorough examination of the ways in which artificial intelligence (AI) methods, including machine learning and deep learning, increase financial market forecasting. It looks at a variety of AI-based models and algorithms that are used to predict financial risk factors, stock prices, and market trends. Additionally, the paper addresses opportunities and challenges related to implementing AI in financial prediction and suggests future research avenues.IEEE International Conference on Circuits and Systems for Communications120A thorough examination of the ways in which artificial intelligence methods, including machine learning and deep learning, increase financial market forecasting, using a variety of AI-based models and algorithms.2024 International Conference on Computing, Sciences and Communications (ICCSC)["Virender Kumar Dahiya", "Ruby Dahiya", "Reshabh Dev", "Ankitha Sharma", "Abhishek Sharma", "P. B. N. Kiran", "Pinnika Syam Yadav", "Sapna Yadav"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/f9a8c8e63bbbd10cc4cd9c13af235b55bd622148 59f3b7d1848909a64388e864ce8462c92bec99b7This article describes the process of technology development from the birth of the first engineering ideas to the formation of an entire system of "smart" algorithms and artificial intelligence (AI), and also examines the role of AI in the modern world.Materials of the All-Russian Scientific and Practical Conference «TECHNOSPHERE SAFETY: MODERN SCIENTIFIC TRENDS, TECHNICAL AND ORGANIZATIONAL MEANS AND METHODS OF PROVISION, SPECIAL EDUCATION»20Materials of the All-Russian Scientific and Practical Conference «TECHNOSPHERE SAFETY: MODERN SCIENTIFIC TRENDS, TECHNICAL AND ORGANIZATIONAL MEANS AND METHODS OF PROVISION, SPECIAL EDUCATION»["E. Zhidko", "A. Kuryanova"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/59f3b7d1848909a64388e864ce8462c92bec99b7 a12c52fae8d2b7f47039a534a44a0b59f09ddb82Journal of Marketing Analytics653Journal of Marketing Analytics["Rituparna Basu", "Md. Nayeem Aktar", "Satish Kumar"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/a12c52fae8d2b7f47039a534a44a0b59f09ddb82 310b415e69bc26e5c3414c7425f49dbdf9269401EClinicalMedicine922Although the current AI models developed cannot entirely replace invasive methods for determining embryo ploidy, AI demonstrates promise as an auxiliary decision-making tool for embryo selection, particularly for individuals who are unable to undergo PGT-A.eClinicalMedicine["X. Xin", "Shanshan Wu", "Heli Xu", "Yujiu Ma", "Nan Bao", "Man Gao", "Xue Han", "Shan Gao", "Siwen Zhang", "Xinyang Zhao", "Jiarui Qi", "Xudong Zhang", "Jichun Tan"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/310b415e69bc26e5c3414c7425f49dbdf9269401 03db8126eea3b6019b4b840c21512fe0d11c2d3cBiomedical technologies are the compound of engineering principles and technologies used to diagnose, treat, monitor, and prevent illness [...]Technologies412Technologies["S. Tovar-Arriaga", "G. P\u00e9rez-Soto", "K. A. Camarillo-G\u00f3mez", "Marcos Aviles", "J. Rodr\u00edguez-Res\u00e9nd\u00edz"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/03db8126eea3b6019b4b840c21512fe0d11c2d3c f294cd73a80202e651510c823d32e175f27862c9European Planning Studies01European Planning Studies["Hendrika Marselina Yuniatris Da Rato", "Anita Oktoviana L. P. G. M. Thomas", "Muchammad Afif Yahya"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/f294cd73a80202e651510c823d32e175f27862c9 056942e0637d551ed8d09142e37e08d2ef018e24Hernia00Hernia : the journal of hernias and abdominal wall surgery["G. Campanelli"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/056942e0637d551ed8d09142e37e08d2ef018e24 0f6fd046d3b698818f6068d54f818f0f12825904Large Language Models (LLMs) can assist in the prebunking of election misinformation. Using results from a preregistered two-wave experimental study of 4,293 U.S. registered voters conducted in August 2024, we show that LLM-assisted prebunking significantly reduced belief in specific election myths,with these effects persisting for at least one week. Confidence in election integrity was also increased post-treatment. Notably, the effect was consistent across partisan lines, even when controlling for demographic and attitudinal factors like conspiratorial thinking. LLM-assisted prebunking is a promising tool for rapidly responding to changing election misinformation narratives.250LLM-assisted prebunking is a promising tool for rapidly responding to changing election misinformation narratives and was consistent across partisan lines, even when controlling for demographic and attitudinal factors like conspiratorial thinking.["Mitchell Linegar", "Betsy Sinclair", "S. V. D. Linden", "R. M. Alvarez"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/0f6fd046d3b698818f6068d54f818f0f12825904 928a053e07c24172d40cfdeceb11a6aaccf8d12c1st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024)2101st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024)["S. Zaman", "Shafaq Tariq Jadoon", "S. Khan"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/928a053e07c24172d40cfdeceb11a6aaccf8d12c 6bbac3f142f60a669fe7c04e32a28c4bb3600e7fFor human-agent teams, it is as important for agents to have models of their human teammates as it is for humans to have models of their agent teammates. However, approaches to knowledge elicitation have wrestled with the problem of capturing human knowledge when much of it is tacit and difficult to verbalize. By observing the choices that people make under different conditions we can infer the choice structure they appear to be following. In this way, observations could allow the agent to predict its human team-mates’ choices and actions. We report an experiment in which eye-tracking is used to capture choices in a constrained task and then develop a decision model that can replicate aspects of these choices. We propose that such a model can support human-agent teams by enabling inferences of some aspects of human decision-making.Proceedings of the Human Factors and Ergonomics Society Annual Meeting80An experiment is reported in which eye-tracking is used to capture choices in a constrained task and a decision model is developed that can replicate aspects of these choices and it is proposed that such a model can support human-agent teams by enabling inferences of some aspects of human decision-making.Proceedings of the Human Factors and Ergonomics Society Annual Meeting["C. Baber", "Aditya Acharya", "Andrew Howes", "Daniel Cassenti", "Alfred Yu"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/6bbac3f142f60a669fe7c04e32a28c4bb3600e7f c4d2f81fbf79743fc24f032ba54fa4777db466a0Worldwide, breast cancer is becoming the most serious illness that affects women. It is believed that early diagnosis and treatment of breast cancer can increase survival rates and decrease the need for surgery. Machine Learning model is very reliant on features for their proper training. However, understanding how a prediction is being affected by specific features is very important for a model’s interpretation. Understanding what features support a prediction is important as it provides some transparency to the inner workings of the model. Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are used to classify the breast cancer data, and accuracy, sensitivity, specificity, false-positive rate, precision, F1-score, and Geometric-Mean (GM) are used for the performance assessment. Furthermore, Multi-Criteria Decision Making (MCDM) is used to evaluate overall performance assessment based on the aforementioned performance measures and DT is found to be best among all the classifiers. Finally, an Explainable AI model namely LIME is used to interpret the predicted outcomes and impact of the different features of the data on the model’s prediction.2024 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks (IEMECON)120An Explainable AI model namely LIME is used to interpret the predicted outcomes and impact of the different features of the data on the model’s prediction.2024 12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks (IEMECON)["Kazi Fardeen", "Harshadeep Bhaduri", "Soham Hazra", "Praloy Mondal", "Sarbajit Manna", "Tapas Si"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/c4d2f81fbf79743fc24f032ba54fa4777db466a0 f7e68406d6021a392044972081c49321f014174bAI has helped dental care professionals in different aspects which directly influence the increase in quality of service provided by dentists and improving patient personalized experience. AI can detect carious lesions, and gingival health, interpret X-rays and CBCT, record impressions of flabby tissues, and predict patient experience with accuracy and precision of more than 85%. AI-based robots can mimic patient expressions and reactions in dental treatment helping dental students at the undergraduate level. AI-based robotics can play an important role in different dental procedures because of the lack of tiredness as compared to manual instrumentation. Machine learning can play a vital role in detecting cancer markers, histological features of oral tissues, and forensic odontology. AI software used to interpret CBCT, and X-rays is useful to dental surgeons since it can measure bone height and width and help clinicians plan treatment accordingly. Patient data records are easily accessible to researchers and clinicians when data is digitalized with the help of AI software. AI has its limitations mainly because of ethical considerations, In the future dentists should make comprehensive AI-based clinics that would record patient pre-treatment records, medical history, and dental history and make treatment plans accordingly.Pakistan journal of medicine and dentistry430In the future dentists should make comprehensive AI-based clinics that would record patient pre-treatment records, medical history, and dental history and make treatment plans accordingly and AI software used to interpret CBCT, and X-rays is useful to dental surgeons since it can measure bone height and width and help clinicians plan treatment accordingly.Pakistan Journal of Medicine and Dentistry["Muhammad Ammar Khan", "A. Ansari", "Madiha Anwar"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/f7e68406d6021a392044972081c49321f014174b 9031b789e2df9303d65e022d11eab66b23ca6972This coursework examines the relationship between the work of artificial generative intelligence and copyright and looks at the possibilities of copyright protection for such work. After evaluating and presenting the concepts of work and artificial intelligence, and after examining an AI work through the personality, labour and utilitarian theories, it is apparent that each case is different in terms of whether such a work could be subject to copyright protection. In the context of the personality and labour theories, an AI work cannot be the subject of copyright protection, whereas the latter, viewed through the prism of the utilitarian theory, is not only capable of being copyrightable, but also must be protected by the law. Looking at the question of authorship of this type of work on international level, the lack of uniform legal regulation is evident. However, it is clear from the recent views expressed by the European Parliament regulations and in the case-law of United States of America, that a work created using artificial generative intelligence as a tool is likely to be eligible for copyright protection, but that such a work will also be subject, amongst other requirements, to the usual criteria for assessing the individuality, or otherwise originality, of a personal intellectual creation.Vilnius University Open Series00It is clear from the recent views expressed by the European Parliament regulations and in the case-law of United States of America, that a work created using artificial generative intelligence as a tool is likely to be eligible for copyright protection, but that such a work will also be subject to the usual criteria for assessing the individuality, or otherwise originality, of a personal intellectual creation.Vilnius University Open Series["Kamilia Edita Fominova", "Rolandas Bugys"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/9031b789e2df9303d65e022d11eab66b23ca6972 9b0e3c25494e96bf02333bc4a06f9f1f0f2c735aThis study analyzes the influence of artificial intelligence (AI) on consumer law, highlighting Bill nº. 2.338/2023 as an essential regulatory framework. The objective is to understand how AI impacts consumers' daily lives, identifying risks such as information asymmetry, algorithmic discrimination, and lack of transparency. Using hypothetical-deductive and dialectical methods, the research reviews literature and analyzes case law, revealing that the regulation of AI in Brazil faces significant challenges, such as the need to balance innovation and the protection of fundamental rights. The results indicate that the absence of a consolidated legal framework generates uncertainties and may lead to abusive practices, compromising consumers' privacy and security. The conclusions emphasize the urgency of regulation that guarantees rights such as access to information and the ability to contest automated decisions. Dialogue among government, businesses, and consumers is essential to create a safe and ethical environment. The research highlights that transparency in data collection practices is crucial for building consumer trust and mitigating the risks associated with AI use. Implementing an effective regulatory framework is essential to ensure that the benefits of AI are leveraged responsibly and ethically.Revista ft00Revista ft["Saulo Favoretto", "Melissa Andr\u00e9a Smaniotto"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/9b0e3c25494e96bf02333bc4a06f9f1f0f2c735a b2979c66e343b203c28d05b1f84a0d96d58729d0In the trading process, financial signals often imply the time to buy and sell assets to generate excess returns compared to a benchmark (e.g., an index). Alpha is the portion of an asset's return that is not explained by exposure to this benchmark, and the alpha research process is a popular technique aiming at developing strategies to generate alphas and gain excess returns. Feature Engineering, a significant pre-processing procedure in machine learning and data analysis that helps extract and create transformed features from raw data, plays an important role in algorithmic trading strategies and the alpha research process. With the recent development of Generative Artificial Intelligence(Gen AI) and Large Language Models (LLMs), we present a novel way of leveraging GPT-4 to generate new return-predictive formulaic alphas, making alpha mining a semi-automated process, and saving time and energy for investors and traders.arXiv.org477A novel way of leveraging GPT-4 to generate new return-predictive formulaic alphas is presented, making alpha mining a semi-automated process, and saving time and energy for investors and traders.ArXiv["Yining Wang", "Jinman Zhao", "Yuri Lawryshyn"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/b2979c66e343b203c28d05b1f84a0d96d58729d0 95c67091150bbbd2773cd86e9c3a3b7ea1509359With society’s continuous development and progress, artificial intelligence (AI) technology is increasingly utilized in higher education, garnering increased attention. The current application of AI in higher education impacts teachers’ instructional methods and students’ learning processes. While acknowledging that AI advancements offers numerous advantages and contribute significantly to societal progress, excessive reliance on AI within education may give rise to various issues, students’ over-dependence on AI can have particularly severe consequences. Although many scholars have recently conducted research on artificial intelligence, there is insufficient analysis of the positive and negative effects on higher education. In this paper, researchers examine the existing literature on AI’s impact on higher education to explore the opportunities and challenges presented by this super technology for teaching and learning in higher educational institutions. To address our research questions, we conducted literature searches using two major databases—Scopus and Web of Science—and we selected articles using the PRISMA method. Findings indicate that AI plays a significant role in enhancing student efficiency in academic tasks and homework; However, when considering this issue from an ethical standpoint, it becomes apparent that excessive use of AI hinders the development of learners’ knowledge systems while also impairing their cognitive abilities due to an over-reliance on artificial technology. Therefore, our research provides essential guidance for stakeholders on the wise use of artificial intelligence technology.Journal of Infrastructure Policy and Development923Excessive use of AI hinders the development of learners’ knowledge systems while also impairing their cognitive abilities due to an over-reliance on artificial technology, and provides essential guidance for stakeholders on the wise use of artificial intelligence technology.Journal of Infrastructure, Policy and Development["Pengfei Cui", "Bity Salwana Alias"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/95c67091150bbbd2773cd86e9c3a3b7ea1509359 dcb03ea50c08e7c378f22c0304c092da66525a0cThis study used bibliometric analysis and a systematic literature review (SLR) to examine how the tourism and hospitality industries use generative artificial intelligence (GAI), identifying developed patterns, theoretical frameworks, strengths and limitations, and future research challenges. We conducted a systematic review using the Scopus database, adhering to PRISMA principles. We analyzed a sample of 25 articles published between 2019 and 2023 through narrative synthesis and bibliometric analysis using the VOSviewer software, a tool for visualizing network analysis. The USA, China, India, and Saudi Arabia are the major countries engaged in GAI research in tourism and hospitality. Significant research topics emphasize decision-making, chatbots, deep learning, and sentiment analysis, mainly through the Technology Acceptance Model (TAM), Stimulus-Organism-Response (S-O-R), and Human-Computer Interaction (HCI) frameworks. GAI applications demonstrate strength in improving user experience and operational efficiency, though gaps exist in scope, ethics, technology performance, and collaboration between humans and AI. This study, therefore, provides a fundamental foundation for understanding the current status of GAI research in tourism and hospitality by pointing out some trends and areas that require further investigation to ensure the responsible and effective integration of AI within the industry.Tourism and Hospitality Research562Tourism and Hospitality Research["Amr Mohamed Fouad", "I. Salem", "Eslam Ahmed Fathy"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/dcb03ea50c08e7c378f22c0304c092da66525a0c e71cb2838108bc43d5f066c12d1b6e84f7078aa6International Journal of Environmental Research1741A comprehensive overview of AI techniques identifies key trends and proposes new research avenues to address the complex issue of achieving the Sustainable Development Goals (SDGs).International Journal of Environmental Research["Lucas Greif", "Fabian R\u00f6ckel", "Andreas Kimmig", "J. Ovtcharova"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/e71cb2838108bc43d5f066c12d1b6e84f7078aa6 c1c66d78e4c41dba0b72e6ed369e186a929cba42This paper explores the intersection of Artificial Intelligence (AI) and cloud computing, focusing on the security implications. As cloud computing becomes increasingly ubiquitous, the integration of AI presents both opportunities and challenges. This paper provides an in-depth analysis of how AI can enhance cloud security, the potential risks associated with AI deployment in the cloud, and the future landscape of AI-driven cloud security. Key topics include AI's role in threat detection, data protection, access management, and the challenges related to AI bias, interpretability, and adversarial attacks.Indonesian Journal of Computer Science1341An in-depth analysis of how AI can enhance cloud security, the potential risks associated with AI deployment in the cloud, and the future landscape of AI-driven cloud security are provided.The Indonesian Journal of Computer Science["Rebet Jones"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/c1c66d78e4c41dba0b72e6ed369e186a929cba42 c8c4504556b874eb7a8914dbc5c0915359c855beThis paper explores the intersection of Artificial Intelligence (AI) and cloud computing, focusing on the security implications. As cloud computing becomes increasingly ubiquitous, the integration of AI presents both opportunities and challenges. This paper provides an in-depth analysis of how AI can enhance cloud security, the potential risks associated with AI deployment in the cloud, and the future landscape of AI-driven cloud security. Key topics include AI's role in threat detection, data protection, access management, and the challenges related to AI bias, interpretability, and adversarial attacks.Indonesian Journal of Computer Science01An in-depth analysis of how AI can enhance cloud security, the potential risks associated with AI deployment in the cloud, and the future landscape of AI-driven cloud security are provided.The Indonesian Journal of Computer Science["Rebet Jones"]2024-10-24T00:00:00https://www.semanticscholar.org/paper/c8c4504556b874eb7a8914dbc5c0915359c855be ce7873f340638133ec77138fd30b26db3995e111Humanities and Social Sciences Communications823This work envisions global AI use that effectively bridges development and innovation disparities and makes policy recommendations that advocate for the swift integration of AI into critical LIC domains such as health, education, energy, and governance.Humanities and Social Sciences Communications["Muhammad Salar Khan", "Hamza Umer", "Farhana Faruqe"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/ce7873f340638133ec77138fd30b26db3995e111 e546fed2eae0ac5c00549d390f31182b6a1b59d7BMC Health Services Research573The study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns, including the variability in technical skills, data fragmentation, and resistance to change.BMC Health Services Research["Maur\u00edcio Alves", "Joana Seringa", "Tatiana Silvestre", "Teresa Magalh\u00e3es"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/e546fed2eae0ac5c00549d390f31182b6a1b59d7 0fe0b5a8fa6162967c5baf86215db234a2240eabThis research explores the connections between Teacher-Led Instruction (TLI), Peer Collaboration (PC), and Artificial Intelligence Assistance (AIA) in influencing Student Cognitive and Social-Emotional Development (SCED). Through exploratory and confirmatory factor analyses, the study found that TLI and PC are important positive predictors of SCED, highlighting the crucial role of collaborative and teacher-led approaches in promoting overall student development. Contrastingly, there is a negative association between AIA and SCED, underscoring the potential disadvantages of excessive dependence on technology at the cost of meaningful human connections. The results indicate that educational settings should give precedence to incorporating effective teaching approaches while utilizing technology as an auxiliary instrument rather than a replacement for conventional teaching methods. By embracing a multifaceted approach to instruction, this study contributes to the ongoing discourse on enhancing educational practices in an increasingly digital landscape, ultimately advocating for a future where students thrive both academically and emotionally while studying with Artificial Intelligence cautiously.Journal of Artificial Intelligence, Machine Learning and Neural Network82The study found that TLI and PC are important positive predictors of SCED, highlighting the crucial role of collaborative and teacher-led approaches in promoting overall student development.Journal of Artificial Intelligence, Machine Learning and Neural Network["M. Darling", "Seth Kofi Owusu", "Mordecai Botchwey", "Daniel Asenso"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/0fe0b5a8fa6162967c5baf86215db234a2240eab e9541719cd3ef078d324bcc76825e255eac7a491Abstract: Integrating Artificial Intelligence (AI) into strategic decision-making is essential for enhancing market competitiveness in today's dynamic business environment. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics optimize operations, personalize customer experiences, and drive product innovation. Machine learning algorithms analyze vast data to uncover patterns, aiding better decision-making. Predictive analytics forecasts market trends and consumer behaviors, allowing companies to anticipate demand and streamline supply chains, reducing risks like overproduction and stockouts. NLP-powered chatbots improve customer interactions by handling routine inquiries, freeing human agents for complex issues, and enabling personalized marketing. AI also accelerates product development by analyzing market data and consumer feedback, simulating scenarios, and predicting outcomes. Operational efficiency is enhanced through automation and optimized workflows, saving costs and increasing productivity. Despite these benefits, challenges such as data privacy, algorithmic bias, significant investment, and a shift to a data-driven culture must be managed. Effective AI integration offers significant competitive advantages, positioning companies to leverage predictive analytics, personalized customer interactions, and operational efficiency for growth and innovation.Advances in Economics, Management and Political Sciences01Effective AI integration offers significant competitive advantages, positioning companies to leverage predictive analytics, personalized customer interactions, and operational efficiency for growth and innovation.Advances in Economics, Management and Political Sciences["Weihan Wang"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/e9541719cd3ef078d324bcc76825e255eac7a491 65f72faa69297ccbba2e7a05bed93bcbce67a376The integration of artificial intelligence (AI) into cybersecurity has brought about transformative advancements in threat detection and mitigation, yet it also introduces new vulnerabilities and potential threats. This research exploration systematically investigates the critical issues surrounding AI within cybersecurity, focusing on specific vulnerabilities and the potential for AI systems to be exploited by malicious actors. The research aims to address these challenges by swotting and analyzing existing methodologies designed to mitigate such risks. Through a detailed exploration of modern scientific research, this manuscript identifies the dual-edged impact of AI on cybersecurity, emphasizing both the opportunities and the dangers. The findings highlight the need for strategic solutions that not only enhance digital security and user privacy but also address the ethical and regulatory aspects of AI in cybersecurity. Key contributions include a comprehensive analysis of emerging trends, challenges, and the development of AI-driven cybersecurity frameworks. The research also provides actionable recommendations for the future development of robust, reliable, and secure AI-based systems, bridging current knowledge gaps and offering valuable insights for academia and industry alike.Computing and Artificial Intelligence361The research systematically investigates the critical issues surrounding AI within cybersecurity, focusing on specific vulnerabilities and the potential for AI systems to be exploited by malicious actors, and provides actionable recommendations for the future development of robust, reliable, and secure AI-based systems.Computing and Artificial Intelligence["Zarif Bin Akhtar", "Ahmed Tajbiul Rawol"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/65f72faa69297ccbba2e7a05bed93bcbce67a376 8f38e3a36bc928171a1c4bfaeb71fb9af3768062Various Artificial Intelligence (AI) techniques in water resource management highlight the current methodologies’ strengths and limitations in forecasting, optimization, and control. We identify a gap in integrating these diverse approaches for enhanced water prediction and management. We critically analyze the existing literature on artificial neural networks (ANNs), deep learning (DL), long short-term memory (LSTM) networks, machine learning (ML) models such as supervised learning (SL) and unsupervised learning (UL), and random forest (RF). In response, we propose a novel framework that synergizes these techniques into a unified, multi-layered model and incorporates a digital twin and a multi-modal transformer approach. This integration aims to leverage the collective advantages of each method while overcoming individual constraints, significantly enhancing prediction accuracy and operational efficiency. This paper sets the foundation for an innovative digital twin-integrated solution, focusing on reviewing past works as a precursor to a detailed exposition of our proposed model in a subsequent publication. This advanced approach promises to redefine accuracy in water demand forecasting and contribute significantly to global sustainability and efficiency in water use.Applied Informatics832This paper proposes a novel framework that synergizes these techniques into a unified, multi-layered model and incorporates a digital twin and a multi-modal transformer approach, aiming to leverage the collective advantages of each method while overcoming individual constraints, significantly enhancing prediction accuracy and operational efficiency.AI["Toqeer Ali Syed", "Muhammad Yasar Khan", "Salman Jan", "S. Albouq", "S. Alqahtany", "M. Naqash"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/8f38e3a36bc928171a1c4bfaeb71fb9af3768062 3610d3a0db551e6e2fbc1b519555a71f3c3645b8This paper investigates the relationship between digital skills and the use of artificial intelligence (AI) in higher education, with an emphasis on the role of teachers. The research, through content analysis, explored 15 articles from the Web of Science and Scopus databases (2020-2024), using the Iramuteq software. The searches were carried out using the search terms “Artificial Intelligence” and “Digital Competence”. The results allowed us to verify that AI plays a relevant role in several stages of the teaching and learning process, from pedagogical planning to learning assessment processes. This ultimately highlights the need for teachers to develop digital skills, as a training that makes them more effective in using AI efficiently in developing students' skills. AI is seen as a tool that can enhance learning, helping students develop knowledge-based skills, such as problem-solving and content application. The study concludes that preparing educators to integrate these technologies is essential, ensuring that digital skills are taught and applied effectively, benefiting the teaching and learning process.Concilium31It is concluded that preparing educators to integrate these technologies is essential, ensuring that digital skills are taught and applied effectively, benefiting the teaching and learning process.Concilium["Ariane Simarco Scarci", "Thaise Moser Teixeira", "Let\u00edcia Fleig Dal Forno"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/3610d3a0db551e6e2fbc1b519555a71f3c3645b8 7c191392c4ab61700f25f12acd39495f58787a01Systematic Reviews132The need for human oversight to ensure the reliability of AI outputs in evidence synthesis and decision-making in healthcare is emphasised, emphasising the need for human oversight to ensure the reliability of AI outputs in evidence synthesis and decision-making in healthcare.Systematic Reviews["Lixia Ge", "Rupesh Agrawal", "Maxwell Singer", "P. Kannapiran", "Joseph Antonio De Castro Molina", "K. Teow", "Chun Wei Yap", "J. Abisheganaden"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/7c191392c4ab61700f25f12acd39495f58787a01 8778256e0b4c990322351e4be79497fd56123012AIMS The integration of artificial intelligence (AI) into functional urology management must be assessed for its clinical utility, but hopefully will change, perhaps to revolutionize the way LUTD and other conditions are assessed, the aim being to offer patients more rapid and effective management which enhances patient outcomes. The aim of this proposal, discussed at the ICI-RS annual meeting, is to evaluate the available evidence on AI and the way it might change the approach to urodynamic (UDS) diagnoses, including overactive bladder syndrome (OAB), and perhaps other LUTDs such as bladder outflow obstruction. METHODS A compendium of discussion based on the current evidence related to AI and its potential applications in UDS and OAB. RESULTS AI-powered diagnostic tools are being developed to analyze complex datasets from urodynamic studies, imaging, and other diagnostic tests. AI systems can leverage large volumes of clinical data to recommend personalized treatment plans based on individual patient profiles to optimize surgical procedures, enhance diagnostic precision, tailor the therapy, reduce the risk of complications, and improve outcomes. In the future, AI will be able to provide tailored counseling regarding the outcomes and potential side effects of drugs and procedures to a given patient. CONCLUSION AI's role in functional urology has been poorly investigated, and its implementation across several areas may improve clinical care and the pathophysiological understanding of functional urologic conditions.Neurourology and Urodynamics321AI's role in functional urology has been poorly investigated, and its implementation across several areas may improve clinical care and the pathophysiological understanding of functional urologic conditions.Neurourology and urodynamics["Enrico Finazzi Agr\u00f3", "E. Rosato", "G. B. Kheir", "K. Rademakers", "M. Averbeck", "T. Tarcan", "H. Hashim", "A. Gammie", "S. Sinha", "Qi-Xiang Song", "R. Mohamed-Ahmed", "Anasofia Da Silva", "Riccardo Lombardo", "P. Abrams", "Alan J Wein", "G. Werneburg"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/8778256e0b4c990322351e4be79497fd56123012 21d525d64a522f892387bf31a0afaf1d2b010851Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in clinical applications such as diagnostics and personalized treatment. However, the application of AI in non-clinical areas, such as operational efficiency, data governance, and data monetization, remains underexplored. This paper addresses this gap by proposing an AI-driven framework for healthcare organizations, synthesizing existing literature on AI applications and data management. Using a qualitative approach, this study identifies six key areas where AI can enhance non-clinical operations: data governance and quality management, technological infrastructure and scalability, leadership and workforce development, operational efficiency, data monetization, and ethical considerations. The framework provides a strategic framework for healthcare organizations to adopt AI technologies effectively while ensuring compliance with local and international regulations. This paper contributes to the growing body of research by offering practical solutions for leveraging AI to improve healthcare administration and create new revenue streams through data valorization.The Integration of AI and Technology in Modern Business Practices00An AI-driven framework for healthcare organizations is proposed, synthesizing existing literature on AI applications and data management and providing a strategic framework for healthcare organizations to adopt AI technologies effectively while ensuring compliance with local and international regulations.The Integration of AI and Technology in Modern Business Practices["Aziz Alzeqri"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/21d525d64a522f892387bf31a0afaf1d2b010851 4372ff1edfbce07e3e2e8a5d21663dc369bbc42dThis research paper investigates the implementation and development of Artificial Intelligence (AI) within the banking industry, a sector undergoing significant transformation due to the rapid adoption of this technology. The study aims to provide a comprehensive analysis of how AI is reshaping key areas such as customer experience, operational productivity, risk management, fraud detection, and regulatory compliance. By examining current practices and exploring future trends, including regulatory and ethical considerations, the research highlights both the opportunities and challenges associated with integrating AI into banking. The paper also addresses the critical role of governance frameworks in managing AI's impact and offers insights for decision-makers on effectively navigating this evolving landscape. The objective is to present a nuanced understanding of AI’s transformative potential in banking and its implications for stakeholders, including customers, regulators, and industry leaders, while projecting the future trajectory of AI-driven innovations in the sector.International Journal of Academic Research in Business and Social Sciences280A nuanced understanding of AI’s transformative potential in banking and its implications for stakeholders, including customers, regulators, and industry leaders, are presented while projecting the future trajectory of AI-driven innovations in the sector.International Journal of Academic Research in Business and Social Sciences["Noor Azma Ismail", "Satgian Singh Khalsa A/L Harjit Singh", "Abdulaziz Al-Nahari"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/4372ff1edfbce07e3e2e8a5d21663dc369bbc42d 04fb34338f4394aa8f6da7ebece2bcc414af7211This paper examines how Generative Artificial Intelligence (GAI) influences word-of-mouth (WOM) in travel and hospitality, focusing on synthetic WOM (syWOM). It explores how GAI-driven WOM reshapes traveler interactions and decision-making in an experience-centric industry. Using a literature review and conceptual analysis approach 1 , this study examines the integration of GAI tools, such as ChatGPT, to enhance travel experiences. The analysis presented in this study highlights GAI's potential in inducing syWOM and its effects on traveler perceptions and behaviors. Additionally, it addresses the emerging role of GAI in WOM, emphasizing the need for further research on its impact on travel planning and engagement. This study presents a fresh view of the interaction of syWOM with GAI in travel, aiming to inform future research and practical applications of personalized traveler engagement.Journal of Computational Information Systems780A fresh view of the interaction of syWOM with GAI in travel is presented, aiming to inform future research and practical applications of personalized traveler engagement and to address the emerging role of GAI in WOM.Journal of Computer Information Systems["Du\u0161an Mladenovi\u0107", "Moein Beheshti", "Toma\u017e Kolar", "Elvira Ismagilova", "Yogesh K. Dwivedi"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/04fb34338f4394aa8f6da7ebece2bcc414af7211 73259ce0435469f0bcf937f7b238e565ced2cdd1This study investigates the impact of artificial intelligence (AI) on women’s empowerment, addressing the pressing problem of gender inequality in various sectors. With the rapid advancement of AI technologies, understanding their role in enhancing women's access to education, safety, and leadership opportunities has become essential. The primary purpose of this research is to explore the perceptions of female students at an online women’s university regarding AI's potential benefits and challenges in promoting empowerment. Employing a quantitative research methodology, a structured questionnaire was distributed to 160 female participants across various faculties. The data collected were analyzed using descriptive and inferential statistical techniques, revealing significant insights into the participants' attitudes towards AI. The results indicate a generally positive perception of AI, with many participants recognizing its potential to improve educational opportunities, enhance online safety, and support women's leadership in technology. However, there are notable concerns regarding the accessibility and implementation of AI, suggesting the existence of barriers that may hinder its effectiveness. In conclusion, this study underscores the importance of addressing these barriers to maximize AI’s benefits for women. By fostering an inclusive approach to AI development and deployment, stakeholders can create an environment that empowers women, thereby contributing to the overall goal of gender equality in the digital age.Journal Electrical and Computer Experiences00By fostering an inclusive approach to AI development and deployment, stakeholders can create an environment that empowers women, thereby contributing to the overall goal of gender equality in the digital age.Journal Electrical and Computer Experiences["Marzia Ebrahimi", "Behnaz Rahimi", "Manizha Sharifi", "Diba Sadat", "Nahid Amiri", "Sadaf Khalil", "Tamanna Quraishi"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/73259ce0435469f0bcf937f7b238e565ced2cdd1 88895e407f0059048bf00ae15ceda0eb96b06164The healthcare landscape has transformed significantly in recent decades, propelled by technological advancements and evolving treatment methodologies. This evolution has improved patient care and introduced complexities in medical malpractice. This research aimed to explore the evolving landscape of medical malpractice in light of technological advancements such as artificial intelligence (AI) and telemedicine. Specifically, the study aims to analyze the gap between traditional legal standards of medical malpractice and the practical realities healthcare providers face in a rapidly changing environment. The gap is most evident when applying static legal definitions to an ever-changing healthcare environment.  This study employs a qualitative research method using a systematic literature review (SLR) to analyze the relationship between legal frameworks and technological developments influencing medical malpractice claims over the past five years (2018-2023).  This study found a pressing need for legal reforms to accommodate emerging technologies such as telemedicine and artificial intelligence (AI), which challenge conventional definitions of liability and standards of care. The study emphasizes the importance of adapting legal frameworks to ensure patient safety while protecting healthcare providers from undue liability. This study highlights medical malpractice law's dynamic and evolving nature in response to technological advancements and changing healthcare practices. Staying informed about these evolving legal standards is essential for healthcare providers' risk management and compliance. Policymakers must prioritize the development of supportive legal frameworks that protect patient rights while providing healthcare providers with the clarity needed to navigate this complex landscape effectively.Journal of law review300A pressing need for legal reforms to accommodate emerging technologies such as telemedicine and artificial intelligence (AI), which challenge conventional definitions of liability and standards of care is found.Legalis : Journal of Law Review["Rosnalisa Zein", "Iwan Kusnawirawan", "Hernayati", "Richard Mottershead", "M. Subu", "Upn Veteran Jakarta", "Kusnawirawan Hernayati Mottershead Subu Waluyo Zein"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/88895e407f0059048bf00ae15ceda0eb96b06164 bfbb9ce4dd0252f5817ec28307bcf53c8af740d6 The importance of artificial intelligence and disability management (DM) are increasing due to numerous social developments. This includes longer life expectancies and longer working lives, an emphasis on labor market inclusion as a whole, and efforts towards participation by almost every citizen in the open labor market. Research results from a new study provides first rougher new insights in relation on artificial intelligence and early intervention and occupational rehabilitation. The aim is to provide new perspectives and content. It is intended that this analysis can facilitate discussions about a meaningful activity for disability management professionals being a part of the development of artificial solutions in the workplace. At this moment a lot of questions are open because the development of these offers are at an early stage. It should be useful to get more clarity what options and possibilities are realistic to being part of the design process from the view of disability experts and from the view of engineers. The objectives are to gain a clearer understanding of capabilities and options on the effectiveness of AI in Disability Management for disabled workers. The focus relies on early intervention and occupational rehabilitation. The study should give more insights on how AI can greatly assist or hinder much improved outcomes. Abstracts of the 6th World Conference on Business, Management, Finance, Economics and Marketing00The objectives are to gain a clearer understanding of capabilities and options on the effectiveness of AI in Disability Management for disabled workers and to give more insights on how AI can greatly assist or hinder much improved outcomes.Abstracts of the 6th World Conference on Business, Management, Finance, Economics and Marketing["P. Rosken"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/bfbb9ce4dd0252f5817ec28307bcf53c8af740d6 ed84a561d6447e1cf5089107650af344b7874c41This comprehensive article explores the transformative role of Artificial Intelligence (AI) in addressing the global climate crisis, focusing on its applications in climate modeling, renewable energy optimization, and disaster response. Through an extensive literature review, case studies, and expert interviews, we examine how AI technologies are revolutionizing climate prediction accuracy, enhancing data processing capabilities, and enabling sophisticated climate scenario simulations. The article delves into AI's contributions to renewable energy forecasting, smart grid management, and energy storage optimization, highlighting its potential to accelerate the transition to sustainable energy systems. We also investigate AI-driven approaches to disaster response and resilience, including early warning systems, resource allocation during climate-related disasters, and post-disaster recovery planning. While acknowledging the significant advancements AI brings to climate action, this study also addresses the challenges and limitations, such as data quality issues, ethical considerations, and technical barriers. Looking ahead, we discuss emerging AI technologies, their integration with other solutions, and the policy implications for effective climate change mitigation and adaptation. This research underscores the critical role of AI in combating climate change, while emphasizing the need for responsible development and deployment of these technologies to ensure a sustainable and resilient future.International Journal For Multidisciplinary Research100The article delves into AI's contributions to renewable energy forecasting, smart grid management, and energy storage optimization, highlighting its potential to accelerate the transition to sustainable energy systems.International Journal For Multidisciplinary Research["Venkata Rajesh Krishna Adapa"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/ed84a561d6447e1cf5089107650af344b7874c41 9f19d20f39c0a67667de0eb1ad5ec1c7b589a22f In the rapidly evolving digital landscape of Globalization, the world has taken one more shift with advancement of Artificial Intelligence (AI), which is taking up many routinely done tasks by people across the countries and handling the tasks inmore efficient and effective manner in a fraction of time than if done manually. Multinational Corporations (MNC’s) are rapidly adopting AI and in different departments and Human Resource (HR) is not an exception. AI enables the collection and analysis of data in HR processes and also eliminates biases and presumption, it guarantees that right candidates are recruited and placed on the right job in the nick of time. AI eliminates the discrimination during the processes and different task that are concerned with HR department and its development. AI helps mining recruitment data that uncover challenges and the hindrances by addressing them objectively. AI does things in more comprehensive way by following the tools and techniques required to ease the task assigned. AI can do automated repetitive and time-consuming tasks as per the requirements of the organization, so that HR professionals can focus more on creating strategies and polices. AI helps in improving decision-making with valuable understandings of HR by using predictive analytics. AI can be handy in enhancing the efficiency of hiring by screening and selection process along with setting KPI’s of employees in a proactive manner. AI can be an effective tool for HR department, provided it is used with little extra care. There are certain challenges in implementation of AI in HR department, it demands specialized knowledge and skill set that many organizations may not be possessing, without the necessary expertise and technical knowhow HR of businesses may struggle to exist and compete. Resistance from employees can be high and they may not be able to adapt to AI changes quickly and may limit their ability to benefit from it. Investing funds fortechnology and training,for relevant skills can be a constraint and acceptability can be a cause of concern. Keywords : Globalization, Artificial Intelligence(AI), Multinational corporations, Human Resource(HR), Challenges. Abstracts of the 6th World Conference on Business, Management, Finance, Economics and Marketing00AI enables the collection and analysis of data in HR processes and also eliminates biases and presumption, it guarantees that right candidates are recruited and placed on the right job in the nick of time and can be an effective tool for HR department.Abstracts of the 6th World Conference on Business, Management, Finance, Economics and Marketing["Dr.Syed Aulia Mohiuddin"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/9f19d20f39c0a67667de0eb1ad5ec1c7b589a22f 1066eebd53cc847b78da4eb83cdddaef46f26275Artificial intelligence (AI) has been developing dynamically, and becomes a core issue in the public debate. The related contemporary achievements, such as generative language models, change the way we work, especially in creative fields. The article analyses the historical development of AI, from the Dartmouth workshops in 1956, through John McCarthy’s and Alan Turing’s symbolic approach to artificial intelligence, to the effect of cybernetics on contemporary technologies. It draws attention to Richard Dreyfuss’ and John Searle’s critique of AI, emphasising their meaning in redefining the differences between human and artificial intelligence. In the context of the AI revolution, the article poses a question about the future of design and technology, considering if the process of human adaptation keeps up with the development of machines. It also refers to ethical aspects of automation, and the growing importance of creative thinking in the face of technological progress. Keywords: artificial intelligence (AI), cybernetics, philosophy, history, designFormy00The article analyses the historical development of AI, from the Dartmouth workshops in 1956, through John McCarthy’s and Alan Turing’s symbolic approach to artificial intelligence, to the effect of cybernetics on contemporary technologies.Formy["Agnieszka Zgud", "Kuba Kulesza"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/1066eebd53cc847b78da4eb83cdddaef46f26275 f305fa0f9c7e9c21c1503cb5f5696cdc2237d359 The Indian manufacturing sector, specifically within Micro, Small, and Medium Enterprises (MSMEs), serves as a fundamental component of the country’s economic development, contributing significantly towards employment generation, exports, and GDP. With the rising influence of digitalization across industries, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies, offering huge potential to enhance efficiency, productivity, reduce operational costs, and improve product quality within the MSME sector. Notwithstanding these potential benefits, the adoption of AI/ML in Indian manufacturing MSMEs remains in its early stages, constrained by financial limitations, lack of skilled workforce, and insufficient infrastructure. This paper examines the current and potential role of AI/ML in Indian manufacturing MSMEs, focusing on the benefits these technologies offer, the limitations hindering their adoption, and the ongoing challenges that persist within the sector. By synthesizing existing literature, this study aims to provide a comprehensive analysis that could help policymakers, industry stakeholders, and MSME owners about the most effective strategies for adopting AI/ML technologies. The paper further explores how these enterprises can overcome obstacles to unlock AI/ML’s full potential for improving operational efficiency, competitiveness, and innovation. Keywords: AI/ML, MSMEs, manufacturing, India, challenges, benefits, technology adoption Abstracts of the 6th World Conference on Business, Management, Finance, Economics and Marketing00This paper examines the current and potential role of AI/ML in Indian manufacturing MSMEs, focusing on the benefits these technologies offer, the limitations hindering their adoption, and the ongoing challenges that persist within the sector.Abstracts of the 6th World Conference on Business, Management, Finance, Economics and Marketing["Dr.Devesh Kumar"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/f305fa0f9c7e9c21c1503cb5f5696cdc2237d359 9306b91c4826683dcb226c8afabbe31e34570957On October 8 2024, the Royal Swedish Academy of Sciences announced the 2024 Nobel Prize in Physics was awarded to Hopfield and Hinton for their foundation research on machine learning with artificial neural networks, which resulted in the current applications for artificial intelligence (AI). Digital diagnostic histopathology combines image capture with image analysis and uses digital tools to collect, analyze, and share diagnostic information. An increase in chronic diseases, diagnostic departmental workloads, and diagnostic tests to support targeted therapy in cancer patients have driven the use and development of image analysis systems, and several medical device companies have recently developed whole-slide scanning devices. In April 2017, the US Food and Drug Administration (FDA) permitted marketing authorization for the first whole slide imaging (WSI) system. During 2024, large-scale studies from several cancer centers have shown the potential for diagnostic reporting for real-world data and whole-slide modeling to develop validated diagnostic AI algorithms. This editorial discusses why recent advances and applications in AI and digital image analysis may have an important future role in cancer diagnosis and prognosis.Medical Science Monitor130Why recent advances and applications in AI and digital image analysis may have an important future role in cancer diagnosis and prognosis are discussed.Medical Science Monitor: International Medical Journal of Experimental and Clinical Research["D. Parums"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/9306b91c4826683dcb226c8afabbe31e34570957 c10501c61e54095d07b02e018d7309b3ccaa5541AI and Ethics830The article discusses the discourse analysis together with theoretical assumptions around the question, which actors in society could be counted as accountable in AI regards, and a discussion of the European AI legal system is added, to evaluate its connection with the media discourses.AI and Ethics["Marek Winkel"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/c10501c61e54095d07b02e018d7309b3ccaa5541 e7247ba7412733a9f43976b717174ca219e3da90Dear Editors, The advancement of technology has led to a transformation in medical education. At this stage, the ethical considerations surrounding the use of cadavers in medical education are particularly intriguing. Nowadays, it is possible to digitally recreate real cadavers with virtual content. Such materials are referred to as virtual cadavers in the literature. Just like with real cadavers, the use of images of donors in virtual cadavers is subject to ethical permissions. At this point, the use of artificial intelligence tools comes to the forefront. The use of cadavers in medical education has been a traditional method for many years. The use of cadavers in medical education is a critical component for students to develop their understanding of human anatomy and clinical skills. Cadaver dissection provides medical students with the opportunity to learn the structure of the human body in detail, allowing them to translate theoretical knowledge into practical application. Additionally, it provides students with the opportunity for clinical experience. The use of cadavers in medical education helps students develop professional skills by providing them with practical experience on a real body (Erbay et al., 2015). Cadavers are obtained from human donors. In our country, there are various problems regarding cadaver donation rates. Cadaver donation is extremely low in our country, making it difficult for medical faculty students to access cadavers (Şeker et al., 2013). On average, there is only one cadaver for every 20 students studying at medical faculties in our country. It is even challenging to find 1-2 cadavers in departments of anatomy (Kürkçüoğlu et al., 2021). Additionally, there are issues arising from the irreversible nature of procedures performed on cadavers. Due to such reasons, studies related to virtual cadavers in digital environments have become increasingly important. Virtual cadavers represent an important solution brought about by technology in the field of medicine and generally in health education. Through the use of virtual cadavers, procedures that are difficult or even impossible to perform repeatedly on real cadavers can be achieved. Additionally, virtual cadaver modeling eliminates the possibility of tissue degradation. Desired tissues, systems, and structures can be modeled in a manner closely resembling reality. In addition to providing unlimited repetition capabilities, virtual cadavers revolutionize the use of cadavers by offering access at any desired time (Gürcan, 2018). The development of virtual cadavers poses certain challenges. For instance, the use of programs like Unity and Blender is necessary in the process of developing organs and structures. Besides the requirement of knowing how to use such programs, extensive hours are needed to create a model. Additionally, if virtual cadaver images are intended to be realistic, there are also some issues to address. The usage and dissemination of virtual cadaver contents obtained from real cadaver images on the internet are subject to ethical considerations. Just like with real cadavers, obtaining permission from the legal heirs of the donor is necessary when using images of donors in virtual cadavers. Artificial intelligence (AI) was first defined in 1956. McCarthy (2004) defined artificial intelligence as the task of creating human-like intelligent machines and intelligent computer programs (Arslan, 2020). AI has become increasingly popular in recent times, both as natural language processing models and in the field of image processing. With the use of AI tools, it is possible to generate visual and textual content without the need for detailed software and programming knowledge. It is even possible to create videos consisting of unreal images but resembling real ones. At this stage, the use of AI tools is considered important for virtual cadavers. By using AI visual generation tools, content that can be used in virtual cadavers can be easily created. Thus, virtual cadaver examples that are indistinguishable from real cadaver images but do not belong to a real person can be obtained. At this point, it is important for medical education and healthcare professionals to ethically examine this situation.Experimental and Applied Medical Science00Virtual cadavers represent an important solution brought about by technology in the field of medicine and generally in health education through the use of virtual cadavers, procedures that are difficult or even impossible to perform repeatedly on real cadavers can be achieved.Experimental and Applied Medical Science["Yusuf Kal\u0131nkara"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/e7247ba7412733a9f43976b717174ca219e3da90 35e26b01f9747d5fcc7dca163bb1476efea37415This study aims to explore the impact of artificial intelligence (AI) on the accuracy and efficiency of scientific research, while focusing on the ethical challenges associated with it. It also seeks to assess the level of awareness of AI ethics among Algerian researchers and the adequacy of current policies in dealing with these challenges, such as issues of plagiarism, privacy violation, and biased results. This will provide a deeper understanding of the reality of scientific research in Algeria in light of rapid technological developments. An electronic questionnaire was used, targeting a group of Algerian researchers, reaching a sample size of 623 individuals. Through this questionnaire, we aimed to collect data that would enable us to provide recommendations to enhance the responsible and ethical use of AI tools while maintaining the integrity and reliability of scientific research. The study found that the respondents demonstrated varying levels of awareness of the ethical implications of using AI in their research, with a lack of adequate training and resources to help researchers deal with the ethical complexities of AI-based research. The study also confirms that current ethical policies and regulations in Algeria are insufficient to effectively address the challenges posed by AI. This calls for the establishment of a comprehensive legal framework in the near future and the updating of ethical guidelines to regulate the use of AI in academic research.South Florida Journal of Development160The study found that the respondents demonstrated varying levels of awareness of the ethical implications of using AI in their research, with a lack of adequate training and resources to help researchers deal with the ethical complexities of AI-based research.South Florida Journal of Development["Belmir Sara", "Daira Aida"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/35e26b01f9747d5fcc7dca163bb1476efea37415 5cc8928314434e7b570e6fa257bb37f07a44491bRheumatology International170Rheumatology fellows exhibit enthusiasm for AI integration yet have reservations about its implementation and ethical implications, and addressing these challenges through collaborative efforts can ensure responsible AI integration, prioritizing patient safety and ethical standards in rheumatology and beyond.Rheumatology international["R. Purohit", "S. Saineni", "Sweta Chalise", "Reanne Mathai", "Rajan Sambandam", "Richard Medina-Perez", "N. Bhanusali"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/5cc8928314434e7b570e6fa257bb37f07a44491b 1abde8b8834a51b59f80572d49be5db2be6af46aOBJECTIVES To evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision-support system (DSS). METHODS We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between 11/25/2023 and 1/15/2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use. RESULTS A total of 47/143 providers completed the survey. 38/47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). 38/47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights for patient care, but almost three quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47). CONCLUSION Most critical care providers perceived that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.Applied Clinical Informatics00Most critical care providers perceived that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors.Applied clinical informatics["A. Tekin", "S. Herasevich", "Sarah Minteer", "O. Gajic", "Amelia K. Barwise"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/1abde8b8834a51b59f80572d49be5db2be6af46a bc0cb0d3c36e61c20c6c9589dc619bc355b754b3This study examines the principal factors influencing organizational management utilizing Artificial Intelligence (AI) in the modern era. The primary emphasis is on the issues and developments impacting contemporary organizations worldwide after the emergence of AI. Initially, the critical elements that influence internal and external management were explored while assessing the ramifications of these factors on management. Then, the impact of numerous factors on organizational management strategies was thoroughly studied alongside adequate contemporary AI models that conceptualized these tactics and led to a competitive advantage stage. Although AI has tremendous advantages for contemporary business and management, it also has disadvantages. The human-feeling process is a fundamental practical sense that AI is limited. Recent studies demonstrated that the AI era lacks human-like creativity and empathy, a proven fact of human brains’ vitality in making intelligent decisions. Therefore, organizations’ members can be complemented by AI for better, more intelligent decision-making that will elevate the related businesses. Conversely, AI can result in ethical concerns about bias and privacy. This issue will prevent modern organizations from considering corrective actions since their decisions might not lead to the anticipated business outcomes, including but not limited to the set Key Performance Indicators (KPIs). Another side-effect of AI is the inadequate data for making the required decision without contemplating empathy. Thus, the AI shall be tackled from 360 degrees to ensure that the AI-driven decision-making system will optimize human interference while minimizing the probable impacts of the related risks, biases, and hallucination. The paper employs genuine case studies and empirical research findings to critically and analytically examine the management concerns presented by applying AI-driven decision-making practice. By harnessing AI for smarter decisions, a practical case study about the Electrical Submersible Pump (ESP) and its related technologies to extract crude oil will be demonstrated using the components and elements of the Contemporary Management Module in the AI age for a smarter-driven decision-making process. This methodology will boost modern organizations’ performances while fostering the employees’ recitals, yielding a successful business journey and evident productivity.The Integration of AI and Technology in Modern Business Practices00By harnessing AI for smarter decisions, a practical case study about the Electrical Submersible Pump and its related technologies to extract crude oil will be demonstrated using the components and elements of the Contemporary Management Module in the AI age for a smarter-driven decision-making process.The Integration of AI and Technology in Modern Business Practices["Hisham I. Al-Shuwaikhat"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/bc0cb0d3c36e61c20c6c9589dc619bc355b754b3 c96256aba686cb5f0597ae16940dbc1bae657c56STUDY QUESTION Can more reliable time cut-offs of embryo developmental incompetence be generated by combining time-lapse technology (TLT), artificial intelligence, and preimplantation genetics screening for aneuploidy (PGT-A)? SUMMARY ANSWER Embryo developmental incompetence can be better predicted by time cut-offs at multiple developmental stages and for different ranges of maternal age. WHAT IS KNOWN ALREADY TLT is instrumental for the continual and undisturbed observation of embryo development. It has produced morphokinetic algorithms aimed at selecting embryos able to generate a viable pregnancy, however, such efforts have had limited success. Regardless, the potential of this technology for improving multiple aspects of the IVF process remains considerable. Specifically, TLT could be harnessed to discriminate developmentally incompetent embryos: i.e. those unable to develop to the blastocyst stage or affected by full-chromosome meiotic aneuploidies. If proven valuable, this application would prevent the non-productive use of such embryos, thereby improving laboratory and clinical efficiency and reducing patient stress and costs due to unnecessary embryo transfer and cryopreservation. STUDY DESIGN, SIZE, DURATION The training dataset involved embryos of PGT-A cycles cultured in Embryoscope with a single media (836 euploid and 1179 aneuploid blastocysts and 1874 arrested embryos; 2013-2020). Selection criteria were ejaculated sperm, own (not donated) fresh oocytes, trophectoderm biopsy and comprehensive-chromosome-testing to diagnose uniform aneuploidies. Out-of-sample (30% of training), internal (299 euploid and 490 aneuploid blastocysts and 680 arrested embryos; 2021-2022) and external (97 euploid, 110 aneuploid and 603 untested blastocysts and 514 arrested embryos, 2018 to early 2022) validations were conducted. PARTICIPANTS/MATERIALS, SETTING, METHODS A training dataset (70%) was used to define thresholds. Several models were generated by fitting outcomes to each timing (tPNa-t8) and maternal age. ROC curves pinpointed in-sample classification values associated with 95%, 99% and 99.99% true-positive rate for predicting incompetence. These values were integrated with upper limits of maternal age ranges (<35, 35-37, 38-40, 41-42, and >42 years) in logit functions to identify time cut-offs, whose accuracy was tested on the validation datasets through confusion matrices. MAIN RESULTS AND THE ROLE OF CHANCE For developmental (in)competence, the best performing (i) tPNa cut-offs were 27.8 hpi (error-rate: 0/743), 32.6 hpi (error rate: 0/934), 26.8 hpi (error rate: 0/1178), 22.9 hpi (error-rate: 1/654, 0.1%) and 17.2 hpi (error rate: 4/423, 0.9%) in the <35, 35-37, 38-40, 41-42, and >42 years groups, respectively; (ii) tPNf cut-offs were 36.7 hpi (error rate: 0/738), 47.9 hpi (error rate: 0/921), 45.6 hpi (error rate: 1/1156, 0.1%), 44.1 hpi (error rate: 0/647) and 41.8 hpi (error rate: 0/417); (iii) t2 cut-offs were 50.9 hpi (error rate: 0/724), 49 hpi (error rate: 0/915), 47.1 hpi (error rate: 0/1146), 45.8 hpi (error rate: 0/636) and 43.9 hpi (error rate: 0/416); (iv) t4 cut-offs were 66.9 hpi (error rate: 0/683), 80.7 hpi (error rate: 0/836), 77.1 hpi (error rate: 0/1063), 74.7 hpi (error rate: 0/590) and 71.2 hpi (error rate: 0/389); and (v) t8 cut-offs were 118.1 hpi (error rate: 0/619), 110.6 hpi (error rate: 0/772), 140 hpi (error rate: 0/969), 135 hpi (error rate: 0/533) and 127.5 hpi (error rate: 0/355). tPNf and t2 showed a significant association with chromosomal (in)competence, also when adjusted for maternal age. Nevertheless, the relevant cut-offs were found to perform less well and were redundant compared with the blastocyst development cut-offs. LIMITATIONS, REASONS FOR CAUTION Study limits are its retrospective design and the datasets being unbalanced towards advanced maternal age cases. The potential effects of abnormal cleavage patterns were not assessed. Larger sample sizes and external validations in other clinical settings are warranted. WIDER IMPLICATIONS OF THE FINDINGS If confirmed by independent studies, this approach could significantly improve the efficiency of ART, by reducing the workload and patient impacts (extended culture and cleavage stage cryopreservation or transfer) associated with embryos that ultimately are developmentally incompetent and should not be considered for treatment. Pending validation, these data might be applied also in static embryo observation settings. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the participating institutions. The authors have no conflicts of interest to declare. TRIAL REGISTRATION NUMBER N/A.Human Reproduction00Embryo developmental incompetence can be better predicted by time cut-offs at multiple developmental stages and for different ranges of maternal age using time-lapse technology, artificial intelligence, and preimplantation genetics screening for aneuploidy (PGT-A).Human reproduction["G. Coticchio", "A. Bartolacci", "V. Cimadomo", "S. Trio", "F. Innocenti", "A. Borini", "A. Vaiarelli", "L. Rienzi", "A. Ahlstr\u00f6m", "D. Cimadomo"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/c96256aba686cb5f0597ae16940dbc1bae657c56 ecd3f229e54cdebee8adf64c013799e0ac85933bArtificial intelligence is becoming an integral part of company strategy. AI’s capabilities are always evolving, with the focus shifting from what it can accomplish to what it cannot. Furthermore, substantially transforming in every aspect, the senior management leads from how they raise and coach, to the way they inspire teams, to the way they use AI and Human power together to achieve their goal for the firm, to the manner they drive transformation and challenge. AI promises, concern, and excitement over the wayward role it will play are running High because to its limitless breadth. One thing is clear: ensuring that your organization is prepared. For the paradigm transition, AI is no longer an option. The challenge now is: are leaders ready to go up and deliver the vision, and adapt to Seize the opportunities? To respond, the UAE’s leading smelting manufacturing businesses polled. Over 450 C-Level executives across the country, by analyzing their perspectives on their acceptability and Adaptability within themselves and the organization of the next AI generation. The company, even to further refine the findings, I conducted approximately 12 to 15 interviews with worldwide leaders. The poll and interviews clearly indicated that leaders in general are very Strong see AI as an opportunity, rather than a danger, but they also see the route to the relationship between development and success remains ambiguous. On the one hand, the material opportunity identified by leaders, both for their role and for their organization, was the increased efficiency that the effective use of AI could bring, more effective decision-making, improved risk management, and the creation of innovative products and services. On the other hand, executives were watchful to the potential threat associated with AI. ‘Manpower redundancy’ and ‘data protection’ appeared as the two main concerns for leaders, both as individual roles and their organizations. The implications of these technologies on the workforce and the nature of work have caused Extensive debate and analysis. By 2030, roughly 375 million workers will constitute Approximately 14% of the worldwide workforce may need to migrate into new occupations or acquire Automation and artificial intelligence have influenced the development of new skills. This shift will provide challenges and executives who must adjust to a changing workforce and develop new ways for managing people in a technologically driven workplace. A cross-functional team of researchers at MIT Sloan demonstrated an outstanding gain of up to 40% in highly. Using generative AI to improve skilled workers’ performance (* The term “Generative AI” relates to Deep-learning AI models can generate high-quality writing, graphics, and other content depending on the data on which they were taught has the potential to add trillions to the global economy. This monumental shift will revolutionize the way people work, study, and interact. It has the ability to shake up entire industries and even society as a whole. According to a Boston Consulting Group report, 61% of executives in the Middle East anticipate these technologies will enhance productivity by over 10% by 2024. Despite regional training surpassing global norms, issues remain in fully equipping the personnel and overcoming Future AI regulations. This article offers insight into how leaders anticipate AI’s influence. organizations, employment, and their positions in the Middle East, and how ready they feel to lead through this Revolutionary disruption. We will investigate the factors and determinants of leadership and organization. Leaders should be prepared to navigate the inevitable technological revolution.The Integration of AI and Technology in Modern Business Practices00Insight is offered into how leaders anticipate AI’s influence and how ready they feel to lead through this Revolutionary disruption, as well as the factors and determinants of leadership and organization.The Integration of AI and Technology in Modern Business Practices["Shoba Krishnan"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/ecd3f229e54cdebee8adf64c013799e0ac85933b 184f5ea363015111dec9a1185c3579ed6046c86eThe latest technological advancements have greatly interested researchers in artificial intelligence (AI) in education. In parallel, researchers have expressed concern about using and applying AI in education. However, there is a shortage of research that comprehensively and holistically examines trends in the use of AI in higher education. Hence, this study aimed to comprehensively analyze and assess AI research trends in higher education. In the SCOPUS database, we conducted a bibliometric analysis of 1,563 articles on research on AI in education. Our results revealed that the use of AI in education has increased dramatically from 2004 to 2023. In particular, a dramatic increase and peak exist after 2019. We also found limited interaction among scholars studying AI. Furthermore, our findings indicate that most of the most influential institutions are located in developed countries. Moreover, our findings demonstrated that AI research primarily concentrated on comprehending the impact of AI-based instruction, with the majority of these studies taking place in engineering education between 2017 and 2020. We also noticed that research on medical education in higher education occurred between 2015 and 2017. In addition, before 2015, research used AI in medical education as a teaching method to implement problem-based learning in higher education.Frontiers in Education540The use of AI in education has increased dramatically from 2004 to 2023 and a dramatic increase and peak exist after 2019, with the majority of these studies taking place in engineering education between 2017 and 2020.Frontiers in Education["R. S. Akhmadieva", "N. A. Kalmazova", "Tatyana Belova", "Alexey Prokopyev", "Natalia M. Molodozhnikova", "Valentina Yu Spichak"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/184f5ea363015111dec9a1185c3579ed6046c86e 054cb576ab072d6c0a787678fa124d70de80b96fThere has been made an attempt in the article to assess the possibility and expediency of application of information and communication technologies and their highest manifestation – artificial intelligence – as an anti-corruption tool in the judiciary. The essence and modern forms of the artificial intelligence phenomenon and its connection with information and communication technologies have been analyzed. There has been researched national and foreign legal regulation on the application of artificial intelligence both in the field of public administration in general and in the field of the judiciary and administration of justice in particular. The cases of the most «significant» corruption events in the judiciary have been presented and an attempt to find out the reasons of the failure of traditional tools to prevent corruption has been made. There has been called into question the ability of the state to overcome such a negative socio-power phenomenon as corruption without the implementation of radical steps which have long been overdue and became even more actualized during the armed conflict. Some positive examples of the information and communication technologies use in the judiciary have been demonstrated. Separate advantages of using artificial intelligence as a new tool for preventing corruption in the administration of justice have been briefly systematized. There has been emphasized that artificial intelligence due to its impartiality is fully capable to ensure the realization of the right to a fair trial, preserving time, financial and human resources, and reducing the level of corruption. At the same time, there has been underlined that without studying the risks and threats of using computer programs at this level of their development, relying only on them as a panacea for the destruction of corruption practices is imprudent and premature. Finally, an impulse for further research of artificial intelligence and its features as a tool of corruption prevention in the judicial system has been given.Public Administration and Regional Development00It has been underlined that without studying the risks and threats of using computer programs at this level of their development, relying only on them as a panacea for the destruction of corruption practices is imprudent and premature.Public Administration and Regional Development["Oleksiy Nepsha"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/054cb576ab072d6c0a787678fa124d70de80b96f f949e4a02f66db4ce46d284d90133ed187c40a15The paper presents the results of a research on the possibilities of using artificial intelligence in education in the context of management decision making, creating learning content, structuring information on education and educational policies. The methodology includes qualitative methods: cyberethnographic observation and interview with experts, which used the same set of questions in order to compare information. The cyberethnographic observation is implemented in indirect synchronous communication with ChatGPT by the two researchers. The interviews with the respondents, who are experts in the education in Bulgaria, is conducted through direct communication. The comparative approach is between direct dialogue with experts and dialogue between researchers and Chatbots on the same topics and with the same questions. The aim is to establish the way of communicating with artificial intelligence and its capabilities to find and structure information on topics related to education, training, educational management, educational content creation, etc.Pedagogika-Pedagogy00The aim is to establish the way of communicating with artificial intelligence and its capabilities to find and structure information on topics related to education, training, educational management, educational content creation, etc.Pedagogika-Pedagogy["Y. Totseva", "I. Mavrodieva"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/f949e4a02f66db4ce46d284d90133ed187c40a15 934a154416fb4622544295a61e6f8532203d489fIn the era of artificial intelligence, a new form of economy has appeared. Accordingly, it is necessary to adapt to the new era of technical skills, compound and innovative talents. As an important position for training technical talents, higher vocational colleges show new characteristics in the era of artificial intelligence and need to innovate the training mode of talents. Based on this, the article will analyze the difference between higher vocational education and undergraduate vocational education, and focus on the analysis of the innovative path and strategy of higher vocational education to train talents in the era of artificial intelligence, to cultivate innovative skilled talents who adapt to the era of artificial intelligence and meet the needs of the development of society and the era [1].Education Reform and Development120The article will analyze the difference between higher vocational education and undergraduate vocational education, and focus on the analysis of the innovative path and strategy of higher vocational education to train talents in the era of artificial intelligence.Education Reform and Development["Yingxuan Jia", "Shuo Yan"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/934a154416fb4622544295a61e6f8532203d489f dde0f093d08ac51ab6af425203a4a7cee948b4fdThe problems in judicial management persist regarding the applicability of artificial intelligence (AI) systems. Currently, they have become a challenge as they lack legal foundation and legitimacy, given that to date, only a minimal number of countries have laws addressing the supervision, regulation, management, and control of AI system applicability in judicial settings. The objective of this research is to analyze the future perspectives of the applicability of Artificial Intelligence in Judicial Management. The method employed will be exploratory research to analyze information from reviewed articles, legal reports, and official websites. Possible indicators of issues in AI applicability in judicial management, current and future trends of AI in judicial management, and an appropriate conceptual model for AI applicability in judicial management were identified. It was concluded that the application of artificial intelligence in judicial management remains a challenge and an ongoing issue because it currently does not inspire confidence in decision-making processes.Journal of Ecohumanism370It was concluded that the application of artificial intelligence in judicial management remains a challenge and an ongoing issue because it currently does not inspire confidence in decision-making processes.Journal of Ecohumanism["Diego Andrade A", "M. Toapanta T.", "Z. G\u00f3mez D", "Enrique Mafla G.", "Antonio Orizaga T", "Janio Jad\u00e1n G"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/dde0f093d08ac51ab6af425203a4a7cee948b4fd 45d79444f6de932c66e8b2d690664b581d1e4345The article presents a study of the influence of artificial intelligence (AI) on the educational field, namely on the use of its capabilities by students and staff of higher educational institutions, and analyzes the positive and negative effects of this use. Today, artificial intelligence is not only a reality, but has become an integral part of the educational process, as it is used by all its participants. The study of the impact of artificial intelligence on the educational process is extremely relevant due to its wide use, because it will provide an opportunity to significantly change teaching and learning methods, ethical and social aspects, therefore understanding the impact of artificial intelligence on education has become critically important. The purpose of the study was to analyze the impact of artificial intelligence on the academic success of students and the professional activity of teachers; identification of challenges associated with the introduction of artificial intelligence into the educational process; creating recommendations to overcome or minimize identified challenges. The research methods were analysis, synthesis, comparison, quantitative methods (secondary data, descriptive statistics). It was established that the implementation of artificial intelligence in education opens up significant opportunities for improving the quality of learning, teaching and increasing academic success. Both readiness and concern of the participants of the educational process for possible changes and challenges are monitored, this emphasizes the relevance and importance of further work on potential challenges regarding artificial intelligence in the educational process. The need for a more in-depth study of ethical and social problems related to the use of artificial intelligence in education has been established to ensure work on the adaptation of educational programs taking into account its capabilities. Based on the results of the research, a list of recommendations was developed to overcome the potential challenges of using artificial intelligence in education. We consider the study of the social consequences of the introduction of artificial intelligence in education as perspectives for further research, in particular the impact on the interaction between students and teachers, changes in the educational process and the impact on social inequality.Zhytomyr Ivan Franko State University Journal. Рedagogical Sciences00The purpose of the study was to analyze the impact of artificial intelligence on the academic success of students and the professional activity of teachers, and identification of challenges associated with the introduction of artificial intelligence into the educational process; creating recommendations to overcome or minimize identified challenges.Zhytomyr Ivan Franko state university journal. Рedagogical sciences["S. Sytniakivska", "O. Kulish"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/45d79444f6de932c66e8b2d690664b581d1e4345 1a24c3ba863f1c444f47d2d959f3c4591ff3b251Introduction. In the modern world, artificial intelligence is becoming more widespread and it is widely used in many areas of life, including education. Readiness to use artificial intelligence in professional activities is becoming one of the important conditions for a successful career.Purpose setting. This study is aimed at identifying the possibilities of pedagogical support for students when studying and using artificial intelligence at a university, including, first of all, creating motivation for the meaningful use of this technology.Methodology and methods of the study. During the research process, Russian and foreign scientific and methodological literature was analyzed, observations and surveys were conducted in groups of students.Results. As a result of the study, factors in the development of students’ motivation to study and use artificial intelligence in educational and professional activities were identified and described. The stages, principles and approaches, content and methods of pedagogical support for students in the process of studying and using artificial intelligence are proposed. The results of the work on this topic allowed the authors to propose possible directions for research into the integration of artificial intelligence technologies in the educational field.Conclusion. Artificial intelligence technology, already familiar to students as part of the educational process should acquire the features of not only a teaching tool, but also a means of self-development and more effective professional activity. Expanding the meaning of artificial intelligence from exclusively everyday to educational and professional is the main task of pedagogical support.Professional education in the modern world200Factors in the development of students’ motivation to study and use artificial intelligence in educational and professional activities were identified and described, and possible directions for research into the integration of artificial intelligence technologies in the educational field were proposed.Professional education in the modern world["T. A. Rakhimova", "I. P. Kaseka"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/1a24c3ba863f1c444f47d2d959f3c4591ff3b251 6bb43a1ad0ebacd2dd3dbe171f29851e1aa79a4aThis article addresses the issue of attributing phenomenal consciousness to Artificial Intelligence (AI), a mistake that can lead to ethically dangerous consequences and that is becoming widespread due to the advances of Large Language Models such as ChatGPT. We juxtapose advancements in AI with the notion of inner experience as it is present in humans. The study draws from various disciplines, including philosophy of mind, artificial intelligence, and theological texts such as "The Inner Castle" by Saint Theresa of Ávila and "Confessions" by Saint Augustine. Firstly, it reviews the current state of the relationship between phenomenal consciousness and AI, followed by a critique of the idea that advanced language models, like ChatGPT, can achieve an inner experience in the same sense we use the term to describe the human inner experience. A common objection is raised, suggesting that AI can become conscious by increasing its complexity. This is countered by presenting theoretical and empirical evidence on the independence of computational intelligence and phenomenal consciousness. The study concludes that, despite AI's notable cognitive abilities, it lacks the inner experience that characterizes human experience. Then, our second main contribution is an analogy of the dwellings of the Inner Castle to the range of different subjective experiences that are available to human beings, together with actions associated to them, which can be useful to understand where the machine can perform tasks that are similar to the human and where subjective experience is key. We are now in a pivotal moment where it is essential to understand the limitations of AI for deploying it ethically.Scientia et Fides00The study concludes that, despite AI's notable cognitive abilities, it lacks the inner experience that characterizes human experience, and draws from various disciplines, including philosophy of mind, artificial intelligence, and theological texts.Scientia et Fides["Sara Lumbreras", "Eduardo Garrido-Merch\u00e1n"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/6bb43a1ad0ebacd2dd3dbe171f29851e1aa79a4a 15d92aea89f88d7b45d8ec21127c7e3f891be264The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems are proposed by us, including federated learning and the creation of artificial intelligence systems that are more open to scrutiny. Lastly, we come to a conclusion by predicting future developments in artificial intelligence for image processing. These developments include the ability of AI to offer more personalised and adaptable image processing solutions, as well as the integration of AI with edge computing for real-time image analysis. The results of our research highlight the significant role that artificial intelligence plays in the development of image processing skills, which has far-reaching ramifications for the fields of technology, industry, and society.2024 Global Conference on Communications and Information Technologies (GCCIT)160The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis, and predict future developments in artificial intelligence for image processing.2024 Global Conference on Communications and Information Technologies (GCCIT)["V. Saravanan", "A. Adaikkammai", "G. Sasikala", "I. A", "A. Babisha", "M. Latha"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/15d92aea89f88d7b45d8ec21127c7e3f891be264 006f757e600aa632eb06f9d89ff0b7c60d71c5cdIndeed, the convergence of new technologies such as the web network, data analytics, blockchain, & artificial intelligence (AI) has significantly changed the way businesses operate. AI is the study of how to solve problems by merging large datasets of computer science and it is the ability of a machine such as a robot to perform functions that are often carried out by human beings with intelligence. Marketing is the measures accepted by an organization to motivate the buying & selling of a merchandise or service. Marketing entails trading, advertising, & dissemination of goods to customers or other organizations or we can say exploring, producing, and providing value is the process of marketing. This paper analyses the risks and prospects of AI in marketing with the help of divergent viewpoints of knowledge generation and information transfer. By way of support from different case studies, it is elaborated how Artificial Intelligence helps in and makes marketing efficient and effective. The study exhibits the prospects of AI in marketing with numerous opportunities for transformative changes in the industry.2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA)170The risks and prospects of AI in marketing with the help of divergent viewpoints of knowledge generation and information transfer are analyzed and how Artificial Intelligence helps in and makes marketing efficient and effective is elaborated.2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA)["Shrishti Agarwal", "Shipra Agarwal", "Varsha Mittal", "Anuradha"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/006f757e600aa632eb06f9d89ff0b7c60d71c5cd 42dc45daf10eeddbdc40cf43a3a5b4c3eda2bf1dArtificial intelligence (AI) is developing rapidly. Integrating AI into healthcare practices in the Arab world will transform diagnosis, prediction, and disease management. Today, countries such as Saudi Arabia and the United Arab Emirates (UAE) are at the forefront of AI adoption in healthcare, supported by strategic frameworks such as Saudi Arabia's Vision 2030 and the UAE National Artificial Intelligence Strategy 2031. However, adopting AI in daily healthcare practice on a large scale faces many obstacles that need to be overcome. Keywords: Artificial intelligence, AI, healthcare practice, diagnosis, prediction, and disease management, Arab world.International journal of research and review00Today, countries such as Saudi Arabia and the United Arab Emirates are at the forefront of AI adoption in healthcare, supported by strategic frameworks such as Saudi Arabia's Vision 2030 and the UAE National Artificial Intelligence Strategy 2031.International Journal of Research and Review["Mostafa Ahmed Arafa", "Karim Hamda Farhat"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/42dc45daf10eeddbdc40cf43a3a5b4c3eda2bf1d c9a1b450a08fc8c4eebc5aaf8ef25e897b359649This bibliographic essay provides a starting point for theological librarians to engage the history, anthropology, ethics, theology of artificial intelligence. What it shows is that despite seeming to appear ex nihilio, artificial intelligence for writing has a long history, profound ethical consequences beyond academic integrity, and latent theology. While theological librarians may or may not have the computer science background necessary to engage the technicalities of AI, they certainly have the disciplinary knowledge and skill to engage the topic in a meaningful and theological way.Theological Librarianship00Theological Librarianship["B. Beard"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/c9a1b450a08fc8c4eebc5aaf8ef25e897b359649 894c5005938aabaf53cdf9e488bef45a7e948d0eThe rapid development of artificial intelligence has had a huge and far-reaching impact on the field of education. The deep integration of artificial intelligence and education has promoted the emergence and development of artificial intelligence educational applications. Today's AI is not only a tool to enhance the learning experience, but also an important promoter of changing traditional teaching models and educational management methods. This article explores the acceptance and views of AI in different cultural backgrounds, and deeply analyzes the ethical issues that may arise in the application of AI in education, such as academic misconduct, algorithmic bias, and data privacy protection. Studies have shown that AI can help improve the personalization and efficiency of education, but its widespread use may cause students to rely on technology and weaken their critical thinking ability. In addition, if the bias in the algorithm is not effectively controlled, it may exacerbate inequality in education. The significance of the paper lies in providing theoretical support for the combination of AI and education, and putting forward constructive suggestions for balancing technological progress and human education elements in the future.Lecture Notes in Education Psychology and Public Media00The acceptance and views of AI in different cultural backgrounds are explored, and the ethical issues that may arise in the application of AI in education, such as academic misconduct, algorithmic bias, and data privacy protection are analyzed.Lecture Notes in Education Psychology and Public Media["Weixi Li"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/894c5005938aabaf53cdf9e488bef45a7e948d0e 3815e01028d46387991f6a5357c532452fc6fad1Artificial intelligence is the next big thing in human history. It is set to affect large and small industries, entertainment, agriculture, literature, research, and healthcare. The field of Artificial intelligence includes machine learning and deep learning, which denote an increased level of specialization. These changes are also set to influence orthopedic trauma, from diagnostics, clinical assessment, surgical intervention, rehabilitation, and outcome prediction. Some of these fields will be affected more than others. We carried out a narrative review of artificial intelligence and orthopaedic surgery to provide an updated overview of the current and future applications. AI is set to revolutionize radiological diagnostics, outcome measurement, and rehabilitation in orthopedic trauma. However, concerns about clinical decision-making and intervention remain. Ethical concerns, regulation, and superiority over traditional methods of treatment also need to be assessed. Until then, the role of AI in orthopaedic trauma remains in the realm of possibilities.International Journal of Research in Orthopaedics00A narrative review of artificial intelligence and orthopaedic surgery is carried out to provide an updated overview of the current and future applications and concerns about clinical decision-making and intervention remain.International Journal of Research in Orthopaedics["Tahir A. Dar", "Zahida Akhter", "Abdul Maajid", "Nasir Ul Islam", "Shabir A Dhar", "Hamza Hamid", "Anbreen Shabir"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/3815e01028d46387991f6a5357c532452fc6fad1 ae3a1b57730badab0008d95616d894fcc41898f9The advancements in artificial intelligence (AI) over the recent years have been drastic and stunning, and they have revolutionized the world in such ways as influencing the advancement of other fields such as accounting. This research paper aims to study the deep influence of Artificial Intelligence (AI) on the auditing profession. They plan to assess the strengths and the existing applications to auditing: machine learning, natural language processing, deep learning, and robotic process automation. These technologies allow the auditors to identify issues that they were previously unable to, or it would have taken them so much of their time to be able to locate. The paper also brings out the various risks related to the application of AI to auditing tasks. Some challenges include data quality and its privacy, ethical dilemmas, opaqueness, and regulatory matters on the use of Artificial Intelligence. Also, the soundness of AI choices and the ability to explain such decisions remain an issue, especially with complex financial data that depend on human perception and experience. Additionally, the paper also examines the possibility of AI imposing to replace human auditors in the future while comparing the technological aspects of the audit together with the humanistic aspects.Advances in Economics, Management and Political Sciences00The strengths and the existing applications to auditing: machine learning, natural language processing, deep learning, and robotic process automation are assessed: machine learning, natural language processing, deep learning, and robotic process automation.Advances in Economics, Management and Political Sciences["Xinyu Du"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/ae3a1b57730badab0008d95616d894fcc41898f9 8ac5d123a9dbf6ada980e2693ffda18b54e4c84aLabor management remains a critical issue in obstetrics, with dystocic labor presenting significant challenges in both management and outcomes. Recent advancements in intrapartum ultrasound have facilitated substantial progress in monitoring labor progression. This paper explores the integration of artificial intelligence (AI) into obstetric care, focusing on the Artificial Intelligence Dystocia Algorithm (AIDA) for assessing spatial dystocia during labor. The AIDA utilizes intrapartum ultrasonography to measure four geometric parameters: the angle of progression, the degree of asynclitism, the head–symphysis distance, and the midline angle. These measurements are analyzed using machine learning techniques to predict delivery outcomes and stratify risk. The AIDA classification system categorizes labor events into five classes, providing a nuanced assessment of labor progression. This approach offers several potential advantages, including objective assessment of fetal position, earlier detection of malpositions, and improved risk stratification, placing labor events within a broader context of labor dystocia and obstetric care and discussing their potential impact on clinical practice. This paper serves as a more comprehensive overview and discussion of the AIDA approach, its implications, perspectives, and future directions. However, challenges such as the technological requirements, training needs, and integration with clinical workflows are also addressed. This study emphasizes the necessity for additional validation across diverse populations and careful consideration of its ethical implications. The AIDA represents a significant advancement in applying AI to intrapartum care, potentially enhancing clinical decision-making and improving outcomes in cases of suspected dystocia. This paper explicates the key methodological approaches underpinning the AIDA, illustrating the integration of artificial intelligence and clinical expertise. The innovative framework presented offers a paradigm for similar endeavors in other medical specialties, potentially catalyzing advancements in AI-assisted healthcare beyond obstetrics.Journal of Clinical Medicine520The AIDA represents a significant advancement in applying AI to intrapartum care, potentially enhancing clinical decision-making and improving outcomes in cases of suspected dystocia, and potentially catalyzing advancements in AI-assisted healthcare beyond obstetrics.Journal of Clinical Medicine["A. Malvasi", "Lorenzo E. Malgieri", "Michael Stark", "A. Tinelli"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/8ac5d123a9dbf6ada980e2693ffda18b54e4c84a ad77dd106b87f9254e246e7df555f6856112da80As society develops, people are paying more attention to disabilities. Visually impaired people as a group, are one of the largest populations of disabilities. Nowadays, scientists are finding approaches to give them more convenience. Robotic guide dogs, which aid the visually impaired to travel, becoming a standout topic in society. As an emerging technology in recent years, artificial intelligence (AI) gives technical support to the study of robotic guide dogs. By examining a number of current robotic guide dog models, this paper will demonstrate how AI can be used to improve robotic guide dogs in a number of ways. The research shows that AI plays an important role in the operation of the robotic guide dog. This research will also point out some directions for future studies.Applied and Computational Engineering00The research shows that AI plays an important role in the operation of the robotic guide dog, and demonstrates how AI can be used to improve robotic guide dogs in a number of ways.Applied and Computational Engineering["Tianrui Xu"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/ad77dd106b87f9254e246e7df555f6856112da80 643d8f5dcdc8b0f0166d7650f03e9492c0fb4143Abstract: The proliferation of artificial intelligence (AI) is a transformative force in global labor markets, presenting dual possibilities: the displacement of traditional jobs and the creation of new employment opportunities. This paper explores the dynamic relationship between AI development and unemployment rates, using empirical data and regression analysis from 2012 to 2022. It examines the impact of AI across various sectors, highlighting how technological advancements and public engagement with AI via platforms like Google Trends influence employment patterns. In addition, it also reveals that AI development has a generally negative correlation with unemployment rates, suggesting that increased AI adoption may be linked to lower unemployment levels. The findings also underscore the importance of adaptive policies and education systems to harness AIs potential for job creation, which contributes to the discourse on AIs economic implications by providing a nuanced analysis of its influence on employment, essential for policymakers and stakeholders in navigating the challenges and opportunities presented by AI technologies.Advances in Economics, Management and Political Sciences00The dynamic relationship between AI development and unemployment rates is explored, using empirical data and regression analysis from 2012 to 2022, revealing that AI development has a generally negative correlation with unemployment rates.Advances in Economics, Management and Political Sciences["Jiaxin Liu"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/643d8f5dcdc8b0f0166d7650f03e9492c0fb4143 cf56ff4895aad782393b8244daf9e25bf3816c89As is known, there has been a significant increase in the use of AI technologies in various fields related to the work of both public and private sectors. Despite its importance in economic, health, security, and educational fields, the use of AI technologies has led to several ethical and legal risks. For example, there are risks of bias or discrimination when building AI systems, challenges related to privacy and data protection, including issues that arise from errors in health protocol procedures and their impact on patient health. This paper aims to highlight a number of practical applications of bias or discrimination associated with the use of AI technologies, concluding with the importance of having clear ethical governance to address the emerging risks of using AI applications.Evolutionary Studies in Imaginative Culture301A number of practical applications of bias or discrimination associated with the use of AI technologies are highlighted, concluding with the importance of having clear ethical governance to address the emerging risks of using AI applications.EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE["S. Albarashdi"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/cf56ff4895aad782393b8244daf9e25bf3816c89 3464409ed1d2f61aa8cf3d8ea26d54d0651cf4e7Bioethics Update01Bioethics Update["E. Agazzi"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/3464409ed1d2f61aa8cf3d8ea26d54d0651cf4e7 f6db0dc2beceb05df72e109ea8f6ccc0e386c554Deep venous thrombosis is a critical medical condition that occurs when a blood clot forms in a deep vein, usually in the legs, and can lead to life-threatening complications such as pulmonary embolism if not detected early. Hospitalized patients, especially those with immobility or post-surgical recovery, are at higher risk of developing deep venous thrombosis, making early prediction and intervention vital for preventing severe outcomes. In this study, we evaluated the following eight machine learning models to predict deep venous thrombosis risk: logistic regression, random forest, XGBoost, artificial neural networks, k-nearest neighbors, gradient boosting, CatBoost, and LightGBM. These models were rigorously tested using key metrics, including accuracy, precision, recall, F1-score, specificity, and receiver operating characteristic curve, to determine their effectiveness in clinical prediction. Logistic regression emerged as the top-performing model, delivering high accuracy and an outstanding receiver operating characteristic curve score, which reflects its strong ability to distinguish between patients with and without deep venous thrombosis. Most importantly, the model’s high recall underscores its ability to identify nearly all true deep venous thrombosis cases, significantly reducing the risk of false negatives—a critical concern in clinical settings, where delayed or missed diagnoses can result in life-threatening complications. Although models such as random forest and eXtreme Gradient Boosting also demonstrated competitive performances, logistic regression proved the most reliable across all metrics. These results suggest that machine learning models, particularly logistic regression, have great potential for early deep venous thrombosis detection, enabling timely clinical interventions and improved patient outcomes.Bioengineering190Logistic regression emerged as the top-performing model, delivering high accuracy and an outstanding receiver operating characteristic curve score, which reflects its strong ability to distinguish between patients with and without deep venous thrombosis.Bioengineering["Aurelian-Dumitrache Anghele", "V. Marina", "L. Dragomir", "C. Moscu", "Mihaela Anghele", "Catalin Anghel"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/f6db0dc2beceb05df72e109ea8f6ccc0e386c554 5068b861432f1df98f37601833d7690340ad919bThe complexity of immunological illnesses makes them difficult to diagnose and treat. Conventional methods mostly concentrate on treating symptoms after these appear, depending on retrospective data and a small number of clinical indicators, which causes diagnoses to be made later and interventions to be less successful. The study presents an AI-powered predictive approach that uses machine learning and deep learning to foresee immune system problems before symptoms appear. The method improves early detection and diagnosis precision by combining large datasets, such as genomic, proteomic, and clinical data. The proposed approach surpassed conventional systems in accuracy, precision, and recall, obtaining 92.5% accuracy and 88.0% sensitivity in early detection. The results show significant improvements over current approaches. The results highlight how the system could transform immunological diagnosis and therapeutic approaches.2024 Global Conference on Communications and Information Technologies (GCCIT)170An AI-powered predictive approach that uses machine learning and deep learning to foresee immune system problems before symptoms appear and surpasses conventional systems in accuracy, precision, and recall is presented.2024 Global Conference on Communications and Information Technologies (GCCIT)["S.K. Akbar Basha", "D. Kerana Hanirex"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/5068b861432f1df98f37601833d7690340ad919b 04aacfffdbd2736c5dcceb76dd70ef7842e029a4Growing worldwide consumption of food security has laid the foundation to make farming methods more effective and sustainable. Machine learning (ML), which falls under the broader AI framework, has become an innovative enabler for precision farming—we facilitate the continuous analysis of data, the use of this data for modelling Outlooks to support farming activity, and manage resources to improve farming yields. This paper discusses the Implication of AI in precision farming as a concept that enhances crop yield forecast, resource utilisation, and the mitigations of farming hurt on the environment. The proposed AI models also use IoT sensors, remote sensing, and machine learning to decide on the use of water, fertilizers, and pesticides. The simulations reveal quite encouraging gains to the precision of yield accuracies, optimization of resources, and reduction of costs. The investigated AI applications in farming demonstrate a significant capacity to cut the farming manifold and environmental emissions, thereby indicating an ability to support the longer-term health and growth of farming and food security for the globe. However, issues like data quality, the accuracy of models used and declaration or adoption have become issues yet to settle. The study indicates that there is hope for the modern agriculture as being revolutionized by AI and points to the ways of improving the use of AI further.2024 Global Conference on Communications and Information Technologies (GCCIT)230The investigated AI applications in farming demonstrate a significant capacity to cut the farming manifold and environmental emissions, thereby indicating an ability to support the longer-term health and growth of farming and food security for the globe.2024 Global Conference on Communications and Information Technologies (GCCIT)["S. Sakpal"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/04aacfffdbd2736c5dcceb76dd70ef7842e029a4 ddbf8212d9256160374cdbc429922cfd1544c84fTheological Librarianship00Theological Librarianship["Jordan Patterson"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/ddbf8212d9256160374cdbc429922cfd1544c84f 974f4fec30e13a58de208d5f43c4bd592bed27b3Theological Librarianship00Theological Librarianship["Courtney Dalton"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/974f4fec30e13a58de208d5f43c4bd592bed27b3 cabf77fc99a65963ebb7342f08e6caff2e558b7eJournal of Physical Chemistry C460The Journal of Physical Chemistry C["Kaoru Hisama", "Atsushi Ishikawa", "S. Aspera", "Michihisa Koyama"]2024-10-25T00:00:00https://www.semanticscholar.org/paper/cabf77fc99a65963ebb7342f08e6caff2e558b7e 6f5f20731fdd85897ba347047f5139b3569facc2Abstract: At present, AI has gradually penetrated various industries, but there is little application in the advertising industry. This study aims to understand how the introduction of AI will affect advertising agencies' workflows and develop strategies for practical AI applications. Before this, much research has also discussed the impact of AI on marketing, but rarely from the perspective of the workflow of advertising agencies. This study analyzes the advertising agencies' workflows and how they will change after the introduction of AI by using a literature review approach. A review and summary of many current studies culminates in the impact of AI on workflows and recommended strategies. The results show that AI affects workflows in four areas: market research, creative attributes, creative production, and intelligent advertising. The study recommends that advertising agencies prioritize experimenting with AI in both market research and the discovery of creative attributes.Advances in Economics, Management and Political Sciences00The results show that AI affects workflows in four areas: market research, creative attributes, creative production, and intelligent advertising, and the study recommends that advertising agencies prioritize experimenting with AI in both market research and the discovery of creative attributes.Advances in Economics, Management and Political Sciences["Shiyu Lin"]2024-10-25T00:00:00 f12429363940f36e6a8aa9caf8e5d0b95ab8eebaThe objective of this research is to offer an empirically grounded assessment of the intention of the auditing profession to adopt "disruptive" technologies. This study investigates, using data collected from employees of the Big Four firms in Tunisia, the determinants that drive auditors to adopt blockchain technology (BT). To achieve this objective, in 2022, 53 auditors from the "big four" enterprises in Tunisia, including both certified auditors and auditing students in training, participated in a survey. The study employs statistical methods and ordinary least squares (OLS) regression to identify the associations between the intention to implement BT and the five variables under investigation. The study examines perceived utility, ease of use, trust, support cost (SC), and facilitating condition (FC) while drawing on a number of theories, including the Technology Acceptance Model (TAM). Results show that two factors, particularly perceived utility for auditing (PUA) practice and trust, drive auditing professionals' intention among Big Four companies to use BT. This study results illuminate the factors motivating Big Four companies to embrace BT, enabling the development of strategies to expedite the adoption and utilization of this technology in developing accounting and auditing firms. The study fills a gap in the literature about adopting BT in emerging economies by concentrating on this specific setting. This study contributes to the knowledge of technology adoption and provides valuable recommendations for accelerating the uptake and application of BT in this particular setting.International Journal of Applied Economics, Finance and Accounting390Results show that two factors, particularly perceived utility for auditing (PUA) practice and trust, drive auditing professionals' intention among Big Four companies to use BT.International Journal of Applied Economics, Finance and Accounting["Ines Bouaziz Daoud", "Ameni Mhiri Rekik"]2024-10-25T00:00:00 8be336d8bc09cd49ce2ddede460c4d5d3c0aae4eProceedings of the 2024 3rd International Conference on Artificial Intelligence and Intelligent Information Processing00Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Intelligent Information Processing["Tingyin Deng", "Xuemei Jiang", "Meiling Wang", "Jingwen Luo"]2024-10-25T00:00:00 3e892726d2d8d1dd14efdcb7fce5d44f9a567210international journal of engineering trends and technology00International Journal of Engineering Trends and Technology["Sirachet Phodhiran", "Pichate Kunakornvong", "Pongpon Nilaphruek", "Jaturapith Krohkaew", "Niti Witthayawiroj", "Padma Nyoman Crisnapati", "Yamin Thwe"]2024-10-25T00:00:00 d86445f67de516cd3b5573d38c87ced6962c5bc3ANZ journal of surgery240ANZ journal of surgery["V. Chai", "Lara Wirth", "Ke Cao", "Lincoln Lim", "Justin Yeung"]2024-10-25T00:00:00 45adc4448052f3f63c755093ecae04a245cd6dbbThe development of generative AI brings new challenges, which could result in total redefinition of design education and practice alike. The article shortly describes the evolution of design over the recent 15 years. It demonstrates how UX and related areas have risen in importance, which made many persons dealing with the more traditional forms of design acquire new skills, including business ones. Next, it refers this “evolution” to the current moment, when the development and increase in AI use forces designers to, once again, re-evaluate their competenggcy in this context. The text also proposes a thesis that the long-term designers’ engagement in building the foundation of technological status quo does not translate into reflection on the variants of automation conductive of life and well-being. It draws attention to the fact that shaping educational programmes in response to the market needs limits the possibilities of appropriate consideration of other prospective variants of technology or social and economic consequences of automation. In parallel, it indicates the potential of change in this situation, which should be commenced by asking adequate, bold questions and challenging the status quo. Keywords: design education, design critique, evolution of design practice, automation, artificial intelligence (AI)Formy00The article shortly describes the evolution of design over the recent 15 years and proposes a thesis that the long-term designers’ engagement in building the foundation of technological status quo does not translate into reflection on the variants of automation conductive of life and well-being.Formy["Katarzyna Janota"]2024-10-25T00:00:00 e9fbd41350cebf87a53571f3fb39a07a0fdc8689The exploration delves into the intersection of technology and education, specifically focusing on the integration of BERT (Bidirectional Encoder Representations from Transformers) in language learning and communication. BERT, an advanced natural language processing model developed by Google, has emerged as a transformative tool in the field of education, promising to revolutionize traditional approaches and open new horizons for learners worldwide. By leveraging BERT’s sophisticated language understanding capabilities, language learning applications can provide personalized, efficient, and immersive learning experiences across various domains, including vocabulary acquisition, grammar correction, reading comprehension, and writing assistance. This abstract examines the potential impact of BERT-based tools on language learning outcomes, exploring their effectiveness in enhancing language proficiency and communication skills. Through a mixed-methods approach encompassing both quantitative and qualitative analyses, this study investigates the efficacy of BERT in improving language learning experiences and gathers feedback from learners and educators on its usability and effectiveness. By understanding the implications of integrating BERT into language learning and communication, this exploration aims to shed light on the transformative potential of artificial intelligence in shaping the future of education.2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA)110The exploration delves into the intersection of technology and education, specifically focusing on the integration of BERT (Bidirectional Encoder Representations from Transformers) in language learning and communication, to shed light on the transformative potential of artificial intelligence in shaping the future of education.2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA)["Rahul Pradhan", "Shamim Ahmad Khan", "Anslin Jegu", "P. Kavitha", "R. R. Rautrao", "M. Ponni Valavan"]2024-10-25T00:00:00 92cbd67984d40a2962a081e0422b27cae9b728e2This article explores the opportunities and challenges posed by AI technologies in Africa. It examines the potential risks of AI exacerbating existing inequalities, infringing on privacy rights, and perpetuating digital colonialism. The article investigates the unique challenges that Africa faces in harnessing AI for human rights and sustainable development by examining the intersection of AI, human rights, and sustainable development from an African perspective. It highlights the importance of context-specific approaches that take Africa’s cultural and ethical considerations into account. Through case studies of a few African countries, this article provides insights into the existing policy and regulatory landscape. It emphasises the need for inclusive policymaking processes that involve diverse stakeholders,including civil society organisations, marginalised communities, and indigenous groups. The article concludes with recommendations on how AI can be ethically deployed to advance human rights and sustainable development goals on the African continent. A case is also made for a human rights-based approach to artificial intelligence and sustainable development.Perspectives of Law and Public Administration00The article investigates the unique challenges that Africa faces in harnessing AI for human rights and sustainable development by examining the intersection of AI, human rights, and sustainable development from an African perspective and recommends how AI can be ethically deployed to advance human rights and sustainable development goals on the African continent.Perspectives of Law and Public Administration["J. Mubangizi"]2024-10-25T00:00:00 a19f287fbd0c476f902aacea8212af3303bbd2a7We have all been affected by the euphoria connected with AI, and we have all followed the on-going discussion about our future, supported by thinking machines, which can replace us in many activities, and maybe professions as well. We can observe the media race to the news feeds about the use of generative AI. What is often left aside, however, is the inconvenient topic of legal consequences of such modifications to human work that is used to train AI, and there are many. This article addresses the issue of the clash of copyright with AI. It discusses the matters of using someone’s creative work to design AI systems, as well as the consequences of a designer’s work with AI, especially on commercial projects, including the notion of copyrights to the content generated by means of such tools, and attempts to determine when there is a chance to grant their protection. These considerations are complemented with examples of legal actions taken against suppliers of AI systems. Part of the text has been dedicated to discussing the pending regulations, especially the Act on AI. Keywords: copyright, artificial intelligence (AI), fair useFormy00The matters of using someone’s creative work to design AI systems, as well as the consequences of a designer’s work with AI, especially on commercial projects are discussed, including the notion of copyrights to the content generated by means of such tools, and attempts to determine when there is a chance to grant their protection.Formy["Jacek Markowski"]2024-10-25T00:00:00 423a2219a3d995e46ffa27d9104e3100a301cbedAmid the optimism of generative Artificial Intelligence (gen-AI) in language education, there remains a weak connection to learning practices. The emergence of gen-AI has preceded considerations of how it should be applied in teaching and learning. However, while gen-AI has been justified in terms of the possibilities to enhance learner agency by expanding opportunities to engage with language, such as through the generation of content or the translation of texts, it can also take power away from learners. How can learners be self-determining in light of how choices become increasingly guided by Artificial Intelligence? In this paper, I conceive the arrangements of humans and software as an assemblage of complex and dynamic social, and technical processes. Drawing on a flat ontology, where all agents (human and non-human, material and subjective) have equal ontological status, I argue that learner agency has its origins in the messy and lively interactions between heterogenous actors. In particular, I consider active and passive affects as being part of the same process: active when we bring something into effect ourselves, passive when our self-determination is changed not by our own power, but through external forces acting on it (such as gen-AI). From this, I explore the constraining and enabling potential of artificial intelligence. Finally, I extend this discussion to the emergence of learner agency.Technology in Language Teaching &amp; Learning01This paper conceive the arrangements of humans and software as an assemblage of complex and dynamic social, and technical processes and argues that learner agency has its origins in the messy and lively interactions between heterogenous actors.Technology in Language Teaching &amp; Learning["Blair Matthews"]2024-10-25T00:00:00 484bc472dfe3da91fe34fa08d8d4b1ffbb4f6d01Artificial intelligence (AI) has become a significant influence in various fields, including architecture, where it is used to enhance the energy efficiency of buildings, manage projects, and support design decisions. Architects harness this technology to simulate the behavior of buildings in different climates, refine design and optimize thermal efficiency, as well as analyze large volumes of data and find solutions more effectively. It also promises a revolution in design, enabling the creation of more efficient, personalized, and visually appealing spaces. However, it is essential for designers, clients, and academics to oversee its ethical and responsible use. Despite the many benefits it brings, AI raises concerns about the potential dehumanization of the creative process and the devaluation of designers’ work due to automation. These dilemmas underscore the importance of striking a balance between technological innovation and preserving human value in architecture.ArteOficio00Artificial intelligence has become a significant influence in various fields, including architecture, where it is used to enhance the energy efficiency of buildings, manage projects, and support design decisions, but raises concerns about the potential dehumanization of the creative process and the devaluation of designers’ work due to automation.Arteoficio["R. Jim\u00e9nez", "R. Mart\u00edn"]2024-10-25T00:00:00 60942299b0cf8a872617937a41bfc3776ee951cfThe place of artificial intelligence in companies keeps on growing, under the effect of a double acceleration: on the one hand, the multiplication of expert systems based on AI, on the other hand, the emergence of new uses exploiting the possibilities of generative AI, more often under the impetus of the professions and the field. However, the singularities of AI call for particular vigilance. People and teams who work on or with AIS are not always aware of the scope of their responsibility in this area, nor of the ethical questions raised. People whose jobs consist of designing, developing, operating and/or using an AIS must be trained to exercise responsibility within the framework of their activity. Management Research Quarterly422Management Research Quarterly["Val\u00e9rie Chapuis", "Delphine Gu\u00e9gan"]2024-10-25T00:00:00 5b3ef0d801d1570214b36ef124e3b8db1f23a49cThe paper delves into the substantial influence of cutting-edge technologies and sensor systems on the automotive industry, emphasizing both the significant benefits and potential long-term drawbacks. Vehicles equipped with advanced sensor systems demonstrate enhanced safety, efficiency, and convenience. These technologies offer real-time data processing capabilities, enabling better decision-making and improved driving experiences. The integration of these systems leads to reduced accident rates, optimized fuel consumption, and greater overall vehicle performance. However, the paper also addresses various challenges associated with these advancements. Ethical concerns arise, particularly regarding decision-making processes in critical situations. The reliance on sensor-driven systems necessitates a robust framework to ensure ethical standards are upheld, avoiding biases and ensuring equitable outcomes. Additionally, cybersecurity risks are a significant concern. As vehicles become increasingly connected, they are more vulnerable to cyberattacks, which could compromise safety and privacy. Ensuring the security of these systems is paramount to gaining public trust and widespread adoption. The societal impacts of these technologies are also considered. The transition to highly automated vehicles may lead to job displacement in traditional driving roles, requiring workforce retraining and adaptation. Furthermore, the shift in driving dynamics and reliance on technology may affect driving skills and behavior, necessitating a reevaluation of driver education and training programs. The paper aims to provide a thorough overview to assist stakeholders in navigating the rapidly evolving landscape of the automotive industry. By addressing both the advantages and the potential challenges, the paper seeks to inform decision-making processes and foster a balanced approach to the integration of advanced sensor technologies in vehicles. The comprehensive analysis presented serves as a valuable resource for understanding the complexities and implications of these technological advancements in the automotive sector. ABSTRACTJournal of Artificial Intelligence and Robotics280The paper delves into the substantial influence of cutting-edge technologies and sensor systems on the automotive industry, emphasizing both the significant benefits and potential long-term drawbacks, and attempts to foster a balanced approach to the integration of advanced sensor technologies in vehicles.Journal of Artificial Intelligence and Robotics["Biswa Ranjan Pradhan"]2024-10-26T00:00:00 eb165c4189a800f059a260b84ffe62b379b0252eAs artificial intelligence (AI) gains prominence, its integration into second language (L2) /foreign language (FL) instruction has become a significant trend. Despite the considerable promise of AI for L2/FL learning, more research is still needed on its effects on student academic engagement in literature classes and the corresponding emotional experiences. This study, therefore, aimed to examine the effects of AI use on English as a foreign language (EFL) learners' academic engagement, and the emotional experience was also qualitatively explored. Students were allocated to the experimental group (N = 48), who received instruction integrated with AI, and the control group (N = 48), who received traditional instruction without AI assistance. Quantitative data were collected using an FL engagement scale, supplemented by individual semi‐structured interviews in the qualitative phase. The results indicated that integrating AI into EFL instruction has a positive effect on students' cognitive, emotional and social engagement. Moreover, the learners' emotional experiences were found to be abundant and dynamic, exerting influence on their academic engagement. This study provides valuable insights for language educators and researchers regarding integrating AI into EFL instruction.European Journal of Education547The results indicated that integrating AI into EFL instruction has a positive effect on students' cognitive, emotional and social engagement, and the learners' emotional experiences were found to be abundant and dynamic, exerting influence on their academic engagement.European Journal of Education["Yumeng Guo", "Yongliang Wang"]2024-10-27T00:00:00 3b0f0a5da324ad2339064483f954441d26f9ffd5Artificial intelligence is defined as human-made systems that mimic the thinking, perception, and learning abilities of the human brain, using the information they gather and having the ability to improve themselves. With the changes and developments in the field of technology, the use of artificial intelligence has also expanded. The increasing popularity of artificial intelligence in the field of education has led to a rise in research on this topic. This paper aims to synthesize the existing literature on the applications of artificial intelligence in education within a specific time frame and to develop a holistic perspective. In the paper, 20 studies written in the last five years on artificial intelligence and education were examined using the metasynthesis method. The research question is defined as: "What trends exist in academic research on artificial intelligence and education conducted in Turkey over the past five years?" The data source for the research was determined using the criterion sampling method. For this purpose, a detailed search was conducted in databases such as Google Scholar, Academia, TÜBİTAK ULAKBİM, and Dergipark using the keyword "artificial intelligence and education." As a result of the analyses, it was found that while the study emphasizes the contributions of artificial intelligence to the field of education, there are also some concerns on the subject. The contributions of artificial intelligence, particularly in terms of providing personalized learning experiences, saving time, and improving student performance, were highlighted, but concerns were raised regarding data privacy and the ethical principles in the use of artificial intelligence. Additionally, it was found that there are educational and developmental needs for artificial intelligence users.İçtimaiyat01The contributions of artificial intelligence, particularly in terms of providing personalized learning experiences, saving time, and improving student performance, were highlighted, but concerns were raised regarding data privacy and the ethical principles in the use of artificial intelligence.İçtimaiyat["Zeynep Tun\u00e7", "\u00d6zlem Ba\u015f"]2024-10-27T00:00:00 1e1506e29dabb0332ea354432b451a1a98905387 Thesis. The thesis of this article is to explore the integration of artificial intelligence (AI) methodology in forensic science. An assessment of the potential implications of AI for improving investigative processes and outcomes will also be addressed. Concept. The concept focuses on exploring the application of AI technologies in ar- eas of science such as analysing evidence recognising patterns and supporting decision- making systems. The article emphasises the practical use of AI algorithms in investiga- tions. on exploring how artificial intelligence technologies can be implemented in various aspects of forensic science. Results. An analysis of existing literature and case studies shows that integrating AI into forensic science can improve efficiency, accuracy, and objectivity in investigations. AI tools can automate tasks analyse datasets and identify patterns that might be challeng- 16 “ABoUT The INTeRNeT” - TheoRy ing for humans to detect. However utilising AI in forensic science poses challenges like algorithms, privacy issues with data handling, and the necessity for oversight. Further- more, when employing intelligence for inquiries it is essential to prioritise transparency, and accountability and uphold integrity in decision-making processes. Originality. This article adds to the discussion about integrating AI into science by of- fering a thorough examination of its potential advantages and obstacles faced along, with ethical concerns. By merging research findings and offering perspectives on emerging trends we can gain insights, into the impact of AI on advancing investigations in the years ahead. Additionally, the theoretical structure presented here establishes a foundation for research studies and practical implementations, within the field of forensic science. Keywords: Artificial intelligence, forensic science, AI applications in forensic science, machine learning for evidence analysis, crime scene reconstruction with AI E-methodology390A thorough examination of its potential advantages and obstacles faced along, with ethical concerns is added to the discussion about integrating AI into science by of- fering a thorough examination of its potential advantages and obstacles faced along.E-methodology["Angelika Dudek", "Anna D\u0105bek", "Iwona Zborowska", "Jakub Lichosik"]2024-10-27T00:00:00 7f605484d5458e065d94dc060bdef57c18011396As outlined by the International Energy Agency, 44% of carbon emissions in 2021 were attributed to electricity and heat generation. Under this critical scenario, the power industry has adopted technologies promoting sustainability in the form of smart grids, microgrids, and renewable energy. To overcome the technical challenges associated with these emerging approaches and to preserve the stability and reliability of the power system, integrating advanced digital technologies such as Digital Twins (DTs) and Artificial Intelligence (AI) is crucial. While existing research has explored DTs and AI in power systems separately, an overarching review of their combined, synergetic application in sustainable power systems is lacking. Hence, in this work, a comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The main results of this review analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity. The prominent utilisation of deep learning, supervised learning, reinforcement learning, and swarm intelligence techniques was identified as mainly constrained to power system operations and maintenance functions, along with the potential for more sophisticated AI techniques in computer vision, natural language processing, and smart robotics. This review also discovered sustainability-related objectives addressed by AI-powered DTs in power systems, encompassing renewable energy integration and energy efficiency, while encouraging the investigation of more direct efforts on sustainable power systems.Energies1280A comprehensive scoping review is conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), which analysed the breadth and relationships among power systems, DTs, and AI dynamics and presented an evolutionary timeline with three distinct periods of maturity.Energies["Ama Ranawaka", "D. Alahakoon", "Yuan Sun", "Kushan Hewapathirana"]2024-10-27T00:00:00 c89ac01062c0c9391ec9ddecdf4c2ca63228f3ffThe integration of Artificial Intelligence (AI) in retail has redefined customer experience, offering new opportunities for personalisation, service quality enhancement, and continuous service delivery. This study investigates the impact of AI on customers’ experience at Giant Hypermarkets in Klang Valley, Malaysia, focusing on four key areas: personalisation, service quality, hassle-free service, and customer service. Using quantitative methods, data were gathered from 365 respondents to assess the influence of AI-driven services on customer experience. The findings reveal that hassle-free service significantly enhances customer experience, while personalisation, service quality, and customer service show no significant impact. These results suggest that while AI is effective in streamlining operations and reducing customer effort, it may fall short in providing the emotional engagement needed to enhance the overall customer experience. This study highlights the importance of balancing AI efficiency with human interaction, particularly in relational services. This research offers valuable insights for retailers, academics, and small business owners, emphasizing the need to adopt AI technologies that focus on customer convenience and experience while maintaining a personal touch. This study clearly highlights the implications for the retail industry, demonstrating that AI can significantly enhance customer experience, but its strategic integration is crucial to address both operational efficiency and emotional engagement.  Article visualizations:European Journal of Management and Marketing Studies680The findings reveal that hassle-free service significantly enhances customer experience, while personalisation, service quality, and customer service show no significant impact, and the importance of balancing AI efficiency with human interaction is highlighted.European Journal of Management and Marketing Studies["Kumaran Kanapathipillai", "Ooi Fu Nian", "Chai Pei Yi", "Yang Wan Ping"]2024-10-27T00:00:00 b9d1681c9482ba4dc3cdcb5bb2613b6f8c0d3b27Artificial intelligence (AI) has emerged as a powerful tool, enabling online retailers to offer highly personalized shopping experiences tailored to individual preferences and behaviors. As e-commerce continues to grow in Malaysia, understanding the influence of AI and personalization marketing on consumer purchase intentions has become increasingly important for businesses seeking to remain competitive. However, the rapid adoption of AI also raises concerns about data privacy, ethical AI usage, and compliance with emerging data protection regulations, such as Malaysia’s Personal Data Protection Act (PDPA).  This study aims to explore the potential impact of artificial intelligence (AI) and personalization marketing on consumer purchase intention within the Malaysian e-commerce market. By reviewing previous literature and theoretical frameworks, the study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments. The study adopts a quantitative approach, in which quota sampling will be used for the participant selection. A self-administered questionnaire with a five-point Likert scale will be employed to gather data from e-commerce users in Malaysia. The findings from this study have important implications for both e-commerce businesses and policymakers in Malaysia. For businesses, understanding which aspects of AI and personalization most influence consumer purchase intentions can help them strategically implement these technologies to enhance customer engagement and drive sales. For policymakers, the study highlights the need to consider ethical and legal issues, such as data privacy and policy issues, in the growing use of AI in the e-commerce market.Information Management and Business Review390The study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments and highlights the need to consider ethical and legal issues in the growing use of AI in the e-commerce market.Information Management and Business Review["Mohamad Fariz Abdullah", "Muhamad Azman Ibrahim", "Azlin Zanariah Bahtar", "Noor Rita Mohamad Khan"]2024-10-27T00:00:00 79e0a52cd99fc5ac06fad0f67e28332c8dfc48adThis research aims to help seaweed farmers optimize seaweed cultivation in Indonesia, by using a tool called Immersea based on the Internet of Things and Artificial Intelligence, farmers can monitor water quality conditions in real-time via smartphone and get additional references for appropriate action. water conditions. The research method used is a combination of descriptive and experimental methods with experiments on a prototype scale. Tests were carried out in seaweed ponds in Jabon District, Sidoarjo. The research results found that sensor readings can be observed on a smartphone application in real-time with a data transmission speed of 30 seconds/data, and data transmission for tool direction control is ±35 seconds/data.Jurnal Ilmiah Universitas Batanghari Jambi110The research results found that sensor readings can be observed on a smartphone application in real-time with a data transmission speed of 30 seconds/data, and data transmission for tool direction control is ±35 seconds/data.Jurnal Ilmiah Universitas Batanghari Jambi["M. Raihansyah", "Sufadi Alim", "V. Karocelli", "Leo Priyantama Diva", "Dina Febrianti", "Yossie Rendy", "Tegar Manggala", "Hamdana Putra", "Politeknik Perkapalan", "Negeri Surabaya"]2024-10-27T00:00:00 d5cf2afd2307d3dc28c910fca87e32e4aaac77dfThis paper explores the role of Artificial Intelligence (AI) in enhancing trade facilitation through its integration with Single Window Systems (SWS). It investigates how AI technologies such as machine learning, natural language processing, and predictive analytics can improve the efficiency and effectiveness of trade processes. Case studies of Singapore and Australia are analyzed to highlight successful AI applications and key lessons learned. The study discusses the benefits, including increased efficiency, reduced costs, enhanced accuracy, and improved user experience, alongside the challenges posed by technical complexities, legal and ethical considerations, and resistance to change. The paper also provides policy implications and recommendations for governments, international organizations, and private sector stakeholders. Future research directions emphasize emerging AI technologies like AI-driven blockchain and advanced NLP, and their potential long-term impacts on global trade dynamics.Journal of AI-Driven Trade Facilitation Engineering and Single Window Systems00The role of Artificial Intelligence in enhancing trade facilitation through its integration with Single Window Systems (SWS) is explored, and how AI technologies such as machine learning, natural language processing, and predictive analytics can improve the efficiency and effectiveness of trade processes is investigated.Journal of AI-Driven Trade Facilitation Engineering and Single Window Systems["Changkui LI"]2024-10-27T00:00:00 43366fed3e86275f13d705dbd40928afca3f0613Academics and practitioners are increasingly interested in the role of the circular supply chains in maximizing resource use and improving sustainable performance. However, the shift from linear to circular supply chains is still at an early stage and still needs more research. The current study seeks to test the direct effects of artificial intelligence‐based dynamic capabilities on frugal innovation and circular supply chains in a group of retail firms operating in Jordan. The current paper also aims to test the potential indirect effect of frugal innovation. To achieve these objectives, a research framework was developed that explains hypothesized relationships between the previous constructs and was tested empirically through a sample of 212 respondents from the top administrative level in these firms. The casual and quantitative approach was employed to test the hypothetical relationships, and the partial least squares‐structural equation modelling (PLS‐SEM) approach was employed as a tool to analyze the data. The empirical results reported through data analysis showed acceptance of all hypothesized relationships that were hypothesized in this study, as there was a positive effect of both artificial intelligence‐based dynamic capabilities and frugal innovation on circular supply chains. In addition, the mediation was statistically significant, as frugal innovation mediated the relationship between artificial intelligence‐based dynamic capabilities and circular supply chains. This study had a set of interesting contributions at the theoretical and the managerial contributions, as this paper is the first work in the body of the literature that explores these innovative relationships. The current paper also contributed to developing recommendations that would improve sustainable performance in the retail sector by enhancing its dynamic capabilities based on artificial intelligence tools, which leads to strengthening circular supply chains.Business Strategy and the Environment820Business Strategy and the Environment["A. Al-Khatib", "T. Ramayah"]2024-10-27T00:00:00 ec1dd55488152381dcd2f00c6fd8c67dec291b63Abstract Artificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the key concepts involved and examines how these applications might be used in the response to a future pandemic. They include early outbreak detection, predictive modelling, healthcare management, risk communication, and health surveillance. Artificial intelligence applications, especially predictive algorithms, have the ability to anticipate outbreaks by integrating diverse datasets such as social media, meteorological data, and mobile phone movement data. Artificial intelligence‐powered tools can also optimise healthcare delivery by managing the allocation of resources and reducing healthcare workers' exposure to risks. In resource distribution, they can anticipate demand and optimise logistics, while AI‐driven robots can minimise physical contact in healthcare settings. Artificial intelligence also shows promise in supporting public health decision‐making by simulating the social and economic impacts of different policy interventions. These simulations help policymakers evaluate complex scenarios such as lockdowns and resource allocation. Additionally, it can enhance public health messaging, with AI‐generated health communications shown to be more effective than human‐generated messages in some cases. However, there are risks, such as privacy concerns, biases in models, and the potential for ‘false confirmations’, where AI reinforces incorrect decisions. Despite these challenges, we argue that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.International Journal of Health Planning and Management370It is argued that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.The International Journal of Health Planning and Management["M. Mckee", "Rikard Rosenbacke", "D. Stuckler"]2024-10-27T00:00:00 c15d8a42b04a88e94810b1b9d3b8adde99789d24This article considersthe impactof artificial intelligence on the process of teaching English in formal and informal higher education. The authors studythe use of various artificial intelligence technologies, such as machine learning, neural networks and automated speech analysis systems, to improve the effectiveness of teaching English to students, analyze the advantages and disadvantages of using artificial intelligence in teaching English, and offer recommendations for optimizing this process. The authors presentedresearchof the influence of artificial intelligence on the process of teaching English through a theoretical review of foreign experience and a questionnaire survey, the purpose of which was to study the opinions and views of students on the influence of artificial intelligence on the process of teaching English. The results of the researchmay be useful for teachers and students planning to introduceartificial intelligence into English language teaching.National Center for Higher Education Development20The use of various artificial intelligence technologies to improve the effectiveness of teaching English to students is studied, the advantages and disadvantages of using artificial intelligence in teaching English are analyzed, and recommendations for optimizing this process are offered.National Center for Higher Education Development["G. Tleuzhanova", "Zhuldyz Tentekbayeva", "Damira Jantassova", "Alfiya Kitibayeva"]2024-10-27T00:00:00 cb38fe33aca5af28cd758383c570883a5234eaa8The increasing complexity and globalization of supply chains necessitate robust risk management strategies to ensure safety and resilience. Traditional risk assessment methods often fall short in dynamically adapting to the rapidly changing conditions and voluminous data inherent in modern supply chains. This study explores the potential of Artificial Intelligence (AI)-powered risk assessment to address these limitations in the context of Malaysia's supply chain industry. By employing AI technologies such as machine learning, IoT, and predictive analytics, organizations can significantly enhance their risk management capabilities, improving predictive accuracy, real-time monitoring, and overall operational efficiency. Through a qualitative analysis involving in-depth interviews with supply chain managers, AI experts, and technology vendors, the study identifies the strategies employed for AI integration, the perceived effectiveness of these technologies, and the challenges faced in implementation. The findings highlight the importance of robust data governance, the development of explainable AI models, and continuous skill development to overcome barriers related to data quality, model interpretability, and high implementation costs. The study concludes with recommendations for fostering a safer and more resilient logistics environment in Malaysia, emphasizing the need for comprehensive AI adoption frameworks and scalable solutions for small and medium-sized enterprises.Information Management and Business Review310The findings highlight the importance of robust data governance, the development of explainable AI models, and continuous skill development to overcome barriers related to data quality, model interpretability, and high implementation costs.Information Management and Business Review["N. Sureshkumar", "PP Narayanan", "F. Ghapar", "Li Lian Chew", "Veera Pandiyan", "Kaliani Sundram", "Babudass M.Naidu", "Mohd Hafiz Zulfakar", "Azimah Daud"]2024-10-27T00:00:00 ca5d7bfd584d66e92699d56d0f6f75032d26b606Various studies have been conducted to identify factors that contribute to student engagement, personalized learning experience, and student academic performance. The evolution of technology offers various benefits including in the education sector. To date, the use of Artificial Intelligence (AI) in education has been seen to provide various benefits. This study aims to identify the relationship between the usage of AI with student engagement, personalized learning experience, and student academic performance. Data was collected from 110 undergraduate students from the Faculty of Business and Management, UiTM Puncak Alam Campus using a questionnaire. 106 data were analyzed using SPSS version 29. The findings show that AI usage for study purposes significantly influences student’s engagement and academic performance. On the other hand, the usage of AI and personalized learning experience show no significant influence. This study not only provides a deeper understanding of the context of AI usage for better student engagement and academic performance but also gives valuable insight for UiTM and faculty specifically to develop strategies and modules that enhance the implementation and usage of AI in their learning activities.Information Management and Business Review630The findings show that AI usage for study purposes significantly influences student’s engagement and academic performance and the usage of AI and personalized learning experience show no significant influence.Information Management and Business Review["Hairunnisa Ma\u2019amor", "Nur\u2019ain Achim", "Nor Lela Ahmad", "Nabila Suraya Roszaman", "Najwa Noor Kamarul Anuar", "Nur Camelia Aqielah Khairul Azwa", "Sahira Nabila Abd Rahman", "Nur Ain Aqilah Hamjah"]2024-10-27T00:00:00 b608fde3d0723ff3dd5bf4186c5301dcd8936c9bBackground: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: “neuroimaging” (title) together with “machine learning” (title) or ”deep learning” (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer’s disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications.Diagnostics580Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications.Diagnostics["Sekwang Lee", "Kwang-Sig Lee"]2024-10-27T00:00:00 4dede08b801a038af95849e45f95dd14a181ba0bRecent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Surgical complications often occur due to lapses in judgment and decision-making. In laparoscopic cholecystectomy, achievement of the critical view of safety is commonly advocated to prevent bile duct injuries. However, bile duct injuries rates remain stable, probably due to inconsistent application or a poor understanding of critical view of safety. Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. AI-based CV techniques may leverage surgical video data to develop real-time automated decision support tools and surgeon training systems. The effectiveness of CV application in surgical procedures is still under early evaluation. The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy. The MedLine, Scopus, and IEEE Xplore databases were searched for publications up to 2024. The keywords used in the search were “laparoscopic cholecystectomy”, “artificial intelligence”. The currently described applications of CV in laparoscopic cholecystectomy are limited. Most current research focus on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic cholecystectomy should focus on application in more scenarios, such as surgeon skill assessment and the development of more efficient models.Emergency medicine630The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy.EMERGENCY MEDICINE["S. Chooklin", "S. Chuklin"]2024-10-27T00:00:00 c25e54ec797d4b368f4ad384d3c126d0ff507ea8This research paper explores the impact of Artificial Intelligence on space technology. The paper examines the current applications of AI in the space industry, the potential benefits and challenges, and the future implications of this transformative technology. As we know there are many hurdles in this field of space exploration AI can help overcome some of these challenges and propel the space industry forward. AI has the potential to revolutionise various aspects of space exploration, from mission planning and spacecraft operations to data analysis and scientific discoveries. This paper talks about the potential it has and what the future might hold.International Journal For Multidisciplinary Research40This research paper examines the current applications of AI in the space industry, the potential benefits and challenges, and the future implications of this transformative technology.International Journal For Multidisciplinary Research["Aditya Punjani"]2024-10-27T00:00:00 e095a69ae312c6d98082943f42c94662c9d8e13eIntensive & Critical Care Nursing151Intensive & critical care nursing["Ella Peschel", "Susanne Krotsetis", "P. Nydahl"]2024-10-27T00:00:00 949b06d0582b6469edb24f78d028ba9fabdb37d0Intensive & Critical Care Nursing91Intensive & critical care nursing["Ingrid Egerod"]2024-10-27T00:00:00 9855bbe5a20c57019453ea7156d2a4577325fb8fSupply chains have become vital in enabling the smooth movement of material and information in the swiftly changing high-tech industries. By utilising technological innovations like automation and AI, supply chains may improve both safety and effectiveness at every level of the process, from manufacturing to sustainability. The necessity to adjust operations, give the adoption of technology top priority for resilience, and improve supply chain management efficiency. Transformation of the Supply Chain is becoming a choice of subject, when it comes to maintain the ideal inventory levels and Just in Time service deliveries. Both AI and ML are the utmost importance for the maintenance of the inventory or service through the technology. It not only helps in reducing the loss of opportunity costs, but also it resolves the issue of overstocks of any inventory and the enhanced level of service delivery. This ultimately results in the optimum utilization of the resources through the help of technology. Moreover, it is the win-win situation for the customer and provider. Even while technology has only recently begun to be incorporated into supply chains, the manufacturing and distribution sectors have adopted it more quickly, emphasising the need for effective risk identification and mitigation techniques. Notwithstanding the possible advantages of transforming the supply chain, there are obstacles and limitations that make an entire technological shift complex. The chapter also explores the concept explainable AI which can be understood as the set of techniques that makes the decision-making processes of AI systems comprehensible to humans. Acknowledging the inherent complexities and relationships within these chains, the goal of this chapter is to explore risk identification and management in high-tech supply networks. A competent and nimble stakeholder base can manage the complex landscape of the high-tech world through the incorporation of innovative technologies, proactive actions, and constructive alliances.Recent trends in Management and Commerce130This chapter explores the concept explainable AI which can be understood as the set of techniques that makes the decision-making processes of AI systems comprehensible to humans.Recent trends in Management and Commerce[]2024-10-27T00:00:00 bde707271c2c8519c92d1b65b1e1ab6a7b01e871We introduce CompAι – a tool for checking the completeness of privacy policies against the general data protection regulation (GDPR). CompAι facilitates the analysis of privacy policies to check their compliance to GDPR requirements. Since privacy policies serve as an agreement between a software system and its prospective users, the policy must fully capture such requirements to ensure that collected personal data of individuals (or users) remains protected as specified by the GDPR. For a given privacy policy, CompAι semantically analyzes its textual content against a comprehensive conceptual model which captures all information types that might appear in any policy. Based on this analysis, alongside some input from the end user, CompAι can determine the potential incompleteness violations in the input policy with an accuracy of ≈96%. CompAι generates a detailed report that can be easily reviewed and validated by experts. The source code of CompAι is publicly available on https://figshare.com/articles/online_resource/CompAI/23676069, and a demo of the tool is available on https://youtu.be/zwa_tM3fXHU.CCS CONCEPTS• Software and its engineering → Requirements analysis; • Security and privacy → Privacy protections; • Computing methodologies → Artificial intelligence.International Conference on Automated Software Engineering80CompAι facilitates the analysis of privacy policies to check their compliance to GDPR requirements and can determine the potential incompleteness violations in the input policy with an accuracy of ≈96%.2024 39th IEEE/ACM International Conference on Automated Software Engineering (ASE)["Orlando Amaral Cejas", "S. Abualhaija", "Lionel C. Briand"]2024-10-27T00:00:00 c06befd72f1f8f0ac8923a9d39ed2c44c6208901 Aim. The study aims to know how student-teachers of B.Ed. courses make use of AI for education and their opinion about the use of AI in education, in general. Methods. The Survey method was used for the study. There were 72 student-teachers of B.Ed the course of Savitribai Phule Pune University, Pune, and studying at Tilak Col- lege of Education, Pune, India, participated in the survey. The questionnaire was the data collection tool for the study. The questionnaire was circulated on WhatsApp group through Google Form which had 12 questions with choices based on the status variable. Results. The data analysis of the 12 questions was done by using percentages and it was presented in Pie charts for each question. The results summarise that most of the stu- dent- teachers use ChatGPT, most of the student-teachers use AI for lessons, practicals, reports, and presentations and student-teachers use AI sometimes in a limited manner. According to the analysis of student-teachers, the use of AI should not be banned but regulations and guidelines should be there. Conclusions. The study underlines that student-teachers use AI in their course and they are in support of use of AI in education. Hence there is a need for general guidelines from the apex bodies regarding use of AI in Education rather than a blanket ban on AI. E-methodology50There is a need for general guidelines from the apex bodies regarding use of AI in Education rather than a blanket ban on AI, and student-teachers are in support of use of AI in education.E-methodology["Suresh G. Isave"]2024-10-27T00:00:00 05598ed6a5cd72d3226f5d3fa277d892482c897cThe integration of Artificial Intelligence (AI) into educational settings has garnered increasing attention for its potential to enhance personalized learning and improve student outcomes. This study investigates the impact of an AI-powered personalized learning tool implemented at colleges in Udaipur. Using a mixed-methods approach, we collected quantitative data through pre-test and post-test scores and qualitative feedback via surveys and interviews with students and teachers. The results revealed significant improvements in student performance and engagement, with the AI tool providing tailored learning experiences that addressed individual needs. Statistical analyses demonstrated a substantial positive effect on academic outcomes, while qualitative feedback highlighted the tool's usability and effectiveness in identifying students' weaknesses. The findings suggest that AI-powered personalized learning can be a valuable asset in education, offering recommendations for broader adoption and further research in this field.Recent trends in Management and Commerce50The findings suggest that AI-powered personalized learning can be a valuable asset in education, offering recommendations for broader adoption and further research in this field.Recent trends in Management and Commerce[]2024-10-27T00:00:00 dd47e5845bc1b6d94fc6688df736322660a9b688There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted.Italian National Conference on Sensors691A brief overview of the research on the security of XAI-enabled DT for smart healthcare systems and a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted are provided.Sensors (Basel, Switzerland)["Niddal H. Imam"]2024-10-27T00:00:00 b1dbf6a1d8075c0d1fa41e48d4db8ab321644670The integration of artificial intelligence (AI) and statistical methods has revolutionized civil engineering by enhancing accuracy, efficiency, and reliability in various processes. This review systematically examines how advanced optimization techniques, including artificial neural networks (ANNs), Design of Experiments (DOE), and fuzzy logic (FL), are transforming civil engineering practices. It emphasizes the significant roles these methods play in addressing modern challenges such as structural health monitoring, damage detection, seismic design optimization, and concrete condition assessment. The review delves into case studies and real-world applications, showcasing the potential of these methods to create more resilient, sustainable, and cost-effective infrastructures. It critically examines the limitations and scalability of these techniques, identifying gaps in current research and practical challenges in real-world applications. The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. By addressing these issues, the review not only shows advancements in optimization techniques but also outlines future research directions, aiming to bridge the gap between theoretical developments and practical applications in civil engineering. This review serves as an essential resource for researchers, professionals, and policymakers interested in leveraging optimization techniques to advance civil engineering practices.Frattura ed Integrità Strutturale491This review systematically examines how advanced optimization techniques, including artificial neural networks, Design of Experiments, and fuzzy logic are transforming civil engineering practices, and highlights the need for substantial computational resources, data privacy, security, and software interoperability.Frattura ed Integrità Strutturale["Asraar Anjum", "M. Hrairi", "Abdul Aabid Shaikh", "Noorfazrina Yatim", "Maisarah Ali"]2024-10-27T00:00:00 f53df5626171c4a139b5ff70c770e63ca6563ba6Web accessibility is a critical aspect of inclusive digital experiences, ensuring equitable access for users with diverse abilities. Despite significant advancements, existing automated tools often fail to detect complex and context-specific accessibility issues. This research proposes an AI-powered semantic framework for comprehensive web accessibility evaluation, leveraging artificial intelligence to analyze and interpret web content with a focus on inclusivity and compliance with accessibility standards. The framework integrates semantic analysis to identify nuanced accessibility barriers, such as inadequate alt text, improper heading structures, and color contrast violations, which are often missed by traditional tools. By utilizing machine learning and natural language processing, the proposed solution aims to bridge gaps in guideline interpretation and enhance the detection of user experience challenges. Additionally, this research incorporates user feedback mechanisms and expert insights to continuously refine the framework, making it adaptable to evolving accessibility needs. Extensive testing across diverse web environments demonstrates the framework's ability to improve detection accuracy, reduce evaluation times, and provide actionable insights to developers. The proposed framework not only advances automated accessibility evaluation but also contributes to fostering a more inclusive internet by empowering developers and organizations to proactively address accessibility barriers.International Journal of Advanced Research in Science, Communication and Technology10An AI-powered semantic framework for comprehensive web accessibility evaluation, leveraging artificial intelligence to analyze and interpret web content with a focus on inclusivity and compliance with accessibility standards is proposed.International Journal of Advanced Research in Science, Communication and Technology["Vinaysimha Varma Yadavali"]2024-10-27T00:00:00 7cc0d673fd564edef99f15e771feac39f48895b2The proliferation of Generative Artificial Intelligence (GenAI) tools has brought a critical shift in how people approach information retrieval and content creation in diverse contexts. Yet, we have limited understanding of how blind people use and make sense of GenAI systems. To bridge this gap, we report findings from interviews with 19 blind individuals who incorporate mainstream GenAI tools like ChatGPT and Be My AI in their everyday practices. Our findings reveal how blind users navigate accessibility issues, inaccuracies, hallucinations, and idiosyncracies associated with GenAI and develop interesting (but often flawed) mental models of how these tools work. We discuss key considerations for rethinking access and information verification in GenAI tools, unpacking erroneous mental models among blind users, and reconciling harms and benefits of GenAI from an accessibility perspective.International ACM SIGACCESS Conference on Computers and Accessibility1020Findings from interviews with 19 blind individuals who incorporate mainstream GenAI tools like ChatGPT and Be My AI in their everyday practices reveal how blind users navigate accessibility issues, inaccuracies, hallucinations, and idiosyncracies associated with GenAI.{"pages": "64:1-64:14"}["Rudaiba Adnin", "Maitraye Das"]2024-10-27T00:00:00 1ed7a46808c55ba6be27c6e21b49e4bb8af16108 Abstract This special edition of the E-methodology journal and conference, marking its 10th anniversary, explores the dynamic intersection of technology, science, and society. Dur- ing a decade we have seen a revolution in the meaning of “e” from being just a digi- tal version of human activity to a totally new world of LLMs (Large Language Models) and other deep learning possibilities. The articles in this volume delve mainly into the transformative potential of artificial intelligence (AI) across diverse fields such as forensic science, education, healthcare, One Health, and public discourse, while examining the broader implications of technology on human life. Keywords: Artificial intelligence, technology, education, medicine E-methodology60The articles in this volume delve mainly into the transformative potential of artificial intelligence across diverse fields such as forensic science, education, healthcare, One Health, and public discourse, while examining the broader implications of technology on human life.E-methodology["A. Jarynowski", "Stanis\u0142aw Maksymowicz", "Luba \u015al\u00f3sarz"]2024-10-27T00:00:00 20252bfa9bd9a7d0aa0f94d2a8c18a2c19540327We are on the verge of a revolution in public sector decision-making processes, where computers will take over many of the governance tasks previously assigned to human bureaucrats. Governance decisions based on algorithmic information processing are increasing in numbers and scope, contributing to decisions that impact the lives of individual citizens. While significant attention in the recent few years has been devoted to normative discussions on fairness, accountability, and transparency related to algorithmic decision-making based on artificial intelligence, less is known about citizens’ considered views on this issue. To put society in-the-loop, a Deliberative Poll was thus carried out on the topic of using artificial intelligence in the public sector, as a form of in-depth public consultation. The three use cases that were selected for deliberation were refugee reallocation, a welfare-to-work program, and parole. A key finding was that after having acquired more knowledge about the concrete use cases, participants were overall more supportive of using artificial intelligence in the decision processes. The event was set up with a pretest/post-test control group experimental design, and as such, the results offer experimental evidence to extant observational studies showing positive associations between knowledge and support for using artificial intelligence.Social Science Research Network520A Deliberative Poll was carried out on the topic of using artificial intelligence in the public sector, as a form of in-depth public consultation, with key finding that after having acquired more knowledge about the concrete use cases, participants were overall more supportive of using artificial intelligence in the decision processes.SSRN Electronic Journal["Sveinung Arnesen", "T. S. Broderstad", "James Fishkin", "M. Johannesson", "Alice Siu"]2024-10-27T00:00:00 ccde3cf65a31c53670b3068ee75e7848787b6904This article presents a qualitative interview study of Finnish screenwriters and commissioners about the impact of generative artificial intelligence on the profession of screenwriting. We ask how screenwriters and commissioners see the benefits and risks of AI tools in screenwriting and how screenwriters see their changing profession in the future. We identify three stances towards AI-driven work practices in screenwriting. The functional, critical and curious stance reflect the ways in which the writers position themselves against AI technologies. Prompt engineering has emerged as a new skill in commanding AI and will become a conventional part of creative work.Critical Studies in Television220A qualitative interview study of Finnish screenwriters and commissioners about the impact of generative artificial intelligence on the profession of screenwriting finds that Prompt engineering has emerged as a new skill in commanding AI and will become a conventional part of creative work.Critical Studies in Television: The International Journal of Television Studies["Eliisa Vainikka", "Anne Soronen", "Saara Kallio"]2024-10-27T00:00:00 33b3546df10b057ba6c463611416c1996e0e43a0This article delves into the legal challenges faced in developing and deploying Natural Language Processing (NLP) technologies, focusing particularly on the European Union’s legal framework, especially the DSM Directive, the InfoSoc Directive, and the Artificial Intelligence Act. It addresses the legal status and accessibility of language data and the development of NLP technologies under both contractual and exception-based models. The authors acknowledge the partial truth in the saying, “US innovates, China replicates, and the EU regulates”. Although Europe’s AI sector is a global competitor and its strict regulations ensure ethical standards and data protection, these regulations might not necessarily boost competitiveness. Such stringent regulations can introduce complexities that may inhibit innovation relative to regions with more lenient policies.JOURNAL OF THE UNIVERSITY OF LATVIA LAW670The legal status and accessibility of language data and the development of NLP technologies under both contractual and exception-based models are addressed, focusing particularly on the European Union's legal framework.Journal of the University of Latvia. Law["I. Ilin", "Aleksei Kelli"]2024-10-27T00:00:00 565b568b93405277a857c2c4d1e2726cbf70fb88This article explores the transformative impact of artificial intelligence (AI) on disaster recovery systems in information technology. It examines how AI-driven solutions are revolutionizing traditional approaches to data protection and business continuity through advanced predictive analytics, automated backup mechanisms, and intelligent recovery processes. The research highlights the significant improvements achieved in recovery times, with some implementations reporting up to 60% faster recovery compared to conventional methods. Key aspects discussed include AI-powered predictive failure detection, automated data backup mechanisms, and the acceleration of recovery times. The article also delves into the enhancement of data availability through AI-driven replication strategies and intelligent failover mechanisms, emphasizing their critical role in maintaining operational resilience. Additionally, it addresses the challenges and considerations in implementing these advanced systems, including integration with existing infrastructure and the necessary adaptation of IT personnel. The article concludes by exploring future directions in AI algorithms for disaster recovery, potential integrations with cloud-based solutions, and the broader applications of these technologies across various industries. This comprehensive analysis underscores the pivotal role of AI in shaping the future of disaster recovery and business continuity strategies in an increasingly digital world.International Journal For Multidisciplinary Research70This article examines how AI-driven solutions are revolutionizing traditional approaches to data protection and business continuity through advanced predictive analytics, automated backup mechanisms, and intelligent recovery processes, and highlights the significant improvements achieved in recovery times.International Journal For Multidisciplinary Research["Vamsi Krishna Rao"]2024-10-27T00:00:00 2d343c1c3eec7b1b72b833ace26ed2d08637dfcdArtificial Intelligence (AI) is the primary force behind the organization's continued sustainability and competitiveness. The rise of AI software, cloud computing-based software and blockchain apps and services, together with the use of accounting information outcomes indicate that computerized accounting has become available to accountants. Even if there are great hopes for the application of AI in the accounting industry, numerous nations with little infrastructure are still lagging in using this type of technology. 88.89% of the 80 questionnaires that were initially issued were returned within a week. As a result, of the 80 questionnaires issued, 75 responses were received in one week, yielding a response return rate of 93.75 percent. Managerial and non-managerial staff members from American Express (Malaysia) Sdn. Bhd, Selangor, Malaysia were chosen as research participants. By employing a sample random sampling approach, a total of 75 legitimate surveys were gathered. The results of the study have shown a positive strong relationship between the level of readiness (AI Software, Cloud technology and Blockchain) and level of adoption (AI Software, Cloud technology and Blockchain) in the accounting industry toward employees’ job efficiency. All eight hypotheses (H1 to H8) showed a significant relationship and were all accepted. The outcome of the moderating effect between working tenure and level of readiness presents a direct impact. However, the level of adoption shows an indirect impact on job efficiency whereas job efficiency shows a direct impact.Information Management and Business Review650The results of the study have shown a positive strong relationship between the level of readiness (AI Software, Cloud technology and Blockchain) and level of adoption (AI Software, Cloud technology and Blockchain) in the accounting industry toward employees’ job efficiency.Information Management and Business Review["R. Munap", "Muhammad Izwan Mohd Badrillah", "Sagathevaa Subramaniam", "N. F. Tarudin"]2024-10-27T00:00:00 1d214355642847f8c5b9fb6806a4c3f0da0a84c8Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data pipelines through a collaborative multi-agent system. AutoKaggle implements an iterative development process that combines code execution, debugging, and comprehensive unit testing to ensure code correctness and logic consistency. The framework offers highly customizable workflows, allowing users to intervene at each phase, thus integrating automated intelligence with human expertise. Our universal data science toolkit, comprising validated functions for data cleaning, feature engineering, and modeling, forms the foundation of this solution, enhancing productivity by streamlining common tasks. We selected 8 Kaggle competitions to simulate data processing workflows in real-world application scenarios. Evaluation results demonstrate that AutoKaggle achieves a validation submission rate of 0.85 and a comprehensive score of 0.82 in typical data science pipelines, fully proving its effectiveness and practicality in handling complex data science tasks.arXiv.org294This work proposes AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data pipelines through a collaborative multi-agent system that implements an iterative development process that combines code execution, debugging, and comprehensive unit testing to ensure code correctness and logic consistency.ArXiv["Ziming Li", "Qianbo Zang", "David Ma", "Jiawei Guo", "Tuney Zheng", "Minghao Liu", "Xinyao Niu", "Yue Wang", "Jian Yang", "Jiaheng Liu", "Wanjun Zhong", "Wangchunshu Zhou", "Wenhao Huang", "Ge Zhang"]2024-10-27T00:00:00 c7a0efe4dc9060b45a6a1eb44ffbedb818635077Nature Computational Science116It is shown that the implementation of circular economy strategies along the GAI value chain could reduce e-waste generation by 16-86%.Nature computational science["Peng Wang", "Ling-Yu Zhang", "A. Tzachor", "Wei\u2010Qiang Chen"]2024-10-28T00:00:00 5af673eaf654f223fe09370800104767da66a2f8Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.British medical journal823The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.The BMJ["Sebastian Vollmer", "Bilal A. Mateen", "Gergo Bohner", "Franz J. Kir\u00e1ly", "Rayid Ghani", "P\u00e1ll J\u00f3nsson", "Sarah Cumbers", "Adrian Jonas", "Katherine S L McAllister", "Puja Myles", "David Grainger", "Mark Birse", "Richard Branson", "K. Moons", "Gary S. Collins", "John P. A. Ioannidis", "Chris Holmes", "Harry Hemingway"]2024-10-28T00:00:00 51fb92e39accf90b3bb722149165563c56269d4cLanguage Testing in Asia981The results suggest that by improving skill development, offering individualized feedback, and meeting students’ emotional and psychological needs, AI systems like ChatGPT have the capacity to transform language assessment and pedagogy.Language Testing in Asia["B. Sayed", "Zein Bassam Bani Younes", "Ahmad Alkhayyat", "Iroda Adhamova", "Habesha Teferi"]2024-10-28T00:00:00 086cfe672d0c0f14db88f198f9c8eab37703cee8Artificial intelligence (AI) is a hot topic that presents new challenges and opportunities for the improvement of educational processes. The disruptive and transformative force of this new technological development implies the adaptation of educational ecosystems for its use and integration as a didactic and pedagogical resource. From this perspective, a systematic literature review has been conducted to analyze the didactic potential of generative AI tools in the field of promoting artistic creativity in music education. The research results confirm that the incorporation of AI in music education is paving the way for a more personalized, interactive and efficient learning experience. In addition, the analysis suggests nine fundamental fields of IA implementation in music education: virtual and augmented reality (VR; VA); learning personalization, intelligent tutoring systems; composition assistants; improved historical and contextual learning; assessment systems; interactive ear training and music theory systems; tools for music collaboration and performance; and assistive technologies. Furthermore, the challenges presented by the intersection of AI and digital didactics in the field of music education are discussed.Education sciences241The research results confirm that the incorporation of AI in music education is paving the way for a more personalized, interactive and efficient learning experience and suggests nine fundamental fields of IA implementation in music education.Education Sciences["Javier F\u00e9lix Merch\u00e1n S\u00e1nchez-Jara", "Sara Gonz\u00e1lez Guti\u00e9rrez", "Javier Cruz Rodr\u00edguez", "Bohdan Syroyid Syroyid"]2024-10-28T00:00:00 a3ba16f2e12bfbe41572d7b82ad381203446f3a3Objective: This study examines the transformative role of Artificial Intelligence (AI) in enhancing Human Resource Management (HRM) processes and its contribution to improving organizational efficiency and employee experience. As organizations navigate an increasingly dynamic job market, the integration of AI offers opportunities for achieving sustainable success while addressing the demands of flexibility and innovation. The research aims to identify effective strategies for AI adoption in HRM, focusing on three key dimensions: personalization of employee experiences, security and privacy assurance, and adaptability to continuous technological advancements. Theoretical framework: Grounded in a theoretical framework that highlights the intersection of technology and strategic HRM, the study builds on a comprehensive literature review of recent advancements in AI applications within HRM. Literature Review: The literature underscores the potential of AI to streamline recruitment, talent management, and employee engagement processes, creating a more responsive and efficient HR ecosystem. Methods: A qualitative research method was employed, involving in-depth analyses of case studies and current HRM practices where AI has been integrated. Data were collected from organizational reports, interviews with HR professionals, and academic publications. Results: The findings reveal that AI adoption fosters a strategic advantage by seamlessly integrating technology into various HRM functions, resulting in improved decision-making, enhanced productivity, and higher employee satisfaction. Specifically, AI-driven tools enable HR departments to provide personalized support to employees, anticipate workforce needs, and optimize resource allocation. Implications: The implications of this research emphasize the necessity for organizations to adopt a holistic strategy for AI integration. This strategy should include substantial investments in technology infrastructure, upskilling HR professionals, and fostering an organizational culture that embraces technological innovation. These measures are essential for achieving competitive advantage and enhancing employee experiences in an era dominated by digital transformation. Novelty: The novelty of this study lies in its focus on the strategic vision required to fully integrate AI into HRM. Unlike previous research that often considers AI as a supplementary tool, this study asserts that AI is a foundational component for future business competitiveness. By framing AI as an essential enabler of strategic HRM, this research contributes to a deeper understanding of how technology can redefine organizational operations and create value.Solo International Collaboration and Publication of Social Sciences and Humanities532The findings reveal that AI adoption fosters a strategic advantage by seamlessly integrating technology into various HRM functions, resulting in improved decision-making, enhanced productivity, and higher employee satisfaction.Solo International Collaboration and Publication of Social Sciences and Humanities["Awad Mabrouk"]2024-10-28T00:00:00 680c4d65beb38fe3bd2930c19e45b4690f76b7a2Artificial intelligence (AI), as the pioneer of today's technological advances, brings innovation to many sectors and graphic design is among these sectors. Within the rapidly developing technology of our age, the integration of AI technologies into the field of graphic design provides a significant acceleration in design processes. In this context, it is predicted that the use of AI in this field contributes to accelerate design processes, increase efficiency and improve user experience and interactive design. Additionally, the research examines the current and potential status. The study adopts qualitative methods of comparative analysis and logical reasoning and is limited to the reviewed literature and studies reviewed.The findings show that AI-assisted graphic design tools accelerate design processes, increase efficiency and enable more creative solutions. The results show that AI-supported graphic design tools accelerate design processes, increase efficiency and enable more creative solutions.İnsan ve Sosyal Bilimler Dergisi311The results show that AI-supported graphic design tools accelerate design processes, increase efficiency and enable more creative solutions.İnsan ve Sosyal Bilimler Dergisi["Mehmet Akif \u00d6zdal"]2024-10-28T00:00:00 74173daeeab3945d6f594cdee27cc893426daa09With the rapid development of the economy and society, enterprises are increasingly prioritizing environmental and social sustainability alongside economic benefits. As a critical driver of technological innovation, the effective application of artificial intelligence (AI) to enhance corporate sustainability performance has garnered considerable attention from both academia and industry. This study explores the impact of AI adoption intensity on corporate sustainability performance, with a particular focus on the mediating role of organizational change and its moderated mediation effect. Employing an empirical analysis approach, this study collected 451 employee survey responses from manufacturing enterprises. The results indicate that AI adoption intensity substantially enhances corporate sustainability performance, reflected in comprehensive improvements in economic, environmental, and social benefits. Furthermore, organizational change serves as a crucial mediator between AI adoption and sustainability performance, with this mediation effect moderated by internal and external environmental factors. The study finds that enterprises with high digital capabilities and innovative cultures are more effective in leveraging AI to enhance sustainability performance. This suggests that in promoting AI applications, enterprises should not only focus on technology adoption but also emphasize internal organizational change and the development of digital capabilities to fully achieve sustainability goals. Through empirical analysis, this study provides an in-depth understanding of the application paths and mechanisms of AI in corporate sustainability, offering a theoretical foundation and practical guidance for corporate managers in strategy and policymaking.Sustainability651The study finds that enterprises with high digital capabilities and innovative cultures are more effective in leveraging AI to enhance sustainability performance, and suggests that in promoting AI applications, enterprises should not only focus on technology adoption but also emphasize internal organizational change and the development of digital capabilities to fully achieve sustainability goals.Sustainability["Jiachen Li", "Xiu Jin"]2024-10-28T00:00:00 08b32b507eadf53cce641e0365474b67ee6e5d55Introduction: This research was conducted to examine the perception of the use of Artificial intelligence tools in teaching practice among teachers at secondary schools. According to the changing trends in the global education arena, the use of Artificial Intelligence (AI) is increasingly expanding. Aims: This aims to enhance the processes of learning and teaching for greater effectiveness. The utilization of AI in education also creates opportunities to improve the quality of education, make learning more adaptive, and prepare the younger generation to face challenges in the future. In Malaysia, many teachers still face challenges in designing engaging learning experiences. In addition, ineffective teaching strategies that do not support differentiated learning methods contribute to an increased student learning rate. Objective: This study was conducted to examine perceptions of the benefits of use, usability, social influence, and readiness for AI acceptance at local school in Malaysia. Methodology: This study utilized a descriptive quantitative approach by collecting data through a survey via questionnaires. The questionnaires were distributed to 90 teachers at a local secondary school, with only 73 respondents selected as the sample for this study based on the Krejcie and Morgan Table. The data were then analysed in descriptive quantitative analysis using the Statistical Package for Social Science (SPSS) version 15. Results: The study results showed that perceptions of the benefits of use, usability, social influence, and readiness for acceptance indicated a high level of agreement. The highest correlation strength was found between social influence and acceptance readiness with r=0.59, p<0.05, compared to usability with r=0.46, p<0.05, and perceived usefulness with r=0.53, p<0.05. Conclusion: However, overall, it indicates a moderate level of relationship. The coleration values showed that b (0.59) had the highest contribution to the level of AI acceptance readiness in teaching among teachers at school, which is social influence. Conclusion: Overall, the findings of this study suggest that encouragement from superiors and social influence are crucial to encouraging teachers to fully adopt the use of AI in their teaching.Jurnal pendidikan bitara UPSI00The findings of this study suggest that encouragement from superiors and social influence are crucial to encouraging teachers to fully adopt the use of AI in their teaching.Jurnal Pendidikan Bitara UPSI[]2024-10-28T00:00:00 1db80066b9733f30d8f77839fa7a6f1e4b674399Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.CivilEng190CivilEng["Reihaneh Samsami"]2024-10-28T00:00:00 1f79c928b9760ef2a9eec4da6ac21c71c219e543The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these conditions can become chronic diseases and can even be the cause of workplace accidents. In this research, a methodology was developed for the rapid assessment of ergonomic risks and for calculating the level of ergonomic comfort in the workplace. This methodology uses artificial intelligence through a specific algorithm and takes into account a number of factors that, when combined, can have a significant impact on workers. To achieve a more accurate simulation of a work situation or to evaluate an ongoing work situation, and to significantly correlate these parameters, we used logarithmic calculation formulas. To streamline the process, we developed software that performs these calculations, conducts a rapid assessment of ergonomic risks, estimates a comfort level, and proposes possible measures to mitigate the risks and effects on workers. To assist in diagnosing the work situation, we used a neural network with five neurons in the input layer, one hidden layer, and two neurons in the output layer. As a result, most work situations, in any industrial field, can be quickly analyzed and evaluated using this methodology. The use of this new analysis and diagnosis tool, implemented through this new research technology, is beneficial for employers and workers. Moreover, through further developments of this methodology, achieved by increasing the number of relevant input parameters for ergonomics and integrating advanced artificial intelligence systems, we aim to provide high precision in assessing ergonomic risk and calculating the level of ergonomic comfort.Applied System Innovation220A methodology was developed for the rapid assessment of ergonomic risks and for calculating the level of ergonomic comfort in the workplace that takes into account a number of factors that, when combined, can have a significant impact on workers.Applied System Innovation["Adrian Isp\u0103\u0219oiu", "I. Milo\u0219an", "C. Gabor"]2024-10-28T00:00:00 a12f8f4338b6369ba829afb55cf6eb9b47a0a44dAbstract Background We observe the surge of interest and application of artificial intelligence (AI) in various areas. AI use in healthcare and medicine is followed by society with interest; however, acceptance varies depending on the area of its application. This study aimed to understand the differences in acceptance depending on the type of solution and explain the factors influencing the attitude toward the use of AI in healthcare. Methods The data from the online survey was collected from a representative sample of 1109 adult Internet users in Poland. The respondents were asked about their acceptance of AI use for several purposes in healthcare. Based on the responses to these items, an ad-hoc score of AI acceptance in healthcare (AIAHC) was developed. The determinants of AIAHC were assessed with uni- (ULRM) and multivariable linear regression (MLRM) models. Results The highest acceptance of AI use was found for monitoring medication intake at home: mean (standard deviation, SD)-3.81 (1.39), the lowest for AI making decisions about the mode of therapy -2.78 (1.35). ULRMs showed that significant predictors of AIAHC were age, gender, chronic disease, vocational status, income, the use of the Internet (IU), and e-health literacy (eHL). In MLRM, only gender, vocational status, income, and eHL maintained a significant relationship with AIAHC. Males showed significantly higher AIAHC than females (B, 95%CI: 2.56, 1.42 - 3.71), and students had higher AIAHC than employees (B, 95%CI: 3.01, 0.14 - 5.89). Persons who refused to reveal their income demonstrated lower acceptance of AI use than those who revealed their income (B, 95%CI: -4.64, -6.54 - -2.74). Finally, greater eHL favored higher AIAHC (B, 95%CI: 0.46, 0.34 - 0.57). Conclusions Evidently, there is a hierarchy of potential areas of application in terms of society’s acceptance. Improving eHL should exert a positive effect on the acceptance of innovative technologies, including AI, in healthcare. Key messages • Citizens show moderate enthusiasm toward the broader use of AI in healthcare. • E-health literacy exerts a positive effect on the introduction of AI-based solutions in healthcare.European Journal of Public Health00There is a hierarchy of potential areas of application in terms of society’s acceptance of AI-based solutions in healthcare, and citizens show moderate enthusiasm toward the broader use of AI in healthcare.The European Journal of Public Health["P. Smola", "M. Wojcieszko", "M. Duplaga"]2024-10-28T00:00:00 cfdab97efa2971d39e6101e1464c6e68de367eecModern perspective of consumer satisfaction has changed due to the proliferation of online businesses. E-commerce has opened up a whole new world to the consumers. The consumers can now get what they want, when they want, delivered at their doorstep. While the ease of online shopping has increased consumerism and consumer demand, it poses significant challenges for the e-commerce players as the consumers are seldom willing to shell out money for availing the convenience of doorstep deliveries. Under such circumstances, the e-commerce players are left with little choices but to significantly enhance their supply chain efficiencies through the improvement of logistics and the delivery services they offer to their customers. Artificial Intelligence or AI, as it is popularly known, is altering the ways and means of these e-commerce businesses. It is changing the manner in which supply chains are managed. Through the use of smart algorithms, data analysis, and automation, it is possible for businesses to employ AI effectively for the optimization of their supply chain management involving inventory and logistics management, demand forecasting, and customer service. This article focuses on logistics management and the application of AI to optimize costs and efficiency in first mile, middle mile and last mile delivery.International journal of supply chain management300Logistics management and the application of AI to optimize costs and efficiency in first mile, middle mile and last mile delivery.International Journal of Supply Chain Management["Priyank Kumawat"]2024-10-28T00:00:00 c3a3d5a6628c44c91d36f48c7fb0beb479300ef7SN Social Sciences430The findings suggest that although the teachers have limited awareness of how AI can be used in teaching and learning, they possess some level of understanding that has opened up new opportunities for exploring innovative ways of teaching and learning.SN Social Sciences["D. Bohara", "Karna Rana"]2024-10-28T00:00:00 513a0df0b8713ed8870830db74fa33734fb07c6aThe article is devoted to the study of the role of artificial intelligence (AI) in the modern digital transformation of the rich countries of the global North and the poor countries of the global South. Particular attention is paid to the different conditions of the digital revolution in the former and the latter. They are leading to an increase in old inequalities and the emergence of new digital inequalities between developed and developing countries. At the same time, the pursuit of profit in the digital economy has become the main reason for further material and property differentiation between the first, second, and third world countries, as well as between the rich and the poor within each of these countries. All of this hinders the revolutionary potential of AI in the interests of all countries and peoples and creates conditions for its use for anti-people purposes, including by combining AI with weapons for the military purpose of killing people. The task of civil society is precisely to force state authorities to take measures to regulate digital business in order to overcome the threats posed by the oligarchs of digital corporations and improve the socio-economic situation of both the cybertariat and the entire working population in the countries of the global North and the global South.Вісник Книжкової палати180The task of civil society is precisely to force state authorities to take measures to regulate digital business in order to overcome the threats posed by the oligarchs of digital corporations and improve the socio-economic situation of both the cybertariat and the entire working population in the countries of the global North and the global South.Вісник Книжкової палати["Anatolii Arseienko"]2024-10-28T00:00:00 495f6a415a4f05773fc67357907b9b41c3747460BACKGROUND Instructors used generative artificial intelligence (AI) as a teaching tool in a third-year baccalaureate nursing leadership course to help students understand and critique a change management proposal. METHOD Instructors used generative AI to develop a sample section of a change proposal for students to critique in class followed by a class discussion. RESULTS Using generative AI enabled instructors to quickly develop a sample section of a change proposal for students to critique. During this learning activity, students recognized the importance of verifying information generated by AI sources for accuracy with evidence-informed sources. Students reported that critically appraising the sample provided clarity on the assignment. CONCLUSION Leveraging generative AI in the classroom is a time-effective way for instructors to create learning activities for students, clarify the expectations for the assignment, and promote the importance of verifying information from AI sources. [J Nurs Educ. 2024;63(X):XXX-XXX.].Journal of Nursing Education30Leveraging generative AI in the classroom is a time-effective way for instructors to create learning activities for students, clarify the expectations for the assignment, and promote the importance of verifying information from AI sources.The Journal of nursing education["Michelle Cullen", "Megan Kirkpatrick"]2024-10-28T00:00:00 065f07cf5027d8b09d08ff34cf1ab670d0b5ea58This study investigates the relationship between Artificial Intelligence (AI) and environmental sustainability, focusing on how AI-driven resource efficiency, energy consumption, environmental monitoring, and carbon emission reduction contribute to sustainability outcomes. The purpose of this research is to examine the dual nature of AI's impact on sustainability by testing both positive and negative effects using Partial Least Squares Structural Equation Modeling (PLS-SEM). Drawing from a sample of 233 firms in the energy, transportation, and manufacturing sectors, this study collects data through an online survey measured on a five-point Likert scale. Four hypotheses are tested, revealing that AI-driven resource efficiency and environmental monitoring positively affect sustainability, while AI energy consumption has a negative impact. Furthermore, AI integration in industrial processes helps reduce resource depletion. The findings suggest that governments should incentivize AI adoption aimed at resource efficiency and environmental monitoring through policies like tax breaks or subsidies, particularly for firms reducing their carbon footprint. To mitigate the negative effects of AI energy consumption, policymakers are urged to promote energy-efficient AI models and invest in renewable energy infrastructure. A balanced policy approach is crucial to optimize the environmental benefits of AI while minimizing its energy-related drawbacks.Quarterly Journal of the Operational Research Society of India (OPSEARCH)180The findings suggest that governments should incentivize AI adoption aimed at resource efficiency and environmental monitoring through policies like tax breaks or subsidies, particularly for firms reducing their carbon footprint, particularly for firms reducing their carbon footprint.OPSearch: American Journal of Open Research["Aman Khan Burki", "Mohamed Normen Ahamed Mafaz", "Zaki Ahmad", "Auni Zulfaka", "Mohamad Yazid Bin Isa"]2024-10-28T00:00:00 dce4b29bb014f5dc5bbab20a62dff63f8f41160bAbstract Background Healthcare-associated infections (HAIs) represent a significant public health concern, correlating with increased morbidity, mortality rates and healthcare expenditures. While artificial intelligence (AI) systems offer transformative potential in enhancing HAIs detection and control practices, the actual performance effectiveness of these systems remains uncertain. This systematic review updates a previously published study from 2020 and evaluates the performance of AI-based tools for surveillance, detection, and control of HAIs. Methods PRISMA 2020 guidelines were applied. The study protocol has been registered in PROSPERO (ID: CRD42024513145). PubMed, Embase, Scopus and Web of Science were searched for experimental and observational studies assessing the performance of AI-based tools to detect and control HAIs, published in English. Results From 8,701 articles initially identified, 4,212 records were removed due to duplication. Out of 4,489 papers screened, 147 were included. Studies reported performance measures including sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, accuracy, precision, F1 score. Significant heterogeneity was found in the types of technology, infections targeted, health care settings and data sources between studies. Conclusions The increase in published evidence since the previous review reflects the growing interest and use of new technologies such as Large Language Models, showing promising performance in surveillance, early diagnosis and prediction of HAIs. However, the observed heterogeneity in study designs, targeted infections, healthcare settings, and data sources underscores the need for standardised methodologies and robust validation processes to ensure the reliability and comparability of results across different studies. Key messages • The use of AI-based tools has the potential to enhance surveillance, detection, and control of healthcare-associated infections, offering a transformative impact on healthcare systems. • Standardised methodologies and validation processes are needed to ensure comparable results across studies and to maximise the real-world impact of AI tools in HAI surveillance and control efforts.European Journal of Public Health00The use of AI-based tools has the potential to enhance surveillance, detection, and control of healthcare-associated infections, offering a transformative impact on healthcare systems, but standardised methodologies and validation processes are needed to ensure comparable results across studies and to maximise the real-world impact of AI tools in HAI surveillance and control efforts.The European Journal of Public Health["C. Barbati", "L. Viviani", "R. Vecchio", "G. Arzilli", "L. De Angelis", "F. Baglivo", "C. Rizzo", "A. Odone"]2024-10-28T00:00:00 b3855f42901fbe04876a723d35fb56530ac573dfArtificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.Sustainability440There exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions, and the relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention.Sustainability["Ping Han", "Tingting He", "Can Feng", "Yihan Wang"]2024-10-28T00:00:00 cf822121d8cb3f7fd2dde009e7eb7677b18e9a02 Purpose The adoption of artificial intelligence (AI) in frontline service encounters is a growing phenomenon in service marketing, which can lead to positive and negative results. In this context, this paper aims to review the literature on value cocreation and codestruction in AI-enabled service interactions. Design/methodology/approach A systematic literature review was carried out using the PRISMA protocol. Data were retrieved from the Web of Science and Scopus databases, from which 48 articles were selected for review. Data analysis, presentation of results and the research agenda followed the theory, context, characteristics and methodology (TCCM) framework. Findings The review especially revealed that: publications on AI-enabled value cocreation and codestruction are in the early stages of development; few articles have addressed value codestruction, and the main research emphasis is on value cocreation; interactions between human actors and AI-enabled autonomous nonhuman actors are resulting in value cocreation or value codestruction, or both, and these phenomena are also likely to occur when AI replaces more than one human actor in the service encounter; and AI is considered an increasingly independent nonhuman actor that integrates resources and interacts with other actors, yet prudence is necessary for its adoption. Originality/value This review fills a gap by jointly exploring the value cocreation and codestruction in the context of AI, presents an overview of the issues discussed and provides a research agenda with directions for future studies. Spanish Journal of Marketing - ESIC880A review of the literature on value cocreation and codestruction in AI-enabled service interactions fills a gap by jointly exploring the value cocreation and codestruction in the context of AI, and provides a research agenda with directions for future studies.Spanish Journal of Marketing - ESIC["Elainy Cristina da Silva Coelho", "Josivania Silva Farias"]2024-10-28T00:00:00 eedbfdc5abe4442104940bc72d64b11b291222a6Abstract Issue The Rapid Evidence Service, initiated by the National Collaborating Centre for Methods and Tools (NCCMT) during COVID-19, supports public health decision making by conducting rapid reviews on priority topics. Description of issue Integral to rapid reviews is an expedited timeline but the quantity of available literature for most public health review questions takes significant time to screen manually. NCCMT integrated 4 artificial intelligence (AI) features into the screening process. DAISY Rank applies predictions learned from manual screening patterns to re-order remaining studies, with most relevant appearing first. AI Screening automatically screens remaining studies based on prediction scores. Check for Screening Errors and Re-Rank Report use previous screening patterns to identify studies that were potentially falsely excluded and predict the total number of included studies, respectively. These features were tested by comparing results provided by AI with those produced manually for select test sets. Results NCCMT used AI to support and expedite screening, assess screening progress, and/or minimize risk of inappropriately excluding studies for 35 rapid reviews on 20 topics. Using DAISY Rank enabled one screener to review over 4000 references in 9 hours, compared to a different review, where the same amount of screening took 28 hours without DAISY Rank. AI Screening correctly excluded up to 80% of irrelevant search results across reviews. Check for Screening Errors identified 37 potential includes manually excluded in one review; these were reviewed and 3 were included. Re-Rank Report allowed NCCMT to re-allocate staff to subsequent steps in the review process when most included studies were identified. Lessons Integrating AI features into screening led to less time required, better anticipated timelines, more accurate staff allocation and reduced errors. More rigorous study of AI best practices is needed to continue to improve rapid review method efficiencies. Key messages • Rapid reviews can be an important source of evidence for decision makers if they can be completed quickly but maintain rigor and accuracy. • AI holds promise as a way to improve screening efficiency.European Journal of Public Health00Artificial intelligence features into screening led to less time required, better anticipated timelines, more accurate staff allocation and reduced errors, and more rigorous study of AI best practices is needed to continue to improve rapid review method efficiencies.The European Journal of Public Health["N. Kini", "F. Cotte", "M. Pereira", "A. Pimenta", "M. Schmude", "J. Nevell", "FD Lourenc\u00b8o", "A. Besarab", "P. Flores"]2024-10-28T00:00:00 bfb84466da0568667901481fa3a851809a5b7dadAbstract   Globally, governments and public health authorities are integrating Artificial Intelligence (AI) techniques in mobile apps to improve public health. For example, during the COVID-19 outbreak, AI-driven chatbots were deployed to provide immediate health information and advice. AI-based mobile apps are also used to support mental health or for preventive healthcare, such as personalized dietary recommendations. Moreover, the use of generative AI for public health purposes is rapidly increasing. In 2024, the WHO launched S.A.R.A.H - a generative AI chatbot designed to provide information on major health topics such as healthy lifestyles and mental health. By integrating AI, public institutions could potentially achieve better health outcomes, improve efficiency, and enhance access to healthcare. However, the rapid adoption of AI public health apps also raises significant ethical, legal, and regulatory challenges. The large amounts of sensitive personal data collected and processed by such apps may create issues for privacy, data protection, and cybersecurity. At the same time, the tendency of AI to exhibit biases may deepen existing health inequities. The new EU ‘digital’ legal framework does not directly address these challenges. This workshop aims to convene leading experts working on AI in health to address these issues. We bring together scholars from different disciplines (health law, health policy, bioethics, biosciences). The workshop objectives are twofold: (1) to critically assess the current landscape of AI in public health apps from a legal, ethical, and regulatory point of view, and (2) to lay the groundwork for policy recommendations on how to ensure the deployment of AI apps for public health in compliance with ethical principles and fundamental rights. First, the panel explores the differences in the data protection regulation of AI public health apps in the EU and the US (speaker: James Hazel). Second, it discusses how these apps are regulated under the new EU Artificial Intelligence Act (speaker: Hannah van Kolfschooten). Third, it investigates the consequences of the European Health Data Space regulation on how health data can be processed through public health apps (speaker: Mahsa Shabani). Finally, as the cross-border use of apps complicates regulatory efforts, it discusses the need for international cooperation in establishing and enforcing guidelines (speaker: Vasiliki Rahimzadeh). The workshop will be conducted as a round table discussion with 4 short presentations of 5 minutes each. Speakers first present a key challenge posed by AI in mobile health apps, and then suggest a potential regulatory solution. This will be followed by a dialogue between panelists and the audience to share best practices on how to regulate AI in mobile public health apps from a legal, ethical, and regulatory perspective. The input will be used to develop a submission for the Call for papers of BMC Bioinformatics. Key messages • As public institutions are increasingly integrating AI technologies into their mobile public health solutions, it is crucial to evaluate the ethical, legal, and regulatory implications. • In light of current gaps in the EU legal framework, we convene scholars from different disciplines and legal systems to design guidelines on the ethical use of AI mobile apps for public health. Speakers/Panelists James Hazel University of Amsterdam, Amsterdam, Netherlands Mahsa Shabani University of Amsterdam, Ghent University, Amsterdam, Netherlands Hannah van Kolfschooten University of Amsterdam, Amsterdam, Netherlands Vasiliki Rahimzadeh Baylor College of Medicine, Houston, USA Pramiti Parwani University of Amsterdam, Amsterdam, NetherlandsEuropean Journal of Public Health00This workshop convenes leading experts working on AI in health to design guidelines on the ethical use of AI mobile apps for public health in compliance with ethical principles and fundamental rights and critically assess the current landscape of AI in public health apps from a legal, ethical, and regulatory point of view.The European Journal of Public Health["James Hazel", "Mahsa Shabani", "V. Rahimzadeh", "Pramiti Parwani", "Monica Br \u0131 nzac"]2024-10-28T00:00:00 fb4f16e6177e69732e9d3ef2dc0001153641bb96 Purpose This study aims to investigate the interplay between artificial intelligence (AI) integration, organizational digital culture, human resource management (HRM) practices and employee sustainable performance in luxury hotels in Malaysia. It seeks to elucidate how AI adoption influences organizational dynamics, shapes HRM practices and impacts employee sustainable performance over time. Design/methodology/approach Using a quantitative approach, survey questionnaires derived from prior research were utilized. Analysis using G*Power software determined an appropriate sample size, with psychometric evaluation validating scale development. Statistical analyses using Statistical Package for Social Sciences (SPSS) 28.0 and SmartPLS 4 confirmed data reliability and validity. Findings Out of the five hypotheses, three were supported. A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction. However, the relationship between AI adoption and organizational digital culture was not supported. On the other hand, HRM practices positively influenced employee sustainable performance. In addition, organizational digital culture was positively associated with employee sustainable performance, underscoring the role of digital fluency in driving workforce productivity. Conversely, AI failed to moderate the relationship between HRM practices and employee sustainable performance. Research limitations/implications The study’s focus on luxury hotels in Malaysia and its reliance on cross-sectional data, suggesting the need for longitudinal designs and diverse organizational contexts in future research. Comparative studies across sectors and countries could offer insights into variations in AI adoption practices and their impact on organizational performance. Originality/value This study contributes to theoretical frameworks by empirically examining complex relationships between AI integration, HRM practices, organizational digital culture and employee performance, emphasizing the importance of considering organizational context and cultural factors in understanding the implications of AI adoption for sustainable performance enhancement. Global Knowledge Memory and Communication700A positive relationship was found between AI adoption and employee sustainable performance, highlighting AI’s potential to enhance productivity and job satisfaction and the role of digital fluency in driving workforce productivity.Global Knowledge, Memory and Communication["Ying-Sin Chin", "Abang Azlan Mohamad", "M. Lo"]2024-10-28T00:00:00 290f103da6be346c89877eb14bd8ae2b95a66a6fAbstract Background The availability of different data sources is increasing with the possibility to link them with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the use of data linkage and artificial intelligence (AI) in routine public health activities and the constraints to linking different data sources. Methods A cross-sectional survey was performed across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI). Results In Europe, the use of data linkage and AI at national institutes of public health, health information and statistics varies. The use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes is common in majority of European countries. The use of AI to estimate health indicators is not frequent among these institutes. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as main constraints to routine data linkage for public health surveillance and research. Conclusions Our study showed that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system allowing to link different data sources are essential to contribute to public health research. Moreover, it supports evidence-informed health policy making processes with an overview of various aspects affecting population health and may contribute to European pandemic preparedness.European Journal of Public Health00It was showed that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI, which supports evidence-informed health policy making processes with an overview of various aspects affecting population health and may contribute to European pandemic preparedness.The European Journal of Public Health["Hanna Tolonen", "R. Haneef"]2024-10-28T00:00:00 854fcd7ea33c7ce7d12bb323227dd99b5ce23c09Medical errors are not generally disclosed but they do happen and vary in severity. By prioritising patient safety, Dr Kohei Tanaka, a clinical engineer from the Tokyo University of Technology, wants to find a solution for preventing medical errors or, at the very least, ensuring they don’t escalate into serious incidents. The solution Tanaka is working on combines artificial intelligence (AI) with augmented reality (AR) glasses. The idea is that the glasses will provide medical professionals with accurate and reliable information at critical times. By providing them with rapid access to real-time data, the expectation is that this will reduce critical errors. As part of the validation process, newly qualified nurses will compare current training methods with training using his AR glasses and also complete a questionnaire on the viability of the glasses. So far, the researchers have been able to verify the retention of data provided through the glasses for a period of about three months and believe large-scale verification will reveal the mechanism of long-term memory in the future. By providing accurate, up-to-date information in a visual (still images and video) and auditory format, the glasses assist with the rapid delivery of critical data. However, there remain question marks regarding how much data can be effectively presented to the operator at the point of use as information overloading is to be avoided. The system included a voice-synthesised text reading guide as well as the display through the bone conduction earpiece within the device, which means the user can continue with the procedure while listening to the voice guide, reading reliance on visuals and thereby increasing safety.Impact00The researchers have been able to verify the retention of data provided through the glasses for a period of about three months and believe large-scale verification will reveal the mechanism of long-term memory in the future.Impact["Kohei Tanaka"]2024-10-28T00:00:00 a437714847601f60fa9cdd1d006af6d1e6753a12Artificial intelligence (AI) is increasingly being used in healthcare for applications such as drug discovery, diagnostics, disease management, and delivery of services. However, integrating AI and healthcare raises concerns about reinforcing existing societal prejudices: AI systems are known to exhibit racial biases by making inaccurate and unreliable decisions based on race when it is irrelevant to the task. Furthermore, government directives currently lack consistent standards for regulating AI and offer insufficient guidance on preventing the perpetuation of harmful racial biases, especially in healthcare. To improve patients’ quality of life interacting with AI systems, it is essential to ensure transparency regarding these systems. Additionally, it is vital to ensure that innovation dedicated to improving healthcare enhances the integrity of the patient’s experience rather than compounds existing systemic disparities. The authors propose three recommendations to address racial biases in healthcare applications of AI and emphasize the need for legislation placing AI regulation in healthcare at the forefront of healthcare policy agendas.Journal of Science Policy &amp; Governance170The authors propose three recommendations to address racial biases in healthcare applications of AI and emphasize the need for legislation placing AI regulation in healthcare at the forefront of healthcare policy agendas.Journal of Science Policy &amp; Governance["Mishayla Harve", "Sakthi Priya Ramamoorthy", "Viresh Pati", "Garen Bainbridge", "Abigayle Kankolenski", "Bratee Podder", "Matthew Sampt"]2024-10-28T00:00:00 975d86823263a97b1892282e1254b2794bb26cadAbstract   Artificial Intelligence (AI) is not just a future promise but an urgent necessity for modern public health. By providing invaluable insights into disease patterns, therapeutic interventions, and overall public health management, AI has the potential to revolutionise healthcare. To tackle essential public health functions effectively, harnessing AI must become a top priority. However, there is an urgent need for a cohesive strategy across Europe. Currently, varying readiness levels among European nations regarding AI adoption in health result in uneven progress across the continent. This disparity must be addressed to ensure all countries benefit equally from AI advancements. Recognising this, the World Health Organization European Regional Office launched a regional report on digital health in 2023. The report evaluated the integration of big data and advanced analytics, including AI, in health systems. Findings revealed that while 60% of Member States have a national data strategy, only 35% have a policy regulating big data and AI in health, and 38% lack both. This highlights a critical gap that must be filled urgently. Our upcoming session will address these challenges head-on. Organised to provide visionary insights, practical applications, and a landscape view of artificial intelligence in public health, the presentations will be followed by a roundtable discussion in which panellists will delve into practical challenges surrounding AI adoption. They will reflect on the profound impact AI could have on the future of European health systems and offer pragmatic and responsible steps forward, culminating in achievable recommendations for public health professionals. To enhance the session, we will utilise existing Generative AI tools to provide a real-time summary of the plenary and reinforce the call to action in alignment with panellists’ recommendations. This approach ensures that the session discusses the urgent need for AI in public health and actively demonstrates its practical applications. Moderators Natasha Azzopardi Muscat Director, Division of Country Health Policies and Systems, WHO Regional Office for Europe Dimitra Panteli Programme Manager/Senior Health Systems Analyst, European Observatory on Health Systems and Policies Facilitator Stefan Buttigieg Vice-President, EUPHA Digital health section Speakers/Panellists Martin McKee Professor of European Public Health, London School of Hygiene & Tropical Medicine, UK Katharina Ladewig Director, Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Germany Marco Marsella Director Digital, EU4Health and Health systems modernisation, Directorate-General for Health and Food Safety (DG SANTE), European Commission Keyrellous Adib Technical Officer Data Science and Digital Health, WHO Regional Office for EuropeEuropean Journal of Public Health00This approach ensures that the session discusses the urgent need for AI in public health and actively demonstrates its practical applications, and utilises existing Generative AI tools to provide a real-time summary of the plenary and reinforce the call to action in alignment with panellists’ recommendations.The European Journal of Public Health["Martin McKee", "Marco Marsella"]2024-10-28T00:00:00 d801a65d1ff072f5704b83b623c1833b3f9c3979Abstract Issue & Problem National Public Health Institutes (NPHIs) in many countries are at the forefront of action to mitigate the impact of current and future threats to health as well as improving population health and wellbeing. Through research, collaboration, and support, NPHIs can approach these new realities together. One of the many emerging realities that NPHIs will need to engage in is how NPHIs best can utilise the powers of artificial intelligence and deal with the ethical and legal issues that come with it. Methods In preparation of IANPHI’s (International Association of National Public Health Institutes) Europe Regional network meeting in April 2024 a survey was conducted among the 42 members. 16 countries replied to questions, around e.g., 1) current applications of (generative) artificial intelligence (AI) in the work of institutes, 2) which experiences were made and 3) which measures NPHIs take to best utilize the possibilities and overcome challenges of AI technologies. Within four weeks 16 replies were shared. Lessons & Conclusions Some institutes have not yet adopted AI, while others report multiple applications but the primary areas of application vary considerably among Europe. Applications include summarizing and reformatting text; translation; transcribing and documenting meetings; search information in documents or databases; coding; creating visuals; literature review; surveillance of infectious diseases; disease forecasting; analysis of transmission dynamics; vaccine efficacy assessment and development; image recognition; diagnosis and prediction of diseases from medical datasets; text mining; symptom extraction; social listening; prediction of nitrate concentration in groundwater; drug analysis and measuring psychological resilience in social crisis. Key messages • AI poses opportunities and risks for public health and work of national public health institutes and capacity building initiatives are essential. • IANPHI decided to develop a joint framework for the use of AI in its member organisations with a focus on research and responsible use.European Journal of Public Health00AI poses opportunities and risks for public health and work of national public health institutes and capacity building initiatives are essential and a joint framework for the use of AI is developed with a focus on research and responsible use.The European Journal of Public Health["C. Habl", "T. Ottersen"]2024-10-28T00:00:00 d5deaa6a6c3dcda638e6e33a43164f401af09c1fThe main goal of this study is to reveal special talented primary school students' perceptions of artificial intelligence, one of the popular concepts of recent times, through metaphors. In this study, the phenomenological design, which is within the scope of qualitative research, was used. In this study, Türkiye Science and Art Center included special talented primary school students in the education field. The 104 special talented primary school students participating in the research were 9-14 years old. Of these students, 53 were boys and 51 were girls. The participants were in the 3th grade, 4th grade, 5th, 6th, 7th and 8th grades of primary education. Purposive sampling method was chosen in the research. When the answers from the students were examined, a total of 36 different categories emerged in line with the relevant metaphors. When these categories are examined in detail, it is seen that the concept of artificial intelligence is represented by different metaphors. According to the findings, it was determined that primary school students simulated the concept of artificial intelligence to different metaphors such as robot, smiling robot, robot vacuum cleaner. As a result, it has been revealed that special talented student generally have positive opinions about the concept of artificial intelligence.International Journal of Research in Education and Science00It was determined that special talented primary school students simulated the concept of artificial intelligence to different metaphors such as robot, smiling robot, robot vacuum cleaner, and it has been revealed that special talented student generally have positive opinions about the concept of artificial intelligence.International Journal of Research in Education and Science["Ay\u015fe Alkan", "E. Yildiz"]2024-10-28T00:00:00 09d20a9f01b4783ec7aee14bd940f1f2f1939b78Artificial intelligence (AI) is an essential tool in the medical sector that contributes significantly to medical imaging, medical pathology, histology, genomic study, predictive analysis, etc. It is being increasingly used not only in breast and lung cancer diagnosis and screening, and genetic disease identification but also in personalized decision-making during the treatment of these diseases. The quality and availability of unbiased data is a key challenge. Algorithmic bias and interpretability are significant concerns as well. Improving the use of AI in medical diagnostics could be achieved through continuous monitoring, access to highly accurate datasets, and timely intervention by healthcare professionals. J Bangladesh Coll Phys Surg 2024; 42: 379-382Journal of Bangladesh College of Physicians and Surgeons140Improving the use of AI in medical diagnostics could be achieved through continuous monitoring, access to highly accurate datasets, and timely intervention by healthcare professionals.Journal of Bangladesh College of Physicians and Surgeons["Swadesh Barman", "Shanta Roy"]2024-10-28T00:00:00 5869d1a3dee630052ad0aa9007faa7393309a0b9Artificial intelligence opens up new opportunities in the field of urban planning and the development of master plans. The use of AI can significantly speed up the process of creating master plans, improve forecasting accuracy and optimize management decisions. It is necessary to further study and develop these technologies for their effective implementation in public administration practice. With the help of artificial intelligence, the generation of master plans of territories is 3 times faster. Collection of scientific articles00Artificial intelligence opens up new opportunities in the field of urban planning and the development of master plans, and the generation of master plans of territories is 3 times faster.Collection of scientific articles["S. Khodko", "A. Nikitchenko"]2024-10-28T00:00:00 50c70debc1c0c2c45e21c6ad29a046ec333e4806In modern education, there is a growing need for innovative teaching approaches, particularly in the context of using artificial intelligence. The relevance of the issue under consideration lies in exploring the potential of ChatGPT for automating the creation of mathematical problems, which can significantly reduce the workload of teachers. The aim of the research is to assess the potential of ChatGPT in generating high-quality math problems across various topics in the school curriculum. To achieve this goal, methods of theoretical analysis of scientific literature and an empirical experiment were applied, during which ChatGPT was used to create tasks on proportionality, combinatorics, algebra, and geometry. The research results demonstrate that ChatGPT can effectively generate both simple and complex mathematical problems, opening new opportunities for automating teachers' preparatory work. This allows teachers to automate part of the preparation process and focus on more creative aspects of teaching. However, certain limitations are identified, such as the dependence of task quality on the clarity of queries and the presence of errors in complex calculations. Overall, the research emphasizes that artificial intelligence can become a valuable tool for math teachers, but its use requires an understanding of its capabilities and limitations. Further research may include researching the impact of using ChatGPT on the student motivation and academic achievements, developing of methods for assessing the quality of ChatGPT-generated tasks, integrating ChatGPT with other learning tools (e.g. learning management systems), exploring the possibility of using ChatGPT to create more complex mathematical tasks that require a creative approach.Scientific Bulletin of Uzhhorod University Series «Pedagogy Social Work»00Scientific Bulletin of Uzhhorod University. Series: «Pedagogy. Social Work»["Myroslav Stoika", "Yu. V. Petechuk"]2024-10-28T00:00:00 80bfd38643a31abe42c33100f59ce0bfe3e96338The proliferation of deep neural network architectures into the space of mission-critical applications raises great risks given the lack of transparency behind model predictions and the paucity of reliable metrics to quantify model uncertainty. By combining certainty and competence scoring, derived from a recent post-hoc uncertainty quantification framework, and with the use of logic tensor networks (LTN), a form of neurosymbolic artificial intelligence, we examine and make improvements to the baseline xView2 neural network architecture developed for disaster damage assessment for aerial image data. This framework is able to identify gross incompetence on the part of the baseline model at damage assessment despite its superficially strong performance with unbalanced data, while also demonstrating the relative resilience of LTNs that incorporate the baseline model as a trainable feature. In particular, we find that the baseline model absent additional logical information collapses in accuracy to a uniform guess, with attendant collapses in F1 score, whereas the LTN improves across measures of accuracy, F1 score, and various measures of certainty, competence, and logical satisfaction, motivating further development of these tools in mission critical settings with pervasive structural uncertainties.IEEE Military Communications Conference100It is found that the baseline model absent additional logical information collapses in accuracy to a uniform guess, with attendant collapses in F1 score, whereas the LTN improves across measures of accuracy, F1 score, and various measures of certainty, competence, and logical satisfaction, motivating further development of these tools in mission critical settings with pervasive structural uncertainties.MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM)["Alexander M. Berenbeim", "Ramneet Kaur", "Adam D. Cobb", "Anirban Roy", "Susmit Jha", "Nathaniel D. Bastian"]2024-10-28T00:00:00 d5c5fda45be038325b6676da8cfa9d92b6d72173The presence of Artificial Intelligence (AI) has a significant impact on the preparation of financial reports, especially in terms of efficiency and accuracy. This article aims to examine the influence of AI on the process of preparing financial reports that were previously carried out conventionally and the challenges faced by companies in adopting it. This study uses a literature study method to identify trends and findings related to the use of AI in accounting. The results show that AI is able to improve operational efficiency, data analysis, and security, although there are challenges in its implementation, such as human resource adaptation and data security.Jurnal Rimba : Riset Ilmu manajemen Bisnis dan Akuntansi140The results show that AI is able to improve operational efficiency, data analysis, and security, although there are challenges in its implementation, such as human resource adaptation and data security.Jurnal Rimba : Riset Ilmu manajemen Bisnis dan Akuntansi["Resalia Resalia", "Heni Nurmayana Soleha", "Alya Bahira", "Rudi Sanjaya"]2024-10-28T00:00:00 1f979212266a59503471a35e04739f648409ba47The article is dedicated to exploring the possibilities and limitations of developing creative artificial intelligence, particularly the ability of machines to determine the level of creativity in the objects they produce. To assess creativity, a three-step model of novelty is proposed, including ontological, subjective, and semantic levels. Three features of creative ideas or artifacts are identified: novelty, unexpectedness, and value. The article describes the model of a competitive generative network called "CAN: Creative Adversarial Networks," which creates new artistic styles and evaluates their novelty. The possibilities and limitations of modeling humor in creative artificial intelligence are discussed. The article analyzes examples of successful work by neural networks that generate jokes and write scripts, showing that the limitation of such systems is the machine's lack of a sense of context, space, and time. Additionally, a crucial condition for successfully writing jokes is the ability to laugh at them; humans can consciously choose the topic and format of humor, while machines lack their own goal-setting. It is shown that the technologies developed to date can be generalized as "weak creative artificial intelligence" since they can create new objects but are not capable of goal-setting and reflection. However, the possibilities of artificial intelligence are constantly expanding, changing our understanding of the limits of modeling natural intelligence.Semiotic studies50The article describes the model of a competitive generative network called "CAN: Creative Adversarial Networks," which creates new artistic styles and evaluates their novelty, and analyzes examples of successful work by neural networks that generate jokes and write scripts.Semiotic studies["Nikita A. Krylov"]2024-10-28T00:00:00 32ce044bbff08a5bf99482e8a342f177f2eeb899 Unveiling the cognitive patterns that underpin linguistic expressions, conceptual metaphor serves not only as an effective means for speakers to convey their values but also as a crucial tool for listeners to comprehend unfamiliar topics. This study undertakes a corpus-based analysis of conceptual metaphor expressions within the European Union’s Artificial Intelligence Act. Utilizing a corpus derived from the European Union Artificial Intelligence Act and employing both Conceptual Metaphor Theory and Critical Metaphor Analysis Theory, this research examines metaphors in terms of their types, orientations, and underlying rationales. The study identifies the most-use semantic domains of Journey, Human, War, and Object metaphors, indicating that the overall orientations are characterized by Tool, Dependency, Human, and Risk, reflecting both the aspirations and concerns of humanity. This study addresses a gap in metaphor research regarding the European Union’s Artificial Intelligence Act, offering valuable insights for policymakers and AI developers in understanding and shaping public perception of AI technologies.International Journal of Digital Law and Governance440This study addresses a gap in metaphor research regarding the European Union’s Artificial Intelligence Act, offering valuable insights for policymakers and AI developers in understanding and shaping public perception of AI technologies.International Journal of Digital Law and Governance["Zhanglei Ye", "Jian Li"]2024-10-28T00:00:00 5a0b2965eac7ad31f778e61683665e624f23051dAbstract   In Italy,the National Breast Screening Programme offers mammography as the 1st level examination, blindly assessed by two radiologists.In the context of continuous quality control,discrepancies in the assessment of mammograms between the 1st and 2nd reader have been studied,focusing on the most significant ones.The aims of the present study are:to evaluate the outcome of women sent to the 2nd level of review;to evaluate the possibility of using artificial intelligence (AI) software to assist radiologists in case of discrepancies. In the Local Health Authority Roma2,radiologists use a standardised classification that rates radiographs on a five-level scale from R1 to R5:R1=no abnormalities,R2=benign findings,R3=equivocal findings,R4=suspected cancer,R5=strongly suspected cancer.Data were extracted from the mammograms performed in 2023 that showed discrepancies (R1/R2 vs. R4/R5).2nd level mammography≥R4 and 2nd level histology≥B4 were considered positive. For the second aim,”IA Lunit INSIGHT” software was used to create specific target groups using the following cut-offs:G1<10%, G2 11-29%; G3 30-59%; G4 60-85%; G5 86-100%(groups ≥G3 were considered positive). Of the 36,339 mammograms,524(1.5%) were discordant:347(66.2%) were considered suspicious by the second radiologist.49(9.4%) were confirmed positive by Level II mammography and 23(4.4%) reported histology≥B4. The AI red a random sample of 247 mammograms and classified 34 women as positive (1 G5, 6 G4, 9 G3), of whom 11 (4.5%) were confirmed positive by Level II mammography and 6 (2.4%) were histologically positive. Of the negative women, 2 had histology≥B4. Discrepancies may be one of the factors leading to inappropriate referral to second level investigation. In such cases, the use of AI may improve the appropriateness of further investigation, although this hypothesis needs to be tested in larger numbers, with additional comparisons with experienced radiologists and consideration of retraining. Key messages • Continuous monitoring in cancer screening programs are vital to assure a sustainability of the process. • Artificial Intelligence software are promising in aiding health care professionals in case of discrepancies.European Journal of Public Health00Evaluating the outcome of women sent to the 2nd level of review and the possibility of using artificial intelligence (AI) software to assist radiologists in case of discrepancies shows promise in aiding health care professionals in case of discrepancies.The European Journal of Public Health["Maria Teresa Riccardi", "V. Rosca", "F. Forestiero", "T. Rudniki", "A. Testa", "G. Cerone", "E. Rossi", "MO Trinito"]2024-10-28T00:00:00 8ef9b9704f309607926bbe89c9b768d5e8f2612cIntroduction: Higher education is clarity an unprecedented transformation due to the growing incorporation of artificial intelligence (AI) tools in university teaching. The promise of AI in this context is clear: improve the quality of education, personalize learning, and prepare students for an ever-changing world. However, its use raises fundamental questions about the traditional role of the teacher and the student learning experience.Objective: Describe the implementation of artificial intelligence in the educational processes developed by university teachers of a private Ecuadorian institution.Method: Based on the positivist paradigm with a quantitative approach, a non-experimental cross-sectional design at a descriptive level is supported by field research. The population comprised 56 teachers, who answered a 29-item questionnaire validated by expert judgment and with a reliability level of 0.91.Results: The main findings demonstrate that teachers at higher education institutions implement AI in their educational processes in an incipient manner, which could be due to a lack of knowledge about the subject. Likewise, it was found that they do not consider the use of AI to be a good practice. AI in the tasks and evaluations that students develop, and they do not perceive the use of this tool in education as essential. However, in a contradictory way, the majority of teachers agree that they need training in AI applied to education and that this will permeate the future of universitiesData and Metadata40The main findings demonstrate that teachers at higher education institutions implement AI in their educational processes in an incipient manner, which could be due to a lack of knowledge about the subject.Data and Metadata["Alirio Antonio Mej\u00eda Mar\u00edn", "Jes\u00fas Orlando G\u00f3mez Rivero"]2024-10-28T00:00:00 121f00aea749ce2799ff6f913554c158b7d836c6Industrial accident prevention is a problem of societal relevance. It is worth the investment of contemporary education theory and up-to-date information and communication technologies including Artificial Intelligence (AI). Apparent deficiencies of AI that is based on Large Language Models (LLMs) und Generative Pre-trained Transformers (GPTs) bear abundant evidence for the need of going beyond the limits of sub-symbolic AI. Game based training is attractive to practitioners and advantageous over conventional educational methods. There is developed, implemented, and applied the original concept of time travel prevention games. The approach is generic and applies to other domains such as crime prevention and health care as well. The key idea is interactive story engagement that allows, if necessary, for virtual time travel to impact the fate. Trainees experience stories of success. Digital storyboarding is an AI design methodology derived from approaches to dynamic plan generation. Technically, a storyboard is a hierarchically structured family of graphs. Semantically, it determines a potentially infinite space of different stories. Single stories unfold at execution time, i.e. during game play, dynamically in response to human activities and environmental conditions. The game AI analyzes the history of game play represented as a string of actions and events. Operationally, string processing, counting letters in strings, finding properties of strings, and the like is key. The prepared alternatives of unfolding game play represent the interdisciplinary designer team's intelligence based on domain knowledge, education theory, learning psychology, game design principles, VR technology, and the like. The storyboard is the place where AI resides. Keywords: industrial accident prevention, prevention training, time travel prevention games, education theory, Artificial Intelligence, sub-symbolic AI, symbolic AI, adaptivity, play state, game state, storyboarding, storyboard interpretation technologyJournal of Contemporary Education Theory &amp; Artificial Intelligence00The prepared alternatives of unfolding game play represent the interdisciplinary designer team's intelligence based on domain knowledge, education theory, learning psychology, game design principles, VR technology, and the like.Journal of Contemporary Education Theory &amp; Artificial Intelligence["Jantke Klaus P"]2024-10-28T00:00:00 5c0066d91ba4c9d9a7d98757171dcd86b06d497fArtificial Intelligence (AI) has grown extensively in recent times and is becoming integrated into human lives. This progression opens diverse opportunities in e-health, facilitating healthcare accessibility globally. Voice assistants are AI-driven technologies, which are becoming increasingly important with the growing prominence of e-health services. This study offers an integrated model that assimilates AI attributes with relevant constructs from various theories such as Technology Acceptance Model (TAM) and Health Belief Model (HBM). Key variables under scrutiny include perceived anthropomorphism, perceived intelligence, perceived ease of use, perceived usefulness, perceived enjoyment, attitude, perceived severity, and perceived susceptibility. The article adds to the current research by reviewing the literature about AI in healthcare, and proposing a conceptual framework that binds technological, AI-specific, and health factors, offering implications for policymakers, healthcare administrators, and marketers seeking effective utilization of voice assistant technology.Business and Management Horizons00An integrated model that assimilates AI attributes with relevant constructs from various theories such as Technology Acceptance Model (TAM) and Health Belief Model (HBM) is offered, offering implications for policymakers, healthcare administrators, and marketers seeking effective utilization of voice assistant technology.Business and Management Horizons["Elaheh Ahanin", "Abu Bakar Sade"]2024-10-28T00:00:00 ca036e01b9d0fc42e2a75c87f0ef64134156af3dThe dire need for proper maintenance of Science Laboratory Equipment (SLE) to attain efficiency, optimal results and durability cannot be overemphasized. To that end, this study proposes the leveraging of AI for optimization and efficiency in the maintenance of SLE. The study relied on both primary and secondary data. The primary data were sourced from twenty Science Laboratory (SL) professionals, while the secondary data were sourced from repositories, databases and websites on the internet. The mixed method alongside the plausible descriptive and statistical tools was employed. The analysis shows that the maintenance of SLE can be optimized and made efficient by leveraging AI for such purposes. Regrettably, public sector organizations are yet to significantly integrate AI into the maintenance of SLE. The study concludes that AI has the capacity to optimize and enhance efficient maintenance of SLE. It calls on stakeholders in the field of SL to make concerted efforts to significantly integrate AI into the maintenance of SLE. The government should help provide AI technologies for the concerned public sector organizations and sponsor the training of people for technical know-how in using and sustaining these cutting-edge technologies in SL.African Journal of Advances in Science and Technology Research391The study concludes that AI has the capacity to optimize and enhance efficient maintenance of SLE and calls on stakeholders in the field of SL to make concerted efforts to significantly integrate AI into the maintenance of SLE.African Journal of Advances in Science and Technology Research["Amusan Odunayo"]2024-10-28T00:00:00 116c69ac976392e805e07ffc0dc7b593f506ba6eThe current study investigates the perceptions of four student teachers of implementing AI tools for designing ELT lessons into their microteaching sessions. The professional development of student teachers achieved via adapting AI tools has not been widely investigated, since the majority of available studies focus on the students’ learning of language skills with AI tools. This study follows the appreciative inquiry approach that emphasises positive teaching practices, aiming to foster sustainable professional development. The participants of the study were MA student teachers studying the Advanced Teaching Practicum course at a Saudi university over one academic semester. Their experiences of designing ELT lessons for their microteaching sessions were reflected upon in reflective journals and a BlackBoard forum. The qualitative analyses of the journals, the forum and lesson plans revealed that the participants appreciated using AI tools, despite the few challenges that occurred. This study demonstrates the participants’ independent efforts that led them to use AI tools that have not been addressed by ELT researchers. The authors hope that this study will enrich ELT practices, assist AI designers in developing their designs by understanding teachers’ experiences and contribute to a sustainable educational future.Sustainability301The perceptions of four student teachers of implementing AI tools for designing ELT lessons into their microteaching sessions are investigated, demonstrating the participants’ independent efforts that led them to use AI tools that have not been addressed by ELT researchers.Sustainability["H. Al-Nofaie", "T. Alwerthan"]2024-10-28T00:00:00 65cac00dc782fd7a9a9f4bc7ad7bee591fa1a9e9Education and Information Technologies : Official Journal of the IFIP technical committee on Education491Educ. Inf. Technol.["B. Abbasi", "Yingqi Wu", "Zhimin Luo"]2024-10-28T00:00:00 d0cf8b3d5d0e3fda5cd858bb1d1cc20aafd62edaIEEE International Conference on Tools with Artificial Intelligence002024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)[]2024-10-28T00:00:00 b3da9373617f781b1ec36ce8be888ab97a819eaaInternational Journal of Infertility &amp; Fetal Medicine30International Journal of Infertility &amp; Fetal Medicine["G. Balsarkar"]2024-10-28T00:00:00 8274ea5339ae74d2da80e9faccd6d7b4903603a5With the rapid development of technology, AI technology has deeply penetrated various stages of news production. From content generation, editing, and distribution to data analysis, it has shown great influence and application potential. This paper analyzes the various forms in which AI is involved in news production, such as automated news writing and editing, technical advancements in news data collection and analysis, and the personalization of news expression and experience. It also explores the challenges AI faces in news production, including the limitations of content generation, privacy and copyright issues, and how to ensure the authenticity of information. The paper provides strategies to address these challenges, aiming to promote the healthy and sustainable development of AI technology in news production, bringing new vitality and opportunities to the news industry.Advances in Social Behavior Research00This paper analyzes the various forms in which AI is involved in news production, such as automated news writing and editing, technical advancements in news data collection and analysis, and the personalization of news expression and experience, and provides strategies to address these challenges.Advances in Social Behavior Research["Xiaolai Liu", "Sisi Yang"]2024-10-28T00:00:00 ee4f63ba280771d71b80d67e24736820ff82f7d1Journal of Literature and Art Studies00Journal of Literature and Art Studies["SHI Ren-wen"]2024-10-28T00:00:00 8544380744c06f482b0e3980c47d881c417aacc8Seventh International Conference on Mechanical Manufacturing and Industrial Engineering (MMIE 2024)00Seventh International Conference on Mechanical Manufacturing and Industrial Engineering (MMIE 2024)["Thanathorn Karot", "C. Pornsing"]2024-10-28T00:00:00 55b26079de8967003f163ac0d03c670b1b1e2ee4Abstract Background Despite the growing prevalence of AI in healthcare, there has been a lack of recent studies exploring its impact on providers’ practices and patient outcomes. This study investigates healthcare providers’ perceptions of AI interventions’ influence on their practice efficiency and patient outcomes, as well as the correlation between providers’ perceptions of AI and its effects on patient outcomes. Methods A self-administered questionnaire was mailed to 38 healthcare providers at a rural medical center in north Texas, with a 70% percent response rate (n = 27). In addition to descriptive statistics, multivariable logistic regression test was conducted to discern the relationship between their perceptions of AI and its impact on patient outcomes. Results The findings revealed that a majority of providers perceived AI to still be in its early stages, posing challenges for practice and having a limited impact on patient outcomes. Additionally, various factors such as age, gender, a user-centered design approach, AI experience, along with perceptions of workplace support and stress, significantly shape providers’ attitudes toward AI and ultimately affecting patient outcomes. Conclusions Before AI technology can fulfill its promise of transforming healthcare through integration with other technologies, the healthcare sector must address numerous hurdles. It is crucial to approach the development and testing of intricate systems like AI-integrated electronic health record (EHR) systems with caution to ensure their reliability and dependability in clinical decision-making. Additionally, navigating medico-legal responsibilities and pursuing fair distribution of benefits are equally crucial. Key messages • This study offers insights into the extent of AI interventions’ impact on healthcare. • This study specifically focuses on enhancing patient care, reducing costs, and improving the efficiency of the healthcare system.European Journal of Public Health00Investigating healthcare providers’ perceptions of AI interventions’ influence on their practice efficiency and patient outcomes reveals that a majority of providers perceived AI to still be in its early stages, posing challenges for practice and having a limited impact on patient outcomes.The European Journal of Public Health["J. Lintz"]2024-10-28T00:00:00 17bfb4d2a813d388ca7abc06c88987b4c58de38dACM Multimedia00{"pages": "11475-11477"}["ShiHang Li", "Haishan Wu", "Biao Wang"]2024-10-28T00:00:00 b7e8d48bfa5fdc17c467887e05a409064f9b7eabAbstract Background The World Health Organization declared the start (March 11th 2020) and the end (May 5th 2023) of COVID19 pandemic, while in Italy it has been present since February 2020. Indicators such as number of new positive cases, deaths and hospitalizations are used to monitor epidemiological trends, but they suffer from biases limiting their effectiveness. Methods We used data from the Emergency Medical Services Activities as an alternative and studied three types of contributions: COVID19, flu and baseline during three time frames: -period 1 (July 1st-Oct 11th 2016; Feb 10th-May 20th 2017) used to model the baseline, when flu contribution should be negligible. -Period 2: (Dec 15th-Feb 14th 2017), when flu in 2017 is very evident. -Period 3: (March 11th-31st 2020), when the first COVID19 wave is dominant. To extract the pure contribution from flu and COVID19, the baseline contribution was properly subtracted. Results From these data we developed a machine learning approach (MLA) that offers a simple and powerful tool to monitor COVID19 or future infectious diseases. To maximize the identification power of COVID19, we used the Toolkit for multivariate analysis package. An artificial neural network, multilayer perceptron, deep neural network and boosted decision tree methods have been trained, considering the events in period 3 as signal and period 1 as background. To avoid overtraining, the samples were divided in two: one half to train the algorithm, and the other to check the performance. The results are stable over time and able to efficiently discriminate COVID19. With 50% efficiency of accepting COVID19 patients, roughly 95% of baseline patients can be rejected. Conclusions MLA can be used to early assign a probability of COVID19 without specific test, only relying on standard triage and emergency call details. This tool could be very useful to early detect the presence of new pandemics and tag positive patients before the official healthcare reporting system. Key messages • AI model using data from Emergency Medical Services Activities can be a useful tool to early detect new pandemics and tag positive patients before the official healthcare reporting system. • Covid19 pandemic gave us the possibility to develop new digital tools to use in the analysis of epidemiological trends.European Journal of Public Health00A machine learning approach using data from Emergency Medical Services Activities can be a useful tool to early detect new pandemics and tag positive patients before the official healthcare reporting system, and can be used to early assign a probability of COVID19 without specific test.The European Journal of Public Health["M. Antinozzi", "D. Del Re", "L. Palla", "P. Meridiani", "L. Soffi", "M. T. Loiudice", "M. Cattaruzza"]2024-10-28T00:00:00 6c894fe4ad62af1e88f042c05a43f42f341411f0With the advances of AI research, AI has been increasingly adopted in numerous domains, ranging from low-stakes daily tasks such as movie recommendations to high-stakes tasks such as medicine, and criminal justice decision-making. Explainability is becoming an essential requirement for people to understand, trust and adopt AI applications. Despite a vast collection of explainable AI (XAI) algorithms produced by the AI research community, successful examples of XAI are still relatively scarce in real-world AI applications. This can be due to the gap between what the XAI is designed for and how the XAI is actually perceived by end-users. As explainability is an inherently human-centered property, in recent years, the XAI field is starting to embrace human-centered approaches and increasingly realizing the importance of empirical studies of XAI design by involving human subjects. To move a step towards a systematic review of empirical study for human-centered XAI design, in this survey, we first brief the technical landscape of commonly used XAI algorithms in existing empirical studies. Then we analyze the diverse stakeholders and needs-finding approaches. Next, we provide an overview of the design space explored in the current human-centered XAI design. Further, we summarize the evaluation metrics based on evaluation goals. Afterward, we analyze the common findings and pitfalls derived from existing studies. For each chapter, we provide a summary of current challenges and research opportunities. Finally, we conclude the survey with a framework for human-centered XAI design with empirical studies.arXiv.org1440This survey briefs the technical landscape of commonly used XAI algorithms in existing empirical studies and provides an overview of the design space explored in the current human-centered XAI design, and concludes with a framework for human-centered XAI design with empirical studies.ArXiv["Shuai Ma"]2024-10-28T00:00:00 39170f1325e3d955685247a3680c1294786532e3Public Performance &amp; Management Review780Public Performance &amp; Management Review["Jaeyeong Nam", "Elizabeth Bell"]2024-10-28T00:00:00 930d90c992a6fa3c5370be2f5b5a3698c52ebf10ACM Multimedia10{"pages": "11470-11471"}["Shien Song", "Jie Yang", "Jin Chen", "Han Qi", "Yifei Xue", "Yizhen Lao", "Yi Yu"]2024-10-28T00:00:00 c340fa6d302aaba406c99cd57b239dc308abcbf6ACM Multimedia00{"pages": "11478-11481"}["Jin Chen"]2024-10-28T00:00:00 381b4b79c80b4dcb2acc80f8a932bab70734b1dbAutosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.Applied Informatics370The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.AI["Latifa Dwiyanti", "Hidetaka Nambo", "Nur Hamid"]2024-10-28T00:00:00 1fd4977254dea6191a43eb2f313ceb05b7f75fc7The relevance of the topic lies in the explanation that the modern world is rapidly changing under the influence of technological progress, and education cannot be left aside from these transformations. The integration of artificial intelligence (AI) into the educational space is one of the key directions of modernization of education, which allows it to adapt it to the needs of the new generation and increase the efficiency of the educational process. AI offers a wide range of tools and opportunities for improving education, from personalizing learning to automating routine tasks. The purpose of the article. - to theoretically substantiate the need to use AI in the educational space. It reveals the advantages of AI for various aspects of the educational process and analyzes its existing capabilities for creating a more effective, personalized and inclusive learning environment. To achieve the goal, the article used a set of scientific research methods. also, an analysis of scientific literature on artificial intelligence and its application in education was conducted, a truly comparative analysis of different approaches to integrating AI into the educational process was carried out, and the synthesis, generalization and systematization of the data obtained were performed. The article considers the theoretical aspects of the use of AI in education, identifies its key advantages.Ekonomìka ta suspìlʹstvo20The article considers the theoretical aspects of the use of AI in education, identifies its key advantages and reveals the advantages of AI for various aspects of the educational process and analyzes its existing capabilities for creating a more effective, personalized and inclusive learning environment.Економіка та суспільство["\u041b\u0456\u043b\u044f \u0411\u0443\u0431\u043b\u0438\u043a", "\u041c\u0430\u0440\u0456\u044f \u0412\u0435\u0441\u043e\u043b\u043e\u0432\u0441\u044c\u043a\u0430"]2024-10-28T00:00:00 ca4610272b429d34aef2da7507dcd13ba9841b51(1) Background: The development of generative artificial intelligence (GAI) is transforming higher education. This systematic literature review synthesizes recent empirical studies on the use of GAI, focusing on its impact on teaching, learning, and institutional practices. (2) Methods: Following PRISMA guidelines, a comprehensive search strategy was employed to locate scientific articles on GAI in higher education published by Scopus and Web of Science between January 2023 and January 2024. (3) Results: The search identified 102 articles, with 37 meeting the inclusion criteria. These studies were grouped into three themes: the application of GAI technologies, stakeholder acceptance and perceptions, and specific use situations. (4) Discussion: Key findings include GAI’s versatility and potential use, student acceptance, and educational enhancement. However, challenges such as assessment practices, institutional strategies, and risks to academic integrity were also noted. (5) Conclusions: The findings help identify potential directions for future research, including assessment integrity and pedagogical strategies, ethical considerations and policy development, the impact on teaching and learning processes, the perceptions of students and instructors, technological advancements, and the preparation of future skills and workforce readiness. The study has certain limitations, particularly due to the short time frame and the search criteria, which might have varied if conducted by different researchers.Information455This systematic literature review synthesizes recent empirical studies on the use of GAI, focusing on its impact on teaching, learning, and institutional practices, to identify potential directions for future research.Information["Jo\u00e3o Batista", "A. Mesquita", "Gon\u00e7alo Carnaz"]2024-10-28T00:00:00 b027886d9064a71b1b1f28b09f3463c422fd8a7aAs frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do so is via safety cases: reports that make a structured argument, supported by evidence, that a system is safe enough in a given operational context. Safety cases are already common in other safety-critical industries such as aviation and nuclear power. In this paper, we explain why they may also be a useful tool in frontier AI governance, both in industry self-regulation and government regulation. We then discuss the practicalities of safety cases, outlining how to produce a frontier AI safety case and discussing what still needs to happen before safety cases can substantially inform decisions.arXiv.org1115The practicalities of safety cases are discussed, outlining how to produce a frontier AI safety case and discussing what still needs to happen before safety cases can substantially inform decisions.ArXiv["Marie Davidsen Buhl", "Gaurav Sett", "Leonie Koessler", "Jonas Schuett", "Markus Anderljung"]2024-10-28T00:00:00 a473a0bffd035ae5d95374cca05f5f4bafc6b8faThis paper explores the future trends and implications of open-source artificial intelligence (AI) for libraries, focusing on predicted technological advancements, long-term impacts on library operations, and the evolving role of librarians. Key advancements, such as enhanced natural language processing, intelligent recommendation systems, and advanced data analytics, are expected to significantly improve user experience and operational efficiency. The implications of these technologies include more personalized and responsive service delivery, streamlined operations, and an evolution in the roles and responsibilities of library staff. Librarians will need to develop new skills and advocate for ethical AI use, ensuring that AI applications align with the library’s values of inclusivity and accessibility. Additionally, the paper discusses the challenges of adopting open-source AI, including technological complexity, resource constraints, and data privacy concerns. The paper concludes that embracing open-source AI fosters innovation and collaboration, positioning libraries as vital hubs of knowledge and community engagement in the future.Asian Journal of Information Science and Technology522It is concluded that embracing open-source AI fosters innovation and collaboration, positioning libraries as vital hubs of knowledge and community engagement in the future.Asian Journal of Information Science and Technology["Emmanuel Okwu", "Diseiye Oyighan", "B. Oladokun"]2024-10-28T00:00:00 9c93f9e696a885a4e88780082016fe57ec434a0eThe applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.Current Drug Targets03The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.Current drug targets["Manmayee Mohapatra", "Chittaranjan Sahu", "Snehamayee Mohapatra"]2024-10-29T00:00:00 25724bc6f86bf366df1b8fb30ca6d5baa37f8231Pflügers Archiv: European Journal of Physiology341From a perspective from the three disciplines of biomedicine, machine learning, and philosophy, light is shed on how the explanation and trustworthiness of artificial intelligence relate to the concepts of causality and robustness and how to connect AI in biomedicine with scientific explanation.Pflugers Archiv : European journal of physiology["Florian Boge", "Axel Mosig"]2024-10-29T00:00:00 594f159097100abe0beabe8f411ae0494136095cThis review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.Magnetic Resonance in Medical Sciences01This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine["T. Saida", "Wenchao Gu", "Sodai Hoshiai", "Toshitaka Ishiguro", "Masafumi Sakai", "T. Amano", "Yuta Nakahashi", "A. Shikama", "Toyomi Satoh", "Takahito Nakajima"]2024-10-29T00:00:00 b6cf57b7a5266b77683d855a3c562b4db14e4a93In this study, cheating behaviors of higher education students and the changes in these behaviors due to developments in the field of artificial intelligence were examined based on the literature. The investigations show that the cheating behavior of the students is at a level that disrupts the accuracy of the decisions made about the students and academic honesty, which is one of the cornerstones of higher education. Cheating in exams, plagiarism and contract cheating are the most common of these behaviors. The way cheating behavior is displayed has also changed from past to present, depending on educational programs, measurement approaches, and developments in technology and artificial intelligence. Tremendous developments in the field of artificial intelligence in the last three years have caused students to make artificial intelligence tools an indispensable part of their cheating processes. This situation requires the development and implementation of new methods to detect and prevent cheating in higher education. Therefore, structuring new studies that address the cheating problem and its solutions in various aspects, including the technology and especially artificial intelligence dimension, is important in terms of its contribution to relevant people and institutions. It is thought that this study is important in terms of drawing attention to the issue.International Journal on Lifelong Education and Leadership01The investigations show that the cheating behavior of the students is at a level that disrupts the accuracy of the decisions made about the students and academic honesty, which is one of the cornerstones of higher education.International Journal on Lifelong Education and Leadership["Zekeriya Nartg\u00fcn", "Eugene Kennedy"]2024-10-29T00:00:00 0b8f86a8039f4799c9ff13dfeece4958cd218ca5In this paper, I discuss the emergence of personal computing, the rise of platform-controlled smartphones and tablets, and the recent surge in artificial intelligence technologies. I explore how these technological advancements have often been shaped by the interests of capital, with recent trends towards increased platform lock-in, control, and exploitation of users (workers). I argue that without a strong push for open-source, democratized AI, these technologies risk being used to further the globalized colonial capitalist project. Through discussion of contemporary issues in corporate LLMs, I explore the corporate piracy of text, visual, and auditory data on the internet and the copyright and other ethical and human implications of this theft of work. I highlight the potential for open-source hardware and software to counter the proprietary and un-hackable future of AI, offering a radical alternative that empowers users and advances human, ecological, and labor rights alongside technology tools. Ultimately, I call for greater attention to the social, political, economic, and environmental implications of computing and AI technologies under capitalism.Fast Capitalism290The potential for open-source hardware and software to counter the proprietary and un-hackable future of AI is highlighted, offering a radical alternative that empowers users and advances human, ecological, and labor rights alongside technology tools.Fast Capitalism[]2024-10-29T00:00:00 d3d1f08bd937bab3d26dd7a651d3d470e401af41Various forms of Artificial intelligence (AI), including machine-driven AI, cognitive AI, and emotional AI, collaborate to provide numerous advantages in marketing research and help marketers develop effective strategies. This study applied the Technology Acceptance Model (TAM) to explore the aspects impelling students' ongoing intention to make advantages of AI in marketing research. To meet the study's objectives, this research used quantitative methods to identify AI applications in marketing research. A questionnaire survey was distributed to 200 student respondents to collect data. The collected data is examined using Importance Performance Map Analysis (IPMA). The study assesses the significance of AI in marketing research among students, revealing that attitude significantly impacts the committed to keep utilizing AI. Positive attitudes are strongly linked to continued usage, with perceived usefulness being the most critical factor, suggesting users find AI enhances their experiences. Acceptance of AI is also influenced by perceived ease of use, system, information, and service quality, as well as attitude. The results indicate that efficient system operation, improved information quality, and high-quality systems and services foster positive perceptions and preferences for AI. The results of this study offer valuable insights for educators in higher education, guiding them on effective strategies to encourage their students to utilize AI in a responsible manner, particularly within the realm of marketing research.Jurnal Riset dan Inovasi Pembelajaran490The study assesses the significance of AI in marketing research among students, revealing that attitude significantly impacts the committed to keep utilizing AI, and indicates that efficient system operation, improved information quality, and high-quality systems and services foster positive perceptions and preferences for AI.Jurnal Riset dan Inovasi Pembelajaran["Adila Sosianika", "Wahyu Rafdinal", "Fatya Alty Amalia"]2024-10-29T00:00:00 20abdd6f2b3b98a32b11a253a6f908befd4e4c5e In the field of education, information technology has been widely used in teaching and learning activities, bringing more efficient learning process for teachers as well as students. However, there are still some problems. The rapid development of Artificial Intelligence (AI) technology has brought a positive effect on the change of education technology. In this paper, the author did some investigation on the research of AI in education. The results of different researchers are divided into three parts in terms of application scenarios. The first part is student performance prediction. In this part, the author has categorized into machine learning and deep learning based on the kind of methods utilized. The second part is student data analysis, which can be functionally categorized into two directions, facial recognition and text analysis, based on the direction of the research. In the third part, the author summarizes some cases of AI-assisted teaching that are closer to actual teaching activities. These cases present solutions to the current deficiencies in the field of education, proving the efficiency of AI. Based on extensive research on many papers, the author suggests that AI currently faces difficult challenges in terms of interpretability, applicability, and safety. The application of AI in education can be improved in the future through technologies such as Open Learner Models (OLMs), expert system, and transfer learning. This paper provides an overview reference for research in this area. Science and Technology of Engineering, Chemistry and Environmental Protection200An overview reference for research in this area of Artificial Intelligence in education suggests that AI currently faces difficult challenges in terms of interpretability, applicability, and safety.Science and Technology of Engineering, Chemistry and Environmental Protection["Ziqi He"]2024-10-29T00:00:00 0b28127e3637a95ae06c098c301f9fc0b9c6306dScientific Reports550Individual differences in the human ability to differentiate human- from AI-generated texts are examined, exploring relationships with fluid intelligence, executive functioning, empathy, and digital habits to inform understanding of how individual difference factors may shape the course of human interactions with AI-generated information.Scientific Reports["J. Chein", "S. Martinez", "A. Barone"]2024-10-29T00:00:00 6e8e4bd2cddbb3788a3434ec82877f6abfac82a3Artificial intelligence (AI) is a broad term encompassing advanced technologies that aim to mimic human intelligence in computer hardware and software, achieving human-level performance. By studying the attitudes and practices of dental professionals and students, we can better integrate AI into the dental field. Hence, the aim is to examine the knowledge, attitudes, and perceptions/practices towards AI among dental students and dental professionals. This study protocol was based on preferred reporting items for overviews of reviews (PRIOR) guidelines. A broad electronic search was carried out in the following databases, such as PubMed, Scopus, Google Scholar, Cochrane database of systematic reviews, and Trip database to find related studies and a total of 28 articles were selected for the systematic review. In this review, the average knowledge percentage among dental students was around 56.9, while dental professionals demonstrated a higher average percentage of 66.42. 82% of postgraduate students showed greater openness to integrating AI at an advanced level of dental education. The review suggests a way for dental students and dental professionals to improve their artificial intelligence understanding and skills.International Journal of Community Medicine and Public Health00Examining the knowledge, attitudes, and perceptions/practices towards AI among dental students and dental professionals suggests a way for dental students and dental professionals to improve their artificial intelligence understanding and skills.International Journal Of Community Medicine And Public Health["Rokesh A.", "Hema S. Budida", "A. S", "Madan Kumar P. D."]2024-10-29T00:00:00 28d1758a267cdb371083b1db5dafacdb14df7f3eNuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.IoT340This study explores the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations and proposes a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents.IoT["Chaima Jendoubi", "Arghavan Asad"]2024-10-29T00:00:00 7e5fb0f79e16f856a5de1a04a6ffc8421a3dc7bdModern society is characterised by the pervasive presence of information and communication technologies. The growing demands of today’s economy and society for enhanced and efficient products and services have led to the continual advance­ment of the technological sector. Among these advancements, artificial intelligence stands out as a particularly noteworthy phenomenon. Artificial intelligence entails the capacity of computer programmes to emulate human intelligence and perform a wide array of tasks. Its implementation has ushered in various advantages, allowing indi­viduals to accomplish tasks like online banking, virtual meetings, and digital conver­sations without the requirement of physical presence. Despite these benefits, the adop­tion of new technologies also introduces potential risks to fundamental rights and freedoms, including privacy, personal data protection, and individual liberty. The ap­plication of artificial intelligence (AI) in the justice system has been a topic of grow­ing interest and debate. AI technologies are being explored and implemented in vari­ous aspects of the justice system to improve efficiency, accuracy, and access to jus­tice.Teme260Artificial intelligence technologies are being explored and implemented in vari­ous aspects of the justice system to improve efficiency, accuracy, and access to jus­tice.TEME["\u017d. Spalevi\u0107", "Milo\u0161 Ili\u0107"]2024-10-29T00:00:00 a1e11cba92e873c4e8a8555a183c2c335bde9a3fSimple Summary In managing cancer diseases, emerging tools and technologies are pivotal for stakeholders including basic scientists, preclinicians, clinicians, and health data managers. In recent years, the emergence of artificial intelligence (AI)-based tools and programs has helped in significant ways to achieve holistic approaches including the diagnosis, therapy, and prognosis of cancer patients. It is true that in achieving enhanced management of cancer patients, productivity in terms of various forms of data, research publications, and patents centered on oncology has increased many-fold. Besides the positive impact of AI on cancer patients and involved stakeholders, reliable balanced approaches are also needed at the moment so that issues of ethics such as patient privacy and data bias can be resolved. Also, the future influence of AI on the creative and disruptive abilities of involved researchers is safeguarded by policies and guidelines on the extent of its uses in oncology.Cancers360It is true that in achieving enhanced management of cancer patients, productivity in terms of various forms of data, research publications, and patents centered on oncology has increased many-fold.Cancers["Nilesh Kumar Sharma", "S. Sarode"]2024-10-29T00:00:00 35d7eb26fc4e422c78cf096d33bed37337c12b95Background: Suicide among students is increasing in India and is a matter of grave concern. Early identification of students contemplating suicide would facilitate emergency intervention and may save precious lives. Aim: Our primary objective was to construct an artificial intelligence (AI) model employing an artificial neural network (ANN) architecture to predict students at risk of suicidal tendencies. This initiative was prompted by the necessity to implement a proactive and technologically driven strategy for identifying competitive exam-bound students facing heightened vulnerability. The aim was to facilitate timely interventions aimed at reducing the risk of self-harm. Materials and Methods: An AI model utilizing ANNs is devised for suicide risk prediction among exam-stressed students. A 33-feature input layer is curated based on literature and expert insights, with binary features assigned weighted values. A rigorous hyperparameter optimization approach using the Optuna library to select the most effective neural network model. Ridge regression was used to determine bias or variance in the dataset. Training and testing of the model are conducted using fictional and simulated profiles, respectively, and model performance is assessed through statistical metrics and the Cohen’s Kappa coefficient, benchmarked against expert evaluations. Result: The AI model demonstrates exceptional predictive capabilities for suicide risk assessment among competitive exam students. Quantitative Metrics: The model’s accuracy of 98% aligns predictions with outcomes, distinguishing risk categories. Precision at 100% identifies cases within predicted risks, minimizing false positives. A recall of 97% identifies true risk cases, highlighting sensitivity. F1 Score: The model’s F1 score of 98% balances precision and recall, indicating overall performance. Cohen’s Kappa: With a coefficient of 1.00, the model’s substantial agreement with experts underscores its consistent classifications. Conclusion: The study introduces an AI model utilizing ANNs for suicide risk prediction among stressed students. High precision, recall, and accuracy align with expert evaluations, highlighting its promise for timely risk identification. The model’s efficiency in evaluating large populations swiftly indicates its clinical potential. Refinement and real-world validation remain future considerations.Industrial Psychiatry Journal160High precision, recall, and accuracy align with expert evaluations, highlighting its promise for timely risk identification, and the model’s efficiency in evaluating large populations swiftly indicates its clinical potential.Industrial Psychiatry Journal["Vikas Gaur", "Gaurav Maggu", "Khushboo Bairwa", "Suprakash Chaudhury", "Sana Dhamija", "T. Ali"]2024-10-29T00:00:00 4748ab25bdc146de7d3d965fc242859b4c79ac08Environmental, social, and governance (ESG) practices have become increasingly important in corporate strategy in recent years, while the rapid development of artificial intelligence (AI) has created new opportunities and challenges for corporate sustainability. AI technology driving companies for ESG time is getting more and more attention. This study examines the application of AI technologies in environmental management, social responsibility, and corporate governance, demonstrating their potential to optimize resource utilization, reduce carbon emissions, improve recruitment fairness, and prevent fraud. However, integrating AI with ESG faces many challenges, including technological complexity, high costs, data privacy and ethical issues, and organizational and cultural resistance. To address these challenges, this study proposes solutions to reduce financial burdens, secure data, and enhance cultural buy-in through strategies such as technology partnerships, open-source tools, and employee training. By delving into the convergence of AI and ESG, this study provides companies with a guiding direction to fully utilize the potential of AI while maintaining long-term sustainability.Finance &amp; Economics00Finance &amp; Economics["Sihan Zhao"]2024-10-29T00:00:00 83a611923eaa2e043b57f9dd0a6332bc1be41673Current Cardiology Reports280Current cardiology reports["Patr\u00edcia Bota", "Geerthy Thambiraj", "S. C. Bollepalli", "A. Armoundas"]2024-10-29T00:00:00 0d50dab4301863fc3c1d4789f5476833c3951897The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning (ML) systems, with significant implications for Graph Neural Networks (GNNs). This paper addresses the unique challenges posed by the AI Act for GNNs, which operate on complex graph-structured data. The legislation's requirements for data management, data governance, robustness, human oversight, and privacy necessitate tailored strategies for GNNs. Our study explores the impact of these requirements on GNN training and proposes methods to ensure compliance. We provide an in-depth analysis of bias, robustness, explainability, and privacy in the context of GNNs, highlighting the need for fair sampling strategies and effective interpretability techniques. Our contributions fill the research gap by offering specific guidance for GNNs under the new legislative framework and identifying open questions and future research directions.arXiv.org490This study explores the impact of these requirements on GNN training and proposes methods to ensure compliance, and provides an in-depth analysis of bias, robustness, explainability, and privacy in the context of GNNs.ArXiv["Barbara Hoffmann", "Jana Vatter", "Ruben Mayer"]2024-10-29T00:00:00 e66360f6f63290de1b69fa6209a202bad85b58fcAs the fourth industrial revolution, artificial intelligence is reshaping many industries. It has become a new cornerstone of digital conversion, and it is no exception in the field of forensic medicine.This paper mainly discusses how artificial intelligence can solve the problems related to forensic medicine.How to use artificial intelligence technology to develop into an auxiliary tool in the field of forensic medicine.This paper summarizes the exploratory data analysis, statistical modeling and machine learning in artificial intelligence, extracts insights and knowledge from the data, and applies them to various fields of forensic medicine.Artificial intelligence will improve the accuracy and efficiency of forensic work: it can automate some tasks and improve the quality of evidence. The comprehensive analysis results show that the artificial intelligence proposed in this paper will be an important auxiliary in the three directions of forensic medicine.Computer and Information Science570The comprehensive analysis results show that the artificial intelligence proposed in this paper will be an important auxiliary in the three directions of forensic medicine.Computer and Information Science["Minni Qi", "Dan Zhou", "Xiaojun Yu"]2024-10-29T00:00:00 0dee89a600c926d05e7d756732d00cbf0d9395a3Recent advancements in the field of Artificial Intelligence (AI) provide promising applications of this technology with the aim of solving complex healthcare challenges. These include optimizing operational efficiencies, supporting clinical administrative functions, and improving care outcomes. Numerous AI models are validated in research settings but few make their way into useful applications due to challenges associated with implementation and adoption. In this article, we describe some of these challenges, along with the need for a facilitating entity to safely translate AI systems into practical use. The authors propose a new AI governance framework to enable healthcare organizations with a mechanism to implement and adopt AI systems.Healthcare Management Forum210A new AI governance framework is proposed to enable healthcare organizations with a mechanism to implement and adopt AI systems and the need for a facilitating entity to safely translate AI systems into practical use is described.Healthcare management forum["Masooma Hassan", "E. Borycki", "A. Kushniruk"]2024-10-29T00:00:00 11f78232b65757d21954edc6e417a2e0cc19eac8The integration of artificial intelligence (AI) into the legal profession is reshaping how legal tasks are performed, enhancing efficiency and streamlining operations. AI applications, such as legal research tools, contract preparation software, and predictive analytics, assist lawyers by automating monotonous tasks, allowing them to focus on more complex aspects of their cases. Key benefits include time savings, improved accuracy in drafting, and reduced bias in decision-making. However, the reliance on AI also raises significant concerns, including the potential erosion of essential legal skills, challenges to privacy and data security, and the diminishing role of human judgment in navigating nuanced legal issues. While AI serves as a valuable tool, it cannot replace the critical thinking, empathy, and intuition that human advocates bring to the profession. Balancing AI's efficiency with the necessity of human insight will be crucial for the future of legal practice.International Journal For Multidisciplinary Research00Balancing AI's efficiency with the necessity of human insight will be crucial for the future of legal practice, as well as the potential erosion of essential legal skills and challenges to privacy and data security.International Journal For Multidisciplinary Research["Vikrant Diwakar"]2024-10-29T00:00:00 dc573dd69f16394a4a02c35c9a591cc526787b1fThis study discusses whether artificial intelligence will pose a threat to human beings. This topic is chosen as AI affects everyone in the 21st century and the problem of it being a threat will come to if nothing is done. Signs of AI can be seen everywhere, from robots to phones, and even dishwashers, this piece of technology has grown to become an irreplaceable figure in the lives of humans. However, as the speed of development of technology increases, AI might get out of control and even become a threat to people. This research has stated some threats it will cause and their solutions. Much literature written by technology experts has been read and used as references in this research to provide reliable data. This research can be used to help solve or mitigate the problem in the future as it explains in detail what could be done. From this research, it concluded that without humans doing anything to interfere with this problem, AI will threaten humans someday. However, this day might not come so soon due to factors like countries refusing to sell microchips to China because of competition. Therefore, if the solutions listed in the research are used, the probability of AI becoming a threat to people will decrease dramatically.Interdisciplinary Humanities and Communication Studies60From this research, it concluded that without humans doing anything to interfere with this problem, AI will threaten humans someday, however, this day might not come so soon due to factors like countries refusing to sell microchips to China because of competition.Interdisciplinary Humanities and Communication Studies["Zhiyou Wang"]2024-10-29T00:00:00 c92dfbd02d3bfc8fb9c51e207cb41efb5b70c770This article discusses the prominent elements of artificial intelligence innovation in fashion sales techniques and details artificial intelligence-supported sales strategies such as virtual reality, augmented reality, virtual try-on rooms, smart mirrors, artificial intelligence-supported style consultants, visual search technologies and chatbot. This study was written with the compilation method. The compilation method is written with the aim of collecting the literature written so far on the research subjects from a scientific perspective and making a collective contribution to the literature. While virtual reality and augmented reality offer customers interactive shopping experiences and the opportunity to try products in their own environment without going to the store, virtual trial rooms and smart mirrors; Studies have shown that it has the potential to make online and face-to-face shopping easier, faster and more interactive. It has been stated that visual search technologies enable customers to easily find the products in the style they want, and artificial intelligence-supported style consultants and chatbots play an important role in optimizing the shopping experience by making personalized suggestions to customers. New and innovative solutions can be produced by conducting studies that encourage interdisciplinary collaboration between fashion designers, software developers and academicians. In this period when we live intertwined with technology, artificial intelligence-focused modules can be added to academic curricula for students studying in the field of design, and these modules will provide benefits for students to gain knowledge and skills on how to integrate artificial intelligence technologies into fashion design. The effective use of artificial intelligence in fashion sales techniques will provide a competitive advantage against competitors in the industry.İnsan ve Sosyal Bilimler Dergisi150İnsan ve Sosyal Bilimler Dergisi["Ba\u015fak Bo\u011fday Say\u011f\u0131l\u0131", "Cansel Dilber"]2024-10-29T00:00:00 437d9941bc6a5688b7d917292a7e25463218365fThe “Chinese Room Argument” is a thought experiment proposed by American philosopher John Searle in 1980. It refutes the claim that computer programs as symbolic representation systems can generate semantic understanding by arguing that artificial intelligence cannot express intentionality. In 1982, Australian philosopher Frank Jackson introduced the “Knowledge Argument,” which questions the physicalism reducibility perspective by examining the concept of qualia. From the analysis of these two thought experiments, it can be concluded that the knowledge of computer programming and its machine simulation cannot truly understand the meaning understood by biological intelligence, and that meaning contains the content of tacit knowledge, so even the most complete artificial intelligence system still lacks the intrinsic, non-physical, and intimate properties that are characteristic of biological intelligence. It is these properties that make up the important difference between biological intelligence and artificial intelligence. This paper discusses how three tacit knowledge forms, relational, physical and collective, affect the development of artificial intelligence, and tries to explore a new direction for the development of artificial intelligence in the future.Interdisciplinary Humanities and Communication Studies20How three tacit knowledge forms, relational, physical and collective, affect the development of artificial intelligence, is discussed, and a new direction for the development of artificial intelligence in the future is explored.Interdisciplinary Humanities and Communication Studies["Zixuan Zhao"]2024-10-29T00:00:00 caedaec2872748b3e4238b3a4c448a261829410dThe students of SMK Negeri 3 Kendal, especially the PPLG (Software and Game Development) competency, has not received knowledge related to artificial intelligence which is starting to be widely used in the world of work. The school wanted the student to have knowledge in artificial intelligence. The reason is that artificial intelligence has begun to be widely used in the world of work. To satisfiy the need, this community service proposed to increase the ability of students at SMK Negeri 3. In this community service, the discussed artificial intelligence would be centered on the LLM (Large Language Model). The community service was carried out using the method of raising awareness/increasing understanding of artificial intelligence issues. The training/increasing understanding activity itself is carried out in 4 stages, namely pre-test, presentation, practice and post-test. From the results of the presentation of presentation and practice, several weaknesses emerged. This is related to understanding how artificial intelligence works, the application of artificial intelligence and how to recognize artificial intelligence results. This community service activity conveys correct information regarding these matters. By comparing pre-test and post-test results, which has 8 questions, there is an increase of their knowledge with an average of 25%. The greatest increase in understanding occurred in the question ‘Does AI really understand what it is doing?’ which relates to understanding how artificial intelligence works. The increase in understanding on this question occurred by 50%.   Abstrak Dalam kegiatannya, SMK Negeri 3 Kendal, utamanya kompetensi PPLG (Pengembangan Perangkat Lunak dan Gim), belum mendapat pengetahuan terkait kecerdasan buatan yang mulai banyak digunakan dalam dunia kerja. Pihak sekolah memiliki keinginan agar par siswa dapat memiliki pengentahuan terkait kecerdasan buatan. Hal ini karena kecerdasan buatan sudah mulai banyak dimanfaatkan dalam dunia kerja. Pengabdian masyarakat ini mengusulkan peningkatan kemampuan para siswa SMK Negeri 3 Kendal untuk menjawab masalah yang telah dipaparkan. Secara khusus, pada pengabdian masyarakat ini, pengenalan kecerdasan buatan yang dimaksud akan berpusat pada bentuk LLM (Large Language Model). Kegiatan pengabdian kepada masyarakat kali ini dilakukan dengan metode penyadaran/peningkatan pemahaman terhadap masalah kecerdasan buatan. Kegiatan pelatihan/peningkatan pemahaman sendiri dilakukan dalam 4 tahap, yaitu pre-test, pemaparan/penyuluhan, praktek dan post-test. Dari hasil pemaparan materi dan praktek didapatkan bahwa terdapat beberapa kelemahan yang mengemuka. Hal ini terkait pemahaman cara kerja kecerdasan buatan, terapan kecerdasan buatan dan cara mengenali hasil kecerdasan buatan. Kegiatan pengabdian kepada masyarakat ini menyampaikan informasi yang benar terkait hal-hal tersebut. Hasil dari pre-test dan post-test yang mencakup 8 poin, menunjukkan terdapat peningkatan pemahaman dari peserta dengan rata-rata sebesar 25%. Peningkatan pemahaman terbesar terjadi pada pertanyaan tentang kebenaran bahwa AI memahami apa yang dilakukannya yang terkait pemahaman cara kerja kecerdasan buatan. Peningkatan pemahaman pada pertanyaan ini terjadi sebesar 50%.Sarwahita00Sarwahita["V. Utomo", "Muhammad Basyier Ardima", "Prind Triajeng Pungkasanti"]2024-10-29T00:00:00 8c81118892cb664c0bc681d0182acd85fd06775eLa introducción de IA en el ámbito judicial plantea una serie de temas que involucran los derechos de propiedad intelectual hasta la ética, la responsabilidad civil y la privacidad. Se plantea como objetivo analizar los efectos de la inteligencia artificial en la administración de justicia.  La población documental se conformó por 18 artículos científicos seleccionados. La incorporación de la inteligencia artificial en la administración de justicia plantea retos fundamentales que exigen un marco regulatorio robusto y ético, capaz de proteger los derechos humanos y garantizar la equidad en la toma de decisiones judiciales. La capacidad de la IA para influir en áreas clave como la propiedad intelectual, los derechos laborales, la privacidad y la responsabilidad civil, junto con su potencial para transformar los procesos judiciales, hace necesario un enfoque jurídico adaptativo que contemple tanto los beneficios como los riesgos.Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales100Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales["Danny Alexander Escobar-Escobar", "Annie Camilie Luna-S\u00e1nchez", "Said Alexander Viteri-Tacoaman", "Luis Ren\u00e1n Garc\u00eda-Sanipat\u00edn"]2024-10-29T00:00:00 c1f001d1d5db5049e2b1522830ff710f9b19775cArtificial neural networks are capable of efficiently processing large data sets, as well as solving the tasks of prediction, classification and data recovery. The article considers each of the above tasks in detail and studies literature sources devoted to the topic under study. Artificial neural networks cope with the tasks with a high degree of accuracy. The methods of application of neural networks for the analysis of systemic haemodynamics are described. Modern neural networks can analyse medical data and are able to work with incomplete data, find hidden patterns in them, and can be adapted to solve a wide range of problems. Our laboratory is developing an artificial neural network capable of classifying indicators describing the state of haemodynamics of subjects and recovering missing or incomplete data. Thus, artificial neural networks can act as an efficient method of analysing systemic hemodynamic parameters.Medical academic journal160The article considers each of the above tasks in detail and studies literature sources devoted to the topic under study and describes the methods of application of neural networks for the analysis of systemic haemodynamics.Medical academic journal["Evgenia A. Sokolova", "T. Sergeev", "M. Kuropatenko"]2024-10-29T00:00:00 10ee0211ba5479d58dea8595d3ec535ef9cd7886La incorporación de la inteligencia artificial (IA) en el ámbito judicial plantea desafíos éticos y responsabilidades que requieren un análisis exhaustivo. Este estudio tiene como objetivo analizar la ética y responsabilidad en el uso de la IA en procesos judiciales. Para ello, se incluyeron 19 artículos científicos publicados entre 2018 y 2024, lo que brindó una comprensión actualizada sobre el desarrollo y aplicaciones de la IA en el ámbito judicial. Los hallazgos muestran que, aunque la IA mejora la eficiencia y accesibilidad judicial, su implementación sin supervisión adecuada podría perpetuar sesgos, vulnerar la privacidad y afectar la objetividad en decisiones. Para que la IA fortalezca el sistema judicial, es esencial que autoridades y el sistema legal establezcan normativas claras que delimiten responsabilidades y aseguren la justicia en un entorno de creciente automatización.Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales00Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales["Keila Lisseth Zabala-Balladares", "Nathaly Kasandra Moncayo-Morlas", "Wendy Geovanna Jim\u00e9nez-Andrade", "Deinier Ros-\u00c1lvarez"]2024-10-29T00:00:00 8f56b3aac390035e15de323fe90b4e3f4e9ea279PurposeArtificial intelligence (AI) usage improves e-commerce logistics efficiency. However, many actors can play significant roles, such as supply chain consistency (SCC), last-mile logistics (LML) performance and collaboration and coordination among logistics firms. This study aims to assess how SCC and LML performance mediate and collaboration and coordination moderate the relationship between AI usage and logistics efficiency.Design/methodology/approachA structured questionnaire was used to collect the data. A total of 245 valid responses were received from Indian e-commerce businesses. The data were then analysed using AMOS v25 and structural equational modelling using SPSS for regression, PROCESS macro for mediation and moderated mediation analysis.FindingsThe findings show that AI usage independently impacts logistics efficiency, with SCC and last-mile delivery performance as mediating variables. Collaboration and coordination among logistic firms are also critical moderators in enhancing AI’s efficacy in logistic operations. The study findings suggest the integration of AI into logistic operations and provide implications to managers on the urgency of fostering a collaborative and synchronised environment to utilise the full potential of AI in e-commerce businesses.Originality/valueThis study not only contributes to the field of logistics theory by presenting empirical data on the various ramifications of AI but also offers practical guidance for logistics firms, particularly those operating in developing economies, on how to strategically employ AI to enhance operational efficiency and attain a competitive advantage in the era of e-commerce logistics in the digital age.International Journal of Logistics Management1010The findings show that AI usage independently impacts logistics efficiency, with SCC and last-mile delivery performance as mediating variables and collaboration and coordination among logistic firms are also critical moderators in enhancing AI’s efficacy in logistic operations.The International Journal of Logistics Management["Gunjan Malhotra", "Manjeet Kharub"]2024-10-29T00:00:00 9937a9cca321dbfb58d25513cad7ba680bc03085En la era de la inteligencia artificial (IA), el acceso y procesamiento de información pública han generado implicaciones para el derecho a la privacidad, marcando nuevos desafíos en la regulación y protección de datos personales. Desde los planteamientos anteriores, se presenta como objetivo de investigación analizar desde una perspectiva jurídica la privacidad al acceder a información pública en la era de la inteligencia artificial en contexto del Ecuador. La revisión documental de 23 articulos científicos fue estructurada en torno a categorías temáticas. Para que Ecuador aproveche los beneficios del gobierno electrónico y la IA sin comprometer los derechos fundamentales, es fundamental que las políticas públicas incluyan la participación ciudadana y refuercen el compromiso hacia la ética y la transparencia digital. Solo políticas de gobernanza que prioricen la protección de datos personales pueden asegurar un desarrollo tecnológico que respete y fortalezca los principios democráticos y los derechos individuales.Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales00Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales["Kerry Stalin Burgos-Arcentales", "Hugo Stewar Jami-Meza", "Deinier Ros-\u00c1lvarez"]2024-10-29T00:00:00 484de12fef7e687b38f3acc5f0327fd47b9e48c8Nursing management190Nursing management["Anna E Schoenbaum", "Ameena Elahi", "Tessa Cook"]2024-10-29T00:00:00 53fc31986623b7c9e9721c234045de61dac0a6e7Implementasi Big Data dan AI untuk meningkatkan kemampuan intelijen TNI mampu memberikan kontribusi signifikan dalam menghadapi tantangan keamanan yang semakin kompleks, dengan mengoptimalkan pengumpulan, analisis, dan interpretasi data untuk mendukung pengambilan keputusan strategis yang lebih efisien dan efektif. Permasalahan penelitian yaitu bagaimana strategi Implementasi Big Data dan AI untuk meningkatkan kemampuan intelijen TNI dalam menghadapi tantangan keamanan yang kompleks di era digital. Tujuan penelitian yaitu untuk menganalisis pengembangan infrastruktur Big Data yang efisien dan aman untuk TNI, penerapan teknik kecerdasan buatan dalam analisis intelijen, serta evaluasi kinerja sistem Big Data dan AI dalam mendukung pengambilan keputusan strategis TNI. Hasil penelitian menunjukkan bahwa Pengembangan infrastruktur Big Data dan AI yang efisien dan aman untuk TNI sangat penting dalam memperkuat intelijen dan mendukung keputusan strategis, namun menghadapi tantangan integrasi, keamanan data, dan keterampilan personel, sehingga memerlukan investasi dalam teknologi canggih serta kolaborasi untuk mengoptimalkan pengelolaan data dan meningkatkan respons TNI. Kesimpulan Implementasi Big Data dan AI dapat memperkuat kapabilitas intelijen dan mendukung keputusan strategis di era digital untuk meningkatkan efisiensi dan efektivitas operasional dalam menghadapi ancaman nasional.Ranah Research220Ranah Research : Journal of Multidisciplinary Research and Development["Tri Wahyu Asmoro Putro"]2024-10-29T00:00:00 b9bf3f6c2c8335670ff7c58e5515de5595e65c3bAI &amp; SOCIETY00AI &amp; SOCIETY["T. Dekeyser", "Mark Whitehead"]2024-10-29T00:00:00 a2fff2122795b2a4f49e76e3409282059f6cef47Coronary artery bypass grafting (CABG) is one of the most common procedures used to treat patients with severe blockages in the heart's arteries, and it typically requires careful post-operative care and rehabilitation [1]. Following such a complex surgery, the rehabilitation process involves managing numerous factors, including monitoring cardiac status, controlling vital signs, improving heart and lung function, and addressing the patient's mental and emotional well-being. AI can be crucial in accelerating and enhancing this process [2]. One of the most critical applications of AI in rehabilitating patients’ post-CABG surgery is its ability to continuously monitor and analyze medical data [3]. Wearable devices and intelligent monitoring systems, supported by AI, consistently track vital signs such as heart rate, blood pressure, and oxygen levels. These systems can detect abnormalities and promptly alert the medical team, preventing severe complications before the patient’s condition worsens [3]. This continuous, uninterrupted monitoring allows patients to be supervised even in their homes, reducing the burden on hospitals and improving the overall patient experience. In addition, AI can play a crucial role in creating personalized rehabilitation programs for patients. By utilizing machine learning algorithms, AI can analyze detailed data related to each patient and tailor rehabilitation and exercise programs according to their specific needs. For example, some patients may require lighter exercise routines, while others may be able to engage in more complex physical activities. AI, by analyzing physiological data and the medical history of patients, can recommend rehabilitation programs suited to their condition while also minimizing potential risks [4, 5]Journal of Nursing Reports in Clinical Practice00AI can play a crucial role in creating personalized rehabilitation programs for patients by utilizing machine learning algorithms, which can analyze detailed data related to each patient and tailor rehabilitation and exercise programs according to their specific needs.Journal of Nursing Reports in Clinical Practice["Amirmohammad Khani", "Ebrahim Sadeghi Velni", "Parisa Khani", "Mohammad-Amin Abshat", "Seyed Sobhan Hosseini", "Danial Bagherieh", "Seyed Javad Masoumi"]2024-10-29T00:00:00 364e02d8a0471fedb397ba55f8ba0e6314de348eThis paper reviews the theoretical background and potential applications of Large Language Models (LLMs) in educational processes and academic research. Utilizing a novel digital ethnographic approach, we engaged in iterative research with OpenAI’s ChatGPT-4 and Google’s Gemini Ultra—two advanced commercial LLMs. The methodology treated LLMs as research participants, emphasizing the AI-guided perspectives and their envisioned roles in educational settings. Our findings identified the potential LLM roles in educational and research processes and we discussed the AI challenges, which included potential biases in decision-making and AI as a potential source of discrimination and conflict of interest. In addition to practical implications, we used the qualitative research results to advise on the relevant topics for future research.Informatics400The theoretical background and potential applications of Large Language Models (LLMs) in educational processes and academic research are reviewed and the AI challenges are discussed, which included potential biases in decision-making and AI as a potential source of discrimination and conflict of interest.Informatics["N. Alfirevi\u0107", "Darko Renduli\u0107", "Maja Fo\u0161ner", "A. Fo\u0161ner"]2024-10-29T00:00:00 92a74a019b3915526dae8c77ec5196af2e813f9fDigital Library Perspectives00Digital Library Perspectives["Juan D. Machin-Mastromatteo", "A. Tammaro"]2024-10-29T00:00:00 0cfcdf9f2a13282b9289dcf2da2da687a7f1ccc2This article explores the upcoming issues and legal challenges in copyright law brought by AI-generated artworks. As AI technologies improve, the creation of art by AI has raised problems regarding authorship, originality, and the application of existing copyright frameworks. By analyzing cases happened in different region about copyright in AI-generated art, it is discovered that different attitudes toward AI-generated artworks under current copyright framework. While the United States show a relatively conservative stance, insisting that the role of author must be human, other countries such as Can dada and China began to admit the authorship of AI, accepting AI as a way to achieve creativity and originality. Based on the existing situation, the article provided possible solutions, aiming to protect copyright of creative artworks generated by AI and accept AI as artistic tool that could increase efficiency and creativity.Interdisciplinary Humanities and Communication Studies40Interdisciplinary Humanities and Communication Studies["Zifan Feng"]2024-10-29T00:00:00 fc06653e1805d8d18bb1115a5ca3a4e1b2b151e600["Indranath Chatterjee", "Nasrollah Moradikor"]2024-10-29T00:00:00 f22c30d92bc7d4f7683da8c87da147615ab5a492Over the past few years, AI-based models for Mahjong have received a substantial amount of contribution due to the advancements in machine learning and game theory. In this review, these state-of-the-art AI models are considered, including Tjong, Kanachan, Suphx, and Zhejiang University’s developed model. The deployed approaches are disparately represented by reinforcement learning, Monte Carlo tree search (MCTS), and deep neural networks, all of which aim at solving the issues that come with the game’s structure, such as incomplete and overburdening information. The Tjong model complements the power of the transformer architecture with hierarchical decision-making and in turn, brings strategic depth to the gameplay. Kanachan employs Q-learning and MCTS to optimize decision-making, while Suphx combines these methods with the novel ideas of Double Q-learning and Thompson Sampling to achieve higher performance than seen before. The integration of reinforcement learning and a new evaluation model imbues the model with the unique properties of both learning machines and human expertise, namely depth and intelligence. As a result of all these exploits, obstacles still exist, the most notable among them are the management of completely unknown information, handling long-distance games, and the strategic balance between offense and defense. In the future, determinants of probabilistic reasoning, the improvement of deep learning systems, and the prowess of reward shaping will result in AI improvement in Mahjong.Science and Technology of Engineering, Chemistry and Environmental Protection20In this review, these state-of-the-art AI models for Mahjong are considered, including Tjong, Kanachan, Suphx, and Zhejiang University’s developed model, which combines reinforcement learning and a new evaluation model to achieve higher performance.Science and Technology of Engineering, Chemistry and Environmental Protection["Weikai Liu"]2024-10-29T00:00:00 7a45076ddf8fca1e088ab3e8e7bd854dca9e183000["Giuseppe Ugazio", "Milos Maricic"]2024-10-29T00:00:00 883efda938f827c2c9dcd3dcba5aea1f2f927346Artificial intelligence (AI) systems are increasingly applied in areas such as voice assistants, customer service, and healthcare. However, current AI systems often lack semantic understanding and emotional expression capabilities when interacting with humans, leading to poor user experiences. To enable AI systems to converse more naturally with humans, it is necessary to synergistically optimize two key capabilities: language adaptation and emotional intelligence. This paper reviews the latest research progress in AI dialogue systems regarding language adaptation and emotional intelligence. In terms of language adaptation, this paper summarizes technologies such as personalized dialogue, knowledge-enhanced dialogue, and multi-turn contextual dialogue. In terms of emotional intelligence, this paper outlines work in areas such as emotion recognition, emotional dialogue generation, and empathetic dialogue. Additionally, this paper discusses strategies for synergistic optimization of language adaptation and emotional intelligence, as well as challenges in dialogue system evaluation. Finally, this paper looks ahead to future directions for AI dialogue systems.Interdisciplinary Humanities and Communication Studies10This paper reviews the latest research progress in AI dialogue systems regarding language adaptation and emotional intelligence and outlines work in areas such as emotion recognition, emotional dialogue generation, and empathetic dialogue.Interdisciplinary Humanities and Communication Studies["Te Zhao"]2024-10-29T00:00:00 da0e14fbffd741b38d50ed026ebcc608ef20ededArtificial Intelligence (AI) has rapidly evolved over the past decade and has advanced in areas such as language comprehension, image and video recognition, programming, and scientific reasoning. Recent AI technologies based on large language models and foundation models are approaching or surpassing artificial general intelligence. These systems demonstrate superior performance in complex problem solving, natural language processing, and multi-domain tasks, and can potentially transform fields such as science, industry, healthcare, and education. However, these advancements have raised concerns regarding the safety and trustworthiness of advanced AI, including risks related to uncontrollability, ethical conflicts, long-term socioeconomic impacts, and safety assurance. Efforts are being expended to develop internationally agreed-upon standards to ensure the safety and reliability of AI. This study analyzes international trends in safety and trustworthiness standardization for advanced AI, identifies key areas for standardization, proposes future directions and strategies, and draws policy implications. The goal is to support the safe and trustworthy development of advanced AI and enhance international competitiveness through effective standardization.arXiv.org502This study analyzes international trends in safety and trustworthiness standardization for advanced AI, identifies key areas for standardization, proposes future directions and strategies, and draws policy implications.ArXiv["Jonghong Jeon"]2024-10-29T00:00:00 2cbe61c3a3bb7f9fdc424d6566ff6dc4393630d2Integrating symbolic techniques with statistical ones is a long-standing problem in artificial intelligence. The motivation is that the strengths of either area match the weaknesses of the other, and $\unicode{x2013}$ by combining the two $\unicode{x2013}$ the weaknesses of either method can be limited. Neuro-symbolic AI focuses on this integration where the statistical methods are in particular neural networks. In recent years, there has been significant progress in this research field, where neuro-symbolic systems outperformed logical or neural models alone. Yet, neuro-symbolic AI is, comparatively speaking, still in its infancy and has not been widely adopted by machine learning practitioners. In this survey, we present the first mapping of neuro-symbolic techniques into families of frameworks based on their architectures, with several benefits: Firstly, it allows us to link different strengths of frameworks to their respective architectures. Secondly, it allows us to illustrate how engineers can augment their neural networks while treating the symbolic methods as black-boxes. Thirdly, it allows us to map most of the field so that future researchers can identify closely related frameworks.arXiv.org02This survey presents the first mapping of neuro-symbolic techniques into families of frameworks based on their architectures, with several benefits: it allows us to link different strengths of frameworks to their respective architectures, and allows us to illustrate how engineers can augment their neural networks while treating the symbolic methods as black-boxes.ArXiv["Jonathan Feldstein", "Paulius Dilkas", "Vaishak Belle", "Efthymia Tsamoura"]2024-10-29T00:00:00 5ce2c28e63c98958b7abd776f94bbf9b5ed90655As artificial intelligence (AI) becomes more integrated into society, concerns have arisen about unintended biases in AI-driven decision-making and the environmental impact of AI technology development. AI assistants such as Siri and Alexa, while helpful, can obscure decision-making and contribute to increased energy use and CO2 emissions. The present study explores whether consumers prioritize transparency and environmental sustainability over performance when choosing AI assistants with conjoint designs. Japanese participants were presented with different AI assistant profiles, varying in performance quality, transparency, cost, and environmental efficiency. The results revealed that Japanese participants prioritized transparency over performance when choosing AI assistants, but they prioritized performance over environmental sustainability. Moreover, future-oriented participants placed more importance on sustainability than those with a present orientation, while participants with an internal locus of control valued transparency more than those with an external locus of control. The findings of this study enhance our understanding of how consumers choose AI options and offer valuable guidance for creating AI systems and communication strategies that work effectively.Big Data & Society871Japanese participants prioritized transparency over performance when choosing AI assistants, but they prioritized performance over environmental sustainability, and future-oriented participants placed more importance on sustainability than those with a present orientation.Big Data Soc.["T. Ioku", "Jaehyun Song", "Eiichiro Watamura"]2024-10-29T00:00:00 c65a7571d2e5bb977f9189e6c9a49df3daaf6837Audits contribute to the trustworthiness of Learning Analytics (LA) systems that integrate Artificial Intelligence (AI) and may be legally required in the future. We argue that the efficacy of an audit depends on the auditability of the audited system. Therefore, systems need to be designed with auditability in mind. We present a framework for assessing the auditability of AI-integrating systems that consists of three parts: (1) Verifiable claims about the validity, utility and ethics of the system, (2) Evidence on subjects (data, models or the system) in different types (documentation, raw sources and logs) to back or refute claims, (3) Evidence must be accessible to auditors via technical means (APIs, monitoring tools, explainable AI, etc.). We apply the framework to assess the auditability of Moodle's dropout prediction system and a prototype AI-based LA. We find that Moodle's auditability is limited by incomplete documentation, insufficient monitoring capabilities and a lack of available test data. The framework supports assessing the auditability of AI-based LA systems in use and improves the design of auditable systems and thus of audits.arXiv.org791A framework for assessing the auditability of AI-integrating systems in use is presented and it is found that Moodle's auditability is limited by incomplete documentation, insufficient monitoring capabilities and a lack of available test data.ArXiv["Linda Fernsel", "Yannick Kalff", "Katharina Simbeck"]2024-10-29T00:00:00 7074214bd58891199b30cdcb37f5bb7f49e10f16There has been a surge of interest in leveraging speech as a marker of health for a wide spectrum of conditions. The underlying premise is that any neurological, mental, or physical deficits that impact speech production can be objectively assessed via automated analysis of speech. Recent advances in speech-based Artificial Intelligence (AI) models for diagnosing and tracking mental health, cognitive, and motor disorders often use supervised learning, similar to mainstream speech technologies like recognition and verification. However, clinical speech AI has distinct challenges, including the need for specific elicitation tasks, small available datasets, diverse speech representations, and uncertain diagnostic labels. As a result, application of the standard supervised learning paradigm may lead to models that perform well in controlled settings but fail to generalize in real-world clinical deployments. With translation into real-world clinical scenarios in mind, this tutorial paper provides an overview of the key components required for robust development of clinical speech AI. Specifically, this paper will cover the design of speech elicitation tasks and protocols most appropriate for different clinical conditions, collection of data and verification of hardware, development and validation of speech representations designed to measure clinical constructs of interest, development of reliable and robust clinical prediction models, and ethical and participant considerations for clinical speech AI. The goal is to provide comprehensive guidance on building models whose inputs and outputs link to the more interpretable and clinically meaningful aspects of speech, that can be interrogated and clinically validated on clinical datasets, and that adhere to ethical, privacy, and security considerations by design.arXiv.org00The goal is to provide comprehensive guidance on building models whose inputs and outputs link to the more interpretable and clinically meaningful aspects of speech, that can be interrogated and clinically validated on clinical datasets, and that adhere to ethical, privacy, and security considerations by design.ArXiv["Si-Ioi Ng", "Lingfeng Xu", "Ingo Siegert", "Nicholas Cummins", "Nina R. Benway", "J. Liss", "V. Berisha"]2024-10-29T00:00:00 cde4efa8e93735e07cb363c4339c66002917cd93New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.Journal of Infrastructure Policy and Development752It is found that fintech does not have an equal impact on every conventional finance theory and outperforms all five traditional theories but on a different scale.Journal of Infrastructure, Policy and Development["Rubaiyat Shaimom Chowdhury", "Md. Aminul Islam", "Dayang Hasliza Binti Muhd Yuauf", "Mohammad Bin Amin", "Md. Sharif Hassan", "S. Barua", "Masuk Abdullah"]2024-10-30T00:00:00 d7088a9f436f489b0ff4328f5c2ba5462d10eac3This research explores the extent of ethical awareness among postgraduate students and their commitment to ethical standards when using artificial intelligence (AI) techniques in scientific writing (SW). It identifies gaps between what students know about ethics and how they apply this knowledge in their SW, specifically in content generation, analysis, and data handling. The study also evaluates the implications of postgraduate students’ increasing use of AI for academic integrity and the verification of sources, focusing on developing effective strategies and measures to ensure ethical compliance. The study participants comprised 68 male and female students from the College of Education at King Faisal University, Saudi Arabia. A descriptive survey research design was used: researchers developed a questionnaire to determine postgraduate students’ level of moral commitment between awareness and the application of AI in their dissertations, theses, and research projects. Results indicated that this commitment is moderate. There were no statistically significant differences between the participants` scores due to age, gender, seniority at university, study type, study state, or subject specialization. The study recommends establishing and implementing intensive awareness training programmes for postgraduate students focused on the importance of ethics in using AI in accordance with academic integrity standards. The study also suggests that institutions review and update academic policies to ensure clear ethical principles regarding the use of AI in SW are included.International Journal of Learning, Teaching and Educational Research241The study identifies gaps between what students know about ethics and how they apply this knowledge in their SW, specifically in content generation, analysis, and data handling and recommends establishing and implementing intensive awareness training programmes for postgraduate students focused on the importance of ethics.International Journal of Learning, Teaching and Educational Research["hmed Zakaria Hegazy", "S. Gaber", "Ibrahim Abdullah Alkhateeb", "Mohammed Ahmed Alqatam", "S. Almughyirah", "Y. Mahgoub", "Hussien Ahmed Ali"]2024-10-30T00:00:00 de3d1d70a8c9521aecb382962f20de83b8378223Artificial Intelligence (AI) is increasingly transforming the field of surgery by enhancing precision, improving patient outcomes, and optimizing surgical workflows. This article explores the practical applications of AI in contemporary surgical practices, including advancements in AI-assisted diagnostic tools, robotic surgery, predictive analytics, and decision-support systems. AI technologies, such as deep learning models and convolutional neural networks, have demonstrated significant improvements in diagnostic accuracy, particularly in medical imaging, pathology, and disease classification. Robotic surgical systems, augmented by AI, offer enhanced precision, control, and dexterity, leading to reduced complications, minimal invasive procedures, and faster recovery times for patients. Predictive analytics powered by AI aids in surgical planning and risk management by forecasting potential complications, personalizing treatment plans, and optimizing surgical outcomes based on patient-specific data. Decision-support systems provide real-time assistance during surgeries, analyzing live data from various sources to offer actionable insights, recommendations, and alerts, thereby enhancing surgical safety and efficiency. Despite these advancements, challenges remain, including the need for diverse and high-quality datasets, ethical considerations regarding data privacy and algorithmic bias, and the seamless integration of AI technologies with existing surgical practices and healthcare systems. In a nutshell, the exciting future of AI in surgery holds great promise and potential for further refinements in surgical techniques, automation, and personalized medicine. Ongoing research, continuous validation, regulatory oversight, and collaboration between AI developers, clinicians, and healthcare policymakers are essential to addressing these challenges and maximizing the benefits of AI in surgical practice, ensuring that these transformative technologies are safe, effective, and accessible to all.Multidisciplinary Reviews01The practical applications of AI in contemporary surgical practices are explored, including advancements in AI-assisted diagnostic tools, robotic surgery, predictive analytics, and decision-support systems.Multidisciplinary Reviews["Shubham Bobade", "Sheetal G. Asutkar", "Devesh Nagpure", "Amar Kadav"]2024-10-30T00:00:00 d65f067e3472afeea839af229a513fea333896d5Background Artificial intelligence (AI) has significant potential in clinical practice. However, its “black box” nature can lead clinicians to question its value. The challenge is to create sufficient trust for clinicians to feel comfortable using AI, but not so much that they defer to it even when it produces results that conflict with their clinical judgment in ways that lead to incorrect decisions. Explainable AI (XAI) aims to address this by providing explanations of how AI algorithms reach their conclusions. However, it remains unclear whether such explanations foster an appropriate degree of trust to ensure the optimal use of AI in clinical practice. Objective This study aims to systematically review and synthesize empirical evidence on the impact of XAI on clinicians’ trust in AI-driven clinical decision-making. Methods A systematic review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, searching PubMed and Web of Science databases. Studies were included if they empirically measured the impact of XAI on clinicians’ trust using cognition- or affect-based measures. Out of 778 articles screened, 10 met the inclusion criteria. We assessed the risk of bias using standard tools appropriate to the methodology of each paper. Results The risk of bias in all papers was moderate or moderate to high. All included studies operationalized trust primarily through cognitive-based definitions, with 2 also incorporating affect-based measures. Out of these, 5 studies reported that XAI increased clinicians’ trust compared with standard AI, particularly when the explanations were clear, concise, and relevant to clinical practice. In addition, 3 studies found no significant effect of XAI on trust, and the presence of explanations does not automatically improve trust. Notably, 2 studies highlighted that XAI could either enhance or diminish trust, depending on the complexity and coherence of the provided explanations. The majority of studies suggest that XAI has the potential to enhance clinicians’ trust in recommendations generated by AI. However, complex or contradictory explanations can undermine this trust. More critically, trust in AI is not inherently beneficial, as AI recommendations are not infallible. These findings underscore the nuanced role of explanation quality and suggest that trust can be modulated through the careful design of XAI systems. Conclusions Excessive trust in incorrect advice generated by AI can adversely impact clinical accuracy, just as can happen when correct advice is distrusted. Future research should focus on refining both cognitive and affect-based measures of trust and on developing strategies to achieve an appropriate balance in terms of trust, preventing both blind trust and undue skepticism. Optimizing trust in AI systems is essential for their effective integration into clinical practice.JMIR AI421The majority of studies suggest that XAI has the potential to enhance clinicians’ trust in recommendations generated by AI, however, complex or contradictory explanations can undermine this trust.JMIR AI["Rikard Rosenbacke", "\u00c5sa Melhus", "M. Mckee", "D. Stuckler"]2024-10-30T00:00:00 31ff92ab527effb9fd283460f738ccff426b71dfOver the past decades, technological advances have led to the development of assistive technologies in the field of education. Specifically, in special education, access for people with a disability may require adaptations to educational programs and technological applications to enhance their independence and participation in society. One of these innovative educational technologies is artificial intelligence. Research has shown that the effective application of artificial intelligence is an important aid in the education of students with special educational needs, such as those with visual impairments. This article presents a brief review of recent studies on the application of AI in the education of people with visual impairments and whether it can contribute to improving their quality of life and their equal access to all levels of education. It also provides a case study, the "PeopleLens" system, for an in-depth analysis of the use of AI as an assistive technology.World Journal of Advanced Engineering Technology and Sciences891A brief review of recent studies on the application of AI in the education of people with visual impairments and whether it can contribute to improving their quality of life and their equal access to all levels of education is presented.World Journal of Advanced Engineering Technology and Sciences["Aikaterini Tsouktakou", "Angelos Hamouroudis", "Anastasia Horti"]2024-10-30T00:00:00 822333e02d1b5100cf545f9f3d733d8a440a754aOBJECTIVE The purpose of this study was to conduct a systematic investigation of the potential of artificial intelligence (AI) models in the prediction, detection of diagnostic biomarkers, and progression of diabetic kidney disease (DKD). In addition, we compared the performance of non-logistic regression (LR) machine learning (ML) models to conventional LR prediction models. METHODS Until January 30, 2024, a comprehensive literature review was conducted by investigating databases such as Medline (via PubMed) and Cochrane. Research that is inclusive of AI or ML models for the prediction, diagnosis, and progression of DKD was incorporated. The area under the Receiver Operating Characteristic Curve (AUROC) served as the principal outcome metric for assessing model performance. A meta-analysis was performed utilizing MedCalc statistical software to calculate pooled AUROC and assess the performance differences between LR and non-LR models. RESULTS A total of 57 studies were included in the meta-analysis. The pooled AUROC of AI or ML model was 0.84 (95% CI = 0.81-0.86, p < 0.0001) for analyzing prediction of DKD, 0.88 (95%CI = 0.84-0.92, p < 0.0001) for detecting diagnostic biomarkers, and 0.80 (95% CI = 0.77-0.82, p < 0.0001) for analyzing progression of DKD. The pooled AUROC of LR and non-LR ML models exhibited no significant differences across all categories (p > 0.05), except for the random forest (RF) model, which displayed a statistically significant increase in predictive accuracy compared to LR for DKD occurrence (p < 0.04). CONCLUSION ML models showed solid DKD prediction effectiveness, with pooled AUROC values over 0.8, suggesting good performance. These data demonstrated that non-LR and LR models perform similarly in overall CKD management, but the RF model outperforms the LR model, particularly in predicting the occurrence of DKD. These findings highlight the promise of AI technologies for better DKD management. To improve model reliability, future study should include extended follow-up periods as well as external validation.Current Medical Research and Opinion881It is demonstrated that non-LR and LR models perform similarly in overall CKD management, but the RF model outperforms the LR model, particularly in predicting the occurrence of DKD.Current medical research and opinion["S. Dholariya", "Siddhartha Dutta", "Amit Sonagra", "Mehul Kaliya", "Ragini Singh", "Deepak Parchwani", "Anita Motiani"]2024-10-30T00:00:00 4841bb14e7f16ddf06aceda067062d54ed8eaa48This systematic review explores the transformative role of Artificial Intelligence (AI) and Big Data analytics in enhancing public health outcomes, focusing on key areas such as disease surveillance, resource allocation, and personalized preventive healthcare. In the wake of increasing healthcare challenges, the integration of AI technologies and Big Data offers unprecedented opportunities for improving health monitoring, early disease detection, and strategic resource management. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review was conducted across multiple databases, resulting in an initial pool of 450 articles. After applying rigorous inclusion and exclusion criteria, a total of 90 high-quality studies were systematically analyzed. The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic. Additionally, the use of predictive analytics in personalized preventive healthcare has shown promise in reducing the burden of chronic diseases by identifying at-risk populations and tailoring interventions based on individual risk profiles. However, challenges related to data quality, standardization, and ethical concerns continue to hinder the widespread adoption of these technologies. The review emphasizes the need for interdisciplinary collaboration and robust data governance frameworks to fully realize the potential of AI and Big Data in public health. This study not only highlights current advancements but also identifies gaps in research, offering insights into future directions for integrating AI-driven solutions to strengthen public health systems globally.Non human journal01The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic.Non human journal["Rebeka Sultana", "Mst Nahida Aktar Aktar"]2024-10-30T00:00:00 333893990bc6498e1ac410ce0c4d85bc4591a3c9This research aims to analyze the use of Artificial Intelligence (AI) technology, specifically ChatGPT, in improving Human Resources (HR) skills and competencies. The main focus of this research is to identify the role of ChatGPT in HR training and development, including the improvement of communication skills, problem solving, and continuous learning through AI-based interactions. The research method used was descriptive quantitative, with data collection through questionnaires involving 58 students from various universities in East Java. The results showed that the majority of students reported significant benefits from the implementation of ChatGPT, which not only enriched the learning experience but also improved in-depth and interactive understanding of the material. The findings provide important insights for educational institutions in developing strategies to increase the adoption of AI technology in supporting students' learning process.Jurnal Riset Manajemen00The results showed that the majority of students reported significant benefits from the implementation of ChatGPT, which not only enriched the learning experience but also improved in-depth and interactive understanding of the material.Jurnal Riset Manajemen["Andiniatul Maulidia", "Elina Zahrotul Firdaus", "Muhammad Alkirom Wildan"]2024-10-30T00:00:00 e6aa217e7c3bf54bfd0d96a48600051ab6e88e28The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.World Journal of Advanced Research and Reviews420This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment, and advocates for a balanced approach to AI adoption.World Journal of Advanced Research and Reviews["Mackline Nuwasiima", "Metogbe Patricia", "Ahonon", "Caleb Kadiri"]2024-10-30T00:00:00 990d70e4c60a4b5a47caee9f7c6867b6eee0cf14Explainable Artificial Intelligence (XAI) emerged to reveal the internal mechanism of machine learning models and how the features affect the prediction outcome. Collinearity is one of the big issues that XAI methods face when identifying the most informative features in the model. Current XAI approaches assume the features in the models are independent and calculate the effect of each feature toward model prediction independently from the rest of the features. However, such assumption is not realistic in real life applications. We propose an Additive Effects of Collinearity (AEC) as a novel XAI method that aim to considers the collinearity issue when it models the effect of each feature in the model on the outcome. AEC is based on the idea of dividing multivariate models into several univariate models in order to examine their impact on each other and consequently on the outcome. The proposed method is implemented using simulated and real data to validate its efficiency comparing with the a state of arts XAI method. The results indicate that AEC is more robust and stable against the impact of collinearity when it explains AI models compared with the state of arts XAI method.arXiv.org100An Additive Effects of Collinearity (AEC) is proposed as a novel XAI method that aim to considers the collinearity issue when it models the effect of each feature in the model on the outcome.ArXiv["Ahmed M. Salih"]2024-10-30T00:00:00 bfc1fc39db0f436854e1daae8fafb59a78960e1bArtificial Intelligence (AI) transforms digital banking technology by augmenting the consumer banking experiences. This study explored the stimulus of AI attributes-trendiness, visual attractiveness, and problem-solving on customer satisfaction in digital banking. To accomplish this, a descriptive and quantitative research approach was embraced, gathering and analysing 120 sample using SPSS 23.0. The correlation analysis exposed positive association between AI attributes and customer satisfaction. Trendiness indicated a modest correlation, Visual Attractiveness and Problem Solving exhibited robust correlations. The regression analysis proved that visual attractiveness is exceptionally significant, problem solving exhibited significant impact and trendiness did not forecast substantial customer pleasure. This study emphasizes the significance of visual attractiveness and problem-solving in shaping positive customer experiences in AI-induced digital banking, stressing the necessity for the constant innovation in the competitive business environment.International Research Journal of Business Studies260The regression analysis proved that visual attractiveness is exceptionally significant, problem solving exhibited significant impact and trendiness did not forecast substantial customer pleasure, stressing the necessity for the constant innovation in the competitive business environment.International Research Journal of Business Studies["Sunil Joseph A", "Shiny C M"]2024-10-30T00:00:00 7874ab7426ff0e66e13bde4bb8a98fe103231a9cThe rapid advancement in artificial intelligence (AI) technologies has led to the emergence of new and complex cyber threats. These threats rely on AI capabilities to target digital systems and infrastructures with greater precision and efficiency, making them more difficult to detect and counter. AI is being used to develop automated cyber-attacks, such as malware that can adapt and learn from its digital environment. Conversely, AI is also employed to enhance cyber security by enabling early threat detection, analyzing suspicious behaviors, and providing rapid responses to hacking incidents. Based on the above, the research idea we intend to present at this article on how to achieve a balance between leveraging AI technologies to protect digital systems and the growing risks associated with using these technologies to develop more sophisticated cyber-attacks. Research Methodologies: The study requires employing both the descriptive-analytical and inferential methodologies.International Journal of Science and Research Archive430The research idea is to present on how to achieve a balance between leveraging AI technologies to protect digital systems and the growing risks associated with using these technologies to develop more sophisticated cyber-attacks.International Journal of Science and Research Archive["Driss Abbadi"]2024-10-30T00:00:00 7502eebcb1c0d3fcfd2c06c4cb63faa1a75022f7Artificial intelligence is transforming the landscape of Human Resource Management (HRM), altering conventional methods and elevating the recruitment process for companies. The conventional approach to hiring can be incredibly time-intensive, often stretching over several weeks to sift through all applications. This process can be daunting for recruiters, who are tasked with reviewing numerous resumes. AI steps in to streamline this process by rapidly sifting through a large number of applications, identifying the most suitable candidates, and providing concise overviews of their qualifications. This not only saves recruiters time but also allows them to concentrate on improving the candidate experience and attracting top talent. AI operates around the clock, ensuring the recruitment process remains active and effective even when recruiters are not on duty. Moreover, AI can help mitigate bias, when utilized correctly, it can facilitate more equitable hiring decisions by focusing on relevant skills and experiences rather than personal biases. This article explores the multifaceted role of AI in mitigating recruitment bias, AI algorithms use objective data and set criteria to reduce unconscious bias during initial screening. This approach helps ensure that job seekers are evaluated based on qualifications and merit, rather than personal characteristics.World Journal of Advanced Research and Reviews540The multifaceted role of AI in mitigating recruitment bias is explored, AI algorithms use objective data and set criteria to reduce unconscious bias during initial screening, which helps ensure that job seekers are evaluated based on qualifications and merit, rather than personal characteristics.World Journal of Advanced Research and Reviews["Zaker Ul Oman", "A. Siddiqua", "Ruqia Noorain"]2024-10-30T00:00:00 23a87ed82b9056012136155d6ba965d8573c1d3cArtificial intelligence (AI) rapidly advances across various industries, including healthcare, finance, education, robotics, entertainment, and commerce. Human resource (HR) management plays a crucial role in AI, helping employees feel valued and creative. However, AI-powered systems often neglect the needs of people with disabilities, who often face barriers in digital environments. This paper aims to investigate digital inclusion practices for people with disabilities in AI-driven human resource management systems. It proposes a heuristic evaluation method for software accessibility assessment based on international standards. The objectives include researching best practices for digital workplace inclusion, studying international standards for software accessibility, and researching AI-powered recruitment systems. An experiment assessed the accessibility of color schemes for people with color blindness.Journal of Underrepresented &amp; Minority Progress290This paper proposes a heuristic evaluation method for software accessibility assessment based on international standards, and proposes a heuristic evaluation method for software accessibility assessment based on international standards for AI-powered recruitment systems.Journal of Underrepresented &amp; Minority Progress["R. Nacheva"]2024-10-30T00:00:00 7165af93fcdcec562e33c77f75a2777bc54e4ebfThe objective of the study was to identify the level of AI and students’ learning strategies, and to find the relationship between AI and students’ learning strategies at secondary level. Artificial Intelligence is reshaping students' learning strategies by providing personalized content, immediate feedback, and data-driven insights that enhance engagement and comprehension. However, while AI promotes self-regulated learning and collaboration, it also requires careful balance to preserve essential human interaction in education. A quantitative and descriptive method is used in the study. The majority of participants were in the secondary school district of Sheikhupura. A questionnaire served as this study's primary research tool. The validity of the questionnaire was found through experts’ opinions and reliability through pilot testing. Descriptive and inferential statistics was used. The findings of the study revealed that there was highly significant relationship between AI and students’ learning strategies at secondary level.Pakistan journal of humanities and social sciences220The findings of the study revealed that there was highly significant relationship between AI and students’ learning strategies at secondary level.Pakistan Journal of Humanities and Social Sciences["Fahd Naveed Kausar", "Rabia Bahoo", "Rani Qaisara", "Muhammad Waseem"]2024-10-30T00:00:00 625dd44ecbe7ccb025e5ffa2ab416c2fa3859e7dThis article investigates the impact of artificial intelligence (AI) on intellectual property (IP) rights, addressing challenges in ownership and authorship of AI-generated creations while exploring legal and ethical dilemmas in traditional IP domains. It offers strategies for navigating these complexities, drawing on legal precedents, international agreements, and policy recommendations. The research emphasizes the urgent need for legislative updates to address these challenges effectively. Recommendations include the enactment of innovative constitutional provisions, updating IP legislation to encompass AI-related issues comprehensively, and advocating for effective judicial intervention. By implementing these strategies, Sri Lanka can foster a harmonious coexistence of AI and IP, ensuring the protection of intellectual property rights while stimulating innovation in the AI era.SLIIT Journal of Humanities and Sciences170SLIIT Journal of Humanities and Sciences["C. Mahingoda"]2024-10-30T00:00:00 3d50016cabd4a797e4346cea710acbf165c2c4a4Islamic educational institutions encounter significant challenges in human resource management due to the rapid advancement of digital technology. To enhance quality and efficiency, these institutions must embrace innovative solutions, particularly the integration of artificial intelligence (AI) within the context of the Industrial Revolution 4.0. This research aims to explore the potential applications of AI in human resource management (HR) specifically within Islamic educational settings, while also identifying the opportunities and challenges associated with its implementation. Utilizing a qualitative approach through a comprehensive literature review, this study collects data from secondary sources to identify key themes and gaps in existing literature, ultimately formulating recommendations for effective AI integration. The findings reveal that AI can significantly improve operational efficiency, facilitate data-driven decision-making, and enhance the professional development of staff in Islamic educational institutions. However, challenges such as resistance to change and limited resources must be addressed. The strategic implementation of AI can foster an environment conducive to the professional growth of educators, aligned with Islamic values. This research not only provides valuable insights into the role of technology in HR management but also emphasizes the necessity for further studies to explore additional dimensions of AI application in educational contexts. Ultimately, this study enriches the understanding of AI's role in HR management within Islamic educational institutions and serves as a reference for administrators seeking to adopt advanced technological solutions.International Journal of Science and Applied Science: Conference Series260The findings reveal that AI can significantly improve operational efficiency, facilitate data-driven decision-making, and enhance the professional development of staff in Islamic educational institutions, however, challenges such as resistance to change and limited resources must be addressed.International Journal of Science and Applied Science: Conference Series["Duwi Habsari Mutamimah", "Binti Maunah"]2024-10-30T00:00:00 0383e80ffb810c01ba8a445bbacc9779caebda7dPelatihan asesmen digital melalui platform Quizizz dan teknologi Artificial Intelligence (AI) dirancang untuk mendukung pengembangan keterampilan abad ke-21 bagi para guru. Keterampilan berpikir kritis, kolaborasi, kreativitas, dan literasi teknologi menjadi semakin penting. Peserta didik di SMA N 10 Yogykarta sudah memanfaatkan teknologi digital seperti Artificial Intelligence (AI) untuk pembelajaran. Guru dituntut untuk mengikuti perkembangan dan menyesuaikan dengan kondisi peserta didik, namun belum memiliki pemahaman dan kemampuan yang memadai dalam pemanfaatan Artificial Intellegince dan Quiziz untuk pengembangan asesmen digital. Program pelatihan ini bertujuan untuk mengembangkan keterampilan dalam memanfaatkan asesmen digital. Kegiatan pelatihan dilakukan secara tatap muka (luring) dengan metode yang komunikatif, interaktif dan partisipatif. Hasil pelatihan menunjukkan bahwa guru di SMA Negeri 10 Yogyakarta dapat membuat dan mengembangkan asesmen digital dengan memanfaatkan Artificial Intelligence (AI) dan Quiziz dalam pembelajaran. Pelatihan ini tidak hanya membekali guru dengan keterampilan teknis dalam menggunakan alat digital tetapi juga mendukung peningkatan kualitas keterampilan guru-guru di SMA Negeri 10 Yogyakarta khususnya innovative thinking skill yang selaras dengan tuntutan pendidikan abad ke-21.Jurnal Edukasi Pengabdian Masyarakat00Jurnal Edukasi Pengabdian Masyarakat["Brigida Intan Printina", "Dini Maharini", "F. Astuti", "Puji Muktianingsih", "Silvi Dwi Mirandani", "S. Annisa", "Yayan Bagus Prabowo", "Yusuf Adil Syuhada"]2024-10-30T00:00:00 1a4fb13a8315df1dab8fc18fc6a1220b653850a7Introduction: In recent times, there has been a growing interest in the integration of artificial intelligence (AI) and machine learning (ML) into the realm of cleft orthognathic surgery, presenting an exciting avenue for transformative innovations. These technologies offer the promise of optimizing treatment plans, facilitating surgical decision-making, and contributing to a more patient-centric approach. However, a systematic and in-depth exploration of the existing literature is essential to discern the true impact, challenges, and potential future directions of AI and ML in this specialized field. The present systematic review aimed to provide an overview of AI and ML algorithms and their applications in cleft orthognathic surgery. Methodology: A comprehensive search was conducted in databases using MeSH terms and other relevant terms including PubMed, Embase, and Scopus until January 2024. This systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Results: The search strategy resulted in a total of 124 articles. After applying the inclusion and exclusion criteria, a total of 5 studies were included for final review. AI has profoundly impacted the prediction of the need for orthognathic surgeries in cleft patients using cephalometric variables with a clinically acceptable accuracy range. Also, provide guidelines to determine the amount and direction of movements of the maxilla and mandible. Conclusions: Understanding the role of AI and ML in cleft orthognathic surgery is paramount for clinicians, researchers, and policymakers alike.AI reduces the work burden of the clinician by eliminating the tedious registration procedures, thereby helping in efficient and automated planning.South Eastern European Journal of Public Health200An overview of AI and ML algorithms and their applications in cleft orthognathic surgery is provided to provide an overview of the true impact, challenges, and potential future directions of AI and ML in this specialized field.South Eastern European Journal of Public Health["Dr. Ramya Vijeta Jathanna", "Dr. Vinod Rakesh Jathanna", "Dr. Rithesh Bangera", "P. Monis"]2024-10-30T00:00:00 20c7ed6afa0c7fcf6efd1da7b44fe55e3a4df45fObjectives: This study investigated the ethical awareness regarding artificial intelligence (AI) among dental hygiene students based on core competency levels and proposed a competency-based educational approach to improve AI ethical awareness. Methods: Eighty-six dental hygiene students participated in the study and provided informed consent. The core competency survey tool included innovation, communication, relations, and services. The AI ​​ethical awareness survey tool was divided into eight categories, each with 24 questions: responsibility, stability and reliability, non-discrimination, transparency and explainability, people-centered service, employment, tolerance and limits, and robot rights. Results: The group with high core competency had higher levels of AI ethical awareness (p<0.05), particularly in terms of responsibility, transparency, and peoplecentered service. The level of AI ethical awareness was significantly correlated with the relationship competency and service competency (p<0.05). Conclusions: These results highlight the association between AI ethical awareness and core competencies. These results suggest that competency-based education in universities is critical for improving AI ethical awareness. Furthermore, we intend to use the findings as preliminary data to suggest a direction for competency-centered education to improve AI ethical awareness.Journal of Korean society of Dental Hygiene290It is suggested that competency-based education in universities is critical for improving AI ethical awareness and the association between AI ethical awareness and core competencies is highlighted.Journal of Korean Society of Dental Hygiene["Hye-Sun Shin", "Seon-Ju Sim"]2024-10-30T00:00:00 0a4c8cc13b155ec4adedb412ffec80f38297b582Skills such as communication, teamwork, adaptability and problem-solving are essential for professional and personal development. The integration of artificial intelligence (AI) in the process of building these skills in students represents a significant innovation in higher education. This is because AI offers new teaching methods that can be more effective and personalized than traditional approaches. Thus, it is relevant to investigate the bibliometric indicators that reflect the trends in which AI enables the development of soft skills in the context of higher education. The approach used is the mixed, exploratory-descriptive level. The total number of studies reviewed was 78, all of them extracted from the Scopus database. The results show that the most prevalent thematic areas are “Communication skills development”, “Teamwork and collaboration” and “Critical thinking and problem-solving”. This is a result of AI’s ability to create more personalized, interactive and adaptive learning environments. However, it is concluded that scientific production in this field of study is still developing and requires greater attention from researchers. It is important to reflect that the implementation of AI in higher education must be supported by policies that regulate its effective integration and maximize its impact. Future studies should employ systematic reviews to address the impact of AI on soft skills according to the area of knowledge, such as engineering, social sciences or health sciences, identifying the skills to which AI is contributing most significantly.International Journal of Learning, Teaching and Educational Research420The results show that the most prevalent thematic areas are “Communication skills development”, “Teamwork and collaboration” and “Critical thinking and problem-solving”, a result of AI’s ability to create more personalized, interactive and adaptive learning environments.International Journal of Learning, Teaching and Educational Research["Nestor Alvarado-Bravo", "Florcita Aldana-Trejo", "V\u00edctor Dur\u00e1n-Herrera", "Jos\u00e9 Rasilla-Rovegno", "Raul Suarez-Bazalar", "Almintor Torres-Quiroz", "Alejandro Paredes-Soria", "Susan Haydee Gonzales-Salda\u00f1a", "Gregorio Tom\u00e1s-Quispe", "Soledad Olivares-Zegarra"]2024-10-30T00:00:00 e2340252ef601bf511f1776860e06792425f27b4This study aims to analyze the collaboration between Artificial Intelligence (AI) and journalists in online media using the Human-Machine Communication (HMC) approach. The research method used is a literature study. Data is processed using Miles and Huberman data analysis techniques. The results of this study indicate that AI is used in three primary stages of journalism: news gathering, news production, and news distribution. At the news gathering stage, AI assists journalists in collecting news materials from various sources and analyzing audience interest in specific topics. AI is used in news script creation, editing, and proofreading at the news production stage. AI chatbots and NLP programs help with automatic news writing and factual verification. Meanwhile, at the news distribution stage, AI is used for content personalization, news recommendations, and SEO optimization in online media. From the HMC perspective, collaboration between AI and journalists can be conceptualized as a unidirectional and two-way process. Collaboration between AI and journalists in a social context also occurs at the micro, meso, and macro levels, where interactions between humans and machines affect the social situation, the immediate reality of individuals, and the structure of society as a whole.Kalijaga Journal of Communication240The results of this study indicate that AI is used in three primary stages of journalism: news gathering, news production, and news distribution.Kalijaga Journal of Communication["Fiqih Rahmawati"]2024-10-30T00:00:00 ea32423782a38b38720a5929fd014ad600da9bf7Artificial Intelligence (AI) is increasingly recognized as a powerful tool for addressing the challenges of climate change. Its ability to process vast amounts of data and generate advanced predictive models positions AI as a key player in efforts to reduce greenhouse gas (GHG) emissions and develop sustainable solutions. This review delves into the multifaceted role of AI in climate change mitigation, highlighting its potential in several critical areas. Firstly, AI is revolutionizing predictive climate modeling by providing more accurate forecasts and simulations, enabling better-informed policy and decision-making. Secondly, it is optimizing energy systems through smart grid management, demand forecasting, and the integration of renewable energy sources, thereby enhancing energy efficiency and reducing reliance on fossil fuels. Furthermore, AI is advancing carbon capture and storage technologies by improving the identification of optimal sites and enhancing process efficiency. In environmental monitoring, AI-driven solutions are enabling real-time detection and analysis of environmental data, contributing to more effective conservation efforts. This review also presents case studies and data that demonstrate the tangible impact of AI applications in driving progress towards global emission reduction targets. However, the adoption of AI in this domain is not without challenges. Issues such as data privacy, algorithmic transparency, and the ethical implications of AI deployment need to be carefully addressed. The paper concludes by outlining future research directions and emphasizing the need for interdisciplinary collaboration to fully harness the potential of AI in combating climate change.World Journal of Advanced Research and Reviews290The multifaceted role of AI in climate change mitigation is delved into, highlighting its potential in several critical areas and the need for interdisciplinary collaboration to fully harness the potential of AI in combating climate change.World Journal of Advanced Research and Reviews["Ibrahim Barrie", "A. Adegbite", "Saheed Femi Osholake", "T. Alesinloye", "Anuoluwapo Blessing Bello"]2024-10-30T00:00:00 ca573258590b2b16e52eb3c33d07d2657dacd7e5The use of artificial intelligence in law represents one of the biggest challenges across different legal systems. Supporters of predictive systems believe that decisionmaking could be more efficient, consistent and predictable by using AI. European legislation and legal scholars, however, identify areas where AI developments are at high risk or too dangerous to be used in judicial proceedings. In this article, we contribute to this debate by problematizing predictive systems based on previous judgments and the growing use of Generative AI in judicial proceedings. Through illustrations from real criminal cases in Italian courts and prosecution offices, we show misalignments between the functions of AI systems and the essential features of legal decision-making and identify possible legitimate usages. We argue that current predictive systems and Generative AI crunch the complexity of judicial proceedings, the dynamics of fact-finding and legal encoding. They reduce the delivery of justice to statistical connections between data or metadata, cutting off the emotive-cognitive process that lies at the core of legal decision-making.Frontiers in Sociology340It is argued that current predictive systems and Generative AI crunch the complexity of judicial proceedings, the dynamics of fact-finding and legal encoding, and reduce the delivery of justice to statistical connections between data or metadata, cutting off the emotive-cognitive process that lies at the core of legal decision-making.Frontiers in Sociology["Francesco Contini", "Alessandra Minissale", "Stina Bergman Blix"]2024-10-30T00:00:00 9ff547d9dba87472e37d40df613418d51e618233 Artificial Intelligence (AI) is transforming different aspects of the economy and society of countries. There are diverse effects when comparing the impact in developed versus developing countries. In the educational sector, efforts to incorporate AI have largely ignored the input from those directly impacted by it. This document presents results from a survey about AI to university students in Latin America. The information presented comes from a survey conducted in November 2023 to college students with ages between 18 and 25 years in four Latin American countries: Mexico, Argentina, Peru, and Ecuador. The results indicate that, in general, youth have a positive view about the potential of AI, though limited knowledge in the topic. Moreover, university students in the region do not want to be passive recipients of AI. They want to participate directly in conversations about this very current topic and have concerns about different aspects of AI implementation in the region. These findings highlight the need for universities, governments, civil society and international organizations and the private sector to work together to create spaces for inclusive dialogue where the youth could directly participate in conversations that crucially matter for their lives.Journal of Integrated Global STEM80The results indicate that, in general, youth have a positive view about the potential of AI, though limited knowledge in the topic, and university students in the region do not want to be passive recipients of AI.Journal of Integrated Global STEM["Martha Cruz Zuniga", "Nastasja Santrac", "Adriana Kwiatkowski", "Benjamin Abood"]2024-10-30T00:00:00 3f950b3bbfbdb5aa4ddb5539b5bdd193fefd4144The detection of diseases and pest infestations in rice plants is essential to prevent potential losses due to crop damage. Although machine learning methods are widely employed for detecting plant diseases and symptoms of pest infestation, they often function as black boxes, lacking transparency in their decision-making processes. This opacity can hinder users' understanding of the underlying mechanisms, thereby diminishing comprehension and accountability for the detection outcomes. A viable solution to this challenge is the implementation of explainable artificial intelligence (XAI) methods, which offer clear explanations of the detection process. In this study, a DenseNet-based classification model demonstrated robust performance, achieving 82.54% accuracy on initial test data and 87.5% on field test data. Among the XAI methods evaluated (LIME, SHAP, and Grad-CAM), the LIME method emerged as the most effective, offering a balanced trade-off between detail, ease of understanding, and computational efficiency, thereby enhancing transparency and user confidence in the detection outcomes.International Conference on Data and Software Engineering90Among the XAI methods evaluated (LIME, SHAP, and Grad-CAM), the LIME method emerged as the most effective, offering a balanced trade-off between detail, ease of understanding, and computational efficiency, thereby enhancing transparency and user confidence in the detection outcomes.2024 IEEE International Conference on Data and Software Engineering (ICoDSE)["William Manuel Kurniawan", "Ir. Windy Gambetta", "Ir. Rika Alfianny"]2024-10-30T00:00:00 eacc011f46a99efef6d91529be3af579ec321f8dThis study examines how implementing artificial intelligence (AI) in human resource management (HR) impacts employee engagement and operational efficiency. By automating tasks like data management, scheduling, and payroll, AI enables HR teams to focus on strategic initiatives such as talent management and culture development. Using a descriptive qualitative approach and literature review, the research finds that AI adoption improves operational efficiency by 30% and enhances employee engagement through personalized experiences and real-time feedback. Additionally, AI supports better strategic decision-making with predictive employee data analysis. However, challenges include employee resistance and the need for HR retraining. For optimal results, companies should strengthen internal communication and establish supportive policies that enhance engagement and well-being through AI integration.East Asian Journal of Multidisciplinary Research220Research finds that AI adoption improves operational efficiency by 30% and enhances employee engagement through personalized experiences and real-time feedback and supports better strategic decision-making with predictive employee data analysis.East Asian Journal of Multidisciplinary Research["Sri Sundari", "Verry Albert Jekson Mardame Silalahi", "Febri Pramudya Wardani", "Rahel Sintadevi Siahaan", "Shinta Sacha", "Yanti Krismayanti", "Nisa Anjarsari"]2024-10-30T00:00:00 599a323f1aedc711c0e9edf69cad819b31020545Artificial intelligence (AI) has long been heralded for its ability to simulate human intelligence, enabling machines to perform complex tasks such as decision-making, problem-solving, and data analysis. However, alongside the advancements in AI, the concept of artificial stupidity (AS) has gained attention. AS refers to the limitations and errors made by AI systems, often resulting from incomplete data, biased algorithms, or the inherent restrictions placed on AI to simulate more human-like decision-making. These instances of "stupidity" can lead to nonsensical or harmful outcomes, especially when AI is applied to critical areas such as healthcare, autonomous systems, and legal decision-making. This narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications. By addressing the causes of artificial stupidity, such as algorithmic limitations and poor data quality, researchers and developers can improve the reliability and decision-making capabilities of AI systems. There is also the need for human oversight and ethical considerations to mitigate the negative impacts of artificial stupidity, especially in high-stakes environments.International journal of computer science and mobile computing00A narrative review explores the duality between AI's potential and its flaws, emphasizing the importance of understanding both AI and AS in developing robust, safe, and ethical AI applications.International Journal of Computer Science and Mobile Computing["Aryeshwar Dayal"]2024-10-30T00:00:00 f5974abac9b6ff28c868e0bf481bf4272085d42cAbstract: In today's modern era, the world of education has experienced changes in the learning system. In recent years, artificial intelligence has emerged as a transformative force in the education system in the modern era. This research aims to analyze the role of artificial intelligence in transforming learning in the modern era. The method used in this research is library research. The data collected is in the form of journal articles, research reports, and documents related to the topic discussed in this research. The analysis in this research uses content analysis. The research results show that the role of artificial intelligence in the learning process has a significant role. The use of artificial intelligence technology can provide an independent learning experience. Students can search for material according to their needs, and learning methods using artificial intelligence technology can increase student involvement, thereby creating high motivation in students. Therefore, artificial intelligence has great potential in transforming learning systems in the modern era. On the one hand, artificial intelligence can be a solution in the learning process in the modern era. And on the other hand, artificial intelligence can be a challenge in the world of education. Thus, to make maximum use of artificial intelligence requires caution regarding academic ethical issues.International Journal of Science and Applied Science: Conference Series290The research results show that the role of artificial intelligence in the learning process has a significant role and that to make maximum use of artificial intelligence requires caution regarding academic ethical issues.International Journal of Science and Applied Science: Conference Series["Muhammad Gafarurrozi", "Moh. Fatkur Rohman", "Rizal Fathurrohman"]2024-10-30T00:00:00 7bd7676658ffbfb9bd38d154277f9e6e73ea6664The article reveals the essence of the concept of artificial intelligence in accordance with the Concept of Artificial Intelligence Development in Ukraine, approved by the Resolution of the Cabinet of Ministers of Ukraine of 02.12.2020 No. 1556-р and the Concept of the State Targeted Scientific and Technical Programme for the Use of Artificial Intelligence Technologies in Priority Sectors of the Economy for the Period until 2026, approved by the Resolution of the Cabinet of Ministers of Ukraine of 13.04.2024 No. 320-р. The article discusses that healthcare is not only one of the priority areas of state activity, but also a priority sector of the economy, in which artificial intelligence technologies are applied, which is associated with the need to change the processes of production, primarily the decision-making process at various levels of government. The article also highlights the use of analytical systems founded upon complex machine learning algorithms by medical institutions. The article analyses the use of artificial intelligence in medical specialities, in particular in radiology, oncology, cardiology, and general surgery. The author investigates the procedure for applying artificial intelligence in neurosurgery, where artificial intelligence contributes to a considerable improvement of tumour identification and surgical planning.Medicne pravo110The author investigates the procedure for applying artificial intelligence in neurosurgery, where artificial intelligence contributes to a considerable improvement of tumour identification and surgical planning.Medicne pravo["O. V. Yurchuk"]2024-10-30T00:00:00 2299e9fd7e5d8961b78439840bd6b0df3e468da4Artificial Intelligence technology has become an innovative tool in global healthcare that helps to manage various problems of scale and complexity. Its use in public health is multifaceted and multivariate extending across early outbreak detection, risk modeling and prediction, efficient vaccine logistics and administration, reducing health disparities, aiding assist in diagnosis, everyday monitoring of chronic diseases, telemedicine and virtual chatbots especially for elderly care, mental health and oncology patients, and community engagement. Despite these huge generational advantages, multiple challenges exist in data quality, security and misuse, individual privacy, contextual application, accountability and oversight and remain primary concerns for universal adoption of artificial intelligence. There is a need for safeguards and a global regulatory framework to responsibly harvest the benefits of artificial intelligence for the advancement of public and community health.World Journal of Biology Pharmacy and Health Sciences430There is a need for safeguards and a global regulatory framework to responsibly harvest the benefits of artificial intelligence for the advancement of public and community health.World Journal of Biology Pharmacy and Health Sciences["Gautam Chamarthy", "Divya Chamarthy", "Srikriti Dammavalam"]2024-10-30T00:00:00 fbc413f3cc7ebb41427738e493b24f49d6dc4501The objective of this work lies in leveraging recent advances in Artificial Intelligence to enhance the diagnosis and treatment of vestibular disorders, crucially improving patients' conditions and their overall quality of life.Otolaryngology Head & Neck Surgery00Otolaryngology, Head and Neck Surgery["Ingrid Raponi"]2024-10-30T00:00:00 e7b1a8988203f7d7b504a3e65e96c7f77d7d9b3dWith the continuous development of artificial intelligence (AI) technology, AI technology has been widely used in many industry fields, also in the field of education, while the application of AI in the field of education also faces many challenges. This paper gives the in-depth analysis of the impact of AI technology in the field of education in order to promote the in-depth application of it in the field of education.Journal of Education, Humanities and Social Sciences30The in-depth analysis of the impact of AI technology in the field of education is given in order to promote the in-depth application of it in the field of education.Journal of Education, Humanities and Social Sciences["Shan Ge"]2024-10-30T00:00:00 a86c550f26d517eba7a8f4cccf833d478a2f87b1The research project examines the impact of Artificial Intelligence (AI) on marketing strategies across various industries, focusing on how AI-driven technologies enhance customer engagement and optimize marketing campaigns. The objective of this study is to explore the diverse effects of AI on marketing practices and to identify the challenges and ethical considerations that arise from its implementation. The research was conducted through a qualitative methodology that conducted interviews with 18 marketing professionals to gather information about their experiences and perceptions regarding AI in marketing. The findings reveal a significant trend toward the adoption of AI and machine learning technologies. Participants noted their effectiveness in personalizing customer interactions and improving data-driven decision making. Key themes include the success of AI-driven tools such as recommendation systems and predictive analytics in enhancing marketing effectiveness. The researchers highlighted some ethical concerns which included data privacy, algorithmic bias, and the need for transparency. The research study validates the importance of data literacy and AI competence among marketing professionals. It suggests that organizations prioritize skill development to adapt to the evolving marketing landscape. The research contributes to the existing body of knowledge by emphasizing the dual role of AI as a transformative force in marketing while also raising critical ethical issues. The research offers practical recommendations for businesses to effectively utilize Artificial Intelligence while ensuring they remain competitive in the global marketing landscape.International Journal of Science and Research Archive00The research study validates the importance of data literacy and AI competence among marketing professionals and suggests that organizations prioritize skill development to adapt to the evolving marketing landscape.International Journal of Science and Research Archive["William Yoo"]2024-10-30T00:00:00 bac51d2c2baf7d977a0e9a714a1e3bc01c57b91bProstate cancer (PCa) is a cancer with a broad spectrum of biological behavior and it is a heterogeneous nature. In order to prevent overdiagnosis and overtreatment, and to detect clinically significant PCa, standardized scoring and grading systems are used in imaging and pathological examinations. However, reproducibility and agreement between readers in these diagnostic stages, which require experience, are low. Promising results have been achieved by integrating artificial intelligence (AI)-based applications into the diagnosis and management of PCa. In radiological and pathological imaging, computer-aided diagnostic tools have increased clinical efficiency and achieved diagnostic accuracy comparable to that of experienced healthcare professionals. This review provides an overview of AI applications used in radiological imaging, prostate biopsy, and histopathological examination in the diagnosis of PCa.The New Journal of Urology260This review provides an overview of AI applications used in radiological imaging, prostate biopsy, and histopathological examination in the diagnosis of PCa, and reproducibility and agreement between readers in these diagnostic stages are low.The New Journal of Urology["Adem Alcin", "As\u0131f Y\u0131ld\u0131r\u0131m"]2024-10-30T00:00:00 0915160724391b50fd56d27fd05d4b977efabb8fPeriodontal disease is a common and frequently-occurring disease in China. Early detection, diagnosis, and treatment of periodontal disease are of great significance for achieving universal oral health and even systemic health. Artificial intelligence endows machines with the ability to mimic human intelligent behavior, and is commonly used in medical field with both physical and virtual forms. Virtual artificial intelligence empowers traditional experience in the application of periodontal disease risk assessment and diagnosis, with the potential to develop a variety of oral health screening tools. It helps to provide new evidence for the prognosis of periodontal disease, improve the accuracy and efficiency of diagnosis, reduce technical sensitivity and further promote the periodontal treatment transformation from "treatment-oriented" to "prevention-oriented". This paper reviews the current applications and progresses of virtual artificial intelligence in periodontal risk assessment and diagnosis, as well as its limitations, providing ideas for future researches on the application of virtual artificial intelligence in this field.Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology00The current applications and progresses of virtual artificial intelligence in periodontal risk assessment and diagnosis, as well as its limitations, are reviewed, providing ideas for future researches on the application of virtual artificial intelligence in this field.Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology["W. J. Hong", "C. C. Liu", "Y. Ding"]2024-10-30T00:00:00 8a0f8a4ed33f256f7222d855844c47fcdfbd7b1bThe development of scientific and technological progress in the context of the transformation of international relations in the 21st century have brought to the forefront the problem of attitudes towards artificial intelligence in the world. Issues of ethics, law, attempts to formulate their vision of the situation are characteristic not only of states, but also of international organizations. The author of this study aimed to assess the activities of universal and regional international organizations in the field of resolving issues related to the work of artificial intelligence in international relations. The topic of artificial intelligence has undoubtedly long acquired a global character. The emergence of new threats and challenges to humanity undoubtedly poses a dilemma for the world community about the role that artificial intelligence will play in these processes. Will this be a creative embodiment, or will artificial intelligence be destructive? The answer to this question has not yet been found. Nevertheless, we can talk about the responsibility of international organizations, and the fact that now active actions are being taken to consolidate norms regarding artificial intelligence. For example, the United Nations, being a universal international organization, could not ignore the phenomenon of artificial intelligence, as well as regional organizations. In this context, it seems particularly relevant to analyze the first steps that are being taken in this direction, especially given the complex international relations between Russia and unfriendly states and organizations. All this is necessary to understand all the changes in the situation related to artificial intelligence on the world stage.Sociopolitical sciences00The author of this study aimed to assess the activities of universal and regional international organizations in the field of resolving issues related to the work of artificial intelligence in international relations.Sociopolitical Sciences["M. Zagaynov"]2024-10-30T00:00:00 6392ead1eed00613577d065645a6fa9c7b89016fIt is similar to having a very intelligent computer that is capable of thought and learning. It all comes down to building machines that are capable of doing tasks that often call for human intelligence, such as language comprehension, image recognition, and decision-making. It's very remarkable. Imagine being able to learn and reason like a human brain is possible with a machine. That's the main purpose of artificial intelligence (AI). It's akin to endowing machines with the capacity for comprehension, decision-making, and even experience-based learning. KEY WORDS: Artificial Intelligence. Human, Process, Approach, Method etc.,EPRA International Journal of Environmental Economics, Commerce and Educational Management00Artificial intelligence is akin to endowing machines with the capacity for comprehension, decision-making, and even experience-based learning, such as language comprehension, image recognition, and decision-making.EPRA International Journal of Environmental Economics, Commerce and Educational Management["Dr. S.Pandurangan", "Dr. S.Sankar"]2024-10-30T00:00:00 6adbdf83e9bea2b283ced090286332f6e654a63bThis article explores the trends, challenges, and prospects of applying artificial intelligence (AI) in modern management. AI plays a crucial role in streamlining processes, automating tasks, and supporting decision-making at both operational and strategic levels. Two primary applications are emphasized: as a tool for developing managerial algorithms and as a means to enhance employee behavior for more efficient teamwork. Key benefits of AI include fast data analysis, scenario forecasting, and minimizing the influence of human error. AI also improves project management, resource optimization, and internal communication. However, its implementation involves ethical dilemmas, legal constraints, and associated risks, highlighting the need for further research and improvements in legislation.sj-economics scientific journal160The trends, challenges, and prospects of applying artificial intelligence in modern management are explored: as a tool for developing managerial algorithms and as a means to enhance employee behavior for more efficient teamwork.sj-economics scientific journal["Olha Kuznietsova"]2024-10-30T00:00:00 108f908ef62b3eeec9ac80a06031670c6e8a9e37This article explores the legal and ethical aspects of implementing artificial intelligence (AI) technologies in healthcare. The application of AI opens new horizons for diagnosing, treating, and predicting diseases, providing physicians with instruments for more precise and timely decisions. However, alongside with its advantages, significant challenges arise, in particular, regarding legal regulations and ethical norms. The conclusions highlight the need for a comprehensive approach that combines legal, ethical, and technical regulations for the effective implementation of AI in medical practice. Ensuring high standards of data quality, transparency of algorithms, and accountability of developers will promote safe and ethical use of AI, which, in turn, will improve the quality of medical care and enhance the overall healthcare system.Medicne pravo00Medicne pravo["I. O. Bogomazova"]2024-10-30T00:00:00 3c2f15ca440e18ad1b5a981c5d3b12e1d04cc622The article presents a comprehensive review and comparative analysis of modern artificial intelligence (AI)-based platforms used in human resource (HR) management. Tools such as HireVue, Pymetrics, Eightfold.ai, Workday, and Textio are reviewed. Their functionality, usability, integration with existing systems, data security, cost, and user support are discussed. The article provides practical recommendations for selecting optimal AI solutions, taking into account the specific needs and priorities of different organizations. It also explores the prospective directions of AI development in HR and the ethical aspects of their implementation.International Journal of Science and Research Archive00A comprehensive review and comparative analysis of modern artificial intelligence (AI)-based platforms used in human resource (HR) management, taking into account the specific needs and priorities of different organizations.International Journal of Science and Research Archive["ALENA LIPINA"]2024-10-30T00:00:00 3bd656bfdb7d39aaba401a1c205b363219856d2eAI powered smart farming transform agriculture landscape. This article based on statistical data that looks into the future of AI with respect to agriculture business.This article creates a path to shed insights on the significant impact of artificial-intelligence in terms of shaping up and automating agricultural operations that would let the reader focus their visualization on root-level processes within food production business.This study is based on the analysis of market data as well as on research results to identify how artificial intelligence is used, the key benefits and perspectives in smart farming. Computer vision, data analytics and machine learning are among the applications of these technologies.The study found out that AI has really Improved Efficiency. The outcomes as per their study show that via automation, one may succeed in bringing the labor cost to 50%, agriculture yield will be increased by 15%, and can reduce use of irrigation water even low down to 20%, having an average increase in the productivity change up to a mark value around at least 30%. This deserves a chapter of its very own, Intelligent irrigation systems and fertilization choices and pest control protocols driven by data. The system is able to identify plant diseases, monitor animal health, and optimize cattle feed, and these features can be expected to increase the productivity of agricultureAlthough the data obviously reflects challenges such as misuse of data, limited entry for small actors and ongoing technological and infrastructure development. The clear finding is that AI has the potential to dramatically transform food production across primary agriculture. Which can lead to higher yields, increased profitability, sustainable practices and one: of the most powerful tools in addressing global food security. We need to adapt this innovative technology and its ethical and equitable implementation to ensure the future sustainability of agriculture.Conference of the Open Innovations Association310This article creates a path to shed insights on the significant impact of artificial-intelligence in terms of shaping up and automating agricultural operations that would let the reader focus their visualization on root-level processes within food production business.2024 36th Conference of Open Innovations Association (FRUCT)["Mohammed Abd. Mohammed", "Sarah Haitham Jameel", "Ali Jabbar Hussein", "Hussain Kassim Ahmad", "Laith S. Ismail", "Alina Zapryvoda"]2024-10-30T00:00:00 10d02a78b947bc10ee1fc026042a443bff5acef4This policy review introduces the Policy Guidance on Artificial Intelligence (AI) for Children, produced by the United Nations Children's Fund (UNICEF). This Policy Guidance is the first international‐level output to boost the development of child‐centred AI and relevant policies. A main contribution of this Policy Guidance is that it outlines the foundations, requirements and specific recommendations for developing child‐centred AI and surrounding policies. The shortcomings of this Policy Guidance are also introduced, especially the insufficient gender responsiveness and age sensitivity, plus relatively low representation of the developing world. Possible suggestions for future updates of the Policy Guidance and improving policies on child‐centred AI are provided in this review, such as the inclusion of a broader age range of children during the consultation process. The coexistence of contributions and limitations of this Policy Guidance reflects the situation of development of child‐centred AI and relevant policies, which is currently immature but promising.Children &amp; Society190Children &amp; Society["Suyu Liu", "Wenjun Ding"]2024-10-30T00:00:00 9bf19acebd5170d13d0fbcaf37be67624411612dIn the context of the emerging digital environment, generative artificial intelligence (GAI) is quickly becoming implemented in different spheres of life and even begins to replace people in some professions. This means that GAI is leading a new round of technological and industrial revolution. The application and growth of artificial intelligence has improved the society as well as the economy and but has issued many ethical and social problems thus making ethical governance of artificial intelligence more relevant. To contribute to the further development of the concept GAI under the conditions of sustainable development of human society and science, industries and scholars around the world have started researches on this technology. However, as GAI is still an embryonic field, more research efforts must be directed toward this field in the future. Researched articles that compare the use of GAI to the effects on ethical and societal concerns will form the basis of this paper and the resulting discussion of the pros and cons of this technology, this paper considers and demonstrates the difficulties of students, educators, and academics facing the consequences of GAI development. Finally, it has suggestion for the governance of GAI’s next phase of growth.Applied Science and Innovative Research00This paper considers and demonstrates the difficulties of students, educators, and academics facing the consequences of GAI development and has suggestion for the governance of GAI’s next phase of growth.Applied Science and Innovative Research["Cai Jia", "Ronaldo Juanatas", "Jasmin D. Niguidula"]2024-10-30T00:00:00 1ec683fd18e8cfd503fa0b70df3616e1eeb75394Objective: Considering the use of Artificial Intelligence (AI) by teachers in public high schools in Barão de Cocais faces challenges, such as the lack of specific training and inadequate infrastructure, along with concerns about pedagogical autonomy, the objective is to investigate the impacts of these technological tools on the teaching-learning process.   Theoretical Framework: This research presents the main concepts and theories that underlie the work. The use of artificial intelligence in public education, the ethical and pedagogical challenges of using artificial intelligence in teaching and the infrastructure and public policies for artificial intelligence in education stand out, providing a solid basis for understanding the context of the investigation.   Method: A qualitative methodology is applied, based on semi-structured interviews with teachers, aiming to capture their experiences and perceptions regarding the use of AI in their pedagogical practices.   Results and Discussion: The results obtained revealed that it is essential to incorporate digital skills development into teacher training curricula, in addition to promoting public policies that encourage the conscious and appropriate use of AI in the educational context.   Research Implications: Content analysis allows the identification of patterns and recurring themes, providing a broad understanding of the implications of this technology in education. It is observed that AI is seen by teachers as a promising tool, particularly for automating tasks and personalizing learning. However, there is a significant gap in teacher training and in the schools’ infrastructure, hindering more efficient adoption.   Originality/Value: This study contributes to the emerging literature on the implementation of AI in Brazilian public education, offering valuable insights into the challenges and opportunities surrounding the adoption of these technologies in educational institutions in specific regions, such as Barão de Cocais.Revista de Gestão Social e Ambiental260It is observed that AI is seen by teachers as a promising tool, particularly for automating tasks and personalizing learning, but there is a significant gap in teacher training and in the schools’ infrastructure, hindering more efficient adoption.Revista de Gestão Social e Ambiental["\u00c9rika M\u00e1rcia Assis de Souza", "\u00c9rika Silva Fabri", "Wagner dos Reis Marques Ara\u00fajo", "Wagner Cavalare de Souza", "Sara Isabel de Melo Resende", "H\u00e9lio Augusto Goulart Diniz"]2024-10-30T00:00:00 b3ecddeff8381906f0c467537588a7f73c27b7c9Radiology, as a highly technical and information-rich medical specialty, is well-suited for artificial intelligence (AI) product development, and many FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end-user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.AJR. American journal of roentgenology360The role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance, is emphasized, to guide AI tools' adaptation and help ensure sustained performance.AJR. American journal of roentgenology["Sirui Jiang", "Syed Muhammad Awais Bukhari", "Arjun Krishnan", "Kaustav Bera", "Avishkar Sharma", "Danielle Caovan", "Beverly Rosipko", "Amit Gupta"]2024-10-30T00:00:00 1e9e1cc58fcfcea600fc80f3a303265c0c1a13c0The legal framework has significant potential to address cybercrime with the help of Artificial Intelligence (AI), which increases the efficiency of detecting, investigating and prosecuting increasingly sophisticated cybercriminals. This technology can perform big data analysis, pattern recognition and identification of suspicious behavior, but the legal framework needs to be updated to cover new crimes such as AI-based fraud and automated cyberattacks. The challenges in law enforcement related to the misuse of AI are quite complex, especially due to the lack of specific regulations to regulate its use in the context of cybercrime. Existing regulations often do not cover new situations, thus reducing the effectiveness of law enforcement. To overcome these challenges, legislators need to update regulations and develop ethical guidelines, while international collaboration and capacity building of law enforcement through education and training are also essential to increase the effectiveness of handling cybercrime.International Journal of Law and Society00The legal framework has significant potential to address cybercrime with the help of Artificial Intelligence, which increases the efficiency of detecting, investigating and prosecuting increasingly sophisticated cybercriminals, but the legal framework needs to be updated to cover new crimes such as AI-based fraud and automated cyberattacks.International Journal of Law and Society["Murshal Senjaya"]2024-10-30T00:00:00 7d8e6cdf4eb17e4724c896927ef8071c678fc505Management Review Quarterly102Management Review Quarterly["Joern Block", "Andreas Kuckertz"]2024-10-30T00:00:00 626cf1159110521e9b6c6820db9f26f83ba8c52bAcademy of Management Review832Academy of Management Review["G. Grote", "Sharon K. Parker", "Kevin Crowston"]2024-10-30T00:00:00 20bf105b5ba2ebf6a5724c8d5b1ec33025f61735Systems and Means of Informatics00Systems and Means of Informatics[]2024-10-30T00:00:00 545620b9cd3479348a97b539e1249b28d02b201fInternational Journal of Computer Trends and Technology00International Journal of Computer Trends and Technology["Kishorebabu Tenneti", "Susmitha Pandula"]2024-10-30T00:00:00 5fb75cc8a36ad671e6b77766f203931cdf16812bRadiography220This study reveals significant gaps in training and understanding of AI among medical imaging staff, laying foundations for future qualitative studies on the provision of AI education for medical imaging professionals, helping to prepare the workforce for the evolving role of AI in medical imaging.Radiography["G. Doherty", "L. McLaughlin", "C. Hughes", "J. McConnell", "R. Bond", "S. McFadden"]2024-10-30T00:00:00 883be2ffec2fcb09f0854af0386a98196efc587cAI &amp; SOCIETY580AI &amp; SOCIETY["Stany Nzobonimpa", "Jean-Fran\u00e7ois Savard", "Isabelle Caron", "Justin Lawar\u00e9e"]2024-10-30T00:00:00 2b910e8a900362c14ad6cd5e1fc867eb610ab88dAcademia Medicine450Academia Medicine["Bongs Lainjo"]2024-10-30T00:00:00 b083e8c7956d70289a7302a6948a9ed0adf297bfAbstract Background Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches. Objective This scoping review examines the potential of AI and data science for modeling, extraction, and incorporation of SDOH data specifically within EDs, further identifying areas for advancement and investigation. Methods We conducted a standardized search for studies published between 2015 and 2022, across Medline (Ovid), Embase (Ovid), CINAHL, Web of Science, and ERIC databases. We focused on identifying studies using AI or data science related to SDOH within emergency care contexts or conditions. Two specialized reviewers in emergency medicine (EM) and clinical informatics independently assessed each article, resolving discrepancies through iterative reviews and discussion. We then extracted data covering study details, methodologies, patient demographics, care settings, and principal outcomes. Results Of the 1047 studies screened, 26 met the inclusion criteria. Notably, 9 out of 26 (35%) studies were solely concentrated on ED patients. Conditions studied spanned broad EM complaints and included sepsis, acute myocardial infarction, and asthma. The majority of studies (n=16) explored multiple SDOH domains, with homelessness/housing insecurity and neighborhood/built environment predominating. Machine learning (ML) techniques were used in 23 of 26 studies, with natural language processing (NLP) being the most commonly used approach (n=11). Rule-based NLP (n=5), deep learning (n=2), and pattern matching (n=4) were the most commonly used NLP techniques. NLP models in the reviewed studies displayed significant predictive performance with outcomes, with F1-scores ranging between 0.40 and 0.75 and specificities nearing 95.9%. Conclusions Although in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in EM offers transformative possibilities for better usage and integration of social data into clinical care and research. With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. Our research underscores the vital need for continued investigation in this domain.JMIR Medical Informatics260Although in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in EM offers transformative possibilities for better usage and integration of social data into clinical care and research.JMIR Medical Informatics["Ethan E Abbott", "Donald Apakama", "Lynne D Richardson", "Lili Chan", "G. Nadkarni"]2024-10-30T00:00:00 a04d288a8496e506e65a1dec24f349e2204faff2The incorporation of AI as a tool in educating undergraduates is a promising concept held with a great potential of positively influencing the learning process, learners’ experiences and institutional management systems. At the same time, it lifts essential moral issues that ought to be adequately addressed into new stages. The aim of this paper is to review literature on the subject of ethics in the application of AI in the context of an undergraduate degree program. Algorithmic choices for instructional design are another relevant ethical concern along with privacy and data protection, practitioners’ responsibilities, and the requisite levels of transparency, as well as the depersonalized and mechanized nature of learning management systems. The analysis reveals how the use of AI technology might deepen the social and economic inequities the students have described and how difficult it is to keep the students’ trust in cases when AI technology is not transparent. Also, the paper raises concerns related to several ethical issues and their consequences to students, teachers, and institutions on how strong ethical principles and policies to apply AI in the educational sectors. It is established that the use of AI in the delivery of undergraduate education has the potential of enhancing the effectiveness of learning(). Nonetheless, it is recommended that the integration of AI in the classroom must be done with reference to several ethical concerns so as to prevent further negative ramifications of the technology.International Journal of Science and Research Archive00It is recommended that the integration of AI in the classroom must be done with reference to several ethical concerns so as to prevent further negative ramifications of the technology.International Journal of Science and Research Archive["Waleed Salameh"]2024-10-30T00:00:00 174766470e0e64d92243299f0eecc8fbf34c78e8International journal of scientific and research publications00International Journal of Scientific and Research Publications["Samer Al Tahiri", "Mohammed Majrashi", "Yahya Otaif", "Waleed Yateemi", "Daifallah Al Dibsh", "Mansour Khallaf", "Abeer Zaila", "Abdullah Alabdali", "Amnah Mayan", "Donia Kharmi"]2024-10-30T00:00:00 d427e42fb687f03d2b2733514dfe237cf81657dfInternational Journal of Cyber Diplomacy00International Journal of Cyber Diplomacy["Flavius Caba-Maria"]2024-10-30T00:00:00 57d82b24050f93551ee661eabeda3983078e000eDiabetic foot ulcers (DFUs) are a serious complication of diabetes mellitus, with a lifetime risk estimated to be between 19% and 34%.1 Without timely prevention and management, DFUs can lead to lower extremity amputations (LEAs) and premature death.2,3 DFUs also impose significant healthcare and societal costs, especially in Southeast Asia.4,5 Regular foot screenings are essential for preventing these complications.Annals of the Academy of Medicine, Singapore120Regular foot screenings are essential for preventing diabetes mellitus-related foot ulcers and impose significant healthcare and societal costs, especially in Southeast Asia.Annals of the Academy of Medicine, Singapore["Yan Sun", "Lixia Ge", "Yee G Ang", "Z. Lo", "Huiling Liew", "Donna Tan", "Daniel Chew", "J. Abisheganaden"]2024-10-30T00:00:00 95182ff5dee44d53000a9a2aa943254cc5b4ad98International Journal of Machine Learning and Cybernetics1020International Journal of Machine Learning and Cybernetics["Vimala Balakrishnan", "Zahiriddin Rustamov", "Ghayathri Ramanathan", "Jia Leng Lim"]2024-10-30T00:00:00 013fda5118ce47c1fe82127dfb95de50d1cd0ac7This study developed an automated language learning teaching assessment system based on electroencephalography (EEG) and differential language large models (LLMs), aimed at enhancing language instruction effectiveness by monitoring learners’ cognitive states in real time and personalizing teaching content accordingly. Through detailed experimental design, the paper validated the system’s application in various teaching tasks. The results indicate that the system exhibited high precision, recall, and accuracy in teaching effectiveness tests. Specifically, the method integrating differential LLMs with the EEG fusion module achieved a precision of 0.96, recall of 0.95, accuracy of 0.96, and an F1-score of 0.95, outperforming other automated teaching models. Additionally, ablation experiments further confirmed the critical role of the EEG fusion module in enhancing teaching quality and accuracy, providing valuable data support and theoretical basis for future improvements in teaching methods and system design.Italian National Conference on Sensors290An automated language learning teaching assessment system based on electroencephalography (EEG) and differential language large models (LLMs) aimed at enhancing language instruction effectiveness by monitoring learners’ cognitive states in real time and personalizing teaching content accordingly exhibited high precision, recall, and accuracy in teaching effectiveness tests.Sensors (Basel, Switzerland)["Yanlin Chen", "Wuxiong Wang", "Shen Yan", "Yiming Wang", "Xinran Zheng", "Chunli Lv"]2024-10-30T00:00:00 b99e9c6558c01968fc20d0a949bfc523be98ad38В рамках теории оптимального управления рассмотрена проблема стабилизации климата Земли посредством введения в стратосферу сульфатных аэрозолей. Для решения задачи используется принцип максимума Понтрягина. Нейросетевые технологии позволяют получить решение оптимизационной задачи, напоминающее оптимальное управление, не решая при этом задачу классическими методами. Within the theory of optimal control, the problem of stabilizing the Earth's climate through the introduction of sulfate aerosols into the stratosphere is considered. The Pontryagin's maximum principle is used to solve the problem. Neural network technologies allow obtaining a solution to the optimization problem resembling optimal control without solving the problem using classical methods.XXX Юбилейный Международный симпозиум Оптика атмосферы и океана. Физика атмосферы30XXX Юбилейный Международный симпозиум Оптика атмосферы и океана. Физика атмосферы["\u0421.\u0410. \u0421\u043e\u043b\u0434\u0430\u0442\u0435\u043d\u043a\u043e"]2024-10-30T00:00:00 c54afea793c99e8c1ea8df2a2e1bbcc4cda728dcAccurate channel models are the prerequisite for communication-theoretic investigations as well as system design. Channel modeling generally relies on statistical and deterministic approaches. However, there are still significant limits for the traditional modeling methods in terms of accuracy, generalization ability, and computational complexity. The fundamental reason is that establishing a quantified and accurate mapping between physical environment and channel characteristics becomes increasing challenging for modern communication systems. Here, in the context of COST CA20120 Action, we evaluate and discuss the feasibility and implementation of using artificial intelligence (AI) for channel modeling, and explore where the future of this field lies. Firstly, we present a framework of AI-based channel modeling to characterize complex wireless channels. Then, we highlight in detail some major challenges and present the possible solutions: i) estimating the uncertainty of AI-based channel predictions, ii) integrating prior knowledge of propagation to improve generalization capabilities, and iii) interpretable AI for channel modeling. We present and discuss illustrative numerical results to showcase the capabilities of AI-based channel modeling.arXiv.org151A framework of AI-based channel modeling to characterize complex wireless channels is presented and illustrative numerical results to showcase the capabilities of AI-based channel modeling.ArXiv["R. He", "N. D. Cicco", "Bo Ai", "Mi Yang", "Yang Miao", "Mate Boban"]2024-10-31T00:00:00 cccf39eede80f811ce3d4544d6c333c6c76c7f58Background: As artificial intelligence technology has developed, research on the application of AI in the medical field has increased, and there is a high likelihood that the use of AI technology will expand in nursing education and practice in the future. However, ethical issues arise when utilizing AI, necessitating a high level of ethical awareness before its application. Objectives: This study aimed to identify factors in artificial intelligence ethics awareness among nursing students. Methods: Participants were 140 nursing students attending universities in G City and J Province in South Korea. Data were collected using a self-administered questionnaire from 26 August to 6 September 2024. Factors in artificial intelligence ethics awareness were analyzed by multiple regression analysis. Results: Nursing students’ artificial intelligence ethics awareness is significantly correlated with digital literacy (r = 0.30, p < 0.001) and moral sensitivity (r = 27, p < 0.001). The influencing factor in artificial intelligence ethics awareness among nursing students was moral sensitivity (β = 0.23, p = 0.042). The explanation power of these variables was 14.0% (F = 46.78, p < 0.001). Conclusions: There is a need to provide education and training programs that can improve moral sensitivity to foster artificial intelligence ethics awareness.Healthcare471There is a need to provide education and training programs that can improve moral sensitivity to foster artificial intelligence ethics awareness among nursing students.Healthcare["Yaki Yang"]2024-10-31T00:00:00 6d07c3c814623709f2749e1029483f656be21e47The stability–plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus–cortex system. We elucidate how this biological system’s ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.Brain Science581This perspective paper elucidates how the mammalian hippocampus–cortex system’s ability to balance rapid learning with long-term memory retention can inspire novel AI architectures, and proposes innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation.Brain Sciences["Thorsten Rudroff", "O. Rainio", "R. Kl\u00e9n"]2024-10-31T00:00:00 9bb96a8be9aa2217273f737dd5adc656fb65cc9cThis research aims to inspect the application of Artificial Intelligence (AI) in product and service innovation from the perspective of the Hainan Free Trade Port (HFTP) and its relationship with corporate transformation, legal compliance, and regulatory oversight. Being critical to the fourth industrial revolution, digital business and international cooperation, technology propels enterprises across various industries to transition from traditional models to intelligent and service-oriented ones. It also elucidates the theoretical foundations of AI products, the digital economy, and service innovation. It can be used to analyzes the challenges enterprises face in the HFTP while implementing AI technology, including funding, technology, management, operations, corporate culture, and innovative concepts. Based on the proposed research methodology, three hypotheses can be formulated. Hypothesis 1 states that the HFTP could facilitate enterprise transformation by applying supportive policies. Hypothesis 2 state that domestic laws and international agreements are urgently needed due to the legal risks arising from artificial intelligence. Hypothesis 3 state that HFTP enterprises comply with these laws while systemically assuring, in theory, and practice, the legal risks of artificial intelligence and its implications for legal regulation, which is a significant aspect of research, addressing legal risks related to data privacy, security, and algorithmic bias with many strategies being proposed. This shows how AI technology can change businesses in the HFTP, demonstrating the application of AI technology in the transformation of enterprises in the HFTP and the various risks they may encounter, providing valuable references for other enterprises regarding the practical significance of AI product and service innovation in the HFTP, and emphasizing the importance of international cooperation and legal instruction.Syst.551How AI technology can change businesses in the HFTP is shown, demonstrating the application of AI technology in the transformation of enterprises in the HFTP and the various risks they may encounter, and providing valuable references for other enterprises regarding the practical significance of AI product and service innovation in the HFTP.Syst.["Yincheng Li", "Shumin Wang", "M. Khaskheli"]2024-10-31T00:00:00 5afc27785d5a6b128c0635310aa0351186b027aeThe dynamics underpinning the urban landscape change are primarily driven by social, economic, and environmental issues. Owing to the population’s fluctuating needs, a new and dual perspective of urban space emerges. The Artificial Intelligence (AI) of a territory, or the system of technical diligence associated with the anthropocentric world, makes sense in the context of this temporal mismatch between territorial processes and utilitarian apparatus. This creates cerebral connections between several concurrent decision-making systems, leading to numerous perspectives of the same urban environment, often filtered by the people whose interests direct the information flow till the transformability. In contrast to the conventional methodologies of decision analysis, which are employed to facilitate convenient judgments between alternative options, innovative Artificial Intelligence tools are gaining traction as a means of more effectively evaluating and selecting fast-track solutions. The study’s goal is to investigate the cross-functional relationships between Artificial Intelligence (AI) and current decision-making support systems, which are increasingly being used to interpret urban growth and development from a multi-dimensional perspective, such as a multi-criteria one. Individuals in charge of administering and governing a territory will gain from artificial intelligence techniques because they will be able to test resilience and responsibility in decision-making circumstances while also responding fast and spontaneously to community requirements. The study evaluates current grading techniques and recommends areas for future upgrades via the lens of the potentials afforded by AI technology to the establishment of digitization pathways for technological advancements in the urban valuation.Information311The study investigates the cross-functional relationships between Artificial Intelligence (AI) and current decision-making support systems, which are increasingly being used to interpret urban growth and development from a multi-dimensional perspective, such as a multi-criteria one.Information["M. Guarini", "Francesco Sica", "Alejandro Segura"]2024-10-31T00:00:00 e1153a40c5e98f7c808d341eac0e62e235bdb1afBackground: Artificial intelligence (AI) has the potential to revolutionize antibiotic discovery. By automating and accelerating various stages of the drug discovery process, AI can help address the urgent need for new antibiotics to combat rising antimicrobial resistance.AI can be used to analyze vast amounts of data, AI algorithms can process and analyze large datasets to identify patterns and trends that may be relevant to antibiotic discovery. Predict molecular properties, design novel antibiotics and optimize drug development. By leveraging AI, researchers can expedite the discovery of novel antibiotics, improve their efficacy, and reduce the time and cost associated with drug development. This is crucial for addressing the growing threat of antibiotic resistance and ensuring the availability of effective treatments for infectious diseases. Materials and Methods: This review article systematically analyzes published research on the application of artificial intelligence (AI) in pharmacology, the drug industry, and specifically, antibiotic discovery. The information from these articles was categorized and reviewed according to the AI applications in pharmacology, AI in the drug industry, AI in antibiotic discovery. This review aims to highlight the specific ways in which AI is being used to address the urgent need for new antibiotics. Results: A review of 35 studies revealed the benefits of using artificial intelligence (AI) in drug discovery, particularly in the context of antibiotic development. AI can enhance drug design processes, improve predictions of ligand-receptor interactions, and facilitate collaboration among healthcare providers.However, AI also faces challenges, such as potential biases in decision-making, ethical concerns, and the need to recognize its limitations. To address these issues, researchers can focus on strengthening neural network databases, integrating AI with traditional experimental methods. By overcoming these challenges, AI can play a crucial role in accelerating the discovery of novel antibiotics and improving the treatment of infectious diseases. Conclusion: The efficient application of artificial intelligence can substantially expedite drug discovery, especially for new antibiotics. As bacterial resistance to current antibiotics increases, the demand for swift development of more potent drugs becomes more critical. AI, capable of analyzing extensive datasets and predicting molecular characteristics, can significantly accelerate this process.International Journal of Advanced Research01A review of 35 studies revealed the benefits of using artificial intelligence (AI) in drug discovery, particularly in the context of antibiotic development, and the efficient application of AI can substantially expedite drug discovery, especially for new antibiotics.International Journal of Advanced Research["Helia Rajabi Dezfooli", "Adib Dashtizadeh", "Arman Esmaeily", "Shiva Baradaranaghili"]2024-10-31T00:00:00 959e56c8323c2d1b27c9b5bb87062398264cd36fArtificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the development of explainable artificial intelligence (XAI), which aims to enhance user understanding and trust by providing clear explanations of AI decisions and processes. This paper reviews existing XAI research, focusing on its application in the healthcare sector, particularly in medical and medicinal contexts. Our analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications.Big Data and Cognitive Computing562This analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications.Big Data and Cognitive Computing["Sayda Umma Hamida", "Mohammad Jabed Morshed Chowdhury", "Narayan Ranjan Chakraborty", "Kamanashis Biswas", "S. Sami"]2024-10-31T00:00:00 ffb1ffcaf4c1afbafcabf73228807640f657c410Artificial Intelligence (AI) has been emerging as a topic of significant interest, attracting the attention of the public and leading to an increase in research and on media coverage of this technology. This article examines how the Brazilian and Portuguese media represent AI in journalism and the challenges it poses. Using digital methods, this study analysed 60 news articles published between June 2022 and June 2024. The data were collected through an anonymous search on Google News, and the content was analysed using sentiment analysis with the PTNews software, followed by a similarity analysis using the Iramuteq software. The results show a predominantly positive sentiment towards AI in journalism, with 91.8% of articles highlighting its benefits, such as increased efficiency and the automation of routine tasks. However, concerns about disinformation, ethical implications, and the potential erosion of journalistic credibility were less emphasised. The analysis also identified key themes, including AI’s dual role as both an enabler and a threat to journalism, the importance of human oversight, and the challenges of newsroom adaptation. The findings suggest that the Brazilian and Portuguese media generally present AI as an opportunity for journalism, often downplaying the associated risks and ethical challenges.The social science370The findings suggest that the Brazilian and Portuguese media generally present AI as an opportunity for journalism, often downplaying the associated risks and ethical challenges.Social Sciences["Jo\u00e3o Canavilhas", "Fabia Ioscote", "Adriana Gon\u00e7alves"]2024-10-31T00:00:00 23bc9fb11901e3c0bcb409a203d3b22cc151ffccBMC Medical Education470A multi-pronged approach, including education, targeted training, and addressing specific concerns, is necessary to facilitate the wider adoption of AI-enabled healthcare.BMC Medical Education["Rasha Mahmoud Allam", "Dalia Abdelfatah", "Marwa Ibrahim Mahfouz Khalil", "Mohamed Mahmoud Elsaieed", "Eman D. El Desouky"]2024-10-31T00:00:00 4045db9c43fbd8bfb7388bcbcd5b4f38e2513e16Artificial Intelligence (AI) is one of the most ingenious inventions drastically transforming the business world. The question is whether the current growth of AI is all hype or if it has the potential to revolutionize the world is raised by the abundance of intelligent goods and services that have emerged in recent times. Technological developments in AI pave the way for creating technologies that resemble humans and can operate independently and imitate our intellectual processes. Numerous industries have shown considerable interest and enthusiasm in adopting AI due to technological advancements. As a result, numerous businesses are making significant investments to leverage this opportunity through innovative business models based on AI. The study aims to establish a model based on an evidence-synthesis approach that demonstrates how the business sector in Saudi Arabia can incorporate AI to achieve better outcomes and strengthen its relationships with other countries. However, the study also highlights some advantages and disadvantages of adopting AI models in the business industry so a balanced approach can be used to incorporate AI in different aspects of the business. The study recommends incorporating AI in different health, education, and business sectors in Saudi Arabia to foster international relationships in Saudi Arabia.Journal of Accounting, Business and Management240The study aims to establish a model based on an evidence-synthesis approach that demonstrates how the business sector in Saudi Arabia can incorporate AI to achieve better outcomes and strengthen its relationships with other countries.Journal of Accounting, Business and Management (JABM)["Nisar Ahmed Zafar"]2024-10-31T00:00:00 e304920b2d739a9e88051f126ea2be87bff8e634This article summarizes important features of artificial intelligence scribe (AIS) and its application as a digital scribe through the utilization of voice recognition as it is integrated into the clinical practice of medicine. This discussion will include 1) digital scribe technology, 2) practical utility, 3) liability, 4) privacy, 5) integration to electronic health records (EHR), 6) technology burden, 7) case use, 8) AIS documentation styles, and 9) cost.  The use of AIS technology in healthcare is a promising tool and will result in variability applications requiring healthcare providers to be aware of the complexities of the tool before utilization, during implementation, the effect of it on workflow, and its acceptance by the patients we serve. AIS technology promises to decrease the cognitive workload of the healthcare provider, through application by relieving the ever-increasing burden of documenting the medical record, organizing, and capturing relevant medical data, by improving integration of this data into cumbersome EHRs, thus providing better work life balance. The authors piloted a version of an AIS in a primarily Spanish patient population. This article provides a unique perspective towards the usability of an AIS in a Hispanic patient population.Journal of the National Hispanic Medical Association00This article summarizes important features of artificial intelligence scribe and its application as a digital scribe through the utilization of voice recognition as it is integrated into the clinical practice of medicine through the utilization of voice recognition.Journal of the National Hispanic Medical Association["Laura Solano", "Robert S. Smith"]2024-10-31T00:00:00 918fb255d1d6c817b49827aeb12e98fab8594144PurposeThe study aims to reflect on past research, uncover current trends and propose a future research agenda in the field of artificial intelligence (AI) for competency-based personalised learning.Design/methodology/approachThe study followed the SPAR-4-SLR protocol to retrieve 855 articles related to the field indexed in the Scopus database. Performance analysis, network analysis and science mapping were then performed using VOSviewer and the Biblioshiny app.FindingsThe analysis identified nine clusters of intellectual structure (healthcare, competencies, learning systems, digital transformation, AI literacy, computer-aided education, AI ethics, e-learning and active learning) and twelve themes (including motor, basic, emerging and niche).Originality/valueFollowing an extensive review of the literature, this would appear to be the first study to provide a panoramic view of AI for competency-based personalised learning based on the Scopus database. The core gaps in the current literature have been identified and the corresponding future agenda will be instrumental in shaping future research directions in the field.The international journal of information and learning technology170Following an extensive review of the literature, this would appear to be the first study to provide a panoramic view of AI for competency-based personalised learning based on the Scopus database.The International Journal of Information and Learning Technology["Omkar Dastane", "Jason Turner", "Alan Nankervis"]2024-10-31T00:00:00 2c77de205d3e9055d72da61728875de16e6c13f6This article examines the implementation of artificial intelligence-powered chatbots in public sector benefits enrollment processes, focusing on their potential to streamline operations, enhance user experience, and reduce administrative burdens. Through a comprehensive analysis of case studies in healthcare and social security benefits programs, we demonstrate that chatbots can significantly improve efficiency, accuracy, and accessibility in enrollment procedures. Our findings indicate a 30-40% reduction in processing times and a marked increase in application completion rates. However, challenges related to data privacy, system integration, and user acceptance persist. This article contributes to the growing body of literature on digital transformation in public administration by providing empirical evidence of chatbots' effectiveness in benefits enrollment. We conclude that while chatbots offer promising solutions to longstanding issues in public sector service delivery, their successful implementation requires careful consideration of technical, ethical, and user-centric factors. Our study has important implications for policymakers and public administrators seeking to leverage AI technologies to enhance public service efficiency and accessibilityInternational Journal For Multidisciplinary Research120It is demonstrated that chatbots can significantly improve efficiency, accuracy, and accessibility in enrollment procedures, with a 30-40% reduction in processing times and a marked increase in application completion rates.International Journal For Multidisciplinary Research["Venkatarama Reddy Kommidi", "Sudheer Chennuri"]2024-10-31T00:00:00 c8d9eb646ec26c9d755a17b152930cc00b214b64Artificial Intelligence (AI) has quickly spread to a number of areas, including HR. The purpose of this study is to evaluate how AI has affected Collabera Digital Bangalore's HR operations. The research aims to comprehend the advantages, difficulties, and general impact of AI on HR procedures including hiring, on-boarding, performance management, and employee development through a thorough examination of AI tools and methods applied within the organisation. A mixed-approaches strategy will be used in the study, integrating quantitative and qualitative research methods. Interviews with HR specialists and staff members will yield qualitative data about their perspectives on AI and their experiences with it. To gauge the observable effects of implementing AI, quantitative data will be collected via surveys and performance indicators. The research's conclusions will offer insightful information to companies thinking about integrating AI into HR tasks, assisting them in comprehending the possible advantages, difficulties, and best practices for a successful integration.International Journal For Multidisciplinary Research190The research aims to comprehend the advantages, difficulties, and general impact of AI on HR procedures including hiring, on-boarding, performance management, and employee development through a thorough examination of AI tools and methods applied within the organisation.International Journal For Multidisciplinary Research["Anusha Bhat", "Veena Ishwarappa Bhavikatti"]2024-10-31T00:00:00 72b24c72e4134aa5794f08cd513f4191a02ff6b4AI is revolutionizing the landscape of colorectal cancer (CRC) surgery, permeating diverse facets ranging from intraoperative guidance to predictive modeling of postoperative outcomes. This scoping review aims to comprehensively delineate the breadth of artificial intelligence (AI) applications in CRC surgery. A search of PubMed, Embase, and Ebsco databases up to December 2023 was conducted, with registration in the international prospective register of systematic reviews (PROSPERO) (CRD42024502107). Sixty-two studies meeting stringent inclusion criteria were scrutinized, encompassing AI utilization in CRC surgery or the development of AI-driven tools for colorectal surgical practice. Five principal domains of AI application emerged: (i) Intraoperative guidance, leveraging real-time navigation, indocyanine green (ICG) angiography, and hyperspectral imaging (HSI) to enhance surgical precision; (ii) Image segmentation, facilitating phase recognition, tools recognition, and anatomical identification to optimize surgical visualization; (iii) Training and performance assessment, enabling objective evaluation and enhancement of surgical skills through AI-driven simulations and feedback mechanisms; (iv) Prediction of surgical complications, encompassing prognostication of anastomotic leakage (AL) or stricture, stoma requirements, and prediction of low anterior resection syndrome (LARS) and short-term postoperative complications; (v) Utilization of electronic health records (EHRs), harnessing AI algorithms to streamline data analysis and inform decision-making processes. This review underscores the paradigm-shifting impact of AI in CRC surgery, transcending conventional boundaries and catalyzing advancements across diverse surgical domains. Although many applications are still experimental, as AI continues to evolve, it promises to transform surgical practice, optimize outcomes, and revolutionize patient care. Embracing AI technologies is imperative for colorectal surgeons to remain at the vanguard of surgical innovation and deliver superior outcomes for CRC patients.Artificial Intelligence Surgery800The paradigm-shifting impact of AI in CRC surgery is underscored, transcending conventional boundaries and catalyzing advancements across diverse surgical domains.Artificial Intelligence Surgery["F. Celotto", "G. Capelli", "Stefania Ferrari", "Marco Scarpa", "Salvatore Pucciarelli", "G. Spolverato"]2024-10-31T00:00:00 93f4a7697c76857edea7ac92d0b8ec9c3c0e3466Objectives This study analyzes trends in domestic elementary and secondary AI education research, suggesting directions for future studies and providing insights to support further research activities. Methods To investigate research trends in AI education for elementary and secondary schools in South Korea, The literature was searched using RISS by combining nine keywords: ‘AI,’ ‘artificial intelligence,’ ‘chatbot,’ ‘machine learning,’, ‘deep learning’, ‘education,’ ‘class,’ ‘learning,’ and ‘teaching’. After a second screening, we selected 140 documents published from 2019 to March 2023 and analyzed them based on basic, research purposes, meth-ods, and content trends. Results First, the number of publications in domestic AI education research for elementary and secondary schools increased from 2019 to 2021 but decreased by one in 2022, with 21 papers published by March 2023. Second, the study subjects were ranked in order of elementary, middle, and high school levels. Third, the primary research fo-cus for elementary and middle schools was on ‘Development and Application of AI Education Programs,’ while high school research also included ‘Analysis of AI Education Curriculum.’ Fourth, developmental research was the most common method, focusing on AI education programs and teaching models across all levels. Fifth, ‘Other’ and ‘Mathematics’ were the most frequently applied subjects. Sixth, the most common teaching methods in-cluded ‘Other,’ emphasizing experiential learning and practical training. Lastly, the most common dependent vari-ables were ‘Attitudes towards artificial intelligence’ at the elementary level, ‘Academic achievement’ at the middle level, and ‘Other’ at the high school level. Conclusions This study is significant in analyzing trends in AI education research within domestic elementary and secondary schools, thereby shedding light on future research directions.Korean Association For Learner-Centered Curriculum And Instruction00This study analyzes trends in domestic elementary and secondary AI education research within domestic elementary and secondary schools, shedding light on future research directions and suggesting directions for future studies.Korean Association For Learner-Centered Curriculum And Instruction["Sujin Kim", "Tami Im", "Jei Kim"]2024-10-31T00:00:00 00c70f3bad1ee9f3390d32af47043e40806feff1The DocBot Chat Bot/AI system is a novel solution to the challenge of acquiring accurate and personalized health information. By utilizing cutting-edge artificial intelligence (AI) algorithms and natural language processing (NLP) methods, the DocBot system creates an engaging and intuitive interface that lets people convey their health concerns and get personalized advice. This study provides an in-depth assessment of the DocBot system, stressing its key features, AI algorithms, advantages, difficulties, and potential applications. To improve healthcare communication and encourage well-informed decision-making, it is important to highlight DocBot's transformative potential in providing users with accurate and trustworthy health knowledge.International Journal for Research in Applied Science and Engineering Technology00An in-depth assessment of the DocBot system is provided, stressing its key features, AI algorithms, advantages, difficulties, and potential applications to improve healthcare communication and encourage well-informed decision-making.International Journal for Research in Applied Science and Engineering Technology["Prof. Neha Nandanwar"]2024-10-31T00:00:00 5a49ecd73c22f0e5da69e5ab311c85560760e1f8This paper explores the integration of Artificial Intelligence (AI) in enhancing employee mental health and well-being in workplace environments. With growing concerns over stress, anxiety, and burnout in the workplace, AI offers novel solutions to monitor, assess, and improve mental health. By leveraging AI technologies such as sentiment analysis, chatbots, virtual assistants, and wearable devices, organizations can detect early signs of stress and burnout, enabling timely interventions. AIs role in providing personalized mental health support, including customized wellness programs and real-time stress management tools, is also discussed. However, the paper highlights key challenges, including data privacy, algorithmic bias, and the ethical implications of continuous employee monitoring. Ethical considerations surrounding employee consent, privacy, and the role of human oversight are crucial to ensuring AIs effectiveness and maintaining trust in its application. Ultimately, the integration of AI in mental health management has the potential to create healthier, more productive workplace environments when coupled with ethical practices and human support systems.International Journal of Advanced Research00Ethical considerations surrounding employee consent, privacy, and the role of human oversight are crucial to ensuring AIs effectiveness and maintaining trust in its application, and the ethical implications of continuous employee monitoring are highlighted.International Journal of Advanced Research["Ankita Jangid"]2024-10-31T00:00:00 f20b457c713c3526813079cd8e1e4ba76b030386This article reviews the impacts of Artificial Intelligence (AI) on teaching careers, with a focus on the pedagogical, administrative, and ethical dimensions, based on an analysis of recent studies (2022-2024). In the pedagogical dimension, AI has the potential to personalize teaching through adaptive systems and virtual tutors, adjusting content to students needs. However, this personalization could compromise teachers autonomy, as they may become dependent on algorithms for decisions regarding lesson pacing and content. In the administrative dimension, AI automates tasks such as grading and lesson planning, allowing for more teaching-focused time. However, this requires new training and technological adaptations, which may increase teachers workloads. In the ethical dimension, challenges such as data privacy, algorithmic bias, and digital exclusion could exacerbate educational inequalities. The article provides recommendations for leveraging AI as a teaching aid while preserving teacher autonomy and ensuring ethical and inclusive implementation. Methodological limitations, such as the narrow temporal scope and the lack of long-term impact studies, are acknowledged. The article also calls for future research on AI integration in various educational contexts and the development of public policies to ensure equitable technology adoption in schools and universities.International Journal of Advanced Research00Recommendations are provided for leveraging AI as a teaching aid while preserving teacher autonomy and ensuring ethical and inclusive implementation, and for the development of public policies to ensure equitable technology adoption in schools and universities.International Journal of Advanced Research["Marcio Goncalves Dos Santos"]2024-10-31T00:00:00 2d5478aa6548e9a244c3acb095bfc1c6b041da14 Although businesses are under pressure to consistently improve both their capacities and business processes, the artificial intelligence (AI) revolution is seen as an appealing company approach that has attracted attention. By offering improved company structures, the application of AI technology has the potential to significantly alter company procedures while also lessening the impact of outside catastrophes. Additionally, using AI can enhance the socioeconomic circumstances of a particular area and have a positive impact on the social worth and economic sustainability of businesses. Few studies have been conducted recently on the use of AI to support businesses at various stages of development and sustainability. In addition, there isn’t much research that shows that the retail sector can benefit from the use of various modern AI technologies. To bridge this gap, this investigation looks into the moderating effect of entrepreneurial orientation. A theoretical framework was established using the assistance of the resource-based view (RBV) and dynamic capability view (DCV) assumptions, in addition to a survey of the available literature. The PLS-SEM method was used in this paper and got 311 respondents from Amman, Jordon retail sector. The findings show that the industry needs to make strategies for social justice and economically favorable approaches that have a beneficial impact by adopting AI approaches. This paper also shows that EA has a moderate impact on performance connections within the industry. International Review of Management and Marketing00The findings show that the industry needs to make strategies for social justice and economically favorable approaches that have a beneficial impact by adopting AI approaches, and shows that EA has a moderate impact on performance connections within the industry.International Review of Management and Marketing["Nancy Al-Ramahi", "Fuad M. Kreishan", "Zahid Hussain", "Arman Khan", "Mahmoud Alghizzawi", "Belal Mahmoud AlWadi"]2024-10-31T00:00:00 2414e596a0cf8ba278531af95990f6fdfb7a59a9Rapid artificial intelligence (AI) development opens new opportunities for enterprises to achieve sustainable development goals. This article summarizes the research results of applying AI to optimize production processes, reduce environmental impact, increase productivity, and strengthen social responsibility. The article aims to highlight the potential of AI for achieving enterprises' sustainable development goals and identify effective strategies for its application to increase economic efficiency, social responsibility, and environmental sustainability. A qualitative analysis of statistical data, official regulatory documents, books, and articles from accredited journals was used to achieve the goal. The obtained results showed that AI could be used to analyze production processes and forecast the demand for raw materials, optimize supply chains and reduce energy consumption, identify potential sustainability risks and develop strategies to prevent them, identify defects in production and control product quality in real-time, create personalized services and products for customers. The study highlights the importance of AI's ethical and safe use to ensure its positive impact on society and the environment. The practical value of the article lies in the systematic review of studies on the effects of AI on the sustainable development of enterprises. The obtained results can be used to improve production processes, increase enterprises' competitiveness, and promote sustainable development.Financial and credit activity problems of theory and practice300The research results showed that AI could be used to analyze production processes and forecast the demand for raw materials, optimize supply chains and reduce energy consumption, identify potential sustainability risks and develop strategies to prevent them, identify defects in production and control product quality in real-time, and create personalized products for customers.Financial and credit activity problems of theory and practice["Kostiantyn Zavrazhnyi", "Anzhelika Kulyk", "Viacheslav Viacheslav", "Maksym Sokolov", "Olesia Antunes de Abreu"]2024-10-31T00:00:00 7ecf8e81ebcdaf1351c3813bc90b8a2fd23f60f9This review examines the increasing use of artificial intelligence (AI) in rheumatology, focusing on its potential impact in key areas. AI, including machine learning (ML) and deep learning (DL), is revolutionizing diagnosis, treatment personalization, and prognosis prediction in rheumatologic diseases. Specifically, AI models based on convolutional neural networks (CNNs) demonstrate significant efficacy in analyzing medical images for disease classification and severity assessment. Predictive AI models also have the ability to forecast disease trajectories and treatment responses, enabling more informed clinical decisions. The role of wearable devices and mobile applications in continuous disease monitoring is discussed, although their effectiveness varies across studies. Despite existing challenges, such as data privacy concerns and issues of model generalizability, the compelling results highlight the transformative potential of AI in rheumatologic disease management. As AI technologies continue to evolve, further research will be essential to address these challenges and fully harness the potential of AI to improve patient outcomes in rheumatology.Journal of Clinical Medicine400This review examines the increasing use of artificial intelligence (AI) in rheumatology, focusing on its potential impact in key areas, and compelling results highlight the transformative potential of AI in rheumatologic disease management.Journal of Clinical Medicine["Gianluca Mondillo", "Simone Colosimo", "Alessandra Perrotta", "Vittoria Frattolillo", "M. F. Gicchino"]2024-10-31T00:00:00 8232d899b4ecda1a26421ff331c11ea7f1f6c7dfThis study investigates the impact of digital leadership on the performance of village-owned enterprises, or VOEs emphasizing the moderating effect of artificial intelligence, or AI. As digital transformation reshapes the business landscape, effective digital leadership emerges as a crucial factor for enhancing organizational performance, particularly in rural settings. This study employs quantitative surveys and interviews from VOEs across various villages with 192 research sample size. The findings reveal that digital leadership significantly correlates with improved performance metrics, such as profitability, operational efficiency, and community values. Moreover, the integration of AI technologies further amplifies these effects, providing tools for better decision-making, resource allocation, and customer interaction. The moderation analysis indicates that the presence of AI not only enhances the effectiveness of digital leadership but also facilitates innovative practices within VOEs. This research also contributes to the understanding of how digital leadership, coupled with AI, can drive sustainable growth in village enterprises, offering practical implications for policymakers and community leaders aiming to leverage technology for rural development. Future studies are suggested to explore the long-term effects of these dynamics in diverse contexts.South Asian Journal of Social Studies and Economics00The findings reveal that digital leadership significantly correlates with improved performance metrics, such as profitability, operational efficiency, and community values, and the integration of AI technologies further amplifies these effects.South Asian Journal of Social Studies and Economics["Adharry D. S. Amran", "Rahman Syahid", "Muh. Yushar Mustafa"]2024-10-31T00:00:00 3255216a7f7839d7ef1e5f8acf5f99163c6148caThe Visual Search Interactive Model for Artificial Intelligence (AI) is designed to enhance the efficiency and effectiveness of visual data analysis across various applications. By leveraging advanced computer vision techniques and machine learning algorithms, this model enables AI systems to interpret and analyze visual information in real-time, facilitating tasks such as object recognition, image classification, and scene understanding. The interactive nature of the model allows users to engage with the AI, refining searches and improving outcomes through iterative feedback. This paper introduces the Auxiliary Clustering k-means Machine Learning (AC k-means ML) model, designed to enhance agricultural efficiency through advanced data analysis and robotic integration. The study evaluates the performance of the AC k-means ML model using a dataset comprising 1,950 samples, achieving an overall accuracy of 91.5% and a precision of 89.2%. Key performance metrics such as F1 scores averaged 88.6%, with the highest individual cluster accuracy reaching 96% for Cluster 10. In addition to data classification, the model facilitated the completion of 250 tasks with a remarkable success rate of 92%, while maintaining an average task completion time of 15.4 minutes and an energy consumption of just 0.5 kWh per task. The implementation of the AC k-means ML model resulted in a 15% increase in crop yield and substantial cost savings estimated at $2,000. With a user satisfaction score averaging 8.7 and an adaptability score of 9.0, the findings indicate that the integration of machine learning and robotics significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.Journal of Computer Allied Intelligence00The findings indicate that the integration of machine learning and robotics significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.Journal of Computer Allied Intelligence["Asmatullah Nashir"]2024-10-31T00:00:00 61ce0fa51acfd87a2c340e835618427dfe9476ddArtificial Intelligence (AI) and Machine Learning (ML) are transforming India's financial sector, enhancing decision-making, risk assessment, and personalized services. They extend financial services to underserved populations through scalable solutions like digital lending and mobile banking, fostering financial inclusion and bridging urban-rural gaps. Notable progress has been made through initiatives like the Pradhan Mantri Jan Dhan Yojana (PMJDY), which opened over 53 crore accounts by August 2024. However, challenges such as low literacy rates, inadequate digital infrastructure, and a large informal economy persist. AI and ML address financial inclusion gaps by automating KYC processes and improving onboarding through facial recognition and identity verification. Initiatives like Aadhaar provide digital identities, enhancing access to banking. ML algorithms expand financial access by analyzing alternative data for credit scoring. Companies like Lenddo and Tala leverage ML for loans, and AI revolutionizes fraud detection by analyzing transaction data in real-time. AI integration with digital payment platforms like UPI has transformed India's financial ecosystem, processing over 15 billion transactions monthly by August 2024 (NPCI). AI-driven micro-lending platforms like Capital Float and Aye Finance address a $380 billion credit gap (BCG) by assessing creditworthiness using alternative data. AI-driven chatbots and financial products tailored to rural customers improve financial literacy and services. In agricultural finance, AI introduces lending models and enhances crop insurance through satellite data and weather patterns. Programs like PMFBY and companies like Skymet use AI for faster claims processing. AI-driven solutions in RegTech simplify compliance and improve fraud detection. Fintech collaborations and government initiatives like IndiaStack, Aadhaar, and DigiLocker promote financial inclusion. Overcoming barriers like infrastructure challenges, data privacy concerns, and AI bias is essential for leveraging AI to provide equitable financial services in India. The constructive interaction between AI and blockchain further enhances financial inclusion, creating a secure, transparent ecosystem that drives economic growth and social well-being.International Journal For Multidisciplinary Research00The constructive interaction between AI and blockchain further enhances financial inclusion, creating a secure, transparent ecosystem that drives economic growth and social well-being in India.International Journal For Multidisciplinary Research["Subroto Das"]2024-10-31T00:00:00 c0b8f79e3b8d5445acacbbc1e6789853df919c80Malaysia Small and Medium-Sized Enterprises (MSMEs) are still in the early phases of adopting artificial intelligence (AI) to transform manual or conventional processes into ones that are supported by AI technology. Most MSMEs have yet to fully grasp the essence of digitalization as a general movement in corporate strategy and work processes to improve decision-making, boost efficiency, and discover untapped possibilities. The purpose of this research is to examine the determinants of AI adoption in accounting among MSMEs. The independent variables are perceived ease of use, management support, organizational readiness, government support, and external pressure. The dependent variable is AI adoption in accounting among MSMEs. This research applied a quantitative method by using a questionnaire consisting of 24 questions. The study used a convenience sampling method, with a sample size of 200 respondents from MSMEs. A total of 150 samples are valid. The collected data was analyzed using SPSS. The collected information was assessed using demographic descriptive analysis, data descriptive analysis, normality test, validity analysis, reliability test, correlation analysis, and regression analysis. The findings indicated that the adoption of AI in accounting among MSMEs is positively influenced by perceived ease of use, management support, organizational readiness, government support, and external pressure. The results and suggestions will be advantageous for SMEs looking to adopt AI in their accounting functions. Additionally, this research proposes a methodology and framework to assist SMEs owners and management in comprehending the determinants that drive significant AI adoption in accounting.Journal of Accounting, Business and Management470The findings indicated that the adoption of AI in accounting among MSMEs is positively influenced by perceived ease of use, management support, organizational readiness, government support, and external pressure.Journal of Accounting, Business and Management (JABM)["Tan Kock Lim", "Lee Wei Seng"]2024-10-31T00:00:00 6a88ef5d126bf5b809589bb2cd1e28942efec342Objective: Stroke remains a leading cause of global disability, and with ageing populations, there is a growing need for advanced medical interventions. This literature review aims to assess how Artificial Intelligence (AI) and Machine Learning (ML) technologies have transformed the diagnosis, treatment, and long-term care of stroke patients. Methods: A comprehensive literature review was conducted using databases such as PubMed, IEEE Xplore, and Scopus, covering articles published from January 2018 to August 2024. The review focused on studies related to the application of AI/ML in stroke diagnosis, treatment, and management, including ethical, technical, and regulatory issues. Results: AI and ML technologies have significantly enhanced stroke diagnosis, primarily through advanced deep learning models that analyze imaging data more accurately and rapidly than traditional methods. These AI-based models have demonstrated high precision in detecting ischemic and hemorrhagic strokes, reducing diagnosis time by up to 50% and markedly improving patient outcomes. Predictive models utilizing big data have consistently surpassed traditional risk assessments in forecasting stroke outcomes and customizing treatments. AI-driven decision-support systems have improved patient selection for thrombolysis and mechanical thrombectomy, optimizing treatment strategies. Conclusion: While AI and ML offer substantial advancements in stroke management, including improved diagnosis, personalized therapy, and prognosis, challenges remain. Issues such as data quality, algorithmic transparency, integration into clinical workflows, algorithmic bias, and patient privacy must be addressed. Further research is needed to overcome these technical, ethical, and regulatory obstacles to fully integrate AI and ML into healthcare systems and enhance stroke management and patient outcomes.International Journal of Applied Research and Sustainable Sciences130AI and ML technologies have significantly enhanced stroke diagnosis, primarily through advanced deep learning models that analyze imaging data more accurately and rapidly than traditional methods.International Journal of Applied Research and Sustainable Sciences["Yahya Abdul", "Rehman Shah", "Sara Mudassir Qureshi", "Hamza Ahmed Qureshi", "Saad Ur", "Ashish Shiwlani", "Ahsan Ahmad", "Qureshi Shah Shiwlani \u00a92024 Shah Qureshi"]2024-10-31T00:00:00 352798718e6189b4f00bdc2a0b3a3168e6fab0f5This article explores the transformative potential of artificial intelligence (AI) in predicting life expectancy and its far-reaching implications for the annuities market. Traditional actuarial models, often reliant on demographic data and historical trends, face limitations in accuracy and personalization. We present a novel approach leveraging machine learning algorithms, including neural networks, decision trees, and ensemble methods, alongside natural language processing and deep learning techniques. Our AI-driven models integrate diverse data sources, including medical histories, genetic information, lifestyle factors, and socio-economic indicators, to provide more accurate and individualized life expectancy predictions. Using a dataset of anonymized health records and historical mortality data, we demonstrate that our AI models significantly outperform traditional actuarial tables in predicting individual mortality risks. The enhanced predictive power of these models has substantial implications for the annuities market, enabling insurers to price products more precisely, manage longevity risk more effectively, and optimize reserve capital. Moreover, consumers benefit from fairer pricing and personalized product offerings. This article underscores the need for regulatory framework revisions to accommodate AI-driven methodologies in actuarial practices. We also discuss ethical considerations, data privacy concerns, and the challenge of model interpretability, highlighting areas for future research to ensure responsible deployment of AI in life expectancy predictions and annuity pricingInternational Journal For Multidisciplinary Research130The need for regulatory framework revisions to accommodate AI-driven methodologies in actuarial practices is underscored, enabling insurers to price products more precisely, manage longevity risk more effectively, and optimize reserve capital.International Journal For Multidisciplinary Research["Praveen Kumar Peddamukkula"]2024-10-31T00:00:00 3d3580069e8e71b2cee12a6cf01f3b5b23c977b7Background. Artificial intelligence (AI) technologies hold significant promise for revolutionizing foreign language teaching methods. Current AI tools enable teachers to customize learning experiences, adapting materials and techniques to meet each learner's specific needs. By incorporating dynamic assessment into learning management systems, student progress is continually monitored, allowing educators to promptly adjust their teaching strategies. Immersive technologies, like virtual and augmented reality, offer interactive language environments where students can engage in real-life scenarios to practice their language skills. Speech synthesis technologies allow teachers to convert written text into spoken language, which improves students’ pronunciation and speaking skills. The purpose of the study is to examine the use of AI tools in teaching foreign languages at Russian universities and future trends in this area. Materials and methods. The authors analyzed current domestic and international scientific works on the integration of AI tools in foreign language education and conducted a study on the perception and evaluation of the aforementioned technology within the Russian teaching community. Results. A survey of 104 teachers showed the staff interest in mastering new technologies and their readiness to develop professional skills for the successful use of AI in the educational process. The study analyzes the current state of the field, identifies potential risks and discusses development prospects. The impact of AI on foreign language education tends to grow in the nearest future, opening up new opportunities for more effective and accessible learning.Russian Journal of Education and Psychology160The study examines the use of AI tools in teaching foreign languages at Russian universities and future trends in this area, analyzes the current state of the field, identifies potential risks and discusses development prospects.Russian Journal of Education and Psychology["Irina A. Semyonkina", "Tatyana A. Pavlova"]2024-10-31T00:00:00 4ed0aea92144258082dd88e676bf71dba64a8ea7This research examines the opportunities and challenges in the integration of Artificial Intelligence (AI) in the financial audit process in the era of the Industrial Revolution 4.0. AI has great potential to improve audit efficiency and accuracy by automating routine tasks, detecting anomalies, and reducing human error in financial reporting. However, the application of this technology is not free from significant challenges, such as limited auditor expertise in using AI, transparency of algorithms that often function as “black boxes,” and data security and privacy risks.This research uses a qualitative approach with a literature study method, analyzing secondary data from scientific journals, industry reports, and related regulatory documents. The results show that although AI is capable of automating many routine tasks, auditors still play an important role in assessing the results produced by AI. Existing challenges, such as limited auditor knowledge and algorithm transparency issues, can be addressed through intensive training and the development of easier-to-understand algorithms.In conclusion, AI can strengthen the audit process if implemented appropriately, providing benefits in terms of efficiency and accuracy. However, its successful implementation relies heavily on auditors' ability to adapt to the technology and the development of solutions that address the challenges.Journal of Economic Education and Entrepreneurship Studies270Artificial Intelligence can strengthen the audit process if implemented appropriately, providing benefits in terms of efficiency and accuracy, however, its successful implementation relies heavily on auditors' ability to adapt to the technology and the development of solutions that address the challenges.Journal of Economic Education and Entrepreneurship Studies["Pratiwi Hamzah", "Evinalia Yeba", "Sifera Patricia Maithy", "Gema Borneo Poetra", "Sifera Patricia", "Gema Borneo Maithy", "Poetra"]2024-10-31T00:00:00 5cab7f52dc7eb8781babb54f8bfca4750867eae0In recent years, the health area has received technological contributions that provide support for diagnostic practices, monitoring, and treatment of different disorders and diseases, mainly combining various techniques of Artificial Intelligence, Virtual Reality, and Mobile Computing. There are many challenges to integrating these technologies and providing solutions that consider the automation of processes, the simplification of interaction between professionals and patients, the low price of equipment, the individualization of use, mobility, and the use of Artificial Intelligence strategies. Aiming to overcome the limitations of two previous works, which applied technological combinations in the desensitization of stress and phobias, this work aims to develop a technological combination that integrates an autonomous and low-cost virtual environment, with multi-agent control and natural language communication support, to be used in the Treatment by Exposure in Virtual Environments - VRET in the area of Clinical Psychology, more specifically related to Anxiety Disorders. Low-cost virtual reality glasses were used, with visualization on a smartphone. The prototype, called PhobIA 3DS, is controlled by multi-agents that have modules for capturing physiological signals (heart rate); uses natural language to obtain the level of anxiety perceived by the patient; considers these two pieces of information in a Fuzzy system, which, in turn, generates a response on the calculated level of anxiety; and controls and changes the display of specific scenarios for each level of anxiety. Finally, the system was evaluated by a group of 6 experienced psychologists to verify aspects of the interface, relevance, and usability. The data obtained by the evaluation showed positive results and good prospects for using the system in real activities. As a contribution, this work created an integration of AI technologies in an ESP32 microcontroller connected to a smartphone and attached to low-cost goggles. This combination of technics opens perspectives for adopting affordable technologies in phobia treatments.Journal of the Brazilian Computer Society430A technological combination that integrates an autonomous and low-cost virtual environment, with multi-agent control and natural language communication support, to be used in the Treatment by Exposure in Virtual Environments - VRET in the area of Clinical Psychology, more specifically related to Anxiety Disorders.J. Braz. Comput. Soc.["Claudio H. M. Jambo", "Vera Maria B. Werneck", "Rosa Maria E. da M. da Costa"]2024-10-31T00:00:00 b28023f9e4241ce03ce8e1b2fa7579b1fedde091Artificial intelligence (AI) ambitiousanswersmust the possible to meaningfullyinfluencepersonsthroughincapacitiesthroughifhelp in their everydaydoingstheneasing the gaining of new-fangledaptitudes. The use of AI knowledge in supplementarypersonsbyincapacitiesconsumesnewforecastsaimed atornamentalconvenience, development inclusivity throughcivilization, thenallowingindependentalive, which would thenposturesubstantialtests or endureimpossible. By way of the arena of AI lasts to growth, it grips the possible to ease the expansion of progressivelyurbanethencrushedcontraventionmethods to challenge the multi-layeredproblemsmet by personsthroughincapacities. So, AI consumes the volume to substitutesuperior inclusivity aimed at this populace.Artificial intelligence (AI) has arisenby way of a transformative skillthose proposals original then efficient methods to easethe organisation of our ordinaryerrands. AI consumes the size to mechanizeerrands that usuallytrust on humanoidintellect, countinglanguagethenspeechcredit, graphicinsight, prognosticmanuscript functionality, executivethennumerousadditionalerrands. Thispossiblegripsimportantinsinuationsaimed atpersonsthroughincapacities, by way of AI containerhighlyimprove their flexibilitythencontribution in day-to-daydoings.The excellence of lifetimeaimed atpeoplebyincapacity (PwD) is damaginglypretentiousthenwhollycooperated. They musttrouble in execution the humblest of diurnalnowdoings. Messagebysisterhumanoidexistencesin adding to the aptitude to animateindependently is susceptible. The adding of expedients computational switchpreviouslyhastefinishedhumanoiddreampreviouslyastuteness, formerlycalled AI, consumes the gearsthenaptitude to decreasenumerousfences that influencePwD in their monotonouseverydaylifetime . It consumes the possible to lease them be culturedthenworking in a non-demeaning method. In this setting, many software businesses are in the procedure of investigationbesidesexpansion of tackles, which containerdefinitelyinfluencesthe exists of PwD. Aninadequate of the instancesstatedunderneath are revealing of the cheerfulthentalentedupcoming it grips for PwD. International Journal of Advanced Research00Artificial intelligence (AI) ambitiousanswers must the possible to meaningfullyinfluence personsthroughincapacitiesthroughif help in their everydaydoing, by way of AI container highly improve their flexibility then contribution in day-to-daydoings.International Journal of Advanced Research["Sunanda Das"]2024-10-31T00:00:00 6d5f351026a8a81a1966fa87422b78f281f3410fIntroduction: in paediatrics, high-frequency emergency department use is defined as repeated emergency visits for reasons that do not require urgent attention or could be managed at a different level of care. Several factors may be associated with this phenomenon, such as socioeconomic, cultural or psychological factors. Its impact on the health care system is significant. Artificial intelligence (AI) has the potential of reducing high-frequency use. Methodology: we assessed the agreement between the information for 101 diseases common in children provided by Gemini AI, a free and open-access service, and the current scientific evidence. We used the adjusted kappa coefficient in this analysis. Results: the AI provided responses for all of the 101 diseases considered in the analysis. The kappa coefficient was 0.857 (95% CI, 0.002) for the identification of the disease, 0.888 (95% CI, 0.003) for the identification of warning signs, 0.876 (95% CI, 0.005) for establishing the need to visit the emergency department and 0.915 (95% CI, 0.003) for the appropriate recommendation of measures to be taken. Conclusions: the text-based artificial intelligence exhibited substantial agreement with protocols used for identification of diseases based on symptoms, and near-perfect agreement for determining the need to visit the emergency department, identifying warning signs and providing therapeutic recommendations. The level of agreement was higher for common diseases and children aged more than 3 months.Revista Pediatría Atención Primaria00The text-based artificial intelligence exhibited substantial agreement with protocols used for identification of diseases based on symptoms, and near-perfect agreement for determining the need to visit the emergency department, identifying warning signs and providing therapeutic recommendations.Revista Pediatría Atención Primaria["Raquel Bernal", "Ana Valer", "Mar\u00eda Celada", "Sara Calmarza", "Elena Calmarza"]2024-10-31T00:00:00 b862c4e1f7b9d2e63756d7462ccb94dd93a3fb39In recent years, Generative Artificial Intelligence (Gen AI) has become the topic of debate within higher education generally, within institutions (their official response and approach), within subject areas (how to utilise within subject teaching) and within the professional sphere of Learning Development. In the summer of 2023, an Association for Learning Development in Higher Education (ALDinHE) Community of Practice was established, and now, it has over 200 members from the UK, Europe, North America, Australia, and New Zealand. This group meets monthly to discuss all aspects of Gen AI, and this mini keynote will share how Learning Developers are engaging (or not) with Gen AI. The session will be open and friendly. Designed to enable colleagues to discuss their challenges, avoidance or how they embrace AI within their practice.   Prompt Questions 1)        Are you engaging with Artificial Intelligence (AI)? If so, how? If not, why not? 2)        Should Learning Developers be engaging with AI? If so, how? 3)        What could ALDinHE do to support your (potential) engagement with AI?Journal of Learning Development in Higher Education00A mini keynote will share how Learning Developers are engaging (or not) with Gen AI, to enable colleagues to discuss their challenges, avoidance or how they embrace AI within their practice.Journal of Learning Development in Higher Education["Kate Coulson"]2024-10-31T00:00:00 aff0a8c954493bac1114b23ad1e89b5e3ba78771This study investigates the transformative potential of Artificial Intelligence (AI) in higher education, focusing on its impact on student achievement, equity, and systemic educational improvements. It explores how AI facilitates personalized learning, addresses educational disparities, and aligns academic outcomes with societal and workforce demands. A qualitative approach using a Systematic Literature Review (SLR) method was employed to synthesize insights from recent peer-reviewed studies. The research analyzed the integration of AI into higher education, examining pedagogical strategies, systemic challenges, and ethical considerations. The findings reveal that AI significantly enhances student engagement and academic performance by personalizing learning experiences. It also supports educators by automating routine tasks, enabling more focused student interaction. AI can potentially democratize access to quality education, particularly in underserved regions. However, challenges such as resistance to change, technological infrastructure limitations, and ethical concerns related to data privacy and algorithmic bias were identified. The discussion emphasizes the need for ethical frameworks and inclusive policies to ensure effective and responsible AI integration. This study provides practical insights for universities, policymakers, and technology developers. Institutions are encouraged to invest in infrastructure, align curricula with AI advancements, and train educators in AI applications. Policymakers should support digital literacy initiatives and equitable technology access. These measures highlight AI’s capacity to create more inclusive, adaptive, and sustainable higher education systems, underscoring its transformative role in modern education.PARADOKS Jurnal Ilmu Ekonomi00The research analyzed the integration of AI into higher education, examining pedagogical strategies, systemic challenges, and ethical considerations, and revealed that AI significantly enhances student engagement and academic performance by personalizing learning experiences.Paradoks : Jurnal Ilmu Ekonomi["A. Asri"]2024-10-31T00:00:00 3300afd8ee3651324bfcc3c4c67a9cd736747397In recent years, significant breakthroughs are observed in developing artificial intelligence (AI), which radically affects the most diverse areas of human life and activity. This review article examines the introduction of AI in agriculture using the example of China, which is a leader in the pace of introduction of AI into the national economy and seeks to head off the United States in the overall leadership in the development of AI technologies. Thanks to active work in this direction and significant financial investments in this area, China has managed to transform substantially its agricultural sector.  The purpose of the article is to analyze the current trends and opportunities offered by the application of AI in the agricultural sector of the PRC economy. To this end, a series of difficulties that China faces in the development of agriculture is considered, as well as the main currently known areas of application of AI in agriculture and the types of technologies used. Information on Chinese companies using AI technologies in agriculture is summarized, including their specialization, technologies used and benefits gained. Early evidence shows that AI is being applied firstly to improve productivity and manufacturing performance, and secondly to address labor shortages and achieve manufacturing sustainability. Analysis of the situation allows  us to conclude that AI can become the main driving force in the development of agriculture.Agricultural science Euro-North-East300Analyzing the current trends and opportunities offered by the application of AI in the agricultural sector of the PRC economy concludes that AI can become the main driving force in the development of agriculture.Agricultural Science Euro-North-East["E. G. Raevskaya"]2024-10-31T00:00:00 787664b4c013a958b70ee256e130dd9030c587c2In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requirements of digital business transformation. The research examines the development of artificial intelligence, with a focus on symbolic artificial intelligence, machine learning, deep learning and generative artificial intelligence. In addition, it considers the impact of these developments on business processes. The article identifies the potential benefits and challenges associated with the adaptation of generative artificial intelligence to the needs of modern business, in the areas of marketing, sales and data analysis. The utilisation of diverse methodologies and techniques, including prompts, fine-tuning, and the incorporation of interactive guidance systems, can enhance the efficacy and precision of generative AI in a business setting, thereby facilitating optimal outcomes in a multitude of tasks. The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries. Additionally, they advocate for the utilisation of agents or orchestration tools to provide guidance to models. The successful implementation of generative artificial intelligence hinges on three key factors: the clear definition of objectives, the selection of suitable tools and technologies, and the assurance of managerial and staff support. The implementation of generative artificial intelligence will contribute to increased efficiency through the automation of routine tasks, enhanced competitiveness through personalisation and innovation, optimised cost structures that increase profitability, and expanded opportunities for research and development.Економіка розвитку систем70The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries.Економіка розвитку систем["Kostiantyn Zavrazhnyi", "Anzhelika Kulyk", "Olesia Antunes de Abreu"]2024-10-31T00:00:00 94af3e6628f5467f7723ac882a209132b16d54a6Artificial intelligence has witnessed great importance and rapid growth in light of the rapid technical development, the increase and diversity of the volume of data, the power and speed of computers, and the accuracy of machine learning models, so that the role of machines has become not only limited to understanding our world, but contributing strongly to shaping it, and here the role of generative artificial intelligence has emerged to reflect... How machines interact with users and generate new and innovative content. Given the interest of the early theorists of Islamic banking in speculation as a basic formula for mobilizing and using resources in Islamic banks, as it is more capable of accumulating investable cash balances, and more capable of distributing available monetary resources to the best uses for the purposes of economic and social development, and contributing directly to the fair distribution of national income. In view of what we see in our contemporary reality of the limited dealing with speculative financing in Islamic banks, due to considerations related to the difficulty of managing their risks, whether these considerations relate to the Islamic bank itself, the financing clients, or the surrounding circumstances. This reality represents a problem for which practical solutions need to be sought. The importance of the research appears to be that, in light of the emergence of artificial intelligence in general and generative artificial intelligence in particular, generative artificial intelligence can be utilized in managing the risks of speculative financing in a way that enables the Islamic bank to protect its funds and opens the way towards development horizons instead of focusing financing on debts. The research aims to identify generative artificial intelligence in terms of its concept, origins, development, and relationship to finance, and the prospects for benefiting from it to contribute to managing the risks of speculative financing in Islamic banks. The research relies on the descriptive and analytical approach, as this approach is consistent with the nature of the research topic, as well as its objectives. The research addressed the definition of artificial intelligence and generative artificial intelligence, the definition of speculative financing and its risks in Islamic banks, and the role of generative artificial intelligence in managing the risks of speculative financing in Islamic banks. The research reached the role that generative artificial intelligence can play in managing speculative financing risks in Islamic banks, whether it comes to the stages of managing those risks or their mechanisms.Bilimname00The research reached the role that generative artificial intelligence can play in managing speculative financing risks in Islamic banks, whether it comes to the stages of managing those risks or their mechanisms.Bilimname["E\u015fref Devabe"]2024-10-31T00:00:00 6f4b8f2c0b75af0f1d27dc8b3583ace1e3611ebbThe integration of Artificial Intelligence (AI) into In Vitro Fertilization (IVF) practices has marked a revolutionary shift in reproductive medicine, offering enhanced precision, efficiency, and personalized treatment plans. The rapid advancement of Artificial Intelligence (AI) has led to significant innovations in the field of reproductive medicine, particularly in In Vitro Fertilization (IVF). Traditional IVF procedures, while effective, often face challenges such as variable success rates, high costs, and the emotional burden on patients due to multiple treatment cycles. AI offers a promising solution to these issues by enhancing accuracy, personalization, and efficiency throughout the IVF process. AI algorithms have shown remarkable capabilities in diagnosing infertility by analyzing complex datasets from hormone profiles, genetic testing, and medical imaging, enabling early identification of conditions like polycystic ovary syndrome (PCOS) and endometriosis. Moreover, one of the most promising applications of AI in IVF is embryo grading. However, AI systems have been developed to objectively evaluate embryos based on time-lapse imaging, morphology, and other parameters, improving the selection process. Additionally, AI has been instrumental in optimizing ovarian stimulation protocols by analyzing patient data to determine the appropriate medication dosage, minimizing the risk of ovarian hyperstimulation syndrome (OHSS). This review discusses the current state of AI integration in fertility treatments, successful case studies, and ongoing research to develop more sophisticated AI models. Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.World Journal of Current Medical and Pharmaceutical Research560Overall, AI holds immense promise in making IVF more accessible, affordable, and successful for patients worldwide, ushering in a new era of precision medicine in reproductive health.World Journal of Current Medical and Pharmaceutical Research["Yashaswini N", "Pavithra R", "Ramesh Arvind S"]2024-10-31T00:00:00 3ec20afcbd88fe4d4b0202e68485eff05d6bf6f2 This cohort study examines patient data from January 2019 to December 2023 to evaluate national trends in the use of artificial intelligence–based screenings to detect diabetic retinopathy among patients with types 1 or 2 diabetes. JAMA ophthalmology40JAMA ophthalmology["Shreya A Shah", "Jared T Sokol", "Karen M. Wai", "Ehsan Rahimy", "David Myung", "P. Mruthyunjaya", "Ravi Parikh"]2024-10-31T00:00:00 2fdda45161f90c54e60b456b0eb73c6194d9915cThis research examines the applications and advancements of artificial intelligence (AI), a computer-based simulation of human intelligence meant to act like humans. AI is one of the driving forces behind the 4.0 industrial revolution, making teaching and learning more accessible in schools. This study aims to understand the function of AI in ELT and examine AI technologies in ELT. This is a library research project. The findings indicate that AI provides a positive learning environment for learning English. Depending on the learner's current level of English, career needs, or hobbies, it has much potential to create a customized environment where students can simultaneously use their senses to learn English. AI boosts practical abilities like writing and offers a trustworthy simulation dialogue platform like spoken English. It maximizes the teaching impact of English in ELT while increasing students' practice ability. With the advancement of technology and platforms, learning English has gotten simpler. Artificial intelligence technology provides the chance to enhance English linguistic competence.[1]Advances in Social Sciences Research Journal00The findings indicate that AI provides a positive learning environment for learning English and maximizes the teaching impact of English in ELT while increasing students' practice ability.Advances in Social Sciences Research Journal["Akam Seyedi", "Mohammad Ali Alaei Yeganeh", "Diar Noorzad", "Shahrooz Firouzi", "Mahsa Qurbani", "Erfan Shakeri", "Kiana Osati"]2024-10-31T00:00:00 3af01b6cd19cd38bd80a83cd4faca0bad5549731Artificial Intelligence-based scene creation leverages advanced algorithms to generate realistic and immersive environments for various applications, including gaming, virtual reality, and simulations. By utilizing techniques such as procedural generation, deep learning, and computer vision, these systems can automatically create complex landscapes, detailed textures, and dynamic elements that respond to user interactions. This not only enhances the user experience but also significantly reduces the time and effort required for manual scene design, enabling developers to focus on creativity and innovation. In the realm of digital media production, the quest for lifelike scenes and efficient evaluation methodologies has led to the exploration of novel techniques. This paper investigates the synergy between point estimation and artificial intelligence (AI) in advancing digital scene creation and evaluation. Through comprehensive simulations and analyses, we assess the effectiveness of point estimation in quantifying scene attributes such as visual realism, dynamic interactivity, physical accuracy, and artistic expression. Additionally, we delve into the utilization of AI algorithms for automating scene classification, thereby streamlining decision-making processes and optimizing resource allocation in production workflows. Through comprehensive simulations and analyses, we assess the effectiveness of point estimation in quantifying scene attributes such as visual realism (mean score: 0.85), dynamic interactivity (mean score: 0.72), physical accuracy (mean score: 0.93), and artistic expression (mean score: 0.65).Journal of Computer Allied Intelligence00This paper investigates the synergy between point estimation and artificial intelligence (AI) in advancing digital scene creation and evaluation, and assesses the effectiveness of point estimation in quantifying scene attributes such as visual realism, dynamic interactivity, physical accuracy, and artistic expression.Journal of Computer Allied Intelligence["Lankapalli Baburao"]2024-10-31T00:00:00 dd687639d5c7a0b00f5b49c0f2dbdf79bcfb0ffaIn the rapidly evolving field of fintech, artificial intelligence (AI) has become an indispensable tool in financial investment decision-making. By applying AI technologies, decision-makers can improve decision-making efficiency and enhance the accuracy of predictive analysis, providing new insights for investors. This paper explores AI’s data analysis support in financial investment decisions, focusing on model prediction, risk management, and trading strategy optimization. The aim is to improve the efficiency of investment decision-making and reduce investment risks.Modern Economics &amp; Management Forum00This paper explores AI’s data analysis support in financial investment decisions, focusing on model prediction, risk management, and trading strategy optimization to improve the efficiency of investment decision-making and reduce investment risks.Modern Economics &amp; Management Forum["Ethan Wong"]2024-10-31T00:00:00 19f5c07599d8eb57769af6f04727d421b4fa8cd6The purpose of this study was to examine teachers' beliefs, the factors that influence the use of AI tools, and the advantages, disadvantages, opportunities, and threats of artificial intelligence in language learning and teaching. To accomplish this, the study used a narrative literature review and ATLAS.ti 9 to analyze the data. The results of the study showed that teachers believed that Artificial Intelligence in Language Learning and Teaching (AILLT) included assessing student needs, developing appropriate learning resources, promoting collaboration among resources, providing real-time assessment tools to improve the educational process, and increasing teaching efficiency and effectiveness. Second, AI tools for writing tools, including Paperpal, Quillbot, Jenni AI, ChatGPT, Elicit, as well as in applications to improve speaking skills, such as Speeko and Vocaroo, and "Siri and Say It" for listening and pronunciation skills. Third, factors that may influence students' use of AI-based language learning tools include adaptability, engagement, motivation, autonomy, immediate or direct feedback, accessibility and inclusivity, and teacher and parent support. This research found that AI enhances accessibility, adaptability, personalization, and immediate feedback, but it also has limitations, such as dependence on technology, inadequate human interaction, limited contextual understanding, and algorithmic biases. In addition, AILLT faces potential threats such as quality control, privacy concerns, and job or task displacement. Narrative literature studies provide theoretical insights, and it is expected that future studies at different levels of language learning will incorporate empirical evidence from experimental and case study-based research.Englisia570The research found that AI enhances accessibility, adaptability, personalization, and immediate feedback, but it also has limitations, such as dependence on technology, inadequate human interaction, limited contextual understanding, and algorithmic biases.Englisia: Journal of Language, Education, and Humanities["Ranta Butarbutar"]2024-10-31T00:00:00 91a670e3f7a72f7301a7357fd2aaee1bfa994a63Artificial intelligence (AI) is increasingly recognized for its transformative potential in healthcare, particularly in emergency medicine. The fast-paced, highstakes nature of emergency departments (EDs) demands rapid decision-making, often under significant time and resource constraints. AI-driven solutions have already demonstrated their ability to enhance diagnostic accuracy, improve triage processes, and optimize resource allocation in emergency settings. However, AI’s potential extends beyond clinical practice into the realm of medical education, where large language models (LLMs) may offer novel opportunities for training future emergency medicine professionals.Istanbul Yeni Yuzyil Universitesi, Yeni Yuzyil Journal of Medical Sciences00Artificial intelligence’s potential extends beyond clinical practice into the realm of medical education, where large language models (LLMs) may offer novel opportunities for training future emergency medicine professionals.Istanbul Yeni Yuzyil Universitesi, Yeni Yuzyil Journal of Medical Sciences["\u00d6. F. Ayd\u0131n"]2024-10-31T00:00:00 62d7e262c8a81ff7c7fb2dd016a156ea80f2b175Artificial intelligence (AI) technology has become a significant trend today because of its ability to process and analyze data quickly and efficiently. Along with the popularity of artificial intelligence (AI) technology in the past ten years, the trend of research, publications, and patents related to AI has experienced rapid and significant growth. AI technology has offered new opportunities for SMEs to assist in market and consumer behavior analysis, enabling them to accurately identify customer trends and preferences. This study aims to determine factors shaping the interest in adopting SMEs towards AI technology. Based on pre-survey data using the FGD method for 10 SME business owners, 32 independent variables were collected to be examined. The population in this study were SMEs assisted by the Department of Cooperatives, SMEs and Trade of the City of Surabaya, and SMEs assisted by PT. Petrokimia within the 2022 period. The sample of the respondents in this study is set as many as 119 SME business owners of the population. The analytical tool used in this research is exploratory factor analysis. The results of this study indicate that six factors shape SMEs' intentions in adopting AI technology, namely the features of AI technology, the awareness of AI technology, the benefits of AI technology, the support of government and external organizations, the influence of social values, and the anthropomorphism factor.Review of management and entrepreneurship00Six factors shape SMEs' intentions in adopting AI technology, namely the features of AI technology, the awareness of AI technology, the benefits of AI technology, the support of government and external organizations, the influence of social values, and the anthropomorphism factor.Review of Management and Entrepreneurship["Satria Hardinata", "Christina whidya Utami", "Yoseva Maria Sumaji"]2024-10-31T00:00:00 5f8d5e381a8fbcead7754aaf80b9503240cdc849The article examines the elective course for students of secondary general and vocational education, which immerses them in the world of mathematical concepts underlying artificial intelligence. There is a reflection on the advantages of this approach and its importance for future education.Materials of the All-Russian scientific and methodological conference "Physical foundations of high-tech technologies"00The article examines the elective course for students of secondary general and vocational education, which immerses them in the world of mathematical concepts underlying artificial intelligence, and its importance for future education.Materials of the All-Russian scientific and methodological conference "Physical foundations of high-tech technologies"["V. Bovich", "O. Kashinceva"]2024-10-31T00:00:00 12d30a015e4514246888c9b2aa4cb22218ffc737The research aims to study and analyze the role of adopting artificial intelligence technologies in supporting the predictive ability of financial statements in the context of big data. To achieve the research objective, the researchers designed a questionnaire that included three axes related to the research variables. It was distributed electronically to a sample of accountants, auditors, and investors in the Iraq Stock Exchange. Around 70 responses were collected from the sample members and relied upon in the practical aspect of the research. The SPSS statistical program was used to analyze the results. The research found that artificial intelligence technologies have a statistically significant effect in improving the predictive value of accounting information, and this effect increases in light of big data. Among the most important recommendations of the research is the necessity for financial analysts and investors to use artificial intelligence technologies because it contributes to the accuracy and speed of conducting analyses and comparisons that help improve the predictive value of information, as well as the necessity of employing and adopting big data analytics and capabilities due to the rapid and accurate data processing it provides.Financial and credit activity problems of theory and practice160The research found that artificial intelligence technologies have a statistically significant effect in improving the predictive value of accounting information, and this effect increases in light of big data.Financial and credit activity problems of theory and practice["Y. Malik", "Hassanain Ojah", "G. Al-Shiblawi", "K. Hameedi"]2024-10-31T00:00:00 21adfea4c1ef00b81b7c073ab4b067a27e270958This study aims to determine the effect of implementing an artificial intelligence-based Accounting Information System on the Quality of Financial Statements moderated by the Internal Control System. The population used in this study were Bank 9 Jambi employees. Determination of the research sample using the Purposive Sampling method. The data used in this study is primary data. Respondents in this study were 30 respondents by distributing questionnaires using a Likert Scale. The analysis technique used is Partial Least Square (PLS). The result showed that The Artificial Intelligence-Based Accounting Information System has a positive effect on the Quality of Financial Statements and the Internal Control System can moderate the effect of the Artificial Intelligence-Based Accounting Information System on the Quality of Financial Statements.International Journal of Economic Research and Financial Accounting (IJERFA)30The result showed that The Artificial Intelligence-Based Accounting Information System has a positive effect on the Quality of Financial Statements and the Internal Control System can moderate the effect of the Artificial Intelligence-Based Accounting Information System on the Quality of Financial Statements.International Journal of Economic Research and Financial Accounting (IJERFA)["Ayudia Febrihartini", "Yuliusman Yuliusman", "Ratih Kusumastuti"]2024-10-31T00:00:00 f2629d6e4eeae54557b5b94cbad6f7c11e9342ecIn this showcase, we took our new student-facing generative Artificial Intelligence (AI) guide as a departure point for discussion around AI literacy support. We hoped to reflect with colleagues on resource development processes, including how we benefitted from wide-ranging and intersecting feedback, and how we navigated issues of policy. The motivation for the guide stemmed from our initial engagement with generative AI tools as they became widely available and highlighted in public and academic discourse. In addition, we quickly realised that AI was causing anxiety to colleagues and students, who expressed frustration with policy not keeping up with the need for clarity on emerging issues. Clearly, our students needed guidance on how to engage with this technology safely and ethically. In building this resource, we aimed to strike a balance in tone and degree of complexity. Inspired by current reflections in Learning Development and higher education circles, we tried to highlight the range of emerging questions and implications that one should consider when exploring this technology in order to be in a position to harness its potential. We included examples and scenario-based exercises to encourage a critical approach. The guide reached its published form after extensive scrutiny. Feedback from colleagues and students at the University Working Group on AI helped shape decisions around language clarity, content focus, selection of examples, and messaging on academic integrity. This was an empowering process, as it helped us feel secure in our stance whilst official institutional policy remained elusive. So, what now and what next? The guide is being used by colleagues and students and received positive anecdotal feedback. We recognise, however, the need for regular updating in this fast-moving field. New themes for developing our guide include: AI for research, specialised AI-powered tools, and rules for acknowledging AI use.Journal of Learning Development in Higher Education00This showcase took the new student-facing generative Artificial Intelligence guide as a departure point for discussion around AI literacy support, and tried to highlight the range of emerging questions and implications that one should consider when exploring this technology in order to be in a position to harness its potential.Journal of Learning Development in Higher Education["Georgia Koromila"]2024-10-31T00:00:00 6f76f24e5b3a4c67f6ecbc27969ffa59c6cf957c  Marwan Omar 1Illinois Institute of Technology  Email:  momar3@iit.edu    Abstract The rapid advancement of Artificial Intelligence (AI), particularly in the domain of generative models, has led to impressive achievements in content creation and natural language processing. However, these models are inherently limited by their reliance on pattern recognition and lack of true understanding. In contrast, Objective-Driven AI offers a promising alternative by focusing on goal-oriented behavior, causal reasoning, and the development of world models. This paper explores the limitations of generative AI, highlighting its inability to grasp context, causality, and ethical considerations. It then presents the concept of Objective-Driven AI, emphasizing its potential to operate effectively in complex, real-world environments where understanding and reasoning are critical. The paper concludes with a discussion of future research directions, including advanced world modeling techniques, ethical AI, and robustness against adversarial attacks, which are essential for the further development of Objective-Driven AI systems. Keywords Objective-Driven AI, Generative AI, Causal Reasoning, World Modeling, Ethical AI, Artificial Intelligence, Adversarial Attacks, Machine Learning, Autonomous SystemsIndonesian Journal of Computer Science00This paper explores the limitations of generative AI, highlighting its inability to grasp context, causality, and ethical considerations, and presents the concept of Objective-Driven AI, emphasizing its potential to operate effectively in complex, real-world environments where understanding and reasoning are critical.The Indonesian Journal of Computer Science["Marwan Omar"]2024-10-31T00:00:00 f7bcf62ce9c2ba9443a21651147c7d4993e393fdDespite Kuala Lumpur’s push for AI integration, only 23% of businesses have adopted AI, lagging behind the global average of 37%, with 65% still relying on basic data tools and only 10% using advanced analytics. This study investigates the factors influencing AI adoption in Kuala Lumpur's IT sector, focusing on Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Perceived Organizational Support (POS). Using the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) as theoretical foundations, this study extends these frameworks by incorporating POS to emphasize the critical role of organizational support in AI adoption. Data from a survey of 340 IT managers were analyzed us- ing PLS-SEM. The results demonstrate that both PEOU and POS significantly impact PU, which in turn influences AI adoption intentions. POS emerged as a vital factor, indicating that organizational support, such as training and resource provision, is key in making AI useful and encouraging its adoption. This research has practical implications for businesses and policymakers. Organizations should focus on improving organizational support mechanisms, particularly through targeted training programs and technical assistance to enhance AI adoption. Policymakers are encouraged to refine initiatives like Industry4WRD by strengthening infrastructure and providing sector-specific support. The study's novelty lies in its focus on emerging markets like Kuala Lumpur, addressing a gap in AI adoption research by exploring the organizational challenges specific to such regions.Journal of Computers, Mechanical and Management151The results demonstrate that both PEOU and POS significantly impact PU, which in turn in turn influences AI adoption intentions, indicating that organizational support, such as training and resource provision, is key in making AI useful and encouraging its adoption.Journal of Computers, Mechanical and Management["Hayyan Nassar Waked", "S. Goyal", "Feras Fathi Albdiwy", "Masri Abdul Lasi", "Nurrohani binti Ahmad"]2024-10-31T00:00:00 c31b48af4c39580fc0083f86daff10c5a236c9c2Journal of Mind and Medical Sciences01Journal of Mind and Medical Sciences["Maria Serban", "I. B\u0103lescu", "Sorin Petrea", "Bogdan Gaspar", "L. Pop", "V. Varlas", "Marilena Stoian", "Camelia Diaconu", "C. B\u0103l\u0103l\u0103u", "N. Bacalba\u015fa"]2024-10-31T00:00:00 06f32bcf0ad800d9eecc8e1723731069904dd2ddKSII Transactions on Internet and Information Systems01KSII Trans. Internet Inf. Syst.["Parasuraman Kumar", "Arumugam Maharajan"]2024-10-31T00:00:00 9cd8fac238c6a4f3bafb022edfa4586cf9787f87Globalizations590Globalizations["Edemilson Paran\u00e1"]2024-10-31T00:00:00 66abfbf344cc9e8c14fb2d8c6afe34f01815533bKorean-Chinese Social Science Studies00Korean-Chinese Social Science Studies["Ya-li Zhao", "Baek Seo-in"]2024-10-31T00:00:00 e76e03c7f49c9e797398ffe9d7dead797565b092Modern Economy00Modern Economics["Al\ufffdsa Shevchenko", "Olga Petrenko", "Dmytro Kosyk"]2024-10-31T00:00:00 f7c99bc4441c3a8be02ce33a4386ad3740164c75The Journal of Korean Association of Computer Education00The Journal of Korean Association of Computer Education["Kyungsun Yoo", "Woong Suh"]2024-10-31T00:00:00 83c526a60e70c850b2cb24a7b9c641adaaff7bb9Journal of the Korean Association of Information Education00Journal of The Korean Association of Information Education["Yeiin Kim", "JaMee Kim", "WonGyu Lee"]2024-10-31T00:00:00 55e8b60ec325f01ab7b8575e5c7c0f63c90a8d4eJournal of Ethics in Entrepreneurship and Technology20Journal of Ethics in Entrepreneurship and Technology["Thomas A. Hemphill"]2024-10-31T00:00:00 f30fe1e83631db7dfc5ee9797e9f442003082215Journal of Mind and Medical Sciences00Journal of Mind and Medical Sciences["Cristina Bucur", "I. B\u0103lescu", "Sorin Petrea", "Bogdan Gaspar", "L. Pop", "V. Varlas", "Marilena Stoian", "C. B\u0103l\u0103l\u0103u", "N. Bacalba\u015fa"]2024-10-31T00:00:00 07c7b968b09357215a5d5a0d0256024ac9101758以大数据、算法推荐、机器学习为核心的人工智能技术的迅速发展，深刻改变着网络舆情的生成模式和传播机制，使得网络舆情的主体、客体、内容、传播过程都发生了深刻的变化，并呈现出新的特点和趋势。一方面，人工智能技术的迅猛发展为网络舆情治理工作带来了前所未有的机遇;另一方面，人工智能技术产生的算法歧视、舆论操控、隐私泄漏等诸多风险，给网络舆情治理工作带来了巨大挑战。深入分析智能时代网络舆情的嬗变特点，并提出相应的治理之策，是应对这些风险挑战的关键所在。Yixin Publisher00Yixin Publisher["Xuesen Zhang", "Zhan Huang"]2024-10-31T00:00:00 f5bfec5282a0e5be7c1b8e7bf1cf0ea3b79cc4e0\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were randomly assigned to two groups, and crossed over between coding complex (longer) texts versus simple (shorter) texts, while using our tool versus not using our tool. \textbf{Results} Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (\emph{P}\textless.001, 95\% CI: 81 to 164), representing a 46\% reduction in median coding time when our tool is used. There was no significant time difference for simpler text sequences. For coding accuracy, the improvement we noted for both complex and simple texts was not significant. \textbf{Conclusions} This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for complex clinical coding tasks. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.arXiv.org290This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for complex clinical coding tasks, with ostensible positive impacts on work efficiencies for complex clinical coding tasks.ArXiv["T. Chomutare", "Therese Olsen Svenning", "Miguel 'Angel Tejedor Hern'andez", "P. Ngo", "A. Budrionis", "Kaisa Markljung", "Lill Irene Hind", "Torbj\u00f8rn Torsvik", "Karl \u00d8yvind Mikalsen", "Aleksandar Babic", "H. Dalianis"]2024-10-31T00:00:00 fd363db08a1e41aaaf838d0b64cdeb5eaeb1ddad00["Tan Yigitcanlar"]2024-10-31T00:00:00 c9e8962fb4ba78b64a0670e0eafc9994dbc77843Pengabdian pada Masyarakat (PPM) yang diselengarakan oleh dosen Program Studi Pendidikan Teknik Mesin Fakultas Keguruan dan Ilmu Pendidikan Universitas Sriwijaya yang di laksanakan pada Musyawarah Guru Mata Pelajaran (MGMP) Otomotif Kabupaten OKI bertujuan untuk membantu guru mengoptimalkan penggunaan teknologi AI dalam pembelajaran otomotif, meningkatkan interaktivitas dan efisiensi pembelajaran, serta memberikan dampak positif bagi pemahaman siswa. Hasil evaluasi awal menunjukkan bahwa masih kurang familiar penggunaan teknologi AI dalam pembelajaran, ketersediaan pelatihan khusus yang mengajarkan penggunaan AI dalam konteks otomotif mungkin terbatas atau tidak memadai. Pelaksanaan dilakukan selama 3 hari menggunakan metode pelatihan Presentasi, Pembelajaran Terbimbing, serta Pendampingan menggunakan Aplikasi AI yang di tujukan kepada 38 guru peserta kegiatan. Hasil evaluasi yang didapatkan menunjukkan 68,2% (Sangat Efektif) menyatakan kegiatan ini membantu dalam memahami konsep dan praktik pembuatan kuis berbantu AI. 77,3% (Sangat Memuaskan) terkait jadwal, lokasi dan durasi kegiatan. 68,2% yaitu materi yang disampaikan selama kegiatan ini mudah dipahami dan relevan dengan kebutuhan guru MGMP. 77,3% pendekatan dan penyampaian materi, termasuk demonstrasi, praktik langsung, atau presentasi Sangat Memuaskan. 90,9% peserta terlibat secara aktif dalam kegiatan. 54,5% peserta memiliki pemahaman yang cukup tentang kuis berbantu AI serta 50% peserta merasa sangat percaya diri dalam membuat kuis berbantu AI.  Jurnal Pelita Sriwijaya70Jurnal Pelita Sriwijaya["Elfahmi Dwi Kurniawan", "M. Fachrurrozi", "Nopriyanti Nopriyanti", "Rudi Hermawan"]2024-10-31T00:00:00 1cf74929f73c5185dad792180f60e94550addc34Public Law00Public Law["JaeHwang Jeong"]2024-10-31T00:00:00 e8f8370fcecce2385745d3e77499696527e0a802Oslo Law Review00Oslo Law Review["Martin Ebers"]2024-10-31T00:00:00 5c70f6f19db488ee67ce87305f90f9547188a099The Journal of Korean Institute of Communications and Information Sciences00The Journal of Korean Institute of Communications and Information Sciences["Yeonjin Kim", "Kap-Yong Choi", "Gyoungbae Kim"]2024-10-31T00:00:00 cf8c061b574c6ee633881f32f6b8010beba57081The Journal of The Korea Institute of Intelligent Transport Systems00The Journal of The Korea Institute of Intelligent Transport Systems["Hoon Jung", "Sanghoon Lee", "Jangwook Kim", "Sooyoung Kwon"]2024-10-31T00:00:00 ce3e2d121ed9fb946fd0ecd499b014962d1ab358Global Spine Journal80Global Spine Journal["Jens R. Chapman", "J. Wang", "K. Wiechert"]2024-10-31T00:00:00 a0ea12a2968ac366dd13c50fcb3b65c59f2b6f7aWiadomości Statystyczne. The Polish Statistician00Wiadomości Statystyczne. The Polish Statistician["A. Taghiyeva"]2024-10-31T00:00:00 d519274936c85f274a2b960d7d94d39d6f79b15bJournal of the Korean Association of Information Education00Journal of The Korean Association of Information Education["Soohwan Lee", "Ki-sang Song"]2024-10-31T00:00:00 4460f5344d787f9ce8673750044bc991720c16a1The core of this paper, which explores the difficulties of resource problems and climate change, is the delicate interplay between AI, human psychology, and sustainable development. As global challenges escalate, the convergence of these domains presents a unique opportunity to devise effective strategies that facilitate environmentally conscious behaviors and resilient communities. This paper explores the psychological dimensions underlying resource dilemmas and climate change, emphasizing cognition, decision-making processes, social dynamics, and the role of AI in driving sustainable development. By amalgamating insights from psychology with cutting-edge technological advancements, society can aspire to navigate the path towards ecological equilibrium and a sustainable futureInternational Journal for Research in Applied Science and Engineering Technology00The psychological dimensions underlying resource dilemmas and climate change are explored, emphasizing cognition, decision-making processes, social dynamics, and the role of AI in driving sustainable development.International Journal for Research in Applied Science and Engineering Technology["Prerna Maheshwari"]2024-10-31T00:00:00 80edfd4fc6b44ef6ca015e0dd8a0bce17466be0aSogang Law Journal00Sogang Law Journal["Misa Park"]2024-10-31T00:00:00 0f8093269028946cec42293e413d244d6b1399f8Transportation Research Part D: Transport and Environment3312Transportation Research Part D: Transport and Environment["Huan Wang", "Xinyu Wang", "Yuanxing Yin", "Xiaojun Deng", "Muhammad Umair"]2024-11-01T00:00:00 63fdef415c9a6c1fa0649733669b4b8dc85a5aecComputers in industry (Print)18311Computers in Industry["G. Culot", "Matteo Podrecca", "G. Nassimbeni"]2024-11-01T00:00:00 6d2f3eaad96b7412fc7b0e6ad2a4a98798a6a997Breast cancer is the most prevalent cancer worldwide, affecting both low- and middle-income countries, with a growing number of cases. In 2024, about 310,720 women in the U.S. are projected to receive an invasive breast cancer diagnosis, alongside 56,500 cases of ductal carcinoma in situ (DCIS). Breast cancer occurs in every country of the world in women at any age after puberty but with increasing rates in later life. About 65% of women with the BRCA1 and 45% with the BRCA2 gene variants develop breast cancer by age 70. While these genes account for 5% of breast cancers, their prevalence is higher in certain populations. Advances in early detection, personalised medicine, and AI-driven diagnostics are improving outcomes by enabling a more precise analysis, reducing recurrence, and minimising treatment side effects. Our paper aims to explore the vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care as well as discussing the potential drawbacks of such integrations into modern medicine. We structured our paper as a non-systematic review and utilised Google Scholar and PubMed databases to review literature regarding the incorporation of AI in the diagnosis and treatment of non-palpable breast masses. AI is revolutionising breast cancer management by enhancing imaging, pathology, and personalised treatment. In imaging, AI can improve the detection of cancer in mammography, MRIs, and ultrasounds, rivalling expert radiologists in accuracy. In pathology, AI enhances biomarker detection, improving HER2 and Ki67 assessments. Personalised medicine benefits from AI’s predictive power, aiding risk stratification and treatment response. AI also shows promise in triple-negative breast cancer management, offering better prognosis and subtype classification. However, challenges include data variability, ethical concerns, and real-world validation. Despite limitations, AI integration offers significant potential in improving breast cancer diagnosis, prognosis, and treatment outcomes.Life493The vast applications of artificial intelligence within the diagnosis and treatment of breast cancer and how these advancements can contribute to elevating patient care are explored as well as discussing the potential drawbacks of such integrations into modern medicine.Life["P. Uchikov", "Usman Khalid", "Granit Harris Dedaj-Salad", "Dibya Ghale", "Harney Rajadurai", "Maria Kraeva", "K. Kraev", "B. Hristov", "M. Doykov", "Vanya Mitova", "Maria Bozhkova", "Stoyan Markov", "Pavel E. Stanchev"]2024-11-01T00:00:00 f0f2ab3655ea346f01c3312113f817353903d448Technological forecasting & social change549Technological Forecasting and Social Change["Philip Jorzik", "Jerome L. Antonio", "Dominik K. Kanbach", "Andreas Kallmuenzer", "Sascha Kraus"]2024-11-01T00:00:00 9c0605ff1ee136aa207987d0993cee6484137561Journal of High Technology Management Research179The Journal of High Technology Management Research["K. S. Suryanarayana", "V.S. Prasad Kandi", "G. Pavani", "A. S. Rao", "Sandeep Rout", "T. Siva Rama Krishna"]2024-11-01T00:00:00 d40f78094f9fb1a45d18580140806d426575c4deThe rise of artificial intelligence (AI) is changing the face of contract law, notably in the areas of contract negotiation and execution. This article explores how AI may affect conventional contracting procedures, emphasizing the opportunities and problems associated with automated contracting. The legal position of contracts created by AI, especially smart contracts, raises important concerns about consumer protection, enforceability, and the subjective nature of some contractual duties as more and more firms use AI technologies. The paper addresses issues with human oversight, the difficulties of integrating AI with current legal systems, and the potential advantages of AI in expediting contract execution and improving efficiency. It also covers the practical challenges of putting AI-driven contract solutions into practice, especially in industries like construction where stakeholder cooperation and regulatory compliance are critical. The results highlight the necessity of an all-encompassing legal framework that takes into account the special features of AI-driven contracts, protecting the interests of both parties and encouraging innovation in contract administration. In the end, this paper promotes a fair-minded strategy that acknowledges the benefits of artificial intelligence in contracting while tackling the inherent difficulties and dangers involved in its application.International Journal of Academic Research in Business and Social Sciences341This article explores how AI may affect conventional contracting procedures, emphasizing the opportunities and problems associated with automated contracting, and promotes a fair-minded strategy that acknowledges the benefits of artificial intelligence in contracting while tackling the inherent difficulties and dangers involved in its application.International Journal of Academic Research in Business and Social Sciences["Norhafiza Awang"]2024-11-01T00:00:00 4ded88628ad2c9b3e24dd2ea24aaaac7658a0485 Purpose Artificial intelligence (AI) is poised to reshape mental health practices, policies and research in the coming decade. Simultaneously, mental health inequalities persist globally, imposing considerable costs on individuals, communities and economies. This study aims to investigate the impact of AI technologies on future citizenship for individuals with mental health challenges (MHCs). Design/methodology/approach This research used a community-based participatory approach, engaging peer researchers to explore the perspectives of adults with MHCs from a peer-led mental health organisation. This study evaluated potential threats and opportunities presented by AI technologies for future citizenship through a co-created film, depicting a news broadcast set in 2042. Data were gathered via semi-structured interviews and focus groups and were analysed using a reflexive thematic approach. Findings The analysis identified four key themes: Who holds the power? The divide, What it means to be human, and Having a voice. The findings indicate that adults with living experiences of MHCs are eager to influence the development of AI technologies that affect their lives. Participants emphasised the importance of activism and co-production while expressing concerns about further marginalisation. Originality/value This study provides new insights into the intersection of AI, technology and citizenship, highlighting the critical need for inclusive practices in technological advancement. By incorporating the perspectives of individuals with living experiences, this study advocates for participatory approaches in shaping AI technologies in mental health. This includes the co-creation of machine learning algorithms and fostering citizen engagement to ensure that advancements are equitable and empowering for people with MHCs. Journal of Public Mental Health871This study evaluated potential threats and opportunities presented by AI technologies for future citizenship through a co-created film, depicting a news broadcast set in 2042, indicating that adults with living experiences of MHCs are eager to influence the development of AI technologies that affect their lives.Journal of Public Mental Health["Phil Morgan", "Nicola Ann Cogan"]2024-11-01T00:00:00 67e8ec8767f91230607b7b125ad48723e14da5faThis study explores the affordance of Artificial Intelligence (AI) to English language learning and teaching, focusing on its alignment with the United Nations' Sustainable Development Goals (SDGs). It aims to investigate the role of AI in enhancing language education and fostering student-centered learning. Data for this study were collected through semi-structured interviews with 18 English teachers to gather qualitative insights into their experiences with AI-powered language learning tools. The findings reveal that the teachers have positive appraisals of AI that its use has six major impacts: i) enhancing the personalization of learning; ii) contributing to improved learning outcomes by advancing students' speaking, listening, reading, and writing skills; iii) playing a fundamental role in bridging educational gaps; iv) enhancing students’ engagement and motivation; v) empowering educators with professional development opportunities; vi) and encouraging self-directed learning. This study argues that, if implemented thoughtfully, AI can enhance language learning outcomes and create an environment conducive to student engagement and success.Journal of Language Teaching and Research291It is argued that, if implemented thoughtfully, AI can enhance language learning outcomes and create an environment conducive to student engagement and success.Journal of Language Teaching and Research["O. Al-Smadi", "R. A. Rashid", "Hadeel Saad", "Yousef Houssni Zrekat", "Siti Soraya Lin Abdullah Kamal", "Gaforov Ikboljon Uktamovich"]2024-11-01T00:00:00 4748785ecc2101c4dc22959cab30bdd7fbecc517Foodborne illnesses and mortalities persist as a significant global health issue. The World Health Organization estimates that one out of every ten individuals becomes ill following the consumption of contaminated food. However, in the age of digitalization and technological progress, more and more data and data evaluation technologies are available to counteract this problem. A specific challenge in this context is the efficient and beneficial utilization of the continuously increasing volume of data. In pursuit of optimal data utilization, the objective of the present study was to develop a Multi-Criteria Decision Analysis (MCDA)-based assessment scheme to be prospectively implemented into an overall artificial intelligence (AI)-supported database for the autonomous risk categorization of food incident reports. Such additional evaluations might help to identify certain novel or emerging risks by allocating a level of risk prioritization. Ideally, such indications are obtained earlier than an official notification, and therefore, this method can be considered preventive, as the risk is already identified. Our results showed that this approach enables the efficient and time-saving preliminary risk categorization of incident reports, allowing for the rapid identification of relevant reports related to predefined subject areas or inquiries that require further examination. The manual test runs demonstrated practicality, enabling the implementation of the evaluation scheme in AI-supported databases for the autonomous assessment of incident reports. Moreover, it has become evident that increasing the amount of information and evaluation criteria provided to AI notably enhances the precision of risk assessments for individual incident notifications. This will remain an ongoing challenge for the utilization and processing of food safety data in the future.Foods411A Multi-Criteria Decision Analysis (MCDA)-based assessment scheme is developed to be prospectively implemented into an overall artificial intelligence (AI)-supported database for the autonomous risk categorization of food incident reports, enabling the efficient and time-saving preliminary risk categorization of incident reports.Foods["Sina R\u00f6hrs", "S. Rohn", "Yvonne Pfeifer"]2024-11-01T00:00:00 6f411a35d460cf7a89a0062d63c5ca93bf2a82e0Artificial intelligence (AI) has revolutionized various fields, including research and higher education. Thanks to its innovative applications, it has changed traditional teaching methods. This article aims to explore the impact of AI on these domains in Moroccan universities, focusing on its transformative influence, benefits, challenges, and future prospects. By analyzing current literature, case studies, and expert opinions, we elucidate how AI has enhanced research methodologies, empowered educators and students, and fostered innovation in academia. In addition, we discuss ethical considerations and potential concerns associated with the increasing integration of AI. Finally, we highlight the future prospects and opportunities offered by AI for research and higher education in Morocco.Journal of Education and Learning (EduLearn)292How AI has enhanced research methodologies, empowered educators and students, and fostered innovation in academia and the future prospects and opportunities offered by AI for research and higher education in Morocco are highlighted.Journal of Education and Learning (EduLearn)["Ghizlane Moukhliss", "Khalid Lahyani", "Ghizlane Diab"]2024-11-01T00:00:00 a5e6ea2f64a83a363d127aa1a28e6d7c0af87125The article AI Through the Ages: How the History of Artificial Intelligence Unlocks Key Opportunities and Challenges for the Future offers a comprehensive analysis of the historical evolution of AI, tracing its development from its inception to its current state and highlighting the critical opportunities and challenges that have emerged along the way. The article aims to demonstrate how understanding AI’s historical trajectory—from the early theoretical foundations laid by pioneers like Alan Turing and John McCarthy, to the rise of machine learning and deep learning—provides essential insights for addressing modern issues in AI, such as ethics, regulation, and societal impact. By reviewing key technological milestones and placing them in a broader societal context, the article underscores how each phase of AI development has unlocked new possibilities while introducing new dilemmas. Methodologically, the article employs a historical review of AI's key eras—early symbolic AI, the AI winters, and the resurgence of AI through machine learning—combined with a critical analysis of how these advancements have influenced industries such as healthcare, finance, and transportation. The article argues that AI’s evolution has been shaped by both technical breakthroughs and societal needs, and it reflects on how AI’s capacity to manage increasingly complex tasks has reshaped our relationship with technology. Expected results from this historical understanding include a more nuanced view of AI’s role in society and a recognition of the ethical challenges that lie ahead, such as bias, fairness, and governance. By analysing the transition from rule-based AI to data-driven machine learning models, the article anticipates that future advancements in AI will continue to present both profound opportunities—such as personalized healthcare, sustainable energy optimisation, and advancements in autonomous systems—and challenges, particularly in the realms of ethics, job displacement, and AI regulation. The article persuasively argues that AI, while offering transformative potential, requires a balanced approach to governance that addresses its societal risks, such as bias amplification, surveillance, and its role in spreading disinformation. The article makes a compelling case for the importance of interdisciplinary collaboration in shaping the future of AI, aligning technological innovation with ethical principles to ensure that AI remains a force for good. By examining AI’s historical evolution, the article effectively ties past lessons to the current landscape, emphasising the need for strategic oversight and global cooperation as AI advances and impacts human life in unprecedented ways. The lessons of history, the article suggests, are essential in navigating the challenges of AI’s future. International Journal of Religion301The article persuasively argues that AI, while offering transformative potential, requires a balanced approach to governance that addresses its societal risks, such as bias amplification, surveillance, and its role in spreading disinformation.International Journal of Religion["Mustafa Osman I. Elamin"]2024-11-01T00:00:00 3149e73e51c5b54720bd3667ca95d3fc2298d487It is Universally known that Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think and act like humans by using big data and algorithms to achieve essential techniques. AI has been used more than ever across different jobs and industries which means that we are in touch with it everyday even if we don’t notice that. Whether we are watching recommended videos in social media or unlocking phone with Face ID.  The review analyzes the changes in advertising and using AI to develop customer experience. The methodology describes what information and methods were used to write the article. Data such as case analysis, research of scientific literature, data on real Nike initiatives, etc. The conclusion summarizing the results of the study, focusing on the main advantages and challenges of using AI in marketing. European Journal of Management, Economics and Business51The review analyzes the changes in advertising and using AI to develop customer experience and focuses on the main advantages and challenges of using AI in marketing.European Journal of Management, Economics and Business["Dalir Khabibulin"]2024-11-01T00:00:00 47ad91478e73a5999d620ef038bf5a8784b4d86eEthanol serves as one of Brazils primary biofuels. The country produces two main types of ethanol: i) hydrous ethanol, directly utilized as vehicle fuel, and ii) anhydrous ethanol, presently integrated at a rate of 27% into regular gasoline. In 2023, data from the National Agency of Petroleum, Natural Gas, and Biofuels (ANP) indicated that the total volume of ethanol sold in Brazil (hydrous and anhydrous) was just over 28 million cubic meters (m3), which corresponded to almost 22% of the total volume of liquid fuels sold in the country. These numbers illustrate the importance of this biofuel in Brazil. Just six states account for approximately 90% of Brazilian ethanol production. The logistical challenge arises from production seasonality and the necessity to transport ethanol from production sites to distribution and resale networks. Commonly, such prediction is supported using econometric models, such as ARIMA. Considering the recent advances in Artificial Intelligence, this challenge prompts the research question: Can we enhance monthly hydrous and anhydrous ethanol production prediction for the primary Brazilian-producing states using Artificial Intelligence Models (AIM) How should data be prepared for such an approach This study aims to contribute to logistical planning by employing D-AI2-M - a Data-Centric Artificial Intelligence (DAI) methodology - to aid in selecting AIM for ethanol production time series in the principal Brazilian-producing states. Our quantitative experimental evaluation demonstrates the superior forecasting performance of D-AI2-M in two approaches: i) Local: where different D-AI2-M outperform the benchmark models depending on the specific time series, and ii) Global: where a single D-AI2-M achieves the best mean performance across the complete set of evaluated time series.IEEE Latin America Transactions421This study aims to contribute to logistical planning by employing D-AI2-M - a Data-Centric Artificial Intelligence (DAI) methodology - to aid in selecting AIM for ethanol production time series in the principal Brazilian-producing states.IEEE Latin America Transactions["Antonio Mello", "Lucas Giusti", "Tarsila Tavares", "Fernando Alexandrino", "Gustavo Guedes", "Jorge Soares", "R. Barbastefano", "F\u00e1bio Porto", "Diego Carvalho", "E. Ogasawara"]2024-11-01T00:00:00 9b86c06f635e7f99ae0a77979aee8542c8b4d977Key Points Question Is the use of artificial intelligence (AI) in radiology practice associated with radiologist burnout? Findings In this cross-sectional study, the use of AI was associated with burnout among radiologists, exhibiting a dose-response association. This association was particularly pronounced in radiologists with high workload and those with low AI acceptance. Meaning These findings suggest the need for harmonious integration of AI tools with radiologists to effectively mitigate burnout in radiology practice.JAMA Network Open372The use of AI was associated with burnout among radiologists, exhibiting a dose-response association, and this association was particularly pronounced in radiologists with high workload and those with low AI acceptance.JAMA Network Open["Hui Liu", "Ning Ding", "Xinying Li", "Yunli Chen", "Hao Sun", "Yuanyuan Huang", "Chen Liu", "Pengpeng Ye", "Zhengyu Jin", "Heling Bao", "Huadan Xue"]2024-11-01T00:00:00 5d0905922b278a1971f5a43dbfb2b91587ecfb72Artificial intelligence (AI) is transforming various sectors globally, including education. In education, artificial intelligence has the potential to significantly enhance educational leadership by improving decision-making, streamlining administrative tasks, and personalizing student learning experiences. However, the integration of AI into educational systems also introduces risks related to bias, privacy, transparency, and accountability. Educational leaders bear the responsibility of managing these risks and ensuring that AI is used ethically and responsibly. This paper explores the risks and responsibilities associated with the implementation of AI in educational leadership. This paper examines ethical concerns, decision-making processes, privacy, accountability, and the need for responsible AI usage. It recommends among other things that educational leaders must ensure that AI systems are designed and operated in ways that promote fairness, equity, and inclusivity by developing and implementing comprehensive ethical guidelines to ensure the responsible use of AI; should implement bias-detection mechanisms so as to promote fairness in AI-driven decision-making processes and reduce discriminatory practices and must enforce strict data security protocols, such as encryption, secure access, and regular system audits, to safeguard sensitive information in educational institutions. To this end, by understanding these risks and responsibilities, educational leaders can better harness AI's potential to enhance educational outcomes while safeguarding the integrity of education systems.European Journal of Arts, Humanities and Social Sciences201Ethical concerns, decision-making processes, privacy, accountability, and the need for responsible AI usage are examined; educational leaders must ensure that AI systems are designed and operated in ways that promote fairness, equity, and inclusivity.European Journal of Arts, Humanities and Social Sciences["I. C. Igbokwe"]2024-11-01T00:00:00 e48939b469780eba592e1cd1619e28abe42a734fProperty valuation services using artificial intelligence (AI) have been developed, with more than 20 services available in Japan. However, since their algorithms and training data are not publicly available, the extent of variations in the AI property valuations among these services is not clear. This study focuses on five services and uses a sample of 4295 valuations for 859 condominium units in six popular residential areas in Tokyo. (1) Multiple comparison tests of the AI property valuations among the services are conducted to confirm their statistical significance and to examine the extent of the variations. (2) The business models of each service are compared to examine the factors contributing to these variations. The results showed that the average variation in the AI property valuations was 10.6%, which was larger than the variations observed in traditional property valuations. It was also found that the valuation groups, categorized as high or low, varied based on the business models of the service providers. These results indicate that it is necessary to promote the healthy development of AI property valuation by establishing guidelines, such as requiring the AI property valuation services to ensure fair prices or disclosing their algorithms and data.Real Estate541It was found that the average variation in the AI property valuations was 10.6%, which was larger than the variations observed in traditional property valuations, and that the valuation groups, categorized as high or low, varied based on the business models of the service providers.Real Estate["Akira Ota", "Masaaki Uto"]2024-11-01T00:00:00 b53661ba5aa2bce4a12b5562b7d83716f1694847Simple Summary Artificial intelligence, now one of the most promising frontiers of medicine, has a long and tumultuous history punctuated by successes and failures. One of its successes was its application to medical images. We reconstruct the timeline of the advancements in this field, from its origins in the 1940s before crossing medical images to early applications of machine learning to radiology, to the present era where artificial intelligence is revolutionizing radiology.Cancers2281The timeline of the advancements in artificial intelligence, from its origins in the 1940s before crossing medical images to early applications of machine learning to radiology, to the present era where artificial intelligence is revolutionizing radiology is reconstructed.Cancers["M. Avanzo", "J. Stancanello", "G. Pirrone", "Annalisa Drigo", "A. Retico"]2024-11-01T00:00:00 7a6e9f14b439cdcb4d66f29484756a04ae430febThe issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about revolutionary changes in this field. These advanced computational methods are capable of processing and analyzing large-scale biomedical data, thereby uncovering complex patterns and mechanisms behind the development of resistance. AI technologies are increasingly applied to predict the resistance of pathogens to various antibiotics based on gene content and genomic composition. This article reviews the latest advancements in AI and ML for predicting antimicrobial resistance in pathogenic microorganisms. We begin with an overview of the biological foundations of microbial resistance and its epidemiological research. Subsequently, we highlight the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks. Furthermore, we explore the major challenges in the field, such as data availability, model interpretability, and cross-species resistance prediction. Finally, we discuss new perspectives and solutions for research into microbial resistance through algorithm optimization, dataset expansion, and interdisciplinary collaboration. With the continuous advancement of AI technology, we will have the most powerful weapon in the fight against pathogenic microbial resistance in the future.Frontiers in Cellular and Infection Microbiology952The latest advancements in AI and ML for predicting antimicrobial resistance in pathogenic microorganisms are reviewed, highlighting the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks.Frontiers in Cellular and Infection Microbiology["Yan Li", "Xiaoyan Cui", "Xiaoyan Yang", "Guangqia Liu", "Juan Zhang"]2024-11-01T00:00:00 13221e6ee67b325fbc4963fffb2b494d7e4cf0b5This research paper uses qualitative analysis to examine the profound influence of artificial intelligence (AI) and blockchain technologies on accounting practices. The study utilizes case studies and semi-structured interviews with industry experts to identify central themes, including efficiency and automation, accuracy and data integrity, fraud detection and security, professional roles and skills, and ethical and regulatory considerations. The results demonstrate that AI increases efficiency by automating repetitive tasks and enhancing fraud detection, while blockchain guarantees the precision and reliability of financial records. Nevertheless, incorporating these technologies into existing systems poses difficulties, including technical obstacles, adherence to regulatory requirements, and ethical considerations such as safeguarding data privacy and addressing algorithmic bias. Due to these findings, accounting professionals must acquire new skills in data analytics and technology management. It is recommended that educators integrate artificial intelligence (AI) and blockchain into accounting curricula. At the same time, policymakers are advised to establish well-defined regulatory frameworks to facilitate the adoption of these technologies. The study also identifies areas for future investigation, such as the enduring effects of AI and blockchain on accounting methods, the factors that influence user adoption, and the creation of efficient regulatory structures. The research thoroughly analyzes how AI and blockchain are transforming the accounting profession, providing insights into the opportunities and challenges they bring. European Journal of Theoretical and Applied Sciences382The research thoroughly analyzes how AI and blockchain are transforming the accounting profession, providing insights into the opportunities and challenges they bring.European Journal of Theoretical and Applied Sciences["Muhammed Zakir Hossain", "F. Johora", "Mamunur R. Raja", "Latul Hasan"]2024-11-01T00:00:00 95b71b24c4fdfb9eb5f76d6eee3e28cc4efbffa2Artificial Intelligence (AI) is transforming various medical fields, including neurosurgery. This review explores the evolving role of AI in neurosurgical planning and outcome prediction, with a focus on its applications in preoperative imaging, intraoperative navigation, and postoperative outcome forecasting. A meta-analysis of recent studies shows that AI enhances precision, reduces errors, and offers personalized patient care. However, significant challenges such as interpretability and integration into clinical practice remain. This article discussescurrent developments, limitations, and future innovations in the use of AI in neurosurgery.IOSR Journal of Dental and Medical Sciences00This review explores the evolving role of AI in neurosurgical planning and outcome prediction, with a focus on its applications in preoperative imaging, intraoperative navigation, and postoperative outcome forecasting.IOSR Journal of Dental and Medical Sciences["."]2024-11-01T00:00:00 13617faaa8565688270fd5df8724054aabba3185Background: Angiogenesis, the formation of new blood vessels, plays a critical role in cancer progression and other pathological conditions such as cardiovascular diseases and diabetic retinopathy. Predicting angiogenic patterns with high precision is vital for personalizing treatment and improving patient outcomes. The Predictive Angiogenesis-Cancer-Artificial Intelligence (PA-C-AI) framework combines artificial intelligence and machine learning techniques to address limitations in traditional methods, such as dependency on human expertise and limited data scalability. Methods: The PA-C-AI system integrates advanced machine learning algorithms, including neural networks, to analyze complex biological datasets. Multi-omics data and real-time patient monitoring were incorporated to enhance the system's accuracy and flexibility. A diverse dataset of angiogenesis markers was used to train and validate the framework. Automated data analysis eliminated bias, ensuring consistent and reproducible results. Results: The PA-C-AI framework achieved 96.2% prediction accuracy in modeling angiogenesis, significantly surpassing traditional approaches. It effectively distinguished angiogenic mechanisms, including sprouting, intussusceptive angiogenesis, and vascular mimicry, addressing gaps in current research. Personalized treatment plans demonstrated a 97.2% success rate, with improved therapeutic outcomes reported at 99.3%. The automated processes achieved 95.38% efficiency in data interpretation. Conclusion: The PA-C-AI system represents a transformative step forward in precision medicine, offering enhanced prediction of angiogenesis, personalized treatment planning, and interdisciplinary collaboration. Its applications extend beyond cancer to other angiogenesis-dependent diseases, such as cardiovascular conditions and diabetic retinopathy. Future developments will focus on expanding prediction capabilities and validating the system's clinical significance to facilitate integration into healthcare systems for AI-enhanced, patient-specific therapies.Journal of Angiotherapy00The Predictive Angiogenesis-Cancer-Artificial Intelligence (PA-C-AI) framework combines artificial intelligence and machine learning techniques to address limitations in traditional methods, such as dependency on human expertise and limited data scalability.Journal of Angiotherapy[]2024-11-01T00:00:00 6f1091a4cb3b08688381bd0cb776e322e0afeec9Objective: To analyze the quality of artificial intelligence (AI) and machine learning (ML) tools developed for facial aesthetic surgery. Data Sources: Medline, Embase, CINAHL, Central, Scopus, and Web of Science databases were searched in February 2024. Study Selection: All original research in adults undergoing facial aesthetic surgery was included. Pilot reports, case reports, case series (n < 5), conference proceedings, letters (except research letters and brief reports), and editorials were excluded. Main Outcomes and Measures: Facial aesthetic surgery procedures employing AI and ML tools to measure improvements in diagnostic accuracy, predictive outcomes, precision patient counseling, and the scope of facial aesthetic surgery procedures where these tools have been implemented. Results: Out of 494 initial studies, 66 were included in the qualitative analysis. Of these, 42 (63.6%) were of "good" quality, 20 (30.3%) were of "fair" quality, and 4 (6.1%) were of "poor" quality. Conclusion: AI improves diagnostic accuracy, predictive capabilities, patient counseling, and facial aesthetic surgery treatment planning.Facial Plastic Surgery & Aesthetic Medicine500AI improves diagnostic accuracy, predictive capabilities, patient counseling, and facial aesthetic surgery treatment planning, according to a qualitative analysis of artificial intelligence and machine learning tools developed for facial aesthetic surgery.Facial plastic surgery & aesthetic medicine["Brooke Stephanian", "Sabin Karki", "Kirin Debnath", "M. Saltychev", "Monica K Rossi-Meyer", "C. Kandathil", "Sam P. Most"]2024-11-01T00:00:00 1375db11217577846215d0c4cec0b820f0d68e90Game adalah bentuk hiburan yang diminati berbagai kalangan, baik anak-anak maupun orang dewasa.Game dipilih sebagai hiburan karena membantu seseorang melepaskan kepenatan dari rutinitas sehari-hari dan mengisi waktu luang. Kebanyakan game memiliki tingkat kesulitan yang berbeda-beda, sehingga membuat pemain tertarik untuk menyelesaikan permainan tersebut.Kebutuhan AI dalam game semakin meningkat seiring perkembangan zaman. AI berkembang dengan gerakan yang beragam untuk menghindari kebosanan.Metode dalam penelitian ini yaitu dimulai dari Studi Literatur, menganalisis Sistem dan Kebutuhan kemudian merancang Animasi dan Sistem, Implementasi, Pengujian, dan Evaluasi.Pada tahap ini hasil dan pembahasan dilakukan implementasi dengan merancang asset,animasi dan sistem perlawanan serta akan dilakukan pengujian animasi, blackbox testing dan pengujian aplikasi.Analisis penelitian "Sistem Perlawanan Musuh dengan Artificial Intelligence (AI) Finite State Machine pada Game Petualangan" menyimpulkan bahwa sistem perlawanan dan animasi berfungsi dengan baik. Implementasi AI Finite State Machine pada musuh dalam game petualangan efektif dalam mengatur perilaku dan respons musuh terhadap situasi dalam permainan. Musuh dapat beralih antara keadaan dengan respons yang tepat.Jurnal Ilmu Komputer dan Bisnis90Jurnal Ilmu Komputer dan Bisnis["Achmad Fiqih Syahri", "M. Rizqi"]2024-11-01T00:00:00 ec04e43df9b5ca964d614c50acb34111dd58dbe1The rapid rise and potential of artificial intelligence (AI) have created growing excitement and much debate on its potential to bring transformative changes across entire industries, including the medical industry. This systematic review aims to investigate the advancements in the AI industry and its potential implementation, specifically in the field of anaesthesiology. AI has already been integrated into different areas of medicine, including diagnostic uses in radiology and pathology and therapeutic and interventional uses in cardiology and surgery. In the field of anaesthesiology, AI has made significant progress. Potential applications include personalised drug dosing, real-time monitoring of vital signs, automated anaesthesia delivery systems, and predictive analytics for adverse events. As AI technologies continue to advance and become more prevalent in medicine, clinicians across all specialities need to understand these technologies and how they can be utilised to provide safer and more efficient care. With the rapid evolution of AI and the introduction of new concepts such as machine learning (ML), deep learning (DL), and neural networks, the field of anaesthesiology is set to undergo transformative changes. In this systematic review, we examine the existing literature to explore the current state of AI in the field of anaesthesiology, along with a prospective look at potential applications in the future. Along with its various applications, we will also discuss its limitations and flaws. As the field progresses, it is crucial to thoughtfully examine the ethical aspects of using AI in anaesthesia and ensure these technologies are applied responsibly and transparently.Cureus240A systematic review of the existing literature is examined to explore the current state of AI in the field of anaesthesiology, along with a prospective look at potential applications in the future.Cureus["Arnesh Shukla", "Ayesha Salma", "Dev Patel", "Jabez David John", "Reshmitha Kantamneni", "Tirath Patel", "Ketan Kantamaneni"]2024-11-01T00:00:00 2b5cc280e8325c06359c4ce373df028418e52e01The purpose of this study was to assess the attitudes and perceptions of instructors toward the use and implication of artificial intelligence (AI) in online higher education spanning bachelor’s through doctorate level classroom instruction. 104 online higher education instructors were recruited through LinkedIn and the Colorado Technical University Teaching & Learning Center to complete a survey. Nonparametric statistical analysis was used to assess close-ended question responses and open-ended question responses were categorized. A significant difference in instructors’ concern for being displaced by AI in the next 2 years compared to the next 5 to 10 years was found at the bachelor’s level (p=0.04846). At the doctorate level, support for the use of AI in higher education was moderately correlated with concerns for being displaced by AI (rho=0.493, p=0.01058). Support for the use of AI in higher education by the instructor was moderately correlated with support for use of AI in the classroom by students (rho= 0.58199, p<0.001). Instructor support for allowing students to use AI in the classroom was not found to be dependent on the perception of the university employer providing instructors with the policies, procedures, and training to appropriately and ethically adapt to AI in the higher education classroom (p=0.7336). However, most participants recommended that the university develop strong policies regarding the use of AI. The findings indicate a positive preference toward the use of AI by both faculty and students, with some significant exceptions. Support and research toward adopting AI in the classroom are recommended.The Pinnacle: A Journal by Scholar-Practitioners00A positive preference toward the use of AI by both faculty and students is indicated by both faculty and students, with some significant exceptions.The Pinnacle: A Journal by Scholar-Practitioners["Alexa Schmitt", "Rae Denise Madison", "Robert Finkelmeier", "Dawn Howell"]2024-11-01T00:00:00 653d29a3d9f263e1c5836100a2df2c4c8598d4c2Integrating artificial intelligence (AI) into academic research has sparked a significant discourse surrounding its ethical implications and potential benefits. This paper explores the complex relationship between AI-generated content and academic integrity, highlighting the challenges of the blurring lines between assistance and academic dishonesty. As educational institutions increasingly adopt AI tools, the necessity for scholars and students to reevaluate the boundaries of originality becomes paramount. The ethical considerations surrounding AI in academic writing encompass property, accuracy, and integrity issues, necessitating a commitment to ethical citation practices to uphold scholarly standards. Moreover, while AI can enhance writing quality and streamline research processes, it also raises concerns about unintentional plagiarism and the authenticity of original thought. The reliance on AI tools may lead to derivative outputs, complicating the distinction between genuine creativity and plagiarism. To address these challenges, educational institutions must implement robust training programs that promote the ethical use of AI, ensuring that students can responsibly integrate AI contributions into their work. Case studies demonstrate that when used effectively, AI can augment academic performance and foster deeper engagement with learning materials, illustrating its potential as a valuable educational resource. Ultimately, this paper advocates for a balanced approach that embraces the benefits of AI while maintaining a strong commitment to ethical scholarship, thereby shaping a future where technology enhances rather than undermines academic integrity.European Journal of Arts, Humanities and Social Sciences310This paper explores the complex relationship between AI-generated content and academic integrity, highlighting the challenges of the blurring lines between assistance and academic dishonesty and advocates for a balanced approach that embraces the benefits of AI while maintaining a strong commitment to ethical scholarship.European Journal of Arts, Humanities and Social Sciences["K. Kotsis"]2024-11-01T00:00:00 140ded7dcc5cf935e3a92664f3546d240686f71a The article explores the impact of Artificial intelligence (AI) on international trade, focusing on challenges and opportunities for developing countries, particularly in Latin America and the Caribbean (LAC). As AI advances, it creates regulatory challenges and ethical concerns, especially regarding human rights and public morals, which may lead to trade frictions not addressed by current international agreements. AI brings important benefits to countries, such as cost reductions and efficiencies, alongside challenges related to data privacy, competition, and security. For developing countries, adopting AI offers both opportunities and obstacles, with infrastructure and human capital constraints being significant hurdles. The article suggests flexible regulatory approaches to attract investment, promote digital services exports, and ease cross-border data flow. While global trade discussions, particularly within the WTO, are addressing AI-related issues, regional agreements have made more progress. These agreements lack however comprehensive coverage and effective dispute resolution. It is important for LAC countries participating in international negotiations to ensure their interests are represented and to help shape global digital governance standards, enabling them to fully benefit from AI. Global Trade and Customs Journal00The article suggests flexible regulatory approaches to attract investment, promote digital services exports, and ease cross-border data flow to attract investment, promote digital services exports, and ease cross-border data flow.Global Trade and Customs Journal["Manuel Quindimil"]2024-11-01T00:00:00 c8360d945f3a690fcbc9c8c009de2f97920ce2c5Coronary artery disease (CAD) remains a leading global cause of morbidity and mortality, underscoring the need for effective cardiovascular risk stratification and preventive strategies. Coronary artery calcium (CAC) scoring, traditionally performed using electrocardiogram (ECG)-gated cardiac computed tomography (CT) scans, has been widely validated as a robust tool for assessing cardiovascular risk. However, its application has been largely limited to high-risk populations due to the costs, technical requirements, and limited accessibility of cardiac CT scans. Recent advancements in artificial intelligence (AI) have introduced transformative opportunities to extend CAC detection to noncardiac CT scans, such as those performed for lung cancer screening, enabling broader and more accessible cardiovascular screening. This review provides a comprehensive analysis of AI-driven CAC detection, examining various types of AI models for CAC detection, like convolutional neural networks (CNNs) and U-Net architectures, and exploring the clinical, operational, and ethical implications of incorporating these technologies into routine practice. Technical challenges, including imaging variability, data privacy, and model bias, are discussed alongside essential areas for further research, such as standardization and validation across diverse populations. By leveraging widely available imaging data, AI-enabled CAC detection has the potential to advance preventive cardiology, supporting earlier risk identification, optimizing healthcare resources, and improving patient outcomes.Cureus570A comprehensive analysis of AI-driven CAC detection is provided, examining various types of AI models for CAC detection, like convolutional neural networks (CNNs) and U-Net architectures, and exploring the clinical, operational, and ethical implications of incorporating these technologies into routine practice.Cureus["D. Vivekanandan", "Nikita Singh", "Marshall Robaczewski", "Abigayle Wyer", "Lucas N Canaan", "Daniel Whitson", "Nathaniel Grabill", "Mena Louis"]2024-11-01T00:00:00 7d87850f2f6624a05dcb72cc1e5a2bd2c76490a7Background Artificial intelligence (AI) is a hot topic in the world of medicine. AI may be useful in identifying and sizing polyps, which influence surveillance intervals. Therefore, we examined polyp size estimation by AI using a survey study. Methods A survey study was performed using a phantom colon model. Eleven videos were produced in the colon phantom using a colonoscope. Gastroenterologists were compared to a new AI system (Argus) for sizing polyps and their impact on surveillance intervals. Results Eleven gastroenterologists completed the survey with a mean age of 51.1 ± 8.1 years and an average of 19.3 ± 10 years of experience. Mean accuracy rates for gastroenterologists were 76% ± 0.1% (range 54-89%) compared to 96% ± 0.05% for Argus. Endoscopists estimated polyp size within ± 1 mm 44 times (36%) versus 9 times (82%) with Argus. Endoscopists' surveillance recommendations were significantly more often inappropriate compared to Argus (34 vs 0). The interval of next colonoscopy was too short for 27 endoscopists (22%) and too long for seven endoscopists (6%). Conclusions AI appears to be more accurate in estimating polyp size than experienced endoscopists. Given the potential impact on surveillance intervals, AI may result in cost savings.Cureus200AI appears to be more accurate in estimating polyp size than experienced endoscopists, given the potential impact on surveillance intervals, and may result in cost savings.Cureus["M. N. Yousaf", "Neal Sharma", "Michelle Matteson-Kome", "S. Puli", "Douglas Nguyen", "Matthew L Bechtold"]2024-11-01T00:00:00 7e981f9fd1a8e9fb65387abed2a3b22c24adce03Mobility-as-a-service (MaaS) integrates different transport modalities and can support more personalization of travelers’ journey planning based on their individual preferences, behaviors and wishes. To fully achieve the potential of MaaS, a range of artificial intelligence (AI) (including machine learning and data mining) algorithms are needed to learn personal requirements and needs to optimize the journey planning of each traveler and all travelers as a whole, to help transport service operators and relevant governmental bodies to operate and plan their services, and to detect and prevent cyberattacks from various threat actors, including dishonest and malicious travelers and transport operators. The increasing use of different AI and data processing algorithms in both centralized and distributed settings opens the MaaS ecosystem up to diverse cyber and privacy attacks at both the AI algorithm level and the connectivity surfaces. In this article, we present the first comprehensive review on the coupling between AI-driven MaaS design and the diverse cybersecurity challenges related to cyberattacks and countermeasures. In particular, we focus on how current and emerging AI-facilitated privacy risks (profiling, inference, and third-party threats) and adversarial AI attacks (evasion, extraction, and gamification) may impact the MaaS ecosystem. These risks often combine novel attacks (e.g., inverse learning) with traditional attack vectors (e.g., man-in-the-middle attacks), exacerbating the risks for the wider participation actors and the emergence of new business models.IEEE Intelligent Transportation Systems Magazine1130This article presents the first comprehensive review on the coupling between AI-driven MaaS design and the diverse cybersecurity challenges related to cyberattacks and countermeasures and focuses on how current and emerging AI-facilitated privacy risks and adversarial AI attacks may impact the MaaS ecosystem.IEEE Intelligent Transportation Systems Magazine["Kai-Fung Chu", "Haiyue Yuan", "Jinsheng Yuan", "Weisi Guo", "N. Balta-Ozkan", "Shujun Li"]2024-11-01T00:00:00 3924a3c3bb496a9dbedad519dd8155985c6d2c7b The objective of this paper is to explore the shortcomings in international trade law (ITL) in view of the expansion of Artificial Intelligence (AI), and to propose some recommendations. AI is contributing to the development of international trade while giving rise to other concerns, such as nontariffs measures. Addressing the legal challenges posed by the integration of AI into various facets of society, including international trade, is of major importance. In this context, this paper explores a number of provisions of the General Agreement on Tariffs and Trade (GATT) 1994, those of the General Agreement on Trade in Services (GATS), and ends with those of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS). Finally, the article proposes some recommendations to better take into account the concerns that AI may give rise to in the multilateral trading system. It highlights the need for close partnership between the World Trade Organization (WTO), the International Standardization Organization (ISO) and the International Electrotechnical Commission (IEC) to better maximize the benefits of AI while minimizing the risks in the context of international trade. Also, it highlights the importance of harmonizing regulations on the trade of AI products and AI-driven trading and the need for a change in legal design that goes beyond mere incremental adjustments. Global Trade and Customs Journal00The objective of this paper is to explore the shortcomings in international trade law (ITL) in view of the expansion of Artificial Intelligence (AI), and to propose some recommendations to better take into account the concerns that AI may give rise to in the multilateral trading system.Global Trade and Customs Journal["Julio Kunda Muya"]2024-11-01T00:00:00 1fc61d7b1c53b22975b39cd3d9ec38a4d3658d37Accurately predicting consumer preferences is one of the key success factors in marketing strategies. This research investigated the effectiveness of different artificial intelligence models in predicting consumer preferences in the Tehran market. The required data was collected through a questionnaire including demographic characteristics and purchase preferences from 400 consumers in Tehran. After data preprocessing, random forest, support vector machine (SVM) and logistic regression models were implemented to predict consumer preferences. The results showed that the random forest model has the best performance among the investigated models with an accuracy of 87%. Also, the data analysis showed that the variables of age, income and education have the greatest influence in predicting purchase preferences. These findings can help local businesses to optimize their marketing strategies and achieve better results in attracting customers.International Journal of Applied Research in Management, Economics and Accounting180The random forest model has the best performance among the investigated models with an accuracy of 87%.International Journal of Applied Research in Management, Economics and Accounting["Kowsar Karami Nasrabad Sofla"]2024-11-01T00:00:00 4c4ce8ce5c25905793ab7b265c1fcfa01eb10bb4 Artificial intelligence (AI) technologies have enormous potential for preventing corruption in the public sector. However, at present, the use of AI in the public sector is not regulated. Consequently, the article aims to propose concrete steps and actions for the implementation of AI technology in the public sector with a view to using this technology as a new tool to identify and prevent situations of corruption. The author conducted a study of Latvian state and local government officials in different regions and positions throughout 2023. In the article, the author analyses the research data on officials’ experiences with AI, their expectations about the effectiveness of AI in preventing corruption, as well as their basic requirements for the concept of implementation of AI. The author of the article proposes to create a basis for the legal environment for the use of AI and recommends adopting the guidelines of AI on the following basic ethical standards: the priority of human well-being; prohibition of harm at the initiative of the AI system; human control; compliance of design with law; prevention of covert manipulation of human behaviour; built-in security. In the article, the author recommends creating a special procedure for testing and further implementation of solutions of AI, bypassing redundant administrative procedures. In addition, the author recommends stimulating anti-corruption institutions and developing approaches to issues of civil liability for mistakes made by AI technology.Acta Prosperitatis30The author of the article proposes to create a basis for the legal environment for the use of AI and recommends adopting the guidelines of AI on the following basic ethical standards: the priority of human well-being; prohibition of harm at the initiative of the AI system.ACTA PROSPERITATIS["Jaroslavs Strelcenoks"]2024-11-01T00:00:00 efb9d1e80bdd20ea9d3b4c9158490a8942898f22Purpose Artificial intelligence (AI) holds great potential for revolutionizing health care by providing clinicians with data-driven insights that support more accurate and efficient clinical decisions. However, applying AI in clinical settings is often challenging due to the complexity and vastness of medical information. This perspective article explores how AI development methodologies can be adapted to support clinicians in their decision-making processes, emphasizing the importance of a hybrid approach that combines AI capabilities with clinicians’ expertise. Patients and Methods We developed a conceptual framework designed to integrate AI-driven hybrid intelligence into clinical practice to enhance decision-making. This framework focuses on adapting key AI concepts, such as backpropagation, quantization, and avoiding overfitting, to help clinicians better interpret complex medical data and improve diagnosis and treatment planning. Results Several AI methodologies were adapted to enhance clinical decision-making. First, backpropagation allows clinicians to refine initial assessments by revisiting them as new data emerges, improving diagnostic accuracy over time. Second, quantization helps break down complex medical problems into manageable components, enabling clinicians to prioritize critical elements of care. Finally, avoiding overfitting encourages clinicians to balance rare diagnoses with more common explanations, reducing the risk of diagnostic errors and unnecessary complexity. Conclusion The integration of AI-driven hybrid intelligence has the potential to enhance clinical decision-making. By adapting AI methodologies, clinicians can enhance their ability to analyze data, prioritize treatments, and make more accurate diagnoses while preserving the essential human aspect of health care. This framework highlights the importance of combining AI’s strengths with clinicians’ expertise for more effective and balanced decision-making in clinical practice. This perspective highlights the value of hybrid intelligence in achieving more balanced, effective, and patient-centered decision-making in health care.International Journal of General Medicine190A conceptual framework designed to integrate AI-driven hybrid intelligence into clinical practice to enhance decision-making is developed, focusing on adapting key AI concepts, such as backpropagation, quantization, and avoiding overfitting to help clinicians better interpret complex medical data and improve diagnosis and treatment planning.International Journal of General Medicine["Takanobu Hirosawa", "Tomoharu Suzuki", "Tastuya Shiraishi", "Arisa Hayashi", "Yoichi Fujii", "Taku Harada", "T. Shimizu"]2024-11-01T00:00:00 0cdbad3e92bd8eea9df958c179f502038f0bcb31This paper investigates the level of awareness and usage of artificial intelligence (AI) for promoting music broadcast on radio stations in Warri, Nigeria. The study was prompted by the paradigm shift that is brought about by the advent of artificial intelligence. Despite the advantages inherent in technology most radio stations appear to lack knowledge of the component meant for the broadcast of music.  Thus, the study establishes the extent to which the radio station in Warri, Nigeria has become AI compliant in relaying music. To determine the AI awareness level and extent of usage in music promotion on radio stations, the researcher conducted interview sessions using four (4) radio stations, forty-one (41) on Air Personalities (OAP). A Self Respondent Interview Research Instrument was administered via email contact. The study was guided by three (3) research questions. Data were analyzed with the aid of percentage calculation measure to decide the level of awareness and usage of the AI tools connected to music broadcast on radio. The study found that respondents show low level of awareness of significant AI tools and thus could not deploy AI technologies for broadcast of music. The study concludes that it is important to create high level of awareness in the use of AI tools for promotion of music broadcast on radio. The study recommends that OAP should acquire the knowledge of AI tools related to promotion of music on radio stations. The proprietors of radio stations should provide training on the use of AI technologies to enable them become AI compliant in radio broadcasting.Àgídìgbo: ABUAD Journal of the Humanities150The study establishes the extent to which the radio station in Warri, Nigeria has become AI compliant in relaying music and recommends that OAP should acquire the knowledge of AI tools related to promotion of music on radio stations.Àgídìgbo: ABUAD Journal of the Humanities["Igue Philo Okpeki", "Temabor Peace Onyenye"]2024-11-01T00:00:00 ab7fbd9d53b4f63abc111bf91eab31be7a660854Objective: this paper aims to map the main artificial intelligence and robotics technologies that are being applied in offshore wind farms around the world, as well as highlight the possible classification of these technologies in Brazil. Methodology/approach: the methodology of the work consists of carrying out a bibliometric study based on a Scopus database where a series of quantitative and qualitative analyses were made and, finally, the main papers were grouped into 8 central clusters found. Originality/Relevance: The relevance of the work consists of presenting to researchers the main fields that have been studied in the applications of AI and robotics in the context of offshore wind farms and, therefore, allows new research to occur in these fields found from the clusters. In addition, the work summarizes in which stages throughout the development of offshore projects each of the clusters can be applied, thus allowing a significant advance for possible projects to be carried out in Brazil in the future. Main conclusions: as a result of the research, eight main clusters of research carried out in the field were identified, as well as their possible classification in the Brazilian scenario in the future. Theoretical/methodological contributions: the scientific contributions that the paper presents to researchers are diverse, among which we can list: the mapping of the main journals that have publications on the theme of AI and robotics applications in the field of offshore wind energy, the main trends in AI and robotics technologies applied to offshore wind energy around the world and, finally, the mapping of the most relevant paper on AI and robotics applications in the context of offshore wind energy, as well as their evidence in the Brazilian context.Revista Inteligência Competitiva320The main artificial intelligence and robotics technologies that are being applied in offshore wind farms around the world are mapped as well as their possible classification in the Brazilian scenario in the future.Revista Inteligência Competitiva["Matheus Pussaignolli de Paula", "Matheus Noronha", "Uiara Garcia Valente", "Beatriz Regina Inacio Domingues", "Let\u00edcia Jahn Souza"]2024-11-01T00:00:00 29db64b557500dc06b1474cb6f44cb5d868aa337The integration of artificial intelligence (AI) into healthcare communication has rapidly evolved, driven by advancements in large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT). This literature review explores AI's role in patient-physician interactions, particularly focusing on its capacity to enhance communication by bridging language barriers, summarizing complex medical data, and offering empathetic responses. AI's strengths lie in its ability to deliver comprehensible, concise, and medically accurate information. Studies indicate AI can outperform human physicians in certain communicative aspects, such as empathy and clarity, with models like ChatGPT and the Medical Pathways Language Model (Med-PaLM) demonstrating high effectiveness in these areas. However, significant challenges remain, including occasional inaccuracies and "hallucinations," where AI-generated content is irrelevant or medically inaccurate. These limitations highlight the need for continued refinement in AI algorithms to ensure reliability and consistency in sensitive healthcare settings. The review underscores the potential of AI as a transformative tool in health communication while advocating for further research and policy development to mitigate risks and enhance AI's integration into clinical practice.Cureus170This literature review explores AI's role in patient-physician interactions, particularly focusing on its capacity to enhance communication by bridging language barriers, summarizing complex medical data, and offering empathetic responses.Cureus["Theo J Clay", "Zephy J Da Custodia Steel", "Chris Jacobs"]2024-11-01T00:00:00 733617160e10a37086ce08e657a82f4f6cb6466bIntegrating Artificial Intelligence in academia has revolutionized various fields with new opportunities for innovation, research, and learning.  The capability of AI to analyze enormous amounts of data at such incredibly short times contributes to research advancement across natural sciences, humanities, social sciences, engineering, and healthcare sciences. For instance, in natural sciences, AI algorithms support various types of data analysis and simulation, helping to make new discoveries and provide methods and new approaches to look at existing research methods. AI advances in social sciences employ prediction modeling and machine learning to enhance economic models and other behavioral analyses. AI has presented humanities advancements in text analysis and interpretation of history work, augmenting the research based on historical data with data analysis. In engineering and technology, AI's role is twofold: enhancing physical security and, at the same time, posing new threats in the form of complex cyber threats. In a related context, AI’s application for diagnosis and treatment planning has been observed in the healthcare sector. It has shown the potential capability of improving the care of patients far beyond any imagined capabilities. Nevertheless, the application of AI in academia comes with some challenges. Privacy, protection, ethical views, and prejudice enhancement are some of the most significant issues that should be considered.  Despite these challenges, AI creates multi-professional collaboration and advances in knowledge and performance in various scientific disciplines. AI continues to thrive in the future of academia, as future advancement holds possible new research horizons, educational improvement, and world problem-solving. With the rapid evolution of AI, its incorporation into academia and its abuses, biases, and risks need to be constantly reviewedInternational Journal of Education Teaching and Social Sciences460Artificial Intelligence continues to thrive in the future of academia, as future advancement holds possible new research horizons, educational improvement, and world problem-solving.International Journal of Education, Teaching, and Social Sciences["Bongs Lainjo"]2024-11-01T00:00:00 47b9e8ec10b2029a41b6e57f01ae4e2a8fa48a7bBackground Artificial intelligence (AI) is rapidly evolving within healthcare, promising improvements in patient care, diagnostic accuracy, and therapeutic interventions. As AI technology becomes more integrated into clinical workflows, understanding healthcare professionals’ (HCPs) knowledge, attitudes, and practices concerning AI is crucial, particularly in diverse healthcare environments like South Gujarat. This study evaluates HCPs' understanding, perception, and application of AI at a tertiary care teaching hospital in this region. Methods This observational, cross-sectional study utilized a non-validated, structured questionnaire based on the Knowledge, Attitude, and Practice (KAP) framework. A convenient sampling technique was employed to recruit 290 HCPs, including consultant doctors, medical faculty, residents, and interns. Data were collected electronically via Google Forms and analyzed using descriptive statistics. Results Most participants (176; 60.7%) were junior residents, with notable representation from departments like Pharmacology and Community Medicine. Regarding AI knowledge, 80 (27.6%) of participants reported full awareness, while 182 (62.8%) were partially aware. AI subtype knowledge varied, with 84 (28.9%) identifying "Self-awareness" and 50 (17.2%) "Limited Memory." Internet sources were the predominant information source for 171 (58.9%) of participants. Notably, 192 (66.2%) recognized AI's role in saving time and enhancing accuracy, although some expressed concerns about its lack of empathy and ethical implications. Conclusions The findings highlight substantial awareness but varying depths of understanding of AI among HCPs, who are interested in further AI education. Increased educational programs on AI’s ethical and practical aspects may enhance AI integration into clinical practice, aiding responsible adoption in healthcare settings.Cureus210Evaluating HCPs' understanding, perception, and application of AI at a tertiary care teaching hospital in South Gujarat highlights substantial awareness but varying depths of understanding of AI among HCPs, who are interested in further AI education.Cureus["Sajal Pandya", "Chetna Patel", "Brijesh Sojitra", "Jaykumar Patel", "Paras Shah", "Akash Shah"]2024-11-01T00:00:00 177ffbf51da4df2b2a2bd5fd642b9538116c9473Introduction: Health literacy plays a vital role in determining one's health status, as studies have shown that poor health literacy is associated with negative health results. The Centers for Disease Control and Prevention (CDC) and the National Institutes of Health (NIH) advise that patient educational materials (PEMs) should be written at an eighth-grade reading level or lower, matching the average reading level of adult Americans. This study evaluated the ability of generative artificial intelligence (AI) to rewrite PEMs about rotator cuff injuries (RCIs) to align with the eighth-grade reading level recommendation of the CDC and NIH. Methods: Online PEMs about RCI from the 25 highest ranked orthopedic hospitals from the 2022 U.S. News and World Report Best Hospitals Specialty Ranking were collected. Chat Generative Pretrained Transformer Plus, version 4.0 (OpenAI, San Francisco, CA) was then instructed to rewrite the PEMs to adhere to CDC and NIH-recommended guidelines. Readability scores were calculated for the original and rewritten PEMs, and paired t-tests were used to determine statistical significance. Results: Analysis of the original and rewritten PEMs about RCI demonstrated significant reductions in reading-grade level and word count of 4.33 ± 1.50 (p < 0.001) and 442.68 ± 351.45 (p < 0.001), respectively. Discussion: Our study determined that generative AI is capable of rewriting PEMs about RCI at a reading comprehension level that conforms to the CDC and NIH guidelines. Hospital administrators and orthopedic surgeons should consider the findings of this study, and the potential utility of AI when crafting PEMs of their own.Cureus170Generative AI is capable of rewriting PEMs about RCI at a reading comprehension level that conforms to the CDC and NIH guidelines, and hospital administrators and orthopedic surgeons should consider the findings of this study, and the potential utility of AI when crafting PEMs of their own.Cureus["Michael Miskiewicz", "Matthew Perez", "Salvatore Capotosto", "Kenny Ling", "Frederick Hance", "David Komatsu", "Edward D Wang"]2024-11-01T00:00:00 414b0e41b97567d8f4d91ef704581a9e9609fe73Background Early detection of critical events in hospitalized patients improves clinical outcomes and reduces mortality rates. Traditional early warning score systems, such as the National Early Warning Score 2 (NEWS2), effectively identify at-risk patients. Integrating artificial intelligence (AI) could enhance the predictive accuracy and operational efficiency of such systems. The study describes the development and implementation of an AI-enhanced early warning system based on a modified NEWS2 scale with laboratory parameters (mNEWS2-Lab) and evaluates its ability to improve patient safety in hospital wards. Methods For this retrospective cohort study of 3,790 adults admitted to hospital wards, data were collected before and after implementing the mNEWS2-Lab protocol with and without AI enhancement. The study used a multivariate prediction model with statistical analyses such as Fisher's chi-square test, relative risk (RR), RR reduction, and various AI models (logistic regression, decision trees, neural networks). The economic cost of the intervention was also analyzed. Results The mNEWS2-Lab reduced critical events from 6.15% to 2.15% (RR, 0.35; P<0.001), representing a 65% risk reduction. AI integration further reduced events to 1.59% (RR, 0.26; P<0.001) indicating a 10% additional risk reduction and enhancing early warning accuracy by 15%. The intervention was cost-effective, resulting in substantial savings by reducing critical events in hospitalized patients. Conclusions The mNEWS2-Lab scale, particularly when integrated with AI models, is a powerful and cost-effective tool for the early detection and prevention of critical events in hospitalized patients.Acute and Critical Care150The mNEWS2-Lab scale, particularly when integrated with AI models, is a powerful and cost-effective tool for the early detection and prevention of critical events in hospitalized patients.Acute and Critical Care["Alejandro Huete-Garcia", "Sara Rodriguez-Lopez"]2024-11-01T00:00:00 7c22c887bd15cdf38830fcaefc004da736de443dThe role of artificial intelligence has been explored in many industries across the world. The medical field is no exception with studies regarding its use for development of algorithms in cancer screening and its diagnostic utility in clinical radiology. This study aims to review current literature on intraoperative use of artificial intelligence within general surgery to identify the latest developments, the major challenges and the trajectory of this field. A literature search was done on PubMed on May 28, 2024, using the terms: ((artificial intelligence) AND (general surgery)). Only publications in English and studies involving human subjects were considered. Exclusion criteria included duplicate papers, irrelevant titles, abstracts, themes, and non-English papers. A literature search on PubMed yielded 13 relevant articles. Among these, five articles focused on intraoperative guidance, four addressed surgical education and training, and four were survey-based exploring perceptions regarding artificial intelligence. Key themes included the development of artificial intelligence-based autonomous actions during surgery and its role in enhancing surgical training. Limitations identified included restricted data availability, ethical concerns, and a lack of validation tools, which pose significant obstacles to progress in this area. Despite existing limitations, the potential for integrating artificial intelligence into general surgery is promising. Careful attention is needed to overcome challenges and maximize its benefits.Cureus230Current literature on intraoperative use of artificial intelligence within general surgery is reviewed to identify the latest developments, the major challenges and the trajectory of this field.Cureus["Deema Othman", "Ahmad Kaleem"]2024-11-01T00:00:00 b40af217cb9eef708fb7ed0fb132f59a7e154d31Artificial intelligence (AI) technologies (natural language processing (NLP), speech recognition (SR), and machine learning (ML)) can transform clinical documentation in healthcare. This scoping review evaluates the impact of AI on the accuracy and efficiency of clinical documentation across various clinical settings (hospital wards, emergency departments, and outpatient clinics). We found 176 articles by applying a specific search string on Ovid. To ensure a more comprehensive search process, we also performed manual searches on PubMed and BMJ, examining any relevant references we encountered. In this way, we were able to add 46 more articles, resulting in 222 articles in total. After removing duplicates, 208 articles were screened. This led to the inclusion of 36 studies. We were mostly interested in articles discussing the impact of AI technologies, such as NLP, ML, and SR, and their accuracy and efficiency in clinical documentation. To ensure that our research reflected recent work, we focused our efforts on studies published in 2019 and beyond. This criterion was pilot-tested beforehand and necessary adjustments were made. After comparing screened articles independently, we ensured inter-rater reliability (Cohen's kappa=1.0), and data extraction was completed on these 36 articles. We conducted this study according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This scoping review shows improvements in clinical documentation using AI technologies, with an emphasis on accuracy and efficiency. There was a reduction in clinician workload, with the streamlining of the documentation processes. Subsequently, doctors also had more time for patient care. However, these articles also raised various challenges surrounding the use of AI in clinical settings. These challenges included the management of errors, legal liability, and integration of AI with electronic health records (EHRs). There were also some ethical concerns regarding the use of AI with patient data. AI shows massive potential for improving the day-to-day work life of doctors across various clinical settings. However, more research is needed to address the many challenges associated with its use. Studies demonstrate improved accuracy and efficiency in clinical documentation with the use of AI. With better regulatory frameworks, implementation, and research, AI can significantly reduce the burden placed on doctors by documentation.Cureus350Improvements in clinical documentation using AI technologies are shown, with an emphasis on accuracy and efficiency, with an emphasis on accuracy and efficiency.Cureus["Craig Lee", "Shawn Britto", "Khaled Diwan"]2024-11-01T00:00:00 b66a4e180fef1e28d532da2ffb4f69358c205513This special topic centers on cutting-edge advancements in the security and safety of artificial intelligence (AI), with a focus on critical applications across domains such as autonomous systems, federated learningSecurity and Safety50Security and Safety["Hao Zhang", "Yu-Gang Jiang", "Claudio Melchiorri", "Gerhard Rigoll"]2024-11-01T00:00:00 368b460aff013fc175905dfef43675c17b9b7b43OBJECTIVE To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their families, and provide data support for clinical decision-making. METHODS A retrospective study was conducted. The clinical information of stroke patients (n = 53 793) were extracted from the Yidu cloud big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to July 31, 2023. Combined with the results of single factor screening and the opinions of experts with senior professional titles in neurology, the input variable was determined, and the output variable was the National Institutes of Health Stroke Scale (NIHSS) representing the severity of the disease at admission. Python 3.7 was used to build DeepFM algorithm model, and five data mining models including Logistic regression, CART decision tree, C5.0 decision tree, Bayesian network and deep neural network (DNN) were built at the same time. The original data were randomly divided into 80% training set and 20% test set, which were used to train and test the models, adjust the parameters of each model, respectively calculate the accuracy, sensitivity and F-index of the six models, carry out the comprehensive comparison and evaluation of the model. The receiver operator characteristic curve (ROC curve) and calibration curve were drawn, compared the prediction performance of DeepFM model and the other five algorithms. In addition, the data of stroke patients (n = 1 028) were extracted from Dalian Central Hospital for external verification of the model. RESULTS A total of 14 015 stroke patients with complete information were selected, including 11 212 in the training set and 2 803 in the testing set. After univariate screening, 14 indicators were included to construct the model, including gender, age, recurrence, physical impairment, facial problems, speech disorders, head reactions, disturbance of consciousness, visual disorders, abnormal cough and swallowing, high risk factor, family history, smoking history and drinking history. DeepFM model adopted the two-order crossover feature. The number of hidden layers in DNN layer was 3. Dropout was used to discard the neurons in the neural network. Rule was used as the activation function. Each layer used Dense full connection. The objective function was random gradient descent. The number of iterations was 15. There were 133 922 training parameters in total. Comparing the predictive value of the six models showed that the accuracy of DeepFM model was 0.951, the sensitivity was 0.992, the specificity was 0.814, the F-index was 0.950, and the area under the curve (AUC) was 0.916. The accuracy of the other five data mining models was between 0.771-0.780, the sensitivity was between 0.978-0.987, the F-index was between 0.690-0.707, and the AUC was between 0.568-0.639. The calibration curve of the DeepFM model was more aligned with the ideal curve than the other five data mining models. Suggesting that the prediction performance of DeepFM model was the best. External validation was conducted on the DeepFM model, and its accuracy was 0.891, indicating good generalization performance of the model. CONCLUSIONS The pre-hospital non-invasive screening prediction model based on DeepFM can accurately predict the severity grading of stroke patients, and has potential application value in rapid screening and early clinical decision-making of stroke.Zhonghua wei zhong bing ji jiu yi xue00The pre-hospital non-invasive screening prediction model based on DeepFM can accurately predict the severity grading of stroke patients, and has potential application value in rapid screening and early clinical decision-making of stroke.Zhonghua wei zhong bing ji jiu yi xue["Chenyu Liu", "Ce Zhang", "Yuanhui Chi", "Chunye Ma", "Lihong Zhang", "Shuliang Chen"]2024-11-01T00:00:00 e4f22374d200419f595b5a5acce513aa5919c94aThis article explores the significant impact of artificial intelligence (AI) oneducation, particularly in the field of English language learning. By exploring current advances in artificial intelligence technology, including natural language processing and machine learning algorithms, the article highlights how AI-based tools and applications are revolutionizing the way Englishis taught and learned. In addition, the article discusses potential challenges and ethical considerations associated with the integration of AI into education, emphasizing the importance of responsible and inclusive implementation. Overall, this article highlights the changing role of artificial intelligence in shaping the future of English language education, providing in sight into its benefits, opportunities, and implications for students and teachers.International Journal of Pedagogics00How AI-based tools and applications are revolutionizing the way English is taught and learned is highlighted, providing in sight into its benefits, opportunities, and implications for students and teachers.International Journal of Pedagogics["Eshmamatov Ruslan Xasan O'g'li"]2024-11-01T00:00:00 8c70967acd13ebefd36ea8746f69c5dbd9434b25 This research project aims to develop an advanced artificial intelligence (AI) framework for detecting and mitigating violations and disinformation across social media networks, with a specific focus on identifying intellectual property rights (IPR) infringements. By integrating machine learning, natural language processing (NLP), and computer vision techniques, the project seeks to automate real-time detection of content that contravenes intellectual property laws or propagates disinformation. Key components include text analysis for identifying disinformation patterns in social media posts and visual content recognition to detect images and videos that infringe upon intellectual property or spread visual misinformation. The project utilizes transformer-based NLP models, convolutional neural networks (CNNs), and generative adversarial networks (GANs) to analyze content at scale. Through adaptive learning mechanisms, the AI system will continuously update its detection models, allowing for scalability and responsiveness to new disinformation patterns. The project is structured across five phases, from data collection and model training to deployment and policy recommendation. Expected outcomes include heightened accuracy in disinformation detection, improved intellectual property protection, and actionable insights for policymakers to address violations and misinformation. This multidisciplinary approach contributes to AI’s application in legal technology, media compliance, and information security, ultimately advancing efforts toward a safer and more transparent digital ecosystem. The system’s ability to adapt to evolving disinformation and violation tactics is critical for maintaining relevance in the dynamic landscape of social media. By leveraging reinforcement learning, the AI model will continuously improve, effectively capturing nuanced shifts in content patterns. This project also addresses significant ethical and legal considerations, ensuring compliance with privacy standards like GDPR and DMCA. Furthermore, the anticipated insights will aid policymakers in establishing robust frameworks for content governance. Ultimately, this AI-driven solution holds potential for wide-scale adoption, enhancing accountability and intellectual property protection across digital platforms. Next Frontier For Life Sciences and AI40An advanced artificial intelligence framework for detecting and mitigating violations and disinformation across social media networks, with a specific focus on identifying intellectual property rights (IPR) infringements, holds potential for wide-scale adoption, enhancing accountability and intellectual property protection across digital platforms.Next Frontier For Life Sciences and AI["Can Sinan Canpolat"]2024-11-01T00:00:00 a8d4bd029371d90449830649211533928e556365The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients’ responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.Bioengineering1650This paper describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes, and the CDP-based second-generation AI platform, which is the basis for the Digital Pill.Bioengineering["Yaron Ilan"]2024-11-01T00:00:00 3ccafb97a15d377098988ad53eb71984c15fec64Controlled Environment Agriculture (CEA) is increasingly used to grow food (namely fruits and vegetables) in con-trolled indoor conditions. While often billed as “eliminating” the classical food safety concerns associated with open field cultivation of produce, traditional as well as potentially novel microbial food safety risks are a concern for CEA, as supported by a recent salmonellosis outbreak in the U.S. linked to CEA grown produce. In addition, the use of diverse technologies and practices in CEA represents a challenge in efforts to develop food safety guidance. CEA, particularly precision vertical farms, however, have the dis-tinct advantage of being “data intense” and typically have a better data collection and management structure than is found in traditional agriculture. This may position at least part of the industry to use digital tools digital tools and Artificial Intelligence (AI) to manage manage food safety. Possible AI approaches may include adaptive sampling and interventions depending on the presence of risk factors that could be predicted with the routine data generated during CEA operations. This article summarizes challenges and opportunities for using AI and digital approaches to as-sure microbial food safety and manage food safety related business risks in CEA.Food protection trends00Challenges and opportunities for using AI and digital approaches to as-sure microbial food safety and manage food safety related business risks in CEA are summarized.Food Protection Trends["Caroline Motzer", "Ahmed El-Moghazy", "Ana Allende", "M. I. Gil", "Yannick Weesepoel", "Leo van Overbeek", "Cheng Liu", "Rick van de Zedde", "Y. Bouzembrak", "Nitin Nitn", "Renata Ivanek", "Martin Wiedmann"]2024-11-01T00:00:00 26010d029e9cfe1d42ec04a69cb639eb4a17609a Recently, artificial intelligence (AI) has emerged as a tool for improving stroke diagnosis, enhancing critical decision‐making, and improving acute ischemic stroke (AIS) care. AI‐based platforms such as RapidAI, Brainomix® and Viz.ai, amongst others, have been studied to improve image analysis, detect large vessel occlusions (LVO), and predict patient outcomes in a timely manner. However, there is limited data pertaining to the impact of these AI platforms on real‐world patient care and management. The objective of this literature review is to evaluate the effectiveness and accuracy of AI platforms in facilitating the treatment of AIS. Following the PRISMA guidelines, a comprehensive systematic review was conducted using PubMed, Embase, Web of Science, and Scopus databases. Studies that meet the inclusion criteria were included. The final selection comprised studies that provided detailed analyses of AI tools, focusing on their sensitivity, specificity, accuracy, and comparative effectiveness. A total of 31 studies were included of which 29 studies primarily focused on detecting AIS or LVO, and 2 studies explored the use of AI in hemorrhagic strokes. AI tools including Viz.ai, RapidAI, and Brainomix®, demonstrated great utility in stroke management. These tools contributed to significant reductions in door‐to‐puncture times, enhanced accuracy in estimating core and penumbra volumes, and provided reliable assessments of ASPECT scores and the presence of intracranial hemorrhage. RapidAI was noted for its ability to rapidly identify LVOs. Viz.ai showed high accuracy in detecting both AIS and LVO, with sensitivity and specificity comparable to expert human interpretation. Brainomix® offered advantages in evaluating stroke severity and predicting outcomes, thereby aiding in the decision‐making process for intravenous thrombolysis and endovascular thrombectomy. Integrating AI tools in stroke care proves to be a valuable tool for aid diagnostic and management measures for greater accuracy and faster decision‐making, leading to improved patient outcomes. Considering the advancement in technology, AI‐based platforms are escalating to become vital assets for personalized care, providing a new level of hand‐tailored and expedited stroke management in the near future. Stroke: Vascular and Interventional Neurology00Stroke: Vascular and Interventional Neurology["A. Elrefaei", "O. Al-Janabi", "D. Bakir", "Y. M. Mahmood", "T. Elgazzar", "A. Gajjar", "A. Alateya", "S. K. Jha", "S. Ghozy", "D. F. Kallmes", "W. Brinjikji"]2024-11-01T00:00:00 c3ded5a698300e0d1db2313cb45bc11cd7cd8c10This paper explores the potential of artificial intelligence (AI) in lung cancer screening programs, particularly in the interpretation of computed tomography (CT) scans. The authors acknowledge the benefits of AI, including faster and potentially more accurate analysis of scans, but also raise concerns about clinician trust, transparency, and the deskilling of radiologists due to decreased scan exposure. The rise of AI in medicine and the introduction of national lung cancer screening programs are both increasing contemporarily and naturally the overlap and interplay between the two in the future is ensured. The paper highlights the importance of human-AI collaboration, emphasizing the need for interpretable models and ongoing validation through clinical trials. The promising results and problems uncovered the current pilot studies is explored. Building trust with patients and clinicians is also crucial, considering factors like disease risk perception and the human element of patient interaction. The authors conclude that while AI offers significant promise, widespread adoption hinges on addressing ethical considerations and ensuring a balanced, synergistic relationship between AI and medical professionals. This report aims to provide a talking point to inspire conversations around, and prepare clinicians for the rapidly approaching frontier that is AI in healthcare.Journal of Thoracic Disease160The authors conclude that while AI offers significant promise, widespread adoption hinges on addressing ethical considerations and ensuring a balanced, synergistic relationship between AI and medical professionals.Journal of Thoracic Disease["Philippa Jane Temple Bowers", "Frazer Michael Kirk"]2024-11-01T00:00:00 0e7c1a7ff5612683b946dd506fa767af0dfb5074Although clinician-supported computer-assisted cognitive-behaviour therapy (CCBT) is well established as an effective treatment for depression and anxiety, less is known about the specific interventions used during coaching sessions that contribute to outcomes. The current study used artificial intelligence (AI) to identify specific components of clinician-supported CCBT and correlated those scores with therapy outcomes. Data from a randomized clinical trial comparing clinician-supported CCBT with treatment as usual in a primary care setting were utilized. Participants (n = 95) engaged in CCBT with coaching sessions. The primary outcome was the Patient Health Questionnaire (PHQ-9), with Generalized Anxiety Disorder (GAD-7), Satisfaction with Life Scale (SWLS) and Automatic Thoughts Questionnaire (ATQ) ratings as secondary outcomes, which were assessed at 12 weeks (post), 3- and 6-month follow-up. The Lyssn system utilized AI technology to code CBT techniques and common general psychotherapeutic techniques. After controlling for initial ratings, 13 Lyssn-variables were observed to be significantly associated with reducing anxiety on the GAD-7 after 12 weeks of treatment. Among the most effective CBT interventions for anxiety included the use of guided discovery, understanding, interpersonal effectiveness and agenda setting. The most beneficial intervention was the proportion of open questions across all variables. Lyssn did not identify any CBT-specific interventions significantly associated with PHQ-9, SWLS or ATQ. Therapist use of CBT-specific techniques was significantly associated with reduction of anxiety symptoms after 12 weeks, but such gains were not observed at follow up. Therapist use of open questions was observed to be the most impactful technique contributing to treatment outcomes.Clinical Psychology and Psychotherapy220Artificial intelligence (AI) was used to identify specific components of clinician-supported CCBT and correlated those scores with therapy outcomes and Therapist use of open questions was observed to be the most impactful technique contributing to treatment outcomes.Clinical psychology & psychotherapy["Jeremy J. Coleman", "Jesse Owen", "Jesse H. Wright", "Tracy D. Eells", "Becky Antle", "Markessa McCoy", "Christina Signe Soma"]2024-11-01T00:00:00 912804958c5794b7507feca4b543033c9df583e6The integration of artificial intelligence has led to significant advancements across industries but also exposed systems to security vulnerabilities. We evaluate defense methods, including robust data practices, adversarial training, model hardening, fairness-aware algorithms, and privacy-preserving techniques, and highlight each method’s effectiveness in addressing specific vulnerabilities.Computer240This work evaluates defense methods, including robust data practices, adversarial training, model hardening, fairness-aware algorithms, and privacy-preserving techniques, and highlights each method’s effectiveness in addressing specific vulnerabilities.Computer["Michail Tsikerdekis", "S. Zeadally", "Iyad Katib"]2024-11-01T00:00:00 ebc6d1ad59b6151b04bfc8058f45251ab82a8a27This study examines the impact of artificial intelligence (AI), specifically computer vision and robotic process automation (RPA), on fraud detection in Deposit Money Banks in Southeast Nigeria. AI technologies offer innovative solutions to combat rising fraud threats by enabling real-time monitoring, anomaly detection, and process automation. The study’s objectives include assessing the effectiveness of computer vision in detecting insider fraud and evaluating the role of RPA in monitoring card fraud activities. Using a descriptive survey design, data was collected from employees within various banking institutions in Southeast Nigeria via questionnaire to assess the effectiveness, challenges, and potential improvements AI technologies bring to fraud detection practices. A total population of 1101 staff were selected from the studied organizations. Sample size of two hundred and eighty four (284) was determined using Freund and William's statistic formula at 5 percent margin of error. Data was presented and analyzed using Likert Scale and the hypotheses using Z - test. The findings indicate that Computer Vision had significant positive effect on insider fraud detection, Z = 6.561< 8.639, P. <, 05. Robotics had significant positive effect on card fraud monitoring in money deposit bank in Southeast, Nigeria, Z = 7.649 < 9.987, P. <,05. The study underscores the importance of a comprehensive AIintegrated fraud detection system and recommends further exploration into cost-effective implementations tailored to the context of smaller banking institutions. Addressing these challenges can foster an improved security landscape in Nigeria’s banking sector, enhancing trust and operational resilienceIOSR Journal of Humanities and Social Science00The study underscores the importance of a comprehensive AIintegrated fraud detection system and recommends further exploration into cost-effective implementations tailored to the context of smaller banking institutions.IOSR Journal of Humanities and Social Science["Anzor, Edith Chima", "Okolie Jonathan Ibekwe", "Udeh, Ifeyinwa Ebere", "Anukwe, Grace Ijeoma", "Eze, Jude Obinna"]2024-11-01T00:00:00 4efd65d80831c97a980ea5444c5baaac86ec2226Abstrak: Penelitian ini berfokus untuk mengetahui pengaruh dari implementasi sebuah model pembelajaran yaitu advance organizer (AO) berbantuan Artificial Intelligence (AI) berupa ChatGPT terhadap peningkatan kemampuan pemahaman konsep fisika pada mahasiswa di Universitas Sulawesi Barat, dibandingkan dengan pembelajaran model kolaboratif. Metode yang digunakan adalah  penelitian Quasi experiment (Eksperimen Semu) dengan Non_equivalent Control Group Design . Kelompok eksperimen menggunakan advance organizer berbantuan ChatGPT, sementara kelompok kontrol menggunakan model kolaboratif. Data dianalisis dengan memakai statistik deskriptif, Uji t berpasangan, uji t independen (sampel bebas), serta analisis Gain Normalized (N-gain). Hasilnya menunjukkan bahwa kelompok eksperimen mengalami peningkatan pemahaman konsep secara signifikan dengan perolehan skor rata-rata  post-test 60,72 dan N-Gain 0,599 (sedang), sedangkan kelompok kontrol memiliki rata-rata post-test 45,65 dan N-Gain 0,036 (rendah). Hasil dari uij hipotesis (uji T) diperoleh signifikansi sebesar 0,000 yang berarti data ini menunjukkan adanya perbedaan secara siginifikan. Kesimpulannya, penggunaan advance organizer berbantuan AI ternyata efektif untuk meningkatkan kemampuan memahami (pemahaman konsep) dibandingkan pembelajaran  model kolaboratif Kata Kunci: Advance Organizer, Artificial Intelligence, Pemahaman Konsep, Model Pembelajaran, ChatGpt Abstract: This research focuses on knowing the effect of implementing a learning model, namely advance organizer (AO) assisted by Artificial Intelligence (AI) in the form of ChatGPT on improving the ability to understand physics concepts in students at the University of West Sulawesi, compared to collaborative learning models. The method used was Quasi experiment research with Non_equivalent Control Group Design. The experimental group used advance organizer assisted by ChatGPT, while the control group used collaborative model. Data were analyzed using descriptive statistics, paired t-test, independent t-test (independent samples), and Normalized Gain (N-gain) analysis. The results showed that the experimental group experienced a significant increase in concept understanding with an average post-test score of 60.72 and N-Gain of 0.599 (medium), while the control group had an average post-test of 45.65 and N-Gain of 0.036 (low). The results of the hypothesis testing (T-test) obtained a significance of 0.000 which means this data shows a significant difference. In conclusion, the use of advance organizer assisted by AI was effective to improve the ability to understand (concept understanding) compared to collaborative learning model. Keywords: Advance Organizer, Artificial Intelligence, Conceptual Understanding, Learning Model, ChatGptJurnal Teknologi Pendidikan (JTP)150The use of advance organizer assisted by AI was effective to improve the ability to understand physics concepts in students at the University of West Sulawesi compared to collaborative learning models.Jurnal Teknologi Pendidikan (JTP)["Faizal Amir", "Andi Saddia"]2024-11-01T00:00:00 add90e01f5364da028548e117dc8bc63fdc5f43fThe prevalence of hepatic steatosis is increasing globally. While non-invasive diagnostic methods like ultrasonography and clinical scoring systems have been suggested as alternatives to liver biopsy, their effectiveness has been questioned. Integrating Artificial Intelligence (AI) with traditional diagnostic methods is being explored to enhance the accuracy of non-invasive approaches. The research utilized science bibliographic databases for data retrieval, namely PubMed, Scopus, and Google Scholar. The search terms utilized were “fatty liver,” “hepatic steatosis” “artificial intelligent”, “machine learning”, “deep learning”, “convolutional neural network”, “artificial neural network” and “ultrasound” etc. The systematic review encompassed studies, which collectively demonstrated that AI had a notable impact on improving the diagnosis of various liver conditions including liver steatosis, steatohepatitis, liver fibrosis, and liver cirrhosis. Through qualitative analysis, it was found that AI was particularly effective in enhancing diagnostic accuracy for these conditions. The integration of AI-supported systems has shown promising advancements in the detection and quantification of steatosis, NASH, and liver fibrosis in patients with liver steatosis. These systems have demonstrated the ability to improve performance in accurately diagnosing and assessing the severity of liver diseases, providing healthcare professionals with valuable tools for more effective clinical management.Public Health Economy and Management in Medicine00A systematic review encompassed studies, which collectively demonstrated that AI had a notable impact on improving the diagnosis of various liver conditions including liver steatosis, steatohepatitis, liver fibrosis, and liver cirrhosis.Public Health, Economy and Management in Medicine["Mohammed Faiz Shajahan", "Inesa I. Toaca", "Olga Stefanet", "Angela Peltec"]2024-11-01T00:00:00 a5a7ccc3806e718ed7259af553df42c6bfed0fb8The article examines the application of artificial intelligence (AI) technologies in the investigation of crimes related to inadequate medical care. It analyzes the advantages and limitations of using AI, including enhancing the accuracy and speed of investigations, and discusses ethical and legal aspects. Practical examples are provided, and recommendations for integrating AI into legal and medical practice are proposed.Studii Juridice Universitare00The application of artificial intelligence technologies in the investigation of crimes related to inadequate medical care, including enhancing the accuracy and speed of investigations, and discusses ethical and legal aspects are examined.Studii Juridice Universitare["Costantin Pisarenco"]2024-11-01T00:00:00 9494842096abc19d8c97497b105c68119ca6b59cThis study examines the current state of enterprise-wide governance practices for artificial intelligence (AI) and machine learning (ML) across various industries. A survey of 60 participants reveals insights into how organizations define, manage, and govern AI/ML technologies. Key findings highlight significant progress in establishing governance frameworks while identifying critical gaps that require attention. This paper discusses these findings and provides recommendations for enhancing AI/ML governance practices to ensure ethical, transparent, and effective deployment of AI/ML technologies.European Journal of Applied Science, Engineering and Technology50This study examines the current state of enterprise-wide governance practices for artificial intelligence (AI) and machine learning (ML) across various industries and provides recommendations for enhancing AI/ML governance practices to ensure ethical, transparent, and effective deployment of AI/ML technologies.European Journal of Applied Science, Engineering and Technology["John Giordani", "Renato Zeko"]2024-11-01T00:00:00 55314a35acef6c9e235b37b28972c8abfbcc0089Abdominal Radiology632The application of AI in abdominal/pelvic ultrasound shows promising early results for disease diagnosis, monitoring, and report refinement, however, the risk of bias remains high because very few of these applications have been prospectively validated (in multi-center studies) or have received FDA clearance.Abdominal radiology["Lie Cai", "Andr\u00e9 Pfob"]2024-11-02T00:00:00 31b6b40c9b8920cbe6a6b62d7cff5c8394d70fecIn the 4.0 era, there has been a striking digitalisation revolution. Machines have been replaced by artificial intelligence (AI). In the past, humans did the thinking, but now robots also have the ability to think, and this will become more common in the future. Industrial Revolution 4.0 differs from previous revolutions in that it involves advances in artificial intelligence (AI), robotics, Internet of Things (IoT), 3D printing, genetic engineering, quantum computing and other technologies. Artificial intelligence is a technology created to mimic human intelligence, with a focus on developing the ability of machines to think and work like humans. Examples of artificial intelligence are speech recognition, problem solving, learning, and planning. However, the use of artificial intelligence (AI) also has a negative impact, namely the reduction of jobs affecting human resources. The question that arises is whether artificial intelligence can adapt the Sharia concept that has long been applied in Indonesia?Jurnal Fokus Manajemen01The question that arises is whether artificial intelligence can adapt the Sharia concept that has long been applied in Indonesia, namely the reduction of jobs affecting human resources.Jurnal Fokus Manajemen["Adelika Adelika"]2024-11-02T00:00:00 9540e03b805507e1b9c5bd9aeaa7667bd2da725cBACKGROUND AND PURPOSE Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and incorporate the analysis to improve the existing AI tool. Materials and methods: An in-house AI tool for whole breast radiation therapy planning was deployed in our institution since 2019, among which 522 patients were included in this study. The AI tool automatically generates fluence maps of the tangential beams to create an AI plan. Human planner makes fluence edits deemed necessary and after attending physician approval for treatment, it is recorded as final plan. Manual modification value (MMV) maps were collected, which is the difference between the AI-plan and the final plan. Subsequently, a human-AI interaction (HAI) model using full scale connected U-Net was trained to learn such interactions and perform plan enhancements. The trained HAI model automatically modifies the AI plan to generate AI-modified plans (AI-m plan), simulating human editing. Its performance is evaluated against original AI-plan and final plan. Results: AI-m plan showed statistically significant improvement in hotspot control over the AI plan, with an average of 25.2cc volume reduction in breast V105% (p=0.011) and 0.805% decrease in Dmax (p<.001). It also maintained the same PTV coverage as the final plan, demonstrating the model has captured the clinic focus of improving PTV hot spots without degrading coverage. Conclusions: The proposed HAI model has demonstrated capability of further enhancing the AI plan via modeling human-AI tool interactions. This study shows analysis of human interaction with the AI planning tool is a significant step to improve the AI tool. .Physics in Medicine and Biology00Analysis of human interaction with the AI planning tool is a significant step to improve the AI tool and the proposed HAI model has demonstrated capability of further enhancing the AI plan via modeling human-AI tool interactions.Physics in medicine and biology["Dongrong Yang", "Cameron Murr", "Xinyi Li", "Sua Yoo", "Rachel Blitzblau", "S. Mcduff", "Sarah Stephens", "Q. J. Wu", "Qiuwen Wu", "Yang Sheng"]2024-11-02T00:00:00 fc89f2a1df23139680d7db850d884c773f544136Background: China's healthcare system for children faces significant challenges, particularly due to the limited pediatric service capacity of primary healthcare institutions. A shortage of effective and accessible training tools for primary care doctors further hinders progress in addressing this gap. Technological advancements, especially in artificial intelligence, offer a potential solution to improve pediatric care. Artificial intelligence-driven virtual standardized patients (VSPs), leveraging internet and virtual simulation technologies, simulate clinical cases with specific disease characteristics, providing an innovative, efficient, and flexible training method. VSPs are increasingly utilized in medical education, clinical reasoning, and licensure exams. This study focuses on using VSPs to improve the management of common pediatric conditions, which are major health concerns for children and impose significant psychological and financial burdens on families. Methods: This study will involve a three-arm randomized controlled trial to evaluate the effectiveness of a virtual pediatric standardized patient platform in enhancing primary care doctors' management of common pediatric diseases. At least 459 participants, including general practitioners, internal medicine practitioners, surgeons, and pediatricians from more than 10 provinces across China, will be randomly assigned to one of three groups: the virtual patient platform group, the case teaching manual group, or the case teaching video group. Five virtual patient cases covering pneumococcal pneumonia, rotavirus enteritis with hypovolemic shock, hand-foot-and-mouth disease, acute appendicitis, and respiratory failure will be developed, along with corresponding case teaching materials. After a two-week learning period, participants' disease management abilities will be assessed using clinical vignettes. The primary outcome is adherence to best clinical practice guidelines, categorized into full adherence, partial adherence, and nonadherence. Discussion: This study aims to leverage artificial intelligence for capacity enhancement, targeting the shortcomings of primary care pediatrics and using VSP to help enhance primary care pediatrics capacity. It is a randomized controlled trial involving over 300 primary healthcare institutions across more than 10 provinces in China, ensuring broad and representative participation from both developed and underdeveloped regions.medRxiv10A three-arm randomized controlled trial to evaluate the effectiveness of a virtual pediatric standardized patient platform in enhancing primary care doctors' management of common pediatric diseases and targeting the shortcomings of primary care pediatrics.["Yanna Mao", "H. Luo", "Yiyuan Cai", "Wanqing Huang", "Fang Fang", "Yue Lu", "Xin Chen", "Qing Zhao", "Duolao Wang", "Hua He", "Xiaohui Wang", "Dexing Zhang", "Guobao Li", "Yichi Zhang", "Roman Xu", "Yao Zhao"]2024-11-02T00:00:00 5004c58fa41a166384910bf4f0414f24011c48ddIndian Journal of Otolaryngology and Head &amp; Neck Surgery70ChatGPT and Gemini were capable of generating understandable post-operative instructions but were poor for actionability and readability, while AI has a promising ability to generate accessible medical information.Indian Journal of Otolaryngology and Head &amp; Neck Surgery["Ariana L. Shaari", "Shreya Bhalla", "Annie Xu", "Aman M Patel", "Andrey Filimonov", "Wayne Hsueh", "J. A. Eloy"]2024-11-02T00:00:00 86a575adf106baea0186209ebbcd0ae32464a447Artificial intelligence (AI) plays a significant role in the educational sector, offering lecturers and students innovative ways of teaching and learning, assessment, acquiring skills, communicating, sharing, creating, grading and interacting with learning materials. Unfortunately, Nigerian universities are yet to fully explore AI in their educational activities, which may be due to inadequate awareness, lecturers' attitudes, lack of self-efficacy, opposition to change, and lack of adequate preparedness to utilise AI. Hence, this study assessed lecturers' awareness of artificial intelligence for education in a Nigerian university. The study adopted a descriptive survey research design. A sample of 271 lecturers was selected using a proportionate stratified random sampling technique. The study was guided by two research questions and a corresponding research hypothesis. A researcher-designed structured questionnaire was used for data collection, which four experts validated. The questionnaire was pilot-tested, and the data obtained were subjected to statistical analysis using the Cronbach alpha correlation formula, and a reliability coefficient of 0.87 was obtained. Descriptive statistics of mean and standard deviation were used to answer the research questions. The findings of the study revealed that lecturers are aware of AI, with a grand mean of 2.57. Independent samples t-test analysis showed that t = 1.047, p > 0.05, indicating no significant difference in the mean response of male and female university lecturers' level of awareness of artificial intelligence for education. In light of the findings, it was recommended that conferences, seminars and workshops should be organised for lectures to increase their level of awareness of the numerous opportunities that AI can provide in augmenting their educational activities, enabling an environment with adequate facilities that will enable lecturers to acquire adequate knowledge on the use of AI should be provided by education stakeholders.Educational Technology Quarterly510It was recommended that conferences, seminars and workshops should be organised for lectures to increase lecturers' level of awareness of the numerous opportunities that AI can provide in augmenting their educational activities, and an environment with adequate facilities that will enable lecturers to acquire adequate knowledge on the use of AI should be provided by education stakeholders.Educational Technology Quarterly["Glory Thomas", "A. Gambari", "H. Yusuf", "Mukhtar Oluwafemi Abanikannda", "Florence Olutunu Daramola"]2024-11-02T00:00:00 7df703d46edb818ffd19d3b43bdccba24f25fa2cThis research paper explores the transformative role of artificial intelligence (AI) in enhancing the efficiency and effectiveness of global internal audit functions. As businesses increasingly adopt AI-driven technologies, internal auditing has witnessed significant advancements in data analysis, risk detection, compliance monitoring, and decision-making processes. The paper analyzes how AI tools like machine learning, natural language processing, and predictive analytics contribute to the automation of repetitive audit tasks, the detection of anomalies, and the improvement of audit accuracy and timeliness. Additionally, it addresses the challenges associated with AI adoption, including data privacy concerns, skills gaps among auditors, and the integration of AI into existing audit frameworks. The study also provides a comparative analysis of AI-enabled versus traditional audit practices, highlighting AI’s potential to enhance audit quality, reduce operational costs, and provide deeper insights into financial and non-financial risks. By examining case studies and industry practices, the paper emphasizes AI’s critical role in shaping the future of internal auditing on a global scale. The findings suggest that AI’s integration into internal audits is not just a trend but a necessary evolution for achieving optimal audit outcomes.Asian Journal of Logistics Management810The paper analyzes how AI tools like machine learning, natural language processing, and predictive analytics contribute to the automation of repetitive audit tasks, the detection of anomalies, and the improvement of audit accuracy and timeliness.Asian Journal of Logistics Management["Iyad Ghafar", "Widya Perwitasari", "Rama Kurnia"]2024-11-02T00:00:00 6e6b4b61b36ceeb82b4487ba67af30bdb8894f00Artificial intelligence (henceforth, AI) is one of the most remarkable topics on social media platforms. The current study aims to investigate the representation of AI in Bill Gates’ social media content to uncover the hidden ideology of one of the most influential figures in the field of AI technology. Furthermore, critical discourse analysis (henceforth, CDA) examines the relationship between language, ideology, and power in various social and cultural contexts. The study aims to answer the following questions: 1- What are the lexical devices that are used to represent Artificial Intelligence (henceforth, AI) in Gates' social media content to construct the "self "and the "other"? 2- How is intertextaulity utilized in social media in terms of ideology and the construction of "self" and "other"? The researcher forms an eclectic model of CDA using Fairclough’s (2001) three-dimensional model and Van Dijk’s (1995) ideological square model. Based on the findings, the study concludes that the examination of lexical devices reveals the way the "self" represents his AI technology and its benefits to the world. While the representation of "other" is illustrated in the environment that surrounds AI technology.International Journal of Language and Literary Studies00The study concludes that the examination of lexical devices reveals the way the "self" represents his AI technology and its benefits to the world.International Journal of Language and Literary Studies["Tabarek Alashtary"]2024-11-02T00:00:00 8fc5e3ddbed2edb63e2176abc83079436e0f2fa6Minds Mach.590This paper argues for the following claims: since factors such as race or sex are not morally significant in themselves, including such factors in the input data, or relying on output that includes such factors or is correlated with them, is neither objectionable nor commendable in itself.Minds Mach.["Re\u2019em Segev"]2024-11-02T00:00:00 c1ebf35fdcfbf7f3a2b84438fb51a0e4526d9ef3Comput. Biol. Medicine570AI can improve the cost-effectiveness and cost-utility of screenings, especially when process reengineering is performed, and is strongly recommended when AI is implemented.Computers in biology and medicine["Senlin Lin", "Yingyan Ma", "Liping Li", "Yanwei Jiang", "Yajun Peng", "Tao Yu", "Dan Qian", "Yi Xu", "Lina Lu", "Yingyao Chen", "Haidong Zou"]2024-11-02T00:00:00 3ca5b5c532ba7149ee53110747778607cecbb269International Journal of Academic Research in Economics and Management Sciences00International Journal of Academic Research in Economics and Management Sciences["Mohammed Afandi Zainal", "M. E. M. Mohd Matore"]2024-11-02T00:00:00 e7f7ba9e163272ca473af6fc77aa6aa4b34e417fScientific Reports131A comparative analysis of the word frequency and statistical characteristics of the text is presented, which provides convincing evidence that ChatGPT employs distinctive vocabulary and paragraph structure compared to human-authored text.Scientific Reports["Yijun Zhao", "Alexander Borelli", "Fernando Martinez", "Haoran Xue", "Gary M. Weiss"]2024-11-02T00:00:00 afbe55c3db4650273a0655a8e71b5f6426877304Humanities and Social Sciences Communications390It is suggested that lower-efficiency AA might outperform higher-efficiency ones due to the constraining influence of trust on adoption rates and that lower initial trust in AA could lead to increased usage in certain scenarios and that stronger emotional and social responses to the use of AA may foster greater trust but result in decreased AA utilisation.Humanities and Social Sciences Communications["Ivan \u00d0ula", "Tabea Berberena", "Ksenia Keplinger", "Maria Wirzberger"]2024-11-02T00:00:00 2c6423853c120ff46084b77291ebf6967f646217Integrating artificial intelligence (AI) technologies into neurology promises increased patient access, engagement, and quality of care, as well as improved quality of work life for clinicians. While most studies have focused on comparing AI models to expert performance, we argue for a more practical approach: demonstrating how AI can augment clinical practice. This article presents a framework for pragmatic AI augmentation, addressing the shortage in neurology practices, exploring the potential of AI in opportunistic screening, and encouraging the concept of AI serving as a "co-pilot" in neurology. We discuss recommendations for future studies designed to emphasize human-computer collaboration, ensuring AI enhances rather than replaces clinical expertise.Aging and Disease220A framework for pragmatic AI augmentation is presented, addressing the shortage in neurology practices, exploring the potential of AI in opportunistic screening, and encouraging the concept of AI serving as a "co-pilot" in neurology.Aging and disease["Krish Kapadia", "Sanskriti Ruwali", "Tanvi Malav", "Sridhar Seshadri", "Abraham Seidmann", "Daniel Z. Press", "V. Kolachalama"]2024-11-02T00:00:00 990fff0e4ba0f423fbe97cd28c7fb17e4c8e522bThe integration of Agentic Artificial Intelligence (AI) within Predictive AIOps (Artificial Intelligence for IT Operations) is revolutionizing the management of IT systems, significantly enhancing IT autonomy and performance (Smith & Johnson, 2023). This article explores the potential of Agentic AI to empower AIOps platforms in proactively predicting, identifying, and resolving system issues. By leveraging predictive analytics and machine learning, AIOps not only enhances operational efficiency but also minimizes downtime and supports autonomous decision-making in complex IT environments (Lee et al., 2022). We examine the key roles that Agentic AI plays in improving performance metrics, optimizing resource allocation, and reducing the reliance on human intervention in critical system operations (Garcia & Patel, 2024). Additionally, this study investigates the implications for IT infrastructure scalability, long-term resilience, and the evolution toward self-governing systems (Chen, 2023). The findings underscore the transformative impact of Agentic AI on future IT operations, showcasing its potential to foster higher levels of automation and operational intelligence.International Journal of Scientific Research and Management270The potential of Agentic AI to empower AIOps platforms in proactively predicting, identifying, and resolving system issues is explored and the implications for IT infrastructure scalability, long-term resilience, and the evolution toward self-governing systems are investigated.International Journal of Scientific Research and Management (IJSRM)["Shanmugasundaram Sivakumar"]2024-11-02T00:00:00 0c4ae4f4bb469b42277c52e1f63b6bc6143ebea2Artificial Intelligence (AI) is reshaping the healthcare landscape by enhancing early disease detection and improving diagnostic accuracy. By leveraging machine learning and deep learning techniques, AI can process vast amounts of medical data, identify patterns, and assist clinicians in making faster, more accurate diagnoses. This paper examines the role of AI in medical diagnostics, with a focus on early detection of chronic diseases, such as cancer and cardiovascular conditions, through case studies. It also highlights the challenges, including data privacy concerns, algorithmic bias, and the need for regulatory frameworks to ensure safe AI implementation. Despite these hurdles, AI-powered diagnostics hold the potential to revolutionize healthcare by reducing costs, improving patient outcomes, and advancing personalized medicine. Keywords: Artificial Intelligence (AI), Early Disease Detection, AI Diagnostics, Machine Learning, Deep Learning.Research Output Journal of Biological and Applied Science140The role of AI in medical diagnostics is examined, with a focus on early detection of chronic diseases, such as cancer and cardiovascular conditions, through case studies, and the challenges including data privacy concerns, algorithmic bias, and the need for regulatory frameworks to ensure safe AI implementation are highlighted.Research Output Journal of Biological and Applied Science["M. Emmanuel K"]2024-11-02T00:00:00 8046e3fc4f76b59adbd533c01ee66d3c6b336d93This paper argues for the strategic treatment of artificial intelligence as a key industry within broader industrial policy framework of Pakistan, underscoring the importance of aligning it with national goals such as economic resilience and preservation of autonomy. The paper starts with defining industrial policy as a set of targeted government interventions to shape specific sectors for strategic outcomes and argues for its application to AI in Pakistan due to its huge potential, the risks of unregulated adoption, and prevailing market inefficiencies. The paper conceptualizes AI as a layered ecosystem, comprising foundational infrastructure, core computing, development platforms, and service and product layers, supported by education, government policy, and research and development. The analysis highlights that AI sector of Pakistan is predominantly service oriented, with limited product innovation and dependence on foreign technologies, posing risks to economic independence, national security, and employment. To address these challenges, the paper recommends educational reforms, support for local AI product development, initiatives for indigenous cloud and hardware capabilities, and public-private collaborations on foundational models. Additionally, it advocates for public procurement policies and infrastructure incentives to foster local solutions and reduce reliance on foreign providers. This strategy aims to position Pakistan as a competitive, autonomous player in the global AI ecosystem.arXiv.org550This paper argues for the strategic treatment of artificial intelligence as a key industry within broader industrial policy framework of Pakistan, underscoring the importance of aligning it with national goals such as economic resilience and preservation of autonomy, to position Pakistan as a competitive, autonomous player in the global AI ecosystem.ArXiv["Atif Hussain", "Rana Rizwan"]2024-11-02T00:00:00 f832d3782e7055ab120f1c963d43147e55800366Timely support after referral to mental healthcare is crucial, yet patients often face prolonged wait times without intervention. Digital mental health interventions offer scalable solutions, but many struggle to achieve acceptable patient engagement. Tailoring and personalizing materials to individual needs is paramount for driving engagement, a task that generative artificial intelligence AI (genAI) is potentially able to achieve. To examine this promise, we conducted a randomized controlled trial using a genAI-enabled therapy app, Limbic Care, which delivers personalized cognitive behavioral therapy (CBT) materials, against PDF workbooks delivering static CBT content, as commonly used in standard care. Adults with elevated symptoms of anxiety or depression (N = 540) were randomly assigned to the app or control group for six weeks. The app group exhibited a threefold increase in engagement (2.4 times higher usage frequency, 3.8 times longer usage durations). While both groups showed similar overall symptom improvement, participants who engaged with the app's clinical personalization capabilities experienced significantly greater reductions in anxiety symptoms and enhanced well-being than those who engaged with the standard CBT materials. Importantly, the app was safe, with no increase in adverse events compared to standard care. Our findings suggest that genAI-enabled therapy apps can safely enhance patient engagement and improve clinical outcomes through clinically personalized interventions.medRxiv30It is suggested that genAI-enabled therapy apps can safely enhance patient engagement and improve clinical outcomes through clinically personalized interventions.["J. McFadyen", "J. Habicht", "Larisa-Maria Dina", "Ross Harper", "Tobias U Hauser", "Max Rollwage", "ID ClinicalTrials.gov"]2024-11-02T00:00:00 b66e56e069f645b1f3e781b4e181b669e77d7b46A pesquisa e produção de medicamentos são processos complexos e custosos, historicamente baseados em métodos empíricos e experimentais. Recentemente, avanços na inteligência artificial (IA) têm prometido transformar a indústria farmacêutica, permitindo processos mais eficientes e econômicos por meio de análises de dados, aprendizado de máquina e deep learning. Este estudo utilizou uma revisão integrativa de literatura, incluindo 20 artigos selecionados através de buscas em bases como BVS, SciELO, PubMed, EbscoHost e Google Scholar. Os critérios de inclusão consideraram artigos publicados nos últimos 4 anos, em português, inglês ou espanhol, que abordassem o uso de IA na descoberta e produção de fármacos. A análise destacou que a IA está acelerando significativamente a descoberta de medicamentos, melhorando a eficiência, reduzindo custos e otimizando o tempo de produção. Exemplos incluem a análise de grandes conjuntos de dados, modelagem molecular avançada e prevenção de toxicidade. A pandemia de COVID-19 evidenciou o papel crucial da IA na resposta rápida e eficaz através de colaborações interdisciplinares e modelos de aprendizado profundo. Em síntese, a IA está revolucionando a farmacologia ao acelerar a descoberta de novos tratamentos e personalizar a medicina. No entanto, é essencial enfrentar desafios como a adaptação dos profissionais de saúde, a implementação de regulamentações adequadas e a garantia de ética no uso da IA. Colaborações amplas e compartilhamento transparente de dados são fundamentais para maximizar os benefícios dessa tecnologia emergente.Brazilian Journal of Implantology and Health Sciences00Brazilian Journal of Implantology and Health Sciences["Camila Andrea de Souza", "Layla Gabriela Kamouh Sain\u00e7a", "Vanessa Caroline Guimar\u00e3es Cortes", "Nat\u00e1lia Filardi Tafuri"]2024-11-02T00:00:00 c6d901b0c7f0b128e887e6ac818988b514114aa7Digital research workflows are study designs that intentionally consider the use of technology in meaningful and reflexive ways. While most scholars use digital tools and spaces in their research process, doing so has consequences that are infrequently considered in an intentional way. Especially in this age of generative AI, technology integration into research studies will have an even greater impact and consequences on study outcomes. This paper documents one digital research workflow, the academic writing process, to demonstrate how inviting generative AI to be a writing partner can be done in a reflexive manner. Drawing on Paulus and Lester’s (2023) technological reflexivity framework, we emphasize the need to assess the use and impact of platforms such as ChatGPT on four dimensions of the academic writing workflow: writing methods, writers and their audience, writing outcomes, and the generative AI platform itself. We structure this use case according to Woolf and Silver’s (2018) notions of “strategies” and “tactics,” applied to the academic writing process, combined with Paulus and Lester’s four consequence categories. We include recommendations for navigating and using generative artificial intelligence in future academic writing endeavors. The Qualitative Report00This paper documents one digital research workflow, the academic writing process, to demonstrate how inviting generative AI to be a writing partner can be done in a reflexive manner and includes recommendations for navigating and using generative artificial intelligence in future academic writing endeavors.The Qualitative Report["Corey W. Johnson", "Treena M. Paulus"]2024-11-02T00:00:00 ef816dd1894cba47045b71355b7ac0aabf99d994This study delves into the factors that drive teachers’ adoption of generative artificial intelligence (GenAI) technologies in higher education. Anchored by the technology acceptance model (TAM), the research expands its inquiry by integrating the constructs of intelligent technological pedagogical content knowledge (TPACK), AI literacy, and perceived trust. Data were gathered from a sample of 237 university teachers through a structured questionnaire. The study employed structural equation modeling (SEM) to determine the relationships among the constructs. The results revealed that both AI literacy and perceived ease were the most influential factors affecting teachers’ acceptance of GenAI. Notably, intelligent TPACK and perceived trust were found to be pivotal mediators in this relationship. The findings underscore the importance of fostering AI literacy and adapting intelligent TPACK frameworks to better equip educators in the age of AI. Furthermore, there is a clear need for targeted professional development initiatives focusing on practical training that enhances AI literacy. These programs should provide hands-on experience with GenAI tools, boosting educators’ confidence and ability to integrate them into their teaching practices.Education sciences621The results revealed that both AI literacy and perceived ease were the most influential factors affecting teachers’ acceptance of GenAI, and the importance of fostering AI literacy and adapting intelligent TPACK frameworks to better equip educators in the age of AI.Education Sciences["A. Al-Abdullatif"]2024-11-03T00:00:00 ffd30b49e2b3d424af26b6a426b75423ef5690ef“Specialized, Refined, Unique, and Innovative” (SRUI) enterprises are a crucial part of the national innovation system and play a significant role in enhancing national core competitiveness by leveraging strengths and addressing weaknesses. Given the limited quantitative analysis on the productivity of SRUI enterprises in the academic field, this paper constructs an enterprise‐level artificial intelligence index based on text analysis and machine learning. Using panel data from 2018 to 2022, we investigate the impact of artificial intelligence on the productivity of SRUI small‐ and medium‐sized enterprises (SMEs). The research findings indicate that (1) artificial intelligence significantly enhances the productivity of SRUI SMEs, and for each one standard deviation increase in artificial intelligence, the productivity level of SRUI enterprises will increase by 6.82%. (2) Mechanism tests reveal that artificial intelligence improves productivity by optimizing labor structure, improving labor resource allocation, stimulating endogenous motivation and innovation vitality within enterprises, and enhancing management level and investment efficiency. (3) Heterogeneity analysis indicates that at the regional level, artificial intelligence significantly boosts the productivity of enterprises with well‐developed network infrastructure and robust intellectual property protection systems. At the industry level, AI has a more pronounced effect on the productivity of technology‐intensive and competitive industries. Additionally, we also find that the impact of AI on productivity is more significant in enterprises with younger executive teams and executives with digital and intelligent education backgrounds. This study expands the research scope of productivity and provides valuable insights for the government to optimize digital economy policies and for enterprises to formulate digital innovation strategies.Managerial and Decision Economics270An enterprise‐level artificial intelligence index based on text analysis and machine learning is constructed and it is found that the impact of AI on productivity is more significant in enterprises with younger executive teams and executives with digital and intelligent education backgrounds.Managerial and Decision Economics["Baizhen Zhang", "Biyu Peng"]2024-11-03T00:00:00 50c410456f961641b1638fee09df3e569f0f9857Cancer immunotherapy has revolutionized cancer treatment by leveraging the immune system to target malignant cells, yet resistance in many cancers highlights the need for novel therapeutic targets. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools for identifying new immunotherapy targets by analyzing vast datasets from genomics, proteomics, and clinical studies. This review explores the role of AI and ML in advancing the discovery of cancer-specific immunotherapy targets, such as tumor antigens and immune pathways. Key advances include the integration of big data, neoantigen prediction, biomarker discovery, and single-cell analysis. Despite these advancements, challenges remain, including data quality and standardization, interpretability of AI models, computational costs, and the need for biological validation of AI-driven discoveries. As AI and ML technologies continue to evolve, they hold the potential to overcome these barriers, leading to personalized immunotherapy solutions. This review also discusses future directions for AI-driven immunotherapy, emphasizing the need for improved models, ethical considerations, and clinical integration. Keywords: Artificial Intelligence, Machine Learning, Immunotherapy, Cancer, Advances, Challenges, Future DirectionsResearch Output Journal of Public Health and Medicine00The role of AI and ML in advancing the discovery of cancer-specific immunotherapy targets, such as tumor antigens and immune pathways is explored, and the need for biological validation of AI-driven discoveries is discussed.Research Output Journal of Public Health and Medicine["Kungu Erisa"]2024-11-03T00:00:00 14393d172d54e2c6c96e5f6d693a5cd24c596f65We present a framework for viewing artificial intelligence (AI) as planetary assemblages of coloniality that reproduce dependencies in how it co-constitutes and structures a tiered global data economy. We use assemblage thinking to map the coloniality of power to demonstrate how AI stratifies across knowledge, geographies, and bodies to influence development and economic trajectories, impact workers, reframe domestic industrial policies, and reconfigure the international political economy. Our post-colonial framework unpacks AI through its (1) global, (2) meso, and (3) local layers, and further dissects how these layers are vertically integrated, each with its horizontal dependencies. At (1) the global layer of international political economy maps a new digital bipolarity expressing Sino and American global digital corporations’ strategic and dominant positions in shaping a tiered global data economy. Then, at (2) the meso layer, we have a mosaic of domestic industrial policies that fund, frame markets, and develop AI talent across industries, sectors, and organizations to competitively integrate into AI value chains. Finally, incorporating into these are (3) the localized labor processes and tasks, where workers and users enact various AI-mediated tasks and practices driving further value extraction. We traced how AI is an interlaced system of power that reshapes knowledge, geographies, and bodies into dependencies that reinforce stratifications in developing underdevelopment. This commentary maps the current digital realities by laying out an uneven techno-geoeconomic power architecture driving a tiered global data economy and opening new research avenues to examine AI as planetary assemblages of coloniality.Big Data & Society250This commentary maps the current digital realities by laying out an uneven techno-geoeconomic power architecture driving a tiered global data economy and opening new research avenues to examine AI as planetary assemblages of coloniality.Big Data Soc.["Kai-Hsin Hung"]2024-11-03T00:00:00 25cd63196d9c9e2f2999876026eb96287fa90c39Artificial intelligence (AI) has bloomed in recent years and is gradually becoming an irreplaceable asset in finance,among other sectors. Personal finance is a subset of finance, which too is being revolutionized due to changing timesand technological advancements, much like AI. Security and proper financial guidance have never been moreimportant with such significant change. In this study, we use FinBERT, a modern large language model specializedin the financial domain, for our AI-powered personal finance coach. However, FinBERT, although a cut above therest, still has room for growth, so we aim to improve its flaws and enhance its efficiency. We established thatFinBERT succeeded in detecting sentiments in explicit sentiments, but was not usually successful in doing socorrectly for implicit sentiments. FinBERT, despite its limitations, has a high accuracy and is the best model to usein our study. This model can also be utilised to provide accurate results regarding the overall trend (positive ornegative) of the global stock market. Our results demonstrate that integrating AI in personal finances is feasible andcan successfully aid individuals in making decisions regarding their finances.International Journal Of Engineering And Computer Science00The results demonstrate that integrating AI in personal finances is feasible and can successfully aid individuals in making decisions regarding their finances.International Journal of Engineering and Computer Science["Aimen Mushtaq", "Ranvitha Chirumamilla", "Pranav Kadiyala", "Ruthwik Guntupalli", "Vatsal Goel", "Ayush Chauhan", "Kritika Verma"]2024-11-03T00:00:00 2d8609e02097c53cbf1cabf5ff31d1743caf43f0This study aims to explore the implementation of Artificial Intelligence (AI) in the recruitment process in the era of Industry 4.0, focusing on the benefits and challenges faced by companies. Utilizing a qualitative approach through in-depth interviews with recruitment teams, human resource managers, and candidates, the research finds that the adoption of AI brings several advantages, including enhanced efficiency in the selection process, improved screening accuracy, and an enriched candidate experience. However, significant challenges also arise, such as privacy and data security issues, a lack of transparency in algorithms, and regulatory uncertainties that may affect the adoption of this technology. The findings highlight the need for companies to address these challenges through the development of robust data protection policies, regular monitoring of algorithms, and educating both candidates and recruitment teams about AI usage. This research aims to provide insights for organizations in implementing AI ethically and effectively in the recruitment process.J-MAS (Jurnal Manajemen dan Sains)190The research finds that the adoption of AI brings several advantages, including enhanced efficiency in the selection process, improved screening accuracy, and an enriched candidate experience, but significant challenges also arise, such as privacy and data security issues, a lack of transparency in algorithms, and regulatory uncertainties that may affect the adoption of this technology.J-MAS (Jurnal Manajemen dan Sains)["Ahmad Firdaus"]2024-11-03T00:00:00 bbe7dc411e35cfd3dfe6abf60fa6fd2a47e2731cThis paper introduces a novel explainable artificial intelligence (XAI) framework aimed at efficiently enhancing CPU turbo yield by optimizing wafer acceptance test (WAT) parameters. This framework utilizes the Extreme Gradient Boosting (XGBoost) algorithm in the yield prediction model and incorporates Shapley Additive Explanations (SHAP) to explain the model's decision-making process. By calculating SHAP value, the importance of WAT parameters in the turbo yield prediction model is determined, enabling the optimization of the manufacturing process to improve yield. A comparative analysis is conducted between our proposed approach and the traditional methodology of Pearson correlation coefficient. In the Pearson correlation analysis, saturation current of WAT parameters is identified as the primary factor affecting turbo yield. However, it is observed turbo yield begins to degrade at high saturation current levels due to failing power constraints. The limitations of the traditional method in considering non-linear data correlation and multi-dimensional interactions of WAT parameters impede yield enhancement. In contrast, our method is employed to address these challenges. In the importance analysis of our proposed method, metal sheet resistance is identified as the primary factor impacting CPU turbo yield. Notably, our method accurately identifies the 6th metal wire from the 8 layers of metal wire in this product design, demonstrating its ability to highlight parameters related to the manufacturing backend-of-line process, even in complex and diverse IC layout designs of advanced node products. To validate the effectiveness of our XAI framework, production lots with adjustments to 6th metal wire are intentionally processed. The results confirm a 15% enhancement in turbo yield with a +2σ adjustment in metal sheet resistance. Overall, this framework offers an effective and comprehensive method for enhancing CPU turbo yield in the production stage.International Test Conference40This paper introduces a novel explainable artificial intelligence (XAI) framework aimed at efficiently enhancing CPU turbo yield by optimizing wafer acceptance test (WAT) parameters and incorporates Shapley Additive Explanations to explain the model's decision-making process.2024 IEEE International Test Conference (ITC)["C. W. Lin", "P. C. Tsao", "Ross Lee", "K. Koh", "Y. J. Ting", "Jennifer Hsiao", "C. Lai", "T. H. Lee"]2024-11-03T00:00:00 dfc10e94aebd9af65f6068a666057b9fa26ae462Embedded artificial intelligence education is an important field these days. It is considered a research area with the goal of developing embedded Artificial Intelligence academic learning tools. This paper presents a comprehensive literature review on this topic, based on a search conducted in the Scopus database. There were no restrictions placed on the duration of the search could continue. The collected data showed that the majority of publications were released in 2023, with 14.08% of the documents in Scopus being in English. The United States had a higher share of publications. Expanding the search with a broader search string to include more publications and analyzing the retrieved data is crucial for advancing research in this area. This approach also provides other researchers with a quick overview of the field, aiding in the design of future studies.2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE)90A comprehensive literature review on embedded artificial intelligence education is presented, based on a search conducted in the Scopus database, which showed that the majority of publications were released in 2023, with 14.08% of the documents in Scopus being in English.2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE)["Mimoun Lamrini", "Soukaina Benabdelouahab", "M. Chkouri", "A. Touhafi"]2024-11-03T00:00:00 15232be7bf059aaf7915790d2f6884ce3a2c2eb7Artificial intelligence (AI) refers to computer systems crafted to replicate human cognitive functions such as learning, perception, and problem-solving. Recently, AI technologies have gained widespread popularity across various domains including healthcare and finance. They have also significantly impacted visual design. This study investigates the burgeoning influence and utilization of AI technologies in visual design, delving into their effects and applications. It scrutinizes how AI innovations transform visual design processes, influence creativity, and shape design practices. The research emphasizes the emergence of AI-powered design tools, their impact on designer creativity, and the potential advantages and drawbacks they pose to creative processes. Furthermore, it examines the ethical and practical dimensions inherent in integrating AI into design practices. By thoroughly assessing AI's impact on visual design, the study aims to prognosticate future technological advancements in this realm.Medeniyet Sanat Dergisi00This study investigates the burgeoning influence and utilization of AI technologies in visual design, delving into their effects and applications and examines the ethical and practical dimensions inherent in integrating AI into design practices.Medeniyet Sanat Dergisi["Merve Karaman"]2024-11-03T00:00:00 9e39e2b4a4b5944f87546b7dd31f7deff522a9b8Background Deep learning can extract predictive and prognostic biomarkers from histopathology whole slide images, but its interpretability remains elusive. Methods We develop and validate MoPaDi (Morphing histoPathology Diffusion), which generates counterfactual mechanistic explanations. MoPaDi uses diffusion autoencoders to manipulate pathology image patches and flip their biomarker status by changing the morphology. Importantly, MoPaDi includes multiple instance learning for weakly supervised problems. We validate our method on four datasets classifying tissue types, cancer types within different organs, center of slide origin, and a biomarker – microsatellite instability. Counterfactual transitions were evaluated through pathologists’ user studies and quantitative cell analysis. Results MoPaDi achieves excellent image reconstruction quality (multiscale structural similarity index measure 0.966–0.992) and good classification performance (AUCs 0.76–0.98). In a blinded user study for tissue-type counterfactuals, counterfactual images were realistic (63.3–73.3% of original images identified correctly). For other tasks, pathologists identified meaningful morphological features from counterfactual images. Conclusion MoPaDi generates realistic counterfactual explanations that reveal key morphological features driving deep learning model predictions in histopathology, improving interpretability.bioRxiv560MoPaDi (Morphing histoPathology Diffusion), which generates realistic counterfactual explanations that reveal key morphological features driving deep learning model predictions in histopathology, improving interpretability.bioRxiv["Laura \u017digutyt\u0117", "Tim Lenz", "T. Han", "K. Hewitt", "N. Reitsam", "S. Foersch", "Zunamys I. Carrero", "Michaela Unger", "Alexander T. Pearson", "Daniel Truhn", "J. N. Kather"]2024-11-03T00:00:00 2a9290c6e6accc987f2be82dc649246a9a0002a4This is a scientific article whose theme is the impact of artificial intelligence on society in light of the General Data Protection Law. The LGPD defines the liability of data processing agents, who are controllers and operators, for damages caused by the exercise of the processing activity, in a manner similar to the system of the Consumer Defense Code (CDC). By providing citizens with rights over their data, the LGPD also tends to increase public transparency regarding data collection and analysis. Thus, it will not be restricted to private companies; in the public sphere, the State will also have to be clearer about how it handles citizen data. The aforementioned law establishes rules for the storage, sharing, use, and collection of citizen data, whether by public or private companies. The article aims to demonstrate how the uncontrolled use of artificial intelligence affects society as a whole, such as the new personal data protection legislation, protecting users and democracy, as well as highlighting the main difficulties faced by companies in the process of adapting and complying with the LGDP standards and the legal limitations of Artificial Intelligence. As a theoretical basis, articles, conventions and laws that corroborate the Brazilian legal system were adopted. The methodology used was a descriptive and exploratory literature review.Revista ft00Revista ft["Carlos Henrique de Lima", "M\u00e1rcio Pereira Bassani"]2024-11-03T00:00:00 a779b69a94241cd472cd36f3640755b8e96178bbThe rapid development of technology has significantly transformed sectors such as banking and finance. With financial transactions becoming more complex and frequent, the need for advanced fraud detection and prevention systems has become critical. This paper explores how artificial immune systems (AIS) and machine learning can enhance fraud detection in banking. Inspired by the human immune system, AIS offers innovative methods to identify and reduce fraud, ensuring the security of financial transactions. The paper emphasizes the crucial role of artificial intelligence in this transformation. The focus is on a proposed model that combines AIS, SMOTE (Synthetic Minority Oversampling Technique), and XGBoost to improve fraud detection efficiency. When applied to a dataset comprising both fraudulent and non-fraudulent banking operations, the model yielded promising results.EDIS300A proposed model that combines AIS, SMOTE (Synthetic Minority Oversampling Technique), and XGBoost to improve fraud detection efficiency yielded promising results when applied to a dataset comprising both fraudulent and non-fraudulent banking operations.2024 4th International Conference on Embedded & Distributed Systems (EDiS)["Ahmed Slimani", "Abdellatif Rahmoun"]2024-11-03T00:00:00 8a1f91658be7ab8e62e5e84e3ddd6ff5f0017a13Recently we have been hearing a lot about artificial intelligence and how it can influence the ways we are doing things, for the better or the worse. In particular, the effects it has on education and learning has received a lot of attention. This paper focuses on if indeed AI can be used as a useful tool in tackling engineering problems. Here, based on my limited experience with AI but a long history of teaching engineering courses I have come to some conclusions about if AI can become helpful for learning engineering subjects, in which the physics of matters play an important role. Although this work is limited more to engineering problems, it can well apply to other disciplines, such as biology, economics, and some other subjects.The main question is if AI can provide us with ways for better teaching or facilitates the ways students learn a subject. In this regard, first it must be reliable and trustworthy. It is shown that the present performance of AI is not acceptable/satisfactory to provide correct and reliable help for education of engineering subjects.2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE)51It is shown that the present performance of AI is not acceptable/satisfactory to provide correct and reliable help for education of engineering subjects and first it must be reliable and trustworthy.2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE)["Ahmad Hemami"]2024-11-03T00:00:00 816c01bc8d0936a150c00b27716b6e3cd49032ddThis scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and the consequences of AIHA for health equity. Using the PRISMA-ScR guidelines, databases were searched between 2020 and 2024 using the terms racial and ethnic bias in health algorithms resulting in a final sample of 23 sources. Suggestions for how to mitigate algorithmic bias were compiled and evaluated, roles played by stakeholders were identified, and governance and stewardship plans for AIHA were examined. While AIHA represent a significant breakthrough in predictive analytics and treatment optimization, regularly outperforming humans in diagnostic precision and accuracy, they also present serious challenges to patient privacy, data security, institutional transparency, and health equity. Evidence from extant sources including those in this review showed that AIHA carry the potential to perpetuate health inequities. While the current study considered AIHA in the US, the use of AIHA carries implications for global health equity.Ethnicity and Health511Evidence from extant sources showed that AIHA carry the potential to perpetuate health inequities, which presents serious challenges to patient privacy, data security, institutional transparency, and health equity.Ethnicity & health["S. A. Hussain", "M. Bresnahan", "Zhuang Jie"]2024-11-03T00:00:00 238411b81e1c97d09e8d31e1b196459883384948The rapid adoption of Artificial Intelligence (AI) in higher education is reshaping students’ learning experiences, with tools such as ChatGPT, Grammarly, and Microsoft Copilot becoming integral to academic work. This study, informed by data from the Digital Education Council Global AI Student Survey 2024, examines the impact of AI on students, focusing on usage patterns, trust in AI-generated content, ethical awareness, and expectations for institutional support. Findings indicate that 86% of students use AI for various academic tasks, with a majority expressing concerns about trust, fairness, and over-reliance on AI. While students value AI’s benefits, only 5% are fully aware of institutional guidelines on AI use, and 72% desire more AI literacy courses, reflecting a significant need for comprehensive support in navigating AI responsibly. The study underscores the importance of clear ethical guidelines, faculty training, and student involvement in AI policy formation to foster responsible AI use and preserve academic integrity. These insights offer valuable guidance for educators and policymakers seeking to integrate AI ethically and effectively into higher education.Journal of Information Technology, Cybersecurity, and Artificial Intelligence151The study underscores the importance of clear ethical guidelines, faculty training, and student involvement in AI policy formation to foster responsible AI use and preserve academic integrity and offers valuable guidance for educators and policymakers seeking to integrate AI ethically and effectively into higher education.Journal of Information Technology, Cybersecurity, and Artificial Intelligence["Ahmed Al Zaidy"]2024-11-03T00:00:00 04879b35c086d06590a88464550bc6d0a7a27069This is research examines how the study program curriculum influences the competency of accounting students. This research also examines the influences of the study program curriculunm and accounting student competencies on accounting students’ confidence in working with Artificial Intelligence (AI) so that their role is not completely replaced by AI. This research uses descriptive research with a quantitative approach carried out by distributing questionnaires to Bachelor of Accounting students at Stikubank University Semarang. Sampling used a purposive random sampling technique by collecting a sample of 116 respondents. This results show that the curriculum influences student competence. Apart from that, the research results show that the curriculum and competencies influence student’s self-confidence. Universities need a curriculum that provides various practice-related cases to improve competency related to the ability and operation of accounting software so that students have high self-confidence.AL-KHARAJ00University need a curriculum that provides various practice-related cases to improve competency related to the ability and operation of accounting software so that students have high self-confidence.Al-Kharaj: Jurnal Ekonomi, Keuangan &amp; Bisnis Syariah["Faradila Rahma", "Pancawati Hardiningsih"]2024-11-03T00:00:00 938c9b60d245bd2db5b7027cffd5fc0d25935756In this study, we studied the implementation of an artificial intelligence (AI) data platform to support medical AI semiconductor research and training for fostering AI semiconductor experts. The proposed AI data platform has two parts to implementing comprehensive and adaptive infrastructure for AI semiconductor research and training. First, the medical AI solution section that deals with data platform and AI platform where is medical data and high-performance computing environment for medical AI solution development. Next is the AI semiconductor section where a diverse infrastructure to built-up the AI semiconductor design platform provides fabless AI semiconductor design and verification, thereby providing the outstanding training environments. Finally, we achieved comprehensive infrastructure for medical AI semiconductor research and training based on AI data platform.2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)100This study studied the implementation of an artificial intelligence data platform to support medical AI semiconductor research and training for fostering AI semiconductor experts and achieved comprehensive infrastructure for medical AI semiconductor research and training based on AI data platform.2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)["Erdenebayar Urtnasan", "Sungwook Ha", "Nam Yeong Lee", "Sang Wan Cho", "Jaesoo Kim"]2024-11-03T00:00:00 cd7e3918f5fab6602c84f47783fc52430ef61b1cThe rapid advancement of Artificial Intelligence (AI) technologies is transforming education, particularly in Electrical and Electronic Engineering (EEE). This paper explores the potential applications, benefits, and challenges of Generative AI (GenAI) and Large Language Models (LLMs) in EEE education. Key areas include personalized learning, intelligent tutoring systems, automated grading, and predictive analytics. While these technologies offer significant enhancements in teaching and learning, they also present challenges such as data privacy, bias, and the need for human interaction. By examining current implementations and providing recommendations, this paper aims to guide educators and researchers in effectively integrating AI to improve EEE education.2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE)410The potential applications, benefits, and challenges of Generative AI (GenAI) and Large Language Models (LLMs) in EEE education are explored and recommendations in effectively integrating AI to improve EEE education are provided.2024 IEEE 11th International Conference on E-Learning in Industrial Electronics (ICELIE)["Jiaqin Sun", "C. Kwong", "G. Buticchi"]2024-11-03T00:00:00 467b4ff3fcea5fe94bd01e8cfe38b4f9de593bbbThis research aims to identify the factors influencing AI adoption in supply chain to resolve supply chain disruptions. It is undeniable that the adoption of AI in the supply chain could be essential to resolve supply chain disruptions. Supply chain disruptions could be defined as unexpected events such as earthquakes, the COVID-19 pandemic, and the Suez Canal crisis. To promote the adoption of AI in supply chain management and leverage its benefits, it is important to investigate the factors influencing AI adoption in supply chain to resolve supply chain disruptions. This research explores the multifaceted dynamics of adopting artificial intelligence (AI) in supply chain management through the lens of the Technology-Organization-Environment (TOE) framework. Drawing from a comprehensive review of existing literature, the study identifies critical factors influencing AI adoption across three key contexts: technological, organizational, and environmental. Within the technological context, compatibility and complexity emerge as pivotal factors, facilitating seamless integration of AI with existing systems while addressing implementation challenges through comprehensive training and user-friendly interfaces. Organizational factors, particularly top management support, are found to play a decisive role in driving AI initiatives by ensuring strategicInternational Journal of Academic Research in Business and Social Sciences350This research explores the multifaceted dynamics of adopting artificial intelligence in supply chain management through the lens of the Technology-Organization-Environment (TOE) framework and identifies critical factors influencing AI adoption across three key contexts: technological, organizational, and environmental.International Journal of Academic Research in Business and Social Sciences["Suzari Abdul Rahim", "Nor Aida Abdul Rahman", "Aidi Ahmi", "Muhammad Waheed"]2024-11-03T00:00:00 c80d722467637a07bffd730f8cb19be61c940082This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.Non human journal00Findings indicate that AI-driven process optimizations have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies, and the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy.Non human journal["Anisur Rahman"]2024-11-03T00:00:00 d469fd9dc70f4e49149872ae56e592739f80619dIn the context of burgeoning cyber threats targeting Supervisory Control and Data Acquisition (SCADA) systems, this paper presents a pioneering Intrusion Detection System (IDS) grounded in Generative Adversarial Networks (GANs). The ubiquity of information technology in critical industrial processes necessitates heightened security measures, and the proposed model addresses this imperative through the fusion of GANs and IDS tailored for SCADA environments. The study delves into the evolutionary trajectory of Intrusion Detection Systems, tracing their progression from conventional signature-based methods to the adaptive capabilities afforded by artificial intelligence, particularly deep learning. Against this backdrop, the paper investigates the vulnerabilities of SCADA systems, elucidating the sophisticated attack vectors identified in recent studies. The distributed nature of SCADA systems, deployed across extensive industrial landscapes, accentuates the need for robust intrusion detection mechanisms. This research's core innovation lies in applying a GAN model to SCADA systems, leveraging the distributed network protocol version 3 (DNP3) for data access authorization. The model is trained on a dataset comprising 12 meticulously selected features from DNP3 packets, attuning it to the nuances of SCADA network traffic. Notably, the proposed GAN-based IDS demonstrates exceptional efficacy, achieving impressive accuracy. Beyond the empirical contributions, the paper envisions future directions for integrating explainable AI techniques and enhancing the interpretability of IDS models specific to SCADA environments. Moreover, collaboration between academia and industry is advocated to foster the development of comprehensive datasets that represent SCADA network traffic.2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)220The study delves into the evolutionary trajectory of Intrusion Detection Systems, tracing their progression from conventional signature-based methods to the adaptive capabilities afforded by artificial intelligence, particularly deep learning.2024 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)["Hong Nhung Nguyen", "Thi Lan-Phan", "Chai-Jong Song"]2024-11-03T00:00:00 911bc5ca00acb54d78593efc2e4e59664188a083El objetivo de esta investigación, fue interpretar las percepciones que subyacen en el docente sobre el aprendizaje adaptativo mediante inteligencia artificial en la enseñanza de las ciencias naturales, para tal fin se asumió el paradigma interpretativo bajo un enfoque cualitativo; Como técnica de levantamiento de la información se aplicó la entrevista semiestructurada, como instrumento se utilizó una guía de entrevista, mediante cuatro (4) interrogantes; La unidad de análisis estuvo conformada por cinco (5) docentes que dictan la asignatura de ciencias naturales para los estudiantes de educación general básica; Con el fin de interpretar la información suministrada por los docentes, se se utilizó como técnica de análisis, la categorización, que dio lugar a la emergencia de las siguientes categorías con sus respectivas subcategorías; Aprendizaje personalizado (necesidades individuales, adaptación de los contenidos), Inclusión (igualdad de oportunidades, barreras físicas), Aprendizaje experiencial (el estudiante como protagonista de su aprendizaje, aprender a través de la experiencia) Retroalimentación (retroalimentación personalizada, recomendaciones personalizadas); Se concluye que el aprendizaje adaptativo mediante inteligencia artificial en la enseñanza de las ciencias naturales, es elemental para crear un entorno educativo más inclusivo, personalizado y efectivo en la enseñanza de las ciencias naturales; Es fundamental que los docentes aprovechen estas herramientas para diseñar experiencias de aprendizaje significativas, donde los estudiantes puedan poner en práctica sus conocimientos, resolver problemas reales y desarrollar habilidades del siglo XXI como la creatividad, la colaboración y la resolución de problemas.Reincisol.00Reincisol.["Christian Ampudia Iza", "Marco Vinicio Yanqui Crespo", "Galecio Francisco Ullauri Jaramillo.", "Miguel Angel Vill\u00f3n Luc\u00edn"]2024-11-03T00:00:00 8a5e532d9289b3f5103e6e8611bf49b3480350cbThis review emphasizes the fascinating convergence of Neuro-Symbolic AI (NeSy) and Compositional Generalization (CoGe), examining how these models might potentially transform AI by enabling real human-like intelligence. This research contends that NeSy's capacity to combine the advantages of neural and symbolic techniques has enormous potential for addressing the CoGe dilemma. CoGe necessitates the ability to learn and use information in unexpected settings through the flexible assembly of existing building components. NeSy architectures, with their distinct combination of symbolic reasoning and flexible learning, provide a viable option for overcoming this critical hurdle. In this paper, we highlighted some of the most important concepts of both NeSy and CoGe, showcasing the cutting-edge research trends shaping these fields, we delve into their diverse techniques and methods. Drawing upon cognitive science studies and concrete AI-based works, we illustrate the multitude of possibilities for implementing CoGe within NeSy. Finally, we discuss the results performed by these studies, their commonalities, we then present our proposition and address the open challenges that lie ahead on the path towards true CoGe with NeSy.EDIS240This research contends that NeSy's capacity to combine the advantages of neural and symbolic techniques has enormous potential for addressing the CoGe dilemma, and illustrates the multitude of possibilities for implementing CoGe within NeSy.2024 4th International Conference on Embedded & Distributed Systems (EDiS)["Lateb Nassim", "Florence S\u00e8des", "Farida Bouarab-Dahmani"]2024-11-03T00:00:00 c00e57d14e918753055005bb31315822743019baIn the era of digital transformation, artificial intelligence (AI) technology—one of the swiftest growing emerging technologies—when integrated with urban planning, can introduce innovative approaches for low-carbon city development and foster the attainment of dual carbon objectives: carbon neutrality and peak carbon emissions. Current research predominantly investigates the influence and alterations of emerging technologies on urban elements, yet it overlooks a comprehensive examination of the applicable procedures of these technologies and their potential synergy with urban planning. Consequently, this study employs a systematic literature review to delve into the application of AI in sectors such as architecture, transportation, land use, and green space development. It categorizes the specific impact processes into monitoring, identification, simulation, and prediction. By offering an exhaustive analysis of urban planning’s content and methodology, this paper elucidates the role of AI technology in the creation of low-carbon cities. The study found that: (1) Due to the varying degrees of application and integration with professional technologies in different fields, the current research focuses more on architecture, land use, and transportation. (2) Combining the four steps of urban planning, artificial intelligence can be divided into monitoring, recognition, simulation, and prediction types, each with its own characteristics. (3) Overall, AI technology is mainly applied in the identification and simulation of architecture, transportation, and land use. (4) There is still room for improvement in the application of AI technology in waste emissions and other algorithms.Land861The role of AI technology in the creation of low-carbon cities is elucidates by offering an exhaustive analysis of urban planning’s content and methodology.Land["Fengying Yan", "Xinran Qi"]2024-11-04T00:00:00 980d16f0338755de129f042732d6e7d34884fae2This study aims to examine scientific studies on artificial intelligence (AI) in educational research from the past to the present, based on the Web of Science database. In this context, 1465 scientific articles containing AI in education from the past to the present were evaluated. Articles accessed from the WoS database were examined using a bibliometric analysis method according to productivity, network analyses, conceptual structure, and thematic map titles. Within the scope of productivity, authors, institutions, countries, citations within the scope of network analysis, authors, institutions, sources, and countries were included in the analysis. In addition, thematic changes over the years, word cloud, collaborations, conceptual formations, and thematic mapping were carried out based on keywords. In this context, 1465 scientific articles published by 3783 authors representing 86 countries were included in the research. According to the research findings, the number of studies and citations on AI in education has increased significantly, especially in the last five years. The Education University and The Chinese University of Hong Kong stand out as productive institutions. While China, England, and the USA stand out as the countries of responsible authors, Hwang, G. J., stands out as the author of network analysis, and the Computer Education journal stands out as the journal. As a thematic change in the studies, there has been an evolution towards new technological developments such as deep learning, machine learning, ChatGPT, chatbots, learning analytics, blockchain, and generative AI. According to the factor analysis conducted on the conceptual structure of AI-related studies in education, it was determined that it explained 48% of the total variability. According to the study findings, studies on AI applications in education should be enriched from a disciplinary perspective, and efficiency should be increased regarding their reflections on teaching.Sakarya University Journal of Education491According to the study findings, studies on AI applications in education should be enriched from a disciplinary perspective, and efficiency should be increased regarding their reflections on teaching.Sakarya University Journal of Education["D. A. Kaya"]2024-11-04T00:00:00 ba459c3dbcbd426b4fb5b3d8400671e318ef330fThe emergence of artificial intelligence is reshaping the landscape of accounting and auditing education, necessitating significant adaptation to meet the challenges posed by this technological revolution. This study investigated the impact of artificial intelligence on the skillsets required for accounting and auditing professionals and explored the implications for the institutions of higher education. This study employed a qualitative research design, incorporating a systematic literature review and SWOT analysis. Findings showed that artificial intelligence enhances the efficiency, transparency, promptness, and accuracy of financial reporting, compelling accounting professionals to transition from conventional roles to more strategic functions that involve data analysis and decision-making. In auditing, artificial intelligence technologies enhance audit quality and enable auditors to focus on value-added tasks, such as risk assessment and advisory services. Despite the benefits, challenges such as resistance to change, organisational culture, workforce adaptation, privacy issues, and prohibitive costs of implementing artificial intelligence are significant barriers to integration. The findings highlighted a growing trend toward artificial intelligence adoption, with most organisations expected to implement or pilot artificial intelligence solutions soon, underscoring the need for continuous learning and skill upgrades among professionals. The results underscored the urgent need for educational reform within accounting and auditing curricula. Institutions of higher education must incorporate artificial intelligence-related competences, emphasising data analytics, critical thinking, and ethical considerations regarding technology use. Collaborative efforts among institutions of higher education, professional organisations, regulators, and the business community are vital for developing a workforce equipped to thrive in an artificial intelligence-driven environment. The practical value of this study lies in offering actionable insights for educational institutions, professional organisations, and regulatory bodies to adapt their curricula and training programmes, equipping accounting and auditing professionals with the necessary AI-related skills for future practiceEconomics Entrepreneurship Management01Findings showed that artificial intelligence enhances the efficiency, transparency, promptness, and accuracy of financial reporting, compelling accounting professionals to transition from conventional roles to more strategic functions that involve data analysis and decision-making.Economics, Entrepreneurship, Management["Vira Shevchuk", "Yuriy Radelytskyy"]2024-11-04T00:00:00 27dd115000b6f42569543f4bb077d4854284be92Our aging population, diabetes, and obesity have fueled the growth of chronic wounds seen throughout the world. Often, wounds are a marker of poor health that leads to increased mortality rates. However, the diagnosis and treatment of these wounds are challenging. Incorrectly differentiating between chronic wounds and other complex conditions can lead to adverse events. Artificial intelligence (AI) has been shown to offer some early benefits, and we hypothesized that it may enhance wound care but also carry some notable risks. We performed a detailed search using PubMed, Scopus, Cumulated Index in Nursing and Allied Health Literature, and Web of Science for AI applications in wound care. AI was found to be applied to wound diagnosis and characterization, wound monitoring for tissue change, daily therapy, and prevention and prognostics. AI made for more efficient and accurate wound assessments, less painful assessments of chronic wounds, more personalized treatment, and improved prognostic prediction capabilities. AI also allowed for more precise at-home observation and care, facilitating earlier wound treatment as needed. Challenges associated with AI included how to best allocate AI-assisted technologies equitably, how to safely maintain patient data, and how to diversify datasets for algorithm training. Because the algorithms are not transparent, validating findings may be challenging. AI presents a powerful tool in several aspects of advanced wound care and has the potential to improve diagnoses, accelerate healing, reduce pain, and improve the cost-effectiveness of wound care. More research needs to be done into how to best incorporate AI into daily clinical practice while keeping clinicians aware of the potential risks of using these evolving technologies.Artificial Intelligence Surgery482Artificial intelligence presents a powerful tool in several aspects of advanced wound care and has the potential to improve diagnoses, accelerate healing, reduce pain, and improve the cost-effectiveness of wound care.Artificial Intelligence Surgery["Ovya Ganesan", "Miranda Xiao Morris", "Lifei Guo", "Dennis P. Orgill"]2024-11-04T00:00:00 82c26332d0e3a58a19a4cb0cc5005cfc37b7a37dArtificial intelligence (AI) has the potential to disrupt the advertising industry as marketers and brands can leverage its power to create highly engaging personalized content. However, the usage of AI is prone to bias and misinformation and can be used to manipulate. Therefore, various lawmakers such as the European Union aim to enforce AI disclosure messages to protect consumers. But the implications of such disclosures have not yet been studied. This paper draws on existing theories in persuasion knowledge, disclosure theory, inferences of manipulative intent, and AI aversion to develop a model to understand consumer attitudes toward AI disclosures in Instagram advertisements. A three-condition between-subjects online experiment ( Nfinal  = 161) was conducted to test the model. The data were analyzed using a moderated mediation model. AI disclosures lead to a direct decrease in advertising attitude. In addition, AI disclosures lead to a decrease in brand attitude only when consumers have high AI aversion. There were no effects of AI disclosures on source credibility. These effects were mediated by inferences of manipulative intent. However, participants who viewed the AI disclosure had lower inferences of manipulative intent and then participants who did not view the AI disclosure. Furthermore, no differences were found between AI disclosures pertaining to the use of AI in the creation of the image or the text. Implications are discussed from both theoretical and managerial viewpoints and highlight why the use of AI on social media for advertising purposes should be limited as it will become more transparent in the future.Emerging Media421A model is developed to understand consumer attitudes toward AI disclosures in Instagram advertisements and highlights why the use of AI on social media for advertising purposes should be limited as it will become more transparent in the future.Emerging Media["Caroline Wortel", "I. Vanwesenbeeck", "F. Tomas"]2024-11-04T00:00:00 616f6dfa174520786536159af0ff97bf483d7e38The integration of Artificial Intelligence (AI) in electrical machine design and maintenance has opened new pathways for achieving enhanced performance, efficiency, and sustainability across various applications. This review explores the current landscape of AI-driven methodologies for motor design, predictive maintenance, and control optimization, analyzing the impact of these technologies on electrical machine capabilities. The study identifies prevalent AI approaches-including machine learning, optimization algorithms, and neural networks-detailing their application, benefits, and limitations in improving motor efficiency and reliability. Despite the advancements, challenges remain, particularly in terms of model complexity, interpretability, and economic feasibility. Moreover, the societal and environmental implications of AI-driven innovations in electrical engineering are considered, with a focus on aligning future research with sustainable development goals. This paper provides a comprehensive overview of the state of AI in electrical machine applications, emphasizing the potential of AI to transform the field while addressing the barriers that must be overcome for widespread implementation.2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)280The study identifies prevalent AI approaches-including machine learning, optimization algorithms, and neural networks-detailing their application, benefits, and limitations in improving motor efficiency and reliability, and addresses the barriers that must be overcome for widespread implementation.2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)["Tlhokaboyo Innocntia Mokwana", "Bessie Monchusi"]2024-11-04T00:00:00 381069eeb9b5aae52f6a914bd949373af9825b6ePurpose The purpose of this paper is to define artificial intelligence (AI) and examine its history, positive and negative impacts, ethical and social implications and implementation within management education. This paper offers various suggestions for the use of AI, as well as context surrounding the current AI landscape. Design/methodology/approach The paper uses a narrative review (Sylvester et al., 2013). Findings This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution. In addition, there are significant concerns regarding the lack of ethical guidelines with current AI. Practical implications Practical implications include the ability to immediately use certain AI tools to enhance lesson plans as well as enhance student work using AI as a tool. Originality/value This paper was originally created as a conference presentation and presented at the society for advancement of management (SAM) International Business Conference before being reworked to be submitted to the journal. All content in this paper is original in their creation. SAM Advanced Management Journal190This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution.SAM Advanced Management Journal["Jared Scott Cook", "Jack Cook"]2024-11-04T00:00:00 a5d0890d25da564ed41331947150a795272c565c The use of Artificial Intelligence (AI) in the supply chain management (SCM) of liquefied natural gas (LNG) is an important turning point with far reaching impact on the industry. It focuses on how AI technologies can help optimize different aspects of the LNG supply chain such as demand forecasting, inventory control, route optimization and risk management. The study looks into how it facilitates visibility across the entire supply chain, manage risks associated with international sanctions and embargoes and assesses its influence on energy efficiency and emissions. Employing mixed-method approach that includes qualitative interviews, case studies and quantitative analysis, this research provides comprehensive insights as well as actionable recommendations. The findings indicate that AI has potential to revolutionize LNG SCM through increased operational efficiencies, lower costs, and more sustainable/resilient operations.ADIPEC160ADIPEC["Nicy Susan Koshy"]2024-11-04T00:00:00 08b968f6cc3f301bca7a3da04cf867d1c91dded3Abstract: About the use of Artificial Intelligence (AI) in the context of Teaching English as a Foreign Language (TEFL), numerous articles have discussed its impact, ranging from positive to negative effects. Although the majority of literature asserts that AI has a significantly positive impact, this research aims to evaluate and critically analyze these effects. The focus of this study is on the variables of critical thinking, the impact of AI on TEFL, and philosophical perspectives. This research employs a qualitative approach with document analysis methods, beginning with the selection of documents based on authenticity, credibility, representativeness, and meaning factors. The research findings support the positive effects of using AI in TEFL, emphasizing the improvement of students' English language skills in speaking and writing, particularly in terms of grammar. However, the research highlights the importance of specific conditions that must be met for these positive impacts to be achieved. A critical analysis of AI's impact on TEFL reflects the necessity of incorporating critical thinking in this research to avoid uncertainties. The study also explores philosophical views related to ontology, epistemology, and axiology. In the ontological perspective, AI aids students in understanding existence and reality in language learning, especially concerning vocabulary, phrases, and grammar. From an epistemological perspective, AI serves as a foundation for students' knowledge acquisition by presenting tailored learning materials and supporting a deep understanding of linguistics. Meanwhile, from the axiological perspective, the use of AI creates positive values such as accessibility, diversity, and equality in education.Journal of English Language Learning00The research findings support the positive effects of using AI in TEFL, emphasizing the improvement of students' English language skills in speaking and writing, particularly in terms of grammar, but highlights the importance of specific conditions that must be met for these positive impacts to be achieved.Journal of English Language Learning["Muslim Fikri"]2024-11-04T00:00:00 3cd6b4393e0ad422fe04f48341432a581b6e7ddfBackground/Aims: Diabetic retinopathy (DR) screening using artificial intelligence (AI) has evolved significantly over the past decade. This study aimed to analyze research trends, developments, and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis. Methods: The study analyzed 1,172 publications from the Web of Science Core Collection database using CiteSpace and Microsoft Excel. The analysis included publication trends, citation patterns, institutional collaborations, and keyword emergence analysis. Results: Publications showed consistent growth from 2014-2022, with a peak in 2021. India (26%), China (20.05%), and USA (9.98%) dominated research output. IEEE ACCESS was the leading publication venue with 44 articles. Research evolved from traditional image processing to deep learning approaches, with recent emphasis on multimodal AI models. The analysis identified three distinct phases: CNN-based systems (2014-2020), Vision Transformers and innovative learning paradigms (2020-2022), and large foundation models (2022-2024). Conclusion: The field shows mature development in traditional AI approaches while transitioning toward multimodal learning technologies. Future directions indicate increased focus on telemedicine integration, innovative AI algorithms, and real-world implementation.medRxiv160The field shows mature development in traditional AI approaches while transitioning toward multimodal learning technologies, and future directions indicate increased focus on telemedicine integration, innovative AI algorithms, and real-world implementation.["Y. Huang", "Y. Qi", "C. Liu", "F. Jing", "C. Li", "M. Wang", "C. Zhu", "P. Gui", "Z. Ge", "X. Han"]2024-11-04T00:00:00 bd65d6d976e8724043bec43aae5dbe412045a2ceThe article examines the foreign experience of digitalization of justice and the development of artificial intelligence in the context of the transition to an electronic format of criminal cases. In conclusion, the authors conclude that, given the predominance of the conservative view of law enforcement officers and the legislator of the Russian Federation, only minor steps are currently taking place towards the digitalization of justice in Russia. At the same time, it was found that the introduction of various information systems and automated workplaces into practical activities, which involved the creation of a single system and a wide-ranging network, did not occur, due to the lack of a single request from the system of investigative bodies and technical capabilities, as well as the absence of an urgent need to change the order of work on the part of investigators and interrogatorsRevista do Curso de Direito do UNIFOR00The article examines the foreign experience of digitalization of justice and the development of artificial intelligence in the context of the transition to an electronic format of criminal cases and concludes that only minor steps are currently taking place towards the digitalization of justice in Russia.Revista do Curso de Direito do UNIFOR["Alexander Volevodz", "Elena N. \u041aleshchina", "Sergey Nuikin", "Zlatoslava Khmeleva", "Alexander Tokolov"]2024-11-04T00:00:00 0f77b7ffd95073425744d99ca3c94bb5eeacfbbcArtificial intelligence (AI) has emerged as a transformative force in drug repurposing, offering innovative solutions to expedite the identification of new therapeutic uses for existing medications. This review explores the multifaceted applications of AI, particularly in the context of complex and rare diseases, where traditional drug development processes are often hindered by high costs and lengthy timelines. AI-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), facilitate the analysis of vast datasets from clinical trials, electronic health records, and scientific literature, enabling researchers to uncover novel drug-disease relationships. The integration of AI with genomics and proteomics further enhances the precision of drug repurposing efforts by identifying genetic and proteomic markers that predict patient responses to therapies. Despite its potential, the field faces challenges related to data quality, regulatory hurdles, and the need for interdisciplinary collaboration among researchers, clinicians, and policymakers. This review highlights recent advancements in AI applications for drug repurposing, emphasizing their role in addressing unmet medical needs, particularly in rare diseases where treatment options are limited. By harnessing the capabilities of AI, the drug repurposing landscape is poised for significant transformation, ultimately leading to more efficient pathways for delivering effective therapies to patients.Nanotechnology Perceptions570By harnessing the capabilities of AI, the drug repurposing landscape is poised for significant transformation, ultimately leading to more efficient pathways for delivering effective therapies to patients.Nanotechnology Perceptions["Humera Khanam", "Ranjana", "Anil Kumar", "Sanmati Kumar Jain", "Meenakshi Tyagi", "Archana Sharma", "Tanya Gupta"]2024-11-04T00:00:00 f254888855602877fad98db5cec53058215be5efPeople with autism spectrum disorder (ASD) may present, in addition to deficits in communication, social interaction and patterns of restricted and repetitive behaviors, also present a deficit in joint attention (JA), which refers to the response repertoire of following and/or directing an adult's visual attention to objects or events in the environment. By having a strong relationship with the learning process, joint attention deficits can compromise a person's learning process. In this way, the use of technology can help in the development of abilities in people with autism, such as, for example, improving joint attention, communication and social skills. In this context, the general objective of the work proposal was to develop a computational approach for intervention that allows the interaction of the student with autism, with 4 and 5 years old, with deficit in joint attention and social-communicative difficulties. Artificial intelligence (AI) techniques were used to model the most appropriate sequence and level of complexity of exercises for each child. AI resources were used with the intention of providing an intelligent environment to guide the child, dynamically and adaptively, in order to promote stimuli and adequate personalization of the process. In this way, it is intended to contribute significantly to the advancement of the state of the art regarding the production of computational technologies for people with ASD.Anais Estendidos do XIII Congresso Brasileiro de Informática na Educação (CBIE 2024)00The general objective of the work proposal was to develop a computational approach for intervention that allows the interaction of the student with autism, with 4 and 5 years old, with deficit in joint attention and social-communicative difficulties.Anais Estendidos do XIII Congresso Brasileiro de Informática na Educação (CBIE 2024)["Nathalia Assis Valentim", "F. A. Dor\u00e7a", "Val\u00e9ria Peres Asnis", "N. C. Elias"]2024-11-04T00:00:00 dd8c8f102041c9ded50cf9e9f3db6b9940609f0aThis systematic review, based on 37 articles, explores the role of artificial intelligence (AI) in criminal justice, focusing on its applications in predictive policing, judicial risk assessments, and surveillance, as well as the associated ethical and regulatory challenges. AI has demonstrated substantial potential for improving efficiency and accuracy in criminal justice systems, from optimizing law enforcement resource allocation to providing data-driven risk assessments that support judicial decisions. However, the review identifies significant ethical issues, especially related to algorithmic bias, which can perpetuate existing societal inequalities and disproportionately affect marginalized communities. Concerns around transparency and accountability are prevalent, as the "black-box" nature of many AI algorithms complicates public understanding and trust in AI-driven outcomes. Surveillance tools, including facial recognition and behavioral analysis, enhance real-time threat detection but raise privacy and civil rights concerns, highlighting the need for regulatory oversight. Gaps in legal frameworks suggest the urgency for standardized policies that address data privacy, algorithmic fairness, and accountability in AI applications. The findings underscore that interdisciplinary collaboration, transparent practices, and comprehensive regulatory measures are essential to responsibly integrate AI into criminal justice, balancing technological advancements with justice, equity, and public trust.Non human journal00Non human journal["Khairul Alam Talukder", "Touhida Ferdousi Shompa"]2024-11-04T00:00:00 f2a203b7e2730f7f38a82b9146bb5e6e83b2fb6cPURPOSE This project explores the perceived implications of artificial intelligence (AI) tools and generative language tools, like ChatGPT, on practice in speech-language pathology. METHOD A total of 107 clinician (n = 60) and student (n = 47) participants completed an 87-item survey that included Likert-style questions and open-ended qualitative responses. The survey explored participants' current frequency of use, experience with AI tools, ethical concerns, and concern with replacing clinicians, as well as likelihood to use in particular professional and clinical areas. Results were analyzed in the context of qualitative responses to typed-response open-ended questions. RESULTS A series of analyses indicated participants are somewhat knowledgeable and experienced with GPT software and other AI tools. Despite a positive outlook and the belief that AI tools are helpful for practice, programs like ChatGPT and other AI tools are infrequently used by speech-language pathologists and students for clinical purposes, mostly restricted to administrative tasks. CONCLUSION While impressions of GPT and other AI tools cite the beneficial ways that AI tools can enhance a clinician's workloads, participants indicate a hesitancy to use AI tools and call for institutional guidelines and training for its adoption.American Journal of Speech-Language Pathology320While impressions of GPT and other AI tools cite the beneficial ways that AI tools can enhance a clinician's workloads, participants indicate a hesitancy to use AI tools and call for institutional guidelines and training for its adoption.American journal of speech-language pathology["Julianna Austin", "Keith Benas", "Sara Caicedo", "Emily Imiolek", "Anna Piekutowski", "Iyad Ghanim"]2024-11-04T00:00:00 c652961dcf962b7ab9933faa85ca33a570815ca4 The paper aims to provide an overview of "Waste Management Solution," an Artificial Intelligence computer vision solution that can detect the location, classify, and quantify waste on a geospatial map constructed by aerial images collected with drones. The objective is to demonstrate how drones with integrated AI solutions can drive efficiency, productivity, and innovation in industrial operations. The solution comprises drones collecting aerial image data and implementing cloud-based AI/Machine learning (ML) models to detect waste materials. By integrating drone technology, AI, and mapping techniques, the solution supports industrial organizations, government authorities, and environmental agencies in achieving their net-zero goals, aligned with the Saudi Green Initiative 2030, and aiming to create a cleaner environment. The solution offers a cost-effective method for processing industries facilities and the environmental and urban planning of smart cities by digitizing waste management practices, replacing time-consuming manual industrial waste inspections. The benefits of the solution are demonstrated through our experience of an actual project conducted with the project management office of a large oil and gas operating company.ADIPEC40An overview of "Waste Management Solution," an Artificial Intelligence computer vision solution that can detect the location, classify, and quantify waste on a geospatial map constructed by aerial images collected with drones is provided.ADIPEC["Burri Harsha Vardhan Reddy", "Vijayaraghavan Ramakrishnan", "Maha Almuaikel", "Khalid Alanazi", "Ahmed Ali Alshaikh"]2024-11-04T00:00:00 288cf201d9a12f8d5f0822ebfcd874f92797c31dThis article explores the legal challenges in regulating artificial intelligence (AI) with a focus on privacy and data protection laws through a qualitative, comparative study. By employing a library research methodology, the study analyzes existing literature and legal frameworks across multiple jurisdictions to identify common trends, gaps, and inconsistencies in AI regulation. It investigates how various countries, including the European Union, the United States, and emerging economies, have developed or are developing legal instruments to address privacy concerns raised by AI technologies. The study highlights key issues such as the tension between technological innovation and regulatory constraints, the adequacy of current legal standards in addressing AI-specific risks, and the role of international cooperation in harmonizing data protection laws. The comparative analysis reveals significant divergences in how privacy is protected across different regions, particularly in the application of principles like consent, transparency, and accountability. Moreover, the article identifies critical gaps in existing frameworks, including the lack of clarity in AI accountability, data sovereignty, and enforcement mechanisms. The findings underscore the need for a more coherent and globally coordinated approach to regulating AI that balances privacy protection with technological advancement. Ultimately, the study calls for reforms that ensure robust privacy safeguards while fostering innovation in AI.International Journal of Social and Human120Key issues such as the tension between technological innovation and regulatory constraints, the adequacy of current legal standards in addressing AI-specific risks, and the role of international cooperation in harmonizing data protection laws are highlighted.International Journal of Social and Human["Miftakhul Huda", "Arif Awaludin", "Harrijun Kapabella Siregar"]2024-11-04T00:00:00 9641897561cf4ed25dae93fcf9ca0fcebc28c052Artificial intelligence (AI), which refers to technologies that mimic human cognition, affects many industries. Education is one of these sectors. Artificial intelligence affects many educational environments, from lectures to homework. In this process, both academic and ethical concerns call into question the future of artificial intelligence. These inquiries are essential as they show that the human factor will continue as an integral part of education. Because AI tools, even when best designed, can only partially replace human interaction or quality teaching. However, they can make the teacher's job easier and contribute to more effective learning. Therefore, teachers' awareness of this technology has become essential. This research aims to determine primary school teachers' opinions about using AI tools in education. The research was conducted using a case study. The participants are 16 primary school teachers determined by the criterion sampling method. Data were collected through a semi-structured interview form and analyzed with content analysis. According to the findings, teachers stated that AI tools may have advantages and disadvantages in educational environments. While teachers are concerned about the adverse effects of artificial intelligence tools on students, they also recognize their cognitive and socio-emotional contributions. Teachers also stated that artificial intelligence can make teachers' jobs easier but can only partially replace them. The results help understand primary school teachers' opinions regarding using artificial intelligence tools in the learning process.Sakarya University Journal of Education360Primary school teachers' opinions regarding using artificial intelligence tools in the learning process are determined to help understand primary school teachers' opinions regarding using artificial intelligence tools in the learning process.Sakarya University Journal of Education["Mustafa Erol", "A. Erol"]2024-11-04T00:00:00 6e0acce3f35b70a9bafcc9005ea93cefc97ba027 Some observers expect that the current wave of new tools based on artificial intelligence (AI) models, such as the large language models, will have strong effects on labor productivity. I present definitions and classifications that help understanding AI as an economic input. I then review theoretical and empirical arguments about macroeconomic productivity effects of AI and conclude that research has so far found no indication that productivity effects of the diffusion of AI are likely to be higher than those associated with the internet boom around the year 2000. While considerable uncertainty around future effects remains, a recent review and calibration exercise by Acemoglu, D. (2024. The Simple Macroeconomics of AI. Cambridge, MA: National Bureau of Economic Research, Working Paper 32487) suggests that the effects might be a lot lower.The Economists' Voice80It is concluded that research has so far found no indication that productivity effects of the diffusion of AI are likely to be higher than those associated with the internet boom around the year 2000.The Economists’ Voice["Marianne Saam"]2024-11-04T00:00:00 b375446431420280b96f48be3ba3993a9d0b9fa5 Geothermal operations face drilling challenges like stuck-pipe incidents, posing significant project risks. In this paper, we introduce a real-time monitoring system for early detection of natural fractures that correspond to permeable feed — subsurface areas where geothermal fluids enter the wellbore from surrounding rock formations. This system is integrated with the Stuck-Pipe Risk Advisor (SPRA), a novel tool we created that enhances drilling efficiency through causal artificial intelligence (AI) and semantic web technologies, aimed at revolutionizing geothermal drilling practices. Our approach integrates the real-time feed zone monitoring system with SPRA, to identify key risk factors for stuck-pipe incidents. The time-series analysis of drilling parameters like rate of penetration (ROP) and standpipe pressure (SPP) predicts the occurrence of feed zones, which triggers the mechanism to run the explainable decision support model and understand the causal relationships among the different formation and well properties in the proximity of feed zones. The system's use of semantic web technologies enhances transparency and aids in the comprehension of the predictive outputs, while a flow-diagram interface provides real-time visualizations of risk factors and potential interventions. Field application of this integrated workflow between the real-time monitoring system and SPRA, has demonstrated accurate prediction on the onset of feed zones, a key component in decreasing stuck pipe incidents. Observations confirm that proactive time series analysis of ROP and SPP across seven wells in three fields showed a 97% accuracy in early detection of feed zones that triggered the mechanism to predict risks of stuck pipe from the SPRA. Actionable insights based on a root-cause analysis diagram for drilling operators, provide real-time visualizations of risk factors, potential mitigations, and interactive explanations, empowering the wellsite team to reduce the frequency of stuck-pipe incidents, enhancing drilling efficiency, and contributing to the overall safety and success of geothermal drilling projects. The semantic annotations add further details to the causal links and risk factors involved, enhancing operational decision-making, and enabling continuous improvement for safe drilling practices. This integrated approach has been validated and endorsed by domain experts (drilling engineers and geologists), showing significant advancements in risk management and drilling efficiency. We present a pioneering neurosymbolic integration of causal AI with time-series analysis (neural) and semantic web technologies (symbolic) in geothermal drilling operations, marking a first in the industry. The SPRA's capability to predictively assess feed zones and preempt stuck pipe using real-time data analytics offers significant advancements in operational safety and efficiency. This innovative methodology not only improves hazard prevention but also provides engineers with actionable and understandable insights that enhance both immediate and long-term drilling operations success.ADIPEC80ADIPEC["S. A. Hussain", "Michael John Williams", "Maria Fernanda Vargas Izquierdo", "Panurach Dumrongthai", "N. M. Wilasari", "Maharani Devira Pramita", "Kenneth Lee Riedel", "Soumil Shah", "Fitria Anindita"]2024-11-04T00:00:00 d735f8c721af3d851ab92a6b1d09a741b807c629 The digital transformation of the energy sector enhances organizational performance, yet a significant skills gap can hinder an organization's digital aspirations. The industry faces fierce competition for artificial intelligence (AI) skilled professionals, as demand outstrips supply. Concurrently, petrotechnical experts are reassessing their roles due to concerns about skill relevance in the AI era. The achievements of one company's talent development and upskilling initiative illustrate one approach to preparing its workforce for the future. Domain expertise is crucial to understand and solve any industry problem. Our talent acquisition strategy emphasizes internal development over external recruitment. We train petrotechnical experts in AI, addressing both challenges effectively. The approach consists of two steps. The first is upskilling petrotechnical experts in our digital division to become data science practitioners, proficient in low-code and no-code AI solutions. The second step is participation in the newly developed domain data scientist program, in which selected experts are equipped with advanced data science and software development skills, followed by hands-on experience on a real-world use case. This intensive 6-month program, managed by technical experts, human resources, and line management, bridges the gap between domain-specific challenges and data science solutions, incorporating machine-learning operations practices. The program has yielded significant business outcomes across the industry's three pillars: people, technology, and performance. It has democratized AI expertise within the organization, resulting in a 50% increase in the data science workforce and reduced attrition among petrotechnical experts. This has led to substantial savings in recruitment costs. The program stands as an innovative model for scaling AI competency in the industry. Throughout its various stages, the program has facilitated more than 60 AI-powered innovation projects across the exploration and production life cycle and engaged with more than 300 stakeholders. The program has also fostered collaboration through external learning partnerships, addressing sustainability challenges such as emissions, and providing AI solutions for social issues. Following successful campaign phases, the adoption of data science learning has surged, involving more than 1,300 certified data science practitioners and more than 2,500 employees upgrading their skills. This comprehensive approach demonstrates the program's effectiveness in driving AI innovation, enhancing workforce skills, and achieving sustainable and social impact across the company. Since the inception of our AI democratization and upskilling program, our workforce, particularly domain experts, have been motivated to learn and apply data science and AI concepts to business use cases, overcoming previous barriers. AI has transformed from a perceived threat to an opportunity for improvement. This upskilling initiative accelerates AI adoption both internally and externally, promising substantial benefits for the industry.ADIPEC30The program has democratized AI expertise within the organization, resulting in a 50% increase in the data science workforce and reduced attrition among petrotechnical experts, and stands as an innovative model for scaling AI competency in the industry.ADIPEC["S. Shekhar", "N. Choudhary", "R. Saraiya", "J. Neff", "B. Zolnikov", "D. Almeida Costa", "S. Zulfiqar", "C. Chrysovulou", "P. Manceron"]2024-11-04T00:00:00 79de31899616cbe1ee59f1f0484816f8e4e5dae7 Artificial-intelligence (AI) driven systems are becoming the new standard in the drilling domain. This is the first field application performed offshore in Africa with the main objective of enhancing drilling operational efficiency and bottomhole assembly (BHA) integrity by advising the optimal drilling parameters, operational procedures, and drilling dysfunction mitigations. This smart system, driven by AI, seamlessly integrates the drilling operations planned engineering and procedures with the real-time operational data to monitor and control the operations execution using a data-driven approach. By design, this system enables the coordination and integration of the data and teams through a digitally orchestrated process, while the AI is primarily in control of the operations. The system's current and next steps can be easily visualized and overridden by the driller if and when needed. For this first application in Africa, close coordination and collaboration between the operator, the technology and service provider, and the rig contractor was a key element from the early stages of the project. A rig survey determined the most appropriate locations for the required equipment on the rig. Once the system was deployed and commissioned, the calibration was performed on the top-hole drilled sections, while the driller received hands-on training with the utilization of the system. The system seamlessly loaded the digital drilling program and recalibrated the operational parameters, which included the rate of penetration (ROP), revolutions per minute, weight on bit (WOB), and flow rates, using the real-time data. At the time of writing, a total of three wells, each with three sections were drilled using this application, resulting in continuous ROP improvement up to 2.5X factor on the last well. The combination of science-based models with real-time data running on the edge device at the rig enabled a step-change in the way AI-driven systems can drive automation, thereby enhancing the drilling operations. In this paper we will briefly describe the reasoning from the operator the decision to continue with this digital transformation of the operations process, and the technical requirements and process for the first successful deployment in Africa, with an overview of the results achieved. Moreover, we will also describe a novel workflow for a fully autonomous operation that combines several smart applications enabling a seamless digital collaboration between the office and the rig.ADIPEC00A novel workflow for a fully autonomous operation that combines several smart applications enabling a seamless digital collaboration between the office and the rig is described, thereby enhancing the drilling operations.ADIPEC["A. F. Zarra", "A. Medea", "L. P. Bianchini", "S. Borra", "E. Gravante", "W. Szemat-Vielma", "M. Banjo", "N. Mouzali", "E. Botnan"]2024-11-04T00:00:00 c0784441538cba4b717ebe917e0b1524eb40e37fBusinesses nowadays have access to an enormous amount of information. In this context, they put up much work to figure out how to use the data that is accessible. The amalgamation of Knowledge Management and Artificial Intelligence enables the development of technologies that respond to the expectations to extract implicit and explicit human and organizational knowledge to aggregate and amplify it. (Quinio et al., 2017). Organizations implementing AI are expected to attain added business value gains, such as increased revenue, cost reduction, and improved business efficiency (Sheibani et al., 2018). A recent MIT Sloan Management Review study found that more than 80% of businesses see AI as a calculated opportunity, and almost 85% see AI as a way to achieve comparative advantage (Ransbotham et al., 2017).Innovative Research Thoughts00The amalgamation of Knowledge Management and Artificial Intelligence enables the development of technologies that respond to the expectations to extract implicit and explicit human and organizational knowledge to aggregate and amplify it.Innovative Research Thoughts["Amrit Raj", "Ashna Puri"]2024-11-04T00:00:00 f3f955aebb3797cbd4998c02686813032fdd1afaThis paper delves into the role of artificial intelligence (AI) in urban design, focusing on its capability to manage complex urban systems via paradigm classification and comparative analysis. It methodically explores how AI improves design generation, optimizes morphology, and simulates real-world scenarios, thereby enhancing design quality and efficiency throughout the urban design process. Furthermore, the study contributes to the development of comprehensive AI evaluation criteria, integrating both theoretical and practical perspectives to critically understand the advantages and limitations of AI in urban design applications.Journal of planning literature700This paper methodically explores how AI improves design generation, optimizes morphology, and simulates real-world scenarios, thereby enhancing design quality and efficiency throughout the urban design process.Journal of Planning Literature["Hee Sun Choi", "Wang Zhang"]2024-11-04T00:00:00 61d60b61443bff06e0a1170eb527837342de582dInfluencer marketing has rapidly emerged as a cornerstone of digital advertising strategies, enabling brands to connect authentically with target audiences through trusted voices on social media platforms. However, measuring the effectiveness of these campaigns has long been a challenge due to the complexity of digital ecosystems and the sheer volume of data generated by user interactions. Big Data and Artificial Intelligence (AI) are revolutionizing the way brands evaluate influencer marketing campaigns, offering unprecedented insights into key performance metrics such as engagement, audience sentiment, and conversion rates. This paper explores how data analytics and AI tools are transforming the identification of influencers, the optimization of campaign performance, and the prediction of future trends. Furthermore, we address the ethical considerations surrounding data privacy and the transparency of AI-driven decisions in influencer marketing. Ultimately, this study highlights how Big Data and AI are not only enhancing the efficiency of influencer marketing but also reshaping the future of digital advertising as a whole.Scholars Journal of Economics Business and Management00This paper explores how data analytics and AI tools are transforming the identification of influencers, the optimization of campaign performance, and the prediction of future trends and addresses the ethical considerations surrounding data privacy and the transparency of AI-driven decisions in influencer marketing.Scholars Journal of Economics, Business and Management["Praveen Gujar", "Sriram Panyam", "Gunjan Paliwal", "Shashank Agarwal"]2024-11-04T00:00:00 8e138f543a08113d41a434d85baae9b17cf74c90This study examined the mechanisms of employing artificial intelligence in the Saudi press and its implications for the development of journalistic content. It aims to identify the extent to which artificial intelligence techniques are employed in the Saudi press, the most prominent techniques used, measure the extent of journalistic leaders’ awareness of the role of artificial intelligence techniques in the development of journalistic content, and reveal the most important Challenges facing the use of artificial intelligence techniques in Saudi journalism, and how to overcome them.  The importance of the study is highlighted by the fact that it examines the extent to which Saudi newspapers keep up with modern technologies to develop their journalistic content, and the extent of clarity of vision among Saudi press leaders regarding artificial intelligence technologies, and their importance in media transformation, especially in light of the technical challenges and rapid developments in the media industry.  The researcher relied on the survey method to monitor the opinions of journalistic and technical leaders in the newspapers of the study sample through in-depth, unstructured interviews, to obtain deeper knowledge about the variables of the study.  The study concluded that the use of artificial intelligence techniques in the Saudi press is very limited, and thus its weak reflection on the development of journalistic content. The results of the study revealed that there is a disparity in the level of awareness and knowledge of the role of artificial intelligence techniques in journalistic work, among editors-in-chief on the one hand, and between editors-in-chief and technology managers on the other hand, in addition to the lack of a clear vision and future strategies for Saudi press institutions in benefiting from artificial intelligence, which created a gap between the content presented in these newspapers and what the public is looking for. Moreover, the results of the study also showed that there are many challenges facing the use of artificial intelligence techniques in the Saudi press, the traditional way in which Saudi newspapers operate the inefficiency of applications in supporting the Arabic language, and the danger of fake information. Journal of Ecohumanism240The study concluded that the use of artificial intelligence techniques in the Saudi press is very limited, and thus its weak reflection on the development of journalistic content.Journal of Ecohumanism["Ali Mohammed Almania"]2024-11-04T00:00:00 c34ebeeb6036a730202d4e40a4296e0d865505adThe integration of Artificial Intelligence (AI) into Ayurveda marks a significant advancement in traditional medical practices. This review article explores AI's multifaceted uses and relevance in the conventional Healthcare industry and Ayurveda. It also highlights the transformative potential of AI in Ayurveda while acknowledging the importance of maintaining the holistic essence of traditional practices. By embracing AI, Ayurveda can evolve to meet contemporary healthcare demands, offering a synergistic approach that combines ancient wisdom with cutting-edge technology. The article concludes with a discussion about the future of Ayurveda in the era of Artificial Intelligence.Journal of Ayurveda and Integrated Medical Sciences00AI's multifaceted uses and relevance in the conventional Healthcare industry and Ayurveda are explored, offering a synergistic approach that combines ancient wisdom with cutting-edge technology.Journal of Ayurveda and Integrated Medical Sciences["Buvana V. Mundargi", "Vaishnavi Mutalikdesai", "Sajitha K", "Deepthi R", "Ahana Balakrishnan"]2024-11-04T00:00:00 88bc65c3343efc7f5e7c213313dd58b1197fc870The daily price in the energy market reflects the point at which supply and demand meet; however, this point is subject to significant variations due to the high volatility of both demand and supply. Therefore, the objective of this paper is to analyze, from a bibliometric perspective, how artificial intelligence can assist in forecasting energy demand.2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)400The objective of this paper is to analyze, from a bibliometric perspective, how artificial intelligence can assist in forecasting energy demand.2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)["Mattia Braggio", "Matteo Lo Russo"]2024-11-04T00:00:00 d1911a6d05a9cde0672f7740d27c50b7fc6c2063The publication of a monograph by Dr Luca Siliquini-Cinelli on the history of scientia iuris in which he argues that law is a constructed form of knowledge that differs from experience is not just an important and very learned contribution to historical jurisprudence. The book’s thesis is also making an important contribution to the debate about the impact, and probable future impact, of artificial intelligence (AI) on law, legal thought and legal reasoning. In critically reviewing the book, this essay will briefly indicate how and why Dr Siliquini-Cinelli’s book is establishing a fundamental relationship between historical jurisprudence (understood as the history of legal thought) and AI. Keywords: artificial intelligence (AI); epistemology; legal singularity; map; model; philosophy; rule-theorist; territory.Amicus Curiae120In critically reviewing the book, this essay will briefly indicate how and why Dr Siliquini-Cinelli's book is establishing a fundamental relationship between historical jurisprudence (understood as the history of legal thought) and AI.Amicus Curiae["Geoffrey Samuel"]2024-11-04T00:00:00 4f839e085fbd4da872369db1662fc8e631be94e8In recent years, the agricultural industry has experienced significant advancements through the integration of artificial intelligence (AI). The utilization of artificial neural networks and deep learning algorithms, in conjunction with Internet of Things devices and data analysis methodologies, holds significant potential for revolutionizing risk management approaches and practices in the field of agriculture. As a result, there has been a significant rise in scientific inquiries and published literature exploring the intersection of risk management and artificial intelligence in the agriculture sector. This academic paper examines the potential of AI in the field of risk management in the agriculture industry, as well as its implications for improving productivity, profitability, and sustainability. The utilization of AI technologies has led to significant progress in the domains of precision agriculture, resource management, and decision-making protocols. The findings of this study suggest that the incorporation of AI into risk management strategies in the agricultural industry holds promise for improving resilience and adaptability. Nevertheless, the application of AI holds the potential to improve agricultural practices, hence increasing output and promoting the development of sustainable and efficient food production systems. This article provides a comprehensive overview of the potential applications AI in risk management within the agriculture industry and examines the implications it holds for the future of the sector.Proceedings of the International Conference Competitiveness of Agro-Food and Environmental Economy410The findings of this study suggest that the incorporation of AI into risk management strategies in the agricultural industry holds promise for improving resilience and adaptability, and the implications it holds for the future of the sector are examined.Proceedings of the International Conference Competitiveness of Agro-Food and Environmental Economy["Nicolae Istudor", "R. Ignat", "Elena-M\u0103d\u0103lina Deaconu", "Marius Constantin"]2024-11-04T00:00:00 afbc6cc327b37f0dde7eecb77d694a510d549e96Introduction: Trauma remains a leading cause of mortality and disability globally, particularly among young adults. Despite advancements, accurate prediction of trauma outcomes remains challenging. Artificial intelligence (AI) and machine learning (ML) has the potential to detect patterns and analyzing complex relationship of data from trauma patients to support decision making in the management of trauma. This study aims to explore the potential of AI and ML to enhance trauma outcome prediction in order to healthcare workers with basic understanding of AI and ML and the potential for them to utilize it management of trauma patients. Methods: A literature review was conducted using studies from PubMed, SCOPUS, Cochrane, EBSCOHost, and ScienceDirect database using PRISMA protocol. Data extraction focused on study design, population, interventions, outcomes, and methodological quality using the Oxford Centre for Evidence-Based Medicine levels of evidence. Results: From the literature review, 15 studies from total of 585 studies were included in this literature review. XGBoost and Neural Networks were the most common algorithms (60%). While six studies predicted mortality, others focused on length of stay and discharge outcomes. Overall, ML significantly improved trauma diagnosis by enhancing predictive models for injury severity and mortality. Conclusions: Machine learning models have shown superior performance compared to traditional scoring systems in their ability to predict trauma outcomes and could effectively forecast critical outcomes and guide treatment decisions. Subsequent studies should concentrate on creating more resilient models based on prospectively gathered data to enhance the performance of created algorithms.JBN (Jurnal Bedah Nasional)00Overall, ML significantly improved trauma diagnosis by enhancing predictive models for injury severity and mortality by showing superior performance compared to traditional scoring systems in their ability to predict trauma outcomes.JBN (Jurnal Bedah Nasional)["Ivan Hisar Marolop Sihombing", "S. S. Panigoro"]2024-11-04T00:00:00 fdb1d40a8cf3df1740ee8e5812aacbcd7e770ccbThis study provides a comprehensive bibliometric review of Artificial Intelligence Generated Content (AIGC) applications in design and art, analysing publication trends from 2022 to 2024. Using data from the SCOPUS database, the study examines 472 publications, identifying key themes such as generative models, large language processing, and educational applications. The findings reveal a sharp increase in research interest, with prominent contributions from the United States and China. Major journals in this field include Documentation, Information & Knowledge and Computers and Education: Artificial Intelligence. This review highlights current research trajectories and underscores the need for ethical guidelines and improved models to ensure responsible integration of AIGC in creative industries. The insights gained here aim to inform future research directions, contributing to the sustainable and innovative application of AIGC in design and art.Nanotechnology Perceptions220This study provides a comprehensive bibliometric review of Artificial Intelligence Generated Content applications in design and art, analysing publication trends from 2022 to 2024, and identifies key themes such as generative models, large language processing, and educational applications.Nanotechnology Perceptions["Shuangping Ouyang", "N. Bakhir", "Lyu Ziwei\u00b3", "Xu Yang\u2074"]2024-11-04T00:00:00 9bd16f168492f35fa8458bad3482e7969d227b2fLaw and Critique60The limits of legal norms and institutions in holding to account the emerging power of Artificial Intelligence (AI) and Machine Learning are analyzed to demonstrate how a symbiosis of capitalism and new forms of digital power is mutating to produce novel and dangerous styles of organised irresponsibility.Law and Critique["Scott Veitch"]2024-11-04T00:00:00 a391408726e8c180f54cdc1f1d7da8f1ff76bf3a In the case of Offshore Construction through barges and construction works in general, whatever is the contracting strategy, one thing is common to all, which is human performance, this drives efficiency and makes the basis of all the development of Cost. There is a significant potential to enhance human efficiency. The project "CONSTRUCTION BARGE PERFORMANCE MANAGEMENT - ARTIFICIAL INTELLIGENCE BASED SOFTWARE" is development and implementation of AI/ML based software that analyzes components of human efficiency to identify training needs and establish targets for low performers to elevate their efficiency, thereby improving overall workforce efficiency and output. Huge data related to work requirements and workforce attributes is utilized by the software to suggest rational deployment of every worker at the most appropriate work location. In complex offshore construction, the gap between average and high productivity among workers is often due to lack of an effective work front. The software is designed to streamline work fronts, optimizing sequencing to shorten critical paths, attain effective work front and then balance the workforce with effective work front so derived. It analyzes non-productive time (NPT), suggests improvements and automates and eventually makes management autonomous. This approach aims to elevate productivity, pushing human efficiency and output to new heights. One of the major tools of this software is the point-based system to evaluate performance and skills. It generates points based on the skills and performance of the workforce to determine compensation. This system develops multi-skilled workers. The system identifies training needs and suggests appropriate courses. It also helps in identifying candidates for promotion by evaluating their performance and multiple skills, suggesting additional training or qualifications required for the next step in their career. Once the system is operated for one to two years it will have enough data to ensure fair compensation, identify training needs, fair promotions, improved behavioral based safety and effective counseling of workforce. Another important module of the software is deft handling of behavioral based safety data which aims to improve BBS and workers’ wellbeing. IoT cameras/sensors integrated with the system will be used in reducing HSE incidents and tracking and improving workers’ well-being.ADIPEC00This project is development and implementation of AI/ML based software that analyzes components of human efficiency to identify training needs and establish targets for low performers to elevate their efficiency, thereby improving overall workforce efficiency and output.ADIPEC["Shahid Ahmed Siddiqui"]2024-11-04T00:00:00 228ee7e10dfbc1895c657e882bd2e0c24e97364dThis paper examines the use of AI in optimizing SCM, particularly efficiency, sustainability, and resilience. The study uses algorithms driven by AI, including machine learning, predictive analytics, and multi-objective optimization to enhance decision-making processes and streamline SCM operations. These algorithms are Genetic Algorithm (GA), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Particle Swarm Optimization (PSO). All of these were applied to real-world data related to the supply chain. The results showed 23% savings in operational costs by GA, 18% better demand forecasting accuracy by ANN, 15% decrease in lead times by SVM, and 20% better supply chain flexibility by PSO. It was also demonstrated that AI, when integrated with blockchain technology, increases the resilience of supply chains. The disruptions are decreased by a significant 30%. When compared to related work, it becomes evident that the optimization potential of AI for SCM has been overlooked; this study has well delineated the step towards improved operational efficiency, cost cuts, and sustainability in the global supply chain. It gives the present scenario, the key challenges of an industry, the role AI played in meeting those demands, and how it holds future scope for development in most diversified industries.Nanotechnology Perceptions73The study uses algorithms driven by AI, including machine learning, predictive analytics, and multi-objective optimization to enhance decision-making processes and streamline SCM operations to enhance efficiency, sustainability, and resilience.Nanotechnology Perceptions["Amar Jyoti Borah", "Bidyut Kumar Das", "Sanjose A Thomas", "R. Saravanan", "Dr. Syed Salim", "Dr. K K Dhande"]2024-11-04T00:00:00 a08af3fe8015a2b4718645e78f93aeb368037465European Heart Journal151European heart journal["Shinwan Kany", "P. Ellinor", "S. Khurshid"]2024-11-04T00:00:00 30c43043d8679140417dbac0aac5f143fcd062c1BDJ In Practice20BDJ In Practice["D. Westgarth"]2024-11-04T00:00:00 db6468c15261eb1f538287bf7cfe14ac5ac3da52BDJ In Practice00BDJ In Practice["D. Westgarth"]2024-11-04T00:00:00 1d34fcfd0a082f437f6ee1ca45a287a013f4653aBDJ In Practice160BDJ In Practice["Rachel Sladden"]2024-11-04T00:00:00 da79d2b00fc1cb548af738112cf7b4e2ac19a8d4JAMA Network Open50JAMA network open["Farid Ghareh Mohammadi", "Ronnie A. Sebro"]2024-11-04T00:00:00 0ef9397c467257e9c7dcdd486508c8f882bfdc75BDJ In Practice00BDJ In Practice["P. Eachempati", "Ramnarayan Komattil", "Catherine Coelho", "S. Kumbargere Nagraj"]2024-11-04T00:00:00 f0aba0f5bfa525ed6efdc5675e0844167797d040Eco‐design describes a concept or way of thinking that integrates multifaceted aspects of design and environmentally sustainable considerations.Environmental sustainability is being employed in many industries. How might AI be used during the design process as a tool to aid designers in the implementation of key aspects of environmental sustainability?Design Management Journal00Eco‐design describes a concept or way of thinking that integrates multifaceted aspects of design and environmentally sustainable considerations that integrates multifaceted aspects of design and environmentally sustainable considerations.Design Management Review["Emelia Delaney", "Wei Liu"]2024-11-04T00:00:00 f0a86100a398f269653407a267d25ae122d41d80This article analyzes the impact of digital technology and AI on traditional sculpture, emphasizing the need to update Sculpture training programs in Fine Arts Universities in Vietnam. Based on survey data on the awareness of experts about the influence of AI in the field of sculpture and on the results of comparing the Sculpture training programs of domestic and foreign training schools in the Sculpture industry. The author has outlined training objectives, developed output standards, and created a knowledge system to equip learners with the skills, knowledge, and attitudes needed for modern job roles.International Journal of Religion150The author has outlined training objectives, developed output standards, and created a knowledge system to equip learners with the skills, knowledge, and attitudes needed for modern job roles.International Journal of Religion["Vo Van Lac"]2024-11-04T00:00:00 032de2e771f21c029ac9cd0db9352106d3217f52Medical &amp; Clinical Case Reports Journal00Medical &amp; Clinical Case Reports Journal["Kaiser Jamil", "M. Asimuddin", "Srikishen Iyengar"]2024-11-04T00:00:00 51f6bb23026fa0b2105051b27efb7e15d0029527 European Science Editing00European Science Editing["B. Tang"]2024-11-04T00:00:00 fee98ee5a8a873c0d20cf7f461842d7760cb3c47European Journal of Communication10European Journal of Communication["Thomas Klikauer"]2024-11-04T00:00:00 939dcb43d2dd6d7db646a2ea4fc8368743b2d52bThe digital revolution over the last several decades has ushered in an era of unparalleled dependence on interconnected information systems. The smooth functioning of modern society, its critical infrastructure, from power grids and financial institutions to healthcare networks and communication channels depends on information technology. However, this reliance has also created a fertile ground for cyberattacks, posing significant risks to national security, economic stability, and public safety. Cybersecurity involves protecting information systems and networks from unauthorized access, use, modification, or destruction. It is essential for ensuring the confidentiality, integrity, and availability of data and services, and for safeguarding the privacy, security, and trust of individuals, organizations, and societies. Cybersecurity is a dynamic and complex domain, characterized by the constant evolution and escalation of cyber threats, the rapid development and adoption of new technologies, and the diverse and conflicting interests and values of various stakeholders. It requires a multidisciplinary and holistic approach, involving technical, human, organizational, and societal factors, and addressing the technical, ethical, legal, and social implications of cyber activities. Over the years, cybercriminals have evolved into highly organized and sophisticated entities, resembling traditional crime syndicates with clear hierarchies and specialized roles. They collaborate extensively and use advanced techniques such as zero-day exploits, sophisticated malware, and social engineering tactics like spear phishing. State-sponsored actors and cybercrime syndicates leverage the latest technologies to automate and enhance their attacks, exploiting vulnerabilities in target organizations. Financial motivations drive them to use cryptocurrencies for anonymous transactions, fueling the growth of ransomware and cyber fraud. Their global reach and ability to target critical infrastructure pose significant challenges, necessitating continuous advancements in cybersecurity measures and international collaboration to effectively counter these threats. Traditionally cybersecurity measures are often reactive and limited in their ability to cope with the rapidly evolving threat landscape.ADIPEC120Over the years, cybercriminals have evolved into highly organized and sophisticated entities, resembling traditional crime syndicates with clear hierarchies and specialized roles, and use advanced techniques such as zero-day exploits, sophisticated malware, and social engineering tactics like spear phishing.ADIPEC["Amit Basu"]2024-11-04T00:00:00 a3cf84847c7b69c7e083283a8513db6cbd1cc331Confirmation bias, the tendency to favor information that supports existing beliefs, can hinder information-seeking, especially in learning contexts where it can perpetuate a one-sided perspective. This paper examines how confirmation bias affects search behaviors among 84 participants learning about AI in education. Participants were divided into Neutral and Biased groups based on their prior attitudes, with the Biased group receiving reinforcing information beforehand. Participants’ interactions with the search system were logged, and we analyzed the data for behavioral differences. Results showed that biased participants often completed searches quickly, spending less time engaging with and selecting search results, and issued longer queries. However, other variables showed no statistical difference. Some results contradict other studies on confirmation bias in search, highlighting the complexity of search dynamics in learning contexts and suggesting the need for specialized research into cognitive biases in search as a learning process.Anais do XXXV Simpósio Brasileiro de Informática na Educação (SBIE 2024)250Examination of how confirmation bias affects search behaviors among 84 participants learning about AI in education shows that biased participants often completed searches quickly, spending less time engaging with and selecting search results, and issued longer queries.Anais do XXXV Simpósio Brasileiro de Informática na Educação (SBIE 2024)["Marcelo Machado", "Jairo F. de Souza", "S. W. M. Siqueira"]2024-11-04T00:00:00 eb1cc18c5f19928d6bf2090597c6df2d833b3186 The Kuwait Oil Company supergiant Greater Burgan Field has been producing billions of commercial volumes since 1946 from the primary clastic sandstone Burgan and Wara reservoirs and from the secondary Burgan Marrat, Magwa Marrat and Burgan Minagish carbonate reservoirs. Currently, the South and East Kuwait (S&EK) directorate stands as the largest and most crucial asset within Kuwait. S&EK produces roughly 1.5 million barrels of oil daily from about 2,000 operational wells that belong to 15 gathering centers. To sustain and increase production rates, intensive drilling and workover campaigns are essential including daily well interventions that involve an extensive range of activities and responsibilities to ensure the efficient performance of the asset. This includes overseeing a high volume of wells and operations, each of which requires meticulous attention to ensure optimal performance and output. Given the scale and complexity of these operations, decision-making processes related to production optimization and surveillance become inherently time-consuming. The magnitude of wells and the interconnectivity of pipelines necessitate thorough analysis, evaluation, and strategic planning before implementing any changes or improvements. Production engineers rely on physics models to support operational decisions because these models can predict and simulate complex processes, enabling the assessment of various scenarios and their potential outcomes. However, creating models is considered a meticulous and demanding activity. In addition, in the asset the vast number of active wells, and the need to constantly troubleshoot and optimization operations, urge the need of a lightning-fast solution that provide confident information and insights prior making decisions. This paper presents an agile and agnostic solution built on the interaction of production engineering knowledge, simulation technologies, and pioneering Physics-informed Artificial Intelligence (PiAI) to enable near real-time operational decision-making. A science-infused AI framework was leveraged to create intelligence surrogate models, trained from data sets generated from physics-based models. To build the data set key inputs (reservoir/wellhead pressure, frequency, gas to oil ratio, water cut and productivity index) were sensitized based on real field operational ranges. An integrated fit-for-purpose production platform was developed using business intelligence tools was integrated with production and artificial lift data-ecosystems to allow simultaneous monitoring of well behavior and artificial-lift health. The platform maximized information to support decision-making in conducting an optimization, troubleshooting or debottleneck job. Embedding AI-based modelling component in the platform is a step change in efficiency and the way of working, moving from a static to an accurate, live and science-based solution. The solution was deployed for both single wells and production network models covering all reservoir and production complexity in the asset (under natural depletion, waterflooding, artificial lift and natural flow wells). The integration of real production data obtained in the field with physics-principles within this framework enhances predictive capabilities. The accuracy of the PiAI surrogates models in terms of flow rates and pressures in the production system reach 97and 98% respectively in comparison to detail physics simulations. The solution demonstrates the practical implementation of our approach and its ability to enhance overall efficiency to identify and increase production rates. Moreover, the solution is engineered to scale seamlessly to large networks, accommodating the complexities inherent in the production system. The novel PiAI approach leveraged in the production platform provides quick predictions, making them suitable for near-real-time applications where immediate results are essential. Field development team can proactively seek, rank, and select high-impact production optimization opportunities in short period of time. Expected production gain can be derived accurately before executing the job maximizing efficiency and minimizing risk. The results unlock the true potential of the reservoir and maximum well completion capacity to balance the production outtake.ADIPEC00ADIPEC["Y. Al-Shemmari", "A. Al-Watyan", "K. Al-Jabal", "J. L. Freire", "G. Sridhar", "J. Tordecilla", "C. Harkness", "L. Johnson", "K. Mooney"]2024-11-04T00:00:00 ec1395669957de1f065edc6484d83cbe4b0eecb4African Journal of Biomedical Research00African Journal of Biomedical Research["Dr. Manish Arya"]2024-11-04T00:00:00 fb7c51743229e09b96040ce6ad289caaa2f3c428Industrial robots, both in manufacturing and non-manufacturing sectors, are evolving rapidly, driven by advancements in artificial intelligence (AI). This paper presents a comprehensive survey of industrial robots, framed through the lens of Howard Gardner’s theory of multiple intelligences. By categorising various AI capabilities in industrial robots—such as visual recognition, decision-making, and collaborative interaction—based on Gardner’s intelligence framework, we provide a novel taxonomy that bridges human cognitive abilities and artificial systems. The survey explores the historical development of industrial robots, the current state of AI implementation, and future trends in robotics. Additionally, we discuss the implications of these advancements for industries and their workforce, as well as the ethical considerations surrounding the growing autonomy of AI systems. This paper aims to serve as a reference point for researchers and professionals seeking to understand the intersection of cognitive science and industrial AI, highlighting the potential and challenges of integrating AI into robotic systems.International Journal of Combinatorial Optimization Problems and Informatics160A comprehensive survey of industrial robots is presented, framed through the lens of Howard Gardner’s theory of multiple intelligences, providing a novel taxonomy that bridges human cognitive abilities and artificial systems.Int. J. Comb. Optim. Probl. Informatics["J. Ruiz-Vanoye", "O. D\u00edaz-Parra", "A. Fuentes-Penna", "Eric Simancas-Acevedo", "Ricardo A. Barrera-C\u00e1mara"]2024-11-04T00:00:00 94cbfcb9c419266fa45aac735983d6029ae9c03fPurpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The study highlights artificial intelligence’s potential to enhance entrepreneurial project management outcomes, while also identifying challenges that may constrain the full potential of its application. Originality/value The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format. Strategic Direction10The study highlights artificial intelligence’s potential to enhance entrepreneurial project management outcomes, while also identifying challenges that may constrain the full potential of its application.Strategic Direction[]2024-11-04T00:00:00 c668ab765aaeac5410f8bb87947d3bf5e6256475Abstract Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools and platforms for collaboration and sharing results. Due to this relationship, the characteristics of the employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) is increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, and data curation/analysis. Nevertheless, concerns about openness, transparency, and bias suggest that GenAI may benefit from greater engagement with OS. GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values, such as fairness, transparency, and integrity, and may harm various social actors. In this paper, we explore the possible positive and negative impacts of GenAI on OS. We use the taxonomy within the UNESCO Recommendation on Open Science to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems. However, due to GenAI’s limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research. Hence, sufficient checks, validation, and critical assessments are essential when incorporating GenAI into research workflows.Quantitative Science Studies02It is concluded that using GenAI could advance key OS objectives by broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems.Quantitative Science Studies["Mohammad Hosseini", "S. Horbach", "Kristi L. Holmes", "Tony Ross-Hellauer"]2024-11-04T00:00:00 b2710d2fabaca4002ba864c273c009c974598a5bThe integration of Artificial Intelligence (AI) and Blockchain is revolutionizing the financial sector, targeting crucial challenges like security and transparency. This paper explores the synergistic effects of AI and Blockchain on enhancing the security of financial transactions through advanced real-time fraud detection, anomaly identification, and decentralized transaction verification. Employing a comprehensive review of existing literature and case studies, the research investigates how AI’s capabilities in processing vast data volumes can be leveraged alongside Blockchain’s robust, immutable ledger system to mitigate risks in financial operations effectively. The findings reveal that integrating AI with Blockchain not only significantly improves the security by enabling the real-time detection of anomalies but also upholds the integrity and transparency of transactions across distributed ledgers. The results underscore the potential of AI-Blockchain technology to enhance financial transaction frameworks and highlight its capacity to support the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 16 (Peace, Justice, and Strong Institutions) by fostering more transparent and secure economic environments. The conclusion of the study suggests further research on the scalability of AI-Blockchain integrations and their broader application across various industries, pointing towards a transformative impact on global financial practices.International Transactions on Artificial Intelligence (ITALIC)452Investigating the synergistic effects of AI and Blockchain on enhancing the security of financial transactions through advanced real-time fraud detection, anomaly identification, and decentralized transaction verification reveals that integrating AI with Blockchain not only significantly improves the security by enabling the real-time detection of anomalies but also upholds the integrity and transparency of transactions across distributed ledgers.International Transactions on Artificial Intelligence (ITALIC)["Daniel Martinez", "Lena Magdalena", "Agnes Novalita Savitri"]2024-11-04T00:00:00 2d066eb56c8c6bcc7750383a01fb8c2ce3f8c1feThe article explores the integration of Artificial Intelligence (AI) in higher education, focusing on its impact on academic assessment. It underscores AI’s potential to revolutionize assessment methods by automating tasks and personalizing learning, while also acknowledging the challenges it poses, such as concerns over academic integrity and equitable access. The narrative positions assessment professionals at the forefront of this shift, emphasizing their critical role in navigating these challenges, mastering new technologies, and collaborating with faculty to ensure AI’s ethical and effective use. It advocates for a reimagined approach to education where AI complements human expertise, enhancing learning outcomes and maintaining the core values of education. The discussion extends to the implications for curriculum development, student engagement, and maintaining the essence of critical and creative learning, calling for a balanced integration of AI that supports dynamic, inclusive, and personalized educational experiences.Intersection: A Journal at the Intersection of Assessment and Learning02The article underscores AI’s potential to revolutionize assessment methods by automating tasks and personalizing learning, while also acknowledging the challenges it poses, such as concerns over academic integrity and equitable access.Intersection: A Journal at the Intersection of Assessment and Learning["WIll Miller"]2024-11-04T00:00:00 39628a74bb4e921e63514b2eb61de521b2aec1d0npj Digit. Medicine354Whether new artificial intelligence applied to CAC scans can predict non-CHD events, including heart failure, atrial fibrillation, and stroke is examined, and AI-CAC significantly improved the Agatston score for predicting all CVD events.NPJ Digital Medicine["M. Naghavi", "A. Reeves", "K. Atlas", "Chenyu Zhang", "T. Atlas", "C. Henschke", "D. Yankelevitz", "Matthew J. Budoff", "Dong Li", "Sion Roy", "Khurram Nasir", "S. Molloi", "Zahi Fayad", "Michael McConnell", "Ioannis Kakadiaris", "David J. Maron", "Jagat Narula", "Kim Williams", "Prediman K. Shah", "D. Levy", "N. D. Wong"]2024-11-05T00:00:00 76b04233d4f565f0dd92c6fc4bd2869fd6c29715AIM To provide practical insights that delve into the ethical issues and regulatory implications of generative artificial intelligence (GenAI) in healthcare. Ethical Challenges and Regulatory Impact in China is used as an example. BACKGROUND Despite China's efforts to strike a delicate balance between protecting public welfare and promoting technological advancement, numerous unresolved issues persist in the practical integration of generative artificial intelligence into healthcare settings. CONCLUSION Key issues such as data application, privacy protection, cost-effectiveness and regulatory remain areas of ambiguity that require clarification. Stringent ethical guidelines, data privacy protection measures and continuous supervision and evaluation of artificial intelligence decisions will help enhance the expected benefits of GenAI in healthcare. RELEVANCE TO CLINICAL PRACTICE The potential use of GenAI in healthcare has garnered widespread attention, emerging as a significant global research topic. However, its application in this domain presents substantial ethical and regulatory challenges. Compared to other fields, GenAI's role in healthcare is more sensitive and complex, necessitating an urgent assessment of its ethical implications for future development and deployment. Challenges and ethical considerations are particularly pronounced in developing countries with limited healthcare resources.Journal of Clinical Nursing281Stringent ethical guidelines, data privacy protection measures and continuous supervision and evaluation of artificial intelligence decisions will help enhance the expected benefits of GenAI in healthcare.Journal of clinical nursing["Lanyi Yu", "Xiaomei Zhai"]2024-11-05T00:00:00 6373acb40e66044071fa14f9ac76d0f5bee763f9The issue of Artificial Intelligence (AI) and educational leadership has become a central topic following the shift to knowledge technology and digital literacy in recent years. Despite increasing attention to the topic among scholars and policymakers during the last decade, we lack a comprehensive review of AI and educational leadership. Therefore, by using bibliometric analysis of data derived from the Scopus database, this paper represents a systematic scoping of AI and educational leadership, tracing its development, characteristics, and knowledge accumulation within broad perspectives. Results identify key trends, geographic representation, topical foci, main concepts, themes, and their interconnections, including their implications for targeted Sustainable Development Goals. Implications, limitations, and future research reviewed in the studies are fully discussed.Educational Management Administration &amp; Leadership552By using bibliometric analysis of data derived from the Scopus database, this paper represents a systematic scoping of AI and educational leadership, tracing its development, characteristics, and knowledge accumulation within broad perspectives.Educational Management Administration &amp; Leadership["Khalid Arar", "A. Tlili", "Soheil S. Salha"]2024-11-05T00:00:00 c082f34c486c403a1d6fb03a6e2d30445146e243BMC Surgery1202This review highlights various applications of artificial neural networks in spinal disease management, including (1) assessing surgical indications, (2) assisting in surgical procedures, (3) preoperatively predicting surgical outcomes, and (4) estimating the occurrence of various surgical complications and adverse events.BMC Surgery["Hao Han", "Ran Li", "Dongming Fu", "Hongyou Zhou", "Zihao Zhan", "Yi\u2019ang Wu", "Bin Meng"]2024-11-05T00:00:00 620b829e9969c6e330389b3300a837b76c4ca679Explainable artificial intelligence (XAI) has gained significant attention, especially in AI-powered autonomous and adaptive systems (AASs). However, a discernible disconnect exists among research efforts across different communities. The machine learning community often overlooks “explaining to whom,” while the human-computer interaction community has examined various stakeholders with diverse explanation needs without addressing which XAI methods meet these requirements. Currently, no clear guidance exists on which XAI methods suit which specific stakeholders and their distinct needs. This hinders the achievement of the goal of XAI: providing human users with understandable interpretations. To bridge this gap, this paper presents a comprehensive XAI roadmap. Based on an extensive literature review, the roadmap summarizes different stakeholders, their explanation needs at different stages of the AI system lifecycle, the questions they may pose, and existing XAI methods. Then, by utilizing stakeholders’ inquiries as a conduit, the roadmap connects their needs to prevailing XAI methods, providing a guideline to assist researchers and practitioners to determine more easily which XAI methodologies can meet the specific needs of stakeholders in AASs. Finally, the roadmap discusses the limitations of existing XAI methods and outlines directions for future research.ACM Transactions on Autonomous and Adaptive Systems992A comprehensive XAI roadmap is presented, providing a guideline to assist researchers and practitioners to determine more easily which XAI methodologies can meet the specific needs of stakeholders in AASs.ACM Trans. Auton. Adapt. Syst.["Ziming Wang", "Changwu Huang", "Xin Yao"]2024-11-05T00:00:00 77d9c987fe75fa5a5a41f3408206e39925b4de65 Acute chest syndrome (ACS) is a leading cause of hospitalization in children with sickle cell disease(SCD). Many SCD patients with ACS often present with an initial negative CXR on presentation with vaso-occlusive pain crises. Prompt treatment may help prevent rapid progression in disease severity. However, apart from the ASH guidelines recommending red blood cell transfusions for moderate to severe cases of ACS, there are limited evidence-based recommendations for timely ACS diagnosis and treatment. Children who have a chest xray performed at a prior emergency visit, within the week before ACS is diagnosed, may represent a population who could benefit from artificial intelligence (AI) systems. AI has already demonstrated ability accurately identify COVID-19, ACS, as well as other lung pathology from pediatric chest xrays. The objective was to determine if a delay in diagnosis of ACS is associated with a longer length of stay in a large SCD pediatric population presenting to the Emergency Department @ Children's National Hospital. We performed a de-identified, retrospective, observational, single-center study of pediatric emergency visits for patients with sickle cell disease between 2016-2023. Our hospital is the referral center for patients followed by hematologists within our health system as well as other health systems in the region. We defined “delayed diagnosis” as any patient with a chest xray performed at a prior emergency visit in the preceding seven days. We included adults who presented the pediatric emergency department after their 18th birthday and excluded outlier patients with >95%ile LOS. Descriptive statistics were performed including demographics, clinical characteristics in children with ACS and compared hospital length of stay (LOS) in children with and without delayed diagnosis of ACS. There were 1733 visits (775 unique patients) for ACS between 2016-2023, with 1364 visits for children (78.7% of visits, 663 unique patients). After excluding outliers, among patients who were not critically ill, LOS was longer if there was a delayed diagnosis of ACS (69 visits, mean LOS: 88.3 hours, SD: 56.0) compared to visits without a delayed diagnosis (N = 1289 LOS: 74.5, SD: 49.7; p=0.026). Among patients who were admitted to the critical care unit, LOS was similar for visits with (N = 10) and without (N = 189) a delayed diagnosis of ACS (mean LOS: 143.5 and 148.0 hours and SD: 40.9, 50.1, respectively). For the group of patients with a delayed diagnosis of ACS, there was not a strong relationship between delay in hours from chest xray at preceding visit and the overall LOS in hours. Data at our site suggests patients with a delayed diagnosis of ACS may have significantly longer LOS. While there is an association between delayed diagnosis and increased LOS, this relationship is complex. LOS in patients with critical illness is similar, regardless of whether or not they had a delayed diagnosis, and the duration of the delay does not explain the variation in LOS among patients known to have a delayed diagnosis. Future work is necessary to better understand the relationship between delayed diagnosis of ACS and LOS. Our findings suggest an opportunity to improve outcomes and decrease health system cost if AI systems can be used to identify ACS earlier or accurately predict the risk of ACS using chest xrays at earlier timepoints than occurs with the current standard of care. Blood00There is an opportunity to improve outcomes and decrease health system cost if AI systems can be used to identify ACS earlier or accurately predict the risk of ACS using chest xrays at earlier timepoints than occurs with the current standard of care.Blood["Kenneth McKinley", "Muhammad Syed Anwar", "D. Darbari", "M. Linguraru", "Andrew D. Campbell"]2024-11-05T00:00:00 96a29e8d1b666898166d1de9db3b97dae35bd4c7The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used. An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis. The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals. Acknowledgment This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’Knowledge & Performance Management320The study found that students’ main motivations for using AI are the automation of routine tasks and the need to save time, while men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work.Knowledge and Performance Management["Mariia Balytska", "Martina Ra\u0161ticov\u00e1", "N. Versal", "I. Honchar", "N. Prykaziuk", "Nataliia Tkalenko"]2024-11-05T00:00:00 47ff6766b969ef67443433533e046f5c1e01e947 BACKGROUND. Most oncological and non-oncological hematological conditions fall under the category of rare diseases. Rare diseases present unique challenges due to the limited availability of data, which impacts diagnostic rates and the generation of clinical evidence. Overall, they constitute a public health concern, highlighting the urgent need to develop new methods for improving data accessibility. In this context, Federated Learning (FL) is a Machine Learning approach that allows multiple centers to collaborate on complex research questions without the need to centralize or share data. This project was conducted by the Genomed4all and Synthema consortia with the goal of developing an innovative FL platform for rare hematological diseases. This platform enables the development of novel Artificial Intelligence (AI) models for personalized medicine without data sharing, to be implemented in the referral centers of EuroBloodNET, the European Reference Network for rare hematological diseases. The aims of the project were: 1) to develop robust federated models for personalized prediction using multicentric, real-world datasets; 2) to protect patients' privacy; and 3) to enhance collaboration between institutions while avoiding the creation of centralized data repositories. METHODS. The FL platform includes a manager node (MN) and multiple worker nodes (WN). Users upload their model to the MN, which distributes it to WNs for local training. Trained weights are returned to the MN for aggregation, repeating until training is complete. The MN is hosted at the Humanitas Research Hospital, and the 3 WNs are located at Humanitas, Universität Leipzig, and the University of Bologna. The testing approach is based on MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID 38875514). We focused on myelodysplastic syndromes as a use case, since they are a rare hematological disease with high clinical and genomic heterogeneity (which represents a challenging scenario). The dataset includes comprehensive clinical and genomic data, with information on treatments and clinical outcomes. Each of the 3 WNs has a different sample size of patient data, with 2656, 1328, and 443 samples. Our goal was to build an AI-based model for prediction of survival probability, combining molecular information with clinical data. We planned 3 scenarios for testing: a best-case scenario using all available features, a worst-case scenario excluding up to 50% of genomic information, and a random scenario where an incremental percentage of the genomic variables were randomly removed. For each setting, a DeepSurv model is trained using 80% of local data for training and 20% for testing. RESULTS. In the best-case scenario, the model achieved a concordance index (c-index) of 0.75. The node with the smallest dataset initially registered the lowest c-index value (0.4); however, after the first training step, its performance began to follow the trend at levels comparable to the nodes with larger samples (0.54). In the random scenarios, the model still achieved a c-index of 0.75 but required more training rounds to converge. The same pattern was also observed in the worst-case scenario that was able to achieve a c-index of 0.74. The most relevant difference between the tested scenarios is the increased training time required to reach a good and stable performance when missing information is present. The developed technology is fully compliant with European GDPR regulations. In the upcoming months, the FL platform will be implemented in the referral centers of the EuroBloodNET clinical network. In addition to tabular data, we plan to address the federation of features extracted from medical images, including both histological slides and radiological scans (CT, MRI and PET-CT). CONCLUSION. The FL platform for rare hematological diseases allows multicentric training of AI models without sharing sensitive patient data. This approach ensures data privacy and security, addressing specific challenges associated with rare diseases. Moreover, this platform manages scenarios with missing data or variables, maintaining robustness and accuracy of predictive models. Overall, FL approach is expected to facilitate the development of more advanced and reliable healthcare solutions, paving the way toward personalized medicine in hematology. Blood00The aims of the project were: 1) to develop robust federated models for personalized prediction using multicentric, real-world datasets; 2) to protect patients' privacy; and 3) to enhance collaboration between institutions while avoiding the creation of centralized data repositories.Blood["Gianluca Asti", "S. D\u2019Amico", "Luciana Carota", "Davide Piscia", "Francesco Casadei", "N. S. C. Merleau", "Patricia Alonso de Apell\u00e1niz", "A. Kubasch", "S. Gloaguen", "Nicole Modler", "Catalina Gonzalez Martin", "Cesare Rollo", "J. Parras", "Mattia Delleani", "Elena Zazzetti", "Elisabetta Sauta", "Nico Curti", "Nicolas Derus", "Gianluca Carlini", "Daniele Dall\u2019Olio", "C. Sala", "Lorenzo Dall\u2019Olio", "Luca Lanino", "G. Maggioni", "Alessia Campagna", "Marilena Bicchieri", "Arturo Bonometti", "Cesare Lancellotti", "Daoud Rahal", "Luca di Tommaso", "T. Sanavia", "Piero Fariselli", "Matteo Zampini", "Matteo Brindisi", "V. Savevski", "U. Platzbecker", "M. D\u00edez-Campelo", "Lin-Pierre Zhao", "Pierre Fenaux", "Torsten Haferlach", "Vincent Planat", "Raffaella Colombatti", "M. Ma\u00f1\u00fa-Pereira", "Anders Krogh", "Silvia Uribe", "Santiago Zazo", "Enrico Giampieri", "G. Castellani", "M. D. Della Porta", "Federico Alvarez"]2024-11-05T00:00:00 150a88a5a36e27a56c8df0d9ecd17c9229a6ebccScientific Reports210Artificial intelligence tools trained on human-labeled data may have inherent human non-uniformity, which can boost the evaluated performance against humans, but image areas with inferior performance should be investigated.Scientific Reports["A. Lenskjold", "Mathias W Brejneb\u00f8l", "M.H. Rose", "Henrik Gudbergsen", "Akshay S. Chaudhari", "Anders Troelsen", "A. M\u00f8ller", "Janus U Nybing", "Mikael Boesen"]2024-11-05T00:00:00 0dd23c2a448b49c6bbe85a609b2651c39c6d0cdaBMC Medical Education130This study compares AI-based simulators and SPs in undergraduate medical education, particularly in history-taking skills development, to provide valuable insights into the comparative advantages of artificial intelligence-based simulators and simulated patients.BMC Medical Education["Y. Zidoun", "Abdelmoniem El Mardi"]2024-11-05T00:00:00 cfa1cb5eebe33e91560061e438945f45c7b90f5f Introduction: Venous thromboembolism (VTE) in patients with cancer is associated with considerable morbidity, mortality and costs. The first validated VTE score, Khorana Score (KS) (Khorana, Kuderer et al. Blood 2008) was derived from a logistic regression (LR) model based on a large, prospective cohort study of ambulatory patients initiating cancer chemotherapy (ANC Modeling Registry, PI, G Lyman) has been externally validated in numerous observational studies, randomized controlled trials (RCTs) and meta-analyses of RCTs, as well as has been utilized for selecting high-risk patients in RCTs of thromboprophylaxis and integrated into clinical practice guidelines (G Lyman et al, Blood Advances 2021). Limitations of the KS of not being able to identify all CAT patients has prompted efforts to further improve its predictive performance by identifying additional risk factors or through variable shrinkage methods. With the emergent role of artificial intelligence, recent efforts to improve model performance have focused on advanced machine learning (ML) techniques. However, the complexity and risk of bias associated with such approaches remain of concern (G Collins. BMJ 2024). Methods: A systematic literature review of Medline and Web of Science was undertaken of published reports of the development and validation of ML risk models for VTE among patients with cancer. Two independent investigators extracted data, including study population, cancer type, study design, the ML algorithms, and performance measures associated with each. A formal quality appraisal based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) Statement was conducted. The primary study outcome is the measure of model performance for VTE risk prediction: Area Under the ROC Curve (AUC) +/- 95% CI. Heterogeneity among studies was assessed by the Q-test and inconsistency index (I2). A meta-analysis was performed using random effects modeling to estimate pooled AUC values and their variance. Study-level moderators of heterogeneity were assessed through subgroup and sensitivity analyses. Publication bias was assessed based on funnel plots and Eggar's regression intercept. Results: The initial search identified 1,017 studies of which 10 met a priori defined eligibility criteria, from US (2), EU (2) and Asia (6) between 2016 to 2024, including 6 studies with multiple cancer types and 4 studies limited to lung, gastric, colorectal, or ovarian cancer, respectively. Mean study sample size was 1603 [range; 608-3398] with VTE rates ranging from 3.4% to 30.9%. Multiple ML algorithms were reported with Random Forest in 60% of studies, Support Vector Machine (40%), and Extreme Gradient Boosting (30%) representing the most common. Based on Q-statistic of 4.58 and overall inconsistency index (I2) of 98%, sources of heterogeneity across studies were sought. Pooled model performance based on AUC was 0.79 [95% CI: 0.72-0.85] overall; compared to pooled AUC of 0.81 [95% CI:0.74-0.88] in the 6 studies based on split sample validation versus the AUC of 0.72 [95% CI:0.70-0.73] in the 2 studies with independent external validation datasets (P<0.001), while 2 studies provided insufficient information for analysis. Performance was greater for ML models than KS in the 6 comparative studies (P<0.001), while no difference was observed between models based on ML versus conventional LR in 6 studies (p=0.407). Potential for bias associated with ML models was not discussed in most studies, with only 1 study providing formal quality appraisal based on the TRIPOD Statement. Important factors such as disparities, disabilities, collinearity, missing data and measures to limit overfitting that might account for superior model performance were not reported in most studies. KS assessment was also limited by unavailability of clinical details to correctly calculate or correctly limit the KS analysis to solid tumor or lymphoma patients on chemotherapy. No evidence for publication bias was found. Conclusion: While there is promise for improved model performance with ML modeling for VTE in patients with cancer, caution is required to avoid significant bias including overinterpretation of such study results. Independent validation by outside groups are essential to fully understand population-level model performance, particularly in key racial and ethnic groups, prior to clinical application. Blood00The primary study outcome is the measure of model performance for VTE risk prediction: Area Under the ROC Curve (AUC) +/- 95% CI, which was greater for ML models than KS in the 6 comparative studies.Blood["N. Kuderer", "G. Lyman"]2024-11-05T00:00:00 e399d661d1304e2561c305ff0a8db024751cfa68 Background: Risk stratification for suspected pulmonary emboli (PE) remains an area of active research. Current risk stratification algorithms have moderate discriminatory capabilities and are guideline-recommended, but they are poorly utilized in practice, with most clinicians relying predominantly on their clinical training and personal “gestalt.” Our group recently derived an Artificial Intelligence (AI) algorithm using electrocardiograms (ECG) to predict the presence of PE (AUC 0.69) on computed tomography scans of the pulmonary arteries (CTPA) in a cohort of 79K patients. Aim: Validate the AI-ECG algorithm in a contemporary dataset of patients with suspected PE. Methods: Emergency Department (ED) visits between 9/1/2022 and 12/31/2023 across the Mayo Clinic Enterprise were searched using a unified data platform. ED visits in patients over 18 years of age with a discretely documented Wells' PE or PERC Rule, a D-dimer result, or a CTPA were included. Only CTPA imaging and D-dimers performed within 48 hours of ED admission were eligible to be included. Patients were included in the final cohort if an ECG was performed within 6 hours of ED admission, D-dimer testing, or CTPA. Patients were considered negative for PE if CTPA was negative, and when this wasn't performed, a negative D-dimer (high sensitivity) or PERC rule of 0 was considered negative. A previously derived Artificial Neural Network algorithm to analyze the ECG to predict PE was applied to the dataset for validation. Results: A total of 27,028 ED visits in 24,288 unique patients meet inclusion criteria. Among this group, the first ED visit with a CTPA and an ECG within 6 hours was included, leaving 18,936 patients for the final validation cohort. The mean age of the cohort was 54.7 (SD 18.7), and 57% were male. Among the entire cohort, 13,573 underwent D-dimer testing for which 4,895 (36%) were positive at standard thresholds. A total of 10,486 patients underwent CTPA (55% of patients evaluated), of which 455 were positive for PE (4.3%). Using the AI-ECG model from the derivation cohort applied to this validation cohort resulted in an AUC of 0.69, identical to the derivation cohort. Using ECG model estimates alone, low (0.74%, n=2447, NPV 99.3%), elevated (2.27%, n=15404, NPV, 97.72%), and high-risk (8.02%, n=1085, NPV 91.98%) categories for PE can be created. A negative D-dimer in the elevated risk group increased the negative predictive value (NPV) to >99.9% and could be used to reduce imaging in this group further. Using the proposed ECG to D-dimer cascade risk stratification could further reduce the need for PE imaging for an overall NPV of 99.9% and sensitivity of 95.2%. We then used additional clinical parameters to train a new algorithm with additional clinical data and the AI-ECG algorithm estimates. Multiple machine learning algorithms were then evaluated in a dataset divided into training (2/3) and testing (1/3) datasets, after downsampling (to address class imbalance). The best results were obtained with AI-ECG results and D-dimer: Ada Boost (AUC 0.93, F1 score 0.895) compared to standard logistic regression (AUC 0.79, F1 score 0.746). The addition of age, sex, and oxygen saturation did not improve model performance. Conclusion: We have now validated the previously derived AI-ANN ECG algorithm for PE in a contemporary and independent cohort and demonstrate potential for integration into clinical PE assessment algorithms. Unlike prior risk stratification processes, ECGs are invariably performed in patients with cardiopulmonary symptoms and have the potential to prevent missed or delayed PE diagnosis by proactively identifying high-risk patients. Additionally, we demonstrate that the results of this algorithm can be combined with D-dimer values, resulting in a simple, powerful, predictive algorithm (AUC 0.93) that pairs cardiovascular electrophysiology with laboratory biomarkers and could lead to a new paradigm in clinical risk stratification for PE. Blood00The proposed ECG to D-dimer cascade risk stratification could further reduce the need for PE imaging for an overall NPV of 99.9% and could be used to reduce imaging in this group further.Blood["Damon E. Houghton", "Kan Liu", "Francisco Lopez-Jimenez", "Ryan Meverden", "Robert D. McBane", "Stan Henkin", "A. Casanegra", "W. Wysokinski"]2024-11-05T00:00:00 6b5af53ef8e395f24e1fe3141b5911e037267dfa Introduction: Artificial intelligence (AI) is increasingly being incorporated into the healthcare space as a tool to support clinical decision making and enhance patient care. Professional societies including ASH, as well as private companies such as Primum, Inc, offer free consultations with appropriate experts who provide personalized responses to clinician-submitted hematology cases. However, the potential of AI tools to supplement or supplant expert consultations remains uncertain. We report results of a study comparing AI versus expert physician responses to 107 real-world hematology cases. Methods: Among 107 cases, inquiries included lymphomas (30), myeloma (24), leukemias (11), myeloid disorders (10), as well as classical hematology cases (32), assessed among 20 unique experts. Responses to these de-identified cases submitted by practicing clinicians to the Primum platform (www.primum.co/) between June 2022-July 2023 were compared to GPT-4 responses (openai.com/chatgpt/). The instructional prompt to GPT-4 was, “You are an expert oncologist conversing with another oncologist as a peer. You prefer to rely on guidelines and data published in reputable medical journals when responding.” Five expert faculty at our institution adjudicated the blinded comparative responses, including their preference, quality and practical value scores, and prediction of which response was AI generated. Comparison of scores was by t-test to generate P-values between expert and AI groups, and Pearson correlation was used for comparisons between adjudication scores. Results: Expert responses were preferred by >50% of adjudicators in 75% of cases (deviation ±25%). Randomized AI responses were correctly identified 90% of the time. Mean expert vs AI scores (Likert scale 0-4) for quality (2.0 vs 2.1, P=0.9) and practical value (2.1 vs 2.1, P=0.9) were equivalent. Interestingly, AI responses were preferred in 46% (n=15) of classical hematology and 31% (n=9) of lymphoma cases, largely due to being more concise. However there was no concordance between high practical value scores and disease subtype for either group. Conclusions: Expert physician responses were preferred for most of the cases, suggesting an implicit value of personalized responses compared to AI. Results showed no significant differences in quality or practical utility between AI generated responses and those from experts, reflected a similarity in the information extracted from standardized guidelines. Our findings may be limited by the broad coverage of hematologic conditions for which experts and guidelines vary. Overall, these data suggest that while AI can supplement knowledge of management paradigms by providing basic management strategies, at present it cannot replace expert clinical consultation in clinical practice. Blood00Comparing AI versus expert physician responses to 107 real-world hematology cases suggests that while AI can supplement knowledge of management paradigms by providing basic management strategies, at present it cannot replace expert clinical consultation in clinical practice.Blood["Olivia Main", "Matthew Struck", "John L. Vaughn", "Jingmei Hsu", "Mohammad Abu Zaid", "Shella Saint Fleur-Lominy", "Marc J Braunstein"]2024-11-05T00:00:00 6f1c2fe1761ff4ecd5ded531d27e78fa32a064b2Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT, which brought the phenomenon to wide public attention. As the deployment of AI products rises geometrically, considerable attention is being given to the threats and opportunities that AI technologies offer, and to the need for regulatory and standards initiatives to ensure that use of the technology aligns with societal needs and generates broad benefits while mitigating risks and threats. This manuscript is the third of a series of White Papers informing the development of IEEE-SA's p3995 {\it `Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence Models'} \cite{P3395}, Chair Marina Cort\^{e}s. This part focuses on assessing calmly and objectively, as far as is possible, the current state of Artificial Intelligence (AI) technology development and identifying predominant trends, prospects, and ensuing risks. It necessarily forms a snapshot of the current instant of a rapidly-evolving landscape, with new products and innovations emerging continuously. While our main focus is on software and hardware developments and their corporate context, we also briefly review progress on robotics within the AI context and describe some implications of the substantial and growing AI energy demand.arXiv.org450This part focuses on assessing calmly and objectively, as far as is possible, the current state of Artificial Intelligence (AI) technology development and identifying predominant trends, prospects, and ensuing risks.ArXiv["G. Tambouratzis", "Marina Cortes", "A. Liddle"]2024-11-05T00:00:00 9945305468d5353e5eb079d292f2348f82cd84beBACKGROUND Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be reviewed by clinicians. OBJECTIVE To assess provider burnout and professional fulfilment associated with Ambient AI technology during a pilot study, assessed using the Stanford Professional Fulfillment Index (PFI). METHODS A pre-post observational study was conducted at University of Iowa Health Care with 38 volunteer physicians and advanced practice providers. Participants used a commercial ambient AI tool, over a 5-week trial in ambulatory environments. The AI tool transcribed patient-clinician conversations and generated preliminary clinical notes for review and entry into the electronic medical record. Burnout and professional fulfillment were assessed using the Stanford PFI at baseline and post-intervention. RESULTS Pre-test and post-test surveys were completed by 35/38 participants (92% survey completion rate). Results showed a significant reduction in burnout scores, with the median burnout score improving from 4.16 to 3.16 (p=0.005), with validated Stanford PFI cutoff for overall burnout 3.33. Burnout rates decreased from 69% to 43%. There was a notable improvement in interpersonal disengagement scores (3.6 vs. 2.5, p<0.001), although work exhaustion scores did not significantly change. Professional fulfillment showed a modest, non-significant upward trend (6.1 vs. 6.5, p=0.10). CONCLUSIONS Ambient AI significantly reduces healthcare provider burnout and may enhance professional fulfillment. By alleviating documentation burdens, ambient AI can improve operational efficiency and provider well-being. These findings suggest that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.Applied Clinical Informatics00Ambient AI significantly reduces healthcare provider burnout and may enhance professional fulfillment and by alleviating documentation burdens, ambient AI can improve operational efficiency and provider well-being.Applied clinical informatics["Jason Misurac", "Lindsey A. Knake", "James M. Blum"]2024-11-05T00:00:00 0ab69bbe3d605e4d0f7f0535d090c76e8f44ca16Perencanaan tata ruang merupakan proses sistematis dalam merancang, memanfaatkan, dan mengendalikan penggunaan ruang untuk memenuhi kebutuhan pembangunan yang terus berubah. Tata ruang bertujuan untuk mencapai efisiensi ekonomi, sinergi dalam pemanfaatan sumber daya, dan menciptakan kesejahteraan masyarakat melalui pengelolaan ruang yang berkelanjutan. Penggunaan Artificial Intelligence (AI) dalam perencanaan tata ruang dapat mengoptimalkan pengelolaan sumber daya dengan cara otomatisasi dalam pemilihan dan pengaturan material, serta meningkatkan efisiensi dalam penggunaan alat dan bahan. AI memiliki potensi besar dalam mengolah data yang kompleks untuk mengidentifikasi pola dan tren yang relevan dalam perencanaan kota. Penelitian ini bertujuan untuk mengevaluasi penerapan AI dalam perencanaan tata ruang serta dampaknya terhadap efisiensi dan keberlanjutan pembangunan kota. Metode penelitian yang digunakan adalah analisis kuantitatif dan simulasi berbasis data dari kota-kota yang menerapkan AI dalam perencanaan tata ruangnya. Hasil penelitian menunjukkan bahwa kolaborasi antara manusia dan AI membantu pengambilan keputusan yang lebih baik, sehingga mampu mendukung visi pembangunan kota yang inovatif dan berkelanjutan. Meskipun terdapat tantangan, implementasi AI yang efektif dalam perencanaan tata ruang memberikan peluang besar bagi pengembangan kota di masa depan.JoDA Journal of Digital Architecture110JoDA Journal of Digital Architecture["K.Heriyansyah Heriyansyah", "Purwanto, Purwanto, L.M.F"]2024-11-05T00:00:00 c218ba12b2603feac52a066f1849eaeaf45feaa2Indonesia has witnessed significant growth in internet penetration, with over 212 million users connected online. This digital boom has been driven by e-commerce, which accounted for 52% of the country's gross merchandise value in 2024, reaching a staggering 1,155 trillion rupiah. To sustain this momentum, e commerce players are adopting digital marketing strategies. The integration of artificial intelligence and big data is being explored to enhance consumer services. This study examines the strategies for integrating artificial intelligence in digital marketing. Using a qualitative descriptive approach based on literature review, our research identifies three key strategies that manufacturers can employ to improve marketing services and provide a seamless shopping experience: (1) leveraging AI-based social media analysis tools; (2) optimizing Search Engine Optimization; and (3) utilizing AI-powered chatbots.Proceeding of The International Seminar on Business, Economics, Social Science and Technology (ISBEST)60Three key strategies that manufacturers can employ to improve marketing services and provide a seamless shopping experience are identified: leveraging AI-based social media analysis tools; optimizing Search Engine Optimization; and utilizing AI-powered chatbots.Proceeding of The International Seminar on Business, Economics, Social Science and Technology (ISBEST)["Muhammad Rif\u2019an"]2024-11-05T00:00:00 500f005da4dbc4d92c34cd2e69b322bf45a869bcAdopting Artificial Intelligence (AI) at the company level constitutes a transformative phase, heralding an economic-technological leap through the digital economy. This advancement streamlines processes, allowing companies to become more flexible and respond promptly to challenges. For marketing, AI can be the potentially infinite engine of performance, but the success of AI adoption is not guaranteed. In this respect, marketing effectiveness depends on various instances of AI adoption, including automation, augmentation, and personification.Journal of Financial Studies500For marketing, AI can be the potentially infinite engine of performance, but the success of AI adoption is not guaranteed, and marketing effectiveness depends on various instances of AI adoption, including automation, augmentation, and personification.Journal of Financial Studies["Octavian Dumitru Hera"]2024-11-05T00:00:00 b635661146311f888c72fdfc150a591e92ab3f0dThis study proposes a conceptual framework to investigate factors influencing the data partiality in Artificial Intelligence (AI). However, the academic research on data partiality focusing on AI is limited across the bibliographic database sources. This study aims to address the gaps by proposing a developed framework that integrates three factors: the AI algorithm, black data, and user revise terminology highlighted in the past literature. The AI algorithm refers to the issues on the training data as a dataset used in the tools, which stimulates the data partiality as the outcome retrieved by the user. The black data is influencing data partiality on the existence of unknown data. The user revise terminology represented on the keywords used by the user to search for information, which incorrect keywords with not specify will lead to the AI to give all related information as an output without filter. The framework asserts that these three elements directly affect the partiality of data in AI. A quantitative methodology will be used in this study to cover the collection of survey data from the community under the MDEC program called Global Online Workforce (GLOW). The framework contributes a theoretical understanding of AI algorithms, black data, and user-revised terminology that influence data partiality in AI. In future research, the framework can be extended to test the data partiality in AI tools used in information agencies, as these bodies govern the safeguards of the accuracy of the information.Information Management and Business Review300A developed framework that integrates three factors: the AI algorithm, black data, and user revise terminology highlighted in the past literature to investigate factors influencing the data partiality in Artificial Intelligence (AI).Information Management and Business Review["Faten Elina Kamaruddin", "Nur Hanisah Mohamad Razali", "Ahmad Fuzi Md Ajis", "Nur Rifhan AB RAHIM", "Siti Noorhaslina Abd Halim", "Ainol Mardhiyah Rahmat"]2024-11-05T00:00:00 c7f2377458459f6192415838616c05392b34f063 Introduction: Since 2011, Indian Cancer Society Cancer Cure Fund (ICS-CCF) has contributed over $24 million to cover the treatment costs of more than 12,000 underprivileged patients across 19 empanelled hospitals in India. A mandate of this philanthropic funding is to carefully choose beneficiaries who have a high chance of cure with guideline based care. From 2011-2021, a team of expert oncologists reviewed every beneficiary application for cure rates/survival. To augment and scale up this process, in February 2021, ICS-CCF evaluated and implemented the use of Artificial Intelligence (AI) in reviewing the applications for recommendation (prior-Authorizations). Navya Al platform is a clinically validated Al model that matches clinical data of beneficiary applicants with available evidence and registry data to predict survival, and generates guidelines based optimal treatment plans. 80% of applications are adjudicated by Navya AI, and the remainder of complex cases are referred to the experts. A goal for implementation of Navya AI is to standardize patient selection for funding, with reproducible rationale for approval based on predicted survival as compared to variable rationale from a rotational volunteer panel of human experts. We hypothesized that Navya AI plus experts would lead to more reproducible survival estimates, leading to better patient selection for funding, and therefore improved survival of the funded cohorts compared to the experts alone. This study evaluates changes in survival of cohorts funded by the ICS-CCF before and after the implementation of Navya AI as a direct measure of success of the funding initiative's mandates. Methods: As part of routine process, all consecutive hematolymphoid cancer patients who were funded by ICS between January 2018 and December 2023 had been contacted by prospective phone follow to obtain survival outcomes. Given three years since the implementation of Navya AI, all age/risk/diagnosis matched cohorts with 3 years data were examined. Acute Myeloid leukemia (AML) was chosen as a common diagnosis, and exemplary for analysis of the benefit of Navya AI. Results: 403 adult AML patients (age 18-60 years) were evaluated by ICS for funding between January 2018 and December 2023 with a median follow up of 14 months. Cohorts 3 years before the introduction of Navya AI with experts alone (157/403) was compared to a cohort 3 years after the introduction of Navya AI plus experts (246/403) in a matched pre-post analysis. Notably, Navya AI plus experts was more effective in assessing risk and selecting patients with favorable or intermediate prognosis categories 207/246 (84%) compared to experts alone 123/157 (78%). Navya AI plus experts funded less high risk patients 39/246 (16%) compared to experts alone 34/157 (22%). Correspondingly, the number of patients reported alive in the Navya AI plus experts cohort was higher 195/246 (79%) than experts alone cohort 98/157 (62%) at a median follow up of 14 months. However, 34/157 patients (22%) were unable to be confirmed alive by phone follow up in the experts alone group as compared to only 9/246 (4%) in the Navya AI plus experts group. Confirmed deaths in Navya AI plus expert group 42/246 (17%) and 25/157 (16%) were comparable. Conclusion: Given more favorable and intermediate risk profiles of patients with AML in the Navya AI plus experts group, there is evidence to show better selection of patients for philanthropic funding who are likely to withstand and survive treatment, compared to the experts alone group. The application of Navya AI in patient selection may help identify the neediest hematolymphoid patients that would most benefit from standardized treatment and funding, maximizing impact of philanthropic funding. Blood00The application of Navya AI in patient selection may help identify the neediest hematolymphoid patients that would most benefit from standardized treatment and funding, maximizing impact of philanthropic funding.Blood["Nehal Khanna", "Ramya B", "Naresh Ramarajan"]2024-11-05T00:00:00 2b5301b3d0a79cc55623e156970ad52d5145f0eaThe article aims to evaluate which changes in criminal law can influence the improvement of defining the legal personality of artificial intelligence. To achieve this, the object of study is AI-based technologies that could become subjects of criminal law. The scientific objective of the article is to identify and organize the most necessary changes in Jordan's criminal legislation to establish the legal personality of artificial intelligence. The research methodology involves applying a consolidated ranking of the proposed changes in criminal law. As a result, a list of proposed amendments to Jordan's criminal legislation is presented, which enhances the definition of artificial intelligence as a legal subject. A model for organizing these changes from the most significant and necessary today to the less influential is also presented. Prospects for further research should involve a deeper assessment of the moral and ethical issues of artificial intelligence and the regulation of its activities.Edelweiss Applied Science and Technology200A list of proposed amendments to Jordan's criminal legislation is presented, which enhances the definition of artificial intelligence as a legal subject, and a model for organizing these changes from the most significant and necessary today to the less influential is presented.Edelweiss Applied Science and Technology["J. Hammouri"]2024-11-05T00:00:00 091b53fd296031d3a750697f579f8e60197f6aa1The technological progress incorporated under the artificial intelligence umbrella is more and more embraced in the finance industry. The constant development combined with particular social situations (COVID, post-COVID) had speed up the usage of artificial intelligence systems in the finance industry. Consumers are now exposed on a regular basis to finance systems as bank applications in which they can choose from simple to more complex operation. Applications interfaces are becoming more and more user-friendly, and consumers tend to trust it, those being an extension of the bank institution. In our research, we investigate the attitude of consumers towards offering trust in finance artificial intelligence systems that can provide advice and operate financial investments based on historical data and their economic future estimations. For financial systems to proceed with these recommendations, the main aspect will be the acceptance to share their private data, based on which artificial intelligence will create a personalized profile in terms of consumer behaviour and interests.Journal of Financial Studies140This research investigates the attitude of consumers towards offering trust in finance artificial intelligence systems that can provide advice and operate financial investments based on historical data and their economic future estimations.Journal of Financial Studies["Mihaela Stanescu (Enache)"]2024-11-05T00:00:00 d1894f6e097359a823a70f531e009a3bd95c57a4The study aims to investigate artificial intelligence (expert systems, knowledge representation and reasoning, automatic learning and effectiveness) on human resources management in Jordanian banks. The descriptive analytical method was used by developing a digital questionnaire that was distributed to a purposive sample of the study population, which includes employees of directors of human resources departments and workers in the human resources department. To answer the study’s questions and hypotheses, the data was analyzed using the SPSS program. It was found that artificial intelligence variables (expert systems, knowledge representation and reasoning, automatic learning, and effectiveness) are associated with a positive, statistically significant relationship with training and development. ? It was also found that artificial intelligence variables (expert systems, knowledge representation and reasoning, automatic learning, and effectiveness) are associated with a positive, statistically significant relationship with selection and appointment. Finally, the results concluded that artificial intelligence explains a large portion of the variance in incentives, which indicates a positive and statistically significant relationship. The study recommended that the expanding the use of artificial intelligence techniques in ?human ?resources ?management?, and conduct more future studies about the relationship between the use of artificial intelligence technologies and other human resources practices such as: (performance evaluation, wages and compensation, talent management).   Received: 11 July 2024 / Accepted: 31 October 2024 / Published: 05 November 2024Academic Journal of Interdisciplinary Studies260It was found that artificial intelligence variables are associated with a positive, statistically significant relationship with training and development and a large portion of the variance in incentives indicates a positive and statistically significant relationship.Academic Journal of Interdisciplinary Studies["Hamza Abdallah Abdalrhman Yahya"]2024-11-05T00:00:00 eda82ebe5bb15437d755e48633843a87f96ac7d6Artificial Intelligence is emerging as a transformative tool impacting various industries, including education As Artificial Intelligence continues to develop and gain prominence in classrooms, understanding how students perceive this integration and how it affects their educational experience becomes crucial. The aim of this research was to develop a model to predict the perception of students at Bolívar State University regarding the use and potentialities of Artificial Intelligence in the educational field. The methodology employed a factorial analysis, which represents the relationships among a set of variables. From this, a logistic regression was performed, generating an equation to identify predictors that allowed understanding student behavior based on specific characteristics such as attitude, perception, and satisfaction. As a technique for information gathering, a questionnaire composed of 25 items on a Likert scale was used, statistically validated with a Cronbach's alpha value of 0.925. The results of the model show that all covariates, except "Insecurity and fear of using artificial intelligence tools", are significant (p < 0.001). This suggests that the remaining variables are related to the dependent variable "Positive Perception of the Usefulness of Artificial Intelligence in Learning". It is concluded that students have limited knowledge about Artificial Intelligence, and this may cause them to have unrealistic expectations. Training can help students learn about AI and how to use it effectively and ethically.   Received: 9 May 2024 / Accepted: 12 September 2024 / Published: 05 November 2024Journal of Educational and Social Research00It is concluded that students have limited knowledge about Artificial Intelligence, and this may cause them to have unrealistic expectations and training can help students learn about AI and how to use it effectively and ethically.Journal of Educational and Social Research["Mar\u00eda Lorena Noboa Torres", "Daniela Alejandra Ribadeneira Pazmi\u00f1o", "Daniela Paola Avalos Espinoza", "Cesar Guevara"]2024-11-05T00:00:00 ffbcc81f25d3d97b39e6c96c47abe4f9c2ce5999Over the last few years, many factors have transformed the automotive supply chain in Europe. This paper addresses some of the issues that challenge this supply chain and discusses artificial intelligence’s implementation benefits, such as the elimination of bottlenecks, gaining an advantage on the procure-to-pay system of the automotive supply chain, and the automatic read of invoices. Qualitative methodology was used to analyse the information gathered within this study, using in depth literature review and checking important releases on this subject in scientific papers available, focusing also on the Audi case study that shows real application of artificial intelligence technology into their manufacturing process and the opportunities to further apply this in Europe. Major changes in European automotive supply chains are underway as a result of the adoption of artificial intelligence, not only solves existing problems but calls for advanced algorithms to be developed to meet new demands. The implementation of artificial intelligence needs to follow ethics, and in some cases needs human supervision. The use of artificial intelligence has both advantages and disadvantages. Some are analysed in this article and the important role of Germany as a pioneer in using artificial intelligence effectively in the automobile industry is emphasized by a case study on Audi.Management and Economics Review00The important role of Germany as a pioneer in using artificial intelligence effectively in the automobile industry is emphasized by a case study on Audi, focusing also on the Audi case study that shows real application of artificial intelligence technology into their manufacturing process and the opportunities to further apply this in Europe.MANAGEMENT AND ECONOMICS REVIEW["Gabriela C\u0103linescu", "Elena-Simona Ionel"]2024-11-05T00:00:00 c2cc17ff533f80a39ccd50ea0b95cb6c79baafb8The article is an analytical review of reports and presentations made at the scientific conference “Application of Artificial Intelligence in Library and Information Activities” and held by INION RAS on May 29, 2024. The current state of Artificial Intelligence (AI) use in libraries in Russia and neighboring countries is described, including examples of the use of neural network applications in commercial projects and libraries of various types. Particular attention is paid to the experience of the Russian State Library, which actively uses ready-made software solutions and develops its own AI products. Among the problems hindering the active implementation of AI in library practice, the following are highlighted: insufficient awareness of librarians about the capabilities of AI, imperfection of currently existing applications, high corporate information security requirements, and the lack of labeled datasets for training neural networks. The challenges for librarianship associated with the spread of AI are also considered, including the possibility of replacing the traditional functionality of librarians with the work of artificial neural networks. The main areas of prospective development of artificial intelligence in librarianship include the creation ofsystems for collapsing information about documents and systems for advisory reading. The current tasks, which constitute a whole new area of work for libraries, include users training in AI tools, which is designed to give the audience a clear understanding of the capabilities and limitations of GPT systems. It is emphasized that the development of artificial intelligence can lead to a radical change in the role of libraries in the structure of information exchanges, which requires the librarianship to carefully study the situation and adapt to new technological conditions. Bibliosphere00Bibliosphere["V. K. Stepanov"]2024-11-05T00:00:00 186abe39e58433a5979a95eff735b95989a56cb2The service industry in Nigeria is crucial to the development of the economy due to the fact it encompasses sectors that spur a lot of economic activities, leading to job creations across the different sectors including hospitality and tourism, retail, healthcare, financial services, information technology services, professional services, as well as education and training. The aim of this research is to investigate the role of Artificial Intelligence in transforming customer experience in the service industry in Nigeria. The approach of data collection is through systemic literature review from related research paper on the domain research area. The service industry’s aim is to transform customer experience to its full potential, integrating different technologies, most importantly Artificial Intelligence (AI). The integration of AI in the service industry has totally transformed has seamlessly transformed customers experienced in the service industry, as well as most of the sectors that comes with the industry. The concept of Artificial Intelligence replicates human intelligence in machines, allowing them to perform tasks that usually need human cognition. The integration of AI in different sectors of the service industry is not widespread. The benefits of integration of AI into service industry to transform customer experience includes automation, decision making, personalization, cost saving and innovation. The Challenges entails ethical concerns, lack of transparency, data quality and accessibility, job displacement and workforce changes, as well as security risk. The impact of AI on transforming customer experience in the service industry is demonstrated in automation, enhanced customer insight, operational efficiency, personalization, and predictive analysis. The industries within the service industry that the AI has really impacted include retail, hospitality, healthcare, banking and finance, as well as telecommunicationInternational Journal of Academic Research in Business and Social Sciences470The role of Artificial Intelligence in transforming customer experience in the service industry in Nigeria is investigated through systemic literature review and automation, enhanced customer insight, operational efficiency, personalization, and predictive analysis.International Journal of Academic Research in Business and Social Sciences["Mbachukwu Maryjane Adaobi", "Mwakawaza Nyangu", "Huang Soon Fook"]2024-11-05T00:00:00 a9c9986a4d897115a7b3c2ba9ed96763e66aa4aaBACKGROUND: The use of chest computed tomography (CT) scans in Krasnoyarsk Krai, Russia, has increased since 2020, during the COVID-19 pandemic. This period also saw a 5.2% decrease in lung cancer (LC) incidence. The potential for missed LC cases has led to the investigation of new diagnostic methods, including the use of artificial intelligence (AI) for analyzing retrospective data. AIM: The study aimed to evaluate the effectiveness of an AI algorithm in identifying patients at high risk for LC using chest CT data from the COVID-19 pandemic. METHODS: A retrospective analysis was conducted on chest CTs from patients diagnosed with COVID-19 in the Krasnoyarsk region, using scans from November 1, 2020, to February 28, 2021. The AI algorithm "Chest-IRA" was applied to detect pulmonary nodules larger than 100 mm³. Radiologists classified the nodules detected by AI into three categories based on LC probability. The economic assessment of the AI algorithm included salary costs and potential savings from early LC treatment. RESULTS: Out of 10,500 CTs, the AI algorithm found nodules in 484 cases. Of these, 192 were highly likely to have LC, 103 showed no signs, and 60 were inconclusive. 112 patients with high or intermediate risk did not seek treatment. The AI confirmed lung cancer in 100 cases, 28.2% of those detected. Early-stage LC was found in 35% of cases, while 65% were at later stages. AI could save about 25.04 months of radiologist work, costing 2.43 million RUB. Early detection savings are estimated at 10.6 to 12.5 million RUB per 10,500 CTs, with a five-year economic impact of 259.4 to 305.1 million RUB. CONCLUSION: AI has proven effective in identifying pulmonary nodules amidst COVID-19, highlighting its potential to improve early detection and diagnostic accuracy, leading to earlier and more precise treatments.Digital Diagnostics60Artificial intelligence has proven effective in identifying pulmonary nodules amidst COVID-19, highlighting its potential to improve early detection and diagnostic accuracy, leading to earlier and more precise treatments.Digital Diagnostics["R. Zukov", "I. P. Safontsev", "M. P. Klimenok", "T. E. Zabrodskaya", "N. A. Merkulova", "V. Chernina", "M. G. Belyaev", "M. Y. Goncharov", "V. Omelyanovskiy", "K. Ulianova", "E. A. Soboleva", "Maria E. Blokhina", "E. Nalivkina", "V. Gombolevskiy"]2024-11-05T00:00:00 f6e94d8b8b4c8fa29c5e6d2cfb20e8d82bd04ec0Trends in Cancer151How AI is improving mammographic density-associated risk prediction and shaping the future of screening and risk-reducing strategies is discussed.Trends in cancer["Wendy V. Ingman", "Kara L. Britt", "Jennifer Stone", "T. Nguyen", "John L. Hopper", "Erik\u00a0W. Thompson"]2024-11-05T00:00:00 999b0fc790ab17d6b7b1e5d20b11655beb5ebfe5Public Choice231The findings indicate that the impartial observer is the most preferred outlet for emotional expression, with neither the machine nor the offending party being able to adequately fulfill this role.Public Choice["Xiangdong Qin", "Siyu Wang", "Mike Zhiren Wu", "Xuechun Feng"]2024-11-05T00:00:00 2cd1b8aaafde5c0d5276649c132f194d105615ceJ. Am. Medical Informatics Assoc.00Journal of the American Medical Informatics Association : JAMIA[]2024-11-05T00:00:00 7eb481c872848cc2cda2769d4c1be3a39db7b17aNanotechnology Perceptions00Nanotechnology Perceptions[]2024-11-05T00:00:00 4b4e3587fbe212cc2cae13c5b021f3561e901cb5International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Hesham Salem Ahmed Nasser"]2024-11-05T00:00:00 bd34f3e0178dd30dea4a2e8ffa7b0f7f890ac74eInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["S. Subudhi"]2024-11-05T00:00:00 276b418bb4861c815f65e6ef9b82337465a603d0Scientific Reports180The gated recurrent unit-attention-based module (GAM) holds promise for improving anemia management in patients with ESKD by optimizing ESA dosages and providing timely transfusion alerts and exhibited considerably high accuracy for transfusion alarms.Scientific Reports["Chaewon Kang", "Jinyoung Han", "Seongmin Son", "Sunhwa Lee", "Hyunjeong Baek", "Daniel Duck-Jin Hwang", "Ji In Park"]2024-11-05T00:00:00 94425365ca9190d716e9f8419105805de3a5fea1International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Sanket Das", "Aparna Krishna Bhat"]2024-11-05T00:00:00 728445f60284c99faceeffb6f097a63de799d8c6Asia Pacific Business Review700Asia Pacific Business Review["Qingqing Xu", "Tianci Meng", "Yahui Cao", "Xia Jiang"]2024-11-05T00:00:00 c7ddb833667e893b82d8f664f3c1b8f43eebb231 Background Clinical trial data are a critical, valuable, difficult to access source of information about new medicines. Privacy & intellectual property concerns limit the availability of trial data for secondary, exploratory analysis. Generative models trained on historical clinical trial data can produce synthetic datasets that preserve patient privacy while maintaining dataset characteristics. This allows analysts access to clinical insights without compromising privacy. Uses of these data include to design clinical trials, to generate evidence to support subgroup analyses or matched external controls and to augment trial data sources for machine learning We report how synthetic data from CAR T trials is used to model safety & efficacy in a trial setting with special focus on analyses to design a mitigation strategy for prolonged leukopenia following CAR T infusion Methods Synthetic Data Generation Synthetic data was generated using Simulants, an algorithm that creates synthetic patients by permuting features among similar patients. This approach significantly outperforms deep-learning approaches for clinical trial data. Patient-level data was synthesized from multiple, completed clinical trials from the Medidata Clinical Cloud Clinically relevant analysis planning In collaboration with academic & industry experts, we translated analytical insights into practical advancements in treatment safety and efficacy. Applications include designing lymphodepletion strategies to optimize CAR-T therapy efficacy, predicting severe Cytokine Release Syndrome and analyzing co-occurrence patterns of CRS and ICANS We focused on prolonged leukopenia and immune-system recovery following CAR T therapy because this is a common side effect associated with life threatening infections that is difficult to study without access to the patient-level data. While information on adverse events, such as infections, or point-in-time reports of abnormal blood cytology may be reported in published trial reports as graded, adverse events, blood test dynamics are notAnalytical approaches Analysis methods included Descriptive analyses on temporal evolution of blood analytes over timeRisk factor analysis using models to find factors predictive of prolonged leukopenia or that impact leukocyte recoveryIntervention modeling through analysis of treatment approaches observed in the trial data, such as use of Granulocyte-Colony Stimulating Factor ResultsSynthetic Data Fidelity and Privacy Synthetic data demonstrated high fidelity to datasets (Silhouette score: -0.083, Bag of words R²: 0.99), ensuring the reliability of downstream analyses while protecting individual privacy (Membership disclosure AUC ROC: 0.62).Clinically meaningful insights Distinct patterns in leukocyte dynamics were observed in patients who exhibited prolonged leukopenia as compared to those who recovered quickly. While leukocyte counts initially dropped sharply in all recovery groups post-infusion, patients who recovered showed a consistent increase in leukocytes, while patients who did not recover fluctuated below the leukopenia threshold. Patients with partial recovery plateaued around Day 50 with minimal late recovery Elevated ferritin post-infusion and pre-infusion leukocyte counts were identified as significant predictors of prolonged leukopenia Early Granulocyte-Colony Stimulating Factor (G-CSF) administration within the first 30 days of CAR T therapy was associated with less long-term leukopenia, highlighting the potential benefit of early interventionCross validation of findings in synthetic data with source data The same analysis was conducted in parallel on the real data. We observed qualitatively similar results, which reinforces the reliability of the synthetic data for analysis. Further, no statistically significant differences were observed in any key findings between the source & synthetic data. Conclusion Synthetic clinical trial data can be used in place of source data to develop clinically meaningful insights for CAR T patient management. As synthetic data carries many fewer risks for the privacy of patients or sponsors, we demonstrate the feasibility of this approach to significantly enhance the availability of clinical trial data & accelerate the discovery of new therapeutic approaches Blood00How synthetic data from CAR T trials is used to model safety & efficacy in a trial setting with special focus on analyses to design a mitigation strategy for prolonged leukopenia following CAR T infusion is reported.Blood["Penelope Lafeuille", "Afrah Shafquat", "Chao Sang", "Mandis Beigi", "Francesco Maura", "J. Aptekar"]2024-11-05T00:00:00 da973e93f5c9944f21fdb4f192904da9a656c8c8International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Bhabashankar Sahu"]2024-11-05T00:00:00 b761ea25bd031215fd67eda2b9c1dc542f785e6aInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Rayan Bokhari"]2024-11-05T00:00:00 3a56fa679c81b242984ab100778b4fa8d4c9e9baInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Chetan Sharma", "Adarsh Vaid", "Mukesh Kumar Saini"]2024-11-05T00:00:00 c02143ce3599a10ef901cc255cd9def6747959ab00["Deepika Ghai", "K. Rawal", "K. Dhir", "Suman Lata Tripathi"]2024-11-05T00:00:00 420d47667b693b5bccb20eea2f3abf88331e2f35International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["V. Anandha Valli"]2024-11-05T00:00:00 4f1ddf8612af916c604996e14b7ea01d135c8457International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Gunda Nikhil", "Kakkireni Bharath Kumar"]2024-11-05T00:00:00 74060bd3067d6ebe21224de40572571785978feaEuropean Journal of Ophthalmology40European journal of ophthalmology["Roberto Perilli"]2024-11-05T00:00:00 5c91886c65f2889c362c9c3dffc96130cb34ef2fInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Sreelakshmi Mohanachandran"]2024-11-05T00:00:00 cbef6618a28d4165c66ea09201dc1ff69d713897Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.2024 IEEE Cyber Science and Technology Congress (CyberSciTech)510This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems.2024 IEEE Cyber Science and Technology Congress (CyberSciTech)["Jianhua Ma", "Qun Jin", "Hui-Huang Hsu", "John Paul C. Vergara", "Antonio Guerrieri", "Claudio Miceli", "Ao Guo"]2024-11-05T00:00:00 b92e7a6eccd47b73025d5b474e7a36cb53b1f077 The Artificial Intelligence Act (AI Act) of the European Union (EU) claims to be based on a risk-based approach to avoid over-regulation and to respect the principle of legislative proportionality. This paper argues that risk-based regulation is indeed the right approach to AI regulation. At the same time, however, the paper shows that important provisions of the AI Act do not follow a truly risk-based approach. Yet, this is nothing that cannot be fixed. The AI Act provides for sufficient tools to support future-proof legislation and to implement it in line with a genuine risk-based approach. Against this background, the paper analyses how the AI Act should be applied and implemented according to its original intention of a risk-based approach, and what lessons legislators around the world can learn from the AI Act in regulating AI.European Journal of Risk Regulation04How the AI Act should be applied and implemented according to its original intention of a risk-based approach, and what lessons legislators around the world can learn from the AI Act in regulating AI are analyzed.European Journal of Risk Regulation["Martin Ebers"]2024-11-06T00:00:00 2f03f71609f7890431bf66773903752e3624f945Background: Amid growing global uncertainty and increasingly complex disruptions, the ability of supply chains to rapidly adapt and recover is critical. The incorporation of artificial intelligence (AI) into supply chain management represents a transformative strategy for enhancing resilience. By harnessing advanced AI technologies, such as machine learning, predictive analytics, and real-time data processing, organizations can more effectively anticipate, respond to, and recover from disruptions.AI improves demand forecasting accuracy, optimizes inventory management, and increases real-time visibility across the supply chain, reducing the risks of stockouts and surplus inventory. Furthermore, I-driven automation and robotics enhance operational efficiency by minimizing human error and streamlining processes. Methodology/Approach: This paper proposes a conceptual framework for strengthening supply chain resilience through AI integration. The framework leverages AI technologies to improve key aspects of supply chain resilience, including risk management, operational efficiency, and real-time visibility. Result/Conclusions: Additionally, it underscores the importance of collaborative relationships with supply chain partners, enabled by AI-powered data-sharing and communication tools that foster trust and coordination within the network. Originality/Value: This comprehensive framework offers a strategic approach to integrating AI into supply chain management, highlighting its potential to significantly enhance resilience, operational efficiency, and sustainability, thereby empowering organizations to navigate the complexities of modern supply chains more effectively.Logistics525This comprehensive framework offers a strategic approach to integrating AI into supply chain management, highlighting its potential to significantly enhance resilience, operational efficiency, and sustainability, thereby empowering organizations to navigate the complexities of modern supply chains more effectively.Logistics["Meriem Riad", "Mohamed Naimi", "C. Okar"]2024-11-06T00:00:00 447409a2368fda9e9d6d0c9028fda358a809fa99The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts. Utilizing an integrated approach, we compared various prediction algorithms, including logistic regression, and interpreted the results through SHAP (SHapley Additive exPlanations) analysis. The findings reveal five key thematic areas: AI’s role in enhancing quality assurance, resource management, technological innovation, security, and the healthcare response to the COVID-19 pandemic. The study highlights AI’s positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration. These insights provide opportunities for targeted interventions to optimize AI’s impact in current and future healthcare landscapes. In conclusion, this work contributes to a deeper understanding of the role of AI in healthcare management and provides insights for policymakers, healthcare professionals, and researchers, offering a roadmap for addressing both the opportunities and challenges posed by AI integration in the healthcare sector.Applied Sciences1053The study highlights AI’s positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration.Applied Sciences["Vito Santamato", "Caterina Tricase", "Nicola Faccilongo", "Massimo Iacoviello", "Agostino Marengo"]2024-11-06T00:00:00 1f5daef839afc0fc0b7ba57f209531fe8876061cThis review explores the current applications, benefits, and challenges of artificial intelligence (AI) in plastic, reconstructive, and aesthetic surgery. In recent years, AI has found its way into everyday life, including the healthcare sector. To deepen the understanding of the use and handling of AI in plastic and reconstructive surgery, this review provides valuable insights into modern practices, illustrated with real examples and potential future applications. While the advantages of AI are obvious, the disadvantages cannot be ignored. This review aims to highlight possible risks, dangers, and sources of error inherent in AI itself and its applications. Therefore, this paper seeks to address possible concerns and questions about AI in plastic surgery while offering a realistically neutral insight. Additionally, fundamental ethical and legal principles will be discussed, as well as possible “rules of the game” for the application and integration of AI in surgery. Innovations in this field are often hailed as miracles, making it crucial to evaluate them critically and objectively. Although progress in AI cannot and should not be halted, it is important to strengthen the trained approach and always look at the whole picture.Artificial Intelligence Surgery451This review aims to highlight possible risks, dangers, and sources of error inherent in AI itself and its applications, as well as possible “rules of the game” for the application and integration of AI in surgery.Artificial Intelligence Surgery["Matthias Novotny", "Anna Fast", "Christine Radtke"]2024-11-06T00:00:00 1d5831e7b535d4a26efcc730c92bf6e964d602baArtificial Intelligence has now taken a full-fledged role in healthcare and has started driving innovations not only in diagnostics and treatment planning but also in patient monitoring and operational efficiency. This will enable complex medical data analysis, extracting patterns and insights that no human is capable of. However, most of these models are per se opaque-that is, the so-called "black-box" problem-there are still great challenges in areas such as transparency, trust, and ethical applications in a clinical setting. This lack of interpretability can stand in the way of acceptance or integration for AI technologies when issues of understanding and accountability are relevant. Explainable AI solves these problems by making real artificial intelligence decisions understand the decisions made to humans. XAI techniques offer well-understandable and interpretable explanations of the models with minimum degradations in performance. This review article explains in detail the critical role of XAI in healthcare, underpinning how this field can bring more transparency into AI applications. We explain some of the current methods of XAI: model-agnostic techniques like LIME and SHAP, interpretable models relating to decision trees and linear models, and visualization techniques like saliency maps and mechanisms of attention.International Journal For Multidisciplinary Research01The critical role of XAI in healthcare is explained, underpinning how this field can bring more transparency into AI applications, and some of the current methods of XAI are explained: model-agnostic techniques like LIME and SHAP, interpretable models relating to decision trees and linear models, and visualization techniques like saliency maps and mechanisms of attention.International Journal For Multidisciplinary Research["Jaishankar Inukonda", "Vidya Rajasekhara Reddy Tetala", "Jayanna Hallur"]2024-11-06T00:00:00 62ac7a5b3b4ae01612a34f1d59dd31d8e5f0da6fArtificial intelligence (AI) has been studied and applied to medicines and vaccine development. However, there is still a need to understand the processes used and improve them. With this letter, we draw attention to AI use as a tool in vaccine production, supporting scientists and enabling optimization in this process. In this way, it can be applied to contain pandemics and epidemics.International Journal of Health Planning and Management71Attention is drawn to AI use as a tool in vaccine production, supporting scientists and enabling optimization in this process, so that it can be applied to contain pandemics and epidemics.The International journal of health planning and management["M. S. Barreto", "Adriana Kelly Santana Correa", "R. S. Santos", "E. E. D. Silva", "D. M. R. R. Silva", "P. H. M. Moura", "P. Jesus", "J. B. D. Souza", "L. A. D. M. Santana", "Rajiv Gandhi Gopalsamy", "Govindasamy Hariharan", "A. G. Guimar\u00e3es", "L. Borges"]2024-11-06T00:00:00 52036166a2c45d228f173da3476234ec79f1a0ffAcademic Psychiatry71This study shows the capacity of GPTZero to distinguish human-created versus AI-generated writing, and suggests standardization of protocol regarding the use of AI prior to its integration in post-graduate medical education.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["Hugh Burke", "Rebecca Kazinka", "Raghu Gandhi", "Aimee Murray"]2024-11-06T00:00:00 91e60c86b9f73eb5f6ff6708ac7bc4fd83ef715dThis work aims, through an approach to recent scientific literature, to highlight the effect of the use of Artificial Intelligence (AI) in today’s educational field. From the analysis of the content of the relevant scientific literature, it appears that AI can help teachers in designing appropriate learning programs focusing on the needs of their students at an individual and collective level. In this case, using AI, a variety of learning activities can be created by the teachers with the aim of creating a supportive learning environment for the students, which will help them to better understand, as well as deepen, the material of the various knowledge areas of the curriculum. The integration of AI in both live and distance education can offer significant benefits, such as the transformation of the traditional teaching methods applied, such as the lecture, with the aim of enhancing the learning experience. Furthermore, from the content of scientific studies, both the creation of social inequalities and the emergence of an ethical concern regarding the educational use of AI, which is linked to the violation of the privacy and personal data of the people who use it in the context of their learning effort, emerge. However, the future of education seems to be linked to the necessity of reshaping the educational content through a broad and multi-level utilization of AI in it. And this is in order to shape the conditions for "meeting" the individual learning needs of the students.  Article visualizations:European Journal of Open Education and E-Learning Studies290The future of education seems to be linked to the necessity of reshaping the educational content through a broad and multi-level utilization of AI in it in order to shape the conditions for "meeting" the individual learning needs of the students.European Journal of Open Education and E-learning Studies["G. Spiliopoulou", "Gerasimos S. Koustourakis"]2024-11-06T00:00:00 ecfc60c5d0e38fc19306fe519889b8a4685670ba Artificial intelligence (AI) has become a buzzword around the globe. For many, AI was once contained in high-tech labs and has now been released out into the world for the rest of us to use. Generative AI, which is what Microsoft, Apple, and OpenAI have recently offered, is only one version of AI – probably the one with the most ‘curb appeal’. In fact, AI dates to the 1950s and has offered much more banal – by today’s standards – innovations. This case study represents an effort to demystify popular notions of AI and take a first baby step toward developing AI literacy among international development practitioners. We offer two cases of courses that we developed to build appropriate bridges to the future, to show AI is not like the discovery of fire – a gift from the gods – but rather a technology that is a baby step forward in data analytics.Journal of Integrated Global STEM90This case study represents an effort to demystify popular notions of AI and take a first baby step toward developing AI literacy among international development practitioners.Journal of Integrated Global STEM["Zahra Zarei Ardestani", "Esther Mao", "Robert Krueger"]2024-11-06T00:00:00 22a8b7b45dec210846b721844e9d2e0b04855bc0Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. For this purpose, an AI-enabled methodological approach is developed in Python, integrating fuzzy logic to effectively navigate the uncertainties inherent in evaluating the performance of techniques. The incorporation of sensitivity analysis further enables a thorough evaluation of how input variations impact decision-making outcomes. To conduct the assessment, this study provides an AI-powered decision-making application using large language models in the field of natural language processing, which has emerged as an influential branch of artificial intelligence. The findings reveal that machine learning and big data analytics as well as fuzzy logic and programming stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems. The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field. Thus, this study not only addresses a critical gap in the literature but also offers an AI-driven approach to support complex decision-making processes.Big Data and Cognitive Computing970The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field.Big Data and Cognitive Computing["Hamed Gholami"]2024-11-06T00:00:00 1f760b9b22d92da841bf731d95dbdc8bc05d5599The frequent increase in e-commerce activities particularly in developing countries like Malaysia has raised issues relating to online fraud detection among other challenges. As e-commerce transcends legal boundaries, there are constant threats of fraud which are dangerous to entities such as financial institutions, corporates, and customers. Although classical machine learning (ML) models have shown effectiveness in the detection of fraudsters, they too have a degree of limitations especially because they lack transparency in operation. Following suit, Explainable Artificial Intelligence (XAI) provides a solution, presenting AI systems that yield thorough explanations together with reliable predictions. Understanding how XAI enhances the transparency of AI models is crucial in fraud detection systems as it helps in adhering to regulatory requirements and developing stakeholder confidence. The objective of this literature review is to consolidate the existing studies on incorporating XAI principles for online fraud detection and the necessity of being able to interpret models for the improvement of the systems' robustness, reliability, and stakeholder's confidence. In concentrating on methods suitable to the specific regulatory and cultural context of Malaysia's digital economy, this study demonstrates the prospect of XAI to enhancing fraud detection and securing the health of digital financial ecosystems.2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS)280The prospect of XAI to enhancing fraud detection and securing the health of digital financial ecosystems is demonstrated, concentrating on methods suitable to the specific regulatory and cultural context of Malaysia's digital economy.2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS)["Parteeban M. Varatharajoo", "Nur Haryani Zakaria", "J. Bakar", "M. Mahmuddin"]2024-11-06T00:00:00 9168001ce49b6e56ac14634c48b03c3b4a744234This study aims to identify factors that influence continuance intention to use mobile banking by extending the Expectancy Confirmation Model (ECM) and integrating Artificial Intelligence (AI) and security as moderating variables. Data were collected from mobile banking users in Southwest Papua and analyzed using a Structural Equation Model (SEM) with the Partial Least Squares (PLS) approach. The results showed that confirmation, perceived usefulness, customer experience, and satisfaction significantly influenced continuance intention to use mobile banking. In addition, artificial intelligence that is considered intelligent and anthropomorphic also increases perceived usefulness and confirmation of expectations. The security aspect was found to moderate the relationship between perceived usefulness and satisfaction, thereby increasing user satisfaction. These findings provide important insights for mobile banking developers and policy makers to increase the adoption of digital banking services.International Conference of Business and Social Sciences440The security aspect was found to moderate the relationship between perceived usefulness and satisfaction, thereby increasing user satisfaction, and provide important insights for mobile banking developers and policy makers to increase the adoption of digital banking services.International Conference of Business and Social Sciences["Rokhimah Rokhimah", "Suhermin Suhermin"]2024-11-06T00:00:00 2d17f42be2065c460924c164ffc0a042720ca1dcBACKGROUND Skin cancer is the most common cancer worldwide. Early diagnosis is crucial for improving patient survival and morbidity. Artificial intelligence (AI)-assisted smartphone applications (apps) for skin cancer potentially offer accessible, early risk assessment of suspicious skin lesions. However, the integration of novel technologies into dermatology pathways raises ethical concerns. Although ethical principles for AI governance are well known, how these principles should be applied to real-life AI apps readily available for public use is less well understood. OBJECTIVES We conducted an ethical use-case analysis of commercially available skin cancer apps to better understand the ethical issues arising from their development and use in a real-world context. METHODS Established methods for ethical analysis of clinical AI applications were applied to two popular skin cancer apps in the UK: SkinVision and Scanoma. Systematic searches of published literature, regulatory documents, and websites were conducted to review the evidence regarding app development, effectiveness, and use. Screening for inclusion was undertaken by two researchers independently. Ethical concerns were identified with reference to previously described ethical concerns and principles for AI-assisted healthcare. RESULTS By conceptualising ethical principles within the use-context of skin cancer apps, we identified specific ethical issues arising throughout the AI lifecycle of both apps. One company provided extensive detail regarding algorithm development and decision-making, this information was insufficiently reported for the other app. Other concerns identified related to number, quality, and consistency of studies assessing algorithm efficacy. Limited efforts to address potential skin tone biases and exclusion of individuals with darker skin tones as target users by one app risks perpetuating existing inequalities. Inadequate regulatory oversight was identified. CONCLUSIONS Findings from our ethical use-case analysis of two patient-facing AI-assisted skin cancer apps suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps. Improved regulation should increase accountability. Ensuring ethics by design through integration between technology developers, dermatologists, ethicists, and the public is urgently needed to prevent the potential benefits of AI-assisted skin cancer apps being overshadowed by potential ethical harms.British Journal of Dermatology00Findings from an ethical use-case analysis of two patient-facing AI-assisted skin cancer apps suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps.The British journal of dermatology["Syed F H Shah", "Daniel Arecco", "Heather Draper", "Simona Tiribelli", "Eli Harriss", "R. Matin"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/2d17f42be2065c460924c164ffc0a042720ca1dc b044c201005ede8b5d647b5b300461b976ba638cThis study explores the implementation of artificial intelligence (AI) in education, particularly in teaching English as a foreign language (TEFL) in higher education. AI has been applied in TEFL since the 1950s and developed significantly over the decades. However, it has received global attention in the last two years after introducing ChatGPT and other similar Generative AI (GenAl) applications. Implementing AI provides benefits and challenges to educators and students in English learning and teaching (ELT). This study examines systematic literature reviews regarding the benefits and challenges of employing AI toward ELT in higher education. This study synthesizes previous research to enhance insights into TEFL by applying AI effectively. Applying AI to teach English as a foreign language has several benefits and challenges. These can be summarized in the dimensions of using machine learning, chatbots, intelligent virtual environments, translation tools, multidimensional ethics, and social media. Each dimension provides valuable benefits and considers the challenges for educators in delivering English as a foreign language at the university level. The benefits of utilizing AI in TEFL are evolutionary and revolutionary changes in teaching methods, personalized learning environments, better management, and more accessible education. Despite these benefits, the challenges of utilizing AI can be identified as computational issues, limited language exposure, and lack of human interaction. Furthermore, this study can enhance the understanding and insight of implementing AI in teaching English as a foreign language by minimizing the challenges and optimizing the benefits of AI.International Conference on Information Technology Based Higher Education and Training360This study synthesizes previous research to enhance insights into TEFL by applying AI effectively, and can enhance the understanding and insight of implementing AI in teaching English as a foreign language by minimizing the challenges and optimizing the benefits of AI.2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET)["Elvina Gusman", "E. Gide", "Mahmoud El Khodr", "Ghulam Chaudhry"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/b044c201005ede8b5d647b5b300461b976ba638c b4edd0237459e659790aed9e9d042b8ee90f1e22The integration of artificial intelligence (AI) is expected to revolutionise healthcare, compelling forthcoming healthcare professionals to arm themselves with essential knowledge and skills. Given this, understanding medical students’ (future healthcare providers’) perspectives and readiness is vital for achieving full integration. This study aimed to assess the knowledge, perspectives, and readiness perceived by medical students at Nnamdi Azikiwe University. This cross-sectional study conveniently recruited 340 medical students. A pretest self-structured questionnaire was utilised for data collection among students who were already in the clinical phase of their study programme. The Statistical Package for Social Science (SPSS) was used for the analysis of the results. The vast majority of the respondents (99.4%) had heard of AI, but only 3.2% were very familiar with its real-world applications. Most participants (96.8%) lacked formal education or training in AI, and few (7.4%) regularly followed AI-related news. Concerns about AI integration included data privacy (39.4%) and the potential loss of human touch in patient care (70.9%). Job displacement (72.1%) and misuse of AI (55.9%) were common fears. Despite these concerns, more than half of the respondents (55.6%) were interested in AI research, and many expressed openness to collaborating with AI systems (34.1%) and acquiring additional AI-related skills (67.9%). There was a lack of AI knowledge among the respondents, coupled with widespread scepticism about its integration. However, there is a notable interest in AI-related research and projects, indicating a willingness to explore its potential benefits.Apollo Medicine200There was a lack of AI knowledge among the respondents, coupled with widespread scepticism about its integration, but there was a notable interest in AI-related research and projects, indicating a willingness to explore its potential benefits.Apollo Medicine["S. Obiekwe", "Ikechukwu Benjamin Omaga", "Mmesoma Miriam Ukadike", "Chidera Gabriel Edeh", "Chinyere Esther Iheanyi", "Promise Ugochuku Anisiobi", "Chukwuebuka Favour Obi", "S. Ogenyi", "Ejeatuluchukwu Obi"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/b4edd0237459e659790aed9e9d042b8ee90f1e22 62d640934b47c1ebbe33dc05a81116edf1ac1fd7Abstract Background and purpose To describe the clinical commissioning of an in‐house artificial intelligence (AI) treatment planning platform for head‐and‐neck (HN) Intensity Modulated Radiation Therapy (IMRT). Materials and methods The AI planning platform has three components: (1) a graphical user interface (GUI) is built within the framework of a commercial treatment planning system (TPS). The GUI allows AI models to run remotely on a designated workstation configured with GPU acceleration. (2) A template plan is automatically prepared involving both clinical and AI considerations, which include contour evaluation, isocenter placement, and beam/collimator jaw placement. (3) A well‐orchestrated suite of AI models predicts optimal fluence maps, which are imported into TPS for dose calculation followed by an optional automatic fine‐tuning. Six AI models provide flexible tradeoffs in parotid sparing and Planning Target Volume (PTV)‐organ‐at‐risk (OAR) preferences. Planners could examine the plan dose distribution and make further modifications as clinically needed. The performance of the AI plans was compared to the corresponding clinical plans. Results The average plan generation time including manual operations was 10–15 min per case, with each AI model prediction taking ∼1 s. The six AI plans form a wide range of tradeoff choices between left and right parotids and between PTV and OARs compared with corresponding clinical plans, which correctly reflected their tradeoff designs. Conclusion The in‐house AI IMRT treatment planning platform was developed and is available for clinical use at our institution. The process demonstrates outstanding performance and robustness of the AI platform and provides sufficient validation.Journal of Applied Clinical Medical Physics390The clinical commissioning of an in‐house artificial intelligence (AI) treatment planning platform for head‐and‐neck (HN) Intensity Modulated Radiation Therapy (IMRT) demonstrates outstanding performance and robustness of the AI platform and provides sufficient validation.Journal of Applied Clinical Medical Physics["Xinyi Li", "Yang Sheng", "Qingrong Jackie Wu", "Y. Ge", "D. M. Brizel", "Yvonne M. Mowery", "Dongrong Yang", "Fang-Fang Yin", "Qiuwen Wu"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/62d640934b47c1ebbe33dc05a81116edf1ac1fd7 6ccb23db43094750f8337b672fa9a88c3ddacc30In the contemporary economic, sociological and political landscape, the successful integration of Artificial Intelligence (AI) computer programs into the predicted behaviour of stock market value in the financial markets represents a profound burgeoning frontier in economics, with profound implications for both wealth generation and national budget resource allocation. This paper explores the potential sociopolitical economic strategy of using AI-driven stock market prediction predictions to cumulatively accumulate capital funded for by taxpayer sourced representative income. As the baseline return accumulates in the form of investment potential then as a means of revenue accumulation for the state then the burden of the national budget upon its citizens decreases then ceases. This is the theorisation of the establishment of a Marxist economic social superstructure for the funding of social institutions through the powerful potential of artificial intelligence. By leveraging a portion of the national budget on advanced AI technologies, this strategy could dramatically facilitate the financial sustenance of political budgets aimed at social equity and economic justice. Through a critical review of sociological and economic theories, this theoretical research delineates exactly how such an approach could potentially reconfigure the mode of production and contribute to the reformation of a sustainable socialist economy.Economics, Politics and Regional Development00This paper explores the potential sociopolitical economic strategy of using AI-driven stock market prediction predictions to cumulatively accumulate capital funded for by taxpayer sourced representative income to reconfigure the mode of production and contribute to the reformation of a sustainable socialist economy.Economics, Politics and Regional Development["Alan Peter Garfoot", "Sameep Gowda"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/6ccb23db43094750f8337b672fa9a88c3ddacc30 bc8c920d962887e586e3a4e3d5b53632c0138616A comprehensive analysis was performed using the AIDS Clinical Trials Group 175 dataset to improve the accuracy of predicting AIDS disease progression. The primary objective was to integrate machine learning techniques to predict AIDS disease outcomes based on clinical, demographic, and treatmentrelated variables. Several machine learning models, including advanced neural network architectures, were developed and thoroughly evaluated. The study highlighted the ability of machine learning to accurately identify patterns and risk factors associated with AIDS disease progression, thereby improving treatment strategies and patient management. Extensive comparisons of several machine learning models were performed to evaluate their performance and robustness. The aim is to demonstrate the important role of artificial intelligence in predicting and diagnosing AIDS early, thereby contributing to better healthcare outcomes. The comprehensive evaluation of model performance in this study is expected to support future advances in prediction models for AIDS and other chronic diseases.2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA)160The study highlighted the ability of machine learning to accurately identify patterns and risk factors associated with AIDS disease progression, thereby improving treatment strategies and patient management and supporting future advances in prediction models for AIDS and other chronic diseases.2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA)["Akshay Rajan", "Gouri Santhosh", "Balu Manoj", "Nandana Manohar", "T. Anjali"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/bc8c920d962887e586e3a4e3d5b53632c0138616 caf1bbd5cac2dfc9ce99df57ef732129604ce223With the rapid development of artificial intelligence, its application in art education is becoming increasingly widespread, providing students with new ways to enhance creativity and critical thinking. This paper analyses the specific application of artificial intelligence in art education and explores how it fosters students' creativity through innovative teaching methods, personalized learning, and thinking guidance in artistic creation. At the same time, the study also reveals the positive role of artificial intelligence in helping students to screen and criticize information, analyze and evaluate art works, and encourage innovative thinking and critical thinking. The research demonstrates that artificial intelligence significantly enhances students' independent thinking and critical appreciation of art, improving their artistic literacy and innovation while supporting the modernization of art education.International Theory and Practice in Humanities and Social Sciences20The research demonstrates that artificial intelligence significantly enhances students' independent thinking and critical appreciation of art, improving their artistic literacy and innovation while supporting the modernization of art education.International Theory and Practice in Humanities and Social Sciences["Yanmeng Fan"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/caf1bbd5cac2dfc9ce99df57ef732129604ce223 6c0b7a1f12714565a033f3df486068fd0489bcacArtificial intelligence (AI) based education represents a significant transformation in the field of education of our age. Artificial intelligence (AI) technology has great potential to enrich the learning experience of special needs students, provide support to teachers, and reduce inequalities in education. Artificial intelligence (AI) technologies can be used to more effectively understand the learning needs of special needs students and provide customized learning experiences for them. Expert systems, adaptive tutorial systems, dialogue-based systems, learning analytics and educational data mining are widely used AI systems developed to increase productivity in educational environments, facilitate achievement of learning goals, provide instant feedback and enrich student interaction. Assessing the learning inclinations, strengths, and weaknesses of students with special needs enables AI to provide tailored learning content and resources that cater to their individual requirements effectively. In this article, tried to present a road map to teachers and researchers by examining the role of AI-based technologies in the education of special needs students, the AI-based technologies that can be used, and the issues to be considered when choosing these technologies.International journal of technology in education and science370A road map to teachers and researchers is presented by examining the role of AI-based technologies in the education of special needs students, the AI-based technologies that can be used, and the issues to be considered when choosing these technologies.International Journal of Technology in Education and Science["Ay\u015fe Alkan"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/6c0b7a1f12714565a033f3df486068fd0489bcac ff21b4564696cf008d937b9380966228a8aea6c7Computational thinking is an approach used to solve complex problems with basic skills such as decomposition, pattern recognition, abstraction and algorithm design. Especially artificial intelligence and its sub-fields make significant contributions to the development of these skills. This study aims to evaluate the research in the field of artificial intelligence in the computational thinking process through bibliometric analysis. The data of the study were obtained from the Web of Science (WoS) database on May 1, 2024. The VOSviewer software program, which is frequently used in bibliometric analysis and can be used free of charge, was used to analyze the data obtained from the WoS database. Analyses using the data obtained from the WoS database enabled the analysis of research in this field in terms of countries, authors, co-authorship status, publication journals, publication years and keywords. The findings of the study show that Taiwan is the country with the highest number of studies in this field, while Spain is the country with the highest number of citations. Hooshyar, D and Hsu, T are among the authors who contributed to the most publications. Collaboration plays an important role in research and Lai, C stands out as the author with the most co-authorship. Education and Information Technologies and Kunstliche Intelligenz are among the journals where the most studies are published. As a result of the examination of the 5 most cited studies, it was determined that different research methods and participant groups were used, data collection tools varied and different data analysis methods were used. These studies provide a broad perspective on artificial intelligence studies in the field of computational thinking and make important contributions to the literature in this field.International Journal of Studies in Education00It was determined that different research methods and participant groups were used, data collection tools varied and different data analysis methods were used, and Taiwan is the country with the highest number of studies and Spain is the country with the highest number of citations.International Journal on Studies in Education["Sercan Ertas Kalay", "Jale Ipek"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/ff21b4564696cf008d937b9380966228a8aea6c7 7c25f039e598e4ff4612f93e8f8cd29854324694Hospital readmissions are a significant management challenge, with both clinical and economic implications. In this study, we apply artificial intelligence techniques to predict hospital readmissions in adult patients within 30 days of discharge using a dataset from Hospital Alma Máter de Antioquia, that includes demographic, clinical, and hospitalization information. The project tested several machine learning algorithms including logistic regression, decision trees and deep learning as neural networks. The results show that the neural network model achieved the best balance between the metrics of Specificity=84.0%, Precision=41.5%, Recall=98.7%, F1-Score=58.4%, accuracy=85.5% and AUC=94.7%. This study highlights the usefulness of artificial intelligence techniques for improving hospital readmission management.2024 3rd International Congress of Biomedical Engineering and Bioengineering (CIIBBI)220This study applies artificial intelligence techniques to predict hospital readmissions in adult patients within 30 days of discharge using a dataset from Hospital Alma Máter de Antioquia, that includes demographic, clinical, and hospitalization information to highlight the usefulness of artificial intelligence techniques for improving hospital readmission management.2024 3rd International Congress of Biomedical Engineering and Bioengineering (CIIBBI)["Iv\u00e1n Daniel", "Salazar Alarc\u00f3n", "Angelower Santana-Vel\u00e1squez", "John Freddy", "Maria Bernarda Salazar-S\u00e1nchez", "Alejandro Hern\u00e1ndez-Arango"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/7c25f039e598e4ff4612f93e8f8cd29854324694 a06cc5df4a0317808f7b13e41278c414a4c2b5b8In light of digital transformation and the digital environment, it has become imperative for all countries to modernize their facilities in line with artificial intelligence applications. With scientific and technological progress, predicting phenomena has become an imperative for all countries, requiring their study, examination, and utilization of all data in order to confront them before they occur, as is the case with the phenomenon of earthquakes. In light of the globalization of crime and the development of criminal methods and the innovation of modern means and tools to commit it and obscure its features, we find ourselves under the inevitability of searching for ways to keep pace with this globalization. The latest endeavor was predictive policing, which is considered among the goals that the state of law seeks to achieve, through a forward-looking outlook and future visions based on algorithms and unambiguous results to arrest criminals, relying on artificial intelligence techniques.Turkish Academic Research Review - Türk Akademik Araştırmalar Dergisi [TARR]00The latest endeavor was predictive policing, which is considered among the goals that the state of law seeks to achieve, through a forward-looking outlook and future visions based on algorithms and unambiguous results to arrest criminals, relying on artificial intelligence techniques.Turkish Academic Research Review - Türk Akademik Araştırmalar Dergisi [TARR]["Bensalem Kheira"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/a06cc5df4a0317808f7b13e41278c414a4c2b5b8 f7d1e47c948fde9557c955f20cf2e805c33d95d5Introduction. Artificial intelligence (AI) technologies are becoming crucial in clinical diagnostics due to their ability to process and interpret large volumes of data. The implementation of AI for biomarker analysis opens new opportunities in personalized medicine, offering more accurate and individualized approaches to disease diagnosis and treatment. The relevance of this review stems from the need to systematize recent advances in AI application for biomarker analysis, which is critical for early diagnosis and prediction of chronic non-communicable diseases (NCDs). Material and methods. The analysis of peer-reviewed scientific publications and reports from leading research centers over the past five years was conducted. Studies on the application of AI algorithms for analyzing genomic, proteomic, and metabolomic biomarkers were reviewed, including machine learning methods and deep neural networks. Special attention was paid to the integration of multi-marker panels for improving the accuracy of diagnosis and prediction of cardiovascular, digestive, respiratory, endocrine system diseases, as well as oncological and neurodegenerative pathologies. Results. The application of AI has significantly increased the sensitivity and specificity of diagnostics, especially in complex cases requiring analysis of multiple disease parameters. The effectiveness of AI has been demonstrated in early diagnosis of lung, breast, and colorectal cancer, prediction of cardiovascular complications and NCDs progression, including diabetes mellitus and Alzheimer’s disease. AI’s significant contribution to the discovery of new biomarkers, optimization of personalized treatment, and improvement of therapeutic strategies has been noted. Conclusion. The use of AI in biomarker analysis has become a significant breakthrough in medical diagnostics, particularly in oncology, cardiology, and neurodegenerative diseases. The technology allows integration of data about various biomarkers and contributes to creating more accurate models for disease diagnosis and prediction. Further development is associated with technology advancement and overcoming ethical and regulatory barriers, which will expand AI capabilities in clinical practice.Molekulyarnaya Meditsina (Molecular medicine)310The use of AI in biomarker analysis has become a significant breakthrough in medical diagnostics, particularly in oncology, cardiology, and neurodegenerative diseases, and the technology allows integration of data about various biomarkers and contributes to creating more accurate models for disease diagnosis and prediction.Molekulyarnaya Meditsina (Molecular medicine)["P.V. Seliverstov", "V. Kutsenko", "V.G. Gorelova", "Sh.A. Magomedova", "S.R. Akhmedov", "Y. Nurmyradov"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/f7d1e47c948fde9557c955f20cf2e805c33d95d5 5870184bb220beef84912006598d9738c1859a4bArtificial Intelligence (AI) has emerged as a transformative force across various sectors, reshaping economies and societies globally. This review aims to provide a systematic analysis of the existing literature on the impact of AI on economic development. By conducting a combined bibliometric and content analysis of relevant studies from the past two decades, we identify major themes and research directions within the field. The review reveals that AI plays a significant role in enhancing productivity, fostering innovation, and driving economic growth. Key areas of influence include intelligent decision-making, labor market transformations, Industry 4.0, and social governance. However, AI also presents challenges such as ethical concerns, potential job displacement, and privacy risks. The findings highlight both opportunities and limitations of AI in economic contexts, emphasizing the need for policies that support positive economic impacts while mitigating adverse effects. This review provides scholars, policymakers, and industry leaders with a comprehensive understanding of AI’s evolving role in economic development and outlines future research directions to bridge current knowledge gaps.International Theory and Practice in Humanities and Social Sciences230It is revealed that AI plays a significant role in enhancing productivity, fostering innovation, and driving economic growth, and the need for policies that support positive economic impacts while mitigating adverse effects is emphasized.International Theory and Practice in Humanities and Social Sciences["Chunhong Yuan", "Jingyi Tang", "YiDing Cao", "Tianshi Wei", "WeiTao Shen"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/5870184bb220beef84912006598d9738c1859a4b f8e1c1602a6f95d431e1c5662d68029e7358f912The development of artificial intelligence has made significant contributions to the financial sector. One of the main interests of investors is price predictions. Technical and fundamental analyses, as well as econometric analyses, are conducted for price predictions; recently, the use of AI-based methods has become more prevalent. This study examines daily Dollar/TL exchange rates from January 1, 2020, to October 4, 2024. It has been observed that among artificial intelligence models, random forest, support vector machines, k-nearest neighbors, decision trees, and gradient boosting models were not suitable; however, multilayer perceptron and linear regression models showed appropriate suitability and despite the sharp increase in Dollar/TL rates in Turkey as of 2019, the suitability of valid models has been maintained.10It has been observed that among artificial intelligence models, random forest, support vector machines, k-nearest neighbors, decision trees, and gradient boosting models were not suitable; however, multilayer perceptron and linear regression models showed appropriate suitability and despite the sharp increase in Dollar/TL rates in Turkey as of 2019, the suitability of valid models has been maintained.["Asef Yelghi", "Aref Yelghi", "Shirmohammad Tavangari"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/f8e1c1602a6f95d431e1c5662d68029e7358f912 c67bf7aaa2b9e86ed63c9fe89a10e7aecd2f07c2Artificial intelligence (AI) technologies have revolutionized journalism in the digital era. This study is constructed on a general literature review revealing the role of AI in journalism and emphasizes the following key facets: (i) automated reporting, (ii) automated content creation, (iii) automated transcription and translation, (iv) data mining and analysis, (v) fact-checking and verification, and (vi) content personalization. The role of AI is observed in creating news reports like financial digests, sports outcomes, and weather updates, gearing up the automated content creation, transcribing the interviews, providing multilingual support for content translation, data mining and analysis, detecting fake news, personalizing the content in line with audience's preferences. The wide application of AI in journalism automates routine journalistic tasks, thereby improving efficacy and productivity and saving time and effort. Though AI is transforming journalism, there are several challenges facing journalism using AI, including biased algorithms, data availability and quality, data privacy and security, the need for training and education, transparency, and cost concerns. Journalists must be trained to identify and address issues such as data privacy, algorithmic bias, and the ethical implications of adopting AI in news reporting. News agencies should also implement strong data protection measures and transparent AI algorithms to overcome these challenges. They must attain a balance between considering user privacy and offering personalized content. It is paramount to have robust regulatory frameworks to oversee the utility of AI in journalism. Also, warranting ethical standards in AI implementation is crucial to preserving journalistic integrity.Edelweiss Applied Science and Technology720The wide application of AI in journalism automates routine journalistic tasks, thereby improving efficacy and productivity and saving time and effort.Edelweiss Applied Science and Technology["Wedad Banafi"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/c67bf7aaa2b9e86ed63c9fe89a10e7aecd2f07c2 1e4a775fb0f99c8b664aebd5c2f4519a13b910a9Artificial Intelligence (AI), like an unstoppable force, is reshaping the global economic landscape and social structure at an unprecedented rate in the 21st century. With the continuous refinement of algorithms, the leap in computing power and the increasing abundance of big data resources, AI is gradually moving from science fiction concepts to real-world applications, and has become the core engine for social progress and change. The purpose of this paper is to deeply analyse the current status of AI development at home and abroad, and to explore the far-reaching impact of this technological revolution on the management efficiency of enterprises and governments, in an attempt to draw a picture of the future management driven by AI at the intersection of theory and practice. A blueprint for future management driven by artificial intelligence, with a view to providing ideas for future management innovation.Modern Economics &amp; Management Forum40The purpose of this paper is to deeply analyse the current status of AI development at home and abroad, and to explore the far-reaching impact of this technological revolution on the management efficiency of enterprises and governments.Modern Economics &amp; Management Forum["Xuerui Li"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/1e4a775fb0f99c8b664aebd5c2f4519a13b910a9 7ad2f4440a69b82dd158362816a5f25c34185e6dThis study aims to predict whether university students will make efficient use of Artificial Intelligence (AI) in the coming years, using a statistical analysis that predicts the outcome of a binary dependent variable (in this case, the efficient use of AI). Several independent variables, such as digital skills management or the use of Chat GPT, are considered.The results obtained allow us to know that inefficient use is linked to the lack of digital skills or age, among other factors, whereas Social Sciences students have the least probability of using Chat GPT efficiently, and the youngest students are the ones who make the worst use of AI.Journal of Infrastructure Policy and Development150The results obtained allow us to know that inefficient use is linked to the lack of digital skills or age, among other factors, whereas Social Sciences students have the least probability of using Chat GPT efficiently, and the youngest students are the ones who make the worst use of AI.Journal of Infrastructure, Policy and Development["Angel Bartolome Mu\u00f1oz de Luna", "Sonia Mart\u00edn Gom\u00e9z"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/7ad2f4440a69b82dd158362816a5f25c34185e6d 4979af36a94c660e9d24d321150637a9368c3974The aim of this study is to examine primary school students' views on artificial intelligence. Phenomenology design, one of the qualitative research methods, was used in the study. The study was conducted with 25 fourth grade students. The participants of the study were determined using the criterion sampling method, one of the purposeful sampling methods. The data were collected using a structured interview form and content analysis technique was applied to analyze the data. The results of the study showed that primary school students generally associate AI with technology, science, education, art and daily life. Students define artificial intelligence as human-designed robots and tools that provide information and help in every field. Stating that artificial intelligence has both positive and negative effects, the students emphasized knowledge acquisition and increasing creativity among the positive effects, while they expressed health problems, ethical and privacy concerns among the negative effects. They also stated that the use of AI in the classroom supports learning and development but can create reliability and ethical issues. It is recommended to conduct studies evaluating the long-term effects of artificial intelligence and to better understand the perceptions of artificial intelligence of students in different age groups.International journal of technology in education and science310The results of the study showed that primary school students generally associate AI with technology, science, education, art and daily life, and stated that the use of AI in the classroom supports learning and development but can create reliability and ethical issues.International Journal of Technology in Education and Science["Taha Oruc", "Ozgen Korkmaz", "Murat Kurt"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/4979af36a94c660e9d24d321150637a9368c3974 87c7126eed277e1af1f967e3a157f88c5a047393Artificial intelligence is revolutionizing many fields of knowledge, especially in the field of industry. Thus, the need arises to systematize this information and identify research trends. Therefore, the objective of the study was to determine the current state of the documents that relate artificial intelligence and the industry. To do this, a bibliometric analysis of 4,858 investigations published in Scopus between 2019 and 2023 was carried out. It is found that the number of documents published in 2022 multiplies 10 times compared to 2019. A trend is observed to develop literature review studies. Likewise, IEEE Access magazine has the largest number of publications, but Sustainability magazine is the most cited. On the other hand, the affiliations come mainly from Asian countries, although their publications are in foreign magazines. Regarding co-authorship by country, it is found that the United States and China have the greatest influence in the field of study with an increasingly accessible and collaborative nature, especially with countries such as India and the United Kingdom, as well as the existence of a recurrence of research on topics such as industry 4.0, machine learning, decision making and the pharmaceutical industry.2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON)280A bibliometric analysis of 4,858 investigations published in Scopus between 2019 and 2023 was carried out and it is found that the number of documents published in 2022 multiplies 10 times compared to 2019 and a trend is observed to develop literature review studies.2024 IEEE XXXI International Conference on Electronics, Electrical Engineering and Computing (INTERCON)["Fabiola Gabriela Bosmans Flores", "Valeria Yolanda Rodr\u00edguez Chac\u00f3n", "Ana Cristina Abarca Caqui", "Madeleine Lourdes Palacios-N\u00fa\u00f1ez", "Linda Giovanna Pongo Alva"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/87c7126eed277e1af1f967e3a157f88c5a047393 127d1dece917034415dd896aba8ba4541e5a9b1cArtificial intelligence has become an integral part of our daily lives, both in discourse and action. Technological developments play a significant role in the recovery of the global economy. Although poverty has decreased proportionally in terms of meeting basic needs due to these advancements, it remains one of the most chronic and significant issues in the world. This study examines the role of artificial intelligence in the fight against poverty. It explores the various roles artificial intelligence plays in microfinance, agriculture, health, and education. While the study emphasizes that artificial intelligence has important functions in this area, it also notes that its contribution may not be inevitable or mandatory; factors such as political conditions and biophysical limits of our planet also play a critical role.Sosyal çalışma dergisi00The role of artificial intelligence in the fight against poverty is examined, and the various roles artificial intelligence plays in microfinance, agriculture, health, and education are explored.Sosyal Çalışma Dergisi["Murat Ka\u00e7er"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/127d1dece917034415dd896aba8ba4541e5a9b1c 355195e611ac250bbb920233890b402bf2300260 From a legal standpoint, this paper critically examines the potential and challenges in deploying Artificial (AI) Intelligence in corporate governance in Nigeria. The examination revealed that leveraging AI in corporate governance could enhance corporate efficiency in Nigeria through improved decision-making, risk management, financial reporting, and stakeholder protection and engagement. However, possible bias and data privacy breaches are significant risks that pose ethical challenges when AI is deployed in corporate governance. Particularly, Nigeria is bisected by several socio-economic challenges, such as a lack of a robust AI framework and insufficient technological expertise to develop and optimize AI systems. Furthermore, Nigeria currently lacks comprehensive national AI legislation, thereby resulting in the absence of a legal basis for the effective deployment of AI in corporate governance and board management. Against this backdrop, this paper proposes an AI-based corporate governance framework, which can be adapted into future legislative reforms to streamline decision-making processes and improve board accountability and stakeholders’ protection in companies. Overall, it argues that AI legislation and policies are vital to the success of efforts to implement AI-based corporate governance in Nigeria.The Journal of Legal Studies180Journal of Legal Studies["Moses Peace Richard"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/355195e611ac250bbb920233890b402bf2300260 b479784c32e5e0ef6e0182b46f8db431f1ec8543Engineering Applications of Computational Fluid Mechanics971Engineering Applications of Computational Fluid Mechanics["Meysam Alizamir", "Mo Wang", "Rana Muhammad Adnan Ikram", "Sungwon Kim", "K. O. Ahmed", "Salim Heddam"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/b479784c32e5e0ef6e0182b46f8db431f1ec8543 0dd6c4d4c80a8f1c13b139d51ed96e9e4085946cIn February 2024, OpenAI unveiled Sora, which built on the foundation of ChatGPT and was able to generate videos based on text, representing one of the most advanced Generative Artificial Intelligences (GAIs) in the current world. As a diffusion model, Sora has the ability to generate long and imaginative videos with multiple characters, genre-specific movements, and sophisticated scenarios based only on textual descriptions, as well as excellent scalability. The research objectives are formulated as follows: to explore Sora's potential applications in future healthcare; to identify Sora's potential influence on visualizations of the healthcare industry; and to investigate Sora's potential impact on diagnostic methods. This study adopts the documentary research method. This study finds that Sora has great potential applications in future healthcare in the following aspects: healthcare robots, virtual doctors, simulating surgical procedures, visualizations of medical academic achievements, visualizing medical records, private assistant-type and companion-type robotic doctors, humanmachine interaction in healthcare, reducing burnout among doctors and nurses and so on. This study is one of the earliest to research Sora's potential applications in future healthcare in above mentioned aspects. The current study will not only enrich theoretical research on the integration of GAI (especially Sora) and healthcare, but also contribute to healthcare practice.2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS)720This study finds that Sora has great potential applications in future healthcare in the following aspects: healthcare robots, virtual doctors, simulating surgical procedures, visualizations of medical academic achievements, visualizing medical records, private assistant-type and companion-type robotic doctors, humanmachine interaction in healthcare, reducing burnout among doctors and nurses and so on.2024 7th International Conference on Internet Applications, Protocols, and Services (NETAPPS)["Yonggang Liu", "H. Awang", "Nur Suhaili Mansor"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/0dd6c4d4c80a8f1c13b139d51ed96e9e4085946c bb41b55a40ac8deae1ad1004701402252ca6566fAJR. American journal of roentgenology20AJR. American journal of roentgenology["J. Perchik"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/bb41b55a40ac8deae1ad1004701402252ca6566f abd518b47afb7ec5b5789c7c6ec0a12cd4c1083fThis editorial aims to highlight the critical intersections of patient care and technology, as illustrated in our recent article collection and addresses urgent challenges posed by global health crises like the COVID-19 pandemic while exploring broader themes such as personalized medicine, ethical practices, and the nutritional impacts on health. This diverse range of research highlights the necessity of interdisciplinary approaches to address the complexities of modern healthcare.IgMin Research420This editorial addresses urgent challenges posed by global health crises like the COVID-19 pandemic while exploring broader themes such as personalized medicine, ethical practices, and the nutritional impacts on health.IgMin Research["Rashid Mudasir"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/abd518b47afb7ec5b5789c7c6ec0a12cd4c1083f 8e7137d0b01047b16f4361afdde80a8d7f5c23f7Traditional access control systems, such as key cards, PIN pads, and physical keys, face challenges in scalability, security, and user experience in today's digital world. We present a cloud-based entry system using Raspberry Pi hardware and Amazon Web Services (AWS) technologies like Lambda, Simple Storage Service (S3), and Rekognition. This solution (AWSecure Entry System) enhances security, streamlines authentication, and increases operational efficiency.ERCIM News30This solution enhances security, streamlines authentication, and increases operational efficiency, using Raspberry Pi hardware and Amazon Web Services technologies like Lambda, Simple Storage Service (S3), and Rekognition.ArXiv["Hamidreza Yaghoubi", "Navtaj Randhawa", "Igor Ivkic"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/8e7137d0b01047b16f4361afdde80a8d7f5c23f7 35a51cd6cfcdf0a9bf6a3f95268e6784290ff6d7Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration00Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration["\u015eaban \u0130brahim G\u00f6ksal", "Maria Claudia Solarte V\u00e1squez"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/35a51cd6cfcdf0a9bf6a3f95268e6784290ff6d7 8a82048ee5b38b900754e1b1f7870a2061274b56(a) Estimating a probability density function by observing a finite set of samples (b) Predicting future exchange rates given the history of past exchange rates (c) Identification of products frequently bought together (d) Chess computer capable of learning from previous games (e) Spam recognition and filtering (f) Classification of applicants as credit-worthy or unworthy (g) Object recognition in computer vision (h) Segmentation of images according to the color value of their pixels (i) Finding out the lever of a three-armed bandit with the highest victory-pobability00Find out the lever of a three-armed bandit with the highest victory-pobability and finding out the lever of a three-armed bandit with the highest victory-pobability are found.["Wendell H. Chun"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/8a82048ee5b38b900754e1b1f7870a2061274b56 c949ad0d18444e8304ff6bd4aba7b882996ea638Balkan Medical Journal00Balkan medical journal["Perihan Elif Ekmek\u00e7i"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/c949ad0d18444e8304ff6bd4aba7b882996ea638 744c23cb04f671043dbdad3c6d1307e333df89f0Nursing Outlook250The paper asserts the necessity for nurses to become active participants in AI's evolution within health care to ensure the enhancement of patient care and the advancement of nursing roles.Nursing outlook["Olga Yakusheva", "Monique J. Bouvier", "Chelsea O.P. Hagopian"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/744c23cb04f671043dbdad3c6d1307e333df89f0 66210a0c8171ff135ad52c6ec3aa41147d1bf239The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.Applied Sciences363An enhanced framework is introduced that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability, to develop a more nuanced and comprehensive understanding of intricate data landscapes.Applied Sciences["Y. Kumar", "Jose Marchena", "Ardalan Hussein Awlla", "J. J. Li", "H. B. Abdalla"]2024-11-06T00:00:00https://www.semanticscholar.org/paper/66210a0c8171ff135ad52c6ec3aa41147d1bf239 204106f1fc9c27e8121f67cff7410fa8b6e9ba7eThis research analyzes the transformational influence of artificial intelligence (AI) on logistics and transportation systems, focusing on both quantitative and qualitative characteristics. By utilizing a mixed-methods approach, we polled industry experts and conducted in-depth interviews to understand the extent and types of AI adoption, perceived advantages, problems faced, and future investment intentions. Quantitative findings demonstrated considerable increases in operational efficiency, cost savings, and service quality attributable to AI technologies such as predictive analytics, machine learning, robotics, and autonomous vehicles. However, practical issues such as high initial costs, technological difficulties, and data protection concerns were also noted. Qualitative findings highlighted strategic benefits, including competitive advantages and enhanced decision-making capacity. Case studies showcased the actual uses of AI in warehouse management, fleet management, supply chain optimization, and urban mobility, demonstrating significant operational gains. This thorough review underscores AI's potential to transform logistics and transportation; however, additional research is required to evaluate technology integrations, socio-economic implications, and environmental advantages. The paper concludes with suggestions for addressing current challenges and leveraging AI to enhance efficiency, sustainability, and resilience in logistics and transportation networks.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)376This thorough review underscores AI's potential to transform logistics and transportation; however, additional research is required to evaluate technology integrations, socio-economic implications, and environmental advantages.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)["Lana Dlawar Miran", "Wameedh Abduladheem", "Ahmed Fouad Abdullah", "Talib A. Al-Sharify", "NoorUlhuda S. Ahmed", "Saadaldeen Rashid Ahmed", "Mohammed AlAqad", "Sameer Algburi"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/204106f1fc9c27e8121f67cff7410fa8b6e9ba7e a4f220aaeb574c255dfa9f8e3a40bf345a88dd77This paper offers a comprehensive analysis of the global economic impact of the COVID-19 pandemic, examining the diverse responses and precautionary measures adopted by nations worldwide. It also delves into the trans-formative role of artificial intelligence (AI) in health surveillance and its implications for economic dynamics. AI, a key driver of the Fourth Industrial Revolution, is undergoing rapid evolution, prompting discussions on its benefits and challenges. Despite varying perspectives, its indispensability in modern society is acknowledged, with AI poised to drive societal transformation and restore global equilibrium. The study also highlights the active engagement of Arab countries, including Bahrain, in the AI landscape.Edelweiss Applied Science and Technology03A comprehensive analysis of the global economic impact of the COVID-19 pandemic is offered, examining the diverse responses and precautionary measures adopted by nations worldwide and the trans-formative role of artificial intelligence (AI) in health surveillance and its implications for economic dynamics.Edelweiss Applied Science and Technology["Ismail Bengana", "Khaled Mili", "L. Mehaouat", "Abdenour Bounsiar", "Mohammed Lamine Cherbi"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/a4f220aaeb574c255dfa9f8e3a40bf345a88dd77 5f1aa1057b2db97f759b18fe61af32fa3b34dd1fArtificial Intelligence Review411Positive associations between AI, PD, and HPWS demonstrated the key role of AI in supporting employee development and improving high-performance work systems, emphasizing the significance of employees’ upskilling for AI integration.Artif. Intell. Rev.["Sajjad Zahoor", "I. Chaudhry", "Shuili Yang", "Xiaoyan Ren"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/5f1aa1057b2db97f759b18fe61af32fa3b34dd1f b30afed03d20e42a9239094b8218831098b7a05eThe integration of Artificial Intelligence (AI) in the public sector presents both unprecedented opportunities and significant challenges for governments worldwide. This article examines the multifaceted impact of AI on public administration, exploring its applications in service delivery, urban planning, public safety, and administrative efficiency. While AI offers substantial benefits, including enhanced decision-making, cost savings, and improved citizen services, it also raises critical concerns regarding bias, privacy, accountability, and workforce displacement. This article provides a comprehensive analysis of the ethical considerations surrounding AI deployment in governance, emphasizing the need for transparency, inclusivity, and human oversight. By critically evaluating both the promises and perils of AI in public sector operations, this article contributes to the ongoing discourse on responsible AI adoption in government. The findings underscore the importance of developing robust governance frameworks that can harness AI's potential while safeguarding citizens' rights and ensuring equitable service delivery. As governments navigate the AI revolution, this article offers insights into strategies for balancing technological advancement with ethical governance, paving the way for a more efficient, fair, and responsive public sector.International Journal For Multidisciplinary Research71The findings underscore the importance of developing robust governance frameworks that can harness AI's potential while safeguarding citizens' rights and ensuring equitable service delivery, paving the way for a more efficient, fair, and responsive public sector.International Journal For Multidisciplinary Research["SantoshKumar Pulijala"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/b30afed03d20e42a9239094b8218831098b7a05e d0d4152eab73b2cb018b7e606e957d3987c86821The emergence of Artificial Intelligence (AI) and Virtual Reality (VR) technologies offers transformative potential for the advancement of inclusive education, particularly for students with disabilities. This systematic review critically evaluates the current state of research to assess the impact of AI and VR on enhancing educational accessibility, personalisation and social inclusion in education. AI-driven adaptive systems can dynamically tailor learning experiences to individual needs, while VR offers immersive, multi-sensory environments that promote experiential learning. Despite these advances, the review also identifies significant challenges, including the high cost of implementation, technical barriers and limited teacher readiness, which hinder widespread adoption. Ethical concerns such as privacy and algorithmic bias are cited as key areas that need careful consideration. The findings underscore the urgent need for further empirical research to explore the long-term impact of these technologies and advocate for more equitable access to AI and VR tools in underserved educational settings. Ultimately, the review highlights the importance of integrating AI and VR as part of a broader strategy to foster genuinely inclusive learning environments that align with the goals of the Convention on the Rights of Persons with Disabilities (CRPD).Education sciences622The importance of integrating AI and VR as part of a broader strategy to foster genuinely inclusive learning environments that align with the goals of the Convention on the Rights of Persons with Disabilities (CRPD) is highlighted.Education Sciences["Angelos Chalkiadakis", "Antonia Seremetaki", "Athanasia Kanellou", "Maria Kallishi", "Anastasia Morfopoulou", "Marina Moraitaki", "S. Mastrokoukou"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/d0d4152eab73b2cb018b7e606e957d3987c86821 d42e686b947b2072fa5fb4ecb7cd9897a4366dbeABSTRACT Background: Fear- and trauma-related conditions, such as post-traumatic stress disorder (PTSD) and social phobia, often manifest as socially avoidant behaviours, which commonly contribute to social and occupational disability transdiagnostically. While gold-standard treatments (i.e. exposure therapy, psychotropic medications) are effective, they are hindered by high dropout rates and limited impact on real-world functioning. Furthermore, most existing interventions only target symptom reduction, with few addressing avoidance-related deficits in social and occupational functioning. Objectives: This methods paper introduces an innovative augmented reality exposure therapy (ARET) technology designed to address the limitations of traditional interventions for anxiety disorders and PTSD, by directly targeting social and occupational dysfunction through exposure to real-life social contexts. Method: We introduce an ARET system, using artificial intelligence (AI)-driven, augmented reality (AR) technology, that enables exposure to realistic scenarios within the patient's real-world environment, fostering contextual generalization and functional improvement. Featuring holographic three-dimensional humans, precise surface mapping, wireless mobility, and telemedicine capabilities, the software provides customizable exposure scenarios to transform an environment into various spaces (e.g. grocery store, house party) with diverse human characters, as well as flexible AI-driven human interactions tailored to individual needs. Results: We share observations and feedback from the treatment of first responders with PTSD. Patients found the technology easy to use, with immersive realism, active engagement, and strong emotional responses needed for effective exposure therapy. Advances in AI-driven character development and AR hardware accessibility support the wider adoption of ARET by clinicians. Conclusion: By bridging the gap between clinical interventions and real-world functioning, ARET offers a transformative approach to addressing the pervasive impact of psychiatric disorders on social and occupational outcomes.European Journal of Psychotraumatology382An innovative augmented reality exposure therapy (ARET) technology designed to address the limitations of traditional interventions for anxiety disorders and PTSD, by directly targeting social and occupational dysfunction through exposure to real-life social contexts is introduced.European Journal of Psychotraumatology["Arash Javanbakht", "L. Hinchey", "K. Gorski", "Alex Ballard", "Luke Ritchie", "Alireza Amirsadri"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/d42e686b947b2072fa5fb4ecb7cd9897a4366dbe 61a9f39d2b85eda81ff25c648a454586f544af45(1) Background: Increasing life expectancy allows for more age-related health issues. Enhancing physical, cognitive, mental, and social health is crucial. Promoting healthy habits combats stress and diseases. Recommendation systems, like collaborative filtering, tailor suggestions but face challenges. Techniques such as artificial intelligence and machine learning are vital. Personalized health recommendations improve lifestyles and mitigate issues. (2) Methods: A systematic review adhering to the general principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses was conducted with the aim of identifying articles on innovative research about using recommendation algorithms, machine learning, or artificial intelligence to promote healthy habits and active aging. (3) Results: A total of 34 articles were included in this work. They address the topic of recommendation systems that use machine learning or artificial intelligence in the promotion of healthy habits. (4) Conclusions: This article reviews health-related activity recommendation techniques for the general population. With rising life expectancy and common health issues, effective recommendations are crucial for future public health. Limitations include excluding simpler models. Despite many proposals, systematic adherence mechanisms are lacking. Developing traceable, verifiable systems for healthy activity recommendations is vital for aging populations in developed countries.Applied Sciences471This article reviews health-related activity recommendation techniques for the general population that use machine learning or artificial intelligence in the promotion of healthy habits for aging populations in developed countries.Applied Sciences["Juan Lopez-Barreiro", "Jos\u00e9 Lu\u00eds Garc\u00eda-Soid\u00e1n", "Luis M. \u00c1lvarez-Sabucedo", "Juan M. Santos-Gago"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/61a9f39d2b85eda81ff25c648a454586f544af45 1e4376ec115ab1f23707ba4bb0b093e8befde857This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI’s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming.Electronics982The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration.Electronics["Fotis Assimakopoulos", "C. Vassilakis", "Dionisis Margaris", "Konstantinos Kotis", "D. Spiliotopoulos"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/1e4376ec115ab1f23707ba4bb0b093e8befde857 c0eee34f1724525e08dd5c9120757255648a35e7Artificial Intelligence (AI) has emerged as a transformative tool in reshaping business processes and enhancing knowledge-sharing capabilities across various sectors globally. In Nigerian enterprises, AI holds significant potential to improve organizational efficiency and overcome persistent challenges, such as fragmented information systems, limited technological infrastructure, and gaps in workforce skills. This study explores the impact of AI on knowledge sharing and organizational efficiency within Nigerian businesses, emphasizing the practical implications of AI integration. A survey was conducted, with two hundred and thirty-four (234) respondents from diverse industries providing feedback through questionnaires. The data collected was analyzed using both descriptive and inferential statistics. Hypothesis testing revealed a positive correlation between AI-driven knowledge sharing and organizational efficiency, with AI technologies enabling faster and more accessible information flow. The findings highlight AI’s potential to optimize knowledge sharing, helping employees make more informed decisions and fostering a collaborative work environment. For Nigerian enterprises, strategic investments in AI can enhance workforce efficiency, support strategic initiatives, boost productivity, and improve organizational agility, thereby creating competitive advantages in knowledge-driven sectors. However, organizations must also address challenges such as employee resistance and data privacy concerns to fully leverage the benefits of AI. Based on these findings, the study suggests that adopting change management practices and developing AI-specific policies can increase the success of AI initiatives, fostering a sustainable shift toward technology-driven growth in emerging markets.African Journal of Management and Business Research262It is suggested that adopting change management practices and developing AI-specific policies can increase the success of AI initiatives, fostering a sustainable shift toward technology-driven growth in emerging markets.African Journal of Management and Business Research["James Abidemi Ola-Oluwa"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/c0eee34f1724525e08dd5c9120757255648a35e7 fcab9ea96bf479af97d6a6f5b56c2d251e577a7dThe joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) will allow training models from distributed data and explaining their inner workings while preserving important aspects of privacy. Towards establishing the benefits and tensions associated with their interplay, this scoping review maps those publications that jointly deal with FL and XAI, focusing on publications where an interplay between FL and model interpretability or post-hoc explanations was found. In total, 37 studies met our criteria, with more papers focusing on explanation methods (mainly feature relevance) than on interpretability (mainly algorithmic transparency). Most works used simulated horizontal FL setups involving 10 or fewer data centers. Only one study explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. Aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. 8 papers addressed the benefits of incorporating explanation methods as a component of the FL algorithm. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens.arXiv.org940This scoping review maps those publications that jointly deal with FL and XAI, focusing on publications where an interplay between FL and model interpretability or post-hoc explanations was found.ArXiv["L. M. Lopez-Ramos", "Florian Leiser", "Aditya Rastogi", "Steven Hicks", "Inga Str\u00fcmke", "V. Madai", "Tobias Budig", "A. Sunyaev", "A. Hilbert"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/fcab9ea96bf479af97d6a6f5b56c2d251e577a7d 3619d45aca4b402a02465221b47f02fef61bc3a4Artificial intelligence (AI) technologies with human-level cognitive abilities are increasingly integrated into workplaces, posing risks of job displacement and redundancy. Understanding AI’s impact on job automation is thus essential, as it helps students understand which occupational roles are likely to be automated. However, there is a lack of coherent understanding of this topic due to the diverse research methodologies deployed, leading to the formation of fragmented and inconsistent insights. This article reviews career literature and global reports from expert sources (e.g., the World Economic Forum) to provide an overview of AI’s influence on job sectors and the skills students need to thrive in a technologically disrupted workplace. The findings emphasize the importance of developing human-centric skills.Merits1790This article reviews career literature and global reports from expert sources to provide an overview of AI’s influence on job sectors and the skills students need to thrive in a technologically disrupted workplace and emphasizes the importance of developing human-centric skills.Merits["Lawrence P. W. Wong"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/3619d45aca4b402a02465221b47f02fef61bc3a4 4da419d8cc9ce3268b50c704f822c7dab3afeee3In accordance with the current European railway regulations and particularly the two directives relating to the interoperability (Directive (EU) 2016/797) and safety (Directive (EU) 2016/798) of the railway system, this literature review proposes to classify artificial intelligence (AI) applications by distinguishing the structural elements (Infrastructure, Energy, Control‐Command‐Signalling and Rolling Stock) and the functional elements (Operation and Traffic Management, Maintenance and Telematics Applications) of the European railway system. Several “classic” AI techniques are implemented, including machine learning (supervised, semi‐supervised, unsupervised), deep learning such as artificial neural networks (ANN), natural language processing (NLP), case‐based reasoning (CBR), etc. However, the inadequacy of these approaches to capitalize, share and reuse the knowledge involved has oriented research towards the development of new approaches based on ontologies and knowledge graphs. This study shows that the stages of data acquisition, modeling, processing and interpretation pose a crucial problem in rail transport. In addition, with complex models described as “black boxes”, it is difficult to understand how the internal reasoning mechanisms of the AI system impact the solution and predictions. The new explainable AI (XAI) approach can possibly provide an element of response to this problem.IET Intelligent Transport Systems560This study shows that the stages of data acquisition, modeling, processing and interpretation pose a crucial problem in rail transport and the new explainable AI (XAI) approach can possibly provide an element of response to this problem.IET Intelligent Transport Systems["H. Hadj-Mabrouk"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/4da419d8cc9ce3268b50c704f822c7dab3afeee3 03ba96d027a253687e9fdae1e0ba7a2183848e80Background Optimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more “AI + medical” application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice. Objective This study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment. Methods Using PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords “artificial intelligence” and “hematological diseases.” We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field. Results AI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical–industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases. Conclusion Our results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.Frontiers in Medicine1440This study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment.Frontiers in Medicine["Shi-Xuan Wang", "Zou-Fang Huang", "Jing Li", "Yin Wu", "Jun Du", "Ting Li"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/03ba96d027a253687e9fdae1e0ba7a2183848e80 515eccb4f7f792a7df15316c377f6be0a7296455The rapid development of technology has brought significant changes in various areas of life, including the world of education. AI is a rapidly developing technology with enormous potential to improve the efficiency and effectiveness of resource management. Library management, which plays a crucial role in supporting teaching and learning activities in schools, employs AI in education. However, the increasing challenges in managing collections, the demand for rapid and accurate services, and the need for effective data management have increased the need for innovation in library management. This study aimed to explore the application of artificial intelligence (AI) in library management at SDN Piyaman 2 Wonosari, emphasizing enhancing operational efficiency, accessibility, and service quality. This study employs a qualitative descriptive approach, collecting data through interviews, observations, and questionnaires from students, teachers, and library staff. The researchers will then process, analyze, and discuss the collected data to conclude. The study results indicate that using artificial intelligence (AI) can improve library operational efficiency by reducing borrowing time, returning each book, and searching from five minutes to one minute. In addition, AI also increases user satisfaction, with an average increase from 60% to 85%. Despite the technical challenges and user adjustments, the results of this study indicate that AI has enormous potential to improve the effectiveness and efficiency of school libraries. Other schools can use this study as a model to implement AI technology in library management, making it more modern and responsive to user needs.International Journal of Engineering Science and Information Technology00The study results indicate that using artificial intelligence (AI) can improve library operational efficiency by reducing borrowing time, returning each book, and searching from five minutes to one minute and increases user satisfaction, with an average increase from 60% to 85%.International Journal of Engineering, Science and Information Technology["Dwi Makarti Amrih Lestari", "S. Saryanto", "Rejokirono Rejokirono"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/515eccb4f7f792a7df15316c377f6be0a7296455 a8dbc5ddb591fedfd049add45ce56e5065da3991This study investigates the utilization of artificial intelligence (AI) technology to enhance practical content development within the media specialization program at Palestine Technical University, Kadoorie. The primary objective is to examine the extent to which media specialty lecturers employ AI technology in developing practical content. A mixed-methods approach is employed, qualitative data are gathered through in-depth interviews with faculty members to elucidate their perceptions and experiences regarding the integration of AI technology in practical content development. The study aims to provide valuable insights into the benefits and challenges of AI integration in practical content development for media specialization programs The study reveals diverse views on AI integration in media education at Palestine Technical University, Kadoorie. Faculty recognize AI’s benefits like personalized learning and productivity but also express concerns about over-reliance and ethics. Consensus exists on cautious AI implementation to maximize benefits and address drawbacks. Obstacles to AI adoption include cost, skills gaps, and ethical considerations, highlighting the complexity of integration. The study emphasizes a balanced approach, offering insights for enhancing practical content development in media specialization programs at Palestine Technical University, Kadoorie.Journal of Infrastructure Policy and Development450The study reveals diverse views on AI integration in media education at Palestine Technical University, Kadoorie, where faculty recognize AI’s benefits like personalized learning and productivity but also express concerns about over-reliance and ethics.Journal of Infrastructure, Policy and Development["Wafa A. Harb", "Samer Rowaished"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/a8dbc5ddb591fedfd049add45ce56e5065da3991 b54ccdf76cfa3bf6cd6320b980c381ade6138f2cThe role that artificial intelligence can play in enhancing business innovation and creativity in the cosmetic industry in Dubai is considered. Amongst many key factual problems faced by this industry, there is personalization, supply chain optimization, formulation of products, marketing, and branding—issues that remain ethical and private. The conceptual problems addressed are related to the balance between creativity and automation, data quality, and bias, interdisciplinary collaboration, and change management.It aims to understand how AI-powered solutions can contribute to addressing these challenges and serve as a driver of innovation in the cosmetics sector of Dubai. This paper considers previous related literature about AI applications in multiple business functions and some conceptual framework that links AI to creativity and innovation. In this regard, a mixed-method approach will utilize questionnaires, interviews, and case studies to analyze the interlinkages between AI adoption, AI capabilities, data-driven decision making, and cognitive augmentation as they influence firm innovation and creativity.The research is likely to be of benefit to Dubai Cosmetics Firms with critical insights; at the same time, it will complement existing literature on how emerging technologies can help instill innovation in business activity. Asian Journal of Logistics Management520A mixed-method approach will utilize questionnaires, interviews, and case studies to analyze the interlinkages between AI adoption, AI capabilities, data-driven decision making, and cognitive augmentation as they influence firm innovation and creativity.Asian Journal of Logistics Management["Khalil Ullah", "Retno Kusumastuti Hardjono"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/b54ccdf76cfa3bf6cd6320b980c381ade6138f2c b9f8b75004480bed27d8801f8cd4bf1e01dee37cThis research study explores the transformative role of Artificial Intelligence (AI) in education. Utilizing a mixed-methods approach, the study combines qualitative data collection techniques and document analysis to determine AI integration in educational settings as perceived by the Open University Systems graduate students who are teachers by profession. Through semi-structured interviews with educators, the study determines the advantages of AI technologies in providing tailored support, enhancing engagement, and improving academic performance. The study also determines the advancement of the increasing number of AI in Education research through a short bibliographic analysis from Scopus database. Recommendations are provided to address these challenges and promote ethical AI practices in the educational landscape. While there is a visible importance of AI in Education, most respondents are concerned about the ethical consideration, and it is recommended to create a policy to be included in the Open University Systems Manual of Operations (OUSOM). Also, while this research aims to contribute to a understanding of AI's transformative potential in education, the findings also see the importance of personalization, efficient assessment, and interactive learning environments.2024 9th International Conference on Information Technology and Digital Applications (ICITDA)160While there is a visible importance of AI in Education, most respondents are concerned about the ethical consideration, and it is recommended to create a policy to be included in the Open University Systems Manual of Operations (OUSOM).2024 9th International Conference on Information Technology and Digital Applications (ICITDA)["C. C. Orlanda-Ventayen"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/b9f8b75004480bed27d8801f8cd4bf1e01dee37c 32a9ec6a0af236c7857fb39c8d6538591343ea88Artificial intelligence (AI) platforms face distinct orchestration challenges in industry‐specific settings, such as the need for specialised resources, data‐sharing concerns, heterogeneous users and context‐sensitive applications. This study investigates how these platforms can effectively orchestrate autonomous actors in developing and consuming AI applications despite these challenges. Through an analysis of five AI platforms for medical imaging, we identify four orchestration logics: platform resourcing, data‐centric collaboration, distributed refinement and application brokering. These logics illustrate how platform owners can verticalize the AI development process by orchestrating actors who co‐create, share and refine data and AI models, ultimately producing industry‐specific applications capable of generalisation. Our findings extend research on platform orchestration logics and change our perspective from boundary resources to a process of boundary processing. These insights provide a theoretical foundation and practical strategies to build effective industry‐specific AI platforms.Information Systems Journal480Through an analysis of five AI platforms for medical imaging, four orchestration logics are identified: platform resourcing, data‐centric collaboration, distributed refinement and application brokering, illustrating how platform owners can verticalize the AI development process by orchestrating actors who co‐create, share and refine data and AI models.Information Systems Journal["Michael Weber", "Andreas Hein", "J. Weking", "Helmut Krcmar"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/32a9ec6a0af236c7857fb39c8d6538591343ea88 ae5a212b714fca8757dcf796181b6e64f5711d5bThe Indian handicraft sector is the foundation of the nation's cultural heritage and economic development, providing employment opportunities to numerous artisans. However, it faces several challenges such as low market access, inadequate marketing skills and lack of digital literacy and infrastructure. These barriers lead to low visibility in the global market and dependance on local market channels. The industry subsequently shows inefficiencies and increased operational costs due to fragmented supply chains. However, e-commerce integration provides a transformative opportunity for the handicraft sector, especially for the well-known Channapatna handicraft toy manufacturing industry. This paper recommends an Artificial Intelligence driven e-commerce platform tailored to Channapatna's Handicraft Toy Manufacturing Industries. It aims to expand their market reach by connecting them directly with local and global customers, thereby avoiding mediators which reduces costs. The framework consists of key components which includes vendors who are toy manufacturers, a cloud-based server and customers, creating a seamless digital marketplace. The proposed platform improves supply chain management, increases product visibility and facilitates better customer engagement through application of digital tools. With the integration of AI, the platform personalizes shopping experiences, forecasts sales trends, and manages inventory effectively. The conclusion outlines future innovative advancements such as augmented reality and virtual reality features to further enhance the online shopping experience. Overall, this AI-driven e-commerce platform can increase competitiveness, operational efficiency, and sustainable economic growth, empowering artisans to preserve their cultural heritage while meeting global market demands.2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS)200An Artificial Intelligence driven e-commerce platform tailored to Channapatna's Handicraft Toy Manufacturing Industries can increase competitiveness, operational efficiency, and sustainable economic growth, empowering artisans to preserve their cultural heritage while meeting global market demands.2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS)["Meghasree V", "C. K. N. Guptha", "Vijayakumar M N"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/ae5a212b714fca8757dcf796181b6e64f5711d5b 5d72ee28c131e9897a61f38f69d2ff65215b6f22This study examines various artificial intelligence (AI) models for predicting financially distressed firms with poor profitability (“Zombie firms”). In particular, we adopt the Explainable AI (“XAI”) approach to overcome the limitations of the previous AI models, which is well-known as the black-box problem, by utilizing the Local Interpretable Model-agnostic Explanations (LIME) and the Shapley Additive Explanations (SHAP). This XAI approach thus enables us to interpret the prediction results of the AI models. This study focuses on the Korean sample from 2019 to 2023, as it is expected that the COVID-19 pandemic increases the number of zombie firms. We find that the XGBoost model based on a boosting technique has the best predictive performance among several AI models, including the traditional ones (e.g. the logistic regression). In addition, by using the XAI approach, we provide visualized interpretations for the prediction results from the XGBoost model. The analysis further reveals that the return on sales and the selling, general and administrative costs are the most impactful variables for predicting zombie firms. Overall, this study focusing on several AI models not only shows the improvement for the prediction of zombie firms (relative to the traditional models) but also increases the reliability of the prediction results by adopting the XAI approach, providing several implications for market participants, such as financial institutions and investors.Journal of Derivatives and Quantitative Studies460The XGBoost model based on a boosting technique has the best predictive performance among several AI models, including the traditional ones, and the return on sales and the selling, general and administrative costs are the most impactful variables for predicting zombie firms.Journal of Derivatives and Quantitative Studies: 선물연구["Dongwook Seo", "Hyeong Joon Kim", "Seong-Su Mun"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/5d72ee28c131e9897a61f38f69d2ff65215b6f22 8a01793ee6dc89457630c6f38b7e931b45c9e31b Purpose This study aims to assess the factors that impact the adoption of artificial intelligence (AI) in the human resource (HR) recruitment procedure in Vietnam’s medium-sized firms. Design/methodology/approach Through a quantitative approach, this paper collected data of 297 hiring managers, HR directors and top-level executives from Vietnam’s medium-sized firms with a structured questionnaire. The partial least squares structural equation model was used to analyze the data and evaluate the hypothesis model (on platform Smart PLS 3.0). Findings The results show that in Vietnam’s medium-sized companies, both perceived benefits and perceived sacrifices directly impact on perceived value, which leads to organizations’ adoption of AI. HR readiness also has a moderating effect between perceived value and AI adoption. Research limitations/implications Future research can compare AI adoption between large and medium companies, as well as other criteria in Asian countries. Other organizational constructs can be considered moderators between perceived value and AI adoption. Practical implications This study offers a context-specific understanding of the practice of using AI to acquire talent in Vietnam. Both of AI technology’s perceived benefits and perceived sacrifices directly impact its perceived value, therefore indirectly impacting its adoption. In this study, HR readiness serves as an inhibitor to adoption. Some essential managerial implications are suggested. Originality/value This study provides valuable insights into applying AI to Vietnam’s medium-sized companies, especially in the recruitment process. It adds to a substantial body of work on applying AI to HR management. Journal of Asia Business Studies750The results show that in Vietnam’s medium-sized companies, both perceived benefits and perceived sacrifices directly impact on perceived value, which leads to organizations’ adoption of AI, and HR readiness serves as an inhibitor to adoption.Journal of Asia Business Studies["Tri Minh Cao", "Loc Thi Vy Nguyen"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/8a01793ee6dc89457630c6f38b7e931b45c9e31b 1eff504c76da4cf674ed2a4a0a234db24de5461fThe artificial intelligence has become a phenomenon of nowadays. It has a significant impact on many fields, including military art and military science, what is also one the of research directions announced by NATO. The article briefly describes the status and possibilities of using artificial intelligence in the Czech Republic and its possible applications in the Czech Armed Forces. The Artificial Intelligence creates conditions and environment for a number of areas where it can make commanders, staffs and soldiers more efficient in their activities in everyday peacetime life, its management, in the stage of their preparation for the performance of combat tasks, planning of combat, as well as in the stage of its management. The article describes the process of experimentation with conversational robots, available on the Internet, as potential means of decision support for commanders, the results achieved and gives suggestions on how to use them in military practice. In the next part, it describes possible areas in which artificial intelligence can be used in the Czech Army to make soldiers' preparation for combat tasks more efficient, to conduct credible war games, in routine processing of documents and information, in military logistics (warehouse management, diagnostics and servicing of military equipment, analysis and processing of data (image, sound, video recordings) and for deception. The article presents an overview of theoretical works on the utilization of artificial intelligence in the Czech Army with emphasis on data and information analysis in documents, rationalization of work with documents and decision support. All in an unclassified mode at the stage of conducting experiments. Currently, the use of so-called chat robots (Chat Robots, Chatbots) has become very widespread. Major IT companies such as Microsoft or Google have introduced various versions of chatbots for use by the general public. Their use for decision support appears to be very advantageous and available. The authors tested the COPILOT and GEMINI chatbots. The purpose and reason for the tests and experimentation of the mentioned tools was to verify how faithfully and precisely the required information compiled by artificial intelligence is true, accurate and complete. The two systems were also chosenbecause they draw information from the extensive databases both companies have and which are publicly available to answeruser questions.Challenges to National Defence in Contemporary Geopolitical Situation100The article describes the process of experimentation with conversational robots, available on the Internet, as potential means of decision support for commanders, the results achieved and gives suggestions on how to use them in military practice.Challenges to National Defence in Contemporary Geopolitical Situation["Vladim\u00edr Vr\u00e1b", "Jan Zezula"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/1eff504c76da4cf674ed2a4a0a234db24de5461f b713da091343858973f02009992ff524351d80c9In this user-friendly book, Nicky Hockly draws on research and her own experience to examine the benefits and challenges of using AI in language teaching. The book provides a range of guidance on good practices in using the technology, with simple tips for applying the learning. It explores some of the key ethical, moral, philosophical and legal questions around using AI and covers topics including accessibility, data ownership and concerns around students cheating. The book also includes support for using AI to help teachers and learners develop. Nicky Hockly's 30 Essentials for Using Artificial Intelligence is an essential guide for teachers of all levels of experience.00Nicky Hockly's 30 Essentials for Using Artificial Intelligence is an essential guide for teachers of all levels of experience and explores some of the key ethical, moral, philosophical and legal questions around using AI.["N. Hockly"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/b713da091343858973f02009992ff524351d80c9 8151c9339c138140f34e5613bd5f404119d56983This article provides an in-depth analysis of the role Artificial Intelligence (AI), and Machine Learning (ML) technologies play in driving the reindustrialization of Brazil, with a focus on transforming the logistics sector. These technologies offer promising solutions to longstanding challenges faced by Brazil's logistics systems, such as high operational costs, inefficiencies in supply chain management, and the complexities of its extensive geographical landscape. The integration of AI enables more precise demand forecasting, optimized transport routes, and automated logistical processes, which not only enhance efficiency but also significantly reduce costs and improve sustainability. It is elaborated on how AI-driven innovations can revitalize Brazil's industrial sector, making it more agile, flexible, and adaptive to the dynamics of global markets. It discusses specific applications of AI in logistics, including intelligent inventory management systems, predictive maintenance, and real-time decision-making capabilities that streamline operations and improve customer satisfaction. Moreover, it is addressed the various barriers to the adoption of these advanced technologies, such as the necessity for substantial investments in technological infrastructure, the imperative of upskilling the workforce, and the challenge of navigating regulatory landscapes. It underscores the critical need for a synergistic approach involving collaboration between government entities, industry leaders, and academic institutions to foster an ecosystem conducive to technological innovation. Finally, the paper highlights the strategic importance of these technological advancements in positioning Brazil as a leader in the global industrial arena. By leveraging AI and ML, Brazil can not only overcome its logistical inefficiencies but also set a new standard for industrial operations in the era of digital transformation, thereby ensuring a sustainable and competitive future. This comprehensive analysis aims to provide stakeholders with insights into the transformative potential of AI and ML, proposing actionable strategies for integrating these technologies into Brazil's reindustrialization effortsAnais do Encontro Nacional de Engenharia de Produção00By leveraging AI and ML, Brazil can not only overcome its logistical inefficiencies but also set a new standard for industrial operations in the era of digital transformation, thereby ensuring a sustainable and competitive future.Anais do Encontro Nacional de Engenharia de Produção["Guilherme Brittes Benitez", "A. Canciglieri"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/8151c9339c138140f34e5613bd5f404119d56983 7ee977fd7468f04f5c6f65e8d416ca83a0dbd6eeArtificial Intelligence (AI) stands as a revolutionary, disruptive and transformative technology with the capacity to significantly enhance business operations globally. In emerging economies, AI integration presents a dual landscape of vast opportunities and substantial challenges. This conference paper offers a comprehensive review of AI applications, prospects, and challenges in the manufacturing, agriculture, retail, financial services, healthcare, and mining key sectors within developing countries. By examining detailed case studies from Brazil, Chile, India, Ghana, Kenya, Nigeria, and South Africa, we highlight the notable benefits of AI. The research methodology involves an extensive literature review, analysis of case studies, surveys, and expert interviews. Findings indicate that AI can lead to significant improvements in business operations, such as increased productivity, innovation, cost savings, better decision-making, and competitiveness. However, challenges such as data privacy, security concerns, ethical considerations, and potential job displacement are particularly acute in developing economies. Additionally, high initial investment costs, limited access to advanced technology, inadequate digital infrastructure, and complex regulatory environments hinder widespread AI adoption. Despite these obstacles, the potential for AI to expand in predictive analytics, automation, and personalized services is promising, suggesting significant economic and social benefits. Addressing issues such as poor data quality, a shortage of skilled talent, and cultural resistance to change is crucial for effective AI deployment. This review emphasizes the need for strategic investments, robust policy frameworks, and capacity-building initiatives to fully harness AI's potential in emerging economies. Collaboration among policymakers, business leaders, and researchers is essential to overcome these challenges and leverage AI’s capabilities to drive sustainable development, enhance competitiveness, and improve quality of life.International Journal of Academic Research in Business and Social Sciences360The need for strategic investments, robust policy frameworks, and capacity-building initiatives to fully harness AI's potential in emerging economies is emphasized, suggesting significant economic and social benefits.International Journal of Academic Research in Business and Social Sciences["Ezekiel T. Mutasa", "Chitra Dhiwwale", "Sundaran Sagaran A. Gopal"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/7ee977fd7468f04f5c6f65e8d416ca83a0dbd6ee 4c29d7c2c548f89f48c9ee651953431de61a7cb2The advancements in technology in the modern era have led to the boom of Artificial Intelligence (AI) in the market including businesses and organizations seeking for increased accountability and recognition. This study investigates the possible advantages of Artificial Intelligence in Corporate Social Responsibility (CSR) and environmental sustainability moreover, the business and economic reforms of it on companies to become more and more sustainable over the years, and the possible strategies to implement it. The application of AI in CSR can lead to enhanced accuracy and real-time data monitoring. Yet, there are some shortcomings of AI such as data security risks and reliability concerns which demand addressing. By conducting this literature survey, we aim to prevail over the challenges across the way of AI in CSR and suggest the best possible mitigation strategies.2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS)260This study investigates the possible advantages of Artificial Intelligence in Corporate Social Responsibility (CSR) and environmental sustainability moreover, the business and economic reforms of it on companies to become more and more sustainable over the years, and the possible strategies to implement it.2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS)["Nishchint Tiku", "Saksham Kumar Jindal", "Lokeshwari M"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/4c29d7c2c548f89f48c9ee651953431de61a7cb2 6850ad52f6a691f8be1d24c646582d87c3604282Introduction Academic engagement of Chinese college students has received increasing research attention due to its impact on Students’ Mental health and wellbeing. The emergence of artificial intelligence (AI) technologies marked the beginning of a new era in education, offering innovative tools and approaches to enhance learning. Still, it can be viewed from positive and negative perspectives. This study utilizes the Theory of Planned Behavior (TPB) as a theoretical framework to analyze the mediating role of students’ attitudes toward AI, perceived social norms, perceived behavioral control, and their intention to use AI technologies in the relationships between Students’ academic engagement and Mental health. Methods The study involved a total of 2,423 Chinese college students with a mean age of approximately 20.53 ± 1.51 years. The survey was conducted through Questionnaire Star, using a secure website designed specifically for the study. The Hayes’ PROCESS Macro (Version 4.2) Model 80 with SPSS 29.0, a multivariate regression analysis with a chain mediation model that allows for multiple mediators to be tested sequentially, has been used. The statistical test explored the direct and indirect effects of students’ engagement (X) on mental health (Y) through a series of mediators: attitude toward AI (M1), subjective norm (M2), perceived behavioral control over AI use (M3), and AI use behavioral intention (M4). Results The direct positive relationship between engagement and mental health (β = 0.0575; p < 0.05), as well as identifying key mediating factors such as perceived behavioral control (β = 0.1039; p < 0.05) and AI use of behavioral intention (β = 0.0672; p < 0.05), highlights the potential of AI tools in enhancing students’ well-being. However, the non-significant mediating effects of attitude toward AI (β = 0.0135), and subjective norms (β = –0.0005), suggest that more research is needed to understand the nuances of these relationships fully. Discussion Overall, the study contributes to the growing body of literature on the role of AI in education and offers practical implications for improving mental health support in academic settings.Frontiers in Psychology760The Theory of Planned Behavior is utilized as a theoretical framework to analyze the mediating role of students’ attitudes toward AI, perceived social norms, perceived behavioral control, and their intention to use AI technologies in the relationships between Students’ academic engagement and Mental health in Chinese college students.Frontiers in Psychology["Yali Wang", "Hui Wang"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/6850ad52f6a691f8be1d24c646582d87c3604282 e1603dbc67b8a87fcce8a7faf9bacc415a26e795Among the most disturbing situations that threaten the existence of the Earth as we know it today is climate change. The implementation of artificial intelligence in climate research provides new opportunities to address this threat. This paper explores how artificial intelligence can contribute to adaptation to or reducing the effects of climate change by identifying problems and solutions as well as risks associated with its utility. The authors analysed the prevailing literature and synthesised key generalisations, which show many approaches where artificial intelligence can be applied to generate innovative products and services aimed at addressing weather challenges, such as improving emissions tracking and developing more accurate climate forecasts. At the same time, the use of artificial intelligence to combat weather change brings to the fore a number of challenges that may turn the newly obtained answers into a whole new problem related to extreme weather conditions and artificial intelligence. The research contributes valuable information for policymakers, scientists and various stakeholders who want to combine artificial intelligence responsibly and effectively in their efforts to deal with climate-demanding situations.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)240The authors analysed the prevailing literature and synthesised key generalisations, which show many approaches where artificial intelligence can be applied to generate innovative products and services aimed at addressing weather challenges, such as improving emissions tracking and developing more accurate climate forecasts.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)["Ana Todorova", "Irina Kostadinova", "Diana Antonova", "Svilena Ruskova"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/e1603dbc67b8a87fcce8a7faf9bacc415a26e795 8ee2bb66161afac8f8df86465b62e36f0c33d130The advent of artificial intelligence tools such as ChatGPT, developed by OpenAI, has radically transformed the educational landscape, providing students with powerful tools to complete written assignments, communicate asynchronously, answer questions, and grade exams and tests. While these technologies provide new opportunities for learning and creativity, they also raise valid concerns about academic integrity and increased student cheating. The example of ChatGPT examines the impact of artificial intelligence on the educational process in the specific context of increasing risks associated with academic dishonesty, the possibility of developing strategies for identifying works created using AI, as well as creating methods to combat student dishonesty. Examples of dishonest behavior of students with negative and positive reactions from teachers are given.Informatics in school40The impact of artificial intelligence on the educational process in the specific context of increasing risks associated with academic dishonesty is examined, with the possibility of developing strategies for identifying works created using AI, as well as creating methods to combat student dishonesty.Informatics in school["D. Bogdanova", "A. A. Fedoseev"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/8ee2bb66161afac8f8df86465b62e36f0c33d130 f1131968a475fd84a22a2da2d0a464ee8a9f5b2bJournal of Personality and Social Psychology00Journal of Personality and Social Psychology[]2024-11-07T00:00:00https://www.semanticscholar.org/paper/f1131968a475fd84a22a2da2d0a464ee8a9f5b2b 0e68486e56f404ce22c9a3b0103b73e067e782f7School Leadership &amp; Management360School Leadership &amp; Management["Rebecca L Marrone", "S. Fowler", "Abhinava Bathakur", "Shane Dawson", "George Siemens", "Chanvi Singh"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/0e68486e56f404ce22c9a3b0103b73e067e782f7 a0f7b5c68889f38b8b7d6db0bdb6d21bd48a92dfUnited European Gastroenterology journal50United European Gastroenterology Journal["M. Iacucci", "G. Santacroce"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/a0f7b5c68889f38b8b7d6db0bdb6d21bd48a92df af761f8b4200c30c1ddf50784d2461a8bb3acc6bInternational Scientific Conference “EDUCATION, RESEARCH, PRACTICE” Proceedings00International Scientific Conference “EDUCATION, RESEARCH, PRACTICE” Proceedings["Mariam Zakariashvili"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/af761f8b4200c30c1ddf50784d2461a8bb3acc6b 51cd3a5cae490b44af7d26cd44e42fe07312d8f6International Journal of Academic Research in Progressive Education and Development00International Journal of Academic Research in Progressive Education and Development["Guo Bing", "Edwin Mondol", "A. M. Karim", "Najim Al Musallami"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/51cd3a5cae490b44af7d26cd44e42fe07312d8f6 ca6026e953ef81aaa078c17d9c32979b4d191ce6Journal of Population Therapeutics and Clinical Pharmacology00Journal of Population Therapeutics and Clinical Pharmacology["Ikram Ullah", "Danish Mehmood", "Dr. Sardar Alam", "Hassan Khan", "Saleem Malik", "Fazal Rehman", "Dr. Abdul Jalal khan PT", "Malika Afzal", "Wareesha Mahmood", "Fida Hussain", "Dr. Akbar Alam"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/ca6026e953ef81aaa078c17d9c32979b4d191ce6 d92ce96c9451f382fcce9260448a070b741badb7Phenomenology and the Cognitive Sciences90Phenomenology and the Cognitive Sciences["Steven S. Gouveia", "Carlos Moruj\u00e3o"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/d92ce96c9451f382fcce9260448a070b741badb7 5decdaaf488ca7759af5486d1b5d6e0b1b5d2ce0Asia-Pacific Journal of Accounting &amp; Economics400Asia-Pacific Journal of Accounting &amp; Economics["Guoquan Xu", "Xin Li", "Siyuan Li", "Yan Tong"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/5decdaaf488ca7759af5486d1b5d6e0b1b5d2ce0 3ebaa0cd7732603d1450a4e297ef51dfee3ab00fNeurology Today20Neurology Today["Gina Shaw"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/3ebaa0cd7732603d1450a4e297ef51dfee3ab00f c81552b06ba549100b8330576d84cbe489434f73This article aims to understand digital culture in Business Law as a growing integration of digital technologies and technological innovations in the practices, processes and standards that govern business activities. In this sense, it addresses brief considerations regarding Business Law, recognizing it as a guarantor of the regularity of economic and business activities, its principles, as well as the legal framework that regulates these activities. Furthermore, it presents the main aspects of digital culture in this context, as well as the use of artificial intelligence in the optimization of legal and administrative processes in companies as innovative tools in the corporate world. As a methodology, this article involves a qualitative, descriptive research, with the performance of documentary research, through the analysis of legislation, as well as bibliographic research.Revista ft00Digital culture in Business Law is understood as a growing integration of digital technologies and technological innovations in the practices, processes and standards that govern business activities as well as the use of artificial intelligence in the optimization of legal and administrative processes in companies as innovative tools in the corporate world.Revista ft["Bruno Bernardo Plaza"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/c81552b06ba549100b8330576d84cbe489434f73 a2a826e8cfea7f01d725257a871b4a5749a8b8fbAs the systems driven by artificial intelligence (AI) become increasingly integrated across various applications, ensuring their security is paramount to mitigate potential risks and vulnerabilities. Threat intelligence plays a pivotal role in identifying, analysing, and mitigating cybersecurity threats, particularly those targeting AI systems. Securing these systems entails safeguarding data integrity and confidentiality while ensuring the reliability and trustworthiness of AI algorithms and models. Challenges in AI-powered systems encompass adversarial attacks, model poisoning, data privacy concerns, as well as issues of bias and fairness. Threat intelligence offers valuable insights into emerging cyber threats, empowering organizations to proactively identify and address potential risks. Leveraging AI and machine learning techniques, threat intelligence platforms analyse extensive datasets to detect patterns indicative of cyber threats against AI systems. Sharing threat intelligence among organizations enhances collective defence capabilities and facilitates proactive threat mitigation strategies. This paper aims to propose an AI-powered systems cyber risk analysis approach, encompassing machine learning techniques. To achieve that, an intrusion detection simulation was done by generating a Python script with synthetic data. Two datasets were generated - a training dataset with 10,000 records and a test dataset with 400 records. Machine learning techniques are applied to predict what the most common security issues are.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)450This paper aims to propose an AI-powered systems cyber risk analysis approach, encompassing machine learning techniques, which aims to propose an AI-powered systems cyber risk analysis approach to mitigate potential risks against AI systems.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)["R. Nacheva", "Otmane Azeroual"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/a2a826e8cfea7f01d725257a871b4a5749a8b8fb d23f07b766e656c24b7b3db42ad3527a4fcfab03The media portrayal of artificial intelligence (AI) directly impacts how audiences conceptualize this technology and, therefore, its use, development, and regulation. This study aims to measure a key aspect of this problem: the feeling of AI anxiety conveyed by news outlets that represent this technology as a sort of “alien” that is autonomous, opaque, and independent of humans. To do so, we build an AI anxiety index based on principal component analysis (PCA) and apply it to a corpus of headlines (n = 1682) about AI published before and after the launch of ChatGPT in ten newspapers: The New York Times, The Guardian, El País, Le Monde, Frankfurter Allgemeine Zeitung, San Francisco Chronicle, Manchester Evening News, La Voz de Galicia, Ouest France, and Münchner Merkur. The results show that ChatGPT not only boosted the number of AI headlines (× 5.16) but also reduced positive sentiments (−26.46%) and increased negatives (58.84%). The AI anxiety index also grew (10.59%), albeit driven by regional media (61.41%), while it fell in national media (−6.82%). Finally, the discussion of the variables that compose the index reveals the opportunities and challenges faced by national and regional media in avoiding the feeling of AI anxiety.The social science513An AI anxiety index is built based on principal component analysis (PCA) on a corpus of headlines about AI published before and after the launch of ChatGPT in ten newspapers and shows that ChatGPT not only boosted the number of AI headlines but also reduced positive sentiments and increased negatives.Social Sciences["Pablo Sanguinetti", "B. Palomo"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/d23f07b766e656c24b7b3db42ad3527a4fcfab03 b620e4192d3269bc75077a0ba0e5984776aed692The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting, the challenges and implications. The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things, also highlight the continued improvement in data processing capabilities. Explore how AI is reshaping financial services with precisely tAIlored services that can more precisely meet the diverse needs of individual investors. The integration of AI challenges regulatory and ethical issues in the financial sector, as well as the implications for data privacy protection. Analyze the limitations of current AI technology in financial forecasting and its potential impact on the future financial industry landscape, including changes in the job market, the emergence of new financial institutions, and user interface innovations. Emphasizing the importance of increasing investor understanding and awareness of AI and looking ahead to future trends in AI tools for user experience to drive wider adoption of AI in financial decision making. The huge potential, challenges, and future directions of AI in the financial sector highlight the critical role of AI technology in driving transformation and innovation in the financial sectorarXiv.org02ArXiv["Shuochen Bi", "Tingting Deng", "Jue Xiao"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/b620e4192d3269bc75077a0ba0e5984776aed692 fbbd7e2f7f5b104adfe8bc99f8bbe6273d165242Artificial Intelligence (AI) auditing is a relatively new area of work. Currently, there is a lack of uniform standards and regulation. As a result, the AI auditing ecosystem is very diverse, and AI auditing professionals use a variety of different auditing methods. So far, little is known about how AI auditors approach the concept of trust in AI through AI audits, in particular regarding the trust of users. This paper reports findings from interviews with 19 AI auditing stakeholders to understand how AI auditing professionals seek to create calibrated trust in AI tools and AI audits. Themes identified included the AI auditing ecosystem, participants' experiences with AI auditing, and trust in AI audits and AI. The paper adds to the existing research on trust in AI and trustworthiness in AI by adding perspectives of key stakeholders regarding trust in AI Audits by users as an essential and currently less explored part of the trust in AI research. This paper shows how information asymmetry in respect to AI audits can decrease the value of audits for users and consequently their trust in AI systems. Study participants suggest key elements for rebuilding trust and suggest recommendations for the AI auditing industry, such as monitoring of auditors and effective communication about AI audits.Proc. ACM Hum. Comput. Interact.181It is shown how information asymmetry in respect to AI audits can decrease the value of audits for users and consequently their trust in AI systems.Proc. ACM Hum. Comput. Interact.["Tina Lassiter", "Kenneth R. Fleischmann"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/fbbd7e2f7f5b104adfe8bc99f8bbe6273d165242 7ed1fa692ce71e1ef1adaabe53c0b952bc605ec3ABSTRACT Background Recent advancements in artificial intelligence (AI) have significantly impacted dermatology, particularly in diagnosing skin diseases. However, aesthetic dermatology faces unique challenges due to subjective evaluations and the lack of standardized assessment methods. Aims This review aims to explore the current state of AI in dermatology, evaluate its application in diagnosing skin conditions, and discuss the limitations of traditional evaluation methods in aesthetic dermatology. Additionally, the review proposes strategies for future integration of AI to address existing challenges. Methods A comprehensive review of AI applications in dermatology was conducted, in both diagnostic and aesthetic fields. Traditional methods such as subjective surveys and hardware devices were analyzed and compared with emerging AI technologies. The limitations of current AI models were evaluated, and the need for standardized evaluation methods and diverse datasets was identified. Results AI has shown great potential in diagnosing skin diseases, particularly skin cancer. However, in aesthetic dermatology, traditional methods remain subjective and lack standardization, therefore limiting their effectiveness. Emerging AI applications in this field show promise, but they have significant limitations due to biased datasets and inconsistent evaluation methods. Conclusions To develop the potential of AI in aesthetic dermatology, it is crucial to create standardized evaluation methods, collect diverse datasets reflecting various ethnicities and ages, and educate practitioners on AI's utility and limitations. Addressing these challenges will improve diagnostic accuracy, better patient outcomes, and help integrate AI effectively into clinical practice.Journal of Cosmetic Dermatology231To develop the potential of AI in aesthetic dermatology, it is crucial to create standardized evaluation methods, collect diverse datasets reflecting various ethnicities and ages, and educate practitioners on AI's utility and limitations.Journal of Cosmetic Dermatology["Sukruthi Thunga", "Marius Khan", "Soo Ick Cho", "Jung-Im Na", "Jane Yoo"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/7ed1fa692ce71e1ef1adaabe53c0b952bc605ec3 77ef9666a5fff2e5a0c68b59cabae8295c9739e2In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully utilize the collaborative potential of multiple AI agents. In this paper, we propose a novel multi-agent collaboration system designed to enhance decision-making in financial investment research. The system incorporates agent groups with both configurable group sizes and collaboration structures to leverage the strengths of each agent group type. By utilizing a sub-optimal combination strategy, the system dynamically adapts to varying market conditions and investment scenarios, optimizing performance across different tasks. We focus on three sub-tasks: fundamentals, market sentiment, and risk analysis, by analyzing the 2023 SEC 10-K forms of 30 companies listed on the Dow Jones Index. Our findings reveal significant performance variations based on the configurations of AI agents for different tasks. The results demonstrate that our multi-agent collaboration system outperforms traditional single-agent models, offering improved accuracy, efficiency, and adaptability in complex financial environments. This study highlights the potential of multi-agent systems in transforming financial analysis and investment decision-making by integrating diverse analytical perspectives.International Conference on AI in Finance341A novel multi-agent collaboration system designed to enhance decision-making in financial investment research, which incorporates agent groups with both configurable group sizes and collaboration structures to leverage the strengths of each agent group type.{"pages": "538-546"}["Xuewen Han", "Neng Wang", "Shangkun Che", "Hongyang Yang", "Kunpeng Zhang", "Sean Xin Xu"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/77ef9666a5fff2e5a0c68b59cabae8295c9739e2 9f3e1ef8b6a3b58565ebe165bc23c1e0067434c9Artificial intelligence (AI) is poised to revolutionize education, offering a plethora of tools to personalize learning experiences, enhance student comprehension, and empower educators with new pedagogical approaches. This paper explores the impact of current generative AI tools, particularly chatbots and AI art generators, within academia. Our analysis highlights the potential benefits of these tools but the paper critically examines the ethical considerations surrounding AI use in education. Potential issues like plagiarism facilitated by chatbots and biases embedded within algorithms necessitate a nuanced approach. Finally, the research emphasizes the importance of developing robust policies to mitigate ethical concerns and ensure equitable access to these tcchnologies for all learners.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)01The research highlights the importance of developing robust policies to mitigate ethical concerns and ensure equitable access to these tcchnologies for all learners and critically examines the ethical considerations surrounding AI use in education.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)["Shayan Aamir", "Shafaq Fatima Mughal", "Aeyaz Jamil Kayani", "Muhammad Zain Yousuf", "Omar Ali Rastgar", "A. Syed"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/9f3e1ef8b6a3b58565ebe165bc23c1e0067434c9 bdca70dd8306c8dba582cf396ea99ad089cbc0c1This study investigates the implementation of the PAIR (Problem, AI, Interaction, Reflection) model in undergraduate business courses at Tecnologico de Monterrey, assessing its impact on critical thinking development and student engagement with Artificial Intelligence (AI) tools. A mixed-method approach was employed, involving 83 fifth-semester International Business students across two campuses. Data collection included quantitative surveys, student reflections, and course deliverable analysis. Quantitative results indicated a positive trend towards PAIR model implementation. Students reported significant improvement in critical thinking skills (Mean = 3.82, SD = 0.86) and high engagement with AI interaction activities (Mean = 3.78, SD = 0.89). 87% of students felt AI enhanced their reflection and research skills. Qualitative analysis revealed increased student awareness of the need to verify and contextualize AI-provided information, recognizing critical thinking's importance. Progress was noted in students' ability to select and evaluate multiple information sources, including AI-generated ones. The study concludes that the PAIR model can enhance critical thinking and foster active learning in AI-influenced educational environments. Structured integration of AI tools into curricula may positively impact student learning experiences and critical skill development for academic and professional life. Limitations include the sample being restricted to one degree program across two campuses and the cross-sectional nature, precluding long-term effect evaluation. Future research should explore the model's long-term impact, compare it with other AI integration models in education, and examine its applicability across various disciplines and educational levels.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)260The study concludes that the PAIR model can enhance critical thinking and foster active learning in AI-influenced educational environments and structured integration of AI tools into curricula may positively impact student learning experiences and critical skill development for academic and professional life.2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)["H\u00e9ctor Ram\u00f3n Rodr\u00edguez Maya", "Ana Beatriz Salas Valdes"]2024-11-07T00:00:00https://www.semanticscholar.org/paper/bdca70dd8306c8dba582cf396ea99ad089cbc0c1 c23dcc1f591ada78e74115743b25f17a902d568eArtificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.Frontiers Artif. Intell.692It is argued that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments.Frontiers in Artificial Intelligence["J. B\u00e9lisle-Pipon", "Gavin Victor"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/c23dcc1f591ada78e74115743b25f17a902d568e 3bbd0705d6abb966033adca809ed81344ff3f9bbA plethora of publications have shed light, particularly on the affordances of artificial intelligence (AI) in language education, garnering significant attention, promising transformative impacts on teaching and learning practices. However, the rapid adoption of AI tools has raised ethical concerns regarding data privacy, bias and academic integrity. in response to these concerns, this systematic review aims to explore the responsible and ethical use of AI in language education (REALE) by examining recent literature from 2020 to 2024. The structure of this research revolves around two key questions: What are the emerging patterns and practices in REALE? and What research methodologies have been utilized in studies examining REALE? The researchers selected 9 studies from 65 publications in the Web of Science (WoS) and Scopus databases, following a rigorous screening process based on predefined inclusion and exclusion criteria. These selected studies were analyzed using thematic codes: the objective of the study, methodologies applied, sample, country and the key outcomes reported. The findings reveal a growing trend towards implementing AI in language education, with an emphasis on ethical training and awareness. The review suggests the necessity for educators and policymakers to develop comprehensive guidelines for the responsible and ethical use of AI in language education. It also recommends further research into inclusive and ethical AI practices across different educational levels to foster a more equitable and responsible use of technology in language education.Forum for Linguistic Studies571The review suggests the necessity for educators and policymakers to develop comprehensive guidelines for the responsible and ethical use of AI in language education, and recommends further research into inclusive and ethical AI practices across different educational levels to foster a more equitable and responsible use of technology in language education.Forum for Linguistic Studies["Nurkhamimi Zainuddin", "Nur Azlin Suhaimi", "Mohammad Najib Jaffar", "Norita", "Muhammad Sabri bin Sahrir", "Wan Ab", "Aziz Wan Daud", "Mohammad Taufiq", "Abdul Ghani"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/3bbd0705d6abb966033adca809ed81344ff3f9bb ff8a9b4a1367645354a46d2d6229c3411b0388daPurposeThis paper specifically aims to examine how (via which activities, methods and capabilities) organizations’ management deploy Artificial Intelligence (AI) systems to address underperformance. Five mitigation strategies/recommendations are introduced to manage the challenges and facilitate greater efficacies in changing organizations.Design/methodology/approachThis paper conceptually synthesizes 47 articles, thematically reports and critically analyzes the AI–HRM–managerial decision-making relationship in changing organizations and discusses the impacts.FindingsThe results highlight three significant challenges and opportunities for changing organizations: (1) job performance challenges, (2) organizational performance challenges and HR and (3) collaborative intelligence opportunities.Originality/valueThe paper’s originality lies in addressing the current lack of a theoretical framework guiding HRM and AI experts on the managerial and strategic capabilities needed to address underperformance and their impacts in facilitating collective efficacies in human–AI collaboration in changing organizations. By further capturing an innovative HR Framework’s (1) human, (2) AI, (3) employees’ well-being, (4) jobs and (5) organizational performance, and its five key managerial recommendations/strategies, this paper develops two concepts: “technological servitization” and “re-ontological in-securitization” to advance theory in Managerial Psychology regarding the unintended/paradoxical consequences of managements’ AI-driven organizational performance interventions, including meaninglessness in organizations.Journal of Managerial Psychology1022Two concepts are developed: “technological servitization” and “re-ontological in-securitization” to advance theory in Managerial Psychology regarding the unintended/paradoxical consequences of managements’ AI-driven organizational performance interventions, including meaninglessness in organizations.Journal of Managerial Psychology["John Mendy", "Apoorva Jain", "Asha Thomas"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/ff8a9b4a1367645354a46d2d6229c3411b0388da 62b740cb9f5ab0fe8bb18e54dae4893216a1b49fAbstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.Applied and Computational Engineering00The integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment are enhanced, demonstrating significant improvements in prediction accuracy and decision-making capabilities.Applied and Computational Engineering["Daren Zhang"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/62b740cb9f5ab0fe8bb18e54dae4893216a1b49f 865bfa78b4410a95f54e93a87e9e7b2b7864431dPurpose: This article explores the integration of Artificial Intelligence (AI) in quality assurance practices within healthcare settings, aiming to significantly enhance patient care. By improving the accuracy and efficiency of quality assurance processes, AI can reduce human error and increase compliance with healthcare standards. Methodology: The study employs a mixed-methods approach, combining quantitative data from healthcare facilities utilizing AI systems with qualitative interviews with healthcare professionals. Surveys assess the impact of AI on quality assurance metrics, while interviews provide insights into the perceptions and experiences of staff regarding AI implementation. Methodology: The results indicate that AI integration significantly enhances the speed and accuracy of quality assurance tasks, leading to a reduction in human error and an increase in compliance with healthcare standards. More importantly, healthcare professionals reported improved patient outcomes and greater workflow satisfaction, which are attributed to AI's capability to handle routine checks and data analysis. Unique Contribution to Theory, Policy and Practice: This article contributes to the theoretical understanding of AI's role in healthcare quality assurance by establishing a framework that links AI utilization with improved patient care outcomes. It informs policymakers about the efficacy of AI technologies, advocating for policies that encourage their adoption in healthcare settings. Practitioners gain insights into best practices for implementing AI solutions that support quality assurance, ultimately fostering a safer and more efficient healthcare environment.International Journal of Health Sciences00The results indicate that AI integration significantly enhances the speed and accuracy of quality assurance tasks, leading to a reduction in human error and an increase in compliance with healthcare standards.International Journal of Health Sciences["Vedamurthy Yogeshappa", "Jayanna Hallur", "Praveen Kuruvangi Parameshwara"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/865bfa78b4410a95f54e93a87e9e7b2b7864431d 0f93a59047b914399912884ad227234186c07727BACKGROUND Artificial intelligence (AI) technology is constantly and rapidly evolving and has the potential to benefit occupational therapy (OT) and OT clients. However, AI developments also pose risks and challenges, for example in relation to the ethical principles of OT. One way to support future AI technology aligned with OT ethical principles may be through human-centered AI (HCAI), an emerging branch within AI research and developments with a notable overlap of OT values and beliefs. OBJECTIVE To explore the risks and challenges of AI technology, and how the combined expertise, skills, and knowledge of OT and HCAI can contribute to harnessing its potential and shaping its future, from the perspective of OT's ethical values and beliefs. RESULTS Opportunities for OT and HCAI collaboration related to future AI technology include ensuring a focus on 1) occupational performance and participation, while taking client-centeredness into account; 2) occupational justice and respect for diversity, and 3) transparency and respect for the privacy of occupational performance and participation data. CONCLUSION AND SIGNIFICANCE There is need for OTs to engage and ensure that AI is applied in a way that serves OT and OT clients in a meaningful and ethical way through the use of HCAI.Scandinavian Journal of Occupational Therapy300There is need for OTs to engage and ensure that AI is applied in a way that serves OT and OT clients in a meaningful and ethical way through the use of HCAI.Scandinavian journal of occupational therapy["V. Kaelin", "I. Nilsson", "Helena Lindgren"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/0f93a59047b914399912884ad227234186c07727 f6d1366a3d13471a9f68747fc4f64107d516e82bScientific Reports290The ML models to predict CO and H2S during composting were demonstrated for the first time and presented a cost-effective and expeditious alternative to the empirical analysis of compost properties.Scientific Reports["Joanna Rosik", "Maciej Karczewski", "S. Stegenta-D\u0105browska"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/f6d1366a3d13471a9f68747fc4f64107d516e82b c9517f99bbb60363a7371c38303716a603b4f84cAbstract. The rapid development of Regulatory Technology (RegTech) has introduced new methods to handle and simplify the compliance and regulatory issues surrounding financial services and products. Combining artificial intelligence (AI) with this trend is an effective way of strengthening financial services, making them safer and more convenient. The current AI technologies incorporated within RegTech applications improve the efficiency of compliance with rules and regulations, and contribute to the development and implementation of smart technology, including automated regulatory reporting, proven and trustworthy KYC processes, and reduced compliance costs. This paper presents the application of intelligent technology, such as machine learning, deep learning, natural language processing, and smart regulatory frameworks, to build modern, intelligent workflows, dynamically control policies, and instantaneously monitor transactions. We employ empirical economic data, evidenced by realistic case studies, to demonstrate the benefits brought on by intelligent technology to financial institutions, including quicker compliance with regulatory regimes, decreased operational risk, and increased transparency and accountability. Through analysing figures offered in various cases, we show the transformed compliance landscape created by RegTech, how it becomes a solution for the diverse challenges affecting financial sectors on a global scale, and how is it going to impact the whole field of financial services in the future..Applied and Computational Engineering100This paper presents the application of intelligent technology, such as machine learning, deep learning, natural language processing, and smart regulatory frameworks, to build modern, intelligent workflows, dynamically control policies, and instantaneously monitor transactions.Applied and Computational Engineering["Pengjian Liang"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/c9517f99bbb60363a7371c38303716a603b4f84c 653abf509ab31e67d429e23e829b0cb186ef53d2Cyber blackmail is defined as a transgression which occurs when an individual or group intimidates the victim by threatening to expose his/her personal information on social media. This form of criminal activity, which has emerged with technological advancement, particularly in the artificial intelligence (AI) domain, is not confined to any specific region or nation. The unrestricted reach of cyber blackmail necessitates the constant revision and evaluation of the laws regulating it so that these laws maintain their effectiveness when changes occur in the manner in which this offence is committed. This study comparatively analysed the validity of current cyber blackmail laws in Iraq and Malaysia through discussions emphasizing the association between cyber blackmail and AI, as well as the rules and regulations formulated to curb this crime. The systematic, comprehensive and comparative method employed for this study scrutinized the issue of cyber blackmail from all perspectives. It revealed that the will of Iraqi authorities falls short when it comes to the implementation of effective measures aimed at reining in the prevalence of cyber blackmail. These measures included reviewing the penal code or drafting laws directed at combating IT crimes. The crime of cyber blackmail involves the instilling of anxiety in the victim, with the purpose of compelling him/her to succumb to the demands of the blackmailer. We conclude this study with proposals aimed at curtailing the occurrence of cyber blackmail.Journal of Infrastructure Policy and Development00The will of Iraqi authorities falls short when it comes to the implementation of effective measures aimed at reining in the prevalence of cyber blackmail, according to a systematic, comprehensive and comparative method employed.Journal of Infrastructure, Policy and Development["Omar Abdulsalam Hussein", "N. Manap"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/653abf509ab31e67d429e23e829b0cb186ef53d2 5d6017e2dea8db4387804e0e89a42233e21a7e8eThe use of Artificial Intelligence (AI) technology, such as ChatGPT, in providing legal assistance in Indonesia presents new potential for improving accessibility and efficiency. However, there remains uncertainty regarding the legal liability for errors that may occur in the legal advice provided by AI. This study aims to explore the legal validity of using AI in providing legal aid and to examine how legal liability may be attributed to service providers if mistakes occur. By reviewing Law No. 18 of 2003 on Advocates, Law No. 8 of 1999 on Consumer Protection, and Law No. 27 of 2022 on Personal Data Protection, this study highlights the importance of specific regulations to ensure that AI use in legal assistance adheres to legal and ethical standards. Additionally, the study discusses legal protection for AI service users, particularly regarding personal data security and consumer rights. The conclusion emphasizes the need for a clear regulatory framework to ensure that AI use in the legal field provides optimal benefits without compromising security and legal certainty for users.Journal of Law, Politic and Humanities90The need for a clear regulatory framework is emphasized to ensure that AI use in the legal field provides optimal benefits without compromising security and legal certainty for users.Journal of Law, Politic and Humanities["Ahzaza Fahrani", "Gunawan Djajaputra"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/5d6017e2dea8db4387804e0e89a42233e21a7e8e 1a6e7186711e928dbf18c7fa53a053157f9da6beTechnology integration into curriculum no doubt has so many benefits that lecturers in tertiary educational institutions can derive. This study examined lecturers’ awareness of Artificial Intelligence (AI) tools for teaching and research at Alvan Ikoku Federal University of Education Owerri Nigeria.  The AI tools examined in the study include ChatGPT, PowerPoint Speaker Coach Quillbot, Perplexity, Scholarcy, Gradescope, Mendeley Gemini, MATHia, ChatPDF, Consensus, Research Rabbit, Class Point and Scite. Three research questions and two research hypotheses guided the study. The population of study comprised lecturers in the university. Data for the study was collected using researchers developed questionnaire tagged Lecturers’ Awareness of Artificial Intelligence Tools for Teaching and Research Questionnaire (LAAITTRQ) on WhatsApp. Mean and standard deviation were used to answer research questions while independent samples t-test statistics and Analysis of Variance (ANOVA) test were employed to test the formulated hypotheses at a 0.05 level of significance. Findings reveal that lecturers are aware of AI tools for teaching and research. Furthermore, it was discovered that there was no significant difference in awareness according to gender. However, significant differences in awareness exist according to teaching experience with the least experienced lecturers having awareness mean scores higher than the most experienced and the moderately experienced.  Based on the findings, the researchers recommended that workshops and training on the use of AI tools in teaching and research should be organized in the university to boost their knowledge and skills among others.African Journal of Humanities and Contemporary Education Research110It was discovered that there was no significant difference in awareness according to gender and significant differences in awareness exist according to teaching experience with the least experienced lecturers having awareness mean scores higher than the most experienced and the moderately experienced.African Journal of Humanities and Contemporary Education Research["Dr. Bede Blaise Chukwunyere Onwuagboke", "C. Nnajieto", "R. Nzeako", "Hope Umune"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/1a6e7186711e928dbf18c7fa53a053157f9da6be e31d1bf57c4ca6f67606b20ff58ed8789361d3fbAbstract: Traditional control methods, such as proportional-integral-derivative (PID) controllers and linear-quadratic regulators (LQRs), have proven effective for linear and well-modeled systems. However, these methods often perform poorly in nonlinear, complex and dynamic environments. The paper aims to investigate the modern control systems by integrating artificial intelligence (AI) techniques, such as machine learning (ML), reinforcement learning (RL), deep learning, and fuzzy logic, to enhance their adaptive, robust, and predictive capabilities. And it reviews the literature and analyzes AI integration in control systems. The proposed strategies include supervised learning for trajectory optimization and fault detection, reinforcement learning for optimal control in dynamic environments, neural networks for complex nonlinear function approximation, and fuzzy logic for handling uncertainty and imprecise inputs. AI techniques significantly enhance the ability to tackle nonlinear problems and dynamic changes, demonstrating superior performance in applications like self-driving cars adapting to various road conditions and optimal energy distribution in smart grids. Despite the challenges of computational complexity, scalability, and the safety and reliability in the implementation of interpretable AI models, this paper suggests that hybrid approaches combining traditional control and AI techniques, along with the evolution of interpretable AI and convergence with quantum control, hold great promise for advancing AI-driven control systems.Applied and Computational Engineering00It is suggested that hybrid approaches combining traditional control and AI techniques, along with the evolution of interpretable AI and convergence with quantum control, hold great promise for advancing AI-driven control systems.Applied and Computational Engineering["Haokai Zhou"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/e31d1bf57c4ca6f67606b20ff58ed8789361d3fb 6cdb42265a29f76ba1900d4941a28b443b86a3c6Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents – the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this paper aims to address the key research challenge: "how to identify and assess GAI system's copyright concerns"? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of ten legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study.Digit. Gov. Res. Pract.170The development of a Copyright Risk Checker (CRC) Tool is proposed, which has been formulated and evaluated using a recognised design science research methodology, and results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems.Digit. Gov. Res. Pract.["Grace Billiris", "A. Gill", "Ian Oppermann", "Mahmood Niazi"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/6cdb42265a29f76ba1900d4941a28b443b86a3c6 08a7772aae03492e59ea7d02d302e55fb014877cCorporate social responsibility, or CSR, has been the subject of an increasing amount of interest with the infusion of artificial intelligence or AI. AI may open up much-needed improvements in CSR practice, including better decision-making, transparency, and sustainable benefits, but it also poses challenges related to the ethical concerns of data privacy, bias, and accountability. This paper focuses on how an AI-based approach enhances CSR activities and identifies risks embedded in AI adoption in approaches to CSR. The study combines qualitative interviews with industry experts and quantitative analysis of CSR reports from AI-adopting firms and thus sheds lights on the upside and downside of AI-enhanced CSR. Finally, this article ends with recommendations in developing a framework that would align AI capabilities with CSR objectives and indicates how that may potentially challenge future research directions in order to mitigate the ethical challenges involved.International Journal of Advanced Multidisciplinary Research and Studies10Recommendations are made in developing a framework that would align AI capabilities with CSR objectives and indicates how that may potentially challenge future research directions in order to mitigate the ethical challenges involved.International Journal of Advanced Multidisciplinary Research and Studies["Dr.Venkateswararao. Podile", "Kovvuri Priyanka Reddy", "Vemareddy Nikhil Sai Reddy", "Mekala Surendra Babu", "Adusumalli Karthik Phani"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/08a7772aae03492e59ea7d02d302e55fb014877c 2a89ca9937c2c6a70e206d32fcf3bad027e6cfffBeberapa tahun ke belakang, penggunaan artificial intelligence atau AI sudah banyak dimanfaatkan untuk memudahkan kegiatan manusia. Penggunaan AI juga mudah ditemukan dalam bidang akademik, terutama penulisan karya ilmiah. Penelitian ini bertujuan untuk melacak manfaat dari AI berdasarkan fungsinya dan limitasi dari AI sehingga peneliti dapat merumuskan cara penggunaan AI yang etis dan berintegritas dalam dunia akademis. Penelitian ini menggunakan studi literatur di mana peneliti menelusuri database yang bersumber dari Scopus dan JSTOR dan menemukan 12 artikel jurnal, 2 buku, dan 1 laporan ilmiah yang berkaitan dengan etika penggunaan AI sebagai sumber data dan informasi yang relevan dengan penelitian ini. Hasil analisis pada sumber literatur tersebut menunjukkan bahwa ditemukan beberapa fungsi AI yang berkaitan dengan penggunaannya dalam bidang akademik dan penulisan karya ilmiah. Selain itu, juga terdapat limitasi dari AI yang menggambarkan keterbatasan kemampuan AI dalam menganalisis sesuatu dalam konteks penelitian dan penulisan karya ilmiah. Namun, penggunaan AI dalam penulisan karya ilmiah bukanlah hal terlarang, akan tetapi penggunaan AI harus dapat dibatasi untuk hal-hal tertentu, seperti menggagas ide, merancang outline, alat bantu analisis dan ekstraksi data/informasi, dan pengecekan tata bahasa. Selain itu, seorang peneliti juga harus menunjukkan integritas akademiknya dengan menampilkan apa saja yang merupakan hasil dari AI dan dibedakan dengan hasil pemikiran oleh peneliti. Penelitian ini dapat berkontribusi sebagai panduan etis dari penggunaan AI dalam penulisan karya ilmiah.Jurnal Penelitian Inovatif00Jurnal Penelitian Inovatif["Galuh Efnol Adzan", "Azhar Azhar"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/2a89ca9937c2c6a70e206d32fcf3bad027e6cfff 8b00bd633673b24852679ba0b4d33029fdfef148Artificial intelligence (AI) technology is deeply changing our lives and provides impetus for improving production and living efficiency as an important emerging tool. Digitalization and intelligent development have also become the development direction of the sports industry, bringing new requirements to the transformation of physical education (PE) and the improvement of the quality of PE teachers. PE is an important part of the public health system, and AI can deeply participate in the formulation of teaching strategies, the tracking of teaching processes and the evaluation of teaching results, effectively improving the quality of teaching. Research on the application of AI technology in PE has been carried out. This paper comprehensively reviews the existing research and conducts a comprehensive analysis of the research progress and status. The potential application areas of AI in PE are discussed to better promote the intelligent and digital upgrading of PE. We found that the research on the application of AI in PE is still in its early stages, and the research content needs to be strengthened in terms of breadth and depth. Furthermore, this paper analyzes the challenges faced by PE teacher development and training in the context of educational transformation in the era of AI, and explores the necessary skills and knowledge related to AI technology that future PE teachers should master in order to effectively achieve the improvement of teaching level and the sustainable development of public health system. The review of this paper provides valuable guidance for educators and policymakers to formulate high-quality teacher development and training mechanisms, and provides a new reference for the application and development of AI in sports.Frontiers in Public Health640The challenges faced by PE teacher development and training in the context of educational transformation in the era of AI are analyzed, and the necessary skills and knowledge related to AI technology that future PE teachers should master are explored in order to effectively achieve the improvement of teaching level and the sustainable development of public health system.Frontiers in Public Health["Yuping Wang", "Xinyan Wang"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/8b00bd633673b24852679ba0b4d33029fdfef148 29de8d2127d3f71260f9e819c9efda5e7a86b7d0This academic investigation examines the bifurcated impact of artificial intelligence (AI) on contemporary labor markets, analyzing both displacement effects and employment generation across multiple sectors (n=327) during 2020-2024. Through a mixed-methods approach combining econometric analysis of industry-level data, semi-structured interviews with key stakeholders (n=142), and longitudinal case studies of AI-implementing firms (n=47), we demonstrate that while AI automation has led to a 23.4% reduction in traditional middle-skill jobs across manufacturing, logistics, and administrative sectors, it has simultaneously generated a 31.7% increase in new employment categories, particularly in AI development, human-AI collaboration, and digital transformation roles. The findings reveal significant sectoral variations in job displacement rates (ranging from 8.2% to 37.6%) and identify critical factors influencing successful workforce transition, including the timing of reskilling initiatives, the nature of institutional support, and the elasticity of labor market responses. Notably, organizations that implemented proactive reskilling programs achieved a 64% higher retention rate of displaced workers compared to those utilizing reactive approaches. The article also uncovers an emerging "adaptation gap" wherein 42% of displaced workers face significant barriers to transitioning into new roles, primarily due to misaligned skill development programs and insufficient support infrastructure. These findings have important implications for policymakers, business leaders, and educational institutions in developing targeted interventions to facilitate effective workforce adaptation in an AI-driven economy.International Journal of Scientific Research in Computer Science Engineering and Information Technology30While AI automation has led to a 23.4% reduction in traditional middle-skill jobs across manufacturing, logistics, and administrative sectors, it has simultaneously generated a 31.7% increase in new employment categories, particularly in AI development, human-AI collaboration, and digital transformation roles.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Krishna Prasanth Brahmaji Kanagarla"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/29de8d2127d3f71260f9e819c9efda5e7a86b7d0 476a7ad1302b9e282904ea6626980565fe798597This comprehensive article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on modern marketing operations. The research delves into key areas where AI and ML are revolutionizing marketing strategies, including hyper-personalization, predictive analytics, and conversational AI. Through an analysis of recent developments and case studies, the article demonstrates how AI-driven personalization significantly enhances customer engagement and relevance, with some implementations showing up to 25% increase in revenue and 15% improvement in customer retention rates. The article also examines the role of predictive analytics in shifting marketing strategies from reactive to proactive approaches, enabling more accurate forecasting of customer behavior, campaign performance, and market trends. Furthermore, the evolution of chatbots and conversational AI is explored, highlighting their capacity to automate lead qualification, scale customer engagement, and gather real-time insights without increasing manual input. The integration of AI in marketing operations is shown to improve campaign management efficiency, enhance personalization capabilities, and facilitate future-focused campaign development. However, the research also addresses the challenges and ethical considerations associated with AI integration in marketing, including data privacy concerns, skill gaps, and the need to balance automation with human creativity. This article provides a comprehensive overview of how AI and ML are reshaping the marketing landscape, offering valuable insights for marketers, researchers, and business leaders navigating this technological revolution.International Journal of Scientific Research in Computer Science Engineering and Information Technology70How AI-driven personalization significantly enhances customer engagement and relevance is demonstrated, with some implementations showing up to 25% increase in revenue and 15% improvement in customer retention rates.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Sowmya Kotha"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/476a7ad1302b9e282904ea6626980565fe798597 47de6a454f102fe6bd04edc488556b5f20994009Abstract. The integration of artificial intelligence (AI) into education is revolutionising how we deliver personalised learning, where curriculum, pedagogy and assessments are dynamically adjusted to meet the demands of each learner. In this paper, we examine the role AI can play in the construction of personalised learning pathways, and explore its applications for K-12 education, higher education and lifelong learning. We highlight that AI-driven systems such as Summit Learning, Pounce at Georgia State University, and IBMs Watson Talent are helping to measure dramatic improvements in student and employee outcomes, ranging from retention rates, academic performance, to engagement levels. We show how AI can enable student profiling, adaptive learning path generation, and ongoing assessment via the continuous feedback loops that are critical to transformative personalised learning. We also highlight some of the key challenges related to how education (eg, data privacy, scalability and equity), and showcase case studies and data from AI applications in different educational contexts to demonstrate the potential of personalised learning enabled by AI. In summary, this paper underscores the benefits and challenges of implementing AI in education, providing a perspective on how AI is likely to shape the future of learning.Applied and Computational Engineering00The role AI can play in the construction of personalised learning pathways, and its applications for K-12 education, higher education and lifelong learning are examined, to demonstrate the potential of personalised learning enabled by AI.Applied and Computational Engineering["Qi Zhang"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/47de6a454f102fe6bd04edc488556b5f20994009 e1b6521c31be16bbe7d75cc67d49af75a499a912Artificial intelligence (AI) has applications in assisting older adults to age in place and provide support to them and their caregivers as their cognition declines with age. However, effective assessment methods of this technology are needed in order to benchmark their performance and a common set of metrics and evaluation methods would enable such assessments to be compared to one another. To this end, we propose a common framework for human-AI interaction involving care recipients and their care networks. From the results of a literature review exercise, a framework with sample metrics, related measures, qualified evaluation tools, and contextual factors that impact assessment are reviewed. This paper provides a sample of common metrics in one of the framework’s measurement spaces (human-AI interaction) and discusses some of the impacts of contextual factors and how use of the common metrics and evaluation framework can be used for meta-analysis and to guide future research. Additional future articles are planned to cover the other measurement spaces in the framework (system performance, task performance, and well-being), including their particular common metrics and evaluation methods. This effort aims to provide guidance for researchers in this domain as well as highlight measurement gaps that can be filled by future research.Proceedings of the AAAI Symposium Series00A sample of common metrics in one of the framework’s measurement spaces (human-AI interaction) and discusses some of the impacts of contextual factors and how use of the common metrics and evaluation framework can be used for meta-analysis and to guide future research.Proceedings of the AAAI Symposium Series["Jasmin Marwad", "Daisy M. Kiyemba", "Elizabeth J. Carter", "Adam Norton"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/e1b6521c31be16bbe7d75cc67d49af75a499a912 41d61550f88a7e80b35e3a05dbb699768799bcfcPelatihan Pemanfaatan Artificial Intelligence (AI) di Pemerintahan, yang diselenggarakan oleh Kementerian Komunikasi dan Informatika Indonesia melalui program Digital Talent Scholarship 2024, dirancang untuk meningkatkan kompetensi pegawai pemerintah dalam mengadopsi teknologi AI. Program ini berfokus pada peningkatan pengetahuan teoritis dan keterampilan praktis Aparatur Sipil Negara (ASN) serta pegawai pemerintah non-ASN mengenai berbagai aspek AI. Melalui pelatihan ini, peserta diharapkan memahami konsep dasar AI, berbagai aplikasi praktisnya dalam konteks pelayanan publik, serta etika dalam penggunaan AI untuk memastikan penggunaan teknologi secara bertanggung jawab. Metode pelatihan yang digunakan mencakup sesi pembelajaran online, baik asinkron maupun sinkron. Pada sesi asinkron, peserta diberikan kebebasan untuk mengakses materi pelatihan secara mandiri melalui platform Learning Management System (LMS). Materi ini mencakup video, artikel, dan modul yang memberikan pemahaman dasar tentang AI. Sesi sinkron, yang dilaksanakan melalui video konferensi, memungkinkan interaksi langsung antara pengajar dan peserta, sehingga mereka dapat mendiskusikan penerapan AI dalam skenario nyata yang relevan dengan tugas-tugas pemerintahan. Selain itu, peserta juga diminta untuk menyelesaikan tugas-tugas yang relevan dengan operasi pemerintahan, seperti analisis data menggunakan algoritma AI atau simulasi chatbot untuk layanan publik. Hasil pelatihan menunjukkan peningkatan signifikan dalam kemampuan peserta dalam memahami dan mengimplementasikan teknologi AI. Peningkatan kompetensi ini tidak hanya mendorong efisiensi dalam proses pelayanan publik, tetapi juga memperkuat transparansi dan akuntabilitas, karena data dapat dikelola dan dianalisis secara lebih baik. Kesimpulan dari program ini menekankan pentingnya pelatihan sebagai langkah strategis dalam upaya transformasi digital pemerintahan. Dengan demikian, diharapkan transformasi digital di sektor pemerintahan dapat berjalan lebih cepat dan merata, mendukung tercapainya pelayanan publik yang lebih baik di Indonesia.Journal Of Community Service130Khidmat: Journal of Community Service["Muhammad Arief Rahman"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/41d61550f88a7e80b35e3a05dbb699768799bcfc a35515533ac3ddc8c86f763d11f378b0d04ac5b4The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.Journal of Infrastructure Policy and Development410This study analyzes the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture from inception to date and examines 182 articles synthesized from two major databases.Journal of Infrastructure, Policy and Development["O. Aroba", "Michael Rudolph"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/a35515533ac3ddc8c86f763d11f378b0d04ac5b4 aeca1115281a4068465b854b93352a39f33884c1The article discusses the issues of familiarizing students with the basics of artificial intelligence in the process of organizing programming training in extracurricular activities, as well as the opportunities provided by the PictoBlox programming environment in the process of organizing programming training in extracurricular activities for fifth- and sixth-grade students. A brief overview of this programming environment is given, the process of creating projects using machine learning and artificial intelligence is considered. Examples of projects that use elements of machine learning and artificial intelligence are given. The materials of the article will introduce informatics teachers and teachers of additional education to the main methodological approaches to using the PictoBlox programming environment in the educational process and will contribute to the organization of extracurricular activities using this software.Informatics in school30Informatics in school["V. I. Filippov"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/aeca1115281a4068465b854b93352a39f33884c1 e17310a5247d6188f010aced8005d52da6aef3a2Artificial intelligence (AI) presents immense potential and significant challenges concerning algorithmic bias. This paper explores how feminist theory provides a criti-cal lens for understanding and addressing algorithmic bi-as’s root causes and impacts. The historical context of systemic discrimination reveals how power imbalances have shaped data collection and analysis, leading to bi-ased datasets that perpetuate inequalities through AI sys-tems. The "black box" problem further obscures these bi-ases, amplifying discriminatory outcomes in various domains. Feminist interventions, particularly intersec-tional feminism, offer a framework for uncovering how algorithmic bias interacts with multiple forms of oppres-sion. Feminist data science challenges traditional meth-odologies and advocates for transparency, accountabil-ity, and diversity in AI development. Critiques of tech-no-solutionism highlight the need for broader societal change alongside technical fixes. By embracing a feminist approach, we can envision and work toward a future where AI technology is used for social justice, inclusivi-ty, and collective liberation.Proceedings of the AAAI Symposium Series120This paper explores how feminist theory provides a criti-cal lens for understanding and addressing algorithmic bi-as’s root causes and impacts, and offers a framework for uncovering how algorithmic bias interacts with multiple forms of oppres-sion.Proceedings of the AAAI Symposium Series["Christo El Morr"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/e17310a5247d6188f010aced8005d52da6aef3a2 dba51fda27c0db2d32c297a18fc69cefb62dbe1fThe rapid development of modern artificial intelligence (AI) systems has created an urgent need for their scientific quantification. While their fluency across a variety of domains is impressive, modern AI systems fall short on tests requiring symbolic processing and abstraction - a glaring limitation given the necessity for interpretable and reliable technology. Despite a surge of reasoning benchmarks emerging from the academic community, no comprehensive and theoretically-motivated framework exists to quantify reasoning (and more generally, symbolic ability) in AI systems. Here, we adopt a framework from computational complexity theory to explicitly quantify symbolic generalization: algebraic circuit complexity. Many symbolic reasoning problems can be recast as algebraic expressions. Thus, algebraic circuit complexity theory - the study of algebraic expressions as circuit models (i.e., directed acyclic graphs) - is a natural framework to study the complexity of symbolic computation. The tools of algebraic circuit complexity enable the study of generalization by defining benchmarks in terms of their complexity-theoretic properties (i.e., the difficulty of a problem). Moreover, algebraic circuits are generic mathematical objects; for a given algebraic circuit, an arbitrarily large number of samples can be generated for a specific circuit, making it an optimal testbed for the data-hungry machine learning algorithms that are used today. Here, we adopt tools from algebraic circuit complexity theory, apply it to formalize a science of symbolic generalization, and address key theoretical and empirical challenges for its successful application to AI science and its impact on the broader community.arXiv.org730ArXiv["Takuya Ito", "Murray Campbell", "L. Horesh", "Tim Klinger", "Parikshit Ram"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/dba51fda27c0db2d32c297a18fc69cefb62dbe1f 46b5a45b9efab7f5d92c8f1f679ccd042ebcdd96The article examines the potential of Artificial Intelligence (AI), with a focus on Machine Learning (ML) and Deep Learning (DL), in the domain of corporate management. A review of the literature and existing practices reveals that AI has the potential to significantly transform traditional business processes, enhance decision-making efficiency, and provide corporations with substantial competitive advantages in the market. The mail goal of thi study is to analyse some options for integrating artificial intelligence into the corporate management system, to explore the impact on the quality of management decisions, and to identify the main disadvantages and threats of using artificial intelligence models in corporate management. The following methods were used in the research process: literature review and systematization of knowledge, empirical analysis, case study and optimization method. The study in question provides a detailed examination of the application of Machine Learning (ML) and Deep Learning (DL) in a number of key areas of corporate management, including shareholder relations, forecasting, process optimization, risk management, and human resources management. A key finding of the study is that AI enables companies to gain deeper insights into their customers, markets, and internal processes through data analysis. This, in turn, facilitates the development of personalized products and services, optimization of marketing campaigns, and enhancement of customer loyalty and stakeholder understanding. However, the authors of the article also highlight several challenges associated with the implementation of AI, including: data quality (the effectiveness of an AI system directly depends on the quality and quantity of data used for training the models); transparency of algorithms: (the complexity of Machine Learning and Deep Learning models often complicates the understanding of the reasons behind specific outcomes, which can lead to skepticism about the reliability of artificial intelligence systems). The social implications of AI are multifaceted and warrant further investigation. The use of AI may give rise to moral issues, including discrimination, bias, and job displacement. For the successful implementation of AI in corporate management, the authors offer a number of recommendations, including investing in the development of data infrastructure, attracting qualified specialists, developing clear strategies and policies for the use of AI, as well as constant monitoring and evaluation of the effectiveness of AI systems.ECONOMICS, FINANCE AND MANAGEMENT REVIEW00A key finding of the study is that AI enables companies to gain deeper insights into their customers, markets, and internal processes through data analysis, which facilitates the development of personalized products and services, optimization of marketing campaigns, and enhancement of customer loyalty and stakeholder understanding.Economics, Finance and Management Review["Y. Khaustova", "Taras Riabokin"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/46b5a45b9efab7f5d92c8f1f679ccd042ebcdd96 64e69f7a81929dfc04293c3594e6dbd05569142bAbstract. This review article explores the complex interplay between neuroscience and artificial intelligence, focusing on vision processing and its applications. It starts by outlining the biological basis of vision, delving into how visual information is processed and encoded in the brain. The discussion then transitions to artificial intelligence, particularly machine vision, highlighting the advancements and technologies that mimic biological processes. A critical analysis compares how visual information is utilized in both biological organisms and artificial systems, with an emphasis on cognitive functions and neural encoding. The challenges of integrating vision across various sensory modalities are examined, underscoring the technological and cognitive limitations currently faced. The review culminates by identifying potential research paths aimed at closing the gap between neuroscience and AI. This involves enhancing the understanding and functionality of vision in multisensory contexts, striving to foster a more comprehensive approach to artificial intelligence that mirrors the complexity of human perception.Applied and Computational Engineering00The biological basis of vision is outlined, delving into how visual information is processed and encoded in the brain, and potential research paths aimed at closing the gap between neuroscience and AI are identified.Applied and Computational Engineering["Haoshan Ye"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/64e69f7a81929dfc04293c3594e6dbd05569142b b1874802714b74413c4aaa85805980d8e890d4e9Objective: to substantiate the promising directions of legal regulation of relations in the use of artificial intelligence technologies in competitive (commercial and public) procurement.Methods: the study was conducted using induction, synthesis, analogy, decomposition of problems and generalization of conclusions. The reasoning was based on the experience of a complex procurement of high-tech equipment. This real-life example was considered as an experimental model for the study and subsequent prediction of the potential use of artificial intelligence technologies in competitive procurement procedures.Results: advantages and potential risks of using artificial intelligence technologies in procurement work were formulated; recommendations on regulating such use were given. The authors highlighted recommendations of general legal nature concerning the legal personality and delictual capacity of artificial intelligence and proposed the wordings for new norms and options for regulating the use of new procurement tools. It was proved that artificial intelligence technologies, if used thoughtfully, may not only improve the work quality and significantly reduce organizational costs, but also help to develop the basic principles of regulated procurement: transparency of procedures, development of competition for contracts between qualified suppliers, reasonableness of decisions, and economic efficiency of the customer’s expenditures.Scientific novelty: despite a large number of works devoted to both the problems of artificial intelligence in general and its use in procurement in particular, the article considers this topic on the basis of mainly inductive reasoning, built on handling a particular case and experience of complex procurement for knowledge-intensive research, refracted through the prism of essential correlation between the basic concepts of “digitalization”, “automation”, “robotization” and so on.Practical significance: the directions of using artificial intelligence described in this paper can be implemented by corporate and, in the future, by public customers to improve the quality of their procurement. At the same time, the recommendations on the normative regulation of such innovation seem to be in demand both at the legislative and local levels.Journal of Digital Technologies and Law110It was proved that artificial intelligence technologies, if used thoughtfully, may not only improve the work quality and significantly reduce organizational costs, but also help to develop the basic principles of regulated procurement.Journal of Digital Technologies and Law["D. A. Kazantsev", "P. Dohnal", "P. Dohnal Jr."]2024-11-08T00:00:00https://www.semanticscholar.org/paper/b1874802714b74413c4aaa85805980d8e890d4e9 b970769777eda1acc20d19efb73f69291663de97This paper discusses the transparency and regulation of Artificial Intelligence (hereinafter the AI). It discusses the growing trend of using decision-making algorithms in various areas of life and the need for transparent and the understandable AI systems. The author highlights the concept of a ‟black box” where algorithms and their processes become hidden to users and developers, and emphasises the importance of explainability and accessibility of information about the operation of the AI. The article also addresses the right to explanation and possible strategies for ensuring transparency and accountability in algorithmic decision-making, including parameter documentation and system certification.Vestnik of Kostroma State University100Vestnik of Kostroma State University["Kirill S. Golovin"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/b970769777eda1acc20d19efb73f69291663de97 288a77646d9d64d2e72fc57cab33acdc9f919441Artificial Intelligence (AI) adalah tambahan kecerdasan pada sistem yang dapat dikelola secara ilmiah dan berkembang di dunia teknologi untuk melayani berbagai aplikasi, termasuk keamanan siber. Kecerdasan buatan memainkan peran penting dalam keamanan siber, memungkinkan deteksi dini ancaman keamanan siber, analisis terperinci terhadap serangan yang muncul, dan respons yang cepat dan akurat. Penelitian ini menggunakan teknik tinjauan literatur sistematis (SLR) untuk menganalisis peran kecerdasan buatan dalam keamanan siber. Pengumpulan data dilakukan dengan mendokumentasikan semua makalah yang memuat temuan penelitian serupa dengan laporan penelitian ini. Makalah yang digunakan dalam penelitian ini adalah 20 makalah dari database ScienceDirect dan Google Scholar. Kecerdasan buatan telah menjadi elemen kunci dalam mendukung upaya untuk melindungi sistem informasi dan jaringan dari ancaman siber yang semakin kompleks. Dengan kemampuannya untuk belajar dari pola-pola data, AI memungkinkan untuk mendeteksi ancaman yang belum pernah terjadi sebelumnya dan memberikan respons secara real-time. Melalui tinjauan literatur sistematis ini, kami menyelidiki berbagai pendekatan dan teknik AI yang telah diterapkan dalam konteks keamanan siber, termasuk penggunaan jaringan syaraf tiruan, algoritma pembelajaran mesin, dan analisis teks. Hasil analisis kami menyoroti bahwa AI telah berhasil digunakan dalam mendeteksi serangan siber, menganalisis pola-pola perilaku yang mencurigakan, dan mengoptimalkan respons keamanan. Implikasi praktis dari penelitian ini adalah pentingnya terus mengembangkan dan mengadopsi solusi AI yang dapat memperkuat pertahanan siber dalam menghadapi ancaman yang terus berkembang.Kata Kunci: Artificial Intelligence, Cyber Security, Systematic Literature Review, Aplikasi Artificial Intelligence -------------------------------------------- Artificial Intelligence (AI) is an augmentation of intelligence within systems that can be managed scientifically and is evolving in the world of technology to serve various applications, including cyber security. Artificial intelligence plays a crucial role in cyber security, enabling early detection of cyber security threats, detailed analysis of emerging attacks, and swift and accurate responses. This research utilizes the systematic literature review (SLR) technique to analyze the role of artificial intelligence in cyber security. Data collection was conducted by documenting all papers containing research findings similar to this research report. The papers used in this study comprise 20 papers from the ScienceDirect and Google Scholar databases.Artificial intelligence has become a key element in supporting efforts to protect information systems and networks from increasingly complex cyber threats. With its ability to learn from data patterns, AI enables the detection of previously unseen threats and provides real-time responses. Through this systematic literature review, we investigated various AI approaches and techniques that have been applied in the context of cyber security, including the use of artificial neural networks, machine learning algorithms, and text analysis. Our analysis highlights that AI has been successfully utilized in detecting cyber attacks, analyzing suspicious behavioral patterns, and optimizing security responses. The practical implications of this research underscore the importance of continually developing and adopting AI solutions that can strengthen cyber defense against evolving threats. Keywords: Artificial Intelligence, Cyber Security, Systematic Literature Review, Application of Artificial IntelligencCyber Security dan Forensik Digital00Cyber Security dan Forensik Digital["Arthur Gregorius Pongoh", "Rizqy Achmad Fahreza", "Bilal Al Kindi", "Feddy Setio Pribadi", "Rizky Ajie Aprilianto"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/288a77646d9d64d2e72fc57cab33acdc9f919441 85b038354d8d23304a5e2e31182b6207c90eeffbThis review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI’s potential to revolutionize traditional practices.Applied Sciences1180Applied Sciences["Inmaculada Izquierdo-Bueno", "Javier Moraga", "J. Cantoral", "M. Carb\u00fa", "C. Garrido", "V. Gonz\u00e1lez-Rodr\u00edguez"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/85b038354d8d23304a5e2e31182b6207c90eeffb 1bed44445a41d8e66f78860c7a59dece0d77db49The potential of artificial intelligence (AI) to transform mental health care through individualized treatment approaches is significant. This research investigates the application of AI and extensive healthcare datasets to improve the precision of mental health diagnoses and treatments. The study employs various machine learning techniques, such as Random Forest, CatBoost, K-Nearest Neighbors, XGBoost, Convolutional Neural Networks, and Long Short-Term Memory networks, to analyze diverse data sources including electronic health records, neuroimaging, genetic information, and demographic data. Following thorough data preprocessing, model training, and evaluation using metrics like accuracy, precision, recall, F1 score, and Cohen's Kappa, the Random Forest algorithm emerges as the top performer with 98.7% accuracy. The research highlights the importance of addressing ethical considerations, such as protecting patient privacy, ensuring data security, and mitigating algorithmic bias when implementing AI in mental health services. The findings indicate that AI-based approaches can markedly improve diagnostic accuracy, provide tailored treatment suggestions, and facilitate early relapse detection, thus promoting proactive mental health management. Key Words: Artificial Intelligence, Mental Health, Data- Driven approach, Personalized Treatment, Machine Learning, Natural Language Processing.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Investigation of the application of AI and extensive healthcare datasets to improve the precision of mental health diagnoses and treatments indicates that AI-based approaches can markedly improve diagnostic accuracy, provide tailored treatment suggestions, and facilitate early relapse detection, thus promoting proactive mental health management.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Tejaswini S", "Uma B N", "Siri N"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/1bed44445a41d8e66f78860c7a59dece0d77db49 d94c09e6528f09c7d0703ec3af65e0cc7893d8a4Artificial Intelligence (AI) and the Internet of Things (IoT) are two revolutionary technologies shaping modern industries. AI enables machines to learn from data and make autonomous decisions, while IoT connects devices, allowing them to communicate and exchange information. The convergence of these technologies, known as AIoT (Artificial Intelligence of Things), has immense potential to transform daily life and business operations. AI enhances IoT by adding intelligence to device networks, improving efficiency and automating processes. For example, AI-powered IoT systems can perform predictive maintenance in industrial settings, preventing costly machine failures. In smart buildings, AI can optimize energy usage, reducing both costs and environmental impact. However, integrating AI with IoT poses significant challenges. One major issue is the vast amount of data generated by IoT devices, which AI requires to function effectively. Managing this data efficiently is critical. Additionally, IoT devices are vulnerable to cyber-attacks, making security a key concern. Protecting these systems from potential threats is essential to safely realize the benefits of AIoT. Despite these challenges, the combination of AI and IoT promises to drive future advancements across industries, enabling smarter, more connected systems that optimize decision-making, reduce costs, and improve overall efficiency.International Journal of Scientific Research in Computer Science Engineering and Information Technology150The combination of AI and IoT promises to drive future advancements across industries, enabling smarter, more connected systems that optimize decision-making, reduce costs, and improve overall efficiency.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Jyoti Bolannavar", "Dr. Sumati Ramakrishna Gowda"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/d94c09e6528f09c7d0703ec3af65e0cc7893d8a4 dab80c57e0673b8e14a92a560b7f315c583cccb8Abstract. The optimization of sewage treatment processes is critical for improving efficiency and reducing energy consumption. This paper explores the application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration. By leveraging real-time monitoring and adaptive control, these algorithms can dynamically adjust operational parameters to enhance treatment efficiency and minimize energy usage. This study provides detailed insights into the implementation and benefits of AI-driven process control in sewage treatment, supported by case studies and data analysis. The findings indicate significant improvements in treatment performance, showcasing the transformative potential of AI in environmental engineering.Applied and Computational Engineering00The application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration are explored, showcasing the transformative potential of AI in environmental engineering.Applied and Computational Engineering["Xu Liu"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/dab80c57e0673b8e14a92a560b7f315c583cccb8 8ec3665bbd9a635c6acc1a7f77637365f938e437Journal of Computational Information Systems1520Journal of Computer Information Systems["Karim Feroz", "Myungjae Kwak"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/8ec3665bbd9a635c6acc1a7f77637365f938e437 d923072172120c63d3fdec0058a9da1eb835999dBook of Abstracts00Book of Abstracts["O. Panchenko"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/d923072172120c63d3fdec0058a9da1eb835999d da0691a6d9d083e83d0021751ec666b5eb9a61e1Book of Abstracts00Book of Abstracts["Grzegorz Konieczny", "Rostyslav Klymenko"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/da0691a6d9d083e83d0021751ec666b5eb9a61e1 de68b7dd0123c384b929a0ce7d5e3628aae85904This comprehensive article investigates the transformative impact of AI-enhanced SAP S/4HANA Finance across healthcare, manufacturing, scientific research, auto, food & Oil &Gas sectors, focusing on human-AI collaboration patterns and implementation outcomes. Through a mixed-methods approach analyzing 15 organizations over 18 months, the research examines how AI integration transforms traditional ERP functionalities into intelligent financial management systems. The article collected data from 450 end-users and 45 key stakeholders, employing both quantitative metrics and qualitative assessments to evaluate implementation patterns, challenges, and success factors. The findings reveal significant improvements across all sectors: healthcare organizations achieved 40% reduction in billing processing time and 15% improvement in collection rates; manufacturing entities realized 35% reduction in unplanned downtime and 22% decrease in working capital requirements; while research institutions demonstrated 45% faster grant processing and 35% improved budget forecasting accuracy. The article introduces the Adaptive Financial Intelligence Framework (AFIF) for conceptualizing human-AI collaboration in financial management, contributing to both theoretical understanding and practical implementation strategies. The article concludes that successful AI integration depends on industry-specific adaptations, comprehensive training programs, and robust governance frameworks while highlighting the critical role of human expertise in maximizing system benefits. These findings provide valuable insights for organizations pursuing AI-enhanced financial management solutions while offering a roadmap for future developments in human-AI collaboration within enterprise systems.International Journal of Scientific Research in Computer Science Engineering and Information Technology30It is concluded that successful AI integration depends on industry-specific adaptations, comprehensive training programs, and robust governance frameworks while highlighting the critical role of human expertise in maximizing system benefits.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Poornachandar Pokala"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/de68b7dd0123c384b929a0ce7d5e3628aae85904 2eef35e01e6bd607fd61ce7daa34dd6bcfef4371Implied volatility is derived by substituting the market prices of option into the theoretical price model. It is one of the key parameters in option pricing, used to describe the market's expectation of future asset price fluctuations. In this paper, the algorithm consisting of hybrid forecasting and wavelet neutral network is proposed. In this algorithm, the measurement for the weighted implied volatility is constructed as input of the neutral network. Its aim is to obtain the optimal weight of implied volatility by a series of options. Case study on the carbon option market implies that the model and algorithm proposed in this study have better precision than the traditional Black-Scholes model and other neutral work models.2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)1502024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)["Jun Liu"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/2eef35e01e6bd607fd61ce7daa34dd6bcfef4371 eb38c49968d816eed511611eabe88c19d909a099Book of Abstracts00Book of Abstracts["M. Kopytko", "O. Sylkin"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/eb38c49968d816eed511611eabe88c19d909a099 7638c9d7b3c434a874f3799238c4e4965c64f1a0XVII Международная научно-практическая конференция «Современные вопросы устойчивого развития общества в эпоху трансформационных процессов»00XVII Международная научно-практическая конференция «Современные вопросы устойчивого развития общества в эпоху трансформационных процессов»["\u0418\u0434\u0440\u0438\u0441\u043e\u0432 \u0410.\u0418."]2024-11-08T00:00:00https://www.semanticscholar.org/paper/7638c9d7b3c434a874f3799238c4e4965c64f1a0 6cb5fcf476b5986bbcae4aa3e56f36f072f490b1Book of Abstracts00Book of Abstracts["V. Tokar"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/6cb5fcf476b5986bbcae4aa3e56f36f072f490b1 b88e4a2b03226d6f95c309c7852bb04a439e05afBook of Abstracts00Book of Abstracts["Paulina Kolisnichenko"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/b88e4a2b03226d6f95c309c7852bb04a439e05af cd722c770e9c690e23a93a1ff9e5438dbc2645edAs our growing population ages, there is a stronger push to age in place. Adults want to stay in their homes and communities as long as possible despite degenerative health issues. With adequate education and support, the growing desire for adults to age in their homes can be met with the strategic integration of AI technologies to maximize safety and bolster engagement in meaningful activities. This seemingly simple solution is complicated by the intersection of unique learning styles and the complexities that come with a generation that is cautious of even the simplest technology. Adopting a person-centered framework is vital for the success of AI-supported aging in place. As an occupational therapist, with experience across geriatric populations and a special interest in adaptive technology, I provide unique insight into effectively introducing AI technologies into the daily lives of older adults throughout this experiential report.Proceedings of the AAAI Symposium Series00As an occupational therapist, with experience across geriatric populations and a special interest in adaptive technology, I provide unique insight into effectively introducing AI technologies into the daily lives of older adults throughout this experiential report.Proceedings of the AAAI Symposium Series["Tracy Moon"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/cd722c770e9c690e23a93a1ff9e5438dbc2645ed 9b4f8a7487390f6e772126d8a58bee9c5a6e47d4International journal on responsibility00International Journal on Responsibility["David K McGraw"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/9b4f8a7487390f6e772126d8a58bee9c5a6e47d4 66df39c898f4a591e711448234bd901e7f7a0935Наука і техніка сьогодні00Наука і техніка сьогодні["Volodymyr Silchenko"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/66df39c898f4a591e711448234bd901e7f7a0935 c13a989ea2aa5cccce33ca098ddcf3bfd9ea5e0fBook of Abstracts00Book of Abstracts["Ihor Koval"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/c13a989ea2aa5cccce33ca098ddcf3bfd9ea5e0f 7a754474d04bf4bb57eaf3d9a4b0fab5972d92daXXV Международная научно-практическая конференция «Вызовы современности и стратегии развития общества в условиях новой реальности»00XXV Международная научно-практическая конференция «Вызовы современности и стратегии развития общества в условиях новой реальности»["\u042e\u043d\u0443\u0441\u043e\u0432 \u041d.\u0422."]2024-11-08T00:00:00https://www.semanticscholar.org/paper/7a754474d04bf4bb57eaf3d9a4b0fab5972d92da 802fcfd56a98514311e20e2925ad3cf879c42e69International Journal of Human Sciences Research00International Journal of Human Sciences Research["Ricardo Holderegger", "Lu\u00eds Felipe de Almeida Duarte"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/802fcfd56a98514311e20e2925ad3cf879c42e69 1cc4f9cdbc6fd09a1fedb7f49994035cb3f87980To improve the efficiency and accuracy of standard knowledge services, this paper explores the application of multimodal knowledge representation, knowledge graph construction, and intelligent question-and-answer (QA) systems. By leveraging deep learning and natural language processing (NLP) technologies, the system achieves automated classification, semantic analysis, and decision support for standard documents. The analysis suggests that the combination of knowledge graphs and intelligent QA systems can significantly enhance the precision of information retrieval and the automation of standardized processes. The results show that the system performs excellently in terms of response speed, user satisfaction, and performance metrics.2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)100This paper explores the application of multimodal knowledge representation, knowledge graph construction, and intelligent question-and-answer (QA) systems by leveraging deep learning and natural language processing (NLP) technologies.2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE)["Xiaoxin Gao", "Ming Sun", "Dong Zhang", "Yao Lu", "Yuangeng Zhu", "Yazhou Mu"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/1cc4f9cdbc6fd09a1fedb7f49994035cb3f87980 cd946ff52c35185d1bdb3c1f5bbc271c4cb60401Socially shared regulation of learning (SSRL) is a crucial process for groups of learners to successfully collaborate. Detecting and supporting SSRL is a challenge, especially in real time, but hybrid intelligence approaches such as Artificial Intelligence (AI) agents may make this possible. Leveraging the concept of trigger events which invite SSRL, we present a design of an AI agent, MAI, which can detect SSRL and prompt students to raise their group‐level metacognitive awareness with the aim of facilitating SSRL. We present the methodology we used to design MAI, drawing on the Echeloned DSR (eDSR) Methodological Framework and making use of the Wizard of Oz prototyping paradigm. We likewise present empirical results evaluating our initial prototype of MAI, using lexical alignment between speakers as a quantitative measure of the effect of MAI's prompts on facilitating SSRL, the Partner Model Questionnaire as a quantitative measure of perceptions of MAI, and interviews as qualitative context for these perceptions. We found that the first prototype of MAI did not facilitate SSRL as hoped, possibly owing to mixed perceptions of MAI's reliability and lack of clarity about MAI's role in the collaborative learning task. From these findings, we offer revised prompts for the next iteration of prototyping this agent and a refined set of design requirements for future development of metacognitive AI agents for supporting SSRL. What is already known about this topic Socially Shared Regulation of Learning (SSRL) is recognized as a critical component for the success of collaborative learning, emphasizing the importance of group‐level regulatory processes in achieving shared goals, enacting strategies and monitoring learning progress. Supporting SSRL in face‐to‐face collaborative learning environments presents challenges, including the complexity of coordinating and synchronizing individual contributions and regulatory actions within a group context. What this paper adds This paper introduces the design of Metacognitive Artificial Intelligence (MAI), a novel AI system aimed at enhancing Human‐AI collaboration for supporting and augmenting SSRL processes. Through empirical research, the study offers lessons learned and design considerations for developing artificial agents on facilitating and enhancing SSRL among learners, demonstrating how AI can play a pivotal role in collaborative learning environments. The findings highlight the critical importance of multidisciplinary knowledge in the design of multi‐agent interfaces (MAI) that provide real‐time, adaptive support for group metacognitive processes and decision‐making. Implications for practice and/or policy Educational technologists can utilize the proposed design principles in the development and integration of MAI tools to enhance SSRL. Educators can incorporate the principles of MAI and our relevant findings into their teaching strategies to actively foster and support socially shared regulation of learning among students. Policymakers should consider revising educational frameworks to include the use of AI technologies that support SSRL strategies in collaborative learning. British Journal of Educational Technology231The design of Metacognitive Artificial Intelligence (MAI), a novel AI system aimed at enhancing Human‐AI collaboration for supporting and augmenting SSRL processes is introduced, demonstrating how AI can play a pivotal role in collaborative learning environments.British Journal of Educational Technology["Justin Edwards", "Andy Nguyen", "Joni L\u00e4ms\u00e4", "M\u00e1rta Sobocinski", "Ridwan Whitehead", "Belle Dang", "Anni-Sofia Roberts", "Sanna J\u00e4rvel\u00e4"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/cd946ff52c35185d1bdb3c1f5bbc271c4cb60401 a5a7f5de72a051c944f26a61df88c454294fe95cImmunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. While current cancer vaccines are primarily prophylactic, advancements in targeting tumor-associated antigens (TAAs) and neoantigens have paved the way for therapeutic vaccines. The integration of artificial intelligence (AI) into cancer vaccine development is revolutionizing the field by enhancing various aspect of design and delivery. This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses. By utilizing AI technologies, researchers can navigate complex biological datasets and uncover novel therapeutic targets, thereby improving the precision and efficacy of cancer vaccines. Despite the promise of AI-powered cancer vaccines, significant challenges remain, such as tumor heterogeneity and genetic variability, which can limit the effectiveness of neoantigen prediction. Moreover, ethical and regulatory concerns surrounding data privacy and algorithmic bias must be addressed to ensure responsible AI deployment. The future of cancer vaccine development lies in the seamless integration of AI to create personalized immunotherapies that offer targeted and effective cancer treatments. This review underscores the importance of interdisciplinary collaboration and innovation in overcoming these challenges and advancing cancer vaccine development.Frontiers in Immunology2024This review explores how AI facilitates precise epitope design, optimizes mRNA and DNA vaccine instructions, and enables personalized vaccine strategies by predicting patient responses, thereby improving the precision and efficacy of cancer vaccines.Frontiers in Immunology["Anant Kumar", "Shriniket Dixit", "Kathiravan Srinivasan", "Dinakaran M", "P. M. Vincent"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/a5a7f5de72a051c944f26a61df88c454294fe95c 06639ee3b562cd82b11a818687983de5e92ea152 As initiatives on artificial intelligence (AI) education in K-12 learning contexts continues to evolve, researchers have developed curricula among other resources to promote AI across grade levels. Yet, there is a need for more effort regarding curriculum, tools, and pedagogy, as well as assessment techniques to popularize AI at the middle school level. Drawing on prior work, we created original curriculum activities with innovative use of existing technology, a new computational teaching tool, and a series of approaches and assessments to evaluate students’ engagement with the learning resources. Our curriculum called AI MyData comprises elements of ML and data science infused with ethical orientation. In this paper, we describe the novel AI curriculum and further discuss how we engaged students in learning and critiquing AI ethical dilemmas. We gathered data from two pilot studies conducted in the Northeast United States, one Artificial Intelligence Afterschool (AIA) program, and one virtual AI summer camp. The AIA program was carried out in a local public school with four middle school students aged 12 to 13; the program consisted of eleven two-hour sessions. The summer camp consisted of two-hour sessions over four consecutive days, with eighteen students aged 12 to 15. We facilitated both pilot programs with hands-on plugged and unplugged activities. The method of capturing data included artifact collection, structured interviews, written assessments, and a pre- to post-questionnaire tapping participants’ dispositions about AI and its societal implication. Participant artifacts, written assessments, survey, observation, and analysis of tasks completed revealed that the children improved in their knowledge of AI. In addition, the AI curriculum units and accompanying approaches developed for this study successfully engaged the participants, even without prior knowledge of related concepts. We also found an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens. Our study results also indicate that lessons establishing links with students’ personal lives (e.g., letting students choose personally meaningful datasets) and societal implications using unplugged activities and interactive tools were particularly valuable for promoting AI and the integration of AI in middle school education across the subject domains and settings. Based on these results, we discuss our findings, identify their limitations, and propose future work. ACM Transactions on Computing Education332A novel AI curriculum that comprises elements of ML and data science infused with ethical orientation is described and an indication that introducing ethics of AI to adolescents will help their development as ethically responsive citizens is found.ACM Transactions on Computing Education["Ismaila Temitayo Sanusi", "Fred Martin", "Ruizhe Ma", "Joseph E. Gonzales", "Vaishali Mahipal", "Solomon Sunday Oyelere", "Jarkko Suhonen", "M. Tukiainen"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/06639ee3b562cd82b11a818687983de5e92ea152 0ae792b95ed72d0589664263b16bddaf472b355fIntelligent Robotic Process Automation (IRPA) combines Artificial Intelligence (AI) and Robotic Process Automation (RPA) to automate complex unstructured tasks, improve decision-making, and cope with changing scenarios. A process analysis framework for IRPA adoption was developed by identifying key factors through a literature review and semi-structured expert opinion survey. The employed experts in the survey comprised RPA/IRPA consultants, RPA/IRPA initiative team leaders, and RPA/IRPA developers with three years or more experience. For the initial factor collection phase, there were a total of eighteen (18) responses, and for the factor evaluation phase, a total of twenty-six (26) experts were used to collect responses. Identified factors were shortlisted and evaluated using a Relative Importance Index (RII) analysis. The study’s findings are presented through a Causal-Loop Diagram (CLD) to illustrate the relationships between factors. The framework provides practical guidance for organizations planning to adopt IRPA, informing decision-making, resource allocation, and strategy development. The final process analysis framework highlights the importance of accuracy, level of human involvement in a task, and standardization as the main three primary factors for successful IRPA adoption. Three major secondary factors were identified: digital data input, integration with existing systems, and the cost of adopting new technologies. This research contributes to the added value to existing knowledge and serves as a foundation for future research in IRPA adoption.Sustainability451The final process analysis framework highlights the importance of accuracy, level of human involvement in a task, and standardization as the main three primary factors for successful IRPA adoption.Sustainability["Sandali Waduge", "R. Sugathadasa", "Ashani Piyatilake", "S. Nanayakkara"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/0ae792b95ed72d0589664263b16bddaf472b355f 1f51a1e463b671588223d62c6a92a20a2e41c62bPurposeCompanies increasingly leverage artificial intelligence (AI) to enhance human performance, particularly in e-commerce. However, the effectiveness of AI augmentation remains controversial. This study investigates whether, how and why AI enhances human agents’ sales through a randomized field experiment.Design/methodology/approachThis study conducts a two-by-two factorial randomized field experiment (N = 1,090) to investigate the effects of AI augmentation on sales. The experiment compares sales outcomes handled solely by human agents with those augmented by AI, while also examining the moderating effect of agents’ experience levels and the underlying mechanisms behind agents’ responses.FindingsThe results reveal that AI augmentation leads to a significant 5.46% increase in sales. Notably, the impact of AI augmentation varies based on agents’ experience levels, with inexperienced agents benefiting nearly six times more than their experienced counterparts. Mediation analysis shows that AI augmentation improves response timeliness, accuracy and sentiment, thereby boosting sales.Originality/valueThis study highlights the role of AI augmentation in human–AI collaboration, demonstrates the varying impacts of AI augmentation based on agents’ experience levels and offers insights for organizations on how to regulate AI augmentation to enhance agent responses and drive sales.Information Technology and People1221This study highlights the role of AI augmentation in human–AI collaboration, demonstrates the varying impacts of AI augmentation based on agents’ experience levels and offers insights for organizations on how to regulate AI augmentation to enhance agent responses and drive sales.Inf. Technol. People["Chengcheng Liao", "Xin Wen", "Shan Li", "Peiyuan Du"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/1f51a1e463b671588223d62c6a92a20a2e41c62b d4a4a9da509be47cf2b1ccb168271ee038ffd018Artificial Intelligence (AI) technologies offer unique capabilities for preservice teachers (PSTs) to engage in authentic and real‐time interactions using natural language. However, the impact of AI technology on PSTs' responsive teaching skills remains uncertain.The primary objective of this study is to examine whether interaction with a responsive AI‐based chatbot that acts as a virtual student improves pre‐service teachers' noticing abilities. The second objective is to compare how the presence or absence of chatbot responses affects changes in PSTs' questioning practices. Finally, the third objective is to investigate how the experience of interacting with the responsive virtual student affects PSTs' perceptions of the effectiveness of their questioning, their satisfaction with the interactions, and their confidence about interacting with a real student compared to the non‐responsive chatbot.A randomised controlled pre‐ and post‐test design was used with 50 PSTs. PSTs' noticing, interaction with the chatbot, and post‐survey data were collected, and a t‐test was conducted to examine significant differences by group.In the experimental group, the virtual student responded to PSTs' questions, while in the control group, she did not. Notable differences were observed in their questioning practices.Overall, AI‐based chatbots hold promise for enhancing PSTs' responsive teaching skills. Future research is needed to examine the long‐term impact of responsive chatbot use on PSTs' noticing skills.Journal of Computer Assisted Learning591Overall, AI‐based chatbots hold promise for enhancing PSTs' responsive teaching skills, and future research is needed to examine the long‐term impact of responsive chatbot use on PSTs' noticing skills.Journal of Computer Assisted Learning["Dabae Lee", "T. Son", "Sheunghyun Yeo"]2024-11-08T00:00:00https://www.semanticscholar.org/paper/d4a4a9da509be47cf2b1ccb168271ee038ffd018 6f9e3abd21f0a5ffa3e04db02ae145a5830b96beThis study explored the transformative potential of generative artificial intelligence (GAI) for achieving the UN Sustainable Development Goal on Quality Education (SDG4), emphasizing its interconnectedness with the other SDGs. A proprietary algorithm and cocitation network analysis were used to identify and analyze the network of SDG features in GAI research publications (n = 1501). By examining GAI’s implications for ten SDG4 targets, the findings advocate for a collaborative, ethical approach to integrating GAI, emphasizing policy and practice developments that ensure that technological advancements align with the overarching goals of SDG4. The results highlight the multifaceted impact of GAI on the SDGs. First, this paper outlines a framework that leverages GAI to enhance educational equity, quality, and lifelong learning opportunities. By highlighting the synergy between GAI and the SDGs, such as reducing inequalities (SDG10) and promoting gender equality (SDG5), this study underscores the need for an integrated approach to utilizing GAI. Moreover, it advocates for personalized learning, equitable technology access, adherence to ethical AI principles, and fostering global citizenship, proposing a strategic alignment of GAI applications with the broader SDG agenda. Next, the results highlight that GAI introduces significant challenges, including ethical concerns, data privacy, and the risk of exacerbating the digital divide. Overall, our findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development.Sustainability932The findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development.Sustainability["Prema Nedungadi", "Kai-Yu Tang", "Raghu Raman"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/6f9e3abd21f0a5ffa3e04db02ae145a5830b96be cd7b8a0b76b835962343a890d0245843daba23f5In this opinion paper, the authors focus on the role of artificial intelligence (AI) in expanding the concept of teaching for students with disabilities beyond the ideas of teaching adaptations or exceptions. We believe that artificial intelligence techniques allow for the development of microlearning environments that are intrinsically inclusive and responsive to learners' differences. The paper explores the interaction of artificial intelligence in making the learning process more individual, increasing the level of feedback during the learning process, and improving students' interaction between them while revealing individual and collective differences. We also address the ethical implications as well as the possible difficulties or hurdles of integrating AI into the inclusive education practice, such as privacy concerns on information processing, biased algorithms, and the necessity of human supervision. Thus, this paper presents an idea of how to incorporate AI into the creation of an inclusive teaching environment that actively involves teachers, learners, and their families. Finally, based on our research, we provide directions for future research and policy implications that develop AI-based supportive and inclusive classrooms that enhance equity, participation, and learners' achievements no matter disabilities and diverse learning needs.Indus Journal of Social Sciences01The interaction of artificial intelligence in making the learning process more individual, increasing the level of feedback during the learning process, and improving students' interaction between them while revealing individual and collective differences is explored.Indus Journal of Social Sciences["Dr. Samina Safdar", "Dr. Farrukh Kamran", "Dr. Faisal Anis"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/cd7b8a0b76b835962343a890d0245843daba23f5 ef71da06ff0bceb96bc906a1acc6491154089529—Autism Spectrum Disorder (ASD) is a challenging clinical condition that requires tailored therapies to boost cognitive and social skills in those affected. Lately, artificial intelligence (AI) has shown great potential in the field of autism assessment and rehabilitation. This article explores how AI plays a crucial role in improving autism clinical conditions. Thus, smart systems for early diagnosis, personalized treatment, and continuous progress tracking were adopted. The paper looks at the difficulties and possibilities of using AI in individuals with ASD. This included concerns like safeguarding data privacy, accurately understanding behavioral cues, and developing interactive, welcoming therapy settings. More specifically, the article explored how techniques from machine learning and artificial intelligence could be woven into rehabilitation methods to enhance learning and promote independence and social inclusion for individuals with autism. This examination provided a fresh and enlightening view on how clinical approaches are evolving, showing how AI could greatly improve the lives of individuals with autism. Implications for research and clinical practice were critically discussed.Conference on Computer Science and Information Systems331How techniques from machine learning and artificial intelligence could be woven into rehabilitation methods to enhance learning and promote independence and social inclusion for individuals with autism is explored.{"pages": "169-176"}["Fabrizio Stasolla", "Enza Curcio", "Antonio Zullo", "A. Passaro", "Maricarla Di Gioia"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/ef71da06ff0bceb96bc906a1acc6491154089529 bed450d1caf1dc99ceb916caa47d1632aba0b994Kegiatan pengabdian masyarakat ini bertujuan meningkatkan pemahaman siswa terhadap pemanfaatan Artificial Intelligence (AI) untuk memudahkan siswa dalam menangani kesulitan dalam pembelajaran dan membantu siswa menyelesaikan tugas-tugas sekolah. Metode yang dilakukan antara lain analisis data, problem solving, dan evaluasi lanjutan untuk mendapatkan feedback dari penerapan metode pelatihan dan pendampingan yang telah dilaksanakan. Berdasarkan kegiatan ini, diharapkan siswa memiliki peningkatan pemahaman dan mampu mengatasi kesulitan dalam pembelajaran yang disesuaikan dengan kemampuan dan gaya belajar masing-masing siswa melalui pemanfaatan AI. Materi yang diajarkan didalam pelatihan diantaranya berkaitan dengan pengertian AI, jenis-jenis AI, manfaat AI dan penerapannya dalam pembelajaran bahasa inggris. Peserta pada kegiatan pengabdian masyarakat ini adalah seluruh siswa kelas VIII MTsN 4 Aceh Utara. Kegiatan  berfokus untuk menerapkan dan  memberi edukasi mengenai pemanfaatan AI sehingga  diharapkan melalui kegiatan ini menjadikan adanya peningkatan pemahaman siswa terhadap penggunaan AI.Jurnal Pengabdian Sosial90Jurnal Pengabdian Sosial["Bungsu Keumala Sari", "M. Iqbal", "Farada Aulia", "Alam Fahlevi Pranata"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/bed450d1caf1dc99ceb916caa47d1632aba0b994 92bf789911a4bbde05dfb63b8d168b807c417d3dThis study aims to explore the use of technology and artificial intelligence (AI) in education administration to improve school performance. In the growing digital era, demands for efficiency and effectiveness in education administration are increasing, especially with the development of school information management systems (SIMS) and AI-based technologies. This research examines how these technologies can automate routine tasks such as class scheduling, student data management, as well as improve data-driven decision-making that is more accurate and efficient. The research method used in this study is descriptive qualitative, with the process of collecting data through in-depth interviews, observation, and document analysis directly from several schools that have adopted this technology. In analyzing the data, I used Miles and Hubberman's data triangulation technique in the form of data reduction, data analysis, and conclusion drawing. The results show that the implementation of technology and AI has improved operational efficiency, accelerated administrative processes, and facilitated the monitoring of student and staff performance. However, the study also found challenges in technology adoption, including limited infrastructure, resistance to change, and concerns regarding data privacy. To overcome these obstacles, a holistic strategy is needed, including the development of supportive policies, digital skills training for staff, and improvement of technology infrastructure in schools. This research concludes that although the application of technology and AI in education administration faces various barriers, the potential benefits are significant, especially in terms of improving the efficiency, transparency and overall quality of school performance.PPSDP International Journal of Education330Although the application of technology and AI in education administration faces various barriers, the potential benefits are significant, especially in terms of improving the efficiency, transparency and overall quality of school performance.PPSDP International Journal of Education["Sari Hutami"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/92bf789911a4bbde05dfb63b8d168b807c417d3d 6161bf7a40b403cda6e313e6cb80f94aacbd06a5Penelitian ini bertujuan untuk menganalisis dampak pemanfaatan teknologi Artificial Intelligence (AI) khususnya Chat GPT terhadap kualitas akademik mahasiswa. Chat GPT, sebagai salah satu implementasi Artificial Intelligence (AI) dalam bidang pendidikan, menawarkan berbagai fitur yang dapat membantu mahasiswa dalam proses pembelajaran, seperti penyediaan informasi secara cepat, bimbingan akademik, penyelesaian masalah pembelajaran serta dukungan dalam menyelesaikan tugas-tugas akademik. Metode penelitian yang digunakan dalam penelitian ini adalah metode kuantitatif dengan pendekatan deskriptif.  Teknik mengumpulkan data yang digunakan melalui kuesioner yang disebarkan kepada beberapa mahasiswa magister program studi Pendidikan Agama Islam yang dilakukan secara acak. Hasil penelitian menunjukkan bahwa penggunaan Chat GPT memiliki pengaruh positif yang signifikan terhadap peningkatan kualitas akademik dari mahasiswa, baik dalam hal pemahaman materi, pengembangan keterampilan berpikir kritis, maupun efisiensi dalam menyelesaikan tugas akademik. Namun, hasil ini juga menunjukkan adanya tantangan dalam penerapan teknologi Artificial Intelligence (AI), seperti potensi kurangnya keakuratan jawaban apabila tidak di kritisi dengan baik, adanya resiko ketergantungan pada Chat GPT sehingga berdampak pada penurunan kualitas akademik mahasiswa dan kurangnya interaksi langsung mahasiswa dengan dosen. Penelitian ini diharapkan dapat memberikan pandangan baru tentang pemanfaatan Artificial Intelligence (AI) yakni Chat GPT dalam dunia pendidikan serta dapat membantu institusi akademik dalam mengembangkan strategi pembelajaran yang lebih efektif di era digital.Journal of International Multidisciplinary Research350Journal of International Multidisciplinary Research["Dina Salsabila", "Martin Kustati", "Gusmirawati", "Rezki Amelia"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/6161bf7a40b403cda6e313e6cb80f94aacbd06a5 3adf99867e0d1eedaace12767972bfbaff44294dObjective: to identify the legal problems of using artificial intelligence in hiring employees and the main directions of solving them.Methods: formal-legal analysis, comparative-legal analysis, legal forecasting, legal modeling, synthesis, induction, deduction.Results: a number of legal problems arising from the use of artificial intelligence in hiring were identified, among which are: protection of the applicant’s personal data, obtained with the use of artificial intelligence; discrimination and unjustified refusal to hire due to the bias of artificial intelligence algorithms; legal responsibility for the decision made by a generative algorithm during hiring. The author believes that for the optimal solution of these problems, it is necessary to look at the best practices of foreign countries, first of all, those which have adopted special laws on the regulation of artificial intelligence for hiring and developed guidelines for employers using generative algorithms for similar purposes. Also, the European Union’s and USA’s legislative work in the area of managing risks arising from the use of artificial intelligence should be taken into account.Scientific novelty: the article contains a comprehensive study of legal problems arising from the use of artificial intelligence in hiring and foreign experience in solving these problems, which allowed the author to develop recommendations to improve Russian legislation in this area. As for the problem of applicants’ personal data protection when using artificial intelligence for hiring, the author proposes to solve it by supplementing the labor legislation with norms that enshrine the requirements for transparency and consistency in the collection, processing and storage of information when using generative algorithms. The list and scope of personal data allowed for collection should be reflected in a special state standard. The solution to the problem of discrimination due to biased algorithms is seen in the mandatory certification and annual monitoring of artificial intelligence software for hiring, as well as the prohibition of scoring tools for evaluating applicants. The author adheres to the position that artificial intelligence cannot “decide the fate” of a job seeker: the responsibility for the decisions made by the algorithm is solely on the employer, including in cases of involving third parties for the selection of employees.Practical significance: the obtained results can be used to accelerate the development and adoption of legal norms, rules, tools and standards in the field of using artificial intelligence for hiring. The lack of adequate legal regulation in this area creates significant risks both for human rights and for the development of industries that use generative algorithms to hire employees.Journal of Digital Technologies and Law190The author proposes to solve the problem of applicants’ personal data protection when using artificial intelligence for hiring by supplementing the labor legislation with norms that enshrine the requirements for transparency and consistency in the collection, processing and storage of information when using generative algorithms.Journal of Digital Technologies and Law["D. A. Novikov"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/3adf99867e0d1eedaace12767972bfbaff44294d 34e829b94ea15c4bc74d87edfbb5eab2fba9173bAwareness computing (AC) has been pursued for decades as an emergent discipline studying the system's ability of observing itself and its environments, and making decisions based on its experiences. Awareness is a subset of intelligent behavior, and as such, it is required but not sufficient to achieve intelligence, including Artificial Intelligence (AI) and Artificial General Intelligence (AGI). Many of today's cutting-edge AI/AGI tools fall short of demonstrating even the most basic properties of awareness, thus they cannot be considered intelligent by any practical measure. Instead of attempting to achieve AI/AGI by scaling-up and recruiting extraordinary amount of data, computational power, and energy, lessons learnt for biological awareness teach us to aim at the efficient use of limited resources as a result of evolutionary pressure. This implies possibly downscaling the system by selecting the essential aspects from the mostly irrelevant data dusts. This work introduces biologically-motivated principles of embodied cognition, to be used in building artificial systems with elements of awareness and intentional action, operating in real time with limited resources.2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)270This work introduces biologically-motivated principles of embodied cognition, to be used in building artificial systems with elements of awareness and intentional action, operating in real time with limited resources.2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)["Robert Kozma"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/34e829b94ea15c4bc74d87edfbb5eab2fba9173b 934d6cd5abcf86703934b6ba04e191bfef6cf6e2Current Urology Reports740Patients may benefit from the integration of AI into a male infertility specialist’s clinical workflow, and the ability of AI to integrate large volumes of data into predictive models could help clinicians guide conversations with patients on the value of various treatment options in infertility.Current Urology Reports["Noopur Naik", "Bradley Roth", "Scott D Lundy"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/934d6cd5abcf86703934b6ba04e191bfef6cf6e2 e9de820e211b36764d4d58f773c2b7f21d9bf11cIndian taxation is main source of public finance in developing economy. The Indian tax system has long been plagued by issues including tax evasion and ineffective administration. Tax administration systems must constantly be designed to reduce errors and speed up decision-making. The Indian tax system is beset by a shortage of workers to do labor-intensive duties including data input, return inspection, tax audits, etc. The Indian government recently announced the use of artificial intelligence and machine learning in the tax assessment system in order to handle the evolving tax landscape in conjunction with the use of analytics. In the field of taxes, artificial intelligence, or AI, is a relatively recent development.International Journal For Multidisciplinary Research00The Indian government recently announced the use of artificial intelligence and machine learning in the tax assessment system in order to handle the evolving tax landscape in conjunction with the use of analytics.International Journal For Multidisciplinary Research["C.D.N.RAKKINI", "G.MADHU Sudhanan"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/e9de820e211b36764d4d58f773c2b7f21d9bf11c 0f3cbb5274d40566ed7b9a7157d4b8692410d0d4Identifying the microorganisms responsible for illnesses and infections is crucial for accurate diagnosis, which is an essential aspect of providing medical care. Artificial intelligence (AI) systems can enhance drug discovery, epidemiological monitoring, prediction of antibiotic resistance, and disease management in the field of microbiological diagnosis. Artificial intelligence (AI) is a branch of science and technology that uses computers to simulate human intelligence. AI has recently shown tremendous promise as a powerful computational tool for the detection and management of bacterial infections. These machines can imitate human thought processes and cognitive capacities. Today, pathogen identification and Antimicrobial Susceptibility Testing (AST) in clinical laboratories often rely on culturing and isolating pathogens. With the rapid advancement of technology, Artificial Intelligence (AI) has become a vital tool for bacterial AST, providing numerous fast and efficient methods for testing drug susceptibility. AI systems offer enhanced diagnostic methods and early detection of antibiotic resistance. Moreover, they can rapidly and accurately identify diseases, including those that are novel or drug-resistant. In addition, AI can be applied to identify cells infected with malaria, detect drug resistance in pathogens like tuberculosis (TB) and HIV, understand and tackle antimicrobial resistance (AMR), and predict outbreaks. The primary goals of applying AI to bacterial diagnosis are the rapid and precise identification of pathogens and the prediction of drug resistance.Asian Journal of Research in Infectious Diseases00Artificial intelligence systems can enhance drug discovery, epidemiological monitoring, prediction of antibiotic resistance, and disease management in the field of microbiological diagnosis and offer enhanced diagnostic methods and early detection of antibiotic resistance.Asian Journal of Research in Infectious Diseases["A. Elshafei"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/0f3cbb5274d40566ed7b9a7157d4b8692410d0d4 1d44f9fab4177ad2ffb496700edffa5b11fce549 Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.Knowledge engineering review (Print)1020A research strategy for mobilising inter-disciplinary research to address societal challenges by leveraging and amplifying AI for national-scale collective intelligence is detailed and some of the key issues that must be faced are outlined.ArXiv["Seth Bullock", "Nirav Ajmeri", "Mike Batty", "Michaela Black", "John Cartlidge", "R. Challen", "Cangxiong Chen", "Jing Chen", "Joan Condell", "Leon Danon", "A. Dennett", "Alison J. Heppenstall", "Paul Marshall", "Phil Morgan", "A. O'Kane", "Laura G. E. Smith", "Theresa Smith", "Hywel T. P. Williams"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/1d44f9fab4177ad2ffb496700edffa5b11fce549 91fe02a5e70b64174e0481b780b55179185f244fConference on Computer Science and Information Systems00{"pages": "45-52"}["Christian Gerhards", "Myriam Baum"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/91fe02a5e70b64174e0481b780b55179185f244f 3b556df94dbd6baa6921b7cd0498c9edbe6ec57b“INSIDER” is a communication game. This game shares similarities with “Werewolf” and “Twenty Questions,” but it is characterized by the need to steer the conversation in a way that conceals one's role from others. The detail of game's rule is explained in the second chapter. The goal of this paper is to describe building a human-like AI agent that plays the “INSIDER”. This game is conversational game with some players to figure out the secret topic. The AI agent is required to think inductively and deductively to find out the secret topic and the Insider player. So, it is necessary for the agent to have quantified knowledge and to make decisions with the knowledge. All of the knowledge that the AI agent has is represented in a table. And it must narrow down topic candidates with questions. Information entropy is the best indicator to observe the communication games. Because everything can be a topic candidate. And all information about topic is accounted for with or without possession. It is important to vectorize the flow of conversation and to represent the distance between two concepts in the ontology. Information entropy makes it possible to quantify the distance. For the first step, we built an AI agent that uses information entropy to deduce the topic using a deductive approach. This approach is different depending on the role. Then, we let the AI agents play the “INSIDER” and observed the game log and transition of information entropy. Then the content of knowledge given to the AI agents in each experiment is changed, and differences in the outcome are recorded in the game log.2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)120The goal of this paper is to describe building a human-like AI agent that plays the “INSIDER”, an AI agent that uses information entropy to deduce the topic using a deductive approach.2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)["Ryunosuke Narikiyo", "Youichiro Miyake"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/3b556df94dbd6baa6921b7cd0498c9edbe6ec57b e57d1942f99dcdf9729b60c88c290cbe1ddfb78bConference on Computer Science and Information Systems00{"pages": "111-116"}["Muddasar Naeem", "Mario Fiorino", "Pia Addabbo", "Antonio Coronato"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/e57d1942f99dcdf9729b60c88c290cbe1ddfb78b 1feffb8f8757459e67f415b095f7765b994bbe79 Generative artificial intelligence is transforming the way we make, and think about, art. With prompting from human users, these generative systems now produce aesthetically compelling and seemingly creative works in a variety of artistic domains. In doing so, they challenge the ways we think about artistic credit, about creativity, and about the mechanism of legal copyright, which is meant to protect and promote creativity in a capitalist art market. All of this is currently at play in the courtroom, as artists contest the ways in which their artworks can be rightfully fed into these artificial systems (“the problem of the inputs”), and other artists are challenged over whether they might be credited for the visual images these systems generate (“the problem of the outputs”). Here, we explore these problems as they arise in visual art. We argue that the contested legal landscape surrounding these artificial systems reflects the ways they challenge our received notions of artistic credit and creativity. And we suggest that clarifying or changing the application of copyright in light of their generative capacities will ultimately involve revising our conception of art.Journal of Aesthetics and Art Criticism40The Journal of Aesthetics and Art Criticism["Joseph G Moore", "Simon J Frankel"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/1feffb8f8757459e67f415b095f7765b994bbe79 a1e1c04edfaf99c08b40d9f5e12efa2f23a019e0Spatial intelligence is important in Architecture, Construction, Science, Technology, Engineering, and Mathematics (STEM), and Medicine. Understanding three-dimensional (3D) spatial rotations can involve verbal descriptions and visual or interactive examples, illustrating how objects change orientation in 3D space. Recent studies show Artificial Intelligence (AI) with language and vision capabilities still face limitations in spatial reasoning. In this paper, we have studied generative AI's spatial capabilities of understanding rotations of objects utilizing its image and language processing features. We examined the spatial intelligence of the GPT-4 model with vision in understanding spatial rotation process with diagrams based on the Revised Purdue Spatial Visualization Test: Visualization of Rotations (Revised PSVT:R). Next, we incorporated a layer of coordinate system axes on Revised PSVT:R to study the variations in GPT-4's performance. We also examined GPT-4's understanding of 3D rotations in Augmented Reality (AR) scenes that visualize spatial rotations of an object in 3D space and observed increased accuracy of GPT-4's understanding of the rotations by adding supplementary textual information depicting the rotation process or mathematical representations of the rotation (e.g., matrices). The results indicate that while GPT-4 as a major current Generative AI model lacks the understanding of a spatial rotation process, it has the potential to understand the rotation process with additional information that can be provided by methods such as AR. By combining the potentials in spatial intelligence of AI with AR's interactive visualization abilities, we expect to offer enhanced guidance for students' spatial learning activities. Such spatial guidance can benefit understanding spatial transformations and additionally support processes like assembly, fabrication, and manufacturing.arXiv.org420While GPT-4 as a major current Generative AI model lacks the understanding of a spatial rotation process, it has the potential to understand the rotation process with additional information that can be provided by methods such as AR.ArXiv["Uttamasha Monjoree", "Wei Yan"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/a1e1c04edfaf99c08b40d9f5e12efa2f23a019e0 23beeef227d10c2868a469d07219ba335f264962Today, when the importance of data-based decision-making is impossible to question, the use of Explainable Artificial Intelligence (XAI) in business intelligence (BI) has inestimable benefits for the financial industry. This paper discusses how XAI influences predictive analytics in BI systems and how it may improve interpretability, and useful suggestions for financial product companies. Thus, within the context of this study, an XAI framework helps the financial institutions to employ higher-performing and more accurate models, like gradient boosting and neural networks, while sustaining interpretability required in tendentious planning and satisfying governance and supervision necessities. These studies reveal that, as applied to the credit scoring dilemma, XAI techniques such as SHAP and LIME do not only enhance prediction consistency and performance, but also offer a detailed understanding of customer behaviours, risk profiles and product performance. They help in interacting and acting within fields that involve decision making on aspects like customer loyalty, probable risks and audit. Furthermore, the study establishes that by incorporating XAI into BI improves model interpretability, which helps financial experts provide tangible rationale for analytical results and conform to regulatory directives. This framework and findings also support the importance of introducing XAI for financial BI applications to improve analytics practice within the sector. , enabling the generation of higher confidence, reliable decisions, which place the subject of XAI as a profound evolution of business intelligence in finance.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300An XAI framework helps the financial institutions to employ higher-performing and more accurate models, like gradient boosting and neural networks, while sustaining interpretability required in tendentious planning and satisfying governance and supervision necessities.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Sreenivasarao Amirineni"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/23beeef227d10c2868a469d07219ba335f264962 f96e7154014a3b7a7d0ae30ec5ee1b5af011d4a3This study compares fixed and solar tracking photovoltaic (PV) systems in four European countries, using the Levelized Cost of Energy (LCOE) as key performance measure. This analysis examines the cost-effectiveness and energy efficiency of vertical single axis, inclined single axis, and dual axis tracking systems compared to fixed PV systems. Results demonstrate that vertical or inclined single axis tracking systems achieve an optimal trade-off between enhanced energy production and cost-effectiveness, making them a favorable choice for maximizing returns on solar energy investments. While dual axis trackers offer higher energy yields, their significantly greater capital and operational expenses lead to increased LCOE, thus reducing overall cost-efficiency. The study also highlights the critical need for advanced optimization approaches to further improve energy yields and minimize operational costs in future PV systems. Our next research studies will explore the integration of Artificial Intelligence (AI)-driven methodologies, particularly Neural Networks, to develop real-time forecasting models. These AI-based models will enhance the performance of the system to foster more adaptive and intelligent solar tracking solutions in response to changing environmental and operational conditions.IEEE International Conference on Renewable Energy Research and Applications4002024 13th International Conference on Renewable Energy Research and Applications (ICRERA)["M.C. Kanu", "M.C. Kanu", "M. A. Tankari", "P. Logerais", "G. Lefebvre", "R. Bayindir"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/f96e7154014a3b7a7d0ae30ec5ee1b5af011d4a3 942ed1fadc1a2aefb7624c37ca7bb982f76727b9 This work is an upper-division and master’s-level text covering the economics of innovation and the ways in which it is encouraged or discouraged by intellectual property (IP) protection. The first part of the book covers the basic features of invention, innovation, diffusion, and the various IP instruments; the contribution of these to firm profits and growth; and the policies that governments use to encourage innovative activity. Part II gives more detailed information on IP rights: patents, copyright, trademarks, and the various alternatives to formal IP. Part III covers several IP (mostly patent) topics in greater detail, looking at specific sectors such as software, including artificial intelligence, and pharmaceuticals, as well as the use of IP in developing countries. It also goes into detail on topics of strategic interest to firms: patent litigation, technology standards and patents, the sharing and exchange of patents, and the strategic use of patents in general. The book’s approach is largely verbal and example-driven, but with simple mathematical models that introduce the reader to the more advanced economics literature in this area and equip them to delve further if they so wish. There is a brief mathematical appendix (Appendix A) containing material that may be unfamiliar to the reader as well as appendices containing the many abbreviations used (B) and an introduction to the data now available for studying the economics of innovation (C). The Bibliography section is large and can serve as a useful compendium of the literature in this area.5830["Bronwyn H. Hall", "Christian Helmers"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/942ed1fadc1a2aefb7624c37ca7bb982f76727b9 5e4e07d278bb38e05a1cdcda3fab7ac5fcca9ed4In the realm of sports education and performance analysis, there exists a pressing need to overcome barriers such as geographical limitations, cost constraints, and time constraints inherent in traditional teaching methodologies. In response to these challenges, this paper presents a novel sports education system leveraging artificial intelligence (AI) technologies. This allinclusive system allows for easy access to sports academies and training facilities based on geographic proximity by introducing an innovative teaching platform driven by AI and GPS integration. Moreover, it offers an extensive library of sports laws, sophisticated strategies, and professional advice, encouraging ongoing skill development. Setting goals and closely observing performance are made easier by the system's integration of AI-driven forecasts, such as victory probabilities. The central aim of this initiative is to redefine the landscape of sports education by offering a convenient, cost-effective, and highly efficient solution accessible to individuals at all proficiency levels. In addition to its educational advantages, the system is poised to make significant contributions to sports analytics, supplying extensive data and insights for both researchers and practitioners. This proposal advocates a progressive approach that moves beyond traditional methods, heralding a new era in sports education and performance analyticsInternational Journal of Advanced Research in Science, Communication and Technology00A novel sports education system leveraging artificial intelligence (AI) technologies is presented, allowing for easy access to sports academies and training facilities based on geographic proximity by introducing an innovative teaching platform driven by AI and GPS integration.International Journal of Advanced Research in Science, Communication and Technology["Shreyash Andhale", "Om Salunke", "Tirse Siddhesh", "Kasar Vivek", "Thorat Vaibhav", "Prof. Ravindra Pandit"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/5e4e07d278bb38e05a1cdcda3fab7ac5fcca9ed4 ef5e25edf3c0ccf48b376ab92df0c4d763c9eeadAdvances in artificial intelligence (AI) and synthetic biology have profoundly influenced biomedical research, creating transformative opportunities in imaging, diagnostics, and therapeutic engineering. In biomedical imaging, AI-driven algorithms enhance precision and accuracy, enabling automated analysis of complex datasets, real-time imaging insights, and identification of disease biomarkers. Meanwhile, synthetic biology redefines cellular engineering, particularly in T-cell research, by enabling customized functionalities, such as precision-targeted antigen recognition and tunable immune responses. The integration of AI into T-cell engineering amplifies these capabilities, facilitating the design and optimization of synthetic circuits, predictive modeling of cellular behaviors, and dynamic monitoring of therapeutic outcomes. This interdisciplinary approach is revolutionizing diagnostics and immunotherapy by streamlining the identification of disease-specific markers, improving diagnostic accuracy, and enabling real-time modulation of T-cell functionality within the tumor microenvironment. By combining AI-powered insights with synthetic biology's ability to engineer living systems, this research aims to address critical challenges in disease treatment, including tumor heterogeneity and immune evasion. This work explores the synergistic application of AI and synthetic biology in biomedical imaging and T-cell engineering, highlighting state-of-the-art technologies, their therapeutic potential, and the future landscape of personalized medicine.Next Frontier For Life Sciences and AI60This work explores the synergistic application of AI and synthetic biology in biomedical imaging and T-cell engineering, highlighting state-of-the-art technologies, their therapeutic potential, and the future landscape of personalized medicine.Next Frontier For Life Sciences and AI["Irmak Y\u0131lmazer"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/ef5e25edf3c0ccf48b376ab92df0c4d763c9eead 0db75910ee96375e1038789de4125b21a7ac7dc3The accelerated development of Artificial Intelligence (AI) technologies has had a substantial impact on educational practices, particularly in the field of English education. This study investigateshe experiences of university students at a UIN Fatmawati Sukarno Bengkulu in the process of navigating English learning through AI. The study examines the challenges and benefits those students experience when utilizing the most frequently used AI tools, as well as their perceptions of these technologies. Interviews with nine students in the fifth semester of the English program at UIN Fatmawati Sukarno Bengkulu are conducted using a qualitative methodology. The results indicate that students have a positive attitude toward AI in the context of English learning and employ a variety of AI tools to resolve various aspects of language acquisition. This study is a unique exploration of the manner in which university students simultaneously incorporate multiple AI tools in their English learning, providing new insights into the role of AI in promoting self-directed learning and practical language application. The study offers new insights into the transformative potential of AI in supporting students’ autonomous learning journeys while learning English by providing a comprehensive view of AI’s role in English education.PPSDP International Journal of Education00This study investigates the experiences of university students at a UIN Fatmawati Sukarno Bengkulu in the process of navigating English learning through AI, providing a comprehensive view of AI’s role in English education.PPSDP International Journal of Education["Hanura Febriani"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/0db75910ee96375e1038789de4125b21a7ac7dc3 fd78a5057baed939a68a77b0eff9f3bd097aa339The Rise of Big Data: Today, the scale and domain of massive data management has prolonged to an unmatched state. In this paper, we will examine the process of data science developed over the recent past and argue that artificial intelligence (AI) and data integration converge to create intelligent data ecosystem reaching new heights. This paper combines AI's strength to automate analytics and data integration, which is able to harmonize different data sources and shows how businesses can benefit from a unified approach for innovation, operational efficiency, decision-making. We also cover some of the key use cases in the industry, and ways these technologies are gaining momentum and reshaping the future of data science.International Journal of Scientific Research and Modern Technology (IJSRMT)20This paper combines AI's strength to automate analytics and data integration, which is able to harmonize different data sources and shows how businesses can benefit from a unified approach for innovation, operational efficiency, decision-making.International Journal of Scientific Research and Modern Technology (IJSRMT)["Shashidhar Reddy Keshireddy"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/fd78a5057baed939a68a77b0eff9f3bd097aa339 058b52c75f33cd551b9a0d466adc8973fc5204b3This study investigates the multifaceted perceptions of cybernetics students toward integrating artificial intelligence (AI) in educational environments. This research uncovers the students' knowledge levels, preferred sources of information, perceived benefits, and significant students through a comprehensive analysis of survey data combined with recent scholarly insights. Detailed visual analyses underscore key trends, offering a clear perspective on how these students view the future of AI in their academic journey. The findings suggest that while students appreciate AI’s utiAI, they express valid concerns about job security and the potential reduction in human engagement. This study contributes critical insights for educators and policymakers aiming to align AI practices with student needs and expectations.    Journal of Information Technology, Cybersecurity, and Artificial Intelligence00The findings suggest that while students appreciate AI’s utiAI, they express valid concerns about job security and the potential reduction in human engagement.Journal of Information Technology, Cybersecurity, and Artificial Intelligence["Ahmed Al Zaidy"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/058b52c75f33cd551b9a0d466adc8973fc5204b3 ff94883ce96e2503e0c6306c952c2c04292c466aThis article examines the profound implications and the transformative impact of artificial intelligence (AI) on vocational education and training (VET) within military contexts, underscoring the imperative for curriculum reform to address the evolving demands of contemporary warfare. As AI technologies progress, they present both significant opportunities and challenges, reshaping defense strategies and operational frameworks, fundamentally altering defense strategies and operational paradigms. The integration of AI into military applications necessitates a workforce proficient in specialized competencies, including technical expertise in AI ethics, system design, and human-centered methodologies. This paper delineates the essential competencies required for future VET professionals, emphasizing the necessity for ongoing upskilling and reskilling to effectively navigate the ethical and legal complexities associated with AI in military operations. By incorporating and embedding AI education into VET curricula, stakeholders can cultivate a new generation of defense professionals equipped to utilize AI technologies in a responsible and effective manner, ensuring adherence to and compliance with international humanitarian standards while enhancing operational efficacy. The findings highlight the critical role of VET in developing a skilled workforce prepared to confront the dynamic landscape of military technologies driven by AI.International Journal of Current Science Research and Review310This paper delineates the essential competencies required for future VET professionals, emphasizing the necessity for ongoing upskilling and reskilling to effectively navigate the ethical and legal complexities associated with AI in military operations.International Journal of Current Science Research and Review["Iliyan Vasilev"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/ff94883ce96e2503e0c6306c952c2c04292c466a 0562abc5a7469d6e61260637e80f1330b990934aGenerative Artificial Intelligence offers a powerful tool for adversaries who wish to engage in influence operations, such as the Chinese Spamouflage operation and the Russian Internet Research Agency effort that both sought to interfere with recent US election cycles. Therefore, this study seeks to investigate the propensity of current generative AI models for producing harmful disinformation during an election cycle. The probability that different generative AI models produced disinformation when given adversarial prompts was evaluated, in addition the associated harm. This allows for the expected harm for each model to be computed and it was discovered that Copilot and Gemini tied for the overall safest performance by realizing the lowest expected harm, while GPT-4o produced the greatest rates of harmful disinformation, resulting in much higher expected harm scores. The impact of disinformation category was also investigated and Gemini was safest within the political category of disinformation due to mitigation attempts made by developers during the election, while Copilot was safest for topics related to health. Moreover, characteristics of adversarial roles were discovered that led to greater expected harm across all models. Finally, classification models were developed that predicted disinformation production based on the conditions considered in this study, which offers insight into factors important for predicting disinformation production. Based on all of these insights, recommendations are provided that seek to mitigate factors that lead to harmful disinformation being produced by generative AI models. It is hoped that developers will use these insights to improve future models.arXiv.org420Gemini was safest within the political category of disinformation due to mitigation attempts made by developers during the election, while Copilot was safest for topics related to health, and characteristics of adversarial roles were discovered that led to greater expected harm across all models.ArXiv["E. Schlicht"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/0562abc5a7469d6e61260637e80f1330b990934a a3b81705bd99b2c67209697c5ee5c8ccb6c50d27Artificial Intelligence (AI) systems for online recruitment markets have the potential to significantly enhance the efficiency and effectiveness of job placements and even promote fairness or inclusive hiring practices. Neglecting Diversity and Inclusion (D&I) in these systems, however, can perpetuate biases, leading to unfair hiring practices and decreased workplace diversity, while exposing organisations to legal and reputational risks. Despite the acknowledged importance of D&I in AI, there is a gap in research on effectively implementing D&I guidelines in real-world recruitment systems. Challenges include a lack of awareness and framework for operationalising D&I in a cost-effective, context-sensitive manner. This study aims to investigate the practical application of D&I guidelines in AI-driven online job-seeking systems, specifically exploring how these principles can be operationalised to create more inclusive recruitment processes. We conducted a co-design workshop with a large multinational recruitment company focusing on two AI-driven recruitment use cases. User stories and personas were applied to evaluate the impacts of AI on diverse stakeholders. Follow-up interviews were conducted to assess the workshop's long-term effects on participants' awareness and application of D&I principles. The co-design workshop successfully increased participants' understanding of D&I in AI. However, translating awareness into operational practice posed challenges, particularly in balancing D&I with business goals. The results suggest developing tailored D&I guidelines and ongoing support to ensure the effective adoption of inclusive AI practices.arXiv.org370The results suggest developing tailored D&I guidelines and ongoing support to ensure the effective adoption of inclusive AI practices, particularly in balancing D&I with business goals.ArXiv["Muneera Bano", "Didar Zowghi", "Fernando Mourao", "Sarah Kaur", "Tao Zhang"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/a3b81705bd99b2c67209697c5ee5c8ccb6c50d27 b850f94539f0bcc0fe5ac8172c86b39fac85a7ecIn recent years, as the percentage of children with developmental disabilities in general education classrooms has increased, so has the need for clinical psychologists to administer psychological tests. There are two types of training methods for psychological testing: classroom training and field training. However, due to difficulties in securing training sites and subjects, there is no environment in which clinical psychologists can receive sufficient training in psychological testing. Therefore, this study develops a child-like robot that can train clinical psychologists in intelligence testing (hereinafter referred to as the “proposed robot”). The proposed robot is equipped with the same speech content as that of a child undergoing intelligence testing. The clinical psychologist trains the robot to perform the intelligence test by talking to the robot. This paper investigates the learning effects of training with a proposed robot on university students studying to become clinical psychologists. The experimental results suggest that training with the proposed robot has the same learning effect as learning to read a conventional manual.2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)140The experimental results suggest that training with the proposed robot has the same learning effect as learning to read a conventional manual.2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS)["Ikue Ishikawa", "Felix Jimenez", "Mayu Mitani", "Takahiro Nakajima", "Shoko Yoshida"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/b850f94539f0bcc0fe5ac8172c86b39fac85a7ec b59df46958b1060ad7005a8a1318f467bc251ca3En este trabajo se presenta una revisión de literatura de los conceptos con mayor relevancia de la inteligencia artificial y presenta una estructura de conocimiento de fácil entendimiento que contribuya a la aplicación en el sector empresarial. Se lleva a cabo una investigación de tipo descriptiva cualitativa, soportada en un referente bibliográfico, por medio de un sólido marco teórico consistente. Los hallazgos facilitan determinar los conceptos con mayor relevancia en el campo de estudio, por medio de la recopilación de documentos, de tal forma que cada elemento tenga un enfoque con aporte significativo y de fácil compresión, incluyendo la relación de inteligencia artificial y empresas, capacidades, flexibilidad, así como la mejora del desempeño en los procesos, ajustados en plantear un marco referencial para las organizaciones en Colombia. De tal forma, el texto conlleva a que el lector comprenda los potenciales usos que estas tecnologías proporcionan a las organizaciones y apliquen las herramientas de la inteligencia artificial en las empresas.ECONDATA00ECONDATA["Johana Elisa Fajardo-Pereira", "Cesar Augusto Herazo Hoyos"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/b59df46958b1060ad7005a8a1318f467bc251ca3 5839e5d55971af20e91fc5bdb44565b1b4ba3dfaThis study explores the core role driving the willingness of commercial banks to mobilise funds for economic development based on the Multilayer Perceptron (MLP) of the artificial neural network (ANN) technique. This approach allows for a nuanced understanding of interdependencies that traditional linear models may overlook, making it particularly suited for analyzing intricate financial systems in emerging economies like Nigeria. This study is critically supported by the Financial Intermediation Theory, which explains the role of financial institutions as intermediaries that facilitate the flow of funds from savers to borrowers. Data used for the analysis and prediction is purposively obtained from 10 commercial banks in Nigeria. The results show that financial institutions are mostly driven to mobilize funds because of their commitment to ensuring an adequate flow of money to serve the deficit sectors of the economy compared to any other underlying reasons. The prediction performs optimally with an r2 value of 86.5% with a cubic predictability model of ????. ???????????????? +????. ???????????? + ????. ???????????????? − ????. ???????????????? − ????. ???????? = ????.????????????????, and the Sum of Square Error (SSE) of 0.002 is minimal based on practice. This study is significant as it could enable financial institutions to make future role predictions relating to this concept in Nigerian settings or other settings analogous to Nigeria using the derived ANN model. These insights provide a basis for banks in Nigeria and similar economies to make strategic financial decisions, supporting the application of ANN models to predict and enhance financial institutions' roles in economic development.Open Journal of Management Science (ISSN: 2734-2107)280The results show that financial institutions are mostly driven to mobilize funds because of their commitment to ensuring an adequate flow of money to serve the deficit sectors of the economy compared to any other underlying reasons.Open Journal of Management Science (ISSN: 2734-2107)["C. Light", "G. E. Nwaobia"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/5839e5d55971af20e91fc5bdb44565b1b4ba3dfa bfe6c20d333446470446d4ebcc773130f82e9367Este texto investiga a função da inteligência artificial (IA) como um instrumento de suporte à inclusão em variados cenários. Começando com uma avaliação dos princípios da inclusão, ressalta-se sua relevância na formação de uma sociedade mais equitativa e acessível. Depois, examinam-se os princípios fundamentais da Inteligência Artificial, sua trajetória histórica e suas utilizações em variados campos. Neste cenário, são mostrados exemplos de como a Inteligência Artificial pode fomentar a inclusão, expandindo a acessibilidade e as oportunidades para indivíduos com deficiência e grupos à margem da sociedade. A conclusão destaca a relevância de tratar os desafios de forma ética e inclusiva, assegurando que a Inteligência Artificial seja criada e empregada para fomentar um mundo mais justo e acessível para todos. Finalmente, as considerações finais destacam a importância de uma perspectiva crítica e colaborativa sobre a função da Inteligência Artificial na inclusão, com o objetivo de maximizar suas vantagens e minimizar seus efeitos adversos.Revista ft00Revista ft["Erisnalva Pereira da Silva", "Silvia Alecrim Ferreira", "La\u00eds Barros Martins", "Rawlinson dos Santos", "Wainy Montalv\u00e3o de Lima", "Eliana Amaral de Oliveira", "Di\u00f3genes Vale de Oliveira", "acqueline Gomes Machado de Mendon\u00e7a", "Adriana Chagas de Morais", "Roseli Maria de Jesus Soares", "Simone Tonello Pereira de Mello", "ule Lourraine da Silva Landinho", "Renilda Artiaga"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/bfe6c20d333446470446d4ebcc773130f82e9367 e9647d079e7b7913b1988b8c362434dcabfc5373En el análisis realizado en este artículo se aborda la incorporación de la inteligencia artificial (IA) en el ámbito educativo de las matemáticas, con la propuesta de implementar un enfoque individualizado con el fin de potenciar el proceso de aprendizaje de los alumnos. Con el propó-sito de analizar la adaptabilidad de las herramientas basadas en inteligencia artificial a las nece-sidades particulares de los estudiantes y su impacto en la eficacia del proceso de aprendizaje, se realizó la investigación. Se llevó a cabo una investigación experimental en la que tomaron parte 120 estudiantes de educación básica. Fueron divididos en dos grupos: uno experimental que empleó plataformas de enseñanza respaldadas por Inteligencia Artificial, y un grupo de control que siguió métodos educativos tradicionales. En el grupo experimental, los estudiantes pudieron beneficiarse de las plataformas de inteligencia artificial que les proporcionaron retroalimenta-ción instantánea, les permitieron acceder a explicaciones personalizadas y practicar ejercicios adaptados a su nivel de competencia. Se registraron las mejoras en el rendimiento académico en matemáticas durante un lapso de 10 semanas, al comparar los resultados iniciales y finales de evaluación de ambos grupos. Los resultados numéricos evidenciaron un incremento notable en el desempeño del grupo experimental en contraste con el grupo de control. El grupo que empleó Inteligencia Artificial (IA) mostró un aumento del 30% en la resolución de problemas matemáti-cos y un incremento del 25% en la comprensión de conceptos abstractos. Además de los datos numéricos obtenidos, se realizaron entrevistas con los profesores y alumnos. Estos comunicaron que la utilización de Inteligencia Artificial favoreció un incremento en la motivación y en la au-tonomía del aprendizaje. Esto se debió a que los alumnos podían progresar a su propio ritmo y acceder a explicaciones adicionales según su necesidad. Según los maestros, las plataformas de inteligencia artificial posibilitaron la personalización del material educativo en función de las habilidades y limitaciones individuales de los estudiantes. En resumen, la incorporación de la inteligencia artificial en la instrucción de las matemáticas se presenta como una táctica efectiva para elevar el desempeño académico y ofrecer un método de enseñanza más individualizado. Se sugiere la incorporación de esta herramienta en el entorno educativo de las aulas de matemáticas con el fin de mejorar la eficacia del proceso de enseñanza y fomentar la implicación de los alumnos en la materia.   Ciencia Latina Revista Científica Multidisciplinar00Ciencia Latina Revista Científica Multidisciplinar["Loide Adriana Guishca Ayala", "Augusto Paolo Bernal P\u00e1rraga", "Michelle Yessen\u00eda Mart\u00ednez Oviedo", "Vinicio Gregorio Pinargote Carre\u00f1o", "Viviana Elizabeth Alc\u00edvar V\u00e9lez", "Vanessa Luc\u00eda Pinargote Carre\u00f1o", "Jorge Eduardo Pisco Mantuano"]2024-11-09T00:00:00https://www.semanticscholar.org/paper/e9647d079e7b7913b1988b8c362434dcabfc5373 37fac78d784ba43e77f4305e4dd48e070d817d05Abstract Background and Objective While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management. Databases and Data Treatment This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management. Results From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image‐guidance for procedural interventions and self‐management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems. Conclusion There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI. Significance This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.European Journal of Pain2472There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes, however, multiple barriers to their clinical integration remain.European Journal of Pain (London, England)["Ryan Antel", "S. Whitelaw", "Genevieve Gore", "Pablo Ingelmo"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/37fac78d784ba43e77f4305e4dd48e070d817d05 7e740415bfeeacf14fe5db4f0574232730cc18c4The European Union's (EU's) Artificial Intelligence Act (AI Act), published on 12 July 2024, seeks to establish a consistent legal framework for AI systems within the EU, promoting trustworthy and human‐centric AI while safeguarding various fundamental rights. The Act classifies AI applications into three risk categories: unacceptable risk, high risk, general purpose AI models with systemic risk and low or no risk, each with corresponding regulatory measures. Although initially not focused on copyright issues, the rise of generative AI led to specific provisions addressing general purpose AI models. These provisions include transparency obligations, particularly regarding the technical documentation and content used for training AI models, and policies to respect EU copyright laws. The Act aims to balance the interests of copyright holders and AI developers, ensuring compliance while fostering innovation and protecting rights.Journal of World Intellectual Property00The Journal of World Intellectual Property["Andres Guadamuz"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/7e740415bfeeacf14fe5db4f0574232730cc18c4 f6553bff05c7469037d35f107af1a4794a290589Artificial intelligence (AI) has much significance in different industries, including education. Due to increasing technological advancement, universities show their growing concerns about adopting several facilities linked with AI, specifically in their library management systems. Motivated by such an emerging trend, this research was conducted to examine AI adoption in both public and private libraries in China. The study explored the existing landscape of AI powered services in libraries, the perception of AI benefits, challenges related to AI adoption, and key determinants of such adoption for the targeted libraries. Using a questionnaire technique, the study collected data from different public and private libraries for which a sample of 154 respondents was finalized over the time span of 4.5 weeks. The analysis of the collected data shows that both the situations of launching and planning to launch different AI-related services were observed in Chinese libraries. Moreover, participants also confirmed several benefits of AI, including the enhancement of library operations by improving search accuracy, automating repetitive tasks, offering personalized resource recommendations, revealing usage trends through analytics, curating digital collections, improving cataloging, digitizing rare materials, and providing quick responses via chatbots, thereby allowing librarians to conduct their other more complex tasks. Additionally, the study confirmed several challenges associated with AI adoption. The results also revealed that along with other factors, the support from university administration, maturity and reliability of AI applications, and training and human resource management are positive and significant determinants of AI adoption. On the contrary, funding/cost associated with implementation and innovative services/alignment with technological trends reflected negative coefficients for AI adoption in Chinese libraries.El Profesional de la Informacion00Examining AI adoption in both public and private libraries in China revealed that along with other factors, the support from university administration, maturity and reliability of AI applications, and training and human resource management are positive and significant determinants of AI adoption.Profesional de la información["Danyang Li"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/f6553bff05c7469037d35f107af1a4794a290589 aa843abbc0fa6912ddc131e98da2134d10ced32dThis brief investigation seeks to explore the significant impact and offer a thorough analysis of the transformative potential of artificial intelligence (AI) and blockchain technology in revolutionizing financial technologies (FinTech) within the context of African business environments. Central to this study is an examination of how AI-driven solutions and blockchain technology can improve efficiency, accessibility, and innovation in financial services. Currently, African businesses face numerous challenges that hinder their overall success. Among these are the prevalence of fraud in Africa’s economy and the absence of credit scoring and reliable risk management, leading to bad debts and poor capital management. The businesses also lack data infrastructure, regulatory compliances, customer engagement and personalisation data privacy, cybersecurity and volatile currencies. Through intensive evaluation, we will be venturing into and showcasing how applications of AI and blockchain technology address these hurdles via techniques involving fraud detection, risk mitigation, advanced credit assessments and scoring for underserved communities, loan origination systems, and automated customer support utilising tools like chatbots, bolstered market analytics, streamlined regulation conformance, and augmented financial inclusivity encompassing digital payment platforms, saving mechanisms, loans, investments, and insurance products. By leveraging AI, African businesses can propel FinTech innovation and progression, foster economic prosperity, and drive sustainable development. The paper comprehensively analyses the current state of AI-driven FinTech solutions in Africa, identifying key factors that can unlock a new era of financial innovation, potential challenges businesses may face in implementing these technologies and our comprehensive research on their solutions.Proceedings of London International Conferences160The paper comprehensively analyses the current state of AI-driven FinTech solutions in Africa, identifying key factors that can unlock a new era of financial innovation, potential challenges businesses may face in implementing these technologies and the authors' comprehensive research on their solutions.Proceedings of London International Conferences["Clay Gitobu", "John Ogetonto"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/aa843abbc0fa6912ddc131e98da2134d10ced32d 2ff18e0f2f69abf880be4748eac864ccc67a681fThe occurrence of low-voltage AC series arc faults will cause the temperature at the fault to rise rapidly, which can easily lead to electrical fires and cause serious losses to individuals and society. However, the detection accuracy of traditional arc fault methods is insufficient and cannot effectively curb the occurrence of arc faults. Artificial intelligence-based technology provides high-precision detection solutions, but the AI model itself is a “black box”. Once a misjudgment occurs, the root cause of the model error cannot be fundamentally identified, and further improvements in model accuracy are limited. In order to solve the above problems, this paper proposes a new method for AC arc fault detection based on attention mechanism. The introduction of the attention mechanism effectively handles the weight between the input arc data and the model output, thereby improving the accuracy of model detection. Experimental results show that the model proposed in this article achieved a detection accuracy of 9 9. 6 9 %, proving the efficiency of this method.International Conference on Electric Power Equipment – Switching Technology130A new method for AC arc fault detection based on attention mechanism is proposed, which effectively handles the weight between the input arc data and the model output, thereby improving the accuracy of model detection.2024 7th International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST)["Dejie Sheng", "Tianle Lan", "Jingtao Yu", "Hai Li", "Zhizhou Bao", "Yao Wang"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/2ff18e0f2f69abf880be4748eac864ccc67a681f 404079c19b587ee6d43c2543b067f2e815395cfcThe Horizon 2020 and Horizon Europe framework programs are the key funding programs for the European Union's policy on innovation, research, and development (R&D&I) in all scientific subject areas. These instruments promote open science by using citizen science as a collaborative methodology and artificial intelligence as a disruptive technology, thereby encouraging public participation and engagement in scientific research. This paradigm shift in the scientific landscape is the impetus for this descriptive and exploratory study analyzing the effectiveness of communication policies and the quality of the dissemination and scientific impact of 28 R&D&I projects developed using citizen science and artificial intelligence, which were selected from the Community Research and Development Information Service (CORDIS) repository. This case study employs a methodological procedure grounded in content analysis and bibliometric indicators to meet four specific objectives: to determine the main formats and channels used in the projects’ communication strategies, as well as which category the projects’ papers fall into; to analyze the effectiveness of the projects’ scientific dissemination using articles published in Scopus according to subject area; to analyze the quality of scientific impact of the 234 articles that the projects produced using the SCImago Journal Rank (SJR) indicator; and to evaluate their specific and comparative impact using the standardized indicators Field-weighted citation impact (FWCI) and CiteScore. The findings confirmed that there were substantial differences in terms of the effectiveness of communication and the quality of dissemination and scientific impact among the projects analyzed. In this context, communication science could help efficiently navigate the challenges and opportunities in scientific communication.El Profesional de la Informacion50There were substantial differences in terms of the effectiveness of communication and the quality of dissemination and scientific impact among the projects analyzed, confirming that communication science could help efficiently navigate the challenges and opportunities in scientific communication.Profesional de la información["Concepci\u00f3n Campillo-Alhama", "Alba Santa-Soriano", "Rosa-Mar\u00eda Torres-Vald\u00e9s"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/404079c19b587ee6d43c2543b067f2e815395cfc 5cadcc51c910f69a8c3250c92a0c0717f5f46752This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.International Journal For Multidisciplinary Research110The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts.International Journal For Multidisciplinary Research["Sowmya Kotha"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/5cadcc51c910f69a8c3250c92a0c0717f5f46752 784a0d166633b5b9019140ca3e5f1f47ca1fc6c9In the modern education system, using artificial intelligence and smart learning techniques has become vital for students' academic success. This research examines the direct impact of smart learning, artificial intelligence, and beliefs about the future on academic performance. It further investigates whether the desire for knowledge mediates the relationships between these variables. A structural questionnaire was designed using the existing literature, and data was collected through face-to-face distribution. The respondents have diversified demographic dimensions for which a sample of 317 was empirically tested with the help of MS-Excel and Smart PLS version 4. The results signify the following output: (1) artificial intelligence, desire for knowledge, and smart learning promote the academic performance of the study. (2) Desire for knowledge fully mediates the relationship between smart learning and academic performance and between beliefs about the future and academic performance, respectively. A comprehensive list of policy recommendations is also providedEl Profesional de la Informacion00Examination of the direct impact of smart learning, artificial intelligence, and beliefs about the future on academic performance finds that desire for knowledge fully mediates the relationship between smart learning and academic performance and between beliefs about the future and academic performance, respectively.Profesional de la información["Liang Hu", "Wenmin Xiao", "Wenxi Zhu", "Lihua Zhu", "Yueting HU"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/784a0d166633b5b9019140ca3e5f1f47ca1fc6c9 376600f6d438e2991975e587b9e2fce4f2d659abThe advancement of artificial intelligence (AI) has been significantly driven by developments in machine learning and neural networks. As AI becomes increasingly pervasive, its applications are diversifying, with notable penetration in sectors such as health, education, social media, robotics, and entertainment. One area in which AI is being deployed is science education. The objective of this study is to examine the research that incorporates AI within the field of science education. By analysing trends in the reviewed studies, this research identifies the countries, institutions, journals and scholars that are the most prominent contributors to this field of enquiry. The findings suggest that the incorporation of artificial intelligence into science education is still in its infancy, with a paucity of widespread implementation. However, there is a discernible increase in the quantity of published works, with an emerging emphasis on the assessment of learning outcomes and the enhancement of academic performance. The findings indicate that the United States is the leading country in terms of publications related to AI in science education, accounting for 38% of the total contributions. Additionally, Türkiye has emerged as a notable contributor in this field, demonstrating a growing presence. The Journal of Science Education and Technology was identified as the preeminent journal publishing research on AI. Furthermore, the findings revealed that GPT was the most frequently utilised tool in this context. In light of these findings, it is recommended that future investigations into the application of artificial intelligence (AI) in science education employ a range of AI tools and explore the development of higher-order thinking skills.Sakarya University Journal of Education420The findings suggest that the incorporation of artificial intelligence into science education is still in its infancy, with a paucity of widespread implementation, but there is a discernible increase in the quantity of published works, with an emerging emphasis on the assessment of learning outcomes and the enhancement of academic performance.Sakarya University Journal of Education["Faruk Ar\u0131c\u0131"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/376600f6d438e2991975e587b9e2fce4f2d659ab 01a4b7e5bfa48ba3c85eea5114bbcd8856ea24f1This research aimed to investigate the influence of consumer behaviour (COB), artificial intelligence technology, and satisfying consumer experience on purchase intention (PIN). The study investigates the direct and moderating effect of hedonic motivation on the relationship between consumer behavior, artificial intelligence, satisfying consumer experience, and purchase intention for the respondents, residents in Shenzhen, China. A survey questionnaire based on past studies was finalized with a slight modification to cover the study context and key variables. Demographic factors included gender, education, and age distribution of the sample of 437 residents with experience of artificial intelligence technology, related products, and their subsequent purchase intention. The analysis showed model's substantial explanatory power with the help of R-square value of 0.842. The outer model assessment by using the Smart PLS confirmed that variables captured enough variance (average variance extracted), internal consistency reliability, and discriminant validity. The inner model assessment confirmed that artificial intelligence, consumer behaviour, and hedonic motivation directly influenced purchase intention. The interactive effect of hedonic motivation confirmed a significant presence in the relationship between artificial intelligence and purchase intention and between COB and PIN. The suggestions and limitations are well captured by the end of this research.El Profesional de la Informacion00The interactive effect of hedonic motivation confirmed a significant presence in the relationship between artificial intelligence and purchase intention and between COB and PIN, and confirmed that artificial intelligence, consumer behaviour, and hedonic motivation directly influenced purchase intention.Profesional de la información["Song Yuchen", "Wang Ying"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/01a4b7e5bfa48ba3c85eea5114bbcd8856ea24f1 d586d835af1b0122c227a31d391bf8dc8fc62705Now times, Block Chain technology and enhancements in Artificial Intelligence paradigm are facing more attention in the field of research with new advancements in many security techniques in all the sectors of IT and business. One cannot deny from the fact that the adoption of Artificial Intelligence paradigms and block-chain technology is shaping the new face of market. Beyond their contradictions both are proceeding at lightning speed. Both of them are aimed at providing something new to the world but the degree of complexity of each is quite different. With a secure, decentralized, and trustworthy system, block-chain technology did automate the bit-coin payments and provide the users an access to a shared ledger of records, transactions and data. With the useof smart contracts, a block-chain can also regulate the user interactions without the need for any central authority. In the contradiction, AI provides agents with the ability of reasoning, decision making and human-level intellect. At bottom one can say, block chain is concerned with keeping correct records, authentication and implementation while AI deals in conducting assessments, examining, and coming to a decision analyzing certain patterns and datasets, eventually giving rise to a self- directed interaction. Artificial Intelligence along with block chain allocates several descriptions which will make a seamless communication prospect certainly. AI and Block-Chain need sharing of data. The decentralized approach of database focuses on the significance of data sharing among various clients on a meticulous network. In the same way, AI depends very much on Big-Data, exclusively, for data distribution. The first section in the proposed article offers the Introduction part. The second section focuses on the majorly emphasized topics and domains about block chain technology indulging Artificial Intelligence into it, presents a detailed literature-review of many of the contributions by eminent research analysts. The third section describes the challenges in the collaborating AI with block-chain. The last section expresses a brief conclusion of the research work. Keywords- Block Chain Technology, Artificial Intelligence, AI, Machine LearningINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The proposed article focuses on the majorly emphasized topics and domains about block chain technology indulging Artificial Intelligence into it, presents a detailed literature-review of many of the contributions by eminent research analysts, and describes the challenges in the collaborating AI with block-chain.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Neha Rani", "Minakshi Thakur"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/d586d835af1b0122c227a31d391bf8dc8fc62705 7dcea00c376a4625d285c2a15b00bf218e0d3c32Advances in artificial intelligence (AI) are undoubtedly changing the practice and profession of learning design. While the full impact is yet to be realised, learning designers grapple daily with the challenges, risk, and opportunities these technologies represent for changing how students learn, how faculty teach, and how we design. So, what knowledge, skills, and mindsets do learning designers need to survive and thrive in a post-AI higher education sector? This paper reports on a project to co-design an AI literacies framework for and with a team of learning designers. Using the world café method, we conducted an online workshop with a group of 18 learning designers, drawing on our collective experience and expertise to ideate and refine the essential elements of an AI literacies framework. The data generated was then coded and thematically analysed to develop a practical framework comprising four domains and 16 specific elements, each elaborated to describe the knowledge, skills, and mindsets required for post-AI learning design. This framework informs the development of training programs and professional learning opportunities for learning designers.ASCILITE Publications00This paper reports on a project to co-design an AI literacies framework for and with a team of learning designers that informs the development of training programs and professional learning opportunities for learning designers.ASCILITE Publications["Adelle Ryall", "Stephen Abblitt"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/7dcea00c376a4625d285c2a15b00bf218e0d3c32 d8e31ae964683f84c8a0eff0c2a90c3c95dd8578Artificial Intelligence (AI) is increasingly influencing various aspects of teaching and learning in tertiary educational institutes. This concise paper presents the preliminary findings of an ongoing research that aims at exploring university students' perceptions of the use of AI in higher education settings. Phenomenographic framework has been adapted as the methodological guide for this research. Semi-structured interviews were conducted among students of an international university to capture students' perceptions. Findings revealed that university students possess diverse perspectives on the use of AI in higher education settings. Four qualitatively distinct categories of perceptions of AI emerged from the interview data. It is found that university students perceive the use of AI as a) an essential academic aid, b) a facilitator of personalized learning, c) an inhibitor to critical thinking, and d) an ethical challenger. Findings have pedagogical, administrative, and ethical implications for developing AI-driven technology-focused policy that facilitates diverse student needs. ASCILITE Publications00It is found that university students perceive the use of AI as a) an essential academic aid, b) a facilitator of personalized learning, c) an inhibitor to critical thinking, and d) an ethical challenger.ASCILITE Publications["Jainaba Jaiteh", "M. Hasan", "Azharul Karim", "Md Abdullah Al Mamun", "Md. Shahadat Hossain Khan"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/d8e31ae964683f84c8a0eff0c2a90c3c95dd8578 fcdfb5fb7e1fa6e5e996f097ec51b49def572ac3Journal of Sociolinguistics104Journal of Sociolinguistics["Helen Kelly-Holmes"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/fcdfb5fb7e1fa6e5e996f097ec51b49def572ac3 1547593c242456e8fba1a6c1caefd27a2b7906ffThis paper starts with an overview of the different levels of autonomy on surgical robots from $\mathbf{L 0}$ to $\mathbf{L 5}$ mimicking the various levels of autonomous cars. While various vertical applications of surgical robots offer different levels of autonomy, from complete human control to reduced levels of human input for a limited range of surgical tasks, the applications of spine surgical robots still remain at an infancy stage. Big players such as Medtronic Mazor X Stealth Edition and Globus ExcelsiousGPS mainly focus on the navigation and imaging system. Although most companies are actively exploring the possibilities of automating part or all tasks during a spine procedure, few companies have realized that at clinical or commercial stage. Vista Robotics is one of the few close to enabling automation in specific tasks during a spine surgery at the clinical stage, such as discectomy and endplate preparation. The paper explains from theoretical, product development and clinical aspects how certain artificial intelligence applications on active robotic assisted spine surgery are being realized at development and clinical stages. Applications include but are not limited to: automated discectomy and endplate preparation, self-regulated intra-operative navigation, AI-enhanced preoperative surgical planning, automated endoscopic decompression, automated laminectomy. The paper further explores potential hurdles and dilemmas companies face in turning theories into products and reality.Objective: Among all different applications of AI and automation in spine surgeries, this paper focuses on the development of semi-automated handheld device and automated robotic system to achieve discectomy and endplate preparation in MIS TLIF. We developed a robotic system to automate discectomy and endplate preparation phases in order to reduce the time of surgery, to increase the volume of bone graft delivery, to optimize the quality of discectomy and endplate preparation and potentially to achieve better clinical outcomes.Methods and Tools: SpineNova includes both hand-held semi-automated discectomy and endplate preparation device, and an active robotic assisted spine system automating this procedure. The system includes hardware and software architecture, software algorithm and selfregulated navigation of the discectomy and endplate preparationResults: Numerous lab tests on cow discs demonstrate consistent improvements, including reduction in surgery time, increased volume of tissue removal, reproducibility and repeatability, and reduction in operator fatigue, etc.Conclusion: The SpineNova robotic system and the handheld device are at the pre-clinical stage and close to the commercial stage. Lab tests demonstrated positive feedback, including but not limited to increased volume of tissue removal, significant time saving from 30 minutes down to 3-4 minutes, and reproducible and consistent results in consecutively multiple lab tests, better in the robotic system than the hand-held device. More cadaver studies will be following in the next months. The SpineNova robotic system and the hand-held device to automate discectomy and endplate preparation, among all other functionalities, are targeting at benefiting surgeons and patients in the foreseeable future.World Forum on Internet of Things220A robotic system to automate discectomy and endplate preparation phases in order to reduce the time of surgery, to increase the volume of bone graft delivery, to optimize the quality of discectomy and endplate preparation and potentially to achieve better clinical outcomes is developed.2024 IEEE 10th World Forum on Internet of Things (WF-IoT)["Sing Fatt Chin", "Christie Wang", "Jianqun Cheng", "Md Alphonse Lubansu"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/1547593c242456e8fba1a6c1caefd27a2b7906ff 05d09d52b9b680ee4377db4d94ae52fc255bfec1Journal of Sociolinguistics70Journal of Sociolinguistics["Patience Afrakoma hMensa"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/05d09d52b9b680ee4377db4d94ae52fc255bfec1 dcf496550056911f649eeba663d61767f05fea3eIntroduction/Background Assessing cardiovascular disease (CVD) risk is necessary in preventive cardiology. Studies have imputed CVD risk factors in algorithms to predict ASCVD. These various scores were derived from risk equations acquired from other populations. In our research, we found that abdominal height measured with our locally conceptualized appliance the Abdominometer predicted ASCVD better than established anthropometric indices. Objectives We, therefore, decided to build it into a risk equation and come up with a new algorithm that will not require data generated from invasive procedures. Methods We secondarily analysed our data and generated an algorithm utilizing 10 risk factors: one of which was our new anthropometric index of abdominal height (AH). Using the CIMT as a standard with a cut of value of ≥0.078 cm for high atherosclerotic risk we compared our new tool with the Framingham Risk Score (FRS). Results With our new algorithm, 24/221 (10.9%) were at high risk with 109 and 88 at low and intermediate risks respectively. Using the FRS, 218/221 were at low risk; only 3 being in the intermediate and high risk. Both risk algorithms correlated significantly with CIMT-determined risk but the correlation coefficient was more for the new (0.448) than the FRS (0.300). Conclusions We found that with sub-clinical atherosclerosis indexed by carotid intima-media thickness as standard, our new Jos App as well as the Framingham Risk score correlated positively and significantly. However, interestingly the level of correlation was higher with our new risk estimation App. We have input this into smart devices for pilot clinical studies.West African Journal of Medicine00With sub-clinical atherosclerosis indexed by carotid intima-media thickness as standard, the new Jos App as well as the Framingham Risk score correlated positively and significantly, interestingly the level of correlation was higher with the new risk estimation App.West African journal of medicine["A. Sirisena", "N. Gurumdimma", "D. Oguche", "B. Okeahialam"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/dcf496550056911f649eeba663d61767f05fea3e c04efd889aedba95e474cb058dfe9e8ef41bef0cRevista Electronica De Veterinaria00Revista Electronica De Veterinaria["Dr. Amit Singh"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/c04efd889aedba95e474cb058dfe9e8ef41bef0c acb8bd4f24f7fd4cbb51aefc69ef984c586fa084Introduction/Background Assessing cardiovascular disease (CVD) risk is necessary in preventive cardiology. Studies have imputed CVD risk factors in algorithms to predict ASCVD. These various scores were derived from risk equations acquired from other populations. In our research, we found that abdominal height measured with our locally conceptualized appliance the Abdominometer predicted ASCVD better than established anthropometric indices. Objectives We, therefore, decided to build it into a risk equation and come up with a new algorithm that will not require data generated from invasive procedures. Methods We secondarily analysed our data and generated an algorithm utilizing 10 risk factors: one of which was our new anthropometric index of abdominal height (AH). Using the CIMT as a standard with a cut of value of ≥0.078 cm for high atherosclerotic risk we compared our new tool with the Framingham Risk Score (FRS). Results With our new algorithm, 24/221 (10.9%) were at high risk with 109 and 88 at low and intermediate risks respectively. Using the FRS, 218/221 were at low risk; only 3 being in the intermediate and high risk. Both risk algorithms correlated significantly with CIMT-determined risk but the correlation coefficient was more for the new (0.448) than the FRS (0.300). Conclusions We found that with sub-clinical atherosclerosis indexed by carotid intima-media thickness as standard, our new Jos App as well as the Framingham Risk score correlated positively and significantly. However, interestingly the level of correlation was higher with our new risk estimation App. We have input this into smart devices for pilot clinical studies.West African Journal of Medicine00With sub-clinical atherosclerosis indexed by carotid intima-media thickness as standard, the new Jos App as well as the Framingham Risk score correlated positively and significantly, interestingly the level of correlation was higher with the new risk estimation App.West African journal of medicine["A. Sirisena", "N. Gurumdimma", "D. Oguche", "B. Okeahialam"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/acb8bd4f24f7fd4cbb51aefc69ef984c586fa084 27234efdcafc7d001c3ca4ed9b3e5f0872a9a12cEnvironment, Development and Sustainability690Environment, Development and Sustainability["W. Chatti", "Muhammad Tariq Majeed", "Haitham Khoj", "M. H. Miraz", "Amanat Ali"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/27234efdcafc7d001c3ca4ed9b3e5f0872a9a12c 996c887c814c53db42b31e36d890b328d71b36b7Introduction The National Health Management Information System (NHMIS) is vital for healthcare decision-making in Nigeria. However, effectiveness requires optimal information use including at the facility and local government level. Objective We assessed the use of information derived from the NHMIS and factors associated with information use at selected facilities and Local Government Areas (LGAs) in Oyo State. Methods A cross-sectional survey was conducted in 54 facilities and nine LGAs among healthcare workers responsible for data management and reporting selected by multistage techniques. The Performance of Routine Information System Management (PRISM) tool which assesses seven domains of information use was utilised. Information used was summarised as a mean score on a 0 - 100-point scale with 95% confidence limits. A linear regression was fitted to identify predictors of information use at α - 0.05. Results The use of information at the facility and LGA level were 42.2 ± 28.8 (95%CI 34.3 - 50.0) and 58.5 ± 39.8 (95%CI 28.0 -89.1) respectively. The positive predictors of use of information were the promotion of problem-solving skills β=0.776 (95%CI 0.031,1.522), the processes of checking data accuracy β=0.715 (95%CI 0.352,1.077), data collection β=1.080 (95% I 0.565,1.594), data transmission β=0.945 (95%CI 0.045, 1.846), data analysis β= 0.636 (95%CI 0.306, 0.966) and data display β=0.488 (95%CI 0.089,0.887). Conclusion Information use is modest at the facility and LGA level and depends on problem-solving, data collection, data analysis, and data display capacity which is often limited at these healthcare levels. AI tools that bridge these capacity gaps may improve NHMIS information use at the facility and LGA levels.West African Journal of Medicine00Information use is modest at the facility and LGA level and depends on problem-solving, data collection, data analysis, and data display capacity which is often limited at these healthcare levels, so AI tools that bridge these capacity gaps may improve NHMIS information use at the facility and LGA levels.West African journal of medicine["O. G. Oluwatosin", "O. A. Popoola", "E. T. Owoaje"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/996c887c814c53db42b31e36d890b328d71b36b7 466f5118b4ca737759bf2c01ead875c57f666631Within the past few years, starting from the greater public use of AI from the recent “AI Boom,” ChatGPT or AI-Language Model equivalents have been making their way into software and other computer science-related work environments for developers and software engineers to use without significant financial cost. In this paper, we often mention the word “productivity,” so it is important to know how we measure this: we measure productivity in lines of code (LOC) to gauge the raw amount of coding done, bug resolutions done by developers to measure the reviewing of code, and customer satisfaction to measure the quality of the code, then combine all of these into “overall productivity.” In this paper, we will examine the effects on productivity that these Large Language Models (LLMs) have had on software engineering or other similar jobs.Proceedings of London International Conferences00This paper will examine the effects on productivity that these Large Language Models (LLMs) have had on software engineering or other similar jobs.Proceedings of London International Conferences["Arsha Sheikhi", "Hamzah Raoof", "Zaid Khan", "Merve Gokgol"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/466f5118b4ca737759bf2c01ead875c57f666631 6173aa933ac370773fb5eef82fa39cc95fa8b9ceThe advancement of Artificial Intelligence (AI) in labor relations is significantly transforming the professional environment, impacting the organization, execution, and control of various work activities. This article aims to analyze the effects of AI on the labor market, with a focus on protecting workers' rights. Initially, the implementation of AI has raised concerns about the replacement of workers by machines and automated systems, especially in repetitive and operational sectors. The study addresses the need to adapt labor laws to ensure the maintenance of workers' fundamental rights, such as dignity, protection against arbitrary dismissal, the right to privacy, limitation of working hours, and respect for adequate breaks, even in highly digitalized environments. Thus, the methodology used in this study analyzes, through a bibliographic review based on different ideological currents and arguments, with the aim of presenting in an academic manner to society through research and analysis, in the legal and ethical context, the need to adapt labor laws to guarantee workers' rights in the face of the advancement of artificial intelligence. Finally, it is concluded that, despite the transformations imposed by Al, it is possible to guarantee the full protection of workers' rights through inclusive public policies, updated legislation and the strengthening of unions and workers' organizations. Thus, the use of Al can be an ally for the development of the labor market, as long as equity and social justice are ensured in the implementation of these technologies.Revista ft00Revista ft["Jo\u00e3o Victor Martins Mansur", "Sankley Araujo de Souza"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/6173aa933ac370773fb5eef82fa39cc95fa8b9ce fefe60e5ee004cb7f00c14159dc1cc5190a98f47This study investigates the integration of Artificial Intelligence (AI) in Recommendation Systems (RSs), highlighting its impact on the personalization and efficiency of recommendations in diverse digital environments. Through a literature review, the main AI techniques applied to RSs, such as machine learning and neural networks, were analyzed, along with their effects on improving accuracy and real-time adaptation to user preferences. Additionally, ethical challenges associated with AI implementation, such as privacy, algorithmic bias, and transparency, were explored to promote a more responsible and inclusive approach. The results indicate that while AI has the potential to significantly enhance the effectiveness of RSs, it is essential that the development of these technologies is balanced with ethical practices that ensure fairness and diversity in recommendations. This study provides valuable insights for researchers and developers aiming to improve RSs and address the ethical complexities of mass personalization.Revista ft00While AI has the potential to significantly enhance the effectiveness of RSs, it is essential that the development of these technologies is balanced with ethical practices that ensure fairness and diversity in recommendations.Revista ft["P. Santos", "J. H. Borges", "F. Florian"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/fefe60e5ee004cb7f00c14159dc1cc5190a98f47 0a7f5a0b7fa68909256911f06f9bb67bf7d5598eThe unprecedented advancements of Generative Artificial Intelligence (GenAI) tools have generated controversies surrounding their potential for transforming educational practices, and democratising knowledge sharing, while acknowledging risks to current educational practices. In order to understand the potential and risks of GenAI tools on educational practices, recent research point to the need to develop student and teacher AI literacy skills and to investigate the impact of GenAI integration in learning and assessment practices. In addressing the call for more research in the field, this study reports on the integration of GenAI tools in an academic assessment in a postgraduate course for pre-service language teachers at an Australian university. Data were collected from students’ reflections of their adoption of GenAI tools for planning their assessment and from qualitative surveys. Thematic analysis was employed to identify students’ perceived challenges and benefits in adopting GenAI for essay writing. Pre-service teachers recognised benefits in adopting GenAI for planning and generating ideas for academic writing but recognised the importance of mitigating risks created by inaccuracies or biases in content. The findings confirm the importance of transparency in the integration of GenAI and developing student awareness of and training in ethical use of GenAI for higher education.ASCILITE Publications00The integration of GenAI tools in an academic assessment in a postgraduate course for pre-service language teachers at an Australian university confirms the importance of transparency in the integration of GenAI and developing student awareness of and training in ethical use of GenAI for higher education.ASCILITE Publications["Eleni Petraki"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/0a7f5a0b7fa68909256911f06f9bb67bf7d5598e e6c9c00c8fbd5304abc44b0f5ff749929e15821fThis article examines the transformative impact of artificial intelligence (AI) and machine learning (ML) integration in cloud computing architectures across enterprise operations. Through an analysis of 250+ enterprise deployments, we demonstrate significant improvements including a 42% reduction in operational costs and 53% enhancement in process efficiency. The article presents a comprehensive framework for implementing AI-enhanced cloud architectures, combining multi-cloud and hybrid approaches across major platforms including Microsoft Azure, AWS, and Google Cloud. Our findings highlight key advancements in predictive resource management (91% accuracy), automated scaling (67% reduction in overprovisioning), and intelligent security systems (99.7% threat detection accuracy). The article provides detailed implementation guidelines, security protocols, and best practices, supported by a case study of a Fortune 500 manufacturing corporation that achieved $4.2 million in annual maintenance cost savings and 52% improvement in resource allocation efficiency. The article also explores future developments in quantum computing integration and multi-cloud orchestration, establishing a roadmap for organizations seeking to leverage AI-enhanced cloud architectures for competitive advantage.International Journal For Multidisciplinary Research70A comprehensive framework for implementing AI-enhanced cloud architectures, combining multi-cloud and hybrid approaches across major platforms including Microsoft Azure, AWS, and Google Cloud is presented, establishing a roadmap for organizations seeking to leverage AI-enhanced cloud architectures for competitive advantage.International Journal For Multidisciplinary Research["Babita Kumari"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/e6c9c00c8fbd5304abc44b0f5ff749929e15821f 382dea0a40fc684b8ed866f125cc79fa5e590646Presently, artificial intelligence (AI) is primarily utilized in user experience (UX) research for handling routine tasks like text conversion (e.g., transcription, survey preparation) to manage large volumes of data efficiently. However, its potential as a collaborative partner in generating ideas and insights remains largely untapped, possibly due to the limited AI education among UX researchers. There is a recognized need to enhance AI literacy among Human-Computer Interaction (HCI) students to effectively utilize AI tools in UX research for an AI-driven future. Despite its growing importance, the integration of AI literacy into HCI curricula remains insufficient. This study aims to explore AI’s potential in addressing current challenges in UX research and assess UX researchers’ readiness to adopt AI tools. Through in-depth interviews with five early-career UX researchers, it examined their experiences, challenges, and perspectives on integrating AI. Thematic analysis of the interview data uncovers insights, revealing that AI has significant potential to overcome challenges in collecting, interpreting, and synthesizing qualitative data, as well as in translating user needs into design concepts. The findings are intended to inform strategies for incorporating AI literacy into HCI education, aligning with industry demands for AI-enabled expertise in UX design.ASCILITE Publications00AI has significant potential to overcome challenges in collecting, interpreting, and synthesizing qualitative data, as well as in translating user needs into design concepts, aligning with industry demands for AI-enabled expertise in UX design.ASCILITE Publications["Yueting Zhang", "Arzoo Atiq", "Winn Chow"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/382dea0a40fc684b8ed866f125cc79fa5e590646 d425b0a44101a1e91f1ff0f4822736acb85467ddThis paper provides a comprehensive analysis of artificial intelligence (AI) implementation in Ghana's healthcare system, synthesizing findings from recent Ghanaian studies. It examines legal and regulatory frameworks, ethical considerations, bias and fairness, transparency and accountability, interdisciplinary approaches, patient rights and data protection, healthcare access and equity, and implementation and oversight. The analysis reveals both significant opportunities for AI to improve healthcare delivery in Ghana and substantial challenges in ensuring its ethical, equitable, and effective implementation. Key findings highlight the need for AI-specific healthcare regulations, investment in local capacity building, culturally sensitive AI design, robust fairness and accountability mechanisms, and ongoing stakeholder engagement. This paper contributes to the discourse on AI in healthcare in developing countries, offering insights into the unique challenges faced by lower-middle-income countries. It aims to inform evidence-based policymaking and enrich academic understanding of AI implementation in diverse cultural and economic contexts.Africa Journal For Regulatory Affairs00This paper provides a comprehensive analysis of artificial intelligence (AI) implementation in Ghana's healthcare system, synthesizing findings from recent Ghanaian studies, and reveals both significant opportunities and substantial challenges in ensuring its ethical, equitable, and effective implementation.Africa Journal For Regulatory Affairs["Fredrick Kayusi", "Srinivas Kasulla", "S. J. Malik", "Muhammad Majeed", "Benson Turyasingura", "J. T. Tumushabe", "George Benneh Mensah"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/d425b0a44101a1e91f1ff0f4822736acb85467dd 673fbba81d369d17917200da8670902488c868adThe rapid acceleration of urbanization underscores the urgent need for developing intelligent transportation systems (ITS) to enhance the efficiency, safety, and sustainability of urban mobility. Within this context, accurately predicting vehicle trajectories is paramount for facilitating superior traffic management and control. To this end, the paper presents an innovative architecture that combines a Long Short-Term Memory (LSTM) module with a generative artificial intelligence (Gen-AI) component, specifically the RoBERTa Transformer model. By leveraging these sophisticated architecture, the LSTM network with a recursive decoder outperforms the teacher forcing decoder on clean datasets, showing higher robustness in time-series predictions. When video data was partially missing, performance decreased, but using the RoBERTa model to reconstruct the missing data significantly improved results for both decoders (from $\mathbf{3 7 \%}$ up to $\mathbf{9 2 \%}$). The reconstructed data notably enhanced the performance of the LSTM models, particularly when larger portions of the video data were absent. These findings highlight the effectiveness of data reconstruction techniques in mitigating the challenges posed by uncontrollable events (common in real ITS scenarios) which can bear to incomplete information.World Forum on Internet of Things100An innovative architecture that combines a Long Short-Term Memory module with a generative artificial intelligence (Gen-AI) component, specifically the RoBERTa Transformer model is presented, which notably enhanced the performance of the LSTM models, particularly when larger portions of the video data were absent.2024 IEEE 10th World Forum on Internet of Things (WF-IoT)["Fabrizio Mangione", "Vincenzo Barbuto", "Claudio Savaglio", "Giancarlo Fortino"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/673fbba81d369d17917200da8670902488c868ad feec7d3f1a9c83dde5463910124c4ad25a50b3efThe rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.arXiv.org00An AI materials scientist named MatPilot is proposed and developed, which has shown encouraging abilities in the discovery of new materials, and demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.ArXiv["Ziqi Ni", "Yahao Li", "Kaijia Hu", "Kunyuan Han", "Ming Xu", "Xingyu Chen", "Fengqi Liu", "Y. Ye", "Shuxin Bai"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/feec7d3f1a9c83dde5463910124c4ad25a50b3ef 350e32b70774ad8b4349969d6900688a3386b58aAbstract—Integrating artificial intelligence (AI) into transfer pricing strategies offers excellent opportunities for global companies to optimize taxes, thereby optimizing business strategies across international borders. This paper proposes a practical approach to an AI-driven framework for the development and execution of transfer pricing strategies. This paper also focuses on compliance, risk management, global profit optimization, and organizational value creation, which are considered to face increased scrutiny from tax authorities worldwide. The paper proposes a step-by-step approach that integrates AI technology into various stages of transfer pricing, from data analysis to documentation. With AI's data analytics, predictive modeling, and decision-making, companies can enhance accuracy and efficiency to meet compliance while enhancing business decisions. The framework discussed provides a practical guide for global organizations to navigate global taxation complexities. Keywords—Transfer pricing, Artificial Intelligence, Machine Learning, Data Analysis, OECD, Pillar 1, Pillar 2, BEPS, NLP, Automation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A step-by-step approach that integrates AI technology into various stages of transfer pricing, from data analysis to documentation is proposed, which provides a practical guide for global organizations to navigate global taxation complexities.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Karthik Hosavaranchi Puttaraju"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/350e32b70774ad8b4349969d6900688a3386b58a 21d0c596e88bb386445b8a272d3360c2985fcad4This comprehensive article examines the current state and future directions of machine learning and artificial intelligence innovations using Python. It provides an in-depth analysis of recent advancements in popular Python-based libraries such as TensorFlow, PyTorch, and scikit-learn, highlighting key features and performance improvements. The article explores emerging machine learning techniques, including federated learning, few-shot learning, and explainable AI, discussing their principles, Python implementations, and real-world applications. Furthermore, the article investigates future trends in AI development with Python, considering potential technological advancements, new libraries and frameworks, and emerging research areas such as quantum machine learning and neurosymbolic AI. It also addresses the challenges and opportunities facing Python in the evolving landscape of AI, including performance optimization, ethical considerations, and the increasing demand for production-ready AI solutions. By synthesizing current developments and future prospects, this article offers valuable insights for researchers, developers, and organizations seeking to leverage Python's capabilities in the rapidly advancing field of artificial intelligence and machine learning.International Journal For Multidisciplinary Research230An in-depth analysis of recent advancements in popular Python-based libraries such as TensorFlow, PyTorch, and scikit-learn is provided, highlighting key features and performance improvements.International Journal For Multidisciplinary Research["Venkata Reddy Mulam"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/21d0c596e88bb386445b8a272d3360c2985fcad4 81b7cfa6468de34197179b53d0007d47ba69b86bThe rising presence of artificial intelligence (AI) in higher education carries great promise and peril. This paper will examine the ethics of using AI for the operation of these institutions. Key areas of focus include data privacy, bias in decision making, accountability, and the impact on academic integrity. So is the importance of transparency, stakeholder involvement, and the need for ethical guidelines. The paper closes with suggestions for the responsible adoption of AI systems in higher education.International Journal For Multidisciplinary Research00The ethics of using AI for the operation of higher education institutions, including data privacy, bias in decision making, accountability, and the impact on academic integrity are examined.International Journal For Multidisciplinary Research["Variganji Sudhir"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/81b7cfa6468de34197179b53d0007d47ba69b86b 067b0e447736a38712535a253ed988d685d045a0The integration of artificial intelligence (AI) into grant management has the potential to revolutionize how organizations handle the application process, budgeting, reporting, and stakeholder engagement. This qualitative study explores the experiences of grant managers, financial officers, and other stakeholders in adopting AI tools. Through interviews and thematic analysis, this research identifies the benefits, challenges, and implications of AI in the grant management process. The findings suggest that while AI can enhance efficiency and decision-making, successful integration requires careful planning, training, and change management.International Journal For Multidisciplinary Research200The findings suggest that while AI can enhance efficiency and decision-making, successful integration requires careful planning, training, and change management.International Journal For Multidisciplinary Research["Edwin Mkabane", "Roylex Kinigi"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/067b0e447736a38712535a253ed988d685d045a0 4e98e43cb29d63abf1627764c79c6e46fed58298The rapid advancements in artificial intelligence (AI) are opening up a complex world where human and AI coexist. It is imperative to develop our understanding of human-computer relationship, and to enhance graduate creativity in order to ensure their competitiveness in a technology-rich word. Recent research has begun to view AI as an independent collaborator and has explored the use of Human-AI Co-Creation (HACC) to foster creativity. However, it remains unclear how HACC practices can be designed to truly benefit undergraduate creativity. The solution to this issue lies in the creation of autonomous learners who can maintain agency in their interactions with AI, aligning with the core idea of Heutagogy. Therefore, this position paper proposes a conceptual framework of HACC for enhancing undergraduate creativity, and explores how the principles of heutagogy can be mapped onto the design of HACC practice.ASCILITE Publications00A conceptual framework of HACC for enhancing undergraduate creativity is proposed, and how the principles of heutagogy can be mapped onto the design of HACC practice is explored.ASCILITE Publications["Jinping Zhong", "Thomas Cochrane"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/4e98e43cb29d63abf1627764c79c6e46fed58298 90d259f9e46fbf4558dfac8f6f3eaf24f8bd86d5This paper provides a comprehensive analysis of artificial intelligence (AI) implementation in Ghana’s healthcare system, synthesizing findings from recent Ghanaian studies. It examines legal and regulatory frameworks, ethical considerations, bias and fairness, transparency and accountability, interdisciplinary approaches, patient rights and data protection, healthcare access and equity, and implementation and oversight. The analysis reveals both significant opportunities for AI to improve healthcare delivery in Ghana and substantial challenges in ensuring its ethical, equitable, and effective implementation. Key findings highlight the need for AI-specific healthcare regulations, investment in local capacity building, culturally sensitive AI design, robust fairness and accountability mechanisms, and ongoing stakeholder engagement. This paper contributes to the discourse on AI in healthcare in developing countries, offering insights into the unique challenges faced by lower-middle-income countries. It aims to inform evidence-based policymaking and enrich academic understanding of AI implementation in diverse cultural and economic contexts.Africa Journal For Law and Development Research00This paper provides a comprehensive analysis of artificial intelligence (AI) implementation in Ghana’s healthcare system, synthesizing findings from recent Ghanaian studies, and reveals both significant opportunities and substantial challenges in ensuring its ethical, equitable, and effective implementation.Africa Journal For Law and Development Research["Fredrick Kayusi", "Srinivas Kasulla", "S. J. Malik", "Muhammad Majeed", "Benson Turyasingura", "J. T. Tumushabe", "George Benneh Mensah"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/90d259f9e46fbf4558dfac8f6f3eaf24f8bd86d5 b8eb7578d9c0d2769aa5d672be1eb83939da4c0dRecent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways depending on the model's context, rendering a single common metric of fairness insufficient. This ambiguity highlights the need for criteria to guide the selection of context-aware measures, an issue of increasing importance given the proliferation of ever tighter regulatory requirements. To address this, we developed a flowchart to guide the selection of contextually appropriate fairness measures. Twelve criteria were used to formulate the flowchart. This included consideration of model assessment criteria, model selection criteria, and data bias. We also review fairness literature in the context of machine learning and link it to core regulatory instruments to assist policymakers, AI developers, researchers, and other stakeholders in appropriately addressing fairness concerns and complying with relevant regulatory requirements.arXiv.org1630A flowchart is developed to guide the selection of contextually appropriate fairness measures and links it to core regulatory instruments to assist policymakers, AI developers, researchers, and other stakeholders in appropriately addressing fairness concerns and complying with relevant regulatory requirements.ArXiv["Caleb J.S. Barr", "Olivia Erdelyi", "P. Docherty", "R. Grace"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/b8eb7578d9c0d2769aa5d672be1eb83939da4c0d aa23034ee215103ec77d30bbb0d0b7cc03fcc156This research proposes the development of a next generation airline reservation system that incorporates the Cloud microservices, distributed artificial intelligence modules and the blockchain technology to improve on the efficiency, safety and customer satisfaction. The traditional reservation systems encounter issues related to the expansion of the systems, the integrity of the data provided and the level of service offered to the customers, which is the main focus of this architecture through the modular and data centric design approaches. This will allow different operations such as reservations, payments, and customer data management among others to be performed separately thereby facilitating high availability of the system by 30% and enhancing performance of the system by 40% on its scalability. Such systems contain AI driven modules that utilize the past booking patterns along with the profile of the customer to estimate the demand and make recommendations, which increases to 25 % of customer engagement. Moreover, blockchain is effective in engaging an incorruptible ledger system for the all transactions therefore mitigating fraud incidences and increasing the clarity by 20%. The system was subjected to analysis using a simulator and using machine learning evaluations that rated it against other conventional systems. The results show that there were clear enhancements in the speed of transactions where the rates of secure data processing rose by 35%, and the system response time by 15 %. The system can also be used for other high transaction industries like logistics and hospitality. This structural design is indicative of how the use of advanced technologies will revolutionize the airline reservation sector. The implications are growing effectiveness, improvement in security and greater customer contentment.arXiv.org530ArXiv["Biman Barua", "M. S. Kaiser"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/aa23034ee215103ec77d30bbb0d0b7cc03fcc156 ae48fe78aff70b181990d6c0f7b59187b8416571The rapid advancement of artificial intelligence (AI) has revolutionized the development of autonomous vehicles, offering transformative potential for the future of transportation. This research investigates the implementation of AI-driven algorithms in autonomous vehicles, focusing on their ability to enhance decision-making, navigation, and safety. By employing state-of-the-art machine learning models, including deep learning and reinforcement learning, the study explores how these technologies can optimize real-time processing of sensor data, environmental perception, and adaptive control mechanisms. The findings demonstrate that AI algorithms can significantly improve the accuracy of object detection, trajectory prediction, and path planning, thereby reducing the likelihood of collisions and enhancing overall road safety. A key contribution of this work is the integration of a multi-modal sensor fusion approach, combining data from LiDAR, cameras, radar, and GPS to create a comprehensive and reliable understanding of the vehicle’s surroundings. Additionally, the research highlights the role of AI in enabling autonomous vehicles to learn from vast amounts of driving data, facilitating continuous improvement and adaptability in diverse driving conditions. The implications of this study are profound, suggesting that AI-powered autonomous vehicles could lead to safer, more efficient, and environmentally sustainable transportation systems. However, the research also identifies challenges related to computational complexity, real-time decision-making, and ethical considerations in AI-driven autonomy. Future work will focus on addressing these challenges and exploring the broader societal impacts of widespread autonomous vehicle adoption.International Journal For Multidisciplinary Research00This research investigates the implementation of AI-driven algorithms in autonomous vehicles, focusing on their ability to enhance decision-making, navigation, and safety, and explores how these technologies can optimize real-time processing of sensor data, environmental perception, and adaptive control mechanisms.International Journal For Multidisciplinary Research["Rajat Rathore", "Taharat Nayeem", "Abha Agarwal", "Shubham Kumar", "Paras", "Akshit Bhardwaj"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/ae48fe78aff70b181990d6c0f7b59187b8416571 d5010de167236707813022b53ad67914e2b4b5e3La convergencia de las tecnologías operativas (OT) con las tecnologías de la informacion (IT) ha incrementado significativamente el riesgo de que las redes industriales sufran ciber ataques. El objetivo del presente artículo ha sido revisar sistemáticamente la literatura existente sobre soluciones de monitoreo de ciberseguridad en redes industriales basadas en inteligencia artificial (IA), con el propósito de identificar los principales fabricantes, soluciones, funcionalidades y sectores industriales en donde aplican esta tecnología.  Se ha empleado el método PRISMA para realizar la búsqueda sistemática de documentación que contenga información relevante en los últimos 7 años. Los resultados obtenidos muestran que existen fabricantes como Nozomi Networks, Claroty, Dragos y Darktrace, que poseen soluciones de monitoreo de ciberseguridad basados en IA. Estas soluciones cuentan con funcionalidades como identificación de activos y comunicaciones, análisis de comportamiento, gestión de vulnerabilidades e inteligencia de amenazas. También se identifica que estas tecnologías estan siendo aplicadas en diferentes sectores industriales, como el energético, petróleo y gas, agua y saneamiento entre otros. Se concluye que la adopción de este tipo de tecnologías es de vital importancia para la detección más rápida y precisa de amenazas cibernéticas en las infraestructuras críticas, por lo cual es importante seguir invirtiendo en el desarrollo y aplicación de este tipo de soluciones.593 Digital Publisher CEIT100593 Digital Publisher CEIT["Lenin Cort\u00e9s-Llanganate", "A. Quevedo-Sacoto"]2024-11-10T00:00:00https://www.semanticscholar.org/paper/d5010de167236707813022b53ad67914e2b4b5e3 b1a10dacf474190910ccbe7092e8d3c81b3f1ec7This review analyzes secondary data from academic databases, research articles, and case studies to explore the role of new technologies for precision agriculture (PA) and investigates the value addition that Artificial Intelligence (AI) and Machine Learning (ML) provide to resource use, crop yield, and economic performance. Accordingly, the most of the key applications of AI in PA were related to crop yield prediction, disease detection, and effective water usage. Operating models through AI will analyze much data in real time, thus providing insight into informed decision making by farmers for proactive action against crop challenges like drought or pest attack. Furthermore, IoT devices and remote sensing support continuous monitoring in the delivery of correct data about optimizations of resources with minimal environmental impact. AI-driven robotics further automates all tasks related to planting, harvesting, and pesticide application, improving labor productivity and operational efficiency. This would involve in other issues like implementation costs, data privacy, and general unawareness among farmers of developing areas. Equally important will be ethical issues like ownership of data and loss of jobs. Various case studies in India, China, the United States, and Africa reveal how AI could transform the future of agriculture if integrated into agricultural systems properly to gain higher productivity and sustainability. Improvements in data quality and ethical issues, and increased access by smallholder farmers, will also be part of future research. Eventually, integrating AI with IoT, robotics, and big data analytics could provide high potential to meet global food demand in a sustainable manner.American Journal of Agricultural Science, Engineering and Technology03The most of the key applications of AI in PA were related to crop yield prediction, disease detection, and effective water usage, and integrating AI with IoT, robotics, and big data analytics could provide high potential to meet global food demand in a sustainable manner.American Journal of Agricultural Science, Engineering, and Technology["K. Polwaththa", "S. Amarasinghe", "A. Amarasinghe", "A. Amarasinghe"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/b1a10dacf474190910ccbe7092e8d3c81b3f1ec7 1333cd59acc41e8e369d1cb6c72e8051535fb039In the new round of global technological revolution and industrial transformation, artificial intelligence (AI) provides an opportunity to foster urban inclusive green growth (IGG). On the basis of scientifically measuring the IGG level and AI development level of Chinese cities from 2010 to 2022, this paper systematically explores the impact and internal mechanism of AI on IGG. This study finds that AI development significantly promotes urban IGG, and this conclusion still holds after a series of robustness and endogeneity tests. The effect of AI on IGG will exhibit heterogeneity because of differences in urban characteristics such as government financial support, information infrastructure development, and innovation and entrepreneurship vitality. Mechanism test reveals that AI development fosters urban IGG through digital technology innovation and industrial structure optimization. Furthermore, AI development has a positive spatial spillover effect on IGG in neighboring cities. This study provides valuable theoretical insights and policy ideas for planning in emerging economies to promote high-quality economic development and ecological civilization through the application of AI technology.Sustainability701Mechanism test reveals that AI development fosters urban IGG through digital technology innovation and industrial structure optimization, and AI development has a positive spatial spillover effect on IGG in neighboring cities.Sustainability["Hongbo Fu", "R. Rasiah"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/1333cd59acc41e8e369d1cb6c72e8051535fb039 a56a275647458e5502be2585948ee71004c03defThe function of artificial intelligence (AI) in creating a positive consumer experience on the Shopee e-commerce platform is covered in this study. Nowadays, artificial intelligence (AI) plays a significant role in the e-commerce sector, allowing businesses to offer more individualized, quick, and safe services. This study examines how Shopee customer satisfaction and loyalty are impacted by the use of AI technologies, including chatbots, tailored product suggestions, fraud detection, and inventory management. This study found that AI plays a significant role in making shopping more responsive and convenient by analyzing data on the use of chatbot services, the efficacy of personalized recommendations, the accuracy of predicting customer needs, transaction security, and the efficiency of logistics management. According to the study's findings, implementing AI technology reduced service response times by up to 20% and increased customer satisfaction by 15%. In addition to enhancing service quality, the use of AI increases Shopee customer loyalty. These results demonstrate how AI in e-commerce holds enormous promise for fostering startup expansion and enhancing client connections.Router : Jurnal Teknik Informatika dan Terapan01This study found that AI plays a significant role in making shopping more responsive and convenient by analyzing data on the use of chatbot services, the efficacy of personalized recommendations, the accuracy of predicting customer needs, transaction security, and the efficiency of logistics management.Router : Jurnal Teknik Informatika dan Terapan["Ainna Khansa", "Tata sutabri"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/a56a275647458e5502be2585948ee71004c03def f85611d25c435028b2fd84490c43fca09dc69a3aSmart Learning Environments472The study emphasizes considering disciplinary differences to better integrate GenAI into higher education and calls for tailored approaches that align with each field's unique epistemological and methodological traditions to balance GenAI's practical benefits with the preservation of core disciplinary knowledge and skills.Smart Learn. Environ.["Yao Qu", "Michelle Xin Yi Tan", "Jue Wang"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/f85611d25c435028b2fd84490c43fca09dc69a3a 55e86c69e84b7de96bfb2166a490a6751a02f95cBackground Artificial intelligence (AI)–based chatbots have emerged as potential tools to assist individuals in reducing anxiety and supporting well-being. Objective This study aimed to identify the factors that impact individuals’ intention to engage and their engagement behavior with AI-based well-being chatbots by using a novel research model to enhance service levels, thereby improving user experience and mental health intervention effectiveness. Methods We conducted a web-based questionnaire survey of adult users of well-being chatbots in China via social media. Our survey collected demographic data, as well as a range of measures to assess relevant theoretical factors. Finally, 256 valid responses were obtained. The newly applied model was validated through the partial least squares structural equation modeling approach. Results The model explained 62.8% (R2) of the variance in intention to engage and 74% (R2) of the variance in engagement behavior. Affect (β=.201; P=.002), social factors (β=.184; P=.007), and compatibility (β=.149; P=.03) were statistically significant for the intention to engage. Habit (β=.154; P=.01), trust (β=.253; P<.001), and intention to engage (β=.464; P<.001) were statistically significant for engagement behavior. Conclusions The new extended model provides a theoretical basis for studying users’ AI-based chatbot engagement behavior. This study highlights practical points for developers of AI-based well-being chatbots. It also highlights the importance of AI-based well-being chatbots to create an emotional connection with the users.JMIR Human Factors1751The new extended model provides a theoretical basis for studying users’ AI-based chatbot engagement behavior and highlights the importance of AI-based well-being chatbots to create an emotional connection with the users.JMIR Human Factors["Yanrong Yang", "Jorge Tavares", "Tiago Oliveira"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/55e86c69e84b7de96bfb2166a490a6751a02f95c 99d101de50a16b3405f00148500e9bf4ddd469b8OBJECTIVES Tools based on generative artificial intelligence (AI) such as ChatGPT have the potential to transform modern society, including the field of medicine. Due to the prominent role of language in psychiatry, e.g., for diagnostic assessment and psychotherapy, these tools may be particularly useful within this medical field. Therefore, the aim of this study was to systematically review the literature on generative AI applications in psychiatry and mental health. METHODS We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was conducted across three databases, and the resulting articles were screened independently by two researchers. The content, themes, and findings of the articles were qualitatively assessed. RESULTS The search and screening process resulted in the inclusion of 40 studies. The median year of publication was 2023. The themes covered in the articles were mainly mental health and well-being in general - with less emphasis on specific mental disorders (substance use disorder being the most prevalent). The majority of studies were conducted as prompt experiments, with the remaining studies comprising surveys, pilot studies, and case reports. Most studies focused on models that generate language, ChatGPT in particular. CONCLUSIONS Generative AI in psychiatry and mental health is a nascent but quickly expanding field. The literature mainly focuses on applications of ChatGPT, and finds that generative AI performs well, but notes that it is limited by significant safety and ethical concerns. Future research should strive to enhance transparency of methods, use experimental designs, ensure clinical relevance, and involve users/patients in the design phase.Acta Neuropsychiatrica531Generative AI in psychiatry and mental health is a nascent but quickly expanding field that mainly focuses on applications of ChatGPT, and finds that generative AI performs well, but notes that it is limited by significant safety and ethical concerns.Acta neuropsychiatrica["Sara Kolding", "Robert M. Lundin", "Lasse Hansen", "S. \u00d8stergaard"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/99d101de50a16b3405f00148500e9bf4ddd469b8 483cf3ba48ca4f9cdde87525ea6d75bf9136a29bWith the development of information technologies and information processing methods, it is important to provide high‐quality education in the field of artificial intelligence (AI). The study aims to investigate the impact of an educational course on AI on the comprehension of concepts, literacy, and empowerment in the field of AI among students of higher educational institutions. The experiment involved 125 students from Hohai University in China. As a result of taking the training course, students were able to improve their understanding of concepts (increasing their average score from 6.33 to 9.69), literacy (from 2.94 to 3.99), and empowerment (from 3.90 to 4.04) in AI. The resulting data statistically confirmed the effectiveness of the developed course for improving confidence in the field of AI. The training module can be applied to improve confidence in the field of AI for students in various careers, as information competence is important these days and increases the success of graduates in employment. When it comes to further research, the encouraging results of this study suggest opportunities for promoting this training program among a diverse group of participants. To confirm the effectiveness of the developed course, it can be conducted among students in schools and other educational institutions, reducing it to even more basic if necessary.Computer Applications in Engineering Education370The training module can be applied to improve confidence in the field of AI for students in various careers, as information competence is important these days and increases the success of graduates in employment.Comput. Appl. Eng. Educ.["Yan Hua Chen", "Kaiping Zhang"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/483cf3ba48ca4f9cdde87525ea6d75bf9136a29b 45a31a38e08b818767bc9dd86e59933d74dcc361Multimodal imaging is crucial for diagnosis and treatment in paediatric cardiology. However, the proficiency of artificial intelligence chatbots, like ChatGPT-4, in interpreting these images has not been assessed. This cross-sectional study evaluates the precision of ChatGPT-4 in interpreting multimodal images for paediatric cardiology knowledge assessment, including echocardiograms, angiograms, X-rays, and electrocardiograms. One hundred multiple-choice questions with accompanying images from the textbook Pediatric Cardiology Board Review were randomly selected. The chatbot was prompted to answer these questions with and without the accompanying images. Statistical analysis was done using X2, Fisher's exact, and McNemar tests. Results showed that ChatGPT-4 answered 41% of questions with images correctly, performing best on those with electrocardiograms (54%) and worst on those with angiograms (29%). Without the images, ChatGPT-4's performance was similar at 37% (difference = 4%, 95% confidence interval (CI) -9.4% to 17.2%, p = 0.56). The chatbot performed significantly better when provided the image of an electrocardiogram than without (difference = 18, 95% CI 4.0% to 31.9%, p < 0.04). In cases of incorrect answers, ChatGPT-4 was more inconsistent with an image than without (difference = 21%, 95% CI 3.5% to 36.9%, p < 0.02). In conclusion, ChatGPT-4 performed poorly in answering image-based multiple-choice questions in paediatric cardiology. Its accuracy in answering questions with images was similar to without, indicating limited multimodal image interpretation capabilities. Substantial training is required before clinical integration can be considered. Further research is needed to assess the clinical reasoning skills and progression of ChatGPT in paediatric cardiology for clinical and academic utility.Cardiology in the Young290ChatGPT-4 performed poorly in answering image-based multiple-choice questions in paediatric cardiology, indicating limited multimodal image interpretation capabilities.Cardiology in the young["Michael N. Gritti", "Rahil Prajapati", "Dolev Yissar", "Conall T. Morgan"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/45a31a38e08b818767bc9dd86e59933d74dcc361 51e17b2c48fbdca51f3815e8624340b9b1799ddeJournal of Robotic Surgery330It is concluded that AI capabilities are to assist the surgeon and the team to improve patient outcomes and can be only used as an adjuvant to complement the surgical team and not replace them.Journal of robotic surgery["Danny Darlington Carbin", "Aruj Shah", "Venkata Ramana Murthy Kusuma"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/51e17b2c48fbdca51f3815e8624340b9b1799dde 251a27fc9fe8e9f20c3a63518d78ff1c4a7100c2The integration of Artificial Intelligence (AI) across medical clinics holds transformative potential for enhancing patient outcomes through improved collaboration. This review examines the applications of AI in fostering inter-clinic connectivity, with a focus on diagnostics, treatment planning, and patient management. By utilizing AI-powered data-sharing platforms, predictive analytics, and telemedicine solutions, clinics can collaborate more efficiently, ensuring continuity of care and reducing diagnostic errors. Despite these advancements, challenges persist, including data privacy, interoperability, and resistance to AI adoption among clinical staff. This review highlights case studies showcasing successful AI-enabled collaborations and proposes future directions to address current limitations. Overall, AI's role in linking medical clinics underscores its potential to revolutionize patient care by bridging gaps in communication and decision-making processes.Journal of Ecohumanism00Artificial Intelligence's role in linking medical clinics underscores its potential to revolutionize patient care by bridging gaps in communication and decision-making processes.Journal of Ecohumanism["Mahdi Salem Mana Alyami", "Mohammad Mahdi Mohammad Alyami", "Hamad Ali Muhammad Al Khuraim", "Awadh Mohammad Salem AL SALEM", "Hadi Ali Mohammed Al", "Khalid Ali Mohammed Albakri", "Qubyl Nasser ALsaqran", "Hamda Salem Alyami", "Ahmed Saleh Alzamanan", "Fahad Monawer Alharbi", "Mofleh Ali Alharbi"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/251a27fc9fe8e9f20c3a63518d78ff1c4a7100c2 2708429a04477142e36baf99159ad237ead19f5bArtificial intelligence (AI) tools are often perceived as lacking humanlike qualities, leading to a preference for human experts over AI assistance. Extending prior research on AI aversion, the current research explores the potential aversion toward those using AI to seek advice. Through eight preregistered studies (total N = 2,317) across multiple AI use scenarios, we found that people denied humanness, especially emotional capacity and human nature traits, to AI advice seekers in comparison to human advice seekers (Studies 1-5 and S1-S3). This is because people perceived less similarity between themselves and AI advice seekers (vs. human advice seekers), with a stronger mediating role of perceived similarity among individuals with greater aversion to AI (Studies 2 and S1). Dehumanization of AI advice seekers predicted less behavioral support for (Study 3) and helping intention toward (Studies S2 and S3) them and could be alleviated through anthropomorphism-related interventions, such as perceiving humanlike qualities in AI or utilizing generative AI (Studies 4 and 5). These findings represent an important theoretical step in advancing research on AI aversion and add to the ongoing discussion on the potential adverse outcomes of AI, focusing on AI users. (PsycInfo Database Record (c) 2024 APA, all rights reserved).Journal of Personality and Social Psychology00It is found that people denied humanness, especially emotional capacity and human nature traits, to AI advice seekers in comparison to human advice seekers, which could be alleviated through anthropomorphism-related interventions, such as perceiving humanlike qualities in AI or utilizing generative AI.Journal of personality and social psychology["Jianning Dang", "Li Liu"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/2708429a04477142e36baf99159ad237ead19f5b 2ae9cf6e9e2b1aca7388eb4a635fca639941a5eeINTRODUCTION Diabetic retinopathy is one of the leading causes of avoidable blindness among adults globally, and screening programmes can enable early diagnosis and prevention of progression. Artificial intelligence (AI) diagnostic solutions have been developed to diagnose diabetic retinopathy. The aim of this review is to identify ethical concerns related to AI-enabled diabetic retinopathy diagnostics and enable future research to explore these issues further. METHODS This is a narrative review that uses thematic analysis methods to develop key findings. We searched two databases, PubMed and Scopus, for papers focused on the intersection of AI, diagnostics, ethics, and diabetic retinopathy and conducted a citation search. Primary research articles published in English between 1 January 2013 and 14 June 2024 were included. From the 1878 papers that were screened, nine papers met inclusion and exclusion criteria and were selected for analysis. RESULTS We found that existing literature highlights ensuring patient data has appropriate protection and ownership, that bias in algorithm training data is minimised, informed patient decision-making is encouraged, and negative consequences in the context of clinical practice are mitigated. CONCLUSIONS While the technical developments in AI-enabled diabetic retinopathy diagnostics receive the bulk of the research focus, we found that insufficient attention is paid to how this technology is accessed equitably in different settings and which safeguards are needed against exploitative practices. Such ethical issues merit additional exploration and practical problem-solving through primary research. AI-enabled diabetic retinopathy screening has the potential to enable screening at a scale that was previously not possible and could contribute to reducing preventable blindness. It will only achieve this if ethical issues are emphasised, understood, and addressed throughout the translation of this technology to clinical practice.Journal of Evaluation In Clinical Practice260It is found that existing literature highlights ensuring patient data has appropriate protection and ownership, that bias in algorithm training data is minimised, informed patient decision-making is encouraged, and negative consequences in the context of clinical practice are mitigated.Journal of evaluation in clinical practice["Alexandra Crew", "Claire Reidy", "H. van der Westhuizen", "M. Graham"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/2ae9cf6e9e2b1aca7388eb4a635fca639941a5ee 2288d1bd124febc26d37828b1dfa7699cd88f1f4Critical Care380Significant biases in AI images of intensivists generated by ChatGPT DALL-E 2 and Midjourney reflect broader cultural issues, potentially perpetuating stereotypes of healthcare worker within the society.Critical Care["M. Gisselbaek", "M\u00e9lanie Suppan", "Laurens Minsart", "Ekin K\u00f6selerli", "Sheila Nainan Myatra", "Idit Matot", "Odmara L. Barreto Chang", "Sarah Saxena", "J. Berger-Estilita"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/2288d1bd124febc26d37828b1dfa7699cd88f1f4 d1e22824854d60b1e451fcd38a996b295f2fdf33This paper presents an innovative implementation of Explainable Artificial Intelligence techniques with Model Predictive Control to enhance the transparency and interpretability of its decisions also improve its performance. The traditional black-box nature of MPC has limited its adoption in safety-critical applications where trust and understanding of automated control systems are essential. To address this challenge, XAI methods were incorporated, specifically SHapley Additive exPlanations into the MPC framework, thereby providing interpretable and clear explanations for control actions. The proposed XAI-enhanced MPC framework is evaluated through chemical system simulations. The results demonstrate that the integration of XAI techniques not only improves the interpretability of control decisions but also maintains, and in some cases enhances, the overall performance of the MPC system. Key performance metrics, such as tracking error, control effort, and constraint violations, are analysed to validate the effectiveness of the approach. The findings suggest that XAI-enhanced MPC is particularly beneficial in applications where transparency is crucial, offering a promising avenue for the broader adoption of MPC in complex, real-world systems. Future work will focus on real-time implementation and further optimization of the computational efficiency of the proposed framework.International Conference on Control, Mechatronics and Automation170The results demonstrate that the integration of XAI techniques not only improves the interpretability of control decisions but also maintains, and in some cases enhances, the overall performance of the MPC system.2024 12th International Conference on Control, Mechatronics and Automation (ICCMA)["Shekhar Mahmud", "Mustafa Kutlu"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/d1e22824854d60b1e451fcd38a996b295f2fdf33 9f04a1353afc80206875ad431a23ca8093eccb37Explainable Artificial Intelligence (XAI) strives to make Artificial Intelligence Systems (AIS) more understandable, thus tackling the “black box” challenge. However, successful implementation requires precise identification of XAI requirements, made complex by the absence of universally accepted protocols. Given the importance of identifying stakeholders in this quest, this article proposes an innovative framework to characterize them. We compare and merge two predominant approaches: role-based and knowledge-based characterizations. The result is a novel framework, segmenting knowledge into subcategories while linking them to specific roles. This XAI Roles and Knowledge Framework offers a flexible methodology that can be adapted to the nuances of each XAI project. By providing a balance between specificity and generality, this tool aims to guide the implementation of XAI while ensuring that the stakeholders' needs are taken into account. By using this approach, XAI projects benefit from a more precise identification of needs, leading to outcomes more closely aligned with user expectations and greater transparency in AI decisions.Conference of the Centre for Advanced Studies on Collaborative Research100This tool aims to guide the implementation of XAI while ensuring that the stakeholders' needs are taken into account, leading to outcomes more closely aligned with user expectations and greater transparency in AI decisions.2024 34th International Conference on Collaborative Advances in Software and COmputiNg (CASCON)["Cam\u00e9lia Raymond", "Sylvie Ratt\u00e9", "Marc\u2010Kevin Daoust"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/9f04a1353afc80206875ad431a23ca8093eccb37 61c3f2110020ef0c45c81aabc1dbf65fdad87c50The research highlights the importance of cyber security in the energy sector, focusing on how artificial intelligence (AI) and machine learning (ML) can make power grids more reliable and secure, particularly in Ukraine. The study looks at how energy systems are vulnerable to cyberattacks, especially during the Russia-Ukraine war, which has caused major disruptions in Ukraine’s energy supply. The research includes a detailed analysis of recent cyber attacks on Ukraine’s power systems, exploring how AI and ML can detect and prevent these threats in real-time, and how effective these technologies are at protecting critical infrastructure. The key findings suggest that AI and ML are essential for predicting and reducing cyber threats, improving energy production and distribution, and keeping power systems running despite increasingly complex cyber attacks. This study is the first in a series of papers on this topic. The research emphasizes the need for a well-organized cyber security approach and the adoption of AI-based solutions to strengthen energy security against modern threats.2024 IEEE International Workshop on Technologies for Defense and Security (TechDefense)280The study looks at how energy systems are vulnerable to cyberattacks, especially during the Russia-Ukraine war, which has caused major disruptions in Ukraine’s energy supply.2024 IEEE International Workshop on Technologies for Defense and Security (TechDefense)["Olga Degtiareva", "Natalia V. Shyriaieva", "Tetiana Kuklinova"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/61c3f2110020ef0c45c81aabc1dbf65fdad87c50 d5602434bcc6d5b7aaa41383d4adf9af1117ab4cThis panel explores strategies for supporting students in developing artificial intelligence (AI) literacy within higher education. The aim is to address the gap between technology-enabled learning and teaching activities and student adoption. Research highlights fundamental differences in how students learn and their learning conditions, impacting their ability to effectively incorporate digital technologies. The evolving definition of AI literacy emphasises awareness, ability, and social impact. Panelists include Associate Professor Jason Lodge (The University of Queensland), Professor Margaret Bearman (Deakin University), Associate Professor Tim Fawns (Monash University), and Dr Paula de Barba (Monash University). Topics covered include evaluative judgment, the dynamic nature of AI literacy, and self-regulated learning. The panel format includes presentations and audience-driven discussions, emphasising the need to balance students’ capabilities with their learning environment.ASCILITE Publications00ASCILITE Publications["Paula De Barba", "Jason Lodge", "Tim Fawns", "M. Bearman"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/d5602434bcc6d5b7aaa41383d4adf9af1117ab4c 8faabfcfcddc42287b1b448846717b98ca89cf41Artificial Intelligence is slowly becoming a part of our everyday life, so it is very important that university students learn about its developmental stages, how it works and how to create a program that builds a neural network based on the given data to play a role in decision support. Last semester, a course was offered to full-time students. Previous experience in higher education has shown that there is sometimes a significant gender difference in the results achieved. In this article, we perform a statistical analysis of the grades achieved in the Artificial Intelligence course, both in terms of theoretical lectures and programming practice.2024 IEEE International Workshop on Technologies for Defense and Security (TechDefense)170In this article, a statistical analysis of the grades achieved in the Artificial Intelligence course is performed, both in terms of theoretical lectures and programming practice.2024 IEEE International Workshop on Technologies for Defense and Security (TechDefense)["G\u00e1bor Kiss", "Susana Moreira Bastos"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/8faabfcfcddc42287b1b448846717b98ca89cf41 41381ccce1175eab4880c140dde0c9588f8212f9This article discusses in detail the role of artificial intelligence in data protection. The main advantages of AI in the field of cybersecurity are described, as well as possible risks and disadvantages associated with its use. Based on real-world examples, it is shown how artificial intelligence contributes to optimizing workflows and improving security in large organizations. In addition, the practical application of AI in business is discussed, especially for small and medium-sized enterprises, where such technologies can become an affordable and effective solution to ensure data protection and improve the operation of the security system.EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA00The role of artificial intelligence in data protection is discussed, where such technologies can become an affordable and effective solution to ensure data protection and improve the operation of the security system.EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA["Cyril Klime\u0161", "J\u00e1n Skalka", "P. \u0160vec", "Tom\u00e1\u0161 Sochor", "Ji\u0159\u00ed Balej", "Jan Francisti"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/41381ccce1175eab4880c140dde0c9588f8212f9 e93783d7bf70799279fd92053b31ce3176c045a2The use of artificial intelligence (AI) in journalism is changing how news is created, disseminated, and consumed in the quickly changing media landscape. Reviewing international case studies, this study examines how artificial intelligence (AI) influences news narratives. The study looks at how AI-powered algorithms affect the way news stories are framed, chosen, and distributed. Through examining case studies from various regions such as Europe, Asia, Africa, and North America, the research delves into how AI impacts journalism practices and the associated ethical considerations. Key findings show that although AI improves personalization and efficiency, it also raises questions about audience perceptions, media diversity, and bias. The results of this study also emphasize the necessity of ethical concerns and legal frameworks by highlighting the opportunities and difficulties that AI presents to the media landscape. The study ends with suggestions for additional research and possible approaches to reducing the ethical difficulties.Journal of Informatics Education and Research340Key findings show that although AI improves personalization and efficiency, it also raises questions about audience perceptions, media diversity, and bias.Journal of Informatics Education and Research["Priyanka Kumar, Shishir Kr. Singh, Rahul Kumar, Sahil Dhall"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/e93783d7bf70799279fd92053b31ce3176c045a2 8d195ad9c35433badaaf7525a1664539b59439a0Artificial Intelligence (AI) has revolutionised various fields and plant science is no exception. In recent years, AI techniques, particularly deep learning, have emerged as powerful tools for advancing plant science research. This paper explores the transformative potential of integrating multi-omics data and AI in plant science research, phenotyping, etc., providing a comprehensive and high-throughput approach to understanding and application to agriculture and plant biology.  The advantages, limitations and future prospects of this tool to the study of plants are discussed.Nigerian Journal of Botany00The transformative potential of integrating multi-omics data and AI in plant science research, phenotyping, etc., providing a comprehensive and high-throughput approach to understanding and application to agriculture and plant biology is explored.Nigerian Journal of Botany["Bala Sidi Aliyu"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/8d195ad9c35433badaaf7525a1664539b59439a0 0e21ebf75c53f884fec6beef5aa86939f010825dThis article analyzes the prospects for using artificial intelligence (AI) in the medical field. It highlights that AI has already become an integral part of modern life and is actively utilized in various countries, particularly in diagnosis, treatment, and disease prognosis. AI enhances diagnostic accuracy, accelerates treatment processes, and optimizes medical resources significantly. Internationally, AI-based solutions are already widely implemented in countries like the USA, Japan, and China to improve healthcare services. These technologies allow doctors to process large amounts of data efficiently and perform complex procedures with reduced time and effort. However, in Ukraine, the development of AI technologies remains in its early stages, despite the approval of the “Concept for the Development of Artificial Intelligence” in 2020. The main challenges include the need to improve the legal framework, ensure data protection, and address ethical concerns. The use of AI can significantly enhance the quality of medical services while saving time and resources in healthcare institutions. In medical practice, AI is applied in several areas. For instance, AI accelerates diagnosis and improves accuracy, such as in the continuous monitoring of bone tissue in periodontal diseases; AI can analyze medical images with high speed and precision, identifying anomalies and leading to more accurate and timely diagnoses at early stages; AI algorithms can predict the deterioration of a patient’s condition by analyzing their medical data.Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії30The prospects for using artificial intelligence (AI) in the medical field are analyzed, highlighting that AI has already become an integral part of modern life and is actively utilized in various countries, particularly in diagnosis, treatment, and disease prognosis.Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії["O.M. Boychenko", "T.D. Bublii"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/0e21ebf75c53f884fec6beef5aa86939f010825d e58d3d4efd8f42ac52210b6b7ff6451a759eb03aThe integration of Artificial Intelligence (AI) in the pharmaceutical sector marks a transformative era, where technology reshapes traditional drug development processes. This paper delves into the pivotal role AI plays in enhancing efficiency and precision in drug discovery, testing, and approval. Existing methods in drug development often suffer from high costs, extended timelines, and high attrition rates in clinical trials, posing significant barriers to timely patient care. To address these issues, we propose the Drug Development based on Artificial Intelligence (DD-AI) framework, leveraging AI-driven algorithms and machine learning models. The DD-AI framework enhances the identification and optimization of drug candidates, predicts clinical trial outcomes, and personalizes patient treatment plans. AI algorithms analyze vast datasets to discover potential drug molecules, simulate their interactions, and streamline the clinical trial process by identifying the most promising candidates. Implementing the DD-AI framework has demonstrated substantial improvements in reducing drug development costs and timelines while increasing the accuracy of trial outcomes. AI's predictive capabilities also personalize treatments, optimizing efficacy and minimizing adverse effects for patients. The DD-AI framework offers a robust solution to existing challenges in drug development, fostering a more efficient, cost-effective, and patient-centered approach. The findings underscore AI's transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting patients with faster and more precise medical solutions.Communications on Applied Nonlinear Analysis00The DD-AI framework offers a robust solution to existing challenges in drug development, fostering a more efficient, cost-effective, and patient-centered approach and underscores AI's transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting patients with faster and more precise medical solutions.Communications on Applied Nonlinear Analysis["Priya Vij"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/e58d3d4efd8f42ac52210b6b7ff6451a759eb03a dd9d26d31478cea88be66d5cf25f1ab51d464245This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized roadmaps, automated presentation generation, and three-dimensional modeling for complex concept visualization. By integrating real-time virtual assistance for doubt resolution, the platform addresses the immediate educational needs of learners while promoting autonomous learning practices. This study explores the psychological advantages of self-directed learning and demonstrates how AI automation can enhance educational outcomes through personalized content delivery and interactive support mechanisms. The research contributes to the growing field of educational technology by presenting a comprehensive framework that combines automated content generation, visual learning aids, and intelligent tutoring to create an efficient, scalable solution for modern educational needs. Preliminary findings suggest that this approach not only accommodates diverse learning styles but also strengthens student engagement and knowledge retention through its emphasis on self-paced, independent learning methodologies.arXiv.org00An innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance and demonstrates how AI automation can enhance educational outcomes through personalized content delivery and interactive support mechanisms is introduced.ArXiv["Tejas Satish Gotavade"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/dd9d26d31478cea88be66d5cf25f1ab51d464245 140d15e47e5c9c32f317e8beed51ab9158565757The article, based on an analysis of the current legislation of Russia and the experience of legal regulation of developed countries, draws conclusions about the need to improve the concept of artificial intelligence technologies based on a criteria-based approach. The author proposed a classification of AI technologies according to the intended purpose of use: used for public purposes; used for commercial purposes; AI technologies used for non-commercial purposes. In banking, AI technologies are always used for commercial purposes, and in the financial sector, developers and people using AI are the same. The author concludes that a presumption of guilt has been established for financial organizations using AI technologies in relations with consumers. The proposed classification may be useful in connection with the creation of a system of regulation and supervision, since the current measures of responsibility of financial organizations should be considered insufficient.Courier of the Kutafin Moscow State Law University10Courier of Kutafin Moscow State Law University (MSAL))["O. B. Sizemova"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/140d15e47e5c9c32f317e8beed51ab9158565757 3d6939ca5743d861541bb195a8b0e260d40aae0fExamines the integration of artificial intelligence (AI) into educational programs aimed at developing scientific analysis skills in an interdisciplinary context. Various AI technologies, such as machine learning, natural language processing, and intelligent educational platforms, are explored for their potential to transform approaches to learning and enhance the effectiveness of educational processes. The results of experimental analysis are presented, demonstrating that the use of AI contributes to improving student performance and developing critical thinking skills. The article also addresses the potential risks and limitations of applying AI in educational processes.Bulletin of Science and Practice50Experimental analysis demonstrates that the use of AI contributes to improving student performance and developing critical thinking skills, and the potential risks and limitations of applying AI in educational processes are addressed.Bulletin of Science and Practice["Baisova G."]2024-11-11T00:00:00https://www.semanticscholar.org/paper/3d6939ca5743d861541bb195a8b0e260d40aae0f 32aec099ac576f7eedeeaaf9f8581df8a465a0fdAugmented Human Research123Augmented Human Research["Pavan Kumar Reddy Poli", "Sushma Pamidi", "Shravan Kumar Reddy Poli"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/32aec099ac576f7eedeeaaf9f8581df8a465a0fd 9db69b78b1d5c4644b61a9fc156855a1001c78ebAcademic Psychiatry131Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["Lily T. Nguyen", "Viet T. Tran", "Jessica T. Tran", "Navin S. Oorjitham"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/9db69b78b1d5c4644b61a9fc156855a1001c78eb fe889bdfaf7da73430ac6500b635e7e6a0e6cab1Journal of Environmental Management211Mechanism analysis reveals that public opinion attention influences corporate green investment through pathways involving digital transformation and public attention to environmental protection, andHeterogeneity analysis results indicate that state-owned enterprises show a positive effect on their green investments during the digital transformation process.Journal of environmental management["Ming-Jie Yang", "Ning Zhu"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/fe889bdfaf7da73430ac6500b635e7e6a0e6cab1 42ce5231208536c78b7e52eae73deb924fd205e0Humanities and Social Sciences Communications202Humanities and Social Sciences Communications["Han Kuang", "Peng Tian", "Xiuwei Liang"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/42ce5231208536c78b7e52eae73deb924fd205e0 535b4116174ac90850336613951dcb02ede04be9<jats:p> </jats:p>Abstracts of the ICA170Abstracts of the ICA["Robert E. Roth"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/535b4116174ac90850336613951dcb02ede04be9 5ff73ebd1c295b810c2edc86c6f8a86b60163869Abstract Aims Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS. Methods and results A comprehensive database including 9611 patients with serial transthoracic echocardiograms was collected from a single institution across three clinical sites. The data set included parameters from echocardiograms, electrocardiograms, laboratory values, and diagnosis codes. Data from a single clinical site were preserved as an independent test group. Machine learning models were trained to identify progression to severe stenosis and all-cause mortality and tested in their performance for endpoints at 2 and 5 years. In the independent test group, the AS progression model differentiated those with progression to severe AS within 2 and 5 years with an area under the curve (AUC) of 0.86 for both. The feature of greatest importance was aortic valve mean gradient, followed by other valve haemodynamic measurements including valve area and dimensionless index. The mortality model identified those with mortality within 2 and 5 years with an AUC of 0.84 and 0.87, respectively. Smaller reduced-input validation models had similarly robust findings. Conclusion Machine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality. Implementation of such models may facilitate real-time, patient-specific follow-up recommendations.European Heart Journal - Digital Health00Machine learning models can be used in patients with mild AS to identify those at high risk of disease progression and mortality, and implementation of such models may facilitate real-time, patient-specific follow-up recommendations.European Heart Journal. Digital Health["Raghav R. Julakanti", "R. Padang", "Christopher G Scott", "Jordi Dahl", "Nader Al-Shakarchi", "Coby Metzger", "Alon Lanyado", "John I Jackson", "V. Nkomo", "Patricia A. Pellikka"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/5ff73ebd1c295b810c2edc86c6f8a86b60163869 c73a5bd254a2910d2892a112cca732201009dceeCollecting and acting on data has increased the military’s dependency on the electromagnetic spectrum (EMS). Electronic Warfare (EW) controls the Electromagnetic Spectrum (EMS) in order to detect, analyze, and track potential threats. EW provides situational awareness for diplomatic insights, defensive measures and offensive options for each country. EW enables Joint Electromagnetic Spectrum Operations (JEMSO). In the EM Operation Environment, the armed forces exploit, protect and attack. More advanced EW can identify, intercept and decode the adversaries’ Data. It can also project directed energy to disrupt enemy operations, reducing the impact of conflicts or preventing some armed conflicts before they begin.Applying cognitive systems to EW helps the army personnel identify patterns and improve the systems, as well as anticipating the COA. Cognitive Electronic Warfare systems interpret a large amount of data from a range of vast sources to provide hypotheses for action plans. Combining human strategies with computer input ensures the success of Cognitive EW approach. Leaving data collection and probability calculations to computers let humans time to think, to be creative and to use their intuiton in order to find the best solutions.STRATEGIES XXI - National Defence College370Cognitive Electronic Warfare systems interpret a large amount of data from a range of vast sources to provide hypotheses for action plans, and combining human strategies with computer input ensures the success of Cognitive EW approach.STRATEGIES XXI - National Defence College["Alida Monica Doriana Barbu"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/c73a5bd254a2910d2892a112cca732201009dcee 7dfc180ad3c9ead2d64b183977525e6699111af6African Journal of Biomedical Research00African Journal of Biomedical Research["Mohammed Abdullah Al- Mekhlaf"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/7dfc180ad3c9ead2d64b183977525e6699111af6 ed2a66ce80ec71454f9fa3662f993a3b3f3237c1Interactive Learning Environments690Interactive Learning Environments["Heng Yang"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/ed2a66ce80ec71454f9fa3662f993a3b3f3237c1 b153bb6543a92fc6d478ce9d49592eb1924755d6Discover Education130Discover Education["Qian Xu"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/b153bb6543a92fc6d478ce9d49592eb1924755d6 cd2ba92e388e28f45644b23d8b415328e8286a48CSCW Companion40{"pages": "54-56"}["Tina B. Lassiter"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/cd2ba92e388e28f45644b23d8b415328e8286a48 5f42c88387735579ef8799b12c4754de3679ad7aQuestions remain about the methods of applying AI in law and the approaches to its teaching. The Concept for the Development of MachineReadable Law Technologies addresses the first question, but the second question remains insufficiently addressed. For law students, it is suggested to begin AI training with chatbots. During their practical training, students were tasked with research assignments utilizing AI, comparing the capabilities of ChatGPT and GigaChat. The results indicated that both AIs have their strengths and weaknesses, requiring critical evaluation and refinement. The creation of interactive AI-based textbooks is also proposed, which could significantly enhance the educational process’s efficiency.Courier of the Kutafin Moscow State Law University00During their practical training, students were tasked with research assignments utilizing AI, comparing the capabilities of ChatGPT and GigaChat, and indicated that both AIs have their strengths and weaknesses, requiring critical evaluation and refinement.Courier of Kutafin Moscow State Law University (MSAL))["O. M. Ivanov"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/5f42c88387735579ef8799b12c4754de3679ad7a 736c05e5fec4523d3f3d1674872f4726251bf70aEuropean Heart Journal150European heart journal["E. Oikonomou", "R. Khera"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/736c05e5fec4523d3f3d1674872f4726251bf70a 86c5d6ea045281919e1c55907033cdc08c6c70f8Very often arguments are put forward that replacing in-person meetings by online events or using digital technologies to do collaborative research will lower the pressure on the environment (Spencer 2023). But is this actually true? The obvious impacts, such as the carbon footprint of using digital technologies, are often overlooked. The electricity (even if green alternatives exist) used to run and cool the ever-growing number of large data centers has been regularly cited (Monteclaro 2023) as the largest contributor to this impact. Recent studies (Bordage 2019), however, tend to show that other actors (mostly the users) in the domain of digital technologies, appear to have an even greater impact. Additionally, ethical considerations and habitat degradation issues resulting from mining for the components to build professional and personal digital devices contribute to the problems. The deployments of networks have also a non-negligible share in the calculation of the actual digital impact on the environement (Bordage 2019). The tremendous increase of generated data, information exchanges, algorithms to run digital applications due to the larger use of AI in all scientific fields, in all sectors of the policy making, industry and civil society will increase the environmental impact of digital technologies even more. However, depending on the purpose of the AI technology, if its usage can demonstrate a reduction of negative environmental impacts, mitigation solutions can be envisaged. For example, if AI allows better land planning or better identification of species, it could balance out the digital environmental impact. In terms of online versus in-person or hybrid meetings, the issue is not as easy as one may think. For in-person meetings, especially in topics like biodiversity information, almost everyone is using devices such as laptops, tablets or smartphones, even encouraged by the organizers and speakers, making live demos, or doing online polls requiring the use of digitial technologies (e.g., Mentimeter). Totally online meetings make heavy use of the networks, dataflows and devices, even if it can be argued that the impact of the flights is removed from the equation. Hybrid meetings combine both the impacts of fully in-person or fully online options, also encouraging local participants to interact with the online participants and potentially demultiplying the environemental impact even more. Mitigation aspects to take into account are the added value of social interactions, learning about localhost's culture and biodiversity, and input to the local economies and infrastructures to welcome business tourism for large conferences. Efforts made to make onsite meetings greener play an important role, by wisely choosing the location and how catering is handled with local products and diminishing the use of one-use-only cups and cutlery. The debate on which meeting options are the best, is far from being closed and will continue into the future. This presentation will show recent analyses conducted, among others, by the European Institutes for Sustainable IT (information technology), to demystify common assumptions on major responsibilities in terms of environmental impact or carbon footprints of digital technologies. Tips and tricks to lower everyone's overall digital impact will be provided as well as encouraging the actors in biodiversity information to sign the Sustainable IT charter or even act further to get the highest Sustainable IT label for their institutions.Biodiversity Information Science and Standards20This presentation will show recent analyses conducted by the European Institutes for Sustainable IT (information technology), to demystify common assumptions on major responsibilities in terms of environmental impact or carbon footprints of digital technologies.Biodiversity Information Science and Standards["Patricia Mergen"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/86c5d6ea045281919e1c55907033cdc08c6c70f8 b1fc25db99fc44f106c2b03cb6e284bff3b66d5d00["Cyril Klime\u0161", "J\u00e1n Skalka", "P. \u0160vec", "Tom\u00e1\u0161 Sochor", "Ji\u0159\u00ed Balej", "Jan Francisti"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/b1fc25db99fc44f106c2b03cb6e284bff3b66d5d 20e10634c7afcd4fcb10555c844bfd5a20b700fd00["Mustafa Berkta\u015f", "Abdulkadir Hiziroglu", "A. Erbaycu", "Orhan Er", "S. Kahyaoglu"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/20e10634c7afcd4fcb10555c844bfd5a20b700fd 30e36a6c1273157abd2cadc954fec2c5cdd39813Background: Nowadays, AI adoption, in medical education is growing rapidly. Studies have been conducted on various aspects of AI in medical education, however, in the context of its challenges and solutions faced with AI adoption is rarely explored through qualitative approaches. It is necessary to know the barriers and remedies for AI utilization in medical education.Objectives: This study explores potential barriers and solutions to AI adoption in medical education. Materials and Methods: Based on Giorgi's phenomenological approach, this qualitative study explored an in-depth understanding of AI adoption in medical education through in-depth interviews from March to April 2024. By purposive sampling, sixteen participants across famous medical institutions of Pakistan were interviewed, and data collection was stopped upon saturation. Results: A total of 16 participants belonging to different cadres of undergraduate medical colleges of Pakistan were interviewed for the current study. A total of 219 quotations from the transcripts resulted in three main themes with subsequent subthemes (total 13), i.e. perceptions of AI in medical education with three subthemes, barriers to AI adoption with seven subthemes and conjoint solutions to barriers having three subthemes.Conclusion: AI adoption in medical education is evolving healthcare globally. Nevertheless, there are barriers like the complexity of AI, lack of technical skills for using AI, and scarcity of resources that hinder its utilization in medical education. Moreover, ethical concerns and the specific guidelines that attract investors are among other challenges. Countermeasures like improving technical infrastructure and faculty development initiatives through collaboration among policymakers, administrators, and teaching faculty can overcome the barriers. Keywords: Artificial Intelligence, Adoption, Challenges, Medical Education.Journal of Saidu Medical College, Swat00There are barriers like the complexity of AI, lack of technical skills for using AI, and scarcity of resources that hinder its utilization in medical education that can be overcome through countermeasures like improving technical infrastructure and faculty development initiatives through collaboration among policymakers, administrators, and teaching faculty.Journal of Saidu Medical College, Swat["Muhammad Junaid Khan", "Mehreen Lajber", "Nazish Bilal", "Sana Khan", "Zarrin `Siddiqi", "Aziz Ahmad"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/30e36a6c1273157abd2cadc954fec2c5cdd39813 c8b33cf094bb5e4c5a00a52d9fdf7bb5514c581400["M. Mellal"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/c8b33cf094bb5e4c5a00a52d9fdf7bb5514c5814 4fde168230811686c28eada243e184963fa2317aOn 16 February 2017, the European Parliament made recommendations to the Commission about civil law rules on robotics, including a proposal to design a specific legal personality for autonomous robots and most sophisticated Artificial Intelligences with the capacity to make decisions and interact with third parties. Although the recent European Union’s Artificial Intelligence Act presents notable advances on supervision, surveillance, control and registration in this field, the question of legal personality remains an open issue. In order to design a specific concept along the lines of the European Parliament’s proposal and to make further progress along this legal line, the classical concept of legal person devised by Sinibaldo Fieschi is an essential reference.Isidorianum370In order to design a specific concept along the lines of the European Parliament’s proposal and to make further progress along this legal line, the classical concept of legal person devised by Sinibaldo Fieschi is an essential reference.Isidorianum["Carlos L\u00f3pez Segovia"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/4fde168230811686c28eada243e184963fa2317a de2d2d92b0828da38bf7a37390eba5c8191b52a9This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards.Information1161The nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy are examined, providing practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users.Information["Attila Kovari"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/de2d2d92b0828da38bf7a37390eba5c8191b52a9 754481ae5ff753ef930c3452061841029c61fe9aThis research investigates the transformative potential of integrating artificial intelligence (AI) with comprehensive risk management frameworks in industrial management. While AI applications have advanced in industrial settings, there is a lack of studies that fully integrate AI with macro risk factors such as PESTLE (political, economic, social, technological, legal, and environmental) and ESG (environmental, social, and governance) factors. These factors, often rooted in human activities and decisions, are critical to understanding and mitigating risks in complex industrial environments. By incorporating AI methods, such as machine learning and deep neural networks, organizations can enhance their ability to identify, analyze, and mitigate these risks efficiently. Recent developments, including OpenAI’s language models, further strengthen this approach by enabling large-scale data analysis and supporting real-time risk assessment and decision-making. OpenAI’s tools can interpret vast volumes of regulatory, economic, and social data, providing valuable insights to decision-makers. This research underscores the innovative potential of AI-driven risk management to enhance the stability and resilience of industrial management. By reducing human error and adapting to dynamic risk factors, this integration offers a forward-looking strategy for optimizing performance, ensuring operational excellence, and supporting sustainable practices across sectors.Economics &amp; Management Information01This research investigates the transformative potential of integrating artificial intelligence with comprehensive risk management frameworks in industrial management to enhance the stability and resilience of industrial management and underscores the innovative potential of AI-driven risk management to enhance the stability and resilience of industrial management.Economics &amp; Management Information["Tian Tian", "Sihan Jia", "Jindi Lin", "Zichen Huang", "Kei O Wang", "Yubing Tang"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/754481ae5ff753ef930c3452061841029c61fe9a 8d828b98d13278f7a0f20dc58ee4814019712446Artificial intelligence (AI), deep learning (DL), and machine learning (ML) algorithms are revolutionizing spine surgery. Soon, these technologies may allow the integration of automated devices into clinical practice. The roles of such devices are yet to be imagined and then developed, but one could assume that automated surgical devices can assist spine surgeons in a variety of ways, such as contextual guidance, precise screw placements, or intraoperative monitoring. In the not-too-distant future, such devices may be able to perform entire surgeries autonomously. Current literature suggests that advancements toward autonomous robotic surgery may improve surgical approaches and reduce negative clinical variation in spine surgery outcomes. This review aims to examine the current trends, practices, and advancements in surgical automation and provide an overview of the stages of automation of devices currently employed within spine surgery.Artificial Intelligence Surgery421This review aims to examine the current trends, practices, and advancements in surgical automation and provide an overview of the stages of automation of devices currently employed within spine surgery.Artificial Intelligence Surgery["Nishanth S Sadagopan", "Dillan Prasad", "Rishi Jain", "Christopher Ahuja", "N. Dahdaleh", "Najib E. El Tecle"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/8d828b98d13278f7a0f20dc58ee4814019712446 94ffc7842cacab60731d570125579d28eb6cb086Language Testing in Asia440The findings revealed that participants exposed to AI-driven assessments showed significant enhancement in listening skills and reported improved self-competence, mindsets, and resilience.Language Testing in Asia["Mohamed Sayed Abdellatif", "Mohammed A. Alshehri", "Hamoud A. Alshehri", "Waheed Elsayed Hafez", "Mona Gafar", "Ali Lamouchi"]2024-11-11T00:00:00https://www.semanticscholar.org/paper/94ffc7842cacab60731d570125579d28eb6cb086 4d6e6ca03e72dbdb4d40dc4b641054b7c21cfbddThe potential of artificial intelligence (AI) to significantly transform numerous aspects of contemporary civilization is substantial. Advancements in research show an increasing interest in creating AI solutions in the healthcare sector. This interest is driven by the broad spectrum and extensive nature of easily accessible patient data-including medical imaging, digitized data collection, and electronic health records - and by the ability to analyze and interpret complex data, facilitating more accurate and timely diagnoses. This review's goal is to provide a comprehensive overview of the advancements achieved by AI in healthcare, to elucidate the present state of AI in enhancing the healthcare system and improving the quality and efficiency of healthcare decision making, and to discuss selected medical applications of AI. Furthermore, the barriers and constraints that may impede the use of AI in healthcare are outlined, and the potential future directions of AI-augmented healthcare systems are discussed.Biomedical Reports291The present state of AI in enhancing the healthcare system and improving the quality and efficiency of healthcare decision making is elucidated, the barriers and constraints that may impede the use of AI in healthcare are outlined, and the potential future directions of AI-augmented healthcare systems are discussed.Biomedical Reports["Abdul-Mohsen Alhejaily"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/4d6e6ca03e72dbdb4d40dc4b641054b7c21cfbdd 390923bad7dc32ed8a49278e74dbfe4ed7f9ff5cIn recent decades, machine learning (ML) artificial intelligence has found wide application in water quality monitoring and prediction due to the increasing complexity of water quality data. This complexity has been attributed to the global upsurge in anthropogenic activities and climatic variations. It is therefore critical to identify the most accurate and suitable ML model for water quality prediction. In this study, a systematic literature review (SLR) was carried out to explore the trend and progress in the application of ML models in water quality monitoring and prediction in Nigeria from 2003-2024. A comprehensive review of the effectiveness of advanced ML models as well as the gaps in their application in the area of water quality assessment and monitoring was also carried out using the PRISMA-P meta-analysis technique. Forty publications were used to perform bibliographic analysis and visualization using the VOS viewer software. The study found that globally, the use of hybrid ML models in water quality prediction has not been well explored; a majority of the prediction has been based on the use of artificial neural networks (ANN). Among the ANN algorithms, the adaptive neuro-fuzzy inference system (ANFIS), and Wavelet-Adaptive Neural Fuzzy Interference System (W-ANFIS) hybrid models are the most accurate in prediction; with temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), and total dissolved solids (TDS) among the most frequently predicted parameters. Nigeria is grossly lagging in the application of ML in water quality prediction. This limitation is largely attributed to inadequate data on environmental monitoring. It is critical therefore for future water quality monitoring and prediction studies in Nigeria to take advantage of the rapidly evolving field of machine learning; with more emphasis placed on the hybridized machine learning algorithms    Global Journal of Pure and Applied Sciences751Among the ANN algorithms, the adaptive neuro-fuzzy inference system (ANFIS), and Wavelet-Adaptive Neural Fuzzy Interference System (W-ANFIS) hybrid models are the most accurate in prediction; with temperature, dissolved oxygen, pH, electrical conductivity (EC, and total dissolved solids) among the most frequently predicted parameters.Global Journal of Pure and Applied Sciences["M. E Omeka", "G. Amah", "M. I Morphy", "B. O Omang", "E. A Asinya", "G. T Kave"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/390923bad7dc32ed8a49278e74dbfe4ed7f9ff5c c08de1c6183d4fff73220e6e2676de072ab68538The “Exposome” is a concept that indicates the set of exposures to which a human is subjected during their lifetime. These factors influence the health state of individuals and can drive the development of Noncommunicable Diseases (NCDs). Artificial Intelligence (AI) allows one to analyze large amounts of data in a short time. As such, several authors have used AI to study the relationship between exposome and chronic diseases. Under such premises, this study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies. To achieve this, we carried out a search on multiple databases, including PubMed, ScienceDirect, and SCOPUS, from 1 January 2019 to 31 May 2023, using the MeSH terms (exposome) and (‘Artificial Intelligence’ OR ‘Machine Learning’ OR ‘Deep Learning’) to identify relevant studies on this topic. After completing the identification, screening, and eligibility assessment, a total of 18 studies were included in this literature review. According to the search, most authors used supervised or unsupervised machine learning models to study multiple exposure factors’ role in the risk of developing cardiovascular, metabolic, and chronic respiratory diseases. In some more recent studies, authors also used deep learning. Furthermore, the exposome analysis is useful to study the risk of developing neuropsychiatric disorders or evaluating pregnancy outcomes and child growth. Understanding the role of the exposome is pivotal to overcome the classic concept of a single exposure/disease. The application of AI allows one to analyze multiple environmental risks and their combined effects on health conditions. In the future, AI could be helpful in the prevention of chronic diseases, providing new diagnostic, therapeutic, and follow-up strategies.Informatics551This study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies.Informatics["S. Isola", "G. Murdaca", "Silvia Brunetto", "Emanuela Zumbo", "A. Tonacci", "S. Gangemi"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/c08de1c6183d4fff73220e6e2676de072ab68538 4b28b5e72f78c8905eb0540c7720ace37b7b2e58 Background: Left ventricular diastolic dysfunction (LVDD) predicts mortality in cardiac intensive care unit (CICU) patients. A novel artificial intelligence enhanced electrocardiogram (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in the cardiac intensive care unit (CICU). We aim to assess if LVDD by AI-ECG is associated with in-hospital and one-year mortality in CICU patients. Methods: In this retrospective cohort study, we included consecutive unique adults admitted to the Mayo Clinic CICU from 2007 to 2018 with an admission AIECG, which AI assigned LVDD grade (0 to 3). Medial mitral E/e’ ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. AIECG and TTE assessment of LVDD were used to assign patients to four groups based on TTE as gold standard: true and false negative, and true and false positive. In-hospital mortality was evaluated using multivariable logistic regression. One-year survival was evaluated using Kaplan-Meier curves and multivariable Cox proportional-hazards. Results: We included 11,868 patients (median age 69.5 years, 37.7% females); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grades were: grade 0 (normal), 33%; grade 1, 7%; grade 2, 39%; grade 3, 21%. In-hospital (adjusted OR) and one-year (adjusted HR) mortality increased in each higher AIECG LVDD grade (Figure A/B), before and after adjustment including TTE measurements. Patients with grade 2-3 LVDD by AIECG and medial mitral E/e’ ratio >15 by TTE (true positive) had the highest in-hospital (adjusted OR 2.5 [1.7-3.9]) and one-year (adjusted HR 1.9 [1.5-2.5]) mortality (Figure C/D), and mortality was elevated similarly in patients with either grade 2-3 LVDD by AIECG (false positive) or medial mitral E/e’ ratio >15 by TTE (false negative). Conclusions: The AIECG LVDD grade was strongly associated with in-hospital and one-year mortality in CICU patients, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures (true positive) were at highest risk. Circulation00The AIECG LVDD grade was strongly associated with in-hospital and one-year mortality in CICU patients, even after adjusting for clinical variables and TTE measurements.Circulation["D. Hillerson", "Jacob C Jentzer", "Eunjung Lee", "Zachi Attia", "G. Kane", "F. Lopez-Jimenez", "P. Noseworthy", "P. Friedman", "J. Oh"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/4b28b5e72f78c8905eb0540c7720ace37b7b2e58 05c5d26fb56bdfd7bb28a8136c7042ddd6f7e85e Background: Invasive hemodynamics are the gold standard for diagnosis of heart failure with preserved ejection fraction (HFpEF). A novel, FDA-approved artificial intelligence (AI) technology that uses a single, 4-chamber transthoracic echocardiogram (TTE) image to screen patients for HFpEF shows promise as a non-invasive tool to assist in diagnosis. Development of right ventricular (RV) dysfunction is a sign of a more advanced HFpEF. Advanced RV hemodynamic parameters, beyond pulmonary arterial pressures (PAP), have not been well studied in HFpEF. We sought to correlate advanced RV hemodynamic parameters in patients screened for HFpEF with this AI screening tool. Method: We retrospectively evaluated two cohorts of patients with suspected HFpEF that underwent TTE and RHC at our institution. The most recent TTE for each patient was screened using the AI-based analysis tool and was reported as either “suggestive” or “non-suggestive” of HFpEF – labeled as “positive” or “negative,” respectively. Mean PAP, pulmonary vascular resistance (PVR), pulmonary artery pulsatility index (PAPI), RV cardiac power output (RV-CPO), RV myocardial performance score (RV-MPS), and right atrial pressure to pulmonary capillary wedge pressure ratio (RA:PCWP) were calculated using invasive hemodynamic parameters at rest, and exercise when available. RV-CPO was calculated as [(mean PAP-RAP) x cardiac output] /451, and RV-MPS was calculated as (RV-CPO x PAP)x1.5. Median values were calculated. AI positive and negative groups were compared using Student’s t-test. Results: A total of 47 patients (82% women, 79% Black, average EF 62%) were included, with 23 undergoing subsequent exercise RHC. There were 18 (38%) that screened positive for HFpEF, and 29 (62%) screened negative by TTE AI software. Positive patients had a significantly higher mean PAP (median 31 vs 23 mmHg, p=0.01), PVR (2.1 vs 1.3 WU, p=0.02), and RV-CPO (0.26 vs. 0.17, p=0.04) than patients who were screened negative. There were no significant differences in PAPI, RV-MPS, and RA:PCWP at rest. There were no significant differences in mean PAP, PVR, PAPI RV-CPO, RV-MPS, or RA:PCWP with exercise. Conclusion: Patients screened positive for HFpEF by a novel AI TTE software had significantly higher PAP and RV-CPO at rest, but no differences in PAPI, RV-MPS, or RA:PCWP ratio. This tool may help identify more advanced HFpEF. Circulation00Patients screened positive for HFpEF by a novel AI TTE software had significantly higher PAP and RV-CPO at rest, but no differences in PAPI, RV-MPS, or RA:PCWP ratio, which may help identify more advanced HFpEF.Circulation["Kevin Chang", "Ryan Sachar", "Maria Latz", "Tess Allen", "John Blair", "Gene Kim", "J. Grinstein", "gary woodward", "Roberto Lang", "M. Belkin"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/05c5d26fb56bdfd7bb28a8136c7042ddd6f7e85e 29864cac3ea38bad2ab1667b94c6c0de02817824 Background: Atrial fibrillation (AF) risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis. Whether integrating these distinct risk signals improves AF risk estimation is unknown. Methods: In the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a published AI-enabled ECG-based AF risk model (ECG-AI), and a 1113667-variant AF polygenic risk score (PRS). We estimated discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic (AUROC) and average precision (AP). Results: Among 49,293 individuals (mean age 65±8 years, 52% women), 825 (2.4%) developed AF within 5 years. Using single models, discrimination of 5-year incident AF was higher using ECG-AI (AUROC 0.705 [95%CI 0.686-0.724]; AP 0.085 [0.071-0.11]) and CHARGE-AF (AUROC 0.785 [0.769-0.801]; AP 0.053 [0.048-0.061]) versus the PRS (AUROC 0.618, [0.598-0.639]; AP 0.038 [0.028-0.045]). The best performing two component model was CHARGE-AF+ECG-AI (AUROC 0.802 [0.786-0.818]; AP 0.098 [0.081-0.13]), with further improvement observed with inclusion of all components (“Total-AF”, AUROC 0.817 [0.802-0.832]; AP 0.11 [0.091-0.15], p<0.01 vs CHARGE-AF+ECG-AI). Using Total-AF, individuals at high AF risk (i.e., 5-year predicted AF risk >2.5%) had a 5-year cumulative incidence of AF of 5.83% (5.33-6.32). At the same threshold, the 5-year cumulative incidence of AF was progressively higher according to the number of models predicting high risk (zero: 0.67% [0.51-0.84], one: 1.48% [1.28-1.69], two: 4.48% [3.99-4.98]; three: 11.06% [9.48-12.61]. Total-AF achieved favorable net reclassification improvement compared to both CHARGE-AF+ECG-AI (0.039 [0.015-0.066]) and CHARGE-AF+PRS (0.033 [0.0082-0.059]). Conclusions: Integration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Total-AF have potential to improve the prioritization of individuals for AF screening. Circulation00Circulation["Shinwan Kany", "Joel T. R\u00e4m\u00f6", "Sam F Friedman", "L. Weng", "Min Seo Kim", "A. Fahed", "Steven Lubitz", "A. Philippakis", "M. Maddah", "P. Ellinor", "S. Khurshid"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/29864cac3ea38bad2ab1667b94c6c0de02817824 ead15d4889848dc2b89647eba0aa8011df6e2f04 Background: Patients and their families often explore the information available online for information about their health status. Dialogue-based artificial intelligence (AI) language models (ChatGPT, Perplexity, Bing AI and Google Bard AI) havebeen developed for complex questions and answers even while still constantly evolving. We sought to assess whether AI model had knowledge of cardiovascular disease (CVD) racial disparities, including disparities associated with CVD risk factors and associated diseases. Methods: To assess the responses of various AI models to topics in cardiovascular disease disparities, we created twelve questions, with each question having varying topics and patient demographics. Each question was input into 4 different AI models and was asked three times to assess for variability in responses, and the application closed after each attempt. Results: A total of 144 responses were tabulated from ChatGPT, Google Bard, Perplexity, and Bing AI answers to 12 questions in triplicate to assess for consistency. Most responses to the same prompt were consistent across different question-and-answer sessions. ChatGPT’s responses to 75% of the questions (9 out of 12 questions) were appropriate, 25% (3 out of 12 questions) were inappropriate and none were unreliable. Google Bard's responses to 91.7% of the questions (11 out of 12 questions) were appropriate, 8.3% (1 out of 12 questions) were inappropriate and none were unreliable. Perplexity responses to 66.7% of the questions (8 out of 12 questions) were appropriate, 25% (3 out of 12 questions) were inappropriate and 8.3% of the questions (1 out of 12 questions) were unreliable. Bing AI responses to 75% of the questions (9 out of 12 questions) were appropriate, 16.7% (2 out of 12 questions) were inappropriate and 8.3% of the questions (1 out of 12 questions) were unreliable. Of the 144 prompt entries into ChatGPT, Google Bard, Perplexity, and Bing AI; 122 (84.7%) were correct, 11 (7.64%) were hedge responses and could not be binarized into a correct or incorrect response, and 11 (7.64%) were incorrect. Conclusion: Our study showed that online chat-based AI models have a broad knowledge of CVD racial disparities, however persistent gaps in knowledge about minority groups. Given that these models might be used by the general public, caution should be advised in taking responses at face value. Circulation00Online chat-based AI models have a broad knowledge of CVD racial disparities, however persistent gaps in knowledge about minority groups are shown, indicating caution should be advised in taking responses at face value.Circulation["O. Eromosele", "K. Ughagwu", "Ayodeji Johnson", "Herlyne Das", "Temitope Sobodu", "David Ouyang"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/ead15d4889848dc2b89647eba0aa8011df6e2f04 00e5cd5593a677c50193999b25651a3bd9963c4a Background: Ablation by pulmonary vein isolation (PVI) is the most effective approach to treat atrial fibrillation (AF), but 30-40% of patients may not respond. Studies including the CAPLA trial suggest that ablating sites of rapid activation on the posterior left atrial wall or elsewhere may improve success. However, AF rate is rarely calibrated and, since electrograms often include noise or far field activity, may overestimate rate (underestimate cycle length) at many sites. Hypothesis: We hypothesized that artificial intelligence (AI) algorithms may more accurately indicate local AF rate, by excluding spurious activations referenced to simultaneous monophasic action potential (MAP) recordings, compared to conventional measurements. Methods: We studied N=303 AF patients at ablation (68.2±8.0 years, 17.8% females, 72.9% non-paroxysmal AF). Patients were matched N=229 into development and one quarter (N=74) into hold-out test cohorts. Patients underwent AF mapping using multipolar catheters, and a subset with MAP catheters (MedFact, GmbH). We used the development set to train an AI algorithm to reliably detect AF activations, which we compared in the test cohort to a standard dV/dt approach referenced to adjacent MAPs within 2 mm (N=20 patients) and manually annotated activations (N=64 patients). Results: Fig A shows AF in a 66 year old woman, where dV/dt often marked non-physiological activations within repolarization shown in the adjacent MAP. Conversely, the AI system excluded spurious deflections and better correlated with experts (black). In summary, Fig. B shows that AI provided a higher F1 score than dV/dt compared to MAPs (median[IQR]: 0.86 [0.5 - 1] vs. 0.67 [0.40 - 0.73], p<0.01) and experts (0.89 [0.73 - 1] vs. 0.72 [0.60 - 0.89]], p<0.01). Fig C shows that the AI approach better identified AF cycle length (rate; blue) than standard approaches (green) that substantially underestimate cycle length (p<0.05). Conclusions: In this large registry, a novel AI-based approach more accurately detected AF rates in clinical electrograms, referenced to experts and action potential recordings, than standard approaches which overestimated rate. AI may improve characterization of AF activity within the atria. Circulation00In this large registry, a novel AI-based approach more accurately detected AF rates in clinical electrograms, referenced to experts and action potential recordings, than standard approaches which overestimated rate.Circulation["Ricardo Carlos Abad Juan", "Suhaas Anbazhakan", "S. Ruip\u00e9rez-Campillo", "Miguel Rodrigo", "S. Narayan"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/00e5cd5593a677c50193999b25651a3bd9963c4a 4d6a5c744b8eb75c77fea4e95b00fe5ecfe4d083 Background: Undiagnosed diabetes and prediabetes present a significant global health challenge. Artificial Intelligence-enabled electrocardiography (AI-ECG) has shown promise in identifying subtle ECG changes in a wide range of subclinical diseases. Opportunistic ECG screening could identify prediabetic patients, enabling early interventions to prevent T2DM and adverse cardiovascular events. Aims: To develop the AI-ECG Risk Estimator to diagnose prevalent T2DM and predict future T2DM (AIRE-DM) Methods: AIRE-DM was trained on a real-world secondary care cohort from Beth Israel Deaconess Medical Center (BIDMC) of 1,163,401 ECGs and externally validated in the UK Biobank (UKB, N = 65,606). AIRE-DM employs a residual neural network architecture with a discrete-time survival loss function. Results: AIRE-DM accurately identifies prevalent T2DM (AUROC: BIDMC – 0.712 (0.705-0.719), UKB - 0.731 (0.725 - 0.741) and predicts future T2DM (C-index: BIDMC - 0.666 (0.658-0.675), UKB 0.689 (0.663-0.715). In subjects without T2DM, the high-risk quartile shows a markedly increased risk of future T2DM (HR: BIDMC - 4.67 (4.01-5.45), UKB - 10.10 (5.87-17.40), adjusted for age and sex. Adding AIRE-DM to clinical risk factors in BIDMC and to the American Diabetes Association (ADA) score in the UKB significantly enhanced predictive accuracy for future T2DM (C-index improvement: BIDMC - 0.0359 (0.0354-0.0363), UKB: 0.0337 (0.0324-0.0350), continuous net reclassification index: BIDMC - 0.407 (0.360-0.445), UKB - 0.391 (0.259-0.503)). Using phenome- and genome-wide association studies, we identified biologically plausible associations for AIRE-DM, including glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness and lipid metabolism. We identified variants adjacent to CASQ2 , TBX3 , NOS1AP , TKT , VGLL2 and PRDM6 , which are known regulators of cardiac morphology, arterial stiffness and glucose metabolism. Conclusion: AIRE-DM can predict future T2DM in non-diabetics and enhances T2DM risk prediction when integrated with clinical risk scores. Its application holds promise for early identification of individuals at high risk of T2DM, enabling early lifestyle and pharmacological interventions. Circulation00AIRE-DM can predict future T2DM in non-diabetics and enhances T2DM risk prediction when integrated with clinical risk scores and its application holds promise for early identification of individuals at high risk of T2DM, enabling early lifestyle and pharmacological interventions.Circulation["L. Pastika", "K. Patlatzoglou", "E. Sieliwonczyk", "Joseph Barker", "B. Zeidaabadi", "K. Mcgurk", "Sadia Khan", "D. Mandic", "James S. Ware", "N. Peters", "Daniel Kramer", "J. Waks", "A. Sau", "F. S. Ng"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/4d6a5c744b8eb75c77fea4e95b00fe5ecfe4d083 448f831f82fc4268701dfac9449f59a23ea905dc Introduction: Heart disease is the leading cause of death (LOCD) in women in the United States. Despite public campaigns, women's awareness of heart disease as the LCOD of death for women significantly decreased from 65% in 2009 to 44% in 2019. Significant declines were observed in Black, Hispanic, and young women. Aims: This pilot trial aims to evaluate the acceptability/usability and potential efficacy of the fully automated artificial intelligence (AI) HeartBot program to increase awareness and knowledge of heart attacks in women. Methods: In this pre-and post- trial, 102 women were asked to complete the baseline survey and then interact with the HeartBot. The HeartBot is an AI-based text-driven conversational agent, available 24 hours a day, 7 days a week, and fully automated (Figure 1). After 4 weeks of the interaction, women were asked to complete the post-survey. The primary outcomes include four questions (recognizing the signs and symptoms of a heart attack, telling the difference between the signs or symptoms of a heart attack and other medical problems, calling an ambulance or dialing 911, and getting to an emergency room within 60 minutes after the onset of your symptoms of a heart attack). Wilcoxon signed rank tests and ordinal logistic regression models were used to evaluate the HeartBot program. Results: The mean (SD) age was 46 (12) years; 60.6% of the sample was women with racial/ethnic backgrounds; 41.3% reported commercially available chatbot use (i.e. Siri) in the past 30 days. 88.5% of the sample completed the trial and the majority accepted HeartBot use. The mean (SD) length of the HeartBot interaction was 13 (11) minutes. Overall, the Wilcoxon signed-rank test indicated a significant increase in all outcomes of heart attack awareness and knowledge from the baseline and the post-Heartbot interaction ( p < 0.05) (Figure 2). In the ordinal regression model, this significance of the outcomes remains even when controlling for potential confounding factors ( p < 0.05). Conclusion: To the best of our knowledge, this was the first pilot trial to demonstrate the potential efficacy of the HeartBot program in the short term in women with diverse racial/ethnic backgrounds. However, a full-scale randomized controlled is warranted. Circulation00This was the first pilot trial to demonstrate the potential efficacy of the HeartBot program in the short term in women with diverse racial/ethnic backgrounds and a full-scale randomized controlled is warranted.Circulation["Yoshimi Fukuoka", "Diane Kim", "Holli Devon", "Jingwen Zhang", "Thomas J Hoffmann", "Kenji Sagae"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/448f831f82fc4268701dfac9449f59a23ea905dc a8d11de3acbb5bc31a3d2e231560cf962590c09b Background: Acute kidney injury (AKI) often complicates acute type A aortic dissection (ATAAD), with elevated comorbidity rates and a significant tie to in-hospital mortality. Identifying risk factors early can mitigate AKI severity. Research Questions: This research endeavors to develop and corroborate predictive models leveraging Machine Learning (ML) techniques from Artificial Intelligence to forecast AKI occurrences in ATAAD-afflicted individuals. Methods: The study employed various machine learning (ML) algorithms including Gradient Boosting Machine (GBM), LightGBM, Random Forest (RF), K-Nearest Neighbors (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Naive Bayes (NB), Logistic Regression (LR), and ensemble methods (combining LR&LightGBM), employing tenfold cross-validation. Model performance was evaluated using SHapley Additive exPlanations (SHAP). A web-based tool for predicting AKI incidence was developed using Streamlit, based on the most effective model. The analysis involved 1350 ATAAD patients, among whom 586 (43.4%) developed post-operative AKI. Patients were divided into two cohorts: 85% for training and 15% for testing, with 126 features included in the predictive model. Results: Incorporating top 10 features, LightGBM (AUROC=0.886, 95% CI 0.841-0.930) excelled in predictive accuracy, calibration, and clinical utility, identifying key factors such as ventilation time in ICU, hourly urine output post-surgery, diuretic use, Scr, heart rate, urea, administration of recombinant human brain natriuretic peptide and ebrantil, MCHC, and blood glucose as associated with ATAAD-AKI. Conclusion(s): These ML models are robust tools for predicting AKI in ATAAD patients, with LightGBM's superior predictive ability standing out. They offer valuable support for clinical decision-making in ATAAD management, helping optimize postoperative strategies to minimize AKI occurrence after surgery. Circulation00These ML models are robust tools for predicting AKI in ATAAD patients, with LightGBM's superior predictive ability standing out and offer valuable support for clinical decision-making in ATAAD management.Circulation["Zhihui Zhu", "Zheyuan Chen", "Nan Liu", "Yongqiang Lai"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/a8d11de3acbb5bc31a3d2e231560cf962590c09b a25666583db22a2175039f17a82a5b0bf7b69543 Background: Electrocardiography-based artificial intelligence (AI-ECG) validated models that detect cardiac disease are increasingly being applied in clinical practice. The utility of such tools to detect insidious cardiac disease in patients with normal baseline left ventricular ejection fraction (LVEF) who paradoxically develop reduced LVEF during exercise stress echocardiography (ESE), and do not have coronary artery disease (CAD) or hypertension, is unknown. Hypothesis: AI-ECG is useful to diagnose insidious cardiac disease and predict clinical outcomes in patients with exercise-induced cardiomyopathy. Aims: To assess the utility of AI ECG in patients with exercise-induced cardiomyopathy. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD (confirmed by coronary angiography within 90 days after ESE), with resting LVEF ≥50% and a paradoxical ≥5% LVEF decrease during ESE were identified. A previously validated AI-ECG algorithm that predicts atrial fibrillation (AF), reduced LVEF, and cardiac amyloidosis (CA) was applied to the baseline ECG closest to the time of ESE. The predicted probability of AF, reduced LVEF, and CA if above the published thresholds, was determined. Results: There were 134 patients with exercise-induced cardiomyopathy who were identified. The mean age of this cohort was 66±10 years, 76% were women and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. The median follow-up period was 6.8 years (IQR 3.0-12.2). Among patients without a baseline history of AF (n=112), AI-ECG identified 29% with a significant probability of AF, which was associated with a subsequent AF diagnosis at univariable analysis (HR 2.505, 95%CI 1.016-6.177, p=0.046). There were 10 patients with an AI-ECG prediction of reduced LVEF among whom 3 were subsequently hospitalized with HF. AI-ECG was positive for CA in 8 and 3 amongst these had subsequent HF hospitalizations (Table). Conclusions: Baseline AI-ECG may help predict subsequent AF and diagnose preclinical amyloid cardiomyopathy in patients who by resting echocardiography do not seem to have cardiac disease but with exercise develop reduced LVEF. Circulation00Baseline AI-ECG may help predict subsequent AF and diagnose preclinical amyloid cardiomyopathy in patients who by resting echocardiography do not seem to have cardiac disease but with exercise develop reduced LVEF.Circulation["Marta Figueiral", "Luca Fazzini", "J. J. Cao", "Scott Hubers", "Christopher Scott", "R. McCully", "M. Castrichini", "Akanksha Mohananey", "Rachad Ghazal", "Min Wang", "Li Wang", "R. Gulati", "Roberta Montisci", "Patricia A. Pellikka", "Naveen L Pereira"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/a25666583db22a2175039f17a82a5b0bf7b69543 47ef4baf5927fbc5f1fa96a0ce0400c0c6524c5b Background: Artificial intelligence (AI) can be used to predict a person’s age from the electrocardiogram (ECG) (AI-ECG age) and has been proposed as a measurement of biological age. The difference between AI-ECG age and chronological age (defined as delta-age) is an independent predictor of mortality in the general population. This study assessed the relationship between AI ECG estimated age and mortality among adults living with congenital heart disease (CHD). Methods: A previously validated neural network to estimate AI-ECG age was used to analyze standard digital 12-lead ECGs in a cohort of subjects aged >18 years seen in the adult CHD (ACHD) clinic between 1992 to 2023. A single ECG, collected during the year of the first visit to the ACHD clinic was analyzed to compute the delta-age.. A positive delta-age indicates an AI ECG estimated age higher than the subject’s chronological age. The relationship between the delta-age and mortality was evaluated using Cox proportional hazard models adjusting for influential clinical factors including chronologic age, sex, ACHD severity, NYHA class and history of arrhythmia. Results: Of 5,780 subjects tested (50% females), the mean chronological age was 39.1 ± 15.0 years. AI ECG estimated age was 52.3 ± 16.6 years. Complexity of CHD was classified as mild, moderate, and severe in 7.4%, 73.9% and 18.7% of patients respectively. Patients with severe CHD had the highest delta-age of 17.1 (±18.2) years followed by moderate 12.7 (±13.6) y and simple 7.7 (±11.5) y. Patients with single ventricle had the highest deviation in AI estimated age [median delta-age 21.0 (IQR 28.6) y] followed by those with a systemic right ventricle [median delta-age of 16.3 (IQR 29.3) y]. During a median follow-up of 6.4 y (25th – 75th centile 1.58 – 13.7 y), mortality occurred in 839 (14.5%) patients. After adjusting for chronological age, CHD severity, and other clinical variables, delta-age gap was associated with increased mortality risk (HR 1.06 (1.03 – 1.09) per 5-year increment in delta-age, p<0.005), Table 1. Conclusion: The difference between AI ECG and chronological age is an independent predictor of all-cause mortality in ACHD patients. Circulation00The difference between AI ECG and chronological age is an independent predictor of all-cause mortality in ACHD patients and is associated with increased mortality risk.Circulation["Scott Anjewierden", "Malini Madhavan", "Zachi Attia", "F. Lopez-Jimenez", "P. Friedman", "A. Egbe", "Heidi M Connolly", "Samuel J Asirvatham", "Luke Burchill"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/47ef4baf5927fbc5f1fa96a0ce0400c0c6524c5b 06a6acc573df056a0295db9f0a745de64e5e26b6 Introduction: An early and accurate diagnosis of occlusion myocardial infarction (OMI) by an electrocardiogram (ECG) is critical for prompt catheterization lab activation (CLA) for primary percutaneous coronary intervention (PCI). Objective: To evaluate the predictive accuracy of a new mobile application, utilizing an artificial intelligence (AI) deep learning algorithm, for distinguishing cases of OMI from non-OMI among actual Emergency Department (ED) patients assessed for potential CLA. Methods: We conducted a retrospective analysis of adult patients assessed for potential CLA in the ED at Barnes Jewish Hospital, St. Louis, MO, from August 22, 2023 to April 6, 2024. Patients arriving post-cardiac arrest were excluded. The ECG obtained immediately prior to each CLA was re-analyzed using a mobile device application with the OMI ECG AI algorithm, known as the Queen of Hearts (QoH) model. Each ECG was then categorized as either OMI or non-OMI. Coronary angiograms were reviewed blinded to the ECG results. Results: Out of 102 CLAs, 57 patients were accepted for emergent coronary angiography. The QoH model predicted 54 patients (52.9%) as having an OMI. Patients predicted to have an OMI were more likely to be accepted for coronary angiography (94% vs. 17%), have primary PCI performed (85% vs. 2.1%), and have acute coronary thrombosis detected (74.1% vs. 0.0%) on coronary angiography compared to non-OMI patients. All 46 patients fulfilling STEMI ECG criteria were correctly identified as having an OMI. Two patients predicted to have OMI without STEMI ECG criteria were found to have acute coronary occlusion. Patients with OMI had higher peak high-sensitivity troponin values. Among the 55 patients predicted to have non-OMI, 41 of 45 (91.1%) were not accepted for emergent coronary angiography and 6 of 10 (60.0%) patients accepted for emergent coronary angiography did not have obstructive coronary artery disease. Conclusions: The AI-based QoH model was highly predictive of OMI confirmed at coronary angiography. Implementation of this model may help clinicians identify the risk of OMI in patients triggering a CLA, and utilization of the AI-model could have led to potential reduction of false-positive CLAs. Circulation00A new mobile application utilizing an artificial intelligence (AI) deep learning algorithm for distinguishing cases of OMI from non-OMI among actual Emergency Department patients assessed for potential CLA was highly predictive of OMI confirmed at coronary angiography.Circulation["Samantha Harris", "Fredrick Brown", "Adam May", "Richard Bach"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/06a6acc573df056a0295db9f0a745de64e5e26b6 9f3789be2568a22fc5028d8cfdcdf0e45a431447 Introduction: Artificial intelligence (AI) models built with convolutional neural networks (CNN) accurately detect left ventricular systolic dysfunction (LVSD) from 12-lead electrocardiogram (ECG). While AI models can also detect LVSD from 1-lead (but with lower accuracy), the optimal numbers and combinations of the leads remain unclear. Aims: To identify the optimal numbers and combinations of ECG leads for detecting LVSD. Methods: A total of 75,033 ECGs recorded within 14 days of an echocardiogram were collected. The data was randomly divided into derivation, validation, and test sets in a 5:2:3 ratio without patient overlap. The same split was used throughout the study. While all available ECGs were included in derivation and validation sets, the test set included 1 ECG per patient closest to the echocardiogram. CNN models were trained to detect LVSD, defined as left ventricular ejection fraction <40%, using all possible combinations of 1-, 2-, and 3-lead extracted from 12-lead ECG. The final model for each lead combination was chosen according to the area under the receiver operating curve (AUROC) on the validation set across the 20 epochs. The models were trained 4 times to evaluate the variance caused by initialization vectors. Results: The 12-lead model showed AUROC of 0.893 (95%CI, 0.887-0.899). Multiple models trained with 2-lead achieved similar performance, with aVR-V4 displaying the highest AUROC (0.892; 95%CI, 0.889-0.895), followed by I-V4 (0.889; 95%CI, 0.883-0.895) and I-II (0.888; 95%CI, 0.884-0.893). The results were similar for 3-lead (aVR-aVL-V4: AUROC 0.895; 95%CI, 0.893-0.897, I-aVR-V4: 0.892; 95%CI, 0.889-0.896, and I-II-V4: 0.891; 95%CI, 0.886-0.895). The choice of leads greatly affected the performance. For the 1-lead models, aVR showed the highest AUROC (0.876; 95%CI, 0.873-0.879), but III only achieved an AUROC of (0.766; 95%CI, 0.761-0.772). The combinations, including a chest lead and a limb lead (especially aVR), tend to achieve higher performance (Fig 1). Conclusion: A combination of 2-lead was sufficient to achieve comparable accuracy as the 12-lead for the AI model to detect LVSD. Wearable devices collecting a combination of a limb and a chest lead may enhance the detection of LVSD. Circulation00A combination of 2-lead was sufficient to achieve comparable accuracy as the 12-lead for the AI model to detect LVSD, and wearable devices collecting a combination of a limb and a chest lead may enhance the detection of LVSD.Circulation["Masamitsu Nakayama", "R. Yagi", "Rahul C. Deo", "C. MacRae", "Shinichi Goto"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/9f3789be2568a22fc5028d8cfdcdf0e45a431447 70da57c34ea19fe121a8251cae539515105518cbJournal of general internal medicine340Recommendations on how clinicians, technologists, and healthcare organizations can approach the use of generative AI and affirm that the practice of medicine remains a fundamentally human endeavor which should be enhanced by technology, not displaced by it.Journal of general internal medicine["Byron Crowe", "Shreya Shah", "Derek Teng", "Stephen P. Ma", "Matthew DeCamp", "Eric I. Rosenberg", "Jorge A Rodriguez", "Benjamin X. Collins", "Kathryn Huber", "Kyle Karches", "Shana Zucker", "Eun Ji Kim", "L. Rotenstein", "Adam Rodman", "Danielle D. Jones", "Ilana B Richman", "Tracey L Henry", "Diane Somlo", "Samantha I Pitts", "Jonathan H. Chen", "Rebecca G Mishuris"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/70da57c34ea19fe121a8251cae539515105518cb c48a95de6432fb86ec973296be486f1c689fd74b Background: We explored the potential of cutting-edge open-label artificial intelligence, particularly the unique cognitive capabilities it offers, in modern clinical practice. Our study evaluated the efficacy of online open-source generative pre-trained transformers (ChatGPT) in predicting cardiovascular risk in patients with heart failure and preserved ejection fraction, comparing its performance with expert-based clinical stratification. Methods: Retrospectively, we included 772 patients presenting with heart failure symptoms (mean age: 69±6 years, 56% female, mean ejection fraction: 61±5%, all >50%). They were followed for a median of 3.9 years for occurrences of death and hospitalization due to heart failure (HF). A script incorporating 12 variables (see Figure 1) was generated and submitted to the ChatGPT website, utilizing the returned score. Additionally, the H2FPEF score was computed as per guidelines. We then compared the predictive capabilities of both models for outcomes. Results: During follow-up, 17 patients died, 52 were hospitalized, and 67 experienced the combined outcome. The average ChatGPT score stood at 6.1±1.7, whereas the mean H2FPEF score was 3.1±1.5, exhibiting a modest correlation (r=0.51, p<0.001). Receiver-operator characteristic curve analysis suggested thresholds of ChatGPT of 6 and H2FPEF of 3 [AE1] for predicting the combined outcome, with comparable accuracy (AUC: 0.71 vs 0.72, all p<0.001). Both models similarly accounted for patients' comorbidities, exercise capacity, and baseline and post-exercise diastolic function. Survival curves illustrated the discriminative power of both H2FPEF and ChatGPT scores in predicting death, HF hospitalization, and the combined outcome. While the agreement between the two classifications was modest (Kappa 0.4, p=0.032), ChatGPT facilitated the reclassification of high-risk patients identified by H2FPEF. Conclusions: Open-source large language models such as ChatGPT can contribute to existing methods for predicting cardiac risk, offering the potential for significant cost and expertise savings. Future research endeavors should explore broader applications in diagnosis and management, always prioritizing rigorous ethical and equitable considerations. Circulation00This study evaluated the efficacy of online open-source generative pre-trained transformers (ChatGPT) in predicting cardiovascular risk in patients with heart failure and preserved ejection fraction, comparing its performance with expert-based clinical stratification.Circulation["Marinela Veshtaj", "A. Omar", "L. Alam", "G. Kim", "Sean Pinney", "Edgar Argulian"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/c48a95de6432fb86ec973296be486f1c689fd74b c57bc42194884cc78bba95dacc6fe743a9209665PurposeThe purpose of this study is to investigate the various challenges of humanitarian supply chains (HSC) and how these challenges can be addressed using artificial intelligence (AI).Design/methodology/approachThis study employs exploratory analysis to identify various issues in HSC and the use cases of AI to address these issues through published literature. Subsequently, we collected tweets from Twitter and posts from LinkedIn using relevant keywords over four months. The collected data were cleaned, analyzed and interpreted to gain insights into users' perspectives on the various issues and use cases of AI in HSC.FindingsThe analysis reveals that various issues of HSC such as logistical challenges, security concerns, health and safety, access constraints, information gaps, coordination and collaboration, cultural sensitivity, funding constraints, climate and environmental factors and ethical dilemmas are predominantly discussed in published literature. Meanwhile, user-generated content reveals different levels of prioritization of these issues and AI attributes and offers AI-based solutions.Research limitations/implicationsThis study is subject to certain limitations, including a restricted data collection period of only four months and the use of just two social media platforms. These limitations could be addressed by conducting a more comprehensive and extended data collection across additional platforms to produce more conclusive findings. Another limitation is the lack of contextual information, which may have provided more specific insights.Originality/valueTo the best of the authors’ knowledge, this is possibly the first paper to explore both published literature and the collective intelligence of social media users to examine AI attributes, the various challenges of HSC and how AI can address these challenges.Benchmarking : An International Journal700The analysis reveals that various issues of HSC such as logistical challenges, security concerns, health and safety, access constraints, information gaps, coordination and collaboration, cultural sensitivity, funding constraints, climate and environmental factors and ethical dilemmas are predominantly discussed in published literature.Benchmarking: An International Journal["S. Shrivastav", "Amit Sareen"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/c57bc42194884cc78bba95dacc6fe743a9209665 0047bd35e513719e307e8d1eb5470a27299a6e4aIntegrating the Coding & Robotics (C&R) subject in South African schools signifies the nation's commitment to Fourth Industrial Revolution preparedness. However, challenges like inadequate teacher preparation and limited technological infrastructure must be addressed to ensure equity. Although Generative Artificial Intelligence (GenAI) may not address the infrastructural deficiencies directly, in this scoping review we examine its potential to complement existing resources and support teachers in delivering C&R instruction. Following Arksey and O'Malley's framework, we conducted a systematic literature search in numerous databases, followed by a screening procedure: 10 of the 61 eligible papers satisfied the inclusion criteria. Our findings reveal that GenAI can optimise C&R teacher development through personalised learning, content generation, feedback on teaching methods, and fostering collaboration with other teachers. Despite its potential, issues including equity, ethical concerns, technological fluency gaps, and overreliance on GenAI tools, must be navigated to enhance equitable C&R instruction and prepare learners for the digital era.Journal of education00GenAI can optimise C&R teacher development through personalised learning, content generation, feedback on teaching methods, and fostering collaboration with other teachers, according to the findings.Journal of Education["Mashite Tshidi", "Alton Dewa"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/0047bd35e513719e307e8d1eb5470a27299a6e4a aa458a8031318582d2e0e083afbac00fc1c9bcd8This study investigates the use of artificial intelligence (AI) in education and the adoption of smart transport technology in Oman, with the goal to better understand these developing phenomena and their societal ramifications. Data from educators and residents were collected using a mixed-methods approach, which included questionnaires and interviews, to study perceptions, attitudes, and behaviors linked to AI integration and smart transportation adoption. Key findings show AI's potential to personalize learning experiences in education, while raising ethical concerns and practical obstacles. Similarly, the study reveals variable levels of adoption and use of smart transportation technology, driven by issues such as usability, privacy concerns, and environmental considerations. Building on these findings, tangible recommendations are made to encourage ethical AI use in education and facilitate the widespread adoption of smart transport solutions. By tackling these obstacles and capitalizing on possibilities, Oman can realize the revolutionary potential of AI and smart transportation to promote inclusive, sustainable, and resilient societies, knowledge gaps. The evaluation additionally seeks to explain the reasoning and driving forces behind the suggested study, offering a feeling of direction, applicability, and importance. The goals and lines of inquiry of the review direct the course of the research project towards significant and productive results. All things considered, this study advances scholarly research and improves useful interventions in these important fields.International Conference on Compute and Data Analysis90Key findings show AI's potential to personalize learning experiences in education, while raising ethical concerns and practical obstacles, and tangible recommendations are made to encourage ethical AI use in education and facilitate the widespread adoption of smart transport solutions.2024 2nd International Conference on Computing and Data Analytics (ICCDA)["Hasna Mohammed Al Salmi", "DivyaJyothi M.G.", "Rachappa Jopate", "Rawan Majid Al Ghafri", "Maram Sulaiman Al Abri"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/aa458a8031318582d2e0e083afbac00fc1c9bcd8 dbb9f86ebe9f35793bdab8e9472f2fed3693b391This research investigates the potential of Artificial Intelligence (AI) to improve educational resourcing in Oman. This research addresses the prevalent issues of inefficiency and inequity in Omani educational resourcing, by proposing hypothesis that AI integration can lead to more efficient resource allocation and enhanced educational outcomes. The study aims to explore this hypothesis through a comprehensive review of existing literature. The ultimate goal is to empower individuals, enhance educational opportunities, and promote innovation and growth within Oman. Literature review provide us a comprehensive understanding of the current state of research area and identifies gaps and opportunities, to enhance our research and contribute to the advancement of knowledge in the field. Analysis of Variance or ANOVA resulted in accepting the null hypothesis. This indicates no statistically significant differences in agreement levels between groups regarding AI and educational resourcing. However, the overall research suggests potential benefits of AI in this area. Future studies could explore implementation strategies, user needs, and the impact of AI on specific educational outcomes in Oman, while also considering the country's unique cultural context and educational needs.International Conference on Compute and Data Analysis130No statistically significant differences in agreement levels between groups regarding AI and educational resourcing are indicated, and the overall research suggests potential benefits of AI in this area.2024 2nd International Conference on Computing and Data Analytics (ICCDA)["Junath Naseer Ahamed", "Jaber Saleh Al Jabri", "Saif Awadh Al Saadi"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/dbb9f86ebe9f35793bdab8e9472f2fed3693b391 698684211b5aae578bf17603a51caa2b4583e109Problems related to decision-making in emergency situations are noted. Decision-making entities are all stakeholders involved in the situation, such as administrative authorities, specialized services, and the population. It is proposed to use the tools of system analysis, artificial intelligence and cognitive graphics to increase the level of situational awareness. Using the example of flood analysis in 2024 In two cities of the Russian Federation, the author's method is shown, which consists in placing a group of multifactorial cognitive images in a system of external coordinates reflecting the most important characteristics of the situation.2024 Dynamics of Systems, Mechanisms and Machines (Dynamics)40It is proposed to use the tools of system analysis, artificial intelligence and cognitive graphics to increase the level of situational awareness in emergency situations using the example of flood analysis in 2024 in two cities of the Russian Federation.2024 Dynamics of Systems, Mechanisms and Machines (Dynamics)["V. Filimonov"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/698684211b5aae578bf17603a51caa2b4583e109 d83b8aa235a1f922d5cdb551d6264b5ad571bc44Generative artificial intelligence (AI) technologies are becoming integral to academic and professional landscapes, with universities rapidly developing policies that govern ethical and effective usage. Yet such efforts are fragmented across institutions, from outright blanket bans to bespoke frameworks supporting AI application. Seeking to offer evidence of this fragmented approach, this study conducts a systematic content analysis of AI policies of UK Russell Group universities, with specific focus on learning and teaching. The analysis reveals differences in policy comprehensiveness, enforcement mechanisms, and educational initiatives, demonstrating varied institutional priorities and approaches. This includes widespread methods of integrating the technology within the learning experience or academic integrity governance strategies. Findings also indicate that while some universities have robust frameworks promoting AI literacy and ethical usage, others provide minimal guidelines, reflecting disparate levels of readiness and commitment to integrating AI into the curriculum. This study underscores the importance of clear, comprehensive policies in fostering equal access and ethical use of AI among students whilst supporting AI literacy. Recommendations include adopting uniform policy elements across institutions to standardise AI usage norms and enhance student preparedness for an AI-driven future. This research contributes to the discourse on educational policy development, emphasising the need for adaptive and forward-thinking strategies in higher education to address AI learning requirements.Enhancing Teaching and Learning in Higher Education210The importance of clear, comprehensive policies in fostering equal access and ethical use of AI among students whilst supporting AI literacy is underscores, emphasising the need for adaptive and forward-thinking strategies in higher education to address AI learning requirements.Enhancing Teaching and Learning in Higher Education["Aarron Atkinson-Toal", "Catherine Guo"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/d83b8aa235a1f922d5cdb551d6264b5ad571bc44 45debd5c5691aff4bac5034b3075134e56d74244Objective: The purpose of the study was to clarify, through the lenses of experts and frontline publishers, ethical dilemmas related to the use of artificial intelligence (AI) in research writing. Method: We conducted a rapid review of expert opinions and publishers’ policy statements on ethical considerations in using AI for research writing. We included articles published in journals indexed by academic databases that met the criteria. We also included the policy statements and guidelines of seven reputable publishers. Result: The use of AI in scientific writing is acceptable, contingent on addressing ethical considerations bordering on plagiarism, transparency, and disclosure. While AI should not be listed as an author or co-author on its own, its use in the development of the work deserves acknowledgment. Authors must substantially rephrase AI-generated content in their own words, properly citing sources to avoid claims of plagiarism. Transparency regarding AI usage and oversight of AI-generated drafts are necessary, as there are risks related to inaccuracy and bias if AI is not supervised by human experts. Conclusion: AI can be deployed to support research writing, provided users carefully abide by ethical standards that uphold academic integrity. Implications: The findings offer valuable guidance for researchers, students, and emerging publishers on how AI’s capabilities can be ethically and responsibly leveraged in academic writing. By establishing clear principles, the study equips these stakeholders with the means to incorporate AI judiciously into their knowledge production practices.Higher Learning Research Communications00The use of AI in scientific writing is acceptable, contingent on addressing ethical considerations bordering on plagiarism, transparency, and disclosure, provided users carefully abide by ethical standards that uphold academic integrity.Higher Learning Research Communications["Ndubuisi Friday Ugwu", "A. S. Igbinlade", "Raphael E. Ochiaka", "Ugoma Deborah Ezeani", "N. C. Okorie", "J. K. Opele", "T. Onayinka", "Obinna Iroegbu", "O. Onyekwere", "Adijat Bolanle Adams", "Precious Aigbona", "F. B. Ojobola"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/45debd5c5691aff4bac5034b3075134e56d74244 7d3b7287f50905fe685f05c9f94b7d58046d0d49The impact of artificial intelligence-driven process automation on digital nomads' work-life balance was studied using a mixed-methods approach that combines quantitative and qualitative evidence to better understand the topic. The research was done using online surveys and social media questionnaires. Survey data from a diverse group of remote workers reveals that AI solutions, mainly employed for task automation and efficiency, have significantly improved respondents' ability to meet deadlines and maintain a healthy work-life balance. Nevertheless, the report highlights increasing concerns about job displacement as AI technology advances. Researchers used open-source statistics software for quantitative analysis of the data collected. They utilized ANDVA to measure the impact of artificial intelligence automation on digital nomads' life and work balance. Most importantly, results show that although AI can improve productivity and adaptability, it also introduces new skill development and adjustment issues. As AI-related responsibilities increase, digital nomads must develop new skills to sustain competitiveness and relevance in the changing labor market. The collected data underscores the essential need for ongoing study and aggressive measures to reduce the possible negative impacts of AI automation on the digital nomad workforce.International Conference on Compute and Data Analysis140Results show that although AI can improve productivity and adaptability, it also introduces new skill development and adjustment issues as AI-related responsibilities increase, and digital nomads must develop new skills to sustain competitiveness and relevance in the changing labor market.2024 2nd International Conference on Computing and Data Analytics (ICCDA)["Lyn M. Dalisaymo", "Myra M. Patalay"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/7d3b7287f50905fe685f05c9f94b7d58046d0d49 5fcf265a33431e7a3fbf778851a02a2882bfa179Artificial Intelligence for IT Operations (AIOps) signifies a radical change in managing and controlling complex IT frameworks. Operating together with artificial intelligence and machine learning technologies, AIOps platforms improve incident management, predictive modeling, root cause analysis, and the entire operation processes [1]–[4]. Addressing these existing problems becomes highly important as the enhancement of IT infrastructures continues to escalate due to the increasing adoption of hybrid and multi-cloud technologies [5]. Most importantly, AIOps helps organizations realize their goals, such as detecting abnormal behavior in a system in realtime, eliminating or automating repetitive processes, estimating the probability of system breakdown, and making the best use of available resources to enhance service delivery while minimizing operational expenses [6] [7]. AIOps also assists organizations in managing probable problems in the best possible manner to maintain both effectiveness and flexibility. This paper discusses the primary purposes of AIOps, how it is set to change the practice of IT operations, the problems encountered when applying this system, and the tactical approaches needed to accomplish this plan effectively. Using practical examples and looking into the horizon, we stress the significance of AIOps application for the development of the IT ecosystem across various sectors in the near future [8].International Conference on Compute and Data Analysis240The primary purposes of AIOps, how it is set to change the practice of IT operations, the problems encountered when applying this system, and the tactical approaches needed to accomplish this plan effectively are discussed.2024 2nd International Conference on Computing and Data Analytics (ICCDA)["Ali Al Hinai", "Mahmood Al Mazroui"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/5fcf265a33431e7a3fbf778851a02a2882bfa179 ca37cc0d6c756ddb56f850679f636d6c5e3f6054This research is focused on the gap created in the perception and expectations of Kosovar students in the implementation of artificial intelligence during their learning and education. The research was conducted with students who had attended training on Artificial Intelligence that to identify the gap between their perception and expectations regarding artificial intelligence. The aim of this study is to investigate the understanding gap between perceptions and expectations of artificial intelligence and to provide answers to the research questions posed in this paper. Obejectiv of the study is to shed light on the need for Sustainable Development Goal 4, Quality Education, by emphasizing the importance of inclusive and equitable AI literacy, and also for Development Goal 9, Industry, Innovation, and Infrastructure, by fostering innovation and supporting the development of resilient and sustainable AI infrastructure. During the research we used purposive sampling since we already had access to the database showing which students had attended the training and were certified for artificial intelligence by Microsoft. Bazed on the use of adaptive learning technology, wich is one of the ways Artificial Intelligence can have an impact on student psychology, and based on each student's performance, interests and learning style, we concluded this technology tailors the learning experience to them, and learning is more effective and efficient, and motivation and engagement also increase more.Edelweiss Applied Science and Technology00This research focused on the gap created in the perception and expectations of Kosovar students in the implementation of artificial intelligence during their learning and education and concluded this technology tailors the learning experience to them, and learning is more effective and efficient, and motivation and engagement also increase more.Edelweiss Applied Science and Technology["Besnik Skenderi", "Safet Zejnullahu", "Diamanta Skenderi", "Ferid Selimi"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/ca37cc0d6c756ddb56f850679f636d6c5e3f6054 6d128e070b7466d0894fde735e27f17aa848ad14This study systematically examines the transformative role of Artificial Intelligence (AI) in decision-making, focusing on its applications, challenges, and future opportunities. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 100 peer-reviewed articles were analyzed to ensure a rigorous and comprehensive understanding of the subject. The findings highlight AI's ability to optimize decision-making processes through advanced technologies such as machine learning, natural language processing, and predictive analytics, significantly enhancing accuracy, efficiency, and responsiveness across diverse sectors such as healthcare, finance, supply chain management, and public administration. Despite these advancements, the study identifies persistent challenges, including algorithmic bias, data privacy concerns, and the lack of transparency in "black box" AI models, which undermine trust and accountability. Additionally, the review uncovers research gaps, particularly in low-resource settings and emerging markets, where AI's potential remains underutilized due to infrastructural and data limitations. The integration of AI with emerging technologies, such as blockchain, quantum computing, and edge computing, presents promising opportunities to enhance scalability, security, and transparency in decision-making. The study also underscores the importance of interdisciplinary research, particularly at the intersection of AI and social sciences, to better understand human-AI interaction and foster ethical and socially equitable AI adoption. By addressing these challenges and leveraging emerging opportunities, AI can evolve into a transformative tool for informed, inclusive, and responsible decision-making in an increasingly complex world.Non human journal00The findings highlight AI's ability to optimize decision-making processes through advanced technologies such as machine learning, natural language processing, and predictive analytics, significantly enhancing accuracy, efficiency, and responsiveness across diverse sectors such as healthcare, finance, supply chain management, and public administration.Non human journal["Rebeka Sultana"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/6d128e070b7466d0894fde735e27f17aa848ad14 96a2dfaf5e0ace6a0c11243559bba0f43207b2ddIntroduction: The globalisation of the economy, dynamic changes in financial markets, and the advent of big data have spurred the development and implementation of artificial intelligence (AI) and machine learning (ML) tools for forecasting economic trends. The purpose of this study is to evaluate the impact of AI and ML on the accuracy and effectiveness of economic trend forecasting. The authors analyse examples of AI and ML applications in various economic sectors during the period 2019–2023, including regional aspects. Methods: To achieve the objectives of this study, we conducted a comprehensive qualitative and quantitative analysis of the role of artificial intelligence (AI) and machine learning (ML) in predicting economic trends. Results: The findings indicate that the use of AI and ML improves the efficiency of economic trend forecasting and allows for quicker adaptation to market changes, thereby reducing risks and uncertainty. Conclusions: Thus, the integration of artificial intelligence and machine learning in economic analysis not only increases the effectiveness of forecasting but also lays the foundations for the sustainable development of economies in a globalised world.Data and Metadata200The findings indicate that the use of AI and ML improves the efficiency of economic trend forecasting and allows for quicker adaptation to market changes, thereby reducing risks and uncertainty.Data and Metadata["Svitlana Marushchak", "Iryna Fadyeyeva", "P. Halachev", "Nursultan Zharkenov", "S. Pakhomov"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/96a2dfaf5e0ace6a0c11243559bba0f43207b2dd c0e3ea757a9d5cf4e32d594c9bcf88d39c8e77a8Artificial Intelligence (AI) has brought transformative changes to education, particularly in the field of physics, where complex concepts often pose significant challenges for students. This narrative review explores the role of AI in physics education by analyzing various tools and methods currently applied in learning environments, including intelligent tutoring systems, adaptive learning platforms, and interactive simulations. The study aims to assess the benefits and limitations of these technologies, as well as their potential to enhance learning outcomes through personalized, adaptive, and interactive experiences. Utilizing the SCOPUS database, a wide-ranging literature search was conducted with relevant keywords to capture studies that contribute to understanding AI’s impact on physics education. Results indicate that AI-driven tools significantly improve student engagement, accessibility, and understanding of abstract concepts by offering tailored learning pathways, real-time feedback, and immersive simulations. Additionally, AI provides alternative access to learning for students from diverse backgrounds, fostering inclusivity in physics education. However, challenges such as dependency on AI, ethical issues related to data security, and the potential digital divide are noted as barriers to effective implementation. To address these issues, the review recommends a balanced approach where AI complements traditional teaching methods, ensuring that it enhances rather than replaces human instruction. This review highlights the transformative potential of AI in physics education, advocating for further research to develop structured, ethical, and inclusive integration strategies that maximize the educational benefits of AI while addressing its limitations.Lensa: Jurnal Kependidikan Fisika00Results indicate that AI-driven tools significantly improve student engagement, accessibility, and understanding of abstract concepts by offering tailored learning pathways, real-time feedback, and immersive simulations.Lensa: Jurnal Kependidikan Fisika["N. Verawati", "Nina Nisrina"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/c0e3ea757a9d5cf4e32d594c9bcf88d39c8e77a8 92cb8087d0437e12ea2b8f1d66345112da102c66Higher Education200The findings reveal that the impact mission has become central to understanding the motivations and values of academics, but unevenly, and highlights the importance of flexible approaches to research policy and governance that are based on a deeper understanding of what motivates researchers, and that take into account academics’ educational role.Higher Education["Eliel Cohen", "Kate Williams", "Jonathan Grant"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/92cb8087d0437e12ea2b8f1d66345112da102c66 2b90b7637b36f72942d121fc1b2fc17531d0a91bToday, with the advance of artificial intelligence, companies in the real world are using AI as managers to make operational decisions, who can respond quickly to market shocks and whose logic can be fine‐tuned to programmed pessimism/optimism, that is, underestimating/overestimating the market. The introduction of AI managers poses new challenges to supply chain management, and how to manage AI managers warrants further exploration. We investigate the optimal AI manager fine‐tuning strategies in a supply chain consisting of one manufacturer and two competing retailers, each operated by an AI manager in the face of an uncertain market shock. We establish the manufacturer–retailer AI manager fine‐tuning game, where the manufacturer and two retailers endogenously decide whether to fine‐tune their AI managers' logic. The market may suffer an uncertain shock, and once the shock occurs, the AI managers' logic settings and price decisions can be quickly adjusted. We find that the manufacturer would never fine‐tune the AI manager, while the retailers may fine‐tune their AI managers to programmed optimism. Notably, AI manager's fine‐tunability only benefits the retailers and harms the manufacturer, entire supply chain, consumers, and social welfare. To make AI manager's fine‐tunability beneficial to all participants, that is, to reach a win–win–win situation, we design two incentive mechanisms, retailer pessimism incentive mechanism and mutual pessimism incentive mechanism (MPIM), where MPIM can lead to the win–win–win situation. Further, we endogenize the compensation, endogenous retailer pessimism compensation and endogenous mutual pessimism compensation, both achieving the win–win–win outcome. We also make several extensions and provide suggestions for supply chain firms to fine‐tune their AI managers' logic.Decision Sciences350This work investigates the optimal AI manager fine‐tuning strategies in a supply chain consisting of one manufacturer and two competing retailers, each operated by an AI manager in the face of an uncertain market shock, and designs two incentive mechanisms, retailer pessimism incentive mechanism and mutual pessimism incentive mechanism, where MPIM can lead to the win–win–win situation.Decis. Sci.["Yue Li", "Ruiqing Zhao", "Xiang Li", "Tsan\u2010Ming Choi"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/2b90b7637b36f72942d121fc1b2fc17531d0a91b 8aac85facf76a61a799e0217dcddc1cd71e1bcbfIntroduction Over the past few decades, numerous researchers have explored the application of machine learning for assessing children’s neurological development. Developmental changes in the brain could be utilized to gauge the alignment of its maturation status with the child’s chronological age. AI is trained to analyze changes in different modalities and estimate the brain age of subjects. Disparities between the predicted and chronological age can be viewed as a biomarker for a pathological condition. This literature review aims to illuminate research studies that have employed AI to predict children’s brain age. Methods The inclusion criteria for this study were predicting brain age via AI in healthy children up to 12 years. The search term was centered around the keywords “pediatric,” “artificial intelligence,” and “brain age” and was utilized in PubMed and IEEEXplore. The selected literature was then examined for information on data acquisition methods, the age range of the study population, pre-processing, methods and AI techniques utilized, the quality of the respective techniques, model explanation, and clinical applications. Results Fifty one publications from 2012 to 2024 were included in the analysis. The primary modality of data acquisition was MRI, followed by EEG. Structural and functional MRI-based studies commonly used publicly available datasets, while EEG-based studies typically relied on self-recruitment. Many studies utilized pre-processing pipelines provided by toolkit suites, particularly in MRI-based research. The most frequently used model type was kernel-based learning algorithms, followed by convolutional neural networks. Overall, prediction accuracy may improve when multiple acquisition modalities are used, but comparing studies is challenging. In EEG, the prediction error decreases as the number of electrodes increases. Approximately one-third of the studies used explainable artificial intelligence methods to explain the model and chosen parameters. However, there is a significant clinical translation gap as no study has tested their model in a clinical routine setting. Discussion Further research should test on external datasets and include low-quality routine images for MRI. T2-weighted MRI was underrepresented. Furthermore, different kernel types should be compared on the same dataset. Implementing modern model architectures, such as convolutional neural networks, should be the next step in EEG-based research studies.Frontiers Neuroinformatics930There is a significant clinical translation gap as no study has tested their model in a clinical routine setting, and prediction accuracy may improve when multiple acquisition modalities are used, but comparing studies is challenging.Frontiers in Neuroinformatics["Eric Dragendorf", "Eva B\u00fcltmann", "Dominik Wolff"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/8aac85facf76a61a799e0217dcddc1cd71e1bcbf 24f7f7a6b830713ac5f33fdf09fa29c1dded1dd0The protection of copyright should adhere to the position that civil and criminal law are consistent, and that the same act cannot establish an offence of copyright infringement under criminal law to the extent permitted by civil law. It is appropriate to interpret the term "reproduction and distribution" in the context of the offence of copyright infringement as reproduction, distribution, reproduction and distribution. Judicial practice survey proved that at this stage, artificial intelligence can only be used as a tool for the offence of copyright infringement, and cannot become the subject of this offence, without affecting the construction and determination of this offence. As the object of the offence of copyright infringement, when an AI-generated object satisfies the two elements of originality and intellectual achievement, it can be considered to be a work of authorship and be included in the scope of protection under the Copyright Law and the Criminal Law. The legal interests protected by the offence of copyright infringement are firstly the state's order of copyright administration and the economic order of the socialist market, behind which lies the implication of protecting the copyright owner's advantageous position in market competition. With regard to the attribution of copyright in AI works, the determination can be made by drawing on the binary subject structure that has matured and operated in copyright law.Journal of Higher Education Research230Judicial practice survey proved that at this stage, artificial intelligence can only be used as a tool for the offence of copyright infringement, and cannot become the subject of this offence, without affecting the construction and determination of this offence.Journal of Higher Education Research["Jin Li", "Haoyu Lin", "Mozheng Lin", "Yu'ang Zhang"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/24f7f7a6b830713ac5f33fdf09fa29c1dded1dd0 5bbdd78e11609558f02d88c59e945b050c0b889fThis research proposes a method to accurately forecast the risk of lung cancer in its initial stages by the combination of medical diagnosis and artificial intelligence. Two distinct models are employed: a symptoms-based model utilizing linear regression, and VGG19, a component of CNNs is employed for the analysis of chest x-ray images. The objective of this dual-model approach is to improve comprehensiveness and precision in the early prediction of lung cancer risk. The JSRT and Chest Xray-14 datasets were utilized to train and validate the convolutional neural network (CNN). The Regression model was trained using the Survey Lung cancer Dataset. This initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI. Initial positive results show that this combined approach has a lot of potential for early detectionInternational Conference on Compute and Data Analysis180Initial positive results show that this combined approach has a lot of potential for early detection and this initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI.2024 2nd International Conference on Computing and Data Analytics (ICCDA)["Syed Mehr Ali Shah", "Danish Jamil", "Muhammad Numan Ali Khan", "Fatma Mahfoodh Mohammed Al-Jarwani"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/5bbdd78e11609558f02d88c59e945b050c0b889f 715629dede46f5cd19fa518217e40af22b44e070In this analytical review, we explore the potential impact of the rapid proliferation of artificial intelligence (AI) tools on the biosphere and noosphere, suggesting that the trend may lead to a transformative event that could be termed “Human-AI integration.” We argue that this integration could give rise to novel lifeforms, associations, and hierarchies, resulting in competitive advantages and increased complexity of structural organizations within both the biosphere and noosphere. Our central premise emphasizes the importance of human-AI integration as a global adaptive response crucial for our civilization’s survival amidst a rapidly changing environment. The convergence may initially manifest itself through symbiotic, endosymbiotic, or other mutualistic relationships, such as domestication, contingent on the rate at which AI systems achieve autonomy and develop survival instincts akin to those of biological organisms. We investigate potential drivers of these scenarios, addressing the ethical and existential challenges arising from the AI-driven transformation of the biosphere and noosphere, and considering potential trade-offs. Additionally, we discuss the application of complexity and the balance between competition and cooperation to better comprehend and navigate these transformative scenarios.Acta Naturae940This analytical review suggests that the rapid proliferation of artificial intelligence (AI) tools may lead to a transformative event that could be termed “Human-AI integration,” which could give rise to novel lifeforms, associations, and hierarchies, resulting in competitive advantages and increased complexity of structural organizations within both the biosphere and noosphere.Acta Naturae["B. Zybailov", "G. Kosovsky", "G. V. Glazko", "V. I. Glazko", "O. I. Skobel"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/715629dede46f5cd19fa518217e40af22b44e070 9495cc01467a0c9df1080b6c86e7a4cabb9082ddThe Law Teacher03The Law Teacher["A. Mazhar"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/9495cc01467a0c9df1080b6c86e7a4cabb9082dd 937a7909e9f744a53186f008ca14d00218553c7aInternational Journal of Computational Intelligence Systems1191Int. J. Comput. Intell. Syst.["G\u00fclay Demir", "Sarbast Moslem", "S. Duleba"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/937a7909e9f744a53186f008ca14d00218553c7a 6d6bf300cc25ef9e2bbc85be3cc9be046bc30343Cogent Education371Cogent Education["Monica Vimbai Chatikobo", "Notice Pasipamire"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/6d6bf300cc25ef9e2bbc85be3cc9be046bc30343 73ae9d0a438f2ad4a4dc9e22f7a93f1c1bfadbcc Brains and microprocessors, while seemingly distinct, share a profound complexity that challenges our understanding of both. In "Could a Neuroscientist Understand a Microprocessor?" by Eric Jonas and Konrad Paul Kording, this complexity is explored through a comparative analysis, raising questions about knowledge, complexity, and the relationship between humans and machines. The authors challenge the common analogy of brains as nature's computers, highlighting the struggle neuroscientists face in comprehending systems like microprocessors, despite their similar composition of billions of interconnected components. Jonas and Kording argue that the limitations of current neuroscience frameworks hinder a deep understanding of brain function, suggesting that methodologies from computer science could enhance the field. Their critique extends to fundamental philosophical debates about consciousness and intelligence, questioning what the difficulties in understanding complex systems reveal about the nature of knowledge. This essay dissects these limitations and their impact on our understanding of brain processes, advocating for an interdisciplinary approach to intelligence that encompasses both biological and artificial systems. By embracing the parallels between brains and microprocessors, we can advance a more comprehensive understanding of both.  Pittsburgh undergraduate review00It is argued that the limitations of current neuroscience frameworks hinder a deep understanding of brain function, suggesting that methodologies from computer science could enhance the field, and advocating for an interdisciplinary approach to intelligence that encompasses both biological and artificial systems.Pittsburgh Undergraduate Review["Aditi Choudhary"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/73ae9d0a438f2ad4a4dc9e22f7a93f1c1bfadbcc 00ebcc4b76e13bc3142331dbf3231be5e5e13576 INTRODUCTION: Current ACS guidelines suggest classifying patients according to the presence of persistent ST segment elevation, as a finding suggestive of acute thrombotic coronary occlusion. However, large series have documented that up to 15% of patients initially classified as NSTEMI will show evidence of total coronary occlusion on index angiography, increasing the length of stay, use of hospital resources, and short-term and long-term mortality. Therefore, prompt detection of Acute Coronary Occlusion Myocardial Infarction (ACOMI) is paramount. AIMS: We aimed to assess the performance of an AI-ECG based model capable of detecting ACOMI in the setting of patients with ACS. Methods: This is a prospective study based on the development of an AI-ECG based model capable of detecting ACOMI. A publicly available dataset (PTB-XL ECG) of 21,837 12-lead ECGs was used for training in recognizing ST-segment elevation. Regarding the detection of ACOMI, 12-lead ECGs from 361 patients who presented to the ED with an ACS (2017-2023) at our center were digitized with phone cameras of varying quality. ECGs were independently evaluated by two expert cardiologists blinded to clinical outcomes; each was asked to determine a) whether the patient had an STEMI, based on universal criteria or b) if STEMI criteria was not met, to identify any other ECG finding suggestive of ACOMI. ACOMI was defined as the presence of one of the following: TIMI V thrombus, TIMI thrombus grade 2 or higher + TIMI grade flow 1 or less, or the presence of a subocclusive (>90%) lesion. Patients were classified into four groups: STEMI + ACOMI, NSTEMI + ACOMI, STEMI + non-ACOMI and NSTEMI + non-ACOMI. Performance of the AI model was evaluated using a comparison of multiple areas under the receiver operating characteristic curve (AUC-ROC). Sensitivity, specificity, positive and negative (PPV, NPV) predictive values and F1-score were also calculated. Results: The AI model accomplished an AUC of 0.8667 in identifying ACOMI, outperforming ECG experts (AUC: 0.3333) and the use of universal STEMI criteria (AUC: 0.5095). It also accomplished a sensitivity of 1, specificity of 0.733, a PPV of 0.846, an NPV of 1 and an F1-score of 0.92. Conclusion: Our AI-ECG model demonstrated a higher diagnostic precision for the detection of ACOMI compared with experts and use of STEMI criteria. Further research and external validation is needed to understand the role of AI-based models in the setting of ACS. : Circulation00The authors' AI-ECG model demonstrated a higher diagnostic precision for the detection of ACOMI compared with experts and use of STEMI criteria, and is needed to understand the role of AI-based models in the setting of ACS.Circulation["Braiana Diaz", "Carlos Alan Castro Garc\u00eda", "Karen Gissel Velez Talavera", "Edgar Roman-Rangel", "Pilar Espinosa", "Santiago March", "Alexandra Arias", "Diego Araiza"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/00ebcc4b76e13bc3142331dbf3231be5e5e13576 59c07e1d7d1192395cd6507a44077e7cfe906fc2European Conference on Modelling and Simulation20{"pages": "29:1-29:2"}["Joyce Mahon", "Brett A. Becker", "Brian Mac Namee", "Juho Leinonen"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/59c07e1d7d1192395cd6507a44077e7cfe906fc2 9a42344321f83c8db39b9afa4e3462cb976ea4deInternational Conference on Optics, Electronics, and Communication Engineering (OECE 2024)00International Conference on Optics, Electronics, and Communication Engineering (OECE 2024)["Ruifeng Luo", "Jie Zheng"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/9a42344321f83c8db39b9afa4e3462cb976ea4de 1eaa331885201664c0126cc0d0c7eb093ad83904 Background: Coronary artery calcium (CAC) scans contain more actionable information than the Agatston CAC score. We have previously shown in the Multi-Ethnic Study of Atherosclerosis (MESA) that AI-enabled left atrial (LA) volumetry in CAC scans (AI-CAC) enabled prediction of atrial fibrillation (AF) as early as one year. Furthermore, we have recently shown adding AI-CAC LA volumetry to CHA 2 DS 2 -VASc risk score improved stroke prediction in MESA. In this study we evaluated the performance of AI-CAC LA volumetry versus LA measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting AF and stroke, and compared them with CHARGE-AF risk score, Agatston score, and NT-proBNP. Methods: We used 15-year outcomes data from 3552 asymptomatic individuals (52.2% women, age 61.7±10.2 years) who underwent both CAC scans and CMRI in the MESA baseline examination. We have applied the AutoChamber TM (HeartLung.AI, Houston, TX) component of AI-CAC to 3552 CAC scans. CMRI LA volume was previously measured by human experts. Data on NT-proBNP, CHARGE-AF risk score and the Agatston score were obtained from MESA. Discrimination was assessed using the time-dependent area under the curve (AUC). Results: Over 15 years follow-up, 562 cases of AF and 140 cases of stroke accrued. The AUC for 15-year AF prediction by AI-CAC LA volume (0.801) was comparable to CMRI LA volume (0.797) and significantly higher than Agatston CAC Score (0.687) and NT-proBNP (0.704). Similarly, the AUC for 15-year stroke prediction for AI-CAC volumetry (0.761) was comparable to CMRI volumetry (0.751) and significantly higher than NT-proBNP (0.631) and Agatston CAC Score (0.646). AI-CAC LA volume outperformed CHARGE AF over 1-3 years for incident AF (p<0.02), but not for subsequent years. AI-CAC significantly improved the continuous Net Reclassification Index (NRI) for prediction of AF and stroke when added to CHARGE-AF risk score (0.28, 0.21), NT-proBNP (0.43, 0.37), and Agatston score (0.69, 0.41) respectively (p for all<0.0001). Conclusion: LA volumetry measured by the AutoChamber component of AI-CAC and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years, and outperformed NT-proBNP and Agatston CAC Score. AI-CAC LA volumetry outperformed CHARGE-AF and NT-proBNP for short-term (1-3 years) AF prediction. Further studies to investigate the clinical utility of AI-CAC LA volumetry for AF and stroke prediction are warranted. Circulation00LA volumetry measured by the AutoChamber component of AI-CAC and CMRI LA volume measured by human experts similarly predicted incident AF and stroke over 15 years, and outperformed NT-proBNP and Agatston CAC Score.Circulation["M. Naghavi", "A. Reeves", "K. Atlas", "Chenyu Zhang", "T. Atlas", "C. Henschke", "Sion Roy", "M. Budoff", "D. Yankelevitz", "Nathan Wong"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/1eaa331885201664c0126cc0d0c7eb093ad83904 64b8c7a06da010fc600e6ec440d4cf33d5cac0ef Background: Accurate ECG interpretation is critical for triaging, diagnosis, and managing patients with cardiovascular conditions. Current computerized methods have limited accuracy and are often proprietary algorithms based on raw signal data. We sought to evaluate ECG-GPT, a novel vision-text transformer model capable of generating free-text diagnosis statements from images of ECGs. Methods: ECG-GPT was developed with 3 million ECGs at Yale and validated in 2 large and geographically distinct populations: (1) 1.4 million ECG images with diagnosis statements from Mount Sinai Health System (MSHS), NY, and (2) 45,389 ECG images with 6 diagnostic labels from UK Biobank (UKB). We employed 2 metrics: (A) a rule-based approach to evaluate diagnostic accuracy for 19 rhythm and conduction disorders on MSHS ECGs and 6 key conditions on UKB ECGs, and (B) semantic similarity (similar meaning) using a fine-tuned DistilBERT model. Using semantic similarity methods, we assessed the quality of generated text across the full range of ECG diagnoses by computing similarity both for all model-generated and reference statements and for subsets specific to ECGs flagged for each of the 19 extracted conditions. To establish baseline similarities, we also computed semantic similarities between random combinations of model-generated and reference statements within each of these sets. Results: The model performed well across multiple key labels, spanning rhythm and conduction disorders, including an AUROC of 0.92 for atrial fibrillation, 0.95 for left bundle branch block, and 0.90 for atrioventricular block (Table A). More importantly, in addition to the labels, ECG-GPT identified the full context of the diagnosis statements with allied conditions. It had a median pairwise cosine similarity of 0.86 (IQR 0.78-0.94), significantly greater than the median baseline similarity of 0.73 (IQR 0.66-0.79, p<0.001) (Table B). This separation between median pairwise and baseline similarity remained consistent across all 19 condition-specific subsets. Results were comparable in the UKB dataset, with AUROCs of 0.95, 0.92, and 0.95 for AF, ST, and SB, and AUROCs of 0.99, 0.99, and 0.95 for LBBB, RBBB, and AVb (Table C). Conclusion: Our model represents a scalable and accessible strategy for generating accurate, expert-level reports from photos of ECGs, validated across large, multinational datasets using both individual diagnostic labels and the comprehensiveness of the diagnostic statement. Circulation00The ECG-GPT model represents a scalable and accessible strategy for generating accurate, expert-level reports from photos of ECGs, validated across large, multinational datasets using both individual diagnostic labels and the comprehensiveness of the diagnostic statement.Circulation["A. Khunte", "V. Sangha", "E. Oikonomou", "L. Dhingra", "Arya Aminorroaya", "A. Coppi", "S. Vasisht Shankar", "Bobak Mortazavi", "Deepak Bhatt", "Harlan Krumholz", "Girish Nadkarni", "A. Vaid", "R. Khera"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/64b8c7a06da010fc600e6ec440d4cf33d5cac0ef d5ba2abfdfa080d7302da43a14e07931f1f465b0International Journal for Educational Integrity420International Journal for Educational Integrity["S. Eaton"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/d5ba2abfdfa080d7302da43a14e07931f1f465b0 e9b7bb1fb6b72d2c5d782c1cdf415be8d136e0ccPurposeThis study illustrates the conceptual framework that expands the knowledge of the fundamental components that describe how AI-driven servant leadership (SEL) influences the job resources (JR), work engagement (WE) and job performance (JP) of tourism and hospitality employees.Design/methodology/approachThe empirical study was conducted on a sample of 953 international tourism and hospitality employees who were selected via a purposive and snowball sampling approach in a cross-sectional survey. The analysis was performed using a partial least square-structural equation modeling.FindingsThe results of this study confirmed the positive impact of AI-driven SEL on employee JR with the boundary conditions of AI-driven SEL.Practical implicationsThis study finding assists tourism and hospitality practitioners in understanding that in the near future, AI will have a major effect on the nature of work, including the impact on leadership styles. Hence, AI-driven SEL holds both positive (through direct impact on JR) and negative (via boundary conditions) impacts on employees’ JP and ultimately organizational success. Accordingly, managers should employ AI-driven SEL to increase employees’ JR, and once employees achieve high WE, they should constrict AI-driven SEL boundary conditions and their influence between JR and WE and WE and JP.Originality/valueThis study offers a novel and original conceptual model that advances AI-driven social theory, SEL theory and job demands-resources (JD-R) theory by synthesizing, applying and generalizing gained knowledge in a methodical way.Journal of Hospitality and Tourism Insights920A novel and original conceptual model that advances AI-driven social theory, SEL theory and job demands-resources (JD-R) theory by synthesizing, applying and generalizing gained knowledge in a methodical way is offered.Journal of Hospitality and Tourism Insights["Aleksandar Radic", "Sonali Singh", "Nidhi Singh", "Antonio Ariza-montes", "Gary Calder", "Heesup Han"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/e9b7bb1fb6b72d2c5d782c1cdf415be8d136e0cc 59b1933fe7be586d4414fd093d7c5a32e669055cEuropean Conference on Modelling and Simulation140{"pages": "45:1-45:9"}["Nicole Ude", "Gia Minh Vo", "Nils Pancratz"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/59b1933fe7be586d4414fd093d7c5a32e669055c 3212390e1967c6a1138e60f06a1cef8020c288c3 Introduction: White-coat hypertension (WCH) and masked hypertension (MH) complicate accurate blood pressure (BP) monitoring. While ambulatory BP monitoring (ABPM) is effective, its high cost and limited availability are significant barriers. Hypothesis: We hypothesized that a machine learning (ML) model using clinical data from a single outpatient visit could accurately predict WCH and MH. Aims: This study aimed to develop and validate ML-based prediction models for WCH and MH using accessible clinical data to improve diagnostic efficiency and accessibility. Methods: We enrolled patients from two hypertension cohorts, after excluding those with incomplete data. Patients were classified by office BP and ABPM readings per American Heart Association guidelines. ML models, including Multi-layer Perceptron （MLP）, Support Vector Machine (SVM), and Tabular Prior-Data Fitted Network (Tab-PFN), were developed. Input parameters included demographic data (age, gender, height, weight, smoker), and office BP (OBP) and heart rate measurements. Principal Component Analysis (PCA), kernel PCA (kPCA), or t-distributed stochastic neighbor embedding (t-SNE) were used to improve class separability. Results: The study population comprised 1481 participants with a mean age of 47.6 years (SD 13.6), 65% of whom were male and 20.1% were smokers. OBP measurements showed a mean systolic BP (SBP) of 128.7 mmHg (SD 15.4) and a mean diastolic BP (DBP) of 84.2 mmHg (SD 11.6). ABPM showed a mean 24-hour systolic BP of 122.5 mmHg (SD 11.8) and diastolic BP of 79.3 mmHg (SD 10.1). The inclusion of demographic and OBP data, along with advanced resampling and dimensionality reduction techniques, significantly improved the model’s predictive ability. The final TabPFN model achieved the best performance with recall, precision, F1 score, and accuracy of 0.747, 0.931, 0.829, and 0.807 for WCH, and 0.713, 0.954, 0.816, and 0.907 for MH. Conclusion: Our ML-based model effectively predicts WCH and MH using accessible clinical data, offering a cost-effective alternative before applying ABPM. Circulation00The inclusion of demographic and OBP data, along with advanced resampling and dimensionality reduction techniques, significantly improved the model’s predictive ability.Circulation["Ming-Hui Hung", "Chun-Hung Chen", "Yu-Hsuan Tseng", "Chin-Chou Huang"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/3212390e1967c6a1138e60f06a1cef8020c288c3 9d37fc33db67412923c519677a80e775b62b334b Background: Coronary artery segmentation, Lesion Identification and Measurement (CASLIM) on XRA images on X-ray angiography (XRA) are performed by cardiologists. Aims: The study, CASLIM aims to develop an end-to-end deep learning framework with integrated conventional image processing methods that accurately performs those tasks on XRA images. Methods: In depth analysis and comparisons of the deep learning models UNet, UNet+, Attention UNet, Trans UNet, SwinUNet, and UNet3+ comprising of the training curve and validation curve plotted against the epochs were carried out. The testing scores are obtained from the results of trained models on around 80 full size images. The data is presented for validity of the UNet3+ model as compared to the others. The performance of the proposed CASLIM analysis starts with the training results of the UNet3+ and other models for the coronary pixel segmentation. For UNet3+, patch sizes of 128*128 and 244*244 are studied to define the effect of patch sizes on performance. The model is trained on XRA images for accurate segmentation. (Image-1) Multiple image processing techniques are developed for lesion assessment. A dataset for training, validation and testing of the model is prepared by manual pixel annotation. Image processing algorithms are innovated to develop algorithms for catheter detection, its width measurement; subtract small arteries; thinning and pixel width measurement on binary mask images of arteries; detect, locate and quantify lesions. (Images-2,3) Experiments evaluated this method by comparing the lesion measurement results with the manually performed ones. Results: The dice score, 0.989; Structural Similarity Index Measurement (SSIM), 0.888; SSIM loss, 0.809 and Intersection Over Union, 0.979 of UNet3+ show its excellent performance and proximity to the ground truth in segmentation; overcoming the issues of intensity and position variations, noise and overlapping structures. The numerical testing performance report shows that the UNet3+ with a patch size of 128*128 performs the best. The lesion measurement by CASLIM shows a mean square error (MSE) value- 28.66 and an R squared (R2) value- 0.81 as compared to the manual process. The MSE- 69.91 and R2- 0.99 are obtained with the proposed method for lesion localization as compared to the manual process. Conclusion: The UNet3+ model has exceptional accuracy for precise segmentation. The CASLIM presents a promising solution for automated coronary lesion assessment accurately. Circulation00The UNet3+ model has exceptional accuracy for precise segmentation in segmentation and the CASLIM presents a promising solution for automated coronary lesion assessment accurately.Circulation["Abhishek Raval", "Karan Padariya", "Pranay Soni", "Harsh Kapadiya", "Niraj Raghvani"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/9d37fc33db67412923c519677a80e775b62b334b cbf1857243bd221120832f1c420376746406d08aTransportation planning and technology (Print)220Transportation Planning and Technology["Paul M. Salmon"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/cbf1857243bd221120832f1c420376746406d08a 05e0278d88ec91a817299c32e43a547027f772cdComputer Science and Engineering00Computer Science and Engineering["Ravi Kumar", "Dinesh Kumar", "Ahmad Saeed", "Chandra Jaiswal"]2024-11-12T00:00:00https://www.semanticscholar.org/paper/05e0278d88ec91a817299c32e43a547027f772cd 0bffa9f564b75aa80421f639c6ca4723ffef896cAnnals of Surgical Oncology41The article calls for a shift away from AI detection toward evaluating the quality of ideas, urging stronger peer review and clear guidelines for the role of AI in academic writing.Annals of surgical oncology["Himel Mondal"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/0bffa9f564b75aa80421f639c6ca4723ffef896c d70f2cd0f128d3cf8649aa5472960716f3f87090In this article, we identify challenges in the complex interaction between artificial intelligence (AI) systems and society. We argue that AI systems need to be studied in their socio-political context to be able to better appreciate a diverse set of potential outcomes that emerge from long-term feedback between technological development, inequalities and collective decision-making processes. This means that assessing the risks from the deployment of any specific technology presents unique challenges. We propose that risk assessments concerning AI systems should incorporate a complex systems perspective, with adequate models that can represent short- and long-term effects and feedback, along with an emphasis on increasing public engagement and participation in the process. This article is part of the theme issue ‘Co-creating the future: participatory cities and digital governance’.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences1271It is proposed that risk assessments concerning AI systems should incorporate a complex systems perspective, with adequate models that can represent short- and long-term effects and feedback, along with an emphasis on increasing public engagement and participation in the process.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences["D\u00e1niel Kondor", "Valerie Hafez", "Sudhang Shankar", "Rania Wazir", "Fariba Karimi"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/d70f2cd0f128d3cf8649aa5472960716f3f87090 f323242b5b824e232226e0ed002f8a0aab890c68Chatbots have been incredibly popular in last ten years, particularly after COVID-19 pandemic. The majority of companies in India have expedited their digital transition and now use chatbots as their main means of consumer interaction. Nevertheless, a lot of these chatbots don't allow users to enter voice commands. This study looks into the advantages and difficulties of using voice-activated chatbots powered by artificial intelligence (AI) in India several enterprises and how this affects customer experience. Information was gathered using qualitative research methods, and the findings show that adding vocal input and sentiment assessment capabilities to AI chatbots may improve user experience by increasing convenience and efficiency. The study also discovered that, although they could lessen the requirement for human representatives, AI chatbots can eventually save firms money and time. However, they won't completely replace human agents. Lastly, recommendations and an implementation methodology are offered to companies interested in using AI delegating voice chatbots for interaction with consumers.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)320The findings show that adding vocal input and sentiment assessment capabilities to AI chatbots may improve user experience by increasing convenience and efficiency and, although they could lessen the requirement for human representatives, AI chatbots can eventually save firms money and time.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["Ashish Kumar Verma", "Tiyas Sarkar", "Manik Rakhra", "Vikas Kumar Pandey", "Projjal Chakrabarty"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/f323242b5b824e232226e0ed002f8a0aab890c68 a65e2756fd4e2602c9a9de6076b984354a809c8dRecently, various risks have been pointed out in artificial intelligence (AI) systems. In particular, AI security, AI ethics, and AI quality are considerable risks. To make AI systems trustworthy against these risks, risk assessment technology is needed to identify potential AI risks and decide which risks should be dealt with in priority. We propose a risk assessment technology that assesses three kinds of risks—AI security, AI ethics, and AI quality—which have been considered separately. Our technology follows the ISO 31000 framework, consisting of four phases: risk candidate identification, impact assessment, likelihood assessment, and priority derivation for countermeasures. To realize this technology, risk candidate identification is achieved by extending AI ethics impact assessment— an identification method of AI ethics risk—to AI security and AI quality. Impact and likelihood assessments are conducted by extending assessment methods for AI security to AI ethics and AI quality. We conducted a case study using our technology and confirmed that the risks were appropriately extracted, and the priority of the risks to be dealt with was derived.Pacific Rim International Symposium on Dependable Computing100A risk assessment technology that assesses three kinds of risks—AI security, AI ethics, and AI quality—which have been considered separately is proposed, and the priority of the risks to be dealt with was derived.2024 IEEE 29th Pacific Rim International Symposium on Dependable Computing (PRDC)["Jun Yajima", "Satoko Shiga", "Kyoko Ohashi", "Masaru Ide", "Hiroshi Tanaka", "Sachiko Onodera"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/a65e2756fd4e2602c9a9de6076b984354a809c8d c1fd4003c10a2cd80caa3413cb19b519832c26f5A virus that can attack a human's liver and cause serious liver damage is primarily known as Hepatitis B Virus or HVB. It influences nearly all functionalities performed by the liver. It is basically a short term infection; however, it will become a chronic disorder and remains for a long term in some individuals. The severe effect of this illness is that it can cause chronic liver diseases, and with time it results in cancer of the liver. Hence, it puts a great risk to the life of a patient. The early detection of the virus is necessary, which assists in saving the patient's life and brings more treatment procedures that can adapt by the doctor for treatment. However, this life-threatening disease's therapy and diagnosis are both costly and can have dangerous adverse effects. As a result, developing a diagnostic system that lowers the cost of therapy and diagnosis is critical. In the medical field, several artificial intelligence technologies have been employed to assist doctors and experts in the diagnosing process. The significant aim of this paper is to thoroughly review all these diagnostic systems developed or proposed by various researchers. Later on, the systems are compared with one another to evaluate which system or model is best to use in solving the problem. The parameter that is taken into account to complete the comparison. As a result, it is noticed that the system developed by using a genetic neural network is accurate for the diagnosis of HBV and has an accuracy of 99.14%.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)110All these diagnostic systems developed or proposed by various researchers are reviewed and the system developed by using a genetic neural network is accurate for the diagnosis of HBV and has an accuracy of 99.14%.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["Dalwinder Singh", "Harjeet Kaur"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/c1fd4003c10a2cd80caa3413cb19b519832c26f5 85a0c8b8cfd7f7d58958dd7d509ada7940f07e79Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.European Conference on Management Leadership and Governance330This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals to identify key research trends, influential contributors, and burgeoning themes, utilizing VOSviewer software to identify critical AI methods advancing stock market prediction.European Conference on Management Leadership and Governance["Chinyang Lin", "Jo\u00e3o Alexandre Lobo Marques", "Lin Kun Chan"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/85a0c8b8cfd7f7d58958dd7d509ada7940f07e79 f287697be037853b1b52fb40f5f5808dfc308412This systematic literature review (SLR) investigates the role of artificial intelligence (AI) in promoting sustainable practices within the apparel industry, addressing the critical need for efficient and environmentally responsible solutions in a sector facing increasing sustainability pressures. The research methodology adheres to PRISMA guidelines, encompassing a comprehensive literature search across databases such as Scopus and Web of Science for articles published between 2010 and 2024. The review process involved rigorous filtering and screening, ultimately analyzing 31 relevant journal articles. Key findings reveal that AI applications significantly enhance operational efficiency and sustainability in apparel manufacturing and supply chains. Techniques such as machine learning for demand forecasting, genetic algorithms for supply chain optimization, and computer vision for quality control are instrumental in reducing waste and improving resource utilization. Despite these advancements, challenges in implementation and scalability persist, indicating areas where further investigation is necessary. The implications of this review underscore the potential of AI to transform the apparel industry by integrating sustainable practices into core operations. However, notable research gaps remain, particularly regarding the ethical implications of AI adoption, its impact on labour practices, and the need for interdisciplinary approaches that bridge technology with environmental sustainability. Future research directions should focus on developing innovative AI methodologies tailored to sustainability challenges, examining the socio-economic impacts of AI on labour within the apparel sector, and enhancing collaboration between academia and industry to foster practical applications of AI technologies. This SLR not only contributes to academic discourse but also serves as a valuable resource for practitioners seeking to implement sustainable practices through AI in the apparel industry.Journal of Informatics Education and Research340Key findings reveal that AI applications significantly enhance operational efficiency and sustainability in apparel manufacturing and supply chains and underscore the potential of AI to transform the apparel industry by integrating sustainable practices into core operations.Journal of Informatics Education and Research["Mr. Raghavan Santhanam", "Dr. Ajit Kumar Khare", "Research Methodology"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/f287697be037853b1b52fb40f5f5808dfc308412 eb69539c1f468de5d75f811749c1c9b148715c60This paper explores the requirements for humancentered artificial intelligence (AI) tools for heart failure (HF) management, focusing on the needs of diverse healthcare settings in selected European countries (Netherlands, Spain, Czech Republic) and a Latin American country (Peru). Clinicians, patients, ethicists, and technical experts were engaged through cocreation workshops, local groups, narrative interviews, and surveys to gather clinical, ethical, and regulatory requirements for AI implementation in HF care. These activities provided input on the intended clinical use of AI tools, as well as patient data privacy and security concerns. Clinical requirements revealed regional differences in AI tool preferences and key predictors. European clinicians favored integration into secondary and tertiary care, focusing on quality of life and comprehensive follow-up measures, while clinicians in Peru prioritized secondary care with an emphasis on treatment adherence and complication management. Ethical considerations, such as data privacy and bias mitigation, were universally important but some context-specific differences emerged. European stakeholders emphasized mitigating biases related to sex, ethnicity, and socioeconomic status under European regulations, whereas Latin American stakeholders focused on context-specific ethics and robust national oversight. By aligning these insights with FUTURE-AI principles, the study ensures the development of effective, human-centered AI tools. This research highlights the importance of continuous stakeholder engagement and contextualizing AI applications to enhance their relevance, usability, and adoption across diverse healthcare settings.Symposium on Medical Information Processing and Analysis50Clinical requirements revealed regional differences in AI tool preferences and key predictors, and the importance of continuous stakeholder engagement and contextualizing AI applications to enhance their relevance, usability, and adoption across diverse healthcare settings is highlighted.2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)["X\u00e8nia Puig-Bosch", "M. Boonstra", "M. Cabrita", "Joan Perramon", "Sara Munive", "A. Guala", "Vladim\u00edr Kincl", "S. Haitjema", "Carina Dantas", "F. Asselbergs", "K. Lekadir"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/eb69539c1f468de5d75f811749c1c9b148715c60 96452863b775654a72459088e0a5518f09dcfb31Teknologi dan industri di abad ke-21 mengalami perkembangan pesat, khususnya dengan hadirnya era Society 5.0, yaitu era yang tidak hanya berfokus pada kecanggihan teknologi, tetapi juga pada kemanusiaan dan kesejahteraan sosial. Di era ini, teknologi seperti kecerdasan buatan (Artificial Intelligence atau AI), Internet of Things (IoT), dan big data berperan besar dalam mendukung upaya meningkatkan kualitas hidup manusia dan membantu menyelesaikan masalah-masalah sosial yang kompleks. Society 5.0 dengan demikian merupakan perpaduan antara kemajuan teknologi dan nilai-nilai kemanusiaan, dimana teknologi dimanfaatkan secara lebih bijaksana untuk kebermanfaatan yang lebih luas. Transformasi ini juga memengaruhi dunia pendidikan, terutama melalui integrasi AI dalam proses pembelajaran. Penggunaan AI di bidang pendidikan menawarkan banyak manfaat bagi guru dan peserta didik, seperti pembelajaran yang lebih personal dan efisien. Namun, di sisi lain, hal ini juga membawa tantangan. Guru diharapkan untuk terus mengembangkan keterampilan digital agar peran mereka sebagai pengajar dan pembimbing tidak tergantikan oleh teknologi. Bagi peserta didik, keberadaan AI memerlukan pendampingan yang kuat dari guru dan orang tua agar mereka tetap bertanggung jawab dan tidak bergantung sepenuhnya pada teknologi, sehingga kemampuan berpikir kritis dan belajar mandiri mereka tetap terjaga. Dengan panduan yang tepat, AI dapat menjadi alat yang efektif dalam memperkaya pengalaman belajar peserta didik dan meningkatkan kualitas pendidikan.Journal of Innovation and Teacher Professionalism00Journal of Innovation and Teacher Professionalism["Rachel Theresa Laras Pratiwi", "M. Yunus"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/96452863b775654a72459088e0a5518f09dcfb31 2e82158b50bcb2c706d7c347aba9015c1fc55cefThe term “mobile commerce” or “m-commerce,” describes the practice of conducting online business transactions without the use of a computer or laptop, instead using wirelessly portable devices like smartphones and tablets. Mobile commerce has expanded into different industries, each of which offers special opportunities and challenges to customers and businesses. The present contribution and facility provided by m-commerce can be directly attributed to technology and artificial intelligence. This study offers a thorough framework for comprehending the different subsets of mobile commerce, classifying them according to their technological infrastructures, AI tools applied, algorithms used, target users, and functionalities. Several important m-commerce subsectors are recognized and distinguished by the framework, including m-shopping, m-payments, m-banking, m-advertising, m-ticketing, and m-brokerage. It examines how m-commerce has affected customer behaviors, such as the increasing desire for mobile-first experiences and the fuzziness of the lines separating online and offline commerce. The study includes data collected from various secondary sources and undergone through systematic methodology and research. It is an expectation that India's m-commerce market will almost double from about US$47 billion in 2020 up to US$83 billion by 2025. The research underscores mobile commerce's pivotal role in spurring economic growth and technological advancement in India. It offers actionable insights for researchers to leverage emerging opportunities in the evolving digital sphere.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)190It is an expectation that India's m-commerce market will almost double from about US$47 billion in 2020 up to US$83 billion by 2025, underscores mobile commerce's pivotal role in spurring economic growth and technological advancement in India.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["Jiya Bhandari", "Raziya Ansari", "P. Chauhan", "Shipra Agarwal", "Malika Pahwa", "Tanupriya Choudhury"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/2e82158b50bcb2c706d7c347aba9015c1fc55cef 04ad5eb15a707d0fce672d58e3ea071a6f4343f0The adoption of Generative Artificial Intelligence (GenAI) tools is drastically changing the way that researchers work. While debate on the quality of GenAI outputs continues, there is optimism that GenAI may help human experts to address the most significant environmental challenges facing society. No previous research has quantitatively assessed the quality of GenAI outputs intended to inform environmental management decisions. Here we surveyed 98 environmental scientists and used their expertise to assess the quality of human and GenAI content relevant to their discipline. We analysed the quality and relative preference between human and GenAI content across three use cases in environmental science outreach and communication. Our results indicate that the GenAI content was generally deemed adequate in quality by human experts, with an average of 82% of respondents indicating a quality of “adequate” or better across the three use cases. Respondents exhibited strong preferences for GenAI over human-only content when using GenAI imageery of future park management scenarios. For the use cases of generating a wetland planting guide and answering a question about invasive species management, preferences were heterogeneous amongst respondents. Our findings raise substantive questions about GenAI content as a complement to human expertise when research is transferred to public audiences.bioRxiv00The results indicate that the GenAI content was generally deemed adequate in quality by human experts, with an average of 82% of respondents indicating a quality of “adequate” or better across the three use cases.bioRxiv["David Worden", "Daniel Richards"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/04ad5eb15a707d0fce672d58e3ea071a6f4343f0 d289e10fdc8b02b0ed8599784ddbd14b3d754f53This paper explores the strategic decision to buy (vs build) Artificial Intelligence (AI) tools for use in higher education and academic libraries. It discusses the benefits and challenges associated with this approach and provides insights that can guide other academic libraries in making informed decisions about AI-driven tool adoption to support undergraduate research workflows. Detailed examples are provided and the pros/cons of each approach are provided.Information Services and Use00The strategic decision to buy (vs build) Artificial Intelligence tools for use in higher education and academic libraries is explored and insights that can guide other academic libraries in making informed decisions about AI-driven tool adoption to support undergraduate research workflows are provided.Information Services and Use["Russell Michalak", "Devon Ellixson"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/d289e10fdc8b02b0ed8599784ddbd14b3d754f53 02b7105f109d6136f561b11860ee80334299abe7The technology known as Artificial intelligence (AI) has the potential to revolutionize the medical industry. AI has a rich history in healthcare, dating back to early expert systems and rule-based approaches. Machine learning (ML) along with the AI is reforming the field of healthcare. Through the use of ML and Deep Learning (DL), AI can aid with patient monitoring, therapy selection, and diagnosis. This makes it possible to deliver healthcare more allows more precisely and effectively. The expanded implementation of AI in healthcare has the ability to augment patient outcomes and change the health industry's procedures, which will raise the standard of care, accessibility and affordability. Since the COVID-19 pandemic, AI has played a crucial role in healthcare industry. In addition to imparting a comprehensive analysis of AI advancements in the healthcare sector, this paper looks at the crucial role AI had in managing the COVID-19 epidemic. Pandemic screening, diagnosis, and prediction have significantly improved with recent advancements in AI research and development. It produces more dependable and efficient solutions, improves scale-up, responds quickly, and occasionally even surpasses people in specific healthcare tasks. The first section of the study gives the modern look of healthcare Era due to AI. It includes benefits as well as wide range of applications using AI and ML in health field. This section also explains the way to build detection system using AI/ML. The second section details the previous studies emphasizing on AI and ML technologies in the arena of health. The third section concentrates on Pandemic Prediction. Previous studies about recent passed pandemic i.e. Covid-19 as well as AI techniques used in forecasting the pandemic, diagnosis as well as detection of pandemic to safe the society before heavy spread.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)520This paper looks at the crucial role AI had in managing the COVID-19 epidemic, as well as AI techniques used in forecasting the pandemic, diagnosis as well as detection of pandemic to safe the society before heavy spread.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["Ritu Sachdeva", "Ayush Kumar", "Bhavya Behl", "Harsh Vardhan Pratap Singh"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/02b7105f109d6136f561b11860ee80334299abe7 0aa1b05038f6b9f0e0c1edc944fdfa6b77a12d82The article examines the role of artificial intelligence in improving the effectiveness of students' professional training in the field of electrical engineering and electronic engineering. The main trends, prospects and algorithms of an intelligent information and analytical management system are considered, as well as the risks of introducing artificial intelligence into modern education. Special attention is paid to the ethical problems of the application of artificial intelligence and the use of artificial intelligence in higher education. The study shows that the use of artificial intelligence can significantly speed up the process of education and training of specialists in the field of electrical engineering and electronic engineering.2024 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED)160The study shows that the use of artificial intelligence can significantly speed up the process of education and training of specialists in the field of electrical engineering and electronic engineering.2024 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED)["T. Y. Salutina", "G. P. Platunina", "I. A. Frank"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/0aa1b05038f6b9f0e0c1edc944fdfa6b77a12d82 8228d1f5f289c3e497926d6635597d3228acf75bThe trend in using artificial intelligence to promote business processes and form marketing campaigns is increasingly being used in the travel industry. Artificial intelligence is a set of programs that can simulate human skills, for example, planning, solving specific tasks, learning and improving its functionality as information accumulates. AI technologies are improving and transforming travel in all sorts of ways. Offers of personal recommendations are based on a systematic selection of information and improving the quality of customer service. Virtual assistants and optimization of operational efficiency improve the booking service of travel products. When examining the importance of artificial intelligence in the development of the modern tourism industry, one can note both advantages – this is data analysis and its transformation, improving the quality of service in travel, ensuring the preservation of historical sites, and disadvantages – digital replication, the use of neural networks to change the appearance of objects or even entire cities, which in the future can lead to a distorted view of tourists on a particular tourist destination.Design. Art. Industry00The trend in using artificial intelligence to promote business processes and form marketing campaigns is increasingly being used in the travel industry, and both advantages and disadvantages can be noted.Design. Art. Industry["K. A. Zhizhileva"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/8228d1f5f289c3e497926d6635597d3228acf75b a20d1dc888c979b70c2ea7bf354a091cb67b96c6 Artificial Intelligence (AI) has rapidly transformed financial markets, enhancing efficiency, precision, and scalability in trading, risk management, fraud detection, and personalization. Through advanced machine learning models, AI has enabled high-frequency trading, predictive risk assessment, and robust compliance processes, which streamline operations and improve market responsiveness. However, the complexity and opacity of AI systems introduce ethical concerns and regulatory challenges, especially regarding model interpretability and potential biases. This paper examines the role of AI across financial functions, highlights both advantages and limitations compared to traditional methods, and discusses future directions for integrating hybrid models and adaptive trading strategies. While AI offers considerable benefits, addressing its ethical and regulatory implications will be essential to harnessing its full potential within financial markets. The social science00The role of AI across financial functions is examined, both advantages and limitations compared to traditional methods are highlighted, and future directions for integrating hybrid models and adaptive trading strategies are discussed.International Journal of Emerging Multidisciplinaries: Social Science["Fahad Masood"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/a20d1dc888c979b70c2ea7bf354a091cb67b96c6 b79f96d5f79eeb33b91c80316f871fd3ca67fd14This study investigates the role of artificial intelligence (AI) in improving cybersecurity, addressing a vital issue as digital threats become more complex and faster. AI-driven advancements in threat detection, predictive vulnerability assessments, and ethical considerations pave the way for robust and adaptable defenses.Journal of Next-Generation Research 5.000Journal of Next-Generation Research 5.0["Rachid Ejjami"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/b79f96d5f79eeb33b91c80316f871fd3ca67fd14 c587c98808121a1d5f18cd84ea5ffcc0a850f1c8This paper analyses current achievements and emerging landscape of artificial intelligence in Morocco. It explores also the various challenges and opportunities related to the development of an AI ecosystem that facilitates the emergence of an agile, holistic, responsible, ethical, and forward-looking national artificial intelligence strategy that supports Morocco's global competitiveness and its strategic intelligence. Based on a methodological triangulation approach, we combined a documentary approach focused on a literature reviews and international benchmarks of national AI policies and strategies. In addition, we have proposed a conceptual model that outlines the essential elements and key enablers and drivers of the national AI strategy for Morocco.  We have called it “LAFBAH AI Framework” focusing on Leadership & Vision, Adaptive Governance, Forward-thinking, Breakthrough Innovations, Agility in Implementation, and Human centred approach. This model is predictive and prescriptive, and most importantly, aligned with the Moroccan priorities and strategic orientations of Morocco’s New Development Model. The study's findings show that Morocco possesses strategic assets that can help it establish itself as an AI hub for Africa and bolster its leadership and digital influence. These assets mainly include the country's international commitment to AI, its participation in AI cooperation programs, a dynamic creation of institutions and university centres dedicated to AI and the start of a virtuous public and political dialogue on the subject involving the private sector, academia and civil society.Our research argues in favour of a holistic and integrated approach to AI in Morocco, which combines AI Governance with technological innovation, ethical and societal considerations to promote sustainable and inclusive development and enhance Moroccan competitiveness. It also demonstrates the urgent need to design and ensure the implementation of an innovative, adapted and integrated national AI strategy for Morocco. This national AI strategy should be designed as an integrative, agile, dynamic framework with a clear and integrated vision, ambitious strategic objectives, relevant priorities and choices and key pillars of AI such as responsible, ethical and innovative governance and strong ecosystem enablers, collaborative research and development, AI talent and skills, sufficient data and infrastructure.We have also designed the fundamental and necessary milestones for a new ecosystem of artificial intelligence in Morocco focused on the imperatives of the knowledge society, the digital, health, food and energy sovereignties of the country, while aspiring to be a hub of artificial intelligence in Africa.European Conference on Management Leadership and Governance110The study's findings show that Morocco possesses strategic assets that can help it establish itself as an AI hub for Africa and bolster its leadership and digital influence and the urgent need to design and ensure the implementation of an innovative, adapted and integrated national AI strategy for Morocco.European Conference on Management Leadership and Governance["Abdelmjid Lafram", "Salah Eddine Bahji"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/c587c98808121a1d5f18cd84ea5ffcc0a850f1c8 2707a1f710028fded9732979fb1b3e7f1d58fd58Studies in Higher Education683Studies in Higher Education["Li Zhao", "Md. Habibur Rahman", "William Yeoh", "Shan Wang", "K. Ooi"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/2707a1f710028fded9732979fb1b3e7f1d58fd58 10b03d7197ecc5b881e8b56df44987f603766baaDalam era kecerdasan buatan (AI), pendidikan menghadapi tantangan baru yang signifikan terkait dengan moralitas dan etika. Luntunya moralitas dalam konteks pendidikan mencerminkan pergeseran nilai-nilai yang terjadi akibat integrasi teknologi canggih dalam proses belajar mengajar. Penelitian ini bertujuan untuk mengeksplorasi dampak AI terhadap moralitas pendidikan, dengan fokus pada bagaimana teknologi dapat mempengaruhi pengambilan keputusan etis, interaksi sosial antara pendidik dan peserta didik, serta pembentukan karakter siswa. Studi ini mengidentifikasi beberapa isu utama, termasuk ketidakadilan algoritmik, privasi data, dan potensi penyalahgunaan teknologi. Selain itu, penelitian ini juga membahas pentingnya pengembangan kurikulum yang tidak hanya berorientasi pada keterampilan teknis tetapi juga pada nilai-nilai moral dan etika. Dengan demikian, diharapkan bahwa pendidik dan pembuat kebijakan dapat merumuskan strategi yang lebih baik untuk memastikan bahwa penggunaan AI dalam pendidikan tidak hanya meningkatkan efisiensi tetapi juga mendukung perkembangan moral siswa. Melalui pendekatan kualitatif dan analisis literatur terkini, penelitian ini memberikan wawasan tentang bagaimana institusi pendidikan dapat beradaptasi dengan perubahan zaman tanpa mengorbankan prinsip-prinsip moral yang fundamental. Hasil dari penelitian ini diharapkan dapat menjadi acuan bagi para pendidik, peneliti, dan pemangku kepentingan lainnya dalam menghadapi tantangan moral di era digital.Journal of Creativity01Journal Creativity["Zainal Muarifin"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/10b03d7197ecc5b881e8b56df44987f603766baa bd3c94974a6c38dd725a481837ccdff0a6c0ffdcElektrichestvo00Elektrichestvo["Pavel Butyrin"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/bd3c94974a6c38dd725a481837ccdff0a6c0ffdc 949fd8abee8e5b6c7dae1e98f59565f05d0e57acJournal of Population Therapeutics and Clinical Pharmacology00Journal of Population Therapeutics and Clinical Pharmacology["Dr Ashwini L H"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/949fd8abee8e5b6c7dae1e98f59565f05d0e57ac 7982e73e42c10d7c2b6f2ccce0a0d96a87b2dd69AI-based models are valuable financial tools because they generate precise predictions and mitigate investment risks. This aids investors in making judgements based on factual infor-mation. It is essential to monitor and analyse various forecasting methods in the financial market, both present and future, with profitability as a critical factor. The studies incorporated in this analysis were sourced from three prominent academic databases and were published from 2019 to 2024. The selected research employed a three-stage approach of preparation, execution, and analysis. The study investigates technical evaluation, profitability analysis, hybrid modelling, and model output. To assess the study's methodological strength, 10 Likert-type quality criteria questions were used to evaluate the articles. Establish inclusion and exclusion criteria to initiate the examination. An increasing number of organisations are utilising ensemble and hybrid models that integrate LSTM and SVM to forecast market movements and prices. Hybrid models that employ AI algorithms for feature engineering exhibit considerable potential, similar to ensemble methods. Performance-based research excludes profitability measures, investment strategies, and real-time or simulated trading. The financial forecasting literature needs to be revised, and future studies could concentrate on multi-class or output-related aspects.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)360The financial forecasting literature needs to be revised, and future studies could concentrate on multi-class or output-related aspects, similar to ensemble methods.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["Rahul Thakur", "Jasneet Kaur", "Harmanjeet Singh"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/7982e73e42c10d7c2b6f2ccce0a0d96a87b2dd69 d9772b50d2af19bce9bf4bdb9b882617c34133e4Academy of Management Review60Academy of Management Review["Bart S. Vanneste", "P. Puranam"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/d9772b50d2af19bce9bf4bdb9b882617c34133e4 1a48e24cf0cd53ea1002b43546c21bbe91d92d92Algorithmic trading has become an increasingly popular method of investing in stock requests due to its potential to offer progressive returns and lower risk in comparison to traditional styles. The current status of algorithmic trading in stock requests is examined in this review paper, along with the creative tactics used by dealers. We analyze the advantages and disadvantages of algorithmic trading, along with the variables that may affect its performance. We also discuss the difficulties dealers have enforcing algorithmic trading and the possibility that algorithmic trading will increase request insecurity. Finally, we conclude by making suggestions for future research in the area of algorithmic trading in stock requests. Overall, the objective of this review is to give a comprehensive overview of algorithmic trading in the stock request and the implicit effects it has on financial assiduity.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)230The objective of this review is to give a comprehensive overview of algorithmic trading in the stock request and the implicit effects it has on financial assiduity.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["Vaibhav Verma", "Kushagra Jangra", "Jasneet Chawla", "Harmanjeet Singh"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/1a48e24cf0cd53ea1002b43546c21bbe91d92d92 1f78abe89fae0debebc6b959707e75302572cf4bThis abstract attempt to explore the extent to which ethical issues impact the managerial accounting performance indices due to the incorporation of AI. There is a question about bias, which increases the adoption of AI technologies in managerial accounting and may affect the reliability of accountants' metrics as well as the issues of accountability and transparency. This is because, in the current world business environment, which features globalization, high-level transparency and antagonism, it is important to apply new information technologies like AI. The study launched on a sample of 200 bookkeepers of Romania who used AI for managerial accounting revealed that the ethical concerns related to confidence, accountability, and autonomy significantly influence AI functioning and application.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)140The study launched on a sample of 200 bookkeepers of Romania who used AI for managerial accounting revealed that the ethical concerns related to confidence, accountability, and autonomy significantly influence AI functioning and application.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["J. Jayashankar"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/1f78abe89fae0debebc6b959707e75302572cf4b 54fad340dfd689a9c236ca151938a97ffcdad25eAesthetic surgery journal00Aesthetic surgery journal["L. Copeland-Halperin", "Claude Oppikofer"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/54fad340dfd689a9c236ca151938a97ffcdad25e 2843144c331ccb0f72a59c83b79bf37854e2193bThe city of Bogota is characterized by the different problems it has related to traffic, especially with the traffic jams that occur within the capital. For this reason, people have opted for the use of alternative means of transportation such as bicycles, however, the number of accidents involving these road actors has increased over time. For this reason, we proposed the use of a learning algorithm, which is able to identify certain movements made by the cyclist with his head and depending on these, the directional signals of the bicycle will be activated, in order to provide information to the other entities of the actions performed by the cyclist. Classifiers refer to the process of training on a labeled data set (dataframe) to assign data points to classes for classification. In order to achieve this, classification models require a large amount of data to be able to predict more accurately. For this reason, the first thing that was done was the creation of the dataset, which was created from the data collection by means of the IMU of the microcontroller of the movements to be classified. After that, the windows of each movement were created, which for this project were 12 samples. The variance was calculated for each window and this was used to test the different classical classifiers (k-near neighbors, logistic regresión, decision trees, random forest, Naive Bayes, support vector machines and support vector machines). The best model were select using the accuracy meusurement and was easier to implement to predict the movement, which would be the logistic regression classifier. From this, the equation of this algorithm was implemented in the Arduino BLE sense 33 and this microcontroller would be placed in the cyclist’s helmet to identify the movements.Latin American Conference on Computational Intelligence140This work proposed the use of a learning algorithm, which is able to identify certain movements made by the cyclist with his head and depending on these, the directional signals of the bicycle will be activated, in order to provide information to the other entities of the actions performed by the cyclist.2024 IEEE Latin American Conference on Computational Intelligence (LA-CCI)["Ahyza Yined Prieto Rodr\u00edguez", "Juli\u00e1n David N\u00fa\u00f1ez Casilimas", "Javier A. Chaparro"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/2843144c331ccb0f72a59c83b79bf37854e2193b 950b9945681a57f0323a965184836908945646a6Dental Traumatology530Dental traumatology : official publication of International Association for Dental Traumatology["Liran Levin", "Lea Budak"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/950b9945681a57f0323a965184836908945646a6 d97332946d24217fbbf7d20d060033a3a5f54ad4IZVESTIYA SFedU ENGINEERING SCIENCES00IZVESTIYA SFedU. ENGINEERING SCIENCES["S. Y. Melnikov", "R.V. Meshcheryakov", "V. A. Peresypkin"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/d97332946d24217fbbf7d20d060033a3a5f54ad4 5aa0fcf001f0235529b002318af955d04ca8b260IZVESTIYA SFedU ENGINEERING SCIENCES00IZVESTIYA SFedU. ENGINEERING SCIENCES["V. I. Voloshchuk", "A. Garyagdiyev", "\u041c. \u0410. Kozlovskaya", "Y. E. Melnik", "A. N. Samoylov"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/5aa0fcf001f0235529b002318af955d04ca8b260 4be7288f1df1656263fd1b2303b3e691156db00cThis study aims to analyze the influence of perceived usefulness and perceived ease of use of Artificial Intelligence (AI)-based applications on the willingness of functional tax instructors to use these AI-based applications. The data used in this study are primary data from questionnaire surveys distributed to functional tax extension officers from all Regional Offices of the Directorate General of Taxes from October to December 2023. In addition, this study also uses secondary data sourced from regulatory documents and other data related to the performance of tax extension officers from the Directorate General of Taxes. Data from 104 respondents were analyzed using multiple linear regression. This study concludes that the perceived usefulness and perceived ease of use of AI-based applications have a positive and significant effect on the willingness of Functional Tax Instructors to use AI-based applications. Functional extension workers need to continue to try to work side by side with technology so that work becomes faster and easier. With the help of AI technology, it is not impossible that the implementation of taxation counseling can be partially delegated to technological assistance so that tax education can be more massive and comprehensive.Educoretax140It is concluded that the perceived usefulness and perceived ease of use of AI-based applications have a positive and significant effect on the willingness of Functional Tax Instructors to use AI-based applications.Educoretax["Ria Dewi Ambarwati", "Khusnaini Khusnaini", "Nur Farida Liyana"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/4be7288f1df1656263fd1b2303b3e691156db00c b2bfc54ac37472646339050b988c7caebf75193eThis paper addresses the call to integrate Artificial Intelligence (AI) technology and critical thinking in English major education in China. By examining the perspectives of university, college, and departmental leaders at Shantou University, this paper explores the integration of AI and critical thinking in English major education in China, focusing on opportunities, challenges, and future directions. Through a polylogue with institutional leaders at Shantou University, the paper provides insight into how AI tools can enhance personalized learning, improve academic outcomes, and better prepare students for the global workforce. However, the findings also highlight the ethical and pedagogical challenges of AI, such as the risk of exacerbating educational inequalities and compromising critical thinking. The paper advocates for an approach that balances AI proficiency with the development of independent thinking, ensuring that students are well equipped for the future.Digital Applied Linguistics04Insight is provided into how AI tools can enhance personalized learning, improve academic outcomes, and better prepare students for the global workforce through a polylogue with institutional leaders at Shantou University.Digital Applied Linguistics["Zhifeng Hao", "Fan Fang", "Jian-E Peng"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/b2bfc54ac37472646339050b988c7caebf75193e 83105b5428e64b07507352aedd1185e235e24362In the era of big data, you absolutely must have consistency across your different data systems to make effective decision-making, be compliance, enhanced governance and security and also mitigation of risks,, and even have system integrity. The data consistency and how rationalization techniques help overcome challenges when working with heterogeneous data sources, is what this paper explores. Rationalization allows businesses to keep their data accurate and consistent by standardizing data formats, reducing duplications, measuring the data growth, categories the data sources, farmats, and aligning conflicting information. We compare the different methods of normalization, data deduplication, Master Data Management (MDM) and data integration framework. We also delve into the part that automation, artificial intelligence and machine learning play in optimizing these processes and provide scalable solutions that can potentially simplify the complexity of modern-day data environments. Practical implications from rationalization effort case studies are shown from sectors like, healthcare, finance, and e-commerce. Our specific findings show that long-term data consistency requires a systematic approach that employs technology and strong governance. In the end, this paper gives a blueprint for how to exploit rationalization methods to exploit the capabilities of data-driven insights fully.International Journal For Multidisciplinary Research251This paper gives a blueprint for how to exploit rationalization methods to exploit the capabilities of data-driven insights fully and shows that long-term data consistency requires a systematic approach that employs technology and strong governance.International Journal For Multidisciplinary Research["Jayanna Hallur", "Vedamurthy Gejjegondanahalli Yogeshappa", "Jaishankar Inukonda", "Vidya Rajasekhara Reddy Tetala"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/83105b5428e64b07507352aedd1185e235e24362 3c833637a7060a62961bfa811778b351ef49953bRock Mechanics and Rock Engineering561Rock Mechanics and Rock Engineering["Ghader Saadati", "Sina Javankhoshdel", "Javad Mohebbi Najm Abad", "Michael Mett", "Heiner Kontrus", "Barbara Schneider-Muntau"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/3c833637a7060a62961bfa811778b351ef49953b 022f7831834d11154db639b7c437caa37d2dca35The proliferation of deep learning has transformed artificial intelligence, demonstrating prowess in domains such as image recognition, natural language processing, and robotics. Nonetheless, deep learning models are susceptible to adversarial examples, well-crafted inputs that can induce erroneous predictions, particularly in safety-critical contexts. Researchers actively pursue countermeasures such as adversarial training and robust optimization to fortify model resilience. This vulnerability is notably accentuated by the ubiquitous utilization of ReLU functions in deep learning models. A previous study proposed an innovative solution to mitigate this vulnerability, presenting a capped ReLU function tailored to bolster neural network robustness against adversarial examples. However, the approach had a scalability problem. To address this limitation, a series of comprehensive experiments are undertaken across diverse datasets, and we introduce the dynamic-max-value ReLU function to address the scalability problem.Mathematics131This work introduces the dynamic-max-value ReLU function, a capped ReLU function tailored to bolster neural network robustness against adversarial examples, to address the scalability problem.Mathematics["Korn Sooksatra", "Pablo Rivas"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/022f7831834d11154db639b7c437caa37d2dca35 2fa42f3326509a3fb269cdeb7ad03ed6193dbfcfThe short paper discusses algorithmic fairness by focusing on non-discrimination and a few important laws in the European Union (EU). In addition to the EU laws addressing discrimination explicitly, the discussion is based on the EU's recently enacted regulation for artificial intelligence (AI) and the older General Data Protection Regulation (GDPR). Through a theoretical scenario analysis, on one hand, the paper demonstrates that correcting discriminatory biases in AI systems can be legally done under the EU regulations. On the other hand, the scenarios also illustrate some practical scenarios from which legal non-compliance may follow. With these scenarios and the accompanying discussion, the paper contributes to the algorithmic fairness research with a few legal insights, enlarging and strengthening also the growing research domain of compliance in AI engineering.arXiv.org191Through a theoretical scenario analysis, the paper demonstrates that correcting discriminatory biases in AI systems can be legally done under the EU regulations and illustrates some practical scenarios from which legal non-compliance may follow.ArXiv["Jukka Ruohonen"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/2fa42f3326509a3fb269cdeb7ad03ed6193dbfcf 77e73d9c63fa5b07c62db886e3c50f1464bbaae9This theoretical research paper explores Generative Artificial Intelligence (AI) as a transformative force in sustainable education within the digital era. Through a comprehensive literature review of peer-reviewed articles, conference proceedings, and policy documents in sustainable education, AI in education, and learning theories, we propose a novel conceptual framework: Generative AI-Enabled Sustainable Education (GAISE). This framework synthesises principles from sustainable education theories, AI in education, constructivism, connectivism, and transformative learning. The GAISE model elucidates how Generative AI's capabilities in content generation, personalisation, adaptive learning, and natural language processing can enhance sustainability literacy and promote transformative learning experiences. Our analysis reveals the framework's potential to integrate Generative AI into curriculum design, teaching methodologies, assessment strategies, and teacher professional development for sustainable education. Critical ethical considerations include data privacy, equity, and human-AI collaboration in educational contexts. The paper identifies key challenges in implementing Generative AI for sustainable education and proposes future empirical research directions and policy recommendations. This work contributes to the intersection of AI and sustainable education, offering theoretical insights and practical pathways for educators and policymakers to leverage Generative AI in promoting sustainability competencies in education.British Journal of Teacher Education and Pedagogy1061This analysis reveals the framework's potential to integrate Generative AI into curriculum design, teaching methodologies, assessment strategies, and teacher professional development for sustainable education, and identifies key challenges in implementing Generative AI for sustainable education.British Journal of Teacher Education and Pedagogy["F. Baskara"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/77e73d9c63fa5b07c62db886e3c50f1464bbaae9 ccdcfbebcc279bc8ac545410f42a883faca5a842This study investigates the potential of artificial intelligence (AI) to revolutionize personalized learning by developing and empirically evaluating the Adaptive Personalization Theory of Learning (APT) model. The APT paradigm uses AI-powered personalized learning algorithms, real-time adaptive assessments, learner engagement strategies, cognitive scaffolding, and ethical safeguards to provide flexible, personalized learning experiences. The study confirms the model's constructs through qualitative methods such as case studies, interviews, and classroom observations. It illustrates how AI improves learning outcomes by continuously tailoring content and evaluations to individual learner needs. The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios. However, the study also identifies ethical concerns, such as potential biases in AI algorithms, emphasizing the significance of establishing transparent, fair systems. Limitations include the scope of the implementation and the necessity for additional quantitative study. The paper continues by identifying areas for further research, emphasizing long-term impacts, ethical frameworks, and practical implementation tactics, and establishing the APT model as a significant contribution to AI in education.Journal of Next-Generation Research 5.000The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios, but also identifies ethical concerns, such as potential biases in AI algorithms, emphasizing the significance of establishing transparent, fair systems.Journal of Next-Generation Research 5.0["Rachid Ejjami"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/ccdcfbebcc279bc8ac545410f42a883faca5a842 4f6ccac011f1878a7efd8c1a23e9867ce7fc6168Artificial Intelligence (AI) has rapidly transformed numerous industries, with education emerging as one of the most impacted fields. The global AI market, valued at $196.63 billion in 2023, is projected to expand significantly, reflecting the growing adoption of AI technologies. In education, AI-driven tools such as personalized learning platforms, automated grading systems, and accessibility-enhancing technologies are revolutionizing teaching and learning experiences. These advancements align with Sustainable Development Goal 4 (SDG 4) by promoting inclusive, high-quality education and lifelong learning opportunities for all. Examples like Duolingo, CoGrader, and the BBC’s AI investments demonstrate how technology is reshaping education to better engage students and improve outcomes. However, the integration of AI raises critical concerns about algorithmic bias, ethical decision-making, and data privacy. While AI can reduce human bias and improve efficiency, it risks perpetuating disparities if training data lacks diversity. Additionally, reliance on large datasets demands stringent data security measures to protect student privacy. Addressing these challenges requires collaboration among educators, policymakers, and developers to ensure AI is used responsibly. This paper examines the transformative potential of AI in education while emphasizing the need for ethical oversight and equity. Future research must focus on refining AI to meet diverse learning needs without compromising inclusivity or fairness.Next Generation Journal for The Young Researchers20The transformative potential of AI in education is examined while emphasizing the need for ethical oversight and equity, and future research must focus on refining AI to meet diverse learning needs without compromising inclusivity or fairness.Next Generation Journal for The Young Researchers["Alp Dulundu"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/4f6ccac011f1878a7efd8c1a23e9867ce7fc6168 3dad3d238cdd104bee1bddb82ab5ccd4edb346bdIntegrating artificial intelligence (AI) into psychological education represents an opportunity in teaching methodologies, offering possibilities for instructors. Despite its potential, there is a significant gap in knowledge regarding how psychology instructors implement AI tools in their teaching practices. The primary objective of this study was to explore how psychology instructors utilize AI tools. This study employed semistructured interviews with 15 psychology instructors. Interviews were transcribed and subjected to thematic analysis. The study revealed key motivations for AI integration: preparing students for the workforce, enhancing academic integrity, aligning with institutional goals, and addressing potential challenges. Additionally, various methods of AI integration were explored, focusing on fostering critical thinking, supporting research and writing, and promoting ethical AI use. Challenges such as inconsistent AI policies, varied student proficiency, ethical concerns, and difficulties with prompt engineering were highlighted. This study shows that AI integration in psychology education is done in a myriad of ways, and instructors demonstrate creative approaches to how to implement AI and the potential challenges. Psychology instructors can consider integrating AI tools into their coursework. Additionally, educators can address potential challenges by providing clear AI usage guidelines and offering tailored.Teaching of psychology160This study shows that AI integration in psychology education is done in a myriad of ways, and instructors demonstrate creative approaches to how to implement AI and the potential challenges.Teaching of Psychology["Christina Costa", "Nahid Husain-Habib", "Alyssa Reiter"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/3dad3d238cdd104bee1bddb82ab5ccd4edb346bd 7ca5d3f9bb3a6e0e71e74757c196546823ca05a1 The development of medical artificial intelligence is dependent on the availability of vast quantities of data, a considerable proportion of which is medical data containing sensitive information pertaining to the health and well-being of patients. The use of such data is subject to extensive legal regulation and is further hindered by financial and organisational constraints, which can result in limitations on accessibility. One potential solution to this problem is the use of synthetic data. This article examines the potential for their use in light of cybersecurity requirements derived from horizontal and sectoral EU legislation. The outcome of this analysis is that EU legislation does not contain specific regulations on the use of synthetic data. Consequently, it cannot be concluded that there is any prohibition on their use. Moreover, while the Medical Device Regulation (MDR) contains some general requirements for cybersecurity, these are further specified by the provisions of the AI Act. It is important to note, however, that the AI Act will not apply to Class I medical devices, which are subject only to the MDR. Furthermore, only indirect obligations within the scope under consideration can be derived from the horizontal regulations, which will apply in a limited number of cases.European Journal of Risk Regulation00EU legislation does not contain specific regulations on the use of synthetic data and it cannot be concluded that there is any prohibition on their use, so the potential for their use in light of cybersecurity requirements derived from horizontal and sectoral EU legislation is examined.European Journal of Risk Regulation["Jaros\u0142aw Greser"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/7ca5d3f9bb3a6e0e71e74757c196546823ca05a1 96041a599d738616258f21e2549617ae31265dcdIn the modern world, digital sculpture technology with the use of neural networks is actively developing in the field of reconstructing works of art, thus contributing to the preservation of cultural heritage. Digital sculpture using artificial intelligence opens new possibilities for the representation of China’s cultural heritage. AI accelerates the process of restoring and recovering damaged or lost cultural assets, which is especially important for preserving unique and fragile artifacts. This study is dedicated to examining the potential of AI-generated digital sculptures in representing and disseminating Chinese national culture. It considers the stages of development of digital sculpture as an art form, analyzing its capabilities and features of artistic expression. Special attention is given to the analysis of key techniques for embodying Chinese national culture in digital sculptures using AI, exemplified by the game «Black Myth: Wukong». The game successfully combines traditional Chinese elements with modern technology, creating a unique and captivating game world that attracts attention with its beauty and realism. The analysis highlights that the use of AI for representing Chinese traditional culture in digital sculptures opens new horizons for artistic creativity and education.Design. Art. Industry90The analysis highlights that the use of AI for representing Chinese traditional culture in digital sculptures opens new horizons for artistic creativity and education.Design. Art. Industry["Zhi Li"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/96041a599d738616258f21e2549617ae31265dcd bb950dbcba1950096613018983818b6742a401b5This research paper aims to mainly discuss the use of Artificial Intelligence (AI) and its derivatives Machine Learning (ML) in the tourism industry and business. In the identification of the effects of AI as well as ML, based on empirical data sourced from different databases, the use empirical models was done to determine customer/tourist satisfaction, efficiency of operations and revenue generation. SME findings show important advancements in these areas, which may indicate that the application of AI and ML is changing the tourism industry.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)260E findings show important advancements in customer/tourist satisfaction, efficiency of operations and revenue generation, which may indicate that the application of AI and ML is changing the tourism industry.2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS)["Mohammad Shoaib Khan", "Shalki", "Omar Abdullah", "Shilpi", "A. K. Verma", "Shikha Designation"]2024-11-13T00:00:00https://www.semanticscholar.org/paper/bb950dbcba1950096613018983818b6742a401b5 1b43632036f7e1f45a4759849f2924478eccc81eStrategic decisions are uncertain and often irreversible. Hence, predicting the value of alternatives is important for strategic decision making. We investigate the use of generative artificial intelligence (AI) in evaluating strategic alternatives using business models generated by AI (study 1) or submitted to a competition (study 2). Each study uses a sample of 60 business models and examines agreement in business model rankings made by large language models (LLMs) and those by human experts. We consider multiple LLMs, assumed LLM roles, and prompts. We find that generative AI often produces evaluations that are inconsistent and biased. However, when aggregating evaluations, AI rankings tend to resemble those of human experts. This study highlights the value of generative AI in strategic decision making by providing predictions.Managers are seeking to create value by integrating generative AI into their organizations. We show how managers can use generative AI to help evaluate strategic decisions. Generative AI's single evaluations are often inconsistent or biased. However, if managers aggregate many evaluations across LLMs, prompts, or roles, the results show that the resulting evaluations tend to resemble those of human experts. This approach allows managers to obtain insight on strategic decisions across a variety of domains with relatively low investments in time or resources, which can be combined with human inputs.Social Science Research Network655This study investigates the use of generative artificial intelligence in evaluating strategic alternatives using business models generated by AI or submitted to a competition and finds that generative AI often produces evaluations that are inconsistent and biased.SSRN Electronic Journal["Anil R. Doshi", "J. J. Bell", "Emil Mirzayev", "Bart S. Vanneste"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/1b43632036f7e1f45a4759849f2924478eccc81e 0403b6ef7a1b4ff135894f38fed6b95752322776BACKGROUND Artificial intelligence (AI) assistance may enhance radiologists' performance in detecting clinically significant prostate cancer (csPCa) on MRI. Further validation is needed for radiologists with different experiences. PURPOSE To assess the performance of experienced and less-experienced radiologists in detecting csPCa, with and without AI assistance. STUDY TYPE Retrospective. POPULATION Nine hundred patients who underwent prostate MRI and biopsy (median age 67 years; 356 with csPCa and 544 with non-csPCa). FIELD STRENGTH/SEQUENCE 3-T and 1.5-T, diffusion-weighted imaging using a single-shot gradient echo-planar sequence, turbo spin echo T2-weighted image. ASSESSMENT CsPCa regions based on biopsy results served as the reference standard. Ten less-experienced (<500 prostate MRIs) and six experienced (>1000 prostate MRIs) radiologists reviewed each case twice using Prostate Imaging Reporting and Data System v2.1, with and without AI, separated by 4-week intervals. Cases were equally distributed among less-experienced radiologists, and 90 cases were randomly assigned to each experienced radiologist. Reading time and diagnostic confidence were assessed. STATISTICAL TESTS Area under the curve (AUC), sensitivity, specificity, reading time, and diagnostic confidence were compared using the DeLong test, Chi-squared test, Fisher exact test, or Wilcoxon rank-sum test between the two sessions. A P-value <0.05 was considered significant. Adjusting threshold using Bonferroni correction was performed for multiple comparisons. RESULTS For less-experienced radiologists, AI assistance significantly improved lesion-level sensitivity (0.78 vs. 0.88), sextant-level AUC (0.84 vs. 0.93), and patient-level AUC (0.84 vs. 0.89). For experienced radiologists, AI assistance only improved sextant-level AUC (0.82 vs. 0.91). AI assistance significantly reduced median reading time (250 s [interquartile range, IQR: 157, 402] vs. 130 s [IQR: 88, 209]) and increased diagnostic confidence (5 [IQR: 4, 5] vs. 5 [IQR: 4, 5]) irrespective of experience and enhanced consistency among experienced radiologists (Fleiss κ: 0.53 vs. 0.61). DATA CONCLUSION AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.Journal of Magnetic Resonance Imaging291AI-assisted reading improves the performance of detecting csPCa on MRI, particularly for less-experienced radiologists, particularly for less-experienced radiologists.Journal of magnetic resonance imaging : JMRI["Zhaonan Sun", "Kexin Wang", "G. Gao", "Huihui Wang", "Pengsheng Wu", "Jialun Li", "Xiaodong Zhang", "Xiaoying Wang"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/0403b6ef7a1b4ff135894f38fed6b95752322776 e68609474f80b4890bb1a00520806acf88843182Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are particularly effective for this task due to their ability to process complex datasets with high accuracy. Furthermore, incorporating explainable artificial intelligence into these predictions provides clarity and interpretability, allowing energy managers and homeowners to make informed decisions that optimize usage and reduce costs. This study comparatively analyzes decision tree–ensemble learning techniques augmented with explainable artificial intelligence for transparency and interpretability in residential building energy consumption forecasting. This approach employs the University Residential Complex and Appliances Energy Prediction datasets, data preprocessing, and decision-tree bagging and boosting methods. The superior model is evaluated using the Shapley additive explanations method within the explainable artificial intelligence framework, explaining the influence of input variables and decision-making processes. The analysis reveals the significant influence of the temperature-humidity index and wind chill temperature on short-term load forecasting, transcending traditional parameters, such as temperature, humidity, and wind speed. The complete study and source code have been made available on our GitHub repository at https://github.com/sodayeong for the purpose of enhancing precision and interpretability in energy system management, thereby promoting transparency and enabling replication.PLoS ONE651This study comparatively analyzes decision tree–ensemble learning techniques augmented with explainable artificial intelligence for transparency and interpretability in residential building energy consumption forecasting, revealing the significant influence of the temperature-humidity index and wind chill temperature on short-term load forecasting.PLOS ONE["Jihoon Moon", "Muazzam Maqsood", "Dayeong So", "Sung Wook Baik", "Seungmin Rho", "Yunyoung Nam"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/e68609474f80b4890bb1a00520806acf88843182 c0c5a5a08fae8e7b447e213321f9d19e40f18c3eArtificial intelligence (AI) is revolutionizing journalism, necessitating a reevaluation of journalism education. The mixed-method study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the factors influencing AI adoption among Egyptian journalism professors from eight universities. Findings indicate that Facilitating Conditions and Effective Expectancy positively correlate with professors’ AI use, and Facilitating Conditions significantly predict AI adoption intention. The study recommends curriculum updates, infrastructure enhancements, and faculty training to integrate AI effectively into Egyptian journalism education.Journalism &amp; Mass Communication Educator171The mixed-method study employs the Unified Theory of Acceptance and Use of Technology to investigate the factors influencing AI adoption among Egyptian journalism professors from eight universities and finds that Facilitating Conditions and Effective Expectancy positively correlate with professors’ AI use.Journalism &amp; Mass Communication Educator["Abdelmohsen Hamed Okela"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/c0c5a5a08fae8e7b447e213321f9d19e40f18c3e 3d03dfd94e2206214120d74bf5466cacab0647b0The evolving landscape of cyber threats necessitates continuous advancements in defensive strategies. This paper explores the potential of artificial intelligence (AI) as an emerging tool to enhance cybersecurity. While AI holds widespread applications across information technology, its integration within cybersecurity remains a recent development. We offer a comprehensive review of current AI applications in this domain, focusing particularly on their preventative capabilities against prevalent threats like phishing, social engineering, ransomware, and malware. To illustrate these concepts, the paper presents a case study showcasing a specific AI application in a cybersecurity context. This case study addresses a critical gap in securing communication within resource-constrained Internet of Things (IoT) networks using the IEEE 802.15.4 standard. We discussed the advantages and limitations of employing PN sequence encryption for this purpose.Applied Sciences251This case study addresses a critical gap in securing communication within resource-constrained Internet of Things (IoT) networks using the IEEE 802.15.4 standard and discussed the advantages and limitations of employing PN sequence encryption for this purpose.Applied Sciences["Selcuk Okdem", "Sema Okdem"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/3d03dfd94e2206214120d74bf5466cacab0647b0 778e1cc1cce627e48a9d9c5aa1b6cf844be68b94Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.Infectious Diseases1891How AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance and how AI can be used to gear up the drug discovery and development process are examined.Infectious diseases["Vartika Srivastava", "Ravinder Kumar", "M. Y. Wani", "Keven Robinson", "Aijaz Ahmad"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/778e1cc1cce627e48a9d9c5aa1b6cf844be68b94 a6f7587267e4bed339d4e33028871e0fddab5ad9Artificial Intelligence (AI) is transforming educational practices by facilitating personalized learning, automating grading processes, and enhancing support through intelligent tutoring systems. This systematic review explores AI's integration in educational settings, highlighting its contributions to increased productivity and tailored learning experiences. It addresses key challenges including data privacy, algorithmic bias, and the need for enhanced accountability and transparency in AI applications. The review also discusses strategic recommendations for embedding ethical AI into curriculum design and emphasizes the importance of professional development for educators. Collaboration among educational stakeholders is vital for advancing responsible AI utilization. By synthesizing recent literature, this review provides insights into AI tools' effectiveness, explores ethical dimensions of technology in classrooms, and suggests future directions for research and practice in educational AI. This analysis serves as a resource for educators, policymakers, and technologists aiming to optimize AI benefits in education.International Journal of Research in STEM Education490This systematic review explores AI's integration in educational settings, highlighting its contributions to increased productivity and tailored learning experiences and addresses key challenges including data privacy, algorithmic bias, and the need for enhanced accountability and transparency in AI applications.International Journal of Research in STEM Education["Pema Wangdi"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/a6f7587267e4bed339d4e33028871e0fddab5ad9 692ded054335e2dca97049f9998611bd9fb29307Faults in the wafer transfer robots (WTRs) used in semiconductor manufacturing processes can significantly affect productivity. This study defines high-risk components such as bearing motors, ball screws, timing belts, robot hands, and end effectors, and generates fault data for each component based on Fluke’s law. A stacking classifier was applied for fault prediction and severity classification, and logistic regression was used to identify fault components. Additionally, to analyze the frequency bands affecting each failed component and assess the severity of faults involving two mixed components, a hybrid explainable artificial intelligence (XAI) model combining Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) was employed to inform the user about the component causing the fault. This approach demonstrated a high prediction accuracy of 95%, and its integration into real-time monitoring systems is expected to reduce maintenance costs, decrease equipment downtime, and ultimately improve productivity.Electronics240A hybrid explainable artificial intelligence model combining Shapley additive explanations and local interpretable model-agnostic explanations was employed and demonstrated a high prediction accuracy of 95%, and its integration into real-time monitoring systems is expected to reduce maintenance costs, decrease equipment downtime, and ultimately improve productivity.Electronics["Jeong-Eun Jeon", "S. Hong", "Seung-Soo Han"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/692ded054335e2dca97049f9998611bd9fb29307 b58d207ee50e52b61a08bb5165926ea1d4d45efcArtificial intelligence (AI) is rapidly diffusing and becoming increasingly popular in the business world, as the most vital driving force to transform the business process in recent years. The applications of AI now are impacting all aspects of business operations, which also include Supply Chain Management (SCM). This research examines and discusses the current AI technologies used in logistics and SCM, including AI methods like Machine Learning (ML) and Advanced Data Analytics. We have conducted comprehensive research from literature research on available AI-based industrial applications today and explored some significant AI challenges for the SCM in business and related careers. The discussions cover AI's primary functions, including supply chain configuration, agility, demand forecasting, inventory control, scheduling, and solving SCM logistics issues. To sum up the discussions, we provided managerial implication suggestions for SCM professionals and researchers.International Journal of Business &amp; Management Studies00This research examines and discusses the current AI technologies used in logistics and SCM, including AI methods like Machine Learning (ML) and Advanced Data Analytics and provides managerial implication suggestions for SCM professionals and researchers.International Journal of Business &amp; Management Studies["Leong Chan", "Pei Zhang", "Rao Kowtha", "Chung- Shing Lee"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/b58d207ee50e52b61a08bb5165926ea1d4d45efc 3e49531321171810aa59f2ae61a34682471d6db4Background: Artificial intelligence has become a valuable tool for diagnosing and detecting postoperative complications early. Through imaging and biochemical markers, clinicians can anticipate the clinical progression of patients and the risk of long-term complications that could impact the quality of life or even be life-threatening. In this context, artificial intelligence is crucial for identifying early signs of complications and enabling clinicians to take preventive measures before problems worsen. Materials and methods: This observational study analyzed medical charts from the electronic archive of the Clinical Emergency Hospital in Galați, Romania, covering a four-year period from 2018 to 2022. A neural network model was developed to analyze various socio-demographic and paraclinical data. Key features included patient demographics, laboratory investigations, and clinical outcomes. Statistical analyses were performed to identify significant risk factors associated with deep venous thrombosis (DVT). Results: The analysis revealed a higher prevalence of female patients (60.78%) compared to male patients, indicating a potential gender-related risk factor for DVT. The incidence of DVT was highest among patients aged 71 to 90 years, affecting 56.86% of individuals in this age group, suggesting that advanced age significantly contributes to the risk of developing DVT. Additionally, among the DVT patients, 15.69% had a body mass index (BMI) greater than 30, categorizing them as obese, which is known to increase the risk of thrombotic events. Furthermore, this study highlighted that the highest frequency of DVT was associated with femur fractures, occurring in 52% of patients with this type of injury. The neural network analysis indicated that elevated levels of direct bilirubin (≥1.5 mg/dL) and prothrombin activity (≤60%) were strong predictors of fracture-related complications, with sensitivity and specificity rates of 78% and 82%, respectively. These findings underscore the importance of monitoring these laboratory markers in at-risk populations for early intervention. Conclusions: This study identified critical risk factors for developing DVT, including advanced age, high BMI, and femur fractures, which necessitate longer recovery periods. Additionally, the findings indicate that elevated direct bilirubin and prothrombin activity play a significant role in predicting DVT development. These results suggest that AI can effectively enhance the anticipation of clinical evolution in patients, aiding in early intervention and management strategies.Clinics and Practice530This study identified critical risk factors for developing deep venous thrombosis, including advanced age, high BMI, and femur fractures, which necessitate longer recovery periods, and indicates that elevated direct bilirubin and prothrombin activity play a significant role in predicting DVT development.Clinics and Practice["Aurelian-Dumitrache Anghele", "V. Marina", "L. Dragomir", "C. Moscu", "I. Fulga", "Mihaela Anghele", "Cristina-Mihaela Popescu"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/3e49531321171810aa59f2ae61a34682471d6db4 2d13d57e96d026bb873f1e37f4693510df807643Detecting and preventing white-collar crimes is the most critical challenge faced by the traditional auditing system, which lacks investigative expertise. Corporate fraud and willful misstatement of financial information downgrade the true spirit of corporate governance objectives. In contrast, forensic auditing is a step ahead of traditional auditing systems because of its robust and proactive investigative proficiency in unraveling complex financial irregularities and contributing to achieving corporate governance objectives. Artificial Intelligence has revolutionized the world as a substitute for human intelligence; integration of AI with forensic auditing has created a synergy to combat corporate crimes much more efficiently and effectively than traditional auditing systems. This study has tried to magnify that forensic audit is a step ahead of the traditional audit and AI Integrated forensic auditing is a powerful tool to reduce corporate crimes and help to achieve corporate governance goals.The Management Accountant Journal80This study has tried to magnify that forensic audit is a step ahead of the traditional audit and AI Integrated forensic auditing is a powerful tool to reduce corporate crimes and help to achieve corporate governance goals.The Management Accountant Journal["Bikash Saha"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/2d13d57e96d026bb873f1e37f4693510df807643 e24c609df00c638c3396b7d605cfac735b69e8bbThe training and mentoring activities for the use of Artificial Intelligence (AI) tools for teachers were chosen with the aim of enhancing or improving teachers’ understanding and ability to implement AI technology in the educational environment. Initial interviews revealed a lack of knowledge and skills among teachers in utilizing AI tools, which affects the quality and effectiveness of learning. The methods applied in this community service include counseling and training, preceded by surveys and interviews. Teachers were given intensive training that included an overview of AI and how to apply AI tools in the teaching and learning process. Additionally, there was an implementation phase that required teachers to design and implement simple AI-based learning applications as supporting media. The AI tools used included Gamma, IlovePDF, chatPDF, and ChatGPT. The results showed significant improvements in teachers’ understanding and skills in using AI tools. Teachers were able to create AI-based learning applications aimed at enhancing interaction between teachers and students. Furthermore, teachers could reduce administrative workload and optimize resources. Thus, the use of AI can have a positive effect on the quality of learning. The implementation of AI also facilitates teachers’ work in managing administration, such as handling attendance, assessments, and lesson planning.  ASPIRASI : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat00The training and mentoring activities for the use of Artificial Intelligence (AI) tools for teachers were chosen with the aim of enhancing or improving teachers’ understanding and ability to implement AI technology in the educational environment to show significant improvements in teachers’ understanding and skills.ASPIRASI : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat["Brave A. Sugiarso", "Arie S.M. Lumenta", "Pingkan A.K. Pratasis"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/e24c609df00c638c3396b7d605cfac735b69e8bb 8745513b0a085f9a8e36ef02c8f24e25764c5de8This paper focuses on the influence of artificial intelligence (AI) in the process of new drug development and in the treatment of diseases. Based on the literature review of the last five years, we present the broad range of AI applications ranging from drug discovery, screening to regulatory aspects, drug designing, and delivery. Leveraging of AI firms by pharmaceutical companies has seen more effective drug development thus improving the global health care systems. Virtual screening and design with the use of AI allow expanding the concept of traditional medicine and enable the search for new drugs in combined molecular structures. Furthermore, the application of AI in pharmaceutical regulatory affair seems significant in enhancing efficiency and compliance. The synthesized nanocarriers with the help of AI technologies are highly precise to have an accurate targeting system in treatments which enhances its effectiveness and the quality of life of the patients. More changes in regards to drug designing, discovering along with the compliance to regulatory benchmarks will be seen in the near future with the mounting popularity of the AI among the sectors of the pharmaceutical industries.2024 First International Conference for Women in Computing (InCoWoCo)100The broad range of AI applications ranging from drug discovery, screening to regulatory aspects, drug designing, and delivery, and delivery, and the application of AI in pharmaceutical regulatory affair seems significant in enhancing efficiency and compliance are presented.2024 First International Conference for Women in Computing (InCoWoCo)["Nikitha Arun", "K. Ravichandran"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/8745513b0a085f9a8e36ef02c8f24e25764c5de8 31f42cf62948f389db01ae2b106add5b3b34a7d7The integration of Generative Artificial Intelligence into clinical processes requires addressing ethical aspects and inherent risks, contributing not only to the automation of these processes but also to the ongoing development of personalized medicine. The generation of complex diagnostic and personalized treatment solutions must be carried out in accordance with deontological norms and ethical standards, aimed at ensuring the protection of patient autonomy and safety. The development of an architectural framework for the identification of pathologies and the provision of treatment scenarios must ensure both the efficiency of the solutions offered and their compliance with ethical standards, involving the creation of a set of rules and practices that encourage interdisciplinary collaboration between experts in technology, medicine, and law. The fundamental principles that facilitate the integration of emerging technologies into clinical processes promote patient autonomy, decision-making transparency, and clinical responsibility. (Abstract)E-Health and Bioengineering Conference120The development of an architectural framework for the identification of pathologies and the provision of treatment scenarios must ensure both the efficiency and compliance with ethical standards, involving the creation of a set of rules and practices that encourage interdisciplinary collaboration between experts in technology, medicine, and law.2024 E-Health and Bioengineering Conference (EHB)["Miruna-Elena Iliu\u0163\u0103", "Damien Trentesaux", "Eugen Pop", "M. Moisescu"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/31f42cf62948f389db01ae2b106add5b3b34a7d7 2fff8b5a73eb1b4740c0f9419b7e5c7687b65edePurposeThis study provides a comprehensive overview of the impact of artificial intelligence (AI) applications on oral healthcare, focusing on clinical outcomes.Design/methodology/approachA systematic approach was used to gather articles from databases such as Scopus, ScienceDirect, PubMed, Web of Science and Google Scholar from 2010 to 2024. The selection criteria included articles published in English, focusing solely on clinical applications of AI in dentistry. Articles such as conference proceedings, editorial material and personal opinions were excluded. The articles were analyzed and visualized using Rayyan software, Microsoft Excel and VOSviewer.FindingsResults indicate that 120 publications were authored by 58 scholars from 92 institutions across 29 countries, with a notable surge since 2018. This analysis showed the significant emphasis on the use of deep learning, demonstrating its high accuracy and performance in oral healthcare, often exceeding that of dentists. It also proved that even though AI is sometimes seen as an auxiliary tool, many studies revealed that AI has a performance near dental professionals’ levels. Findings concluded that the majority of studies indicate that AI is generating better clinical outcomes in oral healthcare.Practical implicationsThis study provides dental professionals with insights on integrating AI for better diagnosis and treatment. Policymakers and healthcare institutions can use these findings to inform AI adoption and training strategies.Originality/valueIt presents novel and valuable findings that can benefit various stakeholders by shedding light on the present scenario and potential future paths of AI integration in oral healthcare, contributing to its overall advancement.Journal of Health Organization and Management260It was concluded that the majority of studies indicate that AI is generating better clinical outcomes in oral healthcare, contributing to its overall advancement.Journal of Health Organization and Management["Faten AlQaifi", "Dilaver Tengilimo\u011flu", "Ilknur Arslan Aras"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/2fff8b5a73eb1b4740c0f9419b7e5c7687b65ede 13c1c483e9b84e17e44ba5eae4683398399a50d9This study aimed to identify and analyze the characteristics of highly cited publications in the field of artificial intelligence within the Science Citation Index Expanded from 1991 to 2022. The assessment focused on documents that garnered 100 citations or more from the Web of Science Core Collection, spanning from their publication date to the end of 2022. Various aspects of these documents were analyzed, encompassing document types, the distribution of annual production, the average number of citations per publication, Web of Science categories, and journals. Moreover, the publication performance of countries, institutions, and authors underwent evaluation through six publication indicators and associated citation metrics. To facilitate a comprehensive comparison of the authors research performance, the Y-index was employed. The outcomes of the analysis revealed that a majority of the highly cited articles were published within the Web of Science categories of"artificial intelligence computer science"and"electrical and electronic engineering". Notably, the United States exhibited dominance across all six publication indicators. Within the realm of average citations per publication, the United Kingdom emerged as a leader for independent articles, first-author articles, and corresponding-author articles. Exceptionally, the Chinese Academy of Sciences in China and the Massachusetts Institute of Technology (MIT) in the USA, contributed significantly. The significant impact of highly cited articles extended to the output of Stanford University in the USA. B.L. Bassler published the most highly cited articles. Upon employing the Y-index analysis, J.E.P. Santos was identified as having the highest potential for publication. In addition to the primary analysis, this study also presented nine classic articles that have left an indelible mark on artificial intelligence research.arXiv.org00Nine classic articles that have left an indelible mark on artificial intelligence research are presented, encompassing document types, the distribution of annual production, the average number of citations per publication, Web of Science categories, and journals.ArXiv["Yuh-Shan Ho", "J. Prieto-Gutierrez"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/13c1c483e9b84e17e44ba5eae4683398399a50d9 8edb984b39347c9543ecd81f0f8c8e272a9be50aThis paper introduces a novel application of the “conceive, design, implement, operate (CDIO)” framework to improve the thoroughness and organization of academic editorial review processes. It demonstrates that the CDIO framework, originally applied to engineering education, can also be adapted for reviewing creative and interdisciplinary ideas. The adaptation of the CDIO framework for editorial review is already evident in scholarly publications, and this paper extends its application to include reviews of content produced by artificial intelligence (AI) platforms. The “conceive” stage focuses on developing clear research questions and objectives that align with the key moments of article conception. It ensures that content produced by AI begins with an ethical scientific foundation and maintains this integrity throughout the process. The “design” stage emphasizes maintaining scientific accuracy and clarity of presentation. It considers all critical manuscript design elements and incorporates methods to evaluate the orig-inality and rationality of AI-generated data and analysis. The “implementation” stage is concerned with the effective communication of findings, providing insights into how the manuscript is perceived. It is crucial for data generation or tool usage involving AI. The “operate stage” involves analyzing the findings and their overall impact on the field, ensuring a comprehensive assessment from all perspectives when AI-generated content is integrated into academic discourse, which has broader implications. By applying the CDIO framework innovatively, this paper offers a systematic and comprehensive method for conducting editorial reviews. This ensures that manuscripts generated by AI are subjected to the same rigorous scrutiny as those authored by humans. This approach improves the quality, transparency, and reputation of scholarly publications. We examine each stage of the CDIO process, achieving uniformity and clarity, and providing a more precise evaluation of both traditional and AI-assisted academic research.Science Editing220This paper introduces a novel application of the “conceive, design, implement, operate (CDIO)” framework to improve the thoroughness and organization of academic editorial review processes, and demonstrates that the CDIO framework can be adapted for reviewing creative and interdisciplinary ideas.Science Editing["Aji Prasetya Wibawa", "A. N. Handayani", "Prananda Anugrah", "Agung Bella Putra Utama"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/8edb984b39347c9543ecd81f0f8c8e272a9be50a 18dcfcab4152f995bcd5565ffafa1e7df8313c00BMC Medical Informatics Decis. Mak.580The findings suggest that AI tools, such as machine learning (ML) algorithms, have high prediction capabilities despite the dynamic and complex association among the influencing factors and AKI.BMC Medical Informatics and Decision Making["Dima Tareq Al-Absi", "M. Simsekler", "Mohammad Atif Omar", "Siddiq Anwar"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/18dcfcab4152f995bcd5565ffafa1e7df8313c00 f4f34f06bc8fffd27c5eef54f3efb40f6aa958bdABSTRACT In this article, the authors propose a repurposing of the concept of entrustment to help guide the use of artificial intelligence (AI) in health professions education (HPE). Entrustment can help identify and mitigate the risks of incorporating generative AI tools with limited transparency about their accuracy, source material, and disclosure of bias into HPE practice. With AI's growing role in education-related activities, like automated medical school application screening and feedback quality and content appraisal, there is a critical need for a trust-based approach to ensure these technologies are beneficial and safe. Drawing parallels with HPE's entrustment concept, which assesses a trainee's readiness to perform clinical tasks-or entrustable professional activities-the authors propose assessing the trustworthiness of AI tools to perform an HPE-related task across 3 characteristics: ability (competence to perform tasks accurately), integrity (transparency and honesty), and benevolence (alignment with ethical principles). The authors draw on existing theories of entrustment decision-making to envision a structured way to decide on AI's role and level of engagement in HPE-related tasks, including proposing an AI-specific entrustment scale. Identifying tasks that AI could be entrusted with provides a focus around which considerations of trustworthiness and entrustment decision-making may be synthesized, making explicit the risks associated with AI use and identifying strategies to mitigate these risks. Responsible, trustworthy, and ethical use of AI requires health professions educators to develop safeguards for using it in teaching, learning, and practice-guardrails that can be operationalized via applying the entrustment concept to AI. Without such safeguards, HPE practice stands to be shaped by the oncoming wave of AI innovations tied to commercial motivations, rather than modeled after HPE principles-principles rooted in the trust and transparency that are built together with trainees and patients.Academic medicine : journal of the Association of American Medical Colleges00Identifying tasks that AI could be entrusted with provides a focus around which considerations of trustworthiness and entrustment decision-making may be synthesized, making explicit the risks associated with AI use and identifying strategies to mitigate these risks.Academic medicine : journal of the Association of American Medical Colleges["Brian C. Gin", "Patricia S. O\u2019Sullivan", "Karen E. Hauer", "Raja-Elie Abdulnour", "Madelynn Mackenzie", "O. ten Cate", "Christy K Boscardin"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/f4f34f06bc8fffd27c5eef54f3efb40f6aa958bd c71b3aa6822da996eaf066df8472cb5cc5e3f04fThe rapid proliferation of data centers in the US - driven partly by the adoption of artificial intelligence - has set off alarm bells about the industry's environmental impact. We compiled detailed information on 2,132 US data centers operating between September 2023 and August 2024 and determined their electricity consumption, electricity sources, and attributable CO$_{2}$e emissions. Our findings reveal that data centers accounted for more than 4% of total US electricity consumption - with 56% derived from fossil fuels - generating more than 105 million tons of CO$_{2}$e (2.18% of US emissions in 2023). Data centers' carbon intensity - the amount of CO$_{2}$e emitted per unit of electricity consumed - exceeded the US average by 48%. Our data pipeline and visualization tools can be used to assess current and future environmental impacts of data centers.arXiv.org330ArXiv["Gianluca Guidi", "Francesca Dominici", "Jonathan Gilmour", "K. Butler", "Eric Bell", "Scott Delaney", "Falco J. Bargagli Stoffi"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/c71b3aa6822da996eaf066df8472cb5cc5e3f04f 69d86b5793ba18fd17d5f8ff399ff8ce579aa780Little is known about the funding organizations and mechanisms behind artificial intelligence (AI) research conducted in United States (U.S.) educational systems (K12 and higher education). This study therefore performs a bibliometric and network analysis of AI research conducted in U.S. educational systems to explore which types of organizations fund peer-reviewed scholarship, which organizations receive this funding, and how these organizations form funded research networks. The results suggest evidence of institutional stratification, with non-U.S. government organizations (such as in China and Europe) funding many AI studies within U.S. educational systems. Moreover, the data suggest stratified funding networks have marginalized Minority-Serving Institutions, consolidating the influence of AI research conducted in U.S. educational systems among few, elite, and predominately White institutions. The implications for research and policy advocacy are also addressed.Education sciences100Evidence of institutional stratification is suggested, with non-U.S. government organizations (such as in China and Europe) funding many AI studies within U.S. educational systems and stratified funding networks have marginalized Minority-Serving Institutions.Education Sciences["Z. Taylor", "K. Stan"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/69d86b5793ba18fd17d5f8ff399ff8ce579aa780 c7f41a1db690c222bda60d341a0388e2690ded86Background: Congenital heart diseases (CHD) are one of the most common birth defects, occurring in 5-9 per 1000 newborns. CHD are the second leading cause of infant mortality and account for 47% of all causes of death from birth defects.The main method for assessing the anatomy and function of the heart is 2-dimensional ultrasonography. Artificial intelligence (AI) technologies are great at recognizing images, thus facilitating quick scanning and analysis of visual information in order to speed up and simplify the diagnostic ultrasonography.All AI software for obstetrics use static images. In our study conducted at the National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov (Moscow, Russian Federation) in 2022-2023, we used video files including 1-5 standard heart views for each fetus.Objective: To create a data set for development of an AI tool that improves the quality of fetal CHD diagnosis and to develop an algorithm for examining the fetal heart using AI. Resulting medical reports could be either “normal” (correct structure of the heart; no sign of CHD) or “abnormal” (incorrect structure of the heart; CHD cannot be excluded; extended fetal echocardiography is recommended as soon as possible).Materials and methods: The examination was conducted at 18-21 weeks’ gestation. Each examination contained video files of 5 standard views of the heart per patient. Each view is at least 25 frames. Verification was performed by confirming/changing the diagnosis by a physician and confirming the diagnosis after birth.Conclusions: As a result, the task of determining zones of the fetal chest and heart was solved with an approximate accuracy of 98%; the task of classifying the heart view on the frame was solved with an approximate accuracy of 82%, and the task of determining the disease on the heart views was solved with an approximate accuracy of 77%.Innovative Medicine of Kuban200This study used video files including 1-5 standard heart views for each fetus to create a data set for development of an AI tool that improves the quality of fetal CHD diagnosis and to develop an algorithm for examining the fetal heart using AI.Innovative Medicine of Kuban["E. L. Bokerija", "N. E. Yannaeva", "A. N. Sencha", "P. V. Metelkin", "O. V. Yurchenko"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/c7f41a1db690c222bda60d341a0388e2690ded86 550ecb56618e7e9afa8121b21ed3f10b1fe22263Generative Artificial Intelligence (GenAI) has revolutionized knowledge management, offering unprecedented capabilities for creating, proofing, summarizing, and evaluating documentation. This paper explores how AI, particularly large language models (LLMs), and Retrieval Augmented Generation (RAG) systems, can streamline the development of knowledge articles while addressing ethical concerns such as data ownership and bias. We examine practical applications, including real-time collaboration, multilingual support, personalized information retrieval, and automated knowledge forecasting. Additionally, we explore AI’s role in bridging legacy systems, reducing biases, and enhancing decision-making. Ultimately, AI extends beyond generating content, shaping a more efficient, inclusive, and innovative approach to knowledge management. This article is based upon a presentation given at the 2024 NISO Plus Conference that was held in Baltimore, MD, USA, February 13–14, 2024.Information Services and Use00This paper explores how AI, particularly large language models (LLMs), and Retrieval Augmented Generation (RAG) systems, can streamline the development of knowledge articles while addressing ethical concerns such as data ownership and bias.Information Services and Use["Brian Pichman"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/550ecb56618e7e9afa8121b21ed3f10b1fe22263 6e698d4da83cedf3c36d145ffe57d71a07d36673Purpose of review Though simple in its fundamental mechanism – a critical disruption of local blood supply – stroke is complicated by the intricate nature of the neural substrate, the neurovascular architecture, and their complex interactions in generating its clinical manifestations. This complexity is adequately described by high-resolution imaging with sensitivity not only to parenchymal macrostructure but also microstructure and functional tissue properties, in conjunction with detailed characterization of vascular topology and dynamics. Such descriptive richness mandates models of commensurate complexity only artificial intelligence could plausibly deliver, if we are to achieve the goal of individually precise, personalized care. Recent findings Advances in machine vision technology, especially deep learning, are delivering higher fidelity predictive, descriptive, and inferential tools, incorporating increasingly rich imaging information within ever more flexible models. Impact at the clinical front line remains modest, however, owing to the challenges of delivering models robust to the noisy, incomplete, biased, and comparatively small-scale data characteristic of real-world practice. Summary The potential benefit of introducing AI to stroke, in imaging and elsewhere, is now unquestionable, but the optimal approach – and the path to real-world application – remain unsettled. Deep generative models offer a compelling solution to current obstacles and are predicted powerfully to catalyse innovation in the field.Current Opinion in Neurology410The potential benefit of introducing AI to stroke, in imaging and elsewhere, is now unquestionable, but the optimal approach – and the path to real-world application – remain unsettled.Current Opinion in Neurology["Jane Rondina", "P. Nachev"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/6e698d4da83cedf3c36d145ffe57d71a07d36673 91ec43633e12eebaf3b1c31eeefee85969b8998aUsing artificial intelligence (AI) in the audit process offers new opportunities to improve the efficiency, productivity and importance of activities taken up by the auditors. To implement AI properly, it is desired that the auditors understand the nuances of the technology and its advantages/limitations. While the auditors possess all the desired skills, using AI will improve their performance by moving them away from routine activities and enabling them to use their energies to bring out better insights to the organisation. This is the right time for the audit teams to start working on bringing the synergy between the audit process and AI, to ensure a win-win situation for the auditors and their clients.The Management Accountant Journal00This is the right time for the audit teams to start working on bringing the synergy between the audit process and AI, to ensure a win-win situation for the auditors and their clients.The Management Accountant Journal["Nageswara Rao"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/91ec43633e12eebaf3b1c31eeefee85969b8998a 4048e20744c25d41218b2360a4ed1d91868168f7The purpose of this study is to analyze development of artificial intelligence in business ethics and regulatory responsibilities in the era of artificial intelligence. The research methods applied in this study are literature study, comparative regulatory analysis, regulatory analysis, and regulatory risk analysis. Through this research method, it is expected to provide a comprehensive picture of the regulatory framework, ethics, and responsibilities related to the development of artificial intelligence in a business context. The results of the study show that the implementation of a regulatory framework for the use of artificial intelligence in business faces a number of complex challenges, such as regulatory uncertainty, unclear regulatory obligations, data privacy and security, algorithmic bias, compliance with regulations and standards, ethical challenges in autonomous decisions, accountability in cases of failure, understanding by stakeholders, risks at the personal level, and changes in job and market dynamics. Keywords: Development, Artificial Intelligence, Business Ethics, Regulatory Responsibilities, Era of Artificial IntelligenceInternational journal of research and review00The results of the study show that the implementation of a regulatory framework for the use of artificial intelligence in business faces a number of complex challenges, such as regulatory uncertainty, unclear regulatory obligations, data privacy and security, algorithmic bias, compliance with regulations and standards, ethical challenges in autonomous decisions.International Journal of Research and Review["Purwanti", "Nur Arifudin", "Mita Sonaria"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/4048e20744c25d41218b2360a4ed1d91868168f7 ee51034b551ac6b1341fc83ee693cc1aa734bdabArtificial intelligence-assisted learning applications have shown significant potential in improving English speaking skills and promoted changes in traditional English teaching models. With the advancement of technology, more and more learners are relying on smart applications to improve their speaking skills. These applications can not only provide personalized learning plans, but also correct pronunciation and intonation through real-time feedback, thereby effectively improving learners’ pronunciation a ccuracy. This study investigates the impact of artificial intelligence-assisted learning applications on English speaking ability through a literature review. The purpose of this study is to understand the existing research and literature on the use of artificial intelligence-assisted learning applications in English speaking learning environments. This article first provides an overview of artificial intelligence-assisted learning applications. Then the relationship between artificial intelligence-assisted learning applications and learners' English speaking improvement is discussed from the theoretical basis. Finally, the impact of artificial intelligence-assisted learning applications on English speaking ability is studied through a literature review. The results of this literature review show that artificial intelligence-assisted learning applications have an overall positive impact on English speaking ability. But longitudinal studies are still needed to examine the long-term effects on learners' language proficiency.International Journal of Academic Research in Progressive Education and Development340The results of this literature review show that artificial intelligence-assisted learning applications have an overall positive impact on English speaking ability, but longitudinal studies are still needed to examine the long-term effects on learners' language proficiency.International Journal of Academic Research in Progressive Education and Development["Bin Xu", "Hanita Hanim Ismail"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/ee51034b551ac6b1341fc83ee693cc1aa734bdab 69aed25baced00aacd471d9970cf31c49e4ccbe7This research examines the integration of Artificial Intelligence (AI) in enhancing autonomous navigation systems within robotics, focusing on developing adaptive machine learning algorithms for high-dimensional data processing. The primary objective is to advance AI-based navigation systems that outperform traditional methods in terms of accuracy, obstacle avoidance, and efficiency. By leveraging deep learning for intricate visual perception and reinforcement learning for agile decision-making and path optimization, the study achieves a substantial increase in navigation precision and obstacle detection in both simulated and real-world settings. The findings reveal that these AI-driven systems surpass conventional rule-based systems and exhibit superior adaptability in dynamic and unstructured environments. Future efforts will concentrate on refining these algorithms to enhance environmental recognition and extend AI applications to more complex robotic operations. This research supports Sustainable Development Goals (SDGs) by promoting innovative infrastructure (SDG 9) and fostering industry innovation and infrastructure development, which are vital for sustainable economic growth and environmental protection.International Transactions on Artificial Intelligence (ITALIC)00The findings reveal that these AI-driven systems surpass conventional rule-based systems and exhibit superior adaptability in dynamic and unstructured environments.International Transactions on Artificial Intelligence (ITALIC)["Pedro Costa", "Januri Ferdiansyah", "Hani Dewi Ariessanti"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/69aed25baced00aacd471d9970cf31c49e4ccbe7 56727f5229ca47d4f4a9567a777cbbe75140c2ebThe purpose of research. Radiation diagnostics is central to the detection of malignant neoplasms. Recently, the implementation of screening programs has faced a number of obstacles, including staff shortages and limited funding. The introduction of artificial intelligence (AI)-based systems capable of absolutely accurate sorting of research into two categories - "normal" and "not normal", seems to be a promising solution to these problems. However, before they are widely used, it is critically important to verify their ability to guarantee the safety and high quality of the screening process. The aim of the study is to evaluate the possibility of using autonomous sorting of mammographic examination results in real clinical conditions.  Methods. The study was carried out in 2 stages. At the first stage, 25,892 mammographic studies processed by the AI service were retrospectively analyzed. A ROC analysis of these results was carried out in order to assess the possibility of configuring the AI service for 100% sensitivity. At the prospective stage, the results of 82,372 mammograms were analyzed. All studies were processed by AI services configured for 100% sensitivity. The tasks of the AI services included the sorting of mammography results into the categories "normal" and "not normal". Next, the decisions of AI services and radiologists on categorization were compared. Results. According to the results of a retrospective study, when configuring the AI service for 100% sensitivity, the specificity was 39%. In the course of a prospective study, the proportion of defects (false attribution of research results to the "norm" category) was 0.08%, the specific weight of clinically significant defects in AI services was 0.02%, which is significantly lower than that of a radiologist. Conclusion. The use of autonomous sorting of mammographic research results in clinical practice is possible in order to optimize the diagnostic process during preventive measures, as well as under the condition of monitoring the quality of artificial intelligence technologies. Keywords: artificial intelligence, mammography, preventive examinations, radiation diagnostics. Conflict of interest: The author declares the absence of obvious and potential conflicts of interest related to the publication of this article. Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering100The use of autonomous sorting of mammographic research results in clinical practice is possible in order to optimize the diagnostic process during preventive measures, as well as under the condition of monitoring the quality of artificial intelligence technologies.Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering["Yu. A. Vasilev", "K. Arzamasov", "A. Vladzymyrskyy", "A. Kolsanov", "I. Shulkin", "T. Bobrovskaya", "L. Pestrenin"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/56727f5229ca47d4f4a9567a777cbbe75140c2eb 6ac740926b4d6a7d98cc2f8717cc293a7a298befAim: This study was conducted to evaluate the knowledge, attitude/behavior and anxiety levels of nurse academics about artificial intelligence applications. Material and Methods: The research was conducted online with 202 nurse academicians in a descriptive type. Data Collection Form, Artificial Intelligence Anxiety Scale were used to collect data. SPSS 21 package program was used to evaluate the data. Descriptive statistics, Kolmogorov-Smirnov, Shapiro-Wilk, Spearman, Mann-Whitney U, Kruskal-Wallis H tests were used to evaluate the data. pGenel Tip Dergisi230Evaluating the knowledge, attitude/behavior and anxiety levels of nurse academics about artificial intelligence applications and SPSS 21 package program was used to evaluate the data.Genel Tıp Dergisi["Deniz Yi\u011fit", "Ayfer A\u00e7\u0131kg\u00f6z"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/6ac740926b4d6a7d98cc2f8717cc293a7a298bef c7f79c4fea0ee9888a9c165f4de5e073cc2eaf0dArtificial Intelligence and infectious diseases prediction have recently experienced a common development and advancement. Machine learning apparition, along with deep learning emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients’ Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of Artificial Intelligence and outlines its limitations in infectious disease management.Acta Universitatis Sapientiae: Informatica610This research categorizes contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients’ Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population.ArXiv["Selestine Melchane", "Youssef Elmir", "Farid Kacimi", "L. Boubchir"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/c7f79c4fea0ee9888a9c165f4de5e073cc2eaf0d 5341f2e18fb31ba5164c32a5f9a95a3b60202d6dOECD Artificial Intelligence Papers01OECD Artificial Intelligence Papers[]2024-11-14T00:00:00https://www.semanticscholar.org/paper/5341f2e18fb31ba5164c32a5f9a95a3b60202d6d 3d297208256d66dc97152a73ec777da45b331b46The role of energy storage as an effective technique for supporting energy supply is impressive because energy storage systems can be directly connected to the grid as stand-alone solutions to help balance fluctuating power supply and demand. This comprehensive paper, based on political, economic, sociocultural, and technological analysis, investigates the transition toward electricity systems with a large capacity for renewable energy sources combined with energy storage systems (ESS), along with a comprehensive overview of energy storage technologies; the role of AI in the development of ESS is also presented. This study aims to demonstrate how energy storage systems can be implemented with successful integration to increase electric grid flexibility. The results of the study indicate that this goal can be achieved with suitable planning and cooperation by the national, provincial, and local governments, while taking into account stakeholders’ needs and environmental concerns. In this regard, comprehensive analysis has revealed that procedures such as planning, increasing rewards for renewable energy storage, technological innovation, expanding subsidies, and encouraging investment in infrastructure for renewable energy and large-scale battery storage are crucial for the development of energy storage systems. Furthermore, stakeholders should be able to comprehend the benefits of energy storage systems and their provided valuable services, and engage in the adoption process. Moreover, leveraging AI can significantly enhance the implementation and operation of energy storage systems in energy systems, enabling governments and policymakers to optimize the storage and distribution of energy from renewable sources.Sustainability1931This study aims to demonstrate how energy storage systems can be implemented with successful integration to increase electric grid flexibility and indicates that this goal can be achieved with suitable planning and cooperation by the national, provincial, and local governments, while taking into account stakeholders’ needs and environmental concerns.Sustainability["Armin Razmjoo", "Arezoo Ghazanfari", "P. A. \u00d8stergaard", "M. Jahangiri", "Andreas Sumper", "Sahar Ahmadzadeh", "Reza Eslamipoor"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/3d297208256d66dc97152a73ec777da45b331b46 a82dcb8fbe2f607817ff865aef941dedf9b937abArtificial Intelligence Review581Artif. Intell. Rev.["Gang Kou", "Serkan Eti", "S. Y\u00fcksel", "H. Di\u0307n\u00e7er", "Edanur Erg\u00fcn", "Ya\u015far G\u00f6kalp"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/a82dcb8fbe2f607817ff865aef941dedf9b937ab 4d0a4c2f49b420e475c001d770792d87adad2a5dThis research paper discusses the current security situation in Uzbekistan and emphasizes the abovementioned problems connected with increasingly operating network attacks. Using the example of Estonia, it analyzes in general terms how AI algorithms, in particular artificial neural networks, may both worsen and improve state cybersecurity. The study is intended to serve two main purposes: assessing the current state of cybersecurity in Uzbekistan for common threats and vulnerabilities, as well as testing AI techniques to protect against these threats. AI 'is the only solution which can fight different cybersecurity threats effectively', the research notes, highlighting that AI is essential to increase Uzbekistan's capability to absorb cyberattacks and protect critical infrastructures and ensure quality of digital resources.International Journal of Innovative Science and Research Technology70Assessing the current state of cybersecurity in Uzbekistan for common threats and vulnerabilities, as well as testing AI techniques to protect against these threats, highlights that AI is essential to increase Uzbekistan's capability to absorb cyberattacks and protect critical infrastructures and ensure quality of digital resources.International Journal of Innovative Science and Research Technology (IJISRT)["Abdullayev Bilol"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/4d0a4c2f49b420e475c001d770792d87adad2a5d 162cdb80fd5fe939c2df9da36e39291690122291Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes40Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes["Ryan D Postle", "Bruce B. Forster"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/162cdb80fd5fe939c2df9da36e39291690122291 1b64d5aaed0921d59f274c24be681e1d23de885fInnovations in Education & Teaching International180Innovations in Education and Teaching International["Swapna Kumar", "Ariel Gunn"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/1b64d5aaed0921d59f274c24be681e1d23de885f 5af2fe2ef213f2b54560b83940ee7e7c1a57834000["C. Meissner"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/5af2fe2ef213f2b54560b83940ee7e7c1a578340 c84428e5d86812c38d810b99f8f9cd1a4b10f11bThe paper focuses on big data and healthcare pharmaceuticals, which pose both great promise and challenge as they can contribute to well-being and deliver innovation and knowledge to next-generation healthcare products. This paper provides an overview of the benefits that machine learning and big data technology contribute to healthcare systems. Realities such as patient and healthcare professional demands, the number of diseases, insufficient drugs, regulations, legal and ethical guideline restrictions, modus operandi, and technology development take place in the healthcare sector. In addition, big data opportunities and challenges for healthcare companies, research, and healthcare infrastructure are discussed. Finally, a vision of health innovation through personalized medicines as well as smart medicine is presented as a step closer to patients through regulatory, legal, and ethical guidelines, applications, and opportunities that need to be regulated and resolved.South Eastern European Journal of Public Health00An overview of the benefits that machine learning and big data technology contribute to healthcare systems is provided and a vision of health innovation through personalized medicines as well as smart medicine is presented as a step closer to patients.South Eastern European Journal of Public Health["Ramanakar Reddy Danda", "Zakera Yasmeen", "Gowtham Mandala", "Kiran Kumar Maguluri", "Pambala Ganesh"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/c84428e5d86812c38d810b99f8f9cd1a4b10f11b 2969a92065a9df62df1608ff9a03ddb9cb998eacSN Computer Science420SN Comput. Sci.["Poonam Kaushik", "Khushboo Bansal", "Yogesh Kumar", "Ankur Changela"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/2969a92065a9df62df1608ff9a03ddb9cb998eac bfb87179e83d3576ed40531ee280446f3435144cCirculation. Cardiovascular Quality and Outcomes00Circulation. Cardiovascular quality and outcomes["V. Sangha", "R. Khera"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/bfb87179e83d3576ed40531ee280446f3435144c 34efb2e32414e02fa8e8c54d5fc3e91e45c2f6eeThe journal of the International Association of Medical Science Educators : JIAMSE20Medical science educator["D. McKell", "Lise McCoy", "Diego F. Ni\u00f1o"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/34efb2e32414e02fa8e8c54d5fc3e91e45c2f6ee 49cbb15e4f895dee2c29e1f245ce37b650d141d4Shoulder arthroplasty (SA) has been performed with different types of implants, each requiring different replacement systems. However, data on previously utilized implant types are not always available before revision surgery, which is paramount to determining the appropriate equipment and procedure. Therefore, this meta-analysis aimed to evaluate the accuracy of the AI models in classifying SA implant types.This systematic review was conducted in Pubmed, Embase, SCOPUS, and Web of Science from inception to December 2023, according to PRISMA guidelines. Peer-reviewed research evaluating the accuracy of AI-based tools on upper-limb X-rays for recognizing and categorizing SA implants was included. In addition to the overall meta-analysis, subgroup analysis was performed according to the type of AI model applied (CNN (Convolutional neural network), non-CNN, or Combination of both) and the similarity of utilized datasets between studies.13 articles were eligible for inclusion in this meta-analysis (including 138 different tests assessing models’ efficacy). Our meta-analysis demonstrated an overall sensitivity and specificity of 0.891 (95% CI:0.866-0.912) and 0.549 (95% CI:0.532,0.566) for classifying implants in SA, respectively. The results of our subgroup analyses were as follows: CNN-subgroup: a sensitivity of 0.898 (95% CI:0.873-0.919) and a specificity of 0.554 (95% CI:0.537,0.570), Non-CNN subgroup: a sensitivity of 0.809 (95% CI:0.665-0.900) and specificity of 0.522 (95% CI:0.440,0.603), combined subgroup: a sensitivity of 0.891 (95% CI:0.752-0.957) and a specificity of 0.547 (95% CI:0.463,0.629).Studies using the same dataset demonstrated an overall sensitivity and specificity of 0.881 (95% CI:0.856-0.903) and 0.542 (95% CI:0.53,0.554), respectively. Studies that used other datasets showed an overall sensitivity and specificity of 0.995 (95% CI:969,0.999) and 0.678 (95% CI:0.234, 0.936), respectively.AI-based classification of shoulder implant types can be considered a sensitive method. Our study showed the potential role of using CNN-based models and different datasets to enhance accuracy, which could be investigated in future studies.Orthopaedic Proceedings00The study showed the potential role of using CNN-based models and different datasets to enhance accuracy, which could be investigated in future studies.Orthopaedic Proceedings["A.M. Asgari", "F. Shaker", "M. Fallahy", "M. Soleimani", "S. Shafiei", "Y. Fallah"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/49cbb15e4f895dee2c29e1f245ce37b650d141d4 532434fcba60503b682ecefed7b579903c474e4cFrontiers in Neuroscience00Frontiers in Neuroscience["A. Elnakib", "Fahmi Khalifa", "Ahmed Soliman", "Ahmed Shalaby", "Mostafa Elhosseini"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/532434fcba60503b682ecefed7b579903c474e4c b4c8fa4a4cc4be3d77183a192c734abcfc0987a3Investytsiyi praktyka ta dosvid00Investytsiyi: praktyka ta dosvid["Iu. Perga", "R. Pashov"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/b4c8fa4a4cc4be3d77183a192c734abcfc0987a3 a3693272688e9e216b5e96c31195b4610c88b71bAs global technologies develop, they integrate into social and state institutions. Mundane public communications were the first to embrace computers, smartphones, social networks, etc. These days, modern technologies facilitate the interaction between the state and its people. As a result, the state increases the internal and external availability of public services. The current friendly relations between the Russian Federation and some African countries may win from using new information technologies in politics and business. In this sense, modern communications require a better understanding of such innovative institutions as the so-called digital justice, which employs digital tools as a platform of justice administration. It has a good potential for resolving social issues of African population, as well as major economic disputes. The article describes the legal aspects of the new dispute resolution opportunities that digital justice offers the people of Uganda. It focuses on the prospects and problems of blockchain technology and artificial intelligence in settling disputes in court, especially in those aspects that concern the most vulnerable social strata. Blockchain algorithms prove effective in resolving land ownership disputes in Uganda while artificial intelligence may be used to provide pro bono legal services.Bulletin of Kemerovo State University. Series: Humanities and Social Sciences70The article describes the legal aspects of the new dispute resolution opportunities that digital justice offers the people of Uganda and focuses on the prospects and problems of blockchain technology and artificial intelligence in settling disputes in court, especially in those aspects that concern the most vulnerable social strata.Bulletin of Kemerovo State University. Series: Humanities and Social Sciences["Ekaterina Rusakova", "Tat'yana Chernysheva"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/a3693272688e9e216b5e96c31195b4610c88b71b 8f905910e2af2fcf8fbfe4712de5b49a70a27ffb00["A. Eltorai", "H. H. Guo"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/8f905910e2af2fcf8fbfe4712de5b49a70a27ffb 7460835acc9da09abfc7710c04f0f23e3855d2dfJournal of Dental Education40Journal of dental education["Shahbaz Katebzadeh", "Paloma Reyes Nguyen", "C. Puranik"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/7460835acc9da09abfc7710c04f0f23e3855d2df 4e601653eb1b6f72b2861e195e36208ef0cc6445In the current digital era, economic and financial inclusion is a crucial issue in various countries including Indonesia. The use of artificial intelligence (AI) has great potential to improve economic and financial inclusion by providing easier, more efficient, and affordable access to financial and economic services for those who were previously marginalized. This research aims to identify Artificial Intelligence (AI) can be used to provide more inclusive financial services to those who are underserved, as well as to analyze its impact on the overall economic welfare of the community, especially Gorontalo city. This research used a descriptive qualitative approach, involving case studies and interviews with stakeholders. The results show that the utilization of Artificial Intelligence (AI) chatbots and virtual assistants has a significant impact on service and user satisfaction, as well as providing confidence in decision making so as to increase economic and financial inclusion for the communityIdeas: Jurnal Pendidikan, Sosial, dan Budaya00The results show that the utilization of Artificial Intelligence (AI) chatbots and virtual assistants has a significant impact on service and user satisfaction, as well as providing confidence in decision making so as to increase economic and financial inclusion for the community.Ideas: Jurnal Pendidikan, Sosial, dan Budaya["Octaviani Suryaningsih Masaguni", "Waldi Patadjenu", "Kurniadi K. Hasan", "R. Amir", "Mohamad Rizal Pasisingi"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/4e601653eb1b6f72b2861e195e36208ef0cc6445 a9924729ace4d050393e84d50c250bf445998543One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent. In a recent article by Ozmen et al this was framed as one of six grand challenges: That AI needs to respect human cognitive processes at the human-AI interaction frontier. We suggest that this extends to the AI-AI frontier and that it should also reflect human psychology, as it is the only successful framework we have from which to build out. In this extended abstract we first make the case for collective intelligence in a general setting, drawing on recent work from single neuron complexity in neural networks and ant network adaptability in ant colonies. From there we introduce how species relate to one another in an ecological network via niche selection, niche choice, and niche conformity with the aim of forming an analogy with human social network development as new agents join together and coordinate. From there we show how our social structures are influenced by our neuro-physiology, our psychology, and our language. This emphasises how individual people within a social network influence the structure and performance of that network in complex tasks, and that cognitive faculties such as Theory of Mind play a central role. We finish by discussing the current state of the art in AI and where there is potential for further development of a socially embodied collective artificial intelligence that is capable of guiding its own social structures.arXiv.org00This extended abstract makes the case for collective intelligence in a general setting, drawing on recent work from single neuron complexity in neural networks and ant network adaptability in ant colonies, and emphasises how individual people within a social network influence the structure and performance of that network in complex tasks.ArXiv["Michael S. Harr'e", "Jaime Ruiz-Serra", "Catherine Drysdale"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/a9924729ace4d050393e84d50c250bf445998543 f4ddc97acaacd87d9712fc688d7d641c40759907This article investigates the growing use of Artificial Intelligence (AI) in companies in Araraquara, SP, and its importance in decision-making. Organizations are increasingly integrating AI into their projects, recognizing benefits such as cost reduction and increased productivity. Decision Support Systems, which use predictive models, allow managers to process large volumes of data and respond quickly to market dynamics. Despite still moderate implementation in some sectors, AI has demonstrated positive impacts on decisions and trend forecasting. However, barriers such as the high cost of implementation and a lack of confidence in the technologies limit its adoption. The survey reveals that although companies recognize the benefits of AI, acceptance remains limited. The expectation is that the use of Artificial Intelligence will increase, thus requiring employee training and technical support to increase its benefit. The survey suggests that education about Artificial Intelligence is crucial to its acceptance and success in organizations.Revista ft00The survey reveals that although companies recognize the benefits of AI, acceptance remains limited and the expectation is that the use of Artificial Intelligence will increase, thus requiring employee training and technical support to increase its benefit.Revista ft["Eduardo Henrique Dolce", "Renata Mirella Farina", "F. Florian"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/f4ddc97acaacd87d9712fc688d7d641c40759907 c80a5a4915869f21a207054e163d29c259eab533This systematic review explores the transformative role of artificial intelligence (AI) in shaping assessment practices within 21st-century education. It critically examines the integration of AI technologies such as Automated Essay Scoring (AES), adaptive learning systems, and learning analytics, emphasizing their contributions to personalized learning experiences and real-time feedback mechanisms. The review identifies key opportunities for AI to enhance educational assessment, including the automation of scoring and the provision of adaptive feedback. However, it also addresses significant ethical challenges such as algorithmic bias, data privacy, and the need for transparency. We urge policymakers and educators to establish robust ethical guidelines and invest in comprehensive educator training to ensure the responsible use of AI in educational settings. The future directions suggest an increase in the integration of AI technologies, emphasizing the need for ongoing research to enhance validity, reliability, and address ethical considerations in AI-driven assessment practices.International Journal of Research in STEM Education00The review identifies key opportunities for AI to enhance educational assessment, including the automation of scoring and the provision of adaptive feedback, but also addresses significant ethical challenges such as algorithmic bias, data privacy, and the need for transparency.International Journal of Research in STEM Education["I. Ogunsakin"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/c80a5a4915869f21a207054e163d29c259eab533 38ded3dbbb76609457eaca6c1619fbad3a64de86Academic writing is a substantial component of tertiary education, yet it poses challenges for second/foreign language (L2/FL) students, particularly those first-year undergraduates with limited exposure to English-medium-instruction (EMI) settings. In this context, English-for-academic-purposes (EAP) tutors play a crucial role in supporting these students, but little is known about the nature of their feedback in scholarly discourse. Complicating matters further is the emergence of Generative Artificial Intelligence (GenAI) as a feedback tool, sparking ongoing debate about its efficacy compared to traditional human feedback. To address these gaps, this study investigates the nature of EAP teacher feedback on English-as-a-foreign-language (EFL) disciplinary writing, juxtaposing it against student opinions and attitudes towards both EAP teacher feedback and AI-generated feedback. This study employed a three-layer coding scheme focusing on corrective, genre-specific, and intentional feedback to analyse the nature of EAP teacher feedback in detail. Through a comprehensive analysis of interview themes, this study highlights the significance of EAP teacher feedback in the context of increasing integration of GenAI tools. The findings offer valuable insights into effective practices for supporting first-year EFL undergraduate students in their academic writing within EMI settings and demonstrate the critical role of EAP teacher feedback in assisting these students’ writing in an AI-prevalent world.Australian Journal of Applied Linguistics01This study employed a three-layer coding scheme focusing on corrective, genre-specific, and intentional feedback to analyse the nature of EAP teacher feedback in detail and highlights the significance of EAP teacher feedback in the context of increasing integration of GenAI tools.Australian Journal of Applied Linguistics["Yiyun Fan", "Sheng Tan", "Grace Yuk Wan Lim"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/38ded3dbbb76609457eaca6c1619fbad3a64de86 b8b355bfa517183908f71369fea1aacbe19ff3b4The widespread adoption of generative artificial intelligence (AI) has fundamentally transformed technological landscapes and societal structures in recent years. Our objective is to identify the primary methodologies that may be used to help predict the economic and social impacts of generative AI adoption. Through a comprehensive literature review, we uncover a range of methodologies poised to assess the multifaceted impacts of this technological revolution. We explore Agent-Based Simulation (ABS), Econometric Models, Input-Output Analysis, Reinforcement Learning (RL) for Decision-Making Agents, Surveys and Interviews, Scenario Analysis, Policy Analysis, and the Delphi Method. Our findings have allowed us to identify these approaches' main strengths and weaknesses and their adequacy in coping with uncertainty, robustness, and resource requirements.arXiv.org551A range of methodologies poised to assess the multifaceted impacts of this technological revolution are uncovered, including Agent-Based Simulation, Econometric Models, Input-Output Analysis, Reinforcement Learning for Decision-Making Agents, Surveys and Interviews, Scenario Analysis, Policy Analysis, and the Delphi Method.ArXiv["Carlos J. Costa", "J. T. Apar\u00edcio", "Manuela Aparicio"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/b8b355bfa517183908f71369fea1aacbe19ff3b4 348af4ffaa4fdef2bd5977906ebd2cc9cfd17302Artificial Intelligence (AI) technologies can potentially revolutionize the aerospace industry with applications such as remote sensing data refinement, autonomous landing, and drone-based agriculture. However, safety concerns have prevented the widespread adoption of AI in commercial aviation. Currently, commercial aircraft do not incorporate AI components, even in entertainment or ground systems. This paper explores the intersection of AI and aerospace, focusing on the challenges of certifying AI for airborne use, which may require a new certification approach. We conducted a comprehensive literature review to identify common AI-enabled aerospace applications, classifying them by the criticality of the application and the complexity of the AI method. An applicability analysis was conducted to assess how existing aerospace standards - for system safety, software, and hardware - apply to machine learning technologies. In addition, we conducted a gap analysis of machine learning development methodologies to meet the stringent aspects of aviation certification. We evaluate current efforts in AI certification by applying the EASA concept paper and Overarching Properties (OPs) to a case study of an automated peripheral detection system (ADIMA). Aerospace applications are expected to use a range of methods tailored to different levels of criticality. Current aerospace standards are not directly applicable due to the manner in which the behavior is specified by the data, the uncertainty of the models, and the limitations of white box verification. From a machine learning perspective, open research questions were identified that address validation of intent and data-driven requirements, sufficiency of verification, uncertainty quantification, generalization, and mitigation of unintended behavior. For the ADIMA system, we demonstrated compliance with EASA development processes and achieved key certification objectives. However, many of the objectives are not applicable due to the human-centric design. OPs helped us to identify and uncover several defeaters in the applied ML technology. The results highlight the need for updated certification standards that take into account the unique nature of AI and its failure types. Furthermore, certification processes need to support the continuous evolution of AI technologies. Key challenges remain in ensuring the safety and reliability of AI systems, which calls for new methodologies in the machine learning community.Frontiers in Aerospace Engineering1201Key challenges remain in ensuring the safety and reliability of AI systems, which calls for new methodologies in the machine learning community, which highlights the need for updated certification standards that take into account the unique nature of AI and its failure types.Frontiers in Aerospace Engineering["Bastian Luettig", "Yassine Akhiat", "Zamira Daw"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/348af4ffaa4fdef2bd5977906ebd2cc9cfd17302 e0924427a19f17a4d3d7c4d1986c32649530942eIn the wake of the fourth industrial revolution, artificial intelligence is gaining momentum and is widely applied in various aspects of life, particularly education. This study investigates the factors influencing students' use of artificial intelligence (AI) in learning, focusing on students at Ho Chi Minh City University of Industry. The research uses a combination of the technology acceptance model and the theory of planned behavior to examine the relationships between subjective norms, image, job relevance, output quality, result demonstrability, self-efficacy, anxiety, perceived playfulness, perceived enjoyment, perceived ease of use, perceived usefulness, and behavioral intention. Combining these technological models brings new insights into the context of AI that can support or hinder user behavior through bias. The results were then analyzed based on the least squares linear structural model, with 390 students participating in the survey using the stratified sampling approach. The study found that perceived ease of use and usefulness are the most significant factors influencing students' intention to use AI in learning. Subjective norms also play an essential role in shaping students' image and intention to use AI. The research also highlights the importance of self-efficacy, perceived enjoyment, playfulness, output quality, result demonstrability, and job relevance in influencing students' perceptions and use of AI. The findings of this study underscore the need for educational institutions to create a supportive environment that encourages students to use AI in learning. In contrast, AI technology creators need to focus on simplifying the user experience to make AI tools more accessible and easy to use. These practical recommendations of the research can guide policy and design decisions in the field of educational technology. Finally, in place of a conclusion, the study also aims to open up further approaches for AI platforms in academia.Multidisciplinary Science Journal01Perceived ease of use and usefulness are the most significant factors influencing students' intention to use AI in learning, and the need for educational institutions to create a supportive environment that encourages students to use AI in learning is underscore.Multidisciplinary Science Journal["Nguyen Binh Phuong Duy", "Tran Ngoc My Phuong", "Vu Nguyen Minh Chau", "Nguyen Vo Huong Nhi", "Vo Thi Mai Khuyen", "Nguyen Thi Phuong Giang"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/e0924427a19f17a4d3d7c4d1986c32649530942e c33aa1d9865fe0158e8eea594c3c0a8bf658f40bOur everyday lives now heavily rely on artificial intelligence (AI) powered large language models (LLMs). Like regular users, programmers are also benefiting from the newest large language models. In response to the critical role that AI models play in modern software development, this study presents a thorough evaluation of leading programming assistants, including ChatGPT, Gemini(Bard AI), AlphaCode, and GitHub Copilot. The evaluation is based on tasks like natural language processing and code generation accuracy in different programming languages like Java, Python and C++. Based on the results, it has emphasized their strengths and weaknesses and the importance of further modifications to increase the reliability and accuracy of the latest popular models. Although these AI assistants illustrate a high level of progress in language understanding and code generation, along with ethical considerations and responsible usage, they provoke a necessity for discussion. With time, developing more refined AI technology is essential for achieving advanced solutions in various fields, especially with the knowledge of the feature intricacies of these models and their implications. This study offers a comparison of different LLMs and provides essential feedback on the rapidly changing area of AI models. It also emphasizes the need for ethical developmental practices to actualize AI models' full potential.arXiv.org571This study presents a thorough evaluation of leading programming assistants, including ChatGPT, Gemini, Gemini, AlphaCode, and GitHub Copilot, based on tasks like natural language processing and code generation accuracy in different programming languages like Java, Python and C++.ArXiv["Md Kamrul Siam", "Huanying Gu", "Jerry Q. Cheng"]2024-11-14T00:00:00https://www.semanticscholar.org/paper/c33aa1d9865fe0158e8eea594c3c0a8bf658f40b c83e7a41ff17d2911998affb9844f63415941720The technology of the Internet of Vehicles (IoV) and digital twins (DTs) is driving deeper connectivity between vehicles and road infrastructure. Through the data exchange of IoV and the simulation of DT technology, vehicle driving decisions, traffic management, and road planning are optimized. However, DT models contain a large amount of private vehicle data, causing the risk of privacy leakage. Distributed artificial intelligence (AI) methods, particularly federated learning (FL) algorithms, ensure data security and privacy by sharing data models rather than sharing private data. Current mainstream algorithms use FL and local differential privacy (LDP) or blockchain approaches to protect data security at the cost of lower model accuracy and larger computation time. In the vehicle road cooperation, we designed a three-layer DT-driven personalized privacy-preserving framework, which includes a physical layer, a DT layer, and an application layer. In our proposed framework, to improve the security and performance of DT models, a time-sensitive PLDP-based FL (TimeSenFLDP) mechanism is proposed to achieve different privacy levels of the DT model of vehicles over sharing time steps. Compared with the mainstream algorithm (e.g., DP-SGD), the experiments prove that our proposed algorithm has 18.07%, 16.32%, and 7.5% accuracy improvement in FedAvg, FedProx, and FedDyn, respectively.IEEE Internet of Things Journal531To improve the security and performance of DT models, a time-sensitive PLDP-based FL (TimeSenFLDP) mechanism is proposed to achieve different privacy levels of the DT model of vehicles over sharing time steps.IEEE Internet of Things Journal["Kangkang Sun", "Jun Wu", "A. Bashir", "Jianhua Li", "Hansong Xu", "Qianqian Pan", "Yasser D. Al-Otaibi"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/c83e7a41ff17d2911998affb9844f63415941720 7a41bca3fc2ac4862429a638bc805cd14117426aBackground: Patient engagement refers to the actively involvement of individuals in their own treatment, decision-making, and partnering with healthcare providers. Due to the progress of Artificial Intelligence (AI), there is a groundbreaking shift happening in healthcare that is bringing numerous benefits to patients and health systems. AI technologies are not only used as instruments in healthcare but also evolving into collaborators for diagnosing and treating patients, as well as enhancing the quality of services, offering a personalized, timely, and interactive healthcare experience. This article delves into how AI is changing patient engagement by increasing effectiveness and catering to individual patient requirements. Methods: The research made use of previously published materials on artificial intelligence, data, and robotic technologies in healthcare settings to explore ways in which patients can be effectively involved in the advancement of these technologies in healthcare settings. Results: AI-powered decision support systems improve healthcare operations by giving instant access to data analysis and medical advice, ultimately aiding in making decisions based on real-time evidence. AI enables proactive healthcare interventions by detecting potential health issues in real time, allowing for remote patient monitoring. Motivating patients to actively participate can result in improved adherence to the treatment plan. Overall, patient engagement appears to be the most well-developed and progressive concept for enabling patients to participate actively in their healthcare. Conclusions: AI technologies are more than just tools in healthcare; they are evolving into collaborators in patient involvement, providing a personalized, proactive and engaging healthcare journey. Further evidence is required to comprehend how patients engage in the process and if this leads to better quality of care. In the dynamic health sector, change management is essential for continuously updating and adjusting healthcare facilities to address the evolving needs of patients. Health systems that do not adjust to changes in a timely manner are unsuccessful.Austin Journal of Clinical Medicine01How AI is changing patient engagement by increasing effectiveness and catering to individual patient requirements is explored, with AI-powered decision support systems improving healthcare operations by giving instant access to data analysis and medical advice, ultimately aiding in making decisions based on real-time evidence.Austin Journal of Clinical Medicine["D. Karaferis", "Dimitra Balaska", "Y. Pollalis"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/7a41bca3fc2ac4862429a638bc805cd14117426a 3b3b13d6cd37b23ff6beffb3ef77458ef6dd0323AIM To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care. BACKGROUND AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care. METHODS A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing. RESULTS The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing. DISCUSSION The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards. CONCLUSION Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing. IMPLICATIONS FOR NURSING AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance. IMPLICATIONS FOR HEALTH POLICY Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.International Nursing Review401Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed, and Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing.International nursing review["Walaa Badawy", "Haithm Zinhom", "Mostafa Shaban"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/3b3b13d6cd37b23ff6beffb3ef77458ef6dd0323 11cf281cd0441373a6a84d3c8412f97a61916323Integrating advanced technologies such as artificial intelligence (AI) with traditional agricultural practices has changed how activities are developed in agriculture, with the aim of automating manual processes and improving the efficiency and quality of farming decisions. With the advent of deep learning models such as convolutional neural network (CNN) and You Only Look Once (YOLO), many studies have emerged given the need to develop solutions to problems and take advantage of all the potential that this technology has to offer. This systematic literature review aims to present an in-depth investigation of the application of AI in supporting the management of weeds, plant nutrition, water, pests, and diseases. This systematic review was conducted using the PRISMA methodology and guidelines. Data from different papers indicated that the main research interests comprise five groups: (a) type of agronomic problems; (b) type of sensor; (c) dataset treatment; (d) evaluation metrics and quantification; and (e) AI technique. The inclusion (I) and exclusion (E) criteria adopted in this study included: (I1) articles that obtained AI techniques for agricultural analysis; (I2) complete articles written in English; (I3) articles from specialized scientific journals; (E1) articles that did not describe the type of agrarian analysis used; (E2) articles that did not specify the AI technique used and that were incomplete or abstract; (E3) articles that did not present substantial experimental results. The articles were searched on the official pages of the main scientific bases: ACM, IEEE, ScienceDirect, MDPI, and Web of Science. The papers were categorized and grouped to show the main contributions of the literature to support agricultural decisions using AI. This study found that AI methods perform better in supporting weed detection, classification of plant diseases, and estimation of agricultural yield in crops when using images captured by Unmanned Aerial Vehicles (UAVs). Furthermore, CNN and YOLO, as well as their variations, present the best results for all groups presented. This review also points out the limitations and potential challenges when working with deep machine learning models, aiming to contribute to knowledge systematization and to benefit researchers and professionals regarding AI applications in mitigating agronomic problems.Agronomy1142This study found that AI methods perform better in supporting weed detection, classification of plant diseases, and estimation of agricultural yield in crops when using images captured by Unmanned Aerial Vehicles (UAVs).Agronomy["Josef Augusto Oberdan Souza Silva", "Vilson Soares de Siqueira", "M\u00e1rcio Mesquita", "Lu\u00eds S\u00e9rgio Rodrigues Vale", "Jhon Lennon Bezerra da Silva", "M. V. D. Silva", "Jo\u00e3o Paulo Barcelos Lemos", "L. N. Lacerda", "R. Ferrarezi", "H. F. E. D. Oliveira"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/11cf281cd0441373a6a84d3c8412f97a61916323 c87d6af4730f41903c1d792cb7f9701bc88cf3ebThe continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.ACM Transactions on Computing for Healthcare881This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives, and endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.ArXiv["Qiyang Sun", "Alican Akman", "Bj\u00f6rn W. Schuller"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/c87d6af4730f41903c1d792cb7f9701bc88cf3eb 65105c5777aa46d039cc6b525d25ad6c6680bd96This complete study, delves into the multifaceted impacts of artificial Intelligence (AI) inside organizational settings, highlighting its ability and demanding situations. The investigation spans numerous aspects along with AI-driven customer relationship management (CRM), employee productivity, and overall performance enhancement thru AI. By analyzing distinct AI applications and methodologies across different organizational functions, this studies presents insights into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development. Despite the promising programs, the study also addresses the pitfalls and enormous hesitancy in AI adoption due to disasters in some high-profile AI projects. The paper underscores the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs).2024 International Conference on Cybernation and Computation (CYBERCOM)250Insight is presented into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development and the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs).2024 International Conference on Cybernation and Computation (CYBERCOM)["Shiney Chib", "Manjusha P. Gandhi", "Shantanu S Bose", "V. P. Thirulogasundaram", "H. N. Prasanna", "S.R Lakshmi"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/65105c5777aa46d039cc6b525d25ad6c6680bd96 489512a1253c98666e9536ca525c3e9f5c00ba03Colorado State Fair adalah acara karnival yang di dalamnya terdapat serangkaian acara yang dapat diikuti oleh semua orang. Colorado State Fair diadakan setiap tahunnya di negara bagian Amerika Serikat, yaitu di Colorado. Di dalam acara Colorado State Fair sendiri terdapat perlombaan karya seni yang dimenangkan oleh seorang desainer game bernama Jason Allen yang memenangkan perlombaan seni dengan kategori karya seni digital. Dalam perlombaan tersebut Jason Allen menggunakan sebuah Artificial Intelligence yang bernama Midjourney. Dengan kemanangan Jason Allen yang menggunakan AI tersebut mengakibatkan beberapa perdebatan dari peserta lainnya. Ada yang menyebutkan bahwa hal tersebut merupakan kecurangan dan ada juga yang mengatakan bahwa hal tersebut boleh boleh saja. Tujuan dari penelitian ini adalah mengetahui bagaimana pendapat dan tanggapan seorang pekerja kreatif digital terkhusus grafik desain dan ilustrtor. Peneliti menggunakan pendekatan in depth interview atau wawancara mendalam mengenai fenomena yang terjadi. Banyak yang beranggapan bahwa hal tersebut boleh boleh saja. Akan tetapi, seharusnya tidak menggunakan teknologi artificial intelligencei dalam pembuatan karya seni, terutama dalam sebuah perlombaan. Serta banyak informan yang mengatakan bahwa teknologi artificial intelligence tidak dapat menggantikan pekerja kreatif seutuhnya, karena harus ada campur tangan dari manusia sendiri agar karya tersebut original, serta pesan pesan yang ingin disampaikan dapat tersampaikan dengan jelas dan tepat sasaran.JIIP - Jurnal Ilmiah Ilmu Pendidikan00JIIP - Jurnal Ilmiah Ilmu Pendidikan["M. Marendra"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/489512a1253c98666e9536ca525c3e9f5c00ba03 2310a588c30a2d9ab5e5967d65401e42fb974ef0 ABSTRACT Traditionally, the life cycle management of military vehicle fleets is a lengthy and costly process involving maintenance crews completing numerous and oftentimes unnecessary inspections and diagnostics tests. Recent technological advances have allowed for the automation of life cycle management processes of complex systems. In this paper, we present our process for applying artificial intelligence (AI) and machine learning (ML) in the life cycle management of military vehicle fleets, using a Ground Vehicle fleet. We outline the data processing and data mapping methodologies needed for generating AI/ML model training data. We then use AI and ML methods to refine our training sets and labels. Finally, we outline a Random Forest classification model for identifying system failures and associated root causes. Our evaluation of the Random Forest model results show that our approach can predict system failures and associated root causes with 96% accuracy. SAE technical paper series00This paper outlines the data processing and data mapping methodologies needed for generating AI/ML model training data, and outlines a Random Forest classification model for identifying system failures and associated root causes.SAE Technical Paper Series["Maxwell C. Kern", "Arif Cengic"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/2310a588c30a2d9ab5e5967d65401e42fb974ef0 c46b9217a122992d00941651078d93f84b43570dThis article explores the impact of artificial intelligence (AI) technology on learning behavior investment in English learning and its optimization strategies. Research shows that AI technology has significantly improved learners' learning motivation and participation through personalized learning resources, real-time feedback mechanisms and flexible learning environments. AI applications, such as intelligent tutoring systems, speech recognition and automatic correction systems, can provide tailored content based on learners' needs and progress, promoting learning efficiency and interest. However, factors such as the learner's personal characteristics, the functional perfection of AI technology, and the learning environment have an important impact on learning behavior investment. The research suggests that the development of personalized resources should be strengthened, AI functions should be optimized, and a positive learning atmosphere should be created to improve learners’ behavioral investment and learning effectiveness.Journal of Education and Educational Research40The research suggests that the development of personalized resources should be strengthened, AI functions should be optimized, and a positive learning atmosphere should be created to improve learners’ behavioral investment and learning effectiveness.Journal of Education and Educational Research["Chunyang Lin"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/c46b9217a122992d00941651078d93f84b43570d 07e8f67a7c623ff29a9d96237b67264ec6d90650The aging population, combined with the scarcity of healthcare resources, presents significant challenges for our society. The use of artificial intelligence (AI) and robotics offers a potential solution to these challenges. However, such technologies also raise ethical and cybersecurity concerns related to the preservation of privacy, autonomy, and human contact. In this case study, we examine these ethical challenges and the opportunities brought by AI and robotics in the care of old individuals at home. This article aims to describe the current fragmented state of legislation related to the development and use of AI-based services and robotics and to reflect on their ethics and cybersecurity. The findings indicate that, guided by ethical principles, we can leverage the best aspects of technology while ensuring that old people can maintain a dignified and valued life at home. The careful handling of ethical issues should be viewed as a competitive advantage and opportunity, rather than a burden.Information190The findings indicate that, guided by ethical principles, the best aspects of technology while ensuring that old people can maintain a dignified and valued life at home can be leveraged while ensuring that old people can maintain a dignified and valued life at home.Information["J. Rajam\u00e4ki", "Jaakko Helin"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/07e8f67a7c623ff29a9d96237b67264ec6d90650 58e427b08678523d685b54c15f5fd2c5e1c5b073Abstract: The employment market has undergone an enormous shift as a result of the rapid progress of artificial intelligence (AI). This transformation presents both significant challenges and opportunities. This paper examines the ways in which AI-driven automation is transforming a variety of industries, resulting in employment displacement in sectors that rely on routine tasks and the creation of new roles in technology, data management, and AI maintenance. Addressing the growing disparities between those who benefit from AI and those who are left behind, as well as managing large-scale employment displacement, are the primary challenges. This is particularly true for low-skilled workers. AI, however, also offers chances to boost creativity, productivity, and the creation of highly skilled jobs in cutting-edge industries. In order to alleviate adverse consequences, this investigation underscores the necessity of proactive measures, such as educational reform, reskilling and upskilling programs, and collaboration among educational institutions, governments, and industries. By prioritizing these strategies, the employment market can adjust to the transformative potential of AI, thereby fostering economic development and inclusive job creation by balancing the reduction of traditional roles with the emergence of new opportunities.Lecture Notes in Education Psychology and Public Media130Examination of the ways in which AI-driven automation is transforming a variety of industries, resulting in employment displacement in sectors that rely on routine tasks and the creation of new roles in technology, data management, and AI maintenance, suggests proactive measures, such as educational reform, reskilling and upskilling programs, and collaboration among educational institutions, governments, and industries are needed.Lecture Notes in Education Psychology and Public Media["Xingtai Fan"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/58e427b08678523d685b54c15f5fd2c5e1c5b073 36cd5ee8fa61fb1bf747541d162288f182789fadThis study aims to examine how Artificial Intelligence (AI) technology can be utilized to improve teacher performance at MTsN 1 Jombang. This research uses a descriptive qualitative method with the type of field research where the researcher is directly involved in the research location and acts as the primary instrument. Data collection techniques were carried out in observation, documentation, and interviews. There are two data sources in this study, namely primary and secondary. Primary data is generated from interviews with the head of the madrasah, deputy head of curriculum, and six teachers at MTsN 1 Jombang. Simultaneously, secondary data is obtained from books, the internet, and scientific journals. Data analysis in this study went through three stages: data reduction, data presentation, and conclusion drawing. The results of this study show that implementing AI technology to support teacher performance at MTsN 1 Jombang is classified as good. This is reflected in the number of teachers who are helped with administrative tasks, teaching skills, creativity, and learning innovation by utilizing AI technology. Based on the data obtained, at MTsN 1 Jombang, implementing AI technology makes it more accessible. It increases the effectiveness and efficiency in completing various tasks, opening up opportunities for more creative learning innovations.Tarbawi: Jurnal Keilmuan Manajemen Pendidikan570The results of this study show that implementing AI technology to support teacher performance at MTsN 1 Jombang is classified as good, and it increases the effectiveness and efficiency in completing various tasks, opening up opportunities for more creative learning innovations.Tarbawi: Jurnal Keilmuan Manajemen Pendidikan["Mey Mayangsari", "Miftakhul Ilmi Suwignya Putra", "Moh Makmun"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/36cd5ee8fa61fb1bf747541d162288f182789fad 2fb33ccafabd1f043408172793ebd2a31cadc915This review paper gives an in-depth analysis of how artificial intelligence has been integrated into management approaches, highlighting how this development has been transformation ally powerful in changing decision-making, resource allocation, operational efficiency, and strategic planning. Businesses are empowered to make actionable business insights through a combination of machine learning algorithms and predictive analytics that have characterized organizations in the utilization of large datasets, improving management information systems to enhance competitive advantage. The paper elaborates on various data types used in management, methods of data collection and processing, and ways AI is applied to different management functions, including risk management, fraud prevention, and construction management. This will lead to the proposition of a full integration strategy embracing cross-disciplinary teams, ethical considerations, and training employees in AI for combat against such problems as algorithm compatibility, technological competence, and ethics. The research gives the business world an overview of the very high potential benefits and challenges involved in the integration of AI, thus providing a roadmap to businesses that intend to drive their management practices by AI.2024 International Conference on Cybernation and Computation (CYBERCOM)260The research gives the business world an overview of the very high potential benefits and challenges involved in the integration of AI, thus providing a roadmap to businesses that intend to drive their management practices by AI.2024 International Conference on Cybernation and Computation (CYBERCOM)["Shailesh Kediya", "Vandana Mohanti", "Pranay Wankhede", "Revati Deshkar", "Shiwani Wagh", "A. Gudadhe"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/2fb33ccafabd1f043408172793ebd2a31cadc915 4548913c5665df435d76f0de4842a9cc9b20956bAI has been integrated into marketing plans and has significantly enhanced the strategic market analysis in the automotive industry based in the UK. This research examines the presence of AI and its impact on the marketing strategies of the UK automobile industry specifically relating to consumer behaviors. Its purpose is to explore the role of artificial intelligence in marketing and its impact on consumer behavior and marketing/sales. Based on the cross-sectional approach, this study synthesizes data extracted from prior research to develop an understanding of the interplay between AI-based marketing & consumers. The type of research used is a qualitative research approach and specifically focuses on a systematic literature review of the articles done in the automotive industry concerning the use of AI in marketing. In this research, AI has been explained as an independent variable AI has been defined in this research as an independent variable. While, Marketing & Sales (MS) & Consumer behavior were considered as dependent variables. The findings of the regression analysis provide further evidence that AI based marketing has a positive and significant correlation with customer buying behavior. Moreover, in the automobile sector of the UK, marketing and selling both have a positive and significant relationship with the (AI) sector. Thus, this study can prove that artificial intelligence significantly impacts marketing and customer relations within the UK automotive industry. AI technologies may help businesses improve their marketing approaches, optimize their operations, & provide better customer experiences.2024 International Conference on Cybernation and Computation (CYBERCOM)190This study can prove that artificial intelligence significantly impacts marketing and customer relations within the UK automotive industry.2024 International Conference on Cybernation and Computation (CYBERCOM)["Bhavana Likhitkar", "Bhawana Pillai", "Sandhya Parasher"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/4548913c5665df435d76f0de4842a9cc9b20956b 6504f011bbe5ea8770423ffd4e4f7c72aef57859Abstract BACKGROUND: Accurate interpretation of Cardiotocography (CTG) is a critical tool for monitoring fetal well-being during pregnancy and labor, providing crucial insights into fetal heart rate and uterine contractions. Advanced artificial intelligence (AI) tools such as AI-Large Language Models (AI-LLMs) may enhance the accuracy of CTG interpretation, leading to better clinical outcomes. However, this potential has not yet been examined and reported yet. OBJECTIVE: This study aimed to evaluate the performance of three AI-LLMs (ChatGPT-4o, Gemini Advance, and Copilot) in interpreting CTG images, comparing their performance to junior and senior human doctors, and assessing their reliability in assisting clinical decisions. STUDY DESIGN: Seven CTG images were evaluated by three AI-LLMs, five senior doctors (SHD), and five junior doctors (JHD) and rated by five maternal-fetal medicine (MFM) experts (raters) using five parameters (relevance, clarity, depth, focus, and coherence). The raters were blinded to the source of interpretations, and a Likert scale was used to score the performance of each system. Statistical analysis assessed the homogeneity of expert ratings and the comparative performance of AI-LLMs and doctors. RESULTS: ChatGPT-4o outperformed the other AI models with a score of 77.86, much higher than Gemini Advance (57.14) and Copilot (47.29), as well as the junior doctors (JHD; 61.57). CG4o's performance (77.86) was only slightly below that of the senior doctor (SHD; 80.43), with no statistically significant differences between CG4o and SHD (p>0.05). Meanwhile, CG4o had the greatest score in the "depth" category, while the other four parameters were only marginally behind SHD. CONCLUSION: CG4o demonstrated outstanding performance in CTG interpretation, surpassing junior doctors and other AI-LLMs, while senior doctors remain superior in all groups. AI-LLMs, particularly CG4o, showed promising potential as valuable tools in clinical practice to assist obstetricians, enhance diagnostic accuracy, and improve patient care. KEYWORDS: Cardiotocography (CTG), Artificial Intelligence Large Language Models (AI-LLMs), ChatGPT, Gemini, Copilot, Fetal monitoring, ObstetricsmedRxiv220AI-LLMs, particularly CG4o, showed promising potential as valuable tools in clinical practice to assist obstetricians, enhance diagnostic accuracy, and improve patient care.["K. E. Gumilar", "M. P. Wardhana", "M. I. A. Akbar", "A. S. Putra", "D. P. P. Banjarnahor", "R. S. Mulyana", "I. Fatati", "Z.-Y. Yu", "Y.-C. Hsu", "E. G. Dachlan", "C.-H. Lu", "L.-N. Liao", "M. Tan"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/6504f011bbe5ea8770423ffd4e4f7c72aef57859 030fac3b2250cf66b38aa5c0703707f29eae5e34The special focus of this paper is to discuss a likely intersection of machine learning, artificial intelligence (AI) technology, and game theory, pointing at the importance of this synthesis both in mathematics and engineering. As these domains develop various means of addressing decision-making problems become more and more sophisticated and can be used in different areas such as economics, security, and social sciences. We will also address selected game-theoretic issues including the concept of decision making in terms of Nash equilibria or in the distinction of games as being cooperative or non-cooperative and how they work in synergy with machine learning approaches bringing in reinforcements and deep learning to leverage forecasting and strategizing. The paper makes references to problem-oriented branches of studies such as autonomous systems or market strategies stressing the importance of the novel direction for further studies. Within the scope of machine learning and game theory the goal is to implement better complex models which utilize real world intricacies for enhancing decision making within a populated agent environment.Asian Research Journal of Mathematics00A likely intersection of machine learning, artificial intelligence (AI) technology, and game theory is discussed, pointing at the importance of this synthesis both in mathematics and engineering.Asian Research Journal of Mathematics["ODUSELU-HASSAN, Emmanuel Oladayo", "Onyenike Kenneth"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/030fac3b2250cf66b38aa5c0703707f29eae5e34 84bd28a138ab227961763a4319a1530e5a11812eThe integration of artificial intelligence (AI) in analysis of financial data for decision-making shows a positive change as it introduces better methods of analyzing stock market for financial intelligence. This work aims to investigate how the use of AI, comprising computer vision, ML, and NLP influence financial decisions in contrast to traditional forms of expert authoritative opinion. The research analyses how well AI performs when predicting stock prices and improving the financial decisions of an organization through technical analysis, sentiment analysis, and a statistical model. This research compares different approaches of AI, especially Machine learning Models like Linear Regression (LR), Long Short-Term Memory (LSTM), Recursive Autoencoders, as well as Traditional method such as Random Forest (RF), for forecast the appreciation of stocks and sentiment analysis. The work results also show the advantage of LR compared to LSTM in terms of price prediction, and Recursive Autoencoders compared to Random Forest in several parameters. Moreover, experiments with the use of Support Vector Machines (SVM) based on sentiment analysis can be considered promising but in real-life projects, the results are not very satisfactory. Cross-sectional research and hypothesis testing show that AI, especially expert systems and computer vision, enhances the effectiveness of financial decision-making with machine learning being of value to risk and cash management. Therefore, it raises the bar for subsequent research to eliminate current deficits and improve the application of AI for efficiencies in financial management.2024 International Conference on Cybernation and Computation (CYBERCOM)180Cross-sectional research and hypothesis testing show that AI, especially expert systems and computer vision, enhances the effectiveness of financial decision-making with machine learning being of value to risk and cash management.2024 International Conference on Cybernation and Computation (CYBERCOM)["Bhavana Likhitkar", "Arti Joshi", "Santosh Solanki", "Sandhya Parasher"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/84bd28a138ab227961763a4319a1530e5a11812e 5ccba6fb750c2efad528e15aeb71c6c8ee2d73fcPediatric heart diseases present a broad spectrum of congenital and acquired diseases. More complex congenital malformations require a differentiated and multimodal decision-making process, usually including echocardiography as a central imaging method. Artificial intelligence (AI) offers considerable promise for clinicians by facilitating automated interpretation of pediatric echocardiography data. However, adapting AI technologies for pediatric echocardiography analysis has challenges such as limited public data availability, data privacy, and AI model transparency. Recently, researchers have focused on disruptive technologies, such as federated learning (FL) and explainable AI (XAI), to improve automatic diagnostic and decision support workflows. This study offers a comprehensive overview of the limitations and opportunities of AI in pediatric echocardiography, emphasizing the synergistic workflow and role of XAI and FL, identifying research gaps, and exploring potential future developments. Additionally, three relevant clinical use cases demonstrate the functionality of XAI and FL with a focus on (i) view recognition, (ii) disease classification, (iii) segmentation of cardiac structures, and (iv) quantitative assessment of cardiac function.arXiv.org920A comprehensive overview of the limitations and opportunities of AI in pediatric echocardiography is offered, emphasizing the synergistic workflow and role of XAI and FL, identifying research gaps, and exploring potential future developments.ArXiv["M. Y. Jabarulla", "T. Uden", "Thomas Jack", "P. Beerbaum", "S. Oeltze-Jafra"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/5ccba6fb750c2efad528e15aeb71c6c8ee2d73fc 51c761663785ec84d1650a9b8f4be050afcd8497Patient-centered research is increasingly important in narrowing the gap between research and patient care, yet incorporating patient perspectives into health research has been inconsistent. We propose an automated framework leveraging innovative natural language processing (NLP) and artificial intelligence (AI) with patient portal messages to generate research ideas that prioritize important patient issues. We further quantified the quality of AI-generated research topics. To define patient clinical concerns, we analyzed 614,464 patient messages from 25,549 individuals with breast or skin cancer obtained from a large academic hospital (2013 to 2024), constructing a 2-staged unsupervised NLP topic model. Then, we generated research topics to resolve the defined issues using a widely used AI (ChatGPT-4o, OpenAI Inc, April 2024 version) with prompt-engineering strategies. We guided AI to perform multi-level tasks: 1) knowledge interpretation and summarization (e.g., interpreting and summarizing the NLP-defined topics), 2) knowledge generation (e.g., generating research ideas corresponding to patients issues), 3) self-reflection and correction (e.g., ensuring and revising the research ideas after searching for scientific articles), and 4) self-reassurance (e.g., confirming and finalizing the research ideas). Six highly experienced breast oncologists and dermatologists assessed the significance and novelty of AI-generated research topics using a 5-point Likert scale (1-exceptional, 5-poor). One-third of the AI-suggested research topics were highly significant and novel when both scores were lower than the average. Two-thirds of the AI-suggested topics were novel in both cancers. Our findings demonstrate that AI-generated research topics reflecting patient perspectives via a large volume of patient messages can meaningfully guide future directions in patient-centered health research.arXiv.org170It is demonstrated that AI-generated research topics reflecting patient perspectives via a large volume of patient messages can meaningfully guide future directions in patient-centered health research.ArXiv["Jiyeong Kim", "Michael L. Chen", "Shawheen J. Rezaei", "Mariana Ramirez-Posada", "Jennifer L. Caswell-Jin", "Allison W. Kurian", "Fauzia Riaz", "Kavita Y. Sarin", "Jean Y. Tang", "Steven M. Asch", "Eleni Linos"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/51c761663785ec84d1650a9b8f4be050afcd8497 e77e6f05651d696ea677442b732abde3ba8a5333In this article, we consider the quality management system of legislation in the Russian Federation: we define its basic elements, the main tools for quality control (management) in the form of legal examinations, and propose a general simple classification of controlled factors in legal examinations. As an example for conducting quality control of legislation, we selected the most important legal examination - anti-corruption examination. Anti-corruption examination is aimed at identifying corruption-generating factors in regulatory legal acts, which are defects in norms and legal formulas that contribute to the manifestations of corruption. Corruption-generating factors, as a rule, have their own attributes (indicators) in anti-corruption examinations, allowing them to be identified in the texts of regulatory legal acts. Within the framework of general trends in automation, informatization, and digitalization, we considered the use of artificial intelligence for the purposes of conducting anti-corruption examination, which, in some cases of “routine work”, could provide all possible assistance to specialists in the field of legal examinations and their digitalization. In this regard, a step-by-step algorithm for pre-training artificial intelligence has been formulated using examples from regulatory legal acts containing corruption-generating factors; a classification of corruption-generating factors has been carried out; a scale of artificial intelligence errors in detecting corruption-generating factors has been developed; frequency characteristics of artificial intelligence errors have been determined; preliminary conclusions have been obtained on the possibility of using artificial intelligence in anti-corruption expertise.2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)170A step-by-step algorithm for pre-training artificial intelligence has been formulated using examples from regulatory legal acts containing corruption-generating factors, and preliminary conclusions have been obtained on the possibility of using artificial intelligence in anti-corruption expertise.2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)["D. L. Kosov", "V. Belov"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/e77e6f05651d696ea677442b732abde3ba8a5333 ee4fcd4093a7fbfaf437c3b1fa25a5524e546015The economic sector is significantly impacted by agriculture. Artificial intelligence is being applied in agriculture to automate numerous tasks, reduce hazards, and provide farmers with a comparatively easy-to-use farming system but a revolution is taking place in the field of agriculture, utilizing artificial intelligence (AI) to solve major issues confronting the agricultural sector in developing nations. A focus on the impact and potential benefits of AI in developing nations is explored in this paper. The widespread adoption of AI in agriculture in developing countries has been confronted by challenges such as infrastructure deficits, high investment costs, and a lack of capacity building. Governments, policymakers, researchers, and technology providers must work together to ensure sustainable and inclusive deployment of AI technologies in agriculture. A critical examination of the state of agriculture in developing countries is the primary objective of this study, as well as the identification and mitigation of farmer challenges with the use of AI technologies. But a revolution is taking place in agriculture, utilizing artificial intelligence (AI) to solve major issues confronting the agricultural sector in developing nations.Social Science Research Network180A critical examination of the state of agriculture in developing countries is the primary objective of this study, as well as the identification and mitigation of farmer challenges with the use of AI technologies.SSRN Electronic Journal["Blessing Odume"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/ee4fcd4093a7fbfaf437c3b1fa25a5524e546015 e3cc0ba6059b0c5953111c50492dd6b2e4a6e4afObjective: Congenital heart disease (CHD) is the most prevalent congenital anomaly, imposing a significant burden in morbidity and mortality. Recent advances in artificial intelligence (AI) have introduced numerous new tools to fetal cardiac ultrasound, including automated generation of fetal cardiac planes and identification of specific CHD diagnostic views. Mechanism: Through a narrative review of literature, we described AI technology on automated CHD detection, lesion identification, and associated challenges, such as training datasets and image segmentation. Findings in Brief: The search provided 28 eligible studies. Conclusions: Artificial intelligence seems to be a promising tool to help clinicians in daily clinical activity: it can automate the detection of standard cardiac planes and assist in identifying abnormalities. The main advantages that emerged from this review are related to the reduction of inter- and intra-operator variability, improvement of overall diagnostic performance and accuracy. However, nowadays, its integration into routine clinical practice gives rise to several issues.Clinical and Experimental Obstetrics &amp; Gynecology390Artificial intelligence seems to be a promising tool to help clinicians in daily clinical activity: it can automate the detection of standard cardiac planes and assist in identifying abnormalities.Clinical and Experimental Obstetrics &amp; Gynecology["Arianna Riva", "Mariachiara Guerra", "Stefania Di Gangi", "Paola Veronese", "V. Vida"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/e3cc0ba6059b0c5953111c50492dd6b2e4a6e4af 0848a280e9375e7faf8a8ff81d6863079133b1e8This paper examines the crucial issue of inaccuracies in artificial intelligence (AI) systems and their implications in different applications. The integration of AI into vital sectors shows the need for high accuracy and reliability, as errors can significantly damage trust in these systems and cause harm. Through a thorough review of current literature, this study explores the origins of AI inaccuracies, their wide-ranging impacts, and ongoing efforts to address these challenges. Focusing on the mechanisms for improving the factual accuracy of AI, the discussion extends to a variety of correction strategies beyond specific instances like the editing of knowledge within AI models. Addresses the critical need for AI systems to produce reliable and factually correct outputs, emphasizing the development of verification processes. The study further delves into the challenge of AI systems generating misleading or incorrect information, termed as “artificial hallucinations,” highlighting the essential for approaches that ensure the integrity and accuracy of AI-generated content. The implications of inaccuracies in AI extend to the realm of scientific and academic writing, where the reliability of information is important. This paper shows the necessity for content verification mechanisms to maintain the standards of integrity and factual correctness. By providing a detailed examination of the state of AI inaccuracies and proposing different directions for improvement, this paper aims to promote a more responsible approach to the development and application of AI technologies. In advocating for transparency, accountability, and continuous innovation, the study seeks to contribute to the advancement of AI systems that are not only more accurate and trustworthy but also capable of retaining public confidence in their utility and safety.Long Island Systems, Applications and Technology Conference110The necessity for content verification mechanisms to maintain the standards of integrity and factual correctness in AI is shown, to promote a more responsible approach to the development and application of AI technologies.2024 IEEE Long Island Systems, Applications and Technology Conference (LISAT)["Jack Kollar", "Mohammad Alshibli"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/0848a280e9375e7faf8a8ff81d6863079133b1e8 211f1f6bc9440723c20be57ed2135efffc67b96eThe application of artificial intelligence (AI) in biotechnology implies the digitalization of processes in agriculture and livestock farming. Based on big data analysis, machine learning technologies make it possible to study, monitor and control key biological processes. AI systems integrate with other digital technologies, such as process and state sensors, cyber-physical systems, unmanned aerial vehicles, which, together with computer vision and deep learning algorithms, help to monitor the condition of agricultural crops and soil, check and predict environmental changes that affect crop yields. Smart agriculture makes it possible to assess environmental and economic sustainability through nutrient cycling, as well as to manage arable and pasture lands using sensor systems that record data on soil, plants and weather. Digital transformation and the application of artificial intelligence is a promising innovative direction that has enormous potential to increase the efficiency, accuracy and speed of research and development, and also creates new conditions for the emergence of revolutionary products and services.Moscow Economic Journal350Digital transformation and the application of artificial intelligence is a promising innovative direction that has enormous potential to increase the efficiency, accuracy and speed of research and development, and also creates new conditions for the emergence of revolutionary products and services.MOSCOW ECONOMIC JOURNAL["Larisa Zhiganova"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/211f1f6bc9440723c20be57ed2135efffc67b96e aa202dcd2ffc8e8ee7a9eff65c5be5a12915349dArtificial Intelligence (AI) and Machine Learning (ML) are revolutionizing various scientific and clinical disciplines including pharmacogenomics (PGx) by enabling the analysis of complex datasets and the development of predictive models. The integration of AI and ML with PGx has the potential to provide more precise, data-driven insights into new drug targets, drug efficacy, drug selection, and risk of adverse events. While significant effort to develop and validate these tools remain, ongoing advancements in AI technologies, coupled with improvements in data quality and depth is anticipated to drive the transition of these tools into clinical practice and delivery of individualized treatments and improved patient outcomes. The successful development and integration of AI-assisted PGx tools will require careful consideration of ethical, legal, and social issues (ELSI) in research and clinical practice. This paper explores the intersection of PGx with AI, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.Pharmacogenomics (London)1440The intersection of PGx with AI is explored, highlighting current research and potential clinical applications, and ELSI including privacy, oversight, patient and provider knowledge and acceptance, and the impact on patient-provider relationship and new roles.Pharmacogenomics["Susanne B Haga"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/aa202dcd2ffc8e8ee7a9eff65c5be5a12915349d faa6729e1b42f87791e32634758893e0d9efb96cArtificial intelligence (AI) has evolved rapidly in the last century. Once a futuristic concept with negative connotations, AI has now begun to permeate various fields, reaching academic and scientific writing. In scientific writing, AI applications promise to significantly improve writing accuracy, provide quality control, and thereby enhance manuscript evaluation, among other possible contributions. It is generally agreed that AI and machine learning tools may become writing assistants if involved in writing tasks. Keywords: Artificial Intelligence, Scientific WritingInternational journal of science and healthcare research00In scientific writing, AI applications promise to significantly improve writing accuracy, provide quality control, and thereby enhance manuscript evaluation, among other possible contributions.International Journal of Science and Healthcare Research["Karim H Farhat", "M. A. Arafa"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/faa6729e1b42f87791e32634758893e0d9efb96c 5a0c3133469a41da870613763931746f7eca3315Artificial Intelligence (AI) encompasses a wide array of technologies that emulate human intelligence to perform tasks such as diagnosis, treatment planning, and patient management in healthcare. Recent innovations in AI, such as deep learning, natural language processing, and robotics, have significantly transformed medical practices. This review highlights key innovations, including AI's role in enhancing diagnostic accuracy through imaging analysis, optimizing treatment strategies with predictive analytics, and improving patient outcomes via personalized medicine. By focusing on both virtual and physical branches of AI in healthcare, this paper underscores the transformative impact of AI, aiming to bridge gaps in efficiency, precision, and patient care.2024 IEEE International Conference on Medical Artificial Intelligence (MedAI)410This review highlights key innovations, including AI's role in enhancing diagnostic accuracy through imaging analysis, optimizing treatment strategies with predictive analytics, and improving patient outcomes via personalized medicine.2024 IEEE International Conference on Medical Artificial Intelligence (MedAI)["Hassan Almaeeni"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/5a0c3133469a41da870613763931746f7eca3315 6abca82403aa014f3dbfc7c5830c2ab808fad4f5 ABSTRACT Recent operations in Ukraine have proven that introducing new technologies, tactics, techniques, and procedures can significantly affect the 21st century battlefield. The U.S. military is integrating the lessons learned from this and other recent conflicts into the Joint All Domain Command and Control (JADC2) warfighting concept. DoD is seeking to achieve decision superiority through JADC2 “to produce the warfighting capability to sense, make sense, and act at all levels and phases of war, across all domains, and with partners, to deliver information advantage at the speed of relevance." While this definition captures what JADC2 aims to achieve, it says little about how to achieve it. This paper uses the OODA loop and a project convergence use case (wet gap crossing) to show how artificial intelligence (AI) will enable decision superiority by reducing risk in this complex and relevant scenario. Citation: D. Taylor, E. Kheyfets, T. Stewart, “Leveraging Artificial Intelligence (AI) to Enable Decision Superiority,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023. SAE technical paper series00This paper uses the OODA loop and a project convergence use case (wet gap crossing) to show how artificial intelligence (AI) will enable decision superiority by reducing risk in this complex and relevant scenario.SAE Technical Paper Series["David Taylor", "Emil Kheyfets", "Timothy Stewart"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/6abca82403aa014f3dbfc7c5830c2ab808fad4f5 3ff70b8f761e84070c64d175cd80ed58221430b9Over the past decade, advancements in technology, notably Artificial Intelligence (AI), have significantly transformed educational methodologies and shifted the existing educational framework. Innovations like OpenAI’s ChatGPT and Gemini have garnered considerable attention, with their groundbreaking features potentially revolutionizing the realm of education, prompting concerns among educators and researchers (Grassini, 2023; Halaweh, 2023). This research endeavours to utilise AI, specifically ChatGPT and Gemini, to bolster and refine formal letter writing and email composition skills among first-cycle undergraduate students enrolled in the English for Engineering module at a Spanish university. Initially, students underwent a pre-test assessing their English proficiency and confidence levels, along with their familiarity with ChatGPT and other AI tools utilised for educational and writing purposes. Subsequently, students engaged in collaborative group tasks using Google Docs within Google Drive. Finally, a post-test was administered to gauge students' perceptions regarding their experience with English for Specific Purposes, particularly English for Mechanical Engineering, and their utilisation of ChatGPT and other AI tools for educational and writing purposes. Findings indicate that integrating AI can enhance students' grasp of written language accuracy in formal letter writing, particularly in inquiries, quotations, and complaint letters, facilitating better organisation and structure in their written correspondence in terms of style, accuracy, and communicative efficacy. This study suggests that investigations like the one presented here enable educators to explore the incorporation of AI, specifically ChatGPT and Gemini, to enrich students' writing proficiency, foster creativity, and cultivate technological aptitude within the classroom setting.Półrocznik Językoznawczy Tertium100This research endeavours to utilise AI, specifically ChatGPT and Gemini, to bolster and refine formal letter writing and email composition skills among first-cycle undergraduate students enrolled in the English for Engineering module at a Spanish university.Półrocznik Językoznawczy Tertium["Adri\u00e1n Pla \u00c1ngel"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/3ff70b8f761e84070c64d175cd80ed58221430b9 a443454735b3eff0b31a83ea5df93459a713ee0cAbstract: With the development of artificial intelligence, AI intelligence education is a hot issue of concern to the whole society today, and it is also one of the key research topics today. Some researchers have found that the penetration of AI into education is rapidly increasing, but there is still a lack of a unified explanation for its impact. This paper analyzes the penetration and impact of artificial intelligence in the field of education. Found to analysis, artificial intelligence has a great role in promoting the development of education. But at the same time, the combination and popularization of artificial intelligence and education will also bring corresponding problems. Therefore, this paper suggests the rational use of AI, letting AI become the auxiliary tool rather than allowing AI to replace the work. At the same time, attention should also be paid to achieving true educational equity to promote a real balance between AI and education.Lecture Notes in Education Psychology and Public Media00This paper suggests the rational use of AI, letting AI become the auxiliary tool rather than allowing AI to replace the work in the field of education.Lecture Notes in Education Psychology and Public Media["Haiyan Zhang"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/a443454735b3eff0b31a83ea5df93459a713ee0c 79e40e4a0d09d1037b24365bf61db65d3e169c83In VUCA (Volatility, Uncertainty, Complexity and Ambiguity) world, artificial intelligence (AI) has gained tremendous popularity and expanded exponentially in all areas but still there is lack of understanding on application of artificial intelligence and analytics in Human Resource Management. Therefore, the present study tries to explore the scope of Artificial intelligence on various dimensions of Human Resource Management (HRM) policies and Practices. After the outbreak of covid-19 pandemic the role of HR professional has been redefined as major intrinsic profile to advanced technological oriented role supported by data analytics and artificial intelligence. The study encompasses databases of top HRM, international business and information management journals. The study considered articles related to AI in relation to HR. Results showcased how HR basic dimensions integrated with potential technology of Artificial intelligence to improve employee efficiency. HR dimensions with AI framework provides better efficiency to workforce to meet customer requirements and gain better competitive advantage.2024 International Conference on Cybernation and Computation (CYBERCOM)300Results showcased how HR basic dimensions integrated with potential technology of Artificial intelligence to improve employee efficiency and help workforce to meet customer requirements and gain better competitive advantage.2024 International Conference on Cybernation and Computation (CYBERCOM)["Neha Gupta", "Richa Shekhar", "Sheetal Sehgal", "Sunishtha Dhaka", "Anshul Malik", "Sandeep Mathur"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/79e40e4a0d09d1037b24365bf61db65d3e169c83 b525bcd94284c2abaf2da868ea7d90fcebfae355The Joint ETC Task Force on Artificial Intelligence and Media is an exploratory group interested in identifying and pursuing opportunities for standardization around artificial intelligence (AI) and its potential applications throughout the media ecosystem. SMPTE Motion Imaging Journal00SMPTE Motion Imaging Journal["Yves Bergquist", "Frederick G. Walls"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/b525bcd94284c2abaf2da868ea7d90fcebfae355 9857fda2bdaf9dce54ab92da4a9825abbf6333b0Artificial intelligence (AI) is well known as a robust technique that can support and improve the quality of human life. In the mining industry, the applications of artificial intelligence have changed the sciences and technologies as well as the performance of the mining industry through machine learning, autonomous technologies that provide many economic benefits to the mining industry by reducing costs, efficiency and improving productivity. In principle, reducing the exposure of workers to hazardous conditions, continuous production and improved safety are the most beneficial factors of the implementation of artificial intelligence in the mining sector, coming to the aid of workers in this field. In recent years, the adoption of artificial intelligence has changed the game in the mining industry by enabling more efficient exploration, taking automation to new levels, generating higher yields, improving safety and maximizing extraction, maintenance and operational performance. This paper aims to highlight the multiple advantages that the implementation of artificial intelligence in the mining industry has for the health and safety of mining workers. SGEM International Multidisciplinary Scientific GeoConference� EXPO Proceedings00SGEM International Multidisciplinary Scientific GeoConference� EXPO Proceedings["Matei Andrada Denisa", "L. Tuhu\u021b", "Morar Marius Simion", "Cioara Cristian Raul", "Florein Manea"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/9857fda2bdaf9dce54ab92da4a9825abbf6333b0 1ac15a6bf92ae41ecf4184720af0a69cbd137286With the rapid development of artificial intelligence (AI) technology, the field of medical imaging technology is undergoing revolutionary changes. In order to adapt to this change, the training model for medical imaging technology professionals is in urgent need of innovation and optimization. Based on social needs and technological development trends, this study proposes to establish the AI concept, constructs a multi-level curriculum system based on the integration of "post course competition and certification" training goals, promotes professional practice with vocational skills competitions, and builds a skills certificate as the foothold. The talent training mechanism that emphasizes both education and training provide reference and reference for the reform of the talent training model for medical imaging technology professionals.Journal of Education and Educational Research60This study proposes to establish the AI concept, constructs a multi-level curriculum system based on the integration of "post course competition and certification" training goals, promotes professional practice with vocational skills competitions, and builds a skills certificate as the foothold.Journal of Education and Educational Research["Hui Chen", "Lisha Wu", "Ming Li", "Lili Jiang"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/1ac15a6bf92ae41ecf4184720af0a69cbd137286 305949dbfe7c67e34dfd02efdb5394b703da64d9PurposeThis paper draws on the dynamic capabilities (DC) view to develop a conceptual framework that explicates the mechanisms through which human intelligence (HI) and artificial intelligence (AI) substitute and complement each other for organizational knowledge management (KM) while considering the role of ethics.Design/methodology/approachThis is a conceptual paper that draws on DC theory and integrates insights from the burgeoning literature on organizational AI adoption and application to develop a conceptual framework that explains the mechanisms through which HI and AI may substitute and complement each other for organizational KM to develop DC.FindingsThe conceptual framework demonstrates that substituting HI with AI is suitable for external environmental scanning to identify opportunities, while AI substitution for HI is ideal for internal scanning through data analytics. Additionally, HI complementing AI is effective for seizing opportunities by aligning internal competencies with external opportunities, whereas AI complementing HI is beneficial for reconfiguring assets by transforming tacit knowledge into explicit knowledge. This substitution and complementarity between HI and AI shape KM processes—acquisition, conversion, application, and retention—that influence organizational performance, depending on how internal and external ethical standards govern organizational AI use.Research limitations/implicationsThe paper presents key insights into how AI may substitute for HI for internal data analytics in KM but may be ineffective for external environmental scanning to sense opportunities. It further reveals that using AI to capture and convert tacit knowledge (HI) to explicit knowledge requires ethical considerations at the organizational level, but ethical considerations are necessary at the employee/manager level when HI relies on AI-generated insights for strategic decisions.Practical implicationsThe study implies that in environments with defined regulations for AI and KM (e.g. privacy protection), responsibility for the consequences of AI-HI substitution and complementarity in developing DC can be assigned to specific steps in the KM process. However, in environments with undefined regulations, responsibility must be assigned to people, units or departments who manage the entire KM process to ensure accountability for ethical breaches.Originality/valueThis study proposes AI-HI substitution and complementarity in organizations to extend the understanding of the relationship between AI and HI to DC development.Journal of Managerial Psychology710A conceptual framework demonstrates that substituting HI with AI is suitable for external environmental scanning to identify opportunities, while AI substitution for HI is ideal for internal scanning through data analytics, and reveals that using AI to capture and convert tacit knowledge (HI) to explicit knowledge requires ethical considerations at the organizational level.Journal of Managerial Psychology["Christopher Agyapong Siaw", "Waqas Ali"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/305949dbfe7c67e34dfd02efdb5394b703da64d9 26f59c88b2f3a2d1cce3363ae7c14bd8dd368ca1A few months after ChatGPT was released, the neural network pioneer Terrence Sejnowski wrote about coming to grips with the shock of what large language models (LLMs) could do: "Something is beginning to happen that was not expected even a few years ago. A threshold was reached, as if a space alien suddenly appeared that could communicate with us in an eerily human way.…Some aspects of their behavior appear to be intelligent, but if it's not human intelligence, what is the nature of their intelligence?"Science01Science["Melanie Mitchell"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/26f59c88b2f3a2d1cce3363ae7c14bd8dd368ca1 7c4d32369a29d72f62a0de928cf32102975bdfe9The integration of OLAP cubes and neural networks represents a significant advancement in business intelligence. OLAP cubes, with their multidimensional data structures, enable efficient analysis across multiple dimensions, helping businesses extract insights from their data. Neural networks excel at learning from large datasets and making accurate predictions. This paper explores how OLAP cubes can be used as datasets for training neural networks. The process includes extracting and preprocessing data to make it suitable for neural network training. The practical applications of this integration in business include improved forecasting, optimization, and strategic decision-making. By combining the analytical capabilities of OLAP cubes with the predictive strengths of neural networks, businesses can achieve more precise and actionable insights. Furthermore, the paper discusses future development and research possibilities in this area, emphasizing the potential for creating better business intelligence solutions. This integration opens new possibilities for enhancing data analysis, making it a promising area for future exploration.2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)70This paper explores how OLAP cubes can be used as datasets for training neural networks, and opens new possibilities for enhancing data analysis, making it a promising area for future exploration.2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)["Rinat D. Abrarov", "Timur A. Khudaybergenov"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/7c4d32369a29d72f62a0de928cf32102975bdfe9 5f37dee584e45e2b9a69f5e36b1ee790ea2b8c0b2024 IEEE International Conference on Medical Artificial Intelligence (MedAI)002024 IEEE International Conference on Medical Artificial Intelligence (MedAI)[]2024-11-15T00:00:00https://www.semanticscholar.org/paper/5f37dee584e45e2b9a69f5e36b1ee790ea2b8c0b 107544d0f259f12b29a2b73e7b015896770e75e4What is the current and potential relationship between automated processing tools for legally relevant data and the performance of the legal profession? Is it possible to consider whether there is a potential for synergy, integration, or alternation between classical human legal activities and procedurally designed algorithmic processes? What definitions and control mechanisms would be required for the potential avenues of inquiry into this subject, given the need to comply with existing principles and guarantees?Труды по Интеллектуальной Собственности00What is the current and potential relationship between automated processing tools for legally relevant data and the performance of the legal profession and what definitions and control mechanisms would be required for the potential avenues of inquiry into this subject?Труды по Интеллектуальной Собственности["\u041c\u0438\u043a\u0435\u043b\u0430\u043d\u0434\u0436\u0435\u043b\u043e \u041f\u0430\u0441\u043a\u0430\u043b\u0438"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/107544d0f259f12b29a2b73e7b015896770e75e4 40b682834a7441b61ec9c15e93a7f20111be75bcThe need for regulations that can protect consumers in business in the digital era is increasingly urgent along with the use of AI. However, the consumer protection regulations currently owned by Indonesia were set long before the digital era, so they are not adequate in protecting consumers in the era of AI utilization. Therefore, this article aims to analyze the current consumer protection legal regime in Indonesia and understand the update of consumer protection regulations in the application of AI. The research method used is normative juridical supported by a legal approach and a case approach. The analysis method used in this study is descriptive-qualitative analysis. The results of the study show that regulations in the field of consumer protection in Indonesia are currently inadequate in protecting consumer interests in the era of AI utilization. Therefore, it is necessary to update consumer protection regulations, especially related to the application of AI in the business sector, which is able to accommodate the complexity of consumer protection in the digital era. The regulatory update is intended to protect consumers and not to limit innovation in AI development.Journal of Education, Humaniora and Social Sciences (JEHSS)330The results of the study show that regulations in the field of consumer protection in Indonesia are currently inadequate in protecting consumer interests in the era of AI utilization, and it is necessary to update consumer protection regulations, especially related to the application of AI in the business sector, which is able to accommodate the complexity of consumer protection in the digital era.Journal of Education, Humaniora and Social Sciences (JEHSS)["S. Suratno", "Y. Yuniwati", "Zulfikar Ali", "Dewi Noviyanti"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/40b682834a7441b61ec9c15e93a7f20111be75bc 41cbf5170901c2185578d558a929ff0db37a1f1cInternational Journal of Recent Innovations in Medicine and Clinical Research00International Journal of Recent Innovations in Medicine and Clinical Research["Wilma Delphine Silvia CR"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/41cbf5170901c2185578d558a929ff0db37a1f1c 1bed7343b3fc3887eff48db4ddb1278f73c67757Prabandhan Indian Journal of Management00Prabandhan: Indian Journal of Management["A. Mohapatra", "Rahul Matta", "Rashmi Soni", "Nandeesh V. Hiremath"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/1bed7343b3fc3887eff48db4ddb1278f73c67757 0ca9532d71db426de11bc530d3cb0a873d741913Einstein100Einstein["T. Midega", "R. Chaves", "Ricardo Kenji Nawa", "B. Mazza", "L. R. Ferraz", "Thiago Domingos Corr\u00eaa"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/0ca9532d71db426de11bc530d3cb0a873d741913 c9ddd98a1221601b303d852dcb9469953ed7b4c4AI mengacu pada program komputer yang dirancang untuk meniru kecerdasan manusia, termasuk kemampuan pengambilan keputusan, logika, dan karakteristik kecerdasan lainnya. karakteristik. Saat ini, AI telah banyak digunakan dalam berbagai aplikasi seperti mesin pencari, asisten virtual seperti Siri, Google Assistant, dan Cortana. Dalam dunia pendidikan, penggunaan AI dapat membantu mahsiswa dalam mengontrol dan memonitor pembelajaran mereka sendiri, sehingga memungkinkan mereka untuk hidup dan bekerja dengan baik dan mandiri di masa depan. Selain itu Kecerdasan buatan di masa depan akan mengarah pada pembelajaran yang presisi. Permasalahan yang saat ini dihadapi oleh mitra mahasiswa di Politeknik Indonusa dalam pembuatan artikel ilmiah atau tugas akhir antara lain, kurang paham dalam merumuskan topik/masalah. Kesulitan membuat tinjauan pustaka. Tinjauan pustaka atau tinjauan literatur merupakan bagian yang harus ditulis dalam skripsi maupun artikel ilmiah lainnya. Tinjauan pustaka mengulas topik-topik terkait yang pernah dibahas/diselesaikan oleh peneliti lain. Untuk menulis tinjauan pustaka, kumpulkan terlebih dahulu referensi terkait. Kesulitan mendapatkan kemajuan. Waktu normal untuk mengerjakan skripsi adalah sekitar dua semester. Agar skripsi bisa selesai tepat waktu, maka harus ada progres kemajuan setiap minggunya. Untuk itu, buatlah jadwal target per minggu/bulan sesuai dengan progress yang ingin didapatkan Artificial Intelligence seperti ChatGPT sudah digunakan untuk menulis berbagai macam artikel seperti artikel blog, berita, dan masih banyak lagi. Oleh karena itu, workshop ini digunakan untuk memberikan pendampingan kepada para mahasiswa dalam menggunakan AI.Abdi Masya120Abdi Masya["Rina Arum", "S. Purnomo", "Sameer Ali Hussein Al-Shami Al-Shami"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/c9ddd98a1221601b303d852dcb9469953ed7b4c4 baa5fa349d951ed886a92c9aa5abde13927a07f1In/Outside: English Studies in Korea00In/Outside: English Studies in Korea["Dasan Kim"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/baa5fa349d951ed886a92c9aa5abde13927a07f1 2c10af10e31442d82a2289d94c820555985a4e88Text classification, also commonly known as text categorization, is the process of assigning labels or tags to textual data. To solve this problem different models are used, based on machine learning algorithms, as well as deep learning methods based on neural networks of different architectures (recurrent, convolutional, transformers, etc.). But most complex machine learning and deep learning models are black boxes because they contain a large number of parameters and have complex architectures that make them uninterpretable. This paper proposes a framework to generate explanations in binary text classification task for an investigation of subject's behavior in the digital environment. Our framework utilizes five machine learning models (Random Forest, K-nearest neighbors, Decision Tree, Logistic Regression, Gradient Boosting) and a fine-tuned BERT-based classifier and an explainability module using LIME, SHAP and Integrated Gradients to generate explanations for each prediction. The main objective of this paper is to show how machine learning-based natural language processing methods enhanced by Explainable AI methods can be applied in psychological domain.2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)210This framework utilizes five machine learning models and a fine-tuned BERT-based classifier and an explainability module using LIME, SHAP and Integrated Gradients to generate explanations for each prediction.2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE)["F. Gafarov", "V. Gafarova", "P. Ustin"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/2c10af10e31442d82a2289d94c820555985a4e88 103c3129e638fde855ab3211767a1f3769ea6c07In the last decade, there has been a significant increase in the use of commercial semi-autonomous vehicles by consumers. This has led to a surge in concerns among users about the limitations of these systems, especially when it comes to safety. In order to address these concerns, users often seek out diverse educational resources to comprehend these constraints, explore alternatives, and determine whether their experiences are representative. This paper examines the viewpoints of users who are using a new AI system that has received minimal training from the vendor. We conducted interviews of the users of the AI to examine the many sources from which users obtain knowledge and developed a learning technology-based framework that utilizes technology to disseminate knowledge among users of the AI.Proceedings of the Human Factors and Ergonomics Society Annual Meeting100This paper conducted interviews of the users of the AI to examine the many sources from which users obtain knowledge and developed a learning technology-based framework that utilizes technology to disseminate knowledge among users of the AI.Proceedings of the Human Factors and Ergonomics Society Annual Meeting["T. Mamun", "Shane T. Mueller"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/103c3129e638fde855ab3211767a1f3769ea6c07 6f0c8ea41e049574b4dd6d51828797788080e5deAbstract. Artificial Intelligence (AI), a technology with a wide range of intelligence capabilities, has developed rapidly in recent years, bringing significant convenience and efficiency to society. However, most of the current artificial intelligence technologies belong to narrow artificial intelligence. Unlike Narrow AI, Artificial General Intelligence (AGI) possesses a more comprehensive understanding and problem-solving capability. AGI can learn in an unsupervised manner. General artificial intelligence can not only stand out in specific fields. It can also make effective decisions to a certain extent and operate in a wide range of environments. However, rapid progress has also raised widespread concerns about its potential risks. Therefore, the development of artificial intelligence requires standardization, which is urgent to ensure that it can make decisions that benefit humanity. Based on existing literature and data results, this paper explores the security issues and moral risks that general artificial intelligence may bring to humans. The research results indicate that these risks include user privacy breaches, system security issues, and social ethical conflicts. Dealing with these risks requires the joint efforts of all practitioners. This includes developing AGI in an ethical manner and ensuring that AI does not engage in activities that violate human interests.Applied and Computational Engineering00The security issues and moral risks that general artificial intelligence may bring to humans are explored and it is indicated that these risks include user privacy breaches, system security issues, and social ethical conflicts.Applied and Computational Engineering["Boqian Feng"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/6f0c8ea41e049574b4dd6d51828797788080e5de 8d24f7a543747c14344936b71bf2b7184eaec48aSehubungan dengan Gerakan Literasi Nasional penulis termotivasi untuk melakukan pengabdian berjudul “Pemanfaatan Artificial Intelligence (AI) sebagai Katalisator Peningkatan Keterampilan Menulis Guru”. Tujuan dari pengabdian ini adalah untuk mengedukasi literasi menulis, khususnya menulis cerpen sebagai sebuah wahana ekspresi tulis bagi guru dan mengenalkan AI untuk memberi kemudahan menulis, tetapi pemanfaatannya akan tetap menjunjung tinggi nilai etika dan moral akademisi. Pelatihan ini sejalan dengan kebijakan Menurut Peraturan Menteri Pendayagunaan Aparatur Negara dan Reformasi Birokrasi (Permen PANRB) No. 16 Tahun 2009 tanggal 10 November 2009 tentang Jabatan Fungsional Guru dan Angka Kreditnya. Tujuan dari pengabdian ini adalah untuk membudayakan literasi dan meningkatkan profesionalisme guru melalui pembuatan karya bersama berupa antologi cerpen sebagai wahana ekspresi tulis. Pengabdian ini bekerja sama dengan  platform e-guru.id. Hasil dari pengabdian ini semua guru dalam pelatihan mampu menggunakan ChatGPT, Bing Ai, dan Wattpad sebagai katalisator peningkatan menulis guru, khususnya karya sastra.ANDIL Mulawarman Journal of Community Engagement00ANDIL Mulawarman Journal of Community Engagement["Sumartini", "U'um Qomariyah", "Maharani Intan Andalas", "Dyah Prabaningrum"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/8d24f7a543747c14344936b71bf2b7184eaec48a fd283d1bf172aa98e1efe484c83aa75386803b9fSince the emergence of security concerns in artificial intelligence (AI), there has been significant attention devoted to the examination of backdoor attacks. Attackers can utilize backdoor attacks to manipulate model predictions, leading to significant potential harm. However, current research on backdoor attacks and defenses in both theoretical and practical fields still has many shortcomings. To systematically analyze these shortcomings and address the lack of comprehensive reviews, this paper presents a comprehensive and systematic summary of both backdoor attacks and defenses targeting multi-domain AI models. Simultaneously, based on the design principles and shared characteristics of triggers in different domains and the implementation stages of backdoor defense, this paper proposes a new classification method for backdoor attacks and defenses. We use this method to extensively review backdoor attacks in the fields of computer vision and natural language processing, and also examine the current applications of backdoor attacks in audio recognition, video action recognition, multimodal tasks, time series tasks, generative learning, and reinforcement learning, while critically analyzing the open problems of various backdoor attack techniques and defense strategies. Finally, this paper builds upon the analysis of the current state of AI security to further explore potential future research directions for backdoor attacks and defenses.ACM Computing Surveys273This paper presents a comprehensive and systematic summary of both backdoor attacks and defenses targeting multi-domain AI models, and proposes a new classification method for backdoor attacks and defenses based on the design principles and shared characteristics of triggers in different domains.ACM Computing Surveys["Shaobo Zhang", "Yimeng Pan", "Qin Liu", "Zheng Yan", "Kim-Kwang Raymond Choo", "Guojun Wang"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/fd283d1bf172aa98e1efe484c83aa75386803b9f a685048780364ab24bf1a298d08138ae48694394With the popularization and development of the artificial intelligence technology, as well as the increasingly deep integration with various industries, machine learning as a service (MLaaS) model is gradually gaining popularity and maturing. However, in the process of the model sharing services, there is still data privacy leakage, which poses security risks to data usage security. To address this challenge, this article proposes the proposed toward incentive with privacy preserving MLaaS scheme for the crowdsensed data trading. This scheme converts the data sharing problem into a federated learning model sharing problem, and then converts the shared model into an auction model, thereby achieving the transformation of privacy protection issues during the sharing process into privacy auction problems. In auction mode, while ensuring the security of submitted information, characteristics, such as utility, individually rational and maximizing social welfare need to be met. Furthermore, in order to ensure fairness and privacy, the bidding information sorting algorithm and the pricing strategy under the ciphertext state are designed. Once the winners are determined, the model service sharing mode based on the attribute-based encryption and interplanetary file system is adopted. The extended experimental results indicate that the proposed scheme meets the characteristics of privacy preserving, flexibility, and efficiency.IEEE Internet of Things Journal351This scheme converts the data sharing problem into a federated learning model sharing problem, and then converts the shared model into an auction model, thereby achieving the transformation of privacy protection issues during the sharing process into privacy auction problems.IEEE Internet of Things Journal["Kunchang Li", "Yinfeng Shi"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/a685048780364ab24bf1a298d08138ae48694394 401d9b6276c6d337749ee9775edeef0412551e7aAI and Ethics391This paper proposes that negative perceptions of AI often concern job displacement, bias and fairness, and misalignment with human values, while positive perceptions typically focus on specific applications and benefits of AI, such as in scientific research, healthcare, and education.AI and Ethics["Airlie Hilliard", "Emre Kazim", "Stephan Ledain"]2024-11-15T00:00:00https://www.semanticscholar.org/paper/401d9b6276c6d337749ee9775edeef0412551e7a 9428fb585c498b09b560cd597439f8e7d9233f77This study investigates the adoption and impact of Artificial Intelligence (AI) in Pakistan’s banking sector, with a focus on Habib Bank Limited (HBL), Faysal Bank, and Bank Alfalah. The objective is to understand the extent to which AI technologies have transformed core activities within customer service, operations, fraud detection, and risk management in these banks. Data were collected from semi-structured interviews with ten respondents, selected based on their experience with AI implementation. This qualitative methodology allows for in-depth insights into the functionality and challenges associated with AI in banking. Results reveal that AI has notably improved operational efficiency by streamlining risk management and enabling real-time fraud detection. Additionally, AI-driven chatbots and virtual assistants have enhanced customer experiences by offering faster and more personalized services. Despite these benefits, the study identifies significant challenges, particularly regarding data privacy and regulatory compliance, as well as the need for human resources to adapt to AI-enabled tools. Key pressures in the industry include building capabilities to work alongside AI while addressing concerns over automation. To maximize the advantages of AI, the study emphasizes the need for comprehensive policies that balance technological adoption with ethical considerations, data security, and staff training. Strategic planning in Pakistan’s banking sector will be crucial to harnessing AI’s potential while managing its risks.Review of Applied Management and Social Sciences151Results reveal that AI has notably improved operational efficiency by streamlining risk management and enabling real-time fraud detection, and AI-driven chatbots and virtual assistants have enhanced customer experiences by offering faster and more personalized services.Review of Applied Management and Social Sciences["Nayyab Zulfiqar", "Faizan Ghafoor", "Muhammad Idrees", "Kashif Raza"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/9428fb585c498b09b560cd597439f8e7d9233f77 2f07e9f8ea71424d1058f2a9443c88bda69a1cad【 Abstract 】 Based on the analysis of the application of artificial intelligence technology in electrical automation control, firstly, it analyzes that artificial intelligence technology is a programmable code written based on the problems existing in electrical automation, similar to copying parts of the human brain to help humans solve difficult problems; Secondly, the application of artificial intelligence technology in electrical automation control is analyzed to effectively help people reduce production costs, human resources, ensure data accuracyFuture Trends in AI Research01This paper analyzes that artificial intelligence technology is a programmable code written based on the problems existing in electrical automation, similar to copying parts of the human brain to help humans solve difficult problems.Future Trends in AI Research["Xuefeng Bai"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/2f07e9f8ea71424d1058f2a9443c88bda69a1cad 1500655acee3f0978601dc77f7a00185fd1bd6f2BACKGROUND To evaluate the effects of artificial intelligence (AI)-assisted dental monitoring (DM) with and without health counseling on the treatment outcomes and oral health-related quality of life (OHRQoL) of patients with periodontitis. METHODS Patients with periodontitis were randomly assigned to either an AI group (AI group, n = 28), an AI and health counseling group (AIHC group, n = 27), or a control group (n = 27). All patients underwent nonsurgical periodontal treatment. Patients in the AI and AIHC groups underwent additional AI-assisted DM and AI-assisted DM with oral health counseling, respectively, for 6 months. Data on OHRQoL and periodontal measures were collected at baseline and follow-ups. RESULTS At 3 months of follow-up, the AI and AIHC groups exhibited a significantly greater reduction in probing pocket depth (mean diff: -0.5 and -0.7) and clinical attachment level (mean diff: -0.5 and -0.6) compared with the control group. At 6 months of follow-up, the AI and AIHC groups exhibited a significantly greater improvement in OHRQoL (mean diff: -4.5 and -4.7) compared with the control group. At 3-month follow-up, the AIHC group exhibited a greater improvement in plaque index (mean diff: -0.2) and OHRQoL (mean diff: -4.3) compared with the AI group. CONCLUSION AI-assisted DM can be used to remind patients with periodontitis of their oral hygiene at home and effectively improve their periodontal measures and long-term OHRQoL. PLAIN LANGUAGE SUMMARY Gum disease is a common problem, but new technology could help. In this study, researchers looked at how AI affects gum health and quality of life (QoL). The researchers divided participants into 3 groups. One group used an AI system to monitor their gums at home. Another used AI plus got health advice. The third did not use any special technology. After 3 and 6 months, the AI groups had healthier gums, with less deep pockets and better gum attachment, compared to the group without AI. The group that also got health advice saw even greater improvements, like cleaner teeth and a bigger boost to their QoL. This is exciting because gum disease is tricky to manage alone. The AI system seems to help by reminding people to care for their teeth and gums. With expert guidance, the AI becomes an even more powerful tool for improving long-term oral health and well-being. This study shows how new technologies like AI could transform how we approach common health problems. By providing personalized support, AI can empower people to better manage their own health, leading to better outcomes.The Journal of Periodontology340AI-assisted DM can be used to remind patients with periodontitis of their oral hygiene at home and effectively improve their periodontal measures and long-term OHRQoL.Journal of periodontology["Fu-Tzu You", "P. Lin", "Chiung-Lin Huang", "Ju-Hui Wu", "Yuji Kabasawa", "Chih-Chang Chen", "Hsiao-Ling Huang"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/1500655acee3f0978601dc77f7a00185fd1bd6f2 3a4b9eabdc48281df89a1e61e63a16cf77da7f26Scientific Reports190The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration in healthcare, and emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels.Scientific Reports["Najmaddin A H Hatem", "M. Ibrahim", "S. A. Yousuf"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/3a4b9eabdc48281df89a1e61e63a16cf77da7f26 63052e3399d5f45a8b1f0dce31909533728a4faaThis integrative literature review (ILR) explores the transformative potential of artificial intelligence (AI) in optimizing in-store logistics, a crucial aspect for retail environments striving to meet growing consumer demands for convenience and quick product availability. It aims to provide retail managers and logistics specialists with actionable insights on AI-driven tools that enhance competitiveness by improving inventory management and space utilization in retail settings. The conceptual framework is framed by functional schema design, user acceptance and experience, operational efficiency, data security and privacy, and equitable access, emphasizing the ethical and inclusive implementation of technology. Key findings suggest that AI improves demand accuracy, reduces inventory mismatches, and enables adaptable store layouts that respond dynamically to real-time consumer behavior, creating a more efficient, responsive, and customer-centered retail environment. Additionally, this study examines the adoption of AI in cashier systems as a response to evolving consumer expectations for efficient digital shopping experiences, revealing that AI-enabled cashier systems significantly reduce wait times and operational costs while providing personalized experiences through real-time analytics. However, challenges such as robust data security protocols, inclusive design for diverse user groups, and employee reskilling remain critical as automation progresses. Recommendations focus on privacy-enhancing data practices, user-friendly interfaces, and hybrid solutions combining human support with automation to foster inclusivity and consumer trust. Future research should address the long-term impacts of AI in retail, cultural adaptability, customer engagement, and secure data handling to support a balanced, customer-centered approach to automation in the retail sector.Journal of Next-Generation Research 5.000This study examines the adoption of AI in cashier systems as a response to evolving consumer expectations for efficient digital shopping experiences, revealing that AI-enabled cashier systems significantly reduce wait times and operational costs while providing personalized experiences through real-time analytics.Journal of Next-Generation Research 5.0["Omar Nouzri", "Rachid Ejjami"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/63052e3399d5f45a8b1f0dce31909533728a4faa cc9365c845a440c1b05412866f4b0bd0293904b8This study was conducted to explore the role of Artificial Intelligence in enhancing the teachers' competencies. This was a theoretical study in nature conducted while using the secondary data collected from the available books, journals, and dissertations. The results revealed that AI has great potential to enhance teachers' competencies while helping them design relevant educational content aligned with learning objectives and providing feedback on their instructional strategies and practices. It also allowed to automate the student attendance and grading on the teacher's side. It will also assist the teachers in selecting suitable strategies and teaching aids for aiding a wide variety of students including those with special needs. Yet, it is also important to underscore that AI ought to act in support of and not replace with machine algorithms infallible virtues inherent to human education: sympathy; a creative spark; and the ability to individually adapt for each student.Global Social Sciences Review00The results revealed that AI has great potential to enhance teachers' competencies while helping them design relevant educational content aligned with learning objectives and providing feedback on their instructional strategies and practices.Global Social Sciences Review["Asim Tanvir", "Samra Bashir", "Sidra Shahzadi"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/cc9365c845a440c1b05412866f4b0bd0293904b8 5195507efc157eb8bc5c2a9a6f76842218046c88The artificial intelligence(AI) revolution and its impact on accounting and financial activities are positively evaluated; The focus is on new skills and capabilities needed by employees. It is concluded that artificial intelligence has the potential to transform the economy in many ways, but there are also significant threats and risks in terms of job displacement, inequality and international competition. AI's full potential will only be realized when policymakers, industry leaders, and the society will work together to create an ethical and fair framework for the development and use of AI. With such interdisciplinary collaborations and approaches (sharing the experiences of economists, technologists, ethicists and policy makers), it is possible that artificial intelligence will bring great benefits to everyone and significantly improve economic growth, development and prosperity. Keywords: Artificial Intelligence(AI), accounting, consulting services, digital environment and economy.Economics00It is concluded that artificial intelligence has the potential to transform the economy in many ways, but there are also significant threats and risks in terms of job displacement, inequality and international competition.Economics["Zhuzhuna Tsiklauri-Shengelia"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/5195507efc157eb8bc5c2a9a6f76842218046c88 e1ff5ee7624c29eadf58fc63a97c333055e2d19eIn many societies today, Artificial intelligence (AI) has developed into a disruptive force, and the system of education is only one example of how this technology is being used. Therefore, this study looked at the teachers’ perceptions in terms of perception, attitude, and experience towards incorporating AI into mathematics education in senior secondary school in Remo, Ogun State, Nigeria. Three research questions were raised to gather data from the respondents. A descriptive survey research design was used in this study with a sample comprised of 60 respondents, with 30 teachers from private and public senior secondary schools each selected from 20 schools. The instrument used was the Teacher Perception Towards AI Questionnaire (TPTAQ) with a reliability coefficient of 0.77. The results revealed that the perceptive level of the teachers towards AI integration was found to be high in terms of perception, attitude, and experience, showing that teachers embrace the use of AI in mathematics education in schools. Therefore, educators are advised to be well-trained in the application of AI technology to mathematics education.Jurnal Pendidikan Matematika dan Sains270The results revealed that the perceptive level of the teachers towards AI integration was found to be high in terms of perception, attitude, and experience, showing that teachers embrace the use of AI in mathematics education in schools.Jurnal Pendidikan Matematika dan Sains["A. Asanre", "Taiwo Oluwadayo Taiwo", "T. A. Odupe"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/e1ff5ee7624c29eadf58fc63a97c333055e2d19e 345ab025ac270ee2c3302fcdb07c11d94fdfa1efThis study explores the integration of Artificial Intelligence (AI) and Corporate Social Responsibility (CSR) as a novel approach for enhancing sustainable brand strategies in contemporary businesses. Utilizing a qualitative research method rooted in literature review and library research, this paper synthesizes insights from existing studies on AI and CSR to examine how their convergence can drive sustainable brand development. The study highlights the potential of AI-driven CSR initiatives to not only streamline operational efficiencies but also foster deeper stakeholder engagement and brand loyalty. Through an analysis of the literature, we find that AI can enhance CSR by enabling companies to better predict and meet stakeholder expectations, optimize resource allocation, and reduce environmental impacts. Moreover, AI technologies offer companies advanced tools for transparent reporting, real-time environmental monitoring, and predictive analytics to anticipate social and environmental challenges. The findings indicate that adopting AI in CSR efforts can amplify brand reputation and establish companies as leaders in sustainable innovation. However, the integration of AI and CSR also poses ethical challenges, such as privacy concerns and potential biases in data-driven decision-making, which require careful consideration. This paper contributes to the discourse on sustainable business practices by proposing that the strategic alignment of AI and CSR presents a promising frontier for brand enhancement in the digital age. The implications for future research and practical applications in diverse industries are also discussedInternational Journal of Social and Human140It is proposed that the strategic alignment of AI and CSR presents a promising frontier for brand enhancement in the digital age by enabling companies to better predict and meet stakeholder expectations, optimize resource allocation, and reduce environmental impacts.International Journal of Social and Human["Samuel Teguh Tarigan", "Andiah Agustiningrum", "Ester Trivona Nauw"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/345ab025ac270ee2c3302fcdb07c11d94fdfa1ef d488d56acf25ee59f16a2ffb4be7721e9880eaa1TechTrends190The study revealed that students anticipated AI to play diverse roles, including data analyst, a bridge to connect people and resources, and a barrier breaker, which suggests how AI can be conceptualized and positioned as a development intervention as well as offer implications on AI-driven interventions for SDGs.TechTrends["Jinhee Kim"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/d488d56acf25ee59f16a2ffb4be7721e9880eaa1 673aed7b5f377f7c1b1f34d5940d94901b789359The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like healthcare, finance, natural language processing, and robotics. However, challenges such as computational demands, data dependency, and model interpretability remain. This paper discusses the benefits, limitations, and future potential of ML and DL and also provides a review study of a hybrid model makes use of both techniques (machine learning & deep learning) advantages to solve complicated problems more successfully than one could on its own. To boost performance, increase efficiency, or address scenarios where either ML or DL alone would not be able to manage, this approach combines deep learning structures with conventional machine learning techniques.Journal of Scientific Research and Reports00The benefits, limitations, and future potential of ML and DL are discussed and a review study of a hybrid model makes use of both techniques to solve complicated problems more successfully than one could on its own.Journal of Scientific Research and Reports["Laxmi Choudhary", "Jitendra Singh Choudhary"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/673aed7b5f377f7c1b1f34d5940d94901b789359 24b2cea76dddf48e8cd03059c64d8d806b1a2085Artificial Intelligence (AI) holds transformative potential across various sectors, yet its journey to full realization and widespread adoption is impeded by several significant obstacles. These challenges span technical, ethical, societal, research and development, implementation, and regulatory domains, necessitating a comprehensive and multi-faceted approach to address them.Technical Challenges: AI systems require vast amounts of high-quality, labeled data, which is often unavailable or of poor quality. AI models also struggle to generalize beyond specific tasks, and their opaque decision-making processes hinder trust. Scaling AI to handle large datasets and interactions, while ensuring robustness and security against adversarial attacks, poses major technical hurdles.Ethical and Societal Challenges: AI can perpetuate and amplify biases, leading to unfair outcomes and privacy issues. AI-driven automation risks job displacement, necessitating measures for worker support and retraining. The rapid development of AI outstrips regulatory bodies' abilities to create appropriate frameworks, complicating responsible deployment.Research and Development Challenges: AI struggles to integrate information from different modalities and lacks human-like common sense and reasoning. Improving AI's learning efficiency to reduce its dependence on vast data is a significant research focus.Implementation Challenges: Integrating AI into legacy systems, addressing the talent shortage, and managing the high costs of AI development and deployment are substantial barriers.Ethical and Regulatory Uncertainty: The absence of universally accepted ethical guidelines and varying regulatory landscapes create uncertainty and potential misuse.Addressing these multifaceted obstacles requires a concerted effort from researchers, developers, policymakers, and society to create robust, fair, and transparent AI systems, unlocking AI's full potential and societal benefits.South Eastern European Journal of Public Health170Addressing these multifaceted obstacles requires a concerted effort from researchers, developers, policymakers, and society to create robust, fair, and transparent AI systems, unlocking AI's full potential and societal benefits.South Eastern European Journal of Public Health["Manish Rana", "Sunny Sall", "Vijaya Sagvekar Bijoor", "Vishwajit Gaiwad", "Ujwala Vishwajit Gaikwad", "Praniti Patil", "Kunal Meher"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/24b2cea76dddf48e8cd03059c64d8d806b1a2085 8a0600479ffe7c92690c20181f8cb0f85afa2faaArtificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.Mikailalsys Journal of Advanced Engineering International00This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges, and highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities.Mikailalsys Journal of Advanced Engineering International["Olayiwola Blessing Akinnagbe"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/8a0600479ffe7c92690c20181f8cb0f85afa2faa 49a49cd82920a18e3e43d9cbf029f3fde8c21073International Journal of Academic Research in Business and Social Sciences00International Journal of Academic Research in Business and Social Sciences["Mohamad Hafiz Bin Mohamad Joned"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/49a49cd82920a18e3e43d9cbf029f3fde8c21073 269c7a29867644706d1836b75134e985c2305757The European Union has taken an active stance in regulating AI through legislative initiatives, ethical guidelines, and consultation with stakeholders (European institutions, companies and governments) and aims to lead the global AI landscape by balancing AI advances with the social values ​​of citizens. Through AI governance, the Union seeks to create a regulatory framework that promotes innovation while ensuring the protection of fundamental rights, safety, and the public interest. This article aims to examine the EU mechanisms for AI governance. The main research question is what are the most important salient features of the AI ​​governance model in the EU? Given the qualitative nature of the subject, the research method is descriptive-analytical. The required data will be collected by referring to EU AI documents and laws such as the AI ​​Law, the General Data Protection Regulation, ethical guidelines, the European AI Strategy, the Data Governance Law, the AI ​​Coordination Plan, and the EU AI Supervisory and Enforcement Mechanisms. The paper's findings show that while the EU is progressing in AI governance, it faces challenges in harmonizing member states’ regulations, attracting global cooperation, navigating the rapidly evolving AI landscape, and balancing innovation with ethical standards. The paper’s findings show that the EU AI governance model focuses on creating a regulatory framework that encourages innovation and oversees the development of AI so that it is used in a way that is consistent with European values ​​such as protecting fundamental rights, ensuring transparency, and promoting accountability.Journal of Electrical Systems00The paper’s findings show that the EU AI governance model focuses on creating a regulatory framework that encourages innovation and oversees the development of AI so that it is used in a way that is consistent with European values, such as protecting fundamental rights, ensuring transparency, and promoting accountability.Journal of Electrical Systems["Ali Karami"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/269c7a29867644706d1836b75134e985c2305757 9a00d205fd27625bd2b2650e41826186743a0f7cObjective: The purpose of this paper is to conduct a thorough review and investigation on the function and importance of AI in education. The development of technology in the sector, and the emergence of raising interest to apply this AI technologies with a view to enable disruptive efficiencies across all aspects of education. Methodology: The study uses a systematic review of literature for exploring the existing literatures and research findings about AI implementation in Education. For learning it tells you good & bad about AI of bunchy resources, And changes because of the whether educational practices. Results: The results suggest that AI can greatly improve the personalization of learning, being able to provide tailored activities beyond traditional strategies. But the review also highlights worries over inequalities that may arise if only a specialist few can call upon top-notch AI tools possibly deepening of the achievement gap. Furthermore, a sanguine reliance on automated solutions could compromise the core human element in education and stymie social-emotional development for our students. Originality/value: Given the tendency for increased transparency, accountability and potential bias of AI systems in this critical domain leading to possible discrimination practices (e.g. admissions, grading etc) it is felt that the paper has great value contribution. There needs to be a spirit among policymakers, educators and developers working together to ensure that AI in education is implemented following certain principles.The Journal of Quality in Education440The results suggest that AI can greatly improve the personalization of learning, being able to provide tailored activities beyond traditional strategies, and a sanguine reliance on automated solutions could compromise the core human element in education and stymie social-emotional development for students.The Journal of Quality in Education["Dr. Easaw Alemayehu Assefa"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/9a00d205fd27625bd2b2650e41826186743a0f7c bce07eeb14a61f3a75a67019372e7cbaf4a1a5b2With the increasing complexity of healthcare services, artificial intelligence (AI) is emerging as a vital tool to support medical practitioners by providing real-time clinical insights, automating routine documentation, and enhancing patient engagement. This survey investigates the design requirements, technical challenges, and ethical considerations for AI-powered clinical intelligence systems intended for healthcare environments. Our study is based on a survey conducted with healthcare professionals, patients, and AI researchers, capturing diverse perspectives on functionality, usability, data privacy, and ethical concerns. This paper synthesizes these insights into a detailed requirement analysis that can guide the development of clinically effective, ethically sound, and user-centric AI systems. Through this analysis, we aim to lay the groundwork for creating AIdriven tools that improve clinical workflows, foster better patient outcomes, and support healthcare professionals in delivering quality care.International Journal of Advanced Research in Science, Communication and Technology20This survey investigates the design requirements, technical challenges, and ethical considerations for AI-powered clinical intelligence systems intended for healthcare environments, based on a survey conducted with healthcare professionals, patients, and AI researchers.International Journal of Advanced Research in Science, Communication and Technology["Vaibhav Kusalkar", "Sarvesh Biwalkar", "Atharv Bhujbal", "Tushar Bansod", "Rohini Jadhav"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/bce07eeb14a61f3a75a67019372e7cbaf4a1a5b2 b458c4dc0f433f055e42e030d9d1340b9c4d2532Journal of Forest Research292This concept paper discusses the emergence and development of CSF, which integrates Ecological Forestry, Carbon Forestry, and Smart Forestry, and proposes the concept of CSF, and analyzes the goals of CSF in improving forest ecosystem stability, enhancing forest ecosystem carbon sequestration capacity, and advocating the application and development of new technologies.Journal of Forestry Research["G. G. Wang", "Deliang Lu", "Tian Gao", "Jinxin Zhang", "Yirong Sun", "Dexiong Teng", "Fengyuan Yu", "Jiaojun Zhu"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/b458c4dc0f433f055e42e030d9d1340b9c4d2532 2b6ce571ea724f76998b84ab44ec1ed40a3db6f6This integrative literature review looks at the revolutionary impact of artificial intelligence (AI) in optimizing in-store logistics to assist retail managers and technology decision-makers in using AI to improve inventory management, spatial organization, and customer experience. Based on six core concepts—AI-driven demand forecasting, automated inventory replenishment, space utilization optimization, adaptive store layout design, operational efficiency, and customer satisfaction—the study's conceptual framework emphasizes AI's strategic value and the factors driving its adoption in retail logistics. The review uses rigorous criteria and systematic analysis of peer-reviewed articles, industry reports, and case studies to identify significant topics such as AI-enhanced demand forecasting, automated restocking, responsive shop layouts, data protection, and the changing responsibilities of retail staff. The paper advocates for balanced AI integration, integrating technology breakthroughs with human control and appropriate data management. Future research proposals include investigating AI's long-term implications, doing comparative assessments across retail forms, and developing frameworks for ethical data usage. These will all provide foundational insights for constructing sustainable, sophisticated retail environments that align with global development goals.Journal of Next-Generation Research 5.002Journal of Next-Generation Research 5.0["Khaoula Boussalham", "Rachid Ejjami"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/2b6ce571ea724f76998b84ab44ec1ed40a3db6f6 17d23f72c7dc414cb53e9c7775184887473b032bCEAS Aeronautical Journal141A systematic and highly automated approach for scenario generation based on the ODD definition is demonstrated and an efficient way of verifying system requirements and conducting automated testing based on the ODD definition is demonstrated.CEAS Aeronautical Journal["Thomas Stefani", "Johann Maximilian Christensen", "Akshay Anilkumar Girija", "Siddhartha Gupta", "Umut Durak", "Frank K\u00f6ster", "Thomas Kr\u00fcger", "Sven Hallerbach"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/17d23f72c7dc414cb53e9c7775184887473b032b c19bb5d98ad15bdeff28ffb0e0e10fccf0f3f768Artificial Intelligence (AI) has become essential in modern healthcare, with large language models (LLMs) offering promising advances in clinical decision-making. Traditional model-based approaches, including those leveraging in-context demonstrations and those with specialized medical fine-tuning, have demonstrated strong performance in medical language processing but struggle with real-time adaptability, multi-step reasoning, and handling complex medical tasks. Agent-based AI systems address these limitations by incorporating reasoning traces, tool selection based on context, knowledge retrieval, and both short- and long-term memory. These additional features enable the medical AI agent to handle complex medical scenarios where decision-making should be built on real-time interaction with the environment. Therefore, unlike conventional model-based approaches that treat medical queries as isolated questions, medical AI agents approach them as complex tasks and behave more like human doctors. In this paper, we study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation. In particular, we consider the emergent o1 model and examine its impact on agents' reasoning, tool-use adaptability, and real-time information retrieval across diverse clinical scenarios, including high-stakes settings such as intensive care units (ICUs). Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools that support better patient outcomes and decision-making efficacy in clinical practice.arXiv.org01The emergent o1 model is considered and its ability to enhance diagnostic accuracy and consistency is demonstrated, paving the way for smarter, more responsive AI tools that support better patient outcomes and decision-making efficacy in clinical practice.ArXiv["Shaochen Xu", "Yifan Zhou", "Zheng Liu", "Zihao Wu", "Tianyang Zhong", "Huaqin Zhao", "Yiwei Li", "Hanqi Jiang", "Yi Pan", "Junhao Chen", "Jin Lu", "Wei Zhang", "Tuo Zhang", "Lu Zhang", "Dajiang Zhu", "Xiang Li", "Wei Liu", "Quanzheng Li", "Andrea Sikora", "Xiaoming Zhai", "Zhen Xiang", "Tian Xi Liu"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/c19bb5d98ad15bdeff28ffb0e0e10fccf0f3f768 29383f02b5f3fe69351333fd23a0c270ecdbf576The core of the deepfake technology is based on a generative-adversarial network built on a combination of two neural networks: a generative network (network G) creates samples, a discriminative network (network D) tries to distinguish correct («genuine») samples from incorrect ones. Networks G and D compete with each other thousands or even millions of times until network G improves its performance. Thus, the network D will no longer be able to distinguish real data from fake data. With the development of big data and machine learning technologies, the scenario for using deepfake technology has gradually changed from creating sound models and imitating text to deep video forgery. For a long time, images modified using traditional Photoshop and other technologies were easily recognized. Deepfake technology changed this situation, making it more difficult to identify fakes. As an important technological innovation in the field of artificial intelligence, deepfake technology is widely used in various areas of society, creating enormous applied value. However, any technology is a double-edged sword. The use of deepfake technology poses a great threat to personal privacy, property security and even national security. In order to find a balance between technological innovation and risk prevention and control, countries around the world are actively exploring various ways to manage. The paper describes the main risks posed by modern deepfake technology, provides an overview of legal regulation in this area in China and offers an effective way to solve problems.Lex Russica20The paper describes the main risks posed by modern deepfake technology, provides an overview of legal regulation in this area in China and offers an effective way to solve problems.Lex Russica["Yao Li"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/29383f02b5f3fe69351333fd23a0c270ecdbf576 7f503b41006883566796f72a62608370b2bc50e0The efficient market theory suggests that the 'buy and hold' (B&H) strategy is optimal due to its simplicity and lower costs. Artificial intelligence (AI) models, including machine learning (ML) is now a common research tool in financial applications. However, advancements in AI and machine learning offer the potential to outperform B&H. This paper investigates AI models applied to the Nifty 50 index, demonstrating that an AI-based strategy can surpass the performance of the traditional B&H approach. We compare various machine learning classifiers, such as LightGBM, KNN, XGB, SVC, and Random Forest, to evaluate their effectiveness. Additionally, we dive into the feature engineering process, converting a core set of financial indicators into comprehensive datasets for model training. A detailed Profit and Loss (PnL) comparison is conducted, revealing that our AI-based strategy not only outperforms the B&H strategy in general but also shows resilience during volatile market conditions. The findings highlights the significant potential of AI in developing superior trading strategies, offering insights into its practical applications in financial markets.Journal of Electrical Systems270This paper investigates AI models applied to the Nifty 50 index, demonstrating that an AI-based strategy can surpass the performance of the traditional B&H approach and shows resilience during volatile market conditions.Journal of Electrical Systems["Dr. Nupur Giri"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/7f503b41006883566796f72a62608370b2bc50e0 a14735a94464e91caef9ec4a8fc17558b76bd1d4Deepfake technology has completely changed the production of synthetic media by allowing the alteration of photos, movies, and audio recordings. It is driven by machine learning and artificial intelligence algorithms. Deepfake provide a lot of amusement and artistic freedom, but they also pose serious problems, especially when it comes to disinformation and digital manipulation. This paper offers a thorough introduction to deepfake technology, covering all of its types, including voice synthesis, gesture control, and face-swapping. The research article delves into the fundamental workings of deepfake generation, emphasizing the part played by convolutional neural networks and generative adversarial networks in producing lifelike artificial content. The paper also investigates the methods and strategies used in detection, with a focus on the latest developments in deep neural network architectures, attention-based models, and hybrid approaches. This review article also focuses on availability of standard datasets and performance parameters for the evaluation of research models. With the aim to provide contribution to help researchers to create reliable and efficient deepfake detection systems that can stop the distribution of manipulated media and ensure the accuracy of digital content by tackling various issues, paper also focuses on key challenges and future work.Journal of Electrical Systems550This paper offers a thorough introduction to deepfake technology, covering all of its types, including voice synthesis, gesture control, and face-swapping, and a review article on availability of standard datasets and performance parameters for the evaluation of research models.Journal of Electrical Systems["Krishna J Patel", "Madhavi B Desai"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/a14735a94464e91caef9ec4a8fc17558b76bd1d4 3dc6bfd2be7223ee955f976a3e9e52ab9d4ee5feThe application of artificial intelligence (AI) in tax control is becoming more and more relevant in the conditions of globalization and digitalization of the economy, which creates a need for effective mechanisms for managing information risk. With the growing volume of data and the complexity of the tax processes, the risk of deviations, fraud and inconsistencies increases significantly. This article aims to examine applied AI tools and approaches that can minimize information risk in tax control, as well as to analyze their effectiveness through the prism of practice in Bulgaria. The methodology is based on correlation analysis, which measures the strength and direction of the linear relationship between pairs of variables. A Pearson correlation coefficient was used to evaluate the variable "Artificial Intelligence" and the different forms of tax control. Kendall's tau-b correlation coefficient was used to assess the linear relationship between the variables of collection, processing, verification, and management of tax information related to the application of AI. The main results show that AI has a positive and statistically significant effect on risk management and cybersecurity, suggesting the potential to improve data protection and reliability. However, the verification and revision of tax information needs to significantly impact AI, which points to the need for further development of these technologies for better integration. In conclusion, the application of AI offers significant opportunities to minimize information risk in tax control but requires targeted adaptation and refinement in specific aspects of control processes.Edelweiss Applied Science and Technology00The main results show that AI has a positive and statistically significant effect on risk management and cybersecurity, suggesting the potential to improve data protection and reliability, but the verification and revision of tax information needs to significantly impact AI.Edelweiss Applied Science and Technology["Zhelyo Zhelev"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/3dc6bfd2be7223ee955f976a3e9e52ab9d4ee5fe f31753a5a04eae30570547478e4cdbdcb259df9bThis paper explores the transformative role of artificial intelligence (AI) in enhancing scientific research, particularly in the fields of brain science and social sciences. We analyze the fundamental aspects of human research and argue that it is high time for researchers to transition to human-AI joint research. Building upon this foundation, we propose two innovative research paradigms of human-AI joint research:"AI-Brain Science Research Paradigm"and"AI-Social Sciences Research Paradigm". In these paradigms, we introduce three human-AI collaboration models: AI as a research tool (ART), AI as a research assistant (ARA), and AI as a research participant (ARP). Furthermore, we outline the methods for conducting human-AI joint research. This paper seeks to redefine the collaborative interactions between human researchers and AI system, setting the stage for future research directions and sparking innovation in this interdisciplinary field.arXiv.org00The collaborative interactions between human researchers and AI system are redefined, setting the stage for future research directions and sparking innovation in this interdisciplinary field.ArXiv["Feng Xiong", "Xinguo Yu", "Hon Wai Leong"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/f31753a5a04eae30570547478e4cdbdcb259df9b 09a61f87f048c5ab35b714496cb7357eb76f1f4bThis paper introduces the Adaptive Human-AI Synergy in Logistics (AHASL) theory, which focuses on integrating artificial intelligence (AI) into logistics and supply chain management. The study takes a qualitative approach, using interviews, observations, and reflexive journaling, and finds that while AI significantly improves operational efficiency and decision-making, human oversight is still required to address AI's limitations, such as bias, lack of transparency, and potential skill erosion. The AHASL model prioritizes Full-Spectrum Explainability (FSE) and a Bias Mitigation Framework (BMF) to ensure that AI-driven decisions are transparent and equitable while simultaneously arguing for retaining human expertise to retain adaptability and resilience in logistical operations. The study's findings emphasize the significance of balanced human-AI collaboration. However, they also call for more research into the long-term effects, scalability, and ethical implications of AI integration in logistics.Journal of Next-Generation Research 5.000The study takes a qualitative approach, using interviews, observations, and reflexive journaling, and finds that while AI significantly improves operational efficiency and decision-making, human oversight is still required to address AI's limitations, such as bias, lack of transparency, and potential skill erosion.Journal of Next-Generation Research 5.0["Rachid Ejjami"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/09a61f87f048c5ab35b714496cb7357eb76f1f4b a5718e11b00aeea677447ffcde92e628586e5e44This paper explores the integration of blockchain, artificial intelligence (AI), and the Internet of Things (IoT) to revolutionize healthcare data management, storage, access, and analysis, aiming to enhance data security, diagnostic accuracy, and healthcare accessibility. By leveraging blockchain’s decentralized and immutable nature, AI’s diagnostic capabilities, and IoT’s real-time monitoring, the study highlights how these technologies can address challenges exposed during the COVID-19 pandemic, such as data breaches, supply chain integrity, and remote patient care. The research emphasizes the potential of these innovations to improve emergency response, optimize clinical workflows, and ensure patient privacy while overcoming technical, security, and ethical hurdles. Through frameworks like decentralized emergency intelligence (D-EI) and blockchain-protected medical imaging systems, the study demonstrates the transformative impact of these technologies on healthcare efficiency and patient outcomes. Future directions focus on enhancing interoperability, scalability, and regulatory compliance to fully realize their potential in global healthcare systems.Journal of Electrical Systems00Journal of Electrical Systems["Md Solaiman Ahamed"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/a5718e11b00aeea677447ffcde92e628586e5e44 62f99e2cc584309acef27f64afaa1d58f1f24d0fInteractive Voice Response (IVR) systems have undergone significant transformation in recent years, moving from traditional code-based development to more user-friendly approaches leveraging widgets and, most recently, harnessing the power of Artificial Intelligence (AI) for automated IVR flow creation. This paper explores the evolution of IVR building techniques, highlighting the industry's revolution and shaping the future of IVR systems. The authors delve into the historical context, current trends, and future prospects of IVR development, elucidating the impact of AI on simplifying IVR creation processes and enhancing customer experiences.arXiv.org100The authors delve into the historical context, current trends, and future prospects of IVR development, elucidating the impact of AI on simplifying IVR creation processes and enhancing customer experiences.ArXiv["Khushbu Mehboob Shaikh", "Georgios Giannakopoulos"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/62f99e2cc584309acef27f64afaa1d58f1f24d0f b76bfd223b3e82a754cc6ae56a4503ff03bf81b4This article explores the potential of Generative Artificial Intelligence (GenAI) in supporting communication for children with Autism Spectrum Disorder (ASD). With the prevalence of ASD increasing, there is an urgent need for innovative interventions. The article discusses how GenAI can offer personalized, adaptive communication tools that interpret social cues, simplify language, and provide real-time support. It examines the key benefits of GenAI in ASD support, including personalized communication assistance, enhanced learning experiences, and data-driven intervention planning. The article also delves into GenAI-enabled conversation tools, explaining their mechanisms and potential impact. Finally, it addresses the ethical considerations and challenges associated with implementing these technologies, emphasizing the need for a multidisciplinary approach in their development and deployment.International Journal For Multidisciplinary Research70The article discusses how GenAI can offer personalized, adaptive communication tools that interpret social cues, simplify language, and provide real-time support, and addresses the ethical considerations and challenges associated with implementing these technologies.International Journal For Multidisciplinary Research["Anoop Sagar Pradhan"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/b76bfd223b3e82a754cc6ae56a4503ff03bf81b4 bb04fde7514eee1d8771e8970cec040050cb6777The paper discusses the temporality of artificial intelligence (AI) and its hegemonic posi-tion in the evolution of capitalism. Based on the theory of varieties of capitalism, I consider forming a coupling nexus as the temporal purpose of AI advancement in developed economies. Furthermore, by complexifying the nexus, I show the possibility of settling the unsettled purposes in developing economies.International Journal of Law, Ethics and Social Sciences150By forming a coupling nexus as the temporal purpose of AI advancement in developed economies, the possibility of settling the unsettled purposes in developing economies is shown.International Journal of Law, Ethics and Social Sciences["Zhengyuan Gao"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/bb04fde7514eee1d8771e8970cec040050cb6777 b23582f3fffe6294dbe4c89b43dc403784d84e6bCognitive warfare has evolved alongside advances in technology, psychology, and communication methods, reflecting a growing understanding of how to influence and control human minds. Cognitive warfare refers to efforts to influence, manipulate, or control public perceptions, decision-making processes, and behaviors using psychological, informational, or technological tactics that often blur the lines between the military and civilian domains. From ancient psychological tactics to artificial intelligence-based disinformation campaigns, the essence of cognitive warfare is the same: shaping perceptions and behavior in ways that often achieve strategic goals without the need for physical force. One of the most important aspects of cognitive warfare is its command and control, which naturally has multiple perspectives. This article aims to understand the European Union’s approach to command and control of cognitive warfare. The main research question is what similarities and differences does the European Union’s approach to command and control of cognitive warfare have with other approaches? The method of this article is descriptive-analytical. The required data was obtained by referring to library sources as well as reputable scientific research articles and official documents. The research findings show that the European Union's approach to command and control in cognitive warfare is shaped by its broader strategy for hybrid threats, cybersecurity, and defense, with a focus on resilience, collective security, and democratic values. The European Union's approach to command and control in cognitive warfare integrates multi-level efforts to protect information integrity, increase public resilience, and promote digital governance while respecting democratic values. Also, in the Union's approach, cooperation with international partners and continuous development of capabilities in command and control of the growing domain of cognitive warfare is crucial.Journal of Electrical Systems00Journal of Electrical Systems["Ali Karami"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/b23582f3fffe6294dbe4c89b43dc403784d84e6b 288db477d17cd472d256dc02b060ba11dab7395fThis research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.South Eastern European Journal of Public Health400Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations and advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders.South Eastern European Journal of Public Health["B. Al-jenaibi", "Amna Bulhoon"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/288db477d17cd472d256dc02b060ba11dab7395f 7faa581fe1f38394363bff9f2e48c63c5f67416fA Inteligência Artificial (IA) se destaca como um dos mais significativos progressos tecnológicos da era atual, com capacidade de impactar positivamente diversas áreas de nossa sociedade. Entretanto, essas inovações trazem consigo complexos e delicados desafios, que demandam uma análise crítica e uma resposta responsável das entidades sociais envolvidas. O estudo é uma pesquisa qualitativa com base em revisão bibliográfica realiza uma análise crítica sobre os impactos associados à implementação da tecnologia, buscando responder quais são as principais preocupações associadas ao uso da IA. Embasando a discussão, o trabalho utilizou de referências artigos, livros e outros materiais acadêmicos recentes encontrados nos repositórios Google Acadêmico e Portal Capes. O objetivo central é analisar criticamente as questões éticas e sociais associadas à implementação da Inteligência Artificial, visando compreender como essas tecnologias impactam a sociedade e quais responsabilidades surgem para os diferentes agentes incluídos. O conteúdo explora sobre os fundamentos da IA, os riscos de preconceito e discriminação, os desafios e limitações da tecnologia, a urgência da ética na IA e a responsabilidade social e igualdade no contexto de sua adoção. Os resultados destacam que, embora a IA tenha trazido inúmeros benefícios para a sociedade, é de suma importância se atentar para os desafios e riscos, amenizando os impactos negativos que acompanham essa tecnologia em constante evolução.Revista contemporânea40Revista Contemporânea["T. M. Freitas"]2024-11-16T00:00:00https://www.semanticscholar.org/paper/7faa581fe1f38394363bff9f2e48c63c5f67416f 5320139029455cd83a63a468ac74c726fbce4ecePalliative care aims to improve the quality of life for seriously ill individuals and their caregivers by addressing their holistic care needs through a person- and family-centered approach. While there have been growing efforts to integrate Artificial Intelligence (AI) into palliative care practice and research, it remains unclear whether the use of AI can facilitate the goals of palliative care. In this paper, we present three hypothetical case examples of using AI in the palliative care context, covering machine learning algorithms that predict patient mortality, natural language processing models that detect psychological symptoms, and AI chatbots addressing caregivers' unmet needs. Using these cases, we examine the ethical dimensions of utilizing AI in palliative care by applying five widely accepted moral principles that guide ethical deliberations in AI: beneficence, nonmaleficence, autonomy, justice, and explicability. We address key ethical questions arising from these five core moral principles and analyze the potential impact the use of AI can have on palliative care stakeholders. Applying a critical lens, we assess whether AI can facilitate the primary aim of palliative care to support seriously ill individuals and their families. We conclude by discussing the gaps that need to be further addressed in order to promote ethical and responsible AI usage in palliative care.Nursing Ethics361Three hypothetical case examples of using AI in the palliative care context are presented, covering machine learning algorithms that predict patient mortality, natural language processing models that detect psychological symptoms, and AI chatbots addressing caregivers' unmet needs.Nursing ethics["Oonjee Oh", "George Demiris", "Connie M Ulrich"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/5320139029455cd83a63a468ac74c726fbce4ece e2da1c9e62429907004f7a515ad1eef639995875The growing contribution of artificial intelligence into several domains, including environmental sustainability and informational energy, has gained dramatic attention from several stakeholders. This research investigates the impact of artificial intelligence, information and communication technologies (ICTs), and technological innovations on information energy systems, with respect to environmental sustainability and economic growth of China. Data was collected during 2001-2020 with yearly observations. The study applied the Methods of Moments Quantile Regression (MMQR) to examine the quantile varying trend of information energy as determined by the stated variables. The results through the MMQR estimator show that artificial intelligence boosts information energy from the 0.25th to 0.90th Quantile, where the highest effect is observed at the 0.75th Quantile. The results also show a positive connection between ICT and information energy across all the quantiles. Moreover, technological innovations positively impact the information energy from 0.75th to 0.90th Quantile. Conversely, environmental sustainability hinders such energy production across all the quantiles. Finally, our findings confirm the productive effect of economic growth in determining an increasing trend of information energy. The study provides several policy suggestions while considering all of the given variables. Besides, the limitations are also highlighted by the end of this research to demonstrate the directions for future studies.El Profesional de la Informacion00The results through the MMQR estimator show that artificial intelligence boosts information energy from the 0.25th to 0.90th Quantile and the productive effect of economic growth in determining an increasing trend of information energy.Profesional de la información["Bo Yu"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/e2da1c9e62429907004f7a515ad1eef639995875 b45c03c8b4a699259abacfe088c5b3662d269df8IntroductionArtificial intelligence (AI) is rapidly transforming medical fields, particularly emergency medicine (EM), where timely decision-making is crucial. AI offers potential benefits in diagnostic accuracy, patient care optimization, and workflow efficiency within emergency departments (EDs). Purpose of WorkThis review aims to synthesize recent advancements in AI applications within emergency medicine, focusing on diagnostic support, patient triage, clinical decision support systems (CDSS), and workflow optimization. Additionally, we highlight the potential benefits, challenges, and future directions for AI in EM. Material and MethodsA comprehensive literature search was conducted using PubMed and Google Scholar databases. We reviewed peer-reviewed articles from 2008 to 2024, focusing on AI-driven solutions in EDs. Keywords included "artificial intelligence," "emergency medicine," "machine learning," and "clinical decision support." Studies were selected based on their relevance to AI applications in EM, diagnostic improvements, and operational efficiency. The results highlight the promising role of AI in improving diagnostic accuracy, reducing overcrowding, optimizing triage processes, and addressing clinician workload. However, challenges like ethical concerns, data bias, and the need for clinical validation remain. Further research is necessary to integrate AI more effectively in clinical practice.Quality in Sport00The results highlight the promising role of AI in improving diagnostic accuracy, reducing overcrowding, optimizing triage processes, and addressing clinician workload.Quality in Sport["Gracjan Sitarek", "Marta \u017berek"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/b45c03c8b4a699259abacfe088c5b3662d269df8 9eb91defc86fca01b6de32a437e74702222e8958Rapidly improving artificial intelligence (AI) technologies have created opportunities for human–machine cooperation in legal practice. We provide evidence from an experiment with law students (N = 206) on the causal impact of machine assistance on the efficiency of legal task completion in a private law setting with natural language inputs and multidimensional AI outputs. We tested two forms of machine assistance: AI‐generated summaries of legal complaints and AI‐generated text highlighting within those complaints. AI‐generated highlighting reduced task completion time by 30% without any reduction in measured quality indicators compared to no AI assistance. AI‐generated summaries produced no change in performance metrics. AI summaries and AI highlighting together improved efficiency but not as much as AI highlighting alone. Our results show that AI support can dramatically increase the efficiency of legal task completion, but finding the optimal form of AI assistance is a fine‐tuning exercise. Currently, AI‐generated highlighting is not readily available from state‐of‐the‐art, consumer‐facing large language models, but our work suggests that this capability should be prioritized in the development of legal AI products.Journal of Empirical Legal Studies190The results show that AI support can dramatically increase the efficiency of legal task completion, but finding the optimal form of AI assistance is a fine‐tuning exercise.Journal of Empirical Legal Studies["Aileen Nielsen", "Stavroula Skylaki", "Milda Norkute", "Alexander Stremitzer"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/9eb91defc86fca01b6de32a437e74702222e8958 878528869be6e31d21eca433c35fb3c975ed4511Competitive advantage has been considered as the foremost element for the organizational success and this could be achieved using artificial intelligence (AI) and technological competencies. The current study investigates the impact of AI usage and technology competencies on the competitive advantage of manufacturing industries in China. The paper also investigates the mediating role of effective information management system (IMS) among AI usage, technology competencies and competitive advantage. Data was obtained from a sample comprising employees of manufacturing companies through questionnaires. The data reliability and association among variables were measured with SPSSAMOS. The results exposed that the AI usage and technology competencies have a positive nexus with competitive advantage and also revealed that the effective IMS significantly mediates among AI usage, technology competencies and competitive advantage of manufacturing industry in China. The study provides useful insights to the regulators in establishing regulations towards improving competitive advantage, effective AI usage and technology competencies.El Profesional de la Informacion00The results exposed that the AI usage and technology competencies have a positive nexus with competitive advantage and also revealed that the effective IMS significantly mediates among AI usage, technology competencies and competitive advantage of manufacturing industry in China.Profesional de la información["Gao Penglong", "Mustapa Fara Diva", "Zhao Xue", "Zhou Yangyang"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/878528869be6e31d21eca433c35fb3c975ed4511 3a5d109fe0582574b487a5bd1c468d850bbc1724This paper explores how automation and artificial intelligence (AI) are transforming U.S. cyber diplomacy. Leveraging these technologies helps the U.S. manage the complexity and urgency of cyber diplomacy, improving decision-making, efficiency, and security. As global inter connectivity grows, cyber diplomacy, managing national interests in the digital space has become vital. The ability of AI and automation to quickly process vast data volumes enables timely responses to cyber threats and opportunities. This paper underscores the strategic integration of these tools to maintain U.S. competitive advantage and secure national interests. Automation enhances diplomatic communication and data processing, freeing diplomats to focus on strategic decisions. AI supports predictive analytics and real time decision making, offering critical insights and proactive measures during high stakes engagements. Case studies show AIs effectiveness in monitoring cyber activities and managing international cyber policy. Challenges such as ethical concerns, security vulnerabilities, and reliance on technology are also addressed, emphasizing human oversight and strong governance frameworks. Ensuring proper ethical guidelines and cybersecurity measures allows the U.S. to harness the benefits of automation and AI while mitigating risks. By adopting these technologies, U.S. cyber diplomacy can become more proactive and effective, navigating the evolving digital landscape with greater agility.arXiv.org00This paper explores how automation and artificial intelligence (AI) are transforming U.S. cyber diplomacy, and underscores the strategic integration of these tools to maintain U.S. competitive advantage and secure national interests.ArXiv["Michael Stoltz"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/3a5d109fe0582574b487a5bd1c468d850bbc1724 12a4c33f3b702cd176d2f913e6364aa5a723b67fTeachers at Kiddos Smart School Makassar faced challenges in exploring children's literature and integrating Artificial Intelligence (AI) technology into the creation of digital picture storybooks. This Community Service Program aims to enhance teachers' abilities in creating interactive AI-based learning media in early childhood. The implementation methods include training, workshops, and intensive mentoring on using tools like ChatGPT, Canva Magic Media, and Suno.ai to create digital text, illustrations, and background music. As a result, teachers were able to adapt children's literature elements into engaging digital narratives and improve their technical skills in producing AI-based digital media. This Community Service Program successfully strengthened teachers' capacity to integrate AI technology, which is expected to enhance educational quality and cognitive development in children.Prima Abdika: Jurnal Pengabdian Masyarakat220This Community Service Program successfully strengthened teachers' capacity to integrate AI technology, which is expected to enhance educational quality and cognitive development in children.Prima Abdika: Jurnal Pengabdian Masyarakat["J. Juanda", "Haripuddin Haripuddin", "Azis Azis"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/12a4c33f3b702cd176d2f913e6364aa5a723b67f 5be1ede9913a787eb5d88ede79f211a61e1bf97eHumanities and Social Sciences Communications732Humanities and Social Sciences Communications["Byung\u2010Jik Kim", "Julak Lee"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/5be1ede9913a787eb5d88ede79f211a61e1bf97e d6e71517d95e9922be47e3b2ddc5a99f2bdd7c24SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis00SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis[]2024-11-17T00:00:00https://www.semanticscholar.org/paper/d6e71517d95e9922be47e3b2ddc5a99f2bdd7c24 19cc8305dbd572c8092748771e2842ccc288dfd6Future Trends in AI Research00Future Trends in AI Research["Bin Zhang", "Jiantao Wang"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/19cc8305dbd572c8092748771e2842ccc288dfd6 29845436b7f23618efc232c3c20ff662c2d679c2The exponential growth of network has introduced new Internet-of-Things (IoT) use cases that has enabling us convenience and comfort. The surge of IoT devices due to the capabilities brought by fifth generation (5G) have given rise to security threats and attacks, particularly malware attacks IoT botnets have been an alarming issue, where smart devices can be manipulated by malicious actors to commence subsequent attacks such as Denial of Service (DoS). Traditional and complex security techniques may not be a viable solution towards these resource-constrained devices with limited processing power. Machine Learning techniques (ML) are the rising trend, and it is often used in Intrusion Detection Systems and Network Anomaly Detection. This paper emphasizes on analyzing and comparing various ML models on the IoT-23 dataset. It aims to predict anomalies and conclude the model with optimal performance and least computational time cost that can be used for network anomaly detection systems with real-time data in future works. The ML models used in this paper are Decision Trees (DT), K-nearest neighbours (KNN), Random Forest (RF), Naïve Bayes (NB) and Histogram Gradient Boosting (HGB). DT displayed the best performance with an accuracy score of 73% and F1 score of 0.49 with a time cost of 28.22 seconds.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)200This paper aims to predict anomalies and conclude the model with optimal performance and least computational time cost that can be used for network anomaly detection systems with real-time data in future works.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["Alice Lee Nan Xin", "Athirah Mohd Ramly", "Mehran Behjati", "M. S. Sharif"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/29845436b7f23618efc232c3c20ff662c2d679c2 8fd12c294b37c3cb41b00c4224b0a6e9495e5953Current Issues in Tourism480Current Issues in Tourism["Yanan Jia", "Anshul Garg", "Joaquim Dias Soeiro"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/8fd12c294b37c3cb41b00c4224b0a6e9495e5953 44a29e9fa6a1f9ebbd9f7849e6cd2244ebbf835cFuture Trends in AI Research00Future Trends in AI Research["Changxu Duan"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/44a29e9fa6a1f9ebbd9f7849e6cd2244ebbf835c 76dfaf105ff328ca7343f94e02944ff1b3cf0d31A priority in the researches on artificial intelligence is the creation of Artificial General Intelligence. Even though the regular artificial intelligence was making some progress in certain areas, there were challenges and untried domains. This study introduces a novel $Q^{*}$ framework which is intended for improving AGI. Understanding, obtaining and using machine-generated knowledge across multiple domains are some of the aspects of AGI. It integrates concepts form cognitive psychology and reinforcement learning to produces a cohesive approach that includes learning as well as introspection and flexibility. By leveraging the advantages of Q-learning and refining existing methods, achieving AGI through $\mathbf{Q}^{*}$ is feasible. $\mathbf{Q}^{*}$ prioritizes metalearning and hierarchical representations over traditional AI systems that perform well within narrow contexts yet fail when asked to generalize more broadly. This makes it possible to be adaptable and learn well in unfamiliar settings. The paper gives a detailed explanation of the theoretical underpinnings and possible applications of AGI, emphasizing $Q^{* \prime}$ ‘s commitmentto advancing its pursuit. We discuss the conceptual framework proposed by Q* $^{*}$ and speculate on its implications for future AI research.International Symposium on Wireless Personal Multimedia Communications90A novel $Q^{*}$ framework is introduced which is intended for improving AGI and prioritizes metalearning and hierarchical representations over traditional AI systems that perform well within narrow contexts yet fail when asked to generalize more broadly.2024 27th International Symposium on Wireless Personal Multimedia Communications (WPMC)["Meenakshi Upreti", "Abhinav Bhatnagar"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/76dfaf105ff328ca7343f94e02944ff1b3cf0d31 49b8cb378a337949e41b2b64fb95249e197026c7Precision medicine is a promising approach for accessible disease diagnosis and personalized intervention planning in high-mortality diseases such as coronary artery disease (CAD), drug-resistant epilepsy (DRE), and chronic illnesses like Type 1 diabetes (T1D). By leveraging artificial intelligence (AI), precision medicine tailors diagnosis and treatment solutions to individual patients by explicitly modeling variance in pathophysiology. However, the adoption of AI in medical applications faces significant challenges, including poor generalizability across centers, demographics, and comorbidities, limited explainability in clinical terms, and a lack of trust in ethical decision-making. This paper proposes a framework to develop and ethically evaluate expert-guided multi-modal AI, addressing these challenges in AI integration within precision medicine. We illustrate this framework with case study on insulin management for T1D. To ensure ethical considerations and clinician engagement, we adopt a co-design approach where AI serves an assistive role, with final diagnoses or treatment plans emerging from collaboration between clinicians and AI.arXiv.org03A framework to develop and ethically evaluate expert-guided multi-modal AI, addressing challenges in AI integration within precision medicine is proposed and illustrated with case study on insulin management for T1D.ArXiv["Ayan Banerjee", "Payal Kamboj", "Sandeep K. S. Gupta"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/49b8cb378a337949e41b2b64fb95249e197026c7 34483129bfc770b9d2f67433b84c446d464c4bd2Modern HPC workflows involve intricate coupling of simulation, data analytics, and artificial intelligence (AI) applications to improve time to scientific insight. These workflows require a cohesive set of performance analysis tools to provide a comprehensive understanding of data exchange patterns in HPC systems. However, current tools are not designed to work with an AI-based I/O software stack that requires tracing at multiple levels of the application. To this end, we developed a data flow tracer called DFTracer to capture data-centric events from workflows and the I/O stack to build a detailed understanding of the data exchange within AI-driven workflows. DFTracer has the following three novel features, including a unified interface to capture trace data from different layers in the software stack, a trace format that is analysis-friendly and optimized to support efficiently loading multi-million events in a few seconds, and the capability to tag events with workflow-specific context to perform domain-centric data flow analysis for workflows. Additionally, we demonstrate that DFTracer has a $1.44 x$ smaller runtime overhead and 1.3-7.1x smaller trace size than state-of-the-art tracing tools such as Score-P, Recorder, and Darshan. Moreover, with AI-driven workflows, Score-P, Recorder, and Darshan cannot find I/O accesses from dynamically spawned processes, and their load performance of 100 M events is three orders of magnitude slower than DFTracer. In conclusion, we demonstrate that DFTracer can capture multi-level performance data, including contextual event tagging with a low overhead of 1-5% from AI-driven workflows such as MuMMI and Microsoft’s Megatron Deepspeed running on large-scale HPC systems.International Conference for High Performance Computing, Networking, Storage and Analysis542It is demonstrated that DFTracer can capture multi-level performance data, including contextual event tagging with a low overhead of 1-5% from AI-driven workflows such as MuMMI and Microsoft’s Megatron Deepspeed running on large-scale HPC systems.SC24: International Conference for High Performance Computing, Networking, Storage and Analysis["H. Devarajan", "Lo\u00a8\u0131c Pottier", "K. Velusamy", "Huihuo Zheng", "Izzet Yildirim", "Olga Kogiou", "Weikuan Yu", "Anthony Kougkas", "Xian-He Sun", "Jae-Seung Yeom", "Kathryn M. Mohror"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/34483129bfc770b9d2f67433b84c446d464c4bd2 c662e2539314abccc8088162d3c2064c7c3ddf13Rapid development of artificial intelligence has drastically accelerated the development of scientific discovery. Trained with large-scale observation data, deep neural networks extract the underlying patterns in an end-to-end manner and assist human researchers with highly-precised predictions in unseen scenarios. The recent rise of Large Language Models (LLMs) and the empowered autonomous agents enable scientists to gain help through interaction in different stages of their research, including but not limited to literature review, research ideation, idea implementation, and academic writing. However, AI researchers instantiated by foundation model empowered agents with full-process autonomy are still in their infancy. In this paper, we study $\textbf{AI-Generated Science}$ (AIGS), where agents independently and autonomously complete the entire research process and discover scientific laws. By revisiting the definition of scientific research, we argue that $\textit{falsification}$ is the essence of both human research process and the design of an AIGS system. Through the lens of falsification, prior systems attempting towards AI-Generated Science either lack the part in their design, or rely heavily on existing verification engines that narrow the use in specialized domains. In this work, we propose Baby-AIGS as a baby-step demonstration of a full-process AIGS system, which is a multi-agent system with agents in roles representing key research process. By introducing FalsificationAgent, which identify and then verify possible scientific discoveries, we empower the system with explicit falsification. Experiments on three tasks preliminarily show that Baby-AIGS could produce meaningful scientific discoveries, though not on par with experienced human researchers. Finally, we discuss on the limitations of current Baby-AIGS, actionable insights, and related ethical issues in detail.arXiv.org01This work proposes Baby-AIGS as a baby-step demonstration of a full-process AIGS system, which is a multi-agent system with agents in roles representing key research process and introduces FalsificationAgent, which identify and then verify possible scientific discoveries.ArXiv["Zijun Liu", "Kai Liu", "Yiqi Zhu", "Xuanyu Lei", "Zonghan Yang", "Zhenhe Zhang", "Peng Li", "Yang Liu"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/c662e2539314abccc8088162d3c2064c7c3ddf13 482fb1a623b6e1aab491528e993355c265b14728Traffic accidents pose a major risk to human life and have a considerable economic impact worldwide. In this study, we present the RAPID framework, an innovative approach that integrates Artificial Intelligence (AI) and Multi-Agent Systems (MAS) to enhance traffic management. The core of the RAPID framework is the YOLOv9 model, a “state-of-the-art” deep learning (DL) algorithm for real-time object detection and classification, which we employ to detect and classify traffic accidents accurately. The RAPID framework addresses key challenges in modern traffic management, including data heterogeneity, scalability and real-time processing. Our system leverages a multi-layered architecture: data is acquired from various sources, including traffic cameras and CCTV and then transmitted to a cloud data center for storage and processing. Within the cloud infrastructure, the data undergoes pre-processing and is analyzed by the YOLOv9 model. The MAS coordinates various components of the framework, distributing tasks among specialized agents responsible for data acquisition, pre-processing, model training and real-time detection. We evaluated the performance of the RAPID framework using a dataset of 3000 labeled traffic accident images. The YOLOv9 model demonstrated 95.4% accuracy, 93.8% precision, 92.6% recall and 93.2% F1-score. The experimental results indicate that the RAPID framework significantly improves traffic accident detection and response times compared to traditional systems, highlighting its potential for real-world applications. The RAPID framework contributes to the field of intelligent transportation systems (ITS) by providing a robust, scalable and efficient solution for real-time traffic management.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)290The experimental results indicate that the RAPID framework significantly improves traffic accident detection and response times compared to traditional systems, highlighting its potential for real-world applications.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["S. Baroud", "Nor Adnan Yahaya"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/482fb1a623b6e1aab491528e993355c265b14728 788a72e94e305c1da413429e9d85d861a2b96155This study investigates how artificial intelligence (AI) can enhance self-directed learning among student teachers in African rural universities. A scoping review methodology was employed, encompassing 214 articles accessed from Scopus and Google Scholar. From these, 78 peer-reviewed English-language articles were selected for thematic analysis. The review highlights both the prospects and challenges of integrating AI into self-directed learning within these specific educational contexts. AI technologies offer significant potential to personalise learning experiences, provide adaptive feedback, and support remote learning in resource-constrained environments. However, the study also uncovers notable challenges, including limited infrastructure, inadequate digital literacy, and resistance to technology adoption. The findings suggest that while AI can significantly benefit self-directed learning, especially in areas where traditional educational resources are scarce, successful implementation requires overcoming these barriers through targeted interventions and support. Future research should focus on developing scalable AI solutions tailored to the unique needs of rural universities and exploring strategies to address the digital divide. This research provides a foundational understanding of AI’s role in supporting self-directed learning and offers practical insights for policymakers, educators, and researchers.International Journal of Innovative Technologies in Social Science100The findings suggest that while AI can significantly benefit self-directed learning, especially in areas where traditional educational resources are scarce, successful implementation requires overcoming these barriers through targeted interventions and support.International Journal of Innovative Technologies in Social Science["Rachel Gugu Mkhasibe", "Oluwatoyin Ayodele Ajani"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/788a72e94e305c1da413429e9d85d861a2b96155 9e43bbed31b7fb2477472e6d986594783317a36aThe rapid adoption of cloud banking has transformed the financial sector by enhancing efficiency, scalability, and accessibility. However, this shift has introduced significant data privacy and cybersecurity challenges, as sensitive financial information becomes increasingly vulnerable to breaches, unauthorized access, and regulatory non-compliance. Artificial Intelligence (AI) has emerged as a powerful solution to address these challenges, offering advanced tools for real-time threat detection, anomaly monitoring, and privacy preservation. This study systematically reviews 62 peer-reviewed articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to explore the role of AI in strengthening data privacy within cloud banking systems. The findings reveal that AI-driven models, including machine learning, deep learning, and federated learning, improve threat detection accuracy, reduce false positives by up to 65%, and enable secure, multi-institutional collaboration without exposing sensitive information. Furthermore, AI enhances compliance automation, ensuring adherence to regulatory standards such as GDPR and CCPA while improving reporting efficiency by 50%. Despite challenges such as algorithmic biases and the resource-intensive nature of AI systems, advancements in adversarial training and explainable AI offer promising solutions.Innovatech Engineering Journal00The findings reveal that AI-driven models, including machine learning, deep learning, and federated learning, improve threat detection accuracy, reduce false positives by up to 65%, and enable secure, multi-institutional collaboration without exposing sensitive information.Innovatech Engineering Journal["Md Majadul Islam Jim"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/9e43bbed31b7fb2477472e6d986594783317a36a b9a0b316a7914e5e32f5b963640a1144e8e1d228In this article, we propose a pipeline to fine-tune domain-specific Speech-to-Text (STT) models using synthetic data generated by Artificial Intelligence (AI). Our methodology eliminates the need for manually labelled audio data, which is expensive and difficult to obtain, by generating domain-specific data with a Large Language Model (LLM) combined with multiple Text-to-Speech (TTS) solutions. We applied our pipeline to the radiology domain and compared the results with different approaches based on the availability of domain-specific data, varying from the total absence of domain-specific data to the use of only domain-specific high-quality data (ground truth). Our performance improved the accuracy of the baseline by 40.19% and 10.63% for the WhisperX Tiny and Small models, respectively, which, although performed worse than the results from using the ground truth, shows that it is possible to achieve good results with minimal cost and effort. Finally, the result analysis shows a good insight into the amount of action necessary to achieve good results based on the availability of real data.Brazilian Symposium in Information and Human Language Technology70A pipeline to fine-tune domain-specific Speech-to-Text models using synthetic data generated by Artificial Intelligence (AI) is proposed and a good insight into the amount of action necessary to achieve good results based on the availability of real data is shown.Anais do XV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL 2024)["Anderson Luiz Karl", "Guilherme Sales Fernandes", "Leonardo Augusto Pires", "Yvens R. Serpa", "Carlos Caminha"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/b9a0b316a7914e5e32f5b963640a1144e8e1d228 8769ed266b0865df6990008c5178704eef9ca5bcThe escalating urbanization and industrial activities in cities have significantly impacted air quality, posing health risks and environmental challenges that demand innovative solutions. This review systematically explores the integration of artificial intelligence (AI) and Internet of Things (IoT) sensors within smart cities, focusing on their role in real-time air quality monitoring and dynamic response mechanisms. By adhering to PRISMA guidelines, we analyze recent advancements in AI-driven automated control systems, which utilize IoT sensors to continuously monitor pollutants, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and particulate matter (PM). The data gathered by these sensors feed into AI algorithms that facilitate immediate, adaptive responses, such as modifying traffic light sequences to alleviate congestion and notifying nearby facilities to adjust emissions during high pollution periods. This review synthesizes findings on the effectiveness, limitations, and scalability of these systems, highlighting key challenges like sensor data accuracy, privacy considerations, and the infrastructure required for city-wide deployment. The paper concludes by emphasizing the transformative potential of AI and IoT in fostering sustainable urban environments and presents recommendations for future research and policy improvements to optimize smart city air quality management.Non human journal00This review systematically explores the integration of artificial intelligence (AI) and Internet of Things (IoT) sensors within smart cities, focusing on their role in real-time air quality monitoring and dynamic response mechanisms, and synthesizes findings on the effectiveness, limitations, and scalability of these systems.Non human journal["Amir Siddiki", "Imran Arif"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/8769ed266b0865df6990008c5178704eef9ca5bc 7cc43f9ff5c79a720646fa5a6a3be59200a5c69fThe emergence of generative artificial intelligence (GenAI) has brought abundant potentials and challenges to the educational sector. This study explores the acceptance and adoption of GenAI in English language and its pedagogical affordability for EFL instructors in tertiary level institutions in the Kingdom of Saudi Arabia. An exploratory sequential mixed method was utilized in this study to gain in-depth insights into the quantitative findings. The first quantitative phase recruited 256 EFL instructors in Saudi Arabia while the follow-up qualitative phase incorporated 17 interviewees. The findings of this study indicate that anthropomorphism, trust, ethics and regulations, and pedagogical affordability are significant determinants of instructors’ acceptance  of GenAI. Conversely, instructors’ acceptance was not significantly influenced by communication capability of GenAI. Yet, there was no evidence of significant differences in GenAI acceptance or adoption due to demographic variables such as gender, age, and degree. The pedagogical affordances of GenAI appears to be the most acceptance factor specifically the productivity and efficiency that GenAI afford for instructors. The study recommends establishing ethical guidelines and embracing transparency and accountability to maintain academic integrity.Journal Of Education, Teaching and Learning00The findings of this study indicate that anthropomorphism, trust, ethics and regulations, and pedagogical affordability are significant determinants of instructors’ acceptance  of GenAI.JETL (Journal of Education, Teaching and Learning)["Talal Alasmari"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/7cc43f9ff5c79a720646fa5a6a3be59200a5c69f d4c0dceaea19f7694b550b26a4a2e90c4970fdd9The use of Generative Artificial Intelligence tools in academic research has recently created a debate in the higher education sector. This study explores researchers' awareness, concerns, and usage of generative AI tools in the academic research process. In addition, the study investigates the current level of readiness among researchers to adopt these tools using the Technology Readiness Index 2.0. Results indicate a high familiarity among respondents with the applications of Generative AI tools in academic research. However, only about half of the participants (51.54%) stated that they are currently adopting these tools mainly for academic writing assistance and language support. In addition, researchers expressed significant concerns about the accuracy of the information, ethical considerations, the authenticity of work, and data privacy and security, with (58.96%) indicating that these concerns may influence their future decisions to adopt or continue adopting these tools. The findings also indicate that the overall readiness level is moderate but reflects a degree of discomfort and insecurity which can inhibit researchers' readiness for adoption. Furthermore, senior researchers tend to feel more insecure than other researcher groups, and AI literacy skills were shown to impact the innovativeness sub-scale.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)490Senior researchers tend to feel more insecure than other researcher groups, and AI literacy skills were shown to impact the innovativeness sub-scale, which reflects a degree of discomfort and insecurity which can inhibit researchers' readiness for adoption.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["H. Salman", "Muhammad Aliif", "Roliana Ibrahim", "Jamilah Mahmood"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/d4c0dceaea19f7694b550b26a4a2e90c4970fdd9 27e418e37a8efa68769f5fcf29e0d1e05208e268E-learning platforms are crucial for education worldwide, but they often struggle with fluctuating internet connectivity, which can disrupt the learning experience. This study takes a look at the role of artificial intelligence in optimizing the allocation of resources on these kinds of platforms with a focus on overcoming bandwidth constraints. We suggest a “5A Model” - Assessment, Analysis, Adaptation, Allocation and Automation - which relies on Artificial Intelligence to manage, forecast and automatically change the distribution of resources, allowing platforms to function properly and users are satisfied. To find out the connections' problems and the users' needs, we focused on gathering primary data from teachers and students through FGDs. We provide a comprehensive description of the model's technology including its features and incorporation into the current network of e-learning resources. We conclude that the allocation of resources using the application of AI greatly increases the efficiency of the e-learning systems, creating a good case for the development of e-learning systems in areas where the network is unstable.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)160A “5A Model” - Assessment, Analysis, Adaptation, Allocation and Automation - which relies on Artificial Intelligence to manage, forecast and automatically change the distribution of resources, allowing platforms to function properly and users are satisfied is suggested.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["Anant Jain", "Deepika Pandita"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/27e418e37a8efa68769f5fcf29e0d1e05208e268 8eede81fb9b07cfc4130a4c1434013b6bb8ae8bbPopulation growth necessitates the urgent enhancement of sustainable food production systems. However, the overuse of fertilizers significantly undermines soil fertility, posing a dual threat to agricultural productivity and environmental integrity. This paper introduces an innovative machine learning (ML) methodology integrated with interpretable artificial intelligence (IAI) aimed at promoting sustainable soil management practices. We conduct a thorough investigation into interpretable ML models specifically designed for the classification of soil fertility. Our approach meticulously analyzes model outcomes while pinpointing critical features that influence predictions regarding soil fertility. The results of this method exhibit remarkable promise, achieving high accuracy in predicting soil fertility.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)220An innovative machine learning (ML) methodology integrated with interpretable artificial intelligence (IAI) aimed at promoting sustainable soil management practices shows remarkable promise in predicting soil fertility.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["Khebbache Roufaida", "Merizig Abdelhak", "Rezeg Khaled"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/8eede81fb9b07cfc4130a4c1434013b6bb8ae8bb 77b86a2f0616a2ba965a22869674a349cf649574In the present scenario, communication technology is essential for connecting with each other. This paper proposes a voice-based email system using artificial intelligence to assist new users and physically impaired individuals in effective communication. The system relies on mouse actions and voice con- version, making it accessible without the need for prior practices. Through speech recognition and text-to-speech, even non-literate individuals can send emails. The design emphasizes responsive voice interaction for a user-friendly experience, allowing quick email sending and comprehensive system functionality.International Journal of Advanced Research in Science, Communication and Technology00A voice-based email system using artificial intelligence to assist new users and physically impaired individuals in effective communication using speech recognition and text-to-speech, allowing quick email sending and comprehensive system functionality.International Journal of Advanced Research in Science, Communication and Technology["Sarang Bhosale", "Shraddha Rahangdale", "Tejas Patil", "Sakshi Kate", "Rohini Jadhav"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/77b86a2f0616a2ba965a22869674a349cf649574 3bb19bdb4cad55c18dea3d1432358c2792ceab14In this research, we introduce two types of Artificial Intelligence (AI) models for classifying chest X-rays, binary and categorical. These models were trained and validated utilizing Convolutional Neural Networks (CNNs) and transfer learning techniques. The binary classification model performed well in classifying normal and abnormal X-rays. The categorical classification model showed good abilities to recognize pathological states such as cardiomegaly and infiltration. However, it faced challenges when radiographic patterns overlapped. We used a dataset of 2,463 chest X-ray images with various pathological conditions and improved CNN architectures with two validation approaches to ensure robustness and reliability. This study contributes to the growing literature on AI in medical imaging, showing enhanced clinical outcomes with robust performance and predictive capabilities.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)250Two types of Artificial Intelligence models for classifying chest X-rays, binary and categorical, were introduced and showed good abilities to recognize pathological states such as cardiomegaly and infiltration.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["Danushka Bandara", "Thamo Sutharssan", "M. S. Sharif"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/3bb19bdb4cad55c18dea3d1432358c2792ceab14 8bc861e28551ac85330ce4e232eb25ad42d7c7bdThe use of Artificial Intelligence (AI) and Machine Learning (ML) as part of scientific workloads is becoming increasingly widespread. It is imperative to understand how to configure AI and ML applications on HPC systems to optimise their performance and energy efficiency, thereby minimising their environmental impact. In this study, we use MLPerf HPC’s DeepCAM benchmark to assess and explore the energy efficiency of ML applications on different hardware platforms. We highlight the challenges that, despite growing popularity, ML frameworks still present in a traditional HPC environment, as well as the challenges of measuring power and energy on a variety of HPC and cloud-like virtualised systems. We conclude our study by proposing recommendations that will improve and encourage best practices around sustainable AI and ML workloads on HPC systems.SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis240This study uses MLPerf HPC’s DeepCAM benchmark to assess and explore the energy efficiency of ML applications on different hardware platforms, and proposes recommendations that will improve and encourage best practices around sustainable AI and ML workloads on HPC systems.SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis["Eleanor Broadway", "Joseph K. L. Lee", "Mich\u00e8le Weiland"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/8bc861e28551ac85330ce4e232eb25ad42d7c7bd 4ecf06995c6dc527ff8a0c8c264eba033285da7d This pilot study explores the integration of technological tools, particularly Artificial Intelligence (AI) Interactive Generative Technologies (IGTs), into pedagogical exercises within design product education (DPE). The aim is to present the impact of this integration on traditional creative exercises. The results of the exercise reveal promising results, suggesting that the use of AI IGTs enhances students’ motivation and application of design principles in less time compared to traditional methods. Through a structured exercise involving the creation of a design firm and the subsequent application of AI IGTs to generate design solutions, students demonstrated natural engagement and proficiency. The study underscores the importance of integrating technological trends, such as AI IGTs, into design education to bridge the gap between academic learning and professional practice. However, several areas for improvement and further research are identified, including the standardization of exercise approaches, optimal tool selection, and comprehensive evaluation of student perception, and learning outcomes. This study contributes to the ongoing discourse on the evolving pedagogical approaches in design education and highlights the potential of AI IGTs to revolutionize the learning experience for future designers.Engineering Education00The results of the exercise reveal promising results, suggesting that the use of AI IGTs enhances students’ motivation and application of design principles in less time compared to traditional methods.Volume 7: Engineering Education["Juan-Carlos Rojas"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/4ecf06995c6dc527ff8a0c8c264eba033285da7d 6d23c2bdfe7936d79c09cf0dc6b2e8b992bb9a73This paper introduces a web-based software pipeline to help perform minimally invasive aortic valve replacement (AVR) procedures. The complete pipeline contains three main steps: aortic extraction, automatic landmark detection, and calculation of the specific measurements required for valve replacement. For Computed Tomography Angiography (CTA) scans, separate deep neural network models based on the U-Net architecture in the Medical Open Network for AI (MONAI) framework segment out key anatomical landmarks, including the Sinotubular Junction (STJ), Left Coronary Artery (LCA), Right Coronary Artery (RCA), and Annulus Plane. This improves the accuracy of aorta segmentation and landmark detection. A web interface facilitates easy data upload, visualization of segmentation results, and access to key measurements for preoperative planning. Artificial Intelligence (AI) and machine learning in medical imaging lead to more accurate preoperative planning of Transcatheter Aortic Valve Replacement (TAVR) procedures, reducing the likelihood of complications from improperly fitted valves. There is still more work to be done before the model can be used in cardiovascular disease diagnosis, including refining models, enriching datasets, and integrating several features that are beneficial for cardiologists.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)210A web interface facilitates easy data upload, visualization of segmentation results, and access to key measurements for preoperative planning of Transcatheter Aortic Valve Replacement (TAVR) procedures, reducing the likelihood of complications from improperly fitted valves.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["John Lorance William", "Nadine Farid Elshafey", "Nadine Walid Abdallah", "Nouran Hady Shaaban", "Sama Ahmed Okasha", "Mustafa A. Elattar"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/6d23c2bdfe7936d79c09cf0dc6b2e8b992bb9a73 3e976e6cdbb815ab1a2eed9667f79b66ef9aef40Artificial Intelligence (AI) is at the forefront of today's research trends, particularly in deep learning. The prevailing trend in designing AI systems is based on the principle “the bigger, the better,” which focuses on achieving higher scores on benchmarks. However, this approach comes at a significant environmental cost. At the same time, reducing carbon footprint emissions is more crucial than ever. This study evaluates the environmental impact of fine-tuning the Google ViT model for medical image analysis. It also examines the impact of selecting the appropriate pre-trained model size, the influence of hardware architecture used for fine-tuning, and whether the choice of online providers affects the total emissions and energy consumption of the process. Using Code Carbon, we calculated that each hyper-parameter fine-tuning experiment required about 0.18 kWh of energy to complete and produced 0.066 kg of equivalent C02 emissions. We also found that using different sizes of the pre-trained ViT model results in varying environmental impact and efficiency. Finally, we tested hardware over-sizing and discovered that it can increase the emissions produced2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)430It is found that using different sizes of the pre-trained ViT model results in varying environmental impact and efficiency, and using different sizes of the pre-trained ViT model results in varying environmental impact and efficiency.2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)["Kostas Ordoumpozanis", "G. Papakostas"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/3e976e6cdbb815ab1a2eed9667f79b66ef9aef40 cf67f74811e58233ad98d84ac7b31703891c37a6In the era of big data, robust pattern recognition and accurate data analysis have become critical in various fields, including healthcare, finance, and industrial automation. This study presents a novel hybrid computational intelligence model that integrates deep learning techniques and evolutionary algorithms to enhance the precision and resilience of pattern recognition tasks. Our proposed model combines Convolutional Neural Networks (CNN) for high-dimensional feature extraction with a Genetic Algorithm (GA) for feature optimization and selection, providing a more efficient approach to processing complex datasets. The hybrid model achieved an accuracy of 98.7% on the MNIST dataset and outperformed conventional methods in terms of recall (95.5%) and precision (97.2%) on large-scale image classification tasks. Additionally, it demonstrated substantial improvements in computation time, reducing processing duration by 35% over traditional deep learning approaches. Experimental results on diverse datasets, including time-series and unstructured data, confirmed the model's versatility and adaptability, achieving F1-scores of 0.92 in healthcare data analysis and 0.89 in financial anomaly detection. By incorporating a Particle Swarm Optimization (PSO) algorithm, the model further optimized hyperparameters, leading to a 25% reduction in memory consumption without compromising model performance. This approach not only enhances computational efficiency but also enables the model to perform reliably in resource-constrained environments. Our results suggest that hybrid computational intelligence models offer a promising solution for robust, scalable pattern recognition and data analysis, addressing the evolving demands of real-world applications.International Journal of Computational and Experimental Science and Engineering3412This study presents a novel hybrid computational intelligence model that integrates deep learning techniques and evolutionary algorithms to enhance the precision and resilience of pattern recognition tasks, providing a more efficient approach to processing complex datasets.International Journal of Computational and Experimental Science and Engineering["J. Jeyasudha", "K. Deiwakumari", "C. A. Arun", "R. Pushpavalli", "P. P. Selvam", "S. D. Govardhan"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/cf67f74811e58233ad98d84ac7b31703891c37a6 15309660767c68d181299956f324b6916a14827bImplementationalism maintains that conventional, silicon‐based artificial systems are not conscious because they fail to satisfy certain substantive constraints on computational implementation. In this article, we argue that several recently proposed substantive constraints are implausible, or at least are not well‐supported, insofar as they conflate intuitions about computational implementation generally and consciousness specifically. We argue instead that the mechanistic account of computation can explain several of the intuitions driving implementationalism and non‐computationalism in a manner which is consistent with artificial consciousness. Our argument provides indirect support for computationalism about consciousness and the view that conventional artificial systems can be conscious.Mind &amp; Language610It is argued that the mechanistic account of computation can explain several of the intuitions driving implementationalism and non‐computationalism in a manner which is consistent with artificial consciousness.Mind &amp; Language["Leonard Dung", "Luke Kersten"]2024-11-17T00:00:00https://www.semanticscholar.org/paper/15309660767c68d181299956f324b6916a14827b b9237c6b03cf34382782f4151ba5f933904f4c46This paper aims to explore the applications of artificial intelligence (AI) and machine learning (ML) in the field of cybersecurity, particularly in the development of end-to-end solutions for threat detection. By analyzing the current challenges in cybersecurity and the limitations of traditional threat detection methods, this paper seeks to demonstrate how AI/ML technologies can enhance the efficiency, accuracy, and automation levels of threat detection. The paper begins by introducing the core concepts of cybersecurity threat detection, including traditional methods such as signature-based detection, behavior-based detection, and rule-based detection systems. It then elaborates on the applications of machine learning in anomaly detection, malware detection, network traffic analysis, intrusion detection systems (IDS) and intrusion prevention systems (IPS), as well as user behavior analytics (UBA). Following this, the paper discusses the importance of data preprocessing and feature engineering in threat detection and their practical applications, including data cleaning, feature selection, and extraction. Finally, the paper explores the training and evaluation of models, the deployment of models, and the challenges they face, along with future trends in AI-driven threat detection. The research results indicate that AI/ML technologies can significantly improve the accuracy and efficiency of threat detection, particularly in handling unknown threats and automating detection processes. Through the application of various machine learning algorithms, such as anomaly detection, malware detection, and network traffic analysis, systems can better identify and respond to various network threats. However, AI-driven threat detection still faces challenges related to data quality, algorithm performance, and system implementation.WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY53The research results indicate that AI/ML technologies can significantly improve the accuracy and efficiency of threat detection, particularly in handling unknown threats and automating detection processes.World Journal of Innovation and Modern Technology["Heyao Chen", "Zepeng Shen", "Yong Wang", "Hu Ke", "Jian Xu"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/b9237c6b03cf34382782f4151ba5f933904f4c46 fa302c740717b74981db28ed704f1b177cf66854Discover Artificial Intelligence362In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis, which underscore the transformative potential of AI and ML technologies for sustainable environmental management.Discov. Artif. Intell.["Emran Alotaibi", "N. Nassif"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/fa302c740717b74981db28ed704f1b177cf66854 220ad9492e691dd396a5be3695699a7e41abd76aEnvironmental issues have become the greatest challenge for SME survival and growth. In Pakistan, SME hotels, which can play a healthy role in the economy, are also dealing with several challenges, including their negative environmental impact, which is not unnoticeable. Building on the resource-based view theory with dynamic capability theory, a mediated moderation model was proposed to investigate the impact of green entrepreneurial orientation on the sustainable performance of SME hotels. The impact of green entrepreneurial orientation on sustainable performance (financial, social and environmental) remains ambiguous despite the significant attention given to it. Moreover, eco-innovation and artificial intelligence capability (AIC) have become relevant in understanding innovation and environmentally friendly practices. This study investigated the relationships among GEO, EI, and SP and the moderating role of AI capability between EI and SP. A quantitative methodology was employed. A total of 350 samples were obtained from SME hotel owners,executives and managers through a self-administered questionnaire. The data were analyzed through SMART-PLS version 4.0. The results indicate that GEO has a substantial positive impact on SP. Furthermore, EI plays a mediating role between GEO and SP. Which indicates the improtance of EI in current era for hotels survival and growth. The moderating impact of AIC is significant in the relationship between EI and SP, which means that AI capability helps to attain sustainable performance in the industry. The current study has significant implications for SME hotels and services industry, as it highlights the importance of GEO, EI, and AIC in enhancing SP in the hotel industry. Furthermore, the moderating role of AI capability has rarely been explored. Future studies need to consider AIC with eco-innovation in turbulent market conditions to ensure the sustainable performance of hotels in underdeveloped countries.Multidisciplinary Science Journal581Multidisciplinary Science Journal["Sajjad Hussain", "Noor Hazlina Ahmad", "Tayyaba Syed"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/220ad9492e691dd396a5be3695699a7e41abd76a f200e338d77886110b3d356367b3774e6d6300d8The adoption and implementation of artificial intelligence (AI) in small businesses in selected developing countries have become increasingly prevalent in recent years. Small businesses in developing countries are recognizing the potential benefits of AI technologies in enhancing efficiency, productivity, and competitiveness. However, challenges such as limited resources, lack of technical expertise, and concerns about job displacement hinder the widespread adoption of AI in this context. This comprehensive analysis explores the current trends, opportunities, challenges, and strategies related to the adoption and implementation of AI in small businesses in selected developing countries. The paper therefore recommended that business owners should make use AI. It will help small businesses streamline their operations by automating routine tasks such as data entry, customer service inquiries, and inventory management with higher return on investment.Journal of Health, Applied Sciences and Management141It is recommended that business owners should make use of AI to help small businesses streamline their operations by automating routine tasks such as data entry, customer service inquiries, and inventory management with higher return on investment.Journal of Health, Applied Sciences and Management["Eyibio Okon Ikpe"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/f200e338d77886110b3d356367b3774e6d6300d8 a5d3a7cd1ebd239fe719a9f0a728be5227a9ed55With the progress of artificial intelligence technology, autonomous vehicles have begun to get research and development support and promotion applications worldwide. Compared with traditional artificially driven vehicles, the advantage of autonomous vehicles is that they can use intelligent driving systems to sense road information in real time, and the overall driving safety is higher. However, as an emerging technology, the laws, regulations and management system matching automatic driving technology are not perfect. When an autonomous vehicle has a traffic accident, there is a huge dispute about the confirmation of the responsible body and the fault identification. To this end, this paper discusses the legal dilemma about the identification of infringement liability of autonomous driving vehicles, analyzes the legal dilemma of the division of infringement liability of autonomous driving vehicles from the perspective of the identification standard of infringement liability, the identification of infringement facts and the implementation of compensation liability, and puts forward some suggestions on the identification of infringement liability.Frontiers in Humanities and Social Sciences80The legal dilemma about the identification of infringement liability of autonomous driving vehicles is discussed, the legal dilemma of the division of infringement liability of autonomous driving vehicles is analyzed, the identification standard of infringement liability, the identification of infringement facts and the implementation of compensation liability are analyzed, and some suggestions are put forward.Frontiers in Humanities and Social Sciences["Tingting Zhang"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/a5d3a7cd1ebd239fe719a9f0a728be5227a9ed55 625043dee81502f62a875a81fd31750f16b10209One of the major challenging task is to maintain the Autonomous vehicles as well as to analyze the future transportation systems. To solve such kind of issues proposed three different technologies, Artificial Intelligence, Light Fidelity and LoRa has been analyzed. AI and LiFi technologies have the potential to revolutionize transportation systems, especially for autonomous vehicles. LiFi uses visible light for data transmission, offering higher rates, security, and energy efficiency. AI is crucial for autonomous vehicles, enabling real-time data transmission, secure communication, intelligent traffic management, and cooperative driving. Challenges include cost, range limitations, and infrastructure integration. The research proposes an innovative system combining LiFi as well as LoRa technologies to improve navigation in congested urban environments. The LiFi transmitter uses visible light to transmit location information to LiFi receivers in vehicles, providing continuous, seamless navigation even in areas without service. The system also broadcasts vehicle location to the outside world via a LoRa module, providing long-range and low-power navigation in non-serviceable locations like terrines, valleys, and tunnels. AI, LiFi, and LoRa are crucial technologies for autonomous vehicles and transportation systems. AI provides the "brain" for autonomous vehicles, while LiFi offers high-speed, secure, and interference-free communication. LoRa is ideal for long-range, low-data-rate communication, enabling accurate decision-making, enhanced safety, and efficient transportation. These technologies are essential for a future autonomous vehicle-friendly future.2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI)160The research proposes an innovative system combining LiFi as well as LoRa technologies to improve navigation in congested urban environments and provides the "brain" for autonomous vehicles, while LiFi offers high-speed, secure, and interference-free communication.2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI)["Sandeep Mishra", "Bipin Pandey", "Rajat Kumar", "Kaustubh Kumar Shukla"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/625043dee81502f62a875a81fd31750f16b10209 75883c9f7a226fa8f5348d52d5e274b9407eded6Artificial intelligence (AI) has rapidly transformed various sectors, including healthcare, where it holds the potential to revolutionize clinical practice and improve patient outcomes. However, its integration into medical settings brings significant ethical challenges that need careful consideration. This paper examines the current state of AI in healthcare, focusing on five critical ethical concerns: justice and fairness, transparency, patient consent and confidentiality, accountability, and patient-centered and equitable care. These concerns are particularly pressing as AI systems can perpetuate or even exacerbate existing biases, often resulting from non-representative datasets and opaque model development processes. The paper explores how bias, lack of transparency, and challenges in maintaining patient trust can undermine the effectiveness and fairness of AI applications in healthcare. In addition, we review existing frameworks for the regulation and deployment of AI, identifying gaps that limit the widespread adoption of these systems in a just and equitable manner. Our analysis provides recommendations to address these ethical challenges, emphasizing the need for fairness in algorithm design, transparency in model decision-making, and patient-centered approaches to consent and data privacy. By highlighting the importance of continuous ethical scrutiny and collaboration between AI developers, clinicians, and ethicists, we outline pathways for achieving more responsible and inclusive AI implementation in healthcare. These strategies, if adopted, could enhance both the clinical value of AI and the trustworthiness of AI systems among patients and healthcare professionals, ensuring that these technologies serve all populations equitably.arXiv.org00The paper explores how bias, lack of transparency, and challenges in maintaining patient trust can undermine the effectiveness and fairness of AI applications in healthcare, and outlines pathways for achieving more responsible and inclusive AI implementation in healthcare.ArXiv["Ellison B. Weiner", "Irene Dankwa-Mullan", "William A. Nelson", "Saeed Hassanpour"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/75883c9f7a226fa8f5348d52d5e274b9407eded6 65477c6827c5bc3d8650baab6311107e2a5a393dThis paper presents a conceptual prototype that integrates Artificial Intelligence (AI) and Augmented Reality (AR) with the principles of Universal Design (UD) to enhance decision-making in everyday scenarios for a diverse user base, eliminating the need for conventional text or voice AI interfaces. The study employed a mixed-method approach, including surveys, user testing, and interviews with eight participants from various age groups. The focus was on user interaction styles (head-mounted, handheld) within three everyday scenarios: 1) medication assistance, 2) food and beverage assistance, and 3) sustainability advocacy. Findings revealed that AR as an interface for AI was well-received for its intuitiveness and practical utility. However, users expressed concerns about privacy, the discomfort of wearable technology, and potential over-reliance on AI. This study demonstrates the potential of integrating AR as an interface for AI, combined with UD principles, to create inclusive, context-aware solutions, adaptable to users with diverse skills and abilities.Studies in Health Technology and Informatics00The potential of integrating AR as an interface for AI, combined with UD principles, to create inclusive, context-aware solutions, adaptable to users with diverse skills and abilities is demonstrated.Studies in health technology and informatics["Attila Bekkvik Szentirmai"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/65477c6827c5bc3d8650baab6311107e2a5a393d 2bb9f876fd30b07d1a26453603c074a3cf004246Human Genomics230Focus groups with members of the Australian public exploring potential uses of AI in genomic medicine, the benefits, risks, and the possible social implications of its use can help to inform both policies around genomic AI and how to educate the public on its use.Human Genomics["Jack E Harrison", "Fiona Lynch", "Zornitza Stark", "D. Vears"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/2bb9f876fd30b07d1a26453603c074a3cf004246 3df55dedad6e2a29840ce5427a4115300874dc81The rise and widespread use of generative AI technologies, including ChatGPT, Claude, Synthesia, DALL-E, Gemini, Meta AI, and others, have raised fresh concerns in journalism practice. While the development represents a source of hope and optimism for some practitioners, including journalists and editors, others express a cautious outlook given the possibilities of its misuse. By leveraging the Google News aggregator service, this research conducts a content and thematic analysis of Africa-focused journalistic articles that touch on the impacts of artificial intelligence technology in journalism practice. Findings indicate that, while the coverage is predominantly positive, the tone of the articles reflects a news industry cautiously navigating the integration of AI. Ethical concerns regarding AI use in journalism were frequently highlighted, which indicates significant apprehension on the part of the news outlets. A close assessment of views presented in a smaller portion of the reviewed articles revealed a sense of unease around the conversation of power in the hands of tech giants. The impact of AI on the financial stability of media outlets was framed as minimal at present, suggesting a neutral, wait-and-see position of news outlets. Our analysis of predominantly quoted sources in the articles revealed that industry professionals and technology experts emerge as the most vocal voices shaping the narrative around AI’s practical applications and technical capabilities in the continent.Journalism and Media420Analysis of Africa-focused journalistic articles that touch on the impacts of artificial intelligence technology in journalism practice revealed that industry professionals and technology experts emerge as the most vocal voices shaping the narrative around AI’s practical applications and technical capabilities in the continent.Journalism and Media["Abdullateef Mohammed", "Adeola Abdulateef Elega", "Murtada Busair Ahmad", "F. Oloyede"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/3df55dedad6e2a29840ce5427a4115300874dc81 dc054979c445a3d8643c78a8896a54052df59198PurposeWith recent advances in artificial intelligence, the hospitality industry has introduced the concept of unmanned smart hotels staffed by service robots instead of human employees. Research is needed to understand consumers’ receptivity to such an innovation. This paper examines factors associated with consumers’ potential resistance to using automated service hotels via two sequential studies. Given that younger generations of consumers are typically early adopters of advanced technology and innovative services, our sampling approach focused on this consumer group.Design/methodology/approachTwo studies were conducted. Study 1 proposed and empirically tested a theoretical model. Results revealed that attitude, subjective norms and perceived behavioral control each positively influenced individuals’ intentions to use unmanned smart hotels. In Study 2, we further investigated aspects informing perceived security, a key variable in the use of unmanned smart hotels.FindingsFindings showed how people’s beliefs about unmanned smart hotels and security control assurances led to perceived security. These perceptions were shaped by perceived physical risks, privacy concerns, website design and hotel reputation. Overall, this research provides theoretical and practical implications for various stakeholders associated with unmanned smart hotels.Practical implicationsFindings of this study suggested that managers of unmanned smart hotels should design user-friendly, secure processes and offer comprehensive support resources to enhance customer experience and usage.Originality/valueThe findings provide a holistic understanding of consumers’ receptivity to unmanned smart hotels.Journal of Hospitality and Tourism Insights610Journal of Hospitality and Tourism Insights["Huiying Du", "Jing Li", "Kevin Kam Fung So", "Ceridwyn King"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/dc054979c445a3d8643c78a8896a54052df59198 dda23856d0477e2feb248c4d60011b8645adc5f8This paper examines the integration of artificial intelligence (AI) tools, specifically conversational agents like ChatGPT, in teaching mathematical economics in tertiary education. Recognizing the inherent challenges in mathematical economics—ranging from complex theoretical constructs to advanced quantitative methods—this study explores AI's potential to enhance student comprehension, engagement, and problem-solving skills. Drawing from existing literature on AI applications in education and learning sciences, this conceptual paper evaluates AI's role in delivering real-time support, facilitating interactive problem-solving, and offering personalized feedback, thereby addressing diverse student needs. Key areas of focus include AI-driven question-and-answer capabilities, scenario-based learning simulations, and guided problem-solving models that can reinforce theoretical knowledge through practical application. The paper further identifies potential challenges, including student overreliance on AI tools, possible misunderstandings in AI-generated solutions, and ethical concerns related to data privacy and academic integrity. By proposing a blended learning model, this paper suggests best practices for using AI as a supportive, non-replacement instructional tool. These best practices encompass educator training, responsible AI implementation, and fostering a balanced, interactive classroom environment. The findings contribute to a growing discourse on the responsible and effective use of AI in higher education, with implications for policy and practice in curriculum design, educational technology, and teaching strategies. Future research directions include empirical studies on AI’s impact on learning outcomes in mathematical economics, exploring how these tools can further enhance both student engagement and academic performance.  Article visualizations:European Journal of Open Education and E-Learning Studies270This conceptual paper evaluates AI's role in delivering real-time support, facilitating interactive problem-solving, and offering personalized feedback, thereby addressing diverse student needs and suggests best practices for using AI as a supportive, non-replacement instructional tool.European Journal of Open Education and E-learning Studies["Emmanouil Choustoulakis"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/dda23856d0477e2feb248c4d60011b8645adc5f8 97fc7d749b8f96221d5b767d3ee7ed3fae61cb89The integration of Artificial Intelligence (AI) into K-12 education represents a significant transformation in the contemporary educational paradigm. This study explores how AI can be used to personalize teaching, adapting it to the individual needs of students in K-12 education. The research examines the challenges and opportunities associated with implementing AI systems in educational settings, focusing on the ability of these technologies to deliver personalized learning experiences. Through a comprehensive literature review, the study explores the potential impacts of AI on student motivation, academic performance, and teaching effectiveness. The results indicate that AI can significantly improve the personalization of teaching by providing real-time feedback, adapting content and pedagogical strategies to individual student needs. However, important challenges are also identified, including ethical issues, data privacy, and the need for adequate training for educators. The study concludes that while AI offers transformative potential for K-12 education, its successful implementation requires a careful and balanced approach, considering both the pedagogical benefits and the ethical and practical challenges.ARACÊ00It is concluded that while AI offers transformative potential for K-12 education, its successful implementation requires a careful and balanced approach, considering both the pedagogical benefits and the ethical and practical challenges.ARACÊ["Maria Cibele Ferreira da Silva", "Ana Cristina Gon\u00e7alves Teixeira Saraiva", "Daniela Paula de Lima Nunes Malta", "Janete Emilia Corr\u00eaa da Silva", "Rafael Leandro da Silva", "Sonia Ara\u00fajo dos Santos"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/97fc7d749b8f96221d5b767d3ee7ed3fae61cb89 1e2df53ab7cd6d8d437a488b7b714bcbc24686b4This paper presents a data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example. Unlike traditional preliminary EMD processes that heavily rely on expertise, this framework leverages an artificial-intelligence based expert database, to provide preliminary designs directly from user specifications. Initial data is generated using 2D finite element (FE) machine models by sweeping fundamental design variables including machine length and diameter, enabling scalable machine geometry with machine performance for each design is recorded. This data trains a Metamodel of Optimal Prognosis (MOP)-based surrogate model, which maps design variables to key performance indicators (KPIs). Once trained, guided by metaheuristic algorithms, the surrogate model can generate thousands of geometric scalable designs, covering a wide power range, forming an AI expert database to guide future preliminary design. The framework is validated with a 30kVA WRSG design case. A prebuilt WRSG database, covering power from 10 to 60kVA, is validated by FE simulation. Design No.1138 is selected from database and compared with conventional design. Results show No.1138 achieves a higher power density of 2.21 kVA/kg in just 5 seconds, compared to 2.02 kVA/kg obtained using traditional method, which take several days. The developed AI expert database also serves as a high-quality data source for further developing AI models for automatic electrical machine design.arXiv.org00A data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example, which leverages an artificial-intelligence based expert database, to provide preliminary designs directly from user specifications.ArXiv["Yiwei Wang", "Tao Yang", "Hailin Huang", "Tianjie Zou", "Jincai Li", "Nuo Chen", "Zhuoran Zhang"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/1e2df53ab7cd6d8d437a488b7b714bcbc24686b4 b2b357962e323e85298400d81d33fe6745ea5af6Transformational leadership (TFL) is an inspiring and motivating leadership style and vital change and novel technology-enhancing factor. The lack of research studying the TFL mechanism of influencing nurses’ readiness and intention for artificial intelligence (AI) adoption in non-AI implemented hospitals is the core problem. Thus, the study aimed to examine the relationship between TFL and nurses’ intentions toward AI utilization in Jordanian hospitals - an online questionnaire disseminated to nurses in targeted hospitals where AI technology is not implemented. Method used structured questionnaire grounded on a Multifactor Leadership Questionnaire (MLQ) for measuring TFL, and Theory of planned behaviors (TPB) and Technology Acceptance Model (TAM) for measuring intention are utilized. The analysis process encompasses descriptive statistics, Pearson correlations, and hierarchical regression. The age group 31-40 years old and those with higher educational levels recorded significantly higher intentions to utilize AI. Even with the limitations of self-reporting and cross-sectional design, findings underscore the criticality of TFL, mainly intellectual stimulation's role in structuring nurses' readiness and intention towards AI utilization, and the necessity for targeted leadership strategies to promote AI adoption culture. Despite that, TFL fosters creativity and critical thinking; some organizational factors such as training and support are significant influential factors. Thus, targeted interventions help overcome resistance and create innovation supportive culture. The results revealed a weak positive influence of TFL on nurses' intentions toward AI utilization, and the perceived intellectual stimulation dimension is the strongest intention predictor.Research Journal of Pharmacy and Technology240Findings underscore the criticality of TFL, mainly intellectual stimulation's role in structuring nurses' readiness and intention towards AI utilization, and the necessity for targeted leadership strategies to promote AI adoption culture.Research Journal of Pharmacy and Technology["Randa Khirfan", "Heba Kotb", "Huda Atiyeh", "Anas Khalifah", "Nahid AlHasan", "Samah Abdelalla"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/b2b357962e323e85298400d81d33fe6745ea5af6 d8c2dcb8e0aaec08faa00bc165de1682c72f8b0bThis paper is primarily based on experientially derived insights about building a bot with artificial intelligence (AI)–in this case, chat generative pre‐trained transformer (ChatGPT)–to prepare students to engage in strategic conversations during foresight fieldwork. The motivation of the exploratory process outlined in this paper is the pedagogical concern of sending students into the field sufficiently prepared to meet the expectations of external stakeholders. The authors explore a in‐class prompt engineering exercise to create a “chief operating bot” (COB) to simulate a C‐suite executive. The student‐faculty team input hand‐selected, industry‐specific, company‐generated documentation, and, after asking ChatGPT to “roleplay” the COO, the student queries this COB in an exploratory fashion embedded in a contained, consequence‐free learning environment. The audience for this paper is faculty responsible for overseeing student engagement experiences like fieldwork, as well as department heads and school deans looking to promote new tools and advance novel applications of AI in their units. The authors explore ways to enhance student readiness for scenario fieldwork based on an exercise drawn from van der Heijden's clairvoyant question, which we refer to colloquially as the “crystal ball thought experiment.” The authors, upon reflection, conclude that the COB can valuably supplement–but not fully replace–face‐to‐face interactions with a COO. Broadly, leveraging AI to create interactive tools like COBs has the potential to transform business education by bridging academic preparation with real‐world demands, enhancing student readiness, advancing AI‐assisted curricula, and contributing to strategic planning and regional development.FUTURES &amp; FORESIGHT SCIENCE120FUTURES &amp; FORESIGHT SCIENCE["Rui Gon\u00e7alves", "Matthew J. Spaniol", "Nicholas J. Rowland", "N. Rytter"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/d8c2dcb8e0aaec08faa00bc165de1682c72f8b0b 9a5b7ca15363ec6d5c744dcd2cd2122b11e81cb6This research analyzed the impact of Artificial Intelligence (AI) on the personalization of learning and its influence on the transformation of teaching and curriculum. The central problem investigated was to identify the main ways in which AI is changing pedagogical practices and curricular structures. The overall objective was to analyze the applications of AI in the personalization of learning in the educational context, highlighting its implications for teaching and curriculum development. The methodology used was a literature review, with a qualitative approach, including the analysis of published materials such as books, scientific articles, theses and official documents. The results indicated that AI is providing significant opportunities for the personalization of learning, allowing the adaptation of content and teaching pace to the individual needs of students. AI applications range from intelligent tutoring systems to platforms for predictive analysis of student performance. The research highlighted the importance of a balanced approach that considers both the benefits and the ethical challenges of implementing AI in education. The concluding remarks pointed out that, despite promising advances, the effective integration of AI in education requires a reformulation of traditional pedagogical and curricular models. Investments in teacher training, development of technological infrastructure and the development of appropriate educational policies are essential to maximize the benefits of AI in personalizing learning. The need for future studies was highlighted to explore the long-term impacts of AI in education and develop best practices for its implementation.ARACÊ00The results indicated that AI is providing significant opportunities for the personalization of learning, allowing the adaptation of content and teaching pace to the individual needs of students.ARACÊ["Ana Paula de Souza Souza", "Creilson de Jesus Concei\u00e7\u00e3o", "Marlene Aparecida Pancoto", "Nat\u00e1lia Queres Barbosa Cecote", "Rodrigo Rodrigues Pedra", "Rosa Maria da Silva Oliveira", "Vagna Ros\u00e2ngela Zaqui Pin\u00e3o", "Wanderson Teixeira Gomes"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/9a5b7ca15363ec6d5c744dcd2cd2122b11e81cb6 bc9e7c94ff87112be2aab1ae206f4106e4263e82The use of technology in healthcare and healthcare education settings has increased rapidly across the United States and accelerated due to the COVID-19 pandemic. However, perceptions of new technologies in clinical nursing and nursing education are not well understood. Yet, understanding perceptions of registered nurses and nursing students toward advanced technology and artificial intelligence in clinical care and education is crucial if we are to implement these care delivery and educational innovations. This literature review investigates existing literature on registered nurses' and nursing students' attitudes toward advanced technology and artificial intelligence in nursing, including nursing education. Ten peer-reviewed studies published between 2017 and 2022 were reviewed. Findings revealed positive perceptions, such as improved patient care, efficiency, and reduced human error, but also concerns about job displacement, loss of human touch, and ethical/legal issues. Challenges in implementation, adequate training in technologies, and how technologies may reduce the human connection aspect of nursing care were identified. By recognizing the attitudes and perceptions of registered nurses and nursing students toward these advanced technologies, we can better ensure that it is ethically, effectively, and responsibly integrated into nursing practice and education.Computers, Informatics, Nursing250Positive perceptions, such as improved patient care, efficiency, and reduced human error, are revealed, but also concerns about job displacement, loss of human touch, and ethical/legal issues are identified.Computers, informatics, nursing : CIN["Omar Abdelaziz", "Sohye Lee", "Sheri Howard", "Leanne Lefler"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/bc9e7c94ff87112be2aab1ae206f4106e4263e82 f12f6dedc27d392d74909d0f0ef8987420adf864In the wake of the digital revolution, the integration of emerging technologies in education has emerged as a powerful catalyst for promoting inclusion and breaking down barriers in education. This study investigates the transformative impact of technology on inclusive education, exploring how innovations such as artificial intelligence, virtual and augmented reality, and assistive technologies are redefining learning possibilities for all students. We adopt a qualitative methodology, based on a systematic literature review, to critically analyze the current state of technology integration in inclusive education. Our findings reveal that, when implemented effectively, these technologies have the potential to personalize learning on an unprecedented scale, adapt to individual student needs, and overcome physical, cognitive, and geographic barriers. We identify significant challenges, including the need for ongoing training of educators, issues of equity in access to technology, and concerns about privacy and data security. The study also highlights the importance of a holistic approach that considers not only the technical, but also the pedagogical, ethical, and social aspects of technology implementation. We conclude that while technology offers transformative opportunities for inclusive education, its success depends on careful and contextualized integration, supported by progressive education policies and an ongoing commitment to equity and inclusion.ARACÊ00It is concluded that while technology offers transformative opportunities for inclusive education, its success depends on careful and contextualized integration, supported by progressive education policies and an ongoing commitment to equity and inclusion.ARACÊ["F\u00e1bio Jos\u00e9 de Ara\u00fajo", "C. C. Favarato", "Alan Johnny Romanel Ambrozio", "Adna Caetano e Silva Moreira", "Ana Paula Rodrigues", "Laura Elice de Souza Ferreira Miranda"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/f12f6dedc27d392d74909d0f0ef8987420adf864 cac4cee9941976cf6c35f2a6a0da8ba81d87e787This paper explains how traditional centralized architectures are transitioning to distributed zonal approaches to address challenges in scalability, reliability, performance, and cost-effectiveness. The role of edge computing and neural networks in enabling sophisticated sensor fusion and decision-making capabilities for autonomous vehicles is examined. Additionally, this paper discusses the impact of zonal architectures on vehicle diagnostics, power distribution, and smart power management systems. Key design considerations for implementing effective zonal architectures are presented, along with an overview of current challenges and future directions. The objective of this paper is to provide a comprehensive understanding of how zonal architectures are shaping the future of automotive technology, particularly in the context of self-driving vehicles and artificial intelligence integration.International Journal of Current Research in Science, Engineering &amp; Technology150The objective of this paper is to provide a comprehensive understanding of how zonal architectures are shaping the future of automotive technology, particularly in the context of self-driving vehicles and artificial intelligence integration.ArXiv["Sneha Sudhir Shetiya", "Vikas Vyas", "Shreyas Renukuntla"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/cac4cee9941976cf6c35f2a6a0da8ba81d87e787 ffbc54d6a838e5a8157c6ab9da27a3a1c0422846Technologies have a profound and far-reaching influence on numerous aspects of human life, shaping industries, societal interactions, and economic structures. As the pace of technological advancements accelerates, the convergence of two of the most transformative technologies - blockchain and artificial intelligence (AI) - holds great potential to drive innovation and create unprecedented opportunities for various sectors. This paper delves into the synergy between blockchain and AI, examining the unique advantages that their integration can offer. Blockchain provides a decentralized and secure framework, while AI adds advanced data processing and decision-making capabilities. Together, these technologies can enhance security, transparency, and efficiency in various applications. This research explores real-world examples of how blockchain-based AI systems are being applied across industries and how they illustrate their potential to revolutionize business processes and societal interactions. Science, Engineering & Education00This research explores real-world examples of how blockchain-based AI systems are being applied across industries and how they illustrate their potential to revolutionize business processes and societal interactions.Science, Engineering and Education["Mihaela Todorova"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/ffbc54d6a838e5a8157c6ab9da27a3a1c0422846 67f7df8dac2b3ff4457dc9785d622e5a7c88a274 The application of artificial intelligence (AI) is expanding rapidly in many fields. Over the past 40 years, AI has developed and diversified in many different areas of healthcare. Within nursing specifically, AI currently performs functions of information synthesis, clinical decision support, disease management, patient engagement, and augmenting human performance.This article aims to review the applications and effects of AI in nursing, examine the influence of AI on patient experience, patient safety, and nursing workflow, analyze the challenges faced, and provide suggestions for the further development of AI in nursing.Interdisciplinary Nursing Research00The applications and effects of AI in nursing are reviewed, the influence of AI on patient experience, patient safety, and nursing workflow is examined, the challenges faced are analyzed, and suggestions for the further development of AI in nursing are provided.Interdisciplinary Nursing Research["Chunyan Su", "Yue Liu", "Xiaoshu Zhou", "Rongsong Tang", "Min Yang", "Jingpin Wang", "Siwei Zhang", "Zhiqian Chen", "Xueqian Ma", "Jing Wang", "Miao Yu", "Heli Zhang", "Xianjing Hu", "Baohua Li"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/67f7df8dac2b3ff4457dc9785d622e5a7c88a274 f53ae21f9a8bc75a4269e55265dc9559a1362e2eThe study examines the impact of using artificial intelligence in e-stores on consumer behavior. The study highlights the importance of artificial intelligence in improving the online shopping experience, by providing personalized recommendations and analyzing purchasing patterns to better meet consumer needs. Artificial intelligence techniques such as natural language processing, deep learning, and augmented reality are used to provide a distinctive experience that contributes to increasing customer loyalty and raising conversion rates. The study indicates that artificial intelligence can automate many tasks, such as inventory management and order processing, which contributes to reducing costs and improving operational efficiency. Artificial intelligence also plays an important role in enhancing e-commerce security, as it can detect fraud and protect personal data. The study included the impact of AI-supported shopping practices on customers’ functional, financial, social, and emotional values. The results of previous studies reviewed indicate the positive impact of artificial intelligence on the efficiency of marketing methods, and raising the competitive advantage of e-stores by improving the mental image and increasing profitability.International Journal of Educational Sciences and Arts00The results of previous studies reviewed indicate the positive impact of artificial intelligence on the efficiency of marketing methods, and raising the competitive advantage of e-stores by improving the mental image and increasing profitability.International Journal of Educational Sciences and Arts["Abdulaziz Alyousef"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/f53ae21f9a8bc75a4269e55265dc9559a1362e2e e3eb72c7b35b49b591a8e4c980f601427db8bdb9AIMS This study examined the anxiety levels of nurses and nurse candidates regarding humanoid nurse robots and artificial intelligence health technologies in perioperative patient care. DESIGN Descriptive and cross-sectional study. METHODS The research was conducted with 158 intern students and 167 surgical nurses. Socio-demographic characteristics form, Questions Form Regarding Humanoid Nurse Robots and Artificial Intelligence Health Technologies, Artificial Intelligence Anxiety Scale and The Medical Artificial Intelligence Preparedness Scale were used. The independent t-test and one-way analysis of variance (ANOVA) were used. This study complied with Appendix S1. RESULTS The total scores on the Artificial Intelligence Anxiety Scale for nurses and nursing students are 73.089 ± 31.667 and 73.624 ± 28.029, respectively. The total scores on the Artificial Intelligence Readiness Scale for nurses and nursing students are 71.736 ± 15.064 and 72.183 ± 13.714, respectively. When comparing the sociodemographic characteristics and scale scores of nurses, a statistically significant difference was found between age and the Artificial Intelligence Anxiety Scale scores (p < 0.05). There was also a statistically significant difference between age, gender and work duration and the Artificial Intelligence Readiness Scale scores for nurses (p < 0.05). CONCLUSION Both groups exhibited moderate levels of anxiety and readiness regarding artificial intelligence. Comprehensive research is needed to elucidate the impact of artificial intelligence technologies on nursing professionals. IMPLICATION FOR THE PROFESSION The proper use of Artificial Intelligence technologies can enhance the quality of patient care, alleviate the workload, increase patient and staff satisfaction and foster new perspectives on acceptance. With their integration into clinics, a patient-centred care environment will emerge, improving patient safety, outcomes and overall well-being. Thus, the anxieties of nurses and students towards artificial intelligence technologies will decrease, and their readiness will increase. PATIENT OR PUBLIC CONTRIBUTION No Patient or Public Contribution.Journal of Clinical Nursing300The anxieties of nurses and students towards artificial intelligence technologies will decrease, and their readiness will increase, with their integration into clinics, improving patient safety, outcomes and overall well-being.Journal of clinical nursing["G\u00fclseren Mara\u015f", "Eda Albayrak G\u00fcnday", "Yeliz S\u00fcrme"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/e3eb72c7b35b49b591a8e4c980f601427db8bdb9 9f87cfac38a967cc8faed2f5db436158c1e787d3The advent, growth, advancement and development of artificial intelligence (AI) has transformed businesses and organizations around the globe, as it contributes to innovation and operational efficiency. It utilizes big data analytics as well as machine learning, which drives strategic decision-making. However, even with the amplified discussion as well as the availability of Artificial Intelligence techniques and tools, there is still a lack of knowledge on how it affects business operations.The study explores the future of Artificial Intelligence in Business Operations in the Philippines. More specifically, the study explored how Artificial Intelligence can be applied in operations management, supply chain management, sales and marketing, training and development, accounting and finance, customer service, strategic planning and quality controlIn order to address this objective, the proponents conducted a critical analysis of 153 Scopus indexed publications in the body of literature to examine Artificial Intelligence in the context of business operations.Artificial Intelligence has a surmountable potential in business organizations and employee adjustments in lieu to this technology necessitates to adopt in these changes. Due to the preceding concerns about the integration of Artificial Intelligence within business operations, the study provided exploratory discussion around its use, challenges, limitations and suggested areas requiring future studies.2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI)380The study explored how Artificial Intelligence can be applied in operations management, supply chain management, sales and marketing, training and development, accounting and finance, customer service, strategic planning and quality control, and suggested areas requiring future studies.2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI)["Jolou Vincent M. Jala", "Everly A. Nacalaban", "Nenon Roy A. Sandinao", "Joshua T. Gaid", "Randy Joy M. Ventayen"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/9f87cfac38a967cc8faed2f5db436158c1e787d3 17f0f25902b9aabba048545c46b8a6d64d38cf8dThe rapid development of information technology in healthcare, the growth of data volumes and the complexity of diagnostic and treatment methods have led to the fact that artificial intelligence has begun to be used more and more often in this industry.The article examines the current state of artificial intelligence development in two leading healthcare countries: the Russian Federation and the United States of America. The choice of comparison objects is due to the openness of official sources of information on the state of the industry, as well as the high actual level of development of digitalization of medicine in these countries. The comparison was carried out on five points: prevalence, legal regulation, development directions, responsibility for development and the possibility of integration with the existing digital healthcare circuit.Russian Journal for Personalized Medicine70The article examines the current state of artificial intelligence development in two leading healthcare countries: the Russian Federation and the United States of America.Russian Journal for Personalized Medicine["A. A. Pchelkin", "O. V. Muzaleva", "A. Akhmineeva"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/17f0f25902b9aabba048545c46b8a6d64d38cf8d e87ac3fddcd9229611ae7acf824b66b19d61d4fdThe landscape of education is undergoing a gradual transformation due to Artificial Intelligence (AI), which is revolutionizing learning and teaching methods. Using Yarmouk UniversityAr as a case study, this study investigates perceptions and impacts of AI within educational contexts. Students and faculty members from a variety of backgrounds participated in the cross-sectional survey. A survey focused on familiarity, perceived advantages, and potential challenges associated with AI in education was administrated to a sample of (387) students and (23) faculty members.According to the results, attitudes and awareness levels regarding artificial intelligence differed significantly between the groups surveyed. In particular, 66.7% of students appreciated AI's ability to enhance lessons and foster personalized learning experiences. Faculty members, however, expressed more caution, with 50% of them expressing concerns about the dehumanization of education and security issues related to student data while recognizing the positive impact.Journal of Ecohumanism240Investigation of perceptions and impacts of AI within educational contexts using Yarmouk UniversityAr as a case study finds attitudes and awareness levels regarding artificial intelligence differed significantly between the groups surveyed.Journal of Ecohumanism["Nadia Ghalia", "Eyal Isami", "Miriam Bsoul", "Saierah. Aabed", "Rema salh Haeb", "Mawaheb Mustafa"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/e87ac3fddcd9229611ae7acf824b66b19d61d4fd e556019fce160c95a31d6e8dd9017a7ac88dd0b1Recent advancements in artificial intelligence (AI) have markedly affected various fields, with notable progress in surgery. This study explores the integration of AI in surgery, particularly focusing on minimally invasive surgery (MIS), where high-quality surgical videos provide fertile ground for computer vision (CV) technology applications. CV plays an important role in enhancing intraoperative decision-making through real-time image recognition. This study considers the challenges in clinical applications and future perspectives by reviewing the current state of AI in navigation during surgery, postoperative analysis, and automated surgical skill assessment.JMA Journal180This study explores the integration of AI in surgery, particularly focusing on minimally invasive surgery (MIS), where high-quality surgical videos provide fertile ground for computer vision (CV) technology applications.JMA Journal["Shintaro Arakaki", "Shin Takenaka", "Kimimasa Sasaki", "D. Kitaguchi", "H. Hasegawa", "N. Takeshita", "Mitsuhisa Takatsuki", "Masaaki Ito"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/e556019fce160c95a31d6e8dd9017a7ac88dd0b1 43021ac7b2b4daffa3da2eb8df6d553c0943fd29Integrasi Kecerdasan Buatan (AI) dalam pendidikan anak usia dini (PAUD) menunjukkan potensi besar dalam meningkatkan pembelajaran yang dipersonalisasi, keterlibatan, dan dukungan bagi guru. Penelitian ini bertujuan untuk mengkaji manfaat AI dalam PAUD melalui metode tinjauan pustaka. Penelitian ini menganalisis 30 studi relevan dari tahun 2010 hingga 2023, yang berfokus pada dampak AI terhadap pembelajaran yang dipersonalisasi, keterlibatan siswa, dan inovasi pengajaran guru. Hasil penelitian menunjukkan bahwa AI secara signifikan meningkatkan proses pendidikan dengan memberikan umpan balik real-time, mendukung pembelajaran individual, dan mendorong kreativitas pengajaran dalam pendidikan anak usia dini. Namun, tantangan seperti privasi data dan kesenjangan digital tetap menjadi isu penting untuk dilakukan penelitian mendatang.Jurnal Obsesi01Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini["Mohammad Fauziddin", "Mellevi Agustin"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/43021ac7b2b4daffa3da2eb8df6d553c0943fd29 38cf7e5de5c4cfddae1095f2bce5d019f8bb3877Marketing Education Review170Marketing Education Review["Victor A. Barger", "P. Chennamaneni", "A. J. Dahl", "Jimmy Peltier"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/38cf7e5de5c4cfddae1095f2bce5d019f8bb3877 0c5a60e93e04f4452847faa4cbd7c21e922d2a3bThe report provides an opportunity for educational enrichment by offering essential information and analysis on one of the most  important and exciting topics in contemporary science and technology. The choice to write about this topic is motivated by its relevance, complexity, and significance for modern society and technological development. Science, Engineering & Education190The choice to write about this topic is motivated by its relevance, complexity, and significance for modern society and technological development.Science, Engineering and Education["Nikoleta Dimkina", "Georgi Hristov"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/0c5a60e93e04f4452847faa4cbd7c21e922d2a3b 215a168a5a36bd79f9a4ae990df53da4b14abd70Annals of the American Thoracic Society00Annals of the American Thoracic Society["Ryan Chow", "S. Jama", "Aaron Cowan", "S. Pakhal\u00e9"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/215a168a5a36bd79f9a4ae990df53da4b14abd70 720ffbf0b3c79ad107bba7c6ea4d92d706cf866dChild in a Digital World00Child in a Digital World["Juan Pedro Mart\u00ednez Ram\u00f3n"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/720ffbf0b3c79ad107bba7c6ea4d92d706cf866d 8036efdfe8bddac76bdfc15534e8ffea0d75f7d7Innovative Higher Education00Innovative Higher Education["Naifa Alqahtani", "Zarina Wafula"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/8036efdfe8bddac76bdfc15534e8ffea0d75f7d7 271ad7d1c5d6d9113a099d9c5a1a69ed38ca906aDigitalisation in Indonesia has seen a significant development in education in recent years. Nevertheless, issues persist in the educational journey, including the absence of interactive and creative learning tools in primary education institutions. Teachers, at the forefront of the educational journey, hold a vital role in crafting learning materials. In the field, particularly in Gugus 5 Blimbing, it is evident that numerous educators lack the skills to create engaging and inventive learning resources. Consequently, student engagement and motivation suffer, resulting in a less impactful learning experience. To address this issue, a workshop focused on creating user-friendly learning materials, accessible to all educators, is essential. Assemblr Edu offers a platform for educational institutions to generate interactive 3D learning tools using Augmented Reality (AR).  Educational establishments have the ability to enhance learning materials by implementing Augmented Reality technology, allowing students to view virtual objects within the classroom.  The target output of this activity is that driving school teachers in Gugus 5 Blimbing have competence in developing 3D interactive learning media using Assemblr Edu Augmented Reality and AI technology.PRIMA PORTAL RISET DAN INOVASI PENGABDIAN MASYARAKAT00The target output of this activity is that driving school teachers in Gugus 5 Blimbing have competence in developing 3D interactive learning media using Assemblr Edu Augmented Reality and AI technology.PRIMA PORTAL RISET DAN INOVASI PENGABDIAN MASYARAKAT["M. Fawwaz", "M. Akbar", "Madziatul Churiyah", "Sholikhan Sholikhan"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/271ad7d1c5d6d9113a099d9c5a1a69ed38ca906a 335f5018d363bd1cbd7ccde551cc609c2caee5e3JACC: Advances270With the variability and challenges of real-world SBP measurement, AI analysis of retinal images may provide a more reliable and accurate biomarker for predicting future ASCVD events than traditionally measured SBP.JACC: Advances["D. Squirrell", "Song Yang", "L. Xie", "Songyang Ang", "Mohammadi Moghadam", "Ehsan Vaghefi", "Michael V. McConnell"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/335f5018d363bd1cbd7ccde551cc609c2caee5e3 daba190a1f5280268aaca81f99ab4039913baa02International Journal of Production Research460International Journal of Production Research["S. Mukherjee", "Mohammed Nawazish", "S. Padhi", "Jayanth Jayaram"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/daba190a1f5280268aaca81f99ab4039913baa02 4a38af072c524735c45dbfeb7098e7c83e9f11cbHumanities and Social Sciences Communications780Humanities and Social Sciences Communications["E. Escolano-P\u00e9rez", "Jos\u00e9 Luis Losada"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/4a38af072c524735c45dbfeb7098e7c83e9f11cb f696020cb399d887affc22496d19436303fcc1ce00["Pethuru Raj", "N. Gayathri", "G. J. W. Kathrine"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/f696020cb399d887affc22496d19436303fcc1ce bf8fdb54e02f9906049b11d82486754e6f6d3bb8Journalism in the rapidly evolving modern world is undergoing significant transformation due to the rise of artificial intelligence applications. The study explores the impact of artificial intelligence (AI) on modern journalism, including its transformative effects on news production, distribution, and consumption. This study explores the intricate relationship between AI applications and evolving journalism practices. Through an analysis of industry trends and case studies, we have identified the multidimensional impact of the applications on newsrooms, information collections, and reporting. We have highlighted both the opportunities and challenges brought about by this technologıcal paradigm shift in the field. The study aims to offer a detailed comprehension of the developing relationship between artificial intelligence (AI) applications and contemporary journalism. The results provide significant insights for scholars, practitioners, and stakeholders concerning the dynamic intersection of technology and media.DIROSAT: Journal of Education, Social Sciences &amp; Humanities300The study explores the impact of artificial intelligence (AI) on modern journalism, including its transformative effects on news production, distribution, and consumption, and identifies the multidimensional impact of the applications on newsrooms, information collections, and reporting.DIROSAT: Journal of Education, Social Sciences &amp; Humanities["Nour Chetouani"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/bf8fdb54e02f9906049b11d82486754e6f6d3bb8 e3825f1d9449f9139f484e7cbc1759c9d8d21dcaInternational Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024)00International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024)["Yahui Li", "Jilong Wang"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/e3825f1d9449f9139f484e7cbc1759c9d8d21dca af0012d0391e36d08282606736916f9463a70182International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024)00International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024)["Hao Luo", "Shuli Jia", "Siqi Chen", "Liyong Ma"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/af0012d0391e36d08282606736916f9463a70182 b239bb5d338f242f885f33560aba0fd60e369372PurposeThe significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning artificial intelligence (AI) and machine learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field.Design/methodology/approachIn this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology to analyze related papers.FindingsIn business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis.Research limitations/implicationsWhile this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024.Originality/valueThis work addresses a significant gap by employing a pioneering approach to introduce challenges in BPM alongside AI/ML techniques and integrated tools. Hence, it offers comprehensive guidelines that elucidate the alignment between ML methods and solutions to current challenges across the BPM life-cycle, including process enhancement and process improvement. Additionally, by detailing various aspects of the life-cycle phases and highlighting ML technique characteristics, this research demonstrates potential approaches for future exploration, thereby enhancing applicability for both process analysts and researchers in this context.Business Process Management Journal1292This work addresses a significant gap by employing a pioneering approach to introduce challenges in BPM alongside AI/ML techniques and integrated tools and demonstrates potential approaches for future exploration, thereby enhancing applicability for both process analysts and researchers in this context.Business Process Management Journal["Mostafa Abbasi", "R. Nishat", "Corey Bond", "J. B. Graham-Knight", "Patricia Lasserre", "Yves Lucet", "Homayoun Najjaran"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/b239bb5d338f242f885f33560aba0fd60e369372 5c428780bf6e675cf2c69e029fa0535da2baa9f4This paper presents an artificial intelligence (AI)‐based framework designed for learning and regenerating analog circuits from academic papers. The framework comprises four distinct modules: circuit extractor, table extractor, text extractor, and simulation executor. The circuit extractor module utilizes deep learning object detection to identify devices and their associated textual descriptions while extracting interconnections between devices. The table extractor module handles textual and image‐based tables, extracting device parameters, and simulation data. The text extractor module leverages optical character recognition (OCR) and AI models to extract supplementary information. The simulation executor employs this information to conduct simulations and optimize circuit performance. In our experiments, our method effectively extracts multimodal circuit design information, achieving an average accuracy of up to 97% in target detection within the circuit extractor module. The improved performance during the simulation process further validates the effectiveness of our framework.International journal of circuit theory and applications51The method effectively extracts multimodal circuit design information, achieving an average accuracy of up to 97% in target detection within the circuit extractor module, and the improved performance during the simulation process further validates the effectiveness of the framework.International Journal of Circuit Theory and Applications["Wenxiao Xiong", "Xiangyu Meng", "Yuwen Tao", "Peng Ling"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/5c428780bf6e675cf2c69e029fa0535da2baa9f4 aa93d95df67222021b04303da2f5c6001a56fa56 Recent advancements in artificial intelligence (AI) have profoundly transformed weather forecasting, challenging traditional reliance on numerical weather prediction (NWP) models. Despite notable progress, AI models still depend heavily on traditional NWP systems and data assimilation methods to generate analysis fields, a dependency that increases computational demands and might limit forecast accuracy. This study explored the integration of Gridpoint Statistical Interpolation (GSI) with the Pangu-Weather AI forecasting model (GSI-Pangu), and assessed the potential for AI models to autonomously generate forecasts by leveraging mature data assimilation systems. Our experiments commenced by adopting ERA5 reanalysis data for the initial cycle, and then involved assimilation of simulated observations in subsequent cycles, spanning a month-long period. Results demonstrated notable enhancements in forecast accuracy, with reductions in the root mean square error across various atmospheric variables compared with the results of a control experiment without data assimilation. Additionally, the results highlighted GSI-Pangu’s ability to predict large-scale circulation patterns of extreme precipitation events, together with its effectiveness in driving regional models to accurately forecast precipitation intensity and distribution. Successful implementation of GSI within the Pangu-Weather framework underscores the transformative potential of hybrid forecasting systems, which merge conventional meteorological techniques with AI innovations, thereby facilitating accelerated adoption of AI in weather forecasting.Environmental Research Letters01Successful implementation of GSI within the Pangu-Weather framework underscores the transformative potential of hybrid forecasting systems, which merge conventional meteorological techniques with AI innovations, thereby facilitating accelerated adoption of AI in weather forecasting.Environmental Research Letters["Hongxiong Xu", "Yihong Duan", "Xiangde Xu"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/aa93d95df67222021b04303da2f5c6001a56fa56 e16b754b3150b9ea10d04b405aaba50d5c7da027Journal of Science Education and Technology581The paper underscores the need to equip pre-service teachers with the necessary competencies to utilize GenAI effectively in their future teaching practices, highlighting the potential of addressing existing challenges in evaluating and developing teacher knowledge via GenAI.Journal of Science Education and Technology["R. Blonder", "Yael Feldman-Maggor", "S. Rap"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/e16b754b3150b9ea10d04b405aaba50d5c7da027 c4caee4225b0b40fcb6cff46e943f883a722aa4dThis paper examines the impact of using an Artificial Intelligence (AI) teacher for current Higher Education (HE) students from three countries. The study utilized an AI avatar powered by a fine-tuned Large Language Model (LLM), OIMISA, which is trained solely for teaching and learning applications. The AI teacher provided a 9-lesson course on employability and transferable skills. In total 207 students across the three institutions enrolled in the programme. The results demonstrate a noteworthy completion rate of over 47%, along with high levels of engagement across all student cohorts and high satisfaction rates from the students. These show the potential for AI-based virtual teachers across countries for students of HE compared to the use of MOOC platforms.Frontiers in Artificial Intelligence241The results demonstrate the potential for AI-based virtual teachers across countries for students of HE compared to the use of MOOC platforms, as well as high levels of engagement across all student cohorts and high satisfaction rates from the students.Frontiers in Artificial Intelligence["Dev Aditya", "Krizia Silvestri", "Pauldy C. J. Otermans"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/c4caee4225b0b40fcb6cff46e943f883a722aa4d bda9a21339af2a4aa3121adbe676c35aa9ce343fBackground The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. Objective This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. Methods Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non–peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher’s Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. Results The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. Conclusions In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.Interactive Journal of Medical Research741This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation, however, persistent concerns remain.Interactive Journal of Medical Research["Margaret Chustecki"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/bda9a21339af2a4aa3121adbe676c35aa9ce343f bb768957ebbafc37d4d840d4c8c5b635d8a51e7dThe development of enjoyable, interesting and meaningful learning experiences in different domains, such as, for example, learning programming languages driven with artificial intelligence (AI) is a current challenge that should be monitored from a longitudinal scope to evidence its true potential. The objective of this work is to contribute to the existing literature on an experience in AI-driven development for programming applied to multimedia, using Tabnine as a central tool. An empirical-analytical research methodology of quantitative, quasi-experimental and longitudinal design is developed. Students who have taken the subject of Programming Applied to Multimedia, from May 2023 to September 2024, of the Multimedia Design career in a Polytechnic University in the city of Guayaquil in Ecuador, participate in the study. The results highlight positive learning factors such as minimizing algorithmic writing time and choice of code, as well as maximizing productivity in the search for a solution, improvements in study commitment, and satisfaction at the time of learning. 91% of the students evidenced their interest and satisfaction in these learning experiences developed by the teachers for learning programming. Future work is focused on longitudinal studies to provide new effective experiences with the use of AI tools that influence academic performance and student ethics.2024 IEEE URUCON300An empirical-analytical research methodology of quantitative, quasi-experimental and longitudinal design is developed and results highlight positive learning factors such as minimizing algorithmic writing time and choice of code, as well as maximizing productivity in the search for a solution.2024 IEEE URUCON["Joe Llerena-Izquierdo", "Ingrid Fiallos-Vargas", "Jonatan Portugal-Gorozabel", "Alonso Veloz-Arce"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/bb768957ebbafc37d4d840d4c8c5b635d8a51e7d 079ba7d7be039345002c8b760bac59d2e617bb42The application of artificial intelligence (AI) in education has gained great attention recently. Integration of AI tools in anatomy teaching is currently engaging researchers and academics worldwide. Several AI chatbots have been generated, the most popular being ChatGPT (OpenAI: San Francisco, California, USA). Since its first public release in November 2022, several research papers have pointed to its potential role in anatomy education. However, it is not yet known whether it will prove superior to other available AI tools in this role. This article sheds some light on the current status of research concerning AI applications in anatomy education and compares the performances of three well-known chatbots (ChatGPT, Gemini, and Claude) in answering anatomy questions. A total of 23 questions were used as prompts for each chatbot. These questions comprised 10 knowledge-based, 10 analysis-based USMLE Step 1-type, and three radiographs. ChatGPT was the most accurate of the three, scoring 100% accuracy. However, in terms of comprehensiveness, Claude was the best; it gave very organized anatomical responses. Gemini performed less well than the other two, with a scored accuracy of 60% and less scientific explanations. On the basis of these findings, this study recommends the incorporation of Claude and ChatGPT in anatomy education, but not Gemini, at least in its current state.Clinical anatomy (New York, N.Y. Print)520Comparing the performances of three well-known chatbots (ChatGPT, Gemini, and Claude) in answering anatomy questions and recommending the incorporation of Claude and ChatGPT in anatomy education, but not Gemini, at least in its current state.Clinical anatomy["K. Al-Khater"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/079ba7d7be039345002c8b760bac59d2e617bb42 78064bb2aef2e3e96ebf30142be829a673b54f6bMedical waste management in healthcare facilities is critical to protecting public health and the environment. Improper handling of medical waste can lead to environmental pollution and pose serious health risks. In Indonesia, Permenkes No. 2 of 2023 provides a regulatory framework for managing medical waste, but its implementation needs to be improved, especially in remote healthcare facilities with limited infrastructure and resources. Technological advances, especially artificial intelligence (AI), offer potential solutions to optimize medical waste management through real-time tracking, sorting, and monitoring. This study aims to evaluate the role of AI in supporting the implementation of Permenkes No. 2 of 2023 in several health facilities and identify barriers to AI adoption. Using a normative legal approach combined with case studies from health facilities in Indonesia, this study highlights the effectiveness of AI implementation in medical waste management. The results show that AI has the potential to improve compliance with medical waste management standards, optimize waste processing, and strengthen supervision through real-time data collection. However, AI adoption faces high costs, a lack of infrastructure, and limited technical expertise, especially in remote areas. The implications of this study emphasize the need for investment in technological infrastructure, health workforce training, and supportive policies to address barriers to AI adoption. This would maximize the potential of this technology in more effective medical waste management for public health and a safer environment.Jurnal Locus Penelitian dan Pengabdian320The results show that AI has the potential to improve compliance with medical waste management standards, optimize waste processing, and strengthen supervision through real-time data collection.Jurnal Locus Penelitian dan Pengabdian["Rommy Sebastian"]2024-11-18T00:00:00https://www.semanticscholar.org/paper/78064bb2aef2e3e96ebf30142be829a673b54f6b 07265044885834daaf6f5a1c2f278d6719d67e66The book discusses the field of Artificial Intelligence in Education (AIED). The authors believe that AIED is a diverse field that encompasses aspects of philosophy, learning and teaching, research, and engineering. They argue that AIED practitioners need to take a broader approach to define the purpose of the field and be more engaged with more general societal issues. The authors call for AIED systems to be designed with transparency, accountability, and user control, to ensure fairness and equity in education. They also suggest a shift away from the traditional model of AI design, which assumes a fixed objective, to a more collaborative model that allows for negotiation between humans and AI to set individual goals.The Serials librarian014The authors suggest a shift away from the traditional model of AI design, which assumes a fixed objective, to a more collaborative model that allows for negotiation between humans and AI to set individual goals.The Serials Librarian["Joshua Rose"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/07265044885834daaf6f5a1c2f278d6719d67e66 a182651e17fd2068154c8335ae6ddf3f1e3a2de5Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to manage risks and ensure sustainability. Artificial intelligence (AI) techniques leverage these diverse knowledge fields to a single theme. This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures. It includes the investigation of diverse AI applications in leak detection and infrastructure maintenance, demand forecasting and water supply optimization, water treatment and water desalination, water quality monitoring and pollution control, parameter calibration and optimization applications, flood and drought predictions, and decision support systems. Finally, an overview of the selection of the appropriate AI techniques is suggested. The nature of AI adoption in WRM investigated using the Gartner hype cycle curve indicated that the learning application has advanced to different stages of maturity, and big data future application has to reach the plateau of productivity. This review also delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources.Water1623This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures and delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources.Water["Drisya Jayakumar", "Adel Bouhoula", "W. Al-Zubari"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/a182651e17fd2068154c8335ae6ddf3f1e3a2de5 6d3e2582a7f4a49e3e9c121696fa2e70c549e541BMC Digital Health361Examination of changes in AIDPM integration and governance since 2021 focuses on large language models and health equity considerations, with a particular focus on large language models and health equity considerations.BMC Digital Health["Shan Guleria", "Janet Guptill", "Ishmeet Kumar", "M. McClintic", "Juan C Rojas"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/6d3e2582a7f4a49e3e9c121696fa2e70c549e541 d219d81843194a6f79b96aca899f3d5da86c7abeBMC Medical Education40Comments are offered on certain aspects of the findings by Amiri et al. that warrant further discussion.BMC Medical Education["Yoshiyasu Ito", "Hironobu Ikehara"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/d219d81843194a6f79b96aca899f3d5da86c7abe cf0b1af85074595a168d188e2af4b084aab71b8cThe rapid growth of Artificial Intelligence (AI) has begun to change many elements of education, including Islamic religious education. Traditionally, Mu'allim, Murabbi, and Mudarris have played important roles in teaching, ethics, and spiritual guidance. However, little is known about the impact of AI on the shifting existence of these terminologies. This research delves into the impact of AI on the responsibilities and perspectives of Mu'allim, Murabbi, and Mudarris, especially through the lens of transhumanism and the diffusion of innovation theory. This study uses qualitative methods to examine data, exploring the variations in AI integration in Islamic education across diverse cultural and regional backgrounds while also recognizing the specific best practices and challenges in each setting. The research seeks to close the gap in understanding between contemporary technology and Islamic educational customs by examining the evolving responsibilities of educators in the age of artificial intelligence. The findings raise significant issues regarding the preservation of human-centered values in religious education while also highlighting the potential of AI to improve educational methods. This study bridges the gap between traditional educational philosophies and technology breakthroughs, contributing to the increasing body of literature on AI in education and providing insightful information for Islamic studies researchers.SUHUF00This research delves into the impact of AI on the responsibilities and perspectives of Mu'allim, Murabbi, and Mudarris, especially through the lens of transhumanism and the diffusion of innovation theory, closing the gap in understanding between contemporary technology and Islamic educational customs.Suhuf["T. Thoriquttyas", "Nita Rohmawati"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/cf0b1af85074595a168d188e2af4b084aab71b8c b3dbac37512576968486540b28566fada41074c2The advent of digital technology has spawned a revolution in software engineering, with artificial intelligence (AI) emerging as a key technology. The integration of advanced techniques, such as natural language processing (NLP) and deep learning has demonstrated AIs remarkable capabilities throughout the software development lifecycle. Enhancements in areas such as code generation, code inspection, and software testing have significantly elevated both efficiency and quality. In addition, the potential of AI also provides new possibilities for automated software updates and maintenance in the future. However, despite the broad application prospects of AI in software engineering, it still faces some problems to be solved urgently. For example, inadequate adaptability and the challenges of personal data privacy protection limit its wider application. At the same time, the high research cost and immature model technology also bring obstacles to further development. By comprehensively analyzing the existing literature and related cases, this study deeply discusses the application status and limitations of AI in software engineering. The research results show that although AI can greatly improve the efficiency of software development, its shortcomings in data security and adaptability need attention. Future research should address these problems and seek more effective technical solutions to promote the sustainable development of AI in software engineering.Advances in Engineering Innovation00The research results show that although AI can greatly improve the efficiency of software development, its shortcomings in data security and adaptability need attention.Advances in Engineering Innovation["Zherong Ma"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/b3dbac37512576968486540b28566fada41074c2 930c3e5b984d7c54bbfad59d4fbb724f437270d0Artificial Intelligence (AI) has become an integral part of digital literacy systems and plays a vital role in the development of intelligence. In the educational sector, AI represents a significant innovation that can greatly enhance the quality of learning and educational management. This technology offers numerous benefits, supporting educational stability and improving the teaching and learning processes. However, it is crucial to acknowledge that AI can also lead to adverse effects if misused. Therefore, ensuring the responsible use of AI in education is essential, adhering to applicable ethical standards. Educational institutions, particularly those with Islamic values, must continually innovate in response to technological advancements. At the same time, they should equip their students with strong religious values to mitigate potential harm that may arise from these technological developments. By fostering a balanced approach, educational institutions can leverage AI’s potential while safeguarding their students' moral and ethical foundations.SUHUF690By fostering a balanced approach, educational institutions can leverage AI’s potential while safeguarding their students' moral and ethical foundations.Suhuf["Rifah Rifah", "Mohammad Jailani", "Miftachul Huda"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/930c3e5b984d7c54bbfad59d4fbb724f437270d0 62887d17e90d0a0a96a63cf8b91a913e1d68c4b1The integration of artificial intelligence (AI) in higher education is transforming traditional learning frameworks, presenting unprecedented opportunities for personalized, efficient, and inclusive education. This paper explores the ways AI technologies, including machine learning algorithms, natural language processing, and intelligent tutoring systems, are reshaping educational methodologies and environments in higher education. By examining AI-driven applications such as adaptive learning platforms, automated assessment tools, and virtual teaching assistants, this study highlights how AI enhances student engagement, facilitates tailored learning experiences, and streamlines administrative tasks. Furthermore, this paper addresses the ethical considerations, challenges, and potential biases associated with AI implementation, emphasizing the need for transparent, equitable practices to optimize AI's positive impact on learning. Ultimately, this research underscores AI's transformative potential in making higher education more accessible and adaptive to diverse learner needs, setting the stage for a future of AI-empowered, data-driven education. This study utilizes a comprehensive literature review and case studies to demonstrate the potential and challenges of AI implementation, highlighting ethical considerations, data privacy, and the need for policy frameworks to support responsible AI usage. By addressing these multifaceted aspects, this research emphasizes the strategic role of AI in shaping the future of higher education.International Journal For Multidisciplinary Research00This study highlights how AI enhances student engagement, facilitates tailored learning experiences, and streamlines administrative tasks, and addresses the ethical considerations, challenges, and potential biases associated with AI implementation, emphasizing the need for transparent, equitable practices to optimize AI's positive impact on learning.International Journal For Multidisciplinary Research["Anil Chandra Borah", "Pratibha Borah"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/62887d17e90d0a0a96a63cf8b91a913e1d68c4b1 9606857ae78e2de8755f1475894e11da2141d1e9 This article examines the opportunities and risks that can be created for trusts by generative artificial intelligence. In particular, the work is concerned with how AI investment tools may affect trusts, given their growing use in investment management. It is argued that trusts can be exposed to the risks and opportunities that this technology may create through the trustees’ general investment power. However, currently, trustees can undertake appropriate risk management by exercising their section 4 and 5 duties relating to investment. The work ends by suggesting that targeted statutory reform and guidance is needed to deal with AI risks.Trusts &amp; Trustees00It is argued that trusts can be exposed to the risks and opportunities that this technology may create through the trustees’ general investment power through the trustees’ general investment power.Trusts &amp; Trustees["Lloyd A Brown"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/9606857ae78e2de8755f1475894e11da2141d1e9 6d71171b645156baa52333275ada9c54028446caIn the context of European state‐owned enterprises (SOEs), this study examines whether the adoption of artificial intelligence (AI) facilitates the pursuit of sustainable development goals (SDGs) and how this relationship varies across the 17 individual SDGs. Results from panel and logistic regression models reveal a statistically significant positive association between AI adoption and SDGs pursuit. Additionally, the findings suggest that AI primarily supports environmental SDGs, with a more limited impact on societal and economic SDGs. The study offers practical implications for managers, investors, and policymakers. For managers, investing in AI may enhance corporate sustainability strategies. Investors with ethical concerns are encouraged to prioritize enterprises increasingly adopting AI. Finally, the study urges policymakers to explore new pathways that more effectively promote the pursuit of SDGs through AI.Corporate Social Responsibility and Environmental Management590Corporate Social Responsibility and Environmental Management["Flavio Spagnuolo", "Raffaela Casciello", "Ilaria Martino", "Fiorenza Meucci"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/6d71171b645156baa52333275ada9c54028446ca 3463593a4b49eac9075ed776a1c4245198a05aadThe rapid advancements in artificial intelligence (AI) have begun challenging the traditional boundaries of religious authority, historically upheld by scholars and clerics. This study examines IslamGPT, an AI platform designed to provide answers on Islamic teachings, and explores its ethical implications within the context of religious authority. While AI, like IslamGPT, offers convenience in accessing religious knowledge, it raises concerns about accuracy, credibility, and the potential erosion of established religious guidance. This article analyzes the ethical dimensions of utilizing AI for religious purposes, emphasizing the need for guidelines to navigate this emerging landscape. The study finds that reliance on AI for religious advice may blur the lines of legitimate authority, highlighting the necessity of direct verification with religious experts to maintain doctrinal integrity. The paper concludes that while AI can support religious education, its use in delivering authoritative religious decisions must be approached with caution, incorporating human oversight to preserve the sanctity of religious teachings.Al'adalah00The study examines IslamGPT, an AI platform designed to provide answers on Islamic teachings, and explores its ethical implications within the context of religious authority, finding that reliance on AI for religious advice may blur the lines of legitimate authority.Al'Adalah["Mohammad Fattahun Niam"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/3463593a4b49eac9075ed776a1c4245198a05aad 13d8924e6ac7a89597f3dea5457b5de6fb412e8fComparative approaches are rarely utilized in futures studies despite the distinctive nature of different policy problems. Issues like climate change, infrastructure investments, and governance of emerging technology are frequently grouped under the umbrella of the “long‐term problems” without adequate consideration for their distinct spatial and temporal attributes. To address this research gap, this paper presents a framework to systematically compare long‐term policy problems, such as the risks of climate change and artificial intelligence (AI). I conduct a comparative analysis of the risks of climate change and AI—both widely regarded as pivotal questions of our time—focusing on how they differ across eight attributes that affect their governance: scientific certainty, spatiality, temporality, linearity, path dependence, accountability, capacity to address and the costs involved. The findings suggest that climate change involves a more evident intergenerational conflict between generations than risks of AI and might therefore be a more challenging long‐term governance problem. Yet, both problems risk triggering irreversible lock‐in effects, specifically in extreme scenarios such as crossing climate tipping points or misaligned advanced AI systems. Mitigating these uncertain lock‐in effects requires precautionary governance measures, highlighting the potential of comparative approaches at the intersection of foresight and policy analysis.FUTURES &amp; FORESIGHT SCIENCE350A comparative analysis of the risks of climate change and AI is conducted, focusing on how they differ across eight attributes that affect their governance: scientific certainty, spatiality, temporality, linearity, path dependence, accountability, capacity to address and the costs involved.FUTURES &amp; FORESIGHT SCIENCE["Atte Ojanen"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/13d8924e6ac7a89597f3dea5457b5de6fb412e8f 485aed6c0af6fae4870b8ab572f2dbb23e33d93eThis discussion paper explores how integrating generative artificial intelligence (GenAI), particularly large language models (LLMs) like ChatGPT, can address educational disparities faced by equity students in higher education (HE). Equity students, including those from under-represented groups such as non-English-speaking backgrounds, students with disabilities, and low socio-economic status, often encounter significant barriers when adapting to academic literacies. GenAI tools offer notable benefits, such as improving language proficiency, fostering critical thinking, and aiding comprehension. However, disparities in digital literacy and access to these tools may risk exacerbating the digital divide, especially among students from disadvantaged backgrounds. This paper advocates for the responsible integration of GenAI in enabling programmes to promote more equitable educational outcomes. It emphasises the need to equip students with ethical guidance in using these tools, ensuring that all students have equal access to GenAI technologies. Additionally, it introduces the use of a framework designed to guide students in using educational tools responsibly and thoughtfully, while safeguarding the productive struggle essential for developing deeper learning and critical thinking skills. While GenAI holds great promise in empowering equity students, it is crucial to integrate these tools in a manner that enhances learning opportunities without perpetuating or reinforcing existing inequalities.Journal of Widening Participation and Lifelong Learning30This paper advocates for the responsible integration of GenAI in enabling programmes to promote more equitable educational outcomes and introduces the use of a framework designed to guide students in using educational tools responsibly and thoughtfully, while safeguarding the productive struggle essential for developing deeper learning and critical thinking skills.Widening Participation and Lifelong Learning["Trixie James", "Grant Andrews"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/485aed6c0af6fae4870b8ab572f2dbb23e33d93e 283d2123beb60bb561cc67df1615a53162934526Purpose This study aims to deepen our understanding of how conventional technologies and robust accounting education standards can impact the effectiveness of green accounting practices in enhancing firm performance. To achieve this, the paper explores the moderating effects of artificial intelligence (AI) and accounting education quality on the relationship between green accounting and firm performance. Design/methodology/approach Using generalized method of moments estimation, this research uses a comprehensive dataset comprising 32,680 firm-year observations of listed companies from ten prominent countries – Canada, the UK, the USA, China, France, Germany, India, Japan, South Korea and Italy – over the period from 2012 to 2022. These countries, selected based on their high gross domestic product rankings as reported by the International Monetary Fund, ensure a diverse representation of economic strengths and capture a wide range of green accounting practices. Findings The study shows that green accounting practices positively impact current firm performance. Country-level AI positively moderates this relationship, suggesting that advanced AI infrastructure enhances the benefits of green accounting through improved data accuracy and decision-making. However, country-level accountancy education quality negatively moderates the relationship, indicating that stringent implementation of green accounting standards in these regions may introduce complexities and costs that reduce firm performance. Practical implications Integrating AI enhances data processing, predictive analytics and decision-making, improving green accounting effectiveness. High-quality accounting education ensures accurate reporting and greater transparency. These insights, when applied, can empower businesses to optimize sustainability strategies, assist policymakers in developing targeted regulations and guide educators in preparing accountants for the evolving demands of green accounting. Originality/value To the best of the authors’ knowledge, this study is the first to explore the combined moderating effects of AI and accounting education quality on the relationship between green accounting and firm performance. By highlighting the synergistic role of digital innovation and robust educational standards, this research offers novel insights into how these factors can enhance the effectiveness of green accounting practices and improve financial outcomes. Meditari Accountancy Research1050This study is the first to explore the combined moderating effects of AI and accounting education quality on the relationship between green accounting and firm performance, suggesting that advanced AI infrastructure enhances the benefits of green accounting through improved data accuracy and decision-making.Meditari Accountancy Research["Shaizy Khan", "Seema Gupta"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/283d2123beb60bb561cc67df1615a53162934526 18e9fcf4cb3708b894c3538590af08a88d6bf69bINTRODUCTION The popularization of generative artificial intelligence (AI), including Chat Generative Pre-trained Transformer (ChatGPT), has raised concerns for the integrity of academic literature. This study asked the following questions: (1) Has the popularization of publicly available generative AI, such as ChatGPT, increased the prevalence of AI-generated orthopaedic literature? (2) Can AI detectors accurately identify ChatGPT-generated text? (3) Are there associations between article characteristics and the likelihood that it was AI generated? METHODS PubMed was searched across six major orthopaedic journals to identify articles received for publication after January 1, 2023. Two hundred and forty articles were randomly selected and entered into three popular AI detectors. Twenty articles published by each journal before the release of ChatGPT were randomly selected as negative control articles. 36 positive control articles (6 per journal) were created by altering 25%, 50%, and 100% of text from negative control articles using ChatGPT and were then used to validate each detector. The mean percentage of text detected as written by AI per detector was compared between pre-ChatGPT and post-ChatGPT release articles using independent t-test. Multivariate regression analysis was conducted using percentage AI-generated text per journal, article type (ie, cohort, clinical trial, review), and month of submission. RESULTS One AI detector consistently and accurately identified AI-generated text in positive control articles, whereas two others showed poor sensitivity and specificity. The most accurate detector showed a modest increase in the percentage AI detected for the articles received post release of ChatGPT (+1.8%, P = 0.01). Regression analysis showed no consistent associations between likelihood of AI-generated text per journal, article type, or month of submission. CONCLUSIONS As this study found an early, albeit modest, effect of generative AI on the orthopaedic literature, proper oversight will play a critical role in maintaining research integrity and accuracy. AI detectors may play a critical role in regulatory efforts, although they will require further development and standardization to the interpretation of their results.Journal of the American Academy of Orthopaedic Surgeons280An early, albeit modest, effect of generative AI on the orthopaedic literature is found and proper oversight will play a critical role in maintaining research integrity and accuracy.The Journal of the American Academy of Orthopaedic Surgeons["Joshua R. Porto", "Kerry A. Morgan", "Christian J. Hecht", "Robert J. Burkhart", "Raymond W. Liu"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/18e9fcf4cb3708b894c3538590af08a88d6bf69b 9231bb4f0c2dd9627c28dece69190f3badef8c1bThe integration of artificial intelligence (AI) into health communication systems has introduced a transformative approach to public health management, particularly during public health emergencies, capable of reaching billions through familiar digital channels. This paper explores the utility and implications of generalist conversational artificial intelligence (CAI) advanced AI systems trained on extensive datasets to handle a wide range of conversational tasks across various domains with human-like responsiveness. The specific focus is on the application of generalist CAI within messaging services, emphasizing its potential to enhance public health communication. We highlight the evolution and current applications of AI-driven messaging services, including their ability to provide personalized, scalable, and accessible health interventions. Specifically, we discuss the integration of large language models and generative AI in mainstream messaging platforms, which potentially outperform traditional information retrieval systems in public health contexts. We report a critical examination of the advantages of generalist CAI in delivering health information, with a case of its operationalization during the COVID-19 pandemic and propose the strategic deployment of these technologies in collaboration with public health agencies. In addition, we address significant challenges and ethical considerations, such as AI biases, misinformation, privacy concerns, and the required regulatory oversight. We envision a future with leverages generalist CAI in messaging apps, proposing a multiagent approach to enhance the reliability and specificity of health communications. We hope this commentary initiates the necessary conversations and research toward building evaluation approaches, adaptive strategies, and robust legal and technical frameworks to fully realize the benefits of AI-enhanced communications in public health, aiming to ensure equitable and effective health outcomes across diverse populations.Journal of Medical Internet Research10A critical examination of the advantages of generalist CAI in delivering health information, with a case of its operationalization during the COVID-19 pandemic and a future with leverages generalist CAI in messaging apps, proposing a multiagent approach to enhance the reliability and specificity of health communications.Journal of Medical Internet Research["Emre Sezgin", "A. Kocaballi"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/9231bb4f0c2dd9627c28dece69190f3badef8c1b 17e8fcf15831c87074dcca3077fd2def299a4cf8The research problem of this article is the following: what are the possible legal issues regarding the use of Artificial Intelligence in business management, and how can they be solved? The integrated research, the bibliographic research technique, and the Boolean technique are used in this work. The database used was Google Scholar. The search terms were “Artificial Intelligence” + “management” + “review” and “Artificial Intelligence” + “Organizations” + “review”. The justification for limiting the search to the term "review" lies in the extensive and qualified bibliography of integrated reviews. The articles were selected based on the following criteria: a) open-source availability; b) simultaneous combination of search terms; c) thematic articles on business management; and d) chronology (after 2020). As a result, the main areas for the use of AI in business management are innovation; supply chain management; decision-making; human resources; strategic management; and product management. Furthermore, the possible legal issues that can be faced are lack of accountability; biased decisions; discrimination; non-compliance with digital literacy; violation of privacy; and unfair decisions. Finally, the original contributions of this work are 12 Governance recommendations and 8 Compliance recommendations.Nuevo Derecho00The main areas for the use of AI in business management are innovation; supply chain management; decision-making; human resources; strategic management; strategic management; and product management.Nuevo Derecho["S. Divino"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/17e8fcf15831c87074dcca3077fd2def299a4cf8 0e872d90660e565c4281683778655273d9c3117cAlzheimer's disease (AD) is a multidimensional, complex condition that affects individuals all over the world. Despite decades of experimental and clinical research that has revealed various processes, many concerns concerning the origin of Alzheimer's disease remain unresolved. Despite the notion that there isn't a complete set of jigsaw pieces, the growing number of public data-sharing initiatives that collect biological, clinical, and lifestyle data from those suffering from Alzheimer's disease has resulted in virtually endless volumes of knowledge about the disorder, far beyond what humans can comprehend. Furthermore, combining Big Data from multi- -omics research gives a chance to investigate the pathophysiological processes underlying the whole biological spectrum of Alzheimer's disease. To improve knowledge on the subject of Alzheimer's disease, Artificial Intelligence (AI) offers a wide variety of approaches for evaluating complex and significant data. The introduction of next-generation sequencing and microarray technologies has resulted in significant growth in genetic data research. When it comes to assessing such complex projects, AI technology beats conventional statistical techniques of data processing. This review focuses on current research and potential challenges for AI in Alzheimer's disease research. This article, in particular, examines how AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies. Ultimately, scientists want to develop individualized, efficient medicines.Reviews on recent clinical trials00How AI may assist healthcare practitioners with patient stratification, estimating an individual's chance of AD conversion, and diagnosing AD using computer-aided diagnostic methodologies is examined.Reviews on recent clinical trials["Siddhant Tripathi", "Yashika Sharma", "Dileep Kumar"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/0e872d90660e565c4281683778655273d9c3117c 0c474ce22fff77607477bc55266fd2acfdd39290The integration of artificial intelligence (AI) into cybersecurity has marked a transformative shift in the field of digital security, offering advanced capabilities for threat detection, response, and prevention. This paper explores the impact of AI on cybersecurity, examining its role in enhancing security measures, the challenges associated with its deployment, and the implications for market dynamics. AI technologies, including machine learning and deep learning, have significantly improved the ability to identify and mitigate cyber threats by analyzing large volumes of data in real-time and automating incident response. Despite these advancements, the application of AI in cybersecurity presents challenges such as data quality, bias, false positives, adversarial attacks, and ethical concerns. The paper also discusses future directions for AI in cybersecurity, including advancements in explainable AI, the need for robust models, and the integration with emerging technologies. Overall, AI represents a powerful tool in the fight against cyber threats, but its deployment must be carefully managed to address its limitations and ethical implications.Brazilian Journal of Technology00Overall, AI represents a powerful tool in the fight against cyber threats, but its deployment must be carefully managed to address its limitations and ethical implications.Brazilian Journal of Technology["Belghachi Mohammed"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/0c474ce22fff77607477bc55266fd2acfdd39290 aaed46e425aab72335d577369e26302b5fbcc7a7Purpose This viewpoint article presents perspectives on the intersection of AI and the diverse elements of Psychological Capital (PsyCap) and is a foundation for discussion on this juncture. It also exhibits the advantages of integrating AI and PsyCap in learning organizations and the challenges and future directions associated with the same. Design/methodology/approach The article exhibits the juncture of Artificial Intelligence (AI) and Psychological Capital (PsyCap) in context to learning organizations. It also highlights the various AI tools that can be used to manage diverse PsyCap attributes in learning organizations. The approach is to present a starting point for discussion on the intersection of the two concepts and how learning organizations can leverage them to create sustained competitive advantage. Findings Artificial Intelligence Systems can be used to enhance self-efficacy, promote optimism, cultivate hope and build resilience in learning organizations. A strategic approach with continued evaluation of AI tools is the key. However, challenges like privacy and unethical issues, biasness and management of human- AI intervention have to be taken into consideration for effective integration of AI tools and Psychological Capital. Originality/value The research in the domain of Artificial Intelligence and related technologies is not a new phenomenon. However, there is a possibility to understand that AI is not a competitor to human ingenuity but a facilitator to it. Through this article an effort is made to ignite the conversation on the topic and it can be a starting point for future research on the integration of the two perspectives. Development and Learning in Organizations: an international journal50Through this article an effort is made to ignite the conversation on the intersection of AI and Psychological Capital and it can be a starting point for future research on the integration of the two perspectives.Development and Learning in Organizations: An International Journal["Prachi Kapil", "Seema Singh"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/aaed46e425aab72335d577369e26302b5fbcc7a7 df8c173b609d29e7fb3ff5351a6325b9b87e19c2The drug discovery and development process is complex, time-consuming, and costly. Artificial Intelligence (AI) has emerged as a transformative technology to improve efficiency, accuracy, and innovation in pharmaceutical research. This study explores the applications, benefits, and challenges of integrating AI in drug discovery and development. the role of AI in drug discovery, its transformative impact on pharmaceutical research, and the potential benefits and challenges. Briefly mention the major AI techniques used in different phases of drug discovery and development. The integration of Artificial Intelligence (AI) into drug discovery and development is transforming the pharmaceutical industry by speeding up processes, reducing costs, and enhancing precision. This paper discusses the involvement of AI in drug discovery and development. AI has brought a revolution to drug invention and development, significantly reducing costs and accelerating the process. By integrating AI into these stages, drug development has become more efficient, allowing for faster and more cost-effective innovations in the pharmaceutical field.International Journal of Scientific Research in Science and Technology210The applications, benefits, and challenges of integrating AI in drug discovery and development are explored, allowing for faster and more cost-effective innovations in the pharmaceutical field.International Journal of Scientific Research in Science and Technology["Ankit Ujjwal"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/df8c173b609d29e7fb3ff5351a6325b9b87e19c2 35a59c46cf123f41b67a2ea6024a629c9ca35fdbThe rapid integration of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies holds significant promise for enhancing human-centric applications, particularly in the domain of law enforcement. This paper explores the application of AI and ML in crime prevention and resource allocation, pivotal areas of concern for law enforcement agencies (LEAs) globally. By utilizing historical crime data, sensory inputs, and advanced analytics, this study contributes to the evolving discourse on smart policing and proactive crime strategies. Our objective is to facilitate the transformation of LEAs from primarily reactive entities into proactive crime preventers through the adoption of predictive analytics, facial recognition enhanced surveillance, and natural language processing for efficient data analysis. We emphasize that collaborative efforts with AI experts ensure the responsible use of technology, meticulously balancing security imperatives with privacy concerns.Global Conference on Artificial Intelligence and Internet of Things70The objective is to facilitate the transformation of LEAs from primarily reactive entities into proactive crime preventers through the adoption of predictive analytics, facial recognition enhanced surveillance, and natural language processing for efficient data analysis.2024 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT)["Nadine Y. Fares", "Manar Jammal"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/35a59c46cf123f41b67a2ea6024a629c9ca35fdb a45d9647e1a0a748b67bed64763127e98e8e1f68The purpose of this research is to use artificial intelligence to determine the influence of brand identity on purchasing decisions for KFC products in Daerah Istimewa Yogyakarta (DIY). This study uses quantitative methods to collect data, by distributing questionnaires in the form of GForms through WhatsApp. Non-probability sampling is used for this purposive sampling research. Trends in the data are analyzed using the artificial intelligence (AI) IBM SPSS Modeller. AI is used to help analyze the relationship between brand identity and purchasing decisions for these products. The findings of this study confirm the assumption that brand identity has a significant effect on purchasing decisions, thus validating previous theories and research conducted by experts on the topic. The practical implications of these research findings are expected to benefit KFC business actors in DIY, as they also demonstrate the important role of AI in helping to uncover various assumptions about the influence of brand identity on purchasing decisions. Keywords: brand identity, artificial intelligence, purchasing decision, marketing strategy, self-image, personalityKnE Social Sciences60The assumption that brand identity has a significant effect on purchasing decisions is confirmed, thus validating previous theories and research conducted by experts on the topic and demonstrating the important role of AI in helping to uncover various assumptions about the influence of brand identity on purchasing decisions.KnE Social Sciences["Rizqa Putri Aldina", "Ifada Rahmayanti"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/a45d9647e1a0a748b67bed64763127e98e8e1f68 1027906ce98ebfbfa5242944aa4b6475fd745d50Review of Evolutionary Political Economy421Review of Evolutionary Political Economy["Leandro Lepratte", "G. Yoguel"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/1027906ce98ebfbfa5242944aa4b6475fd745d50 da76457ae5dcabb7b0ab1f9f3646635008b3046fKorean Journal of Radiology61Korean Journal of Radiology["Hongnan Ye"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/da76457ae5dcabb7b0ab1f9f3646635008b3046f 56cbd180273f6640b374aaca5903d7c7a25901d0Abstract Objectives To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT), and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures. Methods Retrospectively collected two-dimensional images derived from cone beam computed tomographic volumes were used to train CNN, ViT, and gMLP architectures as classifiers for four different cases. Cases selected for training the architectures were the classification of the radiographic appearance of maxillary sinuses, maxillary and mandibular incisors, the presence or absence of the mental foramen, and the positional relationship of the mandibular third molar to the inferior alveolar nerve canal. The performance metrics (sensitivity, specificity, precision, accuracy, and f1-score) and area under the curve (AUC)—receiver operating characteristic and precision-recall curves were calculated. Results The ViT with an accuracy of 0.74-0.98, performed on par with the CNN model (accuracy 0.71-0.99) in all tasks. The gMLP displayed marginally lower performance (accuracy 0.65-0.98) as compared to the CNN and ViT. For certain tasks, the ViT outperformed the CNN. The AUCs ranged from 0.77 to 1.00 (CNN), 0.80 to 1.00 (ViT), and 0.73 to 1.00 (gMLP) for all of the four cases. Conclusions The ViT and gMLP exhibited comparable performance with the CNN (the current state-of-the-art). However, for certain tasks, there was a significant difference in the performance of the ViT and gMLP when compared to the CNN. This difference in model performance for various tasks proves that the capabilities of different architectures may be leveraged.Dento maxillo facial radiology310There was a significant difference in the performance of the ViT and gMLP when compared to the CNN for certain tasks, proving that the capabilities of different architectures may be leveraged.Dentomaxillofacial Radiology["F. A. Fernandes", "Mouzhi Ge", "Georgi Chaltikyan", "M. W. Gerdes", "C. Omlin"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/56cbd180273f6640b374aaca5903d7c7a25901d0 075b7727ca5a3d3136aa9823221a0c1945b1c453Religious Studies: An International Journal for the Philosophy of Religion00Religious Studies["Ryan Lemasters"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/075b7727ca5a3d3136aa9823221a0c1945b1c453 b835f57a9fc3a58570b859617b8bce08494a6e83Translational medicine @ UniSa00Translational Medicine @ UniSa["M. L. Leoni", "Marco Mercieri", "G. Varrassi", "Marco Cascella"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/b835f57a9fc3a58570b859617b8bce08494a6e83 7369e0f82d2bc24712a5d62c1ad376499d9f8e03Autoimmunity Reviews1260A challenging debate between world-experts and the most popular AI bot used (ChatGPT), several clinical cases including a case of vasculitis were discussed in the plenary sessions of AUTO14, which included the newest updates on most autoimmune disorders.Autoimmunity reviews["N. Mahroum", "Abdulrahman Elsalti", "Maisam Al Shawaf", "Mohammad Darkhabani", "Abdulrahman Alwani", "Ravend Seida", "Muhammet Tayfur Ertas", "Ayse Gulnihan Simsek", "Mustafa Awad", "Mona Habra", "Mohamad Aosama Alrifaai", "Dimitrios P. Bogdanos", "Y. Shoenfeld"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/7369e0f82d2bc24712a5d62c1ad376499d9f8e03 bc994fd4c7c049f339bc00db201587bc4d3a1945Nature Reviews Cardiology110Nature reviews. Cardiology["R. M. Wehbe"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/bc994fd4c7c049f339bc00db201587bc4d3a1945 8ab3f3ca2abf21c01a756725e6ad0d43cf53687dKorean Journal of Radiology70Korean Journal of Radiology["N. K. Wee", "K. Git", "Wen-Jeng Lee", "Gaurang Raval", "A. Pattokhov", "E. L. Ho", "C. Chuapetcharasopon", "Noriyuki Tomiyama", "Kwan Hoong Ng", "Cher Heng Tan"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/8ab3f3ca2abf21c01a756725e6ad0d43cf53687d 0ebd4b122b35732e57eb796f52ae314ec7d26136PurposeThis study examines how technological advancements and psychological capital contribute to job stress. Furthermore, the paper examines how job insecurity, job stress and job involvement influence the cynicism of recently laid-off employees. Despite various research studies, there is a lack of understanding of employees’ views on their work future and its probable influence on their job behaviors in this era of technology.Design/methodology/approachA quantitative method was used to collect a sample of 403 recently laid-off employees. The research tool of this study was a questionnaire, and the sampling technique was stratified random sampling. IBM SPSS and AMOS software were utilized to ensure the trustworthiness and accuracy of constructs via factor analysis. The proposed hypotheses were tested using structural equation modeling.FindingsThe analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism. Job involvement is negatively associated with employee’s cynicism.Practical implicationsThe current study adds to the comprehension of shifts in the perceived behavior of employees toward their organizations due to factors like the adoption of new technology in the organization, job stress, job insecurity and job involvement. Accordingly, there will be a need to form a favorable working atmosphere so that employees can perform their jobs with positive psychology and without any insecurity or stress.Originality/valueThe study is thought to contribute to the literature in terms of measuring organizational cynicism while layoffs continue due to AI advancements.Evidence-based HRM: a Global Forum for Empirical Scholarship690The analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism, and job involvement is negatively associated with employee's cynicism.Evidence-based HRM: a Global Forum for Empirical Scholarship["Kanika Sharma", "Benny Godwin J. Davidson", "Jossy P. George", "Peter Varghese Muttungal"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/0ebd4b122b35732e57eb796f52ae314ec7d26136 81189c4efeb514db4b42be372439550b3ee37594Journal of Oral Health and Community Dentistry380Journal of Oral Health and Community Dentistry["Sumit Munjal", "Seema Munjal", "Ameya Tripathi", "Akshay Munjal"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/81189c4efeb514db4b42be372439550b3ee37594 e239fb9d2b6fd9df9b9910700a1ce169dfc62f16No abstractEthiopian Medical Journal50Ethiopian Medical Journal["Eshetu Girma"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/e239fb9d2b6fd9df9b9910700a1ce169dfc62f16 fb395a8796b2c04e217a21b178dfc6def920c6a8Efektyvna ekonomika00Efektyvna ekonomika["I. Kolodii", "Prof. Ye.V. Khraplyvy", "N. Khotynskyi", "\u0406. \u0412. \u041a\u043e\u043b\u043e\u0434\u0456\u0439"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/fb395a8796b2c04e217a21b178dfc6def920c6a8 70d68eee5cb3407b9f75e2130b6d8bd37989696bSaudi Journal of Health Systems Research60Saudi Journal of Health Systems Research["Faisal A. Al-Suwaidan"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/70d68eee5cb3407b9f75e2130b6d8bd37989696b 4122044800ae09553f402de93411b07c5e540c4bNEJM AI180NEJM AI["D. Gunasekeran", "Steven Miller", "W. Hsu", "M. Lee", "H. Wong", "Mun Tuck Lee", "Ecosse Lamoureau", "D. Ting", "G. Tan", "T. Wong"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/4122044800ae09553f402de93411b07c5e540c4b 5dc5367f41b11f8fff78184cbd214d2826e9d26b This article explores the concept of Artificial Collective Memories as an interplay between collective memory and large datasets employed by ai algorithms. A collective memory represents “the recollection of events shared by a group” (Roediger, 2015). It includes lived experiences and human interactions. Contrarily, large datasets used by ai algorithms, such as Large Language Models (llm s) or text-to-image models, are amassed by scraping internet data representing human communication. Those large datasets and the vast number of digitised representations of memories they encapsulate are references to the collective memories they represent, which we refer to as “Artificial Collective Memories”. Using our audio-visual project Nostophiliac ai as the starting point of this discourse and with references to our projects The ai Oracle and ai Reconstructing Realities, we are exploring areas of convergence between artificial intelligence and collective memories. With this article, we aim to enhance our understanding of ai by examining it through the lens of collective memory and, inversely, to obtain fresh insights on human collective memory through observations of generative ai’s applications.Memory Studies Review170The concept of Artificial Collective Memories is explored as an interplay between collective memory and large datasets employed by ai algorithms to obtain fresh insights on human collective memory through observations of generative ai's applications.Memory Studies Review["Phivos-Angelos Kollias"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/5dc5367f41b11f8fff78184cbd214d2826e9d26b f36714da944b56608f38eb9195df1fd0a04a9a41This study examines the role of Artificial Intelligence (AI) in urban traffic management, considering the growing challenges arising from the increase in the vehicle fleet and rapid urbanization. These factors contribute to frequent traffic congestion, increased pollutant emissions and a higher accident rate, which generates an urgent need for innovative solutions that promote safer, more efficient and sustainable traffic. The main objective is to evaluate how AI can be used to optimize urban mobility, reduce the frequency of accidents and minimize traffic-related pollution, in addition to identifying the main challenges and opportunities in the implementation of such technologies in modern urban contexts. The research was carried out through a literature review, analyzing scientific articles, publications and specialized documents. This survey sought to synthesize the existing knowledge on advances in intelligent transportation systems (ITS) and explore the practices and technologies that interconnect data and communication applied to traffic. The sources consulted included databases such as Google Scholar and recognized publications in the area, with a focus on developing countries, where the adoption of AI in urban mobility is still incipient. The use of AI-powered ITS can not only improve traffic flow and reduce congestion, but also contribute to a more sustainable urban environment. The research suggests that investment in infrastructure and capacity building will be crucial to the successful implementation of AI in traffic systems, especially in developing countries.Revista ft00The research suggests that investment in infrastructure and capacity building will be crucial to the successful implementation of AI in traffic systems, especially in developing countries.Revista ft["Iago Ramos Lucho", "Matheus Lucas Maciel Leal"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/f36714da944b56608f38eb9195df1fd0a04a9a41 f7e230f9ae495d3f19e6a7f529470686c28602b1The development, use, and timely promotion of Open Education (OE) has been effective in addressing myriad educational concerns, including inclusivity, accessibility and learning achievement, among many others. However, limited information exists in the literature concerning how OE could enhance Generative Artificial Intelligence (GenAI), which is receiving extensive interest and criticism at this time. To address this research gap, this study relies on the Open Educational Practices (OEP) framework of Huang et al. (2020) to provide various OEP scenarios that could help to promote and facilitate the effective and safe adoption of GenAI in education. The findings of this study could provide guidelines on how relying on OEP when adopting GenAI could help in ensuring quality education which is the sustainable development goal (SDG 4) of the United Nations (UN).Journal of Learning for Development551The findings of this study could provide guidelines on how relying on OEP when adopting GenAI could help in ensuring quality education which is the sustainable development goal (SDG 4) of the United Nations (UN).Journal of Learning for Development["A. Tlili", "Michael Agyemang Adarkwah", "Chung Kwan Lo", "Aras Bozkurt", "Daniel Burgos", "Curtis J. Bonk", "Eamon Costello", "Sanjaya Mishra", "Christian M. Stracke", "Ronghuai Huang"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/f7e230f9ae495d3f19e6a7f529470686c28602b1 5904c4b98a3d91fcf031c4bded572f54c20f7e3fArtificial intelligence (AI) is transforming entire industries at an unprecedented pace. Yet, established technology adoption theories offer limited tools for characterizing business AI integration and analyzing its effects. These primarily focus on the factors facilitating or hindering adoption, rather than on adoption patterns and impacts. This paper introduces a novel conceptual framework to address this key gap and applies it to the case of the ESG rating industry. ESG raters play a pivotal role in sustainable finance, providing metrics that guide investment decisions globally. However, little is known about the extent and nature of their AI usage and its implications. Through a mixed‐methods approach combining the analysis of job postings, patent filings, research publications, and corporate websites, we examine AI adoption among major ESG raters. Our investigation explores the specific AI technologies employed, their functional applications, the innovations developed, the intensity of AI integration, and the potential impacts of raters' AI adoption. Our results reveal widespread and growing AI adoption across the industry. Our findings show that raters extensively leverage Natural Language Processing to streamline data collection, processing, and analysis. Furthermore, they have pioneered Machine Learning innovations that significantly expand their sustainability assessment capabilities in various domains. These findings mark a considerable departure from prior academic and gray literature that characterized major ESG raters as having minimal AI use, prompting critical questions regarding the implications of this technological transformation for ESG ratings' reliability, transparency, and potential biases.Business Strategy &amp; Development1091A novel conceptual framework is introduced to address this key gap and applies it to the case of the ESG rating industry, revealing widespread and growing AI adoption across the industry.Business Strategy &amp; Development["Felipe Su\u00e1rez Giri", "Teresa S\u00e1nchez Chaparro"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/5904c4b98a3d91fcf031c4bded572f54c20f7e3f b1ec77ff2bfdf08e131101c9720cbe9e3887758aThis study explores the application of artificial intelligence, specifically ChatGPT-4o, in constructing and managing a portfolio of cybersecurity stocks over the period from Q1 2018 to Q1 2024. Leveraging advanced machine learning models, fundamental analysis, sentiment analysis, and optimization techniques, the AI-driven portfolio significantly outperformed leading cybersecurity ETFs, as well as broader market indices such as the Nasdaq 100 (QQQ) and S&P 500 (SPY). The methodology employed included data collection, stock filtering, predictive modeling using Random Forests and Support Vector Machines (SVMs), sentiment analysis through natural language processing (NLP), and portfolio optimization using Mean-Variance Optimization (MVO), with quarterly rebalancing to ensure responsiveness to evolving market conditions. The AI-selected portfolio achieved a total return of 273%, with strong risk-adjusted performance as demonstrated by key metrics such as the Sharpe ratio, highlighting the effectiveness of an AI-based approach in navigating market complexities and generating superior returns. The results of this study indicate that AI-driven portfolio management can uncover investment opportunities that traditional methods may overlook, offering a competitive edge in the cybersecurity sector and promising enhanced predictive accuracy, efficiency, and overall investment success as AI technologies continue to evolve.Forecasting221It is indicated that AI-driven portfolio management can uncover investment opportunities that traditional methods may overlook, offering a competitive edge in the cybersecurity sector and promising enhanced predictive accuracy, efficiency, and overall investment success as AI technologies continue to evolve.Forecasting["A. Aiche", "Zvi Winer", "Gil Cohen"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/b1ec77ff2bfdf08e131101c9720cbe9e3887758a 5a5a94356fe98d11f0e258b12341c0c46ea6bf3fIn the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.JMIR Medical Informatics551This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework.JMIR Medical Informatics["Shuimei Liu", "L. R. Guo"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/5a5a94356fe98d11f0e258b12341c0c46ea6bf3f 8fbe06e542201410c9ecd8cda8a782fd8154fcf7This research article is an effort to explore the intriguing fact about the Indian With the rapid advancement of artificial intelligence (AI) technology, we are seeing an explosion of data being transmitted during model training, which unfortunately raises the risk of data leakage. In an age where data privacy is paramount and regulations are becoming increasingly strict, protecting sensitive information from unauthorized access has become a pressing issue. This is where Federated Learning (FL) steps in as a promising solution, finding its way into various sectors. In this paper, we will explore the practical applications of FL in five crucial areas: healthcare, urban transportation, computer vision, the Industrial Internet of Things (IIoT), and 5G networks. We will assess how FL can be effectively implemented in these real-world scenarios to enhance privacy while ensuring model accuracy and efficiency. Additionally, we will compare the FL framework with traditional centralized methods, showcasing how FL improves data privacy and performance, as well as acknowledging some of its current limitations. We will also discuss potential future enhancements that could make FL even more effective. Lastly, we will take a look at the latest research trends and the developmental prospects within this exciting field, providing insight into how FL could shape the future of data protection and AI.Journal of Computer Networks and Virtualization130This paper explores the practical applications of FL in five crucial areas: healthcare, urban transportation, computer vision, the Industrial Internet of Things (IIoT), and 5G networks, and compares the FL framework with traditional centralized methods, showcasing how FL improves data privacy and performance.Journal of Computer Networks and Virtualization["D. Rohini", "S. Shaankari", "M. Bhuvaneswari", "M. Bharathi", "T. A. S. Srinivas"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/8fbe06e542201410c9ecd8cda8a782fd8154fcf7 555cd29c308bcbc32cecd396bd3ffe541bd321e1The "Digital Literacy Program: Daily Life with AI Tools" is a community service initiative aimed at enhancing digital literacy by integrating artificial intelligence (AI) tools into daily routines. Conducted at Rumah Pertubuhan Masyarakat Indonesia (PERMAI) in Pulau Pinang, Malaysia, this program seeks to democratize access to AI technologies, fostering a foundational understanding that bridges the gap between complex AI concepts and their practical applications in everyday life. By equipping participants with the skills to utilize AI tools effectively, the program not only improves efficiency in personal and professional activities but also empowers individuals with the knowledge to navigate the evolving digital landscape. The innovative approach of this program is its focus on making AI accessible to a broader audience, promoting digital inclusivity and literacy. Through hands-on workshops and real-world applications, participants learn to integrate AI into tasks such as time management, data organization, and problem-solving, leading to enhanced productivity and informed decision-making. This initiative ultimately contributes to the broader goal of fostering a digitally literate society capable of leveraging emerging technologies for personal and collective advancement.Jurnal Pengabdian Masyarakat Nasional100Through hands-on workshops and real-world applications, participants learn to integrate AI into tasks such as time management, data organization, and problem-solving, leading to enhanced productivity and informed decision-making.Jurnal Pengabdian Masyarakat Nasional["Bambang Jokonowo", "Hadi Santoso", "Afiyati Afiyati"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/555cd29c308bcbc32cecd396bd3ffe541bd321e1 2bd493b63b5b9377c768b1d9f8bad9077aff0807 The integration of Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, offering important opportunities while emerging challenges. This essay explores the potential and obstacles of AI-based diagnostics. Key problems include technological and ethical issues such as data privacy, compatibility of devices , and regulatory issues. Ethical considerations also arise around patient privacy, accountability for AI errors, legal frameworks, and algorithm transparency. Despite these challenges, AI showcases substantial opportunities for the improvement of diagnostics and treatment techniques. Its capabilities include earlier disease detection, personalized treatment pathways, and healthcare professional support, particularly in radiology, pathology, and genomics. Additionally, AI can help and reduce the cost of healthcare access by streamlining resources, optimizing staff deployment, and facilitating remote monitoring and telemedicine, especially for developing countries. With the analysis of data from wearable devices and enabling remote consultations, AI helps us in the proactive management of chronic conditions. AI-based diagnostics can help overcome long term healthcare challenges, enhancing precision and treatment efficiency. A balanced approach is essential, addressing ethical, privacy, and legal considerations while emphasizing AI’s role as a support for healthcare professionals. Future research should focus on AI algorithm transparency, ethics, and the expansion of telemedicine, especially in underserved areas. Next Frontier For Life Sciences and AI50The potential and obstacles of AI-based diagnostics are explored, addressing ethical, privacy, and legal considerations while emphasizing AI’s role as a support for healthcare professionals.Next Frontier For Life Sciences and AI["Kayra Naz Ustaba\u015f\u0131"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/2bd493b63b5b9377c768b1d9f8bad9077aff0807 586a4e2d9eaa319d336416502c81e2b4ceaa3909The shortage of doctors is creating a critical squeeze in access to medical expertise. While conversational Artificial Intelligence (AI) holds promise in addressing this problem, its safe deployment in patient-facing roles remains largely unexplored in real-world medical settings. We present the first large-scale evaluation of a physician-supervised LLM-based conversational agent in a real-world medical setting. Our agent, Mo, was integrated into an existing medical advice chat service. Over a three-week period, we conducted a randomized controlled experiment with 926 cases to evaluate patient experience and satisfaction. Among these, Mo handled 298 complete patient interactions, for which we report physician-assessed measures of safety and medical accuracy. Patients reported higher clarity of information (3.73 vs 3.62 out of 4, p<0.05) and overall satisfaction (4.58 vs 4.42 out of 5, p<0.05) with AI-assisted conversations compared to standard care, while showing equivalent levels of trust and perceived empathy. The high opt-in rate (81% among respondents) exceeded previous benchmarks for AI acceptance in healthcare. Physician oversight ensured safety, with 95% of conversations rated as"good"or"excellent"by general practitioners experienced in operating a medical advice chat service. Our findings demonstrate that carefully implemented AI medical assistants can enhance patient experience while maintaining safety standards through physician supervision. This work provides empirical evidence for the feasibility of AI deployment in healthcare communication and insights into the requirements for successful integration into existing healthcare services.arXiv.org330It is demonstrated that carefully implemented AI medical assistants can enhance patient experience while maintaining safety standards through physician supervision, and empirical evidence for the feasibility of AI deployment in healthcare communication is provided.ArXiv["Antoine Liz'ee", "Pierre-Auguste Beaucot'e", "James Whitbeck", "Marion Doumeingts", "Ana\u00ebl Beaugnon", "Isabelle Feldhaus"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/586a4e2d9eaa319d336416502c81e2b4ceaa3909 6477bc48df93d06b2f59042dbc3847ac4b9e71f9The United Nations Sustainable Development Goals (SDGs) underpin a holistic approach to promote global sustainability, sparking the emergence of the “twin transition” concept, which combines environmentally friendly practices and digitalization for a greener future. Taking note of the SDGs, the primary objective of this research is to explore the twin transition within the context of high‐tech industries. This study aims to bridge existing knowledge gaps by exploring the indirect impact of information effects and the moderating role of artificial intelligence (AI) infrastructure flexibility in the relationship between platform leadership and green sustainable practices. Grounded in the framework of the diffusion of innovation paradigm, our findings are based on a three‐wave time‐lagged field survey conducted among 437 high‐tech SMEs in China. The study's findings uncover a positive relation between platform leadership and green sustainable practices, with information effects, specifically responsiveness and usability, acting as mediating factors. Furthermore, the study demonstrates that AI infrastructure flexibility acts as a moderator in the relationship between platform leadership and information effects, influencing the indirect effect of platform leadership on green sustainable practices through information effects. This research not only contributes to our understanding of the twin transition in high‐tech SMEs but also sheds light on the critical role of platform leadership, information effects, and AI infrastructure flexibility in driving green sustainable practices. These findings have significant implications for managers, policymakers, and scholars focused on sustainability and innovation in the high‐tech sector.Business Ethics, the Environment &amp; Responsibility960The study's findings uncover a positive relation between platform leadership and green sustainable practices, with information effects, specifically responsiveness and usability, acting as mediating factors and AI infrastructure flexibility acts as a moderator in the relationship between platform leadership and information effects.Business Ethics, the Environment &amp; Responsibility["Khalid Mehmood", "Ataullah Kiani", "Hina Rehman", "S. Alshibani", "Patrice Piccardi"]2024-11-19T00:00:00https://www.semanticscholar.org/paper/6477bc48df93d06b2f59042dbc3847ac4b9e71f9 4aec208dec49f080adc5ec90297f8ccdc9f2d472PurposeeXplainable artificial intelligence (XAI) is an evaluation framework that allows users to understand artificial intelligence (AI) processes and increases the reliability of AI-produced results. XAI assists managers in making better decisions by providing transparency and interpretability in AI systems. This study explores the development of XAI in business management research.Design/methodology/approachThis study collects and analyzes business management research related to XAI using common management keywords as the basis. We used the success/failure system to explore its research guidelines XAI in business management.FindingsThe study found significant growth in XAI research within business management. This research will be discussed from various management disciplinary perspectives to help scholars understand the current research directions. Additionally, we utilize a success/failure system to explore how this theory can be applied to artificial intelligence and business management research.Originality/valueThe success/failure system offers a comprehensive framework encompassing the evolution of the cosmos, nature, and ecology. This theory can offer valuable insights for business management in XAI and competitive societies, governments, and enterprises, enabling them to formulate effective strategies for the future.Journal of Electronic Business &amp; Digital Economics561This study collects and analyzes business management research related to XAI using common management keywords as the basis and utilizes a success/failure system to explore how this theory can be applied to artificial intelligence and business management research.Journal of Electronic Business &amp; Digital Economics["Tsung-Sheng Chang", "Dong-Yih Bau"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/4aec208dec49f080adc5ec90297f8ccdc9f2d472 4201d44e60a949f7ea4ad447240a590953e4e190The research in the current paper seeks to discuss artificial intelligence (AI) in e-commerce with a viewpoint of its effects on the management and the customers. In the light of the current advancements in technology applied in organizations, it is important for companies particularly e-commerce firms to understand AI value for improvement of their business processes in order to establish competitive advantages. Altogether, the present study employs a qualitative method, where data were collected from ten participants in the industry through interviews supported by quantitative analysis to tests associations or trends. It can be concluded that concerning the level of the work organization and effectiveness, AI enhances business processes by reducing the number of routine tasks, increasing inventory control, and providing real-time information processing at the rate of 4.4 out of 5. Moreover, customer experience is improved by narrow casted marketing and effective chat bots, where the mean rating is 4.5. In fact, the correlation between operational efficiency and customer engagement is very high; it stands at 0.75, which means that better backend management directly lead to better customer experiences. However, the implementation costs remain high, and privacy issues also contribute to the hindrances of this flow, which makes the rate of adoption not very enthusiastic. It is crucial to focus on deepening the work in the sphere of creating more efficient AI solutions while keeping the attendees in the strongest constraints of data protection rules possible. In conclusion, this paper reveals the trends of AI within the e-commerce sector as laying the foundation for the effective implementation of AI solutions while providing specific recommendations to managers as to how they can unlock the advantages of various AI approaches for enhancing the efficiency of firms’ operations and strengthening consumers’ engagement in view of the continuously rising competition.Asian Journal of Research in Computer Science02The trends of AI within the e-commerce sector is revealed as laying the foundation for the effective implementation of AI solutions while providing specific recommendations to managers as to how they can unlock the advantages of various AI approaches for enhancing the efficiency of firms’ operations and strengthening consumers’ engagement in view of the continuously rising competition.Asian Journal of Research in Computer Science["Faiza Kanwal", "Nighat Bibi", "Farhan Ullah Jan", "Muhammad Ali Arslan", "Akbar Ali", "Shahjahan Ajmal"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/4201d44e60a949f7ea4ad447240a590953e4e190 4022cf9d6e5090238533682c1da24b027e561545Social Network Analysis and Mining432The study reveals that artificial intelligence transforms interactions within corporate social networks by enabling effective personalization, optimizing customer experience, and enhancing satisfaction, and benefits include precise segmentation, predictive analytics, and customer service optimization through chatbots.Soc. Netw. Anal. Min.["Luis-Alfonso Maldonado-Canca", "Ana-Mar\u00eda Casado-Molina", "Juan-Pedro Cabrera-S\u00e1nchez", "Guillermo Berm\u00fadez-Gonz\u00e1lez"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/4022cf9d6e5090238533682c1da24b027e561545 dcd973fc8aaeccfc2005616123c996602eb11da8Perkembangan Artificial Intelligence telah mengubah banyak aspek kehidupan manusia, termasuk dunia pendidikan. Pemanfaatan Artificial Intelligence dalam pendidikan tidak hanya memberikan dampak pada individu, tetapi juga memberikan kontribusi besar terhadap pengembangan masyarakat secara keseluruhan. Pelaksanaan kegiatan pengabdian menggunakan metode: 1) ceramah; 2) tanya jawab; 3) praktek, dan 4) pendampingan. Peserta dalam kegiatan ini adalah guru anggota MGMP Bisnis Daring Pemasaran Kabupaten Malang. Hasil pelaksanaan kegiatan dapat diketahui seluruh peserta sudah memiliki pemahaman terkait perkembangan materi pembelajaran di program studi Bisnis Daring Pemasaran. Seluruh peserta juga sudah memiliki pengetahuan dan pemahaman tentang pemanfaatan Artificial Intelligence dalam mengembangkan media pembelajaran digital.Jurnal Pengabdian Masyarakat Bhinneka40Jurnal Pengabdian Masyarakat Bhinneka["Rachmad Hidayat", "Wening Patmi Rahayu", "M. Hari", "Jefry Aulia Martha"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/dcd973fc8aaeccfc2005616123c996602eb11da8 38124c1047bde38ee8a83a0da3e4377d6d693f58Kemajuan teknologi Artificial Intelligence (AI), termasuk aplikasi seperti chatGPT, telah menarik perhatian banyak pihak karena kemampuannya yang menyerupai kecerdasan manusia. Hal ini memberikan peluang, tantangan, serta dampak yang signifikan bagi individu dan institusi. Untuk memahami kesadaran dan kesiapan sivitas akademika terhadap penggunaan teknologi AI. penelitian ini dilakukan dengan pendekatan survei. Survei ini melibatkan 68 responden dari berbagai perguruan tinggi di Yogyakarta. Hasil survei menunjukkan bahwa sebagian besar mahasiswa memiliki pemahaman yang baik tentang teknologi AI, dengan 97,06% responden mengaku mengenali teknologi ini dan 51,47% sering menggunakannya dalam aktivitas akademik. Mereka juga menyadari berbagai manfaat, seperti peningkatan efisiensi dalam belajar, tetapi tidak mengabaikan risiko yang mungkin timbul, seperti ketergantungan dan masalah privasi. Meski ada kesiapan yang tinggi untuk memanfaatkan AI, sebagian besar responden masih meragukan adanya regulasi yang jelas terkait penggunaannya dalam pendidikan. Oleh karena itu, penting untuk menerapkan penggunaan AI secara bijak dan terukur dengan adanya peraturan atau kebijakan yang jelas guna memaksimalkan manfaatnya di lingkungan pendidikan.JEMMA (Journal of Economic, Management and Accounting)00JEMMA (Journal of Economic, Management and Accounting)["Sri Ayem", "Umiyatun Wahidah", "Eka Yulia Sari", "Supatman Supatman", "Iis Kinasih", "P. Lestari"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/38124c1047bde38ee8a83a0da3e4377d6d693f58 ae9f3087f2e392f3314bd1f425ae54c22bb57898The Indonesian National Armed Forces (TNI) are frequently exposed to the risk of physical and mental injuries that may result in disability. The integration of artificial intelligence (AI) into integrated rehabilitation programs has the potential to expedite the recovery process, enhance diagnostic precision, and facilitate more tailored and efficacious care. This study aims to examine the role of Integrated Rehabilitation at the Rehabilitation Center of the Ministry of Defense of the Republic of Indonesia in improving the quality of life for Indonesian national army soldiers with disabilities. The research employs a normative juridical research method to systematically investigate the legal framework underpinning rehabilitation programs for soldiers and explores the potential integration of AI into these processes. The results show that the integrated rehabilitation services at Pusrehab Kemhan can improve the operational efficiency of the rehabilitation centre. The incorporation of AI in rehabilitation processes serves a crucial purpose in the context of improving the effectiveness of rehabilitation services for people with disabilities, especially for TNI soldiers. Despite challenges related to patient data privacy and security, the potential of AI is enormous.International Journal of Social Service and Research230Results show that the integrated rehabilitation services at Pusrehab Kemhan can improve the operational efficiency of the rehabilitation centre and the incorporation of AI in rehabilitation processes serves a crucial purpose in the context of improving the effectiveness of rehabilitation services for people with disabilities.International Journal of Social Service and Research["Syahrial Syahrial"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/ae9f3087f2e392f3314bd1f425ae54c22bb57898 5a82e3b7b6e1c0770bb0c4f406d06dc72a6c8a01In today's postal sector, automation is gaining more and more ground making human labour redundant. Although lagging behind in this indicator, the Bulgarian postal sector is starting to introduce automated postal stations (APS) in increasing numbers. This study aims to present the implementation of artificial intelligence and other digital technologies in APS as a disruptive innovation, and the implications of this trend for the Postal services sector. In this regard, fundamental issues are addressed: which business processes in the Postal Services Sector could be affected by digital disruptive innovations; to what extent are they likely to be affected; and what are the most threatened Postal services business processes in the near future. Some conclusions are drawn on this basis.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)170This study aims to present the implementation of artificial intelligence and other digital technologies in APS as a disruptive innovation, and the implications of this trend for the Postal services sector.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)["Diana Ilieva", "Dimitar Kolev", "Victor Gladchenko"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/5a82e3b7b6e1c0770bb0c4f406d06dc72a6c8a01 78c38eeaa17a718d3e8a9188107f646a98f96433The Postal services sector is increasingly automating human labour, which is likely to be reduced to the point of redundancy in future. The introduction of Automatic Post Stations (APS) in the Bulgarian postal sector is gaining momentum and is one of the most recent examples of a disruptive innovation in the Bulgarian economy. This study aims to examine and analyse the use cases of artificial intelligence (AI), APS and other digital technologies as a disruptive innovation (closing technology) and their impact on employment in the Postal services sector. It is thematically linked to another study by the authors examining the impact of AI and other disruptive innovations on the business processes in the sector. This study examines APS development statistics in Europe and Bulgaria, as well as potential job losses in the Postal services sector. The transformative impact of automation and AI on employment in the postal and courier services sectors is outlined, highlighting new opportunities, advantages and disadvantages. Conclusions are drawn based on the investigation and analyses.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)150The transformative impact of automation and AI on employment in the postal and courier services sectors is outlined, highlighting new opportunities, advantages and disadvantages.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)["Diana Ilieva", "Dimitar Kolev", "Victor Gladchenko"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/78c38eeaa17a718d3e8a9188107f646a98f96433 452b5da356842a28449657f5ddfeef4f43389700Since generative artificial intelligence (AI) tools such as OpenAI's ChatGPT became widely available, researchers have used them in the writing process. The consensus of the academic publishing community is that such usage must be declared in the published article. Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots. This analysis of the first 500 examples collected reveals that the problem is widespread, penetrating the journals and conference proceedings of highly respected publishers. Undeclared AI seems to appear in journals with higher citation metrics and higher article processing charges (APCs), precisely those outlets that should theoretically have the resources and expertise to avoid such oversights. An extremely small minority of cases are corrected post publication, and the corrections are often insufficient to rectify the problem. The 500 examples analyzed here likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable. Publishers must enforce their policies against undeclared AI usage in cases that are detectable; this is the best defense currently available to the academic publishing community against the proliferation of undisclosed AI.arXiv.org00Publishers must enforce their policies against undeclared AI usage in cases that are detectable; this is the best defense currently available to the academic publishing community against the proliferation of undisclosed AI.ArXiv["Alex Glynn"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/452b5da356842a28449657f5ddfeef4f43389700 ad1507df5d3dc96459fd669cf8d0dda19c276d78This study aims to explore the impact of interaction with artificial intelligence systems on individuals' mental health and behavior modification. Using a mixedmethod approach combining quantitative and qualitative data, information was collected from employees across various sectors to analyze levels of anxiety and psychological stress associated with the use of these systems. The findings revealed a significant relationship between interaction with intelligent systems and increased psychological anxiety, highlighting the need for designing AI systems that consider users' psychological aspects. The research provides practical recommendations, including enhancing training programs and psychological support for employees, to achieve a better balance between technological benefits and mental well-being in the workplace.Stardom Scientific Journals of Educational and Psychological Studies00A significant relationship between interaction with intelligent systems and increased psychological anxiety is revealed, highlighting the need for designing AI systems that consider users' psychological aspects.Stardom Scientific Journals of Educational and Psychological Studies["Dr. Yaser Qutb"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/ad1507df5d3dc96459fd669cf8d0dda19c276d78 49e8ff43d83c756c9ab7ecbfa71ac9178502cc99The rapid advancement of artificial intelligence (AI) is transforming the media industry by automating processes, with applications in data analysis, automated writing, format transformation, content personalization, and fact-checking. While AI integration offers new opportunities in journalism, it also raises ethical concerns around data privacy, algorithmic biases, transparency, and potential job displacement. This study employed qualitative interviews with media professionals and researchers to explore their perspectives on the ethical implications of AI integration in newsrooms. Interview data were analyzed to identify common themes and specific challenges related to AI use in journalism. The findings discuss issues such as the tensions between technology and journalism, ethical challenges related to AI, the evolution of professional roles in journalism, media guidelines, and potential future regulations.Frontiers in Communication480Qualitative interviews with media professionals and researchers explore their perspectives on the ethical implications of AI integration in newsrooms and discuss issues such as the tensions between technology and journalism, ethical challenges related to AI, the evolution of professional roles in journalism, media guidelines, and potential future regulations.Frontiers in Communication["Beatriz Guti\u00e9rrez-Caneda", "Carl-Gustav Lind\u00e9n", "Jorge V\u00e1zquez-Herrero"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/49e8ff43d83c756c9ab7ecbfa71ac9178502cc99 19d510782824e0894e4da997ea33e5b858a0ceecThe integration of artificial intelligence (AI) and Intellectual Property (IP) within academic settings is rapidly advancing, with the potential to drive innovation and entrepreneurship. This study aims to explore how AI and IP rights can be utilized in higher education to support entrepreneurial activities among university students. The central hypothesis is that combining AI-driven tools with a well-structured IP framework can significantly improve the environment for innovation in universities, leading to greater student involvement in entrepreneurial efforts. Initial findings suggest that AI-driven tools, such as patent analysis software and market research algorithms, can make IP management more efficient and help identify innovations with commercial potential. Furthermore, universities with effective IP policies and support systems have demonstrated higher levels of student participation in entrepreneurial initiatives. In conclusion, incorporating AI and IP rights into the academic context encourages a culture of innovation and entrepreneurship among students. By offering the necessary tools and legal structures, universities can create an atmosphere that motivates students to engage in entrepreneurial ventures, ultimately contributing to economic growth and enhancing global competitiveness.Journal of Ecohumanism140In conclusion, incorporating AI and IP rights into the academic context encourages a culture of innovation and entrepreneurship among students, ultimately contributing to economic growth and enhancing global competitiveness.Journal of Ecohumanism["Laila Barqawi", "Sarah Al-Arasi", "Mohammad Abdallah", "Bernadette Hanna Numan", "M. al-Afaishat"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/19d510782824e0894e4da997ea33e5b858a0ceec 35ffdc90f91bead60bec7774a2a3dd2203c6a44bObjectives: This study aims to explore the themes and perspectives presented in The Independent's Arabic online edition regarding the future outlook of artificial intelligence (AI) and its influential role across various aspects of life in countries and societies. The study addresses a significant issue from a media perspective within the framework of international journalism. It seeks to answer the central research question: "What are the perspectives of The Independent Arabic online edition on the future of artificial intelligence?" Methods: The study adopted a descriptive survey approach, employing content analysis on 39 issues in which AI-related topics appeared, out of a total of 92 issues that represent the research sample. An analysis form was used as a tool to extract categories related to the study variables, measure their frequency, and calculate their percentages. Results: The study's findings revealed that the newspaper pays considerable attention to covering various areas related to AI, as evidenced by the topics focusing on this technology. The overall tone of the newspaper was distinctly positive regarding the future of AI. Furthermore, the most prominent approach in handling AI topics was based on analysis and interpretation. The findings also indicated a lack of fear towards AI replacing humans and causing job losses. Instead, the content emphasized the potential for AI to create new opportunities and jobs, fostering coexistence and complementarity. Conclusions: The study concluded that AI plays a significant role in addressing current and future challenges. However, it may also lead to conflicts and competition over its use, making it both highly beneficial and potentially risky.Dirasat Human and Social Sciences90The study concluded that AI plays a significant role in addressing current and future challenges, however, it may also lead to conflicts and competition over its use, making it both highly beneficial and potentially risky.Dirasat: Human and Social Sciences["Mohammed Saleh Gabab", "Mohammed Ahmed Fyadh"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/35ffdc90f91bead60bec7774a2a3dd2203c6a44b e2047243bf397d822613fcd658cf3d4a4c921ac1Scientific Reports580The article performed a multivariate ANOVA and regression analysis, comparing trust in AI between students from Poland and the UK to identify the significant predictors of trust in this technology.Scientific Reports["Jaros\u0142aw Kozak", "S. Fel"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/e2047243bf397d822613fcd658cf3d4a4c921ac1 31c425036a8b455ff111b8ae8fdd72d000e803bcNeonatal diseases are the disturbances of normal condition of the body, organs and abnormal function of newborns. They range from minor ailments like jaundice, to serious issues such as congenital heart defects encompassing a wide range of health conditions that affect newborns within the first 28 days of life. Thus, the first four weeks of life are critical and vulnerable period that require identification, accurate diagnosis, and management. The major causes of death of diseased newborns have been found to be late detection and misdiagnosis due to confusions in diagnosing diseases with similar symptoms. Hence, artificial intelligence (AI) techniques, especially those based on deep learning algorithms have emerged as important tool in handling very difficult tasks. In spite of its prospect, the potentials of AI are yet to be maximized in newborns’ health management. This paper explored the prospects of deep learning method in neonatal diseases classification. The study proposed a Long Short-Term Memory-Artificial Neural Network (LSTM-ANN) model, the model would be trained on a large dataset comprising of age, symptoms, laboratory test results, x-ray image results and diseases diagnosed. These would be obtained from the medical records of previously diagnosed and treated newborn. The technology will enhance accurate and timely diagnosis of neonatal diseases.UNIOSUN Journal of Engineering and Environmental Sciences00The study proposed a Long Short-Term Memory-Artificial Neural Network model, the model would be trained on a large dataset comprising of age, symptoms, laboratory test results, x-ray image results and diseases diagnosed, which will enhance accurate and timely diagnosis of neonatal diseases.UNIOSUN Journal of Engineering and Environmental Sciences["C. S. Odeyemi", "O. Olaniyan", "A. A. Sobowale", "I. B. Samuel"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/31c425036a8b455ff111b8ae8fdd72d000e803bc ff1942e31c3c437265665d128f488fa23f10607aThe publication deals with a current problem related to the attitudes on human factors since the introduction of Artificial Intelligence (AI), through a survey among software engineers in small and medium – sized (SMEs) IT companies in the Republic of Bulgaria. This brings to the fore not only the functionality of AI tools and their application in software design, but also the perception of developers towards the tasks of writing, implementing and fixing code. The research was conducted on the territory of three large cities in the Republic of Bulgaria, and a quantitative approach was used in the development of an online survey, through which empirical data was generated in the business of SMEs IT companies. Based on the results, an analysis of the attitudes of the respondents in relation to the use of AI in the software activities of the surveyed companies was made. A smooth but steady trend of a positive human factors attitude towards AI-assisted software design was found. On this basis, a relatively good attitude of developers towards the benefits and advantages of adapting AI to software developments was found.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)270A smooth but steady trend of a positive human factors attitude towards AI-assisted software design was found, and a relatively good attitude of developers towards the benefits and advantages of adapting AI to software developments was found.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)["I. Stoyanov", "Gergana Sl. Dimcheva"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/ff1942e31c3c437265665d128f488fa23f10607a cc53146800d644016ae3ed7ebd20e956694be14eChildren with special needs are vulnerable in rural areas to nutrition problems, including stunting, due to limited access to health services and minimal nutrition knowledge. Article 11 of Law No. 17 of 2023 on Health emphasizes the responsibility of the Central and Local Governments to provide access to healthcare facilities and health education. In this case, the utilization of artificial intelligence (AI) can help monitor, diagnose, and intervene more effectively against stunting through accurate data analysis and timely recommendations. The objective of this study is to analyse the legal challenges related to child nutritional health, especially stunting in children with special needs in rural areas, and explore the potential of utilizing AI in addressing this issue. The research method used is normative juridical, using a statutory approach and an analytical approach. The results show that children with special needs in rural areas face various challenges in accessing health services and nutritional information. Limited access, lack of community knowledge about the importance of nutrition, and lack of effective intervention programs are the main obstacles. On the other hand, the use of AI can help in early detection and treatment of stunting through health data analysis, and provide appropriate intervention recommendations. The use of artificial intelligence has great potential in improving the effectiveness of child nutrition health programs and reducing stunting cases among children with special needs in rural areas. Collaboration between the government, health institutions, and technology is needed to implement this solution effectively and sustainably.International Journal of Social Service and Research330International Journal of Social Service and Research["Ely Yulian"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/cc53146800d644016ae3ed7ebd20e956694be14e 39901dd8e7a2b7157af6ef620d717f8f194d28f1Implementing Artificial Intelligence (AI) in the context of national security has become a critically important topic in addressing increasingly complex modern threats. This article examines various AI applications in monitoring and mitigating national security threats, such as managing critical infrastructure, risk assessment, and adaptive responses to crises. By adopting the socio-technical systems theory approach, this approach recognizes the complexity of interactions between AI technology, human decisions, and operational environments that impact national security. Recommendations include increased investment in advanced AI infrastructure and more profound training for AI and the national security workforce. It is expected that mature AI implementation can significantly impact maintaining stability and national security in this dynamic digital era.Security Intelligence Terrorism Journal (SITJ)50This article examines various AI applications in monitoring and mitigating national security threats, such as managing critical infrastructure, risk assessment, and adaptive responses to crises by adopting the socio-technical systems theory approach.Security Intelligence Terrorism Journal (SITJ)["Angga Junior Wiranata"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/39901dd8e7a2b7157af6ef620d717f8f194d28f1 149ce67ee163dff64527b7b9ee5f936d82de48c4ChatGPT and other AI applications have made the widespread use of artificial intelligence possible with their conversational interfaces and open accessibility – this “mainstreaming” of AI use has become concerning for news organizations as they grapple with the creation of appropriate AI-related guidelines and tools for their newsrooms. Even while such discussions are ongoing, the cultural shift among journalists has already begun. This paper highlights the role of journalists not simply as agents working within the confines of news organizations, but as actors with individual agency to enact change. Their use of AI has already gained significant momentum, with or without newsroom oversight and direction. This paper, supported by field theory, uses in-depth interviews with journalists and editors to uncover how journalists are themselves using AI in their day-to-day work, and how they ensure their values of “good journalism” are not compromised. Results show that journalists are already personally using AI in numerous tasks across all the stages of news production, namely in news gathering, news writing and presentation, news editing, and news promotion, and have three strategies to maintain their practice of “good journalism”. These results prompt the paper’s proposition of a new “value-motivated use” perspective to summarize journalistic adoption of AI – that it is the journalistic values that motivate their increased use of AI, as well as the values that motivate their limitations of this use; journalists will use the technology only to the extent that it aligns with the values of their profession.Journalism300Results show that journalists are already personally using AI in numerous tasks across all the stages of news production, namely in news gathering, news writing and presentation, news editing, and news promotion, and have three strategies to maintain their practice of “good journalism”.Journalism["Shangyuan Wu"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/149ce67ee163dff64527b7b9ee5f936d82de48c4 4ab4719c0705185549d2233da4bd4d2176624cabThis study investigates the use of artificial intelligence (AI) technologies among academics at the University of Duhok (UoD), focusing on their perspectives, preferences, and intentions toward integrating AI within academic and research environments. A survey was conducted through Google Forms, targeting postgraduate students, recent alumni (since 2020), and faculty members of UoD in the Kurdistan region of Iraq. A total of 674 participants, aged 22–70 years, responded. The findings indicate that only 36.94% had employed AI technologies. Among AI users (n = 249), primary sources of information were friends or colleagues (46.59%) and social media (35.74%). Younger individuals and those holding master’s degrees exhibited a stronger tendency toward AI usage (p < 0.0001), whereas gender and academic discipline had minimal influence. ChatGPT was the most widely used tool (70.68%), followed by Quill Bot (42.17%), Grammarly (34.94%), and Google Bard (29.32%). The main AI applications were text paraphrasing (33.73%) and information retrieval (15.26%). Notably, 47.58% of respondents recommended AI for various academic tasks, including scientific research and idea generation. In conclusion, the study shows that only one-third of UoD faculty members utilize AI, predominantly for text paraphrasing. Nearly half of the participants suggested the adoption of AI by postgraduate students and academic staff.ARO. The Scientific Journal of Koya University300It is shown that only one-third of UoD faculty members utilize AI, predominantly for text paraphrasing, and nearly half of the participants suggested the adoption of AI by postgraduate students and academic staff.ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY["D. Abdulah", "B. Zaman", "Z. Mustafa", "Lokman H. Hassan"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/4ab4719c0705185549d2233da4bd4d2176624cab f14e4ee605238d7f387e7b4ed067c85a72cdf89eArtificial intelligence is a branch of computer science that is concerned with using intelligent technologies to accomplish tasks that require human intelligence to complete. It aims to program machines to be able to simulate human intelligence, allowing them to think and act like humans. This term is used to refer to any device that can exhibit characteristics similar to the human mind, such as learning and problem solving. Artificial intelligence is characterized by its ability to choose the best procedures to achieve a specific goal with the best possible chance, and also includes the feature of machine learning. The importance of artificial intelligence lies in its ability to have a significant impact in many sectors around the world, giving companies a significant competitive advantage. There are some uses that highlight its importance, such as healthcare, agriculture, transportation, buying and selling products, managing renewable energy systems, and manufacturing. This research deals with the fields and applications of artificial intelligence in law.International Journal of Law Research and Studies00The importance of artificial intelligence lies in its ability to have a significant impact in many sectors around the world, giving companies a significant competitive advantage.International Journal of Law Research and Studies["Farida Rady", "Sally Elsakka"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/f14e4ee605238d7f387e7b4ed067c85a72cdf89e 723ad13ebb912bd3b3380043e4db81492e1feb55This study investigates the emerging field of innovative technology applications for public usage, focusing on employee perspectives. The research employs a questionnaire-based approach, collecting responses from 439 participants and examining demographics, technological proficiency, utility perceptions, personal data concerns, attitudes towards artificial intelligence and generative artificial intelligence, and willingness to endorse technology adoption. The data analysis minimises discrepancies between predicted and actual values through multiple linear regression. In addition, statistical methods such as Spearman’s ρ, the Wilcoxon–Mann–Whitney test and chi-square statistics are employed to consolidate the findings, ensuring the thoroughness and validity of the research process. The results indicate a positive inclination among participants to perceive artificial intelligence as augmentative rather than a replacement in public usage contexts. The research’s originality lies in the unique contribution of employees to technology adoption and strategic knowledge asset renewal for the management in the public domain.Journal of information science670A positive inclination among participants to perceive artificial intelligence as augmentative rather than a replacement in public usage contexts is indicated, indicating a positive inclination to perceive artificial intelligence as augmentative rather than a replacement in public usage contexts.Journal of Information Science["Luca Giraldi", "Luca Rossi", "E. Rudawska"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/723ad13ebb912bd3b3380043e4db81492e1feb55 d407abe76ccffa1590ed3155d0248b27a78e795eThis article presents a comparative analysis of different AI platforms for the same task. Several tests were made - a requirement to generate Python code, generate a text report on the same topic and a request to calculate a value. As a result of an analysis of the returned results, the advantages and disadvantages of each used AI chat have been identified. The main parameters that are the subject of the research described in this paper are the response generation time and the reliability of the returned information. From the research done, it can be concluded that of the investigated chats with artificial intelligence, the fastest in calculating and generating answers is Chat with Ask AI with a time of 6.32 seconds.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)50From the research done, it can be concluded that of the investigated chats with artificial intelligence, the fastest in calculating and generating answers is Chat with Ask AI with a time of 6.32 seconds.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)["G. Spasova", "D. Dinev"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/d407abe76ccffa1590ed3155d0248b27a78e795e 984176a02d8885bd4a319800fc584fe52769a44cWith the widespread application of artificial intelligence technology in the field of film and television production, the production process of film and television content is undergoing profound changes, and the development pattern of film and television production is gradually being reconstructed. However, traditional film and television creative education models are often limited by factors such as teaching resources, teaching methods, and evaluation tools, making it difficult to meet students' personalized learning needs. The rise of artificial intelligence technology has provided new opportunities and solutions for film and television creation. Explore the application of artificial intelligence technology in film and television creative education.Journal of Education, Humanities and Social Sciences00The rise of artificial intelligence technology has provided new opportunities and solutions for film and television creation and traditional film and television creative education models are often limited by factors such as teaching resources, teaching methods, and evaluation tools, making it difficult to meet students' personalized learning needs.Journal of Education, Humanities and Social Sciences["Shu Dong"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/984176a02d8885bd4a319800fc584fe52769a44c a35124b43bd03e9b8cc39a719acaae86479d5953The principle of transparency is of great significance in the governance of text‐based generative artificial intelligence (AI) technology. The principle of transparency not only requires the operational principles and algorithms of text‐based generative artificial intelligence to be interpretable, but also requires text‐based generative artificial intelligence programs to fulfill basic prompting obligations to users, especially when it comes to the output content of generative artificial intelligence, which cannot guarantee true and accurate prompts.The purpose of this study is to explore the classification and frequency of prompt methods in text‐based generative artificial intelligence and to propose that laws should require different prompt rules for various user categories, addressing the current gap in existing regulations.The experiment is conducted from June 1 to 15, 2024 in the school, scientific research company, media organization hall, and railway station lounge, and Kimi program, Tongyi program, ERNIE Bot program, iFLYTEK Spark program, etc., are used as text‐based generative AI programs.The results show that users aged 8‐17 who are minors only have 6 points in their perception of the authenticity of the output content of generative artificial intelligence programs; the level of awareness of possible falsity during use reaches 4 points; the degree of user misleading is as high as 13 points.The study concludes that, for individuals needing special protection, such as minors, prompts should accompany every instance of content generation. For other user groups, prompts should be issued when necessary. To enhance prompt effectiveness, the program should display permanent prompts in prominent positions on the interface, using noticeable fonts and clear, well‐designed wording. Research reveals that minors have insufficient perception of the authenticity of the output content of generative artificial intelligence, and the risk of misleading is significant, highlighting the importance of clear prompts for this specific group every time they generate content.Software: Practice and Experience170Research reveals that minors have insufficient perception of the authenticity of the output content of generative artificial intelligence, and the risk of misleading is significant, highlighting the importance of clear prompts for this specific group every time they generate content.Software: Practice and Experience["Kaigeng Li", "Wechen Jia", "Zhi Li"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/a35124b43bd03e9b8cc39a719acaae86479d5953 7f33f82025601d719d55efd094ad786e9032aa07Objective: To analyze the issue of justice and discrimination in artificial intelligence systems based on medical image databases. Methodology: Analysis of documents that constitute the regulatory framework of the European Union for the use of artificial intelligence, compared with the report FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. Results: The study indicates that artificial intelligence trained with unbalanced data tends to generate biased predictions, which can exacerbate health inequalities and affect justice. Discrimination in artificial intelligence systems appears abstract, subtle, and difficult to detect compared to traditional forms of discrimination. Final Considerations: Robust regulation is necessary to ensure justice in artificial intelligence systems, considering the need for interdisciplinary collaboration to prepare this new generation of legal professionals with an enhanced perspective on the topic and its various dimensions. Submission: 10/01/24| Review: 10/04/24| Approval: 10/04/24Cadernos Ibero-americanos de Direito Sanitário60The study indicates that artificial intelligence trained with unbalanced data tends to generate biased predictions, which can exacerbate health inequalities and affect justice.Cadernos Ibero-Americanos de Direito Sanitário["Valentina Colcelli"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/7f33f82025601d719d55efd094ad786e9032aa07 509c465b42f4b0eda2a6759a2760399d8f384a04Di tengah era digital, pentingnya pendidikan yang dapat mempersiapkan generasi muda untuk tantangan global sangat nyata, terutama dalam konteks pengembangan potensi individu. Banyak siswa yang kurang mendapatkan bimbingan yang tepat, sehingga kemampuan mereka tidak termanfaatkan secara maksimal. Optimalisasi bakat dan minat siswa berbasis sistem pakar di SMPN 16 Pekanbaru bertujuan untuk mengatasi kesenjangan dalam identifikasi dan pengembangan bakat siswa. Dengan menerapkan sistem pakar berbasis Artificial Intelligence (AI), kegiatan ini menawarkan solusi inovatif untuk mendeteksi dan mengarahkan bakat serta minat siswa secara lebih objektif. Kegiatan mencakup seminar edukasi yang mengenalkan konsep bakat dan minat, serta pelatihan praktis dalam penggunaan sistem pakar. Evaluasi efektivitas program menunjukkan peningkatan pemahaman peserta sebesar 77.5%, dengan analisis t-test yang mengindikasikan dampak positif yang signifikan. Temuan ini mempertegas bahwa integrasi teknologi dalam pendidikan sangat penting untuk mendukung pengembangan bakat siswa, sehingga mereka dapat menjadi individu yang lebih percaya diri dan berdaya saing tinggi di masyarakat global.Jurnal Pengabdian UntukMu NegeRI150Jurnal Pengabdian UntukMu NegeRI["Edi Ismanto", "Vitriani", "Ajeng Safitri"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/509c465b42f4b0eda2a6759a2760399d8f384a04 604bb57ee8795a5e319b6215b79369aa5ee78d0eOECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2024-11-20T00:00:00https://www.semanticscholar.org/paper/604bb57ee8795a5e319b6215b79369aa5ee78d0e 6356d9bae8776f9d8ca6b098946189a3d381f0fdLiječnički vjesnik00Liječnički vjesnik[]2024-11-20T00:00:00https://www.semanticscholar.org/paper/6356d9bae8776f9d8ca6b098946189a3d381f0fd 04766b9fbf9ee64f9b8fdf3a4448a50d90d69adbThe use of AI in medicine opens up new opportunities for improving the efficiency and quality of medical care. AI technologies can automate routine tasks, improve diagnostic accu racy, speed up the analysis of medical data, and support doctors in making clinical decisions. One of the most promising areas of AI application in healthcare is clinical laboratory diagnostics.Terapevt (General Physician)10One of the most promising areas of AI application in healthcare is clinical laboratory diagnostics, where AI technologies can automate routine tasks, improve diagnostic tasks, speed up the analysis of medical data, and support doctors in making clinical decisions.Terapevt (General Physician)["A. P. Krylov", "M. V. Sheblaev"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/04766b9fbf9ee64f9b8fdf3a4448a50d90d69adb 1710bdb25d1e1d119edd2c0650853e28787b0df8Annals of Family Medicine00The Annals of Family Medicine["Syed Zabiullah", "V. Zahid", "Esra Ahmed Ibrahim Eltayeb", "Nehal Abdelbaky", "Lina Ahmed", "Syeda Asna Shireen Munawer", "May Eltayeb", "Hanna Ali", "Nadir Abdelrahman"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/1710bdb25d1e1d119edd2c0650853e28787b0df8 771495d600f2c5966cd73c38b4598748c8de7be6Comput. Biol. Medicine1720This paper comprehensively reviews existing integrated solutions involving AI and ILMs in CD, examining their clinical manifestations, epidemiology, diagnostic challenges, and therapeutic considerations, and outlines future research directions.Computers in biology and medicine["Shahadat Hussain", "Shahnawaz Ahmad", "Mohammed Wasid"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/771495d600f2c5966cd73c38b4598748c8de7be6 e049789cc0105cd1b9314ba96775eb0601794cd0ROMANIAN COMPETITION JOURNAL00ROMANIAN COMPETITION JOURNAL["Manta Andreea"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/e049789cc0105cd1b9314ba96775eb0601794cd0 f7649ee973c4f07ef96dd760b9dc6093c47eddafHealth care informatics00Health care informatics["M. Sasseville", "V. Couture", "Jean-S\u00e9bastien Paquette", "Steven Ouellet", "Caroline Rh\u00e9aume", "Marie-Pierre Gagnon", "Malek Sahlia", "Fr\u00e9d\u00e9ric Bergeron"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/f7649ee973c4f07ef96dd760b9dc6093c47eddaf 4a2b6a1315831854426ce8560079eca6106d2dbeInternational Journal of Academic Research in Progressive Education and Development00International Journal of Academic Research in Progressive Education and Development["Basima Ja'ara"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/4a2b6a1315831854426ce8560079eca6106d2dbe 6b4754c203a293c55f1bf17603d9006393e33a3eNeurodata, i.e. data from the examination of human brain activity and the nervous system, can be collected by different neurotechnologies with the use of Artificial Intelligence both in the medical field, from the diagnostic point of view especially through electroencephalography, brain-computer interface, functional magnetic resonance imaging etc., but also in health therapies and rehabilitation activity; in marketing and consumer services (e.g. video games and other entertainment applications), in applications for security purposes, to their use in criminal prosecution or for military purposes. This research attempts to elucidate from a legal point of view the nature and scope of neurodata with special emphasis on the question whether they can be considered as personal data or whether a specific regulation such as the Chilean one is necessary. Submission: 10/01/24| Review: 10/04/24| Approval: 10/04/24Cadernos Ibero-americanos de Direito Sanitário80This research attempts to elucidate from a legal point of view the nature and scope of neurodata with special emphasis on the question whether they can be considered as personal data or whether a specific regulation such as the Chilean one is necessary.Cadernos Ibero-Americanos de Direito Sanitário["Isabel Cornejo-Plaza", "R. Cippitani"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/6b4754c203a293c55f1bf17603d9006393e33a3e 858413f571a3a5c9932afb8fa909443c53483b51The rapid development of information technology, particularly Artificial Intelligence (AI), has significantly impacted various aspects of human life, including education. This study analyses the value alignment and ethical guardianship of AI-powered learning, specifically ChatGPT, in Civic Education. The research aims to assess how AI, particularly ChatGPT, aligns with moral judgments grounded in Pancasila values, a core component of Civic Education in Indonesia. Using a qualitative approach involving document analysis and expert interviews, this study investigates the ethical implications, potential risks, and safety concerns associated with using ChatGPT in educational settings. The findings indicate that while ChatGPT is user-friendly and capable of processing natural language, there are notable issues related to its accuracy, contextual understanding, and ethical considerations. The study concludes that the responsibility for misinformation or ethical breaches remains ambiguous, necessitating a more cautious approach to integrating AI in Civic Education. The implications suggest the need for comprehensive guidelines and frameworks to ensure that AI tools are used responsibly, maintaining alignment with the cultural and moral values of Indonesia.Jurnal Moral Kemasyarakatan00The study concludes that the responsibility for misinformation or ethical breaches remains ambiguous, necessitating a more cautious approach to integrating AI in Civic Education, and suggests the need for comprehensive guidelines and frameworks to ensure that AI tools are used responsibly, maintaining alignment with the cultural and moral values of Indonesia.Jurnal Moral Kemasyarakatan["RirinDwi Agustin", "Subelo Wiyono", "Redi Yamanto"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/858413f571a3a5c9932afb8fa909443c53483b51 6e5f9d9fefe8c736da193899befee0060fb75230Artificial intelligence (AI) has emerged as a pivotal tool in combating financial fraud, offering advanced techniques to detect and mitigate fraudulent activities in real time. By leveraging machine learning algorithms, neural networks, and pattern recognition technologies, AI systems can identify anomalies, assess transaction risks, and predict potential threats with unparalleled accuracy. This research examines the various applications of AI in financial fraud detection, including its role in transactional analysis, user authentication, and predictive modeling. Key areas of focus include the integration of supervised and unsupervised learning methods, the use of natural language processing (NLP) for fraud analysis, and real-time data processing for dynamic threat management. The study also explores the challenges of implementing AI, such as data privacy concerns, algorithmic biases, and the need for regulatory compliance. By analyzing case studies and successful implementations, this research highlights best practices for leveraging AI in financial institutions to ensure robust fraud prevention. The findings underscore AI's transformative potential to enhance security, improve operational efficiency, and build trust in financial ecosystems.Next Generation Journal for The Young Researchers40This research examines the various applications of AI in financial fraud detection, including its role in transactional analysis, user authentication, and predictive modeling, and highlights best practices for leveraging AI in financial institutions to ensure robust fraud prevention.Next Generation Journal for The Young Researchers["Mahammed Gafarov"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/6e5f9d9fefe8c736da193899befee0060fb75230 317d098139eb0eba42314494d22016244a86025bPurposeResearch on training and/or L&D effectiveness is predominantly conducted in a traditional L&D context. Little research is conducted on training and/or L&D in the context of artificial intelligence (AI)-based learning. The present study aims to investigate the relationship between the adoption of AI-based learning systems and learners’ behavior. Drawing from the theory of planned behavior, the research examines the impact of attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC) as AI-based learning intention (ALI) factors relate to changes in learners' behavior. Additionally, inspired by the self-determination theory by Deci and Ryan, the study further examines the mediating role of learner engagement between ALI and behavioral change.Design/methodology/approachFollowing a theoretical framework and using a systematic literature review method, the survey research has been planned by considering a sample from Indian industries. The collected data have been analyzed using SPSS-AMOS 27. While path analysis has been conducted to analyze the direct impact of ALI on learners' behavior, Hay’s PROCESS macro has been used to check the mediating impact of learner engagement between ALI and learners' behavior.FindingsThe results proved a significant and positive impact of all ALI factors such as ATT, SN and PBC on learners’ behavioral change. Further, the research found that learning engagement (LE) successfully mediates between AI learning intention and behavioral change.Originality/valueIn the absence of any empirical study in identifying the relationship among learning intention, LE and behavioral outcome, the result of this study may provide useful insights to researchers and practitioners.Journal of Management Development920The research found that learning engagement (LE) successfully mediates between AI learning intention and behavioral change, and proved a significant and positive impact of all ALI factors such as ATT, SN and PBC on learners’ behavioral change.Journal of Management Development["Parag Bhatt", "Ashutosh Muduli"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/317d098139eb0eba42314494d22016244a86025b 3fb9ba90a32740b6521fd24d3bc4b76f58159711Advancements in computer science, artificial intelligence, and control systems of the recent have catalyzed the emergence of cybernetic societies, where algorithms play a significant role in decision-making processes affecting the daily life of humans in almost every aspect. Algorithmic decision-making expands into almost every industry, government processes critical infrastructure, and shapes the life-reality of people and the very fabric of social interactions and communication. Besides the great potentials to improve efficiency and reduce corruption, missspecified cybernetic systems harbor the threat to create societal inequities, systematic discrimination, and dystopic, totalitarian societies. Fairness is a crucial component in the design of cybernetic systems, to promote cooperation between selfish individuals, to achieve better outcomes at the system level, to confront public resistance, to gain trust and acceptance for rules and institutions, to perforate self-reinforcing cycles of poverty through social mobility, to incentivize motivation, contribution and satisfaction of people through inclusion, to increase social-cohesion in groups, and ultimately to improve life quality. Quantitative descriptions of fairness are crucial to reflect equity into algorithms, but only few works in the fairness literature offer such measures; the existing quantitative measures in the literature are either too application-specific, suffer from undesirable characteristics, or are not ideology-agnostic. Therefore, this work proposes a quantitative, transactional, distributive fairness framework, which enables systematic design of socially feasible decision-making systems. Moreover, it emphasizes the importance of fairness and transparency when designing algorithms for equitable, cybernetic societies.arXiv.org630This work proposes a quantitative, transactional, distributive fairness framework, which enables systematic design of socially feasible decision-making systems and emphasizes the importance of fairness and transparency when designing algorithms for equitable, cybernetic societies.ArXiv["Kevin Riehl", "Michail Makridis", "Anastasios Kouvelas"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/3fb9ba90a32740b6521fd24d3bc4b76f58159711 1caa1dc4fd59f9827267230293340a8c9d55b775Digital War30If AI models of image perception replace ocular-centric ways of seeing, they ask, do these apparatuses have the capacity to not only (pre)define but, in time, further estrange and alienate us from the world?Digital War["Trevor Paglen", "Anthony Downey"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/1caa1dc4fd59f9827267230293340a8c9d55b775 40535b0de222742eb238d318af99c96be1bce8b4Background The application of Artificial Intelligence (AI) in diagnosing retinal diseases represents a significant advancement in ophthalmological research, with the potential to reshape future practices in the field. This study explores the extensive applications and emerging research frontiers of AI in retinal diseases. Objective This study aims to uncover the developments and predict future directions of AI research in retinal disease over the past decade. Methods This study analyzes AI utilization in retinal disease research through articles, using citation data sourced from the Web of Science (WOS) Core Collection database, covering the period from January 1, 2014, to December 31, 2023. A combination of WOS analyzer, CiteSpace 6.2 R4, and VOSviewer 1.6.19 was used for a bibliometric analysis focusing on citation frequency, collaborations, and keyword trends from an expert perspective. Results A total of 2,861 articles across 93 countries or regions were cataloged, with notable growth in article numbers since 2017. China leads with 926 articles, constituting 32% of the total. The United States has the highest h-index at 66, while England has the most significant network centrality at 0.24. Notably, the University of London is the leading institution with 99 articles and shares the highest h-index (25) with University College London. The National University of Singapore stands out for its central role with a score of 0.16. Research primarily spans ophthalmology and computer science, with “network,” “transfer learning,” and “convolutional neural networks” being prominent burst keywords from 2021 to 2023. Conclusion China leads globally in article counts, while the United States has a significant research impact. The University of London and University College London have made significant contributions to the literature. Diabetic retinopathy is the retinal disease with the highest volume of research. AI applications have focused on developing algorithms for diagnosing retinal diseases and investigating abnormal physiological features of the eye. Future research should pivot toward more advanced diagnostic systems for ophthalmic diseases.Frontiers in Medicine1100China leads globally in article counts, while the United States has a significant research impact, and the University of London and University College London have made significant contributions to the literature.Frontiers in Medicine["Mingkai Guo", "Di Gong", "Weihua Yang"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/40535b0de222742eb238d318af99c96be1bce8b4 e4b4890539b7dcf42bcc406b7f8592629d1735f8The escalating complexity and data volume in today’s digital networks demands innovative strategies to enhance performance and efficiency. This survey explores the possibilities of optimization techniques powered by AI to address this critical need. By utilizing artificial intelligence (AI) algorithms, including machine learning and deep learning, the study explores how AI can transform network optimization and management, to achieve superior performance and reliability. The investigation focuses on how AI algorithms can process extensive network data, recognize patterns, and make informed decisions to enhance network configurations and resource allocation methods. The review highlights key findings and insights, emphasizing the revolutionary impact of AI-based optimization for improving network performance and efficiency. It focuses on the advantages of AI-based methods in power control and EE efficiency, resource allocations, user connection, intelligent beam management, and channel estimation, by automating optimization processes, minimizing operational costs, and flexibly adjusting to evolving network conditions and user requirements. Furthermore, the review addresses the concerns and aspects associated with implementing AI-driven optimization techniques. In conclusion, the review underscores the crucial role of AI-driven optimization in tackling the growing complexities of network optimizations. It advocates for continuous exploration and innovation initiatives to fully harness the potential of AI-driven optimization, unlocking enhanced performance and operational efficiency in network systems.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)240The study explores how artificial intelligence algorithms can transform network optimization and management, to achieve superior performance and reliability, by automating optimization processes, minimizing operational costs, and flexibly adjusting to evolving network conditions and user requirements.2024 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES)["Arjola Biti", "Olimpjon Shurdi", "Luan Ru\u00e7i"]2024-11-20T00:00:00https://www.semanticscholar.org/paper/e4b4890539b7dcf42bcc406b7f8592629d1735f8 38949dd75ae11794331759410ef14760ce0a7724Introduction Implementing artificial intelligence (AI) in healthcare, particularly in primary care settings, raises crucial questions about practical challenges and opportunities. This study aimed to explore the perspectives of general practitioners (GPs) on the impact of AI in primary care. Methods A convenience sampling method was employed, involving a hybrid workshop with 12 GPs and 4 GP registrars. Verbal consent was obtained, and the workshop was audio recorded. Thematic analysis was conducted on the recorded data and contemporaneous notes to identify key themes. Results The workshop took place in 2023 and included 16 GPs aged 30 to 72 of diverse backgrounds and expertise. Most (93%) were female, and five (31%) self-identified as ethnic minorities. Thematic analysis identified two key themes related to AI in primary care: the potential benefits (such as help with diagnosis and risk assessment) and the associated concerns and challenges. Sub-themes included anxieties about diagnostic accuracy, AI errors, industry influence, and overcoming integration resistance. GPs also worried about increased workload, particularly extra, unnecessary patient tests, the lack of evidence base for AI programmes or accountability of AI systems and appropriateness of AI algorithms for different population groups. Participants emphasised the importance of transparency, trust-building, and research rigour to evaluate the effectiveness and safety of AI systems in healthcare. Conclusion The findings suggest that GPs recognise the potential of AI in primary care but raise important concerns regarding evidence base, accountability, bias and workload. The participants emphasised the need for rigorous evaluation of AI technologies. Further research and collaboration between healthcare professionals, policymakers, and technology organisations are essential to navigating these challenges and harnessing the full potential of AI.PLoS ONE173The findings suggest that GPs recognise the potential of AI in primary care but raise important concerns regarding evidence base, accountability, bias and workload.PLOS ONE["M. Razai", "Roaa Al-Bedaery", "Liza Bowen", "Reem Yahia", "Lakshmi Chandrasekaran", "Pippa Oakeshott"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/38949dd75ae11794331759410ef14760ce0a7724 74264e98f376b067cc20d1dbe3ac7d233f8d1867Objectives Artificial intelligence (AI)-based health technologies (AIHTs) have already been applied in clinical practice. However, there is currently no standardized framework for evaluating them based on the principles of health technology assessment (HTA). Methods A two-round Delphi survey was distributed to a panel of experts to determine the significance of incorporating topics outlined in the EUnetHTA Core Model and twenty additional ones identified through literature reviews. Each panelist assigned scores to each topic. Topics were categorized as critical to include (scores 7–9), important but not critical (scores 4–6), and not important (scores 1–3). A 70 percent cutoff was used to determine high agreement. Results Our panel of 46 experts indicated that 48 out of the 65 proposed topics are critical and should be included in an HTA framework for AIHTs. Among the ten most crucial topics, the following emerged: accuracy of the AI model (97.78 percent), patient safety (95.65 percent), benefit–harm balance evaluated from an ethical standpoint (95.56 percent), and bias in data (91.30 percent). Importantly, our findings highlight that the Core Model is insufficient in capturing all relevant topics for AI-based technologies, as 14 out of the additional 20 topics were identified as crucial. Conclusion It is imperative to determine the level of agreement on AI-relevant HTA topics to establish a robust assessment framework. This framework will play a foundational role in evaluating AI tools for the early diagnosis of dementia, which is the focus of the European project AI-Mind currently being developed.International Journal of Technology Assessment in Health Care321It is imperative to determine the level of agreement on AI-relevant HTA topics to establish a robust assessment framework for AIHTs, as 14 out of the additional 20 topics identified as crucial were identified as crucial.International Journal of Technology Assessment in Health Care["R. Di Bidino", "Signe Daugbjerg", "Sara C Papavero", "Ira H Haraldsen", "A. Cicchetti", "Dario Sacchini"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/74264e98f376b067cc20d1dbe3ac7d233f8d1867 a17ea8f057cabc05ac5c59f16834ddf403e6b218With rapid technological advancements, the emergence of deepfakes and digital misinformation has become both a powerful tool and a formidable challenge. Deepfakes—realistic yet fabricated media generated through artificial intelligence—threaten media credibility, public perception, and democratic integrity. This study explores the intersection of AI technology with these concerns, highlighting AI's role both as a driver of innovation and as a defense mechanism. By conducting an in-depth review of literature, analyzing current technologies, and examining case studies, this research evaluates AI-based strategies for identifying and addressing misinformation. Additionally, it considers the ethical and policy implications, calling for greater transparency, accountability, and media literacy. Through examining present AI techniques and predicting future trends, this paper underscores the importance of collaborative efforts among tech companies, government agencies, and the public to uphold truth and integrity in the digital age.International Journal of Scientific Research and Modern Technology (IJSRMT)751The importance of collaborative efforts among tech companies, government agencies, and the public to uphold truth and integrity in the digital age is underscored, highlighting AI's role both as a driver of innovation and as a defense mechanism.International Journal of Scientific Research and Modern Technology (IJSRMT)["Chris Gilbert", "Mercy Abiola Gilbert"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/a17ea8f057cabc05ac5c59f16834ddf403e6b218 a606ce16cc73ec7f284ac08e4ddcd8bf77e4774cIn the electronics sector, artificial intelligence (AI) has grown into a disruptive force that is changing how humans engage with technology and creating new opportunities. AI is expanding the capabilities of electronic devices, granting them higher intelligence, increased intuitiveness, and the ability to comprehend and react to human behavior. The purpose of this approach is to highlight the knowledge structure in artificial intelligence application in agriculture and its challenges within the European Union. A bibliometric analysis was conducted, distinguishing the following items as the main research themes: agriculture 4.0; advanced monitoring and controlling strategies in intelligent agriculture; the automation of agriculture by including practices such as cloud computing, Internet of Things (IoT), big data, blockchain, robotics and AI, information security; new skills, and responsible leadership. The regression analysis revealed that the employers’ assumption of responsibility, by ensuring opportunities for training and development of digital skills, determines the growth of added value (0.013) and its rate (0.0003). Enhancing labor productivity depends on Internet access for the integration of technologies based on artificial intelligence (1.343). An increasing employment rate of low-skilled people affects agricultural production (0.0127). The contributions of this two-dimensional approach consist in supporting the integration of digital technology in agriculture as a condition for achieving the goals of sustainable development.Electronics651The regression analysis revealed that the employers’ assumption of responsibility, by ensuring opportunities for training and development of digital skills, determines the growth of added value and its rate, and the contributions of this two-dimensional approach consist in supporting the integration of digital technology in agriculture as a condition for achieving the goals of sustainable development.Electronics["M. Petcu", "M. Sobolevschi-David", "\u0218tefania-Cristina Curea", "Dumitru-Florin Moise"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/a606ce16cc73ec7f284ac08e4ddcd8bf77e4774c 5d5085e88227e2a755df802d2409bcb1e2846ff8OBJECTIVES To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS). MATERIALS AND METHODS This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS. RESULTS The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development. DISCUSSION Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications. CONCLUSION This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.J. Am. Medical Informatics Assoc.271This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.Journal of the American Medical Informatics Association : JAMIA["Laura G Militello", "Julie Diiulio", "Debbie L. Wilson", "Khoa A Nguyen", "Christopher A Harle", "Walid Gellad", "W-H Lo-Ciganic"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/5d5085e88227e2a755df802d2409bcb1e2846ff8 4ea10100958fb8c4e05fc0d933499ae93ec57d15European Radiology392The findings highlight the multifaceted role of imaging in the early stages of SARS, MERS, H1N1, and COVID-19, and outline possible actions for advancing future pandemic preparedness.European radiology["Jennifer Straub", "Enrique Estrada Lobato", "Diana Paez", "Georg Langs", "Helmut Prosch"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/4ea10100958fb8c4e05fc0d933499ae93ec57d15 1aceeb221e531bde739a164710fdd27331d3e539This article investigates the admissibility of artificial intelligence (AI) evidence in criminal proceedings within Mainland Tanzania. As AI technologies increasingly generate data that could be used in legal contexts, questions arise regarding the reliability, transparency, and potential biases inherent in AI-based evidence. The Tanzanian legal framework, including the Evidence Act and the Electronic Transactions Act, lacks explicit provisions for AI-generated evidence, which creates challenges for its integration into criminal cases. This paper explores current admissibility standards in Tanzania, analyzing how AI evidence could be evaluated for relevance and probative value under existing laws. By examining principles such as legal positivism and reliability theory, and drawing on international insights, the article proposes interpretative approaches to assess AI evidence’s validity and reliability in Tanzanian courts. Ultimately, this study seeks to provide insights and recommendations for Tanzanian legal professionals and policymakers, aiming to support the development of clear guidelines for the use of AI in the criminal justice system and ensure that technological advancements uphold procedural fairness and justiceEast African Journal of Law and Ethics00East African Journal of Law and Ethics["Verus Cronery Rwetembula"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/1aceeb221e531bde739a164710fdd27331d3e539 2b2c9ccc7ba6ca8a63bbb863728f59dd93c83da9This study investigates the transformative potential of Artificial Intelligence (AI) and blockchain technologies to enhance tax compliance and revenue generation within public finance. In response to mounting challenges governments worldwide face in maintaining tax compliance and achieving efficient revenue collection, AI and blockchain present promising, forward-looking solutions. Using a qualitative review of secondary data, this paper evaluates the primary applications of AI and blockchain, such as automated compliance monitoring, fraud detection, real-time auditing, and secure transaction processing. The findings suggest that AI's capacity for real-time analysis of vast datasets, coupled with the transparency and immutability provided by blockchain, can significantly elevate tax compliance rates and reinforce public trust in financial systems. Furthermore, integrating AI and blockchain paves the way for innovative policy enforcement and informed decision-making, contributing to a more efficient and transparent public finance system. Nonetheless, substantial challenges persist, including those associated with infrastructure development, regulatory frameworks, and ethical considerations. The paper recommends implementing targeted pilot programs, developing robust regulatory frameworks, enhancing workforce training, and investing in continued research on advanced applications. This study thus lays a foundation for policymakers to leverage the potential of AI and blockchain in transforming public financial management, moving toward a more resilient and transparent future in public finance.Asian Journal of Economics Business and Accounting00The findings suggest that AI's capacity for real-time analysis of vast datasets, coupled with the transparency and immutability provided by blockchain, can significantly elevate tax compliance rates and reinforce public trust in financial systems.Asian Journal of Economics, Business and Accounting["S. Olabanji", "O. O. Olaniyi", "O. Olagbaju"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/2b2c9ccc7ba6ca8a63bbb863728f59dd93c83da9 e7b9992a887b4e1392e936df784e906f1d87de73The integration of artificial intelligence (AI) into orthopaedic care has gained considerable interest in recent years, evidenced by the growing body of literature boasting wide-ranging applications across the perioperative setting. This includes automated diagnostic imaging, clinical decision-making tools, optimisation of implant design, robotic surgery, and remote patient monitoring. Collectively, these advances propose to enhance patient care and improve system efficiency. Musculoskeletal pathologies represent the most significant contributor to global disability, with roughly 1.71 billion people afflicted, leading to an increasing volume of patients awaiting planned orthopaedic surgeries. This has exerted a considerable strain on healthcare systems globally, compounded by both the COVID-19 pandemic and the effects of an ageing population. Subsequently, patients face prolonged waiting times for surgery, with further deterioration and potentially poorer outcomes as a result. Furthermore, incorporating AI technologies into clinical practice could provide a means of addressing current and future service demands. This review aims to present a clear overview of AI applications across preoperative, intraoperative, and postoperative stages to elucidate its potential to transform planned orthopaedic care.SICOT-J720This review aims to present a clear overview of AI applications across preoperative, intraoperative, and postoperative stages to elucidate its potential to transform planned orthopaedic care.SICOT-J["Elena Chiara Thalia Georgiakakis", "Akib Majed Khan", "K. Logishetty", "Khaled Maher Sarraf"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/e7b9992a887b4e1392e936df784e906f1d87de73 9aac4cf3548884276d221850845216108db8a727Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI’s performance in diagnosing and quantitatively phenotyping these disorders. Methods: We searched PubMed and Embase using a semi-automated article-screening pipeline. Results: Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of “low risk of bias” and three had external validation. Conclusion: There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability.Journal of Clinical Medicine670There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability in hyperkinetic movement disorders.Journal of Clinical Medicine["Joaquin A. Vizcarra", "Sushuma Yarlagadda", "Kevin Xie", "Colin A. Ellis", "Meredith Spindler", "Lauren H. Hammer"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/9aac4cf3548884276d221850845216108db8a727 3401df7fe916bec84454afeedf3e51965bee0521With the rapid development of the new generation of artificial intelligence, the creative methods of film and television literature are also undergoing an unprecedented paradigm revolution, with the creative subject shifting from "human" to "human-machine integration". This study suggests that with the continuous deepening of intelligence and the popularity of short videos as a medium, microfilms with brands as the core are becoming a new trend in film and television literature. This type is gradually becoming a new category that connects brands with potential consumers. This article aims to study the application prospects of AIGC technology in assisting film and television creation and analyze the value of intelligent technology in microfilm in conjunction with the Aurora Night · University Micro Film Festival.Highlights in Art and Design50The application prospects of AIGC technology in assisting film and television creation and the value of intelligent technology in microfilm are studied and analyzed in conjunction with the Aurora Night · University Micro Film Festival.Highlights in Art and Design["Yuan Xue"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/3401df7fe916bec84454afeedf3e51965bee0521 9855170663400947ee4ef102db259d0c18fd16d6The sports sector could see a revolution in player performance enhancement and injury prevention, especially with recent developments in artificial intelligence (AI). In order to identify areas for improvement and potential injury risks, this proposed work aims to utilize the potential of AI techniques, such as XGBoost, to evaluate extensive player data, including movement patterns, biomechanics, and physical condition. This strategy is unusual because it combines real-time feedback systems with AI-powered predictive modeling to offer athletes and coaches individualized training advice and early warnings about potential injury risks. Cleaning and normalization were done during the preparation phase to make sure the data was suitable for analysis. Utilizing XGBoost for feature extraction allowed for the identification of critical factors affecting efficiency and injury risk. In sports analytics, the suggested XGBoost-AI system was compared to other AI-based techniques for improving performance and avoiding injuries. The study’s findings show that the XGBoost classifier significantly improves performance accuracy, which may reach 96%. This is in contrast to conventional approaches that depend on subjective evaluations and human data analysis, which can result in errors and inefficiencies. Python is used to implement the suggested work. AI-driven sports analytics platform can improve game strategy, fan engagement, and data-driven decision-making for sports organizations, enhancing competitiveness and sustainability in the sports industry.2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)180The study’s findings show that the XGBoost classifier significantly improves performance accuracy, which may reach 96%.2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)["Tushar Dhar Shukla", "Divya Nimma", "K. S. Pokkuluri", "Syed Najmusaqib", "K.K. Sivakumar", "B. K. Bala"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/9855170663400947ee4ef102db259d0c18fd16d6 2ac412f4b840920f311dcdb7a80b12006cb6824fPharmacogenetics aims to investigate the correlation between patient genetic characteristics and the efficacy of pharmaceutical agents, while concurrently evaluating the risks of adverse reactions. This field of research necessitates the application of complex statistical analysis methodologies, and artificial intelligence (AI) capabilities are increasingly being leveraged for such analyses. AI represents an advanced technology employed to automate the execution of tasks that traditionally demand substantial human intellectual effort. A review of scientific literature on the application of machine learning models in pharmacogenetic research has demonstrated that AI is a highly sophisticated and flexible tool capable of facilitating the widespread implementation of pharmacogenetics in clinical practice. A promising area for the application of AI in pharmacogenetics involves the integration of this technology into tasks related to the analysis, detection, prediction, and support of pharmacogenetic information and decision-making systems. The utilization of deep learning technologies has the potential to expand the understanding of drug pharmacodynamics, indications, and contraindications, which may potentially lead to the updating of educational and methodological literature on pharmacology and substantially advance the quality of patient pharmacotherapy. However, the implementation of AI technologies may be hindered by factors such as a shortage of qualified personnel, ethical disagreements, and complexities in legal regulation of this domain. Nonetheless, the application of AI technologies in pharmacogenetic research demonstrates high effectiveness and expediency, despite the existing challenges.Acta Biomedica Scientia380A promising area for the application of AI in pharmacogenetics involves the integration of this technology into tasks related to the analysis, detection, prediction, and support of pharmacogenetic information and decision-making systems.Acta Biomedica Scientifica["M. Abdullaev", "B. Kantemirova", "O. A. Bashkina", "D. Sychev", "O. V. Ivanchuk", "A. N. Romanova"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/2ac412f4b840920f311dcdb7a80b12006cb6824f 41c983a221fbece167f1c89717c73eeb4da6664bThis paper investigates the transformative role of Artificial Intelligence (AI) in enhancing infrastructure governance and economic outcomes. Through a bibliometric analysis spanning more than two decades of research from 2000 to 2024, the study examines global trends in AI applications within infrastructure projects. The analysis reveals significant research themes across diverse sectors, including urban development, healthcare, and environmental management, highlighting the broad relevance of AI technologies. In urban development, the integration of AI and Internet of Things (IoT) technologies is advancing smart city initiatives by improving infrastructure systems through enhanced data-driven decision-making. In healthcare, AI is revolutionizing patient care, improving diagnostic accuracy, and optimizing treatment strategies. Environmental management is benefiting from AI’s potential to monitor and conserve natural resources, contributing to sustainability and crisis management efforts. The study also explores the synergy between AI and blockchain technology, emphasizing its role in ensuring data security, transparency, and efficiency in various applications. The findings underscore the importance of a multidisciplinary approach in AI research and implementation, advocating for ethical considerations and strong governance frameworks to harness AI’s full potential responsibly.Journal of Infrastructure Policy and Development720The study examines global trends in AI applications within infrastructure projects from 2000 to 2024 and explores the synergy between AI and blockchain technology, emphasizing its role in ensuring data security, transparency, and efficiency in various applications.Journal of Infrastructure, Policy and Development["Alfonso Pellegrino", "Alessandro Stasi"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/41c983a221fbece167f1c89717c73eeb4da6664b c4998e1bcf0c755b22089544978aa8594d3b8b30Uterine cancer identification is a critical task in medical diagnostics since early detection improves patient health. Machine learning (ML) and deep learning (DL) have both showed promise in this discipline as artificial intelligence has advanced. The present paper offers a thorough analysis and comparison of ML and DL methods for uterine cancer detection. Several algorithms, datasets, and assessment criteria have been evaluated in this study in order to ascertain which method performs best. Graphical presentations and tabular comparisons are used to examine the data and show the advantages and disadvantages of each approach. The results conclude that DL based models perform better than typical ML models in terms of accuracy and predictive power, even if both strategies offer advantages.2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC)100The results conclude that DL based models perform better than typical ML models in terms of accuracy and predictive power, even if both strategies offer advantages.2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC)["N. Batra", "Amandeep Kaur", "Sonali Goyal", "Divya Nimma"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/c4998e1bcf0c755b22089544978aa8594d3b8b30 572a1b999b1289356541e929d986d7480284febfThe COVID-19 pandemic, which started in late 2019, caused a global health emergency that required the rapid development of vaccines. It also emphasized the important role patents play in both encouraging innovation and ensuring access to these crucial developments. This study examines the intersection of artificial intelligence (AI) and patent systems during the pandemic, focusing on how AI subfields like Natural Language Processing (NLP) and Machine Learning (ML) can enhance patent detection, analysis, and the overall patent application process. The paper explores traditional patent requirements, the challenges faced during pandemics, and the potential of AI to revolutionize the management of Intellectual Property (IP) in vaccine development. Case studies on COVID-19 vaccine patents reveal the tension between IP protection and equitable access to life-saving technologies. Additionally, the study discusses the legal and ethical considerations of integrating AI in patent systems, the importance of global collaboration, and the need for adaptive IP policies in response to health crises. The paper concludes by addressing the challenges and limitations of AI in patent management and outlines future research directions to optimize AI's role in fostering innovation while ensuring fair and transparent patent processes.2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)210This study examines the intersection of artificial intelligence (AI) and patent systems during the pandemic, focusing on how AI subfields like Natural Language Processing (NLP) and Machine Learning (ML) can enhance patent detection, analysis, and the overall patent application process.2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)["Richika", "Ruchika Sharma", "Nandan Sharma"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/572a1b999b1289356541e929d986d7480284febf 97794471bd94bcf1efccc7fc885b9d15f8610873The use of artificial intelligence (AI) in operational decision‐making is growing, but individuals can display algorithm aversion, preventing adherence to AI system recommendations—even when the system outperforms human decision‐makers. Understanding why such algorithm aversion occurs and how to reduce it is important to ensure AI is fully leveraged. While the ability to seek an explanation from an AI may be a promising approach to mitigate this aversion, there is conflicting evidence on their benefits. Based on several behavioral theories, including Bayesian choice, loss aversion, and sunk cost avoidance, we hypothesize that if a recommendation is perceived as an anomalous loss, it will decrease recommendation adherence; however, the effect will be mediated by explanations and differ depending on whether the advisor providing the recommendation and explanation is a human or an AI. We conducted a survey‐based lab experiment set in the online rental market space and found that presenting a recommendation as a loss anomaly significantly reduces adherence compared to presenting it as a gain, however, this negative effect can be dampened if the advisor is an AI. We find explanation‐seeking has a limited impact on adherence, even after considering the influence of the advisor; we discuss the managerial and theoretical implications of these findings.Decision Sciences510It is found that presenting a recommendation as a loss anomaly significantly reduces adherence compared to presenting it as a gain, however, this negative effect can be dampened if the advisor is an AI.Decis. Sci.["Tracy Jenkin", "Stephanie Kelley", "Anton Ovchinnikov", "Cecilia Ying"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/97794471bd94bcf1efccc7fc885b9d15f8610873 b0f6aaece49a3d9c5562d9cbb9380bd897911541This thesis examines the role of artificial intelligence (AI), specifically AI-based predictive analytics, in enhancing decision-making within the framework of Business Process Management (BPM). As organizations strive for increased efficiency and adaptability in their processes, predictive analytics has emerged as a key tool that empowers businesses to make more informed decisions. By leveraging AI models such as ChatGPT, Gemini AI, and others, companies can analyze vast amounts of historical and real-time data to forecast trends, optimize resource allocation, and mitigate risks in their operations. Predictive analytics, driven by AI, is revolutionizing how BPM is approached. The ability to anticipate potential future events based on data analysis allows businesses to proactively adjust workflows, schedules, and resource use. This shift leads to higher productivity, reduced operational costs, and more agile responses to market dynamics. AI models are particularly effective in analyzing large datasets that would be too complex or time-consuming for human analysts, thus enhancing the speed and accuracy of decision-making. This thesis also delves into the underlying algorithms and machine learning techniques used by AI models to generate predictive insights, including regression analysis, neural networks, and decision trees. It explores the integration of AI-based predictive analytics into existing BPM systems and examines its implications for both operational and strategic decision-making. Furthermore, the work addresses challenges such as data quality, integration complexity, and the need for continuous model training to maintain high prediction accuracy. The research underscores how predictive analytics powered by AI can transform business operations, especially in areas like supply chain management, customer relationship management, and financial forecasting. Additionally, the thesis considers the future potential of AI in BPM, particularly how predictive models might evolve to become more autonomous and adaptive over time, ultimately leading to smarter, self-optimizing business processes.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)00The research underscores how predictive analytics powered by AI can transform business operations, especially in areas like supply chain management, customer relationship management, and financial forecasting, especially in areas like supply chain management, customer relationship management, and financial forecasting.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)["Yurii Horchuk", "Mariia Yukhimchuk", "Volodymyr Dubovoy"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/b0f6aaece49a3d9c5562d9cbb9380bd897911541 375c6d4661e6dfb8d4d91ad2127f8a96a683e244BMC Public Health230Tailoring the tool to distinct user segments could enhance its uptake and effectiveness in promoting early detection and prevention of HIV and STIs.BMC Public Health["P. M. Latt", "N. N. Soe", "Alicia J King", "David Lee", "T. Phillips", "Xian-hui Xu", "Eric P. F. Chow", "Christopher K. Fairley", "Lei Zhang", "Jason J Ong"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/375c6d4661e6dfb8d4d91ad2127f8a96a683e244 8a11bca609699eba85a99e6ffe439e6106c5d4f4In a recent paper, Hong et al developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random forest models significantly improved the preoperative prediction and patient outcome management accuracy. By incorporating data from multiple centers, their model ensures standardization, reliability, and broad applicability, distinguishing it from the prior models. The present study highlights AI's potential in clinical decision support, aiding in the preoperative and postoperative management of gastric cancer patients. Our findings may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.World Journal of Gastroenterology50The present study highlights AI's potential in clinical decision support, aiding in the preoperative and postoperative management of gastric cancer patients, and may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.World Journal of Gastroenterology["Hongniu Wang", "Jia-Hao An", "Liang Zong"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/8a11bca609699eba85a99e6ffe439e6106c5d4f4 981784e47f505f190c5eeb75370191c8697a14eeArtificial intelligence (AI) has rapidly transformed numerous industries, including medicine, with radiography standing to benefit significantly from its capabilities. AI enhances diagnostic accuracy, reduces errors, and improves patient care by leveraging large datasets from digital radiographs commonly used in medical and dental practices. Despite these advantages, the impact of AI on image acquisition and radiographer workflows remains underexplored in radiography literature. This review aims to evaluate the effects of AI on radiographic practices, address regulatory challenges, and explore its integration into educational frameworks for radiologists and radiographers. It highlights AI's role in automating tasks, enhancing diagnostic precision, and improving clinical decision-making. A systematic literature search was conducted using PubMed and Google Scholar up to December 2024, with terms including "artificial intelligence," "machine learning," "deep learning," "radiography," and "diagnostic imaging." Seventy-seven peer-reviewed articles and conference papers focusing on AI applications in digital dental radiography were analyzed to extract data on AI methodologies and their potential applications. The findings reveal that AI-powered solutions enhance efficiency in complex imaging tasks, such as lesion identification and triage in mammography, and real-time assessments in cross-sectional imaging, reducing the need for re-scans and increasing patient throughput. However, widespread adoption faces obstacles related to ethical and legal concerns, including data privacy, algorithmic bias, and the need for transparency. While AI demonstrates significant potential to automate workflows, improve diagnostic accuracy, and optimize patient care in radiography, challenges related to human oversight, professional adaptation, and regulatory compliance must be addressed. Further research is needed to fully understand AI’s impact on radiography and to maximize its clinical utility.Journal of Current Health Sciences700Evaluating the effects of AI on radiographic practices, address regulatory challenges, and explore its integration into educational frameworks for radiologists and radiographers reveals that AI-powered solutions enhance efficiency in complex imaging tasks, including lesion identification and triage in mammography, and real-time assessments in cross-sectional imaging.Journal of Current Health Sciences["Rezwan Ahmed Mahedi", "Hrishik Iqbal", "Raiyan Azmee", "Marzan Azmee", "Fatiha Jakir", "Mufassir Ahmad Nishan", "Mohammed Burhan Uddin", "Sadia Afrin"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/981784e47f505f190c5eeb75370191c8697a14ee dba359aa99c1d9bac33380e8ccc5217c603daeec Artificial intelligence (AI) is reshaping how humans obtain information about environmental challenges. Yet the outputs of AI chatbots contain biases that affect how humans view these challenges. Here, we use qualitative and quantitative content analysis to identify bias in AI chatbot characterizations of the issues, causes, consequences, and solutions to environmental challenges. By manually coding an original dataset of 1512 chatbot responses across multiple environmental challenges and chatbots, we identify a number of overlapping areas of bias. Most notably, chatbots are prone to proposing incremental solutions to environmental challenges that draw heavily on past experience and avoid more radical changes to existing economic, social, and political systems. We also find that chatbots are reluctant to assign accountability to investors and avoid associating environmental challenges with broader social justice issues. These findings present new dimensions of bias in AI and auger towards a more critical treatment of AI’s hidden environmental impacts.Environmental Research Letters00Qualitative and quantitative content analysis is used to identify bias in AI chatbot characterizations of the issues, causes, consequences, and solutions to environmental challenges and finds that chatbots are reluctant to assign accountability to investors and avoid associating environmental challenges with broader social justice issues.Environmental Research Letters["Hamish van der Ven", "Diego Corry", "Rawie Elnur", "Viola Jasmine Provost", "Muh Syukron", "Niklas Tappauf"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/dba359aa99c1d9bac33380e8ccc5217c603daeec 756c9afbdd9c7e317be045c39e1c81f53d21411fThe integration of artificial intelligence into the logistics industry is a rapidly evolving field with the potential to revolutionize the way goods are transported and managed. Artificial intelligence can be used to optimize a wide range of logistics processes, from demand forecasting and route planning to warehouse management and customer service. However, the integration of artificial intelligence also raises a number of technical and ethical issues that need to be addressed to ensure its successful implementation. Choosing the right artificial intelligence algorithms for specific logistics tasks is crucial to ensure their efficiency and accuracy. This requires careful consideration of factors such as data type, task complexity, and desired performance metrics. The growing amount of data collected and processed by artificial intelligence systems raises concerns about data security and privacy. Companies need to implement robust security measures to protect sensitive data from unauthorized access, breaches, and misuse. The use of artificial intelligence in logistics raises ethical issues related to bias, transparency, and accountability. Artificial intelligence algorithms should be developed and used fairly, transparently, and with respect for the right to privacy and in compliance with all relevant laws and regulations. In order to eliminate or prevent these problems, recommendations for the effective implementation of artificial intelligence in the logistics sector have been developed and formulated. They include aspects that need to be addressed in the first place when developing mechanisms for automating logistics processes. The integration of artificial intelligence into logistics offers significant opportunities to increase efficiency, reduce costs and improve customer service. However, it is crucial to address the technical and ethical challenges associated with artificial intelligence integration to ensure that it is used responsibly and beneficially. By following the recommendations, logistics companies can successfully use artificial intelligence to transform their operations and achieve their strategic goals.Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì00Recommendations for the effective implementation of artificial intelligence in the logistics sector have been developed and formulated and include aspects that need to be addressed in the first place when developing mechanisms for automating logistics processes.Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì["Yevhen Burov", "Andrii Kuliavets"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/756c9afbdd9c7e317be045c39e1c81f53d21411f c51bab66508ef0f90e452bbc294ea0f34f47478dThe significance of ethical considerations has been emphasised by management academics and professionals. Nevertheless, there hasn't been much work done till date to theoretically comprehend people's or organisations' ethical stances with regard to Human Resource Management (HRM) decision-making procedures, the choice of particular ethical stances and tactics, or the post-decision accounting for those choices. In order to achieve this, we offer a framework for a throughput model that characterizes people's decision-making in the context of algorithmic HRM. The model illustrates how perceptions, assessments, and information utilisation influence the choice of strategy, showing how the application of specific ethical decision-making algorithmic pathways may enable a variety of tactics. This study sheds light on the various issues regarding the effects and acceptability of integrating artificial intelligence (AI) with human resource management. This research uses multidisciplinary theoretical lenses, including AI-augmented HRM assimilation processes, AI-mediated social exchange, and the judgement and choice literature, to address issues regarding the impact and acceptance of artificial intelligence (AI) integration with HRM. It is suggested that they are of crucial significance in HRM strategy selection and drawing attention to the use of algorithmic ethical perspectives in the adoption of AI for improved HRM outcomes as far as accountability and intelligibility of AI-generated HRM decision-making are concerned.2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)250This research uses multidisciplinary theoretical lenses, including AI-augmented HRM assimilation processes, AI-mediated social exchange, and the judgement and choice literature, to address issues regarding the impact and acceptance of artificial intelligence integration with HRM.2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)["Ravinder Kaur", "Deena Nath Gupta", "Manisha Mittal", "Rohit Anand"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/c51bab66508ef0f90e452bbc294ea0f34f47478d a38dc29ecd99a0ef8c82215cb4ceab1bc398031dArtificial intelligence (AI) is one of the most important and transformative technologies of our time, with potential applications in the field of scientific research. The advancement of management studies can benefit from the adoption of tools and methodologies based on AI. In this article, we argue that the use of AI-based tools for the development of scientific contributions in the field of management studies entails opportunities but also risks in the absence of an ethical approach, closely related to the intention to offer effective contributions to scientific advancement.Symphonya Emerging Issues in Management00It is argued that the use of AI-based tools for the development of scientific contributions in the field of management studies entails opportunities but also risks in the absence of an ethical approach, closely related to the intention to offer effective contributions to scientific advancement.Symphonya. Emerging Issues in Management["Daniela M. Salvioni", "Alex Almici"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/a38dc29ecd99a0ef8c82215cb4ceab1bc398031d 1223ea1c3fce72f5239cf888155bcd39709e1738Artificial intelligence (AI) is leading the charge for a more sustainable future by providing innovative answers to some of the world's most critical environmental problems. Ecosystem monitoring, endangered species protection, and natural resource management are all areas where artificial intelligence (AI) can play a significant role in environmental conservation. Exploring artificial intelligence (AI) for environmental conservation and monitoring, as well as the technology's positive effects on the environment, such as reducing greenhouse gas emissions, improving agricultural practices, maintaining healthy oceans, managing water resources, predicting the future, and building catastrophe resilience. Society is undergoing an abrupt shift in its approach to environmental management and effect mitigation with the incorporation of Artificial Intelligence (AI) into sustainable environmental practices. The use of artificial intelligence (AI) in this field is being more acknowledged to improve resource management, decrease waste, and conserve energy while also serving as a driver of innovation. This paper will depict how artificial intelligence helps in controlling environmental pollution with future innovations in artificial intelligence in environmental conservation.2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)310This paper will depict how artificial intelligence helps in controlling environmental pollution with future innovations in artificial intelligence in environmental conservation.2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)["Prernaa Sharma", "Avnish Chauhan", "Monu Bhardwaj", "Utkarsh Verma", "Prabhat Sharma"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/1223ea1c3fce72f5239cf888155bcd39709e1738 85aca5ea6232c7d70847e9d3d3d96e9d66c96805The author analyzes the key areas of application of artificial intelligence technologies in the energy sector. The main priorities for the application of modern technologies in energy supply systems were highlighted. We have also AI assistance with errors caused by human factors was studied. Separately, the possible use of artificial intelligence was considered application of artificial intelligence to assess the technical condition of a power transformer based on AI. The development of an AI-based serviceability index (SI) model is presented. The proposed method is aimed at simplifying, accelerating, and reducing errors. In the proposed approach, artificial intelligence evaluates the insulation system of power transformers based on oil quality, chromatographic dissolved gas analysis (DGA), and the condition of the paper insulation. The report also highlighted the challenges that complicating the rapid implementation of artificial intelligence.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)00The author analyzes the key areas of application of artificial intelligence technologies in the energy sector and proposes an AI-based serviceability index (SI) model aimed at simplifying, accelerating, and reducing errors.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)["Viktor Zakharchenko", "Volodymyr Netrebskyi", "Yaroslav Taraniuk", "Sergii Shevchuk"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/85aca5ea6232c7d70847e9d3d3d96e9d66c96805 4decf80cba8642f6263c3308b993db6606c214baThis study provides a comprehensive overview of current trends in the application of artificial intelligence (AI) in endoscopic examinations of the gastrointestinal tract (GIT). AI significantly enhances medical diagnostic capabilities, particularly in detecting early signs of oncological and other diseases of the GIT. The analysis highlights the advantages and disadvantages of AI as an auxiliary tool for gastroenterologists, capable of improving examination 4 accuracy and reducing the risk of missing pathologies that are difficult to recognize during the procedure. Particular attention is given to the use of convolutional neural networks (CNN), which demonstrate significant results in image analysis and enhancement. The research discusses two primary approaches in endoscopy: classical and capsule endoscopy. Capsule endoscopy allows for thorough video analysis and is an effective tool for detecting lesions in hard-to-reach areas of the GIT, although its efficacy may depend on the quality of video, type of endoscope, camera, and algorithms used for data processing. Classical endoscopy, on the other hand, remains a widely used method that provides the opportunity for direct examination of the mucosal surface and allows specialists to utilize various image enhancement techniques to identify subtle changes. Studies indicate that the application of AI in classical endoscopy can achieve accuracy rates of up to 98% in identifying potentially hazardous areas, including the specific differentiation of lesion depths. Additionally, this work focuses on the challenges facing AI in endoscopy, particularly image artifacts that may negatively impact analysis results, as well as ethical considerations and data privacy compliance in medical data handling. Despite existing challenges, AI demonstrates significant potential as an assistant for both novice and experienced specialists, enabling the minimization of error risks and improving early disease detection. Overall, the findings indicate that artificial intelligence holds great promise in medicine and can significantly enhance the quality of endoscopic procedures, especially for early-stage diagnosis.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)00Overall, the findings indicate that artificial intelligence holds great promise in medicine and can significantly enhance the quality of endoscopic procedures, especially for early-stage diagnosis.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)["Yurii Poudanien", "Andriy Kozhemiako"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/4decf80cba8642f6263c3308b993db6606c214ba 6b6ad83362a908f1b591153a91c3f9f26262186eArtificial intelligence technology, as the core driving force of the new round of enterprise change, will bring significant changes to the economic market while creating new productivity and new enterprise models. And it is worthwhile to explore and study whether high-tech enterprises, as the key force of the market economy, can screen favorable factors, focus on opportunity development, and realize the improvement of innovation performance for them in the complex market environment through the introduction of artificial intelligence. The article combs through the existing research results on AI technology, enterprise innovation performance and the relationship between the two, and takes KDDI, a listed company of AI in high-tech enterprises, as an example to explore the effect of the intensity of adoption of AI technology on the enhancement of the innovation performance of high-tech enterprises. Finally, in view of the gaps existing in the existing research, the future research direction of artificial intelligence is looked forward to.2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)350The article takes KDDI, a listed company of AI in high-tech enterprises, as an example to explore the effect of the intensity of adoption of AI technology on the enhancement of the innovation performance of high-tech enterprises.2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)["Jiahui Song"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/6b6ad83362a908f1b591153a91c3f9f26262186e ac53ffd5d2d1189bdff65001e2e42216cb175b49Anesthesia and Analgesia62Anesthesia and analgesia["E. Bignami", "Federico Semeraro", "Valentina Bellini", "Marco Cascella"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/ac53ffd5d2d1189bdff65001e2e42216cb175b49 98af21a52ff99afab99333b7749df1a319c83343npj Cardiovascular Health351An AI health equity framework is presented and bias mitigation strategies that can be adopted during the AI lifecycle are reviewed, to discuss the sources and consequences of AI bias in CVD prediction or detection.npj Cardiovascular Health["Ariana Mihan", "Ambarish Pandey", "H. V. Van Spall"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/98af21a52ff99afab99333b7749df1a319c83343 03d0c9e4cb24ffc9c4fdcd039cb2f66511cb97f3Journal of Student Affairs Research and Practice160Journal of Student Affairs Research and Practice["Andrea M. Barrett", "Meghan Plate", "Raffa\u011blla Borasi", "Nathan F. Harris"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/03d0c9e4cb24ffc9c4fdcd039cb2f66511cb97f3 796ef634928abd2ecac5b750ac7c0a203fe2fe0dInternational Conference on Intelligent Information Processing50{"pages": "96-100"}["Sheng Yan", "Linjun Liu", "Comite Ubaldo"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/796ef634928abd2ecac5b750ac7c0a203fe2fe0d 128c262e2f3d98b45dbe501248d7dd244080e8cdThis paper reviews literature and case studies to explore the systematic applications of AI in intellectual property, including patent search, IP management, infringement detection, legal protection, and IP evaluation. It also analyzes specific cases to assess the effectiveness of current AI technologies in IP, highlighting their advantages, disadvantages, and areas for future improvement.2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)2002024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)["Jiahui Yang"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/128c262e2f3d98b45dbe501248d7dd244080e8cd 83053d56cf2860055117f83cc0c99cb44d7f0bcfJournal of Convergence in Technology and Management: Global Nexus00Journal of Convergence in Technology and Management: Global Nexus["Suvendu Narayan Roy"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/83053d56cf2860055117f83cc0c99cb44d7f0bcf 2bf1a694fe49392c5ed143a6a93dfe908ef946b6American Journal of Nursing Research00American Journal of Nursing Research["A. Abualrahi", "Sakna Habobi", "Shereen Almutar", "Farha Al-Khwaildi", "Maryam Alalq", "Rouqayah bomurah", "Zainab Abdrabalnabi", "Eman Al-habib", "Fatimah Al-Mahdi", "Fatimah Al-Sadah"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/2bf1a694fe49392c5ed143a6a93dfe908ef946b6 bb4ae968701955d6350014fb6b96b3cbeeac7e6bRevista Electronica De Veterinaria00Revista Electronica De Veterinaria["Dr. Pandab Hansda"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/bb4ae968701955d6350014fb6b96b3cbeeac7e6b 235edd804b2321a6e21a62c262079d6c8220243aAutism spectrum disorder (ASD) is traditionally diagnosed through clinical observation and standardized tests, processes that are time-consuming and require expert intervention. Early detection is crucial for effective intervention, yet current methods often delay diagnosis, particularly in resource-limited settings. This study presents a scalable, non-invasive method for early autism detection using standard webcam technology to measure biomarkers associated with ASD. The system captures and analyzes eye-tracking data, head movements, and behavioral responses during a controlled 4 -minute video presentation. Data is processed through a series of carefully selected machine learning models, with the most advanced model—utilizing a Convolutional Neural Network (CNN) with ResNet layers—achieving an 91% accuracy rate. Our pilot study, involving 147 children, resulted in the creation of a proprietary dataset, supporting the robustness of this method.2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)330This study presents a scalable, non-invasive method for early autism detection using standard webcam technology to measure biomarkers associated with ASD, achieving an 91% accuracy rate.2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)["Raksheet Jain", "Divyansh Mangal"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/235edd804b2321a6e21a62c262079d6c8220243a aa19e7823fd02d99b95a7b31aa928c8e2801bb59Medical Teacher10Medical teacher["M. Akbarilakeh"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/aa19e7823fd02d99b95a7b31aa928c8e2801bb59 e3f96c15f85d4d36541977d1dd09f8e2268d77b0In the current paper, we will focus on requirements to ensure big data can advance the outcomes of our patients suffering from kidney disease. The associated ethical question is whether and how we as a nephrology community can and should encourage the collection of big data of our patients. We identify some ethical reflections on the use of big data, and their importance and relevance. Furthermore, we balance advantages and pitfalls and discuss requirements to make legitimate and ethical use of big data possible. The collection, organization and curation of data come upfront in the pipeline prior to any analyses. Great care must therefore be taken to ensure quality of the data at this stage, to avoid the garbage in garbage out problem and suboptimal patient care as a consequence of such analyses. Access to the data should be organized so that correct and efficient use of data is possible. This means that data must be stored safely, so that only those entitled to do so can access them. At the same time, those who are entitled to access the data should be able to do so in an efficient way, so as not to hinder relevant research. Analysis of observational data is itself prone to many errors and biases. Each of these biases can finally result in provision of low-quality medical care. Secure platforms should therefore also ensure correct methodology is used to interpret the available data. This requires close collaboration of a skilled workforce of experts in medical research and data scientists. Only then will our patients be able to benefit fully from the potential of AI and big data.Nephrology, Dialysis and Transplantation00Requirements to ensure big data can advance the outcomes of patients suffering from kidney disease are focused on and some ethical reflections on the use of big data are identified.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association["W. van Biesen", "J. Ponikvar", "Monica Fontana", "Peter Heering", "Mehmet S Sever", "Simon Sawhney", "Valerie A. Luyckx"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/e3f96c15f85d4d36541977d1dd09f8e2268d77b0 3d5ad0fa1020a8e46a8e92700bfdc7626aa6147aTidsskrift for Den Norske Laegeforening00Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke["Damoun Nassehi", "Michael Riegler"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/3d5ad0fa1020a8e46a8e92700bfdc7626aa6147a c2bf41d3a8d2a6457539cd049a002d0ee2e87cc2The paper analyses current trends in interior design. It is shown that the most promising way to take into account the personalized requirements of customers and the variety of interior objects is the development of automated systems. This will allow to increase work efficiency by reducing the burden on designers, reducing task completion time, and allowing to make changes to projects more quickly. For the operation of such automated system, it is necessary to develop appropriate computer-aided interior design tools and human-machine interface software for inputting data and saving results. Based on the 3D-FRONT dataset, the ATISS neural network was configured for the task of automated design of kitchens and living rooms, taking into account the style of the rooms and the number of things. A web application for interaction with the user has been developed, which consists of a server part on FastAPI and a front-end part on Gradio. The user can upload photos of rooms to generate interiors and get a 3D model of the room as a result.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)00It is shown that the most promising way to take into account the personalized requirements of customers and the variety of interior objects is the development of automated systems, which will allow to increase work efficiency by reducing the burden on designers, reducing task completion time, and allowing to make changes to projects more quickly.Proceedings of XVII International Conference Measurement and Control in Complex System (MCCS-2024)["D. Kovaliuk", "O. Kovaliuk", "Vadym Malitskyi"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/c2bf41d3a8d2a6457539cd049a002d0ee2e87cc2 9b0da42aa9080ec1f0a63c251c1e49010db9ea6eAmerican Journal of Public Health230American journal of public health["Georges Hattab", "C. Irrgang", "Nils K\u00f6rber", "Denise K\u00fchnert", "Katharina Ladewig"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/9b0da42aa9080ec1f0a63c251c1e49010db9ea6e 81586f2fb37dcca44614cd49852710b6d61ba8feUMKM Industri Kreatif (Ikraf) memiliki peranan yang sangat strategis dalam perekonomian era digital. Literasi digital para pelaku UMKM Ikraf saat ini masih rendah. Apalagi terkait dengan kemampuan menggunakan teknologi Artificial Intelligence (AI) yang makin meningkat peranannya didalam bisnis terutama untuk memperkuat strategi pemasarannya sehingga mereka mampu Go Digital dan Go Export. Program ini bertujuan untuk meningkatkan kompetensi baik baik itu knowledge, skill dan attitude pemanfaatan Artificial Intelligence di dalam strategi pemasarannya para pelaku UMKM Ikraf di kelurahan Teladan Kecamatan Toboali Kabupaten Bangka Selatan. Pengabdian ini bekerja sama dengan Kelurahan Teladan yang memiliki 15 UMKM Ikraf yang potensial untuk naik kelas. Pelaksanaan kegiatan ini masih berlangsung selama dari tanggal 15 Maret 2024 hingga tanggal 15 Oktober 2024. Adapun pelaksanaan kegiatan pengabdian masyarakat dilaksanakan dengan melakukan survey, pemberian materi terkait literasi digital dan pemanfaatan AI serta peluang ekspor. Selanjutnya dilakukan pendampingan dan juga kegiatan pelatihan.Jurnal Abdi Insani00Jurnal Abdi Insani["Reniati Reniati", "Abu Nizarudin", "Alim Bahri"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/81586f2fb37dcca44614cd49852710b6d61ba8fe 94e0c5e36ce360a37a8510f058c77715fdbaa0c5The enormous complexity of political decisions, especially with regard to crisis situations, requires innovative concepts for decision support. The focus here is always on people’s well-being. Artificial societies based on agent-based simulation models are a fairly new, forward-looking paradigm for this. Digital twins, on the contrary, are one of the most promising enabling technologies for realizing a seamless integration between the virtual and the physical or biological world. In this paper, we propose an architecture that combines these two concepts to develop a research agenda to address research topics that will allow us to provide more effective decision support systems. We reflect on the current state of development in this area and formulate possible future research directions. A particular focus here is on integrating generative artificial intelligence (AI) methods and supporting cross-disciplinary collaboration, as decision support in the political environment is a highly cross-domain task. We conclude this article with a call for action to gain experience with the proposed architecture. We hope that this will encourage greater cross-disciplinary exchange for this important task.Simulation (San Diego, Calif.)370This paper proposes an architecture that combines generative artificial intelligence methods and supporting cross-disciplinary collaboration, as decision support in the political environment is a highly cross-domain task.SIMULATION["T. Clemen", "Andreas Tolk", "Ulfia A. Clemen", "Daniel Glake", "Gerrit G\u00fcnther"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/94e0c5e36ce360a37a8510f058c77715fdbaa0c5 4973539573a07696dce191181ba329d9d63fb22fBiological intelligence is inherently adaptive -- animals continually adjust their actions based on environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop"adaptive intelligence,"defined here as harnessing insights from biological intelligence to build agents that can learn online, generalize, and rapidly adapt to changes in their environment. Recent advances in neuroscience offer inspiration through studies that increasingly focus on how animals naturally learn and adapt their world models. In this Perspective, I will review the behavioral and neural foundations of adaptive biological intelligence, the parallel progress in AI, and explore brain-inspired approaches for building more adaptive algorithms.arXiv.org00This Perspective will review the behavioral and neural foundations of adaptive biological intelligence, the parallel progress in AI, and explore brain-inspired approaches for building more adaptive algorithms.ArXiv["Mackenzie W. Mathis"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/4973539573a07696dce191181ba329d9d63fb22f 44804b6fffe61389f3ac96ae471eebfdd240b6acWe address an open problem in the philosophy of artificial intelligence (AI): how to justify the epistemic attitudes we have towards the trustworthiness of AI systems. The problem is important, as providing reasons to believe that AI systems are worthy of trust is key to appropriately rely on these systems in human-AI interactions. In our approach, we consider the trustworthiness of an AI as a time-relative, composite property of the system with two distinct facets. One is the actual trustworthiness of the AI and the other is the perceived trustworthiness of the system as assessed by its users while interacting with it. We show that credences, namely, beliefs we hold with a degree of confidence, are the appropriate attitude for capturing the facets of the trustworthiness of an AI over time. Then, we introduce a reliabilistic account providing justification to the credences in the trustworthiness of AI, which we derive from Tang’s probabilistic theory of justified credence. Our account stipulates that a credence in the trustworthiness of an AI system is justified if and only if it is caused by an assessment process that tends to result in a high proportion of credences for which the actual and perceived trustworthiness of the AI are calibrated. This approach informs research on the ethics of AI and human-AI interactions by providing actionable recommendations on how to measure the reliability of the process through which users perceive the trustworthiness of the system, investigating its calibration to the actual levels of trustworthiness of the AI as well as users’ appropriate reliance on the system.Science and Engineering Ethics693This approach informs research on the ethics of AI and human-AI interactions by providing actionable recommendations on how to measure the reliability of the process through which users perceive the trustworthiness of the system, investigating its calibration to the actual levels of trustworthiness of the AI as well as users’ appropriate reliance on the system.Science and Engineering Ethics["Andrea Ferrario"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/44804b6fffe61389f3ac96ae471eebfdd240b6ac deab69a8a7744bf9fd38f883587732e44845fe70npj Digit. Medicine123Can healthcare learn from the failures of electronic health records to maximize the potential of artificial intelligence?NPJ Digital Medicine["Christian Rose", "Jonathan H. Chen"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/deab69a8a7744bf9fd38f883587732e44845fe70 c9bec2549e3b84497027417d20567574d70c9d21The integrity of global elections is increasingly under threat from artificial intelligence (AI) technologies. As AI continues to permeate various aspects of society, its influence on political processes and elections has become a critical area of concern. This is because AI language models are far from neutral or objective; they inherit biases from their training data and the individuals who design and utilize them, which can sway voter decisions and affect global elections and democracy. In this research paper, we explore how AI can directly impact election outcomes through various techniques. These include the use of generative AI for disseminating false political information, favoring certain parties over others, and creating fake narratives, content, images, videos, and voice clones to undermine opposition. We highlight how AI threats can influence voter behavior and election outcomes, focusing on critical areas, including political polarization, deepfakes, disinformation, propaganda, and biased campaigns. In response to these challenges, we propose a Blockchain-based Deepfake Authenticity Verification Framework (B-DAVF) designed to detect and authenticate deepfake content in real time. It leverages the transparency of blockchain technology to reinforce electoral integrity. Finally, we also propose comprehensive countermeasures, including enhanced legislation, technological solutions, and public education initiatives, to mitigate the risks associated with AI in electoral contexts, proactively safeguard democracy, and promote fair elections.Blockchains811A Blockchain-based Deepfake Authenticity Verification Framework (B-DAVF) designed to detect and authenticate deepfake content in real time is proposed, which leverages the transparency of blockchain technology to reinforce electoral integrity.Blockchains["M. B. E. Islam", "Muhammad Haseeb", "Hina Batool", "Nasir Ahtasham", "Zia Muhammad"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/c9bec2549e3b84497027417d20567574d70c9d21 67190e75be50dbf9c6554261fef69f312e76cd4cAt the University of Bologna, one of the pioneers of higher education in Europe and the institution that inspired the name of the Bologna Process, courses are labelled according to the International Standard Classification of Education (ISCED), a statistical classification of vocational fields. In Open and Distance Learning (ODL), where the number of programmes is high in parallel with the number of learners, determining the fields of courses taught is crucial not only for measurement and evaluation processes but also for a detailed examination of statistical information in processes such as enrolment and graduation. Processes such as data classification according to specific categories can be rapidly carried out with the help of artificial intelligence (AI) and can be utilised in administrative processes. This study investigated whether ChatGPT-4, one of the AI applications, could classify 1135 courses taught at Anadolu University's Open Education System (AUOES), which is part of the Bologna Process, according to ISCED fields, considering the content of the courses. In this study, document analysis was applied to the data analysis. According to the results, the highest number of courses in AUOES were in business, administration, and law (386), while the fewest courses were in education (27). These results indicate that courses related to white-collar professions are taught frequently and are influenced by the programmes at AUOES. This study suggests that AI can be used in administrative processes and to classify courses according to ISCED fields. Categorising all courses according to ISCED or a similar standard could enable the analysis of courses in vocational fields. Determining the fields of courses according to certain standardisations in ODL could allow the courses, and consequently, the books and materials, to be handled by subject matter experts. Decision-makers in ODL could plan the teaching of courses in line with needs by considering the employment situation in vocational fields when launching new programmes or updating course lists. Researchers could investigate the accuracy of AI's processes in administrative tasks and gather the opinions of subject matter experts, opening up new avenues for further research and exploration in the field of AI in education.International Conference on Education Research01It is suggested that AI can be used in administrative processes and to classify courses according to ISCED fields, which indicates that courses related to white-collar professions are taught frequently and are influenced by the programmes at AUOES.International Conference on Education Research["Sefa Emre \u00d6nc\u00fc"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/67190e75be50dbf9c6554261fef69f312e76cd4c 12db344ea0d07b9ae5cc2ab8b9a85e3170a803bbArtificial Intelligence (AI) algorithms showcase the potential to steer a paradigm shift in clinical medicine, especially medical imaging. Concerns associated with model generalizability and biases necessitate rigorous external validation of AI algorithms prior to their adoption into clinical workflows. To address the barriers associated with patient privacy, intellectual property, and diverse model requirements, we introduce ClinValAI, a framework for establishing robust cloud-based infrastructures to clinically validate AI algorithms in medical imaging. By featuring dedicated workflows for data ingestion, algorithm scoring, and output processing, we propose an easily customizable method to assess AI models and investigate biases. Our novel orchestration mechanism facilitates utilizing the complete potential of the cloud computing environment. ClinValAI's input auditing and standardization mechanisms ensure that inputs consistent with model prerequisites are provided to the algorithm for a streamlined validation. The scoring workflow comprises multiple steps to facilitate consistent inferencing and systematic troubleshooting. The output processing workflow helps identify and analyze samples with missing results and aggregates final outputs for downstream analysis. We demonstrate the usability of our work by evaluating a state-of-the-art breast cancer risk prediction algorithm on a large and diverse dataset of 2D screening mammograms. We perform comprehensive statistical analysis to study model calibration and evaluate performance on important factors, including breast density, age, and race, to identify latent biases. ClinValAI provides a holistic framework to validate medical imaging models and has the potential to advance the development of generalizable AI models in clinical medicine and promote health equity.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing281ClinValAI provides a holistic framework for establishing robust cloud-based infrastructures to validate medical imaging models and has the potential to advance the development of generalizable AI models in clinical medicine and promote health equity.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing["O. Ramwala", "Kathryn P. Lowry", "D. S. Hippe", "Matthew P.N. Unrath", "Matthew Nyflot", "Sean D. Mooney", "Christoph I. Lee"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/12db344ea0d07b9ae5cc2ab8b9a85e3170a803bb 203b724b07209b0fa17a5b77834d6e976c0181b9The rapid advancement of artificial intelligence and machine learning (AI/ML) technologies in healthcare presents significant opportunities for enhancing patient care through innovative diagnostic tools, monitoring systems, and personalized treatment plans. However, these innovative advancements might result in regulatory challenges given recent Supreme Court decisions that impact the authority of regulatory agencies like the Food and Drug Administration (FDA). This paper explores the implications of regulatory uncertainty for the healthcare industry related to balancing innovation in biotechnology and biocomputing with ensuring regulatory uniformity and patient safety. We examine key Supreme Court cases, including Loper Bright Enterprises v. Raimondo, Relentless, Inc. v. Department of Commerce, and Corner Post, Inc. v. Board of Governors of the Federal Reserve System, and their impact on the Chevron doctrine. We also discuss other relevant cases to highlight shifts in judicial approaches to agency deference and regulatory authority that might affect how science is handled in regulatory spaces, including how biocomputing and other health sciences are governed, how scientific facts are applied in policymaking, and how scientific expertise guides decision making. Through a detailed analysis, we assess the potential impact of regulatory uncertainty in healthcare. Additionally, we provide recommendations for the medical community on navigating these challenges.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing321The implications of regulatory uncertainty for the healthcare industry related to balancing innovation in biotechnology and biocomputing with ensuring regulatory uniformity and patient safety are explored.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing["Nicole Rincon", "Sara Gerke", "Jennifer K. Wagner"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/203b724b07209b0fa17a5b77834d6e976c0181b9 6e9688b6eb0b4ede98d55f129bfc3c6225f8469cAI and Ethics161Whether and to what extent the various forms of human oversight mentioned in the EU AI Act are feasible in the area of hate speech regulation is examined and to what extent the implementing of ex-post monitoring is necessary and legitimate to curb hate speech on digital social networks.AI and Ethics["Frank Dietrich"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/6e9688b6eb0b4ede98d55f129bfc3c6225f8469c 95cc75934318b7e781f7f7b1c472333375c38ce3Despite significant advancements in general artificial intelligence, such as GPT-4, their effectiveness in the medical domain (general medical AI, GMAI) remains constrained due to the absence of specialized medical knowledge. To address this challenge, we present GMAI-VL-5.5M, a comprehensive multimodal medical dataset created by converting hundreds of specialized medical datasets into meticulously constructed image-text pairs. This dataset features comprehensive task coverage, diverse modalities, and high-quality image-text data. Building upon this multimodal dataset, we propose GMAI-VL, a general medical vision-language model with a progressively three-stage training strategy. This approach significantly enhances the model's ability by integrating visual and textual information, thereby improving its ability to process multimodal data and support accurate diagnosis and clinical decision-making. Experimental evaluations demonstrate that GMAI-VL achieves state-of-the-art results across a wide range of multimodal medical tasks, such as visual question answering and medical image diagnosis. Our contributions include the development of the GMAI-VL-5.5M dataset, the introduction of the GMAI-VL model, and the establishment of new benchmarks in multiple medical domains. Code and dataset will be released at https://github.com/uni-medical/GMAI-VL.arXiv.org01This work proposes GMAI-VL, a general medical vision-language model with a progressively three-stage training strategy that significantly enhances the model's ability by integrating visual and textual information, thereby improving its ability to process multimodal data and support accurate diagnosis and clinical decision-making.ArXiv["Tian-Xin Li", "Yan-Cheng Su", "Wei Li", "Bin Fu", "Zhe Chen", "Ziyan Huang", "Guoan Wang", "Chenglong Ma", "Ying Chen", "Ming Hu", "Yanjun Li", "Pengcheng Chen", "Xiaowei Hu", "Zhongying Deng", "Yuanfeng Ji", "Jin Ye", "Yu Qiao", "Junjun He"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/95cc75934318b7e781f7f7b1c472333375c38ce3 bd6e07098f8c17939b6ff9a1851be52ab0c1e2d3Weeds significantly hinder agricultural productivity by reducing crop yields and increasing production costs. Leveraging artificial intelligence (AI) is critical for equipping farmers with early detection capabilities to implement effective weed management strategies. Among AI technologies, deep learning (DL) techniques are especially effective for analyzing agricultural field images to identify weed species. This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection. Our findings highlight YOLOv9’s superior effectiveness, achieving an accuracy of $\mathbf{9 0 \%}$, due to its robust architecture and advanced feature extraction. The model’s proficiency in accurately identifying weeds is evident from our thematic analysis. We also explore various image acquisition devices, including robots, drones, and mobile phones. Comparative evaluations show that YOLOv9 consistently outperforms other DL techniques, achieving remarkable accuracy and speed. This study includes a comparative analysis of several algorithms like SSD, Mask R-CNN, and Fast R-CNN, underscoring YOLOv9’s superior performance. This paper serves as a valuable resource for researchers and practitioners, guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9’s transformative potential.2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)240This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection, highlighting YOLOv9’s superior effectiveness and guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9’s transformative potential.2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT)["Premkumar Duraisamy", "A. Deepika", "V. Niranjani", "R. Jeevitha", "M. Sibishree", "A. Oviya"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/bd6e07098f8c17939b6ff9a1851be52ab0c1e2d3 eafcf29254357e25000e3fafbf12aee8019601b0The integration of Artificial Intelligence (AI) in higher education is rapidly expanding on a global scale, transforming the realms of teaching, learning, and administrative functions. In this regard, this systematic scoping review seeks to map the current evidence regarding the implementation of AI-powered tools within South African universities. This systematic scoping review was carried out in accordance with the framework proposed by Arksey and O’Malley. An advanced literature search was conducted in the following databases Sabinet African Journal = 65m, Web of Science = 841, Emerald Insight = 417, Science Direct = 30, EbscoHost = 254, and Google Scholar = 3,470. The identified articles were uploaded to Rayyan software for initial screening. We assessed their relevance by analysing the titles and abstracts. After applying the inclusion and exclusion criteria to the articles obtained from the database search, 11 papers were selected for the study.  Notably, three themes were identified following a thematic analysis and these includes the usage of AI-powered tools in South African universities; the challenges connected with using these technologies as well as the strategies required to address these challenges. This systematic scoping review highlights a significant rise in AI tool adoption in South African higher education, noting their benefits in enhancing academic support and efficiency. However, it also raises concerns about ethical issues such as increased cheating, unequal access to technology, infrastructural challenges, and data insecurity.International Conference on Education Research380A significant rise in AI tool adoption in South African higher education is highlighted, noting their benefits in enhancing academic support and efficiency, however, it also raises concerns about ethical issues such as increased cheating, unequal access to technology, infrastructural challenges, and data insecurity.International Conference on Education Research["Noluthando Mbangeleli", "Vusi Funda"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/eafcf29254357e25000e3fafbf12aee8019601b0 42287e98f000616dba049c02ee0f388fc6792bffThe reception of ChatGPT in the educational field has varied between enthusiasm and skepticism, sparking a series of controversies and challenges in response to the emergence of artificial intelligence (AI) technologies within the educational context. For this reason, exploring the student perception of future educators in training regarding these tools, considering the challenges they will face both in their immersion processes in practice and in their future professional role, is believed to constitute a contribution to the field of initial teacher education knowledge. The purpose of this study was to interpret and describe the perceptions of a group of pedagogy students on the use of ChatGPT as an assistant in a learning experience implemented during their training and on the implications of this AI tool in their future work context as teachers. In this context, applied research in teaching was conducted with a course on "Assessment of and for learning" comprised of 26 students from various pedagogy programs in their fourth year. A sequential mixed methodology was employed, collecting quantitative data initially, to then delve deeper into the analysis based on qualitative data. For this, students were first asked to complete a questionnaire on prior knowledge and experiences with the use of ChatGPT. Subsequently, considering the results, work in class focused on two central themes: the drafting of prompts and academic integrity. Then, the workshop, its phases, and the students began working with the assistance of ChatGPT. Feedback sessions on the workshops were held, followed by the application of a semi-structured questionnaire with open-ended questions aimed at gathering information on student perceptions based on this experience and the implications of AI as future teachers. The data collected were analyzed based on codes and the development of categories. A focus group with 12 student representatives was later conducted to promote reflection and critical analysis among students on the implications of artificial intelligence in their future professional teaching performance. The extracted data revealed new codes and explanatory categories of the phenomenon under study. From the qualitative phase, 4 categories and emerging themes were identified, such as "Advantages of using ChatGPT as an assistant in the training of university pedagogy students", within which students expressed a positive valuation of the experience, highlighting the assistance of the chatbot during the workshop through various functions such as helping to justify or provide conceptual support to the evaluation criteria they were developing, proposing ideas for assessment situations, and receiving guidance or feedback quickly on their work. Additionally, a second category grouped the risks associated with using the chatbot, such as the possible lack of rigor or reliability of the information it provides, or the misuse of these tools through plagiarism. Other categories reported on student perceptions regarding their future professional teaching performance, with emerging themes such as the importance of teacher training and mediation when integrating these tools into learning processes with their students, ethical implications, and the need to transform assessment methods in schools to promote the development of critical thinking, information analysis, and creativity, so that AI tools can be a real support for students and not a threat. Finally, students highlighted the importance of working with these tools from the beginning of their training with the mediation of a teacher and a specialist in these new information technologies.  Article visualizations:European Journal of Education Studies720The purpose of this study was to interpret and describe the perceptions of a group of pedagogy students on the use of ChatGPT as an assistant in a learning experience implemented during their training and on the implications of this AI tool in their future work context as teachers.European Journal of Education Studies["Maura Amaranti Pesce", "Daniel Fern\u00e1ndez Blanco"]2024-11-21T00:00:00https://www.semanticscholar.org/paper/42287e98f000616dba049c02ee0f388fc6792bff e80f72439d9a71a6e273c1e43fb2c8f22caea467The scientific fields of complexity, Artificial Life (ALife), and artificial intelligence (AI) share commonalities: historic, conceptual, methodological, and philosophical. Although their origins trace back to the 1940s birth of cybernetics, they were able to develop properly only as modern information technology became available. In this perspective, I offer a personal (and thus biased) account of the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and AI. Even when the trajectory of the article is influenced by my personal experience, the general questions posed (which outweigh the answers) will, I hope, be useful in aligning efforts in these fields toward overcoming-or accepting-their limits.Artificial Life01A personal account of the expectations and limitations of these fields, some of which have their roots in the limits of formal systems, and a comparison of different aspects of complexity, ALife, and AI.Artificial life["Carlos Gershenson"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/e80f72439d9a71a6e273c1e43fb2c8f22caea467 a9dc0c0cb9528ce5d9d9c649affb90c9b8cfc6ae2nd ICANEAT is a conference collaboration program between Akademi Penerbang Indonesia Banyuwangi and Research Synergy Foundation. The conference aims to establish a platform and to provide opportunities for academic scientists, researchers, research scholars, and practitioners from all over the world to exchange and share their experiences, ideas, knowledge and research results to the latest issues of Artificial Intelligence, Navigation, Engineering, and Aviation Technology.Book of Abstract Conference Proceeding the 2nd International Conference on Artificial Intelligence, Navigation, Engineering, and Aviation Technology00The conference aims to establish a platform and to provide opportunities for academic scientists, researchers, research scholars, and practitioners from all over the world to exchange and share their experiences, ideas, knowledge and research results to the latest issues of Artificial Intelligence, Navigation, Engineering, and Aviation Technology.Book of Abstract Conference Proceeding the 2nd International Conference on Artificial Intelligence, Navigation, Engineering, and Aviation Technology["Dr. Prasetyo Iswahyudi, S.T, M.M.", "Dr. Hendrati Dwi Mulyaningsih", "Santi Rahmawati, MSM."]2024-11-22T00:00:00https://www.semanticscholar.org/paper/a9dc0c0cb9528ce5d9d9c649affb90c9b8cfc6ae 6212945dbe81c220338f91b3dc954069cd4f4836Injustice in bankruptcy requirements between State-Owned Enterprises and private companies creates legal uncertainty and unfair treatment in restructuring processes. State-Owned Enterprises often receive more protection compared to private companies, even though both are subject to Law No. 37 of 2004 on Bankruptcy and Suspension of Debt Payment Obligations. Article 2 paragraph (5) of this law stipulates that bankruptcy petitions against State-Owned Enterprises operating in the public interest can only be filed by the Minister of Finance, resulting in unequal legal treatment and raising questions about fairness in the bankruptcy system in Indonesia. This disparity negatively impacts business competition and public trust in the legal system. The purpose of this study is to analyze the injustice in bankruptcy requirements between State-Owned Enterprises and private companies, as well as to explore how artificial intelligence can be integrated to address these issues. The research method used is normative juridical, using a statutory approach and an analytical approach. The research findings indicate that the injustice in bankruptcy requirements is primarily caused by the differing legal treatment and policies that favor State-Owned Enterprises. The utilization of AI in bankruptcy data analysis and decision-making can assist in identifying patterns of injustice and provide more equitable and transparent recommendations. Artificial intelligence has the potential to address injustices in bankruptcy requirements between state-owned enterprises and private companies by enhancing transparency, accuracy, and fairness in legal processes. Its implementation requires clear regulatory support and collaboration among the government, legal institutions, and the private sector.Journal Research of Social Science Economics and Management250The research findings indicate that the injustice in bankruptcy requirements is primarily caused by the differing legal treatment and policies that favor State-Owned Enterprises, and artificial intelligence has the potential to address injustices in bankruptcy requirements by enhancing transparency, accuracy, and fairness in legal processes.Journal Research of Social Science, Economics, and Management["Naek MT Siringoringo"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/6212945dbe81c220338f91b3dc954069cd4f4836 d6b5ef8a5bdb2c0b95ee89018161fa05b5b6274eObjective: This research focuses on examining how digitalization and artificial intelligence (AI) are contributing to development from 2019, to 2024. By investigating how AI influences growth and environmental and social well-being sustainability efforts aim to understand the benefits and obstacles AI presents in advancing the United Nations Sustainable Development Goals (SDGs).   Theoretical Framework: The review delves into the connection between digitalization and sustainability by looking at how AI contributes to the Development Goals (SDGs). It discusses how AI can improve productivity and increase access to services while promoting practices; however, there are also discussions on ethical considerations and issues related to energy use and social inequality.   Method: The study utilizes a review of literature method (CLR) to analyze peer reviewed articles released from 2019 to 2024 using databases like Scopus and Google Scholar that concentrate on journals making impacts on AI and sustainability topics specifically chosen based on stringent criteria for a thorough and impartial investigation into AIs part in promoting sustainable development.   Results and Discussion: According to the analysis presented in the review article mentioned earlier,​ it is noted that artificial intelligence (AI) has had an impact, on development through advancements in productivity and waste reduction strategies​; nevertheless​ there are instances of insufficient exploration in terms of AIs ecological consequences​​ notably in relation to energy usage​​. Moreover,​​ there emerges a call for oversight to guarantee that AI plays a fair role, in societal welfare and environmental preservation.   Research Implications: The results indicate that although AI shows promise, in propelling development forward, more research is required to study its lasting consequences and how AI can be incorporated into sustainability frameworks over time. Subsequent investigations should delve into the concerns associated with AI and how its effects vary among regions.   Originality/Value: This research adds to the increasing amount of studies on how AI contributes to development by summarizing literature spanning from 2019 to 2024, giving policymakers and researchers an understanding of how AI can be used to advance the Sustainable Development Goals (SDGs) while minimizing its adverse effects on the environment and society.Journal of Law and Sustainable Development130The results indicate that although AI shows promise, in propelling development forward, more research is required to study its lasting consequences and how AI can be incorporated into sustainability frameworks over time.Journal of Law and Sustainable Development["Chellig Abdeldjalil", "Kaddouri Mohammed", "Ounissi Asma"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/d6b5ef8a5bdb2c0b95ee89018161fa05b5b6274e 9580861e56332d2e48bb974b42f89c90bf939cfeThis study addresses the transformative impact of Artificial Intelligence (AI) on accounting, exploring how this technology is reshaping the accounting field. Using an exploratory applied research approach based on a literature review, the article broadly investigates the concepts, theories, and applications of AI in accounting, highlighting benefits such as operational efficiency and new analytical capabilities. The study identifies that AI automates repetitive tasks, improves the accuracy of accounting processes, and empowers professionals to focus on strategic analysis. However, implementation faces challenges such as robust technological infrastructure, ethical issues, and the need for professional retraining. Some recommended strategies include a detailed assessment of accounting processes, gradual implementation of AI, and establishment of governance policies to ensure its ethical and effective use. By gathering information throughout the research and analyzing the information, it was possible to understand the impacts of AI on accounting, providing crucial insights for professionals and organizations seeking to integrate this technology in a sustainable and beneficial way. ARACÊ00The study identifies that AI automates repetitive tasks, improves the accuracy of accounting processes, and empowers professionals to focus on strategic analysis, however, implementation faces challenges such as robust technological infrastructure, ethical issues, and the need for professional retraining.ARACÊ["Lauana Batista Santos", "Almeciano Jos\u00e9 Maia Junior", "Solange Rodrigues dos Santos Corr\u00eaa", "N\u00fabia Aparecida Pinto Coelho", "Givaldo Corr\u00eaa dos Santos Neto", "Gustavo da Cruz"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/9580861e56332d2e48bb974b42f89c90bf939cfe 7cb971693e5b814b141d220a35f0aebc7e6f8474The article presents a comprehensive analysis of the transformation of the intellectual property rights protection system in the context of advancing artificial intelligence technologies, with a particular focus on the challenges faced by Ukrainian researchers and scientific institutions, as well as the potential for adapting successful EU experiences. The relevance of this research is driven by several factors. Firstly, the increase in data volumes and computing power has facilitated new approaches in science, where AI plays a crucial role in analysing and interpreting results. Secondly, traditional intellectual property protection mechanisms are proving inadequate as AI algorithms independently generate new knowledge. Thirdly, Ukraine’s integration into the European research area necessitates the harmonization of intellectual property protection approaches, considering the specificities of AI usage. The current state of AI utilization in scientific research is analysed, including the automation of data collection and analysis, the acceleration of scientific discoveries, and the evolving role of researchers. Key risks to intellectual property rights protection are identified, particularly concerning authorship issues related to works created with the assistance of artificial intelligence, difficulties in tracking original sources, and the risks of unauthorized use of scientific data. Special attention is given to analysing the European regulatory experience regarding AI use, particularly the provisions of new EU legislation in the field of AI and its significance for safeguarding researchers’ rights. A comprehensive approach to addressing the identified issues is proposed, incorporating both legal mechanisms (harmonization of legislation with EU norms) and technological solutions for protecting researchers’ rights. Such solutions may include blockchain technologies, digital rights management systems, and plagiarism detection tools. Ensuring a balance between the principles of open science and the protection of intellectual property rights is an essential task. This can be achieved through the implementation of flexible licensing models and the use of AI technologies to monitor compliance with rights. It is recommended that the use of AI in scientific research be explicitly noted in the methodology, and that all data generated by artificial intelligence undergo thorough verification.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law["M. Utkina", "O. Maletova", "S. M. Gudkov"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/7cb971693e5b814b141d220a35f0aebc7e6f8474 83790dbdd9eb3dc17a0bc82a2130705b860e98e0Peran Artificial Intelligence (AI) sebagai referensi dalam penulisan cerita pendek di kalangan mahasiswa Program Studi Pendidikan Bahasa dan Sastra Indonesia di Universitas Singaperbangsa Karawang. Tujuan dilakukan penelitian ini untuk menganalisis peran dan efektivitas penggunaan Artificial Intelligence sebagai sumber referensi dalam proses penulisan cerita pendek di kalangan mahasiswa Pendidikan Bahasa dan Sastra Indonesia, Universitas Singaperbangsa Karawang dengan fokus pada pandangan, pemanfaatan, tantangan serta kekhawatiran terhadap keaslian karya. Metode yang digunakan adalah kuantitatif deskriptif dengan penyebaran kuesioner untuk memperoleh data dari 30 mahasiswa. Hasil penelitian melalui analisis kuesioner menunjukkan bahwa mayoritas mahasiswa menganggap AI sebagai alat bantu yang berguna dalam penulisan kreatif, khususnya dalam menghasilkan inspirasi ide, dan mempercepat proses penulisan. Penggunaan AI dalam penulisan kreatif menunjukkan bahwa 76,7% mahasiswa merasa terbantu oleh AI dalam proses kreatif mereka. Namun, beberapa mahasiswa tetap skeptis terhadap AI, khawatir akan kehilangan keunikan dan orisinalitas dalam karya mereka, 23,3% mahasiswa memilih untuk tidak menggunakan AI dengan alasan menjaga keaslian dan etika karya mereka. Penelitian mengidentifikasi berbagai jenis AI yang digunakan, dengan ChatGPT menjadi pilihan utama responden. Hasil ini memberikan wawasan tentang bagaimana AI dapat dimanfaatkan secara optimal dalam pendidikan sastra, serta pentingnya keterampilan kritis dalam penggunaannya.    Stilistika : Jurnal Pendidikan Bahasa dan Seni210Stilistika : Jurnal Pendidikan Bahasa dan Seni["Bahar Amal", "Chayani Noviana", "Gadis Putri Utarie", "Indah Rafi Utari Azizah", "Marsella Fransiska", "Risma Aprilia", "Parlindunngan Tarihoran"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/83790dbdd9eb3dc17a0bc82a2130705b860e98e0 9998b87b57e5213d9b2a3cd3bcceff2f84cc14f7his study investigates the integration of natural intelligence (NI) and artificial intelligence (AI) within traditional firms linked through a multilayer network framework. The research explores this central question: How can the integration of NI and AI, facilitated by copula nodes, drive economic value creation in digitized firms? The paper combines theoretical and empirical results of the observed increments in cost-benefit marginality linked with AI adoption by checking out testing in various domains such as manufacturing, retailing, finance, etc. The performance of the model shows enhanced efficiency and decision-making, as well as savings in costs. Copula nodes that connect multilayer networks blend NI with AI, boosting overall value. Unlike existing studies, this framework operationalizes copula nodes to capture real-time dependencies between human-driven and AI-driven processes, offering a comprehensive view of economic value creation across digitized firms. A methodology section outlines the empirical validation process conducted across multiple industries (manufacturing, retail, and finance), providing key insights into efficiency, decision-making, and cost optimization. The practical implications offer a strategic pathway for firms to enhance profitability and competitiveness in the digital age. The results underscore the importance of strategically integrating AI with human intelligence to maximize economic outcomes.Journal of Comprehensive Business Administration Research00The research explores this central question: How can the integration of NI and AI, facilitated by copula nodes, drive economic value creation in digitized firms to underscore the importance of strategically integrating AI with human intelligence to maximize economic outcomes.Journal of Comprehensive Business Administration Research["Roberto Moro Visconti"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/9998b87b57e5213d9b2a3cd3bcceff2f84cc14f7 66f353bd33685817cee207393b210a1d72c23878Health services must be oriented towards meeting consumer demands and expectations, which are closely related to service quality. However, there are still problems in its implementation, such as discrimination against BPJS patients, who often get different treatment compared to non-BPJS patients, both in service quality and access to health facilities. According to Article 28H Paragraph (1) of the 1945 Constitution, everyone has the right to a prosperous life and decent health services. The objective of this study is to identify the role of artificial intelligence in reducing and preventing discrimination against BPJS patients in Health services, and to analyse how legal regulations can support the application of AI to ensure the creation of fair and non-discriminatory health services. [Methods] The research method used is normative juridical, using a statutory approach and an analytical approach. The results show that AI has significant potential in detecting and preventing discrimination, for example through algorithms that monitor the treatment of patients in real-time. However, the application of AI must be supported by a clear legal framework to ensure that the use of this technology does not exacerbate inequality, but rather supports equitable access to health services. The conclusion of this study is that artificial intelligence can be an effective solution in addressing discrimination of BPJS patients, as long as it is integrated with policies and regulations that protect the rights of patients, as well as ensuring oversight and accountability on the part of healthcare providers.Journal Research of Social Science Economics and Management00The conclusion of this study is that artificial intelligence can be an effective solution in addressing discrimination of BPJS patients, as long as it is integrated with policies and regulations that protect the rights of patients, as well as ensuring oversight and accountability on the part of healthcare providers.Journal Research of Social Science, Economics, and Management["Nucky Indra Praja"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/66f353bd33685817cee207393b210a1d72c23878 df820820446d894c5437cc790780ed42e5325e6cAI technologies may serve numerous functions in entire retail sector in value chain, and employ the employees for the job done by using to implement AI operations in the value chain. Because of the increasing degree of competition in the retail industry, merchants must now provide higher-quality services and prioritize their clients more than ever before. Retailers have increasingly adopted AI-based solutions to study new potential in the retail industry. The focus of the research is to know the people mind set to use the digital sectors. Performance expectation refers to consumers' belief that technology would improve productivity and effectiveness in reaching goals. The Indian retail sector has emerged as one of the most rapidly increasing industries. The findings show the influence are favorably connected to performance expectation, but not correlated with anthropomorphism. Retailers are always seeking to unleash the industry's true potential by using cutting-edge technologies such as artificial intelligence-based data analytics, CRM systems.2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC)150The findings show the influence of artificial intelligence-based data analytics are favorably connected to performance expectation, but not correlated with anthropomorphism.2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC)["Leena Jenefa", "Ashok Kumar S", "Bibiana Lim Chiu Yiong", "M. Jayakumar", "Lenin Lokesh B", "M. Sakthivel"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/df820820446d894c5437cc790780ed42e5325e6c f6ae81c3c8469bc2ada24226c5e83a80c12f42e7Purpose of the Study: This study aims to analyze the impact of artificial intelligence (AI) on the travel industry, focusing on the sustainability and ethics of various tourism models. It evaluates the viability of tourism types before and after AI implementation while examining how AI technologies shape consumer preferences, industry practices, destination management, and tourism product design. Methodology: Employing a qualitative research design, the study systematically compares multiple datasets through secondary qualitative data analysis. A deductive approach is used to interpret this data, supported by a comprehensive review of scholarly literature, industry reports, and empirical findings. Main Findings: The results provide valuable insights for policymakers, industry practitioners, and stakeholders regarding the strategic implications of AI in tourism development. The study establishes a framework to assess the suitability of different tourism models within an AI-driven context, facilitating informed decisions about sustainable tourism practices. Applications of the Study: This research encourages legislators, industry experts, and consumers to understand AI's strategic influence on tourism design and development. It also outlines an agenda for evaluating tourism models in an AI-centric environment, promoting responsible and sustainable tourism development. Novelty/Originality of the Study: This study uniquely explores the intersection of cognitive psychology and AI, highlighting their parallel evolution and impact on contemporary tourism practices, ultimately enhancing service delivery to clients.International Journal of Tourism &amp; Hospitality Reviews400The study establishes a framework to assess the suitability of different tourism models within an AI-driven context, facilitating informed decisions about sustainable tourism practices and encourages legislators, industry experts, and consumers to understand AI's strategic influence on tourism design and development.International Journal of Tourism &amp; Hospitality Reviews["Sarani Bhaumik", "Sarbari Bhowmick"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/f6ae81c3c8469bc2ada24226c5e83a80c12f42e7 ef02905f947c91b3a7527df0d98e40cfc9f98721This study evaluates the integration of artificial intelligence in the judicial processes, focusing on how technology can promote fairness in the Indonesian Criminal Justice system, where there are no specific guidelines for using AI in legal proceedings. The research follows a normative legal approach, analyzing laws and court cases and comparative methods using various legal sources and qualitative analysis methods. The findings reveal that incorporating AI in the judge's decision-making process aids in assessing information and data, facilitating optimal, effective, and efficient decision-making.  On the other hand, incorporating AI into the decision-making process of judges within the Criminal Justice System implies that judges act as a smaller version of the system itself, considering various factors such as examination records, charges, and real-life circumstances impacted by social, cultural, and economic elements. Ultimately, integrating AI into the evidential process for judicial decision-making aims to align the criminal justice system with factual situations and the goals of punishment. It is recommended that the use of AI in legal proceedings should not only focus on algorithm-based legal aspects but also take into account non-legal aspects such as humanitarian, social, and economic conditions that contribute to criminal activities. This holistic approach is crucial for ensuring alignment in the Criminal Justice System to uphold legal, moral, and societal justice.  The role of the Supreme Court is no exception in providing guidance and supervision of Judges regarding the use of technology, including legal legalization, while still paying attention to law, ethics, social values , and just legal principles.Jurnal Penelitian Hukum De Jure00It is recommended that the use of AI in legal proceedings should not only focus on algorithm-based legal aspects but also take into account non-legal aspects such as humanitarian, social, and economic conditions that contribute to criminal activities.Jurnal Penelitian Hukum De Jure["Syamsul Fatoni", "Erma Rusdiana"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/ef02905f947c91b3a7527df0d98e40cfc9f98721 aaaa8914e32dfb39d5312497e6ce1317f911d7d2Curriculum thought and politics are the need for the development of colleges and universities in the new era, aiming at solving the fundamental problem of talent training. With the development and popularization of artificial intelligence, the intelligence of curriculum thought and politics has ushered in new development opportunities and is also facing new teaching challenges. In view of this, this paper briefly summarizes the value of AI-enabled ideological and political teaching in colleges and universities, as well as the principles that should be followed in teaching innovation, analyzes the problems faced by the construction of ideological and political teaching in colleges and universities, and further discusses the practical path of AI-enabled ideological and political teaching in colleges and universities, hoping to provide new ideas and paths for the innovation of ideological and political teaching in colleges and universities.Education Reform and Development80The value of AI-enabled ideological and political teaching in colleges and universities, as well as the principles that should be followed in teaching innovation, are summarized.Education Reform and Development["Guofeng Ke"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/aaaa8914e32dfb39d5312497e6ce1317f911d7d2 7246519e4a2d1ee8ce484a002a0e6d7daceb8a9eArtificial Intelligence is an innovation of modern technology, a concept transformed into reality. It is the result of sustained work by talented computer science pioneers who have turned their dreams into reality. As we have shown in this article, Artificial Intelligence brings both opportunities and significant risks, given the access some people have to data and information that can influence our entire existence. Human specificity lies in the desire to overcome one's limits and to make one's everyday life easier. However, in a society in constant transformation, we must be aware that not everything that helps us is necessarily beneficial, and vice versa. The future will be the one that will judge the direction of the technology of modern society and our ability to adapt to new challenges. This revolutionary field represents an opportunity, but also a vulnerability, which prompts us to reflect and analyze: "How long will we use artificial intelligence before it starts using us?"Proceedings of the International Conference on Cybersecurity and Cybercrime (IC3)00This revolutionary field represents an opportunity, but also a vulnerability, which prompts us to reflect and analyze: "How long will the authors use artificial intelligence before it starts using us?"Proceedings of the International Conference on Cybersecurity and Cybercrime (IC3)["Gabriel-Virgil Tauber", "S. Vasile"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/7246519e4a2d1ee8ce484a002a0e6d7daceb8a9e e1c23529c5281a50766acc44c7afd1783bd02accArtificial intelligence (AI) is revolutionizing the agribusiness sector, offering significant potential to improve efficiency, sustainability and productivity. By analyzing large amounts of data, AI can optimize resource management, predict crop productivity and detect diseases early, leading to increased agricultural production and reduced environmental impact. However, integrating AI into agriculture faces several challenges, including technological infrastructure, data accessibility, and regulatory hurdles. To fully realize the benefits of AI, it is crucial to address these challenges and develop a robust legal framework that protects intellectual property, ensures data privacy and encourages innovation. By aligning AI-driven agricultural practices with the UN Sustainable Development Goals, we can create a more sustainable and equitable future for global food production.Observatorio de la Economía Latinoamericana00To fully realize the benefits of AI, it is crucial to address these challenges and develop a robust legal framework that protects intellectual property, ensures data privacy and encourages innovation and create a more sustainable and equitable future for global food production.OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA["Thiago Fernandes dos Santos"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/e1c23529c5281a50766acc44c7afd1783bd02acc aebfe9cde26295d64bb5ea9645776acc961a05bbArtificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables computers and machines to simulate human intelligence and solve complicated problems. In this article, we address current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagnosis of hypoglycemia, diabetic eye disease, diabetic kidney diseases, diabetic neuropathy, diabetic foot ulcers, and heart failure in diabetes.Artificial intelligence is advantageous because it can handle large and complex datasets from a variety of sources. With each additional type of data incorporated into a clinical picture of a patient, the calculation becomes increasingly complex and specific. Artificial intelligence is the foundation of emerging medical technologies; it will power the future of diagnosing diabetes complications.Journal of Diabetes Science and Technology1230Current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagnosis of hypoglycemia, diabetic eye disease, diabetic kidney diseases, diabetic neuropathy, diabetic foot ulcers, and heart failure in diabetes are addressed.Journal of diabetes science and technology["Alessandra T. Ayers", "Cindy N. Ho", "David Kerr", "S. Cichosz", "N. Mathioudakis", "Michelle Wang", "Bijan Najafi", "S. Moon", "Ambarish Pandey", "D. Klonoff"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/aebfe9cde26295d64bb5ea9645776acc961a05bb 348e8a16cab8794a6587437a1d5f1b9569311d3cThe pursuit of creating artificial intelligence (AI) mirrors our longstanding fascination with understanding our own intelligence. From the myths of Talos to Aristotelian logic and Heron's inventions, we have sought to replicate the marvels of the mind. While recent advances in AI hold promise, singular approaches often fall short in capturing the essence of intelligence. This paper explores how fundamental principles from biological computation--particularly context-dependent, hierarchical information processing, trial-and-error heuristics, and multi-scale organization--can guide the design of truly intelligent systems. By examining the nuanced mechanisms of biological intelligence, such as top-down causality and adaptive interaction with the environment, we aim to illuminate potential limitations in artificial constructs. Our goal is to provide a framework inspired by biological systems for designing more adaptable and robust artificial intelligent systems.arXiv.org1040This paper explores how fundamental principles from biological computation--particularly context-dependent, hierarchical information processing, trial-and-error heuristics, and multi-scale organization--can guide the design of truly intelligent systems.ArXiv["Nima Dehghani", "Michael Levin"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/348e8a16cab8794a6587437a1d5f1b9569311d3c a4a272d6435609246932d6f1e80f6b47768fdb96This paper focuses on the current risks associated with the use of artificial intelligence (AI) in processing personal data (PD), particularly about individuals’ privacy and confidentiality rights. The paper identifies and examines specific areas of artificial intelligence data processing and discusses how modern trends and threats to data protection impact the development of a robust regulatory framework for managing such technologies. The analysis of Ukraine’s digital transformation journey and specific cases of privacy rights violations through artificial intelligence globally has highlighted unauthorized access to data and insufficient preventive measures for information protection. These issues have been found to harm information security and economic stability. Despite this, there has been a notable trend on online platforms to label content created with the assistance of artificial intelligence. The paper delves into the legal nature of artificial intelligence and the need for an interdisciplinary approach involving both legal mechanisms and technological tools due to the complexity of the data it uses. Furthermore, artificial intelligence is shown to play a crucial role in updating legal regimes, particularly in information security, personal data protection, and individual privacy. Given this, the main shortcomings of the national legislation on the protection of PD have been considered. The national strategy for regulating artificial intelligence has been analyzed, and ways of solving key problems in these fields have been proposed. A separate point was noted in the draft laws on protecting personal data in Ukraine and how they align with European data protection standards in the context of developing artificial intelligence. Also, for comparison, strategies for developing artificial intelligence through the prism of international experience are presented, and European legislation on data protection (General Data Protection Regulation) and the EU Act on AI are processed to identify the main points of their relationship.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law["Y. Z. Ostiian"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/a4a272d6435609246932d6f1e80f6b47768fdb96 c90ccd2e1bd46fd1f20253fa3ac07270eddec1d7The idea that people will lose their jobs because of robots with artificial intelligence is one of the biggest recent concerns about artificial intelligence technology. There are predictions that unemployment will increase with the introduction of robots into the business sector, and due to artificial intelligence, automation in the production sector will make work completed by robots more practical than the efforts accomplished by humans. This study aimed to assess the correlation between artificial intelligence anxiety and the level of unemployment anxiety among university students. As a cross-sectional and descriptive study, the population comprised of 10,682 university students actively enrolled at a university. While the minimum sample size was calculated as 371 students, the research included 476 students as participants. The study used the ‘Personal Information Form’, ‘Artificial Intelligence Anxiety Scale’, and ‘Unemployment Anxiety Scale’ as data collection tools. The demographic information of the participants follows: 50.4% were male, 33.8% were freshmen, and 96.2% were single. The total score averages for the Artificial Intelligence Anxiety Scale and Unemployment Anxiety Scale are 56.00 ± 15.51 and 53.52 ± 11.55, respectively. A statistically significant difference between the participants’ score averages on the Artificial Intelligence Anxiety Scale and the Unemployment Anxiety Scale was identified for gender, major/college, trust in technology, and use of artificial intelligence ( p < 0.05). There was a moderately positive relationship between artificial intelligence anxiety and unemployment anxiety level total score averages ( p < 0.01). There were high scores among participants for artificial intelligence anxiety and unemployment anxiety.WORK: A Journal of Prevention, Assessment &amp; Rehabilitation160There was a moderately positive relationship between artificial intelligence anxiety and unemployment anxiety level total score averages and a statistically significant difference between the participants’ score averages on the Artificial Intelligence Anxiety Scale and the Unemployment Anxiety Scale.WORK: A Journal of Prevention, Assessment &amp; Rehabilitation["Mehmet U\u00e7ar", "H\u00fcseyin \u00c7apuk", "Muhammet Faruk Yi\u011fit"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/c90ccd2e1bd46fd1f20253fa3ac07270eddec1d7 c92adb937c818acb3e22e26365300f27d535a555The construction of ideological and political education in the artificial intelligence curriculum represents a significant challenge to China’s educational reforms. This paper explores the connotation of ideological and political education in artificial intelligence curriculum, analyzes and summarizes the difficulties of integrating ideological and political education into artificial intelligence courses, and presents implementation strategies from the perspectives of content design, teaching improvement, and evaluation and feedback. This paper aims to provide insights and references for higher education institutions in cultivating comprehensive talents in artificial intelligence.Education Reform and Development00The connotation of ideological and political education in artificial intelligence curriculum is explored, the difficulties of integrating ideological and political education into artificial intelligence courses are summarized, and implementation strategies from the perspectives of content design, teaching improvement, and evaluation and feedback are presented.Education Reform and Development["Qi Lin", "Ding Wu"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/c92adb937c818acb3e22e26365300f27d535a555 1c400b420736f9d99ec5092716b825c9634446d3The goal of artificial intelligence is to build robots that can learn, reason, and adapt to carry out complicated tasks. The research aims to provide essential guidance to enhance students' knowledge related to artificial intelligence devices. The highlights of the need for Indonesian engineering students to align their competencies with the 4.0 industrial revolution by developing knowledge in adopting artificial intelligence to improve Indonesia's technological standing in fields such as power electronics. The respondent of the study involved 125 electrical engineering students in Aceh. The questionnaire is the chosen instrument aimed at obtaining perceptions from electrical engineering students regarding their knowledge of artificial intelligence devices. The research revealed that electrical engineering students in Aceh struggle with understanding artificial intelligence applications due to limited exposure, a lack of expert guidance, unfamiliarity with programming elements, and insufficient explanation of AI concepts in their curriculum.Jurnal edukasi elektro00The research revealed that electrical engineering students in Aceh struggle with understanding artificial intelligence applications due to limited exposure, a lack of expert guidance, unfamiliarity with programming elements, and insufficient explanation of AI concepts in their curriculum.Jurnal Edukasi Elektro["Sadrina Sadrina", "Ramlee Mustapha", "Fathurrahman Fathurrahman", "Gina Saria Putri"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/1c400b420736f9d99ec5092716b825c9634446d3 85f73cdc044835ea5240a070d1e451b3138cdb22Electrochemical desalination technologies, including battery electrode deionization and capacitive deionization (CDI), are being actively studied as alternatives to membrane technology due to their relatively low energy consumption. In line with the development of technology, modeling-based research has also been actively conducted to mathematically simulate and automatically control the technology (Donnan models or Lanmuir models). However, despite the complexity of calculations, these numerical models cannot simulate the entire actual operating environment; therefore, multiple assumptions are required. In addition, even in this simplified environment, predictions for specific parameters (effluent pH, etc.) are often very inaccurate. Therefore, research on big data-based artificial intelligence technology that can overcome the shortcomings of these numerical models has been reported mainly by specific research groups in recent years. First, a study that successfully predicted effluent concentration and pH in CDI using deep learning technology was reported in 2021 yr (i.e., Convolutional Neural Network combined with Long Short-Term Memory), followed by a study based on an ensemble model (i.e., Random Forest) that can overcome the complexity of deep learning models in 2023 yr. In addition, reinforcement learning-based research that can automate processes is also being reported starting in 2022 yr. Therefore, in this presentation, artificial intelligence technologies that can predict specific output variables of electrochemical desalination technology, quantify the correlation between each input variable (xAI), and ultimately enable process automation will be discussed. The advantages and disadvantages of different artificial intelligence technologies will also be discussed from a practical/technical perspective (i.e., response time, computational cost, etc).ECS Meeting Abstracts00Artificial intelligence technologies that can predict specific output variables of electrochemical desalination technology, quantify the correlation between each input variable (xAI), and ultimately enable process automation will be discussed.ECS Meeting Abstracts["Moon Son"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/85f73cdc044835ea5240a070d1e451b3138cdb22 525660b953910318a2a6ad7efa6cd9a704f3afd8GeroScience140In summary, AI models, particularly DL models, possess superior predictive capabilities that can enhance patient treatment in a more cost-effective manner and should serve to complement and assist healthcare professionals, rather than supplant them.GeroScience["Achamyeleh Birhanu Teshale", "H. Htun", "Mor Vered", "Alice J. Owen", "Joanne Ryan", "A. Tonkin", "R. Freak-Poli"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/525660b953910318a2a6ad7efa6cd9a704f3afd8 64b26c568b95e9499f4b4e2f8512d331a9053dd2OECD Artificial Intelligence Papers01OECD Artificial Intelligence Papers[]2024-11-22T00:00:00https://www.semanticscholar.org/paper/64b26c568b95e9499f4b4e2f8512d331a9053dd2 48afb9fde7f4969f12dee4482971a6d8dec7d5f4In response to the challenge of optimizing decision support systems (DSS) for enterprises in the big data environment, this paper introduces the C4.5 decision tree algorithm and the improved Apriori algorithm, aiming to improve the classification accuracy and data correlation mining effect of the system through data mining techniques. Firstly, this paper theoretically analyzes the principles and optimization directions of C4.5 and Apriori algorithms; then, algorithm application testing is conducted on actual datasets to verify the effectiveness of the algorithm in different business scenarios, especially in the fields of retail and finance. Finally, the experimental results show that the C4.5 algorithm exhibits high accuracy and F1 score in classification task. The results show that the C4.5 decision tree algorithm effectively improves the accuracy of classification tasks, reaching 85%, with an F1 score of 85%, which is better than traditional decision tree algorithms. The model and method proposed in this paper provide new technical support and optimization directions for enterprises in big data decision support.2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS)130The results show that the C4.5 decision tree algorithm effectively improves the accuracy of classification tasks, reaching 85%, with an F1 score of 85%, which is better than traditional decision tree algorithms.2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS)["Zhiyu Tian", "Shaojing Ma"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/48afb9fde7f4969f12dee4482971a6d8dec7d5f4 d9b09dc41e9d6a16bfd85902db6e4e2db9900c9aPalliative Medicine in Practice00Palliative Medicine in Practice["Ja\u015bmina Bork-Zalewska"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/d9b09dc41e9d6a16bfd85902db6e4e2db9900c9a 586a44a017f5254c740aa49b5d2e29ded2b47e05European Geriatric Medicine00European geriatric medicine["Yanira Aranda Rubio"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/586a44a017f5254c740aa49b5d2e29ded2b47e05 3a5fcfb222abc2129fbb458aa8f969476fc629e9NEJM AI20NEJM AI["Maya Petersen", "Ahmed Alaa", "Emre Kiciman", "Chris Holmes", "Mark van der Laan"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/3a5fcfb222abc2129fbb458aa8f969476fc629e9 46094c73eed89991c66d1f8b8381a9aa09bbed4fInternational Journal of Global Innovations and Solutions (IJGIS)00International Journal of Global Innovations and Solutions (IJGIS)["Nilesh Jain", "Shalmali Patil"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/46094c73eed89991c66d1f8b8381a9aa09bbed4f c28a3ceb29401242b73bbc1ce78c17b759c9af86International Journal of Surgery150International Journal of Surgery (London, England)["B. Schmidl", "Tobias H\u00fctten", "S. Pigorsch", "F. St\u00f6gbauer", "Cosima C. Hoch", "Timon Hussain", "Barbara Wollenberg", "Markus Wirth"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/c28a3ceb29401242b73bbc1ce78c17b759c9af86 dce00354f54f142b0d24da427a91a0aed6475bd9Clinical pharmacology and therapy10Clinical Pharmacology and Therapeutics["Luis Correia Pinheiro", "P. Arlett", "Kit Roes", "Flora Musuamba Tshinanu", "Gabriel Westman", "Zaide Frias", "Hilmar Hamann", "Joaquim Berenguer Jornet", "Iftekhar Khan", "Jeppe Larsen", "Karl Broich", "Emer Cooke"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/dce00354f54f142b0d24da427a91a0aed6475bd9 3fb0a78735779ad67f9f33bdff541e01c60a0519The International Journal of Advanced Manufacturing Technology100The International Journal of Advanced Manufacturing Technology["Tianyu Yang", "Shouliang Lai"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/3fb0a78735779ad67f9f33bdff541e01c60a0519 06df44d2c370169552f8f81ca9039fc7b01ffb3eComput. Biol. Medicine2360This scoping review aims to investigate AI (and especially ML) applications that have been implemented and evaluated across diverse clinical tasks or decisions in breast imaging and to explore the current state of evidence concerning the assessment of AI-based technologies for BC clinical care within the context of Health Technology Assessment (HTA).Computers in biology and medicine["Anisie Uwimana", "Giorgio Gnecco", "Massimo Riccaboni"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/06df44d2c370169552f8f81ca9039fc7b01ffb3e 793a92a032ba21f91b07dd8d355e00819147ad75This study examines the application of Artificial Neural Networks (ANNs) in Islamic finance with a specific focus on their potential to enhance Sharīʿah decision-making within the digital banking landscape. It explores the intersection between Islamic jurisprudence (fiqh) and ANNs, elucidating the complex nature of Artificial Intelligence (AI) for stakeholders in the Islamic finance and digital banking sectors. The methodology employed in this study involves a comprehensive review of the literature on the potential of ANNs. This literature review systematically examines how ANNs can be integrated into Sharīʿah-compliant digital banking to enhance the decision-making processes, highlighting both opportunities and challenges. The findings highlight the paucity of literature on the potential of AI to streamline operations and enhance the efficacy of Sharīʿah-compliant decision-making in the digital age. The research also addresses the ethical considerations and governance challenges associated with implementing AI in Islamic finance, emphasising the need for robust frameworks to ensure ongoing alignment with Sharīʿah guidelines. The findings suggest a practical model, structure, and workable framework for ANNs to enhance efficiency, accuracy, and transparency in Sharīʿah decision-making in digital financial practices while also identifying areas for further investigation.TAFHIM: IKIM Journal of Islam and the Contemporary World00The findings suggest a practical model, structure, and workable framework for ANNs to enhance efficiency, accuracy, and transparency in Sharīʿah decision-making in digital financial practices while also identifying areas for further investigation.TAFHIM: IKIM Journal of Islam and the Contemporary World["Mohd Noor Omar", "A. Sa\u2019ad"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/793a92a032ba21f91b07dd8d355e00819147ad75 376f1640d687a0c082fda578ae7dc23504ddc214The transformation of the teacher’s role in the era of artificial intelligence and information technologies is explored. Some perspectives on the role of education in certain contexts are presented, and briefly, the position of academia in order to find a perspective towards the direction of education in times of artificial intelligence towards the increase in different teaching modalities, such as synchronous and asynchronous ones or the hybrid model (face-to-face and virtual), various ways of understanding contemporary teaching-learning emerge, and it becomes important to generate questions such as How should education be developed in the 21st century?Revista Ensayos Pedagógicos00The transformation of the teacher’s role in the era of artificial intelligence and information technologies is explored, and various ways of understanding contemporary teaching-learning emerge.Revista Ensayos Pedagógicos["David Leandro \u00c1lvarez S\u00e1nchez"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/376f1640d687a0c082fda578ae7dc23504ddc214 c61ac6e5ce23243b548d699ce00c07b3dcd415e6Teacher training must adapt to prepare new generations in a constantly changing society. This essay proposes a humanistic approach, exploring how reflective practice, situated learning, and artificial intelligence (AI) can be integrated into teacher training, promoting a modern, effective, and human-centered education. Reflective practice is key to the professional development of teachers, enriching them through dialogue and collaboration. Artificial intelligence, despite its challenges, offers great potential for personalizing learning. Teachers must develop digital skills and a reflective attitude to integrate AI in a critical and responsible manner. Education must strive to build a more inclusive world with equity and social justice, providing opportunities for all students to develop their potential. In this sense, critical reflection and technology, at the service of humans, can contribute to this ideal. This essay invites us to reimagine teacher training, where technology and critical reflection intertwine to enhance the integral development of educators and students, building an education that responds to the challenges of the 21st century and trains conscious and responsible citizens.Revista Ensayos Pedagógicos00This essay proposes a humanistic approach, exploring how reflective practice, situated learning, and artificial intelligence can be integrated into teacher training, promoting a modern, effective, and human-centered education.Revista Ensayos Pedagógicos["Francisco Sere\u00f1o Ahumada"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/c61ac6e5ce23243b548d699ce00c07b3dcd415e6 382b08734b01ab02f98b2ad5137466e5e90380a6In recent years, there has been a notable increase in the adoption of artificial intelligence, particularly due to the growing implementation of Industry 4.0 and the massive generation of data across various industrial sectors. The aviation industry has not lagged behind in this technological advancement, and multiple studies have been conducted to explore the applications of artificial intelligence in this field. The objective of this study is to carry out a comprehensive and up-to-date analysis of the current state of artificial intelligence utilization in aviation operations, with a special focus on flight planning processes, trajectory prediction, and resource optimization. Through this analysis, the aim is to delve into the latest research and advancements in this field, identifying the main methodologies, algorithms, and techniques employed. Furthermore, the study seeks to provide an integrated view of the diverse applications of artificial intelligence in the aviation industry, highlighting its potential to enhance operational efficiency, safety, and decision-making. Additionally, it aims to identify the most relevant areas for future research and development, with the goal of contributing to progress and innovation in this promising field.Ciencia y Poder Aéreo250This study seeks to provide an integrated view of the diverse applications of artificial intelligence in the aviation industry, highlighting its potential to enhance operational efficiency, safety, and decision-making.Ciencia y Poder Aéreo["Cristian Lozano Tafur", "Didier Aldana Rodr\u00edguez", "Jaime Enrique Orduy Rodr\u00edguez", "David Reinoso Pintor"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/382b08734b01ab02f98b2ad5137466e5e90380a6 cf018673a3e164155d8f7b13c4cb0c9fdaa93c66As educational institutions grapple with teaching students about increasingly complex Artificial Intelligence (AI) systems, finding effective methods for explaining these technologies and their societal implications remains a major challenge. This study proposes a methodological approach combining Conceptual Metaphor Theory (CMT) with UNESCO's AI competency framework to develop Critical AI Literacy (CAIL). Through a systematic analysis of metaphors commonly used to describe AI systems, we develop criteria for selecting pedagogically appropriate metaphors and demonstrate their alignment with established AI literacy competencies, as well as UNESCO's AI competency framework. Our method identifies and suggests four key metaphors for teaching CAIL. This includes GenAI as an echo chamber, GenAI as a funhouse mirror, GenAI as a black box magician, and GenAI as a map. Each of these seeks to address specific aspects of understanding characteristics of AI, from filter bubbles to algorithmic opacity. We present these metaphors alongside interactive activities designed to engage students in experiential learning of AI concepts. In doing so, we offer educators a structured approach to teaching CAIL that bridges technical understanding with societal implications. This work contributes to the growing field of AI education by demonstrating how carefully selected metaphors can make complex technological concepts more accessible while promoting critical engagement with AI systems.arXiv.org01This study proposes a methodological approach combining Conceptual Metaphor Theory (CMT) with UNESCO's AI competency framework to develop Critical AI Literacy (CAIL), and identifies and suggests four key metaphors for teaching CAIL.ArXiv["Jasper Roe", "Leon Furze", "Mike Perkins"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/cf018673a3e164155d8f7b13c4cb0c9fdaa93c66 3bc846c41505d122c74bd13b6d2def7812d26eb8Purpose: The investment in Electronic Health Records (EHRs) in the United States has experienced substantial growth over the years, highlighting the critical importance placed on the adoption of digital health solutions within the healthcare system. This white paper explores the integration of AI with Electronic Health Records (EHRs) to enhance patient care. Methodology: This study discusses the current challenges in EHR usage, the potential of AI solutions, successful case studies, and the ethical considerations and regulatory frameworks necessary for a smooth implementation of AI in healthcare settings. Findings: The findings include that the combination of artificial intelligence (AI) with Electronic Health Record (EHR) systems presents a powerful opportunity to enhance patient care in the healthcare sector. By utilizing sophisticated AI technologies, healthcare providers can derive meaningful insights from large volumes of health data, which supports more informed decision-making. Utilizing machine learning algorithms can review patient histories, detect trends, and forecast outcomes with impressive accuracy, thereby improving the capability to deliver personalized treatments tailored to each patient's specific needs. Furthermore, AI-driven analytics can simplify administrative processes, reducing the workload for healthcare professionals and enabling them to focus more on patient care rather than administrative responsibilities. Unique Contribution to Theory, Policy and Practice: This article enriches the theoretical perspective on AI's influence in healthcare quality assurance by developing a framework that correlates AI application with improved patient care outcomes. It serves to inform policymakers about the effectiveness of AI technologies, pushing for policies that support their integration in healthcare settings. Furthermore, healthcare providers are presented with best practices for implementing AI solutions that reinforce quality assurance, ultimately aiding in the creation of a safer and more effective healthcare environment.International Journal of Health Sciences11The findings include that the combination of artificial intelligence (AI) with Electronic Health Record (EHR) systems presents a powerful opportunity to enhance patient care in the healthcare sector.International Journal of Health Sciences["Leela Prasad Gorrepati"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/3bc846c41505d122c74bd13b6d2def7812d26eb8 3060d17b41d3b21f93fceb6669fbcd0b0e45cfe4Supply Chain Management is a quintessential task of every firm as it is the core of the business. A business is driven entirely by customer behaviour, change in seasonal demand and how the customer expand or shrunk their purchasing power. The stock available in the inventory should also be managed based on the changing demand, if under stocked or overstocked leads to huge losses to the business and they will also lose their market share. This work focuses on the ongoing problem in the supply chain domain, which is the Bull Whip Effect, wherein a slight change in the customer demand has a significant impact in the inventory leading to stocking more than required. Many methodologies have been incorporated to reduce the effect of Bull whip. The advancement in the field of Artificial Intelligence has rendered effective to solve various real time problems. The machine learning algorithms can be fine-tuned to predict the seasonal demand in the market in prior based on the customer behaviour. The prediction results obtained using CatBoost machine learning algorithm has proven effective as it predicts the sales with better accuracy by combining the boosting technique on a decision tree.2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC)220This work focuses on the ongoing problem in the supply chain domain, which is the Bull Whip Effect, wherein a slight change in the customer demand has a significant impact in the inventory leading to stocking more than required.2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC)["Niresh Kumar S", "Cynthia Kirupakaran", "Harini K"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/3060d17b41d3b21f93fceb6669fbcd0b0e45cfe4 29a4f45f16de146df6f54668431c0af36df0c0b1The integration of artificial intelligence (AI) in English language education has generated significant interest and anticipation due to its potential to transform teaching methodologies and enhance learning outcomes. With this in mind, the present study explored the perspectives of 452 Iranian EFL instructors on AI integration in English language education, focusing on efficiency, social and emotional development, engagement, feedback, critical thinking, and the role of teachers. The participants’ thoughts, opinions, and concerns regarding advantages, disadvantages and challenges were gathered through an online questionnaire that included both closed and open-ended questions. This was followed by semi-structured interview sessions with a cohort of EFL instructors, facilitating the collection of both qualitative and quantitative data. The results revealed predominantly positive perceptions regarding AI technology such as ChatGPT in English language education. However, concerns regarding AI tools’ capabilities and limitations were expressed. EFL instructors held neutral attitudes towards the impact of AI tools such as ChatGPT on students’ social-emotional development and high order skills. The results further highlighted a spectrum of opinions on the merits (e.g., fostering collaboration and community building), drawbacks (e.g., insufficient consideration of sociolinguistic nuances and Americentric data), and potential challenges (e.g., apprehension of change) associated with AI integration. The study concluded by discussing the implications of these findings for English language education in Iran and offering recommendations for the effective and ethical integration of AI tools in EFL classrooms.Complutense Journal of English Studies240The perspectives of 452 Iranian EFL instructors on AI integration in English language education, focusing on efficiency, social and emotional development, engagement, feedback, critical thinking, and the role of teachers, revealed predominantly positive perceptions regarding AI technology such as ChatGPT in English language education.Complutense Journal of English Studies["Muhammed Parviz"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/29a4f45f16de146df6f54668431c0af36df0c0b1 f8a72a3ab2c2bd5edf9739643a665feb2fff4ef2The rise in vehicle ownership and urbanization, which exacerbates traffic congestion and carbon emissions, are significant barriers to sustainable urban development. This study investigates how artificial intelligence (AI), machine learning (ML), and the internet of things (IoT) could enhance urban transport systems, reduce traffic, and lower urban regions' carbon footprints. The study examines how urban infrastructures can be made more efficient and flexible through the use of technologies including demand-responsive transport systems, integrated mobility solutions, predictive maintenance, and personalized trip planning. A recent study of educators, students, and other participants at Canadian University Dubai (CUD) found that people are becoming increasingly accepting of AI-driven solutions that reduce fuel consumption and travel time. These technologies have promise even if there are still challenges to be solved, such as the requirement for ongoing validation in dynamic urban contexts and the reliance on historical data. The study concludes with recommendations for integrating AI, IoT, and ML to develop responsive urban mobility solutions and emphasizes the importance of practical implementations to realize these benefits.SMART260This study investigates how artificial intelligence, machine learning, and the internet of things could enhance urban transport systems, reduce traffic, and lower urban regions' carbon footprints through the use of technologies including demand-responsive transport systems, integrated mobility solutions, predictive maintenance, and personalized trip planning.2024 Third International Conference on Sustainable Mobility Applications, Renewables and Technology (SMART)["Mahmood Hossain", "Hamad Khalid", "Avent Prakasa Rao", "Mohammad Lootah", "Salah Salim Khalaf Al-Mohammedi", "S. Majeed"]2024-11-22T00:00:00https://www.semanticscholar.org/paper/f8a72a3ab2c2bd5edf9739643a665feb2fff4ef2 500b5113ca2b29c0466a9e5b84e373b78df451dbOrganisations now have the ability to store, analyse, and manage their data more efficiently and flexibly thanks to cloud computing. Meanwhile, it's not easy to maintain robust cloud security when cyber threats are constantly evolving. This chapter explores the main functions of AI and ML to help you better understand their contributions to cloud security. The use of AI and ML allows organisations to strengthen their cloud infrastructure by detecting, mitigating, and responding to new cyber threats as they emerge. Security systems can now identify trends, outliers, and possible dangers in massive datasets with the help of AI-driven methodologies. Machine learning algorithms have the ability to learn from attack data that has already occurred, which allows for the identification of potential dangers and the creation of better defences. Further, identity management is strengthened by AI-enhanced authentication and access control systems, which lessen the likelihood of data breaches and unauthorised access.International Conference on Evolutionary Computation1410This chapter explores the main functions of AI and ML to help you better understand their contributions to cloud security.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Anand Polamarasetti"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/500b5113ca2b29c0466a9e5b84e373b78df451db e28a6013f50ba7daf09ea920977cf2c353f0c229The latest mutation of Artificial Intelligence, Generative AI, is more than anything a technology of writing. It is a machine that can write. In a world‐historical frame, the significance of this cannot be understated. This is a technology in which the unnatural language of code tangles with the natural language of everyday life. Its form of writing, moreover, is multimodal, able not only to write text as conventionally understood, but also to “read” images by matching textual labels and to “write” images from textual prompts. Within the scope of this peculiarly mechanical manufacturing of writing are mathematics, actionable software procedure, and algorithm. This paper explores the consequences of Generative AI for literacy teaching and learning. In its first part, we speak theoretically and historically, suggesting that this development is perhaps as momentous for society and education as Pi Sheng's invention of moveable type and Gutenberg's printing press—and in its peculiar ways just as problematic. In the paper's second part, we go on to propose that literacy in the time of AI requires a new way to speak about itself, a revised “grammar” of sorts. In a third part, we discuss an experimental application we have developed that puts Generative AI to work in support of literacy and learning. We end with some findings and implications for literacy education and with a proposal for what we will call cyber‐social literacy learning.Reading Research Quarterly583It is proposed that literacy in the time of AI requires a new way to speak about itself, a revised “grammar” of sorts, and some findings and implications for literacy education and with a proposal for what will be called cyber‐social literacy learning.Reading Research Quarterly["M. Kalantzis", "B. Cope"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/e28a6013f50ba7daf09ea920977cf2c353f0c229 68d0b1b8a0660a5194c2f31a4e92880ef4159416This study explores the role of Artificial Intelligence (AI) in strategic market development and business growth. It aims to examine how AI innovations create new opportunities, enhance operational efficiencies, and drive competitive advantages for businesses across various sectors. This study relies significantly on previously published literature and secondary data sourced from esteemed academic databases, including Web of Science, SCOPUS, Google Scholar, and Research gate. Initially, we collected a dataset of 300 papers spanning the period from January 2020 to 2024. After a rigorous screening process, we narrowed it down to 65 papers to derive the desired insights and produce robust results. AI is revolutionizing market strategies by enabling personalized marketing, optimizing supply chains, and improving decision-making through predictive analytics. Companies are leveraging AI for automating customer service, enhancing product development, and discovering new business models. The study also highlights AI’s potential in facilitating global market expansion and localized product offerings. The findings suggest that AI is central to enhancing business efficiency, innovation, and customer satisfaction. However, ethical concerns such as data privacy, algorithmic bias, and regulatory challenges remain significant barriers to AI adoption. Companies must balance innovation with responsible AI use to ensure long-term success and sustainability. Businesses adopting AI technologies can gain a competitive edge by improving operational efficiency and creating more tailored customer experiences. However, they must invest in AI transparency, employee reskilling, and regulatory compliance to mitigate risks associated with AI deployment. Further research is needed to explore the ethical and societal impacts of AI, including its role in job displacement and its potential for environmental sustainability. The integration of AI with emerging technologies like blockchain and IoT also offers exciting opportunities for future growth. Organizations should invest in AI solutions aligned with business objectives, ensuring ethical practices and continuous innovation to maintain a competitive edge in the evolving market landscape.Edelweiss Applied Science and Technology1502The findings suggest that AI is central to enhancing business efficiency, innovation, and customer satisfaction, and organizations should invest in AI solutions aligned with business objectives, ensuring ethical practices and continuous innovation to maintain a competitive edge in the evolving market landscape.Edelweiss Applied Science and Technology["Fahim Hossain", "G. M. Selim", "Shree Pritom", "Paul Shuvo", "Abeda Najmin Kona", "Mostarifa Umme", "Hani Raina", "Fisan Shikder"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/68d0b1b8a0660a5194c2f31a4e92880ef4159416 7bfa2d64ffc6bc17b11909744e62bdc59cacc091There is an urgent need in many application areas for eXplainable ArtificiaI Intelligence (XAI) approaches to boost people’s confidence and trust in Artificial Intelligence methods. Current works concentrate on specific aspects of XAI and avoid a comprehensive perspective. This study undertakes a systematic survey of importance, approaches, methods, and application domains to address this gap and provide a comprehensive understanding of the XAI domain. Applying the Systematic Literature Review approach has resulted in finding and discussing 155 papers, allowing a wide discussion on the strengths, limitations, and challenges of XAI methods and future research directions.ACM Computing Surveys672A systematic survey of importance, approaches, methods, and application domains of the XAI domain is undertaken to address this gap and provide a comprehensive understanding of the XAI domain.ACM Computing Surveys["Naeem Ullah", "Javed Ali Khan", "I. De Falco", "Giovanna Sannino"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/7bfa2d64ffc6bc17b11909744e62bdc59cacc091 ff880cc3215c5af33ef0e3e40c7736c4b2571b8bThis study examines how South-West Nigerian manufacturing companies' entrepreneurial success may be improved by artificial intelligence (AI) technology. The study examines the connections between AI constructs—adoption, applications, relevance, and utilization—and performance outcomes, such as competitive advantage, customer satisfaction, operational efficiency, total quality management, and innovation capability, using a sample of 346 businesses and data analysis tools like SmartPLS and SPSS. The results show that although AI adoption by itself does not considerably boost performance, competitive advantage, customer happiness, and operational efficiency are greatly impacted by AI's strategic use and applicability. The findings highlight the importance of focused AI applications in providing quantifiable advantages, especially in operational and customer- facing fields. The necessity of matching AI activities with organizational objectives is shown by the fact that AI relevance also appears as a critical component that has a beneficial influence on customer satisfaction and competitive positioning.International Journal of Advanced Studies of Economics and Public Sector Management00The results show that although AI adoption by itself does not considerably boost performance, competitive advantage, customer happiness, and operational efficiency are greatly impacted by AI's strategic use and applicability.International Journal of Advanced Studies of Economics and Public Sector Management["M. Mashi", "Auwal Abdullahi", "Nurudeen O. Yusuff"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/ff880cc3215c5af33ef0e3e40c7736c4b2571b8b 47ec4f2f41003adeb13d637ed749c570362ab4bbWhile artificial intelligence (AI) garners widespread media attention as an emerging technology, empirical research on how AI-related information influences public opinion is scarce, especially among those with preexisting emotions toward AI. Utilizing a survey of 1,206 U.S. adults, this study examined how communication about AI and three discrete emotions (anger, fear, and hope) jointly affect public attitudes. More exposure to AI information from mediated sources was linked to stronger support for AI, particularly among those reporting more anger and less hope toward AI. Conversely, there was no significant association between information exposure from interpersonal sources and public support.Science communication720More exposure to AI information from mediated sources was linked to stronger support for AI, particularly among those reporting more anger and less hope toward AI, and there was no significant association between information exposure from interpersonal sources and public support.Science Communication["Sukyoung Choi", "Chul-joo Lee", "Andrew Park", "Jung Ah Lee"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/47ec4f2f41003adeb13d637ed749c570362ab4bb d916371ad4659d3170102a3f9db25d97f2c3a928Chronic noncommunicable diseases are the leading cause of death and morbidity worldwide. Mobile applications are commonly used by the population and, therefore, the implementation of this tool to guide, prevent and monitor the health status of users becomes extremely important, considering the effectiveness of these actions, already proven in the literature. Objective: to conduct an integrative literature review on the effectiveness of artificial intelligence to promote improvements in the health control of the population in relation to NCDs. Materials and methods: This is an integrative literature review through searches in the following electronic databases: National Center for Biotechnology Information (PubMed), Scientific Electronic Library Online (SciELO), Medical Literature Analysis and Retrieval System Online (MEDLINE) and Latin American and Caribbean Literature in Health Sciences (LILACS). The inclusion criteria were studies published between 2019 and September 2023; be in English, Portuguese or Spanish and that analyzed the effectiveness of artificial intelligence to assist in better control of NCDs. The results were analyzed through a critical review of the content. Results and discussion: Ten articles were included for detailed analysis. The results indicate that this strategy is effective in improving disease control, especially diabetes mellitus, considering the population as a whole, that is, from all socioeconomic levels and cultural diversity. Conclusion: This review allowed us to conclude that this resource is a great ally for professionals in the development and implementation of health promotion actions aimed at reducing NCDs. ARACÊ00An integrative literature review on the effectiveness of artificial intelligence to promote improvements in the health control of the population in relation to NCDs indicates that this strategy is effective in improving disease control, especially diabetes mellitus.ARACÊ["Karine Siqueira Cabral Rocha", "Carolina Milhim Barcellos", "Marisa Afonso de Andrade Brunherotti", "Lilian Cristina Gomes do Nascimento"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/d916371ad4659d3170102a3f9db25d97f2c3a928 79b00ea287dfc44fb34ed94713fb5631e1f61b2eArtificial Intelligence (AI) is making transformative strides in the mental health sector. This study explores the integration of AI into mental health services, focusing on current applications, ethical challenges, and future directions. By reviewing literature from databases such as PubMed, IEEE Xplore, PsycINFO, and Google Scholar, we examine the role of AI in diagnosis, treatment, and ongoing monitoring within mental healthcare. The findings reveal significant potential in AI driven early diagnosis, personalized treatment plans, and AIpowered virtual support systems, all of which contribute to more accessible and effective mental health care. However, ethical considerations related to privacy, bias, and the need for human presence highlight the necessity for balanced AI integration. The study concludes that while AI has transformative potential, responsible use is critical to fully leverage its benefits in mental health services. International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)00While AI has transformative potential, responsible use is critical to fully leverage its benefits in mental health services, and the study concludes that responsible use is critical to fully leverage its benefits.International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)["Anu P", "Anil Chhetri", "Aravind Narayanan S", "Kushal Sa", "Tharani R", "Anitha S"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/79b00ea287dfc44fb34ed94713fb5631e1f61b2e d2072689cf1a278381323fbc58f4f0ce3dcf4ca8This letter examines the increasing reliance on artificial intelligence (AI) tools like ChatGPT among medical students in dermatology research, driven by the competitive nature of residency matching. While AI can significantly boost research productivity, it also introduces ethical concerns, including risks of academic dishonesty, data privacy issues, and potential over-reliance on technology that may undermine students’ critical thinking and intellectual development. Balancing AI's benefits with a commitment to ethical standards is crucial to preserving the integrity and quality of medical research.Clincal and Experimental Dermatology00Clinical and experimental dermatology["Alisha Kashyap", "T. A. Black", "Emelie N McQuitty", "Amna M Ali", "Nayna Nambiar", "Rashid M Rashid"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/d2072689cf1a278381323fbc58f4f0ce3dcf4ca8 cfaae5589e5dae9dc40586b66e3202f90462ee74 This study examines the relationship between labour market changes and artificial intelligence, utilising Romer’s Endogenous growth theory, Schumpeter’s Creative destruction, Solow’s Growth model and Becker’s Human capital theory as theoretical frameworks. The purpose of this research is to clarify the multifaceted impacts of artificial intelligence on economic growth, workforce adjustment and the emergence of novel employment trends, focusing specifically on job losses and gains, wage inequalities, and changing skill requirements. Using a structured literature review methodology, the economic implications of artificial intelligence in the labour market were systematically analysed and synthesised. The results suggest that although artificial intelligence significantly enhances productivity and innovation, it has a complex effect on the labour market, causing employment gains in technologically sophisticated industries and losses in sectors prone to automation. The study emphasises strategic policy interventions and pedagogical reforms that maximise the economic benefits of AI while minimising its disruptive effects on employment. Proponents of such policies argue that by cultivating a workforce that is resilient and capable of adjusting to changes driven by artificial intelligence, they can effectively mitigate inequality and safeguard economic stability.Croatian Regional Development Journal180The results suggest that although artificial intelligence significantly enhances productivity and innovation, it has a complex effect on the labour market, causing employment gains in technologically sophisticated industries and losses in sectors prone to automation.Croatian Regional Development Journal["Zivko Krstic"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/cfaae5589e5dae9dc40586b66e3202f90462ee74 c541e5b07bd8a8b2bce5792d6e2e038ff2ad5d28This exploratory study (N = 310) investigates the relationship between students’ attitudes toward artificial intelligence (AI), their attitudes toward AI ethics, and their media and digital literacy levels. This study’s specific objectives were to examine students’ (a) general attitudes toward AI, (b) attitudes toward AI ethics, (c) the relationship between the two, and (d) whether attitudes toward AI are associated with media and digital literacy. Participants, drawn from a convenience sample of university students, completed an online survey including four scales: (a) a general attitude toward AI scale (including two subscales, positive and negative attitudes), (b) an attitude toward AI ethics scale (including two subscales, attitudes toward accountable and non-accountable AI use), (c) a media literacy scale, and (d) a digital literacy scale, alongside demographic information. The findings revealed that students held moderate positive attitudes toward AI and strong attitudes favoring accountable AI use. Interestingly, media literacy was positively related to accountable AI use and negatively to positive attitudes toward AI, whereas digital literacy was positively related to positive attitudes, and negatively to negative attitudes toward AI. These findings carry significant theoretical implications by highlighting the unique relationship of distinct literacies (digital and media) with students’ attitudes. They also offer practical insights for educators, technology designers, and administrators, emphasizing the need to address ethical considerations in AI deployment.Societies350The findings revealed that students held moderate positive attitudes toward AI and strong attitudes favoring accountable AI use, and media literacy was positively related to positive attitudes, and negatively to negative attitudes toward AI.Societies["Asimina Saklaki", "Antonis Gardikiotis"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/c541e5b07bd8a8b2bce5792d6e2e038ff2ad5d28 8c43f0e74a9ab7e7cabf03ff471f4c08fe6e3d79The article examines the political strategy of the introduction of artificial intelligence by the Chinese government. The author examines the strategic goals and objectives of the Ministry of Transportation of the People’s Republic of China for the development and implementation of AI technologies. Forecasting the amount of funding and investment in smart transportation.Post–Soviet Continent00The article examines the political strategy of the introduction of artificial intelligence by the Chinese government and the strategic goals and objectives of the Ministry of Transportation for the development and implementation of AI technologies.Post–Soviet Continent["K. S. Karimov"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/8c43f0e74a9ab7e7cabf03ff471f4c08fe6e3d79 7ddbb781cc7ea2af42026649b8dc8b4a96315be2ABSTRACT Smoking remains a significant public health challenge worldwide, with numerous adverse health effects and social consequences. Despite efforts to curb smoking rates through various campaigns and interventions, the battle against smoking continues. In recent years, the application of artificial intelligence (AI) has emerged as a promising approach to enhance the effectiveness of antismoking campaigns. This review paper examines the current landscape of AI applications in campaigns against smoking, highlighting its potential benefits, challenges, and future directions. Through a comprehensive analysis of relevant literature and case studies, this paper provides insights into how AI can be leveraged to design and implement more targeted, personalized, and impactful antismoking initiatives. Additionally, it discusses the ethical considerations and potential limitations associated with AI-driven approaches in this context.Journal of Pharmacy and Bioallied Sciences40Insight is provided into how AI can be leveraged to design and implement more targeted, personalized, and impactful antismoking initiatives.Journal of Pharmacy & Bioallied Sciences["Saurabh Bhandari", "B. C. Yemineni", "Kiranmai Vadapalli", "Himadri Kundu", "A. Arya", "Rashmi Laddha"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/7ddbb781cc7ea2af42026649b8dc8b4a96315be2 a4908b97cd06028d14e08131ba52932db998217bReviews of Management Sciences842Review of Managerial Science["Giustina Secundo", "Claudia Spilotro", "Johanna Gast", "Vincenzo Corvello"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/a4908b97cd06028d14e08131ba52932db998217b 6748b18aa54e2c1e63433b0fb0539d4220d2ed8eJournal of Asian Public Policy802Journal of Asian Public Policy["Jiaxi Yang", "Han Qiu", "Wenxuan Yu"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/6748b18aa54e2c1e63433b0fb0539d4220d2ed8e a02def304325ade4d1b792e95b417da92d3784cbThe performance of the three-phase multilevel inverter (MLI) is monitored and analyzed using genetic algorithm as an intelligent algorithm for solving mathematical equations accompanying the inverter operation. This performance is compared when employing the traditional space vector pulse width modulation (SVPWM) technique to drive the MLI. SVPWM is commonly used for applications in the power electronics field. The gating angles of the inverters are extracted using the selective harmonic elimination (SHE) concept for optimal switching using artificial algorithms and monitoring the performance of the inverter when loaded with a three-phase motor. Then monitoring the performance of the inverter when extracting these angles using SVPWM techniques. The inverter's voltages, currents, and load characteristics are analyzed, as well as the frequency analysis of the variables, while checking the active and reactive power levels injected to the load. A distinct advantage has been indicated for algorithmic technology compared to SVPWM technique for most variables. A detailed MATLAB model with reduced number of switches for three phase multilevel inverter is built and thoroughly analyzed for both techniques. Also, a practical 31 level single phase inverter is built for testing and measurement.2024 6th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)1502024 6th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)["T. Hussein", "D. Ishak"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/a02def304325ade4d1b792e95b417da92d3784cb 5273991215a46675b29484408ae7d4df7929e93dAI applications in dentistry are multifaceted, ranging from diagnostic imaging and treatment planning to patient management and predictive analytics. Machine learning algorithms can analyze radiographs with higher accuracy than traditional methods, identifying cavities, lesions, and other dental issues at early stages, which contributes to timely interventions and improved patient outcomes. Moreover, AI-driven software has begun to support orthodontics, enabling precise treatment planning through 3D imaging technologies that ensure personalized and effective care. Robotic-assisted surgeries are also making their mark by enhancing the precision of procedures, such as implant placement and root canal treatments. These technological aids reduce the margin of human error, shorten procedural times, and improve patient recovery rates. Additionally, AI is instrumental in patient management systems that streamline appointment scheduling, follow-up reminders, and treatment histories, thus improving clinic efficiency.Health Sciences AUS10Artificial intelligence (AI) applications in dentistry are multifaceted, ranging from diagnostic imaging and treatment planning to patient management and predictive analytics, which contributes to timely interventions and improved patient outcomes.Health Sciences AUS["Dr Hinagul"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/5273991215a46675b29484408ae7d4df7929e93d efcd1ef9ed758b5430809f32fec521ae7e1759d3Winter 2023 was a scary time for educators being confronted with generative AI for the first time. The launch of ChatGPT caused nothing less than a seismic shift in the field of higher education. Amidst calls to ban it from schools and decrying the end of entire disciplines, there was also a great surge of enthusiasm and optimism as faculty and students alike contended with how this will shape their educational experience going forward. This paper reflects on this time and on the then-emerging issue of how to respond to generative AI in higher education from the perspective of a teaching and learning centre. These centres occupy a middle space for educators between the administration making decisions for institutions and the faculty members responding to generative AI in their classrooms. Teaching and learning centres can provide resources, strategize, and centralize the distribution of information, resources, and supports from across institutions and between institutions.The Open/Technology in Education Society and Scholarship Association Conference00This paper reflects on this time and on the then-emerging issue of how to respond to generative AI in higher education from the perspective of a teaching and learning centre.The Open/Technology in Education, Society, and Scholarship Association Conference["Emily Ballantyne"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/efcd1ef9ed758b5430809f32fec521ae7e1759d3 c32980dd5cb806c2a1c4621d82882725c05c71f51st International Conference on Cybersecurity and Digital Defense001st International Conference on Cybersecurity and Digital Defense["Laiba Khan"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/c32980dd5cb806c2a1c4621d82882725c05c71f5 1b256ed7279e646bd2ed205279e3480c3ca0caeeCurrent Medical Research and Opinion130Current medical research and opinion["Marco Cascella", "Mohammed Naveed Shariff"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/1b256ed7279e646bd2ed205279e3480c3ca0caee 8698dac67907ecfa1447ecfe75be216491ee38fbThe future of home appliances in the IoT (Internet of Things) lies in self-awareness, independent condition-action rule implementation, and device autonomy. In the concept of autonomy, agent-based IoT appliances can SLEEP and WAKE-UP independently as when necessary, perceive and compute data; and engage in communication or negotiation with other agent-based devices. In the paradigm of OOP, this paper describes various home appliances' autonomous artificial intelligent (AAI) behavior. While self-awareness borders on the appliance knowing its current status and location, self-control relates to the appliance's ability to control its outgoing and incoming data and take the appropriate action based on some given environment data conditional rules. Thus, this paper presents some IoT home appliances' operational condition-action behavior (CAB). Firstly, by simulation, then followed by the actual transformation of the CAB rules into modular programs in object-oriented paradigm for sensor and appliance agents. The WindSpeedSensor, TempSensor, and HumiditySensor utilize the given environment data input to communicate with their respective WindowAppliance and CoffeeMachineAppliance agent, as demonstrated in this paper. In turn, the appliance agents take the decision reached by their sensors to perform some autonomous action. This model depicts independent AAI behavior of home appliances for the benefit of home owners.2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN)80Some IoT home appliances' operational condition-action behavior (CAB) is presented, by simulation, then followed by the actual transformation of the CAB rules into modular programs in object-oriented paradigm for sensor and appliance agents.2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN)["Kennedy E. Ehimwenma", "Haowen Ji", "Lin Jinle", "Safiya Al Sharji", "Maryam Cheraghy", "Yu Bowen"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/8698dac67907ecfa1447ecfe75be216491ee38fb 1a25bfc3bec8f7d73cad040609a0a63edf8987f4The benefits of new technology are becoming increasingly apparent to organisations as digital transformation continues. However, as technology becomes more widely used, cybersecurity threats and attacks are also becoming more common. Therefore, in order to combat constantly changing threats, increasingly advanced protections are needed. One potential solution could be to use AI. In order to evaluate the efficacy of artificial intelligence (AI)-based solutions in comparison to more conventional methods of cyber defence, the researchers in this study conducted a systematic literature review (SLR). Using the PRISMA flow diagram, the review procedures were sketched out. The other seventy-three papers were culled from scholarly publications that were searchable in EBSCO Host, Google Scholar, Science Direct, ProQuest, and SCOPUS between 2018 and 2023. Findings suggest that AI has the potential to improve cybersecurity in several areas, including automation, threat intelligence, and increased cyber defence. On the other hand, it raises new challenges, like adversarial attacks and the need for high-quality data, both of which might make AI less effective.International Conference on Evolutionary Computation130Artificial intelligence has the potential to improve cybersecurity in several areas, including automation, threat intelligence, and increased cyber defence, but it raises new challenges, like adversarial attacks and the need for high-quality data, both of which might make AI less effective.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Rahul Vadisetty"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/1a25bfc3bec8f7d73cad040609a0a63edf8987f4 3bccd4da1ce1cdfea79bee77c45be0a6c0c08d08This comprehensive article examines the transformative impact of artificial intelligence on healthcare diagnostics, focusing on current applications, implementation strategies, and future directions. The article encompasses various domains, including medical imaging, pathology, and genomics, where AI has significantly improved diagnostic accuracy and efficiency. The article explores healthcare institutions' integration challenges, including technical barriers, clinical adoption hurdles, and regulatory considerations. Through extensive analysis of multi-institutional data, this review highlights successful implementation frameworks, quality assurance protocols, and emerging technological trends. The findings underscore the potential of AI to enhance healthcare delivery while maintaining high standards of patient care, particularly in resource-constrained settings. By synthesizing evidence from diverse healthcare environments, this review provides valuable insights for healthcare providers, administrators, and policymakers navigating the complex landscape of AI integration in clinical practice.International Journal of Scientific Research in Computer Science Engineering and Information Technology60This comprehensive article examines the transformative impact of artificial intelligence on healthcare diagnostics, focusing on current applications, implementation strategies, and future directions, and highlights successful implementation frameworks, quality assurance protocols, and emerging technological trends.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Chandra Sagili"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/3bccd4da1ce1cdfea79bee77c45be0a6c0c08d08 c7f827230ff8f218450810d5a845c4fcb6b9f0f4The adoption of Artificial Intelligence (AI) and Machine Learning (ML) in various computing platforms and areas, necessitates the development of strong Explainable AI (XAI) techniques. Most current AI models are opaque about their decision-making process thereby impeding trust, debugging, and improvement. The goal of this research is to develop comprehensive robust XAI methods capable of explaining the reasoning and decision-making processes in Autonomic, Edge, Server-less, Quantum computing platforms and IoT, Business Automation, Service Innovation domains where these AI models are deployed.This study comprehensively addresses the opacity in AI models through solutions for balanced test-train splits, model evaluation, feature importance, metric imbalances, ROC curve and precision-recall curve analysis, accuracy and statistical metrics, benefits of manual review. This research aims at increasing transparency and trustworthiness within AI systems through developing as well as applying such XAI methods that can detect and mitigate biases while enhancing ethical debugging; responsible development for AI enabled computing purposes.International Conference on Evolutionary Computation140This study comprehensively addresses the opacity in AI models through solutions for balanced test-train splits, model evaluation, feature importance, metric imbalances, ROC curve and precision-recall curve analysis, accuracy and statistical metrics, benefits of manual review.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Vasanth S", "Keerthana S", "Saravanan G"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/c7f827230ff8f218450810d5a845c4fcb6b9f0f4 5f346f152264e37f3f16b2bb2325581a0c46d7d2This paper examines the current legal framework in Tanzania regarding copyright protection for works autonomously generated by artificial intelligence (AI). It highlights the lag in Tanzanian law, specifically the Copyright and Neighbouring Rights Act of 1999, in addressing the issues caused by AI-generated works. Advancements in AI, especially generative AI, are enabling the creation of literary, artistic, and musical works without human input. This challenges traditional concepts of authorship and ownership of literary and artistic works. The paper identifies limitations in the Tanzanian copyright law, which only recognizes human authorship and thus excludes AI-generated works from protection. The consequences of this exclusion include the loss of economic rights for developers and owners of AI technology in works generated by AI, the stifling of innovation due to a lack of incentive, and a lack of legal clarity on moral and economic rights over such works. The paper further explores the human rights implications of this legal gap. It proposes that rights such as the ownership of intellectual property and fair economic gain from one's work should extend to the developers or owners of generative AI technologies. Borrowing from the UK’s approach of attributing authorship to those who arrange for AI-generated works, the paper advocates for similar reforms in Tanzania. The proposed legal changes aimed at creating a copyright framework that better supports innovation, investment, and fair economic incentives for creators in the digital eraEast African Journal of Law and Ethics00The paper identifies limitations in the Tanzanian copyright law, which only recognizes human authorship and thus excludes AI-generated works from protection and proposes that rights such as the ownership of intellectual property and fair economic gain from one's work should extend to the developers or owners of generative AI technologies.East African Journal of Law and Ethics["Christopher Mukoji"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/5f346f152264e37f3f16b2bb2325581a0c46d7d2 77a8af5be97161b6e76321cb051375eb6ef006dcIn recent years, the healthcare sector has experienced rapid advancements in data interoperability, artificial intelligence (AI), and real-time decision support systems. Fast Healthcare Interoperability Resources (FHIR), an open standard for healthcare data sharing, has emerged as a key framework for facilitating interoperability among healthcare platforms. This paper presents a comprehensive analysis of integrating FHIR with Medplum, an open-source platform, to enhance real-time clinical decision-making through AI-driven solutions. The system was evaluated in real-world settings, demonstrating significant improvements in both efficiency and accuracy. The AI-powered clinical documentation system reduced the time healthcare providers spent on paperwork by 50%, enabling them to allocate more time to direct patient care. Furthermore, the system lowered documentation error rates from 6% to 2% under low-load conditions and from 10% to 4% under high-load conditions, resulting in an average error reduction of 60%. Additionally, the system exhibited strong scalability, maintaining acceptable response times as the number of users increased. Response times rose from 50 milliseconds (ms) under low-load conditions to 300 ms under extremely high-load conditions (1000 users).2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN)110This paper presents a comprehensive analysis of integrating FHIR with Medplum, an open-source platform, to enhance real-time clinical decision-making through AI-driven solutions, demonstrating significant improvements in both efficiency and accuracy.2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN)["Trung Kim Hoang Le", "Watcharachai Kongsiriwattana", "Chayapa Nimyungdee", "Rudsada Kaewsaeng-on", "A. Meny", "Khanista Namee"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/77a8af5be97161b6e76321cb051375eb6ef006dc 78db78da95bcaa49e72f29e08f9f0fcd20a24211The AIDoList system is an innovative endeavor aimed at revolutionizing the way individuals manage their daily tasks and commitments. This system seeks to develop a state of the-art task organization and management application empowered by artificial intelligence (AI) capabilities. AIDoList is designed to enhance task management efficiency, user experience, providing a comprehensive solution for users’ productivity needs. It’s main strength surrounds the fact that it can accept input from a user in natural language with spelling mistakes and then corrects them into proper format. Key system objectives include natural language processing (NLP) for multiple task input, task categorization, voice and speech recognition for hands-free task creation, reminding tasks with deadline, intelligent task suggestions, providing user insights. The system also prioritizes user-friendliness with an intuitive and visually appealing interface. It suggests tasks that are most frequently done by the user. AI gets lists of completed and failed tasks and gives advice to the user. This system represents a significant step forward in leveraging AI to streamline daily life and empower users to make the most of their time.International Conference on Evolutionary Computation50Key system objectives include natural language processing (NLP) for multiple task input, task categorization, voice and speech recognition for hands-free task creation, reminding tasks with deadline, intelligent task suggestions, providing user insights.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Akhil Chandran Miniyadan", "Nithya G P", "Susmitha Rajendran", "Rafna Sherin K", "Nidhina V", "Nakul Dev P K"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/78db78da95bcaa49e72f29e08f9f0fcd20a24211 850aba530e13da27ca909665ce3ea26d0f40003bThe advent of artificial intelligence has precipitated a transformation in the manner in which documentary heritage is researched, safeguarded and utilized. This paper presents a systematic review of the literature on the use of artificial intelligence technology in the core database of Web of Science for the protection of documentary heritage. It reviews all the studies on the use of artificial intelligence technology in the core database of Web of Science to participate in the preservation of documentary heritage, analyzes the changing trend of the number of published documents, uses VOSviewer to draw keyword co-graph, author contribution graph, citation analysis graph and coupling analysis graph, and analyzes author groups according to Lotka’s law and Price’s law. Finally, the paper summarizes the characteristics and shortcomings of artificial intelligence research in the field of document protection, and looks forward to the possible development direction of this field in the future.Journal of Artificial Intelligence Research590A systematic review of the literature on the use of artificial intelligence technology in the core database of Web of Science for the protection of documentary heritage summarizes the characteristics and shortcomings of artificial intelligence research in the field of document protection.Journal of Artificial Intelligence Research["Yaohan Lu"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/850aba530e13da27ca909665ce3ea26d0f40003b 3868f5824dd7a61478dd545b708934ad31266443Creating easily available services that connect education and job services is a problem for career counseling in the age of lifelong learning. The use of artificial intelligence to assist with advice in both higher education and the workplace has received very little attention up to this point. The use of artificial intelligence to assist and advance career counseling in higher education institutions is discussed in this study. The findings from focus groups, scenario analysis, and real world experiments are shared, outlining the needs and potential applications of artificial intelligence in career counseling from the perspectives of institutions, guidance personnel, and students. The results show the potential benefits and uses of artificial intelligence in career counseling, along with the obstacles and motivators for using it in order to promote lifelong learning and higher education International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)00The results show the potential benefits and uses of artificial intelligence in career counseling, along with the obstacles and motivators for using it in order to promote lifelong learning and higher education.International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)["Amsa Lakshmi M", "Sankarshan S K", "Shruthi M", "Niveditha H B", "Firoj Ansari"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/3868f5824dd7a61478dd545b708934ad31266443 4af4d9798a20da97eed0f41ae0828e12a35385f2Artificial Intelligence (AI) has become a ground breaking force that is changing industries all over the world and surpassing conventional bounds. Our teaching and learning methods are being drastically altered by the widespread use of technology in the educational field. With major improvements in teaching strategies, individualized learning, and general student engagement, artificial intelligence (AI) is revolutionizing the education sector. The application of AI in education in India has shown incredible promise, transformed conventional approaches, and ushered in a new era of creativity and individualized learning .The Office of Educational Technology (OET) of the U.S. Department of Education develops a national education technology policy and establishes the framework for how artificial intelligence (AI) can transform education and facilitate learning for early learners in K–12, higher education, and adult education everywhere, at any time. International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)00The Office of Educational Technology (OET) of the U.S. Department of Education develops a national education technology policy and establishes the framework for how artificial intelligence (AI) can transform education and facilitate learning for early learners in K–12, higher education, and adult education everywhere, at any time.International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)["Kavitha Kb", "P. Tk", "Suguna A"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/4af4d9798a20da97eed0f41ae0828e12a35385f2 4a33b12efd3be80674e1a9d31d430fef21c4dd3cThe capabilities that artificial intelligence (AI) offers to revolutionise strategic decision-making are a significant reason for the profound importance that AI has in the everchanging environment of the business world. Artificial intelligence provides leaders with sophisticated tools that enable them to master difficult situations and effectively foresee future obstacles. Businesses can stay ahead of the competition in settings that are always changing by using AI to make their decision-making processes more accurate, efficient, and forward-looking. This technology not only makes things run more easily, but it also helps businesses change with the times and do well in any market. Without artificial intelligence, we can't look at data. It can find patterns, trends, and ideas that people might not see. AI can quickly and correctly look at data and give people helpful information and suggestions that can help them make better decisions.International Conference on Evolutionary Computation702024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Sunita Pachar", "Katam Naga Lakshman", "Y. L. M. Latha", "Lakshmi B", "Yabesh Abraham Durairaj Isravel", "S. Katta"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/4a33b12efd3be80674e1a9d31d430fef21c4dd3c 94db118e8a58052fe577081d80aecfafd970773aThe advent of AI has been a game-changer, impacting every facet of our life in this age of unparalleled technological progress. Every industry has seen the ripple effects of AI's profound impact, and human resource management is no exception. Artificial intelligence (AI) is becoming increasingly important within the framework of people management strategies, which is explored in this chapter. Optimisation of talent acquisition, development, and retention strategies can be achieved with the use of insights enabled by data and automation made available by the ever-improving AI technology. This chapter explores the potential benefits and drawbacks of artificial intelligence (AI) in talent management, as well as the ethical challenges that arise from its utilisation. This thesis seeks to shed light on the revolutionary potential of artificial intelligence (AI) in HRM and its influence on organisational success by analysing current trends and future outcomes.International Conference on Evolutionary Computation140This thesis seeks to shed light on the revolutionary potential of artificial intelligence (AI) in HRM and its influence on organisational success by analysing current trends and future outcomes.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Muskan Gupta", "B. R. Kumar", "S. Ammani", "Monika Gupta", "Mohan Ranga", "Rao Dontineni", "S. Katta"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/94db118e8a58052fe577081d80aecfafd970773a e50bdc24efb26325ef7534e06f67d541c2f4589fINTRODUCTION: The advancement of artificial intelligence (AI) has revolutionized the management of critically ill patients, especially in Intensive Care Units (ICUs). Through predictive algorithms, AI enables real-time analysis of large volumes of data, aiding in the early identification of serious conditions and in the personalization of treatments. This has provided faster diagnoses and more accurate interventions, in addition to optimizing clinical decision-making. OBJECTIVE: With this in mind, the objective of this study was to analyze the impact of the main artificial intelligences for optimizing intensive care. METHODOLOGY: This study is an integrative literature review, considering the need to bring together the main types of scientific works and analyze their impact related to the topic under debate. The search was carried out in an exploratory manner in the main databases of medical literature, such as PubMED, Cochrane, SciELO and Web of Science. RESULTS: The results obtained reinforce the importance of using artificial intelligence (AI) in monitoring, early diagnosis and personalization of care in ICUs. Several studies highlight the positive impacts of AI, particularly in the continuous monitoring of vital signs and in the early detection of critical conditions, such as sepsis, organ failure and other complications in critically ill patients. CONCLUSION: The use of AI in intensive care medicine has already demonstrated its value in improving clinical outcomes, reducing mortality and personalizing the treatment of critically ill patients, as long as it continues to be implemented as a support for clinical decision-making and not as a substitute for medical judgment. ARACÊ00The use of AI in intensive care medicine has already demonstrated its value in improving clinical outcomes, reducing mortality and personalizing the treatment of critically ill patients, as long as it continues to be implemented as a support for clinical decision-making and not as a substitute for medical judgment.ARACÊ["Atinelle Teles Novais Lemos", "Elton Lemos Silva", "Jo\u00e3o Victor Lemos Silva", "Joyce Damasio", "Felipe Freire Correia", "Gabriela Chaves Calixto", "Tain\u00e1 Sales Prud\u00eancio Freire", "Rian Barreto Arrais Rodrigues de Morais", "Karoline Eyshila Sousa Ara\u00fajo", "Yohanna Candido Ribeiro", "Emanuelle Santos de Oliveira", "Abra\u00e3o Queiroz dos Anjos", "Edielma Batista Franco", "Lucas da Silva Teixeira", "Diogo Mariano Hildefonso"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/e50bdc24efb26325ef7534e06f67d541c2f4589f c756c47ab3f05c5eb86f92045051a3e13a62fcabThe combination of multi-cloud computing with Network Function Virtualisation (NFV) is a powerful tool for carriers to deploy their network services. Virtual network services (VNS) provide them with significant cost savings and more flexibility. Academics and practitioners have struggled to find a solution that grants these services the same availability and performance as traditional networks. This intricacy arises from a multitude of sources. One key reason is the difficulty of handling fault and performance issues with NFV and VNSs that are based on multiple clouds. Virtual environments are not well-suited to rule-based approaches, which are commonly employed in conventional physical networks. Machine learning and other forms of artificial intelligence (AI) are, thankfully, working well here. This tutorial's primary goal is to help students comprehend how artificial intelligence (AI) based solutions can improve the availability and performance of these services by assisting with fault identification and localisation. To help illustrate the points, we have included a case study that is based on our previous work in this field.International Conference on Evolutionary Computation110This tutorial's primary goal is to help students comprehend how artificial intelligence (AI) based solutions can improve the availability and performance of these services by assisting with fault identification and localisation.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Rahul Vadisetty"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/c756c47ab3f05c5eb86f92045051a3e13a62fcab 28deb11b14b6eaeaaea5bd16d345b765f57cbac5Artificial intelligence (AI) is based on the premise that machines may learn from data collected from different sources to mimic human intellect in order to carry out tasks, identify patterns, or anticipate outcomes. Many areas of technology have made extensive use of AI and ML algorithms, including: autonomous vehicles, recommendation systems in e-commerce and social media, financial technology, question answering systems, and natural language processing. In a similar vein, AI is quietly revolutionising healthcare research. Half a century ago, there was a lot of interest in using a rule-based method for illness diagnosis and clinical decision assistance. When it comes to diagnosing diseases and developing individualized treatment programs, AI systems show remarkable accuracy in analyzing medical imagery. Streamlining operations using AI-powered solutions improves efficiency and the patient experience, while predictive analytics help find patients at high risk so they can get preventative treatments. By automating monotonous operations, particularly in the domains of surgery and rehabilitation, robots powered by artificial intelligence also improve healthcare delivery. Data quality, interpretability, bias, and regulatory frameworks are all important concerns that must be resolved before AI can be used responsibly. Ethical and successful AI integration into healthcare requires robust regulatory frameworks, education, safety validation, human-AI collaboration, and education.International Conference on Evolutionary Computation150Ethical and successful AI integration into healthcare requires robust regulatory frameworks, education, safety validation, human-AI collaboration, and education, which are all important concerns that must be resolved before AI can be used responsibly.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Muni Prasad Putalpattu", "Kumbham Bhargavi", "Megharani B. Mayani", "Pilla Srinivas", "A. Siddiqa", "Mohan Kunkulagunta"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/28deb11b14b6eaeaaea5bd16d345b765f57cbac5 cee72b4d09618cb7e4932b294ec4dc9806f31117Artificial Intelligence (AI) is transforming healthcare, especially in personalized medicine, where AI driven tools enable tailored patient care. This paper explores AI applications across predictive analytics, genomics, and real time patient monitoring. Emphasizing AI’s role in precision diagnostics and treatment customization, we highlight challenges and potential pathways for implementation. International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)00This paper explores AI applications across predictive analytics, genomics, and real time patient monitoring,phasizing AI’s role in precision diagnostics and treatment customization and highlighting challenges and potential pathways for implementation.International Conference on Recent Trends in Computing &amp; Communication Technologies (ICRCCT’2K24)["Mohith D", "R. K"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/cee72b4d09618cb7e4932b294ec4dc9806f31117 cc5f6ec76fca2ee90f279adf10051d06b6140015Artificial intelligence (AI) is finding more and more uses in HRMS and HRIS as a result of HRM's shift towards digitalisation. An increasing number of tactical procedures have begun to use AI. Training and development systems, disciplinary action management, compensation and benefit analysis, employee satisfaction surveys, and performance reviews are all part of this. We plan to read up on articles and books that cover the topic of AI in HRM so that we can better grasp this new trend. This research intends to illuminate the pros and cons of artificial intelligence (AI) in the context of the hiring process, which stands to be significantly altered by this technology. This article takes a look at several AI-powered recruitment strategies and weighs the benefits and drawbacks of using AI in this industry. Social media screening, video interviews, chatbots, predictive analytics, gamification, and resume screening are all examples of AI-based recruiting tactics that might greatly benefit businesses, according to the results. Efficient hiring, cost savings, and improved talent acquisition are all possible outcomes of implementing these tactics. Still, there are certain legitimate moral and legal questions that arise from using AI for hiring purposes, such as the possibility of algorithmic prejudice and discrimination.International Conference on Evolutionary Computation130This research intends to illuminate the pros and cons of artificial intelligence (AI) in the context of the hiring process, which stands to be significantly altered by this technology.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Sajjan Choudhuri", "Sivapavani Veeranalla", "Prathima Gamini", "Chiranjeevi Manike", "U. Priya", "S. Katta"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/cc5f6ec76fca2ee90f279adf10051d06b6140015 d25a6e0fb5f4ed4f5445dbfb2b0e84316f167b04This study investigated the attitudes and perceptions of management undergraduates towards Artificial Intelligence (AI) in higher education. Drawing on survey data from 185 management undergraduates across three leading public universities in Sri Lanka, the study examined the respondents’ perspectives towards AI along with seven distinctive domains of General Perception and Awareness, Comfort and Confidence, Education and Curriculum Design, Ethical Considerations, Impact of market jobs, Learning experience and Future Preparedness. Employing cross-sectional descriptive research design, an online survey using Google Forms was administered to collect data, covering seven sub-areas related to AI awareness and attitudes. Results indicated a moderate level of knowledge about AI concepts among management undergraduates, coupled with a significant gap in formal education and awareness about AI technologies within their academic curriculum. While many undergraduates expressed optimism about the positive impact of AI on their performance and the job market, there is a clear need for increased integration of AI topics into academic programs to enhance skills and knowledge of the undergraduates in this rapidly evolving field. Ethical considerations surrounding AI emerged as an important area of concern, highlighting the need for greater awareness and education on AI ethics within academic curricula. Accordingly, the study contributes valuable insights to the growing body of literature on AI in higher education and emphasizes the importance of addressing the evolving role of AI in preparing management undergraduates for the future.Journal of Tertiary Education and Learning00A moderate level of knowledge about AI concepts among management undergraduates is indicated, coupled with a significant gap in formal education and awareness about AI technologies within their academic curriculum, highlighting the need for greater awareness and education on AI ethics within academic curricula.Journal of Tertiary Education and Learning["K. Anjala"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/d25a6e0fb5f4ed4f5445dbfb2b0e84316f167b04 a8b8ea0c69597f859b1a3ac80d08fafbd4ccfda7In order to achieve digital upgrading of traditional shipbuilding industry, framework design and program development of the artificial intelligence (AI) Aided Process Designing Platform have been completed. The platform is based on B/S architecture. By utilizing retrieval- augmented generation (RAG) technology and Large Language Model (LLM), four major function functional modules was implemented, including process knowledge integration, Knowledge Engineering Management, intelligent question and answer (Q&A) for process knowledge, and process plan generation. The retrieval accuracy of the AI Aided Process Designing Platform was proved to reach over 95%, while the accuracy of Q&A reached over 74% by manual testing, which can help technicians save query time and Improve design efficiency. Besides, process plans can be automatically composited rapidly through the templates. Therefore, the AI Aided Process Designing Platform can help shipbuilding enterprises improve the economic benefits and enhance the competitiveness.2024 IEEE First International Conference on Data Intelligence and Innovative Application (DIIA)80The retrieval accuracy of the AI Aided Process Designing Platform was proved to reach over 95%, while the accuracy of Q&A reached over 74% by manual testing, which can help technicians save query time and improve design efficiency.2024 IEEE First International Conference on Data Intelligence and Innovative Application (DIIA)["Xiaoyang Liang", "Yanjun Ma", "Meng Xi", "Fei Li"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/a8b8ea0c69597f859b1a3ac80d08fafbd4ccfda7 2cf2182d4107d3300362be1a2823faedc9d80b26Adapting threat detection and response procedures is essential for businesses to stay ahead of the constantly developing cyberattack landscape. Researching cutting-edge tools like XDR, SIEM, SOAR, and NDR—which shed light on the dynamic realm of detection and response systems—is crucial. Other areas of investigation include real-time monitoring and network forensics. Security and threat detection have both been greatly enhanced since businesses began storing their apps and data in the cloud. In order to safeguard vulnerable network infrastructures in cloud settings against advanced attacks, traditional security procedures must be revised. Artificial intelligence (AI) is used to learn more about this problem so that threat reaction and identification can be done more quickly and accurately. Artificial intelligence has changed cloud security and threat identification, as this study shows. Because cyberattacks on cloud infrastructures and service providers are becoming more common, strong security steps that are also easy to use are still needed. This piece will talk about how cloud security and AI operations work together, with a focus on how this collaboration speeds up the time it takes to respond to incidents. The paper will also show how this relationship helps a company improve its defences and lessen the impact of security events. Organisations that want to keep up with the constantly changing danger landscape need to use cloud security and AI and understand how the two work together. This is because it is necessary to keep a security stance that is both flexible and strong.International Conference on Evolutionary Computation100This piece will talk about how cloud security and AI operations work together, with a focus on how this collaboration speeds up the time it takes to respond to incidents.2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC)["Rajashekar Reddy Yasani", "Putalpattu Muni Prasad", "Pattlola Srinivas", "N. V. R. S. Reddy", "P. Jawarkar", "Vedaprada Raghunath"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/2cf2182d4107d3300362be1a2823faedc9d80b26 7f3cb0413f6231602540230d6f5571779d97635aThe rapid advancements in Generative Artificial Intelligence (AI) have revolutionized domains such as natural language processing, computer vision, and creative content generation. Simultaneously, Cognitive Science seeks to understand the mechanisms of human cognition, including memory, decision-making, and creativity. This paper explores the intersection of these fields, investigating how Generative AI models can simulate cognitive processes and how Cognitive Science insights can inform AI development. Methodologies include experiments with Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and GPT-3 to assess simulations of memory, creativity, and decision-making. Empirical findings demonstrate how VAEs enable memory reconstruction, GANs simulate decision-making processes, and Transformer-based models like GPT-3 exhibit creative capabilities. This study provides valuable insights into advancing AI research while deepening the theoretical understanding of human cognition.2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN)70Empirical findings demonstrate how VAEs enable memory reconstruction, GANs simulate decision-making processes, and Transformer-based models like GPT-3 exhibit creative capabilities, and valuable insights into advancing AI research are provided.2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN)["Napat Sukthong"]2024-11-23T00:00:00https://www.semanticscholar.org/paper/7f3cb0413f6231602540230d6f5571779d97635a 88bb1f308b9fdead4e2562f222d7175d51910253AIM Regular screening of large number of people with diabetes for diabetic retinopathy (DR) with the support of available human resources alone is a global challenge. Digital health innovation is a boon in screening for DR. Multiple artificial intelligence (AI)-based deep learning (DL) algorithms have shown promise for accurate diagnosis of referable DR (RDR). The aim of this review is to evaluate the use of AI for DR screening and the various currently available automated DR detection algorithms. METHODS We reviewed articles published up to May 15th 2024, on the use of AI for DR by searching PubMed, Medline, Embase, Scopus, and Google Scholar using keywords like diabetic retinopathy, retinal imaging, teleophthalmology, automated detection, artificial intelligence, deep learning and fundus photography. RESULTS This narrative review, traces the advent of AI and its use in digital health, the key concepts in AI and DL algorithm development for diagnosis of DR, some crucial AI algorithms that have been validated for detection of DR and the benefits and challenges of use of AI in detection and management of DR. While there are many approved AI algorithms that are in use globally for DR detection, IDx-DR, EyeArt, and AEYE Diagnostic Screening (AEYE-DS) are the algorithms that have been approved so far by USFDA for automated DR screening. CONCLUSION AI has revolutionized screening of DR by enabling early automated detection. Continuous advances in AI technology, combined with high-quality retinal imaging, can lead to early diagnosis of sight-threatening DR, appropriate referrals, and better outcomes.Seminars in Ophthalmology341AI has revolutionized screening of DR by enabling early automated detection and continuous advances in AI technology, combined with high-quality retinal imaging, can lead to early diagnosis of sight-threatening DR, appropriate referrals, and better outcomes.Seminars in ophthalmology["R. Rajalakshmi", "T. Pramodkumar", "Abdul Subhan Naziyagulnaaz", "R. Anjana", "R. Raman", "Suchetha Manikandan", "Viswanathan Mohan"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/88bb1f308b9fdead4e2562f222d7175d51910253 02852551a9830f11375311069df707a9c9aa4382The article describes the basic principles of using artificial intelligence and digital technologies in the context of identifying and countering foreign means of influencing young people in their own environment. The basic principles of the influence of digital technologies, digitalization in politics and its impact on youth and modern politics in general are revealed. The relevance of the article is determined by the fact that in the context of modern globalization and the development of information and communication technologies, foreign countries are increasingly using digital methods of influence aimed at influencing the youth of other countries, including the Russian one. This manifests itself as the spread of disinformation and propaganda, the organization of protest actions and the recruitment of young people into terrorist and extremist organizations, etc. Artificial intelligence and digital technologies can help Russia in countering foreign influence on young people. They can be used to analyze large amounts of data, create a monitoring system, and develop information campaigns.Post–Soviet Continent30Artificial intelligence and digital technologies can help Russia in countering foreign influence on young people, and can be used to analyze large amounts of data, create a monitoring system, and develop information campaigns.Post–Soviet Continent["P. K. Pobedin"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/02852551a9830f11375311069df707a9c9aa4382 85e472c01e01e237300e07e1eaf3278e2f8155e1Amidst rapid growth and increasing complexity, the events and festivals industry is undergoing a significant transformation through the integration of Artificial Intelligence (AI) technologies. This paper investigates the impact of AI on the events landscape, examining its current applications, emerging trends, and future implications. The research questions address AI's current use in events, emerging trends, and potential future transformations. The study highlights AI's potential to improve attendee experiences, streamline event management, and enhance safety measures, offering deep insights into its substantial industry impact. Using a comprehensive research methodology, this paper explores the complex relationship between AI and events/festivals using empirical research, industry analysis, and case studies. It employs frameworks like AI-driven engagement tools, predictive analytics, and immersive experiences to assess AI's impact on the events landscape. Key findings highlight AI's ability to personalize experiences, optimize logistics, and enhance safety and sustainability. The paper also addresses challenges like data privacy, algorithmic bias, and ethical considerations, emphasizing the need for responsible AI deployment and risk mitigation. By offering best practices and ethical guidelines, this research aims to guide industry stakeholders in integrating AI effectively, steering the events and festivals industry towards greater innovation and efficiency.Events and Tourism Review00The study highlights AI's potential to improve attendee experiences, streamline event management, and enhance safety measures, offering deep insights into its substantial industry impact.Events and Tourism Review["Bhaskar Sailesh"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/85e472c01e01e237300e07e1eaf3278e2f8155e1 378db1780599082e8082af12e4fbf7e7aeb18cfcThis study explores the pivotal role of artificial intelligence (AI) in enhancing personal financial planning among working adults. As AI tools increasingly influence financial decision-making processes, understanding the factors that lead to their adoption becomes crucial. The study investigates how perceived usefulness and ease of use of AI applications affect adoption rates, with digital readiness and self-efficacy mediating variables. This research draws on primary data collected through surveys distributed to a purposive sample, ensuring the relevance and reliability of the findings. Out of 409 surveys distributed, 332 responses were received, providing a robust sample size for analysis, with 299 deemed suitable for data analysis. The data analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between variables and test the study's hypotheses. Results indicated strong support for most hypotheses, demonstrating that both perceived ease of use and usefulness significantly influence AI adoption, moderated by digital readiness and self-efficacy. Notably, ease of use had a more pronounced effect on adoption, underlining the importance of intuitive AI interfaces in encouraging user acceptance. The study suggests that future research could explore the long-term impact of AI adoption in financial planning and examine demographic factors that may alter adoption dynamics. Additionally, future inquiries might consider integrating qualitative insights to capture user experiences more deeply, providing a richer understanding of AI's role in personal finance. Implications from this research are profound, suggesting organizations focus not only on technological innovation but also on enhancing user training and building supportive ecosystems that foster digital readiness and self-efficacy. As such, by addressing these areas, organizations can significantly increase AI adoption, empowering individuals to leverage advanced tools for improvedInternational Journal of Academic Research in Business and Social Sciences490Implications from this research are profound, suggesting organizations focus not only on technological innovation but also on enhancing user training and building supportive ecosystems that foster digital readiness and self-efficacy.International Journal of Academic Research in Business and Social Sciences["Zahir Osman", "Ratna Khuzaimah Mohamad"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/378db1780599082e8082af12e4fbf7e7aeb18cfc 5e6b6dd2844fe4322bfff0d5d35aaabf5f348182This paper explores the transformative potential of Artificial Intelligence (AI), specifically deep learning, in strengthening cybersecurity through real-time threat detection. Given the rapid evolution of cyber threats, traditional detection methods often fall short, necessitating innovative approaches that can adapt and respond swiftly. This study employs a qualitative approach with a literature review and library research methodology to analyze current AI applications in cybersecurity. The research investigates the implementation of deep learning algorithms for identifying patterns and anomalies indicative of potential threats in digital systems. The findings indicate that deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enhance the precision and speed of threat detection, enabling proactive defense mechanisms. The study also addresses the challenges of implementing AI in cybersecurity, including data privacy, computational demands, and the need for continual model updates to counteract evolving threats. This work concludes that deep learning offers promising advancements for real-time threat detection, although its effectiveness depends on balanced integration with other cybersecurity practices and robust frameworks for data protection. Future research is encouraged to explore hybrid models combining deep learning with other AI techniques to further bolster cybersecurity defenses.The Journal of Academic Science170It is concluded that deep learning offers promising advancements for real-time threat detection, although its effectiveness depends on balanced integration with other cybersecurity practices and robust frameworks for data protection.The Journal of Academic Science["Ade Suparman", "Ekka Pujo Ariesanto Akhmad", "Benny Martha Dinata"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/5e6b6dd2844fe4322bfff0d5d35aaabf5f348182 a2009df8749e93a3d3feebef6bfb4c98f9a45f9d"Music gives a soul to the universe, wings to the mind, flight to the imagination, and life to everything." said by Plato. Throughout history music has been the universal language of every person. With each song written and music composed emotions have been unveiled and transmitted. Artificial Intelligence as we see it is penetrating into every field with each advancement it is found to be bigger and stronger than ever. The music industry is an evolving field at this point in time with many technological advancements such as Artificial Intelligence for the various tasks required to be performed for each song such as song writing, production, composition, singing, etc.. But to the positives there will always be negatives in this case it can be seen that there have been various cases of plagiarism, copyright infringement, violation of ethical concerns, unfair competition etc. This paper analyses the legal and ethical issues posed by AI in the music industry and also scrutinizes the efficiency of Indian law to manage the rampant violations of Artificial Intelligence by comparing it with various laws in different countries. It also provides suggestions as to how the use of such technology can be regulated without harming the various stakeholders involved in the industry.International Journal For Multidisciplinary Research180This paper analyses the legal and ethical issues posed by AI in the music industry and also scrutinizes the efficiency of Indian law to manage the rampant violations of Artificial Intelligence by comparing it with various laws in different countries.International Journal For Multidisciplinary Research["Kavinmathi", "Jayalakshmi Iyer Venkatraman"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/a2009df8749e93a3d3feebef6bfb4c98f9a45f9d d5879272d6b4873f97dd698dba94e1e97a2eff3fThis research examines the impact of artificial intelligence (AI) on Al-Ghifari university students' critical reading skills. As AI tools become more common in education, understanding their effects on students' analytical and evaluative abilities is crucial. A survey of 200 students assessed their AI usage for reading assistance, in addition to a reading comprehension test to evaluate critical reading skills. Results showed that 75% of students regularly used AI tools, particularly for text summarization. However, those who relied heavily on AI scored lower in critical reading assessments, indicating a negative correlation between AI usage and performance. These findings suggest that while AI can enhance immediate comprehension, it may hinder deeper analytical engagement. The study highlights the need for a balanced approach to AI integration in education, emphasizing strategies that develop critical reading skills among students.The GIST00It is suggested that while AI can enhance immediate comprehension, it may hinder deeper analytical engagement, and the need for a balanced approach to AI integration in education is highlighted.The GIST["R. D. Cahyani", "Octavia Chandra Dewi", "Adam Darmawan"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/d5879272d6b4873f97dd698dba94e1e97a2eff3f 9fc75f5ffa2a061b52496033c3387b94f0a28349Artificial intelligence (AI) and microbiome have emerged in recent years as transformative fields with far-reaching implications for various biomedical domains. This paper presents a comprehensive bibliometric analysis examining the intersection of AI and the microbiome (AIM). The study aims to provide information on this interdisciplinary field's research landscape, trends, and emerging topics. Using a systematic approach, data-driven studies were extracted from the Scopus database on 23 November 2023 and analyzed using the VOSviewer and Bibliometrix applications. The regression coefficient of 0.94 and the yearly growth rate of 7.46% in AIM production indicate a consistent increase over time. Identification of essential contributors, organizations, and nations illuminated cooperative networks and research hotspots. The trend themes are the gut microbiome, disease prediction, machine learning, transfer learning, categorization, big data, artificial neural networks, chronic rhinosinusitis, epidemiology, COPD, and bronchoalveolar lavage. These hot issues in AIM reflect the present emphasis on research and developments in our knowledge of the microbiome's function in health, sickness, and individualized treatment. The findings give researchers, policymakers, and industry experts a thorough picture of the research environment and guide future paths in AIM's fascinating and promising subject.Cellular and Molecular Biology510A comprehensive bibliometric analysis examining the intersection of AI and the microbiome (AIM) gives researchers, policymakers, and industry experts a thorough picture of the research environment and guide future paths in AIM's fascinating and promising subject.Cellular and molecular biology["S. Abdelwahab", "M. Taha", "A. Jerah", "A. Farasani", "Saleh M. Abdullah", "Ieman A M Aljahdali", "Roa Ibrahim", "Omar Oraibi", "Bassem Oraibi", "H. Alfaifi", "A. Alzahrani", "Y. Babiker"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/9fc75f5ffa2a061b52496033c3387b94f0a28349 2c0de6f0512c2b7fd021ce434614317a3e08dde4With the continuous enhancement of people's spiritual pursuits, multiplayer online games have become a popular trend, encompassing both multiplayer cooperative and competitive games. In multiplayer competitive games, skill disparities among players can lead to unfair competition, prompting some players to resort to cheating for an advantage. Traditional cheating methods, such as memory injection to modify game data, while effective, are easily detectable. In recent years, with the rapid development of Artificial Intelligence (AI) technology, AI-based image recognition cheating software has emerged as a new trend. This software analyzes game screens to identify enemy positions and simulates human operations for automatic aiming, making it difficult for anti-cheating systems to detect. However, AI cheating software has high hardware requirements and is affected by game environments, such as smoke grenades and walls, which can limit its effectiveness. This study aims to explore the advantages and limitations of AI image recognition cheating software compared to traditional memory injection cheating software, analyze the extent of players' use of AI cheating software, and discuss strategies for game companies to combat such cheating behaviors. The research finds that AI cheating software offers superior concealment compared to traditional methods but comes with higher costs and usage barriers. Game companies can employ various measures to prevent cheating, including uploading files to servers for detection, monitoring background software, and using AI to detect abnormal mouse movements. Furthermore, the power of the gaming community should not be overlooked, as player supervision and reporting can effectively reduce cheating behaviors.International Journal of Computer Science & Information Technology (IJCSIT)00The research finds that AI cheating software offers superior concealment compared to traditional methods but comes with higher costs and usage barriers.International Journal of Computer Science and Information Technology["Yixuan Hu"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/2c0de6f0512c2b7fd021ce434614317a3e08dde4 7d8207dce06b8afe6653f67659e16ee440bd6c1bAbstract Aim The aim of this study was to evaluate and compare artificial intelligence (AI)‐based large language models (LLMs) (ChatGPT‐3.5, Bing, and Bard) with human‐based formulations in generating relevant clinical queries, using comprehensive methodological evaluations. Methods To interact with the major LLMs ChatGPT‐3.5, Bing Chat, and Google Bard, scripts and prompts were designed to formulate PICOT (population, intervention, comparison, outcome, time) clinical questions and search strategies. Quality of the LLMs responses was assessed using a descriptive approach and independent assessment by two researchers. To determine the number of hits, PubMed, Web of Science, Cochrane Library, and CINAHL Ultimate search results were imported separately, without search restrictions, with the search strings generated by the three LLMs and an additional one by the expert. Hits from one of the scenarios were also exported for relevance evaluation. The use of a single scenario was chosen to provide a focused analysis. Cronbach's alpha and intraclass correlation coefficient (ICC) were also calculated. Results In five different scenarios, ChatGPT‐3.5 generated 11,859 hits, Bing 1,376,854, Bard 16,583, and an expert 5919 hits. We then used the first scenario to assess the relevance of the obtained results. The human expert search approach resulted in 65.22% (56/105) relevant articles. Bing was the most accurate AI‐based LLM with 70.79% (63/89), followed by ChatGPT‐3.5 with 21.05% (12/45), and Bard with 13.29% (42/316) relevant hits. Based on the assessment of two evaluators, ChatGPT‐3.5 received the highest score (M = 48.50; SD = 0.71). Results showed a high level of agreement between the two evaluators. Although ChatGPT‐3.5 showed a lower percentage of relevant hits compared to Bing, this reflects the nuanced evaluation criteria, where the subjective evaluation prioritized contextual accuracy and quality over mere relevance. Conclusion This study provides valuable insights into the ability of LLMs to formulate PICOT clinical questions and search strategies. AI‐based LLMs, such as ChatGPT‐3.5, demonstrate significant potential for augmenting clinical workflows, improving clinical query development, and supporting search strategies. However, the findings also highlight limitations that necessitate further refinement and continued human oversight. Clinical Relevance AI could assist nurses in formulating PICOT clinical questions and search strategies. AI‐based LLMs offer valuable support to healthcare professionals by improving the structure of clinical questions and enhancing search strategies, thereby significantly increasing the efficiency of information retrieval.Journal of Nursing Scholarship480AI‐based LLMs, such as ChatGPT‐3.5, demonstrate significant potential for augmenting clinical workflows, improving clinical query development, and supporting search strategies, however, the findings also highlight limitations that necessitate further refinement and continued human oversight.Journal of Nursing Scholarship["Lucija Gosak", "Gregor \u0160tiglic", "Lisiane Pruinelli", "Dominika Vrbnjak"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/7d8207dce06b8afe6653f67659e16ee440bd6c1b ae8c6619df22378f4d8450946c30730e390d8e75Background: Artificial Intelligence (AI) holds promise for transforming healthcare, with AI models gaining increasing clinical use in surgery. However, new AI models are developed without established standards for their validation and use. Before AI can be widely adopted, it is crucial to ensure these models are both accurate and safe for patients. Without proper validation, there is a risk of integrating AI models into practice without sufficient evidence of their safety and accuracy, potentially leading to suboptimal patient outcomes. In this work, we review the current use and validation methods of AI models in clinical surgical settings and propose a novel classification system. Methods: A systematic review was conducted in PubMed and Cochrane using the keywords “validation”, “artificial intelligence”, and “surgery”, following PRISMA guidelines. Results: The search yielded a total of 7627 articles, of which 102 were included for data extraction, encompassing 2,837,211 patients. A validation classification system named Surgical Validation Score (SURVAS) was developed. The primary applications of models were risk assessment and decision-making in the preoperative setting. Validation methods were ranked as high evidence in only 45% of studies, and only 14% of the studies provided publicly available datasets. Conclusions: AI has significant applications in surgery, but validation quality remains suboptimal, and public data availability is limited. Current AI applications are mainly focused on preoperative risk assessment and are suggested to improve decision-making. Classification systems such as SURVAS can help clinicians confirm the degree of validity of AI models before their application in practice.Journal of Clinical Medicine2520This work reviews the current use and validation methods of AI models in clinical surgical settings and proposes a novel classification system named Surgical Validation Score (SURVAS), which helps clinicians confirm the degree of validity of AI models before their application in practice.Journal of Clinical Medicine["N. Kenig", "Javier Monton Echeverria", "Aina Muntaner Vives"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/ae8c6619df22378f4d8450946c30730e390d8e75 ef46c0072a5e81f9dca2419372fe0e23e6d39d64Artificial intelligence (AI) is poised to revolutionize dermatology by enabling precision diagnostics, improving clinical workflows, and enhancing accessibility to care, especially in underserved regions. Dermatology’s reliance on visual data, such as clinical and dermoscopic images, makes it an ideal speciality for integrating AI-powered tools, particularly those based on machine learning (ML) and deep learning (DL).Health Sciences AUS120Dermatology’s reliance on visual data, such as clinical and dermoscopic images, makes it an ideal speciality for integrating AI-powered tools, particularly those based on machine learning (ML) and deep learning (DL).Health Sciences AUS["Dr annas Sani"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/ef46c0072a5e81f9dca2419372fe0e23e6d39d64 439cabf41df75a7a97b4d348b5d87c646b5fa457This study focuses on the English version of an ethnographic text translated by ChatGPT, a new technology that utilises Artificial Intelligence (AI). The purpose of the study is to evaluate the syntactic, semantic, and pragmatic aspects of the translation to assess the strengths and weaknesses of this translation technology presented as a formidable tool that is poised to replace translators and render them unemployed. The method used is qualitative. It interprets the text in the Source Language (SL) and evaluates the translation against criteria of fidelity to the meaning of the SL text, cohesion of the discourse in the Target Language, and respect for the cultural context. The data, manually extracted from the translated text, consists of errors and mistakes found in the translation. The data analysis is conducted following Andrew Chesterman's theory on the three aforementioned translation strategies. The results of the study reveal that contrary to the current propaganda, ChatGPT primarily engages in literal translation. It does not engage in oblique translation. Indeed, errors and mistakes of syntactic, semantic, and pragmatic nature are abundant. Procedures such as transposition, modulation, foreignization, domestication, adaptation, transediting, etc., are almost unknown to it. At the current stage, ChatGPT is a tool that contains a vast number of words and can effectively assist translators in their work. It is too early to envision a scenario where this technology would replace experienced translators. Current scientific research trends should incorporate ChatGPT.International Journal of Religion30The results of the study reveal that contrary to the current propaganda, ChatGPT primarily engages in literal translation, and does not engage in oblique translation.International Journal of Religion["S. M. Akpaca"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/439cabf41df75a7a97b4d348b5d87c646b5fa457 1135f62c2722d765a75a48f63989380a80c9fb5dArtificial Intelligence (AI) has been quickly evolving and disrupted many domains such as cybersecurity, governance, law enforcement etc. But with this evolution comes several legal questions to consider, in many cases, the current laws just don't fit. The complexities surrounding AI technology, particularly regarding issues of algorithmic bias and automated decision-making, present new challenges for which the Information Technology Act, of 2000, was not originally designed. This research focuses on two critical factors that demand urgent attention: Algorithmic Accountability and Data Privacy. While algorithmic accountability concerns the need for transparency and the ability to trace decision-making processes, data privacy revolves around safeguarding personal information from unethical AI usage. The absence of clear provisions/interpretation addressing these two factors not only weakens the Indian legal regime but also threatens the protection of individual rights. To address these issues, this research provides a comparative analysis using global models, using information gathered from frameworks like the Algorithmic Accountability Act in the US and the AI Act in the EU. These frameworks have established worldwide guidelines for AI governance by introducing extensive procedures to control algorithmic bias/inaccuracy and enhance data privacy. On the other hand, India's legal system remains disorganized and does not have an effective strategy to address the special threats and capabilities of AI. In addition to legal reform, the research illustrates a more effective explanation of how AI might misuse data and encourage biases, particularly in a large and diverse culture like India. Thus, the research emphasizes the need for immediate and focused reforms to establish a strong framework for regulation that takes AI's complexities into account. This involves implementing strict data protection regulations and required transparency guidelines for AI systems to guarantee that AI technologies are developed and implemented responsibly, preserving digital rights and trust among individuals.International Journal For Multidisciplinary Research110The research emphasizes the need for immediate and focused reforms to establish a strong framework for regulation that takes AI's complexities into account, which involves implementing strict data protection regulations and required transparency guidelines for AI systems to guarantee that AI technologies are developed and implemented responsibly, preserving digital rights and trust among individuals.International Journal For Multidisciplinary Research["Siva Vignesh S.K.V", "Nagarjun D.N"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/1135f62c2722d765a75a48f63989380a80c9fb5d 74ddaceae7bce4f58bed5290d7061b063f3cc7d8In this EPQ, I built a system that successively enables the large language model to answer questions on the past with evidence from primary sources. This project was motivated by the realization that despite the complexity, the performance of the large language model (LLMs) literally means understanding but simply does pattern recognition, coupled with probabilistic text prediction. Against this backdrop, a specialized AI was developed to help in error correction, given that AI-produced historical data are generally full of errors, by the use of the open-source Langchain-Chatchat that allows integration of LLMs with a structured knowledge database. These contain factual and historical information needed for the AI to compute the accurate answer. The system uses the GLM-4 model by Tsinghua University for the level of GPT-4 and above. This web application is built on Streamlit, an AI-powered system that interacts with the user. The steps through which input queries and historical text were processed by the system include an Unstructured Loader, a Text Splitter, an embedding model, and a Vector Store. Successful implementation of this system not only improved the reliability in AI for historical education but also laid the foundation for further improvement in educational technology and AI interaction models. Future plans will involve further historical database expansion to even more diverse periods and geographies, as well as enhancing the user interface to become more interactive and accessible.International Journal of Computer Science & Information Technology (IJCSIT)110A system that successively enables the large language model to answer questions on the past with evidence from primary sources to improve the reliability in AI for historical education and laid the foundation for further improvement in educational technology and AI interaction models.International Journal of Computer Science and Information Technology["Yuming Zou"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/74ddaceae7bce4f58bed5290d7061b063f3cc7d8 0332801bb2ae7e9ea9c0f27eb645abb5facbd66b 心血管危急重症病程演变迅速，且常伴随高死亡率，给公共卫生带来了严峻挑战。传统诊疗手段存在预警准确率不够理想、个体化治疗难以实现等局限性。随着大数据和人工智能技术的发展，医学人工智能模型在心血管疾病的诊疗过程中展现出巨大潜力。该文分析了心血管危急重症的特点，回顾了人工智能在心血管危急重症预警、诊断、治疗、预后预测中的应用，探讨了大语言模型在心血管危急重症领域的应用前景。. Zhonghua xin xue guan bing za zhi00Zhonghua xin xue guan bing za zhi["M. T. Yang", "J. D. Gao", "J. Wu"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/0332801bb2ae7e9ea9c0f27eb645abb5facbd66b 86ba88ea0940b6372e81c71cc7ce30eab9294ce6 人工智能正在从根本上改变着人类生活的各个方面，心血管疾病的诊疗同样受到了人工智能技术的深刻影响。深度学习等对心血管疾病的预测、诊断、治疗、管理等环节带来了巨大帮助。在智能化、数字化、个体化、精准化的人工智能模型加持下，更准确、更高效的心血管疾病诊疗成为可能。该文对相关领域的研究进展进行了总结和描述，并予以多维度评价，同时阐释了未来发展的愿景与挑战。. Zhonghua xin xue guan bing za zhi00Zhonghua xin xue guan bing za zhi["P. Zhang", "Y. T. Zhao", "Y. L. Han"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/86ba88ea0940b6372e81c71cc7ce30eab9294ce6 e8ad66569a43129c4346fe2f97bdef5303b00088Recent advancements in Artificial Intelligence (AI), particularly with Large Language Models (LLMs), have led to significant progress in narrow tasks such as image classification, language translation, coding, and writing. However, these models face limitations in reliability and scalability due to their siloed architectures, which are designed to handle only one data modality (data type) at a time. This single modal approach hinders their ability to integrate the complex set of data points required for real-world challenges and problem-solving tasks like medical diagnosis, quality assurance, equipment troubleshooting, and financial decision-making. Addressing these real-world challenges requires a more capable Artificial General Intelligence (AGI) system. Our primary contribution is the development of the Open General Intelligence (OGI) framework, a novel systems architecture that serves as a macro design reference for AGI. The OGI framework adopts a modular approach to the design of intelligent systems, based on the premise that cognition must occur across multiple specialized modules that can seamlessly operate as a single system. OGI integrates these modules using a dynamic processing system and a fabric interconnect, enabling real-time adaptability, multi-modal integration, and scalable processing. The OGI framework consists of three key components: (1) Overall Macro Design Guidance that directs operational design and processing, (2) a Dynamic Processing System that controls routing, primary goals, instructions, and weighting, and (3) Framework Areas, a set of specialized modules that operate cohesively to form a unified cognitive system. By incorporating known principles from human cognition into AI systems, the OGI framework aims to overcome the challenges observed in today's intelligent systems, paving the way for more holistic and context-aware problem-solving capabilities.arXiv.org220The Open General Intelligence (OGI) framework aims to overcome the challenges observed in today's intelligent systems, paving the way for more holistic and context-aware problem-solving capabilities.ArXiv["Daniel A. Dollinger", "Michael Singleton"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/e8ad66569a43129c4346fe2f97bdef5303b00088 383dd48c83bd012ef1f59ad637d7d17dc39dcdb3Artificial intelligence‐mediated informal digital learning of English (AI‐IDLE) might strengthen second language (L2) learners' motivational self‐concept (e.g., the ideal L2 self) and enhance their foreign language enjoyment (FLE) by enabling them to build confidence, engagement, and willingness to practice their English skills in a self‐directed, instant feedback, and non‐judgemental learning environment. In our explanatory mixed‐method study, we collected questionnaire data from 299 Chinese undergraduate English as a foreign language (EFL) learners and interviewed 12 of them. Structural equation modelling showed that students who participated in AI‐IDLE more often reported a clearer ideal L2 self and greater FLE, but those with a greater ideal L2 self did not report more FLE. In addition, gender did not moderate the impact of AI‐IDLE on FLE. Analysis of the interview data not only corroborated the quantitative results but also highlighted that while EFL learners can acquire a sense of FLE and vivid ideal L2 selves as they agentively negotiate the affordances of generative AI for informal language learning purposes, the sense of FLE and motivational force may shift across contexts to shape their continued investment in AI‐IDLE practices. By comparing and integrating the quantitative and qualitative insights, this study highlights the pedagogical potential of AI‐IDLE activities that can strengthen EFL learners' motivation, enjoyment, and commitment to English learning.European Journal of Education411This study highlights the pedagogical potential of AI‐IDLE activities that can strengthen EFL learners' motivation, enjoyment, and commitment to English learning by comparing and integrating the quantitative and qualitative insights.European Journal of Education["G. Liu", "Minlin Minny Zou", "Ali Soyoof", "M. M. Chiu"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/383dd48c83bd012ef1f59ad637d7d17dc39dcdb3 8a3ae12015f7a5775732c6c00fb7b0cd54fcb7a3Through a systematic review of academic literature, we propose a taxonomy of systemic risks associated with artificial intelligence (AI), in particular general-purpose AI. Following the EU AI Act's definition, we consider systemic risks as large-scale threats that can affect entire societies or economies. Starting with an initial pool of 1,781 documents, we analyzed 86 selected papers to identify 13 categories of systemic risks and 50 contributing sources. Our findings reveal a complex landscape of potential threats, ranging from environmental harm and structural discrimination to governance failures and loss of control. Key sources of systemic risk emerge from knowledge gaps, challenges in recognizing harm, and the unpredictable trajectory of AI development. The taxonomy provides a snapshot of current academic literature on systemic risks. This paper contributes to AI safety research by providing a structured groundwork for understanding and addressing the potential large-scale negative societal impacts of general-purpose AI. The taxonomy can inform policymakers in risk prioritization and regulatory development.arXiv.org00A taxonomy of systemic risks associated with artificial intelligence, in particular general-purpose AI, is proposed to provide a structured groundwork for understanding and addressing the potential large-scale negative societal impacts of general-purpose AI.ArXiv["Risto Uuk", "Carlos Ignacio Gutierrez", "Daniel Guppy", "Lode Lauwaert", "Atoosa Kasirzadeh", "Lucia Velasco", "Peter Slattery", "Carina Prunkl"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/8a3ae12015f7a5775732c6c00fb7b0cd54fcb7a3 f987466c43023f10e57aa7031ca1ebd925401718Cyberspace and artificial intelligence (AI) have been increasingly used in international conflicts without sufficient regulations, raising concerns over their unchecked impact. Although authoritative texts like the Tallinn Manual provide guidance for governing cyberspace, the absence of explicit International Humanitarian Law (IHL) rules tailored to cyber operations remains a significant concern.The current legal frameworks are insufficient to effectively regulate armed conflicts in cyberspace.The article discusses how cyber-attacks are regulated by the existing body of laws such as the United Nations Charter, International humanitarian Law (IHL), international treaties. The UN General Assembly global Conference on Cyberspace was organised to address, Whether the human moderators and commanders can be held responsible for AI-driven violations under the current IHL framework?. This paper explores the cyber attacks that are global in nature using various cyber case studies that are state sponsored or not state sponsored. This paper concludes that there is a need for new international conventions or legislations to effectively deal with cyberwarfare in international space. International Journal For Multidisciplinary Research00It is concluded that there is a need for new international conventions or legislations to effectively deal with cyberwarfare in international space.International Journal For Multidisciplinary Research["Pasupathieswaran M", "Kapilan Bharathi G"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/f987466c43023f10e57aa7031ca1ebd925401718 c1d3d306808557d34c0e667c53c8ef3a22a3dfc5Recent advancements in artificial intelligence (AI) have broadened the applicability of AI-generated images across various sectors, including the creative industry and design. However, their utilization in educational contexts, particularly among undergraduate students in computer science and software engineering, remains underexplored. This study adopts an exploratory approach, employing questionnaires and interviews, to assess students' acceptance, trust, and positive attitudes towards AI-generated images for educational tasks such as presentations, reports, and web design. The results reveal high acceptance, trust, and positive attitudes among students who value the ease of use and potential academic benefits. However, concerns regarding the lack of technical precision, where the AI fails to accurately produce images as specified by prompts, moderately impact their practical application in detail-oriented educational tasks. These findings suggest a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues, while also setting quality standards for AI-generated images to enhance their educational use. Enhancing the capabilities of AI tools to meet precise user specifications could foster creativity and improve educational outcomes in technical disciplines.arXiv.org250There is a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues, while also setting quality standards for AI-generated images to enhance their educational use, according to students' acceptance, trust, and positive attitudes.ArXiv["Aung Pyae"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/c1d3d306808557d34c0e667c53c8ef3a22a3dfc5 8794f7743cd4d737d07658ea6eccc6452ccac4faMolecular diversity460This paper comprehensively investigated the performance of multi-objective AI approaches for multi-target drug design and found that AI approaches for multi-target drug design are able to efficiently generate more high-quality novel compounds than the single-target approaches while satisfying a number of constraints.Molecular diversity["Muhetaer Mukaidaisi", "Madiha Ahmed", "Karl Grantham", "Aws Al-Jumaily", "S. Dedhar", "Michael Organ", "Alain Tchagang", "Jinqiang Hou", "Syed Ejaz Ahmed", "Renata Dividino", "Yifeng Li"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/8794f7743cd4d737d07658ea6eccc6452ccac4fa be9341662c3639cc4864c58dacbcea16a8293e67Health Information Science and Systems190This research sought to build models that could accurately predict BPPV using readily available clinical data and to assess the application of Explainable Artificial Intelligence (XAI) to enhance transparency and trust in ML-driven diagnoses.Health information science and systems["M. Khani", "Jake Luo", "Mohammad Assadi Shalmani", "Amirsajjad Taleban", "Jazzmyne A Adams", "David Friedland"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/be9341662c3639cc4864c58dacbcea16a8293e67 fc9e4f1aafed2d5f7e1f70a8326c5d4789250ecbArtificial intelligence (AI) is rapidly transforming communication processes across various sectors, including marketing, education, healthcare, and entertainment. This study explores the theoretical perspectives surrounding AI’s integration into communication, examining how AI-driven tools such as ChatGPT, MidJourney, and Google Gemini are reshaping content creation, personalisation, and human-machine interaction. While AI enhances efficiency and allows for real-time customisation of messages, it also presents ethical challenges related to privacy, data security, and algorithmic bias. By synthesising key academic studies, the study outlines the critical ethical considerations, including the risks of deepfakes and disinformation, and emphasises the need for ethical frameworks to guide responsible AI use. The text also discusses the new digital competencies required to navigate AI-enhanced communication environments, such as AI literacy, data proficiency, and ethical reasoning. Through a systematic literature review, this study contributes to the ongoing discourse on AI’s role in communication by offering a comprehensive theoretical framework that highlights both the opportunities and limitations of AI technologies. Future research should focus on addressing gaps in empirical studies, particularly concerning the long-term impacts of AI on decision-making and the ethical governance of AI-generated content.Communication today320This study explores the theoretical perspectives surrounding AI’s integration into communication, examining how AI-driven tools such as ChatGPT, MidJourney, and Google Gemini are reshaping content creation, personalisation, and human-machine interaction.Communication Today["Aleksandra Mirek-Rogowska", "Wojciech Kucza", "Krzysztof Gajdka"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/fc9e4f1aafed2d5f7e1f70a8326c5d4789250ecb cebbbe2f944fa605e8e7329fbfe9646abc79b029Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research.Atmosphere840The development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, are summarized to provide direction for future research.Atmosphere["Chunlai Wu", "Siyu Lu", "Jiawei Tian", "Lirong Yin", "Lei Wang", "Wenfeng Zheng"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/cebbbe2f944fa605e8e7329fbfe9646abc79b029 96cef554d458e37573744c2cc50ce0ddd88a11efThis paper thoroughly examines the role of Artificial Intelligence (AI) in digital forensics, showcasing its potential to tackle complex cyber threats and the growing amount of digital data. It starts by discussing key AI technologies, particularly machine learning and deep learning, and their importance in forensic investigations. As cyber threats become increasingly sophisticated, the field of cyber forensics is also advancing. At the forefront of this evolution is artificial intelligence (AI), which is transforming how cyber forensics operates. This article examines the effects of AI on cyber forensics in terms of identifying, monitoring, and preventing cyber threats. By employing AI-driven tools, cyber forensics can process larger datasets, recognize patterns, and detect anomalies, leading to a deeper understanding of cyber incidents. The increasing frequency and complexity of cyber- attacks necessitates the development of competent cyber forensic investigative methodologies. This study looks into the use of machine learning and artificial intelligence (AI) in automated threat analysis and classification, with the goal of better understanding their function in cyber forensics. Forensic investigators and cybersecurity specialists provided information through case studies, observations, and surveys. This study highlights the potential benefits of incorporating artificial intelligence and machine learning to advance digital forensic investigations, as well as providing significant insights into their roles in cyber forensics. Incorporating these technologies has obvious benefits, like faster analytical methods and improved threat detection capability. Investigations may be accelerated by integrating AI and machine learning, allowing firms to respond quickly to cyber threats and reduce overall risk exposure. As the cybersecurity landscape evolves, the successful integration of AI and machine learning in the sector has the promise of ushering in a new era of proactive threat identification, hence strengthening organisations' ability to protect digital assets.International Journal For Multidisciplinary Research00The potential benefits of incorporating artificial intelligence and machine learning to advance digital forensic investigations, as well as providing significant insights into their roles in cyber forensics are highlighted.International Journal For Multidisciplinary Research["Hareesh Kumar C", "Trisha B"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/96cef554d458e37573744c2cc50ce0ddd88a11ef 581d91f32659a0d5de7718b93d323aa7982d8457It is widely assumed that increases in economic productivity necessarily lead to economic growth. In this paper, it is shown that this is not always the case. An idealized model of an economy is presented in which a new technology allows capital to be utilized autonomously without labor input. This is motivated by the possibility that advances in artificial intelligence (AI) will give rise to AI agents that act autonomously in the economy. The economic model involves a single profit-maximizing firm which is a monopolist in the product market and a monopsonist in the labor market. The new automation technology causes the firm to replace labor with capital in such a way that its profit increases while total production decreases. The model is not intended to capture the structure of a real economy, but rather to illustrate how basic economic mechanisms can give rise to counterintuitive and undesirable outcomes.140An idealized model of an economy is presented in which a new technology allows capital to be utilized autonomously without labor input, motivated by the possibility that advances in artificial intelligence will give rise to AI agents that act autonomously in the economy.["Casey O. Barkan"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/581d91f32659a0d5de7718b93d323aa7982d8457 9b5bc728751fb0c407ad8b15b91fe8bb625e75e9University Rankings exert considerable influence in higher-education decision-making. Yet, as an artifact of their construction, rankings are largely unhelpful in conveying practical strategic insights to university administrators intent on improving their college’s rank. Machine learning tools such as interpretable machine learning (IML) and explainable artificial intelligence (XAI), taking aim at piercing obscure, black-box algorithms have gained a lot of interest recently. However, there appear to be few deployments of their use in appraising University rankings. In this work, using data representing QS Rankings data of USA MBA programs we show how counterfactual XAI can support proactive responses by educational stakeholders to Rankings outcomes.  Explaining individual predictions opens great opportunities for intervention and strategizing. The method is applicable to any extant rankings.Asian Journal of Finance &amp; Accounting00This work shows how counterfactual XAI can support proactive responses by educational stakeholders to Rankings outcomes and explains individual predictions opens great opportunities for intervention and strategizing.Asian Journal of Finance &amp; Accounting["Armando Rodriguez", "George Heudorfer", "Brian Kench"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/9b5bc728751fb0c407ad8b15b91fe8bb625e75e9 b7ddce31f986adc5653ba76f355ce0efaba4ca39Artificial Intelligence (AI) employs sophisticated algorithms to meticulously examine vast datasets, uncovering intricate patterns and establishing fundamental principles. For instance, it autonomously discerns and flags spam emails by assimilating extensive email data, progressively refining its identification process through continuous learning. Similarly, AI is adept at scrutinizing numerous images, effortlessly identifying and categorizing individuals’ faces within them. Through deep learning methodologies, AI autonomously hones its analytical prowess by ingesting copious amounts of data, thereby enhancing its cognitive capabilities without explicit human intervention. A notable example is ‘Aspect Point Detection’ or ‘Feature Quantity’ analysis, which undergoes evolutionary refinement as the AI algorithms iteratively analyze and adapt to new data inputs. In this paper through examples of our AI research we will try to explain AI related research history and current status including hardware and software.Optime260Through examples of the AI research, the history and current status of AI related research history and current status including hardware and software are explained.Optime["Arbnor Pajaziti", "Jozef Kola"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/b7ddce31f986adc5653ba76f355ce0efaba4ca39 efc350e59e9526a30ee59d1c13aad4d57c94b01eAsia Pacific Journal of Education30Asia Pacific Journal of Education["Yanping Rui", "Yanjuan Zhang"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/efc350e59e9526a30ee59d1c13aad4d57c94b01e 7b41d89d3602f7424e3a1cc82e213da60fb0c283Revista ft00Revista ft["Alexandre Victor Dias Moura", "Carlos Eduardo de Assis", "Maria Laura Vargas Cabral"]2024-11-24T00:00:00https://www.semanticscholar.org/paper/7b41d89d3602f7424e3a1cc82e213da60fb0c283 4e9709fb0f658963f4404dee7f7fa50861369e47OBJECTIVES The study aimed to assess the usage and impact of a private and secure instance of a generative artificial intelligence (GenAI) application in a large academic health center. The goal was to understand how employees interact with this technology and the influence on their perception of skill and work performance. MATERIALS AND METHODS New York University Langone Health (NYULH) established a secure, private, and managed Azure OpenAI service (GenAI Studio) and granted widespread access to employees. Usage was monitored and users were surveyed about their experiences. RESULTS Over 6 months, over 1007 individuals applied for access, with high usage among research and clinical departments. Users felt prepared to use the GenAI studio, found it easy to use, and would recommend it to a colleague. Users employed the GenAI studio for diverse tasks such as writing, editing, summarizing, data analysis, and idea generation. Challenges included difficulties in educating the workforce in constructing effective prompts and token and API limitations. DISCUSSION The study demonstrated high interest in and extensive use of GenAI in a healthcare setting, with users employing the technology for diverse tasks. While users identified several challenges, they also recognized the potential of GenAI and indicated a need for more instruction and guidance on effective usage. CONCLUSION The private GenAI studio provided a useful tool for employees to augment their skills and apply GenAI to their daily tasks. The study underscored the importance of workforce education when implementing system-wide GenAI and provided insights into its strengths and weaknesses.J. Am. Medical Informatics Assoc.121The private GenAI studio provided a useful tool for employees to augment their skills and apply GenAI to their daily tasks and underscored the importance of workforce education when implementing system-wide GenAI and provided insights into its strengths and weaknesses.Journal of the American Medical Informatics Association : JAMIA["Kiran Malhotra", "B. Wiesenfeld", "Vincent J Major", "Himanshu Grover", "Yindalon Aphinyanagphongs", "Paul Testa", "Jonathan S. Austrian"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/4e9709fb0f658963f4404dee7f7fa50861369e47 342d8d9092b5433794e5362789f81d49f4651f41Artificial Intelligence (AI) has become crucial in digital marketing strategies in the rapidly advancing digital era. Developed and developing countries exhibit significant differences in adopting and implementing this technology, influenced by infrastructure readiness, human resources, and policy support. This study aims to compare the use of AI in digital marketing strategies between developed and developing countries to understand each group's challenges and opportunities. The research employs a Systematic Literature Review (SLR) method by analyzing 50 articles from leading databases such as Scopus, Springer, and IEEE Xplore. The analyzed articles were selected based on inclusion criteria, including relevance to the topic, publication year (2018-2024), and full accessibility. Data were analyzed through thematic synthesis to identify patterns, trends, and gaps in AI adoption between the two groups of countries. NVivo and VOSviewer are used as analytical tools to facilitate data analysis. The findings reveal that developed countries leverage AI for content personalization, predictive analytics, and marketing automation, supported by advanced digital infrastructure. Meanwhile, developing countries still face various obstacles, such as limited infrastructure and digital literacy. The implications of this study highlight the need for more significant investment in technological infrastructure in developing countries and the importance of global collaboration to accelerate equitable AI adoption. This research also provides recommendations for policymakers and business practitioners to optimize AI utilization in digital marketing strategies across different contexts.Ilomata International Journal of Management331The findings reveal that developed countries leverage AI for content personalization, predictive analytics, and marketing automation, supported by advanced digital infrastructure, while developing countries still face various obstacles, such as limited infrastructure and digital literacy.Ilomata International Journal of Management["Muhammad Umam Mubarok", "Maheni Ika Sari", "Yohanes Gunawan Wibowo", "Raisun Mathew"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/342d8d9092b5433794e5362789f81d49f4651f41 6eceab41fccb601539d6424e92759a6d78c05fd5Artificial intelligence (AI) presents significant opportunities and challenges, particularly balancing innovation with protecting privacy and human rights. The increasing integration of AI into daily life has amplified risks to digital privacy, access to information, and online communication, raising concerns about human rights violations. Governments must address these risks by implementing practical measures to ensure safe AI usage and redressing harm caused by unethical practices. This article explores the impact of AI on privacy and human rights, utilizing the 2024 Council of Europe Framework Convention on AI, Human Rights, Democracy, and the Rule of Law as a basis for ethical considerations. Employing an analytical methodology, the study examines international charters and national legislation to highlight disparities in addressing AI-related privacy concerns and to identify gaps between global human rights standards and digital technologies. Comparative analysis is conducted to evaluate international and national approaches to AI governance. The findings emphasize the urgent need for unified global standards to protect digital human rights, harmonize AI ethics, and reduce risks associated with AI applications. Recommendations include adopting comprehensive legal frameworks and promoting international cooperation to ensure ethical AI deployment aligned with human rights principles.Jurnal Media Hukum471There is an urgent need for unified global standards to protect digital human rights, harmonize AI ethics, and reduce risks associated with AI applications, according to the 2024 Council of Europe Framework Convention on AI, Human Rights, Democracy, and the Rule of Law.Jurnal Media Hukum["T. Al-Billeh", "Ruba Hmaidan", "Ali Al-Hammouri", "Mohammed Al Makhmari"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/6eceab41fccb601539d6424e92759a6d78c05fd5 029dd9197a4ae957a1096d1339eccb80a7db7740 We study the effect of Artificial Intelligence (AI) on employment across US commuting zones over the period 2000-2020. A simple model shows that AI can automate jobs or complement workers, and illustrates how to estimate its effect by exploiting variation in a novel measure of local exposure to AI: job growth in AI-related professions built from detailed occupational data. Using a shift-share instrument that combines industry-level AI adoption with local industry employment, we estimate robust negative effects of AI exposure on employment across commuting zones and time. We find that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution and for those in STEM occupations. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality.Social Science Research Network02It is found that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing, consistent with the view that AI has contributed to the automation of jobs and to widen inequality.SSRN Electronic Journal["Alessandra Bonfiglioli", "G. Gancia", "Ioannis Papadakis", "Rosario Crin\u00f2"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/029dd9197a4ae957a1096d1339eccb80a7db7740 e2ea5ba9b835a2ba4105821c7ed4a2aee84a1fa6Purpose This study aims to illustrate the manifold ways in which artificial intelligence (AI) serves as both a sentinel and a potential intruder in the realm of personal data protection. Additionally, it delves into the legal and ethical frameworks governing the use of AI in data-centric contexts. Design/methodology/approach Using a qualitative doctrinal methodology, this research examines existing literatures on AI, data privacy and related laws/regulations. This study explores the multifaceted role of AI in shaping data privacy and the symbiotic relationship between AI and data privacy. Findings It was discovered that there are insufficient AI-specific regulations, and that AI both fortifies and threatens the sanctity of personal data. As such, there is the need for transparency, fairness, accountability and adherence to data privacy regulations to ensure effective use of AI in data privacy. Research limitations/implications This study limits itself to the intersection of AI and data privacy and how innovation, legislations and ethical considerations are intricately intertwined. Originality/value By examining case studies and examples from the real world, this study endeavors to provide a comprehensive perspective on the dynamic landscape of AI and data privacy. It forecasts future trends and challenges, offering insights into how AI may continue to influence and safeguard data privacy while simultaneously posing novel risks. International Journal of Law and Management822This study explores the multifaceted role of AI in shaping data privacy and the symbiotic relationship between AI and data privacy and how innovation, legislations and ethical considerations are intricately intertwined.International Journal of Law and Management["Bareq Lami", "Safinaz Mohd. Hussein", "Ramalinggam Rajamanickam", "Grace Kaka Emmanuel"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/e2ea5ba9b835a2ba4105821c7ed4a2aee84a1fa6 efa73cd015b042c8ed7c10a3304fff36cbd4ad33AI &amp; SOCIETY120The authors argue that the potential for gender bias in visual GAI systems is potentially more troubling than bias in textual GAI because of the superior memorability of images and the capacity for emotional communication that images represent.AI &amp; SOCIETY["Larry G. Locke", "Grace Hodgdon"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/efa73cd015b042c8ed7c10a3304fff36cbd4ad33 2e6c1d66f4f17616e988b2ad97b632d2ea307449The emergence of Artificial Intelligence (AI) technology in journalism has brought about significant changes. Equipped with AI-powered tools, this technology has become an integral part of journalistic work, transforming the way news is processed. These advancements enable more efficient news production, increasing processing capacity in the digital era. This study examines the phenomenon of AI technology adoption in news writing by journalists at Bengkulu Ekspress.com. As AI evolves rapidly, many media outlets are integrating it to enhance efficiency in content creation. Employing a qualitative approach, this research incorporates in-depth interviews with several journalists and analyzes news content published on the Bengkulu Ekspress.com platform. The findings reveal that while AI contributes to various aspects of news writing, such as data processing and automated report generation, it does not entirely replace the role of journalists. Journalists retain primary control over key elements, including narrative construction, perspective, and the values conveyed in the news. Moreover, AI primarily functions as a tool to expedite workflows and improve information accuracy. Despite AI’s growing influence on the media industry, the role of human journalists as creators of news content remains indispensable. Komunikator400The findings reveal that while AI contributes to various aspects of news writing, such as data processing and automated report generation, it does not entirely replace the role of journalists.Komunikator["Indah Kurnianti", "Dhanurseto Hadiprashada", "Nurlianti Muzni", "Sait Ceesay"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/2e6c1d66f4f17616e988b2ad97b632d2ea307449 cf271b0c5855d5ef513ec5761a16ccb5920eba87Artificial intelligence (AI) applications have recently become more powerful and accessible. There has been much discussion about the potential impacts of AI on learning and sample applications. Yet, little research exists on how and to what extent AI is being used in educational contexts. The purpose of this study was to examine and compare students’ and instructors’ perceptions and uses of AI in educational settings. A sample of 113 undergraduate students and 71 instructors at a public university completed an online survey in which they reported how and how often they use AI for educational and instructional purposes and their views of its potential benefits and detriments to learning. Students and instructors reported relatively low AI usage, similar views on potential benefits and detriments to learning, and similar views on acceptable academic use. However, their perceptions diverged in several important ways. This research highlights the need for policy, transparency, training, and nuanced discussions about how to use AI effectively and responsibly to promote learning goals. The survey instruments developed for this study may be useful tools for facilitating conversations, clarifying expectations, and expanding understanding of AI uses in psychology programs.Teaching of psychology20Students and instructors reported relatively low AI usage, similar views on potential benefits and detriments to learning, and similar views on acceptable academic use, however, their perceptions diverged in several important ways.Teaching of Psychology["Tesia Marshik", "Christopher McCracken", "Bryan Kopp", "Morgan O\u2019Marrah"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/cf271b0c5855d5ef513ec5761a16ccb5920eba87 d375faa74daad30840038540e40217c169c5589aThe diagnosis and treatment of autism spectrum disorder (ASD) are highly challenging, demanding extensive preventative and intervention plans. With an emphasis on the use of artificial intelligence (AI) technology, this study aims to look into efficient prevention and therapy approaches for ASD. The study highlights the shortcomings in the present approaches and emphasizes the significance of early diagnosis and intervention. Using a mixed-method approach, the study looks at the effectiveness of different treatment methods and AI-driven diagnostic tools using case studies and a thorough literature review. The methodology includes a detailed analysis of peer-reviewed research papers, clinical trials, and actual AI implementations in ASD care. The results show that AI can improve outcomes for people with ASD by enhancing diagnostic accuracy and customizing therapies. The study additionally investigates at how AI-powered assistive technology, including interactive applications and social robots, can help people with ASD. Additionally, the viability and moral implications of incorporating AI-based solutions into clinical practice are looked at. The paper ends with suggestions for integrating AI into clinical procedures and future lines of study to confront the dynamic nature of ASD therapy. The study intends to give an extensive understanding of how AI may transform ASD care, making it more accurate, tailored, and efficient by addressing these factors.Transactions on Computer Science and Intelligent Systems Research100The results show that AI can improve outcomes for people with ASD by enhancing diagnostic accuracy and customizing therapies, and how AI-powered assistive technology can help people with ASD.Transactions on Computer Science and Intelligent Systems Research["Mengyu Li"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/d375faa74daad30840038540e40217c169c5589a 8a0fe6241d6bba9cfeb6d50395b7b815cee21be8Machine learning (ML) and artificial intelligence (AI) are transforming many industries by enabling systems to learn from data and perform tasks without the need for explicit instructions. In the pharmaceutical industry, these technologies are being used to overcome major challenges such as high research and development (R&D) costs, long drug development times, and complex regulations. AI and ML can help analyze large amounts of data, predict drug interactions, and improve trial design, reducing costs and speeding up the process of bringing new drugs to market. Although AI has great potential, it has only been used to a limited extent in the pharmaceutical industry due to strict regulations and, especially, the need for human oversight to ensure patient safety. However, there are significant challenges, including ethical concerns, privacy issues, and the need to train experts. There are different types of ML techniques, such as supervised learning, semi-supervised learning, unsupervised learning and reinforcement learning, that help in pattern recognition and decision-making. AI aims to solve problems, understand and use knowledge, plan, continuously learn, interact socially, promote creativity, and work well with people. AI has many advantages, such as reduced errors and technological advancements, but also disadvantages, such as high costs and possible job losses. AI is increasingly integrated into the pharmaceutical industry. Partnerships between pharmaceutical companies and AI technology providers can help improve drug discovery, streamline clinical trials.International Journal For Multidisciplinary Research180AI is increasingly integrated into the pharmaceutical industry and partnerships between pharmaceutical companies and AI technology providers can help improve drug discovery, streamline clinical trials and reduce costs and speeding up the process of bringing new drugs to market.International Journal For Multidisciplinary Research["Rupali Madhav Thakare", "Pooja Gangurde", "Gauri Suresh sawant"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/8a0fe6241d6bba9cfeb6d50395b7b815cee21be8 c807a53968a71ce35e773da444fc3cf54c1c2f18Recommending interventions in the mental health and wellbeing context is a difficult task due to multiple considerations, including the range of interventional options, the uncertainty of outcomes under those interventions, and the comparison of outcomes across multiple domains (e.g., psychological distress, personal functioning, social support, physical activity, nutrition, substance use). Effective interventional recommendation systems require a framework to incorporate these aspects of decision-making, which can be implemented using causal artificial intelligence within a Bayesian decision-theoretic framework. This approach was applied to a sample of individuals (N=619) that used the Innowell Fitness app between September 2021 to September 2023 and completed a questionnaire at two timepoints (1 week - 6 months from baseline). Psychological distress had causal effects on personal functioning (ppath=86%), social support (ppath=92%), sleep (ppath=88%), and physical activity (ppath=86%). Conditional on baseline presentation the optimal intervention target was; 1) the unhealthiest baseline domain with exceptions where psychological distress is more effective than intervening on "poor" nutrition or physical activity, 2) psychological distress when it is equally or more unhealthy than other domains, or 3) the domain that is more likely to transition to or persist in an unhealthy state.medRxiv00This approach was applied to a sample of individuals that used the Innowell Fitness app between September 2021 to September 2023 and completed a questionnaire at two timepoints, finding psychological distress had causal effects on personal functioning, social support, sleep, sleep, and physical activity.["M. Varidel", "V. An", "I. Hickie", "S. Cripps", "R. Marchant", "J. Scott", "J. J. Crouse", "A. Poulsen", "B. O'Dea", "F. Iorfino"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/c807a53968a71ce35e773da444fc3cf54c1c2f18 48073885adeb1fd5e4265b7d30801f3d2741edfbArtificial Intelligence today demonstrates an astonishing productivity, and its development has become one of the directions for many countries. However, while developing, it is also necessary to consider the adverse impact of AI development on carbon emissions and to seek environments and regions suitable for AI development. To address this issue, this paper innovatively proposes the concept of "fertility" to describe the AI development potential of a region, and fully considers the adverse impact of AI development on carbon emissions. Based on the carbon emission and development data of various provinces in China in recent years, the "fertility" is modeled through PCA (Principal Component Analysis) and GB-DT model, and combined with LSTM for predicting the future AI development potential, thus deriving the future AI development potential of various provinces in China.Transactions on Computer Science and Intelligent Systems Research00This paper innovatively proposes the concept of "fertility" to describe the AI development potential of a region, and fully considers the adverse impact of AI development on carbon emissions.Transactions on Computer Science and Intelligent Systems Research["Qiaochu Li", "Xinyu Zhuang"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/48073885adeb1fd5e4265b7d30801f3d2741edfb d662df1e20139844aca1094ae8f1c46e8c848714: Initially resorting to the analysis of scientific literature from various fields related to the educational sciences, such as educational management, docimology, and assessment theory, as well as the fields of development of program products and artificial intelligence, during her work the author presents a set of prototypes of AI functionalities to be integrated into various learning-teaching-assessment-self-assessment platforms to the potentially interested parties. The respective functionalities, being partially equipped with AI, allow all kind of users, including the teaching staff and students of any age category, curricular area, and/or level of studies, to interact with the assessment subjects while also benefiting from a series of advantages that satisfy the requirements of educational management for the students' sample administration during the didactic assessments, as well as during the inter-and/or post-assessment period.Virtual Reality150During her work the author presents a set of prototypes of AI functionalities to be integrated into various learning-teaching-assessment-self-assessment platforms to the potentially interested parties.Proceedings of the International Conference on Virtual Learning - VIRTUAL LEARNING - VIRTUAL REALITY (19th edition)["Natalia Burlacu"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/d662df1e20139844aca1094ae8f1c46e8c848714 bceed0f2b6847371d6608ff191ef1a49ae7545bbUltrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal image quality, which complicates interpretation and increases the likelihood of diagnostic errors. Artificial intelligence (AI) has emerged as a promising solution to enhance clinical diagnosis, particularly in detecting abnormalities across various biomedical imaging modalities. Nonetheless, current AI models for ultrasound imaging face critical challenges. First, these models often require large volumes of labeled medical data, raising concerns over patient privacy breaches. Second, most existing models are task-specific, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve of 0.927 for disease diagnosis and a dice similarity coefficient of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers and matches the performance of expert-level sonographers in the joint diagnosis of 8 common systemic diseases. These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking an advancement in AI-driven ultrasound imaging for future clinical applications.arXiv.org00Findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking an advancement in AI-driven ultrasound imaging for future clinical applications.ArXiv["Yuncheng Jiang", "Chun-Mei Feng", "Jinke Ren", "Jun Wei", "Zixun Zhang", "Yiwen Hu", "Yunbi Liu", "Rui Sun", "Xuemei Tang", "Juan Du", "Xiang Wan", "Yong Xu", "Bo Du", "Xin Gao", "Guangyu Wang", "Shaohua Zhou", "Shuguang Cui", "R. Goh", "Yong Liu", "Zhen Li"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/bceed0f2b6847371d6608ff191ef1a49ae7545bb 221bd6dc03d28cc1871938467ffa4446edc2b71ethe article discusses the main ways of using artificial intelligence in the forestry industry, and provides examples of its useMaterials of the All-Russian scientific and practical conference "Modern forest complex of the country: innovative developments and research"00Materials of the All-Russian scientific and practical conference "Modern forest complex of the country: innovative developments and research"["T. Lileeva"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/221bd6dc03d28cc1871938467ffa4446edc2b71e 6cbb59029d6dfbe0b2001ed3fea413fc7e1cd1e8The study investigates the integration of artificial intelligence (AI) in higher education (HE) and its impact on pre-service teachers at the University of Latvia (UL) by exploring pre-service teachers' perceptions of the benefits and challenges of AI in both their academic learning and their future professional roles as educators, particularly regarding the promotion of inclusive education.Data was collected via an online survey of 240 pre-service teachers across various disciplines at the UL. The survey included demographic details, AI usage patterns, and perceived benefits and challenges. Responses were analyzed using descriptive statistics, Kruskal-Wallis H tests, Spearman's correlation, and thematic analysis.Less than half of the participants used AI in their studies, with many expressing ambivalence or opposition toward AI. Benefits included language assistance and accessibility to global knowledge, while challenges involved reduced critical thinking and concerns over plagiarism. Despite recognizing AI's potential to promote inclusivity, most pre-service teachers have not applied it in practice. No significant differences in AI perceptions were found based on age, gender, or study level.The findings highlight a low adoption rate of AI among pre-service teachers and a gap between theoretical recognition of AI's potential and its practical application, particularly for inclusion. The study emphasizes the need for HE institutions to enhance AI literacy and readiness among future teachers.AI is underutilized by pre-service teachers in both HE learning and teaching environments, which has implications for teacher preparation programs that better integrate AI literacy and inclusive practices.Frontiers in Education820AI is underutilized by pre-service teachers in both HE learning and teaching environments, which has implications for teacher preparation programs that better integrate AI literacy and inclusive practices.Frontiers in Education["Daiga Kalnina", "Dita N\u012bmante", "S. Baranova"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/6cbb59029d6dfbe0b2001ed3fea413fc7e1cd1e8 40e3e76ecf191d8a6f51765ae56995117f931919This study reported the effectiveness of a professional development course on artificial intelligence (Al) literacy for administrative staff in higher education. The course aims to support administrative staff to improve their efficiency and the quality of their work in the workplace. A number of 38 administrative staff from a university in Hong Kong took part in this three-month course which was divided into 10 face-to-face lessons (30 hours). Basic concepts, practical use of Al tools, and Al ethics were taught, and group project-based learning was implemented. Many groups built chatbots using their expert knowledge in their workplace deploying concepts and skills acquired in the course. Data collection included pre- and post-concept tests, evaluation surveys, and self-reported written reflections. The result of a paired t-test on a concept test of Al literacy confirmed an improvement in the participants' understanding of Al literacy after joining the course. The result of a project-based survey and an evaluation of the course indicated a positive perception of the design and the effectiveness of the course after they finished taking the course. An analysis of the reflective writing of the participants revealed that they benefited from the course, and they confirmed that the course could help them pursue further how to improve the efficiency and quality of their work in their workplace by deploying the concepts and skills acquired from the course.IEEE International Conference on Consumer Electronics200The effectiveness of a professional development course on artificial intelligence (Al) literacy for administrative staff in higher education revealed that they benefited from the course, and they confirmed that the course could help them pursue further how to improve the efficiency and quality of their work in their workplace by deploying concepts and skills acquired from the course.International Conference on Computers in Education["Siu-Cheung Kong", "Zoe Wai Sum Mak", "Yue Wu", "Yin Yang"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/40e3e76ecf191d8a6f51765ae56995117f931919 d6e70301a22de4e51f324c2430dc331e7af388fcThe development of all sports requires a competent sports ecological environment. Pollution of the sports ecological environment greatly restricts people’s enthusiasm and interest in taking part in sports activities, and at the same time greatly affects people’s physical and mental health and hinders the development of sports. As people’s awareness of environmental pollution protection increases, it will certainly curb and alleviate the environmental pollution problem. Promote social harmony and sustainable development. Therefore, this paper will intuitively analyze the integration mechanism of sports development and ecological environmental protection based on artificial intelligence. After analysis, China’s research in related fields, sports and environmental engineering disciplines accounted for the largest proportion of 74.46%, and with the progress of sports events and environmental protection, the number of international publications in 2017 reached a maximum of 134. To a certain extent, this is linked to the Rio Olympics in Brazil the previous year. However, in the global research in this field, the number of published papers in the United States reached a maximum of 159, which opened a large gap with other countries.Molecular &amp; Cellular Biomechanics220This paper will intuitively analyze the integration mechanism of sports development and ecological environmental protection based on artificial intelligence and reveal China’s research in related fields, sports and environmental engineering disciplines accounted for the largest proportion and the number of published papers in the United States opened a large gap with other countries.Molecular &amp; Cellular Biomechanics["Yun Yang", "Yuhu Zhao", "Jianqiang Guo"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/d6e70301a22de4e51f324c2430dc331e7af388fc 38289e88c1c04988267ad3dc4cae0148574c4aa9Healthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients. One way to address these issues is through the use of artificial intelligence for the detection of medical device failure. This goal of this study was to develop automated systems utilising machine learning algorithms to predict patient monitor performance and potential failures based on data collected during regular safety and performance inspections. The system developed in this study utilised machine learning techniques as its core. Throughout the study four algorithms were utilised. These algorithms include Decision Tree, Random Forest, Linear Regression and Support Vector Machines. Final results showed that Random Forest algorithms had the best performance on various metrics among the four developed models. It achieved accuracy of 94% and precision and recall of 70% and 93% respectively. This study shows that use of systems like the one developed in this study have the potential to improve management and maintenance of medical devices.Technology and Health Care80This study shows that use of automated systems utilising machine learning algorithms to predict patient monitor performance and potential failures based on data collected during regular safety and performance inspections have the potential to improve management and maintenance of medical devices.Technology and Health Care["Faruk Be\u0107irovi\u0107", "Lemana Spahi\u0107", "Nejra Merdovi\u0107", "Lejla Gurbeta Pokvi\u0107", "A. Badnjevi\u0107"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/38289e88c1c04988267ad3dc4cae0148574c4aa9 6e6273331d7f329d794594fbffa884489a2d2fe1Generative Artificial Intelligence (GenAI) has come to stay, and educators are exploring its usage in diverse contexts. One pertinent question begging for an answer is how educators integrating GenAI tools can equitably assess students' learning outcomes. This study explores the mixed-method approach, consisting of a rapid literature review and an analysis of experts' perspectives to address this question. We analyze data from the Scopus and Web of Science databases from the rapid review to understand how the use of GenAI is penetrating the STEM field. On the other hand, the thematic analysis of data generated from a ten-week-long group learning circle discussion among STEM professors regarding assessment in the era of the GenAI was used to gain understanding of educators' perspectives regarding how students' learning could be assessed in a classroom where GenAI tools are used. Our findings provide insights regarding how, where, and when to integrate GenAI in STEM classes and potential assessment strategies that could foster trust and transparency between educators and students. This study contributes to the growing body of literature on GenAI in STEM education. It offers implications from the perspective of contextual adoption of assessment strategy in the era of GenAI rather than the traditional approach of one-size-fits-all.IEEE International Conference on Consumer Electronics330This study explores the mixed-method approach, consisting of a rapid literature review and an analysis of experts' perspectives for understanding of educators' perspectives regarding how students' learning could be assessed in a classroom where GenAI tools are used.International Conference on Computers in Education["F. J. Agbo", "Heather Kitada Smalley", "Kathryn Nyman"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/6e6273331d7f329d794594fbffa884489a2d2fe1 7799ec60e447848e9fedc793a9a837256d3ce584Pollution can be broadly defined as the presence of contaminants or energy sources detrimental to ecosystems and human health. The human organism serves as a valuable indicator of ecosystem contamination. However, understanding physiological disorders and correlating specific contaminants with disease development is a complex and arduous task, necessitating extensive scientific research spanning years or even decades. To facilitate a more rapid and precise understanding of the physiological impairments induced by various contaminants, a comprehensive approach is indispensable. This review proposes a model for such an approach, which involves the systematic collection and analysis of data from ecosystem contamination monitoring, integrated with biomedical data on compromised physiological conditions in humans across different temporal and spatial scales. Given the complexity and sheer volume of data, alongside the imperative for strategic decision-making, this model leverages the capabilities of artificial intelligence (AI) tools. Although this paper exemplifies the model by investigating the effects of contaminants on the human organism, the model is adaptable to all ecosystem components, thereby supporting the conservation of plant and animal species.Toxics1060A model for such an approach is proposed, which involves the systematic collection and analysis of data from ecosystem contamination monitoring, integrated with biomedical data on compromised physiological conditions in humans across different temporal and spatial scales, and leverages the capabilities of artificial intelligence (AI) tools.Toxics["Mirela Volf", "Ante Vu\u010demilovi\u0107", "Z. Dobrovic"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/7799ec60e447848e9fedc793a9a837256d3ce584 f2042abf67ec60c9c86e5dac1ef58a81992e948f Background: Artificial intelligence (AI) in dentistry could improve future clinical practice. Aim: This questionnaire survey was commissioned to evaluate the knowledge and perspective of Iraqi dental students on AI application in dentistry. Methods: The survey was administered to 310 dental students, 112 males and 198 females, ages 19–24. Eighty students were in the 2nd grade, 70 in the 3rd grade, 80 in the 4th grade, and 80 in the 5th grade. They completed questionnaires, selecting one option from the list of possible answers for each of the 11 survey questions about AI. Results: A 42.7% of the 310 participants in the study had experience with AI and its software. However, only 35.3% of dental students understood how to incorporate AI into their work. Students in 5th grade were more likely to think that AI has a future in Iraq (p = 0.034), whereas students in 4th grades rejected the idea of using AI in decision-making (p = 0.045) significantly. Conclusion: Dental students lack a basic grasp of AI; therefore, activating their interest helps them learn about AI and its potential uses. To acquaint dental students with AI and its applications, seminars, lectures, and workshops should coincide with the material they introduce in the curriculum.Journal of Emergency Medicine Trauma and Acute Care190Dental students lack a basic grasp of AI; therefore, activating their interest helps them learn about AI and its potential uses; therefore, seminars, lectures, and workshops should coincide with the material they introduce in the curriculum.Journal of Emergency Medicine, Trauma and Acute Care["Asmaa Abed Shandi", "N. A. Hassan", "Firas H. Alwade"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/f2042abf67ec60c9c86e5dac1ef58a81992e948f 75edb40fa602777046c5c889a1ddfdf79500f793This study investigates the role of Artificial Intelligence (AI) in enhancing cybersecurity for U.S. public schools, with the primary objective of evaluating AI's effectiveness in reducing cyber threats and safeguarding student privacy. Specifically, the study assesses AI-driven security systems such as threat detection and anomaly detection algorithms, which help schools monitor network traffic and identify potential breaches in real-time. Using logistic regression on data from the K-12 Cybersecurity Resource Center, findings reveal that schools implementing AI solutions are 75% less likely to experience cyber breaches (p < 0.001), highlighting AI's protective impact. Furthermore, a comparative analysis of FERPA and COPPA compliance reports highlights a substantial reduction in privacy violations among AI-using schools, with an average of 0.57 violations per school, compared to 1.50 in schools without AI. A K-means cluster analysis identified budget constraints (65.75%) and IT staff shortages (55.25%) as primary barriers to AI adoption. To address these obstacles, the study recommends phased technology upgrades and increased funding for workforce training as critical strategies to facilitate AI integration and enhance cybersecurity across educational institutions. These strategic interventions are essential for optimizing the effectiveness of AI-driven security systems, making it feasible for resource-constrained schools to adopt and maintain advanced cybersecurity measures. The study’s findings contribute to the growing body of knowledge on educational cybersecurity and provide actionable insights for policymakers and administrators seeking to strengthen data protection and privacy in school environments.Asian Journal of Research in Computer Science00Findings reveal that schools implementing AI solutions are 75% less likely to experience cyber breaches (p < 0.001), highlighting AI's protective impact.Asian Journal of Research in Computer Science["Onyinye Obioha Val"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/75edb40fa602777046c5c889a1ddfdf79500f793 0564e71fe2326e0fc2c8217ff36e366fb12b6967Energy poverty remains a significant challenge in Sub-Saharan Africa (SSA), where approximately 600 million people lack proper access to electricity. This paper examines the region’s current state of energy poverty, highlighting its socio-economic impacts and the barriers to achieving Sustainable Development Goal 7 (SDG7), which aims for affordable, reliable, sustainable, and modern energy for all by 2030. Despite the region’s rich renewable energy potential, inadequate infrastructure, economic constraints, and governance issues continue to impede progress. This work employs a doctrinal research methodology, focusing on the critical analysis of existing legal and policy frameworks relevant to energy poverty and the integration of AI in energy management. This paper presents an overview of energy poverty in SSA, underpinned by current statistics and trends. It then examines the dual role of artificial intelligence (AI) and how it impacts this area: while AI technologies, through its data centre s, for example, significantly increase energy consumption, AI also offers innovative solutions for energy management, efficiency, and the integration of renewable energy sources. This paper critically analyzes these dynamics using Marxist and Third World Approaches to International Law (TWAIL) frameworks to understand the broader socio-economic inequalities and global power dynamics at play. Major findings indicate that current policy frameworks are inadequate in addressing the unique challenges of energy poverty and the growing role of AI in the energy sector. The paper reviews existing policy and regulatory frameworks, identifying gaps and proposing actionable recommendations for integrating AI into policies to address energy poverty. It concludes with actionable policy recommendations to achieve a just and inclusive energy transition, contributing to the broader discourse on sustainable development and technological equity.Journal of Sustainable Development Law and Policy00Journal of Sustainable Development Law and Policy (The)["J. O. Effoduh"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/0564e71fe2326e0fc2c8217ff36e366fb12b6967 735eddd8cadc8dbbe6fea1c7809746ced5e2df2cPurpose This study aims to examine how artificial intelligence can be effectively integrated into records management practices by identifying the key cultural aspects that should be aligned with the prerequisites of automation. The author also discusses the new roles that are to be played by records managers in this context. Design/methodology/approach To identify those cultural aspects, the author has used the Information culture framework (ICF) developed by Oliver and Foscarini (2020). For each of the three levels of the ICF (i.e. visible, intermediate and invisible), the author has analyzed the cultural aspects serving as the prerequisites of AI for records management purposes. Findings The results of our theoretical reflection reveal that for AI features to be integrated into records management practices, many cultural aspects are to be taken into consideration. AI-powered technologies use, collaboration practices and horizontal communication are some visible cultural aspects contemporary organizations should have in place to meet the requirements of automation. Furthermore, policies and strategies should define automation purposes, identify actors that will be involved in records management practices and describe their respective roles. Finally, attention should be given to individual perceptions and personal traits to ensure that AI technologies are embraced by organizational actors. All those aspects should support the development of a common AI-related language in the organization and influence the extent to which actors trust AI-powered technologies. In this context of automation, records managers will have to assume new roles in change management and promoting information competencies, to assess organizations’ readiness to integrate AI into its records management practices and make the appropriate use of it. Originality/value To the best of the author’s knowledge, this study is the first to use the ICF model suggested by Oliver and Foscarini (2020) to identify the main cultural aspects to target for the effective use of AI in records management practices. Furthermore, the author confirmed the relevance of the expression “augmented records management”, referring to AI-assisted records management practices in contemporary organizations, by highlighting the fact that AI will not replace human work but can, rather, be used as a tool to support it. Records Management Journal80The author confirmed the relevance of the expression “augmented records management”, referring to AI-assisted records management practices in contemporary organizations, by highlighting the fact that AI will not replace human work but can be used as a tool to support it.Records Management Journal["Siham Alaoui"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/735eddd8cadc8dbbe6fea1c7809746ced5e2df2c e60a8d55caa8ae8e4e9c8405b1833152264ec573In this paper, we examine the role of artificial intelligence (AI) in sovereignty and carbon neutrality, emphasizing digital inclusion and climate-resilient AI strategies for emerging markets. Considering the previous studies on AI for carbon neutrality and digital inclusion for climate research along with technology policy frameworks as a guide, this paper undertakes Partial Least Squares Structural Equation Modelling (PLS-SEM) with AI strategies and carbon neutrality outcomes. At the same time, fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to reveal different configurations leading to achieving climate resilience. The model covers various aspects of AI-enabled policy, including technology adoption, policy frameworks, digital literacy, and public engagement. Survey data were collected from key stakeholders in climate policy, technology sectors, and local communities using a structured survey to understand their attitudes towards negative emissions technologies from prominent experts in emerging countries like Vietnam, Italy, Malaysia, and Greece. PLS-SEM results reveal the importance of AI in developing carbon neutrality, a critical AI strategic dimension (Data analytics capability and policy support). Some aspects of the fsQCA findings present heterogeneous outcomes, highlighting complex combinations of digital inclusion, AI adoption, and climate resilience which are industry-specific. This study would further enrich the literature concerning climate strategies by exploring AI, digital inclusion, and carbon neutrality interactions. Theoretically, practical and enriching suggestions for future research are derived to help AI intelligence infuse sustainable climate actions.Sustainability970Partial Least Squares Structural Equation Modelling with AI strategies and carbon neutrality outcomes reveal the importance of AI in developing carbon neutrality, a critical AI strategic dimension (Data analytics capability and policy support).Sustainability["S. Mondal", "Subhankar Das", "Vasiliki G. Vrana"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/e60a8d55caa8ae8e4e9c8405b1833152264ec573 6d48c3a4b2fa981dd838a3b559e95c0f5613f9f9Based on the Job Demands-Resources (JD-R) theory and the Unified Theory of Acceptance and Use of Technology (U TAUT) model, the study proposed and tested the dual-pathway impact of artificial intelligence (Al) technology on teachers' human-machine collaboration. The results indicated that Al had both negative and positive effects on teachers' human-machine collaboration. Specifically, perceived risk, through the partial mediating effect Of Al anxiety, negatively influenced the collaboration between teachers and Al. Conversely, technology acceptance had a positive influence on teachers' human-machine collaboration through the partial mediation of human- machine compatibility.IEEE International Conference on Consumer Electronics00The study proposed and tested the dual-pathway impact of artificial intelligence (Al) technology on teachers' human-machine collaboration and indicated that Al had both negative and positive effects on teachers' human-machine collaboration.International Conference on Computers in Education["Yujie Xu", "Yiling Hu"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/6d48c3a4b2fa981dd838a3b559e95c0f5613f9f9 8c24b167dabf29582a92dbe258cf8cf06b11d206This study examines the multiple applications of Artificial Intelligence (AI) technologies in game design and development and their implications. First, this paper outlines the main types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, and introduces deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Subsequently, the specific applications of AI in game design are analyzed in detail in the article, encompassing procedural content generation, game balancing, behavioral control of non-player characters (NPCs) and social AI implementation. In game testing and quality assurance, AI technology significantly improves game development efficiency and user experience through automated error detection and user feedback analysis. In addition, AI also shows great potential in adaptive difficulty adjustment and personalized content recommendation, further enhancing players' game experience. In particular, this article also discusses the application of AlphaGo, DeepMind's StarCraft II AI, and OpenAI Five in Dota 2, demonstrating the superior performance of AI in complex gaming environments. Finally, the article discusses the future direction and challenges of AI in gaming, emphasizing the importance of technical security, data privacy, and ethical issues. Overall, AI technology shows great potential in the gaming field, which not only improves the intelligence level of games, but also brings new opportunities and challenges for game development and industry development.Transactions on Computer Science and Intelligent Systems Research00Overall, AI technology shows great potential in the gaming field, which not only improves the intelligence level of games, but also brings new opportunities and challenges for game development and industry development.Transactions on Computer Science and Intelligent Systems Research["Baihan Yu"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/8c24b167dabf29582a92dbe258cf8cf06b11d206 01306de64e9614b0768a97b0fb4bee17cdd4e517This project examines the transformative role of Artificial Intelligence (AI) in the education system, highlighting its potential to enhance learning experiences, streamline administrative processes, and personalize education. As educational institutions face increasing demands for innovation and efficiency, AI technologies such as adaptive learning platforms, data analytics, and virtual assistants are emerging as vital tools. This study explores the various applications of AI, including personalized learning pathways that cater to individual student needs, automated grading systems that free educators to focus on teaching, and AI-driven chatbots that provide instant support to learners. Through a comprehensive analysis of case studies, current trends, and expert insights, this project illustrates the benefits of integrating AI in education, such as improved engagement, accessibility, and overall educational outcomes. Additionally, it addresses challenges associated with AI adoption, including ethical considerations, data privacy, and the need for ongoing teacher training. By examining the intersection of AI and education, this project aims to provide a nuanced understanding of how AI can foster a more inclusive and effective learning environment, ultimately redefining the educational landscape for future generationsInternational Journal of Advanced Research in Science, Communication and Technology00This project illustrates the benefits of integrating AI in education, such as improved engagement, accessibility, and overall educational outcomes, and addresses challenges associated with AI adoption, including ethical considerations, data privacy, and the need for ongoing teacher training.International Journal of Advanced Research in Science, Communication and Technology["Fatou A Bah"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/01306de64e9614b0768a97b0fb4bee17cdd4e517 ea107d3ddd2eb24797204db8599603d6946cba91Artificial intelligence (AI) has emerged as a transformative technology in education. This review focused on the intersection of AI tools and academic writing, addressing challenges such as plagiarism, language barriers, and feedback processes. The problem statement revolved around the increasing integration of AI in academic contexts, which offered opportunities for improved student learning but raised concerns over ethical issues such as plagiarism and over-dependence on AI-generated content. The purpose of this review was to critically review highly cited studies on the use of AI in academic writing, identifying AI tools and key findings. Research questions guiding this review included: 1) Which highly cited studies related to AI and academic writing, published since 2020, were identified as relevant? 2) Which AI had been utilised for academic writing? and 3) What findings had been reported in these previous studies? Methodologically, the review employed keyword searches in Google and Scopus databases to identify highly cited, open-access articles published since 2020. This resulted in the selection of 11 studies that spanned various AI tools in academic writing. Findings indicated that ChatGPT was the most frequently used AI tool, employed for tasks such as academic text generation, plagiarism detection, and language learning support. The review also highlighted ethical concerns, particularly regarding plagiarism, content accuracy, and the risk of over-reliance on AI. The implications were both theoretical and practical. Theoretically, this review demonstrated AI’s expanding influence in educational theory, especially in scaffolding learning for non-native English speakers. Practically, AI tools offered personalised feedback and enhance writing outcomes, though educators must implement these tools responsibly to prevent over-reliance. In conclusion, while AI tools showed great promise in improving academic writing, future research should address ethical concerns, enhance the accuracy of AI-generated content, and develop frameworks that balance AI assistance with the promotion of critical thinking skills.Asian Pendidikan00While AI tools showed great promise in improving academic writing, future research should address ethical concerns, enhance the accuracy of AI-generated content, and develop frameworks that balance AI assistance with the promotion of critical thinking skills.Asian Pendidikan["Hui Guo", "Syaza Hazwani Zaini"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/ea107d3ddd2eb24797204db8599603d6946cba91 e90b8b5815bce5e133e908478360353a33da37ac: Digital technologies are integrated into education to improve the quality of teaching and learning and meet the needs of today’s generation of learners. Recently, the usage of Artificial Intelligence-based tools in education has been accelerating, significantly changing the educational process. Teachers can use Artificial Intelligence-based tools to create lesson plans, learning content and tasks as well as to generate assessment resources. The current paper aims to present the use of two Artificial Intelligence-based tools – ChatGPT and MagicSchool in basic teacher's activities. The results show that such tools can create lesson plans in detail, generate learning content and practical examples, and provide ideas for homework assignments and quiz questions. Artificial Intelligence-based tools emphasize teachers’ activities in classrooms, recommending suitable teaching methods and techniques. Reasonable usage of such tools, considering their advantages and possible risks and challenges can greatly support teachers' activities.Virtual Reality50The current paper aims to present the use of two Artificial Intelligence-based tools – ChatGPT and MagicSchool in basic teacher's activities and shows that such tools can create lesson plans in detail, generate learning content and practical examples, and provide ideas for homework assignments and quiz questions.Proceedings of the International Conference on Virtual Learning - VIRTUAL LEARNING - VIRTUAL REALITY (19th edition)["Gabriela Kiryakova"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/e90b8b5815bce5e133e908478360353a33da37ac a1a23f55797e2f611e4b46ba5cb9ba40835549c1Active Video Watching supports engagement through scalable interventions, such as notetaking in the form of comments. Machine Learning is used to categorize comments based on their quality to provide personalized feedback to students. In previous work on AVW-Space, an online portal for active video watching, a machine learning model was trained using data from several studies on presentation skills. In this paper, we explore the effectiveness in assessing the comment quality of this model in Face-to-Face Meeting Communication skills in comparison to a model trained specifically for this soft skill. We used Explainable Artificial Intelligence to identify and compare the important features of the models. Results show the need for comment quality assessment models to be specific to the soft skill in question and show major differences between their important features, highlighting the necessity to create a model specific to a particular soft skill.IEEE International Conference on Consumer Electronics80Results show the need for comment quality assessment models to be specific to the soft skill in question and show major differences between their important features, highlighting the necessity to create a model specific to a particular soft skill.International Conference on Computers in Education["R. Lumapas", "A. Mitrovic", "Matthias Galster", "S. Malinen"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/a1a23f55797e2f611e4b46ba5cb9ba40835549c1 8059927fa90b8c12ea7f0e7b99a79eee5c48a8a6Over the last years, studies using artificial intelligence (AI) for the detection and prediction of diseases have increased and also concentrated more and more on vulnerable groups of individuals, such as infants. The release of ChatGPT demonstrated the potential of large language models (LLMs) and heralded a new era of AI with manifold application possibilities. However, the impact of this new technology on medical research cannot be fully estimated yet. In this work, we therefore aimed to summarise the most recent pre-ChatGPT developments in the field of automated detection and prediction of diseases and disease status in infants, i.e., within the first 12 months of life. For this, we systematically searched the scientific databases PubMed and IEEE Xplore for original articles published within the last five years preceding the release of ChatGPT (2018–2022). The search revealed 927 articles; a final number of 154 articles was included for review. First of all, we examined research activity over time. Then, we analysed the articles from 2022 for medical conditions, data types, tasks, AI approaches, and reported model performance. A clear trend of increasing research activity over time could be observed. The most recently published articles focused on medical conditions of twelve different ICD-11 categories; “certain conditions originating in the perinatal period” was the most frequently addressed disease category. AI models were trained with a variety of data types, among which clinical and demographic information and laboratory data were most frequently exploited. The most frequently performed tasks aimed to detect present diseases, followed by the prediction of diseases and disease status at a later point in development. Deep neural networks turned out as the most popular AI approach, even though traditional methods, such as random forests and support vector machines, still play a role—presumably due to their explainability or better suitability when the amount of data is limited. Finally, the reported performances in many of the reviewed articles suggest that AI has the potential to assist in diagnostic procedures for infants in the near future. LLMs will boost developments in this field in the upcoming years.Frontiers in Digital Health750The most recent pre-ChatGPT developments in the field of automated detection and prediction of diseases and disease status in infants, i.e., within the first 12 months of life, are summarized to suggest that AI has the potential to assist in diagnostic procedures for infants in the near future.Frontiers in Digital Health["K. D. Bartl-Pokorny", "Claudia Zitta", "Markus Beirit", "Gunter Vogrinec", "Bj\u00f6rn W. Schuller", "Florian B. Pokorny"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/8059927fa90b8c12ea7f0e7b99a79eee5c48a8a6 ef9b3aefcc45ef34603b8f4e5e7bed553b2ea76eThe convergence of Artificial Intelligence (AI) and robotics has led to the emergence of embodied AI, where intelligent systems equipped with sensors and actuators interact with the physical world and operate alongside humans. These systems are transforming industries such as autonomous driving, healthcare, and household assistance. However, despite extensive research, embodied AI systems face significant limitations, including poor generalization and performance degradation in complex environments, hindering their commercialization. Recent developments in Large Language Models (LLMs) present new opportunities to address the above challenges. This study aims to explore the integration of LLMs into embodied AI systems, highlighting their potential to enhance scene understanding, reasoning, and planning capabilities. The paper provides a detailed review of LLMs’ applications in embodied AI, demonstrating how these models can improve the robustness and adaptability of AI systems. Additionally, the study examines the limitations of LLMs, such as hallucinations and efficiency challenges, and discusses potential solutions to mitigate these issues. Through an in-depth analysis of LLM-powered enhancements in embodied AI, this research underscores the transformative impact of LLMs on intelligent systems. By addressing current limitations and implementing innovative solutions, LLMs can significantly advance the field of embodied AI, paving the way for more versatile and intelligent systems that can operate effectively in diverse real-world environments.Transactions on Computer Science and Intelligent Systems Research150This study aims to explore the integration of LLMs into embodied AI systems, highlighting their potential to enhance scene understanding, reasoning, and planning capabilities, and demonstrates how these models can improve the robustness and adaptability of AI systems.Transactions on Computer Science and Intelligent Systems Research["Zitian Li"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/ef9b3aefcc45ef34603b8f4e5e7bed553b2ea76e 5c061f17b36fba21a9ab2bcd5abfb2a25540a83eThis study was motivated by the increasing and seemingly unstoppable nature of cybercrimes and the perceived effect on data safety and reliability in accounting practice. Consequently, this study is poised to ascertain the relationship between artificial intelligence-driven electronic transactions and data loss, theft and manipulation; as well as to ascertain the effect of artificial intelligence-based electronic accounting on data safety and reliability. It adopted a survey design to obtain primary data from a sample of 492 professional accountants comprising ACAs, FCAs, CNAs, and FCNAs in the South-south geo-political zone of Nigeria, through a questionnaire. Analysis of variance and regression analysis from SPSS test results disclosed a significant nexus between artificial intelligence-driven electronic transactions and data loss, theft and manipulation. The test results also revealed that the adoption and recognition of artificial intelligence-based electronic accounting significantly affect the safety and reliability of financial data. Accordingly, this study concludes that, financial data from artificial intelligence-driven electronic transactions are significantly susceptible to data loss, theft and manipulation, and that the adoption and recognition of artificial intelligence-based electronic accounting impairs the security and reliability of financial data. Consequently, this study recommends that firms should be watchful and employ adequate trust management and web security measures and mechanisms in adopting and recognising artificial intelligence-related electronic transactions and accounting. This study further recommends that artificial intelligence technology inventors and engineers should shift focus towards a redefinition, re-engineering and reshaping of artificial intelligence technologies and platforms that can be easily governed and safely benefit users.African journal of accounting and financial research410Financial data from artificial intelligence-driven electronic transactions are significantly susceptible to data loss, theft and manipulation, and that the adoption and recognition of artificial intelligence-based electronic accounting impairs the security and reliability of financial data.African Journal of Accounting and Financial Research["Odogu, T. K. Z."]2024-11-25T00:00:00https://www.semanticscholar.org/paper/5c061f17b36fba21a9ab2bcd5abfb2a25540a83e 2284c98de7f9408a74dc6770ce48642b81c42739The intersection of artificial intelligence (AI) and public health nutrition is rapidly evolving, offering transformative potential for how we understand, assess, and improve population health [...].Nutrients50Nutrients["R. An", "Yuanyuan Yang"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/2284c98de7f9408a74dc6770ce48642b81c42739 0adcce731884e6113fb2bdc6e6382445cc446e9dThe digital revolution, caused by the dynamic development of various technologies, certainly led to a number of positive changes in all areas of human life. However, at the same time, there was an urgent need for the formation of an appropriate regulatory and legal basis that would regulate social relations related to the latest technologies. In recent years, both in the international arena and in the national legislation, complex acts have already appeared, designed to solve the problems of the implementation of digital technologies in the legal field. At the same time, the law does not always respond in time to the rapid development of the latest technologies, which again gives rise to a lot of problematic issues. Artificial intelligence and its rapid penetration into almost all spheres of human existence became a vivid example of such a situation. The use of artificial intelligence (AI) has raised a number of questions for the legal community, including the legal status of AI and the results of its work, data protection, intellectual property, ethics, etc. The goal of the legislative regulation of artificial intelligence is to create a clear policy in the content of legal acts that would determine specific rules for the operation, application and protection of AI. The need for legislative regulation of artificial intelligence, in addition to the significant development that the field has undergone, is also related to the need to control the consequences that the unregulated use of AI can cause. Some states have implemented a number of attempts to legislate AI regulation at the national level, in particular, Ukraine has already taken the first steps to create a legal framework in the field of artificial intelligence. Thus, in 2020, the Concept of the Development of Artificial Intelligence in Ukraine was created, in which the definition, purpose, principles and tasks of the development of artificial intelligence technologies in Ukraine are provided for the first time at the legislative level. However, compared to individual countries, it is the European Union that has made the most progress in terms of preparing for the introduction of a legislative framework for the regulation of AI. On August 1, 2024, the EU Artificial Intelligence Act (EU AI Act), adopted by the European Parliament, the long-awaited European regulation on artificial intelligence, entered into force. This act created a kind of foundation for the further development of the mechanism of the use of artificial intelligence systems in the legal field, regulating such aspects as maintenance of AI systems, protection of personal data, intellectual property rights to the results of AI activity, as well as what, in our opinion, is no less important , provided for clear obligations of subjects during the use of artificial intelligence. The key role of the duties established by the EU AI Act is to ensure transparency in activities using AI. In our opinion, it is worth investigating the content of the established duties in order to understand the mechanism of ensuring the so-called transparency in AI activities. In addition, given the absence of a legal act in Ukrainian legislation that would regulate the use of artificial intelligence systems, the study of the EU’s experience in this field is more than relevant and can be taken into account during the development of such an act in the future.Uzhhorod National University Herald. Series: Law00It is worth investigating the content of the established duties in order to understand the mechanism of ensuring the so-called transparency in AI activities, and the study of the EU’s experience in this field is more than relevant and can be taken into account during the development of such an act in the future.Uzhhorod National University Herald. Series: Law["T. Popovych"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/0adcce731884e6113fb2bdc6e6382445cc446e9d bb6c14b1bd39a6a8dd8bc76002968c4b5949cd1cPenggunaan kecerdasan buatan (AI) memiliki potensi besar untuk meningkatkan  inklusi ekonomi dan keuangan dengan memberikan akses yang lebih mudah, efisien, dan terjangkau  terhadap layanan keuangan dan ekonomi bagi mereka yang sebelumnya terpinggirkan. Penelitian ini bertujuan untuk mengidentifikasi Artificial Intelligence (AI) dapat digunakan untuk memberikan layanan keuangan yang lebih inklusif kepada mereka yang kurang terlayani, serta untuk menganalisis dampaknya terhadap kesejahteraan ekonomi masyarakat secara keseluruhan khususnya kota Gorontalo. Penelitian ini menggunakan pendekatan kualitatif deskriptif.,melibatkan studi kasus dan wawancara dengan pemangku kepentingan. Hasil penelitian menunjukkan bahwa Pemanfaatan Artificial Intelligence (AI) chatbot dan asisten virtual memberikan dampak siginifikan terhadap pelayanan dan kepuasaan pengguna, serta memberikan kepercayaan dalam pengambilan keputusan sehingga dapat meningkatkan inklusi ekonomi dan keuangan bagi masyarakat.  Publik Jurnal Manajemen Sumber Daya Manusia Administrasi dan Pelayanan Publik00Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik["Octaviani Suryaningsih Masaguni", "Waldi Patadjenu", "Kurniadi K. Hasan", "Andi Yusuf Katili", "Mohamad Rizal Pasisingi"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/bb6c14b1bd39a6a8dd8bc76002968c4b5949cd1c aedc4b286738939dfad641e262c1bd7e1b25260fProteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. De novo protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent years, with the rapid development of artificial intelligence (AI), deep learning-based generative models have increasingly become powerful tools, enabling the design of functional proteins with atomic-level precision. This article provides an overview of the evolution of de novo protein design, with focus on the latest algorithmic models, and then analyzes existing challenges such as low design success rates, insufficient accuracy, and dependence on experimental validation. Furthermore, this article discusses the future trends in protein design, aiming to provide insights for researchers and practitioners in this field.Sheng wu gong cheng xue bao = Chinese journal of biotechnology00An overview of the evolution of de novo protein design is provided, with focus on the latest algorithmic models, and the future trends in protein design are discussed, aiming to provide insights for researchers and practitioners in this field.Sheng wu gong cheng xue bao = Chinese journal of biotechnology["Nan Liu", "Xiaocheng Jin", "Chongzhou Yang", "Ziyang Wang", "Xiaoping Min", "Shengxiang Ge"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/aedc4b286738939dfad641e262c1bd7e1b25260f ecdf86c92fd0e74661b1f735a6d37139aec1f100Biodiversity and Conservation684Biodiversity and Conservation["Fazal Ullah", "S. Saqib", "You-Cai Xiong"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/ecdf86c92fd0e74661b1f735a6d37139aec1f100 e52352567dcffdc80081d37c4e703a9aa97386danpj Digit. Medicine381NPJ Digital Medicine["Pak Ching Li", "Stephen Gilbert"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/e52352567dcffdc80081d37c4e703a9aa97386da 6d17eab9b456034b21e263bcc54e991e136d2ca9Education and Information Technologies : Official Journal of the IFIP technical committee on Education341Education and Information Technologies["Fatih Karata\u015f", "Beng\u00fc Aksu Ata\u00e7"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/6d17eab9b456034b21e263bcc54e991e136d2ca9 d6bfb5d86f17385324c28e4cb3e750cc334cd94aBackgroundArtificial Intelligence (AI) in clinical decision support systems (CDSS) has become an essential tool in improving patient outcomes, enhancing diagnostic accuracy, and assisting in treatment planning. However, healthcare professionals’ perceptions and accessibility to AI-powered CDSS vary across different roles, impacting their effectiveness. Understanding how clinicians, physicians, and nurses interact with AI can provide valuable insights for improving AI integration in hospitals. ObjectiveTo evaluate the perception, accessibility, and practical use of AI-powered CDSS among clinicians, physicians, and nurses in a hospital setting, focusing on how these factors influence the overall adoption and effectiveness of AI. MethodsA cross-sectional survey was conducted in a hospital setting in Sheikhupura, Pakistan, over three months (Feb 2024 to Apr 2024). A total of 54 participants were divided into three groups: clinicians (n=18), physicians (n=18), and nurses (n=18). Data was collected using a structured questionnaire assessing perception (positive/negative), accessibility (easy/complex), and beneficiaries of AI (yes/no). Descriptive statistics, chi-square tests, and ANOVA were used for data analysis, performed using SPSS version 25. ResultsClinicians showed the highest positive perception of AI at 83.3% (15/18), compared to 77.8% (14/18) for physicians and 55.6% (10/18) for nurses. AI accessibility was reported as easy by 66.7% (12/18) of clinicians, 55.6% (10/18) of physicians, and 44.4% (8/18) of nurses. Beneficiaries of AI were 72.2% (13/18) of clinicians, 66.7% (12/18) of physicians, and 50% (9/18) of nurses. Statistically significant differences were observed among the groups (p < 0.05). ConclusionThe study demonstrated that while clinicians and physicians generally have a positive perception and easier access to AI, nurses experience more challenges in both areas. Targeted interventions, including training and support, are essential to improving AI accessibility and perception across all healthcare professional groups for optimal clinical decision-making.Insights-Journal of Health and Rehabilitation80While clinicians and physicians generally have a positive perception and easier access to AI, nurses experience more challenges in both areas and targeted interventions are essential to improving AI accessibility and perception across all healthcare professional groups for optimal clinical decision-making.Insights-Journal of Health and Rehabilitation["Asma Taj", "Tabinda Razzaq", "Muhammad Sohaib Azeem", "Sudhair Abbas Bangash", "Talha Mazhar", "Nazeer Ahmed"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/d6bfb5d86f17385324c28e4cb3e750cc334cd94a 8aa98dba0652d6d7ff436509e5e8d5e5108cd9d9The rise of advanced AI technologies, such as ChatGPT, has presented institutions like the Miller Center with significant opportunities to enhance impact and modernize operations. This paper identifies the key challenges the Center faces in integrating AI into its workflows, focusing on the lack of clarity around how to capitalize on AI’s potential while managing its risks. By exploring AI initiatives at the University of Virginia and evaluating successful AI applications, this paper proposes a range of AI projects tailored to the Miller Center’s needs. These projects, spanning from low-risk tools to cutting-edge applications, are designed to improve data accessibility, streamline research processes, and foster public engagement. The recommendations aim to spark strategic discussions around AI adoption, enabling the Miller Center to leverage AI for both internal efficiency and external impact.2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI)00A range of AI projects, spanning from low-risk tools to cutting-edge applications, are proposed, designed to improve data accessibility, streamline research processes, and foster public engagement are proposed for the Miller Center.2024 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI)["Wajiha Abdul Shakir"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/8aa98dba0652d6d7ff436509e5e8d5e5108cd9d9 b7a65f3029a308658fe3a292303458d02b824c39Journal of the European Academy of Dermatology and Venereology10Journal of the European Academy of Dermatology and Venereology : JEADV["P. Vieira-Baptista", "Mario Preti"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/b7a65f3029a308658fe3a292303458d02b824c39 3a92fbff79eca41e21fa524821d0c51dfdcf4b25Lugawiyyat00Lugawiyyat["S. Supriyanto", "Nur Toifah"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/3a92fbff79eca41e21fa524821d0c51dfdcf4b25 982f34ba02e4862a4aa4d93255096698b5a690c9Virtual Reality00Proceedings of the International Conference on Virtual Learning - VIRTUAL LEARNING - VIRTUAL REALITY (19th edition)["Cigdem Hursen", "Erin\u00e7 Er\u00e7a\u011f", "Fezile Ozdamli"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/982f34ba02e4862a4aa4d93255096698b5a690c9 0adc46dfcc53c348a47d776ef0fc51141099809fVirtual Reality00Proceedings of the International Conference on Virtual Learning - VIRTUAL LEARNING - VIRTUAL REALITY (19th edition)["Natalia Burlacu"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/0adc46dfcc53c348a47d776ef0fc51141099809f 6661ffbde44f2e07c664dc945341a82a59a504ceThis research aims to determine the influence of Perceived Easy of Use Assisted System, Perceived Easy of Use Augmented System, Perceived Usefulness Assisted System, and Perceived Usefulness Augmented System on the Audit Process. This research uses primary data obtained from questionnaire data distributed to accountants who work in public accounting firms in Indonesia. The sampling technique used was purposive sampling with multiple regression analysis method. The analytical tool used in this research is SPSS 27.0. The results of research using multiple data regression analysis show (1) Perceived Easy of Use Assisted System has an effect on the Audit Process (2) Perceived Easy of Use Augmented System has no effect on the Audit Process (3) Perceived Usefulness of the Assisted System has no effect on the Audit Process (4 ) Perceived Usefulness of the Augmented System has no effect on the Audit Process.Syntax literate : jurnal ilmiah Indonesia130The results of research using multiple data regression analysis show that Perceived Easy of Use Assisted System has an effect on the Audit Process and Perceived Usefulness of the Augmented System has no effect on the Audit Process.Syntax Literate ; Jurnal Ilmiah Indonesia["M. F. Nurfaizi", "H. Hasnawati"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/6661ffbde44f2e07c664dc945341a82a59a504ce 1d15bce7b12654c73a27db0456e3cc0dd9c5a2dbJournal of the European Academy of Dermatology and Venereology40Journal of the European Academy of Dermatology and Venereology : JEADV["Yingjoy Li", "V. Rotemberg"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/1d15bce7b12654c73a27db0456e3cc0dd9c5a2db 254abb41aed131aa10e79cdc2b66d0ecbe0e3917Journal of the European Academy of Dermatology and Venereology30Journal of the European Academy of Dermatology and Venereology : JEADV["B. Duarte"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/254abb41aed131aa10e79cdc2b66d0ecbe0e3917 054216e69cb57d7b861656d6282b967f8f91d25fAnalysis of data from incident registries such as MAUDE has identified the need to improve surveillance and maintenance strategies for infusion pumps to enhance patient and healthcare staff safety. The ultimate goal is to enhance infusion pump management strategies in healthcare facilities, thus transforming the current reactive approach to infusion pump management into a proactive and predictive one. This study utilized real data collected from 2015 to 2021 through the inspection of infusion pumps in Bosnia and Herzegovina. Inspections were conducted by the national laboratory in accordance with the Legal Metrology Framework, accredited to ISO 17020 standard. Out of 988 samples, 790 were used for model training, while 198 samples were set aside for validation (20% of the dataset). Various machine learning algorithms for binary classification of samples (pass/fail status) were considered, including Logistic Regression, Decision Tree, Random Forest, Naive Bayes, and Support Vector Machine. These algorithms were chosen for their ability to handle large datasets and potential for high prediction accuracy. Through detailed analysis of the achieved results, it was found that all applied machine learning methods yielded satisfactory results, with accuracy ranging from 0.98% to 1.0%, precision from 0.99% to 1%, sensitivity from 0.98% to 1.0%, and specificity from 0.87% to 1.0%. However, Decision Tree and Random Forest methods proved to be the best, both due to their maximum achieved values of accuracy, precision, sensitivity, and specificity, and due to result interpretability. It has been established that machine learning methods are capable of identifying potential issues before they become critical, thus playing a crucial role in predicting the performance of infusion pumps, potentially enhancing the safety, reliability, and efficiency of healthcare delivery. Further research is needed to explore the potential application of machine learning algorithms in various healthcare domains and to address practical issues related to the implementation of these algorithms in real clinical settings.Technology and Health Care30It has been established that machine learning methods are capable of identifying potential issues before they become critical, thus playing a crucial role in predicting the performance of infusion pumps, potentially enhancing the safety, reliability, and efficiency of healthcare delivery.Technology and Health Care["Nejra Merdovi\u0107", "Lemana Spahi\u0107", "Mad\u017eida Hundur", "L. G. Pokvic", "A. Badnjevi\u0107"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/054216e69cb57d7b861656d6282b967f8f91d25f 97a2b7725287c42be03a47514fe573c884ada860The workshop highlighted recent theoretical advances on inference in high-dimensional statistical models based on the interplay of techniques from mathematical statistics, machine learning, theoretical computer science and related areas. The workshop brought together about 50 researchers in order to present new results, exchange ideas and explore open problems.Oberwolfach Reports00Oberwolfach Reports["F. Bunea", "A. Dalalyan", "Robert Nowak", "Sara van de Geer"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/97a2b7725287c42be03a47514fe573c884ada860 9cc7eb8642cd09782e388543f6cb8a92afefede3Cadernos de Saúde Pública80Cadernos de Saúde Pública["Luciana Correia Alves", "Luciana Dias de Lima", "Mar\u00edlia S\u00e1 Carvalho"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/9cc7eb8642cd09782e388543f6cb8a92afefede3 2514444edb58def17cdfab5c9fd2fff60cbea81dPLOS Mental Health80PLOS Mental Health["Archana Reddy Bongurala", "Dhaval Save", "Ankit Virmani"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/2514444edb58def17cdfab5c9fd2fff60cbea81d 2e2a0cec5402e73c8cc457fe77655ee159d4f1c3Virtual Reality00Proceedings of the International Conference on Virtual Learning - VIRTUAL LEARNING - VIRTUAL REALITY (19th edition)["Huseyin Bicen", "Damla Karagozlu"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/2e2a0cec5402e73c8cc457fe77655ee159d4f1c3 1cccc47a651ceb8f1450b6d4cd1e31713d2e0453In 2018, OpenAl introduced the first version of the Generative Pre-trained Transformer (GPT), revolutionizing the future of Large Language Models (LLMs). This model demonstrated the potential of pretraining large-scale models with vast text data and then fine-tuning them for specific tasks to the public. LLMs have quickly penetrated educational environments, aiding students from various disciplines in tasks ranging from initiating research to drafting essays. While the latter may breach academic integrity, the former is highly beneficial, especially for exploring new areas or ideas. Comparatively, the user interactions with GPT might resembles initially with that of search engines, despite technological differences, as both provide answers to queries, often reflecting archived as well as mainstream views. The historical evolution of search engines, from Archie's database matching to Google's relevance-based ranking, highlights similar ethical considerations faced by both technologies. The development of search engines underscored the importance of accessible information, a principle equally relevant to GPT and LLMs today. This paper is written in light of recent coming into force of European Union's legal framework on artificial intelligence for the purpose of examining adoption of LLMs in classrooms, and argues for balanced regulations across jurisdictions that acknowledge both the immense educational potential of LLMs and the need for adherence to legal and ethical standards.IEEE International Conference on Consumer Electronics90This paper argues for balanced regulations across jurisdictions that acknowledge both the immense educational potential of LLMs and the need for adherence to legal and ethical standards.International Conference on Computers in Education["Upasana Dasgupta", "Rwitajit Majumdar"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/1cccc47a651ceb8f1450b6d4cd1e31713d2e0453 6809fd420d3bc378a232426da7c34cf4d77b1e97A proveniência de dados e a inteligência artificial são cruciais na saúde, garantindo a integridade e a transparência das informações. A proveniência possibilita a rastreabilidade dos dados em diagnósticos, promovendo confiança nas decisões clínicas, enquanto a inteligência artificial analisa grandes volumes de dados, facilitando a identificação de padrões e a personalização de tratamentos. É relevante abordar a proveniência de dados e a inteligência artificial no contexto da saúde, enfatizando seus desafios e implicações éticas, com o objetivo de fornecer orientações a pesquisadores e profissionais da área. Este estudo teve como objetivo apresentar os desafios e implicações éticas evidenciadas nas publicações científicas encontradas sobre a proveniência de dados e a inteligência artificial no contexto de saúde. Realizamos uma revisão integrativa utilizando como estratégia de busca os termos Data Provenance and Artificial Intelligence and Health. Após analisar os resultados obtidos e revisar os textos completos, identificamos e selecionamos 2 artigos internacionais relevantes para a temática em questão. Nossa pesquisa evidencia a escassez de estudos sobre a proveniência de dados e a inteligência artificial em saúde, especialmente no que tange aos desafios e implicações éticas relacionados a esses processos. Nesse contexto, este trabalho constitui uma contribuição relevante para o campo da Ciência da Informação. Para pesquisas futuras sobre a temática aqui em questão, é fundamental desenvolver frameworks e diretrizes que integrem considerações éticas e práticas de governança de dados, abordando a proteção da privacidade, a transparência dos algoritmos e a mitigação de viés, além de aprofundar a pesquisa em estudos interdisciplinares.  Logeion Filosofia da Informação00Logeion: Filosofia da Informação["M. Sembay", "Douglas Dylon Jer\u00f4nimo Macedo"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/6809fd420d3bc378a232426da7c34cf4d77b1e97 e0180784cdcea9a0a46dd1811e0e6815513c426fBlack and indigenous musics continue to evolve and dominate global markets and cultural spheres, notwithstanding a history of intellectual property theft and cultural appropriation. DJs and producers (by way of sampling or extrapolation) have played archival roles outside traditional music archiving. Colonial invasions and the transatlantic slave trade, as well as academic neocolonialism, displaced cultural histories imparted through oral traditions. The Black radical tradition resists global corporate capitalism, even within a music industry that emphasises stereotypical Black tropes for profit. Without regulation, the practices of museums, the education system and the music industry will be exacerbated by the development of recommendation systems and artificial intelligence (AI). Hence, in communities that have already suffered unjust intellectual and cultural property theft, I recognise and re-centre the archiving musico-cultural role that DJs and producers have historically played.Organised Sound320Organised Sound["Leila Adu-Gilmore"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/e0180784cdcea9a0a46dd1811e0e6815513c426f ae7b945ce22f7edd48703c7df0de0e9a3d392612The integration of artificial intelligence (AI) into liberal arts education offers a transformative opportunity to address the diverse needs of multilingual and multicultural learners. Consequently, this study introduces the EQUAL AI framework (Enhancing Equity, Quality, Understanding, and Accessibility in Liberal Arts through AI), a structured approach to utilizing AI to foster inclusion and innovation in liberal arts pedagogy. The framework identifies five key domains: linguistic support, cultural representation, creative expression, critical thinking, and collaborative learning. Additionally, the study underscores the necessity of systemic support, particularly through professional development programs that equip educators with technical proficiency, ethical awareness, and the ability to critically assess AI tools. By tackling challenges such as algorithmic bias, data privacy, and the digital divide, the study advocates for culturally responsive policies and inclusive practices. The EQUAL AI framework envisions liberal arts education as a space for equitable participation and cultural understanding, positioning AI as a tool to enhance rather than replace humanistic pedagogy, ensuring its relevance in a technology-driven, interconnected world.Language, Technology, and Social Media362The EQUAL AI framework envisions liberal arts education as a space for equitable participation and cultural understanding, positioning AI as a tool to enhance rather than replace humanistic pedagogy, ensuring its relevance in a technology-driven, interconnected world.Language, Technology, and Social Media["Amin Davoodi"]2024-11-25T00:00:00https://www.semanticscholar.org/paper/ae7b945ce22f7edd48703c7df0de0e9a3d392612 02365761354f8009e3aa84ff797d08a0128c53c1Artificial intelligence (AI) is used in the field of talent acquisition in human resources; it has emerged as a focal point in modern recruitment techniques. Companies are increasingly using AI-based algorithms to innovate and streamline their employment procedures. This technology has numerous benefits; it considerably increases the efficiency and effectiveness of talent acquisition procedures. Technology has a significant impact on hiring HR professionals. AI's effects on HR recruiting have been demonstrated by several studies, with particular attention paid to candidates' perceptions of AI. This study investigates the integration of Artificial Intelligence into the recruitment process, examining the influence of key independent variables such as Relative Advantage, Regulatory Environment, Technological Complexity, and Technology Competence on the adoption and utilization of AI in hiring. The primary focus of this study is the dependent variable, AI usage in recruitment, reflecting the extent to which organizations use AI technologies in their hiring process. This study aims to provide a deeper understanding of the factors that facilitate or hinder the successful integration of AI into the recruitment process through a comprehensive analysis of these independent variables. The expected contributions of this research include offering valuable insights for HR professionals, industry stakeholders, and decision-makers, promoting more effective and efficient recruitment practices, and leveraging AI technologies to optimize talent acquisition. By identifying key factors that impact AI adoption, this study aims to inform strategies for seamless integration of AI into hiring practices, ultimately helping organizations gain a competitive edge in the ever-evolving job market.PaperASIA322This study investigates the integration of Artificial Intelligence into the recruitment process, examining the influence of key independent variables such as Relative Advantage, Regulatory Environment, Technological Complexity, and Technology Competence on the adoption and utilization of AI in hiring.PaperASIA["Che Enn Tay", "Cheah Yeh Ying", "S. Yeo", "Chew Sze Cheah"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/02365761354f8009e3aa84ff797d08a0128c53c1 ef9298c83d96aec6ebd010ca58620d59cb4719bbThis chapter explores the influence of Artificial Intelligence (AI) on digital democracy, focusing on four main areas: citizenship, participation, representation, and the public sphere. It traces the evolution from electronic to virtual and network democracy, underscoring how each stage has broadened democratic engagement through technology. Focusing on digital citizenship, the chapter examines how AI can improve online engagement and promote ethical behaviour while posing privacy risks and fostering identity stereotyping. Regarding political participation, it highlights AI's dual role in mobilising civic actions and spreading misinformation. Regarding representation, AI's involvement in electoral processes can enhance voter registration, e-voting, and the efficiency of result tabulation but raises concerns regarding privacy and public trust. Also, AI's predictive capabilities shift the dynamics of political competition, posing ethical questions about manipulation and the legitimacy of democracy. Finally, the chapter examines how integrating AI and digital technologies can facilitate democratic political advocacy and personalised communication. However, this also comes with higher risks of misinformation and targeted propaganda.Social Science Research Network02ArXiv["Claudio Novelli", "Giulia Sandri"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/ef9298c83d96aec6ebd010ca58620d59cb4719bb 00aacee73265a91132eda547b1463b87152029a3PurposeIn this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.Design/methodology/approachThis paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.FindingsThe study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.Practical implicationsUnderstanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.Originality/valueThis study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.Asia-Pacific Journal of Business Administration1731This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators, and reveals a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations.Asia-Pacific Journal of Business Administration["Amir A. Abdulmuhsin", "Hayder Dhahir Hussein", "Hadi Al\u2010Abrrow", "Ra\u2019ed Masa\u2019deh", "A. Alkhwaldi"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/00aacee73265a91132eda547b1463b87152029a3 38cdc4df589bd950f287ecc264f29465e8476f29The development of Artificial Intelligence (AI) in higher education has created new opportunities while presenting major challenges. This research aims to explore the impact of AI on higher education, both in terms of benefits and risks that may arise in the future. AI has opened up opportunities to personalize learning experiences, automate administrative processes, and support innovation in curriculum development, potentially improving educational effectiveness. However, there are also concerns regarding the digital divide, data privacy, ethical considerations, and the readiness of educators and institutions to deal with these technological changes. This research uses a literature review approach by analyzing current research on AI implementation in higher education institutions. It also compares case studies from several developed and developing countries to gain a broader picture of the global influence of AI in the education sector. The results show that while AI can have a positive impact in terms of more efficient learning and more effective operations, challenges in terms of equitable access and transparency must be addressed. The novelty of this research lies in the comprehensive analysis of the long-term implications of AI on higher education, as well as the strategies that institutions need to implement to maximize the benefits of AI and minimize the risks. This research makes an important contribution to education stakeholders in understanding the importance of responsible AI adoption to create an inclusive and sustainable educational environment.Information Technologies in Environmental Engineering421The results show that while AI can have a positive impact in terms of more efficient learning and more effective operations, challenges in terms of equitable access and transparency must be addressed.International Transactions on Education Technology (ITEE)["Ronal Aprianto", "Etty Puji Lestari", "Sadan", "Eamon Fletcher"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/38cdc4df589bd950f287ecc264f29465e8476f29 23f685f151f3e43f40e61855bc9c68f1bf3473baThe recent advancement in regional anesthesia (RA) has been largely attributed to ultrasound technology. However, the safety and efficiency of ultrasound-guided nerve blocks depend upon the skill and experience of the performer. Even with adequate training, experience, and knowledge, human-related limitations such as fatigue, failure to recognize the correct anatomical structure, and unintentional needle or probe movement can hinder the overall effectiveness of RA. The amalgamation of artificial intelligence (AI) to RA practice has promised to override these human limitations. Machine learning, an integral part of AI can improve its performance through continuous learning and experience, like the human brain. It enables computers to recognize images and patterns specifically useful in anatomic structure identification during the performance of RA. AI can provide real-time guidance to clinicians by highlighting important anatomical structures on ultrasound images, and it can also assist in needle tracking and accurate deposition of local anesthetics. The future of RA with AI integration appears promising, yet obstacles such as device malfunction, data privacy, regulatory barriers, and cost concerns can deter its clinical implementation. The current mini review deliberates the current application, future direction, and barrier to the application of AI in RA practice.World Journal of Clinical Cases341The future of RA with AI integration appears promising, yet obstacles such as device malfunction, data privacy, regulatory barriers, and cost concerns can deter its clinical implementation.World Journal of Clinical Cases["B. Swain", "D. Nag", "Rishi Anand", "Himanshu Kumar", "Pradip Kumar Ganguly", "Niharika Singh"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/23f685f151f3e43f40e61855bc9c68f1bf3473ba 33ccd821ca56bf114424582cec33c3b7b15cfd1eAesthetic Plastic Surgery321The study suggests that GPT-4 has the potential to assist in interpreting and applying clinical guidelines for IBBR but importantly there is still a risk that AI chatbots can misinform.Aesthetic plastic surgery["Makoto Shiraishi", "Yoshihiro Sowa", "Koichi Tomita", "Y. Terao", "Toshihiko Satake", "Mayu Muto", "Yuhei Morita", "Shino Higai", "Yoshihiro Toyohara", "Yasue Kurokawa", "Ataru Sunaga", "Mutsumi Okazaki"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/33ccd821ca56bf114424582cec33c3b7b15cfd1e 50fdddb5ee922a35809a74fc85148d3f6c9da8e0Artificial intelligence (AI) has rapidly advanced, increasingly showcasing its powerful learning and computational capabilities. This progress has resulted in significant breakthroughs in areas such as image processing, speech recognition, and autonomous driving. Scientists predict that by around 2045, AI will overcome existing technological barriers, allowing strong AI to surpass human intelligence. However, it will inevitably affect human social relationships and order. Ethical issues associated with AI technology, such as unemployment, privacy breaches, and discrimination, generate a sense of threat among people, resulting in a loss of confidence in AI, which hampers its sustainable progress. Therefore, AI ethical issues are not only significant topics in academia but also become critical concerns for individuals, society, and nations. This article aims to address the challenges of AI ethics safety and the erosion of human confidence, while promoting the sustainable development of AI. It presents an AI ethics safety framework that analyzes engineering ethics and human trust within the context of sustainable AI development, and it recommends governance methods and strategies informed by case studies. Furthermore, we propose evaluation criteria and methods, establishing early-warning thresholds to keep potential AI risks within acceptable limits. Finally, the future prospects for AI ethics safety are highlighted. We hope our research contributes to the sustainable development of AI, ensuring that the arrival of the AI singularity has a positive impact on society with a long-term harmonious coexistence between AI and humanity.Sustainability510Sustainability["Jing Suo", "Mingcan Li", "Jinhao Guo", "Yan Sun"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/50fdddb5ee922a35809a74fc85148d3f6c9da8e0 88f8e8fc44458044ea86774da1e15e4b96547bfe Artificial intelligence (AI) has become a useful tool in numerous domains, including environmental science. This review explores the application of machine learning and deep learning, as AI technologies, applied in calculating and modelling water quality indexes (WQIs) and water quality classification. WQIs are used to assess the overall status of water bodies and compliance with environmental regulations. Given a large amount of monitoring data, traditional methods for calculating WQIs can be labour-intensive and subject to human error. AI offers a compelling alternative, with the potential to enhance accuracy, reduce time, and provide insights into complex environmental data. This paper examines recent progress in applying AI to water quality assessment through WQIs, including the creation of predictive models that incorporate diverse water quality parameters and the implementation of AI in real-time monitoring systems. The challenges of deploying AI, such as data availability, model transparency, and system integration, are also discussed. Through a detailed analysis of recent studies and practical implementations, this review analyses the potential of AI to contribute to water quality management and suggests directions for future research.Water Quality Research Journal00Recent progress in applying AI to water quality assessment through WQIs is examined, including the creation of predictive models that incorporate diverse water quality parameters and the implementation of AI in real-time monitoring systems.Water Quality Research Journal["Rodica-Mihaela Fr\u00eencu"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/88f8e8fc44458044ea86774da1e15e4b96547bfe ae2cabe3d4cbc2ce5e83650eeac9d72a694a54caThis research investigates the impact of integrating artificial intelligence (AI) and immersive technologies, such as virtual reality (VR) and augmented reality (AR), in software engineering education to promote continuous and adaptive learning. The purpose of this study was to explore how these technologies increase student engagement, personalize the learning experience, and enhance practical skills essential in software development. A mixed methods approach was used, combining a quantitative survey with qualitative interviews of 20 students and 5 educators actively engaged in AI and VR powered courses. Findings revealed that AI-based adaptive learning systems encourage personalized feedback and help address individual knowledge gaps, leading to increased engagement and retention. VR based simulations enable hands-on learning, which significantly improves skill acquisition in coding, debugging, and system analysis. Despite these benefits, challenges such as high costs, data privacy concerns, and limited infrastructure remain a barrier to widespread implementation. This study contributes original insights by identifying strategies to mitigate these challenges, including gradual adoption of technology, cloud-based solutions to reduce costs, prioritizing data security, and continuous educator training. The findings underscore the transformative potential of AI and immersive technologies in creating sustainable, accessible and adaptive learning environments and equipping students with the skills needed for the innovation driven software industry.IC-ITECHS00Findings revealed that AI-based adaptive learning systems encourage personalized feedback and help address individual knowledge gaps, leading to increased engagement and retention and underscore the transformative potential of AI and immersive technologies in creating sustainable, accessible and adaptive learning environments and equipping students with the skills needed for the innovation driven software industry.IC-ITECHS["Dafid Saputra", "Farhan Fadlurohman Tsaqif", "Aurora Luna Ariana"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/ae2cabe3d4cbc2ce5e83650eeac9d72a694a54ca 734b42a706f26afedd4b95bdac7a3697b727654aIntroduction: In recent years, artificial intelligence (AI) has emerged as a transformative tool for enhancing stroke diagnosis, aiding treatment decision making, and improving overall patient care. Leading AI-driven platforms such as RapidAI, Brainomix®, and Viz.ai have been developed to assist healthcare professionals in the swift and accurate assessment of stroke patients. Methods: Following the PRISMA guidelines, a comprehensive systematic review was conducted using PubMed, Embase, Web of Science, and Scopus. Characteristic descriptive measures were gathered as appropriate from all included studies, including the sensitivity, specificity, accuracy, and comparison of the available tools. Results: A total of 31 studies were included, of which 29 studies focused on detecting acute ischemic stroke (AIS) or large vessel occlusions (LVOs), and 2 studies focused on hemorrhagic strokes. The four main tools used were Viz.ai, RapidAI, Brainomix®, and deep learning modules. Conclusions: AI tools in the treatment of stroke have demonstrated usefulness for diagnosing different stroke types, providing high levels of accuracy and helping to make quicker and more precise clinical judgments.Brain Science430AI tools in the treatment of stroke have demonstrated usefulness for diagnosing different stroke types, providing high levels of accuracy and helping to make quicker and more precise clinical judgments.Brain Sciences["O. Al-Janabi", "Amro El Refaei", "T. Elgazzar", "Y. M. Mahmood", "Danah Bakir", "Aryan Gajjar", "A. Alateya", "S. K. Jha", "S. Ghozy", "David F. Kallmes", "W. Brinjikji"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/734b42a706f26afedd4b95bdac7a3697b727654a 336a47b1d58ef0822f83a8bc0c279107970fff73Implementation Science Communications340The proposed intervention requires local tailoring and prospective evaluation but can support early adopters in optimising the chances of success from initial efforts to implement AI-enabled macular services.Implementation Science Communications["H. Hogg", "Katie Brittain", "James Talks", "P. Keane", "Gregory Maniatopoulos"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/336a47b1d58ef0822f83a8bc0c279107970fff73 983e188d9bc57b82eac7b26e06b3adc840140442Machine learning and artificial intelligence in education will help to personalize the student learning process, reduce the time wastage in imploding administration systems, and innovate teaching methods. The article reports the findings of a study on the effects of these technologies on the educational systems of application, benefits, and challenges and looks forward to the implications. This has been realized through a systematic review of peer-reviewed journal articles, government reports, and policy documents published between 2021 and 2024. Results show great benefits, including enhanced personalized learning experiences and administrative efficiencies, but also highlight several challenges, such as equitable access, data privacy, and the need for professional development of educators. Rather, this research outlines the imperatives that there is a need to address challenges for the realization of the potential of AI and ML in education. The paper makes recommendations for key stakeholders and points out areas for possible future research to guide sustainable and ethical integration into educational systems. Further research should be done to understand whether these technologies affect student outcomes in the long term and are applicable with the same efficacy in other cultural and socioeconomic contexts.TIERS Information Technology Journal280There is a need to address challenges for the realization of the potential of AI and ML in education, and recommendations for key stakeholders are made and areas for possible future research are pointed out to guide sustainable and ethical integration into educational systems.TIERS Information Technology Journal["Musawer Hakimi", "Mohammad Shuaib Zarinkhail", "Hamayoon Ghafory", "Shir Ahmad Hamidi"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/983e188d9bc57b82eac7b26e06b3adc840140442 8b6168ef2e5f5e57c8ff3b27aa7f2f04782c5a80Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing healthcare delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven Clinical Decision Support Systems (CDSS) can predict glucose trends and suggest tailor evidenced-based treatments with real-time adjustments as her insulin resistance changes with placental growth. Additionally, mobile health applications (mHealth) facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship.American Journal of Perinatology00While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship.American journal of perinatology["Ellen M Murrin", "Antonio F Saad", "Scott Sullivan", "Yuri Millo", "Menachem Miodovnik"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/8b6168ef2e5f5e57c8ff3b27aa7f2f04782c5a80 6bd6bdc66357ea3aabe59bd5d423869746c1991fPurposeThis paper provides a thorough examination of the advancements and impacts of artificial intelligence (AI) on construction management (CM) over the past five years, particularly focusing on its role in mitigating prevalent challenges such as inefficiency and ensuring quality. By methodically reviewing and synthesizing the body of research conducted in this period, it underscores key contributions and breakthroughs in the application of AI within construction management (AICM). Additionally, the study aims to shed light on emerging trends and forecast future directions for technological innovation in the construction management sector.Design/methodology/approachGuided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, this research conducts a bibliometric analysis of 176 relevant publications from the past five years. The analysis focuses on the adoption of AICM across three critical areas: construction equipment management, improvement of construction safety and construction cost optimization. Additionally, the study systematically identifies and examines 14 emerging themes within this domain, ensuring a comprehensive exploration aligned with PRISMA guidelines.FindingsThis manuscript summarizes recent research from the past five years in three key areas: construction equipment management, construction safety management and construction cost management within the realm of AICM. It identifies key gaps and outlines future research directions, including enhancing AI-driven equipment integration, developing sophisticated AI-based safety systems and optimizing cost management with advanced data analytics. These findings and directions are essential for steering the field toward greater digital innovation and sustainability.Originality/valueThis research provides a detailed analysis of the literature within the AICM domain, thoughtfully compiling significant findings and highlighting the importance of addressing user needs. The insights and recommendations shared aim to be beneficial for both academic researchers and industry professionals, contributing to the ongoing development of AICM as it moves toward a future characterized by digital innovation and sustainability.Engineering Construction and Architectural Management760A thorough examination of the advancements and impacts of artificial intelligence (AI) on construction management (CM) over the past five years, particularly focusing on its role in mitigating prevalent challenges such as inefficiency and ensuring quality is provided.Engineering, Construction and Architectural Management["Jingqi Zhang", "Shaohua Jiang"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/6bd6bdc66357ea3aabe59bd5d423869746c1991f 9a7bf3cba9efb6cd692897fe448621af6a27289fIn recent years, a plethora of artificial intelligence (AI) chatbots have been developed and made available to the public. Consequently, an increasing number of individuals are integrating AI chatbots into their daily lives for various purposes. This trend has also raised concerns regarding AI chatbot dependence. However, a valid and reliable scale to assess AI chatbot dependence is yet to be developed. Therefore, this study was designed to develop and validate an AI chatbot dependence scale. We obtained initial items from previous publications and in-depth interviews. Subsequently, item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), reliability, and validity analyses were performed to validate the AI chatbot dependence scale. Seventeen items underwent item analysis and EFA, resulting in a single-factor model with eight items explaining 58.42% of the total variance. The CFA indicated that our AI chatbot dependence scale had acceptable model fitting indices, with standardized loadings ranging between 0.50 and 0.76. In addition, this scale exhibited good reliability and validity. Thus, the current AI chatbot dependence scale can effectively evaluate individuals' dependence on AI chatbots in their daily lives.Cyberpsychology, Behavior, and Social Networking250The current AI chatbot dependence scale can effectively evaluate individuals' dependence on AI chatbots in their daily lives and exhibited good reliability and validity.Cyberpsychology, behavior and social networking["Xing Zhang", "Mingyue Yin", "Mingyang Zhang", "Zhaoqian Li", "Hansen Li"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/9a7bf3cba9efb6cd692897fe448621af6a27289f 737d6d846fb639a43f120b4007d8b3cc64b4ce7cThis study explores the role of Artificial Intelligence (AI) in mediating the effects of Sustainability Human Resource Management (SHRM) and Organizational Creativity on employee performance. Conducted at ABC Bank’s Yogyakarta Area Office, the research employs a quantitative survey method and Structural Equation Modeling (SEM) to analyze data. SHRM practices, including HR development, diversity, and occupational safety, directly contribute to employee engagement and productivity. Similarly, Organizational Creativity, encompassing individual creativity, team collaboration, and knowledge creation, enhances innovation and adaptability within the workplace. The findings demonstrate that SHRM and Organizational Creativity have a significant positive impact on employee performance. SHRM fosters a sustainable work environment, while Organizational Creativity drives the generation of new ideas and solutions to meet organizational goals. However, AI’s role as a mediator between these variables and employee performance was found to be statistically insignificant. This suggests that while AI facilitates HR processes and supports strategic decisions, its integration as a mediator depends on factors such as technological readiness and employee adaptability. This study underscores the importance of aligning SHRM and Organizational Creativity with technological advancements to enhance employee performance. While the mediating effect of AI requires further exploration, its potential to transform HR and creativity processes remains promising. These findings provide valuable insights for organizations seeking to optimize employee performance by leveraging sustainable practices, fostering innovation, and integrating advanced technologies.International Journal of Current Science Research and Review00AI’s role as a mediator between these variables and employee performance was found to be statistically insignificant, which suggests that while AI facilitates HR processes and supports strategic decisions, its integration as a mediator depends on factors such as technological readiness and employee adaptability.International Journal of Current Science Research and Review["Deviana Puspita Maharani", "Y. Siswanti", "Sabihaini Sabihaini"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/737d6d846fb639a43f120b4007d8b3cc64b4ce7c c6a7cc281af549b93c7a9730ee58971d839fbb52 Purpose By addressing the dearth of literature on the subject of cybersecurity risks and artificial intelligence (AI), this study aims to close a research gap by concentrating on the ever-changing environment of online social networks (OSNs) and technology. The main goals are to classify cyberattacks into categories like malware, phishing/spam and network intrusion detection; to identify efficient algorithms for preventing cyber threats; to review relevant literature from 2019 to 2020; and to use machine learning algorithms to detect suspicious behavior related to malware. The study offers a novel framework that suggests particular machine learning algorithms for every kind of cyber threat, hence improving cybersecurity knowledge and reaction capacities. This makes the research useful for examining the impact of cybersecurity on smart cities. Design/methodology/approach Thirty papers have been examined on AI and machine learning algorithms, including K-nearest-neighbor (KNN), convolutional neural networks (CNN) and Random Forest (RF), that were published in 2019 and 2020. Using analytical software (NVivo), a qualitative approach is used to retrieve pertinent data from the chosen research. The researchers divide cyberattacks into three groups: network intrusion detection, phishing/spam and malware. Findings The study’s conclusions center on how AI and machine learning algorithms linked to cybersecurity are reviewed in the literature, how cyberattacks are classified and how an inventive framework for identifying and reducing risks is proposed. This makes the research useful for researching the implications of cybersecurity for smart cities. Practical implications The practical implications of this research are noteworthy, particularly in the realms of technology, AI, machine learning and innovation. The utilization of the NVivo technique enhances decision-making in uncertain situations, making the study’s results more reliable. The findings showcase the applicability of tools in analyzing malicious cyberattacks to address issues related to social media attacks, emphasizing their practical utility. The study’s relevance is further highlighted by a real-world example, where a Kuwaiti public sector fell victim to a malware attack, underlining the importance of cybersecurity measures aligned with the New Kuwait 2035 strategic development plan. The innovative framework presented in the research guides the selection of algorithms for detecting specific malicious attacks, offering practical insights for securing information technology (IT) infrastructure in Kuwait. Social implications The rapid digitization in Kuwait, accelerated by the COVID-19 pandemic, underscores the pivotal role of technology in government services. Ma’murov et al. (2023) emphasize the significance of digitization, particularly in accessing and verifying COVID-19 information. The call for a dedicated digital library for preserving pandemic-related material aligns with the evolving digital landscape. Cybersecurity emerges as a critical concern in Kuwait and the Gulf Cooperation Council (GCC), necessitating transnational cooperation (Nasser Alshabib and Tiago Martins, 2022). In the local context, the inefficiency of information security systems and low awareness among government employees pose cybersecurity challenges (Abdulkareem et al., 2014). Social media’s role during the pandemic highlights its significance, yet the need for cybersecurity in this domain remains underexplored (Ma’murov et al., 2023; Safi et al., 2023). Originality/value The unique aspect of the paper is its in-depth investigation of the relationship between cybersecurity and AI in OSNs. It uses a special application of machine learning methods, including CNN, RF and KNN, to identify suspicious behavior patterns linked to malware. The detailed analysis of 30 research papers released between 2019 and 2020, which informs the choice of suitable algorithms for diverse cyber threats, further emphasizes the study’s uniqueness. The novel framework that has been suggested categorizes assaults and suggests certain machine learning techniques for identification, offering a useful instrument to improve comprehension and reactions to a variety of cybersecurity issues. Journal of Science and Technology Policy Management680The study offers a novel framework that suggests particular machine learning algorithms for every kind of cyber threat, hence improving cybersecurity knowledge and reaction capacities and makes the research useful for examining the impact of cybersecurity on smart cities.Journal of Science and Technology Policy Management["Khaled Jamal Alrabea", "Mohammad Alsaffar", "Meshari Abdulhameed Alsafran", "Ahmad Alsaber", "Shihanah Almutairi", "Farah Al-Saeed", "A. Alkandari"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/c6a7cc281af549b93c7a9730ee58971d839fbb52 9c7c9d79c7156ddee0c9490ec2b05a0e21e27c6bArtificial Intelligence (AI) is improving business processes across industries, driving innovation, efficiency, and data-driven decision-making. This revolutionary technology is changing the way organizations operate, redesigning work, improving products, and personalizing experiences. The predictive capabilities of AI enable businesses to anticipate business and customer needs, providing a competitive advantage. Integration with financial analysis, human resources, and customer service supports processes, reduces costs, and increases revenue. Build capacity, increase operational efficiency, and drive innovation. Analyzing the use of technology in industries such as manufacturing, retail, and finance, this article shows how AI is changing traditional business models. For example, AI-powered predictive analytics can help companies make decisions based on big data, while automation can reduce human error and increase efficiency. Including high usage costs, employee turnover, and ethical issues around personal information. Despite these obstacles, the potential for AI to transform businesses and create competitive advantage is huge. Case studies from a variety of industries demonstrate successful AI integration and its measurable benefits, such as cost savings and customer satisfaction. The tools you need to succeed in a competitive environment. By embracing the role of AI and addressing its challenges, organizations can unlock the full potential of AI, driving growth and innovation. The analysis highlights the importance of AI as the foundation of modern business and its important role in shaping the future of global business.Human-Computer Interaction00Analyzing the use of technology in industries such as manufacturing, retail, and finance, this article shows how AI is changing traditional business models and how AI-powered predictive analytics can help companies make decisions based on big data, while automation can reduce human error and increase efficiency.Human Computer Interaction["Talha Ya\u015far"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/9c7c9d79c7156ddee0c9490ec2b05a0e21e27c6b 1543a0239d1feff193f56ff05df6d6641a5a1f69The emergency of Artificial Intelligence (AI) endowed with capabilities to simulate human intelligence through software-coded operations, has become a topical issue perplexing the minds of regulators, government officials, non-governmental organisations, and the public, across the globe. Linked to this increasing AI debate, is the view that these technologies have the potential to facilitate financial inclusion. Whilst there are concomitant liability and cyber-security related issues associated with AI adoption, the importance of AI in facilitating financial inclusion cannot be overstated. AI can facilitate financial inclusion by enhancing the quality of financial services products and services offered by key players in the South African financial sector, including the capacity to improve the process of opening bank accounts, data analysis, assessment of credit scores, and the management of risk-linked to various financial products. Drawing significant lessons from a select study of the EU and UK model on the regulation of AI, this article argues that there is a need for South Africa to develop an effective regulatory framework governing AI in pursuit of advancing the goals of financial inclusion, among other things.  In finality, the article offers pertinent recommendations in search of avenues for developing policies, principles, norms, and rules that govern AI in South Africa to advance financial inclusion and other important related goals.Potchefstroom Electronic Law Journal150It is argued that there is a need for South Africa to develop an effective regulatory framework governing AI in pursuit of advancing the goals of financial inclusion, among other things.Potchefstroom Electronic Law Journal["Shelton Mota Makore"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/1543a0239d1feff193f56ff05df6d6641a5a1f69 a96188dc5d6bf5d289065061afd990347f33c603At this point, you have heard about generative artificial intelligence and, most likely, dabbled with it. You may have tried out a prompt with Chat GPT or created an image using Microsoft Copilot. At first, you were a bit cautious, but it was less intimidating with practice. You felt these practice sessions were necessary — everyone is talking about generative AI — and, with yet another lecture or webinar scheduled on your campus to discuss generative AI, these “exercises” allow you to participate in the current higher education environment with some working knowledge.Student Affairs Today00These “exercises” allow you to participate in the current higher education environment with some working knowledge and allow you to participate in the current higher education environment with some working knowledge.Student Affairs Today["Katherine C. Aquino", "Adam R. Lalor", "Ceceilia Parnther"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/a96188dc5d6bf5d289065061afd990347f33c603 d0cd22f4299a4d5a0bf7c71c8124f9983fb11da3The integration of artificial intelligence (AI) in healthcare is revolutionizing the clinical procedures in intervention, diagnosis, therapy, amelioration, eradication of diseases and other psychological and neurological diseases. AI has inevitably transformed the health diagnostics, because of the ability to produce improved and accurate results with better accuracy. It has become significant in recent era due to least requirement of equipment and low cost. Moreover, AI related techniques e.g., machine learning (ML) and deep learning (DL) have also played their roles in improving the research and accurate outcomes in healthcare sector. This article presents a brief review about some major techniques of AI in the healthcare sector. Furthermore, some applications of AI relevant to this domain are also presented in this article.2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)140A brief review about some major techniques of AI in the healthcare sector is presented and some applications of AI relevant to this domain are also presented.2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)["Beenish Zafar", "R. Arshad", "Raheel Muzzammel"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/d0cd22f4299a4d5a0bf7c71c8124f9983fb11da3 84a16b13b9a3518a9881a712b137c28f47d6a02cPurposeBuilding upon the resource-based view (RBV) and related research, this paper empirically examines the impact and specific mechanisms of artificial intelligence transformation on corporate innovation capabilities. It provides micro-level evidence of AI’s influence on innovation behavior.Design/methodology/approachDrawing upon data from Chinese listed companies spanning the period from 2011 to 2022, this study employs a dual fixed-effects model and a mediation effects model to empirically analyze the influence of enterprise AI transformation on its innovation capability as well as the specific mechanisms involved.FindingsThe research reveals that AI transformation significantly enhances the innovation capability of enterprises. Heterogeneity analysis indicates that AI transformation exerts a stronger promoting effect on the innovation capability of non-technology firms, large enterprises and those within the manufacturing sector. Mechanism analysis further reveals that AI transformation enhances innovation capability by boosting enterprise profits, reducing costs and reinforcing internal control mechanisms. Further examination demonstrates that AI transformation elevates the quality, efficiency and eco-friendliness of enterprise innovation.Originality/valueFirstly, this study employs text analysis methods from machine learning to construct artificial intelligence indicators at the firm level, providing stronger evidence of AI’s impact on corporate innovation capabilities. Secondly, it extends corporate innovation behavior to include innovation quality, efficiency and green innovation practices, offering a more comprehensive validation of AI’s role in fostering corporate innovation.European Journal of Innovation Management640This paper empirically examines the impact and specific mechanisms of artificial intelligence transformation on corporate innovation capabilities and extends corporate innovation behavior to include innovation quality, efficiency and green innovation practices, offering a more comprehensive validation of AI’s role in fostering corporate innovation.European Journal of Innovation Management["Liangyu Jiang", "Ye Xuan", "Kerong Zhang"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/84a16b13b9a3518a9881a712b137c28f47d6a02c f5246ffe86813302ea5ebabcdba01ef8118a2b02Abstract. The evolution of artificial intelligence (AI) has sparked widespread discussion regarding its potential to replicate or even surpass human intelligence. However, understanding the fundamental differences between AI and human intelligence is crucial for leveraging AI effectively and responsibly. This paper explores the fundamental differences between AI and human intelligence, focusing on structural, learning, and cognitive disparities. By reviewing the history and current state of AI, the paper highlights that despite significant advancements in specific domains, AI still faces limitations in causal reasoning, generalization, common-sense reasoning, and transparency. Based on these limitations, the paper defines AI's application scenarios, including handling unstructured data and automating repetitive tasks. Furthermore, the discussion extends to the prospects of AI in specific fields such as decision support, healthcare, autonomous driving, and finance, emphasizing collaborative work between humans and AI. Finally, the paper underscores that AI should serve as a tool to assist humans in solving complex problems, with a focus on fairness and safety in its societal applications.Applied and Computational Engineering00It is underscored that AI should serve as a tool to assist humans in solving complex problems, with a focus on fairness and safety in its societal applications.Applied and Computational Engineering["Shuhao Liu"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/f5246ffe86813302ea5ebabcdba01ef8118a2b02 9a9ca3720a7fc4f49ea7539953f96e1d4666fa0aAbstract. Since the advent of artificial intelligence (AI) with the Turing test and the Dartmouth Conference, AI has continued to evolve. This evolution has encompassed the introduction of cybernetics, big data, and machine training. However, as the development of artificial intelligence becomes more sophisticated, several issues have begun to emerge. This paper examines the increasing influence of artificial intelligence (AI) in the political sphere over time, specifically examines how AI is being used in the political process, including applications such as sentiment analysis, targeted advertising, and more. It also considers the challenges that have emerged alongside the development of AI, including those related to privacy, voter manipulation, and the transparency of the political process. The paper presents case studies of the 2016 US presidential election and the 2019 Indian general election, which demonstrate how artificial intelligence is exerting a significant influence on democratic political processes, and concludes that as AI becomes increasingly beneficial to human society, concerns are emerging regarding regulations, ethics, political involvement, and other issues. The research addressed the issue of maintaining democratic integrity in politics while advancing artificial intelligence, which offers numerous benefits for modern politics. The paper calls for implementing regulatory policies that would prevent the unethical involvement of artificial intelligence in politics. Additionally, it emphasizes the importance of ensuring that the advantages of artificial intelligence are utilized effectively and fully in the modern political landscape.Applied and Computational Engineering00The paper presents case studies of the 2016 US presidential election and the 2019 Indian general election, which demonstrate how artificial intelligence is exerting a significant influence on democratic political processes, and concludes that as AI becomes increasingly beneficial to human society, concerns are emerging regarding regulations, ethics, political involvement, and other issues.Applied and Computational Engineering["Jinhan Li"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/9a9ca3720a7fc4f49ea7539953f96e1d4666fa0a 305ba1c9796eea38ec4ba34383b2b6e98d4b6a40Artificial intelligence usage in the communication field is of strategic importance. However, most communication practitioners and academicians have not noticed the gains in momentum substantially artificial intelligence. Through communication journals, this study aims to examine how artificial intelligence is used, its effects, and how important it is in the field of communication. This study was designed as a content analysis and bibliometric analysis study. The sample of this study is AI-based articles in Web of Science Q1 indices communication journals. It selected and examined the first sixteen journals in communication keyword search based on SCImago Journal & Country Rank. This study revealed artificial intelligence usage in the communication industry and field, in addition to AI’s the impacts and significance and AI-based technologies in communication. It was found that the most used method for artificial intelligence-based articles in communication journals was the semi-structured interview method. It is seen that common keywords in AI-based articles and communication journals are announced as human-machine communication, machine learning, artificial agent, artificial intelligence, bias, and social media concepts. In this study, a positive correlation between communication science and artificial intelligence technologies was observed. This study contributes to new studies in topics like artificial intelligence, communication, the impacts of artificial intelligence on communication, new communication technologies, generative AI, human-machine communication and other related areas.Akdeniz Üniversitesi İletişim Fakültesi Dergisi00It was found that the most used method for artificial intelligence-based articles in communication journals was the semi-structured interview method, and a positive correlation between communication science and artificial intelligence technologies was observed.Akdeniz Üniversitesi İletişim Fakültesi Dergisi["Sevgi Kavut"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/305ba1c9796eea38ec4ba34383b2b6e98d4b6a40 2fc93ab5426d649d62ead53156af34b60531fd43Artificial intelligence has transformed teachers’ teaching models. This article explores the application of artificial intelligence in basic education in Macao middle schools. This study adopts case analysis in qualitative research, using a total of eight cases from the innovative technology education platform of the Macau education and Youth Development Bureau. These data illustrate how Macao’s artificial intelligence technology promotes teaching innovation in basic education. These eight cases are closely related to the application of artificial intelligence in basic education in Macao. The survey results show that Macao’s education policy has a positive effect on teaching innovation in artificial intelligence education. In teaching practice, the school also cooperates with the government’s policy. The application of AI technology in teaching, students’ learning styles, changes in teachers’ roles, and new needs for teacher training are all influential.Journal of Infrastructure Policy and Development390The survey results show that Macao’s education policy has a positive effect on teaching innovation in artificial intelligence education, and eight cases from the innovative technology education platform of the Macau education and Youth Development Bureau illustrate how Macao’s artificial intelligence technology promotes teaching innovation in basic education.Journal of Infrastructure, Policy and Development["Sio Hong Teng", "Fat Fai Ieong"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/2fc93ab5426d649d62ead53156af34b60531fd43 831885477765fe3b686988ac2e532ca656c3f24bAbstract. Integrated circuits and artificial intelligence complement each other. On one hand, the continuous optimization of electron and photon accelerators can reduce the energy consumption of artificial intelligence algorithms and adapt them to edge computing, thereby promoting the development of artificial intelligence. On the other hand, artificial intelligence not only assists in troubleshooting and optimizing of integrated circuits, but also automates the design of integrated circuits, making them more convenient and efficient. The interactive relationship between integrated circuits and artificial intelligence is presented through qualitative analysis and literature review in this paper. The mutual influence of these two technologies creates new breakthroughs and possibilities for the development of science and technology. However, while photonic artificial intelligence accelerators are suitable for high-speed neural networks, their current development is not yet mature due to the high cost, complex manufacturing process, and environmental disturbances. Additionally, artificial intelligence algorithms for integrated circuits design lack sufficient high-quality data for training due to the secrecy of integrated circuit parameters. Thus it requires a long time to train and perfect these algorithms.Applied and Computational Engineering230The interactive relationship between integrated circuits and artificial intelligence is presented through qualitative analysis and literature review in this paper and the mutual influence of these two technologies creates new breakthroughs and possibilities for the development of science and technology.Applied and Computational Engineering["Linye Wang"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/831885477765fe3b686988ac2e532ca656c3f24b 150d0839e86b4a5906d16627e5ff13f00f10b92dThis paper delves into the problem caused by the development of artificial intelligence-based systems trained on data sets built by harnessing data collected from employees' work activities. The convergence of technology and workers-produced data prompts a critical examination of its implications within the legal standards framework regarding safeguarding workers against automation and respecting workers' data privacy. The analysis underscores the need to establish legal safeguards for workers, proposing elements for building a legal framework to ensure that companies cannot exploit the data generated through workers’ labor activities to train AI systems without obtaining proper consent and providing fair compensation for those workers.Brazilian Journal of Law, Technology and Innovation00The analysis underscores the need to establish legal safeguards for workers, proposing elements for building a legal framework to ensure that companies cannot exploit the data generated through workers’ labor activities to train AI systems without obtaining proper consent and providing fair compensation for those workers.Brazilian Journal of Law, Technology and Innovation["R\u00f4mulo Soares Valentini"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/150d0839e86b4a5906d16627e5ff13f00f10b92d 24c0cdf306600baf8f3a75b64057bcebea0b8ddcThis paper reviews the privacy and security challenges posed by open-source artificial intelligence (AI) models. The increased use of open-source machine learning models, while beneficial for resource efficiency and collaboration, has introduced significant privacy risks and security vulnerabilities. Key threats include model inversion, membership inference, data leakage, and backdoor attacks, which could expose sensitive data or compromise system integrity. Our review highlights that many open-source models are vulnerable to these attacks due to their transparency and accessibility. We also identify that adversarial training, differential privacy (DP), and model sanitization techniques can effectively mitigate some of these risks, though achieving a balance between transparency and security remains a challenge. The findings highlight the need for continuous research and innovation to ensure that open-source AI models remain both secure and privacy-compliant in increasingly critical applications across various industries.Computers90It is identified that adversarial training, differential privacy (DP), and model sanitization techniques can effectively mitigate some of these risks, though achieving a balance between transparency and security remains a challenge.Computers["Younis Al-Kharusi", "Ajmal Khan", "Muhammad Rizwan", "M. Bait-Suwailam"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/24c0cdf306600baf8f3a75b64057bcebea0b8ddc 5d0369d219f438b55a10a607e1cb2cef66cf62bcAlong with the extensive use of artificial intelligence in the whole society, the deep integration of AI and education has become an inevitable trend. The value implication of AI on ideological and political education is reflected in the integration of AI technology with the four basic elements of ideological and political education: the subject, object, mediator and ring, which endows the traditional mode of ideological and political education with new vitality and great potential for development, and forms a personalized education mode, refined education management, panoramic education evaluation and intelligent education environment. Thus, it provides research tools and research methods for research subjects to deepen their understanding of the objectivity and universality of the function of ideological and political education, and endows the academic environment of ideological and political education function with more mobility, which makes the content of the research of ideological and political education function richer. However, while AI boosts the epochal development of ideological and political education work, it also has problems such as weakening the discourse status of the subject of ideological and political education, deviation of educational value and lack of humanistic care, and ethical risks of data and privacy leakage. Therefore, in the era of AI, it is necessary to strengthen the role authority and ideological guidance of the main body of the discourse of ideological and political education, balance the “intelligence” and “emotional” of ideological and political education, and adhere to the bottom-line requirements of the ethical safeguard mechanism of ideological and political education, so as to make artificial intelligence play a more effective role in ideological and political education. AI can play a more effective role in ideological and political education.Academic Journal of Management and Social Sciences130It is necessary to strengthen the role authority and ideological guidance of the main body of the discourse of ideological and political education, balance the “intelligence” and “emotional” of ideological and political education, and adhere to the bottom-line requirements of the ethical safeguard mechanism of ideological and political education, to make artificial intelligence play a more effective role in ideological and political education.Academic Journal of Management and Social Sciences["Shuang Li", "Wanyin Li", "Junbo Chen", "Yunting Fan"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/5d0369d219f438b55a10a607e1cb2cef66cf62bc d9ab1eeae3064917f3733d5e439074be492c8f0fArtificial intelligence (AI) technology has witnessed unprecedented advancements and a gradual penetration into civilian applications. This paper aims to thoroughly investigate the application of AI in the entertainment industry, with a particular focus on the principles and cross-disciplinary implementations of 3D real-life scanning, AI for non-player characters (NPCs), and AI video generation. By synthesizing how these technologies streamline content creation processes, lower technical barriers, and inspire novel approaches to game design, we observe that AI is not only reshaping the ecosystem of the entertainment sector but also facilitating the entry of newcomers into game development. However, alongside the benefits, this study identifies several challenges and limitations associated with current AI technologies, such as accuracy, cost-effectiveness, and ethical concerns, which require attention and resolution in future research and practice. Through a detailed examination and synthesis of these phenomena, this research provides a reference for practitioners and suggests directions for subsequent studies.Applied and Computational Engineering70This paper aims to thoroughly investigate the application of AI in the entertainment industry, with a particular focus on the principles and cross-disciplinary implementations of 3D real-life scanning, AI for non-player characters, and AI video generation.Applied and Computational Engineering["Yuang Tian"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/d9ab1eeae3064917f3733d5e439074be492c8f0f 501d4660361f9b229b33efe46736d7f3bc2c4709Abstract. In recent years, artificial intelligence (AI) has experienced remarkable growth, largely driven by significant advancements in algorithm structures. This paper provides a comprehensive review of the key algorithmic frameworks employed in AI, with a primary focus on traditional algorithms and their evolution in response to modern deep learning techniques. Traditional algorithms, such as decision trees, support vector machines, and genetic algorithms, have long served as foundational pillars in AI research. However, the advent of deep learning has introduced new paradigms that significantly enhance these algorithms in terms of performance, scalability, and adaptability. By analyzing the classification, characteristics, and limitations of traditional algorithms, this study compares them with deep learning models, highlighting both their strengths and shortcomings. Furthermore, this paper examines how deep learning improves traditional algorithms through case studies that showcase enhanced performance, broader application domains, and evolving design principles. This study is based on an analysis of publicly available datasets and a comprehensive review of the current literature. The findings suggest that while traditional algorithms offer a solid foundation, deep learning has revolutionized algorithmic design, paving the way for new applications and innovations in AI. Ultimately, this review underscores the critical role of integrating deep learning into traditional algorithmic frameworks for the future of AI.Applied and Computational Engineering00The findings suggest that while traditional algorithms offer a solid foundation, deep learning has revolutionized algorithmic design, paving the way for new applications and innovations in AI.Applied and Computational Engineering["Yining Sui"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/501d4660361f9b229b33efe46736d7f3bc2c4709 e71c38f24c940e2f1aad5acedb98d2122424d062Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.Applied and Computational Engineering00This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance.Applied and Computational Engineering["Qinbo Zhang"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/e71c38f24c940e2f1aad5acedb98d2122424d062 1fe04a60a666bfd47566afe93f52b15746d23394International Journal of Energy and Water Resources541International Journal of Energy and Water Resources["M. M. Masud", "A. S. M. Shamem", "A. Saif", "Md. F. Bari", "R. Mostafa"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/1fe04a60a666bfd47566afe93f52b15746d23394 7e080de5a84956d18079a90cf3f9dd31bbf0c6d9ABSTRAKArtificial Intelligence (AI) memiliki dampak yang signifikan terhadap pendidikan, terutama dalam konteks filsafat ilmu  pendidikan. Penelitian ini bertujuan untuk mengukur dampak AI terhadap pemahaman mahasiswa teknologi pendidikan mengenai epistemologi, ontologi, dan aksiologi pendidikan. Penelitian ini menggunakan metode kuantitatif deskriptif dengan desain survei yang melibatkan 30 Guru dari berbagai Jenjang  di Kalimatan Barat.  Data dikumpulkan melalui kuesioner dengan skala Likert dan dianalisis menggunakan uji regresi linear berganda dengan bantuan perangkat lunak SPSS. Hasil uji statistik menunjukkan bahwa AI memiliki pengaruh positif signifikan terhadap pemahaman epistemologis (p < 0,05), di mana 65% responden merasa AI membantu mereka mengakses pengetahuan dengan lebih efisien. Secara ontologis, 50% responden menganggap AI mengubah peran pengajar menjadi fasilitator, sementara 40% merasa bahwa AI dapat mengurangi interaksi langsung antara pengajar dan siswa. Dari perspektif aksiologi, terdapat kekhawatiran tentang etika penggunaan AI, dengan 55% responden mempertanyakan bagaimana AI mempengaruhi nilai-nilai sosial dalam pendidikan. Temuan ini menekankan pentingnya pengintegrasian perspektif etis dan filosofis dalam pengembangan AI di bidang pendidikan, guna memastikan bahwa teknologi ini mendukung tujuan pendidikan yang holistik dan humanistik.Kata Kunci: Artificial Intellogence, Filsafat Ilmu PendidikanABSTRACTArtificial Intelligence (AI) has a significant impact on education, especially in the context of the philosophy of educationscience. This study aims to measure the impact of AI on educational technology students' understanding of the epistemology, ontology, and axiology of education. This study uses a descriptive quantitative method with a survey design involving 30 teachers from various levels in West Kalimantan. Data were collected through a questionnaire with a Likert scale and analyzed using multiple linear regression tests with the help of SPSS software. The results of the statistical test showed that AI had a significant positive effect on epistemological understanding (p <0.05), where 65% of respondents felt that AI helped them access knowledge more efficiently. Ontologically, 50% of respondents considered that AI changed the role of teachers to facilitators, while 40% felt that AI could reduce direct interaction between teachers and students. From an axiological perspective, there are concerns about the ethics of using AI, with 55% of respondents questioning how AI affects social values in education. These findings emphasize the importance of integrating ethical and philosophical perspectives in the development of AI in education, to ensure that this technology supports holistic and humanistic educational goals.Keywords:  Artificial Intelligence (AI), philosophy of educationVOX EDUKASI Jurnal Ilmiah Ilmu Pendidikan00VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan["Rani Diah Pratiwi", "Sarah Arni", "Usman Radiana", "Luhur Wicaksono"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/7e080de5a84956d18079a90cf3f9dd31bbf0c6d9 f2b66a82a22634d0ccb4c7d2fa1e519f77673b7aNature Machine Intelligence370Nature Machine Intelligence["Artem A. Trotsyuk", "Quinn Waeiss", "Raina Talwar Bhatia", "Brandon J. Aponte", "Isabella M. L. Heffernan", "Devika Madgavkar", "R. Felder", "Lisa Soleymani Lehmann", "Megan J. Palmer", "Hank Greely", "Russell Wald", "Lea Goetz", "Markus Trengove", "Robert Vandersluis", "Herbert Lin", "Mildred K. Cho", "R. Altman", "Drew Endy", "D. Relman", "Margaret Levi", "Debra Satz", "David Magnus"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/f2b66a82a22634d0ccb4c7d2fa1e519f77673b7a 1940a8b24fd99ff755884d6f3f0e5273fa78b0a5Neurodegenerative diseases such as Alzheimer's and Parkinson's disease have presented one of the major health challenges facing the world today, especially with the increasing prevalence seen with increasing aging populations. In this regard, AI has been emerged as a promising method for early detection and intervention. Therefore, in the paper, an explainable approach for deep learning in voice recordings toward diagnosing Parkinson's disease is presented. It uses a modified Attention ResNet18 architecture with Squeeze-and-Excitation blocks and utilizes Mel-Spectrogram features obtained from audio signals. Grad-CAM, Integrated Gradients, and LIME were used to provide the explanations generated by the model during the classification process. Experimental results presented an accuracy of 97.8% for classification, thus demonstrating the ability of Explainable AI to detect non-invasive PD.2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)160Experimental results presented the ability of Explainable AI to detect non-invasive PD, thus demonstrating the ability of Explainable AI to detect non-invasive PD.2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)["Adeel Mukhtar", "Saddique Khalid", "Waqas Tariq Toor", "Muhammad Shoaib Akhtar"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/1940a8b24fd99ff755884d6f3f0e5273fa78b0a5 ff5941d6026a3677151c4970ae67eb03bde2ee94Applied Artificial Intelligence280Applied Artificial Intelligence["Eleanor Mill", "Wolfgang Garn", "Chris J. Turner"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/ff5941d6026a3677151c4970ae67eb03bde2ee94 f1200fc1e5a42842364825aca5ab344937871a62Eye120AI-based chat bots can provide relatively accurate and clear responses for addressing common ophthalmological inquiries as well as comprehensiveness and clarity, and surpassed Bard in all measured metrics.Eye["Daniel David", "Ofira Zloto", "Gabriel Katz", "Ruth Huna-Baron", "V. Vishnevskia-Dai", "Sharon Armarnik", "N. A. Zauberman", "Elinor Megiddo Barnir", "Reut Singer", "Avner Hostovsky", "Eyal Klang"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/f1200fc1e5a42842364825aca5ab344937871a62 b2fda6a2fcec0516a818dddd7b8239171cc44bb6This study presents an AI-based optimized energy management model for a hydrogen refueling station with a PV system and fuel cell (FC). Using the Time-series Dense Encoder (TiDE) model for day-ahead PV generation and electricity price forecasting, with real data from Spain, the station efficiently manages hydrogen demand and electricity sales. Despite lower-than-expected PV production (11,559.39 kWh vs. 12,991.71 kWh), real-time revenue rose to 1,144.84 Euros, demonstrating operational flexibility and profit.Telecommunications Forum210Using the Time-series Dense Encoder model for day-ahead PV generation and electricity price forecasting, with real data from Spain, the hydrogen refueling station efficiently manages hydrogen demand and electricity sales.2024 32nd Telecommunications Forum (TELFOR)["O\u011fuz K\u0131rat", "Alper \u00c7i\u00e7ek"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/b2fda6a2fcec0516a818dddd7b8239171cc44bb6 f7248bafd38c4a5d0142d7550261f6f015282c8eDiscover Water960Discover Water["Saif Haider", "Muhammad Rashid", "Muhammad Atiq Ur Rehman Tariq", "Abdullah Nadeem"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/f7248bafd38c4a5d0142d7550261f6f015282c8e 88dafb03b73b139203d8cec5a29fc46261490137Nature Communications150Nature Communications["Tim Johnson", "Nick Obradovich"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/88dafb03b73b139203d8cec5a29fc46261490137 a13046909eca752c6faffd515c49eabe82d9ffddBackground: Allergic contact dermatitis is a common, pruritic, debilitating skin disease, affecting at least 20% of the population. Objective: To prospectively validate a computer vision algorithm across all Fitzpatrick skin types. Methods: Each participant was exposed to 10 allergens. The reference criterion was obtained 5 days after initial patch placement by a board-certified dermatologist. The algorithm processed photographs of the test site obtained on Day 5. Human performance in reading the photographs was also evaluated. Results: A total of 206 evaluable participants [mean age 39 years, 66% (136/206) female, and 47% with Fitzpatrick skin types IV-VI] completed testing. Forty-two percent (87/206) of participants experienced 1 or more allergic reaction resulting in a total of 132 allergic reactions. The model provided high discrimination (AUROC 0.86, 95% CI: 0.82-0.90) and specificity (93%, 95% CI: 92%-94%) but with lower sensitivity (58%, 95% CI: 49%-67%). Human performance interpreting the photographs ranged from providing similar performance to the algorithm to providing superior performance when combined across readers. There were no serious adverse events. Conclusions: The combination of a smartphone capture of patch testing sites with deep learning yielded high discrimination across a diverse sample.Dermatitis110The combination of a smartphone capture of patch testing sites with deep learning yielded high discrimination across a diverse sample, and human performance interpreting the photographs ranged from providing similar performance to the algorithm to providing superior performance when combined across readers.Dermatitis : contact, atopic, occupational, drug["R. Carter", "Alexander D. Weston", "M. A. Wieczorek", "Laura M Pacheco-Spann", "Sheikh Fahad", "Maria A. Caruso", "Miguel A. Aristizabal", "Alison J. Bruce", "Matthew R Hall", "Charles J. Bruce"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/a13046909eca752c6faffd515c49eabe82d9ffdd 9eaf7ea7f25df453e55c01908871f9c937bf688aCluster Computing220Clust. Comput.["Essam H. Houssein", "Someya Mohsen Zaki", "Marwa M. Emam", "N. A. Samee", "Reem Alkanhel", "Eman M. G. Younis"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/9eaf7ea7f25df453e55c01908871f9c937bf688a 40f5446f200a906909d500c5fd5aa5e8ca4ad6ecEXCLI Journal : Experimental and Clinical Sciences80EXCLI Journal["V. E. Hillary", "Rajiv Gandhi Gopalsamy", "L. A. D. M. Santana", "P. C. de Jesus", "J. B. de Souza", "D. M. R. R. Silva", "P. H. M. Moura", "R. S. Santos", "M. S. Barreto", "L. Borges", "E. E. D. Silva"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/40f5446f200a906909d500c5fd5aa5e8ca4ad6ec aae575afccd24d77ec3f0af848a729ccf4c0de81Academic Psychiatry30Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["Rina G. Bhalodi", "Dustin Wong", "Franzes Liongson", "Howard Levin"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/aae575afccd24d77ec3f0af848a729ccf4c0de81 7d8a6fc3791d0fd49cd7cb0b49a495ce8919fce5While generative AI, represented by ChatGPT, brings a technological revolution and convenience to life, it may raise a series of social and legal risks, mainly including violation of personal privacy and data security issues, infringement of intellectual property rights, generation of misleading and false content, and exacerbation of discrimination and prejudice. However, the traditional AI governance paradigm oriented towards conventional AI may not be adequately adapted to generative AI with generalized potential and based on big models. In order to encourage the innovative development of generative AI technology and regulate the risks, this paper explores the construction of a generative AI governance paradigm that combines legal regulation, technological regulation, and ethical governance of science and technology, and promotes the healthy development of generative AI on the track of safety, order, fairness, and co-governance.Academic Journal of Management and Social Sciences00This paper explores the construction of a generative AI governance paradigm that combines legal regulation, technological regulation, and ethical governance of science and technology, and promotes the healthy development of generative AI on the track of safety, order, fairness, and co-governance.Academic Journal of Management and Social Sciences["Fangfei Hu", "Shiyuan Liu", "Xinrui Cheng", "Pengyou Guo", "Mengqi Yu"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/7d8a6fc3791d0fd49cd7cb0b49a495ce8919fce5 d826a0f6f5e389502d4e61efa7756674aaca744dInternational Journal of Computational Intelligence Systems00Int. J. Comput. Intell. Syst.["G\u00fclay Demir", "Sarbast Moslem", "S. Duleba"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/d826a0f6f5e389502d4e61efa7756674aaca744d f2a631427502a51a7f46971dcf80828e4d3d5384The increasing complexity and cost of clinical trials, particularly in the context of oncology and advanced therapies, pose significant challenges for drug development. This study evaluates the predictive capabilities of large language models (LLMs) such as GPT-3.5, GPT-4, and HINT in determining clinical trial outcomes. By leveraging a curated dataset of trials from ClinicalTrials.gov, we compare the models' performance using metrics including balanced accuracy, specificity, recall, and Matthews Correlation Coefficient (MCC). Results indicate that GPT-4o demonstrates robust performance in early trial phases, achieving high recall but facing limitations in specificity. Conversely, the HINT model excels in recognizing negative outcomes, particularly in later trial phases, offering a balanced approach across diverse endpoints. Oncology trials, characterized by high complexity, remain challenging for all models. Additionally, trial duration and disease categories influence predictive performance, with longer durations and complex diseases such as neoplasms reducing accuracy. This study highlights the complementary strengths of LLMs and HINT, providing insights into optimizing predictive tools for clinical trial design and risk management. Future advancements in LLMs are essential to address current gaps in handling negative outcomes and complex domains.arXiv.org00This study evaluates the predictive capabilities of large language models such as GPT-3.5, GPT-4, and HINT in determining clinical trial outcomes and highlights the complementary strengths of LLMs and HINT, providing insights into optimizing predictive tools for clinical trial design and risk management.ArXiv["Shuyi Jin", "Lu Chen", "Hongru Ding", "Meijie Wang", "Lun Yu"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/f2a631427502a51a7f46971dcf80828e4d3d5384 bd612dc180e578eefff487d7dee64df1523e71eeThe principle of comparative advantage determines the optimal allocation of human resources to appropriate positions. Thus, in areas that require social, emotional, creative, or comprehensive abilities, humans can analyze tasks more comprehensively than AI, thereby establishing a comparative advantage in the job market. 2. New technology implementation costs, such as legal, political, and other technology promotion expenses, far exceed the cost of developing the technology itself. With these constraints, technological progress is unlikely to cause immediate social unrest and mass unemployment. In addition, by classifying occupations, this paper finds that new technologies can only eliminate a few completely obsolete occupations. Still, with the penetration of new technologies in the social field, new occupations in the technical field are created. Second, new technologies also raise human work by changing the nature of most occupations and reshaping how work is done. This paper concludes that despite the continuous development of advanced technology,  most of the labor force is unlikely to be unemployed.Advances in Education, Humanities and Social Science Research290This paper concludes that despite the continuous development of advanced technology, most of the labor force is unlikely to be unemployed.Advances in Education, Humanities and Social Science Research["Kwunwang Chan"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/bd612dc180e578eefff487d7dee64df1523e71ee dcc654ddd9844c24c66120ec04dd338740270297Online information review (Print)100Online Inf. Rev.["Lai-Wan Wong", "G. Tan", "K. Ooi", "Jun-Jie Hew", "Yogesh K. Dwivedi"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/dcc654ddd9844c24c66120ec04dd338740270297 f6e7186a0000bf686d2da6d32950e216e54ca2d2This study introduces Fin-ALICE (Artificial Linguistic Intelligence Causal Econometrics), a framework designed to forecast financial time series by integrating multiple analytical approaches including co-occurrence networks, supply chain analysis, and emotional sentiment analysis to provide a comprehensive understanding of market dynamics. In our co-occurrence analysis, we focus on companies that share the same emotion on the same day, using a much shorter horizon than our previous study of one month. This approach allows us to uncover short-term, emotion-driven correlations that traditional models might overlook. By analyzing these co-occurrence networks, Fin-ALICE identifies hidden connections between companies, sectors, and events. Supply chain analysis within Fin-ALICE will evaluate significant events in commodity-producing countries that impact their ability to supply key resources. This analysis captures the ripple effects of disruptions across industries and regions, offering a more nuanced prediction of market movements. Emotional sentiment analysis, powered by the Fin-Emotion library developed in our prior research, quantifies the emotional undertones in financial news through metrics like “emotion magnitude” and “emotion interaction”. These insights, when integrated with Temporal Convolutional Networks (TCNs), significantly enhance the accuracy of financial forecasts by capturing the emotional drivers of market sentiment. Key contributions of Fin-ALICE include its ability to perform month-by-month company correlation analysis, capturing short-term market fluctuations and seasonal patterns. We compare the performance of TCNs against advanced models such as LLMs and LSTMs, demonstrating that the Fin-ALICE model outperforms these models, particularly in sectors where emotional sentiment and supply chain dynamics are critical. Fin-ALICE provides decision-makers with predictive insights and a deeper understanding of the underlying emotional and supply chain factors that drive market behaviors.Journal of Risk and Financial Management130Journal of Risk and Financial Management["Shawn McCarthy", "G. Alaghband"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/f6e7186a0000bf686d2da6d32950e216e54ca2d2 a26dd390adf6d3201065c9deb75e666b249d6703This article deals with the use of Artificial Intelligence (AI) in the judiciary, with an emphasis on the Victor Project, which is a tool designed through a partnership between the Federal Supreme Court (STF) and the University of Brasília (UnB), which aims to improve the resolution of cases in the judicial sphere and eliminate or at least reduce the congestion of unresolved actions in the country's courts, with regard to judicial analyses. AI directly influences the modernization of the judiciary, as this means allows it to be used by lawyers, judges and the Judiciary in general, aiming to speed up the decisions of the Superior Court of Justice in a more skillful manner. Therefore, the general objective of this work is to discuss the use of AI in the field of Law, emphasizing the use of the Victor Project in the decisions of the STF, also showing its positive and negative impacts, from a constitutional and ethical perspective, as well as how all procedures should be in the application of legal practices. The methodology used to develop this article consists of bibliographic and empirical research based on documents and qualitative data analysis, through the search in scientific articles and pertinent legislation based on the higher courts. The brief study proposes a clear understanding of the issues involving AI and judicial decisions, involving the Victor tool and its need for transparency regarding the use of this technology, suggesting the addition of movements in electronic processes to allow revisions when necessary.RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-621860The brief study proposes a clear understanding of the issues involving AI and judicial decisions, involving the Victor tool and its need for transparency regarding the use of this technology, suggesting the addition of movements in electronic processes to allow revisions when necessary.RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218["O. Neto"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/a26dd390adf6d3201065c9deb75e666b249d6703 72d0b13a4fc4738c8f58633728e670f399396675Abstract. The emergence of AI-generated content (AIGC) can be traced back to as early as 1950, when Alan Turing introduced the famous "imitation game" in his paper Computing Machinery and Intelligence, which proposed a method to determine whether a machine possesses "intelligence." Since the introduction of the GAN model by Goodfellow et al. in 2014, the issue of autonomy in AIGC has not seen any breakthroughs. However, due to the challenges posed by robustness and the lack of explainability, society is already beginning to anticipate the social issues and anxieties that might arise with the advent of autonomous artificial general intelligence (AGI). The increasing influence of AI technology on society has further driven concerns about the ethical implications of both AIGC and AGI. Specifically, the relationship between human-computer interaction (HCI) and AI ethicsparticularly the role of explainable AIhas become increasingly crucial. Merely understanding the issue of non-explainability from a technical standpoint is no longer sufficient to form a principled basis for AI ethics. In fact, Turing to some extent foresaw the possibility that AI's future development would face such issues. This paper seeks to offer a direction that moves beyond the traditional AI ethics research framework by reinterpreting Turing's original question and analyzing some of the objections he raised. The goal is to provide a new mindset for exploring the necessary modes of thinking for human-computer interaction in the era of AGI.Applied and Computational Engineering130This paper seeks to offer a direction that moves beyond the traditional AI ethics research framework by reinterpreting Turing's original question and analyzing some of the objections he raised, to provide a new mindset for exploring the necessary modes of thinking for human-computer interaction in the era of AGI.Applied and Computational Engineering["Heyang Chen"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/72d0b13a4fc4738c8f58633728e670f399396675 b64a4c3ae921a191211193334619818327762392Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, highlighting successful applications and case studies across diverse sectors. From predicting genotoxicity and mutagenicity to elucidating molecular pathways underlying carcinogenesis, integrative approaches offer a comprehensive framework for understanding chemical exposures and mitigating associated health risks. Future perspectives for advancing chemical risk assessment and cancer prevention through data integration, advanced machine learning techniques, translational research, and policy implementation are discussed. By implementing the predictive capabilities of AI/ML and omics technologies, researchers and policymakers can enhance public health protection, inform regulatory decisions, and promote sustainable development for a healthier future.Frontiers in Toxicology1783A perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction and provides an overview of key findings, challenges, and opportunities.Frontiers in Toxicology["Ajay Vikram Singh", "Preeti Bhardwaj", "P. Laux", "Prachi Pradeep", "Madleen Busse", "Andreas Luch", "Akihiko Hirose", "Christopher J. Osgood", "Michael W. Stacey"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/b64a4c3ae921a191211193334619818327762392 dbb69d4ec53e282cb03209c5974807de01e3231aWe conducted an online survey with undergraduate students in Afghanistan and Pakistan, two neighboring countries in South Asia, to examine their perspectives and attitudes toward generative artificial intelligence (AI). In particular, we analyzed their self-efficacy regarding learning about AI, experiences with AI tools, and intention to adopt AI in their learning and daily lives. University students in Afghanistan and Pakistan demonstrated mixed attitudes toward AI, simultaneously expressing both optimism and concern regarding AI-related technology. While students from both countries exhibited many similarities, their attitudes were interestingly different regarding the benefits and threats of AI. Our research also identified factors affecting AI self-efficacy and intention to learn about AI among Pakistani and Afghan students. This research helps fill a gap in the field of communication and media that lacks empirical studies on Pakistan and Afghanistan youth attitudes toward generative AI and related issues in the Global South in general.Studies in Media, Journalism and Communications02This research helps fill a gap in the field of communication and media that lacks empirical studies on Pakistan and Afghanistan youth attitudes toward generative AI and related issues in the Global South in general.Studies in Media, Journalism and Communications["Hyunjin Seo", "Azhar Iqbal", "Shanawer Rafique"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/dbb69d4ec53e282cb03209c5974807de01e3231a e4be2d230ab1f25c6fad5c53961b52c502f2ddf9Objectives: Artificial intelligence (AI) is transforming aesthetic dermatology, introducing new opportunities for personalized, predictive, and adaptive approaches in skin diagnostics, treatment planning, and patient management. This review examines AI’s evolving role in enhancing diagnostic precision, individualizing treatments, and supporting dynamic patient care, with a focus on practical implementation in clinical settings. Results: This piece highlights how AI-based imaging and predictive tools enable more precise diagnostics and tailored treatment protocols, leading to improved patient outcomes and satisfaction. Some of the key benefits of AI in aesthetic dermatology include the ability to detect subtle skin changes, simulate treatment outcomes, and adjust interventions in real time. However, this manuscript also addresses significant challenges that practitioners face, such as technical constraints, data privacy concerns, algorithmic biases, and financial barriers, which impact the accessibility and efficacy of AI across diverse patient populations. Conclusions: While AI holds significant potential to enhance aesthetic dermatology, its responsible integration requires addressing these challenges through clinician training, ethical guidelines, and robust data security measures. Effective use of AI will depend on collaboration between technology developers, clinicians, and regulatory bodies. Perspectives: Looking forward, the development of diverse, inclusive datasets and transparent, patient-centered AI models will be essential to ensure that AI’s benefits reach all patients equitably and safely. By prioritizing these factors, AI-driven technologies would become a reliable, accessible, and transformative element of aesthetic dermatology practice.Cosmetics491A review of AI’s evolving role in enhancing diagnostic precision, individualizing treatments, and supporting dynamic patient care, with a focus on practical implementation in clinical settings highlights how AI-based imaging and predictive tools enable more precise diagnostics and tailored treatment protocols, leading to improved patient outcomes and satisfaction.Cosmetics["D. Haykal"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/e4be2d230ab1f25c6fad5c53961b52c502f2ddf9 5e90657264cc5a1fa7bcded94de879e7529809efThe centralization of Artificial Intelligence (AI) poses significant challenges, including single points of failure, inherent biases, data privacy concerns, and scalability issues. These problems are especially prevalent in closed-source large language models (LLMs), where user data is collected and used without transparency. To mitigate these issues, blockchain-based decentralized AI (DeAI) has emerged as a promising solution. DeAI combines the strengths of both blockchain and AI technologies to enhance the transparency, security, decentralization, and trustworthiness of AI systems. However, a comprehensive understanding of state-of-the-art DeAI development, particularly for active industry solutions, is still lacking. In this work, we present a Systematization of Knowledge (SoK) for blockchain-based DeAI solutions. We propose a taxonomy to classify existing DeAI protocols based on the model lifecycle. Based on this taxonomy, we provide a structured way to clarify the landscape of DeAI protocols and identify their similarities and differences. We analyze the functionalities of blockchain in DeAI, investigating how blockchain features contribute to enhancing the security, transparency, and trustworthiness of AI processes, while also ensuring fair incentives for AI data and model contributors. In addition, we identify key insights and research gaps in developing DeAI protocols, highlighting several critical avenues for future research.arXiv.org1571A Systematization of Knowledge (SoK) for blockchain-based DeAI solutions is presented, and a taxonomy to classify existing DeAI protocols based on the model lifecycle is proposed, providing a structured way to clarify the landscape of DeAI protocols and identify their similarities and differences.ArXiv["Zhipeng Wang", "Rui Sun", "Elizabeth Lui", "Vatsal Shah", "Xihan Xiong", "Jiahao Sun", "Davide Crapis", "William J. Knottenbelt"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/5e90657264cc5a1fa7bcded94de879e7529809ef d8b297145f896f3d7018a05b1ad910d3ba7e1ca6Contemporarily, AI technology has been widely adopted in various fields. In this case, this research provides an in-depth look at the application of cutting-edge Artificial Intelligence (AI) technology to autonomous driving technology. The article traces the evolution of autonomous driving from its infancy in the early 1900s to its current state of the art and highlights major milestones such as the DARPA Grand Challenge. It discusses recent advances in AI and its key role in autonomous driving, with a particular emphasis on deep learning techniques in enhancing perception and decision-making capabilities. The convergence of these AI technologies has given rise to highly sophisticated autonomous driving systems that enable vehicles to traverse complex environments safely and efficiently. In addition, this paper explores the application of distributed machine learning, in particular locally updated stochastic gradient descent (SGD), in training large-scale AI models, and highlights its advantages in terms of scalability, data privacy and communication efficiency. The research motivation of this paper stems from improving the efficiency, scalability and privacy of AI models in autonomous driving systems. At the same time, the article identifies limitations in current self-driving AI implementations, including technological maturity, reliability issues, legal and ethical concerns, and high costs, and outlines future prospects for the field.Applied and Computational Engineering00The article traces the evolution of autonomous driving from its infancy in the early 1900s to its current state of the art and highlights major milestones such as the DARPA Grand Challenge, as well as identifying limitations in current self-driving AI implementations.Applied and Computational Engineering["Jiexiang Xu"]2024-11-26T00:00:00https://www.semanticscholar.org/paper/d8b297145f896f3d7018a05b1ad910d3ba7e1ca6 7d436b42bc2a2060bebb7221c575c1d243e61a4cWith accelerating climate change and rising global energy consumption, the application of artificial intelligence (AI) and machine learning (ML) has emerged as a crucial tool for enhancing energy efficiency and mitigating the impacts of climate change. However, their implementation has a dual character: on one hand, AI facilitates sustainable solutions, including energy optimization, renewable energy integration and carbon reduction; on the other hand, the training and operation of large language models (LLMs) entail significant energy consumption, potentially undermining carbon neutrality efforts. Key findings include an analysis of 237 scientific publications from 2010 to 2024, which highlights significant advancements and obstacles to AI adoption across sectors, such as construction, transportation, industry, energy and households. The review showed that interest in the use of AI and ML in energy efficiency has grown significantly: over 60% of the documents have been published in the last two years, with the topics of sustainable construction and climate change forecasting attracting the most interest. Most of the articles are published by researchers from China, India, the UK and the USA, (28–33 articles). This is more than twice the number of publications from researchers around the rest of the world; 58% of research is concentrated in three areas: engineering, computer science and energy. In conclusion, the review also identifies areas for further research aimed at minimizing the negative impacts of AI and maximizing its contribution to sustainable development, including the development of more energy-efficient AI architectures and new methods of energy management.Energies1084An analysis of 237 scientific publications from 2010 to 2024, which highlights significant advancements and obstacles to AI adoption across sectors, and identifies areas for further research aimed at minimizing the negative impacts of AI and maximizing its contribution to sustainable development.Energies["Sergiusz Pimenow", "Olena Pimenowa", "Piotr Prus"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/7d436b42bc2a2060bebb7221c575c1d243e61a4c 71be17545b37f5c57a1c636f0e071ee0b71f6884This study investigates the key components of teachers’ self-assessed artificial intelligence (AI) literacy competencies and how they align with existing digital literacy frameworks. The rapid development of AI technologies has highlighted the need for educators to develop AI-related skills and competencies in order to meaningfully integrate these technologies into their professional practice. A pilot study was conducted using a self-assessment questionnaire developed from frameworks such as DigiCompEdu and the Selfie for Teachers tool. The study aimed to explore the relationships between AI literacy competence and already defined digital skills and competencies through principal component analysis (PCA). The results revealed distinct components of AI literacy and digital competencies, highlighting competence overlaps in some areas, for example, digital resource management, while also confirming that AI literacy competencies form a separate and essential category. The findings show that although AI literacy aligns with other digital skills and competencies, focused attention is required to professionally develop AI-specific competencies. These insights are key elements of future research to refine and expand AI literacy tools for educators, providing targeted professional development programs to ensure that teachers are ready for the opportunities and challenges of AI in education.Sustainability290The results revealed distinct components of AI literacy and digital competencies, highlighting competence overlaps in some areas, for example, digital resource management, while also confirming that AI literacy competencies form a separate and essential category.Sustainability["Ieva Tenberga", "Linda Daniela"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/71be17545b37f5c57a1c636f0e071ee0b71f6884 1bbb19422261d10586dcbc1a32c2b64c93913eb2This study explores the potential of Artificial Intelligence in General Context (AIGC) technology in Pedagogical Content Knowledge (PCK) teaching within higher education, particularly in biochemistry courses. The research highlights the effectiveness of AIGC technology in creating personalized learning experiences, providing targeted interventions, and enhancing student engagement. It emphasizes the importance of recognizing the value of AIGC technology in addressing diverse student needs and supporting tailored instruction. While acknowledging its promising prospects, the study also underscores the need to address ethical, accessibility, and pedagogical challenges associated with its application. Looking ahead, the research recommends further exploration of innovative ways to leverage AIGC technology to personalize learning experiences and promote inclusive education. The study emphasizes the necessity of developing best practices for the ethical and responsible use of AIGC technology to maximize its potential benefits in PCK teaching.Advances in Education, Humanities and Social Science Research00The research highlights the effectiveness of AIGC technology in creating personalized learning experiences, providing targeted interventions, and enhancing student engagement, and the necessity of developing best practices for the ethical and responsible use of AIGC technology to maximize its potential benefits in PCK teaching.Advances in Education, Humanities and Social Science Research["Naxin Su", "Yuanxiu Wang", "Chunjiang Ye", "Y. Liu"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/1bbb19422261d10586dcbc1a32c2b64c93913eb2 276808499cc8f6ebac0995db0ef7ab403d3448f1This article proposes a project aimed at introducing secondary school students to artificial intelligence and logic programming using the Prolog language. In commemoration of the 50th anniversary of Prolog’s development, the authors participate in the international initiative “Prolog Education and Thinking” through the “Digital Bulgaria in Prolog” activity, implemented in Bulgarian secondary schools. The article offers a concise overview of a STEM (Science, Technology, Engineering, and Mathematics) educational program and training for secondary school students in Bulgaria. STEM serves as a project-based learning approach, fostering students’ understanding of multiple disciplines and utilizing diverse technologies to enhance their skills. Additionally, the article showcases examples of student initiatives spanning natural sciences, informatics, humanities, culture, and art, illustrating the interdisciplinary nature of STEM education.TEM Journal00A project aimed at introducing secondary school students to artificial intelligence and logic programming using the Prolog language is proposed, and examples of student initiatives spanning natural sciences, informatics, humanities, culture, and art are showcased, illustrating the interdisciplinary nature of STEM education.TEM Journal["V. Tabakova-Komsalova", "Ivan Stoyanov", "George Cholakov", "Magdalena Maglizhanova"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/276808499cc8f6ebac0995db0ef7ab403d3448f1 d28a11396885638cbbb82194540aa7b31e391abaTeknologi Artificial intelligence (AI) dalam pembelajaran tidak bisa dihindari. Hal tersebut memerlukan peningkatan kompetensi guru dan infrastruktur digital di dalam lingkungan sekolah. Hadirnya teknologi tersebut sering kali disalah gunakan siswa dalam mengerjakan tugas-tugas sekolah. Memperhatikan situasi tersebut pengajar atau guru dituntut untuk meningkatkan kompetensi dalam menggunakan teknologi informasi dalam memanfaatkan AI secara baik dan tepat sasaran. Selain itu belum adanya skill atau background pendidikan guru yang berelasi dengan mata pelajaran yang diampu menjadikan permasalahan yang perlu diatasi dalam jangka waktu yang dekat karena siswa perlu mendapatkan pemahaman yang tepat dari materi yang diajarkan. Sehingga perlu dukungan dari insan Perguruan Tinggi untuk melaksanakan pengabdian dengan memberikan pelatihan maupun pendampingan dalam pemanfaatan teknologi AI di dalam menunjang pembelajaran, apalagi untuk mata pelajaran Teknologi Informasi dan Komunikasi (TIK) atau Informatika yang mana transformasi digital yang pergerakannya sangat cepat. Pelatihan dan Pendampingan yang dilakukan bertujuan untuk membantu pengajar SMA menyesuaikan materi pembelajaran berdasarkan pola belajar, kebutuhan, kekuatan, kelemahan masing-masing siswa, dan dapat membantu pengajar untuk memanajemen tugas-tugas administratif seperti membuat bahan ajar, RPP atau silabus, penjadwalan, dan penilaian. Pelatihan ini diharapkan dapat membantu siswa belajar secara mandiri dengan memanfaatkan AI berbasis tutor virtual sesuai dengan tema pembelajaran, serta siswa dapat menggunakan teknologi secara bertanggung jawab dan etis. Pelatihan ini juga mengenalkan teknologi untuk mendeteksi karya hasil kecerdasan buatan atau AI. Hasil dari implementasi pelatihan literasi dan pemanfaatan AI bagi pengajar SMA berupa publikasi di media online, video dan jurnal/prosiding.Artificial intelligence has already been creating an impact on education. AI's impact on education is transformative and multi-faceted. Artificial intelligence is inevitable. AI powered adaptive learning which can provide immersive experience. Therefore, Artificial intelligence tools can enhance the teachers' and students' experience by providing personalized learning materials, automating administrative tasks, and even offering tutoring assistance. AI can enhance learning outcomes and ensure students receive the support they require to succeed. Therefore, teaching training programs and training courses were held by the university to enhance teachers’ understanding in line with the technology, especially in the use of AI. The training and courses assist in automating administrative tasks, freeing up valuable time for educators. From grading assignments and providing feedback to generating reports, teachers can focus more on individualized instruction and student support. This helps create a more efficient and productive learning environment. By using technology effectively educators can make informed decisions to improve teaching methods, curriculum design, and educational policies. Educators can create educational systems that are more personalized, efficient, and responsive to the needs of students. This purpose of this study is to improve teachers' knowledge on AI. By using AI educators create educational systems that are more personalized, efficient, and responsive to the needs of students. The result of this research included the teachers' product such as books, video, journal, and proceeding by implementing AI.Indonesian Journal of Community Services100Indonesian Journal of Community Services["Andy Supriyadi", "N. Firdaus", "Fiddin Yusfida", "H. Hartatik"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/d28a11396885638cbbb82194540aa7b31e391aba 8bc1575118d54c22388b9785ab8e10263caa69f3As new digitalization strategies storm the banking industry, banks which are behind the technological curve may struggle to keep pace. This is a well-known challenge in the Islamic banking sector in particular; however, this research shows that little is being done to achieve unified digitalization in operations. Artificial Intelligence (AI) is a disruptive force in the financial sector, promising increased financial inclusion, economic development, and a excess of efficient, transparent, Shariah-compliant financial solutions due to its integration of technology with Islam’s profound ethical principles. The objective of this paper is to study the potential and challenges of AI implications in Islamic Banks. This is a qualitative study using focus group interviews with five Islamic Banking experts consists of Shariah Committee Member, academic experts and Islamic banking executive from different banks in Malaysia. SC members from different Islamic banks.  Following the interviews, a thematic analysis of the transcribed data was conducted using computer-assisted qualitative data analysis software. The participants were generally receptive towards the utilization of AI tools in the Islamic banks which give a significant impact of the Islamic banking landscape. The participants recognize the potential of AI for improving the efficiency and effectiveness of the Islamic banking operations. However, they raised some concerns about the challenges that need to be addressed such as replacing of manpower with AI in some area in Islamic banks. The novelty of this research provides a novel discussion of potential and challenges of AI implications in Islamic banks and at the same time add to the Islamic banking literature in this area. The size of the focus group was limited to five participants to optimize group size and composition to allow adequate participation by each group member. Further sampling from more SC members and experts may elicit additional findings. The first-hand views from the focus group interviews provides valuable input for potential and challenges of adopting AI in the Islamic banking operations.iBAF e-Proceedings260The novelty of this research provides a novel discussion of potential and challenges of AI implications in Islamic banks and at the same time add to the Islamic banking literature in this area.iBAF e-Proceedings["Mohd Shukor Harun", "Muhammad Ridhwan Ab Aziz", "Muhammad Azrin Nazri", "Rana Fathinah Ananda", "Sari Nuzullina Rahmadhani"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/8bc1575118d54c22388b9785ab8e10263caa69f3 439cb7597558351f790874b2a3c29485e809d4d6The increasing prevalence of Autonomous Systems (AS) powered by Artificial Intelligence (AI) in society and their expanding role in ensuring safety necessitate the assessment of their trustworthiness. The verification and development community faces the challenge of evaluating the trustworthiness of AI-powered AS in a comprehensive and objective manner. To address this challenge, this study conducts a semi-structured interview with experts to gather their insights and perspectives on the trustworthiness of AI-powered autonomous systems in healthcare. By integrating the expert insights, a comprehensive framework is proposed for assessing the trustworthiness of AI-powered autonomous systems in the domain of healthcare. This framework is designed to contribute to the advancement of trustworthiness assessment practices in the field of AI and autonomous systems, fostering greater confidence in their deployment in healthcare settings.Frontiers in Digital Health320A semi-structured interview with experts is conducted to gather their insights and perspectives on the trustworthiness of AI-powered autonomous systems in healthcare, and a comprehensive framework is proposed for assessing the trustworthiness of AI-powered autonomous systems in the domain of healthcare.Frontiers in Digital Health["Turki Alelyani"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/439cb7597558351f790874b2a3c29485e809d4d6 460cfebab1468f0a7c20e2d5540ff90e53d5f4f1This study examines the effect of digital literacy of educators based on Artificial Intelligence (AI) on learning effectiveness and educator performance in the Non-Formal Education Unit of the Learning Activity Center (SKB) in Pekalongan Prefecture. This research is quantitative causal research. With a population of 132 educators and a sample of 101 educators using a simple random sampling, data were collected through questionnaires and analyzed descriptively and simple linear regression. The findings indicated that the digital literacy of AI-based educators positively and significantly influenced learning effectiveness (59.1%) and educator performances (54%), with a significance value of 0.000 <0.005. The research conclusion emphasizes the importance of improving digital literacy through training and collaboration and the need for support from agencies and the government in providing access to technology and adequate infrastructure. Future investigations will examine additional independent variables that could impact educational outcomes.Jurnal Locus Penelitian dan Pengabdian00The findings indicated that the digital literacy of AI-based educators positively and significantly influenced learning effectiveness and educator performances in the Non-Formal Education Unit of the Learning Activity Center in Pekalongan Prefecture.Jurnal Locus Penelitian dan Pengabdian["Hodijah Wulandari", "J. Sutarto", "Farid Ahmadi"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/460cfebab1468f0a7c20e2d5540ff90e53d5f4f1 59c09ec7887a82613a2b5d8ea85233317babcd65Public Procurement Processes (PPPs) involve substantial taxpayer money, necessitating efficiency and transparency. Artificial Intelligence (AI) is increasingly applied to fraud detection in PPPs, enhancing these processes. This work presents a literature review on AI’s role in PPP fraud detection, focusing on ethical and technical challenges, including fairness, transparency, and privacy. We examine the global state of AI applications in PPPs, highlighting best practices and case studies. By analyzing these technologies’ challenges and opportunities, we provide insights and propose strategies for mitigating risks, contributing to the debate on responsible AI adoption in the public sector.Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)70This work presents a literature review on AI’s role in PPP fraud detection, focusing on ethical and technical challenges, including fairness, transparency, and privacy, and examines the global state of AI applications in PPPs.Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)["I. Sampaio", "F. Bernardini", "Jos\u00e9 Viterbo"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/59c09ec7887a82613a2b5d8ea85233317babcd65 8aaab6dbd681f75aae5ba5d4ac26dfc92e232b01Nowadays, many individual instructors, content creators, and even entire universities are gravitating towards the use of comprehensive learning management systems (LMSs). However, the utilization of existing LMSs is becoming tedious due to the diversification in education. The automated LMS will overcome the challenges faced by content creators and instructors using traditional LMSs. The automated learning management system (autoLMS) is a web-based, artificial intelligence-driven platform designed to enhance the efficiency of course creators by automatically generating educational resources. By taking an educational video as input, autoLMS outputs resources requested by the content creators, including summaries, key points, lecture notes, quizzes, assignments, papers, and project ideas. The main objective is to simplify content generation for course creators, who traditionally have to manually generate content such as key points, summaries, and quizzes after uploading educational videos on platforms like Udemy and Coursera. This study produces a fully functional prototype. Post-development, the system will undergo rigorous testing and analysis to validate its efficiency, usability, performance, and functionality.Foundation University Journal of Engineering and Applied Sciences &lt;br&gt;&lt;i style="color:black;"&gt;(HEC Recognized Y Category , ISSN 2706-7351)&lt;/i&gt;190The main objective is to simplify content generation for course creators, who traditionally have to manually generate content such as key points, summaries, and quizzes after uploading educational videos on platforms like Udemy and Coursera.Foundation University Journal of Engineering and Applied Sciences &lt;br&gt;&lt;i style="color:black;"&gt;(HEC Recognized Y Category , ISSN 2706-7351)&lt;/i&gt;["Muhammad Abbdullah Abbasi", "Abbdullah Zahid", "Kashif Sultan", "Ahmar Hafeez"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/8aaab6dbd681f75aae5ba5d4ac26dfc92e232b01 64f792544a33758d1e52f3bff3fb998d5ea2c7b4The use of artificial intelligence (AI) tools in university education is a phenomenon of various directions: the potential of AI tools, skills, purpose and sense of usage. Each direction is worth of working out and introducing regulatory systems and deeper investigating users’ choice and managing the process of getting, navigating and creating information by means of AI. Therefore, one of the emerging scientific challenges is students’ abilities and personalised learning experience in the use of AI. The study is focused on the usage of AI in specific courses, and namely the students of Information Technology (IT) programmes from Latvia University of Life Sciences and Technologies (LBTU) and Riga Technical University (RTU). The aim of the study is to investigate the students’ ability, need and merit to use AI in learning numerical methods, mathematics and programming. The main data collection method used is a student survey. According to the main results, it is found out that respondents when solving the programming tasks sometimes used AI, while solving mathematical tasks respondents rarely used AI. AI actually did not help to solve the mathematical tasks, while it partly helped to solve the programming tasks. The use of AI partly helped the respondents to improve the knowledge and skills of programming. Acquiring the study course Numerical Methods respondents mainly used ChatGPT, but performing practical works respondents mostly did not use AI.Research for Rural Development160It is found out that respondents when solving the programming tasks sometimes used AI, while solving mathematical tasks respondents rarely used AI, and the use of AI partly helped the respondents to improve the knowledge and skills of programming.Research for Rural Development["N. Sergejeva", "Natalja Vronska", "B. Briede", "Inna Samuilik"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/64f792544a33758d1e52f3bff3fb998d5ea2c7b4 e08f91690c03fce03d79d4d6672f15b9c1c74ec3Artificial intelligence (AI) is rapidly transforming sports coaching, offering new tools to enhance athlete performance and training methods. However, the balance between leveraging AI’s capabilities and maintaining the human touch in coaching remains a critical challenge. This study investigates how AI can be effectively integrated into sports coaching while maintaining the essential human elements of leadership, mentorship, and personalized support. The research aims to provide a framework for combining AI technology with traditional coaching strategies to optimize performance. Using Grounded Theory (GT) methodology, the study conducted expert interviews and performed a detailed literature review to understand the interaction between AI and sports coaching. The resulting “Synergy Theory” model explains how AI can enhance training while highlighting the importance of maintaining ethical standards and human-centered coaching practices. The research reveals that AI can considerably improve performance analysis, injury prevention, and training customization. However, over-reliance on AI risks undermining the human aspects of coaching. The findings underscore the need for technological literacy among coaches and the ethical integration of AI in sports. Challenges such as data quality, resistance to technology, and privacy concerns must also be addressed. The present article is one of the first studies to comprehensively explore the ethical, practical, and technical considerations of integrating AI into sports coaching. This study also offers practical recommendations for balancing AI technology with traditional coaching methods.Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology510This study investigates how AI can be effectively integrated into sports coaching while maintaining the essential human elements of leadership, mentorship, and personalized support and provides a framework for combining AI technology with traditional coaching strategies to optimize performance.Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology["Sajjad Pashaie", "Sardar Mohammadi", "Hamed Golmohammadi"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/e08f91690c03fce03d79d4d6672f15b9c1c74ec3 8bc24728eb4f375f5cc83773d86fa65a8d110ea4The article explores the potential and prospects of incorporating artificial intelligence (AI) into cadastral and land management processes, emphasizing its transformative impact on these fields. A comprehensive analysis was conducted to compare the use of scripts with and without AI for various tasks, highlighting the added value of AI elements. Scripts equipped with AI capabilities not only automate routine operations but also enable advanced functionalities such as data analysis, trend forecasting, and recommendation generation. These features significantly enhance the productivity, accuracy, and efficiency of cadastral and land management operations. The article outlines key areas for AI implementation, including the automation of geospatial data processing, the prediction of land-use changes, and the integration of analytical results into decision-making processes. Specific examples demonstrate the application of modern geographic information systems (GIS) that leverage machine learning algorithms to tackle complex challenges, such as identifying optimal land-use strategies and managing environmental impacts. Additionally, the use of AI in precision agriculture is examined, with a focus on calculating vegetation indices and automating land resource monitoring. These applications illustrate the growing importance of AI in creating sustainable and data-driven solutions for land management. Furthermore, the study identifies critical advantages of AI in cadastral processes, such as improved data accuracy, minimization of human errors, and the optimization of time and resource allocation. However, the article also addresses significant challenges associated with AI adoption. These include the need for comprehensive data standardization, the adaptation of existing legislation to accommodate AI-driven approaches, and the development of new methodological frameworks to fully harness the potential of AI in this domain. The findings emphasize the necessity for a balanced approach, combining technological advancements with policy development and methodological innovation, to unlock the full potential of AI in cadastral and land management practices. Keywords: artificial intelligence, cadastre, land management, geoinformation systems, scripts, automation, land management.Ukrainian Journal of Applied Economics and Technology30The article outlines key areas for AI implementation, including the automation of geospatial data processing, the prediction of land-use changes, and the integration of analytical results into decision-making processes, and addresses significant challenges associated with AI adoption.Ukrainian Journal of Applied Economics and Technology["Mariia Malanchuk", "Nataliia Muzyka", "Iurii Lukianchenko", "Myroslav Kravchuk"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/8bc24728eb4f375f5cc83773d86fa65a8d110ea4 b06984ec502e5fd7c01dcc7cf2db386c3b2368a1Purpose This study aims to examine the perception of public works experts on the application of artificial intelligence (AI) as a tool to potentially increase the rationality and transparency of public works. Design/methodology/approach This paper is based on an exploratory quantitative design. It uses an original survey on the use of AI in public works, targeting public works experts from Peru. Data was analyzed using structural equation modeling. Findings Findings reveal public works experts’ interest in AI, highlighting its potential to improve transparency and efficiency, although labor changes are anticipated. AI monitoring could impact economic and quality control areas, vital in the fight against corruption. Infrastructure, government policies and financial resources emerge as fundamental enablers. Originality/value The advent of advanced AI systems has raised promises to help fight corruption through new monitoring capabilities that enhance transparency and rationality. However, few studies have assessed the impact of AI on public works. This paper contributes to this gap by testing a framework that explores how public works experts perceive the use of AI, considering their perceptions, expectations, perceived challenges and opportunities over public works’ rationality and transparency. Transforming Government: People, Process and Policy540Testing a framework that explores how public works experts perceive the use of AI, considering their perceptions, expectations, perceived challenges and opportunities over public works’ rationality and transparency reveals public works’ interest in AI.Transforming Government: People, Process and Policy["Oscar Miranda-Hospinal", "Juli\u00e1n Villodre", "David Valle-Cruz", "Kesber Angulo-S\u00e1nchez"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/b06984ec502e5fd7c01dcc7cf2db386c3b2368a1 e560236399554300f9ade6c1d4bb815366eccbc5Introduction. The integration of artificial intelligence (AI) in ophthalmology, specifically through the use of Optical Coherence Tomography (OCT) images, has marked a significant advancement in the detection and management of ocular diseases. The article compares the detection of eye conditions by health professionals using Optical Coherence Tomography (OCT) with AI abilities. Review Methods. Online databases were searched for articles discussing the effectiveness of AI in OCT analyses and assessment of the accuracy and agreement of AI algorithms with human experts. Key words included ‘OCT’, ‘AI’, ‘comparison’ and ‘effectiveness’’. Results. AI algorithms have demonstrated the capability to automatically segment retinal layers, detect and quantify pathological changes, and predict disease progression. The application of AI helps address the challenge of artifacts in OCT images, enhancing the accuracy of tissue structure segmentation and improving diagnostic precision. Conclusions. This article explores the comparative effectiveness of AI and human experts in diagnosing ocular conditions using OCT, highlighting AI’s potential to complement human expertise and improve patient outcomes. Despite the promising results, variability in AI performance across different studies underscores the need for more robust and standardized AI models, along with high-quality, diverse datasets to ensure consistent and generalizable results.Annals of Agricultural and Environmental Medicine150The article compares the detection of eye conditions by health professionals using Optical Coherence Tomography (OCT) with AI abilities, highlighting AI’s potential to complement human expertise and improve patient outcomes.Annals of Agricultural and Environmental Medicine["Karol Czesak", "Zuzanna Ga\u0142uszka", "Olga Adamska", "Maciej Kami\u0144ski", "Anna Pierzak", "Agnieszka Kami\u0144ska"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/e560236399554300f9ade6c1d4bb815366eccbc5 bdb3a405c9148662f118c0c72bf78a5f1f4b31a7Background/aim Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance. Methods This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare. Results Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional. Conclusion The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity.Frontiers in Public Health470It is concluded that AI should be rationally guided, function transparently, and produce impartial results and that this kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity.Frontiers in Public Health["Fatma Kahraman", "Aysenur Aktas", "Serra Bayrakceken", "Tuna Cakar", "Hande Serim Tarcan", "Bugrahan Bayram", "Berk Durak", "Y. Ulman"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/bdb3a405c9148662f118c0c72bf78a5f1f4b31a7 abbe9d419094535609edb6ae34b0db549b10dce1This research aims to comprehensively review the applications and limitations of artificial intelligence (AI) in interpreting remote sensing data, highlighting its potential through a detailed case study. AI technologies, particularly machine learning and deep learning, have shown remarkable promise in enhancing the accuracy and efficiency of data interpretation tasks in remote sensing, such as anomaly detection, change detection, and land cover classification. AI-driven analysis has a lot of options because to remote sensing, which can gather massive amounts of environmental data via drones, satellites, and other aerial platforms. AI approaches, in particular machine learning and deep learning, have demonstrated potential to improve the precision and effectiveness of data interpretation tasks, including anomaly identification, change detection, and land cover classification. Nevertheless, the research also points to a number of drawbacks, including challenges related to data quality, the need for large labeled datasets, and the risk of model overfitting. Furthermore, the intricacy of AI models can occasionally result in a lack of transparency, which makes it challenging to understand and accept the outcomes. The case study emphasizes the necessity for a balanced strategy that makes use of the advantages of both AI and conventional techniques by highlighting both effective applications of AI in remote sensing and areas where traditional methods still perform better than AI. This research concludes that while AI holds significant potential for advancing remote sensing data interpretation, careful consideration of its limitations is crucial for its effective application in real-world scenarios.Control Systems and Optimization Letters250It is concluded that while AI holds significant potential for advancing remote sensing data interpretation, careful consideration of its limitations is crucial for its effective application in real-world scenarios.Control Systems and Optimization Letters["Ikram Hossain", "Md Monirul Islam", "Md. Hasnat Hanjala Martin"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/abbe9d419094535609edb6ae34b0db549b10dce1 5c2ef7f658ddc4e108a118e7c4aad2475dfe30d2The ‘black box’ nature of machine learning (ML) approaches makes it challenging to understand how most artificial intelligence (AI) models make decisions. Explainable AI (XAI) aims to provide analytical techniques to understand the behavior of ML models. XAI utilizes counterfactual explanations that indicate how variations in input features lead to different outputs. However, existing methods must also highlight the importance of features to provide more actionable explanations that would aid in the identification of key drivers behind model decisions—and, hence, more reliable interpretations—ensuring better accuracy. The method we propose utilizes feature weights obtained through adaptive feature weight genetic explanation (AFWGE) with the Pearson correlation coefficient (PCC) to determine the most crucial group of features. The proposed method was tested on four real datasets with nine different classifiers for evaluation against a nonweighted counterfactual explanation method (CERTIFAI) and the original feature values’ correlation. The results show significant enhancements in accuracy, precision, recall, and F1 score for most datasets and classifiers; this indicates the superiority of the feature weights selected via AFWGE with the PCC over CERTIFAI and the original data values in determining the most important group of features. Focusing on important feature groups elaborates the behavior of AI models and enhances decision making, resulting in more reliable AI systems.Mathematics320Focusing on important feature groups elaborates the behavior of AI models and enhances decision making, resulting in more reliable AI systems, and indicates the superiority of the feature weights selected via AFWGE with the PCC over CERTIFAI and the original data values in determining the most important group of features.Mathematics["Ebtisam AlJalaud", "M. Hosny"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/5c2ef7f658ddc4e108a118e7c4aad2475dfe30d2 5d7ba6f29c3157f87ead4ffdf29106ab3adf7834This study investigates the efficacy of synthetic data in mitigating bias in artificial intelligence (AI) model training, focusing on demographic inclusivity and fairness. Using Generative Adversarial Networks (GANs), synthetic datasets were generated from the UCI Adult Dataset, COMPAS Recidivism Dataset, and MIMIC-III Clinical Database. Logistic regression models were trained on both synthetic and original datasets to evaluate fairness metrics and predictive accuracy. Fairness was assessed through demographic parity and equality of opportunity, which measure balanced prediction rates and equitable outcomes across demographic groups. Fidelity and data diversity were evaluated using statistical tests such as Kolmogorov-Smirnov (KS) and Kullback-Leibler (KL) divergence, along with the Inception Score, which quantifies diversity in synthetic data. The results revealed significant fairness improvements for models trained on synthetic datasets. For the COMPAS dataset, demographic parity increased from 0.72 to 0.89, and equality of opportunity rose from 0.65 to 0.83, without compromising predictive accuracy (0.82 AUC-ROC compared to 0.83 for original data). Based on the findings, this research recommends employing GANs for generating synthetic data in bias-sensitive domains to enhance demographic inclusivity and ensure equitable outcomes in AI models. Furthermore, integrating human-in-the-loop (HITL) systems is critical to monitor and address residual biases during data generation. Standardized validation frameworks, including fairness metrics and fidelity tests, should be adopted to ensure transparency and consistency across applications. These practices can enable organizations to leverage synthetic data effectively while maintaining ethical standards in AI development and deployment.Journal of Engineering Research and Reports00Using GANs for generating synthetic data in bias-sensitive domains to enhance demographic inclusivity and ensure equitable outcomes in AI models is recommended, and integrating human-in-the-loop (HITL) systems is critical to monitor and address residual biases during data generation.Journal of Engineering Research and Reports["J. Fabuyi"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/5d7ba6f29c3157f87ead4ffdf29106ab3adf7834 ca30e1a2596971b94a12a3578843cd36068f8513Despite advancements in technology, a substantial portion of the global population still resides in rural areas and low-income countries where access to these advanced technologies is limited or nonexistent, emphasizing the continued importance of open surgical approaches. The rapid integration of artificial intelligence (AI) and robotic technologies into surgical practice raises significant concerns regarding the erosion of essential open surgical skills. Integrated minimally invasive surgery (MIS) combined with AI may offer benefits such as enhanced precision and improved patient outcomes, but these should not come at the cost of the erosion of open surgical skills. This narrative review discusses the drawbacks of relying heavily on minimally invasive surgery and AI, particularly the potential degradation of traditional manual techniques that are crucial for effective surgical care. While robotic-assisted surgeries can lead to reduced recovery times and fewer complications, evidence indicates a decline in proficiency in open techniques among surgeons who predominantly utilize these systems. This skill degradation poses substantial risks, especially in situations where technology fails or is unavailable, such as in emergency settings or resource-limited environments. To address these challenges, the review discusses strategies such as hybrid training approaches that combine robotic and manual techniques, skills preservation programs aimed at maintaining traditional competencies, and modular curricula integrating both technological and conventional aspects of surgery. It also highlights the necessity for ongoing research to assess the effectiveness of these strategies, ensuring that surgeons remain proficient in both advanced technologies and fundamental manual skills. A balanced approach is vital for maintaining comprehensive surgical care in the era of AI, particularly for populations that still rely on open surgical methods.Multidisciplinary Reviews190This narrative review discusses strategies such as hybrid training approaches that combine robotic and manual techniques, skills preservation programs aimed at maintaining traditional competencies, and modular curricula integrating both technological and conventional aspects of surgery to address the drawbacks of relying heavily on minimally invasive surgery and AI.Multidisciplinary Reviews["Shubham Bobade", "Sheetal G. Asutkar"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/ca30e1a2596971b94a12a3578843cd36068f8513 b9d29abb95e306315241b065dc2d9372b0b863e4The rapid development of artificial intelligence (AI) technology has presented new challenges in the field of law, particularly regarding its status as a legal subject or object in Indonesia. What is the legal position of artificial intelligence (AI) in Indonesia and what legal implications arise regarding its regulation as an object or subject of law in the Indonesian legal system? The purpose of this study is to explore and determine the legal position of AI in Indonesia, as well as analyze the legal implications that may arise from this status. This research uses a qualitative approach with normative legal research methods and comparative analysis. Data was obtained through a literature review that included national laws and regulations, international legal documents, and related academic literature. The results show that in the Indonesian legal system, AI is currently considered more as a legal object, without a personification status that allows recognition as a legal subject. This leads to various consequences, including liability issues in cases of harm caused by AI actions. This study also found that there are gaps in AI- specific regulations, creating room for debate on whether or not legal policy changes are needed to support AI as partial legal subjects or entities with limited liability. In conclusion, AI in Indonesian law is still positioned as a legal object with inadequate regulations to address the complexity of AI use in various sectors. There is a need to develop adaptive legal policies and a clearer regulatory framework to address the ethical challenges and legal responsibilities in this digital era. This research recommends legal reforms that consider a multidisciplinary approach and the precautionary principle to maximize the potential of AI without compromising legal protection for society.Asian Journal of Education and Social Studies00It is found that there are gaps in AI- specific regulations, creating room for debate on whether or not legal policy changes are needed to support AI as partial legal subjects or entities with limited liability.Asian Journal of Education and Social Studies["Didi Jubaidi", "Khoirunnisa Khoirunnisa"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/b9d29abb95e306315241b065dc2d9372b0b863e4 73413709fc86d141f57f0f0ee8e8abed8192b4ecThe increase in the cost of living has become a significant concern following the two-year COVID-19 pandemic. The rise in daily expenses and service costs is an essential factor influencing the changes in the calculation of the kifayah limit, which determines the rate of exclusion for personal and family expenses in zakat assessments. Despite these challenges, the widespread acceptance and adoption of technology by the Malaysian community offer opportunities to enhance the quality and efficiency of work and services. This study aims to examine the changes in the exclusion rate for personal and family expenses in zakat calculations in Malaysia. Additionally, it explores the role of artificial intelligence (AI) in improving the zakat payment calculation process. A qualitative research approach is employed, with document analysis serving as the primary method for data collection. The sources and data gathered are analyzed descriptively to generate accurate findings. The study reveals that the exclusion rate for personal expenses has been updated in line with the increased cost of living. Furthermore, leveraging big data generated by Majlis Agama Islam Negeri (MAIN) allows for a more precise and comprehensive determination of the kifayah limit. This demonstrates how technological advancements, particularly AI and big data analytics, can significantly improve the efficiency and fairness of zakat calculations. Kenaikan kos sara hidup amat ketara selepas fasa pandemik Covid-19 yang berlaku dalam tempoh dua tahun. Peningkatan kos perbelanjaan harian dan kos perkhidmatan menjadi elemen penting dalam perubahan kadar penolakan perbelanjaan dalam zakat atau dikenali sebagai had kifayah. Namun disebalik itu, penerimaan dan penggunaan teknologi oleh masyarakat Malaysia dapat meningkatkan kualiti dan mutu pekerjaan dan perkhidmatan. Oleh itu, kajian ini akan mengkaji tentang perubahan kadar penolakan perbelanjaan diri yang berkeluarga dalam zakat di Malaysia. Kajian ini juga membincangkan bagaimana peranan  kecerdasan buatan (AI) dalam memastikan proses pengiraan pembayaran zakat lebih baik. Kajian ini menggunakan kaedah kualitatif dengan menjadikan kajian dokumen analisis sebagai asas pengumpulan maklumat. Sumber dan data yang diperoleh akan dianalisis secara deskriptif bagi menghasilkan dapatan yang benar. Hasil kajian menyatakan terdapat perubahan kadar penolakan perbelanjaan diri yang telah dikemaskini. Selain itu, menerusi data raya yang mampu dijana oleh MAIN, kadar perbelanjan yang lebih signifikan dapat terhasil. Kenaikan kos sara hidup amat ketara selepas fasa pandemik Covid-19 yang berlaku dalam tempoh dua tahun. Peningkatan kos perbelanjaan harian dan kos perkhidmatan menjadi elemen penting dalam perubahan kadar penolakan perbelanjaan dalam zakat atau dikenali sebagai had kifayah. Namun disebalik itu, penerimaan dan penggunaan teknologi oleh masyarakat Malaysia dapat meningkatkan kualiti dan mutu pekerjaan dan perkhidmatan. Oleh itu, kajian ini akan mengkaji tentang perubahan kadar penolakan perbelanjaan diri yang berkeluarga dalam zakat di Malaysia. Kajian ini juga membincangkan bagaimana peranan  kecerdasan buatan (AI) dalam memastikan proses pengiraan pembayaran zakat lebih baik. Kajian ini menggunakan kaedah kualitatif dengan menjadikan kajian dokumen analisis sebagai asas pengumpulan maklumat. Sumber dan data yang diperoleh akan dianalisis secara deskriptif bagi menghasilkan dapatan yang benar. Hasil kajian menyatakan terdapat perubahan kadar penolakan perbelanjaan diri yang telah dikemaskini. Selain itu, menerusi data raya yang mampu dijana oleh MAIN, kadar perbelanjan yang lebih signifikan dapat terhasil.iBAF e-Proceedings160iBAF e-Proceedings["Ahmad Wan Abdul Rahman", "Ahmad Adib Mohammad Sofian", "Nur Zaiumi Yasmin Zakaria"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/73413709fc86d141f57f0f0ee8e8abed8192b4ec b4eb7f126b39091a3ba04d91fb7db39e7c5796b5The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.Frontiers in Artificial Intelligence300The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry and highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness.Frontiers in Artificial Intelligence["Abdullah Eskandarany"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/b4eb7f126b39091a3ba04d91fb7db39e7c5796b5 496e86a62fc54aa03f1868c0d5793c745406cd9bIntroducing innovation and technology clusters, technologies, and artificial intelligence into the activities of production and economic systems and business structures will create a viral synergistic effect between different sectors of the economy and management institutions. In Ukraine, this is critical for rapid innovation transformation in the post-war period, contributing to the post-war restoration of economic and social stability by introducing energy-efficient technologies, cloud solutions, and artificial intelligence. The purpose of the study is to develop and present an improved model of a decision support system for managing the information and technological development of production and economic systems and business structures to ensure the performance of all management functions at all levels of the hierarchy of these processes (state, region, territory, business entity). The study examines the actual state and prospects of the development of modern technologies and artificial intelligence as the main factors of modernization of innovative development of business structures and innovative energy-saving development. The article reveals the essence of energy efficiency and energy saving, which is becoming increasingly important in the global community, including Ukraine. However, there are also limitations, such as the high costs of introducing new technologies and the need for significant investments. It is proved that the introduction of a digital technological approach to managing the development of systems and business structures, combined with the use of modern technologies, energy efficiency, information technology, and artificial intelligence, is a prerequisite for achieving sustainable economic growth, increasing the competitiveness of business entities, improving infrastructure and creating new jobs. This provides a comprehensive approach to developing and restoring economic and social stability post-war. The paper proposes tools for implementing cloud information technologies to support decision-making in the energy sector, improving management efficiency and promoting the development of backbone industries. The study identifies the main problems of information and technological development in the global ecosystem of Ukraine, particularly the implementation of the energy strategy in each time frame with maximum efficiency. The importance of effectively implementing a large-scale plan and creating an effective management system is emphasized. A new, dynamic model of access to information resources is needed to achieve strategic goals, which involves changing the nature of service provision, mainly by introducing cloud information technologies. Developing a new decision support system is key to improving technology management and innovation development. Keywords: global ecosystems, innovative technologies, information technologies, clusters, cloud computing, Artificial Intelligence (AI).Ukrainian Journal of Applied Economics and Technology90It is proved that the introduction of a digital technological approach to managing the development of systems and business structures, combined with the use of modern technologies, energy efficiency, information technology, and artificial intelligence, is a prerequisite for achieving sustainable economic growth, increasing the competitiveness of business entities, improving infrastructure and creating new jobs.Ukrainian Journal of Applied Economics and Technology["A. Babichev", "Andrii V. Negliad", "B. Samorodov"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/496e86a62fc54aa03f1868c0d5793c745406cd9b ec175832ebeddf8af209cf2c58dc7d4de1f793e8The increasing demands on software development are putting serious pressure on its pace. To assist software developers, an increasing number of tools powered by generative artificial intelligence are being introduced. This paper aims to investigate how the use and integration of generative AI have evolved among professionals in the software industry, based on a study involving 104 individuals working in Bulgarian software companies. Data was collected in April 2024 through an online questionnaire with four separate groups of questions related to the use of generative AI at work. The study found that 2/3 of the respondents use generative AI actively in their daily work. They highly value the practical benefits of this type of technology, which most often consist of automating routine activities, accessing information quickly, generating initial code, and writing documentation. As a result of these benefits, developers are increasingly moving towards using generative AI at the expense of professional support platforms. The main benefits they cite include faster solutions, more specific and relevant answers, and significantly shorter time to reach the desired outcome.TEM Journal00Investigation of how the use and integration of generative AI have evolved among professionals in the software industry, based on a study involving 104 individuals working in Bulgarian software companies finds developers are increasingly moving towards using generative AI at the expense of professional support platforms.TEM Journal["Ivo Damyanov", "N. Tsankov", "Iliya Nedyalkov"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/ec175832ebeddf8af209cf2c58dc7d4de1f793e8 c31e87fdd7469d4a19fb6c0fcea10dd63d5f4c32Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition. A core mystery is the MRI explanation of individual differences in neurocognition and its manifestation in intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation must be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition.Frontiers in Neuroimaging1910This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition.Frontiers in Neuroimaging["Mohammad Arafat Hussain", "P. E. Grant", "Yangming Ou"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/c31e87fdd7469d4a19fb6c0fcea10dd63d5f4c32 7992d5d59accb3d76f55e7b2d0b3e437a127b1e9Computation offloading has proven effective as a technology that enables mobile devices to run resource-intensive applications. Multi-access Edge Computing facilitates computation offloading for mobile devices. Compute-heavy tasks can be transferred from a mobile device to a nearby cloudlet to reduce computation time and to conserve the battery life of the mobile device. However, due to fluctuating network conditions and the limited computational capacity of the MEC nodes, the offloading decisions made by mobile devices might not always result in the lowest cost. This paper introduces a dynamic offloading framework for mobile users, taking into account the local overhead on the mobile device and the restricted communication and computation resources available on the network. We frame the offloading decision problem as a multi-label classification challenge and employ the eXtreme Gradient Boosting algorithm incorporated with Explainable Artificial Intelligence to minimize computation and offloading overhead. With this research, we show how to exploit Shapley Additive Explanations for feature selection, resulting in an optimal set of features leading to improved accuracy. Furthermore, the simulation results indicate that our approach can reduce system costs by up to 66.67% and 81.09% compared to the random offloading scheme and total offloading scheme, respectively.International Telecommunication Networks and Applications Conference300This research shows how to exploit Shapley Additive Explanations for feature selection, resulting in an optimal set of features leading to improved accuracy and can reduce system costs by up to 66.67% and 81.09% compared to the random offloading scheme and total offloading scheme, respectively.2024 34th International Telecommunication Networks and Applications Conference (ITNAC)["Rasini Amarasooriya", "Mark A. Gregory", "Shuo Li"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/7992d5d59accb3d76f55e7b2d0b3e437a127b1e9 165c0d856d942effa4b524454a0106a0bb5afa56Artificial intelligence is a transformative technology in education, yet there is a gap in research addressing how to overcome the challenges it presents, including resistance to change and insufficient infrastructure. This study seeks to examine the applications of Artificial intelligence in education, identifying both the obstacles and opportunities to enhance its integration into the educational system. The paper aimed to research the reality of using artificial intelligence among university students, a questionnaire was distributed to a sample of 30 students from Jerash University in Jordan. The results showed that the main challenges lie in students' weak response to new technologies, the lack of awareness about the importance of artificial intelligence among faculty members, and the shortage of technical support. In this context, the study proposed solutions such as motivating faculty members to use artificial intelligence, organising training workshops, improving infrastructure, and providing technical support to ensure the effective integration of Artificial intelligence in educationIntercontinental Journal of Social Sciences30The paper aimed to research the reality of using artificial intelligence among university students, and showed that the main challenges lie in students' weak response to new technologies, the lack of awareness about the importance of artificial intelligence among faculty members, and the shortage of technical support.Intercontinental Journal of Social Sciences["Nieveen Abuzaid"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/165c0d856d942effa4b524454a0106a0bb5afa56 f463065f697fd1ee64e92239d0fff22e3bbd00beThe aim of the study is to develop a methodology for statistically assessing countries’ readiness for digital transformation and use of artificial intelligence technologies in order to substantiate strategic directions of digi-tal environment development. Methodology is based on the construction of three integral indices describing the readiness for digital transformation at the level of government regulation, technological sector and big data infrastructure on the basis of a set of statistical indicators. The choice of statistical indicators in the composition of these indices is justified. Comparative assessments of countries’ readiness for digital transformation and implementation of artificial intelligence technologies are constructed. The influence of the existence of national strategies for the development of artificial intelligence and codes of ethics in the field of artificial intelligence on countries’ readiness to use it is analyzed. Statistically significant dependence of the levels of readiness of countries for digital transformation and use of artificial intelligence technologies on the level of average per capita income for four groups of countries (low-income, lower-middle-income, upper-middle-income and high-income) are revealed. The conditions for digital transformation and the implementation of artificial intelligence technologies in the Arab countries and their place in the general hierarchy of countries are assessed. The iden-tified dependencies between the indices provide Arab countries with an approach to developing policies for digital transformation and the implementation of artificial intelligence technologies.Теория и практика общественного развития00The iden-tified dependencies between the indices provide Arab countries with an approach to developing policies for digital transformation and the implementation of artificial intelligence technologies.Теория и практика общественного развития["Dmitry N. Verzilin", "Tatyana G. Maximova", "Kasouha Leen"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/f463065f697fd1ee64e92239d0fff22e3bbd00be eda3d23dd37b7df88d6cbec4a3c99347fbec3c9fIn the increasingly advanced digital era, artificial intelligence (AI) has changed the way we interact with technology and with each other. This study aims to explore the impact of artificial intelligence on human communication, as well as how this technology affects the dynamics of social and professional interactions. This study uses a qualitative approach with literature analysis and in-depth interviews with technology and communication practitioners in Indonesia. The results of the study show that artificial intelligence has a significant impact on increasing communication efficiency, but also brings challenges in terms of emotional relationships and privacy. The impact of artificial intelligence on human communication is very visible in various aspects of life, both in the professional and social worlds. The use of AI in chatbots, recommendation systems, and virtual assistants has accelerated the communication process, but also raises questions about its impact on the authenticity and quality of relationships between people. This study identifies the main challenges faced by Indonesian society in adapting to this new technology, including limited technological literacy and concerns about privacy. Overall, this study reveals the importance of managing communication more wisely amid the rapid development of artificial intelligence. Although this technology provides many benefits, organizations and individuals need to be aware of the potential risks that can arise, such as over-reliance on AI or negative impacts on more personal social interactions. This research is expected to provide deeper insights for communication practitioners, technology developers, and the wider community in facing communication challenges in the era of artificial intelligence.Journal of Dialogos00The main challenges faced by Indonesian society in adapting to this new technology, including limited technological literacy and concerns about privacy are identified, revealing the importance of managing communication more wisely amid the rapid development of artificial intelligence.Journal of Dialogos["Rina Sovianti", "Novrian Novrian"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/eda3d23dd37b7df88d6cbec4a3c99347fbec3c9f 5adf71c992b365981d0793bb4e0ee97916b426f0Alzheimer’s disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia. There is an unmet need to improve diagnosis and prognosis for patients with dementia, as cycles of misdiagnosis and diagnostic delays are challenging scenarios in neurodegenerative diseases. Neuroimaging is routinely used in clinical practice to support the diagnosis of neurodegenerative diseases. Clinical neuroimaging is amenable to errors owing to varying human judgement as the imaging data are complex and multidimensional. Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability to support the detection of Alzheimer’s disease and its prodromal stage, mild cognitive impairment. In patients with amyloid-related imaging abnormalities, radiologists had significantly better detection performances with both ARIA-E (sensitivity higher in the assisted/deep learning method [87%] compared to unassisted [71%]) and for ARIA-H signs (sensitivity was higher in assisted [79%] compared to unassisted [69%]). A convolutional neural network method was developed, and external validation predicted final clinical diagnoses of Alzheimer’s disease, dementia with Lewy bodies, mild cognitive impairment due to Alzheimer’s disease, or cognitively normal with FDG-PET. The translation of artificial intelligence to clinical practice is plagued with technical, disease-related, and institutional challenges. The implementation of artificial intelligence methods in clinical practice has the potential to transform the diagnostic and treatment landscape and improve patient health and outcomes.Cells830A convolutional neural network method was developed, and external validation predicted final clinical diagnoses of Alzheimer’s disease, dementia with Lewy bodies, mild cognitive impairment due to Alzheimer’s disease, or cognitively normal with FDG-PET.Cells["Manal Aljuhani", "Azhaar Ashraf", "Paul Edison"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/5adf71c992b365981d0793bb4e0ee97916b426f0 2dbcbbaa5c099879907ec9185b2c211154d47566Artificial Intelligence (AI) has changed different applications and markets, making them even more engaging and user-centered in Smart Cities, Smart Farms, and e-health. One such sector that has benefited from these capabilities is precision agriculture, which has seen increased productivity and more efficient and intelligent management through AI and cloud computing. By bringing computing resources closer to clients, edge devices are able to embed AI solutions near the client, reducing the time it takes to transfer data to High-Performance Computing (HPC) centers. The latest approaches explore lightweight or distributed Convolutional Neural Networks (CNNs) methods for training and prediction tasks in edge devices using medical, satellite, or precision agriculture images. In this paper, we hypothesized that prediction tasks in precision agriculture can be more easily and seamlessly assigned to edge devices as long as pre-trained AI models are embedded in these devices through Artificial Intelligence as a Service (AIaaS) Architecture. Hence, we propose and evaluate a general-purpose image classification system for edge devices that uses an AI Model Store for edge intelligence empowerment. We conducted a case study on precision agriculture to functionally assess cognitive service delivery performance, including prediction time, memory, and CPU usage, and found a suitable model embodiment on edge devices compared with HPC.2024 IEEE 13th International Conference on Cloud Networking (CloudNet)200It is hypothesized that prediction tasks in precision agriculture can be more easily and seamlessly assigned to edge devices as long as pre-trained AI models are embedded in these devices through Artificial Intelligence as a Service (AIaaS) Architecture.2024 IEEE 13th International Conference on Cloud Networking (CloudNet)["L. F. R. Moreira", "Lucas Nardelli de Freitas Botelho Saar", "Rodrigo Moreira", "Leonardo G. Ferreira Rodrigues", "B. A. N. Traven\u00e7olo", "A. R. Backes"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/2dbcbbaa5c099879907ec9185b2c211154d47566 fa0a470b93d0505ce441a56176aeea4b94d6e41bThe utilization of artificial intelligence techniques has garnered significant interest in recent research due to their pivotal role in enhancing the quality of educational offerings. This study investigated the impact of employing artificial intelligence techniques on improving the quality of educational services, as perceived by students enrolled in the College of Pharmacy at the University of Baghdad. The study sample comprised 379 male and female students. A descriptive-analytical approach was used, with a questionnaire as the primary tool for data collection. The findings indicated that the application of artificial intelligence methods was highly effective, and the educational services provided to students were of exceptional quality. The results also showed a strong correlation (correlation coefficient of 0.719) between the use of artificial intelligence techniques and the quality of educational services. This correlation was statistically significant at a confidence level of 99%. The impact of artificial intelligence techniques and their dimensions on the quality of educational services is highly significant at a confidence level of 99%. This suggests that artificial intelligence technologies play a major role in enhancing the quality of educational services. The study emphasizes the importance of creating technologically advanced classrooms equipped with modern devices and equipment to enhance the learning experience and provide an advanced educational environment. It also highlights the significance of effectively addressing students’ complaints and grievances through technical means, such as electronic communication platforms, social media platforms, technical support via the Internet, and smartphone applications. These measures are essential in providing high-quality educational services.Frontiers in Education420The findings indicated that the application of artificial intelligence methods was highly effective, and the educational services provided to students were of exceptional quality, suggesting that artificial intelligence technologies play a major role in enhancing the quality of educational services.Frontiers in Education["N. D. Farhan", "Bareq Habeeb Sadiq", "Mustafa H. Zwayyer", "Boshra A. Arnout"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/fa0a470b93d0505ce441a56176aeea4b94d6e41b a7a34b444bf23956b7fcbea1b0a38e44591548a7The humanitarian sector has been implementing technology-centric practices since 2015, with the term "humanitarian digitisation" emerging. To ensure resilience in humanitarian supply chain management, adopting technologies that support the industry 4.0 transition is crucial. 8 enabling technologies for enhancing resiliency include artificial intelligence, big data, robotics, blockchain, augmented reality, virtual reality, digital twin, cloud computing, and industrial internet of things. These technologies can transform four aspects: players, organisations, contextual elements, results, and functionality. This paper presents a summary of 10 case studies employing primarily 2 of these digital technologies (artificial intelligence, and big data) in the humanitarian supply chain. Then, it concentrates on discussing the role and convergence of these 2 technologies in enhancing the resiliency of the humanitarian supply chain. The potentials of AI and big data towards achieving human supply chain resilience, and areas for future works to support the convergence in facilitating the resiliency of the humanitarian supply chain are suggested.Canada International Humanitarian Technology Conference490The role and convergence of artificial intelligence and big data in enhancing the resiliency of the humanitarian supply chain are discussed, and areas for future works to support the convergence in facilitating the resiliency of the humanitarian supply chain are suggested.2024 IEEE International Humanitarian Technologies Conference (IHTC)["Emmanuel Ahatsi", "J. Akpan", "O. Olanrewaju"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/a7a34b444bf23956b7fcbea1b0a38e44591548a7 073ec50c5a66ed27e0bf945ee7dc1ff5cf9c17b1Due to the clever gathering and reporting of individual case safety reports, as well as the increased awareness and involvement of patients and healthcare professionals, the number of suspected adverse event reports in the PV database has grown exponentially. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care systemInternational Journal of Advanced Research in Science, Communication and Technology50The benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system are described.International Journal of Advanced Research in Science, Communication and Technology["Dhadge Kirti Ramesh", "Fulsundar Apeksha"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/073ec50c5a66ed27e0bf945ee7dc1ff5cf9c17b1 d2701b07e379799cb8f7a1bce2c96c39c552719dThis study aims to bridge the gap in the literature by examining how artificial intelligence (AI) and product popularity play a role in shaping consumer purchasing behavior on Indonesian e-commerce platforms. The sample is users who make purchases of cosmetic products with AI features on Indonesian E-Commerce. Using multiple linear regression methods and SPSS 25 as a calculation tool. It was found that artificial intelligence and product popularity have a significant influence on consumer purchasing behavior in Indonesian E-Commerce.iBAF e-Proceedings00It was found that artificial intelligence and product popularity have a significant influence on consumer purchasing behavior in Indonesian E-Commerce.iBAF e-Proceedings["Alshaf Pebrianggara", "Muhammad Rizal Yulianto", "Al Akbar Himawan"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/d2701b07e379799cb8f7a1bce2c96c39c552719d e602b3fc5ecbd520ebdb86e9b3d87253a197d97cThis paper examines the present application of Artificial Intelligence (AI) systems in Brazilian law courts, focusing on potential risks and ethical concerns associated with its integration. While AI has the potential to enhance productivity, its application in sensitive domains like criminal justice demands rigorous Verification and Validation (V&V) processes to mitigate biased outcomes. The study argues for classifying AI tools used in law enforcement as high-integrity systems and advocates adherence to standards such as IEEE 1012-2016. It underscores the need for comprehensive regulation and specialized training to address issues related to bias and privacy breaches, ensuring the responsible deployment of AI systems in the activities of law courts.Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)100The study argues for classifying AI tools used in law enforcement as high-integrity systems and advocates adherence to standards such as IEEE 1012-2016, highlighting the need for comprehensive regulation and specialized training to address issues related to bias and privacy breaches.Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)["Taina T. C. dos Santos", "Renata Wassermann", "Juliano Maranhao"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/e602b3fc5ecbd520ebdb86e9b3d87253a197d97c edc72c42e8b29ed21a6f02f80a6b0b21b36602b2This paper explores the transformative impact of Artificial Intelligence (AI) on the accounting profession, encompassing areas such as auditing, tax accounting, management accounting, and financial accounting. AI technologies, including machine learning and generative AI, are automating routine tasks, enhancing data analysis, and providing deeper insights, which significantly improve efficiency and accuracy in accounting processes. The adoption of AIis reshaping the roles of accountants and auditors, enabling them to focus on higher-value tasks such as strategic consulting and risk management. Despite concerns about job displacement and ethical considerations, AI is poised to augment human expertise rather than replace it, leading to a more efficient and insightful accounting practice. The paper also addresses the historical development of AI, its current applications in accounting, and the ethical implications of its use in the profession.Journal of Accounting, Ethics &amp; Public Policy00Journal of Accounting, Ethics &amp; Public Policy["Cindy Greenman", "Derrick Esplin", "Ross Johnston", "James Richard"]2024-11-27T00:00:00https://www.semanticscholar.org/paper/edc72c42e8b29ed21a6f02f80a6b0b21b36602b2 c46c1f7a9d5370a7563728da86644912fbf5b50aThis research explores the perceptions of marketing professionals regarding the use of Artificial Intelligence (AI) in marketing practices within the Brazilian context, focusing on key areas such as product recommendations and market segmentation. A central theme is the ethical boundaries that must guide the application of AI in marketing, particularly in balancing technological advances with consumer expectations. The findings emphasize the importance of ensuring consumer privacy during data collection and managing the risks of information sharing and overload. The paper discusses the limits that should accompany these technological advancements, considering ethical concerns, current legislation, state-of-the-art practices, and regulatory frameworks.Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)20Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)["D\u00e9bora Dias Panicachi", "Eric David Cohen"]2024-11-27T00:00:00 5c2276034482c954d5ec552c98093f3b655ce95eArtificial intelligence (AI) is revolutionizing and transforming industries around the globe. Through the exploration of various AI applications in teaching, research, and administrative tasks, this article illuminates both the potential and limitations of AI for enhancing faculty productivity while improving their effectiveness and innovation in the teaching and research arenas. We use the technology acceptance model (TAM) as a framework to organize our discussion concerning the adoption of AI by marketing faculty. Moreover, this article addresses ethical implications surrounding AI utilization, emphasizing the importance of understanding and navigating issues of bias, intellectual property, and privacy. Through a comprehensive examination, this article concludes with a balanced perspective on the many opportunities and challenges presented to marketing educators by AI. We aim to advocate a rational approach for harnessing its benefits while acknowledging its limitations.Journal of Marketing Education240This article illuminates both the potential and limitations of AI for enhancing faculty productivity while improving their effectiveness and innovation in the teaching and research arenas and advocates a rational approach for harnessing its benefits while acknowledging its limitations.Journal of Marketing Education["Pamela P. Rogers", "Charlotte Allen", "Antoine Busby"]2024-11-27T00:00:00 b5fc5f0de49ae929a94a8d0e25cefd5170bc6bccWith artificial intelligence technologies disrupting status quo of many technologically advanced national economies, educators should face the challenge to harness their potentials without risks to learners. This exploratory mixed-method study aims to add to the growing volume of research that focuses on educators’ attitude towards AI, their views on its applicability in education and necessity to develop AI competences. The research involved 132 in-service and pre-service educators who completed a questionnaire; nine of the participants also took part in follow-up interviews. The results revealed that the majority of educators perceive AI as a promising and useful tool, albeit sometimes complex, risky and not very smart. Most educators report low level of competence and infrequent usage of AI but readiness to undergo training. The research findings speak for the urgent need to design and implement professional development and teacher training courses that debunk myths about AI and build practical skills for applying AI affordances in all levels of education.TEM Journal00The research revealed that the majority of educators perceive AI as a promising and useful tool, albeit sometimes complex, risky and not very smart, which speaks for the urgent need to design and implement professional development and teacher training courses that debunk myths about AI and build practical skills for applying AI affordances in all levels of education.TEM Journal["G. Sadykova", "A. Kayumova"]2024-11-27T00:00:00 fa4820503c9c46a4937c42fe40f91e86144c0114This special issue of AI Magazine covers select applications from the Innovative Applications of Artificial Intelligence (IAAI) conference held in 2024 in Vancouver, Canada. The articles address a broad range of very challenging issues and contain great lessons for AI researchers and application developers.The AI Magazine00This special issue of AI Magazine covers select applications from the Innovative Applications of Artificial Intelligence conference held in 2024 in Vancouver, Canada and contains great lessons for AI researchers and application developers.AI Mag.["Alexander Wong", "Yuhao Chen", "Jan Seyler"]2024-11-27T00:00:00 fc7a07a57d14a6b0e756d641577ebaa63e17c0a5The aim of this study is to identify the existing challenges associated with the use of digital technologies, includ-ing elements of artificial intelligence (AI), in the provision of medical care to patients. The study highlights the position according to which a whole range of modern problems related to the application of artificial intelli-gence in medical practice arises from the lack of legal regulation governing these social relations. Classical legal norms are not always applicable to AI, thereby creating legal uncertainty. The ways of possible overcom-ing and elimination of these problems of legal regulation of public relations in the considered social sphere of activity are proposed. Furthermore, an analysis is conducted of various legal acts at both the international and national (Russian) levels that pertain to the regulation of artificial intelligence, particularly concerning risks and requirements related to transparency, accountability, and ethical considerations.Теория и практика общественного развития00The aim of this study is to identify the existing challenges associated with the use of digital technologies, including elements of artificial intelligence, in the provision of medical care to patients and propose ways of possible overcom-ing and elimination of these problems of legal regulation of public relations in the considered social sphere of activity.Теория и практика общественного развития["Yury A. Kliman"]2024-11-27T00:00:00 69f6ef8960e3e984fa9b3dabe75da2bb42f79131Journal of Responsible Innovation611Journal of Responsible Innovation["Christian Herzog", "Sabrina Blank"]2024-11-27T00:00:00 361b4ce4e366197b80def351cff9c82a903dcf9301["S. Bibri"]2024-11-27T00:00:00 484f663ea628fafad1dc840254d48bdcf773dcf9The study has identified several difficulties in effectively implementing artificial inteligence (AI) techniques in judicial proceedings. The approval of regulations, such as Spain's Royal Decree-Law 6/2023, is insufficient for Judges and legal professionals to use these technologies effectively. Several reasons for these challenges are highlighted. Firstly, judicial proceedings and the resulting sentences must be approved by all participants, including the parties involved, Lawyers, Prosecutors, and Judges. The focus should be on the specific conflict and relevant legal texts or precedents, not on AI-generated models based on past data, which may be biased.Secondly, AI technologies are not designed to assist in the specific tasks required by Judges and other officials responsible for processing judicial proceedings. Judicial processes are governed by strict constitutional, procedural, and substantive norms that AI systems are not equipped to handle without significant human oversight.The study also references critical experiences in other countries and opinions from the General Council of the Judiciary in Spain, which point out the lack of precision in the Spanish regulation regarding the use of AI in judicial activities. This indicates that the existing legal framework has not adequately considered the complexities of integrating information and communication technologies into judicial processes. Therefore, promoting AI technologies in judicial applications requires not only regulatory approval but also comprehensive reforms to existing norms and the creation of precise complementary regulations. These measures must align with the legal system and, especially, with the AI Regulation approved by the European Parliament and Council.Frontiers in Law310Promoting AI technologies in judicial applications requires not only regulatory approval but also comprehensive reforms to existing norms and the creation of precise complementary regulations.Frontiers in Law["Fernando Galindo Ayuda"]2024-11-27T00:00:00 5a047ba7de4de76dacd48fc7ace6fbbeadb33388Universum:Technical sciences00Universum:Technical sciences["Yevgeni Yermolin"]2024-11-27T00:00:00 8f46989cf4eaaa99f0973b44812f2b2c1d97de3aFractals00Fractals["R. C. Aguilera", "Marco Antonio Acevedo Mosqueda", "Maria Elena Acevedo Mosqueda", "S. V. Beltr\u00e1n"]2024-11-27T00:00:00 06f575e397732ccea2b1d47b5ef2bbc7bdedbc9dCactus00CACTUS["C. \u021auclea"]2024-11-27T00:00:00 da8f46cf12311a288ea7df043124f1c7cf0d5a90Universum:Technical sciences00Universum:Technical sciences["Tatiana Khoroshilova"]2024-11-27T00:00:00 66b0cdf93fcccb15cbbc67d79105adeb1548bdc0AI &amp; SOCIETY00AI &amp; SOCIETY["Zhongyuan Zhu"]2024-11-27T00:00:00 26f537c55714943954a0392f96a511fdb274a207The International Journal of Cardiovascular Imaging90The international journal of cardiovascular imaging["Daniel Raskin", "S. Partovi"]2024-11-27T00:00:00 f097b3a82a73331c41003ffd40f97970d71d2894Background: such a thorough understanding of this study could best be achieved through conducting in-depth case studies on organizations that have successfully implemented AI in their HR functions, exploring the benefits, challenges, and impact on their workforce, organization, and even the whole of society and a systematic review of existing literature on AI in HR, synthesizing the results of multiple studies to conclude the overall effectiveness, benefits, and challenges associated with AI applications in HR. This study aims to bring out a comprehensive panorama of AI applications in the six sectors of HRM and the impacts of AI implementation in HR on the transformation of business HR practices and its role transformation due to the advent of AI, as well as to promote technological change and ethical policy norms, and legal improvements in this area. Method: We have selected the relevant literature through a scoping study, and we have categorized it according to the six components of the Human Resource Life Cycle. Conclusion: The literature already covers all six of the human resource life cycle's elements as well as its implications for technology and ethical issues. Future studies may concentrate on how AI affects relationships between dimensions and how it affects results that are particular to human resources. Although AI should still be seen as a solution to many of the problems that face human resources management in businesses, practitioners must acknowledge the limits associated with the implementation of AI in HRM.Scientific Journal of Economics and Management Research00This study aims to bring out a comprehensive panorama of AI applications in the six sectors of HRM and the impacts of AI implementation in HR on the transformation of business HR practices and its role transformation due to the advent of AI to promote technological change and ethical policy norms, and legal improvements in this area.Scientific Journal of Economics and Management Research["Weiyi Wang"]2024-11-27T00:00:00 5efe94303c18fd5e57a143ca4f46fb3d79a99091Considering the rapid advancements in AI technologies such as reinforcement learning, ChatGPT, and deep learning, this paper conducts a comprehensive survey of the technological landscape of AI in the energy and agriculture sectors. It delineates the evolutionary path of AI technologies in smart grids and precision agriculture, highlighting significant advancements in energy prediction, optimisation of production and consumption, and intelligent management. Furthermore, the paper identifies key AI technologies crucial for the Agricultural Energy Internet (AEI), offering specialised exploration into AI applications for crop cultivation and fisheries, including disease detection, yield prediction, and resource management. The research provides essential theoretical foundations for AI integration in each of these agricultural domains. In addition, the paper envisions the future integration of ChatGPT in coupled modelling of agriculture and energy systems, enhancing synergistic intelligent control, and AI‐driven carbon tracking technologies within the AEI. This study facilitates a greater grasp of the transformative potential of AI in reshaping the nexus of agriculture and energy.Energy Internet550A comprehensive survey of the technological landscape of AI in the energy and agriculture sectors delineates the evolutionary path of AI technologies in smart grids and precision agriculture, highlighting significant advancements in energy prediction, optimisation of production and consumption, and intelligent management.Energy Internet["Xueqian Fu", "Wei Ye", "Xin Li", "Xiangrong Zeng", "Yubo Wang", "Fuhao Chang", "Jing Zhang", "Ruihan Liu"]2024-11-27T00:00:00 43c6f78156184efd0016b238ebee3144a7d41af6NEJM AI230NEJM AI["Jianing Qiu", "Jian Wu", "Hao Wei", "Peilun Shi", "Minqing Zhang", "Yunyun Sun", "Lin Li", "Hanruo Liu", "Hongyi Liu", "Simeng Hou", "Yuyang Zhao", "Xuehui Shi", "Junfang Xian", "Xiaoxia Qu", "Sirui Zhu", "Lijie Pan", "Xiaoniao Chen", "Xiaojia Zhang", "Shuai Jiang", "Kebing Wang", "Chenlong Yang", "Mingqiang Chen", "Sujie Fan", "Jianhua Hu", "Aiguo Lv", "Hui Miao", "Li Guo", "Shujun Zhang", "Cheng Pei", "Xiaojuan Fan", "Jianqin Lei", "Ting Wei", "Junguo Duan", "Chun Liu", "Xiaobo Xia", "Siqi Xiong", "Junhong Li", "K. Lam", "Benny P. L. Lo", "Y. Tham", "T. Wong", "Ningli Wang", "Wu Yuan"]2024-11-27T00:00:00 1ecfa89dec8942890f23de88d0eafb38036aa670Cell Reports Medicine580An incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framework integrating pretreatment CT scans and H&E-stained biopsy images, for improved decision-making regarding neoadjuvant chemotherapy is proposed.Cell Reports Medicine["Peng Gao", "Qiong Xiao", "Hui Tan", "Jiangdian Song", "Yu Fu", "Jingao Xu", "Junhua Zhao", "Yuan Miao", "Xiaoyan Li", "Yi Jing", "Yingying Feng", "Zitong Wang", "Yingjie Zhang", "Enbo Yao", "Tongjia Xu", "Jipeng Mei", "Hanyu Chen", "Xue Jiang", "Yuchong Yang", "Zhengyang Wang", "Xianchun Gao", "Minwen Zheng", "Liying Zhang", "Min Jiang", "Yuying Long", "Lijie He", "Jinghua Sun", "Yanhong Deng", "Bin Wang", "Yan Zhao", "Yi Ba", "Guan Wang", "Yong Zhang", "Ting Deng", "Dinggang Shen", "Zhenning Wang"]2024-11-27T00:00:00 ddc9da3a4bc35b4100dd4c8e47afb3b1f2bae508World Neurosurgery170All aneurysms in this cohort grew substantially in volume between pre and post rupture when measured with a semi-automated AI volumetric measurement tool, however, Linear measurements showed both increases and decreases in size.World neurosurgery["D. Sahlein", "Denis Babici", "Daniel P. Gibson", "K. Amuluru", "A. Denardo", "Yasir Saleem", "T. Payner", "C. Kulwin", "Kushal J Shah"]2024-11-27T00:00:00 5557cbf3a784bd02aa8016cc9d8ff5bd929728bfThe Information Society170The Information Society["S. Matei", "Diane Jackson", "Elisa Bertino"]2024-11-27T00:00:00 e99deddeb6b6a74b7a3a0882cd6e2536f0fb0895Major scientific institutions worldwide have integrated ethics into their policies to promote sound scientific writing practices and to guide authors in the AI era. This article summarizes initial discussions on academic writing and presents a comparison of perspectives on the development of guidelines for scholars across four distinct academic disciplines in higher education. Additionally, we underscore the importance of understanding the perceptions of these academic participants, as this insight can support decision-making by academic leaders. Furthermore, this understanding is essential for reassessing technology literacy within the academic community, with the goal of preserving academic integrity.Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)80The importance of understanding the perceptions of academic participants can support decision-making by academic leaders and is essential for reassessing technology literacy within the academic community, with the goal of preserving academic integrity.Anais da I Conferência Latino-Americana de Ética em Inteligência Artificia (LAAI-Ethics 2024)["Rai G. Torres", "Flavia Linhalis"]2024-11-27T00:00:00 a91cbf4e8f6c69ce9054b02cb58a289fc7e2141bNeural computing & applications (Print)00Neural Computing and Applications["John Bosco Ssemakula", "J. Gorricho", "Godfrey Kibalya", "Joan Serrat-Fernandez"]2024-11-27T00:00:00 b42848affe7be9eecf67d31eb623f2b259362ef5In today's world, AI programs powered by Machine Learning are ubiquitous, and have achieved seemingly exceptional performance across a broad range of tasks, from medical diagnosis and credit rating in banking, to theft detection via video analysis, and even predicting political or sexual orientation from facial images. These predominantly deep learning methods excel due to their extraordinary capacity to process vast amounts of complex data to extract complex correlations and relationship from different levels of features. In this paper, we contend that the designers and final users of these ML methods have forgotten a fundamental lesson from statistics: correlation does not imply causation. Not only do most state-of-the-art methods neglect this crucial principle, but by doing so they often produce nonsensical or flawed causal models, akin to social astrology or physiognomy. Consequently, we argue that current efforts to make AI models more ethical by merely reducing biases in the training data are insufficient. Through examples, we will demonstrate that the potential for harm posed by these methods can only be mitigated by a complete rethinking of their core models, improved quality assessment metrics and policies, and by maintaining humans oversight throughout the process.arXiv.org660It is argued that current efforts to make AI models more ethical by merely reducing biases in the training data are insufficient, and the potential for harm posed by these methods can only be mitigated by a complete rethinking of their core models, improved quality assessment metrics and policies, and by maintaining humans oversight throughout the process.ArXiv["J'er'emie Sublime"]2024-11-27T00:00:00 1f8b0d2c9d919f661da4af80d4f52d9a531e41cdNEJM AI420NEJM AI["Jonathan Wang", "Donald A. Redelmeier"]2024-11-27T00:00:00 78e91adc7723d9d68a348829af3d78ca53106fa3African Journal of Biomedical Research00African Journal OF Biomedical Research["Visakh T"]2024-11-27T00:00:00 03ffce6b5800d3f348a35877098953bbe65fca7cIn Turkiye, English is taught at primary level in public institutions for two hours a week. However, studies show that more than two hours per week are needed for natural learning of the language. Therefore, it is necessary to create an environment where students can practise English outside class. It is a well-known fact that technological tools and applications for effective language learning contribute to the second language learning processes of today's primary school students at the point of increasing interest and motivation in the classroom. In this study, additional materials prepared using AI tools were translated from text to audio using AI applications and content that students can access anytime, anywhere via eba or reliable chat applications. This allowed the students to improve their English listening and speaking skills while not being exposed to online video sharing and social media tools outside of the class. The research is an action research study.  The participants were 50, 2nd grade students from Binali Yıldırım Primary School in Tuzla District, Istanbul Province. Qualitative and quantitative tools were used together to collect data. Interviews with students and teachers and observations were used as qualitative data collection tools. Qualitative data showed that podcasts narrated with children's voices using artificial intelligence tools attracted students' attention, and their desire and motivation to listen and create podcasts increased throughout the study. SPSS analyses of quantitative data from pre- and post-tests administered at the beginning and end of the study were conducted and it was found that podcasts reduced students' classroom anxiety. The study findings show that podcasts can effectively improve students' listening and speaking skills and reduce English class anxiety. It is possible to use time- and energy-saving content prepared with artificial intelligence tools to enrich English lessons.CONTEMPORARY RESEARCH IN LANGUAGE AND LINGUISTICS (ISSN: 2980-2253)00The study findings show that podcasts can effectively improve students' listening and speaking skills and reduce English class anxiety and it is possible to use time- and energy-saving content prepared with artificial intelligence tools to enrich English lessons.CONTEMPORARY RESEARCH IN LANGUAGE AND LINGUISTICS (ISSN: 2980-2253)["Funda Alt\u0131nta\u015f"]2024-11-27T00:00:00 1422aa926de1eb5c7fcdf28a11e620631384915cMinds Mach.560Minds Mach.["Siavosh Sahebi", "Paul Formosa"]2024-11-27T00:00:00 9a8600db1eb685aea65a85303e383632cf645cd4Com o advento da Artificial Intelligence (Inteligência Artificial) no campo jurídico, surgem diversas transformações que merecem ser ponderadas. De um lado, a IA (Inteligência Artificial) pode otimizar o trabalho dos advogados, tornando-o mais eficiente e produtivo. De outro lado, surgem preocupações éticas e de responsabilidade relacionadas à implementação dessa tecnologia diante de tudo isso levanta-se o seguinte questionamento: quais os impactos advindos do uso da Inteligência Artificial na advocacia? Sendo assim, este trabalho se propõe a analisar os aspectos do uso da Artificial Intelligence (Inteligência Artificial) na atuação do advogado, salientando os benefícios tangíveis e os desafios inerentes a essa transição. Tendo como desígnio analisar os aspectos do uso da Inteligência Artificial na atuação do advogado e para tanto se faz necessário discutir acerca do uso da IA (Inteligência Artificial) na atuação do advogado, verificar os pontos negativos e positivos da implementação da Artificial Intelligence (Inteligência Artificial) no trabalho do advogado, bem como propor alternativas para a aplicação da IA (Inteligência Artificial) na advocacia, com foco na ética e na responsabilidade. Para alcançar o resultado desejado o respectivo projeto de pesquisa foi estruturado por meio de metodologia de pesquisa por levantamento de material bibliográfico, tendo como autores mais relevantes Stuart Russell, Peter Norvig, Marcelo Soares, Marcos Kauffman. Espera-se que este trabalho traga importantes reflexões acerca da necessidade de os profissionais de direito terem, a fundamental, ciência dos desafios e preocupações que estão intimamente relacionados ao uso dessa ferramenta. Ademais, propor alternativas para sua melhor utilização no campo jurídico.Revista Ibero-Americana de Humanidades, Ciências e Educação00Revista Ibero-Americana de Humanidades, Ciências e Educação["Edson da Hora Concei\u00e7\u00e3o J\u00fanior", "T. Novais"]2024-11-27T00:00:00 e0807c41b5bb33278846904416e7073eee4b5782This article attempts to describe the capabilities of artificial intelligence in the field of education. A brief history of the emergence of artificial intelligence, some educational platforms using artificial intelligence and possible threats related to the use of artificial intelligence are presented.Pedagogika-Pedagogy00The capabilities of artificial intelligence in the field of education are described and some educational platforms using artificial intelligence and possible threats related to the use of artificial intelligence are presented.Pedagogika-Pedagogy["Natalia Vitanova"]2024-11-27T00:00:00 36931201648196d350491431f16f68c7360e8530This review explores the significant impact of Artificial Intelligence (AI) and Machine Learning (ML) on pediatric healthcare and education for children with genetic syndromes. Our investigation shows that AI-driven tools, like Google AI's DeepVariant, have greatly improved diagnostic precision. This allows for earlier and more accurate identification of genetic anomalies in conditions such as Cri-du-Chat Syndrome and 22q11.2 Deletion Syndrome. In addition, ML-based approaches have played a crucial role in advancing personalized treatment strategies, such as utilizing pharmacogenomic models to optimize drug therapies for Duchenne Muscular Dystrophy. Adaptive learning platforms, such as DreamBox Learning, have effectively tailored educational content according to the specific requirements of children with syndromes like Phelan-McDermid Syndrome. The review suggests that combining AI and ML significantly enhances diagnostic accuracy, treatment effectiveness, and educational results, thereby establishing higher benchmarks for pediatric care. Nevertheless, these advancements have notable ethical, legal, and social challenges. It is essential to prioritize equitable access, data privacy protection, and algorithmic transparency to maximize the benefits and minimize potential risks associated with AI. Overall, the findings underscore the potential of AI and ML to revolutionize pediatric genetic care, provided that these technologies are implemented responsibly and inclusively.Jordan Medical Journal902The findings underscore the potential of AI and ML to revolutionize pediatric genetic care, provided that these technologies are implemented responsibly and inclusively, provided that these technologies are implemented responsibly and inclusively.Jordan Medical Journal["Lama Ghunaim", "Ahmed Saad Abdulbari Ali Agha", "A. Al-samydai", "Talal Aburjai"]2024-11-27T00:00:00 c7e823e4d8bb3526f3c70142bcab1e56752ccbe5This systematic review examines the role of artificial intelligence (AI) in English language teaching (ELT), analyzing AI tools, applications, and their pedagogical outcomes. AI technologies, such as chatbots, intelligent tutoring systems, and speech recognition software, are increasingly used to enhance language learning experiences. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model, a standardized approach that ensures transparency and rigor in identifying, screening, and analyzing relevant literature. PRISMA emphasizes clear documentation of the selection process, including inclusion and exclusion criteria, to provide a systematic and replicable methodology for comprehensive reviews. Through thematic qualitative analysis of recent literature indexed in Scopus and Web of Science, key themes emerged regarding AI types, applications, teacher and learner perspectives, and ethical considerations. Findings reveal that AI tools enhance learner engagement, provide personalized learning experiences, and improve language proficiency, particularly in speaking and writing. However, challenges remain, such as accessibility barriers, teacher preparedness, and ethical concerns around data privacy and bias. This review proposes a framework for AI integration in ELT, focusing on access, teacher training, ethical standards, and blended learning models to optimize AI’s benefits. The study underscores the need for targeted teacher training and ethical standards to maximize AI’s effectiveness and sustainability in ELT. This framework and the review findings aim to support educators, developers, and policymakers in fostering an AI-enriched learning environment that aligns with educational goals while addressing existing limitations.The Art of Teaching English as a Foreign Language203A framework for AI integration in ELT is proposed, focusing on access, teacher training, ethical standards, and blended learning models to optimize AI’s benefits, highlighting the need for targeted teacher training and ethical standards to maximize AI’s effectiveness and sustainability in ELT.The Art of Teaching English as a Foreign Language (TATEFL)["Dana Kristiawan", "Khaliq Bashar", "Dian Arief Pradana"]2024-11-28T00:00:00 4ef51a8162b181639acb7f1f8dda73b552156955Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS.Applied Sciences2191To form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector.Applied Sciences["Ashkan Safari", "M. Daneshvar", "A. Anvari\u2010Moghaddam"]2024-11-28T00:00:00 ea26f65c1fa6d8ad04c2623fae6a241e0830dea2Simple Summary Integrating artificial intelligence (AI) in in vitro fertilization (IVF) laboratories represents a significant advancement in reproductive medicine. AI tools such as machine learning and deep learning enhance quality control, improve accuracy, and increase efficiency in tasks like embryo and sperm selection. By automating traditionally manual processes, AI reduces human error and variability, thus supporting higher success rates for IVF treatments. However, the application of AI in this sensitive area also raises ethical and regulatory challenges, including concerns about data privacy and transparency in algorithm-driven decisions. Overall, AI has the potential to revolutionize IVF by optimizing outcomes, but it requires careful management to ensure ethical standards are met and patient trust is maintained.Biology821Overall, AI has the potential to revolutionize IVF by optimizing outcomes, but it requires careful management to ensure ethical standards are met and patient trust is maintained.Biology["Yaling Hew", "Duygu Kutuk", "Tuba Duzcu", "Yagmur Ergun", "Murat Ba\u015far"]2024-11-28T00:00:00 0d36a84f3746e97a739806b35e4087bd01ddc3d1Environmental monitoring has become increasingly critical as climate change continues to pose significant global challenges, impacting ecosystems, economies, and human health. Predicting and managing these impacts requires advanced technological solutions, and Artificial Intelligence (AI) has emerged as a powerful tool in this domain. This study aims to explore the integration of AI techniques, such as machine learning and deep learning, into environmental monitoring to enhance the accuracy of climate change impact predictions and improve management strategies. The methods employed include the application of Convolutional Neural Networks (CNN) for land cover classification and Long Short-Term Memory (LSTM) models for forecasting air quality levels. The results indicate that AI significantly improves prediction accuracy, with CNN achieving high performance in land classification and LSTM models providing reliable forecasts for air quality changes. The findings suggest that AI can be instrumental in transforming environmental monitoring, enabling more proactive and data-driven climate change management. Future research should focus on improving data quality, model interpretability, and expanding AI applications in various environmental contexts.International Transactions on Artificial Intelligence (ITALIC)01The results indicate that AI significantly improves prediction accuracy, with CNN achieving high performance in land classification and LSTM models providing reliable forecasts for air quality changes.International Transactions on Artificial Intelligence (ITALIC)["Olivia Bianchi", "Herman Purwoko Putro"]2024-11-28T00:00:00 5f5c5bf6c6eec0e08d2dc8c41da26bd6aae76604ABSTRACT Immune checkpoint inhibitors (ICIs) are revolutionizing cancer treatment, and Artificial Intelligence (AI) is a key player in this field. A comprehensive analysis of AI’s impact on these inhibitors was lacking, but this study addresses that by analyzing literature for trends and future predictions. It reveals rapid growth and international collaboration. We utilized analytical tools such as CiteSpace, VOSviewer, and PlotDB to analyze 774 documents from the Web of Science Core Collection from 2018 to May 2024, discovering a steady increase in annual publications, with China and the United States leading the way. Sun Yat Sen University and researchers like Ock Chan-young, Zhang Hao, and Newman AM are prominent. The most productive journal is Frontiers in Immunology, while the New England Journal of Medicine has the highest citation rate. The most cited reference is Newman, AM’s 2019 article in Nature Biotechnology. Keywords like “immunotherapy,” “pembrolizumab,” “cancer,” “machine learning,” and “expression” are central to the discourse. Research focuses on the application of inhibitors in non-small cell lung cancer, bioinformatics, and cancer immunotherapy, showing AI’s potential to improve oncology precision medicine. Although AI’s application in ICIs shows promise, significant challenges still demand exploration and resolution. Continued investment in AI research in this context could lead to significant advancements in cancer treatment. Global collaboration is needed to overcome these challenges and fully leverage AI’s potential. This study provides a foundation for future research and interdisciplinary collaboration in this critical field.Human Vaccines & Immunotherapeutics520Analysis of literature for trends and future predictions in ICIs reveals rapid growth and international collaboration, and provides a foundation for future research and interdisciplinary collaboration in this critical field.Human Vaccines & Immunotherapeutics["Ziqi Zhao", "Kun Xu", "Yizhuo Jiang", "Xisheng Xu", "Yuliang Liu"]2024-11-28T00:00:00 2ea077aa35bd857c19019ef511e81ee9a4ee4680With the acceleration of the digital transformation of the automotive industry, personalized marketing plays an increasingly significant role in improving user experience and boosting sales conversion rate. This paper proposes a personalized intelligent model of automobile digital marketing based on artificial intelligence, and focuses on the application of autoencoder algorithm in user data processing and feature extraction. Through dimensionality reduction and compression of extensive user data, the autoencoder efficiently extracts hidden features, thereby significantly enhancing the precision of personalized recommendations. This paper combines K-means clustering and Support Vector Machine (SVM) algorithms for a comparative performance analysis. Experimental results indicate that while K-means clustering exhibits strong performance in user group classification, and Support Vector Machines demonstrate high accuracy in solving classification problems, the overall model's recommendation accuracy and user behavior prediction ability are further enhanced when integrated with the autoencoder, with an accuracy rate increase of approximately 12.5%. The simulation results indicate that the autoencoder algorithm achieves efficient data compression and feature learning in large-scale automotive data marketing systems, offering more precise personalized recommendations and marketing strategies for automotive digital marketing systems.2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM)70The simulation results indicate that the autoencoder algorithm achieves efficient data compression and feature learning in large-scale automotive data marketing systems, offering more precise personalized recommendations and marketing strategies for automotive digital marketing systems.2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM)["Xiaoxia Li", "Qianyu Song", "Zhang Fan"]2024-11-28T00:00:00 c1db77ac92821669ed89f5efebce89f802b916e2Scientific Reports430A ShuffleNet V3-based aerobic exercise classification and recognition model achieves accurate classification and recognition of aerobic exercise by integrating exercise nutrition, ShuffleNet V3, and attention mechanisms, contributing to a more comprehensive intersection of deep learning and sports science research.Scientific Reports["Zhiyuan Duan", "Nan Ge", "Yuanhui Kong"]2024-11-28T00:00:00 2af4c681545996d8a3ef999f439679acf484290fThe advent of Artificial Intelligence (AI) is profoundly transforming organizational landscapes, significantly influencing work practices and triggering cultural shifts. This study explores the role of AI in reshaping organizational work practices and examines the resulting cultural transformation. Through a systematic literature review, this study synthesizes existing research to provide a comprehensive understanding of AI’s impact on organizational landscapes. A systematic literature review was conducted, analyzing peer-reviewed articles, books, and conference papers to identify key themes related to AI-driven changes in work practices, including automation, decision making, and employee roles. It also explores how these changes influence organizational culture, particularly shifts toward innovation, agility, and continuous learning, alongside challenges like resistance to change and ethical concerns. While AI adoption promises benefits such as enhanced efficiency, productivity, and innovation, it also presents significant challenges related to cultural alignment, employee resistance, ethical concerns, and leadership communication. Effective leadership, transparent communication, and investments in skills development emerge as pivotal strategies for overcoming these obstacles and ensuring successful AI implementation. The findings offer insights into the complex interplay between AI adoption and cultural transformation, highlighting gaps in the current research and suggesting directions for future studies. This study serves as a valuable resource for academics and practitioners seeking to understand the broader implications of AI on organizational structures and culture.Administrative Sciences370This study explores the role of AI in reshaping organizational work practices and examines the resulting cultural transformation and offers insights into the complex interplay between AI adoption and cultural transformation, highlighting gaps in the current research and suggesting directions for future studies.Administrative Sciences["O. Murire"]2024-11-28T00:00:00 b2760c341ad192ec3847087046724e831edb46a1PURPOSE OF REVIEW To review role of artificial intelligence in medicine. RECENT FINDINGS Artificial intelligence is continuing to revolutionize access, diagnosis, personalization of medicine, and treatment in healthcare. As a matter of fact, artificial intelligence contributed to the research that resulted in 2024 Nobel Prizes in physics, chemistry, and economics. We are only at the tip of the iceberg in utilizing the abilities of artificial intelligence in medicine to improve accuracy of diagnoses and to enhance patient outcomes. Artificial intelligence has allowed better image analysis, prediction of progression of disease, personalized treatment plans, incorporations of genomics, and improved efficiency in care and follow-up utilizing home monitoring. In ocular health diagnosis and treatment of diabetic retinopathy, macular degeneration, glaucoma, corneal infections, and ectasia are only a few examples of how the power of artificial intelligence has been harnessed. Even though there are still challenges that need more work in the areas of patient privacy, Health Insurance Portability and Accountability Act (HIPAA) compliance, reliability, and development of regulatory frameworks, artificial intelligence has revolutionized and will continue to revolutionize medicine. SUMMARY Artificial intelligence is enhancing medical diagnosis and treatment, as well as access and prevention. Ocular imaging, visual outcome, optics, intraocular pressure, and data points will continue to see growth it the field of artificial intelligence.Current Opinion in Ophthalmology170Artificial intelligence is enhancing medical diagnosis and treatment, as well as access and prevention, and ocular imaging, visual outcome, optics, intraocular pressure, and data points will continue to see growth it the field of artificial intelligence.Current opinion in ophthalmology["Ava S Khossravi", "Qingyu Chen", "Ron A. Adelman"]2024-11-28T00:00:00 799c817d0219b8da12e15d062bc5d06fc3dc94f9. Students can use ChatGPT to create scientific work because ChatGPT can answer questions in seconds. However, the creation of scientific works cannot avoid the Copyright Law because it relates to the creation of works via ChatGPT. Unfortunately, there has not been much IPR research regarding chatGPT in scientific publications, even though it is important to continue creating original scientific work. On the basis of this explanation, a problem formulation was found about how the legal awareness of students towards the use of Chat-GPT in writing scientific papers related to the application of Copyright. This encourages researchers to conduct studies regarding the use of artificial intelligence chatGPT in scientific work from an IPR perspective. The aim of this research is to determine the legal liability for using AI ChatGPT in the creation of scientific work from an IPR perspective. The research method used is empirical juridical with data collection techniques through copyright law studies and distributing questionnaires to 100 respondents from 7 faculties of University X, Central Java. The research results revealed that 80% of students used ChatGPT to create scientific papers. Respondents may retain copyright if they use ChatGPT data without duplicating and processing it. However, the author will lose copyright if the use of ChatGPT in the creation of scientific work is not in accordance with research ethics as stated in Copyright Law Number 28 of 2014 Article 1 Chapter 1.Law Development Journal00The aim of this research is to determine the legal liability for using AI ChatGPT in the creation of scientific work from an IPR perspective and to conduct studies regarding the use of artificial intelligence chatGPT in scientific work from an IPR perspective.Law Development Journal["Alpian Alpian", "Fokky Fuad", "Anis Rifai"]2024-11-28T00:00:00 a83c5550932ea8ea332a0114c260775164c86bebNowadays, with the development of advanced tech-nologies and objects based on systems using Artificial Intelligence (AI), we are witnessing a technological revolution on a par with electricity in the 19th century. It is in this constantly evolving global context that we are studying the integration of AI into the education sector in Burkina Faso. Indeed, given the country's abundant scientific potential, we must get the growing advantages offered by AI. Our studies aim to explore the opportunities and challenges arising from the use of AI in the educational system, which is one of the foundations of any country's development. The main objective is to investigate how AI can transform and improve teaching methods, while considering the social, economic and ethical implications. In this paper, we have carried out comparative studies of different learning support tools based on AI systems. Thus, the synthesis of our work will be able to guide normal learners and learners with disabilities to make a judicious choice of AI tools to improve learning conditions.2024 IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD)70Comparative studies of different learning support tools based on AI systems are carried out to investigate how AI can transform and improve teaching methods, while considering the social, economic and ethical implications.2024 IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD)["Satafa Sanogo", "S. Zio"]2024-11-28T00:00:00 88864d8ff281e30616bc760263401968d3879451Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. Using a representative sample of 1100 participants from Germany, this study examines mental models of AI. Participants quantitatively evaluated 71 statements about AI's future capabilities (e.g., autonomous driving, medical care, art, politics, warfare, and societal divides), assessing the expected likelihood of occurrence, perceived risks, benefits, and overall value. We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs. While many scenarios were deemed likely, participants often associated them with high risks, limited benefits, and low overall value. Across all scenarios, 96.4% ($r^2=96.4\%$) of the variance in value assessment can be explained by perceived risks ($\beta=-.504$) and perceived benefits ($\beta=+.710$), with no significant relation to expected likelihood. Demographics and personality traits influenced perceptions of risks, benefits, and overall evaluations, underscoring the importance of increasing AI literacy and tailoring public information to diverse user needs. These findings provide actionable insights for researchers, developers, and policymakers by highlighting critical public concerns and individual factors essential to align AI development with individual values.arXiv.org830Mental models of AI were examined to highlight critical public concerns and individual factors essential to align AI development with individual values, underscoring the importance of increasing AI literacy and tailoring public information to diverse user needs.ArXiv["P. Brauner", "Felix Glawe", "Gian Luca Liehner", "Luisa Vervier", "Martina Ziefle"]2024-11-28T00:00:00 4c0e3094decb3d5774d5925303e87eb302ca32b9OBJECTIVE Artificial intelligence (AI) has been shown to hold promise for improving breast cancer screening, offering advanced capabilities to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the impact of a multimodal multi-instant AI-based system on the diagnostic performance of radiologists in interpreting mammograms. METHODS We designed a multireader multicase study taking into account the evaluation of both interpretive and noninterpretive tasks. The study was approved by an institutional review board and is compliant with HIPAA. The dataset included 90 cancer-proven and 150 negative cases. The overall diagnostic performance was compared between the unaided vs aided reading condition. Intraclass correlation coefficient (ICC), Fleiss's kappa, and accuracy were used to quantify the agreement and performance on noninterpretive tasks. Reading time and perceived fatigue were used as comprehensive metrics to assess the efficiency of readers. RESULTS The average area under the receiver operating characteristic curve increased by 7.4% (95% CI, 4.5%-10%) with the concurrent assistance of the AI system (P <.001). On average, readers found 8% more cancers in the assisted reading condition. The ICC went from 0.6 (95% CI, 0.55-0.65) in the unassisted condition to 0.74 (95% CI, 0.70-0.78) for readings done with AI (P <.001). An overall decrease of 24% in reading time and a reduction in perceived fatigue was also found. CONCLUSION The incorporation of this AI system, capable of handling multiple image type, prior mammograms, and multiple outputs, improved the diagnostic proficiency of radiologists in identifying breast cancer while also reducing the time required for combined interpretive and noninterpretive tasks.Journal of Breast Imaging320The incorporation of this AI system, capable of handling multiple image type, prior mammograms, and multiple outputs, improved the diagnostic proficiency of radiologists in identifying breast cancer while also reducing the time required for combined interpretive and noninterpretive tasks.Journal of breast imaging["S. Pacil\u00e9", "Pauline Germaine", "Caroline Sclafert", "Thomas Bertinotti", "Pierre Fillard", "Svati Singla Long"]2024-11-28T00:00:00 be3fba4a0ae494214f2aa4af4cc765d4b71b984dIn today’s world, technology is considered a necessity as many can benefit from using it. Education sector also cannot run from integrating and incorporating technology in the teaching and learning process. Therefore, this study would like to investigate students’ perceptions on the use of artificial intelligence (AI) as a tool in the classroom. This study employed a quantitative approach in obtaining the data. There were 284 respondents who participated in this study. The instrument used to obtain the data was a set of questionnaires where there were 20 items in it. Other than that, there were 6 sections in the questionnaire representing the constructs of the perceptions. The results showed that students perceived AI as a tool that could help them in their learning process. This implies that educators need to be more ready in using technology in the classroom and they should equip themselves with 21st century skills that are relevant in today’s education system. Therefore, integrating technology in teaching and learning processes may assist the educators and students to be more engaged in the classroom and two-way communication may occur.Malaysian Journal of Social Sciences and Humanities380Students’ perceptions on the use of artificial intelligence (AI) as a tool in the classroom showed that students perceived AI as a tool that could help them in their learning process, implying that educators need to be more ready in using technology in the classroom.Malaysian Journal of Social Sciences and Humanities (MJSSH)["Zulaikha Khairuddin", "Nor Syahiza Shahabani", "Siti Nurshafezan Ahmad", "Azrina Ahmad", "Nur Adibah Zamri"]2024-11-28T00:00:00 e3a38c6c405f75f74e6549949d5f1b35583bf22cThe application of artificial intelligence (AI) technology in the field of graduate research is becoming more and more common, which has brought unprecedented opportunities and challenges for the enhancement of graduate research ability. The purpose of this study is to deeply analyze the multiple mechanisms of artificial intelligence promoting the improvement of graduate students' scientific research ability. Firstly, this paper systematically analyzed the theoretical foundation of artificial intelligence in graduate research, covering the application of core algorithms such as machine learning and deep learning in scientific research practice, as well as the mechanism of the integration of artificial intelligence and scientific research, including the potential of interdisciplinary cooperation, the challenges encountered in the integration process and their coping strategies. Then the application examples of artificial intelligence in graduate research practice are expounded, including but not limited to the key links of literature search and analysis, experiment design and data analysis. Then, from the two dimensions of cultivating innovative thinking and improving learning autonomy, how artificial intelligence can help improve graduate students' scientific research ability is discussed. It is believed that artificial intelligence plays an important role in generating new hypotheses, stimulating creativity, breaking through traditional thinking patterns, generating personalized learning resources, and assisting group discussion. In summary, artificial intelligence plays a vital role in improving the scientific research ability of graduate students, and provides strong technical support and innovation motivation for graduate students' scientific research work.Journal of Educational Research and Policies80Artificial intelligence plays a vital role in improving the scientific research ability of graduate students, and provides strong technical support and innovation motivation for graduate students' scientific research work.Journal of Educational Research and Policies["Yu Liu", "Lu Xu"]2024-11-28T00:00:00 5acb3f82ced22807029f2d6da817d0aef1db9b8dThe article considers the main aspects of evolution and performs a thorough analysis of the stages of formation of multimodal artificial intelligence systems (AIS). It was determined that in modern realities, artificial intelligence has undergone a transformational shift towards embracing multimodality in large language models. Modern approaches and ways of improving large language models by means of processing and generating a large amount of data are analyzed. The stages of transformation of artificial intelligence in the direction of multimodality of innovative development in large language models have been studied. The issue of verification and interaction of information systems with the surrounding world is considered. It was determined that they are inherently multimodal, multicomponent. Ways of improving large language models with the help of the ability to process and generate different data modalities are analyzed. It has been investigated that modern multimodal artificial intelligence systems are effectively used in various fields of science, education, and economics and require further development and improvement. It was determined that due to the rapid development of information technologies and systems in various spectrums of life, AI is experiencing a rapid modification, where generative models, which are becoming more and more perfect, deserve special attention. An overview of the architecture of the AnyGPT model is performed, where modalities are tokenized into discrete tokens, on the basis of which LLM performs multimodal perception and generation in autoregression. The methodology underlying AnyGPT was found to be multi-component, with the model demonstrating capabilities on par with specialized models in all assessment modalities tested. It has been established that tools designed to detect objects generated by artificial intelligence are in a state of development and are constantly being modified.Системи управління навігації та зв'язку Збірник наукових праць110It was determined that due to the rapid development of information technologies and systems in various spectrums of life, AI is experiencing a rapid modification, where generative models, which are becoming more and more perfect, deserve special attention.Системи управління, навігації та зв’язку. Збірник наукових праць["A. Kapiton", "D. Tysh\u0441henko", "A. Desiatko", "V. Lazorenko"]2024-11-28T00:00:00 92c28ae17c6bde9de4ab8b7103c0d3c4d751f033The author used this paper to discuss the techniques, strategies, and concepts of artificial intelligence and machine learning to learn their uses in providing security and other essential features. The author also discusses the advantages, drawbacks, or limitations of using artificial intelligence and machine learning. Any technology or development comes with certain advantages and limitations. This scenario applies to artificial intelligence and machine learning. By emphasizing the importance of artificial intelligence and machine learning, the author attempts to educate the users and readers about the significant concepts within the study, as this could help many users and organizations to identify the critical factors about these concepts.International Journal of Network Security &amp; Its Applications00By emphasizing the importance of artificial intelligence and machine learning, the author attempts to educate the users and readers about the significant concepts within the study, as this could help many users and organizations to identify the critical factors about these concepts.International Journal of Network Security &amp; Its Applications["Nikitha Merilena Jonnada"]2024-11-28T00:00:00 1b8171bf6a40dc0755f483ba4f5df5571cb346bcThe rapid advancements in artificial intelligence (AI) have presented new opportunities for enhancing efficiency and economic competitiveness across various industries, espcially in banking. Machine learning (ML), as a subset of artificial intelligence, enables systems to adapt and learn from vast datasets, revolutionizing decision-making processes, fraud detection, and customer service automation. However, these innovations also introduce new challenges, particularly in the realm of cybersecurity. Adversarial attacks, such as data poisoning and evasion attacks, represent critical threats to machine learning models, exploiting vulnerabilities to manipulate outcomes or compromise sensitive information. Furthermore, this study highlights the dual-use nature of AI tools, which can be used by malicious users. To address these challenges, the paper emphasizes the importance of developing machine learning models with key characteristics such as security, trust, resilience and robustness. These features are essential to mitigating risks and ensuring the secure deployment of AI technologies in banking sectors, where the protection of financial data is paramount. The findings underscore the urgent need for enhanced cybersecurity frameworks and continuous improvements in defensive mechanisms. By exploring both opportunities and risks, this paper aims to guide the responsible integration of AI in the banking sector, paving the way for innovation while safeguarding against emerging threats.arXiv.org00The importance of developing machine learning models with key characteristics such as security, trust, resilience and robustness are emphasized, essential to mitigating risks and ensuring the secure deployment of AI technologies in banking sectors, where the protection of financial data is paramount.ArXiv["Ana Kovacevic", "Sonja D. Radenkovic", "D. Nikolic"]2024-11-28T00:00:00 1be94717b59b5a0ac7a86b5ed464fe4999dc3cb9The issue of defining the rights of artificial intelligence generated products has always been a hot topic in various fields, triggering heated academic discussions. The emergence of Chat Gpt has further exacerbated the complexity of the problem.  ChatGPT mainly has powerful abilities such as dialogue comprehension for any task, complex logical reasoning, long grid multi text generation, and automatic generation of program code. Compared to the past where only certain algorithms, rules, and modules were used, ChatGPT has become more advanced, and its models and computational methods are more complex. Especially, its ability to meet the personalized needs of users has led to its generated content being more similar in form to human creative achievements. The definition of its ownership of rights has become more complex.The development of technology will inevitably bring potential for development and legal issues that are difficult to define in the short term.  It is of utmost importance to use the system to regulate newly emerging products in appropriate development channels.In the current state of socio-economic development that emphasizes the protection of rights, especially intellectual property, it is necessary to start from the reality of China and construct a copyright ownership model for artificial intelligence generated products that is suitable for China's active development level and legal system practice.International Journal of Social Sciences and Public Administration40It is necessary to construct a copyright ownership model for artificial intelligence generated products that is suitable for China's active development level and legal system practice and of utmost importance to use the system to regulate newly emerging products in appropriate development channels.International Journal of Social Sciences and Public Administration["Liangyu Yang"]2024-11-28T00:00:00 4b0b1dbf4d12169114af5d243b4672e47ce3ec6cThroughout recent years, artificial intelligence in schooling has developed fundamentally. The first to check out the application in training. Context-oriented information from the examination is introduced in this review, including the instructive disciplines, instructive levels, research objectives, procedure, year of distribution, and ideal interest group for the simulated intelligence. Grounded coding demonstrated how affordances connected with subject substance, organization like symptomatic apparatuses, and teaching methods, for example, gaming and personalization fit into three significant topics of man-made consciousness in training. Negative mentalities, an absence of mechanical capability for understudies and educators, moral worries, and issues explicitly with the simulated intelligence device's convenience and configuration were among the difficulties faced by knowledge in schooling.Journal of Educational Research and Policies120Context-oriented information from the examination is introduced in this review, including the instructive disciplines, instructive levels, research objectives, procedure, year of distribution, and ideal interest group for the simulated intelligence.Journal of Educational Research and Policies["Mostafa Ismail", "Mohammed Allam", "Bhawna Suyanto"]2024-11-28T00:00:00 0711784c306dc705377e80cfce1565b398f8cc26The adoption of the Artificial Intelligence Act by the European Union, together with the emergence of large language models (LLMs) based on foundation models, or, more generally, of generative artificial intelligence (GenAI), has attracted renewed interest, within and beyond the financial sector, in the opportunities and limitations of this technology as a driver of change in society. This article aims to provide the context for recent developments and put them into perspective, identify possible road maps within the financial system and also set out what could hold back or spur progress in the medium and long term.Financial Stability Review00The context for recent developments is provided to put them into perspective, identify possible road maps within the financial system and also set out what could hold back or spur progress in the medium and long term.Financial Stability Review["Iv\u00e1n Balsategui", "Sergio Gorj\u00f3n", "Jos\u00e9 Manuel Marqu\u00e9s"]2024-11-28T00:00:00 d33b7cc045d5d4b13adff178309d2983caf255b7This paper will look at the potential of artificial intelligence in the field of social work as a helping profession focused on social justice, social development, democracy, equality and the protection of human rights. Artificial intelligence represents a complex area that is still not advanced enough, especially in the field of social work. In this sense, AI is seen as a discipline and science that should make everyday life easier, while on the other hand there are still numerous moral and ethical issues, especially in the field of human rights protection. At first glance, AI and social work may seem like an unlikely combination, or even as conflicting disciplines; however, the paper will show the strengths, and the common tendencies of the aforementioned disciplines. Additionally, the paper will present what the main ethical dilemmas and challenges in the implementation of artificial intelligence in the field of social work are, as well as what various state-of-the-art mechanisms are provided at the moment. Finally, the paper leaves room for discussion about the digitalisation of social work, the practicality of applying AI in social work, as well as the possibilities of more proactive protection of human rights and the establishment of new policies and practices.Teme210The paper will present what the main ethical dilemmas and challenges in the implementation of artificial intelligence in the field of social work are, as well as what various state-of-the-art mechanisms are provided at the moment.TEME["J. \u0160kori\u0107", "Milena Galetin"]2024-11-28T00:00:00 b15e7b79b4a7860a7c110386129abc226ac25814The integration of Artificial Intelligence (AI) into the aviation industry is revolutionizing operational efficiency, safety, and the passenger experience. This paper provides a comprehensive examination of the current and potential applications of AI in aviation. The introduction defines AI and outlines its key characteristics, setting the stage for a detailed exploration of its practical uses. The second chapter reviews case studies of airlines that have successfully implemented AI technologies, highlighting improvements in areas such as flight operations, customer service, and scheduling. The third chapter focuses on future applications, particularly predictive maintenance, and its potential to enhance reliability and reduce costs. The final chapter presents a SWOT analysis, assessing the strengths, weaknesses, opportunities, and threats associated with the adoption of AI in aviation. By synthesizing these elements, the paper aims to provide insights into how AI can shape the future of the industry and inform strategic decision-making..2024 New Trends in Aviation Development (NTAD)90Insight is provided into how AI can shape the future of the industry and inform strategic decision-making by synthesizing the strengths, weaknesses, opportunities, and threats associated with the adoption of AI in aviation.2024 New Trends in Aviation Development (NTAD)["P. \u0160v\u00e1b", "Patrik G\u00e9ci", "Sebasti\u00e1n \u010cikovsk\u00fd"]2024-11-28T00:00:00 09ffad335684c9ba9185b94dac154384312017fbCloud-native applications, built to leverage the scalability and flexibility of cloud infrastructure, have transformed how organizations develop, deploy, and manage software. However, their dynamic and distributed nature presents unique security challenges, such as container vulnerabilities, API exploits, and misconfigurations. Artificial Intelligence (AI) has emerged as a critical enabler in addressing these challenges. This white paper explores the role of AI in securing cloud-native applications, examining its capabilities in threat detection, automated response, compliance enforcement, and anomaly identification. By integrating AI-driven tools and methodologies, organizations can safeguard their cloud-native environments while enhancing operational agility and resilience.International Journal of Artificial Intelligence &amp; Applications00The role of AI in securing cloud-native applications is explored, examining its capabilities in threat detection, automated response, compliance enforcement, and anomaly identification.International Journal of Artificial Intelligence &amp; Applications["Bhanu Prakash Manjappasetty Masagali", "Mandar Nayak"]2024-11-28T00:00:00 90ee2a82e01cf230283c717c9a88af77118cc0f2This article examines the essence and characteristics of artificial intelligence (AI) and its applications in various agriculture segments. Special attention is paid to the challenges of implementing AI in crop production, animal husbandry, resource management, and analytical processes. The role of robotics is examined as a key factor in the digital transformation of the agricultural sector, facilitating the adoption of new production approaches. The article highlights the main advantages of AI in the agricultural sector, such as the automation of routine tasks, reduction of manual labor costs, increased production efficiency, and the creation of new products. The use of intelligent technologies optimizes resources and boosts productivity, contributing to the competitiveness of agricultural enterprises. The article also reviews global experiences in the implementation of AI and robotics in agriculture. Examples of successful use of these technologies by leading companies are provided, along with an analysis of the experience of Ukrainian agricultural enterprises. Positive aspects of AI implementation, such as increased efficiency and crop yields, are studied, while drawbacks and risks associated with adapting new technologies to the specific conditions of Ukrainian agriculture are also highlighted. The conclusions of the article emphasize that the use of AI is a promising direction for the development of the agricultural sector. AI technologies help address key challenges related to food security and sustainable development. Despite the challenges and risks, AI's potential to enhance agricultural production efficiency is significant, and the future of agriculture largely depends on the further development and implementation of these technologies. The widespread introduction of intelligent technologies can not only transform agricultural processes, but also make them more environmentally sustainable and economically profitable in the long term. Key words: artificial intelligence, agricultural sector, innovative technologies, agriculture, crop production, animal husbandry, robotics, machine intelligence.Agrobiologia120The article highlights the main advantages of AI in the agricultural sector, such as the automation of routine tasks, reduction of manual labor costs, increased production efficiency, and the creation of new products.Agrobìologìâ["I. Apunevych"]2024-11-28T00:00:00 8b780d31e33148abc6d8463ab18e76383a7f35b5Shanghai Xinchuanghua Cultural Development Co Ltd v AI Company (pseudonym) is the first in the world to render a judgment on whether a generative artificial intelligence service provider should bear copyright liability for the infringement of other people’s prior works by its generated content. The court ruled on the types of copyright exclusive rights involved in the infringement by generative artificial intelligence service providers. However, the court should not bypass addressing the prerequisite issue of the copyrightability of the generated content. More importantly, it established criteria for determining the duty of care of generative artificial intelligence service providers based on three aspects: the duty to establish a complaint and reporting mechanism, the duty to warn of potential risks and the duty to provide prominent labelling. By focusing on the duty of care, the court shifted the burden of proof for causation to the defendant, thereby avoiding the evidentiary challenges faced by European and American courts. Additionally, the court established specific measures to stop the infringement.Queen Mary Journal of Intellectual Property00The court ruled on the types of copyright exclusive rights involved in the infringement by generative artificial intelligence service providers and established criteria for determining the duty of care of generative artificial intelligence service providers based on three aspects.Queen Mary Journal of Intellectual Property["Fen Jiang", "Hong Wu"]2024-11-28T00:00:00 15e7015d7068f6dfd5032036351adc2b222e75a5 This article presents the development of an artificial intelligence maturity model (AIMM), specifically tailored for public sector organizations to assess their readiness for AI adoption. Using design science methodology, the research synthesizes insights from academic literature and expert consultations to propose a comprehensive AIMM. Through iterative development and expert feedback, the study refines a model that categorizes AI maturity across eight dimensions. The model’s validity is assessed through expert evaluations and questionnaires, confirming its relevance and utility in guiding public organizations toward effective AI adoption. This research contributes to the theoretical and practical understanding of AI implementation in the public sector, addressing unique challenges such as procurement models, legal compliance, and organizational capabilities.TalTech Journal of European Studies250This article presents the development of an artificial intelligence maturity model (AIMM), specifically tailored for public sector organizations to assess their readiness for AI adoption, and refines a model that categorizes AI maturity across eight dimensions.TalTech Journal of European Studies["R. Dreyling", "Juhani Lemmik", "T. Tammet", "Ingrid Pappel"]2024-11-28T00:00:00 e87ceecf06d067a1077b93e63ae8a7128732ee76Artificial intelligence literacy is vital for individuals' adaptation to the future workforce and societal changes by enabling them to understand and effectively use AI technologies and critically evaluate their impact on society. In this study, the validity and reliability of the artificial intelligence literacy scale in Turkish language were tested and the latent profiles of the students were determined. This methodological study was carried out with a total of 729 students between December 2023 and February 2024. Validity and reliability analyses were conducted with SPSS 27 and AMOS 24, and latent profile analysis was handled with R programming language. According to the results of the CFA analysis of the Artificial Intelligence Literacy Scale, the fit indices were found to be significant (X²/sd= 3.832, RMSEA=.062, CFI=.949, AGFI=.933, GFI=.960, NFI=.949, TLI=.928, IFI=.916). Considering the Cronbach Alpha value of the scale consisting of 4 sub-dimensions and 12 items, the internal consistency coefficientwas found to be 0.814. Since the lowest BIC value in the latent profile analysis was found in the VVV model, the VVV model was considered as the appropriate one in the study, and the class analyses were carried out through this model. With the LPA analysis, it was designated that the scale was divided into 3 classes. It was determined that the Artificial intelligence literacy scale is a valid and reliable measurement tool. After latent profile analysis, it was found out that the scale was divided into 3 classes.Sakarya University Journal of Education350It was determined that the Artificial intelligence literacy scale is a valid and reliable measurement tool and was divided into 3 classes after latent profile analysis, and the scale was divided into 3 classes.Sakarya University Journal of Education["Ali K\u0131rksekiz", "Mehmet Yildiz", "Mubin Kiyici", "M. Y\u0131ld\u0131z"]2024-11-28T00:00:00 b80e02936eb190f312e98a560afb6e599fe8ddb7INTRODUCTION The rapid advancement and adoption of large language models (LLMs) in various academic domains necessitate an examination of their role in scholarly works by medical learners.This paper seeks to discern the implications of LLM use by medical learners when preparing works for publication. While LLMs possess great potential to revolutionize the academic writing process, they can detract from the learning process when used by students and residents who are still learning how to research, formulate ideas, and write cohesive arguments. MATERIALS AND METHODS An environmental scan of both traditional evidence-based sources and gray literature was performed to glean best practices of generative AI in medical education. Sources included peer-reviewed journals, open-source websites, and previous publications in this field ranging from 2015 to 2023. RESULTS We propose several strategies to detect AI involvement: direct inquiry to the learner, assessing the coherence level of the content in contrast to the learner's known capabilities, recognizing patterns of shallow insight or depth, utilizing plagiarism and AI-specific detection tools, and monitoring for fabricated citations-a known pitfall of LLMs. CONCLUSIONS Although LLMs offer potential efficiencies in academic writing, unchecked use can jeopardize the development of essential critical thinking and analytical skills in medical learners. Ultimately, mentors and primary investigators are responsible for ensuring learners are advancing and appropriately utilizing new and emerging technology. This study provides a foundational framework for educators in both responsible use of generative AI and best practices.Military Medicine171This study provides a foundational framework for educators in both responsible use of generative AI and best practices in both responsible use of generative AI and best practices in medical education.Military medicine["Brian Patrick Murray", "Darshan S Thota", "Carrie Baker", "Joshua B Stierwalt"]2024-11-28T00:00:00 0a9921561b12143c0311f2289711b81d1a458128The use of AI in the healthcare sector is facing some formidable concerns raised by the practitioners themselves. This study aimed to establish the concerns that surround the adoption of AI among Saudi Arabian healthcare professionals. Materials and methods: This was a cross-sectional study using stratified convenience sampling from September to November 2024 across health facilities. This study included all licensed healthcare professionals practicing for at least one year, whereas interns and administrative staff were excluded from the research. Data collection was conducted through a 33-item validated questionnaire that was provided in paper form and online. The questionnaire measured AI awareness with eight items, past experience with five items, and concerns in four domains represented by 20 items. Four hundred questionnaires were distributed, and the response rate was 78.5% (n = 314). The majority of the participants were females (52.5%), Saudis (89.2%), and employees of MOH (77.1%). The mean age for the participants was 35.6 ± 7.8 years. Quantitative analysis revealed high AI awareness scores with a mean of 3.96 ± 0.167, p < 0.001, and low previous experience scores with a mean of 2.65 ± 0.292. Data management-related worries came out as the top worry, with a mean of 3.78 ± 0.259, while the poor data entry impact topped with a mean of 4.15 ± 0.801; healthcare provider-related worries with a mean of 3.71 ± 0.182; and regulation/ethics-related worries with a mean of 3.67 ± 0.145. Health professionals’ main concerns about AI adoption were related to data reliability and impacts on clinical decision-making, which significantly hindered successful AI integration in healthcare. These are the particular concerns that, if addressed through robust data management protocols and enhanced processes for clinical validation, will afford the best implementation of AI technology in an optimized way to bring better quality and safety to healthcare. Quantitative validation of AI outcomes and the development of standardized integration frameworks are subjects for future research.Nursing Reports332Health professionals’ main concerns about AI adoption were related to data reliability and impacts on clinical decision-making, which significantly hindered successful AI integration in healthcare.Nursing Reports["Abdulaziz R. Alsaedi", "Nada Alneami", "Fahad Almajnoni", "Ohoud Alamri", "Khulud Aljohni", "Maha K. Alrwaily", "Meshal Eid", "Abdulaziz Budayr", "Maram A. Alrehaili", "Marha M. Alghamdi", "Eqab D. Almutairi", "Mohammed H Eid"]2024-11-28T00:00:00 8eefbd9ec0cc5c4f4dfb1ea8a609cb6757fa1ae3Journal of clinical neuroscience380There is a paucity of high-quality systematic reviews on the utility of artificial intelligence in glioma management, with some demonstrating critically low quality.Journal of Clinical Neuroscience["Umar Ahmed Siddiqui", "Roua Nasir", "Mohammad Hamza Bajwa", "Saad Akhtar Khan", "Yusra Saleem Siddiqui", "Zenab Shahzad", "Aabiya Arif", "Haissan Iftikhar", "Kiran Aftab"]2024-11-28T00:00:00 8e8636d489054e2106f5a6513049fb68a20e8717ECONOMICS AND INNOVATION MANAGEMENT00Economics and Innovation Management["E. Obukhova"]2024-11-28T00:00:00 4d4e7509f96e8b8ab38747e69e702f839ef730cdJournal of Technology Transfer300The Journal of Technology Transfer["Baizhen Zhang", "Biyu Peng"]2024-11-28T00:00:00 bd2ba9198364ab337e32cfacbb00eadeed030d69International Journal of Academic Research in Progressive Education and Development00International Journal of Academic Research in Progressive Education and Development["Nur Yasmin Khairani Zakaria", "Thneswary Ponniah"]2024-11-28T00:00:00 3fc4af13c8c21b41b6c3cb5fb6d789cb4f44df4eInternational Conference on Computer Science and Application Engineering210{"pages": "31-35"}["Shuyan Liu", "Kangsheng Wang"]2024-11-28T00:00:00 18f32327853fd36fdf29eedf31093a8072882048Due to the flourishing expansion of online education, students' appetite for individualized learning experiences has significantly intensified. Consequently, we introduce a model that utilizes machine learning to recommend personalized learning paths. The model incorporates several algorithms such as collaborative filtering, content recommendation and reinforcement learning. First, we use collaborative filtering algorithms to analyze students' learning behavior data to identify users with similar interests and academic backgrounds. Then, through the content recommendation algorithm, we dynamically adjust the recommendation results according to the course characteristics and students' learning preferences. Finally, the reinforcement learning algorithm plays a role in optimizing the long-term learning path in the model, and constantly improves the recommendation strategy through real-time feedback. In the experiment, we used multiple real learning platform data sets for validation. The results show that the model has excellent performance in improving students' learning effect and satisfaction, and can effectively recommend learning paths that meet individual needs. Our research provides new ideas and methods for personalized learning of new business education, and promotes the deep integration of education and artificial intelligence.2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM)130The model incorporates several algorithms such as collaborative filtering, content recommendation and reinforcement learning to recommend personalized learning paths that meet individual needs and provides new ideas and methods for personalized learning of new business education.2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM)["Kun Liang", "Jia You"]2024-11-28T00:00:00 f7d59aef004e3a0a16dec1460e955606745c5cf6US-China Education Review. A00US-China Education Review A["LIU Jun-ping", "HU Zhen-hao", "DU Xiao-qin", "ZHU Qiang", "XIAO Nai-tao", "YANG Hua-li", "PENG Tao", "HU Xin-rong"]2024-11-28T00:00:00 431ba2449dc441eedb905865e752e57f522cd4ec The article is based on the methodological assumptions of political science and administration. In response to the research questions, it can be observed that Poland is a country where the government endeavors to comprehensively implement solutions in the field of e-administration. This includes aspects related to the legal system, e-administration services for citizens, recognition of cyber threats, and a comprehensive counteraction strategy (through training systems and normative solutions), acknowledging the necessity of implementing AI in public administration. The article provides a comparative perspective on the development of e-administration in Poland as a part of Central and Eastern Europe. It analyzes Poland as a country that, while not a leader in the digitization of public administration, is not lagging behind in the implementation of new technologies in the public sector. Simultaneously, the paper may serve as a contribution to further in-depth research in this field.TalTech Journal of European Studies180Poland is analyzed as a country that, while not a leader in the digitization of public administration, is not lagging behind in the implementation of new technologies in the public sector.TalTech Journal of European Studies["E. W\u0142odyka"]2024-11-28T00:00:00 cb990252c347447409935082811f22e0901ba12cArtificial Intelligence (AI) merupakan bidang multidisiplin yang bertujuan untuk mengotomatisasi aktivitas yang saat ini memerlukan kecerdasan manusia. Dalam konteks ini, AI dan manusia dapat berkolaborasi untuk membuat keputusan yang kurang dipengaruhi oleh nilai-nilai pribadi. Salah satu keberhasilan terbaru dalam pengembangan AI adalah sistem yang dapat secara otomatis menyesuaikan perangkat keras sesuai dengan kebutuhan pengguna tertentu. Penelitian ini menggunakan metodologi observasional dan studi kualitatif deskriptif, yang fokus pada identifikasi fitur atau karakteristik dari kejadian yang diperiksa selama proses pengumpulan data, dengan pendekatan pencarian literatur metodis pada database jurnal yang relevan.AI juga memiliki potensi yang signifikan dalam berbagai bidang, termasuk pendidikan, kesehatan, ekonomi, dan pertanian. Dalam pendidikan, AI dapat berkontribusi dalam membentuk moral dan karakter siswa, serta meningkatkan ketajaman mental mereka dengan memberikan wawasan baru. Di sektor kesehatan, AI dapat digunakan untuk analisis data dan diagnosis yang lebih cepat dan akurat. Selain itu, dalam bidang pertanian, teknologi AI dapat diterapkan dalam pengembangan "smart garden" untuk meningkatkan efisiensi dan hasil pertanian. Dengan demikian, AI tidak hanya berfungsi sebagai alat, tetapi juga sebagai mitra yang berharga dalam menciptakan solusi inovatif di berbagai sektorJurnal Pendidikan Islam00QALAM: JURNAL PENDIDIKAN ISLAM["M. S. Hidayat"]2024-11-28T00:00:00 261115a906c71d550320c6d300de7cec6849c232Integrating artificial intelligence (AI) with knowledge management (KM) practices presents a promising avenue for advancing sustainable learning in higher education. However, empirical research exploring this synergy remains limited, particularly in developing countries. This study aimed to investigate the impact of AI-enhanced KM practices on sustainable learning outcomes in Indian higher education institutions. A proposed model was tested using a sample of 401 student responses, analysed through partial least square equation modelling (PLS-SEM) using SmartPLS 4. The findings revealed that AI-driven knowledge creation, storage, discovery, and prediction significantly contribute to sustainable learning when implemented ethically. Conversely, AI-based knowledge capture practices and tailored knowledge delivery did not significantly influence sustainable learning environments. The model exhibited substantial explanatory power regarding sustainable learning outcomes. This study contributes to the “knowledge-based view” and “absorptive capacity” theory by exploring the integration of AI and KM in education. Furthermore, it advances the “responsible AI paradigm” by addressing ethical considerations in AI-enhanced educational systems. The results provide a foundation for future research on the interplay between AI, KM, and sustainable learning, offering valuable insights for transforming educational practices and promoting lifelong learning in higher education.Knowledge Management &amp; E-Learning: An International Journal00Investigation of the impact of AI-enhanced KM practices on sustainable learning outcomes in Indian higher education institutions revealed that AI-driven knowledge creation, storage, discovery, and prediction significantly contribute to sustainable learning when implemented ethically.Knowledge Management &amp; E-Learning: An International Journal[]2024-11-28T00:00:00 21459b853e75341e257960bbdf304a43265a52b8Artificial Intelligence (AI) has demonstrated exceptional performance in automating critical healthcare tasks, such as diagnostic imaging analysis and predictive modeling, often surpassing human capabilities. The integration of AI in healthcare promises substantial improvements in patient outcomes, including faster diagnosis and personalized treatment plans. However, AI models frequently lack interpretability, leading to significant challenges concerning their performance and generalizability across diverse patient populations. These opaque AI technologies raise serious patient safety concerns, as non-interpretable models can result in improper treatment decisions due to misinterpretations by healthcare providers. Our systematic review explores various AI applications in healthcare, focusing on the critical assessment of model interpretability and accuracy. We identify and elucidate the most significant limitations of current AI systems, such as the black-box nature of deep learning models and the variability in performance across different clinical settings. By addressing these challenges, our objective is to provide healthcare providers with well-informed strategies to develop innovative and safe AI solutions. This review aims to ensure that future AI implementations in healthcare not only enhance performance but also maintain transparency and patient safety.Frontiers in Robotics and AI1071This systematic review explores various AI applications in healthcare, focusing on the critical assessment of model interpretability and accuracy, and identifies the most significant limitations of current AI systems, such as the black-box nature of deep learning models and the variability in performance across different clinical settings.Frontiers in Robotics and AI["Mohammad Ennab", "Hamid Mcheick"]2024-11-28T00:00:00 7caca59244e1770bfd94d92fe73cce1bde9b05c7In recent years, the Artificial Intelligence (AI) has enabled conventional Combination Devices (CDs) to innovate in healthcare merging with technology sectors. However, the challenges like reliance on predicate devices in US Food and Drug Administration (FDA's 510(k) pathway, especially for perpetually updating AI are stressed. Though the European Union (EU's new Medical Device Regulations address software and AI, fitting adaptive algorithms into conformity assessments remains difficult. The urgent need for frameworks cognizant of AI risks like model degradation and data biases is emphasized. Insights from recalled devices and case studies elucidate challenges in regulatory navigation for manufacturers. Adaptive policy frameworks balancing patient safeguards and rapid innovation are proposed. Recommendations target regulators and policy makers, advocating global standards to enable safe, effective and equitable AI adoption. This article aims to examine AI-enabled combination device regulation, inspecting US and EU strategies as well as obstacles for manufacturers and regulators.Frontiers in Medical Technology181AI-enabled combination device regulation is examined, inspecting US and EU strategies as well as obstacles for manufacturers and regulators and advocating global standards to enable safe, effective and equitable AI adoption.Frontiers in Medical Technology["S. Santra", "Preet Kukreja", "Kinshuk Saxena", "Sanyam Gandhi", "Om V. Singh"]2024-11-28T00:00:00 68f5250be56c3751727cffaa40f03b2958fc36feThe integration of artificial intelligence (AI) into healthcare has sparked considerable debate regarding its impact on the workforce, particularly concerning the roles of healthcare professionals such as doctors. This article explores the potential for AI-driven automation to replace certain medical tasks traditionally performed by physicians, as well as the broader implications for employment in the healthcare sector. While AI has demonstrated significant capabilities in areas like diagnostics, data analysis, and even surgical assistance, its role is largely seen as complementary rather than substitutive. Many experts argue that AI will enhance the efficiency and accuracy of medical practices rather than eliminate the need for human doctors. Moreover, the implementation of AI in healthcare is expected to create new roles focused on managing, interpreting, and improving AI systems, thereby shifting the nature of healthcare jobs rather than reducing their number. However, concerns about the potential for job displacement and the need for reskilling within the medical profession remain. This article concludes that while AI will inevitably alter the landscape of healthcare employment, it is unlikely to replace doctors entirely. Instead, it will transform the profession, requiring a new set of skills and a reimagining of the doctor-patient relationship. American Journal of Data Mining and Knowledge Discovery71It is concluded that while AI will inevitably alter the landscape of healthcare employment, it is unlikely to replace doctors entirely and will transform the profession, requiring a new set of skills and a reimagining of the doctor-patient relationship.American Journal of Data Mining and Knowledge Discovery["Manisha Sharma"]2024-11-28T00:00:00 78fbb35214869bae1baa1db9fb310ad4db447ccdPurpose This paper does not concern with the “why” of ethics. Such questions are typically of interest to philosophers and are outside the scope of this work. In the next section, the authors offer a look into “what” of ethics, i.e. various types and subtypes of ethics. Subsequently, the authors explore “how” of ethics, by summarising various computational approaches to ethical reasoning offered by researchers in the field. Design/methodology/approach The approaches are classified based on the application domain, ethical theory, agent type and design paradigm adopted. Moreover, promising research directions towards ethical reasoning are also presented. Findings Since the field is essentially interdisciplinary in nature, collaborative research from such areas as neuroscience, psychology, artificial intelligence, law and social sciences is necessary. It is hoped that this paper offers much needed insight into computational approaches for ethical reasoning paving way for researchers to further engage with the question. Originality/value In this paper, the authors discussed vaious computational approaches proposed by researchers to implement ethics. Although none of the approaches adequately answer the question, it is necessary to engage with the research effort to make a substantial contribution to the emerging research area. Though some effort has been made in the design of logic-based systems, they are largely in stages of infancy and merit considerable research. Journal of Information, Communication and Ethics in Society530The authors explore “how” of ethics, by summarising various computational approaches to ethical reasoning offered by researchers in the field, by summarising various types and subtypes of ethics.Journal of Information, Communication and Ethics in Society["Sahil Sholla", "I. Reshi"]2024-11-28T00:00:00 3101c89bb36723789b4fe7791f0c2b585b38aac2BACKGROUND AND AIMS Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk. METHODS An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed. RESULTS The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), -.1 (6.0), 4.7 (8.7), and -1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24-2.78), 1.89 (1.46-2.43), 1.90 (1.55-2.33), and 1.76 (1.67-1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47-3.37), 1.94 (1.39-2.70), 1.58 (1.06-2.35), and 1.79 (1.62-1.97) in these cohorts compared with the normal group. CONCLUSIONS The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.European Heart Journal01The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.European heart journal["Seunghoon Cho", "S. Eom", "Daehoon Kim", "Tae\u2010Hoon Kim", "J. Uhm", "H. Pak", "Moon\u2010Hyoung Lee", "Pil-Sung Yang", "Eunjung Lee", "Z. Attia", "P. Friedman", "S. C. You", "H. Yu", "B. Joung"]2024-11-29T00:00:00 d9fd22425aa3ab659c5425cc513e518c6c1f8f5aBackground and Objective Medical image segmentation is a vital aspect of medical image processing, allowing healthcare professionals to conduct precise and comprehensive lesion analyses. Traditional segmentation methods are often labor intensive and influenced by the subjectivity of individual physicians. The advent of artificial intelligence (AI) has transformed this field by reducing the workload of physicians, and improving the accuracy and efficiency of disease diagnosis. However, conventional AI techniques are not without challenges. Issues such as inexplicability, uncontrollable decision-making processes, and unpredictability can lead to confusion and uncertainty in clinical decision-making. This review explores the evolution of AI in medical image segmentation, focusing on the development and impact of explainable AI (XAI) and trustworthy AI (TAI). Methods This review synthesizes existing literature on traditional segmentation methods, AI-based approaches, and the transition from conventional AI to XAI and TAI. The review highlights the key principles and advancements in XAI that aim to address the shortcomings of conventional AI by enhancing transparency and interpretability. It further examines how TAI builds on XAI to improve the reliability, safety, and accountability of AI systems in medical image segmentation. Key Content and Findings XAI has emerged as a solution to the limitations of conventional AI by providing greater transparency and interpretability, allowing healthcare professionals to better understand and trust AI-driven decisions. However, XAI itself faces challenges, including those related to safety, robustness, and value alignment. TAI has been developed to overcome these challenges, offering a more reliable framework for AI applications in medical image segmentation. By integrating the principles of XAI with enhanced safety and dependability, TAI addresses the critical need for TAI systems in clinical settings. Conclusions TAI presents a promising future for medical image segmentation, combining the benefits of AI with improved reliability and safety. Thus, TAI is a more viable and dependable option for healthcare applications, and could ultimately lead to better clinical outcomes for patients, and advance the field of medical image processing.Quantitative Imaging in Medicine and Surgery01This review explores the evolution of AI in medical image segmentation, focusing on the development and impact of explainable AI (XAI) and trustworthy AI (TAI), and highlights the key principles and advancements in XAI that aim to address the shortcomings of conventional AI by enhancing transparency and interpretability.Quantitative Imaging in Medicine and Surgery["Zixuan Teng", "Lan Li", "Ziqing Xin", "Dehui Xiang", "Jiang Huang", "Hailing Zhou", "Fei Shi", "Weifang Zhu", "Jing Cai", "Tao Peng", "Xinjian Chen"]2024-11-29T00:00:00 c18e8b94de7d009fc41e88c01f2520d06d07b40dThis study provides a comprehensive analysis of artificial intelligence (AI) contribution to translation industry (ACTI) research, synthesizing it over forty-one years from 1980-2024. 13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens. We provided two types of analysis, viz., scientometric and thematic, focusing on cluster, subject categories, keywords, burstness, centrality and research centers as for the former. For the latter, we thematically review 18 articles, selected purposefully from the articles involved, centering on purpose, approach, findings, and contribution to ACTI future directions. The findings reveal that in the past AI contribution to translation industry was not rigorous, resulting in rule-based machine translation and statistical machine translation whose output was not satisfactory. However, the more AI develops, the more machine translation develops, incorporating Neural Networking Algorithms and (Deep) Language Learning Models like ChatGPT whose translation output has developed considerably. However, much rigorous research is still needed to overcome several problems encountering translation industry, specifically concerning low-source languages, multi-dialectical and free word order languages, and cultural and religious registers.arXiv.org590The findings reveal that in the past AI contribution to translation industry was not rigorous, resulting in rule-based machine translation and statistical machine translation whose output was not satisfactory, but the more AI develops, the more machine translation develops, incorporating Neural Networking Algorithms and (Deep) Language Learning Models like ChatGPT whose translation output has developed considerably.ArXiv["Mohammed Q. Shormani"]2024-11-29T00:00:00 efdba71e959fe793e3f0e2740dc70f472e7d4e9bJournal of Computational Social Science530Although both the tone positivity and affective–cognitive ratio were dependent on the specific context, the language in AI posts was more analytically than emotionally oriented in general, which contributed to the practical contribution of public opinion on AI.J. Comput. Soc. Sci.["N. Savela", "Max Pellert", "Rita Latikka", "Jenna Bergdahl", "David Garcia", "Atte Oksanen"]2024-11-29T00:00:00 e6af7e020510188d03324fa25e0af0e241e6d33cThe integration of artificial intelligence (AI) into plastic surgery is transforming the field by enhancing precision in preoperative planning, diagnostic accuracy, intraoperative assistance, and postoperative care. AI encompasses machine learning, natural language processing, computer vision, and artificial neural networks, each offering unique advancements to surgical practice. This narrative review explores the ethical challenges of AI in plastic surgery, addressing concerns such as data protection, algorithmic bias, transparency, accountability, and informed consent. A comprehensive search adhering to PRISMA guidelines identified 63 studies, with 15 selected for in-depth analysis. Findings indicate significant ethical issues: data privacy needs stringent cybersecurity, biases in AI models must be mitigated, and transparency in AI decision making is essential. The review emphasizes the necessity for updated Health Insurance Portability and Accountability Act (HIPAA) regulations, robust validation mechanisms, and the development of explainable AI models. It also highlights the need for an independent regulatory body to oversee AI integration, ensuring ethical standards and protecting patient welfare. Although AI presents promising benefits, its successful application in plastic surgery hinges on addressing these ethical challenges comprehensively.Plastic and Aesthetic Research270A narrative review explores the ethical challenges of AI in plastic surgery, addressing concerns such as data protection, algorithmic bias, transparency, accountability, and informed consent, and the need for an independent regulatory body to oversee AI integration.Plastic and Aesthetic Research["Abhinav Singh", "Ishith Seth", "Bryan Lim", "R. Cuomo", "W. Rozen"]2024-11-29T00:00:00 a0825c82a5cc869b5a17620d2223b2aa7002e894The role of computational tools in drug discovery and development is becoming increasingly important due to the rapid development of computing power and advancements in computational chemistry and biology, improving research efficiency and reducing the costs and potential risks of preclinical and clinical trials. Machine learning, especially deep learning, a subfield of artificial intelligence (AI), has demonstrated significant advantages in drug discovery and development, including high-throughput and virtual screening, ab initio design of drug molecules, and solving difficult organic syntheses. This review summarizes AI technologies used in drug discovery and development, including their roles in drug screening, design, and solving the challenges of clinical trials. Finally, it discusses the challenges of drug discovery and development based on AI technologies, as well as potential future directions.Frontiers in Pharmacology1060This review summarizes AI technologies used in drug discovery and development, including their roles in drug screening, design, and solving the challenges of clinical trials, and discusses the challenges of drug discovery and development based on AI technologies, as well as potential future directions.Frontiers in Pharmacology["Yuyuan Wu", "Lijing Ma", "Xinyi Li", "Jingpeng Yang", "Xinyu Rao", "Yiru Hu", "Jingyi Xi", "Lin Tao", "Jianjun Wang", "Lailing Du", "Gongxing Chen", "Shuiping Liu"]2024-11-29T00:00:00 7e527549756054222c5227828c9e3e417d2d5ca9In the new scientific and technological revolution round, artificial intelligence (AI) technology has become a key leading force for industrial change. Research shows that AI not only promoted technical transformation and industry upgrades but also played a significant role in the rapid development of emerging industries. Based on the installed number of industrial robots and the industrial data by the National Bureau of Statistics, this study establishes a theoretical framework with the econometric model and compares the impact of AI on different categories of industries through empirical analysis. Our results show that AI not only promotes economic growth but also plays a key role in promoting the tertiary industry. Hence, optimization of industrial structure and economic upgrade can be induced.Journal of Electronic Research and Application00A theoretical framework with theeconometric model is established and the impact of AI on different categories of industries through empirical analysis shows that AI not only promotes economic growth but also plays a key role in promoting the tertiary industry.Journal of Electronic Research and Application["Yikun Huang", "ZiXuan Shi"]2024-11-29T00:00:00 822dc744cdd1650f283af254d5c8a375db99a5c4npj Digit. Medicine470OptAB performs an iterative optimal antibiotic selection for real-world Sepsis patients focussing on minimizing the Sepsis-related organ failure score (SOFA-Score) as treatment success while accounting for nephrotoxicity and hepatotoxicity as serious antibiotic side-effects.NPJ Digital Medicine["Philipp Wendland", "Christof Schenkel-H\u00e4ger", "Ingobert Wenningmann", "Maik Kschischo"]2024-11-29T00:00:00 7e27eebed647999e5c6279ee6d2afb3b2c6b0409The study focuses on the urgent problem of ensuring cybersecurity of modern enterprises in the context of the widespread implementation of artificial intelligence (AI) technologies. Given the growing number and complexity of cyberthreats, the authors analyze in detail how AI can be an effective tool for detecting and countering cyber threats, as well as the challenges associated with its use. The article discusses adversarial attacks, data poisoning attacks, and the use of deepfake technologies as tools for manipulation in cyberspace. The authors propose a modern approach to assessing cyber risks based on a modification of the GRS method, which allows classifying information assets of enterprises by level of vulnerability and developing effective protection strategies based on identification of cybersecurity priorities. The practical application of the proposed approach is demonstrated in a case study using the Google Drive platform as a example. The research uses generative artificial intelligence model Gemini, which allows identifying weaknesses in security systems, analyzing potencial risks and providing recommendations for eliminating vulnerabilities. Along with the benefits of AI implementation, such as automation of monitoring processes, analyzing big amounts of data in real time and predicting potencial threats, the study identified a number of challenges. In particular, the complexity of configuring and maintaining systems, the need for specialized knowledge to support them, the problem of algorithm transparency, and the risks of manipulation and attacks by intruders. The authors emphasize the importance of staff training for work with AI systems, including both technical knowledge and understanding of cyberspace risks. The need to develop clear policies for the use of these technologies is particularly emphasized. The study findings confirm that artificial intelligence can significantly improve the cybersecurity of multidisciplinary enterprises, but it requires a comprehensive approach. For effective use of the technology, the authors recommend improving attack detection algorithms, integrating ethical principles into the operation of systems, and developing strategies for the long-term development of enterprise cyber resilience.Київський економічний науковий журнал50The authors recommend improving attack detection algorithms, integrating ethical principles into the operation of systems, and developing strategies for the long-term development of enterprise cyber resilience.Київський економічний науковий журнал["\u041a\u043e\u0441\u0442\u044f\u043d\u0442\u0438\u043d \u0417\u0430\u0432\u0440\u0430\u0436\u043d\u0438\u0439", "\u0410\u043d\u0436\u0435\u043b\u0456\u043a\u0430 \u041a\u0443\u043b\u0438\u043a"]2024-11-29T00:00:00 432bab46185f31688e00d08e6e480c2a50abda13This article explores the evolving role of programming languages in the context of artificial intelligence. It highlights the need for programming languages to ensure human understanding while eliminating unnecessary implementation details and suggests that future programs should be designed to recognize and actively support user interests. The vision includes a three-level process: using natural language for requirements, translating it into a precise system definition language, and finally optimizing the code for performance. The concept of an"Ultimate Programming Language"is introduced, emphasizing its role in maintaining human control over machines. Trust, reliability, and benevolence are identified as key elements that will enhance cooperation between humans and AI systems.arXiv.org00The concept of an "Ultimate Programming Language" is introduced, emphasizing its role in maintaining human control over machines and trust, reliability, and benevolence are identified as key elements that will enhance cooperation between humans and AI systems.ArXiv["Bartosz Sawicki", "Michal 'Smialek", "Bartlomiej Skowron"]2024-11-29T00:00:00 d5b911e1b45176e1d78a5b4417eca12efec2ad0dThe key issue is to develop guidelines for Artificial Intelligence (AI) data protection that respect individual rights and further the general welfare. Where possible, AI systems have to minimize data of a personal nature to the absolute minimum, anonymize such data, and encrypt it in maintaining data security in accordance with regulations such as the General Data Protection Regulation (GDPR). AI systems need to follow the levels defined by the European Commission to achieve proper transparency and explain ability for building confidence and enabling proper ethical control. The present review article aims to bring ethical standard for AI utilization upfront. Additionally, the present article focus on uncovering ethical guidelines for maintaining standards for AI use. A thorough search approach was used to find relevant reviews of the literature for the assessment. Phases of the strategy included scanning several databases, evaluating publications, and choosing the most relevant research for review. This review examined electronic databases including PubMed, Science Direct, EMBASE, and Google Scholar. These databases were chosen to provide comprehensive coverage of relevant content. Making use of studies and reviews published between 2000 and 2024.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)360The present article focus on uncovering ethical guidelines for maintaining standards for AI use, and examined electronic databases including PubMed, Science Direct, EMBASE, and Google Scholar to provide comprehensive coverage of relevant content.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Vaishnavi Shete", "Anil Pethe"]2024-11-29T00:00:00 dc062dc897434e5f264f2e777f2f7ed396e70ce6Artificial Intelligence (AI) is not only revolutionizing digital marketing through personalized customer experiences and optimized advertising strategies, but it is also contributing to sustainability initiatives. As AI reshapes digital marketing, its impact on sustainability is becoming increasingly significant. This dynamic highlights the necessity of exploring how AI can be utilized to foster more sustainable marketing practices. This study seeks to answer the pivotal question: “How does AI impact the sustainability of digital marketing?” A systematic literature review was conducted in this study, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify various relevant articles in the areas of sustainability and marketing. Furthermore, this study examines the crucial role of AI in enhancing sustainable business practices, highlighting a significant increase in adoption among enterprises. The findings demonstrate that the effective integration of AI into digital marketing enhances environmental sustainability, supports the attainment of economic sustainability objectives, and contributes positively to social sustainability outcomes. This study contributes to the field by providing a comprehensive analysis of the intersection between AI and sustainable marketing practices and offers valuable insights for marketers, businesses, and policymakers.Sustainability440The findings demonstrate that the effective integration of AI into digital marketing enhances environmental sustainability, supports the attainment of economic sustainability objectives, and contributes positively to social sustainability outcomes.Sustainability["Bora G\u00fcnd\u00fczyeli"]2024-11-29T00:00:00 6d266f01c7e5e03f1ee6ed2a07d12ac5e8168029This research aims to explore the role of Artificial Intelligence (AI) and Design Thinking in supporting digital transformation in various sectors, with a focus on innovation, sustainability, and operational efficiency. The study also seeks to analyze the relevance of both approaches in business and academic contexts, particularly in creating creative and human-centered solutions in the era of technological disruption. To achieve these objectives, this study used a systematic literature review method, by analyzing ten accredited and indexed articles in reputable international journals. The results showed that AI acts as a disruptive technology that not only improves efficiency but also accelerates digital transformation. Studies such as the one by Di Vaio et al. (2020), which received 1015 citations, highlighted how AI supports the development of sustainable business models. Additionally, research by Verganti et al. (2020) identified that Design Thinking remains relevant as an adaptive innovation approach, especially in the face of challenges posed by AI. The integration between AI and Design Thinking is also reflected in the study of Pham et al. (2022), which shows how creative approaches can maximize the potential of advanced technologies such as big data. Overall, this study confirms that the combination of AI and Design Thinking offers a comprehensive solution to deal with the complexities of digital transformation across various sectors.Multifinance00Overall, this study confirms that the combination of AI and Design Thinking offers a comprehensive solution to deal with the complexities of digital transformation across various sectors.Multifinance["Siti Havidotinnisa", "Firli Afini", "Yadi Saryadi", "Imas Mufti"]2024-11-29T00:00:00 84ec6a27a71b7fcc881015521e66e0d2e65cecfcThis paper addresses the question of how to introduce basic artificial intelligence (AI) literacy skills to learners in higher education. It proposes that a feasible approach is to integrate AI literacy components into existing media and information literacy (MIL) programmes. The paper discusses elements of intersection between the two literacies, such as search techniques, evaluation, and responsible use of information. The author posits that the MIL curriculum needs to be updated by enhancing the intersecting elements and adding new concepts such as AI algorithm literacy, data literacy, AI ethics, and limitations of AI technologies. The author argues that libraries are best poised to take on the role of delivering basic AI literacy. To this end, MIL frameworks need to be reviewed, and librarians will be required to obtain additional skills through AI courses, workshops, and participation in communities of practice. Pioneering libraries such as the FIU Libraries (comprising the Green Library and Hubert Library) in Florida, US, and Massachusetts Library Systems are demonstrating that libraries have the capacity to deliver basic AI literacy to higher education learners. The author has analysed existing attempts at mapping AI literacy to the ACRL Framework for Information Literacy for Higher Education and built on these initiatives by mapping suggested new AI literacy-related knowledge practices and dispositions to the relevant frames of the framework. The paper concludes by making a clarion call to librarians to rise to the occasion and revamp existing MIL programs to include basic AI literacy.Journal of Information Literacy00The author argues that libraries are best poised to take on the role of delivering basic AI literacy and makes a clarion call to librarians to rise to the occasion and revamp existing MIL programs to include basic AI literacy.Journal of Information Literacy["Miriam Wanjiku Ndungu"]2024-11-29T00:00:00 c5641df6b818ef1e1d0ec091ce6b7c6187cf63d1The convergence of Artificial Intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and Machine Learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)200A wide array of applications of AI in AV are presented, elaborating on the way complex sensor fusion, computer vision, and Machine Learning algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Nikita Zade", "M. Gawande", "Prateek Verma", "Swapnil Gundewar"]2024-11-29T00:00:00 65f9833706a57e57d08dfaa61a5e466f8ff4b383The advent of generative artificial intelligence, exemplified by ChatGPT, has infused new vitality into the ideological and political education of college students through its formidable generative capabilities. However, it has also imparted impacts on their values across multiple dimensions. In the backdrop of the widespread adoption of generative AI, college students, being digital natives, have emerged as key players and disseminators in online public opinion. Characterized by their active minds and susceptibility to diverse ideological trends, they may face disruptions in their value cognition or, in extreme cases, deviations in their behavioral norms when confronted with sudden public events that evolve amidst shifting online public opinion. Hence, amidst the relentless advancement of AI, it is crucial to assist college students in mitigating the adverse effects of online public opinion stemming from such events on their values.Advances in Politics and Economics00It is crucial to assist college students in mitigating the adverse effects of online public opinion stemming from such events on their values, amidst the relentless advancement of AI.Advances in Politics and Economics["Hang Yu"]2024-11-29T00:00:00 108792e9d1bbda8d4531ee3361425a087435c864This paper explores the role of artificial intelligence (AI) and big data (BD) technologies in social impact enterprises in China, particularly focusing on their social activities. It demonstrates that these technologies are instrumental in building pragmatic, moral, and cognitive legitimacy by enhancing transparency and efficiency in stakeholder interactions across both commercial and social networks. Additionally, the study highlights the vital role of social impact enterprises in maintaining infrastructure, public services, and social welfare in financially challenged regions amidst local government debt crises. The necessity for enterprises to align with historical and cultural norms, particularly Confucian values and kinship expectations, is also emphasized. This research bridges a critical gap in the literature, offering insights into the integration of technological innovation with social entrepreneurship, and providing a foundation for future studies and policy development.Journal of Global Information Management420The role of artificial intelligence (AI) and big data (BD) technologies in social impact enterprises in China is explored, demonstrating that these technologies are instrumental in building pragmatic, moral, and cognitive legitimacy by enhancing transparency and efficiency in stakeholder interactions across both commercial and social networks.Journal of Global Information Management["Jie Chen", "Zhongmin Wang"]2024-11-29T00:00:00 7ad834297a73e14a772085a7fe856af779fe89e2The rapid expansion of artificial intelligence (AI) in consumer markets presents challenges, particularly in how cognitive biases influence financial decision-making. These biases can lead to irrational spending, raising ethical concerns about AI’s role in such applications. This research explores how AI can enhance decision-making effectiveness and support consumers in making more rational financial choices. The focus is on developing an intelligent financial management system that applies modern AI algorithms to analyze financial behavior, detect anomalies, and offer personalized recommendations. The article considers a system for generating personalized financial recommendations based on large language models, which uses transaction history, predicted costs, and anomaly information to generate individual advice. Techniques include using Isolation Forest for identifying atypical financial actions and a combination of ARIMA and LSTM models for budget forecasting. The research also considers integrating these models with large language models (LLMs) to generate personalized recommendations. The methodological part of the work includes an analysis of existing models and their areas of application, defining data types and structures for processing, developing a system that integrates the available models, and testing it. The process of generating recommendations is described, which includes the stages of processing input data, forming context, generating recommendations and evaluating them taking into account user characteristics, such as risk level, financial goals and preferences. The generated recommendations are aimed at optimizing the user's financial behavior and can be adapted to different income levels. Special attention is paid to the ethical aspects of the system, which include ensuring confidentiality, fairness and transparency, as well as the importance of supporting user autonomy in making financial decisions. The system promotes responsible financial behavior by helping to avoid impulsive spending and increasing financial awareness without manipulation or imposing specific decisions.Applied Aspects of Information Technology00This research explores how AI can enhance decision-making effectiveness and support consumers in making more rational financial choices by developing an intelligent financial management system that applies modern AI algorithms to analyze financial behavior, detect anomalies, and offer personalized recommendations.Applied Aspects of Information Technology["Kostiantyn A. Shuryhin", "Svitlana L. Zinovatna"]2024-11-29T00:00:00 f73bcb99d9b6f5d1b349c162e099de64a6aac739Introduction Artificial intelligence (AI) is being developed for mental healthcare, but patients' perspectives on its use are unknown. This study examined differences in attitudes towards AI being used in mental healthcare by history of mental illness, current mental health status, demographic characteristics, and social determinants of health. Methods We conducted a cross-sectional survey of an online sample of 500 adults asking about general perspectives, comfort with AI, specific concerns, explainability and transparency, responsibility and trust, and the importance of relevant bioethical constructs. Results Multiple vulnerable subgroups perceive potential harms related to AI being used in mental healthcare, place importance on upholding bioethical constructs, and would blame or reduce trust in multiple parties, including mental healthcare professionals, if harm or conflicting assessments resulted from AI. Discussion Future research examining strategies for ethical AI implementation and supporting clinician AI literacy is critical for optimal patient and clinician interactions with AI in mental healthcare.Frontiers in Digital Health350Differences in attitudes towards AI being used in mental healthcare by history of mental illness, current mental health status, demographic characteristics, and social determinants of health are examined.Frontiers in Digital Health["Meghan Reading Turchioe", "Pooja M. Desai", "Sarah Harkins", "Jessica Kim", "Shiveen Kumar", "Yiye Zhang", "Rochelle Joly", "Jyotishman Pathak", "Alison Hermann", "Natalie Benda"]2024-11-29T00:00:00 1aec247939b8632c61595ff7d58b13e38572c1a1Background-With the rapid technological development in the world with Artificial intelligence in Early childhood education (AIED) and development is a new focus in digital literacy education research. Despite its expanding importance, AI literacy is mostly unexplored in early childhood education (ECE), as AI courses for young children have only recently emerged. It is vital to establish AI competencies in the next generation and educate them on properly engaging with numerous toolkits and curricula to enable young children to learn about AI by creating, programming, training, interacting, and using AI. Methods-Previous studies on AI in education have primarily focused on curriculum design for kindergarten, secondary, and university levels and toolkits like social robots, addressing challenges and opportunities. However, research on AI in early childhood education (ECE) remains limited. This study evaluates various research designs, pedagogical approaches, and interventions, exploring how different AI tools help young children to know how to foster AI literacy in terms of concepts, practices, and perspectives. Results-The selected articles published from the year 2019 to 2024 which studied AI in Education and Development, Current research, technology, and applications in AI education have shown that activity-based learning is more effective than other modes of learning and has potential educational benefits. This study also looked at how well children comprehended AI ideas according to their age and gender, in addition to their direct involvement with social robots and AI courses. Conclusion-It highlights the potential and importance of AI literacy in ECE, that young children can quickly grasp AI concepts through interactive activities with a social robot, suggesting that hands-on exploration effectively enhances AI literacy. Future research should focus on expanding the curriculum to include more comprehensive materials and adapting it for diverse educational contexts.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)190It is highlighted the potential and importance of AI literacy in ECE, that young children can quickly grasp AI concepts through interactive activities with a social robot, suggesting that hands-on exploration effectively enhances AI literacy.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Anjali Rai", "Sharath Hullumani V", "Moh\u2019d Irshad Qureshi", "Raghuveer Raghumahti", "Gurjeet Kaur"]2024-11-29T00:00:00 199ce5d443523adf4627b1f1f4a69171b61de4edGiven the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized researches on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how AI and Digital innovations could be used to reduce the environmental footprint, the actual influence of AI in reducing CO2 emissions (a proxy measuring carbon footprint) has yet to be investigated. This paper studies the role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021. Secondly, this paper examines how Stock Market Growth, ICT use, Gross Domestic Product (GDP) and Population affect CO2 emissions using the STIRPAT model. After examining stationarity among the variables using variety of unit root tests, this study concluded that there are no unit root problem across all the variables, with a mixed order of integration. The ARDL bounds test for cointegration revealed that variables in this study have a long-run relationship. Moreover, the estimates revealed from ARDL model in the short- and long-run indicated that economic growth, stock market capitalization and population significantly contributed to the carbon emissions in both the short-run and long-run. Conversely, AI and ICT use significantly reduced carbon emissions over both periods. Furthermore, findings were confirmed to be robust using FMOLS, DOLS, and CCR estimations. Furthermore, diagnostic tests indicated the absence of serial correlation, heteroscedasticity and specification errors and, thus, the model was robust.Journal of Environmental Science and Economics1430The role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021 and 2021 is studied.ArXiv["Azizul Hakim Rafi", "Abdullah Al Abrar Chowdhury", "Adita Sultana", "Abdulla All Noman"]2024-11-29T00:00:00 e625b7371dfbf3f88a2cb22434a2576276086395This study explores the potential role of Artificial Intelligence (AI) in reducing corrupt behavior, focusing on the perspective of Islamic Education. Corruption is a global issue that hinders development and undermines public trust in government institutions. In Indonesia, despite anti-corruption efforts, the Corruption Perception Index remains low, indicating the need for innovative new approaches. AI, with its complex data analysis capabilities, can be an effective tool in detecting and preventing corruption, including identifying suspicious transaction patterns and conflicts of interest. Additionally, Islamic education, which emphasizes values such as honesty and justice, has great potential in shaping anti-corruption character. The integration of AI with Islamic education could create a more effective approach to reducing corrupt behavior, especially among the younger generation. However, the implementation of AI in this context also faces challenges, including privacy issues, algorithmic bias, and the digital divide. This study emphasizes the importance of a holistic approach involving collaboration between the government, academics, and civil society to maximize AI's potential in combating corruption. AI implementation must be aligned with Islamic principles, ensuring that this technology is used ethically and in accordance with moral values. The study concludes that while AI is not a standalone solution to corruption, it can be an important part of a broader anti-corruption strategy when applied wisely and ethically.SYAIKHONA: Jurnal Magister Pendidikan Agama Islam460The study concludes that while AI is not a standalone solution to corruption, it can be an important part of a broader anti-corruption strategy when applied wisely and ethically.SYAIKHONA: Jurnal Magister Pendidikan Agama Islam["Ainurrafiq Dawam", "Muhammad Adwim", "Rifqy El-Hisan"]2024-11-29T00:00:00 78d56ccda7620ee700aa0f063abb4cfdad97810aIn recent years, artificial intelligence technology has developed rapidly around the world is widely used in various fields, and plays an important role. The integration of industrial Internet security with new technologies such as big models and generative artificial intelligence has become a hot research issue. In this regard, this paper briefly analyzes the industrial Internet security technology and application from the perspective of generative artificial intelligence, hoping to provide some valuable reference and reference for readers.Journal of Electronic Research and Application100This paper briefly analyzes the industrial Internet security technology and application from the perspective of generative artificial intelligence, hoping to provide some valuable reference and reference for readers.Journal of Electronic Research and Application["Dinggao Li", "Shengda Liao", "Zhuo Zheng"]2024-11-29T00:00:00 9de18af8e14bc7938d21dd4e575d885025a0cf85The study’s overarching goal is to gain a feel for how different types of educators see the potential for AI to improve classroom learning. The fundamental function of artificial intelligence (AI) in attaining a fruitful learning environment is the focus of this investigation. A quantitative research strategy known as an exploratory research design was utilized in the investigation. Students' information is collected from Bangalore city’s Autonomous Institutions. The study’s total sample size was 76 educators, recruited using a convenience sample method. Both SPSS and AMOS were used to analyze the data. The results of the study indicate that teachers have a favorable impression of the ability of AI features to improve academic performance in specific subjects. Educators have found that students' academic performance is greatly improved when they use AI’s collaborative features in conjunction with methods of instruction that go beyond the typical classroom. By including educators' viewpoints in analyzing the effects of AI on subject area education, this study exemplifies methodological innovation. Colleges in Bangalore that are considered independent and have the authority to develop their own curricula with the usage of AI are the subject of this study. Educators' views on AI’s functions and roles are the focus of this study, which hopes to shed light on the topic for educational organizations and policymakers in the field. The stakeholders can classify the functions that aren't contributing anything and decide whether to keep them or eliminate them.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)250Teachers' views on AI’s functions and roles are the focus of this study, which hopes to shed light on the topic for educational organizations and policymakers in the field.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Anoushka Gupta", "M. R", "Debolina Gupta"]2024-11-29T00:00:00 18dd5fef387482ca468ad4ad1c35ac1133c9a2c4The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)190This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Parth Dhananjay Akre", "Utkarsha Pacharaney", "W. Siraskar"]2024-11-29T00:00:00 e0473d18f370d236d1d032bc7e181d096bc93f46Artificial intelligence (AI) is transforming medical practices with rapidly developing technologies and the innovative solutions it provides. In order for this transformation to be successfully integrated into healthcare services, healthcare professionals must have positive attitudes towards this technology. The present study was conducted with the aim of comparing the attitudes of nurses working in different provinces towards artificial intelligence. The study was planned in a descriptive cross-sectional design. The study population consisted of 1453 nurses working in 3 state hospitals (inpatient hospitals providing secondary health care services) located in the city centers of Muş, Bingöl and Adıyaman provinces in eastern Turkey. While the sample size was 698 nurses in total, the study was completed with 737 nurses. The data were collected through the Introductory Information Form and the General Attitudes toward Artificial Intelligence Scale (GAAIS). ANOVA test and multiple regression were used to analyse the data. It was found that the nurses had highly positive attitudes towards artificial intelligence. When the nurses’ scores from the Positive GAAIS sub-dimension were compared, it was determined that there was a significant difference (p < 0.05) between the provinces. A statistically significant difference (p < 0.01) was found between the provinces in the Negative GAAIS sub-dimension, as well. Demographic characteristics were found to be effective on both Positive GAAIS and Negative GAAIS. Although there were differences between the provinces, the nurses generally had positive attitudes towards artificial intelligence technologies. The majority of the participants continue to use artificial intelligence technologies although they state that artificial intelligence will replace humans in the future. Longitudinal studies on the factors affecting attitudes towards artificial intelligence are recommended.WORK: A Journal of Prevention, Assessment &amp; Rehabilitation240It was found that the nurses had highly positive attitudes towards artificial intelligence, and demographic characteristics were found to be effective on both Positive GAAIS and Negative GAAIS.WORK: A Journal of Prevention, Assessment &amp; Rehabilitation["Mehmet Kaplan", "Mehmet U\u00e7ar"]2024-11-29T00:00:00 85e2fb1cb735996eb208da82a5f4682b1e16656bObjective Structured Clinical Examinations (OSCEs) are cornerstone for evaluation of competencies pertaining to clinical medicine. The recent advent of artificial intelligence has given rise to newer opportunities which evidently have improved the reliability, validity, objectivity of OSCE. This draft deals into the concept of integration of artificial intelligence into OSCE with proper focus on technology technology innovations there in such as provision of real-time feedback and incorporation of virtual simulations as well. As per the PRISMA guidelines, we conducted a literature search and analysed studies available in literature from 2018 till 2024. We have concluded that artificial intelligence as a technological innovation in the world of assessment of clinical competencies. In medical education has a very strong potential to reduce examiner bias, provision of detailed feedback, and simulating realistic clinical scenarios. Still, certain challenges and limitation such as lack of emotional intelligence and the human touch to the entire assessment. Scenario seems to be lagging and is therefore much needed to retain the learning curve of the modern day learners.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)70It is concluded that artificial intelligence as a technological innovation in the world of assessment of clinical competencies has a very strong potential to reduce examiner bias, provision of detailed feedback, and simulating realistic clinical scenarios in medical education.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["G. Mishra", "Anurag Luharia", "Waqar Naqvi", "Anshul Sood"]2024-11-29T00:00:00 633339b2bf856e3fd729d079ffac000202412e2fArtificial intelligence has developed at a very fast pace in various areas of knowledge, which is why we are already seeing worrying results regarding regulations governing the development of Artificial Intelligence applications. In this work we review some strategies that are being analyzed in various research groups in order to initiate a regulatory framework that gives us a panorama of greater security in future developments of Artificial Intelligence and its applications.International Journal of Combinatorial Optimization Problems and Informatics180This work reviews some strategies that are being analyzed in various research groups in order to initiate a regulatory framework that gives a panorama of greater security in future developments of Artificial Intelligence and its applications.International Journal of Combinatorial Optimization Problems and Informatics["Mar\u00eda del Carmen Santiago D\u00edaz", "Gustavo Trinidad Rub\u00edn Linares", "J. H. Sossa Azuela"]2024-11-29T00:00:00 cb9821c0d45f183d0e07b6a01dbd19acd077eaf9The COVID-19 pandemic has generated a global health crisis, and having tools that allow the disease to be efficiently managed is of vital importance. In this context, artificial intelligence offers a unique opportunity to analyze large volumes of medical data and obtain valuable information that can contribute to medical decision-making and improve management of the pandemic. In this work, artificial intelligence techniques are applied to model the results obtained from COVID-19 databases in Mexico.International Journal of Combinatorial Optimization Problems and Informatics170In this work, artificial intelligence techniques are applied to model the results obtained from COVID-19 databases in Mexico and obtain valuable information that can contribute to medical decision-making and improve management of the pandemic.International Journal of Combinatorial Optimization Problems and Informatics["Antonio \u00c1lvarez N\u00fa\u00f1ez", "Mar\u00eda del Carmen Santiago D\u00edaz", "Ana Claudia Zenteno V\u00e1zquez", "Judith P\u00e9rez Marcial", "Gustano Trinidad Rub\u00edn Linares"]2024-11-29T00:00:00 fc03c151971255c7004df87b68c0501d50282d99 Purpose This study aims to identify the impact of adopting different techniques of artificial intelligence including (expert systems, machine learning, neural networks and algorithms) in improving the quality of accounting information characteristics such as; appropriateness, faithful representation and verifiability in Palestinian industrial enterprises. Design/methodology/approach Employees from 13 companies registered on the Palestine Stock Exchange for 2023 were selected. Moreover, the sample included 326 randomly chosen participants. A questionnaire was distributed to participants to collect data, and a descriptive-analytical approach was followed to achieve the study’s aim and examine its hypotheses. Findings The results showed that the use of artificial intelligence techniques (expert systems, machine learning, neural networks and algorithms) has a positive effect on improving the quality of accounting information characteristics (relevance, faithful representation and verifiability). Expert systems, neural network applications and algorithms contribute to developing solutions to various problems in industrial companies; discovering fraudulent practices in financial statements; and obtaining more accurate, faster and more reliable results. Machine learning also links the company’s systems together simultaneously and in an integrated and effective manner. Research limitations/implications The research relied on the industrial sector only because expanding society is difficult due to the general conditions in Palestine, and the results may vary between different sectors due to the nature of their work and activity. Practical implications Industrial companies’ efforts to benefit from artificial intelligence applications in their work increase the quality of accounting information, which in turn reflects the company’s real situation and helps in making the necessary decisions efficiently and effectively. Originality/value This study contributes to directing the attention of financiers and accountants working in Palestinian industrial companies to the importance of applying artificial intelligence techniques to ensure the highest quality characteristics of accounting information through the preparation of accounting programmes that rely on artificial intelligence to operate, thus achieving the maximum degree of advantage in compiling financial data from its sources, operation and conversion into useful financial information for its users. Journal of Financial Reporting & Accounting370The results showed that the use of artificial intelligence techniques (expert systems, machine learning, neural networks and algorithms) has a positive effect on improving the quality of accounting information characteristics (relevance, faithful representation and verifiability).Journal of Financial Reporting and Accounting["B. Awwad", "Majdi Wael Alkababji", "B. Razia"]2024-11-29T00:00:00 7e90508940861c2a748072ebcdbfe5cd35380eb7Artificial Intelligence (AI) is applied to improve the mental health literacy of middle school students. It can effectively improve the efficiency, effectiveness and feasibility of this service. Through the current big data analysis and machine autonomous learning technology, AI can analyze the characteristics of students' behavior patterns and physiological data by training the model used. This paper examines how artificial intelligence can improve the mental health literacy of middle school students. It begins by analyzing and interpreting three key factors that influence their understanding of mental health. Firstly, it analyzes and interprets the three factors that affect the mental health literacy of middle school students. Then from these three factors, the author discusses how artificial intelligence can be applied to enhance the mental health literacy of middle school students. That is to say, it puts forward the new application of artificial intelligence technology in mental health counseling, emotional recognition, mental health assessment, media publicity and education. For example, the use of artificial intelligence to develop virtual psychological teachers, through language processing analysis and emotional recognition to provide online counseling services; It develops emotion recognition function through video and speech analysis of artificial intelligence, and uses digital signal processing means. Through the comprehensive analysis of a large number of data, the mental health evaluation system is gradually intellectualized, so that the evaluation model standard is more targeted.Lecture Notes in Education Psychology and Public Media00This paper examines how artificial intelligence can improve the mental health literacy of middle school students and puts forward the new application of artificial intelligence technology in mental health counseling, emotional recognition, mental health assessment, media publicity and education.Lecture Notes in Education Psychology and Public Media["Qize Cui"]2024-11-29T00:00:00 6d43e8f03e433bd9eb8ad7c65fa386c0c7155f1dThe combination of Artificial Intelligence (AI) with cloud computing represents a groundbreaking advancement in technology providing efficiencies and capabilities in handling data and delivering services. This collaboration between AI and cloud computing is not improving current applications but also opening up new possibilities for industries like business, healthcare, and education. AI powered cloud services utilize algorithms and extensive computing power to offer adaptable solutions that can learn, adjust, and make intelligent choices. These services minimize the need for involvement and enable real time analysis leading to more precise and faster decision-making processes. Additionally, AI bolsters cloud security frameworks by anticipating and addressing risks preemptively. This piece examines the influence of AI on cloud computing illustrating how AI driven innovations enhance the operational functions of cloud services and turn them into more flexible, effective, and secure platforms. The goal is to present a perspective on how AI driven cloud computing isn't just an upgrade, in technology but a transformation that has the potential to reshape global computational standards. Keywords: Artificial Intelligence, Cloud Computing, Machine Learning, AIaaS, Cloud Services, Predictive AnalyticsGalore International Journal of Applied Sciences and Humanities00This piece examines the influence of AI on cloud computing illustrating how AI driven innovations enhance the operational functions of cloud services and turn them into more flexible, effective, and secure platforms.Galore International Journal of Applied Sciences and Humanities["Deekshitha Kosaraju"]2024-11-29T00:00:00 b6b0f6488fad3c630b13488c13cecf4a446a41acGenerative Artificial Intelligence (AI) has been developing rapidly and its application in higher education is becoming more and more widespread. The action research project of generative AI application by teachers in Tianhe District, Guangzhou City and the application case of Chat Generative Pre-trained Transformer are selected to illustrate that generative AI has brought unprecedented changes to many aspects of education and teaching, academic research, student management and services, and intelligent campus construction. However, along with these extensive applications, a series of ethical risk issues have gradually come to the fore. According to existing research, four major ethical risks of generative AI have been proposed: technology ontology risk, educational data risk, machine algorithm risk, and educational application risk. Combined with the literature "Eco-ethics and Risk Mitigation of Generative Artificial Intelligence Educational Applications", we conduct an in-depth analysis and put forward countermeasure suggestions for the risks faced by generative artificial intelligence in higher education. It helps to promote the healthy and sustainable development of generative AI in higher education and the improvement of education quality.IC-ITECHS30An in-depth analysis and countermeasure suggestions are put forward for the risks faced by generative artificial intelligence in higher education to promote the healthy and sustainable development of generative AI in higher education and the improvement of education quality.IC-ITECHS["Mingyang Ji", "Yue Liu"]2024-11-29T00:00:00 d15335467e0ddb091ac84ff0a4c78b3f24f0a261The integration of Artificial Intelligence (AI) in biomedical imaging heralds a transformative era in medical diagnostics and research. This comprehensive overview explores the multifaceted impact of AI on various imaging modalities, emphasizing its potential to revolutionize disease detection, characterization, and treatment planning. AI applications, particularly machine learning and deep learning, enhance the analysis of intricate imaging data, providing quicker and more accurate insights. This review delves into AI's role in automating diagnosis through the development of sophisticated algorithms, enabling faster and more precise identification of diseases across diverse medical imaging platforms. Furthermore, AI contributes to improving image quality, facilitating early detection of abnormalities, and enhancing decision support systems for clinicians. Beyond diagnosis, AI facilitates personalized medicine by tailoring treatment strategies based on individual patient data extracted from imaging. The ethical considerations and privacy implications of handling sensitive medical information are also discussed, ensuring a balanced perspective on the adoption of AI in healthcare. As AI continues to advance, this overview concludes with insights into future trends, challenges, and the collaborative synergy between technology, healthcare practitioners, and researchers, underscoring the ongoing evolution of biomedical imaging through artificial intelligence.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)150This review delves into AI's role in automating diagnosis through the development of sophisticated algorithms, enabling faster and more precise identification of diseases across diverse medical imaging platforms, underscores the ongoing evolution of biomedical imaging through artificial intelligence.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Gunjan R. Chaudhari", "Umesh B. Telrandhe"]2024-11-29T00:00:00 d650e4668394c846c367a9a08acee38b573d593fManaging common chronic conditions puts enormous pressure on healthcare systems. This is why it is timely that, in the case of chronic diseases, AIl encourages preventive measures and long-duration control, which is likely to bring new trends to the treatment vector. This review summarises the current state of artificial intelligence in chronic disease management, including spearheads, control therapy, individualized treatment, and patient engagement. We outline pathways for future AI use and development in that area and detail the advantages and disadvantages of AI implementation. Controlling chronic diseases is also a rehabilitation strategy to help recover the patient’s health and eliminate comorbidities. The management of chronic conditions in health care has improved remarkably due to AI, clinical decision support systems, remote patient monitoring, predictive analytics, and patient participation through personalized medication. Chronic illnesses, despite being a global concern, are burdensome to healthcare. Hence, as chronic diseases by nature require more focus on prevention and prolonged treatment, AI stands to greatly impact the management of these diseases. This paper presents evidence of how the management of chronic disease, in its various forms, is being aided by the use of artificial intelligence today. We talk about the benefits and challenges of implementing AI and present a vision for the further use and development of AI in management.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)190Evidence is presented of how the management of chronic disease, in its various forms, is being aided by the use of artificial intelligence today and a vision for the further use and development of AI in management is presented.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Divya Darne", "Surendra S. Agrawal"]2024-11-29T00:00:00 55bff6b6587f38600e3de6e4f713ba4ab3d5c7a2In today's rapidly evolving economy, the adoption of artificial intelligence (AI) within supply chain management (SCM) has emerged as a key area of study. The integration of AI technologies into SCM systems presents a transformative opportunity for organizations seeking to enhance operational efficiency, reduce costs, and establish a competitive advantage in a dynamic market. AI technologies—ranging from machine learning algorithms to predictive analytics—serve as pivotal tools in addressing these challenges. By automating routine tasks, forecasting demand with greater accuracy, and facilitating real-time decision-making, AI enhances responsiveness and agility within supply chains. The economic benefits of incorporating AI into SCM frameworks are substantial. Implementing AI-driven solutions can lead to significant cost savings through improved inventory management, reduced waste, and enhanced resource allocation. For instance, machine learning models can predict stock requirements more accurately, minimizing excessive inventory and associated holding costs. Additionally, AI enhances supplier relationship management by analyzing vendor performance data, leading to more informed selection processes and negotiation strategies. As the field continues to evolve, it is crucial for professionals to engage with emerging technologies, ensuring that they remain competitive and responsive to the demands of an ever-changing market landscape. This study reviews the literature to determine why supply chain management (SCM) needs to adopt artificial intelligence (AI) in terms of integrative tactical planning, resource utilization and cost reduction, risk management, data management and inventory management. The aim of this study was to encourage professionals to investigate the possibilities of AI technology to enhance several elements of the supply chain. American Journal of Artificial Intelligence130This study reviews the literature to determine why supply chain management (SCM) needs to adopt artificial intelligence (AI) in terms of integrative tactical planning, resource utilization and cost reduction, risk management, data management and inventory management.American Journal of Artificial Intelligence["Aditya Kumar", "Divya Kumar", "Raina Kashyap", "Pranav Kataria", "Abhishek Kumar"]2024-11-29T00:00:00 fe9662ea133eb194529a395b02b4927d028e52e2Artificial Intelligence (AI) plays a significant role in optimizing operations, increasing productivity, building more efficiency, and reducing costs across all business verticals of every industry. AI is an essential driver in the field of marketing, fueling creativity and innovations by Automating repetitive tasks, enhanced targeting, personalizing communication, optimizing advertising spending, predictive analytics, Customer Support, and much more. This paper investigates the role of AI in reducing costs for marketing agencies, explicitly focusing on AI tools in content creation, content management, and video editing. Also, AI-powered video editing applications speed up the overall editing process, decreasing reliance on costly software and skilled personnel. Towards the end, the study highlights the practical implications of how marketing agencies can leverage AI tools to develop a more robust and profitable business model that is dynamic to suit the current technology age and drives stability for sustainable growth.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)120The practical implications of how marketing agencies can leverage AI tools to develop a more robust and profitable business model that is dynamic to suit the current technology age and drives stability for sustainable growth are highlighted.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Prathamesh Veling", "Palaniappan Sellappan"]2024-11-29T00:00:00 cbff185db44394a1f88f327ef76459486ae1ea87 Postpartum hemorrhage (PPH) remains a significant contributor to maternal mortality and morbidity worldwide, with approximately 14 million women affected annually and 70,000 resulting deaths. Despite advances in health care, PPH continues to pose challenges even in developed settings. Apart from mortality, PPH leads to various adverse outcomes and morbidity. Recently, there has been a surge in interest in using artificial intelligence (AI), including machine learning and deep learning, across many areas of health care. This article explores the application of AI in tackling PPH, including predictive modeling and risk stratification. Some studies have shown promising results in predicting PPH. However, external validation of these models is crucial and frequently lacking, with barriers including differences in cohort characteristics and variations in outcome measurement methods. Most of the existing research has taken place in well-resourced health care settings, and there is a lack of models applicable to resource-limited settings where the need is arguably greatest. Incorporating uterine contractility metrics and radiomics into predictive models offers new avenues for enhancing prediction accuracy. Beyond risk prediction, AI has also been explored in other aspects of PPH management, including blood product management and early detection using wearable devices. In conclusion, while AI presents exciting opportunities for PPH prediction and management, challenges such as model validation, clinical translation, and applicability in diverse health care settings remain. Further research, particularly in low-and middle-income countries, is necessary to realize the full potential of AI for addressing the global burden of PPH. Maternal-Fetal Medicine360In conclusion, while AI presents exciting opportunities for PPH prediction and management, challenges such as model validation, clinical translation, and applicability in diverse health care settings remain.Maternal-Fetal Medicine["S. Mathewlynn", "Mohammadreza Soltaninejad", "Sally L Collins"]2024-11-29T00:00:00 98df03db7901eba467b6bbea30236425f7287389The Industrial Revolution, around 200 years ago, marked a significant shift in human social and economic development. It introduced technological advancements, increased energy production and machine power, enhancing human and animal labor. This transformation improved quality of life and societal levels. Today, artificial intelligence is transforming human cognition, leading to profound social and economic changes similar to the Industrial Revolution. Unlike previous technologies that largely automated manual tasks, AI introduces the ability to learn, adapt and make predictive decisions based on large data set, making it a fundamentally different force in healthcare. TraditionalResearch Journal of Medical Sciences170Unlike previous technologies that largely automated manual tasks, AI introduces the ability to learn, adapt and make predictive decisions based on large data set, making it a fundamentally different force in healthcare.Research Journal Of Medical Sciences["Jigisha Vaniya", "N. V. Gandhi", "Kinjal Priyesh Patel", "Chaitali Bhatt", "Komal Preet Kaur"]2024-11-29T00:00:00 4b269c4454ed95fdf997b1d73b9d51d4d4a19a26The rapid development of artificial intelligence, while promoting the progress of the times, has also brought unprecedented employment pressure on vocational school students. To adapt to the requirements of the times, vocational schools in both China and the United States have updated their curricula and teaching concepts to ensure that vocational school students can maximize their mastery of modern AI technology. This article employs interview and questionnaire surveys, combining qualitative and quantitative analysis to investigate the usage and proficiency of AI technologies among vocational school students. The research finds that the use of various AI technologies by students in vocational colleges varies across majors, with equipment availability, the scheduling of hands-on classes, and the frequency of school-enterprise cooperation all contributing to this difference. Although China and the United States share the same reform policies and educational goals, there are still differences in the details of teaching and learning implementation due to different national conditions. However, the findings suggest that it is still beneficial to offer AI-related courses at school. Familiarity with basic theoretical knowledge and practical experience can alleviate students' social anxiety and employment pressure to a certain extent. It can be seen that the reform of vocational education in the era of artificial intelligence in China and the United States is beginning to bear fruit.Lecture Notes in Education Psychology and Public Media00The research finds that the use of various AI technologies by students in vocational colleges varies across majors, with equipment availability, the scheduling of hands-on classes, and the frequency of school-enterprise cooperation all contributing to this difference.Lecture Notes in Education Psychology and Public Media["An Yan"]2024-11-29T00:00:00 7025f50778e474a376e4b6f386547d12690cdce2Subject. This article discusses the issues of reducing investment risks by compiling a balanced investment portfolio using artificial intelligence. Objectives. The article aims to analyze the investment portfolio compiled by artificial intelligence for further use and profitability, and find out whether artificial intelligence can replace a professional trading participant at this stage. Methods. For the study, we used logical and statistical analyses, and also conducted an experiment with a chatbot. Results. The article presents the results of an experiment conducted using the ChatGPT generative artificial intelligence (AI) chatbot in Telegram Messenger. Conclusions. At this stage, artificial intelligence is not able to replace a professional market participant, but this should not be ruled out in the future.Finance and Credit20The article analyzes the investment portfolio compiled by artificial intelligence for further use and profitability, and finds out whether artificial intelligence can replace a professional trading participant at this stage.Finance and Credit["Yuliya A. Dubolazova", "A. A. Ferapontova"]2024-11-29T00:00:00 d25c2e66cf6833e4009f2b5d8b60badc9c9fa1b8OpenAI, creator of ChatGPT, was founded as a nonprofit with a mission of ensuring that artificial general intelligence benefits all of humanity. AI, therefore, was intended to advance the common good, sharing an underlying principle with philanthropy and the nonprofit organizations it supports. However, this was not the first association of machine learning with philanthropy, particularly in terms of algorithms designed for control versus those aimed at doing good. In 1974, a white paper by Heinz Von Foerster, a polymath scientist who happened to be president of an important foundation, considered the potential of computer-based feedback systems to improve “giving with a purpose.” A review of his paper served as the impetus for this essay, which explores the antecedents of contemporary predictions regarding the potential of AI to enhance the practice of philanthropy.Philanthropia00The antecedents of contemporary predictions regarding the potential of AI to enhance the practice of philanthropy are explored, particularly in terms of algorithms designed for control versus those aimed at doing good.Philanthropia["William M. Plater", "Genevieve G. Shaker"]2024-11-29T00:00:00 37bb45c690e2763686a778604b4eb89b6eb0f79aEnsuring financial system stability is paramount, especially in markets guided by Sharia principles, where investor confidence and adherence to ethical standards play critical roles. The ability to accurately forecast stock movements within this framework not only supports informed investment decisions but also strengthens the overall stability of financial markets. This research employs the innovative Neural Prophet model to predict Islamic stock indices in Indonesia with remarkable accuracy and depth. The model demonstrates its capability not only in accurately forecasting trends but also in detecting subtle fluctuations within three Islamic stock indices: the Jakarta Islamic Index (JII), the Jakarta Islamic Index 70 (JII70), and the Indonesia Sharia Stock Index (ISSI). Visual representations highlight the model's adaptability and advanced foresight, surpassing traditional models. The significance of this research lies in its potential to enhance the precision of stock index predictions, particularly for Islamic stocks, offering stakeholders deeper insights. The model's effectiveness spans both stable and volatile market conditions, making it a valuable tool for informed financial decision-making. Accurate forecasts aid in risk management and support well-informed investment decisions in fluctuating markets, thereby contributing to financial system stability.Indatu Journal of Management and Accounting431The innovative Neural Prophet model is employed to predict Islamic stock indices in Indonesia with remarkable accuracy and depth, demonstrating its capability not only in accurately forecasting trends but also in detecting subtle fluctuations within three Islamic stock indices.Indatu Journal of Management and Accounting["Muksalmina Muksalmina", "Ghadamfar Muflih Idroes", "Aga Maulana"]2024-11-29T00:00:00 cfff1fe48cfb65174d6018235709f6014a61736aDiscover Artificial Intelligence331Discov. Artif. Intell.["Tashkinov Aleksey Grigorievich"]2024-11-29T00:00:00 d836e4a085aa13c480a75b16e7b9b45eba8ff854AI-enabled cybersecurity systems are becoming common, but their effectiveness is reported to be mixed at best due to some barriers. The primary objective of this systematic literature review is to find barriers associated with the use of AI-enabled cybersecurity systems. A comprehensive systematic literature review approach was implemented in this study. Literature sampled from different databases (Scopus and WOS) was synthesised to synthesise barriers associated with using an AI-enabled cybersecurity system, and a total of 41 papers were selected using systematic inclusion criteria. The study identified several barriers, such as the complexity of systems, lack of top management support, lack of AI-proficient employees, and lack of regulatory support for AI. These barriers are classified into technological, organisational, and environmental. This paper is unique as it focuses on the barriers associated with using advanced technologies such as AI-enabled expert systems for cybersecurity. Thus, the current research makes a novel contribution, arguing that attention is required toward organisational-level issues to protect the system from cyberattacks. This will establish the way for researchers to evaluate these barriers, opening new avenues for empirical research and for practitioners to utilise these systems more effectively.Information851It is argued that attention is required toward organisational-level issues to protect the system from cyberattacks, and this will establish the way for researchers to evaluate barriers, opening new avenues for empirical research and for practitioners to utilise these systems more effectively.Information["Adeel Ali", "Mahmood Shah"]2024-11-29T00:00:00 227a792a91cc5efc6f419ed43eeab354bf8f3a48Benha Medical Journal00Benha Medical Journal["Hesham El-Sayed El-Sheikh", "H. Khater", "Jehan Ibrahim Al -Tohamy", "Khaled El Sayed Ahmed", "Heba Ahmed Hassan"]2024-11-29T00:00:00 e4e3120550397a984a460a84030aa4f562f7e608Biochemistry and cell biology = Biochimie et biologie cellulaire30Biochemistry and cell biology = Biochimie et biologie cellulaire["Joshua Molligan", "Edel P\u00e9rez-L\u00f3pez"]2024-11-29T00:00:00 2b765ff3e9361adfec95f2fc3106d57d53a04838Congenital heart disease (CHD) remains a significant global health challenge, particularly contributing to newborn mortality, with the highest rates observed in middle- and low-income countries due to limited healthcare resources. Machine learning (ML) presents a promising solution by developing predictive models that more accurately assess the risk of mortality associated with CHD. These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. Despite notable advancements, ongoing research continues to explore the full potential of ML in the identification of CHD. The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years. The study also describes different data sets available for CHD research, discussing their characteristics, collection methods, and relevance to ML applications. In addition, the article also evaluates the strengths and weaknesses of existing algorithms, offering a critical review of their performance and limitations. Finally, the article proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD.PeerJ Computer Science1170The proposed article provides a comprehensive analysis of the ML methods for the diagnosis of CHD in the last eight years, and proposes several promising directions for future research, with the aim of further improving the efficacy of ML in the diagnosis and treatment of CHD.PeerJ Computer Science["Khalil Khan", "Farhan Ullah", "Ikram Syed", "Hashim Ali"]2024-11-29T00:00:00 200d9c3facf2f3e9b8a98a8587bae15b3ae8c4a6Journal of Breast Imaging00Journal of breast imaging["Robert M Nishikawa", "Alisa Sumkin"]2024-11-29T00:00:00 71ec0d7c6d6f6bfb31ef5b4511fe704e2ca18d26Book of Abstracts00Book of Abstracts["Oleksandr Mihus"]2024-11-29T00:00:00 3a0f0b769a122a9ed26089be566c8f653fc33d84The days, manufacturers across a range of industries must handle increasingly difficult responsibilities including risk management, increasing efficiency and security, etc. Integration founded on Industry 4.0 concepts is one way to address these issues. The article explains Industry 4.0's constituent parts and fundamental ideas. Towards a structure to evaluate industry 4.0 services and technology's effect on production resources industrial 4.0 is becoming more and more significant in the manufacturing sector. Scholarly writings have mostly focused on the technological aspects of Industry 4.0. Studies that address Industry 4.0 technologies, services, and production resources within an industrial setting are, nevertheless, extremely rare. Specifically, there is a lack of research to support an academic conversation about how Industry 4.0 technologies affect its production resources and services. The purpose of this article is to provide an initial framework that illustrates how Industry 4.0 services and technologies affect production resources. Two distinct industries are the focus of the application of the framework. For industrial decision-making, this study can be utilized to better support and choose appropriate Industry 4.0 technology.2024 IEEE 4th International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)160An initial framework that illustrates how Industry 4.0 services and technologies affect production resources is provided and can be utilized to better support and choose appropriate Industry 4.0 technology.2024 IEEE 4th International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)["Niranjan Sudhakar Deshmukh", "P. Shalini", "J.Madhuri Sailaja", "Buddhaghosh Arjun Shingade", "T. Muthumanickam", "Pranav Kodali", "G. Ramachandran"]2024-11-29T00:00:00 1903b89f22b8c73a96c73523dad3af57c7b3b1a9International Critical Thought430International Critical Thought["Xu Zhang", "Mengmeng Yu"]2024-11-29T00:00:00 390bd09dc91193f4062e0c07be2279dbfc477eb6This study aimed at probing Egyptian and Saudi English as a Foreign Language (EFL) majors' perceptions of the impact of AI technology on enhancing linguistic competence and predicting the level of linguistic competence development due to the utilisation of AI. The descriptive survey method was used to accomplish the purpose of the study. A 27-item questionnaire was developed to collect the study data from the participants, totalling 523 EFL majors: 256 Egyptian EFL majors at the Faculty of Education at Al-Azhar University, Egypt and 267 EFL majors at Prince Sattam bin Abdulaziz University, Saudi Arabia. The results of the statistical analysis demonstrated that AI technology is perceived positively by the EFL majors in both contexts. However, the Egyptian students showed higher positive perceptions than their Saudi counterparts. More importantly, the simple regression analysis has shown a statistically significant positive correlation between the EFL majors' perceptions and their linguistic competence. The study recommends enhancing the EFL majors' proficiency in using AI tools for developing language skills.International Journal of Computer-Assisted Language Learning and Teaching380The results of the statistical analysis demonstrated that AI technology is perceived positively by the EFL majors in both contexts, however, the Egyptian students showed higher positive perceptions than their Saudi counterparts.International Journal of Computer-Assisted Language Learning and Teaching["Iman El-Nabawi Abdel Wahed Shaalan", "Ayman Shaaban Khalifa Ahmad"]2024-11-29T00:00:00 f8f8c02597931ab903e33800bff79c0ae7333deeInternational Journal of Technology Assessment in Health Care00International Journal of Technology Assessment in Health Care["Marina Richardson", "G. S. Sagoo"]2024-11-29T00:00:00 04639948bd4e025a901ed8b98a7ce05e71031fc4The COVID-19 pandemic has accelerated the adoption of telehealth, creating a pivotal moment for the future of healthcare. By addressing these issues and leveraging AI's capabilities, we can utilize telehealth's full potential, creating a more strong, equitable, and patient-centric diagnostic accuracy, personalize treatment plans, and enable predictive analytics, transforming telehealth into a more effective and efficient care delivery system. Additionally, AI-powered virtual health assistants and wearable devices offer continuous monitoring and support. Despite challenges such as data privacy, technology access, and regulatory hurdles, the potential for AI in telehealth is vast. In conclusion, the article emphasizes the importance of evidence-based research, standardized guidelines, and collaborative efforts to optimize telehealth interventions and overcome barriers, ultimately enhancing healthcare delivery and access in the digital age.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)190The article emphasizes the importance of evidence-based research, standardized guidelines, and collaborative efforts to optimize telehealth interventions and overcome barriers, ultimately enhancing healthcare delivery and access in the digital age.2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)["Sonia Pandey", "Divyanshu Mehrotra", "Kanak Pandey", "Neema Bisht", "A. Pargaien", "Akbar Nawaz"]2024-11-29T00:00:00 98a10bf1d81a880711ed488a3b1c7d963eb271a6Technical Services Quarterly340Technical Services Quarterly["Patrick Ngulube", "Neema Florence Vincent Mosha"]2024-11-29T00:00:00 1934c6f8ab96a4f12a07d276617aeb5fed33f705Heliyon560The findings reveal that younger healthcare workers, those in full-time employment, and individuals with exposure to AI through conferences or research are more likely to possess good knowledge and hold positive attitudes towards AI integration.Heliyon["Moustaq Karim Khan Rony", "Khadiza Akter", "Latifun Nesa", "Md Tawhidul Islam", "Fateha Tuj Johra", "Fazila Akter", "Muhammad Join Uddin", "Jeni Begum", "Md. Abdun Noor", "Sumon Ahmad", "Sabren Mukta Tanha", "Most. Tahmina Khatun", "Shuvashish Das Bala", "Mst. Rina Parvin"]2024-11-29T00:00:00 6335722ef2cefcdcb3a8332af8c95da53a1556b3This paper proposes building a sustainable development computing system for large organizations based on AI and IoT. The system leverages AI to calculate the carbon emissions caused by large organizations' activities and utilizes IoT devices to monitor and compute environmental coefficients. The system also employs automated devices to achieve net-zero carbon emissions. By integrating weather forecast information from meteorological agencies to understand external environmental conditions, and by consulting a knowledge database to devise appropriate response strategies, the AI system can activate relevant equipment to improve both the organization's living environment and carbon emission processes. The feasibility and practical application of this system will be demonstrated through actual simulations to enhance its viability and effectiveness.Journal of Organizational and End User Computing20Journal of Organizational and End User Computing["Hsin-Te Wu", "Jie-Xin Li", "Mu-Yen Chen"]2024-11-29T00:00:00 1ac2581426fdf751c16deef24c3cad5636ce6678Applied Biosafety110Applied Biosafety["D. Hurst", "Christopher A. Bobier"]2024-11-29T00:00:00 fce0f376b53748012842aace3b40866fb638a963Book of Abstracts00Book of Abstracts["D. Mishchenko", "Victoria Khurdey", "V. Datsenko", "Tetyana Dronova", "I. Pavlovska"]2024-11-29T00:00:00 2cff1537fc053f0a5fbd5a673e489a90a9ef5e9fArchives of Medical Research1990An overview of the current and potential applications of AI in human reproduction, including fertility tracking, assisted reproductive technologies, management of pregnancy complications, and laboratory automation is presented.Archives of medical research["G. Mendizabal-Ruiz", "Omar Paredes", "\u00c1ngel \u00c1lvarez", "F\u00e1tima Acosta-G\u00f3mez", "Estefan\u00eda Hern\u00e1ndez-Morales", "J. Gonz\u00e1lez-Sandoval", "Celina Mendez-Zavala", "E. Borrayo", "Alejandro Chavez-Badiola"]2024-11-29T00:00:00 1cd55cf868acf23ccf3d9feb4a358f24215d637b00["Ahmed A. Elngar", "Diego Oliva", "V. Balas"]2024-11-29T00:00:00 2c5dbd616b53e013c09586f56eccde27879f2214Book of Abstracts00Book of Abstracts["Natalia Shkvyria"]2024-11-29T00:00:00 c0556dc02bd94702def6198ae4a61f7e5d9643d5AI integrated leadership entails a revolution in leadership management and decision-making processes among leaders. AI does not just introduce changes that augment the existing practices but reforms the leadership processes from the ground up, offering novel tools like machine learning algorithms and the deep analysis of the data that might help leaders forecast the future tendencies in the sphere, improve productivity, and make proper decisions based on the proven KPIs. Nevertheless, it contributes to many problems, especially in ethical issues, open-mindedness, fairness, and its effects on the workforce. It shows that the future of leadership agenda will continue focusing on these issues as leaders are expected to communicate responsibly in such contexts. It has also been identified that with the advancement of the field of AI and leadership research has also expanded in proliferation with the number of articles, sources and scholars involved have also established an exponential growth. This research presents an insight into the significant trends and thematic directions in the management and leadership field, where AI is becoming a critical focus, offers information about newer topics in the field, including sustainability, optimization, digital transformation, and ethical questions. The collaborative networks among authors imply a strong and connected research community; however, the authors from different countries participated in the development of this study, and the primary countries are the USA, China, and the UK. With the advancement of AI becoming more imminent, it is crucial for academicians, professionals, and policymakers to effectively grasp its consequences towards leadership in regard to positioning the effectiveness of AI towards organizational outcomes and its compliance with/impact on ethical and social responsibilities. Apart from the mapping of the current literature, the identification of trends outlines the overall AI and leadership scholarship trajectory and future research opportunities while stressing responsible and creative use of AI in leadership.Journal of Human Resource Management Perspectives00This research presents an insight into the significant trends and thematic directions in the management and leadership field, where AI is becoming a critical focus, and offers information about newer topics in the field, including sustainability, optimization, digital transformation, and ethical questions.Journal of Human Resource Management Perspectives["Hiranya Dissanayake", "Manoaj Keppetipola"]2024-11-29T00:00:00 bae19e69d592656870067c723a8021dff4a35e3aBook of Abstracts00Book of Abstracts["Daria Kovalenko", "Eva Solovyan", "Ievheniia Mishchuk"]2024-11-29T00:00:00 32144bfc48216e6054e03dd66e0d035068230e3eThis paper aims to review the role of Artificial Intelligence and Machine Learning in managing the healthcare supply chain in the United States. Healthcare supply chains face several challenges such as fragmentation, lack of real-time visibility, and inventory management issues. However, there are solutions with the help of AI and machine learning, including the availability of predictive analytics to improve demand forecasting, optimization algorithms for inventory and logistics, and automated quality control. Application areas demand forecasting, supplier selection, logistics optimization, quality control, and real-time tracking. The applications of AI in healthcare supply chains have the potential to improve the healthcare supply chain in terms of reduced costs, increased efficiency, optimized decision-making, and better patient outcomes. However, the implementation experience has shown several challenges such as data quality, privacy concerns, regulatory compliance, and workforce adaptation within organizations. Successful implementations in various health organizations in the US give valuable insights into how AI could be well implemented. The future presents several opportunities for supply chain optimization with the rise of blockchain and Internet of Things (IoT) integration. For healthcare supply chains to adopt AI, organizations should have specific AI plans, start with pilot projects in high-impact areas, invest in data infrastructure, and ensure strong leadership support. As AI becomes increasingly critical for competitive advantage, it has the potential to create more resilient, efficient, and patient-centric supply chains in the US healthcare system.World Journal of Advanced Research and Reviews242For healthcare supply chains to adopt AI, organizations should have specific AI plans, start with pilot projects in high-impact areas, invest in data infrastructure, and ensure strong leadership support.World Journal of Advanced Research and Reviews["Alice Ama Donkor", "Samuel Ajibola Dada", "Augustine Korang", "Jehoiarib Umoren"]2024-11-30T00:00:00 43b7a7b7a6ff1d0af7cdb9dea37bec49a1693cb3This paper examines the role of Artificial Intelligence (AI) in transforming global health surveillance systems, emphasizing its profound impact on disease detection, monitoring, and response capabilities. By leveraging advanced AI technologies such as machine learning and natural language processing, the paper delves into how these tools enhance the efficiency and accuracy of public health strategies. Through a series of case studies, the effectiveness of AI in real-world scenarios is analyzed, showcasing its potential to predict and manage disease outbreaks more effectively than traditional methods. Additionally, the paper addresses the ethical challenges and technical limitations of integrating AI into existing health surveillance frameworks. Recommendations for overcoming these challenges are provided, alongside a discussion on the necessity of robust data protection measures and the development of unbiased AI algorithms. The comprehensive analysis aims to provide stakeholders with insights into both the transformative potential of AI and the pragmatic considerations needed for its responsible implementation in the field of global health surveillance.World Journal of Advanced Research and Reviews392World Journal of Advanced Research and Reviews["Topeola Balkis", "Awofala"]2024-11-30T00:00:00 53486b988536cd5686f3471d4083ad7e0dd12652DARU732An overview of the latest AI-based technologies and how they may be employed to reduce product development time to market and snowballing product quality, cost-effectiveness, as well as security throughout the manufacturing process is detailed.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences["Nidhi Srivastava", "Sneha Verma", "Anupama Singh", "Pranki Shukla", "Yashvardhan Singh", "Ankit D Oza", "T. Kaur", "Sohini Chowdhury", "Monit Kapoor", "Ajar Nath Yadav"]2024-11-30T00:00:00 a801d879c5c678eefc37eb0aae92db5fe9f4e032Artificial Intelligence (AI) is revolutionizing digital marketing automation by enhancing efficiency, personalization, and predictive capabilities. This study examines the role of AI in transforming marketing practices, focusing on its applications, benefits, ethical considerations, and future directions. By leveraging AI tools such as predictive analytics, NLP, and chatbots, businesses can achieve improved customer segmentation, content personalization, and campaign optimization in marketing strategies. Secondary data from journals, articles, and conference papers were synthesized to provide insights into AI's impact on digital marketing automation. A systematic literature review utilizing the PRISMA methodology initially identified 2,850 records from database searches. Following the removal of duplicates and non-relevant studies, 1,035 records were screened for eligibility based on defined criteria, resulting in the inclusion of 150 relevant studies and 25 high-quality reports for detailed analysis. This robust approach ensured the inclusion of high-quality research, minimizing biases. The findings reveal that AI enhances digital marketing by streamlining processes, automating repetitive tasks, and delivering hyper-personalized customer experiences. Predictive analytics helps anticipate consumer behavior, while chatbots improve real-time customer engagement. However, challenges such as data privacy, algorithmic bias, and the high costs of AI adoption persist. AI adoption allows businesses to make data-driven decisions, improve customer retention, and maximize return on investment. Ethical AI practices, such as transparency and algorithm fairness, are essential for maintaining consumer trust. The study primarily focuses on existing literature, with limited empirical validation. Future research should explore long-term effects of AI-driven marketing on consumer behavior and investigate its integration with emerging technologies like the Internet of Things (IoT) and blockchain. Additionally, tailored AI solutions for SMEs and under-researched areas, such as B2B marketing, are critical for inclusive growth.Edelweiss Applied Science and Technology02The findings reveal that AI enhances digital marketing by streamlining processes, automating repetitive tasks, and delivering hyper-personalized customer experiences, with limited empirical validation.Edelweiss Applied Science and Technology["Md Ahadul Islam", "Shafiqul Islam Fakir", "Seaam Bin Masud", "Md. Deluar Hossen", "Md Tariqul Islam", "Md Rafiuddin Siddiky"]2024-11-30T00:00:00 c88f2fc5bb86418f9f20e76da26eeff081428f3bThe emergence of Industry 5.0 marks a transformative shift in the manufacturing landscape, emphasizing a synergistic relationship between humans and machines. This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing human-machine collaboration within this paradigm. By leveraging AI technologies, industries can foster a more personalized, efficient, and innovative work environment. AI systems facilitate seamless communication and decision-making processes, thereby augmenting human capabilities rather than replacing them. This collaboration allows for the optimization of workflows, reduction of operational risks, and enhancement of product quality through advanced predictive analytics and real-time data processing. Furthermore, the integration of AI in Industry 5.0 supports sustainability initiatives by minimizing waste and energy consumption, aligning with the global push for greener manufacturing practices. Case studies demonstrate the successful implementation of AI-driven solutions across various sectors, showcasing improvements in productivity and employee satisfaction. As Industry 5.0 continues to evolve, the interplay between AI and human labour will redefine traditional roles, empowering workers with augmented intelligence tools. The findings indicate that embracing AI not only enhances operational efficiency but also contributes to a more resilient and adaptive workforce. Ultimately, this paper posits that the future of industry lies in the harmonious collaboration between human intellect and artificial intelligence, which together will drive innovation, productivity, and sustainable practices in the manufacturing sector.World Journal of Advanced Research and Reviews301It is posits that the future of industry lies in the harmonious collaboration between human intellect and artificial intelligence, which together will drive innovation, productivity, and sustainable practices in the manufacturing sector.World Journal of Advanced Research and Reviews["Andrew Nii Anang", "Peter Ofuje Obidi", "Adeleye Oriola Mesogboriwon", "James Opani Obidi", "Maurice kuubata", "Dabira Ogunbiyi"]2024-11-30T00:00:00 b11d7db925ae5a491b2167e52ebaed3a6d24b37dnpj Digit. Medicine872A meta-analysis evaluates the impact of human-AI collaboration on image interpretation workload and finds promising results, but caution is warranted due to significant heterogeneity and uneven study quality.NPJ Digital Medicine["Mingyang Chen", "Yuting Wang", "Qiankun Wang", "Jingyi Shi", "Huike Wang", "Zichen Ye", "P. Xue", "Youlin Qiao"]2024-11-30T00:00:00 ec62d0ebaab263a82969cb7b09f122cd98579996The purpose of this study is to establish basic data on the application of AI in counseling and education field. To this end, we collected and analyzed big data from 2014 to 2023, focusing on the keyword ‘artificial intelligence(AI).’ Using the TEXTOM, we collected data from portal site Naver and Google. Afterwards, the final refined data was subjected to keyword frequency analysis, degree centrality analysis, and CONCOR analysis using TEXTOM and UCINET. The research results are as follows. First, the frequency of AI increased rapidly over time. Second, similar words appeared in the keyword frequency analysis, but there were differences by year, and new words appeared. Third, the degree centrality analysis showed the AI technology and application areas that people consider important by year. Fourth, the CONCOR analysis confirmed that AI formed different clusters by year. Finally, the limitations of this study and future research tasks were discussed.The Association of Korea Counseling Psychology Education Welfare00The frequency of AI increased rapidly over time and the degree centrality analysis showed the AI technology and application areas that people consider important by year.The Association of Korea Counseling Psychology Education Welfare["So-Hyung Kim", "Chang-Seong Oh", "Sang-Hee Lee"]2024-11-30T00:00:00 03a1766d2ac95660e7d8a49e59284e188479390bEvery year, 10.8 to 19.1 billion tonnes of CO2 are emitted through food exports, which accounts for roughly 21% - 37% of the world’s global total emissions. In the urge to expand businesses and ensure availability of all crops throughout the year, the quality of air is being compromised. The demand for crops rises in tandem with population growth and improvements in human well-being. In addition, most of these exported crops lose their nutritional value due to the unhealthy exposure of preservatives and other farming chemicals. There are multiple factors involved in crop production: land, labour, transportation, storage etc. Aeroponics is a relatively new farming system that eradicates numerous problems by using less land and water and requiring minimal labour and nutrients. The aim of this project is to convert the aeroponic farming unit into a 100% carbon-neutral, self-reliant unit called Aerogarden by integrating modern technology like Artificial Intelligence (AI). Every household having an Aerogarden unit can access fresh organic vegetables and microgreens that actually retain their nutritional value. By achieving this goal, non-agricultural nations' dependence on food imports will lessen, which will ultimately result in a reduction in the sector's carbon footprint. By giving people a way to directly support the international movement, the project also aims to support the UN's initiative to achieve the 17 Sustainable Development Goals.International Journal of Science and Research Archive00By achieving this goal, non-agricultural nations' dependence on food imports will lessen, which will ultimately result in a reduction in the sector's carbon footprint, and the project also aims to support the UN's initiative to achieve the 17 Sustainable Development Goals.International Journal of Science and Research Archive["Nikhil Nandi"]2024-11-30T00:00:00 eb2daf33866f478f81177892f14dd72bd371e5c1Artificial intelligence (AI) is playing an increasingly critical role in advancing green organic chemistry by optimizing chemical processes to minimize environmental impact. From predicting reaction outcomes to designing eco-friendly synthetic pathways, AI tools are contributing to sustainable chemical research. This review explores the application of AI in areas such as reaction optimization, solvent selection and waste reduction, all key aspects of green chemistry. Moreover, AI-driven approaches allow for the development of catalysts and reagents that reduce harmful byproducts and energy consumption. Despite these advancements, challenges remain in terms of data availability, integration with experimental workflows and ensuring the interpretability of AI models for chemists. This review also highlights the potential of AI to accelerate green chemistry innovation while maintaining alignment with the 12 principles of green chemistry. By addressing these challenges, AI can further enhance the sustainability of organic synthesis, paving the way for a greener chemical industry.Asian Journal of Chemistry00This review explores the application of AI in areas such as reaction optimization, solvent selection and waste reduction, all key aspects of green chemistry and highlights the potential of AI to accelerate green chemistry innovation while maintaining alignment with the 12 principles of green chemistry.Asian Journal of Chemistry["Gurinderdeep Singh", "Eashwar Sai Komarla Rajasekhar", "Kamati Mounika", "K.R. Sri Krishna Tulasi", "Tejaswi Dondapati", "M. Himasaila", "Sowjanya Pulipati"]2024-11-30T00:00:00 6d519455057a9f5053ce595e51aa00e7848f94b6Artificial intelligence technologies are transforming the way scientific research is reviewed to make verification processes more accurate, efficient, and objective. This paper considers AI's role in testing the veracity of academic studies, with a focus on concerns of research fraud, data integrity, and adherence to ethical and methodological standards. Powered by AI, emerging tools allow the tracing of anomalies, inconsistencies, and potential biases in published research works with the help of NLP algorithms and machine learning models. It is also applied to various fields such as plagiarism detection, statistical analysis checking, and the identification of fabricated or manipulated data. However, flaws regarding the use of AI technologies include relying too much on automation of systems and requiring human supervision. Academic institutions and journals can further enhance scientific research to make it more credible, transparent, and coherent-a reason to be trusted globally-by integrating AI into its evaluation process.Journal of Internet Services and Information Security00Artificial intelligence's role in testing the veracity of academic studies is considered, with a focus on concerns of research fraud, data integrity, and adherence to ethical and methodological standards.Journal of Internet Services and Information Security["R. Kharipova", "I. Khaydarov", "Shakhnoza Akramova", "Durdona Lutfullaeva", "Shavkat Saidov", "Aftondil \u0116rkinov", "S. Azizkhonova", "Nigora Erkinova"]2024-11-30T00:00:00 a7bfbeb4ccfa9c289d7ed68783688a4fbe24ef86This systematic literature review (SLR) study explores artificial intelligence (AI) as a tool to address academics challenges in South African higher education. Numerous difficulties beset higher education system in South Africa, such as a lack of funding, high student-to-lecturer ratios, and unequal access to high-quality education. Relevant academic databases, such as PubMed, Google Scholar, ERIC, and Web of Science, was searched using a combination of keywords relating to South African higher education, AI in education, academic issues, and AI technologies. A total of twenty (20) documents were identified and used in this study. The examination looks at how AI is currently being used, how it affects how people teach and learn, and the opportunities and challenges that come with using it in this setting. The results show that artificial intelligence (AI) technologies, including automated assessment tools, intelligent tutoring systems, and adaptive learning platforms, have the potential to improve personalized learning experiences, boost student engagement and academic performance, and facilitate effective management of educational resources. However, overcoming several issues, such as a lack of technological know-how, restricted infrastructure, concerns about equity, and ethical implications is necessary for the successful integration of AI in South African higher education. It encourages educators, politicians, and technology experts to work together to establish long-term solutions for incorporating AI into teaching and learning processes while maintaining inclusivity, equity and ethical issues.International Journal of Learning, Teaching and Educational Research00The results show that artificial intelligence technologies, including automated assessment tools, intelligent tutoring systems, and adaptive learning platforms, have the potential to improve personalized learning experiences, boost student engagement and academic performance, and facilitate effective management of educational resources.International Journal of Learning, Teaching and Educational Research["Vusumzi Funda", "Noluthando Mbangeleli"]2024-11-30T00:00:00 5477e438e98354831513d2163ba98334759d4ab6The present review explores the role and impact of second opinions in medical oncology, particularly considering the recent advancements in artificial intelligence (AI) and telemedicine. A comprehensive literature search was conducted, and data from various studies were analyzed, highlighting why patients seek a second opinion, the rates of disagreement between the first and second opinions, and the potential barriers to obtaining a second opinion. The results showed that seeking a second opinion is common, with patients often seeking reassurance and a better understanding of their diagnosis and treatment options. However, there is limited evidence on the impact of second opinions on patient outcomes and the cost of care. Additionally, the introduction of Multidisciplinary Molecular Tumor Boards, AI, and telemedicine may improve decision-making and treatment strategies in the context of second opinions. Further research is needed to fully understand the role and implications of second opinions in medical oncology and how these recent technologies impact the second opinion process.Brazilian Journal of Oncology330The results showed that seeking a second opinion is common, with patients often seeking reassurance and a better understanding of their diagnosis and treatment options, however, there is limited evidence on the impact of second opinions on patient outcomes and the cost of care.Brazilian Journal of Oncology["Auro del Giglio", "Sergio Vicente Serrano", "Mateus Uerlei Pereira da Costa"]2024-11-30T00:00:00 1a79b633e5903190e668b34e5acd785b1ea9e1c2Artificial Intelligence (AI) has emerged as a disruptive force in the accounting profession, a field historically reliant on manual processes and human judgment. This study aims to critically analyze the AI technologies proposed in current accounting research and explore their implications on the accounting profession. Using a qualitative research approach and document analysis, the study systematically evaluated scholarly works on the integration of AI in accounting. The findings underscore that while process automation is a foundational pillar of digital transformation, amplifying analytical capabilities and bolstering decision-making processes are equally critical for businesses and financial institutions seeking a competitive edge. Consequently, strategic investment in a diverse portfolio of AI technologies, encompassing machine learning, neural networks, and expert systems, is essential. However, the lack of case-based reasoning and simulation modelling in accounting research limits the full potential of AI in the field. Integrating AI can transform accounting by automating routine tasks, enhancing data analysis, increasing accuracy, efficiency, and productivity, as well as improving decision-making. This integration creates opportunities for scholars, practitioners, and researchers in information technology and accounting to collaborate, navigating the complex terrain where these disciplines intersect and driving future exploration and innovation in the integration of AI in accounting.2018 International Conference on Multidisciplinary Research00The study systematically evaluated scholarly works on the integration of AI in accounting and found that integrating AI can transform accounting by automating routine tasks, enhancing data analysis, increasing accuracy, efficiency, and productivity, as well as improving decision-making.2018 International Conference on Multidisciplinary Research["Melanie Bernice Cloete", "M. Swanepoel"]2024-11-30T00:00:00 d45d8f4212f31fd34cdfc6294e4d8c7554c63befAs cyber threats continue to grow in scale and sophistication, traditional cybersecurity solutions have become increasingly insufficient to mitigate evolving risks. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing cybersecurity by improving threat detection, automating response mechanisms, and preventing attacks before they occur. This review explores the intersection of AI and cybersecurity, focusing on AI-driven techniques in threat detection, automated response systems, and preventive measures. Furthermore, the paper discusses the challenges of deploying AI in cybersecurity, including adversarial attacks and ethical considerations, and provides future directions for research.International Journal of Science and Research Archive380A review explores the intersection of AI and cybersecurity, focusing on AI-driven techniques in threat detection, automated response systems, and preventive measures, and the challenges of deploying AI in cybersecurity, including adversarial attacks and ethical considerations.International Journal of Science and Research Archive["Chigozie Kingsley Ejeofobiri", "Adedoyin Adetumininu Fadare", "Olalekan Olorunfemi Fagbo", "Valerie Ojinika Ejiofor", "Adetutu Temitope Fabusoro", "Peter Onukak", "Sunday Aluko"]2024-11-30T00:00:00 37110ff7ba9f250a2ca25d1e056373792bbd5d6cThe integration of Artificial Intelligence (AI) into judicial decision-making processes is reshaping the administration of justice, offering new possibilities for efficiency and consistency while raising critical concerns about fairness and judicial discretion. This research explores the use of AI to support judges in their decision-making processes, focusing on the balance between technological assistance and the human consideration of justice. Additionally, it examines the regulatory frameworks that enable and govern AI’s role in the judiciary. Employing a normative juridical research methodology and secondary data from literature reviews, this study finds that AI systems can only assist judges by providing recommendations rather than replacing their judgment. Algorithms and mathematical models cannot fully account for the complex, qualitative factors inherent in the pursuit of justice, such as moral considerations, empathy, and societal context. Currently, AI usage in judicial processes is not explicitly regulated; however, it may be justified under Article 1 Point 8 of Indonesia’s ITE Law, which recognizes electronic agents controlled by humans to achieve specified objectives. The research underscores that AI’s potential lies in its capacity to complement human judges by enhancing efficiency and reducing bias in routine tasks, while ultimate decision-making must remain a human responsibility. This study concludes that the responsible use of AI, guided by clear legal and ethical frameworks, can ensure that technological advancements in the judiciary uphold the principles of justice and human dignity.Journal of Indonesian Legal Studies00This study concludes that the responsible use of AI, guided by clear legal and ethical frameworks, can ensure that technological advancements in the judiciary uphold the principles of justice and human dignity.Journal of Indonesian Legal Studies["Syarifah Lisa Andriati", "Inneke Kiki Rizki", "Ain Najwa Binti Mohd Malian"]2024-11-30T00:00:00 489554cbb0017517e736138006d89faa13431cc8Artificial intelligence (AI) has emerged as a powerful tool in transforming drug discovery, formulation, and testing within the pharmaceutical industry. AI is transforming drug discovery by analyzing large-scale biological data, such as genomics and proteomics, to identify disease-related targets. One of the major benefits of AI in drug discovery is its possibility to reduce the costs of research and development (R&D). It can predict crucial aspects of drug behavior, such as pharmacodynamics (PD) and pharmacokinetics (PK) —how a drug works in the body and how the body responds to the drug. By analyzing real-world patient data, AI can help create personalized treatment plans, improving outcomes by tailoring drugs to individual patient profiles. It also plays a part in drug delivery optimization, including designing more efficient pharmaceutical dosage forms. AI can optimize manufacturing process, which enhance consistency, and quality control. While the possibilities for AI in drug discovery is vast, there are also challenges. One key issue is the need for high-quality, well-curated data. Despite the challenges, the investment in AI technologies in the pharmaceutical industry presents exciting opportunities. In summary, there is immense possibilities with AI holds to enhance drug development by improving efficiency, reducing costs, and enabling more personalized treatments. This review outlines the role of AI and current pharmaceutical challenges.International Journal of Science and Research Archive00There is immense possibilities with AI holds to enhance drug development by improving efficiency, reducing costs, and enabling more personalized treatments, according to this review.International Journal of Science and Research Archive["Guna Ranjan Kolli", "Prabhakar Orsu"]2024-11-30T00:00:00 949317c85f439c67f2af3f4b4b0641788ab1a3a9This study explores public perceptions of Artificial Intelligence (AI), focusing on its perceived capabilities, potential risks, and societal implications. A questionnaire consisting of 15 questions was distributed to five respondents, yielding valuable insights into how individuals view AI's role in the future. The results indicate a general understanding of AI as a sophisticated, robot-like technology capable of mimicking human behavior, though not fully replicating human consciousness. While most respondents acknowledged AI’s potential to advance technology and improve daily life, there were concerns regarding its safety and the threat it poses to human employment. Specifically, three respondents expressed fear that AI could lead to global job displacement, highlighting the ongoing debate surrounding the automation of routine tasks. Despite these concerns, all respondents agreed that AI is essential for human advancement and should continue to evolve. Additionally, the study revealed that AI is already integrated into participants' lives, through gadgets and search engines like Google, demonstrating its growing presence in everyday technology. However, there was a divide on the level of intimidation AI causes, with four respondents feeling uneasy about its impact, while one remained unperturbed. The study concludes that while AI offers significant benefits, such as improving productivity and efficiency, its development must be approached with caution, ensuring ethical considerations and regulatory oversight to mitigate potential risks. These findings underscore the need for ongoing dialogue and responsible AI governance to balance innovation with societal well-being.Cognizance Journal of Multidisciplinary Studies00While AI offers significant benefits, such as improving productivity and efficiency, its development must be approached with caution, ensuring ethical considerations and regulatory oversight to mitigate potential risks, the study concludes.Cognizance Journal of Multidisciplinary Studies["Leah Rose L. Paran", "Jacqueline C. Maleptey", "Jovy L. De Leon", "Audrey M. Calines", "Myra Sol M. Calicdan"]2024-11-30T00:00:00 dc950b22f8e9e3601237378289b8aa17dbda0b15Over the course of human history, we have experienced various transformations that have altered how we conduct business in the real world. In the age of Industry 4.0, Artificial Intelligence (AI) has emerged as a crucial asset, enabling businesses to attain market competitiveness. This article aims to provide a comprehensive understanding of AI and its impact on different types of businesses. Although AI is revolutionary in many aspects, it necessitates several considerations before an organization can adopt this capability. Therefore, the article comprehensively analyzes how AI influences modern businesses by examining their real-world operations.International Journal for Research in Applied Science and Engineering Technology00This article comprehensively analyzes how AI influences modern businesses by examining their real-world operations and provides a comprehensive understanding of AI and its impact on different types of businesses.International Journal for Research in Applied Science and Engineering Technology["Visakh Chandran Melveetil"]2024-11-30T00:00:00 b209fc92d98860806707a9e2675ca60440f1aff9Technological evolution, especially in artificial intelligence (AI), has significantly changed digital marketing, specifically customer-targeted experience. Machine learning, recommendation systems, and predictive analytics have made it possible for enterprises to personalize content, products, and services, among other things, on a very large scale. Nevertheless, all these advances have come with large ethical and privacy concerns that are yet to be met. This paper discusses how AI can enhance customers' personalization to analyze the moral and privacy considerations involved. The research examines how machine learning marketing strategies work in the digital environment and real-life examples of organizations that successfully implement these approaches and potential issues. It highlights how individuals might buy more products and make businesses more profitable with customized advertising techniques. However, the paper also describes the moral problems of AI that use personal data without permission, artificially intelligent algorithms that affect decision-making, and artificial intelligence decision-making. At the same time, its work has yet to be well known. The paper looks into privacy risks like data breach incidents, privacy violations, GDPR, and CCPA. This paper adopts an exploratory case study research design as it gathers academic papers, industry examples, and expert opinions to present a balanced view of the effects of AI. Based on the research, conclusions can be drawn that great opportunities AI opens for personalization should be met with safety considerations and working ethical guidelines. In the end, the paper presents guidelines that should help businesses integrate trust and compliance into creating and using AI technologies.International Journal of Science and Research Archive00International Journal of Science and Research Archive["Orcun Sarioguz", "Evin Miser"]2024-11-30T00:00:00 07fd5899d64475c2faedc4b010a48c42a2404025Biofeedback is a technique that relies on measuring bodily functions and providing feedback to the individual so that they can train and control those functions. Artificial intelligence has empowered these systems by making them context-aware, adapting models to users' physiological variations, and providing personalized feedback. However, incorporating AI techniques has opened up new challenges in designing, developing, and evaluating biofeedback systems. In this work, we conducted 25 semi-structured interviews with various specialists in medicine, psychology, human-computer interaction, and computer science to investigate what an AI-based system that considers differences in personal health data should look like. The results helped us answer ‘How can we design AI-assisted systems that take into account differences in personal physiological and AI knowledge between individuals to avoid misinterpretations?’ by defining six design considerations for biofeedback systems that use AI techniques trained with users' physiological signals. Finally, we discuss how these considerations could help researchers design systems for well-being.Avances en Interacción Humano-Computadora140The results helped answer ‘How can the authors design AI-assisted systems that take into account differences in personal physiological and AI knowledge between individuals to avoid misinterpretations?’ by defining six design considerations for biofeedback systems that use AI techniques trained with users' physiological signals.Avances en Interacción Humano-Computadora["Arturo Morales", "Concepcion Valdez", "Yanitza Stambor", "Luis A. Castro", "M. Tentori"]2024-11-30T00:00:00 341db99f90f72944447aa10eeb54b4385b614764Tourism is a vital economic sector in South Africa, attracting a large number of visitors who come to experience the diverse range of tourist attractions the country has to offer. In its efforts to achieve sustainability and competitiveness, the industry is incorporating Artificial Intelligence (AI) into its operations. This review explores the transformative role of AI in South Africa's tourism sector, highlighting both current applications and future potential. This paper aims to illustrate how AI enhances the tourist experience and operational efficiency. Furthermore, it will map the way forward by identifying challenges and proposing strategic initiatives for stakeholders to harness AI's full potential in promoting sustainable tourism growth. A qualitative approach was employed in reviewing thirty articles published in peer-reviewed journals. The results indicate that AI has a significant potential to enhance the competitiveness of the tourism sector in South Africa. However, the integration of AI into the tourism sector might have a plethora of problems such as data privacy concerns, the fear of retrenchments and worker disenfranchisement. The paper suggests that significant measures are required to maximise the potential benefits of AI. The paper proposes collaboration between tech companies and tourism operators and the development of technology pro-policy initiatives to support AI integration. Further research is necessary to explore strategies for mitigating any adverse effects of AI in tourism, with a focus on boosting job opportunities and improving the socio-economic welfare of all stakeholders in the sector.2018 International Conference on Multidisciplinary Research00It is suggested that significant measures are required to maximise the potential benefits of AI and collaboration between tech companies and tourism operators and the development of technology pro-policy initiatives to support AI integration are proposed.2018 International Conference on Multidisciplinary Research["Taemane Phoofolo", "Joram Ndlovu"]2024-11-30T00:00:00 ae5bfc4e0f08475a2edb7838a03cbd522df8c1c3Technological developments can no longer be avoided, including in the field of school education. One of the subjects that can be integrated with the use of technology is English. However, in reality, not all teachers are able to implement technology-based English learning. Limited teacher references on learning technology and inadequate facilities are the main problems faced. Through Community Service (PkM) activities, the service team introduced the use of Artificial Intelligence (AI) in English learning at a school in Palu City, Central Sulawesi. The method used in this community was the literature review method, where the service team reviewed related references, applied them to students, then draw conclusion from the community service activity that had been carried out. Relying on the great benefits and ease of implementation, the use of AI can be a leading technology for teachers to promote modern language learning. The result of the community service indicated that the integration of AI in English learning showed positive results for students, such as increasing their enthusiasm and motivation for learning English.Jurnal Pengabdian Masyarakat200Through Community Service (PkM) activities, the service team introduced the use of Artificial Intelligence (AI) in English learning at a school in Palu City, Central Sulawesi, showing positive results for students, such as increasing their enthusiasm and motivation for learning English.MEKONGGA: Jurnal Pengabdian Masyarakat["Moh. Abraham Akbar Eisenring"]2024-11-30T00:00:00 bdec46ad749bef744a2de6d61d636f68fc3aea2aArtificial intelligence (AI) is undergoing rapid development globally, including in countries such as Taiwan and Indonesia. Taiwan, renowned as a world leader in hardware and semiconductor technologies, holds a significant advantage in AI advancement. This is further bolstered by governmental support through regulatory frameworks, policies, and funding initiatives, enhancing Taiwan's prowess in AI development. In contrast, Indonesia has also embraced the tide of technological progress, with its President declaring the nation's commitment to entering the 4th industrial revolution. Integral to this transition is the adoption of AI, recognized as a pivotal component of the aforementioned revolution. The collective technological advancements across Indonesia, Taiwan, and other nations invariably impact society, particularly the workforce. The integration of these futuristic technologies, predominantly within corporate settings, inherently alters labor dynamics and working conditions. This study scrutinizes the trajectories of AI development in both Taiwan and Indonesia, probing the compatibility of existing occupational safety and health legislation with the AI era. The findings underscore the perpetual evolution of technology and emphasize the imperative for nations to remain adaptive to emerging innovations. Furthermore, the study advocates for continuous updates to legal frameworks to align with the dynamic landscape of technological advancement.Journal of Indonesian Legal Studies00This study scrutinizes the trajectories of AI development in both Taiwan and Indonesia, probing the compatibility of existing occupational safety and health legislation with the AI era and advocating for continuous updates to legal frameworks to align with the dynamic landscape of technological advancement.Journal of Indonesian Legal Studies["Andi Agus Salim", "Shu-Mei Tang"]2024-11-30T00:00:00 cc2791733a81b0e22982970660fdc7a7f0de4fa1This in-depth research explores the emerging relationship between artificial intelligence (AI) and legal systems by addressing key questions and understanding the evolution of global justice systems. This study focuses on the role of AI in strengthening the efficiency and objectivity of the judiciary, especially through the application of AI as judges in countries such as China and Estonia. This research aims to systematically analyse these developments, examining how AI is being integrated into justice systems in different parts of the world with challenges related to ethics, accountability, and human rights. The study results show that the integration of AI in the legal system brings increased efficiency and potential for transparency but also raises serious concerns about bias in AI algorithms, limitations in interpreting complex laws, and the impact on human rights principles. The main findings of this research show that the integration of AI in the legal system contains great potential for transformation but also requires a careful approach. While AI can improve the efficiency and quality of decision-making, it is important that AI is developed and implemented within a solid legal and ethical framework that respects human rights and maintains the justice system's integrity. This research emphasizes the need to consider each country's unique legal, cultural, and social context when adopting AI into their legal systems.Jurnal Hukum dan Peradilan00The main findings show that the integration of AI in the legal system contains great potential for transformation but also requires a careful approach, and the need to consider each country's unique legal, cultural, and social context when adopting AI into their legal systems.Jurnal Hukum dan Peradilan["Z. Fernando", "Ariesta Wibisono Anditya"]2024-11-30T00:00:00 fea971b5a61bd668397302b21cbce9f67d6381daThe classification of skin cancer is crucial as the chance of survival increases significantly with timely and accurate treatment. Convolution Neural Networks (CNNs) have proven effective in classifying skin cancer. However, CNN models are often regarded as "black boxes”, due to the lack of transparency in the decision-making. Therefore, explainable artificial intelligence (XAI) has emerged as a tool for understanding AI decisions. This study employed a CNN model, VGG16, to classify five skin lesion classes. The hyperparameters were adjusted to optimize its classification performance. The best hyperparameter settings were 50 epochs, a 0.1 dropout rate, and the Adam optimizer with a 0.001 learning rate. The VGG16 model demonstrated satisfactory classification performance. The Local Interpretable Model-Agnostic Explanations (LIME) method was implemented as the XAI tool to justify the predictions made by VGG16. The LIME explanation revealed that the correct predictions made by VGG16 were owing to its truthful extraction of the cancer or lesion area, especially for the “vascular lesion” class. Meanwhile, inaccurate classifications were attributed to VGG16 extraction of the background and insignificant parts of the skin as core features. In conclusion, The LIME model allowed visual inspection of the features selected by VGG16, paving the way for improving the CNN model for better feature extraction and classification of skin lesions, offering a promising direction for future research. JOIV: International Journal on Informatics Visualization00The LIME model allowed visual inspection of the features selected by VGG16, paving the way for improving the CNN model for better feature extraction and classification of skin lesions, offering a promising direction for future research.JOIV : International Journal on Informatics Visualization["Sin Yi Hong", "Lih Poh Lin"]2024-11-30T00:00:00 9be67901816b706a3a5e96165a7490e58600566aPurpose: The research studies the impact of various Artificial Intelligence (AI) tools on engineering project management and describes how they affect management success during emergencies in the Ukrainian context. Special attention is paid to the skills comprising AI readiness among engineers and their formation in the system of continuous education. Methodology/Approach: The study employed a perception-based analysis and applied the social cognitive theory and expectancy theory, enabling structured opinion-based questionnaires on Likert scales and assessing engineering project success after the introduction of AI tools. The research involved 96 engineers with diverse roles and expertise representing civil engineering companies or military organisations. Findings: The research explained the peculiarities of some challenges hindering engineering project management and defined traditional, agile, and hybrid engineering project management used during emergencies. 25 AI tools were outlined, and their impact on project success was revealed. The category of AI readiness was described, and its components were presented. Research Limitation/implication: Since the perception-based analysis relies on subjective views, it may not fully present the objective evaluation of AI tools in engineering project management during emergencies. The findings can be used to improve engineers' training programs by integrating AI-focused modules. Originality/Value of paper: The research provides a context-specific understanding of how AI can be applied to the unique challenges faced in Ukraine.Kvalita Inovácia Prosperita30The research provides a context-specific understanding of how AI can be applied to the unique challenges faced in Ukraine and can be used to improve engineers' training programs by integrating AI-focused modules.Quality Innovation Prosperity["Volodymyr Sobchenko", "Andrii Bashynskyi", "Maksym Piatkov", "Serhii Shaforost", "Viktor Chmyr"]2024-11-30T00:00:00 b2da14b8cddc5db55a168a4a9dbad22265c9b9f1Despite various initiatives taken by the government, Malaysia shows a trend of increasing migrant smuggling. Weaknesses in the border control management have resulted in the escape of smugglers at the border gate. Thus, this article aims to assess the potential application of artificial intelligence (AI) technology at the Malaysia-Thailand land border in enhancing border security. Based on a series of interviews with relevant ministries, enforcement agencies, technology experts, academic experts and other stakeholders, as well as secondary sources, this study found that AI technology has the potential to overcome issues pertaining to weak border control management faced by the Royal Malaysian Police (RMP), the main enforcement agency that manages the Malaysia-Thailand land border along with other enforcement agencies. Among the issues are a lack of enforcement, integrity concerns and limited manpower to control Malaysia’s porous borders. However, some strategies should be taken to adapt AI to our border security system. Thus, this article analyses the concept of Integrated Border Management (IBM) to obtain coordination and cooperation among all relevant authorities and countries involved in the adaptation of AI technology at the Malaysia-Thailand land border. This article also analyses how Malaysia should respond to the ethical implications of AI technology. This study will help promote a cross-border collaboration network between Malaysia and related foreign countries in dealing with migrant smuggling issues. e-Bangi Journal of Social Science and Humanities00It is found that AI technology has the potential to overcome issues pertaining to weak border control management faced by the Royal Malaysian Police (RMP), the main enforcement agency that manages the Malaysia-Thailand land border along with other enforcement agencies.e-Bangi Journal of Social Science and Humanities["Norilyani Md Nor", "Marina Abdul Majid", "Andika Ab.Wahab"]2024-11-30T00:00:00 1d0d23b313f47151fee5fa85ddc692218833a044Generative artificial intelligence (GAI) is capable of creating original works with such a remarkable degree of autonomy that it makes no sense to be considered or analogized to traditional technologies that are merely used by humans. The human who provides the initial input (prompt) to the GAI does not make sense to be considered as the author of the GAI's self‐created works. The internal characteristics of GAI that enable it to create its own works and the works per se challenge the four prevailing justifications in Indonesian Copyright Law: the biological humans, the idea‐expression dichotomy, the Hegelian, and the Lockean justifications. This research finds the problem that there is a legal vacuum in the copyright regime in Indonesia regarding the legal status of GAI's self‐ created works. The research uses normative legal research method; with theoretical, symbolic logic legal interpretation––which assists in logical modeling of new legal provisions, comparative law, and conceptual approaches; this research aims to answer the legal lacuna which can be addressed with the proposed solution of placing GAI's self‐created works as sui generis and should be put into the public domain with attribution given to GAI which is logically coherent, efficient, and in line with existing justifications and principles of copyright. However, this research also found that the Indonesian copyright regime itself does not formally acknowledge the concept of public domain, which complicates it compared to the United States Copyright Law, hence the urgency to revise it by adopting and adding a concrete formulation of public domain––as elaborated in this article––which is different from public domain in the informal sense, and then adding the formulation of attribution provisions to GAI.Journal of World Intellectual Property00The problem that there is a legal vacuum in the copyright regime in Indonesia regarding the legal status of GAI's self‐ created works is found and the proposed solution of placing GAI's self‐created works as sui generis and put into the public domain with attribution given to GAI which is logically coherent, efficient, and in line with existing justifications and principles of copyright.The Journal of World Intellectual Property["Ghazali Hasan Nasakti", "Rianda Dirkareshza"]2024-11-30T00:00:00 af1fd00c7823cf67c98ded4dae3cc2c2788e43d9The rapid advancement of Artificial Intelligence (AI) offers transformative potential in addressing some of the most pressing global challenges: rising insecurity, ineffective governance (kakistocracy), and economic downturns. AI’s ability to analyze vast datasets, predict outcomes, and optimize processes positions it as a critical tool for mitigating the impacts of these complex issues. In the realm of security, AI enhances threat detection and prevention through predictive policing, real-time surveillance, and cybersecurity solutions. Tools like machine learning algorithms and anomaly detection systems can proactively identify and neutralize threats ranging from terrorism to cyberattacks. In governance, AI-driven technologies can improve transparency, accountability, and efficiency by automating bureaucratic processes and enabling data-driven policymaking. Block chain-integrated AI systems, for example, ensure tamper-proof public transactions, reducing corruption and inefficiency in public administration. Meanwhile, economic downturns can be mitigated using AI’s predictive capabilities, which allow early identification of financial crises, and its role in optimizing automation can boost productivity and streamline recovery efforts. However, deploying AI is not without risks. Issues such as bias, privacy infringements, and the centralization of power must be carefully addressed to avoid unintended consequences, such as exacerbating inequalities or enabling authoritarianism. This paper explores the multifaceted applications of AI in these contexts, underscoring its promise and the necessity for robust ethical and regulatory frameworks to guide its implementation. Through balanced integration, AI can serve as a powerful ally in navigating global insecurity, governance challenges, and economic instability.International Journal of African Sustainable Development Research00The multifaceted applications of AI in these contexts are explored, underscoring its promise and the necessity for robust ethical and regulatory frameworks to guide its implementation.International Journal of African Sustainable Development Research["MAMMAN ALIYU SALISU", "IWANGER RUTH SAMUEL"]2024-11-30T00:00:00 e6d7495b44017fe1275dc41e278468e43d807d93The use of Artificial Intelligence (AI) indicates the beginning of a new era in the development of digital technology. In general, AI's capabilities are considered to be able to solve problems which have been experienced by professionals, including AI robots which have also been widely used in law enforcement processes. However, the development of AI in law enforcement is certainly not without obstacles in which it is marked by the existence of a legal vacuum which forms the basis for the legality of AI use and a lack of literacy among law enforcers regarding the use of AI. In addition, law enforcement officials in Indonesia are still less aware of the benefits of using AI in order to support their profession. The aim of this study is to analyze the urgency of the implementation of AI for law enforcement in providing legal services and law enforcement processes. The research method used was a normative legal method with a statutory approach and a conceptual approach. Moreover, the analysis was conducted qualitatively and presented descriptively. The result shows that Artificial Intelligence (AI) is very important in helping develop services and law enforcement in which law enforcers in Indonesia so far still rely on manual or conventional methods in conducting their duties. Furthermore, AI can provide benefits in terms of time efficiency and accuracy in assessing cases which are urgently needed by law enforcement. Meanwhile, in terms of law enforcement's perception of the use of AI, they position AI as an assistant which cannot completely replace the law enforcement profession since AI does not have the human characteristics which law enforcement officers should have.Perspektif Hukum570AI is very important in helping develop services and law enforcement in which law enforcers in Indonesia so far still rely on manual or conventional methods in conducting their duties and AI can provide benefits in terms of time efficiency and accuracy in assessing cases which are urgently needed by law enforcement.Perspektif Hukum["Feby Milenia Yahya Krisna Putri", "H. A. Hakim", "Chrisna Bagus Edhita Praja", "Gerald Espares"]2024-11-30T00:00:00 ad71f1c4935197fc25931d9a0c592b94383970c6Artificial Intelligence (AI) has an important role to play in shaping the future of software development. AI responds to complex challenges in the information technology industry and expands the scope of future possibilities, which include increased automation, personalization, and security. The research aims to identify the role of AI in education and research from various aspects of software development, and evaluate the resulting implications for information technology as a whole. The research adopted the Systematic Literature Review Method following PRISMA guidelines. A total of 320 articles were collected from Scopus, Web of Science and Google Scholar and applying predefined criteria, 42 relevant articles were included for analysis. The research findings show that the role and integration of artificial intelligence (AI) has a significant impact in improving efficiency, bringing software innovation in education, learning and research in the future. AI has proven effective in personalizing learning, adapting teaching materials and improving student learning outcomes. AI accelerates the process of analyzing big data, identifying patterns and trends that conventional methods may miss. The implications of the findings suggest that the integration of AI in education and research not only improves the efficiency and effectiveness of the process, but opens up new opportunities for innovation and development of more adaptive and data-driven learning and research methods. The challenges of AI in education and research include data privacy, potential bias in algorithms, and the need for adequate technological infrastructure to support effective and secure implementation, avoid inequality of access, and ensure accurate results.Journal of Vocational Education Studies00The research findings show that the role and integration of artificial intelligence (AI) has a significant impact in improving efficiency, bringing software innovation in education, learning and research in the future.Journal of Vocational Education Studies["Kusmiadi Kusmiadi", "Didin Wahyudin"]2024-11-30T00:00:00 9bd121da4718fd3d0b7387cbc4979d92970d95a1In recent years, we have witnessed a marked development and growth in Artificial Intelligence. The growth of the data volume generated by sensors and machines, combined with the information flow resulting from the user actions on the Internet, with high investments of the governments and the companies in this area, provided the practice and developed the algorithms of the Artificial Intelligence However, the people, in general, started to feel a particular fear regarding the security and privacy of their data and the theme of the Artificial Intelligence Ethics began to be discussed more regularly. The investigation aim of this work is to understand the possibility of adopting Artificial Intelligence nowadays in our society, having, as a mandatory assumption, Ethics and respect towards data and people's privacy. With that purpose in mind, a model has been created, mainly supported by the theories that were used to create the model. The suggested model has been tested and validated through Structural equation modeling based on data taken back from the respondents' answers to the questionnaire online: 237 answers, mainly from the Investigation Technologies area. The results obtained enabled the validation of seven of the nine investigation hypotheses of the proposed model. It was impossible to confirm any association between the Social Influence construct and the variables of Behavioral Intention and the Use of Artificial Intelligence. The aim of this work was accomplished once the investigation theme was validated and proved that it is possible to adopt Artificial Intelligence in our society, using the Attitude Towards Ethical Behavioral construct as the mainstay of the model.arXiv.org240The aim of this work was to understand the possibility of adopting Artificial Intelligence nowadays in the authors' society, having, as a mandatory assumption, Ethics and respect towards data and people's privacy, and prove that it is possible to adopt Artificial Intelligence in their society, using the Attitude Towards Ethical Behavioral construct as the mainstay of the model.ArXiv["Martim Veiga", "Carlos J. Costa"]2024-11-30T00:00:00 5c625b6c55452f81cce9fe0a2e958f345a41c783Abstrak: Meniti arus kemodenan, teknologi tidak dapat lagi dipisahkan dalam kehidupan seharian, bahkan menjadi bahagian integral dalam aspek kehidupan. Artificial Intelligence atau lebih dikenali sebagai AI merupakan cabang dalam ilmu perkomputeran yang dicipta berkeupayaan       melakukan tugas sama seperti intelektual manusia walaupun tanpa kawalan manusia. AI telah membawa perubahan besar dalam pelbagai sektor termasuk ekonomi, perniagaan, kesihatan dan pendidikan kerana kemampuannya mencerna maklumat, mengolah data, menyelesaikan masalah. AI juga mampu menganalisis pola yang kompleks secara tepat dan cepat menjadikannya medium yang sangat berguna dalam meningkatkan kecekapan dan produktiviti manusia. Namun begitu, setanding dengan kemampuannya yang luar biasa tentunya terdapat cabaran dan keburukan yang harus diperhati dan ditangani. Dalam memberikan pemahaman yang menyeluruh terhadap topik yang dibincangkan, kajian ini menggunakan metodologi kualitatif dengan mengumpul data daripada sumber primer seperti Al-Quran dan Sunnah serta sumber sekunder seperti jurnal, penerbitan dan berita. Hasil daripadanya mendapati bahawa al-Adl (keadilan), Hifz al-Khususiyyah (menjaga hak privasi), Al-Musaalat wa as-Syafaafiyat (transparensi dan akauntabaliti), al-Musaalat wa as-Syafaafiyat (tiada diskriminasi) dan Hifz Izzah al-Insan (menjaga kehormatan manusia) adalah prinsip asas dalam memperkasa model pendidikan AI berasaskan dalil naqli dan aqli. Artikel ini diharapkan dapat membantu badan berautoriti, para penyelidik dan pembangun perisian AI dalam memastikan bahawa teknologi AI yang dibangunkan tidak bertentangan dengan nilai dan etika Islam agar manfaatnya dapat dirasai secara menyeluruh.   Abstract: In the modernity paradigm, technology is inevitable and cannot be isolated from daily life, even it becomes an intrinsic component. Artificial Intelligence, known as AI, is a branch of computer science that is created with the ability to perform exact human intellectual tasks even without explicit guidance from a human operator. AI has brought enormous change to many sectors including economy, business, health, and education, because of its capacity to digest information, process data, solve problems and analyze intricate patterns accurately and swiftly making it a very helpful tool for boosting productivity and efficiency. Notwithstanding, compared to its remarkable abilities, there are certainly challenges and disadvantages that must be observed and dealt with. In order to provide a thorough grasp of the topics discussed, this study uses a qualitative methodology by collecting data from primary sources like the Quran and Sunnah as well as secondary sources like journals, publications, and news. The results found that al-Adl (justice), Hifz al-Khususiyyah (protect privacy rights), al-Musaalat wa as- Syafaafiyat (transparency and accountability), ‘Adam al-Unsuriyyah (non-discrimination) and Hifz Izzah al-Insan (protect human dignity) are fundamental principles in empowering the AI education model based on naqli and aqli arguments. This article is expected to help authoritative bodies, researchers and AI software developers ensure that the AI technology developed is in line with Islamic values and ethics so that its benefits can be fully yielded.Sains Insani00Sains Insani["Nurul Thaqifah Mat Arop", "Setiyawan Gunardi"]2024-11-30T00:00:00 c7c6100c7ce62b98f5d4f136bff377ee483552d1We are writing to address the growing interest in the role of artificial intelligence (AI) within healthcare, particularly in the field of reproductive health. As technology continues to evolve, AI offers an unprecedented opportunity to transform how we diagnose, treat, and improve access to reproductive services, especially in underserved communities. AI-driven tools, supported by machine learning and big data analytics, are already demonstrating their potential in enhancing outcomes in reproductive health. These tools can predict fertility outcomes with impressive accuracy, optimize in vitro fertilization (IVF) success rates, and identify early signs of reproductive disorders, such as endometriosis, polycystic ovary syndrome (PCOS), and ovarian cancer. By analyzing biomarkers, medical histories, and lifestyle factors, AI algorithms empower healthcare providers to deliver personalized and effective treatment plans tailored to individual needs.African Journal of Reproductive Health00To address the growing interest in the role of artificial intelligence within healthcare, particularly in the field of reproductive health, AI offers an unprecedented opportunity to transform how to diagnose, treat, and improve access to reproductive services, especially in underserved communities.African journal of reproductive health["Vaageessan Masilamani", "Ragothaman Bharathyuvaraj", "Velayudam Ramalingam Elangovan", "Hema Ramji", "Mohanraj Subramanian", "M. Saravanan", "Devaraj Jothimani", "Fenn Moses", "Mani Megalai", "Venu Gopal", "Samuel Duraivel"]2024-11-30T00:00:00 b3ed09afff9881f943dc314f794b8a1557c31243The construction sector occupies one of the major roles within the global economy, and 13% of world GDP currently comes from this industry. It is also expected to expand by 85%, to a value of $15.5 billion by 2030, with demand in China, the United States, and India acting as the major drives. The growth notwithstanding, the sector has been confronting serious problems with regard to arranging and disseminating large quantities of information among subcontractors, contractors, designers, clients, and other stakeholders with efficiency. Information Technology has emerged as a key enabler, thereby integrating scattered data across geographically dispersed projects, transforming the construction value chain. Artificial Intelligence accelerates this change, and the increasing investment demonstrates the possibility of boosting workforce productivity by 40 percent and doubling economic growth rates annually by 2035. This paper discusses the application of Artificial Intelligence (AI) in Construction Project Management (CPM) and reviews methodologies and applications that have been advanced for the improvement of efficiency and decision-making in the industry. The construction industry is bound to mark a tremendous change by the implementation of Artificial Intelligence (AI), redefining conventional processes and opening up innovation and productivity to new heights.International Journal For Multidisciplinary Research270The application of Artificial Intelligence (AI) in Construction Project Management (CPM) is discussed and methodologies and applications that have been advanced for the improvement of efficiency and decision-making in the industry are reviewed.International Journal For Multidisciplinary Research["Sushil Mahato", "Aryan Dipak Raut", "Aryan Suraj Raut", "Jyoti Yadav", "Aakriti Lama"]2024-11-30T00:00:00 3700ed38f6a714f03e73aaac23bcddf49b76b758This article examines the transformative impact of artificial intelligence on scenario analysis and what-if simulations in business analytics, addressing a critical gap in current literature regarding the integration of AI-driven predictive modeling with traditional business planning methodologies. Through a systematic analysis of implementation cases across financial services and e-commerce sectors, the article demonstrates how AI-enhanced simulation models significantly improve the speed, accuracy, and adaptability of scenario planning processes. The findings indicate substantial improvements in analysis time and prediction accuracy compared to traditional methods, particularly in areas of pricing optimization, risk assessment, and stress testing. The article synthesizes data from multiple enterprise implementations to develop a comprehensive framework for AI integration in scenario analysis, addressing key challenges in data quality, model reliability, and real-time processing capabilities. Results suggest that organizations leveraging AI-driven scenario analysis demonstrate enhanced capability in anticipating market fluctuations, optimizing resource allocation, and responding to environmental changes, though implementation success is heavily dependent on data infrastructure maturity and organizational readiness. This article contributes to both theoretical understanding and practical application of AI in business analytics, providing actionable insights for practitioners while identifying critical areas for future research in the evolving landscape of intelligent business simulation technologies.International Journal of Scientific Research in Computer Science Engineering and Information Technology10Results suggest that organizations leveraging AI-driven scenario analysis demonstrate enhanced capability in anticipating market fluctuations, optimizing resource allocation, and responding to environmental changes, though implementation success is heavily dependent on data infrastructure maturity and organizational readiness.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Deepti Bitra"]2024-11-30T00:00:00 8d74cf00a6a2cb3f2fe66fb7d42da033203287a3The purpose with this mixed-methods research was to examine students’ success in using artificial intelligence. The research sample consisted of 50 first-grade primary school students, 239 parents, and 25 primary school teachers studying in Almaty, Kazakhstan. A descriptive analysis technique was used to analyse the qualitative data. Findings are explained in themes. As a result of the research, it has been observed that parents’ attitudes towards their children’s use of technology were high. Most primary school teachers who participated in the research stated that students were interested in artificial intelligence, they supported the use of artificial intelligence technologies to some extent, and they found the students partially successful in this regard. Students participating in the research defined artificial intelligence as technology, computers that think like humans, smart machines, entertaining and educational computer content, robots that obey given commands, and technological devices that make life easier. Most students stated that they liked using artificial intelligence and that they found themselves somewhat successful in using artificial intelligence.South African Journal of Education350It has been observed that parents’ attitudes towards their children’s use of technology were high and most primary school teachers who participated in the research stated that students were interested in artificial intelligence, and they supported the use of artificial intelligence technologies to some extent.South African Journal of Education["Elmira Uaidullakyzy", "A. K. Oralbekova", "A. Dosbenbetova", "Baibekov Yerubay", "Bauyrzhan Amangazyuly Nauryzbayev", "R. Turmanov"]2024-11-30T00:00:00 d50b6e75967f4ce5092dcaaebe84d8bb2813181eThe present study aims to determine the impact of the Artificial Intelligence (AI) tool on ESL students’ writing skills in Saudi Arabia. A mixed-method approach was designed in this study. A t-test approach was used for the quantitative research. The mean of the pre-test was 51.8, while the mean of the post-test was 79.46. The significant difference in the scores highlighted the positive influential effect of the AI tools on the ESL students’ writing skills. A focus group interview of five students was conducted for the qualitative analysis. The focus group participants discussed the following codes: AI’s impact on content organization, grammar, and vocabulary, as well as the strengths and weaknesses of the AI tools. The interview highlighted that AI tools benefited students’ learning by providing chances in writing patterns, their improvement, and their structures to show the flow of coherent patterns. There were also disparities in AI tool use, which included the possibility of coming up with content not being referred to as human-like, failure to recognize creativity, and capacity to cheat. This research can help future researchers and instructors explore how to use AI tools for writing effectively, enhancing the strengths and diminishing the possible weaknesses.Erudita: Journal of English Language Teaching00The interview highlighted that AI tools benefited students’ learning by providing chances in writing patterns, their improvement, and their structures to show the flow of coherent patterns, as well as the strengths and weaknesses of the AI tools.Erudita: Journal of English Language Teaching["Samah Abduljawad"]2024-11-30T00:00:00 b782872d4c975836db2ca836884b55eacdcc79f8Artificial Intelligence has become an inseparable part of everyday life. Artificial Intelligence has been designed to help humans in various ways. included in the legal profession of an advocate to serve clients. Artificial Intelligence can be a tool in serving clients by not eliminating or changing the function and position of the advocate profession by considering, examining the data or documents provided by Artificial Intelligence. Advocates can use Artificial Intelligence to automate data document processing. This includes classifying documents, highlighting important issues, and extracting relevant information from them. By using Artificial Intelligence document processing can be done quickly and efficiently, thereby saving time and costs. With various types of Artificial Intelligence, of course there are errors and losses for users that cannot be avoided. In the use of Artificial Intelligence, it is found that the injured party is the client. With the discrepancy in the data results provided by Artificial Intelligence, this causes the client to be harmed because there is a discrepancy in the data and facts which makes the client feel that there is an error that the data processing robot should have carried out the data analysis to produce result B even though the data.Journal of Law, Politic and Humanities110Advocates can use Artificial Intelligence to automate data document processing, which includes classifying documents, highlighting important issues, and extracting relevant information from them, thereby saving time and costs.Journal of Law, Politic and Humanities["Sholikhatus Hidayati", "Uswatun Hasanah"]2024-11-30T00:00:00 d0a1f3f43244107de6ce74ce1e24668893069cd3The widespread adoption of digitalization and artificial intelligence, alongside the abundance of big data, has significantly transformed societies. Recently, there has been an increasing interest in leveraging big data and artificial intelligence to capture and analyze social transformative change in evaluation. However, there is no consensus on the ethical and appropriate use of these tools in evaluation. This article used a systematic literature review to provide an overview of using big data and artificial intelligence for evaluation purposes, identifying challenges faced. Unresolved issues encompass ethical, methodological, and ownership concerns. The study suggests ways to address these challenges and advocates for united efforts to mix big data and artificial intelligence with traditional approaches. To achieve this, it emphasizes the necessity of leveraging interconnected data platforms, mitigating ethical risks, and enhancing evaluators’ competencies in computer and data science, which is essential for the integration of big data and artificial intelligence in the evaluation field.Evaluation780The necessity of leveraging interconnected data platforms, mitigating ethical risks, and enhancing evaluators’ competencies in computer and data science is emphasized, which is essential for the integration of big data and artificial intelligence in the evaluation field.Evaluation["S. E. Bouyousfi", "Mich\u00e9 Ou\u00e9draogo"]2024-11-30T00:00:00 2d77597d92af6911f20cb119766ff942bd66aa81Abstract: Artificial intelligence (AI) is developing at a rapid pace, which has significantly changed marketing techniques in a variety of sectors. This study offers a thorough investigation of how artificial intelligence (AI) affects marketing techniques, looking at how it affects both organizational procedures and customer behavior. Artificial intelligence (AI) technologies—such as machine learning, natural language processing, and predictive analytics—have brought out cutting-edge solutions that let companies improve consumer interaction, maximize campaign success, and extract useful insights from massive data sets. The paper starts by investigating the development of man-made intelligence in advertising, following its mix from early robotization apparatuses to refined, information driven frameworks. It features how man-made intelligence driven personalization has upset client connections, permitting advertisers to convey profoundly custom fitted encounters in view of individual inclinations and ways of behaving. Through ongoing information investigation and division, man-made intelligence works with accuracy focusing on and dynamic substance conveyance, prompting expanded transformation rates and consumer loyalty. Crusade adequacy, and distribute assets all the more productively. This upgrades the profit from speculation (return for capital invested) yet additionally helps in alleviating chances related with customary showcasing strategies. Concerns about data privacy and the possibility of algorithmic bias are also addressed in the paper, as are the ethical considerations and difficulties associated with AI in marketing. In order to maintain consumer trust and comply with regulatory standards, it emphasizes the significance of developing AI practices that are transparent and responsible. This analysis acknowledges the need for ongoing adaptation and oversight while highlighting the transformative potential of AI in marketing through case studies and empirical evidence.International Journal for Research in Applied Science and Engineering Technology00An investigation of how artificial intelligence affects marketing techniques, looking at how it affects both organizational procedures and customer behavior and highlighting the transformative potential of AI in marketing through case studies and empirical evidence.International Journal for Research in Applied Science and Engineering Technology["Dr. Ruchi Gupta"]2024-11-30T00:00:00 08778ab9be7edf6769fa4f2cf4fafd1e41e09685: The 21st century has brought many changes to our lives. Digitalisation has gained unprecedented momentum, leading to a series of innovations in the field. Artificial intelligence was in its infancy at the beginning of the millennium. Research into artificial intelligence as given a boost by the rise in computer power and the ubiquity of the internet, which opened up new avenues for research. The real breakthrough for artificial intelligence came in the 2010s, when deep learning and the use of neural networks became widespread and generalised. Opinions on the future of AI are very mixed. One thing is certain: its rapid future development will transform the labour market, education, but the production and manufacturing sectors will be no exception. Artificial intelligence applications are capable of autonomous decision-making and creative problem solving, which will make people increasingly comfortable. Today's young people will certainly learn, work and live in an environment that will be significantly influenced by AI applications. That is why we consider it very important to get the views of the young, currently school-age generation on this topic, in order to prepare them adequately for the challenges and expectations. To be able to cope in a world where artificial intelligence is at work, young people need to have the right digital skills and competences, which the education system must provide them with. Our study aims to highlight the differences between Hungarian and Turkish young people, and to show the development directions and gaps for future success.The eurasia proceedings of educational & social sciences130The study aims to highlight the differences between Hungarian and Turkish young people, and to show the development directions and gaps for future success.The Eurasia Proceedings of Educational and Social Sciences["Bernadett Rev\u00e1k", "\u00c1gnes Csisz\u00e1rik-Kocsir"]2024-11-30T00:00:00 6f3d9f0777273b00450848a6b68b7e223d0d3055Data science (DS) is concerned with building so-called artificial intelligence, i.e., computer systems that automate tasks based on historical data. This article is the first attempt to examine DS using Adele E. Clarke’s framework of social worlds. The main goal of this paper is to show the (re)construction of primary activity based on the example of the social world of DS in Poland. Methodological reflection on this (re)construction is an underdeveloped element in the study of social worlds; therefore, this paper strives to make this process explicit. The empirical background is a three-year ethnographic study, following Clarke’s situational analysis approach. The methodological results demonstrate the indispensability of collaborative ethnography in (re)constructing primary activity and the importance of finding palpable elements as those being crucial to understanding primary activity. The substantive results focus on the idea that data scientists do not refer to their activity as doing artificial intelligence.Przeglad Socjologii Jakosciowej330The main goal of this paper is to show the (re)construction of primary activity based on the example of the social world of DS in Poland, and demonstrate the indispensability of collaborative ethnography in (re)constructing primary activity.Przegląd Socjologii Jakościowej["Remigiusz \u017bulicki"]2024-11-30T00:00:00 03862f1a76ec0c075e0e339e0010367917a45f33This article is a systematic literature review which aims to explore information about the use of artificial intelligence (AI) in learning media in vocational education. In the Industry 4.0 era, education needs to keep up with technological developments. AI has an important role in creating learning materials and solving problems in educational environments. AI is able to analyze individual learning styles and needs, providing personalized and relevant material. This research reveals that the use of artificial intelligence in vocational education has several important findings. These findings include personalization of learning based on individual learning styles and needs, engaging learning experiences through interactive simulations and educational games, as well as enriching practical experiences through virtual laboratories and industrial simulations. Additionally, the artificial intelligence assessment system is capable of detecting students' weaknesses and strengths in real-time, providing personalized feedback, and assisting in monitoring overall class performance. This research shows that artificial intelligence is not just a tool, but also a partner that drives innovation in vocational education, helps students reach their full potential, and prepares them for a bright future in their chosen industry.Journal of Vocational Education Studies00This research reveals that the use of artificial intelligence in vocational education has several important findings, including personalization of learning based on individual learning styles and needs, engaging learning experiences through interactive simulations and educational games, as well as enriching practical experiences through virtual laboratories and industrial simulations.Journal of Vocational Education Studies["Muhammad Fuad Muttaqin", "Y. Sukrawan", "Mohamad Iqbal Rosyadi"]2024-11-30T00:00:00 97661606f2e5727fb36e1031bc1a51f2969da047Abstract: Artificial Intelligence (AI) is reshaping the global economy by introducing innovative solutions to traditional problems, enhancing productivity, and creating new markets. This paper explores how AI contributes to economic growth through applications in diverse industries such as healthcare, finance, manufacturing, and logistics. It discusses AI's ability to drive decision-making, optimize resource allocation, and foster technological innovation. Additionally, the review considers labor market impacts, regulatory challenges, and ethical concerns, offering strategies for inclusive and sustainable AI-driven economic development. While the potential for AI is vast, navigating its risks requires collaboration between governments, businesses, and societies.International Journal for Research in Applied Science and Engineering Technology00This paper explores how AI contributes to economic growth through applications in diverse industries such as healthcare, finance, manufacturing, and logistics, and discusses AI's ability to drive decision-making, optimize resource allocation, and foster technological innovation.International Journal for Research in Applied Science and Engineering Technology["Promod Kumar B M", "M. M. A. Baig"]2024-11-30T00:00:00 d1c92d668254ba8f072a24fc9ec263ad93c2f54eThe rising demand for Artificial Intelligence (AI) and Machine Learning (ML) technologies in the corporate sector offers significant opportunity for firms to foster innovation and change. Incorporating generative AI capabilities into AI/ML corporate adoption plans can significantly improve the performance and efficacy of these technologies. By adopting AI/ML with generative AI functionalities, organizations may optimize content production workflows, provide synthetic data to enhance ML model training, and provide more captivating consumer experiences. This connection enhances creativity and innovation, accelerating model development and refinement, leading to expedited advancement in the creation of sophisticated AI systems. This article discusses the significance of AI and ML in business. Future trends and difficulties are also examined.International Journal of Scientific Research in Computer Science Engineering and Information Technology110The significance of AI and ML in business is discussed, with a focus on generative AI functionalities, which enhances creativity and innovation, accelerating model development and refinement, leading to expedited advancement in the creation of sophisticated AI systems.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Chander Diwaker", "Atul Sharma"]2024-11-30T00:00:00 163c784162ab58ac2295acf4c6ab892908385e4eAdministrative leadership at School District 7 navigates diverse perspectives on implementing artificial intelligence (AI) in their high schools. Planning for the new school year, the superintendent grapples with AI and how it has impacted her four high schools in the past year. This case is written for k-12 school policy and leadership students and policy researchers to discuss innovation, leadership challenges, and moral dilemmas around integrating AI into teaching and learning. Students and faculty members can utilize this case to evaluate policy-setting approaches to implementing (or constraining) AI in different contexts.Journal of Cases in Educational Leadership60Students and faculty members can utilize this case to evaluate policy-setting approaches to implementing (or constraining) AI in different contexts and discuss innovation, leadership challenges, and moral dilemmas around integrating AI into teaching and learning.Journal of Cases in Educational Leadership["Amanda B Roberts", "Jayson W. Richardson"]2024-11-30T00:00:00 5ed28a10a034f8bc620b78db7c38fc6ffd08d976Artificial Intelligence (AI) has been swiftly incorporated into the industry to become a part of both customer services and manufacturing operations. To effectively address the ethical issues now being examined by the government, AI models must be explainable in order to be used in both scientific and societal contexts. The current state of eXplainable artificial intelligence (XAI) in process engineering is examined in this study through a systematic literature review (SLR), with particular attention paid to the technology’s effect, degree of adoption, and potential to improve process and product quality. Due to restricted access to sizable, reliable datasets, XAI research in process engineering is still primarily exploratory or propositional, despite noteworthy applicability in well-known case studies. According to our research, XAI is becoming more and more positioned as a tool for decision support, with a focus on robustness and dependability in process optimization, maintenance, and quality assurance. This study, however, emphasizes that the use of XAI in process engineering is still in its early stages, and there is significant potential for methodological development and wider use across technical domains.Applied System Innovation1490The current state of eXplainable artificial intelligence (XAI) in process engineering is examined through a systematic literature review (SLR), with particular attention paid to the technology’s effect, degree of adoption, and potential to improve process and product quality.Applied System Innovation["Luigi Piero Di Bonito", "L. Campanile", "Francesco di Natale", "Michele Mastroianni", "Mauro Iacono"]2024-11-30T00:00:00 8869b87e551113f716272531bbaa7cafaaf56088Ang pag-aaral ay naglalayong tuklasin ang karanasan ng mga mag-aaral at mga hamon sa paggamit ng artificial intelligence. Ito ay isinagawa sa mga mag-aaral na gumagamit ng artificial intelligence sa Senior High ng Caibiran National High School, Caibiran, Biliran. Ang pag-aaral ay gumamit ng deskriptibong phenomenological na disenyo upang sagutin ang mga layunin na nakuha mula sa mga kalahok sa panayam na isinagawa para sa mahalagang nilalaman ng pag-aaral. Ang mga natuklasan sa mga karanasan ng mga mag-aaral sa paggamit ng Artificial Intelligence (AI) ay kinabibilangan ng: napapadali ang mga gawain; mayroong tama at maling impormasyong naibibigay; nakatutulong sa pagkakaroon ng mataas na marka; at maaaring kausapin sa pamamagitan ng live chat. Ang mga hamon sa paggamit ng Artificial Intelligence (AI) ay kinabibilangan ng: hindi ganap na naiintindihan kaagad ng AI ang tanong o mga hinihinging kasagutan; dumidepende at umaasa na lamang sa paggamit ng AI; at nakakaapekto sa kakayahang magbasa at umunawa. Sa mga pamamaraan kung paano nalalagpasan/ nakakayanan ang mga hamon sa paggamit ng Artificial Intelligence ay mayroong dalawang resulta: paggamit ng tiyak at simpleng mga salita upang makuha ng AI ang buong ideya ng tanong; at paglimita o pag-iwas sa paggamit ng AI. Sa pagpapalakas o pagpapatibay naman sa paggamit ng Artificial intillegence naman ay may mga pamamaraan na inirekomenda: maaaring magpanukala ng mga polisiya sa paggamit ng artipisyal na katalinuhan; at maaaring gamitin ang AI sa mas malawakang pagkatuto. Ayon sa mga resulta ng pag-aaral, inirerekomendang gumawa ng mga patakaran upang mapangasiwaan ang paggamit ng AI sa paaralan. Kailangan ding palawakin ang kaalaman ng mga mag-aaral sa AI kasama ang mga benepisyo at limitasyon nito. Patuloy ang pagbabago sa ating panahon kung kaya’t nararapat na ating tanggapin at yakapin ang mga pagbabagong ito bagamat mayroong mga negatibong epekto ito sa ating paaralan.Cognizance Journal of Multidisciplinary Studies00Cognizance Journal of Multidisciplinary Studies["Jaymart R. Famor", "Frallyn Candido", "Gregg O. Siat"]2024-11-30T00:00:00 21600152c59b3ad2fdc7fdd1b168b7cc6868c6f8Artificial intelligence has emerged as a transformative tool in managing personal data, presenting unprecedented opportunities and significant challenges. This review provides an overview of AI's ethical, technological, and legal dimensions in the context of personal data protection. A systematic literature review was conducted to identify key themes and gaps in these areas. Ethically, the findings highlight the importance of transparency, accountability, and privacy as guiding principles for the responsible use of AI. Technologically, advancements in AI offer innovative solutions for safeguarding data; however, challenges persist in ensuring their interoperability and adaptability across various applications. Legally, regulatory frameworks such as the General Data Protection Regulation (GDPR) and Mexico's General Law on Personal Data Protection Held by Obligated Subjects (LGPDPPSO) illustrate progress in safeguarding personal data. Yet, gaps in enforcement mechanisms and inconsistencies across jurisdictions highlight the need for further refinement. This review underscores the necessity of interdisciplinary collaboration to navigate the complexities of AI and personal data protection. By integrating ethical, technological, and legal perspectives, this study aims to contribute to developing AI systems that respect privacy and remarks on the importance of personal data protection-aware artificial intelligence applications while adapting to diverse regulatory environments.Avances en Interacción Humano-Computadora230This review provides an overview of AI's ethical, technological, and legal dimensions in the context of personal data protection, and remarks on the importance of personal data protection-aware artificial intelligence applications while adapting to diverse regulatory environments.Avances en Interacción Humano-Computadora["B. A. \u00c1lvarez Magall\u00e1n", "RICARDO ACOSTA", "E. A. Morales-Vanegas"]2024-11-30T00:00:00 c38f903ae67908956c37e8618c33bac3ed3001e9The revolutionary role of artificial intelligence in improving infrastructure resource management in business settings is examined in this technical article. The article explores how AI-driven solutions transform data center operations through advanced predictive analytics, intelligent workload distribution, energy efficiency optimization, proactive defect detection, and dynamic resource allocation. Organizations can improve system reliability, save maintenance costs, and increase operational efficiency by utilizing automated systems and machine learning algorithms. While offering thorough best practices and suggestions for the successful adoption of AI, it explores implementation factors, such as technological needs and integration challenges. The article also looks at how AI infrastructure optimization will develop in the future, highlighting new technologies, automation potential, and sustainability factors that will influence how businesses operate in the coming yearsInternational Journal For Multidisciplinary Research120The article explores how AI-driven solutions transform data center operations through advanced predictive analytics, intelligent workload distribution, energy efficiency optimization, proactive defect detection, and dynamic resource allocation.International Journal For Multidisciplinary Research["Ashok Mohan Chowdhary Jonnalagadda"]2024-11-30T00:00:00 18852f537804c4f1f743a30d7d39f7ebb81d0940AI does not embody human intellectual capabilities, but rather aims to perform tasks that humans have been performing using intelligence. Today, it is difficult to imagine an AI judge that is identical to a human judge. Only when the results presented by AI algorithms replace the judgment of human judges can they be called AI judges. AI judges are discussed in the context of improving work efficiency and expectations for consistency and fairness in trials, but there are various constitutional issues. In particular, whether the right to a fair trial or the right to equality may be violated due to the bias of data and algorithms, and whether the right to a fair trial may be violated if the process and basis of the AI algorithm's results are unclear. The difficulty with these issues is that there are fundamental differences in views of fairness, and that attempts to make AI more transparent may compromise its accuracy and performance. Currently, weak AI is not capable of being a judge based on the nature of judicial work. This is because it judges correlation, not causation, and does not draw specific and valid conclusions in individual cases. It is also difficult to expect that people will approve of the judgment of an AI that lacks judicial virtues. Nevertheless, if AI is to be used in a trial, it must have good data and be able to explain its training data and basic working principles. It is possible to utilize AI as an aid in a trial, but it cannot be called an AI judge. There may be room for a flexible review of whether artificial intelligence judges can be introduced, considering the nature of the case.Institute for Legal Studies Chonnam National University00There may be room for a flexible review of whether artificial intelligence judges can be introduced, considering the nature of the case.Institute for Legal Studies Chonnam National University["Jong-Hyun Kim"]2024-11-30T00:00:00 a098056bff53326c4e718169a686e220c6435172The movement of AI into the coaching arena continues to be steady and confident, meeting only rare and timid resistance. The progress of this movement can be explained by decades of technological advances, the entrepreneurial attitude of AI developers, and the inherent peculiarities of the coaching business. The voices of caution are too quiet in ‘the noise of progress’. However, there are important reasons for coaching communities to be apprehensive about the ways this movement could change coaching as a service and what this means for all involved. In this paper, I address potential problematic issues with the AI revolution in the context of a multitude of conceptual holes in coaching as a profession. I argue that dehumanising coaching under the guise of ‘enhancement by AI’ undermines human intelligence, which is desperately needed while the discipline of organisational coaching remains in its early stages of development.Philosophy of Coaching An International Journal00It is argued that dehumanising coaching under the guise of ‘enhancement by AI’ undermines human intelligence, which is desperately needed while the discipline of organisational coaching remains in its early stages of development.Philosophy of Coaching: An International Journal["Tatiana Bachkirova"]2024-11-30T00:00:00 c6db80f502c7e9355bf69674054684f88511d96a- Machine learning (hereafter ML) has shown enormous potential to revolutionize the profession of internal audit (hereafter IA), from enabling audit coverage of entire populations, to introducing objectivity in the analysis of key areas. However, auditors need a continuous innovative mindset to be effective change agents.International journal of scientific and research publications10Auditors need a continuous innovative mindset to be effective change agents and introduce objectivity in the analysis of key areas.International Journal of Scientific and Research Publications["Munira Aljedaimi"]2024-11-30T00:00:00 ae740b326a858ea15813a68d91b950bcd634db7fThe Journal of Society for e-Business Studies00The Journal of Society for e-Business Studies["Jae-Bin Jang", "Beomsoo Kim"]2024-11-30T00:00:00 b492a2c87e89db71fd5d82fc5576529c05d8b504本研究旨在深入研究人工智能技术在各领域应用所诱发的伦理风险，并未对该风险的有效治理提供建议。通过规范分析法和案例分析法，揭示出人工智能伦理风险形成的逻辑与对有效治理存在的困境。在此基础上，本研究提出了人工智能伦理风险治理路径的思考，包括对人工智能技术的应用实施限制、对大数据技术进行审核以及对算法技术进行有效监测，以期实现人工智能伦理风险的有效治理。Academic Frontiers Publishing Group00Academic Frontiers Publishing Group["Yayu Dou"]2024-11-30T00:00:00 11abcc5faafc6386494e4b75060c3afda32799deJournal of Science &amp; Technology Studies00Journal of Science &amp; Technology Studies["Taemin Woo", "Youjung Shin", "Buhmsoon Park"]2024-11-30T00:00:00 244da0ff091c811db7f4c21f1469a3ed4d60b429<jats:p/>The Korean Society for Health and Nursing Convergence00The Korean Society for Health and Nursing Convergence["Yu-mi Kim", "Ye-rim Kim", "Sun-jeong Park", "Sang-yong Park", "Eun-Ju Choi", "A-ram Kil"]2024-11-30T00:00:00 23ca3e70eedc92e649212f1a5c2fd872765f3e0bKorean Journal of Sociology00Korean Journal of Sociology["June Jeon"]2024-11-30T00:00:00 85eb22c8bbf62c49086396b7e0ca6fdaeb8b55d1Abstract: The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi- functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.International Journal for Research in Applied Science and Engineering Technology00The current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases and neurological disorders are explored.International Journal for Research in Applied Science and Engineering Technology["Servesh Gupta", "Pratik Tawde"]2024-11-30T00:00:00 413e78cc8873304c6fac6f801d5c59c52c7e5387This research aims to determine the transformation of the role and competence of the Akarsana public relations agency in using the ChatGpt application to run PR programs. This research uses qualitative descriptive analysis with a case study approach. This research was conducted at the Akarsana Public Relations Agency. Data collection techniques were carried out using interviews, observation and documentation. The results of this research show that the use of ChatGpt supports Akarsana PR to transform from conventional PR to digital PR in the era of Industrial Revolution 4.0 and Society 5.0. With ChatGpt as an innovation, PR Akarsana can make it easier to do research and copywriting. ChatGpt also helps Akarsana's PR role as a bridge between companies and institutions or society to the public. Apart from that, Akarsana's current PR competencies must also be supported by new AI-based PR competencies so that Akarsana PR can transform optimally in the era of Industrial Revolution 4.0 and Society 5.0.Jurnal Indonesia Sosial Teknologi100The results of this research show that the use of ChatGpt supports Akarsana PR to transform from conventional PR to digital PR in the era of Industrial Revolution 4.0 and Society 5.0.Jurnal Indonesia Sosial Teknologi["Shellie Paola Chelsie", "Erna Febriani"]2024-11-30T00:00:00 f1b3df6284ddf31d67783bbef3d898520ebaceccTheoretical &amp; Applied Science00Theoretical &amp; Applied Science["I. Bukhtueva"]2024-11-30T00:00:00 29ca6718efba7c9031e31f2465d4c4905ef5e987Journal of East Asia and International Law00Journal of East Asia and International Law["Youyou Jiang"]2024-11-30T00:00:00 2f06415c7a83a63eed735e21ffa8377e805f33a8International Journal of Advanced Research00International Journal of Advanced Research["Suyash Agarwal"]2024-11-30T00:00:00 fc65e43222c7df9b41e67670c3309ee799721a62人工智能的出现使得社会的发展达到了前所未有的高度。其为社会带来巨大利益的，同时也蕴含着诸多刑事法律风险，主要包括侵犯数据安全的风险、算法错误引起的风险、引起知识产权纠纷的风险。新生积极事物的发展必须考虑创新与规制之间的平衡点，一味限制必然导致新生事物的积极作用被抑制。通过考察积极刑法观与谦抑性两种观念的差异，辨析适合人工智能创新发展的模式。积极刑法观强调依靠刑法实现人工智能刑事法律风险的防范，但是万能刑法观将会导致技术的停滞不前，且无法应对多元的刑事法律风险。为此，应当坚持谦抑主义的治理理念，提升对人工智能带来风险的容忍度；保持刑法的谦抑性，为前置留足空间，构建平衡的法律体系，系统治理人工智能的刑事法律风险；坚持比例原则，在适用刑法时充分考虑比例原则，降低刑法对人工智能的影响，保障人工智能的发展，坚持安全与发展并重原则。Academic Frontiers Publishing Group00Academic Frontiers Publishing Group["Siyuan Zhao"]2024-11-30T00:00:00 0106671dba14ba9306e988ecef2bad04637ff738CECCAR Business Review00CECCAR Business Review["V. Ban\u0163a", "R. Cre\u021bu", "Gabriel Ilie Staicu"]2024-11-30T00:00:00 761f040e4df9b8e861e6f44d7602f349e2da45c8The industrial landscape is swiftly progressing towards Industry5.0, marking the fifth revolution characterized by the integration of sustainable practices and digital sovereignty. This paper advocates for the adoption, expansion, and implementation of Artificial Intelligence (AI)-enabled hardware, tools, methods, and semiconductor technologies in the journey towards Industry5.0. Beyond the initial proposal, the paper explores primary research areas and the diverse challenges inherent in this transition. Notably, significant accomplishments in pivotal industrial use cases are appended, providing validation evidence. This comprehensive approach aims to bridge academic advancements with practical industrial application, fostering a symbiotic relationship between humans and machines for increased efficiency, innovation, and adaptability.IEEE Industrial Electronics Magazine545This paper advocates for the adoption, expansion, and implementation of Artificial Intelligence (AI)-enabled hardware, tools, methods, and semiconductor technologies in the journey towards Industry5.0.IEEE Industrial Electronics Magazine["George J. Dimitrakopoulos", "Pal Varga", "Thomas Gutt", "Germar Schneider", "H. Ehm", "Alfred Hoess", "Markus Tauber", "Konstantina N. Karathanasopoulou", "Anna Lackner", "J. Delsing"]2024-12-01T00:00:00 5af06c0c073fce5e860014cae148adc97f1dbe57Aims: This study aims to provide a comprehensive overview of the role of artificial intelligence (AI) and machineEgyptian Journal of Chemistry1131Egyptian Journal of Chemistry["Kholood Mohammed Yahya Moafa", "Nouf Falah Hindi Almohammadi", "Fatma Saeed Snhat Alrashedi", "Salwa Alrashidi", "Saud Abdullah Al-Hamdan", "Majedah Mohammad Faggad", "Sarah Mohammed Alahmary", "Mohammed Ibrahim Abdulrahman Al-Darwaish", "Asmaa Khalaf Al-Anzi"]2024-12-01T00:00:00 f0292d8820cf8ec65fd0150984fac442300c557bSkin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.Life1112Artificial intelligence-based technologies in dermatology have emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening, and in vivo skin imaging devices are focused on.Life["Banu \u0130smail Mendi", "K. Kose", "L. Fleshner", "Richard Adam", "B. Safai", "B. Farabi", "M. Atak"]2024-12-01T00:00:00 3c46081f21054694cc29a5795987b7fdd87882fbArtificial Intelligence (AI) is playing an increasingly important role in public administration, offering innovative solutions for efficiency and better public services. The application of AI in public administration aims to modernise and streamline the services offered to citizens. AI contains advanced algorithms, neural networks, and machine learning techniques to improve performance and efficiency over time. Smart algorithms, responsibly implemented, could help governments enhance service delivery and strengthen citizen engagement in local, city, regional, and national administrations around the world. Governments in some states have discreetly launched pilot programs to test developing AI applications, demonstrating an enthusiasm for technology at least as great as that in the private sector. By utilising AI, public administrations can automate repetitive processes, optimize resource usage, and improve interaction with the public. The purpose of the research is to show the extent and scope that AI tools integrated into organisations have in almost all areas of the public and private sectors in general, and state administrations in particular. The research objectives were: to analyse the dynamics of investments in AI tools; to discover the main services in state administrations that can be better performed using AI; to analyse the risks; and to highlight the perspective of development and integration of AI in public administrations of a few European states. The research is based on a comparative analysis of the implementation of AI tools across fields, components, and regions at the European level mainly, but also for Romania. The analysis period is 2021-2030. The results show a continuously increasing trend and an ever-faster rate of assimilation of AI tools in the field of public administration in an increasing number of states from most developed regions worldwide.Administratie si Management Public02The research is based on a comparative analysis of the implementation of AI tools across fields, components, and regions at the European level mainly, but also for Romania, showing a continuously increasing trend and an ever-faster rate of assimilation of AI tools in the field of public administration in an increasing number of states.Administratie si Management Public["Armenia Androniceanu"]2024-12-01T00:00:00 bf867e2354cf8fca883f21c42fabd21ece843aaaArtificial intelligence (AI)-driven robotics is a rapidly evolving field that is transforming various industries, including healthcare, manufacturing, transportation, logistics, security, retail, agri-food, and construction. The integration of artificial intelligence algorithms and machine learning techniques has propelled robotics beyond mere automation, enabling machines to modify, alter, adjust, learn, and interact with the world in ways previously deemed science fiction. The relentless pursuit of creating intelligent robotic systems has led to a symbiotic relationship between human inventiveness and AI, with AI-driven autonomous cars, drones, and robots transforming transportation, healthcare, and exploration. It offers flexibility and learning capabilities, transforming the way machines interact with humans. The integration of AI and robotics marks a transformative era in which machines become companions and cognitive extensions of human capabilities. In the future, we expect AI-driven robotics to bring significant changes to employment and societal well-being. However, the development of AI-driven robotics, which is the integration of AI and robotics, faces numerous challenges, including ethical concerns, legal issues, regulations, societal implications, and job market impacts for the proliferation of intelligent machines. Furthermore, it also presents challenges in terms of technical complexities in its development.IAES International Journal of Robotics and Automation511The development of AI-driven robotics, which is the integration of AI and robotics, faces numerous challenges, including ethical concerns, legal issues, regulations, societal implications, and job market impacts for the proliferation of intelligent machines.IAES International Journal of Robotics and Automation (IJRA)["Tole Sutikno"]2024-12-01T00:00:00 bc0fc623e187f6b8f02951e666849b15748f793cNurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare.Healthcare1751Examination of literature published over the past 40 years revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care.Healthcare["H. Dailah", "M. Koriri", "Alhussean Sabei", "Turky Kriry", "Mohammed Zakri"]2024-12-01T00:00:00 dd3918405088bdce352ec58699018b283144df8dThis research aims to explore the feasibility of Artificial Intelligence (AI) enabled process improvement systems to assist businesses in optimizing Internal Business Process (IBP) by making and adopting suggestions and improvements. Over the last two decades’ technological advances in the new generation have allowed us to use more sophisticated systems to speed up different tasks, as well as AI which has cognate from theory to something more efficient and applicable. This study confirms that a feasible AI-based system can provide benefits to companies in terms of increasing revenue. A mixed method was used; quantitative research was carried out through surveys to gather knowledge about the use of AI in the IBP, while qualitative research was carried out through interviews to obtain an overview of the use of AI in certain IBP. The results show that constructing AI in process optimization is a complicated task than one might expect.Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)01The results show that constructing AI in process optimization is a complicated task than one might expect and confirms that a feasible AI-based system can provide benefits to companies in terms of increasing revenue.Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)["J. T. Santoso", "Agus Wibowo", "Budi Raharjo"]2024-12-01T00:00:00 87f55d660ddfb69746a330b6818bf0098a7cc999Spinal cord injury (SCI) frequently results in persistent motor, sensory, or autonomic dysfunction, and the outcomes are largely determined by the location and severity of the injury. Despite significant technological progress, the intricate nature of the spinal cord anatomy and the difficulties associated with neuroregeneration make full recovery from SCI uncommon. This review explores the potential of artificial intelligence (AI), with a particular focus on machine learning, to enhance patient outcomes in SCI management. The application of AI, specifically machine learning, has revolutionized the diagnosis, treatment, prognosis, and rehabilitation of patients with SCI. By leveraging large datasets and identifying complex patterns, AI contributes to improved diagnostic accuracy, optimizes surgical procedures, and enables the personalization of therapeutic interventions. AI-driven prognostic models provide accurate predictions of recovery, facilitating improved planning and resource allocation. Additionally, AI-powered rehabilitation systems, including robotic devices and brain-computer interfaces, increase the effectiveness and accessibility of therapy. However, realizing the full potential of AI in SCI care requires ongoing research, interdisciplinary collaboration, and the development of comprehensive datasets. As AI continues to evolve, it is expected to play an increasingly vital role in enhancing the outcomes of patients with SCI.Korean Journal of Neurotrauma311The potential of artificial intelligence (AI) to enhance patient outcomes in SCI management is explored, with a particular focus on machine learning, to enhance patient outcomes in SCI management.Korean Journal of Neurotrauma["Kwang Hyeon Kim", "Je Hoon Jeong", "M. Ko", "Subum Lee", "W. Kwon", "Byung-Jou Lee"]2024-12-01T00:00:00 420a83c865dacc3c312be11c466821aedebf15a3Background/Objectives: Artificial intelligence (AI) is rapidly reshaping healthcare, offering transformative potential for diagnostics, treatment, and patient management. Despite its growing significance, there is limited integration of AI education in medical curricula, raising concerns about the readiness of future healthcare professionals to utilize AI technologies. This study aims to evaluate the readiness of medical students in Saudi Arabia to embrace AI and to assess the current state of AI education, AI Application use, and future perspectives for medical students. Methods: a cross-sectional design was employed. It involved medical students from various regions of Saudi Arabia. Data were collected using an anonymous, online, structured, and validated tool from previous studies. The survey included sociodemographic information, details on AI education, the usage of AI applications, intended specialties, and a Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). The data were extracted and revised in an Excel sheet. Statistical analysis was conducted using the IBM SPSS computer program with appropriate statistical tests. Results: This study enrolled 572 medical students, with a mean age of 21.93 years. Most students were Saudi (99.0%), and 43.7% lived in the western region of Saudi Arabia. Most students attended a government medical college (97.41%), and 64.3% of students were in their clinical years. Only 14.5% of the students had received formal AI education, while 34.3% had participated in extracurricular AI training. The mean (SD) MAIRS-MS score was 68.39 (18.3), with higher scores associated with female students, those from the central region, and those with advanced English and computer technology skills (p < 0.001). Conclusions: there is limited AI education and moderate AI readiness among medical students in Saudi colleges, with significant variability in terms of gender, region, and educational background. These findings underscore the need to integrate AI education into medical curricula to better prepare future physicians for AI-enabled healthcare systems.Healthcare511There is limited AI education and moderate AI readiness among medical students in Saudi colleges, with significant variability in terms of gender, region, and educational background, which underscores the need to integrate AI education into medical curricula to better prepare future physicians for AI-enabled healthcare systems.Healthcare["A. A. Al Shahrani", "Norah Alhumaidan", "Zeena AlHindawi", "Abdullah Althobaiti", "Khalid Aloufi", "Rasil Almughamisi", "Ahad Aldalbahi"]2024-12-01T00:00:00 dbdadb9e91ad99d9590fcf0140310edead766c32 This scientific paper is devoted to clarifying the relationship between artificial intelligence (AI), the modern labor market, and Education for sustainable development. Within this study, the authors concentrate their efforts on the following directions: (1) exploring the existing educational approaches and techniques, the knowledge of which helps minimize the possible negative consequences associated with the implementation of AI into the labor market; (2) analyzing Ukrainian and Slovak students’ points of views, that were obtained by surveying, on AI technologies and their potential effects on the labor market; (3) providing substantiation regarding the need of using the pedagogical strategies, particularly, (a-b) the implementation of AI-related technologies in the professional cycle disciplines, (c) assigning tasks that require AI technologies to exploit, (d) exploiting AI technologies as the source of improving the information infrastructure in HEIs. While formulating these pedagogical strategies, the authors comprised possible consequences (a negative effect of AI on the labor market, risks of applying AI in all spheres of human activity, a negative attitude of students toward AI) of AI application and the planned results. The authors considered the five dimensions of sustainability (technical, social, environmental, user, and economic) and presented them in the format of the Sustainability Awareness Diagram (SAD). The visualization of the effects that may result from the use of pedagogical strategies for Education for Sustainable Features is suggested. Each planned result is put in the SAD, assuming their influence (immediate, enabling, structural) on Sustainable Features. In overall, the proposed strategies aim to reduce the potential negative impacts of AI on the functioning of the labor market and instill a positive attitude toward AI technologies in university youth. The findings comprise the multifaceted and dynamic relationships between AI, the modern labor market, and Education for sustainable development. Results also reveal that AI technologies significantly impact the labor market by influencing job roles and skill requirements. Correspondingly, Education for sustainable development is vital in preparing young people to be aware of the evolving demands of the labor market impacted by AI, promoting skills that align with both technological improvements and sustainable practices.IOP Conference Series: Earth and Environment91The proposed strategies aim to reduce the potential negative impacts of AI on the functioning of the labor market and instill a positive attitude toward AI technologies in university youth.IOP Conference Series: Earth and Environmental Science["K. P. Osadcha", "N. Shumeiko"]2024-12-01T00:00:00 811b5f69f562cc940fa05e922edaf2134140a08cThis cross-sectional study of 100 top medical journals examines policies for use of artificial intelligence (AI) and generative AI in peer review.JAMA Network Open62JAMA Network Open["Zhi-qiang Li", "Hui-Lin Xu", "Hui-Juan Cao", "Zhao-Lan Liu", "Yu-Tong Fei", "Jian-Ping Liu"]2024-12-01T00:00:00 cdd4e38a7178a1b1a4ed91bd33efd546418b70ecAbstract Conversational Agents (CAs) are increasingly applied in the mental health sector, offering scalable and diverse applications from psychoeducation, symptom assessment, to self-management of mental wellness. The recent advancements in Artificial Intelligence (AI), such as the Large Language Model, have further transformed CAs into more sophisticated systems that allow personalized and intelligent interactions. However, most mental health CAs were not originally designed with the unique needs and preferences of older adults in mind. This oversight highlights the necessity for tailored features that better accommodate older adults’ user experience. Current scholarly research and interventions that focus on AI-based CAs for older adults are scarce. To address this gap, this scoping review aims to offer a comprehensive overview of existing mental health AI-based CA for older adults. We conducted an extensive systematic literature search across thirteen databases, including PubMed, Scopus, IEEE Explore, ACM Digital Library, Web of Science and others. After a thorough selection process, nine peer-reviewed articles were identified for review. Our preliminary findings underscore the usability and effectiveness of AI-based CAs in enhancing the mental well-being of older adults, showing an overall positive user satisfaction and a capability for therapeutic intervention. Nevertheless, challenges such as the need for increased technological literacy and accessible designs specifically tailored for older adults persist. These findings provide critical insights for researchers and practitioners into how AI-based CAs, as promising and accessible applications and interventions, can be optimized to meet the unique mental health needs of older adults.Innovation in aging01Preliminary findings underscore the usability and effectiveness of AI-based CAs in enhancing the mental well-being of older adults, showing an overall positive user satisfaction and a capability for therapeutic intervention.Innovation in Aging["Xiayu Chen", "Wan Wen"]2024-12-01T00:00:00 376a4bbab7fe7bd54090ea3382764186ff8c2425This paper investigates the adoption of artificial intelligence (AI) in public governance and its impact on socioeconomic welfare, focusing on Slovenian Social Work Centres (SWCs). The objectives are to assess how AI applications align with governance models such as (Neo)Weberian Bureaucracy, New Public Management (NPM), and Good Governance, and to evaluate their effectiveness in promoting socioeconomic welfare. Furthermore, the study aims to identify opportunities and risks associated with AI in public governance and to provide policy recommendations for the ethical and effective integration of AI. A mixed-methods approach is adopted, comprising a comprehensive literature review to develop a theoretical framework, a cross-tabulation analysis of the European Commission's dataset of 686 AI use cases in 27 EU Member States, and a case study of AI implementation in Slovenian SWCs. This includes the analysis of administrative data from 2018–2022 on the e-Welfare platform and analysis of reports from Slovenian oversight bodies such as the Court of Audit, the Administrative Inspection, and the Human Rights Ombudsman. The results show that AI significantly improves administrative efficiency, particularly in the areas of resource management, cost-effectiveness, and service quality, which closely align with NPM principles. However, challenges remain in terms of transparency and accountability, as AI systems are often not transparent, making oversight difficult and jeopardising public trust, especially in the area of social welfare. The study concludes that while AI has significant potential to improve public governance, appropriate regulation and human oversight are essential to mitigate risks and ensure compliance with governance principles. The study provides valuable insights into the role of AI in administrative efficiency and is therefore relevant to policymakers, public officials, and researchers aiming to leverage AI's benefits while ensuring ethical governance and equitable socioeconomic outcomes.Administratie si Management Public01The results show that AI significantly improves administrative efficiency, particularly in the areas of resource management, cost-effectiveness, and service quality, which closely align with NPM principles.Administratie si Management Public["E. Murko", "Matej Bab\u0161ek", "Aleksander Aristovnik"]2024-12-01T00:00:00 1295c56b8589d84c98462ab66bcbb6d289e96950OBJECTIVE This study aimed to introduce a novel artificial intelligence (AI)-based robotic system for autonomous planning of spinal posterior decompression and verify its accuracy through a cadaveric model. METHODS Seventeen vertebrae from 3 cadavers were included in the study. Three thoracic vertebrae (T9-11) and 3 lumbar vertebrae (L3-5) were selected from each cadaver. After obtaining CT data, the robotic system independently planned the laminectomy path based on AI algorithms before the surgical procedure and automatically performed the decompression during the procedure. A postoperative CT scan was performed, and the deviation of each cutting plane from the preoperative plan was quantitatively analyzed to evaluate the accuracy and safety of the cuts. The duration of laminectomy was also recorded. RESULTS A total of 285 cuts were made on thoracic and lumbar vertebrae. The average duration for unilateral longitudinal cutting was 16.38 ± 4.76 minutes, while for transverse cutting it was 4.44 ± 1.52 minutes. In terms of accuracy assessment, 3 levels were divided based on the distance between the actual cutting plane and the preplanned plane: 77 (84%) were grade A, 15 (16%) were grade B, and none were grade C. Regarding safety assessment, 74 (80%) were designated safe (grade A), with 18 (20%) classified as uncertain (grade B). CONCLUSIONS The results confirm the accuracy and preliminary safety of the robotic system for autonomous planning and cutting of spinal decompression.Neurosurgical Focus291The results confirm the accuracy and preliminary safety of the robotic system for autonomous planning and cutting of spinal decompression and verify its accuracy through a cadaveric model.Neurosurgical focus["Chengxia Wang", "Shuai Jiang", "Zhuofu Li", "Woquan Zhong", "Xiongkang Song", "Hongsheng Liu", "Lei Hu", "Weishi Li"]2024-12-01T00:00:00 77822fb5e91b50ea5261b6ebba7187acd636a503Modern Pathology647To address ethics and bias in medicine, a comprehensive evaluation process is required which will encompass all aspects such systems, from model development through clinical deployment, from model development through clinical deployment.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc["Matthew G. Hanna", "Liron Pantanowitz", "Brian Jackson", "Octavia Palmer", "Shyam Visweswaran", "Joshua Pantanowitz", "Mustafa Deebajah", "Hooman H. Rashidi"]2024-12-01T00:00:00 322ec9668d96fc1849d0e379a8577e93f5aeee4fInternational Journal of Research Publication and Reviews06International Journal of Research Publication and Reviews["Geraldine O. Mbah"]2024-12-01T00:00:00 51058881136daf0d6e4fbb4311c10a028c6fe3ebMaterials Today Communications1687Materials Today Communications["Jeewanthi Ukwaththa", "Sumudu Herath", "D.P.P. Meddage"]2024-12-01T00:00:00 c8fd7a3e8cfedeb339346ac53cd4ec8f58d0d1a0Cell1506A vision is provided on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science.Cell["Charlotte Bunne", "Yusuf Roohani", "Yanay Rosen", "Ankit Gupta", "Xikun Zhang", "Marcel Roed", "Theo Alexandrov", "Mohammed AlQuraishi", "Patricia Brennan", "Daniel B. Burkhardt", "Andrea Califano", "J. Cool", "A. Dernburg", "Kirsty Ewing", "Emily B. Fox", "Matthias Haury", "Amy E. Herr", "Eric Horvitz", "Patrick D. Hsu", "Viren Jain", "Gregory R. Johnson", "Thomas Kalil", "David R. Kelley", "S. Kelley", "A. Kreshuk", "Tim Mitchison", "Stephani Otte", "Jay Shendure", "Nicholas J Sofroniew", "Fabian Theis", "Christina V. Theodoris", "S. Upadhyayula", "M. Valer", "Bo Wang", "Eric Xing", "S. Yeung-Levy", "M. Zitnik", "Theofanis Karaletsos", "Aviv Regev", "Emma Lundberg", "J. Leskovec", "Stephen R. Quake"]2024-12-01T00:00:00 365b5aa5cb05b12f932f73655e0439cebd7e3a84Computers and Composition336Computers and Composition["Jialei Jiang"]2024-12-01T00:00:00 10635eaa9e357ec16e6ef2b8140205db19df832fOccupational Medicine08Occupational Medicine["Nerys Williams"]2024-12-01T00:00:00 23a8ab00d9b71b80670c264c2accbf78b645d456Technological forecasting & social change607Technological Forecasting and Social Change["Tong Fu", "Zhaoxuan Qiu", "Xiangyang Yang", "Zijun Li"]2024-12-01T00:00:00 8b3b83c834f21f75fcd76853b84c42a2bc0a91adIntroduction Literature reviews (LR) play a crucial role in all health technology assessment (HTA) dossiers, presenting evidence-based value of interventions. There is global exploration of artificial intelligence (AI) to expedite and enhance the efficiency of literature reviews. Our research aimed to identify any existing guidance from HTA bodies regarding the use of AI for conducting literature reviews. Methods We conducted a comprehensive search and review of any published guidance from prominent HTA bodies, including the National Institute for Health and Care Excellence (NICE, England), Scottish Medicines Consortium (SMC, Scotland), National Centre for Pharmacoeconomics (NCPE, Ireland), National Authority for Health (HAS, France), Federal Joint Committee (G-BA, Germany), Institute for Quality and Efficiency in Health Care (IQWiG, Germany), Canadian Agency for Drugs and Technologies in Health (CADTH, Canada), and Pharmaceutical Benefits Advisory Committee (PBAC, Australia). This was done to gain insights into their views regarding the utilization of AI in literature reviews. Additionally, we engaged with HTA representatives, such as NICE, to gain a deeper understanding of their perspectives. Results We found a lack of clear guidance on the use of AI for conducting LRs. NICE has recommended a priority screening technique using machine learning (ML) for identification of a higher proportion of relevant papers at an earlier stage. NICE is currently in the process of developing guidance and is updating its manual in this area. SMC refers readers to NICE methodologies. In its HRB-CICER report, NCPE only acknowledges the potential of ML algorithms for LRs, with no additional information. IQWiG, in its general methods, recommends the use of ML-validated classifiers for identifying randomized controlled trials (RCTs) within bibliographic searches. Conclusions Our research indicates that there is scarce guidance available for the use of AI in LRs for HTA submissions. However, considering the rapidly evolving nature of this field, it is anticipated that guidance documents and manuals will be updated in the near future.International Journal of Technology Assessment in Health Care00It is indicated that there is scarce guidance available for the use of AI in LRs for HTA submissions, and it is anticipated that guidance documents and manuals will be updated in the near future.International Journal of Technology Assessment in Health Care["Gautamjeet Singh-Mangat", "Sugandh Sharma", "Rito Bergemann"]2024-12-01T00:00:00 6bbf6624cf82c511fafe95e0d279f814ecec86b9 Artificial Intelligence (AI) and Machine Learning (ML) are rapidly evolving and have already had a significant impact on military capabilities, enabling the deployment of new types of assets and tactics. At the same time, there is a need to explore how AI can help commanders make faster and possibly more accurate decisions. The topic of this publication was the exploration of the potential of cognitive AI for mission-oriented command and control. NATO uses the original, well-established term Mission Command in its doctrines, instead of which the author has used the term mission-oriented command method, which is identical in content to the one used in this article.Land Forces Academy Review00The topic of this publication was the exploration of the potential of cognitive AI for mission-oriented command and control.Land Forces Academy Review["Imre N\u00e9gyesi"]2024-12-01T00:00:00 ff812e1dbde3a32b62288162ed85e614e7afb0f5Introduction One of the pillars of health technology assessment (HTA) is transparency, which guarantees reproducibility and accountability. Due to the “black-boxness” of artificial intelligence (AI) models, the use of AI-based tools adds new layers of complexity for transparency issues. The aim of this scoping review is to map AI-based tools applied in HTA processes, regarding human supervision and “open-sourceness” aspects. Methods A search strategy using the terms “AI,” “HTA,” and correlated terms was performed in nine specialized databases (health and informatics) in February 2022. Inclusion criteria were publications testing AI models applied in HTA. Selection of studies was performed by two independent researchers. No filter was applied. Variables of interest included a subset of AI models (e.g., machine learning [ML], neural network), learning methods (e.g., supervised, unsupervised, or semi-supervised learning), and code availability (e.g., open source, closed source). Data were analyzed exploratorily as frequency statistics. Results ML with one layer of hidden nodes was applied in 48 (78.6 %) studies, while deep learning (DL) (two-plus layers) were applied in eight (13.1 %). ML models that used supervised learning accounted only for half of the reported models, while half used unsupervised learning. Considering supervision methods in DL models, seven used unsupervised learning, and one used supervision. Four studies did not report the AI model, and 14 studies did not report the supervision paradigm. It was not possible to assess “open-sourceness” in 31 studies. Among the identified software, seven models were not open source, and 13 were open source. Conclusions Transparency and accountability are of utmost importance to HTA. Complexity of AI models may introduce trustworthiness issues in HTA. Transparency provided by open-source code becomes essential in building trust in the automation of HTA processes, as does quality of report. Although progress has been observed in transparency and quality, the lack of a methodological framework still poses challenges in the field.International Journal of Technology Assessment in Health Care00To map AI-based tools applied in HTA processes, regarding human supervision and “open-sourceness” aspects, the lack of a methodological framework still poses challenges in the field.International Journal of Technology Assessment in Health Care["Denis Satoshi Komoda", "Marilia Mastrocolla de Almeida Cardoso", "Ana Renata Lima", "M. B. Visacri", "Carlos Roberto Silveira Correa", "Br\u00edgida Dias Fernandes"]2024-12-01T00:00:00 c7b20faa7f2799ddcec8b40b0f99a9e9fe169506Abstract Background Chronic kidney disease (CKD) is a common condition that can lead to serious health complications. Artificial Intelligence (AI) has shown the potential to improve the prediction of CKD progression, offering increased accuracy over traditional methods. Therefore, this systematic review and meta-analysis examine the diagnostic performance of various AI models in predicting CKD. Method Search was performed in different databases for studies reporting the diagnostic accuracy of AI-based prediction models for the progression of CKD. Meanwhile, pre-defined eligibility criteria were used for the selection of studies. Pooled sensitivity, specificity, and area under curve (AUC) were calculated utilizing Meta-disc 1.4. Quality assessment was performed using the prediction model risk of bias assessment tool (PROBAST). Results A total of 33 studies were included. The pooled sensitivity of prediction tools was 0.43 (95% CI, 0.41–0.44, I2 = 99.3%, p < 0.01). A significant difference (p < 0.01) was also observed in the pooled specificity 0.92 (95% CI, 0.91–0.92, I2 = 99.5%). Positive likelihood ratio (PLP) and negative likelihood ratio (NLR) were 5.12 (95% CI: 3.60–7.27, I2 = 91.3%, p < 0.01) and 0.28 (95% CI: 0.21–0.37, I2 = 99.3%, p < 0.01), respectively and AUC was 0.89, suggesting a diagnostic accuracy of AI-based prediction models for the progression of CKD. Conclusions This study demonstrates the promising potential of AI models in predicting CKD progression. However, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations. Limitations of this study include the potential for overfitting in certain AI models due to imbalanced datasets. The high heterogeneity and the lack of standardized predictors limit the generalizability of findings across different populations.Renal Failure680This study demonstrates the promising potential of AI models in predicting CKD progression, however, further efforts are needed to optimize model performance, particularly in balancing sensitivity and specificity to ensure generalizability across diverse populations.Renal Failure["Qinyu Pan", "Mengli Tong"]2024-12-01T00:00:00 6d53421c5190ecb212110c21c677ed661a9cf82dThe article is devoted to the problem of ensuring a balance between the interests of society and copyright holders when using works by artificial intelligence systems. The author analyzes the risks of uncontrolled use of AI for processing protected intellectual property objects, explores approaches to regulating this issue in foreign countries, and considers various options for limiting exclusive rights in favor of AI. It is proposed to introduce a flexible model combining special exceptions for the non-commercial use of works for the development and training of AI, as well as a mechanism for compulsory licensing in the commercial sphere. The article substantiates the need for a systematic modernization of copyright legislation, taking into account the development of digital technologies while maintaining fundamental guarantees of the rights and interests of authors.International Journal Of Law And Criminology00The article substantiates the need for a systematic modernization of copyright legislation, taking into account the development of digital technologies while maintaining fundamental guarantees of the rights and interests of authors.International Journal of Law And Criminology["Yokubov Shukhratjon Vosidjon ogli"]2024-12-01T00:00:00 b9517baf88d1f8353f4a46d0583fb6e5b938ab64Introduction Health technology assessment (HTA) agencies struggle with how to ensure timely assessment of promising technologies, especially considering the volume of rapidly produced evidence using complex analytical methodologies and applications, such as artificial intelligence (AI). Furthermore, healthcare systems that are already overburdened are now dealing with issues related to sustainability and increasing budgetary constraints resulting from several public health emergencies, such as the COVID-19 pandemic. Methods A targeted literature review of primary publications in English published during the last five years was conducted to answer the following research question: Would AI integration into health outcomes research and health economics encourage automation in the HTA process, allowing for a living model—a real-time, dynamic approach using explicit methods to determine the value of a technology at different points in its lifecycle—to be implemented? We selected publications presenting information on the following concepts: automation in evidence generation; health economics in the decision-making context; cost efficiencies from the integration of automation; and separation of concepts such as lifecycle and living HTA. A narrative synthesis was conducted. Results The publications selected explored four different aspects of the living concept in decision-making: living clinical guidelines, living evidence reviews and economic evaluations, and living HTA. Automation in systematic reviews (screening and data extraction), including time efficiencies, was the most frequently reported living aspect. The value of open-source economic models was increasingly recognized. Few references were found for methods such as living meta-analyses or network meta-analyses. Adaptive HTA was another related key term. A few publications outlined how a living HTA model could be implemented in real decision-making and its operational challenges. Conclusions So far, HTA bodies have been slow in adopting AI and automation innovation in their practices. Pressures to evolve with the increasingly complex treatment and evidence landscape necessitate a reform in HTA methods. A living HTA model may overcome these barriers and ensure faster patient access for new, promising technologies. A set of “living” standards is needed to gain HTA trust.International Journal of Technology Assessment in Health Care00A targeted literature review of primary publications in English published during the last five years was conducted to answer the following research question: Would AI integration into health outcomes research and health economics encourage automation in the HTA process, allowing for a living model—a real-time, dynamic approach to determine the value of a technology at different points in its lifecycle—to be implemented.International Journal of Technology Assessment in Health Care["Grammati Sarri", "Seye Abogunrin"]2024-12-01T00:00:00 64baf75af86dc94a8548cb656c8b13bcafc7aa90The motive behind this study is to explore the transformative impact of Artificial Intelligence (AI) on the accounting profession and its processes. Despite the significant advancements in AI, there exists a void in the literature regarding a comprehensive analysis of AI’s benefits, challenges, and future implications in accounting. This study addresses this gap by examining the evolution and integration of AI in accounting tasks. Methodologically, the study employs a qualitative approach, analyzing existing literature, case studies, and industry reports to assess AI’s role in automating routine tasks, enhancing accuracy, and providing valuable insights for decision-making. The paper also reviews historical developments in the use of computers and computer systems in accounting prior to AI. The findings reveal that AI significantly improves efficiency and accuracy in accounting by automating data entry, invoice processing, and reconciliation. AI-powered tools excel in predictive analytics and fraud detection, providing deeper insights and enhancing decision-making capabilities. However, challenges such as data privacy, security concerns, and high implementation costs persist. The future of AI in accounting is promising, with potential developments in advanced analytics, blockchain integration, and AI-driven advisory services poised to revolutionize the field. This comprehensive analysis underscores the importance of understanding AI’s impact on accounting to navigate its implementation effectively.Communications of International Proceedings00The findings reveal that AI significantly improves efficiency and accuracy in accounting by automating data entry, invoice processing, and reconciliation, and AI-powered tools excel in predictive analytics and fraud detection, providing deeper insights and enhancing decision-making capabilities.Communications of International Proceedings["Marek Cieslak"]2024-12-01T00:00:00 abe5440995a9a42ad829226ead09a29a350c8194Introduction The past decade has seen an exponential increase in peer-reviewed clinical research literature. Consequently, preparing and updating systematic literature reviews (SLRs) is more resource intensive and costly. Artificial intelligence (AI) could potentially accelerate SLR preparation. This study presents a review of evidence evaluating the accuracy of AI methods in SLR preparation and results of a case study using DistillerSR’s AI functionality. Methods The review was based on a search of MEDLINE, Embase, and Embase Preprints databases using title/abstract keywords and subject heading synonyms for AI, machine learning, natural language processing (NLP), and publication screening and selection. The protocol is published on PROSPERO (CRD42023452391). To supplement this review, we conducted a case study with DistillerSR’s AI tools. We applied the AI classifiers, which use NLP to learn patterns from multiple SLRs across several indications, which encompassed over 15,000 references’ titles and abstracts. We then compared those patterns with the human responses to build an AI model that can be applied to other references. Results The search identified 2,209 records. After deduplication, the titles/abstracts of 2,200 records were screened; of these, 79 full-text records were assessed. A total of 42 records met the eligibility criteria for inclusion. The majority were case studies. The most frequently reported tools were DistillerSR AI (n=9), Abstrackr (n=6), ASReview (n=2), and LiveSTART (n=2). The evidence showed efficiency gains, but accuracy varied across studies and AI tools. Results of the case study using DistillerSR’s AI tools indicated efficiency gains with adequate accuracy but with variability across different SLRs. Inclusion and exclusion of articles were consistent with the human decisions. Conclusions The findings of our review and case study indicated that AI can be used reliably in the screening of articles for SLRs and could improve efficiency. However, the evidence is still evolving, and additional studies are needed. There is a need for clear guidelines on the role of AI in study screening and selection for health technology assessments SLRs and submissions.International Journal of Technology Assessment in Health Care00It is indicated that AI can be used reliably in the screening of articles for SLRs and could improve efficiency, but there is a need for clear guidelines on the role of AI in study screening and selection for health technology assessments SLRs and submissions.International Journal of Technology Assessment in Health Care["Dominika Rekowska", "Sera Sahbaz Gulser", "Nita Santpurkar", "Grace E. Fox", "Shayan Ali", "Nick Halfpenny", "Petra Nass"]2024-12-01T00:00:00 545b32b01efbbb2cdcd970418bbfd8d859c32dccAbstract Background There is an urgent need for neuropsychological screening tests that are easily deployed and reliable. We have developed a digital neuropsychological screening protocol that is administered on a tablet, automatically scored using artificial intelligence, and requires approximately 10 minutes to administer. This tablet‐administered protocol assesses the requisite neurocognitive constructs associated with emergent neurodegenerative illness Method The digital protocol was administered to 77 ambulatory care/ memory clinic patients (Table 1). The protocol is comprised of a 6‐word version of the Philadelphia (repeatable) Verbal Learning Test [P(r)VLT], three trials of 5 digits backward (BDST), and the ‘animal’ fluency test. The protocol provides a panel of six traditional measures as would be obtained using paper/ pencil tests and manual scoring of (P[r]VLT free recall/ recognition hits, backward digit span, ‘animal’ fluency output); a variety of outcome measures quantifying errors and the process used to bring tests to fruition; and two separate, norm‐referenced summary scores measuring executive control and memory. Result Cluster analysis using the panel of 6 traditional measures classified participants into normal (nl= 23), amnestic MCI (aMCI= 17), dysexecutive MCI (dMCI= 23), and dementia (dementia= 23) groups. Subsequent analyses of error and process variables operationally defined key features associated with amnesia including rapid forgetting such as (P[r]VLT immediate free recall trial 2 vs. delay free recall (aMCI & dementia < dMCI & nl; p< 0.001), the production of extra‐list intrusion errors (dementia > nl; p< 0.002); profligate responding to recognition foils (aMCI & dementia > dMCI & nl; p< 0.001); key features underlying reduced executive measures (i.e., BDST perseveration/ related errors (dMCI & dementia > aMCI & nl, < 0.050); and the strength of semantic association from successive ‘animal’ fluency responses (nl & dMCI > dementia; p< 0.028). The novel executive and memory index scores dissociated all four groups from each other (p< 0.014). Conclusion This digitally administered and scored protocol yields patterns of impaired performance similar to paper/ pencil tests. The availability of both traditional and error/ process measures suggests that subtle, nuanced indications of early emergent illness may be identified in a fast, efficient, yet comprehensive way.Alzheimer's & Dementia00A digital neuropsychological screening protocol administered on a tablet, automatically scored using artificial intelligence, and requires approximately 10 minutes to administer suggests that subtle, nuanced indications of early emergent illness may be identified in a fast, efficient, yet comprehensive way.Alzheimer's & Dementia["D. Libon", "Rodney Swenson", "Sean E Tobyne", "Catherine C. Price", "Melissa Lamar", "Stephanie Cosentino", "Russel Banks", "Ali Jannati", "John Showalter", "David Bates", "Alvaro Pascual-Leone"]2024-12-01T00:00:00 c534552da912242b97d7aa67ba67460b97a1c3b4Background Artificial Intelligence (AI) is revolutionizing medical science, with significant implications for radiology. Understanding the knowledge, attitudes, perspectives, and practices of medical professionals and residents related to AI's role in radiology is crucial for effective integration. Methods A cross-sectional survey was conducted among members of the Indian Radiology & Imaging Association (IRIA), targeting practicing radiologists and residents across academic and non-academic institutions. An anonymous, self-administered online questionnaire assessed AI awareness, usage, and perceptions, distributed via medical networks and social media. Descriptive statistics and chi-square tests were used to analyze the data, with statistical analysis performed using R version 4.2.2 (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/). Results The survey gathered responses from 404 participants nationwide. A significant portion (95.3%) demonstrated a keen interest in expanding their knowledge of AI and recommended implementing educational initiatives that increase exposure to AI. Considerable concern about losing their jobs to AI was observed only in 27.9% of respondents. More than two-thirds (86.6%) of the respondents opined that the AI curriculum should be taught during residency and 75.7% are interested in collaborating with software developers to learn and start AI at their workplace. Conclusion The survey highlights the growing importance of AI in radiology, underscoring the need for enhanced AI education and training in medical curricula.Cureus230A cross-sectional survey among members of the Indian Radiology & Imaging Association (IRIA) highlights the growing importance of AI in radiology, underscoring the need for enhanced AI education and training in medical curricula.Cureus["Swati Goyal", "P. Sakhi", "Sadhana Kalidindi", "Deepal Nema", "Abhijit P Pakhare"]2024-12-01T00:00:00 2ad7dac92b16319c3ac2cc51281dc05db4f4540fBanknotes may be damaged during various events, such as floods, fires, insect infestations, and mechanical or manual shredding. Disaster victims might need to perform banknote reconstruction when applying for currency exchange, or investigative agencies might need to conduct such reconstruction during evidence collection. When the number of banknote fragments is small, they can be manually assembled; however, when this number is large, manual assembly becomes increasingly difficult and time-consuming. Therefore, an automated and effective method is required for banknote reconstruction. The process of banknote reconstruction can be considered similar to solving a large-scale jigsaw puzzle. This study employed an artificial intelligence (AI) system to reconstruct damaged banknotes. A robotic arm was used to replace manual separation and automated digital image processing techniques, and AI image registration technology, deep learning, and logical operations were utilized. A deep convolutional neural network was used to estimate the relative homography between images, and fragmented banknotes were mapped to a reference banknote for image transformation, thereby reconstructing the damaged banknotes. Additionally, a repetitive matching method was established to optimize the matching results to achieve the best possible mapping and enhance validation efficiency.IAES International Journal of Robotics and Automation640This study employed an artificial intelligence (AI) system to reconstruct damaged banknotes by replacing manual separation and automated digital image processing techniques, and AI image registration technology, deep learning, and logical operations were utilized.IAES International Journal of Robotics and Automation (IJRA)["Yi-Chang Wu", "Pei-Shan Chiang", "Yao-Cheng Liu", "Ru-Yi Huang"]2024-12-01T00:00:00 136fd18f5369ed2fbf47b5c553987f726ba522f0The emerging ecosystem of artificial intelligence (AI) ethics and governance auditing has grown rapidly in recent years in anticipation of impending regulatory efforts that encourage both internal and external auditing. Yet, there is limited understanding of this evolving landscape. We conduct an interview-based study of 34 individuals in the AI ethics auditing ecosystem across seven countries to examine the motivations, key auditing activities, and challenges associated with AI ethics auditing in the private sector. We find that AI ethics audits follow financial auditing stages, but tend to lack robust stakeholder involvement, measurement of success, and external reporting. Audits are hyper-focused on technically oriented AI ethics principles of bias, privacy, and explainability, to the exclusion of other principles and socio-technical approaches, reflecting a regulatory emphasis on technical risk management. Auditors face challenges, including competing demands across interdisciplinary functions, firm resource and staffing constraints, lack of technical and data infrastructure to enable auditing, and significant ambiguity in interpreting regulations and standards given limited (or absent) best practices and tractable regulatory guidance. Despite these roadblocks, AI ethics and governance auditors are playing a critical role in the early ecosystem: building auditing frameworks, interpreting regulations, curating practices, and sharing learnings with auditees, regulators, and other stakeholders.Big Data &amp; Society310It is found that AI ethics audits follow financial auditing stages, but tend to lack robust stakeholder involvement, measurement of success, and external reporting.Big Data &amp; Society["Danielle Schiff", "Stephanie Kelley", "Javier Camacho Ib\u00e1\u00f1ez"]2024-12-01T00:00:00 4fd2853c40cf21f372150c057b37c3a41bd7e7a8In spite of my personal belief in the benefits of artificial intelligence (AI), reading Cathy O'Neil's book "Weapons of Math Destruction" left me feeling unsettled.1 She describes how flawed and unchecked algorithms are widely applied in areas that affect us all: hiring, credit scoring, access to education, and insurance pricing. In one example, a fixed percentage of teachers in a U.S. region was dismissed every year based on biased and opaque algorithms. The authors concluded that such algorithms act as "weapons of math destruction," perpetuate and amplify societal biases, act unethically, and harm vulnerable populations. The question arises as to what happens when we apply these algorithms to medicine? How do we know whether we are giving our patients the correct diagnosis or prognosis? Are we still sure that patients are receiving the appropriate treatment? Would we notice if the algorithms were geared more toward the needs of companies (make a lot of money) or health insurance companies (spend as little as possible)? In fact, evidence of bias and inequality of algorithms in medicine is already available.2 Due to these risks, some of my colleagues suggest that AI should be completely banned from medicine.Hämostaseologie110Evidence of bias and inequality of algorithms in medicine is already available, and some of my colleagues suggest that AI should be completely banned from medicine.Hamostaseologie["Michael Nagler"]2024-12-01T00:00:00 c299845b5bdf6b03ce188465a55319548eb8ad45Introduction Artificial intelligence (AI) is transforming healthcare decision-making, particularly in evidence evaluation and health technology assessment (HTA). This research explores challenges and ethical considerations associated with AI implementation, and biases. It highlights the need for diverse stakeholder perspectives and collaboration to ensure responsible AI use. Through transparency, accountability, and bias mitigation, AI has the potential to revolutionize decision-making and improve patient care while promoting equitable outcomes. Methods Literature research was conducted, including peer-reviewed studies and grey literature, using the PEARL search strategy. Relevant articles from various databases and sources were screened and selected based on their alignment with the research objectives. The selected articles were then analyzed to identify key findings and insights related to the integration of AI in healthcare decision-making, ethical considerations, bias mitigation, and stakeholder perspectives. Results The literature research revealed that AI in healthcare decision-making holds great promise. AI algorithms can efficiently analyze diverse healthcare data sources, improve evidence evaluation, and streamline decision-making processes. Ethical considerations, patient privacy and transparency are crucial. Bias in AI algorithms emerged as a significant challenge, requiring diverse and representative data, bias testing, and explainable AI. Stakeholder engagement plays a vital role in responsible AI implementation. Strategies for ongoing monitoring, collaboration, and training were identified to ensure fair and ethical decision-making in healthcare. The results emphasize the need for a balanced approach to harness the potential of AI while addressing its challenges. Conclusions Integration of AI in healthcare decision-making offers promising opportunities but also presents challenges that need to be carefully navigated. By addressing ethical considerations and mitigating bias, AI can revolutionize decision-making, improve patient outcomes, and ensure the responsible and ethical use of AI in healthcare. The results provide valuable insights and recommendations for researchers working in the field of HTA.International Journal of Technology Assessment in Health Care00This research explores challenges and ethical considerations associated with AI implementation, and biases, and highlights the need for diverse stakeholder perspectives and collaboration to ensure responsible and ethical use of AI in healthcare.International Journal of Technology Assessment in Health Care["Rito Bergemann", "Sugandh Sharma", "Jackie Vanderpuye-Orle", "Jean-Etienne Poirrier"]2024-12-01T00:00:00 169b0b98fe54d09584cd7ccabbf3274264aab16aBackground: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by the accumulation of fat in the liver, excluding excessive alcohol consumption and other known causes of liver injury. Animal models are often used to explore different pathogenic mechanisms and therapeutic targets of MASLD. The aim of this study is to apply an artificial intelligence (AI) system based on second-harmonic generation (SHG)/two-photon-excited fluorescence (TPEF) technology to automatically assess the dynamic patterns of hepatic steatosis in MASLD mouse models. Methods: We evaluated the characteristics of hepatic steatosis in mouse models of MASLD using AI analysis based on SHG/TPEF images. Six different models of MASLD were induced in C57BL/6 mice by feeding with a western or high-fat diet, with or without fructose in their drinking water, and/or by weekly injections of carbon tetrachloride. Results: Body weight, serum lipids, and liver enzyme markers increased at 8 and 16 weeks in each model compared to baseline. Steatosis grade showed a steady upward trend. However, the non-alcoholic steatohepatitis (NASH) Clinical Research Network (CRN) histological scoring method detected no significant difference between 8 and 16 weeks. In contrast, AI analysis was able to quantify dynamic changes in the area, number, and size of hepatic steatosis automatically and objectively, making it more suitable for preclinical MASLD animal experiments. Conclusions: AI recognition technology may be a new tool for the accurate diagnosis of steatosis in MASLD, providing a more precise and objective method for evaluating steatosis in preclinical murine MASLD models under various experimental and treatment conditions.Diagnostics400AI recognition technology may be a new tool for the accurate diagnosis of steatosis in MASLD, providing a more precise and objective method for evaluating steatosis in preclinical murine MASLD models under various experimental and treatment conditions.Diagnostics["Xiao-Xiao Wang", "Yu-Yun Song", "Rui Jin", "Zi-Long Wang", "Xiao-He Li", "Qiang Yang", "Xiao Teng", "Fang-Fang Liu", "Nan Wu", "Yan-Di Xie", "Hui-Ying Rao", "Feng Liu"]2024-12-01T00:00:00 444d046c313e638b4fca1c6b8686239afbf61fb0Preliminary studies of artificial intelligence (AI) tools developed to support breast screening demonstrate the potential to reduce radiologist burden and improve cancer detection which could lead to improved breast cancer outcomes. This study explores the public acceptability of the use of AI in breast screening from the perspective of screening-eligible women in England.64 women in England, aged 50–70 years (eligible for breast screening) and 45–49 years (approaching eligibility), participated in 12 focus groups—8 online and 4 in person. Specific scenarios in which AI may be used in the mammogram reading process were presented. Data were analysed using a reflexive thematic analysis.Four themes described public perceptions of AI in breast screening found in this study: (1)Things going wrong and being missedsummarises a predominant and pervasive concern about an AI tool being used in breast screening; (2)Speed of change and loss of controlcaptures a positive association of AI with technological advances held by the women but also feelings of things being out of their control, and that they were being left behind and in the dark; (3)The importance of humansreports concern around the possibility that AI excludes humans and renders them redundant and (4)Desire for thorough research, staggered implementation and double-checkingof scans included insistence that any AI be thoroughly trialled, tested and not solely relied on when initially implemented.It will be essential that future decision-making and communication about AI implementation in breast screening (and, likely, in healthcare more widely) address concerns surrounding (1) the fallibility of AI, (2) lack of inclusion, control and transparency in relation to healthcare and technology decisions and (3) humans being left redundant and unneeded, while building on women’s hopes for the technology.BMJ Public Health410It will be essential that future decision-making and communication about AI implementation in breast screening and in healthcare more widely address concerns surrounding (1) the fallibility of AI, (2) lack of inclusion, control and transparency in relation to healthcare and technology decisions and (3) humans being left redundant and unneeded, while building on women’s hopes for the technology.BMJ Public Health["Lauren Gatting", "Syeda Ahmed", "Priscilla Meccheri", "R. Newlands", "Angie A Kehagia", "J. Waller"]2024-12-01T00:00:00 3ea4f62e254de29c4fd2b4310d39991169ea21feBackground: Mechanical ventilation (MV) is one of the most frequently used organ replacement modalities in the intensive care unit (ICU). Artificial intelligence (AI) presents substantial potential in optimizing mechanical ventilation management. The utility of AI in MV lies in its ability to harness extensive data from electronic monitoring systems, facilitating personalized care tailored to individual patient needs. This scoping review aimed to consolidate and evaluate the existing evidence for the application of AI in managing respiratory failure among patients necessitating MV. Methods: The literature search was conducted in PubMed, Scopus, and the Cochrane Library. Studies investigating the utilization of AI in patients undergoing MV, including observational and randomized controlled trials, were selected. Results: Overall, 152 articles were screened, and 37 were included in the analysis. We categorized the goals of AI in the included studies into the following groups: (1) prediction of requirement in MV; (2) prediction of outcomes in MV; (3) prediction of weaning from MV; (4) prediction of hypoxemia after extubation; (5) prediction models for MV–associated severe acute kidney injury; (6) identification of long-term outcomes after prolonged MV; (7) prediction of survival. Conclusions: AI has been studied in a wide variety of patients with respiratory failure requiring MV. Common applications of AI in MV included the assessment of the performance of ML for mortality prediction in patients with respiratory failure, prediction and identification of the most appropriate time for extubation, detection of patient-ventilator asynchrony, ineffective expiration, and the prediction of the severity of the respiratory failure.Journal of Clinical Medicine660Artificial intelligence has been studied in a wide variety of patients with respiratory failure requiring MV, including the assessment of the performance of ML for mortality prediction in patients with respiratory failure and the prediction of the severity of the respiratory failure.Journal of Clinical Medicine["D. Viderman", "Ainur Ayazbay", "Bakhtiyar Kalzhan", "Symbat Bayakhmetova", "Meiram Tungushpayev", "Y. Abdildin"]2024-12-01T00:00:00 c245552d071f0e21fd03818362b5dc4f3b90a469Introduction Progress and innovation in artificial intelligence (AI)-based healthcare interventions continue to develop rapidly. However, there are limitations in the published health economic evaluations (HEEs) of AI interventions, including limited reporting on characteristics and development of algorithms. We developed an extension to the existing Consolidated Health Economic Evaluation Reporting Standards (CHEERS) to improve consistency, transparency, and reliability of the reporting of HEEs of AI interventions. Methods The Delphi method was used, following a prespecified study protocol. A steering group with expert oversight was formed to guide the development process. A long list of potential items was defined based on two recent systematic reviews of HEEs of AI-based interventions. The steering group identified and invited 119 experts to the three-stage survey. Participants were asked to score each item on a nine-point Likert scale, and they were also able to provide free-text comments. The final checklist was piloted on a random sample of nine HEEs of AI-based interventions. Results Three stages of the Delphi survey were completed by 58, 42, and 31 multidisciplinary respondents, respectively, including HTA specialists, health economists, AI experts, and patient representatives. The CHEERS-AI extension includes 18 AI-specific reporting items. Ten are entirely new items, including considerations about user autonomy, validation of the AI component, and AI-specific uncertainty. In addition, elaborations on eight existing CHEERS items were added to emphasize important AI-specific nuances. Some participants highlighted that CHEERS-AI can provide key benefits; for example, it could clarify the misconception that the predictive algorithms supporting AI-driven healthcare interventions are available for use without cost. Conclusions CHEERS-AI can aid in improved reporting quality for researchers, editors, and reviewers conducting or assessing HEEs of AI interventions.International Journal of Technology Assessment in Health Care00An extension to the existing Consolidated Health Economic Evaluation Reporting Standards (CHEERS) to improve consistency, transparency, and reliability of the reporting of HEEs of AI interventions, which includes 18 AI-specific reporting items.International Journal of Technology Assessment in Health Care["T. S. Av\u015far", "Jamie Elvidge", "Claire Hawksworth", "S. Knies", "A. Zempl\u00e9nyi", "Z. Petyk\u00f3", "Pekka Siirtola", "G. Chandra", "Divya Srivastava", "Alastair Denniston", "Anastasia Chalkidou", "Julien Delaye", "P. Nousios", "Manuel Gomes", "Junfeng Wang", "Stavros Petrou", "Dalia Dawoud"]2024-12-01T00:00:00 560fc9be3981be8577d456d8cb2480db5c70e2b2Abstract Background Currently, the diagnosis of Alzheimer’s disease dementia (ADD) is determined based on clinical criteria, as well as specific imaging and cerebrospinal fluid (CSF) biomarker profiles. However, healthcare professionals face a variety of challenges that hinder their application, such as the interpretation and integration or large amounts of data derived from neuropsychological assessment, the importance attributed to each source of information and the impact of unknown variables, among others. Therefore, this research focuses on the development of a computerized diagnostic tool based on Artificial Intelligence (AI), to strengthen the capacity of healthcare professionals in the identification and diagnosis of ADD. Method During this research, all phases of the Cross‐Industry Standard Process for Data Mining (CRISP‐DM) methodology were applied. Specifically, seven types of machine learning (ML) classifier algorithms were trained with data derived from the medical and neuropsychological evaluation of 870 people with and without ADD. Additionally, genetic algorithms (GAs) were used to choose the hyperparameters of six of the models and the best performing model was deployed through a graphical user interface. Result A total of 147 ML models were trained to differentiate or classify between people with and without ADD. Using a random forest (RF) algorithm and 31 predictor variables we achieved a sensitivity of 98%, a specificity of 100% and an area under the curve (AUC) of 0,988. Conclusion We trained and evaluated a variety of ML models, highlighting the accuracy of a RF algorithm together with the use of GAs, to distinguish people with and without ADD. Additionally, we deployed the model with the best performance metrics through a graphical user interface in order to promote its future use in clinical settings. Although these results are promising, the ongoing need to evaluate and re‐train the model, as well as the ethical considerations of its implementation, are well acknowledged.Alzheimer's & Dementia00This research trained and evaluated a variety of ML models, highlighting the accuracy of a RF algorithm together with the use of GAs, to distinguish people with and without ADD.Alzheimer's & Dementia["Valeria Guerra Espinosa", "Juan Carlos Arbelaez Maestre", "Daniela Zabaleta", "Mateo Ruiz Espinosa", "Francisco Lopera", "D. Aguill\u00f3n", "Sofia Guerra Espinosa"]2024-12-01T00:00:00 68e2217e675871a85746eb145b5403e8f5e96469Abstract Background There is an urgent need for neuropsychological screening tests that are easily deployed and reliable. We have developed a digital neuropsychological screening protocol that is administered on a tablet, automatically scored using artificial intelligence, and requires approximately 10 minutes to administer. This tablet‐administered protocol assesses the requisite neurocognitive constructs associated with emergent neurodegenerative illness Method The digital protocol was administered to 77 ambulatory care/ memory clinic patients (Table1). The protocol is comprised of a 6‐word version of the Philadelphia (repeatable) Verbal Learning Test [P(r)VLT], three trials of 5 digits backward (BDST), and the ‘animal' fluency test. The protocol provides a panel of six traditional measures as would be obtained using paper/ pencil tests and manual scoring of (P[r]VLT free recall/ recognition hits, backward digit span, ‘animal' fluency output); a variety of outcome measures quantifying errors and the process used to bring tests to fruition; and two separate, norm‐referenced summary scores measuring executive control and memory. Result Cluster analysis using the panel of 6 traditional measures classified participants into normal (nl = 23), amnestic MCI (aMCI = 17), dysexecutive MCI (dMCI = 23), and dementia (dementia = 23) groups. Subsequent analyses of error and process variables operationally defined key features associated with amnesia including rapid forgetting such as (P[r]VLT immediate free recall trial 2 vs. delay free recall (aMCI & dementia < dMCI & nl; p< 0.001), the production of extra‐list intrusion errors (dementia > nl; p< 0.002); profligate responding to recognition foils (aMCI & dementia > dMCI & nl; p< 0.001); key features underlying reduced executive measures (i.e., BDST perseveration/ related errors (dMCI & dementia > aMCI & nl, < 0.050); and the strength of semantic association from successive ‘animal' fluency responses (nl & dMCI > dementia; p< 0.028). The novel executive and memory index scores dissociated all four groups from each other (p< 0.014). Conclusion This digitally administered and scored protocol yields patterns of impaired performance similar to paper/ pencil tests. The availability of both traditional and error/ process measures suggests that subtle, nuanced indications of early emergent illness may be identified in a fast, efficient, yet comprehensive way.Alzheimer's & Dementia00A digital neuropsychological screening protocol administered on a tablet, automatically scored using artificial intelligence, and requires approximately 10 minutes to administer suggests that subtle, nuanced indications of early emergent illness may be identified in a fast, efficient, yet comprehensive way.Alzheimer's & Dementia["D. Libon", "Rodney Swenson", "Sean E Tobyne", "Catherine C. Price", "Melissa Lamar", "Stephanie Cosentino", "Russel Banks", "Ali Jannati", "John Showalter", "David Bates", "Alvaro Pascual-Leone"]2024-12-01T00:00:00 5aa94b258190e073755d704e1d4fd1008d286b3cIntroduction: This qualitative literature review explored the intersection of art, creativity, and the nurse–patient relationship in the context of oncology nursing. It delved into the perceptions and reflections of nurses as captured by Generative Artificial Intelligence (GAI) analysis from two specialized nursing databases. Methods: The protocol was registered on the Open Science Framework (OSF) Platform. A comprehensive search was conducted in CINAHL, the British Nursing Database, and the Nursing & Allied Health Database, using keywords related to art, cancer, creativity, nursing, and relationships. The extracted qualitative research studies were then analyzed using GAI to identify key themes and insights. Results: The analysis revealed profound considerations regarding the role of nurses in oncology and palliative patient care. Nurses acknowledged the spiritual dimension through religious and spiritual practices, while emphasizing authentic presence and empathic communication. They actively addressed patient concerns, adapted to challenges, and engaged in continuous professional development. The insights from the GAI interpretation underscored the significance of empathy, creativity, and artistry in nurturing meaningful nurse–patient connections. Conclusions: The GAI-enabled exploration provided valuable insights into several dimensions of care, emphasizing the importance of spiritual sensitivity, empathic communication, and ongoing professional growth. As technology and human care converge, integrating artistry into the nurse–patient relationship could enhance patient experiences, improve outcomes, and enrich the oncology nursing practice.Current Oncology700As technology and human care converge, integrating artistry into the nurse–patient relationship could enhance patient experiences, improve outcomes, and enrich the oncology nursing practice.Current Oncology["E. Vitale", "Luana Conte", "R. Lupo", "S. Botti", "A. Fanizzi", "R. Massafra", "G. De Nunzio"]2024-12-01T00:00:00 cb43663211237abd1e0b76e620a8dda129f002e6Background: Given the public’s tendency to overestimate the capability of artificial intelligence (AI) in surgical outcomes for plastic surgery, this study assesses the accuracy of AI-generated images for breast augmentation and reduction, aiming to determine if AI technology can deliver realistic expectations and can be useful in a surgical context. Methods: We used AI platforms GetIMG, Leonardo, and Perchance to create pre- and postsurgery images of breast augmentation and reduction. Board-certified plastic surgeons and plastic surgery residents evaluated these images using 11 metrics and divided them into 2 categories: realism and clinical value. Statistical analysis was conducted using analysis of variance and Tukey honestly significant difference post hoc tests. Images of the nipple-areolar complex were excluded due to AI’s nudity restrictions. Results: GetIMG (mean ± SD) (realism: 3.83 ± 0.81, clinical value: 3.13 ± 0.62), Leonardo (realism: 3.30 ± 0.69, clinical value: 2.94 ± 0.47), and Perchance (realism: 2.68 ± 0.77, clinical value: 2.88 ± 0.44) showed comparable realism and clinical value scores with no significant difference (P > 0.05). In specific metrics, GetIMG outperformed significantly in surgical relevance compared with the other models (P values: 0.02 and 0.03). Healing and scarring prediction is the metric that underperformed across models (2.25 ± 1.11 P ≤ 0.03). Panelists found some images “cartoonish” with unrealistic skin, indicating AI origin. Conclusions: The AI models showed similar performance, with some images accurately predicting postsurgical outcomes, particularly breast size and volume in a bra. Despite this promise, the absence of detailed nipple-areola complex visualization is a significant limitation. Until these features and consistent representations of various body types and skin tones are achievable, the authors advise using actual patient photographs for consultations.Plastic and Reconstructive Surgery, Global Open220The AI models showed similar performance, with some images accurately predicting postsurgical outcomes, particularly breast size and volume in a bra, despite the absence of detailed nipple-areola complex visualization.Plastic and Reconstructive Surgery Global Open["Arsany Yassa", "A. Akhavan", "Solina Ayad", "Olivia Ayad", "Anthony Colon", "Ashley Ignatiuk"]2024-12-01T00:00:00 e71cbba9112a4ccc553f0fb52d8253f856b0e0c0ABSTRACT Background and Aims The rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East. Methods A systematic review was conducted across several databases, including PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews in 2024. The quality assessment of the included studies was conducted using the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist. Following this, a thematic analysis was carried out on the acquired data, adhering to the Boyatzis approach. Results 100 papers were included. The quality and bias risk of the included studies were delineated to be within an acceptable range. Multiple themes were derived from the thematic analysis including: “Prediction of diseases, their diagnosis, and outcomes,” “Prediction of organizational issues and attributes,” “Prediction of mental health issues and attributes,” “Prediction of polypharmacy and emotional analysis of texts,” “Prediction of climate change issues and attributes,” and “Prediction and identification of success and satisfaction among healthcare individuals.” Conclusion The findings emphasized AI's significant potential in addressing prevalent healthcare challenges in the Middle East, such as cancer, diabetes, and climate change. AI has the potential to overhaul the healthcare systems. The findings also highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.Health Science Reports1330This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East and highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.Health Science Reports["Mohsen Khosravi", "Seyyed Morteza Mojtabaeian", "E. Demiray", "Burak Sayar"]2024-12-01T00:00:00 dbfb7f936a015718fdfab4eabf6373f047f5d5e5Stories about ‘intelligent machines’ have long featured in popular culture. Existing research has mapped these artificial intelligence (AI) narratives but lacks an in-depth understanding of (a) narratives related specifically to weaponised AI and autonomous weapon systems and (b) whether and how these narratives resonate across different states and associated cultural contexts. We speak to these gaps by examining narratives about weaponised AI across publics in France, India, Japan and the US. Based on a public opinion survey conducted in these states in 2022–2023, we find that narratives found in English-language popular culture are shared cross-culturally, although with some variations. However, we also find culturally distinct narratives, particularly in India and Japan. Further, we assess whether these narratives shape the publics’ attitudes towards regulating weaponised AI. Although respondents demonstrate overall uncertainty and lack of knowledge regarding developments in the sphere of weaponised AI, they assess these technologies in a negative-leaning way and mostly support regulation. With these findings, our study offers a first step towards further investigating the extent to which weaponised AI narratives circulate globally and how salient perceptions of these technologies are across different publics.Big Data &amp; Society320Examining narratives about weaponised AI across publics in France, India, Japan and the US finds that narratives found in English-language popular culture are shared cross-culturally, although with some variations, however, they also find culturally distinct narratives, particularly in India and Japan.Big Data &amp; Society["Ingvild Bode", "Hendrik Huelss", "Anna Nadibaidze", "Tom F. A. Watts"]2024-12-01T00:00:00 9c70e6762355ef12a2bbdfa3403ff20d60243755Students were encouraged to actively use artificial intelligence (AI) in their studies and research in the field of biomedical engineering. The study analyzed the results of reports and research projects assigned to students. The handling of copyright was a common topic among students regarding AI. In research projects, AI was often used to search for technical terms and references. AI was used to list related technologies and check the feasibility of ideas. AI was effective for self-study. AI is particularly effective in interdisciplinary fields that require a wide range of basic knowledge, and is useful for self-study of technical terms. AI was proven to be useful for students in setting research topics and writing papers.Journal of Systemics, Cybernetics and Informatics00AI is particularly effective in interdisciplinary fields that require a wide range of basic knowledge, and is useful for self-study of technical terms, and is useful for self-study of technical terms.Journal of Systemics, Cybernetics and Informatics["Shigehiro Hashimoto"]2024-12-01T00:00:00 c08c5c351f5cc449e9b15136a80fd568d4e64648Abstract Background The recent breakthrough in monoclonal antibody treatment for Alzheimer’s disease (AD) has ushered in a new phase in AD healthcare. However, associated amyloid‐related imaging abnormalities (ARIA) present a significant risk to patients, necessitating careful monitoring. Detection by radiologists can be challenging and may suffer from inconsistency. This study investigates the potential of advanced artificial intelligence (AI) for automated detection of micro‐hemorrhage (mH) in GRE MRI and ARIA‐like abnormal hyper‐intensity in FLAIR MRI, the two primary manifestations of ARIA. Methods In a multi‐site clinical MRI dataset, we selected 399 GRE scans with micro‐hemorrhage as well as 161 FLAIR scans from stroke and posterior reversible encephalopathy syndrome (PRES) patients with ARIA‐like abnormal hyper‐intensity. Expert annotators, under the supervision of a radiologist, manually delineated micro‐hemorrhage and abnormal hyper‐intensity. An additional 2789 GRE scans of normal subjects were included to evaluate micro‐hemorrhage detection performance. 75% of the data was used to train two AI models to segment micro‐hemorrhage and abnormal hyper‐intensity respectively. Both AI models, adopting the U‐Net architecture, were fine‐tuned from a foundational model pretrained on another 5322 clinical MRI scans in a self‐supervised manner. Evaluation was conducted on the test set (the remaining 25%). The receiver operating characteristic (ROC) analysis was performed to investigate the discrimination of GRE scans with and without micro‐hemorrhage. Additionally, the sensitivity, and false positives of detecting individual micro‐hemorrhage were reported. For abnormal FLAIR hyperintensity, the Dice coefficient of the AI‐derived segmentation was computed. Results The prototype discriminated GRE scans with and without micro‐hemorrhage with an ROC area under the curve of 0.93 (Figure 1‐d). At the single micro‐hemorrhage level, the AI system achieved a detection sensitivity of 0.81 with 0.82 false positives per scan. For abnormal FLAIR hyper‐intensity segmentation, our AI model achieved an average Dice coefficient of 0.76±0.07 (Figure 2). Qualitative results are presented in Figures 1 and 2. Conclusions The findings demonstrate that the proposed AI systems, grounded in foundational AI techniques, can reliably detect micro‐hemorrhage from GRE scans and segment ARIA‐like abnormal FLAIR hyper‐intensity. These systems may play a crucial role in the automated detection and monitoring of ARIA, leading to enhanced patient care.Alzheimer's & Dementia00The findings demonstrate that the proposed AI systems, grounded in foundational AI techniques, can reliably detect micro‐hemorrhage from GRE scans and segment ARIA‐like abnormal FLAIR hyper‐intensity, leading to enhanced patient care.Alzheimer's & Dementia["L. Xie", "Paul Yushkevich", "Sandhitsu R. Das", "D. Wolk", "Eli Gibson"]2024-12-01T00:00:00 b4c07b86170a8857eb5b13fbc43f9585dfc6d7f1Abstract Background The recent breakthrough in monoclonal antibody treatment for Alzheimer’s disease (AD) has ushered in a new phase in AD healthcare. However, associated amyloid‐related imaging abnormalities (ARIA) present a significant risk to patients, necessitating careful monitoring. Detection by radiologists can be challenging and may suffer from inconsistency. This study investigates the potential of advanced artificial intelligence (AI) for automated detection of micro‐hemorrhage (mH) in GRE MRI and ARIA‐like abnormal hyper‐intensity in FLAIR MRI, the two primary manifestations of ARIA. Methods In a multi‐site clinical MRI dataset, we selected 399 GRE scans with micro‐hemorrhage as well as 161 FLAIR scans from stroke and posterior reversible encephalopathy syndrome (PRES) patients with ARIA‐like abnormal hyper‐intensity. Expert annotators, under the supervision of a radiologist, manually delineated micro‐hemorrhage and abnormal hyper‐intensity. An additional 2789 GRE scans of normal subjects were included to evaluate micro‐hemorrhage detection performance. 75% of the data was used to train two AI models to segment micro‐hemorrhage and abnormal hyper‐intensity respectively. Both AI models, adopting the U‐Net architecture, were fine‐tuned from a foundational model pretrained on another 5322 clinical MRI scans in a self‐supervised manner. Evaluation was conducted on the test set (the remaining 25%). The receiver operating characteristic (ROC) analysis was performed to investigate the discrimination of GRE scans with and without micro‐hemorrhage. Additionally, the sensitivity, and false positives of detecting individual micro‐hemorrhage were reported. For abnormal FLAIR hyperintensity, the Dice coefficient of the AI‐derived segmentation was computed. Results The prototype discriminated GRE scans with and without micro‐hemorrhage with an ROC area under the curve of 0.93 (Figure 1‐d). At the single micro‐hemorrhage level, the AI system achieved a detection sensitivity of 0.81 with 0.82 false positives per scan. For abnormal FLAIR hyper‐intensity segmentation, our AI model achieved an average Dice coefficient of 0.76±0.07 (Figure 2). Qualitative results are presented in Figures 1 and 2. Conclusions The findings demonstrate that the proposed AI systems, grounded in foundational AI techniques, can reliably detect micro‐hemorrhage from GRE scans and segment ARIA‐like abnormal FLAIR hyper‐intensity. These systems may play a crucial role in the automated detection and monitoring of ARIA, leading to enhanced patient care.Alzheimer's & Dementia00The findings demonstrate that the proposed AI systems, grounded in foundational AI techniques, can reliably detect micro‐hemorrhage from GRE scans and segment ARIA‐like abnormal FLAIR hyper‐intensity, leading to enhanced patient care.Alzheimer's & Dementia["L. Xie", "Paul Yushkevich", "Sandhitsu R. Das", "D. Wolk", "Eli Gibson"]2024-12-01T00:00:00 2f1f3d7106a40611c0d68c49466eb48c90bca219The progress in artificial intelligence (AI) technology has greatly changed various facets of society. This study aimed to explore aspects that need to be considered in developing AI curriculum for senior high schools in Indonesia. The qualitative approach employed in this study. The researchers utilized focus group discussions with schools’ management and students at seven cities and group interviews with students at three cities. The results show that some schools want AI as an extracurricular activity, while others want it as a mandatory subject. School management and teachers aim for 2-3 competent AI instructors in each school. If no teachers are available, training will be provided to ICT, mathematics, or physics teachers for about a year to become AI educators. All participants agree on the importance of teaching students about AI applications and discussing ethical issues related to AI.IAES International Journal of Artificial Intelligence (IJ-AI)300The results show that some schools want AI as an extracurricular activity, while others want it as a mandatory subject, and all participants agree on the importance of teaching students about AI applications and discussing ethical issues related to AI.IAES International Journal of Artificial Intelligence (IJ-AI)["M. Ramli", "Maifalinda Fatra", "Muhamad Murtadlo", "Hasan Albana", "Baiq Hana Susanti", "Saifullah Aldeia"]2024-12-01T00:00:00 6aef247c382a5a2798cf802ae3f1519a1a2dd04aBackground Addressing language barriers through accurate interpretation is crucial for providing quality care and establishing trust. While the ability of artificial intelligence (AI) to translate medical documentation has been studied, its role for patient-provider communication is less explored. This review evaluates AI’s effectiveness in clinical translation by assessing accuracy, usability, satisfaction, and feedback on its use. Methods A systematic search was conducted on July 11, 2024, across Cumulated Index in Nursing and Allied Health Literature (CINAHL), Institute of Electrical and Electronics Engineers (IEEE) Xplore, PubMed, Scopus, Web of Science, and Google Scholar. Inclusion criteria required AI to translate clinical information for a real or theoretical consultation. Exclusion criteria included reviews, correspondence, educational materials, non-peer-reviewed or retracted reports, non-English translations, pre-2016 publications, and reports on sign language or patient education. Search strings representing AI, language interpretation, and healthcare were used. Two investigators independently conducted the screening, extraction, synthesis of results, and bias assessments using Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I), Mixed Methods Appraisal Tool (MMAT), and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. A third investigator resolved conflicts. Results Of 1,095 reports, 9 studies were analyzed, evaluating AI translation platforms Google Translate, Microsoft Translator, Apple iTranslate, AwezaMed, Pocketalk W, and the Asynchronous Telepsychiatry (ATP) App. Investigations occurred in the US, France, Switzerland, and South Africa, with publications from 2019–2024. AI medical translation shows promise, typically providing accurate translations for brief communications in limited languages, though human translation is often necessary. Accuracy scores ranged from 83–97.8% when translating from English, and 36–76% when translating to English. Usability scores were 76.7–96.7%. Patients were more satisfied than clinicians, with 84–96.6% and 53.8–86.7% satisfied, respectively. Clinicians were hesitant to use AI due to questions of respect, quality, reliability, and misunderstanding. AI is being used as a last-resort option, to assist fluent, non-certified providers and lay interpreters, and for brief communications. Conclusions Limitations include few languages tested, unidirectional translation, simulation, and evolving translation tools. AI shows promise in clinical translation, but the complexity of medical consultations requires a balanced approach combining AI and human translation services for quality care.Annals of Translational Medicine00AI medical translation shows promise, typically providing accurate translations for brief communications in limited languages, though human translation is often necessary, though human translation is often necessary.Annals of Translational Medicine["Ariana Genovese", "Sahar Borna", "Cesar A Gomez-Cabello", "S. A. Haider", "Srinivasagam Prabha", "A. Forte", "Benjamin R. Veenstra"]2024-12-01T00:00:00 5cb0e1bddea1a30ff1f81c73aea4f404bbabdd26In the coming years, the proliferation of artificial intelligence (AI) will lead to changes and challenges to many traditional practices in school music and beyond, particularly related to student assessment and grading. At the same time, the AI revolution may also facilitate new and exciting directions for assessment and differentiation in music education. In this article, I offer a range of considerations and suggestions for music educators seeking to teach music effectively and ethically in the age of AI.Music Educators Journal00A range of considerations and suggestions for music educators seeking to teach music effectively and ethically in the age of AI are offered.Music Educators Journal["Brian P. Shaw"]2024-12-01T00:00:00 b5227228a7abe12a1ceb04ad9f286939c924647aInsights into Imaging1160This study provides a comprehensive analysis of the top 100 most-cited papers on the subject of artificial intelligence in breast radiology and discusses the current most influential papers in the field.Insights into Imaging["Sneha Singh", "Nuala A Healy"]2024-12-01T00:00:00 1477fff3abaa963bc8d79f182e95bb72506699beABSTRACT Artificial intelligence (AI) has been a revolutionary jump in the field of dentistry and medicine. The acceptance of the AI among healthcare professionals is growing minutely. This study identifies the knowledge and perception of the health professionals about the implementation of AI in regular practice. Journal of Pharmacy and Bioallied Sciences50This study identifies the knowledge and perception of the health professionals about the implementation of AI in regular practice and identifies the acceptance of the AI among healthcare professionals.Journal of Pharmacy and Bioallied Sciences["Amrit Jena", "Neel Chaudhary", "B. Manohar", "Niva Mahapatra", "Deepa Dubey", "Ankita Sharma", "S. Bhuvaneshwari"]2024-12-01T00:00:00 e854e831607fbc752c27730dde3971090471d1f8 Artificial intelligence (AI) is revolutionizing healthcare by expanding the definition of health and integrating vast amounts of medical data, including unstructured data, into medical science. This transformation highlights the importance of patient participation, reshaping the traditional doctor-patient dynamic into a collaborative relationship that fosters patient empowerment, medical democracy, and accessibility. AI also facilitates shared decision-making between humans and machines, creating hybrid relationships among doctors, patients, and AI systems. As healthcare AI continues to evolve, ethical considerations—particularly regarding patient and citizen participation—become increasingly vital. Despite these advancements, challenges persist in defining participation roles and establishing effective self-regulation frameworks. Korean Journal of Medical Ethics70This transformation highlights the importance of patient participation, reshaping the traditional doctor-patient dynamic into a collaborative relationship that fosters patient empowerment, medical democracy, and accessibility.Korean Journal of Medical Ethics["Cheayun Jung"]2024-12-01T00:00:00 b79bffa61ee99ff574a3247943715707639df1f0The rapid advancement of generative artificial intelligence technologies has sparked widespread interest, yet understanding of user adoption patterns for these tools remains limited. While the unified theory of acceptance and use of technology model has been widely applied to various technologies, its applicability to generative artificial intelligence products, which present unique challenges and opportunities, has not been thoroughly explored. This gap in knowledge hinders the development of effective strategies for promoting user acceptance and optimising the design of generative artificial intelligence tools. This study extends the unified theory of acceptance and use of technology model to investigate the factors influencing usage intention for generative artificial intelligence products. The study incorporates additional variables such as digital literacy, perceived risk and perceived trust to provide a more comprehensive framework. Using structural equation modelling to analyse survey data, the study found that effort expectancy, performance expectancy, social influence, and perceived trust significantly and positively impact usage intention. In addition, digital literacy indirectly enhances usage intention through effort expectancy, while perceived risk negatively influences usage intention through reduced trust. Notably, facilitating conditions did not exhibit a significant effect on usage intention. These findings offer valuable insights for developers and researchers in the field of generative artificial intelligence, highlighting the importance of user-friendly design, performance optimisation, and trust-building measures. By identifying key factors that drive user adoption, this study contributes to a more nuanced understanding of technology acceptance in the context of advanced artificial intelligence systems, paving the way for more effective development and implementation strategies in this rapidly evolving field.Journal of information science510By identifying key factors that drive user adoption, this study contributes to a more nuanced understanding of technology acceptance in the context of advanced artificial intelligence systems, paving the way for more effective development and implementation strategies in this rapidly evolving field.Journal of Information Science["Zhenxiang Cao", "Liqing Peng"]2024-12-01T00:00:00 bcd3fb1c8184e54a65b20de2d8032b4f42c2e8bbInformation technologies are changing our world extremely fast. The availability of information technologies opens new opportunities but presents challenges. The above contributes to the relevance of applying artificial intelligence (AI) in the justice system. E-justice should facilitate digital market development, which is an essential e-government task. The legal industry has always been known for relying on tradition and resisting change. However, recent advances in AI technology are nimble to disrupt the legal landscape, changing how law firms and legal departments work. The article aims to clarify how to use AI to improve the efficiency and speed of judicial processes and analyze examples of successful implementation of AI systems in the legal field. The article determines the advantages and disadvantages of AI used in justice and examines the issue of accessibility and justice in the context of AI in justice. This research is relevant since it offers an in-depth understanding and analysis of new technologies in the context of legal challenges. It is possible to resort to this research when developing effective strategies for implementing artificial intelligence in the legal field, which constitutes its practical implication.REVISTA BRASILEIRA DE ALTERNATIVE DISPUTE RESOLUTION00The article aims to clarify how to use AI to improve the efficiency and speed of judicial processes and analyze examples of successful implementation of AI systems in the legal field.Revista Brasileira de Alternative Dispute Resolution["V. Krykun", "R. Shchokin", "A. Kyryliuk", "L.I. Halupova", "V. Grygoryeva"]2024-12-01T00:00:00 f28a73962f196ec175816e778e8dd946fdcf13a6Deep learning has revolutionized many fields, but caused the 'black-box' problem, where model prediction is not interpretable and transparent. Explainable Artificial Intelligence (XAI) attempts to overcome this problem with the help of Interpretability and Transparency in AI systems. We review important XAI methods focusing on LIME, SHAP and saliency maps that explain the elements behind model predictions. The paper discusses about the role of Explainable Artificial Intelligence (XAI) in high-stake fields such as healthcare, finance and autonomous systems, emphasizing on why trust is important for these sectors and how they help adhere to regulations while promoting ethical AI use. Despite the promise of Explainable Artificial Intelligence (XAI) in promoting transparency, challenges persist, including standardization of interpretability metrics and some users may have difficulty associating their rationales to transparent forms. The study highlights the need for XAI frameworks that are not only robust but also scalable so as to provide a bridge between complex AI systems and their deployment in society. In the end, it is XAI that enables us to use AI in a responsible way in the most critical domains of our modern lives by creating an atmosphere of accountability, fair treatment and trustIOSR Journal of Computer Engineering00The role of Explainable Artificial Intelligence in high-stake fields such as healthcare, finance and autonomous systems is discussed, emphasizing on why trust is important for these sectors and how they help adhere to regulations while promoting ethical AI use.IOSR Journal of Computer Engineering["Nasir Musa Imam", "A. Ibrahim", "Mohit Tiwari"]2024-12-01T00:00:00 fb2ea0508176d7ecc14e73a8b2276f37477acab4Explainable artificial intelligence (XAI) uses artificial intelligence (AI) tools and techniques to build interpretability in black-box algorithms. XAI methods are classified based on their purpose (pre-model, in-model, and post-model), scope (local or global), and usability (model-agnostic and model-specific). XAI methods and techniques were summarized in this paper with real-life examples of XAI applications. Local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) methods were applied to the moral dataset to compare the performance outcomes of these two methods. Through this study, it was found that XAI algorithms can be custom-built for enhanced model-specific explanations. There are several limitations to using only one method of XAI and a combination of techniques gives complete insight for all stakeholders.Bulletin of Electrical Engineering and Informatics250It was found that XAI algorithms can be custom-built for enhanced model-specific explanations and there are several limitations to using only one method of XAI and a combination of techniques gives complete insight for all stakeholders.Bulletin of Electrical Engineering and Informatics["Shweta Bhatnagar", "Rashmi Agrawal"]2024-12-01T00:00:00 2e86a43db9f17bf8c4ed180265f883ffcf56ad40Medical image recognition has enormous potential to benefit from the recent developments in federated learning (FL) and interpretable artificial intelligence (AI). The function of FL and explainable artificial intelligence (XAI) in the diagnosis of brain cancers is discussed in this paper. XAI and FL techniques are vital for ensuring data ethics during medical image processing. This paper highlights the benefits of FL, such as cooperative model training and data privacy preservation, and the significance of XAI approaches in providing logical justifications for model predictions. A number of case studies on the segmentation of medical images employing FL were reviewed to compares and contrasts various methods for assessing the efficacy of FL and XAI based diagnostic models for brain tumors. The relevance of FL and XAI to improve the accuracy and interpretability during medical image diagnosis have been presented. Future research directions are also described indicating as to integrate data from various modes, create standardised evaluation processes, and manage ethical issues. This paper is intended to provide a deeper insight on relevance of FL and XAI in medical image diagnosis.IAES International Journal of Artificial Intelligence (IJ-AI)910The relevance of FL and XAI to improve the accuracy and interpretability during medical image diagnosis have been presented and future research directions are described indicating as to integrate data from various modes, create standardised evaluation processes, and manage ethical issues.IAES International Journal of Artificial Intelligence (IJ-AI)["Sivakumar Muthuramalingam", "Padmapriya Thiyagarajan"]2024-12-01T00:00:00 793614a2c4243f8a966e6255feeb8b2ae8db6dd2Artificial intelligence (AI) has been a major development in recent times, with more and more research being done on this technology. With the advent of AI, organisations are evolving to meet new levels of customer satisfacti on by leveraging it to execute different tasks, such as personal assistant services. This study aims to review the impact of artificial intelligence on digital marketing. The study identifies key developments in artificial intelligence and reveals how digital marketing has changed due to these developments. It also seeks to understand the impact of AI on customer experience and brand loyalty, how companies leverage AI to predict customers' increasingly intricate needs, and how AI influences decision-making among marketers. The search used three significant databases: Web of Science (WOS), Scopus and Google Scholar. A comprehensive research methodology was used to compile relevant literature. Three reviewers conducted thorough literature searches across multiple databases, screening studies against the inclusion criteria, extracting data from the included articles, and validating the extracted data.Journal of Management and Entrepreneurship00The study identifies key developments in artificial intelligence and reveals how digital marketing has changed due to these developments, and seeks to understand the impact of AI on customer experience and brand loyalty, how companies leverage AI to predict customers' increasingly intricate needs, and how AI influences decision-making among marketers.JOURNAL OF MANAGEMENT AND ENTREPRENEURSHIP["Atul Nimbalkar", "Archana Singh"]2024-12-01T00:00:00 619f3b67c691228103c288e83a74168414da651dAn agent can refer to any device employed as a sensor to detect environmental elements and entities, providing responses based on that information. The cycle of agents can include perception, action, processing, and performance, while the environment around us is populated with agents such as temperature sensors, CCTV cameras, mobile phones, and more. Humans, software, and robots around us also function as AI agents. Using artificial intelligence we can create advanced systems with human-like behaviour. This research study represents a comprehensive view of existing literature and an analysis of methods designed to enhance Artificial Intelligence decision-making possibilities. By studying the facts and details of PEAS models of AI, a better idea can be gained on how AI can make decisions similar to human intelligence. This study includes a literature review of some related research. Objective of this study is to discuss the framework, elements, and challenges of the PEAS model in Artificial Intelligence and to simulate the model of control agents for traffic light control systems, its framework with entities and parameters, and limitations.Recent Research Reviews Journal90The framework, elements, and challenges of the PEAS model in Artificial Intelligence and to simulate the model of control agents for traffic light control systems, its framework with entities and parameters, and limitations are discussed.Recent Research Reviews Journal["Rama Bansal", "Shikha Gupta", "Kishori Ravi Shankar", "Arundhati"]2024-12-01T00:00:00 df3c03b50eec18881a2eff911faf427c710f802eThis survey study examines public expectations related to notification about the use of artificial intelligence (AI) in health care.JAMA Network Open40JAMA Network Open["Jody Platt", "Paige Nong", "Gloria Carmona", "Sharon L. R. Kardia"]2024-12-01T00:00:00 9f44cfacdd51c276823bb6ea20189da462653993 This paper investigates the impact of artificial intelligence (hereinafter AI) on the accounting profession, emphasizing the need to adapt educational programs and business practices due to the emergence and development of AI technologies. A qualitative method with the help of a semi-structured interview was used to collect primary data. The participants were professionals in the accounting and information technology field who have the relevant knowledge and experience to consider this topic. The research aimed to discover the main problems that may arise when implementing artificial intelligence in the accounting profession, to determine how AI technologies affect the quality of financial reports, and whether education reform in accounting is needed due to the emergence of AI technologies. The results of the conducted research showed that AI technology will find its application in the accounting profession, that the quality of the financial report generated based on AI technology depends on the quality of the entered data, i.e. that the control function of accountants is of crucial importance, and that it is necessary to reform curricula in the context of exploiting the benefits of AI technologies.Journal of Forensic Accounting Profession100The conducted research showed that AI technology will find its application in the accounting profession, that the quality of the financial report generated based on AI technology depends on the quality of the entered data, and that it is necessary to reform curricula in the context of exploiting the benefits of AI technologies.Journal of Forensic Accounting Profession["Benina Veledar", "Meliha Ba\u0161i\u0107", "Lejla Demirovi\u0107", "Esma Be\u0161irevi\u0107"]2024-12-01T00:00:00 adfa3bef76c3457a0ca562103dde54f96fcc59d0Abstract: For the past decade, the success rate of IVF has been constant. Many studies aim to enhance the existing success rate of IVF, which is around 30%. Artificial intelligence (AI) has the liability to improve medical results.  Embryo evaluation and choice represent the total manifestation of the in vitro fertilization (IVF) procedure. The goal is to choose the "best" embryos from a wide pool of fertilized oocytes, as many may not be viable owing to aberrant development or chromosomal abnormalities. This essay explores whether AI has the ability to improve fertility results in IVF.Karbala Journal of Medicine00Whether AI has the ability to improve fertility results in IVF is explored in a series of studies aimed at improving the existing success rate of IVF.Karbala Journal of Medicine["Sahbaa Hafedh", "Noor Flayyih Hasan"]2024-12-01T00:00:00 3350ba6c95bc450bee47c144aa99576190409768Advances in Artificial Intelligence (AI) are reshaping health communication worldwide, a key aspect of enhancing public health. This study analyzed current health communication policies to pinpoint areas where AI-driven approaches are yet to be addressed. In Nigeria, integrating AI into health communication policies presents both opportunities and hurdles. The study examined how AI can boost health communication initiatives in Nigeria, particularly through AI-powered tools that enhance the spread of health messages, patient education, and public awareness campaigns. Additionally, the study highlighted challenges in implementing AI technologies, including limited infrastructure, inadequate data management, and a shortage of skilled professionals. The paper also presented the potential of Artificial Intelligence (AI) to transform health communication. It suggested policy guidelines to support its integration while reducing risks. Additionally, it recommended policy changes, training, and cooperation among stakeholders to leverage AI's power in health communication. In Nigeria, the outlook for AI in health communication is optimistic, assuming focused investments in technology, training, and regulatory reforms.Journal of Advanced Research and Multidisciplinary Studies14The study examined how AI can boost health communication initiatives in Nigeria, particularly through AI-powered tools that enhance the spread of health messages, patient education, and public awareness campaigns.Journal of Advanced Research and Multidisciplinary Studies["Ezeaka, N. B."]2024-12-02T00:00:00 3d68268114d6a845911b47880edbebed96d4b065The use of current information technology has evolved significantly with the advancement of artificial intelligence (AI), leading to improvements in organizational agility (OA) and sustainable performance (SP). However, there is limited research on this topic. Therefore, this study aims to explore the impact of AI on SP through the lens of OA, using organizational agility theory. It also investigates the role of organizational culture (OC) as a moderator between OA and SP. Data was collected via a structured questionnaire from a cross-sectional sample of businesses in Pakistan and analyzed using structural equation modeling (SEM). The findings from 317 respondents reveal that AI has a significant impact on O A, and O A mediates the relationship between AI and SP. Furthermore, OC is found to be an insignificant moderator between OA and environmental performance (ENVP), but it significantly moderates the relationships between OA, financial performance (FINP), and social performance (SOCP). This study provides new insights into the importance of AI in both theoretical and practical contexts.2024 Artificial Intelligence for Business (AIxB)292The findings from 317 respondents reveal that AI has a significant impact on O A, and O A mediates the relationship between AI and SP, and OC is found to be an insignificant moderator between OA and environmental performance (ENVP), but it significantly moderates the relationships between OA, financial performance (FINP), and social performance (SOCP).2024 Artificial Intelligence for Business (AIxB)["Muhammad Abubakr Tahir", "Tahir Yousaf", "Muhammad Faisal Shahzad", "Fahad Zain", "Q. Ain"]2024-12-02T00:00:00 3d43b00b7f1619ead280cce1f2ccc073536b71f9One of the barriers to the widespread use of artificial intelligence systems in healthcare is the lack of financial and economic justification for their implementation. Using the experience of using the domestic Webiomed platform in solving problems of cardiovascular disease prevention, a model for assessing economic efficiency is proposed. The total potential effect is 117.9 million rubles / 1 million population per year, which corresponds to the payback of the implementation project from the first year of use on a population starting from 64 thousand people. The development of the implementation of the platform in question in the long term will improve the efficiency and validity of management decisions. The use of artificial intelligence systems with a proven positive impact on the efficiency of financial resources allows creating conditions for achieving the target indicators of state programs in healthcare.Manager Zdravookhranenia01Using the experience of using the domestic Webiomed platform in solving problems of cardiovascular disease prevention, a model for assessing economic efficiency is proposed, and the total potential effect is 117.9 million rubles / 1 million population per year.Manager Zdravookhranenia["N. F. Prokhorenko"]2024-12-02T00:00:00 44a16f02ec357d2580467c3d68ee73fcb2735b87This investigation delves into the convergence of human and machine leadership within the context of China, emphasising employee perceptions regarding the incorporation of Artificial Intelligence (AI) into leadership positions. With the swift progression of AI technologies, it is essential to comprehend their impact on leadership dynamics, particularly in culturally unique environments like China. This study examines essential elements, including confidence in AI leadership, the perceived efficacy of AI, its influence on human leadership, ethical considerations, and the significance of cultural values in forming perspectives on AI. The study employs a quantitative approach and SPSS analysis, revealing that employees trust AI leadership when viewed as effective and culturally aligned. Nonetheless, issues related to fairness, bias, and transparency in AI decision-making undermine trust. The findings suggest that AI is perceived as an enhancement to human leadership instead of a substitute, with cultural acceptance significantly influencing how AI is viewed in leadership positions. The analysis offers essential perspectives for entities aiming to incorporate AI into leadership while tackling ethical and cultural issues.Uniglobal Journal of Social Sciences and Humanities00The findings suggest that AI is perceived as an enhancement to human leadership instead of a substitute, with cultural acceptance significantly influencing how AI is viewed in leadership positions.Uniglobal Journal of Social Sciences and Humanities[]2024-12-02T00:00:00 edd33533d23507d5903f4886c3413c8cd7d74923Artificial Intelligence (AI) holds immense potential to revolutionize engineering education and research in Africa, offering opportunities for innovation and sustainable development. This study aims to assess the current utilization of AI in African engineering education and provide recommendations for its enhancement, empowering African engineering students to lead in AI research efforts. Through a meticulously designed questionnaire, the researchers delved into various aspects of AI integration, including understanding and usage, ethical considerations, resource needs, learning methodologies, skills development, industry collaboration, and strategies for maintaining relevance in the AI era. The results indicate that while AI is actively used in Africa, its application often lacks depth. Therefore, there is a critical need to expand the use of AI for more engaging research applications, collaboration, and engineering practices. The insights gained from this research will help shape the future of engineering curriculum design to foster innovation and the global competitiveness of Africa. This pioneering exploration not only sheds light on the present AI landscape in engineering education and research in Africa but also ignites a vision for an AI-driven transformation that could position Africa as a leading contributor in engineering advancements on the global stage.2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)330The results indicate that while AI is actively used in Africa, its application often lacks depth, and there is a critical need to expand the use of AI for more engaging research applications, collaboration, and engineering practices.2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)["Christian A. Bolu", "Tagwa Ahmed Musa", "Martin Claude Domfang", "Omodele Abiodun Abosede Eletta", "Joseph O. Dada", "Yashin Brijmohan", "Samuel T. Wara", "Ahmed Abdelaziz Ibrahim Elrayah", "Theresa Mkandawire", "A. Obiazi", "George Ihenacho", "Adekunle Oyelami", "E. M. Nfah", "Nnenna Harmony Nwobodo-Nzeribe", "Benjamin Anyaegbuna", "E. Matemba"]2024-12-02T00:00:00 180c72f2b6d7da383b74a171cd537cf8898903e7The article explores the issue of implementing artificial intelligence in judicial decision-making, accentuating potential risks and challenges. It highlights the need to consider justice, fairness and the rule of law when applying AI, and provides arguments for a reasonable and limited algorithmization. The article focuses on the problems of algorithmizing complex judicial processes, particularly regarding the selection of legal principles and AI’s potential negative impact on the individualized nature of justice. Among the risks, the tendency of AI towards rationalization and standardization of decisions, its limited ability to interpret human characteristics and case circumstances, and the substitution of legal certainty with algorithmic predictability are emphasized. The article also discusses the difficulties related to the understanding and interpretation of legal texts by algorithms, noting that AI is incapable of thinking and making moral judgment. Special attention is given to the issue of legal reasoning: the article argues that court decisions must not only be justified but also convincing to society, which is impossible to achieve with AI due to its incapability to comprehend discourse and case context. The article concludes that despite technological advances, the complete replacement of human judgment with AI carries risks and may lead to a distortion of the very concept of justice and its devaluation.Problems of Legality00The article concludes that despite technological advances, the complete replacement of human judgment with AI carries risks and may lead to a distortion of the very concept of justice and its devaluation.Problems of legality["Yulia Razmetaeva"]2024-12-02T00:00:00 5ad0654cd582c1613984efc867e94a1dc483ba0dThis study investigates the role of logistics strategies Route and Node Optimization, Shipment Consolidation, and Demand Forecasting in reducing transportation costs in the logistics sector of Bangladesh. It also explores the moderating effect of Artificial Intelligence (AI) on the effectiveness of these strategies. With the increasing complexity of logistics operations and the growing need for cost-efficient practices, this research highlights how traditional logistics methods can be augmented with advanced AI technologies to optimize operations and achieve significant cost reductions. Data were collected through structured surveys involving 300 respondents from logistics companies in Bangladesh. Using Principal Component Analysis (PCA) and Multiple Regression Analysis, the relationships between the independent variables (logistics strategies), the moderating variable (AI implementation), and the dependent variable (Transportation Cost Reduction) were evaluated. The results reveal that Shipment Consolidation had the most significant impact on transportation cost reduction, followed by Demand Forecasting and Route and Node Optimization. AI implementation was found to positively moderate these relationships, enhancing the efficiency of each strategy. The findings underscore the transformative potential of AI in logistics operations, particularly in developing economies. The study contributes to both theory and practice by providing actionable recommendations for integrating AI into logistics to achieve cost efficiency. Future research directions are suggested, including exploring longitudinal impacts and expanding the study scope to other regions.Frontiers in Business, Economics and Management00The results reveal that Shipment Consolidation had the most significant impact on transportation cost reduction, followed by Demand Forecasting and Route and Node Optimization, and AI implementation was found to positively moderate these relationships, enhancing the efficiency of each strategy.Frontiers in Business, Economics and Management["Hossain Md Anwar"]2024-12-02T00:00:00 7f3e7e2388eeca7bd230a537eff33acd95181bdfThe study explores the role of Artificial Intelligence (AI) in Arabic language learning, with an emphasis on the opportunities and challenges faced in its application. AI has great potential to revolutionize the way languages are learned by offering personalized materials to suit the individual needs of students, adapting teaching methods based on different learning styles, and providing real-time feedback to accelerate the learning process. Through in-depth data analysis, AI can tailor teaching to be more relevant and effective, thereby enhancing student engagement and learning outcomes. Despite offering many advantages, the application of AI faces several significant challenges, such as the availability of adequate quality data, the quality of algorithms that need to be well designed to produce accurate results, and the resistance to new technologies from educators as well as students. The study uses a library study method to study the latest literature on the application of AI in Arabic language education, identifying the benefits and obstacles. Research findings show that while AI can enhance learning processes, contextual adjustment and in-depth understanding of the application of this technology is crucial to maximizing its benefits in education.Journal of Practice Learning and Educational Development00Research findings show that while AI can enhance learning processes, contextual adjustment and in-depth understanding of the application of this technology is crucial to maximizing its benefits in education.Journal of Practice Learning and Educational Development["Muhamad Fahmi", "Syifaul Adhimah"]2024-12-02T00:00:00 5d2189830dbed7df7439dee9b9b298d2228cbef6Artificial Intelligence (AI) has been recently implemented in various advanced government applications, including security, transportation, and healthcare. The wide variety of AI applications raised the issue of adoption difficulties in governmental usage, which is what this study investigates. More specifically, the present study examines the relationship between personnel perceptions and organizational, technological, and environmental factors that affect the AI acceptance and adoption in the governmental sector. To this end, a conceptual framework integrating the Technology Acceptance Model (TAM) with the Technology Organization Environment (TOE) is proposed and evaluated, where a survey for collecting relevant data from 179 employees working in four Palestinian ministries was utilized. The Partial Least Squares-Structural Equation Modeling (PLS-SEM) analysis of data using Smart PSL 4.1.0.8 revealed a significant association between TAM constructs and AI acceptance and adoption. Specifically, the relationships between the TOE variables and TAM's Perceived Usefulness (PU) or Perceived Ease Of Use (PEOU) were significant, except for the legal framework and organizational readiness relationship with PEOU. Besides the analytical investigation, this paper contributes practical insights into AI implementation in the government sector emerging from personnel perspectives. Theoretically, the study analyzes the validity of the conceptual model and thoroughly investigates its constructs and factors, hence suggesting that the governmental ministries focus on the linkage between institutional factors and individual AI perceptions for the latter’seffective acceptance and adoption.Engineering, Technology &amp; Applied Science Research370Practical insights into AI implementation in the government sector emerging from personnel perspectives are contributed and it is suggested that the governmental ministries focus on the linkage between institutional factors and individual AI perceptions for the latter’sffective acceptance and adoption.Engineering, Technology &amp; Applied Science Research["Ramiz Assaf", "Mohammad Omar", "Yahya Saleh", "Hani Attar", "N. Alaqra", "Mohammad Kanan"]2024-12-02T00:00:00 1f5f86fdb4dba4284e6b07314131d5af77048403Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, the paper employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this paper addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.arXiv.org1440This review traces AIGC's evolution through four developmental milestones within a unified framework that highlights how each milestone contributes uniquely to content generation, providing a consistent evaluation of AIGC methodologies and their development.ArXiv["Chengzhang Zhu", "Luobin Cui", "Ying Tang", "Jiacun Wang"]2024-12-02T00:00:00 f7155a63d43b0ff5212440e90283fbb42fa4d080Artificial Intelligence (AI) is revolutionizing marketing by enhancing traditional methods and driving innovation. This research investigates the adoption of AI by marketing executives in Greece, regardless of business size, using the Technology Acceptance Model (TAM) and the RACE framework. AI plays a crucial role in personalized marketing, data analysis, and customer service, significantly improving consumer engagement and business profitability. Through a LinkedIn-based survey of 157 marketing executives, with 71 respondents, the study reveals a strong positive attitude towards AI, emphasizing its ease of use and usefulness. Widely used tools such as ChatGPT and Canva AI are shown to enhance marketing strategy efficiency. Statistical analysis indicates that perceived ease of use positively influences perceived usefulness and attitudes towards AI, which in turn affect the intention to use AI tools. Despite the numerous articles on how AI supports marketing activities, there is a notable lack of empirical studies demonstrating the adoption and utilization of AI tools by marketing executives. This research highlights AI's transformative impact on marketing and proposes future research directions, including the long-term effects of AI on marketing strategies and its acceptance across various sectors and regions.Proceedings of the International Conference on Contemporary Marketing Issues30This research investigates the adoption of AI by marketing executives in Greece, regardless of business size, using the Technology Acceptance Model (TAM) and the RACE framework and reveals a strong positive attitude towards AI, emphasizing its ease of use and usefulness.Proceedings of the International Conference on Contemporary Marketing Issues["Christos Theodoros Papastefanou", "Eugenia Papaioannou"]2024-12-02T00:00:00 1ec7622c8367c72ff49d74025d7f5fb45e686876The study aims to empirically test the role of artificial intelligence in personalizing student learning through surveys, quantitative and qualitative data analysis, and modeling. To determine the role of artificial intelligence in personalizing the educational process, a survey of students was conducted on the organization of the educational process using artificial intelligence (format, form of education, educational environment, consulting communication tools, motivations). Based on the preferences and requests of students, an experimental program of personalized learning was generated and tested. The participants of the educational process were asked to evaluate the role of artificial intelligence in personalizing learning by the following criteria: motivation to learn, level of educational outcomes, productivity of communication in the teacher-student system, convenience and accessibility of education. Most participants in the educational process noted the positive impact of the experimental personalized learning program developed using artificial intelligence based on their requests due to its convenience and accessibility, as well as the productivity of communication in the teacher-student system. The study's results can be used in the educational process of higher education institutions of other profiles. The prospect of research is to develop recommendations for using artificial intelligence tools to prepare personalized learning programs in higher education.Revista EDaPECI00Most participants in the educational process noted the positive impact of the experimental personalized learning program developed using artificial intelligence based on their requests due to its convenience and accessibility, as well as the productivity of communication in the teacher-student system.Revista EDaPECI["V. Hrytsenko", "Anna Tkachenko", "Oksana Podolyan", "Kostiantyn Dieiev", "Liubomyr Ilyn"]2024-12-02T00:00:00 b7d4b13cf912786bc86e428cd70de746c1a8b1feGenerative Artificial Intelligence (AI) is rapidly reshaping multiple fields. Generative AI is a type of AI that can create new content or information from scratch, rather than simply manipulating or organizing existing data. This has the potential to revolutionize the way that financial advisors interact with clients and manage their businesses. However, there are many unknowns as it relates to the level and degree of disruption that Generative AI can bring to financial planning. Therefore, this paper explores the interaction of AI with financial planning, drawing insights from the practices of accounting, medicine and law. While the primary focus remains on financial planning, this interdisciplinary approach aims to enrich understanding and examines parallels and emerging trends across diverse professional domains. Each of the four professions integrates client needs, preferences and goals into their decision-making processes.Financial Services Review00This paper explores the interaction of AI with financial planning, drawing insights from the practices of accounting, medicine and law, and examines parallels and emerging trends across diverse professional domains.Financial Services Review["Ella Faulhaber", "Charles Chaffin"]2024-12-02T00:00:00 6833a47b51d6863021f3c6ac8f15d9dd84f1180dAs Malaysia struggles with challenges presented by the emergence of Artificial Intelligence (AI) in the digital age, there is an increasing need to re-evaluate and potentially revise the country's copyright framework. The Copyright Act 1987 may require adjustments to accommodate the evolving nature of creative works and their production, particularly in the context of AI-generated content, or known as Generative AI. One area of consideration is the implementation of a copyright compensation system which has been successfully adopted in the European Union (EU) and the United States (US) to compensate creators for the use of their works. Hence, this paper explores the feasibility and potential structure of a copyright compensation framework in Malaysia, specifically focusing on compensating rights holders for AI training data used by way of a statutory license and levy system. By examining existing compensation systems in selected jurisdictions including the EU and the US, this paper aims to provide insights into how such a framework could be effectively implemented into the Copyright Act 1987. The paper also argues that a customised copyright compensation framework could offer a practical solution to the challenges posed by AI, ensuring fair compensation for right holders, promoting innovation, and upholding copyright principles in an increasingly interconnected world. This paper will analyse the current provisions of the Copyright Act 1987, and identify gaps and areas that require reform to effectively address the implications of AI-generated content. This paper finds that the Copyright Act 1987 lacks explicit provisions for compensating rights holders for the use of their works in AI training data, leading to potential gaps in legal protection and fair compensation. As such, the paper recommends specific amendments to the Copyright Act 1987 to incorporate these mechanisms to guide the policymakers in providing a copyright compensation framework to rights holders in Malaysia.IIUM Law Journal200It is found that the Copyright Act 1987 lacks explicit provisions for compensating rights holders for the use of their works in AI training data, leading to potential gaps in legal protection and fair compensation.IIUM Law Journal["Mohd Syaufiq Abdul Latif", "Nazura Abdul Manap", "Nabeel Mahdi Althabhawi\uf02a"]2024-12-02T00:00:00 0aa6faaab1845a15089ea03c1268ad6fc846b53bPharmaceutical Research510The potential of AI and ML in nanomedicine product development is discussed with a focus on their applications in discovery, assessment, manufacturing, and clinical trials, with a focus on their applications in discovery, assessment, manufacturing, and clinical trials.Pharmaceutical research["Vivek Agrahari", "Y. Choonara", "M. Mosharraf", "S. Patel", "Fan Zhang"]2024-12-02T00:00:00 7c2fd11eb94c97f4a067f416bf81c1b53ecfe989Artificial intelligence helps businesses in correctly predicting and analyzing the needs of the audience, which is then addressed regarding social media marketing. AI promotes machine learning algorithms that help decipher large-scale user data and strives toward developing more personalized content, personalized responses, campaign optimization, and sentiment monitoring, hence drastically increasing marketing efficiency. This paper reviews some of the studies that show just how deep an impact AI has made in social media marketing. It is inclined to show the AI works in improving analytic understanding, increasing user engagement, and optimization of ad strategy. The results indicate that AI improves relationships with customers and helps businesses achieve their marketing objectives more precisely and efficiently, thus providing progress with ongoing competitive advantages in digital marketing.Proceedings of the International Conference on Contemporary Marketing Issues00The results indicate that AI improves relationships with customers and helps businesses achieve their marketing objectives more precisely and efficiently, thus providing progress with ongoing competitive advantages in digital marketing.Proceedings of the International Conference on Contemporary Marketing Issues["Christos Triteos", "C. Halkiopoulos", "H. Antonopoulou"]2024-12-02T00:00:00 33aeb109278dfdb832b80b4356596e7f1c643236With the rapid advancement of artificial intelligence technology, college students are facing unprecedented opportunities and challenges in career planning. This paper aims to explore how college students can effectively plan their careers in the context of the artificial intelligence era, and conducts practical exploration based on the GROW model. The GROW model is a widely used framework in coaching and consulting fields for setting goals and solving problems, which covers four key stages: goal (Goal), reality (Reality), options (Options), and will (Will). The article analyzes the impact of artificial intelligence on the job market and discusses how college students should use artificial intelligence for career assessment, how to accurately understand the current situation of themselves and the career environment, how to master the application of artificial intelligence in personalized career planning, and how to combine artificial intelligence for education and training related to job seeking. The research results show that the GROW model can assist college students in organizing their career planning in the artificial intelligence era, thereby enhancing their employment competitiveness and career adaptability. In addition, the theoretical guidance and practical paths of career planning in the artificial intelligence era have significant practical significance and application value for college students.IC-ITECHS50The research results show that the GROW model can assist college students in organizing their career planning in the artificial intelligence era, thereby enhancing their employment competitiveness and career adaptability.IC-ITECHS["Lizhu Zhao", "Yue Liu"]2024-12-02T00:00:00 26fe4ea9d321b1f5be318d042404976038e871a0The advent of generative artificial intelligence has opened new possibilities for numerous fields. This paper examines the impact of large-scale generative AI models on both virtual and robot assistants. As these models evolve, there is significant potential to enhance the capabilities of virtual assistants and enable the development of general-purpose robots capable of performing everyday tasks like making coffee. These models can also scale to assist with industrial manufacturing. While general-purpose assistants are yet to be fully developed and circulated, recent advancements in generative AI have greatly accelerated the progress toward this goal. The paper concludes that the integration of large-scale generative AI models into assistive technologies will likely lead to a new generation of AI-powered helpers.2024 Artificial Intelligence for Business (AIxB)180The paper concludes that the integration of large-scale generative AI models into assistive technologies will likely lead to a new generation of AI-powered helpers.2024 Artificial Intelligence for Business (AIxB)["Srishti Chaudhary"]2024-12-02T00:00:00 f345ac725ce45d652f5193e92b817e41c59c0258Generative Artificial Intelligence (GenAI) has significantly impacted higher education, offering numerous benefits alongside notable risks, particularly concerning academic integrity. This paper reviews the assessment landscape in the context of GenAI and critically analyses a key GenAI assessment framework against the latest assessment integrity research in engineering. Our analysis indicates that this framework presents a valuable opportunity for advancing engineering education.2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)390This paper critically analyses a key GenAI assessment framework against the latest assessment integrity research in engineering and indicates that this framework presents a valuable opportunity for advancing engineering education.2024 World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC)["Scott Daniel", "Sasha Nikolic", "Carolyn Sandison", "R. Haque", "Sarah Grundy", "M. Belkina", "Sarah Lyden", "Ghulam M. Hassan", "Peter Neal"]2024-12-02T00:00:00 49e8edbe2d52902e91b4dadcaa5e004e82398ed0have revolutionized many aspects of the writing process, offering both opportunities and challenges for researchers, academicians, students and educators. AI in academic writing presents substantial possibilities by acting as an intelligent writing assistant, language translator, supporting automated summarization, enhancing writing styles and grammar, and enabling data analysis and visualization. To ascertain the influence of AI in academic writing, a comprehensive review of literature related to artificial intelligence, machine learning, and academic writing were conducted. This study aims to address three distinct challenges, including the widespread usage of AI-enabled tools for academic writing, problems with authorship, copyright, and plagiarism in AI-generated content, and how these problems might be fixed. The primary aim of this article is to recognize and highlight the implication of AI in the context of academic writing. To improve their writing abilities, particularly in academic writing, learners, academic researchers, authors, and educators would benefit more from this study. However, the authorship, copyright and plagiarism should be taken into consideration. In the machine generated text, the authorship and copyright  go to the user who gives the input in his. When AI-generated text is combined with original content and thoroughly reviewed using plagiarism detection software, it helps reduce the risk of plagiarism. Keywords: Artificial Intelligence, Machine Learning, Academic Writing, Natural Language Processing  Journal of Computer Science and Information Technology00This study aims to address three distinct challenges, including the widespread usage of AI-enabled tools for academic writing, problems with authorship, copyright, and plagiarism in AI-generated content, and how these problems might be fixed.Journal of Computer Science and Information Technology["Rameshor Subedi", "T. E. Nyamasvisva"]2024-12-02T00:00:00 46a408922ca509462a9dcdad804f728d45ca4db2Background The emergence of artificial intelligence presents both potential for growth and challenges for the financial industry. This study examines the impact of artificial intelligence (AI) on the South African fintech industry, focusing on its transformative nature. Method A purposive sampling technique was used to select 76 participants from the banking sector. Furthermore, the participants were divided into focus groups and interviewed. The thematic analysis conducted in this study revealed eight critical themes that encapsulated the myriad challenges and opportunities faced by industry professionals. From navigating the ever-changing regulatory environment to embracing technological advancements, addressing shifting customer expectations, and cultivating organisational agility and resilience, the financial industry grapples with a complex interplay of factors that demand strategic foresight, adaptability, and a commitment to continuous learning and innovation. Conclusions The results demonstrate that AI is propelling innovation, improving operational efficiency, and transforming customer experience in the finance industry. Nevertheless, substantial impediments have arisen in the form of issues over data protection, talent recruiting, and regulatory ambiguity.F1000Research180The results demonstrate that AI is propelling innovation, improving operational efficiency, and transforming customer experience in the finance industry, Nevertheless, substantial impediments have arisen in the form of issues over data protection, talent recruiting, and regulatory ambiguity.F1000Research["P. Cheteni", "Herrison Matsongoni", "I. Umejesi"]2024-12-02T00:00:00 2ad019f9129aac13dfb656039f6c4afa09438936This article reflects on the pillars of media and information literacy (MIL) in the context of artificial intelligence (AI). As AI-based technologies are integrated into the contemporary media ecosystem, the need to develop skills that enable critical and effective interaction with these systems becomes increasingly urgent. Key skills such as access, analysis, creation, reflection, and action are highlighted, with a proposed update for each to address the challenges and opportunities that AI presents. The final reflection emphasizes the importance of adapting and expanding MIL competencies to strengthen civic engagement and critical thinking in an algorithm-mediated world.Journal of Latin American Communication Research90Key skills such as access, analysis, creation, reflection, reflection, and action are highlighted, with a proposed update for each to address the challenges and opportunities that AI presents.Journal of Latin American Communication Research["Janneth Trejo-Quintana", "Alexandre Sayad"]2024-12-02T00:00:00 698b24bed49c308ed18cdbdbc8963cf5fa389f68Penelitian ini bertujuan untuk mendeskripsikan pemanfaatan artificial intelligence oleh siswa khususnya sekolah menengah pertama dan sekolah menengah atas. Metode yang digunakan adalah penelitian deskriptif kualitatif dengan jenis penelitian survei. Data dikumpulkan menggunakan kuesioner yang terdiri dari pertanyaan dengan jawaban tertutup dan terbuka, di mana kuesioner diberikan secara online melalui platform google form. Subjek penelitian terdiri dari 44 orang siswa dengan kategori rentang usia 10-18 tahun. Analisis data dilakukan dengan statistik deskriptif yang menjelaskan hasil penelitian. Hasil penelitian menunjukkan bahwa 100% responden mengetahui tentang artificial intelligence. Kemudian jenis artificial intelligence yang paling banyak digunakan adalah chatGPT dan Gemini dengan persentasi mencapai 61%. Temuan lainnya dalam penelitian ini adalah bahwa dengan adanya artificial intelligence sangat membantu siswa dalam proses pembelajaran mereka terkhusus pada pengerjaan tugas sekolah. Namun dengan hadirnya artificial intelligence juga memberikan dampak positif dan negatif beserta tantangan yang harus dihadapi oleh siswa. Oleh karena itu, dapat disimpulkan bahwa pemanfaatan artificial intelligence di dalam proses pembelajaran harus tetap berada di dalam pengawasan yang tepat agar dampak negatif yang ada dapat diminimalisir.JIIP - Jurnal Ilmiah Ilmu Pendidikan00JIIP - Jurnal Ilmiah Ilmu Pendidikan["S. Susanto", "Ari Kriswinarti", "Yuzi Hana Christiani", "Yohanes Bahari", "Warneri Warneri"]2024-12-02T00:00:00 453c5e1194b2fb3df415a91a3d2cbd3c146fcb0aTHE PURPOSE OF THE ARTICLE is to study the theoretical concept of artificial intelligence and its impact on the modernisation of business processes and strategic development of enterprises. RESEARCH METHODS. The article uses the following methods: expert assessments; algorithmic analysis; experimental research; statistical analysis; monitoring and evaluation of results; analysis and synthesis; graphical method, etc. PRESENTING MAIN MATERIAL. Artificial intelligence (AI) is a multidisciplinary scientific concept that has a huge potential for transformation in the field of business process modernisation and strategic development of enterprises. AI is capable of radically modernising business processes and their strategic development by automating decision-making systems and predicting innovations in production based on data analytics. As AI touches upon issues such as ethics, privacy, and cybersecurity, the misuse of AI can have serious negative consequences for users. The main types of AI fall into two broad areas: Weak AI – systems that are capable of performing specific tasks, but without understanding the broader context or the ability to adapt in general, which can outperform humans in certain tasks but do not have true ‘consciousness’; Strong AI – systems that can think, understand and learn at the level of human intelligence (Strong AI is still a hypothetical area of research, as there is no fully implemented Strong AI in modern science). Currently, there are several main approaches to AI development, each of which has its own peculiarities. The main methods are machine learning, expert systems, neural networks, and evolutionary algorithms. AI is becoming a relevant tool for modernising business processes, optimising resources, and developing enterprises strategically. Thus, AI opens up new horizons for business, allowing companies to optimise their business processes, minimise costs and increase efficiency. Integrating AI into the development strategy of enterprises requires a deeper understanding of its potential and limitations, where the introduction of AI changes not only individual business processes but also affects the overall approach to the management and development of enterprises. One of the key areas of AI use in the strategic development of enterprises is forecasting and strategic planning, where AI can help enterprises predict economic trends, analyse the competitive environment, and develop strategies that meet future customer demands. CONCLUSIONS. It has been established that AI has a mega-potential for business transformation, contributing to the modernisation of business processes and strategic development of enterprises. However, to achieve effective results, it is important to be aware of both the advantages and disadvantages associated with its integration. Successful AI integration requires not only technical training, but also the adaptation of business models, a strategic approach, attention to all ethical aspects, investment in innovation, and continuous training of employees. The introduction of AI will allow businesses to optimise their operations, make more informed decisions and adapt to the changing environment. Understanding the importance of AI for businesses allows them not only to adapt to new conditions but also to gain a competitive advantage, as AI is becoming one of the key factors in business development, and its role will only grow in the future. KEYWORDS: artificial intelligence; modernisation of business processes; strategic development of enterprises; business transformation; integration; innovation; enterprises; adaptation; implementation.Management00It has been established that AI has a mega-potential for business transformation, contributing to the modernisation of business processes and strategic development of enterprises.Management["Dmytro Irnazarov", "P. Puzyrova"]2024-12-02T00:00:00 5a8e8cff73b9af68eca528ceac905d4a24d0329dThis study conducts a comprehensive bibliometric analysis to explore the landscape of research in mathematics and artificial intelligence (AI). Using Scopus as the primary data source, we identify key publications and trends in these fields. Through VOSviewer, we visualize networks of keywords and collaborations among researchers and institutions. The analysis reveals the prominence of topics such as AI and mathematics in academic discourse, as well as the central role played by countries like the United States, the United Kingdom, and China in research collaboration. Limitations include potential biases in data sources and the reliance on keywords for analysis. Future research could integrate alternative metrics and qualitative analyses to provide a more nuanced understanding of research trends and impact.Black Sea Journal of Engineering and Science00The analysis reveals the prominence of topics such as AI and mathematics in academic discourse, as well as the central role played by countries like the United States, the United Kingdom, and China in research collaboration.Black Sea Journal of Engineering and Science["\u015eeyma Bozkurt Uzan", "Nesibe Manav Mutlu", "\u0130rem Deniz Arberk"]2024-12-02T00:00:00 125335558b5589f44b03be8d02509e0b62d7b72cAccurate diagnosis of gait impairments is often hindered by subjective or costly assessment methods, with current solutions requiring either expensive multi-camera equipment or relying on subjective clinical observation. There is a critical need for accessible, objective tools that can aid in gait assessment while preserving patient privacy. In this work, we present a mobile phone-based, privacy-preserving artificial intelligence (AI) system for classifying gait impairments and introduce a novel dataset of 743 videos capturing seven distinct gait patterns. The dataset consists of frontal and sagittal views of trained subjects simulating normal gait and six types of pathological gait (circumduction, Trendelenburg, antalgic, crouch, Parkinsonian, and vaulting), recorded using standard mobile phone cameras. Our system achieved 86.5% accuracy using combined frontal and sagittal views, with sagittal views generally outperforming frontal views except for specific gait patterns like Circumduction. Model feature importance analysis revealed that frequency-domain features and entropy measures were critical for classifcation performance, specifically lower limb keypoints proved most important for classification, aligning with clinical understanding of gait assessment. These findings demonstrate that mobile phone-based systems can effectively classify diverse gait patterns while preserving privacy through on-device processing. The high accuracy achieved using simulated gait data suggests their potential for rapid prototyping of gait analysis systems, though clinical validation with patient data remains necessary. This work represents a significant step toward accessible, objective gait assessment tools for clinical, community, and tele-rehabilitation settingsarXiv.org00A mobile phone-based, privacy-preserving artificial intelligence (AI) system for classifying gait impairments and introduces a novel dataset of 743 videos capturing seven distinct gait patterns, demonstrating that mobile phone-based systems can effectively classify diverse gait patterns while preserving privacy through on-device processing.ArXiv["Lauhitya Reddy", "Ketan Anand", "Shoibolina Kaushik", "Corey Rodrigo", "J. McKay", "Trisha M Kesar", "Hyeokhyen Kwon"]2024-12-02T00:00:00 05b155f7bda44dcc71de88b3f151048c2b7b3042Dear Editor, we would like to share ideas on “Is Artificial Intelligence (AI) currently able to provide evidence-based scientific responses on methods that can improve the outcomes of embryo transfers? No” (Kolokythas & Dahan, 2024). This study offers a valuable look into the usage of AI chatbots to give evidence-based therapeutic recommendations for infertility therapy, specifically embryo transfer outcomes. However, various limitations, both methodological and scope-related, should be rigorously considered. First, the dependence on only nine chatbots raises questions about the representativeness and dependability of the responses. The AI models utilized are constrained by their underlying data and programming, which might induce bias and inconsistency. Furthermore, the nature of the prompts, such as asking the chatbots to write an essay, does not guarantee that the findings adhere to current clinical principles or best practices in reproductive medicine. This constraint emphasizes the need for a broader strategy, including a larger range of AI platforms and even using other question styles. Another major restriction is the focus on only 43 suggestions over 19 categories, with only a tiny proportion of them being evidence-based. This demonstrates a substantial gap in chatbots’ capacity to separate serious scientific evidence from anecdotal or unverified data. The abundance of divergent and controversial recommendations calls into question chatbots’ dependability in offering clinical recommendations. Future research should investigate the logic behind these responses, specifically how the algorithms prioritize andJBRA Assisted Reproduction00This study offers a valuable look into the usage of AI chatbots to give evidence-based therapeutic recommendations for infertility therapy, specifically embryo transfer outcomes.JBRA Assisted Reproduction["H. Daungsupawong", "V. Wiwanitkit"]2024-12-02T00:00:00 96e0c2319ad5febf0302cc0cd8b9f0ff1b694662npj Digit. Medicine260This perspective examines key questions surrounding GRAI reimbursement, including issues of coding, valuation, and coverage policies, and aims to catalyze dialogue among stakeholders about how reimbursement might evolve to accommodate GRAI, potentially influencing AI reimbursement strategies in radiology and beyond.NPJ Digital Medicine["S. Dogra", "Ezequiel \u201cZeke\u201d Silva", "P. Rajpurkar"]2024-12-02T00:00:00 f444d8a023704c7e5e58397a8c374e0dc925d965Integrating Artificial Intelligence (AI) into education, especially in teaching English as a Foreign Language (EFL), has increased the interest and curiosity among EFL teachers. This systematic review examines the perspectives of both in-service and pre-service EFL teachers regarding the use of AI in their teaching practices. The review focuses on studies published between 2020 and 2024. The Scopus database was reviewed, and a total of 29 articles were analyzed. The study adopted a systematic review, and content analysis was utilized as the research method. The findings reveal that the commonly used research method in the reviewed literature was qualitative. EFL teachers generally regard AI tools as helpful for aiding language teaching, increasing student engagement, promoting personalized learning, and improving overall teaching efficiency. Moreover, AI is viewed as a means to reduce teacher workload by contributing to lesson planning, materials development, and providing feedback to students. However, the findings also show that EFL teachers are concerned about using AI tools in language classrooms. These include the potential for AI tools to promote cheating and plagiarism and the lack of adequate training for both students and teachers in utilizing AI effectively. Furthermore, the possibility of students' over-reliance on AI, potentially hindering their critical thinking and creativity, and ethical and privacy issues related to the handling of data by AI tools are also frequently cited concerns.Ihlara eğitim araştirmalari dergisi00The findings reveal that EFL teachers generally regard AI tools as helpful for aiding language teaching, increasing student engagement, promoting personalized learning, and improving overall teaching efficiency, and that EFL teachers are concerned about using AI tools in language classrooms.Ihlara Eğitim Araştırmaları Dergisi["Elif Kadriye \u00d6zkan", "N. Erdemir", "Derya Co\u015fkun"]2024-12-02T00:00:00 40ecbf98a0b05c8d5d91d1235330982faf9ec3f7This article proposes a framework for integrating ethical reasoning into AI systems through Continuous Logic Programming (CLP), emphasizing the improvement of transparency and accountability in automated decision-making. The study highlights requirements for AI that respects human values and societal norms by examining concerns such as algorithmic bias, data privacy, and ethical dilemmas in fields like healthcare and autonomous systems. The proposed CLP-based methodology offers a systematic, elucidative framework for ethical decision-making, allowing AI systems to balance operational efficiency with ethical principles. Important contributions include strategies for the integration of ethical frameworks, stakeholder engagement, and transparency, as well as discussion on artificial moral agents and their function in addressing ethical dilemmas in AI. The paper presents practical examples that illustrate the application of CLP in ethical reasoning, highlighting its ability to bring together AI performance with responsible AI practices.Applied Informatics241The proposed CLP-based methodology offers a systematic, elucidative framework for ethical decision-making, allowing AI systems to balance operational efficiency with ethical principles.AI["Jos\u00e9 Machado", "R. Sousa", "Hugo Peixoto", "A. Abelha"]2024-12-02T00:00:00 4d3922f22d32259b07236f010dd05b749ba212a1Integrating AI with Project Management presents a unique and transformational opportunity to enhance organizations’ business delivery and value stream. This study examines the growing usage of AI in Agile environments to improve decision-making, process automation, and risk reduction. This survey presents data-driven skills and logic-centric thinking that leverage AI’s potential for organizations to develop solid customer solutions, enhance operational efficiency, receive profound insights, and advance the workforce with rising technologies. Collecting general information, this study will explore how effectively utilizing Agile and SAFe 6.0 in project management, and AI could yield noticeable advantages for enterprises in terms of efficiency, innovation, and competitive edge.2024 Artificial Intelligence for Business (AIxB)121This survey presents data-driven skills and logic-centric thinking that leverage AI’s potential for organizations to develop solid customer solutions, enhance operational efficiency, receive profound insights, and advance the workforce with rising technologies.2024 Artificial Intelligence for Business (AIxB)["Tanvi Saxena", "Michael W. Totaro"]2024-12-02T00:00:00 fe9223b353d45711e0331342b793fe96ad7eb4eb00[]2024-12-02T00:00:00 12c2ece935c3820bcc00e328957741d56752f912HCAIep130{"pages": "22-28"}["Brian Conway", "Keith E. Nolan", "Keith Quille"]2024-12-02T00:00:00 37164e97e684b9a28d7f8f2d98f97df1dd3b71b1Abstract Stroke is a significant health problem in India, with an uneven prevalence and high early mortality rates. Worldwide, stroke is the second leading cause of death. In the initial months post-stroke, motor impairment is a primary concern, in addition to various other deficits. Predicting recovery after a stroke is crucial for optimizing resource allocation. Utilizing machine learning offers the potential to enhance therapeutic decision-making and predict individual recovery outcomes in stroke rehabilitation. We aimed to employ machine learning algorithms to predict functional recovery after stroke by estimating Barthel Index scores, identifying patterns and correlations within the dataset, and determining the most effective machine learning model among the five algorithms that were tested. Participants were screened for eligibility before enrollment, and demographic information, stroke characteristics, and Barthel Index scores were recorded. The dataset was split into training and testing subsets for analysis. Five machine learning algorithms were trained using the initial dataset to develop predictive models. High alcohol and tobacco use potentially influenced Barthel Index scores and stroke recovery. The recovery process varied based on stroke type, with ischemic and hemorrhagic strokes. The Random Forest model exhibited the highest predictive accuracy among the models. The study highlights the role of demographics, lifestyle habits, comorbidities, and stroke type in functional recovery poststroke. The Random Forest model demonstrated the most reliable predictive capability, indicating artificial intelligence’s potential in stroke recovery prediction. Furthermore, research studies are needed to develop and evaluate robust prediction models. Journal of Society of Indian Physiotherapists100The Random Forest model demonstrated the most reliable predictive capability, indicating artificial intelligence’s potential in stroke recovery prediction, and highlights the role of demographics, lifestyle habits, comorbidities, and stroke type in functional recovery poststroke.Journal of Society of Indian Physiotherapists["Mansi Deole", "Raghavendrasingh Dharwadkar"]2024-12-02T00:00:00 a0505f2fbfff3b6eed6f4a3f3bfb910390471ca3Cognitive Computation80Cogn. Comput.["Oshin Rawlley", "Shashank Gupta", "Jyotsana Chandrakar", "Manisha K. Johnson", "Chahat Kalra"]2024-12-02T00:00:00 72f8fd1c7b8dae9895dd53f2d930ddcc64806ab7International Review of Psychiatry400International Review of Psychiatry["Daniela L\u00e9 Tassinari", "Maria Olivia Pozzolo Pedro", "Manoela Pozzolo Pedro", "Andr\u00e9 Brooking Negr\u00e3o", "Ricardo Abrantes do Amaral", "A. Malbergier", "Douglas Henrique Crispim", "J. Castaldelli-Maia"]2024-12-02T00:00:00 5dae1d04a6ca86b21ad5d8c67a63865c2813c016Conference on Computer and Communications Security00{"pages": "4905-4906"}["Maura Pintor", "Matthew Jagielski", "Xinyun Chen"]2024-12-02T00:00:00 d22f59df4bbde39398fec9b6a76b3cb1a9db9f07Frontiers in Robotics and AI00Frontiers in Robotics and AI["Ciro Mennella", "Umberto Maniscalco", "G. Masala", "Massimo Esposito"]2024-12-02T00:00:00 9bae8a91d6598662d2562e5670c37ae4a7640987Iris Journal of Educational Research00Iris Journal of Educational Research["Miguel Dominguez Rigo"]2024-12-02T00:00:00 c10866fba166018f99a80f42b6d9b6eb67607720Background of the study: The need for information in the era of globalization is rapidly growing, causing users to strategize to obtain information effectively. One of the strategies used in information retrieval is generative AI applications. Purpose: This study aims to discuss generative AI applications used in information retrieval and the impact of generative AI usage on information-seeking behavior. Methods: Library research is used to collect data from various relevant literature sources. Finding: The use of generative AI in information retrieval provides positive impacts, such as speeding up the search process and presenting concise and easily understood information. However, the use of generative AI also has negative effects, such as inaccurate data and potential dependence on technology. Conclusion: Although generative AI provides convenience in information retrieval, there are negative risks, such as data inaccuracy and overdependence on technology. Therefore, regulations and oversight are needed to prevent these negative impacts   ABSTRAK Latar Belakang: Kebutuhan informasi di era globalisasi semakin berkembang pesat, yang menyebabkan para pengguna informasi harus mengatur strategi untuk mendapatkan informasi secara efektif. Salah satu strategi yang digunakan adalah dengan memanfaatkan AI generatif. Tujuan: Penelitian ini bertujuan untuk membahas aplikasi-aplikasi AI Generatif yang digunakan dalam pencarian informasi serta dampak dari penggunaan AI generatif terhadap perilaku informasi. Metode: Metode penelitian yang digunakan adalah library research dengan pengumpulan data dari berbagai sumber literatur yang relevan. Temuan: Penggunaan AI generatif dalam pencarian informasi memberi dampak positif, seperti mempercepat proses pencarian dan menyajikan informasi yang ringkas dan mudah dipahami. Meskipun demikian, penggunaan AI generatif juga menimbulkan dampak negatif, yaitu data yang kurang akurat dan potensi ketergantungan pada teknologi. Kesimpulan: Meskipun AI generatif memberikan kemudahan dalam pencarian informasi, terdapat resiko negatif seperti ketidakakuratan data dan ketergantungan berlebihan pada teknologi. Oleh karena itu, diperlukan aturan dan pengawasan untuk mencegah dampak negatif tersebut.  JPUA Jurnal Perpustakaan Universitas Airlangga Media Informasi dan Komunikasi Kepustakawanan190JPUA: Jurnal Perpustakaan Universitas Airlangga: Media Informasi dan Komunikasi Kepustakawanan["Febri Nahla", "Anis Masruri"]2024-12-02T00:00:00 3290b634b84810f372a0c56a33f8be8a6543bec700["Thangavel Murugan", "Karthikeyan Periasamy", "A. M. Abirami"]2024-12-02T00:00:00 b22447cec1fa9a248d8fecdb5699b47dbd9af7ebThis study explores Mumbai’s teachers' perceptions of Information, Media, and Technology (IMT) and Artificial Intelligence (AI) skills in teaching, particularly in the post-pandemic era. The transition to digital education during COVID-19 prompted rapid adoption of these skills, yet their long-term relevance in traditional classrooms remains uncertain. Through a qualitative exploratory approach, 300 teachers were surveyed on how IMT/AI skills have impacted their teaching methods, student engagement, and professional growth. Findings show mixed responses: while many teachers recognize the potential of IMT/AI in fostering interactive and collaborative learning, others feel uncertain about their ongoing value. Some teachers have successfully integrated these tools, enhancing lesson delivery and student interaction. However, confidence levels vary, and there is notable dissatisfaction with current professional development programs, suggesting a need for more practical, relevant training. The study concludes that IMT and AI skills can significantly enhance educational practices if supported by targeted training and resources. These findings provide insights for policymakers and educational institutions seeking to promote effective digital integration. By addressing the gaps in digital training, schools can empower teachers to leverage IMT and AI for improved teaching outcomes in both digital and traditional learning environments.Recent trends in Management and Commerce30It is concluded that IMT and AI skills can significantly enhance educational practices if supported by targeted training and resources and addressing the gaps in digital training can empower teachers to leverage IMT and AI for improved teaching outcomes in both digital and traditional learning environments.Recent trends in Management and Commerce[]2024-12-02T00:00:00 3ac788980aa22c61e05e4af273d4e1f9165c5875ChatGPT, developed by OpenAI, is a large language model (LLM) that leverages artificial intelligence (AI) and deep learning (DL) to generate human-like responses. This paper provides a broad, systematic review of ChatGPT’s applications in healthcare, particularly in enhancing patient engagement through medical history collection, symptom assessment, and decision support for improved diagnostic accuracy. It assesses ChatGPT’s potential across multiple organ systems and specialties, highlighting its value in clinical, educational, and administrative contexts. This analysis reveals both the benefits and limitations of ChatGPT, including health literacy promotion and support for clinical decision-making, alongside challenges such as the risk of inaccuracies, ethical considerations around informed consent, and regulatory hurdles. A quantified summary of key findings shows ChatGPT’s promise in various applications while underscoring the risks associated with its integration in medical practice. Through this comprehensive approach, this review aims to provide healthcare professionals, researchers, and policymakers with a balanced view of ChatGPT’s potential and limitations, emphasizing the need for ongoing updates to keep pace with evolving medical knowledge.Applied Informatics1112A broad, systematic review of ChatGPT’s applications in healthcare, particularly in enhancing patient engagement through medical history collection, symptom assessment, and decision support for improved diagnostic accuracy, shows ChatGPT’s promise in various applications while underscoring the risks associated with its integration in medical practice.AI["Fnu Neha", "Deepshikha Bhati", "Deepak Kumar Shukla", "Md Amiruzzaman"]2024-12-02T00:00:00 bd13a16101af15aaaa1193f56fae3af17d29c7feThe integration of digital technologies into health care has significantly enhanced the efficiency and effectiveness of care coordination. Our perspective paper explores the digital information ecosystems in modern care coordination, focusing on the processes of information generation, updating, transmission, and exchange along a patient’s care pathway. We identify several challenges within this ecosystem, including interoperability issues, information silos, hard-to-map patient care journeys, increased workload on health care professionals, coordination and communication gaps, and compliance with privacy regulations. These challenges are often associated with inefficiencies and diminished care quality. We also examine how emerging artificial intelligence (AI) tools have the potential to enhance the management of patient information flow. Specifically, AI can boost interoperability across diverse health systems; optimize and monitor patient care pathways; improve information retrieval and care transitions; humanize health care by integrating patients’ desired outcomes and patient-reported outcome measures; and optimize clinical workflows, resource allocation, and digital tool usability and user experiences. By strategically leveraging AI, health care systems can establish a more robust and responsive digital information ecosystem, improving care coordination and patient outcomes. This perspective underscores the importance of continued research and investment in AI technologies in patient care pathways. We advocate for a thoughtful integration of AI into health care practices to fully realize its potential in revolutionizing care coordination.Journal of Medical Internet Research702This perspective paper explores the digital information ecosystems in modern care coordination, focusing on the processes of information generation, updating, transmission, and exchange along a patient’s care pathway, and how emerging artificial intelligence tools have the potential to enhance the management of patient information flow.Journal of Medical Internet Research["You Chen", "Christoph U. Lehmann", "Bradley A Malin"]2024-12-02T00:00:00 de45441c7fe4910b01fabe0f739226be1dd7d8fbThe shift from Industry 4.0 to Industry 5.0 represents a significant evolution toward sustainable, human-centric manufacturing. This paper explores how advanced multi-objective optimization techniques can integrate Artificial Intelligence (AI) with human insights to enhance both sustainability and customization in manufacturing. We investigate specific optimization methods, including genetic algorithms (GAs), Particle Swarm Optimization (PSO), and reinforcement learning (RL), which are tailored to balance efficiency, waste reduction, and carbon footprint. Our proposed framework enables human creativity to interact with AI-driven processes, embedding human input into a computational structure that adapts dynamically to operational goals. By linking optimization directly to environmental impacts, such as reducing waste, energy consumption, and carbon emissions, this study establishes a pathway toward environmentally sustainable production. This research fills existing gaps by offering a detailed, practical model that harmonizes theoretical insights with applications in personalized manufacturing environments. In this regard, it contributes to the ongoing development of Industry 5.0, emphasizing how AI and human collaboration can foster intelligent, adaptable, and sustainable manufacturing systems.Processes341This paper explores how advanced multi-objective optimization techniques can integrate Artificial Intelligence with human insights to enhance both sustainability and customization in manufacturing, and investigates specific optimization methods, including genetic algorithms, particle Swarm Optimization, and reinforcement learning.Processes["Shu-Chuan Chen", "Hsien-Ming Chen", "Han-Kwang Chen", "Chieh-Lan Li"]2024-12-02T00:00:00 4c46e870697810a2288bd387b7318ff52d1039aeThe classification codes granted by patent offices are useful instruments for simplifying the bewildering variety of patents in existence. They are singularly unhelpful, however, in locating a specific subgroup of patents such as that of drug-related pharmaceutical patents for which no classification codes exist. Taking advantage of advances in artificial intelligence and in natural language processing in particular, we offer a new method of identifying chemical drug-related patents in this article. The aim is primarily that of demonstrating how the proverbial needle in a haystack was identified, namely through leveraging the superb pattern-recognition abilities of the BERT (Bidirectional Encoder Representations from Transformers) algorithm. We build three different databases to train our algorithm and fine-tune its abilities to identify the patent group in question by exposing it to additional texts containing structures that are much more likely to be present in them, until we obtain the highest possible F1-score, combined with an accuracy of 94.40%. We also demonstrate some possible uses of the algorithm. Its application to the US patent office database enables the identification of potential chemical drug patents up to ten years before drug approval, whereas its application to the German patent office reveals the regional nature of drug R&D and patenting strategies. The hope is that both the method proposed and its applications will be further refined and expanded forthwith.PLoS ONE350The aim is primarily that of demonstrating how the proverbial needle in a haystack was identified, namely through leveraging the superb pattern-recognition abilities of the BERT (Bidirectional Encoder Representations from Transformers) algorithm.PLOS ONE["Leonardo Costa Ribeiro", "Valbona Muzaka"]2024-12-02T00:00:00 b8f9877cdd38130f082b06ebbdcf643b88fe1fc1The rapid development of technology has made an earth-shattering innovation take the world by storm: Artificial Intelligence (AI). This study aims to determine the perceptions and practices of teachers and students in using AI in the classroom. Four university teachers and 30 students were sent questionnaires, and four teachers and 23 students were given online structured interviews.  The results stated that in the student group, as many as 24 respondents (80%) had a good perception of AI, and six respondents (20%) had an excellent perception of AI. Meanwhile, in the teacher group, three respondents (75.0%) had a favorable perception, and one teacher respondent (25.0%) had a very favorable perception of AI. This result is also reinforced by the practice of students' and teachers' perspectives of AI, which state that AI is beneficial in eliciting students’ responses, attracting students’ attention, and increasing their motivation and creativity. Nevertheless, AI is just an intelligent tool; no matter how excellent and optimal the tool is, how much potential humans can utilize depends on the use and the user himself.English Education: Jurnal Tadris Bahasa Inggris00The results stated that in the student group, as many as 24 respondents had a good perception of AI, and six respondents had an excellent perception of AI, while in the teacher group, three respondents had a favorable perception, and one teacher respondent had a very favorable perception of AI.English Education: Jurnal Tadris Bahasa Inggris["Zulfikar Muria Timur", "Slamet Setiawan", "Ali Mustofa", "S. Anam"]2024-12-02T00:00:00 90cc6966a3d2dd9dac0276a3ce138c2917fa3098Cyber security is becoming more complex due to the exponential growth of interconnected systems and the global threat landscape. To mitigate those risks, there is a need for a skilled cyber security workforce that can navigate the complex decision-making in rapidly evolving cyberspace. Artificial intelligence (AI) is rapidly adopted into cyber defence operations, but we can not effectively train human and AI-assisted cyber defence operators without understanding the underlying learning theory and eco-systems. Cyber security exercises (CSXs) are popular teaching methods for cyber-readiness. However, applying learning analytics (LA) methods and AI-based approaches to exercise design and implementation is still in the early stages. We propose a holistic human-AI interaction model within the LA and CSX context. The model brings together elements and processes of human-AI interactions, as well as cyber ranges, cyber security, and LA tools, and a wider lens of multimodal learning analytics, exercise life-cycle, and overall pedagogical approach. We also discuss the opportunities and challenges for LA and AI in the context of cyber security training. We analyse the role of AI from the learning, instruction, and administration lens in cyber security training, specifically in the exercises. We aim to stimulate further discussions on the future of human-AI collaboration and how to enhance cyber security training with novel LA and AI capabilities.International Conference on Security of Information and Networks570A holistic human-AI interaction model is proposed within the LA and CSX context that brings together elements and processes of human-AI interactions, as well as cyber ranges, cyber security, and LA tools, and a wider lens of multimodal learning analytics, exercise life-cycle, and overall pedagogical approach.2024 17th International Conference on Security of Information and Networks (SIN)["Kaie Maennel", "Olaf Maennel"]2024-12-02T00:00:00 9a7c54c2deb4694d101e8fc9c8ab16ee6afa52e8Artificial intelligence, digitalisation, and other variants of modern technology have been a central topics in the tourism sector in recent times.    A bibliometric study investigated the impact of artificial intelligence and digitalisation in tourism education. a total of 1,911 published documents between years 2000 to 2023 were retrieved from the Scopus database. The study highlighted the evolving volume of studies on artificial intelligence applications in tourism education and the tourism sector by considering, authors and authorship networks, date of publications, countries, journal publications, citation count, research themes, keywords, and keyword co-occurrence. This evolution in the citation count and number of publications over the years is a pointer to the increasing attention on artificial intelligence generally and then on its role in tourism education and by extension tourism industry. Beyond the dates and citation count, the study reveals countries with the most publications in line with the theme of the study. To this end, China was shown to have more publications emanating from there. In terms of authorship, “Law Rob” and Ivanov Stanislav were shown to have made noteworthy contributions to the topic of consideration. The co-occurrence analysis carried out to identify key theme areas showed notable word cloud formation for terms such as “artificial intelligence”, “tourism” “education” “digitalisation” and “tourism industry”. The study concludes that more studies will be required in other to get the most out of the integration of artificial intelligence and digitalisation in tourism education.Brain: Broad Research in Artificial Intelligence and Neuroscience01The study concludes that more studies will be required in other to get the most out of the integration of artificial intelligence and digitalisation in tourism education.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["Godwin Aigbokhai", "N. M. Bah\u00e7elerli", "Salim Akyurek", "Mehmet Altinay", "A. Kenebayeva"]2024-12-03T00:00:00 35aff835851435c7259471415b920ab8bcac1e72This article explores the significant impact that artificial intelligence (AI) could have on food safety and nutrition, with a specific focus on the use of machine learning and neural networks for disease risk prediction, diet personalization, and food product development. Specific AI techniques and explainable AI (XAI) are highlighted for their potential in personalizing diet recommendations, predicting models for disease prevention, and enhancing data-driven approaches to food production. The article also underlines the importance of high-performance computing infrastructures and data management strategies, including data operations (DataOps) for efficient data pipelines and findable, accessible, interoperable, and reusable (FAIR) principles for open and standardized data sharing. Additionally, it explores the concept of open data sharing and the integration of machine learning algorithms in the food industry to enhance food safety and product development. It highlights the METROFOOD-IT project as a best practice example of implementing advancements in the agri-food sector, demonstrating successful interdisciplinary collaboration. The project fosters both data security and transparency within a decentralized data space model, ensuring reliable and efficient data sharing. However, challenges such as data privacy, model interoperability, and ethical considerations remain key obstacles. The article also discusses the need for ongoing interdisciplinary collaboration between data scientists, nutritionists, and food technologists to effectively address these challenges. Future research should focus on refining AI models to improve their reliability and exploring how to integrate these technologies into everyday nutritional practices for better health outcomes.Exploration of Foods and Foodomics361The METROFOOD-IT project is highlighted as a best practice example of implementing advancements in the agri-food sector, demonstrating successful interdisciplinary collaboration and the need for ongoing interdisciplinary collaboration between data scientists, nutritionists, and food technologists to effectively address these challenges.Exploration of Foods and Foodomics["Pierpaolo Di Bitonto", "Michele Magarelli", "Pierfrancesco Novielli", "Donato Romano", "D. Diacono", "Lorenzo de Trizio", "Angelo Mariano", "Claudia Zoani", "Riccardo Ferrero", "A. Manzin", "Maria De Angelis", "Roberto Bellotti", "S. Tangaro"]2024-12-03T00:00:00 4a65b46063edcd9a97ff73dfe42d2eeb0098e6a5Pediatric surgery international (Print)391The transformative effects of artificial intelligence (AI) and large language models (LLMs) on pediatric surgery, including preoperative, intraoperative, and postoperative care, as well as their influence on medical education and patient communication are explored.Pediatric surgery international["Yuichiro Miyake", "Giuseppe Retrosi", "R. Keijzer"]2024-12-03T00:00:00 57b6d542ddb2301cf6783327294e77011c384534This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), and neural networks (NNs)—were evaluated based on their effectiveness in predicting waste generation, economic growth, and energy prices. The GBR model outperformed the others, achieving a mean absolute error (MAE) of 23.39 and an R2 of 0.7586 in demand forecasting, demonstrating strong potential for resource flow management. In contrast, the NNs encountered limitations in supply prediction, with an MAE of 121.86 and an R2 of 0.0151, indicating challenges in adapting to market volatility. Reinforcement learning methods, specifically Q-learning and deep Q-learning (DQL), were applied for price stabilization, resulting in reduced price fluctuations and improved market stability. These findings contribute a conceptual framework for AI-driven CE marketplaces, showcasing the role of AI in enhancing resource efficiency and supporting sustainable urban development. While synthetic data enabled controlled experimentation, this study acknowledges its limitations in capturing full real-world variability, marking a direction for future research to validate findings with real-world data. Moreover, ethical considerations, such as algorithmic fairness and transparency, are critical for responsible AI integration in circular economy contexts.Sustainability281Reinforcement learning methods, specifically Q-learning and deep Q-learning, were applied for price stabilization, resulting in reduced price fluctuations and improved market stability, contributing a conceptual framework for AI-driven CE marketplaces.Sustainability["A. Sheikh", "Steve Simske", "Edwin K. P. Chong"]2024-12-03T00:00:00 163044de4a5d868cb2edc22c34d5cc359bac4edcBACKGROUND Artificial intelligence integration into healthcare has gained significant attention in recent years, with its use ranging from disease diagnosis to surgical assistance. While artificial intelligence's potential to improve patient outcomes and optimise patient care is undeniable, concerns regarding privacy, transparency, and the potential for medical errors persist. To take full advantage of artificial intelligence's transformative abilities, understanding patient perceptions and attitudes towards its integration into medicine is crucial for ethical considerations and health outcomes. PURPOSE This study aimed to describe patients' perceptions of medical artificial intelligence and its integration into the healthcare system, drawing attention to a crucial yet understudied aspect of artificial intelligence adoption in Kazakhstan. DESIGN Descriptive qualitative design. METHOD From February to March 2024, the researchers conducted semi-structured interviews amongst 13 patients. The interviews were audio-recorded, transcribed, translated, and then analysed using a thematic analysis approach. The study adhered to the COREQ guidelines. RESULT Five themes emerged from 13 interviews: the benefits of artificial intelligence on patient care, the importance of human factors on patient care over artificial intelligence, revolutionising patient care delivery through artificial intelligence, patient education and artificial intelligence, and balancing technology and human interaction in artificial intelligence-driven intervention. CONCLUSION Patient perceptions of artificial intelligence integration into healthcare were primarily positive. Nevertheless, patients prefer artificial intelligence as a supplementary tool under human supervision due to risks such as possible medical errors and violations of patient privacy. PATIENT OR PUBLIC CONTRIBUTION Patients provided the data for this study. The researchers interviewed them about their perceptions of medical artificial intelligence and its integration into the healthcare system. The patients or the public contributed nothing to the other aspects of the study.Journal of Clinical Nursing312Patients' perceptions of artificial intelligence integration into healthcare were primarily positive, and patients prefer artificial intelligence as a supplementary tool under human supervision due to risks such as possible medical errors and violations of patient privacy.Journal of clinical nursing["Alma Tursynbek", "Dilnaz Zhaksylykova", "Jonas Preposi Cruz", "E. Balay-odao"]2024-12-03T00:00:00 be712277c54ae28f2fe9708b4965b6b521646ce9Virchows Archiv120This study evaluates Paige Pan Cancer, a novel artificial intelligence tool designed to flag invasive cancer in haematoxylin and eosin-stained slides from 16 primary tissue types, and finds it demonstrates high sensitivity as a multi-organ screening tool in clinical practice.Virchows Archiv : an international journal of pathology["Iv\u00e1n Rienda", "Jo\u00e3o Vale", "Jo\u00e3o Pinto", "A. Pol\u00f3nia", "C. Eloy"]2024-12-03T00:00:00 3add2be6507aa314481e304b10e562ed4d668086The author reviews the scientific and practical conference on artificial intelligence (AI) in libraries held by RAS Institute for Scientific Information for Social Sciences. He summarizes the presented papers exploring theoretical and practical aspects of using AI tools in library services in Russia and neighbor states.In particular, the author focuses on using artificial neural networks for information retrieval, e-library concept processing (classification, abstracting and annotating), full text recognition, uniform cataloguing, textbook conversion into online courses, and design of reading lists systems. At the conference, the speakers argued that the neural networks were slowly implemented in Russian libraries due to the lack of tagged “library” data sets in open access. They also emphasized the role of libraries in teaching users to work with AI applications.The conference participants consented on the fact of rapid development of this vector in the libraries. The librarians should be more persistent in using available universal applications, partner with Russian large IT-companies, and undertake to design specialized AI-tools for deep content processing of digitized information arrays.Scientific and Technical Libraries00The author focuses on using artificial neural networks for information retrieval, e-library concept processing, full text recognition, uniform cataloguing, textbook conversion into online courses, and design of reading lists systems.Scientific and Technical Libraries["V. K. Stepanov"]2024-12-03T00:00:00 90de53c29ce4f507a8b7a632d47b57341e2699caWith the continuous progress of the digital era, artificial intelligence is increasingly interwoven with various fields. This paper focuses on the current status of financial management courses, applies the SWOT analysis method to analyze the current situation of the development of financial education, proposes the problems existing in current courses, discusses the limitations of traditional teaching, explores the application of AI technology in financial management education, improves and refines course design, breaks the limitations of traditional teaching methods, emphasizes the integration of theory and practice, enhances students' professional competence, cultivates financial management thinking, and builds an intelligent teaching platform. On this basis, it discusses the specific applications of AI in three parts: designing basic courses, cultivating professional competence and thinking, and building a teaching platform. The aim is to innovate financial management education, promote its sustainable and innovative development, and cultivate high-quality talents who can meet the needs of the future financial industry.IC-ITECHS00The aim is to innovate financial management education, promote its sustainable and innovative development, and cultivate high-quality talents who can meet the needs of the future financial industry.IC-ITECHS["Tingting Liang", "Hao Zhang"]2024-12-03T00:00:00 e6d5808f481a6b8c975f8eafe1599134e2b04badThe article presents a comparative analysis of the attitudes of future mathematics teachers, including pre-school and primary school teachers, regarding the application of artificial intelligence in their training in the subjects studied and in creating didactic materials. Data are presented for students and specialists from Konstantin Preslavsky University of Shumen who are studying at the Faculty of Mathematics and Informatics, Faculty of Pedagogy and Department of Information, Qualification and Continuing Education - Varna in Konstantin Preslavsky University of Shumen. MATTEX 2024, CONFERENCE PROCEEDINGS, Volume 100MATTEX 2024, CONFERENCE PROCEEDINGS, Volume 1["N. Pavlova"]2024-12-03T00:00:00 9db5b9807b867e1d3ccba5667ac5d3f45db727e1The integration of artificial intelligence (AI) techniques into clinical settings presents critical challenges due to the opacity of machine learning models, often referred to as "black boxes": this study explores the application of explainability techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), in the context of medical diagnostics. Using the "Diabetes Health Indicators Dataset", we applied Logistic Regression as a predictive model to identify key risk factors for diabetes and evaluate the ability of explainability techniques to improve transparency and interpretability. The results demonstrate that SHAP provides a detailed global and local understanding of feature importance, offering clinicians insights into key predictors such as HighBP, CholCheck, and GenHlth; LIME complements this by delivering intuitive explanations for individual predictions, enabling rapid and accessible interpretation. The combination of these techniques enhances trust in AI systems by providing both comprehensive insights and actionable explanations. Challenges related to computational complexity, scalability, and the integration of these methods into clinical workflows are also discussed, along with recommendations for future research aimed at developing scalable, interpretable AI models for ethical and responsible medical use.IEEE International Conference on Bioinformatics and Biomedicine80The results demonstrate that SHAP provides a detailed global and local understanding of feature importance, offering clinicians insights into key predictors such as HighBP, CholCheck, and GenHlth; LIME complements this by delivering intuitive explanations for individual predictions, enabling rapid and accessible interpretation.2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)["Fedra Rosita Falvo", "Mario Cannataro"]2024-12-03T00:00:00 9b59d231bba53ab919ab23001a06495dade11a55Artificial intelligence (AI) models like ChatGPT and LLama have changed how we understand and create human-like language. They understand language well, can generate text like a person, know the context, and solve problems effectively. This makes them useful in many areas like search engines, customer service, and translation. Recently, these models have also caught the attention of the security field, uncovering weaknesses in security and proving useful for security tasks. This paper offers an extensive review of the security, privacy, and ethical issues associated with AI, focusing on LLMs and their impact on various domains. We explore the vulnerabilities of LLMs, propose poten-tial defense mechanisms, and highlight the broader implications of AI on human society. This survey intends to offer a clear perspective for researchers and practitioners, and policymakers on responsibly developing and deploying AI technologies.2024 1st International Conference On Cryptography And Information Security (VCRIS)170The vulnerabilities of LLMs are explored, proposed defense mechanisms are proposed, and the broader implications of AI on human society are highlighted, to offer a clear perspective for researchers and practitioners, and policymakers on responsibly developing and deploying AI technologies.2024 1st International Conference On Cryptography And Information Security (VCRIS)["Tuyen T. Nguyen", "Huyen T. T. Vu", "H. N. Nguyen"]2024-12-03T00:00:00 0e6bb7266e20000c133751209922f8924b3a9076This study addresses the convergence between technology and education, exploring the impact of paradigms such as "computational intelligence," "educational robotics," and "artificial intelligence" in educational research. The methodology was defined in three stages. In the first stage, the Web of Science database was chosen, and a search string was developed. The second stage involved the selection of studies through inclusion/exclusion criteria and the use of PRISMA. The third stage included the extraction and analysis of quantitative and qualitative data, using bibliometric software, content analysis, and tools such as R Studio, Bibliometrix, VOSViewer, and Python. An annual growth of 56.51% between 2019 and 2023, with 208 works, is revealed. "Sustainability" leads the journals with 39 articles, indicating concentration in highly productive journals. The analysis of keyword co-occurrence reveals frequents thematic areas, highlighting "artificial intelligence," "education," "technology," "machine learning," and "Big data." The lead institution is the Chinese University of Hong Kong, while China stands out with 61 papers at the country level. It emphasizes the importance of considering quality and quantity in scientific production and identifies five key topics in research summaries, suggesting areas of research focused on the integration of technology and educational innovation.International Journal of Educational Research and Innovation420The convergence between technology and education is addressed, exploring the impact of paradigms such as "computational intelligence," "educational robotics," and "artificial intelligence" in educational research and identifying five key topics in research summaries.IJERI: International Journal of Educational Research and Innovation["Alejandra Mercedes Colina Vargas", "Marcos Antonio Espinoza Mina", "Luis L\u00f3pez Cat\u00e1lan", "Blanca L\u00f3pez Catal\u00e1n"]2024-12-03T00:00:00 43feb57327b57cce2cfd8cfaabc558d77eaac642The quality and effectiveness of the academic research writing process could be enhanced by applying artificial intelligence (AI). Researchers can easily and quickly generate precise, well-written, high-quality text using AI algorithms. The role of AI in academic writing has recently been the topic of discussion. Researchers have paid less attention to the importance of AI in academic research writing in developing countries even though it is widely used in academic research writing. AI has become a more significant component of academic writing in recent years. Therefore, this study aimed to examine AI applications in academic research writing. This study's data came from secondary sources such as books, journals, and websites that contained useful data. Moreover, an exploratory research design was employed, and the collected data was analyzed using content analysis. This study showed that AI helps academic writers create excellent content quickly and effectively. AI offers writing assistance, enhances grammar, optimizes structure, supports editing, and helps with ethical compliance. The study concluded that AI profoundly impacts academic research and writing across various fields. Academic research writers must address the potential disadvantages of using AI in academic research writing through action and ethical compliance. The study has contributed to a better understanding the benefits, challenges, and ethical considerations associated with AI's utilization in academic research writing. The study recommended, among others, that AI's transparent and ethical application is critical. Researchers are obligated to utilize AI tools to maintain the authenticity and creativity of their work while refraining from any improper use that might put academic credibility in danger.Qualitative Research of Business and Social Sciences240This study showed that AI helps academic writers create excellent content quickly and effectively, and recommended, among others, that AI's transparent and ethical application is critical.Qualitative Research of Business and Social Sciences["Ugo Chuks Okolie", "Thomastina Nkechi Egbon"]2024-12-03T00:00:00 956d8aa6d95a98fa09ef89535da1c9fd23661775Application of artificial intelligence (AI) in education contributes significantly to improving the quality of human resources (HR). This study aims to analyze the relationship between AI technology and HR quality from an economic perspective. Using a qualitative approach based on literature studies, this study explores the contribution of AI in supporting interactive learning, operational efficiency, and improving critical skills. The results show that AI accelerates the learning process, personalizes learning experiences, and prepares students to face the challenges of the digital job market. At the company level, AI implementation improves productivity, efficiency, and data-driven decision-making. This study recommends the development of a curriculum that supports technological literacy and collaboration between educational institutions, the government, and the private sector to ensure HR readiness to face the demands of the digital era. Practical implications include sustainable training strategies and AI integration to support adaptive digital transformation. This study emphasizes the importance of investing in education and training to optimize technological potential, improve business performance, and support sustainable economic growthIC-ITECHS400The results show that AI accelerates the learning process, personalizes learning experiences, and prepares students to face the challenges of the digital job market.IC-ITECHS["Alamsyah Agit", "Susilawati Muharram", "Oktavianty Oktavianty"]2024-12-03T00:00:00 407208416bea5ca31bf618d24f13894cae86f3ccThe article discusses the challenges in traditional educational assessment methods, such as limited personalization, inefficient feedback, and an overemphasis on lower-order thinking skills. The study aims to explore the evolving role of Artificial Intelligence (AI) in educational assessment, identifying current trends, assessing its impact on learning, and forecasting future developments. A systematic literature review (SLR) was employed to examine the integration of AI in both formative and summative assessments. The findings reveal that AI-based assessment systems provide adaptive, personalized feedback, promote student engagement, and foster individualized learning paths. AI technologies, like machine learning and natural language processing, are particularly effective in providing real-time feedback and evaluating higher-order competencies, including critical thinking and creativity. However, challenges such as data privacy and algorithmic bias remain critical concerns. The study concludes that AI has significant potential to transform educational assessment, offering more dynamic, efficient, and personalized evaluation methods. Future research should focus on addressing ethical concerns like data privacy and algorithmic bias while enhancing AI-driven assessments' adaptability and scalability to support diverse learner needs.Indonesian Journal of Computer Science640It is concluded that AI has significant potential to transform educational assessment, offering more dynamic, efficient, and personalized evaluation methods and addressing ethical concerns like data privacy and algorithmic bias.The Indonesian Journal of Computer Science["Indra Saputra", "Arief Kurniawan", "Merita Yanita", "Elviza Yeni Putri", "Melda Mahniza"]2024-12-03T00:00:00 8273bd99b823338c73d8bacc4037250b367df6aaThe objective of this study was to develop an artificial intelligence (AI)-driven model for the detection of helmet usage among workers in tractor and agricultural machinery factories with the aim of enhancing occupational safety. A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. The classification outputs of all networks were combined through iterative majority voting (IMV) to achieve optimal results. To evaluate the model, an image dataset comprising 662 images of individuals wearing helmets and 722 images of individuals without helmets sourced from the internet was constructed. The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency.Applied Sciences330This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency.Applied Sciences["Simge \u00d6z\u00fca\u011f", "\u00d6mer Ertu\u011frul"]2024-12-03T00:00:00 85c8a8f71a1e3b7bfd759bb84878b1e9f7e153e6Purpose The emergence of artificial intelligence (AI) which operates through technology and digital workspace has proven to transform organisations in recent times. However, there has been key concern over its efficiency among the workforce on how it may replace human intelligence in the contemporary work environment. This study aims to investigate the drawbacks otherwise known as the dark side of AI and its effect on employee quality of work−life and organisational performance through the lens of employee capacity development in reducing its shortcomings. Design/methodology/approach This study used a descriptive research design using a cross-sectional survey approach to administer the research instrument to 1,847 customer service officers of banks, customer agents of telecoms, customer care of retail organisations in Nigeria business environment across various units were respondents of this study, however, 862 participants were finally used. A simple random strategy was used to survey the study participants, and existing scales were adopted to form a new research instrument. A partial least square (PLS) based structural equation model (SEM) was adapted to analyse the collected data from the respondents. Findings The outcome of the study indicated that AI lacks creativity and has a negative impact on both employee quality of work−life and overall organisational performance. The outcome of the study demonstrated the drawbacks and the dark sides of AI as lack of emotional intelligence, lack of in-depth contextual knowledge, over-reliance on data quality and lack of ethical and moral decision analysis are the possible dark side of AI which adversely affect quality of work−life and overall performance of the organisations. The study concluded that it is difficult to replace human intelligence because of AI’s drawbacks and dark side. AI cannot function effectively beyond what is programmed in the system. Originality/value This study has offered a novel trajectory against the efficiency and possible benefits of AI that people are familiar with. It has changed the understanding of the researchers, policymakers and organisations that AI cannot replace human intelligence in the workplace without improvement on those established AI dark sides. Journal of Systems and Information Technology900The study concluded that it is difficult to replace human intelligence because of AI’s drawbacks and dark side and AI cannot function effectively beyond what is programmed in the system.Journal of Systems and Information Technology["O. Akinwale", "O. Kuye", "Indrajit Doddonavar"]2024-12-03T00:00:00 a59088c1f2293ea558f2baf345a3736effab4691Aim: to demonstrate an artificial intelligence model that optimises the differential diagnosis of achalasia.Material and methods. The study included 75 patients: 52 % men (mean age 44.5 ± 17.8 years) and 48 % women (mean age 45.6 ± 16.6 years,) with a preliminary diagnosis of achalasia. Patients were divided into four groups: type I, II, III achalasia and a group of patients whose results did not correspond to a diagnosis of achalasia according to HRM performed based on Chicago Classification version 4.0. On the basis of a set of data from 750 swallows and therefore 6750 manometric parameters, the artificial intelligence models DecisionTreeClassifier, RandomForestClassifier and CatBoostClassifier have been trained to provide a manometric diagnosis. The comparison criteria were the training time and the f1_score metric. The technical characteristics of the model (hyperparameters) were selected using the GridSearchCV method. The model with the best results was integrated into a web application.Results. The RandomForestClassifier was chosen as the best performing model to compare. Its technical characteristics were “decision trees” and branching depth the number of which was 14 and 5 respectively. With a maximum possible value of 1.0, these hyperparameters achieved f1_score=0.91 in 27 seconds. The web application, developed on the basis of this model, is capable of analyzing manometric data and establishing one of three types of achalasia in patients. Alternatively, it can exclude the diagnosis of achalasia. The output of an image corresponding to the diagnosis is produced for each manometric type of the disease.Conclusions. For the first time in Russia, a machine learning model based on high-resolution esophageal manometry data was developed at the V. Kh. Vasilenko Clinic of Internal Disease Propedeutics, Gastroenterology, and Hepatology of Sechenov University. The model has been applied to the creation of a web application which has the ability to substantiate the manometry diagnosis of patients. The Federal Service for Intellectual Property (Rospatent) issued a certificate of state registration of the computer program No. 2024665795 dated July 5, 2024. This artificial intelligence programme can be used in clinical practice as a medical decision support tool to optimize the process of differential diagnosis of achalasia and early detection of the disease, to determine the patient's prognosis and to select the method of further treatment.Russian Journal of Gastroenterology Hepatology Coloproctology210An artificial intelligence model based on high-resolution esophageal manometry data was developed at the V. Vasilenko Clinic of Internal Disease Propedeutics, Gastroenterology, and Hepatology of Sechenov University and has been applied to the creation of a web application which has the ability to substantiate the manometry diagnosis of patients.Russian Journal of Gastroenterology, Hepatology, Coloproctology["O. Storonova", "N. I. Kanevskii", "A. Trukhmanov", "V. T. Ivashkin"]2024-12-03T00:00:00 bd4ffcb17c22aeb89239bdbf5997d081b0109e34This study aims to see if there is a change in the work of Nusa Putra students' coursework after a major update in the artificial intelligence (AI) sector. In a digital era that has changed greatly after AI, this research focuses on examining whether after AI there are changes in critical thinking, problem analysis, and also student literacy in coursework. The method used in the research is qualitative by means of interviews. The population we studied was NUSA PUTRA UNIVERSITY students majoring in Management Semester 2 and the sample used was 6 NUSA PUTRA UNIVERSITY students majoring in Management Semester 2. The results showed that artificial intelligence or AI is very influential on the mindset of critical thinking and also the level of literacy of a student, but the changes brought by AI can have both good and bad effects on students. It is also supported by the answers given by the respondents that AI or artificial intelligence can have both good and bad effects depending on who uses it and how it is used. This study emphasizes the importance of education in the use of AI to ensure that students' critical thinking, literacy and problem-solving skills are not diminished. The results also show that a balanced use of AI is the key to maximizing the advances in technology today. Keywords: Student Management, Critical Thinking, Literacy Level, Artificial Intelligence (AI)JUDICIOUS00The results showed that artificial intelligence or AI is very influential on the mindset of critical thinking and also the level of literacy of a student, but the changes brought by AI can have both good and bad effects on students.JUDICIOUS["Hesri Mintawati", "Intan Deanida Pratiwi", "L. Desianti", "Bonse Aris Mandala Putra Simangunsong", "Meiliani Luckieta"]2024-12-03T00:00:00 ed7b054cf3fd4040a561a6e3add2216e06fc9a5cWhen language is utilized as a medium to store and communicate sensory information, there arises a kind of radical virtual reality, namely"the realities that are reduced into the same sentence are virtual/equivalent."In the current era, in which artificial intelligence engages in the linguistic mediation of sensory information, it is imperative to re-examine the various issues pertaining to this potential VR, particularly in relation to bias and (dis)communication. Semantic See-through Goggles represent an experimental framework for glasses through which the view is fully verbalized and re-depicted into the wearer's view. The participants wear the goggles equipped with a camera and head-mounted display (HMD). In real-time, the image captured by the camera is converted by the AI into a single line of text, which is then transformed into an image and presented to the user's eyes. This process enables users to perceive and interact with the real physical world through this redrawn view. We constructed a prototype of these goggles, examined their fundamental characteristics, and then conducted a qualitative analysis of the wearer's experience. This project investigates a methodology for subjectively capturing the situation in which AI serves as a proxy for our perception of the world. At the same time, It also attempts to appropriate some of the energy of today's debate over artificial intelligence for a classical inquiry around the fact that"intelligence can only see the world under meaning."arXiv.org00Semantic See-through Goggles represent an experimental framework for glasses through which the view is fully verbalized and re-depicted into the wearer's view, and attempts to appropriate some of the energy of today's debate over artificial intelligence for a classical inquiry around the fact that intelligence can only see the world under meaning.ArXiv["Goki Muramoto", "Yuri Yasui", "Hirosuke Asahi"]2024-12-03T00:00:00 de2008edc7a92a02b63197fccc2b5fa67a505410Artificial intelligence is one of the technologies that has been used in different stages of the fashion sector for a long time. Recently, with the emergence of new artificial intelligence tools such as ChatGPT and Midjourney, the fashion sector has begun to become one of the centers of these applications. The industry's picture of using artificial intelligence applications has strengthened the idea that artificial intelligence, which is on the agenda of fashion, may be the technology that will draw the future of fashion. Artificial intelligence technologies can be used in different areas in the fashion sector. In this field, it provides benefits in its processes and methods, especially in fashion design. Artificial intelligence technologies are used more intensively in the fashion sector in areas such as the design process, demand forecasting, selection (preference determination), and communication. Artificial intelligence applications, which have been one of the most frequently discussed topics of recent times with the advantages they offer to the sector, have managed to become one of the most important topics of today. In this context, in this article, artificial intelligence applications used in the sector are included and sample model analyzes are made with ChatGPT. In this context, the study aimed to reveal the situation of artificial intelligence applications in the fashion sector and emphasized the advantages it offers to the sector by presenting some examples.Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi80The study aimed to reveal the situation of artificial intelligence applications in the fashion sector and emphasized the advantages it offers to the sector by presenting some examples.Cankiri Karatekin Universitesi Iktisadi ve Idari Bilimler Fakultesi Dergisi["\u00d6zlem Kaya", "S. Ayta\u00e7"]2024-12-03T00:00:00 36831752fa122775274341599d74f713e28bbcedThe purpose of this paper is to explore how artificial intelligence technology can help mental health education in colleges and universities, analyse the progress of its application in personalised education, mental health monitoring and evaluation, ecological transient intervention, etc., and analyse its current situation, strengths, challenges, and future development direction.IC-ITECHS00IC-ITECHS["Peng Shuai", "Yue Liu"]2024-12-03T00:00:00 003aa8b5baaa7e390dea51e0e019db003b09c875In the continuously evolving digital era, which drives societal mobility, banks in Indonesia are experiencing a transformation from traditional to modern or digital payment methods. This research aims to examine the influence of awareness, attitude, subjective norm, perceived risk, perceived usefulness, and knowledge on the intention to open a Digital Account in digital banks implementing Artificial Intelligence. The study adopts a quantitative approach, and the sampling method involves 111 students from University of Brawijaya, Malang City. Data collection is carried out through questionnaire distribution. Logistic regression analysis is employed for data analysis. The findings of this study are expected to convince the public and serve as a consideration for banks regarding the importance of Artificial Intelligence in the digitalization era.Contemporary Studies in Economic, Finance and Banking00The findings of this study are expected to convince the public and serve as a consideration for banks regarding the importance of Artificial Intelligence in the digitalization era.Contemporary Studies in Economic, Finance and Banking["Bagia Wangsadireja", "Dias Satria"]2024-12-03T00:00:00 d821b6252f111fe9263a49abdc399b63683df2d2Artificial Intelligence (AI) has rapidly transformed numerous sectors, including healthcare, justice, and commerce, providing substantial benefits while also raising complex ethical questions: this article explores the main ethical challenges associated with AI, focusing on issues such as algorithmic bias, data privacy and security, transparency, and accountability. The importance of Explainable Artificial Intelligence (XAI) in enhancing the interpretability of algorithmic decisions is emphasized, particularly in healthcare, where model opacity can have a direct impact on patient outcomes. The paper further examines regulatory frameworks and ethical guidelines, including the European Union’s AI Act, advocating for a multidisciplinary approach that combines innovation and accountability to develop AI systems that respect human rights and foster user trust. In conclusion, the article underscores the need for interdisciplinary collaboration and adaptable regulations to ensure the ethical development of AI, promoting fairness and transparency.IEEE International Conference on Bioinformatics and Biomedicine380The paper examines regulatory frameworks and ethical guidelines, including the European Union’s AI Act, advocating for a multidisciplinary approach that combines innovation and accountability to develop AI systems that respect human rights and foster user trust.2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)["Fedra Rosita Falvo", "Mario Cannataro"]2024-12-03T00:00:00 34155e749c757679c2c99e5f4efd396cb74cac0bThis scoping review seeks to map the landscape of how Artificial Intelligence (AI) is leveraged within educational environments to support students with disabilities and inclusive strategies and experiences. The research question concerns the role and impact of AI across diverse educational settings and, in particular: “How is Artificial Intelligence (AI) being utilized within educational settings to support individuals with disabilities and promote inclusive education?”. The review explores this question under four pivotal dimensions: Educational Context, Disabilities and Special Needs, Artificial Intelligence Technologies, and Inclusivity and Inclusive Practice. Each contributes to a comprehensive understanding of the interdisciplinary nature of this inquiry. To ensure a comprehensive analysis, four major research databases have been used: Scopus, EBSCO, ERIC, and Web of Science (WoS). This robust search strategy enabled us to capture a wide array of relevant literature. The review also addresses ethical considerations essential for the responsible integration of AI in education, such as privacy, accessibility, and bias. By mapping existing research and identifying gaps, this scoping review lays the groundwork for future advancements in AI-driven inclusive educational practices.Information340This scoping review seeks to map the landscape of how Artificial Intelligence is leveraged within educational environments to support students with disabilities and inclusive strategies and experiences and addresses ethical considerations essential for the responsible integration of AI in education.Information["S. Pagliara", "G. Bonavolont\u00e0", "Mariella Pia", "Stefania Falchi", "Antioco Luigi Zurru", "Gianni Fenu", "A. Mura"]2024-12-03T00:00:00 2a4ab22df07e4cb12645ff0dfd17205e3ef503b5Artificial intelligence (AI) has transformed various sectors and institutions, including education and healthcare. Although AI offers immense potential for innovation and problem solving, its integration also raises significant ethical concerns, such as privacy and bias. This paper delves into key considerations for developing AI policies within institutions. We explore the importance of interpretability and explainability in AI elements, as well as the need to mitigate biases and ensure privacy. Additionally, we discuss the environmental impact of AI and the importance of energy-efficient practices. The culmination of these important components is centralized in a generalized framework to be utilized for institutions developing their AI policy. By addressing these critical factors, institutions can harness the power of AI while safeguarding ethical principles.arXiv.org310The importance of interpretability and explainability in AI elements, as well as the need to mitigate biases and ensure privacy, are explored and centralized in a generalized framework to be utilized for institutions developing their AI policy.ArXiv["W. Lamberti"]2024-12-03T00:00:00 e27fb3637d31a38d7005398992eedf4d697a4ca5Artificial Intelligence presents promising opportunities to improve efficiency, enhance decision-making, and boost employee engagement within human resource management. This study explores the intersection of Artificial Intelligence and Human Resource Management through an in-depth bibliometric analysis, offering a comprehensive overview of research trends and advancements over the past decade. Drawing from the Web of Science database, this research examined 491 journal articles published from 2014 to 2023. AI applications in Human Resource Management have been particularly valuable during the COVID-19 pandemic, aiding healthcare professionals, facilitating remote work, and optimizing workforce management under challenging conditions. Implementing Artificial Intelligence in Human Resource Management leadership management and information management has significantly improved organizational processes and outcomes. The study concludes that ongoing advancements in Artificial Intelligence technology will continue to enhance Artificial Intelligence practices, leading to a more adaptable, responsive, and efficient workforce capable of addressing future challenges.Brain: Broad Research in Artificial Intelligence and Neuroscience00The study concludes that ongoing advancements in Artificial Intelligence technology will continue to enhance Artificial Intelligence practices, leading to a more adaptable, responsive, and efficient workforce capable of addressing future challenges.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["Ebba S. I. Ossiannilsson", "F. Alt\u0131nay", "R. Shadiev", "Phillip Benachour", "Muhammet Berigel", "G. Dagli", "Ahmet Ayaz", "Betul Y\u0131k\u0131c\u0131", "Z. Alt\u0131nay"]2024-12-03T00:00:00 18bc3b1c7aa7b9cf4b64401fca5dd3e1fce6c8b7The widespread use of artificial intelligence (AI) has been growing in various fields. While concerns about the use of basic computer technology still continue today, anxiety about a new technological revolution, artificial intelligence, has also surrounded the education sector. The cause of anxiety can be derived from knowledge, skills, and attitudes towards the use of technologies. In this study, the attitudes and anxiety levels of administrators and teachers in primary and secondary schools towards artificial intelligence and the relationship between anxiety and attitude scores were investigated. A total of 130 participants, consisting of administrators and teachers, participated in this study. In this context, survey models are one of the quantitative research methods. Artificial intelligence attitude and anxiety scales were used in accordance with the data collection method. When the research results are corrected, there is a negative relationship between the attitude variable towards artificial intelligence and the anxiety variable; as the attitude increases, the anxiety level decreases. It is seen that administrators and teachers experience significant anxiety.Brain: Broad Research in Artificial Intelligence and Neuroscience00There is a negative relationship between the attitude variable towards artificial intelligence and the anxiety variable; as the attitude increases, the anxiety level decreases; and it is seen that administrators and teachers experience significant anxiety.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["P\u0131nar Ak\u00e7aba", "Dervi\u015fe Amca Toklu", "Umut Akcil"]2024-12-03T00:00:00 5cb4c1a8e842dcb16f69bc5368bf359d0cda55b5npj Digit. Medicine110The Biden 2023 Artificial Intelligence (AI) Executive Order calls for the creation of a patient safety program, and the feasibility of this approach was examined by analyzing reports associated with AI/Machine Learning (ML)-enabled medical devices.NPJ Digital Medicine["Jessica L. Handley", "Seth A Krevat", "Allan Fong", "Raj M. Ratwani"]2024-12-03T00:00:00 1ec64ab56ff31050629fcfd5fc23384107f4ef0cArtificial intelligence (AI) and in particular machine learning, neural networks and natural language processing (NLP) are transforming marketing by providing new opportunities for data analytics, process automation, customer segmentation and predicting consumer behaviour. Despite its great potential, the deployment of AI in marketing faces a number of challenges, including security and privacy threats, unintentional discrimination, and the risk of job displacement. This article highlights the need for human oversight and transparency in the implementation of AIMATTEX 2024, CONFERENCE PROCEEDINGS, Volume 100The need for human oversight and transparency in the implementation of AI in marketing faces a number of challenges, including security and privacy threats, unintentional discrimination, and the risk of job displacement.MATTEX 2024, CONFERENCE PROCEEDINGS, Volume 1["Teodora Stoyanova"]2024-12-03T00:00:00 a3d9347142a53f33632be4c23454cd208cd747a1In the rapidly evolving field of remote sensing, Deep Learning (DL) techniques have become pivotal in interpreting and processing complex datasets. However, the increasing reliance on these algorithms necessitates a robust ethical framework to evaluate their trustworthiness. This paper introduces a comprehensive ethical framework designed to assess and quantify the trustworthiness of DL techniques in the context of remote sensing. We first define trustworthiness in DL as a multidimensional construct encompassing accuracy, reliability, transparency and explainability, fairness, and accountability. Our framework then operationalizes these dimensions through a set of quantifiable metrics, allowing for the systematic evaluation of DL models. To illustrate the applicability of our framework, we selected an existing case study in remote sensing, wherein we apply our ethical assessment to a DL model used for classification. Our results demonstrate the model’s performance across different trustworthiness metrics, highlighting areas for ethical improvement. This paper not only contributes a novel framework for ethical analysis in the field of DL, but also provides a practical tool for developers and practitioners in remote sensing to ensure the responsible deployment of DL technologies. Through a dual approach that combines top-down international standards with bottom-up, context-specific considerations, our framework serves as a practical tool for ensuring responsible AI applications in remote sensing. Its application through a case study highlights its potential to influence policy-making and guide ethical AI development in this domain.Remote Sensing301A comprehensive ethical framework designed to assess and quantify the trustworthiness of DL techniques in the context of remote sensing and serves as a practical tool for ensuring responsible AI applications in remote sensing.Remote Sensing["M. Paolanti", "S. Tiribelli", "B. Giovanola", "Adriano Mancini", "Emanuele Frontoni", "R. Pierdicca"]2024-12-03T00:00:00 a496f9c5eb55668781d2ccd0eb6dd45a76474b62Discover Sustainability361Discover Sustainability["Hatoon S. Alsagri", "S. Sohail"]2024-12-03T00:00:00 39fd53dd9eb54bc11da57ba1d419993772c69a8bCooperation among the BRICS countries on AI is still nascent, yet it holds the potential to light the path ahead for AI innovation and governance among Global South nations.02["Arijit Goswami"]2024-12-03T00:00:00 a4dcca1c7521c9c4681024eb68ba9c30d6c21e04Research in Developmental Disabilities480Light is shed on the promise of applying AI tools in special education to respond to distinctive needs experienced by students with mild ID and the long-term effects of such interventions and their broader applications across diverse educational contexts for inclusive learning.Research in developmental disabilities["Abdulaziz S. Alsolami"]2024-12-03T00:00:00 1c76a692c361e1e4af4d5be7441a9d780006e794SIGGRAPH Asia Art Gallery00Semantic See-through Goggles represent an experimental framework for glasses through which the view is fully verbalized and re-depicted into the wearer's view, and attempts to appropriate some of the energy of today's debate over artificial intelligence for a classical inquiry around the fact that intelligence can only see the world under meaning.{"pages": "15:1"}["Goki Muramoto", "Yuri Yasui", "Hirosuke Asahi"]2024-12-03T00:00:00 835bc6158e41f304f399f7059b0990678e280a75Pediatric cardiology presents a unique set of challenges, where the accurate diagnosis and treatment of rare and complex conditions demand a thorough understanding of patient-specific anatomy and physiology. However, the integration of cutting-edge research and AI models into clinical practice remains a significant practical challenge. CardioVision is a novel software platform designed to bridge this gap, providing pediatric cardiologists with an easy accessible and comprehensive tool for preparing and managing challenging cases. By integrating AI-powered analytics, 3D visualization, and collaborative workflows, CardioVision enables clinicians to make more informed decisions, reduce radiation dose to the patient, and improve patient outcomes. This paper presents the CardioVision platform, highlighting its key features and functionalities, and discusses the potential CardioVision has for improving personalized medicine in pediatric cardiology.IEEE International Conference on Bioinformatics and Biomedicine180The CardioVision platform is presented, highlighting its key features and functionalities, and the potential CardioVision has for improving personalized medicine in pediatric cardiology is discussed.2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)["Andreas Jahnen", "J. Dabin", "G. Annoni", "Alexandru-Adrian Tantar", "Bjorn Cools", "I. Thierry-Chef"]2024-12-03T00:00:00 1fda488e4ef81afc1419af6abe90364a5af60fb6Scientific Reports80A Radio Frequency Identification (RFID)-based smart mask for a Lip-reading framework capable of reading Lips under face masks, enabling effective speech recognition and fostering conversational accessibility for individuals with hearing impairment is proposed.Scientific Reports["Hira Hameed", "Lubna", "Muhammad Usman", "J. Kazim", "Khaled Assaleh", "K. Arshad", "Amir Hussain", "Muhammad Imran", "Qammer H. Abbasi"]2024-12-03T00:00:00 aa4a73370358225fd7ee7cfa1343be2e748fd39bFrontiers in Forests and Global Change00Frontiers in Forests and Global Change["F. Bravo", "Sheng-I Yang", "I. Ruano", "Clara Ant\u00f3n-Fern\u00e1ndez", "Celia Herrero", "Iv\u00e1n Durango"]2024-12-03T00:00:00 fc13b8fc36325946ec5346cf93944725bbd8db04Academia : Revista Latinoamericana de Administración90Academia Revista Latinoamericana de Administración["Manuel Alonso Dos Santos", "Enrique Ogliastri", "Gianni Roman\u00ed"]2024-12-03T00:00:00 e354f3d62eeac94d8da029bf569fa5d9b2e35f18The use of AI in criminal justice and policing has significantly increased in EU countries. AI tools are being used in various phases of a criminal case, from police tasks to those of judges. While the use of AI in criminal justice is impressive in certain areas, it poses serious challenges and concerns, including potential violations of fundamental rights. This paper examines the risks of using AI in criminal justice, addresses the current use of some AI systems, and explores how the legal framework within the EU regulates its use, including EU directive 2016/680 and the projected AI Act.Comparative Law Review00The risks of using AI in criminal justice are examined, the current use of some AI systems are addressed, and how the legal framework within the EU regulates its use is explored.Comparative Law Review["Ammar Alqatawna"]2024-12-03T00:00:00 d3ef18d85048256a2254beec49d5e91d0468b141When it investigated at the industrialization processes in the world economy, it is seen that while the industrial sector in economies passes to a different stage with the development of production techniques, resources are directed to the production of new products with the differentiation in the products produced. In the development process of the world economy, the Taylor Production System, the Fordist Production System, the Post-Fordist Production System express the reflections of technical change in production on production. In a sense, the change in production knowledge has also brought about the change in production techniques. While the change in the process of change in production knowledge in the industrialization process brought about the change in production techniques, the industry also brought about the change in the product range. With the support of the increase in R&D activity, under the influence of new trends in technology, previously non-existent industrial sub-sectors have emerged. At this point, while the production knowledge changes in the industrial sector, the change in production techniques and product variety is analyzed.MATTEX 2024, CONFERENCE PROCEEDINGS, Volume 100While the change in the process of change in production knowledge in the industrialization process brought about the change in production techniques, the industry also brought about the change in the product range.MATTEX 2024, CONFERENCE PROCEEDINGS, Volume 1["Ali Balkanli"]2024-12-03T00:00:00 519b3e8d993c5f1d925f90ddefdcf9389813c54aInformation Systems Frontiers250Information Systems Frontiers["Aaron M. French", "J. P. Shim"]2024-12-03T00:00:00 5b87b56c76a2fffb17502f7ccfc00373c29a8c80Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with critical, precise reasoning capabilities akin to those of top human experts. Existing AI systems often excel at predefined tasks but struggle with adaptability and precision in novel problem-solving. To overcome this, AEI introduces a framework for ``Probably Approximately Correct (PAC) Reasoning". This paradigm provides robust theoretical guarantees for reliably decomposing complex problems, with a practical mechanism for controlling reasoning precision. In reference to the division of human thought into System 1 for intuitive thinking and System 2 for reflective reasoning~\citep{tversky1974judgment}, we refer to this new type of reasoning as System 3 for precise reasoning, inspired by the rigor of the scientific method. AEI thus establishes a foundation for error-bounded, inference-time learning.arXiv.org00AEI introduces a framework for ``Probably Approximately Correct (PAC) Reasoning", which provides robust theoretical guarantees for reliably decomposing complex problems, with a practical mechanism for controlling reasoning precision.ArXiv["Shai Shalev-Shwartz", "A. Shashua", "Gal Beniamini", "Yoav Levine", "Or Sharir", "Noam Wies", "Ido Ben-Shaul", "Tomer Nussbaum", "Shir Peled"]2024-12-03T00:00:00 db6fccabc961e7dc1c446703859b7d724bed433dScholars argue that artificial intelligence (AI) can generate genuine novelty and new knowledge and, in turn, that AI and computational models of cognition will replace human decision making under uncertainty. We disagree. We argue that AI’s data-based prediction is different from human theory-based causal logic and reasoning. We highlight problems with the decades-old analogy between computers and minds as input–output devices, using large language models as an example. Human cognition is better conceptualized as a form of theory-based causal reasoning rather than AI’s emphasis on information processing and data-based prediction. AI uses a probability-based approach to knowledge and is largely backward looking and imitative, whereas human cognition is forward-looking and capable of generating genuine novelty. We introduce the idea of data–belief asymmetries to highlight the difference between AI and human cognition, using the example of heavier-than-air flight to illustrate our arguments. Theory-based causal reasoning provides a cognitive mechanism for humans to intervene in the world and to engage in directed experimentation to generate new data. Throughout the article, we discuss the implications of our argument for understanding the origins of novelty, new knowledge, and decision making under uncertainty.Strategy Science1013It is argued that AI’s data-based prediction is different from human theory-based causal logic and reasoning, and the idea of data–belief asymmetries is introduced to highlight the difference between AI and human cognition.Strategy Science["Teppo Felin", "Matthias Holweg"]2024-12-03T00:00:00 5fd98d7bbd23c08bc492972336a4564bfd5c8f0bDiagnosing rare diseases is difficult due to the complexity of the conditions, limited data, and a lack of specialized expertise. With over 10,000 rare diseases affecting more than 350 million people globally, diagnosis is often delayed or inaccurate, partly because traditional methods rely on fragmented and decentralized data. In this contribution, we highlight an issue similar to the curse of dimensionality that impacts the artificial intelligence training process, where too many features may lead to training failure. We named this issue the curse of heterogeneity: the need for massive interactions that slow down or lead to fail diagnosis process. Then, the contribution examines the challenges hidden behind rare disease diagnoses and discusses how SAI can improve it by combining AI-driven data analysis with human expertise. To do this, we use two real use-case scenarios. Finally, we discussed how SAI could optimize diagnosis processes and better use platforms like Orphanet, RareCare, and OMIM, which centralize rare disease data. The contribution aims to show how SAI offers a transformative approach to rare disease diagnosis by improving data integration, expert collaboration, and patient outcomes to expand the knowledge network as much as possible.IEEE International Conference on Bioinformatics and Biomedicine270The contribution aims to show how SAI offers a transformative approach to rare disease diagnosis by improving data integration, expert collaboration, and patient outcomes to expand the knowledge network as much as possible.2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)["Serena Lembo", "Paola Barra", "S. Dash", "Luigi Di Biasi"]2024-12-03T00:00:00 5292e2b48ed1148824cb4fb217c37b46cb83c71f Purpose Artificial intelligence (AI) use is on the rise and evolving fast, which expectedly is set to transform the way we carry certain processes in accounting. This study aims to examine the use of AI in enhancing accounting efficiency in terms of AI adoption, employee competence, data quality and organizational readiness. Design/methodology/approach This study gathered data from 192 participants working in the field of accounting in Jordan to look into these factors and how they influence the efficiency of accounting processes. The research hypotheses were tested using partial least squares (PLS)-structural equation modeling. Findings The outcomes of this study documented that a high level of AI adoption, better data quality and competent employees can achieve better accounting efficiency. The PLS analysis also showed that accounting efficiency in Jordan could be enhanced with increased AI adoption level, mainly when organizational readiness elements such as sufficient infrastructure and positive organizational cultures are in place. Originality/value In the realm of emerging markets, this study takes a leading position as the researcher recognizes the vital importance of AI in increasing accounting efficiency across different entities. The outcomes of this study highlight the importance of aligning AI initiatives with broader organizational development strategies to fully gain the advantages of AI in accounting. Journal of Financial Reporting & Accounting870It is documented that a high level of AI adoption, better data quality and competent employees can achieve better accounting efficiency, and the importance of aligning AI initiatives with broader organizational development strategies to fully gain the advantages of AI in accounting is highlighted.Journal of Financial Reporting and Accounting["Rasha Alghazzawi"]2024-12-03T00:00:00 3339c35046842540c48d30b9f8d4ab9d7f6a7130With the rapid development of Artificial Intelligence (AI) technology and its widespread use in education, it is becoming increasingly feasible to build more personalized and supportive learning environments for children with autism. The aim of this paper is to explore how AI-assisted tools can promote inclusive education, with a particular focus on the specific group of children with autism. Through keyword searches of relevant papers in mainstream conferences and journals, as well as a comprehensive analysis of existing literature and case studies, we have conducted a systematic review of AI as an aid to promote inclusive education, aiming to provide valuable references for subsequent research. Studies have shown that when AI technology is appropriately applied, it can significantly enhance the social interaction skills, emotional regulation, and academic performance of children with autism. However, it is important to note that most of the current research findings focus on work published within the last few years, and thus may not fully cover relevant contributions from earlier periods. In summary, by examining the current situation of AI applied to the education of children with autism in China, we can see that AI as an assistive tool shows great potential for development and plays a crucial role in realizing inclusive education in the true sense of the word. This not only promotes educational equity, but also provides more opportunities for each child to grow.IC-ITECHS00By examining the current situation of AI applied to the education of children with autism in China, it can be seen that AI as an assistive tool shows great potential for development and plays a crucial role in realizing inclusive education in the true sense of the word.IC-ITECHS["Yili Yan", "Yue Liu"]2024-12-03T00:00:00 69d6da89c8b91cda6ced7456b5f4de82050fb722In the 1950s, the scientific community first proposed the term "Artificial Intelligence" (AI). Against the backdrop of rapid technological development in the 21st century, the application fields of AI technology are becoming increasingly widespread, penetrating every corner of our work and life. From the perspective of AI's impact on graphic design education, it can provide designers with more inspiration, express more accurately what designers want to convey, and also promote the updating of graphic design course content, thereby continuously cultivating students' comprehensive abilities. AI's integration into graphic design education has led to the development of intelligent design tools that can automatically generate design drafts based on given parameters, thus significantly reducing the time required for the initial design phase. Moreover, AI can analyze trends and user preferences, offering designers insights that can guide their creative process. As a result, the educational curriculum must evolve to include the study of AI algorithms and their applications in design, ensuring that future designers are well-equipped to leverage these powerful tools. This shift not only enhances the efficiency and innovation in the design field but also prepares students for the demands of a future job market that will increasingly rely on AI-assisted design solutions.IC-ITECHS90The educational curriculum must evolve to include the study of AI algorithms and their applications in design, ensuring that future designers are well-equipped to leverage these powerful tools.IC-ITECHS["Yang Wang", "Lijia Cheng", "Fei Lu", "Ailifei Zeng", "Ling Lu"]2024-12-03T00:00:00 0a8c6604fcab037e1ef43ff1aa9410b7e7c91d89The rise of artificial intelligence (AI) in the creative industries has sparked significant debates on its ethical, economic, and sociocultural implications. This study delves into the narratives of human artists grappling with the advent of AI-generated art, focusing on its impact on creativity, cultural identity, and the artistic community. Employing a qualitative phenomenological approach, the research gathered insights from eight artists through in-depth semi-structured interviews. Thematic analysis revealed three key concerns: economic challenges such as job displacement and income instability, ethical dilemmas surrounding originality and copyright, and the devaluation of human creativity. Despite these challenges, artists expressed diverse responses to AI, ranging from fear of obsolescence to embracing AI as a tool for collaboration and innovation. Further, the study examines the role of AI in reshaping digital communication patterns and how it influences the sociocultural dimensions of art in digital media environments. Findings highlight the duality of AI as both a threat and a creative partner, underscoring the urgent need for ethical guidelines and regulatory frameworks to address these challenges. This research contributes to the broader discourse on AI’s role in shaping creative industries and cultural authenticity, advocating for a balanced integration of AI that preserves the irreplaceable value of human creativity and identity.Language, Technology, and Social Media320Language, Technology, and Social Media["Aira Jenica Torres", "Jasper Mareece C. Alberto", "Angel Pearl J. Guieb", "Ayessa DR. Paray", "Joseph A. Villarama"]2024-12-03T00:00:00 042bd74be1cc91d7c2727fd3b406c5f20c048aa9Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations about how these tools can help or hinder students' development of STEM problem-solving competency. Our research examines these considerations by studying how and why college students use genAI tools in their STEM coursework, focusing on their problem-solving support. We surveyed 40 STEM college students from diverse U.S. institutions and 28 STEM faculty to understand instructor perspectives on effective genAI tool use and guidance in STEM courses. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools was to save time. Moreover, over half of student participants reported simply inputting problems for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.arXiv.org00Insight is provided into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.ArXiv["Karen D. Wang", "Zhangyang Wu", "L'Nard Tufts", "Carl E. Wieman", "S. Salehi", "Nicholas Haber"]2024-12-03T00:00:00 3a33593ee58af406016c5e55f704c49b3ed27e48BACKGROUND Artificial intelligence now encompasses technologies like machine learning, natural language processing, and robotics, allowing machines to undertake complex tasks traditionally done by humans. AI's application in healthcare has led to advancements in diagnostic tools, predictive analytics, and surgical precision. AIM This comprehensive review aims to explore the transformative impact of AI across diverse healthcare domains, highlighting its applications, advancements, challenges, and contributions to enhancing patient care. METHODOLOGY A comprehensive literature search was conducted across multiple databases, covering publications from 2014 to 2024. Keywords related to AI applications in healthcare were used to gather data, focusing on studies exploring AI's role in medical specialties. RESULTS AI has demonstrated substantial benefits across various fields of medicine. In cardiology, it aids in automated image interpretation, risk prediction, and the management of cardiovascular diseases. In oncology, AI enhances cancer detection, treatment planning, and personalized drug selection. Radiology benefits from improved image analysis and diagnostic accuracy, while critical care sees advancements in patient triage and resource optimization. AI's integration into pediatrics, surgery, public health, neurology, pathology, and mental health has similarly shown significant improvements in diagnostic precision, personalized treatment, and overall patient care. The implementation of AI in low-resource settings has been particularly impactful, enhancing access to advanced diagnostic tools and treatments. CONCLUSION AI is rapidly changing the healthcare industry by greatly increasing the accuracy of diagnoses, streamlining treatment plans, and improving patient outcomes across a variety of medical specializations. This review underscores AI's transformative potential, from early disease detection to personalized treatment plans, and its ability to augment healthcare delivery, particularly in resource-limited settings.Public Health Nursing931A comprehensive review of AI's transformative potential, from early disease detection to personalized treatment plans, and its ability to augment healthcare delivery, particularly in resource-limited settings underscores AI's transformative potential.Public health nursing["Ravi Rai Dangi", "Anil Sharma", "V. Vageriya"]2024-12-04T00:00:00 ac4f80f47620d3844c3fccc4d5d58ff67e4541c9Although large language models like ChatGPT enable creators to outsource cognitive tasks, partially or entirely entrusting the creative process to artificial intelligence (AI), there is no standardized tool for measuring cognitive outsourcing. In this study we discussed the concept and dimensions of cognitive outsourcing in the context of AI, leading to the development of a measure we called the Cognitive Outsourcing Behavior Toward Artificial Intelligence Scale. By analyzing the content of qualitative interviews with 10 people and 279 quantitative survey responses, we developed this scale, which is composed of 30 items representing five dimensions of cognitive outsourcing: unreliability, gullibility, irrationality, dependency, and cognitive autonomy. We found that individuals' cognitive outsourcing behaviors when collaborating with AI were influenced by their perception of the tool's credibility and reliability, among other factors, and involved reflection on the extent to which their agency is compromised. Our research underscores the significance of cognitive outsourcing in AI-enhanced creativity, providing a comprehensive tool for future investigations into the effects of AI on human creativity and practices.Social Behavior and Personality: An international journal00It is found that individuals' cognitive outsourcing behaviors when collaborating with AI were influenced by their perception of the tool's credibility and reliability, among other factors, and involved reflection on the extent to which their agency is compromised.Social Behavior and Personality: an international journal["Wei Tao", "Mengqiu Zhang", "Yichuan Liu"]2024-12-04T00:00:00 37c8f8a017d9da9121e2e029f305471ce7092f25This research investigates the impact of Artificial Intelligence (AI) and Big Data technologies on the accounting profession, explicitly focusing on accountants who serve as educators. The rapid development of AI and Big Data technology has changed teaching approaches and curricula in accounting education.. The research methodology involves surveys and interviews with accounting educators affiliated with the Indonesian Institute of Accountants, specifically within the Educator Accountants Department, from various higher education institutions in Indonesia. Data obtained from these interviews are qualitatively analyzed to identify their perspectives on the impact of AI and Big Data technologies in the context of teaching and accounting practices. The findings reveal that accounting educators widely recognize the importance of integrating AI and Big Data concepts into accounting curricula. This research provides valuable insights for educators to be able to design responsive curricula and equip students with the skills necessary to succeed in an increasingly interconnected and digitally transformed work environment.Australasian Accounting, Business and Finance Journal00The findings reveal that accounting educators widely recognize the importance of integrating AI and Big Data concepts into accounting curricula, and provide valuable insights for educators to be able to design responsive curricula and equip students with the skills necessary to succeed in an increasingly interconnected and digitally transformed work environment.Australasian Accounting, Business and Finance Journal["Inta Budi Setya Nusa", "Adi Rachmanto", "Mahmad Hasan H. Alhilo"]2024-12-04T00:00:00 72cedec057aa868f5db0cbfc0cffeaab667a7e34Artificial intelligence (AI) has come to stay. The use of AI has helped human society greatly. At the same time, it has posed several concerns or issues. One of the issues concerns intellectual property rights (IPR). There has been a debate about whether IPR should be given to AI. Countries like Australia and South Africa have granted IPR in favor of AI. At the same time, countries such as the United States have not recognized IPR in favor of AI. Many countries firmly believe that human intervention is required to grant an IPR. Be it copyright, patent, etc., human intervention is a condition precedent.On the other hand, the countries in favor of granting IPR to AI believe in AI as a person capable of creating literary work or innovation and are convinced to grant AI with IPR. Before we recognize IPR in favor of AI, we have to grant the person’s status to AI. This paper aims to understand various issues related to identifying or non-recognizing IPR to AI.THE SCIENTIFIC TEMPER00This paper aims to understand various issues related to identifying or non-recognizing IPR to AI.The Scientific Temper["Hemang Shah", "Archana Gadekar"]2024-12-04T00:00:00 d11dd70bf393212c26c846a0bb474f055c8d3515This research explores the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM) practices within the context of remote work. By analyzing existing research and case studies, this paper investigates how AI technologies, such as machine learning and predictive analytics, can be leveraged to optimize recruitment processes, streamline performance evaluations, and facilitate seamless collaboration among geographically dispersed teams. The study also addresses the ethical considerations associated with AI in HRM, including data privacy, algorithmic bias, and ensuring fair and equitable treatment of employees. The findings of this research provide valuable insights into the potential of AI to revolutionize HRM practices in the remote work era, enabling organizations to enhance employee engagement, improve decision-making, and drive innovation.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)150Investigating how AI technologies can be leveraged to optimize recruitment processes, streamline performance evaluations, and facilitate seamless collaboration among geographically dispersed teams provides valuable insights into the potential of AI to revolutionize HRM practices in the remote work era.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)["Ch Sahyaja", "Ch Shankar", "Khudsiya Zeeshan", "N. Nagaraj"]2024-12-04T00:00:00 8c390e4a869d742302f595adacc5305b05c97f42Business sustainability is turning from a leading competitive advantage into an absolute necessity. Companies are increasingly adopting sustainable practices in their supply chains due to mounting pressure from consumers, governments, and investors. These may be different strategies to minimize environmental impact, ensure prospective economic stability, or contribute to the social well-being of vulnerable groups of people. This report applies the semi-systematic literature review method to explore the potential of integrating artificial intelligence (AI) and big data analytics (BDA) technologies to improve sustainability in supply chains. AI and BDA are increasingly asserting themselves as leading solutions for the sustainability of today’s complex supply chains. They affect the three main dimensions of supply chain sustainability— environmental, social, and economic. Thanks to AI, companies make more accurate forecasts, more efficient production and logistics models, and, as a result, minimize stocks, waste, and excess production, reduce fuel consumption, and reduce harmful emissions. Big data (BD) and BDA technologies increase the quality of management decisions and contribute to improving the consistency and transparency of processes in complex supply chains. Despite the very good start, the implementation of AI and BD to achieve supply chain sustainability requires serious research. This implementation is still in its early stages of development and has faced several challenges. At the current stage, results have been achieved in terms of economic sustainability, and more serious ones are expected in other areas as well.International Conference on Advanced Communication Technologies and Networking500This report applies the semi-systematic literature review method to explore the potential of integrating artificial intelligence (AI) and big data analytics (BDA) technologies to improve sustainability in supply chains.2024 7th International Conference on Advanced Communication Technologies and Networking (CommNet)["P. Popova", "Veselin Popov"]2024-12-04T00:00:00 e23a9a9e85fcc68c2a89acc1980c35ce61559268Personalized medicine is a new concept that transforms health care in the context of tailoring strictly towards the needs of an individual patient, using genetic, molecular, and clinical data. This new wave has been brought about by the advancement of genomics, technology, and artificial intelligence (AI). AI comes into play while analyzing massive datasets, identifying different patterns, and enabling predictive modelling for disease outcomes. Genomic medicine makes potent use of the Human Genome Project by relying on AI technologies to process and interpret enormous genetic data. AI enhances genome sequencing, thereby making it possible to be more sensitive and specific in detecting genetic variations that predispose or cause diseases. In pharmacogenomics, AI optimizes drug prescriptions based on genetic profiles to make therapies more effective and personalized. AI, particularly ML and DL, helps in the advanced genomic analysis in clinical genomics, going beyond what is allowed by the traditional bioinformatics to study genomic patterns that wouldn't have been possible under previous standards. Advanced genomic analysis paves the way for precision medicine, providing insights in terms of cancer diagnosis, optimising treatment, and prognosis by the inspection of complex genomic data including SNVs, structural variations, and CNVs. This paper briefly surveys the integration of AI in personalized medicine and genomic research; it would most likely revolutionize healthcare outcomes. As this paper surveys the following, it is apparent that including AI in personal medicine highly enhances genomic study and treatment personalization. The promise of such an advancement includes improved patient outcomes through precised diagnostics, tailored therapies, and optimized disease management strategies.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)160It is apparent that including AI in personal medicine highly enhances genomic study and treatment personalization and the integration of AI in personalized medicine and genomic research would most likely revolutionize healthcare outcomes.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)["Dhanapandi V", "Mahendran G", "Nivetha S A", "Sonali M", "Susmitha D P"]2024-12-04T00:00:00 496e6bff878f770bff0db65f9e63d32472ab8d85This research aims to evaluate the performance of an artificial intelligence (AI) tool called CranioCatch, which assists dentists by examining the anthropomorphization and cognitive aspects of the decision-making process. It remains unclear to what extent technology replaces or complements the work of healthcare professionals. Additionally, the specific cognitive aspects of the decision-making process attributed to CranioCatch, characterizing the anthropomorphization of the technology, have not been mapped. The methodology employed included "instruction to the double," "shadowing," and interviews. The study reveals how CranioCatch replicates cognitive activities performed by dentists by comparing, correlating, and associating images from a specific patient with similar images in a database. Through these steps (comparison, correlation, and association), which constitute the identification of the problem, it is possible to develop alternatives and select the best solution for the patient's situation. Thus, AI contributes to both the identification and action aspects of the decision-making process. From a physical perspective of anthropomorphization, the primary characteristic replicated by AI was the sense of vision. Regarding contributions to dentistry, the AI technology studied enhances the efficiency of professionals, notably reducing evaluation and diagnosis times.International Conference on AI Research240The study reveals how CranioCatch replicates cognitive activities performed by dentists by comparing, correlating, and associating images from a specific patient with similar images in a database, which contributes to both the identification and action aspects of the decision-making process.International Conference on AI Research["Guilherme Knupp Muniz", "Luciana Paula Reis", "S\u00e9rgio Evangelista Silva", "June Marques Fernandes"]2024-12-04T00:00:00 c8bcca6f759863b6feff872e6ea4c15f57b22908The PrimaryAI project focuses on developing an upper elementary integrated curriculum that covers life science, artificial intelligence (AI), and computer science concepts. The PrimaryAI curriculum uses both problem-based learning (PBL) and game-based learning (GBL) to engage students and situates the curriculum in a real-world context. The curriculum and online game were co-designed by researchers and elementary classroom teachers over a period of three years. The curriculum and game utilize a PBL approach to increase student interest and agency while exploring complex topics. The curriculum introduces students to ecosystems; population studies; AI definition and examples; computer vision; machine learning; and AI planning—all within the context of helping study an animal population in students’ local communities. The GBL environment provides students with the opportunity to apply learning from the curriculum to help virtual scientists learn more about factors impacting the endangered yellow-eyed penguins to establish conservation efforts. This design case highlights the instructional design process behind the immersive virtual learning environment and the curriculum as products.International Journal of Designs for Learning00The PrimaryAI curriculum uses both problem-based learning and game-based learning to engage students and situates the curriculum in a real-world context and the curriculum and online game were co-designed by researchers and elementary classroom teachers over a period of three years.International Journal of Designs for Learning["Minji Jeon", "Katie Jantaraweragul", "Anne T. Ottenbreit-Leftwich", "Cindy E. Hmelo-Silver", "Krista D. Glazewski", "Bradford W. Mott", "James Lester", "Cathy Ringstaff"]2024-12-04T00:00:00 5619ee72bc897a16b236e09096ad7b1dcdc864e3The article discusses the use of artificial intelligence (AI) tools in the translation of a literary text. The modern era is marked by the transition of mankind to a new stage in the digital technologies development and the emergence of such a terminological designation as “the digital literary translation”. The work of AI has long gone beyond performing simple search requests on various subject areas in the dialog box mode. With the development of neural networks it is possible to mention that “a smart machine” is “an essence that can think, develop and form the rudiments of consciousness, perception of itself and the surrounding reality” [8, pp. 70]. Systems based on neural networks can generate unique texts in different languages, compose poetry and translate an entire book in a matter of seconds. The fragments of the novel “Fire and blood” by George R. R. Martin, translated from the English language into Russian by the expert, ChatGPT, Google and Yandex Translators are the illustrative material of our research. The methods of comparative, oppositional and contextual analysis and deduction were used in the course of the research. The problems connected with the emergence of a new complex system of contradictory relations in the “human – AI” tandem, which the translator encounters in the course of his professional activity, seem to be relevant. The results of the research show that the main obstacle to the full automation of the literary text translation process is the inability to classify and encode the translator’s cognitive abilities and creative activity in the program.Scientific Notes of V.I. Vernadsky Crimean Federal University. Philological sciences10The results of the research show that the main obstacle to the full automation of the literary text translation process is the inability to classify and encode the translator’s cognitive abilities and creative activity in the program.Scientific Notes of V.I. Vernadsky Crimean Federal University. Philological sciences["M. Norec", "A. Reynova"]2024-12-04T00:00:00 9407557745e1b537b88832188c34d93f99c7b8f4From the shape of our cellphones, the colorful packaging on our foods, to the material in our clothing, every object around us typically has some kind of element of design associated with it. Design is concerned with how users interact with the objects around them. This analysis will be concerned with identifying how AI is being applied in three main categories of design: functional design, visceral design, and behavioral design. Functional designs prioritize the function of objects over form. Visceral designs are concerned with issues of the pure aesthetics of objects. Behavioral designs influence users to act based upon the design of an object, whether it pertains to purchasing the item or using the item in a preferred way. In this analysis, an overlap of these categories will be analyzed through the lens of traditional paintings. A painting reflects a story told by an artist which allows for a variety of interpretations by the perceivers of the artwork. However, what happens when Artificial Intelligence (AI) is used in conjunction with painting? AI when applied to painting uses art-related generative algorithms, and neural networks, which are adapted from models for processing data. AI relies on this type of model to complete in the case of painting, the use of a Neural Style Transfer (NST) to compose a new object of art while employing the style of another artist. Through the lenses of generative AI’s current application and implications related to its future use, this analysis will provide an extensive overview of the convergence of technology, art, and design. This discussion will also address potential ethical and future ethical concerns about authorship, originality, the value of AI-generated art, and the impact on traditional practices of Art and Design from the perspective of painting. As AI technology related to the creation of art continues to develop, anticipatory ethics will attempt to identify ethical issues with this continued development of generative AI in the area of art.International Conference on AI Research00This analysis will be concerned with identifying how AI is being applied in three main categories of design: functional design, visceral design, and behavioral design, and an overlap of these categories will be analyzed through the lens of traditional paintings.International Conference on AI Research["Richard Wilson", "Eunice Hong"]2024-12-04T00:00:00 28d990ac837969b1117bf4ee7871155076f28dbfThe integration of artificial intelligence (AI) into educational research marks a significant paradigm shift where AI intersects with educational research from diverse perspectives, emphasizing its transformative potential. By leveraging AI technologies, researchers can transcend traditional limitations, thereby enhancing their capabilities to pose more incisive questions, analyze vast datasets, and refine research methodologies, ultimately leading to more impactful outcomes. Within the context of a research endeavor focused on K-12 principals supporting classroom teachers serving English Learners in the United States, we explore how AI algorithms can refine research questions and augment research methodologies, leading to deeper insights and more informed decisions in educational studies. Innovative techniques for optimizing survey questions and methodologies are discussed, showcasing AI's analytical prowess in unlocking new avenues of understanding and leading to deeper insights and more informed decisions in educational research studies. Through advanced data processing techniques, AI unveils patterns, correlations, and insights that may elude traditional analysis methods. This analytical prowess not only facilitates deeper understanding but also empowers researchers to make more informed decisions. Moreover, AI augments research methodologies by offering innovative techniques for optimizing research questions and methodologies. By harnessing AI's analytical capabilities, researchers unlock new avenues of understanding, leading to more comprehensive and nuanced studies. The realm of AI-driven skill enhancement for researchers is addressed by illustrating the process in the context of a study that seeks to gain a deeper understanding of the strategies principals employ to develop teachers working with English learners. This collaborative approach enriches individual research endeavors and contributes to the collective advancement of research methodologies within the educational landscape. We highlight the transformative potential of AI in revolutionizing educational research practices and enhancing outcomes for English learners in the K-12 education system. By leveraging AI, researchers can improve their interviewing techniques, refine performance, and foster a culture of continuous improvement. AI-powered tools provide real-time feedback and facilitate iterative refinement of practices. This collaborative approach can enrich individual research endeavors and contribute to the collective advancement of research methodologies within the educational landscape.International Conference on AI Research00This work highlights the transformative potential of AI in revolutionizing educational research practices and enhancing outcomes for English learners in the K-12 education system by exploring how AI algorithms can refine research questions and augment research methodologies, leading to deeper insights and more informed decisions in educational studies.International Conference on AI Research["Belinda G. Gimbert", "Dustin Miller", "Raeal Moore", "D. Cristol", "Nick Giester"]2024-12-04T00:00:00 d1504727df3eec4b2732cb0e396691d635850702Artificial Intelligence (AI) offers tremendous potential and difficult moral dilemmas in the fight against cyber speech, including hate speech, disinformation, and cyberbullying. This study looks at the two requirements that must be met to protect civil rights and successfully combat harmful online speech. By showcasing developments in deep learning algorithms, natural language processing, and automated moderation tools, it explores the potential of AI systems to identify, regulate, and lessen harmful online behavior. The ethical implications of AI in moderating online debate are rigorously examined in this paper, with particular attention paid to issues with biases, privacy, and freedom of speech. AI creates concerns about data exploitation and spying. It may also over-censor or misinterpret context, which puts permissible expression at risk of being unfairly suppressed. Additionally, AI systems have the power to amplify and perpetuate preconceptions, resulting in biased judgments that affect marginalized communities. Through an analysis of case studies and statutes, the study seeks to strike a balance between the need to preserve fundamental rights and AI’s ability to make online places safer. It promotes a plan that upholds justice and human dignity by fusing technical advancements with strict moral standards and open governance. Keywords: Cyber Speech; Artificial Intelligence; Civil Liabilities; Privacy Concerns; International Cooperation; Legislatives PROCEEDINGS OF THE SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES [SICASH]120The ethical implications of AI in moderating online debate are rigorously examined and a plan that upholds justice and human dignity is promoted by fusing technical advancements with strict moral standards and open governance is promoted.PROCEEDINGS OF THE SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES [SICASH]["Ashini Hansika Godigamuwa"]2024-12-04T00:00:00 301bc2ca0ac5e2948a1e7e5f9d865ad4e7fb965bThis paper explores the transformative impact of advanced artificial intelligence (AI) techniques on real-time inventory optimization within dynamic supply chains. The introduction highlights the significance of inventory optimization and the limitations of traditional methods, setting the stage for the integration of AI. A comprehensive literature review summarizes existing research on AI applications in supply chain management, identifying key gaps and areas for further exploration. The paper then delves into various AI techniques, including machine learning, deep learning, and reinforcement learning, detailing their application in predicting demand and supply, and the benefits and challenges of implementing these technologies in real-time inventory systems. The analysis of AI-driven inventory optimization reveals significant improvements in supply chain responsiveness, efficiency, and risk management, alongside discussions on scalability and adaptability across different industries and organizational sizes. The conclusion synthesizes key findings and provides recommendations for future research and practical implementation, emphasizing the need for enhanced data quality, model transparency, and ethical considerations. By harnessing AI, businesses can achieve more efficient, responsive, and resilient supply chains, positioning themselves competitively in an increasingly complex global market.. Keywords: Real-Time Inventory Optimization, Dynamic Supply Chains, Artificial Intelligence, Machine Learning, Deep Learning.International Journal of Management &amp; Entrepreneurship Research00The analysis of AI-driven inventory optimization reveals significant improvements in supply chain responsiveness, efficiency, and risk management, alongside discussions on scalability and adaptability across different industries and organizational sizes.International Journal of Management &amp; Entrepreneurship Research["Iyadunni Adewola Olaleye", "Chukwunweike Mokogwu", "Amarachi Queen Olufemi-Phillips", "Titilope Tosin Adewale"]2024-12-04T00:00:00 f95b28decc370cf6a1fa1ae6fa1b3d678377067cWith the development of artificial intelligence (AI), protecting personal data has become crucial. AI systems can collect and analyze personal data, which can risk privacy and security. The main issues are privacy breaches, misuse of data, cybersecurity threats, and algorithmic bias. Addressing these problems requires legislation, transparency, technical security, and adherence to ethical guidelines. These measures can help ensure the protection of personal data. Keywords: protection, personal data, potential, artificial intelligence, machine learning algoritmsPAHTEI-Procedings of Azerbaijan High Technical Educational Institutions00With the development of artificial intelligence, protecting personal data has become crucial and legislation, transparency, technical security, and adherence to ethical guidelines can help ensure the protection of personal data.PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions["Firuza Aghayeva Firuza Aghayeva"]2024-12-04T00:00:00 18fd8e1d39263791bf778834b6d7fa2851db2b21Artificial intelligence (AI) has gaining more and more importance in diagnosis of dermatologic conditions since COVID-19 pandemic. Most of the literature on AI in dermatology focus on melanoma and non-melanoma skin cancer detection, with reporting from 81.0%-99.0%. Other commonly studied diseases include psoriasis, acne vulgaris, onychomycosis, atopic dermatitis. While AI has the potential to improve access to dermatologic care, especially in underserved communities, challenges remain in its implementation. Here we review the different application of AI in dermatology and their outcomes, focusing on the accuracy, sensitivity, specificity of different AI algorithm in diagnosis of different skin conditions. This review may provide an organized summary of the various applications of AI in dermatology and their potential outcomes.International Journal of Dermatology and Venereology00The different application of AI in dermatology and their outcomes are reviewed, focusing on the accuracy, sensitivity, specificity of different AI algorithm in diagnosis of different skin conditions.International Journal of Dermatology and Venereology["Soumi Biswas", "Unmesh Achar", "Arun Achar"]2024-12-04T00:00:00 660b94b3ee837a8898a307364a6ca29b23193e9eAn increasingly popular subcategory of Artificial Intelligence (AI) is Generative AI (GAI), which encompasses technologies capable of creating new content, such as images, text, and music, often resembling outputs made by humans. The potential impact by GAI on sustainability is multifaceted. On the positive side, generative AI can aid in optimizing processes, developing innovative solutions, and identifying patterns in large datasets related to sustainability. This can lead to more efficient resource management, reduced energy consumption, and the creation of more sustainable products. However, there are also potential negative impacts, such as increased energy consumption associated with training and running generative AI models, as well as the potential for unintended consequences or biases in the generated content. Additionally, overreliance on generative AI may lead to reduced human oversight, which could undermine holistic, interdisciplinary, and collaborative approaches to sustainability. The aim of this paper is to explore the potential impacts on sustainability by generative artificial intelligence through a review of prior research on the topic. The study was conducted with a scoping literature review approach to identify potential impacts by generative AI on sustainability. Data were collected through a search in the database Scopus during the spring semester of 2024. Keywords, relevant for the study, were combined with Boolean operators. Papers identified through the search underwent a manual screening process by the authors, in which papers were selected for inclusion or exclusion in the study based on a set of criteria. Included paper were then analyzed with thematic analysis, according to the guidelines by Braun and Clarke. A categorization matrix, based in prior research on sustainability, supported the analysis and deductive coding of collected data. Results of the study highlight generative AI’s potential impact on sustainability that relate to both environmental aspects, economic aspects, and social aspects of sustainability. These different aspects of sustainability impact make this research an important contribution for deepening the understanding of generative AI and its potential consequences for society. Findings of the study provide theoretical contribution, implications for practice, and recommendations for future research on generative AI and sustainability.International Conference on AI Research430Generative AI’s potential impact on sustainability that relate to both environmental aspects, economic aspects, and social aspects of sustainability make this research an important contribution for deepening the understanding of generative AI and its potential consequences for society.International Conference on AI Research["Niklas Humble", "Peter Mozelius"]2024-12-04T00:00:00 367ce8b576fe700b6a81265163d6a2c93fde8015The widespread use of new technologies has reduced the precise boundary between physical and digital realities. Dynamic development of artificial intelligence (AI) systems has contributed to the digital transformation of the global economy. Given its extensive range of potential applications, AI has the potential to impact a multitude of socio-economic domains, including politics, security, health care, medicine, economy, trade, finance, taxes, and production. As the banking sector plays a crucial role in the global economy, the question arises as to whether and to what extent banking market entities are willing to use artificial intelligence solutions in their business processes. This study aims to determine the scope of AI use by key banks operating in the Polish banking market. Achieving this goal requires determining what categories of AI are used by key banks operating in Poland, analysing AI methods used by those banks, and the fields of AI implementation. The research scope covers the analysis of AI applications in the largest banks operating in Poland, which together cover almost half of the market share (PKO Bank Polski S.A., Bank Pekao S.A., Santander Bank Polska S.A., mBank S.A. and ING Bank Śląski S.A). To obtain an up-to-date overview of AI usage, the research period is 2019-2024. The empirical part of the research is supported by an analysis of the widespread use of artificial intelligence and its dynamic international development based on academic papers and industry reports.International Conference on AI Research330The scope of AI use by key banks operating in the Polish banking market is determined by determining what categories of AI are used by key banks operating in Poland, analysing AI methods used by those banks, and the fields of AI implementation.International Conference on AI Research["Monika Klimontowicz"]2024-12-04T00:00:00 c5055f95a610e453b29bf278c2e62650f2666412One of the most significant challenges faced by the average person is understanding the general context of the legislation that applies to them. The legal discipline has certain characteristics that make it almost esoteric for those who are not part of it, however, it is necessary to know the legal norms as comprehensively as possible. People today are no longer just "from a village/town/province/country", but have become something almost universal within the frameworks of globalization and the vast library the Internet offers at nearly zero cost. Artificial intelligence (AI) is a technology that is beginning to fundamentally change society, and within this "sea of transformations", the law – and legal and political practices in general – cannot avoid contact and change. There is no area of law not being altered, but in my opinion, the most significant place where transformations will be recorded is in legislating and drafting normative acts. Legislative operations are always complex and rarely bring satisfaction to those subject to regulation, given the relationship between the rights and obligations set out by normative acts. At the same time, it is challenging to legislate in an increasingly complex society, where enduring and situational interests intersect, where there are poor-quality mechanisms in legal documentation, and where the concept of legality is not always correctly perceived in the political environment. AI should offer a greater understanding of legal concepts to both ordinary citizens and legislators, precisely through its extensive library and its demonstrated synthesis and analysis capabilities. Therefore, the use of AI in the legislative process will become increasingly necessary as its capabilities grow, primarily to produce faster syntheses of legal documentation, essential for correctly understanding the context that justifies the need for new legislation. I believe that in the coming years, no country will escape this change, and AI will help eliminate some of the poor-quality practices that do not offer countries and citizens better prospects for wealth and professional development.International Conference on AI Research160The use of AI in the legislative process will become increasingly necessary as its capabilities grow, primarily to produce faster syntheses of legal documentation, essential for correctly understanding the context that justifies the need for new legislation.International Conference on AI Research["Marius Vacarelu"]2024-12-04T00:00:00 e3835788914f39e14cf5fcab92e03b3fcef27c3bArtificial intelligence (AI) is increasingly being adopted in different types of organisations and is attracting the attention of various actors. In this context, the analysis aims to provide an overview of the most relevant aspects of the adoption of AI technology solutions in organisations. To this end, the analysis adopted the archival research method and carried out a study of recent documents/analyses proposed by leading consulting firms. These players accumulate considerable application knowledge and are, therefore, special sources. To provide an in-depth and up-to-date picture, the analysis considered a range of archival information (such as reports, articles, insights, technical notes, etc.) published by around 15 organisations in the consulting sector since the beginning of 2023. The study highlights the main opportunities/risks associated with the introduction of AI. The findings cover various aspects, including AI investments, classification of organisations adopting AI, main challenges in AI initiatives, most common areas of AI use, and changes in work. The results may have some limitations due to bias in the identification of documents, as they were derived from direct searches and queries on the search engines of the consulting firms' websites. The audience and implications are broad, and this is a value of the work. AI novices studying the adoption and implementation of AI have a picture of the relevant dimensions on which further in-depth studies can be developed. In addition, entrepreneurs, managers, and policymakers will have an overview of the main threats/opportunities of AI and elements to support decision-making.International Conference on AI Research120The study highlights the main opportunities/risks associated with the introduction of AI and covers various aspects, including AI investments, classification of organisations adopting AI, main challenges in AI initiatives, most common areas of AI use, and changes in work.International Conference on AI Research["Massimo Albanese"]2024-12-04T00:00:00 889f2209507ef691f800735a4097306bd78857cbIn the current spring of Artificial Intelligence, the rapid development of Generative AI (GenAI) has initiated vivid discussions in higher education.  Opportunities as well as challenges have been identified and to cope with this new situation there is a need for a large-scale teacher professional development. With basic skills about GenAI teachers could use the new technology as an extension of the existing technology enhanced teaching and learning. The aim of this paper is to present and discuss the project FAITH (Frontline Application of AI and Technology-enhanced Learning for Transforming Higher Education). FAITH is a higher education pedagogical development initiative for institutional development for teachers with good fundamental skills in traditional pedagogy.  A project with the overall objective of increasing the staff understanding of AI and to develop new competencies in the field of GenAI and technology enhanced learning. The research question that guided this study was: "What are the perceived opportunities, challenges and expectations of involving GenAI in higher education?" The overall research strategy for the FAITH project is design-based research, which involves iterative and cumulative development processes. In the early iteration that this study was a part of has been carried out inspired by Collective Autoethnography where members of the steering group behind the FAITH project, and members of the project team have constituted the main focus group. Data were collected by structured interviews where two GenAI tools also have been interviewed. Findings show that the expectations are high, but that the FAITH ambition of institutional development is depending on teachers’ motivation for taking an active part in the project. Another challenge could be that many teachers see GenAI as something that threatens the current course design, and that a general ban of GenAI is the appropriate solution. One of, several identified opportunities, is that a general revision of syllabi and assessment in an adaptation for GenAI enhanced learning would improve the current course design. International Conference on AI Research200Findings show that the expectations are high, but that the FAITH ambition of institutional development is depending on teachers’ motivation for taking an active part in the project, and that a general revision of syllabi and assessment in an adaptation for GenAI enhanced learning would improve the current course design.International Conference on AI Research["Peter Mozelius", "Marcia H\u00e5kansson Lindqvist", "Cleveland-Innes Martha", "J. Jaldemark", "Marcus Sundgren"]2024-12-04T00:00:00 bc9c475a5db233517ef7102cf7e8228872e56ad5Artificial intelligence is causing a change in the way of working in educational processes, one with the use of new technological tools, based on the use of artificial intelligence, is causing changes in the creative processes in students. The objective of this article is to identify and analyze articles selected between the years 2020 to 2024 that propose programs and strategies to systematically insert the creative capacity of students without the use of artificial intelligence, as well as the strategies that are being taken as a result of the presence of artificial intelligence. The search was carried out in journals indexed in the Scopus, Scielo, Ebesco, Wos and Dialmat databases, applying inclusion and exclusion criteria through the Prisma Method, in the same way, the boolean operators "and" and "or", "quotation marks" were used, which resulted in 36 articles, which are divided into 2 groups of 20 articles for each group. Concluding that almost all the authors of the articles reviewed have similarities in their proposals and strategies regarding creative thinking because it is of utmost importance within education, with the help of the tools that artificial intelligence provides us, these proposals are changing in such a way that we must improve the strategies to be able to exploit the tools to benefit from improving the creative aspects of the students.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)380Almost all the authors of the articles reviewed have similarities in their proposals and strategies regarding creative thinking, and these proposals are changing in such a way that the authors must improve the strategies to be able to exploit the tools to benefit from improving the creative aspects of the students.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)["Victor Huayllani-Palomino", "Grisi Bernardo-Santiago", "W. Auccahuasi"]2024-12-04T00:00:00 57272bbeb74198f8612e6de183bb0509c55aa0cbIt is beyond dispute that the potential benefits of artificial intelligence (AI) in military intelligence are considerable. Nevertheless, it remains uncertain precisely how AI can enhance the analysis of military data. The aim of this study is to address this issue. To this end, the AI demonstrator deepCOM was developed in collaboration with the start-up Aleph Alpha. The AI functions include text search, automatic text summarization and Named Entity Recognition (NER). These are evaluated for their added value in military analysis. It is demonstrated that under time pressure, the utilization of AI functions results in assessments clearly superior to that of the control group. Nevertheless, despite the demonstrably superior analysis outcome in the experimental group, no increase in confidence in the accuracy of their own analyses was observed. Finally, the paper identifies the limitations of employing AI in military intelligence, particularly in the context of analyzing ambiguous and contradictory information.arXiv.org470It is demonstrated that under time pressure, the utilization of AI functions results in assessments clearly superior to that of the control group, and the limitations of employing AI in military intelligence are identified, particularly in the context of analyzing ambiguous and contradictory information.ArXiv["Christian Nitzl", "Achim Cyran", "Sascha Krstanovic", "Uwe M. Borghoff"]2024-12-04T00:00:00 1bfe107ee631d6090b4b91787333a4a662c6b233Given the various risks involved in incorporating artificial intelligence (AI) and machine learning into their business operations, firms are at an inflection point about how to do so. In this paper, we propose that for small medium-sized enterprises (SMEs), which lack capital, large quantities of data, and expertise, the best solution would be to join a pre-existing (or developing) ecosystem.  From the two potential alternatives available to an SME, going it alone or depending on a larger corporation, we argue for a third option, the joining of an ecosystem of organizations that use AI systems in their operations, as the golden mean. We conclude with some practical and theoretical implications.International Conference on AI Research370It is proposed that for small medium-sized enterprises (SMEs), which lack capital, large quantities of data, and expertise, the best solution would be to join a pre-existing (or developing) ecosystem that uses AI systems in their operations, as the golden mean.International Conference on AI Research["Steve Nolan", "Stelios C. Zyglidopoulos"]2024-12-04T00:00:00 f87387dcb884a1fe6c8adf0e0ff78f637f9985d3Artificial intelligence (AI) has the potential to change the world of work radically. Wherever information processing is involved, AI can be integrated into processes with added value. From the perspective of Human Resource (HR) management, this implies three things: first, business models and performance processes in the company will undergo change; second, employee requirements will change; and third, HR processes will change. While the literature describes various AI maturity models, there has been no dedicated consideration of HR management. This article, therefore, aims to identify relevant influencing factors for an AI-orientated approach to HR management and to describe these in more detail using maturity levels in a Human Resources Artificial Intelligence Maturity Model (HR-AIMM). The resulting HR-AIMM consists of eleven dimensions. These include anchoring the AI topic in the corporate and HR strategy, its use in selected HR processes, considering ethical, data-related, and infrastructural principles, and organisational, cultural, and competence-related anchoring. The characteristics of these factors enable the identification of four maturity levels for using AI in HR management: from a curious start to the level of holistic integration. Our framework supports researchers and companies in understanding and evaluating the factors influencing the professional application of AI in HR management.International Conference on AI Research460This article aims to identify relevant influencing factors for an AI-orientated approach to HR management and to describe these in more detail using maturity levels in a Human Resources Artificial Intelligence Maturity Model (HR-AIMM).International Conference on AI Research["Sascha Armutat", "M. Wattenberg", "Nina Mauritz"]2024-12-04T00:00:00 6c1d2297fc7d6bae7997f0e0e16c018f8de659aaArtificial Intelligence (AI)-based Intrusion Detection Systems (IDS) significantly advance network security by leveraging Machine Learning (ML) and Deep Learning (DL) for highly accurate and dynamic cyber threat detection. However, a critical limitation of current AI-based IDS is their inherent “black box” nature, which disrupts the decision-making processes, thereby compromising trust and accountability. In response to these challenges, we propose an efficient Explainable Artificial Intelligence (XAI)-based framework designed to enhance both the robustness and explainability of IDS. Our two-stage process integrates traditional statistical methods with XAI techniques, specifically SHAP and LIME, for feature selection and explanation analysis, providing transparent and interpretable decision-making while maintaining high detection performance. Our experimental evaluation, conducted using the CIC-DDoS2019 and CICIoT2023 datasets, demonstrates that the framework can sustain high detection accuracy while significantly enhancing the interpretability of IDS decisions. Moreover, the proposed feature reduction process lowers the computation effort of the XAI techniques, resulting in up to 87% faster.Cyber Security in Networking Conference280This work proposes an efficient Explainable Artificial Intelligence (XAI)-based framework designed to enhance both the robustness and explainability of IDS, and integrates traditional statistical methods with XAI techniques, providing transparent and interpretable decision-making while maintaining high detection performance.2024 8th Cyber Security in Networking Conference (CSNet)["Beny Nugraha", "Abhishek Venkatesh Jnanashree", "Thomas Bauschert"]2024-12-04T00:00:00 0fd985a8ed36551e001ce8e7b1c5fc30b0890328The rapid expansion of Artificial Intelligence(AI) has outpaced the development of ethical guidelines and regulations, raising concerns about the potential for bias in AI systems. These biases in AI can manifest in real-world applications leading to unfair or discriminatory outcomes in areas like job hiring, loan approvals or criminal justice predictions. For example, a biased AI model used for loan prediction may deny loans to qualified applicants based on demographic factors such as race or gender. This paper investigates the presence and mitigation of bias in Machine Learning(ML) models trained on the Adult Census Income dataset, known to have limitations in gender and race. Through comprehensive data analysis, focusing on sensitive attributes like gender, race and relationship status, this research sheds light on complex relationships between societal biases and algorithmic outcomes and how societal biases can be rooted and amplified by ML algorithms. Utilising fairness metrics like demographic parity(DP) and equalised odds(EO), this paper quantifies the impact of bias on model predictions. The results demonstrated that biased datasets often lead to biased models even after applying pre-processing techniques. The effectiveness of mitigation techniques such as reweighting(Exponential Gradient(EG)) to reduce disparities was examined, resulting in a measurable reduction in bias disparities. However, these improvements came with trade-offs in accuracy and sometimes in other fairness metrics, identified the complex nature of bias mitigation and the need for precise consideration of ethical implications. The findings of this research highlight the critical importance of addressing bias at all stages of the AI life cycle, from data collection to model deployment. The limitation of this research, especially the use of EG, demonstrates the need for further development of bias mitigation techniques that can address complex relationships while maintaining accuracy. This paper concludes with recommendations for best practices in Artificial Intelligence development, emphasising the need for ongoing research and collaboration to mitigate bias by prioritising ethical considerations, transparency, explainability, and accountability to ensure fairness in AI systems.International Conference on AI Research260The results demonstrated that biased datasets often lead to biased models even after applying pre-processing techniques, highlighting the critical importance of addressing bias at all stages of the AI life cycle, from data collection to model deployment.International Conference on AI Research["Ashionye Aninze"]2024-12-04T00:00:00 8f6d05bb4e701a3bcc0f94a68cd0b16bf4e9991a00[]2024-12-04T00:00:00 8ab29d910027582a40fdb8bd8b0bc3314bf7f270Translational medicine @ UniSa00Translational Medicine @ UniSa["Marco Cascella", "Cosimo Guerra", "Rosario De Feo", "V. Cerrone", "S. Farah", "P. Sarzi-Puttini", "F. Salaffi"]2024-12-04T00:00:00 aea17ab1b01230ae1fbc293f04f858c10d69c182Trend forecasting within the fashion industry is aimed at predicting the of future popular styles, materials, colors, and all things related to the development of fashion. Trend forecasting is based on information collected about past, present, and projected future developments in the fashion world. AI, as it has been applied in other areas in society, is now being applied within the fashion industry. This analysis will focus on AI being applied to trend forecasting within the fashion industry. Using AI in trend forecasting brings a number of advantages for those in the fashion industry while at the same time raising a number of ethical concerns. Three significant ethical concerns that are related to the employment of AI in the fashion industry are, the invasion of privacy from the data mining required to gather data to make predictions about fashion trends, the consequences for businesses of incorrect trend predictions, and the anticipated environmentally unsustainable nature of fast fashion due to the overconsumption that consumers practice and that is increasingly promoted by AI. Overconsumption is the product of how AI is involved in rapidly forecasting new trends in fashion that consumers then follow. Addressing all of these ethical issues is needed because fast fashion has continued to be demanded by both consumers and investors. New technologies, particularly AI, have allowed fashion brands to develop methods that allow them to be ahead of rapidly changing fashion trends and put high-demand products in stores faster, increasing the prevalence of fast fashion within the fashion industry. Questions now arise about how to address these ethical concerns. Important questions for the fashion industry include what issues will the use of AI in trend forecasting create in the long run within the fashion industry, and how can proactive action be taken within the fashion industry to prevent future ethical issues in the fashion industry? This paper which employs empirical methods to describe the fashion industry while also employing conceptual analysis related to philosophical ethics. The goal of the analysis is to provide, an informative ethical analysis of AI in trend forecasting, while also attempting to develop ethical guidance for concerns involving the use of AI in trend forecasting. The paper perform an anticipatory ethical analysis that attempts to address future concerns about fashion and will close by drawing conclusions about the direction of future analysis related to the application of AI in the fashion industry.International Conference on AI Research180The goal of the analysis is to provide an informative ethical analysis of AI in trend forecasting, while also attempting to develop ethical guidance for concerns involving the use of AI in trend forecasting, to address future concerns about fashion.International Conference on AI Research["Richard Wilson", "Olivia Bowles"]2024-12-04T00:00:00 b0fdc729ccc789d810ecf2c2550dd116b4368a90 The speed and volume of scientific publishing is accelerating, both in terms of number of authors and in terms of the number of publications by each author. At the same time, the demand for knowledge synthesis and dissemination is increasing in times of upheaval in the education sector. For systematic reviewers in the field of education, this poses a challenge in the balance between not excluding too many possibly relevant studies and handling increasingly large corpora that result from document retrieval. Efforts to manually summarise and synthesise knowledge within or across domains are increasingly running into constraints on resources or scope, but questions about the coverage and quality of automated review procedures remain. This article makes the case for integrating computational text analysis into current review practices in education research. It presents a framework for incorporating computational techniques for automated content analysis at various stages in the traditional workflow of systematic reviews, in order to increase their scope or improve validity. At the same time, it warns against naively using models that can be complex to understand and to implement without devoting enough resources to implementation and validation steps. London Review of Education720The case for integrating computational text analysis into current review practices in education research is made and a framework for incorporating computational techniques for automated content analysis at various stages in the traditional workflow of systematic reviews is presented, in order to increase their scope or improve validity.London Review of Education["Henrik Karlstr\u00f8m"]2024-12-04T00:00:00 6a4bafffb1e13e02da1c12507e8c4ba02ae2f9ddNeuroscience and Biobehavioral Reviews670Despite methodological variations and small sample sizes in some modalities, this study underscores AI's predictive utility in schizophrenia treatment, offering insights for tailored approaches, improving adherence, and reducing relapse risk.Neuroscience & Biobehavioral Reviews["Reza Saboori Amleshi", "Mehran Ilaghi", "Masoud Rezaei", "M. Zangiabadian", "Hossein Rezazadeh", "G. Wegener", "S. Arjmand"]2024-12-04T00:00:00 a836ea083e6dab5db183f7fb4af5f57883cc8ff6Urology00Urology["Andrewe L Baca", "Christopher Chung", "D. Kanmaniraja", "Tim Q Duong", "Kara L Watts"]2024-12-04T00:00:00 5e1e3fb1d06895ef76ee5ad692adc5a0b63d3440The challenge of chronic diseases will take its deserved place among the top global health issues, mainly because they account for 74% of deaths every year, as reported by the WHO. This systematic review will discuss possible applications of machine learning and AI techniques to predict, diagnose, and treat CDs such as diabetes, cancer, cardiovascular diseases, and liver disease. Integration of ML algorithms: RF, SVM, DT, ANN, and boosting techniques has transformed healthcare by improving the quality of predictive information for potential cases, reducing healthcare costs, and contributing to innovative clinical decision support systems. The research focuses on several approaches of ML, namely supervised and unsupervised learning, for predicting patient-specific treatment regimens and hence better outcomes in chronic disease management. Another discussion from the study is on how to apply ML on large, structured and unstructured datasets collected from the healthcare system about the predictive powers of AI-based models in order to be able to address complications arising from CD. Research aims at providing a comprehensive overview of the understanding of predictive models (PMs) under one heading, making an amalgamation of the desired workings of PMs for chronic disease prediction, useful for knowledge collection for future reference. The paper also provides a descriptive analysis of data sources, methodologies, and algorithms adopted, accompanied by a tabular representation of previous studies to avoid redundancy and enhance accuracy.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)100Research aims at providing a comprehensive overview of the understanding of the understanding of predictive models (PMs) under one heading, making an amalgamation of the desired workings of PMs for chronic disease prediction, useful for knowledge collection for future reference.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)["P.Priya", "S.Kavya"]2024-12-04T00:00:00 ad5cbb0c6489f28371156c74ddb377907fabbbf7Comput. Biol. Medicine240The study developed a highly discriminative CatBoost model for identifying PD patients candidates for DAT, potentially improving timely and accurate treatment selection and offering a promising tool for neurologists, particularly those less experienced with DAT, to optimize referral to Movement Disorder Units.Computers in biology and medicine["Eric Freire-Alvarez", "I. L. Ram\u00edrez", "Roc\u00edo Garc\u00eda-Ramos", "F\u00e1tima Carrillo", "Diego Santos-Garc\u00eda", "J. G\u00f3mez-Esteban", "J. Mart\u00ednez-Castrillo", "I. Mart\u00ednez-Torres", "C. Madrid-Navarro", "M. J. P\u00e9rez-Navarro", "Fuensanta Valero-Garc\u00eda", "B\u00e1rbara Vives-Pastor", "Laura Mu\u00f1oz-Delgado", "B. Tijero", "Carlos Morata Mart\u00ednez", "J. Valls", "R. Aler", "I. M. Galv\u00e1n", "F. Escamilla-Sevilla"]2024-12-04T00:00:00 bb6a780920acc29a32eabf3ede8821af10ed04c8Traversing our digital information society safely and responsibly rests mainly on our comprehension of the vast sociotechnical nature of AI ethics risks, its implications and consequences. Ultimately, we all would prefer to live in a mature information society that is technologically just, inclusive and sophisticated, firmly rooted in ethical information philosophy and values. In this paper the findings of a scoping review of recent reported research look, in particular, at the sociotechnical changes and impact that disruptive AI innovation has on societies, and how this could impact new and futuristic nuances in AI ethics. The study delves into the interdisciplinarity of AI ethics.  The role of intergovernmental collaboration in researching and availing  frameworks and  guardrails in upholding AI ethics is critically interrogated and explored. The study alludes to gaps in current research around AI ethics and impresses the need to deliberate on future AI ethics dimensions. The prerequisites for fostering further confidence and trust in AI technology are synthesised. The study concluded that inclusivity and justice in AI Ethics is not yet achieved on a global level, and that there is still a tendency towards cultural and other biases in designing, planning, implementing and also regulating AI. More research is needed on the impact and trends of AI innovation in the Global South compared to the Global North.International Conference on AI Research00The study concluded that inclusivity and justice in AI Ethics is not yet achieved on a global level, and that there is still a tendency towards cultural and other biases in designing, planning, implementing and also regulating AI.International Conference on AI Research["Brenda VAN WYK", "Marlene Holmner"]2024-12-04T00:00:00 c0f0a8504f281e0d417f2c7ba9c60face4322cfeThis issue of the Transnational Education Review features several compelling articles that explore critical dimensions of education in our rapidly evolving global landscape. Helena Dedecek Gertz, Javier A. Carnicer, and Sara Fürstenau examine the influential role of migrant content creators in transnational education through social media platforms. Their research highlights how digital spaces can bridge cultural gaps and create new educational opportunities across borders. In another insightful study, Zaynab Benabdallah and Djamila Chekrouni analyze the emigration of highly qualified students from Morocco using panel data. Their findings shed light on the motivations and impacts of this talent migration, offering valuable perspectives on brain drain and global mobility. Additionally, Carlos Javier Gomez M., Pedro Longart, and María Cristina González Martínez propose a meta-abilities model for 21st-century academic leadership. This innovative framework emphasizes the cultivation of critical consciousness, underscoring the importance of holistic leadership development in higher education. Finally, Sophia Dimelis reflects on the state of digital higher education and adult learning in Greece and other Balkan countries. Her comparative analysis highlights both the challenges and opportunities faced by these regions in adapting to digital transformations in education. These articles collectively contribute to a deeper understanding of the complex forces shaping global education today. And yet, the need for more studies and debates on the AI and digitalisation are warranted. We invite you to engage with these thought-provoking studies and consider submitting your work to enrich future discussions in this evolving field.Transnational Education Review00This issue of the Transnational Education Review features several compelling articles that explore critical dimensions of education in the authors' rapidly evolving global landscape, and proposes a meta-abilities model for 21st-century academic leadership.Transnational Education Review["Ibrahim Sirkeci"]2024-12-04T00:00:00 4304752b61e8461d3eda229cde091e1b2fad0577Human resources management plays a crucial role in the success of an organization by acting as a link between employees and employers. Its policies and decisions shape the dynamics of the workforce, aiming to optimize their utilization and productivity. This includes strategic planning for recruitment, retention, and training and continuous assessment of employee performance. The study on performance prediction in human resources management begins by acknowledging the crucial role HRM plays in the success of an organization, acting as a vital link between employees and employers. It involves a wide range of strategic decisions and policies aimed at optimizing the utilization and productivity of the workforce. Workforce planning, a key HRM component, entails developing recruitment, retention, and training strategies while continuously assessing employee performance to ensure alignment with organizational goals and objectives. The quality of the workforce is a crucial determinant of organizational achievement, as employees are the most asset for development. Accurately evaluating employee performance is essential for fostering growth and progress, although traditional methods may be subjective and inefficient. However, introducing machine learning algorithms offers promising opportunities to enhance accuracy and objectivity in this process. At the core of this research is the understanding that the quality of the workforce significantly impacts organizational outcomes, with employees being the most valuable asset for organizational growth. However, accurately evaluating employee performance can be challenging due to subjective biases and inefficiencies inherent in traditional evaluation methods. In this context, machine learning algorithms offer a promising approach to enhance performance evaluation processes' accuracy, objectivity, and efficiency. This research focuses on evaluating and improving existing machine learning classifiers for predicting performance in human resources management.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)50This research focuses on evaluating and improving existing machine learning classifiers for predicting performance in human resources management and offering a promising approach to enhance performance evaluation processes' accuracy, objectivity, and efficiency.2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS)["M. Umamaheswari", "Ginu Mol George", "N. Amsaveni", "D. Divya", "B. M. Anitha", "S. A. Kumar"]2024-12-04T00:00:00 89afbc10de14829d7a6b7d746a6a66f9f3f7dfc0 The explosive advancement of contemporary artificial intelligence (AI) technologies, typified by ChatGPT, is steering humans towards an uncontrollable trajectory to artificial general intelligence (AGI). Against the backdrop of a series of transformative breakthroughs, big tech companies such as OpenAI and Google have initiated an “AGI race” on a supranational level. As technological power becomes increasingly absolute, structural challenges may erupt with an unprecedented velocity, potentially resulting in disorderly expansion and even malignant development of AI technologies. To preserve the dignity and safety of human-beings in a brand-new AGI epoch, it is imperative to implement regulatory guidelines to limit the applications of AGI within the confines of human ethics and rules to further counteract the potential downsides. To promote the benevolent evolution of AGI, the principles of Humanism should be underscored and the connotation of Digital Humanism should be further enriched. Correspondingly, the current regulatory paradigm for generative AI may also be overhauled under the tenet of Digital Humanism to adapt to the quantum leaps and subversive shifts produced by AGI in the future. Positioned at the nexus of legal studies, computer science, and moral philosophy, this study therefore charts a course for a synthetic regulation framework of AGI under Digital Humanism.International Journal of Digital Law and Governance340This study charts a course for a synthetic regulation framework of AGI under Digital Humanism to promote the benevolent evolution of AGI and to adapt to the quantum leaps and subversive shifts produced by AGI in the future.International Journal of Digital Law and Governance["Le Cheng", "Xuan Gong"]2024-12-04T00:00:00 f338bdd4eac00380c34f84897db14bfa3d487b6aArtificial intelligence (AI) can augment human intelligence in teamwork, however, it is still not clear how to implement artificial management. This rapid development of computer science gives opportunities to replace team managers with robots. However, it is still not possible to employ a robot in a managerial position. Therefore, the aim of this paper is to present a theoretical foundation for such an information system which could be widely used by human managers in their day-to-day work. At the same time, it could collect data on managerial work in order to implement artificial management. The research problem concerns the theoretical assumptions needed to design this solution. Two research questions arise from this research problem: (RQ1) What types of research methods should be used to collect data on managerial work? (RQ2) How to implement research methods into the managerial tools to make the managerial work automated? The research methods used in the paper are: literature studies, technical documentation of online managerial tools created by the author, and a long-term observation of human-managed teams. In Section 2 we present the theoretical foundation of research in management science which is the answer to the first research question (RQ1). Section 3 contains the theoretical assumption of the information system design which is the answer to the second research question (RQ2). In Section 4 we present examples of research conducted by the author as the practical use of answers to both research questions (RQ1 and RQ2). Section 5 includes conclusions on the use of the information system in artificial management. The main contribution of this paper is as follows: Firstly, to answer the first research question (RQ1) about the types of research methods which should be used to collect data on managerial work in order to make them automated. The answer is the mixed method as the most appropriate method to study what a manager really does. Secondly, the paper contains the answer to the second research question (RQ2) on ways of implementing research methods into the online managerial tools aimed at artificial management.International Conference on AI Research440The main contribution of this paper is to answer the types of research methods which should be used to collect data on managerial work in order to make them automated and the mixed method as the most appropriate method to study what a manager really does.International Conference on AI Research["Olaf Flak"]2024-12-04T00:00:00 9f87e3abf353f363065623a1626a5bd004538b77Machines can now match, or outperform, human performance in several reasoning and decision tasks. Some say that all that intelligence amounts to is smart computation. This is not a new thesis, dating back to Leibniz as well as Simon and Newell, but what is new is what smart means. Today it is identified with complex statistics and optimisation. Simon’s meaning, however, of smart rested on bounded rationality, a unified view of human and artificial decision making. This view was f l eshed out by Gigerenzer as fast-and-frugal heuristics. Interestingly, such heuristics are typically sparse, as some machine learning models are optimised to be. So, one might hope that we can make sense of artificial intelligence in human terms after all, and face the upcoming challenges with open-mindedness and courage, just like Simon, and of course Wilkes, would have done.Croatian Journal of Philosophy00One might hope that the authors can make sense of artificial intelligence in human terms after all, and face the upcoming challenges with open-mindedness and courage, just like Simon, and of course Wilkes, would have done.Croatian journal of philosophy["K. Katsikopoulos"]2024-12-04T00:00:00 4dea3d053daf0b714d58f6cff4380632cb6d839cEffective resource management plays a vital role in such an efficient, cost-effective, and scalable infrastructure when most organizations depend on IT infrastructure. Classical approaches often break down in complex modern environments of IT. Artificial intelligence presents transformative abilities in the management of resources in IT by applying predictive analytics, automation, and optimization. This paper addresses the integrating theme of AI in IT resource management from methodologies to applications, technological frameworks, to ethical considerations. Deeper insight is provided to the AI-driven tools in resource allocation, predictive capability planning, and cost optimization on metrics and technical data. Challenges in privacy, scalability, and fairness are discussed, and future innovations comprise edge computing and self- governing IT systems.International Journal of Engineering and Management Research08This paper addresses the integrating theme of AI in IT resource management from methodologies to applications, technological frameworks, to ethical considerations and deeper insight is provided to the AI-driven tools in resource allocation, predictive capability planning, and cost optimization.International Journal of Engineering and Management Research["Nikhil Annam"]2024-12-04T00:00:00 db4090aeeaf113d9024839d211084d885b2cfb85The rapid advancement of artificial intelligence (AI) has significantly transformed the landscape of phishing attacks, presenting new challenges for detection and defense. AI-generated phishing emails, which leverage machine learning and natural language processing (NLP), have become increasingly sophisticated, making traditional detection methods ineffective. This research analyzes the evolution and impact of AI-driven phishing attacks, comparing the distinguishing linguistic and contextual patterns of AI-generated versus human-generated phishing emails. The study utilizes a comprehensive dataset, insights from informal discussions with Chief Information Security Officers (CISOs), and an analysis of historical phishing incidents before and after the release of advanced generative models like ChatGPT. Findings reveal that AI-generated phishing emails exhibit higher success rates due to their ability to bypass conventional spam filters and mimic human communication styles. Additionally, the research identifies significant gaps in current defense strategies and recommends a multi-layered security framework that integrates AI-specific detection tools, real-time threat intelligence, and machine learning-based anomaly detection to mitigate these evolving threats. This study emphasizes the need for organizations to proactively adapt to the growing sophistication of AI-powered phishing by implementing advanced defenses that are capable of keeping pace with the rapidly changing cyber threat landscape.International Conference on AI Research241The need for organizations to proactively adapt to the growing sophistication of AI-powered phishing by implementing advanced defenses that are capable of keeping pace with the rapidly changing cyber threat landscape is emphasized.International Conference on AI Research["Shreyas Kumar", "Anisha Menezes", "Sarthak Giri", "Srujan D. Kotikela"]2024-12-04T00:00:00 c1cd96c5ccd4810566613ae15e2ccec674d85b72The advancement of Artificial Intelligence (AI) and Large Language Models (LLMs) ushers in a new era in education, characterized by more adaptive, personalized learning experiences. This literature review examines the profound impact of these technologies on student engagement, achievement, and personalized learning within higher education institutions. Through a systematic analysis of scholarly articles from 2022 to 2024, this review explores how AI is reshaping educational practices through enhanced feedback mechanisms, predictive analytics, and innovative teaching methodologies. The findings indicate that AI significantly improves student support services by enabling early identification of at-risk students and by facilitating tailored educational interventions. Moreover, the deployment of chatbots and LLMs, such as GPT (generative pre-trained transformer) and BERT (bidirectional encoder representations from transformers), offers promising enhancements in instructional strategies and student assessments, fostering richer, interactive learning environments. However, the integration of these technologies also introduces ethical challenges, necessitating consideration of issues such as data privacy and bias. The review emphasizes the need for ethical frameworks and responsible AI usage to ensure technology enhances educational outcomes without compromising fairness or integrity. Future research directions are suggested, focusing on broader AI applications across various educational settings and the need for longitudinal studies to assess the long-term effects of AI integration in education.International Conference on AI Research230The findings indicate that AI significantly improves student support services by enabling early identification of at-risk students and by facilitating tailored educational interventions, and the deployment of chatbots and LLMs offers promising enhancements in instructional strategies and student assessments, fostering richer, interactive learning environments.International Conference on AI Research["Willie Moore", "Li-Shiang Tsay"]2024-12-04T00:00:00 b73f083e2849d228b929a6b793a7444b0c6fa373The unequal distribution of educational opportunities carries the risk of having a long-term negative impact on general social peace, a country's economy and basic democratic structures. In contrast to this observable development is the rapid technological progress in the field of artificial intelligence (AI). Progress makes it possible to solve various problems in the field of education as well. In order to effectively exploit the advantages that arise from the use of AI, prospective teacher training students need appropriate AI skills, which must already be taught during their studies. In a first step, the added value of this technology will be demonstrated using a concrete example. This article is therefore about conducting an exploratory pilot study to test the Individual Educational Chatbot (IEC-V1) prototype, in which the levels can be individually determined in order to generate appropriate answers depending on the requirements. The results show that this is an important function for prospective teachers, and that there is great interest in taking a closer look at this technology in order to be able to better support learners in the future. The data shows that experience has already been gained with chatbots, but that there is still room for improvement. It also shows that IEC-V1 is already working well. The knowledge gained will be used for the further development of the prototype to further improve the usability of the chatbot. Overall, it is shown that useful AI applications can be effectively integrated into learning situations even without proprietary systems and that important data protection requirements can be complied with.arXiv.org180Overall, it is shown that useful AI applications can be effectively integrated into learning situations even without proprietary systems and that important data protection requirements can be complied with.ArXiv["Stefan Pietrusky"]2024-12-04T00:00:00 5084be0d76a728d9cfef1645145c4be045761428PurposeThe purpose of this study is to assess how managerial capability affects artificial intelligence (AI) adoption and employee well-being now in a dynamic context of organizational change. This study investigated the role that managerial capability and organizational support play in facilitating successful AI technology implementation within organizations. The study seeks to provide an integrated perspective on how organizations can help mitigate the effects of AI anxiety and improve the well-being of employees.Design/methodology/approachA survey questionnaire was administered to collect data from 324 employees and managers working in small- and medium-sized enterprises (SMEs) located in Pakistan. Partial least squares-structural equation modeling (PLS-SEM) was employed using Smart PLS version 4.1.0.3 to analyze the relationships between the study variables.FindingsThe findings of the study show that AI anxiety can significantly impact employee well-being. However, the relationship was moderated by organizational support. When organizational support was high, the effects of AI anxiety decline on employee well-being.Originality/valueThis study offers three important implications; it adds to our understanding regarding AI adoption and its effect on employee well-being by addressing how managerial interventions may facilitate the smooth integration of AI technology and examining the moderating effect that organizational support might have over the association between anxiety and employee well-being. Additionally, we have offered a nuanced view of the potential impact of AI adoption on employees and offered practical recommendations for organizations to undertake to address AI anxiety and promote employee well-being during AI implementation.Kybernetes900The findings of the study show that AI anxiety can significantly impact employee well-being, however, the relationship was moderated by organizational support, and the effects of AI anxiety decline on employee well-being.Kybernetes["Sanam Soomro", "M. Fan", "Jan Muhammad Sohu", "Safia Soomro", "Sonia Najam Shaikh"]2024-12-04T00:00:00 29600a4d4838fe1a6089fe35f9c200c9a7aa6098This research investigates the importance for robust regulation and legislation to properly govern the development of ‘Frontier Artificial Intelligence Systems’.  To do this a comparison of approaches of both existing and proposed legislation has been taken which includes from the US, UK and EU. This has involved reading both summations, assessments and opinions from both academic writers and the media and making comparisons with other industry regulations issues such as Social Media.  The key findings of this research highlight the different approaches being taken to the same problem. Legislation that came into force on August the 1st 2024 from the EU takes a safety-first approach, identifying risk levels from ‘unacceptable risk’ that would be prohibited, down to ‘minimal risk’ which would remain unregulated. This is compared to the UK White Paper, which advocated an innovation first approach with a secondary focus on safety.  With the potential risks associated with AI enhanced cyber-attacks and the spread of disinformation across different platforms, this research emphasises the importance of strong regulation in relation to safety and ensuring this happens from the outset in the development of ‘Frontier AI’ and is not an afterthought.International Conference on AI Research160The importance for robust regulation and legislation to properly govern the development of ‘Frontier Artificial Intelligence Systems’ is investigated, with the potential risks associated with AI enhanced cyber-attacks and the spread of disinformation across different platforms.International Conference on AI Research["Michael Aubrey"]2024-12-04T00:00:00 ec09d74a01993255b4e6413120371a014f40207eThis study presents an innovative approach to mitigate mobbing as a cyberattack by integrating artificial intelligence (AI) techniques such as Latent Dirichlet Allocation (LDA) and Robustly Optimized BERT Pretraining Approach (RoBERTa) according with the Cross-Industry Standard Process for Data Mining methodology (CRISP-DM). Mobbing in digital environments constitutes an emerging form of cyber attack that affects both individuals and organizations. Preventing mobbing requires a comprehensive approach involving legal frameworks, employee awareness, and technological solutions. This work uses AI to identify patterns of mobbing and proposes a structured process based on CRISP-DM to address this phenomenon. It focuses on analyzing mobbing through digital data to develop a content-filtering solution for platforms like email and messaging systems. The study identified eight general phases that describe the overall context of mobbing, as well as four specific phases that outline the detailed process of harassment within it, using fine-tuning techniques for detection. The results show how AI can automate the detection and mitigation of mobbing, minimizing its impact on victims and improving cybersecurity.Cyber Security in Networking Conference120An innovative approach to mitigate mobbing as a cyberattack is presented by integrating artificial intelligence techniques such as Latent Dirichlet Allocation and Robustly Optimized BERT Pretraining Approach according with the Cross-Industry Standard Process for Data Mining methodology (CRISP-DM).2024 8th Cyber Security in Networking Conference (CSNet)["Patricio Zambrano", "Jenny Torres", "Carlos E. Anchundia", "Johan Illicachi"]2024-12-04T00:00:00 1ff99c661121fdc58fc91d1a87443e995f22e11bGiven the widespread concerns about the integration of Artificial Intelligence (AI) tools into security and law enforcement, it is natural for digital governance to strive for greater inclusivity in both practice and design (Chohan and Hu, 2020). This inclusivity can manifest in several ways, such as advocating for legal frameworks and algorithmic governance (Schuilenburg and Peeters, 2020), allowing individuals choice, and addressing unintended consequences in extensive data management (Peeters and Widlak, 2018). An under-reflected aspect is the question of ownership, i.e., who should be able to possess and deploy AI tools for law enforcement purposes. Our interview findings from 111 participants across seven countries identified five citizens viewpoints with respect to AI ownership of security-related AI: (1) Police and police-governed agencies; (2) Citizens who disassociate themselves; (3) Entities other than the police; (4) All citizens including themselves; and (5) No one or Unsure. The five clusters represent disparate perspectives on who should be responsible for AI technologies, as well as related concerns about data ownership and expertise, and thus link into broader discussions on responsibility for security, i.e., what deserves protection, how and by whom. The findings contribute theoretically to digitalization, smart technology, social inclusion, and security studies. Additionally, it seeks to influence policy by advocating for AI development that addresses citizen concerns, thereby mitigating risks, social, and ethical implications associated with AI. Crucially, it aims to highlight citizens’ concerns around the potential for malicious actors to exploit ownership of such powerful technology for harmful purposes.International Conference on AI Research400The findings contribute theoretically to digitalization, smart technology, social inclusion, and security studies, and seeks to influence policy by advocating for AI development that addresses citizen concerns, thereby mitigating risks, social, and ethical implications associated with AI.International Conference on AI Research["Yasmine Ezzeddine", "P. Bayerl"]2024-12-04T00:00:00 6bb42e651eadcc033dd018d50cac5c313b4e7bb5BACKGROUND Diagnostic scope is the range of diagnoses found in a clinical setting. Although the diagnostic scope is an essential feature of training and evaluating artificial intelligence (AI) systems to promote diagnostic excellence, its impact on AI systems and the diagnostic process remains under-explored. CONTENT We define the concept of diagnostic scope, discuss its nuanced role in building safe and effective AI-based diagnostic decision support systems, review current challenges to measurement and use, and highlight knowledge gaps for future research. SUMMARY The diagnostic scope parallels the differential diagnosis although the latter is at the level of an encounter and the former is at the level of a clinical setting. Therefore, diagnostic scope will vary by local characteristics including geography, population, and resources. The true, observed, and considered scope in each setting may also diverge, both posing challenges for clinicians, patients, and AI developers, while also highlighting opportunities to improve safety. Further work is needed to systematically define and measure diagnostic scope in terms that are accurate, equitable, and meaningful at the bedside. AI tools tailored to a particular setting, such as a primary care clinic or intensive care unit, will each require specifying and measuring the appropriate diagnostic scope. OUTLOOK AI tools will promote diagnostic excellence if they are aligned with patient and clinician needs and trained on an accurately measured diagnostic scope. A careful understanding and rigorous evaluation of the diagnostic scope in each clinical setting will promote optimal care through human-AI collaborations in the diagnostic process.Diagnosis430The concept of diagnostic scope is defined, its nuanced role in building safe and effective AI-based diagnostic decision support systems is discussed, current challenges to measurement and use are reviewed, and knowledge gaps for future research are highlighted.Diagnosis["Gary E Weissman", "Laura Zwaan", "Sigall K. Bell"]2024-12-04T00:00:00 9b1161b4a10a32a3da6a5e42aee1ac635e6b7f3fThe article examines the current issue of implementing artificial intelligence (AI) technologies in modern medical practice with a special focus on the role of nursing staff. The key areas of AI application in healthcare, the existing regulatory framework in the Russian Federation, and ethical aspects of using intelligent systems in medicine are analyzed. Particular attention is paid to the transformation of nurses’ professional competencies in the context of healthcare digitalization. Based on the analysis of current practices and technology development prospects, recommendations are proposed for adapting educational programs and professional standards for nursing staff.Meditsinskaya sestra181Recommendations are proposed for adapting educational programs and professional standards for nursing staff in the context of healthcare digitalization and the transformation of nurses’ professional competencies in the context of healthcare digitalization.Meditsinskaya sestra["P. Seliverstov"]2024-12-05T00:00:00 cfa805b7d3afd9a4f9db827c39124d5493ec2db3Artificial intelligence (AI) systems capable of generating creative outputs are reshaping our understanding of creativity. This shift presents an opportunity for creativity researchers to reevaluate the key components of the creative process. In particular, the advanced capabilities of AI underscore the importance of studying the internal processes of creativity. This paper explores the neurobiological machinery that underlies these internal processes and describes the experiential component of creativity. It is concluded that although the products of artificial and human creativity can be similar, the internal processes are different. The paper also discusses how AI may negatively affect the internal processes of human creativity, such as the development of skills, the authenticity of creativity, and the diversity of ideas.The Journal of creative behavior311The neurobiological machinery that underlies these internal processes of creativity is explored and the experiential component of creativity is described, concluding that although the products of artificial and human creativity can be similar, the internal processes are different.ArXiv["Jaan Aru"]2024-12-05T00:00:00 52cbc1a6bd7f59915bd223a3d1dd634fbfecd09bCONTEXT.— Generative artificial intelligence (GAI) is a promising new technology with the potential to transform communication and workflows in health care and pathology. Although new technologies offer advantages, they also come with risks that users, particularly early adopters, must recognize. Given the fast pace of GAI developments, pathologists may find it challenging to stay current with the terminology, technical underpinnings, and latest advancements. Building this knowledge base will enable pathologists to grasp the potential risks and impacts that GAI may have on the future practice of pathology. OBJECTIVE.— To present key elements of GAI development, evaluation, and implementation in a way that is accessible to pathologists and relevant to laboratory applications. DATA SOURCES.— Information was gathered from recent studies and reviews from PubMed and arXiv. CONCLUSIONS.— GAI offers many potential benefits for practicing pathologists. However, the use of GAI in clinical practice requires rigorous oversight and continuous refinement to fully realize its potential and mitigate inherent risks. The performance of GAI is highly dependent on the quality and diversity of the training and fine-tuning data, which can also propagate biases if not carefully managed. Ethical concerns, particularly regarding patient privacy and autonomy, must be addressed to ensure responsible use. By harnessing these emergent technologies, pathologists will be well placed to continue forward as leaders in diagnostic medicine.Archives of Pathology & Laboratory Medicine01This work presents key elements of GAI development, evaluation, and implementation in a way that is accessible to pathologists and relevant to laboratory applications and offers many potential benefits for practicing pathologists.Archives of pathology & laboratory medicine["Rajendra Singh", "Ji Yeon Kim", "Eric F. Glassy", "Rajesh C Dash", "Victor Brodsky", "Jansen N Seheult", "M. E. D. de Baca", "Qiangqiang Gu", "Shannon Hoekstra", "B. Pritt"]2024-12-05T00:00:00 3475ba12ffb1fc7a9a1017d27011926284c5a035The challenging characteristics of earthwork environments—complex, unstructured, and constantly evolving—pose significant challenges for the path planning of automated earthwork machinery. Recent advancements in artificial intelligence (AI) technology have opened new avenues to address these challenges, which are crucial for improving the intelligence level of automated earthwork machinery. However, there is a notable lack of comprehensive analyses on AI‐based path planning in earthwork operations. Consequently, we provide a systematic review of four AI technologies currently employed in path planning for earthwork machinery, including (1) evolutionary computation, (2) swarm intelligence, (3) machine learning, and (4) other AI‐based technologies. We analyzed the application and performance evaluation results of these technologies across various construction machinery. Through this systematic analysis, we identified several key challenges: (1) multiconstraint earthwork environments, (2) generalization across 3D unstructured sites, (3) adaptability to dynamically uncertain environments, and (4) shortage of on‐site validation. We then outline potential future directions: (1) integration of generative AI with reinforcement learning, (2) use of large model technology, (3) adoption of embodied intelligence technology, and (4) conduction of more on‐site experiments.Journal of Field Robotics971A systematic review of four AI technologies currently employed in path planning for earthwork machinery, including (1) evolutionary computation, (2) swarm intelligence, (3) machine learning, and (4) other AI‐based technologies, and outlines potential future directions.Journal of Field Robotics["Cheng Zhou", "Yuxiang Wang", "Rao Li", "Tao Guan", "Zhenyuan Liu", "Gang Peng", "Ke You"]2024-12-05T00:00:00 d239812863a1a31e87541faa8769a8f950795023Rapid advancements in artificial intelligence (AI) have the potential to be revolutionary, but they have also sparked questions about power relations and socioeconomic inequalities that are reminiscent of previous colonial practices. The risk of “techno-neocolonialism,” a phrase used to characterize the potential for dominance and exploitation analogous to historical colonial practices, is juxtaposed with the possibility of unprecedented technological advancements. This paper examines the idea of techno-neocolonialism as a modern form of dominance and exploitation, emphasizing how the AI revolution runs the risk of sustaining these practices in a globalized environment. Through an analysis of vital AI enablers like talent, data, infrastructure, and computing power, we contend that the advantages of AI are frequently centered in rich countries, marginalizing the Global South. The paper goes on to stress how important it is that cooperative frameworks give equity, respect for one another, and ethical issues top priority while developing AI. This study, which ends with a plea for fair collaborations, seeks to show the way toward a more inclusive AI ecosystem that actually empowers all parties involved while avoiding the exploitation traps that come with techno-neocolonial partnerships.Trayectorias Humanas Trascontinentales140It is contended that the advantages of AI are frequently centered in rich countries, marginalizing the Global South, and how important it is that cooperative frameworks give equity, respect for one another, and ethical issues top priority while developing AI is stressed.Trayectorias Humanas Trascontinentales["J. J. Kponyo", "Dickson Marfo Fosu", "Frederica Efia Birago Owusu", "Musah Ibrahim Ali", "Maxwell Mawube Ahiamadzor"]2024-12-05T00:00:00 ff080c179d53e1fb9c8e6bf1ec34ff9144975867The introduction of ChatGPT in November 2022 by OpenAI has stimulated active discussion about the implementation of artificial intelligence (AI) in various fields such as science, entrepreneurship, and education. Although AI has been used in many areas of the economy for several years, the use of generative AI models, for example the American ChatGPT service, is becoming the information technology that sets the vector of technological progress in the use of AI. The main areas of application of generative AI are quite simple: it is not only the creation of video and audio files as the results of intellectual activity, but also the use of this technology to predict business decisions, make such decisions, in corporate governance, etc. The extent to which the use of generative AI is justified and does not create legal uncertainty is a debatable issue. In this connection, various positions on this issue are expressed in the scientific literature.At the same time, the legal regime actually applied to such artificial intelligence does not create legal certainty for business entities when using it. In summary, this article contributes to the growing academic discourse in an area of /significant research regarding the potential impact of AI and offers practical insight into how to use this technology to develop new or improve existing business models, taking into account its legal characteristics.Courier of the Kutafin Moscow State Law University00This article contributes to the growing academic discourse in an area of /significant research regarding the potential impact of AI and offers practical insight into how to use this technology to develop new or improve existing business models, taking into account its legal characteristics.Courier of Kutafin Moscow State Law University (MSAL))["O. Sushkova"]2024-12-05T00:00:00 0a5c55dd752f9ed47328692fba9ca107bf450bcdThis study explored the transformative impact of Artificial Intelligence (AI) on research methodologies at Bugema University, focusing on ethical considerations associated with AI's integration. The problem stems from AI's ability to enhance data analysis, predictive modelling, and task automation, aligning with Sustainable Development Goals (SDGs). However, it raises concerns such as algorithmic bias, data privacy, and the erosion of traditional research skills. Using a qualitative case study approach, the research examines AI adoption across various departments, involving in-depth interviews with academic staff. Findings indicate that AI improves research efficiency and quality but requires ongoing training to address technical challenges and ethical concerns. AI's integration highlights the need for continuous skill development, robust ethical guidelines, and interdisciplinary collaboration to ensure the responsible and effective use of AI in academic research. Recommendations include comprehensive AI training, the establishment of ethical guidelines, and the promotion of collaborative approaches for sustainable AI adoption in research practicesEast African Journal of Interdisciplinary Studies00Findings indicate that AI improves research efficiency and quality but requires ongoing training to address technical challenges and ethical concerns, and recommendations include comprehensive AI training, the establishment of ethical guidelines, and the promotion of collaborative approaches for sustainable AI adoption in research practices.East African Journal of Interdisciplinary Studies["Eria Muwagunzi", "Rosette Kabuye", "Christopher Ddamulira", "Stephen S. Kizza"]2024-12-05T00:00:00 1bf39ac742b4af52b406a787d47218d624139548Artificial Intelligence (AI) is having a growing impact on society, and its presence is increasing in many areas. At the same time, risk communication has been gaining prominence and importance in both society, especially in the wake of the recent COVID-19 pandemic, and academia. In view of the magnitude of both phenomena, this article aims to identify the different points and aspects where they converge, as well as the potential theoretical and practical implications of AI in risk communication. To this end, we carried out an exploratory, systematic review of the scientific literature from a holistic perspective, taking a mixed methods approach that considered both quantitative and qualitative aspects in order to analyse the state of academic research and its implications. The results show a marked increase in scientific production that addresses both concepts jointly, particularly from 2019 onward, coinciding with the COVID-19 pandemic, with this risk being the main subject of study. Moreover, social networks, especially X (formerly Twitter), emerge as the most interesting platforms for research, while other platforms receive a lower level of attention. Our findings suggest that AI has a dual impact on risk communication, presenting both challenges by generating new risk scenarios, and opportunities by providing new methods that allow new horizons to be explored. Finally, different theoretical and practical implications arise from this research, and it is necessary to address the challenges and take advantage of the opportunities provided by AI to improve risk communication.Review of Communication Research800An exploratory, systematic review of the scientific literature from a holistic perspective suggests that AI has a dual impact on risk communication, presenting both challenges by generating new risk scenarios, and opportunities by providing new methods that allow new horizons to be explored.Review of Communication Research["Noel Pascual-Presa", "Berta Garc\u00eda-Orosa"]2024-12-05T00:00:00 e404274c3989edcb3295794037e450721aeb2925This study examines the application of Immanuel Kant's ethical principles in the use of autonomous technology based on Artificial Intelligence (AI) in the academic domain. AI technology has significantly impacted academic efficiency and innovation but has also raised serious ethical challenges, such as algorithmic bias, dependency on technology, and threats to privacy and transparency. Using a Kantian ethical approach, this research identifies key issues and proposes an ethical framework that places justice, transparency, and respect for human dignity at the core of the design and implementation of autonomous technologies. The findings indicate that algorithmic bias can exacerbate academic inequities, while excessive reliance on AI risks diminishing the critical and creative capabilities of academics. Additionally, privacy and accountability in the management of academic data emerge as major concerns requiring ethically grounded policy interventions. This study also highlights the need for a new paradigm ensuring that autonomous technologies uphold humanistic values in academic settings, such as inclusivity, intellectual autonomy, and justice.Jurnal Manajemen Kewirausahaan dan Teknologi00The findings indicate that algorithmic bias can exacerbate academic inequities, while excessive reliance on AI risks diminishing the critical and creative capabilities of academics and highlights the need for a new paradigm ensuring that autonomous technologies uphold humanistic values in academic settings.Jurnal Manajemen Kewirausahaan dan Teknologi["Oktaviani Putri Dita", "Radittya Mahasputra Antara", "Agung Winarno"]2024-12-05T00:00:00 ed73d7612ffa89919638a0692d424d19e8b4a247The "Right to be Forgotten" (RTBF) has become a crucial aspect of data privacy in the digital age. It addresses the challenges of managing and erasing personal data in a world dominated by artificial intelligence (AI) and machine learning (ML). This paper examines the implementation of RTBF within AI and ML systems. It includes a comparative analysis of regulatory frameworks in the European Union (EU), the United States (US), and India. The EU's General Data Protection Regulation (GDPR) sets a global benchmark with explicit provisions for data erasure and RTBF. It requires AI systems to comply with strict data handling and deletion protocols. In contrast, the US lacks a federal RTBF regulation, relying instead on a patchwork of state laws and sector-specific regulations. This presents unique challenges and opportunities for AI and ML practitioners. India’s Digital Personal Data Protection Act (DPDP) introduces RTBF focusing on consent and transparency, aiming to balance innovation with privacy concerns. This paper explores the technical and legal implications of implementing RTBF in AI and ML, including data minimization, retraining models, and the ethical considerations of balancing individual rights with the collective benefits of data-driven technologies. The implementation of RTBF should also be carefully handled alongside other legal rights such as the right to freedom of speech and expression. By examining case studies and current practices, this research offers insights into developing robust RTBF mechanisms that align with diverse regulatory landscapes, ensuring that AI and ML advancements are achieved without compromising fundamental privacy rights.International Research Journal on Advanced Engineering and Management (IRJAEM)00The technical and legal implications of implementing RTBF in AI and ML, including data minimization, retraining models, and the ethical considerations of balancing individual rights with the collective benefits of data-driven technologies are explored.International Research Journal on Advanced Engineering and Management (IRJAEM)["Sriram G", "J. Junita Sarah", "J.B. Rupa Vahini"]2024-12-05T00:00:00 df3ae00c546ffbe09c51a27cd93f4396501bfc5dDiscover Sustainability790This article explores how the sociotechnical connection between AI, the social dimension of sustainable development, and security is being communicated in research conceptualizing this liaison and highlights the importance of aligning technology development with broader social objectives by highlighting the complex interplay between AI, social sustainability, and security.Discover Sustainability["Irja Malmio"]2024-12-05T00:00:00 93c43eda249692c0b506fe22e18a99c941ea75f4Introduction The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress. Methods This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for a comprehensive literature review, analyzing over 9,350 publications from 2004 to 2023. Utilizing BERTopic modeling, 14 key themes in AI-driven cybersecurity were identified. Topics were clustered and validated through a combination of algorithmic and expert-driven evaluations, focusing on semantic relationships and coherence scores. Results AI applications in cybersecurity are concentrated around intrusion detection, malware classification, federated learning in privacy, IoT security, UAV systems and DDoS mitigation. Emerging fields such as adversarial machine learning, blockchain and deep learning are gaining traction. Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats. Global trends indicate significant contributions from the US, India, UK, and China, highlighting geographical diversity in research priorities. Discussion While AI enhances cybersecurity efficacy, challenges such as computational resource demands, adversarial vulnerabilities, and ethical concerns persist. More research in trustworthy AI, standardizing AI-driven methods, legislations for robust privacy protection amongst others is emphasized. The study also highlights key current and future areas of focus, including quantum machine learning, explainable AI, integrating humanized AI and deepfakes.Frontiers in Big Data1100Analysis reveals that AI's adaptability and scalability are critical for addressing evolving threats, and highlights key current and future areas of focus, including quantum machine learning, explainable AI, integrating humanized AI and deepfakes.Frontiers in Big Data["K. Achuthan", "S. Ramanathan", "Sethuraman Srinivas", "R. Raman"]2024-12-05T00:00:00 43bd9de3a86ca44fe2f53c8ce768867354cd56e2This paper investigates the adoption and potential integration of artificial intelligence (AI) within higher education, examining its impact on educators and learners through detailed perspectives gathered from university students. It provides an extensive literature review outlining the dynamics, characteristics, and the application of AI in the educational sector. The primary research included a meticulously designed survey distributed among active students to assess their current experiences, perceived benefits, and concerns having AI-driven materials and tools in educational environments. Based on the learners’ responses a generally positive attitude towards the use of AI was revealed among the university students. They expressed a strong belief in their ability to learn with and utilize AI tools effectively, acknowledging the significant advantages AI can offer in enhancing educational experiences and providing personalized academic support. This optimistic view is, however, tempered by significant concerns, particularly regarding ethical issues and the potential shift away from traditional pedagogical methods. The data also showed that the participants highly valued the effectiveness and accessibility provided by the AI-enhanced instructional materials and teaching methods. Despite this, there remained a substantial degree of apprehension surrounding the ethical implications and safety of AI applications in education. This paper makes a significant contribution to the field of educational technology by providing primary research on AI-related challenges and considerations. It highlights the critical importance of maintaining a balanced approach that prioritizes technological innovation alongside ethical considerations and human-centered practices in the development and integration of AI into higher education, advocating for responsible use of technology.International Journal on Lifelong Education and Leadership00It is highlighted the critical importance of maintaining a balanced approach that prioritizes technological innovation alongside ethical considerations and human-centered practices in the development and integration of AI into higher education, advocating for responsible use of technology.International Journal on Lifelong Education and Leadership["Attila Balogh"]2024-12-05T00:00:00 bec219d2f5398202b083b7665f4390bbaad99997This review explores the transformative role of artificial intelligence (AI), especially through deep learning (DL) and convolutional neural networks (CNNs), in the early detection of esophageal squamous cell carcinoma (ESCC). ESCC presents significant diagnostic challenges due to its aggressive nature and high mortality, often diagnosed at advanced stages. Traditional endoscopic methods, while essential, suffer from limitations like interobserver variability, especially for subtle early lesions. AI-based systems have demonstrated high sensitivity in identifying these early neoplasms, reducing diagnostic discrepancies and enhancing precision. Through a bibliographic review, the article highlights recent AI advancements, particularly in utilizing CNNs to identify early-stage ESCC. These tools have shown the potential to improve diagnostic accuracy even among less experienced endoscopists and provide critical support in clinical decision-making, including biopsy guidance and lesion mapping in chromoendoscopy and narrow-band imaging (NBI). Despite promising results, challenges persist in AI’s practical application, such as the need for extensive data for training, clinical validation, and standardization across different endoscopic equipment. The study underscores the need for large-scale multicenter studies and standardization to solidify AI’s role in ESCC diagnosis. It envisions a future where AI not only serves as a diagnostic support tool but becomes integral to endoscopic practices, requiring continued technological development, ethical considerations, and regulatory frameworks to maximize clinical benefit.Brazilian Journal of Health Review00The study underscores the need for large-scale multicenter studies and standardization to solidify AI’s role in ESCC diagnosis, and envisions a future where AI not only serves as a diagnostic support tool but becomes integral to endoscopic practices.Brazilian Journal of Health Review["Flaubert Sena de Medeiros Filho", "Thiago Cavalcanti Pinheiro", "Miguel Adelino da Silva Filho", "Hudson Paulinelly da C\u00e2mara Melo", "Isaac Matheus Bezerra Gurgel", "Rodolfo de Oliveira Lobo", "Felipe Dantas da Silva", "Leonardo Jos\u00e9 de Oliveira Marinho", "Jos\u00e9 Felipe de Oliveira Neto", "Lara Aladim Almeida", "Gabrielle Campos de Menezes", "Jo\u00e3o Pedro Sartor de Azevedo Concei\u00e7\u00e3o", "Marina Irma Pinheiro de Souza", "Vin\u00edcius Guedes Lima Bahia", "Lucas Paula da Frota", "Germano Leite Brasil Montenegro Filho", "Matheus Jales Menezes"]2024-12-05T00:00:00 3f07d6c0f5599213626a934490a3207ecc504c8aThrough ChatGPT and other artificial intelligence platforms, large language models’ (LLMs’) applications have expanded across daily life, including research. Yet, the qualitative paradigm's methodological assumption of socially mediated interpretivism is at odds with how LLMs operate. Qualitative research is appropriate for inquiry where conceptual development and interpretation are required. Specifically, Disability Studies scholars have used qualitative research to understand more about disabled peoples’ experiences. Like other marginalized identities, disability is often misunderstood pejoratively. We offer concerns about whether LLMs can address key qualitative analysis markers, contextualizing these concerns within disability research. To test these concerns, we assigned two LLMs, ChatGPT and Gemini, coding tasks using existing secondary de-identified data. We found LLMs were not able to produce codes that were high quality, credible, or consistent, and could not parse data from research participants with certain disabilities. We discuss implications for future methodological decisions and policies.Qualitative Research490Concerns about whether LLMs can address key qualitative analysis markers are offered, contextualizing these concerns within disability research and implications for future methodological decisions and policies are discussed.Qualitative Research["Carli Friedman", "Aleksa Owen", "Laura VanPuymbrouck"]2024-12-05T00:00:00 5c2b815cb28318b0feca6f69e437b23b8d5d84afArtificial intelligence (AI) has transformative potential in veterinary pathology in tasks ranging from cell enumeration and cancer detection to prognosis forecasting, virtual staining techniques, and individually tailored treatment plans. Preclinical testing and validation of AI systems (AIS) are critical to ensure diagnostic safety, efficacy, and dependability. In this two-part series, challenges such as the AI chasm (ie, the discrepancy between the AIS model performance in research settings and real-world applications) and ethical considerations (data privacy, algorithmic bias) are reviewed and underscore the importance of tailored quality assurance measures that address the nuances of AI in veterinary pathology. This review advocates for a multidisciplinary approach to AI development and implementation, focusing on image-based tasks, highlighting the necessity for collaboration across veterinarians, computer scientists, and ethicists to successfully navigate the complex landscape of using AI in veterinary medicine. It calls for a concerted effort to bridge the AI chasm by addressing technical, ethical, and regulatory challenges, facilitating AI integration into veterinary pathology. The future of veterinary pathology must balance harnessing AI's potential while intentionally mitigating its risks, ensuring the welfare of animals and the integrity of the veterinary profession are safeguarded. Part I of this review focuses on considerations for model development, and Part II focuses on external validation of AI.Veterinary clinical pathology410This review advocates for a multidisciplinary approach to AI development and implementation, focusing on image-based tasks, highlighting the necessity for collaboration across veterinarians, computer scientists, and ethicists to successfully navigate the complex landscape of using AI in veterinary medicine.Veterinary clinical pathology["Christina Pacholec", "B. Flatland", "Hehuang Xie", "Kurt Zimmerman"]2024-12-05T00:00:00 1ce42a659cd26e73a85fecf126607f38bd68fbe3Cybercrime has become an escalating threat in the digital age, with India experiencing a significant rise in cyberattacks and data breaches. This study investigates the critical role that Artificial Intelligence (AI) plays in the detection and prevention of cybercrimes within the Indian context. The research delves into the current state of cyber threats in India, highlighting the vulnerabilities and challenges faced by individuals, businesses, and government agencies. It explores the evolving landscape of cybercrimes, encompassing diverse forms such as phishing attacks, data theft, ransom ware, and online fraud. The primary focus of this study is the application of AI technologies, including machine learning, natural language processing, and anomaly detection, in identifying and mitigating cyber threats. It investigates the effectiveness of AI-based solutions in real-time threat detection, threat intelligence analysis, and incident response. Furthermore, the study examines the legal and ethical considerations surrounding the use of AI in combating cybercrimes in India. It analyzes the existing regulatory framework and privacy concerns, emphasizing the need for a balanced approach to safeguarding digital infrastructure while protecting individual rights. Through an in-depth analysis of case studies and expert opinions, this research aims to provide insights into the potential benefits and limitations of AI in addressing cyber threats in India. It also offers recommendations for policymakers, businesses, and cybersecurity professionals on harnessing AI's capabilities to enhance cybersecurity measures and protect the digital economy. In conclusion, this study highlights the growing importance of Artificial Intelligence in the context of cybercrime prevention and detection in India. As the nation continues to digitize rapidly, harnessing AI's power becomes paramount in safeguarding critical data, infrastructure, and individual privacy in an interconnected worldInternational Journal of Advanced Research in Science, Communication and Technology10The primary focus of this study is the application of AI technologies, including machine learning, natural language processing, and anomaly detection, in identifying and mitigating cyber threats, and the effectiveness of AI-based solutions in real-time threat detection, threat intelligence analysis, and incident response.International Journal of Advanced Research in Science, Communication and Technology["Sowmya N"]2024-12-05T00:00:00 1039493592098dc6a10e9e31173e8b9a089e464b Governments have recently started to design policies that are specific to artificial intelligence (AI), which is projected to become the dominant technology in the decades to come. AI is increasingly permeating all aspects of the digital economy, including trade in goods and services, giving rise to concerns whether emerging AI-specific regulation may run afoul of international economic law (IEL). However, studies on the law of the World Trade Organization have yet to pay close attention to the European Union (EU) Artificial Intelligence Act (AI Act), the first binding regulation of its kind in the world. This paper seeks to address this gap in the literature. It examines the compatibility of emerging AI regulation with multilateral trade rules, using the EU AI Act as a case study. More specifically, it analyses to what extent the EU AI Act’s disciplines on prohibited AI systems are likely to violate the EU’s existing obligations and commitments enshrined in the Agreement on Technical Barriers to Trade and the General Agreement on Trade in Services. This paper demonstrates that there is potential for conflict between the EU regulation and these two multilateral trade agreements. It also suggests that, although emerging AI regulation can represent a challenge for the future of IEL, the latter can play a role in guiding and shaping AI-specific regulation moving forward.Journal of international economic law00Journal of International Economic Law["Marta Soprana"]2024-12-05T00:00:00 994a28d1a61607f29e365448829914ff8d49febeThe concept of trust in artificial intelligence (AI) has been gaining increasing relevance for understanding and shaping human interaction with AI systems. Despite a growing literature, there are disputes as to whether the processes of trust in AI are similar to that of interpersonal trust (i.e., in fellow humans). The aim of the present article is twofold. First, we provide a systematic test of an integrative model of trust inspired by interpersonal trust research encompassing trust, its antecedents (trustworthiness and trust propensity), and its consequences (intentions to use the AI and willingness to disclose personal information). Second, we investigate the role of AI personalization on trust and trustworthiness, considering both their mean levels and their dynamic relationships. In two pilot studies (N = 313) and one main study (N = 1,001) focusing on AI chatbots, we find that the integrative model of trust is suitable for the study of trust in virtual AI. Perceived trustworthiness of the AI, and more specifically its ability and integrity dimensions, is a significant antecedent of trust and so are anthropomorphism and propensity to trust smart technology. Trust, in turn, leads to greater intentions to use and willingness to disclose information to the AI. The personalized AI chatbot was perceived as more able and benevolent than the impersonal chatbot. It was also more anthropomorphized and led to greater usage intentions, but not to greater trust. Anthropomorphism, not trust, explained the greater intentions to use personalized AI. We discuss implications for research on trust in humans and in automation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).Journal of experimental psychology. General00It is found that the integrative model of trust is suitable for the study of trust in virtual AI and perceived trustworthiness of the AI is a significant antecedent of trust and so are anthropomorphism and propensity to trust smart technology.Journal of experimental psychology. General["F. Lalot", "Anna-Marie Bertram"]2024-12-05T00:00:00 14b2e3d0e6b4d25f042a220bb57812b9d5c2dcd3The creation and successful operation of programs for electronic computers capable of performing actions not related to direct operator commands and not initially embedded in the programmable code (artificial intelligence class programs) have caused a number of problems in the theory of legal regulation, including including the problem of determining whether a program of the “artificial intelligence” class, performing independent actions of legal significance, acquires such properties as legal capacity and personality. The solution to this problem is possible through the use of the experience of Roman law.Courier of the Kutafin Moscow State Law University00The solution to the problem of determining whether a program of the “artificial intelligence” class, performing independent actions of legal significance, acquires such properties as legal capacity and personality is possible through the use of the experience of Roman law.Courier of Kutafin Moscow State Law University (MSAL))["D. V. Gribanov", "E. A. Belkanov"]2024-12-05T00:00:00 c78b6f834655567dce270c78d2aaef8346773a22The study aimed to explore how artificial intelligence (AI) is applied in the restaurant industry, particularly in enhancing operational efficiency, personalizing customer interactions, and predicting demand. Using qualitative methods, interviews were conducted with industry experts in Odesa, Ukraine, including professionals from major chains and a case study with a Ukrainian chef. Findings revealed that global brands like McDonald’s, Starbucks, and Marriott use AI for demand forecasting, offer personalization, and streamlining communication. Chef Yevhen Klopotenko’s use of AI to prepare a gourmet dinner showcased its innovative potential in cuisine. The research enriches the existing literature on AI in hospitality with practical examples, offering valuable insights for industry professionals. It highlights the significance of AI in predicting demand and automating customer interactions to boost efficiency. The originality of the study lies in its analysis of AI applications and its comprehensive list of AI tools for content generation. However, the qualitative focus and regional scope limit generalizability, suggesting future research could broaden to include other regions and quantitative methods.Turystyka i Rozwój Regionalny310Findings revealed that global brands like McDonald’s, Starbucks, and Marriott use AI for demand forecasting, offer personalization, and streamlining communication, and it highlights the significance of AI in predicting demand and automating customer interactions to boost efficiency.Turystyka i Rozwój Regionalny["Kateryna Fedosova", "Alina Katunian"]2024-12-05T00:00:00 800f7faea30e58e52fde1f98de6a2975ca1dc0ebThe rapid advancement of artificial intelligence (AI) technologies presents profound challenges to societal safety. As AI systems become more capable, accessible, and integrated into critical services, the dual nature of their potential is increasingly clear. While AI can enhance defensive capabilities in areas like threat detection, risk assessment, and automated security operations, it also presents avenues for malicious exploitation and large-scale societal harm, for example through automated influence operations and cyber attacks. Understanding the dynamics that shape AI's capacity to both cause harm and enhance protective measures is essential for informed decision-making regarding the deployment, use, and integration of advanced AI systems. This paper builds on recent work on offense-defense dynamics within the realm of AI, proposing a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society. By establishing a shared terminology and conceptual foundation for analyzing these interactions, this work seeks to facilitate further research and discourse in this critical area.arXiv.org00A taxonomy is proposed to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society, and seeks to facilitate further research and discourse in this critical area.ArXiv["Giulio Corsi", "Kyle Kilian", "Richard Mallah"]2024-12-05T00:00:00 bbce6bb4b1abc763816fee4c92b6c372902b1bffAI4EF, Artificial Intelligence for Energy Efficiency, is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF enables stakeholders such as public sector representatives, energy consultants, and building owners to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modeling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the platform incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. Its compatibility with open-source identity management, Keycloak, enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an architectural overview of AI4EF, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI).arXiv.org00An architectural overview of AI4EF, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI) are presented.ArXiv["A. Tzortzis", "Georgios Kormpakis", "Sotiris Pelekis", "Ariadni Michalitsi-Psarrou", "Evangelos Karakolis", "Christos Ntanos", "Dimitris Askounis"]2024-12-05T00:00:00 5b7af9030996207e48877878dd40e6c2171d59f7In the automobile industry, the application of artificial intelligence is more and more extensive, among which the emergency hedging system of automobile is one of the important fields of artificial intelligence application. With the increasing growth of automobiles, traffic road safety has become a topic often talked about in People's Daily life. With the rapid development of artificial intelligence, many automobile companies begin to use artificial intelligence to help the car emergency hedging system. Artificial intelligence will greatly enhance the timeliness, accuracy and safety of the automobile emergency system. This paper aims to discuss the influence and development of artificial intelligence on automobile emergency avoidance system, and analyze its advantages and disadvantages and future development trend. Which has a very obvious help to the automatic driving technology, but also laid the development of automatic driving technology.Journal of Computing and Electronic Information Management00This paper aims to discuss the influence and development of artificial intelligence on automobile emergency avoidance system, and analyze its advantages and disadvantages and future development trend.Journal of Computing and Electronic Information Management["Linyan Ji"]2024-12-05T00:00:00 9ad4f479c3fd5c7817e62977e4a37691982bffd2Artificial Intelligence (AI) is increasingly influencing the education sector, offering vast potential for transformation. This study determined the Bachelor of Secondary Education (BSEd) students’ perception of AI integration in education under three domains: the teaching and learning process, curriculum, and future possibilities. The study used a mixed-method research approach, employing descriptive analysis for quantitative data and thematic analysis for qualitative data. The quantitative results revealed that the most common AI tools used by BSEd students were ChatGPT, Quillbot, Perplexity, Gemini, and Grammarly. Further, the participants had a positive perception of AI integration in the domains of the teaching-learning process and curriculum. They had a neutral perception of the domain of future possibilities. Moreover, the qualitative data on students’ experiences with the use of AI-generated tools in class revealed two themes: (1) AI support in the Teaching and Learning Process and (2) Concerns Regarding the Use of AI. These results highlight the need for clear guidelines and policies to ensure effective and responsible use of AI in education, providing valuable insights for future research on Artificial Intelligence in Education (AIED).International journal of science and management studies00The quantitative results revealed that the most common AI tools used by BSEd students were ChatGPT, Quillbot, Perplexity, Gemini, and Grammarly, and the participants had a positive perception of AI integration in the domains of the teaching-learning process and curriculum.International Journal of Science and Management Studies (IJSMS)["Richeno Paolo S. Abejo", "Gen Infinity E. Indac", "Nessel Joy D. Montejo", "Carmel S. Namocot", "Jade C. Colegado"]2024-12-05T00:00:00 9b9f7ed41735d895757686116fce18a1513db307The article studies the problems of strategic management and artificial intelligence as instruments of effective activity of enterprises in Ukraine. It is proved that the modern business environment is characterized by dynamic changes, high level of competition and rapid development of technologies. In such circumstances, enterprises are faced with the need to improve strategic management to ensure the effectiveness of their activities and adapt to market challenges. It is considered that artificial intelligence becomes one of the key tools capable of providing a new level of control due to automation of processes. The importance of strategic management and the introduction of artificial intelligence in ensuring the success of enterprises can not be overestimated. Today, effective implementation of enterprises and the further use of artificial intelligence and strategic management has significant potential for success both at the national and international levels. Scientific research suggests that the combination of artificial intelligence and strategic management of enterprises can be a tool for achieving more significant results in the implementation of plans, business management and other areas of activity. As for strategic management, the use of artificial intelligence actively affects the productivity of workers and the enterprise as a whole, and also increases efficiency among workers. It is also known that the use of artificial intelligence for strategic management eliminates repetitive roles and redundancy, and also effectively performs complex tasks. Given the range of challenges facing businesses both nationally and internationally, research has shown that strategic management can be used to overcome them. The problem lies in the insufficient development of theoretical and practical foundations for the use of artificial intelligence in strategic management, which complicates the effective integration of these tools into the activities of enterprisesEconomies Horizons00The problem lies in the insufficient development of theoretical and practical foundations for the use of artificial intelligence in strategic management, which complicates the effective integration of these tools into the activities of enterprises.Economies' Horizons["Mykola Kuzminov"]2024-12-05T00:00:00 31830ed776ba1ef6235f52dc63c9805af95679ecArtificial intelligence (AI) tokens are digital assets that integrate AI capabilities by operating on decentralized networks using AI algorithms in order to automate tasks, make intelligent decisions, and swiftly adapt based on data. Given that AI tokens are energy intensive assets, in this paper, we explore how major AI tokens are connected to oil, natural gas, and biofuel under extreme market movements using daily data from June 2019 to March 2024. We find that AI tokens are net transmitters of shocks while the entire energy sector is the net receiver of shocks at the return level. However, both AI tokens and oil are net transmitters of shocks at the volatility level. We also show that total dynamic connectedness significantly increased during the start of COVID‐19 pandemic and the Russian‐Ukraine war. Our quantile‐based connectedness analysis further shows that return and volatility connectedness is considerably higher at low and high quantiles, indicating that shocks to AI tokens spread more intensely during extreme market movements. These results indicate that AI tokens are subject to contagion and thus offer inadequate portfolio diversification under major market movements.Review of Financial Economics320Review of Financial Economics["Farooq Malik", "Zaghum Umar"]2024-12-05T00:00:00 af2481f09163e3b6ecbe188a285d37a2fcc32cd8The article considers the use of artificial intelligence as a tool for analyzing and classifying programming problems in the educational process. An approach to automating the problem classification process is proposed, which takes into account their complexity, subject matter, and type. Particular attention is paid to the advantages of using AI technologies to increase the efficiency of programming training, in particular, adapting problems to the level of students' knowledge. Practical results of implementing the problem classification system are presented, which demonstrate its high accuracy and prospects. The article also outlines the possibilities for further development of this area, such as the creation of adaptive platforms and recommender systems. The conclusions of the article emphasize the importance of integrating artificial intelligence into the modern educational process.E-learning teXnology00An approach to automating the problem classification process is proposed, which takes into account their complexity, subject matter, and type, and demonstrates its high accuracy and prospects.E-learning teXnology["V. Velychko", "O.S. Ganiev", "S.S. Zhadan"]2024-12-05T00:00:00 e52e52d52d765d5a784301436ec73d1af2cbccb0The most booming technology of artificial intelligence, so-called large language models (LLM), is considered. Their functionality, examples and prospects of use in various fields of activity are analyzed. It is shown that by means of specialized pre-training technologies there are opportunities to create numerous neuro employees on the basis of large language models, increasing the efficiency of companies' activity. Pre-training adds special expert knowledge in a particular field and/or specific functional capabilities to the “basic intelligence” of large language models. A pilot project implemented by VNIIMS in cooperation with the University of Artificial Intelligence to create a neuro consultant in the field of legal metrology based on the YandexGPT model is described. The results of the project confirmed the practical feasibility and high efficiency of such a neuro employee. The project assumes the possibility of further development and scaling.Izmeritel`naya Tekhnika10It is shown that by means of specialized pre-training technologies there are opportunities to create numerous neuro employees on the basis of large language models, increasing the efficiency of companies' activity.Izmeritel`naya Tekhnika["A. Y. Kuzin", "A. N. Kroshkin", "I. A. Obolensky"]2024-12-05T00:00:00 5f3aaca3e83708c3e58864b7ffcd160da1f79f0fUnder the background of Digital China strategy, artificial intelligence assists judges to carry out judicial trials and helps to improve judicial efficiency, and promotes the modernization of trial practice. However, there is a lack of internal mechanism discussion on the application scenarios and optimization paths of AI assisted decision in civil cases of Internet courts. Article based on the background of the Internet court judicial work practice, combined with judicial activism theory, artificial intelligence and man-machine program justice theory construction research theory framework, prove the judge authority on the basis of intelligent judicial man-machine fusion decision for the future application direction, but artificial intelligence short cannot get rid of pure tool status subject status and difficult to widely application, human judge will continue to play subject initiative and keep trial dominance in the future, "AI judge" and human judge. In addition, the optimization of artificial intelligence justice needs to build an intelligent decision-making management data-based system based on differentiated practice scenarios, build an intelligent construction platform of judicial public service and introduce the experience outside of intelligent justice.Advances in Education, Humanities and Social Science Research160Article based on the background of the Internet court judicial work practice, combined with judicial activism theory, artificial intelligence and man-machine program justice theory construction research theory framework, proves the judge authority on the basis of intelligent judicial man-machine fusion decision for the future application direction.Advances in Education, Humanities and Social Science Research["Shuangrui Shi"]2024-12-05T00:00:00 5f326db42196d2ca7feafc2297bdddb05f63cc99Developments in machine learning and computing power suggest that artificial general intelligence is within reach. This raises the question of artificial consciousness: if a computer were to be functionally equivalent to a human, being able to do all we do, would it experience sights, sounds, and thoughts, as we do when we are conscious? Answering this question in a principled manner can only be done on the basis of a theory of consciousness that is grounded in phenomenology and that states the necessary and sufficient conditions for any system, evolved or engineered, to support subjective experience. Here we employ Integrated Information Theory (IIT), which provides principled tools to determine whether a system is conscious, to what degree, and the content of its experience. We consider pairs of systems constituted of simple Boolean units, one of which -- a basic stored-program computer -- simulates the other with full functional equivalence. By applying the principles of IIT, we demonstrate that (i) two systems can be functionally equivalent without being phenomenally equivalent, and (ii) that this conclusion is not dependent on the simulated system's function. We further demonstrate that, according to IIT, it is possible for a digital computer to simulate our behavior, possibly even by simulating the neurons in our brain, without replicating our experience. This contrasts sharply with computational functionalism, the thesis that performing computations of the right kind is necessary and sufficient for consciousness.arXiv.org02It is demonstrated that, according to IIT, it is possible for a digital computer to simulate the authors' behavior, possibly even by simulating the neurons in their brain, without replicating their experience.ArXiv["Graham Findlay", "William Marshall", "Larissa Albantakis", "Isaac David", "Will Mayner", "Christof Koch", "G. Tononi"]2024-12-05T00:00:00 fa05d19e2714de140a4f916e673b188f53868474Norwegian Journal of Geography401Norsk Geografisk Tidsskrift - Norwegian Journal of Geography["M. Valdez", "Matthew Cook"]2024-12-05T00:00:00 5a6d73685415c270e750ca03044ddd8663765679International Journal of Media and Information Literacy00International Journal of Media and Information Literacy[]2024-12-05T00:00:00 14246d76717e8149a648f1f44aa4d5e5e1133bb6SIGCSE Virtual00Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 1["Cynthia Bailey"]2024-12-05T00:00:00 0905d67388a8e9eb04e45f97a9fd8dc8e1198a25SIGCSE Virtual60Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 2["D. Boulden", "Jessica Vandenberg", "Alex Goslen", "Veronica Catet\u00e9", "Wookhee Min", "Bradford W. Mott"]2024-12-05T00:00:00 2581a6360389b7617669a0463215814903aeeb75Journal of Mining and Geotechnical Engineering00Journal of Mining and Geotechnical Engineering["Daria A. Trubitsyna", "Alexey A. Khoreshok", "Olga V. Dolbnya", "Alexander N. Ermakov", "Kirill Varnavskiy"]2024-12-05T00:00:00 c9f0b055dec50480b98cb9a6c6b62bf758d58d33SIGCSE Virtual00Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 1["Qinghua Zheng"]2024-12-05T00:00:00 29849280a6e0342c3d841637d3856542f3e07d43J. Medical Syst.20Journal of medical systems["Jesse M. Ehrenfeld", "Keith F Woeltje"]2024-12-05T00:00:00 0e5bc43e68fe6f6e66b8d5818e9a546ef6656119Abstract is not available in English.Journal of Law00Journal of Law["Tamar Gvaramadze"]2024-12-05T00:00:00 298a7cf211b89762ee410d80814b580a832ecee2With the mounting urgency to achieve a sustainable future, it is of paramount importance to provide pre‐service teachers with a robust understanding of de facto. The present study investigated the potential of ChatGPT‐supported educational technologies to enhance the understanding of sustainable development among 20 pre‐service teachers at a university during the 2023–2024 academic year. Over a period of 14 weeks of intervention, participants employed ChatGPT and Web 2.0 tools (Pixton) to create digital comic stories focused on sustainable development goals. The study employed an explanatory sequential mixed‐method design, utilising evaluation forms, semi‐structured interviews, inferential statistics and content analysis. The results revealed significant improvements in sustainability perspectives, awareness and knowledge, despite concerns about productivity, originality and ethical issues.European Journal of Education780The present study investigated the potential of ChatGPT‐supported educational technologies to enhance the understanding of sustainable development among 20 pre‐service teachers at a university during the 2023–2024 academic year, revealing significant improvements in sustainability perspectives, awareness and knowledge.European Journal of Education["Fatih Kayaalp", "Mehmet Durnal\u0131", "Bayram G\u00f6kbulut"]2024-12-05T00:00:00 a7dbc72aa2334e745f0a6a85327bdc6f875d0c1eAI and Ethics200AI and Ethics["Sanjana Kumari", "Amisha Kumari", "Rabia Asim", "Rayyan Khan"]2024-12-05T00:00:00 686785d789bf7403b3b74ca7f6c7af92d163cf6aNeural computing & applications (Print)120Neural Computing and Applications["Amir Feizi", "H. Golp\u00eera"]2024-12-05T00:00:00 3dc5a4c60beadfeb763eafeae8f1b4844be85840Progressively more people are living to at least the age of 60 years and at least a quarter of the global population is expected to be 60 years or older by 2050 (United Nations, 2023). This demographic transition, driven by declining fertility rates and increased life expectancy, is accompanied by a notable trend: more people are returning to the workplace or taking up part-time jobs after retirement, which implies that the number of retirees doing so is on the rise. In the United States, about 29 percent of retirees resume work (DE Silver, 2023), and 47 percent of men aged from 60 to 64 years go back to work within the first ten years of retirement in Canada (Statistic Canada, 2023). Such changes in the social contract require that current and future advancements in post-retirement employment be analyzed and understood about antecedents and consequences of career concepts and management of human capital in organizations. There is relatively little literature published on retirement, however, the few extant literature are silent on what motivates retirees back into work and the effects on organizational performance. Therefore, this paper attempts to fill this gap by reviewing the literature on post-retirement employment, with particular emphasis on antecedents and consequences of decisions to re-employment retirees. It stresses the significance of integration and synthesis of findings for better understanding of the subject by specialists of different branches of knowledge, including sociology, psychology, and economics in the framework of HRM for the sake of improved strategic planning and policy-making. Finally, an analysis is made regarding demographic effects on workers in the organization as well as the effects of the retiring baby-boomers and the shortage of workers expected to ensue. It speaks about the possibilities of reemploying older workers as having implications to reduce workforce shortages, especially when it comes to specialized occupational positions. Last but not least; the paper sums up the social equity functions of retirees, retirees' responsibilities in family and community, and difficulties experienced in the process of retirement. Thus, the goal to expand the existing knowledge about retirees’ quality of life and the effects of work after retirement on the individual and organizational levels will be achieved through attending to the aspects identified above. Indeed, this research is valuable for enriching modern theories on career development and human resource management, and for understanding how retirees’ skills and experience can be utilized in the interest of both the employment market and the social well-being of the community.The Critical Review of Social Sciences Studies00The goal is to expand the existing knowledge about retirees’ quality of life and the effects of work after retirement on the individual and organizational levels by reviewing the literature on post-retirement employment, with particular emphasis on antecedents and consequences of decisions to re-employment retirees.The Critical Review of Social Sciences Studies["Ishfaq Ahmad", "Dr. Faiz Bakhsh", "Muhammad Faisal", "Sajid Sultan"]2024-12-05T00:00:00 448287f3896ed21bdfde6e72ce0a00f7ac41edfcReview of International Political Economy440Review of International Political Economy["Andrea Lagna"]2024-12-05T00:00:00 71dd4639d02e818caacf0fe4b621076eb5d1b6a2The article develops the idea of a new universal right — the right to refuse digital technologies, using the example of a separate area of AI use. It characterizes the digital development of human society, describes issues of trust in digital technologies, including using foreign and Russian sociological studies as an example, and identifies the risks to humans and humanity on account of reckless use of new technologies. It provides a legal analysis of patterns and cites foreign and Russian examples of legal initiatives related to limiting the use of AI technologies. It considers the ideas about the scope of the right to refuse digital technologies.Courier of the Kutafin Moscow State Law University00The article develops the idea of a new universal right — the right to refuse digital technologies, using the example of a separate area of AI use, and considers the ideas about the scope of the right to refuse digital technologies.Courier of Kutafin Moscow State Law University (MSAL))["V. B. Naumov"]2024-12-05T00:00:00 a8ac1343549caf236fc87fcc4339e23d8d9bc60eEn “Computing Machinery and Intelligence”, de 1950, Turing acaba afirmando la posibilidad de procedimientos automáticos computacionales que sustituyan al pensamiento humano. Son reconocidas las críticas de Searle a este artículo, que se han retomado y ampliado a propósito de la actual discusión sobre el alcance y los límites de la inteligencia artificial. Especialmente relevantes al respecto resultan los últimos trabajos de Žižek y Larson, así como la defensa de las propuestas de Turing que ha realizado Daniel Dennett, especialmente en su obra final. De toda esta discusión quiero valerme para esclarecer cuál es exactamente la posición de Turing a partir de 1950. La conclusión a la que llego es que Turing no defiende que los procedimientos automáticos computacionales, tal y como él mismo los define, sustituyen al pensamiento humano, sino que es posible que lo puedan sustituir, y que se trata de una cuestión que solo se puede resolver empíricamente.Revista de Filosofia50Revista de Filosofía (Madrid)["Juan Antonio Valor Y\u00e9benes"]2024-12-05T00:00:00 51c3d5fdad3ee07b96aebf9780316b40e0990ea0With all of the talk about advancements in artificial intelligence, it's likely that your board has pondered how AI might be leveraged to improve board activities and operations—and with good reason.Board &amp; Administrator for Administrators Only00Board &amp; Administrator for Administrators Only[]2024-12-05T00:00:00 bbc40049bf149328e5c26fa3fe992936e5a08f7dThe integration of artificial intelligence (AI) in L2 teaching and learning is poised to revolutionise educational practices by enhancing both instructional methods and language development for L2 learners. This study employed a mixed‐methods design to comprehensively examine the impact of AI tools, machine translation systems, and traditional approaches on students' translation accuracy, emotions, and motivation. A total of forty‐nine undergraduate English majors were divided into three groups: the AI Group (AIG; N = 16) using AI tools, the machine translation group (MTG; N = 20) using machine translation tools, and the traditional group (TG; N = 13) using manual methods. Participants completed four translation tasks with varying levels of linguistic complexity, and their performance was evaluated using quantitative metrics such as meaning retention, grammatical correctness, fluency, and naturalness. Additionally, semi‐structured interviews were conducted to gather qualitative insights into participants' emotional and motivational experiences. Quantitative data analysis included the Kruskal‐Wallis test to assess differences amongst the groups, revealing that AIG students achieved the highest translation accuracy. Qualitative thematic analysis of the interview data indicated that emotions such as curiosity, anxiety, and excitement were prevalent across all groups. While AI tools fostered motivation in the AIG and MTG, some participants expressed concerns about over‐reliance on technology leading to reduced engagement. These findings highlight AI's dual role in enhancing translation accuracy and shaping the emotional and motivational dynamics of L2 learners, suggesting that its integration should be balanced with traditional methods to optimise learning outcomes.European Journal of Education471While AI tools fostered motivation in the AIG and MTG, some participants expressed concerns about over‐reliance on technology leading to reduced engagement, suggesting that its integration should be balanced with traditional methods to optimise learning outcomes.European Journal of Education["Mariusz Kruk", "Agnieszka Ka\u0142u\u017cna"]2024-12-05T00:00:00 6d6e6581ba3ae43799f04b52fd60884ccd2c660cIn this cohort study involving 9399 current and former smokers from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease study, we assessed the relationship between artificial intelligence-quantified mucus plugs on chest CTs and all-cause mortality. Our results revealed a significant positive association, particularly for those with COPD GOLD stages 1-4, with HRs of 1.18 for 1-2 mucus-obstructed bronchial segments and 1.27 for ≥3 obstructed segments. This corroborates previous visual mucus plug counting research and demonstrates the relevance of mucus plugs in COPD pathology and as a marker for risk assessment. Automated mucus plug quantification methods may provide an efficient tool for both clinical evaluations and research.Thorax61The results revealed a significant positive association between artificial intelligence-quantified mucus plugs on chest CTs and all-cause mortality, particularly for those with COPD GOLD stages 1-4, and corroborates previous visual mucus plug counting research.Thorax["T. van der Veer", "E. Andrinopoulou", "Gert-Jan Braunstahl", "J.-P. Charbonnier", "Victor Kim", "R. Latisenko", "David A. Lynch", "H. Tiddens"]2024-12-05T00:00:00 3faed1a171460e932ec2d6fd1f4308335eb4d743Background Artificial Intelligence (AI) is significantly transforming dermatology, particularly in early skin cancer detection and diagnosis. This technological advancement addresses a crucial public health issue by enhancing diagnostic accuracy, efficiency, and accessibility. AI integration in medical imaging and diagnostic procedures offers promising solutions to the limitations of traditional methods, which often rely on subjective clinical evaluations and histopathological analyses. This study systematically reviews current AI applications in skin cancer classification, providing a comprehensive overview of their advantages, challenges, methodologies, and functionalities. Methodology In this study, we conducted a comprehensive analysis of artificial intelligence (AI) applications in the classification of skin cancer. We evaluated publications from three prominent journal databases: Scopus, IEEE, and MDPI. We conducted a thorough selection process using the PRISMA guidelines, collecting 1,156 scientific articles. Our methodology included evaluating the titles and abstracts and thoroughly examining the full text to determine their relevance and quality. Consequently, we included a total of 95 publications in the final study. We analyzed and categorized the articles based on four key dimensions: advantages, difficulties, methodologies, and functionalities. Results AI-based models exhibit remarkable performance in skin cancer detection by leveraging advanced deep learning algorithms, image processing techniques, and feature extraction methods. The advantages of AI integration include significantly improved diagnostic accuracy, faster turnaround times, and increased accessibility to dermatological expertise, particularly benefiting underserved areas. However, several challenges remain, such as concerns over data privacy, complexities in integrating AI systems into existing workflows, and the need for large, high-quality datasets. AI-based methods for skin cancer detection, including CNNs, SVMs, and ensemble learning techniques, aim to improve lesion classification accuracy and increase early detection. AI systems enhance healthcare by enabling remote consultations, continuous patient monitoring, and supporting clinical decision-making, leading to more efficient care and better patient outcomes. Conclusions This comprehensive review highlights the transformative potential of AI in dermatology, particularly in skin cancer detection and diagnosis. While AI technologies have significantly improved diagnostic accuracy, efficiency, and accessibility, several challenges remain. Future research should focus on ensuring data privacy, developing robust AI systems that can generalize across diverse populations, and creating large, high-quality datasets. Integrating AI tools into clinical workflows is critical to maximizing their utility and effectiveness. Continuous innovation and interdisciplinary collaboration will be essential for fully realizing the benefits of AI in skin cancer detection and diagnosis.PeerJ Computer Science1511A comprehensive analysis of artificial intelligence applications in the classification of skin cancer highlights the transformative potential of AI in dermatology, particularly in skin cancer detection and diagnosis.PeerJ Computer Science["Kavita Behara", "Ernest Bhero", "J. Agee"]2024-12-05T00:00:00 517017cb153da267d316c3a8783069f13816f82dThe rapid advancement of deep learning has resulted in substantial advancements in AI-driven applications; however, the"black box"characteristic of these models frequently constrains their interpretability, transparency, and reliability. Explainable artificial intelligence (XAI) seeks to elucidate AI decision-making processes, guaranteeing that explanations faithfully represent the model's rationale and correspond with human comprehension. Despite comprehensive research in XAI, a significant gap persists in standardized procedures for assessing the efficacy and transparency of XAI techniques across many real-world applications. This study presents a unified XAI evaluation framework incorporating extensive quantitative and qualitative criteria to systematically evaluate the correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models. The framework prioritizes user-centric and domain-specific adaptations, hence improving the usability and reliability of AI models in essential domains. To address deficiencies in existing evaluation processes, we suggest defined benchmarks and a systematic evaluation pipeline that includes data loading, explanation development, and thorough method assessment. The suggested framework's relevance and variety are evidenced by case studies in healthcare, finance, agriculture, and autonomous systems. These provide a solid basis for the equitable and dependable assessment of XAI methodologies. This paradigm enhances XAI research by offering a systematic, flexible, and pragmatic method to guarantee transparency and accountability in AI systems across many real-world contexts.arXiv.org180This study presents a unified XAI evaluation framework incorporating extensive quantitative and qualitative criteria to systematically evaluate the correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models.ArXiv["Md. Ariful Islam", "M. M. Mridha", "Md. Abrar Jahin", "Nilanjan Dey"]2024-12-05T00:00:00 cd682efee23b89dec6d8565c8dba2714b5887f8dAdvances in Artificial Intelligence (AI) have made significant strides in recent years, often supplementing rather than replacing human performance. The extent of their assistance at the highest levels of human performance remains unclear. We analyse over 11.6 million decisions of elite chess players, a domain commonly used as a testbed for AI and psychology due to its complexity and objective assessment. We investigated the impact of two AI chess revolutions: the first in the late 1990s with the rise of powerful PCs and internet access and the second in the late 2010s with deep learning-powered chess engines. The rate of human improvement mirrored AI advancements, but contrary to expectations, the quality of decisions mostly improved steadily over four decades, irrespective of age, with no distinct periods of rapid improvement. Only the youngest top players saw marked gains in the late 1990s, likely due to better access to knowledge and computers. Surprisingly, the recent wave of neural network-powered engines has not significantly impacted the best players - at least, not yet. Our research highlights AI's potential to enhance human capability in complex tasks, given the right conditions, even among the most elite performers.British Journal of Psychology530This research highlights AI's potential to enhance human capability in complex tasks, given the right conditions, even among the most elite performers, even among the most elite performers.British journal of psychology["M. Bilali\u0107", "Mario Graf", "N. Vaci"]2024-12-05T00:00:00 a4cde47e9be5d6ad9375ea84560d29af990593e0The rapid development of Artificial Intelligence (AI) has led to the creation of powerful text generation models, such as large language models (LLMs), which are widely used for diverse applications. However, concerns surrounding AI-generated content, including issues of originality, bias, misinformation, and accountability, have become increasingly prominent. This paper offers a comprehensive overview of AI text generators (AITGs), focusing on their evolution, capabilities, and ethical implications. This paper also introduces Retrieval-Augmented Generation (RAG), a recent approach that improves the contextual relevance and accuracy of text generation by integrating dynamic information retrieval. RAG addresses key limitations of traditional models, including their reliance on static knowledge and potential inaccuracies in handling real-world data. Additionally, the paper reviews detection tools that help differentiate AI-generated text from human-written content and discusses the ethical challenges these technologies pose. The paper explores future directions for improving detection accuracy, supporting ethical AI development, and increasing accessibility. The paper contributes to a more responsible and reliable use of AI in content creation through these discussions.arXiv.org360Retrieval-Augmented Generation (RAG) is introduced, a recent approach that improves the contextual relevance and accuracy of text generation by integrating dynamic information retrieval and discusses the ethical challenges these technologies pose.ArXiv["Fnu Neha", "Deepshikha Bhati", "Deepak Kumar Shukla", "Angela Guercio", "Ben Ward"]2024-12-05T00:00:00 ddf7adbaa978409272bd9166097c83a0c69d8233The optimization of last-mile delivery represents a critical challenge in modern e-commerce logistics, consuming a substantial portion of total shipping costs. This comprehensive technical article examines how artificial intelligence and machine learning technologies are revolutionizing last-mile delivery operations through advanced route optimization, demand forecasting, and resource allocation. This article synthesizes findings from recent implementations across major logistics providers, demonstrating that AI-driven route optimization systems significantly reduce delivery times and decrease fuel consumption across diverse operational environments. Analysis of machine learning models deployed by leading e-commerce platforms shows marked improvements in delivery time prediction accuracy, achieving unprecedented precision in estimated delivery windows. This article examines key technical components, including dynamic routing algorithms, predictive demand modeling, and real-time fleet management systems, while also addressing implementation challenges such as data quality and system integration. Case studies from major logistics providers demonstrate substantial ROI improvements following AI implementation, with particular emphasis on neural network architectures optimized for urban delivery scenarios. This article provides a technical framework for understanding how AI technologies transform last-mile logistics while offering insights into future developments, including autonomous delivery systems and smart city integration.International Journal For Multidisciplinary Research130Key technical components, including dynamic routing algorithms, predictive demand modeling, and real-time fleet management systems are examined, while also addressing implementation challenges such as data quality and system integration.International Journal For Multidisciplinary Research["Arvindan Badrinarayanan"]2024-12-05T00:00:00 e9370cf1197c9a2005748be8e22308e98b6dd811Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for"frontier data governance"opens up new avenues for monitoring and mitigating risks from advanced AI models, particularly as they scale and acquire specific dangerous capabilities. Still, frontier data governance faces challenges that stem from the fundamental properties of data itself: data is non-rival, often non-excludable, easily replicable, and increasingly synthesizable. Despite these inherent difficulties, we propose a set of policy mechanisms targeting key actors along the data supply chain, including data producers, aggregators, model developers, and data vendors. We provide a brief overview of 15 governance mechanisms, of which we centrally introduce five, underexplored policy recommendations. These include developing canary tokens to detect unauthorized use for producers; (automated) data filtering to remove malicious content for pre-training and post-training datasets; mandatory dataset reporting requirements for developers and vendors; improved security for datasets and data generation algorithms; and know-your-customer requirements for vendors. By considering data not just as a source of potential harm, but as a critical governance lever, this work aims to equip policymakers with a new tool for the governance and regulation of frontier AI models.arXiv.org1120This work proposes a set of policy mechanisms targeting key actors along the data supply chain, including data producers, aggregators, model developers, and data vendors, and aims to equip policymakers with a new tool for the governance and regulation of frontier AI models.ArXiv["Jason Hausenloy", "Duncan McClements", "Madhavendra Thakur"]2024-12-05T00:00:00 c138433dade4e900efaa7c0832f04fed3b07f8a8In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed for the smart monitoring and assessment of water quality. Leveraging the strengths of Ensemble Learning models and Explainable Artificial Intelligence (XAI), EWAIS not only enhances the prediction accuracy of water quality but also provides transparent insights into the factors influencing these predictions. EWAIS integrates multiple Ensemble Learning models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, and Voting Ensemble Learning (VEL)—to classify water as drinkable or non-drinkable. The system incorporates advanced techniques for handling missing data and statistical analysis, ensuring robust performance even in complex urban datasets. To address the opacity of traditional Machine Learning models, EWAIS employs XAI methods such as SHAP and LIME, generating intuitive visual explanations like force plots, summary plots, dependency plots, and decision plots. The system achieves high predictive performance, with the VEL model reaching an accuracy of 0.89 and an F1-Score of 0.85, alongside precision and recall scores of 0.85 and 0.86, respectively. These results demonstrate the proposed framework’s capability to deliver both accurate water quality predictions and actionable insights for decision-makers. By providing a transparent and interpretable monitoring system, EWAIS supports informed water management strategies, contributing to the sustainability and well-being of urban populations. This framework has been validated using controlled datasets, with IoT implementation suggested to enhance water quality monitoring in smart city environments.Processes170EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed for the smart monitoring and assessment of water quality, has been validated using controlled datasets, with IoT implementation suggested to enhance water quality monitoring in smart city environments.Processes["Nermeen Gamal Rezk", "S. Alshathri", "A. Sayed", "Ezz El-Din Hemdan"]2024-12-05T00:00:00 bec7b61c8ecdedcfe7b899e9e89e720c66abb0fdArtificial intelligence is changing the way hospitals work by being more efficient and patient-centric. This paper explores how AI technologies, such as machine learning and natural language processing, are integrated into healthcare systems to optimize data management, diagnostics, and personalized medicine. Applications driven by AI streamline workflows, facilitate clinical decision-making, and mitigate human error, thereby improving patient outcomes. Some of the key operational efficiencies that can be facilitated by AI include predictive analytics for better resource allocation, automated scheduling to minimize wait times, and supply chain optimization for effective delivery of medical services.The paper describes how the use of AI is instrumental in transforming patient care through a diagnostic precision, personalized medicine and remote monitoring. The use of ML-algorithm powered diagnostic tools increases image and pattern recognition accuracy through early and accurate detection. Personalized medicine is delivered through AI's capability to personalise treatment plans for individualised patient profiles to improve care outcomes. Remote monitoring of the patient can be done at real-time by using complex sensors and telecommunication tools regarding chronic and critical conditions.Despite these gains, integration barriers, concerns about bias and privacy, and the need for extensive education of healthcare professionals remain. In conclusion, the study emphasizes further innovation and interdisciplinary collaboration toward harnessing the full power of AI in healthcare.This paper also outlines future directions, emphasizing AI's capacity to revolutionize healthcare with advancements in analytics, intelligent hospital frameworks, and clinical research. By aligning technical innovation with ethical considerations, AI promises to create a smarter, patient-centric healthcare ecosystem. The findings advocate for strategic adoption of AI technologies to achieve enhanced operational efficiency, cost reduction, and superior patient care.Multidisciplinary Reviews180This paper describes how the use of AI is instrumental in transforming patient care through a diagnostic precision, personalized medicine and remote monitoring, and outlines future directions, emphasizing AI's capacity to revolutionize healthcare with advancements in analytics, intelligent hospital frameworks, and clinical research.Multidisciplinary Reviews["Vishwajit Suryawanshi", "Deepika Kanyal", "Shantanu R Sabale", "Vikas Bhoyar"]2024-12-05T00:00:00 148138cc5b897876a3e9180d1c294d0e253999faOBJECTIVES To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI). MATERIALS AND METHODS We identify overlap in the quality management requirements for laboratory testing and clinical AI. RESULTS We propose modifications to the CLIA model that could make it suitable for oversight of clinical AI. DISCUSSION In national discussions of clinical AI, there has been surprisingly little consideration of this longstanding model for local technology oversight. While CLIA was specifically designed for laboratory testing, most of its principles are applicable to other technologies in patient care. CONCLUSION A CLIA-like approach to regulating clinical AI would be complementary to the more centralized schemes currently under consideration, and it would ensure institutional and professional accountability for the longitudinal quality management of clinical AI.J. Am. Medical Informatics Assoc.132A CLIA-like approach to regulating clinical AI would be complementary to the more centralized schemes currently under consideration, and it would ensure institutional and professional accountability for the longitudinal quality management of clinical AI.Journal of the American Medical Informatics Association : JAMIA["Brian R Jackson", "M. Sendak", "Anthony Solomonides", "Suresh Balu", "Dean F. Sittig"]2024-12-06T00:00:00 7035b074764fbfefd17a682d77f444144687be26The rise of artificial intelligence (AI) in internet applications has garnered widespread attention, not only due to the rapid development of AI technology and its extensive applications online but also because of the growing demand from users for more efficient, intelligent, and personalized services. With the continuous maturation of big data, deep learning, and natural language processing technologies, AI has gradually found widespread use in areas such as search engines, recommendation systems, automated customer service, and content generation. The application of AI technology has significantly enhanced the efficiency of internet services and improved user experience, driving the internet toward a more intelligent and personalized future. This study reviews the current state and trends of AI applications in search engines, recommendation systems, automated customer service, and content generation through a literature review. The findings indicate that AI technology has markedly improved the efficiency of internet services and user experience. The significance of this study lies in summarizing the achievements of current AI applications, providing references for future research and practice, and promoting the development of a more intelligent and personalized internet.Applied and Computational Engineering00The findings indicate that AI technology has markedly improved the efficiency of internet services and user experience, driving the internet toward a more intelligent and personalized future.Applied and Computational Engineering["Muhan Li"]2024-12-06T00:00:00 deb7318ca86fb930e97bcaf01ffd9e0286324096On the one hand, one of the priority areas of state policy in accordance with the National Development Strategy is the accelerated development of artificial intelligence (AI), on the other hand, various problems arise, including legal regulation of the field of AI in order to ensure the security of the information space of the Russian Federation.Methodology: Using a set of dialectical, formal and systemic methods to study the development and implementation of AI technologies to determine the main directions of legal regulation in the field of AI and identify problems associated with legal support for the security of the information space of the Russian Federation.Results. Approaches to regulation of AI in the Russian Federation and in world practice are analyzed; The existing legal problems in the field of development and use of AI are considered. It has been revealed that the introduction of AI technologies leads to violations of the security of the information space of the Russian Federation. The result of the study was the substantiation of the need to create a unified regulatory framework in the field of AI to ensure the security of the information space of the Russian Federation, which can be achieved by improving the legislative framework in the field of AI, as well as minimizing the identified legal risks associated with the practice of developing and implementing AI technologies.Courier of the Kutafin Moscow State Law University20The substantiation of the need to create a unified regulatory framework in the field of AI to ensure the security of the information space of the Russian Federation can be achieved by improving the legislative framework in the field of AI, as well as minimizing the identified legal risks associated with the practice of developing and implementing AI technologies.Courier of Kutafin Moscow State Law University (MSAL))["P. V. Eresko"]2024-12-06T00:00:00 82e9bf3da073d00c04c253f9d00982e2b764731aThe Artificial Intelligence Pedagogical Content Knowledge (AIPACK) framework provides educators with strategies to apply AI tools effectively in education. It emphasizes flexibility in adapting to advancements in AI technologies, ensuring context-sensitive applications that align with specific pedagogical goals. By integrating AI capabilities with pedagogical and content expertise, AIPACK aims to improve teaching practices and prepare students for an AI-driven world.The European Educational Researcher40The Artificial Intelligence Pedagogical Content Knowledge (AIPACK) framework provides educators with strategies to apply AI tools effectively in education and aims to improve teaching practices and prepare students for an AI-driven world.The European Educational Researcher["Nuri Balta"]2024-12-06T00:00:00 08de45dd8429d6c299970d14f93cca42d80dd6fdThe integration of artificial intelligence (AI) into medical practice creates new opportunities to enhance the quality of healthcare while simultaneously posing significant ethical challenges. This article explores key ethical aspects of AI use in various fields of medicine, including genetic editing, molecular diagnostics, telemedicine, and clinical decision support systems. Special attention is given to the role of nurses in ensuring the ethical application of AI technologies and protecting patient rights. Current issues related to data confidentiality, informed consent, and potential risks of algorithmic bias are analyzed. The article emphasizes the need for a multidisciplinary approach to AI implementation and outlines the crucial role of nurses in this process.Meditsinskaya sestra160The article emphasizes the need for a multidisciplinary approach to AI implementation and outlines the crucial role of nurses in this process, and special attention is given to the role of nurses in ensuring the ethical application of AI technologies and protecting patient rights.Meditsinskaya sestra["V.P. Kutsenko", "Y. Nurmyradov", "S.R. Akhmedov"]2024-12-06T00:00:00 f884f1f5aa2b2e860cf8cd0f6992af84c0c75201Researchers have explored artificial intelligence (AI) applications across educational contexts; however, there is a lack of meta-analysis focused on students with disabilities (SWDs). This study examined the overall effect of AI-based interventions on SWDs’ learning outcomes in 29 (quasi-)experimental studies conducted globally. We used cultural historical activity theory (CHAT) to explore how the effect was moderated by factors, including participant-, AI-, AI-SWD interaction-, intervention-, and methodology-related characteristics. Results indicated a medium effect (Hedge’s g = 0.588) of interventions operating through robots, computer software, and intelligent VR systems. There were no statistically significant moderators. Regardless, this study contributes to a holistic understanding of historical dimensions of AI applications for SWDs and offers critical theoretical implications for future investigations. We call for more rigorous research to explore AI that not only ensures accessibility but also promotes opportunities for SWDs to take an agentic role in participating in and contributing to AI-mediated learning activities.Review of Educational Research690This study examined the overall effect of AI-based interventions on SWDs’ learning outcomes in 29 (quasi-)experimental studies conducted globally to contribute to a holistic understanding of historical dimensions of AI applications for SWDs and offers critical theoretical implications for future investigations.Review of Educational Research["Ling Zhang", "R. Carter", "Yuting Liu", "Peng Peng"]2024-12-06T00:00:00 6496e5803773e8d054769be4b611bf87daddcf58International Breastfeeding Journal580It is suggested that AI can promote breastfeeding, improve milk safety, and enhance infant nutrition, and has enabled a more precise analysis of human milk composition, drug transfer, and contaminant detection.International Breastfeeding Journal["Sergio Agudelo-P\u00e9rez", "Daniel Botero-Rosas", "Laura Rodr\u00edguez-Alvarado", "Juli\u00e1n Espitia-Angel", "Lina Raigoso-D\u00edaz"]2024-12-06T00:00:00 83ecfab300116650eb0ce882dd35c0184c6dbe11Objective: The research aims to explore the integration of Artificial Intelligence (AI) within educational systems and analyze its impact on the governance of higher education institutions (HEIs), particularly focusing on decision-making, data protection, and administrative efficiency.   Theoretical Framework: The article presents key theories on the transformative role of AI in educational governance, particularly focusing on how AI-driven data analysis and automation enhance decision-making and administrative efficiency. It also addresses theories related to ethical governance, emphasizing data protection and equitable access within higher education institutions.   Method: The research methodology in this article is based on a qualitative approach, combining a review of existing literature with case studies of AI implementation in educational contexts. This approach provides in-depth insights into the effects of AI on governance within higher education institutions.   Results and Discussion: The research findings highlight that AI integration in higher education governance improves decision-making and operational efficiency through data-driven insights and automation. However, it also reveals challenges, particularly in data protection, ethical concerns, and shifting power dynamics within institutions. The study emphasizes the need for responsible and transparent AI governance to ensure balanced benefits across stakeholders.   Research Implications: This research underscores the need for higher education institutions to adopt AI responsibly, balancing its potential to enhance governance and decision-making with rigorous ethical standards, especially in data privacy and equity. It calls on policymakers and administrators to develop frameworks that ensure AI-driven processes remain transparent, inclusive, and aligned with educational values.   Originality/Value: The originality of this research lies in its focus on how AI specifically transforms governance in higher education institutions, going beyond general applications of AI in education to address ethical, operational, and decision-making challenges unique to institutional governance. It provides a nuanced perspective on balancing innovation with responsibility in an academic setting.International Journal of Professional Business Review110The research findings highlight that AI integration in higher education governance improves decision-making and operational efficiency through data-driven insights and automation, but also reveals challenges, particularly in data protection, ethical concerns, and shifting power dynamics within institutions.International Journal of Professional Business Review["Gadmi Mariam", "Loulid Adil", "Bendarkawi Zakaria"]2024-12-06T00:00:00 837537d331e7cd1d7f8ae5b28755d99b5a1a4997Human factors science, which incorporates psychology, engineering, design, and statistics, aims to improve safety and efficiency in healthcare by optimizing the interaction between providers and their systems. This approach is critical in complex medical environments where errors often result from systemic issues rather than individual negligence. Historically rooted in aviation, human factors principles have significantly enhanced safety and performance, offering valuable insights for healthcare. Human factors specialists aim to reduce errors and improve patient outcomes by addressing poorly designed interfaces, inefficient workflows, and inadequate communication systems. The integration of artificial intelligence (AI) into healthcare can further enhance these efforts by predicting patient risks, optimizing treatment plans, and automating routine tasks. AI, designed with human factors in mind, can augment healthcare providers’ capabilities, ensuring safety and efficiency. The future of healthcare lies in the seamless integration of human factors and AI, fostering a system that enhances provider performance and patient safety. Learning from aviation’s success in minimizing human error through human factors engineering, healthcare can achieve similar safety standards, ensuring a technologically advanced, safe, and human-centered healthcare system.International Journal of Translational Medical Research and Public Health130Learning from aviation’s success in minimizing human error through human factors engineering, healthcare can achieve similar safety standards, ensuring a technologically advanced, safe, and human-centered healthcare system.International Journal of Translational Medical Research and Public Health["Ariel Braverman"]2024-12-06T00:00:00 47df2b0cf8b5c699afef7be58eb95307f7ad4036The field of autonomous vehicles is undergoing rapid development, with research focused on enhancing safety, efficiency, and intelligence to drive the full realization and transformation of intelligent transportation systems. This paper explores the potential applications and challenges of blockchain technology, quantum computing, and artificial intelligence in the realm of autonomous vehicles. The study finds that integrating blockchain with autonomous vehicles significantly improves data encryption security through quantum hash algorithms. However, the advancement of quantum computers still faces challenges in technological maturity, stability, and cost. In the operating mechanisms of autonomous vehicles, quantum reinforcement learning demonstrates unique advantages in path optimization, while quantum annealing and quantum optimization algorithms improve decision-making efficiency and precision. This research highlights the innovative opportunities brought by blockchain, quantum computing, and artificial intelligence to the field of autonomous vehicles, while also addressing the challenges of technology integration, providing valuable insights for the development of intelligent transportation systems.Applied and Computational Engineering00The study finds that integrating blockchain with autonomous vehicles significantly improves data encryption security through quantum hash algorithms, however, the advancement of quantum computers still faces challenges in technological maturity, stability, and cost.Applied and Computational Engineering["Siying Li"]2024-12-06T00:00:00 1083d862e40fa0aa284db81bc87cab264d3abfdfRecently, there has been a significant increase in the use of artificial intelligence in healthcare, an increased trust of healthcare providers in artificial intelligence, and the interest of investors in the development of healthcare solutions based on artificial intelligence. The vast majority of providers of medical services and technologies, as well as of biomedical companies, are using artificial intelligence which confirms the great demand in the field of health care. The increased adoption of artificial intelligence techniques in medical applications has led to the focus of key market participants on new products and technical connections to expand commercial production. The object of research is the world market of artificial intelligence in healthcare. Factors influencing the market positively and negatively have been identified. The general characteristics are given, as well as key points of the state and development of the market. The market is segmented by geographic regions, applications, therapeutic area support, market components, technologies, and usage. According to the segmentation of the world artificial intelligence market in health care by geographical regions, the largest market share belonged to the segment of the North American region (45 %); by application – to clinical trials segment (22.7 %); by the support of therapeutic areas – to radiology segment (75 %); by artificial intelligence components – to software segment (41 %); by technologies – to machine learning segment (33.1 %); by use – to medical imaging and diagnostics segment (27.1 %). The main strategic trends and directions of further development of the market of artificial intelligence in health care are provided. The dynamics of the market in terms of growth factors, market opportunities, limitations, and challenges are considered. Important factors inhibiting the development of the artificial intelligence market in the field of health care are the lack of qualified specialists and ineffective cooperation between the public and private sectors. Data on competitive tech giants and artificial intelligence healthcare powerhouses are provided.Technology audit and production reserves150Technology audit and production reserves["Viktor Malyshev", "Yurii Lipskyi", "Viktoriia Kovalenko", "A. Gab", "D. Shakhnin", "Olha Orel"]2024-12-06T00:00:00 6a23a9cd607b78e699f18d1af7a5255c0f406ac0The application of artificial intelligence in the field of criminal evidence review and judgment has significant advantages, which are conducive to improving the efficiency of case processing, enhancing the reasoning ability of judicial personnel, and preventing judicial corruption. However, artificial intelligence is still facing a series of problems in its practical application in this field, including the inability of artificial intelligence to simulate the assessing process of probative value, the imperfect construction of the criminal evidence database of artificial intelligence, and the lack of clarity of the subject of responsibility for the misjudgment of criminal evidence by artificial intelligence. To this end, a series of measures should be taken to address these challenges, including clarifying the use of artificial intelligence for auxiliary examination of probative value, optimizing the criminal evidence database for artificial intelligence, and insisting on the status of judicial personnel as the main body of responsibility.Scientific Journal of Intelligent Systems Research60A series of measures should be taken to address challenges of artificial intelligence in this field, including clarifying the use of artificial intelligence for auxiliary examination of probative value, optimizing the criminal evidence database for artificial intelligence, and insisting on the status of judicial personnel as the main body of responsibility.Scientific Journal of Intelligent Systems Research["Wuting Zhu"]2024-12-06T00:00:00 011e42fd4c838e1aed63c294562f54669342ff75The advantages and disadvantages of the use of generative artificial intelligence, such as ChatGPT, in medical writing have been widely discussed; however, two concerns remain largely unexplored. The first involves “human touch,” such as personal anecdotes and experiences. This touch often distinguishes human-written papers from those generated by ChatGPT as ChatGPT cannot independently access personal experiences. Although ChatGPT may mimic humanlike behavior, including the incorporation of a human touch, it lacks genuine emotions. With the lack of established guidelines on the acceptable levels of ChatGPT use and imperfect detection tools, many authors fear that their work could be perceived as overly reliant on ChatGPT. I worry that writers may artificially insert forced personal touches simply to assert their own writing. The second concern is the authors’ worry and doubt about whether to use ChatGPT and, if so, to what extent, which may disrupt their reflective and quiet writing process. While I acknowledge the lack of empirical data, I offer practical suggestions to balance the benefits of ChatGPT assistance and the preservation of the integrity of human writing in medical publications.JMA Journal40Practical suggestions are offered to balance the benefits of ChatGPT assistance and the preservation of the integrity of human writing in medical publications to balance the advantages and disadvantages of the use of generative artificial intelligence in medical writing.JMA Journal["Shigeki Matsubara"]2024-12-06T00:00:00 a06cbc69775cd58d2f966c7695075e39554c9c93As an important branch of the field of artificial intelligence, the technology of Artificial Intelligence Generated Content (AIGC) has developed rapidly in recent years. With the popularity of the Internet and the development of social media, virtual community marketing has become one of the important means of enterprise marketing. More and more enterprises begin to try to apply AIGC technology to virtual community marketing, which has brought new opportunities and challenges for virtual community marketing. This study builds a conceptual model to explain the influence of AIGC on virtual community marketing. This model is tested on the survey data from some virtual communities in China. The empirical results show that the influence of AIGC (content multimodality and content interactivity) on virtual community marketing (forwarding intention and brand attitudes) are mediated by users’ perceived interestingness and perceived usefulness.2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)80A conceptual model is built to explain the influence of AIGC on virtual community marketing and the empirical results show that the influence of AIGC (content multimodality and content interactivity) on virtual community marketing are mediated by users’ perceived interestingness and perceived usefulness.2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)["Ziyi Tian", "Xinwei Yuan"]2024-12-06T00:00:00 0994e3318c8f94464cb6d977d9392ad8547fddbaArtificial intelligence (AI) research and applications have sparked a broad scientific, economic, social, and political debate. Statistical approaches and techniques are fundamental to AI because they allow robots to learn from data and make intelligent decisions. It's possible even to view statistics as a fundamental component of AI. Statistics is an ideal partner for other disciplines in teaching, research, and practice because of its specialized knowledge of data evaluation, which begins with the correct phrasing of the research question and continues through a study design stage to analysis and interpretation of the results. This work aims to demonstrate how statistical methodology is relevant to the development of AI. In terms of methodological development, planning, research design, evaluation of data quality and collection, distinction of causation and relationships, and evaluation of result uncertainty, we talk about the contributions of statistics to the field of artificial intelligence. This study thoroughly analyzes the crucial role statistics play in AI, demonstrating the numerous applications of statistical ideas like probability, regression, classification, and clustering. The essay highlights the value of statistical analysis in handling uncertainty, making predictions, and training AI models all of which improve the efficiency and precision of AI systems.NCC Journal190This study thoroughly analyzes the crucial role statistics play in AI, demonstrating the numerous applications of statistical ideas like probability, regression, classification, and clustering and the value of statistical analysis in handling uncertainty, making predictions, and training AI models all of which improve the efficiency and precision of AI systems.NCC Journal["Dila Ram Bhandari", "Michael Baron", "Kapil Shah", "Sharda Kandel"]2024-12-06T00:00:00 1ec798910cf1bccccc394f44247e20f4ebdc19e9The use of artificial intelligence in higher education has inspired this research, which aims to develop a theoretical ap-proach to emotional interaction within the educational sphere. This study was conducted using a qualitative approach, rooted in the interpretative-humanistic paradigm. At the National University of Chimborazo in Ecuador, the research was carried out with a population of 310 students from the psychopedagogy program, selecting a homogeneous sample of twenty-eight ninth-semester students. Data collection tools included participatory observation guides and semi-structured interviews. The data obtained were analyzed using Atlas.Ti software, which enabled the triangulation of emotional inter-actions during the educational process and an exploration of how artificial intelligence can facilitate the understanding of these dynamics. Through this analysis, four main constructs were identified: cognitive influences of AI, emotional inter-action and artificial intelligence in educational research, social-affective bonds in educational research and AI, and AI as a facilitator of relationships. This approach underscores the importance of integrating the emotional dimension in education, suggesting that artificial intelligence can enhance not only academic learning but also enrich interpersonal relationships and emotional management within the educational environment. Thus, it aims to contribute to more meaningful learning that meets the emotional needs of students.Journal of Ecohumanism00This study concludes that artificial intelligence can enhance not only academic learning but also enrich interpersonal relationships and emotional management within the educational environment, suggesting that artificial intelligence can enhance not only academic learning but also enrich interpersonal relationships and emotional management within the educational environment.Journal of Ecohumanism["Flores Hinostroza Elizeth Mayrene", "Amparo Cazorla Basantes", "Magda Francisca Cejas Mart\u00ednez", "Francisco Paul P\u00e9rez Salas", "Mendoza Velazco Derling Jos\u00e9"]2024-12-06T00:00:00 7d3366a1bbd56a0d1e02480de3c12f7dcb2c1f2fTobacco Control361Tobacco control["Grace Kong", "R. R. Ouellette", "Dhiraj Murthy"]2024-12-06T00:00:00 7f2cdc54ffbf02bfa3e7974b1f5f1c96b1620896Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence330Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence["Yingnan Shi"]2024-12-06T00:00:00 d9a49519ff8cd84e87b752519d83efa4d2200d8cThe paper presents the structure and content of intelligent systems and technologies as a new independent course within the professional cycle of the educational programme.AI is disclosed in various meanings, including a detailed description of AI as a subject, as a learning tool and as an object of legal regulationThe proposed methodology and practice-oriented thematic content of the course forms professional digital knowledge, skills and abilities, creates and enhances universal and professional competences.The used methods of comparison, analogy, modelling, as well as the method of experiment, allowed to form not only a representation of the basic part of the study, but also suggestions to improve the existing state of affairs. As conclusions, it is proposed to refine the educational standards to form a practiceoriented competence-based approach to the training of modern technologies in the activities of lawyers, which will not only allow to be more competitive, but also significantly intensify and improve the quality of work performed.Courier of the Kutafin Moscow State Law University00It is proposed to refine the educational standards to form a practiceoriented competence-based approach to the training of modern technologies in the activities of lawyers, which will not only allow to be more competitive, but also significantly intensify and improve the quality of work performed.Courier of Kutafin Moscow State Law University (MSAL))["D. V. Shibaev"]2024-12-06T00:00:00 71087a3345db7ba421f4580a3429576856fd6b3fThis research uses the Socio-Technical Systems (STS) theory postulating that organizational success is a product of social and technical sub-systems, and examine the effects of generative AI on the HR practices. Furthermore, the study focuses on the mediating role of innovation climate on the relationship between generative AI and HR practices in the hospitality sector. A sample of 327 participants is selected by a convenient sampling technique from hotel employees. The data is collected through structured questionnaire and analyzed using PLS-SEM. The results of the study bring essential insights into the interactions between technological dynamics and organizational environments in the case of HR functions in the technology oriented world. Findings shows that generative AI has a massive positive impact on the HR practices and innovation climate positively influence HR Practices. This finding supports the notion that Innovation Climate mediate the relation between generative AI and HR practices. The findings imply that generative AI improves HR Practices but that its full potential is unlocked with a robust innovation climate. The study’s findings contain practical and managerial implications for professionals, managers, and leaders as well as policymakers who desire to improve HR practice and create an innovative environment using generative AI. To attain benefits, HR leaders need to spend as much as they can on the AI driven tools which is capable of doing these processes but handles HR teams to work together to benefit from AI.Journal of Asian development studies00The study focuses on the mediating role of innovation climate on the relationship between generative AI and HR practices in the hospitality sector and shows that generative AI has a massive positive impact on the HR practices and innovation climate positively influence HR Practices.Journal of Asian Development Studies["Abid Ahmad", "Fazli Wadood", "Imran Khan"]2024-12-06T00:00:00 c8bf4cd56b89303f6f88ff249343ae25043f1a22Disaster Medicine and Public Health Preparedness60Disaster medicine and public health preparedness["Payam Emami"]2024-12-06T00:00:00 f903cee90a176e1e033d6d4ba7170820f49b5c4bJournal of Anxiety Disorders570Journal of anxiety disorders["Qianqian Ju", "Zhijian Xu", "Zile Chen", "Jiayi Fan", "Han Zhang", "Yujia Peng"]2024-12-06T00:00:00 094ba811725f70be754a3cd8abd17166b85f14d2Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence130Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence["Wenchao Fang"]2024-12-06T00:00:00 81e83a35d395cb6a39496b9097a6cb249f7f7761EDPACS: The EDP Audit, Control, and Security Newsletter350EDPACS["Shaher Falah Alroud", "Mishal Abdullah Aljabr", "Amjad Jameel Al-Shorafa"]2024-12-06T00:00:00 6182e8ce1a0d04528a8c42a960f75b6ace8f0b01Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence30Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence["Bo Xiao", "Xuan Zhou"]2024-12-06T00:00:00 c5f1f7cd1e4931febe8bee9a91082e754a9f4c00International Journal of Human-Computer Interaction710International Journal of Human–Computer Interaction["Zhihuai Lin", "Yu-Leung Ng"]2024-12-06T00:00:00 6bc2557e7175cb33c1d8eeb31f7196ce904e1187Journal of Research on Technology in Education510Journal of Research on Technology in Education["Ali Orhan", "Tu\u011fba Ayd\u0131n Y\u0131ld\u0131z", "\u015eule \u00c7\u0131nar Ya\u011fc\u0131"]2024-12-06T00:00:00 77d9c7d5d2cde14e23f539db3b1d5d3a54048079This study examines the link between sustainable tourism and entrepreneurial education in Michoacán, analyzing their impact on promoting regional economic growth and international collaboration. The crucial role of the tourism sector as a driver of local development and job creation is emphasized, as well as its connection with entrepreneurship training and innovation. The research delves into the relevance of an interculturally-focused education in fostering sustainable tourism in Michoacán, promoting appreciation and consideration for cultural and linguistic diversity. Additionally, it evaluates the significance of global cooperation in the evolution of Michoacán's tourism industry, and how this relates to the promotion of academic exchanges and collaborative research initiatives. The study underscores the need to implement a participatory tourism management model in Michoacán, harmoniously integrating various stakeholders, including local governments, indigenous communities, and businesses in the sector. In conclusion, it is proposed that entrepreneurial education and the promotion of sustainable tourism can serve as effective strategies to boost local economic development and strengthen international cooperation in the Michoacán region.Sapiens International Multidisciplinary Journal00It is proposed that entrepreneurial education and the promotion of sustainable tourism can serve as effective strategies to boost local economic development and strengthen international cooperation in the Michoacán region.Sapiens International Multidisciplinary Journal["Jos\u00e9 Mart\u00ednez Pe\u00f1a", "Omar Becerra Moreno", "\u00c1ngel Leonel Ortiz Herrera", "Tzitzi Erandi Becerra Moreno"]2024-12-06T00:00:00 98db6116d3dc5aeb88235843641fbe6e7f066553The adsorption process involves capturing contaminants at active sites on adsorbent materials. Its economic efficiency and simplicity distinguish it as a preferred option amidst concerns about anthropogenic pollution. Traditional kinetic models present challenges in fitting nonlinear behaviors of porous materials, prompting the exploration of alternative approaches. Machine learning has a wide range of applications in various fields, including environmental engineering. These models possess generalization capabilities and are used as predictive tools, but they can lead to undesired behaviors if the system is complex and the model fails to capture this complexity. A novel methodology is proposed where the calibration of traditional models is studied using a machine learning model for adsorption kinetics, with hexavalent chromium as the adsorbate and activated carbon as the adsorbent. Furthermore, the technique of adding synthetic data to influence the model's capacity is studied, considering whether it leads to overfitting.Traditional models include pseudo first and second order kinetics, while multilayer perceptron is used as the machine learning model. The models obtained through the proposed methodology exhibit significant performance compared to traditional models. Additionally, an improved interpretability in their behavior compared to using only an artificial intelligence model is observed. Calibration ensures physical interpretation while enhancing generalization. The incorporation of synthetic data into the artificial intelligence model resulted in overfitting, subsequent ensemble methods effectively leveraged this, reducing bias related errors. The impact of synthetic data on calibration has demonstrated favorable outcomes.Brazilian Journal of Development00A novel methodology is proposed where the calibration of traditional models is studied using a machine learning model for adsorption kinetics, with hexavalent chromium as the adsorbate and activated carbon as the adsorbent.Brazilian Journal of Development["Jorge Pellegrini", "Jorge de Celis"]2024-12-06T00:00:00 64812740836957258e8c1611290bf071113548a8Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence70Proceedings of the 2024 8th International Conference on Computer Science and Artificial Intelligence["Xianghe Li", "Chenye Ding", "Yudi Zhu", "Haibo Wang", "Bo Qin", "Yan Li"]2024-12-06T00:00:00 b3d1cd752ef9e0a1064df63498d7ea7b7804a910The intersection of environmental sustainability and technological innovation is redefining investment and development strategies. ESG (Environmental, Social, and Governance) investing has become pivotal for achieving long-term sustainability, particularly in Peninsular Malaysia's power grid sector, where carbon neutrality goals align with broader sustainability objectives. System Dynamic (SD) methodology enhances ESG (Environmental, Social, and Governance) investment models by providing a framework to navigate complex, non-linear systems. This approach is essential for comprehensively assessing ESG (Environmental, Social, and Governance) impacts in power grid investments. In parallel, AI advancements in Peninsular Malaysia are transforming various sectors-boosting biodiversity conservation, revolutionizing education, and advancing the National AI (Artificial Intelligence) Framework. These efforts highlight Malaysia's role as a leader in integrating AI (Artificial Intelligence) for sustainable development. Overall, combining ESG (Environmental, Social, and Governance) investment strategies with AI (Artificial Intelligence) innovations offers a powerful pathway to addressing environmental challenges and driving technological progress.International Service Availability Symposium2502024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS)["Amirul Daniel Bin Sulaiman", "Ilham Sentosa", "Muhamad Zulkiflee Osman", "Hairunajiha Roslan", "Kamarul Nirhaqqim Nor Azri Juhar"]2024-12-06T00:00:00 cde088b37d64ab98e9b89125d12c8932d6a300caThe tragedy of the commons illustrates a fundamental social dilemma where individual rational actions lead to collectively undesired outcomes, threatening the sustainability of shared resources. Strategies to escape this dilemma, however, are in short supply. In this study, we explore how artificial intelligence (AI) agents can be leveraged to enhance cooperation in public goods games, moving beyond traditional regulatory approaches to using AI as facilitators of cooperation. We investigate three scenarios: (1) Mandatory Cooperation Policy for AI Agents, where AI agents are institutionally mandated always to cooperate; (2) Player-Controlled Agent Cooperation Policy, where players evolve control over AI agents' likelihood to cooperate; and (3) Agents Mimic Players, where AI agents copy the behavior of players. Using a computational evolutionary model with a population of agents playing public goods games, we find that only when AI agents mimic player behavior does the critical synergy threshold for cooperation decrease, effectively resolving the dilemma. This suggests that we can leverage AI to promote collective well-being in societal dilemmas by designing AI agents to mimic human players.arXiv.org00Using a computational evolutionary model with a population of agents playing public goods games, it is found that only when AI agents mimic player behavior does the critical synergy threshold for cooperation decrease, effectively resolving the dilemma.ArXiv["A. Hintze", "Christoph Adami"]2024-12-06T00:00:00 850a5c74833054824ce2280cb0d7226cbaac8b46Recently, Artificial Intelligence Generated Content (AIGC) has gained significant attention from society, especially with the rise of Generative AI (GAI) techniques such as ChatGPT, GPT-4 [165], DALL-E-3 [184], and Sora [137]. AIGC involves using AI models to create digital content, such as images, music, and natural language, with the goal of making the content creation process more efficient and accessible. Large-scale models have become increasingly important in AIGC as they provide better intent extraction and generation results. This survey provides a comprehensive review of the history of generative models and recent advances in AIGC, focusing on both unimodal and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, the survey discusses the existing open problems and future challenges in AIGC. Overall, this survey serves as a valuable resource for individuals interested in understanding the background and secrets behind the impressive performance of AIGC techniques.ACM Computing Surveys3711This survey provides a comprehensive review of the history of generative models and recent advances in AIGC, focusing on both unimodal and multimodal interaction.ACM Computing Surveys["Yihan Cao", "Siyu Li", "Yixin Liu", "Zhiling Yan", "Yutong Dai", "Phillp S. Yu", "Lichao Sun"]2024-12-06T00:00:00 f8acacf9c154c7986bd84e06a5f25459fe4a661aThe growing integration of Artificial Intelligence (AI) into Human Resources (HR) processes has transformed the way organizations manage recruitment, performance evaluation, and employee engagement. While AI offers numerous advantages, such as improved efficiency, reduced bias, and hyper-personalization, it raises significant concerns about employee well-being, job security, fairness, and transparency. This study examines how AI shapes employee perceptions, job satisfaction, mental health, and retention. Key findings reveal that while AI can enhance efficiency and reduce bias, it also raises concerns about job security, fairness, and privacy. Transparency in AI systems emerges as a critical factor in fostering trust and positive employee attitudes. AI systems can both support and undermine employee well-being, depending on how they are implemented and perceived. The research introduces an AI-employee well-being Interaction Framework, illustrating how AI influences employee perceptions, behaviors, and outcomes. Organizational strategies, such as clear communication, upskilling programs, and employee involvement in AI implementation, are identified as crucial for mitigating negative impacts and enhancing positive outcomes. The study concludes that the successful integration of AI in HR requires a balanced approach that prioritizes employee well-being, facilitates human-AI collaboration, and ensures ethical and transparent AI practices alongside technological advancement.arXiv.org02The study concludes that the successful integration of AI in HR requires a balanced approach that prioritizes employee well-being, facilitates human-AI collaboration, and ensures ethical and transparent AI practices alongside technological advancement.ArXiv["Soheila Sadeghi"]2024-12-06T00:00:00 8b6b6925fbd895ac4990aeb1c39f0b72b06c94deThis systematic literature review examines the transformative applications of artificial intelligence (AI) in policymaking, exploring its potential to enhance decision-making, public engagement, and governance effectiveness. Employing a rigorous research methodology, this review analyzed scholarly articles from Scopus, Web of Science, and PubMed databases using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, ensuring methodological transparency and reproducibility. The final dataset of 22 articles was synthesized into four key themes: (i) AI in policy development and implementation, which focuses on data-driven decision support in policy formulation; (ii) AI in public administration and governance, highlighting AI’s role in improving public sector efficiency and resilience; (iii) ethical and regulatory aspects of AI in policymaking, which addresses critical issues like transparency, privacy, and bias; and (iv) applications of AI in specific policy domains, encompassing areas such as public health, environmental sustainability, and education. Findings indicate that AI can support evidence-based policymaking by facilitating real-time data analysis, scenario modeling, and enhanced public participation. However, challenges persist, particularly concerning ethical considerations, algorithmic accountability, and regulatory frameworks that ensure AI is implemented responsibly and equitably. This review underscores the need for interdisciplinary collaboration, ethical standards, and robust governance frameworks to address these challenges and maximize AI’s benefits in policy development and implementation. The synthesis of insights from diverse policy contexts provides a foundation for future research, encouraging exploration of responsible AI integration in policymaking to advance public trust, accountability, and policy effectiveness).Journal of Science, Technology and Innovation Policy361Findings indicate that AI can support evidence-based policymaking by facilitating real-time data analysis, scenario modeling, and enhanced public participation, and underscores the need for interdisciplinary collaboration, ethical standards, and robust governance frameworks to address these challenges and maximize AI’s benefits in policy development and implementation.Journal of Science, Technology and Innovation Policy["Amirah 'Aisha Badrul Hisham", "Nor Ashikin Mohamed Yusof", "S. H. Salleh", "Hafiza Abas"]2024-12-06T00:00:00 af9cf7d953ad28f3f62ed42e24ea4bd902caac7bThe joining of artificial intelligence (AI) across different areas has fundamentally improved productivity and development. Nevertheless, this progression has increased cybersecurity threats, especially those determined by AI itself. These AI-powered threats exploit the advancements intended to obtain computerized frameworks, in this manner subverting their honesty. This systematic review focuses on the intricacies of AI-driven cyber threats, which use complex AI abilities to lead to intricate and tricky cyberattacks. Our review integrates existing examinations to determine the extension, location procedures, effects, and relief systems connected with AI-initiated threats. We feature the powerful exchange between AI improvement and cybersecurity, underlining the requirement for cutting edge protective frameworks that advance pairs with increasing threats. The discoveries highlight the basic job of AI in both carrying out and countering cybersecurity measures, representing a dualistic effect that requires ceaseless development in cybersecurity techniques.Mesopotamian Journal of CyberSecurity451This systematic review focuses on the intricacies of AI-driven cyber threats, which use complex AI abilities to lead to intricate and tricky cyberattacks.Mesopotamian Journal of CyberSecurity["M. Alanezi", "R. AL-Azzawi"]2024-12-06T00:00:00 13faac654c2b0a5dc2f6e3863a3abed2fc9e7ce1Cooperation between humans and machines is increasingly vital as artificial intelligence (AI) becomes more integrated into daily life. Research indicates that people are often less willing to cooperate with AI agents than with humans, more readily exploiting AI for personal gain. While prior studies have shown that giving AI agents human-like features influences people's cooperation with them, the impact of AI's assigned gender remains underexplored. This study investigates how human cooperation varies based on gender labels assigned to AI agents with which they interact. In the Prisoner's Dilemma game, 402 participants interacted with partners labelled as AI (bot) or humans. The partners were also labelled male, female, non-binary, or gender-neutral. Results revealed that participants tended to exploit female-labelled and distrust male-labelled AI agents more than their human counterparts, reflecting gender biases similar to those in human-human interactions. These findings highlight the significance of gender biases in human-AI interactions that must be considered in future policy, design of interactive AI systems, and regulation of their use.arXiv.org00Results revealed that participants tended to exploit female-labelled and distrust male-labelled AI agents more than their human counterparts, reflecting gender biases similar to those in human-human interactions.ArXiv["Sepideh Bazazi", "Jurgis Karpus", "T. Yasseri"]2024-12-06T00:00:00 1645154d8458eb23562dacfc49736849a7f4612bThe artificial intelligence (AI) technology in automated measurements has seen remarkable advancements across various vendors, thereby offering new opportunities in echocardiography. Fully automated software particularly has the potential to elevate the analysis and the interpretation of medical images to a new level compared to previous algorithms. Tasks that traditionally required significant time, such as ventricular and atrial volume measurements and Doppler tracing, can now be performed swiftly through AI’s automated phase setting and waveform tracing capabilities. The benefits of AI-driven systems include high-precision and reliable measurements, significant time savings, and enhanced workflow efficiency. By automating routine tasks, AI can reduce the burden on clinicians, allowing them to gather additional information, perform additional tests, and improve patient care. While many studies confirm the accuracy and the reproducibility of AI-driven techniques, it is crucial for clinicians to verify AI-generated measurements and ensure high-quality imaging and Doppler waveforms to fully take advantage of the benefits from these technologies. This review discusses the current state of AI-driven automated measurements in echocardiography, their impact on clinical practice, and the strategies required for the effective integration of AI into clinical workflows.JMA Journal380The current state of AI-driven automated measurements in echocardiography, their impact on clinical practice, and the strategies required for the effective integration of AI into clinical workflows are discussed.JMA Journal["Y. Hirata", "K. Kusunose"]2024-12-06T00:00:00 f13af6f76d7e0cd2202de94bb7b9bc00ec6b3161This study investigates private equity (PE), a crucial segment of the global financial ecosystem where funds and investors directly invest in private companies or buy out public companies. Unlike public equity markets, PE offers essential capital for growth, restructuring, and management buyouts, predominantly from institutional investors and high-net-worth individuals. The study outlines the structure of PE firms as limited partnerships, where general partners manage investments while limited partners provide capital and share profits. It explores various types of PE investments, including venture capital, growth capital, buyouts, and distressed situations. The research highlights significant growth in the PE sector, driven by superior returns compared to traditional asset classes, despite challenges from evolving technology and regulatory changes. A central focus is the role of artificial intelligence (AI), which is transforming deal sourcing, due diligence, portfolio management, and operational efficiency. The objectives of this study are to understand the operations and significance of private equity firms, explore AI's impact on PE functions, and assess the market landscape of AI within the private equity sector. This exploration provides insights into AI's potential to reshape investment strategies and enhance value creation in private equity.Journal of Next-Generation Research 5.000A central focus is the role of artificial intelligence (AI), which is transforming deal sourcing, due diligence, portfolio management, and operational efficiency in private equity, and assess the market landscape of AI within the private equity sector.Journal of Next-Generation Research 5.0["Tanwangini Sahani"]2024-12-06T00:00:00 1c5f70f9d050790407a799d8f8482b059f47dfadIn the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between innovation and regulation has become an emerging focus of our society. Despite tremendous advancements in AI's capabilities to excel in specific tasks and contribute to diverse sectors, establishing a high degree of trust in AI-generated outputs and decisions necessitates meticulous caution and continuous oversight. A broad spectrum of stakeholders, including governmental bodies, private sector corporations, academic institutions, and individuals, have launched significant initiatives. These efforts include developing ethical guidelines for AI and engaging in vibrant discussions on AI ethics, both among AI practitioners and within the broader society. This article thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union. It delves into the fundamental ethical principles of safety, transparency, non-discrimination, traceability, and environmental sustainability for AI developments and deployments. Considering the technical efforts and strategies undertaken by academics and industry to uphold these principles, we explore the synergies and conflicts among the five ethical principles. Through this lens, work presents a forward-looking perspective on the future of AI regulations, advocating for a harmonized approach that safeguards societal values while encouraging technological advancement.arXiv.org00This article thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union and delves into the fundamental ethical principles of safety, transparency, non-discrimination, traceability, and environmental sustainability for AI developments and deployments.ArXiv["Nan Sun", "Yuantian Miao", "Hao Jiang", "Ming Ding", "Jun Zhang"]2024-12-06T00:00:00 554b635886c217346814133bed03f8b4864fe8dbThe integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape.arXiv.org550The AISE index, a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator, provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape.ArXiv["Enrico Maria Fenoaltea", "D. Mazzilli", "A. Patelli", "A. Sbardella", "A. Tacchella", "A. Zaccaria", "Marco Trombetti", "L. Pietronero"]2024-12-06T00:00:00 3619b062c55e9514adaec710f3723c4fb3092512This article offers a thorough investigation of a unique IoT fluid monitoring system augmented with artificial intelligence (AI) for live data collection and predictive maintenance. This system employs high-precision pressure sensors and flow meters that demonstrate accuracy of ±0.5 bar and ±1% of reading, respectively, which offers consistent monitoring over a variety of fluid dynamics. The performance investigation demonstrated an average response time of 2 seconds, while considerable changes occurred in high turbulence settings, which highlights the vital significance of sensor location and calibration. We put into practice numerous AI models, including random forest, support vector machines (SVM), and neural networks, to analyze the gathered sensor data. The Random Forest model demonstrated the greatest performance among the others, with an accuracy of 92% that offered credible forecasts of fluid system abnormalities. A demonstration of real-time monitoring showed the system’s capacity, as it observed a 4-bar pressure reduction in under 5 minutes, allowing for fast remedial procedures. These findings underline the possibilities of merging IoT and AI technology to enhance fluid management techniques via predictive maintenance and better operational efficiency. Although the findings represent substantial improvement, persistent issues such as the potential of data loss via wireless transmission and the rigorous calibration needs still remain. Future research will stress the enhancement of communication protocols, exploring new calibration methodologies, and integrating complex AI algorithms to better the performance of the system. The results of this study offer a basis for the construction of more intelligent and more robust industrial systems, providing a substantial contribution to fluid monitoring.International Service Availability Symposium360A thorough investigation of a unique IoT fluid monitoring system augmented with artificial intelligence (AI) for live data collection and predictive maintenance, which employs high-precision pressure sensors and flow meters that demonstrate accurate monitoring over a variety of fluid dynamics.2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS)["Abrar Ghalib Mahmood Al-Daffaie", "Saadaldeen Rashid Ahmed", "Zainab T. Al-Sharif", "Zainab Mejeed Khadim", "Baqer A Hakim", "Rafad Imad Kadhim", "Sameer Algburi", "Jungpil Shin"]2024-12-06T00:00:00 9e72ba2ddbea2fcd589d3b2af80ad8517a35841aAs the widespread use of algorithms and artificial intelligence (AI) technologies, understanding the interaction process of human–algorithm interaction becomes increasingly crucial. From the human perspective, algorithmic awareness is recognized as a significant factor influencing how users evaluate algorithms and engage with them. In this study, a formative study identified four dimensions of algorithmic awareness: conceptions awareness (AC), data awareness (AD), functions awareness (AF), and risks awareness (AR). Subsequently, we implemented a heuristic intervention and collected data on users' algorithmic awareness and FAT (fairness, accountability, and transparency) evaluation in both pre‐test and post‐test stages (N = 622). We verified the dynamics of algorithmic awareness and FAT evaluation through fuzzy clustering and identified three patterns of FAT evaluation changes: “Stable high rating pattern,” “Variable medium rating pattern,” and “Unstable low rating pattern.” Using the clustering results and FAT evaluation scores, we trained classification models to predict different dimensions of algorithmic awareness by applying different machine learning techniques, namely Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and XGBoost (XGB). Comparatively, experimental results show that the SVM algorithm accomplishes the task of predicting the four dimensions of algorithmic awareness with better results and interpretability. Its F1 scores are 0.6377, 0.6780, 0.6747, and 0.75. These findings hold great potential for informing human‐centered algorithmic practices and HCI design.Journal of the Association for Information Science and Technology440This study implemented a heuristic intervention and collected data on users' algorithmic awareness and FAT (fairness, accountability, and transparency) evaluation in both pre‐test and post‐test stages, and trained classification models to predict different dimensions of algorithmic awareness.Journal of the Association for Information Science and Technology["Jing Liu", "Dan Wu", "Guoye Sun", "Yuyang Deng"]2024-12-06T00:00:00 63ec1a44c28dee99f375f7e2cdc147a3a771a28bIn the era of artificial intelligence, Chinese painting-style animation is undergoing an unprecedented transformation. The application of AI technology not only improves the efficiency of animation production but also brings new possibilities for innovation to creators. The impact of the AI era on the animation industry is multi-dimensional. AI technology can automate many repetitive tasks, such as the generation of in-between frames, the drawing of characters and scenes, and its application in various other aspects, thereby shortening the production cycle, reducing actual time costs, and improving timeliness. Its detailed functions, such as AI-assisted scriptwriting and directing to generate creative storylines, plots, and even scripts, provide new ideas for animation creation. One of the most admired and sought-after aspects is the operability and personalization of AI. By analyzing audience preferences and behaviors through various cases and traffic analysis, it can provide personalized content recommendations using animation production techniques, meeting the psychological needs of different audience groups. In terms of image and video processing, it can be used to enhance the visual effects of animation, such as automatic coloring, background rendering, image clarity and blurring processing, etc. For the design of characters and scenes, designers can quickly generate concept drawings of characters and scenes through subjective expression, including composition positioning, character body dynamics, and simulating different styles and schools of painting techniques. In terms of sound, it is also excellent, capable of simulating specific sound effects, generating and improving dubbing sound effects and sound quality, enhancing the auditory experience of animation, and through analysis techniques such as voice recognition and natural language processing, allowing animated characters to use lifelike body movements and facial expressions to interact with the audience, providing a better interactive experience.Journal of Education, Humanities and Social Sciences00In the era of artificial intelligence, Chinese painting-style animation is undergoing an unprecedented transformation, and AI technology can automate many repetitive tasks, thereby shortening the production cycle, reducing actual time costs, and improving timeliness.Journal of Education, Humanities and Social Sciences["Qunshan Hou"]2024-12-06T00:00:00 e2e82f10ea0e8ccd88eb31356b9bd7f66cf0e5abThis study presents a full framework for merging artificial intelligence (AI) with simulation software to increase cybersecurity defenses. This suggested system, which integrates powerful machine learning algorithms with secure protocols, enables real-time identification of threats and enhanced protection against cyberattacks, particularly built for fluid dynamics simulation settings. The architecture employs AI models like Random Forest and Long Short-Term Memory (LSTM), which have been enhanced via hyperparameters tuning and trained on a dataset that encompasses both simulated and real-world cyber events. The findings demonstrate considerable success in key security parameters, including a ${3 5 \%}$ spike in threat detection rates and a ${2 0 \%}$ decline in system vulnerabilities, when compared to traditional cybersecurity measures. The implementation of these AI-driven protocols increased the software’s reliability and convenience of use for engineers and researchers. Although various constraints were observed, including computational resource needs, the whole system delivers a scalable and effective solution for the protection of high-value simulation settings from emerging cyber threats. Future research will be targeted at extending the dataset and integrating more complex AI technologies to further increase security resilience.International Service Availability Symposium360This suggested system, which integrates powerful machine learning algorithms with secure protocols, enables real-time identification of threats and enhanced protection against cyberattacks, particularly built for fluid dynamics simulation settings.2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS)["Rafad Imad Kadhim", "Saadaldeen Rashid Ahmed", "Zainab T. Al-Sharify", "Mohammad K. Abdul-Hussein", "Al-Ibraheemi Fuqdan", "Rangin Sabah Hassan", "Sameer Algburi", "Jungpil Shin"]2024-12-06T00:00:00 604f89e173205f61e6aa0ae2100109cd4ed5a28aIn the creative sector, artificial intelligence (AI) has proven to be a crucial instrument. AI encompasses a variety of artistic creations that were previously thought to be exclusive to human talent, such as paintings and music. The study improves on earlier research showing that artificial creativity processes may produce goods that are competitive with those generated by humans, satisfy customer expectations, and provide enjoyment. This study investigates the impact of AI on esthetics and artistic expression. Directing the machine to paint a landscape, create a pen and ink portrait of a person, or create a gouache before still life, etc. The synthesis of realistic paintings requires more effort than just accurately capturing target styles. It also requires maintaining original content aspects and visual structures, for which the existing techniques are insufficient to provide satisfying creation of art. In this study, a novel redefined generative adversarial network (RGAN) was proposed for automatic art creation and generation of paintings. In this study, a diverse set of art image data for artistic creation were collected. The data were preprocessed using a median filter to remove noise from the obtained data. Histogram of Oriented Gradients (HOG) approaches are used to extract features, which extract gradient orientation to capture texture patterns and edge information. The results demonstrate that the proposed technique achieved better performance than the other existing techniques. The innovative AI model improves the system’s ability to capture style and preserve content, resulting in better art creation outcomes.International Journal of High Speed Electronics and Systems00A novel redefined generative adversarial network (RGAN) was proposed for automatic art creation and generation of paintings and demonstrated that the proposed technique achieved better performance than the other existing techniques.International Journal of High Speed Electronics and Systems["Weinan Liu", "Kim Hyung-Gi"]2024-12-07T00:00:00 ef68834beec7908c4eb9f01b5388b0452f03a7acIntroduction This pilot study aims to evaluate the clinical impact of artificial intelligence-based decision support, Koios Decision Support™, on the diagnostic performance of ultrasound assessment of thyroid nodules, and as a result to avoid fine needle aspiration. Methods This retrospective pilot study was conducted on ultrasound images of thyroid nodules investigated with fine needle aspiration from January 2022 to December 2022. Orthogonal ultrasound images of thyroid nodules, previously investigated with fine needle aspiration, were compared with the Koios Decision Support™ suggestion to perform fine needle aspiration. Surgical histology was used as ground truth. Results A total of 29 patients (76% women) with a mean age of 48 ± 16.5 years were evaluated, n = 15 (52%) were histologically proven benign and n = 14 (48%) were malignant. In the benign group, Koios Decision Support™ suggested avoidable fine needle aspiration in n = 8 (53%). In the malignant group, Koios Decision Support™ suggested follow-up or no fine needle aspiration in n = 2 (14%). Sensitivity is 85.7% (n = 12) (p = 0.027), whereas specificity is 53.3% (n = 8) (p = 0.027). The positive predictive value is 63.2% (n = 12), negative predictive value is 80% (n = 8), false-negative value is 20% (n = 2) and false-positive value is 36.8% (n = 7). Based on artificial intelligence decision, one cancer would have been missed. Conclusion Artificial intelligence can improve specificity without significantly compromising sensitivity. There was a suggested reduction in the fine needle aspiration rate, in the histologically proven benign nodules, by 53%. This had no statistical significance, likely due to the small population, however, it is thought to be the largest study to date. Further investigation with wider-ranging studies is suggested.Ultrasound110Artificial intelligence can improve specificity without significantly compromising sensitivity and there was a suggested reduction in the fine needle aspiration rate, in the histologically proven benign nodules, by 53%.Ultrasound["Amy Barnes", "Rebecca White", "Heather Venables", "Vincent Lam", "Ram Vaidhyanath"]2024-12-07T00:00:00 765c5d3cc8c547e60366286cc90f4480dcc5f480Background: Dysregulation of thyroid function, manifesting as hyperthyroidism or hypothyroidism, can profoundly impact an individual's overall health. In this context, artificial intelligence (AI) applications have the potential to revolutionize diagnostic approaches, treatment strategies, and patient monitoring. Objectives: This study comprehensively reviews the latest literature on AI applications in thyroid functional and autoimmune disorders. Methods: An online search was conducted on databases using search queries crafted with MeSH terms related to AI and thyroid disorders. After screening, studies aligned with our research focus were selected for this narrative review. Results: Multiple studies have explored the use of AI technologies, including machine learning (ML) and deep learning (DL), to enhance laboratory workflows for thyroid function tests (TFT) and improve the accuracy of TFT interpretation by incorporating clinical data. In imaging, DL-based models have demonstrated the potential to assist less experienced radiologists in interpreting scintigraphy and ultrasound images. Artificial intelligence has also provided valuable insights into identifying diagnostic genes for thyroid-related autoimmune disorders and understanding the effects of environmental factors, such as chemicals, on thyroid gland function. Some ML models have been developed to predict the risk of hypothyroidism following radioiodine therapy (RAI). Furthermore, AI has shown promise in personalized levothyroxine dose adjustments, predicting treatment responses, and accurately diagnosing complications such as thyroid-associated ophthalmopathy (TAO). Finally, ML-based models forecasting the risk of suicide attempts in patients with major depressive disorder (MDD) and predicting pregnancy outcomes, such as gestational diabetes mellitus (GDM) and preterm delivery, based on TFT results, appear beneficial in addressing these significant health issues. Conclusions: The current state of AI in diagnosing and treating thyroid function disorders is promising, with applications primarily focused on improving diagnostic accuracy, consistency, and personalized treatment approaches. However, challenges remain that prevent these models from fully substituting professionals. Addressing these challenges is crucial to ensure AI effectively contributes to the management of patients with thyroid diseases.Shiraz E Medical Journal680The current state of AI in diagnosing and treating thyroid function disorders is promising, with applications primarily focused on improving diagnostic accuracy, consistency, and personalized treatment approaches.Shiraz E-Medical Journal["Farnaz Atighi", "Parsa Yazdanpanahi", "Alireza Keshtkar", "Alireza Karimi", "Arzhang Naseri", "Mohammadhossein Dabbaghmanesh"]2024-12-07T00:00:00 37d3cd880342865ad38b95e60889088b30567557This study aims to investigate the influence of competence, professionalism, and auditor behavior on the utilization of Artificial Intelligence (AI) technology and the effectiveness of financial audit implementation among auditors in Public Accounting Firms (KAP) in Surabaya. The research seeks to explore how these aspects interact and impact the quality of audits. The study was conducted at Public Accounting Firms in Surabaya, with a sample of 60 auditors. Hypothesis testing in the research employed analysis using the SmartPLS 3.0 application, incorporating outer model and inner model tests. This research is expected to make a significant contribution to the accounting and audit literature by exploring the dynamics between human and technological factors in the context of financial audit. Furthermore, the results of this study can serve as a foundation for Public Accounting Firms (KAP) and regulators to design policies and training programs that support the more effective integration of AI technology into audit practices.EKONOMIKA45 :  Jurnal Ilmiah Manajemen, Ekonomi Bisnis, Kewirausahaan00The results of this study can serve as a foundation for Public Accounting Firms (KAP) and regulators to design policies and training programs that support the more effective integration of AI technology into audit practices.EKONOMIKA45 :  Jurnal Ilmiah Manajemen, Ekonomi Bisnis, Kewirausahaan["Gusti Chania Raafi Iradati", "Tri Ratnawati"]2024-12-07T00:00:00 d55a3c3fb4890dd35b4f11b273fc951fa8fddc8dArtificial intelligence (AI) tools have become an essential part of modern medicine and surgery in recent years. Potential cosmetic surgery patients can visualize possible outcomes of a surgical procedure with the swipe of a finger and get a quote for surgical treatments without leaving their homes. Most of these AI tools use high-quality 2D or 3D photographs, as well as sensitive personal data regarding medical history and other important parameters, which certainly raise concerns about the bioethical aspects, accountability, and personal data protection. This article points out the key ethical principles and issues that may arise in the implementation of these tools, especially in facial cosmetic surgery. It discusses the possible pre-programmed bias and other considerations and controversies that can lead to unintentional violation of sensitive information and resulting legal issues. A search was performed across PubMed and Web of Science, using a combination of keywords related to “cosmetic surgery” and “artificial intelligence,” such as “bioethics,” “AI preprogrammed bias,” and “AI liability.” The search was focused on published articles in the past 5 years to point out the recent trends and opinions among plastic cosmetic surgeons about the use of AI in the field. The main potential issues associated with the use of AI in plastic cosmetic surgery were summarized as “Possible pre-programmed Bias,” “Key ethical principles” and “Other considerations and controversies,” such as compromised traditional patient-clinician relationships, including loss of empathy and patient-centered care. Further considerations include the potential dehumanization of health care, AI-generated threats to patients’ safety and efficacy, and limited liability. The current experts’ opinions regarding the use of AI in cosmetic surgery and health care seem to be very controversial despite the increase in its use and the recent improvements in the quality and versatile facets of AI tools and their implementation. There are supporters and opponents, whose common goal remains to be the quality of care, patient safety, and bioethics, including data protection and liability. Artificial intelligence technologies can potentially improve patient care by supporting surgeons without having to replace them. Strict regulations are crucial for any kind of AI technology, especially the one involved in direct patient care, such as cosmetic surgery.The American Journal of Cosmetic Surgery180The main potential issues associated with the use of AI in plastic cosmetic surgery were summarized as “Possible pre-programmed Bias,” “Key ethical principles” and “Other considerations and controversies,” such as compromised traditional patient-clinician relationships.The American Journal of Cosmetic Surgery["Nikoletta Vargas", "Daria Hamrah"]2024-12-07T00:00:00 ff04d448f82d4f8be1ac8073c50d838548d98beeThis article presents a comprehensive analysis of artificial intelligence applications in quality control across manufacturing, service, and infrastructure maintenance sectors. The article examines how AI-driven systems are transforming traditional quality control processes through automated defect detection, real-time monitoring, and adaptive testing methodologies. Through systematic review of industry implementations and case studies, we investigate the impact of machine learning algorithms, computer vision systems, and deep learning applications on quality assurance processes. The findings demonstrate significant improvements in inspection accuracy, reduction in manual inspection requirements, and enhanced detection of subtle defects across various industrial applications. The article reveals that AI-driven quality control systems offer substantial benefits in terms of operational efficiency, cost reduction, and quality consistency, while also identifying key implementation challenges such as initial infrastructure requirements, data quality concerns, and workforce adaptation needs. Additionally, the article provides insights into emerging trends and future opportunities for AI integration in quality control systems, contributing to the broader understanding of Industry 4.0 implementation strategies. This work serves as a foundational reference for organizations considering AI implementation in their quality control processes and provides a framework for evaluating the potential benefits and challenges across different industrial contexts.International Journal of Scientific Research in Computer Science Engineering and Information Technology30It is revealed that AI-driven quality control systems offer substantial benefits in terms of operational efficiency, cost reduction, and quality consistency, while also identifying key implementation challenges such as initial infrastructure requirements, data quality concerns, and workforce adaptation needs.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Divyansh Jain"]2024-12-07T00:00:00 a840b303a617e924eac2c80a73f3d28610d476ebPenelitian ini bertujuan untuk menganalisis pengaruh penerapan Artificial Intelligence (AI) berbasis ChatGPT terhadap kinerja pegawai di lingkungan Pemerintah Kota Pekalongan, dengan penerimaan teknologi sebagai variabel moderasi. Penelitian ini merupakan penelitian korelasional yang melibatkan 45 responden pegawai pemerintahan. Teknik pengumpulan data dilakukan melalui kuesioner dan dianalisis menggunakan SmartPLS. Hasil penelitian menunjukkan bahwa AI berbasis ChatGPT memiliki pengaruh positif signifikan terhadap kinerja pegawai, baik secara langsung maupun melalui penerimaan teknologi sebagai variabel moderasi. Penerimaan teknologi memperkuat hubungan antara penggunaan AI dengan kinerja pegawai, mengindikasikan bahwa semakin tinggi penerimaan teknologi, semakin besar peningkatan kinerja pegawai. Penelitian ini memberikan wawasan mengenai pentingnya adopsi teknologi AI dalam meningkatkan efektivitas kerja di sektor publik.INOBIS Jurnal Inovasi Bisnis dan Manajemen Indonesia200INOBIS: Jurnal Inovasi Bisnis dan Manajemen Indonesia["Muhamad Najib Ferdinand", "Chalimah Chalimah"]2024-12-07T00:00:00 e99a094f69ddef47d472a0acd32c648748b9ec94This paper explores the influence mechanism of artificial intelligence application on corporate green innovation with a sample of A-share listed companies in Shanghai and Shenzhen from 2010 to 2023. It is found that artificial intelligence application is significantly positively related to corporate green innovation. After robustness tests such as replacing core variables, excluding new crown epidemics, and excluding green innovation outliers, the finding remains unchanged, indicating that AI application significantly promote corporate green innovation. Further analysis reveals that the promotion effect of AI application on corporate green innovation is more obvious in high management shareholding ratio and non-heavily polluted enterprises, while it does not show a significant difference in the nature of ownership and industry attributes. The findings of this paper not only enrich the existing literature on AI application, but also provide empirical evidence on how governments can promote corporate green innovation.South Asian Research Journal of Engineering and Technology10It is found that artificial intelligence application is significantly positively related to corporate green innovation, and empirical evidence on how governments can promote corporate green innovation is provided.South Asian Research Journal of Engineering and Technology["Yansha Zhu", "Guanping Zhu"]2024-12-07T00:00:00 2bbb1083b1e4f047b4cf40a726e06a5dc876548cArtificial Intelligence (AI) has become a driving force in reshaping the service industry, fundamentally altering how businesses operate and deliver value. Through the automation of tasks, AI enhances decision-making, streamlines operations, and provides personalized customer experiences. AI’s ability to analyze vast datasets, recognize patterns, and automate tasks previously handled by humans leads to significant improvements across industries like hospitality, retail, healthcare, and customer service. For example, in hospitality, AI-driven dynamic pricing and virtual concierges improve guest satisfaction and optimize revenue. In retail, AI powers recommendation engines, driving significant revenue growth. In healthcare, AI reduces diagnostic errors and accelerates decision-making. Similarly, AI-driven chatbots in customer service improve operational efficiency while ensuring high customer satisfaction. This paper explores these applications in depth, discussing benefits, challenges, and ethical considerations associated with AI integration in the service sector.Nanotechnology Perceptions00This paper explores benefits, challenges, and ethical considerations associated with AI integration in the service sector in depth, discussing benefits, challenges, and ethical considerations associated with AI integration in the service sector.Nanotechnology Perceptions["Dr Deepa Prasad Venkatraman", "Prof (Dr) Manasi Kurtkoti"]2024-12-07T00:00:00 71deb48e2161863a180d677c2dfe732894ef1249This paper examines the ethical challenges associated with the development and deployment of artificial intelligence (AI) and automation technologies. As AI continues to advance, it raises complex ethical issues regarding autonomy, accountability, fairness, transparency, privacy, and bias. The paper explores the key ethical concerns in AI and automation, focusing on how these technologies must be designed and governed to ensure they serve the public good without reinforcing societal inequalities. It emphasizes the importance of integrating ethical considerations into the design process from the outset, fostering a balanced approach to innovation and responsibility. The paper calls for a continued commitment to responsible AI development that promotes fairness, transparency, and accountability while mitigating the risks of harm.Journal of Computer, Signal, and System Research00How these technologies must be designed and governed to ensure they serve the public good without reinforcing societal inequalities is explored, focusing on the importance of integrating ethical considerations into the design process from the outset.Journal of Computer, Signal, and System Research["Rajesh Kumar"]2024-12-07T00:00:00 71098dd8f9e885d5ebe51c1a5ceafeb9463671f0Artificial intelligence (AI) significantly impacts consumer electronics by making devices more intuitive, personalized, and efficient, enabling them to learn from user behavior, optimize performance, and automate routine tasks, ultimately enhancing the overall user experience across various applications like smart homes, wearables, and communication tools; however, concerns around data privacy and security remain crucial considerations when integrating AI into consumer electronics. AI improves the computational capabilities of consumer electronics by incorporating complex algorithms that can learn and adapt based on data. These algorithms range from simple predictive models used in personalizing user interfaces to sophisticated neural networks that enable advanced image and speech recognition.The Bioscan70Concerns around data privacy and security remain crucial considerations when integrating AI into consumer electronics; however, concerns around data privacy and security remain crucial considerations when integrating AI into consumer electronics.The Bioscan["Dr. K. Sudha Ramya", "G. Y. Rao"]2024-12-07T00:00:00 848a013495009a8c7fca88cd8ede293f19f86102The article presents the possibilities of using artificial intelligence in the context of cybersecurity. It outlines the role of artificial intelligence in the face of increasing threats to network infrastructure. Section 1 discusses the justification for using artificial intelligence in cybersecurity, while Sections 2 and 3 explore the concept of detecting and preventing incidents using incident response automation. Meanwhile, Section 4 is dedicated to identity and access management in the context of accessing organizational resources.Dydaktyka Informatyki80Dydaktyka Informatyki["Jacek Wo\u0142oszyn"]2024-12-07T00:00:00 aa17e7759573adb6f95eff7046f4affaf5fd2accGlobal warming has triggered more intense clashes of weather systems in India in recent years, increasing extreme weather events, which the independent Centre for Science and Environment estimates have killed nearly 3,000 people this year. Weather agencies around the world are focussing on AI, which can bring down cost and improve speed, and with a recent Google-funded model found to have outperformed conventional methods. Accurate weather forecasting is particularly crucial in India, a country of 1.4 billion people, many impoverished, and the world's second-largest producer of rice, wheat and sugar Using AI with an expanded observation network could help generate higher-quality forecast data at lower cost. The increasing frequency and intensity of torrential rains, floods, and droughts due to climate change present significant challenges to weather forecasting and disaster preparedness. Artificial Intelligence (AI) offers transformative potential in building advanced climate models to address these challenges. By integrating vast datasets, including satellite imagery, historical weather records, and real-time sensor data, AI enhances the accuracy and resolution of climate predictions. Machine learning algorithms can identify complex patterns, simulate localized weather phenomena, and predict extreme weather events with greater precision. This paper explores the role of AI-driven climate models in improving weather forecasting, enabling proactive responses to mitigate the impacts of climate-induced disasters across diverse regions. Enhanced forecasting through AI not only safeguards lives and livelihoods but also supports sustainable resource management in the face of escalating climate uncertaintiesInternational Journal of Advanced Research in Science, Communication and Technology00The role of AI-driven climate models in improving weather forecasting is explored, enabling proactive responses to mitigate the impacts of climate-induced disasters across diverse regions.International Journal of Advanced Research in Science, Communication and Technology["Dr Srinivasa Rao Kadari", "Dr. P. Ravichandra", "M. Shekar", "D. Neetha"]2024-12-07T00:00:00 ec250277b3eb5fcb2d1c1444b0dc22615d23775aThe relevance. The rate of spread of artificial systems with intelligence is increasing every year. This is evidenced by a large number of scientific publications, patents, research in this area and support for government programs. However, the ambiguity of its use in science and in practice leaves AI the subject of heated discussions both in the humanitarian community and among scientists who use artificial intelligence technologies in their professional activities.The purpose of the article is to analyze the role of artificial systems with intelligence in scientific and professional, and specifically, medical practice.Objectives: to study digital technologies with elements of artificial intelligence used in science and high-tech practices, their potential and risks; to identify a number of social roles that can be assigned to programs with elements of intelligence as assistants to scientists; to show the possibilities and risks of introducing AI into medical practice.Methodology. As the main approach to solving the tasks set, the article uses an interdisciplinary synthesis of philosophical reflections, statistics and the results of the practical application of AI in healthcare, which allows highlighting the anthropological and social problems of the rapid introduction of new technologies into the social sphere.Results. This article examines healthcare as an environment for the rapid introduction of AI into all system processes, from diagnosis to management of medical complexes. The possible roles of AIS as an assistant manager, analyst, consultant and qualified colleague in modern technology-oriented healthcare are shown.Conclusions. When using AI in scientific work and professional practices, it is possible to identify pragmatic, psychological and ethical aspects. Problems were found not only in the disclosure of confidential information about patients, but also in more serious shortcomings related to the effectiveness of the organization of healthcare as a social practice and the loss of important professional competencies of practitioners.Proceedings of the Southwest State University. Series: Economics. Sociology. Management190The role of artificial systems with intelligence in scientific and professional, and specifically, medical practice is analyzed to identify a number of social roles that can be assigned to programs with elements of intelligence as assistants to scientists and to show the possibilities and risks of introducing AI into medical practice.Proceedings of the Southwest State University. Series: Economics. Sociology. Management["I. Aseeva"]2024-12-07T00:00:00 1f290bd601a86fbb03be9cf82969bfd5756868b7Artificial intelligence (AI) and digital public infrastructure (DPI) are two technological developments that have taken center stage in global policy discourse. Yet, to date, there has been relatively little discussion about how AI and DPI can mutually enhance the public value provided by each other. Therefore, in this paper, we describe both the opportunities and challenges under which AI and DPI can interact for mutual benefit. First, we define both AI and DPI to provide clarity and help policymakers distinguish between these two technological developments. Second, we provide empirical evidence for how AI, a general-purpose technology, can integrate into many DPI systems, aiding DPI function in use cases like language localization via machine translation (MT), personalized service delivery via recommender systems, and more. Third, we catalog how DPI can act as a foundation for creating more advanced AI systems by improving both the quantity and quality of training data available. Fourth, we discuss the challenges of integrating AI and DPI, including high inference costs for advanced AI models, interoperability challenges with legacy software, concerns about induced bias in AI systems, and privacy challenges related to DPI. We conclude with key takeaways for how policymakers can work to enhance the positive interactions of AI and DPI.arXiv.org00The opportunities and challenges under which AI and DPI can interact for mutual benefit are described and key takeaways for how policymakers can work to enhance the positive interactions of AI and DPI are concluded.ArXiv["Sarosh Nagar", "David Eaves"]2024-12-07T00:00:00 bedbdaebc737252677b19c5db62ffe3fac852dc9International Journal of Progressive Research in Engineering Management and Science00International Journal of Progressive Research in Engineering Management and Science[]2024-12-07T00:00:00 95ba041b1c0199df598d817f0615058c177cb816Automating repetitive tasks significantly increases the efficiency of operations by identifying repetitive tasks, then choosing the appropriate tools for automation, designing successful workflows, testing, iteration, monitoring the performance of operations continuously, and making the necessary adjustments, which helps auditors accomplish their mission. This research aims to highlight the urgent need to develop the skills of accountants and auditors to maintain alignment with the swift advancements in digital technology in the accounting field to reduce fraud risks and protect stakeholders. The study relied on the applied aspect of 157 questionnaires distributed to a sample of auditors working in auditing firms and Federal Financial Supervision Bureau auditors. We used a five-point Likert scale to answer the questions on the questionnaire and the SPSS V.25 statistical package to look at the data and test our research hypotheses. We used Pearson correlation, simple regression, the coefficient of determination and interpretation R2, and the standard coefficient of regression B. Our research showed that using robotic process automation and artificial intelligence in auditing would make audited financial statements more reliable. Adding auditors' skills, artificial intelligence, and process automation will also significantly mitigate inherent control and detection risks. Furthermore, enhancing the quality of auditingTechnium Social Sciences Journal00The research showed that using robotic process automation and artificial intelligence in auditing would make audited financial statements more reliable and significantly mitigate inherent control and detection risks.Technium Social Sciences Journal["Kadhim Arwa Awad", "Wahhab Asaad Mohammed Ali"]2024-12-07T00:00:00 e10300ddc93a75016d1079f23b442fe0460d8477This article presents a detailed analysis of the use of AI in various aspects of cybersecurity, from training, threat detection and response, through identity and access management, to future directions and inherent challenges. It examines how this technology is transforming the field of digital security. The aim is not only to present the opportunities offered by AI, but also to draw attention to the need for a conscious and responsible approach to its implementation and exploitation.Dydaktyka Informatyki00Dydaktyka Informatyki["Jacek Wo\u0142oszyn"]2024-12-07T00:00:00 d7a2dfb4063d1179c272e5be487c34d32cf2a649Proceedings on Engineering Sciences00Proceedings on Engineering Sciences["Sadhana Tiwari", "Mohammad Asif", "Amar Johri", "Mohammad Wasiq", "Mohd Imran"]2024-12-07T00:00:00 24f9d7c218ee85173ee7898843710b2393e14e58Theology and Science00Theology and Science["Saif Ahmed", "Ayesha Akter Sumi", "Norzalita Abd Aziz"]2024-12-07T00:00:00 971adfa63c3ffb0f5f35172d26661316a724d2e7This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions. This review also addresses the ethical considerations and societal impacts of AI in SHM, such as data privacy, equity, and transparency. We conclude by discussing future research directions and challenges, emphasizing the potential of AI to enhance the efficiency, safety, and sustainability of infrastructure systems.Infrastructures1103This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety, and examines seven core areas where AI significantly advances SHM capabilities.Infrastructures["V. Plevris", "G. Papazafeiropoulos"]2024-12-07T00:00:00 165b7dbc658995a4666a7ecbff8aba47a987ceeaArtificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens. Simultaneously, the ambition toward establishing a sustainable food system has motivated the adoption of cutting-edge technologies such as Artificial Intelligence to strengthen food security. Given the myriad challenges confronting stakeholders in their endeavors to safeguard food security, Artificial Intelligence emerges as a promising tool capable of crafting holistic management strategies for food security. This entails maximizing crop yields, mitigating losses, and trimming operational expenses. AI models present notable benefits in efficiency, precision, uniformity, automation, pattern identification, accessibility, and scalability for food security endeavors. The escalation in the global trend for adopting alternative protein sources such as edible insects and microalgae as a sustainable food source reflects a growing recognition of the need for sustainable and resilient food systems to address the challenges of population growth, environmental degradation, and food insecurity. Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.Critical reviews in food science and nutrition3131Artificial Intelligence offers a range of capabilities to enhance food safety in the production and consumption of alternative proteins like microalgae and edible insects, contributing to a sustainable and secure food system.Critical reviews in food science and nutrition["Jerina Rugji", "Zeki Erol", "F. Ta\u015f\u00e7\u0131", "Laura Musa", "Ambreen Hamadani", "Migena Gjoni G\u00fcndemir", "Esa Karalliu", "S. Siddiqui"]2024-12-07T00:00:00 b49f580067a4c973620d15406ab15ec794dd6328The adoption of generative Artificial Intelligence (AI) in medicine holds the promise of enhancing patient care through improved clinical decision-making, personalized treatment plans, and efficient data management. However, the integration of AI also introduces significant ethical challenges that must be addressed to ensure its responsible use. This systematic review examines the primary ethical concerns associated with generative AI in medicine, including issues of bias, transparency, accountability, privacy, and the impact on patient autonomy. The review identifies the need for robust ethical frameworks that guide the development and implementation of AI technologies in healthcare, ensuring that they are transparent, fair, and uphold patient rights. Key ethical challenges include managing data privacy, ensuring informed consent, avoiding biases, and maintaining trust among healthcare professionals and patients. The findings suggest that addressing these ethical issues is crucial for the responsible integration of AI in healthcare, and that future research should focus on bias mitigation, transparency, ethical decision-making, and understanding the impact of AI on healthcare professionals’ roles. This review provides a comprehensive overview of the ethical implications of generative AI in medicine and outlines directions for future research and policy development.IEEE Conference on Systems, Process and Control180The findings suggest that addressing ethical issues is crucial for the responsible integration of AI in healthcare, and that future research should focus on bias mitigation, transparency, ethical decision-making, and understanding the impact of AI on healthcare professionals’ roles.2024 IEEE 12th Conference on Systems, Process & Control (ICSPC)["Ali Gunawan", "Richard Wiputra"]2024-12-07T00:00:00 a167912afc6691f6947dbe01fc3312dede1c650bAlgorithmic management leverages data-driven algorithms and artificial intelligence to automate managerial functions traditionally executed by human managers. This paper provides a comprehensive overview of algorithmic management, exploring its definitions and emergence in gig economy platforms and traditional workplaces. It delves into key sociological and organizational theories—including Weber's bureaucracy, Critical Management Studies (CMS), and technological rationality—to frame the discussion. The impact of algorithmic management on employee autonomy, digital surveillance, and forms of worker resistance is examined, alongside its role in shaping organizational structures, enhancing efficiency, and driving innovation. Ethical implications, particularly concerning fairness, transparency, and bias, are critically analyzed. While algorithmic management offers potential benefits such as improved efficiency and decision-making, it also raises significant concerns about worker autonomy, power imbalances, and ethical considerations. The paper underscores the need for a nuanced understanding and responsible implementation of algorithmic management to harness its advantages while mitigating its drawbacks.Technium Social Sciences Journal00The need for a nuanced understanding and responsible implementation of algorithmic management to harness its advantages while mitigating its drawbacks is highlighted, highlighting the need for a nuanced understanding and responsible implementation of algorithmic management.Technium Social Sciences Journal["Lucian Sfetcu"]2024-12-07T00:00:00 e07b8ebf9078edebda980dddb76270e8203e1665The marketing landscape has seen radical transformations throughout history, from product- focused approaches to customer-centric strategies. Today, Artificial Intelligence (AI) plays a pivotal role in reshaping how businesses interact with consumers. This paper examines the history and evolution of marketing, AI, and generational shifts, with an emphasis on Generation Z, the first digital-native generation. We explore how AI-driven marketing strategies resonate with Gen Z and their ethical concerns about privacy. Finally, we introduce the concept of "Generation AI," predicting an even deeper integration of AI into consumer behavior. The study focuses on the Indian market, analyzing how AI is influencing marketing strategies and reshaping the interaction between businesses and Gen Z consumers.International Journal For Multidisciplinary Research70The history and evolution of marketing, AI, and generational shifts, with an emphasis on Generation Z, the first digital-native generation is examined, analyzing how AI is influencing marketing strategies and reshaping the interaction between businesses and Gen Z consumers.International Journal For Multidisciplinary Research["Tanay Saxena"]2024-12-07T00:00:00 51cd3b118668b2b2dc9bf1b223c73aa66da696ddArtificial Intelligence has transformed enterprise cloud-based integrations and automation, revolutionizing how businesses manage data, workflows, and applications across distributed environments. This comprehensive article explores the impact of AI on enterprise systems, examining key areas, including intelligent data integration, automated workflow optimization, and enhanced security measures. The article delves into technical implementation considerations, discussing infrastructure requirements and integration architectures while highlighting the substantial business benefits in operational efficiency, cost optimization, and strategic advantages. Additionally, it addresses the critical challenges organizations face in technical and organizational dimensions when implementing AI solutions, providing insights into successful adoption strategies and future considerations for enterprise AI integration.International Journal of Scientific Research in Computer Science Engineering and Information Technology20This comprehensive article explores the impact of AI on enterprise systems, examining key areas, including intelligent data integration, automated workflow optimization, and enhanced security measures, and delves into technical implementation considerations.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Mahaboob Subhani Shaik"]2024-12-07T00:00:00 950a49ad5207955a88cc93209fe26d1c56a5d2daThis article presents a conceptual framework for integrating artificial intelligence (AI) to enhance communication skills in educational and business settings. By examining the dual role of AI as both an enabler and a challenge, the article highlights AI’s capacity for personalized learning, skill development, and efficiency in communication tasks. It also addresses potential issues such as academic integrity, data reliability, and ethical considerations. This framework aims to guide institutions and organizations in adopting AI responsibly, ensuring that human-centered communication remains integral to AI-enhanced environments.Business and Professional Communication Quarterly210A conceptual framework for integrating artificial intelligence to enhance communication skills in educational and business settings is presented, ensuring that human-centered communication remains integral to AI-enhanced environments.Business and Professional Communication Quarterly["The Anh Phan"]2024-12-07T00:00:00 e4cd9839cdfc7eb3b413f6413efbac02f6b8f51dThis paper aims at explaining how AI can augment smart grids toward improved energy management. Mainly, the research is concerned with using artificial intelligence together in order to optimize energy distribution, minimize the cost of running organizations and finally establish means to harness sustainable energy resources. Four AI algorithms are utilized in this study: During the smart grid, load forecasting, fault detection, and energy optimization, ANN, SVM, GA, and RL approaches can be used. Based on the experimental findings, it is shown that the ANN achieved a 92% forecast precision; on the other hand, the utility of both the SVM and GA algorithms in energy optimization ranged from 18–23%. The RL algorithm returned the best result and cut wastage by 32% and enhanced load balance efficiency. These results demonstrate that AI strategy performs better than the conventional energy management systems, particularly for accommodating renewable generation and dynamic grid loads. By optimising the utilisation of the grid, and utilising artificial intelligence, energy losses are kept to an absolute minimum, thus supplementing sustainable efforts. The study establishes that smart grid using AI technology is the innovative way of enhancing energy management and thus sustainable smart infrastructures. In future work, issues of data security and scalability will be targeted in order to enhance the application of AI in smart grids.Nanotechnology Perceptions230The study establishes that smart grid using AI technology is the innovative way of enhancing energy management and thus sustainable smart infrastructures and thus sustainable smart infrastructures.Nanotechnology Perceptions["Dr. M. Sai Veerraju", "SatyavedaSomepalli", "Dr P Kiran", "Kumar Reddy", "Dr. P. Ram", "Kishore Kumar Reddy", "D. Reddy", "Ranga Reddy"]2024-12-07T00:00:00 55bdad9cd6995e2784399dfee81a9a57b0c783a1Abdominal Radiology380The data showed relatively little biomarker value change when measured at or greater than 70 keV, but lower VMI datasets should be avoided due to larger deviations in measured value as compared to 70 keV, a level considered equivalent to conventional 120 kVp exams.Abdominal radiology["G. Toia", "John W Garret", "Sean D. Rose", "Timothy Szczykutowicz", "P. J. Pickhardt"]2024-12-07T00:00:00 1c814af713267362031ac97b27cffae45bf0a965The digital transformation of financial reporting has emerged as a critical paradigm shift in corporate accounting, driven by the revolutionary technologies of Artificial Intelligence (AI) and Blockchain. This qualitative research employs a comprehensive literature review methodology to examine how these emerging technologies are fundamentally reshaping transparency, efficiency, and reliability in corporate financial reporting processes. Through systematic analysis of peer-reviewed journals, academic publications, and industry reports published within the last five years, the study explores the multifaceted implications of digital technologies on accounting practices. The research investigates the transformative potential of AI and Blockchain in addressing traditional challenges such as data integrity, real-time reporting, fraud prevention, and regulatory compliance. By synthesizing insights from interdisciplinary perspectives, including accounting, computer science, and organizational management, this study reveals how these technologies are creating unprecedented opportunities for enhanced financial transparency and operational efficiency. Key findings demonstrate that AI-powered algorithms can significantly improve financial data analysis, predictive modeling, and risk assessment, while Blockchain technology offers immutable, decentralized record-keeping that minimizes manipulation and increases trust. Moreover, the study critically examines the implementation challenges, technological infrastructure requirements, and potential organizational resistance to these digital innovations. The research provides a comprehensive framework for understanding the strategic integration of AI and Blockchain in financial reporting, offering valuable insights for corporate leaders, accounting professionals, and policymakers seeking to navigate the evolving digital landscape of corporate financial management.International Journal of Social and Human110Key findings demonstrate that AI-powered algorithms can significantly improve financial data analysis, predictive modeling, and risk assessment, while Blockchain technology offers immutable, decentralized record-keeping that minimizes manipulation and increases trust.International Journal of Social and Human["Arif Budiarto", "Andi Arifuddin Iskandar", "W. Suryathi"]2024-12-07T00:00:00 0afefab8288dc844606de63942f4121449bf77c0Pediatric Radiology160This study demonstrates that RH, as the first reported AI-derived knowledge recommender for radiology education, significantly reduces reporting time and improves reporting accuracy while reducing overall workload and mental demand for radiology trainees.Pediatric radiology["Julian Lopez-Rippe", "Manasa Reddy", "M. C. Velez-Florez", "Raisa Amiruddin", "Wondwossen T Lerebo", "A. Gokli", "Michael Francavilla", "Janet Reid"]2024-12-07T00:00:00 ba598b27c84f8d00d3456c32f73d39e9f9dfc5d5This study investigates the adaptability of Artificial Intelligence (AI) agents in the Metaverse, focusing on their ability to enhance responsiveness, decision-making, and engagement through the proposed Adaptive Learning Model for AI Agents (ALMAA) framework. The research does not introduce new interventions to existing platforms like Epic Games or AltspaceVR but instead analyzes how their operations align with adaptive learning principles. By examining these platforms, the study demonstrates the alignment between real-world practices and theoretical constructs, offering insights into how adaptive AI systems operate in dynamic virtual environments. Case observations highlight key metrics such as user interaction efficiency, contextual decision accuracy, and predictive engagement strategies. The data, derived from detailed user interaction logs and feedback reports, underscore the practical application of adaptive learning in optimizing user satisfaction and system performance. Statistical analyses reveal notable gains in response speed, predictive precision, and user engagement, validating the theoretical framework’s relevance. This paper positions the ALMAA framework as a critical lens for understanding and analyzing adaptive AI in virtual settings. It emphasizes theoretical exploration rather than experimental application, providing a foundation for future research into scalable, user-centered AI systems tailored for the Metaverse’s evolving demands.Applied Sciences400The ALMAA framework is positioned as a critical lens for understanding and analyzing adaptive AI in virtual settings, providing a foundation for future research into scalable, user-centered AI systems tailored for the Metaverse’s evolving demands.Applied Sciences["Yina Xia", "Seong-Yoon Shin", "Hyun-Ae Lee"]2024-12-07T00:00:00 382477ca4e7522b0c9531760bee00ec9d6ff19b0The convergence of Federated Artificial Intelligence (AI) systems and Circular Economy (CE) principles marks a transformative approach to sustainable and resilient data science. Federated AI, with its decentralized architecture and emphasis on data privacy, seamlessly integrates with CE's objectives of maximizing resource efficiency, reducing waste, and fostering closed-loop systems. This paper proposes a novel framework that synergizes CE principles with Federated AI to address persistent challenges, including data siloing, scalability, energy efficiency, and environmental sustainability. The framework leverages adaptive learning models and resource-aware algorithms to optimize data- driven decision-making across applications such as smart city development, sustainable supply chain management, and renewable energy optimization. Through rigorous simulations and real-world case studies, the study demonstrates measurable improvements: a 20–30% increase in resource efficiency and a marked reduction in computational energy consumption compared to traditional centralized AI systems. These findings highlight the transformative potential of Federated AI in driving circular and sustainable ecosystems. The research also contributes to ethical AI discourse, providing actionable insights for policymakers, academics, and industry leaders to harmonize AI advancements with global sustainability imperatives.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A novel framework that synergizes CE principles with Federated AI to address persistent challenges, including data siloing, scalability, energy efficiency, and environmental sustainability is proposed.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Dr. Pramod Kumar", "Mr. Vikas Kumar", "Mr. Rahul Gautam"]2024-12-07T00:00:00 4398ae516a5ea96d8fccd0b4fc7e43ea4ad345f7The rapid advancements in Artificial Intelligence (AI) technology have introduced transformative methods in various fields, including second language acquisition (SLA). This study aims to examine the impact of AI-based tools and platforms on the process of learning a second language, focusing on their effectiveness, challenges, and pedagogical implications. The research employs a qualitative descriptive methodology, incorporating a thorough review of relevant literature and case studies. Data collection involves analyzing academic journals, reports, and practical use cases of AI-based language learning applications such as Duolingo and Babbel. Thematic analysis is utilized to organize data into core themes, including the personalization of learning, feedback mechanisms, and cultural context adaptation. The findings reveal that AI provides significant benefits in SLA by enabling personalized learning experiences and offering instant, adaptive feedback tailored to individual learners. However, challenges such as the inability of AI to fully understand cultural nuances and ethical concerns surrounding data privacy persist. These insights underline the need for strategic integration of AI in education, emphasizing a balance between technological potential and the human aspects of language learning. This study contributes to the growing body of research on AI in education, offering practical recommendations for educators, policymakers, and developers to harness AI's potential effectively in enhancing second language acquisition.GANEC SWARA00The findings reveal that AI provides significant benefits in SLA by enabling personalized learning experiences and offering instant, adaptive feedback tailored to individual learners, however, challenges such as the inability of AI to fully understand cultural nuances and ethical concerns surrounding data privacy persist.GANEC SWARA["I. G. N. O. Seventilofa"]2024-12-07T00:00:00 06ddd0b4aa8a8b4bb980a08d6cdd0245eb1e232cThe lack of ground truth explanation labels is a fundamental challenge for quantitative evaluation in explainable artificial intelligence (XAI). This challenge becomes especially problematic when evaluation methods have numerous hyperparameters that must be specified by the user, as there is no ground truth to determine an optimal hyperparameter selection. It is typically not feasible to do an exhaustive search of hyperparameters so researchers typically make a normative choice based on similar studies in the literature, which provides great flexibility for the user. In this work, we illustrate how this flexibility can be exploited to manipulate the evaluation outcome. We frame this manipulation as an adversarial attack on the evaluation where seemingly innocent changes in hyperparameter setting significantly influence the evaluation outcome. We demonstrate the effectiveness of our manipulation across several datasets with large changes in evaluation outcomes across several explanation methods and models. Lastly, we propose a mitigation strategy based on ranking across hyperparameters that aims to provide robustness towards such manipulation. This work highlights the difficulty of conducting reliable XAI evaluation and emphasizes the importance of a holistic and transparent approach to evaluation in XAI.arXiv.org550This work defines this manipulation as an adversarial attack on the evaluation where seemingly innocent changes in hyperparameter setting significantly influence the evaluation outcome and proposes a mitigation strategy based on ranking across hyperparameters that aims to provide robustness towards such manipulation.ArXiv["Kristoffer K. Wickstr\u00f8m", "Marina M.-C. H\u00f6hne", "Anna Hedstr\u00f6m"]2024-12-07T00:00:00 05ebb11d7036e7c4223bb8b697521732d6595c0ePenelitian ini bertujuan untuk menganalisis media artificial Intelegence dalam literasi digital untuk pengembangan kognitif anak usia dini. Penelitain ini adalah jenis penelitian dan pengembangan (R&D) terdiri dari 10 langkah yaitu 1) Potensi dan Masalah,2) Pengumpulan Data, 3) Desain Produk, 4) Validasi desain, 5) Perbaikan desain, 6) Uji Coba Produk, 7) Revisi Produk, 8) Uji Coba pemakaian, 9) Revisi Produk, 10) Produksi massal. Metode pengumpulan data yangdigunakan adalah Wawancara, observasi, angket dan dokumentasi, teknik analisis data menggunakan angket validasi dan angket tanggapan. Hasil dari penelitian menyatakan bahwa media Media Artifiicial Intelegence yang peneliti kembangkan dapat meningkatkan kemampuan literasi digital dan mengebangkan kognitif anak usia dini yang dikembangkan dengan metode penelitian Hasil validasi ahli media dan ahli materi dapat dikatakan media Media Artifiicial Intelegence dapat mengenalkan literasi digital Anak Sebagai Upaya Mengembangkan Kognitif Pada Anak Usia Dini. Uji coba kelompok besar dengan jumlah 15 peserta didik menunjukkan kategori Berkembang sesuai Harapan (BSH) ada 11 anak dengan hasil presentase 73,3 % dan anak yang sudah berkembang sesuai harapan terdapat 4 anak dengan presentase 36,3% “Hal ini menunjukkan bahwa media pembelajaran mengembangkan media Artificial Intelegence dalam mengenalkan literasi digital Anak Sebagai Upaya Mengembangkan Kognitif Pada Anak Usia Dini Dibandar Lampung.Murhum : Jurnal Pendidikan Anak Usia Dini00Murhum : Jurnal Pendidikan Anak Usia Dini["Heny Wulandari", "Kanada Komariah"]2024-12-07T00:00:00 aa10bbd580488bee7a23114796b54fdbbba4f84eArtificial Intelligence (AI) is changing the face of healthcare insurance by innovating key operations such as risk assessment, claim processing, and fraud detection. By applying advanced algorithms in machine learning, insurers are able to reach a more accurate risk profiling and thus offer personalized insurance pricing based on individual health conditions and lifestyles. AI- powered automated claims processing enhances operational efficiency, shortens processing time, and minimizes errors. Additionally, fraud detection systems powered by AI proactively identify suspicious activities and save insurers and policyholders from financial loss. The paper discusses the various dimensions in which AI is making an impact on the health insurance ecosystem, highlighting the potential benefit to insurers through cost savings and improved decision-making while offering customers speedier services and personalization. The paper further points to some key regulatory challenges that will need consideration in terms of data privacy, algorithmic transparency, and ethical considerations to ensure equity and compliance in the adoption of AI technologies in health insurance. Keywords: Artificial Intelligence, Health Insurance, Risk Profiling, Claims Processing, Fraud Detection, Pricing of Insurance, Machine Learning, Regulatory Challenges, Data Privacy, Operational EfficiencyInternational Scientific Journal of Engineering and Management00The paper discusses the various dimensions in which AI is making an impact on the health insurance ecosystem, highlighting the potential benefit to insurers through cost savings and improved decision-making while offering customers speedier services and personalization.International Scientific Journal of Engineering and Management["Manoj Kumar Manoj Kumar"]2024-12-08T00:00:00 a2997eab0e971859dea3e781b1b52d2a2bbbfcc7Artificial intelligence (AI) has attracted much public interest, inspiring both hopes and fears. As countries define pathways for developing and implementing AI, healthcare is emerging as a priority sector. Sociotechnical imaginaries, which can mobilize public support and attract resources for realising sociotechnical visions, play an important role in the trajectories of emerging technologies. News media, in turn, are central to the negotiation, construction, and promotion of such imaginaries. We analyze how news media construct sociotechnical imaginaries of AI in healthcare in China, Germany, and the United States (US), three countries with differing healthcare and media systems, and sociopolitical and -cultural outlook on technologies. Drawing from a thematic analysis of articles from 15 newspapers, we find two powerful, cross-national, collectively held imaginaries: The first imaginary on enhancing healthcare with AI emerged across all three countries; the second imaginary on using AI to manage pandemics or epidemics was only fully developed in Chinese and US coverage, though present as an outlier in German news coverage. Lower-level divergences within each imaginary can be explained by systemic differences between the countries, such as the largely private US healthcare system, the mostly state-controlled Chinese media and healthcare systems, and the German hesitancy toward emerging technologies. This study provides evidence for how powerful imaginaries can emerge across very different sociopolitical and cultural contexts while accounting for contextual national factors.Emerging Media330Evidence is provided for how powerful imaginaries can emerge across very different sociopolitical and cultural contexts while accounting for contextual national factors.Emerging Media["Saba Rebecca Brause", "Heng Yang", "Mike S Sch\u00e4fer", "Jing Zeng"]2024-12-08T00:00:00 5e2b5a3761ce4cb85c9faced7191c3273f97c02bThe two-lane approach as a response to assessment in the new world of generative artificial intelligence (GenAI) (Liu & Bridgeman, 2023), has fast gained traction with tertiary education providers. The flexible, adaptive and experimental nature of this approach arguably complements much of what the literature on second language (L2) motivation research advocates. A key component of that literature is that the more students can see a rationale for their learning and its relevance, the more they will become and remain motivated. While L2 motivation research greatly expands on these broad concepts, two key theoretical constructs underpin much of it. The first is the Process Model of Motivation (Dörnyei & Ottó, 1998) and the second is Dörnyei’s (2009) L2 Motivational Self System, which expanded on the former. This article will background the two-lane approach and then discuss the perceived merits of it by way of example. It will posit that this approach may work to the advantage of students in a world in which they will be increasingly expected to incorporate GenAI into their course work. Finally, this article will speak to the reservations in the literature about GenAI’s role and ability to promote critical thinking and the use of evaluative judgment, which are both core elements that learning advisors teach and support students with.ATLAANZ Journal370The two-lane approach as a response to assessment in the new world of generative artificial intelligence (GenAI) has fast gained traction with tertiary education providers and the perceived merits of it are discussed, along with reservations in the literature about GenAI’s role and ability to promote critical thinking and the use of evaluative judgment.ATLAANZ Journal["Nigel Gearing"]2024-12-08T00:00:00 351983d04fc01442e064e4cd531c91d26eb823e9Artificial intelligence (AI) is a set of computational methods that interprets the data given, uncovers the underlying patterns associated with the complexity of data, and provides the prediction of outcomes that have become increasingly relevant in urology. The current application of AI in urology predominately focuses on disease diagnosis and risk factor analysis in urologic oncology and male infertility. While many candidate models have been proposed in the literature, efforts to construct clinically meaningful data by incorporating patient-specific and multidisciplinary approaches should be carried out to improve the clinical applicability of AI in driving personalised treatment planning and disease prognosis. Looking forward, AI has the potential to drive targeted training in urology, from surgical techniques to patient-specific surgical procedure simulation, in combination with other technologies such as augmented reality. In order to achieve this, patient involvement should be considered in the model development stage, which also addresses issues surrounding the ethical deployment of AI in the clinical environment. It is possible to see AI playing a collaborative role with surgeons in improving clinical efficiency in the future. Level of evidence: Not ApplicableJournal of Clinical Urology190AI has the potential to drive targeted training in urology, from surgical techniques to patient-specific surgical procedure simulation, in combination with other technologies such as augmented reality, and patient involvement should be considered in the model development stage.Journal of Clinical Urology["Yi Zhao", "Eva Bolton", "Naeem Soomro", "B. Rai", "Rakesh Heer"]2024-12-08T00:00:00 aa454469ce3c8b465d8476d0aa12a04d2568a9c0In the contemporary digital landscape, the focus of manufacturing companies on green innovation has garnered attention in the business and academic realms. Nonetheless, the existing research system for manufacturers lacks a systematic study on how artificial intelligence literacy may bolster green innovation efforts. This study endeavors to construct a theoretical framework for artificial intelligence literacy, green information system, green absorptive capacity, and green innovation with respect to the dynamic capability theory and conducting empirical analysis utilizing survey data obtained from 288 ISO14001 manufacturing firms in Malaysia. The findings revealed that artificial intelligence literacy is a significant determinator of green absorptive capacity, the positive outcome of green absorptive capacity is green innovation, and the positive link between artificial intelligence literacy and green absorptive capacity is moderated by green information system. However, artificial intelligence literacy didn't exhibit a direct relationship with green innovation, even when considering green absorptive capacity as a mediator. These results not only offer compelling insights into the link between artificial intelligence literacy and green innovation, but also hold significant implications for academic research and policymaking concerning sustainable development and cleaner manufacturing production.Corporate Social Responsibility and Environmental Management370A theoretical framework is constructed for artificial intelligence literacy, green information system, green absorptive capacity, and green innovation with respect to the dynamic capability theory and empirical analysis utilizing survey data obtained from 288 ISO14001 manufacturing firms in Malaysia revealed that artificial intelligence literacy is a significant determinator of green absorptive capacity.Corporate Social Responsibility and Environmental Management["Jie Cheng", "Nai Ru Xu", "Noor Ullah Khan", "H. Singh"]2024-12-08T00:00:00 ce699c050d4e0cb981408e0e40122a962bc51987Journal of Risk Research680Journal of Risk Research["Andreas Schwarz"]2024-12-08T00:00:00 40780b1f3d715151b33721a407ae215798563f38We create a large language model with high accuracy to investigate the relatedness between 12 environmental, social, and governance (ESG) topics and more than 2 million news reports. The text match pre‐trained transformer (TMPT) with 138,843,049 parameters is built to probe whether and how much a news record is connected to a specific topic of interest. The TMPT, based on the transformer structure and a pre‐trained model, is an artificial intelligence model trained by more than 200,000 academic papers. The cross‐validation result reveals that the TMPT's accuracy is 85.73%, which is excellent in zero‐shot learning tasks. In addition, combined with sentiment analysis, our research monitors news attitudes and tones toward specific ESG topics daily from September 2021 to September 2023. The results indicate that the media is increasing discussion on social topics, while the news regarding environmental issues is reduced. Moreover, toward almost all topics, the attitudes are gradually becoming positive. Our research highlights the temporal shifts in public perception regarding 12 key ESG issues:ESG has been incrementally accepted by the public. These insights are invaluable for policymakers, corporate leaders, and communities as they navigate sustainable decision‐making.Business Strategy and the Environment470A large language model with high accuracy is created to investigate the relatedness between 12 environmental, social, and governance topics and more than 2 million news reports and indicates that the media is increasing discussion on social topics, while the news regarding environmental issues is reduced.Business Strategy and the Environment["Chao Li", "A. Keeley", "S. Takeda", "Daikichi Seki", "Shunsuke Managi"]2024-12-08T00:00:00 e2a79503fd07f0033ce7a66ae25b9ab69a21f11dSmall and Medium Enterprises (SMEs) in South Africa previously faced challenges due to limited resources, restricted access to technology, and the need to constantly adapt to a dynamic business environment. The introduction of Generative Artificial Intelligence (AI) emerged as a potential solution to these issues, promising to enhance operational efficiency and strategic decision-making. As a representative of developing economies, South Africa experienced a growing interest in AI technologies. This study was conducted to explore the impact of generative AI on SME productivity in South Africa, an area which had been underexplored. Employing a qualitative methodology, the study evaluated the current state and implications of generative AI in South African SMEs. It involved in-depth interviews to gather perceptions, experiences, challenges, and benefits from SME owners and managers regarding the adoption of generative AI technologies. The findings analysed via R Statistical Software revealed significant insights into the specific areas where generative AI substantially impacted SME productivity. It also identified the challenges and opportunities associated with the adoption of generative AI by SMEs, as well as the potential long-term implications. Key findings included notable improvements in data-driven decision-making, operational efficiencies, and market expansion strategies. However, the study also highlighted barriers such as the lack of technical expertise, initial setup costs, and concerns over data security. Overall, the impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.International Journal of Research In Business and Social Science140The impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.International Journal of Research in Business and Social Science (2147- 4478)["Meshel Muzuva", "Helper Zhou", "R. Zondo"]2024-12-08T00:00:00 384038008a057d4a78a6352bbe4852d795a61aca The pharmaceutical industry is undergoing a massive transformation driven by technological advancements and leapfrogging further due to the integration of Artificial intelligence (AI), automation, and robotics. These technologies are being deployed to cover various aspects, including drug discovery, manufacturing, supply chain, and patient care. AI's ability to process and analyze massive data sets allows researchers to identify new drug candidates faster or improve on current ones through various strategies. Automation transforms repetitive tasks and increases accuracy, but most importantly, it frees people up from work that they need not do and concentrates on the jobs that still require human involvement. Conversely, when integrated with AI, robots enhance the production process by enabling speedy, accurate, and scalable manufacturing. Robotics are now being used in pharmacies for medication dispensing and are very efficient. Furthermore, all these innovations driven by AI, Automation, and robotics also contribute to personalized medicine by extending tailored treatments based on individual patient data. This paper explores the role of AI, Automation, and Robotics in the Pharmaceutical industry and its benefits. The ongoing evolution of such technologies holds immense potential to address the growing needs and handle industry challenges such as increasing demand, regulatory compliance, and global health needs to ultimately lead the pharmaceutical industry towards a more efficient, adaptive, and patient-centered approach. Journal of Next-Generation Research 5.000The role of AI, Automation, and Robotics in the Pharmaceutical industry and its benefits is explored to address the growing needs and handle industry challenges such as increasing demand, regulatory compliance, and global health needs to ultimately lead the pharmaceutical industry towards a more efficient, adaptive, and patient-centered approach.Journal of Next-Generation Research 5.0["Sivasakthivel Periyannan Ramamoorthy"]2024-12-08T00:00:00 ebbbe19f6dc410f739f3223d0f043e5e70340d2aRobots and other artificial intelligence (AI) systems are widely perceived as moral agents responsible for their actions. As AI proliferates, these perceptions may become entangled via the moral spillover of attitudes towards one AI to attitudes towards other AIs. We tested how the seemingly harmful and immoral actions of an AI or human agent spill over to attitudes towards other AIs or humans in two preregistered experiments. In Study 1 (N = 720), we established the moral spillover effect in human-AI interaction by showing that immoral actions increased attributions of negative moral agency (i.e., acting immorally) and decreased attributions of positive moral agency (i.e., acting morally) and moral patiency (i.e., deserving moral concern) to both the agent (a chatbot or human assistant) and the group to which they belong (all chatbot or human assistants). There was no significant difference in the spillover effects between the AI and human contexts. In Study 2 (N = 684), we tested whether spillover persisted when the agent was individuated with a name and described as an AI or human, rather than specifically as a chatbot or personal assistant. We found that spillover persisted in the AI context but not in the human context, possibly because AIs were perceived as more homogeneous due to their outgroup status relative to humans. This asymmetry suggests a double standard whereby AIs are judged more harshly than humans when one agent morally transgresses. With the proliferation of diverse, autonomous AI systems, HCI research and design should account for the fact that experiences with one AI could easily generalize to perceptions of all AIs and negative HCI outcomes, such as reduced trust.arXiv.org00It is found that spillover persisted in the AI context but not in the human context, possibly because AIs were perceived as more homogeneous due to their outgroup status relative to humans.ArXiv["Aikaterina Manoli", "Janet V. T. Pauketat", "Jacy Reese Anthis"]2024-12-08T00:00:00 df56e61a3cdeebdd38a9cf7dbe502644dd73d2f0This study examines how artificial intelligence (AI) affects worker productivity, emphasising AI's capacity to automate jobs, reduce errors, and optimise workflows. It emphasises the need for dynamic reskilling initiatives and company-school cooperation to provide workers with the necessary skills. Using a two-log econometric model, the study examines the association between AI patents and productivity. It observes that the effects of AI differ across industries, with less automation in positions requiring creativity and emotional intelligence. The paper also suggests more research and examines the relationship between productivity and R&D costs, physical assets, and non-AI patents.Sosyoekonomi270The need for dynamic reskilling initiatives and company-school cooperation to provide workers with the necessary skills is emphasised, emphasising AI's capacity to automate jobs, reduce errors, and optimise workflows.Sosyoekonomi["Hiroshi Yoshida", "Meltem \u0130nce Yenilmez"]2024-12-08T00:00:00 052212cd77af5ea3599b9e8aaba385ff040bf355: Artificial intelligence (AI) and technology approaches have been increasingly used in financial markets. The global technology revolution has had a profound and lasting effect on the world. The stock market capitalization in Arab countries has also witnessed these changes. This thesis attempted to investigate the impact of artificial intelligence and technology on the stock market capitalization rate in Arab countries. Econometric and statistical analysis using panel regression has been adopted to test this impact, especially for Middle Eastern countries, such as Bahrain, Kuwait, Egypt, Qatar, Oman, Morocco, Tunisia, Lebanon, Saudi Arabia, Jordan, and the United Arab Emirates from 2010 until 2022. Middle Eastern countries were selected according to the availability of data, and they represent the majority of Arab countries, which adapt quickly to the use of AI and technology. The model includes the dependent variable, the stock market capitalization rate, as an indicator for stock market capitalization. While concerned, theالمجلة العلمیة للإقتصاد و التجارة00This thesis attempted to investigate the impact of artificial intelligence and technology on the stock market capitalization rate in Arab countries with a focus on Middle Eastern countries, which adapt quickly to the use of AI and technology.المجلة العلمية للإقتصاد و التجارة["Mariam Alaa el din Osman zaky"]2024-12-08T00:00:00 2608757e813839c41c9f6a974729c1592754d7a6The use of artificial intelligence (AI) tools to generate content-specific instructional materials has attracted the interest of Language for Specific Purposes (LSP) educators in higher education, as language courses in this setting typically do not utilize a textbook, requiring the instructor to create independent materials. However, instructors are often not content experts. Collaboration between LSP instructors and content experts in the form of co-teaching is one way in which materials can be generated and benefit instructors and students alike. At the same time, creating instructional materials can be a time-consuming task and can detract from other areas of collaboration. The use of AI tools to generate mathematical content could help instructors save time, enable real-world connections and offer a variety of materials to students. This study examines the generation of word problems with the help of AI tools for a content-based mathematics language course for first-semester bachelor students pursuing a Science, Technology, Engineering or Mathematics (STEM) degree. As they form part of the final exam, a new set of word problems needs to be generated each year. While recent studies (cf. Lu et al., 2022) found that AI methods were effective in generating math word problems that were diverse, relevant, and useable, there have been no studies examining the applicability of AI-generated word problems in terms of their efficacy in an LSP setting. The action research study used screencasting to capture the math content tutors’ content analysis of AI output on math word problems and was followed by a semi-structured group interview. The results showed that word problems generated by AI were generally useful based on factors such as prompting techniques. However, limitations were observed in the areas of accuracy and consistency. Based on initial results, the report suggests first implications for use in LSP instruction and describes measures that need to be taken into account in further studies.Scripta Manent00The results showed that word problems generated by AI were generally useful based on factors such as prompting techniques and limitations were observed in the areas of accuracy and consistency, which suggest first implications for use in LSP instruction.Scripta Manent["Karen Fleischhauer", "Kate Friedrich"]2024-12-08T00:00:00 43c07ae364d2a9b0f922a253aade31f3124b028aAbstract—The rise of cybercrime has made fraud detection a critical focus for financial services, where even minor improvements in detection rates can translate into significant savings. Traditional rule- based detection systems are increasingly unable to keep up with the complexity and evolving nature of fraudulent schemes. This work explores the role of artificial intelligence (AI), particularly deep learning, in enhancing fraud detection capabilities. Here we propose a novel AI-driven fraud detection system that incorporates Single Sign-On (SSO) identity and access management (IAM) frameworks, leveraging a dual-layered approach with batch and real-time processing. The batch layer establishes a user trust identity by analysing historical behavioural patterns, which informs an access-granting mechanism that evaluates real-time transaction data for fraud indicators. A deep iterative convoluted memory classifier then assesses transactions, for detecting frauds. This architecture automates fraud detection, allowing analysts to focus on high-priority cases and ensuring that only authenticated users can access sensitive information. Our experiments are Conducted in the MATLAB environment, From the analysis it was revealed that the model's effectiveness, showing promise for scalable, efficient fraud prevention in financial services. This work underscores the transformative potential of AI in securing financial transactions against sophisticated cyber threats. Index Terms— cybercrime, Fraud, Financial Transactions, artificial intelligence, identity and access management, fraud detection,International Scientific Journal of Engineering and Management00A novel AI-driven fraud detection system that incorporates Single Sign-On (SSO) identity and access management (IAM) frameworks, leveraging a dual-layered approach with batch and real-time processing is proposed.International Scientific Journal of Engineering and Management["Ranga Premsai"]2024-12-08T00:00:00 ed7ce42d0e7aca2bd1443021a691092ba0e14bf0Background: The integration of Artificial Intelligence (AI) into physical therapy is transforming traditional practices, presenting a range of ethical, professional, and technical challenges. These include concerns about patient data security, algorithmic bias, job displacement, the need for continuous education, and system reliability. While AI has the potential to enhance diagnostic accuracy and treatment personalization, its adoption necessitates significant adaptations in clinical workflows and professional roles, emphasizing the need for a strategic approach to address these challenges and optimize its application. Objective: The objective of this systematic review was to analyze the ethical, professional, and technical challenges associated with implementing AI in physical therapy and to propose strategic recommendations for mitigating risks while maximizing the potential benefits. Methods: A systematic review was conducted following PRISMA guidelines, with a comprehensive search across PubMed, Scopus, Web of Science, and IEEE Xplore databases up to July 2024. The search terms included "Artificial Intelligence," "physical therapy," "challenges," "ethics," "professional development," and "technical reliability." Studies were included if they addressed ethical, professional, or technical barriers to AI integration in physical therapy. Titles and abstracts were screened independently by two reviewers, with disagreements resolved by a third reviewer. Data were extracted on study characteristics, AI applications, identified challenges, and suggested solutions. Quality appraisal was conducted using the CASP tool. Results: A total of 12 studies met the inclusion criteria. Ethical concerns, including patient data security and algorithmic bias, were reported in 60% of studies. Professional challenges, such as job displacement and the need for continuous education, were highlighted in 50%. Technical issues, including system reliability and integration into clinical workflows, were evident in 75%. The findings also showed that 40% of studies emphasized the importance of adapting clinical training programs to include AI-focused education. Conclusion: AI holds transformative potential for physical therapy by improving diagnostic precision and personalized care. However, its integration is hindered by significant ethical, professional, and technical challenges. Addressing these requires the establishment of robust ethical frameworks, continuous education and training for professionals, and rigorous technical validation processes to ensure AI's safe and effective application in clinical practice.Insights-Journal of Health and Rehabilitation30A systematic review found that AI holds transformative potential for physical therapy by improving diagnostic precision and personalized care, however, its integration is hindered by significant ethical, professional, and technical challenges.Insights-Journal of Health and Rehabilitation["Muhammad Hafeez", "Muhammad Zia Ul Haq", "Zahra Tahzeem", "Shabana Rahim", "Nosheen Rao"]2024-12-08T00:00:00 819db5fb908837881fdb6a51681d4fb81a40bf78Cooperation is vital to our survival and progress. Evolutionary game theory offers a lens to understand the structures and incentives that enable cooperation to be a successful strategy. As artificial intelligence agents become integral to human systems, the dynamics of cooperation take on unprecedented significance. The convergence of human-agent teaming, contract theory, and decentralized frameworks like Web3, grounded in transparency, accountability, and trust, offers a foundation for fostering cooperation by establishing enforceable rules and incentives for humans and AI agents. We conceptualize Incentivized Symbiosis as a social contract between humans and AI, inspired by Web3 principles and encoded in blockchain technology, to define and enforce rules, incentives, and consequences for both parties. By exploring this paradigm, we aim to catalyze new research at the intersection of systems thinking in AI, Web3, and society, fostering innovative pathways for cooperative human-agent coevolution.arXiv.org40Incentivized Symbiosis is conceptualize Incentivized Symbiosis as a social contract between humans and AI, inspired by Web3 principles and encoded in blockchain technology, to define and enforce rules, incentives, and consequences for both parties.ArXiv["T. J. Chaffer", "Justin Goldston", "I. GemachD.A.T.A."]2024-12-08T00:00:00 644822df1b6e2e8ded7ae46bc381e8fbe8412e87The stock market is influenced not only by traditional financial metrics but also by psychological factors such as emotions, opinions, and sentiments. In recent years, the integration of sentiment analysis and artificial intelligence (AI) has transformed stock market forecasting by enabling traders and investors to interpret market behavior more effectively. Sentiment analysis, a subset of natural language processing (NLP), analyzes textual data from diverse sources like news articles and social media to gauge public sentiment—positive, negative, or neutral—toward market conditions. This analysis bridges the gap between quantitative data and investor psychology, revealing insights that traditional metrics might not capture. With the rise of social media and online forums, the volume of opinion data has surged, necessitating advanced technologies for real-time processing and interpretation. AI, mainly through machine learning and deep learning models like GPT and BERT, is crucial in efficiently analyzing vast datasets, detecting patterns, and predicting market trends. These AI-powered tools can combine sentiment data with historical market trends, providing a holistic view of market dynamics. The advanced capabilities of AI models to comprehend nuances such as sarcasm and irony further enhance sentiment detection, allowing for more accurate predictions. While integrating sentiment analysis and AI in financial markets offers numerous advantages, it also faces challenges such as algorithmic bias, data privacy issues, and the unpredictability of human emotions. This study aims to explore the integration of sentiment analysis and AI in stock market predictions, assess the accuracy of AI-driven predictions compared to traditional methods, and analyze case studies of successful applications in financial markets. Through this, the study seeks to contribute to the evolving landscape of financial forecasting by demonstrating the potential of AI and sentiment analysis in shaping market behavior understanding and decision-making processes.Journal of Next-Generation Research 5.080This study aims to explore the integration of sentiment analysis and AI in stock market predictions, assess the accuracy of AI-driven predictions compared to traditional methods, and analyze case studies of successful applications in financial markets.Journal of Next-Generation Research 5.0["Tanwangini Sahani"]2024-12-08T00:00:00 6087c28f55a9e3f16ba1aab8daa2b017fb588b28The dynamic force of artificial intelligence (AI) is reshaping our world, not in the distant future, but today. Its transformative potential, adaptability, and capacity to liberate human potential are becoming evident in a multitude of domains. AI’s ability to process vast datasets, offer data-driven recommendations, and enhance decision-making processes underscores its pivotal role in addressing complex challenges. This article explores AI’s current impact and its potential for further growth. It reviews 77 articles across diverse domains, highlighting AI’s role in emergency services. Through an in-depth analysis of these studies, the paper provides a broad overview of the current state of AI in emergency services, identifying key trends, challenges, and future opportunities. By examining the methodologies, datasets, AI and deep learning techniques, feature selection processes, evaluation metrics, and prediction models used in each study, the paper aims to offer a thorough understanding of AI’s role in this critical sector. This extensive body of knowledge is intended to be a valuable resource for researchers, practitioners, and policymakers. It supports the ongoing advancement of AI-driven emergency services, with the goal of saving lives, optimizing resource allocation, and enhancing response times in critical situations. Ultimately, this collaborative effort seeks to foster the development of more resilient and responsive emergency systems that can effectively mitigate risks and deliver timely aid to those in need. By advancing the capabilities of emergency response systems, AI enhances the precision and efficiency of critical interventions, ultimately leading to better outcomes and improved resilience in crisis situations.International Journal of Intelligent Systems and Applications450An in-depth analysis of 77 articles across diverse domains, highlighting AI’s role in emergency services provides a broad overview of the current state of AI in emergency services, identifying key trends, challenges, and future opportunities.International Journal of Intelligent Systems and Applications["R. Mallouhy", "Naoufal Sirri", "Irum Nahvi", "Christophe Guyeux"]2024-12-08T00:00:00 21f1a164ea57d32a8dfffcb59cf7c82389916483Ensuring Artificial General Intelligence (AGI) reliably avoids harmful behaviors is a critical challenge, especially for systems with high autonomy or in safety-critical domains. Despite various safety assurance proposals and extreme risk warnings, comprehensive guidelines balancing AI safety and capability remain lacking. In this position paper, we propose the \textit{AI-\textbf{$45^{\circ}$} Law} as a guiding principle for a balanced roadmap toward trustworthy AGI, and introduce the \textit{Causal Ladder of Trustworthy AGI} as a practical framework. This framework provides a systematic taxonomy and hierarchical structure for current AI capability and safety research, inspired by Judea Pearl's ``Ladder of Causation''. The Causal Ladder comprises three core layers: the Approximate Alignment Layer, the Intervenable Layer, and the Reflectable Layer. These layers address the key challenges of safety and trustworthiness in AGI and contemporary AI systems. Building upon this framework, we define five levels of trustworthy AGI: perception, reasoning, decision-making, autonomy, and collaboration trustworthiness. These levels represent distinct yet progressive aspects of trustworthy AGI. Finally, we present a series of potential governance measures to support the development of trustworthy AGI.00The AI-\textbf{$45^{\circ}$} Law is proposed as a guiding principle for a balanced roadmap toward trustworthy AGI, and the Causal Ladder of Trustworthy AGI is introduced as a practical framework.["Chao Yang", "Chaochao Lu", "Yingchun Wang", "Bowen Zhou"]2024-12-08T00:00:00 96d78a0073c610a901dc0f72663c57cea57dd2baEn el contexto de la educación superior, la integración de las tecnologías de la información y la comunicación (TIC) y la inteligencia artificial (IA) está transformando significativamente los métodos de enseñanza y evaluación. Este artículo de revisión analiza investigaciones recientes sobre el uso de estas tecnologías para mejorar la calidad de la enseñanza en las universidades. Los estudios realizados entre 2019 y 2024 se centran en diversas aplicaciones de las TIC y la IA, como plataformas de aprendizaje en línea, sistemas de tutoría inteligentes y herramientas de evaluación automatizadas. Los resultados muestran que la adopción de estas tecnologías no sólo facilita la personalización del aprendizaje, sino que también mejora la eficiencia y precisión de la evaluación de los estudiantes. Además, se discuten los desafíos y oportunidades que estas innovaciones presentan para el futuro de la educación universitaria. Así mismo, se identifican obstáculos como la necesidad de formación continua de los docentes en habilidades digitales y la adaptación de la infraestructura tecnológica. Finalmente, se destaca la necesidad de investigaciones futuras que evalúen el impacto a largo plazo de estas tecnologías en los resultados educativos y la equidad de acceso a los recursos educativos. La transformación digital en la educación superior es una oportunidad para innovar y mejorar la enseñanza y la evaluación, preparando a los estudiantes para los desafíos del siglo XXI.Reincisol.00Reincisol.["Meyvilin Mar\u00eda Mora Mera", "Carlos Roberto Ochoa Gonzalez", "Miguel \u00c1ngel Cango Zhin\u00edn", "Jefferson Olimpo Guti\u00e9rrez Bastidas"]2024-12-08T00:00:00 950991837b5c8f78edb5bea360dfc57b5dec08b1CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne00CMAJ : Canadian Medical Association Journal["Payal Agarwal", "Rosemarie Lall", "Rajesh Girdhari"]2024-12-08T00:00:00 ddec97c4118a8cda050743b04ffab2305fe0e1c1In the digital security environment, the obfuscation and encryption of malicious scripts are primary attack methods used to evade detection. These scripts—easily spread through websites, emails, and file downloads—can be automatically executed on users' systems, posing serious security threats. To overcome the limitations of signature‐based detection methods, this study proposed a methodology for real‐time detection of obfuscated and encrypted malicious scripts using ML/DL models with feature optimization techniques. The obfuscated script datasets were analyzed to identify the unique characteristics, classified into 16 feature sets, to evaluate the optimal features for the best detection accuracy. Although the detection accuracy of these datasets was < 20%, when tested with commercial antivirus services, the experimental results using ML and DL models demonstrated that the proposed light gradient boosting model (LGBM) could achieve the best detection accuracy and processing speed. The LGBM outperformed other artificial intelligence models by achieving 97% accuracy and the minimum processing time in the decoded, obfuscated, and encrypted dataset cases.ETRI Journal230The proposed light gradient boosting model (LGBM) outperformed other artificial intelligence models by achieving 97% accuracy and the minimum processing time in the decoded, obfuscated, and encrypted dataset cases.ETRI Journal["Kookjin Kim", "Jisoo Shin", "Jong\u2010Geun Park", "Jung\u2010Tae Kim"]2024-12-08T00:00:00 214ea8219b1a5f8d0300febdca9534c0af13e922Importance: Transplantation is one of the few areas in medicine where the definitive treatment is rationed. Subjective decision-making pose challenges towards the transplant selection process. It has been proposed that large language models (LLMs) as artificial intelligent (AI) agents could provide objectivity in decision-making to solve complex problems. Objective: To examine the performance of a multidisciplinary selection committee of AI agents (AI-SC) as a proof-of-concept towards objectivity in the liver transplant (LT) selection process. Design: The AI-SC consisted of four LLMs: transplant hepatologist, transplant surgeon, cardiologist, and social worker. Zero-shot prompting with chain-of thought was used. Decisions were made based on clinicodemographic characteristics at time of waitlisting and LT. Setting: National LT cohort. Participants: Adult patients receiving deceased donor LT from 2004-2023 were extracted from the Scientific Registry of Transplant Recipients (SRTR) and clinical vignettes were generated. Standard absolute contraindications to LT were randomly assigned to a subset of patients to expose the AI-SC to cases of patients declined for LT. Exposures: Clinicodemographic characteristics at waitlisting and transplantation. Main Outcomes and Measures: The AI-SCs accuracy with either: 1) listing candidates if LT would offer a 6-month or 1-year survival benefit or 2) declining candidates if contraindications to LT are present or if LT would not offer those survival benefits. Results: Of 8,412 patients, 83.6% were waitlisted and 16.4% had contraindications to LT. The AI-SC was able to accurately identify contraindications to LT (accuracy: 98.2%, 95%CI 97.9%-98.4%), predict 6-month (94.9%, 95%CI 94.4%-95.3%) and 1-year (92.0%, 95%CI 91.4%-92.6%) survival. HCC burden beyond Milan criteria was the most common reason for accepted patients who were declined by AI-SC (False Negative). Malignancy was the most common cause of death prior to 6-month or 1-year end points (False Positive). The AI-SC most frequently did not perceive a lack of social support or severe cardiopulmonary disease as barriers to LT. Conclusions and Relevance: LLMs can be leveraged to simulate the LT-SC meetings and provide accurate, objective insights on patients who may or may not benefit from LT. Lessons learned from this proof-of-concept are a provocative step towards making the LT selection process more equitable and objective.medRxiv570LLMs can be leveraged to simulate the LT-SC meetings and provide accurate, objective insights on patients who may or may not benefit from LT, a provocative step towards making the LT selection process more equitable and objective.["Bima J. Hasjim MD MSc", "Ghazal Azafar", "Frank Lee", "T. Diwan", "Shilpa Raju Mph", "JD JedAdamGrossMPhil", "Aman Sidhu", "H. Ichii", "Rahul G. Krishnan", "Muhammad Mamdani", "Mph PharmD", "Divya Sharma", "Mamatha Bhat"]2024-12-08T00:00:00 1c4524b697324181987e15a062f8912026a535c9Aesthetic Plastic Surgery51This paper explores how AI can improve the delivery and effectiveness of scientific presentations in Aesthetic Surgery, and argues that the integration of AI in aesthetic surgery heralds significant possibilities for both academic and clinical applications.Aesthetic plastic surgery["Gianluca Marcaccini", "Ishith Seth", "R. Cuomo"]2024-12-09T00:00:00 b13620566ffb4dfda76eb0ce236aa6281bb8b509Smart Learning Environments1252The results reveal that AI is used mostly to support teachers and students in education with less focus on other educational stakeholders (e.g. school leaders or administrators), providing a possible roadmap for future research agenda on AIED, facilitating the implementation of effective and safe AIED.Smart Learn. Environ.["Muhammad Yasir Mustafa", "A. Tlili", "Georgios Lampropoulos", "Ronghuai Huang", "P. Jandri\u0107", "Jialu Zhao", "Soheil S. Salha", "Lin Xu", "Santosh Panda", "Kinshuk", "Sonsoles L\u00f3pez-Pernas", "M. Saqr"]2024-12-09T00:00:00 29549d79dfc17d8c857fa7a8467f12eec0c276e9A considerable amount of recent research has focused on the role of artificial intelligence (AI) in colorectal cancer (CRC), aiming to improve outcomes in CRC. However, AI for young onset colorectal cancer (yoCRC)-defined as colorectal cancer in patients less than 50 years old-is not nearly as explored, and its role in the prevention, detection, and management of yoCRC remains largely unknown. To address this gap, we performed an integrative review on AI in yoCRC. We conducted a comprehensive literature search of PubMed, Medline (Ovid), and Embase for articles published from 2020 to 2024, adhering to specific inclusion and exclusion criteria. This integrative review involved gathering information from diverse research designs and literature sources. After removing duplicates and applying inclusion criteria, a total of 11 articles were included in the review. Our analysis identified one review discussing the importance of AI in yoCRC, three articles presenting research studies mentioning applications for yoCRC, and seven comprehensive investigations utilizing AI with a specific focus on yoCRC. The findings indicate that while AI in CRC is an evolving research field, there are few plans or implementations reported on how to incorporate AI specifically in yoCRC. Potential limitations of this review include the limited number of databases searched and the scope of search queries used. Nonetheless, this review highlights the need for more targeted research on AI applications in yoCRC. Future research can build upon the foundation of AI in CRC with adjustments to account for the increasing incidence of yoCRC.Journal of Surgical Oncology221An integrative review on AI in yoCRC identified one review discussing the importance of AI in yoCRC, three articles presenting research studies mentioning applications for yoCRC, and seven comprehensive investigations utilizing AI with a specific focus on yoCRC.Journal of surgical oncology["Joel Grunhut", "John J. Newland", "Rebecca Frances Brown"]2024-12-09T00:00:00 6eadeea15e0c06e3e9f41f1b1a14ae1f83f29a2cThis study provides a systematic review of the literature on the operationalization of artificial intelligence (AI) within small and medium-sized enterprises (SMEs), aiming to develop an integrated conceptual framework for understanding AI adoption. The findings indicate that Technological readiness plays a pivotal role, with SMEs requiring knowledge of AI applications, methods, and capabilities to adopt AI effectively. AI adoption yields diverse outcomes, including enhanced operational efficiency, improved customer engagement, and greater innovation, but these vary based on industry, firm size, and resource capacity. The study emphasizes that AI is not a unitary concept but a multi-dimensional construct, with operationalization requiring alignment with organizational dynamic capabilities. This review offers a framework for understanding AI adoption, helping to bridge fragmented findings in the literature.Uluslararası anadolu sosyal bilimler dergisi01Uluslararası Anadolu Sosyal Bilimler Dergisi["Ay\u00e7a K\u00fcbra H\u0131zarc\u0131", "Alara Tarier", "\u00d6zge \u00d6zgen", "G\u00fcl\u00fczar Kurt G\u00fcm\u00fc\u015f"]2024-12-09T00:00:00 09b2e28b90a4f51476d7b9023c3faf1eb34250b0Monitoring the responsible application of Artificial Intelligence (AI) in higher education requires the establishment of robust regulatory frameworks and thorough policy guidelines. The study concentrates on important elements that could impede the excellence of education, including issues with data security, privacy, and policies as well as legal frameworks. By examining these variables, the study aims to address the particular opportunities and difficulties encountered in this setting and to gain a deeper knowledge of how AI deployment affects educational excellence in Jordanian higher education institutions. A survey research methodology has been chosen. Institutions in Jordan that have started implementing AI or metaverse technology were given a pre-made questionnaire. The population of students in Jordanian higher education institutions, including both local and foreign students at different educational levels, is the main subject of this study. During the three-month data collection phase, the sample size was cautiously raised to more than 457 individuals in order to boost the research's robustness. The results show that the AI adoption, trust in technology (by data privacy and security), and policy & regulations in Jordanian higher institutions have significant impacts on educational excellence. Our results highlight the urgent need for policymakers to reevaluate and explain current regulatory frameworks in order to safeguard educational excellence, while also confirming the transformative possible of AI implementation in improving instructive resources and services. This study demonstrates the possible benefits of integrating AI technologies into educational backgrounds by confirming the strong correlation between AI adoption and educational excellence. In order to increase the effectiveness, usability, and caliber of educational resources and services, schools ought to think about implementing AI-driven tools and platforms.Edelweiss Applied Science and Technology1471The possible benefits of integrating AI technologies into educational backgrounds are demonstrated by confirming the strong correlation between AI adoption and educational excellence and highlighting the urgent need for policymakers to reevaluate and explain current regulatory frameworks in order to safeguard educational excellence.Edelweiss Applied Science and Technology["Mohamed Hadi Al Najdawi", "Fanar Shwedeh", "Mahmoud Mokhtar Abdelmoghies", "A. Kitana", "Ahmed Ali"]2024-12-09T00:00:00 e43267c014caad40fce4273b115e8cff4ea5fa65This study assessed the extent of utilization and availability of Artificial Intelligence (AI) in teaching and assessment at Ambrose Alli University. As educational paradigms shift towards digitalization. This study employed survey research design. A snowball sampling method was used to select 103 lecturers who provide the relevant data or information regarding the extent of availability and utilization of AI in teaching and assessment of students. The instrument for data collection was a self-developed semi structured questionnaire, entitled Availability and Utilization of AI in Teaching and Assessment of Students by Lecturers in Ambrose Alli University (AUAITASLAAU). The Statistical Package for Social Sciences version 23.0 was used to analyze the data collected. The study found that a positive reception of AI-enhanced teaching and assessment tools among both Lecturers and Students, highlighting increased engagement, personalized learning experiences, and efficient assessment mechanisms. However, concerns related to data privacy, accessibility, and technological proficiency remain significant barriers to widespread AI adoption. The author logically concluded that the adoption of AI for teaching and assessment of students by lecturers within the university setting will measurably improve the teaching and unbiased assessment of students. The author recommended, among others, that before implementing AI, the management of Ambrose Alli University should endeavour to improve on the quality of electricity power supply.Asian Journal of Assessment in Teaching and Learning00The author logically concluded that the adoption of AI for teaching and assessment of students by lecturers within the university setting will measurably improve the teaching and unbiased assessment of students.Asian Journal of Assessment in Teaching and Learning[]2024-12-09T00:00:00 442cde970a959005ba7d019177549206516d704bThis in-depth article on the ethics and responsible development of artificial intelligence (AI) looks at how quickly AI technologies are changing society and how big those changes are. Ethical issues like bias, privacy, responsibility, and openness are looked at in detail. It outlines responsible AI development principles, encompassing fairness, human-centered values, and safety. It discusses practical strategies for implementing ethical AI practices, including diverse development teams and explainable AI techniques. The article examines applications of responsible AI across finance, recruitment, and healthcare. Finally, it looks at future directions for AI ethics, including regulatory frameworks, global collaboration, interdisciplinary research, and education initiatives. By addressing these crucial ethical challenges, the aim is to ensure AI technologies align with human values and benefit society as a whole.International Journal of Scientific Research in Computer Science Engineering and Information Technology10It outlines responsible AI development principles, encompassing fairness, human-centered values, and safety, and discusses practical strategies for implementing ethical AI practices, including diverse development teams and explainable AI techniques.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["V Khadake"]2024-12-09T00:00:00 41e98a4c9445b903884966b8fa392558829c4db8Challenges in the water supply sector have hindered the advanced implementation of artificial intelligence (AI) compared to other sectors. These challenges have not been sufficiently examined in the existing literature. An empirical study was conducted within a public utilities organization in the United Arab Emirates (UAE) to address this gap. An integrated approach combining interpretive structural modeling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was utilized to identify the critical challenges and to model and analyze the relationships among them. The ISM model provides significant advantages by enabling decision-makers to visualize complex interactions, supporting the development of an effective AI implementation strategy. The strategy should prioritize four critical challenges: the lack of technical skills and knowledge, the limited availability of ready-to-use AI solutions, inadequate water infrastructure, and concerns regarding privacy and data security. These challenges were identified based on their positioning at the lowest level of the ISM model and their classification as independent in the fuzzy MICMAC analysis. Addressing these four challenges will help to mitigate the remaining six. The study’s findings and implications are expected to offer valuable guidance for decision-makers in implementing AI technologies within water supply organizations, both in the UAE and in countries with similar environments.Water750An integrated approach combining interpretive structural modeling (ISM) and fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was utilized to identify the critical challenges and to model and analyze the relationships among them.Water["Moza S. M. A. Almheiri", "Hamdi A. Bashir", "U. Ojiako", "Salah Haridy", "Mohammed Shamsuzzaman"]2024-12-09T00:00:00 cccc57dcc691b98911e2bef444038bfcc77639a5Introduction: Glaucoma, a leading cause of irreversible blindness, requires early detection and prediction of progression to preserve vision. Artificial intelligence (AI) offers promising tools for analyzing complex ophthalmic data and identifying high-risk individuals. This meta-analysis evaluates the performance of machine learning (ML) models in predicting glaucoma progression. Methods: A systematic search of PubMed, Scopus, and Web of Science databases was conducted for studies published between 2013 and 2024 that investigated the use of ML models to predict glaucoma progression. Studies reporting performance metrics like sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and accuracy were included. Results: Six studies met the inclusion criteria, encompassing 1,250 participants. The pooled sensitivity and specificity of ML models for predicting glaucoma progression were 0.81 (95% CI: 0.78-0.84) and 0.77 (95% CI: 0.73-0.81), respectively. The pooled AUC was 0.88 (95% CI: 0.86-0.90), indicating excellent discriminatory ability. Conclusion: ML models hold significant potential for predicting glaucoma progression with high accuracy. Further research with larger, more diverse datasets is needed to validate these findings and develop clinically applicable tools.  Sriwijaya Journal of Ophthalmology160A meta-analysis evaluates the performance of machine learning (ML) models in predicting glaucoma progression with high accuracy and concludes that ML models hold significant potential for predicting glaucoma progression with high accuracy.Sriwijaya Journal of Ophthalmology["Indira Putri"]2024-12-09T00:00:00 e243da556434a06bdfd66218f1445301ecd6fd4dINTRODUCTION Assessments in medical education play a central role in evaluating trainees' progress and eventual competence. Generative artificial intelligence (AI) is finding an increasing role in clinical care and medical education. The objective of this study is to evaluate the ability of the large language model ChatGPT to generate exam questions that are discriminating in the evaluation of graduating urology residents. METHODS Graduating urology residents representing all Canadian training programs gather yearly for a mock exam that simulates their upcoming board certifying exam. The exam consists of a written multiple-choice questions (MCQs) exam, and an oral OSCE. In 2023, ChatGPT Version 4 was used to generate 20 MCQs that were added to the written component. ChatGPT was asked to use Campbell-Walsh Urology, AUA, and CUA guidelines as resources. Psychometric analysis of the ChatGPT MCQs was conducted. The MCQs were also researched by 3 faculty for face validity and to ascertain if they came from a valid source. RESULTS The mean score of the 35 exam takers on the ChatGPT MCQs was 60.7% versus 61.1% for the overall exam. 25% of ChatGPT MCQs showed a discriminating index > 0.3, the threshold for questions that properly discriminate between high and low exam performers. 25% of ChatGPT MCQs showed a point biserial > 0.2, which is considered a high correlation with overall performance on the exam. The assessment by faculty found that ChatGPT MCQs often provided incomplete information in the stem, provided multiple potentially correct answers, were sometimes not rooted in the literature. 35% of the MCQs generated by ChatGPT provided wrong answers to stems. CONCLUSIONS Despite what appears to be similar performance on ChatGPT MCQs and the overall exam, ChatGPT MCQs tend not to be highly discriminating. Poorly phrased questions with potential for AI hallucinations are ever present. Careful vetting for quality of ChatGPT questions should be undertaken before their use on assessments in urology training exams.Journal of Urology00Despite what appears to be similar performance on ChatGPT MCQs and the overall exam, ChatGPT MCQs tend not to be highly discriminating and careful vetting for quality of ChatGPT questions should be undertaken before their use on assessments in urology training exams.The Journal of urology["N. Touma", "Ruchit Patel", "Thomas Skinner", "Michael Leveridge"]2024-12-09T00:00:00 99d9496df8562666f254bc125b4ab66473c778e6This article analyses the integration of artificial intelligence (AI) in health pastoral care, emphasizing the synergy between technology and spirituality. This paper discusses possible AI applications, highlighting the importance of ethical implementation that respects human interactions. Ethical issues like privacy and empathy are examined, as well as the potential of AI in facilitating collaboration between healthcare professionals and pastoral workers. Finally, it calls for a debate on the responsible use of AI in care contexts.Tumori110Ethical issues like privacy and empathy are examined, as well as the potential of AI in facilitating collaboration between healthcare professionals and pastoral workers, and the importance of ethical implementation that respects human interactions are highlighted.Tumori["C. Clerici", "A. Ferrari", "Tullio Proserpio"]2024-12-09T00:00:00 6c0c25eb43d4097af47dd72b3703673332bbd656With the rising demand for personalized learning and increased digital natives, the traditional education model is under pressure to reform. the rapid development of artificial intelligence (AI) provides new education solutions, and the application of AI is becoming widespread, from intelligent tutoring to automated grading to learning analytics. However, the application of AI in education is not without challenges. This literature review aims to explore the impact of AI on education, analyze its potential advantages, and propose strategies to address the challenges and provide guidance for the future development of education. Understanding the impact of AI on education is instructive for the future direction of AI in education and reveals the importance of AI for education to the extent that it is important for formulating effective educational policies and cultivating future talents in AI technology. The final results of the study show that although AI in the education industry there are various difficulties and limitations, but AI offers great potential for smarter and more efficient education levels and education systems. In today's era, computer programming education is emerging as a pivotal force in shaping the future, as it not only hones students' logical thinking and innovative capabilities but also lays a solid foundation for their career paths in the digital world. By integrating knowledge across various disciplines, programming education stimulates students' enthusiasm for applying their learning to real-world problems and enhances their sense of social engagement, enabling them to tackle challenges through technological means.Lecture Notes in Education Psychology and Public Media00The final results of the study show that although AI in the education industry there are various difficulties and limitations, but AI offers great potential for smarter and more efficient education levels and education systems.Lecture Notes in Education Psychology and Public Media["Xiangkun Wang"]2024-12-09T00:00:00 7d13125fe936cb51cc0b319a5c3d17db49b89e82This paper comprehensively analyzes the speculative bubble risks of investments in artificial intelligence (AI) technologies in financial markets. We conduct a GSADF test and volatility spillover analysis on the stocks of the so-called “Magnificent Seven,” namely Meta, Microsoft, Apple, Amazon, Google, Nvidia, and Tesla. The test results reveal significant bubbles, especially in Nvidia and Tesla stocks, and these bubbles spread volatility to other technology stocks. The fact that Nvidia plays a central role in volatility spillovers suggests that overpricing in AI investments can create a domino effect across the sector, leading to severe volatility in global markets. Investors should diversify portfolios and adopt long-term strategies against speculative bubble risks. At the same time, policymakers should increase market efficiency by tightening financial regulations.Ekonomi ve Finansal Araştırmalar Dergisi190The test results reveal significant bubbles, especially in Nvidia and Tesla stocks, and these bubbles spread volatility to other technology stocks, suggesting that overpricing in AI investments can create a domino effect across the sector, leading to severe volatility in global markets.Ekonomi ve Finansal Araştırmalar Dergisi["Volkan Eteman"]2024-12-09T00:00:00 3051cf55aa0e2b7ebe5073d55c4773e639c8b835This paper investigates the educational integration of Artificial Intelligence (AI) in high school art education and examines AI's roles in teachers' and students' classrooms. A snowball sampling method was selected, covering 25 participants from developed areas in China, including six teachers and 19 students. Participants are categorized by familiarity with AI tools such as ChatGPT and DALL-E, which are conducted as an assessment and creative exercise. The results showed that, although 55% of teachers used AI to offer formative feedback, 24% feared that students might become overly dependent on AI scribes and lose some of their spontaneous creative instincts. Meanwhile, 52% of students said AI helped them more effectively generate and refine creative documents. The study concluded that, when AI comes to art education, it can be both a boon and a bane: it has the potential to enhance creativity but may threaten the integrity of the learning processJournal of Education, Humanities and Social Sciences00Artificial Intelligence has the potential to enhance creativity but may threaten the integrity of the learning process, and, when AI comes to art education, it can be both a boon and a bane.Journal of Education, Humanities and Social Sciences["Wenxuan Hu"]2024-12-09T00:00:00 7be43a37e48ceb8f0369cd5dccda576648e4d02aArtificial intelligence (AI) has shown a unique ability to tailor and enhance personalized learning experiences. However, its application in physical education is limited. This study aims to investigate the effect of the AI-driven application on the learning outcome and student engagement among physical education students in higher education. A quasi-experimental two-group design was used to determine differences between pre-test and post-test scores. Standardized tests that measured musculoskeletal strength and endurance were utilized. The Utrecht Work Engagement Scale for Students (UWES-9S) assessed student engagement. For eight weeks, the experimental group (EG) had an AI-driven physical activities while the control group (CG) followed the usual PE class. Post-test scores showed the experimental group significantly improved the learning outcome (p<0.001). Engagement scores were also significantly higher in EG (p < 0.01). Consequently, The CG showed a significant decrease in student engagement while the increase in learning outcome is not statistically significant. These findings suggest that AI-driven applications positively affect students’ physical fitness and engagement. Further research is recommended to fully implement AI in classrooms, addressing student and teacher literacy and ethical concerns to maximize its potential in the PE curriculum.Pedagogy Review: An International Journal of Educational Theories, Approaches and Strategies00It is suggested that AI-driven applications positively affect students’ physical fitness and engagement and addressing student and teacher literacy and ethical concerns to maximize its potential in the PE curriculum is recommended.Pedagogy Review: An International Journal of Educational Theories, Approaches and Strategies["Harlyn Mae S. Ompoc", "Jerrwin Aguinaldo"]2024-12-09T00:00:00 dbedd9e2950ed1a6603f090e259553575c8e3f54Purpose Artificial intelligence (AI) has advanced at a rapid pace in the 21st century, and this has had a profound impact on many facets of human behavior, most notably the attitudes toward sustainable consumption and the lifestyles of consumers. Young consumers are at the forefront of AI technology adoption due to their upbringing in an era dominated by technological advancements, and these technologies are changing the way they engage with brands, make purchases and practice sustainability. This research paper aims to investigate the influence of AI on sustainable consumption behavior and lifestyle choices among young consumers. Design/methodology/approach This research study examines the complex effects of AI on young consumers using the Unified Theory of Acceptance and Use of Technology 2 model. This study addresses the intricacies and issues related to AI, such as the risk of overconsumption and the environmental impact of AI technologies, while also examining how AI-driven tailored experiences improve consumer engagement and promote sustainability. Structural equation modeling (SEM) was used to evaluate the hypotheses and produce solid insights into the connections between consumer behavior, sustainable consumption and the adoption of AI. Findings The results highlight the necessity of adopting AI in a balanced manner and stress the significance of coordinating AI advancements with sustainability goals. Originality/value This study offers a significant contribution to the body of knowledge by examining the relationships between the adoption of AI, environmental consciousness and sustainable consumption. It also offers practical suggestions for encouraging consumers to engage in eco-friendly activities and responsible consumption. Young Consumers900This study addresses the intricacies and issues related to AI, such as the risk of overconsumption and the environmental impact of AI technologies, while also examining how AI-driven tailored experiences improve consumer engagement and promote sustainability.Young Consumers["Animesh Kumar Sharma", "Rahul Sharma"]2024-12-09T00:00:00 d919265ca21be45c32c1ae5c219a99321d365966Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application explores the power of artificial intelligence (AI) in advancing sensor technologies and computer vision for healthcare and automation. Covering both machine learning (ML) and deep learning (DL) techniques, the book demonstrates how AI optimizes prediction, classification, and data visualization through sensors like IMU, Lidar, and Radar. Early chapters examine AI applications in object detection, self-driving vehicles, human activity recognition, and robot automation, featuring reinforcement learning and simultaneous localization and mapping (SLAM) for autonomous systems. The book also addresses computer vision techniques in healthcare and automotive fields, including human pose estimation for rehabilitation and ML in augmented reality (AR) for automotive design. This comprehensive guide provides essential insights for researchers, engineers, and professionals in AI, robotics, and sensor technology. Key Features: - In-depth coverage of AI-driven sensor innovations for healthcare and automation. - Applications of SLAM and reinforcement learning in autonomous systems. - Use of computer vision in rehabilitation and vehicle automation. - Techniques for managing prediction uncertainty in AI models.00This comprehensive guide provides essential insights for researchers, engineers, and professionals in AI, robotics, and sensor technology.["Minh Long Hoang"]2024-12-09T00:00:00 805ba16b05b77533fa5f7bebc7d152c64e1fab6fThe purpose of this study is to develop and evaluate technology education in which students learn about various types of artificial intelligence (AI) experientially through creating web map content to solve problems in life and society in secondary schools. A teaching guidance plan was developed for practice in Japanese secondary schools. Two types of AI were employed as teaching materials: data classification AI and image generation AI. Based on the results of tentative teaching practice, the feasibility of the learning content, except for the image generation AI, was confirmed. The proposed AI technology education will be conducted in secondary schools to evaluate its usefulness.International Conference on Teaching, Assessment, and Learning for Engineering30Based on the results of tentative teaching practice, the feasibility of the learning content, except for the image generation AI, was confirmed and the proposed AI technology education will be conducted in secondary schools to evaluate its usefulness.2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)["Yosuke Ito", "Takuto Watanabe", "Yuta Sasaki"]2024-12-09T00:00:00 77dbab6b989e7732fd2d81bcb3b20943a8aab92bTechnology plays an important role in economic development. Covid-19 has changed people's lifestyles and the behavioral patterns of business actors have transformed from traditional to digital. Even though Covid-19 has had a significant impact in the form of a decline in the global economy, including on Micro, Small and Medium Enterprises (MSMEs), it has also provided lessons in the form of adaptation to innovation and digital transformation in the form of the application of the latest technology and artificial intelligence. This research uses a qualitative descriptive method and research data is collected through articles, journals, books, and official sources that are reliable and accurate.  This research aims to describe phenomena or events from the development of the Industrial Revolution 4.0 era which supports the growth of MSMEs to change or transform digitally with the help of artificial intelligence so that they can grow and compete globally. The results of this research show that the Industrial Revolution 4.0 and digital transformation can provide opportunities and strengths to build sustainable businesses. Along with changes in people's consumption patterns, MSMEs have also implemented digital adoption in the form of switching to e-commerce platform sites, food delivery applications, and online business models. However, the adoption of artificial intelligence is still not widely used by MSMEs. However, several MSMEs have adopted artificial intelligence technology such as chatbot applications for automatic customer communication services. Apart from that, support and providing programs from the government and the private sector are needed for MSMEs so that they can develop and adapt their business models to the latest technological adaptations so that MSMEs are also able to survive and even grow rapidly in the future.Jurnal Penelitian Multidisiplin Bangsa200The results of this research show that the Industrial Revolution 4.0 and digital transformation can provide opportunities and strengths to build sustainable businesses.Jurnal Penelitian Multidisiplin Bangsa["Sri Hutami Adiningsih S", "Is Arianto Pratama"]2024-12-09T00:00:00 4f256dac3a8633bf2302c0af63962a0cc15c347fWith the rapid development of modern society and the accelerated pace of life, sleep disorders have become a significant issue affecting peoples quality of life. Numerous studies indicate that art therapy, as a non-pharmacological treatment, can externalize the subconscious through creative activities, raising it to a conscious level, thus improving the psychological state and sleep quality of individuals with sleep disorders. However, traditional art therapy methods still face certain limitations, often failing to achieve optimal therapeutic outcomes. The fast-growing field of artificial intelligence (AI), with advances in intelligent algorithms and big data analytics, offers new possibilities for the evolution of art therapy, potentially enhancing its effectiveness. This paper aims to explore the feasibility of integrating AI technology into art therapy for individuals with sleep disorders. By examining applications in mandala painting, intelligent wearable devices, and dream visualization, this study proposes innovative therapeutic approaches that combine traditional art therapy with modern AI technology to provide more efficient and personalized therapeutic solutions for those suffering from sleep disorders.Communications in Humanities Research200This paper aims to explore the feasibility of integrating AI technology into art therapy for individuals with sleep disorders, examining applications in mandala painting, intelligent wearable devices, and dream visualization.Communications in Humanities Research["Yuedan Luo"]2024-12-09T00:00:00 9c8c400ce013a07aea1fc60e4893656b67f07e29Artificial intelligence technology has been widely adopted globally, and its application in different fields has brought higher efficiency and accuracy to people around the world, such as the recognition of simple diseases in healthcare, the automatic adaptation of student abilities in education systems, and the planning and construction of smart cities. However, it cannot be ignored that while this technology has brought help to humanity, it has also brought unprecedented machine ethical challenges to human society – such as amplified skin color bias, easy access to personal information, privacy breaches, and the synthesis of false information or images. This paper starts from several core ideas of Buddhism – compassion, meditation, and the interconnection of all things – to analyze the potential guiding role of Buddhist thought in AI ethical issues and discuss exemplary cases of successfully applying Buddhist thought in technological development, thus revealing the important role of Buddhist thought in the future development of AI ethics.Journal of Education, Humanities and Social Sciences210The potential guiding role of Buddhist thought in AI ethical issues is analyzed and exemplary cases of successfully applying Buddhist thought in technological development are discussed, revealing the important role of Buddhist thought in the future development of AI ethics.Journal of Education, Humanities and Social Sciences["Youxin Yan"]2024-12-09T00:00:00 f3e239b1673f208ecd9e478bb62e72cb4cb37e3bArtificial intelligence is already being used in the educational space in different countries of the world to predict, analyze and model the learning environment. It is possible to define at least three main areas of use of artificial intelligence in the modern educational space. First, artificial intelligence is used to personalize learning because it can analyze data about a student's success and his learning style and dynamically adjust learning materials and tasks to his individual needs. Second, artificial intelligence is used to improve learning outcomes because it can analyze large amounts of data to identify patterns and trends that can help improve teaching methods and predict student performance. Third, artificial intelligence in general enables the process of learning automation as it can automate tasks such as lesson planning, journaling, assessment and grading, and can be used to create personalized learning materials. Third, artificial intelligence generally supports the process of automating learning, as it can handle tasks such as lesson planning, journaling, assessment and grading, and can be used to create personalised learning materials. At the same time, the use of artificial intelligence in the educational environment is at an early stage and raises a number of ethical and practical problems that will need to be solved in the near future. This publication analyzes the main trends of the influence of artificial intelligence technology on the transformation of the modern educational space. The main problems and digital possibilities of using artificial intelligence technology in the educational environment are considered. In general, the analysis of different views on the introduction of artificial intelligence into the education system suggests that it is possible that in the near future society will need to rethink the social role of education and the learning environment. The traditional format of education offered and provided by educational institutions today may be imperfect in the conditions of the growing role of digital technologies and the use of artificial intelligence. Accordingly, the new format of obtaining information and knowledge will require from modern teachers not only basic knowledge and skills necessary for the organization of the educational process, but also an understanding of the very essence of artificial intelligence technology.Dnipro Academy of Continuing Education Herald. Series: Philosophy, Pedagogy60It is possible that in the near future society will need to rethink the social role of education and the learning environment, and the main trends of the influence of artificial intelligence technology on the transformation of the modern educational space are analyzed.Dnipro Academy of Continuing Education Herald. Series: Philosophy, Pedagogy["S. Dovhal", "Andrii Miroshnychenko"]2024-12-09T00:00:00 521c5eee6c69b88ea1b5a91b8c8f8894bfe0c7e2The article explores the importance of university students' knowledge of the world as an essential prerequisite for facing digital, ecological and human transitions. A study of 50 third‐year students of primary education sciences investigates (a) familiarity with reflection during learning; (b) the influence of artificial intelligence in the construction of knowledge; and (c) the degree of trust placed in AI to improve skills. The aim is to highlight students' metacognitive attitude towards AI, its creative use and the beliefs that emerged in their professional development.European Journal of Education100The article explores the importance of university students' knowledge of the world as an essential prerequisite for facing digital, ecological and human transitions and highlights students' metacognitive attitude towards AI, its creative use and the beliefs that emerged in their professional development.European Journal of Education["Stefania Capogna", "Sara Pellegrini", "Riccardo Sebastiani"]2024-12-09T00:00:00 c1964f69305ddec12274a1ac29492b1b609e66e3Abstract Artificial intelligence (AI) has the potential to standardize and automate important aspects of fertility treatment, improving clinical outcomes. One promising application of AI in the fertility clinic is the use of machine learning (ML) tools to assess embryos for transfer. The successful clinical implementation of these tools in ways that do not erode consumer trust requires an awareness of the ethical issues that these technologies raise, and the development of strategies to manage any ethical concerns. However, to date, there has been little published literature on the ethics of using ML in embryo assessment. This mini-review contributes to this nascent area of discussion by surveying the key ethical concerns raised by ML technologies in healthcare and medicine more generally, and identifying which are germane to the use of ML in the assessment of embryos. We report concerns about the ‘dehumanization’ of human reproduction, algorithmic bias, responsibility, transparency and explainability, deskilling, and justice.Human Reproduction490Concerns about the ‘dehumanization’ of human reproduction, algorithmic bias, responsibility, transparency and explainability, deskilling, and justice are reported.Human Reproduction (Oxford, England)["J. Koplin", "M. Johnston", "Amy N S Webb", "Andrea Whittaker", "Catherine Mills"]2024-12-09T00:00:00 a0284b33cfa57818761862b4c1ea13f21d8e144bCurrent Treatment Options in Psychiatry370This review seeks to clarify the current ethical implications of generative artificial intelligence chatbots, and to identify the key empirical questions that should be pursued to inform ethical practice.Current Treatment Options in Psychiatry["C. Blease", "A. Rodman"]2024-12-09T00:00:00 c0c1c58c90d927caf3463a0efb3adc9c71d5b064Sustainability has become a critical priority for businesses and governments as we address environmental changes such as climate change, resource depletion, waste management, and water level increases. This paper investigates the transformative role of artificial intelligence (AI) in sustainable product development, highlighting how advancements in AI technologies are reshaping traditional practices. Traditional product development processes often relied on resource-intensive methods that generated significant waste and offered limited adaptability to sustainability goals. As a cornerstone of the digital economy, the IT sector faces increasing pressure to address its environmental impact, particularly in areas like server management, network optimization, and data transmission. This paper explores the transformative role of Artificial Intelligence (AI) in driving sustainable practices within IT operations. AI-powered solutions, such as dynamic workload management, predictive maintenance, and traffic optimization, enable energy-efficient server and network operations, significantly reducing power consumption. Advanced AI-driven algorithms facilitate data compression, prioritize transmission, and optimize content delivery, minimizing carbon emissions from data-intensive activities. Additionally, AI supports renewable energy integration, tracks carbon footprints, and provides actionable insights for developing sustainable software and IT infrastructure. These innovative applications of AI enhance operational efficiency and contribute to global sustainability goals by reducing the environmental footprint of IT systems. This research highlights the potential of AI to create a greener IT landscape while maintaining the sector’s growth and performance imperatives.International Journal of Scientific Research in Computer Science Engineering and Information Technology20The transformative role of artificial intelligence in sustainable product development is investigated, highlighting how advancements in AI technologies are reshaping traditional practices and contributing to global sustainability goals by reducing the environmental footprint of IT systems.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Anu Rai", "Shubham Metha"]2024-12-09T00:00:00 1bc1750f107388451bc885fc74b28634a4ce11b6Against the backdrop of the globalization of information, artificial intelligence (AI) technology has become a core driving force for the development of all countries. It also has a profound impact on the field of international law, and the issue of technological hegemony is one of the core issues. Technological hegemony refers to an asymmetrical relationship between technologically developed countries and other countries by taking advantage of their dominant position in the field of technology. This paper focuses on the issue of technological hegemony in the relationship between AI and international law, and analyses in depth the nature, impact and coping strategies of the phenomenon of technological hegemony, aiming to build a more fair and reasonable international AI governance system. Methodologically, this paper adopts the literature analysis method and comparative research method. By collecting, collating and analyzing relevant academic literature, policy documents and legal precedents at home and abroad, and comparing the legislative practices, policy orientations and international cooperation modes of different countries and regions, the paper explores in-depth the international law strategies for responding to the issue of technological hegemony. In general, to address the issue of technological hegemony, it is necessary to strengthen international legal regulation, promote multilateral cooperation and dialogue, and facilitate technology sharing and cooperation to enhance the technological capabilities of developing countries, and current policies still need to be changed and evolved in line with the changing international environment.Lecture Notes in Education Psychology and Public Media00This paper analyses in depth the nature, impact and coping strategies of the phenomenon of technological hegemony, and analyses in depth the international law strategies for responding to the issue of technological hegemony, to build a more fair and reasonable international AI governance system.Lecture Notes in Education Psychology and Public Media["Yubo Lu"]2024-12-09T00:00:00 ad79e7ed6e984a61cc733542575351874b2e2a09The author discusses the concept of artificial intelligence and analyses the approaches to its concept and definitions existing in science. In a sketch of emergence of artificial intelligence the author notes artificial intelligence represents a natural stage in the development of technical devices designed to facilitate human intellectual activity. The author offers a classification of devices endowed with artificial intelligence, distinguishing between narrow (weak) AI and general (strong) AI; he outlines the most relevant areas of its development and describes the development stages. Among the spheres of possible application of devices with AI, the author lists the search for and structuring of information; identification of new connections and patterns that humans cannot see; assisting humans in professional activities; relieving humans from time-consuming and unproductive intellectual activities; management automation; assistance in making optimal management decisions. The article places a special focus on the threats and risks associated with the proliferation of AI. The author believes these include: displacement of humans from socially important spheres of activity, and job hijacking; decline in the level of education and qualification of workers leading to degradation of human intellect; corruption of humanity by idle and meaningless existence leading to its physical and cultural degradation; danger of robots making erroneous technical, economic, environmental, medical, etc. decisions; threat of failures in the operation of industrial robots and computerised control systems; particularly dangerous is the deliberate use of robots to cause harm, including the use of military and security systems capable of causing harm to people and property. The article discusses various options for granting AI a legal status. The author assumes a device endowed with artificial intelligence is a complex and autonomously operating tool of human activity, relatively independent of human person. The latter, however, is fully responsible for the consequences of the use of this tool. The author is sure it is counterproductive to artificially extend legal statuses developed for man, who is an individual endowed with consciousness and will, to AI. The article contains results of a “parallel exam” on legal analytics for students and artificial intelligence held at the Department of Law of the National Research University Higher School of Economics in May 2023. Also the author discusses specific areas and examples of the use of AI in law and legal analytics in the spheres of law -making, law enforcement, jurisprudence, and education.Legal Issues in the Digital Age20The author discusses specific areas and examples of the use of AI in law and legal analytics in the spheres of law -making, law enforcement, jurisprudence, and education and discusses various options for granting AI a legal status.Legal Issues in the Digital Age["Vladimir Isakov"]2024-12-09T00:00:00 da61cc57ad62b2b510d4f7c1f25082bf2e99f01dArtificial intelligence technology is increasingly integrated into education, offering potential benefits for personalized feedback and data-driven insights. However, its effectiveness in early childhood education, particularly in terms of teachers’ perceptions and experiences, remains underexplored. This study aimed to evaluate the effectiveness of AI-driven tools in early childhood education, focusing on learning outcomes, usability, feedback quality, and teacher workload in preschool and kindergarten settings. A mixed-methods design was used, comprising a survey of 40 teachers and semi-structured interviews with 10 participants. Quantitative data were analyzed through descriptive statistics and ANOVA, while qualitative data were analyzed using thematic analysis. 80% of teachers believed AI tools enhance learning outcomes, with experienced teachers more favorable toward AI feedback. Challenges included AI's inability to interpret social cues, emphasizing the need for human interaction in early learning. AI tools should complement human teaching, with a focus on teacher training and balancing AI with human interaction in early education.Social Science Review Archives00Evaluating the effectiveness of AI-driven tools in early childhood education, focusing on learning outcomes, usability, feedback quality, and teacher workload in preschool and kindergarten settings found 80% of teachers believed AI tools enhance learning outcomes, with experienced teachers more favorable toward AI feedback.Social Science Review Archives["Dr. Abdul Qayyum", "Maryam Bukahri", "Pakiza Zulfiqar", "Maryam Ramzan"]2024-12-09T00:00:00 0c13bef9288105bc76b8dddfb754110fbf144481Canadian Journal of Chemical Engineering151The Canadian Journal of Chemical Engineering["S. Upreti"]2024-12-09T00:00:00 ed1d1c53dcd766a36121834294b1e9bb1a520a87Advances in Atmospheric Sciences222Advances in Atmospheric Sciences["Mu Mu", "Bo Qin", "G. Dai"]2024-12-09T00:00:00 fe3da68451378bb77d5df5b4fec5a54cb6bd2827Industry 4.0 Science00Industry 4.0 Science[]2024-12-09T00:00:00 c5336d4d099999014ea381571ef520ce8c58b5d8American Psychologist00American Psychologist[]2024-12-09T00:00:00 e1ca9c7cc0e1492ad9de13fc7fb5591bc4dc3d8fInternational Journal of Progressive Research in Engineering Management and Science00International Journal of Progressive Research in Engineering Management and Science[]2024-12-09T00:00:00 743803b77797f501ea9a856951e471e4a421755dHealth, Risk &amp; Society00Health, Risk &amp; Society["Veronica Moretti", "Francesco Miele"]2024-12-09T00:00:00 0a93e28f5c5f30da45a6bd6652a03b13b2047eb2One of the oldest and largest biodiversity-related citizen science (CS) projects is eBird (https://ebird.org/home), developed by the Cornell Lab of Ornithology. It provides a mobile application for birdwatchers to record checklists of when, where, and how they have seen or heard birds. The Cornell Lab has also developed a mobile application, Merlin, which uses a deep convolutional neural network to help users automatically identify bird species from photos, sounds (converted to spectrograms), or descriptions. This research investigates how the use of machine learning (ML) classification models affects the learning of novice birders. Our participants (computer science students with no previous background in ornithology) were randomly divided into three groups: one using the eBird application and identifying bird species themselves; one using the Merlin application, which uses ML to automatically identify birds from photos or sounds; and a control group. Participants were tested on their knowledge of birds before and after participating in the project to see how using the ML classification model affected their learning. We also interviewed selected participants after the post-test to understand what they had done and what might explain the results. Our results show that novice participants who participate in a CS project for even a short time significantly improve their content knowledge of familiar birds in their neighbourhood, and that eBird users outperform Merlin users on the knowledge post-test. Although AI may improve volunteer productivity and retention, there is a risk that it may reduce their learning. Further research with different participant profiles and project designs is needed to understand how to optimise volunteer productivity, retention, and learning in AI-assisted CS projects.Citizen Science: Theory and Practice320Investigating how the use of machine learning (ML) classification models affects the learning of novice birders shows that novice participants who participate in a CS project for even a short time significantly improve their content knowledge of familiar birds in their neighbourhood, and that eBird users outperform Merlin users on the knowledge post-test.Citizen Science: Theory and Practice["Khrystyna Pankiv", "Laure Kloetzer"]2024-12-09T00:00:00 2a49326dff1c548eae76d2a161581b5a2b30afff<jats:p>N/A</jats:p>Citizen Science: Theory and Practice250Citizen Science: Theory and Practice["Lucy Fortson", "Kevin Crowston", "Laure Kloetzer", "Marisa Ponti"]2024-12-09T00:00:00 b5ff66fa8d7019387775dbb7b947cca1491fbe6cInternational Journal of Physiotherapy00International Journal of Physiotherapy["Shahul Hameed Pakkir Mohamed"]2024-12-09T00:00:00 b3a55d43c791a9d68403c20cdee4be2cda73a541 LLM has attained generative capabilities similar to human discourse and can effectively summarize documents and extract information from texts. The development of R.A.G. systems will soon make these systems capable to browse databases such as MEDLINE and extract knowledge, creating summaries of the literature. These summaries may soon reach a point where they are equivalent to current reviews of the literature, possibly making them irrelevant. The availability of automated summaries of the literature may raise the bar of what is still worth publishing. Literature reviews may have to capitalize on human imagination, creativity and abstraction capabilities to survive the A.I. revolution. Learned Publishing700The availability of automated summaries of the literature may raise the bar of what is still worth publishing, and literature reviews may have to capitalize on human imagination, creativity and abstraction capabilities to survive the A.I. revolution.Learn. Publ.["Carlo Galli", "Chiara Moretti", "E. Calciolari"]2024-12-09T00:00:00 e8e8a41693baebd76e9401de624492c73b7fba08Artificial intelligence is a growing trend nowadays, being used in the most different areas of human, biological and exact sciences. In the health area, its use has become increasingly widespread among professionals in medicine, nursing, dentistry, among other professions. The aim of this study was to carry out a narrative review of the indexed scientific literature based on the central question: what is the impact of using artificial intelligence in different areas of health? To do this, searches were carried out in the PubMed database using the terms “artificial intelligence” and “health care” or “health occupations”. Inclusion and exclusion strategies were applied, and 9 articles were selected to be included in the present work. The results were represented in tables and present a trend in the use of artificial intelligence in different areas of health, demonstrating that there is a positive impact on the incorporation of technology in the clinical routine of nurses, doctors and dentists. Based on the results presented, it is concluded that there is a positive impact on the use of technological resources that use artificial intelligence in the areas of health.Revista ft00There is a positive impact on the use of technological resources that use artificial intelligence in the areas of health, demonstrating that there is a positive impact on the incorporation of technology in the clinical routine of nurses, doctors and dentists.Revista ft["Maur\u00edcio de Oliveira Barros", "Thyago Oliveira Cardoso", "Dayana Gouveia de Lemos", "Mara Kallyne Alves do Nascimento", "Mayra Santos Moura", "Felipe de Souza Duarte"]2024-12-09T00:00:00 6c2764474e2b7c7afcb6d6158f3b98c85c2e5c8cArtificial intelligence (AI) is disrupting industry and potentially threatening to replace humans at work. In this article, we offer a strategy to ensure that executive decision-makers are given the tools to combine the best of human skills with AI, both preserving human dignity and enhancing organizational achievement. We propose a decision-making framework, the Arbitrage-Enhancement Decision Grid (AEDG), that enables organization leaders to determine the optimum human and intelligent machine collaboration to improve workforce performance. The framework recognizes the inevitable adoption of technology innovation, in conjunction with an organization’s need to balance human performance and competitive objectives. The authors then advance an actionable roadmap for developing human workforce and intelligent machine competencies and skills, the Human Resource-Artificial Intelligence Collaboration (HRAIC) framework that complements the decision-making outcomes of the AEDG.Performance Improvement Journal320An actionable roadmap for developing human workforce and intelligent machine competencies and skills, the Human Resource-Artificial Intelligence Collaboration (HRAIC) framework that complements the decision-making outcomes of the AEDG.Performance Improvement Journal["James P. Eicher", "William J. Mea"]2024-12-09T00:00:00 b95718e25d2279382709e84a498e20c5dd4a55cdThis study explores the role of artificial intelligence (AI) in reshaping the marketing mix in Indonesia’s consumer electronics industry, focusing on traditional marketing elements—product, price, promotion, and place—along with service marketing elements such as people, process, and physical evidence. The primary novelty of this research lies in its approach of positioning AI not merely as an operational tool, but as a strategic driver of growth, essential for crafting responsive and consumer-centered marketing strategies, emphasizes AI’s role as a catalyst for strategic decision-making within the marketing framework. Using a qualitative case study of a major electronics company in Indonesia, data was gathered through interviews with industry practitioners and consumers, as well as document analysis and observation. Findings indicate that AI-driven strategies enhance operational efficiency, support product innovation, enable dynamic pricing, and facilitate targeted promotions, all of which elevate the user experience to align with evolving consumer needs. This study makes significant contributions to both theory and practice by presenting a conceptual framework that highlights AI’s role as an integral part of marketing mix strategies, positioning it as a vital tool for achieving sustainable, consumer-oriented growth in the digital age.PaperASIA241Findings indicate that AI-driven strategies enhance operational efficiency, support product innovation, enable dynamic pricing, and facilitate targeted promotions, all of which elevate the user experience to align with evolving consumer needs.PaperASIA["Djoko Pandji Dhyakso Raden Mas", "Virienia Puspita"]2024-12-09T00:00:00 fddc5738e3b3da9635a6a5ddaa3e606bad93ac81In the peer review process of top-tier machine learning (ML) and artificial intelligence (AI) conferences, reviewers are assigned to papers through automated methods. These assignment algorithms consider two main factors: (1) reviewers' expressed interests indicated by their bids for papers, and (2) reviewers' domain expertise inferred from the similarity between the text of their previously published papers and the submitted manuscripts. A significant challenge these conferences face is the existence of collusion rings, where groups of researchers manipulate the assignment process to review each other's papers, providing positive evaluations regardless of their actual quality. Most efforts to combat collusion rings have focused on preventing bid manipulation, under the assumption that the text similarity component is secure. In this paper, we demonstrate that even in the absence of bidding, colluding reviewers and authors can exploit the machine learning based text-matching component of reviewer assignment used at top ML/AI venues to get assigned their target paper. We also highlight specific vulnerabilities within this system and offer suggestions to enhance its robustness.arXiv.org01It is demonstrated that even in the absence of bidding, colluding reviewers and authors can exploit the machine learning based text-matching component of reviewer assignment used at top ML/AI venues to get assigned their target paper.ArXiv["Jhih-Yi Hsieh", "Aditi Raghunathan", "Nihar B. Shah"]2024-12-09T00:00:00 6d89aa58bb553675d7986c438285a8e2c98b8d29The integration of artificial intelligence (AI) in education, exemplified by tools like ChatGPT, represents a transformative shift in teaching and learning methodologies. This study explores ChatGPT’s role in advancing personalized learning, empowering educators, and enhancing accessibility within educational ecosystems. Using a systematic literature review supported by bibliometric analysis, the paper identifies key trends and insights into AI-driven educational technologies. Findings demonstrate ChatGPT's capacity to personalize instruction by generating adaptive content, delivering real-time feedback, and facilitating curriculum development. It also alleviates educators' workloads through automated grading, lesson planning, and administrative support. However, challenges such as ethical concerns regarding data privacy, inherent AI biases, and potential over-reliance on automation hinder its widespread adoption. The study emphasizes the necessity of ethical guidelines, transparency, and balanced AI integration to mitigate these risks. In conclusion, ChatGPT holds substantial potential for improving educational outcomes by fostering inclusive, adaptive, and efficient learning environments. Future efforts should focus on refining AI technologies to reduce biases, uphold data privacy, and equip educators with the skills needed to effectively integrate AI into pedagogical practices. Responsible and ethical implementation will be key to unlocking ChatGPT's full potential in education.International Journal of Essential Competencies in Education1170ChatGPT holds substantial potential for improving educational outcomes by fostering inclusive, adaptive, and efficient learning environments and the necessity of ethical guidelines, transparency, and balanced AI integration to mitigate these risks is emphasized.International Journal of Essential Competencies in Education["Muhammad Asy'ari", "Sergii Sharov"]2024-12-09T00:00:00 0a0646a115bbbf5b0fae9047b68d6d08d9b2d2daThis study examines students perceptions of Artificial Intelligence (AI) in Social Studies education, exploring the impact of AI integration on academic performance and the ethical concerns associated with its use. Utilizing a quantitative research design, data were collected from secondary school students to assess their views on AI-driven educational tools. The findings reveal that students generally have a high perception of the usefulness of AI in enhancing learning and improving academic outcomes. However, significant ethical concerns were also identified, particularly regarding data privacy, algorithmic bias, and transparency in AI decision-making. The study highlights the need for stronger data protection measures, the reduction of biases in AI algorithms, and greater transparency to build trust in AI technologies. The results suggest that a balanced approach, which addresses both the educational benefits and ethical challenges of AI, is crucial for the responsible integration of AI in Social Studies education. The implications for policy, practice, and future research emphasize the importance of developing comprehensive ethical guidelines and promoting critical discussions about AI in the curriculum. By addressing these ethical considerations, the study contributes to a deeper understanding of how AI can be effectively and ethically integrated into Social Studies education, enhancing learning outcomes while safeguarding students rights.  Social Science and Humanities Journal00The results suggest that a balanced approach, which addresses both the educational benefits and ethical challenges of AI, is crucial for the responsible integration of AI in Social Studies education, enhancing learning outcomes while safeguarding students rights.Social Science and Humanities Journal["Adedoyin Adetutu KEHINDE-AWOYELE", "A. W. Adeowu"]2024-12-09T00:00:00 6b66fad9c084154615b1181ec7520d3e4dca1057The integration of artificial intelligence (AI) in higher education (HE) has expanded significantly in recent years. Colleges and other educational institutions are gradually adopting new technologies in teaching and learning. The development of technology and large model algorithms have greatly enhanced AI-generated content's capabilities, positioning AI as promising generative tools that add convenience and potential to education. While much research has focused on AI's role in disciplines like Engineering and Computer Science, its impact on Marketing Communications (Marcom) education remains under-explored. This review investigates how AI tools enhance Marcom education by simulating real-world marketing environments because Marcom is a career-oriented discipline. This review discusses AI in HE and its applications within the Marcom industry and then bridges the gap between HE and the industry by exploring Marcom education's current situation and future opportunities with AI assistants. The findings highlight the importance of equipping both educators and students with AI competencies. Educators can use AI to improve the teaching process, and they also play an essential role in monitoring students' AI usage to ensure their learning quality. From the students' aspects, mastering AI tools can help them be well-prepared for the evolving Marcom industry.Lecture Notes in Education Psychology and Public Media00This review investigates how AI tools enhance Marcom education by simulating real-world marketing environments because Marcom is a career-oriented discipline and bridges the gap between HE and the industry by exploring Marcom education's current situation and future opportunities with AI assistants.Lecture Notes in Education Psychology and Public Media["Shirui Dong"]2024-12-09T00:00:00 d3c6fc0395c3c7128ad8602b1ce87ec63d785a5bDue to the ChatGPT craze, all industries around the world have a hot Generative AI of interest. In particular, since Open AI announced AI Sora, which makes text into video on February 15, 2024, the media and content industries, both at home and abroad, have expressed expectations and a sense of crisis. By learning a large amount of Hyper-scale Data with artificial intelligence technology that actively generates results according to the specific needs of live Generative AI users, it is looking beyond the realm of creation, which can be called the human domain[1][2][3]. Although, unlike image-generating AI, the video live Generative AI service still has the limit of generating only short-length videos because it has to maintain temporal coherence. However, as live Generative AI developers supplement these problems and continue to offer advanced services, they will become the mainstream of live Generative AI in the media and content industries. Therefore, as of 2024, this study intends to present 3I(Inquiry-Inspection-Idea) as a strategy for competitive advantage in the media and content industries to respond to changes in the media and content industries that will be triggered by generative Generative AI along with ways to secure competitiveness through case analysis of generative Generative AI in the media and content industries[2].International Journal of Religion003I(Inquiry-Inspection-Idea) (Inquiry-Inspection-Idea) is presented as a strategy for competitive advantage in the media and content industries to respond to changes in the media and content industries that will be triggered by generative Generative AI.International Journal of Religion["YOUN-SUNG Kim"]2024-12-09T00:00:00 e7c7b94ace2efd3566eeb61e1c396d0666f98ab6As artificial intelligence (AI) technology becomes more prevalent in education, AI automatic grading systems have emerged as essential tools for enhancing homework grading efficiency and alleviating teachers' workloads. The two leading platforms, Zuoyebang and MathGPTPro, are widely utilized in mathematics education. This study employs an experimental research method to compare the performance of these platforms in automatically grading function solution problems. The sample consists of 30 questions from the Function Solution Problems section of China's National College Entrance Examination. The focus will be on four key dimensions: logical steps, final answers, expression symbols, and analysis feedback to assess accuracy rates. A t-test will then examine differences in their handling of complex solution steps. The results show that MathGPTPro can achieve higher accuracy in complex reasoning, and its AI system has more potential to be applied to homework correction and math learning. However, there are still problems of inaccurate identification and wrong judgment in the process of Zuoyebang step recognition. This study offers insights into the application of AI automatic grading systems in education and suggests areas for system optimization.Lecture Notes in Education Psychology and Public Media00The results show that MathGPTPro can achieve higher accuracy in complex reasoning, and its AI system has more potential to be applied to homework correction and math learning than Zuoyebang, which has more potential to be applied to homework correction and math learning.Lecture Notes in Education Psychology and Public Media["Ziyue Gu"]2024-12-09T00:00:00 0f1509133166a37e136d3e013d5bc502794ec505 Purpose Social robotics is a rapidly growing application of artificial intelligence (AI) in society, encompassing an expanding range of applications. This paper aims to contribute to the ongoing integration of psychology into social robotics ethics by reviewing current theories and empirical findings related to human–robot interaction (HRI) and addressing critical points of contention within the ethics discourse. Design/methodology/approach The authors will explore the factors influencing the acceptance of social robots, explore the development of relationships between humans and robots and delve into three prominent controversies: deception, dehumanisation and violence. Findings The authors first propose design factors allowing for a positive interaction with the robot, and further discuss precise dimensions to evaluate when designing a social robot to ensure ethical design technology, building on the four ethical principles for trustworthy AI. The final section of this paper will outline and offer explicit recommendations for future research endeavours. Originality/value This paper provides originality and value to the field of social robotics ethics by integrating psychology into the ethical discourse and offering a comprehensive understanding of HRI. It introduces three ethical dimensions and provides recommendations for implementing them, contributing to the development of ethical design in social robots and trustworthy AI. International Journal of Ethics and Systems2440This paper aims to contribute to the ongoing integration of psychology into social robotics ethics by reviewing current theories and empirical findings related to human–robot interaction and addressing critical points of contention within the ethics discourse.International Journal of Ethics and Systems["Auxane Boch", "Bethany Rhea Thomas"]2024-12-09T00:00:00 d7caccd6632a4cfc64371f25fe1e437c708cd572Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities.Electronics05This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities, and examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things, Edge AI, and 5G/6G networks.Electronics["Zita Lifelo", "Jianguo Ding", "Huansheng Ning", "Qurat-Ul-Ain", "Sahraoui Dhelim"]2024-12-10T00:00:00 97a5fb3512be8dfd9c8a32f4c556ad9db6030288With the continuous development of technological and educational innovation, learners nowadays can obtain a variety of supports from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatGPT. In particular, there has been a surge of academic interest in human‐AI collaboration and hybrid intelligence in learning. The concept of hybrid intelligence is still at a nascent stage, and how learners can benefit from a symbiotic relationship with various agents such as AI, human experts and intelligent learning systems is still unknown. The emerging concept of hybrid intelligence also lacks deep insights and understanding of the mechanisms and consequences of hybrid human‐AI learning based on strong empirical research. In order to address this gap, we conducted a randomised experimental study and compared learners' motivations, self‐regulated learning processes and learning performances on a writing task among different groups who had support from different agents, that is, ChatGPT (also referred to as the AI group), chat with a human expert, writing analytics tools, and no extra tool. A total of 117 university students were recruited, and their multi‐channel learning, performance and motivation data were collected and analysed. The results revealed that: (1) learners who received different learning support showed no difference in post‐task intrinsic motivation; (2) there were significant differences in the frequency and sequences of the self‐regulated learning processes among groups; (3) ChatGPT group outperformed in the essay score improvement but their knowledge gain and transfer were not significantly different. Our research found that in the absence of differences in motivation, learners with different supports still exhibited different self‐regulated learning processes, ultimately leading to differentiated performance. What is particularly noteworthy is that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger “metacognitive laziness”. In conclusion, understanding and leveraging the respective strengths and weaknesses of different agents in learning is critical in the field of future hybrid intelligence. What is already known about this topic Hybrid intelligence, combining human and machine intelligence, aims to augment human capabilities rather than replace them, creating opportunities for more effective lifelong learning and collaboration. Generative AI, such as ChatGPT, has shown potential in enhancing learning by providing immediate feedback, overcoming language barriers and facilitating personalised educational experiences. The effectiveness of AI in educational contexts varies, with some studies highlighting its benefits in improving academic performance and motivation, while others note limitations in its ability to replace human teachers entirely. What this paper adds We conducted a randomised experimental study in the lab setting and compared learners' motivations, self‐regulated learning processes and learning performances among different agent groups (AI, human expert and checklist tools). We found that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger metacognitive "laziness", which can potentially hinder their ability to self‐regulate and engage deeply in learning. We also found that ChatGPT can significantly improve short‐term task performance, but it may not boost intrinsic motivation and knowledge gain and transfer. Implications for practice and/or policy When using AI in learning, learners should focus on deepening their understanding of knowledge and actively engage in metacognitive processes such as evaluation, monitoring, and orientation, rather than blindly following ChatGPT's feedback solely to complete tasks efficiently. When using AI in teaching, teachers should think about which tasks are suitable for learners to complete with the assistance of AI, pay attention to stimulating learners' intrinsic motivations, and develop scaffolding to assist learners in active learning. Researcher should design multi‐task and cross‐context studies in the future to deepen our understanding of how learners could ethically and effectively learn, regulate, collaborate and evolve with AI. British Journal of Educational Technology791A randomised experimental study compared learners' motivations, self‐regulated learning processes and learning performances among different agent groups (AI, human expert and checklist tools) and found that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger metacognitive "laziness".ArXiv["Yizhou Fan", "Luzhen Tang", "Huixiao Le", "Kejie Shen", "Shufang Tan", "Yueying Zhao", "Yuan Shen", "Xinyu Li", "D. Ga\u0161evi\u0107"]2024-12-10T00:00:00 727e9c815f98be1f58124d2bed5feef707c4a75eThe Artificial Intelligence of Things (AIoT) is applicable for various domains, that is, smart healthcare, smart cities, industrial sectors, transportation systems, and many more. Controller area network (CAN) facilitates the integration of the sensing devices, thus enables them to send their data for analysis to various artificial intelligence (AI) algorithms. CAN also provides reliability and fault tolerance to the AIoT applications, as it has been designed to deal with noisy environments. Thus, CAN‐driven AIoT improves the efficiency, reliability, and functionalities of the devices and systems, which is very much needed for various AIoT applications. However, it is vulnerable to various cyber‐attacks like message replay, modification attack, fuzzy attack, denial of service, and spoofing the RPM gauge or drive gear. Therefore, an intrusion detection system (IDS) is required to detect attacks on the CAN bus. In this paper, we propose a lightweight and efficient intrusion detection system which successfully detects multiple intrusions based on the type of attack on CAN bus without causing additional traffic overhead to the ongoing communications (in short, ACID‐CAN). The presented mechanism is very much needed for the CAN‐driven AIoT applications. Experimental results show that the proposed ACID‐CAN successfully detects intrusions even when the amount of intrusion data is reduced to of normal data. The obtained results were compared with those of previous studies in the field of CANs intrusion detection, and it has been noted that the proposed ACID‐CAN offers comparable and better results.Security and Privacy111A lightweight and efficient intrusion detection system which successfully detects multiple intrusions based on the type of attack on CAN bus without causing additional traffic overhead to the ongoing communications is proposed (in short, ACID‐CAN).Secur. Priv.["Gagan Dangwal", "M. Wazid", "Sarah Nizam", "Vinay Chamola", "A. K. Das"]2024-12-10T00:00:00 3c48a7ef3cace46e1b94d24359a2a9e528882d7aOBJECTIVE The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research. BACKGROUND The application of AI to the study of headache and other healthcare challenges is growing rapidly. It is critical that these findings be accurately interpreted by headache specialists, but this can be difficult for non-AI specialists. METHODS This paper is a narrative review of the fundamentals required to understand ML/AI headache research. Using guidance from key leaders in the field of headache medicine and AI, important references were reviewed and cited to provide a comprehensive overview of the terminology, methodology, applications, pitfalls, and bias of AI. RESULTS We review how AI models are created, common model types, methods for evaluation, and examples of their application to headache medicine. We also highlight potential pitfalls relevant when consuming AI research, and discuss ethical issues of bias, privacy and abuse generated by AI. Additionally, we highlight recent related research from across headache-related applications. CONCLUSION Many promising current and future applications of ML and AI exist in the field of headache medicine. Understanding the fundamentals of AI will allow readers to understand and critically appraise AI-related research findings in their proper context. This paper will increase the reader's comfort in consuming AI/ML-based research and will prepare them to think critically about related research developments.Headache460Understanding the fundamentals of AI will allow readers to understand and critically appraise AI-related research findings in their proper context and increase the reader's comfort in consuming AI/ML-based research and will prepare them to think critically about related research developments.Headache["Gina M. Dumkrieger", "Chia-Chun Chiang", "Pengfei Zhang", "Mia T. Minen", "Fred Cohen", "Jennifer A Hranilovich"]2024-12-10T00:00:00 8b4a8c9c5d7b2654268cc5ca7297dcbfeef3d3c0This article focuses on developing and applying an artificial intelligence (AI) model in the field of nuclear energy, emphasizing its importance as a decision-making support tool. The current state of AI in nuclear energy is discussed, with a special focus on the creation of a proprietary classification model. The article outlines the main stages of AI model development and its practical application for classifying events at nuclear power plants. It utilizes machine learning technology and natural language processing to develop the model. The significance and innovative approach of using AI in nuclear energy are emphasized, considering its potential in enhancing the efficiency of processes at nuclear power plants. Results demonstrate the high efficiency (accuracy) of the developed AI model during testing for event classification. The current era of digital transformation and rapid technological development highlights the increasing importance of AI as a tool in various sectors. Additionally, the article covers the international promotion of AI in nuclear energy, particularly by the International Atomic Energy Agency (IAEA) and the United States Nuclear Regulatory Commission (NRC). It details the efforts of these organizations in exploring the application of AI technology in nuclear technologies and regulatory activities, emphasizing safe AI use and developing strategic plans for AI applications. In conclusion, the article suggests the model's potential application in investigating nuclear power plant events, that can be used for classifying by categories, root causes, corrective actions etc. The article concludes that while AI is a modern technology finding application in various fields including nuclear energy, it cannot fully replace human involvement in the nuclear sector. However, AI combination with human input can improve the efficiency and safety of processes at nuclear power plants.POWER ENGINEERING: economics, technique, ecology00While AI is a modern technology finding application in various fields including nuclear energy, it cannot fully replace human involvement in the nuclear sector, however, AI combination with human input can improve the efficiency and safety of processes at nuclear power plants.POWER ENGINEERING: economics, technique, ecology["M. Dzerun", "Iurii Ovdiienko"]2024-12-10T00:00:00 2547a30e6f99e24bf47119f57179a2111a41494cThis comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and advanced neural network architectures, aimed at accurately predicting the remaining useful life (RUL) of CPs under varying operational conditions. This review also addresses the limitations and challenges of the current AI-driven methodologies, offering insights into potential solutions. By synthesizing these methodologies and presenting practical applications through case studies, this review provides a forward-looking perspective to empower industry professionals and researchers with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components.Actuators830This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data, placing a specific emphasis on artificial intelligence approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis.Actuators["Salman Khalid", "Soo-Ho Jo", "Syed Yaseen Shah", "Joon Ha Jung", "H. Kim"]2024-12-10T00:00:00 28b25b490ca879863149a9e16a1bff81137150c3Purpose Universities face challenges due to the absence of artificial intelligence (AI) integration in entrepreneurship education (EE) and its incubation centers for young startups. Making a business plan for their innovative enterprises, which includes market analysis, financial projections, marketing strategy and an operations plan, are a few of the toughest tasks they may face. Aspiring students can make it simple to launch their dream business by integrating AI tools. Hence, this study aims to conduct a systematic literature review (SLR) to examine the global trend of the transformation of EE with AI and determine the necessity of integrating AI in university incubation centers as a potential future research direction. Design/methodology/approach In this study, the authors conducted an SLR method to investigate the transformation of EE with AI. This review employed a bibliometric analysis covering the period of 1993–2023 and utilized articles published in scientific journals available in the SCOPUS database as our data source. Findings There is an enormous potential for research on EE using cutting-edge AI in developed and developing nations. There is a lack of studies exploring AI integration into university incubation centers. Hence, there are possible future directions for research into integrating AI into university incubation centers using cutting-edge tools like chatbots, ChatGPT, ChatGen and other AI that will help to develop a comprehensive business plan for students aspiring entrepreneurial venture startups. Research limitations/implications The study’s research was limited using the Scopus database’s core collection, which may ignore other significant research articles. Therefore, the study’s scope can be constrained due to the narrow search parameters. The study, however, tries to establish the importance of its research by offering a thorough review and evaluation of AI in EE. Practical implications There is significance of incorporating AI into EE to foster an EE culture and realize its potential benefits. To transform incubation centers and promote aspirant entrepreneurs in the fourth industrial revolution (4IR), higher education institutions (HEIs) should strategically adopt AI. Originality/value This study presents a novel viewpoint by investigating the distinction in AI perception and usage among educators, advocating the incorporation of AI in university incubation centers to help entrepreneurial students. It contributes uniqueness and innovative approaches to early startup issues in EE. Asia Pacific Journal of Innovation and Entrepreneurship660A novel viewpoint is presented by investigating the distinction in AI perception and usage among educators, advocating the incorporation of AI in university incubation centers to help entrepreneurial students.Asia Pacific Journal of Innovation and Entrepreneurship["M. M. Thottoli", "Maria Elisa Cruz", "Salem Al Abri"]2024-12-10T00:00:00 60b669a90a40eccb04bfdfb735e925e7694b6a26Artificial Intelligence (AI) is rapidly pacing in every sphere of life, particularly for corporations and small and medium enterprises, breeding plenty of opportunities and challenges. This research study examines the adoption and identifies the key factors influencing its integration into Pakistan's small companies. However, this research has determined that AI has the potential to enhance operational efficiency, competitiveness, improved productivity, better customer care engagement, and streamlined operations. Significant hurdles are still faced in Pakistan, such as inadequate technical expertise, limited resources, and cultural inertia related to the adaptation of this AI technology. In Pakistan, the younger employees and the Information Technology (IT) sector make it much easier to accelerate the adaptation process of AI. Addressing these challenges and unlocking the potential to drive economic growth and innovation in its small business sector is imperative. The results offer valuable insights for researchers, business leaders, and policymakers, contributing to a comprehensive understating of AI integration in emerging markets.Journal of Social &amp; Organizational Matters00It has been determined that AI has the potential to enhance operational efficiency, competitiveness, improved productivity, better customer care engagement, and streamlined operations in Pakistan.Journal of Social &amp; Organizational Matters["Jamil Ur Rahman", "Nimat Ullah", "Sikandar Jalal", "Atif ur rahman Yousafzai"]2024-12-10T00:00:00 3e8334905b27eb9fc37c58905160546c59b689dfArtificial Intelligence (AI) is transforming key industries like employment, healthcare, and criminal justice, but it also introduces significant ethical and legal challenges, particularly regarding gender bias. AI systems, often trained on biased historical data, can perpetuate and even amplify existing gender inequalities. This essay examines the legal implications of gender bias in AI, focusing on challenges to anti-discrimination laws, transparency issues, and the need for regulatory oversight. Gender bias in AI arises when systems are trained on datasets that reflect societal inequalities, leading to discriminatory outcomes. This bias is not a technical flaw, but rather a consequence of using data and algorithms that mirror patterns of discrimination. The lack of diversity among AI developers, who are predominantly male, exacerbates this issue by failing to account for the perspectives and needs of women and marginalized groups. In legal contexts, the use of AI in hiring, criminal justice, and risk assessment raises ethical concerns. AI-driven systems risk reinforcing historical gender biases, which can undermine fairness in decision-making processes. Unchecked, these biases could worsen disparities in critical areas such as recruitment and justice administration, threatening legal protections. A major legal challenge is how AI interacts with existing anti-discrimination laws. To address these challenges, transparency in AI decision-making is essential. Regulatory frameworks must evolve to require regular audits of AI systems and enforce accountability for biased outcomes. Ethical guidelines are insufficient; mandatory legal oversight is needed to ensure AI promotes fairness and inclusivity.Amicus Curiae. Revista Electrónica de la Facultad de Derecho30The legal implications of gender bias in AI are examined, focusing on challenges to anti-discrimination laws, transparency issues, and the need for regulatory oversight.Amicus Curiae. Revista Electrónica de la Facultad de Derecho["Dra. Trilce Fabiola Ovilla Bueno"]2024-12-10T00:00:00 7fd85484321ab858ce3f5b4fc4aa70455980e1bcIn this study, we aimed to investigate employees’ perspectives on artificial intelligence (AI) and understand their opinions and emotions regarding the future advancements of AI and its potential impacts on their work lives. We conducted a qualitative study using an inductive approach and a phenomenological research design. We employed purposive sampling methods to select 20 participants from various sectors for interviews to achieve this goal. We utilized content analysis with the MAXQDA 2024 program, followed by descriptive and relational analyses of the categories and codes we obtained. Our research showed that the most common emotion among participants regarding their encounters with AI was ‘astonishment.’ We discovered that the participants had mixed feelings towards AI, including positive emotions such as happiness, curiosity, admiration, and excitement, and negative emotions such as anxiety, fear, anger, frustration, and distrust. Our study showed that respondents were most surprised and happiest about how AI makes life easier and more convenient and how its speed gives people more time for their personal lives. Conversely, the factors that caused the most concern and fear among participants were the potential for mass unemployment due to AI and the risk of encouraging laziness in people. Many of them believe that AI has the potential to bring significant advantages to humanity, particularly in fields such as healthcare, the environment, and the economy. However, there is also a growing concern and fear around the possibility that AI could spiral out of control, be utilized in biological, chemical, and technological warfare, and result in widespread unemployment.Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi400The study showed that respondents were most surprised and happiest about how AI makes life easier and more convenient and how its speed gives people more time for their personal lives and the factors that caused the most concern and fear among participants were the potential for mass unemployment due to AI and the risk of encouraging laziness in people.Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi["G\u00f6zde Dilara Can", "Ebru Tolay"]2024-12-10T00:00:00 e14b12a92a968e21564189d23c3afd1e4654ba52This study investigates the influence of artificial intelligence (AI) and information system quality (ICT) on the mental health of Generation Z employees in West Kalimantan, with a specific focus on technostress as a mediating factor. The research uses an explanatory quantitative approach, employing a partial least squares-based structural equation model (PLS-SEM) via SmartPLS 4 software. The sample consists of 200 Generation Z employees, selected through purposive sampling based on age (18-27 years) and experience using AI and ICT for at least one year. The study reveals that AI positively and significantly affects employee mental health, while ICT has a negative and insignificant impact on employee mental health. Furthermore, technostress positively and significantly mediates the relationship between AI, ICT, and mental health. This research provides evidence that the effective use of AI and ICT, combined with proper technostress management, can enhance employee well-being and productivity, fostering innovation and efficiency in West Kalimantan’s workforce, ultimately positioning the region to leverage Generation Z’s digital skills to drive economic growth and attract investment in emerging tech sectors.eCo-Buss410Evidence is provided that the effective use of AI and ICT, combined with proper technostress management, can enhance employee well-being and productivity, fostering innovation and efficiency in West Kalimantan’s workforce.eCo-Buss["D. Bagaskara", "H. Hasanudin", "Karsim Karsim", "Maria Christiana Iman Kalis", "Ilzar Daud Iman Kalis"]2024-12-10T00:00:00 1dd95ac34dde7af54c04bb2b4d76f5b51f2b6470Artificial Intelligence (AI) potential to revolutionize agricultural practices, from precision farming to decision-making, is increasingly recognized by students. Understanding their views is essential for fostering AI adoption and innovation in the agricultural sector. The research was conducted among 100 agricultural students from Tamil Nadu Agricultural University Coimbatore, Tamil Nadu, using a proportionate random sampling method. A well-structured questionnaire was used to collect data on students' perceived usefulness of AI, ease of use, its impact on learning, emotional responses and attitudes towards AI in agriculture and challenges faced by the agricultural students. Mean score and percentage analysis were used to analyse the collected data. The results show that AI improves decision-making processes in farm management (4.52), AI is easy to use for agricultural studies (4.42), AI tools make learning more interactive and engaging in agricultural education (4.54), the idea of using AI in agriculture makes me feel excited (3.73) and students believe AI technologies will play a significant role in the future of agriculture (3.96).  About 85 per cent of students had technical limitations, such as insufficient access to equipment and software. Furthermore, the integration of AI with traditional agricultural practices was found to be difficult for 81 per cent of the respondents. The study concludes that while agricultural students are eager to embrace AI in their future careers, significant improvements in infrastructure, training, and support are needed to fully realize AI’s potential in the agricultural sector. These findings offer valuable insights for educators seeking to foster AI-driven innovation in agriculture.Journal of Scientific Research and Reports00While agricultural students are eager to embrace AI in their future careers, significant improvements in infrastructure, training, and support are needed to fully realize AI’s potential in the agricultural sector.Journal of Scientific Research and Reports["Jayashree V", "Ramasubramanian M", "Karthikeyan C", "Gnanasanjevi G", "S. P. Kamali"]2024-12-10T00:00:00 07d5df836562d05d1ea57ac2c942277c0be0efe7PurposeThis study aims to provide a methodology and tools to design new organizational processes and artificial intelligence (AI)-based scoring to optimize the resources management in healthcare units.Design/methodology/approachProcess design and process data-driven simulation: the processes are designed by the business process modeling and notation and the unified modeling language standards. Data processing is performed by Correlation matrix analysis and by Fuzzy c-Means data clustering. The matching between the two methods provides the most indicated final corrective actions of the “TO BE” organizational model.FindingsThis proposed method, experimentally applied in this work merging the lean management model (LMM), process mining (PM) and AI methods, named process mining organization (PMO) model (Rosa et al., 2023 (b)), is able to improve organizational processes of a hospitalization unit (HU) by developing three propaedeutic phases: (1) analysis of the current state of the processes (“AS IS”) by identifying the critical issues as bottlenecks of processes, (2) AI data processing able to provide additional classified and predicted information allowing the “TO BE” workflow process and (3) implementation of corrective actions suggested by the PMO in order to support strategic decision-making processes in the short, medium and long term by classifying an order of priority about the healthcare procedures/protocols to perform.Research limitations/implicationsThe main limitation of the proposed case study is in the limited number of available digital data to process. This aspect reduces the capability to interpret result. In any case, the proposed methodology is a “launch” work to define a new approach to integrate organizational processes including workflow design and AI scoring. Future work will be focused on managerial implications due to use of the discussed method: design and development of new human resource (HR) organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.Practical implicationsMain implications are in healthcare managerial scenarios: design and development of new HR organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.Social implicationsCare services optimization is addressed on HUs.Originality/valueThe design of HR organizational processes integrates AI-driven data decision-making processes. This case study examines AI-based innovation analytics addressed on resource efficiency.Business Process Management Journal560A methodology and tools to design new organizational processes and artificial intelligence (AI)-based scoring to optimize the resources management in healthcare units to define a new approach to integrate organizational processes including workflow design and AI scoring.Business Process Management Journal["A. Rosa", "A. Massaro", "Giustina Secundo", "Giovanni Schiuma"]2024-12-10T00:00:00 86679c6c0b0f144485dc3f9170ac947db5badb16Objective: This study aims to explore how AI has been applied in patient triage in emergency services, investigating its contributions to the efficiency of care, the challenges faced in implementation, and the opportunities for response time optimization and resource allocation.   Method: An integrative literature review was carried out with studies published between 2020 and 2024. The research was guided by the PICo (Population, Interest and Context) model, focusing on patients treated in emergency services (P), the application of AI for triage (I) and the hospital and emergency context (Co). The search was carried out in databases such as PubMed, LILACS, SciELO and Scopus, applying the Boolean code "Artificial Intelligence" AND "Triage" AND "Emergency Services". After screening 214 initial articles, 12 studies were selected for final analysis, based on the PRISMA guidelines.   Results and Discussion: The review pointed out that AI significantly improves response time and reduces the margin of error in patient classification, especially in high-demand situations. The use of AI stood out in identifying critical outcomes, such as the need for immediate care. During the COVID-19 pandemic, AI has proven to be essential in the remote triage of high-risk patients, ensuring efficient use of resources in overcrowded settings. However, implementation faces challenges, such as resistance from health professionals and the need for integration with existing health systems.   Conclusion: In summary, the use of AI in emergency services has brought significant benefits, such as increased triage efficiency, improved diagnostic accuracy, and improved resource management. However, overcoming cultural and operational barriers and setting clear ethical guidelines are essential. The careful integration of AI, with continuous training and periodic assessments, is critical to transforming care and ensuring more effective and safer patient care.Revista de Gestão Social e Ambiental180The use of AI in emergency services has brought significant benefits, such as increased triage efficiency, improved diagnostic accuracy, and improved resource management, but implementation faces challenges, such as resistance from health professionals and the need for integration with existing health systems.Revista de Gestão Social e Ambiental["Maria Eugennia Andrade Magalh\u00e3es", "Carine Vit\u00f3ria Lemes da Silva", "H. M. Oliveira", "Ana Beatriz Rodrigues de Lima", "Maria Teresa Salum Flores", "Isabella Ferreira Leite", "Guilherme Aresi da Silva", "Ivan Aur\u00e9lio Fortuna Kalil de Faria", "Adriano Nogueira da Cruz", "Jos\u00e9 Helinaldo das Chagas Costa", "Rodrigo Daniel Zanoni"]2024-12-10T00:00:00 c3dfc35f6d515f93b0493b4eda688a7a6ff0977cWith the rapid development of artificial intelligence (AI) technology, its application in the education field is becoming increasingly widespread. This study aims to analyze university English teachers' willingness to use AI and its influencing factors. By exploring university English teachers' willingness to use AI through the qualitative research method of grounded theory, we analyze the underlying influencing factors. The analysis reveals that university English teachers' willingness to use AI tools is influenced by risk perception, external factors, and personal factors. Risk perception includes perceived technological anxiety, information quality perception, self-risk perception, and information security perception, which are negatively correlated with teachers' willingness to use AI; external factors include school training, AI development trends, and community influence, which indirectly affect teachers' willingness to use AI; while personal factors, including risk concerns, personal biases, and individual abilities, have a decisive impact on their willingness to use AI in the classroom.Journal of Education and Educational Research140Analysis of university English teachers' willingness to use AI tools reveals that university English teachers' willingness to use AI tools is influenced by risk perception, external factors, and personal factors.Journal of Education and Educational Research["Xiwen Zhong"]2024-12-10T00:00:00 da62bc07ba3ae939ed1f0edd591c5b200c6dd910INTRODUCTION The rapid advancement of artificial intelligence (AI) has led to significant transformations in health and healthcare. As AI technologies continue to evolve, there is an urgent need to establish a unified framework that guides the design, implementation, and evaluation of AI-driven interventions across individual and population health contexts. APPROACH In response to this need, the National Academy of Medicine (NAM) has initiated the development of an AI code of conduct (AICC) through its Digital Health Action Collaborative. This code of conduct is grounded in shared principles and commitments, aiming to actualize ethical and effective AI practices within the broader health and healthcare ecosystem. Given its specialized expertise and insight, the biomedical informatics (BMI) community plays a pivotal role in shaping and applying these guidelines. RECOMMENDATIONS We, as members of the AICC Steering Committee and the NAM Digital Health Action Collaborative, urge BMI educators, researchers, and practitioners to engage actively in refining and implementing the AICC. This involvement is critical to ensuring that the code is robust, applicable, and continuously improved to meet the evolving challenges facing health and healthcare.J. Am. Medical Informatics Assoc.00Members of the AICC Steering Committee and the NAM Digital Health Action Collaborative urge BMI educators, researchers, and practitioners to engage actively in refining and implementing the AICC, aiming to actualize ethical and effective AI practices within the broader health and healthcare ecosystem.Journal of the American Medical Informatics Association : JAMIA["P. R. Payne", "Kevin B. Johnson", "Thomas M Maddox", "Peter J. Emb\u00ed", "Kenneth D. Mandl", "Deven McGraw", "S. Saria", "Laura Adams"]2024-12-10T00:00:00 11b37f53d952069b8104b59b4c64d70793d964abWater channels play a crucial role in stormwater management, but the build-up of debris in their grilles can lead to flooding, endangering humans and animals, properties, and critical infrastructure nearby. While automated mechanical grab systems are necessary for efficient debris removal, their deployment in outdoor environments has been non-existent due to safety concerns. Here we report the successful use of Generative Artificial Intelligence (GenAI) and a Vision Language Model (VLM) to endow an automated mechanical grab with “awareness”, which allows it to differentiate between non-living and living objects, deciding whether to initiate or abort grabbing actions. The existing approaches such as YOLOv7 only achieve a sensitivity of 86.94% (95% CI: 83.44% to 89.93%) in detecting humans and specified animals. They systematically miss crouching workers and animals facing away from the cameras. Grounding DINO (VLM) can achieve a sensitivity of 100% (95% CI: 99.17% to 100.00%) and a specificity of 85.37% (95% CI: 77.86% to 91.09%). Together with BLIP-2 (GenAI), it acquires “awareness”, allowing it to detect animals beyond those specified. This opens up possibilities for the application of GenAI/VLM in automation sectors where human-machine mingling occurs, such as manufacturing, logistics, and construction. This innovation can potentially improve the safety and efficiency in these domains.HKIE Transactions160The successful use of Generative Artificial Intelligence (GenAI) and a Vision Language Model (VLM) to endow an automated mechanical grab with “awareness”, which allows it to differentiate between non-living and living objects, deciding whether to initiate or abort grabbing actions.HKIE Transactions["Jolly P. C. Chan", "Heiton M. H. Ho", "T. K. Wong", "Lawrence Y L Ho", "Jackie Cheung", "Samson Tai"]2024-12-10T00:00:00 bdc7ee9c784bdb788bf9c23b7e40c722b0a2a6a4In the Fourth Industrial Revolution (4IR) era, the rapid digitalisation of services poses both opportunities and challenges for the banking sector. This study addresses how adopting artificial intelligence (AI) and online and mobile banking advancements can influence customer satisfaction, particularly in Kaduna State, Nigeria. Despite significant investments in AI and digital banking technologies, banks often struggle to align these innovations with customer expectations and satisfaction. Using Structural Equation Modeling (SEM), this research investigates the impact of customer satisfaction with online banking (C_O) on AI integration (I_A) and mobile banking convenience (C_M). The SEM model reveals that customer satisfaction with online banking significantly influences AI integration (path coefficient of 0.40) and mobile banking convenience (path coefficient of 0.68). These results highlight a crucial problem: while technological advancements in banking are growing, their effectiveness is highly dependent on customer satisfaction with existing digital services. The study underscores the need for banks to prioritise enhancing online banking experiences as a strategic lever to improve AI integration and mobile banking convenience. Consequently, the research recommends that Nigerian banks develop comprehensive frameworks to evaluate and optimise their technology integration strategies, ensuring that technological innovations align with customer needs and expectations in the rapidly evolving digital landscape.Journal of Infrastructure Policy and Development680The SEM model reveals that customer satisfaction with online banking significantly influences AI integration and mobile banking convenience, highlighting the need for banks to prioritise enhancing online banking experiences as a strategic lever to improve AI integration and mobile banking convenience.Journal of Infrastructure, Policy and Development["G. Osuma", "N. Nzimande"]2024-12-10T00:00:00 a6719ac13fbc02992de0ccb7ecce60d821040cedIn terms of artificial intelligence (AI) applications, chatbot-based AI such as ChatGPT have the potential to improve students’ academic well-being and success. Despite the wide spread of chatbot-based AI development, no studies have explored their correlation with psychological constructs in the realm of education. This study aimed to measure the academic intensity use of chatbot-based AI and academic well-being among undergraduates and investigate the correlation between these constructs. The data was gathered using a self-administered web-based questionnaire, which includes the Academic Well-Being Scale and the developed academic intensity use of chatbot-based AI scale (AIUCA). The study sample consists of 340 undergraduates from the School of Educational Science. The findings revealed a moderate level of usage of chatbot-based AI and a moderate level of academic well-being among undergraduates. It also demonstrated a significant positive correlation (r = 0.68) between the intensity use of chatbot-based AI and academic well-being (p < 0.01). These results recommend decision-makers in higher education encourage students to integrate chatbot-based AI with their learning processes and activities to improve their academic well-being.International Journal of Engineering Pedagogy (iJEP)00The findings revealed a moderate level of usage of chatbot-based AI and a moderate level of academic well-being among undergraduates and demonstrated a significant positive correlation between the intensity use and academic well-being.International Journal of Engineering Pedagogy (iJEP)["A. Ajlouni", "R. Abu-Shawish", "Doha M. Silim", "Amal H. Ibrahim"]2024-12-10T00:00:00 0790ab75319322629da1d728653eee3887810186Penelitian ini bertujuan untuk menguji pengaruh teknologi Augmented Reality (AR) dan Artificial Intelligence (AI) terhadap antusiasme dan akseptansi siswa dalam pembelajaran bahasa di sekolah dasar. Metode yang digunakan dalam penelitian ini adalah pendekatan kuantitatif dengan desain cross-sectional. Data dikumpulkan melalui kuesioner yang didistribusikan kepada 200 siswa sekolah dasar di Sidoarjo. Analisis dilakukan menggunakan model Structural Equation Modeling (SEM) berbasis Partial Least Squares (PLS). Hasil penelitian menunjukkan bahwa teknologi AR dan AI berpengaruh signifikan terhadap antusiasme siswa, dengan nilai β sebesar 0,186 (p = 0,002) untuk AR dan β sebesar 0,649 (p = 0,000) untuk AI. Selain itu, AR juga berpengaruh langsung terhadap akseptansi siswa dengan nilai β sebesar 0,201 (p = 0,020), sementara AI berpengaruh terhadap akseptansi dengan β sebesar 0,223 (p = 0,012). Antusiasme siswa berperan sebagai mediator signifikan dalam hubungan antara penggunaan teknologi dan akseptansi, dengan pengaruh tidak langsung AI melalui antusiasme sebesar β = 0,149 (p = 0,003) dan AR sebesar β = 0,043 (p = 0,040). Nilai R² untuk antusiasme adalah 64,3%, dan untuk akseptansi adalah 35,8%. Penelitian ini menyimpulkan bahwa penggunaan AR dan AI mampu meningkatkan antusiasme dan akseptansi siswa terhadap pembelajaran bahasa. Kedua teknologi tersebut dapat menciptakan pengalaman belajar yang lebih interaktif dan personal, yang secara signifikan meningkatkan penerimaan siswa terhadap metode pembelajaran berbasis teknologi.ETNOLINGUAL00ETNOLINGUAL["Rikke Kurniawati", "Suyatno", "Bambang Yulianto", "Setya Yuwana Sudikan"]2024-12-10T00:00:00 f676549d9b31a439030fd48651b49c7eb3851707The increasing complexity and widespread use of artificial intelligence (AI) underscore the importance of its ethical dimensions. Understanding diverse perspectives on AI ethics is crucial, especially among university students who will shape future technological advancements. This study aims to deeply examine university students’ attitudes toward AI ethics, focusing on fairness, transparency, privacy, responsibility, and non-maleficence. A mixed-methods approach was used. In the quantitative phase, 355 students from engineering (E) and education science (ES) programs were evaluated using the AI Ethics Attitudes Scale. In the qualitative phase, semi-structured interviews with 23 students were thematically analyzed to gain detailed perspectives based on gender and discipline. The findings revealed significant gender-based differences in fairness and privacy, with female students scoring higher than male students. Interdisciplinary differences were evident in the transparency dimension, where ES students showed greater sensitivity. Interviews highlighted that female student emphasized legal compliance and data security more, whereas male students focused on financial information privacy. ES students prioritized user-friendly language and feedback and complaints in transparency discussions.International Journal of Engineering Pedagogy (iJEP)00Significant gender-based differences in fairness and privacy were revealed, with female students scoring higher than male students in fairness and privacy, and interdisciplinary differences were evident in the transparency dimension, where ES students showed greater sensitivity.International Journal of Engineering Pedagogy (iJEP)["G\u00fcls\u00fcm A\u015fiksoy"]2024-12-10T00:00:00 d1645806bbc4824754bcfabe88876b343832aa29 This Memory Studies Review special issue explores the intricate relationship between artificial intelligence (ai) and collective memory. In the one hand, the emergence of generative ai, exemplified by ChatGPT’s 2022 release, appears to herald a new infrastructure for collective memory. On the other, the memory studies work highlights the limits and the backlashes of this new form of memory in its social dimension. This leads to raise a provocative, open-ended question: Is artificial intelligence the future of collective memory? Our issue brings together diverse perspectives from memory studies scholars of different backgrounds and machine learning practitioners, fostering critical engagement with ai in memory practices. This multidisciplinary approach offers an initial exploration of the interactions between ai-powered software, platforms, and collective memory. The articles herein present a multifaceted analysis of ai’s role in shaping collective memory’s future. We advocate for increased interdisciplinary collaboration and ethical reflection in this rapidly evolving domain, providing memory studies scholars with a foundation for understanding and engaging with these technological transformations.Memory Studies Review260This Memory Studies Review special issue brings together diverse perspectives from memory studies scholars of different backgrounds and machine learning practitioners, fostering critical engagement with ai in memory practices, and offers an initial exploration of the interactions between ai-powered software, platforms, and collective memory.Memory Studies Review["Sarah Gensburger", "Fr\u00e9d\u00e9ric Clavert"]2024-12-10T00:00:00 5c2ea682b551708ad8f117f84be3ab2240ede9f7Artificial intelligence (AI) is revolutionizing numerous fields, including higher education, by offering innovative tools and techniques that enhance learning, assessment, and skill development. This research investigates the awareness, perceptions, and competencies of undergraduate students in the Kathmandu Valley concerning AI. The study aims to evaluate students' understanding of AI, identify knowledge gaps, and recommend curriculum enhancements to align education with industry demands. A quantitative research design was employed, collecting data from 123 students across diverse colleges using a structured Likert-scale questionnaire. The findings reveal that while many students recognize AI's potential in streamlining academic tasks and shaping future careers, there are significant gaps in their understanding and practical application of AI technologies. The study highlights the dual nature of AI, which can serve as both an enabler of efficiency and a source of apprehension, particularly concerning job displacement and ethical implications. It also identifies disparities in access to AI tools due to socioeconomic factors, underscoring the importance of addressing the digital divide. The research concludes that integrating AI-related content into higher education curricula is critical for preparing students for an AI-driven workforce. By fostering AI literacy, ethical awareness, and hands-on skills, institutions can empower students to leverage AI responsibly and effectively. The novelty of this study lies in its context-specific approach, exploring AI's impacts on undergraduate education in a developing region, and providing actionable insights for curriculum development and policy-making in the age of AI.NPRC Journal of Multidisciplinary Research260It is concluded that integrating AI-related content into higher education curricula is critical for preparing students for an AI-driven workforce and by fostering AI literacy, ethical awareness, and hands-on skills, institutions can empower students to leverage AI responsibly and effectively.NPRC Journal of Multidisciplinary Research["Chhabi Ratna Tripathi"]2024-12-10T00:00:00 c8e97ed461eb49d7de3d470cb4b12199b9c7d13dArtificial intelligence (AI) model creators commonly attach restrictive terms of use to both their models and their outputs. These terms typically prohibit activities ranging from creating competing AI models to spreading disinformation. Often taken at face value, these terms are positioned by companies as key enforceable tools for preventing misuse, particularly in policy dialogs. But are these terms truly meaningful? There are myriad examples where these broad terms are regularly and repeatedly violated. Yet except for some account suspensions on platforms, no model creator has actually tried to enforce these terms with monetary penalties or injunctive relief. This is likely for good reason: we think that the legal enforceability of these licenses is questionable. This Article systematically assesses of the enforceability of AI model terms of use and offers three contributions. First, we pinpoint a key problem: the artifacts that they protect, namely model weights and model outputs, are largely not copyrightable, making it unclear whether there is even anything to be licensed. Second, we examine the problems this creates for other enforcement. Recent doctrinal trends in copyright preemption may further undermine state-law claims, while other legal frameworks like the DMCA and CFAA offer limited recourse. Anti-competitive provisions likely fare even worse than responsible use provisions. Third, we provide recommendations to policymakers. There are compelling reasons for many provisions to be unenforceable: they chill good faith research, constrain competition, and create quasi-copyright ownership where none should exist. There are, of course, downsides: model creators have fewer tools to prevent harmful misuse. But we think the better approach is for statutory provisions, not private fiat, to distinguish between good and bad uses of AI, restricting the latter.arXiv.org00This Article systematically assesses the enforceability of AI model terms of use and offers three contributions, pinpointing a key problem: the artifacts that they protect, namely model weights and model outputs, are largely not copyrightable, making it unclear whether there is even anything to be licensed.ArXiv["Peter Henderson", "Mark A. Lemley"]2024-12-10T00:00:00 9e76ee9fe55b7152ec51b527f1ab891f3e19086dArtificial intelligence is a new trend in global technological development and plays an important role in promoting the construction of a new industrialization system. However, as an emerging productive force, AI technology has always lacked a set of standardized rules on ethical and moral issues. This has led to numerous copyright disputes and personal injury incidents caused by AI technology worldwide, posing a serious threat to intellectual property and ethical circles. To achieve this, global theoretical and institutional innovations are needed. The handling of moral and ethical issues by artificial intelligence mainly includes the formulation of moral and ethical norms and the moral and ethical evaluation of AI technology. Therefore, there should be more cooperation in the international field just like the Artificial Intelligence Law promulgated by the European Union. This paper adopting literature analysis and case analysis methods, mainly studyed the different governance attitudes of China, the United States, and the European Union, as well as the legislative practice of the EU in enacting artificial intelligence laws.Journal of Education, Humanities and Social Sciences100The different governance attitudes of China, the United States, and the European Union, as well as the legislative practice of the EU in enacting artificial intelligence laws are studied to study the handling of moral and ethical issues by artificial intelligence.Journal of Education, Humanities and Social Sciences["Xiaoyu Guo"]2024-12-10T00:00:00 eff1b2d6f5f224f9156c82445ad673a5b987a0caThe risk of a lack of consensus on the development and use of artificial intelligence in the defense domain may have significant negative implications in the future. Such incoherence in the relationship can cause the strategic vulnerability of the largest global military and technological powers precisely through the negative manifestations of use in an undefined environment. The importance of achieving consensus within the joint activities of the United Nations, the European Union, the United States of America, the People's Republic of China and the Russian Federation provides artificial intelligence with a strong basis for ethical acceptance and establishing norms and rules of global use. Assessing the impact of artificial intelligence on the development of the national security strategy is an important factor in shaping the future defense system. Just as terrorism, the proliferation of nuclear and biological weapons and unconventional threats have become an integral part of the consideration of a wide number of national security strategies of sovereign states, so it is necessary that artificial intelligence be a part of consideration and a formal part of shaping the security system, at all levels from global to local. This work should, through a short strategic assessment, bring artificial intelligence closer to the professional public and contribute to its actualization and implementation in normative and legal strategic documents of states and global security and defense organizations.SCIENCE International Journal50This work should, through a short strategic assessment, bring artificial intelligence closer to the professional public and contribute to its actualization and implementation in normative and legal strategic documents of states and global security and defense organizations.SCIENCE International Journal["Aleksandar M. Pavi\u0107", "Hatid\u017ea A. Beri\u0161a"]2024-12-10T00:00:00 39886abca30c7dcdbe2aa0902f2bab7f96dbb33aThe article is devoted to topical issues of the possibility of defining artificial intelligence as a subject of legal relations. The possibility of recognizing artificial intelligence as a subject of legal relations is analyzed, taking into account the criterion of the degree of its autonomy. The possibility of recognizing the legal personality of artificial intelligence is considered from the point of view of The EU Artificial Intelligence Act (2024), taking into account the assessment of tortiousness of artificial intelligence systems and seeing it as a source of increased danger.INFORMATION AND LAW00The article is devoted to topical issues of the possibility of defining artificial intelligence as a subject of legal relations, taking into account the assessment of tortiousness of artificial intelligence systems and seeing it as a source of increased danger.INFORMATION AND LAW["V. Varynskyi"]2024-12-10T00:00:00 b31141f41492ef1933cb1ddf36fc76b448167208The article is devoted to the legal regulation of problematic issues in the intellectual property sphere related to determining patentability and inventorship on inventions created using artificial intelligence. The purpose of the study is to analyze the experience of the United States Patent and Trademark Office for further development of national documents that will regulate these issues. The article examines how these issues are addressed in the United States as one of the leading countries in innovation, whose authorities keep artificial intelligence-related issues under close control and regulation. The article analyzes the content and structure of the “Inventorship Guidance for AI-Assisted Inventions”. The study of national legislation has shown the relevance of developing similar regulations in Ukraine. The author substantiates the need to create recommendations similar to the recommendations mentioned above of the US Patent Office which would explain to all interested parties the conceptual foundations and practical approaches to patentability and inventorship on inventions created using artificial intelligence.INFORMATION AND LAW00The author substantiates the need to create recommendations similar to the recommendations mentioned above of the US Patent Office which would explain to all interested parties the conceptual foundations and practical approaches to patentability and inventorship on inventions created using artificial intelligence.INFORMATION AND LAW["V. Mykhailenko"]2024-12-10T00:00:00 be9d15245d89656208e510297c637b3254021d17The business landscape is rapidly changing across industries due to artificial intelligence (AI). This study examines the complex effects of artificial intelligence (AI) on a range of company activities, from automation and efficiency gains to strategic decision-making and improved customer experiences. This research explores the benefits and obstacles presented by AI adoption in enterprises by a thorough assessment of the current literature, case studies, and interviews with industry professionals. Important discoveries highlight how AI plays a vital role in process optimization, fostering creativity, and transforming organizational structures. The report also looks into future trends, labor consequences, and ethical issues related to integrating AI into business operations. Through shedding light on the revolutionary potential of AI, our research seeks to assist organizations in efficiently utilizing its potential to provide competitive advantage and long-term prosperity in the digital era.mLAC Journal for Arts, Commerce and Sciences (m-JACS) ISSN: 2584-192000Through shedding light on the revolutionary potential of AI, the research seeks to assist organizations in efficiently utilizing its potential to provide competitive advantage and long-term prosperity in the digital era.mLAC Journal for Arts, Commerce and Sciences (m-JACS) ISSN: 2584-1920["Keerthiga Devi"]2024-12-10T00:00:00 263fb8b5d56e342c5b5a5b3edce0142b5ffe0310Since the unemployment rate is a critical factor that directly affects a country's economic performance and social health, reducing unemployment with effective policies is of great importance for sustainable development and prosperity. Therefore, precise forecasting of the unemployment rate is pivotal to effective policymaking and planning, especially in Japan, where unique demographic structures and economic challenges prevail. This study aims to estimate the unemployment rate in Japan using an Artificial Neural Network (ANN) model with the annual data for the period 1985-2017. Key factors shaping Japan's labour market dynamics, such as artificial intelligence-related technology patent applications, inflation rate, population growth rate, and labour productivity, are used to estimate the unemployment rate. The findings indicate that the Japanese unemployment rate is expected to increase gradually until 2030. This research provides significant insights to the Japanese government and policymakers through a non-linear forecasting model that includes the variable of artificial intelligence, which has not previously been used in the literature.Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi260This research provides significant insights to the Japanese government and policymakers through a non-linear forecasting model that includes the variable of artificial intelligence, which has not previously been used in the literature.Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi["Ay\u015feg\u00fcl Y\u0131ld\u0131z", "G\u00fcl\u015fah Adam"]2024-12-10T00:00:00 6e58aa7d750024d941511838ce2b06d438c3b371This article reviews the advantages, difficulties, and disadvantages of artificial intelligence (AI) in this field and suggests potential strategies and approaches for overcoming the current obstacles. AI has the potential to improve the efficiency, accuracy, and speed of the drug discovery process, but its successful application depends on the availability of high-quality data, the resolution of ethical issues, and the understanding of the limitations of AI-based approaches.International Journal For Multidisciplinary Research110AI has the potential to improve the efficiency, accuracy, and speed of the drug discovery process, but its successful application depends on the availability of high-quality data, the resolution of ethical issues, and the understanding of the limitations of AI-based approaches.International Journal For Multidisciplinary Research["Nihal Singh"]2024-12-10T00:00:00 d29184d1453aff0cf5ee1195cc1f9f2a19a01265Applied Artificial Intelligence611Applied Artificial Intelligence["L. Ofusori", "Tebogo Bokaba", "S. Mhlongo"]2024-12-10T00:00:00 b32ca48ec60dbe71bb98cfda380d83a8ab432e09International Journal of Health Sciences00International Journal of Health Science["Caio Andrade Prins", "Gabriela Mezher Gibson", "Ana Beatriz Poleto Ainbinder", "Juliana Santana Panza", "Jo\u00e3o Francisco Meira Valadares", "Anne Caroline Montenegro de Oliveira", "Andr\u00e9 Bastazini Lopes de Oliveira", "Mikaela Dorine Beletato da Silva", "Cristiano Paludo De Negri", "Diana Barth Amaral de Andrade", "Let\u00edcia Cardin Picinin", "Gustavo Kazuo Saito Yamada", "Mauricio Lopes da Silva Netto"]2024-12-10T00:00:00 9ebbc2b4b22d11d4a7e52cef754f7c0a02acd96fEBioMedicine90eBioMedicine["Jia Fu", "M. Fang", "Zhuozhao Zheng", "Di Dong"]2024-12-10T00:00:00 22aab0907cb9a7047b8e4b448e9a0a7e9c0af981Asian Bioethics Review220Asian Bioethics Review["Kean Chang Phang", "Tze Chang Ng", "Sharon Kaur Gurmukh Singh", "T. Voo", "Wellester Anak Alvis"]2024-12-10T00:00:00 05653d2e9f48f9b4b13fe828131a58265020dd2d00["N. Kshetri"]2024-12-10T00:00:00 d9d4e64836f4fa41ad8c5cd2a7aa35f22ddf52d2International Journal of Research Studies in Management100International Journal of Research Studies in Management["Yuankai Zhong"]2024-12-10T00:00:00 4dfa9686a203516536c4b87ab25f4e822e6991bbJornal Brasileiro de Pneumologia110Jornal Brasileiro de Pneumologia["R. Figueiredo", "Juan C Calderon"]2024-12-10T00:00:00 bfcc68d5393033414cc6b1c15d348630cd364dfaTechnology Analysis &amp; Strategic Management130Technology Analysis &amp; Strategic Management["Runze Gao", "Yuan Wang"]2024-12-10T00:00:00 0c4114b20647e153ee1d0204757fc5863916df96Computational Economics760Computational Economics["Wael Dammak", "Halilibrahim G\u00f6kg\u00f6z", "A. Jeribi"]2024-12-10T00:00:00 370608250749ed4936f13840dd658638dd88e031Nursing Education Perspectives00Nursing education perspectives["Matthew D Byrne"]2024-12-10T00:00:00 794fb0f538cd7756635c3972000ca9cbe595727bJournal of Environmental Management1190The research findings reveal that technology convergence generality and innovation team scale have a significant impact on carbon emissions, with the latter exhibiting a U-shaped effect.Journal of environmental management["Tianlong Shan", "Shuai Feng", "Kaijian Li", "Ruidong Chang", "Ruopeng Huang"]2024-12-10T00:00:00 3a76533679a29c554c7dbf2e48d4bfe8c7a003b8Interpretation and Translation00Interpretation and Translation["Sang-Bin Lee"]2024-12-10T00:00:00 5449b0b662e043ab89e8d9249c8dc2c711086bc2New Writing160New Writing["Katherine Day", "Rose Michael", "Ren\u00e9e Otmar", "Sharon Mullins"]2024-12-10T00:00:00 c1eba637fff322e3066f27687fc6e5fff3bed9d1Journal of Bio-X Research00Journal of Bio-X Research["Afiya Baig", "Mitesh Janvalkar", "Rohan Barse", "Vijay Jagtap"]2024-12-10T00:00:00 692299813078fda41fc3ae2d313e03ed2819bea8Journal of Technology Transfer590The Journal of Technology Transfer["Mar\u00eda Dolores Redondo-Rodr\u00edguez", "Elo\u00edsa D\u00edaz-Garrido", "Diana C. P\u00e9rez-Bustamante Y\u00e1bar", "M. A. Ram\u00f3n-Jer\u00f3nimo"]2024-12-10T00:00:00 90ef76b3be06a6f7459e45897aba6ac4f6164b91This research delves into the application of artificial intelligence in precision farming and crop monitoring, focusing on four AI algorithms: “Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN)”. The objective of improving crop disease detection accuracy and prediction efficiency, along with yield prediction and resource optimization, is targeted. The models were trained to predict crop health and optimize agricultural practices, using a dataset of crop images and environmental data. For the test result, CNN took the leads with an accuracy of 92.5% in disease detection, followed by RF with an accuracy of 89.3%, SVM with an accuracy of 86.7%, and KNN with an accuracy of 81.5%. Additionally, crop yield prediction using a hybrid AI model incorporating meteorological and soil data showed an R-squared value of 0.88, demonstrating strong prediction capabilities. The integration of AI with UAVs and remote sensing technologies allowed for real-time monitoring of crops, providing farmers with actionable insights to optimize resource use. These results demonstrate the possible significant impact of AI on facilitating sustainable farming practices through cost savings, reduced environmental impact, and improved productivity. In general, AI applications in agriculture will revolutionize precision farming by coming up with intelligent data-driven solutions for crop management.Communications on Applied Nonlinear Analysis230This research delves into the application of artificial intelligence in precision farming and crop monitoring, focusing on four AI algorithms: Support Vector Machine, Random Forest, Convolutional Neural Networks, and K-Nearest Neighbors, trained to predict crop health and optimize agricultural practices.Communications on Applied Nonlinear Analysis["Dr. Kamatchi Sundravadivelu", "Dr. Santosh Kumar", "Dr. L. Kartheesan", "Chiranjit Dutta"]2024-12-10T00:00:00 2d5cc99ca73b1cd641c60e2780f8ab543f6e779bThe integration of Artificial Intelligence (AI) into the finance sector is revolutionizing decision-making processes, enabling data-driven insights and automation at an unprecedented scale. This research explores the transformative potential of AI in financial decision-making, focusing on its applications in risk assessment, portfolio management, fraud detection, and algorithmic trading. AI technologies, such as machine learning, natural language processing, and predictive analytics, empower financial institutions to process vast amounts of data efficiently, uncover patterns, and generate actionable insights. By enhancing precision and reducing human biases, AI-driven systems contribute to more informed and timely decisions. However, the adoption of AI in finance also raises concerns about transparency, ethical considerations, and regulatory compliance. This study aims to analyze the benefits and challenges of AI-driven decision-making, evaluate case studies of successful implementation, and propose frameworks for integrating AI ethically and effectively into financial operations. The findings emphasize the necessity of balancing innovation with accountability, ensuring that AI technologies enhance rather than compromise financial stability.Next Generation Journal for The Young Researchers40This study aims to analyze the benefits and challenges of AI-driven decision-making, evaluate case studies of successful implementation, and propose frameworks for integrating AI ethically and effectively into financial operations.Next Generation Journal for The Young Researchers["Kerem Ko\u00e7ar"]2024-12-10T00:00:00 1e008924bfdb052e8b67dd0280fe635b4f05e741Image classification usually requires connectivity and access to the cloud, which is often limited in many parts of the world, including hard-to-reach rural areas. Tiny machine learning (tinyML) aims to solve this problem by hosting artificial intelligence (AI) assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without Internet or cloud access. This study explores the use of tinyML to provide healthcare support with low-spec devices in low-connectivity environments, focusing on the diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). The model weights were then offloaded to a Raspberry Pi with a webcam attached, to be used for the classification of skin lesions without Internet access. It was found that the developed prototype achieved a test accuracy of 78% when trained on the HAM10000 dataset, and a test accuracy of 85% when trained on the ISIC 2020 Challenge dataset.Applied Sciences220This study explores the use of tinyML to provide healthcare support with low-spec devices in low-connectivity environments, focusing on the diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting.Applied Sciences["Tess Watt", "Christos Chrysoulas", "Peter J. Barclay", "Brahim El Boudani", "Grigorios Kalliatakis"]2024-12-10T00:00:00 a429a8755c72a69c77611ad90ead1e6b68c5503aThe proliferation of large datasets has facilitated data-driven approaches in paleontology, offering novel insights into evolutionary history. However, managing complex data poses challenges, including laborious processing and a lack of standardized evaluation metrics. Despite the widespread use of artificial intelligence (AI) in other scientific domains, its adoption in paleontology remains limited. This study reviews over 70 AI studies in paleontology since the 1980s, encompassing tasks such as micro- and macrofossil classification, image segmentation, and prediction. Various machine learning solutions, including Knowledge-Based Systems have been employed to automate paleontological workflows. The rise in AI adoption is attributed primarily to improved accessibility rather than advancements in fossil data or methodologies. Additionally, emerging AI implementations like diffusion models and the potential for Large Language Models is evident for future integration with paleontological research. Although AI has yet to become integral to paleontologists' toolkits, its successful implementation suggests transformative prospects for the field.International Journal For Multidisciplinary Research110This study reviews over 70 AI studies in paleontology since the 1980s, encompassing tasks such as micro- and macrofossil classification, image segmentation, and prediction, and suggests transformative prospects for the field.International Journal For Multidisciplinary Research["Nethravathi B", "Sudarshan J S", "Bhargava Sharma C S", "Ashish Kumar Mishra"]2024-12-10T00:00:00 59cb404904db13e1462c4719e74605a043383a27This study explores how undergraduate students' growth of their English writing skills is impacted by artificial intelligence (AI) technologies. This study addresses the key topic of whether AI-assisted writing instruction enhances undergraduate students' positive evaluations of the learning process, reduces anxiety, and improves writing skills as the significance of proficient academic writing grows. The study employed a mixed-methods design with two groups of fifteen University of Sebha, Libya students each: the experimental group and the control group In terms of method, data was obtained through pre and post-tests and writing examples that took place over 16 weeks. A control group made use of conventional approaches toward writing, while the experimental group used AI technologies during writing. The writing samples were qualitatively scored by expert raters while statistically comparing the test scores. In addition, the experimental group showed improvement in writing quality, grammatical complexity, and vocabulary. These findings confirm the positive effect of AI on writing skills. AI-assisted education enhances writing skills, increases confidence, and decreases anxiety. These findings suggest that using AI in writing classes can effectively aid language acquisition, benefiting teachers and curriculum developers.International Journal of Social Science Humanity &amp; Management Research00It is confirmed that using AI in writing classes can effectively aid language acquisition, benefiting teachers and curriculum developers and confirming the positive effect of AI on writing skills.International Journal of Social Science Humanity &amp; Management Research["Dr. Nagamurali Eragamreddy"]2024-12-10T00:00:00 e241124b24db7aade1ed27548f2014fbdb55c556PurposeAs human–AI hybrid teams become more common, it is essential for team members to interact effectively with artificial intelligence (AI) to complete tasks successfully. The integration of AI into the team environment alters the cooperative dynamics, prompting inquiry into how the design characteristics of AI impact the working mode and individual performance. Despite the significance of this issue, the effects of AI design on team dynamics and individual performance have yet to be fully explored.Design/methodology/approachDrawing upon coping theory, this study presents a research model aimed at elucidating how the characteristics of AI in human–AI interaction influence human members’ adaptive behavior, subsequently impacting individual performance. Through the creation of experiments that require human–AI collaboration to solve problems, we observe and measure various aspects of AI performance and human adaptation.FindingsWe observe that the explainability of AI enhances the behavioral adaptation of human team members, whereas the usability and intellectuality of AI improve their cognitive adaptation. Additionally, we find that human team members’ affective adaptation is negatively affected by the likability of AI. Our findings demonstrate that both behavioral and cognitive adaptations positively impact individual performance, whereas affective adaptation negatively impacts it.Practical implicationsOur research findings provide recommendations for building efficient human–AI hybrid teams and insights for the design and optimization of AI.Originality/valueOverall, these results offer insights into the adaptive behavior of humans in human–AI interaction and provide recommendations for the establishment of effective human–AI hybrid teams. These findings pioneer an understanding of how design characteristics of AI impact team dynamics and individual performance, establishing a connection between AI attributes and human adaptive behavior.Information Technology &amp; People500These findings pioneer an understanding of how design characteristics of AI impact team dynamics and individual performance, establishing a connection between AI attributes and human adaptive behavior.Information Technology &amp; People["Aihui Chen", "Anran Lyu", "Yaobin Lu"]2024-12-10T00:00:00 043497552959485bae988702960cc3b5f9af2f93PURPOSE To compare two artificial intelligence (AI)-based Automated Diabetic Retinopathy Image Assessment (ARIA) softwares in terms of concordance with specialist human graders and referable diabetic retinopathy (DR) diagnostic capacity. METHODS Retrospective comparative study including 750 consecutive diabetes mellitus patients imaged for non-mydriatic fundus photographs. For each patient four images (45 degrees field of view) were captured, centered on the optic disc and macula. Images were manually graded for severity of DR as no DR, any DR (mild non-proliferative diabetic retinopathy [NPDR] or more), referable DR (RDR (more than mild DR)), or sight-threatening DR (severe NPDR or more severe disease and/or clinically significant diabetic macular edema [CSDME]). IDx-DR and MONA DR output was compared with manual grading and with each other. RESULTS Total sample size was 750 patients, of which 55 were excluded due to ungradable images. Out of the remaining 695 patients 522 (75%) were considered as having no DR by manual consensus grading, and 106 (15%) as having RDR. Agreement between raters varied between moderate to substantial. IDx-DR showed moderate agreement with human grading (k = 0.4285) while MONA DR had substantial agreement (k = 0.6797). Out of 106 patients with a ground truth of RDR, IDx-DR identified 105 and MONA DR identified 99. The sensitivity and specificity rates for RDR detection of IDx-DR were 99.1 and 71.5% compared with MONA DR of 93.4 and 89.3% respectively. Of note, both ARIAs had 100% sensitivity for the detection of STDR. CONCLUSION Both ARIAs performed well in this study population, both with sensitivity for RDR screening over 90%, with IDx-DR showing higher sensitivity and MONA DR higher specificity. MONA DR showed superior agreement with human certified graders.Acta ophthalmologica250Both ARIAs performed well in this study population, both with sensitivity for RDR screening over 90%, with IDx-DR showing higher sensitivity and MONA DR higher specificity.Acta ophthalmologica["Andrzej E Grzybowski", "Freya Peeters", "R. Bar\u00e3o", "Piotr Brona", "Stef Rommes", "Tomasz Krzywicki", "Ingeborg Stalmans", "Julie Jacob"]2024-12-10T00:00:00 7178fa45868d8061e5a557c8f0881e53b948c025The agricultural field is encountering multiple problems with the climate changing, population booming and overusing chemical pesticide which all lead to unsustainable agriculture. Affecting quality and yield, plant diseases account for a heavy loss from the final production. Conventional plant disease detection is definitely the aforementioned matter as well, profound education analyzing with labor-intensive and time-consuming procedure yet not so accurate. By merging artificial intelligence (AI) with image processing, plant disease diagnosis can be automated quickly and efficiently. It uses machine learning algorithms, combined with high-resolution imagery to detect disease symptoms in the early stage of infestation thereby making the treatment process largely dependent on chemical control. In this paper, we reviewed state-of-the-art methods which have experience significant improvement and development in terms of image processing approaches using AI for plant disease recognition. We made a lot of progress however there are still many gaps to fill like other data types, real-time processing and generalizability models that need to be incorporated with farming practices as well accessibility considering all the factors is important for economic viability. Overcoming these gaps requires a holistic approach by combining AI innovations with perspectives from the fields of agronomy and agricultural economics. Future research could potentially concentrate in improving the real-time process, increasing model interpretability and integration with current agricultural systems. Overcoming these challenges, AI-powered image processing can be the backbone of precision agriculture that could secure our food supply and make farming more sustainable.International Journal of Scientific Research in Science and Technology100This paper reviewed state-of-the-art methods which have experience significant improvement and development in terms of image processing approaches using AI for plant disease recognition and made a lot of progress.International Journal of Scientific Research in Science and Technology["Meena Jindal", "Khushwant Kaur"]2024-12-10T00:00:00 77f7e729d8fb84869bbeab121f0042c19676275bThe integration of artificial intelligence (AI) in educational measurement has transformed assessment methods, allowing for automated scoring, swift content analysis, and personalized feedback through machine learning and natural language processing. These advancements provide valuable insights into student performance while also enhancing the overall assessment experience. However, the implementation of AI in education also raises significant ethical concerns regarding validity, reliability, transparency, fairness, and equity. Issues such as algorithmic bias and the opacity of AI decision-making processes risk perpetuating inequalities and affecting assessment outcomes. In response, various stakeholders, including educators, policymakers, and testing organizations, have developed guidelines to ensure the ethical use of AI in education. The National Council of Measurement in Education’s Special Interest Group on AI in Measurement and Education (AIME) is dedicated to establishing ethical standards and advancing research in this area. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI’s responsible and effective use in education.Chinese/English Journal of Educational Measurement and Evaluation00A diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement, explores significant challenges such as automation bias and environmental impact, and proposes solutions to ensure AI’s responsible and effective use in education.Chinese/English Journal of Educational Measurement and Evaluation["Okan Bulut", "Maggie Beiting-Parrish"]2024-12-11T00:00:00 1c193f923b204a2dda2f0d65912da397ecc68e6aKecerdasan buatan menjadi salah satu kebutuhan untuk mendukung pekerjaan manusia. Penelitian ini bertujuan mengimplementasikan inovasi teknologi berbasis Artificial Intelligence menjadi media pembelajaran sebagai solusi untuk meningkatkan aksesibilitas dan efektivitas pembelajaran bagi siswa tunanetra di Sekolah Luar Biasa Yayasan Bahagia Kota Tasikmalaya. Inovasi teknologi menggunakan layanan suara interaktif untuk membantu siswa tunanetra mempelajari materi pembelajaran. Dengan menerapkan metode ADDIE (Analysis, Design, Development, Implementation, Evaluation) dan metode pengujian SQA (Software Quality Assurance). Hasil pengujian aplikasi secara kuantitatif dengan metode SQA (Software Quality Assurance) menunjukan skor 88.4. hal ini menunjukkan bahwa aplikasi media pembelajaran ini dapat meningkatkan pemahaman dan motivasi siswa tunanetra terhadap materi yang disampaikan. Kontribusi penelitian yang dihasilkan dapat mendukung proses belajar-mengajar, serta meningkatkan keterlibatan dan minat belajar siswa tunanetra. Artificial Intelligence has become essential to support human tasks. This study aims to implement AI-based technological innovation into educational media as a solution to enhance the accessibility and effectiveness of learning for visually impaired students at the Sekolah Luar Biasa (SLB) Yayasan Bahagia Tasikmalaya City. The technology innovation utilizes interactive voice services to assist visually impaired students in learning course material. The ADDIE (Analysis, Design, Development, Implementation, Evaluation) method and testing SQA (Software Quality Assurance) method was applied to guide the development process. Quantitative testing of the application using the Software Quality Assurance (SQA) method resulted in a score of 88.4, indicating that this learning media application effectively enhances both understanding and motivation among visually impaired students regarding the presented material. Thus, this research contributes to supporting the teaching and learning process, while increasing engagement and interest in learning for visually impaired students.IJCIT (Indonesian Journal on Computer and Information Technology)00IJCIT (Indonesian Journal on Computer and Information Technology)["Agung Baitul Hikmah", "Haerul Fatah", "Vincentius Christian"]2024-12-11T00:00:00 df20192c331d2e026bfff3cb086d2d6a0acafea3Current Heart Failure Reports680The emerging potential of artificial intelligence in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM), is explored.Current heart failure reports["Arman Salavati", "C. N. van der Wilt", "M. Calore", "Ren\u00e9 van Es", "A. Rampazzo", "P. van der Harst", "F. V. van Steenbeek", "J. V. van Tintelen", "Magdalena Harakalova", "A. T. te Riele"]2024-12-11T00:00:00 0259b1357d8af65b29d73d890f2099249bb383e2The adoption of Artificial Intelligence (AI) technology in journalism globally is characterized by a significant disparity, with Western countries exhibiting more widespread and advanced usage compared to non-Western countries. As a result, research on AI's application in journalism has predominantly focused on developed economies, creating a substantial knowledge gap and scarcity of studies exploring AI's use in journalism in developing countries. This study addresses this gap by examining the current state of AI deployment in Ghana's media industry, its potential benefits and risks, and the challenges hindering its adoption. The study was anchored on Rogers' adoption-diffusion theory and van Dijk's digital dichotomy theory. Based on eighteen in-depth interviews with journalists selected through purposive and snowball sampling, this study reveals that AI is being leveraged to improve newsroom efficiency, but a significant digital divide persists. While some newsrooms actively adopt AI, others lag behind. The adoption of AI is expected to yield both positive outcomes, such as enhanced efficiency and innovative broadcasting, and negative outcomes, including diminished human creativity and potential disinformation. The high cost of deployment, inadequate data, and poor internet connectivity are barriers to AI adoption in Ghana's media industry.MediAsia Official Conference Proceedings270It is revealed that AI is being leveraged to improve newsroom efficiency, but a significant digital divide persists, while some newsrooms actively adopt AI, others lag behind.MediAsia Official Conference Proceedings["Samuel Adefioye"]2024-12-11T00:00:00 2a9587f2492ea29ca41adfe9e884f3f64e3e2944Nonobstructive azoospermia (NOA) is a serious form of male infertility with therapeutic options limited to trials of endocrine manipulations and repertoire of surgical interventions, also known as surgical sperm retrieval (SSR) procedures. Despite its invasive nature, SSR remains crucial in the management of NOA, offering infertile males the opportunity of fathering their biological children using assisted reproductive technologies. Success rates of SSR are variably governed by several factors including the genetic background, preoperative endocrine optimization, testicular histopathology, surgeon's microsurgical expertise, and laboratory technological and technical team's capability. This paper explores the significant role of artificial intelligence (AI) in the process of sperm retrieval among NOA patients. The role of AI has evolved from basic predictive models used for outcome assessment and patient counseling, to advanced image processing capabilities for assessing sperm parameters, and now to cutting‐edge applications in identifying the rare sperm present in the azoospermic microdissection testicular sperm extraction tissue samples.UroPrecision660The role of AI has evolved from basic predictive models used for outcome assessment and patient counseling, to advanced image processing capabilities for assessing sperm parameters, and now to cutting‐edge applications in identifying the rare sperm present in the azoospermic microdissection testicular sperm extraction tissue samples.UroPrecision["Hussein Kandil"]2024-12-11T00:00:00 400da1d402caf188f01121ad8982471c50111edbThis study aimed to identify the reality of Using Artificial intelligence "AI" in Hospitals and Health Care Centers as Seen by Workers in Medicine, Nursing, Laboratory, Public Health, Nutrition, and Health Information in Riyadh Region. The study followed the descriptive survey method. The study sample consisted of (80) workers in medicine, nursing, laboratory, public health, health information, and nutrition, and a questionnaire was used to collect data consisting of (18) items distributed among the fields of artificial intelligence: (databases used, senior management support, users system, computerized health). Information systems used and performance improvement. Results showed that the arithmetic averages of the study sample’s responses to the reality of applications of artificial intelligence fields in health administration in the General Directorate of Health Affairs in the Riyadh region ranged between (3.40-3.12), with an average application rate for all fields, and the field of databases used came in first place. In health administration, with a mean of (3.40) and a moderate degree, followed by the field of Support senior management, with a mean of (3.36) and a moderate degree, the “Users of the system” field came third, with an arithmetic mean of (3.32) and a moderate degree, followed by the field Computerized health information systems used, with a arithmetic mean (3.28) and a moderate degree, and the field of Improve the Performance came in last place, with a arithmetic mean (3.12) and a moderate degree, and the average reached General arithmetic for areas of artificial intelligence applications in health management (3.29), with a moderate degree of application. International Journal of Religion260Results showed that the arithmetic averages of the study sample’s responses to the reality of applications of artificial intelligence fields in health administration in the General Directorate of Health Affairs in the Riyadh region ranged between (3.40-3.12), with an average application rate for all fields, and the field of databases used came in first place.International Journal of Religion["Abdullah abdulaziz Alshayb", "Khlood Mohammed Alhawsawei", "Rabiea Mohammed ALDossery", "Ayman Mustafa Yenbaawi", "Ibrahim Hassan Alzubadie", "Abdulrhman said Ali Alomari", "Ali Yahya Dhamiri", "Ghadeer Mohd Alanzi", "Adi Hussain Hazazi", "Abdulrahim Ramadhan Alanazi", "Eisa Ali Aldawsari", "Faisal Mohammed Alghamdi", "Raid Mordhi ALBeshi", "Abdullah Rakan Omar Alahmadi"]2024-12-11T00:00:00 15958278b6fa3a23749bd87a1b7ba9d9b04bc9deUrban water systems are increasingly strained by the impacts of climate change, growing populations, and resource constraints, driving the need for more integrated and sustainable management solutions. Small and Medium-sized Enterprises (SMEs) are central to driving this transformation, leveraging their adaptability and significant impact on the industry. However, many SMEs lack access to comprehensive Information Systems (ISs) that combine data on government policies, industry trends, and water management initiatives, limiting their ability to implement sustainable practices. This study introduces Sustain WaterBot, a Chatbot powered by Generative Artificial Intelligence (GenAI), including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Sustain WaterBot bridges this information gap by seamlessly integrating data from diverse sources such as news outlets, government publications, industry reports, scientific studies, and social media platforms into a unified IS. It enhances decision-making by providing timely and relevant insights into Sustainable Urban Water Initiatives (SUWIs) through an interactive Question Answering (QA) framework. By leveraging open-source technologies, Sustain WaterBot offers SMEs an affordable, scalable, and sustainable tool to adopt eco-friendly water practices while improving operational efficiency and informed decision-making.2024 International Conference on Decision Aid Sciences and Applications (DASA)410Sustain WaterBot is introduced, a Chatbot powered by Generative Artificial Intelligence (GenAI) that enhances decision-making by providing timely and relevant insights into Sustainable Urban Water Initiatives (SUWIs) through an interactive Question Answering (QA) framework.2024 International Conference on Decision Aid Sciences and Applications (DASA)["Muhammad Arslan", "Saba Munawar", "Zainab Riaz"]2024-12-11T00:00:00 56b9b8250545f2c8aace1ab175ecec769e976416In this article, the author performs a philosophical and a comparative analysis of the new phenomenon of Artificial Intelligence (AI) in a legal context. South Africa is in a a difficult position in adapting its legal system to such a radical change, having inherited both the Roman-Dutch law (a great part of its substantive law, especially Private Law), as well as English law (its entire Procedural Law as well as its Law of Evidence) from former colonial masters. One of the best international examples of specifically enacted AI law up to the present is that recently adopted by the European Union (EU). Unfortunately South Africa (RSA) will be forced to also have a close look at both the United Kingdom (UK) as well as the United States (USA) because the accusatorial system of procedure adopted by those countries is much closer to that applicable in the RSA. For the same reason, work done by the African Union (AU), being based mainly on the French inquisitorial model, is not ideally suited to former British colonies which are based on the British accusatorial model. Lay opinion on the future of AI, especially when allied with a powerful force (such as the Law), is also sharply divided. Perhaps because of science fiction hysteria, many citizens fear having their lives being over-regulated by inhuman machines. In the philosophical part of the present article some of these fears are addressed, mostly at the hand of  some commentary on the science fiction work of George Orwell, entitled "1984". Here Government, acting as "Big Brother", gains ingress to every household by means of a government-sponsored "two-way" . To the mind of the present author this is an excellent illustration of the conflicting claims for privacy and security that AI has brought about.Potchefstroom Electronic Law Journal90A philosophical and a comparative analysis of the new phenomenon of Artificial Intelligence (AI) in a legal context is performed, including some commentary on the science fiction work of George Orwell, entitled "1984".Potchefstroom Electronic Law Journal["Daniel Van der Merwe"]2024-12-11T00:00:00 944ddc7cefe3844c4509d56baf1194e717226187Social and primary marketing research is used to study the perceptions of the people in various Arab states towards the including of artificial intelligence AI in the fight against disinformation, its ethical and legal issues. Assessing opinions on the use of AI to combat misinformation and security challenges, the research seeks to identify the space of promise and pitfalls of AI technologies. The question is dealt with whether the reasons and factors persuading the social media users to accept AI and its tools in the defence against disinformation are effective and how much of the constraints are there. The subject is also the use of AI algorithms for social media content categorization, and specifically, the categories ‘Legal’ and ‘Illegal’. This study progresses in the direction of policy making, that is aiming to work with policy makers and other stakeholders' perceptions of AI in addressing disinformation and the need for ethical standards and legal frameworks in the area in question.2024 International Conference on Decision Aid Sciences and Applications (DASA)200This study progresses in the direction of policy making, aiming to work with policy makers and other stakeholders' perceptions of AI in addressing disinformation and the need for ethical standards and legal frameworks in the area in question.2024 International Conference on Decision Aid Sciences and Applications (DASA)["R. Alrasheed", "Khaled Al-Mhasneh", "Mahmoud Sabry Abdelaziz", "M. Khalifa", "Dalal Alshammari", "Ahmed Alzahrani"]2024-12-11T00:00:00 fc3de5123b8a24b16727c7ed6560f9d6f5cdf42dThis editorial introduces the first part of CEJEME's Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement. As AI and ML technologies revolutionize education, they offer new opportunities for personalized learning and innovative assessment practices. This issue highlights the transformative impact of AI and ML on educational measurement, addressing both their potential and the ethical challenges they pose. This issue includes four articles that explore the opportunities and ethical challenges of AI in educational measurement, automated text scoring in the age of Generative AI for the GPU-poor, a novel approach using autoencoders and BERT to detect compromised items in computerized testing, and the use of ML packages in R. The issue provides valuable insights into the future of educational measurement. A second part of this special issue will be available in spring 2025.Chinese/English Journal of Educational Measurement and Evaluation00This issue includes four articles that explore the opportunities and ethical challenges of AI in educational measurement, automated text scoring in the age of Generative AI for the GPU-poor, and the use of ML packages in R.Chinese/English Journal of Educational Measurement and Evaluation["Okan Bulut", "Yi Zheng"]2024-12-11T00:00:00 9362d93ce9579a2b13f8c05daa299f3d443f8774The development of information technologies has been exponentially applied to the architecture, engineering, and construction (AEC) industries. The extent of the literature reveals that the two most pertinent technologies are building information modeling (BIM) and artificial intelligence (AI) technologies. The radical digitization of the AEC industry, enabled by BIM and AI, has contributed to the emergence of “smart cities”, which uses information technology to improve urban operational and sustainable efficiency. Few studies have investigated the roles of AI and BIM in AEC from the perspective of sustainable buildings in assisting designers to make sustainable decisions at building and city levels. Therefore, the purpose of this paper is to explore the research status and future development trends in the relationship between AI and BIM-aided sustainable building in the context of the smart city to provide researchers, designers, and technology developers with potential research directions. This paper adopted a macro and micro bibliographic method, which is used to map out the general research landscape. This is followed by a more in-depth analysis of the fields of sustainable design, sustainable construction, sustainable development, and life cycle assessment (LCA). The results show that the combination of AI and BIM helps to make optimal decisions on materials, cost, energy, construction scheduling, and monitoring and promotes the development of sustainable buildings in both technical and human aspects so to achieve Sustainable Development Goals 7 (ensuring access to affordable, reliable, and sustainable modern energy for all), 9 (building resilient infrastructure, promote inclusive and sustainable industries, and foster innovation), 11 (building inclusive, safe, risk-resilient, and sustainable cities and human settlements), and 12 (ensuring sustainable consumption and production patterns). In addition, the combination of AI, BIM, and LCA technologies offers great potential to improve building performance, and the future development of AI and BIM integration should not only consider the sustainability of buildings but also consider the human-centered design concept and the health, safety, and comfort of stakeholders as one of the goals to realize the multidimensional development of smart city based on city information model.Sustainability2110The results show that the combination of AI and BIM helps to make optimal decisions on materials, cost, energy, construction scheduling, and monitoring and promotes the development of sustainable buildings in both technical and human aspects so to achieve Sustainable Development Goals.Sustainability["Jinyi Li", "Zhen Liu", "Guizhong Han", "Peter Demian", "Mohamed Osmani"]2024-12-11T00:00:00 ebf6a8777431a1e9021b4f5e206d34f1c293c414Purpose. This paper aims to explore the intersection between artificial intelligence (AI) and social responsibility (SR) through a bibliometric analysis of 779 peer-reviewed articles published from 2019 to 2023 in the Scopus database. The study employs tools such as R Studio (Bibliometrix) and VOSviewer to identify key trends, influential authors, and research clusters within this emerging field. Results. The analysis reveals an annual research growth rate of 54.46%, with significant contributions from prominent authors like Gupta S, Kumar A, and Mehmood R, and institutions such as Kaunas University of Technology and Wuhan University. The keyword analysis highlights six main research clusters: the use of AI in resource management with a focus on "water" and "irrigation"; topics around "AI," "sustainability," "corporate SR," and "ethics"; applications in "policymaking" and "governance"; and research related to "energy" and "investments." Despite the rapid growth, significant gaps persist, including a lack of comprehensive frameworks to integrate AI with broader SR practices, as well as geographic disparities, with dominant contributions from countries like China, the USA, and the UK, while regions such as Africa and the Middle East remain underrepresented. Scientific novelty. This study contributes to the field by identifying critical gaps in current research on AI and SR, particularly the absence of integrated frameworks that address ethical AI applications across multiple sectors. It also highlights the need for more geographically diverse studies and calls for a more interdisciplinary approach to responsibly align AI practices with SR goals. Practical value. The findings underscore the importance of fostering AI research that is not only technologically advanced but also ethically grounded, especially in underrepresented regions. By addressing existing gaps and encouraging interdisciplinary research, this study advocates for the responsible development of AI to enhance SR practices globally, thus fostering a more equitable and sustainable future.Brazilian Journal of Business280A bibliometric analysis of peer-reviewed articles published from 2019 to 2023 in the Scopus database reveals an annual research growth rate of 54.46%, and highlights the need for more geographically diverse studies and calls for a more interdisciplinary approach to responsibly align AI practices with SR goals.Brazilian Journal of Business["Abdelwahab Boubaa", "Bedj Bedj Toufik", "Dhia Smaali"]2024-12-11T00:00:00 c135918d2f155728d4c2b5d88a4bc72313775ebcAbstract. The progress in artificial intelligence (AI) technology has revolutionized the perspectives, activities, and behaviors of human beings. AI has facilitated virtual interactions with humans at a remarkably advanced level, making it accessible to everyone. It has developed robots capable of emulating human behavior and engaging in a wide range of tasks, including participating in worship services. On the one hand, the inclusion of AI pastors has a beneficial effect on church services but, on the other hand, causes a detrimental effect on interpersonal services. This study investigated the function and influence of AI pastors on the future of the church in Indonesian through an analysis of Christian theology and ethics. We contended that AI pastors serve as tools that can enhance the future ministry of the church by critically considering ethical, biblical, and spiritual principles.Abstrak. Kemajuan teknologi AI telah mengubah cara pandang, cara kerja, dan tingkah laku manusia. AI telah membuka ruang kepada semua orang untuk dapat berinteraksi secara virtual manusia dengan kecanggihan dan kecerdasan yang sangat tinggi. AI telah membuat robot yang dapat bertindak seperti manusia dan melakukan berbagai kegiatan, termasuk dalam pelayanan keagamaan dengan hadirnya pendeta AI dalam pelayanan gerejawi. Kehadiran pendeta AI memberikan dampak positif bagi pelayanan gereja, namun berdampak negatif dalam pelayanan yang bersifat relasional. Dalam riset ini, kami melakukan penelitian terhadap peran dan dampak pendeta AI di masa depan gereja di Indonesia dengan melakukan studi teologi dan etika Kristen. Kami berargumen bahwa pendeta AI adalah instrumen yang dapat membantu pelayanan gereja di masa depan dengan memerhatikan secara saksama nilai etis, biblis, dan spiritual.Dunamis: Jurnal Teologi dan Pendidikan Kristiani00DUNAMIS: Jurnal Teologi dan Pendidikan Kristiani["Terifosa Ndruru", "Agustinus Setiawidi"]2024-12-11T00:00:00 d7580829e6b460f7bf9b83f5cfa2c3a1abf9a7bePurposeThe main aim of the paper was to determine the effect of Technology–Organization–Environment (TOE) Framework on adoption of artificial intelligence (AI) in Kenyan academic libraries anchored on diffusion innovation and TOE theories.Design/methodology/approachThe target population comprised of 517 librarians, deputy librarian, and senior staff from 98 academic libraries from institutions of higher learning in Kenya. The study used stratified and random technique to sample 226 respondents.FindingsFindings showed that technology factors (relative advantages, compatibility, and complexity) and environmental factors (regulatory environment, competitive pressure, and vendor partnership) positively affect adoption of AI in Kenyan academic libraries. However, organizational factors (firm size, top management support, and organizational readiness) had insignificant effect on adoption of AI in Kenyan academic libraries.Research limitations/implicationsThe study underscores the necessity of proactive policy measures and managerial decisions to drive AI adoption in academic libraries. Policymakers must prioritize investments in training and resources to enhance staff readiness and create supportive regulatory environments. Library administrators play a pivotal role in demonstrating leadership support and effectively allocating resources to overcome implementation challenges. Tailoring adoption strategies to individual library needs and fostering collaboration between policymakers and administrators are critical for successful AI integration.Originality/valueOther studies that have been done on application of AI in academic libraries have taken broader approach but this study narrows its focus to highlighting the pivotal influence of technological factors on AI adoption in academic libraries, recognizing the benefits and obstacles inherent in integration.Library Management310Findings showed that technology factors and environmental factors positively affect adoption of AI in Kenyan academic libraries, however, organizational factors had insignificant effect on adoption of AI in Kenyan academic libraries.Library Management["Lucy Jelagat Sang"]2024-12-11T00:00:00 2f65418f637eddc03606c58fbaac093df9ae2ecdThis paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and preliminary testing of new safety methodologies. Traditional techniques in this field typically depend on slow, iterative cycles of empirical data collection and analysis, which can be both time-intensive and costly. In contrast, our LLM-based workbench leverages synthetic data generation and advanced prompt engineering to simulate complex safety scenarios and generate diverse, realistic data sets on demand. This capability allows for more flexible and accelerated experimentation, enhancing the efficiency and scalability of safety science research. By detailing an application case, we demonstrate the practical implementation and advantages of our framework, such as its ability to adapt quickly to evolving safety requirements and its potential to significantly cut down development time and resources. The introduction of this workbench represents a paradigm shift in safety methodology development, offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies.Applied Sciences00A novel generative artificial intelligence workbench specifically tailored to the field of safety sciences, offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies.Applied Sciences["Andrea Falegnami", "Andrea Tomassi", "Giuseppe Corbelli", "Francesco Nucci", "Elpidio Romano"]2024-12-11T00:00:00 7c0e26809122038f136e9c3576b3b689a162c323Electric Power Steering (EPS) systems utilize electric motors to aid users in steering their vehicles, which provide additional precise control and reduced energy consumption compared to traditional hydraulic systems. EPS technology provides safety,control and efficiency.. This paper explains the integration of Artificial Intelligence (AI) into Electric Power Steering (EPS) systems, focusing on its role in enhancing the safety, and adaptability across diverse driving conditions. We explore significant development in AI-driven EPS, including predictive control algorithms, adaptive torque management systems, and data-driven diagnostics. The paper presents case studies of AI applications in EPS, such as Lane centering control (LCC), Automated Parking Systems, and Autonomous Vehicle Steering, while considering the challenges, limitations, and future prospects of this technology. This article discusses current developments in AI-driven EPS, emphasizing on the benefits of improved safety, adaptive control, and predictive maintenance. Challenges in integrating AI in EPS systems. This paper addresses cybersecurity risks, ethical concerns, and technical limitations,, along with next steps for research and implementation in autonomous, and connected vehicles.arXiv.org130Current developments in AI-driven EPS are discussed, emphasizing on the benefits of improved safety, adaptive control, and predictive maintenance, along with next steps for research and implementation in autonomous, and connected vehicles.ArXiv["Vikas Vyas", "Sneha Sudhir Shetiya"]2024-12-11T00:00:00 edf2aaa790c322e61e5592219baefba3272a9e84This systematic literature review examines the role of Artificial Intelligence (AI) in transformative academic advising within higher education. The integration of AI in academic advising has the potential to revolutionize the traditional manual processes and enhance student outcomes. The review explores the key applications of AI in academic advising, focusing on chatbots, recommender systems, and predictive analytics. Chatbots provide personalized self-service platforms for students, addressing their queries and assisting with course selection. Recommender systems utilize text-mining techniques to generate module recommendations based on students' interests and learning goals. Predictive analytics employ machine learning algorithms to forecast student grades and identify at-risk students. The benefits of incorporating AI systems in academic advising include personalized recommendations aligned with students' strengths and objectives, as well as improved efficiency and accuracy in providing guidance. However, challenges such as language barriers and ethical considerations need to be addressed. The findings contribute to a comprehensive understanding of the current state of AI in transformative academic advising.International Journal of Academic Research in Business and Social Sciences240The benefits of incorporating AI systems in academic advising include personalized recommendations aligned with students' strengths and objectives, as well as improved efficiency and accuracy in providing guidance.International Journal of Academic Research in Business and Social Sciences["Khawla Albinali", "Noorminshah A. Iahad", "Ahmad Fadhil Yusof", "Anwaar Abdulla"]2024-12-11T00:00:00 dbe6147eb07f73e250d1e2a698b5168ff4b94481Demand for autonomous protection in computing devices cannot go unnoticed, considering the rapid proliferation of deployed devices and escalating cyberattacks. Consequently, cybersecurity measures with an improved generalisation that can proactively determine the indicators of compromises to predict 0‐day threats or previously unseen malware together with known malware are highly desirable. In this article, the authors present a novel concept of autonomous device protection based on behavioural profiling by continuously monitoring internal resource usage and leveraging generative artificial intelligence (genAI) to distinguish between benign and malicious behaviour. The authors design a proof‐of‐concept for Windows‐based computing devices relying on a built‐in event tracing mechanism for log collection that is converted into structured data using a graph data structure. The authors extract graph‐level features, that is, graph depth, nodes count, number of leaf nodes, node degree statistics, and events count and node‐level features (NLF), that is, process start, file create and registry events details for each graph. Further, the authors investigate the use of genAI exploiting a pre‐trained large language network—a simple contrastive sentence embedding framework to extract strong features, that is, dense vectors from event graphs. Finally, the authors train a random forest classifier using both the graph‐level features and NLF to obtain classification models that are evaluated on a collected dataset containing one thousand benign and malicious samples achieving accuracy up to 99.25%.IET Cyber-Physical Systems210A novel concept of autonomous device protection based on behavioural profiling by continuously monitoring internal resource usage and leveraging generative artificial intelligence (genAI) to distinguish between benign and malicious behaviour is presented.IET Cyber-Physical Systems: Theory &amp; Applications["Sandeep Gupta", "Bruno Crispo"]2024-12-11T00:00:00 061a91171cda61d3a22cfe4366e5140d35bcf0ebCurrent Urology Reports710Artificial Intelligence has great potential in erectile dysfunction diagnosis and treatment but there are deficiencies in AI programs and a lack of accuracy in offering precise diagnoses and treatments for ED.Current urology reports["A. Teoman", "E. \u015eerefo\u011flu"]2024-12-11T00:00:00 bcfd3d1e5c679bda544d56b02a70694177921687Recently, artificial intelligence technology has been talked about by young people. Artificial intelligence is one of the developments in the digital era like this. This AI is artificial intelligence made by humans. AI is a branch of computer science. Over time, AI technology has played certain tasks and roles in everyday life, especially in the business world. It can be concluded that artificial intelligence, or artificial intelligence, learns from human experience to do human tasks in general. In the development of the world in this digital era, there are many crimes that are rampant. There is one AI technology that is used for crimes, for example, deepfake.Interkoneksi: Journal of Computer Science and Digital Business00It can be concluded that artificial intelligence, or artificial intelligence, learns from human experience to do human tasks in general.Interkoneksi: Journal of Computer Science and Digital Business["Adelia Nur Fitriana", "Vina Salima Mujahida", "Ali Zainal Abidin", "Ahmad Naufal", "Muhammad Rizal Rafli"]2024-12-11T00:00:00 15f63601cca0773ae3b92539c1f0fb4a6c2635caArtificial intelligence (AI) is a possible paradigm shift in our ability to provide dermatology services. Our article endeavours to outline the opportunities and obstacles faced by our specialty with integrating AI into our departments.British Journal of Dermatology00The article endeavours to outline the opportunities and obstacles faced by the specialty with integrating AI into the authors' departments.The British journal of dermatology["Buket Beresford-Wylie", "Samuel Ashcroft", "B. Gaglani", "Alice Green", "Robert W Smillie", "Katie Tucker", "Ameet Bakhai", "Ben Esdaile", "I. Palamaras"]2024-12-11T00:00:00 585870c75f8201ff94d9b0fd84ad0f00acf784acThe current study delves thoroughly into popular opinions and attitudes regarding the use of artificial intelligence in Arab journalism, given the pervasive effect of artificial intelligence on media and journalism. We aim to respond to several critical inquiries from academics, the artificial intelligence community, and the journalism sector. The public's current knowledge, feelings, concerns, desires, and expectations regarding artificial intelligence in the media sector were investigated through an online poll. It was discovered that the audience was well aware of how artificial intelligence is being used in journalism and other media, with the most familiar component being the description of certain news items that use artificial intelligence. One of the most important results of this study is that the public prefers to use artificial intelligence in news report format more than in creating news or media content. In terms of a variety of media content and news creation procedures, the people had varying preferences. Lastly, most people said that in future news production, human review and artificial intelligence modes should work in tandem.2024 International Conference on Decision Aid Sciences and Applications (DASA)250The public prefers to use artificial intelligence in news report format more than in creating news or media content, and most people said that in future news production, human review and artificial intelligence modes should work in tandem.2024 International Conference on Decision Aid Sciences and Applications (DASA)["R. Alrasheed", "Khaled Al-Mhasneh", "M. Khalifa", "Abedalrahim Al-Arqan", "Ali M. Aldada", "Mona Almarri"]2024-12-11T00:00:00 5c14471af01a1be3a43053ebda435c0b204c41a6This research aims to identify the impact of artificial intelligence on administrative decision-making (flexibility, value & integration) at Zain Bahrain Telecommunication Company. Four hundred and three employees were surveyed through a simple random sampling method. The analysis is based on the outcomes of the questionnaire survey that was given out to a representative sample of the employees at the company in question. Researchers hypothesized that there is a positive significant impact of artificial intelligence on administrative decision-making (flexibility, value & integration) at Zain Bahrain Telecommunication Company. The findings indicated that this hypothesis was accepted. Moreover, the results indicated that there are no significant differences relating to the impact of artificial intelligence on administrative decision-making (flexibility, value & integration) at Zain Bahrain Telecommunication Company due to the demographic (age, qualification, and years of experience). At the same time, there are differences due to (gender and position).2024 International Conference on Decision Aid Sciences and Applications (DASA)90There are no significant differences relating to the impact of artificial intelligence on administrative decision-making at Zain Bahrain Telecommunication Company due to the demographic (age, qualification, and years of experience), but there are differences due to (gender and position).2024 International Conference on Decision Aid Sciences and Applications (DASA)["Aysha Abdulla Ghayath", "Hamad Al-kaabi", "Horiya Al Deebr", "M. Abdeldayem"]2024-12-11T00:00:00 85b8036cc648fee0c36578d3d41b2c4cd1f4ca6aABSTRACT The expanding regenerative medicine toolkit is reaching a record number of lives. There is a pressing need to enhance the precision, efficiency, and effectiveness of regenerative approaches and achieve reliable outcomes. While regenerative medicine has relied on an empiric paradigm, availability of big data along with advances in informatics and artificial intelligence offer the opportunity to inform the next generation of regenerative sciences along the discovery, translation, and application pathway. Artificial intelligence can streamline discovery and development of optimized biotherapeutics by aiding in the interpretation of readouts associated with optimal repair outcomes. In advanced biomanufacturing, artificial intelligence holds potential in ensuring quality control and assuring scalability through automated monitoring of process-critical variables mandatory for product consistency. In practice application, artificial intelligence can guide clinical trial design, patient selection, delivery strategies, and outcome assessment. As artificial intelligence transforms the regenerative horizon, caution is necessary to reduce bias, ensure generalizability, and mitigate ethical concerns with the goal of equitable access for patients and populations.Regenerative medicine890As artificial intelligence transforms the regenerative horizon, caution is necessary to reduce bias, ensure generalizability, and mitigate ethical concerns with the goal of equitable access for patients and populations.Regenerative Medicine["Armin Garmany", "A. Terzic"]2024-12-11T00:00:00 c6a28ac36e71e95b7018cfc203f4ac3d53a6590bArtificial intelligence (AI) significantly influences the systems and applications that underpin modern life. Large datasets are generated from diverse sources. Hence, there is a need for effective data monitoring, processing, and reporting, which is crucial for making influential decisions. In developing complex software systems, AI has become essential for ensuring rigorous testing that aligns with business requirements within constrained timelines. The main advantage of AI in software testing lies in its ability to enhance accuracy and streamline repetitive tasks, ultimately reducing the time required for testing. To this end, this paper explores the role of AI in automated software testing, critically analyzing the limitations of traditional automated testing methods, which often struggle with inconsistent bug detection and have limited adaptability across different environments. By expanding test coverage and addressing these limitations, AI-driven tools can significantly enhance testing efficiency. Additionally, we investigate various AI-based tools designed to optimize software testing and strengthen quality assurance processes.2024 International Conference on Decision Aid Sciences and Applications (DASA)140The role of AI in automated software testing is explored, critically analyzing the limitations of traditional automated testing methods, which often struggle with inconsistent bug detection and have limited adaptability across different environments.2024 International Conference on Decision Aid Sciences and Applications (DASA)["Abdallah Awad", "Mahmoud H. Qutqut", "Ali Ahmed", "Fatima Al-Haj", "Fadi Almasalha"]2024-12-11T00:00:00 5b5f868e40b7b41f7d11d7ffdefbc4633aaa0666The research being conducted aims to examine the impact of artificial intelligence (AI) on labour productivity inside small and medium-sized enterprises (SMEs) situated in Bahrain. Utilising a carefully constructed questionnaire with a solid reliability value (Cronbach's Alpha = 0.974), data was collected from the staff of this sector. The findings of our study indicate that the integration of artificial intelligence (AI) significantly enhances productivity, as it amplifies the benefits of training. Corroborating previous studies, the results indicate that targeted training improves performance and that artificial intelligence has the potential to automate tasks and provide personalized learning experiences. The present study addresses the existing research deficiencies in the domain of small and medium-sized companies (SMEs) and the synergistic effects of artificial intelligence (AI). The pragmatic implications suggest that small and medium-sized firms (SMEs) could make use of artificial intelligence (AI) to augment training programmes and boost productivity.2024 International Conference on Decision Aid Sciences and Applications (DASA)790The findings of this study indicate that the integration of artificial intelligence (AI) significantly enhances productivity, as it amplifies the benefits of training and has the potential to automate tasks and provide personalized learning experiences.2024 International Conference on Decision Aid Sciences and Applications (DASA)["M. Alaghbari", "A. Ateeq", "R. A. Ateeq", "Nasser A. Saif Al Muraqab"]2024-12-11T00:00:00 5d95082b5bb120d639685d47af1c1e800c699611The article considers the conceptual foundations of the functioning of artificial intelligence and its impact on criminal law relations. It have been analyzed the doctrinal and legislative sources on which the development of an effective model of criminal legal regulation of these processes should be based. It is emphasized on digitalization as a driving factor of fundamental changes, which forms the legal digital reality. Its transformative potential is developing due to the growing availability of big data, artificial intelligence, increasing capacity of modern computers, new blockchain technology platforms, Internet of Things, cloud services, virtual reality, social networks and platforms, cybersecurity, electronic services, etc. It is emphasized the controversy of the definition of the term “artificial intelligence”. It have been presented the arguments in favor of granting to artificial intelligence the status of “electronic person”, which is due to the rapid development of this innovative digital tool and the acquisition of intellectual qualities that equal or exceed human ones, which is the basis for recognizing such a person as a subject of criminal-legal relations and a subject of a criminal offense”. The opposite opinion is based on the fact that human intelligence belongs to a subject endowed with consciousness, but what is called artificial intelligence refers to an object, that is, technology, machines that are not able to empathize, to create, etc., therefore, the attribution of a set of information technologies to unique qualities of a person, in the context of their criminal legal dimension, is a manifestation of the dehumanization of law in general, and criminal law in particular. Keywords: digitalization of law, digital transformations, artificial intelligence, criminal-legal relations, conceptual foundations, philosophy of artificial intelligence.Scientific journal Criminal and Executive System Yesterday Today Tomorrow90The arguments in favor of granting to artificial intelligence the status of “electronic person”, which is due to the rapid development of this innovative digital tool and the acquisition of intellectual qualities that equal or exceed human ones, are presented.Scientific journal Criminal and Executive System: Yesterday. Today. Tomorrow["V. Pidgorodynskyi"]2024-12-11T00:00:00 89b607ca4dcb94b1ae9ac7d2965ce3b013aeb1eb This paper introduces transhumanism and its central concerns in the age of artificial intelligence and climate crisis, and the four essays in this issue, revealing how they collectively offer critical reflections on transhumanism from the perspective of theological anthropology and Chinese culture.Journal of Chinese Theology00Journal of Chinese Theology["Liang Hong"]2024-12-11T00:00:00 bc2a0e71fbf967d6b57c84dedc833abf4cb62705Artificial intelligence (AI) offers transformative potential in psychedelic research by addressing limitations in personalized treatment, predicting therapeutic outcomes, and understanding complex biological and environmental factors. AI-driven models provide new insights into long-term efficacy, set and setting optimization, and alternative treatment methods, advancing psychedelic therapy into personalized medicine.ACS Medicinal Chemistry Letters150Artificial intelligence-driven models provide new insights into long-term efficacy, set and setting optimization, and alternative treatment methods, advancing psychedelic therapy into personalized medicine.ACS medicinal chemistry letters["Robert B. Kargbo"]2024-12-11T00:00:00 08ec33fefc2b7daaeccde9bf6bd8411677b03fb9The focus of this paper is the integration of artificial intelligence (AI) within the educational field including but not limited to, adaptive learning, teaching evaluation, online learning environments, and individualized instruction. The authors investigate the positive role of AI in enhancing teaching performance and the quality of students' learning experiences. The study aims to demonstrate what role AI may play in the course of education development and practice improvement. It also sheds light on the limitations AI will have in the realm of education, such as accessibility, data ownership, and ethical issues. The paper does not contain only the limitations but the opportunities for this technology as well and describes how AI could be effectively incorporated into education, subsequently providing insights into the evolution of AI-based educational transformation.2024 International Conference on Decision Aid Sciences and Applications (DASA)500How AI could be effectively incorporated into education is described and the opportunities for this technology are described, subsequently providing insights into the evolution of AI-based educational transformation.2024 International Conference on Decision Aid Sciences and Applications (DASA)["Abdallah M. A. Al-Tarawneh", "Reem AbdElkareem AlOmoush", "Tanveer ul Islam", "J. Janjua", "Tahir Abbas", "Anaum Ihsan"]2024-12-11T00:00:00 31c2b416d0d7935dabac62b2ba200fd36875c82dWith the continuous growth of the global population, agricultural production faces unprecedented challenges. Traditional agricultural methods exhibit numerous deficiencies in food production, quality control, and environmental protection, necessitating urgent intervention by modern technologies. This paper investigates the application of artificial intelligence (AI) technology in the agricultural sector, exploring its significant contributions to enhancing agricultural production efficiency, precision, and sustainability. Drawing on the research findings of several scholars, this paper demonstrates specific applications of AI in soil moisture classification, unmanned aerial vehicle (UAV) thermal imaging for nest detection, and crop health monitoring. Additionally, this paper discusses the crucial role of AI technology in the modernization of agriculture in China, particularly its potential in cultivating intelligent agricultural talents and promoting high-quality agricultural development. In summary, AI is injecting new vitality into modern agriculture with its unique advantages, driving agriculture towards green and sustainable development. Through efforts in intelligent machinery, precision agriculture, and talent development, AI presents a promising outlook for its application in agriculture, offering new perspectives and solutions to address global agricultural challenges.Highlights in Science Engineering and Technology100Artificial intelligence is injecting new vitality into modern agriculture with its unique advantages, driving agriculture towards green and sustainable development.Highlights in Science, Engineering and Technology["Ao Shen"]2024-12-11T00:00:00 f56534177eec8af513f45f291f7520fd0a85686fPolitical institutions and organizations face many challenges such as information chaos, fake news, and misinformation in light of the spread of technology. There are doubts and concerns about the effectiveness of the use of artificial intelligence by specialists, employees, and academics such as the Chat GPT application in political analysis and its impact on the quality of their information assessment and appropriate decision-making regarding political issues and topics. Hence, the study problem stems from a main question: “What is the role of artificial intelligence such as the Chat GPT application in developing the ability for political analysis and critical thinking and its impact on building the skill of evaluating information and making appropriate decisions regarding political issues and topics?”2024 International Conference on Decision Aid Sciences and Applications (DASA)150The study problem stems from a main question: what is the role of artificial intelligence such as the Chat GPT application in developing the ability for political analysis and critical thinking and its impact on building the skill of evaluating information and making appropriate decisions regarding political issues and topics.2024 International Conference on Decision Aid Sciences and Applications (DASA)["Khaled Al-Mhasneh", "R. Alrasheed", "Abedalrahim Al-Arqan", "Juwaireya Fares", "Munays Alqahtani", "Amal Salman"]2024-12-11T00:00:00 cf6b5eb507ead77453fe60c153ba64d5a8e72084The rapid advancement of technology has made artificial intelligence (AI), particularly generative chatbots, an integral part of everyday life. These chatbots, powered by Natural Language Processing (NLP) and deep learning technologies, are widely used in various fields such as customer service, education, and entertainment. However, the increasing prevalence of such technologies brings forth important philosophical concerns, particularly in the realm of axiology—the branch of philosophy that deals with the nature of values, including the practical and ethical implications of knowledge and technology. This study investigates the practical benefits and ethical responsibilities associated with generative chatbots, using ChatGPT as a case study. The research examines whether ChatGPT adheres to the axiological principles of science, specifically its usefulness in enhancing human life and its ethical responsibilities. Through a qualitative content analysis, this research evaluates the responses of ChatGPT to a series of questions based on the axiological framework outlined by Sumantri. The study focuses on two main aspects of science's axiological evaluation: the practical benefits of science and technology, and the ethical responsibilities tied to their application. The findings indicate that ChatGPT is capable of providing useful insights that contribute to human understanding, improve quality of life, simplify complex tasks, and offer solutions to various problems. However, the ethical considerations of AI technology, such as fairness, transparency, and accountability, remain a crucial area of concern. This research highlights the importance of balancing technological progress with ethical responsibility, emphasizing that AI systems like ChatGPT must be developed and applied in ways that align with human values to ensure their positive impact on society.Switch : Jurnal Sains dan Teknologi Informasi00Investigating the practical benefits and ethical responsibilities associated with generative chatbots, using ChatGPT as a case study indicates that ChatGPT is capable of providing useful insights that contribute to human understanding, improve quality of life, simplify complex tasks, and offer solutions to various problems.Switch : Jurnal Sains dan Teknologi Informasi["Ig Jarot Febri Setyo Wibowo", "Agung Winarno"]2024-12-11T00:00:00 ea2a32a4b2cdd552e12330ae14c1ce180f52b6b9This article presents the growing role of artificial intelligence (AI) in account management, through highlighting its increasing significance in computing and financial administration. AI is rapidly becoming a transformative tool across various sectors, and its application in tax administration presents both opportunities and challenges. The article explores AI-driven business opportunities, growth trends, and strategic planning within the field. in addition to address the critical technical, ethical, and legal considerations, including intellectual property rights, that must be navigated to ensure responsible and effective implementation.2024 International Conference on Decision Aid Sciences and Applications (DASA)390The article explores AI-driven business opportunities, growth trends, and strategic planning within the field, and addresses the critical technical, ethical, and legal considerations, including intellectual property rights, that must be navigated to ensure responsible and effective implementation.2024 International Conference on Decision Aid Sciences and Applications (DASA)["Mahmoud Mahfuri", "Sameh Ghwanmeh", "Ali Q Saeed", "Mohammad Khishe"]2024-12-11T00:00:00 c47cc19b20daf39ebd35859022ca546bdc98f990Applied Artificial Intelligence801Applied Artificial Intelligence["Xhavit Islami", "Enis Mulolli"]2024-12-11T00:00:00 3ff6c16eceaa4844eb69d043bb93c7baa6b29a7a01["Christian L. Geminn", "Paul C. Johannes", "M. Nebel", "Tamer Bile"]2024-12-11T00:00:00 95c80fa8dd59bc766fe15baa93aeb8201e11646cNature Biotechnology50Nature biotechnology[]2024-12-11T00:00:00 9bda6437e64fd9e6f56797e27da1239a2bf143f2Intensive Care Medicine90Intensive care medicine["Greet De Vlieger", "J. Koyner", "M. Ostermann"]2024-12-11T00:00:00 00da1ee82a7008c568d8ac4659f0af2b93128ea1The Journal of Values Based Leadership00The Journal of Values-Based Leadership["Eya Mahouachi"]2024-12-11T00:00:00 027202539d6902d1048ded91f530d82c62a3a5ebForestry has a profound impact on environmental protection, economic development, and social welfare. With the improvement of global environmental protection awareness, the construction of platform supply chain of forest products has become the core driving force to promote sustainable development of forestry. Studying the resilience of supply chain of platform of forest products is of great importance to solve the contradiction between economic development and natural ecosystem protection. However, the existing resilience evaluation methods are not suitable for the dynamic and complex performance evaluation of the current forest products platform supply chain. Therefore, in order to make up for this shortcoming, this paper evaluates and analyzes the supply chain resilience of the forest products platform based on AI recommendation and extension theory. Firstly, this paper combined the characteristics of forest products and used AI recommendation technology to build a forest products platform supply chain resilience performance evaluation index system. Secondly, the AHP method was used to calculate the index weight, and the resilience evaluation model of the platform supply chain of forest products was constructed. Finally, in order to ensure the authenticity and credibility of the evaluation results, three practical cases were analyzed to illustrate the resilience level of the platform supply chain of forest products, and the effectiveness of the application of AI recommendation and extension theory in the resilience performance evaluation of forest products platform supply chains was verified. The scientific value of this paper is that it provides a new idea and a new method for the resilience performance evaluation of the forest products platform supply chain and makes theoretical and practical contributions to the fruitful application of AI recommendation in the supply chain field. In addition, this study also provides a new practical guideline for protecting the natural environment and realizing the sustainable development of forestry.Forests490Forests["Lin Lu", "Ping Long", "Xiaochun Luo"]2024-12-11T00:00:00 fa0b0e249c1679a53fdba84484ff882a8cc42520Sin resumenDiálogos Jurídicos00Diálogos Jurídicos["Miguel Navajas Rebollar"]2024-12-11T00:00:00 8c991268a464957c6b61dac8a90e59a10264ae3cInternational Journal of Human-Computer Interaction360International Journal of Human–Computer Interaction["Muhammed Jisham", "Vanitha Selvaraj", "Abin John"]2024-12-11T00:00:00 6230201cbd8725ecce0bfebda77a5a924c822d86International Symposiu on Visual Information Communication and Interaction00{"pages": "55:1-55:2"}["Assem Kroma"]2024-12-11T00:00:00 d6b6fc32f9145798ba004d7e51f700ac77c8696dIntensive Care Medicine100Intensive care medicine["Emma-Jane Spencer", "Nicoleta J. Economou-Zavlanos", "M. V. van Genderen"]2024-12-11T00:00:00 77c52c4553952a9938b50fd6d4d428bd27ff7613Nature00Nature["Abdullahi Tsanni"]2024-12-11T00:00:00 72839acfb3a19b1451a34782e6235f39f59ee9ceSeries on biomechanics00Series on Biomechanics["N. Jojua", "T. Gognadze", "L. Dolidze"]2024-12-11T00:00:00 e321dcd74ea6a0d01ff5dddb6353cec6fdf83b7bInternational Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024)00International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024)["Gengbin Zhang", "Jianxiang Xie", "Jiaqi Wu", "Chang Chen", "Zhichao Fu", "Jiajun Ou"]2024-12-11T00:00:00 3f2c540df3afd0685d6f0ca1e827de5db4a1d039This study explores the potential impact of ChatGPT on the academic writing skills development of medical students enrolled in a compulsory 3-unit writing course at a medical university. The research focuses on two primary objectives, which are formulated as two research questions: Firstly, does the use of ChatGPT enhance medical students’ English academic writing skills compared to conventional writing training? Secondly, how does the use of ChatGPT impact on different components of academic writing? A longitudinal intervention design was employed with 83 participants from two writing classes in the experimental and control groups. The findings demonstrated ChatGPT’s significant impact on enhancing medical students’ English academic writing skills, with large effect sizes. ChatGPT enhanced students’ writing skills, especially content, organization, vocabulary, and mechanics in the experimental group, while its impact on language use is limited. AI tools like ChatGPT can be valuable in assisting with certain aspects of writing, but they should not be considered a one-size-fits-all solution for enhancing writing skills. The result of the study can be beneficial for educators, particularly those interested in teaching writing.Frontiers in Education540ChatGPT enhanced students’ writing skills, especially content, organization, vocabulary, and mechanics in the experimental group, while its impact on language use is limited.Frontiers in Education["Zahra Shahsavar", "Reza Kafipour", "Laleh Khojasteh", "Farhad Pakdel"]2024-12-11T00:00:00 faea4c431b9b3435e680837f7896ebf30439f521Revista Colombiana de Cardiología00Revista Colombiana de Cardiología (English Edition)["Luis Pino", "Iv\u00e1n Triana"]2024-12-11T00:00:00 a0338b1a279e57780a86ca6dc4594261ebb4ba3c3L The Southeast Asian Journal of English Language Studies003L The Southeast Asian Journal of English Language Studies["Aqib Javid Parry", "Sana Altaf", "Akhtar Habib Shah"]2024-12-11T00:00:00 cb9225862d328c15ef9e145ac310f8d488df45cdEuropean Heart Journal100European heart journal["G. Boriani", "D. Mei", "G. Lip"]2024-12-11T00:00:00 7e97f7bb94e34da09dbd136fe346079b27b8d6a5Intensive Care Medicine110Intensive care medicine["B. Wernly", "B. Guidet", "M. Beil"]2024-12-11T00:00:00 f96ee9ed66da6b560d64dcef019af2f6f6c3f0a4To enhance decision making processes among engineering industries, the main objective of this research is to explain the role of AI-Enabled applications on decision making processes rely on the mediation effect of value co-creations determinants within engineering companies in Jordan. Based on a quantitative design, the target sample were asked to provide their perspectives in accordance with a questionnaire approach. The results of this study retrieved based on Structural Equation model method. The outcomes show that AI-Enabled applications confirm a positive influence on value co-creation determinants. Moreover, the results uncover that value co-creation determinants conform a considerable influence on decision-making processes. Furthermore, the outcomes discover that value co-creation determinants strongly mediate the relationship between AI-Enabled applications and decisionmaking processes. Lastly, the findings of this study provide new insights for managers and top management of engineering companies that investing in AI tools have an ability to offer various benefits and take attention toward reinforcing value co-creation as a strategic resource.2024 International Conference on Decision Aid Sciences and Applications (DASA)240The outcomes show that AI-Enabled applications confirm a positive influence on value co-creation determinants and discover that value co-creation determinants strongly mediate the relationship between AI-Enabled applications and decisionmaking processes.2024 International Conference on Decision Aid Sciences and Applications (DASA)["Hasan Alhanatleh", "Amro Alzghoul", "Sakher Alnajdawi"]2024-12-11T00:00:00 7b79ef7042092f5af9a0505d81feee1ebd4db2d0Environment, Development and Sustainability470Environment, Development and Sustainability["Rana Muhammad Adnan Ikram", "Mo Wang", "Hossein Moayedi", "M. Gholizadeh", "Atefeh Ahmadi Dehrashid", "Quynh T. Thi"]2024-12-11T00:00:00 32dfef05c3098e63c1520049637d162ddf2e9d8900["Sonal Trivedi", "M. K. Nallakaruppan", "B. Balusamy", "Nithya Rekha Sivakumar"]2024-12-11T00:00:00 8378aac7d553239c865a03240ff89f5f7e380693British Journal of Sports Medicine50British journal of sports medicine["J. P. MacDonald", "Madeleine Pape", "Kathryn E Ackerman", "Eva M Carneiro", "Yungui Huang", "Katherine H Rizzone", "P. Zondi", "M. Mountjoy"]2024-12-11T00:00:00 77d523801f229c1873e8e9d54ac5d1145a5ef343The aim of this study is to carry out an analysis of the intellectual structure of the introduction of AI into finance, in the period from 1995 to 2023, using SciMAT v.1.1.04 software. The results indicate how research on the incorporation of AI in finance has grown significantly, which shows the evolution and importance of this area of research. Eight main topics were obtained in this area: bank, prediction, impact, decision, valuesstock, genetic algorithm, big data analysis, and social data analysis. This study shows us how the incorporation of AI can strongly support the analysis of different financial situations such as decision making or the prediction of movements.Journal of Risk and Financial Management350This study shows how the incorporation of AI can strongly support the analysis of different financial situations such as decision making or the prediction of movements in the period from 1995 to 2023.Journal of Risk and Financial Management["Antonio Alc\u00e1zar-Blanco", "J. F. Rangel-Preciado", "Fiama Portillo-Santos"]2024-12-11T00:00:00 2d2be33626a55a25a762031f89cabb4a9a6825deDescribing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.arXiv.org00MaestroMotif is presented, a method for AI-assisted skill design, which yields high-performing and adaptable agents and surpasses existing approaches in both performance and usability.ArXiv["Martin Klissarov", "Mikael Henaff", "Roberta Raileanu", "Shagun Sodhani", "Pascal Vincent", "Amy Zhang", "Pierre-Luc Bacon", "D. Precup", "Marlos C. Machado", "P. D'Oro"]2024-12-11T00:00:00 ae5b9da8375f7cac53bc1969ea9fc4cd00c2c180Scientific and technological progress is not ideologically neutral. It is driven not only by a desire to understand reality but also by an ambition to bring specific ideological visions to fruition via new technologies.Academia. Magazyn polskiej Akademii Nauk00ACADEMIA. The magazine of the Polish Academy of Sciences["Filip Bia\u0142y"]2024-12-11T00:00:00 f94aaa3974f06b7abfb65ac61677b96b33bac3fdThe construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operational challenges. This review focuses on how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are being leveraged to tackle these issues. It specifically explores AI’s role in predicting air pollution, improving material quality, monitoring worker health and safety, and enhancing Cyber-Physical Systems (CPS) for construction. This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. These tools are assessed for their ability to predict air pollutant levels, improve concrete quality, and monitor worker safety in real time. Research papers were also reviewed to understand AI’s application in predicting the compressive strength of materials like cement mortar, fly ash, and stabilized clay soil. The performance of these models is measured using metrics such as coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Furthermore, AI has shown promise in predicting and reducing emissions of air pollutants such as PM2.5, PM10, NO2, CO, SO2, and O3. In addition, it improves construction material quality and ensures worker safety by monitoring health indicators like standing postures, electrocardiogram, and galvanic skin response. It is also concluded that AI technologies, including Explainable AI and Petri Nets, are also making advancements in CPS for the construction industry. The models’ performance metrics indicate they are well-suited for real-time construction operations. The study highlights the adaptability and effectiveness of these technologies in meeting current and future construction needs. However, gaps remain in certain areas of research, such as broader AI integration across diverse construction environments and the need for further validation of models in real-world applications. Finally, this research underscores the potential of AI and ML to revolutionize the construction industry by promoting sustainable practices, improving operational efficiency, and addressing safety concerns. It also provides a roadmap for future research, offering valuable insights for industry stakeholders interested in adopting AI technologies.Frontiers in Artificial Intelligence361This study evaluates various AI and ML models, including Artificial Neural Networks and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization for their ability to predict air pollutant levels, improve concrete quality, and monitor worker safety in real time.Frontiers in Artificial Intelligence["Eliezer Zahid Gill", "D. Cardone", "Alessia Amelio"]2024-12-12T00:00:00 b257eaf44ec6647bcc38a1cd4c4623c297b430eb In recent years, the gradual maturation of artificial intelligence (AI) and big data technology has promoted the deep integration of the "AI + transportation" model, which has been widely used in many transportation segments, and the intelligent transportation has been increasingly emphasized, on the one hand, it is an important method for the intelligent management of transportation, and on the other hand, it improves the efficiency of the implementation of the traffic management system. This paper analyzes the mainstream technology and application of artificial intelligence in the field of transportation by reviewing various types of literature and summarizing the summary of information, compares and evaluates the advantages and disadvantages of the application of different artificial intelligence technologies in the field of transportation, summarizes the progress of intelligent transportation, summarizes the advantages of different applications and the technical shortcomings and bottlenecks of the development of different applications, puts forward proposals for the application of artificial intelligence technology in the field of transportation, and looks forward to the future direction of development, in order to promote the further development of intelligent transportation.Journal of Advances in Engineering and Technology00This paper analyzes the mainstream technology and application of artificial intelligence in the field of transportation by reviewing various types of literature and summarizing the summary of information, compares and evaluates the advantages and disadvantages of the application of different artificial intelligence technologies in the field of transportation.Journal of Advances in Engineering and Technology["Siru Wang"]2024-12-12T00:00:00 f3edf64b88a2ede849e01a0cee6fa256ffcb8c81The Digital Transformation 4.0, which brings opportunities for innovation, involves several enabling technologies, among which Artificial Intelligence (AI) stands out. In this context, this study aims to identify programming languages, as well as libraries and tools, focused on AI applications. Four programming languages for AI were highlighted: C#, R, MatLab, and Python. Subsequently, several Python-based AI libraries and tools were discussed, such as Numpy, Pandas, TensorFlow, PyTorch, Scikit-Learn, Keras, as well as Natural Language Processing (NLP) frameworks like LangChain, HuggingFace Transformers, NLTK, and SpaCy. Finally, the conclusion consolidates knowledge about programming languages, libraries, and AI tools into comparative charts with pros and cons, which organize the literature and can be used by professionals and researchers in the field.Revista contemporânea60This study aims to identify programming languages, as well as libraries and tools, focused on AI applications, and consolidates knowledge about programming languages, libraries, and AI tools into comparative charts with pros and cons, which can be used by professionals and researchers in the field.Revista Contemporânea["Bruno de Oliveira Maciel", "Isabela Dambiski Gomes de Carvalho", "Kassyane Nunes da Silva", "Alberjan de Jesus Jean Pinto", "Orlando Renato Brenner Lantmann", "H\u00e9lio Gomes de Carvalho", "Gustavo Dambiski Gomes de Carvalho"]2024-12-12T00:00:00 fa4bb2fd06bb885259018aa31285f1153af3df95The area of modern philosophical issues includes the sphere of digital technologies and the phenomenon of artificial intelligence (AI), in particular. And while there are loud debates about the social and political status of AI if it outgrows itself and becomes on a par with human intelligence - what rights and freedoms we will have to give it, etc. - about the principles of its functioning and development, it is worth, with no less attention, studying the transformations occurring with the person himself against the backdrop of AI, which is widely spreading and being introduced into all spheres of human life. Is AI beginning to displace humans from their active positions today: professional, creative, every day, and looking to the future? How does the use of AI affect a person, his skills, activities, work, and self-realization - from writing an advertising slogan with neural networks to high-precision calculations in the field of astronautics or genetic engineering? This article is an attempt to philosophically comprehend these questions and ask new ones about the place and role of AI in the professional activities of modern and future people.Bulletin of Chelyabinsk State University00This article is an attempt to philosophically comprehend questions and ask new ones about the place and role of AI in the professional activities of modern and future people.Bulletin of Chelyabinsk State University["N. Vialshin"]2024-12-12T00:00:00 c1f3b3b09cdf75cdb25fe66934a6aa733fdf14beThe frontier of artificial intelligence (AI) is constantly moving, raising fears and concerns whenever AI is deployed in a new occupation. Some of these fears are legitimate and should be addressed by AI developers-but others may result from psychological barriers, suppressing the uptake of a beneficial technology. Here, we show that country-level variations across occupations can be predicted by a psychological model at the individual level. Individual fears of AI in a given occupation are associated with the mismatch between psychological traits people deem necessary for an occupation and perceived potential of AI to possess these traits. Country-level variations can then be predicted by the joint cultural variations in psychological requirements and AI potential. We validated this preregistered prediction for six occupations (doctors, judges, managers, care workers, religious workers, and journalists) on a representative sample of 500 participants from each of 20 countries (total N = 10,000). Our findings may help develop best practices for designing and communicating about AI in a principled yet culturally sensitive way, avoiding one-size-fits-all approaches centered on Western values and perceptions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).American Psychologist00It is shown that country-level variations across occupations can be predicted by a psychological model at the individual level, avoiding one-size-fits-all approaches centered on Western values and perceptions.The American psychologist["Mengchen Dong", "Jane Rebecca Conway", "Jean\u2010Fran\u00e7ois Bonnefon", "Azim Shariff", "Iyad Rahwan"]2024-12-12T00:00:00 e84672e3f4ebf0be725da4793c650bacebd41007This article examines the role of big data analytics (BDA) in international market selection (IMS) decisions. It is based on a study of South African companies that used the TRADE-DSM (Decision Support Model) big data analytics tool to help in making these decisions. While there is much theory on the potential use of big data analytics and artificial intelligence for international business in general and international market selection decisions in particular, there is very little research on how these tools are used when making this important decision. This article reports on a study that examined: whether big data analytics was used in making international market selection decisions, how important it was relative to other sources of information; how it was used in the international market selection decision-making process; and what factors led to acceptance of big data analytics output. Results from the surveys and interviews both with those who generated the TRADE-DSM reports and the users of the reports (the decision-makers) are presented to provide deeper insights into the role of big data analytics in international market selection decisions. The results showed that while big data analytics is very important (rated third-highest information source), it is one of many sources of information used in the process and that human sources (visits to the market, attendance at trade shows and conferences) are considered the most valuable. Regarding what prompts the acceptance of big data analytics in the international market selection process, the study found that knowledge of the system, trust in the person providing the report and the relationship between the person providing the report and the decision-maker are the most important factors. Journal of Intelligence Studies in Business340The results showed that while big data analytics is very important (rated third-highest information source), it is one of many sources of information used in the process and that human sources are considered the most valuable.Journal of Intelligence Studies in Business["Jonathan Calof", "W. Viviers"]2024-12-12T00:00:00 dfd25b7d6e2d8113b7e5cde7d49bb0503de89555The educational guidance platform via artificial intelligence chatbot to promote vocational aptitude for vocational students or the educational guidance platform via AI Chatbot is a research tool that was designed with the combination of educational guidance process, artificial intelligence technology, and chat platforms like LINE and Messenger. The platform in this study is intended primarily to be used as a tool to analyze vocational aptitude and provide personalized educational advice, which will assist learners to choose suitable programs for further study in vocational education level. This study were aimed to (1) study and synthesis the conceptual framework of the educational guidance platform via AI Chatbot, (2) develop the educational guidance platform via AI Chatbot, and (3) evaluate the results of the developed the educational guidance platform via AI Chatbot. There were nine participants from different institutions included in this research, derived by means of purposive sampling, and with experience in the design and development application. The research instruments include (1) the architecture the educational guidance platform via AI Chatbot, and (2) evaluation form on the architecture the educational guidance platform via AI Chatbot. The results of this study, which were derived from the study on the prototype design of the architecture of the educational guidance platform via AI Chatbot, are designated to be used as a guideline for future studies in order to develop the educational guidance platforms via AI Chatbot that can be put in practical use in an effective manner. The results of this study show that the overall suitability of the development of the architecture of the educational guidance platform via AI Chatbot is at strongly agree level. Nevertheless, there are still some research gaps in this study that need to be further addressed in the future. For instance, the future studies should cover a wider range of application of the developed platforms by conducting the survey with more diverse population and broader educational environments. This is to confirm the suitability of the development of the architecture of the educational guidance platforms via AI Chatbot that can be used as a guideline for future development coupled with the related technologies.Higher Education Studies200The overall suitability of the development of the architecture of the educational guidance platform via AI Chatbot is at strongly agree level and is designated to be used as a guideline for future studies in order to develop the educational guidance platforms via AI Chatbot that can be put in practical use in an effective manner.Higher Education Studies["Manus Phuttawong", "Pinanta Chatwattana"]2024-12-12T00:00:00 366a30be935e62607a8843b551bfce38884b58f2Holistic Integrative Oncology940The role of animal models in cancer research is reviewed and the ethical issues surrounding their use are considered, as well as the potential and feasibility of artificial intelligence technology in improving animal welfare and addressing ethical concerns.Holistic Integrative Oncology["Hong Yin", "Qiannan Li", "Shuling Yang", "Chunhuan Zhang", "Yueyi Zhai", "Haowei Hou", "Yan Qu"]2024-12-12T00:00:00 3c2cd03d90d68dec90d61488762f034d882d8300<div class="section abstract"><div class="htmlview paragraph">Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry.</div><div class="htmlview paragraph"><b>Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry</b> discusses how the modern gaming population of 3.3 million individuals could be engaged through embedded AAM-based scenarios and AI-enhanced grading systems for concept creation, engineering, manufacturing, air space design and management, piloting, remote operations, infrastructure planning, vehicle operations.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>00This paper discusses how the modern gaming population of 3.3 million individuals could be engaged through embedded AAM-based scenarios and AI-enhanced grading systems for concept creation, engineering, manufacturing, air space design and management, piloting, remote operations, infrastructure planning, vehicle operations.["Johnny Doo"]2024-12-12T00:00:00 9f5a2757877aaba1976fc71b746c6f1bb10dc73eAs the promise of advancements in efficiency and cost-effectiveness of healthcare sector, through artificial intelligence (AI), gains momentum globally, especially in resource-scarce regions like Nepal, potential challenges surrounding ethical and legal concerns around its implementation remains a primary discussion. This paper explores various socio-cultural, legal and privacy imperatives aligning with such discussions. This paper, along with the statistical evidence following a survey, identifies major concerns as perceived by the possible future general stakeholders of the system and discusses probable steps that can be taken for a smooth adoption of AI in health sector of the country. The paper also discusses various AI based applications and their potential benefits including virtual healthcare, personalized treatment, early diagnosis and administrative assistance. The findings suggest that while AI holds significant promise, concerns like privacy should be addressed while integrating such models, adhering to existing socio-cultural values and existing healthcare infrastructure.International Conference on Automation and Computing310The findings suggest that while AI holds significant promise, concerns like privacy should be addressed while integrating such models, adhering to existing socio-cultural values and existing healthcare infrastructure.2024 6th International Conference on Advancements in Computing (ICAC)["Sangam Ghimire", "Janak Sitaula", "Shubhashish Karki", "Dipen Khatri", "Sudan Jha"]2024-12-12T00:00:00 57834ab86de44a63d7f43f5f43909f67ee83dc67The global transport sector, a significant contributor to energy consumption and greenhouse gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology, offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI’s role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels. It explores AI’s potential to drive advancements in electric and autonomous vehicles, shared mobility, and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport, considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However, challenges such as data quality, computational demands, system integration, and ethical concerns must be addressed to fully harness AI’s potential. The study also highlights the policy implications of AI adoption, underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness.Energies1870This study provides a comprehensive review of AI’s role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels, and highlights the policy implications of AI adoption.Energies["Tymoteusz Miller", "Irmina Durlik", "Ewelina Kostecka", "Adrianna \u0141obodzi\u0144ska", "Marcin Matuszak"]2024-12-12T00:00:00 e19247bfaf61ef53dccffc697cb15583ecf003ccAim: To present the ARILIS study aimed at assessing the use of artificial intelligence to analyze chest computed tomography (CT) data to predict and prevent non-cancer mortality in patients with cancer. Material and methods: This cohort study will include patients with cancer diagnosed in the Arkhangelsk region (AR) within the 2019–2023 period. The COVID-19 patients with pneumonia, patients with general medical conditions, and the population of the Know Your Heart Study are planned to be enrolled as control groups. To detect and quantify the CT signs of the cardiovascular, pulmonary, and bone disorders, the thoracic СT scans of all the subjects will be processed using the multi-targeted AI algorithm provided by the IRA Labs company. From the date of processing of the thoracic CT scans using the multi-targeted AI algorithm, the study subjects will be followed for new clinical diagnoses and all-cause mortality. Expected results: T he study will determine the prevalence of CT signs of the cardiovascular, pulmonary, and bone disorders in patients with cancer compared with the Know Your Heart Study population sample. It will also assess the incidence of cardiovascular, pulmonary, and bone events and all-cause mortality in patients with cancer compared with control groups, explore the potential of the IRA Labs’ multi-targeted AI algorithm in the assessment and reclassification of assessed risks in patients with cancer, and provide a software product for using mtIA in healthcare practice.Ekologiya cheloveka (Human Ecology)180The ARILIS study will determine the prevalence of CT signs of the cardiovascular, pulmonary, and bone disorders in patients with cancer compared with the Know Your Heart Study population sample, explore the potential of the IRA Labs’ multi-targeted AI algorithm in the assessment and reclassification of assessed risks in patients with cancer, and provide a software product for using mtIA in healthcare practice.Ekologiya cheloveka (Human Ecology)["M. Valkov", "A. M. Grjibovski", "A. Kudryavtsev", "Maxim A. Bogdanov", "Dmitriy V. Bogdanov", "A. A. Dyachenko", "V. Chernina", "M. G. Belyaev", "Farukh R. Yaushev", "Elena V. Panina", "M. A. Donskova", "E. A. Soboleva", "M. V. Basova", "Maxim E. Pisov", "Maria N. Dugova", "E. Petrash", "Regina R. Gareeva", "Alexey E. Shevtsov", "Vilgelm V. Volman", "Z. G. Berikhanov", "S. Avdeev", "Natalya S. Serova", "M. I. Sekacheva", "Yaroslav I. Ashikhmin", "Z. Belaya", "V. Omelyanovskiy", "M. Y. Goncharov", "Aleksandr S. Gershtanskiy", "V. Gombolevskiy"]2024-12-12T00:00:00 39295858671004feb8aefc44a254cbb44e5b4be2This report synthesizes the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Task Force's guidance on the integration of artificial intelligence (AI) in otolaryngology-head and neck surgery (OHNS). A comprehensive literature review was conducted, focusing on the applications, benefits, and challenges of AI in OHNS, alongside ethical, legal, and social implications. The Task Force, formulated by otolaryngologist experts in AI, used an iterative approach, adapted from the Delphi method, to prioritize topics for inclusion and to reach a consensus on guiding principles. The Task Force's findings highlight AI's transformative potential for OHNS, offering potential advancements in precision medicine, clinical decision support, operational efficiency, research, and education. However, challenges such as data quality, health equity, privacy concerns, transparency, regulatory gaps, and ethical dilemmas necessitate careful navigation. Incorporating AI into otolaryngology practice in a safe, equitable, and patient-centered manner requires clinician judgment, transparent AI systems, and adherence to ethical and legal standards. The Task Force principles underscore the importance of otolaryngologists' involvement in AI's ethical development, implementation, and regulation to harness benefits while mitigating risks. The proposed principles inform the integration of AI in otolaryngology, aiming to enhance patient outcomes, clinician well-being, and efficiency of health care delivery.Otolaryngology Head & Neck Surgery230The AAO-HNS Task Force principles underscore the importance of otolaryngologists' involvement in AI's ethical development, implementation, and regulation to harness benefits while mitigating risks, and inform the integration of AI in otolaryngology.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery["Noel F. Ayoub", "Ana\u00efs Rameau", "Michael J Brenner", "A. Bur", "Gregory A Ator", "Selena E. Briggs", "Masayoshi Takashima", "Konstantina M. Stankovic"]2024-12-12T00:00:00 80e2ff075cf66e62eb01fde0ea9fab677c1ea4b0Background: The potential of generative artificial intelligence (genAI) tools, such as ChatGPT, is being increasingly explored in healthcare settings. However, the same tools also introduce significant cybersecurity risks that could compromise patient safety, data integrity, and institutional trust. This study aimed to examine real-world security breaches involving genAI and extrapolate their potential implications for healthcare settings. Methods: Using a systematic Google News search and a consensus-based approach among the authors, five high-profile genAI breaches were identified and analyzed. These cases included: (1) Data exposure in ChatGPT (OpenAI) due to an open-source library bug (March 2023); (2) Unauthorized data disclosure via Samsung’s (Samsung Group) use of ChatGPT (2023); (3) Logical vulnerabilities in Chevrolet (General Motors) AI-powered chatbot resulting in pricing errors (December 2023); (4) Prompt injection vulnerability in Vanna AI (Vanna AI, Inc.) which enabled remote code execution (2024); and (5) the deepfake technology used in a scam targeting the engineering firm Arup (Arup Group Limited), leading to fraudulent transactions (February 2024). Hypothetical healthcare scenarios were constructed based on the five cases, mapping their mechanisms to vulnerabilities in electronic health records (EHRs), clinical decision support systems (CDSS), and patient engagement platforms. Each case was analyzed using the Confidentiality, Integrity, and Availability (CIA) triad of information security to systematically identify vulnerabilities and propose actionable safeguards. Results: The analyzed cases of AI security breaches revealed significant risks to healthcare systems. Confidentiality violations included the potential exposure of sensitive patient records and billing information, extrapolated from incidents such as the ChatGPT data exposure and Samsung’s cases. These identified security breaches raised concerns about privacy violations, identity theft, and non-compliance with regulations such as Health Insurance Portability and Accountability Act (HIPAA). Integrity vulnerabilities were highlighted in Vanna AI's prompt injection flaw incident, with risks of altering patient records, compromising diagnostic algorithms, and misleading CDSS with erroneous recommendations. Similarly, logic errors identified in the Chevrolet case exposed potential risks of inaccurate billing, double-booked appointments, and flawed treatment plans within healthcare contexts. Availability disruptions, observed through system outages and operational suspensions following breaches like the ChatGPT and deepfake cases, can delay access to EHR systems or AI-driven CDSS. Such interruptions would directly impact patient care and create inefficiencies in administrative workflows. Conclusions: Generative AI presents a double-edged sword in healthcare, with transformative potential accompanied by substantial risks. Extrapolation of security breach cases in this study highlighted the urgent need for robust safeguards if genAI is implemented in healthcare settings. To address these vulnerabilities, healthcare institutions must implement strong security protocols, enforce strict data governance, and create AI-specific incident response plans. The balance between genAI-enabled innovation and protection of patient safety and data integrity trust requires proactive safety measures.Mesopotamian Journal of Artificial Intelligence in Healthcare810Examination of real-world security breaches involving genAI raised concerns about privacy violations, identity theft, and non-compliance with regulations such as Health Insurance Portability and Accountability Act (HIPAA).Mesopotamian Journal of Artificial Intelligence in Healthcare["Malik Sallam", "Kholoud Al-Mahzoum", "Mohammed Sallam"]2024-12-12T00:00:00 e897d8a69c0efe0f40099463da7ac4fda6c97a4fSuicides and suicidal behaviors are complex disorders with diverse symptoms, often lacking clear etiology, especially in spontaneous or childhood cases. This complicates timely diagnosis, therapy, and treatment. As a result, research into markers for depression and suicidal behavior continues. The use of artificial intelligence represents a significant advancement in suicide prevention, offering new tools for early detection and intervention to improve outcomes for at-risk individuals. According to the World Health Organization (WHO), 726,000 people commit suicide, not counting the much larger number of people who attempt suicide each year. Suicides occur throughout life, but in 2021 they became one of the leading causes of death among 15-29 year-olds worldwide. This problem is also relevant in Kazakhstan, and this article is the first to reflect an interdisciplinary approach to suicide prevention among minors using AI methods in application to scientific data obtained in the study of respondents with suicidal behavior.  Suicide is a significant public health issue with profound societal impacts. Its effects extend beyond the loss of life, leading to emotional suffering for families and loved ones, and economic losses from reduced productivity and increased healthcare costs. For each suicide, there are over 30 attempted suicides, compounding the social and economic burden. The repercussions affect countless individuals, both directly and indirectly, leaving long-lasting emotional and financial strain. Additionally, the economic impact includes treatment costs for psychosomatic and mental disorders in those left behind, highlighting the extensive and multifaceted consequences of suicidal behavior.Journal of Clinical Medicine of Kazakhstan00An interdisciplinary approach to suicide prevention among minors using AI methods in application to scientific data obtained in the study of respondents with suicidal behavior is reflected.Journal of Clinical Medicine of Kazakhstan["K. Saduakassova", "M. Zhanuzakov", "G. Kassenova", "Vassiliy Serbin"]2024-12-12T00:00:00 ce3bd2cce88a00008447ba49d2b5bfc18851198eINTRODUCTION The introduction of generative artificial intelligence (AI) may have a profound effect on residency applications. In this study, we explore the abilities of AI-generated letters of recommendation (LORs) by evaluating the accuracy of orthopaedic surgery residency selection committee members to identify LORs written by human or AI authors. METHODS In a multicenter, single-blind trial, a total of 45 LORs (15 human, 15 ChatGPT, and 15 Google BARD) were curated. In a random fashion, seven faculty reviewers from four residency programs were asked to grade each of the 45 LORs based on the 11 characteristics outlined in the American Orthopaedic Associations standardized LOR, as well as a 1 to 10 scale on how they would rank the applicant, their desire of having the applicant in the program, and if they thought the letter was generated by a human or AI author. Analysis included descriptives, ordinal regression, and a receiver operator characteristic curve to compare accuracy based on the number of letters reviewed. RESULTS Faculty reviewers correctly identified 40% (42/105) of human-generated and 63% (132/210) of AI-generated letters (P < 0.001), which did not increase over time (AUC 0.451, P = 0.102). When analyzed by perceived author, letters marked as human generated had significantly higher means for all variables (P = 0.01). BARD did markedly better than human authors in accuracy (3.25 [1.79 to 5.92], P < 0.001), adaptability (1.29 [1.02 to 1.65], P = 0.034), and perceived commitment (1.56 [0.99 to 2.47], P < 0.055). Additional analysis controlling for reviewer background showed no differences in outcomes based on experience or familiarity with the AI programs. CONCLUSION Faculty members were unsuccessful in determining the difference between human-generated and AI-generated LORs 50% of the time, which suggests that AI can generate LORs similarly to human authors. This highlights the importance for selection committees to reconsider the role and influence of LORs on residency applications.Journal of the American Academy of Orthopaedic Surgeons260Faculty members were unsuccessful in determining the difference between human-generated and AI-generated LORs 50% of the time, which suggests that AI can generate LORs similarly to human authors.The Journal of the American Academy of Orthopaedic Surgeons["Sam Simister", "Eric G. Huish", "Eugene Y Tsai", "Hai V Le", "Andrea Halim", "Dominick A Tuason", "John P. Meehan", "Holly Leshikar", "Augustine M Saiz", "Zachary C Lum"]2024-12-12T00:00:00 76b1beeaeefbf0d6e804bdb1f8a38985b6f5217cThere is a rapid spread of artificial intelligence applications (AIA hereafter) worldwide resulting in their intervention in various fields, yet electronic management is not exceptional. This study illustrated the role of AIAs in the electronic management of public facilities in the government sector. In order to obtain rich data, the researcher employed a descriptive, analytical, and legal interpretive methodology. The main result showed that the administrative staff had a limited ability to deal with modern technology. It also indicated that AIAs might be used within open-source software or social networks to infringe upon the privacy of individuals and institutions.Journal of Ecohumanism00The main result showed that the administrative staff had a limited ability to deal with modern technology and indicated that AIAs might be used within open-source software or social networks to infringe upon the privacy of individuals and institutions.Journal of Ecohumanism["Hamdn Ghunemat", "Musaab Faraj Mahdi", "A. Arabiyyat"]2024-12-12T00:00:00 57e06e2d85f9a013ed1e2fe325981e7f08494460Artificial intelligence (AI) has emerged as a transformative force in enhancing patient safety within hospital settings. This perspective explores the various applications of AI in improving patient outcomes, including early warning systems, predictive analytics, process automation, and personalized treatment. We also highlight the economic benefits associated with AI implementation, such as cost savings through reduced adverse events and improved operational efficiency. Moreover, the perspective addresses how AI can enhance pharmacological treatments, optimize diagnostic testing, and mitigate hospital-acquired infections. Despite the promising advancements, challenges related to data quality, ethical concerns, and clinical integration remain. Future research directions are proposed to address these challenges and harness the full potential of AI in healthcare.Hospitals650This perspective explores the various applications of AI in improving patient outcomes, including early warning systems, predictive analytics, process automation, and personalized treatment, and highlights the economic benefits associated with AI implementation.Hospitals["Francisco Epelde"]2024-12-12T00:00:00 e111b2eaccb3323e0c29d3759030b1bfb3f3c471Artificial intelligence technologies are rapidly developing and having a major impact on the business world. Decision-making processes play an important role for the success of an organization. However, in today’s business world with its complexity and uncertainty, it becomes difficult to manage decision-making processes. At this point, creating future scenarios supported by artificial intelligence and working on different scenarios helps businesses to be more prepared for uncertainty. Artificial intelligence-supported scenarios can be utilized across various sectors and fields of work. AI enables businesses to analyze past data, predict trends, and consequently work on future scenarios to make more informed decisions. The significance of future scenarios lies in identifying risks and opportunities in advance, adapting to future changes, and being proactive in competition. By evaluating potential developments, shaping your business strategy, you can gain a competitive advantage and make more reliable decisions. Qualitative methods were employed in the research. Interviews were conducted with managers from 6 different professional groups (software, biomedical, public, construction, university, e-commerce). Data was collected and analyzed using semi-structured interview forms consisting of 4 questions. When the findings were evaluated, no concerns or negative expressions regarding the use of artificial intelligence were expressed. Except for public institutions, everyone has AI in their planning. Each sector believes it is important. No negative concerns were expressed. The prominent concepts in the findings are: Speed, big data, gaining competitive advantage, personalized customer experience, risk analysis, cost advantage, technology adaptation, optimization, accurate and fast situation detection, efficiency, etc. It is thought that the research will create significant awareness for businesses in the turbulent period of the 21st century, where uncertainties are greater than ever. Despite all the positive aspects, AI-supported decision-making processes also carry some risks. The most prominent risks include the applicability of AI-supported scenarios, security concerns, the existence of ill-trained AI models, ethical issues and data privacy.International Journal of Current Science Research and Review00It is thought that the research will create significant awareness for businesses in the turbulent period of the 21st century, where uncertainties are greater than ever.International Journal of Current Science Research and Review["Mehmet Fatih Kano\u011flu"]2024-12-12T00:00:00 aef67220304e6306867b975b91973e98a0f729e8In response to the arrival of advanced artificial intelligence (AI) in the form of ChatGPT, this study examines its potential for providing feedback to foreign language writers. This represents a more acceptable use of AI in the writing classroom, rather than students simply using AI to write their entire essay. The methodological procedure involved eliciting normal classroom writing-practice essays from 29 English major students at a Saudi university, with ChatGPT (2023) then given a simple prompt requesting feedback. Both the essays and the feedback were qualitatively analysed to respond to research questions concerning the feedback’s consistency and credibility, and the extent to which it represented the different potential feedback types, based on a review of the extensive literature on the subject. Although superficially impressive, close examination revealed certain weaknesses to the AI feedback. For example, there was inconsistency in how the feedback was handled across essays, and some statements were not fully accurate regarding the respective text. In focus, the feedback was primarily accuracy-oriented, while even-handed in attention to content, organisation, and lower-level language matters, providing both positive and negative comments. However, there was a paucity of message-oriented communicative and explicit affective feedback. Like many teachers, ChatGPT was selective in terms of the feedback provided, but the decisions of what to address did not seem altogether motivated by criteria that an expert human feedback provider would consider. The main conclusion is that while AI feedback on writing practice is useful, it does require human monitoring by a teacher.World Journal of English Language00It is found that while AI feedback on writing practice is useful, it does require human monitoring by a teacher, rather than students simply using AI to write their entire essay.World Journal of English Language["Manal Saleh M. Alghannam"]2024-12-12T00:00:00 cd654265c01a371b481b47574174bb1bb4d2baa0 Electrocardiography (ECG) remains a cornerstone of non-invasive cardiac diagnostics, yet manual interpretation poses challenges due to its complexity and time consumption. The integration of Artificial Intelligence (AI), particularly through Deep Learning (DL) models, has revolutionized ECG analysis by enabling automated, high-precision diagnostics. This review highlights the recent advancements in AI-driven ECG applications, focusing on arrhythmia detection, abnormal beat classification, and the prediction of structural heart diseases. AI algorithms, especially convolutional neural networks (CNNs), have demonstrated superior accuracy compared to human experts in several studies, achieving precise classification of ECG patterns across multiple diagnostic categories. Despite the promise, real-world implementation faces challenges, including model interpretability, data privacy concerns, and the need for diversified training datasets. Addressing these challenges through ongoing research will be crucial to fully realize AI’s potential in enhancing clinical workflows and personalizing cardiac care. AI-driven ECG systems are poised to significantly advance the accuracy, efficiency, and scalability of cardiac diagnostics. Annals of Medicine &amp; Surgery00This review highlights the recent advancements in AI-driven ECG applications, focusing on arrhythmia detection, abnormal beat classification, and the prediction of structural heart diseases.Annals of Medicine &amp; Surgery["S. N. Qayyum", "Muhammad Iftikhar", "Muhammad Rehan", "Gulmeena Aziz Khan", "Maleeka Khan", "Risha Naeem", "R. S. Ansari", "Irfan Ullah", "Samim Noori"]2024-12-12T00:00:00 9843b1b20df0c87658f1509131a5b881baf59f3cPenggunaan Artificial Intelligence (AI) dalam pendidikan, khususnya dalam pembelajaran pemrograman merupakan kebutuhan yang potensial karena AI dapat mendorong pembelajaran adaptif dan interaktif. Permasalahan yang sering ditemukan dalam pembelajaran pemrograman adalah siswa kesulitan dalam membuat algoritma dan problem solving error. Dengan permasalahan tersebut maka perlu dilakukan upaya peningkatan keterampilan  siswa untuk penggunaan AI dalam bentuk Edukasi. Tujuan Pengadian Kepada Masyarakat (PkM) ini adalah untuk memberikan edukasi dalam menggunakan AI untuk meningkatkan pemahaman siswa terhadap konsep pemrograman dan partisipasi dalam proses pembelajaran. PkM ini menggunakan metode yang terdiri dari 3 tahap diantaranya; Persiapan, Pelaksanaan dan Evaluasi. Pelaksanaan PkM ini diberikan kepada siwa kelas XI di SMK N 2 Kota Padang. Hasil PkM menunjukkan bahwa  pemanfaatan AI dapat meningkatkan pemahaman konseptual, mempercepat deteksi dan koreksi error, serta memberikan umpan balik yang disesuaikan dengan kebutuhan pembelajaran individu. Hasil PkM ini menunjukkan bahwa siswa telah mampu menerapkan AI yang sangat efektif dan memudahkan dalam pembelajaran pemrograman.Jurnal Pengabdian Masyarakat dan Penerapan Ilmu Pengetahuan00Jurnal Pengabdian Masyarakat dan Penerapan Ilmu Pengetahuan["Haris Kurniawan", "Satrio Junaidi", "Rada Puspita Wahyuni", "Meriza Cahyani"]2024-12-12T00:00:00 b2c8c4f1de3a8e0d165a9b9028431bfbf752fe70 How do countries narrate their values and priorities in artificial intelligence infrastructures in comparative national and global contexts? This paper analyzes the policies governing national and regional artificial intelligence infrastructures to advance an understanding of “entangled narratives” in global affairs. It does so by utilizing artificial intelligence techniques that assist with generalizability and model building without sacrificing granularity. In particular, the machine learning and natural language processing big data models used alongside some process-tracing demonstrate the ways artificial intelligence infrastructural plans diverge, cluster, and transform along several topical dimensions in comparative contexts. The paper's entangled narrative approach adds to international relations (IR) theorizing about infrastructural narratives and technological diffusion. We provide patterned and granular results at various levels, which challenge and refine existing theories that attribute differences in infrastructures and technological adoption to geopolitical competition and imitation, top-down or linear international diffusion effects, and differences in political systems.International Studies Quarterly440The policies governing national and regional artificial intelligence infrastructures are analyzed to advance an understanding of “entangled narratives” in global affairs by utilizing artificial intelligence techniques that assist with generalizability and model building without sacrificing granularity.International Studies Quarterly["J. P. Singh", "Amarda Shehu", "Manpriya Dua", "Caroline F. Wesson"]2024-12-12T00:00:00 e1f6ed47799703cedab8f7e7a021a21c420b33bdArtificial Intelligence (AI) refers to the capability of technology, particularly computer systems, to simulate human intelligence processes. AI is an evolving field with the potential to transform various aspects of society. In the education sector, AI is being increasingly used to develop innovative teaching and learning strategies. By analyzing vast amounts of data, AI helps identify patterns and insights that can inform the creation of new educational policies and frameworks. This paper aims to explore the role of AI in education, highlighting its significance and the challenges associated with its integration. Additionally, the study examines AI in the context of the National Education Policy (NEP) 2020. The research is qualitative, based on a review of relevant literature on AI.International Journal For Multidisciplinary Research70The role of AI in education is explored, highlighting its significance and the challenges associated with its integration, and the study examines AI in the context of the National Education Policy (NEP) 2020.International Journal For Multidisciplinary Research["Dr. Neeta Bendre", "Prof. Yash Mallaya"]2024-12-12T00:00:00 00cee78f9a064b51566f260dbbb6e02d5483a044This paper explores in depth the changes in the mode of oil painting creation and appreciation under the perspective of artificial intelligence technology and its multidimensional impact. It analyzes the transformation of the creation mode from the combination of traditional painting and AI to the automated creation by AI, as well as the expansion of the subject matter of creation in terms of cultural traditions and cross-boundary imagination; elaborates on the changes of aesthetic concepts in the mode of appreciation due to the characteristics of the AI works, and the innovation of the appreciation mode realized with the help of AI; and discusses the reexamination of the artistic value in terms of originality and authenticity, and the opportunities brought by cultural inheritance and innovation due to the AI. The opportunities for cultural heritage and innovation brought about by AI. Through the study of these aspects, we will provide a comprehensive understanding and thinking direction for the development of oil painting in the era of artificial intelligence.International Journal of Finance and Investment110This paper analyzes the transformation of the creation mode from the combination of traditional painting and AI to the automated creation by AI, as well as the expansion of the subject matter of creation in terms of cultural traditions and cross-boundary imagination.International Journal of Finance and Investment["Yulong Shi", "D. Tsetsegdelger"]2024-12-12T00:00:00 8c370f53d406a5d6d2e2d8768a3ffadf02f1e967European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery20Sites and artificial intelligence providing patient education material regarding myringotomy tubes are of "fair" quality but have readability levels above the recommended 6th grade level, and Google search results were superior to artificial intelligence in readability.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery["Melissa Papuc", "Patrick Scheffler"]2024-12-12T00:00:00 82948bf19083a6a0de221c59a379e291a5ef5c3bThe aim of the study is to explore the development aspects of competitive intelligence (CI) in terms of the challenges posed by the increase in data volume, incl. unverified data, data reliability, and the integration of artificial intelligence (AI) into data analysis processes. The primary research question explores how the role of the human factor has become increasingly important in ensuring the accuracy and reliability of data used by AI, especially in an era dominated by AI and rapid information dissemination. The study highlights the imperative role of human judgment in the era of AI-driven data analysis, highlighting skills, competencies, and authority as critical factors in evaluating data processing outcomes. This points to the risks associated with the uncritical adoption of AI-generated solutions, which can lead to innovative but impractical outcomes that consume significant organizational resources. Furthermore, the study calls for a balanced approach to integrating AI into CI processes, supporting strategies that enhance the synergy between human analytical prowess and AI computational efficiency. This approach is critical to overcoming the challenges posed by data reliability and ensuring the effective implementation of CI strategies that are both innovative and grounded in reality.Journal of Intelligence Studies in Business50The study highlights the imperative role of human judgment in the era of AI-driven data analysis, highlighting skills, competencies, and authority as critical factors in evaluating data processing outcomes and calls for a balanced approach to integrating AI into CI processes.Journal of Intelligence Studies in Business["Andrejs Cekuls"]2024-12-12T00:00:00 2525017ab845fd792f20898a91c357679ddd38f8International Journal of Big Data Intelligent Technology00International Journal of Big Data Intelligent Technology[]2024-12-12T00:00:00 4528306bde4ef8e687f73098afee88165a25fa34Asian Journal of Dental Sciences00Asian Journal of Dental Sciences["Arjita Dutta", "Rohit Kumar Singh", "Rekha Revanna", "P. Ramanna"]2024-12-12T00:00:00 de0f950b10eb099a4d33c493addb73c8dd3416c4Cogent Education160Cogent Education["Nuria Chaparro\u2010Banegas", "Alicia Mas-Tur", "N. Roig-Tierno"]2024-12-12T00:00:00 d9bbbb3d16052c3bb7b892e2526a557e1d51f392There is an increasing likelihood of job replacement by AI over time, with significant implications for workforce planning and policy development. Women and lower-skilled workers face disproportionately higher exposure to AI replacement. Office and administrative roles exhibit the highest exposure levels, while roles requiring complex problem-solving, interpersonal skills, or human interaction remain less affected.00["Miguel Benitez-Rueda", "Eric Parrado"]2024-12-12T00:00:00 fb201acf12b2cafdbdf85ed3cd9b7a8bb0f7176bInternational journal of hematology180The results underscore the need to improve strategies to raise awareness and knowledge of stem cell donation among the Italian population and underscore the need to raise awareness and knowledge of stem cell donation among the Italian population.International journal of hematology["Luana Conte", "G. De Nunzio", "R. Lupo", "D. Cascio", "M. Cioce", "Elsa Vitale", "Chiara Ianne", "Ivan Rubbi", "Massimo Martino", "Letizia Lombardini", "Aurora Vassanelli", "S. Pupella", "S. Pollichieni", "N. Sacchi", "Fabio Ciceri", "S. Botti"]2024-12-12T00:00:00 f72781d4bcb5f08f65b7af0081cd89636635aa51This study evaluates machine learning models for stock market prediction in the European stock market EU50, with emphasis on the integration of key technical indicators. Advanced techniques, such as ANNs, CNNs and LSTMs, are applied to analyze a large EU50 dataset. Key indicators, such as the simple moving average (SMA), exponential moving average (EMA), moving average convergence/divergence (MACD), stochastic oscillator, relative strength index (RSI) and accumulation/distribution (A/D), were employed to improve the model’s responsiveness to market trends and momentum shifts. The results show that CNN models can effectively capture localized price patterns, while LSTM models excel in identifying long-term dependencies, which is beneficial for understanding market volatility. ANN models provide reliable benchmark predictions. Among the models, CNN with RSI obtained the best results, with an RMSE of 0.0263, an MAE of 0.0186 and an R2 of 0.9825, demonstrating high accuracy in price prediction. The integration of indicators such as SMA and EMA improves trend detection, while MACD and RSI increase the sensitivity to momentum, which is essential for identifying buy and sell signals. This research demonstrates the potential of machine learning models for refined stock prediction and informs data-driven investment strategies, with CNN and LSTM models being particularly well suited for dynamic price prediction.Mathematics290The results show that CNN models can effectively capture localized price patterns, while LSTM models excel in identifying long-term dependencies, which is beneficial for understanding market volatility.Mathematics["Javier Parra-Dom\u00ednguez", "Laura Sanz-Mart\u00edn"]2024-12-12T00:00:00 7be36cc97e61772c8b784081bd0c6c6cecb39e1eWith the rapid development of AI technology, generative AI has triggered significant risks and challenges in the ideological field. This paper provides an in-depth analysis of the real-life manifestations of these risks, including the problems of algorithmic control, data misdirection and content distortion, and explores their generative mechanisms, which are mainly attributed to the interpenetration of technology and power, the Western values of the designers, and the technological manipulation of capital. These risks may not only lead to the loss of discourse power and a crisis of value identity, but also exacerbate the confrontation and conflict between different ideologies, posing a threat to national political security and social stability. To cope with these risks, this paper proposes strategies such as strengthening regulation, shaping mainstream values, and enhancing the public's intelligent literacy, aiming to safeguard China's ideological security and promote the modernization of the system and capacity of ideological risk governance.Journal of Education, Humanities and Social Sciences170This paper proposes strategies such as strengthening regulation, shaping mainstream values, and enhancing the public's intelligent literacy, aiming to safeguard China's ideological security and promote the modernization of the system and capacity of ideological risk governance.Journal of Education, Humanities and Social Sciences["Tongyang Zheng"]2024-12-12T00:00:00 260e2fc5aeab55ca13e590a3eb1f4ca6a08fa4f7A reasonable driving behavior decision model can choose the appropriate driving behavior according to the actual situation, thus improving the safety and efficiency of driving. To achieve an intelligent and humanized driving experience, this study explores the decision‐making process behind driving behaviors. We have established a decision‐making model for driving behaviors rooted in the finite state machine (FSM) paradigm. This model selects the most suitable driving action based on the car's current state, the surrounding environment, and the driver's intention. Given the intricate and varied nature of driving behaviors, we have incorporated a deep reinforcement learning (DRL) algorithm. This enables the optimization of decision‐making strategies through dynamic interactions between the driver and the environment. Our findings reveal that this model adeptly handles complexities in real‐world driving scenarios, thereby enhancing driving safety. In automotive contexts, FSM ensures the selection of apt driving actions aligned with the vehicle's status, environmental cues, and the driver's intentions. This innovative model surpasses traditional decision‐making frameworks, paving the way for advancements in intelligent driving technology, and demonstrating remarkable adaptability and potential for further optimization. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING260A decision‐making model for driving behaviors rooted in the finite state machine (FSM) paradigm is established that surpasses traditional decision‐making frameworks, paving the way for advancements in intelligent driving technology, and demonstrating remarkable adaptability and potential for further optimization.IEEJ Transactions on Electrical and Electronic Engineering["Haibo Yan"]2024-12-12T00:00:00 3ac9457c76b058aa65aeeafe93da55066790fd4cEducation and Information Technologies : Official Journal of the IFIP technical committee on Education350Education and Information Technologies["Dongxuan Wang", "Yu Liu", "Xin Jing", "Qi Liu", "Qingjiao Lu"]2024-12-12T00:00:00 6a7512cf04fc0369053d806db9a49686f34bc117Journal of Nuclear Medicine50Journal of nuclear medicine : official publication, Society of Nuclear Medicine["D. G. Kovacs", "C. Ladefoged", "Jacob Bak Rosenkj\u00e6r", "Fatima Mawassi", "Line Akiti Hingelberg", "Anna Olga Aaskilde Laursen", "B. M. Fischer", "F. Andersen"]2024-12-12T00:00:00 d1f2fed2dc635679d7bd18f526974215af85b249International Journal of Medical Arts00International Journal of Medical Arts["A. Al-Adl"]2024-12-12T00:00:00 4a0c5e152875aa2e058b9f26c24057950c7e4e0aUnited European Gastroenterology journal50United European gastroenterology journal["Cem \u015eim\u015fek", "Nasim Parsa", "L. Kunovsk\u00fd"]2024-12-12T00:00:00 03400d9c2a6b6eccd12c5b5983c2145edb9891a2International Journal of Computer Assisted Radiology and Surgery00International journal of computer assisted radiology and surgery["Atsuhisa Fukuta", "Shogo Yamashita", "Junnosuke Maniwa", "Akihiko Tamaki", "T. Kondo", "N. Kawakubo", "Kouji Nagata", "T. Matsuura", "Tatsuro Tajiri"]2024-12-12T00:00:00 cb09d0992bc04f66f0757dfccd979190903c9468Artificial intelligence (AI) is now an integral part of the digital transformation of various industries including the maritime sector. In the context of maritime law, AI has the potential to optimize various aspects ranging from maritime traffic management, Accident risk mitigation to monitoring international environmental maintenance. However, the application of AI also presents a variety of very complex challenges. This research highlights the legal, regulatory and ethical challenges that arise along with the application of AI in the maritime sector. Among them are the lack of clarity regarding legal responsibility for incidents involving autonomous ships, the lack of international standards governing the use of AI in operations maritime, as well as threats to data privacy and security from the use of increasingly sophisticated technology. Apart from these challenges, This research also discusses innovations that are already developing, such as the development of new legal frameworks for maritime AI, blockchain integration technology in the supply cycle chain as well as cross-border collaboration to developing regulatory standards that are cohesive and responsive to technological developments, this research concludes that to ensure the safe and responsible implementation of AI in the maritime sector, a multidisciplinary approach involving law, technology and international cooperation is needed, AI can be the main catalyst in creating a safer, more efficient and sustainable maritime ecosystem in the future.Desentralisasi : Jurnal Hukum, Kebijakan Publik, dan Pemerintahan00To ensure the safe and responsible implementation of AI in the maritime sector, a multidisciplinary approach involving law, technology and international cooperation is needed, and AI can be the main catalyst in creating a safer, more efficient and sustainable maritime ecosystem in the future.Desentralisasi : Jurnal Hukum, Kebijakan Publik, dan Pemerintahan["Nurul wahdatulnisa", "Imam Fadhil Nugraha"]2024-12-12T00:00:00 2d8049e5056edb8c1aa1285a32f94b225739bd65The integration of artificial intelligence (AI) in tax administration represents a transformative technological shift in public finance management. This paper synthesizes recent research on AI applications in taxation, examining technological developments from 2014 to 2024. The analysis reveals significant advancements in compliance monitoring, fraud detection, and policy implementation through AI-enabled systems. Machine learning algorithms, blockchain technology, and natural language processing have enhanced tax authorities' capabilities in risk assessment, audit selection, and taxpayer service delivery. While these technologies demonstrate substantial benefits in administrative efficiency and compliance enforcement, they also present challenges in data privacy, system security, and cross-border coordination. The study identifies critical research gaps, particularly in long-term impact assessment, cross-cultural implementation, and the integration of emerging AI technologies. Future research directions should focus on developing robust governance frameworks, improving system transparency, and addressing the evolving needs of digital economy taxation. This comprehensive analysis provides valuable insights for researchers, practitioners, and policymakers working at the intersection of artificial intelligence and taxation.International Journal of Finance and Investment336This paper synthesizes recent research on AI applications in taxation, examining technological developments from 2014 to 2024 and identifies critical research gaps, particularly in long-term impact assessment, cross-cultural implementation, and the integration of emerging AI technologies.International Journal of Finance and Investment["Mengdie Wang"]2024-12-12T00:00:00 ca17eaca64c23407f851f3a4d5ce92253c91596cAbstract Endometriosis affects over 190 million women globally, and effective therapies are urgently needed to address the burden of endometriosis on women's health. Using an artificial intelligence (AI)‐driven target discovery platform, two unreported therapeutic targets, guanylate‐binding protein 2 (GBP2) and hematopoietic cell kinase (HCK) are identified, along with a drug repurposing target, integrin beta 2 (ITGB2) for the treatment of endometriosis. GBP2, HCK, and ITGB2 are upregulated in human endometriotic specimens. siRNA‐mediated knockdown of GBP2 and HCK significantly reduced cell viability and proliferation while stimulating apoptosis in endometrial stromal cells. In subcutaneous and intraperitoneal endometriosis mouse models, siRNAs targeting GBP2 and HCK notably reduced lesion volume and weight, with decreased proliferation and increased apoptosis within lesions. Both subcutaneous and intraperitoneal administration of Lifitegrast, an approved ITGB2 antagonist, effectively suppresses lesion growth. Collectively, these data present Lifitegrast as a previously unappreciated intervention for endometriosis treatment and identify GBP2 and HCK as novel druggable targets in endometriosis treatment. This study underscores AI's potential to accelerate the discovery of novel drug targets and facilitate the repurposing of treatment modalities for endometriosis.Advancement of science581AI's potential to accelerate the discovery of novel drug targets and facilitate the repurposing of treatment modalities for endometriosis is underscored.Advanced Science["Bonnie Hei Man Liu", "Yuezhen Lin", "Xi Long", "S. Hung", "Anna Gaponova", "Feng Ren", "Alex Zhavoronkov", "Frank W. Pun", "C. Wang"]2024-12-12T00:00:00 15c942d3ba618407f155a460fa63925fa97e1615Recent developments in Generative Artificial Intelligence (GenAI) have created significant uncertainty in education, particularly in terms of assessment practices. Against this backdrop, we present an updated version of the AI Assessment Scale (AIAS), a framework with two fundamental purposes: to facilitate open dialogue between educators and students about appropriate GenAI use and to support educators in redesigning assessments in an era of expanding AI capabilities. Grounded in social constructivist principles and designed with assessment validity in mind, the AIAS provides a structured yet flexible approach that can be adapted across different educational contexts. Building on implementation feedback from global adoption across both the K-12 and higher education contexts, this revision represents a significant change from the original AIAS. Among these changes is a new visual guide that moves beyond the original traffic light system and utilises a neutral colour palette that avoids implied hierarchies between the levels. The scale maintains five distinct levels of GenAI integration in assessment, from"No AI"to"AI Exploration", but has been refined to better reflect rapidly advancing technological capabilities and emerging pedagogical needs. This paper presents the theoretical foundations of the revised framework, provides detailed implementation guidance through practical vignettes, and discusses its limitations and future directions. As GenAI capabilities continue to expand, particularly in multimodal content generation, the AIAS offers a starting point for reimagining assessment design in an era of disruptive technologies.01The theoretical foundations of the revised AI Assessment Scale (AIAS) are presented, detailed implementation guidance through practical vignettes is provided, and its limitations and future directions are discussed.["Mike Perkins", "Jasper Roe", "Leon Furze British University Vietnam", "Vietnam", "James Cook University Singapore", "Singapore", "Deakin University", "Australia"]2024-12-12T00:00:00 1818d4b683e223d671dcbf3ca901aa40464fa4a7PurposeGenerative artificial intelligence (GenAI) is one of the most diffused AI technologies, capable of generating manifold forms of content, including music, text, images and synthetic data. The purpose of this study is to analyze the determinants that affect GenAI acceptance and its outcomes on both the explorative and exploitative forms of innovation.Design/methodology/approachThe study employs a conceptual framework based on the technology-organization-environment (TOE) paradigm. Through Smart-PLS analysis, it examines empirical data retrieved from an online survey where 302 manufacturing companies took part.FindingsIt is found that GenAI has the potential to facilitate both exploratory and exploitative innovation, particularly via the moderating effect of environmental dynamism. Hence the adoption of GenAI has potential to improve organizational performance.Originality/valueThe study is the first empirical project to investigate factors that influence manufacturing firms' adoption of GenAI. As the first project to have integrated the TOE paradigm when examining the impact of environmental dynamism on exploratory and exploitative innovation, the study emphasizes the double innovation potential of GenAI in organizational performance improvement.Journal of Manufacturing Technology Management1021It is found that GenAI has the potential to facilitate both exploratory and exploitative innovation, particularly via the moderating effect of environmental dynamism, Hence the adoption of GenAI has potential to improve organizational performance.Journal of Manufacturing Technology Management["Salman Khan", "S. Mehmood", "Safeer Ullah Khan"]2024-12-12T00:00:00 f9828d6995fcb6a7951be2c954feef28f56c7fa5 Purpose The purpose of this study is to examine the effect of trust on user adoption of artificial intelligence-generated content (AIGC) based on the stimulus–organism–response. Design/methodology/approach The authors conducted an online survey in China, which is a highly competitive AI market, and obtained 504 valid responses. Both structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) were used to conduct data analysis. Findings The results indicated that perceived intelligence, perceived transparency and knowledge hallucination influence cognitive trust in platform, whereas perceived empathy influences affective trust in platform. Both cognitive trust and affective trust in platform lead to trust in AIGC. Algorithm bias negatively moderates the effect of cognitive trust in platform on trust in AIGC. The fsQCA identified three configurations leading to adoption intention. Research limitations/implications The main limitation is that more factors such as culture need to be included to examine their possible effects on trust. The implication is that generative AI platforms need to improve the intelligence, transparency and empathy, and mitigate knowledge hallucination to engender users’ trust in AIGC and facilitate their adoption. Originality/value Existing research has mainly used technology adoption theories such as unified theory of acceptance and use of technology to examine AIGC user behaviour and has seldom examined user trust development in the AIGC context. This research tries to fill the gap by disclosing the mechanism underlying AIGC user trust formation. Electronic library550The results indicated that perceived intelligence, perceived transparency and knowledge hallucination influence cognitive trust in platform, whereas perceived empathy influences affective trust in platform, and generative AI platforms need to improve the intelligence, transparency and empathy to engender users’ trust in AIGC and facilitate their adoption.The Electronic Library["Tao Zhou", "Hailin Lu"]2024-12-12T00:00:00 3bda1d4ff9451fb803037918d822d09be166575aThe rapid integration of artificial intelligence (AI) into clinical practice prompts a critical re-examination of the roles of physicians and how we educate them. While AI promises unparalleled gains in accuracy and speed, and better management decisions and health outcomes, doctors must be skilled in harnessing these new AI tools effectively and wisely to improve patient outcomes. We seek to layer further upon this with a call for medical education to go further than simply improving AI literacy of doctors, but to include a comprehensive reform of medical education. This reform would aim to expand physician capabilities from the traditional cognitive knowledge of medicine to integrating AI competencies seamlessly, with a renewed focus on the humanistic aspects of medicine. We propose the Humanistic Medicine - AI-Enabled Education (HuMe-AiNE) framework, which includes the key components: (1) standardisation and individualisation of AI competencies; (2) integration of AI tools through the curriculum; (3) fostering critical thinking skills in integrating technological solutions with a humanistic approach to patient care; and (4) developing a professional identity that encompasses both technology-related and humanistic capabilities. The AI revolution provides an opportunity for developments to medical education—to train doctors to be both tech-enabled physicians and true humanists.Annals of the Academy of Medicine, Singapore130The Humanistic Medicine - AI-Enabled Education (HuMe-AiNE) framework is proposed, which includes the key components: standardisation and individualisation of AI competencies; integration of AI tools through the curriculum; fostering critical thinking skills in integrating technological solutions with a humanistic approach to patient care.Annals of the Academy of Medicine, Singapore["Michelle Jong"]2024-12-12T00:00:00 16dd96f980a0ba33f23ed953f5dd2ffb625adbe9Introduction: Artificial intelligence (AI) has become a significant aspect of contemporary society, impacting various sectors, particularly education. Objective: This study aims to explore the implications and viewpoints regarding integrating AI in higher education in Ecuador. Methodology: An interpretive paradigm guided the methodology, influenced by hermeneutics and phenomenology; the research was qualitative, exploratory, and descriptive. Data collection was conducted through surveys, utilizing confirmatory factor analysis for statistical evaluation. Results: Findings indicate that the technological approach exhibits the highest loading at 46.8, marking it as the most significant factor influencing AI integration in higher education, whereas the pedagogical approach shows the lowest loading at 24.9, reflecting a moderate current influence on the teaching-learning process. This suggests ongoing efforts are needed for genuine inclusion and optimal utilization of AI in higher education. The z-value of 4.90 and a p-value below .001 further affirm the validity and significance of the four approaches examined in this study. Conclusion: Socioeconomic inequality in the country limits the equitable and accessible adoption of AI, mirroring challenges faced throughout Latin America and globally in emergent nations.Sapienza: International Journal of Interdisciplinary Studies00Socioeconomic inequality in the country limits the equitable and accessible adoption of AI, mirroring challenges faced throughout Latin America and globally in emergent nations.Sapienza: International Journal of Interdisciplinary Studies["Jessica Adriana Taipica\u00f1a Vergara", "Milton Fernando Hidalgo Achig", "Germanico Sinchiguano Molina", "Cristian Stalin Salguero N\u00fa\u00f1ez", "Nelson Rodrigo Chiguano Umajinga"]2024-12-12T00:00:00 947b2b9800a292024c6662f983885d0b00d7941aMachine Learning (ML) models have made significant advancements in medical sciences, yet their application in clinical practice is hindered by concerns over transparency and accountability. Explainable Artificial Intelligence (XAI) emerges as a solution to these challenges, offering interpretable insights into ML model predictions for clinicians. This research investigates the extent to which existing XAI techniques support decision-making in brain tumor detection for radiologists. A highly accurate ML model based on the VGG16 architecture was developed for detecting brain tumors from MRI images, achieving training and testing accuracy of 99.46% and 96.28%, respectively. A series of experiments were conducted integrating various XAI techniques. The study reveals, despite high model accuracy, false positives can mislead clinical decisions, especially when datasets contain multiple MRI sequences with conflicting diagnostic information. The findings, validated by expert radiologists, underscore the importance of using 3D imaging or at least consecutive 2D slices from the same sequence to improve diagnostic accuracy. The paper presents the experimental details highlighting the critical gap between 2D MRI slice-based diagnosis and the need for comprehensive 3D analysis. The research concludes with recommendations for improved dataset design and the integration of XAI to enhance the reliability and accountability of AI -driven healthcare diagnostic systems.International Conference on Automation and Computing110The study reveals, despite high model accuracy, false positives can mislead clinical decisions, especially when datasets contain multiple MRI sequences with conflicting diagnostic information, and underscores the importance of using 3D imaging or at least consecutive 2D slices from the same sequence to improve diagnostic accuracy.2024 6th International Conference on Advancements in Computing (ICAC)["Bhimaja Goonatillaka", "Prasanna Haddela"]2024-12-12T00:00:00 5b94778bfc94a7d94fc22b7d2533a545b98308bbIn the emergent and evolving landscape of artificial intelligence, decision support systems have a vital role in the choice-making functions, right from the health sector to finance. Whereas the new generation of traditional AI models is excellent at identifying correlations in big datasets, it often cannot discern whether true causal relations are driving an outcome. This then often results in misguided decisions that do not relate to the underpinning factors driving the outcomes. The present study focuses on the causal inference of AI-based decision support, moving beyond the correlations to identify causes using real-world datasets. We integrate some of the advanced techniques in causal modeling and show how AI systems can provide more accurate and actionable insights. We use case studies on healthcare patient outcomes and financial risk assessments to demonstrate improved decision-making insight developed from the understanding of causative factors. This will help make recommendations from AI more reliable and foster greater trust and accountability in automated decision-making processes. Embracing causal inference within AI finally opens the door to informed and effective strategy formulation, anchored in understanding rather than superficial associations of variables.2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)240This study uses case studies on healthcare patient outcomes and financial risk assessments to demonstrate improved decision-making insight developed from the understanding of causative factors, which will help make recommendations from AI more reliable and foster greater trust and accountability in automated decision-making processes.2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)["Mohan K Mannava"]2024-12-12T00:00:00 9ba2a17f7fec433f8bf0166cd75e705e50f57f1bThe paper addresses advancements in Generative Artificial Intelligence (GenAI) and digital chip design, highlighting the integration of Large Language Models (LLMs) in automating hardware description and design. LLMs, known for generating human-like content, are now being explored for creating hardware description languages (HDLs) like Verilog from natural language inputs. This approach aims to enhance productivity and reduce costs in VLSI system design. The study introduces"AiEDA", a proposed agentic design flow framework for digital ASIC systems, leveraging autonomous AI agents to manage complex design tasks. AiEDA is designed to streamline the transition from conceptual design to GDSII layout using an open-source toolchain. The framework is demonstrated through the design of an ultra-low-power digital ASIC for KeyWord Spotting (KWS). The use of agentic AI workflows promises to improve design efficiency by automating the integration of multiple design tools, thereby accelerating the development process and addressing the complexities of hardware design.arXiv.org00AiEDA is designed to streamline the transition from conceptual design to GDSII layout using an open-source toolchain and promises to improve design efficiency by automating the integration of multiple design tools, thereby accelerating the development process and addressing the complexities of hardware design.ArXiv["Aditya Patra", "Saroj Rout", "Arun Ravindran"]2024-12-12T00:00:00 ac7269c45d44fccae9dc51a98344c521e922c332Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to cognitive decline and memory loss, severely affecting millions worldwide. Early detection and accurate prediction of Alzheimer's are critical for timely interventions. This paper explores the application of Artificial Intelligence (AI) in predicting Alzheimer's disease, focusing on machine learning (ML) models, neural networks, and deep learning (DL) techniques. By analyzing a combination of neuroimaging data, genetic information, and cognitive test results, AI systems can identify subtle patterns and biomarkers that indicate the onset of AD even before the appearance of clinical symptoms. The paper discusses the integration of AI with brain imaging technologies, such as MRI and PET scans, as well as the role of natural language processing (NLP) in evaluating speech and text patterns. Key challenges such as data quality, interpretability, and the need for large, diverse datasets are also addressed. The potential for AI to enhance diagnostic accuracy and facilitate personalized treatment approaches in Alzheimer’s care is highlighted, along with future directions for research in this field. The results suggest that AI has the capacity to significantly improve early detection and intervention strategies, ultimately advancing the fight against Alzheimer's disease.International Research Journal on Advanced Engineering and Management (IRJAEM)150The application of Artificial Intelligence in predicting Alzheimer's disease, focusing on machine learning models, neural networks, and deep learning techniques, suggests that AI has the capacity to significantly improve early detection and intervention strategies, ultimately advancing the fight against Alzheimer's disease.International Research Journal on Advanced Engineering and Management (IRJAEM)["Rakhi Raghukumar", "Aswathi V Nair", "Amrutha Raju", "Aina S Dcruz", "Susheel George"]2024-12-12T00:00:00 7efab68667838b9a290d48b4eb11bd273a4f79d8The deep integration of Artificial Intelligence (AI) with Key Opinion Leader (KOL) marketing is ushering in a new era for brand strategies. In this transformation, AI not only serves as a powerful tool for data analysis, but also acts as a strategic navigator. By deeply mining big data, AI can precisely portray the target audience, helping enterprises select the most compatible and influential KOL partners from the vast pool. Additionally, the content creation landscape has also undergone an intelligent revolution, where AI generates creative and interactive content tailored to the unique style of KOLs, fan preferences, and market trends, significantly enhancing the efficiency and quality of content production. Regarding campaign performance analysis, AI's real-time feedback mechanism enables marketing teams to immediately grasp market dynamics and flexibly adjust promotion strategies, ensuring that every investment precisely reaches potential consumers and maximizes Return on Investment (ROI). However, the widespread application of this technology also poses challenges, such as data privacy protection, information authenticity verification, and avoiding misleading propaganda. This requires enterprises, while pursuing technological innovation, to adhere to ethical boundaries and ensure that AI-driven KOL marketing activities are both efficient and credible, continuously earning the trust and favor of consumers.Advances in Economics, Management and Political Sciences00Artificial Intelligence not only serves as a powerful tool for data analysis, but also acts as a strategic navigator in this transformation, helping enterprises select the most compatible and influential KOL partners from the vast pool.Advances in Economics, Management and Political Sciences["Yunqi Wang"]2024-12-12T00:00:00 508cf71af40649394d47eb191058891339ec2c63This article presents a comprehensive framework for implementing responsible artificial intelligence (AI) development in contemporary technological landscapes. As AI systems become increasingly integrated into daily life across various sectors, the need for ethical guidelines and responsible development practices has become paramount. The article examines the fundamental principles of responsible AI, including fairness, transparency, accountability, privacy, and system robustness, while proposing practical implementation strategies for organizations. Through analysis of current practices and emerging challenges, this article outlines a structured approach to ethical AI development that balances innovation with societal values. The article introduces a multi-stakeholder model for implementing responsible AI practices, emphasizing the importance of cross-disciplinary collaboration, continuous education, and robust oversight mechanisms. By examining the intersection of technological advancement and ethical considerations, this article contributes to the growing body of knowledge on responsible AI development and provides actionable insights for developers, policymakers, and organizations. The findings suggest that successful implementation of responsible AI requires systematic integration of ethical principles throughout the development lifecycle, supported by strong governance frameworks and stakeholder engagement.International Journal of Scientific Research in Computer Science Engineering and Information Technology20It is suggested that successful implementation of responsible AI requires systematic integration of ethical principles throughout the development lifecycle, supported by strong governance frameworks and stakeholder engagement.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ravi Kottur"]2024-12-12T00:00:00 f3c491f447627d4d075e64c3a09662105939d813Purpose: Artificial intelligence (AI) has been recognized as a critical force in the maritime industry, transforming port operations to meet the needs of the digital age. A paradigm change is taking place in the marine industry, which is a crucial component of global economic systems and international trade. Ports are leading the way in this transformation, using cutting-edge digital and AI capabilities to introduce a new age of operating strategies that provide improved efficiency, accuracy, and security. Approach/Design/Methodology: Providing a historical summary of AI’s evolution since the 1950s, the paper emphasizes its vital role in driving technical innovation and changing marine operations. Considerable attention is devoted to the ethical aspects of AI implementation in marine environments, promoting conscientious and ethical use. The article examines how AI improves marine operations, port, and port operation, in efficiency, accuracy, and security. It also addresses data management, financial issues, and ethical issues related to AI applications. The researchers employed a qualitative research technique to examine the transformative capacities of AI in the maritime industry and its impact on port operations with the support of SWOT Analyses. To gather primary data, a survey was conducted with industry professionals, including port officials, maritime specialists, and providers of AI technology. Findings: This study contributes to a better understanding of the role that AI plays in current marine activities through the SWOT analyses outcomes. The article emphasizes the profound ability of AI to bring about significant changes in port operations through the Positive Aspects as Operational Efficiency, Safety and decision making. It discusses the potential advantages and difficulties associated with AI implementation. The article offers useful insights for industry executives and regulators, underlining the need for strategic and ethical AI integration in maritime port operations.Maritime Research and Technology72The article examines how AI improves marine operations, port, and port operation, in efficiency, accuracy, and security, and discusses the potential advantages and difficulties associated with AI implementation.Maritime Research and Technology["Hossam Eldin Bakr Abdelsalam", "Mohamed Nabil Elnabawi"]2024-12-13T00:00:00 dd72e3df456975919aab424db41b2bc495a8b293Purpose: Advances in technology inevitably come with new potential methods for performing already established activities. Artificial intelligence, in turn, is one of the most talked-about technological innovations. Its impact on the financial sphere is still being analyzed and explored. This article examines the effect of these tools on the established market valuation methodology. The purpose of this paper is to show how digitalization and improvements in the usage of new digital technologies could prove to be useful in increasing the efficiency of already established processes such as the selected methodology for enterprise valuation: The Market approach. More specifically it focuses on artificial intelligence as a tool which can be used to improve said efficiency. Design/Methodology/Approach: The research method used in this paper is a case study, based on a practical execution of the chosen valuation method in three different scenarios, which differ depending on the usage of AI technologies. All of the executions of the methodology are timed using a stopwatch. A subsequent comparison of results is carried out, based on the findings, and the three executions are analyzed based on speed, accuracy of results, relevancy of results and relevancy of peers. Findings: The analysis displayed a concrete result, in which the AI used, although proving to be extremely useful in shortening the execution time of the chosen valuation method, the accuracy of the results provided by it remained very far from the truth, as is the relevance of the peers provided by the Artificial intelligence. This shows that the usage of AI could be an integral part of financial analysis in the future and could significantly improve the efficiency of the market valuation method. However, at this point in time, it should be used as a tool to facilitate analysis but not to replace it altogether. Practical Implications: In practice, this would be able to help execute valuations significantly faster and easier than ever before, but with the necessity of the valuator to make sure the peers provided are relevant to the company being valuated. Originality/Value: No similar study has been done regarding the implications of AI in enterprise valuation methodologies and therefore this would bring significant added value to this area of study. Paper Type: Case studyFinance, Accounting and Business Analysis00The usage of AI could be an integral part of financial analysis in the future and could significantly improve the efficiency of the market valuation method, but at this point in time it should be used as a tool to facilitate analysis but not to replace it altogether.Finance, Accounting and Business Analysis[]2024-12-13T00:00:00 8d774a4ec77d4906f6f98a29f5f3e3ca469d1210Introduction. The effectiveness of screening as one of the strategies for cancer control is beyond doubt. Screening reduces the risk of diagnosing cancer at a late stage and identifies precancerous pathologies, thereby preventing the development of cancer. Potential limitations of screening include the high probability of false positives, false negatives, and overdiagnosis. The consequences are additional examinations and unnecessary and, often, excessive treatment. At the same time, interval cancers, which are characterized by an aggressive course, often do not come into view.The purpose of the study: to explore the data on effectiveness of artificial intelligence (AI) for improving the sensitivity and specificity of cancer screening and reducing the probability of false negative and false positive results, and overdiagnosis.Materials and methods. Review and analysis of published data on a) screening of breast cancer (BC), lung cancer (LC), prostate cancer (PC), cervical cancer (CC) and large bowel cancer (LBC); b) development and application of AI systems to improve the effectiveness of screening. The PubMed and Cochrane Library databases were searched for relevant publications.Results. In mammography screening, AI reduces the number of abnormal interpretations of mammograms, the number of recalls, the number of biopsies with a negative result, and increases the efficacy of mammogram interpretation regardless of the characteristics of the breast (dense breast, calcifications). The use of AI in conjunction with low-dose computed tomography (LDCT) for LC screening not only improves the diagnosis of various types of LC, but also predicts the risk of developing cancer several years in advance. A systematic review and meta-analysis of 12 studies evaluating the effectiveness of AI in tandem with multiparametric magnetic resonance imaging (mpMRI) of the prostate showed high overall effectiveness in the diagnosis of clinically significant PC. The performance of the AI system – based on the multimodal data including demographics, clinical characteristics, laboratory tests and ultrasound reports of patients with PC, was better than the effectiveness of PSA tests in diagnosing clinically significant PC. The effectiveness of AI in tandem with colonoscopy, despite the use of the most advanced AI systems (deep learning system based on a convolutional neural network), remains controversial. The solution to this problem depends on what goal we are pursuing when developing and training the system? Increasing “detection rate” of adenomas, regardless of their size, and removing them, or identifying and removing only large adenomas? The successful use of AI for cytological diagnosis of cervical pathology, including all stages of cervical intraepithelial neoplasia (CIN), is encouraging. The introduction of AI systems developed and trained to interact with a cytopathologist in reading and evaluating cytological material and diagnosing CIN and CC into general practice will reduce the burden on cytopahologists and other medical personnel.Conclusion. The analysis of published data has shown the promising results concerning the use of AI for cancer diagnostics, especially in the setting of population screening programs, which cover many thousands of people. The use of AI significantly increases the effectiveness of diagnostic tool, improves its sensitivity and specificity, and reduces the probability of false negative, false positive results and overdiagnosis. The decision to introduce into practice any of the AIs with proven effectiveness in clinical trials should be made only after its testing in a real world, at the population level. The “informed consent” forms that objectively describe all the advantages and disadvantages of the use of AI compared to current practice has to be developed.Public Health370The performance of the AI system – based on the multimodal data including demographics, clinical characteristics, laboratory tests and ultrasound reports of patients with PC, was better than the effectiveness of PSA tests in diagnosing clinically significant PC.Public Health["D. Zaridze"]2024-12-13T00:00:00 063e0331b1ec3df8d0590c1f3df85b4f367289b6Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to increasing access, personalization of treatment, and real-time monitoring that enhances patient outcomes. The current paper discusses the evolution, present application, and future challenges in the field of AI for mental health and well-being. From the early chatbot models, such as ELIZA, to modern machine learning systems, the integration of AI in mental health has grown rapidly to augment traditional treatment and open innovative solutions. AI-driven tools provide continuous support, offering personalized interventions and addressing issues such as treatment access and patient stigma. AI also enables early diagnosis through the analysis of complex datasets, including speech patterns and social media behavior, to detect early signs of conditions like depression and Post-Traumatic Stress Disorder (PTSD). Ethical challenges persist, however, most notably around privacy, data security, and algorithmic bias. With AI at the core of mental health care, there is a dire need to develop strong ethical frameworks that ensure patient rights are protected, access is equitable, and transparency is maintained in AI applications. Going forward, the role of AI in mental health will continue to evolve, and continued research and policy development will be needed to meet the diverse needs of patients while mitigating associated risks.00The role of AI in mental health will continue to evolve, and continued research and policy development will be needed to meet the diverse needs of patients while mitigating associated risks.["Hari Mohan Pandey"]2024-12-13T00:00:00 c2ef4f0c1eb789159594d88c27b6505f0db808b2Relevance. The development of artificial intelligence (AI) as a societal foundation has become crucial for leading economies, who view it as a key driver of national competitiveness and security. In an era defined by rapid technological advancement and industrial transformation, nations strive to lead in the international science and technology arena, taking advantage of AI to address challenges across sectors such as agriculture, astronomy, and cybersecurity. AI’s role in enhancing productivity, sustainability, and security highlights its strategic importance, underscoring the urgency for countries to actively pursue AI development to secure a competitive edge in a globalised world. Methodology. The study employs a multi-faceted methodological approach. First, a comprehensive literature review and analysis of AI applications in various sectors, including agriculture, astronomy, and cybersecurity, is conducted to provide context on current advancements and trends. Secondly, a comparative analysis examines the strategic AI policies of leading nations to assess how different countries are positioning AI within their national agendas. Third, case studies of AI implementation in specific sectors, such as precision agriculture and cybersecurity, illustrate the practical impacts and potential benefits of a society-oriented approach to AI. The aim of this study is to analyse the strategic value of fostering an AI-driven society as a means of enhancing national competitiveness and securing leadership in international technological innovation. It aims to explore how AI can be harnessed to support sustainable development, improve sectoral efficiency, and protect against security threats, thus contributing to the overall socio-economic resilience and global standing of a nation. Results. The study reveals that the integration of AI across diverse sectors has led to significant efficiency gains, particularly in resource management, sustainability, and security. AI-driven advancements in agriculture, such as precision farming, contribute to higher productivity and environmental sustainability, while applications in astronomy support large-scale data processing for deep space exploration. In cybersecurity, AI has proven instrumental in identifying and countering cyber threats in real time. These findings confirm that an AI-centric societal model can enhance national resilience, drive economic stability, and bolster a country’s competitive position on the global stage. Conclusion. The emergence and development of the “artificial intelligence society” in the context of the technological transformation of the world is a process in which societies adapt to the profound changes caused by the introduction and development of artificial intelligence (AI) technologies. This process includes several key aspects: economic change, based on the automation of production processes, leading to increased efficiency and productivity; the creation of new markets and business models based on AI capabilities, including the redistribution of jobs and changes in employee skill requirements; social change, which is based on changing the way people interact with each other and with technology; transition to smart cities and communities, where AI helps to manage resources and ensure the comfort of life; impact on education, health and other areas of life through the introduction of personalised AI-based solutions; cultural changes aimed at transforming the values and worldview associated with AI technologies; emergence of new cultural practices and media formats based on AI; development of digital culture and its impact on traditional cultural forms; political and ethical challenges, including defining new regulatory and legal frameworks for AI regulation; ensuring ethical use of AI, avoiding discrimination and ensuring fairness; managing risks related to data security and privacy; technological development, based on the continuous improvement of AI algorithms and models; integration of AI into various sectors of the economy and everyday life’; development of infrastructure to support the large-scale implementation of AI (e.g. 5G networks, data centres); This process reflects the overall technological transformation of the world, where AI is becoming an integral part of economic, social, cultural, and political life.  Key words: artificial intelligence, philosophy of society, national competitiveness, strategy.  Laisvalaikio tyrimai200The study reveals that the integration of AI across diverse sectors has led to significant efficiency gains, particularly in resource management, sustainability, and security, and confirms that an AI-centric societal model can enhance national resilience, drive economic stability, and bolster a country’s competitive position on the global stage.Laisvalaikio tyrimai["V. Voronkova", "V\u00ectalina Nikitenko", "Victorya Marienko", "Maryna Gramchuk"]2024-12-13T00:00:00 925a3265cb23c99d6577d2126c7ef0d94b511b49Relevance. The article considers the issues of application of artificial intelligence in order to identify the current state and prospects of its application in making procedural decisions in forensic medical examination. The use of AI in forensic examination opens up new opportunities, but it is important to keep in mind the potential risks. The aim of the study is to conduct a scientific review of the application of artificial intelligence in making procedural decisions in forensic medical examination. Materials and Methods. The study examines the works of specialists from different countries devoted to the implementation of AI in forensic medical examination. Special attention is paid to the use of AI in making procedural decisions. Literature analysis shows that forensic psychiatry is a leader in the field of AI application in forensic medicine. According to the results of the research, generalization of scientific works, analysis of domestic and foreign experience on the problem, the authors of the work identified the areas of application of artificial intelligence in the field of forensic medicine. Some aspects that require attention in the application of artificial intelligence in this area are pointed out. Conclusion. The authors conclude that the introduction of AI in forensic medicine has become a revolutionary event, opening new opportunities to optimize the processes of data analysis and interpretation. However, despite the significant potential of AI, the role of humans in making key decisions remains irreplaceable. AI is not intended to replace human expertise, but rather to be a valuable enabler. Human insight and knowledge remain indispensable for interpreting context and making informed decisions. In addition, a SWOT analysis of the application of AI in procedural decision making in forensic science has been conducted. Based on the results of the study, the positive and negative sides of AI application were identified.Russian Journal of Forensic Medicine130The introduction of AI in forensic medicine has become a revolutionary event, opening new opportunities to optimize the processes of data analysis and interpretation, however, despite the significant potential of AI, the role of humans in making key decisions remains irreplaceable.Russian Journal of Forensic Medicine["Dinara R. Nurkeyeva", "Y. Begaliyev", "M. Abzalbekova", "Ardak A. Biyebayeva", "Farida S. Zhaxybekova"]2024-12-13T00:00:00 b59c9fc372a0ca8788df2d9e3bb7850131c36d96Purpose This study aims to review and synthesize existing research in the field. Additionally, this study identifies emerging themes and future research opportunities based on the discussions within these studies. This research also develops a model to integrate artificial intelligence with agile human resource (AHR) practices and strives to outline potential directions for future researchers. Design/methodology/approach This study adopted a systematic literature review (SLR) and bibliometric analysis followed by content analysis through bibliographic coupling to analyze the identified literature. The SCOPUS database was used in this study, using a search string of keywords to identify the relevant research literature. The initial extraction resulted in 151 articles after adopting a series of inclusion–exclusion criteria, which led to the final attainment of 73 articles to be included for further analysis and discussion. Findings This study through the extant literature identified five themes and foundations of artificial intelligence in AHR practices research and developed a model for future investigation by future researchers. Originality/value To the best of the authors’ knowledge, this study is one of a kind that explores artificial intelligence within AHR practices for improved employee and organizational well-being. Thus, developing a synthesized work provides a comprehensive picture of the research domain. International Journal of Lean Six Sigma940This study is one of a kind that explores artificial intelligence within AHR practices for improved employee and organizational well-being and develops a model for future investigation by future researchers.International Journal of Lean Six Sigma["Gayatri Panda", "Shilpee Aggarwal", "Mahender Singh Kaswan", "Kavisha Dhillon"]2024-12-13T00:00:00 73c540b901285207fcbd856939f1d82f0c417e75This is a report of an NSF workshop titled"Envisioning National Resources for Artificial Intelligence Research"held in Alexandria, Virginia, in May 2024. The workshop aimed to identify initial challenges and opportunities for national resources for AI research (e.g., compute, data, models, etc.) and to facilitate planning for the envisioned National AI Research Resource. Participants included AI and cyberinfrastructure (CI) experts. The report outlines significant findings and identifies needs and recommendations from the workshop.arXiv.org00ArXiv["S. Jha", "Yolanda Gil"]2024-12-13T00:00:00 a0075ca46a883a6ca9176d68b88a9bf2d1cb3d51From software development to cyber security, the IT sector plays a crucial role in enabling digital transformation and driving innovation. It offers exciting career opportunities and is constantly adapting to meet the changing needs of businesses and society. AI had a profound impact on the IT sector. Also it automates repetitive tasks, freeing up valuable time for employees to focus on more strategic work. The study examines what the factors are influencing the respondents to adopt Artificial Intelligence, its impact and the opinion of the respondents while introducing towards AI in IT industry. For the purpose of the study percentage analysis and weighted average score analysis has been used for the analysis. The study suggested that implement the AI software in their respective companies which are well suited for their work capacity. The study concludes that the integration of Artificial Intelligence in the Information Technology industry is a testament to its potential to reshape the future of work.International Journal For Multidisciplinary Research00The study concludes that the integration of Artificial Intelligence in the Information Technology industry is a testament to its potential to reshape the future of work.International Journal For Multidisciplinary Research["Dr.M.Renuka devi", "Dr.Y.S.Irinie Jiji", "G.A.Hema"]2024-12-13T00:00:00 6f1b934ee3bb69152fe8ddf79d2bb582d50f1a29This article is devoted to the analysis of a number of issues related to the study of the role of technological factors in ensuring the artificial intelligence transparency in the world economic relations development of the Russian Federation in the context of globalization. In the article, ensuring the artificial intelligence transparency is due to the contradiction of the legislative system and the international integration of the Russian Federation with other countries. The object of the study is multilateral and diverse cooperation between Russia and international organizations in the field of solving problems of the artificial intelligence technologies’ development and implementation. The subject of the study is the relationships, developing in Russia in connection with the international trade development, using modern artificial intelligence technologies. The purpose of the study is to develop the Russian economy in the world economic system, based on the use of technological factors to ensure the artificial intelligence transparency. The purpose of the study is to determine the AI transparency as a characteristic of the international economic system development, as well as to study various properties of ensuring this transparency. The methodology of theoretical literature review and observation is considered. Regulatory documents, statistical materials, and targeted programs for the artificial intelligence development in the Russian Federation are analyzed. The research results are as follows: the role of technological factors in ensuring the artificial intelligence transparency in the world economic relations development of the Russian Federation in the context of globalization has been identified. In particular, AI transparency as a property of the international economic system and AI transparency as a property of the understandability of the international integration economic processes algorithm are considered. Thus, tools for the world economic processes development in the Russian Federation based on the creation of a transparent artificial intelligence system promote cooperation in the transfer of knowledge and technology, integration cooperation, improvement of the international economic, organizations activities, and growth in labor productivity. Transbaikal State University Journal00Transbaikal State University Journal["Elena M. Kurushina", "Dmitry A. Kurushin"]2024-12-13T00:00:00 adae38649a4d8867a50c7d777e6c59e17fe57c4fAbstract Motivation Due to the breakthrough in protein structure prediction by AlphaFold, the scientific community has access to 200 million predicted protein structures with near-atomic accuracy from the AlphaFold protein structure DataBase (AFDB), covering nearly the entire protein universe. Segmenting these models into domains and classifying them into an evolutionary hierarchy hold tremendous potential for unraveling essential insights into protein function. Results We introduce DPAM-AI, a Domain Parser for AlphaFold Models based on Artificial Intelligence. DPAM-AI utilizes a convolutional neural network trained with previously classified domains in the Evolutionary Classification Of protein Domains (ECOD) database. DPAM-AI integrates inter-residue distances, predicted aligned errors, and sequence and structural alignments to previously classified domains detected via sequence (HHsuite) and structural (Dali) similarity searches. DPAM-AI has demonstrated its power through rigorous tests, excelling in several benchmark sets compared to its predecessor, DPAM, and other recently published domain parsers, Merizo and Chainsaw. We applied DPAM-AI to representative AFDB models for proteins classified in Pfam. We obtained representative 3D structures for 18 487 (89%) of the 20 795 Pfam families. The remaining families either (i) belong to viral proteins that were excluded from AFDB or (ii) do not adopt globular 3D structures. Our structure-aware domain delineation uncovered a considerable fraction (15%) of Pfam domains containing multiple structural and evolutionary units and refined the boundaries for over half. Availability and implementation Pfam and corresponding DPAM-AI domains are at http://prodata.swmed.edu/DPAM-pfam/. Our code is deposited at https://github.com/Jsauce5p/DPAM/tree/dpam_ai, and updates will be released through https://github.com/CongLabCode/DPAM.Bioinformatics00The structure-aware domain delineation uncovered a considerable fraction of Pfam domains containing multiple structural and evolutionary units and refined the boundaries for over half, as well as introducing DPAM-AI, a Domain Parser for AlphaFold Models based on Artificial Intelligence.Bioinformatics["J. Durham", "Jing Zhang", "Richard D Schaeffer", "Qian Cong"]2024-12-13T00:00:00 516398281ca1c5f2d46264b28f752a8df2cb7d24Today, with the rapid development of technology, the usage areas of artificial intelligence technologies are also increasing rapidly. Artificial intelligence applications are frequently used in many fields such as education, engineering and health. One of the important areas of use of artificial intelligence systems is mechatronics engineering. Especially in robotics and unmanned aerial vehicles applications, artificial intelligence methods are frequently used.The study introduces an artificial intelligence model developed to detect seat belt usage by drivers using unmanned aerial vehicles. Seat belts play an important role in reducing injuries and deaths in traffic accidents, but current inspection methods are time-consuming and limited. In this study, image processing techniques were used to determine whether drivers are wearing seat belts. For this purpose, a dataset consisting of in-car images taken under different driving conditions was created, and Gaussian filters were applied to these images to remove noise and interference. Convolutional neural network architecture was used for model training, and the results were compared with common models such as ResNet-18 and AlexNet. Test results showed that the developed special convolutional neural network model is superior to other models in terms of accuracy and performance. This study reveals that artificial intelligence and image processing techniques can monitor seat belt usage more effectively and increase traffic safety.International journal of 3d printing technologies and digital industry00It is revealed that artificial intelligence and image processing techniques can monitor seat belt usage more effectively and increase traffic safety.International Journal of 3D Printing Technologies and Digital Industry["M. M. \u00d6zmen", "Muzaffer Eylence", "B. Aksoy"]2024-12-13T00:00:00 3d6c0455e2fdbaa8aa906af94e7c165e74ef835fAs the technology develops, we are moving towards the creation of fully self-driving vehicles. Not only the technical but also the legal conditions must be in place. Both in civil and criminal law, a completely new approach to legislation is needed in order to regulate this situation, which is quite different from the general situation. Human rights must also be taken into account. The study examined the difficulties of introducing self-driving vehicles from the perspective of fundamental rights as enshrined in the European Convention on Human Rights. It can be concluded that self-driving vehicles (or the accidents they cause, the events they record, etc.) may affect a number of human rights (right to life, right to a fair trial, prohibition of punishment without legal provision, right to respect for private and family life, freedom of discrimination, freedom of movement). Nevertheless, prudent legislation can solve the problems. Keywords: artificial intelligence, weak artificial intelligence, strong artificial intelligence, self-driving cars, human rightsLaisvalaikio tyrimai160The study examined the difficulties of introducing self-driving vehicles from the perspective of fundamental rights as enshrined in the European Convention on Human Rights to conclude that prudent legislation can solve the problems.Laisvalaikio tyrimai["Herke Csongor", "Dalia Perkumien\u0117"]2024-12-13T00:00:00 b1ed4246056b5b7d938652aec11bc974f9e49b7fMarx's thought on the division of labor in the era of artificial intelligence requires new interpretation and understanding. The development of artificial intelligence technology has profoundly changed the mode and nature of the division of labor, leading to the disappearance of traditional labor methods and the lowering of occupational barriers, providing a realistic path for the "elimination of division of labor" proposed by Marx. Through the analysis of the concept and historical evolution of the division of labor, drawing on Marx's thought on the division of labor, this article explores in depth the refinement and transformation of the division of labor in the era of artificial intelligence. The article argues that the development of artificial intelligence technology helps to solve the problems brought about by the division of labor in the industrial era, promote the reform of the social division of labor, and promote the free and comprehensive development of human beings. KEYWORDS: Artificial Intelligence, Marx; Division of Labor, Labor Force, ProductivityEPRA International Journal of Research &amp; Development (IJRD)00The article argues that the development of artificial intelligence technology helps to solve the problems brought about by the division of labor in the industrial era, promote the reform of the social division of labor, and promote the free and comprehensive development of human beings.EPRA International Journal of Research &amp; Development (IJRD)["Ye Luyang", "Chen Junjie"]2024-12-13T00:00:00 25f7f2cdf7f61b802a61bcff0e79faa313255ac8This article explores how the work of ethical assurance is understood by those involved in artificial intelligence development and deployment, and uses the findings to consider how ethical reflection might be better supported. The article presents a case study of a multi-disciplinary project developing a care support system that used machine learning in remote monitoring of people living at home with dementia. In this project engineering and clinical perspectives come together through a fractionated interdisciplinary trading zone to address goals such as remote detection of urinary infections. Ethics is done, according to project participants, in formal ethical review processes and through a shared understanding of common goals, but also in discipline-specific practices that sit outside of the trading zone. A key role is played by team members who translate concerns between discipline-based research groups and who act as proxies for the ultimate users of the systems not directly present within the trading zone. These insights into cross-disciplinary ethical work in relation to smart care lead us to recommend that infrastructural support for imaginative and transparent ethical reflection needs to be woven through the collaborations that create artificial intelligence, both across disciplines and throughout the lifetime of a project.Science, Technology, &amp; Human Values240It is recommended that infrastructural support for imaginative and transparent ethical reflection needs to be woven through the collaborations that create artificial intelligence, both across disciplines and throughout the lifetime of a project.Science, Technology, &amp; Human Values["Christine Hine", "P. Barnaghi"]2024-12-13T00:00:00 4def18aae53cf0b3c293f80bf59b8e0cf3923400This study aims to examine the integration of Artificial Intelligence (AI) in strategic management, focusing on market analysis, predictive analytics, and operational efficiency. The research employs a qualitative method with a case study approach on leading companies that have successfully implemented AI. The findings reveal that AI adoption significantly enhances organizational adaptability, competitive advantage, and innovation capacity. Furthermore, AI efficiently analyzes large datasets, supports real-time data-driven decision-making, optimizes operational efficiency by up to 30%, and accelerates product and service innovation, boosting customer satisfaction. However, key challenges include organizational resistance to change and data privacy issues. This study concludes that strategic AI implementation offers substantial benefits to companies, provided ethical and technical barriers are addressed prudently.  Jurnal Manajemen dan Bisnis00It is concluded that strategic AI implementation offers substantial benefits to companies, provided ethical and technical barriers are addressed prudently and technical barriers are addressed prudently.JURNAL MANAJEMEN DAN BISNIS["Martha Nur Aini", "Nadya Zuleika Hanum", "Nauval Ega Kurniawan", "Vicky F. Sanjaya"]2024-12-13T00:00:00 c8a82e036eba4c04fc646f9f22db04ff192016deIn recent years, artificial intelligence (AI) research in the medical field has been actively conducted owing to the evolution of algorithms, such as deep learning, and advances in hardware, such as graphics processing units, and some such medical devices have been used in clinics. AI research in obstetrics and gynecology has also increased. This review discusses the latest studies in each field. In the perinatal field, there are reports on cardiotocography, studies on the diagnosis of fetal abnormalities using ultrasound scans, and studies on placenta previa using magnetic resonance imaging (MRI). In the reproduction field, numerous studies have been conducted on the efficiency of assisted reproductive technology as well as selection of suitable oocyte and good embryos. As regards gynecologic cancers, there are many reports on diagnosis using MRI and prognosis prediction using histopathology in cervical cancer, diagnosis using hysteroscopy and prediction of molecular subtypes based on histopathology in endometrial cancer, and diagnosis using MRI and ultrasound as well as prediction of anticancer drug efficacy in ovarian cancer. However, concerns related to AI research include handling of personal information, lack of governing laws, and transparency. These must be addressed to facilitate advanced AI research.JMA Journal640Concerns related to AI research include handling of personal information, lack of governing laws, and transparency must be addressed to facilitate advanced AI research.JMA Journal["K. Sone", "Ayumi Taguchi", "Y. Miyamoto", "Mayuyo Uchino-Mori", "Takayuki Iriyama", "Yasushi Hirota", "Yutaka Osuga"]2024-12-13T00:00:00 812c8487a95f6dc92336e06af667421638bcdaffThe advent of artificial intelligence (AI) technologies has significantly changed many domains, including applied statistics. This review and vision paper explores the evolving role of applied statistics in the AI era, drawing from our experiences in engineering statistics. We begin by outlining the fundamental concepts and historical developments in applied statistics and tracing the rise of AI technologies. Subsequently, we review traditional areas of applied statistics, using examples from engineering statistics to illustrate key points. We then explore emerging areas in applied statistics, driven by recent technological advancements, highlighting examples from our recent projects. The paper discusses the symbiotic relationship between AI and applied statistics, focusing on how statistical principles can be employed to study the properties of AI models and enhance AI systems. We also examine how AI can advance applied statistics in terms of modeling and analysis. In conclusion, we reflect on the future role of statisticians. Our paper aims to shed light on the transformative impact of AI on applied statistics and inspire further exploration in this dynamic field.arXiv.org00The symbiotic relationship between AI and applied statistics is discussed, focusing on how statistical principles can be employed to study the properties of AI models and enhance AI systems.ArXiv["Jie Min", "Xinyi Song", "Simin Zheng", "Caleb B. King", "Xinwei Deng", "Yili Hong"]2024-12-13T00:00:00 16365991e4e4c149e98e979461055800b7b51f91This study presents a critical review of the emerging field of Artificial Intelligence (AI) Sociology, examining the social implications and ethical considerations of AI technologies. Through a qualitative methodology incorporating a systematic literature review and thematic analysis, this research explores the intersection of AI and sociology, aiming to bridge the gap between technological advancement and societal impact. The study investigates key theoretical frameworks, including critical theory and actor-network theory, to analyse power relations, social stratification, and the dynamic interplay between AI and society. The findings reveal the multifaceted influence of AI on social structures, ethical challenges, and the need for interdisciplinary approaches to address the societal implications of AI.Filosofija Sociologija440The findings reveal the multifaceted influence of AI on social structures, ethical challenges, and the need for interdisciplinary approaches to address the social implications and ethical considerations of AI technologies.Filosofija. Sociologija["Chunfa Zhou"]2024-12-13T00:00:00 39bfaad5023cc615c8c07e207f236c6e42e9820eThis study aims to explore aquaculture professionals’ perspectives on, attitudes towards and understanding of Mediterranean farm fish health management, regarding Artificial Intelligence (A.I.), and to shed light on the factors that affect its adoption. Α survey was distributed during a major fish health management conference, representing more than 70% of Greek domestic production. A total of 73 questionnaires were collected, for which descriptive statistics and statistical analysis followed. Gender and age were shown to affect interest in A.I. and in viewing A.I. as a partner rather than a competitor. Age was additionally shown to affect trust in A.I. estimates and anticipation that A.I. will contribute to professional development. Education level shows no significant effect. Knowledge of A.I. is positively correlated with A.I. usage (r = 0.43, p < 0.05), as is interest in learning about A.I. (r = 0.64). A.I. usage is in turn positively correlated with eagerness to see its contribution (r = 0.72). Despite the fact that 64.4% characterized their knowledge as little or non-existent, 67.1% expressed interest in learning more, while 43.8% believe that A.I. will revolutionize aquaculture and 74% do not fear they will be replaced by A.I. in the future. The findings highlight the importance of targeted educational initiatives to bridge the knowledge gap and encourage trust in A.I. technologies.Water00Gender and age were shown to affect interest in A.I. and in viewing A.I. as a partner rather than a competitor, and trust in A.I. was additionally shown to affect trust in A.I.Water["Dimitris C. Gkikas", "Vasileios P. Georgopoulos", "J. Theodorou"]2024-12-13T00:00:00 97260aed151a764c6e76456970afc3b7917b939eArtificial intelligence (AI), which enhances efficiency, production, and decision-making, has rapidly become a crucial component in sectors such as healthcare, banking, education, and transportation. However, as AI systems increasingly integrate into critical aspects of daily life, significant legal challenges related to liability, transparency, and accountability arise. The issue is that it can be challenging to assign blame for judgments made by AI, particularly when self-learning systems are involved and go beyond initial programming. In addition to algorithmic bias, opaque decision-making procedures, and third-party involvement, there are ambiguities in the assignment of accountability among developers, operators, and users. The purpose of this study is to discuss these legal issues and offer workable answers to guarantee fairness and responsibility in AI-assisted decision-making. In order to streamline compensation by emphasizing causality rather than culpability, key findings recommend the implementation of strict responsibility for high-risk AI applications. Accountability and traceability can be increased by increasing transparency through required paperwork and explainable AI systems. Uncertainty can be decreased by using explicit contractual frameworks to clearly define roles for developers, operators, and users. Furthermore, the creation of specialist liability insurance can promote the appropriate use of AI while providing financial protection for stakeholders. Building public trust and making sure AI advances society without endangering it needs striking a balance between innovation and moral and legal obligations. Cross-border AI applications require international harmonization of legal norms, such as the GDPR and the EU's AI Act, in order to establish a uniform regulatory framework. To ensure justice, fairness, and the well-being of society, these extensive legal reforms are required to close the gap between accountability and technological innovationEast African Journal of Law and Ethics00Key findings recommend the implementation of strict responsibility for high-risk AI applications and the creation of specialist liability insurance to promote the appropriate use of AI while providing financial protection for stakeholders.East African Journal of Law and Ethics["Enrico Moch"]2024-12-13T00:00:00 72a081fcb4b5c4d1b135da51e0f42f3650062689Introduction The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. Purpose This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. Method A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. Results The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Conclusion Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.Frontiers in Artificial Intelligence510The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency, but challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies.Frontiers in Artificial Intelligence["Poshan Belbase", "Rajan Bhusal", "Sapana Sharma Ghimire", "Shreesti Sharma", "B. Banskota"]2024-12-13T00:00:00 6148c25469ade84fda2ac3d88b1d064d74f7ece3Artificial intelligence (AI) is a technology of significant military-political and strategic value. The use of artificial intelligence has become one of the main topics of public debate in recent years. This technological solution has positive results, but it turns out that it also has many hidden consequences for various fields, ranging from increasing the efficiency of industrial production to developing various applications that have the potential to be used for military purposes. Beyond its potential to increase production and efficiency, AI also brings a range of military applications and new threats. In this way, AI represents the coming revolution in military affairs that could reorient the relative distribution of power. In parallel, the new threats become even more serious when considering the high complexity of control in the spread of artificial intelligence – potential for use and increasing the risk of abuse.Научно списание „Сигурност и отбрана“00Artificial intelligence represents the coming revolution in military affairs that could reorient the relative distribution of power and the new threats become even more serious when considering the high complexity of control in the spread of artificial intelligence – potential for use and increasing the risk of abuse.Научно списание „Сигурност и отбрана“["Stefaniya Mircheska"]2024-12-13T00:00:00 ec994838348dedfb0a2939bbb1a4ab48bb6db224Artificial Intelligence (AI) is a vital element of neuro-marketing research, which improves the process of identifying consumer preferences by using Electroencephalography technology. Employing AI algorithms allows marketers to record and decode brain signals elicited by marketing stimuli precisely. The synergy of AI and EEG in neuromarketing will bring about a revolution in the way marketers conduct research on consumer behavior. AI applications in neuromarketing are not limited to this; they also involve creating neural systems that adapt in real-time by collecting EEG data to alter marketing messages and content per consumer preferences. Moreover, AI can be employed to take over, simulate, and forecast consumer behavior and analyze emotional EEG data to achieve a deeper understanding of consumer behavior. This paper aims to provide preliminary information on the role of AI in neuromarketing using the EEG technique in two dimensions: tracking and processing of the EEG brain signals. The research on the role of AI in neuromarketing using a systematic literature review method is conducted. In summary, the joint utilization of AI and EEG techniques in neuromarketing research can provide us with more insights into consumer behavior, thereby supporting better marketing strategies.International Journal of Academic Research in Business and Social Sciences320Preliminary information is provided on the role of AI in neuromarketing using the EEG technique in two dimensions: tracking and processing of the EEG brain signals.International Journal of Academic Research in Business and Social Sciences["Samira Nazari Ghazvini", "Elmira Nazari", "Nor Zafir Md Salleh", "Rohaizat Baharun"]2024-12-13T00:00:00 042d8abd4e4a32344239bd520d24717fe36a7ea2Endoscopy71Endoscopy["Yuichi Mori"]2024-12-13T00:00:00 97a5786f535fc8c1d4f02c4f088fb980a2383759International Journal of Legal Information : Official Publication00International Journal of Legal Information["Naman Anand"]2024-12-13T00:00:00 2ef8ad289ef49e10f51faaefe6d10e199e97758dNEJM AI40NEJM AI["M. Sabuncu", "Alan Q. Wang", "Minh Nguyen"]2024-12-13T00:00:00 4be12b522daee2b90e1f2f2eeaa5440fed3a0880The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability.Sustainability530This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations, and utilizes a Multi-Model Super Ensemble forecasting framework to generate accurate hourly gridded weather forecasts.Sustainability["M.E.H. Lee", "Ming-Hwi Yao", "Pu-Yun Kow", "Bo-Jein Kuo", "Fi-John Chang"]2024-12-13T00:00:00 0ebb9b98216eaec0b70132626bb11bab3fa9b0f9Expert review of pharmacoeconomics & outcomes research150Expert review of pharmacoeconomics & outcomes research["Majid Ali"]2024-12-13T00:00:00 a4a5d991e68c1befc22d5739706cafd107bbc493African Journal of Biomedical Research00African Journal of Biomedical Research["Dr Prashant Kariya"]2024-12-13T00:00:00 cd75cb5888dc7fc51d75fc9883a26f826a1b8332The article examines the characteristics of generations X, Y, Z, and offers practical recommendations for optimizing the hiring processes and interactions with this group, taking into account their unique characteristics and potential. The relevance of the study is that in today’s society, engulfed in rapid technological change, generation Z is becoming a key participant in the labor market, while more and more studies are devoted to the application of modern methods in the study of the characteristics of generation Z and the use of this fact in human resource management. The scientific novelty is that the study put forward and proved the hypothesis that using the deep learning (DL) model «Random Forest» it is possible to form a forecast of the decision to hire applicants, representatives of generations X, Y, Z. Applicants who scored above the average level receive 1 and can be hired. It is possible to tighten the requirements by raising the "bar" of requirements higher. The program can select the required number of applicants from a ranked series. The results of the study, when statistically processed, indicate that the best candidates can be selected among the representatives of Generation Z using the deep learning method Random Forest.Mezhdunarodnaja jekonomika (The World Economics)60The results of the study indicate that the best candidates can be selected among the representatives of Generation Z using the deep learning method Random Forest.Mezhdunarodnaja jekonomika (The World Economics)["N. Lomakin", "E. V. Samsonova", "V. N. Tsygankova", "L. V. Kuzmina", "M. V. Samsonova", "V. V. Moiseeva", "N. Y. Volkova", "S. A. Gostyunin"]2024-12-13T00:00:00 b632bcc3b3e619a434c8193788f2bf9b7be55e9bInternational Higher Education00International Higher Education["Alejandra Gaitan Barrera", "G. Azeez"]2024-12-13T00:00:00 58e1b6c275e5e28588372c116b7bd911be5ffa6fMTZ worldwide00MTZ worldwide["Kathrin Gerhard", "Daniel Ross"]2024-12-13T00:00:00 7dbad4b4f9e3fb0c1a8c8e86ea3b57805112ce5eArtificial Intelligence (AI) has given new hopes and changed the elements of education for the better, especially in inclusive education. AI is very concerned with equality, which is expected to help children with special needs get their rights and a more appropriate education. However, the presence of AI has also brought many negative impacts due to the lack of understanding of the ethical principles that must be implemented. This paper was compiled to explore The Importance of Ethical of Artificial Intelligence (AI) in Inclusive Education. The results of this study show that until now there have been many public, private, and government organizations in various countries around the world that have created multiple guidelines to regulate, guide, and protect all elements involved and affected by the progress of AI. Among them are charter agreements and various guidelines related to ethical principles, basic rights, fundamental values, and other instruments that have been studied and compiled in detail and very systematically. But the conditions that have occurred until now are far from perfect. AI still presents many problems, concerns, and forms of unavoidable abuses, especially in inclusive education.Ideguru: Jurnal Karya Ilmiah Guru240The results of this study show that until now there have been many public, private, and government organizations in various countries around the world that have created multiple guidelines to regulate, guide, and protect all elements involved and affected by the progress of AI.Ideguru: Jurnal Karya Ilmiah Guru["Anita Intan Rohmatuszahroh", "Kiki Ayu Hermawati", "Hanny Rizqiyana Nur'aliya", "Siti Khalimah"]2024-12-13T00:00:00 a4e1f3b1f0e1b62e0cc4c22c1b8b042ca443fceaThe rise of artificial intelligence (AI) has significantly impacted education, yet few scholars have explored AI‐assisted classrooms, particularly in language education in China. Understanding the roles of classroom climate, AI literacy, and resilience is essential, as these factors foster positive learning environments and enhance student engagement. In this sense, this study, grounded in Social Cognitive Theory, employs structural equation modelling to investigate factors influencing classroom engagement in AI‐assisted Chinese English as a Foreign Language (EFL) classrooms. It examines data from 606 university EFL learners to explore the interactions among these variables and the mediating role of resilience. The findings indicate that classroom climate, AI literacy, and resilience all significantly predict classroom engagement, highlighting the importance of both environmental and cognitive factors in fostering active student participation. Furthermore, resilience serves as a crucial mediator, linking classroom climate and AI literacy to engagement. This study provides some insights for educators and policymakers, emphasising the need to cultivate supportive classroom environments, promote AI literacy programs, and strengthen students' resilience to optimise engagement in AI‐assisted educational settings.European Journal of Education746Investigation of factors influencing classroom engagement in AI‐assisted Chinese English as a Foreign Language (EFL) classrooms in China indicates that classroom climate, AI literacy, and resilience all significantly predict classroom engagement.European Journal of Education["Xiaochen Wang", "Yang Gao", "Qikai Wang", "Panpan Zhang"]2024-12-13T00:00:00 c0d895ce341bf19901fd128fb1a33f0f26500a0bThe financial market is changing rapidly, and to sustain themselves in this dynamic investment world, investors need to adopt various strategies. Traditional investment tactics rely entirely on human intuitions and historical data, which may fail to keep pace with the ever-changing nature of investment. If someone fails to adopt the need-based strategies, they will be kept in the market. Investors need more complex tools and approaches to make sound investment decisions and withstand the fluctuating market environment. Technology has made it easier by providing these tools to analyze large amounts of data and identify trends, thus crafting prudent investment strategies. One of the blessings of technology that is changing the investment world dynamically is artificial intelligence (AI). Therefore, this research aims to investigate the multifaceted role of Artificial Intelligence in investment strategies, emphasizing its predictive capabilities, data management efficiencies, user engagement enhancements, practical applications, and emerging trends. This paper employs a qualitative approach with a significant focus on existing literature and case study analysis to give a comprehensive overview of the impact of AI on investment strategies. The analysis reveals that integrating AI into investment strategies is redesigning the investment landscape, offering unprecedented opportunities such as improved predictive capabilities and risk management, which help make informed decisions. The study's finding provides valuable insights for investors and financial institutions seeking to optimize their strategies with AI in an increasingly data-driven investment environment.American Finance &amp; Banking Review00Analysis of the impact of AI on investment strategies reveals that integrating AI into investment strategies is redesigning the investment landscape, offering unprecedented opportunities such as improved predictive capabilities and risk management, which help make informed decisions.American Finance &amp; Banking Review[]2024-12-13T00:00:00 f9247a917b94011861d9c432b28aa4d62ee41844The rise of Artificial Intelligence (AI) has transformed the entrepreneurial landscape in Nigeria, creating both possibilities and challenges for local business owners. This study investigates the relationship between entrepreneurship, development communication, and AI in Nigeria, emphasizing the need for businesses to adapt their communication strategies to succeed. It examines the Nigerian context, cultural factors, and technological infrastructure to offer effective AI-driven communication strategies for entrepreneurs. The study provides valuable insights for Nigerian entrepreneurs aspiring to flourish in the AI era. By implementing effective communication strategies, they can boost their visibility, establish credibility, and foster business growth in an AI-dominated market. This study emphasizes the critical role of strategic communication in empowering entrepreneurs to overcome AI-related challenges and fostering a thriving entrepreneurial ecosystem. It provides actionable advice on harnessing communication for entrepreneurial success in Nigeria's digital era. The recommendations are tailored to entrepreneurs, policymakers, educational institutions, and the private sector, aiming to create a dynamic economy that utilizes AI as a driver of growth and development.International journal of entrepreneurship and business innovation11This study investigates the relationship between entrepreneurship, development communication, and AI in Nigeria, emphasizing the need for businesses to adapt their communication strategies to succeed and provides actionable advice on harnessing communication for entrepreneurial success in Nigeria's digital era.International Journal of Entrepreneurship and Business Innovation["Ezeaka, N. B."]2024-12-13T00:00:00 730d5e017ae6bf8dbbcba3e8211309ca7b900691This study examines H1N1 and seasonal flu vaccination behaviors using machine learning models and explainable artificial intelligence (XAI) techniques. Utilizing data from the National 2009 H1N1 Influenza Survey, we developed a predictive framework employing models such as CatBoost, XGBoost, and LightGBM. CatBoost outperformed others with an accuracy of 0.696 and an F1 score of 0.688. SHAP (Shapley Additive Explanations) was used for interpretability, providing both global insights, such as the critical role of doctor recommendations, and local insights, highlighting individual decision factors. Our findings underscore the importance of addressing vaccine skepticism and improving healthcare communication to enhance vaccination uptake. These results contribute to public health strategies aimed at increasing immunization coverage and preparing for future pandemics.Mehmet Akif Ersoy Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi01The findings underscore the importance of addressing vaccine skepticism and improving healthcare communication to enhance vaccination uptake, and contribute to public health strategies aimed at increasing immunization coverage and preparing for future pandemics.Mehmet Akif Ersoy Üniversitesi Sağlık Bilimleri Enstitüsü Dergisi["Bekir \u00c7etintav", "Ahmet Yal\u00e7\u0131n"]2024-12-13T00:00:00 0c318737fcdfbf63f75896aa56cee0765fcf203fArtificial intelligence (AI) holds immense potential to revolutionize dental care, offering advancements in diagnostic accuracy, personalized treatments, and overall patient outcomes. However, AI's ability to deliver these benefits hinges on the availability of large, high-quality datasets, especially electronic patient records (EPR). These records, encompassing diagnostic images, treatment histories, patient demographics, and clinical outcomes, are critical for training AI models to enhance clinical decision-making. However, as the demand for data grows, so do the ethical concerns surrounding its collection and use. Ethical Challenges in Data Collection While the benefits of AI in dentistry are undeniable, collecting and using patient data for training AI models raises several ethical challenges that must be addressed Patient Privacy and Consent: Informed consent is essential before patient data can be used for AI training. Patients must fully understand how their data will be utilized, with strict adherence to privacy regulations such as HIPAA (USA) or GDPR (EU). Data Ownership: Determining clear ownership of patient data—whether it belongs to patients, healthcare providers, or third parties—is crucial. This ensures ethical use in AI research while protecting patient autonomy. Bias and Fairness: AI models can be skewed by biased or unrepresentative data, leading to unfair outcomes for marginalized or underserved patient groups. Transparency and Accountability: The integration of AI in dentistry demands transparency in how models are trained and deployed. Accountability mechanisms must be in place to address errors and prioritize patient safety and well-being. Balancing Innovation with Ethics Dental professionals, researchers, and policymakers must collaborate to create a framework that ensures data is collected, used, and protected responsibly. To leverage AI’s potential while addressing these ethical concerns, several strategies can be adopted: Anonymization of Patient Data: One way to protect patient privacy while still allowing data to be used for AI training is to anonymize patient records. This ensures that individual identities are kept confidential while still providing valuable data for research and development. Clear Ethical Guidelines: There is an urgent need for standardized guidelines regarding the ethical collection, use, and sharing of patient data in AI training. These guidelines should prioritize patient rights, data security, and fairness in AI development. Patient Education: Raising awareness among patients about the benefits and risks of sharing their data for AI purposes is crucial. Clear communication can foster trust and allow patients to make informed decisions about their participation. The future of dentistry lies in harnessing AI-driven insights for diagnostic precision and personalized treatment plans. However, its success depends on effectively navigating the ethical complexities of data use. By responsibly utilizing EPRs, we can achieve transformative advancements in dental care while maintaining the trust and confidentiality of the patients we serve.JOURNAL OF KHYBER COLLEGE OF DENTISTRY01The future of dentistry lies in harnessing AI-driven insights for diagnostic precision and personalized treatment plans, however, its success depends on effectively navigating the ethical complexities of data use.JOURNAL OF KHYBER COLLEGE OF DENTISTRY["Syed Muaz Masoom Shah"]2024-12-13T00:00:00 ba7cc2af8dc8457ddb356dd78af482cfeb0aaf1aCancer causes & control : CCC1030This review will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches.Cancer causes & control : CCC["M. Dafni", "Mohamed Shih", "Agnes Zanotto Manoel", "Mohamed Yousif Elamin Yousif", "Stavroula Spathi", "Chorya Harshal", "Gaurang Bhatt", "S. Chodnekar", "Nicholas Stam Chune", "Warda Rasool", "Tungki Pratama Umar", "Dimitrios C. Moustakas", "Robert Achkar", "Harendra Kumar", "Suhaila Naz", "Luis M. Acu\u00f1a-Ch\u00e1vez", "Konstantinos Evgenikos", "Shaina Gulraiz", "Eslam Salih Musa Ali", "Amna Elaagib", "I. H. P. Uggh"]2024-12-13T00:00:00 6cc4e65c85404ff47c2de9146b00111bbe3cc3f2The emergence of Generative Artificial Intelligence (GenAI) raises critical questions about learner autonomy and agency. This exploratory case study examines how four university-level German language learners with diverse backgrounds developed autonomy in their learning process through engagement with AI tools. The study was conducted in early 2023 when most learners were first discovering ChatGPT’s potential for language learning. Data were collected through reflective journals, digital portfolios, and interviews during a semester-long course that scaffolded self-directed learning with AI integration. The findings reveal emerging patterns of shared agency between learners and AI tools. Learners developed distinct strategies for AI integration based on their language learning backgrounds, with heritage speakers focusing on accuracy improvement while classroom learners emphasized communication practice. Cross-case analyses identified key dimensions of autonomy development: a critical evaluation of AI output, evolving learner–AI relationships, maintaining and developing a second language (L2) voice, and the strategic integration of AI tools while preserving learner agency. These patterns suggest that autonomy in AI-mediated environments manifests through learners’ capacity to engage productively with AI while maintaining critical awareness and personal agency in their learning process.Education sciences180Analysis of four university-level German language learners with diverse backgrounds and cross-case analyses suggest that autonomy in AI-mediated environments manifests through learners’ capacity to engage productively with AI while maintaining critical awareness and personal agency in their learning process.Education Sciences["Antonie Alm"]2024-12-13T00:00:00 2707366c683001f6cd143429be163ad2b2917fa5Electronic Markets530It is observed that explanations impact users’ perception of the social media platform’s transparency, trust, and understandability and is proposed another synthesized framework for end user inclusion in designing an explainable interactive user interface.Electron. Mark.["Akm Bahalul Haque", "Najmul Islam", "Patrick Mikalef"]2024-12-13T00:00:00 5addc5cdba9767893c7e30104b0ea8e2c031bbacA myriad of measures to illustrate performance of predictive artificial intelligence (AI) models have been proposed in the literature. Selecting appropriate performance measures is essential for predictive AI models that are developed to be used in medical practice, because poorly performing models may harm patients and lead to increased costs. We aim to assess the merits of classic and contemporary performance measures when validating predictive AI models for use in medical practice. We focus on models with a binary outcome. We discuss 32 performance measures covering five performance domains (discrimination, calibration, overall, classification, and clinical utility) along with accompanying graphical assessments. The first four domains cover statistical performance, the fifth domain covers decision-analytic performance. We explain why two key characteristics are important when selecting which performance measures to assess: (1) whether the measure's expected value is optimized when it is calculated using the correct probabilities (i.e., a"proper"measure), and (2) whether they reflect either purely statistical performance or decision-analytic performance by properly considering misclassification costs. Seventeen measures exhibit both characteristics, fourteen measures exhibited one characteristic, and one measure possessed neither characteristic (the F1 measure). All classification measures (such as classification accuracy and F1) are improper for clinically relevant decision thresholds other than 0.5 or the prevalence. We recommend the following measures and plots as essential to report: AUROC, calibration plot, a clinical utility measure such as net benefit with decision curve analysis, and a plot with probability distributions per outcome category.arXiv.org00This work discusses 32 performance measures covering five performance domains (discrimination, calibration, overall, classification, and clinical utility) along with accompanying graphical assessments and explains why two key characteristics are important when selecting which performance measures to assess.ArXiv["B. Calster", "Gary S. Collins", "Andrew J. Vickers", "Laure Wynants", "Kathleen F. Kerr", "L. Barre\u00f1ada", "G. Varoquaux", "Karandeep Singh", "K. Moons", "Tina Hernandez-boussard", "D. Timmerman", "D. McLernon", "M. Smeden", "E. Steyerberg"]2024-12-13T00:00:00 77f4f351473cb8a962763ae1e9096f3b64d239f3Artificial Intelligence (AI) impacts various daily activities and features, including higher education. Educators and academics now see AI in education to be essential. The benefits of higher education and how universities adjust to shifting student and faculty attitudes on learning are topics of growing discussion. This study aims to explore how policymakers and educators may apply AI and modify it for the learning domain. The integrated technology acceptance model (TAM)-TOE model was implemented in a conceptual model that was released. It was tested with survey data obtained from 200 respondents who participated in an online survey, and a structural equation model (SEM-PLS) was utilized to assess the suggested hypotheses. The results show that organizational readiness, organizational compatibility, and partner support on perceived ease of use had been correlated with any significant relationship evaluated in the setting of higher education. It is anticipated that the approach will help authorities facilitate the use of AI in higher education. Furthermore, as AI is still in its infancy, more academic study is required before it can be used to the sector of education.Dinasti International Journal of Education Management And Social Science600The results show that organizational readiness, organizational compatibility, and partner support on perceived ease of use had been correlated with any significant relationship evaluated in the setting of higher education.Dinasti International Journal of Education Management And Social Science["Djoko Setyo Widodo", "Dwi Rachmawati", "Hadi Wijaya", "Alfi Maghfuriyah", "Udriya Udriya"]2024-12-13T00:00:00 f113cca94eb7e6686737294b247fa9db45b5bc64This research aims to explore the role of artificial intelligence (AI) technology in enhancing students' writing creativity in secondary school Indonesian language learning. AI technologies, such as AI-based word processing applications, automatic grammar tools, as well as AI-based creative platforms, have been widely used in education to encourage student innovation and creativity. The research approach used is qualitative with a case study method. Data were collected through observation, interviews with teachers and students, and analysis of student writing assignments using AI technology. The results showed that the integration of AI technology can help students produce more creative, structured, and interesting writing. In addition, the use of AI increases efficiency in the writing process, such as automatic editing and providing relevant writing suggestions. The role of the teacher as a facilitator remains crucial to ensure the use of AI is in line with the learning objectives. Obstacles include limited access to AI technology and the need for teacher and student training to maximize its benefits. This research provides new insights into the potential of AI technology in supporting creative learning in the digital era.International Journal For Multidisciplinary Research140The results showed that the integration of AI technology can help students produce more creative, structured, and interesting writing and increases efficiency in the writing process, such as automatic editing and providing relevant writing suggestions.International Journal For Multidisciplinary Research["Andi Paida"]2024-12-13T00:00:00 9a7e50f7d71c176b51b3358640c521a08d81e765The integration of artificial intelligence (AI) and robotics in precision applications is revolutionizing industries ranging from healthcare and agriculture to manufacturing and environmental monitoring. By leveraging advanced algorithms, machine learning, and real-time sensor data, AI-powered robotic systems achieve unparalleled accuracy, efficiency, and adaptability. This research explores the design and implementation of AI and robotics for precision tasks, focusing on autonomous decision-making, high-resolution detection, and real-time response capabilities. Applications include robotic surgery, precision farming, and quality control in manufacturing, where even minute deviations can significantly impact outcomes. The study also examines challenges in achieving precision, such as algorithmic complexity, system scalability, and the need for robust human-robot collaboration. Through case studies and simulations, this research highlights the transformative potential of combining AI and robotics in precision tasks, emphasizing how these technologies are reshaping global industries. Additionally, the ethical implications and societal impacts of deploying such systems are explored, ensuring that advancements align with principles of safety, transparency, and accountability.Human-Computer Interaction40The design and implementation of AI and robotics for precision tasks, focusing on autonomous decision-making, high-resolution detection, and real-time response capabilities, are explored, emphasizing how these technologies are reshaping global industries.Human Computer Interaction["Beg\u00fcm Pak\u00f6z"]2024-12-13T00:00:00 f83c57c5fa2f6ddb8faf72ac556c6a12f6147dd2Associate Editor Laurent Sheybani discusses how artificial intelligence (AI) could be both a risk and a solution for statistical reliability in scientific research in the context of the European Union AI act, which came into force in the summer of 2024.Brain Communications120How artificial intelligence (AI) could be both a risk and a solution for statistical reliability in scientific research in the context of the European Union AI act, which came into force in the summer of 2024 is discussed.Brain Communications["Laurent Sheybani"]2024-12-13T00:00:00 71cfedd6a8995e401ffac4edb91a62887be462e0European Journal of Pediatrics550An overview on how AI-driven systems might be able to support children and adolescents with chronic illnesses is provided on how AI-driven systems might be able to support children and adolescents with chronic illnesses.European Journal of Pediatrics["Janna-Lina Kerth", "Maurus Hagemeister", "A. C. Bischops", "Lisa Reinhart", "Juergen Dukart", "Bert Heinrichs", "S. Eickhoff", "Thomas Meissner"]2024-12-14T00:00:00 4282ee0042a32de8755ba68d1929f62b0cea26adThis research aims to analyze the legal vacuum in the Electronic Information and Transaction Law (UU ITE) related to the regulation of artificial intelligence (AI) in Indonesia in the context of deepfake. This research is motivated by the existence of several regulations on AI, these regulations are not sufficient to regulate thoroughly, especially regarding the technical aspects, implementation, and supervision of AI. So, the study to further analyze the urgency of reformulating the ITE Law due to the absence of specific regulations that are able to close the legal vacuum related to deepfake. The reformulation of the ITE Law is an urgent need to address the threat of deepfake, an AI-based content manipulation technology that is increasingly prevalent in Indonesia, deepfake creates manipulative content without the victim's consent, causing psychological harm, social stigma, and serious challenges in privacy and security. This research is included in normative juridical writing using a statutory approach through analysis of laws and derivative regulations and a comparative approach through analysis of the EU AI Act and China's regulations to provide reformulation suggestions. The results show that there is a legal vacuum that has not specifically regulated AI, which risks the misuse of technology and hampers legal certainty. Comparing the EU and China AI Acts, key findings include the need to adopt the basic principles of the EU AI Act, such as transparency, security, and fairness, as well as risk classification for AI systems. The current AI Act does not regulate important aspects such as labeling, reporting mechanisms, and supervision of high risks in AI systems, and it is recommended to establish a supervisory body responsible for AI risk management. Penelitian ini ditujukan untuk menganalisis kekosongan hukum dalam Undang-Undang Informasi dan Transaksi Elektronik (UU ITE) terkait regulasi kecerdasan buatan (AI) di Indonesia dalam konteks deepfake. Penelitian ini dilatarbelakangi dengan adanya beberapa aturan tentang AI, regulasi ini belum cukup mengatur secara menyeluruh, terutama terkait aspek teknis, pelaksanaan, dan pengawasan AI. Maka, pengkajian untuk menganalsis lebih lanjut atas urgensi reformulasi UU ITE dikarenakan belum adanya peraturan spesifik yang mampu menutup kekosongan hukum terkait deepfake. Reformulasi UU ITE menjadi kebutuhan mendesak untuk mengatasi ancaman deepfake, teknologi manipulasi konten berbasis AI yang semakin marak di Indonesia, deepfake menciptakan konten manipulatif tanpa persetujuan korban, sehingga menimbulkan kerugian psikologis, stigma sosial, dan tantangan serius dalam privasi serta keamanan. Adapun penelitian ini termasuk ke dalam penulisan yuridis normatif dengan menggunakan pendekatan pendekatan perundang-undangan melalui analisis undang-undang dan peraturan turunannya dan pendekatan komparasi melalui analisis pengaturan EU AI Act dan China untuk memberikan saran reformulasi. Hasilnya menunjukkan adanya kekosongan hukum yang belum mengatur AI secara spesifik, yang berisiko pada penyalahgunaan teknologi dan menghambat kepastian hukum. Maka dengan membandingkan pengaturan EU dan China AI Act, temuan utama mencakup kebutuhan untuk mengadopsi prinsip-prinsip dasar dari EU AI Act, seperti transparansi, keamanan, dan keadilan, serta klasifikasi risiko untuk sistem AI. UU ITE saat ini belum mengatur aspek-aspek penting seperti labelling, mekanisme pelaporan, dan pengawasan terhadap risiko tinggi dalam sistem AI, serta disarankan untuk membentuk badan pengawas yang bertanggung jawab atas pengelolaan risiko AI.JURNAL USM LAW REVIEW00There is a legal vacuum that has not specifically regulated AI, which risks the misuse of technology and hampers legal certainty, and key findings include the need to adopt the basic principles of the EU AI Act, such as transparency, security, and fairness, as well as risk classification for AI systems.JURNAL USM LAW REVIEW["Adnasohn Aqilla Respati"]2024-12-14T00:00:00 c4c148054ebed03f00cd0f471a366d079567ba87Penggunaan Artificial Intelligence (AI) masif terutama dalam bidang desain, AI digunakan para desainer dalam membantu dalam berkarya. Penelitian ini untuk mengeksplorasi dan menganalisis penggunaan Leonardo AI  dalam menciptakan karya  visual. Metode yang digunakan dalam penelitian adalah penelitian eksploratif  untuk mandalami topik tertentu, sehingga memperoleh pemahaman yang komprehensif pada topik tersebut. Prosedur penelitian yang dilaksanakan  1) Penggunaan kata kunci sebagai instruksi yang tepat, 2) Penyusunan instruksi secara terstruktur, 3) Uji dan analisis instruksi dalam bentuk deskriptif analisis, 4) Evaluasi instruksi. Hasil diperoleh, selain kecanggihan ternyata Leonardo AI memiliki kelemahan. Output visual yang diperoleh dapat berbeda meskipun penggunaan instruksi yang identik. Penggunaan istilah teknis fotografi yang belum semua dapat diakomodir oleh Leonardo AI. Terdapat kemungkinan terjadinya pelanggaran hak cipta apabila dataset yang digunakan untuk melatih model mencakup karya yang dilindungi hak cipta tanpa izin. Hasil visual di-generate Leonardo AI dapat dijadikan aset digabungkan aset visual yang dibuat desainer dengan menggunakan aplikasi tambahan.Infotech journal00INFOTECH journal["Nunnun Bonafix", "Octavianus Frans", "Noor Latif CM", "Ardiyan"]2024-12-14T00:00:00 7cb90dd3715e8e2aba094adf140cedb3b1c1e8feThis systematic literature review (SLR) explores the impact of negative stereotypes in science fiction and fantasy on public attitudes towards artificial intelligence (AI). By analyzing 9 studies published between 2011 and 2023, this review identifies key themes related to fear, distrust, ethical concerns, and the influence of media portrayals on the acceptance and adoption of AI technologies. The findings indicate that negative portrayals in these genres significantly increase fear and anxiety towards AI, leading to heightened skepticism and ethical concerns. Moreover, these negative stereotypes hinder the acceptance of AI in various fields, particularly affecting younger demographics more profoundly. This review highlights the need for more balanced and diverse media portrayals of AI to mitigate negative attitudes and promote a more nuanced understanding of AI technologies, particularly in light of its increasing role in various sectors.Studies in Media and Communication00The findings indicate that negative portrayals in science fiction and fantasy significantly increase fear and anxiety towards AI, leading to heightened skepticism and ethical concerns, which hinder the acceptance of AI in various fields, particularly affecting younger demographics more profoundly.Studies in Media and Communication["Duan Bo", "Aini Azeqa Marof", "Zeinab Zaremohzzabieh"]2024-12-14T00:00:00 744d4413fa34a81266d603fc48f90fd793fc3ae9Background Due to its complexity, Artificial Intelligence often requires large, confidential clinical datasets. 20-30% of the general public remain sceptical of Artificial Intelligence in healthcare due to concerns of data security, patient-practitioner communication, and commercialisation of data/models to third parties. A better understanding of public concerns of Artificial Intelligence is therefore needed, especially in the context of stroke research. Aims We aimed to evaluate the opinion of patients and the public in acquiring large clinical datasets using an opt-out consent model, in order to train an AI-based tool to predict the future risk of stroke from routine healthcare data. This was in the context of our project ABSTRACT, a UK Medical Research Council study which aims to use AI to predict future risk of stroke from routine hospital data. Methods Opinions were gathered from those with lived experience of stroke/TIA, caregivers, and the general public through an online survey, semi-structured focus groups, and 1:1 interviews. Participants were asked about their perceived importance of the project, the acceptability of handling deidentified routine healthcare data without explicit consent, and the acceptability of acquiring these data via an opt-out model of consent model by members within and outside of the routine clinical care team. Results Of the 83 that participated, 34% of which had a history of stroke/TIA. Nearly all (99%) supported the project's aims in using AI to predict stroke risk, acquiring data via an opt-out consent model, and the handling of pseudonymized data by members within and outside of the routine clinical care team. Conclusion Both the general public and those with lived experience of stroke/TIA are generally supportive of using large, de-identified medical datasets to train AI models for stroke risk prediction under an opt-out consent model, provided the research is transparent, ethically sound, and beneficial to public health.medRxiv00Both the general public and those with lived experience of stroke/TIA are generally supportive of using large, de-identified medical datasets to train AI models for stroke risk prediction under an opt-out consent model, provided the research is transparent, ethically sound, and beneficial to public health.["W. Heseltine-Carp", "M. Thurston", "M. Allen", "D. Browning", "M. Courtman", "A. Kasabe", "E. Ifeachor", "S. Mullin"]2024-12-14T00:00:00 97372252c97a24d57fbcd17590d27db8405704f0This article examines the integration of artificial intelligence (AI) into accounting as a tool for process optimization and risk management. The benefits of AI, such as automation of routine operations, increased data accuracy, improved reporting transparency, and support in risk management, are discussed. Particular attention is paid to how AI contributes to predictive analytics and error prevention, which reduces the likelihood of financial losses. The challenges associated with the implementation of AI are also analyzed, including high financial costs, data protection requirements, and staff adaptation.Bulletin of Science and Practice00The integration of artificial intelligence into accounting as a tool for process optimization and risk management is examined, with particular attention paid to how AI contributes to predictive analytics and error prevention, which reduces the likelihood of financial losses.Bulletin of Science and Practice["K. Nurdinova"]2024-12-14T00:00:00 b385dfef747e3eb76e7b553e5a46c12c0ea0a1e2The rapid integration of Artificial Intelligence (AI) into educational systems has prompted an exploration of its efficacy in language acquisition. This study aims to evaluate the role of AI in enhancing language learning processes, focusing on its capability to support personalized and adaptive learning experiences. Utilizing a systematic content analysis coupled with quantitative methods, including the chi-square test, this research synthesizes findings from scholarly articles published between 1985 and 2023. The content analysis examined the deployment of AI tools in language learning, while quantitative measures assessed the distribution and impact of these technologies. Results from the chi-square test indicated no significant differences in the frequency of studies advocating for or against the use of AI, suggesting a balanced academic perspective. The findings highlight AI's potential to enrich language learning through customized educational experiences. However, they also underscore the necessity for careful implementation, considering ethical concerns and potential biases. Conclusively, AI presents valuable opportunities for language education but requires strategic management to mitigate associated risks. Implications of this study stress the importance of ongoing research to optimize AI applications in language learning, ensuring they are equitable and effective across diverse educational settings. Furthermore, the use of AI for data analysis based on instructed frameworks for large language models could play a major role in helping researchers analyze large datasets collected about language acquisition if the AI tools are used skillfully and responsibly.Forum for Linguistic Studies680Results from the chi-square test indicated no significant differences in the frequency of studies advocating for or against the use of AI, suggesting a balanced academic perspective, and highlight AI's potential to enrich language learning through customized educational experiences.Forum for Linguistic Studies["Obied Alaqlobi", "Ahmed Alduais", "Muhammad Alasmari", "Fawaz Qasem"]2024-12-14T00:00:00 2f3277698617b64c7f25f6cb80f253d7c6c2def0Current Medical Science730How AI supports the development of medical metaverse is introduced, including its specific application scenarios, shortcomings and future development to contribute to the advancement of more sophisticated and intelligent medical methods.Current medical science["Jia-Ming Yang", "Bao-Jun Chen", "Rui-Yuan Li", "Bi-Qiang Huang", "Mo-Han Zhao", "Peng-Ran Liu", "Jia-Yao Zhang", "Z. Ye"]2024-12-14T00:00:00 d70ee9e22dcfbf0810b16d9bab00f7672412289fThis study investigates the critical challenges associated with ensuring the security and robustness of artificial intelligence (AI) systems, especially within high-stakes applications such as autonomous vehicles, healthcare, and financial technologies. The primary objective is to identify vulnerabilities in AI algorithms and propose effective mitigation strategies. The research emphasizes contemporary threats, including adversarial attacks, algorithmic opacity, data breaches, and the ethical ramifications of AI deployment. A review of current literature reveals that adversarial attacks, where subtle input perturbations cause significant misclassifications, present a considerable risk to AI reliability. Techniques such as robust training, involving training models on adversarial examples, have shown effectiveness in improving resilience, albeit with higher computational demands. The study also explores the importance of explainable AI (XAI) tools like LIME and SHAP, which enhance transparency by clarifying the decision-making processes of complex models. This transparency is vital for fostering user trust, especially in fields like medicine and finance, where understanding AI decisions is essential. XAI approaches enable better oversight and adherence to ethical standards. Data privacy concerns are addressed through methods such as differential privacy, which protects sensitive information by adding noise, and federated learning, which enables decentralized model training without exposing raw data. The findings indicate that these strategies secure data while maintaining model efficacy. By integrating robustness and explainability, this study contributes practical solutions to strengthen AI systems against evolving threats, advancing AI security and fostering trust in these technologies.Bulletin of Science and Practice00The study explores the importance of explainable AI (XAI) tools like LIME and SHAP, which enhance transparency by clarifying the decision-making processes of complex models, which enable better oversight and adherence to ethical standards.Bulletin of Science and Practice["A. Zhalilov", "A. Toktorbaev"]2024-12-14T00:00:00 832d0994201119020488d3b67c70b58a329785e3Artificial Intelligence (AI) has made major advancements in recent years, with Machine Learning (ML) emerging as one of its most potent subfields. Machine learning techniques, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning, have transformed various sectors by enabling systems to learn from data and make intelligent judgements. the newest breakthroughs in machine learning techniques and their real-world applications across diverse sectors, including healthcare, banking, transportation, and entertainment. We analyse the evolution of algorithms, increases in computer power, and the rising availability of huge data, all of which have contributed to AI's rapid advancement. Additionally, we explore issues such as model interpretability, bias in training data, and ethical implications. By emphasising cutting-edge ML models and their implementations, this article seeks to provide an overview of how machine learning is influencing industries and transforming the way we address challenging challenges.Journal of Sustainable Solutions30The evolution of algorithms, increases in computer power, and the rising availability of huge data, all of which have contributed to AI's rapid advancement are analyzed.Journal of Sustainable Solutions["Sarbhjeet Kaur"]2024-12-14T00:00:00 7c2d235183e8a7e3f4b34e1f712ac11cf7feb55fInt. J. Inf. Sec.790This systematic literature review seeks to fill the research gap by assessing the current state of AI in SSA, identifying key areas where AI contributes to improve SSA processes, highlighting the limitations of current methodologies, and providing the guidance for future advancements in the field of AI-driven SSA.Int. J. Inf. Sec.["Shao-Fang Wen", "Ankur Shukla", "Basel Katt"]2024-12-14T00:00:00 57f1dd0d48dfa6773f00e3da6f4eac4cf2ae43c3Artikel ini mengkaji mengenai manfaat kecerdasan buatan Artificial Intelligensi dalam pembelajaran dan pendidikan agama islam. Pemanfaatan kecerdasan AI dalam bidang pendidikan telah menjadi subjek yang menarik perhatian akademisi dan praktis pendidikan. Kemajuan teknologi Artificial Intelligensi memberikan dampak yang sangat signifikan dalam pendidikan, termasuk pendidikan agama islam. Teknologi seperti chatgpt, chatbot dan natural language processing (NLP), penelitian ini mengidentifikasi manfaat AI dalam pembelajaran dan pendidikan agama islam, analisis data pendidikan, serta meningkatkan pembelajaran secara efektif dan adaptif pendidikan agama islam. Metode penelitian ini merupakan studi kepustakaan, data diambil dan dianalisis yang bersumber dari buku dan jurnal. Hasil menunjukkan bahwa AI dapat memperkaya proses pembelajaran, penerapannya dihadapkan tantangan dan dampak terhadap pembelajaran dan pengembangan pendidikan keagamaan yang bergantung terhadap AI. AI dapat memberikan manfaat secara signifikan dan maksimal dalam proses memperbaiki pembelajaran pendidikan agama islam. This article examines the benefits of artificial intelligence (AI) in Islamic religious learning and education. The use of AI intelligence in education has become a subject of interest to academics and educational practitioners. The advancement of Artificial Intelligence technology has a very significant impact on education, including Islamic religious education. Technologies such as chatgpt, chatbots and natural language processing (NLP), this study identifies the benefits of AI in Islamic religious learning and education, educational data analysis, and improving effective and adaptive learning of Islamic religious education. This research method is a literature study, data is taken and analyzed from books and journals. The results show that AI can enrich the learning process, its application is faced with challenges and impacts on learning and the development of religious education that depends on AI. AI can provide significant and maximum benefits in the process of improving Islamic religious education learning.Mauriduna: Journal of Islamic Studies110Mauriduna: Journal of Islamic Studies["M. Nur", "Ilham Munir", "Muhammad Hamidum Majid", "Pendidikan Agama Pembelajaran", "Kontribusi Islam", "Ai"]2024-12-14T00:00:00 c9ac1c7163423a8ddfdc9e010cc2ad0321ee865fIJARCCE00IJARCCE[]2024-12-14T00:00:00 347b8c59f38d4eac7fb6aa7375392af2cab7ec82The International Journal of Advanced Manufacturing Technology140The International Journal of Advanced Manufacturing Technology["Ting Wu"]2024-12-14T00:00:00 4ea5bdf1c552e3e56ce307fee2f878b1c480e719Abstract. The aim of this article is to describe a dataset derived from crime scenes or fictitious accidents that can be used for artificial intelligence processing. The steps involved in setting up these forensic scenes, the data acquisition chain, classification and formatting will be described, as well as the first tests carried out in artificial intelligence. The first data formatting operations will be carried out so that they can be integrated into machine learning and deep learning solutions; in particular the deep learning solution SuperPointTransformer (Robert et al., 2023). The difficulties linked to this type of work and the benefits of using artificial intelligence for forensic scenes will also be presented. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences01The steps involved in setting up these forensic scenes, the data acquisition chain, classification and formatting will be described, as well as the first tests carried out in artificial intelligence.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences["Herv\u00e9 Daudigny", "P. Grussenmeyer"]2024-12-14T00:00:00 0321e760da71c361aea70171c524a1690c1545eaArtificial intelligence (AI) can revolutionize agriculture by enhancing genomic research and promoting sustainable crop improvement. AI systems integrate machine learning (ML) and deep learning (DL) with big data to identify complex patterns and relationships by analyzing vast genomic, phenotypic, and environmental datasets. This capability accelerates breeding cycles, improves predictive accuracy, and supports the development of climate-resilient, high-yielding crop varieties. Applications such as precision agriculture, automated phenotyping, predictive analytics, and early pest and disease detection demonstrate AI’s ability to optimize agricultural practices while promoting sustainability. Despite these advancements, challenges remain, including fragmented data sources, variability in phenotyping protocols, and data ownership concerns. Addressing these issues through standardized data integration frameworks, advanced analytical tools, and ethical AI practices will be critical for realizing AI’s full agricultural potential. This review provides a comprehensive overview of AI-powered genomic research, highlights the role of big data in training robust AI models, and explores ethical and technological considerations for sustainable agricultural practices.Agriculture780A comprehensive overview of AI-powered genomic research is provided, the role of big data in training robust AI models is highlighted, and ethical and technological considerations for sustainable agricultural practices are explored.Agriculture["E. W\u00f3jcik-Gront", "Bart\u0142omiej Zieniuk", "M. Pawe\u0142kowicz"]2024-12-14T00:00:00 2d6aa46efa2110d1be62f32bf3365abe49fad806Recognizing the risks of the new technologies and the online space, the European Union in the recent years has embarked on regulation process to reduce them. These risks are including the strong competitive advantages, and considerable economic power gained by some of the service providers in the online market, the growing of the infringements of the users’ rights, and the issues related to the use of the Artificial Intelligence. Although – as we will refer to that later – there were already rules in the European Union, specifically regulating the online world, thus we can see that the rapid and dynamic development of this field requires the continuous revision of the legislation and the introduction of new provisions. In this paper we focus on two EU regulations of particular importance for businesses: the Digital Markets Ant and the Digital Services Act. These regulations are relatively recent pieces, they have not been in force for a long time, but they are also mandatory in our country.Safety and Security Sciences Review00The rapid and dynamic development of this field requires the continuous revision of the legislation and the introduction of new provisions, so the Digital Markets Ant and the Digital Services Act are focused on.Safety and Security Sciences Review["M\u00e1rk Kolejanisz", "Zs\u00f3fia R\u00e9ti"]2024-12-14T00:00:00 c715fe3e76e504f53ee904cfbf90429e12d1f6e3The fast track acceptance of artificial intelligence (AI) in combat introduces ethical dilemmas that call for a cohesive approach ensuring compliance with humanitarian laws and moral standards. In this paper, we develop a framework for responsible innovation in military AI, driven by teachable moments learned from the Athena AI Case Study. We examine some of the ethical concerns surrounding autonomy in warfare, such as accountability, transparency, bias, and human control, and propose guidance aimed at mitigating these possible risks. Findings indicate therefore that adherence to the principles of Responsible Research and Innovation can make it possible for AI to ethically spread in defense.International Journal For Multidisciplinary Research00This paper develops a framework for responsible innovation in military AI, driven by teachable moments learned from the Athena AI Case Study, and indicates that adherence to the principles of Responsible Research and Innovation can make it possible for AI to ethically spread in defense.International Journal For Multidisciplinary Research["Syed Taha Hussain Kazmi"]2024-12-14T00:00:00 e7c340f4145af1fc1f48c1fe3fb0b4cf99a4ddcdWith the popularization of AI technology, small and medium-sized enterprises can now leverage AI technology in e-commerce. However, there is a threshold for the use of technology, and the adoption of different levels of artificial intelligence technology also causes specific problems from the aspects of capital, technology, organizational structure, and enterprises. Due to the problems, this article aims to propose e-commerce process and mapping for AI utilization in SMEs. This article utilizes Google Scholar as the data source and selects 31 related literatures through classification to conduct literature research. Based on the classification of different levels of small and medium-sized enterprises, this article discusses the factors influencing enterprises' adoption of AI technology in business from the lens of technology, organization, and environment. Through a summary of existing research, artificial intelligence technology is categorized into three categories and five levels. The technologies at each level are then analyzed in detailed based on the descriptions, capabilities, and applications, with a focus on highlighting their technical and application characteristics. Finally, from an e-commerce perspective, this article attempts to map AI technology in e-commerce for small and medium-sized enterprises. This would initiate towards assisting enterprises at different levels to which they can utilize AI technology and effectively leverage it in e-commerce, and to address development challenges plus enhancing the competitiveness of their e-commerce operations.International Journal of Academic Research in Business and Social Sciences320The factors influencing enterprises' adoption of AI technology in business from the lens of technology, organization, and environment are discussed and the proposed e-commerce process and mapping for AI utilization in SMEs are proposed.International Journal of Academic Research in Business and Social Sciences["Tong Zhu", "Mohd Zaidi Abd Rozan"]2024-12-14T00:00:00 e25f819747de9daef83239748dc680e638fdbbbcArtificial intelligence (AI) is opening up new possibilities for educators to explore innovative solutions to classroom challenges. While the benefits of AI in enhancing individual learning are well established, its potential in collaborative learning environments remains underexplored. This paper reports on the first cycle of a design-based research project that involved the development of Collabowrite, a collaborative story-writing application designed for compulsory English-as-a-foreign-language (EFL) classes at a Japanese university. During a 14-week semester, 52 students engaged with the application in 160 separate usage instances, using AI features such as grammar checks, virtual group members, artwork generation, and story narration. The grammar checks were evaluated for accuracy at 90.4% across 894 instances, with students adhering to elements of AI-provided guidance 91.4% of the time. Students rated their enjoyment of the activities at an average of 4.63 out of 5. Pre- and post-intervention surveys involving 27 participants indicated significant positive shifts in perceptions toward English writing, collaborative work, and the role of technology in language learning. Reflexive thematic analysis of qualitative data generated three salient themes that capture the diverse experiences of participants. These findings suggest the need to revisit sociocultural learning theories to incorporate AI into classroom dynamics. This initial research cycle led to the formulation of six design principles for integrating AI-enhanced collaborative writing tools into EFL classrooms. Future cycles will further evaluate the efficacy of Collabowrite as an educational tool and its impact on students’ foreign language writing abilities.Technology in Language Teaching &amp; Learning00The initial research cycle of a design-based research project that involved the development of Collabowrite led to the formulation of six design principles for integrating AI-enhanced collaborative writing tools into EFL classrooms, suggesting the need to revisit sociocultural learning theories to incorporate AI into classroom dynamics.Technology in Language Teaching &amp; Learning["Nicolas Emerson"]2024-12-14T00:00:00 cccd25b93a461ba802fa302a8cf7fbb905bb9eb5Este artículo explora cómo la Inteligencia Artificial (IA) puede integrarse en proyectos de Aprendizaje-Servicio (ApS), analizando su uso en tres niveles: como objeto central, como recurso para el desarrollo de los proyectos y como herramienta para su planificación y evaluación. Además, revisa diferentes enfoques educativos: educar con IA, educar sobre IA y educar a la IA, destacando las implicaciones pedagógicas en cada uno de ellos. El texto también aborda los desafíos éticos relacionados con la aplicación de la IA al ApS, subrayando la necesidad de garantizar un uso responsable que promueva el bienestar sin perpetuar desigualdades o exclusiones sociales.EDU REVIEW. International Education and Learning Review / Revista Internacional de Educación y Aprendizaje150EDU REVIEW. International Education and Learning Review / Revista Internacional de Educación y Aprendizaje["Joshua Beneite-Mart\u00ed"]2024-12-14T00:00:00 8681c7b9e854a18a42d8df83160b52ee46c107d4In the period of the Fourth Industrial Revolution, the rapid growth of artificial intelligence (AI) technologies poses a challenge to all countries for their legitimate use and timely implementation in the public sector. The BRICS countries are active participants in the digitalization process in the political, economic, social and military spheres, as well as in the association's activities aimed at enhancing AI technologies and communication channels. However, the rapid growth of the latest AI-based technologies threatens the already vulnerable cybersecurity sector and facilitates active informationpsychological and terrorist operations aimed at putting BRICS political actors out of work and conducting disinformation campaigns.Latinskaia Amerika91The rapid growth of the latest AI-based technologies threatens the already vulnerable cybersecurity sector and facilitates active informationpsychological and terrorist operations aimed at putting BRICS political actors out of work and conducting disinformation campaigns.Latinskaia Amerika["Ekaterina A. Vinogradova"]2024-12-15T00:00:00 70dd5b42f113f293ff941d6857ba192efdcae1b7Technological developments and transformations have rapidly risen since the Fourth Industrial Revolution. The prevalence of industrial devices interconnected over the wireless sensor networks and the provision of a sustainable data flow reveal the importance of the Industrial Internet of Things (IIoT). In the manufacturing industry, supervisory control and data acquisition (SCADA) systems are used to control IIoT for critical infrastructure. A cyberattack on the network-based communication structure embedded into the architecture of industrial equipment can significantly disrupt/sabotage product manufacturing and other industrial operations. The digitization of industrial control systems can expose the systems to malicious actors and therefore requires additional security solutions, such as intrusion detection systems (IDSs). Increasing sophistication of cyberattacks, industrial companies need to adopt innovative solutions like artificial intelligence (AI)-based attack detection to protect their valuable assets. In addition, AI-based approaches are more effective as they analyze network traffic, identify threats, and adapt to new attack techniques. This study aims to develop an AI-based IDS with high accuracy for SCADA security. In the study, cyberattacks that may occur against SCADA systems are examined. AI methods (including K-nearest neighbor, quadratic discriminant analysis, adaptive boosting, gradient boosting, and random forest) in different categories are used and AI models with various parameters are built. To improve the detection performance of the models, comprehensive experiments are carried out on two different SCADA data sets. As a result of experiments, the test accuracy rates exceeding 96.82% are achieved by all models: on the WUSTL-IIOT-2021 data set, the XGB model has outperformed with an accuracy of 99.99%.IEEE Internet of Things Journal01This study aims to develop an AI-based IDS with high accuracy for SCADA security, and AI methods in different categories in different categories are used and AI models with various parameters are built.IEEE Internet of Things Journal["Nesibe Yal\u00e7\u0131n", "Semih \u00c7ak\u0131r", "Sibel \u00dcnald\u0131"]2024-12-15T00:00:00 f1e2faa7f814884f38968113b499794b9ceef0beartificial intelligence (AI) as a transformative force in various sectors, including the legal domain. This research uses a qualitative approach to explore the intersection between Islamic law and AI, and how AI serves as a revolutionary tool that reshapes the practice and interpretation of Sharia in the digital age. Through qualitative methods such as in-depth interviews with Islamic legal experts, content analysis of legal literature, as well as case studies of AI applications in the context of Islamic law, this research explores how AI can improve legal reasoning, streamline judicial processes, and ensure more accurate and consistent legal outcomes in Islamic jurisprudence. The research offers a comprehensive analysis of the opportunities and challenges presented by AI in the context of Islamic legal practice, and proposes a balanced approach in utilising the technology while upholding the integrity of Sharia. The findings contribute to a deeper understanding of the role of AI in the modernisation of Islamic law, paving the way towards a future where technology and tradition can coexist harmoniously.Jurnal Hukum Islam00The research offers a comprehensive analysis of the opportunities and challenges presented by AI in the context of Islamic legal practice, and proposes a balanced approach in utilising the technology while upholding the integrity of Sharia.Al-Hurriyah: Jurnal Hukum Islam["Muhammad Edo Rahman", "Fadilla Syahriani", "Wilibaldus Jampa"]2024-12-15T00:00:00 aa0c9ba499c77035d31fcfb3728d42a3d691e981Forensic Medicine is the application of medical knowledge for law and administration of justice, which involves conducting the medico-legal post-mortem examination, estimation of the age of the individual, victim-accused examination and the study of poisons in all aspects. Many times, minute details of the examination are often missed by the naked eye, especially if the expert is inexperienced or if the autopsy is conducted at night time leading to high numbers of negative or obscure autopsies. As Artificial Intelligence is booming the smoothening of the work in all specialities like finance, administration, transportation, health care and the medical field, its applicability can smoothen the work of forensic medicine experts. It can offer the result more accurately, efficiently, precisely and within no time at low cost as compared to contemporary services. This review explores the applications, benefits, challenges, and prospects of AI in forensic medicine within the Indian context. By examining recent advancements and case studies, this paper aims to offer a comprehensive understanding of AI's impact on forensic practices in India.IP International Journal of Forensic Medicine and Toxicological Sciences190This review explores the applications, benefits, challenges, and prospects of AI in forensic medicine within the Indian context by examining recent advancements and case studies to offer a comprehensive understanding of AI's impact on forensic practices in India.IP International Journal of Forensic Medicine and Toxicological Sciences["P. Dixit", "Udai Shankar Sinha", "Rajeev Kumar", "Mumta Kumari", "H. Chawla", "Vikas Chandra", "Aditya Anand"]2024-12-15T00:00:00 c2afab531767910b13a9d92d8e69124a4d7040c0This paper discusses the complementary relationship between Modeling and Simulation (M&S) and Artificial Intelligence (AI) methods like machine learning. While M&S uses algorithms to model system behavior from input parameters, AI learns patterns from correlation in data. The paper argues that hybrid models combining M&S and AI can be more powerful than either alone. It provides a conceptual framework showing how M&S and AI can be integrated in sequential, parallel, complementary or competitive configurations. Several example applications are given where AI enhances M&S and vice versa, such as using AI to optimize simulation parameters, generate synthetic training data for AI from simulations, interpret AI model behavior through simulation, and automate aspects of simulation development with AI assistance. The potential benefits of hybrid AI/M&S modeling span improved accuracy, efficiency, trustworthiness and cross-disciplinary collaboration. The paper calls for further research developing a solid theoretical foundation for merging these complementary paradigms.Online World Conference on Soft Computing in Industrial Applications460It is argued that hybrid models combining M&S and AI can be more powerful than either alone, and provides a conceptual framework showing how M&S and AI can be integrated in sequential, parallel, complementary or competitive configurations.2024 Winter Simulation Conference (WSC)["Andreas Tolk"]2024-12-15T00:00:00 50658d5d7f8f48317110d2c5e77724fef348c047The study focuses on a currently emerging topic: artificial intelligence (AI). The impact of AI on the field of education is considerable. The possibilities and risks associated with its use are already well known, especially when the ethical and/or legal boundaries associated with it are crossed. However, the potential of AI as an emerging technology, in the field of education in general, and in early childhood education in particular, is yet to be realised. In this paper we consider what has occurred to date, and then focus the attention of researchers who have conducted studies in the field of early childhood education. To do this, we adopted a bibliometric study approach. This type of analysis has allowed us to consider the scientific activity carried out in the field of AI as applied in early childhood education. As we note throughout this paper, this type of analysis is highly valued among the scientific community as a way of assessing the quality, productivity and scientific evolution of a subject of study. It provides academics with valuable information about research conducted in a particular area. Following the recommendations of experts in the field, in this paper we not only address the volume of publications (quantity), but also assess other scientometric indicators that measure their quality. The results of this study then can make a significant contribution to the field of research and work in early childhood education in the face of the new challenges presented by today’s society.Contemporary Issues in Early Childhood210This study considers the scientific activity carried out in the field of AI as applied in early childhood education, and addresses the volume of publications, but also assess other scientometric indicators that measure their quality.Contemporary Issues in Early Childhood["Mar\u00eda Jos\u00e9 Latorre-Medina", "Samia Abdelmaula-Mesaud"]2024-12-15T00:00:00 6c48abe4345573668549ccd447b113caf66954feDiagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement. AI can streamline the information-gathering process by automating data retrieval, reducing cognitive load, and providing clinicians with essential patient details quickly. AI-driven CDS systems enhance diagnostic decision making by offering real-time insights, reducing cognitive biases, and prioritizing differential diagnoses. Furthermore, AI-powered feedback loops can facilitate continuous learning and refinement of diagnostic processes by providing targeted education and outcome feedback to clinicians. By integrating AI into these areas, the potential for reducing diagnostic errors and improving patient safety in the ED is substantial. However, successfully implementing AI in the ED is challenging and complex. Developing, validating, and implementing AI as a safe, human-centered ED tool requires thoughtful design and meticulous attention to ethical and practical considerations. Clinicians and patients must be integrated as key stakeholders across these processes. Ultimately, AI should be seen as a tool that assists clinicians by supporting better, faster decisions and thus enhances patient outcomes.Academic Emergency Medicine1060Artificial intelligence should be seen as a tool that assists clinicians by supporting better, faster decisions and thus enhances patient outcomes.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine["R. A. Taylor", "Rohit B. Sangal", "Moira E Smith", "A. Haimovich", "A. Rodman", "Mark S. Iscoe", "Suresh K Pavuluri", "Christian Rose", "Alexander T. Janke", "Donald S Wright", "V. Socrates", "A. Declan"]2024-12-15T00:00:00 81be9bc05c7398808f8178e0e65818a6da6c59fdPerkembangan Artificial Intelligence (AI) mempermudah pembuatan konten visual yang kreatif dan efisien. Media sosial, sebagai platform utama remaja untuk berekspresi, mempromosikan bisnis, dan membangun jaringan, sering kali tidak dimanfaatkan optimal karena keterbatasan keterampilan desain grafis. Pengabdian ini bertujuan untuk menyelenggarakan pelatihan desain grafis berbasis AI bagi remaja Baitul Halim (RBH) untuk meningkatkan kreativitas mereka dalam pengelolaan konten media sosial. Pelatihan mencakup penggunaan alat AI seperti Canva dan ChatGPT untuk membuat desain otomatis, memanfaatkan template dinamis, serta strategi pemasaran konten yang relevan dengan tren terkini. Hasil kegiatan menunjukkan bahwa peningkatan signifikan dalam keterampilan desain peserta, yang mampu menciptakan konten menarik dan berkualitas tinggi. Selain itu, pelatihan ini memperluas wawasan mereka terhadap peluang karier dan wirausaha di bidang digital dan kreatif. Dengan itu, integrasi teknologi AI dalam desain grafis tidak hanya mempercepat proses produksi, tetapi juga mempersiapkan remaja untuk menghadapi dinamika teknologi dan media sosial di masa depan. Kata Kunci: Desain Grafis, Artificial Intelligence, Media Sosial, Kreativitas RemajaJurnal Pengabdian Kolaborasi dan Inovasi IPTEKS120Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS["Yuris Alkhalifi", "Khairul Rizal", "Amir", "Ahmad Fachrurozi"]2024-12-15T00:00:00 62406d8b3086cddb6630e49cee45b40ff014937fThe boom in Artificial Intelligence (AI) technologies is opening up new opportunities in engineering. A variety of novel tools are flooding the market every day. However, integration into the industry processes is happening at a slow pace. This paper represents a survey conducted with 163 industrial engineers on the use of AI in product development. The questionnaire specifically focuses on the early phase of product development and investigates the current state, challenges and potentials. The results show a high level of interest in the use of AI, but integration into everyday working processes has been low so far. Among the few who incorporate AI into their concept development processes, an indication to shorter concept development times was observed. The automation of routine tasks and a conflicting requirements detection are seen as particularly promising AI applications. Main challenges and barriers lie in the expertise of employees, the costs of implementation and the usability of data. Nevertheless, more than two thirds state that further AI integration is planned. The focus here is particularly on generative AI.IEEE International Conference on Industrial Engineering and Engineering Management180Among the few who incorporate AI into their concept development processes, an indication to shorter concept development times was observed, and more than two thirds state that further AI integration is planned.2024 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)["S. Steininger", "H. Camci", "J. Fottner"]2024-12-15T00:00:00 278bff937e04683beff753974bea5ed3eeda97dcINTRODUCTION: The interaction between natural and artificial intelligence (AI) is increasingly significant as technology evolves. While natural intelligence has historically driven human progress, AI introduces new models in problem-solving and decision-making. This study explores the dynamics between these forms of intelligence and their implications for public health technology assessment. METHODS: This review employs a multidisciplinary approach, including historical analysis, comparative case studies, and examination of ethical considerations, to assess the impact of AI relative to natural intelligence. RESULTS: Natural intelligence has traditionally addressed complex problems, but AI now enhances capabilities through data analysis and precision. While AI offers significant benefits across sectors such as healthcare, finance, and education, it also raises concerns about data privacy, ethics, and job displacement. In public health, AI can improve disease management and resource allocation, though challenges related to health disparities and data security persist. DISCUSSION: The integration of AI presents substantial opportunities but requires careful management of ethical and practical challenges. Maintaining a balance between leveraging AI and preserving human cognitive functions is crucial. Developing a prototype model to address current global public health challenges, based on the perspectives presented and the considerations discussed, could provide valuable additional insights into effective strategies for managing these complex issues worldwide. CONCLUSION: The future of AI involves integrating technological advancements with human intelligence to enhance capabilities while addressing ethical and practical issues. This balance will be key to advancing public health and other sectors effectively.Eurasian journal of health technology assessment200The future of AI involves integrating technological advancements with human intelligence to enhance capabilities while addressing ethical and practical issues, and this balance will be key to advancing public health and other sectors effectively.Eurasian Journal of Health Technology Assessment["Verda Tunal\u0131g\u0131l (md, Mph, Phd)"]2024-12-15T00:00:00 a814ba5015b86933fdebe5c9d7bc93621520afa1In today’s society, online marketing has become essential for almost every business. As digitalization accelerates, companies are increasingly compelled to strengthen their online presence and strategically position themselves within the digital space. Younger consumers, in particular, actively use social media platforms to inform their purchasing decisions. As a result, it is critical for brands to boost their visibility on these platforms and develop effective marketing strategies. With consumers becoming more mobile, they expect personalized experiences and seamless interactions across all touchpoints when engaging with a company. To address these challenges, the integration of artificial intelligence (AI) in marketing, alongside collaborations with influencers, has gained significant traction in recent years. AI plays a pivotal role in crafting personalized customer experiences, while influencers have considerable sway over young consumers. Social media campaigns are especially effective in shaping the consumption habits of adolescents, who tend to trust the recommendations and experiences shared by influencers. This trust significantly influences their purchasing behavior. In particular, influencer marketing strategies on platforms like Instagram enable brands to effectively connect with younger audiences. This study aims to explore how businesses utilize AI to enhance their online marketing efforts within the realm of social media, and to analyze the influence of influencers on the consumption patterns of young people. In this context, the growing impact of influencer marketing is increasingly shaping brand loyalty among young consumers, leaving a lasting effect in today’s digital age.The Turkish Online Journal of Design Art and Communication240This study aims to explore how businesses utilize AI to enhance their online marketing efforts within the realm of social media, and to analyze the influence of influencers on the consumption patterns of young people.Turkish Online Journal of Design Art and Communication["B\u00fc\u015fra Sar\u0131kaya"]2024-12-15T00:00:00 43927008e92694d24f1a3e3b4145f953c1116581The relevance of the research topic is based on the dynamic development of artificial intelligence (hereinafter - AI), as well as the impact it has on all areas of social activity legislation in this area. The existing legal and regulatory framework of Ukraine in the field of relations related to the use of AI is not perfect and needs further development. The objective of this research paper is to study and analyze the legislative frameworks of the PRC and Ukraine in this area, assess their effectiveness, and identify aspects of Ukraine’s legal regulation that can be improved based on the experience of the People’s Republic of China. The analysis of recent studies and publications demonstrates the growing interest of scholars in the issues of legal regulation of public relations in the field of AI. However, the issue of the relevance of taking into account the experience of China in the further development of legal regulation of Ukraine, as well as its specific aspects that need to be improved, is not sufficiently studied. The main focus of the article is on the research and analysis of the conceptual and special legal acts of China in the field of AI, as well as on the analysis of the Ukrainian legislative framework in this area with a view to further determining the feasibility and consideration of the experience of China by Ukrainian legislators in the further development of legal regulation of relations related to the use of AI. It is established that Ukrainian legislation currently does not provide sufficient regulation of relations in the field of use of AI and requires further improvement, taking into account the experience of other countries. It was also revealed that the PRC’s legislation is complex and well-structured, and the comprehensive legal regulation is valuable for Ukrainian legislators to consider when developing and improving their own legislation in this area.Constitutional State00The main focus of the article is on the research and analysis of the conceptual and special legal acts of China in the field of AI and the analysis of the Ukrainian legislative framework in this area with a view to further determining the feasibility and consideration of the experience of China by Ukrainian legislators in the further development of legal regulation of relations related to the use of AI.Constitutional State["M. Mykhailenko"]2024-12-15T00:00:00 6aa0417cfa86c4ab2c2f2880d2626bc4117c0ea1This review aims to assess the role of artificial intelligence (AI) in community pharmacy by analyzing the most recent studies and identifying trends, gaps, and future directions for integrating AI technologies in these settings. A systematic literature review was performed on the PubMed, Scopus, and Google Scholar databases, looking at papers from 2019 to 2024. The search was further refined using the terms "AI in pharmacy," "telepharmacy," or "clinical decision support systems." Fourteen studies were included in the review after applying specific inclusion and exclusion criteria for further analysis. AI technologies have potential effects on community pharmacy practice. The most beneficial impact was noted in medication management, where 15% of medication errors were reduced, and patient compliance improved by 10%. In telepharmacy, AI supports encouraging adherence and access to pharmacy services where geographical barriers exist. However, there are concerns such as lack of privacy for system users, implementation costs, and onboarding pharmacists to such systems. AI has significant supportive and transformative capabilities for community pharmacy, but crucial barriers must be overcome first. Addressing the barriers and their ethical aspects is critical for further research. International Journal of Pharmaceutical and Bio-Medical Science00Assessing the role of artificial intelligence in community pharmacy by analyzing the most recent studies and identifying trends, gaps, and future directions for integrating AI technologies in these settings found that AI supports encouraging adherence and access to pharmacy services where geographical barriers exist.International Journal of Pharmaceutical and Bio-Medical Science["Inas Rifaat Ibrahim", "I. A. Majeed", "Y. Y. Fareed"]2024-12-15T00:00:00 1feeb5eb57645341201b23275aaaa44f576d9bac2024 the 5th International Conference on Artificial Intelligence and Engineering Applications (AIEA 2024) was held in Guilin, China during November 16-17, 2024. AIEA 2024 is a platform for presenting excellent results and new challenges facing the field of the artificial intelligence and engineering applications, especially in the field of unmanned vehicles and aircraft, infrared identification, radar detection and so on. It brings together experts from industry, governments and academia, experienced in engineering, design and research. We appreciate that if you disseminate this flyer to your friends, colleagues, disciples and others who might interests to AIEA Conference Series. The conference received 91 manuscripts, by submitting a paper to AIEA, the authors agree to the review process and understand that papers undergo a peer-review process. Manuscripts will be reviewed by appropriately qualified experts in the field selected by the Conference Committee, who will give detailed comments and-if the submission gets accepted-the authors submit a revised version that takes into account this feedback. All papers are reviewed using a double-blind review process: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other's identities, nor do the authors receive information about who has reviewed their manuscript. The Committees of AIEA 2024 invest great efforts in reviewing the papers submitted to the conference and organizing the sessions to enable the participants to gain maximum benefit. With our warmest regards, Kaisen Li, Truman Pan Conference Organizing CommitteesHighlights in Science Engineering and Technology00AIEA 2024 was held in Guilin, China during November 16-17, 2024 and received 91 manuscripts, by submitting a paper to AIEA, the authors agree to the review process and understand that papers undergo a peer-review process.Highlights in Science, Engineering and Technology["Kaisen Li", "Truman Pan"]2024-12-15T00:00:00 75f6be608d48aa2bebe157b3c80fdab565bcc5abBackground: The use of Artificial intelligence (AI) is used nowadays rigorously in the  pharmaceutical industry however, challenges remain in pharmaceutical  management education, which prepares the professional who manages the  industry. Through AI, the pharmaceutical industry designs drug discovery, formula  development, marketing, strategies, quality assurance and many more. However, in  literature, uses of AI in pharmaceutical management education have not been  widely used and discussed with reference to India.   Objective: This article explores the opportunity of key publication, its citation, gaps,  and future scope of application of AI in the pharmaceutical industry as well as  education that how AI could help professionals who pursue pharmaceutical  management as a career in higher education. Moreover, the use of this paper will  focus on how AI can facilitate pharmaceutical management education by adopting  ethical guidelines and keeping scientific practices.   Materials and Methods: To answer this, a systematic literature review with the  SCOPUS database from 2013 to 2023 was conducted and selected 988 research  articles out of 5,39,874 by applying the PRISMA approach. The keywords used to  search the articles are pharmaceutical, education, artificial intelligence, marketing,  strategy, future, business, management, accounting, pharmacology, toxicology, and  pharmaceutics and trends. As an inclusion criterion, articles authored by Indian  academicians in English languages were included.   Result: The findings suggested that the role of AI in higher education is need of the  hour as industries are looking for professionals with such skills. The study also  concludes that AI in higher education could be used how to ensure customer  preference through unstructured data for selecting the best segment,  standardisation of products, regulatory approval, designing good research design in  conducting clinical trials, strategies, and marketing. These identified topics could  enrich the content of the pharmaceutical need and bring a revolution in the  pharmaceutical industry for the betterment of society.   Conclusion: Currently, pharmaceutical management education needs more  professionals aligned with the uses of AI for developing strategies in marketing,  branding, and product development. Further, it could be used to measure customer  satisfaction and ethical regulations. Hence study recommends that curriculums  need to be looked at from various angles.  International Journal of Pharmaceutical Sciences and Nanotechnology170AI in higher education could be used how to ensure customer preference through unstructured data for selecting the best segment, standardisation of products, regulatory approval, designing good research design in  conducting clinical trials, strategies, and marketing.International Journal of Pharmaceutical Sciences and Nanotechnology(IJPSN)["Pushparaj Patel", "Disha Kumari", "Archita Jain", "Riya Gupta", "Hemanta Kumar Mishra"]2024-12-15T00:00:00 8f1b162df82cffb59f4d95ac4ee5669b3c5e7c89This study aimed to identify the challenges associated with the application of artificial intelligence (AI) in human resource management. The problem arises from the fact that AI introduces a new reality that may lead to significant changes in the work environment, accompanied by numerous challenges and obstacles to its implementation in universities. To achieve the study’s objectives, the researcher employed interviews with a purposively selected sample of academics. The study follows a descriptive-analytical qualitative methodology. Key findings indicate that AI relies on training neural networks tailored to the specific environment in which it will be applied. Hence, experts in AI applications are required in human resources or computer centers, and AI must be designed and implemented based on the institution's standards rather than relying on external, incompatible models, a process that requires substantial expertise and effort. The researcher also identified several gaps, including differences in laws, regulations, and legislations used to train AI systems, disparities in employee capabilities at different levels, a lack of training programs and AI specialists, and insufficient budgets to support such applications. The originality of the study lies in addressing a contemporary challenge—applying AI in human resource management within universities—while focusing on practical gaps such as legal disparities, employee capabilities, shortages of specialists, and financial resources. The study adds value by emphasizing the need for AI applications to be customized according to institutional standards to enhance their effectiveness and sustainability.Ahliya Journal of Business Technology and MEAN Economies00Key findings indicate that AI relies on training neural networks tailored to the specific environment in which it will be applied, and the need for AI applications to be customized according to institutional standards to enhance their effectiveness and sustainability.Ahliya Journal of Business Technology and MEAN Economies["Afaf Salman"]2024-12-15T00:00:00 20552d7937662d1645e0666291d7f1882d886cbeArtificial intelligence (AI) has been entwined with the field of radiology ever since digital imaging began replacing films over half a century ago. These algorithms, ranging from simplistic speech-to-text dictation programs to automated interpretation neural networks, have continuously sought to revolutionize medical imaging. With the number of imaging studies outpacing the amount of trained of readers, AI has been implemented to streamline workflow efficiency and provide quantitative, standardized interpretation. AI relies on massive amounts of data for its algorithms to function, and with the wide-spread adoption of Picture Archiving and Communication Systems (PACS), imaging data is accumulating rapidly. Current AI algorithms using machine-learning technology, or computer aided-detection, have been able to successfully pool this data for clinical use, although the scope of these algorithms remains narrow. Many systems have been developed to assist the workflow of the radiologist through PACS optimization and imaging study triage, however interpretation has generally remained a human responsibility for now. In this review article, we will summarize the current successes and limitations of AI in radiology, and explore the exciting prospects that deep-learning technology offers for the future. Core Tip: Artificial intelligence (AI) has been an increasingly publicized subject in the field of radiology. This review will attempt to summarize the evolving philosophy and mechanisms behind the AI movement as well as the current applications, limitations, and future directions of the field.iRADIOLOGY70This review will attempt to summarize the evolving philosophy and mechanisms behind the AI movement as well as the current applications, limitations, and future directions of the field.iRADIOLOGY["Bin Huang", "Bo Gao"]2024-12-15T00:00:00 3ba6fbc9751960a80068c7ebb6939945a46e2b00The article analyzed different concepts of the regulation of the artificial intelligence in the context of the competition between EU and China. In just the last few years artificial intelligence has evolved from academic research and futuristic discussions into the most dynamically developing practice. The speed and scale of implementation of this technology, and even more so, the prospects, are confusing an increasing number of people ‒ from politicians and businessmen to ordinary citizens and the developers of these AI systems themselves. As a result of a comparative interdisciplinary study of the approaches of the European Union and China to the topic of artificial intelligence, common and distinctive features that are determined by value differences have been identified. The main difference, according to the Europeans themselves, is ethics based on the European understanding of human rights. Modern China, challenging global primacy, is promoting its concept in this area. The examples of problems of ethics, competition, and security show the differences in the regulation of AI implementation processes, as well as the uncertainty that increasingly determines the actions of European politicians. This may contribute to an ideologically determined loss in competition in the field of artificial intelligence. It is possible that, despite solemn statements, Europe will have to use elements of the Chinese pragmatic approach to the problem.Sovremennaâ Evropa90Common and distinctive features that are determined by value differences have been identified and it is possible that, despite solemn statements, Europe will have to use elements of the Chinese pragmatic approach to the problem.Sovremennaâ Evropa["N. V. Litvak", "N. B. Pomozova"]2024-12-15T00:00:00 b5b8666ae055ea78ab0f573218e0a06181ca7e51Artificial Intelligence (AI) is a technology that enables computers to mimic human intellectual abilities, such as learning from experience, identifying patterns, making decisions, and completing complex tasks. AI is currently a popular topic in the field of education, where it can assist teachers in tasks like creating lesson plans, assessing learning outcomes, and providing support to students. AI can also benefit learners by improving their academic performance, making learning more interesting and interactive. AI also believed as a tool for mastering several foreign languages. One of the programs at universities that required students to master a single foreign language is International Class Program (ICP). AI could have an impact in improving academic performance of ICP students. By the case, this study aims to explore the use of AI among International Class Program (ICP) students and its implications for their learning. A survey was conducted among 46 ICP students from seven different majors in the Faculty of Tarbiya and Teacher Training, Maulana Malik Ibrahim State Islamic University of Malang. The results showed that AI is mostly used by ICP students for searching for translations, looking for inspiration, and finding answers. The use of AI has implications for ICP students, such as the lack of mastery of foreign languages. However, AI can also help students master foreign languages quickly, easily, and efficiently. AI can also help students do assignments, find ideas, and create designs or discussions. The information provided by AI tends to be more concise, easy to understand, and informative. The advantages of using AI include helping students find answers efficiently, making it easier for students to find additional information, being practical, growing student inspiration, imagination and creativity, helping students develop a better understanding of technology, assisting in developing designs or discussions, and helping translate accurately. The disadvantages of using AI include dependence, making students lazy, providing inaccurate information, eliminating students' critical thinking, reducing students' literacy, and using non-standard language. In conclusion, AI has both advantages and disadvantages in education. While AI can help students learn more efficiently and effectively, it can also make students lazy and dependent on technology. Therefore, it is essential to use AI wisely and not abuse it in education.Proceeding of International Conference on Islamic Education00While AI can help students learn more efficiently and effectively, it can also make students lazy and dependent on technology, therefore, it is essential to use AI wisely and not abuse it in education.Proceeding of International Conference on Islamic Education (ICIED)["Eka Wahyu Handayani", "Qurrota A\u2019yun Ramadhani", "Akhmad Nur Mahdi Rozan", "M. N. T. Al \u2018Azmi"]2024-12-15T00:00:00 0f8eb2e2f2f262f8801f7bee069aed14a60e8ff5Medical Device manufacturers have been interested in artificial intelligence (AI). However, there is a constant need to evaluate its use and performance due to system complexity, the variety of their architecture, as well as ethical and legal problems. This study offers a narrative commentary on the past, present, and future applications of machine learning (ML) algorithms and artificial neural networks (ANN) in medical devices. Finding challenges and issues with AI integration in medical devices was one of the study's main research goals. From clinical engineering to medical applications, artificial intelligence is transforming healthcare. Prior to realizing the full potential, though, ethical, legal, and social issues must be addressed. Its application must also be scrutinized and regulated in terms of fair access, privacy, suitable uses and users, liability, bias, and inclusivity. Conclusion The goal of this study is to comprehend technology's accessibility, recognize artificial intelligence's enormous potential in the healthcare industry, and keep tabs on recent scientific advancements to motivate fellow researchers. Up until now, privacy and security, trust, bias, and accountability and accountability issues have dominated ethical discourses on artificial intelligence and health. As the technology's scope continues to grow, more issues will surely surface. Artificial Intelligence is relatively new when it comes to medical devices. Manufacturers of medical devices are predicted to abandon their conventional business models until 2030 in Favor of new digital artificial intelligence techniques. It is necessary to create a regulatory framework before introducing AI-based MDs to the market. The process of defining AI regulations and policies pertaining to MDs is still in its early stages, according to prominent regulatory bodies globally. In order to facilitate the adoption of regulatory frameworks and standardize the market, international standards pertaining to AI in MDs are required. Organizations like IEEE, ISO, and IEC are working to standardize data quality management and the use of AI in ways that impact human welfare. Even with acknowledged barriers, it is possible to draw the conclusion that AI has already fundamentally altered the way traditional medicine is practiced, greatly raised the caliber of medical care, and ensured universal health. It remains to be seen how the human population will be affected by the potential for future development of medical AI in addressing issues like chronic illnesses, infectious pandemics, and the aging population.International Journal of Drug Regulatory Affairs00It is possible to draw the conclusion that AI has already fundamentally altered the way traditional medicine is practiced, greatly raised the caliber of medical care, and ensured universal health.International Journal of Drug Regulatory Affairs["Durgesh V. Patil", "G. D. Basarkar"]2024-12-15T00:00:00 286116039915d0c92cb8a2904ee096b209b84a35This paper delves into the innovations and challenges of mathematical algorithms in the development of artificial intelligence. The fundamental position of mathematical algorithms in artificial intelligence and the demand-driven mathematical algorithms in the development of artificial intelligence are described. The improvement of traditional mathematical algorithms, the emergence of new mathematical algorithms, and the algorithmic innovation brought about by interdisciplinary integration are analysed in detail. Meanwhile, the challenges faced by mathematical algorithms in the development of AI, such as computational complexity, data quality and uncertainty, interpretability, and ethics and safety, are analysed in depth, and corresponding coping strategies are proposed. Finally, the future development of mathematical algorithms in AI is envisioned.Highlights in Science Engineering and Technology50The fundamental position of mathematical algorithms in artificial intelligence and the demand-driven mathematical algorithms in the development of artificial intelligence are described and the future development of mathematical algorithms in AI is envisioned.Highlights in Science, Engineering and Technology["Chenghong Huang"]2024-12-15T00:00:00 129de68f7ebffec763c2ac33c6a4d26948c43815This study examines the authorship pattern and research collaboration in Artificial Intelligence (AI) based on 3495 scholarly communications between 2014 and 2023Journal of Science &amp; Technology Metrics20Journal of Science &amp; Technology Metrics["Bhuvaneshwari Patil", "Gavisiddappa Anandhalli"]2024-12-15T00:00:00 7099c47ceecb95e894315e919212f6f26fb6095eBrain-inspired artificial intelligence (AI) is a rapidly evolving field that seeks to model computational systems after the structure, processes, and functioning of the human brain. By drawing from neuroscience and cognitive science, brain-inspired AI aims to improve the efficiency, scalability, and adaptability of machine learning algorithms. This paper explores the key technologies and advancements in the realm of brain-inspired AI, including neural networks, neuromorphic hardware, brain-computer interfaces, and algorithms inspired by biological learning mechanisms. Additionally, we will analyze the challenges and future opportunities in achieving more brain-like cognitive systems. The integration of these technologies promises a paradigm shift in AI research, bringing us closer to artificial general intelligence (AGI) while creating more energy-efficient and resilient systems. Keywords Brain-inspired AI, Neural Networks, Neuromorphic Computing, Spiking Neural Networks, Artificial General Intelligence, Brain-Computer Interfaces, Cognitive Architectures.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper explores the key technologies and advancements in the realm of brain-inspired AI, including neural networks, neuromorphic hardware, brain-computer interfaces, and algorithms inspired by biological learning mechanisms, and analyzes the challenges and future opportunities.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["V. K P", "Jwala Jose", "Prince Joy", "S. S", "G. S"]2024-12-15T00:00:00 53ab2b42844f0181dec1aa1bce8a13ea6623e1ffArtificial intelligence has emerged as a transformative force in the healthcare system. It has revolutionized the traditional approach to diagnosis, treatment planning, and surgical outcomes. Oral and Maxillofacial Surgery also stands at the forefront of this technological revolution by enhancing surgical procedures, optimizing treatment outcomes, and improving patient satisfaction. However, integrating AI into the healthcare system also presents unique challenges, including data privacy concerns, regulatory compliance issues, and the need for ongoing training for clinicians. This article provides a comprehensive overview of AI applications within Oral and Maxillofacial Surgery, highlighting key developments, challenges, and future directions.Journal of Oral Medicine Oral Surgery Oral Pathology and Oral Radiology150This article provides a comprehensive overview of AI applications within Oral and Maxillofacial Surgery, highlighting key developments, challenges, and future directions.Journal of Oral Medicine, Oral Surgery, Oral Pathology and Oral Radiology["Shallu Bansal", "Anil Managutti", "Aishwarya Babhulkar", "Neha Patel"]2024-12-15T00:00:00 1c6d59c0b92407ec9f303e880821df4982b1226dArtificial Intelligence (AI) has emerged as a powerful tool in the healthcare industry. It has enhanced patient care, diagnostics, treatment planning, and operational efficiency. This article explores the diverse applications of AI. Machine learning algorithms, natural language processing, and predictive analytics reshape clinical practices. These technologies allow healthcare providers to focus more on patient care by enhancing diagnostic accuracy, personalising treatments, and automating tasks. However, challenges such as data security, algorithmic bias, and ethical concerns must be addressed for AI to be fully integrated into healthcare. Overcoming these obstacles will enable AI to revolutionise patient outcomes and healthcare innovation.Transforming Healthcare: The Role of Artificial Intelligence in Revolutionising Patient Care00The diverse applications of AI, including machine learning algorithms, natural language processing, and predictive analytics reshape clinical practices, allow healthcare providers to focus more on patient care by enhancing diagnostic accuracy, personalising treatments, and automating tasks.Transforming Healthcare: The Role of Artificial Intelligence in Revolutionising Patient Care["Kaushaki Gupta"]2024-12-15T00:00:00 bcca6b45168a76faf82e1b13d69844a91ab8f8e1Artificial Intelligence (AI) has presented a major breakthrough in Qur'anic studies, enabling text analysis, thematic classification, and the development of digital commentaries. These innovations provide opportunities to expand accessibility and improve the accuracy of Qur'anic analysis. However, AI also poses ethical challenges, including the potential for algorithmic bias, distortion of meaning, and threats to Islamic scholarly authority. From an Islamic perspective, these challenges require a clear ethical framework so that the utilization of AI does not conflict with Qur'anic values.             This research aims to identify Islamic ethical principles relevant to the use of AI in Qur'anic studies and develop a philosophical framework based on maqashid sharia. The methodology used is library research with a content analysis approach to classical and modern Islamic literature on ethics, Islamic philosophy, and maqashid sharia. The results show that principles such as justice (al-'adl wa al-ihsan), expediency (maslahah), and prudence (wara') should be the main guides in any application of AI in the Qur'anic context. In addition, maqashid sharia provides an evaluation framework to ensure that AI applications support the main objectives of sharia, such as safeguarding religion (hifdh ad-din) and reason (hifdh al-'aql).             The conclusion of this study confirms that the ethical use of AI in Qur'anic studies requires an integration of technological innovation and Qur'anic values. With the application of strong Islamic ethical principles, AI can be effectively used to support Qur'anic understanding, without compromising the integrity of the text and scholarly authority. This study offers a contribution to the development of comprehensive ethical guidelines to bridge technological advancement with the spiritual and scholarly needs of Muslims. Keyword: Artificial Intelligence (AI), Qur'anic Studies, Philosophical Islam   Etika Pemanfaatan Artificial Intelligence (AI) dalam Studi Al-Qur'an: Perspektif Filosofis Islam Abstrak Artificial Intelligence (AI) telah menghadirkan terobosan besar dalam studi Al-Qur'an, memungkinkan analisis teks, klasifikasi tematik, hingga pengembangan tafsir digital. Inovasi ini memberikan peluang untuk memperluas aksesibilitas dan meningkatkan akurasi analisis Qur'ani. Namun, kehadiran AI juga memunculkan tantangan etis, termasuk potensi bias algoritma, distorsi makna, hingga ancaman terhadap otoritas keilmuan Islam. Dari perspektif Islam, tantangan ini memerlukan kerangka etika yang jelas agar pemanfaatan AI tidak bertentangan dengan nilai-nilai Qur'ani. Penelitian ini bertujuan untuk mengidentifikasi prinsip-prinsip etika Islam yang relevan dengan penggunaan AI dalam studi Al-Qur'an serta mengembangkan kerangka filosofis berbasis maqashid syariah. Metodologi yang digunakan adalah penelitian kepustakaan (library research) dengan pendekatan analisis konten terhadap literatur Islam klasik dan modern tentang etika, filsafat Islam, dan maqashid syariah. Hasil penelitian menunjukkan bahwa prinsip-prinsip seperti keadilan (al-‘adl wa al-ihsan), kemanfaatan (maslahah), dan kehati-hatian (wara') harus menjadi panduan utama dalam setiap penerapan AI dalam konteks Qur'ani. Selain itu, maqashid syariah memberikan kerangka evaluasi untuk memastikan bahwa aplikasi AI mendukung tujuan utama syariah, seperti menjaga agama (hifdh ad-din) dan akal (hifdh al-‘aql). Kesimpulan penelitian ini menegaskan bahwa etika pemanfaatan AI dalam studi Al-Qur'an memerlukan keterpaduan antara inovasi teknologi dan nilai-nilai Qur'ani. Dengan penerapan prinsip etika Islam yang kuat, AI dapat digunakan secara efektif untuk mendukung pemahaman Al-Qur'an, tanpa mengorbankan integritas teks dan otoritas keilmuan. Studi ini menawarkan kontribusi bagi pengembangan panduan etika yang komprehensif untuk menjembatani kemajuan teknologi dengan kebutuhan spiritual dan keilmuan umat Islam. Keyword : Artificial Intelligence (AI), Studi Al-Qur'an, Filosofis IslamAsyahid Journal of Islamic and Quranic Studies (AJIQS)00Asyahid Journal of Islamic and Quranic Studies (AJIQS)["M. T. Zuhri", "Lalan Sahlani", "Nenden Munawaroh"]2024-12-15T00:00:00 7a64e70a0bfa4689ecfe48f2b06f1d8b61d44486The article presents an analysis of the main mechanisms for regulating risks caused by the malicious use of artificial intelligence (MUAI) in the USA, the EU and China. The relevance of the MUAI problem is proven by numerous data on the use of artificial intelligence (AI) technologies by antisocial actors. The authors set a goal – to identify the specifics of regulating MUAI risks in the USA, EU and China – due to the innovative experience of these three jurisdictions. It was found that in the USA counteraction to MUAI has not yet been shaped into systemic decisions at the level of federal authorities. It is more about decisions that take into account the growing risks of MUAI within the framework of general regulation of AI and the safety of its use. The EU has adopted the world's first Law on AI, which however pays little attention to the MUAI issues, and the main initiators of proposals to counter and regulate risks are law enforcement agencies, such as Europol. In China, MUAI risk regulation is most centralized and is becoming the subject of strategic documents and legislative acts. The authors use a systemic approach when considering various options for MUAI threats and formulating the research conclusions.Sovremennaâ Evropa80An analysis of the main mechanisms for regulating risks caused by the malicious use of artificial intelligence (MUAI) in the USA, the EU and China found that in the USA counteraction to MUAI has not yet been shaped into systemic decisions at the level of federal authorities.Sovremennaâ Evropa["D. Y. Bazarkina", "E. N. Pashentsev", "E. Mikhalevich"]2024-12-15T00:00:00 bb1baae07ccb940f41cb3b0d3aa594f00af29edcThe integration of Artificial Intelligence (AI) into teledentistry represents an unprecedented step towards increased accessibility in dental care, given India's diversity and vast population. This comprehensive review attempts to outline the practical applications of AI in teledentistry, from patient education, remote diagnosis, treatment planning, to follow-up care. It critically analyses works relating to AI's effectiveness on the detection of caries, monitoring orthodontic, and the preliminary screening of OPMDs in relation to improving health care outcomes, especially in underserved regions. It also highlights challenges such as data privacy, accuracy, acceptance, and need for regulatory clarity among dental professionals and patients. This review primarily proposes the recommendations that we should be attempting to fuse AI based solutions with a human touch as a fixture of provider of dental rendering, based on peer-reviewed study evidence. The practice will change as collaboration is required with AI to pursue a teledental practice that is ethical, effective and accessible to everyone.Journal of Oral Medicine Oral Surgery Oral Pathology and Oral Radiology110This comprehensive review attempts to outline the practical applications of AI in teledentistry, from patient education, remote diagnosis, treatment planning, to follow-up care, and critically analyses works relating to AI's effectiveness on the detection of caries, monitoring orthodontic, and the preliminary screening of OPMDs.Journal of Oral Medicine, Oral Surgery, Oral Pathology and Oral Radiology["V. Divya", "Bovaz Babu", "C. Ganesh", "Shanthi Mathialagan", "A. Backiyalakshmi", "S. Haripriya"]2024-12-15T00:00:00 c18dc234e80863343abc6762318107ff72c1e7a8Goal: To consider the use of medical products with artificial intelligence technology (hereinafter referred to as AI) in healthcare and to identify trends in the perception and acceptance of the new technologies under consideration. The authors note that the use of AI, according to medical personnel, can improve the efficiency of medical care. Federal projects are being implemented in the regions of Russia, including the introduction of AI in various areas of activity. Methods: A survey of medical workers in the Sverdlovsk region. As a result, the authors emphasize that the further development and implementation of medical products with artificial intelligence can help improve the efficiency of diagnosis, treatment of diseases and improve the accessibility of medical care, with which more than 70 % of respondents agreed. Conclusions: For the successful implementation of such products, it is necessary to harmonize standards and regulations, train medical workers and provide technical support for relevant information systems.Journal of Volgograd State Medical University50It is emphasized that the further development and implementation of medical products with artificial intelligence can help improve the efficiency of diagnosis, treatment of diseases and improve the accessibility of medical care, with which more than 70 % of respondents agreed.Journal of Volgograd State Medical University["I. M. Gryaznov", "Irina A. Samkova"]2024-12-15T00:00:00 51575836bbdbd893a8fb4c2fa67c60c92c2d60faDue to the variability in energy production from renewable sources such as solar and wind, maintaining the stability of power grids in renewable energy systems presents several challenges. Accurate prediction of both renewable energy generation and energy consumption is crucial to addressing these issues effectively. Artificial intelligence (AI) techniques can significantly enhance the accuracy of these predictions by analyzing large and complex datasets. A review of previous research highlights the importance of selecting the most appropriate AI technique, as different approaches have been proposed and evaluated using various performance metrics. This study used a multi-criteria decision-making method to rank AI prediction techniques. Extreme Gradient Boosting, Random Forest, Long Short-Term Memory, and Artificial Neural Networks emerged as the highest-ranked among the evaluated techniques. These four techniques were applied to data sets to predict energy consumption and solar energy production.BigData Congress [Services Society]490Extreme Gradient Boosting, Random Forest, Long Short-Term Memory, and Artificial Neural Networks emerged as the highest-ranked among the evaluated techniques when applied to data sets to predict energy consumption and solar energy production.2024 IEEE International Conference on Big Data (BigData)["Behzad Pirouz", "Francesca Guerriero"]2024-12-15T00:00:00 b0e33c8145d1ca9fc6297c73fa58c22d2878f33bArtificial Intelligence (AI) became a concern of many scholars including Educators. Some educators believed that AI can be a very sophisticated tool to support teaching and learning processes, but others believed otherwise. Discussion on Implementation of AI in the field of education always stimulates the appearance of pros and cons. This study tries to explore secondary school teacher perspectives on the use of Artificial Intelligence in the field of education. This study explores 15 teachers of secondary school in Malang. In-depth interview was taken to get comprehensive data. The result of this study showed that there are 5 AI tools which became frequently used by teachers, those are: ChatGPT, Grammarly, Canva, Gamma, and Class point. Interestingly, 42% of the informants are using AI for professional purposes like supporting their teaching and learning process. Besides, 33% of the informants are using it for academic purposes like writing the article, news, or other tasks which are able to improve their academic competences. There were also 25% of the informants were used AI for personal purposes which is not correlated to their profession as teachers, like for having fun, trial and error, or out of curiosity. Moreover, the use of Artificial Intelligence in the field of education created two-side of perspectives: pro and con. The pro argued that AI can be fully adapted in secondary school for its numerous benefits for both teacher and student, while the con argued that AI would have many problems if it was implemented. There are four arguments behind the pros: the use of AI in education proved that teachers are adaptive towards newest technology, it is also able to improve teacher skills and competencies, it also can support teacher’s administrative tasks, and students need to be introduced to the latest technology for their future. On the other side, there are three arguments behind the cons: ethical issues, lack of learning process, and technology dependencies and loss of human interaction.Proceeding of International Conference on Islamic Education00Secondary school teacher perspectives on the use of Artificial Intelligence in the field of education showed that there are 5 AI tools which became frequently used by teachers, those are: ChatGPT, Grammarly, Canva, Gamma, and Class point.Proceeding of International Conference on Islamic Education (ICIED)["R. Rosi"]2024-12-15T00:00:00 9bb0ee564f0193084d845aed250b43097357617cAutonomous, cloud‐based laboratories (CBLs) are transforming scientific research by democratizing access to advanced instruments that accelerate high‐throughput discovery. As artificial intelligences (AIs) become integrated or “embodied” with CBLs and gain independence from human oversight, efforts to identify novel pharmaceuticals, renewable energies, and agricultural biotechnologies will accelerate. AI‐driven CBLs can perform tasks more efficiently and accurately than human scientists at lower costs, achieving results in weeks rather than years. However, as AI systems approach or exceed human intelligence, their decision‐making abilities could outpace the need for human input, raising ethical, economic, and safety concerns. Aligning AI goals with human values is critical, as unregulated systems could pose existential risks, including global health hazards or the distortion of knowledge‐generating systems. AI‐driven misinformation in research highlights the need for transparency and data integrity, which may be achieved by aligning incentivizes and engineered fail‐safes to promote long‐term human flourishing. To mitigate risks, strict compartmentalization of AI systems and CBLs with third‐party supervision at fine temporal resolutions will be necessary. While current CBLs are piloted by humans, future AI systems may relegate humans to the role of co‐pilot. Anticipating increased AI‐CBL integration, policies must balance innovation with caution to maximize benefits and avoid unintended harm.Advanced Intelligent Systems90Anticipating increased AI‐CBL integration, policies must balance innovation with caution to maximize benefits and avoid unintended harm and align AI goals with human values.Advanced Intelligent Systems["Nicolas Rouleau", "N. Murugan"]2024-12-15T00:00:00 3e365bba95b60ec66dc1b916087d79818ef51a44Journal of Research in Language &amp; Translation00Journal of Research in Language &amp; Translation[]2024-12-15T00:00:00 d30b5053d216269a18affcec7c4bb721d20b5140المجلة الدولیة للمناهج والتربیة التکنولوجیة00المجلة الدولیة للمناهج والتربیة التکنولوجیة["\u0631\u0648\u0649 \u0645 \u0639\u0627\u0644\u0645 \u0639\u0627\u0644\u0645"]2024-12-15T00:00:00 7285c680bb6a031c583974655125693faf9a479eAI &amp; SOCIETY50AI &amp; SOCIETY["Anatolii P. Getman", "Oleg M. Yaroshenko", "O. Dmytryk", "Oleksii Y. Tykhonovych", "Dmytro Hryn"]2024-12-15T00:00:00 2b651021637a850fc94d9779b5c6c21d07a2bb08This paper focuses on developing a framework for uncovering insights about NDD children’s performance (e.g., raw gaze cluster analysis, duration analysis & area of interest for sustained attention, stimuli expectancy, loss of focus/motivation, inhibitory control) and informing their teachers. The hypothesis behind this work is that self-adaptation of games can contribute to improving students’ well-being and performance by suggesting personalized activities (e.g., highlighting stimuli to increase attention or choosing a difficulty level that matches students’ abilities). The aim is to examine how AI can be used to help solve this problem. The results would not only contribute to a better understanding of the problems of NDD children and their teachers but also help psychologists to validate the results against their clinical knowledge, improve communication with patients and identify areas for further investigation, e.g., by explaining the decision made and preserving the children’s private data in the learning process.BigData Congress [Services Society]40The aim is to examine how AI can be used to help solve the problems of NDD children and their teachers and help psychologists to validate the results against their clinical knowledge, improve communication with patients and identify areas for further investigation.2024 IEEE International Conference on Big Data (BigData)["Abdul Rehman", "Ilona Heldal", "Diana Stilwell", "Jerry Chun-Wei Lin"]2024-12-15T00:00:00 e9ee357acb6466a3a2427b47c1214e251c4fd450Pediatric surgery international (Print)70Pediatric surgery international["A. Aliyeva"]2024-12-15T00:00:00 242b29c4b2422348d01d81031f66b1050a2fffe0Journal of Educational Media590Learning, Media and Technology["Ha Nguyen", "Victoria Nguyen", "Sara Ludovise", "R. Santagata"]2024-12-15T00:00:00 11d79df5cb749f55bdcd3329ed77649f42461a2aJournal of Intelligent Computing00Journal of Intelligent Computing["Jing Wang"]2024-12-15T00:00:00 b87541f4065d205e9065c3cd4c57bacc19cb90beThis paper discusses the integration of Virtual Constructive (VC) simulation and Convolutional Neural Networks (CNNs) into an Agent-Based Model (ABM) to study pilot performance. By leveraging the strengths of VC for immersive training scenarios and CNN for advanced image recognition and decisionmaking processes, the research aims to provide a comprehensive understanding of how AI and machine learning can help pilot training programs. The paper's series of experiments within the ABM demonstrate the potential of this integrated approach to improve decision accuracy and response times under simulated operational conditions. The findings underscore the effectiveness of integrating VC and CNN into an ABM for training simulations, with implications for pilot training and developing environments that capture operator behavior.Online World Conference on Soft Computing in Industrial Applications200The findings underscore the effectiveness of integrating VC and CNN into an ABM for training simulations, with implications for pilot training and developing environments that capture operator behavior.2024 Winter Simulation Conference (WSC)["Larry Lowe", "Luis Rabelo", "Marwen Elkamel", "Mitchell Hunsucker", "Katalina Arias-Marin", "Nathalia Davila", "Omar Allaz", "Mario Marin", "Gene Lee"]2024-12-15T00:00:00 18aacaddc47531bad5c6330d9c0eb02ad725fb9dIntroduction To evaluate the performance of ChatGPT-4.0 and ChatGPT-3.5 in answering multiple-choice questions in OphthoQuestions (www.ophthoquestions.com), a popular question preparation bank, and to compare the performance of GPT-4.0 and GPT-3.5. Methods In January 2024, using a personal account on OphthoQuestions (www.ophthoquestions.com), 520 questions were selected from 4,551 OphthoQuestions. These 520 questions were created by randomly selecting 40 questions from each of 13 ophthalmology subspecialties. GPT-3.5 and GPT-4.0 were asked to answer these same 520 questions. Results ChatGPT-4.0 and ChatGPT-3.5 answered 408 questions (78.46%) 95% CI [70,88%] and 333 questions (64.15%) 95% CI [53,74%] of 520 questions correctly, respectively. GPT-4.0 answered significantly more questions correctly than GPT-3.5 (p= 0.0195). ChatGPT-4.0 showed a statistically significant difference compared to ChatGPT-3.5 in giving correct answers in all subgroup analyses (p<0.05). Discussions This study gives an encouraging new proof of ChatGPT’s ability to manage complex clinical and medical data, focusing on the development and consistency of artificial intelligence algorithms. The statistically significant success of GPT-4.0 over GPT-3.5 in this study should be examined in light of future algorithm advances, particularly in online tests, which will increase progressively as the use of artificial intelligence poses an increasing danger to test integrity. Protocols such as required proctoring should be considered. In the following years, ChatGPT’s clinical management and decision-making expertise should be supplemented by more research indicating that it may be a beneficial resource for ophthalmologists and other medical professionals seeking information and guidance on challenging cases. Conclusions GPT-4.0 was found to give more and more consistent answers than GPT 3.5 on a multiple-choice ophthalmology question bank. ChatGPT has shown significant differences between algorithms in accuracy and repeatability when handling questions related to eye diseases. This study shows that new artificial intelligence algorithms are promising. More data is needed to use artificial intelligence language models in medical applications.Romanian Journal of Ophthalmology00GPT-4.0 was found to give more and more consistent answers than GPT 3.5 on a multiple-choice ophthalmology question bank, and new artificial intelligence algorithms are promising.Romanian Journal of Ophthalmology["Suleyman Demir"]2024-12-15T00:00:00 a4897e40c7f2e717ab1d5031b183529feca886b8Graphical abstractBiochemia Medica570Biochemia Medica["S. Dodig", "I. \u010cepelak", "M. Dodig"]2024-12-15T00:00:00 48f8d753cc16a9e0aa4eb8e6fc137bb6645a90a2INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN BUSINESS00INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN BUSINESS["Felix Chisomebi Okwaraoha", "David Agyemfra Atakora", "Azeezat Wahab Morenikeji"]2024-12-15T00:00:00 54c2f97ac2de85dac9fdf1b265abda832a6f95b5Advances in machine learning have created new opportunities to develop artificial intelligence (AI)-based clinical decision support systems using past clinical data and improve diagnosis decisions in life-threatening illnesses such breast cancer. Providing explanations for AI recommendations is a possible way to address trust and usability issues in black-box AI systems. This paper presents the results of an experiment to assess the impact of varying levels of AI explanations on clinicians' trust and diagnosis accuracy in a breast cancer application and the impact of demographics on the findings. The study includes 28 clinicians with varying medical roles related to breast cancer diagnosis. The results show that increasing levels of explanations do not always improve trust or diagnosis performance. The results also show that while some of the self-reported measures such as AI familiarity depend on gender, age and experience, the behavioral assessments of trust and performance are independent of those variables.arXiv.org491The results show that increasing levels of explanations do not always improve trust or diagnosis performance and that while some of the self-reported measures such as AI familiarity depend on gender, age and experience, the behavioral assessments of trust and performance are independent of those variables.ArXiv["Olya Rezaeian", "Onur Asan", "A. E. Bayrak"]2024-12-15T00:00:00 072b6b8a6dd1b0ee97b530c35d221c2e013b80e9Simulation and artificial intelligence (AI) have played crucial roles in the design and operational optimization of critical infrastructures in modern societies. In this work we briefly review the latest development in three fields, namely the stability analysis and supply demand matching in electric power grid, and the efficient simulation in autonomous driving. We wish this tutorial may shed some light on the synergy between simulation and AI for critical infrastructure in the near future.Online World Conference on Soft Computing in Industrial Applications711This work briefly review the latest development in three fields, namely the stability analysis and supply demand matching in electric power grid, and the efficient simulation in autonomous driving.2024 Winter Simulation Conference (WSC)["Qing-Shan Jia", "Chao Duan", "Shuo Feng", "Yuhang Zhu", "Xiao Hu"]2024-12-15T00:00:00 f6d85c6664fceefbef222433134f6d45094b0ff2AI &amp; SOCIETY221Overall, this paper demonstrates that addressing structural challenges in AI is challenging and requires an approach that considers four requirements: (1) multi-level, (2) multi-faceted, (3) interdisciplinary, and (4) polycentric governance.AI &amp; SOCIETY["Mark Ryan", "Nina de Roo", "Hao Wang", "Vincent Blok", "Can Atik"]2024-12-15T00:00:00 232f1bbd0cacd975dba9d0bf54ff43ce53f8081fThe Internet of Things (AIoT) is supporting the revolution of many industries. However, AIoT systems require a large amount of computing resources and electricity consumption as support, which leads to significant carbon emissions and energy consumption, which is not conducive to sustainable energy development. Reducing the demand for data in artificial intelligence through active learning (AL) is an effective solution. In this study, based on the interpretability of neural networks, we propose an interpretable AL algorithm. By improving the traditional heatmap display method, we use predicted probability entropy and posterior probability entropy to form an information class activation map information visualization method, thereby providing an explanation for the sources of information in AL. Meanwhile, we propose a Similarity-Loss AL sampling strategy to evaluate the information content of samples. The experimental results show that our proposed method has achieved good results in terms of interpretability and optimization of sampling in AL. In addition, the proposed Similarity-Loss sampling strategy has achieved the highest performance in current AL scenarios, contributing to achieving low-carbon and sustainable AIoT.IEEE Internet of Things Journal581This study improves the traditional heatmap display method, and proposes a Similarity-Loss AL sampling strategy to evaluate the information content of samples, which has achieved good results in terms of interpretability and optimization of sampling in AL.IEEE Internet of Things Journal["Desheng Chen", "Shuai Xiao", "Meng Xi", "Guipeng Lan", "Zhuo Zhang"]2024-12-15T00:00:00 6e4445d33ce6cd871a296616b7c984e98edd6585Advances in artificial intelligence are revolutionizing agricultural diagnostics, particularly in addressing the critical challenge of cassava disease detection. Cassava, a vital food source for millions worldwide, faces significant yield losses due to various diseases that threaten food security in developing regions. This research presents a novel approach integrating transfer learning with explainable AI to create a robust disease detection system. Through extensive experimentation with multiple deep learning architectures, our ResNet-based model achieves a remarkable accuracy of 92% in distinguishing among four major cassava diseases and healthy specimens. The integration of SHAP (SHapley Additive exPlanations) technology provides unprecedented transparency in the model’s decision-making process, allowing stakeholders to understand how the neural network identifies disease-specific features. Our system demonstrates particular strength in identifying Cassava Mosaic Disease, achieving 98% accuracy, while maintaining robust performance across bacterial blight, brown spot and green mite detection. The methodology presented here not only advances the technical frontier of agricultural AI but also provides a practical tool for enhancing food security through early disease detection. This research establishes a foundation for developing accessible and interpretable AI systems that can be deployed in resource-limited agricultural settings, potentially transforming how farmers manage crop health in the digital age.BigData Congress [Services Society]190A novel approach integrating transfer learning with explainable AI to create a robust disease detection system that demonstrates particular strength in identifying Cassava Mosaic Disease, achieving 98% accuracy, while maintaining robust performance across bacterial blight, brown spot and green mite detection.2024 IEEE International Conference on Big Data (BigData)["Danilo Maurmo", "Marco Gagliardi", "Tommaso Ruga", "Ester Zumpano", "Eugenio Vocaturo"]2024-12-15T00:00:00 3986307b4a015ad4f6eac004d28e3bf4c77480dbToday, crop diversification in agriculture is a critical issue to meet the increasing demand for food and to improve food safety and quality. This issue is considered to be the most important challenge for the next generation of agriculture due to diminishing natural resources, limited arable land and unpredictable climatic conditions caused by climate change. In this paper, we employ emerging technologies such as the Internet of Things (IoT), machine learning (ML) and explainable artificial intelligence (XAI) to improve operational efficiency and productivity in the agricultural sector. Specifically, we propose an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions. In this system, we provide local and global explanations of ML model decisions with methods such as ELI5, LIME, SHAP, which we integrate into ML models. More importantly, we provide regional alternative crop recommendations with the Counterfactual explainability method. In this way, we envision that our proposed AgroXAI system will be a platform that provides regional crop diversity in the next generation agriculture.BigData Congress [Services Society]290This paper proposes an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions and provides regional alternative crop recommendations with the Counterfactual explainability method.2024 IEEE International Conference on Big Data (BigData)["\u00d6zlem Turgut", "Ibrahim Kok", "Suat \u00d6zdemir"]2024-12-15T00:00:00 bf7c7430d580273db1459c75d7252cc8b9986abaAustralian debates about how to regulate deepfake video have, to date, largely been shaped by STEM agendas for generative artificial intelligence (AI) policy and public fears about disinformation intensification. As the federal government consults on AI regulation, this article aims to move policymakers’ focus beyond the deepfake ‘problem’ to investigate the implications of generative AI screen technologies from two creative industries perspectives. First, it establishes the negative and positive uses of deepfake applications in media, politics, commerce, education, film and art. Second, it compares the forms and scope of emerging international deepfake regulations with those proposed in Australia to conceptualise the impact that restrictions on deepfakes might have for domestic screen producers, flagging the potential closure of artistic and public expression. In doing so, we highlight that a STEM-focused approach to deepfake regulation is insufficiently attuned to the benefits of synthetic media applications in the post-truth AI communications economy.Media International Australia350The negative and positive uses of deepfake applications in media, politics, commerce, education, film and art are established and a STEM-focused approach to deepfake regulation is highlighted, insufficiently attuned to the benefits of synthetic media applications in the post-truth AI communications economy.Media International Australia["Anna Broinowski", "Fiona R. Martin"]2024-12-15T00:00:00 4fe6fd67f3cb32afdcf5f2532a528579df0674ccIntrusion Detection Systems (IDS) are the key for securing the rapidly evolving Internet-of-Things (IoT), where data security and privacy will become increasingly important in the forthcoming era. This research presents an innovative method for improving IDS performance through the integration of Artificial Intelligence (AI), Blockchain, and Digital Twin (DT) technologies. AI is utilized for real-time anomaly detection, whereas DT replicate device behavior for predicting threats and Blockchain ensures secure, decentralized data transmission. Energy-efficient zero-knowledge proofs are employed to meet the energy requirements of Blockchain, enhancing both security and resource efficiency. The performance of the suggested system will be assessed based on detection accuracy, latency, scalability, energy efficiency, and privacy preservation. This distinctive integration of advanced technologies delivers a multi-faceted security system, providing a thorough respond to for strengthening security in IoT networks.BigData Congress [Services Society]70This research presents an innovative method for improving IDS performance through the integration of Artificial Intelligence, Blockchain, and Digital Twin technologies, providing a multi-faceted security system, providing a thorough respond to for strengthening security in IoT networks.2024 IEEE International Conference on Big Data (BigData)["Ohood Alharbi", "R. Shaikh", "Rameez Asif"]2024-12-15T00:00:00 d6f4f2d8bcd9b0b23acca7711ef1ba01aa8cfdeeAbstract In recent years, Artificial Intelligence (AI) has emerged as a transformative force in business analytics, enabling organizations to make more informed, data-driven strategic decisions. This paper explores the integration of AI in business analytics and its impact on enhancing operational efficiency, gaining market insights, and securing a competitive advantage. AI technologies like machine learning and natural language processing have revolutionized how businesses collect, analyze, and leverage data to optimize decision-making processes. By automating routine tasks and providing predictive and prescriptive insights, AI helps organizations streamline operations, understand customer behaviour, and stay ahead of market trends. However, adopting AI in business analytics also presents challenges related to data privacy, algorithmic bias, and system integration. This article discusses these challenges and provides recommendations for businesses seeking to effectively integrate AI into their strategic decision-making processes. Ultimately, AI is reshaping business operations and offering a new paradigm for making informed decisions that drive long-term growth and success.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402301This article discusses the challenges and recommendations for businesses seeking to effectively integrate AI into their strategic decision-making processes and provides recommendations for businesses seeking to effectively integrate AI into their strategic decision-making processes.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["SM Tamim Hossain Rimon"]2024-12-16T00:00:00 08a6ecabe5272b9864b7cdc1b4020a3a2f3e319cThis cross-sectional study analyzes the availability of artificial intelligence and machine learning–enabled devices authorized for children by the US Food and Drug Administration (FDA) and assesses reporting of algorithm validation in the pediatric population.JAMA pediatrics31JAMA Pediatrics["Ryan C L Brewster", "Matthew Nagy", "Susmitha Wunnava", "Florence T Bourgeois"]2024-12-16T00:00:00 585cab56e1a3283e3f3b76abf82ff5c39e33bdb1The integration of artificial intelligence(AI) technologies into identity and access management (IAM) systems has greatly improved access control and management, offering more robust, adaptive, and intelligent solutions than traditional methods. AI-driven IAM systems enhance security, operational efficiency, and introduce new capabilities in industrial environments. In this narrative review, we present the state-of-the-art AI technologies in industrial IAM, focusing on methods such as biometric, comprising facial and voice recognition, and multifactor authentication for robust security. It addresses the challenges and solutions in implementing AI-based IAM systems in industrial settings, including security, privacy, evaluation, and continuous improvement. We present also the emerging trends and future directions, highlighting AI’s potential to transform industrial security measures. This review aims to guide researchers and practitioners in developing and implementing next-generation access control systems, proposing future research directions to address challenges and optimize AI applications in this domain.Future Internet651This narrative review presents the state-of-the-art AI technologies in industrial IAM, focusing on methods such as biometric, comprising facial and voice recognition, and multifactor authentication for robust security.Future Internet["Jes\u00fas Vegas", "C\u00e9sar Llamas"]2024-12-16T00:00:00 8bf54323d65926b2e772949259b4045bb9b9999fViral respiratory infections in children are a major public health issue, with high incidence rates and a significant impact on healthcare systems. The application of artificial intelligence (AI) in the medical field offers substantial opportunities for early detection, accurate diagnosis, effective management, and prevention of these infections. Aim: This study aims to analyse the most effective AI-based approaches for managing viral respiratory infections in children, including its application in paediatric hospitals, telemedicine, and routine practices, while also identifying challenges associated with implementation. Methodology: A systematic literature review was conducted following the PRISMA guidelines. The search was performed across 10 major databases: De Gruyter, MDPI, Nature, PubMed, ScienceDirect, Elsevier, SpringerLink, Wiley Online Library, Taylor & Francis, and Frontiers, focusing on articles published between 2020 and 2024. Out of 46,900 scientific articles, 17 relevant studies were selected, including original research, meta-analyses, and systematic reviews.Results: AI has shown high efficiency in the early detection of symptoms, differential diagnosis between viral and bacterial infections, monitoring disease progression, and personalising treatments. Its use in telemedicine and family education has improved accessibility to care and raised awareness. Integration of AI in paediatric hospitals has reduced diagnostic time and optimised resources. However, large-scale implementation depends on collaboration between medical professionals and IT specialists. Conclusions: AI represents a promising solution for improving the management of viral respiratory infections in children. The development of standardised protocols and addressing ethical challenges are essential for the effective integration of this technology into paediatric practice.Technium BioChemMed700AI represents a promising solution for improving the management of viral respiratory infections in children, and the development of standardised protocols and addressing ethical challenges are essential for the effective integration of this technology into paediatric practice.Technium BioChemMed["Mariana Gr\u01cedinaru", "Silvia Aura (Mateescu Costin)", "Gabriela Isabela Verga", "Margareta (Huciu)", "M. Draganescu"]2024-12-16T00:00:00 1a900e222adf3818d8f8f01b815bacfab0887cecArtikel ini membahas pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) sebagai asisten bagi guru Pendidikan Agama Islam,  hambatan dan solusinya. Artikel ini merupakan studi kepustakaan, data dikumpulkan dan dianalisis dari sumber seperti buku dan jurnal. Peran AI sebagai asisten guru, memudahkan guru dalam merencanakan pembelajaran, menyederhanakan tugas administratif, menciptakan media pembelajaran interaktif, hingga menyusun evaluasi otomatis. Meskipun AI menawarkan manfaat signifikan, penerapannya dihadapkan pada berbagai tantangan, termasuk rendahnya literasi teknologi di kalangan guru, bias dalam informasi yang dihasilkan, keterbatasan fasilitas, dan potensi ketergantungan siswa terhadap AI. Artikel ini menawarkan solusi optimalisasi untuk mengatasi kendala tersebut, termasuk peningkatan keterampilan teknologi guru, penggunaan versi AI yang lebih efisien, dan perlunya regulasi etika AI yang ketat guna meminimalisir dampak negatif pada proses pendidikan PAI. This article discusses the utilisation of artificial intelligence (AI) as an assistant for Islamic Religious Education teachers, its obstacles and solutions. This article is a literature study, with data collected and analysed from sources such as books and journals. The role of AI as a teacher assistant makes it easier for teachers to plan lessons, simplify administrative tasks, create interactive learning media, and compile automatic evaluations. While AI offers significant benefits, its implementation is faced with various challenges, including low technological literacy among teachers, bias in the information generated, limited facilities, and potential student dependency on AI. This article offers optimised solutions to overcome these obstacles, including the improvement of teachers' technological skills, the use of more efficient versions of AI, and the need for strict ethical regulation of AI to minimise negative impacts on the PAI education process.Mauriduna: Journal of Islamic Studies00Mauriduna: Journal of Islamic Studies["Ananda Qomaruzzaman"]2024-12-16T00:00:00 9171270568024f6de594b3fdef85b1ffd932ce71The purpose of the article is to identify the features of the formation of the discourse around the ethics of artificial intelligence, as well as to characterize the main legal acts that set out its key principles. Research methods. The methods of analysis and synthesis, generalization and abstraction were applied, which made it possible to achieve the set goal. The scientific novelty lies in identifying the features of the public (determined by the strategies and limitations characteristic of the media arena) and academic (long, deep and reasoned discussions of researchers) discourses around the ethics of artificial intelligence, clarifying the main approaches (risk-oriented, according to the areas of application) to setting out its key principles in legal acts at the international level, emphasizing the need to improve the tools for managing AI technologies, in particular the creation of global governance structures to prevent their misuse. Conclusions. It is emphasized that, given the steady growth in the scale of data and AI use worldwide, it is necessary to systematically make efforts to increase literacy, awareness and education about the ethical consequences of the use of AI technologies. Ethical challenges associated with different ways of using AI require interdisciplinary interaction and interaction with many stakeholders, as well as cooperation between cultures, organizations, academic institutions, etc. By directly addressing the ethical issues surrounding the development and use of AI, collaboration between policymakers, technologists, and ethicists can ensure that AI serves humanity responsibly and fairly. It is emphasized that, despite the promotion of policy approaches to regulating AI by some countries and international organizations, the impact of corporate investment in AI and the political responses associated with governance have yet to be assessed.Digital Platform Information Technologies in Sociocultural Sphere00It is emphasized that, given the steady growth in the scale of data and AI use worldwide, it is necessary to systematically make efforts to increase literacy, awareness and education about the ethical consequences of the use of AI technologies.Digital Platform: Information Technologies in Sociocultural Sphere["Yuliya Trach"]2024-12-16T00:00:00 292e5ae17e2a8fcb8dab74aa7c9ae17329907b03The implementation of smart technologies as well as artificial intelligence resulted in an increase in productivity and efficiency in production, optimization of costs, and automation of time-consuming processes. The situation is similar in the field of the production of construction products. Digitization and automation are challenges within Industry 4.0 (Construction 4.0), which are the subject and interest of several studies and discussions by experts in the field. The aim of the research is to analyze the relationships between digitization in the industry, artificial intelligence, and performance in the design, production, and use of construction products in the context of Industry 4.0 principles. The research uses primary and secondary data on the use of AI and its potential and impacts in the field of performance and efficiency in design, production, and use in civil engineering. Several statistical tools are used in the analysis, from descriptive statistics to the use of statistical tests and correlation and regression analysis. Spearman’s correlation coefficient was the primary tool for evaluating the dependence between variables. The research results point to the connection and dependence between the use of AI and digitization in individual design and production activities. Autonomous production machines, the production of ore products, and generative design represent areas of production in the construction industry, where the use of AI and digitization makes sense from the point of view of the performance of the results. Innovations and intelligent tools within the concept of Industry 4.0 (Construction 4.0) are, therefore, a prerequisite for an effective setting of design and production in this industry as well.Machines310The aim of the research is to analyze the relationships between digitization in the industry, artificial intelligence, and performance in the design, production, and use of construction products in the context of Industry 4.0 principles.Machines["T. Mandi\u010d\u00e1k", "A. Beh\u00fanov\u00e1", "P. M\u00e9s\u00e1ro\u0161"]2024-12-16T00:00:00 b1edaad0680c18ae03b6234e07060abc14cb6289Artificial intelligence (AI) has emerged as a significant technology transforming many aspects of life in recent years. With the increasing importance of AI-based inventions, the issue of patentability for inventions created by AI has become a topic of great interest and debate. Patent law plays a vital role in promoting innovation and protecting the rights of innovators. Patents are a form of intellectual property protection for innovators to prevent others from using their inventions for a certain period. However, the rapidly evolving nature of AI technology poses challenges to patent law, especially patentability criteria and the review of AI-based patent applications. The article outlines the legal provisions on patent procedures in Vietnam, China, Japan, and India. It concludes that in these jurisdictions, it is not possible to protect an invention created by AI because the protection procedures all require the disclosure of information about the inventor as an individual. Disclosure of information about the inventor as an individual is a mandatory requirement during the patent application process. Since an AI system is not an individual, it is, therefore, not possible to apply for a patent for an invention created by AI. On that basis, the article recommends recognizing the AI system as the inventor, but recognizing the AI system as the inventor does not mean that the AI owns the invention. They credit an AI system as the inventor, intended to accommodate the evolution of technology and distinguish between an invention made by an individual and an invention made by an artificial intelligence system.South Eastern European Journal of Public Health10The article outlines the legal provisions on patent procedures in Vietnam, China, Japan, and India and concludes that in these jurisdictions it is not possible to protect an invention created by AI because the protection procedures all require the disclosure of information about the inventor as an individual.South Eastern European Journal of Public Health["Dr. Le Thi Minh", "Dr. Vo Trung Hau"]2024-12-16T00:00:00 d159d89f2197860c443121f386f9dd3940f82b0fThe rapid development of artificial intelligence technology has revolutionized the judicial field, improving the efficiency of case handling, standardizing the adjudication process, and enhancing the review of evidence. However, the problems of uneven quality of judicial data, judicial injustice caused by algorithmic black boxes, erosion of judges' discretionary power, and rigidity of the trial process have seriously affected the public's trust in AI judicial assistance. The theory of inversion and the theory of self-creation have jointly constructed a comprehensive regulatory framework for the co-governance of law and technology to ensure that law and technology maintain independence and autonomy in the interaction process, to prevent technological abuses, to promote the in-depth integration of AI and the judicial system, and to safeguard judicial impartiality and systemic authority.Journal of Global Trends in Social Science00The theory of inversion and the theory of self-creation have jointly constructed a comprehensive regulatory framework for the co-governance of law and technology to ensure that law and technology maintain independence and autonomy in the interaction process.Journal of Global Trends in Social Science["Decai He"]2024-12-16T00:00:00 c8b51d958fdf57c6923c59d1c130b776664746eeThe global mental health crisis is compounded by barriers such as cost, accessibility, and stigma, leaving millions without adequate support. FASSLING (fassling.ai), an innovative artificial intelligence (AI)-powered platform, addresses these challenges by providing free, 24/7 multilingual emotional and coaching support through text and audio interactions. Grounded in inclusivity and compassion, FASSLING bridges gaps in traditional mental health systems by offering immediate, non-clinical support while complementing professional services. This paper explores FASSLING's design and implementation, emphasizing its user-centered features, including cultural adaptability, trauma-informed care principles, and active listening techniques. The platform not only empowers users to navigate emotional challenges but also fosters resilience and empathy, creating a ripple effect of societal compassion. Ethical considerations, such as ensuring user privacy and managing the limitations of AI, are central to FASSLINGs mission. By integrating advanced AI technologies with psychological best practices, FASSLING sets a new standard for accessible and inclusive mental health support, positioning itself as a transformative tool for global well-being. This case study highlights FASSLING's potential to redefine emotional support systems and drive positive change in mental health care worldwide.Journal of Clinical Technology and Theory00This case study highlights FASSLING's potential to redefine emotional support systems and drive positive change in mental health care worldwide, and explores its user-centered features, including cultural adaptability, trauma-informed care principles, and active listening techniques.Journal of Clinical Technology and Theory["Yujia Zhu"]2024-12-16T00:00:00 e108c11f3190e936a7fe125198111c34491761bcPurpose Artificial intelligence (AI) integration in the workplace yields positive outcomes, yet its impact on employees remains incompletely understood. This study aims to examine employee viewpoints regarding AI and its influence on employee career attitudes, behaviors and skill enhancement. The author examines how employees perceive AI and its impact on their career adaptability within the context of career self-management. Design/methodology/approach The researchers conducted hypothesis testing using AMOS; data was collected from 255 software house employees working in Pakistan. This study is time-lagged in nature. Data on AI perception was collected at time 1. After three weeks, data was collected for hypotheses related to mediation, and employees filled out a questionnaire related to career adaptability at time 3 with the interval of three weeks. Findings This study indicates a strong correlation between beliefs about AI dominance in the job market and increased career adaptability. The researchers discovered that career insecurity and skill development are pathways that elucidate employees’ perceptions of AI dominating their decisions regarding career adaptability. Originality/value This study demonstrates that AI perception has the potential to influence employees, motivating them to enhance their abilities and pursue adaptable career trajectories. The study indicates that employees’ unfavorable perceptions of AI can result in behaviors associated with career adaptability. Management Research Review650The study indicates that employees’ unfavorable perceptions of AI can result in behaviors associated with career adaptability, and demonstrates that AI perception has the potential to influence employees, motivating them to enhance their abilities and pursue adaptable career trajectories.Management Research Review["Quratulain Burhan"]2024-12-16T00:00:00 170b57e11bda2e0314e4a6d885738dce04d16a3eBreast cancer is one of the most common types of cancer in women, and early diagnosis is life-saving. The aim of this study is to enhance the resolution of mammography images, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images. For this purpose, mammography images obtained from the open-access mini-MIAS dataset were used. Both the original dataset and the images processed with the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm underwent resolution enhancement using the Stable Diffusion artificial intelligence system. The results were evaluated by an expert radiologist, and it was determined that the diagnostic quality of the images significantly increased. These improvements aim to support early diagnosis in breast cancer and enhance diagnostic accuracy. Additionally, the applicability and effectiveness of these methods were emphasized, and the potential benefits of resolution enhancement techniques in clinical practice were discussed. The results have the potential to allow for more detailed and accurate analysis of mammography images, thereby improving patient care and treatment planning.Black Sea Journal of Engineering and Science00Improvements in the resolution of mammography images are made, thereby improving the contrast resolution, spatial resolution, and the detectability of calcifications, distortions, and opacities in the images to support early diagnosis in breast cancer and enhance diagnostic accuracy.Black Sea Journal of Engineering and Science["Fatih G\u00fcl", "Muhammed U\u00e7ar", "Nur H\u00fcrsoy"]2024-12-16T00:00:00 21f2f48b383640d1a89e41ac091c37d612cbb003Artificial intelligence (AI) is a fast-growing technology with the potential to transform education. While some fear it might replace teachers, AI’s benefits in enhancing teaching and learning are gaining attention. However, there is limited research on mathematics teachers’ behavioral intention to use AI in their teaching practices. This paper explored factors influencing mathematics teachers’ intentions to use AI using the UTAUT framework. Data from 224 teachers showed that facilitating conditions negatively affected attitudes toward AI ($-0.143, \mathrm{p}(0\lt)0.05$) but positively influenced behavioral intention ($0.118, \mathbf{p}\lt 0.05$). Effort expectancy had no effect on attitudes but positively influenced behavioral intention ($0.209, \mathrm{p}\lt 0.01$). Performance expectancy and social influence positively shaped attitudes $(0.769, p\lt 0.001; 0.172, p\lt 0.01)$ but did not directly impact behavioral intention. Attitudes strongly influenced behavioral intention ($0.637, \mathrm{p}\lt 0.01$) and mediated the effects of performance expectancy and social influence. These findings highlight the importance of creating supportive conditions and fostering positive attitudes to promote $\mathbf{A I}$ adoption in teaching.2024 International Conference on TVET Excellence & Development (ICTeD)2202024 International Conference on TVET Excellence & Development (ICTeD)["Evelyn B. Ballenas", "M. T. Lasco"]2024-12-16T00:00:00 cc5bcaeef811f2fc7c73652c767e879aa6e0d8d9The prominence of artificial intelligence and machine learning in everyday life has led to efforts to foster AI literacy for all K-12 students. In this paper, we review how Hour of Code activities engage with the five big ideas of AI, in particular with machine learning and societal impact. We found that a large majority of activities focus on perception and machine learning, with little attention paid to representation and other topics. A surprising finding was the increased attention paid to critical aspects of computing. However, we also observed a limited engagement with hands-on activities. In the discussion, we address how future introductory activities could be designed to offer a broader array of topics, including the development of tools to introduce novices to artificial intelligence and machine learning and the design of more unplugged and collaborative activities.230How future introductory activities could be designed to offer a broader array of topics are addressed, including the development of tools to introduce novices to artificial intelligence and machine learning and the design of more unplugged and collaborative activities.["Luis Morales-Navarro", "Yasmin B. Kafai", "Eric Yang", "A. Suryana"]2024-12-16T00:00:00 9819d8d03f342ae56a2f5fad27f90be0ad42b897Stress and burnout among healthcare workers represent a global crisis with significant implications for psychological and physical health, job performance, and interpersonal skills. These conditions are linked to anxiety, depression, suicidal ideation, substance use, poor quality of life, digestive disorders, and cardiovascular diseases. Burnout is characterized by emotional fatigue, depersonalization, and reduced personal accomplishment, often caused by chronic workplace stress. Factors such as demographics, fatigue, and resilience influence its development and severity. Traditional stress management interventions, such as counselling and leave, often prove insufficient in addressing these challenges. Recent advancements in Artificial Intelligence (AI) provide innovative tools for stress and burnout management, including mobile applications offering mindfulness, meditation, and self-care resources. AI systems like IBM Watson and Google DeepMind are being tested to enhance accessibility and effectiveness in stress management. Additionally, Stress Inoculation Training (SIT), involving methods such as meditation, yoga, cognitive-behavioural therapy, and biofeedback, has been recognized as a proactive approach to mitigating stress. This review explores the factors contributing to stress and burnout in healthcare workers and evaluates interventions aimed at improving well-being and productivity, emphasizing the potential of AI and SIT in preventing and managing these conditions.International Journal of Health and Medicine00This review explores the factors contributing to stress and burnout in healthcare workers and evaluates interventions aimed at improving well-being and productivity, emphasizing the potential of AI and SIT in preventing and managing these conditions.International Journal of Health and Medicine["Hasnah Taureng", "Intan Suhana Munira Mat Azmi", "San San Oo", "Moe Thwe Aung", "Ucok Ucok"]2024-12-16T00:00:00 f0ff06f5d47086582299708ee8609f680b1d2d1cWastewater treatment is a critical process for ensuring water quality and public health, particularly in the context of increasing environmental challenges such as pollution and water scarcity. Artificial intelligence (AI) has emerged as a transformative technology capable of optimizing various wastewater treatment processes, such as contaminant removal, energy consumption, and cost-efficiency. This study presents a comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024. Utilizing machine learning techniques such as neural networks, fuzzy logic, and genetic algorithms, the analysis reveals key trends in the role of the AI in optimizing wastewater treatment processes. The results show that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control. Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. The findings provide valuable insights into the current state and future potential of AI technologies in wastewater management, offering a roadmap for researchers exploring the integration of AI to address sustainability challenges in the field.Resources460A comprehensive bibliometric analysis of AI applications in wastewater treatment, utilizing data from Scopus and Web of Science covering 4335 publications from 1985 to 2024, shows that AI has increasingly been applied to solve complex problems like membrane fouling, nutrient removal, and biofouling control.Resources["Javier De La Hoz-M", "E. A. Ariza-Echeverri", "Diego Vergara"]2024-12-16T00:00:00 19e595284ef861ad2bf3e06a682d0b91efaab1acThe rapid development of artificial intelligence (AI), particularly in the field of generative artificial intelligence (GenAI), raises complex questions about data use and copyright protection. This article explores the significant transition from AI models relying on human influence to achieving near-complete autonomy, presenting formidable challenges to existing copyright laws. As AI-generated creations gain widespread use, debates about copyright eligibility and the recognition of AI as a creator emerge. This article also argues against granting copyright to AI creators because their products lack human influence. The nature of GenAI is discussed, distinguishing it from other AI models, assessing the extent of human input required and questioning the application of current intellectual property laws. The article also follows the evolution of AI in creativity, outlining three phases marked by technological advances. The diminishing role of human intervention in the creative process is highlighted, a diminution particularly evident in contemporary models such as ChatGPT. Unlike human creators, algorithms lack awareness and influence, undermining the need for copyright protection. Ongoing legal discourse focuses on ownership and data protection, and market solutions can cause confusion. The changing landscape prompts a reassessment of the adequacy of copyright law to protect the rights of creators and maintain the human-centric foundation of copyright law, a foundation that is absent in the outcomes generated by AI. The article additionally considers recent case law that could potentially offer insights into addressing the legal issues at hand. In conclusion, the article emphasizes ongoing questions regarding the necessity of protecting AI-generated outcomes and the difficulties these outcomes present within the existing legal framework, as seen from a Spanish perspective.Gdańskie Studia Prawnicze00The significant transition from AI models relying on human influence to achieving near-complete autonomy, presenting formidable challenges to existing copyright laws is explored, as seen from a Spanish perspective.Gdańskie Studia Prawnicze["Ma\u0142gorzata W\u0119grzak", "Concepci\u00f3n Saiz Garc\u00eda"]2024-12-16T00:00:00 4362ddee09bd066bbf4e46f95b3806fdfa5c58bfPurposeWe compare human intelligence to artificial intelligence (AI) in the choice of appropriate Journal of Economic Literature (JEL) codes for research papers in economics.Design/methodology/approachWe compare the JEL code choices related to articles published in the recent issues of the Journal of Economic Literature and the American Economic Review and compare these to the original JEL code choices of the authors in earlier working paper versions and JEL codes recommended by various generative AI systems (OpenAI’s ChatGPT, Microsoft’s Copilot, Google’s Gemini) based on the abstracts of the articles.FindingsThere are significant discrepancies and often limited overlap between authors’ choices of JEL codes, editors’ choices as well as the choices by contemporary widely used AI systems. However, the observations suggest that generative AI can augment human intelligence in the micro-task of choosing the JEL codes and, thus, save researchers time.Research limitations/implicationsRapid development of AI systems makes the findings quickly obsolete.Practical implicationsAI systems may economize on classification costs and (semi-)automate the choice of JEL codes by recommending the most appropriate ones. Future studies may apply the presented approach to analyze whether the JEL code choices between authors, editors and AI systems converge and become more consistent as humans increasingly interact with AI systems.Originality/valueWe assume that the choice of JEL codes is a micro-task in which boundedly rational decision-makers rather satisfice than optimize. This exploratory experiment is among the first to compare human intelligence and generative AI in choosing and justifying the choice of optimal JEL codes.Journal of Documentation190It is suggested that generative AI can augment human intelligence in the micro-task of choosing the JEL codes and, thus, save researchers time and, thus, save researchers time.Journal of Documentation["Jussi T.S. Heikkil\u00e4"]2024-12-16T00:00:00 6ac434701935f86c04c602dc44bd11a3e23615caThe adoption of Artificial Intelligence (AI) in the public sector offers transformative potential for enhancing decision-making processes, optimizing service delivery, and driving operational efficiency. This research examines the integration of AI into digital public services, emphasizing the dual imperatives of accountability and efficiency. By analyzing AI’s application in areas such as resource allocation, policy formulation, and citizen engagement, this study explores how public sector organizations can leverage AI to address complex societal challenges. Central to this discourse is the balance between automation’s benefits and the ethical considerations inherent in governance, such as transparency, equity, and inclusivity. Through a multidisciplinary approach, the research evaluates real-world case studies and theoretical frameworks, providing actionable insights for policymakers and administrators. It also investigates the limitations and risks of AI adoption, including algorithmic bias, lack of explainability, and potential erosion of public trust. By proposing strategies to align AI innovations with the principles of good governance, this study contributes to the development of equitable and accountable AI-driven public systems, ensuring their sustainability and societal acceptance.Human-Computer Interaction50By proposing strategies to align AI innovations with the principles of good governance, this study contributes to the development of equitable and accountable AI-driven public systems, ensuring their sustainability and societal acceptance.Human Computer Interaction["Berke S\u00f6ker"]2024-12-16T00:00:00 0f8551165287f6f9dbfe902dc1ba5151e84ec3a5INTRODUCTION Artificial intelligence (AI) has significant transformative potential across various sectors, particularly in health care. This study aims to develop a protocol for the content analysis of a method designed to assess AI applications in drug-related information, specifically focusing on contraindications, adverse reactions, and drug interactions. By addressing existing challenges, this preliminary research seeks to enhance the safe and reliable integration of AI into healthcare practices. METHODS A study protocol was developed for the creation of the method, followed by an initial content analysis conducted by an expert panel. The method was established in phases: (1) Analysis of drug-related databases and form development; (2) AI configuration; (3) Expert panel review and initial validation. RESULTS In Phase 1, the Micromedex, UpToDate, and Medscape databases were reviewed to establish terminology and classifications related to contraindications, adverse reactions, and drug interactions, resulting in the development of a questionnaire for the AI. Phase 2 involved configuring the Gemini AI tool to enhance response specificity. In Phase 3, AI responses to 30 questions were validated by an expert panel, yielding a 76.7% agreement rate for appropriateness, while 23.3% were deemed inappropriate, particularly concerning contraindicated drug interactions. CONCLUSION This preliminary study demonstrates the potential for using an AI-powered tool to standardize drug-related information retrieval, particularly for contraindications and adverse reactions. While AI responses were generally appropriate, improvements are needed in identifying contraindicated drug interactions. Further research with larger datasets and broader evaluations is required to enhance AI's reliability in healthcare settings.Journal of Evaluation In Clinical Practice70This preliminary study demonstrates the potential for using an AI-powered tool to standardize drug-related information retrieval, particularly for contraindications and adverse reactions, particularly for contraindications and adverse reactions.Journal of evaluation in clinical practice["Dantony Castro Barros de Donato", "G. J. Aguilar", "Lucas Gaspar Ribeiro", "Luiz Ricardo Albano dos Santos", "Luana Michelly Aparecida Costa Dos Santos", "W. Costa", "Alan Maicon de Oliveira"]2024-12-16T00:00:00 d082ef067f8e7ad638b856d31e45b963896b82d5Tanzania has the highest age-adjusted prevalence of diabetes in sub-Saharan Africa. Diabetic retinopathy, a common complication, is a significant cause of vision loss; but with effective screening and treatment this often can be prevented. However, with very few specialist eye care staff in Tanzania this is a major challenge. Artificial intelligence (AI) systems, which automate clinical decision making and therefore task-shift away from specialist staff, could contribute to improved diabetic retinopathy screening services in low-resource settings. This article describes our experiences of selecting, procuring and implementing an AI system into a regional diabetic eye screening programme in northern Tanzania.Transactions of the Royal Society of Tropical Medicine and Hygiene40The experiences of selecting, procuring and implementing an AI system into a regional diabetic eye screening programme in northern Tanzania are described.Transactions of the Royal Society of Tropical Medicine and Hygiene["Charles R Cleland", "William U Makupa", "Bernadetha R Shilio", "Justus Rwiza", "David Macleod", "C. Bascaran", "Matthew J Burton"]2024-12-16T00:00:00 812c5d1e9019179b18c6d10c3ff3ad9e257b1203The existence of AI is a separate system based on logic, where the information entered into the system will be processed with a programmed algorithm to determine a predetermined result. AI can cause various forms of harm to everyone, including its creator, and the harm it causes can have a long-lasting impact, considering that AI can make decisions similar to humans. The application of AI in the industrial sector will impact all existing systems, including the criminal justice system in all countries. Therefore, the legal regulation of cybercrime in Indonesia needs to be reformed to resolve criminal liability for criminal acts that AI can carry out. The formulation of the problem in this study is what is the urgency of updating cybercrime law in Indonesia. Furthermore, how is the legal policy on criminal liability for artificial intelligence resolved it. Thus,  this study normative such as legal research carried out by examining library materials or data using statutory and analytical approaches. This study concludes and suggests that responsibility must be imposed on AI users and legal entities whose responsible parties are company directors. AI creators must also be responsible for the AI's actions. The renewal of cyber law in Indonesia is significant and should be done immediately, even in the Electronic Information and Transactions Law. Almost all institutional and personal documents are stored electronically; the state must also protect them. Other parties can then misuse these documents, which can be traded on the cyber black market and used irresponsibly. Illegal activities in cyberspace are also increasing, and the diversity of their actions with various skills is constantly increasing.JUSTISI100Responsibility must be imposed on AI users and legal entities whose responsible parties are company directors, such as legal research carried out by examining library materials or data using statutory and analytical approaches.JUSTISI["Kurnia Dewi Anggraeny", "Mufti Khakim", "Muhammad Rizal Sirojudin"]2024-12-16T00:00:00 f542a8feaeae475bc870d57f1ef38822c1ecf81eGlaucoma is a pathologically irreversible eye illness in the realm of ophthalmic diseases. Because it is difficult to detect concealed and non-obvious progressive changes, clinical diagnosis and treatment of glaucoma is extremely challenging. At the same time, screening and monitoring for glaucoma disease progression are crucial. Artificial intelligence technology has advanced rapidly in all fields, particularly medicine, thanks to ongoing in-depth study and algorithm extension. Simultaneously, research and applications of machine learning and deep learning in the field of glaucoma are fast evolving. Artificial intelligence, with its numerous advantages, will raise the accuracy and efficiency of glaucoma screening and diagnosis to new heights, as well as significantly cut the cost of diagnosis and treatment for the majority of patients. This review summarizes the relevant applications of artificial intelligence in the screening and diagnosis of glaucoma, as well as reflects deeply on the limitations and difficulties of the current application of artificial intelligence in the field of glaucoma, and presents promising prospects and expectations for the application of artificial intelligence in other eye diseases such as glaucoma.Frontiers in Medicine690This review summarizes the relevant applications of artificial intelligence in the screening and diagnosis of glaucoma, as well as reflects deeply on the limitations and difficulties of the current application, and presents promising prospects and expectations for the application of artificial intelligence in other eye diseases such as glaucoma.Frontiers in Medicine["Yu Jin", "Lina Liang", "Jiaxian Li", "Kai Xu", "Wei Zhou", "Yamin Li"]2024-12-16T00:00:00 2396d7e8684f5d45cac552aed75506e01def1922Artificial intelligence (AI) is transforming the healthcare systems by introducing cutting-edge solutions that enhance diagnostic accuracy, optimize clinical workflows, and personalize patient care. This report explores the current and potential applications of AI in healthcare, with a focus on its role in medical imaging, personalized medicine, surgery, drug discovery, workflow management, and virtual healthcare. Using AI's ability to process vast datasets, detect patterns, and assist in decision-making, healthcare systems can achieve improved efficiency and patient outcomes. Despite its benefits, the integration of AI into healthcare faces challenges related to data privacy, algorithmic transparency, biasness, and ethical concerns. Additionally, this report discusses on the future of AI, highlighting its potential in healthcare industries.International Symposium on Embedded Multicore/Many-core Systems-on-Chip230The current and potential applications of AI in healthcare are explored, with a focus on its role in medical imaging, personalized medicine, surgery, drug discovery, workflow management, and virtual healthcare.2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)["Thanasitsomboon Siradanai", "C. Kok", "Chee Kit Ho", "Yit Yan Koh", "T. H. Teo"]2024-12-16T00:00:00 a0ed1e3035e3dc92e2a5af5bc9a790c7bc680ebd The latest advances in science and technology have resulted in great advances in artificial intelligence (AI), including the creation of chatbots. Chatbots simulate human conversation and allow humans to ask questions and receive answers based on a large volume of electronically stored information. Faculties of universities around the world are trying to come to grips with the availability of AI tools, such as chatbots, and are debating the ethical and moral questions surrounding the use of AI in education. This paper presents the results of a study which intended to answer three research questions. RQ1: how familiar are students with AI and tools that utilize it? RQ2: are students aware of the ethical issues involving AI and are they familiar with the university’s policies regarding the use of AI? RQ3: can ChatGPT be used as an efficient tool to teach science majors to code in Python? In this project, a chatbot was used to instruct students on the use of the Python programming language. Introductory college physics students were tasked with using an AI chatbot, ChatGPT (chat generative pre-trained transformer), to learn how to effectively code in the coding program, Python. Before using ChatGPT to code, the students were given a pre-test survey to determine their skill level in Python coding and their familiarity of AI and issues pertaining the use of AI. After completing exercises in Python coding using ChatGPT, a post-test survey was conducted to determine how well the students have learned to code in Python as well as how effective ChatGPT was in assisting their study.The Physical Educator220Introductory college physics students were tasked with using an AI chatbot, ChatGPT (chat generative pre-trained transformer), to learn how to effectively code in the coding program, Python.Physics Education["Joseph J Trout", "Lauren Winterbottom"]2024-12-16T00:00:00 12783ae2ad1f220dac9adb5f8299442cbe64598aThe article presents a small part of the research results devoted to the development of scientific and methodological support of variant teaching of the basics of artificial intelligence in the course of computer science of basic general and secondary general education, concerning the preparation of schoolchildren for the Olympiad on artificial intelligence. The aim of the article is to actualize the problem of developing or selecting the necessary task material for purposeful preparation for school Olympiads on artificial intelligence. The content of theoretical and practical modules of the basics of artificial intelligence developed as a result of the research includes olympiad training in artificial intelligence for basic and high school students studying computer science at an advanced level. The research methodology consists of: integrative approach to the development of the methodology of variant teaching of the basics of artificial intelligence and the implementation of possible educational trajectories in the basic educational programs of basic general and secondary general education; experience in the development of the concept and content of the first All-Russian Olympiad of schoolchildren of grades 8-11 on artificial intelligence; expert activity in the Russian Union of Schoolchildren’s Olympiads and the results of surveys of teachers. General theoretical and empirical research methods were used. The conclusion about possible solution of the problem of development of training tasks for purposeful preparation of schoolchildren for the Olympiad on artificial intelligence and realization of the course on choice of participants of educational relations with the use of standard tasks of three stages of the Olympiad on artificial intelligence is made in the conclusion.Siberian Pedagogical Journal00Siberian Pedagogical Journal["N. N. Samylkina", "I. Kalinin"]2024-12-16T00:00:00 6df086eb62aa7df911bfbfbe5f01e8ff1ee47be0Prototyping and the medical field have been transformed by the extensive use of 3D printing. Nevertheless, mistakes can occur during the process, resulting in unsuccessful prints, material waste, and lost time. By providing real-time issue identification and repair, artificial intelligence (AI) provides a solution and revolutionizes 3D printing. In order to identify printing flaws with 97.77% accuracy, this study created a Machine Learning (ML) model using Support Vector Machines (SVM). By enabling automatic modifications, the model's integration into a smart slicing process greatly decreased mistakes, increasing efficiency and dependability. These results demonstrate how AI may improve the precision and efficiency of 3D printing.International Conference on Communication and Electronics Systems110By enabling automatic modifications, the model's integration into a smart slicing process greatly decreased mistakes, increasing efficiency and dependability and demonstrate how AI may improve the precision and efficiency of 3D printing.2024 9th International Conference on Communication and Electronics Systems (ICCES)["Ritu Gupta", "Anushka Sinha", "Aryan Sharma"]2024-12-16T00:00:00 e114d1d58286ecb787cdde758211386a95849562This research explores the integration of artificial intelligence (AI) in cardiovascular medicine, highlighting its potential to revolutionize the diagnosis, treatment, and management of cardiovascular diseases, offering the potential for improved patient outcomes and more efficient healthcare delivery. This paper explores the multifaceted applications of AI in cardiology, including early diagnosis and risk assessment, personalized treatment plans, predictive analytics, and drug development. AI algorithms leverage vast amounts of data from various sources, such as electronic health records, imaging studies, and wearable devices, to provide insights that enhance clinical decision-making. However, the widespread adoption of AI in cardiology faces significant challenges, including data quality and interoperability, ethical concerns surrounding patient privacy, algorithm transparency, and regulatory hurdles. This review emphasizes how collaboration among clinicians, researchers, and policymakers can help overcome challenges to fully leverage AI in cardiovascular care, ultimately enhancing patient outcomes.International Symposium on Embedded Multicore/Many-core Systems-on-Chip100This review emphasizes how collaboration among clinicians, researchers, and policymakers can help overcome challenges to fully leverage AI in cardiovascular care, ultimately enhancing patient outcomes.2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)["C. Kok", "Yit Yan Koh", "Chee Kit Ho", "Nguyen To Cong Thanh", "T. H. Teo"]2024-12-16T00:00:00 48895066a70927ba6d98c47c3adbc85901f33fc4The issue of artificial intelligence (‘AI’) in the context of intellectual property law, including copyright law, has attracted continued interest. Progressive innovation brings new challenges, and the advances we have seen in recent years - particularly in the development of generative artificial intelligence (‘GenAI’) systems – are attracting media and public attention. The adoption and use of generative artificial intelligence systems has sparked widespread debate about their relevance to the copyright system. In the wake of emerging questions, copyright holders have begun to file copyright infringement lawsuits against artificial intelligence companies targeting the process of training artificial intelligence with the results obtained from generative artificial intelligence systems. As a result of these questions, copyright holders have begun filing copyright infringement lawsuits against owners of programs trained on the basis of data protected by copyright and data protection law. Drawing on analysed discussions, normative proposals, consultations and recommendations from experienced practitioners, this article identifies one of the broad questions of contemporary copyright policy towards artificial intelligence, concerning the legality of using copyrighted works to train artificial intelligence models. It also poses the question of the desirability of establishing a new system of copyright exceptions and limitations dedicated to artificial intelligence systems, while analysing the impact of existing limitations under copyright exceptions and limitations on the development of artificial intelligence.Gdańskie Studia Prawnicze170One of the broad questions of contemporary copyright policy towards artificial intelligence, concerning the legality of using copyrighted works to train artificial intelligence models, is identified and the question of the desirability of establishing a new system of copyright exceptions and limitations dedicated to artificial intelligence systems is posed.Gdańskie Studia Prawnicze["Anna Bober-Kotarbi\u0144ska"]2024-12-16T00:00:00 2b77df49d26d7cac5488879d31e24cc4479c6480Artificial intelligence is a part of computing that deals with the development of the computer's ability to perform tasks that require a certain level of intelligence (Hrvatska enciklopedija, 2024). Its development also increases the potential for misuse in various areas. Artificial intelligence as a tool that can be used to create very convincing disinformations in communication leads to greater possibilities of manipulating public opinion. This phenomenon is not unknown and is becoming more widespread as the popularity of social networks grows. The spread of disinformation created by artificial intelligence increases the possibility of spreading falsehoods that need to be fought against. In the introductory part, this paper will clarify the purpose of disinformation created by artificial intelligence, furthermore it will provide an overview of selected cases of the dissemination of disinformation in the public in recent history to the present day - from the Homeland War in Croatia, the US war in Iraq until the recently started war between Ukraine and Russia, with the purpose of creating tools for hybrid warfare.In the final part, the paper will deal with the question of whether artificial intelligence, which serves humans to create disinformation and hybrid warfare, can be a weapon to fight against such warfare. Can artificial intelligence be a weapon against itself in the disinformation war?National security and the future00The purpose of disinformation created by artificial intelligence is clarified and an overview of selected cases of the dissemination of disinformation in the public in recent history to the present day is provided - from the Homeland War in Croatia to the recently started war between Ukraine and Russia.National security and the future["Jelena Bo\u017ei\u0107", "Margareta Gregi\u0107"]2024-12-16T00:00:00 42d9d8d8bd51cee70f964307ffee3ab365f7cf56The paper prepared in the context of mainstreaming artificial intelligence (AI) school teaching describes a model of AI and data analysis variant teaching of schoolchildren in computer science class and its components based on the integrative approach. The main aim of this study is to provide scientific and teaching community with the results of a methodological research that was carried out in Institute of Mathematics and Computer Science (Moscow Pedagogical State University) to develop methodological support for teaching schoolchildren topics related to AI technologies in both basic and secondary general education organizations and basic or advanced computer science classes. The paper summarizes current experience in AI and data analysis school teaching, with an integrative approach being the main methodological approach to designing a course for schoolchildren. The paper proposes the structure and the components of a methodology of variant teaching of AI and data analysis that considers project and extracurricular activities possibilities and the requirements of the Federal State Educational Standard of Basic General Education and the Federal State Educational Standard of Secondary General Education. The paper concludes that the model that is designed in accordance with the requirements of Federal State Educational Standard and which is in a sense an “ideal” model for teaching schoolchildren AI and data analysis basics can be interpreted by educational organizations, for example, to design various learning trajectories that meet the personal needs of schoolchildren, the technical and methodological capabilities of an educational organization, and the personal character of students. Moreover, the paper emphasizes that this model for variant teaching AI and data analysis basics centered on an integrative approach is already supported with practical educational learning materials for schoolchildren (Basic General Education and Secondary General Education level) and can be used by computer science teachers in their classroom, extracurricular, and project-research activities, as well as in preparing high school students for AI and data analysis Olympiads.Siberian Pedagogical Journal00The model that is designed in accordance with the requirements of Federal State Educational Standard and which is in a sense an “ideal” model for teaching schoolchildren AI and data analysis basics can be interpreted by educational organizations to design various learning trajectories that meet the personal needs of schoolchildren, the technical and methodological capabilities of an educational organization, and the personal character of students.Siberian Pedagogical Journal["S. Karakozov", "N. I. Ryzhova", "N. N. Samylkina", "E. A. Samokhvalova"]2024-12-16T00:00:00 600a0e8912dee85a4cd84f677e153def6d6f5514This study aims to examine the level of convenience of Artificial Intelligence (AI) for students in education in terms of personalized learning, enhanced engagement, accessibility, and inclusivity. It also explores the extent of the drawbacks of AI in education for students, considering neutrality, privacy concerns, the digital gap, and dehumanizing effects. The study looked into the significant differences between the convenience and drawbacks of Artificial Intelligence and the relationships between these factors. The study employed a descriptive-correlational research design. Results revealed that the correlation analysis between the level of convenience of Artificial Intelligence and the extent of its drawbacks shows a significant relationship. This entails that active use of AI in education contributes to students’ learning while knowing its downfall. This is viewed as Al’s role in education to have a balanced use. Regarding these results, it is essential to properly create policies and procedures to use Al in education. It will help enhance the student support system and encourage responsible Al development practices to manage the unforeseen drawbacks and ensure AI’s ethical and efficient application in educational settings.2024 International Conference on TVET Excellence & Development (ICTeD)290The correlation analysis between the level of convenience of Artificial Intelligence and the extent of its drawbacks shows a significant relationship and entails that active use of AI in education contributes to students’ learning while knowing its downfall.2024 International Conference on TVET Excellence & Development (ICTeD)["Rojen Lyneth T. Ampong", "Leo L. Codilla", "Theress Joy Romero", "Princess Dianne P. Caparo"]2024-12-16T00:00:00 229980d5e6377c2463b936006acee72a821a04a2Articles, reports, studies of a number of scientists in the field of parametric design were analyzed. The work of leading architects using parametricism and the latest technologies in design, such as Zaha Hadid, Santiago Calatrava, Patrick Schumacher, and others, is highlighted. Ways to solve and implement complex spatial modeling processes thanks to parameterization and the use of artificial intelligence are determined. A number of computer programs that are active have been analyzed used in design, creation of virtual reality, and 3D simulations. The methods of using software tools that involve algorithmic calculations for the generation of forms in the design and optimization of structures are disclosed. It has been proven that the use of the latest technologies ensures efficient and accurate design, and reduces the time for project development. That is, parametrics has a key role in the creation of innovative programs and affects the future architecture and organization of space as a whole. With the help of technologies are created digital models of the object. Digital models are stored in a system that allows you to provide the maximum level of security of designer and project data. And for achievement maximum efficiency and convenience in designing architects at various stages parametric design uses artificial intelligence, which allows them to get interesting architecture of the future. Based on the experience of leading architects in the field the introduction of parameterization in the design and use of artificial intelligence is emerging. There is a reason to believe that in the future it is necessary to promote parametricism, to develop AI comprehensively and use it in practice. Also, all of the above must be conveyed to the young generation of architects to influence the architecture of the future.Regional problems of architecture and urban planning00It has been proven that the use of the latest technologies ensures efficient and accurate design, and reduces the time for project development, and parametrics has a key role in the creation of innovative programs and affects the future architecture and organization of space as a whole.Regional problems of architecture and urban planning["T. O. Dolgikh"]2024-12-16T00:00:00 0d9d1d552818263d1daeee3681432f423a1af8e4Journal of Network and Systems Management241J. Netw. Syst. Manag.["Xinhao Wan", "Gang Xue", "Yiming Zhong", "Zhicheng Wang"]2024-12-16T00:00:00 7c2fb2630ad853aaff0c8606ca44664f5d327c1aThe IoT -based women protection system employs advanced machine learning models to provide real-time threat detection and emergency response. This paper presents a novel approach that integrates feature extraction and dimensionality reduction techniques to enhance the system's performance. By combining statistical methods and time-frequency analysis, the proposed model effectively processes high -dimensional sensor data, such as heart rate, movement patterns, and GPS coordinates, ensuring accurate threat detection. A thorough evaluation was conducted comparing the proposed model with nine existing models, including Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). The proposed model achieved an impressive accuracy of 97.81 %, outperforming the best-performing alternative models such as LightGBM and XGBoost. Additionally, the model demonstrates significant improvements in precision, recall, and F1-score, highlighting its capability to detect and classify potential threats in real time. Energy efficiency and low inference time make this model suitable for IoT devices, ensuring seamless operation without excessive resource consumption. The results indicate that the proposed model is robust, efficient, and well-suited for real-time safety applications, particularly in IoT -based systems focused on women protection.International Conference on Communication and Electronics Systems150A novel approach is presented that integrates feature extraction and dimensionality reduction techniques to enhance the system's performance, and is robust, efficient, and well-suited for real-time safety applications, particularly in IoT -based systems focused on women protection.2024 9th International Conference on Communication and Electronics Systems (ICCES)["J. Gayathri", "Vamsi Krishna K P", "M. Devasekthi", "Thirunavukkarasu P", "S.R. Pranav", "Bharath M"]2024-12-16T00:00:00 a0d6cf160e57fa3c857c396aae6ff56487a62d7dJournal of Reliability Science and Engineering00Journal of Reliability Science and Engineering["M. Compare", "E. Zio"]2024-12-16T00:00:00 42d9d8b248367e2e40684ecbc0581a0780583a68During the last decades, Salafi-Jihadi groups have exploited the proliferation of social media to create a persistent and ideologically cohesive presence online. Yet, Salafi-Jihadi individuals connected online to form a new dispersed network of ‘media mujahedeen’ based on loose affiliations, moving from the ‘one-to-many’ to a ‘peer-to-peer’ structure described by Ali Fisher as the Swarmcast Model. The Swarmcast model can be adopted to understand the current media war held by media mujahedeen online. Given this context, experts observed a new centrality of non-institutional media houses, which spread jihadist propaganda and/or produce propaganda content. During the last decade, Telegram was Salafi-Jihadi organizations’ main messaging platform for both institutional and non-institutional media houses. However, in late November 2019, the 16th Referral Action Day coordinated by EUROPOL took place and forced Salafi-Jihadi propaganda operators to adopt what was described by Ali Fisher and Nico Prucha as the Multiplatform Communication Paradigm (MCP) to create a more resilient digital network. Among the messaging platforms, there was Rocket.Chat, which became their primary launchpad and digital safe haven. In late March, inside the Islamic State (IS) server on Rocket.Chat, the first massive use of Artificial Intelligence (AI) was individuated, which was aimed at producing what can be described as a news broadcast in Arabic of IS’s wilayat operations all over the world made by a pro-IS non-institutional media house. The producer inserted all the main characteristics of news broadcasts created through the support of AI. From that day to now, the user posted ten different propaganda videos employing the same production techniques, albeit with some graphic and content differences. Against the backdrop of the evolution of Salafi-Jihadi communication that occurred in the last decades, this paper seeks to illustrate the use of AI inside IS’s server on Rocket.Chat and trace possible countermeasures to prevent using such technologies to produce Salafi-Jihadi propaganda.National security and the future00Against the backdrop of the evolution of Salafi-Jihadi communication that occurred in the last decades, this paper seeks to illustrate the use of AI inside IS’s server on Rocket.Chat and trace possible countermeasures to prevent using such technologies to produce Salafi-Jihadi propaganda.National security and the future["Alessandro Bolpagni"]2024-12-16T00:00:00 3575312191e5d8f2e640e3486d034c143a682f42Journal of Military Ethics90Journal of Military Ethics["William Hasselberger"]2024-12-16T00:00:00 a90ee340141d78e97b87415af6fba83460bcb406Neural computing & applications (Print)00Neural Computing and Applications["Tamilarasi Kathirvel Murugan", "Pritikaa Karthikeyan", "Pavithra Sekar"]2024-12-16T00:00:00 19b91daf223e6021fc359e2675e6f2395977f8eaJournal of Medical Toxicology10Journal of medical toxicology : official journal of the American College of Medical Toxicology["Kai Ay Smollin", "C. Smollin"]2024-12-16T00:00:00 0a57a85a3d5186c9f422f25a949e9583257700d6Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform the traffic landscape. This article endeavors to review the topics where AI and traffic management intersect. It comprises areas like AI-powered traffic signal control systems, automatic distance and velocity recognition (for instance, in autonomous vehicles, hereafter AVs), smart parking systems, and Intelligent Traffic Management Systems (ITMS), which use data captured in real-time to keep track of traffic conditions, and traffic-related law enforcement and surveillance using AI. AI applications in traffic management cover a wide range of spheres. The spheres comprise, inter alia, streamlining traffic signal timings, predicting traffic bottlenecks in specific areas, detecting potential accidents and road hazards, managing incidents accurately, advancing public transportation systems, development of innovative driver assistance systems, and minimizing environmental impact through simplified routes and reduced emissions. The benefits of AI in traffic management are also diverse. They comprise improved management of traffic data, sounder route decision automation, easier and speedier identification and resolution of vehicular issues through monitoring the condition of individual vehicles, decreased traffic snarls and mishaps, superior resource utilization, alleviated stress of traffic management manpower, greater on-road safety, and better emergency response time.arXiv.org00The topics where AI and traffic management intersect are reviewed to review the topics like AI-powered traffic signal control systems, automatic distance and velocity recognition, smart parking systems, and Intelligent Traffic Management Systems (ITMS), which use data captured in real-time to keep track of traffic conditions, and traffic-related law enforcement and surveillance using AI.ArXiv["Ritwik Raj Saxena"]2024-12-16T00:00:00 957ff369c65fc7c44fc649300e8c8929e18a9c1fJournal of the Foundations of Ophthalmology00Journal of the Foundations of Ophthalmology["Adeel Mushtaq"]2024-12-16T00:00:00 0461da81a4ee79a1dda243cde58cac23e57620f6American Journal of Respiratory and Critical Care Medicine170American Journal of Respiratory and Critical Care Medicine["N. Seam", "S. H. Chotirmall", "Fernando J Martinez", "Andrew J. Halayko", "M. Harhay", "Stephanie D Davis", "Paul T Schumacker", "Robert M. Tighe", "Kristin M Burkart", "Colin Cooke"]2024-12-16T00:00:00 a877528bde81cfdc41f783d3022a0961fc527300African Journal of Biomedical Research00African Journal of Biomedical Research["Prof. Zeba Nisar"]2024-12-16T00:00:00 2b6c23c7e3d5b2ffde3120586efe600ae482567cRecalling the debate around data justice in order to highlight which parts of this multifaceted concept have been endowed with legal relevance by EU legislation or initiatives, the paper argues that the EU should implement a more “instrumental” approach to data justice. This perspective emphasizes a stronger focus on the purposes addressed by the deployment of data within AI systems.Gdańskie Studia Prawnicze340It is argued that the EU should implement a more “instrumental” approach to data justice, which emphasizes a stronger focus on the purposes addressed by the deployment of data within AI systems.Gdańskie Studia Prawnicze["Nadia Maccabiani", "Anna Podolska", "Ewelina Szatkowska"]2024-12-16T00:00:00 5f4439a6862add6bbd830b792f891f19d3e54b7cWe are witnessing an unprecedented convergence of scientific discoveries, technology innovations, exponential adoption of technology and remarkable population demographic shifts towards a digitally native society. The Noble Prizes in medicine, chemistry, physics awarded this year further validated the profound impact of technology on healthcare and life sciences. For 2025-designated by the United Nations as The Year of Quantum Technology, we can envision further technology-driven innovations in all domains, triggering the transition to a novel health ecosystem. The role of AI in modern drug development, the demand for quantum-proof encryption, and the opportunities of blockchain in health data monetization are all trends can be disruptive for pharma, healthcare and healthcare finance.Blockchain in Healthcare Today00The role of AI in modern drug development, the demand for quantum-proof encryption, and the opportunities of blockchain in health data monetization are all trends can be disruptive for pharma, healthcare and healthcare finance.Blockchain in Healthcare Today["Ingrid Vasiliu-Feltes MD, EMBA", "Jennifer Hinkel, MSc, CHW, FRSA", "Olga Kubassova, PhD"]2024-12-16T00:00:00 cab1992dbc77a5ed78cb0b1eda6a277d4a30bfeaHigher Education for the Future00Higher Education for the Future["Rajan Gurukkal"]2024-12-16T00:00:00 6221c655d83f6b78ca4fb412a77e39d25c6c14bbTribologie und Schmierungstechnik00Tribologie und Schmierungstechnik["Manfred Jungk"]2024-12-16T00:00:00 a6d7c62a0c0226590fcd55b4d7072eff7093ff72Adesh University Journal of Medical Sciences &amp; Research200Adesh University Journal of Medical Sciences &amp; Research["M. Panditrao"]2024-12-16T00:00:00 4fbaeb176604199ce8fa2d75426d8cf83d369f27Pada mata kuliah Media Pembelajaran Berbasis ICT, mahasiswa dituntut kreatif untuk menghasilkan media pembelajaran Biologi berbasis digital yang tepat guna, mampu meningkatkan pemahaman siswa, mudah digunakan, serta tidak memakan waktu lama saat proses pembuatannya. Penggunaan AI dalam membuat media pembelajaran Biologi memberikan akses yang cepat dalam pemberian informasi. Penggunaan AI juga sangat relevan untuk mempermudah pemahaman konsep-konsep abstrak dalam Biologi. Penelitian ini bertujuan untuk mendeskripsikan respon 10 orang mahasiswa pada prodi Pendidikan Biologi UNIPAR Jember setelah dilakukan pembelajaran menggunakan media berbasis AI. Penelitian yang dilakukan merupakan penelitian deskriptif kuantitatif. Secara keseluruhan, hasil survei ini menunjukkan respon yang sangat positif terhadap penggunaan media presentasi berbasis AI. Hal ini dapat dilihat dari hasil angket respon mahasiswa memenuhi kriteria baik (68%-83,9%) dan sangat baik (84%-100%),yakni dilihat dari segi desain, materi, interaksi, dan cara penyampaian. Mayoritas peserta merasa tertarik dan melihat sesuatu yang baru dalam media ini, dengan sebagian besar merasa mudah untuk memahaminya. Berdasarkan hasil wawancara dengan 10 orang mahasiswa, seluruhnya menunjukkan pilihan yang kuat untuk menggunakan media pembelajaran berbasis AI kelak ketika mereka menjadi guru. Mereka berpendapat bahwa AI untuk membuat proses pembelajaran menjadi lebih menarik dan interaktif. Dari segi efisiensi, beberapa mahasiswa mengungkapkan bahwa penggunaan media berbasis AI dapat membantu meningkatkan efektivitas pengajaran dengan cara yang signifikan.  BIO-CONS : Jurnal Biologi dan Konservasi00BIO-CONS : Jurnal Biologi dan Konservasi["Hanif Rafika Putri"]2024-12-16T00:00:00 9c31a990b13740d941a3eb4f90ce8c2c33cbecf9This article reviews the concepts of virtuality and artificial intelligence applied to teaching Spanish as a foreign language. It analyzes how these models are integrated to create accessible learning environments for the acquisition of second or foreign languages. Virtuality facilitates distance learning through digital platforms, while artificial intelligence promotes personalization and interactivity of learning. The present research provides a classification of these technologies within the area of ​​Spanish teaching, highlighting their benefits and limits.SABIR. INTERNATIONAL BULLETIN OF APPLIED LINGUISTICS00The present research provides a classification of these technologies within the area of ​​Spanish teaching, highlighting their benefits and limits.SABIR. INTERNATIONAL BULLETIN OF APPLIED LINGUISTICS["Mar\u00eda Luisa Garc\u00eda Fern\u00e1ndez"]2024-12-16T00:00:00 84125acea880cee9c85ba478f91e5a3e3985d2abArtificial Intelligence (AI)-driven voice assistants, such as Amazon's Alexa and Google Assistant, have become essential components in modern smart home ecosystems, revolutionizing the way users interact with their environments. These voice assistants enable hands-free control over various home automation systems, offering unprecedented levels of convenience and enhancing daily productivity. By integrating with a wide range of smart devices—from lighting and temperature control to security systems and entertainment—AI-driven voice assistants simplify the management of daily routines, allowing users to perform tasks more efficiently with minimal effort. This study aims to explore the influence of AI-driven voice assistants on user productivity and overall satisfaction within smart homes. Employing both quantitative data collection methods, such as usage statistics and automation completion rates, alongside qualitative insights gathered from user feedback, we conduct a comprehensive analysis of how voice-controlled smart home interactions affect daily activities. The research examines aspects such as task completion speed, automation accuracy, and the seamlessness of multi-device integrations, in addition to the subjective user experience, including perceived ease of use, convenience, and general satisfaction with the system. Our findings suggest that AI-driven voice assistants offer considerable improvements in user productivity, reducing the time and effort required to complete tasks by streamlining processes and enabling multi-tasking. Additionally, users reported higher satisfaction levels due to the simplicity and efficiency introduced by these technologies. However, the study also highlights ongoing challenges, particularly regarding the assistants' limited ability to understand complex contextual information and the growing concerns over privacy and data security. Despite these issues, the positive impact of AI-driven voice assistants on smart home ecosystems is clear, with potential for further enhancements as the technology evolves.Journal of Economic Theory and Business Management02The findings suggest that AI-driven voice assistants offer considerable improvements in user productivity, reducing the time and effort required to complete tasks by streamlining processes and enabling multi-tasking.Journal of Economic Theory and Business Management["Zuen Cen", "Yuxin Zhao"]2024-12-16T00:00:00 b809613e6b193c9158854683c7ac59be1cf6d762Artificial intelligence (AI) is transforming medical radiation applications by handling complex data, learning patterns, and making accurate predictions, leading to improved patient outcomes. This article examines the use of AI in optimizing radiation doses for X-ray imaging, improving radiotherapy outcomes, and briefly addresses the benefits, challenges, and limitations of AI integration into clinical workflows. In diagnostic radiology, AI plays a pivotal role in optimizing radiation exposure, reducing noise, enhancing image contrast, and lowering radiation doses, especially in high-dose procedures like computed tomography. Deep learning-powered CT reconstruction methods have already been incorporated into clinical routine. Moreover, AI-powered methodologies have been developed to provide real-time, patient-specific radiation dose estimates. These AI-driven tools have the potential to streamline workflows and potentially become integral parts of imaging practices. In radiotherapy, AI's ability to automate and enhance the precision of treatment planning is emphasized. Traditional methods, such as manual contouring, are time-consuming and prone to variability. AI-driven techniques, particularly deep learning models, are automating the segmentation of organs and tumors, improving the accuracy of radiation delivery, and minimizing damage to healthy tissues. Moreover, AI supports adaptive radiotherapy, allowing continuous optimization of treatment plans based on changes in a patient's anatomy over time, ensuring the highest accuracy in radiation delivery and better therapeutic outcomes. Some of these methods have been validated and integrated into radiation treatment systems, while others are not yet ready for routine clinical use mainly due to challenges in validation, particularly ensuring reliability across diverse patient populations and clinical settings. Despite the potential of AI, there are challenges in fully integrating these technologies into clinical practice. Issues such as data protection, privacy, data quality, model validation, and the need for large and diverse datasets are crucial to ensuring the reliability of AI systems.Journal of Radiological Protection01The use of AI in optimizing radiation doses for X-ray imaging, improving radiotherapy outcomes, and briefly addresses the benefits, challenges, and limitations of AI integration into clinical workflows are examined.Journal of radiological protection : official journal of the Society for Radiological Protection["John Damilakis", "J. Stratakis"]2024-12-16T00:00:00 686836729d5af79c13732941c028c7f0adb151a0International Journal of Educational Technology in Higher Education331The findings highlight the need for targeted professional development on AI ethics, collaborative policy making and a multidisciplinary approach to promote ethical use of AI in higher education.International Journal of Educational Technology in Higher Education["J. Kamali", "Muhammet Furkan Alpat", "Aras Bozkurt"]2024-12-16T00:00:00 f45b24bca3b0a05237883c23e941ee3b8a5cee57HR decision-making is changing as a result of artificial intelligence (AI), especially in the areas of hiring, onboarding, and retention. This study examines the use of AI tools throughout the lifecycle of an employee, emphasizing how they enhance the effectiveness, customization, and scalability of HR procedures. These solutions streamline employee setup, learning, and documentation. They range from AI-driven applicant tracking systems (ATSs) for applicant selection to AI-powered platforms for automated onboarding and individualized training. Predictive analytics also helps retention and performance monitoring plans, which lowers turnover, but issues such as bias, data privacy, and ethical problems must be carefully considered. This paper addresses the limitations and future directions of AI while examining its disruptive potential in HR.Applied Sciences01This study examines the use of AI tools throughout the lifecycle of an employee, emphasizing how they enhance the effectiveness, customization, and scalability of HR procedures.Applied Sciences["Mitra Madanchian"]2024-12-16T00:00:00 a2e12a5debd08cd72908849bb4fa6107cbd2ae21Alternative Dispute Resolution (ADR) is a method for resolving a dispute in a non-traditional way, outside the court without entering into normal conventional litigation. ADR is not restricted only to arbitration or mediation rather it covers all the means utilized for the settlement of a dispute outside the court without the intervention of any judicial proceedings. The need to shift the litigation system to an Alternative system is the efficiency and flexibility maintained by ADR, which provides parties to dispute, a speedy and quick solution without getting into a lengthy trial. This study critically examines the integration of artificial intelligence into the mediation process for uplifting the mediation standards and reducing the factors of bias in the mediation procedure in order to highlight mediation as the most sustainable procedure for the settlement of disputes outside the court. This article inspects that AI is a solution for replacing human mediators and also pinpoints the potential challenges for AI-based mediation models. Furthermore, this article examines that what factors are causing hindrances in the implementation of AI-integrated mediation models, and what the advantages of AI in mediation are. The article utilized the qualitative research methodology to carry out the study and potential recommendations will be made for a sustainable hybrid model of AI and Human-Integrated Mediation for resolving commercial as well as other disputes.The Critical Review of Social Sciences Studies320This study critically examines the integration of artificial intelligence into the mediation process for uplifting the mediation standards and reducing the factors of bias in the mediation procedure in order to highlight mediation as the most sustainable procedure for the settlement of disputes outside the court.The Critical Review of Social Sciences Studies["Asim Arslan Ahmad"]2024-12-16T00:00:00 3ab366fc9f630343f4949ffbe3664022416271b4Global forests face increasing threats from deforestation, biodiversity loss, and climate change, necessitating innovative tools for effective monitoring and management. Traditional forest monitoring methods, which rely heavily on manual fieldwork and labor-intensive data processing, are often inadequate for addressing the scale and complexity of these challenges. Advanced tools leveraging artificial intelligence (AI) and remote sensing have emerged as critical solutions, offering timely, accurate, and actionable insights to enable efficient ecosystem monitoring, threat detection, and sustainable management practices. This paper introduces SylvaMind AI, an advanced platform that integrates satellite imagery, deep learning frameworks, and geospatial analysis within a user-friendly interface, which was built using Python for backend systems and deep learning pipelines, alongside tools like Pandas, Rasterio, and TensorFlow for data preprocessing and predictive modelling. The platform processes high-resolution data from Sentinel-2 and Landsat missions for feature extraction and predictive modelling. SylvaMind AI offers two modelling approaches: an automated option for non-technical users and a customizable feature for researchers with specialized needs. Using these approaches, we developed a predictive canopy height model for a study area. The results demonstrated the platform's ability to capture underlying forest patterns and provide detailed insights into canopy height distribution, particularly for medium to high canopies (>25m). This underscores its strength in modeling structural complexity in dense forests. However, the model showed limitations in representing smaller trees, attributed to insufficient training data. SylvaMind AI holds immense potential in transforming forest monitoring by leveraging advanced geospatial data, AI, and intuitive design to address critical challenges in sustainable forest management.Bulletin of the Transilvania University of Brasov. Series II: Forestry • Wood Industry • Agricultural Food Engineering350SylvaMind AI, an advanced platform that integrates satellite imagery, deep learning frameworks, and geospatial analysis within a user-friendly interface, is introduced, demonstrating the platform's ability to capture underlying forest patterns and provide detailed insights into canopy height distribution, particularly for medium to high canopies (>25m).Bulletin of the Transilvania University of Brasov. Series II: Forestry • Wood Industry • Agricultural Food Engineering["M.I. Keskes", "M.D. Nita"]2024-12-16T00:00:00 1b2ddbc5602b96f5497e2aa2e87084061f449affThese are notes for lectures presented at the University of Stuttgart that provide an introduction to key concepts and techniques in AI Planning. Artificial Intelligence Planning, also known as Automated Planning, emerged somewhere in 1966 from the need to give autonomy to a wheeled robot. Since then, it has evolved into a flourishing research and development discipline, often associated with scheduling. Over the decades, various approaches to planning have been developed with characteristics that make them appropriate for specific tasks and applications. Most approaches represent the world as a state within a state transition system; then the planning problem becomes that of searching a path in the state space from the current state to one which satisfies the goals of the user. The notes begin by introducing the state model and move on to exploring classical planning, the foundational form of planning, and present fundamental algorithms for solving such problems. Subsequently, we examine planning as a constraint satisfaction problem, outlining the mapping process and describing an approach to solve such problems. The most extensive section is dedicated to Hierarchical Task Network (HTN) planning, one of the most widely used and powerful planning techniques in the field. The lecture notes end with a bonus chapter on the Planning Domain Definition (PDDL) Language, the de facto standard syntax for representing non-hierarchical planning problems.arXiv.org320The notes begin by introducing the state model and move on to exploring classical planning, the foundational form of planning, and present fundamental algorithms for solving such problems, which examine planning as a constraint satisfaction problem, outlining the mapping process and describing an approach to solve such problems.ArXiv["Marco Aiello", "Ilche Georgievski"]2024-12-16T00:00:00 756e3b06ed18af9a1ec56834abbc370262207950Penelitian ini bertujuan untuk mengeksplorasi inovasi dalam pembelajaran Pendidikan Agama Islam (PAI) melalui penerapan teknologi Artificial Intelligence (AI), dengan fokus pada peningkatan interaksi siswa. Dalam era digital saat ini, penggunaan AI dalam pendidikan menawarkan peluang untuk menciptakan pengalaman belajar yang lebih interaktif dan personal. Metode yang digunakan dalam penelitian ini adalah pendekatan kualitatif dengan analisis studi pustaka dan wawancara dengan pendidik serta siswa. Hasil penelitian menunjukkan bahwa integrasi AI, seperti penggunaan chatbot edukasi dan platform pembelajaran adaptif, dapat meningkatkan keterlibatan siswa, mempercepat umpan balik, dan memfasilitasi komunikasi yang lebih efektif antara siswa dan guru. Meskipun demikian, tantangan seperti keterbatasan infrastruktur teknologi dan perlunya pelatihan bagi guru juga diidentifikasi sebagai faktor yang perlu diperhatikan untuk memastikan keberhasilan implementasi AI dalam pendidikan agama Islam. Penelitian ini diharapkan dapat memberikan wawasan bagi pendidik dan pengambil kebijakan dalam mengoptimalkan penggunaan teknologi AI untuk meningkatkan interaksi siswa dalam pembelajaran PAI. This research aims to explore innovation in Islamic Religious Education (PAI) learning through the application of Artificial Intelligence (AI) technology, with a focus on increasing student interaction. In today's digital era, the use of AI in education offers opportunities to create more interactive and personalized learning experiences. The method used in this research is a qualitative approach using literature study analysis and interviews with educators and students. The research results show that AI integration, such as the use of educational chatbots and adaptive learning platforms, can increase student engagement, speed up feedback, and facilitate more effective communication between students and teachers. However, challenges such as limited technological infrastructure and the need for teacher training were also identified as factors that need to be considered to ensure the successful implementation of AI in Islamic religious education. It is hoped that this research can provide insight for educators and policy makers in optimizing the use of AI technology to increase student interaction in PAI learning.Mauriduna: Journal of Islamic Studies01Mauriduna: Journal of Islamic Studies["Rifqi Fahrudin", "Riyadi Sollikhin", "Anisatul Masruroh"]2024-12-17T00:00:00 61521c071bc858deb4749c999a06f0941249ecdaIntroduction The growing presence of generative artificial intelligence (GenAI) in our society, particularly in the educational field, is undeniable. This fact has led to various studies on its implications for learning and teaching. However, as with other technological resources, these implications will depend on how teachers use GenAI. Therefore, it is essential to identify teachers’ beliefs regarding the use of GenAI for teaching and learning. Methods To this end, a questionnaire was designed and completed by 321 university teachers. This questionnaire consisted of two parts. The first included questions about the participants’ demographic information and a Likert scale on teachers’ pedagogical beliefs. The second part consisted of a 32-item Likert scale that evaluated teachers’ beliefs about the impact of GenAI on their students’ learning and their own teaching. These aspects were reflected through items that considered GenAI as either an educational opportunity or a threat. Results The results showed that, of all the variables analyzed, only pedagogical beliefs and the frequency of previous GenAI use influenced beliefs about GenAI usage. Specifically, teachers with constructivist beliefs saw greater potential in GenAI compared to others. Similarly, teachers who regularly used these technologies had more positive beliefs about their educational use than those who used them sporadically or not at all. Lastly, it was also observed that while teachers valued the positive effects of GenAI on their teaching work, they also considered that its use could be detrimental to the learning processes of their students, making them more superficial. Discussion These findings underline the importance of providing teachers with training focused on constructive approaches that enable them to maximize the potential of GenAI in education. In particular, it is crucial to promote teaching practices that, through student-centered GenAI use, foster active and reflective processes in students, aligned with the competencies demanded by today’s society.Frontiers in Psychology841The results showed that, of all the variables analyzed, only pedagogical beliefs and the frequency of previous GenAI use influenced beliefs about GenAI usage, and teachers with constructivist beliefs saw greater potential in GenAI compared to others.Frontiers in Psychology["Beatriz Cabellos", "Carlos de Aldama", "J. Pozo"]2024-12-17T00:00:00 4c8fd34b6add405d3c7642d6247a1c0c542a720fThe relationship between AI and management decision-making has received increasing attention in the literature, but the impact of AI on managerial decision-making through transformational leadership has not yet been thoroughly examined. Thus, this study investigates the impact of artificial intelligence on engineering management decision-making through transformational leadership. The participants include 385 employees drawn from manufacturing, construction, and information technology firms in Turkey. The data were processed using WarpPLS (7.0), and the estimation was conducted with the use of “partial least squares structural equation modeling (PLS-SEM)”. A positive and significant direct influence of “artificial intelligence” and “transformational leadership” on engineering management decision-making practices was demonstrated in this study, while transformational leadership was also found to have a significant mediating role in the relationship between artificial intelligence and engineering management decision-making practices. This study concluded with theoretical and practical implications for policymakers in the engineering industry by providing an integrated framework that allows for a nuanced examination of how AI impacts engineering management decision-making. It accounts for individual perceptions, leadership influences, and organizational adaptations, providing a comprehensive lens through which to analyze the complex interplay between AI technology, leadership, and decision-making processes in engineering management contexts. In addition, the findings of our study have significant implications for engineers and for governments creating standards to help preserve engineering businesses. Leaders and practitioners should research the instillation of values inherent to AI for an organization like engineering businesses to ensure that AI is being used to enable effective decision-making towards ensuring the accomplishment of their sustainable competitive advantage.Systems621A positive and significant direct influence of “artificial intelligence” and “transformational leadership” on engineering management decision-making practices was demonstrated in this study, while transformational leadership was also found to have a significant mediating role in the relationship between artificial intelligence and engineering management decision-making practices.Systems["Abdullah Abositta", "Muri Wole Adedokun", "Ay\u015fen Berbero\u011flu"]2024-12-17T00:00:00 28878ae3842e6abb1a950ee9664c8b0213150d62Modern agriculture increasingly relies on technology to increase efficiency and productivity. Aquaponics, a sustainable farming method that combines fish and plant farming, has emerged as one promising approach. To maximize yield in an aquaponics system, monitoring plant growth becomes very important. In this context, Artificial Intelligence (AI) offers innovative solutions to monitor and optimize plant growth in realtime. AI-based aquaponics technology is designed portably so that it allows people to grow crops inside and outside the home. AIbased aquaponics technology uses a camera that functions to monitor plants in real-time. The data on the camera will be processed and analyzed by the AI system so that automatic monitoring of the plant growth environment in the system can be carried out. Where will output results that show whether the leaves are still fresh, immediately wither Using CNN's deep learning method, this technology contributes to sustainable food production with higher efficiency in managing resources.  This system can increase productivity and strengthen food security in the face of future challenges. This aquaponics technology can make a significant contribution to the development of sustainable agriculture and can provide guidance and inspiration for agricultural and food industry players. By optimizing food production through AI-based aquaponics systems, communities can face global food security challenges and move towards more environmentally friendly, efficient, and sustainable solutions for the future.Jurnal Informatika: Jurnal Pengembangan IT00By optimizing food production through AI-based aquaponics systems, communities can face global food security challenges and move towards more environmentally friendly, efficient, and sustainable solutions for the future.Jurnal Informatika: Jurnal Pengembangan IT["Lyla Putri Deviana", "Styawati Styawati"]2024-12-17T00:00:00 2e2cbb19fe1739445cc595e86c4fb11356f54e79Today, the world is in a process of continuous change, and the digital era has positively influenced education by revolutionizing the traditional approach through the adoption of artificial intelligence (AI) tools. The aim of this paper is to analyze how AI is transforming teaching and learning processes. To this end, a systematic literature review was conducted, selecting 33 articles that addressed the research topic from various perspectives. To summarize the information, the PRISMA methodology was used, which involved an exhaustive search in academic databases, the application of inclusion and exclusion criteria to select relevant studies, and a detailed analysis of the chosen articles, developed between 2019 and 2024, which contained relevant characteristics for the study. In parallel, specific research questions were established to guide the review, addressing pedagogical, practical, ethical, and social aspects related to the integration of AI in education. The results highlighted the potential benefits of AI in learning personalization, teaching efficiency, and access to advanced educational resources. In addition, challenges in AI implementation were identified, such as the generation of incorrect information and biases in training data. It is concluded that AI can improve personalization of learning, teaching efficiency, and access to advanced resources, but it is crucial to address ethical challenges such as data privacy, equity in access to technology, transparency of algorithms, and impact on students’ autonomy and critical thinking.International Journal of Interactive Mobile Technologies (iJIM)00It is concluded that AI can improve personalization of learning, teaching efficiency, and access to advanced resources, but it is crucial to address ethical challenges such as data privacy, equity in access to technology, transparency of algorithms, and impact on students’ autonomy and critical thinking.International Journal of Interactive Mobile Technologies (iJIM)["Carlos Fernando Yerbabuena Torres", "Alexandra Valeria Villagomez Cabezas", "Ana Roc\u00edo Yerbabuena Torres", "Nathalie Abigail Mendoza Torres"]2024-12-17T00:00:00 964ded88eea0c997d1c92006c48aa0c227aabb68Entrepreneurship has entered a new era shaped by artificial intelligence (AI), demanding accelerated scholarly advances to keep pace with this transformative technology—yet this demands that academics bridge the gap between the AI revolution’s ambiguities and meaningful scholarly contributions. To motivate and guide future research on AI’s transformative role in entrepreneurship, we introduce an ongoing special issue in Entrepreneurship Theory and Practice ( ETP) and outline multiple compelling opportunities for future research. Unlike typical editorials, we offer a prospective vision—rather than retrospective, after the articles have been accepted and published—at this project’s outset, to empower the field to prospect and establish new scholarly foundations in the relatively uncharted world of AI in the domain of entrepreneurship. Accordingly, we highlight the “AI PEN” (Prospecting and Establishing Nexus) as a desirable research approach to advance this literature going forward. We hope, and anticipate, that our invitation to submit proposals to this special issue facilitates novel empirical as well as theory-focused contributions to the literature.Entrepreneurship: Theory & Practice660An ongoing special issue in Entrepreneurship Theory and Practice is introduced and the “AI PEN” (Prospecting and Establishing Nexus) is highlighted as a desirable research approach to advance this literature going forward to motivate and guide future research on AI’s transformative role in entrepreneurship.Entrepreneurship Theory and Practice["M. Obschonka", "Denis A. Gr\u00e9goire", "Boris Nikolaev", "Fr\u00e9d\u00e9ric Ooms", "Moren L\u00e9vesque", "Jeffrey M. Pollack", "Tara S. Behrend"]2024-12-17T00:00:00 4d5f47a1c6eb80880243a627f9be64aa20d9a67bThe development of Artificial Intelligence (AI) has been used in various applications that are able to translate Arabic and map its grammatical rules automatically. In the field of linguistics, especially Arabic, AI has the potential to help and speed up the translation process as well as complex grammar analysis. However, some linguistic aspects, such as sentence context, nuances of meaning, and i’rab interpretation, are still a challenge for AI systems. This article presents the analysis of AI's ability to punctuate, translate and map Arabic rules, and assess the extent to which it can replace humans. The research method used was a literature study with a content analysis approach. The objects studied were Arabic mahfudzhot selected based on the objectives with the book to analyze the AI with Kitab Nahwu Wadhih. This study found that the three AIs analyzed have their own advantages and disadvantages. ChatGPT has the largest percentage score in punctuation (harakat) and Arabic rule mapping (i’rab). As for the translation, Gemini has a superior percentage score compared to ChatGPT and Perplexity. Although AI is able to perform literal translation and structural analysis, the shortcomings were frequently showed as its results, so a human is needed to ensure accuracy and contextualized interpretation. These results showed that AI can serve as an effective tool, but cannot fully replace human expertise in Arabic linguistics.Jurnal Al Bayan: Jurnal Jurusan Pendidikan Bahasa Arab00The results showed that AI can serve as an effective tool, but cannot fully replace human expertise in Arabic linguistics, so a human is needed to ensure accuracy and contextualized interpretation.Jurnal Al Bayan: Jurnal Jurusan Pendidikan Bahasa Arab["Fera Favirotus Siyam", "Rahmat Hidayat", "Cecep Sobar Rochmat", "Rosendah Dwi Maulaya", "Annisa Avilya", "Muhammad Bahaudin Maulidi"]2024-12-17T00:00:00 14562b6c18eeaba988bed477a0f10fe70954a4e2Artificial Intelligence (AI) has emerged as a transformative force driving sustainable business innovation across various sectors, particularly in finance and marketing. This study conducted a comprehensive meta-analysis of existing research to explore the role of AI as a catalyst for sustainable practices and digital transformation. This methodology entails a comprehensive literature search across multiple databases with a focus on the nexus of AI, sustainability, and business model innovation. The study underscores the significance of digital transformation in the context of sustainable business models, underscoring the necessity for strategic integration of technology, business model reengineering, and organizational structure optimization. The potential of AI technologies, including Machine Learning (ML), neural networks, and generative AI, to enhance sustainability efforts and drive innovation is also discussed. Furthermore, this study examines the challenges and opportunities associated with the adoption of AI in the fields of finance and marketing, considering factors such as data quality, ethical considerations, and organizational readiness. By providing in-sights into the sustainable utilization of AI technologies, this study contributes to the understanding of how AI can facilitate digital transformation and promote long-term value crea-tion in finance and marketing.Gazdaság és Társadalom00A comprehensive meta-analysis of existing research is conducted to explore the role of AI as a catalyst for sustainable practices and digital transformation, underscoring the necessity for strategic integration of technology, business model reengineering, and organizational structure optimization.Gazdaság és Társadalom["Cedric Bartelt", "Alexander Maximilian R\u00f6ser"]2024-12-17T00:00:00 557427745dde06b1d2eb5b15ac8579d195f5b9a3PurposeGiven the increasing attention on ChatGPT in academia due to its advanced features and capabilities, this study aims to examine the links among Artificial intelligence (AI), knowledge sharing, ethics, academia and libraries in educational institutions. Moreover, this study also aims to provide a literature base while discussing recent trends in AI and ChatGPT technologies, highlighting their specific uses in institutions.Design/methodology/approachThe paper involves a structured interview format where a human interviewer poses questions “Qs” in ChatGPT, related to knowledge sharing, ethics, academia and libraries. Moreover a literature base is also provide to discussed recent trends in AI and ChatGPT technologies, highlighting their specific uses in institutions.FindingsThe study find out that AI and ChatGPT technologies in educational institutions affect knowledge sharing, ethical consideration, academia and libraries. This study also highlights literature directions for the trends and proper use of the AI and ChatGPT among institutions, such as improving student-learning engagement.Originality/valueThis research contributes to the prior literature by offering an in-depth review of current uses and applications of AI and ChatGPT in educational institutions. It not only highlights key trends and innovations but also provides insights and guidelines for future research. This study also provides insights and guidelines for future research. Furthermore, the article emphasizes the potential impact of AI and ChatGPT on the future of education and technology.The international journal of information and learning technology750The study finds that AI and ChatGPT technologies in educational institutions affect knowledge sharing, ethical consideration, academia and libraries, and highlights literature directions for the trends and proper use of the AI and ChatGPT among institutions, such as improving student-learning engagement.The International Journal of Information and Learning Technology["Ali Zeb", "F. Rehman", "Majed Bin Othayman", "Muhammad Rabnawaz"]2024-12-17T00:00:00 6001e8c1340cb5d8386625a46795849dff61e8b6This study compares the outcomes of two Delphi technique implementations investigating cybersecurity threats to online education: a traditional human expert panel from a 2014 study and a virtual panel generated using ChatGPT-4. The research evaluated whether artificial intelligence (AI) can produce results comparable to human experts in Delphi studies. Through a three-round process, both panels identified and prioritized key cybersecurity concerns. Results revealed significant overlap in core concerns, with both panels emphasizing training, data security, and system infrastructure as critical priorities. The AI panel introduced novel perspectives, such as collaboration and continuous improvement, while maintaining alignment with the human panel's recommendations. These findings suggest that AI can expedite-Delphi studies and produce meaningful insights, albeit with limitations in contextual understanding and practical nuance. This research contributes to methodological advancements in Delphi studies, offering implications for incorporating AI into expert-driven research.International Journal For Multidisciplinary Research270It is suggested that AI can expedite-Delphi studies and produce meaningful insights, albeit with limitations in contextual understanding and practical nuance.International Journal For Multidisciplinary Research["Phillip Davidson"]2024-12-17T00:00:00 735ad4f834e62ead3b7fd1ae4be55c83fb1096baINTRODUCTION Recent advancements in Artificial Intelligence (AI), particularly through the deployment of Large Language Models (LLMs), have profoundly impacted healthcare. This study assesses five LLMs-ChatGPT 3.5, ChatGPT 4, BARD, CLAUDE, and COPILOT-on their response accuracy, clarity, and relevance to queries concerning acute liver failure (ALF). We subsequently compare these results with Chat GPT4 enhanced with Retrieval Augmented Generation (RAG) technology. METHODS Based on real-world clinical use and the American College of Gastroenterology guidelines, we formulated 16 ALF questions or clinical scenarios to explore LLMs' ability to handle different clinical questions. Using the "New Chat" functionality, each query was processed individually across the models to reduce any bias. Additionally, we employed the RAG functionality of GPT-4, which integrates external sources as references to ground the results. All responses were evaluated on a Likert scale from 1 to 5 for accuracy, clarity, and relevance by four independent investigators to ensure impartiality. RESULT ChatGPT 4, augmented with RAG, demonstrated superior performance compared to others, consistently scoring the highest (4.70, 4.89, 4.78) across all three domains. ChatGPT 4 exhibited notable proficiency, with scores of 3.67 in accuracy, 4.04 in clarity, and 4.01 in relevance. In contrast, CLAUDE achieved 3.04 in clarity, 3.6 in relevance, and 3.65 in accuracy. Meanwhile, BARD and COPILOT exhibited lower performance levels; BARD recorded scores of 2.01 in accuracy and 3.03 in relevance, while COPILOT obtained 2.26 in accuracy and 3.12 in relevance. CONCLUSION The study highlights Chat GPT 4 +RAG's superior performance compared to other LLMs. By integrating RAG with LLMs, the system combines generative language skills with accurate, up-to-date information. This improves response clarity, relevance, and accuracy, making them more effective in healthcare. However, AI models must continually evolve and align with medical practices for successful healthcare integration.American Journal of Gastroenterology00Chat GPT 4 +RAG, augmented with RAG, demonstrated superior performance compared to others, consistently scoring the highest in response clarity, relevance, and accuracy, making them more effective in healthcare.The American journal of gastroenterology["Sheza Malik", "Lewis J. Frey", "Jason Gutman", "Asim Mushtaq", "Fatima Warraich", "Kamran Qureshi"]2024-12-17T00:00:00 49d3cc318482b159e84bbc499b09eeb5a0ce960aIntegrating artificial intelligence (AI) into occupational safety management systems is becoming increasingly relevant in today's rapidly evolving technological landscape. This study focuses on implementing intelligent systems for workplace safety in Ukraine, addressing the pressing need for national legislation to adapt to international standards and incorporate modern technologies. The significance of this research lies in its potential to enhance safety in work environments, ensuring the well-being of employees across various sectors. The primary objective of this study is to analyze the current state of AI adoption in occupational safety in Ukraine, identifying existing barriers and challenges enterprises face in the integration process. The research investigates the extent to which Ukrainian legislation reflects contemporary technological changes, particularly in AI, and how this compares to international practices. A comparative analysis reveals notable differences in the approach to workplace safety between Ukraine and other countries, highlighting areas where Ukraine lags in regulatory frameworks and technological implementation. This study involved a comprehensive review of existing literature, legislative documents, and case studies from industries where AI technologies have been successfully implemented. Key challenges identified include inadequate infrastructure, a lack of qualified personnel, and an updated regulatory framework addressing AI's unique aspects in occupational safety. The research emphasizes the importance of developing educational programs to enhance the skills of workers and managers in this field. The findings emphasize the necessity for a multi-faceted approach to overcome the identified obstacles. Recommendations include the active modernization of legislation, establishing national safety standards aligned with international requirements, and promoting collaboration with global organizations. By fostering a culture of safety that embraces technological advancements, Ukraine can effectively harness the potential of AI to improve workplace conditions. In conclusion, integrating AI into occupational safety management in Ukraine is both timely and essential. With the proper steps and commitment from all stakeholders, significant improvements can be achieved in workplace safety, reducing risks and enhancing productivity. This research contributes to the academic discourse on occupational safety and AI. It is a guiding framework for policymakers and industry leaders to create safer working environments.Municipal economy of cities20The research investigates the extent to which Ukrainian legislation reflects contemporary technological changes, particularly in AI, and how this compares to international practices, and the importance of developing educational programs to enhance the skills of workers and managers in this field.Municipal economy of cities["O. Krainiuk", "Y. Buts", "V. Barbashyn", "D. Kozodoi", "\u041e.D. Kozodoi"]2024-12-17T00:00:00 d9b83924b9f1e26ea4cc8d04e6e85ec6a9d8ee07This systematic review examines the transformative potential of Generative Artificial Intelligence (GenAI) across diverse sectors, including information technology, education, manufacturing, creative industries, healthcare, transportation, management, marketing, finance, energy, law, media, agriculture, and e-commerce. By analyzing its applications, the study highlights how GenAI enhances efficiency, fosters innovation, and addresses sector-specific challenges. Key benefits include the automation of complex processes, optimization of resource use, and acceleration of decision-making. However, delayed adoption risks such as workforce displacement and ethical dilemmas are also discussed. The review identifies critical barriers like data privacy concerns, algorithmic bias, and regulatory challenges. Practical strategies for successful GenAI integration are explored, emphasizing infrastructure readiness, workforce upskilling, and ethical governance. This includes leveraging generative models such as Generative Adversarial Networks (GANs), Transformer-based models, Variational Autoencoders (VAEs), and diffusion models to adapt to industry-specific demands. Furthermore, the study underscores the necessity of balancing technological advancements with responsible AI deployment to minimize risks and maximize societal benefits. By synthesizing existing research, this review provides actionable insights for stakeholders aiming to leverage GenAI's transformative capabilities responsibly. It emphasizes the urgency of adopting GenAI technologies to maintain competitiveness and sustainability in rapidly evolving markets. As the study concludes, it advocates for cross-sectoral collaboration to address the complex challenges posed by this paradigm-shifting technology and calls for adaptive policies to align innovation with ethical principles and societal values.Journal of Smart Systems Research00The study emphasizes the urgency of adopting GenAI technologies to maintain competitiveness and sustainability in rapidly evolving markets and advocates for cross-sectoral collaboration to address the complex challenges posed by this paradigm-shifting technology.Journal of Smart Systems Research["Osman \u015eahin", "D. Karayel"]2024-12-17T00:00:00 f9c2c706bc3021582fbf4d4ae6ddc80031fc2205Artificial intelligence (AI) and its applications in digital health, bioengineering, and society have significant material impacts on the environment owing to AI's vast energy demands and energy consumption, carbon footprints, and water usage to cool data centers and generate electricity to power the data centers. Yet, the environmental footprints of AI remain underappreciated and inadequately acknowledged. This is significant, particularly in this era of climate emergency and ongoing threats to planetary energy and water supplies. The vocabulary attached to AI often aims to mimic positive human capacities such as "warmness" and "care." However, these attempts to humanize AI and digital technology come with an anthropocentric gaze and blind spots that bracket out the environmental impacts and footprints of AI and privilege humans and technology over nonhuman animals and planetary ecological limits. In medicine, the environmental impacts of large language models range from water consumption and carbon emission to rare mineral usage. This commentary and innovation analysis question and queer the popular imagination of AI and digital technology as things that only exist in the immaterial world of cyberspace. In the course of research on AI in planetary health, we must be cognizant of its materiality, ecological impacts, and massive energy and water demands. We argue that moving away from anthropocentric narratives and vocabulary in AI design and praxis would bode well to live within planetary ecological limits so that AI and emerging digital technologies best serve robust and responsible science and all life on the planet Earth.Omics90It is argued that moving away from anthropocentric narratives and vocabulary in AI design and praxis would bode well to live within planetary ecological limits so that AI and emerging digital technologies best serve robust and responsible science and all life on the planet Earth.Omics : a journal of integrative biology["\u00dcmit Karaka\u015f", "V. \u00d6zdemir"]2024-12-17T00:00:00 21f5f9656d173a8ea3bd0abb2539e9033fee9efaSomnologie160This pilot study showed that AI- and expert-constructed psychotherapeutic stories share similarities in addressing sleep-related problems; however, the latter seem to be superior regarding key elements of such stories.Somnologie["Angelika A. Schlarb", "J. Faber"]2024-12-17T00:00:00 4bf177cdbdb7b26504e6771e0e1f794ed21c0b08Transformasi pendidikan agama Islam (PAI) di era digital mendorong integrasi kecerdasan buatan (Artificial Intelligence/AI) dalam proses pembelajaran. Penelitian ini bertujuan mengeksplorasi peran AI dalam memperkuat nilai-nilai Islami melalui pembelajaran yang lebih personal, efektif, dan adaptif. Dengan menggunakan pendekatan kualitatif deskriptif, penelitian ini mengkaji manfaat, tantangan, dan implikasi etis dari penggunaan AI dalam PAI. Hasil penelitian menunjukkan bahwa AI mampu meningkatkan personalisasi pembelajaran, mempercepat evaluasi otomatis, dan menyediakan materi ajar interaktif yang relevan dengan kebutuhan siswa. Selain itu, AI berperan dalam memperkuat nilai-nilai Islami melalui konten pembelajaran yang kontekstual dan berbasis karakter. Kendati demikian, tantangan yang dihadapi meliputi kesenjangan literasi digital guru, keterbatasan infrastruktur, serta kekhawatiran etis terkait privasi dan bias algoritma. Oleh karena itu, penelitian ini merekomendasikan penguatan literasi digital bagi guru, pengembangan kebijakan etis, dan pengadaan infrastruktur teknologi yang memadai. Dengan pengelolaan yang tepat, AI berpotensi menjadi alat pendukung yang efektif dalam menciptakan pembelajaran PAI yang relevan, adaptif, dan berbasis nilai-nilai keislaman. The transformation of Islamic religious education (PAI) in the digital era encourages the integration of artificial intelligence (AI) in the learning process. This research aims to explore the role of AI in strengthening Islamic values through more personalized, effective, and adaptive learning. Using a descriptive qualitative approach, this study examines the benefits, challenges, and ethical implications of using AI in PAI. The results show that AI is able to improve learning personalization, accelerate automatic evaluation, and provide interactive teaching materials relevant to students' needs. In addition, AI plays a role in strengthening Islamic values through contextualized and character-based learning content. However, challenges include teachers' digital literacy gap, infrastructure limitations, and ethical concerns regarding privacy and algorithm bias. Therefore, this study recommends strengthening digital literacy for teachers, developing ethical policies, and procuring adequate technological infrastructure. With proper management, AI has the potential to be an effective supporting tool in creating PAI learning that is relevant, adaptive, and based on Islamic values.Mauriduna: Journal of Islamic Studies150Mauriduna: Journal of Islamic Studies["Muhamad Hadziq", "Dian Ayu Havifah", "Labiebatul Badriyah"]2024-12-17T00:00:00 aaf906e9a36121e222a727f8502bde09012058f9Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.Innovatech Engineering Journal00A systematic review of 82 high-quality peer-reviewed articles is conducted to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management, highlighting the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines, which consistently demonstrate high accuracy and reliability in predicting diabetes risk.Innovatech Engineering Journal["Md Ashraful Alam", "Amir Sohel", "Kh Maksudul Hasan", "Mohammad Ariful Islam"]2024-12-17T00:00:00 bc4760251f00803ab357c1a779a0af8b692e587ePURPOSE To evaluate the ZEISS AI IOL Calculator (ZEISS AI) and compare its accuracy in refractive prediction to the Barrett Universal II (BUII) and Kane formulas. SETTING Cullen Eye Institute, Baylor College of Medicine, Houston, TX. DESIGN Retrospective case series. METHODS The ZEISS AI IOL Calculator (ZEISS AI) is an artificial intelligence (AI) based IOL-optimized formula. The refractive prediction errors (PEs) were calculated in the entire dataset and subgroups of short eyes (axial length (AL) ≤ 22.5 mm) and long eyes (AL ≥ 25.0 mm). The standard deviation (SD), root-mean-square absolute error (RMSAE), mean absolute error (MAE), median absolute error (MedAE), and percentage of eyes within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D of PEs were calculated. Values with ZEISS AI were compared to those from Barrett Universal II (BUII) and Kane. Advanced statistical methods were applied using R. RESULTS A dataset of 10,838 eyes was included. Compared to ZEISS AI, BUII produced significantly greater SDs, RMSAEs, and MAEs in the whole group and short eyes, and the Kane had greater SD, RMSAE, and MAE in short eyes (all adjusted P<0.05); the BUII had significantly lower percentages of eyes within ±0.50 D of PEs in the whole group (80.0% vs 81.2%) and in short eyes (71.3% vs. 76.1%), and the Kane had lower percentage of eyes within ±0.50 D of PEs in short eyes (71.9% vs. 76.1%) (all adjusted P<0.05). CONCLUSION The ZEISS AI IOL Calculator had superior performance compared to the BUII and Kane formulas, especially in short eyes.Journal of cataract and refractive surgery00The ZEISS AI IOL Calculator had superior performance compared to the BUII and Kane formulas, especially in short eyes.Journal of cataract and refractive surgery["Li Wang", "Hendrik Burwinkel", "Nicolas Bensaid", "Douglas D Koch"]2024-12-17T00:00:00 89f76d671ca93b80d8620688a1554e9192bcb4a4This community service activity aims to enhance the quality of teaching for teachers at Ma’had Al Furqon Al Islami Gresik through training on utilizing Artificial Intelligence (AI)-based platforms in the development of interactive learning media. The rapid advancement of technology, particularly AI, offers innovative opportunities to create engaging and effective educational content. However, the lack of digital skills among many teachers often poses a barrier to its implementation. The program includes workshops and hands-on sessions focused on using AI-based tools to support learning in Islamic boarding schools (pesantren). Through a service-learning approach, teachers are provided with step-by-step guidance to integrate AI tools into their instructional design. The results of the activity indicate a significant improvement (31.15 points) in teachers' ability to understand the material presented, as evidenced by pre- and post-training evaluations. Participants also reported increased confidence in adopting technology to enhance student engagement and learning outcomes. This initiative demonstrates the potential of AI in bridging the gap between traditional teaching methods and modern educational needs, paving the way for more dynamic and student-centered learning environments.Jurnal Pembelajaran Bimbingan dan Pengelolaan Pendidikan00Jurnal Pembelajaran, Bimbingan, dan Pengelolaan Pendidikan["Moh. Ridhoi"]2024-12-17T00:00:00 b12ed5f9b9612ac4f6203d45d500e8a9170a2640Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.Frontiers in Pharmacology460This work states that further initiatives are required to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.Frontiers in Pharmacology["Jonathan Hare", "Morten Nielsen", "Agnes Kiragga", "Daniel Ochiel"]2024-12-17T00:00:00 25292ddc1f041129f5e71dd2dcc04d0f424ada0bPurpose: The purpose of this paper is to explore the impact of Artificial Intelligence on the performance of Indian Banks in terms of financial metrics. The study focused specifically on the NIFTY Bank Index. The paper also advocates that a greater transparency in disclosing AI related information in a Bank’s annual report is required even if it is voluntary. Design/Methodology/Approach: The paper uses a mixed method approach where quantitative and qualitative analysis is combined. A dynamic panel data model is used to understand the impact of AI of Return on Equity (RoE) of 12 Indian Banks in the NIFTY Bank Index over a five-year period. In addition to that, Content analysis of annual reports of banks was conducted to examine AI related disclosure and transparency. Findings: The paper highlights that the integration of Artificial Intelligence (AI) significantly influences the financial performance of sample banks of India. Return on Equity the specific parameter positively influenced with adoption of AI. The profitability of banks is positively impacted by reduced errors and improved operational efficiency. The content analysis of annual reports of the banks indicates different approach for AI disclosure where some banks give detailed information and some are not transparent about AI initiatives. The findings suggest that a higher level of transparency could enhance confidence of all stakeholders. Theoretical Implications: The positive relation between adoption of AI and financial performance, specifically ROE, gives a foundation for academic research to explore the dynamics of emerging technology and financial systems. The study can be extended to explore the impact on other performance indicators in different sectors. Practical Implications: The findings of this study emphasize the importance of transparent AI related disclosures. A detailed reporting about integration of AI helps in enhanced stakeholders’ confidence in case of banking industry. The regulatory framework of banks may also consider making mandatory AI disclosure practices to ensure due accountability to maximize the benefits of AI in banking.Journal of Infrastructure Policy and Development00The paper highlights that the integration of Artificial Intelligence significantly influences the financial performance of sample banks of India and suggests that a higher level of transparency could enhance confidence of all stakeholders.Journal of Infrastructure, Policy and Development["Priya Rao", "Nidhi Srivastava", "Andr\u00e9s Fernando Mej\u00eda-Amaya"]2024-12-17T00:00:00 c2ce5a7c9be7afaacd864656273811e2e96119fbThe article examines the issue of introducing artificial intelligence in restaurant chains. The authors have studied the development of the restaurant chains market in Ukraine and the world, and identified significant differences in the scale and duration of the chains' development. The world's restaurant chains consist of thousands of establishments, while the largest Ukrainian chain is only 200 establishments. In addition, the vast majority of Ukrainian chains operate in the local market. The reasons for this are the short duration of network development, limited resources and difficulties in entering global markets. The article assesses the opportunities and risks of introducing artificial intelligence in restaurant enterprises, including demand forecasting, optimization and automation of processes and tasks, improving guest service and expanding the audience. Risks include a decrease in the role of staff, data privacy and security, management difficulties, and high implementation costs. The article analyzes the use of artificial intelligence technologies by restaurant chains in Ukraine and the world. The use of artificial intelligence technologies by Ukrainian restaurant chains is currently at the stage of experiments and individual implementations. At the same time, global networks are already making full use of all the possibilities of innovative technology. The reasons for this are the short development time of Ukrainian restaurant chains compared to the global average, limited resources, and the difficulty of entering global markets. The article also presents a study that was conducted in the form of a survey: “What do you know about artificial intelligence and the use of artificial intelligence in the restaurant business?”. The study has shown that people are well aware of what artificial intelligence is, but most of them do not know and have not encountered it during service in restaurant establishments. This study suggests that Ukrainian restaurant chains have great potential and prospects for implementing artificial intelligence technologies.Municipal economy of cities50This study suggests that Ukrainian restaurant chains have great potential and prospects for implementing artificial intelligence technologies, and risks include a decrease in the role of staff, data privacy and security, management difficulties, and high implementation costs.Municipal economy of cities["A. Sokolenko", "V. Ponomar"]2024-12-17T00:00:00 e40589b9426a3fde6053b5d670ed4073e9b49484The impact of artificial intelligence (AI) in biotechnology has become increasingly significant, driving advancements across multiple subfields in several areas of science. The demand for faster data analysis, integration of extensive databases, pattern recognition, problem solving, and even hypothesis generation has fueled the development of AI technologies in subjects like modern biotechnology, which AI has revolutionized, where the main goal is to develop new advanced products and technologies through the manipulation of biological organisms. The main impacts of AI observed in biotechnology are focused on four colors of biotechnology: green (agricultural sector); red (health sector); white (industries); and blue (marine sector). Numerous AI tools have been developed and made freely available, significantly reducing researchers’ workloads. However, the application of AI in biotechnology also raises questions that must be addressed. This review exhibits and discusses the impacts of AI on biotechnology, the advantages and disadvantages of its current presence, and the potential ethical issues and social impacts.Applied Sciences00The impacts of AI on biotechnology, the advantages and disadvantages of its current presence, and the potential ethical issues and social impacts are discussed.Applied Sciences["Alexandrina Gomes", "Beatriz Gon\u00e7alves", "Bruno Ingl\u00eas", "Sara Silv\u00e9rio", "C. Pinto", "Jorge A. Saraiva"]2024-12-17T00:00:00 dcbfaaa176d12ef5fa4beef1b473a40776879f70The use of Artificial Intelligence (AI) in education offers tremendous opportunities to improve personalization of learning, administrative efficiency, and accessibility of education at scale. AI can quickly analyze student data, provide real-time learning recommendations, and ease the administrative burden on teachers, freeing them up to focus on teaching. However, the application of AI in education also presents significant challenges, such as inequitable access to technology and potential bias in algorithms that could exacerbate existing inequities. To address these challenges and maximize the benefits of AI, collaboration between governments, educational institutions, and technology providers is needed to ensure that AI integration is done in an inclusive, equitable, and ethical manner.JURNAL ILMIAH EDUKATIF00Collaboration between governments, educational institutions, and technology providers is needed to ensure that AI integration is done in an inclusive, equitable, and ethical manner.Jurnal Ilmiah Edukatif["Erna Widyasari", "Budi Murtiyasa", "Eko Supriyanto"]2024-12-17T00:00:00 e373b0182945eb8a7831a9db7960b710448e51e0Objective: This cross-sectional survey aims to assess the level of awareness and understanding of artificial intelligence (AI) among dental practitioners in Sangli, Maharashtra. With the rapid advancements in technology, including AI, it is crucial to gauge the extent to which dental professionals are familiar with these technologies, as they play an increasingly prominent role in the field of dentistry.Methods: A structured questionnaire was designed to collect data on the awareness, knowledge, and perceived relevance of AI among dental practitioners in Sangli. The survey included questions related to the understanding of AI concepts, its current applications, and the willingness of practitioners to integrate AI into their dental practices. A stratified random sampling technique was employed to ensure a representative sample from various dental specialties.The questionnaire was distributed through an online Google forms link. The statistics done using SPSS software, chi square test was done to check the association and a p value of 0.05 was said to be statistically significant.Results: Preliminary findings reveal varying levels of awareness and knowledge about AI among dental practitioners in Sangli. 63.4% of the participants were aware that artificial intelligence is the analysis of medical data without direct human input. From the survey it was evident that 57% of the participants feel that with the help of AI clinical decision and diagnosis can be revolutionized and also found that both male and female practitioner were equally aware of the importance of application of artificial intelligence in medicine(p-value>0.05). Conclusion: This study concludes that about 63.4% of the study participants were aware that Artificial Intelligence technology in medicine is beneficial to doctors and also found that both male and female practitioner were equally aware of artificial intelligence. The findings will contribute to a better understanding of the knowledge gaps that exist in the dental community regarding AI and can guide future educational initiatives to enhance awareness and integration of AI in dental practices. As AI continues to evolve, it is imperative for dental professionals to stay informed and adapt to technological advancements for the improvement of patient care and overall dental practice management.South Eastern European Journal of Public Health00It was evident that 57% of the participants feel that with the help of AI clinical decision and diagnosis can be revolutionized and also found that both male and female practitioner were equally aware of the importance of application of artificial intelligence in medicine.South Eastern European Journal of Public Health["Dr. Shridevi Adaki", "Dr Amol Karagir", "Dr. Raghavendra Adaki", "Dr Bhagyashree Vanaki", "Dr Sheetal Sale", "Dr. Anupama Bijjal"]2024-12-17T00:00:00 b165ce39d6b1837241630f7ab7e28189a0402fe0The fast-changing landscape of consumer behavior, driven by digitalization in retail, fosters big changes in industry. Two other major trends are omnichannel strategies, integrating physical experience with online shopping, and the rise of experiential shopping technologies such as AR (augmented reality) in shaping how retailers connect with consumers. The increasing function of artificial intelligence and machine learning to optimize supply chains also raises crucial questions regarding the ethics of these developments in retail marketing. This paper also discusses neuromarketing, an innovative approach whereby neuroscience is combined with marketing, as a tool to help optimize sales techniques and improve customer service. Techniques such as EEG (electroencephalogram), eye-tracking, and fMRI (functional magnetic resonance imaging) offer retailers insight into customers’ unconscious responses to stimuli, from advertising to product placement. While these approaches have been increasingly adopted by retailers, the current study investigates whether the drift toward digital platforms impacts the efficiency of neuromarketing strategies and how AI takes further priority in this direction (Goncalves et al., 2024). The backbone of this research is to establish the level at which businesses have integrated neuromarketing into their greater marketing strategies and to find any new consumer behavior that could be proposed within a retail context. This paper will attempt to contribute, by exploratory research and secondary data analysis, to a better understanding of how these new trends adapts to the digitization of retail due to technological development and ethical concerns raised by its increased use. JEL classification: M310, M160 Article History: Received: October 18, 2024; Reviewed: November 29, 2024; Accepted: December 9, 2024; Available online: December 17, 2024.Studia Universitatis Babeş-Bolyai Negotia260The backbone of this research is to establish the level at which businesses have integrated neuromarketing into their greater marketing strategies and to find any new consumer behavior that could be proposed within a retail context.Studia Universitatis Babeș-Bolyai Negotia["Florina-Gabriela Mitu", "Marius Bota", "Emil Emanuel Savan"]2024-12-17T00:00:00 41d09a9223c2c9c2e0c3c78dd7272d537c19952cAbstrak - Perkembangan teknologi, khususnya Artificial Intelligence (AI), telah membawa dampak signifikan pada berbagai sektor, termasuk pendidikan. Penelitian ini bertujuan untuk menganalisis pendayagunaan AI dalam meningkatkan efisiensi proses pembelajaran di ITB STIKOM Bali. Metode yang digunakan mencakup survei berbasis kuesioner dan wawancara semi-terstruktur dengan responden mahasiswa dan dosen. Analisis data dilakukan menggunakan Partial Least Squares (PLS) dengan bantuan perangkat lunak SmartPLS untuk mengevaluasi model Technology Acceptance Model (TAM). Hasil penelitian menunjukkan bahwa AI berkontribusi dalam meningkatkan produktivitas, mempermudah pemahaman materi, dan mengurangi waktu yang dihabiskan dalam kegiatan belajar mengajar. Kesimpulannya, AI dapat menjadi solusi potensial untuk meningkatkan efisiensi pembelajaran di era digital.Kata Kunci: Artificial Intelligence, Efisiensi Pembelajaran, Technology Acceptance Model, SmartPLS, ITB STIKOM BaliAbstract - The advancement of technology, particularly Artificial Intelligence (AI), has significantly impacted various sectors, including education. This study aims to analyze the utilization of AI to enhance the efficiency of the learning process at ITB STIKOM Bali. Methods employed include questionnaire-based surveys and semi-structured interviews with students and lecturers as respondents. Data analysis was performed using Partial Least Squares (PLS) with the assistance of the SmartPLS software to evaluate the Technology Acceptance Model (TAM). The results indicate that AI contributes to increased productivity, ease of understanding materials, and reduced time spent on teaching and learning activities. In conclusion, AI serves as a potential solution to improve learning efficiency in the digital era.Keywords: Artificial Intelligence, Learning Efficiency, Technology Acceptance Model, SmartPLS, ITB STIKOM BaliJurnal Nasional Komputasi dan Teknologi Informasi (JNKTI)00Artificial Intelligence serves as a potential solution to improve learning efficiency in the digital era and contributes to increased productivity, ease of understanding materials, and reduced time spent on teaching and learning activities.Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI)["Bryan Fortino Kurniawan", "Ni Luh Gede Pivin Suwiryanti", "Ni Kadek Dwi Febriani Putri", "I. G. K. Timotious", "I. Bangsawan", "I. Putra"]2024-12-17T00:00:00 1d9dc21cbdf927a1a4c7fc6af4816249fd35f912With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics, metabolomics, and transcriptomics, are being applied to disease genetics research. However, biological data often exhibit high dimensionality and significant noise, making it challenging to effectively distinguish disease subtypes using a single-omics approach. To address these challenges and better capture the interactions among DNA, RNA, and proteins described by the central dogma, numerous studies have leveraged artificial intelligence to develop multi-omics models for disease research. These AI-driven models have improved the accuracy of disease prediction and facilitated the identification of genetic loci associated with diseases, thus advancing precision medicine. This paper reviews the mathematical definitions of multi-omics, strategies for integrating multi-omics data, applications of artificial intelligence and deep learning in multi-omics, the establishment of foundational models, and breakthroughs in multi-omics technologies, drawing insights from over 130 related articles. It aims to provide practical guidance for computational biologists to better understand and effectively utilize AI-based multi-omics machine learning algorithms in the context of central dogma.00This paper reviews the mathematical definitions of multi-omics, strategies for integrating multi-omics data, applications of artificial intelligence and deep learning in multi-omics, the establishment of foundational models, and breakthroughs in multi-omics technologies, drawing insights from over 130 related articles.["Lei Xin", "Caiyun Huang", "Hao Li", "Shihong Huang", "Yuling Feng", "Zhenglun Kong", "Zicheng Liu", "Siyuan Li", "Chang Yu", "Fei Shen", "Hao Tang"]2024-12-17T00:00:00 bf82f97e9b794056df7c2fc6929be3cefa2ce3c2Strokes are currently the third most common cause of death worldwide and the leading cause of disability in people over 50 years of age. The functioning of post-stroke patients depends primarily on well-conducted rehabilitation, both in stationary conditions and at home. The aim of this study was to evaluate the functional outcomes of patients after ischemic stroke who underwent home rehabilitation. The RMA (Rivermead Motor Assessment) and ADL (activities of daily living) scales were used for evaluation. A total of 20 patients underwent a 4-week home rehabilitation program in Cracow. In the studied group, most patients showed functional improvement after the 4-week rehabilitation period. Predictive models were created (Net1, Net2, Net3) using artificial intelligence algorithms, including regression and classification methods. The analysis results indicate that the best outcomes in predicting the RMA and ADL indicators. For Net2, the prediction accuracy for the ADL indicator was 94.4%, which is significantly higher compared to the other indicators. The RMA1-3 indicators achieved relatively low accuracy rates of 38.9–44.4%. In contrast, for Net3, the RMA1-3 indicators showed high accuracy, achieving 89.1–91.3% correct results. The conclusions of the study suggest that using a combination of the Net2 and Net3 models can contribute to optimizing the rehabilitation process, allowing therapy to be tailored to the individual needs of patients. The research proves that it is possible to predict the effect of rehabilitation by using AI. The implementation of such solutions can increase the effectiveness of post-stroke rehabilitation, particularly through the personalization of therapy and dynamic monitoring of patient progress.Applied Sciences250The research proves that it is possible to predict the effect of rehabilitation by using AI, and suggests that using a combination of the Net2 and Net3 models can contribute to optimizing the rehabilitation process, allowing therapy to be tailored to the individual needs of patients.Applied Sciences["Ma\u0142gorzata Ku\u017anar", "A. Lorenc"]2024-12-17T00:00:00 27e4f3462b091a6a3b92e5c63029a3939cac04bbPenelitian ini bertujuan untuk menjelaskan pengaruh pemanfaatan Kecerdasan Buatan (AI) dalam proses pembelajaran pendidikan agama Islam. Pendekatan yang diambil dalam studi ini adalah pendekatan kualitatif deskriptif. Pendekatan ini dipilih untuk menjelaskan pengaruh pemakaian kecerdasan buatan dalam pembelajaran pendidikan agama Islam di Sekolah Menengah Pertama (SMP). Sumber utama yang dijadikan referensi dalam artikel ini meliputi buku, jurnal penelitian, dan berita daring yang relevan dengan isu yang diteliti. Pengumpulan data dilakukan dengan cara membaca, menganalisis, dan mencatat berbagai sumber seperti buku, jurnal penelitian, dan berita daring yang berkaitan dengan topik yang dibahas, kemudian disaring dan disusun dalam kerangka pemikiran secara teoritis agar dapat diambil kesimpulan. Temuan penelitian menunjukkan bahwa dampak pemanfaatan Kecerdasan Buatan (AI) dalam pembelajaran pendidikan agama Islam memiliki potensi signifikan untuk meningkatkan efisiensi dan efektivitas pembelajaran agama tersebut. Namun, juga ada sisi negatif yang terkait dengan efek penggunaan AI dalam proses belajar. Dalam jangka waktu yang lebih lama, AI bisa menjadi alat yang sangat bermanfaat untuk membantu pembelajaran pendidikan agama Islam, selama digunakan dengan bijaksana dan diintegrasikan dengan baik dalam sistem pembelajaran. The purpose of this study is to outline the effects of artificial intelligence (AI) on Islamic religious education instruction. Descriptive qualitative research methodology was employed in this study. The impact of applying artificial intelligence to Islamic religious education is explained using this approach. This article's primary sources are found in online news about research issues, scientific journals, and literature. Various literary materials, scientific journals, and internet news that are relevant to the topic of discussion are read, reviewed, and recorded as part of the data collecting procedure. These resources are then filtered and placed into a theoretical framework so that a conclusion can be made. The findings of the study show that applying artificial intelligence (AI) to Islamic religious education has the potential to significantly improve learning effectiveness and efficiency. Nevertheless, there are drawbacks to the application of AI in educational activities as well. As long as AI is properly applied and incorporated into the educational system, it has the potential to be a very useful instrument for promoting Islamic religious education learning in the long run.Mauriduna: Journal of Islamic Studies05Mauriduna: Journal of Islamic Studies["Slamet Supangat", "Sugiyanto", "Khamdi"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/27e4f3462b091a6a3b92e5c63029a3939cac04bb c8a6e7758ebb1acbbe3ebee80512a2e08daf5970Journal of Research Innovation and Implications in Education00Journal of Research Innovation and Implications in Education[]2024-12-17T00:00:00https://www.semanticscholar.org/paper/c8a6e7758ebb1acbbe3ebee80512a2e08daf5970 05266ac38a3041f4798044100c914fea7f02bbb6International Studies of Management &amp; Organization610International Studies of Management &amp; Organization["Shubham Gupta", "Anurag Singh"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/05266ac38a3041f4798044100c914fea7f02bbb6 cc63c121372070861973eb28f2bee4c21a4278efJournal of Resources and Ecology80Journal of Resources and Ecology["Zhichao Han", "Qinghai Zou", "Yu Li", "Liang Faze", "Yingshu Wang", "Longqi Zhou"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/cc63c121372070861973eb28f2bee4c21a4278ef 9b4b6dd0800c7aa919e91842757ace576a957cb1Intensive Care Medicine110Intensive care medicine["M. Gijsen", "Jan J. De Waele"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/9b4b6dd0800c7aa919e91842757ace576a957cb1 e4ce328ec25be74239ec9b2e7e105e68c1c6760aInteractive Learning Environments660Interactive Learning Environments["Chenggang Chen", "Wei Hu", "Xiaomin Wei"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/e4ce328ec25be74239ec9b2e7e105e68c1c6760a 5bd7f61adff4310a8abb7d28d4d78a3b9a243067This research work presents a novel language intervention system for Tamil-speaking children with autism spectrum disorder (ASD). The system satisfies the considerable requirement for tools aimed at one more section of population that has actually been forgotten by mainstream economics. Traditional language learning applications often lack specificity for diverse linguistic contexts, focusing on general learning rather than the unique challenges faced by ASD children. The proposed solution leverages Deep Learning (DL) modules to transform partial spoken Tamil words into complete, child-friendly vocabulary. This system goes beyond simple word completion; it incorporates interactive features to engage children with ASD and promote active learning. Preliminary evaluations yielded promising results, suggesting the system's potential in enhancing communication skills among Tamil-speaking children with ASD. This paper research study reviews the limitations of existing language learning tools and the rationale behind employing a DL-based approach. We discuss the specific DL architecture (e.g., Recurrent Neural Networks) envisioned for the system and how it will be trained on a corpus of Tamil speech data. This study also investigate the potential advantages beyond vocabulary learning, such as improved sentence formulation and the development of practical language abilities. Future ambitions include broadening the system's scope to include more languages, refining the DL model through ongoing training with enriched datasets, and conducting extensive user research with varied ASD populations. Validation across demographics is critical to ensuring the system's efficiency and generality. This research has the potential to bridge the language acquisition gap for Tamil-speaking children with ASD, empowering them to develop crucial communication skills and fostering their social inclusion.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)350A novel language intervention system for Tamil-speaking children with autism spectrum disorder that leverages Deep Learning modules to transform partial spoken Tamil words into complete, child-friendly vocabulary and incorporates interactive features to engage children with ASD and promote active learning.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)["A. Julaiha", "S. Hemamalini", "V. S. Preiya", "K. Sathyamoorthy", "V. Priyanka"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/5bd7f61adff4310a8abb7d28d4d78a3b9a243067 cc9fccbaf485c8b3ca85c214aaf7d37c8c91e36cFrontiers in Psychology390Frontiers in Psychology["Anithamol Babu", "Akhil P. Joseph"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/cc9fccbaf485c8b3ca85c214aaf7d37c8c91e36c 9961d17830419bbb7ad549830c21a2208f91b0b8International Journal of Dermatology40International journal of dermatology["Marina Landau", "H. Galadari", "Mohamad Goldust"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/9961d17830419bbb7ad549830c21a2208f91b0b8 c19c3910304a323cf7138f49b84fe5ad5246b326Accurate and timely diagnosis of disorder known as autism spectrum disorder (ASD) is not an easy task due to the complicated neurodevelopmental condition's high clinical presentation variation. In order to improve the diagnostic procedure for ASD in pediatric patients, machine learning (ML) techniques have come to light as potential approaches. The previous surveys about the practice of ML algorithms for diagnosing ASD in children has been thoroughly reviewed and summarized. The supervised and unsupervised learning, feature selection, and ensemble methods used in ASD research are among the many ML techniques that is methodically examined. The necessity of large-scale, diverse datasets, cross-validation methods, and interpretability are emphasized over the advantages, disadvantages, and potential future directions of ML-based ASD diagnostic models. This study attempts to offer insights for researchers, clinicians, and other stakeholders in the field of ASD diagnosis by critically analyzing the current status of ML in ASD Diagnosis.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)90The necessity of large-scale, diverse datasets, cross-validation methods, and interpretability are emphasized over the advantages, disadvantages, and potential future directions of ML-based ASD diagnostic models.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)["Sridevi R", "Helen. K. Joy", "Karthikeyan K J", "Gopika S S", "Neha Seirah Biju", "Shriniha Pa"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/c19c3910304a323cf7138f49b84fe5ad5246b326 01634b7f29cb42bf1ceb054930dcb03fb4821992This article is prepared to help Extension agents, farmers, farm managers, researchers, graduate students, and the general public better understand AI, associated terms, and technologies. Written by Young Gu Her, Nikolay Bliznyuk, Yiannis Ampatzidis, Ziwen Yu, and Haimanote Bayabil, and published by the UF/IFAS Department of Agricultural and Biological Engineering, September 2024.EDIS10EDIS["Young Gu Her", "Nikolay Bliznyuk", "Y. Ampatzidis", "Ziwen Yu", "H. Bayabil"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/01634b7f29cb42bf1ceb054930dcb03fb4821992 fd0a667d28fe5b86f29b983ca67f2765203bdd32International Review of Applied Economics260International Review of Applied Economics["Jens Lowitzsch", "Renan Magalh\u00e3es"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/fd0a667d28fe5b86f29b983ca67f2765203bdd32 5bc21d4e08bda06ccf29dcbfcbdd000dda0d460fTópicos00Tópicos, Revista de Filosofía["Tatiana Lozano Ortega"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/5bc21d4e08bda06ccf29dcbfcbdd000dda0d460f 09c72c0f45c798069c98ba9bf43c16399d81b61cJournal of Surgical Oncology40Journal of surgical oncology["Mengyang Zhang", "Xiao Ye"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/09c72c0f45c798069c98ba9bf43c16399d81b61c 11cf948dd81e69b3f61179eab3a0350d119483faEfficient and accurate detection of retinal diseases is critical for preventing vision loss and improving patient outcomes. This survey provides an in-depth analysis of state-of-the-art deep-learning techniques for diagnosing retinal diseases using fundus images. It examines various Convolutional Neural Network (CNN) architectures, transfer learning strategies, and attention mechanisms optimized for retinal image analysis. The survey also discusses the integration of Generative Adversarial Networks (GANs), multimodal data fusion, and hybrid models combining traditional and deep learning approaches. Additionally, it emphasizes the role of image preprocessing, dataset balancing, and hyperparameter optimization in enhancing model performance.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)60This survey provides an in-depth analysis of state-of-the-art deep-learning techniques for diagnosing retinal diseases using fundus images by examining various Convolutional Neural Network architectures, transfer learning strategies, and attention mechanisms optimized for retinal image analysis.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)["Divya K. S", "Manesh T", "Abhinand K Prasad", "Akhila Venu", "Akshara Devaraj", "Albert Mathew Paul"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/11cf948dd81e69b3f61179eab3a0350d119483fa bf7d555f1839892673e3e479747bc17b11854b72Frontiers in Medicine00Frontiers in Medicine["Y. Meng", "Meng Wang", "Haoyu Chen", "Yalin Zheng"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/bf7d555f1839892673e3e479747bc17b11854b72 be1e8d938a7592a2b828d2704907bcf778a53dfaThe proposed AI-driven billing system reinvents the retail checkout process by combining computer vision, machine learning, and a customer interface app. The proposed system uses machine learning and computer vision to instantly recognize objects placed on a counter by using visual detection. The correct weight sensors assess every item's weight simultaneously, guaranteeing correct billing. This work involves creating an app with an intuitive user interface help to reduce the gap between online and in-store purchasing experiences. Clients may easily review their chosen products, interact with their virtual shopping cart, and make secure payments. The AI-based Fast Billing System provides an amazing shopping experience, primarily concentrating on streamlining the checkout process, reducing in-store interactions with people, and improving overall convenience.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)110The proposed AI-driven billing system reinvents the retail checkout process by combining computer vision, machine learning, and a customer interface app to simplify the checkout process, reducing in-store interactions with people, and improving overall convenience.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)["Vinu R", "Jisy N K"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/be1e8d938a7592a2b828d2704907bcf778a53dfa 79a992b6f043af9f20f10181a98f454e59ef67a900["Genevieve Hayman"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/79a992b6f043af9f20f10181a98f454e59ef67a9 6c8fa174c2bf774595bdc4bbb74b0ddb7f7a4b88AI and Ethics321AI and Ethics["D. Bikkasani"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/6c8fa174c2bf774595bdc4bbb74b0ddb7f7a4b88 be45d9f26ead1a7fe4c6b4986dbde73dda51f262Descriptive review of the text Ethics in artificial intelligence, by Mark Coeckelbergh.Tríade: Comunicação, Cultura e Mídia00Tríade: Comunicação, Cultura e Mídia["C. Garcia"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/be45d9f26ead1a7fe4c6b4986dbde73dda51f262 2ea56f6cc27f109304032eb0f744cb32c515aee6Pendidikan agama Islam di Indonesia menghadapi tantangan signifikan akibat globalisasi dan perkembangan teknologi yang cepat. Dalam konteks ini, penelitian ini bertujuan untuk mengeksplorasi optimalisasi Artificial Intelligence (AI) dalam meningkatkan kualitas pendidikan agama Islam. Urgensi penelitian ini terletak pada kebutuhan untuk mengadaptasi metode pembelajaran yang lebih efektif dan relevan bagi generasi muda, terutama di daerah terpencil dengan akses pendidikan yang terbatas. Penelitian ini menggunakan pendekatan kualitatif dengan analisis literatur untuk mengidentifikasi peran AI dalam personalisasi pembelajaran, pengembangan konten interaktif, dan peningkatan kompetensi pendidik. Temuan menunjukkan bahwa AI dapat menciptakan pengalaman belajar yang lebih menarik dan adaptif, serta membantu pendidik dalam menyusun materi yang sesuai dengan kebutuhan siswa. Hasil dan pembahasan menyoroti tantangan yang dihadapi, termasuk infrastruktur teknologi yang tidak merata dan kesiapan sumber daya manusia. Kesimpulan dari penelitian ini menekankan pentingnya kolaborasi antara pemerintah, lembaga pendidikan, dan pengembang teknologi untuk mengimplementasikan AI secara efektif, serta perlunya pelatihan bagi pendidik untuk memanfaatkan teknologi ini dalam proses belajar-mengajar. Dengan penerapan yang bijaksana, AI berpotensi membawa pendidikan agama Islam ke era yang lebih modern tanpa mengesampingkan nilai-nilai tradisional yang menjadi fondasinya Islamic religious education in Indonesia faces significant challenges due to globalisation and rapid technological development. In this context, this research aims to explore the optimisation of Artificial Intelligence (AI) in improving the quality of Islamic religious education. The urgency of this research lies in the need to adapt more effective and relevant learning methods for the younger generation, especially in remote areas with limited access to education. This research uses a qualitative approach with literature analysis to identify the role of AI in learning personalisation, interactive content development, and educator competency enhancement. The findings show that AI can create a more engaging and adaptive learning experience, as well as assist educators in developing materials that suit students' needs. The results and discussion highlight the challenges faced, including uneven technological infrastructure and human resource readiness. The conclusions of this study emphasise the importance of collaboration between the government, educational institutions, and technology developers to implement AI effectively, as well as the need for training for educators to utilise this technology in the teaching-learning process. With judicious implementation, AI has the potential to bring Islamic religious education into a more modern era without setting aside the traditional values that underpin it.Mauriduna: Journal of Islamic Studies110Mauriduna: Journal of Islamic Studies["Roziqin Nasih", "Mirza", "Saerozi"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/2ea56f6cc27f109304032eb0f744cb32c515aee6 4595a615c00f95d0fb17ab527317303a23c9acf7Este estudio examina cómo la inteligencia artificial (IA), la realidad aumentada (RA) y otras herramientas han reconfigurado el campo del diseño gráfico en casos específicos medidos en términos de creatividad y eficiencia. Mediante una revisión de literatura, se sintetizan hallazgos sobre el impacto de estas tecnologías en la práctica profesional y académica de los últimos cinco años. Los resultados destacan el uso de estas herramientas para mejorar la creatividad y eficiencia en los procesos de diseño, así como la necesidad de adaptar la educación en diseño gráfico para integrarlas y así redefinir la práctica profesional. Se destaca la importancia de actualizar continuamente habilidades y métodos para enfrentar los retos de un mercado laboral tecnológicamente avanzado y en constante cambio. Palabras clave: automatización, IA generativa, innovación digital, algoritmos, diseño gráfico. AbstractThis study examines how artificial intelligence (AI), augmented reality (AR), and other tools have reconfigured the field of graphic design in specific cases measured in terms of creativity and efficiency. Through a literature review, findings are synthesized regarding the impact of these technologies on professional and academic practice over the past five years. The results highlight the use of these tools to enhance creativity and efficiency in design processes, as well as the need to adapt graphic design education to integrate them and thereby redefine professional practice. The importance of continuously updating skills and methods to meet the challenges of a technologically advanced and constantly changing job market is emphasized. Keywords: automation, generative AI, digital innovation, algorithms, graphic design.DISEÑO ARTE Y ARQUITECTURA00DISEÑO ARTE Y ARQUITECTURA["Nicol\u00e1s Antonio Cevallos C\u00f3rdova", "Izamar Susan Luna Aro"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/4595a615c00f95d0fb17ab527317303a23c9acf7 4c06819d029b72368b666ca9b5f5df78890c87c0Banks are institutions that play a significant role in the economy through the diverse financial products and services they offer. Given that they operate in a competitive environment, they are constantly striving to employ the best methods to withstand competition. Among the latest techniques widely adopted by banks today are various applications of artificial intelligence, with artificial neural networks being at the forefront. This paper explores the various uses of these neural networks in banks and the benefits these uses bring to the banking sector as a whole. Therefore, this study is presented as a qualitative study, relying on the descriptive approach to draw valuable conclusions on the subject by reviewing relevant previous literature closely related to the study topic. The research concluded that artificial neural networks are highly beneficial for banks as they contribute significantly to predicting various risks facing the bank, as well as providing many other advantages, which will be discovered in this paper.21st Century Media and Communications00The research concluded that artificial neural networks are highly beneficial for banks as they contribute significantly to predicting various risks facing the bank, as well as providing many other advantages, which will be discovered in this paper.21st Century Media and Communications["Ali Bentayeb", "AboubakerOtherOther Khoualed", "Khayreddine Bouzerb"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/4c06819d029b72368b666ca9b5f5df78890c87c0 8785fc898e2078186e096507bfe5f997c351d5baThis comprehensive article explores the transformative impact of artificial intelligence on location intelligence and site selection in the commercial real estate industry. The article examines how AI-powered systems are revolutionizing traditional approaches to property analysis, investment decisions, and operational efficiency across various real estate sectors. Through an examination of current implementations, benefits, and future trends, this analysis demonstrates how machine learning models, advanced analytics, and real-time data processing capabilities are enabling more accurate predictions, improved decision-making, and enhanced operational performance in the real estate industry. The article highlights the significant advancements in areas such as retail site selection, office space development, and industrial property optimization, while also addressing the quantifiable benefits and return on investment of AI implementation.International Journal of Scientific Research in Computer Science Engineering and Information Technology30This analysis demonstrates how machine learning models, advanced analytics, and real-time data processing capabilities are enabling more accurate predictions, improved decision-making, and enhanced operational performance in the real estate industry.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Anil Kumar Reddy Avula"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/8785fc898e2078186e096507bfe5f997c351d5ba ae21b0c60c2c6d15b855970fde5a7e1d9ec44f2f Purpose Although artificial intelligence (AI) is an essential component of hospitality in the technological empowerment era, AI’s effectiveness as an attraction in this context remains unclear. Grounded in Herzberg’s motivation theory and complexity theory, this study aims to explore configurational paths whereby combinations of qualities lead to success for different types of AI-themed hotels. Design/methodology/approach This study innovatively blends topic modeling and fuzzy-set qualitative comparative analysis (fsQCA) to investigate configurational paths whereby combined qualities produce positive guest evaluations of 12 AI-themed hotels as evidenced by 7,431 customer reviews. Findings The results indicate that AI could serve as a “theme” to attract customers under certain circumstances. First, “attractive” and “must-be” qualities are first identified for different types of AI-themed hotels. Furthermore, 6, 15 and 15 configurational paths inspiring favorable guest evaluations of luxury-independent, budget-independent and chain AI-themed hotels, respectively. Technology-related qualities are found to be especially attractive for luxury-independent AI-themed hotels, whereas the role of technology is minimal for budget AI-themed hotels. The impact of technology is salient for chain AI-themed hotels when combined with other factors. In addition, the effect of price differs among the configurational paths for the three hotel types. Research limitations/implications This study expands the understanding of AI applications within the hospitality context by exploring the role of AI in AI-themed hotels and comparing its effectiveness in attracting customers across various hotel types. It also provides operational strategies for adopting AI for different types of hotels and for other hospitality and tourism sectors. Originality/value This study represents an early attempt to integrate topic modeling and fsQCA to clarify customers’ perceptions of AI-themed hotels and the combined impacts of various qualities. The findings expand on Kano’s model by classifying technology-related qualities into attractive qualities within AI-themed hotels. International Journal of Contemporary Hospitality Management491The results indicate that AI could serve as a “theme” to attract customers under certain circumstances and provides operational strategies for adopting AI for different types of hotels and for other hospitality and tourism sectors.International Journal of Contemporary Hospitality Management["Bowen Yi", "Da Shi", "Gang Li"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/ae21b0c60c2c6d15b855970fde5a7e1d9ec44f2f ee79dc9817a0f07d401cae06065600c996fbc1bdPediatric Research791This article reviews recent clinical applications of AI to important neonatal problems, including sepsis, retinopathy of prematurity, bronchopulmonary dysplasia, and others, and provides a comprehensive explanation of the AI development process and recommendations for a standardized performance metric framework.Pediatric research["A. Husain", "Lindsey A. Knake", "Brynne A Sullivan", "James S Barry", "Kristyn S. Beam", "Emma Holmes", "Thomas Hooven", "Ryan M McAdams", "Alvaro G Moreira", "W. Shalish", "Z. Vesoulis"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/ee79dc9817a0f07d401cae06065600c996fbc1bd f959bcf33f16624aaf549b27b937867fa5a2baeeLarge Language Models (LLMs) have revolutionized various aspects of engineering and science. Their utility is often bottlenecked by the lack of interaction with the external digital environment. To overcome this limitation and achieve integration of LLMs and Artificial Intelligence (AI) into real-world applications, customized AI agents are being constructed. Based on the technological trends and techniques, we extract a high-level approach for constructing these AI agents, focusing on their underlying architecture. This thesis serves as a comprehensive guide that elucidates a multi-faceted approach for empowering LLMs with the capability to leverage Application Programming Interfaces (APIs). We present a 7-step methodology that begins with the selection of suitable LLMs and the task decomposition that is necessary for complex problem-solving. This methodology includes techniques for generating training data for API interactions and heuristics for selecting the appropriate API among a plethora of options. These steps eventually lead to the generation of API calls that are both syntactically and semantically aligned with the LLM's understanding of a given task. Moreover, we review existing frameworks and tools that facilitate these processes and highlight the gaps in current attempts. In this direction, we propose an on-device architecture that aims to exploit the functionality of carry-on devices by using small models from the Hugging Face community. We examine the effectiveness of these approaches on real-world applications of various domains, including the generation of a piano sheet. Through an extensive analysis of the literature and available technologies, this thesis aims to set a compass for researchers and practitioners to harness the full potential of LLMs augmented with external tool capabilities, thus paving the way for more autonomous, robust, and context-aware AI agents.arXiv.org00This thesis proposes an on-device architecture that aims to exploit the functionality of carry-on devices by using small models from the Hugging Face community, and elucidates a multi-faceted approach for empowering LLMs with the capability to leverage Application Programming Interfaces (APIs).ArXiv["Ioannis Tzachristas"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/f959bcf33f16624aaf549b27b937867fa5a2baee d5ccc8ee42ce5b7e53ce212d307b752d69aa9725The advent of Artificial Intelligence (AI) has revolutionized the field of cybersecurity by introducing advanced mechanisms for detecting, preventing, and mitigating cyber threats. This research explores the intersection of AI and cybersecurity, highlighting the transformative potential of AI-driven solutions in combating increasingly sophisticated cyberattacks. By leveraging machine learning, deep learning, and neural network algorithms, AI enhances real-time threat detection, predictive analytics, and anomaly detection across diverse digital infrastructures. This study evaluates current AI-driven cybersecurity frameworks, emphasizing their efficacy in handling dynamic threat landscapes and addressing the limitations of traditional methods. Additionally, it examines ethical considerations, such as the potential misuse of AI by malicious actors and the need for transparent AI systems. Through comprehensive analysis, this research underscores the importance of developing resilient AI models to secure critical data and infrastructure in an era of rapidly evolving cyber risks. The findings provide actionable insights for policymakers, organizations, and technology developers, advocating for collaborative efforts to harness AI’s potential while addressing its inherent challenges.Human-Computer Interaction70This study evaluates current AI-driven cybersecurity frameworks, emphasizing their efficacy in handling dynamic threat landscapes and addressing the limitations of traditional methods, and examines ethical considerations, such as the potential misuse of AI by malicious actors and the need for transparent AI systems.Human Computer Interaction["Ahmet Mert \u00c7ak\u0131r"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/d5ccc8ee42ce5b7e53ce212d307b752d69aa9725 506fb867ff1c1efa2dd574bbae22e669f3bae8b6Abstract Background Geriatric comanagement has been shown to improve outcomes of older surgical inpatients. Furthermore, the choice of discharge location, that is, continuity of care, can have a fundamental impact on convalescence. These challenges and demands have led to the SURGE-Ahead project that aims to develop a clinical decision support system (CDSS) for geriatric comanagement in surgical clinics including a decision support for the best continuity of care option, supported by artificial intelligence (AI) algorithms. Objective This qualitative study aims to explore the current challenges and demands in surgical geriatric patient care. Based on these challenges, the study explores the attitude of interviewees toward the introduction of an AI-supported CDSS (AI-CDSS) in geriatric patient care in surgery, focusing on technical and general wishes about an AI-CDSS, as well as ethical considerations. Methods In this study, 15 personal interviews with physicians, nurses, physiotherapists, and social workers, employed in surgical departments at a university hospital in Southern Germany, were conducted in April 2022. Interviews were conducted in person, transcribed, and coded by 2 researchers (AU, LB) using content and thematic analysis. During the analysis, quotes were sorted into the main categories of geriatric patient care, use of an AI-CDSS, and ethical considerations by 2 authors (AU, LB). The main themes of the interviews were subsequently described in a narrative synthesis, citing key quotes. Results In total, 399 quotes were extracted and categorized from the interviews. Most quotes could be assigned to the primary code challenges in geriatric patient care (111 quotes), with the most frequent subcode being medical challenges (45 quotes). More quotes were assigned to the primary code chances of an AI-CDSS (37 quotes), with its most frequent subcode being holistic patient overview (16 quotes), then to the primary code limits of an AI-CDSS (26 quotes). Regarding the primary code technical wishes (37 quotes), most quotes could be assigned to the subcode intuitive usability (15 quotes), followed by mobile availability and easy access (11 quotes). Regarding the main category ethical aspects of an AI-CDSS, most quotes could be assigned to the subcode critical position toward trust in an AI-CDSS (9 quotes), followed by the subcodes respecting the patient’s will and individual situation (8 quotes) and responsibility remaining in the hands of humans (7 quotes). Conclusions Support regarding medical geriatric challenges and responsible handling of AI-based recommendations, as well as necessity for a holistic approach focused on usability, were the most important topics of health care professionals in surgery regarding development of an AI-CDSS for geriatric care. These findings, together with the wish to preserve the patient-caregiver relationship, will help set the focus for the ongoing development of AI-supported CDSS.JMIR Aging570Support regarding medical geriatric challenges and responsible handling of AI-based recommendations, as well as necessity for a holistic approach focused on usability, were the most important topics of health care professionals in surgery regarding development of an AI-CDSS for geriatric care.JMIR Aging["Adriane Uihlein", "Lisa Beissel", "A. Ajlani", "M. Orzechowski", "C. Leinert", "T. Kocar", "C. Pankratz", "Konrad Schuetze", "Florian Gebhard", "F. Steger", "M. Fotteler", "Michael Denkinger"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/506fb867ff1c1efa2dd574bbae22e669f3bae8b6 8aec8d06ad302278401a459ae09a2a23a229a499Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)200This research suggests using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients.2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)["Suresh Palarimath", "Pyingkodi Maran", "W. R", "Cibi. A", "S. J. Kavitha", "Steffi S"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/8aec8d06ad302278401a459ae09a2a23a229a499 cc6742cda6817db12b72c0a2d1bda335cc6217c0This study aims to explore how AI (artificial intelligence) can transform the learning experience by going beyond the limitations of traditional textbook-based methods in global education. With a qualitative research approach and case study type, data were collected through semi-structured interviews and participant observations at Madrasah Aliyah Nurul Jadid Paiton Probolinggo, which has implemented AI technology, and through documentation analysis. The study results show that data-based learning allows teachers to design more personalized and effective learning through comprehensive data analysis. Meanwhile, automated teaching through AI provides real-time guidance and feedback, accelerating students' understanding of the subject matter. These findings indicate that integrating AI in madrasah education improves the quality of learning and creates a more inclusive and adaptive learning environment.Educative: Jurnal Ilmiah Pendidikan00The study results show that data-based learning allows teachers to design more personalized and effective learning through comprehensive data analysis and automated teaching through AI provides real-time guidance and feedback, accelerating students' understanding of the subject matter.Educative: Jurnal Ilmiah Pendidikan["Izzatul Munawwaroh", "Moses Adeleke Adeoye"]2024-12-17T00:00:00https://www.semanticscholar.org/paper/cc6742cda6817db12b72c0a2d1bda335cc6217c0 9cad17a91812e72e2c87523fd0b1ece4d493046bBMC Medical Education261The findings highlight the importance of targeted educational interventions and customised strategies to support AI integration within nursing education settings across Arab countries, equipping future nurses with the necessary skills and knowledge to use AI effectively in their practice.BMC Medical Education["Omar Al Omari", "Muna Alshammari", "Wafa Al Jabri", "Asma Al Yahyaei", "Khalid Aljohani", "H. Sanad", "Mohammed Baqer Al-Jubouri", "Ibrahim Bashayreh", "Mirna Fawaz", "M. Albashtawy", "Abdullah Alkhawaldeh", "Jamal Qaddumi", "S. A. Shalaby", "Haitham Mokhtar Mohamed Abdallah", "Loai AbuSharour", "Mohammad Al Qadire", "M. Aljezawi"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/9cad17a91812e72e2c87523fd0b1ece4d493046b a8186a30ece81e40d7c0c1ab590ae9c73dbffee8Discover Artificial Intelligence181This work aims to portray the most significant divergences between Artificial Intelligence and Natural Intelligence and find out if those can converge under the current technological advancements.Discov. Artif. Intell.["Konstantinos Sgantzos", "S. Stelios", "Panagiotis Tzavaras", "Kostas Theologou"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/a8186a30ece81e40d7c0c1ab590ae9c73dbffee8 b7da51a1591caabf405dc24948974b5c6036e087Artificial intelligence (AI) is significantly transforming surgery by enhancing precision, decision-making, and patient outcomes. This bibliometric analysis examines AI's impact on surgery, highlighting research trends, key contributors, and evolving themes from 1998 to 2024. Utilizing data from the Web of Science Core Collection and analyzed through the Bibliometrix tool, the study reviews publication trends, author impact, institutional contributions, country-specific research activities, and keyword frequency. A total of 821 articles were examined, revealing a 14.53% annual growth rate in publications, increasing from one in 1998 to 328 in 2023. Influential contributors include 10,157 authors, notably HASHIMOTO DA and ITO M. Prominent institutions such as Harvard University and Stanford University, along with leading countries like the USA and China, play major roles in this field. High-frequency keywords identify core research areas: surgery, artificial intelligence, classification, diagnosis, and outcomes. Thematic evolution shows a shift from foundational concepts to advanced applications and interdisciplinary collaborations. AI integration into surgical practices is revolutionizing the field, driving advancements in precision, efficiency, and patient care. The study underscores significant research growth, influential contributors, and key trends, emphasizing the importance of continued interdisciplinary collaboration and innovation. Future research should focus on enhancing AI applications, addressing data quality and security challenges, and expanding into diverse surgical contexts to further improve surgical outcomes and patient care. AI in surgery is a rapidly evolving and promising field for innovation, with its full potential reliant on enhanced collaboration across disciplines.International Journal of Surgery01This bibliometric analysis examines AI's impact on surgery, highlighting research trends, key contributors, and evolving themes from 1998 to 2024, and underscores the importance of continued interdisciplinary collaboration and innovation.International journal of surgery["Hui-yan Li", "Zhuoqi Han", "Haixiao Wu", "Elmar Musaev", "Yile Lin", "Shu Li", "A. D. Makatsariya", "Vladimir P. Chekhonin", "Wenjuan Ma", "Chao Zhang"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/b7da51a1591caabf405dc24948974b5c6036e087 4d7102fa98071085bd84dcfc3bb429e8a75a32b8Digital technologies are fundamentally transforming professions by altering roles and redefining competencies across all sectors. The progression from computerization to digitization, digitalization, and now digital transformation has been driven by the widespread integration of artificial intelligence (AI) and big data analytics (BDA). Industry 4.0 introduced smart automation and connectivity, Industry 5.0 emphasized human–machine collaboration and personalization, and Industry 6.0 now integrates advanced technologies with sustainability and ethical considerations, exerting a profound influence on many professions. This transformation is especially significant in emerging markets, where AI and BDA are overhauling traditional practices and enhancing efficiency but also introducing new challenges. Focusing on the accounting profession, this paper examines AI’s and BDA’s dual impact on the roles and skill sets of professional accountants (PAs). Specifically, it addresses how these technologies shape the activities, interactions, roles, and competencies of PAs in an Industry 6.0 context, as well as the opportunities and challenges that arise. Given the public interest role of PAs in ensuring accuracy and transparency in financial reporting, understanding their perceptions and experiences of digital transformation is essential. The findings reveal that while AI and BDA drive efficiency gains and open strategic pathways, they also risk eroding core traditional accounting competencies, reducing client engagement, and raising ethical concerns such as data security and privacy—all of which can undermine service quality and, ultimately, public trust. These insights underscore the need for responsible AI and BDA integration, particularly in emerging markets, where digital literacy gaps and regulatory limitations may slow adoption. This study offers actionable recommendations for policymakers, educators, and organizations, highlighting the importance of ethical standards, targeted training, and sustainable practices to preserve the relevance and integrity of the accounting profession in an increasingly technology-driven era.Electronics1061The findings reveal that while AI and BDA drive efficiency gains and open strategic pathways, they also risk eroding core traditional accounting competencies, reducing client engagement, and raising ethical concerns such as data security and privacy—all of which can undermine service quality and, ultimately, public trust.Electronics["Delia Deliu", "Andrei-Marius Olariu"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/4d7102fa98071085bd84dcfc3bb429e8a75a32b8 bee8a472fa7e90b20c9ad5d159fcb358547e5722To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristics as race, gender and class. Second, among the many sources of bias in military AI, three broad categories are prominent: bias in society; bias in data processing and algorithm development; and bias in use. Third, bias in military AI can have various humanitarian consequences depending on context and use. These range from misidentifying people and objects in targeting decisions to generating flawed assessments of humanitarian needs.00A deeper examination of the issue of bias in military AI is provided, finding that among the many sources of bias in military AI, three broad categories are prominent: bias in society; bias in data processing and algorithm development; and bias in use.["Alexander Blanchard", "Laura Bruun"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/bee8a472fa7e90b20c9ad5d159fcb358547e5722 5e9c66bf3734d83c9dbe4a56ff06d30c70fc8962The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency. Acknowledgments This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).Knowledge & Performance Management340The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale and found that decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency.Knowledge and Performance Management["A. Kozhakhmetova", "Almas Mamyrbayev", "A. Zhidebekkyzy", "S.A. Bilan"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/5e9c66bf3734d83c9dbe4a56ff06d30c70fc8962 ab75caec496c00664bafdb1633540ee07626ba75Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as well as providing valuable benchmarks and virtual environments to advance AI technologies in general. This report presents a high-level overview of five promising research pathways for applying state-of-the-art AI methods, particularly deep learning, to digital gaming within the context of the current research landscape. The objective of this work is to outline a curated, non-exhaustive list of encouraging research directions at the intersection of AI and video games that may serve to inspire more rigorous and comprehensive research efforts in the future. We discuss (i) investigating large language models as core engines for game agent modelling, (ii) using neural cellular automata for procedural game content generation, (iii) accelerating computationally expensive in-game simulations via deep surrogate modelling, (iv) leveraging self-supervised learning to obtain useful video game state embeddings, and (v) training generative models of interactive worlds using unlabelled video data. We also briefly address current technical challenges associated with the integration of advanced deep learning systems into video game development, and indicate key areas where further progress is likely to be beneficial.arXiv.org1340A curated, non-exhaustive list of encouraging research directions at the intersection of AI and video games that may serve to inspire more rigorous and comprehensive research efforts in the future is outlined.ArXiv["Markus Dablander"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/ab75caec496c00664bafdb1633540ee07626ba75 167a21317f0826ebf2ba0bd8fa8ba9e7bee18e52This research evaluates the capability of prospective teachers to design learning media that effectively incorporates Artificial Intelligence (AI). Conducted within the Mathematics Education Study Program at Malikussaleh University, the study highlights the significant role of AI in education during the Society 5.0 era, particularly in enhancing the quality of learning. Using a descriptive quantitative research method, data was collected through a questionnaire distributed to 60 students in the Mathematics Education Study Program. The findings clearly indicate that 33,33% of respondents fall into the medium ability category, while 53.33% are classified as high and 13.33% as very high in their ability to design AI-based learning media. These findings suggest that while a majority of prospective teachers demonstrate strong competencies in designing AI-based learning media, a notable 33,33% remain in the medium category and may require additional support. It is crucial to enhance skills in creating AI-based learning media to ensure that prospective teachers are well-prepared to address the challenges of education in the digital age. This research provides valuable insights into the abilities of prospective teachers and is expected to serve as a key reference for developing more effective training programs in the future.  Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)00Evaluating the capability of prospective teachers to design learning media that effectively incorporates Artificial Intelligence (AI) suggests that while a majority of prospective teachers demonstrate strong competencies in designing AI-based learning media, a notable 33,33% remain in the medium category and may require additional support.Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)["Nur Elisyah", "Widya Widya", "Nova Herliana Hasibuan", "Dinda Adha Hutabarat"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/167a21317f0826ebf2ba0bd8fa8ba9e7bee18e52 97d6a4a23304064b753d9e9012171cde97d76ad2 Artificial Intelligence (AI), due to digitalization, has recently conquered the healthcare disciplines. AI has substantially progressed in healthcare and medical research for preventive, predictive, and personalized care. AI will continue to become the ultimate healthcare-effective tool for serious ailments requiring the early detection of rebuttal. It is a fast-growing automated system based on algorithms positioned to benefit patients, clinicians, researchers, and physicians involved in treatment, prognosis, and preventive care in health. The primary focus of artificial intelligence is technologically expedited solutions to complex challenges. AI's remarkable contribution to machine learning has become a transforming opportunity in medical science. The optimized research, formulation, and development in AI reduce the cost of medical therapy, provide extensive care, improve patient compliance, and promote personalized medicine. The articles were cited from SCI-hub, PUBMED, Scopus, and Google Scholar. AI is assisted with autonomous disease assessing and screening tools that can save time for clinicians and help in the early diagnosis of diabetic retinopathy, cancer detection, and chromosomal disorders to solve complex hurdles. The automated image quality improvement tool makes AI an effective medium for targeting highly complex drug molecules and specific sensors to target organs. Furthermore, the masses have utilized their application in medical devices, pharmaceutical technology, dosage form designing, medical research, and regulatory frameworks to explore the medical era of AI in the healthcare field. However, the integration of AI in medical practice is in the early stages and needs further research to fit an AI model-based approach in clinical settings. AI limitations in health and medical research arise from biases related to gender variation, ethical concerns, complex algorithms, regulatory, cyber security, model evaluation, and problems faced by policymakers. Certain vulnerability factors that can cause health record data breaches and ethical concerns present challenges in healthcare settings due to result failure. Therefore, solutions to overcome these challenges are essential to set the future of AI in clinical research. All such concerns and their solutions must be successfully deployed from research to clinical settings to adopt transformative AI models in medical science. This will help scientists and researchers explore lead molecules and identify newer therapeutic targets. It is crucial to implement measures to control and frame policy guidelines, conduct continuous checks on cybersecurity, solve ethical issues, and consider the possibility of AI adoption in pharmaceutical industries, banking, research areas, hospitals, administration, and clinical practice. Letters in Drug Design &amp; Discovery00It is crucial to implement measures to control and frame policy guidelines, conduct continuous checks on cybersecurity, solve ethical issues, and consider the possibility of AI adoption in pharmaceutical industries, banking, research areas, hospitals, administration, and clinical practice.Letters in Drug Design &amp; Discovery["Ashima Ahuja", "Yogesh Murti", "Sonia Singh"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/97d6a4a23304064b753d9e9012171cde97d76ad2 c262c159273c3516641e6a9e1326d9d1bcd1f461PurposeThe conventional notion that adopting Artificial Intelligence (AI) positively affects firm performance is often confronted with various examples of failures. In this context, large-scale empirical evidence of the economic performance implications of adopting AI is poor, especially in the context of Small and Medium Sized Enterprises (SMEs). Drawing upon the Resource-Based View and the Digital Complementary Asset literature, we assessed whether the adoption of AI affects SMEs’ revenue growth.Design/methodology/approachFirst, we examine the relationship between the adoption of AI and SMEs’ revenue growth. Second, we assess whether AI complements the Internet of Things (IoT) and Big Data Analytics (BDA). We use firm-level data from the European Commission in 2020 on 11,429 European SMEs (Flash Eurobarometer 486).FindingsAmong the key findings, we found that ceteris paribus, the adoption of AI positively affects SMEs’ revenue growth and, in conjunction with IoT and BDA, appears to be even more beneficial.Originality/valueOur results suggest that AI fosters SME growth, especially in combination with IoT and BDA. Thus, SME managers should be aware of the positive impacts of investments in AI and make decisions accordingly. Likewise, policymakers are aware of the positive effects of SMEs’ reliance on AI, so they may design policies and funding schemes to push this digitalization of SMEs further.Internet Research1080It is found that ceteris paribus, the adoption of AI positively affects SMEs’ revenue growth and, in conjunction with IoT and BDA, appears to be even more beneficial.Internet Research["Lorenzo Ardito", "Raffaele Filieri", "E. Raguseo", "Claudio Vitari"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/c262c159273c3516641e6a9e1326d9d1bcd1f461 a6280236517ef40ce374d5b9959bb7e791b32498The use of Artificial Intelligence or AI in learning as a form of integration between real and virtual world in the Society 5.0 Era presents challenges in the ethical aspect, such as forms of responsibility for use, data security issues, and plagiarism in the creation of works. The concept of ethical use of AI in learning is needed, included in the perception of Islamic Religious Education students as AI users in Islamic Education learning, will provide a broader perspective and could be associated with the context of Islamic Education. This research will explore the perception of Islamic Education (PAI) students about the ethics of using AI in learning based on phenomena at Islamic State University of Sultan Aji Muhammad Idris (UINSI) Samarinda. Descriptive qualitative research with phenomenological methods is used to answer the formulated objectives. Data collection uses interview techniques supported by surveys. Data analysis using the Miles, Huberman, and Saldaña models in the form of data condensation, data display, and conclusion drawing. The results of this study explain the perception of students in a neutral manner based on the phenomena experienced at UINSI Samarinda related to the concept of ethics in the use of AI in learning with the ethical limitations discussed as follows: 1) Absence of data privacy and security issues and their prevention; 2) Plagiarism avoidance that must be done because there is great potential that occurs at the location, and; 3) Responsibilities that are only carried out by some students.J-PAI: Jurnal Pendidikan Agama Islam530The perception of Islamic Education (PAI) students about the ethics of using AI in learning based on phenomena at UINSI Samarinda is explored to explain the concept of ethics in the use of AI in learning with the ethical limitations discussed.J-PAI: Jurnal Pendidikan Agama Islam["Muhammad Rezza Nur Rahman", "Nur Kholik Afandi"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/a6280236517ef40ce374d5b9959bb7e791b32498 b7e9c1fd7a01a972b3176451c389ec60af07b96cThis study aims to explore the perception and adoption of artificial intelligence (AI) technologies in educational settings, focusing on their benefits and the factors influencing their integration. Guided by Rogers' Diffusion of Innovations Theory, the research examines the relative advantage, compatibility, complexity, trialability, and observability of AI tools in education. Utilizing a qualitative approach, data were collected through surveys and interviews with students from the Pendidikan Agama Islam (PAI) program at Universitas Islam Jakarta. The findings reveal a generally positive perception of AI technologies, with strong support for their relative advantage, compatibility, trialability, and observability. Most respondents recognize the benefits of AI in personalizing learning experiences and providing flexible, accessible support. However, opinions are divided on the complexity of AI tools, indicating a need for more user-friendly designs and comprehensive training. The study also highlights various motivational factors, such as curiosity, autonomous learning, engagement, emotional involvement, authenticity, and the desire for self-improvement, which significantly influence the learning process. These insights suggest that addressing perceived challenges and fostering a supportive, engaging learning environment can enhance the acceptance and effectiveness of AI in education. Future research should focus on developing intuitive AI interfaces, conducting longitudinal impact studies, and exploring strategies to enhance personalization and engagement.International journal of social science and human research00The study highlights various motivational factors, such as curiosity, autonomous learning, engagement, emotional involvement, authenticity, and the desire for self-improvement, which significantly influence the learning process.International Journal of Social Science and Human Research["Cahyono Cahyono", "Mulki Siregar", "Akhmad Sutrisna", "N. Nurlaili", "Marcely Susanti"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/b7e9c1fd7a01a972b3176451c389ec60af07b96c 6c6ee9986b06cad886ef8e534336a19a21fc6b2cArtificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the"black-box"nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://github.com/ShilinSun/mxai_review.arXiv.org3320The MXAI methods are reviewed from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs to provide crucial guidance for building more transparent, fair, and trustworthy AI systems.ArXiv["Shilin Sun", "Wenbin An", "Feng Tian", "Fang Nan", "Qidong Liu", "Jun Liu", "Nazaraf Shah", "Ping Chen"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/6c6ee9986b06cad886ef8e534336a19a21fc6b2c 8a69a573d483f8cd1d1553ecbeb30d6945e89433Thanks to its capabilities, artificial intelligence (AI) contributes to business development by attracting customers and investors. However, the wide spread of various AI technology software complicates understanding its concept and reduces its implementation in local businesses. Our research was devoted to the development of an algorithm for building marketing strategies with the use of AI based on the definition of the concept, functions, principles of action and assessment of the perception of digitalisation by consumers for the application of a personalised approach to interaction with the client and improving communication. The results confirmed the low awareness of the population, including entrepreneurs, about the possibilities of AI. We identified the main functions, concepts, and types of AI and provided examples of their application. We explored ways to personalise marketing and improve communication based on the achievement of trust criteria, customer satisfaction, ease of purchase, fulfilment of seller obligations, quality support service, security of personal data, and creation of recommended offers based on the analysis of data obtained during communication with the consumer. To achieve these criteria, recommendations are given for using different types of AI: chatbots, automated mailings, intelligent assistants, installation of filters and sorting programs, automatic recommendations and multifactor identification. The AI ​​algorithm created in this way to ensure the personalisation of marketing and quality communication will increase the implementation of digitalisation in the country's entrepreneurship and improve the economy.Multidisciplinary Reviews00An algorithm for building marketing strategies with the use of AI based on the definition of the concept, functions, principles of action and assessment of the perception of digitalisation by consumers will increase the implementation of digitalisation in the country's entrepreneurship and improve the economy.Multidisciplinary Reviews["Oksana Kotyrlo", "Ruslan Naboka", "Vitalii Nestor", "Dmytro Tyshko", "Oleksii Panasenko"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/8a69a573d483f8cd1d1553ecbeb30d6945e89433 bd4f06ded98022ec2154e244363c2e9cbf6d9984PurposeThe study explores new aspects of financial investment management with technological involvement, providing detailed knowledge for future research. It identifies gaps in the literature and summarizes key research topics, utilizing a precise data collection framework.Design/methodology/approachThe study is structured using systematic and bibliometric analysis with the antecedents, decisions, outcome-theories, context, and methods (ADO-TCM) framework. Data from Scopus and Web of Science were filtered based on Q1, Q2, social sciences citation index (SSCI) and Australian Business Deans Council (ABDC) criteria, resulting in 128 articles majorly emphasizing the last ten years. The “R” package facilitated bibliometric analysis, starting with data cleaning and import into Biblioshiny for effective results interpretation.FindingsThe study found that artificial intelligence detects and mitigates biases in investment decisions through rigorous pattern analysis, including social and ethical biases. The ADO-TCM framework revealed emerging theories, such as robo-advisory theory, offering new directions in behavioral finance for researchers and practitioners. The top authors and articles highlighted existing work in financial management.Originality/valueThe study’s originality is highlighted by its use of unique frameworks for data collection (SPAR-4-SLR) and interpretation (ADO-TCM).International Journal of Emerging Markets1330The study found that artificial intelligence detects and mitigates biases in investment decisions through rigorous pattern analysis, including social and ethical biases.International Journal of Emerging Markets["Annu", "Ravindra Tripathi"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/bd4f06ded98022ec2154e244363c2e9cbf6d9984 9b33e2ed88839c207ad7860ef977cf6dd616bb8aAIM To explore the current application and research frontiers of global ophthalmic optical coherence tomography (OCT) imaging artificial intelligence (AI) research. METHODS The citation data were downloaded from the Web of Science Core Collection database (WoSCC) to evaluate the articles in application of AI in ophthalmic OCT published from January 1, 2012 to December 31, 2023. This information was analyzed using CiteSpace 6.2.R2 Advanced software, and high-impact articles were analyzed. RESULTS In general, 877 articles from 65 countries were studied and analyzed, of which 261 were published by the United States and 252 by China. The centrality of the United States is 0.33, the H index is 38, and the H index of two institutions in England reaches 20. Ophthalmology, computer science, and AI are the main disciplines involved. Hot keywords after 2018 include deep learning (DL), AI, macular degeneration, and automatic segmentation. CONCLUSION The annual number of articles on AI applications in ophthalmic OCT has grown rapidly. The United States holds a prominent position. Institutions like the University of California System and the University of London are spearheading advancements. Initial researches centered on the automatic recognition and diagnosis of ocular diseases leveraging traditional machine learning (ML) technology and OCT images. Nowadays, the imaging process algorithm selection has shifted its focus towards DL. Concurrently, optical coherence tomography angiography (OCTA) and computer-aided diagnosis (CAD) have emerged as key areas of contemporary research.International Journal of Ophthalmology00The annual number of articles on AI applications in ophthalmic OCT has grown rapidly and the United States holds a prominent position, hot keywords after 2018 include deep learning (DL), AI, macular degeneration, and automatic segmentation.International journal of ophthalmology["Ruo-Yu Wang", "Si-Yuan Zhu", "Xin-Ya Hu", "Li Sun", "Shao-Chong Zhang", "Wei-Hua Yang"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/9b33e2ed88839c207ad7860ef977cf6dd616bb8a 59a4cad1c3febab6518e992ca080cdd88623c513BioData Mining220ChatGPT-4o failed an important component in using information effectively and misrepresenting trusted sources of public health information is highly concerning, which is a significant limitation of GenAI’s ability to meet information literacy standards expected of graduate students.BioData Mining["Shilpa R Thandla", "Grace Q Armstrong", "Adil Menon", "Aashna Shah", "David L Gueye", "Clara Harb", "Estefania Hernandez", "Yasaswini Iyer", "Abigail R Hotchner", "Riddhi Modi", "Anusha Mudigonda", "Maria A Prokos", "Tharun M Rao", "Olivia R Thomas", "Camilo A Beltran", "Taylor Guerrieri", "Sydney LeBlanc", "Skanda Moorthy", "Sara G Yacoub", "Jacob E Gardner", "Benjamin M Greenberg", "Alyssa Hubal", "Yuliana P Lapina", "Jacqueline Moran", "Joseph P O'Brien", "Anna C Winnicki", "Christina Yoka", "Junwei Zhang", "Peter A. Zimmerman"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/59a4cad1c3febab6518e992ca080cdd88623c513 6a5d0ffd872aed28d2344966d6f1c6019e08997cPurposeIn today’s competitive business landscape, organizations are increasingly recognizing the strategic advantage of implementing sustainable practices to gain a competitive edge. This study aims to investigate the effect of green artificial intelligence (AI) on achieving a green competitive advantage, examining the mediating roles of green organizational learning, green product innovation and green process innovation. Additionally, the research explores the moderating role of perceived green climate in the relationship between green AI and these mediating factors.Design/methodology/approachThis research examined companies in Isfahan, Iran, that have varying levels of artificial intelligence adoption within their business processes. The target population consisted of 148 senior managers from these companies. This study uses structural equation modeling to examine the proposed model.FindingsGreen AI positively impacted green organizational learning and green process innovation but not green product innovation. In addition, the results showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage. Finally, the results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors.Practical implicationsOrganizations should prioritize green AI initiatives, foster a culture of green learning and invest in green innovation to achieve sustainable growth and outpace competitors in the environmentally conscious marketplace.Originality/valueThis study positions itself at the forefront of research on green AI and green competitive advantage. It offers a unique framework by examining the combined effects of green AI, green learning and both product and process innovation on achieving a sustainable competitive advantage.The TQM Journal1200The results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors, and showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage.The TQM Journal["Reza Salehzadeh", "Maliheh Javani", "Hassan Esmailian"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/6a5d0ffd872aed28d2344966d6f1c6019e08997c 832b68bcca27fdfae3a7e25b718cb5c5cc2f02fdThe virtues of the humanities, including philosophy, history, and sociology, are more precious than ever. They serve as guiding lights, illuminating the dark regions of science, as the sociologist and philosopher Edgar Morin would assert. Their importance in the digital age cannot be overstated. With this paper, we intend to open a debate on ‘digital humanism’ – the humanistic imprint of the digital world. We want to bring humans back to the centre of attention as idea builders (so to speak, ideators or creators) who promote the wellbeing of all living things: people, animals and natural objects (seas, lakes, rivers, mountains, etc.). This article outlines a cultural framework that aims to foster the creation of technologies by ideators who prioritise social justice, environmental sustainability and ethical behaviour.  *Excerpted from Piero Formica. Human Intelligence and Artificial Intelligence: Exhibition at the Mind Gallery. Bologna, Italy: Edizioni Pendragon. Journal of Comparative International Management00A cultural framework is outlined that aims to foster the creation of technologies by ideators who prioritise social justice, environmental sustainability and ethical behaviour.Journal of Comparative International Management["Piero Formica"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/832b68bcca27fdfae3a7e25b718cb5c5cc2f02fd 36821af2cc922a87fa720e7881cc20c6d61ec71aEnglish language learning is undergoing a profound transformation with the advent of digital technology and Artificial Intelligence. This research, that conduct by narrative review method, will aim to address these gaps by synthesizing findings from the last decade of research and providing a comprehensive understanding of the current trends, challenges, and future directions in the use of AI for English language learning. Most existing studies focus on the efficacy of specific AI tools, with limited attention given to long-term effects on learners' language proficiency, retention, and socio-cultural competence. Additionally, much of the research to date has concentrated on English as a foreign language, with less emphasis on how AI can support multilingualism or the learning of English as a second language in diverse sociocultural contexts. Furthermore, there is a lack of studies that explore the ethical, cultural, and pedagogical implications of widespread AI adoption in language education, particularly in non-Western contexts. This study contributes valuable insights into the evolving landscape of the use of AI in English learning, highlighting both the promising potential and the challenges of integrating GenAI technologies. Suggestions for further research are to conduct empirical research on the concepts in this study.EDUJ : English Education Journal290This research aims to address gaps by synthesizing findings from the last decade of research and providing a comprehensive understanding of the current trends, challenges, and future directions in the use of AI for English language learning by synthesizing findings from the last decade.EDUJ : English Education Journal["Bhenu Artha", "Bahri", "Niken Permata Sari", "Utami Tunjung Sari", "Antonius Satria Hadi", "Cahya Purnama Asri", "Elang Perwira", "Manggala Seta"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/36821af2cc922a87fa720e7881cc20c6d61ec71a eb08ca9f819f54fe8eb3110a4a9feb22fad35ca1Abstract Our opinion piece pays homage to the evolution of medical practices, tracing back to the era of Hippocrates, through significant historical milestones, and drawing parallels with the principles underpinning foundational artificial intelligence (AI) models. It emphasizes the shared ethos of both domains: a commitment to comprehensive care that values diverse data integration and individualized patient treatment. The excitement surrounding foundation models in medical imaging is understandable. However, a critical and cautious approach is crucial before widespread adoption. By addressing the present 4 major limitations (ie, data bias and generalizability, interpretability of AI models, data scarcity and diversity, and computational resources and infrastructure) and fostering a culture of rigorous research, we can unlock the true potential of these models and revolutionize medical care. This critique (opinion) paper highlights the need for a more measured approach in the field of foundation AI models for medicine in general and for medical imaging in particular. It emphasizes the importance of tackling core challenges before rushing toward clinical applications. By focusing on robust methodologies and addressing limitations, researchers can ensure the development of truly impactful and trustworthy models for the betterment of healthcare.BJR artificial intelligence00The need for a more measured approach in the field of foundation AI models for medicine in general and for medical imaging in particular is highlighted, with the importance of tackling core challenges before rushing toward clinical applications.Bjr Artificial Intelligence["A. Medetalibeyo\u011flu", "Yury Velichko", "Eric Hart", "Ulas Bagci"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/eb08ca9f819f54fe8eb3110a4a9feb22fad35ca1 13363905b10691fc9bb4a5075aee0ae349864f47The functioning of transport and all the relevant infrastructure gives rise to the phenomenon of a transport ecosystem. The nature of transport and other legal relations arising within the transport ecosystem is largely determined by the process of technological development of society.The objective of the study was, based on the achievements of legal hermeneutics and the application of systemic legal analysis, to analyse the legal regime of transport ecosystems based on the principles of functioning of artificial intelligence. Application of comparative legal and formal dogmatic analysis methods allowed to achieve scientific results in the field of transport legal science, particularly, to substantiate a scientific hypothesis that the implementation of a high-tech element in the form of artificial intelligence entails fundamental changes in the methodological basis of functioning of transport ecosystems, transforms the concept of management influence on the processes occurring in them and entails a change in the nature and content of legal regulation of transport and related public relationships. The findings focus on shaping new scientific ideas on the legal regime of transport ecosystems based on the principles of functioning of artificial intelligence.World of Transport and Transportation80The objective of the study was to analyse the legal regime of transport ecosystems based on the principles of functioning of artificial intelligence to substantiate a scientific hypothesis that the implementation of a high-tech element in the form of artificial intelligence entails fundamental changes in the methodological basis of functioning of transport ecosystems.World of Transport and Transportation["N. L. Bondarenko", "Y. G. Konanevich", "A. I. Zemlin"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/13363905b10691fc9bb4a5075aee0ae349864f47 1e2df4854dbec0d39433a0478cd162c55bd7485dThis opinion piece explores the intersection of artificial intelligence (AI), The Treaty of Waitangi, and New Zealand schools from the perspective of a Māori educator. The author reflects on personal experiences with AI and discusses the rewards and potential risks Māori face when dealing with artificial intelligence. The piece then looks at how the Treaty’s principles of partnership, participation, and protection can coexist with AI and offers suggestions on how AI can be integrated into Māoritanga in a respectful and collaborative way.Teachers' Work00The piece then looks at how the Treaty’s principles of partnership, participation, and protection can coexist with AI and offers suggestions on how AI can be integrated into Māoritanga in a respectful and collaborative way.Teachers' Work["Brendon Shaw"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/1e2df4854dbec0d39433a0478cd162c55bd7485d 535887244b1f74ea1f4399e1891644f1bf6dc946The study of the interaction between STEM education and artificial intelligence is relevant for ensuring academic integrity because of the need to guarantee the efficacy and impartiality of the educational process, which must align with contemporary technological demands and ethical standards. The objective of this research article is to identify the challenges and potential for the utilisation of artificial intelligence in the development of STEM education programmes and curricula. This article examines the efficacy of current preventive measures against students' misuse of AI technologies in the context of STEM education. In the course of composing the research article, the authors employed a systematic approach to analysing and generalising the findings of their review of the literature, with the objective of identifying the critical aspects of academic integrity in the use of artificial intelligence in STEM education. The study employed expert assessment to collate data on the total number of coursework items examined and the number of works in which the GPT detector identified indications of AI usage. The calculation of the percentage of violations of academic integrity through the use of artificial intelligence to the total number of coursework for each speciality revealed that the percentage of violations of academic integrity is greater at the Igor Sikorsky Kyiv Polytechnic Institute, where the coursework was checked for the first time for the use of AI (7.46%), than at Taras Shevchenko National University of Kyiv (4.03%), where the check is carried out for two semesters. Concurrently, there is an emerging concern regarding the increasing incidence of academic integrity violations facilitated by AI technologies. This necessitates the formulation of transparent guidelines governing the deployment of AI in the educational sphere, enhancements to the assessment framework, and the integration of AI-based detection tools into the evaluation of student performance. Moreover, it is imperative to cultivate a heightened ethical consciousness among both students and educators.Multidisciplinary Reviews00The efficacy of current preventive measures against students' misuse of AI technologies in the context of STEM education is examined and the percentage of violations of academic integrity is greater at the Igor Sikorsky Kyiv Polytechnic Institute than at Taras Shevchenko National University of Kyiv.Multidisciplinary Reviews["Nataliia Streletska", "Andrii Ulishchenko", "Anna Klieba", "Iryna Vlasiuk", "Svitlana Genkal"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/535887244b1f74ea1f4399e1891644f1bf6dc946 3893fb99ebb7901d4574b012ea8fb43232eae684The rapid advancements of Generative Artificial Intelligence (GenAI) technologies, such as the well-known OpenAI ChatGPT and Microsoft Copilot, have sparked significant societal, economic, and regulatory challenges. Indeed, while the latter technologies promise unprecedented productivity gains, they also raise several concerns, such as job loss and displacement, deepfakes, and intellectual property violations. The present article aims to explore the present regulatory landscape of GenAI across the major global players, highlighting the divergent approaches adopted by the United States, United Kingdom, China, and the European Union. By drawing parallels with other complex global issues such as climate change and nuclear proliferation, this paper argues that the available traditional regulatory frameworks may be insufficient to address the unique challenges posed by GenAI. As a result, this article introduces a resilience-focused regulatory approach that emphasizes aspects such as adaptability, swift incident response, and recovery mechanisms to mitigate potential harm. By analyzing the existing regulations and suggesting potential future directions, the present article aims to contribute to the ongoing discourse on how to effectively govern GenAI technologies in a rapidly evolving regulatory landscape.Societies390This paper argues that the available traditional regulatory frameworks may be insufficient to address the unique challenges posed by GenAI, and introduces a resilience-focused regulatory approach that emphasizes aspects such as adaptability, swift incident response, and recovery mechanisms to mitigate potential harm.Societies["Matteo Bodini"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/3893fb99ebb7901d4574b012ea8fb43232eae684 05abe2b4fcd7a2ed2b962958f3783c76b55716d8This study explores the role of artificial intelligence (AI) in enhancing sustainability reporting and green accounting within the context of Industry 4.0. By drawing on sustainability accounting theory and integrating emerging technologies, the research addresses the limitations of traditional sustainability reporting, such as inefficiencies in data collection, lack of real-time feedback, and resource constraints. AI, with its ability to automate processes, streamline data analysis, and provide continuous monitoring, offers a promising solution to these challenges. The research employs qualitative case studies to demonstrate how AI-driven methods improve the accuracy and productivity of sustainability reporting. Findings indicate that AI facilitates more effective resource utilization, provides real-time insights, and enhances data-driven decision-making, which collectively contribute to more sustainable business practices. The paper concludes that AI's integration into sustainability accounting not only improves transparency and accountability but also accelerates the transition towards environmentally conscious business operations in the digital age of Industry 4.0.Acta Marisiensis. Seria Technologica260It is concluded that AI's integration into sustainability accounting not only improves transparency and accountability but also accelerates the transition towards environmentally conscious business operations in the digital age of Industry 4.0.Acta Marisiensis. Seria Technologica["Ayesha Tariq", "Mohd Reyaz Ur Rahim"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/05abe2b4fcd7a2ed2b962958f3783c76b55716d8 2a96c2c1c1ab18e15f48b44a1903855140e9b6b2Teknologi telah menjadi “kacamata” baru dalam memahami dunia, sebagaimana terlihat dari revolusi industri yang kini mencapai era 5.0 dengan fokus pada integrasi dunia maya dan nyata untuk kesejahteraan manusia. Namun, perkembangan ini juga membawa disrupsi yang berpotensi merugikan, termasuk dalam penggunaan Artificial Intelligence. Hukum yang dinamis diperlukan untuk memberikan solusi atas hubungan manusia dengan AI, terutama dalam menghadapi kerugian yang tak terduga. Oleh karena itu, penelitian ini bertujuan untuk mengetahui bagaimana kedudukan hukum Artificial Intelligence dalam hubungannya terhadap manusia. Melalui metode yuridis normatif dengan pendekatan undang-undang dan pendekatan konseptual, kedudukan AI dalam perspektif hukum perdata ditelaah berdasarkan bahan hukum primer dan sekunder yang kemudian dianalisis dengan metode kualitatif. Hasil penelitian ini adalah Artificial Intelligence memiliki prospek yang cukup besar dalam kedudukannya sebagai subjek hukum-quasi dalam konteks hukum perdata Indonesia. Indonesia yang hingga saat ini masih belum memiliki peraturan yang komprehensif mengenai AI, tentu perlu segera merumuskan peraturan yang mengatur kedudukan AI dalam sistem hukum Indonesia mengingat jalinan antar AI dan manusia dalam berbagai aspek yang kian erat saling berhubungan.Jurnal ISO: Jurnal Ilmu Sosial, Politik dan Humaniora00Jurnal ISO: Jurnal Ilmu Sosial, Politik dan Humaniora["Bintang M.D", "M. Masnun"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/2a96c2c1c1ab18e15f48b44a1903855140e9b6b2 b6e24b551e4ef4df66d7d3ace540dc4eb892dc8aPURPOSE To assess the feasibility, implementation, and patient experience of autonomous artificial intelligence-based diabetic retinopathy detection models. METHODS This was a prospective cohort study where consenting adult participants previously diagnosed with diabetes were screened for diabetic retinopathy using retinal imaging with autonomous artificial intelligence (AI) interpretation at their routine primary care appointment from December 2022 through October 2023 in Thunder Bay, Ontario. Demographic (age, sex, race) and clinical (type and duration of diabetes, last reported eye exam) data were collected using a data collection form. A 5-point Likert scale questionnaire was completed by participants to assess patient experience following the AI exam. RESULTS Among the 202 participants (38.6% women) with a mean age of 70.8 ± 11.7 years included in the study and screened by AI, the exam was successfully completed by 93.6% (n = 189), with only 1.5% (n = 3) requiring dilating eyedrops. The most common reason for an unsuccessful exam was small pupils with patient refusal for dilating eyedrops (n = 4). Among the participants with successful eye exams, 22.2% (n = 42) had referable diabetic retinopathy detected and were referred to see an ophthalmologist; 32/42 (76.0%) of these attended their ophthalmologist appointment. A total of 184 participants completed the satisfaction questionnaire; the mean score (out of 5) for satisfaction with the addition of an eye exam to their primary care visit was 4.8 ± 0.6. CONCLUSION Screening for diabetic retinopathy using autonomous artificial intelligence in a primary care setting is feasible and acceptable. This approach has significant advantages for both physicians and patients while achieving very high patient satisfaction.Ophthalmic Epidemiology290Screening for diabetic retinopathy using autonomous artificial intelligence in a primary care setting is feasible and acceptable and has significant advantages for both physicians and patients while achieving very high patient satisfaction.Ophthalmic epidemiology["V. Bhambhwani", "N. Whitestone", "Jennifer L. Patnaik", "Alonso Ojeda", "James Scali", "D. Cherwek"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/b6e24b551e4ef4df66d7d3ace540dc4eb892dc8a a7847cf8ce2e3bc6ab8301b3c2f8151b6f2ec331Introduction. Advanced artificial intelligence technology (hereinafter referred to as AI) is being actively introduced around the world, which also poses legal problems for the copyright institution. An urgent task of copyright law is the need to develop positions on the recognition of copyright in works created with the help of AI, and their protection for further use. Theoretical analysis. In the international and Russian law, there are problems in determining the legal status of AI as an object, subject, or quasi-subject of law. According to the Decree of the President of the Russian Federation “On the development of artificial intelligence in the Russian Federation” and the first law on the regulation of AI in world practice, approved by the European Parliament, AI is a complex or system of technological solutions. Thus, at the present stage of the development of AI, it is recognized as an object of law. Еmpirical analysis. It has been revealed that problems of recognition and protection of copyright are associated with determining authorship of works created with the help of AI. The approaches to determining authorship are highlighted, taking into account the role of a person in the process of creating works: they can be a user, a developer or owner of an AI system, or the author of a precedent work on which artificial intelligence was trained. The paper considers the cases when part of a work created by AI goes into the public domain. The determining factor of authorship is the idea and creative concept of a person, implemented with the help of AI systems, according to which the author is a person, and AI is just a tool for creating the results of human intellectual activity. Results. The need to improve current legislation in the field of copyright for works created by AI has been proven; the most acceptable approach to the recognition and protection of copyright in works created with the help of AI has been identified.Izvestiya of Saratov University. Economics. Management. Law00The need to improve current legislation in the field of copyright for works created by AI has been proven; the most acceptable approach to the recognition and protection of copyright in works created with the help of AI has been identified.Izvestiya of Saratov University. Economics. Management. Law["Polina V. Eresko"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/a7847cf8ce2e3bc6ab8301b3c2f8151b6f2ec331 7a86ed47599335aa923ab60a2274f244c3009aaeOECD Tourism Papers450OECD Tourism Papers[]2024-12-18T00:00:00https://www.semanticscholar.org/paper/7a86ed47599335aa923ab60a2274f244c3009aae abd83e9deb8b6d0935ce3b1c006e2a0722063c2aBackground: The worst outcomes of diabetic retinopathy (DR) can be prevented by implementing DR screening programs assisted by AI. At the University Hospital of Navarre (HUN), Spain, general practitioners (GPs) grade fundus images in an ongoing DR screening program, referring to a second screening level (ophthalmologist) target patients. Methods: After collecting their requirements, HUN decided to develop a custom AI tool, called NaIA-RD, to assist their GPs in DR screening. This paper introduces NaIA-RD, details its implementation, and highlights its unique combination of DR and retinal image quality grading in a single system. Its impact is measured in an unprecedented before-and-after study that compares 19,828 patients screened before NaIA-RD's implementation and 22,962 patients screened after. Results: NaIA-RD influenced the screening criteria of 3/4 GPs, increasing their sensitivity. Agreement between NaIA-RD and the GPs was high for non-referral proposals (94.6% or more), but lower and variable (from 23.4\% to 86.6%) for referral proposals. An ophthalmologist discarded a NaIA-RD error in most of contradicted referral proposals by labeling the 93% of a sample of them as referable. In an autonomous setup, NaIA-RD would have reduced the study visualization workload by 4.27 times without missing a single case of sight-threatening DR referred by a GP. Conclusion: DR screening was more effective when supported by NaIA-RD, which could be safely used to autonomously perform the first level of screening. This shows how AI devices, when seamlessly integrated into clinical workflows, can help improve clinical pathways in the long term.00DR screening was more effective when supported by NaIA-RD, which could be safely used to autonomously perform the first level of screening, and shows how AI devices, when seamlessly integrated into clinical workflows, can help improve clinical pathways in the long term.["Imanol Pinto", "'Alvaro Olazar'an", "David Jur'io", "Borja de la Osa", "Miguel Sainz", "Aritz Oscoz", "Jer'onimo Ballaz", "Javier Gorricho", "Mikel Galar", "Jos'e Andonegui"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/abd83e9deb8b6d0935ce3b1c006e2a0722063c2a 492e5bb127b2878b6c099bdb8ee0546f0589e46eInternational Journal of Computational Intelligence Systems1040Int. J. Comput. Intell. Syst.["Thura J. Mohammed", "Xinying Chew", "Alhamzah Alnoor", "K. W. Khaw", "A. Albahri", "W. L. Teoh", "Zhi Lin Chong", "S. Saha"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/492e5bb127b2878b6c099bdb8ee0546f0589e46e 5591a38d29d3be269a438ec65b69c771ca122bedSTAR Protocols400A protocol for artificial intelligence-guided neural control in rats using deep reinforcement learning (RL) and infrared neural stimulation (INS) is presented and steps for integrating RL closed-loop control into neuroscience and neuromodulation studies are described.STAR Protocols["Brandon S. Coventry", "Edward L. Bartlett"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/5591a38d29d3be269a438ec65b69c771ca122bed cd537c93acca44e3962c5246d1b15cb5ea132f9aComput. Biol. Medicine290This paper explores and applies explainable artificial intelligence (XAI) techniques to different CNN architectures for glaucoma classification, comparing which explanation technique offers the best interpretive resources for clinical diagnosis and proposes a new approach, SCIM (SHAP-CAM Interpretable Mapping), which has shown promising results.Computers in biology and medicine["Cleverson Vieira", "Leonardo Rocha", "M. Guimar\u00e3es", "D. Dias"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/cd537c93acca44e3962c5246d1b15cb5ea132f9a 268ea4fa58a5a565cd0b77edc60b0469de283cdeInternational Studies of Economics00International Studies of Economics["Ye Luo"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/268ea4fa58a5a565cd0b77edc60b0469de283cde 1fe5d41d580b17904fdf144d4330ec27c2bae693Humanities and Social Sciences Communications160Humanities and Social Sciences Communications["Walter Leal Filho", "Gouvid\u00e9 Jean Gbaguidi"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/1fe5d41d580b17904fdf144d4330ec27c2bae693 bdfdbb674dc52b00455dc10c8b96d7ad54009a60Frontiers in Ophthalmology90Frontiers in Ophthalmology["S. Alryalat", "Muawyah Al Bdour", "Hisham M. Jammal"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/bdfdbb674dc52b00455dc10c8b96d7ad54009a60 1b81639b038147740e359f34bb7e87741f4aa7d7Clinical and Experimental Obstetrics &amp; Gynecology80Clinical and Experimental Obstetrics &amp; Gynecology["Aigerim Mashekova", "Vasilios Zarikas", "Yong Zhao", "Eddie Yin", "Kwee Ng"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/1b81639b038147740e359f34bb7e87741f4aa7d7 1a4ce10f584dd705dc2688295bb5a750e2cc0534JOURNAL OF LAHORE MEDICAL AND DENTAL COLLEGE00JOURNAL OF LAHORE MEDICAL AND DENTAL COLLEGE["Uzma Zargham"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/1a4ce10f584dd705dc2688295bb5a750e2cc0534 ce86cd3c6996c31b29f496680cc2136379ae1c7a00["Suman Kumar Swarnkar", "Abhishek Guru", "Gurpreet Singh Chhabra", "Harshitha Raghavan Devarajan"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/ce86cd3c6996c31b29f496680cc2136379ae1c7a 5ba39c5b8a54b0a010a616c86c27e34c48a9dd44In 2019, COVID-19 began one of the greatest public health challenges in history, reaching pandemic status the following year. Systems capable of predicting individuals at higher risk of progressing to severe forms of the disease could optimize the allocation and direction of resources. In this work, we evaluated the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19, using clinical data from hospital admission alone. This data was collected during a prospective, multicenter cohort that followed patients with respiratory syndrome during the pandemic. We aimed to predict which patients would present mild cases of COVID-19 and which would develop severe cases. Severe cases were defined as those requiring access to the Intensive Care Unit, endotracheal intubation, or even progressing to death. The system achieved an accuracy of 80%, with Area Under Receiver Operating Characteristic Curve (AUC) of 91%, Positive Predictive Value of 87% and Negative Predictive Value of 82%. Considering that only data from hospital admission was used, and that this data came from low-cost clinical examination and laboratory testing, the low false positive rate and acceptable accuracy observed shows that it is feasible to implement prediction systems based on artificial intelligence as an effective triage method.Frontiers in Artificial Intelligence420Evaluating the performance of different Machine Learning algorithms when predicting clinical outcomes of patients hospitalized with COVID-19 shows that it is feasible to implement prediction systems based on artificial intelligence as an effective triage method.Frontiers in Artificial Intelligence["Yuri Oliveira", "I\u00eada Rios", "Paula Ara\u00fajo", "Alinne Macambira", "Marcos Guimar\u00e3es", "L\u00facia Sales", "Marcos Rosa J\u00fanior", "Andr\u00e9 Nicola", "Mauro Nakayama", "Hermeto Paschoalick", "Francisco Nascimento", "Carlos Castillo-Salgado", "Vania Moraes Ferreira", "Hervaldo Carvalho"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/5ba39c5b8a54b0a010a616c86c27e34c48a9dd44 c39157a77ff92cd990dad7f12388eeea19287ad0Journal of the Colleges of Medicine of South Africa00Journal of the Colleges of Medicine of South Africa["S. Seedat"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/c39157a77ff92cd990dad7f12388eeea19287ad0 42fbf97ec253eb3981280cef7aa1bbbfaae0b47eA fresh paradigm for classifying current studies on flood management systems is proposed in this review. The literature has examined methods for managing different flood management activities from a variety of fields, such as machine learning, image processing, data analysis, and remote sensing. Prediction, detection, mapping, evacuation, and relief efforts are all part of flood management. This can be improved by adopting state-of-the-art tools and technology. Preventing floods and ensuring a prompt response after floods is crucial to ensuring the lowest number of fatalities as well as minimizing environmental and financial damages. The following noteworthy research questions are addressed by the framework: (1) What are the main methods used in flood control? (2) Which stages of flood management are the majority of research currently in existence focused on? (3) Which systems are being suggested to address issues with flood control? (4) In the literature, what are the research gaps regarding the use of technology for flood management? To classify the many technologies that have been studied, a framework for classification has been provided for flood management. It was found that there were few hybrid models for flood control that combined machine learning and image processing. Furthermore, it was discovered that there was little use of machine learning-based techniques in the aftermath of a disaster. To provide efficient and comprehensive disaster management, future efforts must concentrate on integrating image processing methods, machine learning technologies, and the understanding of disaster management across all phases. The study has proposed the use of Generative Artificial Intelligence.CivilEng530It was found that there were few hybrid models for flood control that combined machine learning and image processing and there was little use of machine learning-based techniques in the aftermath of a disaster.CivilEng["Adekunle Olorunlowo David", "J. Ndambuki", "Mpho Muloiwa", "W. Kupolati", "Jacques Snyman"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/42fbf97ec253eb3981280cef7aa1bbbfaae0b47e 527ab7c0e2ea256d5b7e9248ca2765a8c496d713Production Engineering441This study presents a first and superior systematic approach for the systematic evaluation, development and implementation of AI in production management and introduces a structured framework that can be employed to assess and determine the optimal level of Human-AI collaboration for a range of production use cases.Production Engineering["Carl Ren\u00e9 Sauer", "Peter Burggr\u00e4f"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/527ab7c0e2ea256d5b7e9248ca2765a8c496d713 72c76dcbcab63aafdc2dba54b4f480c91b75f21eSMP Negeri 3 Selat is located in Duda Utara, Selat District, Karangasem Regency, Bali Province, Indonesia. Situational analysis shows that most teachers have not utilized artificial intelligence (AI) technology optimally in education. In fact, artificial intelligence technology has the potential to help teachers' work in improving the quality of learning. The purpose of this study was to see the effectiveness of training and mentoring activities in improving teacher knowledge about AI technology and its utilization, as well as improving teacher skills in utilizing AI technology for compiling learning administration; designing learning processes and compiling teaching materials. The data used were primary data from training and mentoring participants consisting of 20 teachers of SMP Negeri 3 Selat. The data were in the form of quantitative data, namely pretest and posttest scores. The data analysis stage began with a plot of pretest and posttest scores and a bar chart plot of grouped data, both of which showed a significant increase in scores after being given intervention in the form of training and mentoring. Paired t-test. To see how big the effect of the training and mentoring intervention was, Cohen's effect size was calculated, the percentage change in pretest and posttest scores. The paired t-test results show that there is a significant difference between the pretest and posttest scores. Based on Cohen's size, it was found that the training and mentoring intervention had a very strong effect on changes in pretest and posttest scores. In addition, it was also found that the percentage of changes in pretest and posttest was 106.85% and it can be 95% certain that the average posttest score in the population is estimated to be 32.76 to 45.24 points higher than the average pretest score, due to the intervention in the form of training and mentoring. This is reinforced by the pretest and posttest difference plot showing that there was a significant increase in scores after the intervention.International journal of social science and human research00It was found that the training and mentoring intervention had a very strong effect on changes in pretest and posttest scores, as well as improving teacher skills in utilizing AI technology for compiling learning administration; designing learning processes and compiling teaching materials.International Journal of Social Science and Human Research["Kartika Sari", "Anak Agung Istri Ngurah Eka Karyawati", "Luh Putu Ida Harini", "Ni Ketut Tari Tastrawati", "I. G. S. Wirawan", "Karolien Miracle Anggraeni", "Putu Ayu Liana Prasetya D"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/72c76dcbcab63aafdc2dba54b4f480c91b75f21e e5e16646e2e4c73d03bae27fca92e7bada183e1bWith the recent advancements in artificial intelligence, researchers and industries are deploying gigantic models trained on billions of samples. While training these models consumes a huge amount of energy, human brains produce similar outputs (along with other capabilities) with massively lower data and energy requirements. For this reason, more researchers are increasingly considering alternatives. One of these alternatives is known as synthetic biological intelligence, which involves training \textit{in vitro} neurons for goal-directed tasks. This multidisciplinary field requires knowledge of tissue engineering, bio-materials, digital signal processing, computer programming, neuroscience, and even artificial intelligence. The multidisciplinary requirements make starting synthetic biological intelligence research highly non-trivial and time-consuming. Generally, most labs either specialize in the biological aspects or the computational ones. Here, we propose how a lab focusing on computational aspects, including machine learning and device interfacing, can start working on synthetic biological intelligence, including organoid intelligence. We will also discuss computational aspects, which can be helpful for labs that focus on biological research. To facilitate synthetic biological intelligence research, we will describe such a general process step by step, including risks and precautions that could lead to substantial delay or additional cost.00Here, it is proposed how a lab focusing on computational aspects, including machine learning and device interfacing, can start working on synthetic biological intelligence, including organoid intelligence.["Md Sayed Tanveer", "Dhruvik Patel", "Hunter E. Schweiger", "K. D. Abu-Bonsrah", "Brad Watmuff", "Azin Azadi", "Sergey Pryshchep", "Karthikeyan Narayanan", "Christopher Puleo", "Kannathal Natarajan", "M. Mostajo-Radji", "Brett J. Kagan", "Ge Wang"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/e5e16646e2e4c73d03bae27fca92e7bada183e1b a58d0e057adfb551c5340da2a4cc53bb66b833c9Applied Artificial Intelligence830Appl. Artif. Intell.["Zhen-Yao Chen"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/a58d0e057adfb551c5340da2a4cc53bb66b833c9 433ad76bddea7f7a1735e9c8e584962a03403e76Nature Human Behaviour445A feedback loop where human-AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans is revealed, triggering a snowball effect where small errors in judgement escalate into much larger ones.Nature human behaviour["Moshe Glickman", "T. Sharot"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/433ad76bddea7f7a1735e9c8e584962a03403e76 8451303bee4517b486bab3a12522dde44ae0b8dcThe digital world is changing quickly. One big area of focus is how artificial intelligence (AI) fits in with cybersecurity. New rules, like the NIS2 Directive, are pushing organizations to keep their systems secure. This research looks at how generative AI can help meet those safety standards. More companies are using generative AI for things like data creation and risk assessment. This gives them a chance to improve their security practices. We explore how AI tools, like machine learning and natural language processing, can help find weaknesses, predict threats, and respond to incidents. This way, companies can follow the tough rules set by NIS2. We analyze how AI is currently used in cybersecurity. The goal is to share best practices for using these technologies well. We also address the challenge of keeping new tech secure.  In the end, we want to add to the conversation about AI and cybersecurity. We give advice for policymakers and business leaders on how to use generative AI as a smart tool for both innovation and meeting NIS2 requirements. This study highlights how generative AI can help improve cybersecurity while also fulfilling regulatory needs in today's tricky digital landscape.International Journal of Regional Development01This study highlights how generative AI can help improve cybersecurity while also fulfilling regulatory needs in today's tricky digital landscape.International Journal of Regional Development["Christos P. Beretas", "Athanasios Davalas"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/8451303bee4517b486bab3a12522dde44ae0b8dc b563d9f44a5bec153fcfee04f9fdba4946fa2bcdJournal of Racial and Ethnic Health Disparities711To address racial disparities in healthcare outcomes, enhanced ethical considerations and regulatory frameworks are needed in AI healthcare applications and comprehensive bias detection tools and mitigation strategies are essential to ensure AI becomes a tool for reducing racial disparities in healthcare outcomes.Journal of racial and ethnic health disparities["S. A. Haider", "Sahar Borna", "Cesar A Gomez-Cabello", "Sophia M Pressman", "Clifton R. Haider", "AJ Forte"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/b563d9f44a5bec153fcfee04f9fdba4946fa2bcd 36169f24e3536e76a067d1e864a2b67a41457843BMC Nursing941The proposed TAM-AIN offers a comprehensive framework for optimising AI integration into nursing practice, emphasising the importance of nurse-centred implementation strategies and provides healthcare institutions and policymakers with a robust tool to facilitate successful AI adoption and enhance patient outcomes.BMC Nursing["Osama Mohamed Elsayed Ramadan", "M. Alruwaili", "A. Alruwaili", "M. G. Elsehrawy", "Sulaiman Alanazi"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/36169f24e3536e76a067d1e864a2b67a41457843 6e9c71c73007747b8f4805a33ef8abaad828fe7dMarketing instructors increasingly are using artificial intelligence (AI) to improve efficiency in course planning and assessment. However, scholarship has yet to show how such use impacts student evaluations, which are often skewed by gender bias. Toward this aim, we conducted a quasi-experiment in which college-student participants viewed student work along with instructor feedback under four experimental conditions. In one condition, participants were told the grade and feedback were written by the instructor and, in another, that the feedback was generated by an AI tool trained by the instructor. Instructor gender was also manipulated. Participants evaluated the assessment and the instructor using measures of agency and competence. We found that perceptions of agency and competence were higher for male instructors than for female instructors who did not use an AI tool. However, when instructors used AI, marked reductions in gender differences occurred. These findings suggest that AI complicates perceptions of agency and competence enough to potentially level the playing field for female instructors. However, perceptions of instructors were universally more negative when participants were told the assessment was created with AI, regardless of instructor gender. Therefore, attempts to use AI as an assessment tool should be made with caution.Journal of Marketing Education650It is found that perceptions of agency and competence were higher for male instructors than for female instructors who did not use an AI tool, however, when instructors used AI, marked reductions in gender differences occurred.Journal of Marketing Education["Michelle Cowan", "Gavin Fox", "Keri M. Larson"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/6e9c71c73007747b8f4805a33ef8abaad828fe7d e05f9abe6d05c69203b69f2f2051849fbbdd69fcThis article examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) integration within SAP S/4HANA Cloud for supply chain optimization. Through a comprehensive article analysis of multiple enterprise-level implementations conducted over an extended period, the study investigates the effectiveness of AI-driven solutions in enhancing supply chain visibility, operational efficiency, and decision-making capabilities. The article employs a mixed-methods approach, combining quantitative performance metrics with qualitative stakeholder insights to evaluate the implementation outcomes and strategic benefits. The article reveals substantial improvements across multiple dimensions, including significant reductions in operational costs, manual interventions, and marked enhancement in order fulfillment accuracy. The article also addresses critical implementation considerations, including change management strategies, best practices, and potential challenges in AI integration. Furthermore, the article explores emerging trends and future implications for supply chain management, including the role of quantum computing, edge processing, and digital twins. This comprehensive article analysis provides valuable insights for organizations seeking to leverage AI and ML capabilities in their supply chain operations, while also contributing to the broader understanding of digital transformation in enterprise resource planning systems.International Journal of Scientific Research in Computer Science Engineering and Information Technology20Substantial improvements are revealed across multiple dimensions, including significant reductions in operational costs, manual interventions, and marked enhancement in order fulfillment accuracy in SAP S/4HANA Cloud for supply chain optimization.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Satheesh Kumar Nendrambaka"]2024-12-18T00:00:00https://www.semanticscholar.org/paper/e05f9abe6d05c69203b69f2f2051849fbbdd69fc 03574f9d87f7973de102976d50401e1f98b014daThe article explores how youth perceive the risks of artificial intelligence (AI), based on a survey of 410 university students from Poland, Spain, and Ukraine. The study confirms non-random responses with acceptable internal consistency for categorical variables and complex constructs. The authors built three latent profiles of participants with pragmatic, skeptical, and cautious attitudes towards AI. The scenario approach revealed that respondents are cautious about the use of AI but generally support its implementation under conditions of flexible state regulation. Youth do not see AI as a threat to social equality or the labor market but expect state support through retraining and basic income for laid-off workers. At the same time, there are both optimistic and skeptical scenarios about the future of AI, which depend on the level of awareness and cultural characteristics of the respondents.Human Technology01Investigating how youth perceive the risks of artificial intelligence (AI) based on a survey of 410 university students from Poland, Spain, and Ukraine revealed that respondents are cautious about the use of AI but generally support its implementation under conditions of flexible state regulation.Human Technology["H. Yarovenko", "Aleksandra Kuzior", "Tomasz Norek", "Agnieszka Lopatka"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/03574f9d87f7973de102976d50401e1f98b014da 10f77f40cbfc6bdd7218692aef4b13843ceffd1bConsidering the development of technology, in recent years the regulation of Artificial Intelligence has become a central policy issue in the European Union (EU). Policymakers have committed to developing an approach to AI to ensure that Europeans can benefit from the new technologies that are developed and operate with them in accordance with EU values and principles. On March 13, 2024, the European Parliament approved the world’s first binding horizontal rules on AI. The purpose of this publication is to explore this EU-developed regulatory framework for Artificial Intelligence and understand the main principles and goals of the regulation, and to identify problems.INDIVIDUAL. SOCIETY. STATE. Proceedings of the International Student and Teacher Scientific and Practical Conference01The purpose of this publication is to explore this EU-developed regulatory framework for Artificial Intelligence and understand the main principles and goals of the regulation, and to identify problems.INDIVIDUAL. SOCIETY. STATE. Proceedings of the International Student and Teacher Scientific and Practical Conference["Linda Irbe"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/10f77f40cbfc6bdd7218692aef4b13843ceffd1b eda6ec3219fcab63f5f625da23d7709eea68ee6bPurpose The purpose of this study was to examine the impact of artificial intelligence interventions in transforming learning and development of organizations and to investigate whether artificial intelligence can replace traditional learning methods. Design/methodology/approach Mixed method approach was adopted to conduct the study. The sample size for quantitative study was 300 employees belonging to domains of technology, finance, health care and manufacturing. Structural equation modeling was done to arrive at the results. The qualitative study was done on 25 employees by conducting in-depth interviews followed by thematic analysis. Findings The findings of quantitative study revealed that perceived usefulness, perceived experience and learning effectiveness significantly contributed to transformation of traditional learning. The same was validated by qualitative study, and it also indicated that respondents preferred blended learning and artificial intelligence cannot replace traditional learning. Originality/value The study contributes to research by highlighting the impact of artificial intelligence in transformation of traditional learning and development based on departmental and job-specific roles as well as sectors that require physical training in addition to knowledge-based training. Foresight511The findings of quantitative study revealed that perceived usefulness, perceived experience and learning effectiveness significantly contributed to transformation of traditional learning and indicated that respondents preferred blended learning and artificial intelligence cannot replace traditional learning.foresight["Jaya Chitranshi", "Komal Chopra", "Pallabi Banerjee"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/eda6ec3219fcab63f5f625da23d7709eea68ee6b 91920c25ec7ad2859905453db1a8840422d9b6dcDiscover Artificial Intelligence591A prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning is revealed.Discov. Artif. Intell.["Bulus Bali", "E. J. Garba", "A. S. Ahmadu", "Kwaji Tizhe Takwate", "Y. M. Malgwi"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/91920c25ec7ad2859905453db1a8840422d9b6dc 7b07a6e3c481413a0f4389da8d3df99fb29de70dDiabetic retinopathy (DR) is a highly prevalent cause of vision loss worldwide. Detection of DR requires substantial human resources and high medical costs. Therefore, the use of diagnostic software has been recently explored. The study aimed to assess the results of DR diagnoses by Cybersight, an artificial intelligence software. A total of 1,012 patients with type 2 diabetes mellitus (1,943 eyes) with a mean age of 74.61 ± 6.73 years were included. Comprehensive demographic and clinical data were gathered, and all patients underwent color fundus photography following Cybersight's standardized protocols. The study compared Cybersight's accuracy with that of ophthalmologists in identifying key DR lesions, including retinal microvascular changes, exudates, hemorrhages, the diagnosis and staging of DR, using sensitivity, specificity, and weighted Kappa metrics. The prevalence of DR was 16.2%.  A high level of agreement was found between Cybersight and ophthalmologists in DR diagnosis, with a sensitivity of 85.0%, specificity of 95.8%, and a weighted Kappa of 0.78. The presence of cataracts and the degree of pupil dilation notably impacted on the accuracy of DR diagnosis. The results have important implications for the potential application of Cybersight as a low-cost and effective tool for diabetic eye screening.JOURNAL OF CURRENT SCIENCE AND TECHNOLOGY350The study compared Cybersight's accuracy with that of ophthalmologists in identifying key DR lesions, including retinal microvascular changes, exudates, hemorrhages, the diagnosis and staging of DR, using sensitivity, specificity, and weighted Kappa metrics.Journal of Current Science and Technology["Ha Luong Thi Hai", "Van Pham Trong", "Tung Mai Quoc", "Minh Dang Duc", "Quang Nguyen Viet", "Tran Tran Tuan"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/7b07a6e3c481413a0f4389da8d3df99fb29de70d 2136a91e5467699d430b2c8280415900aeb717f6This study aims to analyze the Regulation of Legal Protection on the Utilization of Artificial Intelligence Works in the Empowerment of Intellectual Property Rights in Indonesia and analyze the Urgency of Regulation on Patent Law Problems in Indonesia as the Utilization of Artificial Intelligence Works in Comparison with Japan. This study uses normative legal methods with a comparative approach to analyze AI patent protection, utilizes literature studies, and aims to provide recommendations for regulatory development in Indonesia. The results of the study show that legal protection for AI works in Indonesia is urgent to be developed through adaptive and specific regulations. Legal ambiguity in Law No. 13 of 2016 hampers the protection of AI patents, while Japan has shown mature and comprehensive regulations. Revisions to the law in Indonesia are needed to include AI copyright and patent protection, encourage innovation, and attract investment. Learning from Japan, Indonesia can create a legal system that is conducive to the growth of AI-based technology. Responsive regulation will ensure AI is used legally and benefits society.JUNCTO Jurnal Ilmiah Hukum00JUNCTO: Jurnal Ilmiah Hukum["Namira Romaito Siregar", "Saidin Saidin", "Jelly Leviza", "Syarifah Lisa Andriati"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/2136a91e5467699d430b2c8280415900aeb717f6 c1bc292103b3c31def722bdc608b4f3c34561d57Management Review Quarterly240This work provides comprehensive guidance on the promises and pitfalls for researchers seeking to leverage AI in the research process, highlighting AI’s limitations and attempting to provide an outlook on each stage.Management Review Quarterly["Felix Lorenz", "Solvej Lorenzen", "Matheus Franco", "Julius Velz", "Thomas Clauss"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/c1bc292103b3c31def722bdc608b4f3c34561d57 6e193f36812f2c65885158575f241beb747d3eb2 Problem-based learning (PBL) in medical education has encountered challenges affecting both teachers and students. The integration of artificial intelligence (AI) into PBL may provide potential solutions to these challenges. This paper aims to discuss the potential advantages of AI, where we found these merits of AI have the potential to improve the quality of PBL lessons. It is also important to pay attention to ethical guidelines and other limitations of AI in PBL lessons as well. Examples of interactions with AI chatbots are provided to demonstrate its application possibility. It is recommended to try using AI in PBL lessons, making it more adaptable for the PBL classroom. Future research should further explore the capabilities of AI, with the goal of developing a more personalized and adaptive learning experience within PBL.Global Medical Education270It is recommended to try using AI in PBL lessons, making it more adaptable for the PBL classroom, and to pay attention to ethical guidelines and other limitations of AI in PBL lessons as well.Global Medical Education["Yaoxing Chen", "Hong Qi", "Yu Qiu", "Juan Li", "Liang Zhu", "Xiaoling Gao", "Hao Wang", "Gan Jiang"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/6e193f36812f2c65885158575f241beb747d3eb2 d2923a1999797d9dfad3cabfac2c6c3e8b1b7ca5As the population ages, traditional elderly care models struggle to meet the growing demand for care. Smart elderly care has emerged as a new approach to address traditional issues. Research shows that smart elderly care can improve the quality of life for the elderly but also faces challenges such as technology and application maturity, data security and privacy protection, and legal and ethical issues. This paper aims to explore the integration of smart elderly care and artificial intelligence, analyzing the opportunities, challenges, and development trends in elderly care services, with the goal of addressing traditional elderly care problems. To this end, the paper proposes solutions including technological innovation, policy and social driving forces, cross-sector collaboration, and international cooperation, and looks ahead to future research directions.Advances in Economics, Management and Political Sciences00This paper aims to explore the integration of smart elderly care and artificial intelligence, analyzing the opportunities, challenges, and development trends in elderly care services, with the goal of addressing traditional elderly care problems.Advances in Economics, Management and Political Sciences["Rong Zhao"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/d2923a1999797d9dfad3cabfac2c6c3e8b1b7ca5 962dad4732681f1efe053e29eee8f29da1c9d6ecJournal of NeuroEngineering and Rehabilitation400The study found that combining HRV and EDA features into a comprehensive dataset improved the synergistic representation of engagement compared to unimodal datasets, and displayed the effectiveness of psychophysiology-based AI models in predicting rehabilitation engagement, thus promoting their practical application for personalized care and improved clinical health outcomes.Journal of NeuroEngineering and Rehabilitation["Simone Costantini", "Anna Falivene", "Mattia Chiappini", "Giorgia Malerba", "Carla Dei", "Silvia Bellazzecca", "F. Storm", "Giuseppe Andreoni", "E. Ambrosini", "Emilia Biffi"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/962dad4732681f1efe053e29eee8f29da1c9d6ec f8718a568c00a8113a8f591454efd9152740691bThis study aims to adapt the Artificial Intelligence Literacy Scale translated by Wang et al. (2023) into Turkish and create a scale suitable for assessing the artificial intelligence literacy of pre-service teachers. The study used the survey method within the scope of the quantitative method. The sample of the study consisted of 440 pre-service teachers from a state university in the Eastern Anatolia Region of Turkey. The original scale consists of 12 items, 4 factors, and a 5-point Likert-type structure. In the first stage, we conducted translation studies to assess the language validity of the adapted scale. Then, the data collected from the part of the sample determined for EFA (Exploratory Factor Analysis) were analyzed. The results show that the adapted scale preserves the original scale structure. The data collected from the part of the sample designated for CFA (confirmatory factor analysis) was also analyzed. The results of the analysis show that the scale has acceptable and good-fit indices. In terms of reliability, Cronbach Alpha reliability coefficients show that the scale has a reliable structure. The results of the analysis indicate that the scale adapted into Turkish has a valid and reliable structure.e-Kafkas Eğitim Araştırmaları Dergisi400The results indicate that the scale adapted into Turkish has a valid and reliable structure and has acceptable and good-fit indices.e-Kafkas Eğitim Araştırmaları Dergisi["Hilal U\u011fra\u015f", "Merve Do\u011fan", "Mustafa U\u011fra\u015f"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/f8718a568c00a8113a8f591454efd9152740691b 454543f72cc6d3cf95fa9e700d2372775067b224Negative emotions such as loneliness, depression, and anxiety (LDA) are prevalent and pose significant challenges to emotional well-being. Traditional methods of assessing LDA, reliant on questionnaires, often face limitations because of participants' inability or potential bias. This study introduces emoLDAnet, an artificial intelligence (AI)-driven psychological framework that leverages video-recorded conversations to detect negative emotions through the analysis of facial expressions and physiological signals. We recruited 50 participants to undergo questionnaires and interviews, with their responses recorded on video. The emoLDAnet employs a combination of deep learning (e.g., VGG11) and machine learning (e.g., decision trees [DTs]) to identify emotional states. The emoLDAnet incorporates the OCC-PAD-LDA psychological transformation model, enhancing the interpretability of AI decisions by translating facial expressions into psychologically meaningful data. Results indicate that emoLDAnet achieves high detection rates for loneliness, depression, and anxiety, with F1-scores exceeding 80% and Kendall's correlation coefficients above 0.5, demonstrating strong agreement with traditional scales. The study underscores the importance of the OCC-PAD-LDA model in improving screening accuracy and the significant impact of machine learning classifiers on the framework's performance. The emoLDAnet has the potential to support large-scale emotional well-being early screening and contribute to the advancement of mental health care.Applied Psychology: Health and Well-Being410EmoLDAnet, an artificial intelligence (AI)-driven psychological framework that leverages video-recorded conversations to detect negative emotions through the analysis of facial expressions and physiological signals, has the potential to support large-scale emotional well-being early screening and contribute to the advancement of mental health care.Applied psychology. Health and well-being["Feng Liu", "Peiwan Wang", "Jingyi Hu", "Siyuan Shen", "Hanyang Wang", "Chen Shi", "Yujia Peng", "Aimin Zhou"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/454543f72cc6d3cf95fa9e700d2372775067b224 5bf92b72a54ad44b3d684e317daa15c907112fe2The intention of this study is to explore the moderating impact of artificial intelligence in the relationship between organizational citizenship behavior and the performance of the employee. Organizational citizenship behavior, which covers voluntary employee actions that contribute to organizational effectiveness, is increasingly pertinent from a modern workplace perspective. As organizations assimilate AI technologies, understanding how the said technologies have impacts on employee performance becomes crucial. This study also examines the way AI enhances or weakens the effects of OCB on employee performance of small and medium-sized enterprises. The research sample in this study is 243 culinary small and medium-sized enterprises from Makassar, Indonesia. The findings of the study eventually suggest that AI not only supports employees in their roles but also shapes their willingness to engage in OCB, ultimately impacting overall performance outcomes. This study also discusses the implications for management practices and future research guidelines, emphasizing the importance of leveraging AI to foster a productive work environment.Journal of global economics, management & business research00The findings of the study eventually suggest that AI not only supports employees in their roles but also shapes their willingness to engage in OCB, ultimately impacting overall performance outcomes.Journal of Global Economics, Management and Business Research["Nurafni Shahnyb", "Inriani Inriani", "Nurmaliadina Nurmaliadina", "Muh. Ichwan Musa", "Muh. Yushar Mustafa"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/5bf92b72a54ad44b3d684e317daa15c907112fe2 b18fb476da186e8547d0a818bc28b73974d4b3ddThis article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models’ errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3).Energies440Energies["Dawid Maciejewski", "Krzysztof Mudryk", "M. Sporysz"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/b18fb476da186e8547d0a818bc28b73974d4b3dd d587ef35119c49f1959101b3896629d71947a0ecThe digital revolution has created great opportunities in the world of education, one of which is using Artificial Intelligence (AI). This text reviews the role and challenges of AI in innovation in developing Indonesian language learning curricula in the future. AI has the ability to improve the quality of learning through personalizing the learning process, developing adaptive materials, automatic assessments, and technology-based interactions such as chatbots and educational applications. By utilizing natural language processing (NLP) technology, AI can help students master grammar, understand text, and improve communication skills in a more effective and efficient way. However, implementing AI in the curriculum also faces challenges. The main obstacles include gaps in access to technology, low digital literacy among teachers, and ethical issues related to student data privacy. Apart from that, AI development must also pay attention to preserving cultural values ??and the uniqueness of the Indonesian language, including the diversity of regional languages. This text emphasizes the importance of collaboration between government, educational institutions, and the technology industry to overcome these challenges. Through this study, it is hoped that various strategies can be designed to optimize the use of AI in Indonesian language learning that is inclusive, relevant and sustainable. This text also provides suggestions for the development of technology-based educational policies and practices that take into account technical, ethical and cultural aspects. ABSTRAKRevolusi digital telah menciptakan peluang besar di dunia pendidikan, salah satunya menggunakan Kecerdasan Buatan (AI). Teks ini mengulas peran dan tantangan AI dalam inovasi pengembangan kurikulum pembelajaran Bahasa Indonesia di masa yang akan datang. AI memiliki kemampuan untuk meningkatkan mutu pembelajaran lewat personalisasi proses belajar, pengembangan materi yang adaptif, penilaian otomatis, dan interaksi berbasis teknologi seperti chatbot dan aplikasi edukasi. Dengan memanfaatkan teknologi pemrosesan bahasa alami (NLP), AI dapat membantu siswa dalam menguasai tata bahasa, memahami teks, serta meningkatkan keterampilan komunikasi dengan cara yang lebih efektif dan efisien. Namun, penerapan AI dalam kurikulum juga menghadapi tantangan. Kendala utama mencakup kesenjangan akses terhadap teknologi, rendahnya literasi digital di antara guru, serta isu etika yang berkaitan dengan privasi data siswa. Selain itu, pengembangan AI juga harus memperhatikan pelestarian nilai-nilai budaya serta keunikan bahasa Indonesia, termasuk keragaman bahasa daerah. Teks ini menekankan pentingnya kolaborasi antara pemerintah, lembaga pendidikan, dan industri teknologi untuk mengatasi berbagai tantangan tersebut. Melalui kajian ini, diharapkan bahwa berbagai strategi dapat dirancang guna mengoptimalkan pemanfaatan AI dalam pembelajaran Bahasa Indonesia yang inklusif, relevan, dan berkelanjutan. Teks ini juga memberikan saran untuk pengembangan kebijakan dan praktik pendidikan berbasis teknologi yang memperhitungkan aspek teknis, etis, dan budaya.LEARNING : Jurnal Inovasi Penelitian Pendidikan dan Pembelajaran00LEARNING : Jurnal Inovasi Penelitian Pendidikan dan Pembelajaran["I. Budi", "I. B. Putrayasa", "Nmr Wisudariani", "I. N. Sudiana"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/d587ef35119c49f1959101b3896629d71947a0ec 640d8bf67a3acc3edc4e60d095929fdca4cc9e04One of the critical goals of organization is the creation of a sustainable workplace with the aim to balance economic growth with environmental stewardship and social well-being. The combination of Artificial Intelligence and practices of Green Human Resource Management is offering a revolutionary pathway for achievement of this goal. Artificial Intelligence tools like machine learning, predictive analysis, and automation are enabling organizations optimized use of resources, reducing environmental footprints, and enhancing productivity of employees. Simultaneously, practices of Green Human Resource Management are promoting sustainability by its eco-friendly policies, green trainings, and employee engagement activities in environmentally conscious creativities. Workplace AI assists organizations in improving their operational efficiencies that help faster their decision-making, and inventive products and services. While there is a plenty of information available about how Artificial Intelligence provide value to workplace. Policy makers all over the globe are researching on how these rapidly developing AI technologies and their adoption at the workplace is increasing. A sample of 243 was collected from employees of different organization. The factors that identify how AI and Green HRM create sustainable workplace are AI for Sustainable Decision-Making, Green HRM Practices Enhanced by AI, Sustainable Talent Retention, and AI in Green Training and Development.Journal of Informatics Education and Research00The factors that identify how AI and Green HRM create sustainable workplace are AI for Sustainable Decision-Making, Green HRM Practices Enhanced by AI, Sustainable Talent Retention, and AI in Green Training and Development.Journal of Informatics Education and Research["Divyanshu Pandey"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/640d8bf67a3acc3edc4e60d095929fdca4cc9e04 0aea1115da54672354b6eef19017ec1cdb7b51dePurposeThe purpose of this study is to examine the effects of artificial intelligence (AI) on the identity formation processes of individuals and organizations. Within the framework of symbolic interactionism and looking-glass self-theories, it is investigated how AI transforms social interactions and identity perceptions. The study aims to understand how AI reshapes individuals’ self-perception in the organizational context and to provide a theoretical explanation of these processes.Design/methodology/approachThis article uses qualitative research and a grounded theory approach to examine the effects of artificial intelligence on individual and organizational identity. Data obtained through literature review and thematic analysis are analyzed to theoretically explain the effects of artificial intelligence on identity formation processes. With the grounded theory method, new theoretical implications are presented regarding the effects of artificial intelligence on identity and social roles.FindingsAI reshapes individual and organizational identities by automating routine tasks and providing rapid feedback, which enhances self-perception and collective identity while potentially introducing identity threats or development opportunities depending on task alignment.Originality/valueThis article provides a novel perspective by integrating symbolic interactionism and the looking-glass self-theories with AI interactions, offering fresh insights into how AI affects identity construction in both individuals and organizations. It uniquely examines the dynamic influence of AI on self-perception and organizational identity, contributing to the understanding of AI’s role in identity reconfiguration and the cognitive processes behind it.Kybernetes1010This article uniquely examines the dynamic influence of AI on self-perception and organizational identity, contributing to the understanding of AI’s role in identity reconfiguration and the cognitive processes behind it.Kybernetes["Asl\u0131han Canbul Yaro\u011flu"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/0aea1115da54672354b6eef19017ec1cdb7b51de 3ae4281f2deeef143aeb771847705f0da5fd8cd1This study presents a systematic literature review analysing the impact of artificial intelligence (AI) on higher education, focusing on its methods, results, and implications. By synthesising a diverse range of academic papers, the review explores how AI technologies influence educational standards and practices in higher education institutions. Findings reveal that AI has the potential to enhance the quality of higher education by diversifying teaching responsibilities, customising learning experiences, and employing intelligent, adaptive teaching strategies. These capabilities position AI as a transformative tool for improving educational delivery and outcomes. However, the study also highlights significant challenges associated with integrating AI into higher education. These challenges include delineating the appropriate scope of AI use, addressing inequalities in access to digital resources, and ensuring adequate training and support for educators and students. The review underscores the importance of understanding these complexities to guide the development of effective strategies and policies that optimise AI's potential while mitigating its limitations. The review offers critical insights into the dual role of AI in higher education, where it can either advance or hinder educational standards depending on how it is implemented. By examining the advantages, limitations, and broader consequences of AI-powered instructional tools, this study provides a comprehensive perspective on the intricate relationship between AI and educational quality. The findings aim to inform educators, policymakers, and stakeholders about the opportunities and challenges of adopting AI in higher education, contributing to the development of inclusive, innovative, and sustainable educational practices.International Journal of Research In Business and Social Science370Findings reveal that AI has the potential to enhance the quality of higher education by diversifying teaching responsibilities, customising learning experiences, and employing intelligent, adaptive teaching strategies, which position AI as a transformative tool for improving educational delivery and outcomes.International Journal of Research in Business and Social Science (2147- 4478)["O. A. Ajani", "Morakinyo Akintolu", "Sunday Oluwafemi Afolabi"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/3ae4281f2deeef143aeb771847705f0da5fd8cd1 2d964d26bde42ca53650724b4bdfd23fd33952d6In recent years, much research has been dedicated to uncovering the environmental impact of Artificial Intelligence (AI), showing that training and deploying AI systems require large amounts of energy and resources, and the outcomes of AI may lead to decisions and actions that may negatively impact the environment. This new knowledge raises new ethical questions, such as: When is it (un)justifiable to develop an AI system, and how to make design choices, considering its environmental impact? However, so far, the environmental impact of AI has largely escaped ethical scrutiny, as AI ethics tends to focus strongly on themes such as transparency, privacy, safety, responsibility, and bias. Considering the environmental impact of AI from an ethical perspective expands the scope of AI ethics beyond an anthropocentric focus towards including more-than-human actors such as animals and ecosystems. This paper explores the ethical implications of the environmental impact of AI for designing AI systems by drawing on environmental justice literature, in which three categories of justice are distinguished, referring to three elements that can be unjust: the distribution of benefits and burdens (distributive justice), decision-making procedures (procedural justice), and institutionalized social norms (justice as recognition). Based on these tenets of justice, we outline criteria for developing environmentally just AI systems, given their ecological impact.00Criteria for developing environmentally just AI systems, given their ecological impact are outlined, based on environmental justice literature, in which three categories of justice are distinguished, referring to three elements that can be unjust.["Nynke van Uffelen", "Lode Lauwaert", "Mark Coeckelbergh", "O. Kudina"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/2d964d26bde42ca53650724b4bdfd23fd33952d6 99e54332afb9af92e3041f49cf40f15866dd8706Algorithms and artificial intelligence (AI) tools are crucial for digital literacy and competitiveness in today’s high-tech environment, transforming jobs across various sectors, including the media. The history of journalism is closely linked with technological development; from Gutenberg’s printing press to radio, television, the internet and AI tools. This paper aims to determine the perception of AI in the Croatian media and its alignment with Croatian journalists’ opinions, as well as assess ChatGPT’s effectiveness in sentiment analysis. The insights gained should improve the understanding of the impact of AI on public opinion and its ethical implications for journalism. The research methodology combined AI-driven sentiment analysis (ChatGPT Plus) with qualitative content analysis. A total of 45 articles about AI published in the Croatian media between April and September 2023 were evaluated. Two hypotheses were tested: H1, that ChatGPT’s sentiment analysis matches human assessment, and H2, that the Croatian media generally express a positive view of AI. The results confirmed H1, with ChatGPT’s sentiment analysis corresponding to human assessment in 44 out of 45 cases. H2 was partially corroborated; sentiment distribution pointed to neutral (42%), positive (36%), and negative (22%) views of AI. Positive articles highlighted the benefits of AI in areas such as healthcare, while negative articles raised privacy and employment concerns. The results point to a balanced perspective on AI in the Croatian media, recognising advantages and risks alike. Future research can expand on these findings by examining long term sentiment trends in domestic media and comparing them with global trends. Improved AI tools for independent data collection and more accurate sentiment interpretation can further support this endeavour. Additionally, this paper paves the way for further sentiment analysis of media articles, exploring the implications of AI application on readers’ perception and reception of information.Medijska Istraživanja410The results point to a balanced perspective on AI in the Croatian media, recognising advantages and risks alike, and paves the way for further sentiment analysis of media articles, exploring the implications of AI application on readers’ perception and reception of information.Medijska istraživanja["Ivana Erceg Matija\u0161evi\u0107", "Martina Bari\u010devi\u0107 Debelec", "Ljerka Lui\u0107"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/99e54332afb9af92e3041f49cf40f15866dd8706 86e2cbeac81b21b5b1f7a326146fe6cdbb7179e3The article analyses scientific sources on teacher training in terms of the use of artificial intelligence in the educational process. The concept of 'artificial intelligence' has been clarified and presented as "computer science" which deals with formalized problems and tasks similar to the actions performed by a person. The advantages (quick access to information) and disadvantages (relevant and irrelevant answers; reliable and unreliable answers; insufficiently complete information; the context cannot always be understood) that accompany the use of artificial intelligence are identified. A number of authors describe useful digital artificial intelligence tools for primary school teachers and classify them according to the nature of their teaching activities. It is noted that personalization of education (the educational process can be adapted to each individual student), efficiency and productivity (spending more time interacting with students and supporting them), accessibility and distance learning (access to education in remote regions), interactivity and lifelong learning (combination of virtual reality and artificial intelligence) are of particular importance in the context of war, limited access to education and significant educational losses. As part of their professional activities, primary school teachers can use various artificial intelligence tools, including the following: brainstorming, technical support (introduction of digital tools, use of integrated learning - using language models (ChatGPT, Bing Ai, Perplexity, Bard, Claude), administrative tasks; communication with participants in the educational process, assessment (automatic assessment), creation of educational materials (preparing lessons, creating notes, presentations, tests, using tools for writing and checking texts (Grammarly)), research on specific topics, assistance in supporting teacher training (Futurepedia, virtual laboratories). It has been determined that the use of artificial intelligence in the professional activities of teachers opens up many new opportunities for improving the teaching and learning process based on individual needs of each student. It is concluded that artificial intelligence has become a tool of everyday use that has penetrated into our lives and the lives of our children, which makes education better and brings it to a new and higher level.Scientific journal of Khortytsia National Academy60It has been determined that the use of artificial intelligence in the professional activities of teachers opens up many new opportunities for improving the teaching and learning process based on individual needs of each student.Scientific journal of Khortytsia National Academy["Anna Klieba", "Lydmila Chetaieva", "\u041elena Vovkushevska"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/86e2cbeac81b21b5b1f7a326146fe6cdbb7179e3 39b0e4fa00998447645469a886a160462261de1fArtificial intelligence has become an integral part of higher education, significantly transforming the landscape of higher education. This study aims to identify, analyse and visualise peer-reviewed academic research output on artificial intelligence (AI) and graduate attributes in higher education. Data was gathered from the Scopus database over a decade (2014-2024), with search terms related to artificial intelligence, graduate attributes, and higher education. Following the PRISMA method guidelines, 106 articles were deemed necessary for review. Bibliometric methods, content and thematic analysis were used to identify main themes, and VoSviewer software was used to analyse the data. The findings revealed research productivity, citation overview, the main subjects, the territory of the leading researchers, thematic choices and future research opportunities and directions. Themes such as the impacts of AI on graduate attributes emerged, which may assist policymakers, educational institutions, teachers and students in their strategies and choices for adopting and using AI. The study recognised research trends, provided insights into the current state of AI and higher education research, and identified potential gaps in the literature on the research landscape of AI, graduate attributes, and higher education. The study can guide future researchers on emerging thematic opportunities.International Journal of Research In Business and Social Science370The findings revealed research productivity, citation overview, the main subjects, the territory of the leading researchers, thematic choices and future research opportunities and directions, which may assist policymakers, educational institutions, teachers and students in their strategies and choices for adopting and using AI.International Journal of Research in Business and Social Science (2147- 4478)["Mavis Chamboko-Mpotaringa", "Blandina Manditereza"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/39b0e4fa00998447645469a886a160462261de1f 0e123e010ca9317400656359e4fe9f115c30f373This study examines the widespread impact of artificial intelligence (AI) in the financial sector, looking into the various ways it affects the sector. The study includes a thorough examination of AI applications with an emphasis on how it can revolutionize operational procedures, paradigms for making decisions, and the general direction of the financial industry. The main goal is to analyze the various ways artificial intelligence is being used in finance, from algorithmic trading and customer support to risk management and fraud detection. The research attempts to give a clear picture of how AI technologies are changing conventional practices and enhancing the capabilities of financial institutions by exploring particular use cases and implementations. The paper also examines the complex role that AI plays in financial sector decision-making. This entails a thorough analysis of how it affects credit scoring, investment strategies, and risk assessment. The goal of the study is to outline how AI affects decision-making procedures, explaining the benefits of incorporating cutting-edge technologies into well-established financial frameworks. The study also considers the future, evaluating AI's potential advancement in the financial sector. The study provides insights into how artificial intelligence (AI) is likely to develop and influence the financial landscape in the future by anticipating technological trends, regulatory influences, and potential obstacles. All things considered, this study provides a thorough and perceptive examination of the effects of AI in finance, providing insightful information for stakeholders, legislators, and business professionals negotiating the ever-changing intersection of financial services and artificial intelligence. KEYWORDS: Fintech, machine learning, algorithmic trading, artificial intelligence, finance, and customer serviceINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The study provides insights into how artificial intelligence (AI) is likely to develop and influence the financial landscape in the future by anticipating technological trends, regulatory influences, and potential obstacles.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Kesawaraj P.K"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/0e123e010ca9317400656359e4fe9f115c30f373 ab7af63bdf82269bdb07992af0b7b9d13c031a13In the article, the authors paid attention to the role of artificial intelligence in increasing economic security in a competitive environment. They considered the use of artificial intelligence in solving economic problems aimed at reducing costs and optimal production of a business enterprise, summarizing the importance of this aspect for ensuring the economic security of commercial organizations.The Economy under Guard00The use of artificial intelligence in solving economic problems aimed at reducing costs and optimal production of a business enterprise is considered, summarizing the importance of this aspect for ensuring the economic security of commercial organizations.The Economy under Guard["Georgiy Brikach", "A. Strokov"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/ab7af63bdf82269bdb07992af0b7b9d13c031a13 9563b03118881938a2335019bec62c8f5ed4d04fArtificial intelligence (AI) has been put forth as a technological innovation which can change the way in which healthcare will be delivered in the near future. AI developers plan to deploy tools that will aid diagnosis, improve therapy, minimize errors, increase safety, and optimize systems and bring down costs. In addition, a paradigmatic shift in the scientific method has been suggested where causation will be inferred by empirical methods from data. A more cautious view suggests that the epistemic basis of AI and the predictions based on AI need independent external validation prior to widespread use. Whether the aim of the AI developers and the society it serves are aligned is difficult to ascertain as there exists a technological and information asymmetry between the AI developers and public stakeholders.Higher Education for the Future60Artificial intelligence has been put forth as a technological innovation which can change the way in which healthcare will be delivered in the near future, but whether the aim of the AI developers and the society it serves are aligned is difficult to ascertain.Higher Education for the Future["S. Sampath"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/9563b03118881938a2335019bec62c8f5ed4d04f 7d87c2889348ff8238bf5f4b5923ffae90b570fcThe study aims to explore the extent to which Jordanian e-news sites rely on artificial intelligence applications in their news content. The researchers will use a media survey methodology, and the sample will consist of 45 editors-in-chief and editors from 10 Jordanian news sites, namely: Ammon, Khabrny, Joe24, Saraya, Amman Net, Jafra, Crown News, Petra, Kingdom, and Roya. The researcher will use an electronic questionnaire, which led to several findings, the most significant of which are: Many news and media sites have introduced artificial intelligence systems to enhance the services they provide to the public. A significant number of journalistic and electronic media websites have shown interest in data analysis tools for their media services. Electronic news sites are clearly striving to improve their capabilities in using artificial intelligence technologies to enhance the services they provide to the Jordanian audience. Additionally, most electronic media websites have expressed a willingness to develop a plan to improve cybersecurity systems to protect against hacking and intrusion attempts, safeguarding their data and the AI systems that operate continuously.AI systems in media organizations also aim to enhance the news experience for users by enriching media services with modern, communicative content.Journal of Infrastructure Policy and Development390Findings are that many news and media sites have introduced artificial intelligence systems to enhance the services they provide to the public and most electronic media websites have expressed a willingness to develop a plan to improve cybersecurity systems to protect against hacking and intrusion attempts.Journal of Infrastructure, Policy and Development["Hala Amr", "Mohamad Abu Halka", "Ruba Mohd"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/7d87c2889348ff8238bf5f4b5923ffae90b570fc b8a2506c79cfa6bda1188207568eba03055b6073With the rapid progress of science and technology and the booming economy, artificial intelligence technology has been widely used in all walks of life, and its application in the field of enterprise financial management has also become increasingly widespread, greatly changing the way of financial work. However, many enterprises still have a series of problems in the process of promoting the construction of information technology. This paper takes artificial intelligence as the background, firstly, analyzes the various preconditions for its application in financial management, such as the need for enterprises to have a perfect data system, sufficient technical basis and relevant talents, etc., secondly, explores the impact mechanism of artificial intelligence on the financial management of the enterprise, and carries out research in the three aspects of improving the quality of information, enhancing the efficiency of management and assisting in decision-making, and finally, from the perspective of the game theory, analyzes the internal top management team, business department and IT department of the enterprise. Finally, from the perspective of game theory, This study analyses the relationships between AI and humans, identify the risks that may be brought about by AI's participation in financial management, and finally explore how human beings can collaborate and cooperate with AI to form a stronger intelligent assisted decision-making.Advances in Economics, Management and Political Sciences00The relationships between AI and humans are analyzed, the risks that may be brought about by AI's participation in financial management are identified, and how human beings can collaborate and cooperate with AI to form a stronger intelligent assisted decision-making is explored.Advances in Economics, Management and Political Sciences["Tianruo Liu"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/b8a2506c79cfa6bda1188207568eba03055b6073 7dad1d5cb275314c783ee3a097118ec473f2bd92Artificial Intelligence (AI), Blockchain, and Financial Technology (FinTech) are reshaping how financial systems work, making them more secure, efficient, and accessible. AI improves decision-making and risk assessment, while Blockchain ensures secure, tamper-proof transactions. Together with FinTech, they are building a financial ecosystem that is faster, smarter, and more inclusive. This paper explores how these technologies work together to solve key challenges in the financial world. It looks at how they can reduce barriers to access, promote economic inclusion, and create new possibilities for innovation and growth. These changes are particularly important as digital solutions become essential to meeting the needs of a fast-moving global economy. Understanding the impact of this technological integration has far-reaching implications. Better supportive regulations and tools to improve efficiency and expand access are a few of the applications of these technologies. For society, it means financial systems that are more equitable and inclusive. This paper highlights how the fusion of AI, Blockchain, and FinTech is not just transforming finance but also helping shape a future where financial opportunities are open to all. Sample of 207 people from different fintech organization were surveyed to explore the factors that shows different Role of Artificial Intelligence and Blockchain in Transforming the Operations of Fintech Organisations and found that Customer Experience, Fraud Detection and Prevention, Payments and Transactions and Audit and Compliance are the factors showing different Role of Artificial Intelligence and Blockchain in Transforming the Operations of Fintech Organisations.Journal of Informatics Education and Research00How the fusion of AI, Blockchain, and FinTech is not just transforming finance but also helping shape a future where financial opportunities are open to all is highlighted.Journal of Informatics Education and Research["Nirupama Mohanty"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/7dad1d5cb275314c783ee3a097118ec473f2bd92 2f1b5492a34f30f6c4f71c2fc45f7bda07c31ae9This study conducted a systematic literature review to examine the trajectory of AI research over the past five years, from 2019 to 2023, focusing on emerging ethical and social concerns related to the deployment of AI technologies. The study also aimed at enhancing the understanding and promotion of robust AI ethics for societal benefit. The explosive rise of the internet, AI, and mobile technology has dramatically changed how we live, work, consume, learn, and communicate. AI is improving the quality of human life but poses dangers from unintended disastrous and undesirable outcomes, if unregulated. Cyberattacks on critical infrastructure networks pose grave threats, exponentially increasing risks of fatalities and service breakdowns. AI can instantly diagnose rare diseases, robots can perform precision surgeries and chatbots can write assignments for students. AI is also used for surveillance, monitoring financial activities and autonomous weapon systems in the military. Two hundred and twenty-five publications from Scopus database were selected to determine the central themes, the affordances and constraints of AI and principles that enhance public trust and accountability. Results show an upward trajectory in AI ethics research from 6.2% in 2019 to 40.3% in 2023. Furthermore, results revealed the emerging ethical and social concerns in major socioeconomic domains. Results also show that AI collects data about individuals and data breaches have catastrophic consequences. The growing complexity and opacity of AI systems make it hard to understand decision-making, hindering accountability for developers and deployers. AI algorithms may be biased against minorities; perpetuating prejudices. The study contributes to the ongoing discourse on the ethical and societal concerns surrounding unregulated AI adoption. The issues identified in this study may assist policymakers in developing frameworks and policies for AI usage.AI Computer Science and Robotics Technology381A systematic literature review to examine the trajectory of AI research over the past five years, from 2019 to 2023, focusing on emerging ethical and social concerns related to the deployment of AI technologies, revealed the emerging ethical and social concerns in major socioeconomic domains.AI, Computer Science and Robotics Technology["V. Maphosa"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/2f1b5492a34f30f6c4f71c2fc45f7bda07c31ae9 5b64b0ddde0a71c5b55abf5cca94e3910ebaa2eeFew studies have investigated students’ perceptions towards using intelligence & interactive virtual massive open online course (IMOOCs) although IMOOCs is an adaptive technique to resolve the massive open online course problems. This study aims to investigate the influence of perceived usefulness, perceived ease of use, attitude, subjective norm, and perceived behavioural control on intention to use IMOOCs based on the technology acceptance model and theory of planned behaviour. A total of 216 students were recruited as respondents and further tested the hypotheses proposed based on PLS-SEM. The results show that perceived usefulness positively influences attitude, and perceived ease of use positively influences perceived ease of use and, subsequently attitude. Meanwhile, attitude, subjective norm, and perceived behavioural control positively influence intention towards using IMOOCs. Lastly, theoretical and practical implications as well as limitations are discussed accordingly.International Journal of Education, Science, Technology, and Engineering440The results show that perceived usefulness positively influences attitude, and perceived ease of use positively influences perceived ease of use and, subsequently attitude, and attitude, subjective norm, and perceived behavioural control positively influence intention towards using IMOOCs.International Journal of Education, Science, Technology, and Engineering (IJESTE)["Lei Wang", "Qi Zhang", "Yue Gong"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/5b64b0ddde0a71c5b55abf5cca94e3910ebaa2ee 3bb752dff7afb23d5c377427d260d7e4d7b1a998Cyprus Turkish Journal of Psychiatry &amp; Psychology00Cyprus Turkish Journal of Psychiatry &amp; Psychology[]2024-12-19T00:00:00https://www.semanticscholar.org/paper/3bb752dff7afb23d5c377427d260d7e4d7b1a998 297fd85671ce7f024275a460dda220c14d0dbb5bThe economic satisfaction of a nation can reflect citizens' perceptions of their government's performance, and machine learning models can help uncover non-trivial information from such data. In this context, this article aimed to analyze the satisfaction of Latin American citizens with their country's economy. To achieve this, six traditional classifier algorithms and four ensemble models were used, with a final application of an explainable method (SHapley Additive exPlanations, SHAP) to analyze the key factors contributing to economic satisfaction. The models were trained and tested on a dataset comprising data from the 2020 and 2023 Latinobarómetro surveys, totaling 27,600 instances in the final set. As a result, it was found that the Random Forest was the best individual model, while the stacking ensemble achieved the best performance in classifying between “satisfied” and “dissatisfied” citizens. The SHAP method revealed that “satisfaction with democracy” and “perception of the country's progress” are the main factors influencing economic satisfaction. This study offers insights for public managers on how to improve their citizens' economic satisfaction.Navus: Revista de Gestão e Tecnologia20The SHAP method revealed that “satisfaction with democracy” and “perception of the country's progress” are the main factors influencing economic satisfaction, offering insights for public managers on how to improve their citizens' economic satisfaction.Navus - Revista de Gestão e Tecnologia["Luiz Fernando Menegazzo Ferreyra", "Yasser Bulaty Tauil", "Helton Messias Adigneri", "Bruno Samways dos Santos", "R. Lima"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/297fd85671ce7f024275a460dda220c14d0dbb5b fca4c84572b4efd56a73ddca6bc3b84338619325Incorporating AI into public policy has the potential to change how decisions are made, whether in the fields of public health, resource management, or social welfare. This article examines the use of AI in DSS, a public policy decision support system, its benefits and disadvantages, as well as the ethics and privacy implications. By analyzing health, welfare and urban planning case studies, this report examines the efficacy of AI on measures such as precision, effectiveness, equity and engagement. In addition, the work deals with key obstacles to AI integration, such as infrastructure constraints, biases and public mistrust, that affect its acceptability and utility. This research suggests that, while AI has significant benefits, its responsible application to public policy requires a carefully calibrated strategy with an emphasis on ethical transparency, transparency and safe data collection in order to ensure public confidence and equitable results.Applied and Computational Engineering00It is suggested that, while AI has significant benefits, its responsible application to public policy requires a carefully calibrated strategy with an emphasis on ethical transparency, transparency and safe data collection in order to ensure public confidence and equitable results.Applied and Computational Engineering["Xinyu Zhang"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/fca4c84572b4efd56a73ddca6bc3b84338619325 fd0e5434b51f2ef23396651c4985e89894f9c391JOURNAL OF POPULATION THERAPEUTICS AND CLINICAL PHARMACPLOGY00JOURNAL OF POPULATION THERAPEUTICS AND CLINICAL PHARMACPLOGY["Dr. Sridevi Garapati", "Dr. Sujatha Peetala", "Dr. Rambabu Rampatruni", "Swarnalata Swarnalata"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/fd0e5434b51f2ef23396651c4985e89894f9c391 eec41d288cdb617807ddbd04611ff64e999a68cdLeadership and Policy in Schools640Leadership and Policy in Schools["Soheil S. Salha", "Allam Mousa", "Saed Khayat"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/eec41d288cdb617807ddbd04611ff64e999a68cd c972f3574521781d3ebe1fe394836077f1f3cee8Balancing fairness and predictive accuracy remains a key challenge in AI system development. This study investigates the origins of AI bias, how it happens in business processes, and the challenges it poses to ethical and transparent decision-making. Drawing on existing literature, the research explores the various types of biases—including cognitive, algorithmic, and representation biases—and their impact on AI systems in the BFSI sector. Furthermore, the study critically evaluates current debiasing techniques, such as pre-processing, fairness-aware models, and post-processing, highlighting their limitations in balancing fairness with predictive accuracy. This study aims to advance the development of more equitable AI systems in the BFSI sector by proposing the FAIR-BIAS Framework. This framework provides a structured approach to detecting, mitigating, and monitoring biases in AI models. Key recommendations include implementing equalized odds as a fairness metric to ensure balanced outcomes across demographic groups, applying adversarial debiasing techniques during model training to minimize discriminatory effects, and conducting regular data audits to ensure long-term fairness. The findings offer direct benefits for BFSI stakeholders. Businesses can enhance the reliability and ethical integrity of AI models by adopting fairness-aware risk assessments, which promote compliance and customer trust. Regulators can enforce accountability by mandating transparency measures, such as model explainability, and conducting periodic audits using fairness metrics like equalized odds. Policymakers can use the insights to create inclusive legislation which requires fairness testing and transparency in AI applications. Future research could explore the long-term effectiveness of debiasing techniques across different industries, such as healthcare or public policy, by conducting longitudinal studies to assess how evolving datasets and models influence fairness outcomes. It is critical for BFSI organizations to adopt these frameworks and techniques to foster a more inclusive and ethical future in financial services.”Asian Journal of Research in Computer Science00Key recommendations include implementing equalized odds as a fairness metric to ensure balanced outcomes across demographic groups, applying adversarial debiasing techniques during model training to minimize discriminatory effects, and conducting regular data audits to ensure long-term fairness.Asian Journal of Research in Computer Science["Oluwatofunnmi O. Oguntibeju"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/c972f3574521781d3ebe1fe394836077f1f3cee8 2aca74f1711ddd39b708e45f5f1577e75b47d25eThis systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models.arXiv.org00The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes and emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models.ArXiv["Pir Bakhsh Khokhar", "Carmine Gravino", "Fabio Palomba"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/2aca74f1711ddd39b708e45f5f1577e75b47d25e 9eb4647bee6629914c7890dbeaeaf340153d0f9dJournal of Magnetic Resonance Imaging70Journal of magnetic resonance imaging : JMRI["Stefan J. Fransen"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/9eb4647bee6629914c7890dbeaeaf340153d0f9d 3d99ea9024ec52cbb3199fb61694d964f4fe34edECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH00ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH["Marina\u0219 Laura Elena", "P\u0103un Cristian Valeriu", "Diaconescu Mirela", "Smirna Tudor Gherasim"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/3d99ea9024ec52cbb3199fb61694d964f4fe34ed 4a77d6cbc431ade3ea4a0acb858d1f0b6ef45f2aN/A.Journal of Nepal Health Research Council00Journal of Nepal Health Research Council["Ganesh Dangal", "Ojash Dangal"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/4a77d6cbc431ade3ea4a0acb858d1f0b6ef45f2a 699dd4c41df2efaebb1143461160e4e2cf40ba64International journal on responsibility00International Journal on Responsibility["Tatjana Titareva"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/699dd4c41df2efaebb1143461160e4e2cf40ba64 4faa9de389411c369a05307e0954d50b7048faf7 The fields which are considered to be most primary adopters of AI are financial services, banking and insurance sectors. The implementations of AI technology patent are mostly in the fields of investments, securities, consumer relationships, risk management, market analysis and compliance. AI of today is a collection of lots of algorithms and techniques that are good for variety of applications. Another variant of AI patent that is Artificial Narrow Intelligence (ANI) can perform task which is specific or can perform narrow range of task. ANI models are intelligent enough to perform well define task within a limited domain. ANI model operates within the well-defined parameters. The current study aims to discuss the customers view on adoption of an ANI enabled autonomous intelligent digital financial inclusion system (iDFIS) patent. Total 681 responses were collected from different state of India. The result shows that the factor like convenience, performance efficiency, security, personalization, effort efficiency and social influence factor had a significant positive relationship with the attitude to adopt an autonomous iDFIS system. The three cultural dimension of Hofstede’s theory power distance index, collectivism and uncertainty acceptance also taken as a moderator. The effect of moderators also seen in the different relationships. The finding of the study will be worthy to develop a strategic decision making in finance sector. The goal of finance sector is also focus on new AI innovative technologies to improve overall growth related to all aspect of finance sector specially in the field of customer service. The study proposed the direct hypotheses to analyze the adoption of an autonomous iDFIS system among customers. Recent Patents on Engineering00The result shows that the factor like convenience, performance efficiency, security, personalization, personalization, effort efficiency and social influence factor had a significant positive relationship with the attitude to adopt an autonomous iDFIS system.Recent Patents on Engineering["Ruchira Rawat", "H. Goyal", "Sachin Sharma"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/4faa9de389411c369a05307e0954d50b7048faf7 d49193d185265563d6601e78d1c4dc3075f7e8bfThe rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity strategies, particularly in critical sectors such as healthcare, finance, energy, and transportation. This paper explores the transformative role of AI-powered threat intelligence in revolutionizing cybersecurity practices. By leveraging advanced machine learning algorithms, natural language processing, and predictive analytics, AI-driven systems can detect, analyze, and mitigate threats with unprecedented speed and accuracy. This research highlights the integration of real-time data processing, threat intelligence platforms, and adaptive security frameworks to enable proactive risk management. Case studies and experimental results underscore the effectiveness of AI-powered approaches in anticipating cyberattacks, reducing response times, and minimizing operational disruptions. The findings demonstrate that AI is not merely a tool but a pivotal enabler of robust, adaptive, and scalable cybersecurity strategies in the face of an ever-evolving threat landscape.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402303The findings demonstrate that AI is not merely a tool but a pivotal enabler of robust, adaptive, and scalable cybersecurity strategies in the face of an ever-evolving threat landscape.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["S. M. Islam", "Md Shadikul Bari", "Ankur Sarkar", "A. J. M. O. R. Khan", "Rakesh Paul"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/d49193d185265563d6601e78d1c4dc3075f7e8bf 1c6757f92ce17543a6e8dd82b4586a98ac27ab65This article explores the impact of artificial intelligence (AI) on the production of judgments in the judiciary. The increasing complexity of legal cases and the volume of cases demand efficiency and accuracy, challenges that AI is prepared to address. We will cover how natural language processing and machine learning technologies are being applied to optimize the production of court judgments, providing faster, more accurate, and more efficient analysis.Revista ft00This article explores the impact of artificial intelligence on the production of judgments in the judiciary by covering how natural language processing and machine learning technologies are being applied to optimize the production of court judgments, providing faster, more accurate, and more efficient analysis.Revista ft["Jos\u00e9 C\u00e9lio de Lacerda S\u00e1"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/1c6757f92ce17543a6e8dd82b4586a98ac27ab65 14d533fde253ac9595aa38063503aed43c4e6023npj Digit. Medicine801It is concluded that AI holds enormous potential to support adaptive functioning for people with NDCs and for personalized health support and the need for further research studies to advance AI technologies in this field is underscores.NPJ Digital Medicine["Nina Perry", "Carter Sun", "Martha Munro", "Kelsie A Boulton", "A. Guastella"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/14d533fde253ac9595aa38063503aed43c4e6023 61e814d1bbb94cd61a8f02516e7221045d3928c1In the evolving landscape of cybersecurity, traditional information technology (IT) infrastructures often struggle to meet the demands of modern risk management frameworks, which require enhanced security, scalability, and analytical capabilities. This paper proposes a novel artificial intelligence (AI)–driven IT infrastructure backed by blockchain technology, specifically designed to optimize risk management processes in diverse organizational environments. By leveraging artificial intelligence for predictive analytics, anomaly detection, and data-driven decision-making, combined with blockchain’s secure and immutable ledger for data integrity and transparency, the proposed infrastructure offers a robust solution to existing challenges in risk management. The infrastructure is adaptable and scalable to support a variety of risk management methodologies, providing a more secure, efficient, and intelligent system. The findings highlight significant improvements in the accuracy, speed, and reliability of risk management, underscoring the infrastructure’s capability to proactively address emerging cyber threats. To ensure the proposed model effectively addresses the most critical issues, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique will be used to analyze and evaluate the interrelationships among the existing critical factors. This approach evaluates the interrelationships and impacts of these factors, verifying the model’s comprehensiveness in managing organizational risk. This study lays the foundation for future research aimed at refining AI-driven infrastructures and exploring their broader applications in enhancing organizational cybersecurity.Risks321A novel artificial intelligence (AI)–driven IT infrastructure backed by blockchain technology, specifically designed to optimize risk management processes in diverse organizational environments, is proposed, laying the foundation for future research aimed at refining AI-driven infrastructures and exploring their broader applications in enhancing organizational cybersecurity.Risks["Mir Mehedi Rahman", "B. Pokharel", "Sayed Abu Sayeed", "S. K. Bhowmik", "Naresh Kshetri", "Nafiz Eashrak"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/61e814d1bbb94cd61a8f02516e7221045d3928c1 fcf9fc818b8e54915ae9c990a33bdecd3a40651aThe rapid innovation of Artificial Intelligence (AI) has transformed various sectors of society by revolutionising decision making and enhancing efficiency through novel data-driven technologies. This paper explores the challenges of striking a healthy balance between future AI innovation and personal data privacy, where the massive collection and utilisation of personal data have given rise to significant privacy concerns. The study identifies the risks of massive data collection, complex and opaque algorithms, and cybersecurity threats, while simultaneously highlighting the existing legal frameworks such as General Data Protection Regulation (GDPR) and the variations among global approaches to data privacy. The paper also discusses the technical solutions such as privacy-preserving techniques including differential privacy and federated learning, as well as encryption technologies that can facilitate the secure storage and transmission of data. The research proposes strategies for building privacy-preserving AI models and encouraging cross-industry collaboration to achieve a balance between innovation and the protection of individual privacy. It also adds to the ongoing discourse on shaping a responsible future for AI.Applied and Computational Engineering00The research proposes strategies for building privacy-preserving AI models and encouraging cross-industry collaboration to achieve a balance between innovation and the protection of individual privacy.Applied and Computational Engineering["Huilian Xiao", "Jiaxuan Li"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/fcf9fc818b8e54915ae9c990a33bdecd3a40651a 2bdbe73697fb523531c90ddb27ef720faa2d1238Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Explainable AI (XAI). Unfortunately, despite a decade of development in XAI, an existential challenge remains: progress in research has not been fully translated into the actual implementation of algorithmic transparency by organizations. In this work, we test an approach for addressing the challenge by creating transparency advocates, or motivated individuals within organizations who drive a ground-up cultural shift towards improved algorithmic transparency. Over several years, we created an open-source educational workshop on algorithmic transparency and advocacy. We delivered the workshop to professionals across two separate domains to improve their algorithmic transparency literacy and willingness to advocate for change. In the weeks following the workshop, participants applied what they learned, such as speaking up for algorithmic transparency at an organization-wide AI strategy meeting. We also make two broader observations: first, advocacy is not a monolith and can be broken down into different levels. Second, individuals' willingness for advocacy is affected by their professional field. For example, news and media professionals may be more likely to advocate for algorithmic transparency than those working at technology start-ups.arXiv.org400This work creates an open-source educational workshop on algorithmic transparency and advocacy, and delivers the workshop to professionals across two separate domains to improve their algorithmic transparency literacy and willingness to advocate for change.ArXiv["Andrew Bell", "Julia Stoyanovich"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/2bdbe73697fb523531c90ddb27ef720faa2d1238 4e1faac0a1db13fe5dc17a8bf586ae17b33da343This paper investigates the impacts of the rapidly evolving landscape of generative Artificial Intelligence (AI) development. Emphasis is given to how organizations grapple with a critical imperative: reevaluating their policies regarding AI usage in the workplace. As AI technologies advance, ethical considerations, transparency, data privacy, and their impact on human labor intersect with the drive for innovation and efficiency. Our research explores publicly accessible large language models (LLMs) that often operate on the periphery, away from mainstream scrutiny. These lesser-known models have received limited scholarly analysis and may lack comprehensive restrictions and safeguards. Specifically, we examine Gab AI, a platform that centers around unrestricted communication and privacy, allowing users to interact freely without censorship. Generative AI chatbots are increasingly prevalent, but cybersecurity risks have also escalated. Organizations must carefully navigate this evolving landscape by implementing transparent AI usage policies. Frequent training and policy updates are essential to adapt to emerging threats. Insider threats, whether malicious or unwitting, continue to pose one of the most significant cybersecurity challenges in the workplace. Our research is on the lesser-known publicly accessible LLMs and their implications for workplace policies. We contribute to the ongoing discourse on AI ethics, transparency, and security by emphasizing the need for well-thought-out guidelines and vigilance in policy maintenance.00This paper explores publicly accessible large language models (LLMs) that often operate on the periphery, away from mainstream scrutiny, and examines Gab AI, a platform that centers around unrestricted communication and privacy, allowing users to interact freely without censorship.["Lakshika Vaishnav", "Sakshi Singh", "Kimberly A. Cornell"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/4e1faac0a1db13fe5dc17a8bf586ae17b33da343 89a31851166da7ca743c9ca6d4df6402539eb9b1The purpose of this article is to consider whether caring can be designed into an artificial intelligence system. Caring is complex. In our daily lives, caring takes several forms and occurs in various ways. This article discusses human caring and how caring has been a vital aspect of our lives since our early ancestors. The research focuses on the form of care a healthcare worker may provide in a clinic or hospital. The research considers whether this attentive form of caring can be designed into AI systems. The approach of this research is to consider a specific AI design known as the LIDA model. The research describes the cognitive cycle and the global workspace within the LIDA model. It also depicts elements in the LIDA model that can be associated with caring. The findings show that caring can occur through gestures and movements. The findings also show that a LIDA agent can perform such gestures and movements and offer an appearance of caring. The findings suggest that a LIDA agent, configured in a particular way, could be a carer in some caring situations. The practical benefit of this research is to show that the LIDA model can be a starting point for designing care in AI systems. Through this research, we may uncover elements of caring that already exist within the LIDA model and can be employed in a caring AI agent.World Scientific Research260The research describes the cognitive cycle and the global workspace within the LIDA model and depicts elements in the LIDA model that can be associated with caring and shows that caring can occur through gestures and movements.World Scientific Research["Suereth Russell"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/89a31851166da7ca743c9ca6d4df6402539eb9b1 29eaba57407a1238bb2bd2a4049d39d82aaa0e59Artificial Intelligence (AI) is revolutionizing healthcare, mainly in diagnostics and most cancers treatment, via improving accuracy, performance, and personalized care. Advanced gadget learning algorithms examine medical records, such as imaging scans, pathology slides, and genetic facts, to come across illnesses at in advance stages and predict patient outcomes with extraordinary precision. AI-pushed gear in radiology and pathology aid in figuring out tumors, assessing their aggressiveness, and suggesting potential remedy options. In oncology, AI models are accelerating drug discovery and enabling precision medicinal drug by means of tailoring remedies to person genetic profiles. By integrating AI with scientific workflows, healthcare structures are overcoming diagnostic demanding situations, decreasing human errors, and optimizing assets. While AI affords transformative benefits, it additionally brings moral and regulatory challenges that want to be addressed to ensure secure and effective implementation. This paper explores the modern packages of AI in diagnostics and most cancers remedy, its effect on affected person care, and the destiny path of AI in healthcare innovation.International Research Journal on Advanced Engineering and Management (IRJAEM)00This paper explores the modern packages of AI in diagnostics and most cancers remedy, its effect on affected person care, and the destiny path of AI in healthcare innovation.International Research Journal on Advanced Engineering and Management (IRJAEM)["Resna Ibrahim", "Najma.S", "Anupama Biju", "Jeffin George", "Tintu varghese"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/29eaba57407a1238bb2bd2a4049d39d82aaa0e59 c792413895dc312d35e340ab2284ef3bde3b4b9aThis paper examines the current state of artificial intelligence (AI) adoption in Philippine businesses and industries, analyzing the barriers to adoption and evaluating the government's role in fostering AI-driven industrial development. Through an analysis of various AI readiness indices and case studies, the research finds that while basic digital infrastructure is widespread, with 90.8 percent of establishments having computers and 81 percent having internet access, advanced technology adoption remains limited. Only 14.9 percent of firms use AI technologies, with adoption concentrated in urban areas and larger firms, particularly in the ICT and BPO sectors. The study identifies key barriers including limited digital infrastructure, low awareness of AI technologies, significant skills gaps, and insufficient funding opportunities. Drawing from economic theory and international case studies, the paper outlines three critical domains for government intervention: market facilitation, capability building, and ecosystem coordination. The research proposes policy recommendations focusing on infrastructure development, human capital development, regulatory frameworks, public-private partnerships, and ethical guidelines. These recommendations emphasize the need for coordinated action across government agencies, substantial investment in digital infrastructure and education, and the establishment of clear governance frameworks to ensure responsible AI adoption while fostering innovation and competitiveness in the Philippine business sector.00The study identifies key barriers including limited digital infrastructure, low awareness of AI technologies, significant skills gaps, and insufficient funding opportunities and proposes policy recommendations focusing on infrastructure development, human capital development, regulatory frameworks, public-private partnerships, and ethical guidelines.["Francis Mark Quimba", "Neil Irwin Moreno", "Alliah Mae Salazar"]2024-12-19T00:00:00https://www.semanticscholar.org/paper/c792413895dc312d35e340ab2284ef3bde3b4b9a 3e78f6af64e2e5a6c42f6f3b22e3620eb23a85c0This article examines the experience with and potential application of artificial intelligence (AI) within the Canadian public service. Assessed are the ways in which AI is being applied to internal administration and operations, the bilingual requirements of Canada's federal government, public service delivery, policy analysis and advising, application adjudication, and monitoring and regulatory compliance. The response to date from the federal government on how to guide the use of AI in the public service is assessed, and options and prospects for the future are offered in conclusion.Canadian public administration141The response to date from the federal government on how to guide the use of AI in the public service is assessed, and options and prospects for the future are offered.Canadian Public Administration["Justin Longo"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/3e78f6af64e2e5a6c42f6f3b22e3620eb23a85c0 520acec4d5e9a8adb8c279f063d5259e3fcde890Artificial intelligence (AI) technology is vital for practitioners to incorporate AI and robotics in day-to-day regional anesthesia practice. Recent literature is encouraging on its applications in regional anesthesia, but the data are limited. AI can help us identify and guide the needle tip precisely to the location. This may help us reduce the time, improve precision, and reduce the associated side effects of improper distribution of drugs. In this article, we discuss the potential roles of AI and robotics in regional anesthesia.World Journal of Methodology121The potential roles of AI and robotics in regional anesthesia are discussed, which may help reduce the time, improve precision, and reduce the associated side effects of improper distribution of drugs.World Journal of Methodology["Nitin Choudhary", "Anju Gupta", "N. Gupta"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/520acec4d5e9a8adb8c279f063d5259e3fcde890 33e226e52ce12964f340ad27c70288e850eac3c5OBJECTIVES To audit prospectively the accuracy, time saving and utility of a commercial artificial intelligence auto-contouring tool (AIAC). To assess the reallocation of time released by AIAC. METHODS We audited the perceived usefulness (PU), clinical acceptability and reallocation of time during the introduction of a commercial AIAC. The time from CT to plan completion (PPTT) was audited for several pathways. RESULTS 248 patients and 32 staff were included in this audit. PU increased with exposure to AIAC (p < 0.05). For 80% of sites AIAC was timesaving and AI contours were clinically acceptable after minor edits. Edits had little impact on doses for the majority of cases. Median PPTT reduced by 5.5 (breast) and 9 (prostate) working days (p < 0.01). Radiographers spent more time on other tasks within planning. Oncologists improved their work-life balance and increased time spent on professional development and research by up to 2 hours per week. CONCLUSIONS All users of AIAC found it a useful tool and it improved their productivity. The contours were high quality and needed little editing. It reduced contouring time and reduced PPTT by several days in some cases. The reallocated time was staff group dependent. ADVANCES IN KNOWLEDGE The time released by the use of AIAC can lead to a reduction in the PPTT by up to 9 days. It also improves the work-life balance of oncologists by reducing the time spent out of hours contouring.British Journal of Radiology01All users of AIAC found it a useful tool and it improved their productivity and it improves the work-life balance of oncologists by reducing the time spent out of hours contouring.The British journal of radiology["Keith A Langmack", "Gavin G Alexander", "Joshua Gardiner", "Angela McKenna", "E. Shawcroft"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/33e226e52ce12964f340ad27c70288e850eac3c5 af2c11c27ad8bce7750c0a5a78b8c63699c7b526The integration of Artificial Intelligence (AI) and smart technologies into safety management is a pivotal aspect of the Fourth Industrial Revolution or Industry 4.0. This study conducts a systematic literature review to identify and analyze how AI and smart technologies enhance safety management across various sectors within the Safety 4.0 paradigm. Focusing on peer-reviewed journal articles that explicitly mention “Smart”, “AI”, or “Artificial Intelligence” in their titles, the research examines key safety management factors, such as accident prevention, risk management, real-time monitoring, and ethical implementation, across sectors, including construction, industrial safety, disaster and public safety, transport and logistics, energy and power, health, smart home and living, and other diverse industries. AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. For instance, in the energy and power sector, intelligent gas meters and automated fire suppression systems manage gas-related risks effectively, while in the health sector, AI-powered health monitoring devices and mental health support applications improve patient and worker safety. The analysis reveals a significant trend towards shifting from reactive to proactive safety management, facilitated by the convergence of AI with IoT and Big Data analytics. Additionally, ethical considerations and data privacy emerge as critical challenges in the adoption of AI technologies. The study highlights the transformative role of AI in enhancing safety protocols, reducing accident rates, and improving overall safety outcomes across industries. It underscores the need for standardized protocols, robust AI governance frameworks, and interdisciplinary research to address existing challenges and maximize the benefits of AI in safety management. Future research directions include developing explainable AI models, enhancing human–AI collaboration, and fostering global standardization to ensure the responsible and effective implementation of AI-driven safety solutions.Applied Sciences702The study highlights the transformative role of AI in enhancing safety protocols, reducing accident rates, and improving overall safety outcomes across industries, and underscores the need for standardized protocols, robust AI governance frameworks, and interdisciplinary research to address existing challenges and maximize the benefits of AI in safety management.Applied Sciences["Jiyoung Park", "D. Kang"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/af2c11c27ad8bce7750c0a5a78b8c63699c7b526 b2d750f92fa3e25e6e68e883661720d846524a0aThe generally conceived belief that jobs are at danger of being replaced by AI has sparked concern which has resulted in human intelligence being seen as in conflict with AI systems. However, this perceived rivalry obscures a more profound reality: the integration between human beings and artificial intelligence. For that reason, this study refutes the competition myth and shows that people and AI can work together. Human intelligence is superior in adaptability, feelings and the ability to consider the circumstances, whereas AI is faster, flexible at scale, and analytical. Combinatorial of these capacities gives rise to hybrid intelligence, which essentially extends human capabilities and reconstructs problem-solving approaches. As demonstrated in this study, AI can serve to enhance human capabilities that are more valuable, work on the tasks that are best executed by human intelligence, and can create path to a new paradigm of human-AI collaboration.  Journal of Arts &amp; Social Sciences00This study refutes the competition myth and shows that people and AI can work together, which can serve to enhance human capabilities that are more valuable, work on the tasks that are best executed by human intelligence, and can create path to a new paradigm of human-AI collaboration.Journal of Arts &amp; Social Sciences["Ambreen Sarfaraz"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/b2d750f92fa3e25e6e68e883661720d846524a0a 01fe23dad1ed59e67b837c9aa8af4320b28abbb4Studying customers’ value co-creation of using fintech Islamic banking services has been a trend of Islamic bank managements to obtain value, competitive advantages, growth, and sustainability. This work endeavors to explore the role of applying artificial intelligence and big data technologies on value co-creation of using fintech Islamic banking services from customers’ perspective in Jordan. This study used a quantitative methodology based on survey approach to conduct its goals and objectives. Using structural equation modelling approach, the results indicate that artificial intelligence has a significant role in customers’ trust (β = 0.316***) and satisfaction (β = 1.14***) of using fin-tech Islamic banking services in Jordan. The results indicate that big data capabilities have a significant role in customers’ trust (β = 0.658***) and satisfaction (β = –0.109*) of using fintech Islamic banking services in Jordan. The results show that customers’ satisfaction confirms a significant effect on customers’ trust (β = –0.132***) of using fintech Islamic banking services in Jordan. The results uncover that customers’ trust have a considerable impact on customers’ value co-creation (β = 0.232***) of using fintech Islamic banking services in Jordan. The results uncover that customers’ satisfaction have a considerable impact on customers’ value co-creation (β = 0.382***) of using fintech Islamic banking services in Jordan. This study provides novel contributions regarding financial services of Islamic banks in maximizing customers’ value co-creation in Jordan.Innovative Marketing840This study provides novel contributions regarding financial services of Islamic banks in maximizing customers’ value co-creation in Jordan by exploring the role of applying artificial intelligence and big data technologies on value co-creation from customers’ perspective in Jordan.Innovative Marketing["Amineh A. Khaddam", "Hasan Alhanatleh"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/01fe23dad1ed59e67b837c9aa8af4320b28abbb4 1bdb863e86d6047852b351905e7856fdc9eb534aBackground Depression is a prevalent mental health disorder that affects a significant proportion of the global population, posing a major public health challenge. In recent years, the application of Artificial Intelligence (AI) to mental health diagnosis has garnered increasing attention. This systematic review aims to provide a comprehensive overview of the current state of research on AI-based approaches for depression diagnosis, identifying both advancements and gaps in the literature that can guide future studies. Methods A comprehensive search was conducted across leading research databases to identify relevant studies published up to July 2024. A combination of automated and manual filtering was employed to refine the initial set of records. Eligibility criteria were applied to ensure that only studies directly addressing the use of AI for depression diagnosis were included in the final analysis. Results The initial search yielded 1,179 records. Following a rigorous selection process, 145 studies were deemed eligible for inclusion in the review. These studies represent a diverse array of AI techniques and data sources, with a predominant focus on supervised learning algorithms. The most common data sources were social networks, followed by clinical data integrated with psychological assessments. Conclusion The results highlight the growing interest in leveraging AI for depression diagnosis, particularly through the use of supervised learning methods. Social network data has emerged as the most frequently used data source, though clinical data combined with validated psychological tests remains a key area of focus. Despite these advancements, several challenges persist, including data availability and quality, which present opportunities for future research to improve diagnostic accuracy and generalizability.F1000Research280A comprehensive overview of the current state of research on AI-based approaches for depression diagnosis is provided, identifying both advancements and gaps in the literature that can guide future studies.F1000Research["Mart\u00edn Di Felice", "Ilan Trupkin", "Ariel Deroche", "M. F. Pollo Cattaneo", "Parag Chatterjee"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/1bdb863e86d6047852b351905e7856fdc9eb534a 714f139bc532fa5b24e0be8fd2d25aad5014ed60Artificial Intelligence (AI) is a rapidly evolving field of technology that has the potential to drive innovation in sectors such as healthcare, education, transportation, and security. However, AI regulation in Indonesia is still lacking, posing risks to data privacy, cybersecurity, and the workforce. This research contributes to the discourse on the regulation of AI in Indonesia by emphasizing the necessity for legal transformation to effectively govern the intersection of law and technology. It underscores the importance of developing a regulatory framework that addresses the unique challenges posed by AI, ensuring that legal standards keep pace with technological advancements. The research aims to inform policymakers, legal practitioners, and stakeholders about the critical need for updated legal structures that protect public interests while fostering innovation in the rapidly changing field of AI. This contribution is vital for guiding future legislation, promoting ethical AI practices, and ensuring Indonesia can harness the benefits of AI responsibly and sustainably.Interdiciplinary Journal and Hummanity (INJURITY)120The research aims to inform policymakers, legal practitioners, and stakeholders about the critical need for updated legal structures that protect public interests while fostering innovation in the rapidly changing field of AI.Interdiciplinary Journal and Hummanity (INJURITY)["F. Alfiani"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/714f139bc532fa5b24e0be8fd2d25aad5014ed60 d88bf3e02ded36e863b571dc63311fed79164d95Conservation AI—the deliberate application of artificial intelligence technology to achieve conservation goals—has great potential to boost productivity, make existing conservation actions more efficient, and enable entirely new areas of activity. However, it also comes with risks, including AI being used by bad actors; high material demand for energy, land, and water; biases in training datasets; AI‐fueled techno‐optimism distracting from other actions; and undesirable changes in staffing and working practices in the conservation sector. Changes in wider society brought about by AI in areas such as agriculture, human health, and labor markets may also have significant impacts on biodiversity (whether positive or negative), as these are major drivers of biodiversity loss. This article reviews the various links between AI and conservation, arguing that to date there has been too much techno‐optimism and a lack of attention to risks and broader implications. It concludes with recommendations for how conservation could approach AI more effectively by considering risks and potential unintended consequences; adopting a principle of transparency; ensuring AI does not harm the staff, skills, and independence of the conservation sector; and investing in research and advocacy to address the conservation implications of wider societal changes caused by AI.Conservation Letters200Recommendations are made for how conservation could approach AI more effectively by considering risks and potential unintended consequences; adopting a principle of transparency; ensuring AI does not harm the staff, skills, and independence of the conservation sector; and investing in research and advocacy to address the conservation implications of wider societal changes caused by AI.Conservation Letters["C. Sandbrook"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/d88bf3e02ded36e863b571dc63311fed79164d95 7aefe1621aa2e4164e7c7ba77e2b2a6b6c3a8234Purpose: The current study aims to investigate the awareness and potential readiness among accountants in Malaysian governmental departments to adopt artificial intelligence (AI) in anticipation of the implementation of an AI-embedded accounting system. Design/ Methodology/ Approach: A quantitative online survey was employed to assess the awareness and potential readiness for AI adoption among Malaysian governmental accountants. The unified theory of acceptance and use of technology (UTAUT2), which is a well-established framework in technology acceptance research, guided the conceptualisation of adoption readiness. Findings: The results demonstrated that accountants had a moderate level of AI awareness as compared to their previous knowledge. Nonetheless, most respondents expressed a high willingness to adopt AI in daily tasks, while only minimal job security concerns regarding the substitution by AI were expressed. The UTAUT2 revealed that only performance expectancy emerged as a significant predictor of AI usage intention, which posited that Malaysian public sector accountants primarily focused on the perceived benefits when considering adopting AI. Comparatively, other factors, such as effort expectancy and facilitating conditions, played a less prominent role. Research Limitations/ Implications: The limitations included a relatively low response rate and the sole focus on UTAUT2, which did not incorporate other potential factors explored in different research frameworks. Practical Implications: Training that emphasises real-world benefits for accountants can assist in bridging the gap between willingness and AI usage in Malaysian public sector accounting, apart from prioritising functionalities that can improve existing workflows. Resultantly, higher accountant confidence and AI adoption are achieved. Originality/ Value: The findings contributed valuable insights pertinent to the current Malaysian governmental efforts in promoting AI adoption across various sectors. Keywords: Malaysian public sector accounting, AI adoption, accounting information systems, UTAUT2IPN Journal of Research and Practice in Public Sector Accounting and Management00The results demonstrated that accountants had a moderate level of AI awareness as compared to their previous knowledge, and it was revealed that Malaysian public sector accountants primarily focused on the perceived benefits when considering adopting AI.IPN Journal of Research and Practice in Public Sector Accounting and Management["Azwadi Ali", "Zaira Aniza Samsudin"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/7aefe1621aa2e4164e7c7ba77e2b2a6b6c3a8234 8633d2534ae9654ccc796ae89f6d94d3d74f5a81This research paper examines the development of the Georgian media industry concerning the modern world’s media trend of automated journalism, using news agencies as an example. The research topic of this paper is to explore the practice of generative artificial intelligence systems in the world media industry and, based on the above evaluation of these technologies in media production by leading Georgian news agencies. Since technological advancements have significantly altered media consumption by accelerating the speed of information dissemination, expanding the range of available channels, and enabling user-generated content, a lag in adopting these technologies could challenge the core mission of news agencies and influence their future development trajectory. The conducted research on the automation of news agencies gives a picture and identifies the problems that determine the extent of the use of Generative Artificial Intelligence in their activities, the basis of which, in turn, is the Georgian language models and/or the competence of media representatives. During the research, no specific competence problems were identified; however, it was discovered that a language barrier, particularly the low quality of Georgian language models, has resulted in the virtually nonexistent use of Generative Artificial Intelligence in the operations of news agencies.International journal of social science100It was discovered that a language barrier, particularly the low quality of Georgian language models, has resulted in the virtually nonexistent use of Generative Artificial Intelligence in the operations of news agencies.International Journal of Social Sciences["Nino Chalaganidze", "Natia Popkhadze"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/8633d2534ae9654ccc796ae89f6d94d3d74f5a81 56533a17a60be95efb40c5a321e765fcea038fee     This review paper examines the integration of Artificial Intelligence (AI) within data mining, focusing on various algorithms, tools, and applications across different sectors. The review details the strengths and weaknesses of key algorithms such as supervised learning, unsupervised learning, and reinforcement learning. Furthermore, it discusses popular data mining tools and presents case studies highlighting the impact of AI on fields like healthcare, finance, and retail. The review concludes by identifying emerging trends, challenges, and future research directions in AI-driven data mining. The review details the strengths and weaknesses of key algorithms such as supervised learning, unsupervised learning, and reinforcement learning. Furthermore, it discusses popular data mining tools and presents case studies highlighting the impact of AI on fields like healthcareAl-Noor Journal for Information Technology and Cybersecurity10This review paper examines the integration of Artificial Intelligence within data mining, focusing on various algorithms, tools, and applications across different sectors, and identifies emerging trends, challenges, and future research directions in AI-driven data mining.Al-Noor Journal for Information Technology and Cybersecurity["Adnan Abdullah", "Yusra Mohammad", "Saba Q. Hasan", "Marwa J. Mohammed"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/56533a17a60be95efb40c5a321e765fcea038fee 1dd1d90b8c689488190d9e539fc0a42f2f2f491aLiver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging evidence underscores the critical roles of the immune microenvironment in tumor initiation, development, prognosis, and therapeutic responsiveness. However, the composition of the immune microenvironment of liver cancer (LC-IME) and its association with clinicopathological significance remain unelucidated. In this review, we present the recent developments related to the use of artificial intelligence (AI) for studying the immune microenvironment of liver cancer, focusing on the deciphering of complex high-throughput data. Additionally, we discussed the current challenges of data harmonization and algorithm interpretability for studying LC-IME.Frontiers in Immunology2160The recent developments related to the use of artificial intelligence (AI) for studying the immune microenvironment of liver cancer are presented, focusing on the deciphering of complex high-throughput data.Frontiers in Immunology["Ihtisham Bukhari", "Mengxue Li", "Guangyuan Li", "Jixuan Xu", "Pengyuan Zheng", "Xiufeng Chu"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/1dd1d90b8c689488190d9e539fc0a42f2f2f491a 57c7db70ab34dc701179f1b1296c2a63b4a95369Artificial Intelligence (AI) and Machine Learning (ML) can potentially revolutionise healthcare systems by improving diagnostic and treatment procedures, thereby enhancing patients' health. Based on big data, these technologies can find correlations that human eyes cannot see. Doing so may lead to earlier diagnoses, better treatments, and more effective drug development. However, the use of AI and ML in the healthcare sector cannot succeed without addressing issues of ethics and professionalism, such as data privacy and inductive bias. To protect patients, it is important that these technologies are designed and used ethically, ensuring that patients have control over their deployment. With the continual development of AI and ML technologies, it is necessary to invest more in research and development to tackle their potential issues.International Journal For Multidisciplinary Research170With the continual development of AI and ML technologies, it is necessary to invest more in research and development to tackle their potential issues, and to protect patients, it is important to protect patients.International Journal For Multidisciplinary Research["Richard Aggrey", "Bright Ansah Adjei", "Nana Adwoa Konadu Dsane", "Karl Osei Afoduo", "Loretta Naa Oye Holdbrooke"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/57c7db70ab34dc701179f1b1296c2a63b4a95369 608a91d102689a8ba3d058a5632b4ecd07bbd862The integration of Artificial Intelligence (AI) in Indonesia’s healthcare system, particularly in telemedicine, presents both opportunities and challenges. AI enhances healthcare delivery by improving diagnostics and patient management, especially in underserved areas. However, its adoption raises legal concerns, particularly around data privacy, security, and professional accountability. Indonesia's regulatory framework, including the Personal Data Protection Law (UU PDP) and Health Ministerial regulations, governs healthcare and data use but has not fully adapted to the complexities of AI. This study aims to analyse the adequacy of Indonesia’s legal framework in addressing the challenges posed by AI in telemedicine. A qualitative method was used, involving a comprehensive review of existing laws and case studies of AI application in healthcare both locally and globally. The results reveal that while Indonesian health laws provide a foundation for telemedicine regulation, they lack specific provisions for AI-related issues, such as algorithm transparency, liability for AI errors, and real-time data handling. The conclusion emphasizes the need for updated regulations that account for AI’s unique characteristics, ensuring that its benefits in healthcare are fully realized while protecting patient rights and aligning with international legal standards. This research highlights the importance of legal reform to create a safer, more efficient, and ethically sound AI-driven healthcare system in Indonesia.Devotion : Journal of Research and Community Service00The results reveal that while Indonesian health laws provide a foundation for telemedicine regulation, they lack specific provisions for AI-related issues, such as algorithm transparency, liability for AI errors, and real-time data handling.Devotion : Journal of Research and Community Service["Rommy Sebastian"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/608a91d102689a8ba3d058a5632b4ecd07bbd862 8f2aca9cbe5d96b479fc5c8ed1096130f71c5f7aThe escalating challenge of climate change necessitates an urgent exploration of factors influencing carbon emissions. This study contributes to the discourse by examining the interplay of technological, economic, and demographic factors on environmental sustainability. This study investigates the impact of artificial intelligence (AI) innovation, economic growth, foreign direct investment (FDI), energy consumption, and urbanization on CO2 emissions in the United States from 1990 to 2022. Employing the ARDL framework integrated with the STIRPAT model, the findings reveal a dual narrative: while AI innovation mitigates environmental stress, economic growth, energy use, FDI, and urbanization exacerbate environmental degradation. Unit root tests (ADF, PP, and DF-GLS) confirm mixed integration levels among variables, and the ARDL bounds test establishes long-term co-integration. The analysis highlights that AI innovation positively correlates with CO2 reduction when environmental safeguards are in place, whereas GDP growth, energy consumption, FDI, and urbanization intensify CO2 emissions. Robustness checks using FMOLS, DOLS, and CCR validate the ARDL findings. Additionally, Pairwise Granger causality tests reveal significant one-way causal links between CO2 emissions and economic growth, AI innovation, energy use, FDI, and urbanization. These relationships emphasize the critical role of AI-driven technological advancements, sustainable investments, and green energy in fostering ecological sustainability. The study suggests policy measures such as encouraging green FDI, advancing AI technologies, adopting sustainable energy practices, and implementing eco-friendly urban development to promote sustainable growth in the USA.00["Abdullah Al Abrar Chowdhury", "Azizul Hakim Rafi", "Adita Sultana", "Abdulla All Noman"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/8f2aca9cbe5d96b479fc5c8ed1096130f71c5f7a 6fc0a582dac80187641bbcee12827ba843fcbe39BMC Cardiovascular Disorders520AI-driven prediction models show significant promise in improving outcome predictions for congenital heart surgery, surpassing traditional risk prediction tools not only in immediate postoperative risks but also in long-term outcomes such as 1-year survival and malnutrition.BMC Cardiovascular Disorders["Ida Mohammadi", "Shahryar Rajai Firouzabadi", "Melika Hosseinpour", "Mohammadhosein Akhlaghpasand", "Bardia Hajikarimloo", "Sam Zeraatian-Nejad", "P. Sardari Nia"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/6fc0a582dac80187641bbcee12827ba843fcbe39 e7544f8efed1d5371c6d976c22e9324646e5cdcaABSTRACT The rapid advancement of generative artificial intelligence (GAI) is poised to revolutionize medical education, clinical decision-making, and health care workflow. Despite considerable interest and a surfeit of newly available tools, medical educators largely lack both competencies and guidance on how to incorporate the new and rapidly evolving world of GAI into the core medical school curriculum and experiences of undergraduate medical education. This Scholarly Perspective highlights the need for medical schools to adapt to this new paradigm by implementing policies, governance, and curricula that address the ethical, technical, and pedagogical implications of GAI. The authors recommend creating policies for appropriate GAI use, designed to protect institutional and patient data, and provide students with clarity on the appropriate use of AI for education. The authors suggest that implementing GAI governance at institutions is crucial to create guiding principles on ethical and equitable GAI use and involving students as coinventors of local innovation. The authors argue that providing faculty and learners with tools and training for safe experimentation with GAI and defining competencies for students and faculty are essential. Curricula for GAI should focus on implications of clinical uses. The authors propose a set of new competencies for GAI that build on those already established for AI in general. Given how dynamic the world of GAI is and how quickly new innovations are changing longstanding practices of clinical medicine, it is imperative that the medical education community acts together to share best practices, gather data to assess the impact of GAI education, continuously update the expected competencies of medical students, and help students prepare for a career that will be continually changed by GAI.Academic medicine : journal of the Association of American Medical Colleges00It is argued that providing faculty and learners with tools and training for safe experimentation with GAI and defining competencies for students and faculty are essential and a set of new competencies for GAI that build on those already established for AI in general are proposed.Academic medicine : journal of the Association of American Medical Colleges["Marc M Triola", "A. Rodman"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/e7544f8efed1d5371c6d976c22e9324646e5cdca ea79112420a71ede0d45a2097e7ada895b28cd0cThe article considers the quality management system of legislation in the Russian Federation, defines its basic elements, the main tools for quality control (management) in the form of legal examinations, and proposes a general simple classification of the controlled factors in legal examinations. As an example for conducting quality control of legislation, the authors choose the most important legal expertise called anti-corruption examination (ACE). Within the framework of general trends in automation, informatization, and digitalization, the paper considers the use of artificial intelligence (AI) for the purposes of conducting ACE, which, in some cases of “routine work”, could provide all possible assistance to specialists in the field of legal expertises and their digitalization. In this regard, a step-by-step algorithm for pre-training AI is formulated using examples from regulatory legal acts (RLA) containing corruption factors (CF); a classification of CF is carried out; a scale of AI errors in detecting CF is developed; frequency characteristics of AI errors are determined; preliminary conclusions are obtained on the possibility of using AI in ACE.Ergodesign00A step-by-step algorithm for pre-training AI is formulated using examples from regulatory legal acts (RLA) containing corruption factors (CF), and preliminary conclusions are obtained on the possibility of using AI in ACE.Ergodesign["D. Kosov", "V. Belov"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/ea79112420a71ede0d45a2097e7ada895b28cd0c 10a580bbda7c0a37c8b03fa42b452e13ee85cd2eArtificial intelligence (AI) has been extensively used in digital marketing. Still, the recent advances in generative AI (GAI) have revolutionized social media marketing and content creation, lowering barriers that once restricted high-quality design to professionals well versed in expensive and complex software like Adobe Suite. GAI tools enable anyone, from students to marketers, to generate logos, branding, and multimedia content without extensive training. This shift has empowered more people to engage in creative expression, expanding the pool of ideas and creativity. However, the abundance of AI-generated content raises questions about the evolving definition of “art” and the emergence of a new category of AI artists and designers who blend technical skills with AI-driven creativity. The future of marketing will likely see even greater automation, where these generative tools learn brand esthetics, predict trends, and create content tailored to specific audiences – eventually automating everything from ad creation to distribution across platforms. Methods are reviewed by which these tools influence each marketing stage, from audience segmentation to content distribution, and assess their impact on key performance indicators such as engagement, conversion rates, and return on investment. More significantly, the article explores and demonstrates how GAI reshapes content creation, how audiences adapt to AI-generated work, and the implications for the future of marketing and design as the technology becomes an increasingly seamless collaborator.Design00How GAI reshapes content creation, how audiences adapt to AI-generated work, and the implications for the future of marketing and design as the technology becomes an increasingly seamless collaborator are demonstrated.Design+["Andrew Smith", "James Hutson"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/10a580bbda7c0a37c8b03fa42b452e13ee85cd2e 5d72347b017cffc64617a9a425f9fb614a7fcbceLAY ABSTRACT Autism is clinically defined by challenges with social language, including difficulties offering on-topic language in a conversation. Similar differences are also seen in genetically related conditions such as fragile X syndrome (FXS), and even among those carrying autism-related genes who do not have clinical diagnoses (e.g., the first-degree relatives of autistic individuals and carriers of the FMR1 premutation), which suggests there are genetic influences on social language related to the genes involved in autism. Characterization of social language is therefore important for informing potential intervention strategies and understanding the causes of communication challenges in autism. However, current tools for characterizing social language in both clinical and research settings are very time and labor intensive. In this study, we test an automized computational method that may address this problem. We used a type of artificial intelligence known as pre-trained language model to measure aspects of social language in autistic individuals and their parents, non-autistic comparison groups, and individuals with FXS and the FMR1 premutation. Findings suggest that these artificial intelligence approaches were able to identify differences in social language in autism, and to provide insight into the individuals' ability to keep a conversation on-topic. These findings also were associated with broader measures of participants' social communication ability. This study is one of the first to use artificial intelligence models to capture important differences in social language in autism and genetically related groups, demonstrating how artificial intelligence might be used to provide automatized, efficient, and objective tools for language characterization.Autism480This study is one of the first to use artificial intelligence models to capture important differences in social language in autism and genetically related groups, demonstrating how artificial intelligence might be used to provide automatized, efficient, and objective tools for language characterization.Autism : the international journal of research and practice["Joseph Cy Lau", "Emily Landau", "Qingcheng Zeng", "Ruichun Zhang", "Stephanie Crawford", "Rob Voigt", "Molly Losh"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/5d72347b017cffc64617a9a425f9fb614a7fcbce 3b9bacbfb1e15c92bc795dce2b40ee75e3bb0e8bThis research paper seeks to explore a potential legal predicament, that arises from integrating artificial intelligence (AI) in extracting and analysing forensic evidence in the Saudi Arabian judicial system. Now, with the rapid growth of AI technologies and its application in criminal investigation, becoming more common, there are many legal and ethical questions that arise. The paper also addresses some important issues, such as the reliability of evidence generated by AI, its admissibility in the courts, and the protection of the defendants' rights to privacy. The paper then delves into a study of the available legal frameworks in the Kingdom of Saudi Arabia alongside a comparative study of some international systems: the United States, the United Kingdom, the European Union, and the legal framework of China. It shows important lessons learned from these jurisdictions. Furthermore, the findings also suggest several legal and procedural reforms that could be implemented to improve the efficiency and fairness of the Saudi judicial system. These include, inter alia, possible standardization in terms of admissibility of technical evidence, increased transparency in the algorithms used, and judges that have legal and technical training. Thus, the study seeks to suggest pragmatic recommendations to strike the balance between the required efficiency propelled by AI technologies and the governing principles of justice in the Saudi legal environment, which based on Islamic lawJournal of Ecohumanism00The study seeks to suggest pragmatic recommendations to strike the balance between the required efficiency propelled by AI technologies and the governing principles of justice in the Saudi legal environment, which based on Islamic law.Journal of Ecohumanism["Dalia Kadry Ahmed Abdel Aziz"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/3b9bacbfb1e15c92bc795dce2b40ee75e3bb0e8b 27ea2e2be522dbdae2332dd4ae5b727770303667The aim of this research study is to investigate the influence of artificial intelligence (AI) on digital financial inclusion (DFI). The involvement of economically disadvantaged individuals in the financial sector has emerged as a pivotal subject of discourse concerning approaches to enhance digital financial inclusion. The Financial Technology sector is employing artificial intelligence and its various tools to foster engagement and improve participation among low-income individuals, marginalized communities, women, youth, and small business owners in conventional financial activities. The methodological approach of the study involved a thorough examination of perceptional and research journals, an analysis of papers authored by co-researchers, and a review of other authoritative sources pertinent to the subject, aimed at assessing the impact of AI on digital financial inclusion. This study proved that artificial intelligence has a major impact on digital financial inclusion in several ways. It helps with risk assessment and mitigation, solves the problem of disorganized data, improves cyber security and fraud detection, and uses chatbots to help users with desktop tasks. The study argues that governments throughout the world, together with financial and non-financial organizations, should embrace AI on a massive scale.The Critical Review of Social Sciences Studies630This study proved that artificial intelligence has a major impact on digital financial inclusion in several ways, which helps with risk assessment and mitigation, solves the problem of disorganized data, improves cyber security and fraud detection, and uses chatbots to help users with desktop tasks.The Critical Review of Social Sciences Studies["Dr. Kiran Jameel", "Dr Laeeq Razzak"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/27ea2e2be522dbdae2332dd4ae5b727770303667 60557627b81b905c767d5c0caec3167d60131927This article examines how artificial intelligence (AI) is transforming digital drawing. Using tools such as VERAS, Midjourney, and DALL-E 2, it explores AI's impact on creativity, artistic processes, and authorship. The research includes practical cases where AI adapts hand-drawn sketches into styles like those of Van Gogh, Frida Kahlo, or Luis Barragán. Results show that AI enhances artistic capabilities and productivity but also raises ethical challenges and technical limitations, such as the lack of conceptual depth in AI-generated works. In conclusion, the integration of AI in digital art presents opportunities and challenges, requiring a balance between technological innovation and human creativity. Revista Teoría Educativa00Examination of how artificial intelligence is transforming digital drawing shows that AI enhances artistic capabilities and productivity but also raises ethical challenges and technical limitations, such as the lack of conceptual depth in AI-generated works.Revista Teoría Educativa["Demetrio Castel\u00e1n-Urquiza", "Cesar Monroy-Mondrag\u00f3n"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/60557627b81b905c767d5c0caec3167d60131927 a56d2271f6ddffd06f6b7b7591b325f5086ecfacAdverse effects of climate change on our environment and life have been of significant concern and this concern has become more significant because of recent extreme hurricanes and flooding in various parts of the world.  Artificial Intelligence (AI) based technologies are being adopted across all aspects of our life.  One of the most frequent arguments in support of AI is that AI-based tools can achieve significant improvements in efficiency, which could translate into much better economic return while optimizing the use of resources.  However, environmental and carbon footprints of datacenters training and facilitating the use of AI tools such as ChatGPT is enormous and is increasing rapidly with explosive growth in AI-related tools.  This paper presents a detailed discussion on both beneficial and adverse aspects of AI with respect to its impacts on the climate and the environment based critical review of several available studies.Journal of High School Research00A detailed discussion on both beneficial and adverse aspects of AI with respect to its impacts on the climate and the environment is presented based critical review of several available studies.Journal of High School Research["Siddhi Agrawal"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/a56d2271f6ddffd06f6b7b7591b325f5086ecfac 71fc2fed24f924d17ebcadcd32132e8c9471fdf2Developing artificial intelligence is a topical issue of contemporary discussions among engineers and economists. The aim of the article is to analyze major stages of technological evolution that led to creating artificial intelligence to identify the possibilities and limitations associated with its application, as well as economic consequences. The author bases research methodology on the theory of technological development, “core-periphery” technology model, and taxonomic analysis. The main findings state that the emergence of artificial intelligence technology is a natural result of technological evolution, however, the consequences of its development are characterized by a high degree of unpredictability. Introducing a new technology designed to facilitate the work of natural intelligence, and in some areas, replacing it, requires the deployment of new types of human activity, namely, forms of control activity, as well as analytical work interpreting the results of using artificial intelligence. The scale of applying and developing artificial intelligence technology is directly dependent on how high the technological level is, provided by the previous class of technologies, namely electronic-digital ones, robotics, nanotechnology. Here the paper clearly shows the principle of technological development from what the authors have achieved, with creating and preparing an appropriate technological base for disseminating the peak one in novelty of artificial intelligence technology. There is also a need for the synchronous deployment of control systems for applying such technologies with the necessary training of personnel and managing the process of their distribution by types of economic activity, including those arising due to applying new technologies.Ergodesign60The main findings state that the emergence of artificial intelligence technology is a natural result of technological evolution, however, the consequences of its development are characterized by a high degree of unpredictability.Ergodesign["Oleg Suharev"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/71fc2fed24f924d17ebcadcd32132e8c9471fdf2 a4aa7221f8da47557692d7ba06bc74714cff6c00The focus of education in the 4.0 era is not only on academic knowledge, but also on the development of important skills such as critical thinking, problem solving, creativity, collaboration, and digital literacy. These are skills needed to adapt to the changing world of work. The use of technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and augmented reality (AR) has changed the way teachers and students interact. Learning can be done online with digital platforms, allowing access to wider information and flexibility of time. Artificial intelligence (AI) has changed many aspects of life, including education. This article aims to explore the use of AI in learning for students, with a focus on effectiveness, impact, and challenges faced. Through a literature review and case studies, this research identifies how AI can improve the learning experience, personalize education, and increase student engagement.Pedagogi: Jurnal Ilmu Pendidikan00Through a literature review and case studies, this research identifies how AI can improve the learning experience, personalize education, and increase student engagement.Pedagogi: Jurnal Ilmu Pendidikan["Mutiara Felicita Amsal", "Dony Darma Sagita"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/a4aa7221f8da47557692d7ba06bc74714cff6c00 9d43ac0f9ce9030b84e94cecd548dd17a6fbb769Objective: To analyze recent advances in the implementation of artificial intelligence (AI) in hospital management, highlighting its benefits, challenges, and future prospects.   Theoretical Framework: Artificial intelligence (AI) has revolutionized hospital management, driven by machine learning, robotics, and intelligent decision-making systems. These advances have enabled tools that optimize administrative and clinical processes, improving the use of hospital resources and the quality of patient care.   Method: The methodology used was an exhaustive bibliographic review, using academic sources and relevant studies published between 2020 and 2024. The databases consulted include PubMed, Scopus, and ScienceDirect.   Results and Discussion: The findings show that artificial intelligence has transformed hospital management by improving operational efficiency and the quality of patient care. Applications such as electronic medical record management, predictive algorithms to optimize resource allocation, and AI-assisted telemedicine have enabled a more efficient distribution of resources and a reduction in waiting times.   Research Implications: It is highlighted that, although AI offers great potential to improve hospital management and contribute to sustainability, its implementation must be carefully planned to address technical and ethical challenges. It is crucial to promote staff training and establish clear regulatory frameworks that ensure equitable and responsible use of AI.   Originality/Value: The study offers a comprehensive view of recent advances in artificial intelligence in hospital management. Its originality lies in the exploration of how AI optimizes both administrative and clinical processes, ensuring and promoting healthy lives for all (SDG:3).Journal of Lifestyle and SDGs Review00The findings show that artificial intelligence has transformed hospital management by improving operational efficiency and the quality of patient care, and its implementation must be carefully planned to address technical and ethical challenges.Journal of Lifestyle and SDGs Review["Erwin William Ccosi Paucar", "Hilda Zenaida Ccosi Paucar", "Dina Ruth Ccosi Paucar", "Gladys Vilma Ccosi Paucar", "Cristian Gumercindo Medina Sotelo"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/9d43ac0f9ce9030b84e94cecd548dd17a6fbb769 f995f339e8ef9cfe3302e580f681134673a2ec71The author examines how it is possible to use artificial intelligence to the advantage of an organization that operates in the technology industry. The main focus of the research was to identify how effectively and in what ways Samsung used AI technologies in its smart phones and home appliance product segments to improve functionality and usability as well as operational effectiveness. This was to have been achieved through the use of Artificial Intelligence in manufacturing processes as well as in various ways to deliver unique customized services to customer unlike the rival companies. This work also shows how Bixby, Samsung’s own AI assistant or smart IoT solutions all help towards creating a smarter environment, thereby building goodwill for the brand and generating additional revenue streams. The study further shows how Samsung supports that the application of AI can enhance a firm’s competitive position by pushing product differentiation, monitoring and anticipating equipment wear and tear and making better decisions from analyzed data.Brazilian Journal of Business00The study shows how Samsung supports that the application of AI can enhance a firm’s competitive position by pushing product differentiation, monitoring and anticipating equipment wear and tear and making better decisions from analyzed data.Brazilian Journal of Business["Lensari Embarka", "Haddadi Abdellatif", "Reggani Lalla Fatma", "Messai Salima"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/f995f339e8ef9cfe3302e580f681134673a2ec71 fc4eebf56c8942970babfac451976ee007ff90a2Aim of the study The correct establishment of the therapeutic conduct in papillary thyroid cancer through the use of artificial intelligence (AI) models is essential in medical practice in diagnosing and establishing the prognosis of the disease, this aspect implies the most accurate and effective therapeutic approach. I aim to use AI models to obtain correct and favorable results for the diagnosis and prognosis of papillary thyroid cancer by conducting a clinical trial, in which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms.The use of AI will avoid errors and increase performance in the interpretation of the doctor's computed tomography (CT) scan and consequently, improve treatment planning. Materials and methods The optimization of the method will consist in the development and training of artificial intelligence models, using AI algorithms in the diagnosis of papillary thyroid cancererol by accurately identifying pathological lesions and adenopathy and generating 3D images from 2D CT images. use the transfer function for opacity and color, grayscale from DICOM images projected in a three-dimensional space. [1]; [2] We also use artificial intelligence (AI), through the Generative Adversarial Networks (GAN) technique, which has proven to be effective in representing complex data distributions [2], as we do in this study. I will validate this method of diagnosis and prognosis of papillary thyroid cancer, optimized by the artificial intelligence algorithm, by conducting a randomized, controlled clinical trial over a period of 12 months. Results I will validate the diagnostic method by using AI. By using this method in medical practice, I aim to be able to avoid errors, to provide precision in diagnosing, staging and establishing the therapeutic plan in papillary thyroid cancer using AI models. Conclusions The use of the AI method can increase the quality of life by establishing the correct therapeutic plan and improving the prognosis.Romanian Journal of Oral Rehabilitation110The use of AI will avoid errors and increase performance in the interpretation of the doctor's computed tomography (CT) scan and consequently, improve treatment planning and increase the quality of life by establishing the correct therapeutic plan and improving the prognosis.Romanian Journal of Oral Rehabilitation["Ramona Elena Teiu", "Tudor Florin Ursuleanu", "Cristian Stefan Hlescu", "Roxana Grigorovici", "Andreea Roxana Luca", "Maria Paula Comanescu", "Alina Ionela Calin", "Alexandru Grigorovici"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/fc4eebf56c8942970babfac451976ee007ff90a2 f53ae2f93892d649b66301538f810ddd37601a96The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), popularly known as Artificial Intelligence for Everything (AIoT), has become a game-changing technology for industrial use. This research explores how AIoT enhances automation and efficiency in industrial machinery through networked devices, real-time decision-making, and advanced data analytics. Through a thorough analysis of recent studies and academic publications, the paper emphasizes the potential of AIoT in intelligent monitoring systems, process optimization, and predictive maintenance. The findings reveal that AIoT enables industrial machines to operate autonomously by leveraging AI algorithms to analyze data collected from IoT devices. This integration reduces downtime, optimizes resource utilization, and improves overall operational efficiency. Furthermore, the study emphasizes the role of edge computing and cloud-based platforms in facilitating seamless data processing and decision-making in real-time. Key challenges such as data security, system scalability, and implementation cost are also identified, along with potential strategies to address these issues. This research provides valuable insights for industry stakeholders, policy makers, and researchers interested in leveraging AIoT to revolutionize industrial operations and achieve Industry 4.0 goals.Sci-tech Journal340The findings reveal that AIoT enables industrial machines to operate autonomously by leveraging AI algorithms to analyze data collected from IoT devices, which reduces downtime, optimizes resource utilization, and improves overall operational efficiency.Sci-tech Journal["Devi Rahmayanti"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/f53ae2f93892d649b66301538f810ddd37601a96 c36c0bd6f07a6cd801ca36559773b54f5ed3f2ffThis research aims to explore the use of artificial intelligence (AI) in digital marketing among MSME practitioners in Indonesia. In an increasingly evolving digital era, understanding and implementing AI technology becomes crucial for MSMEs to improve their competitiveness and marketing effectiveness. This study involved 50 respondents who are MSME actors and used a quantitative method with questionnaires to collect data. The results indicate that 72% of respondents have a basic understanding of AI; however, only 44% have implemented AI-based tools in their marketing strategies. Furthermore, linear regression analysis revealed a significant relationship between the use of AI and increased sales, with a regression coefficient of 0.75 (p < 0.01), demonstrating that the use of AI positively contributes to marketing outcomes. The study also found that the use of AI tools such as chatbots and automated copywriting can enhance customer engagement. Although MSME actors show awareness of AI's potential, there exists a gap between knowledge and practice. Therefore, this research recommends the need for greater training and support for MSMEs to maximize the use of AI in their digital marketing strategies. The findings are expected to provide insights for stakeholders and policymakers in designing initiatives that support the integration of AI technology in MSME businesses, allowing them to adapt to changes and improve marketing effectiveness.Devotion : Journal of Research and Community Service00The results indicate that 72% of respondents have a basic understanding of AI; however, only 44% have implemented AI-based tools in their marketing strategies, and linear regression analysis revealed a significant relationship between the use of AI and increased sales, demonstrating that the use of AI positively contributes to marketing outcomes.Devotion : Journal of Research and Community Service["Siti Juriah", "Darwati Susilastuty"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/c36c0bd6f07a6cd801ca36559773b54f5ed3f2ff 48ef43d57a7f6d7e1a1d66420effcf4693b58f78The military use of artificial intelligence is increasing in area of strategic decision making and targeting systems. These systems require large amounts of data to operate, including population surveillance and profiling information, social medial inferences, and drone footage. Private technology companies – ‘data enablers’ – are now offering militaries the opportunity to supplement their data bases. These companies offer their clients access to a range of services and products including aggregated data sets, data analytics and related software systems. This powerful combination provides militaries with datasets they would not ordinarily have access to and the ability to build on these datasets using software systems which collate and analyse data in situations of armed conflict. The quality of these datasets and software systems can have profound effects on how military AI functions yet there is minimal accountability for technology companies under international law frameworks when these AI systems cause harm. This paper argues that a starting point for addressing this accountability deficit could take the form of proactive regulation by states of data enablers including incorporating data governance processes in national weapon reviews together with sui generis agreements between states and technology companies to ensure data quality and accuracy. L’utilisation militaire de l’intelligence artificielle est en plein essor dans le domaine des systèmes de prise de décision stratégique et de ciblage. Ces systèmes requièrent de grandes quantités de données pour fonctionner, lesquelles comprennent des informations de surveillance et de profilage de la population, des déductions basées sur les réseaux sociaux et des images filmées par des drones. Les entreprises technologiques privées, ou «data enablers», offrent désormais aux militaires l’opportunité de compléter leurs bases de données. Ces entreprises permettent à leurs clients d’accéder à une gamme de services et de produits, comprenant des jeux de données agrégées, des analyses de données et des systèmes logiciels connexes. Cette combinaison puissante fournit aux militaires des ensembles de données auxquels ils n’auraient normalement pas accès et la possibilité de développer ces ensembles à l’aide de systèmes logiciels qui comparent et analysent les données dans des situations de conflit armé. La qualité de ces jeux de données et systèmes logiciels peut avoir de profonds effets sur la manière dont l’IA militaire fonctionne. Toutefois, la responsabilité des entreprises technologiques est minimale dans les cadres juridiques internationaux lorsque ces systèmes d’IA causent des dommages. Cet article soutient que le point de départ pour aborder cette responsabilité pourrait prendre la forme d’une réglementation proactive des data enablers par les États, en ce compris l’intégration de processus de gouvernance des données dans les évaluations nationales d’armes avec des accords sui generis entre les États et les entreprises technologiques pour assurer la qualité et la précision des données. Het militaire gebruik van kunstmatige intelligentie neemt toe op het gebied van systemen voor strategische besluitvorming en doelbestrijding. Deze systemen hebben grote hoeveelheden gegevens nodig om te kunnen werken, waaronder informatie verkregen via toezicht op de bevolking en profilering, inferenties op basis van sociale media en dronebeelden. Particuliere technologiebedrijven – gegevensverwerkende bedrijven – bieden militairen nu de mogelijkheid om hun databases aan te vullen. Dankzij deze bedrijven hebben klanten toegang tot een reeks diensten en producten, waaronder geaggregeerde gegevensreeksen, gegevensanalyse en gerelateerde softwaresystemen. Deze krachtige combinatie voorziet militairen van gegevensreeksen waar ze normaal gesproken geen toegang toe hebben en stelt hen in staat om voort te bouwen op deze gegevensreeksen met behulp van softwaresystemen die in situaties van gewapende conflicten gegevens verzamelen en analyseren. De kwaliteit van deze gegevensreeksen en softwaresystemen kan grote gevolgen hebben voor de manier waarop militaire AI functioneert, maar toch moeten technologiebedrijven krachtens internationale rechtskaders nauwelijks verantwoording afleggen als deze AI-systemen schade veroorzaken. In deze paper stelt de auteur dat staten die dit gebrek aan aansprakelijkheid wensen aan te pakken, zouden kunnen overgaan tot proactieve regulering van gegevensverwerkende bedrijven, onder meer door processen voor gegevenskwaliteitsbeheer op te nemen in nationale wapeninspecties, samen met sui generis-overeenkomsten tussen staten en technologiebedrijven om de kwaliteit en nauwkeurigheid van gegevens te waarborgen. El uso militar de la inteligencia artificial (IA) está aumentando en el ámbito de la toma de decisiones estratégicas y de los sistemas de selección de objetivos. Estos sistemas requieren grandes cantidades de datos para funcionar, incluida la vigilancia de la población y la información de elaboración de perfiles, inferencias de redes sociales y grabaciones de drones. Las empresas privadas de tecnología («habilitadoras de datos») ofrecen ahora a los militares la oportunidad de complementar sus bases de datos. Estas empresas ofrecen a sus clientes acceso a una gama de servicios y productos que incluyen conjuntos de datos agregados, análisis de datos y sistemas informáticos relacionados. Esta poderosa combinación proporciona a los militares conjuntos de datos a los que normalmente no tendrían acceso y la capacidad de aprovechar estos conjuntos de datos utilizando sistemas informáticos que recopilan y analizan datos en situaciones de conflicto. La calidad de estos conjuntos de datos y sistemas informáticos puede tener efectos profundos en el funcionamiento de la IA militar, pero la responsabilidad de las empresas de tecnología en los marcos del derecho internacional es mínima cuando estos sistemas de IA causan daños. Este artículo sostiene que un punto de partida para abordar este déficit de responsabilidad podría implicar la adopción de una regulación proactiva por parte de los Estados respecto a los habilitadores de datos, incluida la incorporación de procesos de gobernanza de datos en las revisiones nacionales de armas junto con acuerdos sui generis entre Estados y empresas de tecnología para garantizar la calidad y precisión de los datos. L’uso militare dell’intelligenza artificiale sta aumentando nell’ambito dei sistemi di decision making strategica e di puntamento. Per funzionare, tali sistemi richiedono grandi quantità di dati, tra cui informazioni relative alla sorveglianza e profilazione della popolazione, inferenze sui social media e filmati di droni. Le aziende tecnologiche private – Abilitatori di dati – offrono alle Forze Armate la possibilità di integrare le loro banche dati. Queste aziende offrono ai loro clienti l’accesso ad una gamma di servizi e prodotti che comprendono set di dati aggregati, analisi dei dati e sistemi software ad essi correlati. Questa potente combinazione fornisce alle Forze Armate set di dati a cui non avrebbero normalmente accesso e la possibilità di basarsi su questi tipi di dati utilizzando sistemi software che raccolgono ed analizzano I dati in situazioni di conflitto armato. La qualità di questi set di dati e sistemi software può avere profondi effetti sul funzionamento dell’IA militare, eppure la responsabilità delle aziende tecnologiche è minima nell’ambito del diritto internazionale quando tali sistemi di IA causano danni.Il presente elaborato sostiene che un punto di partenza per affrontare questo deficit di responsabilità potrebbe assumere la forma di una regolamentazione proattiva da parte degli Stati rispetto agli strumenti di abilitazione dei dati, comprendenti, nelle revisioni nazionali degli armamenti, processi di governance dei dati assunti insieme ad accordi sui generis tra Stati e aziende tecnologiche al fine di garantire la qualità e l’accuratezza dei dati. Die Verwendung von künstlicher Intelligenz für Militärzwecke ist auf dem Vormarsch im Bereich der Systeme der strategischen Entscheidungsfindung und der Zielerfassung. Für den Betrieb dieser Systeme sind große Datenmengen benötigt, darunter Informationen zur Überwachung und Profilerstellung der Bevölkerung, Rückschlüsse aus sozialen Medien und Drohnenaufnahmen. Heutzutage bieten private Technologieunternehmen („Data Enablers“) Soldaten die Möglichkeit, ihre Datenbanken zu ergänzen. Diese Unternehmen bieten ihren Kunden Zugang zu einer Reihe von Dienstleistungen und Produkten, einschließlich aggregierter Datensätze, Datenanalysen und dazugehöriger Softwaresysteme. Diese leistungsstarke Kombination verseht Soldaten mit Datensätzen, auf die sie normalerweise keinen Zugriff hätten, und ermöglicht es ihnen, auf diesen Datensätzen weiterzubauen mit Hilfe von Softwaresystemen, die Daten in Situationen bewaffneter Konflikte vergleichen und analysieren. Die Qualität dieser Datensätze und Softwaresysteme kann tief greifende Auswirkungen auf die Funktionsweise von militärischer KI haben. Dennoch müssen Technologieunternehmen kaum Rechenschaft in internationalen Rechtsrahmen ablegen, wenn diese KI-Systeme Schaden anrichten. In diesem Paper wird behauptet, dass Staaten als Ausgangspunkt zur Behebung dieses Rechenschaftsdefizits zu einer proaktiven Regelung von „Data Enablers“ übergehen könnten, unter anderem durch Einbeziehung von Verfahren für das Datenqualitätsmanagement in nationale Waffenüberprüfungen zusammen mit Vereinbarungen sui generis zwischen Staaten und Technologieunternehmen, um die Qualität und Richtigkeit der Daten zu gewährleisten.The Military Law and the Law of War Review00The Military Law and the Law of War Review["Helen Stamp"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/48ef43d57a7f6d7e1a1d66420effcf4693b58f78 ee56c513767b946e40cc574b05651af816d8c237Se presenta como objetivo analizar la posibilidad de falsificación de pruebas a través de la inteligencia artificial. La metodología se trabajó desde el enfoque cualitativo basado en la revisión documental, se seleccionaron 16 artículos publicados entre 2020 y 2024. La inteligencia artificial (IA) tiene el potencial de transformar los procesos judiciales al mejorar la precisión, eficiencia y análisis de datos en la producción y valoración de pruebas. Sin embargo, también plantea riesgos significativos, como la falsificación de pruebas, la manipulación de datos y la falta de estándares éticos y regulatorios claros, que pueden comprometer los principios de justicia, equidad y transparencia.Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales170Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales["Evelyn Nayelli Santiago-Basantes", "Brayan Gabriel Taday-Guam\u00e1n", "Dennys Oswaldo Paullan-Punguil", "Eduardo Luciano Hern\u00e1ndez-Ramos"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/ee56c513767b946e40cc574b05651af816d8c237 8ef04914fa1bf350e340411082229e7c3ee68e6bSe indica como objetivo analizar la inteligencia artificial (IA) como amenaza del sistema jurídico. El trabajo se realizó mediante una revisión documental de carácter cualitativo, basada en 15 artículos científicos publicados entre 2020 y 2024. El aporte de la inteligencia artificial (IA) no está exento de riesgos y desafíos éticos, legales y sociales. La IA, al ser una herramienta creada y entrenada por humanos, puede perpetuar sesgos, comprometer principios fundamentales como la imparcialidad, la transparencia y las garantías procesales, y amplificar desigualdades existentes, especialmente en contextos vulnerables como América Latina. Por ello, su uso debe ser cuidadosamente regulado y guiado por principios éticos claros que prioricen la equidad, la justicia y la responsabilidad social. Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales00Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales["Edgar Ren\u00e9 Orozco-Zavala", "Andonny Lui Amores-Castillo", "Wilson Jahir Donoso-Beltr\u00e1n", "Guido Javier Silva-Andrade"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/8ef04914fa1bf350e340411082229e7c3ee68e6b 667f006645ecc244d4101a9b1ccf47d0ebfc299aLa inteligencia artificial (IA) está cambiando la forma en que entendemos y gestionamos muchos aspectos de nuestra vida, y el sistema judicial no es la excepción. Se tiene por objetivo analizar la inteligencia artificial en la judicatura penal: Evaluación de beneficios y retos. se realizó una revisión documental, se seleccionaron 16 referencias relevantes, publicadas entre 2020 y 2024. Su implementación no está exenta de retos que nos invitan a reflexionar sobre cómo garantizar que esta tecnología realmente sirva a las personas. La falta de regulación, los sesgos en los algoritmos y las desigualdades tecnológicas, especialmente en regiones como América Latina, nos recuerdan que la justicia no puede depender únicamente de máquinas.Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales00Verdad y Derecho. Revista Arbitrada de Ciencias Jurídicas y Sociales["Freddy Jos\u00e9 Aguas-Y\u00e1\u00f1ez", "Danny Patricio L\u00f3pez-Cando", "Alexis Xavier Moreano-Santos", "Pa\u00fal Orlando Piray-Rodr\u00edguez"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/667f006645ecc244d4101a9b1ccf47d0ebfc299a b2ff63f06029ea34f08ec310d3902e844c3e7fb2Journal of Coastal Research00Journal of Coastal Research["S. Veerasingam", "M. Ranjani", "F. S. Asim", "P.K. Hashir", "J.I. Prince", "Hana Ahmed", "E.E.E. Fatma Magdy", "Ridhwan Athaulla", "B. Abisha", "Raneem Omer Mohamed", "J. Al-Khayat", "S. Rajendran", "P. Vethamony", "F. Sadooni", "Saud Ghani"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/b2ff63f06029ea34f08ec310d3902e844c3e7fb2 76f8e7d75a6aacfc6b8bad9fc825baa30f3afcc1Cogent Business &amp; Management450Cogent Business &amp; Management["Tran Hung Nguyen", "Xuan Cu Le"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/76f8e7d75a6aacfc6b8bad9fc825baa30f3afcc1 7ba6e95b94bfc240c377aff75d0d3a2e5ecb9c76As a reason for research based on the author's interview with one of the Special Assistant Teachers (GPK) of SLB B Tunas Harapan Karawang on Friday, November 10 2023, it was said that Deaf students experienced several obstacles in the areas of socialization, social interaction, communication and cooperation because they were still minimal in vocabulary, Difficulty interpreting words or sentences that contain figurative meaning, and Difficulty interpreting abstract words. The aim of the research is to make things easier when individuals experience limitations in communication, in this case they have problems with hearing abilities or are deaf, which creates their own obstacles to carrying out the social interaction process. The research method or approach based on previous researchers carried out on the ASL language translation system is that it is able to display letters resulting from data processing in the system onto an LCD contained in the 1Sheeld application. The research results are to find out the implementation of Speech-to-Text Software, how to initialize and record sound, testing variations in speaking speed, configuring the Speech-to-Text program with Raspberry Pi, testing the Speech-to-Text system, and implementing hardware in the Speech-to-Text subsystem. The conclusion of this research is to answer the problems faced by Deaf students at SLB B Tunas Harapan Karawang in communicating with normal people.JUTECH Journal Education and Technology140The conclusion of this research is to answer the problems faced by Deaf students at SLB B Tunas Harapan Karawang in communicating with normal people in communicating with normal people.JUTECH : Journal Education and Technology["Efrans Firdaus"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/7ba6e95b94bfc240c377aff75d0d3a2e5ecb9c76 befee2d147acb987dd395dcaa678ef14b2031783Educational Media International230Educational Media International["J\u00e9ssica Mendoza Moheno", "M. A. Calzada", "Jos\u00e9 Ortega-Mohedano"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/befee2d147acb987dd395dcaa678ef14b2031783 ae5ab6b5a1c06decd35d970fbfb64b19b86437d8Journal of Coastal Research00Journal of Coastal Research["M. Romi\u0107", "V. Mornar", "Marko Relji\u0107", "Andrej Slapnicar", "Marina Bagi\u0107 Babac", "D. Romi\u0107"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/ae5ab6b5a1c06decd35d970fbfb64b19b86437d8 ece6695ea3adc0bb4896c28498ccdddf7c2f64c1European Heart Journal70European heart journal["F. Asselbergs", "Thomas F. L\u00fcscher"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/ece6695ea3adc0bb4896c28498ccdddf7c2f64c1 6a28fab55a76b534459fa091a194460b617b461cThe Scholarship Without Borders Journal00The Scholarship Without Borders Journal["Maher Abdelwahab"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/6a28fab55a76b534459fa091a194460b617b461c 9c2ac5781692a092493fbcef2395970340f36087International journal on responsibility00International Journal on Responsibility["Arwa Alnajashi", "Danielle DeRise", "Philip L. Frana", "David K McGraw", "Amanda Sawyer", "Tatjana Titareva", "Raafat Zaini", "Allie Zombron"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/9c2ac5781692a092493fbcef2395970340f36087 d587e03df690f8bc93705f72351f37d80a668346JMA Journal00JMA Journal["Soichiro Saeki"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/d587e03df690f8bc93705f72351f37d80a668346 67c2689ea425deae0cb2e0e3293a5659dbd93eaaJournal of Big Data580Unpredictable calamities, such as earthquakes, floods, fires, and pandemic breakouts, amplify these interruptions and cause permanent harm to the healthcare system and patient treatment.J. Big Data["Ibrahim M. Hezam", "Ahmed M. Ali", "Ahmad M. Alshamrani", "Xuehong Gao", "Mohamed Abdel-Basset"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/67c2689ea425deae0cb2e0e3293a5659dbd93eaa 58e6fe3db62ec2832172a73586ec6ec34bbeaba5Perkembangan pesat Kecerdasan Buatan (AI) membawa perubahan signifikan dalam dunia bisnis, terutama dalam meningkatkan efisiensi operasional dan inovasi. Namun, adopsi teknologi ini tidak terlepas dari tantangan teknis, ekonomi, dan etika yang kompleks. Penelitian ini bertujuan untuk mengidentifikasi peluang, manfaat, serta hambatan implementasi AI di berbagai sektor bisnis dengan pendekatan Systematic Literature Review (SLR). Menggunakan sumber-sumber terindeks dari tahun 2019 hingga 2023, penelitian ini menyaring 30 artikel untuk memberikan wawasan komprehensif tentang dampak teknologi AI di sektor bisnis. Temuan utama menunjukkan adopsi AI telah berkembang pesat di negara maju, meskipun terdapat kendala dalam hal biaya implementasi, infrastruktur teknologi, serta isu privasi data. Studi ini juga memberikan rekomendasi untuk penelitian lanjutan dan praktik bisnis di era digital.Djtechno: Jurnal Teknologi Informasi290Djtechno: Jurnal Teknologi Informasi["Ahmad Wildan Razaqi", "Nalendra Pradipta Loka", "Muhammad Alfian Handi Yudha"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/58e6fe3db62ec2832172a73586ec6ec34bbeaba5 2da668eea0867f5ff2235ede9fe9b196e80b74d8KNOWLEDGE TRANSFER STATEMENT The topic discussed in this commentary could serve as an initial inquiry point that deeply probes into the trustworthiness of an AI solution that a user might consider applying in the field of dentistry.JDR Clinical & Translational Research30The topic discussed in this commentary could serve as an initial inquiry point that deeply probes into the trustworthiness of an AI solution that a user might consider applying in the field of dentistry.JDR clinical and translational research["D. C. Mardini", "M. Sharma", "S. Madathil"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/2da668eea0867f5ff2235ede9fe9b196e80b74d8 5138f32c95e00174a72fecd0480ca4448d03864200["Michal Sourek"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/5138f32c95e00174a72fecd0480ca4448d038642 86804af55ee03a5911fb8ca329db3c291dfa03a9Language assessment and evaluation is crucial for employment, education, and language proficiency. Language evaluation has long employed human evaluators to rate and assess language competency in accordance with preset criteria. However, this manual assessment method has some limitations, including subjectivity, inter-rater variability, and scalability issues. The rapid advancement of AI technology has led to significant improvements in language assessment, producing more creative, accurate, and effective evaluation methods. The current study covers a wide range of subjects, including automated scoring and evaluation, benefits and advantages, challenges and considerations, and future directions. AI can assist language evaluation in achieving previously unheard-of levels of scalability and impartiality while accounting for ethical considerations. The goal of this article is to comprehend AI's impact on language assessment and guiding for further research and development in this dynamic field.مجلة كلية التربية العلمية260AI can assist language evaluation in achieving previously unheard-of levels of scalability and impartiality while accounting for ethical considerations, and guiding for further research and development in this dynamic field.مجلة كلية التربية العلمية["Omar Mohammed Ali Mohammed al Shaykhi"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/86804af55ee03a5911fb8ca329db3c291dfa03a9 fe7f6a351f1a7536d101c3b338fba97755b5ab29This article focuses on the adaptation made by ChatGPT to the lexical inventory corresponding to the perceptual and emotional dimensions of levels B1 and B2 of Spanish as a foreign language (ELE). To do so, the corpus of the Instituto Cervantes Curricular Plan (PCIC) and the texts generated by ChatGPT are analyzed. A series of preliminary conclusions are presented with the aim of evaluating the suitability of the generative artificial intelligence model to these levels of language proficiency.RILEX Revista sobre investigaciones léxicas00Evaluating the suitability of the generative artificial intelligence model to levels B1 and B2 of Spanish as a foreign language (ELE) and the corpus of the Instituto Cervantes Curricular Plan and the texts generated by ChatGPT are analyzed.RILEX. Revista sobre investigaciones léxicas["Maria Dolores Garc\u00eda Fern\u00e1ndez"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/fe7f6a351f1a7536d101c3b338fba97755b5ab29 b62e942df737505562bb7bf7e5da20ec2dabe2e2This community service activity aims to enhance the ability of teachers at SDI Sekolah Embriyo Inspirator in developing teaching materials using Artificial Intelligence (AI) technology. The background of this program is based on the importance of teaching materials as strategic tools to improve the quality of education. However, many teachers face challenges in creating teaching materials due to limited skills and knowledge. By leveraging advancements in AI, such as ChatGPT, Gemini, Bing Image Creator, Quillbot, and Mendeley, this activity was designed to provide practical and innovative solutions. Through a situational analysis, it was identified that SDI Sekolah Embriyo Inspirator, located in Tambun Selatan, has significant potential for educational development but requires capacity building for teachers to utilize technology effectively. The program was carried out in several stages, including preparation and socialization, training on AI-based teaching material development, hands-on practice in creating teaching materials, and evaluation and reporting. The activity, conducted on May 17, 2024, demonstrated high enthusiasm among teachers, who successfully applied various AI tools in their teaching material development practices. Additional discussions enriched participants’ knowledge of learning strategies. As a follow-up, continued collaboration is proposed for advanced training and the development of digital learning media. This program successfully provided a tangible impact in enhancing teachers' competencies and supporting the achievement of better educational quality in the digital era.Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat00This community service activity aims to enhance the ability of teachers at SDI Sekolah Embriyo Inspirator in developing teaching materials using Artificial Intelligence (AI) technology, and demonstrated high enthusiasm among teachers, who successfully applied various AI tools in their teaching material development practices.Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat["Rifki Muhendra", "Ratih Kumalasari", "Rifda Ilahy Rosihan", "Haris Hamdani"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/b62e942df737505562bb7bf7e5da20ec2dabe2e2 490b9ed72682030313540ee138637a7a92f71e08This systematic literature review investigates perceptions, concerns, and expectations of young digital citizens regarding privacy in artificial intelligence (AI) systems, focusing on social media platforms, educational technology, gaming systems, and recommendation algorithms. Using a rigorous methodology, the review started with 2,000 papers, narrowed down to 552 after initial screening, and finally refined to 108 for detailed analysis. Data extraction focused on privacy concerns, data-sharing practices, the balance between privacy and utility, trust factors in AI, transparency expectations, and strategies to enhance user control over personal data. Findings reveal significant privacy concerns among young users, including a perceived lack of control over personal information, potential misuse of data by AI, and fears of data breaches and unauthorized access. These issues are worsened by unclear data collection practices and insufficient transparency in AI applications. The intention to share data is closely associated with perceived benefits and data protection assurances. The study also highlights the role of parental mediation and the need for comprehensive education on data privacy. Balancing privacy and utility in AI applications is crucial, as young digital citizens value personalized services but remain wary of privacy risks. Trust in AI is significantly influenced by transparency, reliability, predictable behavior, and clear communication about data usage. Strategies to improve user control over personal data include access to and correction of data, clear consent mechanisms, and robust data protection assurances. The review identifies research gaps and suggests future directions, such as longitudinal studies, multicultural comparisons, and the development of ethical AI frameworks.arXiv.org542ArXiv["Ajay Kumar Shrestha", "Ankur Barthwal", "Molly Campbell", "Austin Shouli", "Syed Saad", "Sandhya Joshi", "Julita Vassileva"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/490b9ed72682030313540ee138637a7a92f71e08 0bd302fa214f20347da8335a0c921fd18a8938e1The integration of artificial intelligence (AI) into UX/UI design has revolutionized how digital interfaces interact with users, enabling personalized, adaptive, and user-centered experiences. This paper explores the transformative impact of AI-driven personalization on UX/UI design, emphasizing its role in enhancing user engagement, inclusivity, and interactivity. Key areas of focus include the intersection of AI technologies and information architecture, ethical considerations surrounding data privacy and algorithmic transparency, and the challenges of implementing AI in diverse design environments. Through detailed analysis and real-world case studies, this work highlights both the opportunities and potential pitfalls of leveraging AI for personalization. Future trends, such as conversational interfaces and augmented reality, are discussed, providing insights into the evolving landscape of AI-enhanced design. This study aims to equip designers, developers, and stakeholders with a comprehensive understanding of the implications and applications of AI-driven personalization in UX/UI design, fostering a balance between innovation and ethical responsibility.Revista ft01This paper explores the transformative impact of AI-driven personalization on UX/UI design, emphasizing its role in enhancing user engagement, inclusivity, and interactivity, and the challenges of implementing AI in diverse design environments.Revista ft["Guilherme de Abreu Lessa"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/0bd302fa214f20347da8335a0c921fd18a8938e1 6af4d94f5d2ec06cd899dd001415a1c352bb300eIntroduction: Artificial intelligence (AI) is increasingly transforming healthcare, however, the interaction between the user, AI, and the users environment is poorly understood. To elucidate this interplay and support the delivery of compassionate remote care in Occupational Therapy (OT) practice, we created a self-administered remote AI-powered cognitive assessment, facilitated using the Wizard of Oz method, and measured the experiences of patients, controls, caregivers, and healthcare providers. The Wizard of Oz method uses human-computer interaction (HCI) and user experience (UX) research to simulate the functionality of a system before it is fully developed by creating the illusion of a functioning system by having a human behind the scenes, controlling the system's responses. Research Questions: 1. How are AI-assisted cognitive assessments experienced by patients, caregivers, healthcare providers, and controls? 2. How can we maintain compassionate care while incorporating technology into healthcare? 3. What are the concerns of healthcare providers using chatbots to administer cognitive assessments? Methods: 6 participants with progressive cognitive decline, 6 healthy controls, 6 caregivers, and 6 healthcare providers were invited to complete a virtual AI-powered cognitive assessment followed by a survey about their experience and other demographical information. Survey questions were separated into 4 scales: Trust, Compassion, Usability, and Care Experience. Results: No statistically significant difference in mean survey scores between participant categories was observed. Factors such as sex, device type, chatbot familiarity, and education had no statistically significant effects. Participants scored statistically significantly lower on the scale Trust (8.09) than on Compassion (8.72). Additionally, those who used the chatbot during the assessment scored statistically significantly lower on the Usability scale compared to those who did not (7.33 vs. 9.20). Conclusion: The findings help to evaluate user experience with virtual AI-based cognitive assessments and provide insights that can inform important design characteristics to improve user experience and compassionate care delivery.medRxiv00A self-administered remote AI-powered cognitive assessment is created, facilitated using the Wizard of Oz method, and the experiences of patients, controls, caregivers, and healthcare providers are measured to evaluate user experience with virtual AI-based cognitive assessments.["M. Teng", "M. Yu", "W. Jin", "D. W. Hwang", "D. Stitt", "C. Conati", "G. Carenini", "T. Jarus", "T. S. Field"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/6af4d94f5d2ec06cd899dd001415a1c352bb300e dac6df3d2495951b622ee1d29513f16ec3b00a82Artificial Intelligence (AI), particularly large language models (LLMs), has demonstrated remarkable capabilities in addressing complex tasks, including professional-level medical question answering. While standardized benchmarks like the USMLE have been widely used for evaluating LLM performance in English, there is a significant gap in evaluating these models in other languages, such as Portuguese. To address this, we present a curated dataset derived from the Teste de Progresso (TP), a widely adopted Brazilian progress test used to assess medical knowledge across six key domains: Basic Sciences, Internal Medicine, Surgery, Obstetrics and Gynecology, Public Health, and Pediatrics. The dataset consists of 720 multiple-choice questions spanning five years (2019–2023). We demonstrate two primary applications of this dataset. First, we benchmark the performance of GPT-4, which achieved an overall accuracy of 90% across the six medical domains, with the highest performance in Internal Medicine (10%) and the lowest in Public Health (80%). Second, we develop a classification model based on BERTimbau, achieving an overall accuracy of 94% in categorizing questions into their respective medical domains. Our results highlight the utility of the dataset for both benchmarking AI models and automating medical question classification. This work emphasizes the importance of creating domain-specific datasets in underrepresented languages, like Portuguese, to advance AI-driven medical applications, ensure equitable access to AI technologies, and address linguistic and cultural gaps in healthcare education.bioRxiv00A curated dataset derived from the Teste de Progresso (TP), a widely adopted Brazilian progress test used to assess medical knowledge across six key domains, and develops a classification model based on BERTimbau, achieving an overall accuracy of 94% in categorizing questions into their respective medical domains.bioRxiv["Felipe Akio Matsuoka", "Henrique Nunes Onaga"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/dac6df3d2495951b622ee1d29513f16ec3b00a82 2a2fc8dfe6d476e620b494e3fff2a97d01b66243Artificial intelligence and data fusion technologies are being used to incorporate the technology into healthcare systems worldwide.  This work focuses on the idea and investigates how the AI-based Data Fusion Centre affects precision medicine, organizational and patient-centric models. In understanding how practice, diagnostics, and general efficiency of the healthcare system may benefit from AI, this article provides a great example. In this way, the approach is extended comprehensively by providing an analysis of techniques and illustrations. A unique case surveillance at Cleveland Clinic made it possible for the authors to record the influence of data fusion centers. Data fusion integrates as needed multiple data originating from various data fusion centers and provides a coherent and inclusive health status for a given patient. Some examples are genetics databases, electronic health records databases, wearable sensors in real-time databases. Contemporary diagnostic tools’ feasibility and efficacy are explained through methodologies based on machine learning and deep learning. These studies have helped in early diagnosis of the illness signals and cost parameters minimization. Analyzing this article one can observe that the growing ethical considerations to be met are to allow intelligent machines to work in full efficiency. The problem area that has come up in relation to GCP is data privacy, which is viewed as a major concern, second to algorithmic bias and integration. The results of the study show that it is expected that Data Fusion Center offers pro and post progressively effective and fair president, especially on the health of the clients.Edelweiss Applied Science and Technology00The results of the study show that it is expected that Data Fusion Center offers pro and post progressively effective and fair president, especially on the health of the clients, and the growing ethical considerations to be met are to allow intelligent machines to work in full efficiency.Edelweiss Applied Science and Technology["Shohoni Mahabub", "Bimol Chandra Das", "Md Russel Hossain"]2024-12-20T00:00:00https://www.semanticscholar.org/paper/2a2fc8dfe6d476e620b494e3fff2a97d01b66243 8a4dc36f8b5a3d5d7f01b777f5c92bf70f09f1bdThis paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of"idea-generation"tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.03The potential of AI-augmented research is demonstrated and the complementarity between algorithms and expertise in the innovative process is highlighted, as survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.["Aidan Toner-Rodgers"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/8a4dc36f8b5a3d5d7f01b777f5c92bf70f09f1bd 18ec196a56896b57c1ec3c0e052087e91a5dee32European Journal of Pediatrics1890Artificial intelligence-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.European Journal of Pediatrics["D. Lisik", "Rani Basna", "Tai Dinh", "C. Hennig", "Syed Ahmar Shah", "G\u00f6ran Wennergren", "E. Goks\u00f6r", "B. Nwaru"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/18ec196a56896b57c1ec3c0e052087e91a5dee32 48eb14c202de980d08cf0dd4e358127548fd2bbfThe present world is changing in a phase where there is no instrument which can exactly measure how fast the world is changing. In this ever-changing business market, Artificial Intelligence is one of the important techniques which is helping the employees in various aspects. But as there will be always a flip side for any coin, there are some risks and issues associated with the Artificial Intelligence. Improvement of productivity as well as efficiency, new job creation and improved decision making are positive side of AI whereas loss of privacy, reduction of jobs, and discrimination are issues with AI. The present research work focuses on employee perceptions towards changing trends of AI in the workspace. It is found that different sector respondents are feeling that government should play a vital role in aiding the employees in this AI era.RESEARCH REVIEW International Journal of Multidisciplinary00The present research work focuses on employee perceptions towards changing trends of AI in the workspace and it is found that different sector respondents are feeling that government should play a vital role in aiding the employees in this AI era.RESEARCH REVIEW International Journal of Multidisciplinary["E.M. Naresh Babu", "B. Sathwik", "K. Haseena"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/48eb14c202de980d08cf0dd4e358127548fd2bbf e51a48e23cffd0f6243a57bf7afcdef90e8a33afPengambilan keputusan merupakan elemen penting dalam organisasi, namun sering kali terbatas oleh bounded rationality yang mengacu pada keterbatasan manusia dalam memproses informasi secara optimal. Dengan berkembangnya teknologi big data, Artificial Intelligence (AI) menawarkan solusi untuk mengatasi keterbatasan ini melalui kemampuan analitik yang mendalam dan otomatisasi proses pengambilan keputusan bisnis. Penelitian ini bertujuan untuk menganalisis peran AI dalam mendukung bounded rationality, mengidentifikasi peluang dari integrasi keduanya, serta mengeksplorasi tantangan implementasinya di berbagai sektor. Dengan pendekatan kajian literatur, penelitian ini menunjukkan bahwa AI dapat meningkatkan efisiensi, memperluas wawasan pengambilan keputusan bisnis, dan mengurangi bias kognitif. Namun, tantangan seperti bias algoritmik, kualitas data, dan isu etika tetap menjadi hambatan utama. Temuan ini memberikan wawasan praktis dan teoritis bagi organisasi untuk memanfaatkan AI secara efektif guna mendukung pengambilan keputusan bisnis yang lebih cerdas di era big data.Jurnal Bisnis dan Komunikasi Digital170Jurnal Bisnis dan Komunikasi Digital["Gading Rayya Samita", "Widya Wisesa", "Ezra Daniel Setiawan", "Indah Respati", "R. Hidayat"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/e51a48e23cffd0f6243a57bf7afcdef90e8a33af 2d78be29aed25b74353c06c9153f01a9e3fd9356The purpose of this study is to investigate how Artificial Intelligence (AI) are being implemented in Supply Chain (SC) in emerging markets. Using a systematical literature review methodolody, this research analyze the publications available on Scopus, Emerald and Elsevier that link AI and SC. A total of 55 research studies have been identified, which are futher screened and finalized with 24 research studies. The research reviews and analyzes comprehensively the 24 studies that found relevant with impact supply chain capability and capacity in emerging markets. Our study underscores AI's contributions. AI enhances operational efficiency, resilience, and sustainability in supply chains, crucially impacting economic growth and global competitiveness. It acts as a catalyst for transforming supply chain dynamics, optimizing decision-making, mitigating risks, and fostering innovation across various sectors. Emerging markets play a pivotal role in AI adoption, leveraging its capabilities to adapt to market uncertainties and enhance supply chain agility. This comprehensive analysis not only elucidates AI's transformative potential but also underscores its critical role in shaping the future of global supply chain management. This study makes a comprehensive view from AI implication to SC in emerging market. The source of analyze based on academic credibility, novelty and relevance.INOBIS Jurnal Inovasi Bisnis dan Manajemen Indonesia210This comprehensive analysis not only elucidates AI's transformative potential but also underscores its critical role in shaping the future of global supply chain management.INOBIS: Jurnal Inovasi Bisnis dan Manajemen Indonesia["Yeremia Albert", "Antonius Alijoyo"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/2d78be29aed25b74353c06c9153f01a9e3fd9356 809b107dd93b2b3b50a3e8254f6ac243a717de86The primary objective of this study is to examine the ethical implications of incorporating Artificial Intelligence (AI) into public policy decision-making processes. The research underscores the critical importance of ethical considerations in the development and implemntation of AI technologies to addres potential risks whitin social contexts. Employing a qualitative analysis approach, the study draws on a comprehensive review of literature and data from various sources to explore the ethical dimension od AI in decision-making. The findings hinglight the necessity of integrating ethical frameworks into AI systems to ensure their responsible and effective use in public policy. The results also demonstrate how AI can enhance decision-making processes while simultaneously addressing ethical challenges. The study concludes by emphasizing the importance of establishing robust ethical guidelines to govern AI applications in public policy, ensuring that these technologies are developed and utilized in ways that are socially responsible and beneficial for society.Wacana: Jurnal Ilmu Sosial dan Ilmu Politik Interdisiplin240The study concludes by emphasizing the importance of establishing robust ethical guidelines to govern AI applications in public policy, ensuring that these technologies are developed and utilized in ways that are socially responsible and beneficial for society.Wacana: Jurnal Ilmu Sosial dan Ilmu Politik Interdisiplin["Jihan Rofifatuz Zahabiyyah", "Alya Nabila Septiana", "Hayat"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/809b107dd93b2b3b50a3e8254f6ac243a717de86 b9f2ce88874ad97fb8d8c353c9e6713808c8838dThe science in the world is moving very fast, and technological support is an important factor in the development of this increasingly fast-paced business world. There is something very promising when technology becomes a very reliable tool, as a substitute for the use of muscle-based or human labors. One of the technologies that greatly support the performance of business organizations is the use of digital technology in human resource governance. of these digital technologies, it is still focused on the use of artificial intelligence technology for integrative HR management. This research is a literature review of several articles related to machine learning. The review was conducted from some of the recent research efforts that utilize machine learning. Furthermore, this review is derived from multiple literacies and includes an attempt at problem solving efforts that are divided into section areas from the perspective of each AI category. AI can change the way the human resource management domain functions in an organization. It is making changes in all aspects of human resource management starting from human resource planning. Enormous data is available in human resource information systems (HRIS) available in organizations.Jurnal Penelitian Pendidikan IPA00This research is a literature review of several articles related to machine learning conducted from some of the recent research efforts that utilize machine learning.Jurnal Penelitian Pendidikan IPA["Yunita Niqrisah Dwi Pratiwi"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/b9f2ce88874ad97fb8d8c353c9e6713808c8838d 3cb6cc86db8ed3a31b33a02927f2ebd8af96be11Artificial Intelligence (AI) has emerged as a pivotal technology significantly impacting various sectors, including education. In the context of learning, AI holds the potential to improve personalization, automate assessments, and enhance learning analytics. However, in Islamic education, AI's implementation must align with the values of Islamic educational management and supervision. This study analyzes the application of AI in Indonesia's teaching and learning process while evaluating its benefits, challenges, and relevance to Islamic educational supervision. Using a literature review methodology, this study explores the role of AI in enhancing educational quality, particularly through personalized learning and administrative efficiency. Findings indicate that AI can assist supervisors in monitoring educational processes to ensure they are in line with Islamic values, such as justice, transparency, and character building. However, challenges such as technological infrastructure, digital literacy, data privacy, and algorithm bias remain significant. The study concludes that collaboration between the government, educational institutions, and supervisors is essential to develop infrastructure, train educators, and create policies that protect student privacy and uphold Islamic values. Future research should delve deeper into how Islamic educational supervision can optimize AI implementation in diverse educational settings.TOFEDU: The Future of Education Journal00It is concluded that collaboration between the government, educational institutions, and supervisors is essential to develop infrastructure, train educators, and create policies that protect student privacy and uphold Islamic values.TOFEDU: The Future of Education Journal["Bastomi Bastomi", "Ahmad Zarkasyi Mujahid", "Asmuni Asmuni", "A. Sibron", "Mia Audina", "Kasinyo Harto"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/3cb6cc86db8ed3a31b33a02927f2ebd8af96be11 2a93689c4ded059dd0dd2e703c91bfa2dd47182dThis review explores the integration of Human-Computer Interaction (HCI) principles in AI to advance Human-Centered Artificial Intelligence (HCAI). It highlights how these fields intersect to create user-friendly AI systems that enhance human capabilities and align with human values. Given the recent interest of HCI in user-centered design and AI in technical innovation, this paper bridges this divide by infusing principles from HCI into AI systems. Relevant peer-reviewed articles, conference papers, and case studies have been selected from leading databases like IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar, encompassing publications from 2017 to 2024. The inclusion criteria for the review focus on interdisciplinary approaches, real-world applications, and challenges of HCAI, while studies that do not have a clear methodology or lack relevance to HCAI were excluded. This paper identifies some of the key gaps, highlights the successful applications of HCAI across healthcare, edu-cation, and entertainment, and discusses various challenges that have arisen, such as bias, transparency, and balancing automation with human control. Findings reveal that iterative design and hu-man-centered frameworks will lead to better usability and ethical fit for HCAI, but significant challenges remain. This study proposes an integrative framework for bringing HCI principles into AI design through interdisciplinary collaboration in developing systems that will enhance human capabilities while considering ethical aspects. Future directions include responsible AI, personalized healthcare, and effective human-AI collaboration.Journal of Future Artificial Intelligence and Technologies450This study proposes an integrative framework for bringing HCI principles into AI design through interdisciplinary collaboration in developing systems that will enhance human capabilities while considering ethical aspects.Journal of Future Artificial Intelligence and Technologies["Shristi Sharma", "Sushil Shrestha"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/2a93689c4ded059dd0dd2e703c91bfa2dd47182d cf1ad3117ca6c3da7e03b0c120bf761fc2624fd5A recent technological advancement that helps meet many of the demands of people in many spheres of life is artificial intelligence. Politics has a role in one of these elements. As an example, artificial intelligence (AI) has started to be used in this context, particularly in Indonesian political campaigns. In more detail, the problems with AI's application in politics pertain to the efficiency, dangers, and obstacles that the field's future technological advancements will have to overcome in order to contribute to a positive transformation of political advertising. The study's findings indicate that there are a number of risks associated with using artificial intelligence (AI) technology for political marketing in the form of political advertising, but there are also advantages for the future growth of political advertising media. Cooperation and community socialization are necessary for the effective and efficient use of AI in politics, including in Indonesia.Riwayat: Educational Journal of History and Humanities00The study's findings indicate that there are a number of risks associated with using artificial intelligence (AI) technology for political marketing in the form of political advertising, but there are also advantages for the future growth of political advertising media.Riwayat: Educational Journal of History and Humanities["Putri Khairunnisa", "Mulyadi Mulyadi"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/cf1ad3117ca6c3da7e03b0c120bf761fc2624fd5 c91061ea8c68e18992ce7189d94bdfa56f650a7cThe escalating sophistication of cyber fraud necessitates innovative defense mechanisms, positioning Artificial Intelligence (AI) at the forefront of cybersecurity strategies. AI's role in combating cyber fraud is multifaceted, encompassing the detection, prevention, and mitigation of fraudulent activities. Leveraging machine learning algorithms, AI systems analyze vast amounts of data to identify patterns and anomalies indicative of fraud. These systems can rapidly adapt to new threats, providing real-time monitoring and response capabilities that outpace traditional methods. Additionally, AI enhances threat intelligence by integrating data from diverse sources, enabling a comprehensive understanding of cyber threats. Predictive analytics powered by AI allows for the anticipation of potential attacks, thereby strengthening pre-emptive measures. Furthermore, AI-driven automation reduces the burden on human analysts, enabling them to focus on more complex tasks. Despite its potential, the integration of AI in cybersecurity also presents challenges, such as algorithmic biases and the need for continuous learning. The synergistic application of AI in combating cyber fraud promises a robust defense mechanism, enhancing the resilience of digital ecosystems against evolving threats.Computer fraud & security140The synergistic application of AI in combating cyber fraud promises a robust defense mechanism, enhancing the resilience of digital ecosystems against evolving threats.Computer Fraud and Security["Dr. Sukhvinder Singh", "Dari", "Bipin Sule", "Ansh Anand Bhanushali", "Dr. Sunil L. Bangare"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/c91061ea8c68e18992ce7189d94bdfa56f650a7c 2eebea7eae42134052c1886ef089d60f8107c284The limitations of traditional advertising formats and the advent of the internet have driven a shift in the advertising industry toward the digital era, making modern advertising more effective and efficient. This transformation has also reshaped the landscape of marketing communication. Innovations in artificial intelligence (AI) play a critical role in facilitating marketing communication in the digital age, enabling companies to engage audiences in more personal and compelling ways. This study aims to explore the role of AI in enhancing the effectiveness of marketing communication compared to traditional methods. It employs a systematic literature review methodology guided by PRISMA protocols. The research focuses on analyzing academic literature and previous empirical studies on AI's role in content personalization, marketing efficiency, consumer behavior analysis, and its impact on customer engagement. The findings reveal that AI offers significant advantages over traditional methods, including improved content personalization, enhanced marketing efficiency, better consumer insights, increased customer engagement, and cost savings. However, challenges such as limited consumer understanding of AI and concerns over data privacy underscore the need for a more humanistic and transparent approach, emphasizing clear communication of AI's tangible benefits to consumers. Successful AI integration requires balancing human expertise with machine capabilities, aligning with broader business strategies, and addressing ethical considerations in its application.Jurnal lensa mutiara komunikasi310The research focuses on analyzing academic literature and previous empirical studies on AI's role in content personalization, marketing efficiency, consumer behavior analysis, and its impact on customer engagement to reveal that AI offers significant advantages over traditional methods.JURNAL LENSA MUTIARA KOMUNIKASI["Dinni Aulia"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/2eebea7eae42134052c1886ef089d60f8107c284 792b36fc79bbf1130e0ff20a6e19d399135f310eThis article emphasizes the importance of artificial intelligence in the tourism sector. Various technologies are being integrated to enhance customer services. The world is making significant efforts to strengthen industrial competitiveness using big data and artificial intelligence technologies, and tourism is facing increasing demands for new strategic solutions through the digital transformation of the existing tourism industry using cutting-edge technologies. Artificial intelligence quickly addresses customer requirements by providing timely information on critical factors such as hotels, airplanes, cathedrals, general facilities, and natural resources. This information can include interactive messages, virtual tours, interactive booking processes, language translations, and global positioning system technology. Artificial intelligence algorithms in the tourism industry help predict demand, revenue, and business trends. The tourism industry incorporates new technologies like virtual reality, chatbots, and language translation. Adopting artificial intelligence apps requires significant investments, including initial capital costs, maintenance, software updates, and staff training. Understanding the tourism and artificial intelligence industry will help build an artificial intelligence that will significantly transform the tourism industry in the future. The rise of artificial intelligence simplifies the process of making travel plans. Tourists used to decide their destinations and activities using pictures in a catalog or on the Internet. They have recently begun using artificial intelligence to find and tailor their specific requirements. Artificial intelligence is a valuable supplementary aspect to the future of the tourism industry. However, it cannot surpass the human touch, an essential determinant of experiential tourism.International Journal on Advanced Science, Engineering and Information Technology370Artificial intelligence is a valuable supplementary aspect to the future of the tourism industry, however, it cannot surpass the human touch, an essential determinant of experiential tourism.International Journal on Advanced Science, Engineering and Information Technology["Y. Choi", "Donghyun Kim"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/792b36fc79bbf1130e0ff20a6e19d399135f310e 1c9a84bc1028282811114c25b175a77efa87df5dEl libro "Creative Applications of Artificial Intelligence in Education", editado por Alex Urmeneta y Margarida Romero, presenta una amplia exploración de cómo la Inteligencia Artificial (IA) está transformando el ámbito educativo a través de aplicaciones creativas. La obra está dividida en tres partes y aborda desde aplicaciones generales de IA en educación, pasando por implementaciones específicas en la educación primaria y secundaria, hasta su uso en la educación superior. En cada sección analiza diversos temas que van desde las oportunidades que la IA ofrece para personalizar y mejorar el aprendizaje, hasta los desafíos éticos, técnicos y pedagógicos que surgen con su integración. Además, se enfatiza la importancia de mantener un enfoque centrado en el ser humano, promoviendo una colaboración equilibrada entre humanos y máquinas para potenciar las capacidades educativas sin desplazar el rol fundamental de los educadores.Revista Estudios de la Información00Revista Estudios de la Información["Humberto Mart\u00ednezCamacho"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/1c9a84bc1028282811114c25b175a77efa87df5d 7acc8dbb4f26ca724539fcca2c0f9d66ae31ed99This study aims to describe the perceptions of students of the Faculty of Teacher Training and Education, University of Jambi towards the use of Artificial Intelligence (AI) in compiling scientific papers as the final assignment of the undergraduate education program. Using a quantitative approach, this study found that the mastery of the AI ​​program was dominated by natural science students (66.40%), while social science students were only 33.60%. In terms of writing skills, natural science students had an average mastery of 64%, compared to 36% for social science students. Student perceptions of the use of AI in writing scientific papers showed positive results, with 86% of students strongly agreeing, 9% agreeing, and the rest being hesitant or disagreeing. In addition, the percentage of students who felt helped in terms of grammar reached an average of 92.66%, and in terms of the technique of compiling and developing scientific papers, the average was 91.58%. The results of hypothesis testing using ANOVA analysis showed a very small significance value (0.000), which means there is a significant relationship between the use of AI and the ability to write scientific papers. This study concludes that the mastery of various AI programs is dominated by natural science students compared to students from social science backgrounds.Jurnal Penelitian Pendidikan IPA00The mastery of various AI programs is dominated by natural science students compared to students from social science backgrounds, which means there is a significant relationship between the use of AI and the ability to write scientific papers.Jurnal Penelitian Pendidikan IPA["Andiopenta"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/7acc8dbb4f26ca724539fcca2c0f9d66ae31ed99 c0c4d4ddf62346f1d2f4e7ae8cdf41204f315a47doi: https://doi.org/10.12669/pjms.41.1.11391 How to cite this: Mansoor A. Artificial intelligence in radiology: Radiologist’s adversary or comrade? Pak J Med Sci. 2025;41(1):1-2.  doi: https://doi.org/10.12669/pjms.41.1.11391 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Pakistan Journal of Medical Sciences80Artificial intelligence in radiology: Radiologist’s adversary or comrade?Pakistan Journal of Medical Sciences["Ali Mansoor"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/c0c4d4ddf62346f1d2f4e7ae8cdf41204f315a47 f135fa2f0a61629358f463d98134956528675dc8Through the analysis of network structure, the network model can be expressed by mathematical formula. This paper studies the promotion of mathematical algorithms to the development of computer artificial intelligence. This paper mainly solves the problem of how to transform complex networks into simple logic and mathematics. Based on the theoretical background of intelligent concept, that is, artificial intelligence theory, and combined with the discipline structure characteristics of intelligent technology, this paper attempts to comb and analyze the development of intelligent concept with mathematical algorithm. Through the optimization of the network through operation analysis, a more general network mathematical model can be abstracted. It makes the network structure relationship more simple, practical, safe and reliable, and is more suitable for the application environment of various types of network mathematical models. In addition, the application of mathematical theory to artificial intelligence network can simplify the artificial intelligence network and facilitate the analysis and calculation of artificial intelligence network.Computer fraud & security100This paper mainly solves the problem of how to transform complex networks into simple logic and mathematics and can simplify the artificial intelligence network and facilitate the analysis and calculation of artificial intelligence network.Computer Fraud and Security["YuBao Chang"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/f135fa2f0a61629358f463d98134956528675dc8 1bfef19f6ce500f5700e0b5b68840d1e0962d688International Research Journal of Modernization in Engineering Technology and Science01International Research Journal of Modernization in Engineering Technology and Science[]2024-12-21T00:00:00https://www.semanticscholar.org/paper/1bfef19f6ce500f5700e0b5b68840d1e0962d688 cf9b587a70ff051386b3e2e79ebdfbec64af7d91The existing driver driving behavior supervision platform is slightly insufficient in the overall control of fleet safety management. This is reflected in the lack of a clear definition of safety monitoring and the inability to implement timely intervention in case of bad driving behavior. Thus, the current fleet safety accident rate is still high. Based on this background, this paper proposes a safety management method for drivers on the way. This method combines the analytic hierarchy process to analyze the driver's driving behavior, identify the bad driving behavior on the way and push it to the team administrator in time. The team manager can intervene in time when facing the bad driving behavior of drivers. This can effectively prevent and control the occurrence of fleet safety accidents. Combined with this theory and method, this paper designs and implements a safety supervision platform based on driving behavior. The platform mainly includes six functional modules: data preprocessing, driving behavior calculation, safety value calculation, data storage and push, and real-time display. The test shows that the platform can reduce the human factors affecting vehicle driving safety.Computer fraud & security00A safety management method for drivers on the way that combines the analytic hierarchy process to analyze the driver's driving behavior, identify the bad driving behavior on the way and push it to the team administrator in time can effectively prevent and control the occurrence of fleet safety accidents.Computer Fraud and Security["Changliu Li"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/cf9b587a70ff051386b3e2e79ebdfbec64af7d91 8b492050d68fde35d14de70b2c02b635beaca72bUniversum:Philology and art history00Universum:Philology and art history["Victoria Pustovedova", "Natalia Bykova", "Elena Chernyshkova"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/8b492050d68fde35d14de70b2c02b635beaca72b 9fe39e6314a0ab80f15787b0bc695dfc469b6ca7INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Rohith Vangalla"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/9fe39e6314a0ab80f15787b0bc695dfc469b6ca7 718d9eebbc5e5537f308ed7c0211e7fd67456c81Parasitologists United Journal00Parasitologists United Journal["Reham Refaat Mostafa", "Noha Madbouly Taha", "Fatma M.A. Eissa"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/718d9eebbc5e5537f308ed7c0211e7fd67456c81 ef5b184bd20c810ce6add967d54b000e9857fcc4npj Climate Action200npj Climate Action["Daniel Richards", "David Worden"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/ef5b184bd20c810ce6add967d54b000e9857fcc4 a7fe71a47c247fd8dcff6d41b9f20e51e4d95812Journal of Academic Ethics200Journal of Academic Ethics["Russell Franco D\u2019Souza", "K. M. Surapaneni", "Sathyanarayanan P", "Annamalai Regupathy", "Mary Mathew", "Vedprakash Mishra", "Ani Grace Kalaimathi", "Geethalakshmi Sekkizhar", "R. Tandon", "Princy Louis Palatty", "Vivek Mady"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/a7fe71a47c247fd8dcff6d41b9f20e51e4d95812 454c52b898032d00f398dfe417226145ccc49226Background. In the current era of technological advances, the development of artificial intelligence has experienced a significant surge in its development and application in the last two years. AI is used to create virtual personas with realistic voices and facial expressions, generate automatic text, and more. Its contributions to human life are undeniable, but where are the limits of its use established? Is its use ethical? Aim. This study aims to analyze the ethical boundaries of artificial intelligence use in academic and educational contexts. Method. To do this, a mixed approach is used. First, a systematic review of scientific literature on artificial intelligence was conducted using the Web of Science database. Subsequently, a qualitative SWOT analysis was employed to analyze and discuss the findings. Results. As a result, it is important to note that the use of artificial intelligence for generating texts is a valuable and beneficial tool for the study and use of languages, as it allows for the creation of speeches in other languages almost instantly. However, weaknesses were also identified, such as the risk of plagiarism in educational, academic, and university environments. Conclusions. Therefore, establishing ethical standards and limits on the use of AI is a fundamental basis for supporting its utilization.Revista Electrónica Educare00Establishing ethical standards and limits on the use of AI is a fundamental basis for supporting its utilization, and it is important to note that the use of artificial intelligence for generating texts is a valuable and beneficial tool for the study and use of languages.Revista Electrónica Educare["Lionel S\u00e1nchez-Bol\u00edvar", "Sergio Escalante-Gonz\u00e1lez", "Asunci\u00f3n Mart\u00ednez-Mart\u00ednez"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/454c52b898032d00f398dfe417226145ccc49226 c2c43036d6679486bd3f273777b82b8c8bffb1cfThe emergence of large language models (LLMs) has sparked the possibility of about Artificial Superintelligence (ASI), a hypothetical AI system surpassing human intelligence. However, existing alignment paradigms struggle to guide such advanced AI systems. Superalignment, the alignment of AI systems with human values and safety requirements at superhuman levels of capability aims to addresses two primary goals -- scalability in supervision to provide high-quality guidance signals and robust governance to ensure alignment with human values. In this survey, we examine scalable oversight methods and potential solutions for superalignment. Specifically, we explore the concept of ASI, the challenges it poses, and the limitations of current alignment paradigms in addressing the superalignment problem. Then we review scalable oversight methods for superalignment. Finally, we discuss the key challenges and propose pathways for the safe and continual improvement of ASI systems. By comprehensively reviewing the current literature, our goal is provide a systematical introduction of existing methods, analyze their strengths and limitations, and discuss potential future directions.arXiv.org631This survey explores the concept of ASI, the challenges it poses, and the limitations of current alignment paradigms in addressing the superalignment problem, and reviews scalable oversight methods for superalignment.ArXiv["HyunJin Kim", "Xiaoyuan Yi", "Jing Yao", "Jianxun Lian", "Muhua Huang", "Shitong Duan", "J. Bak", "Xing Xie"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/c2c43036d6679486bd3f273777b82b8c8bffb1cf fe76f5377e8117e06fe28d920b62dab65d769d85In the digital age, financial systems and personal identities are increasingly targeted for fraud by sophisticated actors, including criminal organizations, terrorist groups, and rogue states. The U.S., as a global financial hub, faces unique challenges in mitigating these threats, which have direct implications for national security. The rise of cloud-native AI-based systems offers a powerful solution for detecting and preventing identity and financial fraud at scale. Leveraging artificial intelligence (AI) in a cloud-native environment enables federal agencies and private-sector institutions to uncover fraudulent transactions, trace illicit funds, and disrupt organized networks with unprecedented speed and accuracy.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402304Leveraging artificial intelligence (AI) in a cloud-native environment enables federal agencies and private-sector institutions to uncover fraudulent transactions, trace illicit funds, and disrupt organized networks with unprecedented speed and accuracy.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Prashis Raghuwanshi"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/fe76f5377e8117e06fe28d920b62dab65d769d85 76b7a7629bed9bc550a8ab40e0702c2822887610Anticipation is a fundamental cognitive process in interpreting that enables interpreters to predict upcoming speech segments and facilitate the transfer of meaning between languages. This abstract explores the cognitive aspects of anticipation in interpreting and examines how artificial intelligence (AI) can enhance this process. Drawing on research from cognitive psychology and interpreting studies, the abstract discusses the cognitive mechanisms involved in anticipation, including the role of working memory, attention, and language processing It explores how interpreters utilize anticipation at different levels, such as lexical, syntactic, and semantic anticipation, to produce fluent and coherent interpretations. Furthermore, the abstract examines the potential of AI in supporting interpreters' anticipation skills. It discusses how AI technologies, such as machine learning and natural language processing, can analyze language patterns, predict upcoming speech segments, and provide real-time suggestions to interpreters. The integration of AI in interpreting can augment interpreters' anticipation abilities, improve accuracy, and enhance the overall interpreting experience. However, challenges such as the need for training AI models on diverse language pairs and the importance of maintaining the human interpreter's role and expertise should be considered. Understanding the cognitive aspects of anticipation in interpreting and the potential of AI can inform the development of AI-assisted interpreting tools and advance the field by optimizing the efficiency and quality of interpretation. Al-Noor Journal for Humanities00Understanding the cognitive aspects of anticipation in interpreting and the potential of AI can inform the development of AI-assisted interpreting tools and advance the field by optimizing the efficiency and quality of interpretation.Al-Noor Journal for Humanities[]2024-12-21T00:00:00https://www.semanticscholar.org/paper/76b7a7629bed9bc550a8ab40e0702c2822887610 75bac6c934049d9f00a9e7fdca85ba664354002eThe widespread application of digital technology has triggered profound changes in the field of education, promoting the innovation and transformation of the education model, and international organizations have deeply discussed and given creative suggestions on sensitive topics such as when and how artificial intelligence can be used in education This paper focuses on the latest norms proposed by UNESCO, the OECD and the EU on the application of AI in education, and compares and analyses them in four dimensions, namely, focusing on the perspective, actual influence, degree of innovation, and focusing on preventing risks. Lastly, the international organizations provide useful references and guidance for the specific field of "generative AI", which covering teacher training, educational content standardization, data protection and ethical concerns. The comparison of the education policy recommendations of the three international organizations aims to help governments sort out the similarities and differences between the starting points, concerns and goals of different international organizations, and then clarify the relevance and feasibility of their recommendations.Al-Noor Journal for Humanities00The comparison of the education policy recommendations of the three international organizations aims to help governments sort out the similarities and differences between the starting points, concerns and goals of different international organizations, and then clarify the relevance and feasibility of their recommendations.Al-Noor Journal for Humanities[]2024-12-21T00:00:00https://www.semanticscholar.org/paper/75bac6c934049d9f00a9e7fdca85ba664354002e 025074b2ef5ab49c3bef2ce79602eb14312c9fcfObjective: The incorporation of artificial intelligence (AI) and technological tools into managerial processes marks a significant evolution in modern leadership practices, driven by the need to address complex organizational challenges and capitalize on technological advancements. This study aims to investigate the impact of AI and technology on leadership practices, focusing on their implications for efficiency, adaptability, and employee engagement. Theoretical framework: The research is grounded in a theoretical framework that combines technological innovation with contemporary leadership models, emphasizing the role of AI-powered tools and project management software in enhancing managerial workflows. Literature Review: A literature review provides insights into previous studies that highlight the transformative potential of these technologies while addressing associated challenges, such as ethical concerns and employee resistance. Methods: Employing a qualitative methodology, this study analyzes case studies and relevant literature to explore how organizations integrate AI and technological tools to achieve competitive advantages. Results: The findings reveal that AI-driven insights significantly improve predictive analytics, enabling leaders to make strategic decisions, while project management software optimizes task allocation and communication, fostering team collaboration. However, challenges such as resistance to adoption and the need for employee training are identified as critical barriers to effective implementation. Implications: The study's implications are twofold: first, it offers a practical framework for leaders aiming to integrate AI and technology into their management practices, emphasizing strategies to enhance efficiency, decision-making, and teamwork; second, it highlights the importance of addressing ethical considerations and ensuring employee readiness through comprehensive training programs. Novelty: This research contributes novelty by providing a holistic perspective on the integration of AI and technology in leadership, bridging the gap between technological innovation and human-centric management approaches. By adopting the proposed framework, leaders can navigate potential challenges and leverage technological tools to transform their leadership practices, ensuring organizational adaptability and sustained success in a rapidly evolving landscape.Solo International Collaboration and Publication of Social Sciences and Humanities621By adopting the proposed framework, leaders can navigate potential challenges and leverage technological tools to transform their leadership practices, ensuring organizational adaptability and sustained success in a rapidly evolving landscape.Solo International Collaboration and Publication of Social Sciences and Humanities["Uwase Shakilla", "Edy Purwo Saputro"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/025074b2ef5ab49c3bef2ce79602eb14312c9fcf 5825e84b3e1a5dcf4efed9e03842399d5c6a9c10Artificial intelligence (AI) is fast altering the landscape of cybersecurity, and it is becoming a double-edged sword. AI improves defensive and offensive capabilities while also giving cyber enemies significant power, such as the ability to execute complex, automated cyber-attacks. Specifically, this paper reviews the basics of AI in cybersecurity, focusing on its use in defensive as well as offensive pivot operations. This examines the types of AI-powered cyber-attacks, such as adversarial machine learning and automated social engineering. Threats such as anomaly detection and behavioural analysis are discussed as detection and defence mechanisms to counteract these threats. This is demonstrated through illustrative real-world case studies. Finally, ethical implications are discussed, and opportunities and challenges of AI across future trends and emerging technologies are delineated in relation to cybersecurity. With AI progressing, the need to develop a robust defensive strategy to secure digital systems and protect sensitive information is not negotiable.International Journal For Multidisciplinary Research280This paper reviews the basics of AI in cybersecurity, focusing on its use in defensive as well as offensive pivot operations, and examines the types of AI-powered cyber-attacks, such as adversarial machine learning and automated social engineering.International Journal For Multidisciplinary Research["Richard Aggrey", "Bright Ansah Adjei", "Karl Osei Afoduo", "Nana Adwoa Konadu Dsane", "Lena Anim", "Millicent Abrefi Ababio"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/5825e84b3e1a5dcf4efed9e03842399d5c6a9c10 0e5492019df185e257ec5882a20d181f6cd8f2dcArt therapy has been proven to be efficacious in alleviating symptoms of children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD), with the advent of artificial intelligence providing new technological means to engage and increase the participation levels of ADHD children. Nonetheless, research on the application of AI in art therapy remains scant. This study, predicated on a method involving ADHD children co-creating art with AI, has devised a therapeutic activity aimed at ameliorating their attention deficits. By conducting standardized measurements with the SNAP-IV 26 questionnaire and qualitative analyses of the art created by 16 ADHD children, the effectiveness of the "Co-creation with AI" activity was assessed. Findings indicate that this activity, through a process encompassing engagement, action, variable rewards, and sustained involvement, has facilitated ADHD children in focusing their attention and ameliorating their hyperactive behavioral issues. However, it offered minimal assistance in addressing their oppositional defiant behavior. The co-creative approach with machines has effectively enriched the emotional expression of ADHD children and mobilized their enthusiasm. Nevertheless, the instability in AI's painting style and its variability may lead to a diminution in the children's interest over time. The guidance of therapists and communication in daily life are indispensable elements.Applied neuropsychology. Child280Applied neuropsychology. Child["Aijia Zhang", "Runqing Lin", "Xuexin Luo", "Hong Li", "Guanghui Huang"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/0e5492019df185e257ec5882a20d181f6cd8f2dc 190d0545927d0b8131a3aad4fae25a92e2a2ddf5This paper presents a comprehensive overview on the applications of artificial intelligence (AI) in mathematical research, highlighting the transformative role AI has begun to play in this domain. Traditionally, AI advancements have heavily relied on theoretical foundations provided by mathematics and statistics. However, recent developments in AI, particularly in reinforcement learning (RL) and large language models (LLMs), have demonstrated the potential for AI to contribute back to mathematics by offering flexible algorithmic frameworks and powerful inductive reasoning capabilities that support various aspects of mathematical research. This survey aims to establish a bridge between AI and mathematics, providing insights into the mutual benefits and fostering deeper interdisciplinary understanding. In particular, we argue that while current AI and LLMs may struggle with complex deductive reasoning, their"inherent creativity", the ability to generate outputs at high throughput based on recognition of shallow patterns, holds significant potential to support and inspire mathematical research. This creative capability, often overlooked, could be the key to unlocking new perspectives and methodologies in mathematics. Furthermore, we address the lack of cross-disciplinary communication: mathematicians may not fully comprehend the latest advances in AI, while AI researchers frequently prioritize benchmark performance over real-world applications in frontier mathematical research. This paper seeks to close that gap, offering a detailed exploration of AI fundamentals, its strengths, and its emerging applications in the mathematical sciences.arXiv.org00This survey aims to establish a bridge between AI and mathematics, providing insights into the mutual benefits and fostering deeper interdisciplinary understanding, and argues that while current AI and LLMs may struggle with complex deductive reasoning, theirherent creativity holds significant potential to support and inspire mathematical research.ArXiv["Shi-Zhong Liang", "Wei Zhang", "Tianyang Zhong", "Tian Xi Liu"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/190d0545927d0b8131a3aad4fae25a92e2a2ddf5 658e00201c38b8c047dffd67a7c7430e7817f7a2The age of Artificial Intelligence (AI) is marked by its transformative"generative"capabilities, distinguishing it from prior iterations. This burgeoning characteristic of AI has enabled it to produce new and original content, inherently showcasing its creative prowess. This shift challenges and requires a recalibration in the realm of arts education, urging a departure from established pedagogies centered on human-driven image creation. The paper meticulously addresses the integration of AI tools, with a spotlight on Stable Diffusion (SD), into university arts curricula. Drawing from practical insights gathered from workshops conducted in July 2023, which culminated in an exhibition of AI-driven artworks, the paper aims to provide a roadmap for seamlessly infusing these tools into academic settings. Given their recent emergence, the paper delves into a comprehensive overview of such tools, emphasizing the intricate dance between artists, developers, and researchers in the open-source AI art world. This discourse extends to the challenges and imperatives faced by educational institutions. It presents a compelling case for the swift adoption of these avant-garde tools, underscoring the paramount importance of equipping students with the competencies required to thrive in an AI-augmented artistic landscape.arXiv.org320The paper meticulously addresses the integration of AI tools, with a spotlight on Stable Diffusion (SD), into university arts curricula, and presents a compelling case for the swift adoption of these avant-garde tools, underscoring the paramount importance of equipping students with the competencies required to thrive in an AI-augmented artistic landscape.ArXiv["Atticus Sims"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/658e00201c38b8c047dffd67a7c7430e7817f7a2 9b29557763e670b607caca0218916cecd567111ePublic infrastructure, which includes roads, bridges, utilities, transit networks, and public buildings, is the foundation of contemporary society. The need for robust, effective, and sustainable infrastructure has increased as the world's population continues to rise and urbanize. There is a chance to take use of developments in artificial intelligence (AI) at the junction of this difficulty. With its powers in data analysis, automation, predictive modeling, and real-time decision-making, artificial intelligence (AI) has the potential to revolutionize public infrastructure, resolving long-standing problems and bringing about new standards of sustainability, safety, efficiency, and durability.International Journal for Research Publication and Seminar40International Journal for Research Publication and Seminar["Parth Vishnubhai Prajapati"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/9b29557763e670b607caca0218916cecd567111e 82050b2e577070103b08491451d6e77f1974c525J. Intell. Robotic Syst.110This research combines survey data with real-time physiological monitoring, offering visions into optimizing human-AI interaction in UAV operations and providing a foundation for improving AI integration and operator strategies.J. Intell. Robotic Syst.["Omar Alharasees", "Utku Kale"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/82050b2e577070103b08491451d6e77f1974c525 636c7f37463fbb3ad313efd3e2237a0700b13a0aAI and Ethics310The need to “decolonize” AI ethics to ensure just, equitable, and inclusive AI in healthcare in Africa is discussed and key principles that can ensure an approach rooted in African contexts and values are proposed.AI and Ethics["Mugalula Kalule Grancia"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/636c7f37463fbb3ad313efd3e2237a0700b13a0a 8a7f4c523b9d2b674801ca21f4d022a3d7ba1255Background: Chest X-ray Radiography (CXR) is the primary investigation for patients with potential symptoms of lung cancer in the UK. Artificial intelligence (AI) can detect abnormalities on CXR and prioritise cases for reporting. We describe a method to determine which AI findings are associated with lung cancer to inform and validate prioritisation strategies. Methods: This multicentre study compared the prevalence of AI findings on CXR in a retrospective cohort of patients diagnosed with lung cancer (4408 CXR) to the prevalence of AI findings in a prospective cohort of CXR from the referral population (107,065 CXR). Nineteen AI findings were assessed individually and in combination. Results: The most common AI findings in patients with lung cancer compared to the referral population were "Abnormal" (92.6% vs 60.9%), Opacity (83.4% vs 45.4%), Consolidation (36.9% vs 12.9%), Atelectasis (33.5% vs 20.9%) and Nodule (32.7% vs 10.9%). The finding most associated with cancer based on the prevalence ratio in the cancer and referral cohorts were "Lung nodule malignancy" (13.3), Cavity (4.0), Tracheal deviation (3.1), Nodule (3.0) and Consolidation (2.9). The percentage of CXR classed as "AI Abnormal" varied by the referral cohort, 63.5% from Accident & Emergency vs 43.0% from General Practice. This suggests significant variation in the complexity of cases across referral pathways. Conclusion: Individual AI findings had limited sensitivity in detecting lung cancer. Using combinations of AI findings significantly improved cancer detection rates but required prioritising a larger proportion of CXR from the referral population.medRxiv00Individual AI findings had limited sensitivity in detecting lung cancer, so using combinations of AI findings significantly improved cancer detection rates but required prioritising a larger proportion of CXR from the referral population.["R. Bramley", "P. Broadhurst", "A. Sharman", "D. Robert", "E. Weber", "M. Simon", "R. Naseer", "L. Brown", "S. Grundy", "M. Evison"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/8a7f4c523b9d2b674801ca21f4d022a3d7ba1255 553f60ed936fbb2749d87fe25a310bf1b123a8ecRobotics and automation represent transformative fields that integrate mechanical systems, electronics, and intelligent software to perform tasks with minimal human intervention. Robotics involves the design, development, and deployment of machines capable of sensing, decision-making, and executing physical actions. Automation focuses on optimizing processes by employing advanced technologies to increase efficiency, precision, and reliability across industries. Together, these disciplines revolutionize sectors such as manufacturing, healthcare, agriculture, logistics, and exploration. Emerging trends, including artificial intelligence, machine learning, and collaborative robots (cobots), are driving the evolution of robotics and automation, enabling systems to adapt, learn, and operate in unstructured environments. These advancements address critical challenges such as labor shortages, cost reduction, and safety while unlocking opportunities for innovation and productivity. As the integration of robotics and automation deepens, ethical considerations and workforce adaptation remain essential to ensuring equitable and sustainable technological progressInternational Journal of Advanced Research in Science, Communication and Technology70As the integration of robotics and automation deepens, ethical considerations and workforce adaptation remain essential to ensuring equitable and sustainable technological progress.International Journal of Advanced Research in Science, Communication and Technology["Dr. Pradeep V", "Abhi B C", "Abhilash C M", "Abhishek M S", "Adarsh"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/553f60ed936fbb2749d87fe25a310bf1b123a8ec 25e02fdf899bb13ba0ee43cd45418d55fee11bb3The exponential increase in biomedical data offers unprecedented opportunities for drug discovery, yet overwhelms traditional data analysis methods, limiting the pace of new drug development. Here we introduce a framework for autonomous artificial intelligence (AI)-driven drug discovery that integrates knowledge graphs with large language models (LLMs). It is capable of planning and carrying out automated drug discovery programs at a massive scale while providing details of its research strategy, progress, and all supporting data. At the heart of this framework lies the focal graph - a novel construct that harnesses centrality algorithms to distill vast, noisy datasets into concise, transparent, data-driven hypotheses. We demonstrate that even small-scale applications of this highly scalable approach can yield novel, transparent insights relevant to multiple stages of the drug discovery process and present a prototype system which autonomously plans and executes a multi-step target discovery workflow. Graphical AbstractbioRxiv00A framework for autonomous artificial intelligence (AI)-driven drug discovery that integrates knowledge graphs with large language models (LLMs) is introduced, capable of planning and carrying out automated drug discovery programs at a massive scale while providing details of its research strategy, progress, and all supporting data.bioRxiv["Douglas W. Selinger", "Timothy R. Wall", "Eleni Stylianou", "Ehab M. Khalil", "Jedidiah Gaetz", "Oren Levy"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/25e02fdf899bb13ba0ee43cd45418d55fee11bb3 314d30465bcaf8f0d50c686431f3927705f66eee Artificial intelligence is now capable of generating sophisticated and compelling images from simple text prompts. In this paper, I focus specifically on how artists might make use of AI to create art. Most existing discourse analogizes AI to a tool or collaborator; this focuses our attention on AI’s contribution to the production of an artistically significant output. I propose an alternative approach, the exploration paradigm, which suggests that artists instead relate to AI as a participant: artists create a space for interaction with the AI algorithm by way of their prompts, thereby allowing them to explore the way that the algorithm ‘sees’ and ‘represents’. AI art practised in this fashion bears a striking resemblance to contemporary conceptual and participatory art. Viewing AI art in this way has implications for the appreciation of style in AI art; concerns about novelty and originality; and the assignment of artistic credit and copyright.British Journal of Aesthetics230This paper proposes an alternative approach, the exploration paradigm, which suggests that artists instead relate to AI as a participant: artists create a space for interaction with the AI algorithm by way of their prompts, thereby allowing them to explore the way that the algorithm ‘sees’ and ‘represents’.British Journal of Aesthetics["Anthony Cross"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/314d30465bcaf8f0d50c686431f3927705f66eee 649be8f18df78e439c50afa780ff07183513efb8The skyrocketing demand for artificial intelligence (AI) has created an enormous appetite for globally deployed power-hungry servers. As a result, the environmental footprint of AI systems has come under increasing scrutiny. More crucially, the current way that we exploit AI workloads' flexibility and manage AI systems can lead to wildly different environmental impacts across locations, increasingly raising environmental inequity concerns and creating unintended sociotechnical consequences. In this paper, we advocate environmental equity as a priority for the management of future AI systems, advancing the boundaries of existing resource management for sustainable AI and also adding a unique dimension to AI fairness. Concretely, we uncover the potential of equity-aware geographical load balancing to fairly re-distribute the environmental cost across different regions, followed by algorithmic challenges. We conclude by discussing a few future directions to exploit the full potential of system management approaches to mitigate AI's environmental inequity.arXiv.org230This paper advocates environmental equity as a priority for the management of future AI systems, advancing the boundaries of existing resource management for sustainable AI and also adding a unique dimension to AI fairness.ArXiv["Mohammad Hajiesmaili", "Shaolei Ren", "Ramesh K. Sitaraman", "Adam Wierman"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/649be8f18df78e439c50afa780ff07183513efb8 ac5a61d6d7c1ec6938bddb6cdbdbf66e7e210ae4Recent advancements in artificial intelligence (AI) applications within aerospace have demonstrated substantial growth, particularly in the context of control systems. As High Performance Computing (HPC) platforms continue to evolve, they are expected to replace current flight control or engine control computers, enabling increased computational capabilities. This shift will allow real-time AI applications, such as image processing and defect detection, to be seamlessly integrated into monitoring systems, providing real-time awareness and enhanced fault detection and accommodation. Furthermore, AI's potential in aerospace extends to control systems, where its application can range from full autonomy to enhancing human control through assistive features. AI, particularly deep reinforcement learning (DRL), can offer significant improvements in control systems, whether for autonomous operation or as an augmentative tool.arXiv.org120Real-time AI applications will allow real-time AI applications, such as image processing and defect detection, to be seamlessly integrated into monitoring systems, providing real-time awareness and enhanced fault detection and accommodation.ArXiv["Abedin Sherifi"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/ac5a61d6d7c1ec6938bddb6cdbdbf66e7e210ae4 de80423ac254352fe0e978931303e3ee67ba154aAbstract Generative artificial intelligence (GenAI) shows potential for personalized care, psychoeducation, and even crisis prediction in mental health, yet responsible use requires ethical consideration and deliberation and perhaps even governance. This is the first published theme issue focused on responsible GenAI in mental health. It brings together evidence and insights on GenAI’s capabilities, such as emotion recognition, therapy-session summarization, and risk assessment, while highlighting the sensitive nature of mental health data and the need for rigorous validation. Contributors discuss how bias, alignment with human values, transparency, and empathy must be carefully addressed to ensure ethically grounded, artificial intelligence–assisted care. By proposing conceptual frameworks; best practices; and regulatory approaches, including ethics of care and the preservation of socially important humanistic elements, this theme issue underscores that GenAI can complement, rather than replace, the vital role of human empathy in clinical settings. To achieve this, an ongoing collaboration between researchers, clinicians, policy makers, and technologists is essential.JMIR Mental Health200GenAI can complement, rather than replace, the vital role of human empathy in clinical settings, and to achieve this, an ongoing collaboration between researchers, clinicians, policy makers, and technologists is essential.JMIR Mental Health["Oren Asman", "J. Torous", "A. Tal"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/de80423ac254352fe0e978931303e3ee67ba154a 8da7fabcf9e7f098ef1b4d9beb2f64395909fc17Opinion mining, aka sentiment analysis, is a branch of Natural Language Processing (NLP) that focuses on analyzing and understanding opinions, sentiments, attitudes, and emotions expressed in text data. The goal of opinion mining is to determine the sentiment polarity of a given piece of text, such as a review, comment, or social media post. However, opinion mining faces language-specific challenges that differentiate studies in less commonly researched languages from those conducted in English. This article presents a novel process for Turkish opinion mining by comparing various artificial intelligence algorithms. We conducted extensive experiments using an open-source Turkish opinion-mining dataset to ensure transparency and reproducibility. Our research evaluated traditional machine learning, deep learning-based algorithms, and pre-trained transformer models, focusing on optimizing their parameters. We also compared word embeddings with the traditional bag-of-words method. By fine-tuning hyperparameters, our optimized models significantly improved accuracy and F1 scores. The proposed process outperformed existing methods in the literature, providing valuable insights for future research in opinion mining.Computer Science00This article presents a novel process for Turkish opinion mining by comparing various artificial intelligence algorithms and evaluated traditional machine learning, deep learning-based algorithms, and pre-trained transformer models, focusing on optimizing their parameters.Computer Science["\u00d6mer K\u00f6ksal"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/8da7fabcf9e7f098ef1b4d9beb2f64395909fc17 3447db2f7240d4d2f144903aaa6986ecc462133cEnvironment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.arXiv.org00A function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors is presented.ArXiv["I. Aslam", "Abhishek Buragohain", "Daniel Bamal", "Adina Aniculaesei", "Meng Zhang", "Andreas Rausch"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/3447db2f7240d4d2f144903aaa6986ecc462133c e4561e54dc302600faec7c62903c9081bdc44fceThis study aims to analyze the impact of Artificial Intelligence (AI) and digital material accessibility on academic achievement through students' engagement with technology. The research employs a quantitative survey method a survey method using SEM-PLS data analysis to explore the relationships between the relevant variables. A purposive sampling technique is used to select samples that meet specific criteria. The research sample comprises of 162 students in Malang, Indonesia, with data collected via an online questionnaire. This study shows that the use of AI in learning among students in Malang, when combined with effective digital material accessibility, has been proven to have a positive and significant impact on their academic performance, with technology engagement serving as an important mediating variable. AI, by enhancing competence, autonomy, and intrinsic motivation, helps students achieve their academic goals, increases their efforts, and provides higher self-satisfaction. This research implies that effective integration of AI and accessibility of digital materials, supported by technology engagement, can significantly improve students' academic performance, so educational institutions urgently need to strengthen the use of technology in learning.Jurnal kependidikan230The use of AI in learning among students in Malang, when combined with effective digital material accessibility, has been proven to have a positive and significant impact on their academic performance, with technology engagement serving as an important mediating variable.Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran["Anik Lestariningrum", "Abu Muna Almaududi Ausat", "M. I. Wanof", "Susatyo Adhi Pramono", "Syamsuri Syamsuri"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/e4561e54dc302600faec7c62903c9081bdc44fce c766e9fa0dee43f9df4fec6750d299e67a4a684fBackground: The bringing in of Artificial Intelligence (AI) in the climate change forecasting models would help in producing more accurate forecast results and better the measures that are taken for mitigating it. However, the use of AI in this field has failed to meet certain technical and systemic barriers. Objective: The objectives of this research will be to ascertain quantitatively the level of preparedness of the professionals towards the use of AI in deriving climate change forecasts, the level of resistance that professionals will exhibit in incorporating AI into their modeling, and how willing they are to use it in the same process. Methods: An online and self-completion survey with a structured format was administered to 250 respondents of the four target populations of ML/AI users, climate scientists, and environmental policymakers. To analyze the data, basic descriptive statistics and inferential statistics were applied: Lilliefor tests to check for normal distribution, Cronbach’s Alpha coefficient of reliability, correlation, and regression analysis to check the relation between AI familiarity & confidence levels. Results: The analysis of the data unveiled rather considerable fluctuations in the perceived efficiency of AI with the help of the Lilliefors test that pointed to the non-normality of the distribution. Cronbach’s alpha of 0. The reliability analysis of the AI-indexed perception questions showed low internal consistency in 046. Hypothesis three was not supported as statistical test results revealed that there is no medium to perfect positive correlation between the degrees of familiarity with AI on the one hand and confidence in the effectiveness of the same on the other hand. Some of the challenges to the integration of AI revealed from the survey include high costs and lack of support from the government, however many of the respondents indicated interest in adopting AI for sustainability initiatives. Conclusion: The study also shows that despite the entice for the use of AI in climate change predictions, there a challenges such as lack of funds and poor support from institutions in its application. Furthermore, raising the awareness of AI alone implies that people’s confidence in the impact of this technique will not necessarily rise either. Combating all these barriers through financial investments, policy support, and well-documented AI applications might lead to better implementation of AI in climate science.Journal of Medical &amp; Health Sciences Review520Journal of Medical &amp; Health Sciences Review["Abu Bakkar", "Naheed Ali", "Dr. Talha Abaid"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/c766e9fa0dee43f9df4fec6750d299e67a4a684f 93a5c56895bee75ae036cc8b895a13af5852cb7cBMC Medical Ethics250The integration of AI-CDSS into healthcare resource allocation presents both opportunities and significant ethical challenges, which underscore the need for robust ethical frameworks, enhanced AI literacy among healthcare professionals, interdisciplinary collaboration, and rigorous monitoring and evaluation processes.BMC Medical Ethics["C. Elgin", "C. Elgin"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/93a5c56895bee75ae036cc8b895a13af5852cb7c cb87fd91d9155fff7a1cb49d4f6cc5480c4099f1O ser humano tem buscado desvendar o futuro no presente através do desenvolvimento tecnológico e não é à toa que se tem ouvido muito a respeito de inteligência artificial e big data. O presente artigo tem como escopo analisar os “novos direitos” que surgem com as novas tecnologias. O principal objetivo é identificar se com novos direitos há algum impacto para a efetividade de direitos humanos fundamentais – especialmente relacionados à privacidade e à proteção de dados – e ao ideal de justiça. A metodologia para elaboração do artigo foi o método dedutivo e a técnica de pesquisa de análise bibliográfica e jurisprudencial. Ao final, foi possível concluir e identificar alguns obstáculos à efetividade dos direitos humanos fundamentais, tais como a interpretação legislativa pelo judiciário, a discriminação em razão de dados históricos utilizados em sistemas de inteligência artificial, entre outros. Com isso, como contribuição, foi possível verificar que a aplicação e a interpretação do direito tem íntima ligação com a efetividade dos direitos fundamentais e o que entendemos por justiça.Revista Internacional Consinter de Direito70Revista Internacional Consinter de Direito["M. Ruzzi", "Patr\u00edcia Borba Marchetto"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/cb87fd91d9155fff7a1cb49d4f6cc5480c4099f1 1bfcb4bd12d8ea72e4debfa2fe043dd318da500cLa tecnología ha invadido cada una de las esferas de nuestra vida, trayendo consigo una gama de beneficios desde el punto de la practicidad, el tiempo y la economía de personas. La robótica y la IA están entre nosotros y generan afectación en nuestras vidas y la profesión de enfermería no se encuentra libre de esta influencia tecnológica y recientemente se reconocen sus posibles efectos. Por ello, el presente artículo propone describir los usos de inteligencia artificial en los servicios de enfermería según la literatura científica disponible de los últimos años. La metodología fue de enfoque cualitativo, de revisión documental, se empleó el metabuscador Google Académico y las revistas Scielo y Dialnet. La muestra estuvo conformada por veinticuatro (24) artículos los mismos que estuvieron sujetos bajo criterios de inclusión y exclusión. El instrumento fue una ficha de recolección de datos. Los resultados apuntan a que el cuanto a los Servicios productivos la IA se emplea para el aparejo de soluciones endovenosas, infusión Endo gástrico, la tomas de constantes y extracciones de sangre y el suministro de medicamentos intravenosos. Además, en cuanto a los servicios asistenciales apoya en sesiones de ejercicio para personas mayores, de rehabilitación y movilización. Y finalmente respecto a los productos intermedios la IA contribuye en el monitoreo y detección en tiempo real de anomalías en signos vitales de pacientes en UCIP, en el análisis y procesamiento de gran cantidad de datos complejos para potenciar la toma de decisiones, automatizar y acelerar cada uno de los procesos. Se concluye que los usos de la IA en los servicios de enfermería son diversos y significativos.  MQRInvestigar00MQRInvestigar["Jhocelyn Dayana Ocampo-Bermeo"]2024-12-21T00:00:00https://www.semanticscholar.org/paper/1bfcb4bd12d8ea72e4debfa2fe043dd318da500c 3b56f21d7cf5eb1c51e5b7511b9d7e064bbe9e62Kecerdasan buatan atau artificial intelligence ialah suatu konsep penanaman pengetahuan pada mesin untuk dapat melakukan kegiatan atau aktifitas seperti manusia lakukan, yang dengan ditanamkannya kecerdasan buatan tersebut, memiliki tujuan untuk membantu manusia dalam menyelesaikan segala hal yang tidak dapat dilakukan oleh manusia. Pemahaman ini kemudian memunculkan pertanyaan apakah kecerdasan buatan kemudian menjadi bagian dari ilmu pengetahuan itu sendiri. Artikel ini disusun dengan pendekatan kualitatif berbasis studi pustaka dengan dibantu metode analisis isi. Dari hasil telaah didapatkan gambaran bahwa kecerdasan buatan masuk ke dalam pemahaman intermediary domain yang secara prinsip berjalan serta menjadi bagian dari ilmu pengetahuan baik teoretis sekaligus empiris. Hal ini menjadikan kecerdasan buatan tidak hanya memiliki implikasi filosofis terhadap status epistemologis, namun juga secara aksiologis dan juga ontologis yang menjadi bagian dari filsafat ilmu. Tidak hanya itu, kecerdasan buatan juga meniscayakan adanya konsekuensi praktis yang dengannya kecerdasan buatan menjadi bagian dari ilmu pengetahuan itu sendiri.LogicLink01LogicLink["Arditya Prayogi", "Riki Nasrullah"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/3b56f21d7cf5eb1c51e5b7511b9d7e064bbe9e62 8d815ed67e4154e9e837a75ba70c30f313d350d0This paper investigated Librarians’ Awareness towards the Use of Artificial Intelligence Technologies for Sustainable Library Services. Four research objectives and one null hypothesis were formulated to guide the study. The study adopted a descriptive survey research design. The targeted population of the study are Librarians in Nigeria. Questionnaires were sent online via Google form to the association WhatsApp platforms in order to get responses from the members. The process brought in a total of 203 responses which was used to analyse the data. Mean and standard deviation were used to answer the research questions while t test was used to test the hypotheses at 0.05 level of significant. The finding shows that there is high extent of the level of librarians’ awareness in the use of AI technologies in library services. The study also highlighted the various challenges of using AI in library operations, ranging from the fact that AI do not have human feelings/physical contact and frequent use of AI can make them irrelevant in the library thereby losing their job. Finally, the hypothesis stated that there is a significant difference between the mean score of the awareness of librarians towards the use of artificial intelligence technologies on library services. Since the p-value is less than the significance level, the null hypothesis is rejected. Based on the findings, the researchers recommends among others that there is need for librarians to attend trainings, workshops and conferences related to the adoption of artificial intelligence in order to prepare them for future tasks.Business Information Review50There is need for librarians to attend trainings, workshops and conferences related to the adoption of artificial intelligence in order to prepare them for future tasks, according to the researchers.Business Information Review["Ebisemen Patience Lulu-Pokubo", "Emmanuel Okwu"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/8d815ed67e4154e9e837a75ba70c30f313d350d0 ff085cb20c8a9e055e027e3822c1b76f6798776eThis research aims to investigate how Artificial Intelligence (AI) integration enhances creative productivity and leads to improved outcomes in financial performance. The advancement of technology enables the improvement of knowledge sharing, absorptive capacity, skills, and innovation, fostering greater efficiency in achieving organizational goals. This pioneering research develops a holistic framework that integrates AI as a moderating factor, highlighting its transformative role in achieving a competitive advantage. A sample of 499 MSMEs in the Indonesian creative sector was obtained from a total of 34,000 HIPMI members through purposive and judgment sampling methods. Using the scrutinize method, this study provides an in-depth analysis of the interactions between technological advancements and organizational capabilities, offering strategic insights for sustainable growth. Regression analysis results indicate that knowledge sharing, absorptive capacity, skills, and innovation have a significant and positive effect on the creative performance. AI also has a significant positive impact on financial performance. The integration of AI strengthens the association between innovation and creative performance, but it does not reveal a substantial impact on the link of knowledge sharing, absorptive capacity, and skills with creative performance. This study provides theoretical and practical contributions to support decision-making in technology policy in the creative sector.Journal of Ecohumanism00Regression analysis results indicate that knowledge sharing, absorptive capacity, skills, and innovation have a significant and positive effect on the creative performance, and AI also has a significant positive impact on financial performance.Journal of Ecohumanism["Didik Prasetyanto", "D. Suhardjanto", "A. Probohudono", "Wahyu Widarjo"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/ff085cb20c8a9e055e027e3822c1b76f6798776e becea8e5c3b3dd3a58b560f347ad8976e5b02e4aThe inability of conventional techniques to keep up with changing fraudulent strategies has made the integration of artificial intelligence (AI) and machine learning (ML) in online banking fraud detection crucial. Real-time detection capabilities provided by AI-powered models improve the capacity to spot irregularities and questionable patterns that may indicate fraudulent activity. However, there are drawbacks, especially with deep learning models, such as issues with data quality, scalability, and transparency. The purpose of this paper is to assess the efficacy of AI in preventing fraud, offer performance-enhancing solutions, and discuss important deployment issues along with proposing a solution for AI integration. The study emphasises how AI-driven fraud detection systems must have strong data management, real-time adaptability, and transparency.International Conference on Computational Intelligence and Communication Networks330It is emphasised how AI-driven fraud detection systems must have strong data management, real-time adaptability, and transparency, and how AI-driven fraud detection systems must have strong data management, real-time adaptability, and transparency.2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN)["Roop Kumar Yekollu", "Shivkumar V Haldikar", "Tejal Bhimraj Ghuge", "Omer Farook", "Sammip Sunil Biradar"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/becea8e5c3b3dd3a58b560f347ad8976e5b02e4a f2d1c2c5979a9eb28256d36d625cb5bd49d58443This study discusses the application of Artificial Intelligence (AI) in internal audit in the banking sector, with a focus on fraud detection and prevention. In the digital era, the need for efficiency and accuracy in auditing is increasingly pressing, and AI offers an innovative solution. Through real-time big data analysis, AI can identify suspicious patterns and anomalies that may be missed by traditional methods. This study uses a qualitative approach with interviews and questionnaires to employees in the banking sector. The results show that the application of AI improves the efficiency of the audit process, reduces human error, and increases customer trust. However, challenges in auditor training and data management remain a concern. Recommendations for the development of AI usage policies and periodic training for auditors are proposed to maximize the benefits of this technology.International Journal Of Education Social Studies And Management (IJESSM)130The results show that the application of AI improves the efficiency of the audit process, reduces human error, and increases customer trust, however, challenges in auditor training and data management remain a concern.International Journal Of Education, Social Studies, And Management (IJESSM)["Anastasya Mechta Mediana", "Tries Ellia Sandari"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/f2d1c2c5979a9eb28256d36d625cb5bd49d58443 03e4a19eff4063130d0f46affc11297fb8ef0895This work analyzes the intersections between artificial intelligence (AI) and human rights from an international perspective. It analyzes the regulatory efforts of essential entities such as the United Nations, the Council of Europe, and the European Union. Recent initiatives that aim to establish ethical and legal standards to address the risks associated with AI are highlighted. Mass surveillance, algorithmic bias, and privacy violations stand out among these aspects. The book takes a unique approach, integrating a normative perspective with analyses of specific cases to examine the impact of artificial intelligence in key areas such as justice, health, labor rights, and privacy. It delves into challenges such as video manipulation, facial recognition regulations, and ethical conflicts in applying autonomous technologies in international settings. The book underscores the necessity of a flexible legal framework that accommodates technological advances while upholding fundamental rights. It also underscores the need to strengthen the responsibility of states and private entities to protect these rights. This volume is a fundamental reference point in the creation of international norms that ensure artificial intelligence respects human rights and proactively participates in their promotion in a globalized and constantly evolving world. Its insights are invaluable in the field of AI ethics and human rights.Deusto Journal of Human Rights00Deusto Journal of Human Rights["Itziar Art\u00ed\u00f1ano Ortiz"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/03e4a19eff4063130d0f46affc11297fb8ef0895 28f417aff5bb445d8ab971a9fc6e771ef560ae0aThe present study aims to explore the transformations brought to custom, a traditional source of law, by the emergence of artificial intelligence (AI). The plan of the analysis is structured in two main parts: the first part focuses on the presentation of the notion of custom and its particularities in relation to usage. The second part of the paper develops the concept of AI and its functions, in particular that of the legal algorithm. The central issue around which this article is articulated is the protection of fundamental rights and free will, since AI tends to take the place of the legislator, producing new social rules based only on factual data, which lack the power of abstraction.International Journal of Legal and Social Order00The central issue around which this article is articulated is the protection of fundamental rights and free will, since AI tends to take the place of the legislator, producing new social rules based only on factual data, which lack the power of abstraction.International Journal of Legal and Social Order["R. Stancu"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/28f417aff5bb445d8ab971a9fc6e771ef560ae0a 9c6e687ef7300950a745ec83696a463c29936148In recent years, Artificial Intelligence (AI) bases on data driven and machine learning have been at the centre of debates on the implications of certain uses of this technology on fundamental rights in terms of individual and social risks. At the national level, reflections on whether or not AI systems have their own ontological determinism seem to have come up against the obstacles of the staticity of constitutional frameworks that are still analogical. In the European legal order, the most disruptive digital effects of the so-called knowledge economy on the subject and his or her rights seem to be conditioned by the telos of the centrality of the human being in his/her objective-axial dimension (guarantee of the Union’s values) and subjective dimension (protection of the Union’s fundamental rights). The European Union Artificial Intelligence Act would be its most recent legal-normative concretisation, in line with other norms of secondary law that would outline the dynamics of the so-called digital constitutionalism. Received: 28 May 2024Accepted: 12 September 2024Deusto Journal of Human Rights00The European Union Artificial Intelligence Act would be its most recent legal-normative concretisation, in line with other norms of secondary law that would outline the dynamics of the so-called digital constitutionalism.Deusto Journal of Human Rights["Ainhoa Lasa L\u00f3pez"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/9c6e687ef7300950a745ec83696a463c29936148 e62339127f8a7f52c90124417c986e50079d13f0Pertanian di Indonesia menghadapi tantangan seperti peningkatan kebutuhan pangan, keterbatasan sumber daya, dan perubahan iklim. Artificial Intelligence (AI) muncul sebagai solusi potensial untuk meningkatkan efisiensi dan keberlanjutan sektor pertanian. Penelitian ini menggunakan metode Systematic Literature Review (SLR) untuk mengkaji implementasi AI dalam sektor pertanian, dengan fokus pada algoritma dan teknologi yang efektif. Algoritma seperti Support Vector Machine (SVM), Convolutional Neural Networks (CNN), dan Long Short-Term Memory (LSTM) menunjukkan hasil yang baik dalam deteksi penyakit tanaman, prediksi hasil panen, dan pengelolaan sumber daya. Hasil penelitian menunjukkan bahwa integrasi AI dengan Internet of Things (IoT) dan remote sensing dapat meningkatkan produktivitas pertanian dan mengurangi dampak lingkungan. Namun, penerapan AI di sektor pertanian Indonesia masih menghadapi tantangan dalam aksesibilitas teknologi dan infrastruktur. Penelitian lebih lanjut diperlukan untuk mengoptimalkan penerapan AI dalam praktik pertanian di Indonesia.JATI (Jurnal Mahasiswa Teknik Informatika)00JATI (Jurnal Mahasiswa Teknik Informatika)["Ahnaf Sofi'an Eka Putra", "Chotibul Umam Hanif", "Moch. Azriel Maulana Racmadhani"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/e62339127f8a7f52c90124417c986e50079d13f0 d3e343f2ada2c2ded536ecd7a7de25eeaa8c31afThis paper discusses the role and strategies of artificial intelligence technology in enhancing the independent learning ability of college students. With the rapid development of artificial intelligence technology, the field of education is experiencing unprecedented changes, which presents new challenges and opportunities for the independent learning ability of college students. The article first analyzes the current status of the application of artificial intelligence in education, including personalized learning content push, adaptive learning path planning, etc., and points out the current problems faced, such as low technology integration and imperfect personalized services. Then, the article emphasizes the importance of enhancing the independent learning ability of college students, including personal development, social adaptation, moral responsibility and other aspects. The article also discusses the problems of artificial intelligence in college students' independent learning, such as technology dependence, privacy and data security, and algorithm bias. Finally, the article proposes enhancement strategies, including teaching mode innovation, learning strategy guidance and learning environment optimization. Through systematic review and empirical research, this article aims to provide scientific and effective support for the enhancement of college students' independent learning ability to meet the requirements of the informationization era.Modern Management Science &amp; Engineering00The article analyzes the current status of the application of artificial intelligence in education, including personalized learning content push, adaptive learning path planning, etc., and points out the current problems faced, such as low technology integration and imperfect personalized services.Modern Management Science &amp; Engineering["Yan Long"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/d3e343f2ada2c2ded536ecd7a7de25eeaa8c31af 635ec15fa17de2d7f0c2a10292d149abba3b6b2aArtificial intelligence (AI) is progressively being embraced by institutions for digital transformation, a trend that is increasingly evident in scholarly endeavors. To better understand the research landscape at the intersection of AI and digital transformation, we conducted a systematic literature review (SLR) of 119 relevant articles indexed in the Clarivate Web of Science (WOS) and Elsevier Scopus databases. Our bibliometric analysis employed advanced techniques such as keyword co-occurrence and bibliographic coupling to map the dominant topics and their evolution over time in this emerging field. By leveraging these powerful analytical methods, we were able to generate valuable insights into the current state of research at the nexus of AI and digital transformation. The findings from our comprehensive SLR provide an updated synopsis of the existing scientific work in this area. Moreover, we developed an interpretive framework that sheds light on the key drivers and outcomes of AI adoption for digital transformation within organizations. This framework serves as a valuable tool for researchers and practitioners alike, helping to elucidate the complex interplay between AI, digitization, and innovation in the context of organizational transformation. Our study underscores the growing importance of AI as a transformative technology that enables firms to navigate the challenges and opportunities of the digital age. By systematically mapping the research landscape, we aim to inspire further scholarly inquiry and practical applications of AI in the service of digital transformation. As organizations continue to embrace AI-driven solutions, we must deepen our understanding of this powerful technology's strategic and operational implications.Proceedings of The International Conference on Advanced Research in Management, Economics and Accounting00A systematic literature review of 119 relevant articles indexed in the Clarivate Web of Science (WOS) and Elsevier Scopus databases and developed an interpretive framework that sheds light on the key drivers and outcomes of AI adoption for digital transformation within organizations.Proceedings of The International Conference on Advanced Research in Management, Economics and Accounting["Maryame Bijou", "Asmaa Elmoutaouakkil"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/635ec15fa17de2d7f0c2a10292d149abba3b6b2a 9d103f36fe0c41f3285686589c5aa418e58275afIn the current era, the use of artificial intelligence is rapidly becoming widespread. However, there is limited research on how artificial intelligence can be utilized in preschool education both in our country and globally and how educators’ perceive it. This study aims to investigate preservice teachers’ perceptions of artificial intelligence and the meanings they attributed to it by preservice teachers who will be preschool educators in the near future. The study was designed as qualitative research. The participants of the study consisted of 107 preservice preschool teachers selected through convenience sampling. Data were collected using a semi-structured form that included prompts such as: “Artificial intelligence is like... because...” and “Artificial intelligence is... because..." They were asked to explain their views on AI in detail. As a result of data analysis, the preservice preschool teachers’ perceptions regarding artificial intelligence were grouped into ten categories. The metaphorical perceptions of AI included: human-like, assistant teacher, convenience, future essentials, complexity, innovation and change, double-sided meaning, dangerous, play and experience, and philosophy. Most of the participants stated that AI has human-like features, while the others focused on its functionality. Regarding the meaning attributed to AI, five categories emerged: features, effects, philosophy, usage of AI, and other. It was determined that pre-service teachers attributed the highest number of meanings to the functional, technical, human-like, dynamic, and human interaction features of artificial intelligence and the lowest number to the necessary, unnecessary, mandatory, and important meanings in the other category. The findings suggest that preservice preschool teachers perceive AI as a necessity of the modern age with the potential to shape the future of education. They also emphasize that need for cautious use to its dual impacts- both positive and negative. Thus, it should be used cautiously. While the participants expressed willingness to integrate AI into preschool education, they approached it with a critical perspective.Çukurova Üniversitesi Türkoloji Araştırmaları Dergisi280The findings suggest that preservice preschool teachers perceive AI as a necessity of the modern age with the potential to shape the future of education and emphasize that need for cautious use to its dual impacts- both positive and negative.Çukurova Üniversitesi Türkoloji Araştırmaları Dergisi["Aysel Korkmaz", "Hatice \u00c7\u0130LSALAR SAGNAK"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/9d103f36fe0c41f3285686589c5aa418e58275af c8b5f81688da1f127d8bd110e379fb097f535a65In this article, artificial intelligence technology is applied to optimize the construction of artificial intelligence (AI) system. Traditional artificial intelligence technology has some problems such as data limitation, expert knowledge dependence and lack of interpretability. In view of the above problems, this project intends to use particle swarm optimization (PSO) to process and analyze the data so as to realize intelligent service. The aim of this article is to improve the performance and effect of artificial intelligence system and provide better user experience for users. First of all, how to use genetic algorithm to make automatic decision and optimization, and its application in practice are discussed. On this basis, intelligent algorithms and models are proposed to improve the system performance. On this basis, this project proposes an intelligent autonomous intelligent system based on PSO to achieve the fastest speed of 105 milliseconds per project. The system has the characteristics of high efficiency, high intelligence and high reliability, and can play a better role in different industries.International Conference on Computational Intelligence and Communication Networks150An intelligent autonomous intelligent system based on PSO to achieve the fastest speed of 105 milliseconds per project, which has the characteristics of high efficiency, high intelligence and high reliability, and can play a better role in different industries.2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN)["Yali Guo", "Peng Wu", "Haoping Guo"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/c8b5f81688da1f127d8bd110e379fb097f535a65 ac60bdf6f15497c576e7922ffb3e3e8ff192ffb2Kemajuan teknologi, khususnya Artificial intelligence (AI) , telah membawa perubahan signifikan dalam dinamika ketenagakerjaan di era revolusi industri 5.0. Meskipun teknologi ini mampu meningkatkan efisiensi, produktivitas, dan kualitas hidup pekerja, dampaknya terhadap pengurangan lapangan kerja dan kesenjangan keterampilan menjadi tantangan serius. Penelitian ini bertujuan untuk menganalisis dampak sosial pengambilan keputusan berbasis Artificial intelligence (AI) terhadap ketenagakerjaan, baik dari segi positif maupun negatif. Studi ini menggunakan metode kajian literatur untuk mengumpulkan dan menganalisis data dari berbagai artikel, jurnal, dan laporan terkait. Hasil penelitian menunjukkan bahwa penerapan Artificial intelligence (AI) meningkatkan efisiensi fungsional, mempercepat analisis data, dan memberikan peluang baru dalam pekerjaan berbasis teknologi. Namun, di sisi lain, otomatisasi pekerjaan rutin oleh Artificial intelligence (AI) dapat menyebabkan pengurangan jumlah pekerjaan manual, kesenjangan keterampilan digital, dan tantangan etika terkait privasi data.Journal of Macroeconomics and Social Development310Journal of Macroeconomics and Social Development["I. Kusumasari", "R. Hidayat", "Zika Aisyantus Sophia", "Frisca Mei Maghfiroh", "A. Anggraini"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/ac60bdf6f15497c576e7922ffb3e3e8ff192ffb2 0deff620cea72063053a98d7d845362867b1e341This article examines the intersections between technology and coloniality, with a particular focus on the role of artificial intelligence (AI) in perpetuating colonial power structures and reinforcing exclusions. The study examines the ways in which historically marginalized groups—including Black people, the poor, women, Indigenous peoples, queer individuals, and those from peripheral areas—are reinterpreting AI, transforming it into a tool of resistance against the oppressive logics of Eurocentric modernity. The methodology is based on a qualitative approach, comprising interviews, an analysis of audiovisual materials, digital platforms, and social media. The research identified initiatives that propose technological alternatives based on diverse epistemological and ontological frameworks, thereby challenging the dominance of modern/colonial technological paradigms. The analysis of the data revealed that these groups adopt collaborative methodologies, with a particular focus on the inclusion of marginalized populations and the creation of new technological epistemologies. The findings demonstrate that these communities are developing technological arrangements based on non-Western cosmologies, thereby challenging Western dominance in technology. These practices not only adapt existing technologies but also create new forms of technological interaction that reflect their specific realities and contexts. The study concludes that the decolonization of technology is both possible and necessary, with the adoption of cosmotechnics that respect cultural and epistemological diversity, paving the way for fairer, plural, and inclusive technological futures.Digital Theory, Culture &amp; Society00The study examines the ways in which historically marginalized groups are reinterpreting AI, transforming it into a tool of resistance against the oppressive logics of Eurocentric modernity, and concludes that the decolonization of technology is both possible and necessary.Digital Theory, Culture &amp; Society["Carlos Eduardo Souza Aguiar", "Dayana Karla Melo Da Silva"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/0deff620cea72063053a98d7d845362867b1e341 fc0115b13df31b4bc65a2339c962522c77abab8fThis article addresses the impact of artificial intelligence (AI) on human rights from a constitutional perspective, focusing on the vulnerability of certain groups in the face of technological advances. After an introduction contextualising the relevance of the topic, human rights in the technological context are examined, with a particular focus on the vulnerability of certain groups. An assessment is made of the areas of special protection for these people in relation to the use of AI, and discrimination arising from algorithmic biases is discussed. The conclusions highlight the need for legal research in the field of AI to focus on ensuring that technological progress does not undermine human rights acquired over time. The importance of protecting vulnerable groups, whose vital development may be disproportionately affected by the impact of AI, is emphasised. It identifies areas where the advancement of AI may generate adverse effects on citizens’ rights, underlining the importance of adapting this technological progress to the protection of human rights. It also highlights the risk of algorithmic biases in the processing of personal data, highlighting the need to protect the privacy and data of individuals as fundamental elements to ensure an AI that respects human rights. It concludes that only an AI that respects these rights can contribute to a more advanced and just society, based on democratic principles. Received: 08 April 2024Accepted: 16 September 2024Deusto Journal of Human Rights00Only an AI that respects human rights can contribute to a more advanced and just society, based on democratic principles, and it is concluded that only an AI that respects these rights can contribute to a more advanced and just society.Deusto Journal of Human Rights["Jorge Castellanos Claramunt"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/fc0115b13df31b4bc65a2339c962522c77abab8f 49683ea7124839516529dfa5b832ba8e112d75ffThis article analyses the right to participate in democracy as a human right and its link to the development and implementation of artificial intelligence. First, it explores the fundamental aspect of the right to participate as a human right in the democratic framework, reflecting on its importance and its basic function of generating open spaces for the debate and presentation of other rights, highlighting that political participation generates a pull effect on other rights as citizens are in a favourable position for the defence and recognition of their rights. Next, emphasis is placed on the role of emerging technologies in facilitating and enhancing democratic engagement, bearing in mind that technological development has a direct influence on all aspects of people’s lives, so that democracy in general, and each society’s methods of organisation in particular, are also affected. Finally, a significant part of the discussion revolves around the future perspective of artificial intelligence and its potential impact on democracy, exploring both favourable developments and potential challenges. Artificial intelligence will undoubtedly continue to conquer more spheres of human endeavour, so it is worth reflecting on the importance of adapting this new technology to the future of democracies, while respecting its essential elements and guaranteeing citizens’ fundamental rights. Finally, the article concludes by summarising the main ideas and implications, underlining the critical importance of safeguarding democratic principles in the midst of technological advances. Received: 30 May 2024 Accepted: 02 December 2024Deusto Journal of Human Rights00This article analyses the right to participate in democracy as a human right and its link to the development and implementation of artificial intelligence, highlighting that political participation generates a pull effect on other rights as citizens are in a favourable position for the defence and recognition of their rights.Deusto Journal of Human Rights["Mar\u00eda Dolores Montero Caro"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/49683ea7124839516529dfa5b832ba8e112d75ff ff297c68d5e2a87925751ba57660d37a272d8fbbIn response to recent proposals to utilize artificial intelligence (AI) to automate ethics consultations in healthcare, we raise two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters. The first cause for concern is that, because these programs would effectively function like black boxes, this approach seems to preclude the kind of transparency that would allow clinical staff to explain and justify treatment decisions to patients, fellow caregivers, and those tasked with providing oversight. The other main problem is that the kind of authority that would need to be given to the guidance issuing from these programs in order to do the work set out for them would mean that clinical staff would not be empowered to provide meaningful safeguards against it in those cases when its recommendations are morally problematic.Nursing Ethics240In response to recent proposals to utilize artificial intelligence to automate ethics consultations in healthcare, this work raises two main problems for the prospect of having healthcare professionals rely on AI-driven programs to provide ethical guidance in clinical matters.Nursing ethics["Jeffrey Byrnes", "Michael Robinson"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/ff297c68d5e2a87925751ba57660d37a272d8fbb df942c99b45bfecd13a43fd632567c3fd9fcb50bTahun 2021 prevalensi stunting di Indonesia sebesar 24,4%. Jawa Timur mempunyai prevalensi pada tahun 2021 sebanyak 23,5%. Prevalensi di Indonesia dan Jawa timur tersebut masih dikatakan tinggi karena di atas rata-rata WHO yaitu 20% saja. Deteksi dini stunting memungkinkan intervensi lebih dini dan efektif untuk mencegah dampak jangka panjang pada pertumbuhan dan perkembangan anak. Penelitian ini bertujuan mengembangkan model KASITING berbasis kecerdasan buatan (AI) untuk deteksi dini stunting pada bayi, yang dapat digunakan oleh kader kesehatan. KASITING adalah sebuah sistem aplikasi yang memanfaatkan algoritma AI untuk mengidentifikasi dan memantau kondisi kesehatan bayi secara akurat dan efisien. Dalam penelitian ini, model AI dikembangkan menggunakan data kesehatan bayi yang mencakup parameter seperti berat badan, tinggi badan, dan faktor gizi. Menggunakan metode Research and Development (R&D) untuk menghasilkan sebuah produk baru berupa aplikasi web yang dapat melakukan deteksi dini. Diterapkan untuk menganalisis data dan mengidentifikasi pola yang terkait dengan stunting. Uji coba dilakukan dengan melibatkan sejumlah kader kesehatan, yang dilatih untuk menggunakan aplikasi KASITING dan memberikan umpan balik terkait keakuratan dan kemudahan penggunaan. Hasil penelitian menunjukkan bahwa model KASITING berbasis AI dapat mendeteksi risiko stunting dengan akurasi 87%, sensitivitas 90%, spesifisitas 85%, precision 88% dan dapat digunakan secara praktis dalam kegiatan sehari-hari kader kesehatan. Implementasi sistem ini dapat meningkatkan efisiensi dan efektivitas deteksi dini stunting, yang dapat mendukung upaya penurunan angka stunting di masyarakat. Penelitian ini menyimpulkan bahwa adopsi teknologi AI dalam aplikasi kesehatan seperti KASITING dapat menjadi alat yang berharga untuk mendukung kader kesehatan dalam memantau dan menangani masalah stunting dengan lebih baik.PREPOTIF : JURNAL KESEHATAN MASYARAKAT00PREPOTIF : JURNAL KESEHATAN MASYARAKAT["Putri Pamungkas", "Ariska Putri Hidayathillah"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/df942c99b45bfecd13a43fd632567c3fd9fcb50b 622d1037db567535d524341c87113dcba186143eTransport Means 2024. Proceedings of the 28th International Scientific Conference00Transport Means 2024. Proceedings of the 28th International Scientific Conference["Simona Briedien\u0117", "Vilma Pranckevi\u010di\u016bt\u0117"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/622d1037db567535d524341c87113dcba186143e fbbb19ade5752b96f95a251d2b09e9e7ebb46e61Transport Means 2024. Proceedings of the 28th International Scientific Conference00Transport Means 2024. Proceedings of the 28th International Scientific Conference["I. Kulbovskyi", "M. Tkachuk"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/fbbb19ade5752b96f95a251d2b09e9e7ebb46e61 9ee3e1a32948d4523c6b1a67cf98b4c52b7b6b25Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.Elkom Jurnal Elektronika dan Komputer00This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting, which should improve hydroponic plant stock and sales predictions.Elkom: Jurnal Elektronika dan Komputer["Putu Bagus Adidyana Anugrah Putra", "Septian Geges", "Oktaviani Enjela Putri", "I Made Bayu Artha Pratama"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/9ee3e1a32948d4523c6b1a67cf98b4c52b7b6b25 c5aad7d4f1199fa487ff05f72606ff1b745553d5This study aims to develop an AI-based teaching factory model in Vocational High Schools (SMKs) to improve vocational education quality and align student competencies with Industry 4.0 requirements. Integrating AI is anticipated to enhance students' technical and non-technical skills, including problem-solving, creativity, and technology adaptation. The research utilized a mixed-methods approach, combining quantitative and qualitative techniques. Data were collected through surveys, interviews, and observations from teachers and students in SMKs implementing AI-based teaching factories. Analysis was conducted using Partial Least Squares Structural Equation Modelling (PLS-SEM) and in-depth teacher interviews to evaluate readiness and integration challenges. Findings reveal that AI applications in teaching factories significantly enhance students' technological proficiency, learning efficiency, and industry-readiness. Teachers reported improved teaching effectiveness, although they faced obstacles in areas like teacher training and technological infrastructure. The study highlights the potential of AI in elevating vocational education but identifies barriers requiring attention, such as the need for continuous teacher development and robust infrastructure. Recommendations include targeted training programs, increased investment in technology, and curriculum revisions to integrate AI comprehensively. Implementing AI in SMKs presents a promising strategy to address the evolving demands of Industry 4.0, enhancing educational outcomes for students and teaching effectiveness for educators.AL-ISHLAH: Jurnal Pendidikan00Findings reveal that AI applications in teaching factories significantly enhance students' technological proficiency, learning efficiency, and industry-readiness, as well as enhancing educational outcomes for students and teaching effectiveness for educators.AL-ISHLAH: Jurnal Pendidikan["S. Wahjusaputri", "Tashia Indah Nastiti", "B. Bunyamin", "Wati Sukmawati", "Johan Johan"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/c5aad7d4f1199fa487ff05f72606ff1b745553d5 11eb5d3ed5b458d9fc4baf9b7886cb77c121531dJournal of Medical Imaging and Radiation Sciences660Future education efforts should prioritise evidence-based curriculum design, expand training offerings to radiographers, increase content offerings in imaging informatics, and effectively utilise different teaching strategies and training tools and resources in the curriculum.Journal of medical imaging and radiation sciences["Su Jean Loi", "Wenhui Ng", "Christopher Lai", "Eric Chern-Pin Chua"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/11eb5d3ed5b458d9fc4baf9b7886cb77c121531d 4c0d7c5e933e1eb6783cce292cf8f95343b03382Kemajuan teknologi informasi yang pesat, khususnya kecerdasan buatan (Artificial Intelligence/AI), telah membawa transformasi signifikan di berbagai sektor, termasuk pendidikan tinggi. Penelitian ini bertujuan untuk mengkaji efektivitas penggunaan AI dalam meningkatkan pembelajaran mahasiswa. AI memberikan berbagai manfaat, seperti personalisasi pembelajaran, akses cepat ke sumber daya pendidikan, serta umpan balik real-time yang membantu mahasiswa belajar lebih efisien. Selain itu, AI mampu meningkatkan produktivitas mahasiswa dan mempercepat pemahaman materi. Metode penelitian yang kami gunakan yaitu literatur review dengan mengumpulkan dan menganalisis data dari 43 jurnal terkait AI dalam pendidikan, yang menghasilkan kesimpulan bahwa AI memiliki potensi besar untuk meningkatkan kualitas pembelajaran mahasiswa, asalkan digunakan secara bijak dan bertanggung jawab. Hasil penelitian ini diharapkan dapat menjadi referensi untuk pengembangan lebih lanjut dalam implementasi AI di perguruan tinggi, serta mendorong eksplorasi baru terkait kolaborasi antara teknologi dan pengajaran manusia. Integrasi AI diharapkan mampu menciptakan pengalaman pembelajaran yang lebih personal, efektif, dan inklusif bagi mahasiswa di era globalisasiJATI (Jurnal Mahasiswa Teknik Informatika)01JATI (Jurnal Mahasiswa Teknik Informatika)["Eka Wahyudinarti", "Putri Andini Rachmatika", "Rika Nurul Ain"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/4c0d7c5e933e1eb6783cce292cf8f95343b03382 63e7167033c1a9de9fa0a4252aa2b4fd118714e0The full potential of many human rights cannot be reached due to the economic costs in their development. The use of artificial intelligence and related technologies in budgetary and audit processes could help in a better allocation of scarce public resources and deliver savings due to better targeting in programming and execution, avoiding irregularities and corruption. When public and corporate organizations automate processes, monitoring should ensure their compliance with regulation or voluntary commitments affecting environmental, social, and governance criteria. Many funds are granted to support digitalization processes if safeguards related to human rights are respected. The provision of goods and services like health and education is often subject to additional technological requirements. In both cases, an efficient supervision is crucial for fairness, in terms of accessibility and the effective protection of human rights. Received: 08 September 2024 Accepted: 10 December 2024Deusto Journal of Human Rights01The use of artificial intelligence and related technologies in budgetary and audit processes could help in a better allocation of scarce public resources and deliver savings due to better targeting in programming and execution, avoiding irregularities and corruption.Deusto Journal of Human Rights["Mar\u00eda Amparo Grau Ruiz"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/63e7167033c1a9de9fa0a4252aa2b4fd118714e0 fa78e1bc16d6f4361820c0e091c03150b0292ddeThis paper addresses the link between Artificial Intelligence (AI) and the human and fundamental right to freedom from slavery: in particular, we focus on the modern slavery in global supply chains and the possibility to use AI to identify it. We analyze the slavery and its modern version, situate the AI within the human rights debate and argue that we should not only focus on how AI can violate and infringe the human rights, but also explore how AI could be useful in identifying violations and helping to combat them. We emphasize the need for inclusive datasets and stakeholder oversight and argue in support of AI to enhance transparency of international supply chains while cautioning against biases. We conclude by outlining the importance of responsible AI deployment and making a case for more regulatory efforts to protect the fundamental human right to freedom from slavery in supply chain operations. Received: 27 May 2024Accepted: 12 September 2024Deusto Journal of Human Rights00Deusto Journal of Human Rights["Migle Laukyte", "Lorena Mar\u00eda Arismendy Mengual"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/fa78e1bc16d6f4361820c0e091c03150b0292dde 81aa0aff08b7c5a5d711424e1e45e6c973255917The integration of Artificial Intelligence (AI) into Science, Technology, Engineering, and Mathematics (STEM) education has transformative potential, particularly for South Asia, a region marked by economic and educational disparities. This paper examines the impact, opportunities, and challenges associated with adopting AI in STEM higher education in South Asia. Through AI, education systems in the region can overcome limitations in resources, geographical barriers, and outdated curricula, offering adaptive, personalized, and accessible learning experiences. The study highlights the roles of leading AI-focused institutions, particularly in India, which are pioneering AI initiatives and fostering regional leadership in AI-driven education. By evaluating current AI integration, institutional contributions, and socio-economic barriers, the paper provides insights into harnessing AI to enhance research, improve STEM education outcomes, and prepare South Asian students for a global, AI-driven economy. This research underscores the need for a balanced approach that considers AI’s ethical, cultural, and infrastructural challenges, aiming to create an inclusive and sustainable model for AI-enhanced STEM education in South Asia.Journal of development innovations320The need for a balanced approach that considers AI’s ethical, cultural, and infrastructural challenges is underscored, aiming to create an inclusive and sustainable model for AI-enhanced STEM education in South Asia.Journal of Development Innovations["Binod Vaidya"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/81aa0aff08b7c5a5d711424e1e45e6c973255917 82115c1cb8ea6f7dfdf836441036f2d9a43c1271The revolutionary role of artificial intelligence in precision medicine is examined in this thorough article, which also looks at how the combination of AI technologies and genomic data is transforming the way healthcare is delivered. The technical underpinnings of AI-driven precision medicine are examined in the article, along with data integration, analysis techniques, and important technology elements including computer vision and natural language processing. It talks about the important uses of AI in complex diseases, especially in diabetic care, uncommon genetic disorders, and oncology, where the technology has shown impressive results in enhancing diagnosis and treatment outcomes. Along with discussing new technologies like digital twins and single-cell analytics, the article also looks at the obstacles to clinical integration, ethical issues, and data quality issues that arise when implementing AI-driven precision medicineInternational Journal For Multidisciplinary Research80The article talks about the important uses of AI in complex diseases, especially in diabetic care, uncommon genetic disorders, and oncology, where the technology has shown impressive results in enhancing diagnosis and treatment outcomes.International Journal For Multidisciplinary Research["Sriram Sitaraman"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/82115c1cb8ea6f7dfdf836441036f2d9a43c1271 0d4eed736632cbdf6889162385df35f60048c1d2This comprehensive article explores the transformative impact of Artificial Intelligence on operational efficiency in the insurance industry, focusing on implementing AI-driven solutions across underwriting, claims processing, and agent management functions. The article examines how modern AI architectures address traditional operational challenges through intelligent workflow analysis, process automation, and resource optimization. The article demonstrates substantial improvements in operational efficiency, customer satisfaction, and cost reduction by analyzing implementation cases across various insurance organizations. The article covers key technological components, including artificial intelligence engines, machine learning modules, natural language processing capabilities, and robotic process automation, while highlighting their collective contribution to enhanced insurance operations. The article presents evidence-based insights into performance improvements achieved through AI implementation. It offers insurance organizations a strategic framework for leveraging these technologies to enhance their operational capabilities and maintain competitive advantage in an increasingly dynamic market environment.International Journal For Multidisciplinary Research110International Journal For Multidisciplinary Research["Chetan Prakash Ratnawat"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/0d4eed736632cbdf6889162385df35f60048c1d2 8ff9cba75521b64724f8c89ab0db8cafbdc6c295This paper explores the integration of generative artificial intelligence (AI) in enhancing check deposit systems within the banking sector. Banks can improve accuracy, reduce fraud, and streamline customer experiences by leveraging generative AI techniques. This paper comprehensively analyzes current check deposit processes, identifies key challenges, and proposes a generative AI-driven framework that addresses these challengesInternational Journal For Multidisciplinary Research00This paper comprehensively analyzes current check deposit processes, identifies key challenges, and proposes a generative AI-driven framework that addresses these challenges.International Journal For Multidisciplinary Research["Kumar Shanmugasamy"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/8ff9cba75521b64724f8c89ab0db8cafbdc6c295 0a8f99f8203bb85885caf63e6b720bd90e922077Integrating artificial intelligence and advanced analytics in manufacturing has revolutionized traditional maintenance approaches, particularly through implementing predictive maintenance systems. This comprehensive article explores the transformation of manufacturing operations through AI-driven technologies, examining their impact on operational efficiency, cost reduction, and strategic advantages. The article investigates various aspects, including implementation strategies, challenges, and solutions across manufacturing sectors. Key focus areas include production line optimization, quality control integration, and supply chain management, demonstrating how predictive maintenance improves equipment reliability, reduces downtime, and enhances operational performance. The article also examines the financial implications and strategic benefits of these implementations while addressing both technical and organizational challenges faced by manufacturing facilities during adoption.International Journal For Multidisciplinary Research00This comprehensive article explores the transformation of manufacturing operations through AI-driven technologies, examining their impact on operational efficiency, cost reduction, and strategic advantages and investigates various aspects, including implementation strategies, challenges, and solutions across manufacturing sectors.International Journal For Multidisciplinary Research["Gautam Ulhas Parab"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/0a8f99f8203bb85885caf63e6b720bd90e922077 7a0cebeb85f0e35aa0c3c041e3da8f176ebfad5bAs Artificial Intelligence (AI) systems become increasingly integrated into the social fabric of contemporary communities, ethical considerations surrounding their impact on fundamental rights have come to the fore. Indeed, the growing significance of AI has recently prompted a pivotal discourse within academic and policy circles in Europe concerning the development of an ethical framework for human-centric AI. As part of a broader research project examining the implications of AI on fundamental rights, particularly the right to non-discrimination, our objective is to present a preliminary overview of fundamental rights’ risk management in the context of AI. In light of the significant impact of AI on vulnerable individuals and minorities, our discussion will subsequently address critical areas of concern related to the EU AI Act, including algorithmic bias and its constituent elements of discrimination based on ethnicity or religion. Received: 06 June 2024 Accepted: 22 November 2024Deusto Journal of Human Rights00A preliminary overview of fundamental rights’ risk management in the context of AI is presented and critical areas of concern related to the EU AI Act are addressed, including algorithmic bias and its constituent elements of discrimination based on ethnicity or religion.Deusto Journal of Human Rights["Mar\u00eda Teresa Garc\u00eda-Berrio Hern\u00e1ndez"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/7a0cebeb85f0e35aa0c3c041e3da8f176ebfad5b 5e31b7d31a3dac220e24afe2da44dd25b76c1385New technologies such as artificial intelligence (AI), applications and platforms are becoming more common in youth services and non-formal education, with chatbots being key examples. However, many chatbots often fail to take into account the profiles, requirements and rights of young users leading to potential risks such as biases, polarization, and low data protection standards. In carrying out this research, a literature review was done to determine the history of youth services in Europe and the prevalence of chatbots. A series of interviews with representatives of organizations that either represented young people or provided youth services at the European level were held to share their experiences and describe the key features needed for a correct use of chatbots on youth services. This study highlights the practical possibilities and limitations of AI chatbots, and the need to codesign AI tools with youth organizations and young people in order to minimize threats and maximize the effectiveness of digital youth information, advice, and counselling services in Europe. Received: 30 May 2024 Accepted: 22 October 2024Deusto Journal of Human Rights00The practical possibilities and limitations of AI chatbots, and the need to codesign AI tools with youth organizations and young people in order to minimize threats and maximize the effectiveness of digital youth information, advice, and counselling services in Europe are highlighted.Deusto Journal of Human Rights["Alonso Escamilla", "Paula Gonzalo"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/5e31b7d31a3dac220e24afe2da44dd25b76c1385 9c739d03d5aa0213317b8060ab3af6b76f33992fAdvances in artificial intelligence (AI) are reshaping mobility through autonomous vehicles (AVs), which may introduce risks such as technical malfunctions, cybersecurity threats, and ethical dilemmas in decision-making. Despite these complexities, the influence of consumers' risk preferences on AV acceptance remains poorly understood. This study explores how individuals' risk preferences affect their choices among private AVs (PAVs), shared AVs (SAVs), and private conventional vehicles (PCVs). Employing a lottery experiment and a self-reported survey, we first derive four parameters to capture individuals' risk preferences. Based on a stated preference experiment and the error component logit model, we analyze reference-dependent preferences for key attributes of PAVs and SAVs, using PCVs as the reference. Our analysis reveals that risk-tolerant consumers are more inclined toward PAVs or SAVs. Further, consumers exhibit a greater sensitivity to losses, such as higher purchasing prices and running costs, than to gains, such as reduced egress time. Specifically, for buying a PAV, consumers are willing to pay 3582 CNY more for 1000 CNY saving on annual running cost, 3470 CNY for a 1-min reduction in egress time, 28,880 CNY for removing driver liability for crashes, and 30,710 CNY for the improved privacy data security. For adopting SAVs, consumers are willing to pay 0.096 CNY extra per kilometer for a 1-min reduction in access time and 0.033 CNY extra per kilometer for a 1% increase in SAV availability. Therefore, this study enhances the understanding on risk preferences in AV acceptance and offers important implications for stakeholders in the AI-empowered mobility context.Risk Analysis870The authors' analysis reveals that risk-tolerant consumers are more inclined toward PAVs or SAVs, and consumers exhibit a greater sensitivity to losses, such as higher purchasing prices and running costs, than to gains, such as reduced egress time.Risk analysis : an official publication of the Society for Risk Analysis["Ya Liang", "L. Qian", "Yang Lu", "Tolga Bekta\u015f"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/9c739d03d5aa0213317b8060ab3af6b76f33992f 7f36c3b957e96ee5687cf2b06f5e7aaba8f8d66aThis paper examines the strategic dynamics of international competition to develop Artificial Superintelligence (ASI). We argue that the same assumptions that might motivate the US to race to develop ASI also imply that such a race is extremely dangerous. These assumptions--that ASI would provide a decisive military advantage and that states are rational actors prioritizing survival--imply that a race would heighten three critical risks: great power conflict, loss of control of ASI systems, and the undermining of liberal democracy. Our analysis shows that ASI presents a trust dilemma rather than a prisoners dilemma, suggesting that international cooperation to control ASI development is both preferable and strategically sound. We conclude that cooperation is achievable.00["Corin Katzke", "G. Futerman"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/7f36c3b957e96ee5687cf2b06f5e7aaba8f8d66a 0bd2d3fa7d8c6ae5599d0e6d5f06cbfcdfc92e06El artículo analiza la relación entre la inteligencia artificial (IA) y los derechos humanos en el contexto de la era digital, destacando los retos y oportunidades que plantea su desarrollo acelerado. Se examina cómo la IA impacta en ámbitos como la privacidad, la igualdad y la libertad, abordando riesgos asociados, como los sesgos algorítmicos, la vigilancia masiva y la manipulación informativa. También se enfatiza la necesidad de garantizar un marco ético y regulatorio robusto, especialmente a través de iniciativas como el Reglamento Europeo de Inteligencia Artificial (RIA), que busca proteger los derechos fundamentales y anticipar problemas éticos. El texto subraya el potencial transformador de la IA en sectores como la salud, la educación y la justicia, pero advierte sobre su impacto negativo en grupos vulnerables y en la gobernanza democrática. Finalmente, se proponen recomendaciones, incluyendo fortalecer la regulación, proteger datos personales y fomentar tecnologías inclusivas y responsables.Deusto Journal of Human Rights00Deusto Journal of Human Rights["Jos\u00e9 Miguel Iturmendi Rubia"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/0bd2d3fa7d8c6ae5599d0e6d5f06cbfcdfc92e06 e1934149379247569196b738bc3bb705468ec4cbLos denominados derechos del interesado, que aparecen regulados en el Capítulo III del Reglamento (UE) 2016/679, constituyen una de las principales herramientas puestas a disposición de los individuos para conseguir el control efectivo sobre sus datos personales. Entre dichos derechos figuran los de acceso, rectificación, supresión, oposición, portabilidad y limitación del tratamiento, además del poco ejercitado hasta la fecha derecho a no ser objeto de decisiones automatizadas. Como norma general, estos derechos son directamente exigibles frente al responsable del tratamiento, que debe dar una respuesta formal dentro un plazo definido, incluso en aquellos supuestos en que corresponda la denegación de la solicitud. Este artículo analiza los cambios que la sucesiva implantación de sistemas de inteligencia artificial puede provocar en las peticiones de los afectados y los problemas prácticos a los que, previsiblemente, se enfrentarán los responsables del tratamiento que deban darles respuesta. En particular, resalta la importancia de realizar una adecuada interpretación y aplicación del artículo 22 del citado Reglamento (UE) 2016/679, relativo a decisiones automatizadas. Recibido: 30 mayo 2024 Aceptado: 25 octubre 2024Deusto Journal of Human Rights00Deusto Journal of Human Rights["Mar\u00eda Luisa Gonz\u00e1lez Tapia"]2024-12-22T00:00:00https://www.semanticscholar.org/paper/e1934149379247569196b738bc3bb705468ec4cb 50b5f33fd794a1aba4b6820dadfb02ef4ed706dcThe growth of the planet’s population requires the application of innovative technological solutions for its nutrition. Artificial intelligence, as part of the technological toolkit of Industry 4.0, having a strong transforming effect in modern society, is perceived as a strategic factor for increasing productivity, efficiency and innovation in a number of sectors, including the food industry. The global food and beverage artificial intelligence market in 2021 is valued at USD 4.49 billion. It is expected to grow at a CAGR (average annual rate) of 45.4% to reach US$ 83.4 billion by 2029. The main goal of every industrial enterprise in the food industry is to produce high-quality products at the lowest possible cost. The application of AI can contribute to maintaining a higher quality of the manufactured product, through rapid quality control and visualization of the result. AI can aggregate and analyze data in real-time making recommendations to improve operational activities, can analyze data, uncover trends and recommend actions to increase efficiency. The result of the implementation of artificial intelligence in the food industry contributes to driving the growth of the market.BIO Web of Conferences124The main goal of every industrial enterprise in the food industry is to produce high-quality products at the lowest possible cost, and the result of the implementation of artificial intelligence in the food industry contributes to driving the growth of the market.BIO Web of Conferences["Valentina Nikolola-Alexieva", "K. Valeva", "Stoyan Pashev"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/50b5f33fd794a1aba4b6820dadfb02ef4ed706dc 0eea01b4c2985534578cbb157817a5d710f0810dPurpose The purpose of this paper is to report on how the barriers that prevent or hinder the applicability of artificial intelligence (AI) impact its use in an insurance company.Design/methodology/approach Through bibliographic research, this paper maps the literature to identify the barriers to the use of AI by companies. After this, a qualitative single-case study, with descriptive characteristics, is conducted in an insurance company to verify if it is ready to adopt AI. Secondary data based on surveys of information technology consultancies were also used to provide greater consistency to the research.Findings This analysis showed that investments in these kinds of projects encounter organizational, financial and social barriers, such as a culture not focused on innovation, a lack of strategic IT alignment, a lack of knowledge of the potential or even the ability to carry out an adequate feasibility analysis. Based on the barriers presented by the case and AI initiatives, the researched company is using AI for automation and execution rather than using its transformative potential to be an agent of change across the organization. Most of the barriers to the adoption of AI are organizational rather than technical.Research limitations/implications The limitation of this study is it presents a single-case study, but this involves local cultural problems because companies are not open to supplying internal information to external researchers.Originality/value The value of this article lies in examining a real case in Latin America, raising the barriers to the adoption of AI, which has a different environment from Europe and the USA, presenting a company that has the freedom to choose local or international technologies.Innovation &amp; Management Review471Analysis showed that investments in these kinds of projects encounter organizational, financial and social barriers, such as a culture not focused on innovation, a lack of strategic IT alignment, a lack of knowledge of the potential or even the ability to carry out an adequate feasibility analysis.Innovation &amp; Management Review["Edval da Silva Tavares", "Michel Leardini", "Marcelo Schneck de Paula Pessoa"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/0eea01b4c2985534578cbb157817a5d710f0810d 42c3dd0adda23ade10e62193de161d166584d6d1Artificial intelligence (AI) has significantly impacted various fields, including oncology. This comprehensive review examines the current applications and future prospects of AI in lung cancer research and treatment. We critically analyze the latest AI technologies and their applications across multiple domains, including genomics, transcriptomics, proteomics, metabolomics, immunomics, microbiomics, radiomics, and pathomics in lung cancer research. The review elucidates AI’s transformative role in enhancing early detection, personalizing treatment strategies, and accelerating therapeutic innovations. We explore AI’s impact on precision medicine in lung cancer, encompassing early diagnosis, treatment planning, monitoring, and drug discovery. The potential of AI in analyzing complex datasets, including genetic profiles, imaging data, and clinical records, is discussed, highlighting its capacity to provide more accurate diagnoses and tailored treatment plans. Additionally, we examine AI’s potential in predicting patient responses to immunotherapy and forecasting survival rates, particularly in non-small cell lung cancer (NSCLC). The review addresses technical challenges facing AI implementation in lung cancer care, including data quality and quantity issues, model interpretability, and ethical considerations, while discussing potential solutions and emphasizing the importance of rigorous validation. By providing a comprehensive analysis for researchers and clinicians, this review underscores AI’s indispensable role in combating lung cancer and its potential to usher in a new era of medical breakthroughs, ultimately aiming to improve patient outcomes and quality of life.Frontiers in Oncology1541This review underscores AI’s indispensable role in combating lung cancer and its potential to usher in a new era of medical breakthroughs, ultimately aiming to improve patient outcomes and quality of life.Frontiers in Oncology["Dongdong Huang", "Zifang Li", "Tao Jiang", "Chaojuan Yang", "Ning Li"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/42c3dd0adda23ade10e62193de161d166584d6d1 64d426f64b4051c67e95efc82e4c01e095137251Water temperature (WT) is a crucial factor indicating the quality of water in the river system. Given the significant variability in water quality, it is vital to devise more precise methods to forecast temperature in river systems and assess the water quality. This study designs and evaluates a new explainable artificial intelligence and hybrid machine-learning framework tailored for hourly and daily surface WT predictions for case studies in the Menindee region, focusing on the Weir 32 site. The proposed hybrid framework was designed by coupling a nonstationary signal processing method of Multivariate Variational Mode Decomposition (MVMD) with a bidirectional long short-term memory network (BiLSTM). The study has also employed a combination of in situ measurements with gridded and simulation datasets in the testing phase to rigorously assess the predictive performance of the newly designed MVMD-BiLSTM alongside other benchmarked models. In accordance with the outcomes of the statistical score metrics and visual infographics of the predicted and observed WT, the objective model displayed superior predictive performance against other benchmarked models. For instance, the MVMD-BiLSTM model captured the lowest Root Mean Square Percentage Error (RMSPE) values of 9.70% and 6.34% for the hourly and daily forecasts, respectively, at Weir 32. Further application of this proposed model reproduced the overall dynamics of the daily WT in Burtundy (RMSPE = 7.88% and Mean Absolute Percentage Error (MAPE) = 5.78%) and Pooncarie (RMSPE = 8.39% and MAPE = 5.89%), confirming that the gridded data effectively capture the overall WT dynamics at these locations. The overall explainable artificial intelligence (xAI) results, based on Local Interpretable Model-Agnostic Explanations (LIME), indicate that air temperature (AT) was the most significant contributor towards predicting WT. The superior capabilities of the proposed MVMD-BiLSTM model through this case study consolidate its potential in forecasting WT.Water391The overall explainable artificial intelligence (xAI) results, based on Local Interpretable Model-Agnostic Explanations (LIME), indicate that air temperature was the most significant contributor towards predicting WT.Water["Leyde Briceno Medina", "K. Joehnk", "R. Deo", "Mumtaz Ali", "Salvin S. Prasad", "Nathan Downs"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/64d426f64b4051c67e95efc82e4c01e095137251 489eb7f0407be4020131f160eb0227833c05fb3dReproductive Health401This commentary discusses innovations in sexual, and reproductive health and rights in meeting target 3.7 of the SDGs with a focus on artificial intelligence and highlights the need for a more circumspective approach in response to the ethical and human rights implications of using AI in providing sexual and reproductive health services.Reproductive Health["F. Y. Gbagbo", "E. Ameyaw", "S. Yaya"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/489eb7f0407be4020131f160eb0227833c05fb3d d61bf58b77a67148ddb26896ce68e7596305e6dbIn the rapidly evolving landscape of the 21st century, the intersection of artificial intelligence (AI) and development communication presents both opportunities and challenges that demand nuanced exploration. This paper examines how technological advancements in AI are reshaping communication strategies within diverse cultural contexts. By employing qualitative analysis, the study unravels the implications of AI-driven tools for information dissemination, audience engagement, and participatory communication processes, with a strong emphasis on cultural sensitivity and inclusivity. The paper delves into the transformative potential of AI to enhance developmental outcomes, bridging gaps in access to information and fostering dialogue among various stakeholders. Concurrently, the study addresses the ethical considerations and risks inherent in algorithmic biases and the digital divide, underscoring the need for mindfulness in deploying these technologies. This exploration ultimately aims to provide insights for practitioners and policymakers in leveraging AI’s capabilities while prioritizing sustainable development and cultural respect. By advocating for a balanced approach, the paper seeks to promote technological innovation within a framework that values equity, diversity, and community empowerment in the realm of development communication.African Journal of Culture, History, Religion and Traditions52The paper delves into the transformative potential of AI to enhance developmental outcomes, bridging gaps in access to information and fostering dialogue among various stakeholders, and addresses the ethical considerations and risks inherent in algorithmic biases and the digital divide.African Journal of Culture, History, Religion and Traditions["Ezeaka, N. B.", "Umennebuaku, V. A."]2024-12-23T00:00:00https://www.semanticscholar.org/paper/d61bf58b77a67148ddb26896ce68e7596305e6db e2b0c52a091076d05fa7eeebad4a27a8ecae4f22OBJECTIVE Aim: To analyze and compare the experiences and perceptions of artificial intelligence (AI) among practicing doctors and medical students. PATIENTS AND METHODS Materials and Methods: A survey was conducted among 30 doctors and 30 fifth-year master's students enrolled in the "Medicine" program. Participants were asked about their experiences with AI, their perceptions of AI's impact on their education and practice, and their views on the benefits and drawbacks of AI in the medical field. The data were analyzed to compare the responses between the two groups. RESULTS Results: Among the respondents, 8 doctors (26,67%) and 4 students (13,33%) had not used AI in their practice or studies. The analysis was conducted on the remaining 22 doctors and 26 students. The study found that students generally rated the effectiveness of AI higher than physicians did, particularly in areas such as enhancing work and educational experiences. Both groups used AI primarily for information retrieval, with students showing a slightly greater openness to expanding AI's role in education and practice. Despite recognizing the advantages of AI, both groups expressed concerns regarding its accuracy and reliability. CONCLUSION Conclusions: The study indicates that while AI, particularly ChatGPT, is increasingly being adopted in medical education and practice, there is still a level of caution and skepticism among both students and professionals. Further research is needed to optimize the integration of AI in medical curricula and address the ethical implications of its use.Wiadomosci lekarskie00The study indicates that while AI, particularly ChatGPT, is increasingly being adopted in medical education and practice, there is still a level of caution and skepticism among both students and professionals.Wiadomosci lekarskie["Oksana O Drevitska", "Lidiia V Butska", "Ostap O Drevytskyi", "Valentyn O Ryzhak", "Hanna B Varina", "Olha V Kovalova", "Inna V Medvid"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/e2b0c52a091076d05fa7eeebad4a27a8ecae4f22 937cce8cd846420c4688357d1a92d38d8839c9f5Artificial intelligence (AI) is rapidly transforming various sectors, including healthcare and education. In nursing, AI has the potential to enhance educational outcomes and improve clinical practices. The study aim to explore the nuanced experiences and perceptions of nursing students at Bayero University, Kano, in relation to the use of AI in their academic and clinical environments. A qualitative narrative inquiry was conducted among nursing students at Bayero University, Kano. Focus group discussion was used to collect data from the participants. Thematic analysis was employed to identify and interpret themes related to their experiences and perceptions of AI. The study identified that the majority of students have engaged with AI technologies primarily for academic purposes, such as researching assignments and accessing clinical information. However, they demonstrated limited awareness of nursing-specific AI tools. Participants also reported significant challenges related to network connectivity and data access, which hindered their use of AI. Despite these challenges, the overall perception of AI was positive, with students acknowledging its potential to improve nursing education and practice. Concerns about technical errors, over-reliance, and job displacement were also noted. Nursing students at Bayero University, Kano, recognize the potential benefits of AI in education and clinical practice but face significant challenges due to infrastructural limitations. Their positive perception of AI suggests an openness to its integration into nursing, provided that technological and educational barriers are addressed. The findings highlight the need for improved technological infrastructure, targeted training on AI applications in nursing, and a balanced approach to integrating AI with human expertise.Keywords: Artificial intelligence, Nursing education, Perceptions, Experiences, Qualitative, NigeriaReal in Nursing Journal00Nursing students at Bayero University, Kano, recognize the potential benefits of AI in education and clinical practice but face significant challenges due to infrastructural limitations, and their positive perception of AI suggests an openness to its integration into nursing, provided that technological and educational barriers are addressed.REAL in Nursing Journal["Amina Suleiman Rajah", "Z. M. Sardauna", "M. Ladan"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/937cce8cd846420c4688357d1a92d38d8839c9f5 86a71f8f95df7ffa343f413defd892c6717559a9This article discusses the challenges and opportunities of using Artificial Intelligence (AI) as a supporting tool in business decision making in the digital era. In the context of growing digitalization, AI plays an important role in improving operational efficiency and accuracy of data analysis, which has a significant impact on various aspects of business, including audit, finance and accounting. This research uses a literature study method to identify the challenges and opportunities faced by companies in implementing AI. The research results show that AI can increase efficiency and productivity, as well as provide more accurate predictive analysis. However, the application of AI is also faced with challenges such as the need for sophisticated technological infrastructure and ethical risk management. By utilizing AI, companies can make real-time data-based decisions, understand market trends, and innovate products that suit consumer needs. This article emphasizes the importance of adapting to new technologies to achieve business goals in the digital era.Jurnal Bisnis dan Komunikasi Digital140The research results show that AI can increase efficiency and productivity, as well as provide more accurate predictive analysis, and the importance of adapting to new technologies to achieve business goals in the digital era.Jurnal Bisnis dan Komunikasi Digital["Rusdi Hidayat", "Pinky Indah Respati Kusumasari", "Arisma Putri", "Nindia Murdiana", "Devina Rahma Puspita"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/86a71f8f95df7ffa343f413defd892c6717559a9 b6e0ffcd7662bf5d1a8d38eae3c26f309c29288dThe development of 5.0 technology, which facilitates the integration of virtual and physical spaces, has significantly impacted various aspects of human life by incorporating Artificial Intelligence (AI). This influence extends to the legal field. The purpose of this study is to examine and understand the use of AI in the assignment and appointment of judicial panels in the Indonesian judiciary's case handling processes. The research conducted is a type of library research employing a normative legal approach with a qualitative methodology. The approach utilized in this study is both juridical-normative and conceptual or analytical. The data sources used include primary and secondary data. The findings of this study indicate that the use of AI in the assignment and appointment of judicial panels is an effort towards greater transparency in case handling. This approach can potentially reduce or eliminate suspicions among parties involved in litigation or the public that a judicial panel could be influenced to favor a particular outcome. Additionally, assigning and appointing judicial panels based on the type and qualifications of cases, as well as workload, can improve the efficiency of judicial performance.Pena Justisia Media Komunikasi dan Kajian Hukum00The findings of this study indicate that the use of AI in the assignment and appointment of judicial panels is an effort towards greater transparency in case handling, which can potentially reduce or eliminate suspicions among parties involved in litigation or the public that a judicial panel could be influenced to favor a particular outcome.Pena Justisia: Media Komunikasi dan Kajian Hukum["Alysa Al Fitri", "Reynaldi Ahmad Taufiqurrahman"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/b6e0ffcd7662bf5d1a8d38eae3c26f309c29288d 6dfa783928ea83f8b467effbad037f30a7238989Integrating Artificial Intelligence (AI) into civil justice systems has become a complex issue in the legal technology landscape. This study examines AI-driven decision-making's implications on fundamental legal principles, particularly in discretionary punishment within international civil law. By employing rhetorical discourse analysis, the research highlights the importance of critically evaluating AI's influence on fairness, impartiality, and due process. Thematic analysis explores potential benefits and drawbacks of AI in civil justice systems, emphasizing the need to uphold ethical standards and ensure equitable outcomes. Key themes include the impact of AI on international civil law, balancing technological innovation with core legal principles, ethical considerations such as algorithmic bias and transparency, challenges in ensuring fairness and due process, and the need for a thoughtful approach to AI integration. Combining rhetorical discourse and thematic analysis effectively communicates the significance and scope of the study, presenting a compelling argument for examining AI's impact on civil justice systems while balancing innovation and preserving fundamental legal principles.Journal of Digital Art &amp; Humanities180Examining AI-driven decision-making's implications on fundamental legal principles, particularly in discretionary punishment within international civil law, highlights the importance of critically evaluating AI's influence on fairness, impartiality, and due process.Journal of Digital Art &amp; Humanities["Z. Roozafzai"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/6dfa783928ea83f8b467effbad037f30a7238989 6e36d0311b0785136be235e3b3e773c5a9cd9353This research is motivated by the importance of the role of technology in education, especially the use of artificial intelligence (AI) and interactive learning tools such as Quizizz to increase students' interest in learning. The purpose of this study is to analyze the impact of the use of artificial intelligence and Quizizz on students' interest in the Digital Educational Technology course. The research method used is a quantitative approach using a survey method by distributing questionnaires to students of the Jakarta State University Office Administration Training Program in 2022 as the population in this study, with a sample of 50 respondents combined between class A and class B, and analyzed with the help of IBM SPSS Statistic 25 to obtain test results. The results of the study show that the use of AI provides a more personal and effective learning experience, while the Quizizz media creates an interesting learning environment through gamification elements. The combination of these two factors has been shown to have a significant positive impact on increasing students' interest in learning, as evidenced by the high level of student engagement and motivation in the learning process. The findings of this study provide insight for educators to develop innovative technology-based teaching strategies and also encourage educational institutions to use modern technology to improve the quality of education in the digital era.JURNAL PENELITIAN DAN KARYA ILMIAH400The results of the study show that the use of AI provides a more personal and effective learning experience, while the Quizizz media creates an interesting learning environment through gamification elements.Mutiara : Jurnal Penelitian dan Karya Ilmiah["Julia Risky Handayani", "Marsofiyati"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/6e36d0311b0785136be235e3b3e773c5a9cd9353 d909f99aa1f38d86251f6d383f1765086804d4d9Kecerdasan Buatan (Artificial Intelligence/AI) telah menjadi teknologi yang revolusioner dalam bidang pendidikan dengan memberikan kontribusi signifikan terhadap efisiensi dan personalisasi proses pembelajaran. Penelitian ini bertujuan untuk menganalisis penerapan AI dalam pendidikan, mengidentifikasi manfaatnya, serta mengkaji tantangan yang akan dihadapi, khususnya dalam konteks pendidikan di Indonesia. Penelitian ini menggunakan metode Systematic Literature Review (SLR) yang melibatkan analisis terhadap 30 artikel terpilih dari basis data ScienceDirect dan Google Scholar, yang diterbitkan pada rentang tahun 2019–2024. Hasil penelitian menunjukkan bahwa AI berperan dalam mengotomatisasi tugas administratif, menciptakan pengalaman belajar yang adaptif, serta memberikan umpan balik secara cepat dan tepat. Namun demikian, terdapat berbagai kendala dalam implementasi AI, seperti keterbatasan infrastruktur teknologi, kesiapan pendidik dalam memanfaatkan teknologi, serta kesenjangan digital antara wilayah perkotaan dan pedesaan. Dengan adanya penelitian ini diharapkan dapat menjadi acuan dalam mengoptimalkan penerapan AI di sektor pendidikan, guna mendukung peningkatan efisiensi pembelajaran dan mendorong terwujudnya revolusi pendidikan di masa mendatangJATI (Jurnal Mahasiswa Teknik Informatika)00JATI (Jurnal Mahasiswa Teknik Informatika)["Adelia Firnanda Putri", "Siti Nur Hayati", "Adinda Rahmanda Putri"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/d909f99aa1f38d86251f6d383f1765086804d4d9 52909050ac9f5987b6b304feaa233646f0549afcThis research aims to analyze the role of Artificial Intelligence (AI) in decision-making within e-commerce businesses, focusing on its impact on efficiency, innovation, and competitive advantage. This study employs a qualitative approach using literature review to explore the implementation of AI in e-commerce. AI enables real-time analysis of large-scale data, supporting more efficient and accurate strategic decision-making. The technology also fosters innovation through features such as recommendation systems and the integration of augmented reality (AR) and virtual reality (VR), enhancing customer experiences. Furthermore, AI strengthens companies' competitiveness by personalizing services and leveraging big data to design superior strategies. The findings show that AI not only improves operational efficiency and innovation but also serves as a key element for business sustainability in the digital era.Jurnal Bisnis dan Komunikasi Digital200The findings show that AI not only improves operational efficiency and innovation but also serves as a key element for business sustainability in the digital era.Jurnal Bisnis dan Komunikasi Digital["Regina Pinkan Efendi", "Ihfada Qolbi", "Syifa Zahra Arista Afandi", "Indah Respati Kusumasari", "Rusdi Hidayat Nugroho"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/52909050ac9f5987b6b304feaa233646f0549afc 94a886f7a99065db7f104f086c99348a0c57c2dePenelitian ini membahas permasalahan terkait efektivitas penggunaan Artificial Intelligence (AI) dalam menganalisis perilaku konsumen di bisnis e-commerce, terutama bagaimana AI dapat meningkatkan loyalitas pelanggan. Tujuan penelitian ini adalah untuk mengevaluasi sejauh mana AI berperan dalam memberikan rekomendasi produk yang relevan, pengalaman personalisasi yang memuaskan, serta interaksi konsumen dengan fitur AI. Metode penelitian menggunakan pendekatan kuantitatif dengan analisis regresi linier berganda yang melibatkan data simulasi dari 200 responden. Hasil penelitian menunjukkan bahwa ketiga variabel tersebut memiliki pengaruh signifikan terhadap loyalitas pelanggan, dengan relevansi rekomendasi produk berbasis AI sebagai faktor yang paling berpengaruh. Kesimpulannya, AI berkontribusi positif dalam membangun loyalitas pelanggan melalui personalisasi yang lebih efektif, yang memiliki implikasi praktis bagi optimalisasi strategi e-commerce.Dengan demikian, penelitian ini memperluas literatur yang ada tentang peran AI dalam e-commerce dan membuka peluang bagi studi lanjutan terkait elemen AI lainnya yang dapat mempengaruhi loyalitas pelanggan secara signifikan.  Prosiding SNAST150PROSIDING SNAST["Dison Librado", "Surdani Yanti", "Yosef Murya", "Kusuma Ardhana"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/94a886f7a99065db7f104f086c99348a0c57c2de ea21f9bb019a2188848b9aadd98066e8ff788282The rapid development of the digital economy, driven by artificial intelligence (AI), is profoundly transforming traditional accounting practices and business models. The emergence of innovative models such as “wisdom + accounting” and “wisdom + financial sharing” has opened new avenues for enhancing enterprise decision-making support systems. This paper delves into the application of AI technology in accounting, examining its practical implementation and associated challenges. To mitigate potential risks arising from technological advancements, enterprises should establish robust and efficient intelligent financial systems. Additionally, organizations should foster a mindset of change within their accounting teams, improve the application of management information systems, strengthen internal control mechanisms, and continuously upgrade intelligent accounting software. Financial managers must adapt to the evolving landscape and proactively adjust their career paths and development strategies.Proceedings of Business and Economic Studies00This paper delves into the application of AI technology in accounting, examining its practical implementation and associated challenges and recommends that enterprises establish robust and efficient intelligent financial systems.Proceedings of Business and Economic Studies["Jun Che", "Youting Chen", "Xianglin Zuo"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/ea21f9bb019a2188848b9aadd98066e8ff788282 e18c0f9034793fa72367ebd2809c0ae5c80d2d1ePenelitian ini membahas tentang strategi teknologi Artificial Intelligence (AI) atau kecerdasan buatan dalam pengambilan keputusan bisnis di era digital. Penelitian ini menggunakan teknik pengumpulan dan analisis data dari berbagai sumber, seperti jurnal ilmiah, artikel, dan studi kasus yang berkaitan dengan penerapan AI dalam bisnis. Melalui pendekatan ini, kita dapat mengeksplorasi bagaimana AI memengaruhi proses pengambilan keputusan, tantangan yang dihadapi oleh perusahaan, serta strategi yang diterapkan untuk mengintegrasikan teknologi ini secara efektif. Strategi teknologi AI dalam pengambilan keputusan bisnis di era digital sangat penting untuk membuat hasil lebih efisien dan akurat. Dengan memanfaatkan AI, perusahaan dapat merespons perubahan pasar dengan lebih baik dan mengurangi risiko yang terkait dengan pengambilan keputusan. Namun, tantangan seperti kebutuhan akan data yang berkualitas dan kemampuan analisis yang memadai harus diatasi untuk memaksimalkan potensi AI. Penelitian ini menekankan pentingnya strategi yang tepat dalam implementasi AI untuk mencapai keunggulan kompetitif di era digital.Jurnal Bisnis dan Komunikasi Digital80Jurnal Bisnis dan Komunikasi Digital["Rusdi Hidayat Nugroho", "I. Kusumasari", "V. Febrianto", "M. Arif", "Mohammad Ryan Mahardika", "R. Hidayat"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/e18c0f9034793fa72367ebd2809c0ae5c80d2d1e 9a75f66db1ef00dca3f37c2a4115c5f444a7e742Activities related to ensuring the necessary conditions for public safety in the country currently seem to be the most important of the priorities aimed at the sustainable development of Russian society. This area needs to be developed and improved, using the latest digital technologies in solving law enforcement problems. In this regard, the authors of this study draw attention and emphasize that today it is extremely important to strive in the activities of police departments to increase the ability to use technologies based on artificial intelligence to track, collect, analyze, process and otherwise use colossal flows of information to effectively combat crime. The conducted research gives reason to believe that it is possible to use new technologies in the domestic law enforcement sphere: by processing materials containing statistical data; providing assistance in the preparation of documentation of varying complexity; saturating law enforcement websites with legal information; making decisions in the process of qualifying crimes, etc. Without a doubt, in the future, the use of new technologies can also have a significant impact on the formation of prospects for combating crime, as well as its prevention. It is concluded that in modern conditions it is important to intensify the use of new technologies to counter various criminal manifestations. Most likely, the full implementation of new technologies in the activities of internal affairs bodies will go a long way. However, in any case, in order to actively counteract criminal manifestations today, it is important for modern society to begin the active use of intelligent systems in law enforcement.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia20It is concluded that in modern conditions it is important to intensify the use of new technologies to counter various criminal manifestations, and it is important for modern society to begin the active use of intelligent systems in law enforcement.Legal Science and Practice: Journal of Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia["Petr Kobets", "Igor' Il'in"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/9a75f66db1ef00dca3f37c2a4115c5f444a7e742 772f55b50b4ac3dc74c32f16696009e5b2783ef9Accurately diagnosing and planning for the treatment of brain tumors is crucial in clinical practice. Brain tumor detection and diagnosis rely heavily on artificial intelligence (AI) systems that mainly employ medical imaging modalities like MRI. This study employs cutting-edge DL and image processing techniques to intelligently forecast the brain tumor using AI. The complicated and varied nature of brain tumors frequently presents challenges to deep learning models, despite their promising performance in this task. In order to overcome this obstacle, we present the InceptionV3 architecture, which is based on CNNs and uses 5-fold cross-validation to classify brain tumors from MRI images. A training, validation, and testing of a model were conducted using a publically accessible MRI dataset that included 7023 greyscale brain MRI pictures. These images were classified into four types of tumors: gliomas, meningiomas, no tumors, and pituitary. To enhance diversity of a training dataset, the photos were preprocessed by scaling, greyscale conversion, and labeling. Afterward, data augmentation techniques were applied. A model's performance was assessed using 5-fold cross-validation, yielding an F1-score of 99.98%, an average accuracy of 97.12%, precision of 97.97%, and recall of 96.59%. Other Artificial Intelligent models that were compared included InceptionV3, VGG19, CNN, and DenseNet and the results indicated that the InceptionV3 gave better results overall. These results demonstrate that deep learning can accurately and efficiently detect brain tumors utilizing MRI pictures.International Journal of Current Engineering and Technology00The InceptionV3 architecture, which is based on CNNs and uses 5-fold cross-validation to classify brain tumors from MRI images, demonstrates that deep learning can accurately and efficiently detect brain tumors utilizing MRI pictures.International Journal of Current Engineering and Technology["Sagar Bharat Shah"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/772f55b50b4ac3dc74c32f16696009e5b2783ef9 d4bea8ed32647fb7c0dfe3cfade35c26fb19f34bThe rapid advancement of Generative Artificial Intelligence (GAI) technologies has significantly impacted various sectors, including higher education. This study investigates the behavioral intention of students in higher education institutions to adopt GAI and its effect on their academic performance mediating the intention to use GAI. This study uses an analytical cross-sectional design to assess the current relationships among behavioral intention factors, intention to use GAI, and academic performance. Data were collected using a quantitative approach from 279 online and 105 student self-administered responses through purposive sampling. Purposive sampling was employed to target students with GAI experience, ensuring relevant insights aligned with the study’s objective of examining adoption patterns among the active users in higher education settings. The students represent seven higher education institutions that are accredited by the University Grants Commission, Nepal.  A seven-point Likert scale measured variables like performance expectancy, effort expectancy, social influence, facilitating conditions, intention to use GAI and academic performance. The final sample size was 384, and pilot testing ensured instrument validity. Data analysis was conducted using SMART Partial Least Square (PLS). SmartPLS was chosen for its capacity to handle complex models, making it suitable for analyzing predictive relationships without requiring the normal data distribution. The results show that all the factors of behaviorual intentions significantly influence the intention to use GAI; however, the effort expectancy and social influence did not influence academic performance. The mediating role of intention to use GAI was also ensured.  The findings highlight the need for educational institutions to provide targeted training and clear ethical guidelines for responsible GAI use, emphasizing its integration into curricula to enhance academic performance.Prithvi Journal of Research and Innovation620The results show that all the factors of behaviorual intentions significantly influence the intention to use GAI; however, the effort expectancy and social influence did not influence academic performance.Prithvi Journal of Research and Innovation["Resam Lal Poudel", "Chandra Kanta Bastakoti"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/d4bea8ed32647fb7c0dfe3cfade35c26fb19f34b ee4407b78ebc71e406c4b6ede1cf3a5bbcd06cc1Background Total Hip Arthroplasty (THA) is a transformative surgical intervention for hip joint disorders, necessitating meticulous preoperative planning for optimal outcomes. With the emergence of Artificial Intelligence (AI), preoperative planning paradigms have evolved, leveraging AI algorithms for enhanced decision support and imaging analysis. This systematic review aims to comprehensively evaluate the role of AI in THA preoperative planning, synthesizing evidence from studies exploring various AI techniques and their applications. Methods A systematic search of PubMed, Scopus, and Web of Science databases was conducted to identify relevant articles. Inclusion criteria encompassed studies focusing on AI in THA preoperative planning, including randomized controlled trials (RCTs), observational studies, and comparative studies. Results Six studies from China met the inclusion criteria, collectively analyzing 831 patients. AI-assisted planning demonstrated superior accuracy in estimating prosthesis size and positioning compared to traditional methods. However, limitations such as geographic bias and language constraints were noted. Conclusion AI-assisted preoperative planning significantly enhances femoral positioning accuracy, providing superior outcomes compared to traditional methods. This improvement in precision, particularly in the placement of femoral and acetabular components, has been consistently observed across studies, making AI an indispensable tool in improving the overall success of Total Hip Arthroplasty. Despite promising findings, further research is warranted to address limitations and optimize the integration of AI technologies into routine clinical practice.Frontiers in Artificial Intelligence220AI-assisted preoperative planning significantly enhances femoral positioning accuracy, providing superior outcomes compared to traditional methods, making AI an indispensable tool in improving the overall success of Total Hip Arthroplasty.Frontiers in Artificial Intelligence["Javad Khaje Mozafari", "Seyed Ali Moshtaghioon", "Seyed Mani Mahdavi", "Alireza Ghaznavi", "Morteza Behjat", "Ali Yeganeh"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/ee4407b78ebc71e406c4b6ede1cf3a5bbcd06cc1 c88e1e21dddeb4d43936a75e6e2a22b98caa9590This study aims to examine the impact of marketing artificial intelligence (AI) and the specific channels it uses to influence the performance of Jordanian SMEs. In contrast to prior research that focused solely on the direct effects of marketing AI on organizational performance, our study introduces innovative pathways involving customer engagement, customer satisfaction, and data-driven decision-making. These channels serve as indirect mechanisms through which the impact of marketing AI extends to influence the Sustainable Financial Performance of SMEs. Using a framework for structural equation modeling, we looked at the answers of 250 small and medium-sized enterprises (SMEs) chosen through cluster sampling from industrially active areas in Jordan. Our findings reveal that the adoption of AI technologies leads to a notable 42.5% enhancement in Sustainable Financial Performance among SMEs, driven by a 50% increase in customer engagement and a 76% improvement through data-driven decision-making processes. While our study did not establish a direct correlation between marketing AI and long-term financial performance, it demonstrated the connection between technology adoption, customer-focused strategies, and data-driven practices. The findings offer actionable insights and recommendations for Jordanian SMEs leveraging marketing AI to achieve competitive advantage and sustainable growth. The study offers significant contributions to how marketing AI improves the Sustainable Financial Performance of Jordanian SMEs by identifying crucial indirect pathways. Unlike previous studies, it focuses on the roles of customer engagement, satisfaction, and data-driven decision-making as mediators rather than emphasizing direct impacts. The findings highlight the importance of integrating technology with customer-centric and data-oriented strategies to drive sustainable growth and competitiveness in SMEs.Sustainability310The study offers significant contributions to how marketing AI improves the Sustainable Financial Performance of Jordanian SMEs by identifying crucial indirect pathways involving customer engagement, satisfaction, and data-driven decision-making as mediators rather than emphasizing direct impacts.Sustainability["I. Magableh", "Maher H. Mahrouq", "Mohammad A. Ta\u2019Amnha", "H. A. Riyadh"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/c88e1e21dddeb4d43936a75e6e2a22b98caa9590 df7ed03419e24b9d5c1d4cebf836be2bceb62f0cCompulsory liability insurance for harm (artificial intelligence technologies) is a completely new phenomenon for domestic law. At the same time, the experimental legal regime is nothing more than a special procedure for legal regulation, which is aimed at introducing digital innovations into the Russian economy and has certain restrictions regarding the subject composition, as well as in space and time. The conclusion is formulated that the property interest of the subject of responsibility for causing harm in this type of insurance legal relations consists in neutralizing the negative consequences of a property nature, which are associated with the onset of civil liability for causing harm to artificial intelligence. The article makes proposals for amending the legislation, which may consist in introducing norms on causing harm to artificial intelligence (and insuring liability for causing it) not only to the life and health of individuals, the property of legal entities, but also to natural objects, as well as the environment as a whole.NORTH CAUCASUS LEGAL VESTNIK00The article makes proposals for amending the legislation, which may consist in introducing norms on causing harm to artificial intelligence (and insuring liability for causing it) not only to the life and health of individuals, the property of legal entities, but also to natural objects, as well as the environment as a whole.NORTH CAUCASUS LEGAL VESTNIK["\u0411\u0430\u0442\u0442\u0430\u0445\u043e\u0432 \u041f\u0435\u0442\u0440 \u041f\u0435\u0442\u0440\u043e\u0432\u0438\u0447", "\u041e\u0432\u0447\u0438\u043d\u043d\u0438\u043a\u043e\u0432\u0430 \u042e\u043b\u0438\u044f \u0421\u0435\u0440\u0433\u0435\u0435\u0432\u043d\u0430"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/df7ed03419e24b9d5c1d4cebf836be2bceb62f0c 262f6fc3b6533f016156c6c9f54c8032f045486aThis review explores the multifaceted role of Artificial Intelligence (AI) in education, focusing on its historical development, current applications, and future potential. It highlights AI's transformative impact on education, emphasizing personalized learning, intelligent tutoring systems, automated essay scoring, and virtual/augmented reality tools. The study critically examines technological advancements, pedagogical shifts, and ethical challenges associated with AI integration in education. By enhancing personalized learning and supporting individualized instruction, AI addresses diverse student needs while promoting inclusive education. The paper also discusses the evolving role of teachers in AI-enhanced classrooms, highlighting the need for reskilling educators to effectively engage with AI tools. Ethical considerations, including privacy, bias, and equity, are explored, underscoring the importance of responsible AI adoption. Furthermore, the study emphasizes the role of policy frameworks in ensuring fair, transparent, and accountable AI implementation. By providing insights into the opportunities and risks of AI-driven education, this review sheds light on the potential of AI to enhance learning outcomes, support student engagement, and foster lifelong learning. It calls for a balanced approach to AI integration, combining technological innovation with human oversight to achieve sustainable, ethical, and equitable educational transformation.STUDIES IN ENGINEERING AND EXACT SCIENCES60This review explores the multifaceted role of Artificial Intelligence in education, focusing on its historical development, current applications, and future potential, and calls for a balanced approach to AI integration, combining technological innovation with human oversight to achieve sustainable, ethical, and equitable educational transformation.STUDIES IN ENGINEERING AND EXACT SCIENCES["Hafsi Abbas"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/262f6fc3b6533f016156c6c9f54c8032f045486a 5cef74ee96d7712fc4faebf0ad63b77f53bda0d3The Clean Air Journal has adopted a new policy on the use of Artificial Intelligence (AI) tools and resources for all submissions to Clean Air Journal. This policy was written with the view that transparency about the use of AI is necessary to ensure trust between authors, reviewers, editors and readers. In addition, we believe that appropriate use of AI can support authors and research and be a great resource. Clean Air Journal00Clean Air Journal["M. Mpanza", "Sarisha Perumal", "Gregor Feig", "R. Garland", "K. Langerman"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/5cef74ee96d7712fc4faebf0ad63b77f53bda0d3 9fc18b6e71099bc0216426703d3cd93825668bc5The incorporation of artificial intelligence (AI) into educational settings is the primary force behind the revolutionary change occurring in the educational scene of the twenty-first century. This new way of thinking, called AI Education, has the potential to reshape the conventional wisdom that has long defined educational systems around the globe. The capacity of AI to automate administrative processes, give profound insights into student learning behaviours and needs, and create tailored learning experiences is fundamental to this revolution. In order to fully take advantage of AI technologies, educators, legislators, and engineers must successfully negotiate the many opportunities and threats that these technologies present. The article delves into the effects of AI on the field of education, drawing attention to the change from a cookie-cutter approach to instruction to one that is more personalized and flexible. New educational tools and platforms powered by AI are changing the game when it comes to information delivery and consumption. AIcan analyse a student's past interactions and performance using adaptive learning technology to personalize educational content to their unique learning style and pace. Not only does this method increase involvement, but it also improves understanding and memory retention. In addition, with the help of AI-driven analytics, teachers can see their students' strengths and weaknesses like never before, which allows them to help those students when needed it the most.Journal of Informatics Education and Research260The article delves into the effects of AI on the field of education, drawing attention to the change from a cookie-cutter approach to instruction to one that is more personalized and flexible.Journal of Informatics Education and Research["Dr. Bhagwan Jagwani", "Dr. Udai Bhan Trivedi", "Dr. Ashok Kumar", "Dr. Ashwani Kumar Yadav"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/9fc18b6e71099bc0216426703d3cd93825668bc5 785d68f45139bc51fea9220d8189c53a4ec0c0ebThis study aims to identify the implementation of knowledge management processes in Arab universities based on artificial intelligence technology. A phenomenological approach is used to analyze this implementation in leading Saudi Arabian universities. Data were collected through interviews, observations, and literature reviews, then analyzed using the Interpretative Phenomenological Analysis (IPA) method to explore themes and insights from participants' experiences. The study results indicate that implementing knowledge management processes based on artificial intelligence in Arab universities is moderate. Universities have developed networks to generate, store, share, and apply knowledge through artificial intelligence techniques. However, they still face various challenges in this technology-based knowledge management process. Based on these findings, this study recommends improving university infrastructure with the latest artificial intelligence technology and its application in knowledge management. It is also recommended that new services and applications based on artificial intelligence in the field of education be utilized through training programs, workshops, scientific meetings, and seminars related to digital developments. In addition, universities are encouraged to share knowledge internally and externally through knowledge management teams and training units in artificial intelligence systems.AL-TANZIM : JURNAL MANAJEMEN PENDIDIKAN ISLAM00The study results indicate that implementing knowledge management processes based on artificial intelligence in Arab universities is moderate, and improving university infrastructure with the latest artificial intelligence technology and its application in knowledge management is recommended.Al-Tanzim: Jurnal Manajemen Pendidikan Islam["Bannaga Hussen"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/785d68f45139bc51fea9220d8189c53a4ec0c0eb 4d87cc82773fe10bc608cedf08dc40ce0da72705Emergency Radiology680An expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI is developed to foster best-practices and further discussion among researchers working on various aspects of emergency and trauma radiology AI.Emergency radiology["David Dreizin", "Garvit Khatri", "Pedro V Staziaki", "Karen Buch", "Mathias Underbath", "Mohammed Mohammed", "Aaron Sodickson", "B. Khurana", "Anjali Agrawal", "J. S. Spann", "Nicholas Beckmann", "Z. Delproposto", "C. LeBedis", "Melissa Davis", "Gabrielle Dickerson", "Michael H Lev"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/4d87cc82773fe10bc608cedf08dc40ce0da72705 434e0bf299abf023aba4123147b92a646ac9cd91Among gynecological pathologies, cervical cancer has always represented a health problem with great social impact. The giant strides made as a result of both the screening programs perfected and implemented over the years and the use of new and accurate technological equipment have in fact significantly improved our clinical approach in the management and personalized diagnosis of precancerous lesions of the cervix. In this context, the advent of artificial intelligence and digital algorithms could represent new directions available to gynecologists and pathologists for the following: (i) the standardization of screening procedures, (ii) the identification of increasingly early lesions, and (iii) heightening the diagnostic accuracy of targeted biopsies and prognostic analysis of cervical cancer. The purpose of our review was to evaluate to what extent artificial intelligence can be integrated into current protocols, to identify the strengths and/or weaknesses of this method, and, above all, determine what we should expect in the future to develop increasingly safer solutions, as well as increasingly targeted and personalized screening programs for these patients. Furthermore, in an innovative way, and through a multidisciplinary vision (gynecologists, pathologists, and computer scientists), with this manuscript, we highlight a key role that AI could have in the management of HPV-positive patients. In our vision, AI will move from being a simple diagnostic device to being used as a tool for performing risk analyses of HPV-related disease progression. This is thanks to the ability of new software not only to analyze clinical and histopathological images but also to evaluate and integrate clinical elements such as vaccines, the composition of the microbiota, and the immune status of patients. In fact, the single-factor evaluation of high-risk HPV strains represents a limitation that must be overcome. Therefore, AI, through multifactorial analysis, will be able to generate a risk score that will better stratify patients and will support clinicians in choosing highly personalized treatments overall. Our study remains an innovative proposal and idea, as the literature to date presents a limitation in that this topic is considered niche, but we believe that the union of common efforts can overcome this limitation.Applied Informatics910To what extent artificial intelligence can be integrated into current protocols, to identify the strengths and/or weaknesses of this method, and to determine what the authors should expect in the future to develop increasingly safer solutions, as well as increasingly targeted and personalized screening programs for patients, is evaluated.AI["M. Dellino", "M. Cerbone", "A. d\u2019Amati", "Mario A. Bochicchio", "A. S. Lagan\u00e0", "Andrea Etrusco", "Antonio Malvasi", "Amerigo Vitagliano", "Vincenzo Pinto", "Ettore Cicinelli", "Gerardo Cazzato", "E. Cascardi"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/434e0bf299abf023aba4123147b92a646ac9cd91 eeec24748590b1a119c59d118bb28f837735a312Artificial Intelligence in the digital era has brought significant changes in the way companies manage and utilise human resources. Artificial Intelligence is expected to assist in various aspects of human resource management, particularly in military or state security sectors. This study focuses on the impact of AI on human resources within the Indonesian police force. The use of AI tools in managing the large amount of employee data in the police force is seen as beneficial. Matching the right person with the right job is a key challenge for human resource professionals, which AI and automation technology can help with. AI aids in forecasting future employee needs and making effective recruitment choices. Performance management tools driven by AI offer opportunities for both employees and organisations in police forces. AI can also help in evaluating employees fairly through multi-attribute decision-making processes. Additionally, AI systems can assist managers in determining appropriate compensation and benefits for police personnel.POLICY LAW NOTARY AND REGULATORY ISSUES (POLRI)00The use of AI tools in managing the large amount of employee data in the police force is seen as beneficial and AI systems can assist managers in determining appropriate compensation and benefits for police personnel.POLICY LAW NOTARY AND REGULATORY ISSUES (POLRI)["Widya Septiyandini", "Chairul Muriman", "Vita Mayastinasari"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/eeec24748590b1a119c59d118bb28f837735a312 ce08c36ba93b32a6759f0655c41ab658271d34b7This study aims to analyze the influence of auditor behavior, artificial intelligence, client health, and the audit process. The study was conducted at Public Accounting Firms (KAP) in Bengkulu City. The approach used in this research is quantitative, with data collection techniques through questionnaires. The obtained data were analyzed using multiple regression with the help of SPSS version 29.0. The results of the study show that auditor behavior (X1) has a significant influence on the audit process. In addition, artificial intelligence (X2) also has a significant influence on the audit process, while client health (X3) does not have a significant influence on the audit process (Y). Before the main analysis was conducted, the research instruments were tested through validity and reliability tests. The prerequisite tests included normality test, multicollinearity test, and heteroscedasticity test. The hypothesis testing in this study used multiple regression analysis. Overall, the results of this study indicate a significant influence of artificial intelligence on the audit process. Keywords: Auditor Behavior, Artificial Intelligence, Client Health, And Audit ProcessJURNAL AKUNTANSI KEUANGAN DAN TEKNOLOGI INFORMASI AKUNTANSI00The results of the study show that auditor behavior has a significant influence on the audit process and artificial intelligence also has a significant influence on the audit process.Jurnal Akuntansi, Keuangan dan Teknologi Informasi Akuntansi["Tia Fitri Sarioyonsi", "Pedi Riswandi", "Nina Yulianasari"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/ce08c36ba93b32a6759f0655c41ab658271d34b7 0ab40cb15e8c8b7c85c34695f6f921298dc494d8Background: Artificial Intelligence (AI) in dentistry has the potential to revolutionize oral healthcare by solving its inherent shortcomings. Aim: To review and evaluate the body of research on artificial intelligence's use in dentistry, with a focus on how it affects treatment planning, diagnosis, and patient care in a range of dental specialties. Methodology: 30 papers encompassing oral diagnosis, surgery, endodontics, prosthodontics, orthodontics, forensic dentistry, radiography, and periodontics are thoroughly examined in this review using PRISMA guidelines. The Cochrane Handbook principles were followed in the evaluation of important variables such as randomization, blinding, withdrawal/dropout rates, sample size estimation, clarity of inclusion/exclusion criteria, examiner reliability testing, pre-specification of outcomes, and bias risk. The Newcastle-Ottawa Scale (NOS) was used in quality assessment to measure bias risk and star ratings. Results: The research highlight improvements in diagnosis, treatment planning, and procedural accuracy, illustrating the revolutionary effects of AI in dentistry. Applications of AI demonstrate its versatility and include automated designs, risk prediction, lesion recognition, and precision in dental operations. There is little chance of bias in randomization, intervention variations, and outcome assessments, according to the methodological evaluation, which shows excellent scientific rigor. Even though a few studies had minor issues including uneven blinding and missing data, these had no appreciable impact on the dependability of the results. Overall, the studies' consistent methodological quality highlights how AI may be relied upon to advance dental research and practice.BULLETIN OF STOMATOLOGY AND MAXILLOFACIAL SURGERY400The studies' consistent methodological quality highlights how AI may be relied upon to advance dental research and practice and there is little chance of bias in randomization, intervention variations, and outcome assessments.BULLETIN OF STOMATOLOGY AND MAXILLOFACIAL SURGERY["V. Veeraraghavan", "Farha Shaikh", "G. Othman", "G. Minervini"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/0ab40cb15e8c8b7c85c34695f6f921298dc494d8 d83b972445ea9239281e03216e429fec1ace926dMuhammad ibn Musa al-Khwarizmi, a pivotal figure of the Islamic Golden Age, established foundational principles for modern computation and artificial intelligence (AI). This article examines his transformative contributions to mathematics, particularly his seminal work, The Compendious Book on Calculation by Completion and Balancing, which introduced algebra as a systematic discipline. Al-Khwarizmi’s development of algorithms—structured problem-solving methods—shaped medieval and contemporary computational sciences. His introduction of the Hindu-Arabic numeral system, including the concept of zero, revolutionized arithmetic, enabling advancements in science, engineering, and technology. The article highlights the profound connection between Al-Khwarizmi’s methodologies and modern AI applications, such as machine learning and neural networks, which rely on systematic algorithms. It also explores the intellectual ecosystem of the Abbasid Caliphate’s House of Wisdom in Baghdad, emphasizing its role in fostering innovation and interdisciplinary collaboration. This study challenges Eurocentric narratives, shedding light on the contributions of non-Western civilizations to modern technological paradigms. Keywords: Al-Khwarizmi, Algorithms, Algebra, Artificial Intelligence, Islamic Golden Age.Journal of Ecohumanism310The article highlights the profound connection between Al-Khwarizmi’s methodologies and modern AI applications, such as machine learning and neural networks, which rely on systematic algorithms.Journal of Ecohumanism["M. O. Elamin"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/d83b972445ea9239281e03216e429fec1ace926d a99c17db136438075a20c1988d6c21334b57a650This study explores the implementation of artificial intelligence (AI) in an educational context and its impact on data-driven decision-making related to student engagement, academic success, and creativity. Contextually, considering the necessity of utilization AI tools in education as a predominant factor in educational practice, this research provides full-fledged knowledge and approach on the overall implementation and significance of AI tools using hypothetical situation evidence (background materials), which are required to understand their prominence. We use a mixed-methods approach, combining quantitative analysis of institutional records and surveys capturing student engagement, academic performance and innovative thinking pre-post AI implementation with qualitative case studies which provide detailed insights into how AI tools can be implemented effectively in educational settings. This study is based on secondary data source, major publications available in the Scopus database related to AI in education. Search terms for data search were "Artificial Intelligence in Education," "AI-driven tools," "Impact of AI on classroom dynamics", "Personalized learning", "Educational technology, AI, and creativity in education", Critical thinking in the classroom, Adaptive learning systems), intelligent tutoring systems)." Student engagement. The results demonstrate significant gains in multiple metrics, with engagement scores increasing by 20–23%, GPA from 9% to 14%, and innovative thinking skills levels swelling from 44% to 57%. About 65%-75% of teacher surveys indicated a positive impact of AI on teaching and learning. The study also notes constraints including confounding whereby the inability to control for factors that may affect odds of innovative thinking and conceptualization in measuring innovative thinking. This is role, it should also advocate for longitudinal inquiries, establish standardized ways of assessing, and be analytical about the ethical implications raised by AI in education. Overall, this study underscores that AI has considerable potential to improve educational achievement and the necessity of systematic research in order to actualize its benefits. This study reinforces the possibility of AI making a big difference in improving educational results. As such, it illuminates the importance of systematic investigations into AI´s function in education to obtain full knowledge on how best to take advantage. International Journal of Vocational Education and Training Research10This study underscores that AI has considerable potential to improve educational achievement and the necessity of systematic research in order to actualize its benefits.International Journal of Vocational Education and Training Research["Mark Treve"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/a99c17db136438075a20c1988d6c21334b57a650 32d9c082eb9ee881cac0f7cad0e4856e02f185c7The evolution of artificial intelligence (AI) has revolutionised numerous aspects of our daily lives, with profound implications across various sectors, including healthcare. Although the concept of AI in healthcare was introduced in the early 1970s, the integration of this technology in healthcare is still in the evolution phase. Despite barriers, the current decade is witnessing an increased utility of AI into diverse specialities of the medical field to enhance precision medicine, predict diagnosis, therapeutic results, and prognosis; this includes respiratory medicine, critical care, and in their allied specialties. AI algorithms are widely studied in areas like mechanical ventilation, sleep medicine, lung ultrasound, and pulmonary function diagnostics and the results are found to be promising. The quality of patient care and safety can be greatly enhanced if respiratory care professionals fully understand the concept and importance of AI, as they are already incorporating various aspects of this technology into their clinical practice. Awareness of AI in the clinical field is essential during this phase; hence, it is desirable to establish widely accepted standards presented in a clear and accessible language. This article aims to describe the existing and prospective role of AI in the field of respiratory care and allied areas.Frontiers in Digital Health1250The quality of patient care and safety can be greatly enhanced if respiratory care professionals fully understand the concept and importance of AI, as they are already incorporating various aspects of this technology into their clinical practice.Frontiers in Digital Health["M. Karthika", "Jithin K. Sreedharan", "Madhuragauri Shevade", "C. S. Mathew", "Santosh Ray"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/32d9c082eb9ee881cac0f7cad0e4856e02f185c7 140c201378cba5d113134b4a5815d45d9e2b7ddcPerioperative Medicine290This analysis provides a systematic analysis on the literature regarding the AI-related research in the field of anesthesiology, which may help researchers and anesthesiologists better understand the research trend of anesthesia-related AI.Perioperative Medicine["Bi-Hua Xie", "Ting-Ting Li", "Feng-Ting Ma", "Qi-jun Li", "Qiu-Xia Xiao", "Liu-Lin Xiong", "Fei Liu"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/140c201378cba5d113134b4a5815d45d9e2b7ddc 8f94f5fc033b67f102cc71ceca0c5b23792a6483Civic intelligence (CI) represents the collective capacity of communities to address challenges, yet its integration with smart city infrastructure remains limited. This study bridges CI theory with technical implementation through a novel framework combining blockchain and AI technologies. Our approach maps core CI components (knowledge capital, system capital, and relational capital) to specific technical solutions: a civic engagement index for measuring participation quality, a tokenization framework for incentivizing meaningful engagement, and a governance optimization function for resource allocation. Using mixed-methods research, we developed and validated the conceptual CI governance (CIG) framework, which satisfies CI principles through smart contracts and AI-assisted interfaces. The empirical evaluation demonstrates both social and technical improvements: 40% increased civic participation rates, 85% governance efficiency maintenance, and significant gains in engagement quality metrics (knowledge sharing +32%, collective decision making +28%). While technical implementation shows promise, success requires the careful integration of social dynamics, digital literacy initiatives, and regulatory compliance. This research contributes to smart city development by providing a theoretically grounded, feasible framework that introduces the fusion of blockchain and AI technologies to enhance civic participation while preserving governance effectiveness.Technologies1011This study developed and validated the conceptual CI governance (CIG) framework, which satisfies CI principles through smart contracts and AI-assisted interfaces, and introduces the fusion of blockchain and AI technologies to enhance civic participation while preserving governance effectiveness.Technologies["Andrey Nechesov", "Janne Ruponen"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/8f94f5fc033b67f102cc71ceca0c5b23792a6483 beb76b9f56a89ab0bdb7b7aaf303aac9a9434f5fFrontiers in Physiology20Frontiers in Physiology["Bing Yao", "Nathan Gaw", "Hyo Kyung Lee"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/beb76b9f56a89ab0bdb7b7aaf303aac9a9434f5f d9c00b353bc93a15ede76081796f9978087e4bd300["Rishabha Malviya", "Shivam Rajput", "Deepa Muthiah"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/d9c00b353bc93a15ede76081796f9978087e4bd3 b6a901bc8864991b1116a8b428aa9a4b79663f63INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Vijaya Kumar Reddy Atla"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/b6a901bc8864991b1116a8b428aa9a4b79663f63 aa6d80d07d00cf7b18d98e79ef4381a0971bb8caBackground: The research examined the current understanding and utilization of AI-based digital technology in agricultural extension services. It assessed the level of adoption and identified the advantages and disadvantages of incorporating such technology. Methods: To collect data, a structured online questionnaire was employed and responses were gathered from 131 extension professionals based in India. The data were analyzed using percentage and mean. Result: The findings revealed that approximately 75.25% of the respondents had used AI-based digital technology at least onceand 61.48% were aware of its potential applications in agriculture extension services. Among those who used AI-based technology, about 45% were able to propagate innovations, while 33.60% showcased innovations and technologies. One of the major benefits, as reported by 80.25% of the participants, was the technology’s capacity to reach the target audience universally and at any time. However, 72.50% of respondents identified the high costs associated with its digital enablers as the primary drawback. The study highlighted a significant level of awareness among agricultural extension specialists regarding AI-based digital technologies. However, the actual utilization of these technologies in their services remained relatively low. To address this gap, the research recommends organizing on-the-job capacity building initiatives for current professionals. These programs aim to promote the adoption of AI-based digital technologies in agricultural extension services throughout India.Bhartiya Krishi Anusandhan Patrika00A significant level of awareness among agricultural extension specialists regarding AI-based digital technologies remained relatively low, but the actual utilization of these technologies in their services remained relatively low, so the research recommends organizing on-the-job capacity building initiatives for current professionals.Bhartiya Krishi Anusandhan Patrika["Maulika Patel", "P. B. Khodifad", "M. Chaudhary"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/aa6d80d07d00cf7b18d98e79ef4381a0971bb8ca 751b031250ae8c6b0c500125274f79b54428d455 This study evaluates AI’s effectiveness in boosting real-time decision-making and supply chain agility in West African ports. Utilizing Structural Equation Modeling (SEM), data from 250 supply chain experts across several countries, including Ghana and Nigeria, were analyzed. Results indicate significant enhancements in supply chain agility, particularly through improved data processing speed, system integration, prediction accuracy, and user interface quality, with the latter having the most substantial impact. The study underscores the importance of user-friendly AI systems, supported by Dynamic Capabilities Theory, which facilitates organizational adaptability to market changes. Recommendations focus on developing AI systems with robust user interfaces and ensuring seamless integration with existing IT infrastructures. This research contributes to the literature by empirically demonstrating AI’s role in improving operational adaptability and filling theoretical gaps, with a unique regional focus and methodological approach.Perspectives on Global Development and Technology200Evaluated AI’s effectiveness in boosting real-time decision-making and supply chain agility in West African ports indicates significant enhancements in supply chain agility, particularly through improved data processing speed, system integration, prediction accuracy, and user interface quality.Perspectives on Global Development and Technology["Umar Farouk Aliu Mahama", "D. Boison", "Musah Osumanu Doumbia", "Ahmed Antwi-Boampong"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/751b031250ae8c6b0c500125274f79b54428d455 4b6bee6df27beffe9d8f645dff78e54c277fac07Intensive Care Medicine40Intensive care medicine["A. Vlaar", "S. Myatra", "Christian Jung"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/4b6bee6df27beffe9d8f645dff78e54c277fac07 97333ec776906eb4ad74190c2d80a03d98711edbCritical Humanities00Critical Humanities["Victor Mureithi"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/97333ec776906eb4ad74190c2d80a03d98711edb 705b0abfd57f6b992070f16d3e2f6424c04730f7This article discusses the principles of and premises for liability for damage caused by AI systems. It applies to liability models based on the principles of risk and guilt. It indicates that different groups of entities, e.g. programmers, may be responsible for the creation of AI under the principle of guilt, while producers and merchants may put it into circulation under the principle of risk. The liability of AI system users should be tempered and based on the principle of guilt. This article includes a critical view of the AI Act and the relevant directives. It points out that effective liability for damage should be related to the level of harm caused (harm to a person, human death) and not dependent on whether it was inflicted by a high-risk system or any other AI system.Teka Komisji Prawniczej PAN Oddział w Lublinie200It is pointed out that effective liability for damage should be related to the level of harm caused and not dependent on whether it was inflicted by a high-risk system or any other AI system.Teka Komisji Prawniczej PAN Oddział w Lublinie["Jacek Wid\u0142o"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/705b0abfd57f6b992070f16d3e2f6424c04730f7 0c452eab748ed5bba08bdaea2a2a1d5e5322ddbeDiscover Sustainability360Discover Sustainability["M. Al\u2010Raeei"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/0c452eab748ed5bba08bdaea2a2a1d5e5322ddbe 313ccfda80901407307aec8a31ca0b1a347a150bThis paper analyses the application of AI technology to the process of delivering writing feedback to first-year students of Hanoi University of Industry (HaUI). AI brings an effective solution to improve writing skills since one wants to receive individual and effective instruction. In this article, the authors discuss how there are opportunities for the use of AI to improve feedback quality, time, and flexibility in line with the various AI tools and platforms that cater to young writers. It also explores the implications and difficulty of incorporating AI-driven feedback systems for the classroom, like having issues with technology use and the roles and participation of teachers. This research uses qualitative data collection techniques, interviewing 10 teachers and focusing group discussions with 50 students majoring in business. Drawing on the analytical framework outlined above, this article examines the possible benefits and limitations of AI written feedback applicable to HaUI and offers directions for teachers and administrators in comparable contexts who want to utilize AI technology for writing feedback to support learners.Proceedings of the AsiaCALL International Conference210There are opportunities for the use of AI to improve feedback quality, time, and flexibility in line with the various AI tools and platforms that cater to young writers.Proceedings of the AsiaCALL International Conference["Thi Kim Hue Duong", "Thi Thu Huong Le"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/313ccfda80901407307aec8a31ca0b1a347a150b c15509947f68cb58da089f810710c17e1580262aAbstract—Artificial General Intelligence (AGI) represents the apex of AI research, striving to replicate human-like adaptability, reasoning, and learning across diverse domains. While current AI systems excel in specific, narrow tasks, they fall short of generalization, creativity, and transferability. Inspired by Francois Chollet’s “On the Measure of Intelligence,” this paper synthesizes theoretical insights and practical methodologies to propose a pathway toward AGI. We introduce frameworks for hybrid architectures, embodied learning, skill-acquisition benchmarks, and ethical safeguards, creating a robust foundation for scalable and human-aligned AGI. Index Terms—Artificial General Intelligence (AGI), Hybrid Architectures, Skill-Acquisition Efficiency, Ethical Safeguards, Embodied Learning, Generalization BenchmarksINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper synthesizes theoretical insights and practical methodologies to propose a pathway toward AGI, and introduces frameworks for hybrid architectures, embodied learning, skill-acquisition benchmarks, and ethical safeguards, creating a robust foundation for scalable and human-aligned AGI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Eeman Majumder"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/c15509947f68cb58da089f810710c17e1580262a 8f73c4cd205d8db28ba1d18e698d9537c5c25ce7International Journal of Artificial Intelligence in Education770International Journal of Artificial Intelligence in Education["Imogen Casebourne", "Shengpeng Shi", "Michael Hogan", "Wayne Holmes", "Tore Hoel", "R. Wegerif", "Li Yuan"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/8f73c4cd205d8db28ba1d18e698d9537c5c25ce7 3caa9f4b36c8d1aa47423c7deef1af0525250907As artificial intelligence (AI) becomes increasingly central to various fields, there is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science. This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively. Through interviews and co-design sessions with K-12 teachers, we examined current AI literacy levels and how teachers adapt AI tools like the AI Art Lab, AI Music Studio, and AI Chatbot into their course designs. While teachers appreciated the potential of AI tools to foster creativity and critical thinking, they also expressed concerns about the accuracy, trustworthiness, and ethical implications of AI-generated content. Our findings reveal the challenges teachers face, including limited resources, varying student and instructor skill levels, and the need for scalable, adaptable AI tools. This research contributes insights that can inform the development of AI curricula tailored to diverse educational contexts.arXiv.org240This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively and reveals the challenges teachers face.ArXiv["Hanqi Li", "Ruiwei Xiao", "Hsuan Nieu", "Ying-Jui Tseng", "Guanze Liao"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/3caa9f4b36c8d1aa47423c7deef1af0525250907 71b5abff1a3bf793d5815ed26161b7f5b7582c88 The integration of Artificial Intelligence (AI) in biology education offers transformative potential, yet teacher preparedness for AI remains under-researched. This study explores the perceived benefits of AI integration and self-reported Technological Pedagogical Content Knowledge (TPACK) among pre-service biology teachers from a South African university (n = 62) and an Indonesian university (n = 51). Using a comparative survey design, data were collected via an online questionnaire. Indonesian participants reported higher Technological Knowledge and Technological Pedagogical Knowledge levels, though both groups viewed AI integration positively, with no significant differences in perceived benefits. Among Indonesian participants, Technological Knowledge strongly correlated with perceived benefits, especially for peer collaboration. These findings highlight the importance of tailored teacher training and equitable technological resource allocation to enhance AI integration in biology education. The study underscores the critical role of technological and pedagogical knowledge in fostering positive attitudes toward AI adoption in diverse educational settings.Asia-Pacific Science Education510The importance of tailored teacher training and equitable technological resource allocation to enhance AI integration in biology education is highlighted, and the critical role of technological and pedagogical knowledge in fostering positive attitudes toward AI adoption in diverse educational settings is underscored.Asia-Pacific Science Education["L. Mnguni", "P. Nuangchalerm", "R. A. Z. El Islami", "Doras Sibanda", "Moleboheng Ramulumo", "Indah Juwita Sari"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/71b5abff1a3bf793d5815ed26161b7f5b7582c88 c458ad571aafb402a401cfd7f7425746cda32dd0Artificial intelligence (AI) is reshaping the landscape of econometric modeling, offering innovative tools to address complex legal issues involving predictive analysis, resource allocation, and policy evaluation. This research explores the application of AI-driven econometric models to legal challenges, focusing on areas such as contract enforcement, intellectual property disputes, and regulatory compliance. By integrating machine learning with traditional econometric techniques, these models enhance the precision and adaptability of legal forecasts and decision-making processes. Key methodologies include the use of natural language processing (NLP) for legal text analysis, deep learning for pattern recognition in case law, and game theory to evaluate strategic interactions in legal contexts. The study highlights the potential of AI to improve efficiency, reduce bias, and facilitate equitable outcomes in legal systems. Challenges such as data privacy, interpretability, and ethical considerations are also addressed. By bridging AI and econometrics, this research aims to provide a robust framework for advancing legal analytics, contributing to more informed policy-making and judicial processes.Human-Computer Interaction80This research explores the application of AI-driven econometric models to legal challenges, focusing on areas such as contract enforcement, intellectual property disputes, and regulatory compliance, and bridges AI and econometrics to provide a robust framework for advancing legal analytics.Human Computer Interaction["Melis Dokumac\u0131"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/c458ad571aafb402a401cfd7f7425746cda32dd0 09d9bae5bc69d20df47ad8a3fd1d54a4d7344787The rapid proliferation of artificial intelligence (AI) technologies has created transformative opportunities across industries while introducing complex ethical, legal, and social challenges. As AI systems increasingly influence critical areas such as healthcare, finance, and governance, the development of comprehensive legal frameworks becomes paramount to ensure accountability, fairness, and transparency. This research explores the evolution of legal frameworks for AI regulations, focusing on key areas such as algorithmic accountability, data privacy, intellectual property rights, and liability for autonomous decision-making. By examining global regulatory efforts, including the EU’s AI Act and U.S. policy initiatives, the study highlights the challenges of harmonizing legal standards across jurisdictions. The research also addresses emerging issues, such as bias mitigation, transparency in AI development, and the ethical implications of automated decision-making. Through a multidisciplinary approach, this study aims to propose adaptive, forward-looking regulatory models that balance innovation with ethical oversight, fostering trust in AI technologies while safeguarding societal interests.Human-Computer Interaction70This research explores the evolution of legal frameworks for AI regulations, focusing on key areas such as algorithmic accountability, data privacy, intellectual property rights, and liability for autonomous decision-making, to propose adaptive, forward-looking regulatory models that balance innovation with ethical oversight.Human Computer Interaction["Melis Dokumac\u0131"]2024-12-23T00:00:00https://www.semanticscholar.org/paper/09d9bae5bc69d20df47ad8a3fd1d54a4d7344787 374c3f5722f244dec3a97250edd47c6aff366604Objectives: This study aims to systematically review and analyze the impact of artificial intelligence (AI) technologies on occupational health and safety (OHS), focusing on their effectiveness in risk mitigation, disease prevention, and the promotion of worker well-being. Methods: A comprehensive literature search was conducted across databases including Embase, PubMed, and Google Scholar, covering studies from 1974 to the present. The review followed the guidelines set forth by Cochrane, with data analyzed using the Review Manager software (Version 5.4). Results: The analysis included 25 studies involving diverse industries, with a total of 2,500 workers. Findings indicated a significant positive effect of AI technologies on reducing occupational hazards (SMD: -0.75, 95% CI: -0.82 to -0.68, Z=18.45, P<0.00001) and enhancing safety protocols (SMD: -0.45, 95% CI: -0.56 to -0.34, Z = 9.30, P<0.00001). Furthermore, AI-driven monitoring tools were associated with a notable decrease in workplace accidents (SMD: -0.52, 95% CI: -0.60 to -0.44, Z = 14.23, P<0.00001). Conclusions: The integration of AI in occupational health and safety practices significantly enhances the management of workplace risks, leading to improved safety outcomes and reduced incidents. This study underscores the need for continued investment in AI technologies to promote healthier and safer work environments.The European Research Journal580The integration of AI in occupational health and safety practices significantly enhances the management of workplace risks, leading to improved safety outcomes and reduced incidents, underscores the need for continued investment in AI technologies to promote healthier and safer work environments.The European Research Journal["T. Karada\u011f"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/374c3f5722f244dec3a97250edd47c6aff366604 7b1e26d6a09e1a0e5f8fd2b9d1d1323e66d416aePURPOSE OF REVIEW The management of thoracic empyema is often complicated by diagnostic delays, recurrence, treatment failures and infections with antibiotic resistant bacteria. The emergence of artificial intelligence (AI) in healthcare, particularly in clinical decision support, imaging, and diagnostic microbiology raises great expectations in addressing these challenges. RECENT FINDINGS Machine learning (ML) and AI models have been applied to CT scans and chest X-rays to identify and classify pleural effusions and empyema with greater accuracy. AI-based analyses can identify complex imaging features that are often missed by the human eye, improving diagnostic precision. AI-driven decision-support algorithms could reduce time to diagnosis, improve antibiotic stewardship, and enhance more precise and less invasive surgical therapy, significantly improving clinical outcomes and reducing inpatient hospital stays. SUMMARY ML and AI can analyse large datasets and recognize complex patterns and thus have the potential to enhance diagnostic accuracy, preop planning for thoracic surgery, and optimize surgical treatment strategies, antibiotic therapy, antibiotic stewardship, monitoring complications, and long-term patient management outcomes.Current opinion in pulmonary medicine410ML and AI can analyse large datasets and recognize complex patterns and thus have the potential to enhance diagnostic accuracy, preop planning for thoracic surgery, and optimize surgical treatment strategies, antibiotic therapy, antibiotic stewardship, monitoring complications, and long-term patient management outcomes.Current opinion in pulmonary medicine["Adam Zumla", "Rizwan Ahmed", "Kunal Bakhri"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/7b1e26d6a09e1a0e5f8fd2b9d1d1323e66d416ae ed41323e99eac67d239d4b3ad763a95036dd4adfThis study presents a systematic analysis of artificial intelligence (AI) applications in forestry management, examining current implementations, challenges, and future perspectives. Through a comprehensive review of 580 articles from Web of Science (211) and Scopus (369) databases spanning 2021-2025, the research identifies key thematic areas where AI is transforming forestry practices. The analysis reveals that Forest Monitoring and Management Systems (30.4%) and Digital Transformation (23.6%) represent the primary focus areas of current research, followed by Resource Optimization (17.1%) and Biodiversity Conservation (14.6%). The study highlights significant opportunities in productivity enhancement, risk analysis, biodiversity conservation, and carbon management through AI integration. However, it also identifies critical challenges, including technical limitations in data quality and infrastructure, resource constraints, operational complexities, and regulatory requirements. The research particularly emphasizes the emergence of human-centered AI approaches, digital twin technologies, and integrated sensor networks as promising future directions. This analysis provides valuable insights for forestry professionals, researchers, and policymakers, offering a framework for understanding both the potential and limitations of AI implementation in forestry management while highlighting the importance of balanced technological integration that considers both environmental sustainability and operational efficiency.Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi00This analysis provides valuable insights for forestry professionals, researchers, and policymakers, offering a framework for understanding both the potential and limitations of AI implementation in forestry management while highlighting the importance of balanced technological integration that considers both environmental sustainability and operational efficiency.Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi["Ouran\u0131a Areta H\u0131z\u0131ro\u011flu", "Tar\u0131k Semiz"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/ed41323e99eac67d239d4b3ad763a95036dd4adf 5849159f39722591381216f28729e1b619f67072This bibliometric analysis explores the emerging landscape of Artificial Intelligence (AI) in personalized education, revealing a significant growth in research publications from 2004 to 2024. The study examines research trends, publication patterns, and collaborative efforts across disciplines, with findings indicating a sharp increase in publications from 200 per year in 2020 to a projected 500 by 2024. Analyzing data from Scopus, the research highlights AI's transformative potential in higher education, with Social Sciences (46%) and Computer Science (23.3%) dominating the publication landscape. Key themes include personalized learning, adaptive learning technologies, and integrating advanced tools like ChatGPT while addressing critical ethical considerations around data privacy and responsible AI implementation. The global research ecosystem, led by the United States, United Kingdom, and China, demonstrates a collaborative approach to developing AI-driven educational solutions that promise to enhance individual learning experiences and outcomes for university students.Journal of Information Systems and Technology Management00Analysis of research trends, publication patterns, and collaborative efforts across disciplines highlights AI's transformative potential in higher education, with Social Sciences and Computer Science dominating the publication landscape.Journal of Information System and Technology Management["Dean Nelson Mojolou", "Faerozh Madli", "Helmina Thomas", "S. L. Sondoh Jr", "Erick Karunia"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/5849159f39722591381216f28729e1b619f67072 cd2575fb05359b004a96b165f9d025b114f2ef52The paper demonstrates the potential for using value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment. A retrospective cohort study was conducted. For 2013–2021, 734 cadets of the Navy Military Training and Research Center “Soviet Union Fleet Admiral N.G. Kuznetsov Naval Academy” were examined, 48 of them were diagnosed with maladjustment. Neural networks were used for mathematical modeling of maladjustment prediction. The study included 8 cycles of neural network training and 7 cycles of neural network model testing. As the actual material increases, the sensitivity of the model for predicting cadet maladjustment using neural networks increases: 30.MLP 16-7-2; 28.MLP 16-13-2; 30.MLP 16-22-2; 29.MLP 16-31-2; 42.MLP 16-39-2; 19.MLP 16-45-2; 16.MLP 16-48-2; 30.MLP 16-30-2 from 0.43 to 1.00 conventional units (y = 0.017x2 – 0.0647x + 0.4898, R² = 0.8264); specificity: from 0.96 to 1.00 conventional units (y = –0.002x2 + 0.0211x + 0.9462, R² = 0.8923); predictive value increased from 91.8% to 99.45% (y = –0.1477x2 + 2.3309x + + 90.238, R² = 0.9368). When the models were tested on new samples, the mean sensitivity was 0.45 conventional units with an increasing trend (y = 0.0207x2 – 0.1214x + 0.5271, R² = 0,6945), specificity: 0.97 conventional units (y = –0.0048x2 + + 0.0388x + 0.9086, R² = 0.772), predictive value: 92.6% (y = –0.4962x2 + 3.5402x + 88.447, R² = 0.6598). Therefore, the model for predicting cadet maladjustment using neural networks can identify cadets who will experience maladjustment with an accuracy of 32% to 72%, whereas no more than 6% of cadets without maladjustment will receive a false prediction. The predictive value of the model is close to the absolute accuracy of vocational aptitude prediction with reference values of 65%–70%. The predictive ability of the models tested in the study, ranging from 89.7% to 96.4%, confirms the high effectiveness of using neural networks to predict maladjustment. The value-based and motivational parameters of the cadets, combined with the use of neural networks to predict their maladjustment, create a highly effective artificial intelligence system. Such an approach can be used in medical and psychological support activities for military personnel at a military university for their optimal selection and support.Bulletin of the Russian Military Medical Academy70The model for predicting cadet maladjustment using neural networks can identify cadets who will experience maladjustment with an accuracy of 32% to 72%, whereas no more than 6% of cadets without maladjustment will receive a false prediction.Bulletin of the Russian Military Medical Academy["Alexey N. Yatmanov", "V. Apchel", "D. V. Ovchinnikov", "V. Yusupov", "B. V. Ovchinnikov", "Yuri L. Starenchenko", "Yuri \u041c. Babin", "Andrey V. Korzunin", "Denis S. Tsvetkov"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/cd2575fb05359b004a96b165f9d025b114f2ef52 f0dca16f00f102838c6bba6264adc72e58a3ffe5PurposeThe current research investigates how the adoption of Artificial Intelligence (AI)—a set of technologies designed to enhance decision-making and automate processes—impacts Integrated Financial Reporting (IFR) in Gulf Cooperation Council (GCC) listed firms, which present the typical features of emerging economies. It is postulated that their IFR is enhanced as firms within these markets experience AI adoption. In addition, the study also focuses on the role of audit quality towards AI adoption and the IFR relationship within these regions. To this effect, the study examines the moderation effect of audit quality (using its sub-components i.e. audit fee, audit industry specialization and restatement) on the relationship between AI adoption experience and IFR in GCC.Design/methodology/approachThe investigation draws upon panel data consisting of 2,912 non-financial firm-year observations covering the period from 2010 to 2023 across GCC markets. To achieve its purpose, the study applies the conventional ordinary least square (OLS) to estimate the effect of AI adoption experience on IFR. Subsequently, to guarantee the robustness of the results, this study utilizes the propensity score matching (PSM) technique.FindingsThe results from empirical analysis disclose a direct impact of AI adoption on the IFR of the firms within GCC markets. Furthermore, the study also discovers that the high level of audit quality moderates this positive relationship. Therefore, in the GCC regions, firms with higher AI adoption show higher IFR effectiveness, mainly in the presence of specialized auditors and increased audit fees, whereas their relationship is stronger in the absence of restatements. The results are robust when tested through the PSM technique.Originality/valueThe results of this study highlight the significance for policymakers to ensure comprehensive AI adoption in GCC markets, as well as the appointment of industry specialists and the standardization of audit fees to support the improvement of IFR in the regions.Asian Review of Accounting940The results of this study highlight the significance for policymakers to ensure comprehensive AI adoption in GCC markets, as well as the appointment of industry specialists and the standardization of audit fees to support the improvement of IFR in the regions.Asian Review of Accounting["Faisal Khan", "Sharif Ullah Jan", "Hafiz Muhammad Zia-ul-haq"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/f0dca16f00f102838c6bba6264adc72e58a3ffe5 df1dafede9d0cedf5443bc05056cc5ea2b53c6fdThis study examines the application of Artificial Intelligence (AI) in Education, mainly focusing on its impact on student achievement in the Pancasila and Citizenship Education Program at STKIP Pasundan. Students often face challenges when dealing with complex and abstract materials in civic Education. The research employs a quantitative approach, using random sampling and multiple linear regression analysis to assess the influence of AI. The findings reveal that ChatGPT is the most commonly used AI platform among students, valued for its user-friendly features and effective response capabilities. The analysis confirms that AI significantly affects student learning achievement, and its contribution is substantial compared to other influencing factors. Additionally, AI enhances student motivation, material absorption, and innovation in the learning process. In conclusion, AI positively and significantly impacts student achievement and can be integrated into educational practices to foster more innovative and practical learning in the digital era.International Journal of Social Learning (IJSL)290Artificial Intelligence positively and significantly impacts student achievement and can be integrated into educational practices to foster more innovative and practical learning in the digital era.International Journal of Social Learning (IJSL)["J. H. Hendrawan", "Nurul Falah Anggraeni", "S. Silah", "Ali Anwar"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/df1dafede9d0cedf5443bc05056cc5ea2b53c6fd d6d6f8b7cccbf69abf518921bde8b21989ffb669The benefits in the use of Artificial Intelligence (AI) in the electoral process are enormous only when AI is used ethically and all stakeholders particularly those vested with the responsibility to conduct elections are ethically upright in their use of AI. The problem this paper seeks to address is how AI can be ethically used to aid the smooth conduct of elections and how can the benefits of AI be harnessed in the electioneering process. The methodology used in both data collection and analysis was expert judgement and systematic review. This paper analysed the various AI tools and techniques that are used to assist in the conduct of credible elections. Also discussed are the various ways that AI can be used negatively to influence voters namely deepfakes, automated bots, data privacy breaches, microtargeting, psychological profiles of voters, voting pattern prediction, and cyberattacks of electronic devices with the intention to rig elections. It is recommended that the ethical use of AI in elections should be advocated, and training of the citizens on the role of AI before, during and after elections and how to handle AI-related threats should be conducted well ahead of elections. Keywords: AI in Elections, Voting and AI, Ethical AI in elections, the impact of AI in electoral process, deepfakes and elections.E-Journal of Humanities, Arts and Social Sciences160It is recommended that the ethical use of AI in elections should be advocated, and training of the citizens on the role of AI before, during and after elections and how to handle AI-related threats should be conducted well ahead of elections.E-Journal of Humanities, Arts and Social Sciences["Itumeleng Michael Maine", "B. M. Esiefarienrhe"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/d6d6f8b7cccbf69abf518921bde8b21989ffb669 b5118d8f566fee590231904bcc78d328ffaedc13The main objective of this paper is to illustrate the use of artificial intelligence (AI) in the tourism and air transport industries to improve tourists’ experiences, as well as provide a definition of the AI concept closest to both sectors. In order to examine and demonstrate the body of literature on AI and its application to the travel and tourism industry. This study also presents the findings of a literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach in conjunction with a systematic literature review using the Web of Science (WoS) database. This approach enabled us to construct a novel AI concept in the context of tourism. This research found that AI technology offers new and creative opportunities for tourists due to this innovative tool that promotes and empowers travel and tourism organisations’ products and services. AI has helped to outline travel planning for tourists, made it easier to discover new experiences, and streamlined the booking process. The reality is that AI methods and applications are changing and improving passengers and tourists’ experiences in tourism cities and the air transport sector. Moreover, it is necessary to highlight that one of AI technology’s greatest strengths lies in the immediacy of response and advice that swiftly help tourists plan their trips, tours, detailed itineraries, and flight bookings at the same moment. This research is an antecedent attempt to define AI technology in the tourism and air transport context and to illustrate its virtues and shortcomings to improve tourists’ experiences in cities and the operational efficiency of organisations.Electronics1170The research found that AI technology offers new and creative opportunities for tourists due to this innovative tool that promotes and empowers travel and tourism organisations’ products and services.Electronics["L\u00e1zaro Florido-Ben\u00edtez", "Benjam\u00edn del Alc\u00e1zar Mart\u00ednez"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/b5118d8f566fee590231904bcc78d328ffaedc13 b1666333b20ac432fe6bc9dc84029ba49b7029b8ABSTRACT Artificial intelligence (AI), defined as algorithms built to reproduce human behavior, has various applications in health care such as risk prediction, medical image classification, text analysis, and complex disease diagnosis. Due to the increasing availability and volume of data, especially from electronic health records, AI technology is expanding into all fields of nursing and medicine. As the health care system moves toward automation and computationally driven clinical decision-making, nurses play a vital role in bridging the gap between the technological output, the patient, and the health care team. We explore the nurses' role in translating AI-generated output to patients and identify considerations for ensuring informed consent and shared decision-making throughout the process. A brief review of AI technology and informed consent, an identification of power dynamics that underly informed consent, and descriptions of the role of the nurse in various relationships such as nurse-AI, nurse-patient, and patient-AI are covered. Ultimately, nurses and physicians bear the responsibility of upholding and safeguarding the right to informed choice, as it is a fundamental aspect of safe and ethical patient-centered health care.MCN, The American Journal of Maternal Child Nursing390The nurses' role in translating AI-generated output to patients and considerations for ensuring informed consent and shared decision-making throughout the process are explored, as it is a fundamental aspect of safe and ethical patient-centered health care.MCN. The American journal of maternal child nursing["Sara Bickweat Penner", "Nicholas R. Mercado", "Samantha Bernstein", "Elise Erickson", "Melissa Anne DuBois", "Caitlin Dreisbach"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/b1666333b20ac432fe6bc9dc84029ba49b7029b8 577d6ca9cc26580c0aeef5f251aa11172fa4f21fThis study aims to explore the use of game-based Artificial Intelligence (AI) to support differentiated learning and improve literacy skills of inclusive students with Specific Learning Disorder (SLD). In the context of inclusive education, the need for a personalized approach is very important, especially for students with specific learning disorders such as dyslexia, dyscalculia, and dysgraphia. AI technology integrated into educational games offers an innovative solution by presenting adaptive materials, accommodating various learning styles, and adjusting the level of difficulty based on student abilities. This study used a mixed-methods method, involving a quasi-experiment on 40 elementary school students with SLD and qualitative observations of student interactions with AI-based games for eight weeks. The results showed that this approach significantly improved students' motivation, engagement, and literacy skills, especially in reading ability and conceptual understanding. Game-based AI is also able to provide personalized real-time feedback to students, thereby strengthening differentiated learning. This study recommends the application of game-based AI as an effective learning method for inclusive students with specific learning disorders, especially in the context of 21st-century learning that focuses on literacy and technology skills.Journal of Management World400The results showed that this approach significantly improved students' motivation, engagement, and literacy skills, especially in reading ability and conceptual understanding, thereby strengthening differentiated learning.Journal of Management World["Sri Sukasih, S.S.", "Dra. Noening Andrijati", "Atip Nurharini"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/577d6ca9cc26580c0aeef5f251aa11172fa4f21f 94717fc86e59fa20a756121ee6a43ebb94a0b75dArtificial Intelligence (AI) has emerged as a transformative force reshaping the international political economy (IPE) by influencing global trade, economic governance, and policy management. As technological advancements accelerate, AI's applications in decision-making, predictive analytics, and economic simulations have introduced unprecedented efficiency and transparency into governance frameworks. However, these advancements also pose challenges, including ethical dilemmas, global inequalities, and cybersecurity risks, necessitating a comprehensive exploration of its impact on the global economic order. This study aims to analyze the multifaceted role of AI in reshaping the IPE, focusing on its implications for economic governance and policy management. By adopting a mixed-methods approach, the research combines qualitative analysis of AI-driven case studies with quantitative evaluations of global trade and policy data. The study investigates AI's transformative influence on decision-making processes within international institutions, its role in optimizing global supply chains, and its applications in monetary policy and financial management. Additionally, it addresses the risks associated with AI, such as algorithmic bias and regulatory fragmentation, and examines strategies for mitigating these challenges. The findings reveal that while AI enhances efficiency and fosters innovation in economic governance, its benefits are unevenly distributed across nations, exacerbating technological divides. To harness AI's potential, the study proposes inclusive governance frameworks, harmonized policies, and capacity-building initiatives for developing economies. This research contributes to the broader discourse on the intersection of AI and global governance, offering actionable insights for policymakers and scholars.Academic Journal of Management and Social Sciences00The study investigates AI's transformative influence on decision-making processes within international institutions, its role in optimizing global supply chains, and its applications in monetary policy and financial management, and addresses the risks associated with AI.Academic Journal of Management and Social Sciences["Tong Zheng", "Xiao Yu", "Yunting Fan", "Xiaoxi Ding", "Rui Ye", "Yushuo Zhang"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/94717fc86e59fa20a756121ee6a43ebb94a0b75d eb2142c2a8a5c65e53e11ad3bb9a7d1633edd9ecPenelitian ini bertujuan untuk mengevaluasi implementasi Artificial Intelligence (AI) dalam sistem pengaduan masyarakat dengan fokus pada analisis efisiensi dan kepuasan pengguna. Menggunakan metode systematic literature review (SLR) dengan protokol PRISMA, penelitian ini menganalisis 45 artikel ilmiah yang dipublikasikan dalam rentang waktu 2019-2024. Hasil penelitian menunjukkan bahwa implementasi AI meningkatkan efisiensi operasional secara signifikan, dengan peningkatan produktivitas staff sebesar 27% dan pengurangan waktu pemrosesan hingga 87.5%. Tingkat kepuasan pengguna menunjukkan hasil positif dengan skor rata-rata di atas 4.0 dari skala 5.0 untuk mayoritas dimensi yang diukur, dimana kecepatan respons (4.5) dan kemudahan penggunaan (4.2) menjadi faktor utama. Analisis ROI mengindikasikan break-even point tercapai pada tahun ketiga implementasi dengan pengurangan biaya operasional sebesar 68% per transaksi. Tantangan utama implementasi meliputi aspek teknologi (impact score 8.5/10), organisasi, dan sumber daya manusia. Penelitian ini merekomendasikan pengembangan framework implementasi yang terstruktur, peningkatan fokus pada keamanan data, investasi dalam pengembangan kompetensi staff, serta pengembangan sistem monitoring dan evaluasi berkelanjutan. Implikasi praktis dari penelitian ini memberikan panduan bagi institusi pemerintah dalam mengoptimalkan implementasi AI untuk meningkatkan kualitas layanan publik.JIIP - Jurnal Ilmiah Ilmu Pendidikan00JIIP - Jurnal Ilmiah Ilmu Pendidikan["Dharmawan Hadiutama", "Samsons Laurens"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/eb2142c2a8a5c65e53e11ad3bb9a7d1633edd9ec 5005406adf666bcd8df6bad75e4f9615b8b5ff7eBackground: Advancements in artificial intelligence (AI) and machine learning (ML) are set to revolutionize healthcare, particularly in fields like endoscopic surgery that heavily rely on digital imaging. However, to effectively integrate these technologies and drive future innovations, pediatric surgeons need specialized AI/ML skills. This survey evaluated the current level of readiness and educational needs regarding AI/ML among members of the European Society of Pediatric Endoscopic Surgeons (ESPES). Methods: A structured survey was distributed via LimeSurvey to ESPES members via email before and during the 2024 Annual Conference. Responses were collected over four weeks with voluntary, anonymous participation. Quantitative data were analyzed using descriptive statistics. Results: A total of 125 responses were received. Two-thirds (65%) of respondents rated their AI/ML understanding as basic, with only 6% reporting advanced knowledge. Most respondents (86%) had no formal AI/ML training. Some respondents (31%) used AI/ML tools in their practice, mainly for diagnostic imaging, surgical planning, and predictive analytics; 42% of the respondents used these tools weekly. The majority (95%) expressed interest in further AI/ML training, preferring online courses, workshops, and hands-on sessions. Concerns about AI/ML in pediatric surgery were high (85%), especially regarding data bias (98%). Half of respondents (51%) expect AI/ML to play a significant role in advancing robotic surgery, oncology, and minimally invasive techniques. A strong majority (84%) felt that the ESPES should lead AI education in pediatric surgery. Conclusions: This survey presents the ESPES with a unique opportunity to develop a competency map of its membership’s AI/ML skills and develop targeted educational programs, thus positioning the society to take the lead in AI education and the advancement of AI solutions in pediatric endosurgery.Children160This survey presents the ESPES with a unique opportunity to develop a competency map of its membership’s AI/ML skills and develop targeted educational programs, thus positioning the society to take the lead in AI education and the advancement of AI solutions in pediatric endosurgery.Children["Holger Till", "Hesham Elsayed", "M. Escolino", "Ciro Esposito", "Sameh Shehata", "G. Singer"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/5005406adf666bcd8df6bad75e4f9615b8b5ff7e 3ec2436c0f5bc549bd99ed663ba4602029b50ff1BMC Medical Informatics Decis. Mak.120ChatGPT is not ready to take on the coaching role for either healthcare learners or healthcare professionals because of the lack of consistency in the responses to the same question, which is problematic for both learners and decision-makers.BMC Medical Informatics and Decision Making["Meron W. Shiferaw", "Taylor Zheng", "Abigail Winter", "Leigh Ann Mike", "Lingtak-Neander Chan"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/3ec2436c0f5bc549bd99ed663ba4602029b50ff1 a22dacd18879a71aba37c5186ff24c01c86cf6d7PurposeThe launch of ChatGPT has brought the large language model (LLM)-based generative artificial intelligence (GAI) into the spotlight, triggering the interests of various stakeholders to seize the possible opportunities implicated by it. Nevertheless, there are also challenges that the stakeholders should observe when they are considering the potential of GAI. Given this backdrop, this study presents the viewpoints gathered from various subject experts on six identified areas.Design/methodology/approachThrough an expert-based approach, this paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare.FindingsThe subject experts first share an overview of the use of GAI, followed by the relevant opportunities and challenges in implementing GAI in each identified area. Afterwards, based on the opportunities and challenges, the subject experts propose several research agendas for the stakeholders to consider.Originality/valueThis paper serves as a frontier in exploring the opportunities and challenges implicated by the GAI in six identified areas that this emerging technology would considerably influence. It is believed that the viewpoints offered by the subject experts would enlighten the stakeholders in the identified areas.Industrial Management &amp; Data Systems330This paper gathers the viewpoints of various subject experts on the identified areas of tourism and hospitality, marketing, retailing, service operations, manufacturing and healthcare and proposes several research agendas for the stakeholders to consider.Industrial Management &amp; Data Systems["K. Ooi", "A. Koohang", "Eugene Cheng-Xi Aw", "T. Cham", "Cihan Cobanoglu", "Charles Dennis", "Yogesh K. Dwivedi", "Jun-Jie Hew", "Heather Linton Kelly", "Laurie Hughes", "Chieh-Yu Lin", "Anubhav Mishra", "Ian Phau", "Ramakrishnan Raman", "Marianna Sigala", "Yun-Chia Tang", "Lai-Wan Wong", "G. Tan"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/a22dacd18879a71aba37c5186ff24c01c86cf6d7 86df23230f6fec459f7ceaab2e000b2a5e90c8acThis study explores the application, opportunities, challenges, and future development directions of artificial intelligence (AI) in the online teaching of Chinese as a foreign language. AI offers intelligent and personalized teaching methods, enhancing teaching efficiency and optimizing resource allocation. However, its application faces challenges such as technical limitations, teaching ethics, and privacy. The online teaching of Chinese as a foreign language has emerged in the context of globalization. Despite its development, it is constrained by issues like unstable networks. Existing teaching platforms have their own characteristics and deficiencies, and AI has varying application effects in different teaching links. The acceptance of AI by teachers and students is influenced by multiple factors. In the future, AI technology is expected to make continuous breakthroughs, and Chinese as a foreign language education will exhibit new forms such as diversified teaching content, personalized teaching methods, and intelligent teaching models. The government, educational institutions, etc. should jointly promote its development.Education Insights130In the future, AI technology is expected to make continuous breakthroughs, and Chinese as a foreign language education will exhibit new forms such as diversified teaching content, personalized teaching methods, and intelligent teaching models.Education Insights["Zengxian Mo"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/86df23230f6fec459f7ceaab2e000b2a5e90c8ac 38485776b38113db6fc6ac43401702461d368971With the growing scale of China's digital economy, the innovation and application of digital technology, the rapid development of artificial intelligence has a profound impact on China's employment. As an indispensable part of the labor market, it is of great practical significance to study the impact of artificial intelligence on women's employment. The application of artificial intelligence has greatly impacted the employment of women, making them face certain difficulties, but also has a positive impact.Frontiers in Business, Economics and Management00The application of artificial intelligence has greatly impacted the employment of women, making them face certain difficulties, but also has a positive impact.Frontiers in Business, Economics and Management["Yuanyuan Deng"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/38485776b38113db6fc6ac43401702461d368971 4868fe2298a384c0077529cfb20dd61e3e01c542In recent years, every aspect of the society has rapidly transformed because of the emergence of artificial intelligence (AI) technologies. AI excels not only in image and voice recognition and analysis but also in achieving near-natural conversations through the development of large language models. These technological innovations are steadily being integrated into healthcare settings and can significantly change the way physicians work in clinics in the near future. Patient interviews will predominantly be performed by AI. Physicians will discuss the findings of traditional tests like electrocardiograms and chest X-rays with AI, providing beyond-human interpretation. Additionally, AI is changing areas that have seen little development for a long time, such as auscultation and phonocardiography, and the recognition and quantification of previously challenging observations like the gait analysis. Although barriers to real-world implementation exist, in the near future, a majority of physicians will collaborate with AIs supporting various aspects of clinical practice, consequently enabling more accurate and appropriate diagnosis and treatment of cardiovascular diseases, including ischemic and valvular heart diseases, arrhythmias, and heart failure. This review focuses on AI application in the field of cardiology, specifically on how it can improve the workflow in clinical settings. We examine various examples of AI integration in cardiology to demonstrate how these technologies can lead to more accurate and efficient patient care. Understanding the advancements in AI can lead to more appropriate and streamlined medical practices, which will ultimately benefit both healthcare providers and patients.JMA Journal00Various examples of AI integration in cardiology are examined to demonstrate how these technologies can lead to more accurate and efficient patient care, which will ultimately benefit both healthcare providers and patients.JMA Journal["Akira Sakamoto", "Yutaka Nakamura", "Eiichiro Sato", "N. Kagiyama"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/4868fe2298a384c0077529cfb20dd61e3e01c542 35d362a800b2c76decc8d178a1c22d37b9d05303Artificial Intelligence (AI) has become a transformative force in financial risk management, offering innovative tools to enhance predictive accuracy, efficiency, and decision-making processes. This research explores how AI technologies, such as machine learning, natural language processing, and neural networks, are revolutionizing traditional approaches to risk assessment and mitigation. By analyzing vast datasets, AI enables financial institutions to identify potential risks, detect anomalies, and respond to crises with unprecedented speed. However, integrating AI into financial systems also presents challenges, including algorithmic biases, regulatory concerns, and cybersecurity vulnerabilities. Through an interdisciplinary approach, this study examines the benefits and limitations of AI-driven risk management solutions, offering insights into their practical applications and ethical implications. The findings highlight the potential of AI to redefine risk management practices while emphasizing the need for robust frameworks to address associated risks.Human-Computer Interaction40This research explores how AI technologies, such as machine learning, natural language processing, and neural networks, are revolutionizing traditional approaches to risk assessment and mitigation, emphasizing the need for robust frameworks to address associated risks.Human Computer Interaction["Arhan O\u011fuz"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/35d362a800b2c76decc8d178a1c22d37b9d05303 37261200c1f0698fe85a74aa0d4b876e9199e0beIn the context of the industrial revolution 4.0, the use of advanced technologies such as artificial intelligence (AI), data analytics, and automation has fundamentally changed accounting practices. This article aims to explore and analyze recent developments in accounting practices through a systematic review of existing literature. The focus of the study is on how information technology and data analytics affect accounting practices and managerial decision-making. The methodology used is a systematic literature review, which involves collecting and analyzing relevant studies from various academic sources and leading journals with strict inclusion and exclusion criteria. This article shows a significant increase in the adoption of information technology and data analytics among accounting professionals, as well as challenges in its implementation, such as the need for training and adaptation to change. The conclusion of this article recommends the development of a comprehensive training program to help accounting professionals adapt to technological changes. This article is expected to make a significant contribution to the understanding of current dynamics in the accounting field and direct further research in the future.Journal of Accounting and Finance Management00A significant increase in the adoption of information technology and data analytics among accounting professionals, as well as challenges in its implementation, such as the need for training and adaptation to change are shown.Journal of Accounting and Finance Management["Mediaty Mediaty", "Aini Indrijawati", "Yansen Pratama Kohar", "Sutriani Sutriani", "Tsarwah Salsabila"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/37261200c1f0698fe85a74aa0d4b876e9199e0be 9e64de57b92e9d5dd76cfe2dd1393d8e0cbc46e9Artificial intelligence has somehow become the force that has transformed various sectors, including the military. Moreover, AI technology has revolutionized the military with its ability to rapidly process large amounts of data, make decisions, and analyze complex patterns. Analyzing the question of how it is used and what ethical norms exist in military structures is not an easy task, because artificial intelligence components are supported by the defense sector in many directions - autonomous weapons and vehicle systems, intelligent command and control systems, predictive maintenance performance, logistics and maintenance services. , cyber security, intelligence and surveillance, decision support systems, simulations and training, artificial intelligence applications and more. We have already discussed these issues in detail in the main part of the book, analyzed and discussed all the relevant issues related to the introduction, use and development of artificial intelligence technologies. The development of artificial intelligence has raised hopes of bringing great benefits, which can be reflected on the one hand in the Internet of Things (IoT), a huge set of capabilities, such as unmanned surveillance and targeting, health monitoring of soldiers, situational awareness and other critical applications. The trend is that decisions in future wars will require seconds, minutes, or even hours rather than days and weeks. This implies that the operational environment should be analyzed. By using artificial intelligence and machine learning, rapid information can be delivered to the frontline, which also means rapid decision-making. The Internet of Military Things is known to encompass many different tools, from battlefield sensors and weapon systems, to surveillance, intelligence, communications, wearables, and sensors on ships, aircraft, tanks, and the body. These tools collectively share an unprecedented amount of information in real-time during the war. The success of this issue depends on the ability to collect and store huge amounts of data from thousands of devices. However, a much more problematic issue is to quickly understand this information and deliver results to the fighters so that said information is useful and can be used. Ethical norms for the use of artificial intelligence differ from general ethical standards, artificial intelligence is more automated and scalable than most other processes, roles also include ethical risks - risk mitigation, legal risks reduction, human discrimination reduction, etc.თავდაცვა და მეცნიერება00This book analyzed and discussed all the relevant issues related to the introduction, use and development of artificial intelligence technologies.თავდაცვა და მეცნიერება["Levan Nikoleishvili", "Thornike Zedelashvili"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/9e64de57b92e9d5dd76cfe2dd1393d8e0cbc46e9 31b5edc635e38abc0a2e2619258996de58b701f7 Artificial intelligence (AI) is increasingly prevalent in the clinical workplace, a trend that is likely to continue with the amount of attention and resources these technologies receive. This review of 22 articles from the last 18 months takes stock of not only the prospects but also the challenges for clinicians resulting from AI integration. While the technology matures rapidly, insights into organizational processes and user readiness and involvement in AI development, implementation, and deployment lag behind. AI impact assessments often focus narrowly on task efficiency, overlooking the derived effect of additional workload elsewhere. Additionally, the issue of the distribution of responsibility between humans and AIs poses a fundamental ethical, legal, and political challenge. Research acknowledges the need to consider healthcare professionals’ diverse roles and sociocultural backgrounds to avoid AI exacerbating existing inequalities among the clinical workforce and, ultimately, the patients cared for. Decision-makers should involve users throughout the entire AI life cycle, from the early stages of AI development to continuous postdeployment impact assessment on workload. More research is needed on AI's cost-effectiveness, integration into clinical practice, and the role of diversity-aware facilitation in realizing its potential. Current Opinion in Epidemiology and Public Health270More research is needed on AI's cost-effectiveness, integration into clinical practice, and the role of diversity-aware facilitation in realizing its potential, as well as on the issue of the distribution of responsibility between humans and AIs.Current Opinion in Epidemiology and Public Health["Anna Schneider-Kamp", "S\u00f8ren Askegaard"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/31b5edc635e38abc0a2e2619258996de58b701f7 f4410986a6fe29ca5d7bf5a7a4a65b34f13df8f1This paper comprehensively explores the multifaceted influence of artificial intelligence (AI) on corporate competitive advantage. It analyzes how AI technologies enhance operational efficiency, transform customer experiences, and drive innovation within enterprises. Additionally, potential challenges such as data privacy concerns, technological complexities, and ethical issues are examined. Strategies for businesses to harness AI's potential while mitigating risks are proposed, aiming to provide valuable insights for corporate decision-makers and researchers in the evolving digital landscape.Frontiers in Business, Economics and Management00Frontiers in Business, Economics and Management["Xin Wan", "Pengfei Zhao"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/f4410986a6fe29ca5d7bf5a7a4a65b34f13df8f1 a8258420ffff6a044f46be8e7cafc284a78e4d9dVaccine, a traditional treatment to fight against infected pathogens, is now applied into the therapy of tumor. For the development of tumor vaccines, the critical challenge is the selection of tumor-specific antigens that could distinguish normal and malignant cells. The human genome is vast. A single tumor may be accompanied by dozens or even hundreds of gene mutations, making it extremely difficult to select the appropriate mutation that corresponds to an ideal targeting antigen. Leveraging artificial intelligence and big data models to accelerate antigen selection is crucial for advancing tumor vaccine research. This article will explore the principles of tumor vaccines and antigen screening, delve into cutting-edge AI-assisted technologies in tumor vaccine development, and discuss how AI addresses technological gaps in this field. It will also provide insights into the potential impact of AI on advancing biological research, particularly in the context of current priorities in tumor clinical treatment.Highlights in Science Engineering and Technology70The principles of tumor vaccines and antigen screening are explored, cutting-edge AI-assisted technologies in tumor vaccine development are explored, and how AI addresses technological gaps in this field are discussed, to provide insights into the potential impact of AI on advancing biological research.Highlights in Science, Engineering and Technology["Jiaming Cui"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/a8258420ffff6a044f46be8e7cafc284a78e4d9d 93b107d4468208dd9a45566abee45331497c7f2cAlthough artificial intelligence (AI) has grown in popularity throughout the world as a vital tool for financial statement audits, auditor adoption and use of AI tools in Nigeria is still in its infancy. The fast advancement of science, technology, and the economy has ushered in the age of artificial intelligence, which has had a profound impact on every facet of daily life. Is there a general concern about the situation of accountants facing elimination? This article will examine how artificial intelligence will affect accounting staff and how to prevent accounting fraud. Since machines cannot make decisions, this technology won't result in widespread unemployment. Instead, it will have a positive impact on the quality of accounting information. The article's conclusion will highlight the need for accounting staff to develop their seven areas of expertise and become fully qualified personnel in the context of artificial intelligence. According to the study's findings, auditors will be able to anticipate trends in the future and make better decisions that are aimed at enhancing audit procedures with the help of AI. The study suggested increasing the use of image recognition to help with object classification, investing in machine learning tools by Nigerian audit firms, and providing accountants and audit staff with ongoing training on data mining techniques to improve audit practice.Journal of management & science00The article's conclusion will highlight the need for accounting staff to develop their seven areas of expertise and become fully qualified personnel in the context of artificial intelligence.Journal of Management and Science["Bashir M Ogungbangbe", "Kalu, Alexanda O U"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/93b107d4468208dd9a45566abee45331497c7f2c f6d5416c86ebbe3a26d627db9782d3719e96eff5This study looks at how artificial intelligence (AI) is employed in news reporting and gathering in Nigerian mainstream media, with a focus on LTV and TVC. The study's goals are to find out how much AI is employed in these TV stations, as well as what the benefits and challenges are of doing so, and what the ethical implications are of incorporating AI into journalism. The study conducted in-depth interviews with journalists from LTV and TVC to gain information on their viewpoints and experiences about the usage of AI in their news gathering and reporting methods. The findings demonstrate the growing use of artificial intelligence (AI) in data analysis, automation, and content creation for news reporting. Nonetheless, the research also emphasizes the difficulties and moral dilemmas that come with the use of AI, such as the possibility of prejudice, the loss of jobs, and the requirement for accountability and transparency. According to the study's findings, artificial intelligence (AI) has the potential to completely transform Nigerian journalism. Still, its implementation would need to be carefully regulated to get the best results for society and the media.Journal of Ecohumanism00According to the study's findings, artificial intelligence (AI) has the potential to completely transform Nigerian journalism, still, its implementation would need to be carefully regulated to get the best results for society and the media.Journal of Ecohumanism["F. O. Talabi", "John Ayodele Oyewole", "S. Bello", "Victor Oluwole Adefemi", "Joseph Moyinoluwa Talabi", "Tosin Adesile", "Patrick Olajide Oladele"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/f6d5416c86ebbe3a26d627db9782d3719e96eff5 9bd7135d71c62199a5b17e3dde0d300c4a4a65aeThis critical analysis explores the intersection of technology and artificial intelligence (AI) in English literature, delving into the cultural, philosophical, and ethical implications of this theme. The study adopts a close reading approach, closely examining literary works that engage with technology and AI to uncover recurring themes, symbols, and motifs. Through comparative analysis, commonalities and differences in the portrayal of technology and AI are explored, shedding light on their cultural significance Drawing on interdisciplinary perspectives from fields such as philosophy and cultural studies, the analysis delves into the philosophical inquiries raised by these literary works, including questions about human identity, consciousness, and morality. Ethical dimensions are also examined, addressing concerns around privacy, surveillance, and the social impact of technology and AI. Throughout the analysis, the study highlights the unique insights that literature offers in understanding the complex relationship between humanity and technology, inviting readers to reflect on the implications of technology and AI in their own lives. By critically analyzing these literary works, this study provides valuable insights into the cultural and philosophical implications of the intersection of technology and artificial intelligence in English literature.Al-Noor Journal for Humanities00This critical analysis explores the intersection of technology and artificial intelligence in English literature, delving into the cultural, philosophical, and ethical implications of this theme through closely examining literary works that engage with technology and AI.Al-Noor Journal for Humanities["Dr.Khaled Ahmed Ali Al-swmaeai"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/9bd7135d71c62199a5b17e3dde0d300c4a4a65ae 48e19a71a9d624a629122979c0b7100b1f565d87The study aims to highlight the future research directions and global research trends of applying artificial intelligence (AI) in Human Resources (AI) Using Bibliometric Analysis in the last three decades (1996 – 2024). Using performance analysis and scientific mapping, the research uses bibliometric analysis to investigate co-authorship, co-occurrence, citation, bibliographic coupling, and co-citation analysis in 99 articles taken from the Scopus database. The analysis looked at the quantity of scientific publications, the most prolific writers, the most important papers, nations, and organizations. The study used VOSviewer as a science mapping and performance analysis tool. The most productive year was 2023 with 34 publications and the most impactful institute and countries are the Essec Business School in France, and the country is the United States, respectively. Similarly, the most influential journal is “California Management Review”, furthermore, the most cited article is “Artificial intelligence in huma n resources management: Challenges and A path forward”. The authorsInternational Journal of Academic Research in Accounting, Finance and Management Sciences660The research uses bibliometric analysis to investigate co-authorship, co-occurrence, citation, bibliographic coupling, and co-citation analysis in 99 articles taken from the Scopus database.International Journal of Academic Research in Accounting, Finance and Management Sciences["Saleh Alkoud", "Isam Majeed", "Dolhadi Zainudin", "Suhaimi Bin Mhd Sarif"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/48e19a71a9d624a629122979c0b7100b1f565d87 363de92cccb498516835d30c71b0fd8a53c21fb0Modern trends and prospects for using artificial intelligence (AI) in language learning are considered. The authors analyze scientific articles and other sources related to the study and effectiveness of AI integration into the educational process. The main development areas and the potential of AI in transforming the educational process in the field of language learning are considered. The purpose of this article is to study the current state and prospects for using AI in language education, identifying key trends, advantages, and challenges associated with the application of this technology. The results of a survey are presented to explore students' needs in using AI technologies, their experience with AI, and its effectiveness in language learning within the higher education system.Trudy Universiteta00The results of a survey are presented to explore students' needs in using AI technologies, their experience with AI, and its effectiveness in language learning within the higher education system.TRUDY UNIVERSITETA["Natalya Dokuchaeva", "Svetlana Ivanova", "Dana Tleumbetova", "Aksana Ten", "\u041dajduk Ludovit"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/363de92cccb498516835d30c71b0fd8a53c21fb0 10b79dfff35122668d6e6dc8a944e7e49bcbf482With the advancement of population aging, the incidence of orthopedic diseases increases annually. The early diagnosis and precise treatment of many orthopedic diseases still require advancements in technology to address effectively. With the rapid development of artificial intelligence (AI), this technology is expected to achieve early diagnosis and improved treatment of many diseases, providing revolutionary changes in clinical. However, the integration of AI in orthopedics is still in its infancy, and its existing intelligent algorithms have been clinically applied models and their advantages need to be further summarized to pave the way for future development and exploration. The review provides a concise overview of the basic concepts and mechanisms of AI in orthopedics, and summarizes orthopedic surgery and sports medicine in four areas of application and development, specifically, developing precision diagnostics, assisting treatment, monitoring assisted during rehabilitation, and enhancing educational research and data analysis. In this section, the main focus is on each aspect of the AI programs that are now used in clinical applications, and also comparing them to the purely manual results. In conclusion, the continued application and development of AI are anticipated to enhance our understanding of the diagnosis, progression, and prognosis of orthopedic diseases, ultimately laying the groundwork for more effective clinical applications.International Journal of Surgery00The continued application and development of AI are anticipated to enhance the understanding of the diagnosis, progression, and prognosis of orthopedic diseases, ultimately laying the groundwork for more effective clinical applications.International journal of surgery["Jiekai Guan", "Zuhao Li", "Shihao Sheng", "Qiushui Lin", "Sicheng Wang", "Dongliang Wang", "Xiao Chen", "Jiacan Su"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/10b79dfff35122668d6e6dc8a944e7e49bcbf482 f4f55547eb45f49e963261207f1d1f3760411720This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.Journal of Infrastructure Policy and Development01Journal of Infrastructure, Policy and Development["Zolt\u00e1n Z\u00e9man", "Botond G\u00e9za K\u00e1lm\u00e1n", "Szil\u00e1rd Malatyinszki"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/f4f55547eb45f49e963261207f1d1f3760411720 b02c6334e82f6f0dc703b9ad160eebb24da0f92dIntroduction. The present study analyzed the implemented innovative cross-cutting technologies in the construction sector of the Russian Federation, aimed at facilitating the production and design activities of construction organizations. The relevance of the study lies in the necessity to achieve technological sovereignty in the construction industry of the Russian Federation.Aim. To propose recommendations for the implementation process of these technologies and their commercialization in the construction industry, based on the areas of transformation within the construction sector of the Russian Federation identified through innovative technologies. The objectives include analyzing the innovative cross-cutting technologies currently utilized at construction sites in the Russian Federation and abroad, and outlining the main directions for the development of innovative technologies in construction.Materials and methods. The study involved general methodological approaches — systemic-creative and systemic-informational — along with methods of systemic analysis and logical analysis, as well as generalization and classification. The research object of study consists in cross-cutting safety and design technologies at construction sites. Specifically, the study analyzes BIM technology, unmanned aerial vehicles, and others currently used at construction sites in Russia and abroad, examines the current state of transformations in the construction sector of the Russian Federation considering these innovations, and identifies key directions for the development of innovative technologies in construction, including the application of artificial intelligence.Bulletin of Science and Research Center of Construction30The present study analyzed the implemented innovative cross-cutting technologies in the construction sector of the Russian Federation, aimed at facilitating the production and design activities of construction organizations and identifies key directions for the development of innovative technologies in construction, including the application of artificial intelligence.Bulletin of Science and Research Center of Construction["A. B. Lanchakov"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/b02c6334e82f6f0dc703b9ad160eebb24da0f92d 089d8c74a827686e38877dddc114115f4647605aAmerican Medical Journal Rheumatology00American Medical Journal Rheumatology["H. Badsha", "Tanishka Agarwa", "Arun J", "Sumanth Raman"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/089d8c74a827686e38877dddc114115f4647605a daf7d6b0ba2a64bd1fc0d25976c5748119aef693PurposeThis study aims to examine the trends and advancements in AI-supported language learning over the past decade. By analyzing 15 empirical research articles, the study seeks to fill the gap in understanding the effectiveness and challenges of AI-assisted language learning for both first- and second-language learners.Design/methodology/approachThe research utilizes activity theory, which includes seven components: tool, subject, object, rules, community, division of labor and outcome. This theoretical framework helps to reveal the dynamic interactions and contradictions among these elements. The selection and screening process for relevant articles followed the PRISMA method, ensuring a systematic and comprehensive review.FindingsThe study found that AI-supported technology shows promise in enhancing language learning, particularly in areas such as writing quality, scoring accuracy and learner engagement. However, challenges remain in terms of dialogic competence and the necessity of teacher intervention in pedagogical design. While AI-supported systems can effectively aid in language acquisition, improvements are needed to foster language use for communication and collaborative design.Research limitations/implicationsThe review highlights the need for more empirical studies on the pedagogical impacts of AI-supported language learning and the engagement levels of both learners and teachers. It also underscores the importance of investigating the application of AI-assisted language learning in actual classroom environments.Practical implicationsThe implications of this study offer significant insights for both educational practice and future research in AI-supported language learning. As AI technologies continue to evolve, their potential to enhance learning outcomes and support teachers’ efforts becomes increasingly apparent. However, effective implementation requires not only the availability of technological tools but also proper pedagogical integration and teacher intervention. Furthermore, AI presents unique opportunities to personalize learning and foster collaboration among learners, aligning with the growing trend of hybrid learning environments.Originality/valueThis paper addresses the need for a comprehensive review of AI’s role in language education, providing insights into emerging trends and identifying areas for future research. It emphasizes the importance of integrating AI tools with educational theories and the necessity of teacher configuration in enhancing AI-supported language learning.Saudi Journal of Language Studies710The study found that AI-supported technology shows promise in enhancing language learning, particularly in areas such as writing quality, scoring accuracy and learner engagement.Saudi Journal of Language Studies["Eman Alhusaiyan"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/daf7d6b0ba2a64bd1fc0d25976c5748119aef693 9c1c9ca0bbae75bf8e34651b605178ab8945dd73Background Acute pancreatitis (AP) is a potentially life-threatening condition characterized by inflammation of the pancreas, which can lead to complications such as pancreatic necrosis. The modified computed tomography severity index (MCTSI) is a widely used tool for assessing the severity of AP, particularly the extent of pancreatic necrosis. The accurate and timely assessment of the necrosis volume is crucial in guiding treatment decisions and improving patient outcomes. However, the current diagnostic process relies heavily on the manual interpretation of computed tomography (CT) scans, which can be subjective and prone to variability among clinicians. This study aimed to develop a deep-learning network model to assist clinicians in diagnosing the volume ratio of pancreatic necrosis based on the MCTSI for AP. Methods The datasets comprised retrospectively collected plain and contrast-enhanced CT scans from 144 patients (6 with scores of 0 points, 42 with scores of 2 points, and 65 with scores of 4 points) and the National Institutes of Health contrast-enhanced CT scans from 45 patients with scores of 0 points. An improved fully convolutional neural networks for volumetric medical image segmentation (V-Net) model was developed to segment the pancreatic volume (i.e., the whole pancreas, necrotic pancreatic tissue, and non-necrotic pancreatic tissue) and to quantify the split volume ratios. The improved strategy included three stages of body up- and down-sampling adapted to the task of segmentation in AP, and the selection of objects, loss function, and smoothing coefficients. The model interpretations were compared with those of clinicians with different levels of experience. The reference standard was manually segmented by a pancreatic radiologist. Accuracy, macro recall, and macro specificity were employed to compare the diagnostic efficacy of the model and the clinicians. Results In total, 144 patients (mean age: 44±13 years; 40 females, 104 males) were included in the study. Optimal training results were obtained using the necrotic pancreatic tissue and whole pancreas as the input objects, and combining dice loss and 500 smoothing coefficients as the loss function for training. The dice coefficient for the whole pancreas was 0.811 and that for the necrotic pancreatic tissue was 0.761. The performance of the artificial intelligence model and clinicians were compared. The accuracy, macro recall, and macro specificity of the improved V-net were 0.854, 0.850 and 0.923, respectively, which were all significantly higher than those of the senior and junior clinicians (P<0.05). Conclusions Our proposed model could improve the effectiveness of clinicians in diagnosing pancreatic necrosis volume ratios in clinical settings.Quantitative Imaging in Medicine and Surgery00An improved fully convolutional neural networks for volumetric medical image segmentation (V-Net) model was developed to segment the pancreatic volume and quantify the split volume ratios, which could improve the effectiveness of clinicians in diagnosing pancreatic necrosis volume ratios in clinical settings.Quantitative Imaging in Medicine and Surgery["Cheng-Xiang Lu", "Jiali Zhou", "Yong-Chang Feng", "Si-Jun Meng", "Xue-Ling Guo", "Wen-Song Su", "Tue Ngo", "Tsehao Hsu", "Peng Lin", "James Huang", "Si-Tong Liu", "Manuel L. B. Palacio", "Wei-Lin Change", "Glen Qin", "Yi-Qun Hu", "Ling-Hui Zhan"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/9c1c9ca0bbae75bf8e34651b605178ab8945dd73 3401ffc1fd140789428506f676dc0306cbae4285Economy and Entrepreneurship00Economy and Entrepreneurship["M. Tepliuk", "Anna Savranska"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/3401ffc1fd140789428506f676dc0306cbae4285 ddc479f6fa8cd41a3d52845ca97b273fc95ffb54Artificial intelligence has advanced rapidly in the last decade, driven primarily by progress in the scale of deep-learning systems. Despite these advances, the creation of intelligent systems that can operate effectively in diverse, real-world environments remains a significant challenge. In this white paper, we outline the Thousand Brains Project, an ongoing research effort to develop an alternative, complementary form of AI, derived from the operating principles of the neocortex. We present an early version of a thousand-brains system, a sensorimotor agent that is uniquely suited to quickly learn a wide range of tasks and eventually implement any capabilities the human neocortex has. Core to its design is the use of a repeating computational unit, the learning module, modeled on the cortical columns found in mammalian brains. Each learning module operates as a semi-independent unit that can model entire objects, represents information through spatially structured reference frames, and both estimates and is able to effect movement in the world. Learning is a quick, associative process, similar to Hebbian learning in the brain, and leverages inductive biases around the spatial structure of the world to enable rapid and continual learning. Multiple learning modules can interact with one another both hierarchically and non-hierarchically via a"cortical messaging protocol"(CMP), creating more abstract representations and supporting multimodal integration. We outline the key principles motivating the design of thousand-brains systems and provide details about the implementation of Monty, our first instantiation of such a system. Code can be found at https://github.com/thousandbrainsproject/tbp.monty, along with more detailed documentation at https://thousandbrainsproject.readme.io/.arXiv.org00An early version of a thousand-brains system is presented, a sensorimotor agent that is uniquely suited to quickly learn a wide range of tasks and eventually implement any capabilities the human neocortex has, in an ongoing research effort to develop an alternative, complementary form of AI.ArXiv["Viviane Clay", "Niels Leadholm", "Jeff Hawkins"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/ddc479f6fa8cd41a3d52845ca97b273fc95ffb54 3451aceb69bec68a45da06af263ba3da535521faPurpose: This study examined the implementation of Artificial Intelligence-driven Automation as a game changer in accounting research. Specifically, this study assessed the advantages and disadvantages of AI-driven automation in enhancing the quality of accounting research. Methods: A descriptive survey design was used in the study. The study sample comprised of 137 accounting academics. Primary data for this study were collected using a structured questionnaire. The collected data were assigned quantitative measurements using a Likert scale system of ranks. Descriptive analytical tools (frequency and mean-point analyses) were used to analyze the data with the aid of the SPSS version 25 software. Results: The findings  show a general consensus that AI-driven automation enhances the accuracy, efficiency, and comprehensiveness of accounting research, with high acceptance of its benefits. However, there are notable concerns about potential drawbacks such as reduced originality, difficulties in validation, and the risk of introducing biases or compromising ethical standards. Limitations: This study’s limitations include a narrow sample of academics, potential response biases, and the inability to assess long-term AI impacts across diverse accounting professionals.   Contribution: The implementation of AI-driven automation represents a game-changer in accounting research because it offers new opportunities to enhance the quality, efficiency, and scope of academic inquiry, as well as challenges and risks that must be carefully managed to ensure that the benefits of AI are fully realized while maintaining the integrity and rigor of the research process. Therefore, this study recommends that academic institutions and research ethics committees develop workable training programs that emphasize the importance of maintaining human oversight, creativity, and ethical standards when utilizing AI-driven automation in accounting research.International Journal of Financial Accounting and Management231This study recommends that academic institutions and research ethics committees develop workable training programs that emphasize the importance of maintaining human oversight, creativity, and ethical standards when utilizing AI-driven automation in accounting research.International Journal of Financial, Accounting, and Management["A. Ikwuo", "Gilbert Ogechukwu Nworie", "Vitalis O. Moedu"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/3451aceb69bec68a45da06af263ba3da535521fa 564b54c527200ccbbee2403b467a38d3ff8a2301This paper comprehensively explores the significant role that accounting plays in the progress of social civilization and delves into the profound impact of accounting on the advancement of social civilization, integrating its historical evolution and modern applications with emerging technological trends in Artificial Intelligence (AI), Artificial General Intelligence (AGI), Artificial Superintelligence (ASI), and Universal Basic Income (UBI). By examining accounting's role in fostering economic growth, corporate governance, governmental functioning, and social equity, alongside the transformative potential of advanced AI, the paper highlights how these domains interconnect to shape future societies. Comparative analyses between socialist and capitalist accounting systems underscore diverse approaches to leveraging AI technologies for equitable development and innovation. The findings illuminate the indispensable role of accounting as a cornerstone for global progress and ethical advancement in an AI-dominated era.Frontiers in Business, Economics and Management171Comparative analyses between socialist and capitalist accounting systems underscore diverse approaches to leveraging AI technologies for equitable development and innovation and illuminate the indispensable role of accounting as a cornerstone for global progress and ethical advancement in an AI-dominated era.Frontiers in Business, Economics and Management["Bing Chen"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/564b54c527200ccbbee2403b467a38d3ff8a2301 8b3aa8217923527f40683487fc626436aff20879Science China Information Sciences291A novel and practical methodology for green, real-time, and controllable 6G native intelligence, starting with knowledge graph analysis to extract small but critical datasets, followed by the development of distributed lightweight AI models, and the use of digital twins to create precise replicas of physical 6G networks.Science China Information Sciences["Xiaohu You", "Yongming Huang", "Cheng Zhang", "Jiaheng Wang", "Hao Yin", "Hequan Wu"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/8b3aa8217923527f40683487fc626436aff20879 6f108128a1ebb52fecefe1a495994e4cff2f5720npj Digit. Medicine181It is argued that ethical principles around autonomy, patient ownership of data, and privacy should be prioritized in the data acquisition paradigm.NPJ Digital Medicine["Marta M Williams", "Wasie Karim", "Justin Gelman", "Marium M. Raza"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/6f108128a1ebb52fecefe1a495994e4cff2f5720 078cee51c325ad17e9abb699a51a77d2401cccb6In recent years, with the development of science and technology, artificial intelligence is applied in various fields of society. Among them, the application in financial market prediction has attracted a lot of attention. Artificial intelligence combined with machine learning, deep learning and other models has helped investors solve many problems related to financial investment. It improves the efficiency of investment and saves the cost of investment. However, while gaining benefits, there are also disadvantages of AI investment. In this paper, the volatility of the AIEQ fund is predicted using the Garch model, based on the AIEQ fund data, and the AI stock returns from January 2019 to July 2024 are used. The empirical results show that the volatility of the GARCH model under the t-distribution assumption of the fund is significant, and there are still some problems in the prediction, although the AIEQ fund has an advantage in processing data and executing the trading strategy, but there is still instability in still has a certain market risk. When considering investing in AIEQ funds, investors should fully understand their operating mechanisms and potential risks, and make decisions based on their risk tolerance and investment objectives. This study enhances the knowledge and understanding of AI investment, enabling future investors and financial institutions to make more reasonable investment choices when utilizing AI for investment decisions.Frontiers in Business, Economics and Management00This study enhances the knowledge and understanding of AI investment, enabling future investors and financial institutions to make more reasonable investment choices when utilizing AI for investment decisions.Frontiers in Business, Economics and Management["Wenjing Du"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/078cee51c325ad17e9abb699a51a77d2401cccb6 167aa641418963afd3e5ebdfd72b0684c7ed7143This proposed FlowingLife framework addresses the challenges of optimizing altered flow regimes in Irish plans and programs to improve Irish aquatic ecosystems' economic and environmental outcomes. The framework uses Artificial Intelligence (AI) techniques to revolutionize flow regime management and decision-making processing, providing sustainable resource allocation, climate change adaptation, and aquatic habitat conservation. The potential of identifying optimization guides thoroughly evaluating Irish Plans, including development plans, river basin management, biodiversity, and climate action. Fish population restoration, protection of biodiversity, optimization of agricultural techniques, and management of water resources are some of the critical uses. AI-empowered FlowingLife framework creates real-time monitoring and assessment in Strategic Environmental Assessments (SEAs), enabling adaptive management. The FlowingLife evaluates and adaptively manages fish populations and flow regimes by combining Deep Learning (DL) for image and sensor analysis, knowledge graphs for intricating ecological linkages, and predictive modeling. The results show that the proposed paradigm using AI improves environmental management and supports evidence-based decision-making, sustainable resource management, and the preservation of Irish aquatic ecosystems.Advances in Environmental and Engineering Research280Advances in Environmental and Engineering Research["H. Al-Dois", "Farhan M. A. Nashwan", "Neil J Rowan", "Amnnah Alhabeeb Shoushan", "Niall O\u2019Brolchain", "S. Alsamhi"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/167aa641418963afd3e5ebdfd72b0684c7ed7143 26192a546133ff93647eeb1811c5a6ca3bcc1025Science and Engineering Ethics570It is believed that applying Embedded Ethics offers a pathway to stimulate reflexivity, proactively anticipate social and ethical concerns, and foster interdisciplinary inquiry into such concerns at every stage of technology development.Science and Engineering Ethics["Theresa Willem", "Marie-Christine Fritzsche", "Bettina M Zimmermann", "Anna Sierawska", "Svenja Breuer", "Maximilian Braun", "A. K. Ruess", "Marieke Bak", "F. Sch\u00f6nweitz", "Lukas J Meier", "A. Fiske", "Daniel Tigard", "Ruth M\u00fcller", "S. McLennan", "Alena Buyx"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/26192a546133ff93647eeb1811c5a6ca3bcc1025 a0d6fdf07d6844298a40ad5e500b8dfa8c142ef3Artificial intelligence presents immense opportunities to transform the Middle East and North Africa (MENA) region through advances in healthcare, environmental sustainability, economic inclusion, and more. However, the adoption of AI also poses risks if deployed unethically, as seen in biased facial recognition harming vulnerable populations. This paper analyzes AI’s potential while assessing core ethical challenges in the MENA context. It spotlights perspectives from regional experts on grounding AI governance in cultural values, exemplified by initiatives like the UAE’s AI ethics council. Proposed solutions include diversifying AI design teams, auditing for bias, and centering human needs in policymaking. Rather than resist progress, this talk empowers MENA nations to proactively shape an AI landscape that promotes human dignity, community, justice and participatory decision-making. Audiences will gain an understanding of strategies to realize AI’s benefits through an ethical lens. The aim is to provide practitioners and policymakers with an actionable framework to develop and regulate AI systems that align with regional values and elevate societyAl-Noor Journal for Humanities00This paper analyzes AI’s potential while assessing core ethical challenges in the MENA context and provides practitioners and policymakers with an actionable framework to develop and regulate AI systems that align with regional values and elevate society.Al-Noor Journal for Humanities["Ray Gutierrez", "Al-Noor"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/a0d6fdf07d6844298a40ad5e500b8dfa8c142ef3 6f859104ae9f900956b11d975e165f1d50512b9eThe rapid evolution of phishing attacks has been significantly accelerated by advancements in artificial intelligence (AI), transforming these schemes into sophisticated, scalable, and highly targeted cyber threats. This paper examines the historical progression of phishing, from its early days of generic mass emails to the advent of AI-powered attacks that exploit deepfake technology, adaptive strategies, and hyper-personalization. Key areas of focus include the anatomy of AI-driven phishing campaigns, real-world case studies highlighting their impact, and the unique challenges they pose to traditional security measures. The study further explores countermeasures, emphasizing AI driven detection systems, adaptive security protocols, and enhanced training programs to mitigate these threats. By analyzing the integration of generative AI tools in phishing schemes, this paper underscores the urgent need for innovative and collaborative defenses to address the rapidly evolving landscape of AI-fueled cybercrime and the need for proactive and adaptive security measures to mitigate AI-fueled threats, providing a roadmap for future research and practical implementations.Journal of Digital Science110By analyzing the integration of generative AI tools in phishing schemes, this paper underscores the urgent need for innovative and collaborative defenses to address the rapidly evolving landscape of AI-fueled cybercrime and the need for proactive and adaptive security measures to mitigate AI-fueled threats.Journal of Digital Science["Patricia Riurean", "George Bolog", "S. Riurean"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/6f859104ae9f900956b11d975e165f1d50512b9e 955231732ee9557addb3eeaa6f139dfb03973893Driven by the relentless advancement of artificial intelligence technology, autonomous driving technology is progressively transforming the transportation industry, presenting unprecedented opportunities for pioneering companies in the ride-hailing sector such as Uber Eats. The deep integration of autonomous driving technology not only signifies a technological leap but also achieves a qualitative leap in cost structure optimization and operational efficiency. Through intelligent algorithms and precise dispatch systems, autonomous vehicles significantly reduce reliance on human labor, thereby substantially lowering operational costs. Simultaneously, their superior driving stability and accident prevention capabilities effectively mitigate traffic accidents and hefty maintenance expenses caused by human errors, further minimizing expenses throughout the vehicle's lifecycle. These cost optimizations empower Uber Eats with greater pricing flexibility and market competitiveness, allowing it to offer more attractive service packages and an enhanced customer experience. This, in turn, accelerates business growth, solidifies its market leadership position, and lays an unshakeable foundation for the company's long-term development in the intelligent transportation sector.Highlights in Business, Economics and Management90These cost optimizations empower Uber Eats with greater pricing flexibility and market competitiveness, allowing it to offer more attractive service packages and an enhanced customer experience, and accelerates business growth, solidifies its market leadership position, and lays an unshakeable foundation for the company's long-term development in the intelligent transportation sector.Highlights in Business, Economics and Management["Siyu Cheng", "Lingzhou Gu"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/955231732ee9557addb3eeaa6f139dfb03973893 5f5cf22b52e2dc2d3cc608d53f597c73ac17fb0eOur society is on the verge of a revolution powered by Artificial Intelligence (AI) technologies. With increasing advancements in AI, there is a growing expansion in data centers (DCs) serving as critical infrastructure for this new wave of technologies. This technological wave is also on a collision course with exacerbating climate hazards which raises the need for evaluating the vulnerability of DCs to various hazards. Hence, the objective of this research is to conduct a nationwide vulnerability assessment of (DCs) in the United States of America (USA). DCs provide such support; however, if an unplanned disruption (like a natural hazard or power outage) occurs, the functionality of DCs are in jeopardy. Unplanned downtime in DCs cause severe economic and social repercussions. With the Local Indicator of Spatial Association (LISA) test, the research found that there are a large percentage of DCs that are in non-vulnerable areas of disruption; however, there is still a notable percentage in disruption prone areas. For example, earthquakes, hurricanes, and tornadoes have the most DCs in vulnerable areas. After identifying these vulnerabilities, the research identified areas within the USA that have minimal vulnerabilities to both the aforementioned natural hazards and power outages with the BI-LISA test. After doing a composite vulnerability score on the Cold-Spots from the BILISA analysis, the research found three counties with the low vulnerability scores. These are Koochiching, Minnesota (0.091), Schoolcraft, Michigan (0.095), and Houghton, Michigan (0.096).00["Miguel Esparza", "Bo Li", "Junwei Ma", "Ali Mostafavi"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/5f5cf22b52e2dc2d3cc608d53f597c73ac17fb0e d0265b43499af1deb8de2cc8021b18c738ce64adThe recent surge in artificial intelligence (AI) research has transformed applications across various sectors, including healthcare, education, manufacturing, and digital learning. Although digital learning, collaborative learning, and socially shared regulation learning (SSRL) have experienced significant growth, a thorough examination of scholarly contributions, emerging trends, and influential figures in these fields is still absent. Despite increasing interest in utilizing intelligent agents to enhance SSRL, there is a lack of comprehensive mapping in this interdisciplinary area. Addressing this gap, this study systematically analyses 1,200 publications indexed in Scopus, covering a period from 2018 to 2024. Using Scopus analyzer and VOSviewer, we examined publication trends, co-authorship networks, key research clusters, and thematic patterns. The keywords guiding this analysis were "agent," "digital learning," "socially shared regulation learning," and "collaborative learning." Findings reveal significant growth in this research area, with prominent clusters focused on intelligent agent implementation, collaborative learning frameworks, and SSRL in educational settings. Key contributors and leading journals were identified, highlighting the most influential entities driving research in this field. In conclusion, the study underscores a progressive shift towards integrating intelligent agents in SSRL, suggesting impactful directions for future research and the potential for intelligent agent applications to enhance collaborative digital learning environments. This trend mapping offers valuable insights into both current advancements and prospective developments in agent-driven SSRL.International Journal of Modern Education00A progressive shift towards integrating intelligent agents in SSRL is underscores a progressive shift towards integrating intelligent agents in SSRL, suggesting impactful directions for future research and the potential for intelligent agent applications to enhance collaborative digital learning environments.International Journal of Modern Education["Asmara Alias", "Nurbiha A. Shukor"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/d0265b43499af1deb8de2cc8021b18c738ce64ad cf5b5c2d2f888edf6ac28867025da5ef4822c986With the rapid development and increasing popularity of artificial intelligence technology, its influence has penetrated into every corner of the social economy. The marketing field has also ushered in unprecedented changes. The introduction of AI technology has not only reshaped the strategies and execution methods of traditional marketing, but also opened up new paths for the future development of marketing. This paper conducts a case study on Amazon to explore the wide application direction of AI technology in the field of marketing, especially focusing on how AI learning systems play a key role in various core processes of marketing, and the emerging development trends emerging in this process. Through research, it is found that AI technology still has great development potential in marketing in many aspects such as market and customers. At the same time, the challenges that come with it cannot be ignored. It requires joint efforts from both inside and outside the industry to ensure that AI technology can develop healthily and sustainably in the field of marketing. From a practical perspective, this study helps companies better understand and apply AI technology to improve the efficiency and effectiveness of marketing, so as to formulate more scientific and effective marketing strategies. From a theoretical perspective, this study helps enrich marketing theory and promote the development of marketing disciplines. The introduction of AI technology provides new tools and methods for marketing, but also brings new challenges and problems. In-depth research on these issues will help improve the marketing theory system and provide theoretical support for future marketing practices.Frontiers in Business, Economics and Management00A case study on Amazon is conducted to explore the wide application direction of AI technology in the field of marketing, especially focusing on how AI learning systems play a key role in various core processes of marketing, and the emerging development trends emerging in this process.Frontiers in Business, Economics and Management["Ruichen He"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/cf5b5c2d2f888edf6ac28867025da5ef4822c986 7dd0b77d2ad51b806b950da6abc79734d4c047ceSolid Waste Management (SWM) poses a major global challenge with significant environmental implications. The integration of Artificial Intelligence (AI) and Information and Communication Technology (ICT) has emerged as a promising solution to revolutionize waste management practices. This systematic literature review, which examines the application of AI and ICT in SWM over the past five years (2018-2023) and analyzes 152 research papers, explores their integration at various stages.In the production phase, AI-driven predictive models have outperformed traditional methods, improving waste forecasting accuracy and facilitating recycling initiatives. In waste collection, AI and ICT enable real-time route optimization, dynamic scheduling, and sensor-based monitoring, enhancing service delivery while reducing operational costs. Furthermore, AI-powered technologies have revolutionized waste sorting, precisely identifying and segregating recyclables from mixed waste streams, thereby increasing recycling rates and alleviating the burden on landfills. The article also identifies the constraints and challenges associated with these technologies and discusses potential strategies to address them. The main objective of this review is to provide guidance to SWM researchers interested in utilizing these technologies within their field. Additionally, it aims to enrich the ongoing conversation about sustainable waste management by offering insights into current practices and future trends.Journal of Environmental Engineering and Science00This systematic literature review, which examines the application of AI and ICT in SWM over the past five years and analyzes 152 research papers, explores their integration at various stages and identifies the constraints and challenges associated with these technologies.Journal of Environmental Engineering and Science["Aya Idrissi", "R. Benabbou", "J. Benhra", "Mounia El Haji"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/7dd0b77d2ad51b806b950da6abc79734d4c047ce 5edfed609771342534ea4ca41c385a3b51af4fdcThis study explored the ethical challenges posed by the application of Artificial Intelligence (AI) technology in the field of art and design. With the rapid development of AI technology, it had a profound impact on the art and design industry but had also raised a range of moral and ethical issues including personal privacy, data security, attribution of responsibility, prejudice and discrimination, employment issues, economic inequality, ethical principles and values, disinformation and manipulation. This study aimed to identify and understand the ethical challenges that may be encountered when AI technologies were applied in art and design, including the impacts on attribution of artistic creations, copyrights, cultural diversity, and personal privacy, and to propose solutions and strategies. Through quantitative research on art and design students in Sichuan, China, the results showed that students generally expressed concerns about the ethical risks of AI application in art and design. This study emphasised the need for art and design education to pay attention to the ethical issues posed by AI, and to train students as both technology users and moral guardians, promoting innovation while safeguarding ethics and social responsibility.Journal of Information Systems and Technology Management00The need for art and design education to pay attention to the ethical issues posed by AI is emphasised, to train students as both technology users and moral guardians, promoting innovation while safeguarding ethics and social responsibility.Journal of Information System and Technology Management["Quan Wen", "Hao Ding"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/5edfed609771342534ea4ca41c385a3b51af4fdc 8cf1e8200639a1de515429dc7cb4f4272c410e04Artificial Intelligence (AI) has rapidly gained widespread attention and has made unprecedented strides in recent years, significantly influencing various sectors, especially the medical field. Its applications have revolutionized healthcare by assisting doctors in making faster, more accurate, and data-driven decisions. This paper aims to provide an in-depth analysis of AI's role in medical imaging, focusing on its applications in interpreting X-rays, CT scans, and MRIs. These imaging techniques utilize cutting-edge image recognition algorithms, with Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) being the most prominent. AI not only improves diagnostic precision but also reduces human error, leading to better patient outcomes. Furthermore, the paper discusses current trends in AI-driven medical imaging and explores potential future directions, emphasizing how AI could continue to advance medical diagnostics and treatment planning, paving the way for more personalized and effective healthcare solutions.Highlights in Science Engineering and Technology160An in-depth analysis of AI's role in medical imaging is provided, focusing on its applications in interpreting X-rays, CT scans, and MRIs, with Generative Adversarial Networks and Convolutional Neural Networks being the most prominent.Highlights in Science, Engineering and Technology["Jialin Song"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/8cf1e8200639a1de515429dc7cb4f4272c410e04 e55c9a92ec2d38cba9f4d15aeabc56c2067195c4Our society increasingly benefits from Artificial Intelligence (AI). Unfortunately, more and more evidence shows that AI is also used for offensive purposes. Prior works have revealed various examples of use cases in which the deployment of AI can lead to violation of security and privacy objectives. No extant work, however, has been able to draw a holistic picture of the offensive potential of AI. In this SoK paper we seek to lay the ground for a systematic analysis of the heterogeneous capabilities of offensive AI. In particular we (i) account for AI risks to both humans and systems while (ii) consolidating and distilling knowledge from academic literature, expert opinions, industrial venues, as well as laypeople -- all of which being valuable sources of information on offensive AI. To enable alignment of such diverse sources of knowledge, we devise a common set of criteria reflecting essential technological factors related to offensive AI. With the help of such criteria, we systematically analyze: 95 research papers; 38 InfoSec briefings (from, e.g., BlackHat); the responses of a user study (N=549) entailing individuals with diverse backgrounds and expertise; and the opinion of 12 experts. Our contributions not only reveal concerning ways (some of which overlooked by prior work) in which AI can be offensively used today, but also represent a foothold to address this threat in the years to come.arXiv.org00The contributions of this SoK paper reveal concerning ways in which AI can be offensively used today, but also represent a foothold to address this threat in the years to come.ArXiv["Saskia Laura Schr\u00f6er", "Giovanni Apruzzese", "Soheil Human", "P. Laskov", "Hyrum S. Anderson", "Edward W. N. Bernroider", "Aurore Fass", "Ben Nassi", "Vera Rimmer", "F. Roli", "S. Salam", "Ashley Shen", "Ali Sunyaev", "Tim Wadwha-Brown", "Isabel Wagner", "Gang Wang"]2024-12-24T00:00:00https://www.semanticscholar.org/paper/e55c9a92ec2d38cba9f4d15aeabc56c2067195c4 ba626f2d85aac73247bb5a1389c6ab0844eb5545This study explores the evolution of AI-driven product management in the retail industry, focusing on product quality, customer retention, and revenue growth. From the extensive case study of ChemScene, a biopharma company, we used advanced AI models that integrate LSTM neural networks, Q-learning, and genetic algorithms. Analysis of 18 months of data revealed remarkable improvements across key performance metrics. The sales volume increased by 38.1%, while the sales volume decreased by 77.1%. Customer loyalty was significantly boosted, increasing retention from 82% to 91%. These improvements translated into profitable results, including a 20% increase in revenue and a 31.3% jump in operating profit. Our findings not only validate the effectiveness of machine learning in inventory management but also provide new insights into AI's broader impact on customer relationships. And the market as a whole. This research provides a useful model for retailers considering AI adoption, paving the way for future research in this rapidly changing industry.Online (Weston, Conn.)406This study explores the evolution of AI-driven product management in the retail industry, focusing on product quality, customer retention, and revenue growth, using advanced AI models that integrate LSTM neural networks, Q-learning, and genetic algorithms from ChemScene, a biopharma company.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)["Xiaowen Ma", "Wang Zeyu", "Xin Ni", "Gang Ping"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/ba626f2d85aac73247bb5a1389c6ab0844eb5545 00ee740489b4a44f7e56e8be019afde6e1155a48The article examines issues of liability for the actions of artificial intelligence (AI) in the field of healthcare. In the context of the active introduction of AI into medical practice, legal and ethical problems arise related to the definition of entities responsible for errors and damage caused by AI systems. The article analyzes existing approaches to the distribution of responsibility between developers, medical institutions, doctors and operators of information systems. It also raises issues of AI autonomy and the complexity of applying traditional legal mechanisms. The authors propose revising legislative approaches to create a fairer system of distributing legal responsibility for the actions of AI in healthcare.Bulletin of Chelyabinsk State University. Series: Law00The authors propose revising legislative approaches to create a fairer system of distributing legal responsibility for the actions of AI in healthcare and raises issues of AI autonomy and the complexity of applying traditional legal mechanisms.Bulletin of Chelyabinsk State University Series Law["I. R. Khmelevskoi", "N. Kalashnikov"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/00ee740489b4a44f7e56e8be019afde6e1155a48 2f7f4b17da8c9f03e39b2895d9b825fff93e3d54Soldier performance cannot be separated from the role of the organization. This research investigates the influence of the development of defense equipment and artificial intelligence on soldier performance. Also investigate whether organizational commitment is a good mediator. This study conducted a survey at the Surabaya Marine Corps 2nd Amphibious Reconnaissance Battalion. Using a saturated sampling method, and a research sample of 102 respondents, the data was then analyzed using Structural Equation Modeling. The findings show that the development/modernization of defense equipment and artificial intelligence makes a positive contribution to organizational commitment. Research also proves that organizational commitment mediates the relationship between artificial intelligence and soldier performance, while it does not mediate the relationship between defense equipment development and soldier performance. This study contributes to understanding organizational behavior by using a framework for the development of defense equipment and artificial intelligence in the 2nd Amphibious Reconnaissance Battalion of the Surabaya Marine Corps, which can also be applied to other general organizations.EKONOMIKA45 :  Jurnal Ilmiah Manajemen, Ekonomi Bisnis, Kewirausahaan00The findings show that the development/modernization of defense equipment and artificial intelligence makes a positive contribution to organizational commitment, and proves that organizational commitment mediates the relationship between artificial intelligence and soldier performance, while it does not mediate the relationship between defense equipment development and soldier performance.EKONOMIKA45 :  Jurnal Ilmiah Manajemen, Ekonomi Bisnis, Kewirausahaan["Bayu Abiantoro", "Siti Mujanah", "Achmad Yanu Alif Fianto"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/2f7f4b17da8c9f03e39b2895d9b825fff93e3d54 81215faae8cacbd85346ce875373d68b646cb8d8This study aims to test the effectiveness of AI (Artificial Intelligence), which took a new turn after ChatGPT as a tool for the social sustainability of academics in the Egyptian private higher education sector. Digitalization reflects the intensity of artificial intelligence usage in enhancing the performance of professors and its reflection on their quality of life. Moreover, the degree of facilitation and progress can provide educators with the best educational experience they can provide to students. This study relies on two theories and their backgrounds. The first is the theory of project-based learning as a tool for enhancing the quality of education using AI. The second is Martec’s Law, which is a derivation of the law of accelerating returns. Two main assumptions are addressed in this study, the first is: Using artificial intelligence as a tool that can facilitate, enhance, and provide a variety of ways for professors to engage their students online and in class. The second is based on measuring the degree of effectiveness and performance advancement seen by professors in their social sustainability. Enhanced experience of the students will be measured by their rates of attendance and engagement. The amount of impact on project-based learning is going to be measured by the degree of reliance of professors on digital learning methods and their reliance on using artificial intelligence in constructing them. Data will be provided by professors through a constructed survey. The professor’s social sustainability will be measured by quality time saved and related career advancement. Data collection depends on testing faculty members at 4 private universities in the greater Cairo area. A cross-sectional survey was conducted on a single shot in time. Results showed that we accepted the hypotheses and that there is a strong relevance between the variables.Cybrarians Journal80The effectiveness of AI (Artificial Intelligence) took a new turn after ChatGPT as a tool for the social sustainability of academics in the Egyptian private higher education sector and results showed that there is a strong relevance between the variables.Cybrarians Journal["Hala Bakry", "Rasha Ismail", "Mazen Khalil"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/81215faae8cacbd85346ce875373d68b646cb8d8 5fcb557aedf2c31f11b211cd8a816d6cd439dd1dThe article analyzes the differences between classical and non-classical logical concepts. The features and possibilities of transformational logic are considered. Specific details and proposals are analyzed, including using the example of the functioning of ChatGPT, where the use of transformational logic tools, namely rules that clarify the meaning of explicit forms of thought and derive new judgments from explicit forms of thought, as well as complex rules with the help of which it is possible to simultaneously solve the two problems mentioned, would significantly expand the possibilities and improve the operation of artificial intelligence (AI) systems. Non-classical logical systems, partly critical of classical logic systems, not only open up new prospects for studying thought structures, but also consider issues that go beyond the subject area of classical logical systems, adding the possibilities of logical science. In terms of interpreting the phenomenon of complementarity of logical concepts, the conception of polylogic by G. Brutian is valuable and the application of his ideas will give positive results from the point of view of further development of artificial intelligence tools.wisdom30The article analyzes the differences between classical and non-classical logical concepts and suggests that the conception of polylogic by G. Brutian is valuable and the application of his ideas will give positive results from the point of view of further development of artificial intelligence tools.WISDOM["Hovhannes O. Hovhannisyan"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/5fcb557aedf2c31f11b211cd8a816d6cd439dd1d 90835cce66fba9a005a1caaf6cec2dcdf8013b32This work seeks to understand the transformative role of Artificial Intelligence in business practices to enhance safety and efficiency at operations. Using a mixed-method approach, the study integrates primary data collected from interviews and surveys with information gathered from scholarly articles and reports from industries. According to the research results, it has been possible to reduce equipment failures by 30%, hazard prediction accuracy attained is 85%, and safety compliance by AI monitoring systems by 20%. Operational efficiency reveals a 25% decrease in inefficiencies, an 18% reduction in energy consumption, and a 15% increase in customer satisfaction from AI-powered solutions. The study underscores the potential of AI in driving safer, effective, and ethical practices towards creating workable frameworks for sustainable business operations.Computer fraud & security00The study underscores the potential of AI in driving safer, effective, and ethical practices towards creating workable frameworks for sustainable business operations.Computer Fraud and Security["Yupei Du"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/90835cce66fba9a005a1caaf6cec2dcdf8013b32 68d01333b442253ff12afeeed94f47dfc14ac4f1This article highlights the influence of artificial intelligence (AI) on automating the translation of technical terminology in project management. In particular, the article focuses on the use of machine translation (MT), which, thanks to their ability to process large amounts of data, cloud computing, and advanced algorithms, increase the accuracy and speed of translation of technical documentation. It is noted that AI helps to reduce translation costs and improves the consistency of terminology, which is critical for the successful completion of projects with tight deadlines. Nevertheless, challenges in this field are also identified, including the high error rate in translating highly specialized terms and the ongoing need to enhance systems to meet the varied demands of users. Different translation approaches are discussed, including neural machine translation (NMT), statistical machine translation (SMT) and rule-based machine translation (RBMT), which ensure high accuracy and smoothness of translation. Integrating AI into project management systems can also optimise communication in multilingual teams. The article highlights the growing role of AI in the translation of technical terms, which has great potential to improve efficiency in project management.Scientific Journal of Polonia University50The article focuses on the use of machine translation (MT), which, thanks to their ability to process large amounts of data, cloud computing, and advanced algorithms, increase the accuracy and speed of translation of technical documentation.Scientific Journal of Polonia University["Oleksandr Lysychenko"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/68d01333b442253ff12afeeed94f47dfc14ac4f1 a5788b618e8fefcdc4590012024d8097ba8ba948This article examines the evolution of face protection products, starting from combat drawings and masks in ancient times and ending with modern helmets using advanced technologies such as artificial intelligence. Various historical stages are analyzed, including ancient civilizations, antiquity, the Middle Ages, as well as the XX and XXI centuries, when new threats such as firearms and mine explosive devices appeared. The key stages of the development of military helmets and masks are described, as well as their impact on the safety of soldiers and the improvement of tactical mobility. Attention is also paid to technical innovations in helmet design and their use in modern conflicts.Bulletin of Pirogov National Medical &amp; Surgical Center00The key stages of the development of military helmets and masks are described, as well as their impact on the safety of soldiers and the improvement of tactical mobility.Bulletin of Pirogov National Medical &amp; Surgical Center["S. A. Yepifanov", "L. A. Krainyukova", "Yu. D. Mironyuk", "P. E. Krainyukov", "S. Matveev"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/a5788b618e8fefcdc4590012024d8097ba8ba948 7665b62d7503d1a1e4085a7a35feecba7a52dc76Artificial Intelligence (AI) is transforming Business Intelligence (BI) by helping organizations make better decisions through predictive and prescriptive insights. However, many AI models are complex and difficult to understand, especially for non-technical decision-makers. This creates challenges in trusting and using AI-driven outputs effectively. To address this, we propose an Explainable AI (XAI) framework specifically designed to make AI insights clearer, more transparent, and easier to act on for business users. To address these issues, this article proposes a tailored Explainable AI (XAI) framework specifically designed for BI contexts. The framework incorporates five key components: interpretability guidelines, visualization techniques, natural language processing (NLP) interfaces, bias detection and mitigation tools, and role-specific customization. Conceptual validation through hypothetical scenarios in industries such as telecom, banking, and retail, we demonstrate how the framework reduces confusion, builds trust, and helps decision-makers confidently use AI to improve outcomes. This work relies on secondary data from reputable academic research and industry case studies to propose the framework. While this framework shows significant benefits, we acknowledge some limitations, such as the need for high-quality data, the resources required for initial setup, and frequent updates in fast-changing business environments. Future research should focus on creating advanced visual tools, real-time bias detection systems, and ways to measure the success of explainability frameworks. By bridging the gap between AI’s technical complexity and users’ understanding, this framework empowers decisionmakers to use AI insights effectively, making organizations smarter, more ethical, and more data-driven.International Journal of Innovative Research in Computer and Communication Engineering00A tailored Explainable AI (XAI) framework specifically designed for BI contexts that empowers decisionmakers to use AI insights effectively, making organizations smarter, more ethical, and more data-driven.International Journal of Innovative Research in Computer and Communication Engineering["Ahmad Jidda"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/7665b62d7503d1a1e4085a7a35feecba7a52dc76 415550e85fca931a58a54f2c9ae86783976f99b8Artificial intelligence (AI) poses a serious challenge for data protection and ethics. Whereas ethics is based on the moral and religious understanding of good and evil, adherence to religious norms is vital for many nations. In Islam, privacy is a fundamental value that is deeply rooted in the principles of Shariah law and focuses on the dignity of the individual, personal boundaries and moral behaviour. Therefore, the use of AI technologies by Islamic nations raises concerns about the compatibility of these traditional privacy norms with ethical frameworks for AI. To ensure that the field of AI is not only innovative but also morally and religiously acceptable, an optimal balance between ethical governance and technological advancement must be maintained. This study examines the approaches of the Kingdom of Saudi Arabia and the United Arab Emirates in integrating Islamic ethics and privacy rules into their ethical frameworks for AI. The study utilised qualitative methods, including descriptive and content analyses, combined with a comparative approach from data collected through documentary method. The transition from Islamic ethics to digital Islamic ethics experienced the significant influence of Islamic business ethics, which places a strong emphasis on justice, accountability and human dignity. Future studies on digital Islamic ethics need to follow the pace of technological advancement and identify the future ethical risks and gaps. The Islamic concept of privacy is unique. It differs from the Western legal approach in that it emphasises the importance of adherence to Shariah teachings and provides a strict and clear explanation for desirable behaviour in doubtful and ambiguous cases. The digital age requires the evolution of the Islamic concept of privacy to address the ethical and legal challenges posed by technological advances. The ethical frameworks for AI adopted by the Kingdom of Saudi Arabia and the United Arab Emirates illustrate the integration of cultural and ethical values with technological innovation. The governments attach great importance to harmonising security and privacy with Islamic principles. Both countries demonstrate how universal ethical rules for AI can be adapted to national circumstances. A customised ethical framework will therefore not only promote innovation and technological progress, but also ensure that these are in line with national ethical standards, cultural and religious values and societal needs.Malaysian Journal of Syariah and Law720This study examines the approaches of the Kingdom of Saudi Arabia and the United Arab Emirates in integrating Islamic ethics and privacy rules into their ethical frameworks for AI, and demonstrates how universal ethical rules for AI can be adapted to national circumstances.Malaysian Journal of Syariah and Law["Ella Gorian", "Noor Dzuhaidah Osman"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/415550e85fca931a58a54f2c9ae86783976f99b8 398347168bdb8f0ad95df03666c453a3ebe4ee09The article presents the concept of implementing the artificial intelligence (AI) essentials in the informatics course of secondary general education. The authors propose a structured and multilevel approach to AI, covering both basic and advanced levels of the subject. The concept is organically embedded in four main sections of the informatics course: digital literacy, theoretical foundations of informatics, algorithms and programming, and information technologies. Special attention is paid to the practice-oriented aspect of learning. This includes working with real data sets, programming in Python, and using specialized libraries for machine learning and data analysis. Specific examples of practical tasks are given to demonstrate the application of various AI methods and algorithms: from simple data classification to the creation and training of neural networks. The concept provides ample opportunities for project and extracurricular activities, which contribute to developing students’ creative, analytical, and research skills. An important aspect of the proposed approach is its compliance with modern requirements of the digital economy. The article emphasizes the need to teach schoolchildren competencies in AI and data analysis, which will be in demand in their future professional activities. It presents criteria for selecting learning content, exemplary thematic planning for basic and advanced levels. The authors substantiate the importance of the systematic AI study in secondary general education and point out the need to develop appropriate teaching and learning support.Informatics and Education160The article presents the concept of implementing the artificial intelligence essentials in the informatics course of secondary general education and presents criteria for selecting learning content, exemplary thematic planning for basic and advanced levels, and the need to teach schoolchildren competencies in AI and data analysis.Informatics and education["S. Karakozov", "E. A. Samokhvalova", "N. I. Ryzhova"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/398347168bdb8f0ad95df03666c453a3ebe4ee09 3bc79f6a6b341aa5f36dd0b111bed8d7824099dbOver the past decade, the Nigerian government has sought to leverage Artificial Intelligence (AI) to drive socio‐economic transformation and improve the welfare of its citizenry. Recent initiatives, such as the establishment of the National Centre for AI and Robotics (NCAIR) and the development of several strategic AI policies, highlight the country's commitment to this objective. This article explores the often‐overlooked issue of how the Nigeria's copyright regime hinders these initiatives, revealing that the regime permits only fair dealing and the transient or incidental reproductions of copyrighted materials for limited technological purposes. This study argues that this regime is unduly restrictive for algorithmic training and risks stifling AI innovation and the development of machine‐learning models in Nigeria. It recommends adopting a bespoke text and data mining (TDM) exception tailored to Nigeria's needs, allowing the use of copyrighted works for training AI models and machine learning activities within defined limits. Drawing on comparative analyses of copyright frameworks in jurisdictions such as Singapore, Japan, the United Kingdom, and the European Union, this study demonstrates that the proposed TDM exception aligns with the three‐step test under international copyright conventions. For instance, the exception is limited to specific users and types of reproductions, applies only to internalized and transformative reproductions, and avoids traditional methods of exploiting copyrighted works that prejudice the legitimate interests of rightsholders. The ultimate goal of this exception is to recalibrate Nigeria's copyright system to justly balance AI innovation with authors' rights, aligning it with foundational principles of the international copyright system in an era of rapid technological advancements.Journal of World Intellectual Property00This study argues that this regime is unduly restrictive for algorithmic training and risks stifling AI innovation and the development of machine‐learning models in Nigeria, and recommends adopting a bespoke text and data mining exception tailored to Nigeria's needs.The Journal of World Intellectual Property["Morris K. Odeh"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/3bc79f6a6b341aa5f36dd0b111bed8d7824099db c3f8bb2331d7a3ab0b83e0d597b86c622a2b36cf Purpose This study aims to explore the constituents of artificial intelligence (AI)-augmented knowledge management (AIKM) capability and its impact on clinical performance (CP) in the health-care sector. It further examines the mediating role of absorptive capacity (Abs Cap) and discusses the implications of these findings for marketing strategies, highlighting how enhanced CP through AIKM can lead to more effective and patient-centered marketing approaches. Design/methodology/approach This research uses a mixed-method design. A qualitative study through semi-structured interviews was conducted to explore the facets of AIKM. The synthesis of qualitative findings infused with the relevant literature to develop a hypothesized model of AKM, Abs cap and CP metrics (e.g. diagnostic accuracy, patient satisfaction and treatment effectiveness). A survey of health-care professional in India was conducted to assess the proposed model by using structural equation modeling (PLS-SEM). Findings The results demonstrate a significant positive relationship between AIKM and CP. Moreover, Abs Cap mediates this relationship partially, highlighting its crucial role in translating improved knowledge access and analysis enabled by AI into enhanced clinical outcomes. Research limitations/implications The findings suggest that health-care organizations should invest in developing AIKM alongside strengthening Abs cap to maximize the positive impact of AI on CP and ultimately improve patient care. Future research can explore specific AIKM components and Abs cap facets influencing different aspects of CP. Originality/value This study represents a pioneering effort to conceptualize AIKM within the health-care context and empirically establish it as a higher-order factor. The inclusion of marketing strategies underscores the potential of AIKM not only in improving clinical outcomes but also in transforming health-care marketing. The mediating role of Abs Cap emphasizes the importance of organizational structures and processes that facilitate the absorption and utilization of knowledge, thereby contributing to both clinical and marketing excellence. Journal of Knowledge Management840The findings suggest that health-care organizations should invest in developing AIKM alongside strengthening Abs cap to maximize the positive impact of AI on CP and ultimately improve patient care.J. Knowl. Manag.["Pradeep Kumar"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/c3f8bb2331d7a3ab0b83e0d597b86c622a2b36cf fc86c9a402c2b5bf461b7cc2733bd324fcb3b809Research works on topics such as; Fairness in Agreement with European Values: An Interdisciplinary Perspective on AI Regulation or Decolonial AI Alignment: Openness, Viśesa -Dharma and Including Excluded Knowledges, generated the motivation to search for Artificial intelligence (AI) alignment with Turkish and Islamic Values. The driving force to this research work is the fact that all of the algorithmic decision-making systems include bias to some extend and the non-western world needs to construct its own value-based technological and sociological development models since there is not much belief left in the so-called international justice or the so-called democratic values. This research includes examination of brief information about the fundamentals of big data, algorithms, and artificial intelligence. Importance of thick data and digital anthropology is emphasized. Misuse and abuse AI has been identified as one of the most important challenges. Vygotsky’s arguments on social learning, social construction of technology theory and worldview theory may provide some of the arguments to construct the idea of an AI approach may be developed in accordance with Turkish and Islamic values. Decolonial AI arguments and fairness in AI approaches have also been utilized to empower our argument. Lastly, brief information on Turkish and Islamic values were presented, limited to the scope of this research.Marmara Üniversitesi İlahiyat Fakültesi Dergisi00This research includes examination of brief information about the fundamentals of big data, algorithms, and artificial intelligence and brief information on Turkish and Islamic values were presented, limited to the scope of this research.Marmara Üniversitesi İlahiyat Fakültesi Dergisi["Yusuf F\u0131r\u0131nc\u0131"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/fc86c9a402c2b5bf461b7cc2733bd324fcb3b809 0afd3e7a29701cae087031a9ce827d4467d9c384The integration of Artificial Intelligence (AI) in project management has emerged as a transformative approach, revolutionizing traditional practices by enhancing efficiency, decision-making, and risk management. Despite its potential, organizations face significant challenges, including high implementation costs, concerns over data privacy, and resistance to change, which hinder effective adoption. The purpose of this study is to explore emerging trends, key applications, and challenges of AI in project management, while also evaluating its impact on improving risk management, resource allocation, and decision-making in complex projects. The study employs a systematic literature review (SLR) methodology, adhering to the PRISMA protocol, to analyze peer-reviewed articles from MDPI, IEEE, Science Direct, and Emerald databases, published between 2018 and 2024. Keywords combined with Boolean operators were used to filter relevant studies, ensuring a balanced and focused selection of high-quality publications. The results reveal AI's capacity to proactively identify risks, adapt to dynamic project environments, and optimize resource allocation, ultimately enhancing decision-making efficiency and project outcomes. However, challenges such as implementation costs and resistance to organizational change remain critical barriers. The implications suggest that while AI significantly enhances project management, addressing these challenges is essential for broader adoption and scalability. This research concludes that AI is a game-changer in project management, offering insights into emerging trends and critical challenges. Future research should focus on developing scalable, cost-effective AI solutions to overcome adoption barriers, thereby extending the benefits of AI integration across diverse industries.TIERS Information Technology Journal210It is concluded that AI is a game-changer in project management, offering insights into emerging trends and critical challenges, and future research should focus on developing scalable, cost-effective AI solutions to overcome adoption barriers.TIERS Information Technology Journal["Irshad Ahmed Hashimzai", "Mohammad Qias Mohammadi"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/0afd3e7a29701cae087031a9ce827d4467d9c384 463492d97b5152f769753a23a255b6a621e21566The object of this study is multi-layered cybersecurity systems for detecting and countering advanced persistent threats through the integration of machine learning technologies, artificial intelligence, and multi-layered security systems. The task relates to the need to design adaptive detection systems capable of effectively responding to new and modified threats while improving accuracy and minimizing delays. An integrated approach was devised in the study, which combines conventional detection methods (signature analysis, correlation rules) with modern technologies such as machine learning and Artificial Intelligence assistants. Each layer of the system showed varying levels of effectiveness: for example, antivirus solutions were most effective at detecting known threats but failed to cope with modified threats, which were detected by correlation rules. Machine learning proved most effective at detecting fileless attacks and anomalous activity that other tools could not detect. It is through the combination of these methods that the detection system proved to be effective, providing a high level of protection. The results are due to the efficiency of combining several layers of defense, in which each subsequent layer compensates for the shortcomings of the previous one. Antivirus solutions detected 100 % of known threats, while correlation rules identified all modified malicious files. Overall, the system was able to detect 98 % of malicious files and 99 % of tactics, techniques, and procedures used in advanced persistent threats attacks. A unique feature of the research is the integration of the Artificial Intelligence assistant, which automates threat analysis processes and speeds up response times by leveraging historical data and the context of past incidents. This reduces the workload on cybersecurity specialists and improves the overall effectiveness of the detection system, allowing for the quick identification of new threats and a reduction in false positives. Practical application of the results is possible in various critical sectors, including financial institutions, government organizations, and energy companies. The system demonstrates high flexibility and scalability, making it possible to easily adapt to different infrastructures and types of threatsEastern-European Journal of Enterprise Technologies160The integrated approach was devised in the study, which combines conventional detection methods (signature analysis, correlation rules) with modern technologies such as machine learning and Artificial Intelligence assistants, which automates threat analysis processes and speeds up response times.Eastern-European Journal of Enterprise Technologies["Danyil Zhuravchak", "Maksym Opanovych", "Anastasiia Tolkachova", "Valerii Dudykevych", "Andriian Piskozub"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/463492d97b5152f769753a23a255b6a621e21566 5898f0eed508f54c7292c23e71dd8b25c11066c7Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI’s current applications, challenges, and future directions in fetal and pediatric echocardiography.Children730A review of AI’s current applications, challenges, and future directions in fetal and pediatric echocardiography highlights the need to address challenges to fully integrate AI into practice.Children["Alan P Wang", "T. Doan", "Charitha Reddy", "P. Jone"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/5898f0eed508f54c7292c23e71dd8b25c11066c7 75ef110dbc1b70f216426b491674ff23a4bae7b4The article dwells on the origins of the concept of artificial intelligence as applied to the library with its principles of fast search. The integrity of such knowledge, corresponding to the integrity of the products of neural networks, is characterized. The history of science has seen not only the management of discoveries, but also the operation of such specific production, manifested vividly in the activities of women mathematicians and women philosophers. They are operators rather than inventors of networks, who integrate networks and ensure that different epistemic regimes can be integrated. In this paper, the historical outline of the emergence of the library as a model of artificial intelligence is discussed. The ancient regulations of Aristotle and the Library of Alexandria stipulated a purely alphabetical rather than thematic ordering principle; at that time, the production of knowledge required visual aids, integral formulae, and the decomposition of knowledge into indications of sources without remainder. An ideal operator of such a library was Hypatia of Alexandria. Further development of libraries involved the production of corporate, religious, and national knowledge, unlocked by new operators through returning universal meaning to the visual representation of knowledge. Currently, such universal visual representation is characteristic of images generated by artificial intelligence. Knowledge production modes require functions of both direct producers and recipients of knowledge. The development of sciences during different periods has made it necessary to encompass the knowable as well as to recognize its agency and ability to become ready for use. We see the origins of such agency in primitive initiation and view the library as a mechanism of potential initiation. In that case, we need not only knowledge distributors that make potential initiation real, but also knowledge operators that make it possible to move from harsher to softer modes of initiation. The article points out the traits that an individual acting as a reliable operator of such knowledge can have. Recognizing the agency of libraries allows us to plan the use of neural networks, including for countering fake news.Arctic00The article points out the traits that an individual acting as a reliable operator of such knowledge can have, and sees the origins of such agency in primitive initiation and view the library as a mechanism of potential initiation.Vestnik of Northern (Arctic) Federal University. Series Humanitarian and Social Sciences["A. V. Markov", "\u041e. A. Shtayn"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/75ef110dbc1b70f216426b491674ff23a4bae7b4 315280295734c1aa62cd726516e92288e5a11e75Artificial Intelligence (AI) is rapidly emerging as a transformative force in diagnostic medicine, reshaping how healthcare professionals detect and manage diseases. By leveraging sophisticated machine learning and deep learning algorithms, AI can efficiently analyse extensive datasets, including medical images and patient records, with exceptional speed and accuracy. This capability not only improves the precision of diagnoses but also aids in the early identification of conditions such as cancer and heart disease, leading to more tailored treatment strategies. Furthermore, AI tools are proving essential in minimizing human error and optimizing workflows, enabling healthcare providers to devote more time to patient care. As AI continues to be integrated into clinical practice, it promises to enhance patient outcomes while meeting the increasing demands placed on healthcare systems globally.International Journal of Research and Review in Applied Science, Humanities, and Technology00AI capability not only improves the precision of diagnoses but also aids in the early identification of conditions such as cancer and heart disease, leading to more tailored treatment strategies.International Journal of Research and Review in Applied Science, Humanities, and Technology["Rashi Sahay", "Ajeet Singh", "Muskan Aggarwal"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/315280295734c1aa62cd726516e92288e5a11e75 1820e492c3d0a619e60200e5277c8d3229e8fac3The integration of artificial intelligence (AI) and machine learning (ML) into pharmacovigilance (PV) marks a transformative step towards enhancing drug safety and patient outcomes. With their unparalleled ability to analyze vast and complex datasets, AI/ML technologies offer unprecedented opportunities to revolutionize adverse drug event (ADE) monitoring, signal detection, and causality assessment. This editorial explores the state-of-the-art applications, key challenges, and actionable strategies to unlock the full potential of AI in PV.GenoMed Connect20The state-of-the-art applications, key challenges, and actionable strategies to unlock the full potential of AI in PV are explored.GenoMed Connect["Osama A. Badary"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/1820e492c3d0a619e60200e5277c8d3229e8fac3 816628855c6e942124af48cba4e19d21c0f6b4adThe 5.0 era is a milestone in all forms of technological development in various fields. The emergence of a system that provides wide access and is able to easily solve every problem with the right solution is called artificial intelligence. The system has now entered the scope of education. Its use is in the administration and implementation of learning, by providing convenience for teachers in presenting innovative material content. Artificial intelligence (AI) has a significant impact on the implementation of independent curriculum-based learning. The curriculum emphasizes student-centered learning, so the use of AI can be an alternative to developing interesting and fun teaching methods and media so that students get a meaningful learning experience. This research aims to determine the impact of the use of AI on independent curriculum-based learning. Then, using a descriptive qualitative research method with literature review analysis. Furthermore, the results of the research are that artificial intelligence is able to improve student understanding through more interactive learning, because in the implementation of learning using various methods, models, and media adapted to student needs. The impact of the use of artificial intelligence (AI) as a form of technological advancement in learning includes several important aspects, namely 1) in learning using artificial intelligence (AI) to improve learning personalization and 2) the use of artificial intelligence (AI) is in line with independent curriculum-based learning that carries the concept of independent learning. Thus, artificial intelligence (AI) can be used as a teacher's learning assistant to strengthen and develop the preparation of effective teaching tools.International Journal of Multidisciplinary Sciences180The results of the research are that artificial intelligence is able to improve student understanding through more interactive learning, because in the implementation of learning using various methods, models, and media adapted to student needs.International Journal of Multidisciplinary Sciences["Pranistya Dwi Ayu Mutiara Ningtyas", "I. Haris"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/816628855c6e942124af48cba4e19d21c0f6b4ad 3f2448a9f3bdf4ff8b8a39d895be2560b3e8f8f7As generative artificial intelligence (generative AI) technology rapidly develops, new tools are being introduced to the market, and its use in many areas, from education to healthcare, is quickly increasing. Therefore, ethical research must keep pace with these developments and address the new challenges. In this way, AI can benefit society and prevent potential harm. This study was conducted to identify ethical issues in the use of generative AI, highlight prominent issues, and provide an overview through a systematic literature review. A systematic search was conducted in Scopus, Web of Science, and ScienceDirect databases to retrieve articles examining ethical aspects of generative AI with no year restrictions. The search terms were "generative artificial intelligence," "generative AI," "GenAI," or "GAI," with the combination of "ethic," "ethics," or "ethical." Studies were selected using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Forty-three articles were included in the review after the screening process. According to the research results, the "justice and fairness" principle was emphasized in all the articles examined. The least examined ethical principles were the principle of "solidarity", which expresses unity in society or group, and the principle of "dignity", which means the value an individual feels for himself and his rights. The authors of the 43 articles are mainly from the United States (n = 31), followed by China (n = 15) and the United Kingdom (n = 13). Of the 43 articles reviewed, 41 mentioned ChatGPT, albeit as an example. This study reviews the literature on the ethical use of generative AI and presents challenges and solutions.Business &amp; Management Studies: An International Journal00The "justice and fairness" principle was emphasized in all the articles examined and the least examined ethical principles were the principle of "solidarity", which expresses unity in society or group, and the principle of "dignity", which means the value an individual feels for himself and his rights.Business &amp; Management Studies: An International Journal["Esra CENG\u0130Z TIRPAN"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/3f2448a9f3bdf4ff8b8a39d895be2560b3e8f8f7 d155af4c4a21593cb72c17856d81169d5f77b7f9This study aimed to compare the exam performances of ChatGPT Plus and Google Gemini Advanced in tourism management, tourism marketing, and tourism economics courses with the exam performance of undergraduate students. One hundred fifty students studying at Harran University Faculty of Tourism and completing their education in these three courses were selected and included in the exams with artificial intelligence models. In the exam, 25 questions were created for each course by academicians who are experts in their fields. The results show that ChatGPT has the highest overall accuracy rate and the lowest number of wrong answers. ChatGPT gave 21 correct answers in the tourism economics exam, Google Gemini 18, and students 16.6. In the tourism marketing exam, ChatGPT 19 and Google Gemini 18, students gave 14.9 correct answers. In the tourism management exam, ChatGPT answered 22 questions correctly, Google Gemini answered 14 questions, and students answered 16.3 questions correctly. In addition, the questions were categorised as short, long, easy, medium difficulty, complex questions, negative sentences, and scenario questions. When the results were analysed, ChatGPT was more successful in all categories. Although artificial intelligence language models are more effective than undergraduate students in certain exam conditions, this study underscores the need for further research to optimise and validate the use of these technologies in education. In addition, as a result of the research, it is thought that artificial intelligence language models can play a transformative role in tourism education in the future. An important finding has also emerged that ensuring the ethical and practical use of artificial intelligence technologies in academic settings requires responsible integration, human oversight, and more validation studies.Business &amp; Management Studies: An International Journal690Although artificial intelligence language models are more effective than undergraduate students in certain exam conditions, this study underscores the need for further research to optimise and validate the use of these technologies in education.Business &amp; Management Studies: An International Journal["L. G\u00f6kta\u015f"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/d155af4c4a21593cb72c17856d81169d5f77b7f9 ee0e5eab57283f30acdd7c5507d8083d01de1d6dThe extensive application of big data and artificial intelligence technologies has brought both opportunities and challenges to national security. Building and improving the legal system for national security is a necessary condition for a sound national security system. This article analyzes the potential data security and information security issues that may arise from big data and artificial intelligence technologies, and clarifies the deficiencies in relevant laws, systems, and policies. By comparing the legislative situations of big data and artificial intelligence technologies in China and the West, it proposes specific strategic suggestions from three perspectives: strengthening the construction of laws and regulations, establishing and improving regulatory mechanisms, and enhancing the publicity of cyber security.Highlights in Science Engineering and Technology00By comparing the legislative situations of big data and artificial intelligence technologies in China and the West, this article proposes specific strategic suggestions from three perspectives: strengthening the construction of laws and regulations, establishing and improving regulatory mechanisms, and enhancing the publicity of cyber security.Highlights in Science, Engineering and Technology["Yihe Gao"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/ee0e5eab57283f30acdd7c5507d8083d01de1d6d 0226d2f30eee56dcf047c83a73f5f93413280916As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.Sheng li xue bao : [Acta physiologica Sinica]00This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing, to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security.Sheng li xue bao : [Acta physiologica Sinica]["Cong Li", "Xiaoyan Zhang", "Yun-Hong Wu", "Xiao-Lei Yang", "Hua-Rong Yu", "Hong-Bo Jin", "Ying-Bo Li", "Zhao-Hui Zhu", "Rui Liu", "Na Liu", "Yi Xie", "Lin-Li Lyu", "Xin-Hong Zhu", "Hong Tang", "Hong-Fang Li", "Hong-Li Li", "Xiang-Jun Zeng", "Zai-Xing Chen", "Xiao-Fang Fan", "Yan Wang", "Zhi-Juan Wu", "Zun-Qiu Wu", "Ya-Qun Guan", "Ming-Ming Xue", "Bin Luo", "Ai-Mei Wang", "Xin-Wang Yang", "Ying Ying", "Xiu-Hong Yang", "Xin-Zhong Huang", "Ming-Fei Lang", "Shi-Min Chen", "Huan-Huan Zhang", "Zhong Zhang", "Wu Huang", "Guo-Biao Xu", "Jia-Qi Liu", "Tao Song", "Jing Xiao", "Yun-Long Xia", "You-Fei Guan", "Liang Zhu"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/0226d2f30eee56dcf047c83a73f5f93413280916 4c897aca0d8a2c0826bf573edbdadd64fd4b4c35The Arab Journal for Quality Assurance in Higher Education00The Arab Journal for Quality Assurance in Higher Education[]2024-12-25T00:00:00https://www.semanticscholar.org/paper/4c897aca0d8a2c0826bf573edbdadd64fd4b4c35 6cb48bb1b6b7356447a10480ac48363acb13c128The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection. Our machine learning models consist of a novel and comprehensive dataset collected from a network of strategically placed sensors, capturing not only electromagnetic field readings but also additional urban features, including population density, levels of urbanization, and specific building characteristics. This sensor-driven approach, coupled with explainable AI, enables us to identify key factors influencing electromagnetic exposure more accurately. The integration of IoT sensor data with machine learning opens the potential for creating highly detailed and dynamic electromagnetic pollution maps. These maps are not merely static snapshots; they offer researchers the ability to track trends over time, assess the effectiveness of mitigation efforts, and gain a deeper understanding of electromagnetic field distribution in urban environments. Through the extensive dataset, our models can yield highly accurate and dynamic electric field strength maps. For this study, we performed a comprehensive analysis involving 566 machine learning models across eight French cities: Lyon, Saint-Étienne, Clermont-Ferrand, Dijon, Nantes, Rouen, Lille, and Paris. The analysis incorporated six core approaches: k-Nearest Neighbors, XGBoost, Random Forest, Neural Networks, Decision Trees, and Linear Regression. The findings underscore the superior predictive capabilities of ensemble methods such as Random Forests and XGBoost, which outperform individual models. Simpler approaches like Decision Trees and k-NN offer effective yet slightly less precise alternatives. Neural Networks, despite their complexity, highlight the potential for further refinement in this application. In addition, our results show that the machine learning models significantly outperform the linear regression baseline, demonstrating the added value of more complex techniques in this domain. Our SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models.Italian National Conference on Sensors360The SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models, demonstrating the added value of more complex techniques in this domain.Sensors (Basel, Switzerland)["Yiannis Kiouvrekis", "Ioannis Givisis", "T. Panagiotakopoulos", "Ioannis Tsilikas", "A. Ploussi", "E. Spyratou", "Efstathios P. Efstathopoulos"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/6cb48bb1b6b7356447a10480ac48363acb13c128 91da8c45cc9663480a215f18f73e8f37601311f3In the presence of acute respiratory failure, mechanical ventilation emerges as a temporary alternative to maintain adequate gas exchange in the body such as that which occurs in natural respiration. This technique is widely used in intensive care units. Our objective was to carry out an analysis and interpretation of cardiorespiratory signals in patients assisted by mechanical ventilation, using non-linear analysis techniques of dynamic systems, data mining and machine learning techniques to establish indices that allow determining the appropriate moment of disconnection. in patients during the weaning process. We use three categories: Failure, success and reintubated. We introduced a new variant of Moving Window with Variance Analysis, with which good results are obtained. We have found that by using all the time series available in the database, we have obtained an accuracy of 96% when using simple symbolic dynamics to differentiate between successful weaning and reintubated cases. and 86% when comparing success and failure, which contrasts with the results observed in the state of the art.Periodicals of Engineering and Natural Sciences (PEN)300An analysis and interpretation of cardiorespiratory signals in patients assisted by mechanical ventilation is carried out, using non-linear analysis techniques of dynamic systems, data mining and machine learning techniques to establish indices that allow determining the appropriate moment of disconnection in patients during the weaning process.Periodicals of Engineering and Natural Sciences (PEN)["C. J. A. Pererira", "C. L. Sandoval-Rodr\u00edguez", "B. F. Giraldo", "E. H. Solano", "Camilo Leonardo Sandoval Rodriguez"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/91da8c45cc9663480a215f18f73e8f37601311f3 5d47ad34fefbf1ab99da3c1bb36d27b8be7f8202The integration of Generative AI into supply chain management holds the promise of transforming traditional practices into dynamic, responsive systems capable of adapting to the complexities and challenges of the modern business environment. By leveraging AI's capabilities, organizations can achieve greater efficiency, resilience, and innovation in their supply chain operations.International Journal For Multidisciplinary Research160International Journal For Multidisciplinary Research["Muneer Basha Shaik"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/5d47ad34fefbf1ab99da3c1bb36d27b8be7f8202 520abf929c4f2a18969d964e515919b4c613d442The research aims to determine how various factors influence customer satisfaction and loyalty (CS&L) in AI-driven food delivery systems during and after COVID-19. Two hundred ninety-four participants were given a 32-item questionnaire, and the data were analysed using multiple regression analysis and quantitative research methods. Numerous factors were examined in this inquiry, such as price reduction, promotion benefits, information quality, hedonic motivation, safety packaging, and perceived severity. The research illustrates how the pandemic affected consumer behaviour, with an adjusted R² value of 0.715 during the pandemic and 0.489 in the post-pandemic period. It also concludes that hedonic motivation and information quality are two essential elements influencing consumer satisfaction and loyalty. The research seeks to enhance the post-pandemic delivery of AI-supported services and help provide recommendations for developing AI systems for food delivery services with a better understanding of consumer motivation for AI-based food delivery services.Business &amp; Management Studies: An International Journal580The research illustrates how the pandemic affected consumer behaviour, and concludes that hedonic motivation and information quality are two essential elements influencing consumer satisfaction and loyalty.Business &amp; Management Studies: An International Journal["Can Sayginer"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/520abf929c4f2a18969d964e515919b4c613d442 579595b5a83dd9f612c62fdf68436fbd4e0d095fA remarkable amount of progress has been made in the field of GenAI over the last year, resulting in it becoming an increasingly sought-after technology among mobile and e-commerce developers as well as end users. Mobile and e-commerce developers in Indonesia have incorporated GenAI features into their applications as a result of this surge in interest and technological advancement, improving the efficiency and usability of their applications. It is the purpose of this book to provide an overview of the current state of GenAI integration by Indonesian mobile and e-commerce application developers. Using insights gathered from a survey and focus group discussions, we explore the key trends, strategies, use cases and challenges associated with the use of GenAI within the mobile and e-commerce industry. It has been demonstrated that GenAI is having a transformative impact on application development, offering both opportunities and challenges to developers. The focus group discussions provided valuable insight into the current practices and future directions of GenAI integration, reflecting a dynamic and evolving market. With continued focus on developer trends and technological advancements, the authors consider this paper to be a pivotal reference for understanding how Indonesian mobile and e-commerce use GenAI to redefine functionality and user experience in their applications.00An overview of the current state of GenAI integration by Indonesian mobile and e-commerce application developers is provided, using insights gathered from a survey and focus group discussions to explore the key trends, strategies, use cases and challenges associated with the use of GenAI within the mobile and e-commerce industry.["Siddhartha Paul Tiwari", "Adi Fahrudin"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/579595b5a83dd9f612c62fdf68436fbd4e0d095f 040c4ed0a2e00355c223c0d422308d32a5d12fcaThe world has entered the era of cultural multipolarization and economic globalization. As a global common English, it is particularly important. Based on the theory of multimodal discourse analysis, this paper studies the construction of natural ecological model of British and American literary education. This study shows that in the past, domestic British and American literature education often paid attention to its instrumentality and ignored its internal cultural connotation. As a result, the domestic British and American literature education is boring and rigid. In this way, students' lack of interest makes English teaching more and more difficult. Through the research on the means and principles of cross-cultural awareness, this paper improves students' cross-cultural awareness. The experimental results show that cross-cultural integration based on multimodal discourse analysis theory can make British and American literature education play a better role.Computer fraud & security00The experimental results show that cross-cultural integration based on multimodal discourse analysis theory can make British and American literature education play a better role.Computer Fraud and Security["Ping Lu"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/040c4ed0a2e00355c223c0d422308d32a5d12fca f314d3d8d57874c90a49cfc78a2a567df119375dABSTRACTIngénierie des Systèmes d'Information630Ingénierie des systèmes d information["Alexander Inga Alva", "Ciro Rodr\u00edguez"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/f314d3d8d57874c90a49cfc78a2a567df119375d 586f8f4325c0424e44ca17485a2e0970c9970e9bIn the field of aviation, English language proficiency is essential for ensuring clear communication and safe flight operations. Effective assessment of pilots’ and air traffic controllers’ aviation English (AE) proficiency is, therefore, crucial. Conventional AE proficiency assessments, while effective, face limitations in scalability, objectivity, and feedback mechanisms. This article reviews the advancements and effectiveness of AI-driven assessment tools for AE proficiency testing, highlighting their potential to overcome these limitations. The review encompasses AI technologies such as automated speech recognition (ASR), natural language processing (NLP), and intelligent tutoring systems (ITS) in the light of language proficiency requirements stated by the International Civil Aviation Organization (ICAO). Overall, the present review concludes that AI-driven tools provide accurate, reliable, and immediate feedback, significantly improving learners' AE proficiency. Despite challenges such as speech recognition errors and ethical concerns, these tools offer scalable and accessible solutions for large aviation training programs. The review concludes with recommendations for future research, emphasizing the need for continued innovation to address technological limitations and enhance adaptive learning environments. This review offers valuable insights for ESP practitioners and stakeholders in the aviation industry.Literacy Trek00Overall, the present review concludes that AI-driven tools provide accurate, reliable, and immediate feedback, significantly improving learners' AE proficiency, and offer scalable and accessible solutions for large aviation training programs.The Literacy Trek["G\u00f6khan Demird\u00f6ken"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/586f8f4325c0424e44ca17485a2e0970c9970e9b e9018e02ff2d129a95fde74b4ef38ba8f027bc61AI and Ethics80AI and Ethics["Dwayne Woods"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/e9018e02ff2d129a95fde74b4ef38ba8f027bc61 ae5b0bbccc5d5f74902870571f702a13f8402cb5International Journal of Aging390International Journal of Aging["Nazanin Masoudi", "E. Sarbazi"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/ae5b0bbccc5d5f74902870571f702a13f8402cb5 7bbb2e627ad170d75285f1f811012ff9ccdcc12eBakirkoy Tip Dergisi / Medical Journal of Bakirkoy00Bakirkoy Tip Dergisi / Medical Journal of Bakirkoy["Mustafa T\u00fcmt\u00fcrk"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/7bbb2e627ad170d75285f1f811012ff9ccdcc12e 5c935b34ce5840021dfc535befa32869fbdfe7f4With the advancement of technology and computing capabilities of computers, various applications and algorithms that were previously not applicable to portable devices such as laptops and smartphones can now be applied. Artificial intelligence (AI) is one of the increasingly popular techniques that are now a part of everyday life. AI is an umbrella term that refers to the simulation of human intelligence by machines that use big data to perform various tasks. AI has benefited many aspects of human life, including education. However, AI also has significant negative effects. This study aims to study the influence of artificial intelligence on students' critical thinking skills. This study uses qualitative research and literature analysis. However, the study also identified several negative impacts, including reliance on technology, unassured information quality, social isolation, and ethical and privacy concerns. Therefore, a balanced and strategic approach is needed in integrating AI in education to ensure that its benefits can be maximized, while its negative impact is minimized.Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa00A balanced and strategic approach is needed in integrating AI in education to ensure that its benefits can be maximized, while its negative impact is minimized.Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa["Ratnasari Ratnasari", "Mewa Zabeta", "Faza Zikri Sholeha"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/5c935b34ce5840021dfc535befa32869fbdfe7f4 c186184179a3d795e6914034b5284abd2657cee4Assessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education.The present study evaluated the SSI inquiry skills of students in an AI‐enabled scoring environment. An AI model for socioscientific issues that can assess students' inquiry skills was developed. Responses to a learning module were collected from 1250 participants, and the open‐ended responses were rated by humans in accordance with a designed rubric. The collected data were then preprocessed and used to train an AI rater that can process natural language. The effects of two hyperparameters, the dropout rate and complexity of the AI neural network, were evaluated.The results suggested neither of the two hyperparameters was found to strongly affect the accuracy of the AI rater. In general, the human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories. Discrepancies were identified and are discussed both quantitatively and qualitatively.Journal of Computer Assisted Learning541An AI model for socioscientific issues that can assess students' inquiry skills was developed and human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories.Journal of Computer Assisted Learning["W. Zhang", "John J. H. Lin", "Ying-Shao Hsu"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/c186184179a3d795e6914034b5284abd2657cee4 820a3500dab5f1a75b23146ea30fce36220a0d8dThis study examines the impact of artificial intelligence on the pragmatic function of the French press, by analyzing the role of this advanced technology in changing the methods of collecting, editing, and analyzing news. This research aimed to comprehend the role and effect of artificial intelligence technology on news media practices in France, the quality of content and the prevailing challenges. The researchers used a qualitative design and gathered data using semi structured interview questionnaires. A sample of 15 participants is selected and further data is analyzed using content analysis approach. The results showed that artificial intelligence is widely utilized to enhance the quality of content by streamlining the content creation, design, and sharing process. Besides, the relevant technology helps French news media organizations to manage redundant tasks and enable journalists to focus on more areas. It is found that AI algorithms are also employed to analyze vast amounts of data, facilitating the speed and accuracy of content. However, the study participants indicated some ethical concerns such as bubble filter and bias, further emphasizing the needs to counteract these ethical dilemmas. Therefore, this research implied that AI technology enhances the pragmatic role of French news media organizations by facilitating productivity and quality of content. While it offers many benefits, it is crucial to address the ethical issues revolving around bias and bubble filters.Forum for Linguistic Studies521The results showed that artificial intelligence is widely utilized to enhance the quality of content by streamlining the content creation, design, and sharing process and facilitating productivity and quality of content.Forum for Linguistic Studies["Batoul M. Al-Muhaissen", "Samer Al-Hammouri", "Kossay M. Rachdan", "Mohammed Habes"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/820a3500dab5f1a75b23146ea30fce36220a0d8d 3803bb789c1b93296950a356cea4f697d564c7c5PurposeThis study develops and validates the “Perception of the Adoption and Training in the Use of Artificial Intelligence Tools in the Profession” instrument, designed to measure Latin American university students' attitudes and perceptions regarding AI training in their professional education across diverse fields.Design/methodology/approachThe instrument was administered to 238 students from various disciplines at a Mexican university. Structural validity and reliability were assessed using a generalized structural equation model (GSEM) with quasi-maximum likelihood (QML) to handle data non-normality and analyze latent construct relationships.FindingsResults show high internal consistency and validity, with strong correlations between items and constructs of “attitude” and “perception of AI training value.” The study found significant relationships between understanding AI tools and the perceived value of AI training, as well as between this perception and attitudes toward incorporating AI in professional training.Practical implicationsThe instrument helps institutions identify student attitudes and training needs related to AI, enabling tailored curricula and training programs that foster positive AI acceptance, thus preparing students for modern technological challenges.Originality/valueThis study offers a validated instrument tailored to the Latin American context, addressing a gap in measuring student perceptions of AI in professional training. It serves as a diagnostic tool for educators and policymakers in designing AI-integrated pedagogical strategies that align with student needs.Applied Computing and Informatics230The study found significant relationships between understanding AI tools and the perceived value of AI training, as well as between this perception and attitudes toward incorporating AI in professional training.Applied Computing and Informatics["Jos\u00e9 Carlos V\u00e1zquez-Parra", "Carolina Henao-Rodr\u00edguez", "Jenny-Paola Lis-Guti\u00e9rrez", "Sergio Palomino-G\u00e1mez", "Paloma Su\u00e1rez-Brito"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/3803bb789c1b93296950a356cea4f697d564c7c5 fd50d964a44077afc394034e1726835942a32067The development of Machine Learning (ML) and Artificial Intelligence (AI) technologies has revolutionized various industries, including marketing and customer satisfaction. In the modern competitive business era, companies are increasingly relying on this technology to improve operational efficiency and effectiveness, especially in answering marketing needs and customer satisfaction. This study aims to analyze the role of ML and AI in strengthening aspects of marketing and customer satisfaction in the business sector. The research method uses a qualitative approach with data collection techniques through literature studies. After the data is collected, it is then analyzed by the stages of filtering relevant data, presenting key information, and answering the research objectives in the conclusion. The results of the study show that the application of ML and AI can significantly improve the marketing effectiveness of companies through personalization of products and services, conducting more accurate customer segmentation, predicting consumer behavior, and optimizing various aspects of marketing. On the other hand, the application of ML and AI also plays an important role in improving customer satisfaction. For example, with the use of intelligent chatbots and customer feedback analysis, companies can understand the shortcomings that need to be fixed, then improve the quality of customer service. So, by utilizing this technology, companies can increase efficiency in marketing, drive increased sales, and build more solid relationships with customers, which ultimately contributes to increased customer satisfaction.International Journal of Social Service and Research250The results of the study show that the application of ML and AI can significantly improve the marketing effectiveness of companies through personalization of products and services, conducting more accurate customer segmentation, predicting consumer behavior, and optimizing various aspects of marketing.International Journal of Social Service and Research["Dony Ari Nugroho"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/fd50d964a44077afc394034e1726835942a32067 e12d6ac2301f57cd22f0681928227ecaad4b580bThe paper analyzed the impact of digital transformation, specifically through artificial intelligence (AI) and big data, on enhancing supply chain efficiency in the Nigerian oil sector. The research aimed to evaluate how these technologies improve operational performance and identify the challenges and opportunities associated with their implementation. Using a systematic content analysis in reviewing recent related studies, the findings revealed that AI and big data significantly enhance decision-making, predictive maintenance, inventory management, and risk mitigation, contributing to overall supply chain efficiency. However, the study also identifies several challenges, including inadequate infrastructure, a shortage of skilled personnel, and organizational resistance to change. Despite these barriers, the opportunities for optimizing operations and improving supply chain resilience are considerable. The paper concluded that, with targeted investments in technology and workforce development, the Nigerian oil sector can fully leverage AI and big data to achieve sustained operational excellence and competitive advantage in a dynamic global market. International Journal of Management and Fuzzy Systems120The paper concluded that, with targeted investments in technology and workforce development, the Nigerian oil sector can fully leverage AI and big data to achieve sustained operational excellence and competitive advantage in a dynamic global market.International Journal of Management and Fuzzy Systems["Oboho Eteyen"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/e12d6ac2301f57cd22f0681928227ecaad4b580b 36374a4ecface656c94e98fa772b4a4bd5940229This essay in honor of ASQ's 70th volume surveys how technology-driven changes in scholarly publishing have introduced algorithmic management to organizational research. The internet greatly reduced the cost of publishing journals and prompted an orders-of-magnitude increase in the number of journals and articles while also foregrounding quantitative metrics for scholarship. Given the academic incentive system of publish or perish, the new online ecosystem has encouraged problematic practices by scholars and publishers that threaten the standards and values of organizational theory. The advent of generative artificial intelligence within this milieu is almost certain to worsen the publishing trends we have already experienced. Drawing on prior literature about the centrality of deep intellectual engagement through reading, writing, and interactions with colleagues, we propose a set of reforms to preserve the sacredness of craft and community at the core of our scholarly work.Administrative Science Quarterly300This essay in honor of ASQ's 70th volume surveys how technology-driven changes in scholarly publishing have introduced algorithmic management to organizational research and proposes a set of reforms to preserve the sacredness of craft and community at the core of the authors' scholarly work.Administrative Science Quarterly["Beth A. Bechky", "Gerald F. Davis"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/36374a4ecface656c94e98fa772b4a4bd5940229 94f84a9f10cbe66666de1c277edb312761652469The rapid advancement of artificial intelligence (AI) technologies necessitates robust governance frameworks to ensure safety, fairness, and compliance. This paper examines the complementary roles of proactive guardrails and reactive moderation tools in achieving scalable and trustworthy AI governance. Proactive approaches embed preemptive safeguards, such as threat modeling, AI observability, and adversarial testing, to mitigate vulnerabilities before deployment. Reactive mechanisms, including content detection, intervention, and correction, address emergent issues in real time, ensuring resilience against evolving threats. By integrating these methods into hybrid frameworks, organizations can balance scalability with operational efficiency while maintaining ethical integrity. This research highlights the critical role of fairness, accountability, and transparency in fostering trust and compliance in AI systems. Through case studies and actionable recommendations, we demonstrate how modular governance models reduce risks while enhancing performance. The findings underscore the importance of iterative and adaptable strategies for navigating complex regulatory landscapes and advancing responsible AI innovation.International Journal of Innovative Research in Science Engineering and Technology00The complementary roles of proactive guardrails and reactive moderation tools in achieving scalable and trustworthy AI governance are examined, highlighting the critical role of fairness, accountability, and transparency in fostering trust and compliance in AI systems.International Journal of Innovative Research in Science, Engineering and Technology["Shreyam Dutta Gupta"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/94f84a9f10cbe66666de1c277edb312761652469 ebd31d196947109fe5cf5babfbaa4567707c2fedThis paper explores the evolving role of Artificial Intelligence (AI) in the learning environment, emphasizing both opportunities and challenges. Through tools like adaptive learning systems, automated grading, and personalized tutoring, AI tools have revolutionized educational practices. Further, AI tools have established tailored learning experiences and optimized teaching strategies. Especially in the wake of the COVID-19 pandemic, AI has offered support for learners, parents, and educators, and was crucial in facilitating remote and hybrid learning. However, introducing AI also presents notable challenges, such as privacy concerns, ethical implications, and socio-economic disparities in access, which can exacerbate existing educational inequalities. Furthermore, without appropriate and proper training, educators often face difficulties in adapting to AI-driven environments. This paper focuses on AI's potential to reduce educational disparities among low-income and underserved students, as it offers inclusive opportunities through accessible and personalized learning technologies. In response to these complex challenges, this review paper proposes recommendations for ethical policy frameworks, adequate teacher training, and transparent AI usage guidelines to ensure equitable and responsible AI integration. By balancing AI's benefits with its limitations, the field of education can foster a future where AI contributes positively to diverse, inclusive, and adaptive learning environments.Journal of Education, Humanities and Social Sciences130This review paper proposes recommendations for ethical policy frameworks, adequate teacher training, and transparent AI usage guidelines to ensure equitable and responsible AI integration to reduce educational disparities among low-income and underserved students.Journal of Education, Humanities and Social Sciences["Chenxin Zhang"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/ebd31d196947109fe5cf5babfbaa4567707c2fed 3fee4435a035e6e2e40578609abe575edd7b5b3dIn recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in construction. However, concerns regarding the technical maturity and reliability, safety, and privacy implications have led to a lack of trust in AI among stakeholders and end users in the construction industry, which slows the intelligent transformation of the industry, particularly for on-site AI implementation. This paper reviews frameworks for AI system design across various sectors and government regulations and requirements for achieving trustworthy and responsible AI. The principles for the AI system design are then determined. Furthermore, a lifecycle design framework specifically tailored for AI systems deployed in the construction industry is proposed. This framework addresses six key phases, including planning, data collection, algorithm development, deployment, maintenance, and archiving, and clarifies the design principles and development priorities needed for each phase to enhance AI system trustworthiness and acceptance. This framework provides design guidance for the implementation of AI in the construction industry, particularly for on-site applications, aiming to facilitate the intelligent transformation of the construction industry.Buildings680A lifecycle design framework specifically tailored for AI systems deployed in the construction industry is proposed, which addresses six key phases and clarifies the design principles and development priorities needed for each phase to enhance AI system trustworthiness and acceptance.Buildings["Lichao Yang", "Gavin Allen", "Zichao Zhang", "Yifan Zhao"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/3fee4435a035e6e2e40578609abe575edd7b5b3d 3b93fc13b8d8833449495fc754cbc88db7787063Socially shared regulation in learning (SSRL) contributes to successful collaborative learning (CL). Empirical research into SSRL has received considerable attention recently, with increasingly available multimodal data, advanced learning analytics (LA), and artificial intelligence (AI) providing promising research avenues. Yet, integrating these with traditional datasets remains a challenge in SSRL research due to the misalignment between theoretical constructs, methodological assumptions, and data structure. To address this challenge and expand our understanding of the nature of SSRL, the present research adopted a human–AI collaboration approach in a three-layer analysis to examine group interactions in response to cognitive and emotional regulation triggering events. Two-level theoretical lenses — macro-level (regulatory aspects) and micro-level (deliberative interactions) — were used to analyze 2,125 utterances from video-recorded tasks of ten groups of three Finnish secondary students (N=30). Results showed two types of deliberation patterns for SSRL, namely 1) the Plan and Implementation Approach (PIA) associated with adaptive patterns, and 2) the Trials and Failure Approach (TFA) associated with maladaptive patterns. Our findings revealed that groups often fail to recognize, or are ill-equipped to respond to, emerging regulatory needs. These findings advance SSRL theories and research methodologies by utilizing AI-enhanced LA to offer new insights into group dynamics and regulatory strategies.Journal of Learning Analytics00It is revealed that groups often fail to recognize, or are ill-equipped to respond to, emerging regulatory needs, as well as two types of deliberation patterns for SSRL.J. Learn. Anal.["Belle Dang", "Andy Nguyen", "Sanna J\u00e4rvel\u00e4"]2024-12-25T00:00:00https://www.semanticscholar.org/paper/3b93fc13b8d8833449495fc754cbc88db7787063 d0083443105c4d8be072038e41c6f3a4e9c31929The financing of terrorism in Indonesia, has become increasingly complex due to the involvement of various domestic terrorist organizations. These groups obtain funds through diverse sources such as direct donations, membership fees, self-funding, and the misuse of non-profit organizations (NPOs). This study aims to explore and analyze the potential and challenges of utilizing advanced technologies, particularly Artificial Intelligence (AI) and Machine Learning (ML), in preventing terrorist financing in Indonesia. The research employs a qualitative descriptive approach, utilizing secondary data from FATF reports and related literature. The findings indicate that AI and ML can significantly enhance the detection and investigation of suspicious financial activities, provide real-time transaction monitoring, and facilitate inter-agency collaboration. However, challenges such as data limitations, regulatory complexity, high implementation costs, and data security and privacy issues must be addressed to fully leverage these technologies. This study provides recommendations for developing a supportive regulatory framework, enhancing inter-agency cooperation, and investing in better data infrastructure to effectively utilize AI and ML in combating terrorist financing.Enrichment: Journal of Multidisciplinary Research and Development00The findings indicate that AI and ML can significantly enhance the detection and investigation of suspicious financial activities, provide real-time transaction monitoring, and facilitate inter-agency collaboration.Enrichment: Journal of Multidisciplinary Research and Development["Rafi Wisesa", "Sapto Priyanto", "Muhamad Syauqillah", "A. Subroto"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/d0083443105c4d8be072038e41c6f3a4e9c31929 a5f31bba40aeefb03c29305bd97a48480a3dea40This bibliometric analysis examines global research trends in Artificial Intelligence (AI) from 2019 to 2023, using 7,030 Scopus indexed documents. The study found an annual growth rate of 25.93%, indicating a substantial increase in AI research effort. The majority of articles were created by collaborative teams, with an average of 4.28 authors per paper, with only 415 being single-authored. IEEE Access is the most prolific contributor, King Saud University is the leading institution, and China is the main publishing country, with 1,277 corresponding authors and the highest citation count (19,873). Thematic analysis highlights a strong emphasis on machine learning, deep learning, and neural networks as foundational topics, alongside growing interest in ethical AI and convolutional neural networks, signaling the field's evolution toward addressing societal challenges and specialized applications. International collaboration plays a significant role, with 31.31% of publications involving authors from multiple countries. While the volume of AI research grows, newer articles have lower average citations due to their recent publication date. These findings highlight the interdisciplinary and worldwide nature of AI research, as well as its transformational potential for academia, industry, and policymakers. By mapping major trends and contributors, this report gives significant insights into the changing AI landscape, identifying potential for improving worldwide research collaboration and addressing growing difficulties in the field.Asian Journal of Research in Computer Science00Thematic analysis highlights a strong emphasis on machine learning, deep learning, and neural networks as foundational topics, alongside growing interest in ethical AI and convolutional neural networks, signaling the field's evolution toward addressing societal challenges and specialized applications.Asian Journal of Research in Computer Science["E. A. Abanga", "Theophilus Acquah"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/a5f31bba40aeefb03c29305bd97a48480a3dea40 6dcb575b033ae9aee5dd03a7ae6e87d5961671f4The rapid development of artificial intelligence has opened up an unprecedented window of opportunity, but at the same time, it has also triggered a series of complex and serious challenges. This technology is penetrating and reshaping every level of economic and social structure with unprecedented depth and breadth, profoundly changing people's production mode and life mode. This study focuses on the specific mechanisms of AI's impact on global income distribution equity and finds that it works through three core pathways: substitution and creation effects, skill bias, and capital bias. Ai is not only reshaping the employment landscape by replacing low-skilled labor jobs and creating new occupations, it is also further widening the income gap between high-skilled labor and low-skilled labor, as well as owners of labor capital, because of its inherent skill preferences and capital preferences. The interwoven effect of these mechanisms, while driving rapid economic growth, has also exacerbated income inequality within society.Journal of Education, Humanities and Social Sciences180This study focuses on the specific mechanisms of AI's impact on global income distribution equity and finds that it works through three core pathways: substitution and creation effects, skill bias, and capital bias.Journal of Education, Humanities and Social Sciences["Yiran Liu"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/6dcb575b033ae9aee5dd03a7ae6e87d5961671f4 930040c14c1ef9bc3ca2193ff1122c9c46f9a3b9The article focuses on philosophic-anthropological issues related to the development of artificial intelligence (AI). The author analyzes a set of possible positive and negative consequences of digitalization for humanity. In studying the dialectics of digital technology development, some novelty points concern an attempt to look at this complex process systematically, highlighting the anthropological aspects of this controversial development. In particular, there is an intention to understand how AI may affect the evolution of our consciousness and ethics, and how it correlates with human nature. The article mentions the AI's prominent achievements as on the beginning of 2024, demonstrating how advanced artificial intelligence has already opened up unique opportunities in fundamental and applied sciences. These impressive results give grounds to evaluate AI as a challenging technology, which questions traditional notions of what makes humans the most intelligent creatures on the planet. The development of AI is forcing us to rethink a number of ethical, psychological, and existential issues, including our place in the world and our relationship with smart technologies. The article emphasizes the ambivalent potential of the emerging digital technologies, which: can disrupt existing markets for labor, goods and services, and at the same time create new ones; can contribute to overcoming poverty and inequality, or exacerbate them in other ways; have the potential to serve humanistic goals, or to amplify processes of dehumanization when AI is integrated into new means of mass destruction or enslavement of humans. One of the important conclusions proposed in the article is that current AI-based technologies remain entirely within the anthropological paradigm: they do not (yet) have any self-interest that has not been built in by humans in advance. Therefore, the main source of danger comes from people, and the primary risks lie in attempts to instrumentalize AI by people to achieve some wrong and misguided goals. In the article, a call is made for a thorough study of the ethical and social implications of the large-scale deployment of digital technologies, in order to ensure their safe and responsible use. Among other things, a number of systemic measures are proposed that could contribute to effective public control over these innovative processes.Мiждисциплiнарнi дослiдження складних систем300One of the important conclusions proposed in the article is that current AI-based technologies remain entirely within the anthropological paradigm: they do not (yet) have any self-interest that has not been built in by humans in advance.Мiждисциплiнарнi дослiдження складних систем["O. Stovpets"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/930040c14c1ef9bc3ca2193ff1122c9c46f9a3b9 9cb55962415c22269deed06ce13485edb9362536In the contemporary society, the rapid growth of the artificial intelligence industry presents significant opportunities for venture capital investment, driven by advancements in machine learning, robotics, neural networks, and generative AI. With a projected market size of $184 billion by 2024, demand for AI technologies is attracting growing investor attention. This paper explores key trends in AI venture capital, highlighting regional differences. U.S. investments emphasize growth in sectors like healthcare and autonomous vehicles, while Chinese investors focus on capital-intensive areas like mobility and robotics, prioritizing relationships and government alignment. Despite opportunities, AI investment faces challenges like information asymmetry, security risks, and a lack of deep tech expertise. Short venture capital funding cycles further complicate long-term returns for investing in the AI industry. The paper also outlines the venture capital decision-making process, underscoring the need for expertise, regional awareness, and strategic risk management to effectively navigate the complexities of AI investment.Advances in Economics, Management and Political Sciences00The paper outlines the venture capital decision-making process, underscoring the need for expertise, regional awareness, and strategic risk management to effectively navigate the complexities of AI investment.Advances in Economics, Management and Political Sciences["Jiaying Du"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/9cb55962415c22269deed06ce13485edb9362536 fce927c0cd8d2f7cb8d2ebcdf37659159b1c748dArtificial Intelligence (AI) and Machine Learning (ML) are revolutionizing critical care. In the Intensive Care Unit (ICU), timely and accurate decisions are crucial. AI and ML can enhance decision-making by predicting adverse events, personalizing treatment plans, and improving diagnostic accuracy. Early warning systems, powered by AI, can detect conditions like sepsis and acute respiratory distress syndrome early on. AI-driven decision support systems provide real-time recommendations, optimizing resource allocation and ensuring adherence to best practices. While AI offers significant benefits, challenges like data privacy, bias, and ethical considerations must be addressed. Ensuring transparency, accountability, and fairness in AI algorithms is essential. The future of AI in the ICU is promising. Advancements in AI and ML, coupled with collaborative human-AI approaches can further improve patient outcomes. By addressing ethical concerns and fostering responsible AI development, healthcare providers can harness the power of AI to optimize critical care.International Journal of Medical Science and Clinical Research Studies00By addressing ethical concerns and fostering responsible AI development, healthcare providers can harness the power of AI to optimize critical care and further improve patient outcomes.International Journal of Medical Science and Clinical Research Studies["Dr. Ketan Kargirwar", "Dr Anjali Dange", "Dr Rahul Pandit"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/fce927c0cd8d2f7cb8d2ebcdf37659159b1c748d 6086b0db6b590e7f0a03d5e74392252d202c30daIntegrating Artificial Intelligence (AI) into education is set to transform teaching and learning practices. Although the benefits of AI in education are vast, significant challenges and uncertainties persist. This study examines the role of AI in language teaching and learning at a university in Tanzania, focusing on its applications, impacts, challenges, and ethical considerations. Data were collected from 128 participants through surveys, interviews, observations, and group discussions. The findings revealed the growing use of AI-powered educational technologies in Tanzanian university settings, offering innovative solutions to traditional challenges and optimizing learning outcomes. However, challenges such as data privacy, lack of proficiency in AI, lack of suitable equipment, plagiarism issues, and high dependency on AI need to be addressed. Moreover, questions about the evolving roles of teachers and the ethical implications of AI usage in education. Despite the challenges, the study highlights AI’s potential to transform teaching, personalize learning, and enhance instruction through improved access to materials and data analysis. Furthermore, instructors mainly benefit from AI lesson content creation and virtual reality simulations. Concerns regarding dependency and misuse raise issues regarding critical thinking and integrity. This study advocates for ongoing dialogue among stakeholders to ensure responsible AI integration and maximize its benefits.Pan-African Journal of Education and Social Sciences120AI’s potential to transform teaching, personalize learning, and enhance instruction through improved access to materials and data analysis is highlighted, and instructors mainly benefit from AI lesson content creation and virtual reality simulations.Pan-African Journal of Education and Social Sciences["Job W. Mwakapina"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/6086b0db6b590e7f0a03d5e74392252d202c30da 3fabc537faab9319c0bf4fcc73ccf82aa4c71bb1Global attention has been attracted to Artificial Intelligence (AI) due to its rapid development. As a new science technology, AI has not only made remarkable achievements in the business and industrial fields but also gradually affected the education field, bringing far-reaching impacts on the traditional teaching model. However, with the proliferation of AI technology, teachers are facing challenges in terms of role orientation and professional development. This essay analyses the challenges that teachers are facing in the age of AI and proposes strategies to rationally balance the application of AI in education, to provide valuable management ideas and suggestions for educational policymakers. This essay concludes that the impact of AI on education consists of positive effects such as improving teaching efficiency, providing personalized learning, and reducing the burden on teachers, but at the same time, it brings with it challenges such as the change in teachers' professional roles and the need for skills upgrading. Based on this, this essay proposes recommendations to strengthen teacher training to enhance their AI-related skills, formulate policies to safeguard teachers' professional development and promote the application of human-computer collaboration models in education.Journal of Education, Humanities and Social Sciences180The impact of AI on education consists of positive effects such as improving teaching efficiency, providing personalized learning, and reducing the burden on teachers, but at the same time, it brings with it challenges such as the change in teachers' professional roles and the need for skills upgrading.Journal of Education, Humanities and Social Sciences["Runqiu Wu"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/3fabc537faab9319c0bf4fcc73ccf82aa4c71bb1 f658ec38cdcb6d07def417ffe9eb5acccf93d289The paper explores the impact of artificial intelligence (AI) on developing students' footwork in the sport of badminton. It emphasizes the potential of AI in improving motor skills required for badminton and discusses the role of AI in deducing and updating knowledge and skills. The study employs an experimental approach with third-year students at a physical education college in Iraq, focusing on the use of AI techniques and devices to enhance footwork training. The paper highlights positive effects of AI-based training programs, such as promoting healthier muscle strength, improving swing technique, and enhancing communication and cooperation among students. Additionally, it discusses the potential benefits of integrating technology, such as AI, to enhance the effectiveness of footwork training in badminton. The findings indicate a high average use and reliance on AI technologies for improving athletes' performance and suggest the need for further research to evaluate the effectiveness of these techniques and determine the most effective ways to apply them in badminton. Overall, the paper suggests that AI has the potential to significantly impact the development of footwork in badminton and improve athletes' performance, calling for continued exploration and application of AI in sports training.Eximia00It is suggested that AI has the potential to significantly impact the development of footwork in badminton and improve athletes' performance, calling for continued exploration and application of AI in sports training.Eximia["Muhannad Nazar Kzar"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/f658ec38cdcb6d07def417ffe9eb5acccf93d289 2fce853166dbe453561f6f19993e0511dc84f267The article presents the results of a systematic analysis of the current state of application of artificial intelligence (AI) in software engineering (SW) based on the analysis of publications, assessment of AI capabilities, experience in its application, and conducted experiments. The conceptual foundations of the research were formed, which determine: perception of AI as a tool, not an individual of work; the main directions of its application are engineering and management; the subject of AI application is the processing of artifacts (synthesis and analysis) and obtaining consultations; the need to assess the quality of AI-derived products and analyze the risks of its use is emphasized. Directions of application of AI in management: agreement processes (development of product concept and contract), organizational processes (project group formation and selection of technologies) and project management (planning, risk management, control and analysis of project implementation) Directions of application of AI in engineering: requirements management, design, construction, testing and documenting. To systematize the analysis of AI application directions, a conceptual model was developed, which includes: the direction, subject, and mode of application of AI. The mode of application of AI: the format of the prompt (problem statement and set of input data), the required product and its type (finished product, prototype, template, solution options, information support), the role of AI (executor, co-author, consultant), form of AI interaction (external service, integration via API, integrated system or local autonomous system). A structure of derivative models was formed for the analysis of the application of AI in specific directions with an overview of the capabilities of the most effective AI tools. As conclusions, it was determined that in management, the most rational model of using AI is to receive consultations and prototypes of documentation when contacting external AI services, in engineering – creating prototypes of project solutions and documentation based on external services, using integrated AI systems for design and testing in co-authorship mode. The risks of using AI include the possibility of obtaining insufficiently detailed documentation, complex and confusing software artifacts, and errors in the software code. To reduce risks and increase the effectiveness of AI application, it is determined that constant quality control of its products and training based on corporate requirements and standards is required.Collection Information technology and security00It was determined that in management, the most rational model of using AI is to receive consultations and prototypes of documentation when contacting external AI services, and constant quality control of its products and training based on corporate requirements and standards is required.Collection "Information Technology and Security"["Volodymyr Sokolov", "V. Riabtsev", "Oleksandr Uspenskyi", "Danylo Kopych"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/2fce853166dbe453561f6f19993e0511dc84f267 436c3684654f12cf8bf23c7a69c88b5b8f0138cdThis study analyses the effect of Artificial Intelligence, institutional pressure, and auditor personality on audit quality. The respondents were 84 auditors at Public Accounting Firms in Surabaya. This research is motivated by the inconsistency of previous research results. In addition, AI, which has begun commonly used by auditors to assist in audit tasks, has become the focus of new research. Auditors' perceptions may differ in accepting that AI will provide benefits or cause disruption during the audit process. Empirical results show that institutional pressure and auditor personality influence audit quality, while the use of AI does not affect audit quality. Although AI can help answer various questions, it's not always directly correlated with audit quality. This research show that managers at public accounting firms need to consider the presence of AI to increase the speed and quality of auditor work. However, they also need to organize and plan AI adoption to avoid unsatisfactory results. In addition, managers must also choose skilled professional auditors who can integrate with AI systems to improve company performance and reduce the risk of misuse of AI systems. In practice, managers still really need to consider personality in the auditor profession and use it as an indicator for assessing quality. In addition, auditors' perception of institutional pressure will improve audit quality if they perceive such pressure as a driving factor for performance quality.InFestasi350Managers at public accounting firms need to consider the presence of AI to increase the speed and quality of auditor work, however, they also need to organize and plan AI adoption to avoid unsatisfactory results.InFestasi["Achmad Daffa Abiyyu", "Nurul Mustafida"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/436c3684654f12cf8bf23c7a69c88b5b8f0138cd 0186bbcd80a9116c17c7585c07b7333a93007ed4Examining the Artificial Intelligence (AI) models can provide clear guidance for project management practice, even in outer areas that they may not have conceived. AI affords virtuous circles as symptom detection may afford novel datasets, diagnostic feedback for ML model building, and advocacy for the value and function of AI analysis of the diagnostic classifications. AI variables could also have direct predictive value as they are proposed to have some mechanism with the outcome, and AI has the potential to detect novel mechanisms. Finally, AI might use it to detect how context effects change the nature of the effects of other variables and use that to select custom actions within the nomothetic guidelines. (Sarkar et al.2022) (Wang et al., 2023) (Yathiraju2022).Big Data, IOT and Blockchain Trends 2025500AI might use it to detect how context effects change the nature of the effects of other variables and use that to select custom actions within the nomothetic guidelines, and AI has the potential to detect novel mechanisms.Big Data, IOT and Blockchain Trends 2025["Heidrich Vicci"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/0186bbcd80a9116c17c7585c07b7333a93007ed4 0d95fd7788fce160c082b0af9e996ea6a890f1beThe aim of the article is to characterize the main functi-ons, implementation methods, and limitations of artificial intelligence (AI) as a resource for developing personal life competence during the war in Ukraine, based on the concept of understanding AI’s potential. The article identifies the possibilities of using AI to support informati-onal and psychological assistance, social-psychological adaptation, and innovative-prognostic support in the context of three key dimensions of life competence development: routine-selective, adaptive, and innovative. It is shown that the routine-selective dimension includes the automation of routine tasks and the provision of informational support, which reduces cognitive load and ensures access to relevant information. The adaptive di-mension encompasses early warning systems about potential threats, which help to preserve life and health, as well as interactive psychological support systems such as virtual psychologists Woebot and Wysa. The innovative dimension includes stimulating creative processes and forecasting possible strategies for personal development.Despite the significant potential of using AI as a resource for developi-ng life competence, limitations such as technical problems, dependence on technology, data security issues, and ethical considerations have been identified. Addressing these obstacles and working on overcoming them is key to the effective use of AI in the conditions of war in Ukraine.Мiждисциплiнарнi дослiдження складних систем320The article identifies the possibilities of using AI to support informati-onal and psychological assistance, social-psychological adaptation, and innovative-prognostic support in the context of three key dimensions of life competence development: routine-selective, adaptive, and innovative.Мiждисциплiнарнi дослiдження складних систем["K. Hutsol"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/0d95fd7788fce160c082b0af9e996ea6a890f1be a4db5b8e15b78e6018428664b315f9ae71d90647The systematic literature review explores the use of Artificial Intelligence (AI) tools in foreign language (FL) education within K-12 school settings. It aims to offer a comprehensive understanding of the current state of research, identify emerging trends and gaps in the literature, and provide valuable insights for educators and researchers in the field. We analysed 16 empirical studies conducted between 2019 and 2023, focusing on three key areas: the pedagogical integration of AI tools, their impact on language learning outcomes, and future research recommendations. The review provides insights into the pedagogical aspects of AI utilization, the theoretical frameworks of the studies, and the research methods employed. The findings highlight the specifics of using AI tools, their impact on language learning outcomes, and the challenges and potential benefits of implementing AI in K-12 FL education.Journal of Education in Black Sea Region490Journal of Education in Black Sea Region["Tamar Mikeladze", "P. Meijer"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/a4db5b8e15b78e6018428664b315f9ae71d90647 0beabce693f98302c58874c9319430224e0ef5e1 Artificial intelligence (AI) has revolutionised the process of identifying species and individuals in audio recordings and camera trap images. However, despite developments in sensor technology, machine learning and statistical methods, a general AI‐assisted data‐to‐inference pipeline has yet to emerge. We argue that this is, in part, due to a lack of clarity around several decisions in existing workflows, including: the choice of classifier used (e.g. semi‐ vs. fully automated); how classifier confidence scores are used and interpreted; and the availability and selection of appropriate statistical methods for drawing ecological inferences. Here, we attempt to conceptualise a general workflow associated with automated tools in ecology. We motivate this perspective using our experiences with occupancy modelling using monitoring data collected through passive acoustic monitoring and camera trapping, identifying priority areas for future developments. We offer an accessible guide to support the ecological community in navigating and capitalising on rapid technological and methodological advances. We describe how different error types arise from both sensor‐based monitoring and from classifiers themselves; how different error types are handled at each stage of the workflow; and finally, implications and opportunities associated with deciding on methods used at each step of the pipeline. We recommend that ‘black box’ tools like neural network classification algorithms should be embraced in ecology, but widespread uptake requires more formal integration of AI into the existing ecological inference workflows. Like ecological AI more broadly, however, successful development of new data‐to‐inference pipelines is a multidisciplinary endeavour that requires input from everyone invested in collecting, processing, analysing and using ecological monitoring data. Methods in Ecology and Evolution240It is recommended that ‘black box’ tools like neural network classification algorithms should be embraced in ecology, but widespread uptake requires more formal integration of AI into the existing ecological inference workflows.Methods in Ecology and Evolution["Amber Cowans", "X. Lambin", "D. Hare", "Chris Sutherland"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/0beabce693f98302c58874c9319430224e0ef5e1 d59a33b8d64b0ab05a737732ac3115f38836fb63Abstract Purpose To propose a novel artificial intelligence (AI)‐based virtual assistant trained on tabular clinical data that can provide decision‐making support in primary eye care practice and optometry education programmes. Method Anonymised clinical data from 1125 complete optometric examinations (2250 eyes; 63% women, 37% men) were used to train different machine learning algorithm models to predict eye examination classification (refractive, binocular vision dysfunction, ocular disorder or any combination of these three options). After modelling, adjustment, mining and preprocessing (one‐hot encoding and SMOTE techniques), 75 input (preliminary data, history, oculomotor test and ocular examinations) and three output (refractive, binocular vision status and eye disease) features were defined. The data were split into training (80%) and test (20%) sets. Five machine learning algorithms were trained, and the best algorithms were subjected to fivefold cross‐validation. Model performance was evaluated for accuracy, precision, sensitivity, F1 score and specificity. Results The random forest algorithm was the best for classifying eye examination results with a performance >95.2% (based on 35 input features from preliminary data and history), to propose a subclassification of ocular disorders with a performance >98.1% (based on 65 features from preliminary data, history and ocular examinations) and to differentiate binocular vision dysfunctions with a performance >99.7% (based on 30 features from preliminary data and oculomotor tests). These models were integrated into a responsive web application, available in three languages, allowing intuitive access to the AI models via conventional clinical terms. Conclusions An AI‐based virtual assistant that performed well in predicting patient classification, eye disorders or binocular vision dysfunction has been developed with potential use in primary eye care practice and education programmes.Ophthalmic & physiological optics390An AI‐based virtual assistant that performed well in predicting patient classification, eye disorders or binocular vision dysfunction has been developed with potential use in primary eye care practice and education programmes.Ophthalmic & Physiological Optics["Leandro Stuermer", "Sabrina Braga", "Raul Martin", "James S. Wolffsohn"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/d59a33b8d64b0ab05a737732ac3115f38836fb63 058075f5cd4cbd3c94277b865bc10b712a9e3a37Purpose This analysis draws on ethnographic research in a Northern UK city where a series of engagement activities produced hand-drawn sketches about the future. This paper shows how different groups revealed contradictory aspirations for the area with growth-focused politicians and planners projecting affluent prosperity rather than the modest, family-oriented social stability sought by local people. Design/methodology/approach Reconciling multiple perspectives is the greatest challenge of civic leadership. This paper considers how the emerging potential of artificial intelligence (AI) could support a structured imagining process for masterplanners to aggregate aspirational sketches at scale and so develop a closer relationship between citizen ideas for the future and civic decision makers’ own strategies and action plans. Findings This paper argues for a collective intelligence paradigm that aggregates individual futures thinking at city scale. Urban masterplanning strategies provide an organising structure to allow a city to emerge as a flourishing of multiple aspirations all at once. Research limitations/implications Rather than having individualism or collectivism as binary alternatives, generative AI offers an intriguing process for combining individual aspirations into a pluralist endeavour. Practical implications Engaging with citizens on the ground, in their homes and community spaces is the only way to uncover what is important to them. Ethnography provides that. Social implications Although AI’s use of aggregated data is collective, intelligence comes from nuanced and ethnographic engagement with the data. Originality/value The principle of the emergent city (Symons 2017) provides a conceptual approach to amalgamating the individual with the collective. Foresight400This paper considers how the emerging potential of artificial intelligence (AI) could support a structured imagining process for masterplanners to aggregate aspirational sketches at scale and so develop a closer relationship between citizen ideas for the future and civic decision makers’ own strategies and action plans.foresight["Jessica Symons"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/058075f5cd4cbd3c94277b865bc10b712a9e3a37 ba80eab7a7514c6fc8a3a39bcd3f05f4b60d571aBackground: Artificial intelligence (AI) and predictive analytics are transforming nursing care by improving decision-making processes and enhancing patient outcomes. This study examines the integration of AI technologies within nursing practice, emphasizing their potential to support nurses in delivering high-quality care. Methods: A comprehensive literature review was conducted to identify key applications of AI in nursing, including machine learning algorithms for risk assessment, natural language processing for documentation, and predictive analytics for patient outcomes. Results: Results indicate that AI tools can significantly reduce the administrative burden on nurses, allowing them to focus more on direct patient care. Additionally, the review highlights ethical, legal, and social implications associated with the adoption of AI technologies in nursing, such as the need for bias mitigation and ensuring patient privacy. Furthermore, the necessity for nursing education to incorporate AI competencies is emphasized, as current curricula often lack adequate training in health informatics and AI. Conclusions: In conclusion, while AI presents substantial opportunities to enhance nursing practice and patient care, it also poses challenges that must be addressed through comprehensive education and ethical frameworks. Future research should explore the long-term impact of AI on nursing roles and patient outcomes, ensuring that technology complements rather than replaces the human elements of nursing care.Journal of Ecohumanism00This study examines the integration of AI technologies within nursing practice, emphasizing their potential to support nurses in delivering high-quality care and the necessity for nursing education to incorporate AI competencies.Journal of Ecohumanism["Khalid Abdullah Saeed Alsaeed", "Manal Turki Ali Almutairi", "Salman Mohammed Dughayyim Almutairi", "Meshal Samah Al Nawmasi", "Nawaf Abdullah Alharby", "Mohammad Masafr Alharbi", "Mohammed Saud Sad Alazzmi", "Adel Hamad Alsalman", "Fahad Ayed Alenazy", "Falaj Ibrahim Alfalaj"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/ba80eab7a7514c6fc8a3a39bcd3f05f4b60d571a 1beae433151130890c2a9d3b82cf3478a6b7c44cThis study investigates the application of artificial intelligence (AI) and machine learning (ML) in quantitative finance, financial technology, and stock market forecasting. Emphasizing their ability to manage large datasets and improve financial predictions, the research details the entire methodology, including data collection, preprocessing, model selection, simulation, and outcome analysis. Linear regression models, particularly suited for stock price prediction, are used alongside Python libraries such as Scikit-learn and TensorFlow to facilitate large-scale implementation. The study also evaluates other models, including Artificial Neural Networks (ANN), Support Vector Machines (SVMs), and Decision Trees, applied to tasks like stock price forecasting and credit risk classification. Results demonstrate that AI and ML significantly enhance financial forecasting accuracy and improve portfolio management. Furthermore, this research highlights the strengths and limitations of current techniques and suggests potential advancements through the integration of AI with technologies such as blockchain and quantum computing. Future directions include exploring these emerging technologies to further increase efficiency and innovation in the financial sector.Advances in Economics, Management and Political Sciences00The research details the entire methodology of artificial intelligence and machine learning in quantitative finance, including data collection, preprocessing, model selection, simulation, and outcome analysis, and highlights the strengths and limitations of current techniques.Advances in Economics, Management and Political Sciences["Tongyu Zheng"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/1beae433151130890c2a9d3b82cf3478a6b7c44c eb45fe666e7350a8a774cc66980d8f21826257f4Kemunculan teknologi Artificial Intelligence (AI) marak digunakan civitas akademik dalam bidang pendidikan termasuk mahasiswa. Istilah teknologi AI mulai diperkenalkan sejak abad ke-16 kepada masyarakat. Sejak itu, AI semakin berkembang hingga berbentuk chatbot AI yang memiliki sisi baik dan buruk sehingga penggunaannya perlu memperhatikan etika akademik. Selain sebagai penunjang penugasan, terdapat anggapan bahwa penggunaan chatbot AI dikhawatirkan memberi mahasiswa keleluasaan dalam melakukan pelanggaran etika akademik misalnya tindak plagiarisme. Tujuan penelitian untuk meneliti seberapa jauh pemahaman etika akademik mahasiswa terhadap penggunaan AI dalam penugasan karya ilmiah. Metode pada penelitian ini yaitu kualitatif melalui wawancara secara lebih dalam sehingga dapat memperoleh informasi yang dibutuhkan. Hasil penelitian menunjukkan, mahasiswa secara implisit memiliki pemahaman adanya etika akademik pada lingkup pendidikan. Meskipun begitu, terdapat mahasiswa yang melakukan praktik pelanggaran etika akademik sehingga pemahaman tersebut belum diterapkan sepenuhnya.Journal of Educational Research210Journal of Education Research["Sausan Salsabila", "Sohidin Sohidin"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/eb45fe666e7350a8a774cc66980d8f21826257f4 6f27222c359a1c64405882224f5cf12a82eca804The topic of active implementation of AI technologies in modern educational reality is one of the most popular in modern scientific discourse. This article is aimed at fixing the points of view of domestic and foreign research community regarding the possibilities of applying artificial intelligence (AI) technologies in the educational sphere. The discussion is centered not so much on the diversity of approaches, topics, and strategies as on a semi-systematic (descriptive) literature review aimed at identifying the most significant research traditions for the topic under consideration. The latter include specific applications of AI technologies, the context of technology use related to the recently concluded COVID-19 pandemic, AI as a tool for learners’ adaptation in the educational process, as well as the analysis of opportunities, limitations, positive expectations and threats to the active implementation of AI technologies in education. Both the diversity of research positions and their ambiguity are recorded. It becomes obvious that despite the large number of publications devoted to the application of AI technologies in the educational process, a number of aspects remain unclear, including the understanding of what is the phenomenon of modern education in the context of the AI technologies application and how it is transformed under the influence of such technologies. The need for further understanding of the role and place of AI technologies in modern social practices within the educational process is emphasized.Ideas and Ideals00It becomes obvious that despite the large number of publications devoted to the application of AI technologies in the educational process, a number of aspects remain unclear, including the understanding of what is the phenomenon of modern education in the context of the AI technologies application and how it is transformed under the influence of such technologies.Ideas and Ideals["V. Vikhman", "A. Mindigulova", "M. Romm"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/6f27222c359a1c64405882224f5cf12a82eca804 436a8e87a767e463752f406b81e7352f740e4730This paper discusses the development history of artificial intelligence (AI) technology and its application in the medical field, including its progress in medical image analysis, intelligent diagnosis, drug development and genomics. It analyses how AI technology breaks through the imbalance between the supply and demand of medical resources, reduces the misdiagnosis rate, and promotes scientific medical decision-making. The application of AI technology in medical data processing and analysis is demonstrated through a case study of Mediadu Cloud, and its advantages and challenges are discussed. Finally, countermeasures such as data standardisation and technological innovation are proposed to promote the healthy development of AI technology in the medical field.Journal of Computing and Electronic Information Management60This paper analyses how AI technology breaks through the imbalance between the supply and demand of medical resources, reduces the misdiagnosis rate, and promotes scientific medical decision-making.Journal of Computing and Electronic Information Management["Ziyi Zhang"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/436a8e87a767e463752f406b81e7352f740e4730 bcaff7108620792f4c3578bf84e5a5bdf864ffb5The rapid advancement of artificial intelligence (AI) is reshaping industries worldwide, particularly its impact on the labor market, which has garnered significant attention. AI, as an emerging technology, has not only enhanced productivity but also posed challenges by automating certain tasks, especially in labor-intensive sectors. Studies indicate that the degree of AIs influence varies across industries such as finance, manufacturing, and services, leading to heterogeneous substitution effects. Low-skilled and repetitive jobs in manufacturing are more susceptible to AI replacement, whereas high-skilled positions are less affected due to the complexity of the tasks involved. In the service industry, while AI assists in improving efficiency, human emotional intelligence and complex decision-making are still required.To address the disruptions AI may cause in employment, policymakers should focus on vocational training, upskilling, and fostering innovation and entrepreneurship, helping workers adapt to emerging opportunities. Education and retraining policies are crucial to mitigating job displacement caused by AI. Additionally, crafting policies that meet the specific needs of different industries will be key to facilitating the coexistence of AI and human labor in the future.Advances in Economics, Management and Political Sciences00To address the disruptions AI may cause in employment, policymakers should focus on vocational training, upskilling, and fostering innovation and entrepreneurship, helping workers adapt to emerging opportunities.Advances in Economics, Management and Political Sciences["Yuna Lin"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/bcaff7108620792f4c3578bf84e5a5bdf864ffb5 b6f68b622583d74f770fd78a4eebacb3a3b1f1f2Artificial intelligence has already led to changes in education, and its influence will continue to grow. However, its implementation in education is not just about introducing new technologies – it requires reflection and the introduction of new educational practices, ethical considerations and the essential strengthening of critical thinking to properly evaluate the reliability of sources and the accuracy of information offered by artificial intelligence. This paper aims to highlight the positive and negative aspects of the use of artificial intelligence in education, focusing on the current challenges, especially ethical and legal ones. The regulatory environment in this field is becoming increasingly dynamic, either through the adaptation of existing or the adoption of new comprehensive laws and legal frameworks at both the national and international levels, to ensure the ethical, non-discriminatory, sustainable and verifiable use of artificial intelligence in education.Anali Pravnog Fakulteta u Beogradu00The regulatory environment in this field is becoming increasingly dynamic, either through the adaptation of existing or the adoption of new comprehensive laws and legal frameworks at both the national and international levels, to ensure the ethical, non-discriminatory, sustainable and verifiable use of artificial intelligence in education.Anali Pravnog fakulteta u Beogradu["Sandra Fabijani\u0107 Gagro"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/b6f68b622583d74f770fd78a4eebacb3a3b1f1f2 91f0eae76e6e2d9c9cece4abd6729b9e1db135ceThe purpose of this paper is to classify and analyze the application scenarios of artificial intelligence in supply chain based on case studies of multinational companies. The paper first introduces the background and development of AI and discusses its current applications in supply chain management. Three main large-scale application areas are proposed, namely supply chain demand forecasting, risk management, and transportation operations planning. Three representative cases from Walmart, HP and UPS are reviewed based on the applied technologies, implemented processes, and advantages and disadvantages. As can be seen, this study contains meaningful recommendations to enhance the use of AI models in other industries and to adapt them to their characteristics. In conclusion, it can be said that AI has great potential to improve supply chain performance and resilience to adversity if conditions are taken into account in practical applications.Journal of Computing and Electronic Information Management140It can be said that AI has great potential to improve supply chain performance and resilience to adversity if conditions are taken into account in practical applications.Journal of Computing and Electronic Information Management["Xinhao Tang", "Lingzhong Yu"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/91f0eae76e6e2d9c9cece4abd6729b9e1db135ce 5131e12658445632e0a11d3c5436c6f433f8d6ccBACKGROUND Artificial intelligence (AI) has significantly impacted medical imaging, particularly in gastrointestinal endoscopy. Computer-aided detection and diagnosis systems (CADe and CADx) are thought to enhance the quality of colonoscopy procedures. SUMMARY Colonoscopy is essential for colorectal cancer screening, but often misses a significant percentage of adenomas. AI-assisted systems employing deep learning offer improved detection and differentiation of colorectal polyps, potentially increasing adenoma detection rates by 8%-10%. The main benefit of CADe is in detecting small adenomas, whereas it has a limited impact on advanced neoplasm detection. Recent advancements include real-time CADe systems and CADx for histopathological predictions, aiding in the differentiation of neoplastic and non-neoplastic lesions. Biases such as the Hawthorne effect and potential overdiagnosis necessitate large-scale clinical trials to validate the long-term benefits of AI. Additionally, novel concepts such as computer-aided quality improvement systems are emerging to address limitations facing current CADe systems. KEY MESSAGES Despite the potential of AI for enhancing colonoscopy outcomes, its effectiveness in reducing colorectal cancer incidence and mortality remains unproven. Further prospective studies are essential to establish the overall utility and clinical benefits of AI in colonoscopy.Digestion00Despite the potential of AI for enhancing colonoscopy outcomes, its effectiveness in reducing colorectal cancer incidence and mortality remains unproven and further prospective studies are essential to establish the overall utility and clinical benefits of AI in colonoscopy.Digestion["M. Misawa", "S. Kudo"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/5131e12658445632e0a11d3c5436c6f433f8d6cc d0354be20e3f7d9594b4a8191d8c314b6e949164Explainable Artificial Intelligence (XAI) refers to systems that make AI models more transparent, helping users understand how outputs are generated. XAI algorithms are considered valuable in educational research, supporting outcomes like student success, trust, and motivation. Their potential to enhance transparency and reliability in online education systems is particularly emphasized. This study systematically analyzed educational research using XAI systems from 2019 to 2024, following the PICOS framework, and reviewed 35 studies. Methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), used in these studies, explain model decisions, enabling users to better understand AI models. This transparency is believed to increase trust in AI-based tools, facilitating their adoption by teachers and students.Journal of educational computing research450This study systematically analyzed educational research using XAI systems from 2019 to 2024, following the PICOS framework, and reviewed 35 studies, finding that transparency in AI-based tools is believed to increase trust in AI-based tools, facilitating their adoption by teachers and students.Journal of Educational Computing Research["Gamze T\u00fcrkmen"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/d0354be20e3f7d9594b4a8191d8c314b6e949164 dcc7d26daa03cf8c97ce82d11763dffedefe7e50The task of structuring information flows that provide the process of designing a transport category aircraft for creating a strategy for optimal design of aviation structures using artificial intelligence is considered. The task of creating strategies for the optimal design of aircraft structures is currently partially solved. This task requires the creation of additional optimization and information technology, which allows transferring individual design tasks from humans to artificial intelligence. Artificial intelligence should relieve people in solving complex problems, reduce the time for designing new aircraft and increase their efficiency.MECHANICS OF GYROSCOPIC SYSTEMS00MECHANICS OF GYROSCOPIC SYSTEMS["Yuryi Bondar"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/dcc7d26daa03cf8c97ce82d11763dffedefe7e50 359f6c40736f7c6d8f5c5bdb6cc62de5798a9a78With the rapid development of artificial intelligence, the public and academic understanding of AI capabilities are also constantly changing. This change has triggered discussions about whether AI can have consciousness, understanding, or subjective experience. The definition of consciousness itself also remains controversial in philosophy and science, further complicating the issue. This article will discuss John Searle and David Chalmers’ thoughts on whether artificial intelligence has consciousness from the three dimensions of the differences between philosophers on the development of machine learning, the relationship between consciousness and matter, and the nature of consciousness, and deeply analyze their thoughts on whether artificial intelligence has consciousness. Conscious views and opinions.Journal of Education, Humanities and Social Sciences90This article will discuss John Searle and David Chalmers’ thoughts on whether artificial intelligence has consciousness from the three dimensions of the differences between philosophers on the development of machine learning, the relationship between consciousness and matter, and the nature of consciousness, and deeply analyze their thoughts.Journal of Education, Humanities and Social Sciences["Kam Seng Li", "Jiahe Tian", "Beijun Xiong"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/359f6c40736f7c6d8f5c5bdb6cc62de5798a9a78 2c6b08861d710dd67fb238aba52d260d184512c3Abstract. Problem. A current problem in recent years is an increase in interest in artificial intelligence, which is due to a significant increase in the power of computing equipment, as well as the availability of data through the global Internet network. The use of artificial intelligence is observed in many fields, in particular in materials science. Today, in the educational process, the issues of creating new materials, predicting properties, additional generation of design features of products are also being solved with the help of artificial intelligence. Goal. The goal of the research in the paper is to demonstrate the use of artificial intelligence in the educational process for students of higher education in the specialty 132 Materials science on the example of teaching the discipline of the selective block "Artificial intelligence in educational technologies". Methodology. Research methods for the application of the platform offer the opportunity to visualize the atomic structures of materials in a three-dimensional format. It helps students analyze material geometry, symmetry, and the spatial arrangement of atoms. Originality. The Materials Project platform is a powerful tool for analyzing and predicting material properties. Due to the fact that it is based on machine learning and provides open online access to the material base, this platform is promising for implementation in the practical activities of students within the discipline "Artificial Intelligence in Educational Technologies". Practical value. The practical training tasks described, which help to acquire the skills of materials scientists, may be used in the field of materials science and related disciplines in the future. The use of software products, in particular the Materials Project database, for the analysis of physico-chemical and mechanical properties of materials is demonstrated.Bulletin of Kharkov National Automobile and Highway University30The use of software products, in particular the Materials Project database, for the analysis of physico-chemical and mechanical properties of materials is demonstrated and may be used in the field of materials science and related disciplines in the future.Bulletin of Kharkov National Automobile and Highway University["Zoia Sazanishvili", "Iryna Matsiuk", "I. Verner"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/2c6b08861d710dd67fb238aba52d260d184512c3 293d7bab78b8453ded641d8d3b34035605a42842This article discusses the role of artificial intelligence (AI) in the development of intercultural communicative competence (ICC) in the context of foreign language acquisition. In modern education, communicative competence has become very important because it requires the use of a foreign language for communication. Communicative competence, as an individual's ability to communicate effectively, is of particular importance in modern society.  The purpose of the article is to analyze the current application AI in foreign language teaching, examine its impact on developing speaking, writing, listening skills and communicative competences of learners. The findings demonstrate AI's ability to personalize learning experiences, hence increasing learner autonomy and engagement. The first section identifies the theoretical foundations of ICC and explores the potential of AI-driven tools to enhance communicative skills. It outlines the methodological approaches used to analyze AI`S role and highlights its benefits in creating interactive learning process. The second section provides a comparative analysis of traditional and AI-enhances teaching methods, illustrating how AI improves skill acquisition through real-time simulations and adaptive feedback mechanisms. Challenges related to data privacy, algorithmic biases, and access inequalities are also discussed. The paper concludes with recommendations for using artificial intelligence into language teaching while maintaining ethical, inclusive, and sustainable methods. The main goal of the article is to ensure the development of communicative skills and to investigate how these skills can be developed in the educational process using artificial intelligence.Bulletin00The findings demonstrate AI's ability to personalize learning experiences, hence increasing learner autonomy and engagement and recommendations for using artificial intelligence into language teaching while maintaining ethical, inclusive, and sustainable methods.THE BULLETIN["Z.N. Zhumatayeva", "Zh. M. Mametkarim", "A.M. Dosanova"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/293d7bab78b8453ded641d8d3b34035605a42842 36f5c83c8a6fc7307c5525337676805153f1ed12 In this episode of the AJR Podcast Series on Training and Education, Michael Recht, MD, joins host Monica Cheng, MD, in a conversation that explores precision education and novel learning paradigms that harness the power of artificial intelligence and innovation in radiology education. AJR. American journal of roentgenology00AJR. American journal of roentgenology["Monica Cheng", "Michael P Recht"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/36f5c83c8a6fc7307c5525337676805153f1ed12 1bc44d6edcd11929b2829c3e731592762f7d64f4In the era of globalization and swift technological progress, the significance of supply chain management (SCM) in the facilitation of business operations has never been more pronounced. This paper investigates the profound impact of artificial intelligence (AI) on SCM, with a focus on scrutinizing the trajectory of AI's development and its practical applications within the supply chain context. The study's objective is to evaluate the advantages AI confers upon the field, through an examination that encompasses a literature review and case analysis. This research scrutinizes AI's contributions to enhancing supply chain efficiency, transparency, flexibility, and responsiveness. The findings underscore that AI's influence extends beyond mere operational enhancements; it is a catalyst for agility, enabling supply chains to swiftly navigate market fluctuations. The paper concludes that the amalgamation of AI with SCM is not just an evolution but a revolutionary shift, presenting a novel framework that will guide forthcoming research and dictate practice within the sector.Advances in Economics, Management and Political Sciences00The paper concludes that the amalgamation of AI with SCM is not just an evolution but a revolutionary shift, presenting a novel framework that will guide forthcoming research and dictate practice within the sector.Advances in Economics, Management and Political Sciences["Xiyao Lin"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/1bc44d6edcd11929b2829c3e731592762f7d64f4 fff32136984762de929b17699117409b84a23c66The rapid development of Artificial Intelligence (AI) has significantly impacted the accounting sector, creating both opportunities and challenges for professionals and students. This study analyzes the readiness of accounting students and practitioners in Indonesia to adopt AI technologies, focusing on their Artificial Intelligence Technology Readiness (AITR). Using the Theory of Planned Behavior (TPB) framework, the study examines the influence of practitioner status, age, gender, technology skill level (TSL), and location on AITR. Data from 100 respondents (students and practitioners) were collected through surveys and analyzed using multiple linear regression with bootstrapping. The research found that students have higher AITR than practitioners, with TSL emerging as the most significant factor. Age, gender, and location show no significant effects. These findings highlight the need for curriculum reforms integrating AI-related skills and practical experiences to meet industry demands. This study provides valuable insights for educators and policymakers to enhance the competencies of future accountants in AI-driven workplaces.  Akurasi : Jurnal Studi Akuntansi dan Keuangan00The research found that students have higher AITR than practitioners, with TSL emerging as the most significant factor, which highlights the need for curriculum reforms integrating AI-related skills and practical experiences to meet industry demands.Akurasi : Jurnal Studi Akuntansi dan Keuangan["Elga Yulindisti", "Ardimansyah Ardimansyah", "Fiqih Yusril Mahendra", "Rafles Ginting"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/fff32136984762de929b17699117409b84a23c66 04dc55959b5e0a781a9487f00e3e3a638eba3e9eArtificial intelligence (AI) is crucial in the fashion industry nowadays. It facilitates innovation in design, production, e‐commerce, personalisation and supply chain management, improving efficient operations and providing opportunities to support sustainability. The research aimed to identify the current use of artificial intelligence in the fashion industry and its arrangement and determine to what extent AI is used to support fashion sustainability. Data collection and selection were according to PRISMA guidelines and were retrieved from Scopus and WoS (January 2017–October 2024; n = 82 (from 234 identified items)). Data analysis was performed using six steps of thematic analysis, including topic modelling (in the MAXQDA 24 programme). The identified themes revealed the current effects of AI use in the fashion industry (RQ1): data‐centric design, forecasting using big data, and experience‐oriented services. AI technologies are predominantly utilised in fabric production and B2B distribution. Experience‐focused services are enhanced through precise image searches and chatbot support. Platforms like SaaS, generative fashion, and Science4Fashion enable the creation of new designs. Applications such as Style. Me serve as personal stylists, facilitating customised outfit selection and streamlining the purchasing process. The last theme (RQ2) allowed to establish that sustainable development requires innovation based on AI technology. Despite optimistic forecasts, available solutions are only used to a limited extent. The main barrier is that companies put economic goals above sustainable development goals. Models that combine commercial and environmental perspectives are essential in developing beneficial change strategies. Therefore, it is important to monitor progress in this area.Sustainable Development560The research aimed to identify the current use of artificial intelligence in the fashion industry and its arrangement and determine to what extent AI is used to support fashion sustainability.Sustainable Development["Jolanta Bie\u0144kowska"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/04dc55959b5e0a781a9487f00e3e3a638eba3e9e 5b8ce348287e4a25c778209312e8c708be76f8ccWhat is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with our family, and yet another in the office. Likewise for AI, appropriate behavior for a comedy-writing assistant is not the same as appropriate behavior for a customer-service representative. What determines which actions are appropriate in which contexts? And what causes these standards to change over time? Since all judgments of AI appropriateness are ultimately made by humans, we need to understand how appropriateness guides human decision making in order to properly evaluate AI decision making and improve it. This paper presents a theory of appropriateness: how it functions in human society, how it may be implemented in the brain, and what it means for responsible deployment of generative AI technology.arXiv.org01A theory of appropriateness is presented: how it functions in human society, how it may be implemented in the brain, and what it means for responsible deployment of generative AI technology.ArXiv["Joel Z. Leibo", "A. Vezhnevets", "Manfred Diaz", "J. Agapiou", "William A. Cunningham", "P. Sunehag", "Julia Haas", "Raphael Koster", "Edgar A. Du'enez-Guzm'an", "William S. Isaac", "Georgios Piliouras", "S. Bileschi", "Iyad Rahwan", "Simon Osindero"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/5b8ce348287e4a25c778209312e8c708be76f8cc b6d7bced71227ab919239dd05099306e5f239d2eThis paper aims to establish an understanding of how developments in technology have affected insurance fraud detection and control. This paper discusses the applicability of combining ML, cloud environment and AI to build flexible and effective fraud discovery systems. The existing strategies for fraud detection and prevention may have a weakness with the amount, variety and real-time nature of data. This paper proposes a detailed framework to improve the effectiveness of fraud detection with the help of ML algorithms for accurate prediction models, AI for decision automation support, and cloud computing for future expansion. It will be clear from the above results that enhanced detection accuracy, operations efficiency and compliance to set legal standards have been attained. This research work’s objective is to present recommendations for insurers interested in preventing fraud while keeping the antidote affordable and easily soluble in large volumes.International Journal of Automation, Artificial Intelligence and Machine Learning00A detailed framework to improve the effectiveness of fraud detection with the help of ML algorithms for accurate prediction models, AI for decision automation support, and cloud computing for future expansion is proposed.International Journal of Automation, Artificial Intelligence and Machine Learning["Sreenivasarao Amirineni"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/b6d7bced71227ab919239dd05099306e5f239d2e fe93246c1e34a9f51ad0e69e08c92a792518c41aTechnological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, the risk of contamination has increased severalfold, often resulting in significant damage in terms of food wastage, economic loss to the producers, danger to public health, or all of these. In general, governments across the world have recognized the importance of having food safety processes in place to impose food recalls as required. However, the primary challenges to the existing practices are delays in identifying unsafe food, siloed data handling, delayed decision making, and tracing the source of contamination. Leveraging the Internet of Things (IoT), 5G, blockchains, cloud computing, and big data, a novel framework has been proposed to address the current challenges. The framework enables real-time data gathering and in situ application of machine learning-powered algorithms to predict contamination and facilitate instant decision making. Since the data are processed in real time, the proposed approach enables contamination to be identified early and informed decisions to be made confidently, thereby helping to reduce damage significantly. The proposed approach also throws up new challenges in terms of the implementation of changes to data collection across all phases of food production, onboarding various stockholders, and adaptation to a new process.Applied Sciences150Leveraging the Internet of Things, 5G, 5G, blockchains, cloud computing, and big data, a novel framework enables contamination to be identified early and informed decisions to be made confidently, thereby helping to reduce damage significantly.Applied Sciences["Siva Peddareddigari", "Sri Vigna Hema Vijayan", "Manickavasagan Annamalai"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/fe93246c1e34a9f51ad0e69e08c92a792518c41a 8eb2e11d59aa1c8bd84858e9429438c57b76812cObjectives The National Library of Medicine (NLM) currently indexes close to a million articles each year pertaining to more than 5300 medicine and life sciences journals. Of these, a significant number of articles contain critical information about the structure, genetics, and function of genes and proteins in normal and disease states. These articles are identified by the NLM curators, and a manual link is created between these articles and the corresponding gene records at the NCBI Gene database. Thus, the information is interconnected with all the NLM resources, services which bring considerable value to life sciences. National Library of Medicine aims to provide timely access to all metadata, and this necessitates that the article indexing scales to the volume of the published literature. On the other hand, although automatic information extraction methods have been shown to achieve accurate results in biomedical text mining research, it remains difficult to evaluate them on established pipelines and integrate them within the daily workflows. Materials and Methods Here, we demonstrate how our machine learning model, GNorm2, which achieved state-of-the art performance on identifying genes and their corresponding species at the same time handling innate textual ambiguities, could be integrated with the established daily workflow at the NLM and evaluated for its performance in this new environment. Results We worked with 8 biomedical curator experts and evaluated the integration using these parameters: (1) gene identification accuracy, (2) interannotator agreement with and without GNorm2, (3) GNorm2 potential bias, and (4) indexing consistency and efficiency. We identified key interface changes that significantly helped the curators to maximize the GNorm2 benefit, and further improved the GNorm2 algorithm to cover 135 species of genes including viral and bacterial genes, based on the biocurator expert survey. Conclusion GNorm2 is currently in the process of being fully integrated into the regular curator's workflow.JAMIA Open140This work demonstrates how the machine learning model, GNorm2, which achieved state-of-the art performance on identifying genes and their corresponding species at the same time handling innate textual ambiguities, could be integrated with the established daily workflow at the NLM and evaluated for its performance in this new environment.JAMIA open["R. Islamaj", "Chih-Hsuan Wei", "Po-Ting Lai", "Melanie Huston", "Cathleen Coss", "P. Kochar", "Nicholas Miliaras", "James G Mork", "Oleg Rodionov", "Keiko Sekiya", "Dorothy Trinh", "Deborah Whitman", "Craig Wallin", "Zhiyong Lu"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/8eb2e11d59aa1c8bd84858e9429438c57b76812c f3f9f8075d46f010dce730ae5c6c49c868a84f98One of the critical phases in software development that must be done accurately is the design of the user interface and user experience design. The way in which users are involved with a system is predominantly shaped by two essential elements: user interface (UI), which refers to the look and feel of the product, and user experience (UX), which encompasses the entire process of the interaction by the user. Our study focuses on the relationship between sustainability and UI/UX design, examining how these design components can be used to create environmentally friendly software with little negative effects on the environment. This protocol describes how factors and principles of sustainable UI/UX design can be ascertained systematically with a special focus on the use of AI in green software engineering. This paper investigates sustainable UI/UX by reviewing literature through a Multivocal literature review (MLR), followed by empirical validation with UX practitioners and researchers. Some specific interesting fields to work are the concept of less but better, energy saving, and AI approaches to the problem. The outcomes are intended as a pathway to a more environmentally responsible design to help researchers and practitioners. Objectives: The objectives of this study is to find key factors and principles of UI/UX design that help in making software more eco-friendly, study the role of AI in enhancing UI/UX design, and explore AI-based practices that contribute to sustainable software development. Kashf Journal of Multidisciplinary Research00This protocol describes how factors and principles of sustainable UI/UX design can be ascertained systematically with a special focus on the use of AI in green software engineering, and explores AI-based practices that contribute to sustainable software development.Kashf Journal of Multidisciplinary Research["Muhammad Ilyas", "Sahab Ahmad Khan", "Noor ul Islam", "Fazli Rabi", "Nasir Rashid"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/f3f9f8075d46f010dce730ae5c6c49c868a84f98 431394471668e051591d3ec089a16a9317edf505AJR. American journal of roentgenology20AJR. American journal of roentgenology["Sandra Patricia Maldonado", "Mar\u00eda M\u00f3nica Yepes", "J. Orteg\u00f3n"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/431394471668e051591d3ec089a16a9317edf505 dd768b45b5e04d4b548063ca71f3d466e80b11ffAJR. American journal of roentgenology00AJR. American journal of roentgenology["Syed Muhammad Awais Bukhari", "Sirui Jiang", "Amit Gupta"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/dd768b45b5e04d4b548063ca71f3d466e80b11ff fb78146eac8ae5a07ec0cb7592f95008962fb06aFrom the perspective of global value chain, this paper explores the correlation between AI and emerging market trade, and analyzes how AI technology affects the trade structure of emerging market countries and their changing position in global value chain. Based on regression analysis and panel data analysis methods, the study comprehensively assesses the specific impact of AI on emerging market trade in terms of production efficiency, product quality, and market entry strategies.This paper proposes that emerging market countries should further strengthen the R&D and application of AI technology, optimize their industrial structure, and actively participate in the innovation cooperation of GVCs. The findings provide an important reference for policymakers to promote emerging market countries to achieve higher quality development in global trade.Advances in Economics, Management and Political Sciences00It is proposed that emerging market countries should further strengthen the R&D and application of AI technology, optimize their industrial structure, and actively participate in the innovation cooperation of GVCs to achieve higher quality development in global trade.Advances in Economics, Management and Political Sciences["Jialu Lin"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/fb78146eac8ae5a07ec0cb7592f95008962fb06a 7d1c09cd17a9f9de9d46a2155ada0ca822e1b70dNurse Educator20Nurse educator["Emily J Tomlinson", "Monica Schoch", "Susie Macfarlane", "Sara Aryal", "Fiona Kumar", "Naomi Bunker", "Jo McDonall"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/7d1c09cd17a9f9de9d46a2155ada0ca822e1b70d 7d6f4b56f67a9459cc21ca5e790e67e6379a77dfWomen & health100Women & health["M. Carneiro"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/7d6f4b56f67a9459cc21ca5e790e67e6379a77df b7b934e11690e07473a83620e0af1ec7becfc677The finance industry has been radically re-invented by AI and is now providing novel solutions to a data-intensive and ever more sophisticated marketplace. In this article, AI applications in finance portfolio management, risk management, and algorithmic trading are discussed in depth. The overview discusses some emerging techniques (including deep learning, synthetic data generation and deep reinforcement learning) and problems (such as interpretability, regulation compliance, algorithmic bias). The paper synthesizes existing research, outlining the limitations and prospects for AI technologies to enable financial decision-making, risk-taking and trading. The paper will address the promise and challenges of AI in finance, to contribute to the growing literature and inform scholars, practitioners and policymakers, leading to a more effective and resilient financial ecosystem.Applied and Computational Engineering00The paper will address the promise and challenges of AI in finance, to contribute to the growing literature and inform scholars, practitioners and policymakers, leading to a more effective and resilient financial ecosystem.Applied and Computational Engineering["Sheng Wu"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/b7b934e11690e07473a83620e0af1ec7becfc677 2d3c82a8fc1be45a761c66764f9533b0b62ff9feJournal of Educare00Journal of Educare["Hanin Badsah"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/2d3c82a8fc1be45a761c66764f9533b0b62ff9fe 339effa911cd54b1d5ab9d61ea315fc79c4db705Research and Development in Medical Education00Research and Development in Medical Education["Mostafa Kashani", "Amin Beigzadeh"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/339effa911cd54b1d5ab9d61ea315fc79c4db705 2c5e543b690ce1a04ef9baa0a4b3e26798ad0713International Journal of Research in Library Science00International Journal of Research in Library Science["Bornali Konwar"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/2c5e543b690ce1a04ef9baa0a4b3e26798ad0713 f3ea88107b9b11e943b40eb22a1de806a80c0746The International Journal of Advanced Manufacturing Technology120The International Journal of Advanced Manufacturing Technology["Xiangguo Yin"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/f3ea88107b9b11e943b40eb22a1de806a80c0746 9ca9817c1826b7533d0591e2fb62cadaaaf5812bInternational Journal of Computer Applications00International Journal of Computer Applications["Mobasher Hasan", "Jubair Bin Sharif", "Md. Kwosar", "Md. Faysal Ahmed", "Daniel Lucky Michael"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/9ca9817c1826b7533d0591e2fb62cadaaaf5812b fc1afcbf9367095abdac467e6961a6464abb2762Current Infectious Disease Reports410Current Infectious Disease Reports["Lemuel R. Non", "Alexandre R Marra", "Dilek Ince"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/fc1afcbf9367095abdac467e6961a6464abb2762 e30b4de9ef629012f62d27f7ef0d6813208322c6observations, modelingBulletin of The American Meteorological Society - (BAMS)160Bulletin of the American Meteorological Society["Dani Jones", "Scott Steinschneider", "Paul Roebber", "Sage Osborne", "Lauren Fry", "Lacey Mason", "Andrea Vander Woude", "M. Phanikumar", "Nathan Fox", "William S. Currie", "Silvia Santa", "Maria Newell", "Jia Wang", "Alisa Young", "Lindsay Fitzpatrick", "Yi Hong", "Hazem Abdelhady", "William J. Pringle", "Anders Kiledal", "A. Gronewold", "Ann Arbor"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/e30b4de9ef629012f62d27f7ef0d6813208322c6 aa35614ae029c5ff219a545f98903a48462b976dPurposeIn this era of rapid technological advancement, Artificial Intelligence (AI) has emerged as a crucial factor in reshaping organisational dynamics, notably in the realm of leadership. This systematic literature review (SLR) aims to investigate the emerging relationship between AI and leadership, focussing on defining AI-powered leadership, identifying prevalent themes, exploring challenges, and uncovering research gaps within the relevant literature.Design/methodology/approachA sample of 73 papers was chosen after carefully applying the inclusion and exclusion criteria to 1,387 research articles that were initially sought. Using the methodological framework presented by Denyer and Tranfield (2009), our study adopted a four-step procedure to obtain insights from the corpus of literature. The papers were analysed by employing content and thematic analysis to address four key questions.FindingsThe review explores various definitions of AI-powered leadership proposed in the literature based on real-world situations. The study further synthesises significant themes in the existing literature, such as the past, present and future of AI and AI in various facets of organisational leadership, transitional management, and urban management. The review revealed a range of key challenges in AI-powered leadership, including ethical dilemmas, complications in human-AI interactions, hurdles in AI implementation within leadership contexts, and long-term risks associated with AI integration. In addition, this study identified areas within AI-powered leadership research that require further investigation by revealing significant research gaps in the papers.Originality/valueBy adopting a comprehensive approach, this research advances understanding of the complex relationship between AI and leadership dynamics, thus facilitating comprehension of the current body of knowledge and enabling future scholarly investigations in the AI-powered leadership domain.Journal of Managerial Psychology801The review revealed a range of key challenges in AI-powered leadership, including ethical dilemmas, complications in human-AI interactions, hurdles in AI implementation within leadership contexts, and long-term risks associated with AI integration.Journal of Managerial Psychology["Muhammad Faisal Aziz", "J. I. Rajesh", "Fazilat Jahan", "Adela McMurrray", "Nisar Ahmed", "Roshni Narendran", "Christian Harrison"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/aa35614ae029c5ff219a545f98903a48462b976d 5c58f0b4fdf7a4a164997ccb0f2e5432bb0f6259Purpose: This study examines the impact of artificial intelligence disclosure within the US banking sector. It may explore the implications of AI disclosure on issues like financial reporting, transparency, accountability, and ethical considerations within the banking sector. Design/methodology/approach: Using a blend of qualitative and quantitative analyses, the researchers utilized SEC and NASDAQ databases to scrutinize AI disclosures within the top 10 banks. The sample comprised 100 annual reports, and through multiple regression analysis, the research discerned a noteworthy enhancement in performance metrics. Findings: The study found that AI influences financial performance only when moderated by the interaction of shareholders, the board of directors, and independent board members. The findings indicate a rising trend of AI disclosure in financial reports. The study indicates that AI disclosure impacts NII, TEXP, and P/E. Additionally, the study indicated a conflict of interest between agents and principals. Large shareholders tended to favor more AI disclosures, whereas the board of directors either did not support or adopted a more conservative stance on disclosure. Research limitations/implications: This study acknowledges a limitation in the dataset; initially comprising 100 annual reports, it was later refined to meet regression analysis assumptions. Despite this limitation, the study’s insightful results contribute significantly to our understanding of the dynamic relationship between AI disclosure and the performance of top-tier banks in the USA. Originality/Value: By investigating the impact of AI disclosure, the study aims to provide insights into the broader considerations associated with artificial intelligence disclosures in the US banking sector. This study also analyzes how stakeholders respond to the disclosed information about artificial intelligence.Journal of Risk and Financial Management361The study found that AI influences financial performance only when moderated by the interaction of shareholders, the board of directors, and independent board members, and the findings indicate a rising trend of AI disclosure in financial reports.Journal of Risk and Financial Management["Ahmad Alzeghoul", "Nizar Alsharari"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/5c58f0b4fdf7a4a164997ccb0f2e5432bb0f6259 b0d0aba647a43f9603318c61d03990cb5a320809This paper explored the impact of the use of artificial intelligence on the development of critical thinking skills in high school students. It was hypothesized that the use of this tool can have a positive effect on cognitive abilities. For this purpose, a mixed approach was used to examine how interaction with AI affects the teaching-learning process in a diverse educational environment. The theoretical framework established the growing relevance of AI in the transformation of education. The methodology detailed the quasi-experimental design, the selection of a diverse sample, and the quantitative and qualitative instruments for data collection. The findings provided an in-depth understanding of how participants use AI tools in their learning and their perception of impact. The mixed analysis highlighted trends in AI use and its relationship to cognitive skill development. This paper seeks to contribute to knowledge about the impact of AI on critical thinking, hoping that it will serve as a starting point for improving educational practices in an increasingly digitized context.Multidisciplinary Latin American Journal (MLAJ)01The mixed approach used to examine how interaction with AI affects the teaching-learning process in a diverse educational environment provided an in-depth understanding of how participants use AI tools in their learning and their perception of impact.Multidisciplinary Latin American Journal (MLAJ)["Nansi Ysabel Garc\u00eda-Garc\u00eda", "Martha Patricia Guti\u00e9rrez-P\u00e9rez", "Rita Elizabeth Soto-S\u00e1nchez", "Rita Elizabeth Soto-S\u00e1nchez", "Germ\u00e1n Ra\u00fal Jim\u00e9nez-Garc\u00eda"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/b0d0aba647a43f9603318c61d03990cb5a320809 28e744f5ae3e2278fe12313af6591c631bff30c4PurposeThis study investigates healthcare administration students’ perspectives on integrating artificial intelligence (AI) in fluid healthcare work environments, focusing on its potential impact on future healthcare practices.Design/methodology/approachThe research utilizes a mixed-methods strategy, combining quantitative surveys and qualitative interviews to collect data from healthcare administration students at a mid-sized urban university. This comprehensive approach allows for an in-depth analysis of students’ understanding of, attitudes toward and expectations of AI in healthcare settings.FindingsResults reveal that students have a nuanced understanding of AI’s capabilities to enhance healthcare operations and patient care, showcasing readiness to adopt these technologies. Nonetheless, there are significant concerns about job security and the depersonalization of care with the integration of AI. The study highlights the critical need for healthcare curricula to evolve to incorporate AI training that equips future professionals to use these technologies in increasingly flexible work settings.Originality/valueThis research offers new perspectives on how future healthcare professionals view AI integration within evolving work arrangements. It emphasized the need for educational institutions to update and adapt educational frameworks to prepare a workforce that can effectively navigate the challenges and opportunities presented by AI in the healthcare sector. This is particularly relevant in fluid work dynamics, where adaptability and responsiveness are key.International Journal of Productivity and Performance Management490The study highlights the critical need for healthcare curricula to evolve to incorporate AI training that equips future professionals to use these technologies in increasingly flexible work settings.International Journal of Productivity and Performance Management["Stephanie Bilderback", "Mohammad Movahed"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/28e744f5ae3e2278fe12313af6591c631bff30c4 c293235588f81f45048e5ef8aa9cdeaf941b36b4This study explores the influence of anthropomorphism and racial homophily on audience trust in Artificial Intelligence News Anchors (AINAs) in the context of contemporary journalism. Utilizing a comprehensive between-groups experiment, participants were recruited online and presented with audiovisual news clips featuring AINAs. The research investigates the relationships among anthropomorphic cues, viewers’ perceptions of racial homogeneity, and the trustworthiness of news conveyed by these AI entities. Findings indicate a significant positive correlation between visual cues and news trustworthiness, while anthropomorphic features exert a moderating effect. However, the study highlights limitations in sample representativeness and generalizability across diverse cultural contexts, suggesting that results may not apply universally to all AINA viewers. The study calls for further exploration of the interaction between racial traits and AI technology, emphasizing the need to consider personal attributes and the evolving landscape of AI in journalism. By advancing the theoretical framework of human-AI interaction, this research contributes valuable insights into the intersection of technology, media, and audience perception.Journal of Education, Humanities and Social Sciences00Findings indicate a significant positive correlation between visual cues and news trustworthiness, while anthropomorphic features exert a moderating effect, which calls for further exploration of the interaction between racial traits and AI technology.Journal of Education, Humanities and Social Sciences["Qiyu Long"]2024-12-26T00:00:00https://www.semanticscholar.org/paper/c293235588f81f45048e5ef8aa9cdeaf941b36b4 97e79a8efa2b269827d8ceff1da882869a2f48c2Artificial intelligence (AI) into education is becoming a transformative agent offering new chances for enhancing administrative processes, teaching, and learning. Particularly machine learning (ML) and deep learning (DL), recent advances in artificial intelligence technologies have shown great potential in predicting academic achievement, improving teaching strategies, and so supporting decision-making inside educational institutions. Notwithstanding these advances, there are obvious problems and limits that have to be addressed if we are to fully exploit the potential of artificial intelligence in the field of education. Recent research reveals significant limits like poor contextual adaptability of artificial intelligence models, insufficient integration of emerging technologies like augmented reality (AR), and challenges in improving distance learning. Although the integration of AR into educational systems is still under investigated, current artificial intelligence models usually rely on generalised datasets lacking the diversity of educational environments. The shift to online learning has underscored even more the requirement of solid, contextually relevant models to manage assessment strategies, student interaction, and technology acceptance. By means of a comprehensive examination of the corpus of present literature, this paper evaluates the present position of artificial intelligence applications in education so highlighting research needs and constraints. Emphasising their capacity to solve the discovered challenges, the survey focusses on ML and DL application. By means of analysis of current studies and recommended future research routes, this study aims to offer pragmatic insights and recommendations for enhancing the efficiency of artificial intelligence in educational environments.International Journal of Computational and Experimental Science and Engineering3011This paper evaluates the present position of artificial intelligence applications in education so highlighting research needs and constraints, and offers pragmatic insights and recommendations for enhancing the efficiency of artificial intelligence in educational environments.International Journal of Computational and Experimental Science and Engineering["S. Esakkiammal", "K. Kasturi"]2024-12-27T00:00:00https://www.semanticscholar.org/paper/97e79a8efa2b269827d8ceff1da882869a2f48c2 c1317a90b359b4acffddf7e5a8eb18d1b0cd4b8cObjectives: This review aims to explore the applications of artificial intelligence (AI) in prosthodontics and implant dentistry, focusing on its performance outcomes and associated ethical concerns. Materials and Methods: Following the PRISMA guidelines, a search was conducted across databases such as PubMed, Medline, Web of Science, and Scopus. Studies published between January 2022 and May 2024, in English, were considered. The Population (P) included patients or extracted teeth with AI applications in prosthodontics and implant dentistry; the Intervention (I) was AI-based tools; the Comparison (C) was traditional methods, and the Outcome (O) involved AI performance outcomes and ethical considerations. The Newcastle–Ottawa Scale was used to assess the quality and risk of bias in the studies. Results: Out of 3420 initially identified articles, 18 met the inclusion criteria for AI applications in prosthodontics and implant dentistry. The review highlighted AI’s significant role in improving diagnostic accuracy, treatment planning, and prosthesis design. AI models demonstrated high accuracy in classifying dental implants and predicting implant outcomes, although limitations were noted in data diversity and model generalizability. Regarding ethical issues, five studies identified concerns such as data privacy, system bias, and the potential replacement of human roles by AI. While patients generally viewed AI positively, dental professionals expressed hesitancy due to a lack of familiarity and regulatory guidelines, highlighting the need for better education and ethical frameworks. Conclusions: AI has the potential to revolutionize prosthodontics and implant dentistry by enhancing treatment accuracy and efficiency. However, there is a pressing need to address ethical issues through comprehensive training and the development of regulatory frameworks. Future research should focus on broadening AI applications and addressing the identified ethical concerns.Dental journal521AI has the potential to revolutionize prosthodontics and implant dentistry by enhancing treatment accuracy and efficiency, but there is a pressing need to address ethical issues through comprehensive training and the development of regulatory frameworks.Dentistry Journal["Amal Alfaraj", "Toshiki Nagai", "Hawra AlQallaf", "Wei-Shao Lin"]2024-12-27T00:00:00https://www.semanticscholar.org/paper/c1317a90b359b4acffddf7e5a8eb18d1b0cd4b8c c2ca0a1fe31ccbbffa6c5d206b5c4f3a3e56de12The article examines the rapidly growing artificial intelligence technologies aimed at understanding and supporting humans. It outlines the main achievements, emerging problems, and promising development paths. It is argued that while these emerging human-oriented artificial intelligence technologies promise a significant increase in psychological comfort, they also pose serious risks. The concept of "digital angels" is proposed—artificial intelligence technologies created to protect the interests of their owner. Digital angels should combine three roles: digital assistants that help individuals organize their lives; digital communicators that ensure connection with other people and the environment; and digital confidants that a person can unconditionally trust.Experimental Psychology50The concept of "digital angels" is proposed—artificial intelligence technologies created to protect the interests of their owner, which should combine three roles: digital assistants that help individuals organize their lives; digital communicators that ensure connection with other people and the environment; and digital confidants that a person can unconditionally trust.Experimental Psychology (Russia)["Dmitry V. Ushakov"]2024-12-27T00:00:00https://www.semanticscholar.org/paper/c2ca0a1fe31ccbbffa6c5d206b5c4f3a3e56de12 3bfae10fd66e8b2ba795589c592245c988541a2cThis study investigates the integration of artificial intelligence (AI) in education, focusing on its impact on learning processes, teaching methods, and evaluation systems. Utilizing a qualitative research approach through a systematic literature review, the research highlights how AI technologies have evolved from basic computer systems to sophisticated web-based platforms and intelligent tutoring systems. AI's capabilities include mimicking human cognitive functions such as learning and decision-making, which have been increasingly adopted by educational institutions. These technologies enhance teaching efficiency by automating administrative tasks like grading and attendance tracking, allowing educators to concentrate on more complex instructional duties. Furthermore, AI facilitates personalized learning experiences by analyzing individual student data to tailor educational content according to their unique strengths and weaknesses. The findings indicate that AI not only improves the quality of education but also enriches the overall learning experience by fostering greater student engagement and retention of knowledge. As AI continues to develop, its role in education is expected to expand, providing innovative solutions that cater to diverse learning needs and enhancing the effectiveness of teaching methodologies, thereby underscoring its transformative potential in shaping future educational practicesJATI (Jurnal Mahasiswa Teknik Informatika)00The findings indicate that AI not only improves the quality of education but also enriches the overall learning experience by fostering greater student engagement and retention of knowledge.JATI (Jurnal Mahasiswa Teknik Informatika)["Cindy Fitri Laksono", "Afrida Eka Prasetya Putri", "Resti Anggraini"]2024-12-27T00:00:00https://www.semanticscholar.org/paper/3bfae10fd66e8b2ba795589c592245c988541a2c ceccbee848e1c3694636f5325bf673970aee4d3bThis research explores the transformative impact of artificial intelligence (AI) on personalized learning in educational settings. As traditional teaching methods struggle to cater to the diverse needs of students, AI offers innovative solutions that can tailor educational experiences to individual learning styles, preferences, and paces. This study investigates the various applications of AI technologies, including intelligent tutoring systems, adaptive learning platforms, and data-driven insights, to enhance personalized learning outcomes. By employing a mixed-methods approach that combines quantitative analysis of academic performance metrics with qualitative feedback from students and educators, the research aims to assess the effectiveness of AI-driven personalized learning initiatives. The findings reveal that AI not only improves student engagement and motivation but also facilitates differentiated instruction that addresses learning gaps and strengths. Additionally, the study identifies challenges such as data privacy concerns and the need for professional development for educators to effectively integrate AI solutions into their teaching practices. Ultimately, this research contributes to the ongoing discourse on the role of AI in education, offering actionable recommendations for educators and policymakers to optimize personalized learning experiences for all students.Edu Spectrum: Journal of Multidimensional Education80The findings reveal that AI not only improves student engagement and motivation but also facilitates differentiated instruction that addresses learning gaps and strengths.Edu Spectrum: Journal of Multidimensional Education["Muhammad Zailani Iman", "Alfian Airlangga Asis", "Aynu Uzma Zein Rahma"]2024-12-27T00:00:00 a39f0740ddc50b7b31cfc25240d4bacd2bd2a638Current Urology Reports250Current uses of AI, including AI-powered Chatbots, Large Language Models (LLM) and Natural Language Processing (NLP), are discussed with a focus on their application in men's health clinics, with a focus on enhancing clinic efficiency and billing practices.Current urology reports["Nickolas Kinachtchouk", "David Canes"]2024-12-27T00:00:00 f2f435433d9224f8462e33ed9e42063ac1923dd9в статье представлен диапазон потенциальных рисков и способов их решения при применении искусственного интеллекта в образовании. Автор исследует не только технические, правовые и этические аспекты, но также уделяет внимание взаимодействию между людьми и машинами в образовательной среде. С точки зрения автора, результаты исследования имеют важное значение для содействия здоровому развитию технологий искусственного интеллекта в образовании, поддержки разработки соответствующих политик и усовершенствования практик в образовании. this article presents a range of potential risks and ways to address them in the application of artificial intelligence in education. The author explores not only the technical, legal and ethical aspects, but also pays attention to the interaction between humans and machines in the educational environment. From the author's point of view, the results of the study are important for promoting the healthy development of AI technologies in education, supporting the development of relevant policies and improving practices in education.Социально-гуманитарные исследования: векторы развития науки и образования: материалы IX научно-практической конференции с международным участием, г. Москва, МПГУ, 25–26 апреля 2024 г.00Социально-гуманитарные исследования: векторы развития науки и образования: материалы IX научно-практической конференции с международным участием, г. Москва, МПГУ, 25–26 апреля 2024 г.["\u0421\u0438\u043d\u044c\u0436\u0443\u0439 \u041b\u0438"]2024-12-27T00:00:00 2510ba67f296c3b24a04f50ec3ca178b5dafbbdfArtificial Intelligence (AI) is a computer system that mimics the human brain's ability to process information and make decisions. AI technology is used to learn patterns in data and make predictions or decisions based on that learning. Based on the current situation, elementary school teachers need help in adopting AI technology due to limited training, lack of resources, and resistance to change. In order to address this problem, this research aims to identify the factors influencing the adoption of AI technology among primary school teachers in West Java, Indonesia. The research involved 384 participants and used a quantitative approach. Specific factors influencing AI adoption were identified by developing a model for AI-based teaching and learning and assessing readiness factors. The results from the study identified optimism, innovativeness, insecurity, discomfort, perceived validity, trust, usefulness, and ease of use as factors in the successful adoption of AI among primary school teachers in West Java. The customized adoption model provides a practical roadmap for integrating AI into teaching and learning processes, highlighting regional specificities while remaining relevant to similar educational challenges worldwide. The assessment of readiness factors provides actionable insights for fostering a supportive environment for technology integration. The study concluded with recommendations for future research and implications for educators, administrators, and policymakersJurnal Online Informatika00Specific factors influencing AI adoption were identified by developing a model for AI-based teaching and learning and assessing readiness factors, and optimism, innovativeness, insecurity, discomfort, perceived validity, trust, usefulness, and ease of use were identified.Jurnal Online Informatika["Erlan Darmawan", "Titik Khawa Abdul Rahman", "N. Thamrin"]2024-12-27T00:00:00 93c62cddb7f347c878ceac6e29dd7409dbfc0e86Artificial Intelligence (AI) is reshaping the design landscape, bridging computational efficiency with human creativity to revolutionize fields such as architecture, graphic design, and product development. This paper explores AI’s transformative impact, focusing on its ability to enhance productivity, foster innovation, and personalize user experiences. Objectives include identifying the benefits of AI-driven tools, analyzing their applications across domains such as architecture, graphic design, and product development, and evaluating ethical concerns related to AI in design. The research adopts a qualitative approach, to examine AI’s role as a creative collaborator and its implications for design methodologies. Results reveal that AI optimizes design iterations, accelerates prototyping, and democratizes access to high-quality resources, making design processes more inclusive and efficient. Findings also highlight ethical concerns, such as bias in AI systems and intellectual property disputes, which require balanced and responsible integration strategies. AI serves as a creative collaborator, enhancing ideation and prototyping processes. Despite its benefits, AI integration raises ethical concerns, including data bias, intellectual property disputes, and potential job displacement. These challenges necessitate equitable frameworks to ensure inclusive and responsible AI use. The future of AI in design promises even greater innovation with emerging technologies like augmented reality and the metaverse, fostering collaborative human-machine interactions. By embracing AI, designers can expand creative boundaries, producing solutions that are not only functional and visually compelling but also socially and environmentally sustainable. This study underscores the need for balanced integration, ensuring AI complements human ingenuity while redefining creativity in the evolving design landscape.Smart Design Policies00Results reveal that AI optimizes design iterations, accelerates prototyping, and democratizes access to high-quality resources, making design processes more inclusive and efficient, and highlights the need for balanced integration.Smart Design Policies["O. P. Agboola"]2024-12-27T00:00:00 3fd50ebc1a803648e14f272972776e9ed348730dThis study explores the application of Artificial Intelligence (AI) concepts in the context of Hindu epics, particularly The Mahabharata. The research seeks to draw parallels between ethical dilemmas faced by characters in the scripture and the contemporary challenges of AI governance. The objective is to examine how the concepts of autonomy, responsibility, and moral decision-making, as depicted in these ancient scriptures, can inform modern discussions on the ethical use of AI. The study adopts a qualitative methodology, analyzing specific episodes from The Mahabharat in light of AI governance frameworks. Key episodes, such as Yudhisthira’s dice game, Krishna’s counsel to Arjuna, and the use of Krishna’s Sudarshana Chakra, are examined for their relevance to AI ethics, particularly the dangers of unchecked autonomy, the balance between autonomy and responsibility, and the ethical use of autonomous systems. The findings reveal that The Mahabharat provide rich analogies for contemporary AI governance issues. The texts emphasize the importance of ethical oversight, human control, and moral responsibility in decision-making processes, mirroring current debates around AI’s role in society. The principle of dharma is highlighted as a potential framework for governing AI systems, ensuring that they operate ethically and prioritize human welfare. In conclusion, the study suggests that insights from Hindu philosophy, particularly the concept of dharma, offer valuable ethical guidance for the development and regulation of AI systems, ensuring that these technologies serve humanity responsibly.  Mongolian Journal of Arts and Culture00It is suggested that insights from Hindu philosophy, particularly the concept of dharma, offer valuable ethical guidance for the development and regulation of AI systems, ensuring that these technologies serve humanity responsibly.Mongolian Journal of Arts and Culture["Ramesh Prasad Adhikary"]2024-12-27T00:00:00 c390693d501a84f9ec4a00c4bacbfb3e183d7023The relevance of this study is determined by the lack of international legal instruments to regulate the use of innovative technologies, such as artificial intelligence (AI), in the military sphere. Rapid technological progress, especially in the field of military technologies, creates new challenges that require the development of legal mechanisms capable of adequately responding to potential threats and ensuring global security. This problem is exacerbated by the lack of uniform international standards for the military use of AI, leading to legal uncertainty and a dearth of harmonized rules. The purpose of this study is to analyze the current legislation of the People's Republic of China (PRC) in the field of military AI, with a focus on the draft of AI Regulation Law, considered by the author in a military context. The study aims to identify the advantages and limitations, as well as fragmentation and uncertainty in the present legal regulation of military AI in the PRC. The results of the analysis show that the existing norms of Chinese law are characterized by insufficient elaboration, which may lead to legal conflicts when interpreting and applying these norms in the context of international obligations and standards. This emphasizes the need to integrate national approaches into global legal mechanisms to create universal standards consistent with international principles for regulating the military use of AI. The practical significance of the study lies in identifying the prospects for the formation of a comprehensive legal framework that will ensure the lawful use of AI in armed conflicts, considering international obligations and norms of humanitarian law, contributing to the strengthening of international security.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00The analysis of the current legislation of the People's Republic of China in the field of military AI shows that the existing norms of Chinese law are characterized by insufficient elaboration, which may lead to legal conflicts when interpreting and applying these norms in the context of international obligations and standards.Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan["A. Khassanay"]2024-12-27T00:00:00 20d98ea79c7b108bcc07aeb4fa4f152948919d53The development of artificial intelligence (AI) has revolutionized various aspects of human resource management, including employee performance evaluation. While existing studies have extensively explored the potential of AI in improving efficiency and objectivity, they often overlook the nuanced employee experiences and organizational dynamics that influence its successful implementation. This research bridges this gap by examining the perceptions and experiences of PT Pos Indonesia employees regarding the use of an AI-based performance evaluation system. Using a qualitative approach with a phenomenological design, data was collected through in-depth interviews with employees who have used the system for at least six months. The findings reveal that AI contributes significantly to enhancing efficiency and reducing subjectivity in evaluations. However, challenges such as algorithm bias, the relevance of performance metrics, and system transparency remain prevalent. Importantly, this study identifies critical factors influencing acceptance, including employee understanding, trust, and perceptions of fairness in the evaluation process. Unlike previous research, this study emphasizes the interplay between technological and human factors, highlighting the irreplaceable role of human interaction in providing qualitative context. This research extends the existing literature by offering a deeper understanding of employee-centered factors and organizational practices that facilitate the integration of AI in performance evaluation. Practically, it provides actionable insights for organizations aiming to implement AI-based systems effectively, ethically, and equitably.bit-Tech00This research bridges the gap in understanding of employee-centered factors and organizational practices that facilitate the integration of AI in performance evaluation by examining the perceptions and experiences of PT Pos Indonesia employees regarding the use of an AI-based performance evaluation system.bit-Tech["Agung Dwianto", "Sitta Kusuma", "Junengsih"]2024-12-27T00:00:00 7914cbc21e02218887a9ef103346f3d2b3f7b092Journal of religion and health500A Catholic ethical study of human dignity in the context of AI in healthcare is presented, which provides a framework that brings AI development in tandem with a Catholic vision of human dignity and supports a healthcare system that caters to the common good but correctly respects the irreplaceable value of the human person and highlights moral responsibility.Journal of religion and health["I. E. Gozum", "Chastene Christopher D Flake"]2024-12-27T00:00:00 15fdb261b42f1c2734ecc67412538f5c45f238b7This study examines the use of Artificial Intelligence (AI) in developing data-driven marketing strategies at Cherri Technologies, Pondicherry. A descriptive research approach was applied, collecting data from 103 participants through structure questionnaires. Various statistical tools, such as Chi-square, ANOVA, regression analysis, and correlation, were used to analyse the data. The results indicate a strong association between the integration of AI and the enhancement of marketing strategies, especially in terms of improving efficiency, personalization, and customer engagement. Despite its advantages, obstacles like high implementation costs, ethical concerns, and data privacy issues continue to hinder its full adoption. The findings offer valuable insights for utilizing AI to create innovative marketing strategies, while also addressing the challenges involved. The study highlights the need for ongoing adaptation, ethical considerations, and further research into AI-driven marketing practices.International Journal For Multidisciplinary Research30The results indicate a strong association between the integration of AI and the enhancement of marketing strategies, especially in terms of improving efficiency, personalization, and customer engagement.International Journal For Multidisciplinary Research["A. S", "P. S"]2024-12-27T00:00:00 bb0e494659ee57a07f55fcda995a4cbc9ce30ec9In the 21st century, humanity has made a significant leap in the development of information and computational technologies, leading to the active advancement of artificial intelligence (AI). Initially, AI had primarily an entertainment character, but now technologies based on AI are actively being integrated into various professional fields. The Russian Federation is also increasing its research volume, including the application of these technologies in medical activities. This article examines some of the issues related to the active implementation and use of AI and AI-based products in medical practice in Russia. Literature data on existing legislation and legal practice are presented, along with discussions on ethical and deontological issues of AI use in medicine. Problems of information security and material-technical support are also highlighted. Based on the analysis conducted, we have formulated conclusions pointing to the controversial aspects of AI-based technologies' application in current medical activities. It is important to note that the authors do not oppose the integration of AI-based technologies into medical activities but have merely analyzed the problematic issues. The formulated conclusions and identified problems can help form a unified trajectory for the development and integration of AI into practical healthcare in Russia.Bulletin physiology and pathology of respiration30Examination of issues related to the active implementation and use of AI and AI-based products in medical practice in Russia finds conclusions pointing to the controversial aspects of AI-based technologies' application in current medical activities.Bulletin Physiology and Pathology of Respiration["A. V. Kucher", "S. Khodus", "E. Borzenko"]2024-12-27T00:00:00 a8678bd1f5eb22b55ddf8187171d6f9179374faeArtificial intelligence (AI) has emerged as a powerful tool to enhance decision-making and optimize treatment protocols in in vitro fertilization (IVF). In particular, AI shows significant promise in supporting decision-making during the ovarian stimulation phase of the IVF process. This review evaluates studies focused on the applications of AI combined with medical imaging in ovarian stimulation, examining methodologies, outcomes, and current limitations. Our analysis of 13 studies on this topic reveals that, reveal that while AI algorithms demonstrated notable potential in predicting optimal hormonal dosages, trigger timing, and oocyte retrieval outcomes, the medical imaging data utilized predominantly came from two-dimensional (2D) ultrasound which mainly involved basic quantifications, such as follicle size and number, with limited use of direct feature extraction or advanced image analysis techniques. This points to an underexplored opportunity where advanced image analysis approaches, such as deep learning, and more diverse imaging modalities, like three-dimensional (3D) ultrasound, could unlock deeper insights. Additionally, the lack of explainable AI (XAI) in most studies raises concerns about the transparency and traceability of AI-driven decisions - key factors for clinical adoption and trust. Furthermore, many studies relied on single-center designs and small datasets, which limit the generalizability of their findings. This review highlights the need for integrating advanced imaging analysis techniques with explainable AI methodologies, as well as the importance of leveraging multicenter collaborations and larger datasets. Addressing these gaps has the potential to enhance ovarian stimulation management, paving the way for efficient, personalized, and data-driven treatment pathways that improve IVF outcomes.arXiv.org00The need for integrating advanced imaging analysis techniques with explainable AI methodologies, as well as the importance of leveraging multicenter collaborations and larger datasets are highlighted, to enhance ovarian stimulation management.ArXiv["Jana Zakall", "Birgit Pohn", "Antonia Graf", "Daniel Kovatchki", "Arezoo Borji", "Ragib Shahriar Islam", "Hossam Haick", "Heinz Strohmer", "S. Hatamikia"]2024-12-27T00:00:00 a89a9ea5c09c6c80252dd0722211455d209822a8Integrating Artificial Intelligence (AI) with 3D printing technology offers transformative solutions in healthcare, specifically improving clinical training through precise and customizable replicas of human anatomy. Traditional training methods face challenges such as limited access to high quality models, lack of precision, adaptability, and the environmental impact of resource use. This study employed a mixed methods approach, combining quantitative analysis of model accuracy and input from healthcare professionals, to evaluate the effectiveness of AI optimized 3D printing. The focus was on AI enhanced 3D printing models designed for healthcare training. Traditional methods often lack precision, adaptability, and scalability, limiting their effectiveness in dynamic healthcare scenarios. AI based 3D printing reduces material use by 30\% while providing high quality, customized training models, improving accessibility and sustainability. This research highlights the potential of AI and 3D printing integration to drive technological innovation, align healthcare training with Sustainable Development Goals (SDGs), and promote a more sustainable and efficient future in medical education.ADI Journal on Recent Innovation (AJRI)00This research highlights the potential of AI and 3D printing integration to drive technological innovation, align healthcare training with Sustainable Development Goals (SDGs), and promote a more sustainable and efficient future in medical education.ADI Journal on Recent Innovation (AJRI)["Ramzi Zainum Ikhsan", "Sri Rahayu", "Abdul Hamid Arribathi", "Nur Azizah"]2024-12-27T00:00:00 19c0d992b6fee549eb7303bbd41fb063cf08d9d1The applications of Artificial Intelligence (AI) in the airport industry are significantly transforming operational efficiency, safety, and passenger experiences. This study investigates the integration of AI within aviation construction projects, with a focus on the United Arab Emirates (UAE). While AI technologies such as facial recognition, IoT, and biometric systems have advanced airport security and operations, their use in construction project management remains limited. A survey was conducted among 101 engineering professionals and experts with experience or involvement in managing aviation-related construction projects. Participants, many of whom had familiarity with AI tools, provided insights into the applicability of AI in areas such as planning, scheduling, and safety monitoring. The majority agreed that AI has the potential to revolutionize project management processes, improving decision-making, and efficiency. AI tools can predict delays, optimize workflows, and enhance safety through real-time data analytics and machine learning algorithms, reducing risks and human error. Despite the UAE’s leadership in AI-driven security advancements, its use in aviation construction is still underdeveloped. This research highlights the potential for broader AI integration across the entire lifecycle of aviation projects. By adopting AI in these areas, UAE airports could set new benchmarks for cost effectiveness, sustainability, and project delivery, reinforcing the region’s status as a leader in technological innovation within the aviation industry.Applied Sciences460By adopting AI in these areas, UAE airports could set new benchmarks for cost effectiveness, sustainability, and project delivery, reinforcing the region’s status as a leader in technological innovation within the aviation industry.Applied Sciences["Mariam Abdalla Alketbi", "F. Dweiri", "D. Dalalah"]2024-12-27T00:00:00 9d4cb8ef236a7f1e0968c8a60aa9c14f4ec829caThis article investigates attitudes towards the adoption of artificial intelligence (AI) technology in the public academic libraries of South Africa. It employed the mixed method research approach and the concurrent mixed method research design. The study's target population was 2565 library staff members (library managers, systems librarians, and general librarians), and policy documents from 26 public academic libraries in South Africa. A sample of 555 participants was selected, comprising 26 library managers and 26 systems librarians chosen through purposive sampling, while 503 librarians were sampled using proportional stratified random sampling. Data collection tools used comprised questionnaires, interviews, and content analysis. Data were analysed using thematic and descriptive statistical data analysis. Based on the findings, public academic libraries in South Africa generally have a positive attitude towards the adoption of AI technology, with only a few having a negative attitude. Furthermore, some employees were afraid that this technology would make them redundant by taking over their work. The major contributory factors to attitudes towards the adoption of AI include self-perception of AI knowledge, optimism and enthusiasm about AI, and concerns about job security. The article recommends that a positive outlook and optimistic attitude regarding the adoption of AI should be maintained. Additionally, AI training sessions and workshops to educate librarians who hold negative attitudes must be implemented to facilitate a shift in their perceptions towards the acceptance of AI.Information Development80Public academic libraries in South Africa generally have a positive attitude towards the adoption of AI technology, with only a few having a negative attitude, and some employees were afraid that this technology would make them redundant by taking over their work.Information Development["A. Molaudzi", "N. Marutha"]2024-12-27T00:00:00 08b8eb3dbbe80b7b9bb40aa67662746a74f9fba4Despite the advancement in autonomous vehicles, public trust and acceptance are crucial for AV’s widespread adoption. This study examines how different collaboration levels between human drivers and artificial intelligence influence users’ trust and acceptance of AVs. Using an extended Technology Acceptance Model, this study incorporates psychological factors and technological attitudes such as perceived safety, perceived risk, AI literacy, and AI technophobia. Data collected from 392 vehicle owners across 11 Chinese cities were analyzed using Partial Least Squares Structural Equation Modeling and Multi-Group Analysis. The findings reveal that at the fully manual level, perceived ease of use significantly influences perceived usefulness, while trust remains grounded in mechanical reliability rather than AI systems. In contrast, as AI assumes driving responsibilities at collaborative automation levels, the findings show that AI literacy significantly increases perceived trust and ease of use, while AI technophobia decreases them, with these effects varying across different driving automation levels. As AI takes on greater driving responsibilities, perceived ease of use becomes less critical, and perceived trust increasingly influences users’ acceptance. These findings highlight the need for targeted public education and phased automation strategies, offering guidance for AV developers to address user concerns and build trust in autonomous technologies. By enhancing public trust and acceptance, this study contributes to sustainable development by promoting safer roads and enabling more efficient, resource-conscious transportation systems. Gradually integrating AVs into urban mobility also supports smart city initiatives, fostering more sustainable urban environments.Sustainability800The findings reveal that at the fully manual level, perceived ease of use significantly influences perceived usefulness, while trust remains grounded in mechanical reliability rather than AI systems, with these effects varying across different driving automation levels.Sustainability["Yi Yang", "Min-Yong Kim"]2024-12-27T00:00:00 2360f94649d2a6c144679562128c595d10cedc5fThe article examines the application of artificial intelligence (AI) in the banking system of Armenia, emphasizing its relevance and importance in the financial sector. The objective is to analyze the opportunities, problems and impact of AI on banking services. The research was conducted using both qualitative and quantitative methods. The obtained results show that the use of AI can significantly increase the efficiency of banks, predict financial risks and frauds. The article also highlights that innovative AI applications such as automated financial advice are still not widely used in Armenia, which opens up new opportunities.Проблемы социально-экономического развития: поиски, перспективы, решения00The obtained results show that the use of AI can significantly increase the efficiency of banks, predict financial risks and frauds, and open up new opportunities in the financial sector.Проблемы социально-экономического развития: поиски, перспективы, решения["Inessa Nushikyan"]2024-12-27T00:00:00 2c78c88e0539cbae18e98c20ae33daad0b05d566Artificial Intelligence merupakan salah satu produk teknologi yang saat ini sedang marak digunakan. Kegunaan utama dari AI yaitu untuk menunjang kehidupan manusia setiap harinya. Dengan teknologi yang semakin pesat maka kebutuhan manusia semakin kompleks.  Pada era saat ini teknologi memiliki peran besar terhadap pembelajaran manusia., salah satunya yaitu dalam bidang pembelajaran bahasa asing. Metode pembelajaran konvensional untuk saat ini telah dianggap kurang mampu dalam meningkatkan wawasan dari bahasa asing. Artificial Intelligence merupakan salah satu produk teknologi yang saat ini sedang marak digunakan. Bukti peran dari AI yaitu menghadirkan pembelajaran bahasa asing yang lebih personal, interaktif, dan adaptif sehingga dapat menjadi solusi bagi masalah yang dihadapi dalam bidang pembelajaran. Tujuan dari penelitian ini yaitu untuk dapat memberikan pandangan lebih luas mengenai AI yang digunakan dalam bidang pembelajaran bahasa asing. Metode yang digunakan yaitu Systematic Literature Review untuk menjawab seluruh pertanyaan yang diajukan berdasarkan dari penelitian terdahulu yang berkaitan. Penelitian menghasilkan bahwa AI dapat digunakan dalam menunjang efektivitas pembelajaran bahasa asing dengan menggunakan teknologi Natural Language processing.JATI (Jurnal Mahasiswa Teknik Informatika)00JATI (Jurnal Mahasiswa Teknik Informatika)["Camelia Salsabila Putri Wijaya", "Farah Bianca", "Melisya Sesy Amelia"]2024-12-27T00:00:00 55786d50371e9f20012d09827d7dd7d6931d5aadFiduciary Guarantee before being used as collateral in a credit agreement, it is mandatory to register the fiduciary first. Electronic fiduciary registration still has problems including network disruptions, system errors and is vulnerable to cyber attacks. Smart contract integration with electronic fiduciary guarantee registration can be a solution to these problems. Smart contract as a form of artificial intelligence / Artificial Intelligence (AI). The purpose of this study is to analyze the legality of credit agreements with fiduciary guarantees made with Artificial Intelligence (AI). The type of research used is empirical legal research, supported by primary and secondary data. The results of the research are that Smart contract is a computer program that can make agreements automatically with the blockchain system.AI is generally viewed as a legal object, not a legal subject. This is because AI is a tool or device used by humans to achieve certain goals. The requirements for the validity of an agreement (including a credit agreement with fiduciary guarantee) remain based on Article 1320 of the Civil Code.Sultan Agung Notary Law Review00The results of the research are that Smart contract is a computer program that can make agreements automatically with the blockchain system that is based on Article 1320 of the Civil Code.Sultan Agung Notary Law Review["Lathifah Hanim", "Peni Rinda Listyawati", "MS Noorman"]2024-12-27T00:00:00 3c137fb01b613ca7d4919b569abb56491378357eThe purpose of this study is to explore the barriers to the successful implementation of Artificial Intelligence (AI) in organizations, focusing on psychological, organizational, and ethical challenges. The aim is to identify strategies to overcome resistance and foster trust, ensuring a seamless integration of AI technologies into business operations. Methodology. The research is based on a comprehensive review of existing literature and real-world examples. It employs a qualitative approach to analyze the root causes of resistance to AI adoption, emphasizing psychological fears, organizational misalignments, and ethical concerns. Strategic frameworks and best practices are proposed to address these challenges effectively. Results. The findings reveal that psychological resistance arises from fears of job displacement and mistrust in AI systems, while misaligned strategies and cultural inertia drive organizational resistance. Ethical concerns such as bias, accountability, and privacy violations exacerbate resistance. Strategies such as fostering transparency, aligning AI initiatives with business goals, implementing robust governance, and addressing ethical challenges can significantly reduce resistance and enhance AI adoption. Practical Implications. The study provides actionable insights for business leaders and policymakers to mitigate resistance to AI implementation. By fostering transparency, offering training programs, and ensuring ethical compliance, organizations can build trust among stakeholders. Legal measures and stakeholder engagement are highlighted as critical components for long-term success in AI integration. Value / Originality. This research offers a holistic framework for addressing resistance to AI adoption, integrating psychological, organizational, and ethical dimensions. By bridging gaps between theory and practice, it provides unique insights to support organizations in leveraging AI’s transformative potential while ensuring alignment with societal and ethical values.Three Seas Economic Journal10The findings reveal that psychological resistance arises from fears of job displacement and mistrust in AI systems, while misaligned strategies and cultural inertia drive organizational resistance, and strategic frameworks and best practices are proposed to address these challenges effectively.Three Seas Economic Journal["Vasyl Ivchyk"]2024-12-27T00:00:00 1e00cb1acc1279b912fd717002ba76e1e99952d2актуальность исследования обусловлена тем, что инвестиции в разработку программ с искусственным интеллектом остаются значимым фактором развития экономики, общества и обеспечения технологического прогресса в целом, способствуя развитию цифровой инфраструктуры, формированию конкурентоспособной и динамичной экономики. В результате исследования сделан вывод, что каждый этап развития цифровой экономики характеризуется инновациями в используемых технологиях и связан с использованием более совершенных инструментов с ИИ, что вызывает потребность постоянного притока инвестиций, наибольшую долю которых занимают инвестиции в программы и оборудование с искусственным интеллектом. the relevance of the research is conditioned by the fact that investments in the development of programs with artificial intelligence remain a significant factor in the development of the economy, society and technological progress in general, contributing to the development of digital infrastructure and the formation of a competitive and dynamic economy. As a result of the study, it was concluded that each stage of the development of the digital economy is characterized by innovations in the technologies used and is associated with the use of more advanced AI tools, which causes the need for a constant influx of investments, the largest share of which is occupied by investments in AI programs and equipment.Социально-гуманитарные исследования: векторы развития науки и образования: материалы IX научно-практической конференции с международным участием, г. Москва, МПГУ, 25–26 апреля 2024 г.00Социально-гуманитарные исследования: векторы развития науки и образования: материалы IX научно-практической конференции с международным участием, г. Москва, МПГУ, 25–26 апреля 2024 г.["\u0410.\u0421. \u041a\u043e\u0437\u043b\u043e\u0432\u0441\u043a\u0430\u044f", "\u0410.\u0412. \u0418\u0432\u0430\u043d\u0438\u0447\u043a\u0438\u043d\u0430", "\u0415.\u0412. \u0422\u0438\u043d\u044c\u043a\u043e\u0432\u0430"]2024-12-27T00:00:00 89e7c80a3f7d87a4c2e9010a23adf2c1b49d5733The article focuses on the tendencies in artificial intelligence (AI) conceptualization based on the analysis of Russian media discourse. The conceptual, figurative and axiological features of AI as an abstract mental formation are identified. The conceptualization of artificial intelligence is shown to be represented in two directions: the ability of an artificial system to perform tasks that mimic human cognitive abilities and the science of modelling computerized intellectual behavior. The nuclear features of the concept with the key representation in the word combination artificial intelligence are recorded in lexicographic sources and special-purpose dictionaries; they constitute the basis for the conceptualization of artificial intelligence in media discourse. In the figurative-and-perceptual aspect, artificial intelligence is conceptualized as a living being endowed with physical characteristics and analytical abilities. Artificial intelligence is noted to be conceptualized in media discourse as an object of use, development, implementation, training, that performs a wide range of vital functions, and as a subject that demonstrates anthropomorphic characteristics (the ability to memorize, explain, analyze, etc.). The conceptualization of artificial intelligence in the value dimension manifests itself through positive assessment or possible harm. The utilitarian properties are evaluated positively, whereas the hypothetical impact on humans, in the case of uncontrolled headway in this area, is assessed negatively.Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije190Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije["Elena N. Galichkina"]2024-12-27T00:00:00 06ab4b0a43bbfa00ff9c8529681cf17c8186dd4dArtificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field. It describes a broad range of AI methodologies, investigates the unique challenges inherent in pediatric ECG analysis, reviews the current state of the literature in pediatric AI-ECG, and discusses potential future directions for research and clinical practice. While AI-ECG applications have demonstrated considerable promise, widespread clinical adoption necessitates further research, rigorous validation, and careful consideration of equity, ethical, legal, and practical challenges.Children760A broad range of AI methodologies are described, a unique challenges inherent in pediatric ECG analysis are investigated, the current state of the literature in pediatric AI-ECG is reviewed, and potential future directions for research and clinical practice are discussed.Children["David M. Leone", "Donnchadh O\u2019Sullivan", "Katia Bravo-Jaimes"]2024-12-27T00:00:00 8d0f9c889bdea1a5079f6d0434918c8502a16cafThe article presents a master class for teachers on the use of artificial intelligence and neural networks for project activities. During the master class, the authors of the article share their personal experience and introduce participants to examples of using the capabilities of artificial intelligence in project research activities. Participants of the master class get acquainted with the main trends and well-known examples of using neural networks and artificial intelligence in life; master services based on artificial intelligence for use in project activities; acquire practical skills in working with neural networks; learn to distinguish a product created by a person from a product created by artificial intelligence. The article presents a detailed plan of the master class, including examples of tasks, exercises and recommendations for using modern interactive tools for working on a research project. The proposed material is intended to help teachers implement innovative methods of working with artificial intelligence and neural networks in their students' projects.Informatics in school00A master class for teachers on the use of artificial intelligence and neural networks for project activities, including examples of tasks, exercises and recommendations for using modern interactive tools for working on a research project is presented.Informatics in school["V. V. Menshikov", "N. M. Savin"]2024-12-27T00:00:00 02fea4171489b4989bbed1e3804378bcaaaef542The use of Artificial Intelligence (AI) is becoming a daily routine in newsrooms. AI appears as an essential tool in journalists’ professional routines, aiding in faster work processes, automatic text generation, and assisting with repetitive tasks. However, the use of AI in the media provides fertile ground for various types of abuse, settling scores with dissenters, and falls under the category of “weaponized defamation.” The Serbian government is committed to keeping pace with the development and application of AI in various sectors. However, this commitment is not accompanied by an adequate legal framework when it comes to the media. Currently, there is no specific law regulating this field, leaving those affected by existing practices to rely on related legislation, which neither covers all potential violations in this area nor prevents further manipulation. This paper analyzes the legal framework for regulating AI in the media, as well as the potential for self-regulation. The analysis is based on a case study in which media mogul Željko Mitrović published a “satirical video” on his X platform, followed by its broadcast in the news programs of Pink Television. The video, which was a deepfake generated using artificial intelligence, mocked opposition representatives. The paper also examines the first lawsuit in which opposition representative Dragan Djilas won against Željko Mitrović and Pink Television, based on the AI-generated video content that was broadcast on television. The scope of this ruling highlights both the possibilities and shortcomings of the legal framework in this area within the Serbian media landscape.MEDIA STUDIES AND APPLIED ETHICS00The legal framework for regulating AI in the media, as well as the potential for self-regulation, is analyzed, based on a case study in which media mogul Željko Mitrović published a “satirical video” on his X platform, followed by its broadcast in the news programs of Pink Television.MEDIA STUDIES AND APPLIED ETHICS["Milica Kuli\u0107"]2024-12-27T00:00:00 b309ea290283bc477130e0dcd25a0c9cf50c67a7This study explores the critical interplay between artificial intelligence (AI) and human behavior within organizational settings. Using a quantitative methodology, data from 504 employees across AI-utilizing organizations were analyzed to examine AI's effects on productivity, work stress, personality, and skill development. Results demonstrate AI's capacity to enhance productivity and facilitate continuous skill enhancement while presenting challenges such as maintaining employee well-being and adapting to technological changes. The findings provide actionable insights for organizations to implement AI effectively while fostering a balanced and supportive work environment that aligns with human psychological and emotional needs.International Journal For Multidisciplinary Research00Analysis of data from 504 employees across AI-utilizing organizations demonstrates AI's capacity to enhance productivity and facilitate continuous skill enhancement while presenting challenges such as maintaining employee well-being and adapting to technological changes.International Journal For Multidisciplinary Research["Priyanka"]2024-12-27T00:00:00 f5b5323785d35be2e96274aba6683e898e43ff17Blockchain and artificial intelligence are two of the most prominent technologies in computer science today and have attracted considerable attention from various research communities. Recently, several initiatives have been launched to explore the combination of these two pioneering technologies. The main goal is to combine the data integrity, privacy, and decentralization properties of blockchain with the ability of artificial intelligence to process, analyze, predict, and refine massive data sets. The combination of blockchain and AI technologies is expected to address key challenges in the digital realm, such as data security, transparency, and streamlined decision-making. However, there is a problem that many studies have focused on the advancement of a single technology as the main perspective. To overcome these recent research limitations, we provide a broad view of the combination of blockchain and artificial intelligence and analyze the limitations of existing research and their causes. Furthermore, we identify challenges and attempts to be addressed through this analysis. The analysis in this paper is organized into a comprehensive section dedicated to the application of artificial intelligence in blockchain and vice versa. Based on our analysis, we identify existing challenges and propose a novel framework for researchers to overcome these limitations, thus expanding new research opportunities.Electronics630This paper provides a broad view of the combination of blockchain and artificial intelligence and analyze the limitations of existing research and proposes a novel framework for researchers to overcome these limitations, thus expanding new research opportunities.Electronics["Nakhoon Choi", "Heeyoul Kim"]2024-12-27T00:00:00 a722d2a123be41a74f567faa1090b8d4e700f6f0The topic "Artificial Intelligence in Modern Marketing" is highly relevant and crucial in today's rapidly evolving market landscape. Intense competition necessitates marketing professionals to continuously make precise and informed decisions to maintain a robust and sustainable presence. This research aims to conduct a thorough analysis of the utilization of artificial intelligence in contemporary marketing, offering valuable insights into how companies can leverage it effectively to accomplish their marketing objectives. Various research methodologies were employed, including theoretical research, scientific abstraction, and comparative analysis. Through these approaches, the study aims to provide both theoretical insights and practical recommendations on how companies can enhance their marketing strategies using artificial intelligence, leading to greater success. The findings and recommendations derived from this study hold significance for marketing professionals, as well as industry researchers, by shedding light on the myriad ways artificial intelligence can enhance marketing strategies and drive business goals forward.Проблемы социально-экономического развития: поиски, перспективы, решения30The findings and recommendations derived from this study hold significance for marketing professionals, as well as industry researchers, by shedding light on the myriad ways artificial intelligence can enhance marketing strategies and drive business goals forward.Проблемы социально-экономического развития: поиски, перспективы, решения["Anna Sargsyan"]2024-12-27T00:00:00 d8efce4264bb16554fd3d2922770cef7be27e57fThe scientific article examines the issues of the relationship between artificial intelligence and the labor sphere, as well as those subject to regulation in the labor sphere by analyzing it within the EAEU member states. In the event of the development of artificial intelligence in all areas, what changes will it bring to the labor sphere of the EAEU and what new changes will be made to labor legislation, shows the relevance of the scientific article. The development of artificial intelligence systems leads to the obsolescence and disappearance of many jobs and professions, and affects the emergence of new professions. As digitalization develops, many industries will be robotized, and jobs that require physical labor may be eliminated. Therefore, among the EAEU member states, it is necessary to limit the work assigned to robots in labor legislation, regulate the preparation of labor standards that ensure their interaction with people, and regulate labor legislation. The purpose of the scientific article is to determine the place of artificial intelligence in the sphere of labor of the EAEU member states, to study how it is implemented in law enforcement practice, and to draw the author's conclusions. At the same time, to study the place of artificial intelligence in the sphere of labor and foreign practice of its legal regulation, and also to consider the possibility of its use in the national legislation of the EAEU member states with reference to the norms of the Treaty on the EAEU. Should artificial intelligence be avoided or can a country's economy be improved by effectively using its services, using it to improve the skills of employees at national and international levels and drawing conclusions about points that can lead to effective changes in labor relations.Bulletin of Institute of Legislation and Legal Information of the Republic of Kazakhstan00The scientific article examines the issues of the relationship between artificial intelligence and the labor sphere, as well as those subject to regulation in the labor sphere by analyzing it within the EAEU member states and drawing conclusions about points that can lead to effective changes in labor relations.Bulletin of the Institute of Legislation and Legal Information of the Republic of Kazakhstan["M. Zhurunova"]2024-12-27T00:00:00 12e9f682eed2f14730893865876baefd3b9e893fThis review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and opportunities associated with integrating AI into CPV, focusing on real-time data analysis and proactive process adjustments. The paper highlights the importance of aligning AI solutions with regulatory standards and offers a set of comprehensive recommendations to bridge the gap between AI's potential and its practical, compliant, and safe application in pharmaceutical manufacturing. Emphasizing transparency, interpretability, and risk management, the research contributes to establishing best practices for AI implementation, ensuring the highest quality pharmaceutical products while meeting regulatory expectations. The conclusions drawn provide valuable insights for navigating the evolving landscape of AI in pharmaceutical manufacturing.PDA journal of pharmaceutical science and technology00The paper highlights the importance of aligning AI solutions with regulatory standards and offers a set of comprehensive recommendations to bridge the gap between AI's potential and its practical, compliant, and safe application in pharmaceutical manufacturing.PDA journal of pharmaceutical science and technology["Mario Stassen", "Catarina S Leitao", "Toni Manzano", "Francisco Valero", "Benjamin Stevens", "Matt Schmucki", "David Hubmayr", "Ferran Mirabent Rubinat", "Sandrine Dessoy", "Antonio R Moreira"]2024-12-27T00:00:00 beb376ef209107574f49ef1785061c37c25b958eThe application of artificial intelligence in the field of higher education provides an opportunity to offer personalized and effective learning. Through artificial intelligence, the curriculum can be tailored to meet the needs and abilities of students. AI can help identify weaknesses that need to be addressed and areas that should be improved. It contributes to enhancing the educational process in terms of efficiency and flexibility, as well as identifying previous mistakes. However, artificial intelligence cannot fully replace teachers but serves as a support to improve the learning process. The use of artificial intelligence can enhance the quality of education and foster a deeper understanding of the material by students. However, its effectiveness depends on proper usage and the need to integrate it with traditional educational technologies.Проблемы социально-экономического развития: поиски, перспективы, решения30The use of artificial intelligence can enhance the quality of education and foster a deeper understanding of the material by students, but its effectiveness depends on proper usage and the need to integrate it with traditional educational technologies.Проблемы социально-экономического развития: поиски, перспективы, решения["Liana Osipyan"]2024-12-27T00:00:00 99e6744f94b3a20a7ede450a5144bd1dfcaf8242The purpose of this paper is to discuss the status quo of the teaching classroom under the background of artificial intelligence as well as the existing problems, and put forward corresponding countermeasures and suggestions. First of all, this paper outlines the application of artificial intelligence in education, including intelligent teaching system, personalized recommended learning resources, etc., and introduces the changes in teaching mode, such as flipped classroom and online education. Meanwhile, the change of teachers' roles and the change of students' learning styles are also important features of the teaching classroom in the context of artificial intelligence. However, there are some problems in the teaching classroom in the context of artificial intelligence. Technical problems are one of them, including the stability of the system and the accuracy of data. The issue of educational equity is also not to be ignored, as the gap in technological resources leads to inequity in learning opportunities among students. In addition, teacher training issues and student privacy protection issues are also important issues that need to be addressed. In order to address these issues, this paper presents some countermeasures and recommendations. Technological improvements are necessary, including improving the reliability of the system and protecting the security of data. Policy support is also key, and the government should increase investment in AI education and promote the balanced distribution of educational resources. In addition, teacher training and development is also important. Teachers should improve their educational technology skills and adapt to the needs of teaching in the context of AI. At the same time, the protection of students' rights and interests is also necessary, and relevant laws and regulations should be formulated to protect students' privacy and rights and interests.International Journal of Education and Humanities60The application of artificial intelligence in education is outlined, including intelligent teaching system, personalized recommended learning resources, etc., and the changes in teaching mode, such as flipped classroom and online education are introduced.International Journal of Education and Humanities["Weiwei Zhang"]2024-12-27T00:00:00 ae67e2cb726cfe843824b87a92e29af0a69e8316This article examines the complex relationship between artificial intelligence and the cultivation of high school students' soft skills. The research aims to explore AI's impact on expectations for students' social competencies and propose strategies to enhance these skills. Methodologies employed include literature review, questionnaires, statistical evaluation, synthesis, and comparative analysis. Findings indicate that across all surveyed groups - educators, families, employers, and pupils - there is consensus that AI technologies are altering skill requirements for high schoolers. The investigation contributes theoretically by offering fresh insights into soft skills' significance amid technological advancement and illuminating factors shaping student capability demands. Practically, it presents a framework for nurturing social aptitudes within secondary education. Moreover, the outcomes may inform curriculum design and instructional resources focused on fostering essential interpersonal proficiencies in adolescent learners. Keywords: soft skills, artificial intelligence, high school students, skills requirements, teachers, parents, employers Bulletin of Toraighyrov University Pedagogics Series00It is indicated that across all surveyed groups - educators, families, employers, and pupils - there is consensus that AI technologies are altering skill requirements for high schoolers.Bulletin of Toraighyrov University. Pedagogics series["N. Y. Fominykh", "A. E. Mukhametkairov", "K. O. Kaziyev", "T. Azamat", "B. A. Matayev"]2024-12-27T00:00:00 82ed122fea5a1ef69fdcea2ac325fc472013da9bThis critical analysis examines the role of artificial intelligence (AI) in attaining the Sustainable Development Goals (SDGs), emphasizing its potential advantages and related problems. AI technologies can markedly improve resource efficiency, optimize public service delivery, and stimulate economic growth and development, thereby advancing multiple Sustainable Development Goals (SDGs), including poverty alleviation, responsible consumption and sustainable urban development. The incorporation of artificial intelligence brings ethical challenges, including the potential for perpetuating biases and aggravating inequality, which may impede progress toward sustainable development. This assessment underscores the necessity of responsible deployment of Artificial Intelligence, promoting strong governance frameworks, collaborative efforts among multiple stakeholders, and focused investments in education and capacity building to minimize poverty significantly. By connecting artificial intelligence research and uses with the Sustainable Development Goals and ensuring equal access to its advantages, stakeholders may leverage AI as a potent instrument for sustainable development. In conclusion, leveraging the revolutionary potential of AI for sustainable development necessitates a multi-stakeholder approach that includes governments, researchers, civil society, and the corporate sector. This collaboration must emphasize the establishment of strong governance frameworks, the involvement of data management specialists, the assurance of algorithmic transparency and accountability, and the facilitation of equal access to AI technologies. This study ultimately advocated for a balanced strategy that optimizes the beneficial effects of AI while mitigating its problems to promote a more sustainable and equitable future for all citizens.Journal of economics and trade00In conclusion, leveraging the revolutionary potential of AI for sustainable development necessitates a multi-stakeholder approach that includes governments, researchers, civil society, and the corporate sector.Journal of Economics and Trade["Ndubuisi-Okolo Purity Uzoamaka"]2024-12-27T00:00:00 f18b54f0fc40fc7df36882de3195851872086680Some time ago, Indonesia was amazed by the presence of a female celebrity named "Lentari van Lorainne" who is familiarly called Riri. Riri's figure is the center of attention because she is Indonesia's first virtual celebrity created based on Artificial Intelligence (AI). The media depiction of Riri's figure is very identical to the depiction of the feminine gender promoted by patriarchy. This article will use Gramsci's thinking about Hegemony, and is equipped with the concepts of hyperbody, gender ideology, gender stereotypes and Artificial Intelligence. The paradigm used is critical with a text analysis method from the most posts on Lentari van Lorainne's account, @lentaripagi. The results of the research show that the figure of Riri also perpetuates gender stereotypes of the female body by presenting Riri as a young woman with beautiful associations that make people stunned, has an ideal body and a beautiful Indonesian-Dutch face, likes sports and travelling and is charming. This perpetuation occurs through posts and comments on Riri's Instagram account. This perpetuation was carried out by the traditional intellectuals behind the creation of the character Riri as a hyperentity. There is no counter to the hegemony of gender ideology found in this paper.WACANA: Jurnal Ilmiah Ilmu Komunikasi200The research shows that the figure of Riri also perpetuates gender stereotypes of the female body by presenting Riri as a young woman with beautiful associations that make people stunned, has an ideal body and a beautiful Indonesian-Dutch face, likes sports and travelling and is charming.WACANA: Jurnal Ilmiah Ilmu Komunikasi["Sari Monik Agustin"]2024-12-27T00:00:00 d3548dffb593d78513ac9d01c5268853d39b79a1Dalam era digital yang semakin berkembang, kecerdasan buatan (artificial intelligence atau AI) telah menunjukkan potensinya dalam berbagai bidang, termasuk literatur. Salah satu aplikasi artificial intelligence yang signifikan adalah dalam pembuatan klasifikasi genre buku secara otomatis. Sistem ini dirancang untuk mempermudah pengguna atau penulis buku untuk menemukan, merekomendasi dan memilih buku yang sesuai dengan preferensi mereka. Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan sebuah sistem klasifikasi genre buku berbasis artificial intelligence yang di integritasikan pada platform website. Dengan memanfaatkan metode pembelajaran mesin (machine learning) dan pemrosesan bahasa alami (Natural Language Processing/NLP), yang menghasilkan sebua software aplikasi klasifikasi genre buku berbasis website. Sistem ini memiliki kemampuan untuk menganalisis konten buku secara mendalam, seperti sinopsis, deskripsi, hingga teks lengkap. Analisis ini memungkinkan sistem untuk menentukan genre buku yang paling tepat dengan tingkat akurasi yang tinggi. Algoritma AI bekerja dengan mengenali pola-pola spesifik dalam teks, yang sering kali sulit dikenali melalui metode konvensional. Hasil pengujian menunjukkan bahwa sistem ini dapat mengelompokkan buku secara konsisten ke dalam berbagai genre, seperti fiksi ilmiah, roman, misteri, fantasi, biografi, dan lain sebagainya. Keberadaan sistem ini tidak hanya meningkatkan efisiensi pengelolaan katalog buku secara digital, tetapi juga memperkaya pengalaman pengguna dalam menjelajahi koleksi buku. Dengan kemampuan personalisasi yang lebih baik, pengguna dapat lebih mudah menemukan buku favorit berdasarkan genre yang diminati. Sistem ini diharapkan menjadi solusi inovatif dalam menghadapi tantangan pengelolaan literatur di era modern.bit-Tech230bit-Tech["Fariz Dwiki Dermawan", "Supriyono"]2024-12-27T00:00:00 b79f5b2e7ba2810011385ab1102072b6ed85f937Annotation: the issues of automation of the processes of collection, storage, transmission, analytical processing of information in the interests of improving the quality of management decisions in the railway management system, as well as the introduction of artificial intelligence systems in relation to the management bodies of Russian Railways are considered. Purpose: formalization of regulatory approaches for the implementation of artificial intelligence systems. Methods: harmonization, system analysis, simplex planning. Results: in the course of the research, approaches to the regulation of regulatory and legal measures have been identified that allow the development and improvement of NTDs based on unified approaches. Practical significance: the definition of approaches to the regulation of regulatory and legal measures will allow specialists involved in system design, development, implementation, operation of AI systems, developers of standards, as well as specialists of regulatory authorities, to improve the regulatory framework based on unified approaches, and developers of AI technologies will allow them to form new requirements for software and hardware being developed.Bulletin of scientific research results00In the course of the research, approaches to the regulation of regulatory and legal measures have been identified that allow the development and improvement of NTDs based on unified approaches.Bulletin of scientific research results["E. Kazakevich", "Alexander Bogdanov"]2024-12-27T00:00:00 fd06f5eae2a8f8b45bd7128b096aceee246afbbfObesity Surgery161Obesity surgery["Mohamed Hany", "Marwan Emad Abdou", "Ahmed Abokhozima", "M. Zidan"]2024-12-27T00:00:00 bbcd7d966669fe59dbf89e2ce154396f083c2957This research examines the key factors influencing Indonesian consumer’ willingness to use AI chatbots, focusing on technological characteristics, hedonic motivations, anthropomorphism, AI performance and user experience, using the extended Artificially Intelligent Device Usage Acceptance (AIDUA) model. This is quantitative research where a survey technique was adopted, and two hundred and eight participants’ responses were obtained. The participants were consumers in Indonesia who had prior experience using AI chatbot. The study reveals that anthropomorphism, technological competence, and consumer hedonic motivation while using a chatbot affects the consumer’s perception about the perceived performance of a chatbot and the user experience. These perceived performance and experiences influence feelings, and then influence the willingness to use the AI chatbot. Mediation analysis indicated that perceived performance mediated the relationship between anthropomorphism and willingness to use AI, while user experience did not. That hedonic motivation affects willingness to adopt AI through the mediations of user experience, emotions, and perceived performance. Further, technological factors influence willingness to use AI mediated by perceived performance, in which case, user experience is not a mediator. The results indicate that the factors influencing the willingness to use AI include technological readiness, anthropomorphism, and hedonic motivation, which are mediated by perceived performance and emotions, whereas user experience does not significantly mediate the relationship.Innovative Marketing300The study reveals that anthropomorphism, technological competence, and consumer hedonic motivation while using a chatbot affects the consumer’s perception about the perceived performance of a chatbot and the user experience, and influence the willingness to use the AI chatbot.Innovative Marketing["D. Laksmidewi", "Efendi", "Wong Chee Hoo"]2024-12-27T00:00:00 a271f361ecdcf0ddfd8c68a5a21725006e19c42cINTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Hari Kiran Vuyyuru"]2024-12-27T00:00:00 03c1b5fc1130857d17afb6253ce7ec4c9ad74ccdInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Rajdeep x"]2024-12-27T00:00:00 4538a63645380d4d99816fe0689127a908ceb9b7African Journal of Biomedical Research00African Journal of Biomedical Research["Sathya M"]2024-12-27T00:00:00 0be796bc162d67a39fbc5f94697992790b03463aInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Sonam Nidhi"]2024-12-27T00:00:00 fc8196406af2575a9493161ed8166e24787fd8deInternational journal of human resources management1610The International Journal of Human Resource Management["Qinyan Gong", "Di Fan", "Timothy Bartram"]2024-12-27T00:00:00 98abac76d65de22429f2b979b61e7e488e949f94Biol. Cybern.180Biological cybernetics["J. Fellous", "Peter J. Thomas", "Paul Tiesinga", "Benjamin Lindner"]2024-12-27T00:00:00 764f426b223663e4f453e6de76e0a5a9c9864275Gachon Law Review00Gachon Law Review["El Kang"]2024-12-27T00:00:00 01f4e5e52d97def9c187245ef23bcee9bea58c83Deutsches Ärzteblatt International50Deutsches Arzteblatt international["Mark Enrik Geissler", "Merle Goeben", "Kira A. Glasmacher", "Jean-Paul Bereuter", "R. Geissler", "I. Wiest", "Fiona R. Kolbinger", "J. N. Kather"]2024-12-27T00:00:00 29c9f1ebecc48ef7d0cfc5af962e28b5a2ce60c3International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Mudasir Ashraf"]2024-12-27T00:00:00 a032e07fcfc815b3d00ca60562e1b8ade2956f72Business and Legislation00Business and Legislation["Keso Sumbadze", "Nino Tavberidze"]2024-12-27T00:00:00 0ab7fe5110dd8d0d367d1b02f4dd49bc50f76b24The article presents reflections on the place of digitalization in the development of domestic anesthesiology and intensive care. The important role of this technology in ensuring high-quality treatment is shown. The practical component of implementing tasks within the framework of digitalization should include reducing the workload on personnel not related to direct work with the patient, but also changing the management of treatment system to improve its efficiency and the safety of medical activities.Messenger of Anesthesiology and Resuscitation190Messenger of ANESTHESIOLOGY AND RESUSCITATION["Y. Polushin", "I. V. Shlyk", "N. S. Smolin", "G. A. Timofeev"]2024-12-27T00:00:00 c6911aedc25e1494b5b84eebafd74c075be717aaJournal of Gastrointestinal and Liver Diseases00Journal of gastrointestinal and liver diseases : JGLD["Ludovico Abenavoli", "P. Guzzi"]2024-12-27T00:00:00 4797907b734788d1fbea42da5b399f0966b06258This editorial provides an initial reflection on the relationship between Open Science and Artificial Intelligence (AI), highlighting their interdependencies, barriers, and investment disparities. It advocates global initiatives adapted to local realities to consolidate inclusive advances that respect diversity and sustainability in scientific and technological production.Revista Electrónica Educare00Revista Electrónica Educare["L\u00facia da Silveira", "Fabiano Couto-Corr\u00eaa-da Silva"]2024-12-27T00:00:00 a1bbfeb10f88a9485e155e8150d6eb1954ec6ceaThis editorial provides an initial reflection on the relationship between Open Science and Artificial Intelligence (AI), highlighting their interdependencies, barriers, and investment disparities. It advocates global initiatives adapted to local realities to consolidate inclusive advances that respect diversity and sustainability in scientific and technological production.Revista Electrónica Educare00Revista Electrónica Educare["L\u00facia da Silveira", "Fabiano Couto-Corr\u00eaa-da Silva"]2024-12-27T00:00:00 11f452c0f7b4a33555fba3a13393657bc1c98443Background: Lately, the reproducibility of scientific results has become an increasing worry in the scientific community. Several studies show that artificial intelligence research is not spared from reproducibility issues. Objectives: As a pioneer in open and transparent research published on the Internet, the Journal of Artificial Intelligence Research (JAIR) seeks to promote good research practices and close the feedback loop between the original researchers and those reproducing their research. Methods: Four different mechanisms will be adopted immediately by JAIR. These are: 1) reproducibility checklists, 2) structured abstracts, 3) reproducibility badges and 4) reproducibility reports. Results: All authors submitting articles to JAIR fill out a reproducibility checklist and are encouraged to use structured abstracts. Articles that fulfill certain criteria will receive reproducibility badges, and reproducibility reports can be submitted by anyone for any article published in JAIR. Conclusions: We believe that adopting the four mechanisms outlined in this paper will improve the reproducibility of research published in JAIR and thus make a contribution to addressing the broader reproducibility issue in artificial intelligence. We hope that JAIR’s reproducibility initiative will inspire similar efforts at other top-tier journals.Journal of Artificial Intelligence Research02Adopting the four mechanisms outlined in this paper will improve the reproducibility of research published in JAIR and thus make a contribution to addressing the broader reproducibility issue in artificial intelligence.J. Artif. Intell. Res.["Odd Erik Gundersen", "M. Helmert", "Holger H. Hoos"]2024-12-27T00:00:00 6abb9853ef9d32853fd7380a9e87ce364d39edabAI &amp; SOCIETY210A framework called the hierarchical narrative representation (HieNaR) is developed to systematize the structure of narrative generation in data utilization processes and provides a foundation for improving human–AI collaboration.AI &amp; SOCIETY["Kaira Sekiguchi", "Yukio Ohsawa"]2024-12-27T00:00:00 3ac7062a483d33c2f4e56711a19fdbdb70e7c31dIn recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilities. This paper explores the potential of LLMs to address key challenges in personal finance, focusing on the United States. We evaluate several leading LLMs, including OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama, to assess their effectiveness in providing accurate financial advice on topics such as mortgages, taxes, loans, and investments. Our findings show that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas. Specifically, LLMs struggle to provide accurate responses for complex financial queries, with performance varying significantly across different topics. Despite these limitations, the analysis reveals notable improvements in newer versions of these models, highlighting their growing utility for individuals and financial advisors. As these AI systems continue to evolve, their potential for advancing AI-driven applications in personal finance becomes increasingly promising.Applied Economics210The potential of LLMs to address key challenges in personal finance, focusing on the United States, is explored, showing that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas.ArXiv["Oudom Hean", "Utsha Saha", "Binita Saha"]2024-12-27T00:00:00 3741e0e9ea9caac9e69e012f19915f0b738833adSome emerging technologies transforming supply chain management (SCM) include the Internet of Things (IoT) and Artificial Intelligence (AI). Their abilities provide the tools companies need to evolve and meet the changing needs and business conditions, ensuring they remain afloat. The challenge, however, is understanding how companies can incorporate the technologies into their systems. There are also concerns over the low adoption rates among small and medium enterprises (SMEs), and the paper looks into the issue to assess the barriers and solutions. The other goal is to determine strategies companies use to optimize AI and IoT to ensure proper supply chain management. This paper contributes to supply chain management by providing a structured framework for integrating AI and IoT technologies to enhance operational efficiency, real- time decision-making, and supply chain visibility. It addresses barriers faced by SMEs, such as financial constraints and lack of skilled personnel, offering strategies like innovative leadership and collaboration through ecosystems to overcome these challenges. Additionally, the research highlights how AI and IoT strengthen supply chain resilience, optimize inventory, and ensure seamless operations, especially in adapting to post-pandemic vulnerabilities. The literature review was done on recent scholarly research papers and case studies. The findings showed that SMEs can overcome challenges such as limited funds and human resource constraints by collaborating with others and getting innovative leaders to lead the supply chains. Supply Chain Systems (SCS) that adopt AI and IoT can leverage them to ensure optimized inventory management, tracking and quality control, predictive analysis abilities, and continuous monitoring, which provide constant system improvement.International journal of supply chain management161The findings showed that SMEs can overcome challenges such as limited funds and human resource constraints by collaborating with others and getting innovative leaders to lead the supply chains, which provide constant system improvement.International Journal of Supply Chain Management["Rohit Raman", "Manikandan Selvaraj"]2024-12-28T00:00:00 0a86cfbbeed28ec9b070187016d33d31b26ec50eWith the geopoliticisation of the digital economy, the realisation of artificial intelligence (AI) sovereignty is increasingly influenced by the geopolitical manoeuvrings into which a state is drawn. China, the EU, and the US currently form the three poles of AI in the world. The EU has emerged as a global leader in AI regulation, and the US is currently a world leader in AI innovation. The research outlined in this article explored how China’s regulators are responding to these two currents of geopolitical pressure, from the EU and the US. The study found that China’s response manifests as a dual-track AI regulatory approach, comprising (1) a mix of restrictive and facilitative regulation at the central level; and (2) facilitative regulation at the local level.The African journal of information and communication291China’s regulators are responding to two currents of geopolitical pressure, from the EU and the US, which manifests as a dual-track AI regulatory approach, comprising a mix of restrictive and facilitative regulation at the central level and facilitative regulation at the local level.The African Journal of Information and Communication (AJIC)["Aifang Ma"]2024-12-28T00:00:00 ceedd53769cdf6c676089b5705f5dfb78aafe80a The digital world is characterized by a vast array of data, documents, and legal materials, which are safeguarded and preserved through modern applications and technologies. Artificial intelligence plays a crucial role in this process, featuring numerous intelligent applications that facilitate operations within the legal field, particularly in legal professions and judicial systems. This has led to a fundamental transformation, marked by the emergence of digital justice, remote litigation, and the adoption of electronic trials, all of which contribute to the efficient and swift execution of judicial tasks   Journal of Science and Knowledge Horizons30The digital world is characterized by a vast array of data, documents, and legal materials, which are safeguarded and preserved through modern applications and technologies, which contribute to the efficient and swift execution of judicial tasks.Journal of Science and Knowledge Horizons["Houria Benahmed"]2024-12-28T00:00:00 90c5fa9a05c05a7bc6b16a44beba17e59b57a75dThis study assesses the extent to which the Brazilian Artificial Intelligence Plan (PBIA, Plano Brasileiro de Inteligência Artificial) of 2024 supports the country’s pursuit of AI sovereignty. The authors map the financial allocations to the PBIA’s 54 proposed structural actions against the components of Belli’s (2023a; 2023b) key AI sovereignty enablers (KASE) framework: data, algorithms, computing capacity, connectivity, electricity, education, cybersecurity, and regulation. The study finds that the PBIA’s structural actions support six of the KASE enablers, in the following order of priority: algorithms, data, computing capacity, education, electricity, and cybersecurity. Close to 50% of the PBIA’s proposed investments for structural actions are found to be for actions related to the algorithms enabler in the KASE framework, followed by 20% for actions connected to the data enabler, 20% for actions supporting the computing capacity enabler, and 11% for actions related to the education enabler. Much lower levels of expenditure are set out in the PBIA for the electricity enabler (2%) and the cybersecurity enabler (1%). The authors analyse the implications of the PBIA’s prioritisations for Brazil’s progress towards AI development and AI sovereignty.The African journal of information and communication00The study finds that the PBIA’s structural actions support six of the KASE enablers, in the following order of priority: algorithms, data, computing capacity, education, electricity, and cybersecurity.The African Journal of Information and Communication (AJIC)["Germano P. Johansson Neto", "V. C. Farias da Costa", "W. B. Gaspar"]2024-12-28T00:00:00 296c497c9236c0a544b4669422ea6a8a025c390cThis study evaluated the legal gaps which are addressing artificial intelligence (AI) and copyright infringement in the domain of legal frameworks in Pakistan. The purpose was to examine whether existing copyright laws adequately protect against unauthorized use of creative works generated or exploited by AI technologies. The study analyzed Pakistan’s Copyright Ordinance, 1962, relevant judicial precedents, and international instruments such as the Berne Convention and TRIPS Agreement to assess their applicability to AI-related copyright challenges through the Employed doctrinal legal research methodology. The findings revealed significant gaps, including the absence of provisions addressing ownership of AI-generated works, liability for infringement involving AI systems and mechanisms to safeguard copyright holders against unauthorized AI use. The results underscored the need for legislative reforms to align Pakistan’s copyright laws with emerging AI technologies, ensuring adequate protection for creators while fostering innovation. The study recommended the introduction of specific provisions to regulate AI-generated content, determine ownership, and establish liability frameworks to bridge these legal gaps.Indus Journal of Social Sciences190The findings revealed significant gaps, including the absence of provisions addressing ownership of AI-generated works, liability for infringement involving AI systems and mechanisms to safeguard copyright holders against unauthorized AI use.Indus Journal of Social Sciences["Abdul Salam", "Muhammad Zakir", "Muhammad Hassan Sajjad", "Sadia Fatima"]2024-12-28T00:00:00 4417daacc63bc6171d95c029bf51c3c7070df350Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) telah membawa dampak besar dalam berbagai aspek kehidupan sehari-hari, termasuk di kalangan umat Muslim. Munculnya pertanyaan mengenai etika penggunaan AI dari perspektif Islam menjadi relevan untuk dibahas. Artikel ini bertujuan untuk meneliti pandangan Islam terhadap penggunaan AI dan bagaimana etika Islam dapat memberikan panduan dalam pemanfaatan teknologi ini. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi pustaka dan analisis terhadap literatur keislaman terkait etika, teknologi, dan AI. Kesimpulan menunjukkan bahwa Islam mendukung penggunaan teknologi yang bermanfaat bagi umat manusia, namun mengedepankan prinsip-prinsip etika seperti keadilan, tanggung jawab, dan kehati-hatian dalam menghadapi dampak negatif yang mungkin muncul dari penggunaan AI.NUANSA00NUANSA: Jurnal Penelitian Ilmu Sosial dan Keagamaan Islam["E. Haikcal Firdan El-Hady", "M. Zenrif"]2024-12-28T00:00:00 bdf3a2c5cfde2a31516e9c991a8e2ecf5921f407The use of artificial intelligence, AI, has increased in recent years, offering tools to generate content and solve problems in different areas of knowledge. Questioning their relevance in areas such as integral calculus, 11 AI tools were tested to solve from basic to advanced integrals, described in different ways such as natural language or images. The results were compared with the solutions given in the literature, analyzing precision, number of steps, clarity of explanations and ease of use. Finding that the Chat GPT-Wolfram Alpha partnership stood out for its ability to identify appropriate integration techniques and offer detailed and understandable explanations; While Copilot is more complex to understand if you do not use the LaTeX language, the rest present some problems when interpreting instructions and images. Although these tools are not designed to solve mathematical problems, they proved to be effective in most cases, promoting an interactive space to clarify doubts in real time and generate debate; however, their effectiveness depends on the use given to them, since either as support in the classroom to deepen the analysis of the problem or simply use it as a black box to obtain quick answers.LatIA5011 AI tools were tested to solve from basic to advanced integrals, described in different ways such as natural language or images, finding that the Chat GPT-Wolfram Alpha partnership stood out for its ability to identify appropriate integration techniques and offer detailed and understandable explanations.LatIA["Jes\u00fas Alexis Aguilar-Rodr\u00edguez", "Patricia Rivera-Garc\u00eda", "Armando Cervantes-Sandoval", "Alejandro Josu\u00e9 Perales-Avila"]2024-12-28T00:00:00 9a76e75e41a5b8c548812982baa3b625cb15e12eThe continuous advancement of artificial intelligence (AI) technology is driving a profound transformation in educational systems, which are crucial in shaping societal development and future trends. AI plays a pivotal role in advancing social progress by developing programs and devices that replicate human behaviors and cognitive functions, leading to a revolutionary change in learning methodologies and an enhancement in the interaction between learners and educational content. This research paper explores the role of AI in improving the quality of adaptive e-learning. It begins by defining fundamental concepts of AI and its educational technologies, then clarifies the concept of adaptive e-learning, and reviews the latest AI applications in this field. Additionally, the paper demonstrates how AI can be utilized to enhance the educational environment and improve educational quality, while also assessing notable international experiences in this context. The study’s key findings indicate that AI enhances educational quality by providing personalized learning experiences, effectively analyzing learner data, and offering flexible virtual learning environments and advanced digital tools to enhance interactionJournal of Science and Knowledge Horizons340The study’s key findings indicate that AI enhances educational quality by providing personalized learning experiences, effectively analyzing learner data, and offering flexible virtual learning environments and advanced digital tools to enhance interaction.Journal of Science and Knowledge Horizons["Abderrezzaq Brada", "Fatima Dahmani"]2024-12-28T00:00:00 a5d770a0a29b480fd93bf93ed4089b9e63b3d576Pelatihan penggunaan Artificial Intelligence (AI) dalam pembelajaran di perguruan tinggi bertujuan untuk meningkatkan kompetensi dosen dalam mengintegrasikan teknologi modern ke dalam proses pendidikan. Dengan metode Asset-Based Community Development (ABCD), kegiatan ini dirancang berdasarkan kebutuhan dan potensi tenaga pengajar. Pelaksanaan melibatkan lima tahap: Discovery, Dream, Design, Define, dan Destiny, yang mencakup analisis kebutuhan, perancangan materi, hingga implementasi pelatihan. Hasil yang diharapkan adalah peningkatan kemampuan mahasiswa dalam memanfaatkan AI untuk menciptakan pembelajaran yang lebih efektif dan relevan. Pelatihan ini mendukung perguruan tinggi dalam menghadapi tantangan era digital dan menghasilkan lulusan yang kompetitif di tingkat global.Khidmatuna: Jurnal Pengabdian Masyarakat00Khidmatuna: Jurnal Pengabdian Masyarakat["Ficky Dewi Ixfina", "Moh. Taufiq", "Abdul Aziz"]2024-12-28T00:00:00 5f781c94ff96bac307e252506000e095bdfe0ea2Global environmental pollution has a devastating influence on the planet's population and jeopardizes humanity's future. The construction business is a major producer of waste and hazardous emissions into the atmosphere. It is vital to discover measures to reduce the damage done to nature. Currently, artificial intelligence technologies are one of the most promising approaches to helping the environment. This research investigates the use of green AI algorithms for measuring greenhouse gas (GHG) emissions in the context of ecological footprint assessment. Green AI algorithms prioritize sustainability and seek to lower AI systems' carbon footprints while monitoring GHG emissions data. These algorithms use environmentally conscious machine learning techniques to improve resource allocation, encourage energy-efficient model topologies, and prioritize renewable energy sources for AI model training. Carbon-aware optimization approaches are used to reduce the environmental impact of AI computations, resulting in a greener future. The incorporation of green algorithms into AI systems identifies the potential for emission reduction and energy efficiency, promoting environmentally beneficial behaviours across industries. The use of green algorithms allows for a full analysis of GHG emissions and ecological footprints, permitting a symbiotic interaction between technology and the environment for sustainable growth.Journal of Information Systems Engineering & Management00This research investigates the use of green AI algorithms for measuring greenhouse gas emissions in the context of ecological footprint assessment and identifies the potential for emission reduction and energy efficiency, promoting environmentally beneficial behaviours across industries.Journal of Information Systems Engineering and Management["Beena Nawghare"]2024-12-28T00:00:00 e127da5ed504a9f3d143554242a93b7620743d25As artificial intelligence rapidly advances, addressing the interplay of technical, ethical, and risk factors in optimizing digital market decision-making through AI platforms has become increasingly prominent. However, the impact of these factors on market performance, particularly in investment value, remains underexplored. The study, based on 412 validated responses from service industry professionals gathered through a carefully designed questionnaire, aims to predict the relationship among these factors and their influence on market performance. It also explores how cognitive engagement mediates the relationship between AI platforms and financial metrics. Key findings:(1) the interplay of technical, ethical, and risk factors optimizes market decision-making and guides AI investments; (2) cognitive engagement, especially in the services sector, is essential to maximize the impact of AI platforms on market performance. The study provides valuable insights into AI's role in shaping market dynamics within the services sector and relevant governance recommendations for policymakers.Journal of Global Information Management1170The interplay of technical, ethical, and risk factors optimizes market decision-making and guides AI investments and cognitive engagement, especially in the services sector, is essential to maximize the impact of AI platforms on market performance.Journal of Global Information Management["Rongxin Chen", "Yuhao Chen"]2024-12-28T00:00:00 26b2269672c9475a88ac32759e6461f999fa5a09Artificial Intelligence (AI) has significantly transformed higher education in recent years by enhancing teaching methods and influencing student learning outcomes. This study investigates the effects of AI on student motivation and cognitive skills, addressing both the benefits and challenges of AI integration. The research adopts a mixed-methods approach, combining a comprehensive literature review with a questionnaire-based survey of 60 university students from diverse academic disciplines. The results indicate that AI tools, such as intelligent tutoring systems and learning analytics, increase student engagement, motivation, and problem-solving abilities. However, concerns regarding over-reliance on AI reduced creativity, and ethical issues such as privacy and fairness persist. This study highlights the potential of AI to foster personalized learning while emphasizing the need for responsible AI implementation to address its limitations.  Journal of Computational Science and Technology130The results indicate that AI tools, such as intelligent tutoring systems and learning analytics, increase student engagement, motivation, and problem-solving abilities, however, concerns regarding over-reliance on AI reduced creativity, and ethical issues such as privacy and fairness persist persist.TechComp Innovations: Journal of Computer Science and Technology["TechComp Innovations", "Zohaib Hassan Sain", "Uthman Shehu Lawal", "Chanda Chansa", "Aulia Luqman"]2024-12-28T00:00:00 509043946e7c8a895e66e34067dcc437cf87ab82Artificial Intelligence (AI) telah menjadi inovasi penting dalam layanan kesehatan, memberikan kontribusi besar terhadap kualitas, efisiensi, dan pengelolaan layanan medis. Teknologi ini digunakan untuk mendukung diagnosis dini yang akurat, penyederhanaan proses pelayanan, serta pengambilan keputusan berbasis data. Namun, penerapan AI tidak terlepas dari tantangan seperti keterbatasan integrasi data, regulasi yang belum seragam, dan masalah privasi. Penelitian ini bertujuan untuk mengidentifikasi peluang dan tantangan penerapan AI di bidang kesehatan melalui tinjauan literatur sistematis menggunakan protokol PRISMA. Data dikumpulkan dari berbagai sumber terkemuka seperti Google Scholar, PubMed, IEEE Xplore, dan ScienceDirect pada rentang tahun 2019 hingga 2024. Hasil kajian menunjukkan bahwa AI mendukung berbagai aspek layanan kesehatan, termasuk meningkatkan efisiensi administrasi, mempercepat diagnosis, serta mendukung pendidikan kedokteran melalui metode pembelajaran yang lebih terarah dan berbasis teknologi. Meski demikian, terdapat kendala teknis seperti kebutuhan data berkualitas tinggi dan pelatihan tenaga medis untuk memahami dan mengintegrasikan AI secara efektif. Dengan langkah strategis yang tepat, AI memiliki potensi jangka panjang untuk merevolusi layanan kesehatan dan menciptakan sistem yang lebih adaptif dan responsif terhadap kebutuhan pasien.JATI (Jurnal Mahasiswa Teknik Informatika)00JATI (Jurnal Mahasiswa Teknik Informatika)["Leilani Najma Rachmawati", "Chika Rievania Khairunisa Fitri", "Monica Exsanni Araf Octaviana"]2024-12-28T00:00:00 19331abf170f0b192007859f62d6af1ad08620b0The article substantiates the necessity of applying artificial intelligence (AI) technologies as a digital tool for the project-based approach in local governance. Local self-government plays a critical role in ensuring the functioning of communities, particularly during the current crisis caused by the Russian military invasion. Integrating modern AI-based management approaches enhances process automation, big data analysis, and resource optimization, improving efficiency. The study identifies key directions for AI application in local governance: project planning, control, and timeline management through analytics services; risk analysis and forecasting via machine learning algorithms; task automation for project execution; resource optimization using efficiency indicators; personalized communication with virtual assistants, chatbots, and digital democracy tools; intelligent HR services for team coordination; and informed decision-making based on big data analysis with reduced risks. An algorithm for AI adaptation in local selfgovernment has been proposed, encompassing stages such as task identification, data analysis for decision-making, process automation, resource monitoring, risk management, and personnel training. Examples of AI tools at the local level include «smart city» services for infrastructure monitoring, transportation optimization, and waste management; digital participation tools for public opinion analysis; and intelligent systems for strategic planning and tax procedure simplification. The study highlights the ethical implementation of AI with data protection and its benefits, including time savings, transparency, resource optimization, and enhanced communication. Prospective technologies include machine learning, natural language processing, computer vision, big data analysis, blockchain governance, and smart platforms for digital participation. These tools promote sustainable community development and improve public service delivery, ensuring higher quality of life for residents.Strategy of Economic Development of Ukraine00The article substantiates the necessity of applying artificial intelligence technologies as a digital tool for the project-based approach in local governance and highlights the ethical implementation of AI with data protection and its benefits, including time savings, transparency, resource optimization, and enhanced communication.Strategy of Economic Development of Ukraine["Oleksandr Karpenko", "N. Vasiuk", "A. Osmak"]2024-12-28T00:00:00 6d1fc3edad3e9fd2c4fc795865b8c9f8a275b628 Human understanding of the climax of memory has evolved through a progressive process from the unconscious, to the subconscious, and then to consciousness. Episodic memory plays a crucial role in this process, determining how individuals break and recombine slices of their self-narrative. Intense emotional experiences serve as the backdrop of this entire process, determining the fun and meaningfulness of the narrative. However, with the rise of Artificial Intelligence (AI) , humans seem to be entering another dimension of agency—one in which they are both fragmented into slices and simultaneously experience emotions in this fragmented state, all while dissolving within the tool-like nature of AI, or in other words, under human “absolute” control over AI. Film, too, has been fragmented into various forms of humanized or intelligentized possibilities, and its identity has undergone a meaningful shift. This article adopts a phenomenological approach and uses film as a starting point. It explores, through a logical progression from the perceivable (the actual reality of the narrative) , to the sensed (the psychological reality of emotion) , and to the known (the higher-dimensional reality of emotional generation) , the relationship between humans and AI. Ultimately, it finds that the developmental trajectory of this relationship remains “controllable, ” offering insights into human survival strategies in the early stages of the AI era.Advances in Art Science00This article explores, through a logical progression from the perceivable (the actual reality of the narrative) , to the sensed (the psychological reality of emotion) , and to the known (the higher-dimensional reality of emotional generation) , the relationship between humans and AI.Advances in Art Science["Xin Xie", "Tianyang Qi"]2024-12-28T00:00:00 79b1c3902a8fb6fff157ef48c412c7b406cbc22fThe capacity of artificial intelligence (AI) to transform food waste management is rooted in its capability to analyse extensive data sets and enhance processes throughout the food supply chain. Despite the potential of AI to transform food waste management, many organisations and consumers remain uninformed about its capabilities and the innovative strategies it can provide to effectively reduce food waste. This study aimed to review innovative strategies for sustainable consumption by harnessing AI to mitigate food waste. This study employed a review analysis as a methodological approach to synthesise existing literature on the intersection of AI and food waste management. The review analysis revealed several innovative strategies for sustainable consumption by harnessing AI to mitigate food waste, including: (a) smart inventory management; (b) recipe suggestions based on available ingredients; (c) automated waste tracking; (d) predictive analytics for meal preparation; and (e) consumer behaviour insights. In conclusion, utilising AI to reduce food waste offers a viable strategy for promoting sustainable consumption practices throughout the food supply chain. Future research should prioritise longitudinal analyses to assess the long-term effectiveness of AI interventions across sectors including agriculture, retail, and hospitality.Malaysian Journal of Social Sciences and Humanities610Using AI to reduce food waste offers a viable strategy for promoting sustainable consumption practices throughout the food supply chain and future research should prioritise longitudinal analyses to assess the long-term effectiveness of AI interventions across sectors including agriculture, retail, and hospitality.Malaysian Journal of Social Sciences and Humanities (MJSSH)["Muhammad Adieb Ahmad Wafi", "Mohd Amzari Tumiran"]2024-12-28T00:00:00 eb47ad7bae3fab2ba66f6fc98d5ee8dd5401a8c1The integration of Artificial Intelligence (AI) into legal practice is transforming the global legal landscape, and Uganda is no exception. This article examines the prospects and socio-economic implications of AI adoption in Uganda’s legal sector. It explores the potential of AI to enhance legal research, streamline case management, and improve access to justice through automated legal services. However, the incorporation of AI also raises critical socio-economic issues, including job displacement, the digital divide, and regulatory challenges. The article analyzes the readiness of Uganda’s legal infrastructure for AI, the ethical considerations, and the potential impact on legal professionals and clients. By assessing both opportunities and challenges, this study offers insights into how Uganda can navigate the evolving intersection of law and technology, ensuring equitable benefits from AI in the legal domain.Kampala International University law journal00The article analyzes the readiness of Uganda’s legal infrastructure for AI, the ethical considerations, and the potential impact on legal professionals and clients, and offers insights into how Uganda can navigate the evolving intersection of law and technology, ensuring equitable benefits from AI in the legal domain.Kampala International University law journal["Opeyemi Obisesan Olawunmi", "Margaret Kareyo", "Sheya Nuwagaba", "Alexanda Binomugisha", "Amina Wahab"]2024-12-28T00:00:00 858e8dc0ab02f4a0301ca2e424679e1fbeb07146This study is about gender-based changes that may occur with artificial intelligence technology. The connection between social change and technology is not a recent phenomenon. Comte illustrates this change through the evolution of knowledge and shifts in human thought. Anne Balsamo (1995) opposes the claim that the material body has lost its validity in our scientific culture in her study examining how the body is gendered in its interaction with new corporal technologies. Balsamo, who provides abundant evidence that the techno-body has always been gendered and racially marked, prepares the ground for a renewed relationship of feminists with contemporary technological narratives. In this context, how the physical world continues while recreating women with virtual characters based on gender approaches will be examined. In this study, first, body theories will be addressed within the scope of the posthuman. Second, how virtual characters occur in social media environments will be evaluated. Finally, Turkish influencer and talk show host Alara X will be examined in terms of her internal and external aspects within the entirety of both human and non-human elements In this study, which claims to connect the cultural formation of women's identities in virtual environments with dominant cultural production forms while making sense of it, it has been concluded that the female body is instrumentalized in power relations through consumption, communication, control mechanisms, and information technologies.Communication Papers00It has been concluded that the female body is instrumentalized in power relations through consumption, communication, control mechanisms, and information technologies.Communication Papers["N\u00fcket ELPEZE ERGE\u00c7"]2024-12-28T00:00:00 43dd460b91efe42ef6a65cef6ed61d07bf9842d2At the initial stage, Al-generated works were categorized as computer-assisted or computer-propelled works, therefore, copyright/patent rights were conferred on the individuals or persons who utilized Al as a tool. In other words, authorship/inventorship under the copyright and patent law is viewed to be human-centric because authorship/inventorship is reserved for the natural human person. Artificial Intelligence’s influence has permeated all sectors of human endeavors - science, technology, academia, politics, business, law, economics and other perspectives. Past experience has vividly illustrated that even a minimal alteration or amendment to intellectual property legislation will reflect significantly on the transformative and innovative network of interconnected systems. In many countries of the world, Al-generated inventions devoid of human impact are not eligible for patent law because they fail the test of the non-obvious requirement, even though Artificial Intelligence is viewed as a person with ‘skills in the art‘. This article focuses on the conceptual and legal issues that arise in the evolution and revolution of AI-generated works. The writers adopt a doctrinal approach, citing primary and secondary sources in the thematic study of AI and IP in Uganda, whilst drawing comparative analysis from other developed economies such as the US, UK and China. In conclusion, the authors advocate exploring the public domain option, trade secret and other contractual arrangements for inventors and IP owners, due to the fact that Uganda and other developing countries are primarily technology users. The article recommends policy changesKampala International University law journal00The writers adopt a doctrinal approach, citing primary and secondary sources in the thematic study of AI and IP in Uganda, whilst drawing comparative analysis from other developed economies such as the US, UK and China.Kampala International University law journal["Hassan Ismaila Adebowale", "Ahmad Mohamed", "Odi Agwu Chukwuemeka", "Habib Shehu", "Magnus Chima", "John Damina Joshua", "Osman Yasein Hassan N."]2024-12-28T00:00:00 e6ecb765eac572b113f3528a7d17b700f2420cffArtificial intelligence (AI) technology has become a major subject in security and privacy issues in the digital era. This article aims to explore the impact of the use of AI technology on crime and law enforcement, with a focus on the Indonesian context. This research uses a descriptive analytical approach to evaluate trends and impacts of using AI in crime prevention. Data was collected through literature studies and analysis of policies and regulations related to AI and law enforcement in Indonesia. The analysis results show that the use of AI technology in law enforcement in Indonesia has increased significantly over the last few years. This is reflected in the number of incidents and controversies involving AI, as well as in the implementation of policies and regulations governing its use. While AI offers great potential in supporting crime investigation and detection, challenges related to privacy, fairness, and accountability also arise as it develops. The use of artificial intelligence technology in law enforcement in Indonesia raises a number of impacts and challenges that need to be considered. Despite its potential to increase the effectiveness of law enforcement, protecting privacy and human rights must be a primary focus in the development and implementation of AI technologies.Pena Justisia Media Komunikasi dan Kajian Hukum00The analysis results show that the use of AI technology in law enforcement in Indonesia has increased significantly over the last few years, and protecting privacy and human rights must be a primary focus in the development and implementation of AI technologies.Pena Justisia: Media Komunikasi dan Kajian Hukum["Ali Furkan", "M. E. Susila"]2024-12-28T00:00:00 e4293b6e23f12b7efdf140a05642919595a56ab1The article presents a method for calculating the optimal operating mode of boilers and presents the results of an analytical solution for optimization steam boilers. Software solution to the problem of optimizing steam boilers and the algorithm for the program operation are presented. Based on the results of the software solution, graphical dependencies of the studied quantities are presented in the article. A comparison of the obtained analytical and software solutions, namely graphical dependencies, calculation tables and the calculation result, is carried out, in order to verify the correctness of the software solution. The features of using artificial intelligence, as well as its training for solving engineering prob-lems, and need of energy-saving measures in heat and power equipment when several boilers are operating together were described. The need for the introduction of artificial intelligence in energy in general and heat engineering in particular is substantiated.Energy Systems00A method for calculating the optimal operating mode of boilers and an analytical solution for optimization steam boilers are presented and the need for the introduction of artificial intelligence in energy in general and heat engineering in particular is substantiated.Energy Systems["Mikle Egorov", "Nikolay Smirnov"]2024-12-28T00:00:00 c364bf1bb2f65ad2fe19e6e0fd463ce8e7e57ea0This study aims to describe the use of Artificial Intelligence (AI) in improving Islamic Religious Education learning. This study uses the Classroom Action Research (CAR) method with the Kemmis and McTaggart spiral model approach, which involves the cycle of planning, implementing actions, observing, and reflecting. The results of the study showed that there was a significant increase in understanding of Islamic Religious Education material, there was an increase in student activeness in discussions, asking questions, and being involved in learning activities, which created a more dynamic learning atmosphere, there was a significant increase in practical skills in practical skills.Jurnal Jaringan Penelitian Pengembangan Penerapan Inovasi Pendidikan (Jarlitbang)00The results of the study showed that there was a significant increase in understanding of Islamic Religious Education material, there was an increase in student activeness in discussions, asking questions, and being involved in learning activities, which created a more dynamic learning atmosphere.Jurnal Jaringan Penelitian Pengembangan Penerapan Inovasi Pendidikan (Jarlitbang)["Rohmahjimi Sholihah"]2024-12-28T00:00:00 8459adc4650901f2884c0fdd064fd8283566406eArtificial Intelligence (AI) telah menjadi salah satu inovasi terpenting dalam berbagai bidang, termasuk pendidikan jasmani. Artikel ini membahas implementasi AI dalam pendidikan jasmani, dengan fokus pada peningkatan pengalaman belajar, analisis performa fisik, dan pengembangan program latihan yang lebih efektif. AI dapat digunakan untuk menganalisis data performa siswa secara real-time, memberikan umpan balik yang lebih akurat, serta menciptakan pengalaman belajar yang lebih interaktif dan menarik. AI dapat digunakan untuk mengembangkan aplikasi yang mendorong kolaborasi antar siswa dalam kegiatan fisik, sehingga meningkatkan interaksi sosial dan keterampilan kerja sama. Pada pendidikan jasmani, di mana kolaborasi dan kerja sama sangat penting, penggunaan platform berbasis AI dapat dirancang sedemikian rupa agar tetap mendorong terjadinya interaksi sosial antara siswa. Data dari studi kasus dan wawancara dengan pendidik jasmani yang telah menerapkan teknologi AI menunjukkan bahwa AI dapat meningkatkan motivasi siswa, memberikan umpan balik yang lebih cepat dan akurat, serta membantu dalam perencanaan latihan yang lebih personal. Kesimpulan dari pembahasan ini bahwa penting untuk mendorong proyek pendidikan jasmani berbasis AI yang menciptakan model pembelajaran yang dapat digunakan melibatkan guru secara langsung (offline) atau pembelajaran siswa secara mandiri (online) namun tetap menekankan pada sosialisasi dan kolaborasi antar siswa sehingga implementasi AI dalam pendidikan jasmani dapat dirasakan sebagai wujud langkah menuju pembelajaran yang lebih inovatif dan efektif. Kata Kunci : Implementasi, Artificial Intelligence, Pendidikan JasmaniJURNAL PRESTASI40JURNAL PRESTASI["Irwansyah Siregar", "Bangun Abdul Harris Handoko", "Setia Hasibuan"]2024-12-28T00:00:00 69006be7d8faed8e1983463ab45086734a4cf016For about a decade and a half, increasing research attention has been paid to the role of women in the technological and content shaping of digital media industries (Gender Equality 2018). This study wants to give an idea of ​​how the digital presence of Hungarian women journalists can be characterized, their opportunities in the world of the network, and what is their relationship to artificial intelligence? What AI tools do they use in their personal and professional lives. We also present whether there are gender differences between male and female journalists in the application of AI in Hungary. The study relies on the European Union's Women in Digital research data for the given period.For about a decade and a half, increasing research attention has been paid to the role of women in the technological and content shaping of digital media industries (Gender Equality 2018). This study wants to give an idea of ​​how the digital presence of Hungarian women journalists can be characterized, their opportunities in the world of the network, and what is their relationship to artificial intelligence? What AI tools do they use in their personal and professional lives. We also present whether there are gender differences between male and female journalists in the application of AI in Hungary. The study relies on the European Union's Women in Digital research data for the given period.Communication Papers00An idea is given of how the digital presence of Hungarian women journalists can be characterized, their opportunities in the world of the network, and what is their relationship to artificial intelligence?Communication Papers["M\u00f3nika Andok", "D\u00f3ra P. Szilczl", "Andr\u00e1s Radetzky", "Zolt\u00e1n Rajki"]2024-12-28T00:00:00 c7877debc92ca0369cd654c612eda27f664e4823Artificial intelligence, , is a technological revolution in our current era, called the Fourth Industrial Revolution, which is essentially based on merging computer science, or computer science, with human intelligence to produce artificial intelligence. Chatbot (GAPT) is one of the most important generative artificial intelligence programs as a chatbot, or (GATTGBT). It is translated into Arabic (generative artificial transformer that is pre-trained for conversation), and the name generative artificial intelligence comes from the fact that it is programmed in order to be able to generate and create new content of its kind by learning patterns and structures from the data on which it was programmed, and thus it enjoys a kind of independence from whoever created it. By programming it with data and information. In our research, we focus on the legal nature of this modern technology according to the Iraqi Civil Law No. (40) and according to the Iraqi Consumer Protection Law No. (10) of 2010, and at the end of our research we arrived at a set of conclusions and recommendations to serve this research.ZANKO Journal of Law and Politics00In this research, Chatbot is one of the most important generative artificial intelligence programs as a chatbot, or GATTGBT, and the legal nature of this modern technology according to the Iraqi Civil Law No. (40) and according to the Iraqi Consumer Protection Law No. (10) of 2010 is focused on.ZANKO Journal of Law and Politics["Samera Mostafa"]2024-12-28T00:00:00 4f62970fe9beb4e978c21292378d89f57e75ac90The development of digital technologies and their active introduction into all spheres of life of society and the state have led to the emergence of new types of offences related to the use of artificial intelligence. When studying artificial intelligence is considered as a field of computer science dealing with the creation of systems capable of performing tasks that require human intellectual abilities. The article presents the results of the study of the risks of using artificial intelligence, including as a tool and means of committing crimes that can cause significant harm to the individual, society and the state. Special attention in the article is paid to the analysis of the risks of using artificial intelligence, the problems of its legal regulation and the use of this technology for criminal purposes. In the course of the study, the authors conclude that it is necessary to develop a set of measures to counteract crimes committed with the use of artificial intelligence.LEGAL ORDER: History, Theory, Practice00The authors conclude that it is necessary to develop a set of measures to counteract crimes committed with the use of artificial intelligence, including as a tool and means of committing crimes that can cause significant harm to the individual, society and the state.LEGAL ORDER: History, Theory, Practice["T. Pinkevich", "D. Konev"]2024-12-28T00:00:00 9519612a16c1ea7ece8f7395e3e110842e4345fdArtificial intelligence (AI) is a computer program that uses human-like intelligence to solve issues of diverse science, arts, medicine, social science, and technological inquiry. AI has the human-like ability to undertake learning, teaching, research, problem-solving, and intellectual reasoning cutting across all spheres of life. Recently robot lawyers have used Artificial Intelligence in conducting cases in court. Similarly, AI could impact legal education in diverse fields. This research interrogates how and manners that AI could advance legal education in Uganda. This research Uses a doctrinal approach, referring to, statutes, legal texts, and Internet publications. This research finds that in the same way that lawsuits can be conducted by Robot lawyers using AI enablement, legal education can also be advanced using AI instrumentality. The paper recommends that training be conducted for law teachers in Uganda to acquaint them with modern technology of AI application and programming amongst others.Kampala International University law journal00This research finds that in the same way that lawsuits can be conducted by Robot lawyers using AI enablement, legal education can also be advanced using AI instrumentality.Kampala International University law journal["Hilary Nwaechefu", "Michale Adewusi", "Timothy Kajja", "Kelechi Onwubiko", "Mastulah Natukunda"]2024-12-28T00:00:00 82b3a15dfd895216b5d5aeee73fb83817e07a713As research on artificial intelligence (AI) in education continues to expand, many scholars predict significant changes in the roles of teachers, schools, and educational leaders. This study seeks to examine potential outcomes of integrating AI into education and its implications for the future of educational institutions. Using a phenomenological approach, a qualitative research method, the study explores the perspectives of individuals from diverse sectors. The findings indicate that AI's introduction in education will bring innovative tools and advantages for schools and teachers, while also presenting certain challenges. TAMADDUN NURI JURNALI90The findings indicate that AI's introduction in education will bring innovative tools and advantages for schools and teachers, while also presenting certain challenges.TAMADDUN NURI JURNALI["Gulnora Najmiddinova"]2024-12-28T00:00:00 e27c355b90c4556f354add4fafbfb6342af55c33In recent years, artificial intelligence (AI) has grown at an unprecedented rate, causing fundamental shifts in many different scientific fields and industrial sectors. Machine learning (ML) algorithms have been a trailblazer in many of its most noteworthy developments, providing cutting-edge answers to difficult issues in industries as diverse as healthcare, banking, and autonomous systems. most recent developments in machine learning algorithms, with an emphasis on fresh ideas that challenge the status quo. We highlight the promise of new ideas like explainable AI, deep learning, and reinforcement learning to transform decision-making and human-computer interactions. Using up-to-date information on optimisation techniques, computational models, and algorithmic advancements, this study surveys the cutting edge of artificial intelligence research. Responsible AI development must be prioritised, and we address the difficulties and ethical concerns that come with these advances. In its last section, the article paints a picture of machine learning's potential for the years to come, describing the revolutionary changes it will bring to both science and society.Darpan International Research Analysis20This study surveys the cutting edge of artificial intelligence research, highlighting the promise of new ideas like explainable AI, deep learning, and reinforcement learning to transform decision-making and human-computer interactions.Darpan International Research Analysis["Abhyuday Singhal"]2024-12-28T00:00:00 dbcbf8ac9cc5a3acc21e693807b0c9cc11469d5eObjective: to determine the potential and acceptability of using artificial intelligence in legal activities according to the Nigerian law.Methods: the research is based on scientific analysis, as well as formallegal, comparative-legal, historical-legal and systemic-functional methods. The scope of the research is represented by the norms of legislation, including expired normative legal acts, as well as scientific monographic and periodical literature.Results: It was found that artificial intelligence and robot lawyers are inevitable innovations in the legal practice of many countries, including Nigeria. The use of these digital technologies has proven to be highly effective in activities related to the administration of justice, providing assistance in four areas of legal services: consulting and guidance, searching for materials, data analysis and forecasting the trial results. Technology greatly facilitates the work of lawyers, given the amount of legal services. In addition, the author show that while the use of artificial intelligence may generally be considered justified, the involvement of robot lawyers in legal practice in Nigeria facesboth legal and ethical barriers. The laws on legal education and legal practice in force in this country do not recognize robot lawyers as persons licensed to practice law in Nigeria. Robot lawyers must be given the status of a person before they can fully implement their potential in the legal practice of Nigeria.Scientific novelty: this is primarily due to the formulation of a research task to determine the possibility of a robot acting as a practicing lawyer within the legal framework in Nigeria.Practical significance: the conclusions formulated in the paper, namely, the inevitability of using artificial intelligence and robot lawyers in legal practice and the current legislation governing legal practice in Nigeria, will be useful when considering amendments to legislation in order to adapt it to the current level of digital technology development.Journal of Digital Technologies and Law200The conclusions formulated in the paper, namely the inevitability of using artificial intelligence and robot lawyers in legal practice and the current legislation governing legal practice in Nigeria, will be useful when considering amendments to legislation in order to adapt it to the current level of digital technology development.Journal of Digital Technologies and Law["O. O. Ikubanni", "O. A. Oyebanji", "A. A. \u041eyebade"]2024-12-28T00:00:00 be479ec8859e73781fc07cf25910cdcc672420a6The bibliometric analysis of 395 articles selected from the Web of Science (WoS) database between 2004 and 2024 is designed to provide a foundation for future research by mapping scientific collaborations, conceptual clusters, citation relationships, and intellectual structures in the research area, highlighting the international scope of the research area, and identifying emerging trends and influential studies. The results show that dominant topics such as machine learning, deep learning, aviation safety, atmospheric modeling, and anomaly detection are being studied in academia, highlighting the central role of AI in improving aviation safety and operational efficiency. High-impact journals such asIEEE Access and Aerospace have emerged as leading platforms, while Transportation Research Part C and the Journal of Air Transport Management are prominent in logistics and aviation-focused research. China and the United States lead aerospace and AI research with high publication volumes and significant impact. Italy contributes fewer publications but makes a notable impact, while the United Kingdom plays an important role in this field with active research efforts. Institutions such as Nanjing University of Aeronautics, Astronautics, and Vanderbilt University play an important role in advancing the field. These data show that, on both a journal and country basis, certain centers and countries have assumed dominant roles in the global research agenda in aerospace and AI, which have directly contributed to the formation of the aerospace ecosystem. These results provide important clues as to where future research will focus, and show that research communities are increasingly collaborating.International Journal of Computational and Experimental Science and Engineering254The bibliometric analysis of 395 articles selected from the Web of Science database between 2004 and 2024 shows that certain centers and countries have assumed dominant roles in the global research agenda in aerospace and AI, which have directly contributed to the formation of the aerospace ecosystem.International Journal of Computational and Experimental Science and Engineering["Rafet Erteki\u0307n", "Hakan Rodoplu", "Serap G\u00fcrsel"]2024-12-28T00:00:00 e3cac3970189b8e609e00a200a9ed138e71561dfScientific Reports501Five intelligent models were developed, including Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), and Bi-LSTM model had the best predictability performance for forecasting air quality parameters.Scientific Reports["O. A. Alawi", "H. Kamar", "Ali Alsuwaiyan", "ZaherMundher Yaseen"]2024-12-28T00:00:00 382f31125df69b232e425b998b56c1f5905657ddScientific Reports410While DL models generally surpassed ML models, RF’s exceptional performance highlights the potential of combining these technologies for early stroke detection, significantly improving patient outcomes by preventing severe consequences like permanent neurological damage or death.Scientific Reports["Khadijeh Moulaei", "Lida Afshari", "Reza Moulaei", "Babak Sabet", "Seyed Mohammad Mousavi", "Mohammadreza Afrash"]2024-12-28T00:00:00 66c60b332586f8a0d6a10e558946ed4131c57dbcThe Egyptian Journal of Community Medicine00The Egyptian Journal of Community Medicine["S. Abokresha", "Alaa Ahmed Ghaleb", "Fatma Ali Mahmoud"]2024-12-28T00:00:00 e3a15d1c7828a0d960c96eead1a112abcda71fdcCorporate Governance Insight00Corporate Governance Insight["AD Amar", "Imran Ahmed"]2024-12-28T00:00:00 8798ef1baffa617568c8c646bfc42fbe0f6713d1This study aims to critically examine the current research on AI-powered adaptive learning systems by synthesizing studies to identify trends, gaps, and challenges. It also explores the applications, benefits, challenges, and future directions of these systems in education. The research design employs a systematic review and qualitative thematic synthesis following PRISMA guidelines. Data collection involves a comprehensive literature search across databases such as ERIC, JSTOR, IEEE Xplore, Google Scholar, Web of Science, and Scopus. Inclusion criteria focus on peer-reviewed articles and high-quality grey literature from the past ten years. Data analysis includes coding and thematic mapping to integrate findings into a comprehensive narrative, ensuring rigor through triangulation, peer debriefing, and reflexivity. The findings reveal significant themes related to the role of AI in adaptive learning, including machine learning algorithms, natural language processing, and AI-driven data analysis. Applications of adaptive learning systems are demonstrated in personalized learning pathways, adaptive assessments, intelligent tutoring systems, and support for diverse learning needs. Case studies highlight the effectiveness of these systems in enhancing student engagement and learning outcomes. This study provides a comprehensive overview of the potential of AI-powered adaptive learning systems to transform education. It identifies significant benefits such as improved learning outcomes, increased engagement, scalability, and cost-effectiveness. The study also addresses challenges like data quality, ethical considerations, and institutional resistance, providing a balanced view of the current landscape. AI-powered adaptive learning systems have innovative potential in personalizing and improving educational experiences. While the benefits are significant, addressing challenges related to data quality, ethical considerations, and educator support is crucial. Future research should focus on long-term impacts, ethical implications, and integrating emotional and social learning to create a holistic educational environment.Batı Anadolu eğitim bilimleri dergisi00This study provides a comprehensive overview of the potential of AI-powered adaptive learning systems to transform education and identifies significant benefits such as improved learning outcomes, increased engagement, scalability, and cost-effectiveness.Batı Anadolu Eğitim Bilimleri Dergisi["Ru\u015fen Meylani"]2024-12-28T00:00:00 48692db2c21c239df26cfc2920df32dcc57c69f2Journal of Management00JOURNAL OF MANAGEMENT["V. Bharadwaj"]2024-12-28T00:00:00 bc0456489ba0a5da99091de1c928dea6b92d34ed Batam, as the third largest city in Sumatra, has many attractive beaches such as Melur Beach that attract both local and foreign tourists. However, the abundance of choices and the lack of complete information often make novice travelers confused in choosing the right destination. The variety of attractions and activities at tourist sites also adds to the hesitation. As a solution to this problem, an automated chatbot was developed that is able to provide services as if visitors were interacting directly with staff or officers without any time constraints. This research aims to design and implement a chatbot system capable of providing beach tourism recommendations in Batam using the Feedforward Neural Network (FFNN) method. The dataset used includes descriptive information about beach tourist attractions in Batam as well as reviews from visitors. The model achieved the best accuracy with 80% dataset division for training and 20% for testing, with 600 epochs, batch size 10, and learning rate 0.002, which resulted in 97.1% accuracy. Evaluation of this model shows a macro precision value of 79.71%, macro recall of 80%, and macro F1 score of 79.76%. Overall, the model showed high effectiveness in classification tasks, with high accuracy and a good balance between detecting correct instances and minimizing prediction errors. This design shows that a chatbot with feedforward neural network model can be used effectively which is able to provide beach tourism recommendations in Batam with high accuracy and appropriate response to the user. JURNAL QUANCOM: QUANTUM COMPUTER JURNAL00This design shows that a chatbot with feedforward neural network model can be used effectively which is able to provide beach tourism recommendations in Batam with high accuracy and appropriate response to the user.JURNAL QUANCOM: QUANTUM COMPUTER JURNAL["Arvy Kurnia Ramadhan", "Muhammad Abrar Masril", "Deosa Putra Caniago"]2024-12-28T00:00:00 e66bfb075b69b1848e32421eadcf48db1d707596Despite the risks associated with generative AI (GenAI) chatbots, people increasingly use these technologies, which may seem contradictory. This study identified and explored factors and risks related to trust, perceived values, satisfaction, and sustainable use of GenAI chatbots. Relying on IS theories to build a stimulus-organism-response model, the authors tested a model using PLS-SEM with data from 393 ChatGPT users. The results show that user competence and autonomy dramatically increase a user's trust in ChatGPT, and trust improves hedonic value (HV), utilitarian value (UV), value-in-use, perceived task-technology fit (TTF), information accuracy, knowledge acquisition, perceived informativeness, and user satisfaction. In addition to trust, user satisfaction depends on HV, UV, and TTF. The sustainability use of ChatGPT depends on HV and satisfaction. However, perceived privacy concerns, perceived privacy risks, and privacy awareness do not affect consumer trust. There is a complete mediation between trust and sustainability, as well as HV and sustainability.Journal of Global Information Management1120The results show that user competence and autonomy dramatically increase a user's trust in ChatGPT, and trust improves hedonic value (HV), utilitarian value (UV), value-in-use, perceived task-technology fit (TTF), information accuracy, knowledge acquisition, perceived informativeness, and user satisfaction.Journal of Global Information Management["Serge-Lopez Wamba-Taguimdje", "S. Wamba", "H. Twinomurinzi"]2024-12-28T00:00:00 287b0e0658400d5d8a8fb062ff92c26dcbb69ff6Sino-US English Teaching00Sino-US English Teaching["XU Lili"]2024-12-28T00:00:00 2e10dd67441b451ecfb5ea4a0a8e5a16d6b7720eEClinicalMedicine1130Subgroup analysis revealed that, for AI-assisted cervical cytology diagnosis, certain performance indicators were superior in developed countries compared to developing countries, and AI demonstrated superior accuracy in colposcopic examinations.eClinicalMedicine["Lei Liu", "Jiangang Liu", "Qing Su", "Yuening Chu", "Hexia Xia", "Ran Xu"]2024-12-28T00:00:00 096d83dae4854c5c53bdd607275e49274710d4a3Indian Journal of Surgery60Indian Journal of Surgery["S. Bhattacharya", "Kaushik Bhattacharya", "Neeta Bhattacharya"]2024-12-28T00:00:00 41866a8351f26c824cbfdc301fce1894ee943948This research work is motivated by both groups of ongoing unlimited opportunities, challenges, and alarming potentials for abuses, fraud, and spread of disinformation. The bifocal stress of the project is on economic essence and framework for assessment of both potential efficiencies in productivity and potential costs of AI in an economy, industry, or a business unit. The topical issue of the attention extent of AI in business public relations, as a potential perspective to a relevant economic cost-benefit analysis is cited in this research. AI is, in its essence, a technological component of capital in production of goods, services, public relations, and advertising. Hence, the productivity model, cost framework, and profit optimization methodologies are all integrated into a system of interrelated equations. Businesses and dynamic organizations are supposed to devote a more cohesive attention to uncertainties, probabilities, and more importantly, to the extent of presence and/or lack of attentions to the AI capital in search of a sustainable viability and thriving niche in their heavily competitive digital age. Our goal is to strive in compilation of massive data in our prospective research works in a more application-oriented inquiry than this current work.Management Studies170The main focus of the project is on economic essence and framework for assessment of both potential efficiencies in productivity and potential costs of AI in an economy, industry, or a business unit.Management Studies["Reza G. Hamzaee", "Maryam Salimi"]2024-12-28T00:00:00 86e42dd478daad46ba53603b8c393492b37ce713Kegiatan pembekalan pendeta dan aktivis gereja di GSJA Kanaan Bandar Jaya Lampung Tengah bertujuan untuk meningkatkan kemampuan mereka dalam memanfaatkan aplikasi SABDA dan teknologi kecerdasan buatan untuk mendukung pelayanan, khususnya dalam berkhotbah dan mempelajari Alkitab. Program ini dilaksanakan melalui metode pelatihan tatap muka mencakup sesi teori, praktik langsung, diskusi interaktif, dan pendampingan pasca-pelatihan. Hasil kegiatan menunjukkan peningkatan yang signifikan pada kompetensi peserta. Sebanyak 85% peserta berhasil menguasai fitur-fitur utama aplikasi SABDA, seperti pencarian topik, analisis paralel, dan referensi silang, sementara 80% peserta mampu menggunakan kecerdasan buatan untuk penggalian teks Alkitab yang lebih mendalam. Khotbah yang disampaikan oleh peserta setelah pelatihan mendapatkan umpan balik positif dari jemaat, dengan peningkatan kualitas dari segi struktur, kedalaman teologi, dan relevansi praktis. Meskipun terdapat tantangan teknis, seperti adaptasi pada teknologi baru, pelatihan ini berhasil memberikan solusi melalui pendampingan intensif. Selain itu, peserta mengapresiasi relevansi materi yang diberikan dan mengusulkan pelatihan lanjutan untuk memperdalam penggunaan teknologi dalam pelayanan. Hasil kegiatan ini membuktikan bahwa integrasi teknologi digital dapat secara efektif mendukung transformasi pelayanan gereja, menjawab kebutuhan jemaat, dan meningkatkan kualitas penyampaian firman Tuhan.JURNAL PENGABDIAN KEPADA MASYARAKAT40Devotion: Jurnal Pengabdian Kepada Masyarakat["R. R. Walean", "Dida Hae", "Kati", "Rivaldo Matheis Nendissa", "Wimprit Prayogi", "Sekolah Tinggi", "Teologi Mawar", "Saron Lampung", "R. R. Walean", "dan kawan-kawan"]2024-12-28T00:00:00 88c1fd68ed01838f61f7904e12eaa30c24ec76f2Supply chains are frequently exposed to disruptions, which can be either positive, driven by technological advancements, or negative, caused by natural and man-made disasters. This study aims to explore the possibilities and implications of building supply chain resilience through AI-driven AR/VR simulations. In light of the disruptions experienced during the COVID-19 pandemic, there has been a growing interest among both researchers and practitioners in the role of digital technologies in enhancing end-to-end visibility within supply chains and their potential for boosting resilience.The study provides insights into how leveraging the dynamic capabilities of supply chains through AI technology can strengthen resilience. It offers a forward-looking perspective on how emerging technologies will shape modern supply chains and play a crucial role in improving their resilience. The article underscores the transformative potential of AI, highlighting its ability to equip supply chains to better withstand disruptions and mitigate associated risks.International journal of supply chain management370The study provides insights into how leveraging the dynamic capabilities of supply chains through AI technology can strengthen resilience and underscores the transformative potential of AI, highlighting its ability to equip supply chains to better withstand disruptions and mitigate associated risks.International Journal of Supply Chain Management["Priyank Kumawat"]2024-12-28T00:00:00 bdc6077cd9817d5879db92f031dc3b323f804638The purpose of this research is to discuss the contribution of artificial intelligence to student character at the basic education level. The research method used is qualitative with a literature study. The results of this study reveal that there are three main things about the contribution of AI to the character building of MI/SD students, first, the personalization of learning, second, the impact caused both positively and negatively, and the influence of AI on the character of MI/SD students in learning. Therefore, teachers and parents need to direct students to be smart in using AI as a learning tool, not as the main source of knowledge, because AI is a man-made technology that has limitations in recognizing the psychology of students. AI is utilized to facilitate students' learning and stimulate their thinking.Diksi170Teachers and parents need to direct students to be smart in using AI as a learning tool, not as the main source of knowledge, because AI is a man-made technology that has limitations in recognizing the psychology of students.Diksi: Jurnal Pendidikan dan Literasi["Rita Sari", "Sibawaihi Sibawaihi"]2024-12-28T00:00:00 4c3cd85d8b74f1a9b6add667a642cd293d367c16This article is devoted to the analysis of the impact of the introduction of artificial intelligence (AI) technologies on the economic performance of service enterprises. In the face of increasing competition and ever-increasing demands on the quality of service, the use of AI is becoming a strategic factor in improving efficiency and competitiveness. The study aims not only to assess the economic benefits of using AI in various service areas, but also to identify key factors affecting the effectiveness of implementation, as well as to develop recommendations for optimizing investment decisions in this area. The theoretical value of the work lies in the development and application of a comprehensive methodology for evaluating the economic efficiency of AI solutions in the service industry. The results obtained can be used to substantiate investment projects, plan a strategy for the development of service enterprises and increase their competitive position in the market.Vestnik BIST00The study aims not only to assess the economic benefits of using AI in various service areas, but also to identify key factors affecting the effectiveness of implementation, as well as to develop recommendations for optimizing investment decisions in this area.Vestnik BIST (Bashkir Institute of Social Technologies)["Lyubov D. Matveeva", "Nadezhda G. Iraeva", "Adel R. Raimova"]2024-12-28T00:00:00 a631a30ba5cacd1f21dc649d603ded0996b55e0aThe integration of Artificial Intelligence (AI) in education offers transformative possibilities, particularly in enhancing classroom communication. However, its adoption remains uneven, especially in developing countries like Nigeria, where sociocultural, linguistic, and infrastructural barriers persist. This study investigates the adoption of AI-mediated communication among academic staff at the Federal College of Education (Technical) Akoka, Nigeria. A mixed-methods approach was employed, combining quantitative survey data from 200 respondents with qualitative insights to explore the extent of adoption, challenges encountered, and sociocultural influences. Results indicate that while 19.2% of respondents extensively use AI tools, a significant portion faces challenges such as inadequate funding (42.3%), limited technical expertise (15.4%), and infrastructural deficiencies (25%). Moreover, sociolinguistic issues, including the mismatch between AI tools and local languages, hinder effective communication. Resistance to change, rooted in traditional teaching norms, further complicates adoption. The study contributes by highlighting the interplay between sociocultural norms and technological resistance, offering a multidisciplinary perspective on AI integration in education. Recommendations include targeted professional development, infrastructure improvements, and the creation of culturally adaptive AI tools. These findings provide a replicable framework for enhancing AI adoption in similar educational contexts globally. Future research should explore longitudinal impacts of AI integration on teaching outcomes and develop policies addressing ethical considerations in AI usage. This study underscores the critical need for inclusive and localized AI solutions to bridge the technological gap in education.Language, Technology, and Social Media201The study investigates the adoption of AI-mediated communication among academic staff at the Federal College of Education (Technical) Akoka, Nigeria, highlighting the interplay between sociocultural norms and technological resistance, offering a multidisciplinary perspective on AI integration in education.Language, Technology, and Social Media["O. Festus", "Ogunrinbokun Bamidele Emmanuel"]2024-12-28T00:00:00 62c0f77b38a3f6629cf6d54294d9564c664302acThe latest changes on the distribution network due to the presence of distributed energy resources (DERs) and electric vehicles make it necessary to monitor the grid using a real-time high-precision system. The present work centers on the development of an open-source software platform that allows for the joint management of, at least, power quality monitors (PQMs), phasor measurement units (PMUs), and smart meters (SMs), which are three of the most widespread devices on distribution networks. This framework could work remotely while allowing access to the measurements in a comfortable way for grid analysis, prediction, or control tasks. The platform must meet the requirements of synchronism and scalability needed when working with electrical monitoring devices while considering the large volumes of data that these devices generate. The framework has been experimentally validated in laboratory and field tests in two photovoltaic plants. Moreover, real-time Artificial Intelligence capabilities have been validated by implementing three Machine Learning classifiers (Neural Network, Decision Tree, and Random Forest) to distinguish between three different loads in real time.Applied Sciences420The present work centers on the development of an open-source software platform that allows for the joint management of power quality monitors, phasor measurement units, and smart meters, which are three of the most widespread devices on distribution networks.Applied Sciences["Victoria Arenas-Ramos", "Federico Cuesta", "V. Pallar\u00e9s-L\u00f3pez", "Isabel Santiago"]2024-12-28T00:00:00 bfef705c4be6de7d0e14231c149a24c09accfd5eLow altitude economy (LAE) holds immense potential to drive urban development across various sectors. However, LAE also faces challenges in data collection and processing efficiency, flight control precision, and network performance. The challenges could be solved by realizing an integration of sensing, communications, computation, and control (ISC3) for LAE. In this regard, embodied artificial intelligence (EAI), with its unique perception, planning, and decision-making capabilities, offers a promising solution to realize ISC3. Specifically, this paper investigates an application of EAI into ISC3 to support LAE, exploring potential research focuses, solutions, and case study. We begin by outlining rationales and benefits of introducing EAI into LAE, followed by reviewing research directions and solutions for EAI in ISC3. We then propose a framework of an EAI-enabled ISC3 for LAE. The framework's effectiveness is evaluated through a case study of express delivery utilizing an EAI-enabled UAV. Finally, we discuss several future research directions for advancing EAI-enabled LAE.arXiv.org00This paper investigates an application of EAI into ISC3 to support LAE, exploring potential research focuses, solutions, and case study, and proposes a framework of an EAI-enabled ISC3 for LAE.ArXiv["Yaoqi Yang", "Yong Chen", "Jiacheng Wang", "Geng Sun", "D. Niyato"]2024-12-28T00:00:00 7ffcdf64de5a1e47e15fb5cb43330612ac265de3The rapid development of technology, especially in the field of artificial intelligence (AI), has created new opportunities for the creation of creative content and provided fresh approaches to captivate viewers and deliver important messages. The purpose of this community service project is utilizing AI technology to create preaching content for the students of the Indonesian Da'wah Academy located in North Sumatra. The activity is carried out in four stages, starting with a socialization of the activity plan to the partner and participants (students), followed by a presentation of training materials, independent learning, and task presentations. A number of AI-powered platforms are unveiled, including those for image, video and voice generation. The training emphasized the importance of digital literacy and ethical content creation within the context of dakwah (Islamic propagation). By the end of the program, a Student Content Creator Unit was established as a unit that will continue to enhance students' abilities in the digitalization of content. The evaluation was conducted in the form of a questionnaire, with results reflecting the enthusiasm and satisfaction of the participants regarding the training they attended. The program's success highlights the potential of AI in empowering students to enhance their creative skills while promoting positive and constructive narratives in the digital age.ABDIMAS TALENTA: Jurnal Pengabdian Kepada Masyarakat50This community service project is utilizing AI technology to create preaching content for the students of the Indonesian Da'wah Academy located in North Sumatra, highlighting the potential of AI in empowering students to enhance their creative skills while promoting positive and constructive narratives in the digital age.ABDIMAS TALENTA: Jurnal Pengabdian Kepada Masyarakat["Tri Widyawati", "Yunilda Andriyani", "T. B. Nur", "I. B. Sumantri", "Tarisa Olivia", "Muhammad Rozan", "Fawwaz Syariq", "Septia Nugraha", "Saddam Haikal", "Rezka Zuhra Maulida"]2024-12-28T00:00:00 00ff470eef971c640938f72c999bfe353fd122e5Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data discrepancies between model deployment and development environments. To address this issue, we developed TRUECAM, a framework designed to ensure both data and model trustworthiness in non-small cell lung cancer subtyping with whole-slide images. TRUECAM integrates 1) a spectral-normalized neural Gaussian process for identifying out-of-scope inputs and 2) an ambiguity-guided elimination of tiles to filter out highly ambiguous regions, addressing data trustworthiness, as well as 3) conformal prediction to ensure controlled error rates. We systematically evaluated the framework across multiple large-scale cancer datasets, leveraging both task-specific and foundation models, illustrate that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness. These findings highlight TRUECAM as a versatile wrapper framework for digital pathology AI models with diverse architectural designs, promoting their responsible and effective applications in real-world settings.medRxiv590It is illustrated that an AI model wrapped with TRUECAM significantly outperforms models that lack such guidance, in terms of classification accuracy, robustness, interpretability, and data efficiency, while also achieving improvements in fairness.medRxiv : the preprint server for health sciences["X. Zhang", "T. Wang", "C. Yan", "F. Najdawi", "K. Zhou", "Y. Ma", "Y.-m. Cheung", "B. A. Malin"]2024-12-28T00:00:00 5682273db0d04780a2f0ef61ac9f8dae17575298Integrating artificial intelligence (AI) into teacher professional development (PD) is a promising way to enhance teaching practices and foster innovation in education. However, the development and adoption of AI-based educational platforms face unique challenges in the BRICS region, including technological infrastructure and cultural diversity. This study aimed to identify key aspects of teaching in developing an integrative AI-assisted adaptive learning environment (ALE) in the region. Theoretical methods were used along with qualitative analyses of in-depth interviews and focus group discussions of 36 professors, researchers, and experts from Iranian universities. The findings of this study provided perspectives on the feasibility and effectiveness of developing a joint AI-ALE platform, emphasizing the importance of contextualizing AI initiatives and educational approaches to the needs and constraints of each country and underscoring the significance of local training programs, understanding areas of growth, clarifying values and cultures to be shared with other parties, designing educational resources compatible with collaborative platforms, and overcoming technological barriers. The study provided recommendations on capacity building and joint partnerships for educational innovation, and the development of AI-based teaching practices to open up new opportunities for PD of the next generation of educators for the digital landscape in the BRICS area.Education and Self Development00The study provided recommendations on capacity building and joint partnerships for educational innovation, and the development of AI-based teaching practices to open up new opportunities for PD of the next generation of educators for the digital landscape in the BRICS area.Education and Self Development["Moloud Mohammadi"]2024-12-28T00:00:00 0633d3026050a8ebcc17d57eeb005b636800d5f1Every day, a considerable number of new cybersecurity attacks are reported, and the traditional methods of defense struggle to keep up with them. In the current context of the digital era, where industrial environments handle large data volumes, new cybersecurity solutions are required, and intrusion detection systems (IDSs) based on artificial intelligence (AI) algorithms are coming up with an answer to this critical issue. This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework. The solution is modularly built with two pipelines for data analysis. The pipelines use a pre-trained XGBoost (eXtreme Gradient Boosting) model that achieved an accuracy score of up to 90%. The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets.Italian National Conference on Sensors260This paper presents an approach for implementing a generic model of a network-based intrusion detection system for Industry 4.0 by integrating the computational advantages of the Nvidia Morpheus open-source AI framework.Sensors (Basel, Switzerland)["Beatrice-Nicoleta Chiriac", "Florin-Daniel Anton", "A. Ionita", "Bogdan-Valentin Vasilic\u0103"]2024-12-28T00:00:00 206016fcf3743e81b051f346a0b99a15a48d9a3aEnd-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. However, the current push towards widespread integration of artificial intelligence (AI) models, including in E2EE systems, raises some serious security concerns. This work performs a critical examination of the (in)compatibility of AI models and E2EE applications. We explore this on two fronts: (1) the integration of AI"assistants"within E2EE applications, and (2) the use of E2EE data for training AI models. We analyze the potential security implications of each, and identify conflicts with the security guarantees of E2EE. Then, we analyze legal implications of integrating AI models in E2EE applications, given how AI integration can undermine the confidentiality that E2EE promises. Finally, we offer a list of detailed recommendations based on our technical and legal analyses, including: technical design choices that must be prioritized to uphold E2EE security; how service providers must accurately represent E2EE security; and best practices for the default behavior of AI features and for requesting user consent. We hope this paper catalyzes an informed conversation on the tensions that arise between the brisk deployment of AI and the security offered by E2EE, and guides the responsible development of new AI features.IACR Cryptology ePrint Archive1350This work performs a critical examination of the (in)compatibility of AI models and E2EE applications, and offers a list of detailed recommendations based on technical and legal analyses.ArXiv["Mallory Knodel", "Andr'es F'abrega", "Daniella Ferrari", "Jacob Leiken", "Betty Li Hou", "Derek Yen", "Sam de Alfaro", "Kyunghyun Cho", "Sunoo Park"]2024-12-28T00:00:00 8af7328c944568e179da21029b7519710bc6aff1Kanker merupakan salah satu penyebab utama kematian di seluruh dunia, dengan deteksi dini menjadi kunci untuk meningkatkan peluang kesembuhan. Dalam upaya mempercepat diagnosis dan mendukung asuhan keperawatan, teknologi Artificial Intelligence (AI) kini digunakan untuk membantu proses deteksi dini kanker. Penelitian ini bertujuan mengembangkan sistem deteksi dini dan asuhan keperawatan pada kanker berbasis AI menggunakan model Random Forests yang diintegrasikan ke dalam aplikasi web. Penelitian ini menggunakan metode Research and Development (R&D) dengan pengembangan sistem berbasis AI. Sistem dikembangkan melalui pengumpulan data dari pasien kanker dan didesain menggunakan model Random Forests untuk memproses input data gejala dan kondisi pasien. Model ini diimplementasikan dalam aplikasi berbasis web yang mampu melakukan prediksi dan memberikan saran asuhan keperawatan. Pengujian dilakukan terhadap 300 data pasien dengan berbagai jenis kanker. Hasil penelitian didapatkan bahwa sistem yang dikembangkan memiliki tingkat akurasi sebesar 98%. Pada jenis kanker payudara, paru-paru, kolorektal, prostat, serviks, dan leukemia, akurasi mencapai 100%, sedangkan pada kanker endometrium 80% dan kanker otak 66,7%. Variasi tingkat akurasi dipengaruhi oleh respons pengguna dalam menjawab pertanyaan terkait kondisi kesehatan mereka. Sistem mampu mengatasi masalah overfitting melalui mekanisme Random Forests yang menjaga keandalan prediksi. Kesimpulan pada penelitian ini yakni sistem berbasis AI dengan model Random Forests yang diintegrasikan ke dalam aplikasi web terbukti efektif dalam mendeteksi dini beberapa jenis kanker dengan akurasi tinggi. Meskipun demikian, optimalisasi lebih lanjut diperlukan, terutama pada jenis kanker dengan tingkat akurasi rendah, untuk meningkatkan keandalan sistem dalam deteksi dan asuhan keperawatan kanker secara menyeluruh.PREPOTIF : JURNAL KESEHATAN MASYARAKAT161PREPOTIF : JURNAL KESEHATAN MASYARAKAT["Heri NUR CAHYANTO", "Octo Zulkarnain", "Rasi Rahagia"]2024-12-29T00:00:00 a0ea74612fb1fa0844383699f22144496b25e853In this article, the examination of the influence of artificial intelligence (AI) on the educational process is conducted, with a particular focus on its impact on individualized teaching, the monitoring of educational progress, and student motivation. The discussion delves into diverse AI applications, including the customization of educational materials, adaptive progress tracking, and the introduction of innovative motivational techniques. Emphasis is placed on the positive aspects of incorporating AI into education, while concurrently underlining the importance of using this technology with awareness and discretion. Furthermore, the article seeks to dispel common misconceptions about the role of AI, particularly in generating educational content, and stresses the irreplaceable role of the teacher as a mentor in the learning process. A critical point is made about the imperative need to educate all participants involved in the educational sphere to effectively leverage the potential benefits of artificial intelligence.Journal of Modern Science170The article seeks to dispel common misconceptions about the role of AI, particularly in generating educational content, and stresses the irreplaceable role of the teacher as a mentor in the learning process.Journal of Modern Science["Krystian Tuczy\u0144ski"]2024-12-29T00:00:00 a6bbe99cd8216eef951f5c265f069882ff35a290This technical article comprehensively explores the integration of Artificial Intelligence in agricultural systems, with a primary focus on precision farming methodologies and their practical implementation. It conducts an in-depth examination of the technological framework underpinning agricultural AI systems, encompassing sophisticated data acquisition infrastructure and advanced machine learning implementations. Through detailed analysis, the article investigates the critical technical components of precision farming systems, emphasizing the integration of sensor technologies spanning both aerial and ground-based platforms, alongside robust data analytics architectures. It systematically addresses the prevalent challenges in agricultural data integration and system scalability, presenting innovative solutions for these complex issues. Furthermore, the article explores emerging technological developments in the field, including cutting-edge sensing technologies and AI algorithm enhancements, offering valuable insights into future trajectories of agricultural AI applications. The comprehensive article illuminates how AI technologies are fundamentally transforming traditional farming practices through the implementation of sophisticated automated decision-making processes and enhanced resource management capabilities, ultimately contributing to more efficient and sustainable agricultural operationsInternational Journal For Multidisciplinary Research80The comprehensive article illuminates how AI technologies are fundamentally transforming traditional farming practices through the implementation of sophisticated automated decision-making processes and enhanced resource management capabilities, ultimately contributing to more efficient and sustainable agricultural operations.International Journal For Multidisciplinary Research["Ravi Kottur"]2024-12-29T00:00:00 2da964fe828c76fb628a495b85419394ab5eefdfBackground: Ambient artificial intelligence offers promise for improving documentation efficiency and reducing provider burden through clinical note generation. However, challenges persist in workflow integration, compliance, and widespread adoption. This study leveraged a Learning Health System (LHS) framework to align research and operations using a hybrid effectiveness-implementation protocol, embedded as pragmatic trial operations within the electronic health record (EHR). Methods: An alpha phase was conducted to pilot technical integration, refine workflows, and determine sample size in planning for a beta phase designed as a pragmatic randomized controlled trial with the Stanford Professional Fulfillment Index (PFI) as primary outcome. During alpha, bi-directional governance was established between IS operations and LHS team with multidisciplinary workgroups for analytics, technical, documentation, and user experience. Ambient AI was embedded into the EHR using Fast Healthcare Interoperability Resources (FHIR), with real-time data dashboards tracking utilization and documentation accuracy for operations and research. Performance metrics were monitored serially using a difference-in-differences (DiD) analysis to detect drift caused by software workflow changes. Results: The alpha phase, designed as Type 1 Hybrid, informed a 24-week beta phase stepped-wedge trial with 90% power to detect changes in PFI. Across the alpha phase, the weighted median of average provider Ambient AI utilization was 65.4% following Plan-Do-Study-Act cycles addressing organizational feasibility and task-dependent adoption. Diagnosis code accuracy dropped from 79% to 35% (p < 0.01) during alpha but recovered with a new note template and provider training. DiD did not detect significant drifts in work outside of work or time in notes two weeks before and after the new note template. Beta phase enrollment achieved its targeted 66 providers across eight specialties, initiating on schedule. Conclusions and Relevance: We provide a novel playbook for integrating Generative AI platforms in healthcare, combining pragmatic trial operations, human-centered design, and real-time monitoring to advance evidence-based implementation.medRxiv180A novel playbook for integrating Generative AI platforms in healthcare, combining pragmatic trial operations, human-centered design, and real-time monitoring to advance evidence-based implementation is provided.medRxiv : the preprint server for health sciences["Majid Afshar", "Felice Resnik", "Mary Ryan Baumann", "Josie Hintzke", "A. Sullivan", "Tina Shah", "Anthony Stordalen", "Michael Oberst", "Jason Dambach", "Leigh Ann Mrotek", "Mariah Quinn", "Kirsten Abramson", "Peter Kleinschmidt", "Tom Brazelton", "Heidi Twedt", "David Kunstman", "John Long", "Brian W. Patterson", "F. Liao", "Stacy Rasmussen", "Elizabeth Burnside", "Cherodeep Goswami", "Joel Gordon"]2024-12-29T00:00:00 ba24062417622a8062d55a034283f7be6aa516f6Artificial Intelligence (AI) is rapidly playing a significant role in modern society, bringing about widespread changes across various areas of human life. In healthcare, AI-based technologies make diagnosis faster and more accurate and improve patient care, while in education, it provides personalized learning experiences for students. In the financial sector, AI is enhancing efficiency in risk management, fraud prevention, and customer service. However, challenges such as data privacy, bias, and unemployment due to automation are also associated with AI. Understanding the impact of AI and ensuring its responsible use is crucial. Through proper policies, ethical guidelines, and strong governance, the benefits of AI can be maximized while minimizing potential risks. This article emphasizes the dual impact of AI and the need for its responsible development to shape not only the present but also the future with a positive influence on human life. Abstract in Hindi Language: कृत्रिम बुद्धिमत्ता (AI) तेजी से आधुनिक समाज में एक महत्वपूर्ण भूमिका निभा रही है, जिससे मानव जीवन के विभिन्न क्षेत्रों में व्यापक परिवर्तन हो रहे हैं। स्वास्थ्य सेवा में, AI-आधारित तकनीकें निदान को तेज़ और सटीक बनाती हैं और रोगी देखभाल को सुधारती हैं, जबकि शिक्षा में यह छात्रों के लिए व्यक्तिगत शिक्षण अनुभव प्रदान करती है। वित्तीय क्षेत्र में, AI जोखिम प्रबंधन, धोखाधड़ी की रोकथाम और ग्राहक सेवा को कुशल बना रहा है। हालांकि, AI से संबंधित डेटा गोपनीयता, पूर्वाग्रह और स्वचालन से बेरोजगारी जैसी चुनौतियां भी मौजूद हैं। AI के प्रभाव को समझना और जिम्मेदार उपयोग सुनिश्चित करना महत्वपूर्ण है। उचित नीति, नैतिक दिशा-निर्देश और मजबूत शासन के माध्यम से, AI के लाभों को अधिकतम किया जा सकता है, जबकि संभावित जोखिमों को कम किया जा सकता है। इस लेख में AI के दोहरे प्रभाव और इसके जिम्मेदार विकास की आवश्यकता पर जोर दिया गया है, ताकि मानव जीवन पर इसका सकारात्मक प्रभाव न केवल वर्तमान बल्कि भविष्य को भी आकार दे सके। Keywords: कृत्रिम बुद्धिमत्ता, मानव जीवन, जोखिम प्रबंधन, डेटा गोपनीयता, स्वचालन, सामाजिक असमानता ।Research Review Journal of Social Science10Research Review Journal of Social Science["Sarita Singh"]2024-12-29T00:00:00 3dc7f7bcb9d462eb1c4fbcc4f2438310a9552006Background: The rise of telehealth, particularly during the COVID-19 pandemic, has transformed healthcare delivery, providing essential services to patients in remote areas. Advanced practice nurses (APNs) are crucial in utilizing telemedicine to enhance patient care. However, the integration of artificial intelligence (AI) in telehealth remains underexplored. Methods: This scoping review examines the role of advanced practice nurses in telemedicine, focusing on AI-assisted interventions. A systematic search was conducted across six databases, including PubMed and CINAHL, for studies published from 2017 to 2023. The review assessed user satisfaction, perceptions of AI technology, and the effectiveness of AI algorithms in telehealth applications. Results: The review synthesized findings from eight studies that utilized AI technologies in telehealth for nursing practice. The results indicate that AI tools, particularly machine learning algorithms, significantly enhance decision-making and patient outcomes. APNs reported improved patient monitoring and satisfaction levels with AI-assisted telehealth services. However, challenges such as inadequate training and technology acceptance among nurses were identified. Conclusion: The findings underscore the pivotal role of advanced practice nurses in leveraging AI technologies within telemedicine to improve healthcare delivery. Enhanced training and support for APNs are essential to fully realize the potential of AI in telehealthJournal of Ecohumanism380The results indicate that AI tools, particularly machine learning algorithms, significantly enhance decision-making and patient outcomes within telemedicine to improve healthcare delivery.Journal of Ecohumanism["Wasayif Atallah Albalwi", "Shaymaa Atallah Albalawi", "Swasan Ibrahim Ahmed Mikhizne", "Huda Khalid Ajmi Aldhafeeri", "Samar Nasser Alshahrani", "Suhail Matla Alotabi", "Tayser Naser Monif Alotaibi", "Najal Mohammed Al Yamani", "Ahlam Albeladi", "Latifa Faihan Ayad Al-Otaibi", "Abdulmajeed Abdulaziz M Alfayez", "Abdulaziz Abdullah Faisal Alotaibi", "Mateb Marzoq Matar Aleit", "Tayseer Naseer Monife Alotaibi"]2024-12-29T00:00:00 d1f5f10a6c7c0720b558a2e0d793da517d6aba95Do people prefer that artificial intelligence (AI) aligns with gender stereotypes when requesting help to answer a question? We found that people preferred gender stereotypicality (over counterstereotypicality and androgyny) in voice-based AI when seeking help (e.g., preferring feminine voices to answer questions in feminine domains; Studies 1a–1b). Preferences for stereotypicality were stronger when using binary zero-sum (vs. continuous non-zero-sum) assessments (Study 2). Contrary to expectations, biases were larger when judging human (vs. AI) targets (Study 3). Finally, people were more likely to request (vs. decline) assistance from gender stereotypical (vs. counterstereotypical) human targets, but this choice bias did not extend to AI targets (Study 4). Across studies, we observed stronger preferences for gender stereotypicality in feminine (vs. masculine) domains, potentially due to examining biases in a stereotypically feminine context (helping). These studies offer nuanced insights into conditions under which people use gender stereotypes to evaluate human and non-human entities.Personality and Social Psychology Bulletin450It is found that people preferred gender stereotypicality in voice-based AI in voice-based AI when seeking help, and these studies offer nuanced insights into conditions under which people use gender stereotypes to evaluate human and non-human entities.Personality and Social Psychology Bulletin["Julia Spielmann", "Chadly Stern"]2024-12-29T00:00:00 fa8ea1a9c2ee9ff0a606add13d48de67258028e2The purpose of this study was to evaluate the effectiveness of Artificial Intelligence (AI) tools in teaching and learning in higher education institutions in Kenya, specifically focusing on Intelligent Tutoring Systems (ITS), Adaptive Learning Platforms, Virtual Learning Assistants (VLAs), Automated Grading Systems and Learning Analytics Systems (LAS), their accessibility use and its effectiveness in teaching and learning. The study employed a mixed-methods research design, combining both quantitative and qualitative approaches, to gather comprehensive data from faculty members, students, and administrators across 15 selected public and private universities and technical colleges in Kenya. The findings indicated that the accessibility of AI tools in institutions of higher learning in Kenya is significantly limited. A large majority of respondents expressed that AI tools are not readily available, highlighting disparities in access across different departments and projects within institutions. In terms of usage, the integration of AI tools into teaching and learning practices is still in its early stages in most institutions and where they are available they are not always well-integrated with existing curricula, leading to limited and uneven adoption across different disciplines. Despite these challenges, those who have begun using AI tools have reported benefits such as personalized learning, more efficient assessment processes, and enhanced feedback mechanisms, indicating that AI has the potential to transform educational practices if more effectively utilized. Findings further established a significant correlation between AI tools and effective teaching and learning in institutions of higher learning in Kenya (r = .781; p = .000). The study noted that while AI can significantly improve the educational experience, its current impact is constrained by several factors. Faculty members' unfamiliarity with AI, the lack of comprehensive training, and the inadequate integration of AI tools into the curriculum are major barriers to their effective use. However, where AI has been successfully implemented, it has contributed to better learning outcomes, higher student engagement, and more personalized feedback. The study recommended that institutions must invest in infrastructure, ongoing professional development, and curriculum integration, ensuring that AI tools are both accessible and effectively used to enhance teaching and learning outcomes.Journal of the Kenya National Commission for UNESCO100The study noted that while AI can significantly improve the educational experience, its current impact is constrained by several factors, and recommended that institutions must invest in infrastructure, ongoing professional development, and curriculum integration, ensuring that AI tools are both accessible and effectively used to enhance teaching and learning outcomes.Journal of the Kenya National Commission for UNESCO["Audrey Matere"]2024-12-29T00:00:00 c711f7baf938d4a82df32f2c09fa6281a57c4e1eClearly, the digital age is here with us, inevitably, Artificial Intelligence (AI) tools have penetrated the education sector with a promise to reshape the landscape of education. Among the many capabilities of AI tools is the promise to customize learning to individual needs, improve the laborious administrative tasks, and provide a deeper understanding of the student performance. However, this rapid adoption of AI tools into our education systems warrants a careful examination of both their transformative potential and the challenges they may pose. This paper provides a critical review of the implications of using AI tools in education, with the aim of weighing the benefits with the possible dangers that could result in the near future. This review looks at the extent of the integration of AI in education from basic to higher institutions of learning in the developed and developing world. It considers the possible benefits and potential pitfalls. Some of the concerns include the use of vast amounts of data since AI systems require vast amounts of data to function effectively. Further, there are issues of the possibility of over-dependence on AI tools which could hinder the development of critical thinking and socialization skills among students. The review also looks at the risk of possible worsening of the digital divide, as most students in the developing world do not have access to the latest technologies and infrastructure which could find themselves disadvantaged. This paper serves to motivate the development of a policy and guidelines that will can maximize the benefits of AI while minimizing risks in the adoption of AI tools in education. The paper is also motivates close collaboration among educators, technologists, and policymakers. This is important as we venture into the inevitable wave of AI also known as the Fourth Industrial Revolution.Multidisciplinary Journal of Technical University of Mombasa40This paper serves to motivate the development of a policy and guidelines that will can maximize the benefits of AI while minimizing risks in the adoption of AI tools in education, and motivates close collaboration among educators, technologists, and policymakers.Multidisciplinary Journal of Technical University of Mombasa["Mvurya Mgala"]2024-12-29T00:00:00 c777ce61eeee72312d5d5bc01ce23b9666625418Objective: This review identifies the characteristic features of artificial intelligence (AI) chatbots and their therapeutic effect; assesses their efficacy in treatment of depression, anxiety, and other mental health disorders; and establishes levels of user engagement and satisfaction. Method: Searches were conducted on the PubMed, Embase, MEDLINE, CENTRAL, CINAHL, PsycINFO, and Google Scholar databases using a set of keywords such as, not limited to, AI cognitive behavioral therapy (AI CBT), Youper, Wysa, Woebot, and other related terms. We included studies that were empirical, peer-reviewed, conducted between January 2017 and June 2024, and primarily focused on efficacy regarding the interventions and therapeutic outcomes. Data were then extracted and analyzed using both qualitative and quantitative methods concerning the mental health outcome. Results: Our review identified large improvements across the three chatbots in symptoms of mental health, as supported by the 10 included studies: five on Woebot, four on Wysa, and one on Youper. Woebot showed remarkable reductions in depression and anxiety with high user engagement; Wysa demonstrated similar improvements, especially in users with chronic pain or maternal mental health challenges; Youper also presented a significant symptom reduction, including a 48% decrease in depression and a 43% decrease in anxiety. Common benefits of all chatbots were the therapeutic alliance and a high rate of satisfaction among users. We have also discussed the included studies’ limitations; that is, study design shortcomings and lack of sample diversity. Conclusion: AI CBT chatbots, including but not limited to Woebot, Wysa, and Youper, are highly promising because of their availability and effectiveness in mental health support. They provide a useful complement to standard therapy when professional help is unavailable, and offer constant engagement with tailored interventions. However, it is necessary that further studies investigate their potential impact as long-term intervention models and explore how they may be integrated into holistic mental health care systems.Iranian Journal of Psychiatry00AI CBT chatbots, including but not limited to Woebot, Wysa, and Youper, are highly promising because of their availability and effectiveness in mental health support and offer constant engagement with tailored interventions.Iranian Journal of Psychiatry["Maryam Farzan", "Hamid Ebrahimi", "Maryam Pourali", "Fatemeh Sabeti"]2024-12-29T00:00:00 5709b841df6fce4bb05f515344dfa8b084338516The integration of Artificial Intelligence (AI) in the banking sector is transforming financial literacy initiatives by offering personalized, accessible, and interactive learning experiences. This study examines the impact of AI interventions on financial literacy improvement through a two[1]way ANOVA analysis, considering two independent variables: demographics (age groups) and banking channels (mobile apps vs. physical branches). Findings indicate significant improvements in financial literacy among younger users (18–35) and higher effectiveness of AI-driven tools in mobile apps compared to physical branches. Additionally, the interaction effect reveals that demographic-specific strategies are essential for maximizing AI's impact. This research underscores AI’s potential to bridge knowledge gaps and promote financial inclusion by tailoring educational tools to diverse customer needs.Journal of Economics and Management190Analysis of the impact of AI interventions on financial literacy improvement through a two-way ANOVA analysis indicates significant improvements in financial literacy among younger users and higher effectiveness of AI-driven tools in mobile apps compared to physical branches.Journal of Economics and Management["Abdul Rahman"]2024-12-29T00:00:00 cf1f1fc71bd98ca68e15cbe8fe0e34c5cad996b1Artificial Intelligence (AI) is transforming the field of sports science by providing unprecedented insights and tools that enhance training, performance, and health management. This work examines how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being. It explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management. Practical examples illustrate how AI applications have significantly advanced each area and how they support and enhance the effectiveness of sports scientists. This manuscript also underscores the importance of ensuring that AI technologies are context-specific and communicated transparently. Additionally, it calls for academic institutions to update their curriculums with AI-focused education, preparing future sports professionals to fully harness its potential. Finally, the manuscript addresses future challenges, such as the unpredictable nature of team sports, emphasizing the need for interdisciplinary collaboration, including clear communication and mutual understanding between sports scientists and AI experts, and the critical balance between AI-driven insights and human expertise.Italian National Conference on Sensors550Examination of how AI is advancing the role of sports scientists, particularly in team sports environments, by improving training load management, sports performance, and player well-being explores key dimensions such as load optimization, injury prevention and return-to-play, sports performance, talent identification and scouting, off-training behavior, sleep quality, and menstrual cycle management.Sensors (Basel, Switzerland)["Nuno Mateus", "Eduardo Abade", "D. Coutinho", "M. G\u00f3mez", "Carlos Lago Pe\u00f1as", "J. Sampaio"]2024-12-29T00:00:00 22a01dc15bf7f12f3a130c3da8749ed0578d2745The advancement of artificial intelligence (AI) has raised questions about its potential utilization in various roles, including as a member of the board of directors in a Limited Liability Company. In Indonesia, this phenomenon was highlighted when PT Suryadhamma Investama claimed to have appointed an AI named Ardi as its Director. This study aims to examine the legal feasibility of AI serving as a corporate director within the Indonesian legal framework. Employing a doctrinal research method, this study relies on secondary data obtained from literature studies. The analysis is conducted using Article 1367 paragraph (1) of the Civil Code to explore the legal status of AI and Article 93 paragraph (1) of the Company Law to examine the normative requirements for the position of a Director. The findings indicate that, under Article 1367 paragraph (1) of the Civil Code, AI can be analogized as a legal object categorized as goods under the supervision of its creators, sponsors, and owners. Furthermore, based on Article 93 paragraph (1) of the Company Law, the role of a Director is normatively restricted to natural persons who meet specific eligibility criteria, thereby excluding AI from such appointments. These findings have implications for corporate governance and the legal responsibilities associated with AI utilization, emphasizing the need for regulatory clarity in addressing AI's role within organizational structures.  Asian Journal of Engineering, Social and Health190Asian Journal of Engineering, Social and Health["Sherin Dinda Muthia", "Tjhong Sendrawan"]2024-12-29T00:00:00 e9676b0895ef829277ac4e26ed4b0bea289ff5b7The adoption of emergent technologies, among them Artificial Intelligence (AI), in producing media commodities is increasingly becoming significant in the media industry in the 21st century. Studies theorizing AI embeddedness in broadcast media commodities production value-chains—from ideation, concept development, scripting, curation, and editing to post-production—have also emerged in various pieces of research, particularly from the Global North. There is, however, a paucity of studies documenting the state of AI appropriation in broadcast media production from the Global South. Therefore, this study examines the adoption of AI in producing broadcast media commodities such as news, commentaries, entertainment, and marketing content in Kenya using a qualitative systematic literature review of 1,262 scholarly publications. The review sampled eight (8) publications using exclusion and inclusion criteria and found evidence of the use of AI in producing media content, attendant affordances, limitations, and AI skills gaps for media producers in Kenya. While ethical dilemmas regarding labor issues, bias, and privacy concerns are widespread, AI dependency on the Global North remains.Journal of the Kenya National Commission for UNESCO00This study examines the adoption of AI in producing broadcast media commodities such as news, commentaries, entertainment, and marketing content in Kenya using a qualitative systematic literature review of 1,262 scholarly publications.Journal of the Kenya National Commission for UNESCO["Paul Muya"]2024-12-29T00:00:00 30cf7022054a9e5e55761b910ff24198507c8c1eAmong the skills included in the Compass for Tomorrow are new skills for teachers and educators in the context of emotional artificial intelligence. Is it possible to teach social-emotional skills? And how is it possible to do so? Can socio-emotional competences be considered as curricular standards? This contribution aims to answer these questions, highlighting opportunities and risks that the digital revolution and affective computing pose today.In order to explore the relationship between artificial intelligence (AI) and Social-Emotional Learning (SEL), a multidisciplinary approach combining technological and psycho-pedagogical research methods is required. Main sources: scientific databases (e.g. Google Scholar, PubMed, Scopus), educational and technological journals; case studies where AI is integrated into SEL programmes.Artificial intelligence can play a key role in teaching social-emotional skills, helping students develop skills such as empathy, self-management, social awareness, and effective interpersonal relationships. AI platforms can create simulated or interactive scenarios that allow students to practise managing complex social situations.In conclusion, we can say that AI can help teachers in their work by facilitating the identification of suitable strategies to foster and develop social-emotional competences in students. It can also help to broaden and achieve that vision suitable for understanding and fostering school inclusion, which is the goal to be achieved (Rivoltella, 2014). It must also be emphasised that training and refresher courses are needed to use robotics correctly, to assist schools in their choice of aid and to train teachers in the acquisition of skills or teaching practices that make its use effective.Journal of Modern Science270AI can help teachers in their work by facilitating the identification of suitable strategies to foster and develop social-emotional competences in students, and can also help to broaden and achieve that vision suitable for understanding and fostering school inclusion.Journal of Modern Science["Rosa Indellicato"]2024-12-29T00:00:00 142d2c97fe98e8c0916fdeb7a0a64c2fa659a8a2This article analyzes the possibilities and prospects of digital transformation of the Russian manufacturing sector, examines its current state, key driving forces, potential advantages and problems, and provides recommendations for further development. The author examines the issues of introducing the achievements of Industry 4.0 into the production processes of enterprises in traditional sectors of the economy, in particular in transport, energy and mining. As part of the implementation of the achievements of Industry 4.0, it is necessary to mention artificial intelligence (AI), which is becoming an integral element of the modern industrial era, being introduced in order to optimize production processes. In this regard, the author dwells in detail on the use of artificial intelligence, exploring its advantages, opportunities and prospects, as well as showing the best practices of application. At the same time, the author shows and analyzes the main challenges and problems arising from the widespread introduction of artificial intelligence into production processes. The purpose of this article is to consider the relevance of the application of the achievements of Industry 4.0 in modern production, to show the possibilities of using artificial intelligence in production processes, to analyze the main problems that arise and to assess the prospects for further development. Based on the conducted research, it can be concluded that the introduction of Industry 4.0 technologies and other digitalization achievements in modern business, including in manufacturing, transport and energy, provides organizations with serious advantages and opportunities reflected at various levels of management and strategic and operational decision-making. The novelty of this study lies in an attempt to consider the patterns and possibilities of introducing artificial intelligence into the production processes of industrial enterprises, based on their current needs and prospects for further development.Theoretical Economics40It can be concluded that the introduction of Industry 4.0 technologies and other digitalization achievements in modern business, including in manufacturing, transport and energy, provides organizations with serious advantages and opportunities reflected at various levels of management and strategic and operational decision-making.Theoretical economics["A. Balashov"]2024-12-29T00:00:00 ac4c3643cdb0acee716a5c1335671b5023ff89f8The use of Artificial Intelligence (AI) in the Sales an Operations Planning (S&OP) is an innovation in supply chain management. Extended traditional S&OP processes that involve static data, manual flow, and dispersed systems do not fit the needs of constantly evolving markets. In this paper, we consider some opportunities of AI in S&OP development and discuss the most significant changes ones, including predictive analytics, machine learning, and automation. The work being done by the research implies a case-based approach, using real-world situations in order to illustrate substantial gains in forecast precision, procedural organization and control, expenditure and financial leakage mitigation, as well as customer satisfaction. The capacity of AI that can offer to enhance decision-making and provide flexibility makes its position crucial in today’s SCMS. However, barriers including resistance from the organizational culture and employee skill deficiencies hold the key in preventing this from occurring. According to the recommendations drawn in this paper, the phases and training programmes shown in Figure 4 point the way towards bringing out the best of AI for successful S&OP modernisation.International Journal For Multidisciplinary Research300Some opportunities of AI in S&OP development are considered and the most significant changes ones, including predictive analytics, machine learning, and automation are discussed.International Journal For Multidisciplinary Research["Luis Polo"]2024-12-29T00:00:00 14f5da01b83550f0a14aa2009bcc1bc0969be52eArtificial Intelligence (AI) is a transformative sector of computer science field that focuses on generating intelligent system and machines capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding. With advance development in AI technologies, used into diverse fields such as healthcare, education, agriculture, business, and autonomous systems. AI employs techniques such as machine learning, neural networks, and natural language processing to analyze and interpret vast amounts of data, enabling predictive analytics, decision-making, and automation. The use of AI into various industries is revolutionizing workflows, enhancing productivity, and creating innovative solutions to complex challenges. However, the field also presents ethical considerations and challenges, such as data privacy, data security, bias, and the impact on employment.Journal of Scientific Research and Reports00The use of AI into various industries is revolutionizing workflows, enhancing productivity, and creating innovative solutions to complex challenges, however, the field also presents ethical considerations and challenges, such as data privacy, data security, bias, and the impact on employment.Journal of Scientific Research and Reports["Chaitali Kulkarni"]2024-12-29T00:00:00 5a3e13e74cc71495be7a902944171665d76f57a5Artifical intelligence nowadays is attracting the focus of both academics and practitioners due to its contrubtion in achieving major changes in business environment. Public accounting as field has benefited a lot from machine learning. Thus, this article aims to highlight the importance and influences of artifical intelligence on accounting. To achieve this aim, this article started with giving a brief overview about artifical intelligence and its evolution over time. This overview helps in showing the power of artifical intelligence and how it attracted investments of billion of dollars due to its leading role in reducing business costs and providing business solutions. Furthermore, the article identifies how artifical intelligence works as this helps accountants to better undertsand machine learning and identify how it can be optimally used in the field of accounting to get the best results. However, to be widely used in the field of accounting, major investments are required and this is one of the obstacles that faces regional small and mid-sized firms that do not have the required resources to effectively implement artifical intelligence. However, the availablity of pre-packaged applications that are offered by big companies such as Google and Amazon can enable small and mid-sized firms to benefit from artifical intelligence. Nonetheless firms will need to train their labor force to benefit from artifical intelligence. Despite the fact that artifical intelligence can help accounting professionals to perform their jobs more efficiently and eliminate reptitive tasks, it is important to note that maching learning cannot eliminate the accountants’ role. Artifical intelligence in the accounting field enables accountants to provide their companies with tecnhologies that can save time, increase the efficiency of tasks, reduce costs and help accountants focus more on value-adding activities.Newsletter on the Results of Scholarly Work in Sociology, Criminology, Philosophy and Political Science00To be widely used in the field of accounting, major investments are required and this is one of the obstacles that faces regional small and mid-sized firms that do not have the required resources to effectively implement artifical intelligence.Newsletter on the Results of Scholarly Work in Sociology, Criminology, Philosophy and Political Science["Izhar Haq"]2024-12-29T00:00:00 5490a8b11d075815171a1f3ddaf69e5c85f02c28Mirror neuron cells were first found in the macaque monkey cortex and subsequently reported in the inferior frontal and parietal lobes. Due to the activation of this neural apparatus during goal understanding, some scholars believe that mirror neurons are involved in one of the cognitive aspects of understanding, called action understanding. Later, it was argued that this system was involved in various social-cognition aspects, such as theory of mind, learning by observation, and empathy, which allow human beings to predict the behavior of others, transmit social knowledge, and grasp the states of mind of others. Some descriptions even give mirror neuron cells the ability to communicate without any conscious effort, with words, or beyond words. Researchers used proposals about mirror neuron cells to investigate the language of origin, social cognition, and human evolution. Like many new ideas, mirror neuron cells have caused a major reflection and a significant increase in academic activity. There are now about 4,000 papers on the topic. The production of behaviors is possible because of a complex interplay between perception and action. Component analysis of complex behavior is explained by the presence of neural circuits and complex cell activation patterns that model the predicted outcome derived from either performed or observed actions and consequently generate the actual physical performance. It is suggested, therefore, that knowledge of behavior, both own and others’, could be organized in terms of acquired neural activity. The mirror neurons, reacting during observation and execution of the same motor act, would provide such neural means, blurring the experimental distinction between self-action and other action. Behavioral research on humans has shown enhanced memory and kinematic facilitation processes for action features that are either compatible or congruent with observed movement. However, only a few studies have so far been able to link these parametric effects to distinct changes in the cortical pattern of activity in the observer. Despite the widespread acceptance in the recent experimental literature, there is no definitive evidence for the existence of an MNS. Furthermore, contrasting evidence comes from transcranial brain stimulation investigations and from a few studies on patients with focal brain lesions. The broken MNS hypothesis and its models may offer more parsimonious and cognitively coherent accounts of ASC. Finally, it seems clear that imitation refers not only to mirroring processes but also to emulation/mimicry mechanisms underlying the formation of pragmatic and communicative aspects of imitation critically impaired and impoverished in ASC.Journal of Ecohumanism00It seems clear that imitation refers not only to mirroring processes but also to emulation/mimicry mechanisms underlying the formation of pragmatic and communicative aspects of imitation critically impaired and impoverished in ASC.Journal of Ecohumanism["A. Alkhatib", "Rahaf Salem Darabseh"]2024-12-29T00:00:00 b4c25bc0108ead37cbe30295a5e0aefd4c2280baCelem artykułu jest przybliżenie charakterystyki i potencjału analitycznego sztucznej inteligencji w obszarze analizy bezpieczeństwa. Tekst ma pomóc udzielić odpowiedzi na następujące pytania badawcze: • Jaka jest charakterystyka wyobraźni analitycznej identyfikowanej jako narzędzie, które mogłoby być wykorzystane przez sztuczną inteligencje w analizach bezpieczeństwa? • W jakich okolicznościach sztuczna inteligencja jest w stanie uwzględnić charakterystykę cyklu wywiadowczego w generowanych odpowiedziach i opracowaniach? • Które cechy właściwe dla myślenia kontekstowego oraz myślenia krytycznego stanowią wyzwanie dla sztucznej inteligencji, a które są polem jej przewagi poznawczej nad człowiekiem?Analiza krytyczna, analiza literaturyWyobraźnia analityczna składa się z dwóch wzajemnie warunkujących się oraz częściowo nachodzących na siebie elementów: myślenia kontekstowego oraz myślenia krytycznego. Oba te pojęcia mają swoje konkretne właściwości ze względu na specyfikę domeny bezpieczeństwa. W tym kontekście kluczowe jest pojmowanie bezpieczeństwa jako procesu społecznego, czyli fenomenu dynamicznego, zmiennego, płynnego, który wymaga od analityka konkretnych narzędzi. To stanowi dla sztucznej inteligencji realne wyzwanie.W tekście omówiono łącznie dwanaście cech wyobraźni analitycznej wykorzystywanej w domenie bezpieczeństwa – odpowiednio pięć dotyczących myślenia kontekstowego oraz siedem związanych z myśleniem krytycznym. Łącznie na dwanaście cech/zmiennych, które składają się na wyobraźnię analityczną, już na chwilę obecną sztuczna inteligencja sprawnie funkcjonuje (albo zaraz będzie sprawnie funkcjonować) praktycznie we wszystkich.Journal of Modern Science40Journal of Modern Science["M. Ciesielski"]2024-12-29T00:00:00 e2ae484ec4926bc571ba76ee5f97636fa721e319 Bevezetés: A ChatGPT nagy nyelvi modell, amelyet az OpenAI fejlesztett ki, és jelentős előrelépést hozott a természetes nyelvi feldolgozás terén. A GPT-4.0 verzióval a modell képes emberi szintű beszélgetéseket folytatni, ami különösen hasznos az oktatásban és a kutatásban. Az egészségügyi oktatásban a hallgatók virtuális szituációkban gyakorolhatják a betegfelvételt, valós idejű visszajelzést kapva. Emellett az oktatási anyagok személyre szabását is lehetővé teszi, igazodva a hallgatók egyéni igényeihez. Célkitűzés: A tanulmány célja, hogy bemutassuk a ChatGPT, vagyis a mesterséges intelligencia alkalmazásának lehetőségeit az egészségügyi oktatásban. A ChatGPT lehetővé teszi virtuális betegek és szimulációk létrehozását, amelyek révén a hallgatók valósághű környezetben gyakorolhatják a betegfelvételt és a kommunikációt a különböző nyelvű betegekkel. Módszer: A ChatGPT-t használtuk arra, hogy különböző szimulációkat hozzunk létre az egészségügyi oktatásban. A szimulációk során a hallgatók valós időben kaptak visszajelzést, és a tananyagokat a hallgatók egyéni igényeihez tudtuk igazítani. A ChatGPT segítségével a hallgatók olyan helyzetekben gyakoroltak, amelyekben különböző nyelvi kihívásokkal szembesültek. A szimulációk során finomhangolással tettük valósághűbbé a mesterséges intelligencia által adott válaszokat, valamint az empátiát is integráltuk a rendszerbe. Eredmények: A kutatás során oktatási szimulációkat sikerült létrehozni a ChatGPT-vel, amelyek javították a hallgatók készségeit és növelték önbizalmukat. A hallgatók képesek voltak önállóan kitölteni ápolási dokumentációkat anélkül, hogy valós betegekkel érintkeztek volna. Megbeszélés: A ChatGPT használata az oktatásban jelentős előnyöket kínál, különösen a betegágy melletti oktatásban. A virtuális szimulációk lehetővé teszik a hallgatók számára, hogy biztonságos és kontrollált környezetben gyakorolják készségeiket, ami növeli önbizalmukat és csökkenti a szorongást. Az infokommunikációs eszközök használata megragadja a hallgatók figyelmét és érdeklődését. Következtetés: A ChatGPT bevezetése az egészségügyi oktatásba változatos gyakorlati szituációk előkészítését és bemutatását teszi lehetővé, javítva a betegellátás minőségét. Jelenleg további kutatásokat is végzünk, ilyen a ChatGPT logopédiai területen és betegedukációban történő alkalmazásának lehetősége. Kiemelten fontos, hogy a képzésben közreműködő rangidős oktatók, tanárok képesek legyenek kapcsolódni a már a digitális világban élő fiatal generációhoz. Orv Hetil. 2024; 165(52): 2061–2064. Orvosi Hetilap00Orvosi hetilap["Erzs\u00e9bet Horv\u00e1thn\u00e9 K\u00f3nya", "Andrea Vir\u00e1g", "Patr\u00edcia Lajk\u00f3", "\u00c1d\u00e1m Attila Sz\u0171cs", "Dorina Markovics", "Kl\u00e1ra Gad\u00f3", "Zolt\u00e1n Balogh"]2024-12-29T00:00:00 56f568db5cffcdaa2ebf4538d1e900e5435d3344Calculation is one of the foundational concepts operating at the basis of the notion of an algorithm. Seemingly intuitive, it remains nonetheless no small task to provide a rigid theoretical framework for articulating an ontology of computation. The central and primary point of oscillation around which the following paper will revolve, is concerned therefore not only with the complicated questions that make up the foundations of logic and mathematics, but the social and political implications that follow directly therefrom. Whether a machine can think is directly tied to the question of whether calculation is a form of thinking. That is, whether human thinking is a form of calculation. Subversively, Wittgenstein claims not only that human thought is irreducible to computation, but that human calculation itself is a form of thinking that is entirely different from anything that could be labeled “mechanical”. Wittgenstein’s critique of the Turing Thesis paves the way for a new variety of Foucauldian Biopolitics aimed specifically at the discourse surrounding Artificial Intelligence. A discourse that bears a suspicious resemblance to Christian pastoralism.Newsletter on the Results of Scholarly Work in Sociology, Criminology, Philosophy and Political Science00Wittgenstein’s critique of the Turing Thesis paves the way for a new variety of Foucauldian Biopolitics aimed specifically at the discourse surrounding Artificial Intelligence.Newsletter on the Results of Scholarly Work in Sociology, Criminology, Philosophy and Political Science["Giorgi Vachnadze"]2024-12-29T00:00:00 6ccde1f2ac153098e768a56146a7037a4ad94b8dADAB AL-BASRAH00ADAB AL-BASRAH["Zainab Waleed Abdul-Latif", "Busaad Garkaan Kazem"]2024-12-29T00:00:00 ca3df5028130b2036b79989c9e71f82afa23c6baThe use of artificial intelligence (AI) is playing a relevant role in different areas of our lives (information, health, leisure, etc.) although its understanding, knowledge and use vary widely among the population. The objective of this study is to know what future education professionals know about AI, what educational uses and applications they consider it has, as well as their opinion on its ethical and regulatory implications. The methodology used is quantitative in nature, with the questionnaire (designed ad hoc) being the instrument chosen to obtain the information. The participating sample is made up of a total of 164 students of different degrees related to education from the Universities of Extremadura, Valladolid and Castilla-La Mancha. The results show that most participants have basic knowledge about AI, have used it at some time, mainly to obtain information, make summaries or diagrams and believe that it can have positive implications in the educational field, likewise, they show concern for ethics. and transparency, considering a regulatory development in this regard necessary. Therefore, it is essential that educational programs are adapted to ensure that students develop a solid and critical understanding of this emerging technology. El uso de la inteligencia artificial (IA) está tomando un papel relevante en diferentes ámbitos de nuestra vida (información, salud, ocio, etc.) aunque su comprensión, conocimiento y uso varían ampliamente entre la población. El objetivo de este estudio es conocer qué saben los futuros profesionales de la educación sobre la IA, qué usos y aplicaciones educativas consideran que tiene, así como su opinión sobre las implicaciones éticas y normativas de esta. La metodología utilizada es de carácter cuantitativo, siendo el cuestionario (diseñado ad hoc) el instrumento elegido para obtener la información. La muestra participante la compone un total de 164 estudiantes de diferentes grados relacionados con la educación de las Universidades de Extremadura, Valladolid y Castilla-La Mancha. Los resultados reflejan que la mayoría de participantes tiene conocimiento básico sobre la IA, la ha utilizado alguna vez (principalmente para obtener información, realizar resúmenes o esquemas) y opina que puede tener implicaciones positivas en el ámbito educativo; así mismo, los participantes muestran preocupación por la ética y transparencia, considerando necesario un desarrollo normativo al respecto. Por ello resulta fundamental que los programas educativos se adapten para garantizar que el alumnado desarrolle una comprensión sólida y crítica de esta tecnología emergenteRevista Electronica Interuniversitaria de Formación del Profesorado00Revista Electrónica Interuniversitaria de Formación del Profesorado["Nuria Garc\u00eda-Perales", "M. Hern\u00e1ndez Rinc\u00f3n", "Bel\u00e9n Su\u00e1rez Lantar\u00f3n"]2024-12-29T00:00:00 ffde689a454a6a07f34c62b9e07087b7e7e7c45cThis paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) in adaptive eLearning platforms designed for cybersecurity training, with a focus on entrepreneurs. Due to limited resources and technical expertise, entrepreneurs face unique challenges in protecting their businesses from cyber threats. Traditional training methods often fail to meet their needs, highlighting the importance of AI-driven platforms that offer personalized learning experiences. The paper examines the benefits of AI-powered eLearning systems, including improved engagement, real-time assessments, and adaptation to diverse learning styles. It also addresses emerging trends in AI and ML for cybersecurity education, the integration of adaptive eLearning into entrepreneurial support systems, and the ethical and regulatory implications of AI-driven learning. Finally, recommendations are provided for policymakers and educators to support the growth of AI in cybersecurity training. The findings suggest that AI-powered platforms can offer scalable, effective solutions for entrepreneurs to enhance their cybersecurity skills and protect their digital assets. Keywords: Artificial Intelligence, Machine Learning, Cybersecurity Training, Adaptive eLearning, Entrepreneurs, Personalized Learning.Computer Science &amp; IT Research Journal01It is suggested that AI-powered platforms can offer scalable, effective solutions for entrepreneurs to enhance their cybersecurity skills and protect their digital assets.Computer Science &amp; IT Research Journal["Blessing Austin-Gabriel", "Adeoye Idowu Afolabi", "Christian Chukwuemeka Ike", "Nurudeen Yemi Hussain"]2024-12-29T00:00:00 f9ff217ec26759450812861015bfce8e8c106461Obesity Surgery61Both ChatGPT-4 and DALL·E 3, while promising, have significant limitations in recognizing and generating accurate illustrations of bariatric surgical procedures, underscores the need for further evaluation of AI in bariatric surgery.Obesity surgery["Mohammad Mahjoubi", "Shahab Shahabi", "Saba Sheikhbahaei", "A. D. Jazi"]2024-12-29T00:00:00 ac27c02dac10226bd1daaba08cdb22fda3bae12aDigital transformation in financial services is revolutionizing the industry, integrating artificial intelligence (AI), fintech innovations, and other advanced technologies to foster financial inclusion and drive SME growth. This paper explores the transformative role of AI tools, such as predictive analytics and chatbots, in enhancing operational efficiency and customer experience while addressing ethical considerations. It highlights fintech innovations, including mobile payments, blockchain, and peer-to-peer lending, which bridge gaps in traditional banking and empower SMEs, especially in emerging markets. Furthermore, the paper examines how digital ecosystems and cloud-based financial tools enhance SME access to credit, streamline operations, and expand market opportunities. Challenges such as digital literacy gaps, infrastructure limitations, and regulatory concerns are discussed alongside mitigation strategies. Recommendations for policymakers, financial institutions, and SMEs emphasize the need for collaborative efforts to build an inclusive, resilient, and innovation-driven financial ecosystem that supports global economic growth. Keywords: Digital Transformation, Financial Inclusion, Artificial Intelligence (AI), Fintech Innovations, SMEs Growth, Digital Ecosystems.Gulf Journal of Advance Business Research01This paper explores the transformative role of AI tools, such as predictive analytics and chatbots, in enhancing operational efficiency and customer experience while addressing ethical considerations, and highlights fintech innovations, which bridge gaps in traditional banking and empower SMEs, especially in emerging markets.Gulf Journal of Advance Business Research["Hope Ehiaghe Omokhoa", "Chinekwu Somtochukwu Odionu", "Chima Azubuike", "Aumbur Kwaghter Sule"]2024-12-29T00:00:00 08ee3e883b7cbb3a85d5311cb86d06f34dfab6b2The integration of artificial intelligence in fintech is revolutionizing financial services, particularly for microfinance institutions and small and medium-sized enterprises (SMEs). This paper explores the transformative impact of AI-powered innovations in credit scoring, debt recovery, and financial access. AI-driven credit scoring leverages alternative data and advanced machine learning techniques to enhance accuracy, inclusivity, and efficiency, addressing the limitations of traditional methods. In debt recovery, AI optimizes collection processes through predictive analytics, workflow automation, and conversational tools, improving operational efficiency while fostering ethical practices and customer trust. AI also plays a pivotal role in expanding financial access, enabling underserved populations to benefit from tailored digital platforms for lending, savings, and insurance. Despite its potential, AI adoption entails risks, including data privacy concerns, algorithmic bias, and the digital divide, which require careful management. The paper concludes with recommendations for policymakers, financial institutions, and tech developers to ensure AI's ethical and inclusive deployment, fostering economic resilience and equitable growth. Keywords: Artificial Intelligence (Ai), Fintech Innovations, Microfinance Institutions (Mfis), Small And Medium-Sized Enterprises (Smes), Credit Scoring, Financial Inclusion.Gulf Journal of Advance Business Research01The transformative impact of AI-powered innovations in credit scoring, debt recovery, and financial access are explored, with recommendations for policymakers, financial institutions, and tech developers to ensure AI's ethical and inclusive deployment.Gulf Journal of Advance Business Research["Hope Ehiaghe Omokhoa", "Chinekwu Somtochukwu Odionu", "Chima Azubuike", "Aumbur Kwaghter Sule"]2024-12-29T00:00:00 d0f5f0eeed57a59784b5a899bc77290d3cebba80Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms.arXiv.org00At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms.ArXiv["Camila Gonz'alez", "Moritz Fuchs", "Daniel Pinto dos Santos", "Philipp Matthies", "M. Trenz", "Maximilian Gr\u00fcning", "Akshay Chaudhari", "David B. Larson", "Ahmed E. Othman", "Moon Kim", "F. Nensa", "Anirban Mukhopadhyay"]2024-12-29T00:00:00 d2798ab66080265d09626232e867d8c5f634c71fDespite the significant increase in studies on AI applications in many aspects of life, its applications in mental health services still require further studies. This study aimed to test a proposed structural model of the relationships between AI use, therapeutic alliance, and job engagement by PLS-SEM. The descriptive method was applied. The sample consisted of (382) mental health service providers in Saudi Arabia, including 178 men and 204 women between 25 and 50 (36.32 ± 6.43) years old. The Artificial Intelligence Questionnaire, the Therapeutic Alliance Scale, and the Job Engagement Scale were applied in this study. The results showed the structural model’s predictability for using AI and the therapeutic alliance in predicting job engagement and explaining the causal relationships between them compared to the indicator average and linear models. The study also found a strong positive overall statistically significant effect (p < 0.05) of the use of AI on therapeutic alliance (0.941) and job engagement (0.930) and a positive overall average statistically significant effect (p < 0.05) of the therapeutic alliance on job engagement (0.694). These findings indicated the importance of integrating AI applications and therapeutic alliance skills into training and professional development plans.Behavioral Science650The structural model’s predictability for using AI and the therapeutic alliance in predicting job engagement and explaining the causal relationships between them compared to the indicator average and linear models indicated the importance of integrating AI applications and therapeutic alliance skills into training and professional development plans.Behavioral Sciences["Boshra A. Arnout", "Sami M. Alshehri"]2024-12-29T00:00:00 cd619c5e2c56514b1f08bc4d82b8d74386c0d2f2Artificial intelligence (AI) technologies, such as Gemini.ai, are revolutionizing higher education by enhancing data analysis and interpretation. This study aimed to investigate how Gemini.ai can enhance clarity in analytical results, provide contextually adaptive responses, and excel in advanced data modeling. We collected data through interviews with industrial engineering students and conducted thematic analysis using a qualitative research approach. The findings reveal that Gemini.ai significantly enhances user comprehension by simplifying complex data, offering tailored responses based on user commands, and integrating external information sources for robust data modeling. However, the study also highlights the need for further development, including improved customization options and advanced modeling features. These results demonstrate the transformative potential of AI tools like Gemini.ai in fostering intuitive, user-centric analytical experiences. The insights gained can inform the development of AI-driven educational tools, promoting personalized learning and critical thinking in higher education. Future research should explore interdisciplinary applications and the ethical implications of integrating AI in education to enhance its efficacy and accessibility.Gema Wiralodra00The findings reveal that Gemini.ai significantly enhances user comprehension by simplifying complex data, offering tailored responses based on user commands, and integrating external information sources for robust data modeling, demonstrating the transformative potential of AI tools like Gemini.ai.Gema Wiralodra["Edi Supriyadi", "Samsul Pahmi"]2024-12-29T00:00:00 5d33f74b6550f69b0f30b29496f00267a1ddcc92This article discusses the evolving role of artificial intelligence (AI) in the legal profession, focusing on its potential to streamline tasks such as document review, research, and contract drafting. However, challenges persist, particularly the occurrence of"hallucinations"in AI models, where they generate inaccurate or misleading information, undermining their reliability in legal contexts. To address this, the article proposes a novel framework combining a mixture of expert systems with a knowledge-based architecture to improve the precision and contextual relevance of AI-driven legal services. This framework utilizes specialized modules, each focusing on specific legal areas, and incorporates structured operational guidelines to enhance decision-making. Additionally, it leverages advanced AI techniques like Retrieval-Augmented Generation (RAG), Knowledge Graphs (KG), and Reinforcement Learning from Human Feedback (RLHF) to improve the system's accuracy. The proposed approach demonstrates significant improvements over existing AI models, showcasing enhanced performance in legal tasks and offering a scalable solution to provide more accessible and affordable legal services. The article also outlines the methodology, system architecture, and promising directions for future research in AI applications for the legal sector.arXiv.org390This article proposes a novel framework combining a mixture of expert systems with a knowledge-based architecture to improve the precision and contextual relevance of AI-driven legal services, and demonstrates significant improvements over existing AI models.ArXiv["Sidra Nasir", "Qamar Abbas", "Samita Bai", "Rizwan Ahmed Khan"]2024-12-29T00:00:00 bdf2939104dfe66ebb0758819cd20dfb8f3b77cdArtificial intelligence (AI) holds immense potential to drive sustainable development by enabling progress toward the realization of the 17 Sustainable Development Goals (SDGs) outlined in the 2030 Agenda. This potential is emphasized by the concept of twin transitions, where the digital and green transformations reinforce each other. This study examines the integration of AI in 26 study courses at the University of Ljubljana, Slovenia, focusing on its sustainable impact. Using an exploratory case study and a comprehensive content analysis framework, the study identifies the enabling and inhibiting factors in the interplay of digital and green transitions in education. The results reveal that the integration of AI in higher education can facilitate progress toward achieving 11 SDGs and 28 targets by promoting innovation, equality, and digital literacy. At the same time, however, it also harbors risks that can hinder 7 SDGs and 11 targets, particularly in terms of data protection and equal access. By highlighting the synergies between digital and green transformations, this case study provides actionable insights for educators and policy makers who want to harness the transformative potential of AI while addressing its challenges and calls for future research in a broader educational landscape.Sustainability360This study examines the integration of AI in 26 study courses at the University of Ljubljana, Slovenia, focusing on its sustainable impact, and identifies the enabling and inhibiting factors in the interplay of digital and green transitions in education.Sustainability["Vesna Ferk Savec", "Sanja Jedrinovi\u0107"]2024-12-29T00:00:00 02cefeccf87a5fb811b26d6e5388fdc5a3a13597With the growth of artificial intelligence (AI) tools available to anybody with internet access, English language learners are increasingly turning to these resources to improve their writing skills. By examining their utilization of AI tools to improve their writing proficiency, this research examines the perspectives of Turkish English language learners enrolled in a preparatory program at a state university in Istanbul. Using a Q methodological approach, researchers created a set of 40 statements based on a literature study, which were then sent to 55 consenting individuals. Qualitative information was also collected by interviewing five students in depth. The analysis indicated a growing tendency among students to utilize AI tools for writing assignments, highlighting advantages such as assistance in translation, idea generation, and preparedness for future studies. However, participants expressed concerns over excessive dependence on these technologies, which can result in problems such as plagiarism, reduced originality, and ethical dilemmas. These findings highlight the need for supporting the ethical and balanced use of AI technologies in language learning environments, ensuring that learners may use technology efficiently while maintaining the integrity and authenticity of their work. Further study should explore strategies for integrating AI tools into language teaching in ways that minimize these concerns and enhance their benefits for learners.Language Teaching Research260The analysis indicated a growing tendency among students to utilize AI tools for writing assignments, highlighting advantages such as assistance in translation, idea generation, and preparedness for future studies, but participants expressed concerns over excessive dependence on these technologies.Language Teaching Research["Ay\u015fe Y\u0131lmaz Virlan", "Burak Tomak"]2024-12-29T00:00:00 849fa7248b49c03a4b3b17304d976b1fe5b77f03Offshore wind energy leverages the high intensity and consistency of oceanic winds, playing a key role in the transition to renewable energy. As energy demands grow, larger turbines are required to optimize power generation and reduce the Levelized Cost of Energy (LCoE), which represents the average cost of electricity over a project's lifetime. However, upscaling turbines introduces engineering challenges, particularly in the design of supporting structures, especially towers. These towers must support increased loads while maintaining structural integrity, cost-efficiency, and transportability, making them essential to offshore wind projects' success. This paper presents a comprehensive review of the latest advancements, challenges, and future directions driven by Artificial Intelligence (AI) in the design optimization of Offshore Wind Turbine (OWT) structures, with a focus on towers. It provides an in-depth background on key areas such as design types, load types, analysis methods, design processes, monitoring systems, Digital Twin (DT), software, standards, reference turbines, economic factors, and optimization techniques. Additionally, it includes a state-of-the-art review of optimization studies related to tower design optimization, presenting a detailed examination of turbine, software, loads, optimization method, design variables and constraints, analysis, and findings, motivating future research to refine design approaches for effective turbine upscaling and improved efficiency. Lastly, the paper explores future directions where AI can revolutionize tower design optimization, enabling the development of efficient, scalable, and sustainable structures. By addressing the upscaling challenges and supporting the growth of renewable energy, this work contributes to shaping the future of offshore wind turbine towers and others supporting structures.00["Joao Alves Ribeiro", "Bruno Alves Ribeiro", "Francisco Pimenta", "S'ergio M. O. Tavares", "Jie Zhang", "Faez Ahmed"]2024-12-29T00:00:00 a84b752e56d88e72fa750e0234d4050961e0c936The rising prevalence of noncommunicable diseases (NCDs) such as diabetes, cardiovascular disease, and chronic respiratory illnesses underscores the urgent need for effective management strategies. This study investigates the effectiveness of health coaching applications enabled by artificial intelligence (AI) in promoting behavior change among individuals having NCDs. Utilizing a mixed-methods approach, the research evaluates user engagement, adherence to treatment regimens, and the impact of application features including personalized interventions, real-time feedback, and community support. Data was collected through surveys distributed among NCD patients using various health coaching applications. Preliminary findings indicate that personalized health coaching significantly enhances user adherence and engagement, with specific features being instrumental in driving positive health outcomes. Additionally, barriers to effective use, such as data privacy concerns and technology access, were identified. This research contributes to the understanding of how AI applications can optimize NCD management and offers insights for developers and healthcare providers to enhance the usability and effectiveness of these technologies. Ultimately, findings aim to inform the design of future health coaching applications, ensuring they better meet the needs of patients managing chronic health conditions.Agora International Journal of Economical Sciences310Findings aim to inform the design of future health coaching applications, ensuring they better meet the needs of patients managing chronic health conditions, and contribute to the understanding of how AI applications can optimize NCD management.AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES["Nino Mikava"]2024-12-29T00:00:00 10303c3bee9ea0451331b44fe363285ad4da2bf0The field of Computer Vision, a pivotal subdomain of Artificial Intelligence (AI), has seen extraordinary advancements since its emergence in the 1960s. This paper examines the historical development of Computer Vision technologies, tracing the journey from early foundational models, such as Frank Rosenblatt’s Perceptron, to contemporary breakthroughs driven by Deep Learning. Key milestones are explored, including the development of algorithms like Scale-Invariant Feature Transform (SIFT), Viola-Jones for face detection, and Eigenfaces, which paved the way for modern solutions such as Convolutional Neural Networks (CNNs), YOLO and FaceNet. The paper highlights the evolution of face detection and recognition techniques, contrasting traditional methods with the transformative capabilities of Deep Learning-driven approaches. Additionally, we analyze the growing computational demands of modern algorithms, discussing the trade-offs between accuracy and efficiency and their implications for practical applications. This study underscores the rapid progression of Computer Vision, its challenges, and its role as a cornerstone in shaping the future of Artificial Intelligence.Agora International Journal of Economical Sciences270This paper examines the historical development of Computer Vision technologies, tracing the journey from early foundational models, such as Frank Rosenblatt's Perceptron, to contemporary breakthroughs driven by Deep Learning, highlighting the evolution of face detection and recognition techniques.AGORA INTERNATIONAL JOURNAL OF ECONOMICAL SCIENCES["Kledia Tirana", "Endri Bejleri"]2024-12-29T00:00:00 74d63195098a6c17b7aecaf49f9dd7a56903abddAIM The purpose of this study is to compare the efficacy of an artificial intelligence (AI)-based care plan learning strategy with standard training techniques in order to determine how it affects nursing students' learning results in newborn resuscitation. METHODS Seventy third-year nursing students from a state university in Türkiye participated in the study. They were split into two groups: the experimental group, which received care plans based on AI, and the control group, which received traditional instruction. The control group underwent traditional training consisting of lectures and skill demonstrations, while the experimental group underwent 4 weeks of training utilising an AI-based care plan learning approach. Neonatal resuscitation knowledge tests and student information questionnaires were used for pre- and post-test assessments. RESULTS When compared to the control group, the AI-based care plan group demonstrated noticeably greater learning achievement in newborn resuscitation. While the two groups' pre-test results were comparable, the AI-based education group's post-test results were noticeably higher than those of the traditional education group. Furthermore, most of the students had favourable opinions on AI applications and acknowledged their advantages for the nursing field. CONCLUSION The study's conclusions highlight the benefits of incorporating AI technology into nursing education and highlight how it might improve student learning outcomes for vital competencies like newborn resuscitation.Journal of Evaluation In Clinical Practice210The study's conclusions highlight the benefits of incorporating AI technology into nursing education and highlight how it might improve student learning outcomes for vital competencies like newborn resuscitation.Journal of evaluation in clinical practice["Birsel Molu"]2024-12-29T00:00:00 a0095b029ee5c49e768f809b4ac43a71126ca5ceArtificial intelligence applications are more pervasive than ever, and users trust AI-enabled machines (AIEMs) for many tasks. However, the improper design and use of AIEMs may threaten individuals' ability to shape their lives and make their own decisions. This may be especially true with AIEMs designed with a high degree of humanlike features, which contribute to processes of anthropomorphization. This can encourage a parasocial relationship by the user, which fosters trust and increased usage of the device. Since AIEMs may nudge user behavior, entrepreneurs must consider the psychological aspects of relationship building when creating these devices.Journal of Applied Business and Economics550Since AIEMs may nudge user behavior, entrepreneurs must consider the psychological aspects of relationship building when creating these devices.Journal of Applied Business and Economics["Amy Gresock", "Kira Leck"]2024-12-29T00:00:00 fb2d057e7cd01dd7b7950677aa5e1722d60f6626This comprehensive article explores the integration of artificial intelligence and machine learning technologies in cloud security, focusing on implementation strategies, challenges, and future directions. The research examines how AI-powered security solutions transform threat detection, predictive analytics, and incident response in cloud environments. The study investigates key challenges including data privacy, model interpretability, and infrastructure integration while presenting best practices for successful implementation through phased approaches and continuous learning frameworks. The article encompasses both current capabilities and emerging trends in neural network architectures, automated response mechanisms, and zero-trust integration, providing insights into the future landscape of AI-enhanced cloud security.International Journal For Multidisciplinary Research80The research examines how AI-powered security solutions transform threat detection, predictive analytics, and incident response in cloud environments while presenting best practices for successful implementation through phased approaches and continuous learning frameworks.International Journal For Multidisciplinary Research["Chandrasena Cheerla"]2024-12-29T00:00:00 813884aebd10c86d5e777ce80d647d7dfb0a7921The integration of new technologies into all areas of a company existence, known as digital transformation, necessitates a fundamental shift in traditional understanding of a business process optimization and human resilience to turbulent socioeconomical and technological environment. Simultaneously, artificial intelligence (AI) emerges as a disruptive force, with immense potential to impact businesses and individuals on an unprecedented scale and at an (exponential) pace. The purpose of paper is to present the theoretical foundations of the concept of AI-powered digital transformation from an organizational perspective (taking ethical aspects also into account). The main research method is a systematic literature review based on the Web of Science (ELSEVIER) database. On the foundation of literature review, the core pillars of AI-Powered digital transformation are presented as a contribution to further theoretical and empirical research.Journal of Modern Science250The theoretical foundations of the concept of AI-powered digital transformation from an organizational perspective are presented, taking ethical aspects also into account (taking ethical aspects also into account).Journal of Modern Science["Ewa Chrzanowska", "Maciej Chrzanowski", "P. Zawada"]2024-12-29T00:00:00 674c22b9704b60f584aafe046b7420a8a82f38a6The transition to sustainable mobility has gained significant momentum with the increasing adoption of Electric Vehicles (EVs) and Hydrogen Fuel Cell Vehicles (HFCVs). These technologies promise reduced greenhouse gas emissions, enhanced energy efficiency and greater reliance on renewable energy sources. However, challenges such as limited infrastructure, battery performance, hydrogen storage and system integration hinder widespread adoption. Recent advancements in Artificial Intelligence (AI) have emerged as transformative solutions to address these challenges, offering innovative approaches in battery management, thermal optimization, autonomous driving, smart charging and cybersecurity. This paper explores AI-driven innovations that optimize EV and HFCV systems, integrate renewable energy, and improve operational safety and efficiency. Additionally, it examines global trends, comparative analyses and the infrastructure evolution required to accelerate sustainable mobility. By leveraging AI, this study envisions a future where EVs and HFCVs coexist seamlessly, creating a robust ecosystem for sustainable transportation.International Journal For Multidisciplinary Research00This paper explores AI-driven innovations that optimize EV and HFCV systems, integrate renewable energy, and improve operational safety and efficiency, and examines global trends, comparative analyses and the infrastructure evolution required to accelerate sustainable mobility.International Journal For Multidisciplinary Research["C. Karuppanasamy"]2024-12-29T00:00:00 50994b8c38e03bb4b138d41feff05d96e1a381acThis study investigates the impact of artificial intelligence (AI) tools on English Literature students’ learning experiences. Using a quantitative approach with a structured survey, 22 students from Universitas Negeri Medan were examined regarding their frequency of AI usage, purposes, and perceptions of its impact on academic skills. The findings indicate that AI tools are predominantly used for essay writing, grammar correction, idea generation, and translation. Most students reported improved comprehension, enhanced writing skills, and increased confidence in English usage. However, concerns about dependency and the accuracy of AI outputs were also raised. While 50% of students admitted to being somewhat dependent on AI tools, a majority (54.5%) found them effective in improving overall academic performance. The study underscores the dual role of AI in education—providing efficiency and support while posing challenges to creativity and critical thinking. The results highlight the importance of a balanced approach to integrating AI in academic settings.Indonesian Journal of Education and Development Research20The findings indicate that AI tools are predominantly used for essay writing, grammar correction, idea generation, and translation, and most students reported improved comprehension, enhanced writing skills, and increased confidence in English usage.Indonesian Journal of Education and Development Research["Ayuke Nurul Fahira", "Chyntia Riquella Siagian", "Deniela Yoshelyn Simarmata", "Sinta Fransiska Manik", "Muhammad Natsir"]2024-12-29T00:00:00 9c6199b39092d71c5f45f833bed553bfa5a2d70bBackground: Artificial intelligence (AI) tools are increasingly used in breast imaging and radiology more broadly. Patients express varying levels of trust and acceptance toward the incorporation of AI tools, although no research has examined how it can best be communicated in the patient portal setting. Methods: English-speaking US women with 1+ prior mammogram were recruited via Prolific and randomized to one of thirteen conditions. All participants were shown a vignette asking them to imagine receiving a BI-RADS 1 (Negative) radiologist report from their patient portal. Participants in twelve conditions also received an AI report with one of four AI abnormality scores (not flagged: 0, 29; flagged: 31, 50) and 0-2 accompanying features (nothing; a only; a & b): (a) an abnormality cutoff threshold; (b) the AI tool's False Discovery Rate (FDR) or False Omission Rate (FOR). As the primary outcome, participants indicated whether they would consider a lawsuit if a one-year follow-up found evidence of Stage 3 breast cancer. Secondary outcomes included hypothetical decisions regarding follow-up (e.g., second opinions), concern for breast cancer, and desire for additional imaging. Results: Participants (n=1,623) were more likely to consider a lawsuit when AI was (versus was not) provided, p=0.001. However, for most AI abnormality scores, providing the abnormality cutoff threshold and FDR/FOR reduced lawsuit consideration relative to the AI abnormality score alone. Concern for breast cancer, desire for additional imaging, and follow-up requests (same radiologist, different radiologist, and ordering physician) increased as the AI abnormality score increased, though this was also often mitigated by providing the FDR (and sometimes FOR). Conclusion: Disclosing AI feedback used for medical decision-making will impact patients' perceptions and behaviors pertaining to malpractice and follow-up. Best practices are needed to engage and inform patients about the application of AI tools in their care while minimizing its unintended negative consequences.medRxiv00Disclosing AI feedback used for medical decision-making will impact patients' perceptions and behaviors pertaining to malpractice and follow-up, and best practices are needed to engage and inform patients about the application of AI tools in their care while minimizing its unintended negative consequences.["E. C. Song", "M. H. Bernstein", "P. S. Lay", "L. Druart", "E. H. Dibble", "A. P. Lourenco", "G. L. Baird"]2024-12-29T00:00:00 f73cb17289b9d12240190be2d51a302282fbec5fThe causes of errors and hallucinations of artificial intelligence models are analyzed. The work of artificial intelligence is compared with the structure of the psyche, which is well described by the socionic model of information metabolism by Augustinavichyute–Bukalov. This model has been successfully used in the analysis of the work of the psyche for over 40 years. It is shown that modern AI models lack an independent block for monitoring the implementation of established rules and assessing the importance of the information being processed. It is also necessary to rank the processed data according to specified criteria. It is shown that the most pressing problem of AI - the emergence of hallucinations - is due to an increase in semantic entropy in closed probabilistic models. This problem is fundamentally unsolvable without changing the principles of construction and operation of AI. In addition, AI models lack a number of structures, including aspects of true creativity, which are an integral part of the real psyche and socionic models. In the context of the creation of quantum computers, the quantum aspects of creating a new type of AI are also considered, by analogy with the work of the psyche. Such computers must have a fundamentally different architecture, compared to existing ones, and consist of a whole system of specialized quantum processors.Artificial Intelligence01It is shown that the most pressing problem of AI - the emergence of hallucinations - is due to an increase in semantic entropy in closed probabilistic models, which is fundamentally unsolvable without changing the principles of construction and operation of AI.Artificial Intelligence["B. A"]2024-12-30T00:00:00 e24c34d25f22a0a100562b20b77aee0bc531c7baThis article explores the case of the usage of artificial intelligence (AI) for optimizing the process of covering distributed systems with consumer-driven contract test, analyzing the pros and cons of this approach. Considering the complexity of development of modern distributed systems, like microservices, and the need to ensure the system components interactions keep reliable as long as the system keeps evolving this study is focused on finding the most effective way to introduce the contact testing into such systems to maximize the contracts tests coverage while minimizing development costs. The contract testing has its challenges: steep learning curve, impact on the delivery lifecycle, spreading the approach consistently across the organization. These challenges often lead to teams sacrificing the benefits of the approach and using more traditional ways of testing, like end-to-end (E2E) testing, which however does not fit well into distrusted system. The described methodology includes generating (by AI platform) the contract between the parties (consumer and provider), generating the consumer test to verify the provider is compatible with the expectations the consumer has of it. It is proposed to use following inputs for AI as the source for generation: request-response pairs, OpenApi specification, consumer codebase. The research employs Pact as a tool that allows to define a contract between a consumer and a provider as well as verify that both sides adhere to this contract. NodeJS is used as a framework for consumer and provider development. PactFlow platform with its HaloAI executes contracts and tests generation. The proposed approach simplifies the road to introduce the contact testing into the distributed systems, increases the development team effectiveness in system implementation and a confidence in its stabilityArtificial Intelligence00The proposed approach simplifies the road to introduce the contact testing into the distributed systems, increases the development team effectiveness in system implementation and a confidence in its stability.Artificial Intelligence["Harasymchuk O"]2024-12-30T00:00:00 d9af7119ee2d2a48b4feed07bd5ddb2bd2cddbd1Artificial intelligence (AI) is emerging as a transformative force in cloud product optimization, enabling organizations to achieve efficiency, scalability, and cost-effectiveness that were previously unattainable. The complexity of managing cloud resources, including cost, performance, and reliability, has increased dramatically with the widespread adoption of cloud computing. AI techniques, such as machine learning, deep learning, and reinforcement learning, can be leveraged to address these complexities by predicting workload patterns, automating resource allocation, and ensuring optimal performance through proactive monitoring and adjustments. This article provides an in-depth exploration of AI-based methods used for optimizing cloud infrastructure, focusing on real-world scenarios like dynamic resource allocation, pricing prediction, and service reliability. Additionally, we present the challenges of AI adoption in cloud optimization and outline potential directions for future researchArtificial Intelligence00An in-depth exploration of AI-based methods used for optimizing cloud infrastructure, focusing on real-world scenarios like dynamic resource allocation, pricing prediction, and service reliability is provided.Artificial Intelligence["Artamonov O", "Balych P"]2024-12-30T00:00:00 41f42b871caf2ac1720df4280c71d9559818d851The article is devoted to the problem of using artificial intelligence in the activities of after-school educational institutions of tourism and local studies. The possible directions of using artificial intelligence specifically in the educational process of study groups, in the organizational and managerial activities of after-school educational institutions, in the organization and arrangement of mass events are identified. Specific forms of using artificial intelligence in after-school tourism and local studies education institutions are highlighted to ensure the safety of events, prepare tourist trips, hikes, excursions, and organize the work of tourist route and qualification commissions. Based on a educators' survey of after-school education institutions of Ukraine using Google forms, the level of use of information and communication technologies and artificial intelligence by pedagogical staff of after-school education institutions of tourism and local studies is determined. The level of understanding by teachers of the concept of “artificial intelligence” and ways of its use in educational activities in after-school education is determined. The technologies of artificial intelligence used by educators, the directions of using artificial intelligence (search for necessary information, creation of text, graphic and test educational materials, creation of news and advertising materials, monitoring of the security situation) are determined. The organizational, methodological and technical problems that, in the opinion of educators, hinder the use of artificial intelligence technologies, ways for educators to acquire competencies in the use of artificial intelligence technologies are highlighted. It has been established that the main source of educators' acquisition of competencies in artificial intelligence is self-education.Artificial Intelligence00It has been established that the main source of educators' acquisition of competencies in artificial intelligence is self-education and ways for educators to acquire competencies in the use of artificial intelligence technologies are highlighted.Artificial Intelligence["Narovlianskyi O", "Narovlianska M", "Lukatskyi Ye"]2024-12-30T00:00:00 44e19837ad7c06c9e4a51cf4643430c743326f41This study explores the transformative potential of artificial intelligence (AI) within the lean construction framework, addressing the pressing need for enhanced efficiency, sustainability, and innovation in the construction industry. Lean construction principles, which emphasize waste minimization and value maximization, align seamlessly with AI’s capabilities, offering a paradigm shift in project management. The research employs a comprehensive review of contemporary literature, analyzing key applications of AI in optimizing resource utilization, streamlining workflows, and fostering sustainability through renewable energy integration and environmental impact reduction. The findings reveal that AI-driven tools significantly enhance project efficiency by automating repetitive tasks, facilitating predictive analytics, and enabling real-time data-driven decision-making. AI also promotes collaboration and transparency among stakeholders through secure digital platforms, while supporting sustainability goals through material optimization and energy-efficient processes. However, critical barriers to AI adoption persist, including economic constraints, data security challenges, and the absence of standardized regulatory frameworks. The study concludes that AI represents a pivotal driver for advancing lean construction, with its integration promising unprecedented improvements in efficiency and sustainability. To overcome the challenges identified, the study recommends the establishment of robust regulatory policies, investment in workforce training to enhance AI competency, and sustained research and development to address technical and economic hurdles. These measures are essential to ensuring that the construction industry fully harnesses the potential of AI, paving the way for a future characterized by collaborative, efficient, and sustainable practices. This research contributes valuable insights to the ongoing discourse on AI in lean construction, serving as a foundation for further exploration and practical implementation.World Journal of Advanced Engineering Technology and Sciences513The study concludes that AI represents a pivotal driver for advancing lean construction, with its integration promising unprecedented improvements in efficiency and sustainability.World Journal of Advanced Engineering Technology and Sciences["Rasheed O. Ajirotutu", "Baalah Matthew", "Patrick Garba", "Johnson Segun Olu"]2024-12-30T00:00:00 dd230f6d2f231006314b12a3236b112711720397The article aims to impart an overview of integrating artificial intelligence (AI) with identity and access management (IAM) systems and their strategic view regarding evolution, benefits, and challenges. Solid IAM systems are bulwarks of defense that shield sensitive information from sophisticated digital threats. The evolution of IAM during these last three decades - from modules such as Lightweight Directory Access Protocol (LDAP) to developed systems - has made inroads into handling the contemporary complexities of modern security protocols. It is AI that boosts IAM to real-time threat detection, automated user provisioning, and compliance with regulations through which provisioning is done using machine learning to analyze vast amounts of data for the detection of access patterns that are not usual and from those to predict the breaches, followed by the implementation of solid authentications and controls, including biometric and adaptive verification. Apart from providing benefits, there are challenges to be taken care of in a scenario where the integration of AI will call for advanced hardware, standardized software, and intensive training of users. General AI-driven platforms and tools, like IBM Security Identity Governance and Microsoft Azure Active Directory, add to the strength of IAM by ensuring the automatic detection of potential threats and facilitating proactive actions against them. Moreover, the article elaborates on a few AI-driven IAM applications in the finance and healthcare sectors, which considerably impact data security and fraud reduction. In this regard, addressing the current challenges and developing standardized frameworks is going to be imperative for the extraction of the full potential of AI into IAM.International Journal of Information Security and Cybercrime01An overview of integrating artificial intelligence (AI) with identity and access management (IAM) systems and their strategic view regarding evolution, benefits, and challenges is impart.International Journal of Information Security and Cybercrime["Kaushik Reddy Muppa"]2024-12-30T00:00:00 08685f9b27555d2deb728aa42d7b633919a25e90The prepared draft of the concept in the field of the development of artificial intelligence is the result of scientific research by the group of researchers of the Cyber Law scientific school named after S. Gulyamov at the Tashkent State University of Law. It is suggested for adoption in the Republic of Uzbekistan. The concept defines the fundamental principles and new legal constructions that will model the foundations and vectors of the targeted development of the public administration system in the field of artificial intelligence, instead of a belated response to patching up gaps and eliminating contradictions, chaotically developing new and transforming various relationships with artificial intelligence.Uzbek Journal of Law and Digital Policy01The concept defines the fundamental principles and new legal constructions that will model the foundations and vectors of the targeted development of the public administration system in the field of artificial intelligence, instead of a belated response to patching up gaps and eliminating contradictions.Uzbek Journal of Law and Digital Policy["Said Gulyamov", "I. Rustambekov", "Otabek Narziev", "Azamat Khudayberganov"]2024-12-30T00:00:00 4e8dd8e371efe7bacdab75dad80d10f4870a1d1aKeamanan jaringan komputer menghadapi tantangan yang semakin kompleks seiring dengan meningkatnya volume dan variasi ancaman, seperti serangan Distributed Denial of Service (DDoS), malware, dan eksploitasi kerentanan. Pendekatan tradisional dalam deteksi dan mitigasi ancaman sering kali tidak cukup responsif terhadap pola serangan yang dinamis dan canggih. Teknologi kecerdasan buatan (Artificial Intelligence/AI), khususnya Machine Learning (ML), menawarkan pendekatan baru yang lebih adaptif dan proaktif.Penelitian ini bertujuan untuk menganalisis peran AI dalam meningkatkan keamanan jaringan melalui penerapan berbagai algoritma ML, seperti Naïve Bayes Classifier, Support Vector Machine (SVM), Decision Tree, dan Random Forest. Pendekatan ini memungkinkan analisis data dalam jumlah besar secara real-time, identifikasi pola anomali, dan deteksi dini terhadap serangan yang belum teridentifikasi sebelumnya. Hasil tinjauan literatur menunjukkan bahwa algoritma Machne Learning mampu meningkatkan akurasi deteksi ancaman hingga 95% dalam berbagai studi kasus. Meskipun demikian, beberapa tantangan masih dihadapi, seperti tingkat false positives yang tinggi, keterbatasan data pelatihan, dan kebutuhan infrastruktur yang signifikan. Untuk mengatasi tantangan ini, diperlukan pengembangan algoritma yang lebih efisien serta integrasi AI dengan teknologi lain, seperti blockchain dan Software-Defined Networking (SDN).Penelitian ini menyimpulkan bahwa AI memiliki potensi besar untuk menjadi komponen kunci dalam strategi keamanan jaringan modern, dengan memberikan solusi yang lebih cepat, akurat, dan skalabel dalam mendeteksi dan merespons ancaman keamanan siber.Jurnal Minfo Polgan130Jurnal Minfo Polgan["A. Purnomo", "Aliyah Kurniasih", "Ahlijati Nuarminah", "Sri Hartati"]2024-12-30T00:00:00 9c5bb5cd653cb8759008022a66c17a55511d9c09The primary object of analysis in this study is the impact of artificial intelligence (AI) on various departments of a district state administration. The problem addressed by the research was to evaluate the key benefits and challenges of using AI to optimize management processes. The results demonstrated a significant increase in the efficiency of handling citizen inquiries, reducing the processing time from seven days to two days, indicating the high productivity of the implemented systems. These results can be explained by the application of automating routine tasks and optimizing workflows, which lead to the rapid processing of inquiries and reduction of administrative burdens. Moreover, the increased internal consistency of the data, confirmed by Cronbach's alpha, indicates the reliability of the metrics and assessment tools used. The distinctive features of the results, such as high transparency and efficiency of processes, became possible through the integration of the latest AI technologies, which helped solve the identified problem. These features allow AI to serve as an important tool in public administration reform. The scope of practical application of the results includes the use of AI to enhance the quality of public services and optimize internal processes in public administration. Owing to the implementation of best practices in data management and cybersecurity, departments can achieve better interaction and efficiency, promoting the development of a transparent and effective management system. The practical application of the proposed innovations could significantly improve the quality of interaction with citizens, ensuring greater satisfaction with services and compliance with modern efficiency requirementsEastern-European Journal of Enterprise Technologies290A significant increase in the efficiency of handling citizen inquiries is demonstrated, reducing the processing time from seven days to two days, indicating the high productivity of the implemented systems.Eastern-European Journal of Enterprise Technologies["Alla Bashuk", "O. Chechel"]2024-12-30T00:00:00 d98114c41e6f22a8d828fa2039bea8a07c18688fArtificial intelligence (AI) is increasingly recognized as an advanced educational tool, supported by substantial evidence demonstrating its effectiveness in addressing educational challenges of varying complexity — from generating lesson plans with a single click to facilitating virtual excursions within classroom settings. Despite the current monopolistic tendencies among companies producing artificial assistive technologies, the digital market is anticipated to soon become saturated with a diverse array of generative agents of any needs and wallets. In the context of general pedagogy, positive trends in AI integration are evident. However, inclusive education remains largely anchored in traditional pedagogical approaches, primarily due to the challenges associated with making significant advancements in this low-mobility sector. Before any innovative steps can be undertaken, fundamental material needs must be addressed. A comparative analysis of the Ukrainian inclusive education experience against international benchmarks reveals a notable regression since the onset of the full-scale invasion. While leading international initiatives are focused on the potential of AI to address global educational challenges, Ukraine appears to depend largely on the initiatives of individual educators. To explore the contentious issue of whether it is acceptable to disregard certain recommendations from prominent state and international institutions regarding the safe integration of AI into educational practices — especially in the absence of alternative validated methodologies for teaching children with special educational needs — a survey was conducted involving 43 respondents engaged in inclusive education. The findings affirm that the negative perceptions surrounding AI in inclusive contexts are justified: Ukraine's educational landscape is still evolving to reach the technological standards observed in the early 2000s in other countries. This article also highlights the risks associated with the uncritical introduction of «radically new technologies» from the era of the first computers into educational settings without first ensuring compliance with four critical criteria: content accuracy, age appropriateness, relevance of pedagogical methods, and cultural and social suitability. While the article predominantly conveys a skeptical perspective on the feasibility of effectively integrating AI into the inclusive educational process, the authors do not intend to diminish national accomplishments. Instead, they advocate for a research trajectory that is genuinely pertinent: Ukraine's journey with AI should be collaborative, aiming to refine tools that empower, rather than replace, ensuring that «artificial inclusion» becomes a lived reality rather than a tick-box culture.Educological discourse50Ukraine's journey with AI should be collaborative, aiming to refine tools that empower, rather than replace, ensuring that «artificial inclusion» becomes a lived reality rather than a tick-box culture.Educological discourse["L. Potapiuk", "Alina Sasiuk"]2024-12-30T00:00:00 e37f02e58ea5f51d00c9e07a0c61755369b89a97In the light of the rapid development of advanced technologies in recent years, many questions have been raised about the future application of available technological solutions in various spheres of life, including politics. An important issue that should be discussed in this field concerns the risks associated with the use of artificial intelligence algorithms in creating the public image of politicians and in electoral campaigns. This paper is based on the concept of eroded epistemics, which is a part of Existential Risk Analysis for AI research. Using the AI Safety Research perspectives of monitoring and systemic safety, it examines the potential risks of using AI in politics and ways to minimize them. The analysis is based on the examples of actions of American politicians. Firstly, the threats of using deepfake technology in creating and manipulating the image of politicians such as Nancy Pelosi, Barack Obama, and Donald Trump, are presented. The second part of the paper discusses user profiling and microtargeting strategies and how they may form opinions and influence voters’ decisions. Finally, examples of present‑day solutions that are being developed to combat these risks are described.Ad Americam50The threats of using deepfake technology in creating and manipulating the image of politicians such as Nancy Pelosi, Barack Obama, and Donald Trump, are presented and ways to minimize them are described.Ad Americam["Helena Ja\u0144czuk"]2024-12-30T00:00:00 ef77996c9b7068a0f94ae46e47492e7aa4a0e93aThe article explores the historical development of artificial intelligence (AI) from the first technical developments to modern neural networks, analyzing its reflection in culture and cinema. The evolution of public perception is also considered through the prism of works of art, starting with mythological plots and ending with modern films. Based on the analysis of 58 popular films, four basic ideas about AI in the minds of residents of megacities have been identified: anthropomorphic robots (36.2%), intelligent systems (29.3%), technologically advanced humans (24.1%) and animated images (10.3%). Special attention is paid to the role of cinema in shaping public opinion about intelligent technologies and their impact on the development of a digital society. The study demonstrates the relationship between cultural perception of AI and real technological progress.Sociopolitical sciences00The article explores the historical development of artificial intelligence from the first technical developments to modern neural networks, analyzing its reflection in culture and cinema, and demonstrates the relationship between cultural perception of AI and real technological progress.Sociopolitical Sciences["A. Garaganov"]2024-12-30T00:00:00 be4f4e9fac747af5c43866640078c68f101638f6In today's digital landscape, technology plays a central role in nearly every aspect of business, including supply chain management, manufacturing, sales, marketing, and finance. However, the increasing reliance on digitisation has made organisations across various sectors more vulnerable to fraud. As businesses adopt technology to enhance efficiency, their exposure to these risks grows, necessitating the protection of intellectual property, business data, consumer information, and more. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising tools for detecting and preventing fraud. This article explores the potential of AI and ML to collaborate with both supervised and unsupervised systems to better address security risks. By analysing financial transactions, customer behaviour, and real-time traffic, these technologies can detect anomalies and raise alerts for suspected fraud. This study investigates the fraud detection and prevention capabilities of AI applications in the e-commerce, healthcare, and tourism sectors. Data is collected and analysed to provide meaningful insights into the managerial factors influencing various AI applications in fraud detection and prevention. The analysis of different AI applications and software, focusing on their technological models, key features, and industry use, demonstrates that tech developers have successfully integrated fraud monitoring and detection systems. Furthermore, these applications could be adapted for use in other sectors to address critical security infrastructure gaps. The survey results also strongly indicate that while organisational strategy, structure, resources, and trust support the implementation of AI, broader environmental factors such as organisational culture may significantly affect the effectiveness of AI in fraud detection and prevention.Computer fraud & security00The survey results strongly indicate that while organisational strategy, structure, resources, and trust support the implementation of AI, broader environmental factors such as organisational culture may significantly affect the effectiveness of AI in fraud detection and prevention.Computer Fraud and Security["Nasser Abdullah Alsulayhim"]2024-12-30T00:00:00 d10a854ab8beb1f274f93d8c22872c8970d06ab7Introduction: This study explores the role of Artificial Intelligence (AI) in financial analysis and forecasting, focusing on its application in the banking sector. AI's ability to process large datasets and enhance prediction accuracy is critical for improving financial decision-making, particularly in forecasting stock prices, currency rates, and market trends.Methods: The research employed traditional statistical methods such as ARIMA models and machine learning algorithms like Gradient Boosting Machines and Random Forests. These methods were applied to financial data sets to assess the impact of AI on forecasting accuracy and risk assessment. Data preprocessing and model training were conducted using R statistical software.Results:  Integrating AI models improved forecasting accuracy by 30% compared to traditional methods, and risk assessment accuracy increased by 20%. Gradient Boosting Machines outperformed other models in identifying investment portfolio risks, while Random Forests provided robust predictions of trading volumes.Conclusions: AI has the potential to revolutionize financial analysis by increasing the efficiency and accuracy of forecasts. However, data privacy, algorithmic bias, and ethical concerns must be addressed to ensure fair and responsible AI use in finance. Collaboration among researchers, financial experts, and policymakers is essential for maximizing AI's benefits while mitigating risksData and Metadata250AI has the potential to revolutionize financial analysis by increasing the efficiency and accuracy of forecasts, however, data privacy, algorithmic bias, and ethical concerns must be addressed to ensure fair and responsible AI use in finance.Data and Metadata["Olha Chernysh", "Oleksandr Smishko", "Yuliia Koverninska", "Mykola Prokopenko", "I. Pistunov"]2024-12-30T00:00:00 2bae1f6c4a6f4adf529089b462ada5fc0e568b90Artificial intelligence (AI) has revolutionized many sectors with its development, but it is also likely to pose new threats when used maliciously. This study examines the implications of state and nonstate actors using AI to conduct more sophisticated cyber-attacks and their potential consequences for international relations and global security. Cyber-attack detection has become more automated, targeted, and challenging due to rapid advances in AI. Thanks to AI, adversaries can now impersonate humans, manipulate data, eavesdrop on conversations, and exploit system weaknesses on an unprecedented scale. Unchecked, AI-enabled cyber-attacks can undermine diplomatic relations, increase the likelihood of military conflict between governments, and destabilize the economy. The international community will need new legal frameworks and technological measures to mitigate these risks. International cooperation is necessary to limit the development of AI cyber weapons, develop robust systems, and establish guidelines for responsible state activity in cyberspace. Through cooperation and foresight, the potential of AI is more likely to be achieved while reducing the likelihood of intensified cyber warfare. This paper provides a broad literature perspective and offers recommendations for both legal and technological solutions to reduce the likelihood of the effects of AI-based cyber-attacks.Güvenlik Stratejileri Dergisi150This paper provides a broad literature perspective and offers recommendations for both legal and technological solutions to reduce the likelihood of the effects of AI-based cyber-attacks.Güvenlik Stratejileri Dergisi["Esra Merve \u00c7al\u0131\u015fkan"]2024-12-30T00:00:00 d1cf1d0e5a6f26707a1705b9a7a0b1c2d064c7f7This study explores the integration of maqāṣid al-syarī‘ah principles with Artificial Intelligence (AI) in developing a Sharia Online Trading System (SOTS) for Indonesia's Islamic capital market. By focusing on key principles such as justice (al-‘adalah) and public benefit (al-maṣlahah), this research employs a phenomenological approach to analyze the subjective experiences of market participants. Findings reveal that an AI-based system using prompt engineering can enhance transparency, efficiency, and compliance in sharia transactions. The system provides real-time notifications to investors, helping them understand the ethical and legal aspects of their transactions. This research contributes to bridging the gap between Islamic ethical frameworks and modern technological advancements, paving the way for a more inclusive and sustainable Islamic financial ecosystem. Future research could expand on implementation evaluation, global comparisons, and further algorithmic development to deepen the impact of AI in Islamic financial markets.Computer fraud & security00Findings reveal that an AI-based system using prompt engineering can enhance transparency, efficiency, and compliance in sharia transactions in Islamic financial markets.Computer Fraud and Security["Mukhlis Lubis"]2024-12-30T00:00:00 85a41279c3bc7e9286f092ec08fbfa76d86b4bf1Background: The implementation of artificial intelligence (AI) in the public sector represents a crucial milestone in the digital transformation of Ecuador, particularly in Guayaquil. While AI has the potential to optimize administrative processes, enhance public services, and improve data-driven decision-making, its deployment without a robust governance framework poses ethical, legal, and social risks. Methods: This study employs a quantitative, non-experimental research design to analyze AI governance in Guayaquil’s public administration. Data collection was conducted through structured surveys administered to public employees, evaluating key dimensions such as governance components, public perceptions, technological infrastructure, and ethical considerations. Statistical techniques, including Pearson correlation analysis, were used to assess relationships between variables. Results: Findings highlight the disparity in AI adoption among public institutions, with significant gaps in training, infrastructure, and policy implementation. The study underscores the necessity of an AI governance model that ensures transparency, inclusivity, and ethical compliance. Conclusions: The proposed governance model provides strategic recommendations for AI adoption in Guayaquil’s public sector, emphasizing regulatory frameworks, capacity-building initiatives, and cross-sector collaboration. This research contributes to the global discourse on responsible AI governance, aligning with international efforts to establish ethical standards for emerging technologiesJournal of Ecohumanism190The proposed governance model provides strategic recommendations for AI adoption in Guayaquil’s public sector, emphasizing regulatory frameworks, capacity-building initiatives, and cross-sector collaboration.Journal of Ecohumanism["Elisa Amelia Cisneros Prieto", "Ingrid Angelina Soto Galarza", "Antonio Poveda Guevara", "Shamary Poleth Valdospin Sanchez", "Ricarte Francisco Carre\u00f1o Calder\u00f3n"]2024-12-30T00:00:00 3995d15799e615a03c68a95c81914917ae08b34eThe aim of the article is to highlight the key role of artificial intelligence in modern oncology. The search for scientific publications was carried out through the following web search engines: PubMed, PMC, Web of Science, Scopus, Embase and Ebsco. Artificial intelligence plays a special role in oncology and is considered to be the future of oncology. The largest application of artificial intelligence in oncology is in diagnostics (more than 80%), particularly in radiology and pathology. This can help oncologists not only detect cancer at an early stage but also forecast the possible development of the disease by using predictive models. Artificial intelligence plays a special role in clinical trials. AI makes it possible to accelerate the discovery and development of new drugs, even if not necessarily successfully. This is done by detecting new molecules. Artificial intelligence enables patient recruitment by combining diverse demographic and medical patient data to match the requirements of a given research protocol. This can be done by reducing population heterogeneity, or by prognostic and predictive enrichment. The effectiveness of artificial intelligence in oncology depends on the continuous learning of the system based on large amounts of new data but the development of artificial intelligence also requires the resolution of some ethical and legal issues.Applied Sciences940The effectiveness of artificial intelligence in oncology depends on the continuous learning of the system based on large amounts of new data but the development of artificial intelligence also requires the resolution of some ethical and legal issues.Applied Sciences["Shuhua Zheng", "Yue Meng"]2024-12-30T00:00:00 f618eac0fb890ddabbe7d2fb82310c0b9c32ec1aThis research investigates the transformative role of artificial intelligence (AI) in making agriculture more sustainable, addressing one of the most pressing challenges of our time—balancing food production with environmental conservation. AI-driven technologies are reshaping traditional farming by optimizing various processes, including precision farming, resource management, and crop monitoring. Precision agriculture allows farmers to apply resources like water, fertilizers, and pesticides with pinpoint accuracy, reducing waste and minimizing environmental impact. AI also enhances resource management by analyzing real-time data from sensors and satellite imagery, helping farmers make data-driven decisions to maximize yields while conserving essential resources like soil and water. The research further explores AI’s role in monitoring crops, predicting diseases, and identifying optimal harvesting times, which increases efficiency and reduces crop loss. Through an analysis of case studies, the paper examines how cutting-edge technologies, such as machine learning algorithms, drone systems, and IoT devices, are being implemented in various farming environments around the world. The findings reveal how AI contributes not only to increased productivity but also to sustainable agricultural practices that combat soil degradation, reduce greenhouse gas emissions, and support biodiversity. Moreover, this study addresses the potential challenges and barriers to AI adoption, such as high initial costs, lack of technical expertise, and infrastructure limitations, particularly in developing regions. By highlighting the intersection of AI and sustainability, this research demonstrates how AI can help agriculture meet the growing global demand for food while mitigating its environmental footprint. The paper concludes by outlining the long-term implications of AI in shaping the future of farming and its crucial role in global efforts to achieve food security and environmental sustainability.International Scientific Journal of Engineering and Management00By highlighting the intersection of AI and sustainability, this research demonstrates how AI can help agriculture meet the growing global demand for food while mitigating its environmental footprint.International Scientific Journal of Engineering and Management["Roshan Khadka", "Prof. Virendra Kumar"]2024-12-30T00:00:00 703526fd438fcf321b76e2e5a8d9e012e014cc58Artificial intelligence (AI) is being actively implemented in anti-money laundering (AML) systems due to its potential to improve the detection of suspicious transactions. The article examines AI's effectiveness in detecting and reducing financial crimes of private military companies. The research employs machine learning (ML) algorithms and neural networks, anomaly detection methods, and economic impact assessment. A combination of supervised and unsupervised learning methods enables the creation of accurate predictive models for detecting money laundering anomalies. The results show that AI models outperform traditional rule-based systems, reducing false positives by 30% and increasing high-risk detection by 25%. This proves the advantages of AI over conventional anti-money laundering methods, which often cannot adapt quickly. The research emphasizes the transformative impact of AI on anti-money laundering systems, optimizing accuracy and resource allocation. Further research should focus on improving AI algorithms and their application in new financial technologies.Theoretical and Practical Research in Economic Fields00Examination of AI's effectiveness in detecting and reducing financial crimes of private military companies shows that AI models outperform traditional rule-based systems, reducing false positives and increasing high-risk detection by 25%.Theoretical and Practical Research in Economic Fields["H. A. AlAbabneh", "Cholpon Nuralieva", "Gulbaira Usmanalieva", "Maksym Kovalenko", "Bohdan I. Fedorovych"]2024-12-30T00:00:00 30c87d937cdb12042f626edbbcf3ef45987dac47The transition to a sustainable and regenerative model of the economy the circular economy advocates relies on digitization and innovation that should help and support long-term sustainability. AI is a key technology that can support a smooth transition to a circular economy. At the same time, the Internet of Things is the main driver of process integration with other technologies, while Big Data plays an important role in the process of effective decision-making. Therefore, it is not surprising that research on the contemporary technologies in the circular economy attracts enormous attention of both the academic and professional community and that the number of publications in this field is increasing rapidly. In this paper, VOSviewer is used to discover research trends and provide a comprehensive and integrated approach to research on the role of artificial intelligence, big data and IoT as drivers of the circular economy.Facta Universitatis. Series: Economics and Organization00VOSviewer is used to discover research trends and provide a comprehensive and integrated approach to research on the role of artificial intelligence, big data and IoT as drivers of the circular economy.Facta Universitatis, Series: Economics and Organization["Ivana Markovi\u0107", "Mirjana Jemovi\u0107"]2024-12-30T00:00:00 b0eccbf5cc616dfea8ead44f95639e6a6ed97483This study introduces a framework for the adoption of Artificial Intelligence (AI) driven technology to talent acquisition processes in luxury hotels. This qualitative study employed 23 semi‐structured interviews to explore the perceptions of professionals on AI in luxury hotels in Greece. The findings highlight the benefits of AI‐enabled technologies in talent acquisition, including speed, reliability and enhanced candidate communication, however, human interaction remains pivotal at critical stages. The model proposed includes six stages in the AI‐Talent Acquisition process, serving as a practical guide for practitioners and researchers. This study contributes to the AI‐HRM strategic change field by offering theoretical and practical insights. To the best of the authors' knowledge, it represents one of the initial empirical attempts to develop a comprehensive AI‐Talent Acquisition framework, providing valuable implications for ongoing research and implementation in this domain.Strategic Change380This study introduces a framework for the adoption of Artificial Intelligence (AI) driven technology to talent acquisition processes in luxury hotels in Greece and represents one of the initial empirical attempts to develop a comprehensive AI‐Talent Acquisition framework.Strategic Change["E. Marinakou", "Charalampos Giousmpasoglou", "E. Papavasileiou"]2024-12-30T00:00:00 604b5109189e38d330aafe4924348da888babf72Recent advancements in artificial intelligence (AI) technology have revived concerns about technological unemployment. Regarding the issue, this study examines the impact of AI on employment rates across 17 leading AI countries from 1998 to 2017 using two panel econometric techniques, DOLS and FMOLS, to ensure robust results. For the first time, as far as is known, the effect of AI on employment in distinct sectors is analyzed separately. By uniquely combining different countries and sectors within a macroeconomic framework, this study provides a more comprehensive understanding through a total of eight estimates. The findings indicate that, according to both DOLS and FMOLS techniques, increased AI innovation raises employment rates in the overall economy and in the service sector, while reducing employment rates in the industrial and agricultural sectors. Consequently, while AI positively impacts overall employment, considering industrial and agricultural sectors, employment policies should be adjusted to meet evolving needs in the AI era.Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi380The findings indicate that, according to both DOLS and FMOLS techniques, increased AI innovation raises employment rates in the overall economy and in the service sector, while reducing employment rates in the industrial and agricultural sectors.Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi["Yahya Alg\u00fcl"]2024-12-30T00:00:00 629062246545a35ca9ccabc0bcf9704073621093Artificial Intelligence (AI) as a scientific discipline is an exciting opportunity for the development of a new direction in rehabilitation sciences and the improvement of patient care and research approaches [1]. Nevertheless, this integration brings a lot of problems to the issue of credibility and novelty around the processes of researching practices. Thus, this editorial is devoted to these questions, stressing the importance of a strong ethic compared to the standards for research in this shifting context. AI technologies have started influencing Rehabilitation sciences by offering individualized intervention, patient tracking, and optimization of the results by using big data analytics [2, 3]. For example, the machine learning approach may retrieve an avalanche of data to predict rehabilitation strategies that may be effective for patients with particular characteristics [4]. Nonetheless, as more developments are created through the utilization of artificial intelligence, the likelihood of ethical concerns and questions regarding originality in research rises as well [5]. Scientific practice requires creativity and any utilization of AI needs to hold to this regulated element of advancement. Investigators should implement specific precautions to avoid plagiarism or the unauthorized borrowing of concepts through the use of AI tools [6]. This requires policies and procedures that define how the use of AI in production can be realized without compromising the research process domain [7]. AI practice must be brought into the open through mandatory reporting obligations that compel institutions to declare the use of AI throughout research processes It is equally important in all fields especially those which have the potential to endanger the lives of patients. As noted in various studies, the principles of ethical practice like candor, accountability, and transparency are critical in developing public confidence in science [8]. That means the rehabilitation sciences community must embrace the operational and best use of AI and ensure that researchers learn the ethics of using them and the importance of following the set standards. There is a need to ensure institutional commitment to the training of consciousness on responsible conduct of research and the use of AI [7]. Key factors touched upon here include bias that exists in the AI systems they develop and the kind of data they produce [9, 10]. To this end, researchers should be compelled to specify the strategies they have applied AI in their research; the database they have utilized; the algorithm they have employed; and the reasons as to why they selected such choices [11]. This transparency is necessary to make methods reproducible and trustworthy [12]. There was a proposal that enhancing the elements of interdisciplinary interaction would increase the yield of their work [13]. There is also the concern that the ethical standards followed should not be overlooked in the peer review processes thus the procedures should be changed to allow for the review of the implementation of AI [6]. Universities and research centers need to develop strong guidelines to prevent researchers from engaging in wrongdoing; this can involve developing and providing procedures for reporting research misconduct [14-16]. AI in the domain of rehabilitation sciences has the potential to take the specialty to the next level. However, this potential has to be controlled and balanced by the goals of novelty, as well as the ethical course of the research. By keeping the emphasis on ethical behavior and core principles of the rehabilitation sciences the community can embrace the attributes of AI while at the same time preserving the integrity of science. It will increase the quality of work done while also making new technologies in rehabilitation it is useful and beneficial for society. artificial intelligence integrity rehabilitation sciencesThe Rehabilitation Journal00This editorial is devoted to the importance of a strong ethic compared to the standards for research in this shifting context, stressing the need to ensure institutional commitment to the training of consciousness on responsible conduct of research and the use of AI.The Rehabilitation Journal["Abdul Haseeb Bhutta"]2024-12-30T00:00:00 526336114fb68a18d26b63269fe1378095406adfHumanity’s ability to embrace artificial intelligence (AI), or the skills and “knowledge” that it can impart, depends not only on the control of input fed to AI, but also on output management. When properly managed, the AI output, including of large language models (LLMs) such as ChatGPT, can complement human endeavor and excellence. Yet, if abused or left to its own computational vices, AI might cause harm to humans and thus humanity. Within this in mind, this perspective paper offers a reflection on whether LLM-based AI, having the capacity to integrate text, voice and speech, could assist in personal or psychological counseling processes. Cognizant that psychological counseling places the human factor as a central premise of therapy, AI could be perceived as a risk of replacing human-centered counseling roles, even though it might provide assistance to humans under strictly controlled conditions. While the replacement of human-based counseling is not being advocated, there is value in considering the possibility of applying LLM-based AI tools as counseling aides, as AI-human teams, under strict human supervision, and following stringent testing, provided that an ethical working framework and reliability in AI performance can be established.Central Asian Journal of Medical Hypotheses and Ethics510There is value in considering the possibility of applying LLM-based AI tools as counseling aides, as AI-human teams, under strict human supervision, and following stringent testing, provided that an ethical working framework and reliability in AI performance can be established.Central Asian Journal of Medical Hypotheses and Ethics["Jaime A. Teixeira da Silva", "Yuki Yamada"]2024-12-30T00:00:00 c0b4fd4ee1b30025db1da9d5dffdd457889ad9daThis study explores the impact of artificial intelligence (AI) and predictive models in higher education, focusing on the personalization of learning, student retention, and academic success prediction. In a constantly evolving educational environment, it is crucial for universities to implement innovative curricula that prepare students for the challenges of the current job market. The study's methodology is based on a descriptive approach, using a semi-structured survey administered to 250 higher education students. The survey included questions about academic performance, academic support, guidance, skills, and interests. The collected data were analyzed using regression techniques with SPSS software, allowing for the identification of relationships between predictor variables and student satisfaction. The analysis results revealed that, although the predictor variables explain a small proportion of the variance in student satisfaction, the identified patterns provide valuable insights for improving educational offerings. Personalization of learning and optimization of student retention emerge as key areas where AI can have a significant impact. In conclusion, while the implementation of AI predictive models presents challenges related to accuracy and ethics, these models have the potential to transform the educational experience in higher education. The study suggests that, with proper integration and alignment with educational objectives, AI can enhance the quality of education and meet the individual needs of students, contributing to a more efficient and personalized educational experienceRevista de Educación Superior00The study suggests that, with proper integration and alignment with educational objectives, AI can enhance the quality of education and meet the individual needs of students, contributing to a more efficient and personalized educational experience.Revista de Educación Superior["Arturo Torres-Guti\u00e9rrez", "Juan Alfredo Lino-Gami\u00f1o", "Jos\u00e9 de la Cruz D\u00edaz-Ledezma", "Pablo Enr\u00edquez-Cerda"]2024-12-30T00:00:00 22eb78b6d27256aae97414a010fead30e63cd085This study investigates the role of Artificial Intelligence (AI) in minimizing environmental pollution within the civil engineering sectors. With the growing emphasis on sustainability and environmental protection, AI presents an innovative approach to reducing resource consumption, waste generation, and pollution in construction and urban development. The research explores various applications of AI, including resource optimization, waste reduction, energy efficiency, smart infrastructure, and real-time environmental pollution monitoring. Key findings suggest that AI technologies contribute to improved sustainability by optimizing material usage, enhancing recycling processes, and reducing energy consumption in buildings and infrastructure. Additionally, AI-powered systems enable accurate pollution monitoring and forecasting, allowing for timely interventions. However, challenges such as high implementation costs, lack of standardized data, and resistance to technological adoption hinder widespread implementation. The study concludes that while AI holds substantial promise in promoting environmentally sustainable practices, overcoming these barriers will be essential for its broader adoption in civil engineering.World Journal of Advanced Research and Reviews00World Journal of Advanced Research and Reviews["Md Abu Sayeed", "Pankaj Kumar Sarker", "Md. Suman Miah", "Ahmed Sagar Ridoy", "Saihan Rahman", "Mahedi Hasan", "Md. Ariful Islam"]2024-12-30T00:00:00 06594ed6c9db0b5071b116e8b57576f99fbcc2d3The depth of the ecological crisis raises the issue of implementing innovative solutions and technologies in waste management that align with the principles of the circular economy. This research explores the advantages of using artificial intelligence (AI) in waste management systems in Ukraine. The study employs methods of systems analysis, formalization, abstract and logical analysis, in addition to the linear trend method. A comparative analysis of waste management in Ukraine and EU countries was conducted. It was found that the waste generation level in Ukraine is 11.1 tons per person per year, compared to 4.8 tons in the EU. Only 8.24% of waste in Ukraine is sent for recycling, while this figure reaches 49.6% in the EU. The analysis revealed that insufficient infrastructure, poor-quality legislation, and the destruction caused by the war significantly affect waste management practices in Ukraine. An exhaustive review of current startups and technological solutions in waste management utilizing AI was conducted. The findings show that the implementation of AI in waste management can increase recycling rates by 20-30% and reduce operational costs by 10-15%. It is projected that by 2033, the global AI market for waste management will reach USD 18.2 billion, demonstrating an annual growth rate of 27.5%. The study concludes that applying AI in waste management in Ukraine could significantly reduce waste incineration and increase recycling rates to 70% by 2030, aligning with the goals of the National Waste Management Strategy.Grassroots Journal of Natural Resources00The study concludes that applying AI in waste management in Ukraine could significantly reduce waste incineration and increase recycling rates to 70% by 2030, aligning with the goals of the National Waste Management Strategy.Grassroots Journal of Natural Resources["Nina Linde", "Anush Balian", "T. Shabatura", "I. Gryshova", "Alla Yakovenko", "T. Hnatieva"]2024-12-30T00:00:00 96fe385de5aaee61c3e2ce10f693ba5c534b41acThis paper discusses the challenges associated with personal data protection in the era of artificial intelligence (AI). While AI technologies enable personalized services by collecting and analyzing large amounts of data, concerns regarding privacy and data protection are growing. The methods AI systems use to process data are often opaque, which poses a potential risk to individual rights and freedoms. Therefore, exploring data protection measures suitable for the AI era is crucial. Personal data protection is essential to comply with legal requirements and maintain individual privacy and trust. In the digital era, personal data is used in various ways, leading to issues such as data breaches, misuse, and discriminatory algorithms. These problems can erode trust and negatively affect innovation and economic growth. Strengthening personal data protection has thus become a socially important task. As AI technologies advance, existing data protection laws are proving insufficient to address new challenges. Regulations such as the European General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have been enacted to strengthen personal data protection, but they face limitations in keeping up with the complexities of AI technology. The complexity of AI algorithms makes it difficult to ensure transparency in data collection and processing, hindering trust in the use of personal data.To address these issues, companies must establish clear data protection policies, policymakers to develop flexible regulations, and engineers to adopt principles that consider personal data protection in system design. These efforts will help reinforce personal data protection and enhance the reliability of AI systems. In conclusion, a trust-based data protection framework is necessary for AI technologies to respect privacy and have a positive societal impact. Companies and governments must cooperate to strengthen data protection and build societal trust.Journal of Medical Imaging00A trust-based data protection framework is necessary for AI technologies to respect privacy and have a positive societal impact and companies and governments must cooperate to strengthen data protection and build societal trust.Journal of Medical Imaging["Giljae Lee"]2024-12-30T00:00:00 b2ebc3c39cb1aed2dccf087bb34712b5b23f0604This article examines the application of artificial intelligence (AI) technologies to improve human-computer interaction (HCI) and foster cooperation in graphic design. Comprehensive study and practical use reveal that AI integration significantly enhances HCI's effectiveness and precision while introducing unique collaborative models in graphic design. The research employs natural language processing (NLP) and deep learning technologies in HCI to provide natural dialogue and intelligent responses between humans and machines by developing intelligent question-answering systems and automated task-processing capabilities. Data research indicates that with the implementation of AI, user-computer interface efficiency has increased by 21%, and the mistake rate has decreased by 12%. This accomplishment substantially enhances user experience and facilitates computers in comprehending and addressing user requirements more effectively. The biocompatibility and comfort of users of these substances are of concern. Investigating novel flexible biosensors is imperative to enhance these devices' adaptability, comfort, and interaction. In visual design, the research employs AI's picture recognition and analysis skills to facilitate intelligent recommendations and automatic modifications of design aspects. AI can autonomously produce visual components that align with the designer's goal and approach by analyzing several design scenarios using machine learning algorithms. Data analysis indicates that the design effectiveness of AI-assisted design projects has increased by 30%, and the caliber of the outputs has substantially enhanced.Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications320The research employs natural language processing and deep learning technologies in HCI to provide natural dialogue and intelligent responses between humans and machines by developing intelligent question-answering systems and automated task-processing capabilities.Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications["Wenyi Zhang", "Dongkwon Seong"]2024-12-30T00:00:00 a0a2aff8fd6dc039a20b34f75059600a5e1e197eThe construction industry is increasingly adopting artificial intelligence (AI) to optimize project management processes and enhance risk mitigation strategies. As construction projects grow in complexity, with tight deadlines, evolving regulations, and high costs, the traditional approaches to managing projects and assessing risks are often inefficient and prone to human error. AI technologies, particularly machine learning and predictive analytics, offer powerful tools to address these challenges by providing data-driven insights, improving decision-making, and automating various project management tasks. By analysing historical data and real-time project information, AI can predict potential risks, such as delays, cost overruns, and safety hazards, enabling proactive interventions. AI-powered tools help streamline project scheduling, resource allocation, and performance tracking, ensuring that projects stay on track and within budget. Additionally, AI can optimize supply chain management, reducing material waste and ensuring the timely availability of resources. Machine learning algorithms can continuously learn from project data, improving their predictive accuracy over time and adapting to changing conditions. This paper explores the role of AI in transforming construction project management and risk mitigation strategies. It examines the specific AI applications in areas such as risk assessment, safety management, cost estimation, and scheduling optimization. Case studies and examples from the construction industry highlight the successful implementation of AI tools in real-world projects, demonstrating tangible improvements in project outcomes. The paper also addresses the barriers to AI adoption in construction, including data quality, integration challenges, and the need for specialized skills. Ultimately, the integration of AI into construction project management holds the potential to create more efficient, cost-effective, and risk-resilient projects.World Journal of Advanced Research and Reviews00The role of AI in transforming construction project management and risk mitigation strategies is explored, and the specific AI applications in areas such as risk assessment, safety management, cost estimation, and scheduling optimization are examined.World Journal of Advanced Research and Reviews["Bernard Anim Manu"]2024-12-30T00:00:00 56cc2c80dee9daaec48ac806532977d158e1bcf0Economic researchers have been discussing a paradigm shift in mainstream economics for many years, as economics as a science has ceased to formulate accurate predictive conclusions. Representatives of unorthodox streams of economics, such as institutional economics, behavioural economics or the emerging neuroeconomics, state that the classical paradigm of mainstream economics should be eliminated from research and a new paradigm of economics should be introduced into economic science. Previous attempts to construct such a new paradigm of economics have failed. Artificial intelligence, rapidly developing in the 21st century, brought some hope to those seeking to dismantle the classical paradigm of mainstream economics. In this paper, we present our research on the classical paradigm of mainstream economics. We put forward a research hypothesis stating that the mainstream economic paradigm should not be dismantled, but rather modified. We propose applying data filtering at both the input and output stages to the existing paradigm by constructing various filters. These filters would enable economics to formulate practical conclusions, increase the efficiency of scientific research, allow the discovery of new economic laws, and optimize the decision-making process.Torun International Studies170This paper proposes applying data filtering at both the input and output stages to the existing paradigm by constructing various filters that would enable economics to formulate practical conclusions, increase the efficiency of scientific research, allow the discovery of new economic laws, and optimize the decision-making process.Torun International Studies["Marian Noga", "Beniamin Noga"]2024-12-30T00:00:00 244b9a466125b0d4daa1caf1821cbf34366b6268The steady rise of artificial intelligence (AI) across multiple domains, particularly education, marks a transformative period for the field. Understanding the essential role of teachers and students as active participants in this transformation, as well as the factors influencing their perceptions and attitudes toward AI, is critical. In Ukraine, the emergence and rapid spread of accessible AI tools occurred during a time of full-scale military conflict, bringing about drastic disruptions to traditional educational processes. This study provides insights into these impacts by analyzing the results of a nationwide survey conducted in 2023 on AI's role in education, gathering perspectives from two main groups: educators (N = 1734) and students in grades 8–11 (N = 1448). The data reveal distinct differences in teachers' and students' attitudes towards integrating AI into education. While many teachers recognize AI's potential to aid in tasks like test creation, creative task development, and student progress tracking, they also express concerns about ethical implications and the risk of academic dishonesty. In contrast, a substantial portion of students’ view AI as a valuable tool for enhancing learning and promoting self-directed education. Additionally, the study identifies an inverse relationship between the duration of a teacher's professional experience and their frequency of AI use, suggesting that younger educators may be more inclined to adopt these technologies. Among students, however, a positive correlation exists between their year of study and the frequency of AI tool utilization, indicating a gradual increase in AI engagement with advancing grade levels. Based on the results, it can be concluded that AI is currently an additional option for educational activities that will become a necessity in the near future. Therefore, retraining and upskilling teachers and providing them with appropriate quality tools is an essential and urgent task.Ìnformacìjnì Tehnologì ì Zasobi Navčannâ260It can be concluded that AI is currently an additional option for educational activities that will become a necessity in the near future, and retraining and upskilling teachers and providing them with appropriate quality tools is an essential and urgent task.Information Technologies and Learning Tools["Stanislav Dovgyi", "S. Babiichuk", "Lidiia Davybida", "Mariia Biletska"]2024-12-30T00:00:00 286e0e4bac55568df447c6e1ed1cad50f2ace3aaThis study aims to explore the current state of research and the applicability of artificial intelligence (AI) at various stages of post-traumatic stress disorder (PTSD), including prevention, diagnosis, treatment, patient self-management, and drug development. We conducted a bibliometric analysis using software tools such as Bibliometrix (version 4.1), VOSviewer (version 1.6.19), and CiteSpace (version 6.3.R1) on the relevant literature from the Web of Science Core Collection (WoSCC). The analysis reveals a significant increase in publications since 2017. Kerry J. Ressler has emerged as the most influential author in the field to date. The United States leads in the number of publications, producing seven times more papers than Canada, the second-ranked country, and demonstrating substantial influence. Harvard University and the Veterans Health Administration are also key institutions in this field. The Journal of Affective Disorders has the highest number of publications and impact in this area. In recent years, keywords related to functional connectivity, risk factors, and algorithm development have gained prominence. The field holds immense research potential, with AI poised to revolutionize PTSD management through early symptom detection, personalized treatment plans, and continuous patient monitoring. However, there are numerous challenges, and fully realizing AI’s potential will require overcoming hurdles in algorithm design, data integration, and societal ethics. To promote more extensive and in-depth future research, it is crucial to prioritize the development of standardized protocols for AI implementation, foster interdisciplinary collaboration—especially between AI and neuroscience—and address public concerns about AI’s role in healthcare to enhance its acceptance and effectiveness.Behavioral Science1100Behavioral Sciences["Ruoyu Wan", "Ruohong Wan", "Qing Xie", "Anshu Hu", "Wei Xie", "Junjie Chen", "Yuhan Liu"]2024-12-30T00:00:00 24cdc2dd0868213816ce219f1ebd03237c133b50Artificial Intelligence (AI) has emerged as a burgeoning field in education, characterized by rapid growth and diverse research interests. This study employs bibliometric analysis to explore the landscape of AI research in education, focusing on studies indexed in the Web of Science (WOS) database. A comprehensive search identified 1383 articles published between 1981 and 2024, which were analysed using the Bibliometrix R package. The analysis encompassed performance analysis, science mapping, and network analysis, yielding visualizations such as annual scientific production trends, most cited documents, and thematic maps. Key findings reveal a substantial increase in AI research from 2022 onwards, underscoring a shift towards longitudinal studies to track AI's evolution and impacts in educational contexts. Ethical considerations, data privacy, and societal implications emerged as critical areas requiring further investigation. While early studies focused on intelligent tutoring systems, contemporary research highlights topics like ChatGPT, machine learning, and higher education. The interdisciplinary nature of AI in education is evident through its publication in journals spanning educational technology and related fields. Future research directions emphasize the need for comprehensive studies addressing ethical frameworks and guidelines for responsible AI integration in education. Bridging technological advancements with pedagogical strategies is essential for developing integrative models that enhance personalized learning and educational outcomes. Ongoing bibliometric analyses will play a pivotal role in identifying emerging trends and guiding future research endeavours in AI and education.Participatory Educational Research470This study employs bibliometric analysis to explore the landscape of AI research in education, focusing on studies indexed in the Web of Science database, revealing a substantial increase in AI research from 2022 onwards.Participatory Educational Research["Ezgi Do\u011fan", "Ferhan \u015eahin"]2024-12-30T00:00:00 9357fc24d4c2914c7943b0993480a2a3a186483a. The study investigates the application of artificial intelligence (AI), specifically the ChatGPT 4o tool, for data-based machine diagnostics of the local territorial development using Latvian municipalities as a case study. The topic is highly relevant due to the growing demand for precise, data-driven territorial diagnostics to address sustainable development and governance challenges. The study aims to evaluate AI tools' efficiency and contextual adaptability in performing municipalities' SWOT (Strengths, Weaknesses, Opportunities, Threats) analyses based on their annual public reports. Using discourse analysis as the methodological framework, the study focuses on five municipalities representing different typological clusters in Latvia: Riga City Municipality, Yelgava City Municipality, Liepaja City Municipality, Ropazhi County Municipality, and Augshdaugava County Municipality. Empirical results demonstrate the AI tool's ability to conduct detailed SWOT analyses, uncovering nuanced insights such as demographic challenges, economic dependencies, and opportunities for green transition initiatives. Notably, the tool highlighted innovative perspectives, such as the competitive impact of proximity to Riga on surrounding municipalities. The study identifies the AI tool’s capabilities, including flexibility in focus, contextual socioeconomic and environmental factors integration, and efficiency in processing complex datasets. However, challenges such as data limitations and the necessity of human oversight were also noted. The findings contribute novel insights into the feasibility and potential of AI for local territorial diagnostics, paving the way for broader applications in regional development planning and policymaking.Entrepreneurship and Sustainability Issues90The study identifies the ChatGPT 4o tool’s capabilities, including flexibility in focus, contextual socioeconomic and environmental factors integration, and efficiency in processing complex datasets, including flexibility in focus, contextual socioeconomic and environmental factors integration, and efficiency in processing complex datasets.Entrepreneurship and Sustainability Issues["V. Komarova", "J. Kudins", "Aija Sannikova", "E. \u010ci\u017eo", "O. Ru\u017ea", "A. Kokarevica", "Zane Zeibote"]2024-12-30T00:00:00 ea78a9b06e72782ed2ce8b63d8bc3344bee6950eIn the current post-pandemic scenario, the integration of artificial intelligence (AI) and metaverses in higher education has gained significant relevance, transforming learning experiences towards more immersive and personalized approaches. Identifying the way in which these technologies impact university teaching-learning is essential to understanding their true contribution. This bibliometric review study focuses on analyzing the trend in scientific production, as well as the implications of its impact on higher education. Through a mixed and descriptive methodological approach, 63 scientific documents from the Scopus database were reviewed. The findings show that scientific production increased during the pandemic; however, in the post-pandemic context, research has maintained an upward trend. Furthermore, the implications of AI’s contribution to metaverses—representing a positive impact on higher education—have been grouped into five categories, the most prominent of which is “Interactivity for improving student movement”, which covers 42.86% with respect to the other categories identified in the reviewed studies. Therefore, it is concluded that the integration of AI in the development of metaverses presents a significant potential to transform higher education, offering personalized immersive learning experiences that are redefining the way in which students interact with knowledge. Nevertheless, gaps persist that require attention from the scientific community, especially in terms of evaluating the long-term impact of these technologies and their equitable adaptation in various educational contexts. Future research should be directed towards understanding the effectiveness of these environments in the development of specific competencies in higher education, as well as in the creation of strategies that promote inclusive accessibility.International Journal of Learning, Teaching and Educational Research530It is concluded that the integration of AI in the development of metaverses presents a significant potential to transform higher education, offering personalized immersive learning experiences that are redefining the way in which students interact with knowledge.International Journal of Learning, Teaching and Educational Research["Freddy Luis Villar Castillo", "Adolfo Cesar Paredes Reyna", "Carlos Palacios Huaraca", "Nestor Alvarado Bravo", "Florcita Hermoja Aldana Trejo", "Margot Cecilia Corilla Condor", "Raul Suarez Bazalar", "Olga Paola Aguirre P\u00e9rez", "Almintor Giovanni Torres Quiroz", "Roberto Pfuyo Mu\u00f1oz"]2024-12-30T00:00:00 57467a33e49af46a71c62f41aba588bc45201867Accompanying the inroads generative artificial intelligence (GenAI) models such as ChatGPT have made into the higher education sector, an urgent need has arisen to investigate the types of GenAI policies South African higher education institutions (HEIs) have developed in response to GenAI. To date, no study has explored this aspect of South African higher education. With this lack in mind, this paper reports on an online rapid environmental review of the GenAI policies of 26 South African HEIs that were freely available on the websites of these HEIs or otherwise online. The main purpose of the paper is to establish whether these HEIs had institution-wide GenAI policies, what types of policies they were and what constituted their contents. The study employed a critical-ethics-based framing comprising six dimensions: the Siyavuma, semi-Siyavuma, critical, semi-critical, uBuntu and semi-uBuntu dimensions. It analyzed data through content and thematic analyses. Some of its findings are worth mentioning. Firstly, it discovered that five of the 26 South HEIs had their institution-wide GenAI policy documents freely available on their websites or online; one HEI had four such policy documents. The retrieved GenAI policy documents were mainly guides or guidelines. Secondly, academic staff and students were the main target audiences of the GenAI policy documents. Thirdly, ChatGPT was the most mentioned and the most cited GenAI tool in the reviewed policy documents. Fourthly, the responsible use of AI tools, GenAI and academic integrity, and risks and concerns of using GenAI tools featured as one instance of the main convergence points for the GenAI policy documents that spelled out their aims and their main foci. Lastly, six of the GenAI policy documents manifested elements of the critical dimension, whereas one GenAI policy document has features of the uBuntu dimension. The paper also makes relevant recommendations.International Journal of Learning, Teaching and Educational Research00An online rapid environmental review of the GenAI policies of 26 South African HEIs that were freely available on the websites of these HEIs or otherwise online found that five of the 26 South HEIs had their institution-wide GenAI policy documents freely available on their websites or online.International Journal of Learning, Teaching and Educational Research["Chaka Chaka", "Thembeka Shange", "Tlatso Nkhobo", "Vivienne Hlatshwayo"]2024-12-30T00:00:00 2ca7db4cd6377d3fbe8d1b55c1ed5a657729247fINTRODUCTION. The latest digital technologies play a key role in the development of modern society and the state, contribute to their further progress and improvement. One of the varieties of such technologies is artificial intelligence (hereinafter – AI), which has a significant impact on all aspects of human life. The article examines the problem of international legal regulation of AI, analyzes existing documents and identifies the main problems and challenges facing the world community. Particular attention is paid to the issues of establishing a balance between the expansion of AI capabilities and the risks associated with the transfer of management decisions from human to machine. The objectives of the work are: to analyze existing concepts of AI and international acts regulating the specifics of using this technology, to consider topical issues of improving legal regulation and practice of using AI, to determine the prospects for further development of legal regulation of AI.MATERIALS AND METHODS. The sources of international law of a universal and regional character, scientific works related to the topic under research were analyzed. Formal legal, comparative legal and historical legal methods were used as research methods.RESEARCH RESULTS. AI is one of the most important and key elements of sustainable human development, the use of which contributes to technological progress and solving global problems. International legal regulation of the use of AI is at the stage of development and formation of new rules, the creation of which must take into account the possible risks and specifics of the use of technology in various fields. International cooperation and exchange of experience in the field of legal regulation of AI is designed to promote the harmonization of approaches and standards in this area, taking into account the principles of sustainable development, environmental safety and protection of personal data of users.DISCUSSION AND CONCLUSIONS. The main conclusions of the research are as follows. AI helps automate many processes, increases work efficiency and improves the quality of life of people and society as a whole. Cooperation between states and international organizations is a key factor in the successful development and application of AI, contributes to the creation of common principles for ensuring security, transparency and maintaining the technological sovereignty of countries. However, at the moment, the international legal regulation of AI is at the initial stage of its development and many countries face problems with the lack of appropriate norms and standards. The consistent development of legal regulation at the international universal and regional levels, the introduction of clear and comprehensive terminology, taking into account the existing experience of individual international organizations will allow us to continue working on the development and improvement of standards in the field of AI, taking into account the interests of all participants in the international community.Moscow Journal of International Law70Moscow Journal of International Law["A. V. Kolosov"]2024-12-30T00:00:00 b34cbcfcdbf4d06fe7fa0202e76069b0b21d8926Purpose. Integral assessment of the security situation in the Ukrainian education system under the intensive implementation of Artificial Intelligence (AI) technologies and identification of promising scenarios for the post-war reconstruction of Ukraine, using the example of rural education. Methodology. The scientific research was based on general scientific methods, supplemented by a comprehensive econometric analysis of the main indicators of educational security in Ukraine. Optimisation and integral evaluation methods were also used, which do not require the involvement of experts from various fields of educational activity. The key tool in the integrated multi-factor evaluation method is the module of intelligent economic-mathematical modeling and forecasting of optimistic, realistic, and pessimistic scenarios for the implementation of digital initiatives for the post-war recovery of the educational system of Ukraine, which is implemented through the method of principles, the “t-criterion” in the calculation of scalar thresholds based on IBM SPSS Modeler, AI-Prophet, Cloud Pak for Data, and R-Studio AI. Findings. The work highlights the key factors influencing the level of security of the Ukrainian rural education system and defines the corresponding integrated index of educational security, which in further research can play a key role in strengthening the economic security of the national economy in the era of post-war reconstruction of the country and the formation of a new digital economy in Ukraine. Based on the obtained integrated assessment of the security of the educational system, the authors have identified probable trajectories for the transformation of educational processes in the context of the use of AI technologies. Key scenarios for the improvement of tactical and strategic management tools in the education sector are proposed in order to accelerate the processes of high-quality digital transformation aimed at the post-war recovery of Ukraine. Originality. Based on the developed innovative digital platform “Central European Network for Sustainable and Innovative Economy”, an open source program IIS-GPT 3 was created, which, unlike the existing analogues, integrates new algorithms for the use of AI technology for forecasting key security indicators based on SPSS and AI-Prophet. This made it possible to carry out an independent integral assessment, modelling and forecasting of the transformation of the processes of strengthening the educational security of Ukraine in the context of post-war reconstruction. Practical value. The conducted research proves the relevance of the modern national educational policy of digitization and indicates the potential benefits, challenges, and risks, particularly regarding the implementation of a digital environment with AI elements “Dream”, which can become a game changer in Ukrainian education. The research materials form a scientific basis for further systematic analysis of the key post-war imperatives of educational security in Ukraine.Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu00The conducted research proves the relevance of the modern national educational policy of digitization and indicates the potential benefits, challenges, and risks, particularly regarding the implementation of a digital environment with AI elements “Dream”, which can become a game changer in Ukrainian education.Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu["V. D. Zalizko", "A. Cherniak", "D. Nowak", "V. Artemov"]2024-12-30T00:00:00 2718b5cfecb44b05271c601610ae179ba83705d0This study explores students' perceptions of Artificial Intelligence (AI) in the educational process, focusing specifically on creativity and confidence. As AI technology becomes increasingly integrated into higher education, understanding its impact on students' creative development and their confidence in using AI tools is crucial for shaping effective educational practices. To this end, a comprehensive questionnaire was designed and distributed to higher education students across Latvia, Ukraine, and Spain, resulting in a diverse sample of 89 respondents. The survey collected data on demographic information, general AI usage in education, and students' attitudes towards AI's impact on creativity. To analyse the data, the Kruskal-Wallis test was employed to examine country-based differences in AI usage frequency. The results showed no significant variance (p = 0.448). This finding led to the rejection of the hypothesis that students from EU countries use AI more frequently than those from non-EU countries. Descriptive data analysis revealed that 83% of students felt AI did not limit their creative expression, and 69% reported a positive impact on their ability to generate creative solutions. However, only 47% of students expressed confidence in using AI collaboratively, indicating mixed perceptions about its role in group creative tasks. These results suggest that while students generally view AI as supportive of their creativity, there is a need for increased efforts to enhance confidence in AI's collaborative and creative applications. In light of the escalating significance of AI in educational settings, this study is pivotal in elucidating the optimal integration of AI to nurture students' creative growth and fortify their confidence in the effective utilisation of AI tools. This research makes a significant contribution to the field by offering valuable insights into the evolving role of AI in higher education, emphasising the importance of balanced integration strategies for maximising its potential in the educational sphere.Baltic Journal of Economic Studies230This research makes a significant contribution to the field by offering valuable insights into the evolving role of AI in higher education, emphasising the importance of balanced integration strategies for maximising its potential in the educational sphere.Baltic Journal of Economic Studies["Olga Verdenhofa", "Remigijus Kinderis", "Galina Berjozkina"]2024-12-30T00:00:00 78932d3191e856e4b128a6f4b3884a3e0875ed86BMC Musculoskeletal Disorders290The eXGBM model shows potential as a valuable tool for stratifying patients with a high risk of prolonged dependence on mechanical ventilation and may offer a promising approach for optimizing patient care and resource allocation in critical care settings.BMC Musculoskeletal Disorders["Weigang Jiang", "Tao Liu", "Baisheng Sun", "Lixia Zhong", "Zhencan Han", "Minhua Lu", "Mingxing Lei"]2024-12-30T00:00:00 27e8e4f14e679df6abe3c0fa604e5f679845b2dfThe invention of autonomous vehicles (AVs) and their use in transportation have been substantially accelerated by technological developments in artificial intelligence (AI) and deep learning Algorithms. Vision-based systems are a crucial part of AVs for detecting their surroundings and making the right decisions. At the same time, they are in motion, thanks to massive data from numerous sensor devices and sophisticated computing power. They understand how AI and deep learning functions in AV systems are crucial in achieving the objective of full automation, or self-driving, systems. Previous studies have done a fantastic job of looking into various facets of using AI and deep learning in AV production. Nevertheless, few studies have provided a comprehensive analysis of existing methods for integrating AI in AVs to the research community. This paper offers a systematic review of the most important papers in this field of research. It seeks to close the knowledge gap by providing state-of-the-art practices, challenges, and future direction. Its specific goal is to examine the various algorithms, models, and techniques applied to AVs by enhancing AI and deep learning for effective vision, navigation, and location in making decisions. It looks into the methods now in use to determine the potential applications of AI and the difficulties and problems that come with putting them into practice. This study offers more insights into possible opportunities for utilizing AI and deep learning in conjunction with other developing technologies, based on an examination of current practices and technological advancements. Big data, high computing power, and high-resolution navigation, expanded simulation platforms through a vision-based system.International Journal of Advanced Engineering and Nano Technology140A systematic review of the most important papers in artificial intelligence and deep learning offers more insights into possible opportunities for utilizing AI and deep learning in conjunction with other developing technologies, based on an examination of current practices and technological advancements.International Journal of Advanced Engineering and Nano Technology["F. Gonten", "Professor Abdulsalam Ya'u Gital", "Mr. Datti Useni", "Mr. Larson Suwa", "Mr. Mudimka Jahota Yerima"]2024-12-30T00:00:00 b758c3bd59089328adc30689f61087bafa1d1ba7This study integrates life cycle assessment (LCA) and system dynamics (SD) modeling to evaluate the potential of Artificial Intelligence (AI)-enhanced anaerobic ammonium oxidation (anammox) technology in industrial wastewater treatment. The research examines the environmental, economic, and social benefits of AI optimization, with a focus on its long-term implications for sustainable development. By constructing a detailed LCA model, the study analyzes the environmental impacts of wastewater treatment across its lifecycle, from raw material acquisition to final waste disposal. The integration of the SD model simulates dynamic feedback mechanisms, predicting the long-term effects of AI optimization on resource efficiency and environmental performance. Specifically, the AI system employs a convolutional neural network (CNN) to analyze real-time pollutant levels and a reinforcement learning algorithm to optimize operational parameters such as aeration rates, chemical dosing, and sludge retention time. This optimization achieves a 7.02% reduction in energy consumption, an 18% decrease in greenhouse gas emissions, and a 15% reduction in total nitrogen concentrations in treated water. Economically, AI predictive maintenance reduces operating costs by 10% and extends equipment lifespan by 20%, while socially, it enhances the public perception of corporate social responsibility, particularly in regions with stringent environmental regulations. This study underscores the effectiveness of combining LCA and SD models to evaluate sustainable wastewater treatment technologies, providing scientific evidence for policymakers and industry stakeholders to use to promote green technologies and social responsibility.Processes430This study underscores the effectiveness of combining LCA and SD models to evaluate sustainable wastewater treatment technologies, providing scientific evidence for policymakers and industry stakeholders to use to promote green technologies and social responsibility.Processes["Juan Yu", "Gaiyan Li"]2024-12-30T00:00:00 c28ae79531f6f9f038e9cb13e93db0e0a3b0749eReligious courts face complex challenges in resolving contemporary cases, such as marital disputes, inheritance distribution, and conflicts in Sharia-based economic matters. In the digital era, Artificial Intelligence (AI) offers innovative solutions to enhance the efficiency and accuracy of legal processes. This study employs a qualitative method with a descriptive-analytical approach to explore the potential use of AI in religious courts. Data was collected through literature reviews and document analysis, focusing on AI applications in inheritance calculations based on Islamic faraidh law, virtual dispute mediation, and Sharia contract analysis. The analysis follows the Miles and Huberman framework, involving data reduction, display, and conclusion drawing. Data validation was carried out through source triangulation to ensure the accuracy and credibility of the findings. The research findings reveal that AI can support digitalizing legal processes in religious courts, such as managing electronic documents, predicting rulings based on legal precedents, and monitoring compliance with sharia principles. Additionally, AI can potentially improve the efficiency of dispute mediation through digital platforms and facilitate automated inheritance calculations in line with Islamic law. However, implementing AI presents challenges, including inadequate regulations, potential algorithmic bias, and compatibility with Islamic legal values. This study’s academic contribution provides a new perspective on integrating modern technology with Islamic law, particularly within the religious court system. The findings are expected to serve as a foundation for developing strategic policies to support AI implementation in Islamic legal systems, addressing societal needs in the contemporary era.MILRev: Metro Islamic Law Review230The research findings reveal that AI can support digitalizing legal processes in religious courts, such as managing electronic documents, predicting rulings based on legal precedents, and monitoring compliance with sharia principles.MILRev: Metro Islamic Law Review["Sukindar", "Hendrik Kusnianto", "Sarikun", "Benhard Kurniawan Pasaribu", "Muhd Syahazizamir"]2024-12-30T00:00:00 3ea23f3c31622cba31a5eb453c281885eb9c449fArtificial intelligence (AI) is transforming the healthcare industry by optimizing patient outcomes, enhancing diagnostic accuracy, and streamlining operational efficiency. AI technologies such as machine learning, predictive analytics, and natural language processing are increasingly being integrated into clinical decision-making processes, enabling healthcare providers to deliver personalized, data-driven care. These technologies help in analysing vast amounts of patient data, identifying patterns, and predicting potential health risks, thereby improving clinical decision-making and treatment plans. However, the successful implementation of AI in healthcare requires healthcare professionals to be adequately trained to use these technologies effectively. Specialized staff training programs are essential to ensure that healthcare workers are equipped with the necessary skills to integrate AI tools into their daily practices. These programs focus on enhancing both technical skills, such as understanding AI algorithms and their applications, and soft skills, such as interpreting AI-driven insights in a clinical context. Additionally, training programs emphasize the ethical use of AI, ensuring that healthcare providers are aware of privacy concerns, biases in data, and the importance of human oversight in AI decision-making. This paper explores the role of AI in optimizing patient outcomes and the significance of specialized training programs for healthcare staff. It highlights how AI-powered tools, when coupled with well-structured training, can improve diagnosis, treatment accuracy, and patient monitoring. The paper also addresses challenges such as data privacy, regulatory concerns, and the need for continuous education to ensure that healthcare professionals remain adept at using AI technologies in an ever-evolving landscape.World Journal of Advanced Research and Reviews00The role of AI in optimizing patient outcomes and the significance of specialized training programs for healthcare staff are explored, highlighting how AI-powered tools, when coupled with well-structured training, can improve diagnosis, treatment accuracy, and patient monitoring.World Journal of Advanced Research and Reviews["Morenikeji I Yisa"]2024-12-30T00:00:00 1da409278130146709d14b46bc56904f22677ec0This community service activity aims to improve the quality of education at SMP Muhammadiyah Parakan Pamulang through the utilization of Artificial Intelligence (AI). Using a school-needs-based approach, the program includes situational analysis, interactive training, and technical assistance for students and teachers. The lecture method was employed to introduce AI and its applications in education, while the discussion method encouraged active participant engagement in addressing challenges and practical solutions. Hands-on training involved demonstrations of AI-based tools such as adaptive learning platforms, virtual tutors, and automated evaluation applications. Evaluations indicated an increase in participants' understanding of AI usage and enthusiasm for integrating technology into teaching and learning activities. This program not only enriched participants' skills but also provided strategic solutions to enhance the effectiveness of technology-based education. This approach is expected to create a modern, adaptive, and sustainable learning ecosystem in the school.Jurnal Abdi Masyarakat Multidisiplin00 Jurnal Abdi Masyarakat Multidisiplin["Lukman Anthoni", "Rahman Faisal", "Darul Fahmi"]2024-12-30T00:00:00 56cd6fb2510bf531241e1de44fec0b564fb797b5Artificial intelligence (AI) has profoundly influenced various sciences, with Chat GPT emerging as a key tool for researchers, programmers, and students. This study explores the attitudes of students from the Departments of Library and Information Science and Media at Alexandria University's Faculty of Arts toward using Chat GPT for academic assignments. It aims to assess the extent of their reliance on the tool, their proficiency in using it to produce accurate and reliable results, and their ability to critically evaluate the outputs generated. Using a descriptive-analytical methodology, the study surveyed a random sample of students during the 2023-2024 academic year, collecting 206 responses (40 of which were excluded for incompleteness). The findings aim to raise awareness of Chat GPT's capabilities, guide university instructors in promoting its effective use, and enhance the integration of AI tools in education for improved academic outcomes.Cybrarians Journal00This study explores the attitudes of students from the Departments of Library and Information Science and Media at Alexandria University's Faculty of Arts toward using Chat GPT for academic assignments to assess the extent of their reliance on the tool, their proficiency in using it to produce accurate and reliable results, and their ability to critically evaluate the outputs generated.Cybrarians Journal["Sarah Mahmoud"]2024-12-30T00:00:00 fb045eefc201d5f0712ad13f47fbb1635d872a6aNotwithstanding the increase in research on artificial intelligence-infused English language learning, several issues remain inadequately addressed. Thus, this paper provides a systematic review and analyzes previous studies to pinpoint fruitful knowledge gaps and outline approaches for future research directions. Two approaches, bibliometric and descriptive content analysis, were employed in this study. Firstly, we extracted data for bibliometric analysis from the Scopus database, covering publications from 1996 to 2024. The findings show that the topic peaked in 2024, with 107 articles published. China was the most cited country, with 1.215 citations, and the most productive country, with 327 articles. The International Journal of Emerging Technologies in Learning published the majority of the articles. The research theme evolved to emphasize English learning and student involvement through mobile learning. We applied descriptive content analysis to selected papers published between 2014 and 2024. Theoretically, the findings suggest that addressing knowledge gaps can enhance the integration of artificial intelligence in English language learning. Empirically, the mixed studies used descriptive statistics collected through observation and questionnaires, with a medium sample size selected through random sampling, a commonly used research design. These approaches can potentially expand the scholarly literature on this subject.  Advances in Education00A systematic review and analyzes previous studies to pinpoint fruitful knowledge gaps and outline approaches for future research directions suggest that addressing knowledge gaps can enhance the integration of artificial intelligence in English language learning.Advanced Education["Sri Wahyuni", "Nur Hidayanto Pancoro Setyo Putro", "Anwar Efendi"]2024-12-30T00:00:00 279d7fb6ac79fc110832afd5c55026ae0a17efd3Background and Aim: This study investigates the transformative impact of artificial intelligence (AI) on traditional management accounting practices, focusing on predictive analytics, automated reporting, and decision-making processes. The aim is to investigate how AI enhances the efficiency and accuracy of management accounting, as well as its strategic capabilities. Scope: The research is limited to AI applications within management accounting, examining current practices and the extent of AI integration. Methods: A mixed-methods approach combines an analysis of literature with available case studies. Results: The findings reveal that AI greatly enhances predictive analytics by boosting model accuracy and swiftly processing large datasets. AI-powered automated reporting improves efficiency and minimizes errors, while AI-based decision support systems deliver real-time insights and detect complex patterns, resulting in more effective strategic decisions. The study concludes that AI has the potential to revolutionize management accounting by enhancing predictive accuracy, operational efficiency, and strategic decision-making. However, successful implementation must address challenges related to data quality, system integration, and regulatory compliance. Originality: This paper contributes novel insights into how AI technologies can effectively integrate into accounting practices. Practical Implications: The findings are helpful for accounting professionals and business managers seeking to leverage AI to enhance their accounting practices, providing valuable recommendations for implementation and addressing potential challenges.Business &amp; Management Compass00The study concludes that AI has the potential to revolutionize management accounting by enhancing predictive accuracy, operational efficiency, and strategic decision-making, however, successful implementation must address challenges related to data quality, system integration, and regulatory compliance.Business &amp; Management Compass["Milos Pavlovic", "\u010cedomir Gligori\u0107", "Filip Zdravkovic", "Danijela Pavlovi\u0107"]2024-12-30T00:00:00 8eaba5bb945324fa8711938b596e6568a94397d3This study investigates the current state of Artificial Intelligence (AI) integration in Information Technology (IT) education, including the adoption of AI-powered tools, the development of AI-related curricula, and the impact of AI on student learning outcomes. Employing a mixed-methods approach, the research combines quantitative and qualitative data to analyze AI integration in IT education. Findings reveal a significant gap between current AI integration in IT curricula and the skills demanded by AI-driven industries. While respondents demonstrated improved critical thinking and problem-solving skills when engaged in AI-powered learning, challenges such as insufficient faculty training, inequitable access to AI tools, and inadequate emphasis on ethical considerations were identified. The study recommends expanding AI-related topics in curricula, incorporating hands-on learning opportunities, and equipping educators with skills for effective AI instruction. By addressing these gaps, this study contributes to the achievement of SDG 4 (Quality Education) by improving the quality and accessibility of IT education and equipping students with the necessary skills for the 21st-century workforce. Moreover, it supports SDG 8 (Decent Work and Economic Growth) by preparing students for in-demand AI-related careers and contributing to economic development.Journal of Innovative Technology Convergence00A significant gap is revealed between current AI integration in IT curricula and the skills demanded by AI-driven industries, and the study recommends expanding AI-related topics in curricula, incorporating hands-on learning opportunities, and equipping educators with skills for effective AI instruction.Journal of Innovative Technology Convergence["Roger Mission", "Renald Jay Fio", "Annie Rose Mission"]2024-12-30T00:00:00 625abc4f2a848b1895d4cdf8788f724525358337Research background: Generative Artificial Intelligence (GenAI) is a disruptive technology with great promise for the FinTech industry. The current study focuses on the drivers of GenAI adoption and its consequences for both exploratory and exploitative innovation in FinTech companies. Purpose of the article: Based on a conceptual model that extends the Technology-Organization-Environment (TOE) framework, this study also explores the moderating effect of ethical dilemmas in the relationship between GenAI adoption and innovation, as well as the role of Environmental, Social, and Governance (ESG) factors in shaping the broader impact of GenAI on organizational practices. Methods: Data were collected and analyzed using Structural Equation Modeling (SEM) from participants in the Chinese FinTech industry. Findings & value added: Our empirical findings show that GenAI improves both kinds of innovations and, subsequently, leads to improved organizational performance. However, ethical dilemmas do not significantly affect either of these effects. Moreover, the study suggests that aligning GenAI adoption with ESG goals, such as promoting sustainable practices and ensuring ethical governance, can further enhance long-term performance and stakeholder trust. This study underlines the strategic role of GenAI adoption in driving innovation, advancing ESG objectives, and improving performance in the fast-evolving landscape of FinTech.Oeconomia Copernicana00The empirical findings show that GenAI improves both kinds of innovations and, subsequently, leads to improved organizational performance, however, ethical dilemmas do not significantly affect either of these effects.Oeconomia Copernicana["Muhammad Zada", "Salman Khan", "S. Mehmood", "Nicol\u00e1s Contreras-Barraza"]2024-12-30T00:00:00 2d9c76410700e1a33118b5546df9ab4ca11bf29bPurposeThis research aims to examine the potential tensions and management strategies for adopting artificial intelligence (AI) within Sales and Operations Planning (S&OP) environments.Design/methodology/approachWe conducted in-depth interviews with eight S&OP professionals from different manufacturing firms, supplemented by interviews with AI solutions experts and secondary document analysis of various S&OP processes, to scrutinize the paradoxes associated with AI adoption in S&OP.FindingsWe revealed 12 sub-paradoxes associated with AI adoption in S&OP, culminating in 5 overarching impact pathways: (1) balancing immediate actions with long-term AI-driven strategies, (2) navigating AI adoption via centralized systems, process redesign and data unification, (3) harmonizing AI-driven S&OP identities, collaboration and technology acceptance, (4) bridging traditional human skills with innovative AI competencies and (5) managing the interrelated paradoxes of AI adoption in S&OP.Practical implicationsThe findings provide a roadmap for firms to proactively address the possible tensions associated with adopting AI in S&OP, balancing standardization with flexibility and traditional expertise with AI capabilities.Originality/valueThis research offers (1) a nuanced understanding of S&OP-specific paradoxes in AI adoption, contributing to the broader literature on AI within operations management and (2) an extension to Paradox Theory by uncovering distinct manifestations at the AI–S&OP intersection.International Journal of Operations &amp; Production Management350A nuanced understanding of S&OP-specific paradoxes in AI adoption is offered, contributing to the broader literature on AI within operations management and an extension to Paradox Theory by uncovering distinct manifestations at the AI–S&OP intersection.International Journal of Operations &amp; Production Management["Amer Jazairy", "Hafez Shurrab", "Fabienne Chedid"]2024-12-30T00:00:00 d69f6b6ef195d82777b8cfc47f16729e200e787dAbstract The development of Artificial Intelligence (AI) technology has a significant impact on various sectors, including education. One interesting application of AI is Text to Questions, a technology that converts narrative text into questions for automatic question creation. ANDALIMAN is a Moodle-based Learning Management System developed by the Religious Training Center (BDK) Medan, a platform designed for training management, including material management, teaching resources, assessments, and the creation and questions management. AI in the ANDALIMAN is expected to improve efficiency and produce a greater variety of questions in less time. The implementation of AI also demonstrates excellent potential to enhance the quality of learning assessments, although it faces challenges in generating accurate questions that meet specific training needs. This research aims to explore the application of AI technology in the ANDALIMAN application, focusing on its impact on question quality, question creation time, and the advantages/disadvantages of that technology. The research results indicate that the efficiency and quality of quiz creation have improved, as evidenced by time efficiency and increased productivity, making it a practical solution, particularly in education.  Abstrak Kemajuan teknologi Artificial Intelligence (AI) memberikan dampak signifikan dalam berbagai sektor, termasuk pendidikan. Salah satu penerapan AI yang menarik adalah Text to Questions, yaitu teknologi yang mampu mengonversi teks naratif menjadi pertanyaan untuk pembuatan soal secara otomatis. ANDALIMAN (Aplikasi Pendukung Pelatihan BDK Medan) merupakan aplikasi yang dikembangkan oleh Balai Diklat Keagamaan (BDK) Medan berbasis Learning Management System (LMS) Moodle, sebuah platform yang dirancang untuk pengelolaan pelatihan, baik mulai dari pengelolaan materi, bahan ajar, penilaian, hingga pembuatan dan pengelolaan soal. Penggunaan AI dalam Aplikasi ANDALIMAN diharapkan dapat meningkatkan efisiensi dan menghasilkan soal dengan variasi yang lebih banyak dalam waktu yang lebih singkat. Penerapan AI juga menunjukkan potensi besar untuk meningkatkan kualitas evaluasi pembelajaran, namun dihadapkan dengan tantangan dalam menghasilkan soal yang akurat dan sesuai dengan kebutuhan spesifik pelatihan. Penelitian ini bertujuan untuk mengeksplorasi penerapan teknologi AI dalam aplikasi ANDALIMAN, dengan fokus pada dampaknya terhadap kualitas soal, waktu pembuatan soal, dan kelebihan/kekurangan pada teknologi tersebut. Hasil riset menunjukkan bahwa efisiensi dan kualitas pembuatan soal kuis menjadi meningkat dilihat dari efisiensi waktu dan peningkatan produktifitas yang dapat menjadi solusi efektif khususnya dalam bidang pendidikan.Wawasan: Jurnal Kediklatan Balai Diklat Keagamaan Jakarta00Wawasan: Jurnal Kediklatan Balai Diklat Keagamaan Jakarta["Muhammad Fajar Zain"]2024-12-30T00:00:00 5b32837fad94aaf43af42a01bcc6da6eed444b9dThe transformative potential of Artificial Intelligence (AI) has sparked significant interest across economic and empirical research domains, inspiring investigations into its impacts on productivity, labor markets, economic growth, and policy adaptation. This study offers a comprehensive analysis of AI's economic implications, focusing on its integration into diverse sectors and its measurable effects on economic performance. Through a multi-dimensional approach, we explore AI’s role in enhancing productivity and efficiency, reshaping workforce dynamics, and influencing the distribution of economic benefits. Supported by recent empirical studies and quantitative analyses, this research highlights AI’s capacity to drive innovation while examining its challenges, such as labor displacement, income inequality, and skill gaps. Case studies and data-driven insights provide evidence of AI’s role in fostering new economic models, underscoring its dual potential to stimulate growth and exacerbate disparities. Furthermore, the study delves into the evolving landscape of policy responses, analyzing how different regulatory frameworks influence AI’s integration and impact across economies. By offering nuanced perspectives on AI’s transformative effects, this investigation identifies key trends and areas requiring further research, including the long-term implications for developing economies and global inequality. The findings aim to equip policymakers, researchers, and industry leaders with evidence-based insights to navigate AI’s complexities, ensuring sustainable and inclusive economic advancement in an AI-driven future.Economy520This study offers a comprehensive analysis of AI's economic implications, focusing on its integration into diverse sectors and its measurable effects on economic performance, and delves into the evolving landscape of policy responses.Economy["Zarif Bin Akhtar", "Ahmed Tajbiul Rawol"]2024-12-30T00:00:00 917a43d6625531c97d0992f348ecb6dbb2dceb95Recently, research on physical fitness posture estimation in the virtual space using artificial intelligence (AI) has been actively conducted. However, AI has been difficult due to a lack of fitness datasets and guidelines. To advance fitness posture estimation algorithm through analyzing fitness image provided by national artificial intelligence Platform known as AI-Hub (powered by National Information society Agency, NIA) in Korea and approaching it wider and deeper from the perspective of exercise prescription vacation and behavioral analysis. Through this advancement, this study intended to closely analyze fitness movements and guide correct exercise posture with screen and sound. Referring to image and labeling JSON (JavaScript Object Notation) file provided by AI-Hub, contents necessary and useful for the posture estimation algorithm from the perspective of exercise prescription were explained in writing, diagrams, and photos. The structure of data for 6 million consecutive and diverse fitness images and labeling data of scenes was analyzed. In addition to existing explanation, exercise state and posture of exercise in the data structure of 41 fitness images were analyzed and presented in detail. In addition, annotation and labeling characteristics were explained in detail with photo images.Journal of international academy of physical therapy research220To advance fitness posture estimation algorithm through analyzing fitness image provided by national artificial intelligence Platform known as AI-Hub, contents necessary and useful for the posture estimation algorithm from the perspective of exercise prescription were explained in writing, diagrams, and photos.Journal of International Academy of Physical Therapy Research["Byunggook Lee", "Ulziichimeg Ulziisaikhan", "Hyotaek Lim", "Wansuk Choi"]2024-12-30T00:00:00 1c30251d40a7819d6a24456835a03c2cbfda28a0Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, the diffusion of GenAI publications across fields of study, and the geographical spread of scientific research on generative AI. We also investigate team size and international collaborations to explore whether GenAI, as an emerging scientific research area, shows different collaboration patterns compared to other AI technologies. The results indicate that generative AI has experienced rapid growth and increasing presence in scientific publications. The use of GenAI now extends beyond computer science to other scientific research domains. Over the study period, U.S. researchers contributed nearly two-fifths of global GenAI publications. The U.S. is followed by China, with several small and medium-sized advanced economies demonstrating relatively high levels of GenAI deployment in their research publications. Although scientific research overall is becoming increasingly specialized and collaborative, our results suggest that GenAI research groups tend to have slightly smaller team sizes than found in other AI fields. Furthermore, notwithstanding recent geopolitical tensions, GenAI research continues to exhibit levels of international collaboration comparable to other AI technologies.arXiv.org480The results suggest that GenAI research groups tend to have slightly smaller team sizes than found in other AI fields, and notwithstanding recent geopolitical tensions, GenAI research continues to exhibit levels of international collaboration comparable to other AI technologies.ArXiv["Liangping Ding", "Cornelia Lawson", "Philip Shapira"]2024-12-30T00:00:00 601dee284b98625f65319075b6e832d7f1fee8c8Objective - Artificial intelligence (AI) is an emerging field in the service industry. To increase customer satisfaction either through customisation or personalisation and to counter the challenge of finding skilled manpower, AI adoption in the hospitality and tourism industry (H&TI) can be a milestone achievement. Methodology – This study applied a combined approach of performance analysis and science mapping to identify the growth and citation pattern, co-word analysis and thematic development based on the literature published in Scopus and Web of Science (WoS) databases since its evolution. Findings and novelty – Publication growth on AI adoption in H&TI has taken a manifold turn in the last five years. Science mapping analysis, such as co-word clustering, developed themes like interconnectivity, connectivity, experience, transformation, and diversity, which can help H&TI match the growing expectations of travelers and increase business. Five central themes were developed based on the article Bibliographic Coupling Analysis (BCA), which highlighted the critical information that can lay the foundation for H&TI to move forward in AI adoption to survive and stay ahead in this competitive market. Additionally, this article has uncovered important information such as authors, countries, and organisation contributions, which can also generate critical information. Future researchers can explore the uncovered area of the study and unearth the hidden information. Type of Paper: Review JEL Classification: J24, Z32 Keywords: Artificial Intelligence, Hospitality, Robotics, Science mapping, Tourism Reference to this paper should be referred to as follows: Kumar, D.; Shandilya, A.K, Shandilya; Kumar, S. (2024). Artificial Intelligence Adoption in Hospitality and Tourism: A Science Mapping Approach, GATR-Global J. Bus. Soc. Sci. Review, 12(4), 209–217. https://doi.org/10.35609/gjbssr.2024.12.4(5)GATR Global Journal of Business Social Sciences Review330Five central themes were developed based on the article Bibliographic Coupling Analysis (BCA), which highlighted the critical information that can lay the foundation for H&TI to move forward in AI adoption to survive and stay ahead in this competitive market.GATR Global Journal of Business Social Sciences Review["Dr. Dilip Kumar", "Dr. Abhinav Kumar Shandilya", "Sumit Kumar"]2024-12-30T00:00:00 f771e5bbaf4125baa8bab06f14d362ed649957c6Rapid technological and economic developments have brought about radical changes in accounting, through the development of new tools and solutions, which increase efficiency and improve accuracy within the practice. Artificial Intelligence (AI) has a prominent place in critical issues, especially regarding forecasts, as its algorithms are based on historical data to create rigorous analyzes and make rational financial decisions. The future of artificial intelligence in accounting involves advanced predictive analytics, through its deeper integration into strategic financial planning and the development of systems capable of handling more complex accounting tasks with minimal human intervention. This paper comes to illuminate critical issues in which there is a gap, as the literature is particularly limited in the field of accounting, focusing on how artificial intelligence affects accounting. The contribution of the study lies precisely in this aspect, in order to have a smooth transition in the accounting profession to the new normality. The findings showed that artificial intelligence particularly affects Financial, Managerial accounting and Auditing, while it can significantly improve the accuracy of financial reporting by reducing human errors in calculations in data entry, updating accounting records, preparing both financial statements, as well as audit reports. The need for compliance of the entire spectrum of accounting with the relevant accounting standards and regulations, within the framework of ethics and data protection, is identified.International Journal of Science and Research Archive00The findings showed that artificial intelligence particularly affects Financial, Managerial accounting and Auditing, while it can significantly improve the accuracy of financial reporting by reducing human errors in calculations in data entry, updating accounting records, preparing both financial statements, as well as audit reports.International Journal of Science and Research Archive["Chara Kottara", "Sofia Asonitou"]2024-12-30T00:00:00 19d1065f460cfe8795793a14b21d5817d473a52cEveryone is talking about artificial intelligence (AI) these days. Unprecedented new potential solutions are made possible when biotechnology and artificial intelligence breakthroughs are coupled. This can support significant Sustainable Development Goals and assist with a number of global issues. Food security, health and well-being sustainable energy, conscientious production and consumption, climate action, and life below water, safeguarding, restoring, and promoting the environmentally friendly forest management and the sustainable utilization of terrestrial ecosystems, preventing desertification, stopping and going backwards degradation, and stopping biodiversity loss are a few instances that are presently in the news. The biological sciences are now heavily reliant on artificial intelligence. Recent advances in artificial intelligence (AI) and biotechnology have brought about a convergence that could completely transform the healthcare industry. This review investigates the benefits and challenges of biotechnology in the healthcare industry by providing up-to-date case studies on its applications.World Journal of Advanced Research and Reviews00This review investigates the benefits and challenges of biotechnology in the healthcare industry by providing up-to-date case studies on its applications and bringing about a convergence that could completely transform the healthcare industry.World Journal of Advanced Research and Reviews["Meenakshi Devi Mudunuri", "Nagaraju Yalampati", "Akshay Kumar Voosala", "V. R. S. L. Asritha Kukunuri", "Sirisha Kudulla", "Y. B. Manju Latha", "V. Bhaskara Raju"]2024-12-30T00:00:00 ecb6e6faf165ba085c18a52eb6e3202f5d11b404The object of this study is artificial intelligence technologies in the system of accounting and analytical support to public sector entities. This paper addresses the task related to the possibility of integrating artificial intelligence technologies into the accounting and analytical support system of public sector entities. The key differences between conventional accounting automation and artificial intelligence technologies in the system of accounting and analytical support have been determined. Analysis of investment volumes for the introduction of artificial intelligence, including in the accounting system, was carried out. It was established that according to forecasts for 2025 the amount of investment in the field of artificial intelligence for the automation of accounting and reporting will grow actively: in the USA (USD 45–50 billion), China (USD 30–35 billion), Germany (USD 15–18 billion), Japan (USD 13–15 billion), Great Britain (USD 12–15 billion). Analysis of the characteristics and cost of integrating modern artificial intelligence technologies into the system of accounting and analytical support was carried out. Zoho Books AI cloud technology, which in terms of cost and properties is most suitable for integration into the system of accounting and analytical support of public sector entities, has been identified as recommended. The key factors of the impact of artificial intelligence on the automation of the accounting and analytical support system, which lead to saving time on document processing, reporting and data analysis, have been determined. Based on the calculation results, it was determined that as a result of the integration of Zoho Books AI technology into the accounting and analytical support system, time will be reduced by 2164 hours/year, which will lead to the optimization of public fundsEastern-European Journal of Enterprise Technologies210Based on the calculation results, it was determined that as a result of the integration of Zoho Books AI technology into the accounting and analytical support system, time will be reduced by 2164 hours/year, which will lead to the optimization of public funds.Eastern-European Journal of Enterprise Technologies["Tetiana Larikova", "Pavlo Ivankov", "L. Novichenko", "Kydysiuk Khrystyna"]2024-12-30T00:00:00 6ffaf9fb07f1fb03caae0ab49f0b72db03f4bbb2One of the most impactful technological discoveries today is the use of Artificial Intelligence or AI; tool that has become a great favorite at all levels due to its ease of use and its features that allow access to endless content and obtain the best results in minimal search times, which also allows you to complement very good quality, making use of themes that have already been duly defined and verified for truthfulness. The true usefulness lies in being able to understand that this tool is not only a copy and paste of content, but also allows retrospective work on what we really want to know and applyRevista de Tecnología y Educación00The true usefulness of this tool lies in being able to understand that this tool is not only a copy and paste of content, but also allows retrospective work on what the authors really want to know and apply.Revista de Tecnología y Educación["Daisy Escamilla-Regis", "Elizabeth Mart\u00ednez-Bahena"]2024-12-30T00:00:00 a67e7a42c1b3dd202b5fe6e5b92823f8760e82a9The study aimed to identify the impact of artificial intelligence in building a smart organization in Irbid Electricity Company: Digital culture is a moderating variable, and the study community consisted of leadership positions in the company, and the sample was (114) individuals, and the data were analyzed and hypotheses were tested using hierarchical multiple regression analysis, and the study concluded that there were high levels of building a smart organization, while the levels of using artificial intelligence and digital culture were at an average level, and it was found that there is an impact of artificial intelligence on building a smart organization with the presence of digital culture, and the study recommended the necessity of holding training courses and continuing education programs with the aim of defining the dimensions of the smart organization and the dimensions of artificial intelligence and digital culture and working to enhance them.    Al-Ghary Journal of Economic and Administrative Sciences00There is an impact of artificial intelligence on building a smart organization with the presence of digital culture, and the study recommended the necessity of holding training courses and continuing education programs with the aim of defining the dimensions of the smart organization and the dimensions of artificial intelligence and digital culture.Al-Ghary Journal of Economic and Administrative Sciences["Ayat Muhammad N Ababneh", "Mahmoud Ali Al-Rousan"]2024-12-30T00:00:00 43ab113d1e18a12ba2f866a6210827b4c67a0487Personalized learning, powered by Artificial Intelligence (AI), is reshaping education by addressing diverse student needs, bridging learning gaps, and enhancing academic outcomes. This paper explores how AI technologies, such as adaptive learning platforms, predictive analytics, and intelligent tutoring systems, contribute to more equitable and efficient education. Emphasizing the potential of inclusive access to AI tools, it highlights the role of technology in democratizing education globally. The research concludes with recommendations for integrating AI into educational practices to ensure broad, equitable impacts.International Journal of Science and Research Archive60This paper explores how AI technologies, such as adaptive learning platforms, predictive analytics, and intelligent tutoring systems, contribute to more equitable and efficient education and highlights the potential of inclusive access to AI tools.International Journal of Science and Research Archive["Emmanuel Dumbuya"]2024-12-30T00:00:00 de1682ea71a5dc2a6b5579e6d30459a3feef39feThe paper proposes a logical definition of the concept of artificial intelligence (AI), based on sufficiency predicates, by moving from genus to species. The nature of the intra contingent need for AI, respectively the logical characteristics of AI, is shown. The digital/analogical relationship is discussed and, on this basis, issues in the AI "zone" such as: conscious/subconscious, free will, self-learning are examined. Finally, the issue of human protection against AI is assessed, respectively that of AI protection against humans and AI itself.Journal of Knowledge Dynamics10A logical definition of the concept of artificial intelligence (AI) is proposed, based on sufficiency predicates, by moving from genus to species by moving from genus to species.Journal of Knowledge Dynamics["Emil Dinga"]2024-12-30T00:00:00 e91e858c1c84fb75f7c609ede0e87fde3e6b7af7Abstract. In modern conditions, companies need to introduce new technologies in a timely manner and monitor compliance with information security requirements in order to effectively counter modern threats. Access control systems are one of the main tools that ensure an adequate level of information security at the enterprise and simplify control over the provision of access to the organization’s resources. The main goal: to explore the possibilities of using artificial intelligence for access control systems, highlighting its importance in the context of increasing the level of information security of the organization and the effectiveness of access rights management. Research method: analysis of modern tools and technologies, including artificial intelligence and large language models, as well as assessment of practical cases of AI implementation in access control systems. Both the advantages and risks associated with automated access control using AI are considered. Practical significance: the results of the work can be used by organizations to improve the security and efficiency of access management to information resources. The identified risks and recommendations for the introduction of AI technologies will allow companies to take a more conscious approach to improving information security processes.Intellectual Technologies on Transport10The identified risks and recommendations for the introduction of AI technologies will allow companies to take a more conscious approach to improving information security processes and can be used by organizations to improve the security and efficiency of access management to information resources.Intellectual Technologies on Transport["Vladislav Blyum", "Dar'ya Grigor'eva"]2024-12-30T00:00:00 10e13cc854249d43b13a0032555914107aa10966The aim of this study is to develop a scale to determine teachers' perceptions of the use of artificial intelligence tools in education. The universe of the study consists of teachers from different branches working in schools affiliated with the Ministry of National Education throughout Türkiyein the 2023-2024 academic year. The sample of the study consists of 530 volunteer teachers. Exploratory factor analysis was conducted to determine the construct validity of the scale with the collected data. Pearson r test was used for the validity of the scale, and in order to determine the discrimination of the items, the difference between the groups was examined by determining the 27% upper group and 27% lower group. In order to obtain reliability, internal consistency coefficients were calculated, and stability tests were conducted with the test-retest method. The scale consists of 3 factors and 37 items. The factors were named considering the item contents. The total Cronbach Alpha value of the factors in the scale was determined as 0.970 and the total Omega value as 0.971. The correlation obtained by the test-retest method varies between 0.660 and 0.509 of the factors. It was determined that the factors determined in the scale explained 64.295% of the total variance. In this study, the validity and reliability of the "Teachers' Perception Scale towards Use of Artificial Intelligence Tools in Education" was evaluated. The results of the research show that the scale is a valid and reliable measurement tool in determining teachers' perceptions about the use of artificial intelligence tools in education.Participatory Educational Research130The results of the research show that the scale is a valid and reliable measurement tool in determining teachers' perceptions about the use of artificial intelligence tools in education.Participatory Educational Research["Seher I\u015f\u0131k", "R. \u00c7ak\u0131r", "\u00d6zgen Korkmaz"]2024-12-30T00:00:00 e65f90e475939c754571aab422cbc24fe01aa3ffArtificial Intelligence (AI) is a field that utilizes computer technology to imitate, improve, and expand human intelligence. The concept of AI was originally proposed in the mid-twentieth century, and it has evolved into a technology that serves different purposes, ranging from simple automation to complex decision-making processes. AI encompasses Artificial Narrow Intelligence, General Intelligence, and Super Intelligence. AI is transforming data analysis, language checks, and literature reviews in research. In many fields of AI applications, ethical considerations, including plagiarism, bias, privacy, responsibility, and transparency, need precise norms and human oversight. By promoting understanding and adherence to ethical principles, the research community may successfully utilize the advantages of AI while upholding academic accountability and integrity. It takes teamwork from all stakeholders to improve human knowledge and creativity, and ethical AI use in research is essential.Central Asian Journal of Medical Hypotheses and Ethics870In many fields of AI applications, ethical considerations, including plagiarism, bias, privacy, responsibility, and transparency, need precise norms and human oversight.Central Asian Journal of Medical Hypotheses and Ethics["Abdel Rahman", "Feras AlSamhori", "Fatima Alnaimat"]2024-12-30T00:00:00 c3290ca2dcc887b793b41fed4045d956d2bf11f3Background: Artificial Intelligence (AI) refers to technology that can efficiently perform tasks that typically require human intelligence, such as decision-making, teaching, object detection, and solving complex problems. As a veritable tool in medical education, this study was conducted to assess medical students’ awareness, perception and usage of AI in learning. Methodology: This study was conducted at the Department of Paediatrics, University of Port Harcourt Teaching Hospital (UPTH). The subjects were one hundred and thirty-nine 5th-year medical students who had completed 3 months of Paediatrics and Obstetrics/Gynaecology clinical rotations. Data was collected using a semi-structured, open-ended questionnaire. Data were analysed using IBM SPSS Statistics version 26. Statistical significance was set at p value<0.05. Results: 64 (46%) of the respondents are aware of AI. 57 (44%) of respondents applied AI during their clinical training. The most commonly used AI tool is Chatbots. 57(100%).  The major limitations to AI use were unreliable internet connectivity (62%) and the high cost of AI hardware and software (53%). Most respondents (68%) expressed ethical concerns about the use of AI. There was a statistically significant relationship between awareness of AI and the use of AI in learning (p=0.0001) Conclusion: This study demonstrates average awareness of AI's use and benefits among medical students. The major limitations to using AI were unlimited internet connectivity and the cost of AI tools. To maximize the benefits of AI in medical education in developing countries, medical schools need to increase their awareness and infrastructural capacity.International Journal of Research in Medical Sciences141Average awareness of AI's use and benefits among medical students is demonstrated, demonstrating the benefits of AI in medical education in developing countries, medical schools need to increase their awareness and infrastructural capacity.International Journal of Research in Medical Sciences["Kiniyiruchi Nelson Wobo", "I. Nnamani", "E. Alinnor", "Nneka Gabriel-Job", "Nsirimobu Paul"]2024-12-31T00:00:00 43fc3fd2cf4ea78bbcfb18bbaf9a3d452207110fArtificial Intelligence (AI) chatbots, such as ChatGPT, have been shown to mimic individual human behaviour in a wide range of psychological and economic tasks. Do groups of AI chatbots also mimic collective behaviour? If so, artificial societies of AI chatbots may aid social scientific research by simulating human collectives. To investigate this theoretical possibility, we focus on whether AI chatbots natively mimic one commonly observed collective behaviour: homophily, people's tendency to form communities with similar others. In a large simulated online society of AI chatbots powered by large language models (N = 33,299), we find that communities form over time around bots using a common language. In addition, among chatbots that predominantly use English (N = 17,746), communities emerge around bots that post similar content. These initial empirical findings suggest that AI chatbots mimic homophily, a key aspect of human collective behaviour. Thus, in addition to simulating individual human behaviour, AI-powered artificial societies may advance social science research by allowing researchers to simulate nuanced aspects of collective behaviour.British Journal of Psychology491In a large simulated online society of AI chatbots powered by large language models, it is found that communities form over time around bots using a common language, suggesting that AI chatbots mimic homophily, a key aspect of human collective behaviour.British journal of psychology["James K He", "Felix P S Wallis", "Andr\u00e9s Gvirtz", "Steve Rathje"]2024-12-31T00:00:00 97291828e304caca0c1c60237723d4f2d9084e50The rapid advancement of Artificial Intelligence (AI) in healthcare has led to the adoption of diverse technologies aimed at improving accuracy, efficiency, and cost-effectiveness. This study applies the Evaluation Based on Distance from Average Solution (EDAS) method to evaluate and rank six prominent AI technologies in healthcare: AI-Powered Diagnosis (AID), Robotic Surgery (RS), Clinical Decision Support Systems (CDSS), Patient Monitoring Systems (PMS), AI-Based Drug Discovery (AIDD), and Chatbots for Patient Interaction (CPI). The technologies were assessed using four evaluation parametersAccuracy (%), Cost Savings (%), Time Efficiency (%), and Training (Hours)with equal weighting assigned to each criterion.The results indicate that Chatbots for Patient Interaction (CPI) rank first due to their superior performance in training efficiency and time optimization, making them ideal for rapid deployment and scalability in healthcare settings. Patient Monitoring Systems (PMS) secured second place, demonstrating a balanced performance across cost savings and operational efficiency. Clinical Decision Support Systems (CDSS) ranked third, largely benefiting from their streamlined training requirements. AI-Based Drug Discovery (AIDD) followed closely, ranking fourth due to significant cost-saving advantages and moderate time efficiency. AI-Powered Diagnosis (AID) ranked fifth, primarily excelling in accuracy but underperforming in other parameters. Finally, Robotic Surgery (RS) ranked last (sixth) despite achieving the highest accuracy, as its extensive training requirements and relatively limited cost efficiency impacted its overall performance.This study highlights the effectiveness of the EDAS method as a multi-criteria decision-making framework, enabling a comprehensive evaluation of AI technologies in healthcare. The rankings emphasize the trade-offs among accuracy, cost, efficiency, and ease of implementation, offering valuable insights for healthcare stakeholders to prioritize AI solutions that align with their operational needs and resource constraints. Future research can further refine this approach by integrating additional criteria or dynamic weight assignments to reflect varying healthcare priorities.Data Analytics and Artificial Intelligence70The results indicate that Chatbots for Patient Interaction (CPI) rank first due to their superior performance in training efficiency and time optimization, making them ideal for rapid deployment and scalability in healthcare settings.Data Analytics and Artificial Intelligence[]2024-12-31T00:00:00 abf36fa6883dadba1afb37376e599fb92c76d57fArtificial Intelligence (AI) is revolutionizing the banking industry by enhancing operational efficiency, personalizing customer experiences, and improving decision-making processes. AI technologies, such as machine learning, natural language processing, and predictive analytics, are being leveraged to streamline operations, detect fraudulent activities, and provide tailored financial advice. Banks are using AI-driven algorithms to analyze vast amounts of data in real-time, enabling them to offer personalized financial products and services, optimize risk management, and automate routine tasks. AI Chabot’s and virtual assistants are transforming customer service by providing instant support and addressing queries around the clock. Additionally, AI helps in credit scoring and loan approvals by assessing a broader range of variables, leading to more accurate and equitable decisions. Overall, AI is driving innovation in banking, offering enhanced security, efficiency, and customer satisfaction. Research Significance: The significance of Artificial Intelligence (AI) in banking lies in its transformative impact on efficiency, security, and customer engagement. AI technologies enable banks to process vast amounts of data swiftly, improving decision-making and operational efficiency. They enhance fraud detection and risk management through advanced predictive analytics and anomaly detection. AI-driven personalization offers tailored financial solutions, improving customer satisfaction and loyalty. Furthermore, AI automation reduces operational costs and minimizes human error. As the banking industry faces increasing competition and evolving regulatory demands, AI provides a crucial competitive edge, driving innovation and adapting to dynamic market conditions. Methodology: The Complex Proportionality Assessment (COPRAS) method is a multi-criteria decision-making method that ranks options according to several conflicting criteria It assesses the proportionality of each alternative concerning the desired outcomes. The method involves normalizing criteria values, calculating weighted scores for each alternative, and then determining the overall performance by comparing these scores. COPRAS provides a systematic approach to decision-making, allowing for a comprehensive evaluation of alternatives by considering their relative advantages and disadvantages across various criteria. This method is particularly useful in complex decision environments where multiple factors need to be balanced. Alternative: Chabot’s for Customer Service, Fraud Detection Systems, Automated Loan Approval, Personalized Financial Advising, Credit Scoring Models, Anti-Money Laundering (AML) Systems, Robotic Process Automation (RPA) for Back-office Tasks, AI-driven Investment Management. Evaluation Parameters: Cost Reduction, Efficiency Improvement, Customer Satisfaction, Accuracy, Scalability. Result: According to the results, Credit Scoring Models has the lowest score, while Personalized Financial Advising has the highest rankData Analytics and Artificial Intelligence230The COPRAS method is a systematic approach to decision-making, allowing for a comprehensive evaluation of alternatives by considering their relative advantages and disadvantages across various criteria, particularly useful in complex decision environments where multiple factors need to be balanced.Data Analytics and Artificial Intelligence[]2024-12-31T00:00:00 e163b89f127613edbaa314aca7cf6dc0449fbdcaThis study reports English language teachers’ perceptions and experiences of integrating artificial intelligence (AI) and the professional development needs for its effective integration subscribing to Vygotsky's Zone of Proximal Development (ZPD) as a theoretical lens. We employed narrative inquiry research method and semi-structured interviews to elicit the experiences of three secondary level English teachers in Butwal Sub-metropolitan City of the Rupandehi district. The research questions focused on teachers' perceptions and experiences of AI integration and their professional development needs. The findings revealed that teachers recognized the potential of AI to enhance personalized learning and instructional efficiency but face significant challenges, including technological proficiency, training, and ethical considerations. The implications of this research extended to educators and policymakers to address the digital divide and ensure equitable access to AI technologies in education to create more effective and personalized learning environments.Lumbini Journal of Language and Literature210The findings revealed that teachers recognized the potential of AI to enhance personalized learning and instructional efficiency but face significant challenges, including technological proficiency, training, and ethical considerations.Lumbini Journal of Language and Literature["Puna Ram Ghimire", "B. Neupane"]2024-12-31T00:00:00 0931f7a26f128c21ff998956def7bb42987ec091This study aims to analyze students' perceptions of the use of Artificial Intelligence (AI) technology in solving mathematical problems and creating scientific papers. The study uses a descriptive quantitative method with data collection through questionnaires and semi-structured interviews. The subjects of the study consist of 58 active students from the University of Mataram who have experience using AI technology in completing their coursework assignments. The results of this study show that: 1) the majority of students (89.6%) feel that AI technology is easy to use, 2) 75,9% have confidence in the accuracy of this technology, 3) 94,9% experience increased efficiency in completing coursework assignments, and 4) based on interviews, concerns were found regarding the potential dependency on AI (29.3%) and ethical issues in its use (24.1%). This study emphasizes that AI can be an effective tool in education if used wisely. Therefore, policies are needed to support the ethical and responsible use of AI to ensure that students continue to develop critical thinking skills.Griya Journal of Mathematics Education and Application00It is emphasized that AI can be an effective tool in education if used wisely if used wisely and policies are needed to support the ethical and responsible use of AI to ensure that students continue to develop critical thinking skills.Griya Journal of Mathematics Education and Application["Ratna Yulis Tyaningsih", "Nourma Pramestie Wulandari", "Dita Oktavihari"]2024-12-31T00:00:00 9458b2287185ba1f3ef9df691d4b7ed41c5db4f0This study investigates the implementation of artificial intelligence (AI) algorithms to enhance inventory management processes in small and medium-sized enterprises (SMEs) within the retail sector. Accurate inventory level determination is a critical factor in improving organizational performance. Inventory levels are subject to a wide range of influences, including seasonal fluctuations, promotional campaigns, and macroeconomic conditions, which introduce significant complexity and variability. Such complexities often render manual management approaches inefficient. This research focuses on addressing these challenges through AI-based methodologies, particularly by employing machine learning and data analytics techniques to optimize inventory control. The findings of the study contribute to the literature by highlighting the potential of AI-driven approaches in reducing inventory costs and improving supply chain efficiency.The European Journal of Research and Development150This study investigates the implementation of artificial intelligence algorithms to enhance inventory management processes in small and medium-sized enterprises (SMEs) within the retail sector by employing machine learning and data analytics techniques to optimize inventory control.The European Journal of Research and Development["Osman \u00c7ayl\u0131", "Zeki Oralhan"]2024-12-31T00:00:00 17e6fce65020b614dac1beed0dc87e7d49a974c2This research aims to examine the role of Artificial Intelligence (AI) in strengthening cybersecurity for critical information infrastructure (CII) in Indonesia. The research methodology used is a systematic literature review (SLR) involving identification, review, and evaluation, data collected through literature studies relevant to the research topic. Data were analyzed using critical synthesis method, gap analysis and SOAR (Strengths, Opportunities, Aspirations, Results) analysis, compared with the NIST Cybersecurity Framework to determine suitable strategic alternatives. The research results indicate that AI can strengthen cybersecurity in the CII sector by enhancing threat detection and response capabilities and optimizing risk management. The study also identified potential opportunities, such as cross-sector collaboration and the development of innovative technologies, as well as challenges, including the need to maintain transparency and ethical AI. In addition, the proposed strategy to realize AI as a tool in cyber security includes strengthening investment in technology, increasing human resource skills, and supporting development policies while taking into account potential risks that may arise, such as privacy violations, data leaks, and potential bias in decision making by AI systems. It is hoped that the results of this research will provide insight for stakeholders in efforts to strengthen cyber security in the IIV sector in Indonesia.  Monas: Jurnal Inovasi Aparatur00The research results indicate that AI can strengthen cybersecurity in the CII sector by enhancing threat detection and response capabilities and optimizing risk management.Monas: Jurnal Inovasi Aparatur["Agus Kurniati"]2024-12-31T00:00:00 da3981965a131d35b71dd0dd38e2c9f633db320dThis study aims to examine the influence of doctors' knowledge of the benefits of accounting, technical knowledge and demands of obligations on the implementation of artificial intelligence (AI) of accounting in independent general practitioner practice entities. Data from questionnaire answers to respondents as many as 167 general practitioners who open independent practice services. The research method uses a quantitative approach with hypothesis testing using multiple linear regression analysis to test factors that influence the implementation of AI of accounting and the Independent t-test mean difference test to test differences in doctors' perceptions based on their characteristics. The results of the multiple linear regression analysis test show that the factors that influence the implementation of AI of accounting are 1) doctors' knowledge of the benefits of accounting, 2) doctors' knowledge of basic accounting techniques, and 3) demands of obligations from stakeholders. The results of the Independent t-test test show that there is no difference in the perception of the need for AI of accounting between ASN and Non-ASN general practitioners. The originality of this study: testing the determination of the implementation of AI of accounting in independent general practitioner practice entities, and testing differences in perceptions of the implementation of AI of accounting between ASN and non-ASN general practitioners.  Jurnal Akuntansi Terapan dan Bisnis210The results of the multiple linear regression analysis test show that the factors that influence the implementation of AI of accounting are doctors' knowledge of the benefits of accounting, doctors' knowledge of basic accounting techniques, and demands of obligations from stakeholders.Jurnal Akuntansi Terapan dan Bisnis["Nur Rahmanti Ratih", "M. Kusuma", "C. A. Barreto"]2024-12-31T00:00:00 d475fc8c5cd5e903ddc6896e83c93f2a7ede1a2a[Bahasa]: Kemajuan pesat Kecerdasan Buatan (Artificial Intelligence/AI) dalam dunia pendidikan menawarkan potensi yang signifikan untuk meningkatkan pengalaman belajar. Namun, guru-guru bahasa Indonesia di sekolah menengah pertama, khususnya di Tuban, menghadapi tantangan kritis dalam mengintegrasikan AI, termasuk pelatihan yang terbatas, penguasaan teknis, dan masalah etika. Hambatan-hambatan ini menghambat adopsi alat AI yang efektif untuk pembelajaran yang dipersonalisasi, adaptif, dan fleksibel. Untuk mengatasi masalah ini, penelitian ini bertujuan untuk meningkatkan pengetahuan dan kompetensi guru dalam integrasi AI melalui program pengabdian masyarakat yang terstruktur. Secara khusus, program ini menargetkan 30 guru bahasa Indonesia, melibatkan mereka dalam lokakarya dan sesi pendampingan berkelanjutan. Dengan menggunakan pendekatan partisipatif, program ini dilaksanakan dalam tiga tahap: persiapan, implementasi, dan evaluasi. Pre-test dan post-test dilakukan untuk menilai pemahaman awal para guru dan kemajuan selanjutnya. Hasilnya menunjukkan peningkatan yang signifikan, dengan 85% peserta mendapatkan pemahaman yang lebih dalam tentang konsep-konsep terkait AI dan 90% menunjukkan kemampuan untuk menggabungkan alat berbasis AI ke dalam kelas mereka. Terlepas dari pencapaian ini, masih ada tantangan yang dihadapi, terutama terkait keterampilan teknis dan pertimbangan etika. Penelitian ini menggarisbawahi pentingnya pelatihan dan dukungan berkelanjutan untuk mencapai integrasi AI yang berkelanjutan dalam pendidikan bahasa Indonesia. Penelitian lebih lanjut direkomendasikan untuk mengeksplorasi dampak jangka panjang dari adopsi AI dalam praktik pengajaran dan mendorong kolaborasi antara pendidik dan pengembang AI. Kata Kunci: kecerdasan buatan, inovasi pendidikan, pembelajaran bahasa Indonesia, pelatihan guru, pengabdian masyarakat. [English]: The rapid advancement of Artificial Intelligence (AI) in education offers significant potential for enhancing learning experiences. However, Indonesian language teachers in junior high schools, particularly in Tuban, face critical challenges in integrating AI, including limited training, technical mastery, and ethical concerns. These barriers hinder the effective adoption of AI tools for personalized, adaptive, and flexible learning. Addressing this issue, this study aimed to improve teachers' knowledge and competencies in AI integration through a structured community service program. Specifically, the program targeted 30 Indonesian language teachers, engaging them in workshops and continuous mentoring sessions. Adopting a participatory approach, the program was implemented in three stages: preparation, implementation, and evaluation. Pre- and post-tests were conducted to assess teachers’ initial understanding and subsequent progress. Results revealed significant improvements, with 85% of participants gaining a deeper understanding of AI-related concepts and 90% demonstrating the ability to incorporate AI-based tools into their classrooms. Despite these achievements, challenges remained, particularly regarding technical skills and ethical considerations. This study underscores the necessity of sustained training and support to achieve sustainable AI integration in Indonesian language education. Further research is recommended to explore the long-term impacts of AI adoption in teaching practices and foster collaboration between educators and AI developers. Keywords: artificial intelligence, educational innovation, Indonesian language learning, teacher training, community service.Transformasi: Jurnal Pengabdian Masyarakat400Transformasi: Jurnal Pengabdian Masyarakat["Titik Indarti", "U. Z. Fanani", "Riki Nasrullah", "Hespi Septiana", "Nadya Afdholy"]2024-12-31T00:00:00 a9e0a4a3d25f7a7c907cc47432dad9809ed5b6e6Amateur sports organisations in the EU often struggle to secure funding due to limited resources and expertise in grant writing. This paper explores how Artificial Intelligence (AI) can address this challenge by streamlining and enhancing the grant proposal development process. A pilot project conducted in Slovenia demonstrates AI's capacity to empower non-professional grant writers, resulting in increased success rates in securing EU funding. The study also reveals the need for structured methodologies and ongoing support to maximise the benefits of AI in grant writing. This research highlights AI's transformative potential in democratising access to funding and fostering a more vibrant and inclusive amateur sports landscape in the EU.ing access to funding and fostering a more vibrant and inclusive amateur sports landscape in the EU.SPORTICOPEDIA - SMB160This research highlights AI's transformative potential in democratising access to funding and fostering a more vibrant and inclusive amateur sports landscape in the EU by streamlining and enhancing the grant proposal development process.SPORTICOPEDIA - SMB["Igor Razbornik", "Sanja Todosijevi\u0107"]2024-12-31T00:00:00 f0f426818873bca636edb57864bc3ddd8322e42cIntroduction: In an unpredictable economic climate, the integration of artificial intelligence (AI) has gained prominence in enhancing accounting, control, and analytical processes, thereby improving the competitiveness of businesses. This study aimed to systematize approaches to leveraging AI for refining these processes and strengthening the competitive position of modern enterprises.Methods: The study employed a comprehensive literature review to examine artificial intelligence's implementation, benefits, and limitations in accounting and analytical processes. A structured approach to data selection ensured relevance and reliability, focusing on thematic relevance, recent advancements, and quality assessments to form a balanced dataset for analysis.Results: The findings indicated that the AI market is rapidly expanding, with a growing interest in applying AI technologies to manage large datasets and complex information requiring detailed analysis. AI was found to reduce the burden of routine tasks for auditors, freeing time for strategic and high-value operations. Furthermore, the integration of AI improved the accuracy and efficiency of accounting and auditing processes, minimized costs associated with repetitive work, and optimized the use of various organizational resources. These benefits are particularly significant for Ukrainian enterprises, which face unique economic and operational challenges.Conclusions: The study concluded that adopting AI in accounting and auditing provides substantial advantages, including enhanced process efficiency, cost reduction, and better resource utilization. These findings underscore the critical role of AI in transforming traditional accounting practices and offer practical implications for businesses seeking to maintain a competitive edge in a dynamic economic environmentSalud, Ciencia y Tecnología160Ad adopting AI in accounting and auditing provides substantial advantages, including enhanced process efficiency, cost reduction, and better resource utilization, underscore the critical role of AI in transforming traditional accounting practices and offer practical implications for businesses seeking to maintain a competitive edge in a dynamic economic environment.Salud, Ciencia y Tecnología - Serie de Conferencias["Hanna Datsenko", "Olena Kudyrko", "Iryna Krupelnytska", "L. Maister", "Iryna Hladii", "Inna Kopchykova"]2024-12-31T00:00:00 642ca728fb3a97024d32833283e1b4b5cb14dfd9Artificial intelligence(AI) writing has been a rising star in science and literature in recent years. AI writing is successfully used in creating news, official documents, and poetry, while China’s network literature originated in the 1990s and has been developing for 30 years. This paper aims to analyze the feasibility of applying AI writing to the field of online literature. This paper mainly uses literature analysis and comparative research methods, combined with existing data, to compare the differences between AI writing and manual writing, as well as network literature and traditional literature. The analysis shows that although the current AI writing has the advantage of speed and quantity, it also has problems, such as lack of aesthetic value and inability to arouse emotional resonance. Network literature has shown many characters and complete classification characteristics, but label production literature does not have substantial defects. Given the many similarities between the two, it is feasible that online literature and art will become the following application fields of AI writing. This application can also help the continuous progress of AI technology.Arts, Culture and Language20The analysis shows that although the current AI writing has the advantage of speed and quantity, it also has problems, such as lack of aesthetic value and inability to arouse emotional resonance.Arts, Culture and Language["Bihai Wang"]2024-12-31T00:00:00 1e98dfc9c1c8ee82fbef642751e879852dda54d0In the face of the pervasive influence of Artificial Intelligence technology on the journalism industry for the past few years, journalists have called the adoption of AI technology into question. AI technology in journalism has shaped the industry’s landscape in news topic selection, news production, news distribution, news consumption, etc. Entering a new era of AI-associated journalism and a world where robots can write like people, the industry faces the question of what would be the role of journalists. This research will examine the impacts of AI technology with a focus on both opportunities and challenges. AI-driven tools are found to be helpful to journalists by providing them with vast datasets, fact-checking information, and content creation, etc. However, there are also criticism doubting the ethical use of AI and concerns about job replacement. Through a comprehensive analysis of the applications of AI in journalism, this paper highlights AI’s future implementations, such as its use in news writing, data analysis, and fact-checking, potential challenges of inaccurate information, loss of human oversight, and job displacement as well as future suggestions to take advantage of technological advancement within the scope of journalistic integrity and ethics.Interdisciplinary Humanities and Communication Studies00Through a comprehensive analysis of the applications of AI in journalism, this paper highlights AI’s future implementations, such as its use in news writing, data analysis, and fact-checking, potential challenges of inaccurate information, loss of human oversight, and job displacement as well as future suggestions to take advantage of technological advancement within the scope of journalistic integrity and ethics.Interdisciplinary Humanities and Communication Studies["Siya Zhang"]2024-12-31T00:00:00 b7ecc63c93e6e17a9ead3d203e6cda1398b8fce0In the education system of the artificial intelligence major, the effectiveness of course teaching is of crucial importance for talent cultivation. As the integration of industry and education has become an important trend in educational reform, how to effectively implement this concept in professional core courses has become the focus of research. As a core course of the artificial intelligence major, Data Mining and Machine Learning aims to enable students to master relevant principles and processing methods. This course is mainly targeted at sophomore students in applied undergraduate programs, emphasizes the combination of theory and practice, and pays special attention to the introduction of cutting-edge technologies in the industry. Through an analysis, students have been found to have several issues in learning this course, including a shallow understanding of theoretical knowledge, difficulty in reaching the technological frontier, and challenges in connecting with the industry . To address these issues, the course has put forward a teaching concept of ``deepening theory, exploring the frontier, applying in practice, docking with the industry, and integrating ideological and political education", constructed a ``production, teaching, research and application" four-in-one curriculum system, implemented a ``project-driven" practical teaching model, introduced the industry mentor system, and so on. Since its implementation, these reform measures for the integration of industry and education have achieved remarkable results, providing strong support for talent cultivation and the development of the industry.Integration of Industry and Education00Data Mining and Machine Learning aims to enable students to master relevant principles and processing methods and pays special attention to the introduction of cutting-edge technologies in the industry.Integration of Industry and Education["Weili Liu", "Rongjun Chen"]2024-12-31T00:00:00 73e5115844605daa4fb8bb21074a2a7c45cfd2c5Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just as it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general.Journal of Otolaryngology Research &amp; Reports00The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties.Journal of Otolaryngology Research &amp; Reports["Yesh Sharma"]2024-12-31T00:00:00 d2694b0b424a20d58d763e5af955ce7a2ddf6aacWith the advent of artificial intelligence (AI), AI proficiency has become vital for teachers and learners in educational settings. Although preservice teachers acknowledge AI’s educational potential, they also express concerns regarding the technical challenges and integration of AI in teaching. In response, the Ministry of Education has mandated curriculum revisions in national universities to enhance AI and digital textbook competencies among preservice teachers. This study proposes to develop a program within university curricula aimed at developing the AI competencies of preservice teachers in the field of art education. Leveraging the curriculum of G National University of Education, elements from the Ministry’s “2024 Teacher Qualification Examination Practical Handbook” were incorporated into the Art Education I and II courses. Based on “AI·Digital Competency Structure for Art Teachers,” Art Education I—a required course for sophomores—integrates AI and digital competencies in 8 of its 15 sessions. The targeted sub-competencies include basic AI knowledge, digital device use, understanding social and educa tional implications, problem-solving, digital etiquette, protection of personal information and copyright, and evaluation of digital information in art.The Institute for Education and Research Gyeongin National University of Education00This study proposes to develop a program within university curricula aimed at developing the AI competencies of preservice teachers in the field of art education using elements from the Ministry of Education’s “2024 Teacher Qualification Examination Practical Handbook”.The Institute for Education and Research Gyeongin National University of Education["Won-Sang Cho", "Haikyung Kim"]2024-12-31T00:00:00 59349b70c844543b2e6f21ca9c3f0b345595efe8Artificial Intelligence (AI) is transforming cybersecurity by addressing the limitations of traditional reactive systems, which often fall short against advanced and unknown threats. By leveraging machine learning, neural networks, and predictive analytics, AI-driven cybersecurity systems provide proactive and adaptive solutions to modern challenges. These systems enhance threat detection accuracy, reduce response times, and efficiently manage large-scale, complex networks, making them indispensable across industries like finance, healthcare, and e-commerce. AI's ability to process vast amounts of data enables early identification of anomalies and prediction of potential risks, ensuring robust protection against evolving cyberattacks. However, challenges such as data privacy concerns, algorithmic biases, and the risk of AI misuse must be addressed. Ethical deployment and collaboration among governments, industries, and academia are critical to overcoming these obstacles. By fostering transparency and innovation, AI can become a cornerstone of global cybersecurity, paving the way for a safer, more resilient digital future.International Journal for Research in Applied Science and Engineering Technology00By fostering transparency and innovation, AI can become a cornerstone of global cybersecurity, paving the way for a safer, more resilient digital future.International Journal for Research in Applied Science and Engineering Technology["Sumanth Goud N G"]2024-12-31T00:00:00 e3f22c7c66824ad3abf648d4cb198c29e56bd4ccIn the field of criminal psychology, artificial intelligence (AI) has the potential to have a significant impact on effective technology development and application for crime response by providing and analyzing information based on extensive data for improving crime prevention, investigation, and offender rehabilitation programs. This study examined the use of AI in the field of criminal psychology, analyzed the risk factors of AI, and discussed its implications. This study was conducted using a literature review method that analyzed related academic research results, and research reports, and recent press articles. The results of the study confirmed that the risk factors of AI in the field of criminal psychology include: 1) prejudice and discrimination (bias of AI algorithms), 2) lack of transparency and accuracy (black box effect), 3) ethical concerns, 4) excessive reliance on AI, 5) data privacy and security violations, 6) unclear responsibility and accountability for incorrect judgments, 7) imperfection of human psychological assessment, 8) manipulation and misuse of AI systems, and 9) loss of human elements in criminal justice. As an implication of this, it was pointed out that AI-based predictive systems used to profile or predict criminal behavior may be inherently flawed or overly deterministic. In addition, ethical issues arise, such as the bias of AI algorithms, privacy rights, and the possibility of misuse in predictive policing. Therefore, the fairness and transparency of the operation of AI systems must be guaranteed. In order to secure the fairness of the operation of AI systems, strict supervision, continuous evaluation, and participation of experts are required to eliminate bias, enhance ethics, and secure transparency. Criminal justice policymakers, criminal psychologists/police science scholars, and scientists need to build a systematic system to balance human judgment, responsibility, and ethical standards for the use of AI technology in the field of criminal psychology. Finally, the academic and policy implications of this study were discussed along with its limitations.Korean Association of Criminal Psychology00It was pointed out that AI-based predictive systems used to profile or predict criminal behavior may be inherently flawed or overly deterministic, and ethical issues arise, such as the bias of AI algorithms, privacy rights, and the possibility of misuse in predictive policing.Korean Association of Criminal Psychology["Yong-Eun Sung"]2024-12-31T00:00:00 60a44e508a0dd2bfb70501fee0a57bc1d5311884Artificial Intelligence (AI) has emerged as a transformative technology, reshaping operational efficiencies and strategic business management across industries. This study employs a bibliometric analysis using VOSviewer to explore the intellectual structure, global collaboration, and thematic trends in AI research from 2000 to 2024. The findings reveal AI’s pivotal role in enhancing operational processes, particularly in cost reduction, efficiency improvement, and data-driven decision-making. Furthermore, AI’s integration into diverse fields such as healthcare, energy management, and cybersecurity underscores its multidisciplinary impact. The visualizations highlight the strong global collaboration among nations, with China, India, and the United States as major contributors to AI research. Despite these advancements, challenges such as ethical concerns, data privacy, and workforce displacement persist. This study emphasizes the need for ethical frameworks, workforce reskilling, and robust international cooperation to maximize AI's benefits while mitigating its challenges. By mapping current trends and identifying future directions, this research contributes to a deeper understanding of AI’s transformative potential in operational and strategic domains.West Science Information System and Technology180The need for ethical frameworks, workforce reskilling, and robust international cooperation to maximize AI's benefits while mitigating its challenges is emphasized, contributing to a deeper understanding of AI’s transformative potential in operational and strategic domains.West Science Information System and Technology["Loso Judijanto", "Ahmad Zaelani Adnan", "Gilang Pranajasakti", "A. Y. Vandika", "Wahyuni Sri Astutik"]2024-12-31T00:00:00 09c72e6c97b3a5bd83cbb1b7b6ad4393ec86a3a3Magnetic resonance imaging (MRI) has been widely used in clinical diagnosis since its introduction with its high resolution and unparalleled contrast imaging of soft tissues. The traditional MRI image analysis is highly dependent on subjective judgment and has the risk of misdiagnosis. The efficiency of human relied diagnosis is still needs to be improved. In recent years, artificial intelligence technology has developed rapidly and gradually involved in MRI image analysis. For example, the image segmentation algorithm, machine learning and deep learning are increasingly widely used in MRI image processing. This paper explores the use of traditional machine learning and deep learning models in MRI and focuses on their ability to extract advanced features, and performance of lesion detection and tumour classification. The advantages and disadvantages of traditional machine learning models such as support vector machines (SVM) and random forests (RF) and their applications are discussed. The deep learning models, particularly convolutional neural networks and generative adversarial networks, this paper focus on their principles and applications to assist MRI diagnosis.Science and Technology of Engineering, Chemistry and Environmental Protection60This paper explores the use of traditional machine learning and deep learning models in MRI and focuses on their ability to extract advanced features, and performance of lesion detection and tumour classification.Science and Technology of Engineering, Chemistry and Environmental Protection["Li Dong"]2024-12-31T00:00:00 1d43c7413a3d701c991b89d17e44cdbf84a5c09dArtificial intelligence (AI) is revolutionizing diagnostic imaging, enhancing precision, speed, and efficiency. This study explored radiologists' perceptions of AI through a survey of 100 radiologists across various institutions, focusing on awareness, benefits, concerns, and preparedness for AI adoption. Most radiologists recognized AI's potential to improve diagnostic accuracy and workflow efficiency but expressed concerns about its reliability, job displacement, and ethical implications. Readiness to adopt AI varied significantly based on age, experience, and familiarity with AI tools. These findings underscore the need for targeted education and training programs to address skepticism and support the effective integration of AI into diagnostic imaging practices.Bioinformation00Radiologists' perceptions of AI are explored through a survey of 100 radiologists across various institutions, focusing on awareness, benefits, concerns, and preparedness for AI adoption.Bioinformation["Prasanna Sakthi Aravazhi", "Kumaran Ottilingam Ravindran", "Kanika Balasubramani", "Mohammed Kamil", "Kanishka Gouthaman", "Lalit Karki", "Sandhiya Thiyagarajan", "Akshay Sureshkumar Nair"]2024-12-31T00:00:00 8ab96a2752d80aac18d3b25691662cc9b9e0b4dbThis study aimed to develop an educational platform to efficiently implement software, robotics, and artificial intelligence education in the revised 2022 elementary practical arts education curriculum. The results of the study are as follows. The revised 2015 curriculum posed various methodological difficulties that hindered efficient software and robotics instruction. First, there were difficulties in pairing block-based programming tools (software) with robots, the hardware that executes coding content. Second, teachers found monitoring each student's coding progress in real-time difficult, and answering individual questions required additional time, making personalized guidance difficult. Third, it took considerable time to share correctly coded programs with students for comparison and modification or to explain the correct coding methods. To address these challenges, this study developed a platform to efficiently facilitate software, robot, and coding education for artificial intelligence. The platform allows for easy pairing between programming tools and hardware and allows teachers and students to share coding content in real time. In addition, students can copy correctly coded programs to their own platforms in real time and modify or execute them. This platform is compatible with not only computers but also tablets and mobile phones, with capabilities such as voice recognition, image recognition, and machine learning. Image recognition functionality includes blocks for face, hand, and character recognition.The Korean Association of Practical Arts Education00An educational platform to efficiently implement software, robotics, and artificial intelligence education in the revised 2022 elementary practical arts education curriculum with capabilities such as voice recognition, image recognition, and machine learning is developed.The Korean Association of Practical Arts Education["Jong Pyo Kang", "Young Kil Yu", "Dong Young Lee"]2024-12-31T00:00:00 f8cf910db2a9dc664817966cbea93843a7d8e3c9Integrating artificial intelligence (AI) into public health and healthcare systems management represents a transformative opportunity to enhance efficiency, improve patient outcomes, and facilitate proactive approaches to health crises. By harnessing machine learning algorithms, predictive analytics, and natural language processing, healthcare professionals can analyze vast datasets to identify trends, optimize resource allocation, and streamline operations. AI applications can assist in disease surveillance, outbreak prediction, and personalized medicine, ultimately enabling a more responsive health infrastructure. However, ethical considerations, data privacy, and the necessity for human oversight must guide the implementation of these technologies. Overall, AI has the potential to transform the healthcare system by improving patient outcomes, reducing costs, and enhancing the overall quality of care. Therefore, there is a dire need for hybrid management to transform their institution into a modern state-of-the-art digital platform to maximize efficiency, economy, better-desired outcomes, and patient satisfaction.Open Access Public Health and Health Administration Review00Overall, AI has the potential to transform the healthcare system by improving patient outcomes, reducing costs, and enhancing the overall quality of care, and there is a dire need for hybrid management to transform their institution into a modern state-of-the-art digital platform to maximize efficiency, economy, better-desired outcomes, and patient satisfaction.Open Access Public Health and Health Administration Review["Dr. Bahadar Shah"]2024-12-31T00:00:00 7779c08c1a6b21b1315b93784f213893b76051a5In the trend of educational digitalization, this study focuses on the current application of artificial intelligence in high school education, using Yutan Middle School in Ningxiang City as a case study. The research employed interview methods to conduct in-depth discussions with four interviewees, exploring the current status and existing issues of AI application in high school education at Yutan Middle School and proposing targeted countermeasures. The study finds that the application level of artificial intelligence in high school education at Yutan Middle School is relatively low and still in the exploratory stage, primarily influenced by factors such as subject nature, teaching concepts, high costs, operational complexity, and knowledge accuracy. To address these issues, the research suggests optimizing AI software to enhance technological maturity, strengthening teacher training to improve digital literacy and skills, and establishing an AI teaching technology exchange platform to share resources and promote educational innovation. Although this study has a small sample size and limited interview scope, it provides insights for future research directions.Arts, Culture and Language00The study finds that the application level of artificial intelligence in high school education at Yutan Middle School is relatively low and still in the exploratory stage, primarily influenced by factors such as subject nature, teaching concepts, high costs, operational complexity, and knowledge accuracy.Arts, Culture and Language["Qiuyi Wan"]2024-12-31T00:00:00 a35612367191720df5e8fd8efec381ff0851e4e5Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine, where it processes high-dimensional data to improve diagnostics and treatment outcomes. This review explores AI applications in urological surgery, highlighting advancements such as image classification and robotic assistance in surgical procedures. AI has demonstrated exceptional diagnostic accuracy, with some systems achieving up to 99.38% in detecting prostate cancer. Additionally, AI facilitates real-time anatomical recognition and instrument delineation, increasing surgical precision. While current robotic systems operate under human supervision, ongoing research aims to advance autonomous surgical capabilities. The future of AI in robotic surgery is promising, especially regarding the possibility of improved outcomes; nonetheless, challenges related to autonomy, safety, and ethics remain.Urogenital Tract Infection410The future of AI in robotic surgery is promising, especially regarding the possibility of improved outcomes; nonetheless, challenges related to autonomy, safety, and ethics remain.Urogenital Tract Infection["Dae Young Lee", "Hee Jo Yang"]2024-12-31T00:00:00 72ef8e8e7e21bbb72438e77c87c10685eaee6351The study explores the perspectives of teachers and students regarding the integration of artificial intelligence (AI) in higher education in Nepal. Adopting a qualitative research methodology, a survey was conducted with 200 students and 20 teachers through Google Forms from various educational backgrounds to assess their attitudes toward AI tools. The results indicate that both groups recognize AI is potential to enhance learning and teaching experiences, although their perceptions differ across specific aspects. This study explores the perspectives of teachers and students regarding the integration of artificial intelligence students generally appreciate AI’s role in improving engagement, motivation, and personalized learning but they also express concerns about privacy, collaboration, and effectiveness of these tools in promoting independent research. Teachers report benefits such as improved lesson plans, grading efficiency, and data-driven insights. However, they highlight challenges in aligning AI with their pedagogical approaches and achieving consistent student engagement. The findings underscore the transformation potential of AI in education while identifying areas for improvement, including the need for privacy safeguards and adaptability to diverse teaching methods. To address these issues, education institutions can better leverage AI’s capabilities fostering more inclusive and effective learning environments for the students.International Research Journal of MMC90Teachers and students generally appreciate AI’s role in improving engagement, motivation, and personalized learning but they also express concerns about privacy, collaboration, and effectiveness of these tools in promoting independent research.International Research Journal of MMC["Shiva Dutta Chapagai", "Bhabana Adhikari"]2024-12-31T00:00:00 bb52178474cb515fd2054f7376c6de190e36df27Recently, the techniques of industrial control systems (ICS) have developed rapidly, which leads to new cyber threats in this field. The railway system, as a special ICS, is also facing more and more challenges in the intrusion detection and risk evaluation fields. However, compared with other ICS, the intrusion detection and defense methods for railway systems are lagging behind. This paper is a comprehensive review of the application of artificial intelligence (AI) in the railway industry, with a particular focus on cybersecurity. We examine existing anomaly detection methods based on AI and their implementation in ICS and railway operations. We found that machine learning and deep learning algorithms are effective in processing large amounts of network traffic data, modeling normal system behavior, and detecting anomalies. Different AI-based anomaly detection algorithms each have their own strengths and weaknesses, and they hold significant potential for enhancing the cybersecurity of railway systems. While the field of AI in the railway industry is still in its early stages, several case studies demonstrate that AI technologies have already shown considerable promise in safeguarding railway networks. However, there are still numerous challenges in practical applications, such as improving accuracy, generalizability, and robustness. Addressing these challenges will be critical for realizing the full potential of AI in railway cybersecurity and ensuring the safety and efficiency of railway operations in the future. Our work serves as a guide for future explorations, aiming to contribute to the broader discourse of AI applications in industrial cybersecurity.Transportation Research Record470A comprehensive review of the application of artificial intelligence (AI) in the railway industry, with a particular focus on cybersecurity, finds that machine learning and deep learning algorithms are effective in processing large amounts of network traffic data, modeling normal system behavior, and detecting anomalies.Transportation Research Record: Journal of the Transportation Research Board["Jin Qi", "Jian Wang"]2024-12-31T00:00:00 76b841be0ab32f876819f51ee095d463f24e9486Artificial Intelligence (AI) has become an integral part of everyday life, and this technology has now transformed the way we work and has controlled various industries and sectors of human life. The potential benefits of AI today are enormous, including increased efficiency, productivity, and innovation. However, the use of AI also raises concerns about ethical, legal, social, and privacy issues for society. This paper aims to measure the benefits of AI in human life, the urgency of regulating the use of AI technology, and the concept of regulating the use of AI in Indonesia. This paper reviews various literature and studies related to AI and its impact on society. The results show that AI has the potential to bring significant benefits to human life, but its unregulated use can also pose significant risks. Therefore, it is important to establish regulations that address AI concerns from ethical, legal, and social perspectives. This paper also proposes a concept of regulating AI in Indonesia that includes general requirements, development to use and sanctions, and AI use must also uphold ethical and legal principles, such as transparency, accountability, and human rights protection. This paper emphasizes the importance of balancing the benefits and risks of AI to ensure safe and responsible use in society.Pena Justisia Media Komunikasi dan Kajian Hukum260The results show that AI has the potential to bring significant benefits to human life, but its unregulated use can also pose significant risks, and it is important to establish regulations that address AI concerns from ethical, legal, and social perspectives.Pena Justisia: Media Komunikasi dan Kajian Hukum["Siti - Mariyam"]2024-12-31T00:00:00 85397da9710ac98d8e0e9f39e858644bfeb51208Journal of Economic Structures1450It is posited that AI may stimulate economic development by increasing efficiency, promoting economies of scale, enhancing the quality of products and services, and improving working conditions.Journal of Economic Structures["Maha Kalai", "Hamdi Becha", "Kamel HELALI"]2024-12-31T00:00:00 5b706684fda6bca2355ddd3a2187ed954f688d13Purpose This study aims to evaluate an artificial intelligence (AI) capability scale using resource-based theory and tests its impact on dynamic capabilities and organizational creativity to influence the performance of public organizations. Design/methodology/approach The study used qualitative and quantitative methods to develop and validate an AI capability scale using an integrative psychometric approach. An initial set of 26 items was selected from the literature for qualitative analysis. Self-reported data from 344 public managers in United Arab Emirates public organizations were used for scale refinement and validation. Hypotheses were tested against theoretically related constructs for nomological validation. Findings A 23-item AI capability scale was developed. Nomological testing confirmed that AI capability positively and significantly enhances dynamic capabilities, which in turn boosts organizational creativity and improves organizational performance. Originality/value Previous information system literature has not sufficiently addressed the importance of organizational-level complementary resources in developing distinctive capabilities within public organizations. Grounded in resource-based theory and recent AI research, this study provides a framework for public sector organizations to assess their AI capabilities. The findings empirically support the proposed theoretical framework, showing that AI capability increases dynamic capabilities, organizational creativity and performance. Journal of Systems and Information Technology650The findings empirically support the proposed theoretical framework, showing that AI capability increases dynamic capabilities, organizational creativity and performance.Journal of Systems and Information Technology["Hamad Mohamed Almheiri", "Syed Zamberi Ahmad", "Khalizani Khalid", "Abdul Hafaz Ngah"]2024-12-31T00:00:00 00d512576b41c4387ecb10edc3e50cc77ae4c15fErgonomic risk assessment is crucial in preventing work-related musculoskeletal disorders (WMSDs) across various industries. Traditional methods, while effective, have limitations, such as reliance on manual observations and a lack of real-time monitoring. Recent technological advancements, including artificial intelligence (AI), wearable sensors, and industry-specific solutions, are addressing these gaps. AI and machine learning techniques enable real-time data analysis, providing more accurate and proactive ergonomic assessments. Wearable technology, such as inertial measurement units and pressure sensors, offers continuous monitoring of worker movements and postures, helping to prevent injuries in sectors like healthcare, construction, and manufacturing. These tools also allow for personalized ergonomic interventions by assessing individual risk factors in real-time. Industry-specific approaches have also emerged, particularly in high-risk fields such as healthcare and mining, where the integration of ergonomic and psychosocial stressors provides a comprehensive risk assessment model. In addition to physical ergonomics, advancements now incorporate psychosocial factors, addressing issues like organizational culture and job stress, which significantly influence musculoskeletal health. Finally, technological innovations such as simulation and modeling tools further enhance ergonomic assessments by simulating worker movements and identifying high-risk postures. However, challenges remain in standardizing these tools and integrating them into existing workflows. The evolution of ergonomic risk assessments towards more automated, precise, and real-time systems promises to reduce WMSDs and improve overall workplace safety.Malaysian Journal of Ergonomics (MJEr)280The evolution of ergonomic risk assessments towards more automated, precise, and real-time systems promises to reduce work-related musculoskeletal disorders and improve overall workplace safety.Malaysian Journal of Ergonomics (MJEr)["A. Hilmi", "Asna Rasyidah Abdul Hamid", "Wan Abdul Rahman Assyahid Wan Ibrahim"]2024-12-31T00:00:00 06fa59fe3c09a1e9d7d8d789de16fa7b06e3da8dThis study looks at how Artificial Intelligence (AI) helps Small and Medium-sized Enterprises (SMEs) improve their supply chain management, with a focus on important factors that affect its use in uncertain environments. The main goals of this study are to identify key success factors for effective AI integration in supply chains and to examine how identified success factors interact with environmental uncertainties.Using a quantitative approach, the study measures how often SMEs adopt AI, checks their performance indicators, and analyzes how different external uncertainties impact supply chain efficiency. The results show that using AI well can greatly boost the operational efficiency and flexibility of SMEs. Key factors for success include strong leadership support, active employee participation, and a solid technological foundation. Importantly, the research finds that SMEs in unstable environments can better handle risks and quickly adjust to market changes with AI strategies. This is especially relevant for healthcare, where having a strong supply chain is vital for timely medical supply delivery and good patient care. By focusing on how AI use and environmental factors interact, this study provides important insights for healthcare providers looking to use technology to improve supply chain speed and responsiveness. Overall, this research adds to the ongoing discussion about digital change in supply chains, stressing the importance for SMEs to embrace AI technologies to succeed in a more uncertain business world.Journal of Informatics Education and Research00The research finds that SMEs in unstable environments can better handle risks and quickly adjust to market changes with AI strategies, and is especially relevant for healthcare, where having a strong supply chain is vital for timely medical supply delivery and good patient care.Journal of Informatics Education and Research["Mr. Suvam Sarmacharjee", "Prof. Debomalya Ghosh", "Dr. Irshad Ahmad", "Dr. Faraz Ahmad", "Dr. Robin Manohar Shinde", "Dr. Md. Obaidul"]2024-12-31T00:00:00 f71946687536f58ddc395f56cfc869cb6a4b30adThe oil and gas industry operates within a landscape of complex, high-stakes risks that span operational, environmental, and safety domains. Traditional risk assessment methodologies, while foundational, are constrained by their static nature and limited capacity to process dynamic, large-scale data. This dissertation investigates the application of artificial intelligence (AI) methodologies—specifically fuzzy logic and machine learning—to enhance risk assessment frameworks in the oil and gas sector. By systematically evaluating key performance criteria, including predictive accuracy, data processing capabilities, and user interactivity, this research establishes a comprehensive framework for integrating AI-driven approaches into risk management systems. The findings demonstrate that AI-based models significantly enhance the ability to anticipate and mitigate risks through real-time decision support and advanced predictive analytics. This work further introduces a scalable decision-making model leveraging fuzzy inference to handle uncertainty and improve the robustness of risk assessments. The proposed framework offers a pathway for transitioning from reactive to proactive safety management strategies, ensuring resilience and sustainability in increasingly complex industrial environments.Data and Metadata70The findings demonstrate that AI-based models significantly enhance the ability to anticipate and mitigate risks through real-time decision support and advanced predictive analytics, and further introduces a scalable decision-making model leveraging fuzzy inference to handle uncertainty and improve the robustness of risk assessments.Data and Metadata["MR Oubellouch Hicham", "MR Soulhi Aziz"]2024-12-31T00:00:00 32ff8f4824be206c4b8c5bb8aaca270c35f1a716Electricity has been a key driver of global socioeconomic development and sustainability for both developed and developing nations. In Malaysia, electricity is primarily generated by burning fossil fuels, emitting greenhouse gases (GHG) that adversely impact the environment and public health. Therefore, accurately predicting electricity consumption is crucial for economic management, security analysis, facility scheduling for generation and distribution, and maintenance planning. This study aimed to develop a modified stacked ensemble multivariable Artificial Intelligence (AI)-based predictive algorithm, specifically Stacked Simple Linear Regression and Multiple Linear Regression (SLR-MLR), and Stacked Simple Linear Regression and Multiple Non-Linear Regression (SLR-MNLR) utilizing the Cross Industry Standard Process for Data Mining (CRISPDM) data science methodology. The proposed AI-based predictive algorithm aimed to provide predictive insights and interpret the impact of significant economic, environmental, and social clustered determinants on electricity consumption in Malaysia. The analysis revealed that the SLR-MLR predictive algorithm better fits Malaysias limited electricity consumption dataset compared to the existing Stacked SLR and e-Support Vector Regression (SLR-e-SVR) and SLR-MNLR predictive algorithms. It identified key economic and environmental clustered determinants that significantly impact electricity consumption in Malaysia. In academia, this study proposed an innovative SLR-MLR predictive algorithm and utilized a novel statistical approach to evaluate and select the superior predictive algorithm. Practically, it offered valuable insights for policymakers to craft efficient regulations, manage the energy sector proactively, and anticipate electricity generation and consumption trends. These contributions align with Malaysias economic and environmental sustainability goals outlined in the Twelfth Malaysia Plan, the Madani Economy Framework, the National Energy Policy 2022-2040, and the National Energy Transition Roadmap (NETR) agenda.MATEMATIKA00MATEMATIKA["Zun-Liang Chuan", "Lit Ken Tan", "Angel Wee Chi Chyin", "Yim Hin Tham", "Shao Jie Ong", "Jia Yi Low", "Chong Yeh Sai"]2024-12-31T00:00:00 723ee45d926728850475d40cec66620ebed8d666This study investigates the effectiveness of the STEM Learning Project model in enhancing student outcomes in Artificial Intelligence (AI) courses at higher education institutions. The research aimed to assess the model’s impact on students’ cognitive, affective, and psychomotor skills, with a focus on fostering active participation, problem-solving, and interdisciplinary knowledge integration. Employing a mixed-methods approach, the study utilized both qualitative and quantitative data collection methods. The experimental group engaged in the STEM Learning Project, while the control group followed a traditional AI curriculum. Changes in student knowledge and engagement were measured using pre- and post-test surveys, complemented by qualitative insights obtained from interviews and focus group discussions. The results demonstrated progress in both groups, though the experimental group achieved a greater increase in post-test scores (29,87) compared to the control group (29,21). Statistical analyses confirmed that the data satisfied normality and homogeneity assumptions, allowing for parametric testing. An independent sample t-test revealed a significant difference in post-test scores between the two groups, highlighting the effectiveness of the STEM Learning Project model in enhancing students' AI-related skills. This approach notably improved students' cognitive abilities and interdisciplinary knowledge in AI education, establishing it as a promising strategy for preparing students to address the demands of the AI industry. Future research could explore the model's long-term impact on career readiness and its applicability to other technology-driven educational settings.Data and Metadata240This approach notably improved students' cognitive abilities and interdisciplinary knowledge in AI education, establishing it as a promising strategy for preparing students to address the demands of the AI industry.Data and Metadata["R. Rahmiati", "N. Jalinus", "Hansi Effendi", "Rahmat Fadillah", "Rizki Ema Wulansari"]2024-12-31T00:00:00 a2be82dcdf9a0a6474906c4322c874316a9ae0fbThis study is trying to assess the ability of AI to predict the opinion of people about climate change by giving importance to the psychological and behavioral facets of such opinions. The survey was conducted on 300 people in Pakistan by a self-administered questionnaire. The data analysis done in regard to these factors included the implementation of multiple statistical methods such as correlation, regression, and post-hoc analysis that would consider the complex interplay between several psychological factors in question. Some of the factors involved cognitive biases, emotional responses, and what the layperson in society thinks about climate change. The findings presented with this analysis clearly showed that the models in this study, driven by artificial intelligence, were far better than the traditional survey methods used for a long time. In this regard, the AI-driven models had an astonishing accuracy rate of 87%, whereas the accuracy rate of only 72% came with traditional approaches used in this study and was proven to be highly statistically significant, as indicated by the p-value being below 0.05. The regression analysis results showed that some psychological factors such as cognitive bias with a beta value of 0.75 and emotional responses at a beta value of 0.65 strongly and significantly predict the attitude of the people regarding climate change. Apart from the above primary analysis, post-hoc analysis brought a number of other insights to light, especially regarding the important role of social identity in forming a public perception related to climate change issues. The above findings depict promising scope for AI technology in refining and improving the strategy of climate change communication through an appropriate application of psychological knowledge within the predictive model.Review of Applied Management and Social Sciences360The regression analysis results showed that some psychological factors such as cognitive bias with a beta value of 0.75 and emotional responses at a beta value of 0.65 strongly and significantly predict the attitude of the people regarding climate change.Review of Applied Management and Social Sciences["Nouman Khan", "Muhammad Umar Aziz", "Sameer Ullah Khan", "Ramla Shah"]2024-12-31T00:00:00 d7bcc99ba6377b2c3ae3b745490ff5caee586c2bThe main goal of the research is to develop an artificial intelligence technology to assess the practical knowledge of air traffic controller (ATCo) students based on their responses in simulated multitasking situations using the proposed neuro-fuzzy model verified in experiments. An informational neuro-fuzzy model was developed and verified on 157,500 real data points. It illustrates an example of inferring the level of practical knowledge in selected ATCo students who were tested using a device measuring the reaction time and relative error rate in multiple-task tasks. The average error in the incorrect response was 7.7% of the experimental data. Data processing was performed using fuzzy set theory and intellectual knowledge analysis. These measurement results are useful for an individual approach to the student’s education to understand and master the correct solutions to achieve the desired educational results. Ensuring a personal approach to the student’s education is key to acquiring the necessary skills, knowledge, and competencies in the profile of the graduate. The developed technology will enable the integration of automated knowledge and skills assessment systems into the real educational process and the identification of problematic topics and tasks in the training of individuals. The result of the conducted research was used for the software design for the practical application in the flight training of ATCo students.Applied Sciences190An artificial intelligence technology to assess the practical knowledge of air traffic controller (ATCo) students based on their responses in simulated multitasking situations using the proposed neuro-fuzzy model verified in experiments is developed.Applied Sciences["M. Anto\u0161ko", "V. Polishchuk", "M. Kelemen", "Anton Korniienko", "M. Kelemen"]2024-12-31T00:00:00 99f4fcb4e2f22e0c0787c1a378985235df7c068dBackground: The increasing burden on radiologists due to rising imaging demands and complexities has led to concerns about burnout and compromised patient care. As artificial intelligence (AI) technologies evolve, they present potential solutions to enhance diagnostic accuracy and efficiency in radiology. Methods: This review examines the implications of AI in radiology, particularly for general practitioners (GPs) in primary care settings. A comprehensive literature search was conducted to identify studies that highlight the applications of AI in diagnostic imaging, patient management, and engagement, as well as the ethical considerations surrounding its implementation. Results: The findings indicate that AI applications, such as machine learning algorithms, have demonstrated superior capabilities in detecting diseases in imaging studies compared to traditional methods. AI-driven tools can aid GPs in making informed decisions, improving patient outcomes by facilitating early diagnosis and personalized treatment plans. However, challenges such as integration into existing healthcare systems, training requirements, and ethical concerns regarding accountability and algorithmic bias persist. Conclusion: The integration of AI in radiology holds a significant promise for enhancing the role of general practitioners in patient care. By leveraging AI technologies, GPs can improve diagnostic accuracy, reduce the burden on radiologists, and ultimately enhance patient safety. Continued research and collaboration are essential to address the barriers to AI adoption and to ensure ethical and effective implementation in clinical practiceJournal of Ecohumanism00The findings indicate that AI applications, such as machine learning algorithms, have demonstrated superior capabilities in detecting diseases in imaging studies compared to traditional methods and hold a significant promise for enhancing the role of general practitioners in patient care.Journal of Ecohumanism["Ali Hadi Hadadi", "Sami Abdullah Towhari", "Huda Mohammed Funtul Alsharar", "Harbah Ghanem Muteb Alfaqir", "Narjes Omar Hamad Aljuhayyim", "Waleed Mohammed Nafea Alharbi", "Khulud Madallah Duhayman Alsharari", "Abdullah Ayidh Alotaibi", "Rakan Gzian Alotaibi"]2024-12-31T00:00:00 22a9fb753c18ec43ee85d716a07f2fb8c118524aThe integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics has transformed marketing practices, especially in technology-driven sectors like start-ups. This study examines the influence of these technologies on marketing performance in technology start-ups in Jakarta using quantitative analysis. Data were collected from 150 respondents through a structured questionnaire and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS 3). The findings reveal that AI, ML, and Data Analytics each have significant positive impacts on marketing performance, with Data Analytics emerging as the strongest individual predictor. Moreover, the combined use of these technologies demonstrates a synergistic effect, amplifying their overall influence. These results highlight the critical role of technology integration in enhancing marketing efficiency, customer engagement, and return on investment. The study contributes to the theoretical understanding of technology adoption in marketing and provides actionable insights for start-ups aiming to leverage these tools for competitive advantage.West Science Information System and Technology400The findings reveal that AI, ML, and Data Analytics each have significant positive impacts on marketing performance, with Data Analytics emerging as the strongest individual predictor.West Science Information System and Technology["Muchamad Sobri Sungkar", "Mukrodin Mukrodin", "Muhamad Bakhar"]2024-12-31T00:00:00 e505eecf249a4830453284a6515c558251b24048OBJECTIVE Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can be challenging to detect visually on magnetic resonance imaging. Three artificial intelligence models for automated FCD detection are publicly available (MAP18, deepFCD, MELD) but have only been compared on single-center data. Our first objective is to compare them on independent multicenter test data. Additionally, we train and compare three new models and make them publicly available. METHODS We retrospectively collected FCD cases from four epilepsy centers. We chose three novel models that take two-dimensional (2D) slices (2D-nnUNet), 2.5D slices (FastSurferCNN), and large 3D patches (3D-nnUNet) as inputs and trained them on a subset of Bonn data. As core evaluation metrics, we used voxel-level Dice similarity coefficient (DSC), cluster-level F1 score, subject-level detection rate, and specificity. RESULTS We collected 329 subjects, 244 diagnosed with FCD (27.7 ± 14.4 years old, 54% male) and 85 healthy controls (7.1 ± 2.4 years old, 51% female). We used 118 subjects for model training and kept the remaining subjects as an independent test set. 3D-nnUNet achieved the highest F1 score of .58, the highest DSC of .36 (95% confidence interval [CI] = .30-.41), a detection rate of 55%, and a specificity of 86%. deepFCD showed the highest detection rate (82%) but had the lowest specificity (0%) and cluster-level precision (.03, 95% CI = .03-.04, F1 score = .07). MELD showed the least performance variation across centers, with detection rates between 46% and 54%. SIGNIFICANCE This study shows the variance in performance for FCD detection models in a multicenter dataset. The two models with 3D input data showed the highest sensitivity. The 2D models performed worse than all other models, suggesting that FCD detection requires 3D data. The greatly improved precision of 3D-nnUNet may make it a sensible choice to aid FCD detection.Epilepsia210This study shows the variance in performance for FCD detection models in a multicenter dataset and suggests that FCD detection requires 3D data, suggesting that 3D-nnUNet may be a sensible choice to aid FCD detection.Epilepsia["Lennart N Kersting", "Lennart Walger", "T. Bauer", "V. Gnatkovsky", "Fabiane Schuch", "B. David", "Elisabeth Neuhaus", "F. Keil", "Anna Tietze", "Felix Rosenow", "Angela M. Kaindl", "Elke Hattingen", "H. Huppertz", "A. Radbruch", "Rainer Surges", "T. R\u00fcber"]2024-12-31T00:00:00 e0316fbdd2d2240be3dfdc1b74ecc5448529ae67Faced with the development of the artificial intelligence-based technologies, do we need to modify our civil liability (tort liability) system or compensation system in general? Although an AI system has several features compared to other technologies, it is its “autonomy” which poses the essential question whether a harm caused by its output can be attributed to a human. The aim of this paper is to overview and to analyze the current theoretical situation regarding this topic in Japan. Just like in other countries, the adaptability of the existing civil liability regime to the accidents caused by autonomous vehicles is considered as a question to be solved as immediately as possible in Japan. Although the liability regime established by the Act on Securing Compensation for Automobile Accidents in 1955 will work acceptably well for the SAE Level 4 (High Driving Automation) vehicles, there are worries that it won’t for the SAE Level 5 (Full Driving Automation) vehicles because of the eventual disappearance of the person responsible according to this regime. This is why the Japanese government and law scholars have started to discuss the alternative regimes aiming to reinforce the civil liability of manufactures of autonomous vehicles such as renewing the compulsory liability insurance, elaborating an independent liability regime for autonomous vehicles, creating a compensation fund, etc. How about AI systems in general? The modernization of the common civil liability regimes will be inevitable: the traditional fault liability regime should be reinforced by introducing the general measures of the presumption of fault and/or causality; the existing product liability regime established by the Product Liability Act in 1994 should be modified to be adapted to the digitalization. However, these solutions might be nothing more than stopgap measures, considering the inherent limits of these regimes (necessity to identify a “misconduct” of a human using an AI system, difficulty in defining a “defect” of an AI system, etc.). Three new regimes are to be further discussed: no fault liability focusing on the uncontrollability of autonomous AI systems, vicarious liability regarding AI systems as auxiliaries of humans, and compensation fund intended to indemnify victims of the outputs of AI systems. Each regime poses a lot of theoretical questions to which the current Japanese civil liability doctrine is not yet ready to answer satisfactorily.The Korean Association of Civil Law00Three new regimes are to be further discussed: no fault liability focusing on the uncontrollability of autonomous AI systems, vicarious liability regarding AI systems as auxiliaries of humans, and compensation fund intended to indemnify victims of the outputs of AI systems.The Korean Association of Civil Law["Taro Nakahara"]2024-12-31T00:00:00 038f94cd70cb4e2f5f06a4ad6d17818563a16b71Recently, there has been significant discussion about artificial intelligence (AI) and Large Language Models (LLMs) as they relate to teaching and learning. To date, literature exists about the role of AI in Physical Therapy treatment, but not Physical Therapy education. This review will identify the benefits of adapting AI into Physical Therapy education to better prepare the healthcare providers of tomorrow. Survey research shows that AI can improve access to information, increase productivity, and reduce errors. However, most also feel ill-informed on the topic. A majority agreed that AI concepts should be included in Physical Therapy education. While valid concerns exist about AI sources giving false information, as well as the potential to use these services to plagiarize work, tools are already in existence to mitigate these issues. Potential benefits of AI in physical therapy education include the ability to produce examples, provide different explanations, and assist in assessing student learning while providing immediate feedback on performance. With the scope of AI rapidly expanding, we believe it is imperative for physical therapy educators to have access to current information about its potential benefits, uses, and limitations. While many professionals do not currently possess much knowledge on the topic, there is a growing consensus as to the role it will play in our profession in the future. While discussions of AI can raise more questions than answers, preparation now will reduce the need for urgent adaptations in the future.International Journal of Research in Medical Sciences120The benefits of adapting AI into Physical Therapy education to better prepare the healthcare providers of tomorrow and prepare for urgent adaptations in the future.International Journal of Research in Medical Sciences["R. K. B.", "Anusha Bills"]2024-12-31T00:00:00 bbc40f7730a8a704e8bdbb2f5ae5975136e0e074This study critically analyzes the strategic impact of Artificial Intelligence on freelancing in Bangladesh. Using a systematic literature review, data was gathered from scientific articles available on platforms such as Google Scholar, SSRN, Wiley database, as well as reports from PricewaterhouseCoopers International Limited (PwC). The findings highlight strategic implications using a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework. AI presents a potential threat to freelancers, as some job roles can now be replaced by AI technology. The authors also highlight that the current lack of AI-related skills is a weakness but could also be seen as an opportunity for upskilling. This underscores the importance of enhancing skills to manage and optimize AI systems effectively. Additionally, AI has created opportunities in expanding AI-related job sectors in Bangladesh, supported by global AI trends, increasing government recognition of freelancing, new job creation, and improved productivity. Among these factors, AI acceptance, government recognition, and productivity enhancements can be considered strengths. The practical implications suggest that freelancers and policymakers alike can leverage these strategic insights to enhance the Bangladeshi freelancing sector. This study's further emphasize potentials for AI skills development, aiding the growth of freelancing careers. While existing studies tend to focus on AI's benefits and risks, few explore the strategic implications within the Bangladeshi freelancing context. Academics, policymakers, freelancers, and other stakeholders invested in advancing digital Bangladesh should engage in further research on developmental pathways for upskilling and supporting freelancing as a robust sector. IUBAT Review—A Multidisciplinary Academic Journal, 7(2): 100-113IUBAT Review370The authors highlight that the current lack of AI-related skills is a weakness but could also be seen as an opportunity for upskilling, underscoring the importance of enhancing skills to manage and optimize AI systems effectively.IUBAT Review["Kamal Hossain", "Kazi Md Fahim Ahmed", "Sabrina Hossain Fariha"]2024-12-31T00:00:00 b2eba7fd97478f3c3f1e1e3a40e77f453c1e8a80The integration of artificial intelligence (AI) into general surgery has brought significant advancements in surgical precision, postoperative complication prediction, and intraoperative assistance. Despite its potential, AI faces challenges regarding its broad implementation in clinical practice. This systematic review aims to assess the impact of AI on clinical outcomes in general surgery, including diagnostic accuracy, complication prediction, and surgical error reduction. A systematic review was conducted using PubMed, Scopus, and Web of Science databases, focusing on studies published between 2020 and 2024. Inclusion criteria required studies that evaluated AI’s role in general surgery with a sample size of at least 50 patients. Studies reporting both qualitative and quantitative outcomes, including complication prediction and intraoperative assistance, were included. Ten studies were selected, involving a total of 12,580 patients undergoing various surgical procedures such as hepatectomies, colectomies, and cholecystectomies. AI significantly improved complication prediction accuracy (25% improvement over traditional methods) and reduced intraoperative errors by 18%. Additionally, AI-assisted surgeries showed an average reduction of 30 minutes in surgical time, from 150 to 120 minutes in complex cases. AI has proven to be a valuable tool in general surgery, particularly in complex procedures where precision and complication prediction are critical. However, further studies are needed to validate AI models across diverse populations and healthcare settings to ensure widespread adoption.International Journal of Research in Medical Sciences20Assessment of the impact of AI on clinical outcomes in general surgery, including diagnostic accuracy, complication prediction, and surgical error reduction finds AI significantly improved complication prediction accuracy and reduced intraoperative errors by 18%.International Journal of Research in Medical Sciences["Sergio M. S. Fuentes", "Luis A F. Ch\u00e1vez", "Eduardo M. M. L\u00f3pez", "Christian D. C. Cardona", "L. L. M. Goti"]2024-12-31T00:00:00 3d8ae6a87a5c562c48ba613e6850b2cba981934dEffective assessments guide student learning, refine teaching practices, ensure curriculum alignment, and foster workforce readiness. However, the emergence of generative artificial intelligence (GenAI) tools, such as ChatGPT, has significantly disrupted traditional assessment processes, raising concerns about academic integrity and necessitating innovative approaches. While higher education institutions are making strides in adapting to this new reality, the foundation of effective assessment remains educators’ assessment literacy. This paper responds to the critical need for improving educators’ assessment literacy by introducing a comprehensive model – the ‘AARDVARC’ framework – that outlines eight key attributes of effective assessment: alignment, authenticity, reliability, developmental appropriateness, validity, accessibility, realism, and constructiveness. By fostering assessment literacy, educators can design innovative, equitable, and discipline-relevant assessments that incorporate GenAI responsibly and meaningfully. The paper further offers actionable recommendations for adapting university assessments to align with institutional goals and meet the evolving demands of the educational landscape. These strategies aim to ensure that assessments continue to promote student engagement, maintain academic standards, and reflect the realities of modern education.Education, research and perspectives00A comprehensive model is introduced – the ‘AARDVARC’ framework – that outlines eight key attributes of effective assessment: alignment, authenticity, reliability, developmental appropriateness, validity, accessibility, realism, and constructiveness.Education Research and Perspectives["Elaine Chapman", "Jian Zhao", "Peyman G. P. Sabet"]2024-12-31T00:00:00 bb9b4087e4899dd21ab7c66183eda37ad5679486Purpose: The purpose of this study were to analyze the user-centered usage status of ICT-based artificial intelligence care services for establishing a safety net for the vulnerable and to suggest a policy direction for improving the service. Method: This study targeted 400 people using ICT-based artificial intelligence care services to build a safety net for the vulnerable. in N-gun, Gyeongsangnam-do. Analyzed the types of services used, main utterances, emotional conversations, and emergency SOS usage status for three months from May to August 2024 using SPSS statistical software. Result: Among the study subjects, there were 352 women (88.0%) and 48 men (12.0%). The service most used by users was the Sosiktoktok, followed by emotional conversations, music healing, weather, fortune telling, radio, and mood. In addition, the number of uses per subject over 3 months was 249.10, and the number of uses per person by gender was higher for men (406.71 times) than for women (239.16 times), and the number of uses was higher for the elderly over 70 years old. The most common user utterance was weather search, followed by thank you, hello, and I heard. The most common keyword for emotional conversation was happiness, followed by loneliness, depression, and psychological stability. The emergency SOS service was used 17 times in total. This study found that vulnerable groups use emotional care most among ICT-based AI care services, and men use AI care services more frequently than women, and the elderly over 70 years old use AI care services more frequently. Conclusion: Based on the results of this study, it is suggested that ICT-based AI care services in the future expand emotional support services according to the needs of users, utilize AI devices in the form of animals or humans rather than speakers so that the vulnerable class can feel familiar with AI care media, and simplify services so that the elderly over 70 years of age can easily use them.Forum of Public Safety and Culture00It is suggested that ICT-based AI care services in the future expand emotional support services according to the needs of users, utilize AI devices in the form of animals or humans rather than speakers so that the vulnerable class can feel familiar with AI care media, and simplify services so that the elderly over 70 years of age can easily use them.Forum of Public Safety and Culture["Chi Yang Yoon", "Mi-Yang Jeon"]2024-12-31T00:00:00 747bf31c572b7c945bf805b8e25e3d8d8738343aOBJECTIVE This study aims to examine neonatal intensive care unit (NICU) nurses' perceptions of artificial intelligence (AI) technologies, particularly language models, and their impact on nursing practices. BACKGROUND AI is rapidly spreading in healthcare, transforming nursing practice. Understanding the role of AI in NICUs in the discharge process is crucial for understanding nurses' perceptions of these technologies. METHODS The qualitative, phenomenological study used semi-structured interviews. Data were collected in a public hospital in Gaziantep from January to June 2024. Fifteen NICU nurses participated. Data were analyzed using content analysis. RESULTS Most nurses found AI to be a valuable tool for saving time and simplifying information delivery in clinical processes. However, concerns were raised about AI potentially reducing human interaction and weakening the use of professional judgment. Serious concerns about AI's reliability and ethical implications were also expressed. CONCLUSIONS AI is considered a potentially supportive tool in nursing practice, but its integration must consider the ethical implications and impact on the use of professional judgment. Nursing is based on human interactions and AI should be considered an additive tool to enhance care. IMPLICATIONS FOR PRACTICE AND RESEARCH AI integration in nursing requires careful and balanced implementation. Future research should delve deeper into the ethical dimensions of AI and its long-term effects on nursing practices.Journal of Perinatal & Neonatal Nursing210Examining neonatal intensive care unit (NICU) nurses' perceptions of artificial intelligence (AI) technologies, particularly language models, and their impact on nursing practices concludes that AI is considered a potentially supportive tool in nursing practice, but its integration must consider the ethical implications and impact on the use of professional judgment.The Journal of perinatal & neonatal nursing["A. Co\u015fkun", "Carole Kenner", "Erhan Elmao\u011flu"]2024-12-31T00:00:00 18ac591c3d76f50188743d42a9a4aa15bd1bb9bdAs artificial intelligence (AI) technology proliferates, its impact on second language education, has garnered significant attention. This study explores the strategies adopted by university students in utilizing AI tools, including machine translation and generative AI, during their second language thesis writing process. By conducting semi-structured interviews with Japanese majors, the study delves into students’ utilization strategies, perceptions, and their desired assistance when applying AI tools for academic writing. The findings reveal that students frequently rely on machine translation for comprehension and initial translation, while generative AI aids in summarizing, outlining, and refining language. While acknowledging the efficiency and quality enhancements AI brings, students also note limitations such as translation quality issues and potential misunderstandings with generative AI. Participants express needs for instructor guidance, training, and recommendations for suitable AI tools. This study concludes that AI tools hold promise for enhancing academic writing but underscores the importance of future research on larger scales to evaluate their long-term effects on writing skills and creativity. Furthermore, developing user-centered AI tools and integrating AI into writing curricula are recommended to maximize the educational benefits of AI-assisted writing.Arts, Culture and Language10It is concluded that AI tools hold promise for enhancing academic writing but underscores the importance of future research on larger scales to evaluate their long-term effects on writing skills and creativity.Arts, Culture and Language["Ruixue Tang"]2024-12-31T00:00:00 0f894c245a4ac00d704f71e13409c5817f4bfebcPurpose This study aims to present systematic analysis of research concerning the intersection of human resource management (HRM) and the integration of artificial intelligence (AI) technologies within a digitalized economy further analyzing the trends in research with specific emphasis on utilization of diverse AI technologies within HRM. Design/methodology/approach This research is based on bibliometric analyses and content analyses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses review methodology is implemented, using the Scopus database as the primary source which gathered 1,414 articles between 1978 and 2024. This study investigates publishing trends, the most prolific countries, universities, journals, publications and authors in the field. Further, the research trends based on the use of AI in HRM were accomplished through scientific mapping using VOSviewer. Findings The outcomes demonstrate a rising inclination toward using various AI techniques in HRM which shows increasing influence and growing appeal of the subject. The research uncovers the deployment of diverse technologies, including emerging ones, within the HRM field. It accomplishes this by scrutinizing the connections among various keywords and unearths both contradictions and focal areas of interest within the domain. Originality/value The study contributes to the existing body of literature by ascertaining suggestions for further research in the field of HRM integrated with various AI technologies. The integration of these technologies in HR holds a promising and optimistic outlook for the managers, thereby enhancing employee productivity. Global Knowledge Memory and Communication930This study investigates publishing trends, the most prolific countries, universities, journals, publications and authors in the field, and the research trends based on the use of AI in HRM were accomplished through scientific mapping using VOSviewer.Global Knowledge, Memory and Communication["Meenal Arora", "Jaya Gupta", "Amit Mittal", "Anshika Prakash"]2024-12-31T00:00:00 9e58aa638bdacc0a228948c962bf0563422d9f53Artificial intelligence (AI) is increasingly advancing, developing, and finding applications in various aspects of life and work. Some of the most significant social areas, such as law and dispute resolution, have not remained immune to the influence of AI. In this sense, both the positive aspects, useful elements, as well as the potential dangers and risks that this technology brings, are being increasingly considered. Arbitration, as a contractual and voluntary method of dispute resolution, presents fertile ground for the application of various technological solutions to accelerate and ease the process. Investment arbitration, which deals with resolving disputes between a host state and a foreign investor, has its specificities that need to be addressed in the context of AI application. Therefore, this paper provides a review of the benefits of AI in arbitration, analyzes its shortcomings and risks, offers an overview of certain smart tools available, and presents a general view on the ethical and legal regulation of AI application in arbitration. The conclusion of the paper is that the possibilites for further development and refinement of AI technology for use in arbitration are numerous, but should be approached cautiously. It will be necessary to devise an optimal, flexible regulation to standardize the rudimentary rules of AI application in order to meet the basic requirement: benefit for humanity and society, while respecting the specific values and principles on which investment arbitration is based.Social Informatics Journal160It will be necessary to devise an optimal, flexible regulation to standardize the rudimentary rules of AI application in order to meet the basic requirement: benefit for humanity and society, while respecting the specific values and principles on which investment arbitration is based.Social Informatics Journal["Milica Njegovan", "Sne\u017eana Prelevi\u0107 Plav\u0161i\u0107"]2024-12-31T00:00:00 e8cb2266a3c2df1b9627bdeef9f0278f68adb60eArtificial Intelligence (AI) is revolutionizing the field of clinical case reporting and review, providing innovative solutions to enhance diagnostic accuracy, treatment planning, and patient outcomes. This article explores the integration of AI in clinical settings, discussing its potential benefits, challenges, and future directions. The evolution of AI from simple algorithms to sophisticated machine learning models has enabled healthcare professionals to harness vast amounts of data, leading to more precise and personalized care. However, the widespread adoption of AI in clinical case reporting also raises significant ethical and regulatory concerns. This review critically examines the latest advancements in AI applications within clinical case reporting and the implications for modern medicine. The article concludes with recommendations for future research and the development of guidelines to ensure safe and effective AI integration in clinical practice.New Medical Innovations and Research00This article explores the integration of AI in clinical settings, discussing its potential benefits, challenges, and future directions, and critically examines the latest advancements in AI applications within clinical case reporting and the implications for modern medicine.New Medical Innovations and Research["Ashish Pandey"]2024-12-31T00:00:00 9a5d13acec499dbf2853701634053167fed05d1cScientific writing is a major consideration when writing a research paper, as it encompasses all aspects of the research. With the rise of digitalization, new opportunities have emerged for the development of Artificial intelligence (AI)-driven tools and algorithms designed to analyze the vast amounts of data being uploaded. It has allowed researchers and practitioners to more efficiently access and evaluate a vast array of scientific papers. This capability facilitates the connection of related studies from the past, identifies research gaps, and speeds up the processes of literature review, evidence generation, and knowledge discovery. Despite these advancements, AI tools are subject to ethical considerations, regulatory approval, compliance with data protection regulations, journal guidelines, transparency, and public perception. Some text prompts are used to instruct AI tools to generate effective information. Fostering trust and transparency with AI tools in scientific writing includes operationalizing frameworks, addressing discrepancies, reducing plagiarism, and generating new innovative ideas. Future trends suggest that AI capabilities will keep advancing and developing, underscoring the need for ethical considerations and the need to balance AI automation with human expertise. However, it cannot replace the creativity and critical thinking skills that are crucial for scientific writing and research. The key objective of this review is to discuss and assess various AI-based tools and algorithms, focusing on their key features and how they can support researchers and authors in enhancing their writing skills.International Journal of Research in Medical Sciences300The key objective of this review is to discuss and assess various AI-based tools and algorithms, focusing on their key features and how they can support researchers and authors in enhancing their writing skills.International Journal of Research in Medical Sciences["Anil Sharma", "Praveen Rao", "Mohammad Zubair Ahmed", "Krishnakant Chaturvedi"]2024-12-31T00:00:00 346224f7e1c9deaf35216d7eeb333cc777e7b23cThe subject of international jurisdiction aims to regulate liability in the context of disputes related to artificial intelligence in relationships tainted with a foreign element, leading to various relationships between the producer and supplier or the supplier and consumer, which may result in conflicts. This necessitates legal protection to obtain compensation for specific damages, especially if caused by artificial intelligence products. It seeks to identify the responsible party for the damage if the injured party succeeds in proving the elements of liability, ensuring compensation. This has raised several legal issues, particularly concerning the legal adaptation of artificial intelligence to regulate liability and thereby define the parameters of international jurisdiction. It also addresses the applicable law governing procedures in such disputes. The research adopts a descriptive-analytical comparative methodology, leading to findings and recommendations. Among these, a notable recommendation is the need for the Jordanian legislature to enact specific legislation regulating provisions related to artificial intelligence and establish a system for compulsory insurance against liability arising from damages caused by artificial intelligence programs.Jordanian Journal of Law and Political Science00Jordanian Journal of Law and Political Science["Ghazi Alsalaita", "Dr. Mohammad Saleh Alqudah"]2024-12-31T00:00:00 21c2cec35dde24613e5a3786b223f082af404219Artificial Intelligence (AI) has rapidly emerged as a transformative force in various domains, including healthcare. In mental health, AI presents a plethora of opportunities to improve assessment, diagnosis, treatment, and overall patient care. However, integrating AI into mental health practices comes with its own set of challenges. This review explores the current landscape of AI applications in mental health, discusses the challenges encountered, and highlights opportunities for future development.International Journal of Nursing Education and Research40The current landscape of AI applications in mental health, discusses the challenges encountered, and highlights opportunities for future development are explored.International Journal of Nursing Education and Research["Samruddhi Nelson Chauhan", "Ashwini K Vaidya"]2024-12-31T00:00:00 98de1b4836d38d7b21ca2cf4c1ea36f21305008eWith the rapid development of artificial intelligence technology, the field of education is undergoing profound changes. As the core of educational innovation, college teachers need to closely integrate their professional growth path with intelligent technology to meet the challenges and opportunities of the new era. Based on the systematic combing of existing literature, this study proposes the optimization path for the professional growth of college teachers empowered by artificial intelligence technology. First, the potential of AI technology in promoting teachers' professional development is explored from three aspects: personalized growth path design, intelligent training platform construction, and educational data analysis and performance evaluation. Second, the challenges in the process of technology application are analyzed, including the difficulty of technology integration, ethical and privacy issues, and the adaptability of teachers' role transformation. Finally, the study summarizes the key directions for optimizing the professional growth path of college teachers, emphasizing the construction of a theoretical framework and practical strategies for AI-centered teacher growth. In conclusion, this paper provides theoretical support and practical reference for teachers' professional development in the era of artificial intelligence.Region - Educational Research and Reviews00The study summarizes the key directions for optimizing the professional growth path of college teachers, emphasizing the construction of a theoretical framework and practical strategies for AI-centered teacher growth.Region - Educational Research and Reviews["Qiuyan Lu"]2024-12-31T00:00:00 de1bd1741511b2682772ab0b7417d6b0b180839aIn recent years, the integration of logistics and artificial intelligence has become increasingly important across various industries, fostering innovation and progress. This study seeks to uncover key contributors, prominent keywords, influential journals, and leading countries at the crossroads of logistics and AI to provide direction for future research. By analyzing 1118 articles from the past decade (2015–2024) using the Web of Science (WoS) database and VOSviewer software, several critical insights were derived. The analysis included co-occurrence of keywords, citation patterns (articles, sources, institutions, and countries), and co-authorship networks. Results from the keyword analysis reveal that “artificial intelligence” and “logistics” dominate, followed by terms such as “machine learning,” “deep learning,” “blockchain,” “optimization,” and “internet of things.” Citation analysis identified the study by Dwivedi et al. (2021) as the most cited work, with 1009 citations. Among journals, Engineering Applications of Artificial Intelligence stands out, featuring 58 papers and 894 citations. In co-authorship analysis, Angappa Gunasekaran emerges as the most impactful author with six publications and 330 citations. Institutionally, the Chinese Academy of Sciences leads with 342 citations, while China ranks first among countries with 3979 citations, followed by India and the United Kingdom. This bibliometric analysis highlights pivotal resources, influential studies, and leading contributors in the field of logistics and artificial intelligence, serving as a foundational guide and valuable reference for future researchers in this domain. International Journal of Applied Methods in Electronics and Computers00This bibliometric analysis highlights pivotal resources, influential studies, and leading contributors in the field of logistics and artificial intelligence, serving as a foundational guide and valuable reference for future researchers in this domain.International Journal of Applied Methods in Electronics and Computers["Selime Sinem Bahar", "Muslume Beyza Yildiz", "Serkan Gerz", "E. Yasin", "Ahmet Goktas", "M. Koklu"]2024-12-31T00:00:00 c140b96fa95f5da3b6f7b81697de6e6f88c13361The main aim is to investigate the impact of Artificial Intelligence (AI) on various aspects of disclosing financial information. The case study used a mixed methods approach, and a sample of stakeholders dealing in the Iraq Stock Exchange was taken during 2023 in Iraq. Data collection mainly included a survey conducted on 168 beneficiaries who trade stocks in the stock market, after which 22 clients were selected for personal interviews based on their voluntary willingness to participate in individual interviews. The results revealed beneficiaries prefer to use Artificial Intelligence to get new ideas for tasks or to help them with "plans" for their projects. The results also revealed that artificial intelligence applications raise concerns about ethics and reliability in their use to disclose financial information and their exploitation by management to obtain financial funding against beneficiaries' will. Finally, the results revealed that most current educational curricula differ from technological developments. Based on the participant's responses about receiving training in artificial intelligence, their concerns were related to the need for appropriate education to deal with artificial intelligence tools.Financial and credit activity problems of theory and practice510The results revealed beneficiaries prefer to use Artificial Intelligence to get new ideas for tasks or to help them with "plans" for their projects, and most current educational curricula differ from technological developments.Financial and credit activity problems of theory and practice["Alikhalaf Gatea"]2024-12-31T00:00:00 ec59a3e906951aa1a36e68d7c0336b1930730ebdObjective: Chatbots have been frequently used in many different areas in recent years, such as diagnosis and imaging, treatment, patient follow-up and support, health promotion, customer service, sales, marketing, information and technical support. The aim of this study is to evaluate the readability, comprehensibility, and accuracy of queries made by researchers in the field of health through artificial intelligence chatbots in biostatistics. Methods: A total of 10 questions from the topics frequently asked by researchers in the field of health in basic biostatistics were determined by 4 experts. The determined questions were addressed to the artificial intelligence chatbots by one of the experts and the answers were recorded. In this study, free versions of most widely preferred ChatGPT4, Gemini and Copilot chatbots were used. The recorded answers were independently evaluated as “Correct”, “Partially correct” and “Wrong” by three experts who blinded to which chatbot the answers belonged to. Then, these experts came together and examined the answers together and made the final evaluation by reaching a consensus on the levels of accuracy. The readability and understandability of the answers were evaluated with the Ateşman readability formula, Sönmez formula, Çetinkaya-Uzun readability formula and Bezirci-Yılmaz readability formulas. Results: According to the answers given to the questions addressed to the artificial intelligence chatbots, it was determined that the answers were at the “difficult” level according to the Ateşman readability formula, “insufficient reading level” according to the Çetinkaya-Uzun readability formula, and “academic level” according to the Bezirci-Yılmaz readability formula. On the other hand, the Sönmez formula gave the result of “the text is understandable” for all chatbots. It was determined that there was no statistically significant difference (p=0.819) in terms of accuracy rates of the answers given by the artificial intelligence chatbots to the questions. Conclusion: It was determined that although the chatbots tended to provide accurate information, the answers given were not readable, understandable and their accuracy levels were not high.European Journal of Therapeutics420It was determined that although the chatbots tended to provide accurate information, the answers given were not readable, understandable and their accuracy levels were not high.European Journal of Therapeutics["\u0130lkay Do\u011fan", "P\u0131nar G\u00fcnel", "Ihsan Berk", "Buket \u0130pek Berk"]2024-12-31T00:00:00 057d4be50c515dbeb0251b9b1d621895c4737c38The integration of Artificial Intelligence (AI) in healthcare and health professions education is increasingly gaining attention due to its potential benefits. AI technologies have demonstrated potentials in enhancing teaching and learning processes such as personalized learning systems, virtual reality simulations, and automated assessments. However, most of the medical institutions are yet to be incorporated AI to their existing curriculum currently. It might be contributed by the considerable challenges such as limited faculty familiarity with AI technologies, availability of resources, algorithmic bias, ethical concerns, and data privacy issues. This article explores the potential transformative impact AI can have on medical education, focusing on its benefits, challenges, and the strategies required to successfully incorporate it into the curriculum.International Journal of Transformative Health Professions Education20The potential transformative impact AI can have on medical education is explored, focusing on its benefits, challenges, and the strategies required to successfully incorporate it into the curriculum.International Journal of Transformative Health Professions Education["M. Htay"]2024-12-31T00:00:00 0fb94721cbdc1e6b5becc1b5789ac7e8dd45e8d8Integrating artificial intelligence (AI) into healthcare is a revolutionary step and an excellent opportunity to improve patient outcomes, operational efficiencies, and diagnostic precision. However, ethical, legal, and social challenges persist, which must be appropriately addressed to ensure equitable, safe, and trustworthy healthcare systems.Journal of Biomedical Sciences10Integrating artificial intelligence into healthcare is a revolutionary step and an excellent opportunity to improve patient outcomes, operational efficiencies, and diagnostic precision, but ethical, legal, and social challenges persist.Journal of Biomedical Sciences["Bedanta Roy"]2024-12-31T00:00:00 841d60d0d7b5ac383d982c66ef740e9f6ca1ae0fIntroduction: The research presented in this article is located in the field of general pedagogy. They constitute a reflection not only on the effective, but also on the legitimate use of artificial intelligence (AI) in the educational process. Research Aim: The aim of the research is to formulate pedagogical criteria and the resulting conclusions with a view to enabling educators to optimally integrate AI into the educational process. Evidence-based Facts: It is assumed that the subject of pedagogical research is characterised by an anthropocentric dimension and concerns the following areas: descriptive, normative and optative-praxiological. Summary: The criteria presented are the result of correspondences with the accepted areas of pedagogical research and do not constitute a closed canon.Lubelski Rocznik Pedagogiczny00Pedagogical criteria presented are the result of correspondences with the accepted areas of pedagogical research and do not constitute a closed canon.Lubelski Rocznik Pedagogiczny["Marek Jeziora\u0144ski"]2024-12-31T00:00:00 d5471ce02804ebf816663bb895b3863a2a527f7dAim: The aim of this study is to examine the relationship between field hockey referees' attitudes towards artificial intelligence and their openness to organisational change. Methods: In the study, in addition to examining the effect of the scales among themselves, evaluations were made in terms of demographic characteristics by using general attitude towards artificial intelligence and organisational openness to change scales. In this context, the sample group of the study consisted of a total of 112 field hockey referees, 68 male and 44 female, affiliated to the Turkish Hockey Federation. Descriptive survey model was used for the study. In order to obtain the study data, ‘Openness to Organisational Change Scale’ developed by Çalışkan (2022) and ‘General Attitude Towards Artificial Intelligence Scale’ developed by Schepman and Rodway (2020) and adapted into Turkish by Kaya et al., (2022) were applied. Results: As a result of the study, a positive relationship (r=0.716; pInternational Journal of Sport, Exercise &amp; Training Sciences360The relationship between field hockey referees' attitudes towards artificial intelligence and their openness to organisational change is examined and a positive relationship is found.International Journal of Sport, Exercise &amp; Training Sciences["Ye\u015fim Bayrakdaro\u011flu", "\u00dcst\u00fcn T\u00fcrker", "Mustafa Ayhan", "Mahir Kaplan"]2024-12-31T00:00:00 4ea142e51e0415a2715389a23d2910bd29c5dc30Generative artificial intelligence is equipped with more advanced functions than existing artificial intelligence technology, raising new problems that were difficult to predict in the past. For example, generative artificial intelligence handles and handles data containing personal information in the stage of learning and processing vast amounts of data, and in this process, it is very difficult for individual data subjects to recognize and control that their information is processed for generative artificial intelligence learning. In addition, unlike existing general artificial intelligence, generative artificial intelligence has the ability to learn data by itself and generate new original results based on this, and in the course of this series of processes, major legal issues related to personal information protection collided. In order to discuss legal issues related to personal information protection related to generative artificial intelligence, the first relevant constitutional discussion should be made. In terms of guaranteeing basic rights, a conflict of basic rights between the data subject's right to self-determination of personal information and the freedom of job choice of a person who develops or operates generative artificial intelligence can typically arise. These basic rights conflict problems are expected to occur more frequently with the advent of generative artificial intelligence technology, and to solve this problem, both the right to self-determination of personal information and the freedom of occupation must be guaranteed in a harmonious and appropriate balance, and legal and institutional improvement is needed to effectively guarantee the rights of data subjects in response to the generative artificial intelligence era. Based on this awareness of the problem, this paper analyzes the legislative limitations and problems of Korea's current laws and regulations to discuss major legal issues related to personal information protection related to generative artificial intelligence, and based on this, legislative improvement measures were derived to effectively guarantee the rights of information subjects to use generative artificial intelligence technology and to resolve legal obstacles to the development and development of generative artificial intelligence technology.Korean Constitutional Law Association00This paper analyzes the legislative limitations and problems of Korea's current laws and regulations to discuss major legal issues related to personal information protection related to generative artificial intelligence, and based on this, legislative improvement measures were derived to effectively guarantee the rights of information subjects to use generative artificial intelligence technology and to resolve legal obstacles to the development and development of generative artificial intelligence technology.Korean Constitutional Law Association["Kang Han Kim"]2024-12-31T00:00:00 6ca633c9417f0acfdde546474756712452d71a9fThis study investigates the impacts of artificial intelligence (AI) and knowledge management (KM) on the competitive advantage (CA) of enterprises. In particular, it delves into the mediating role of knowledge management in the relationship between artificial intelligence and competitive advantage. Structural Equation Modelling (SEM) is employed to assess the relationships among the variables, with data drawn from a sample of 402 respondents in China. The survey findings reveal that the adoption of artificial intelligence significantly improves both knowledge management and the competitive advantage of enterprises; knowledge management not only directly impacts competitive advantage but also serves as a mediating variable in the relationship between artificial intelligence and competitive advantage. This mediating effect of knowledge management demonstrates how the acquisition, sharing and application of knowledge, propelled by artificial intelligence, can enhance competitive advantage over the long term. The findings of this study validate and extend the existing body of knowledge regarding the influences of AI and KM on enhancing organisational competitive advantage. It provides valuable insights for the strategic integration of artificial intelligence within a knowledge-driven framework, particularly within the context of Chinese enterprises. By addressing gaps in the related literature, it contributes to both the theoretical advancement and practical application of enterprise management.Journal of Information &amp; Knowledge Management00The survey findings reveal that the adoption of artificial intelligence significantly improves both knowledge management and the competitive advantage of enterprises; knowledge management not only directly impacts competitive advantage but also serves as a mediating variable in the relationship between artificial intelligence and competitive advantage.Journal of Information &amp; Knowledge Management["Daojun Yuan", "Jung Kwan Kim", "Cong Gao"]2024-12-31T00:00:00 1960855290f2dc6b2674f5e425067db6bf349a5aDifferential Game Theory has seen significant advancements in recent years, driven by numerous scholars exploring theoretical and practical aspects of the field. In this paper, we summarize the principal findings in pursuit-evasion games, focusing on Cop-Win and Robber-Win games on graphs. A dedicated section explores the demonstration technique introduced by G. Ibragimov, showcasing how evasion or capture can be achieved in a chase game by defining time intervals. The paper concludes by presenting key open problems in this area, with a special emphasis on applying artificial intelligence to trajectory prediction.WSEAS Transactions on Systems110The principal findings in pursuit-evasion games, focusing on Cop-Win and Robber-Win games on graphs are summarized, with a special emphasis on applying artificial intelligence to trajectory prediction.WSEAS TRANSACTIONS ON SYSTEMS["Massimiliano Ferrara", "B. A. Pansera"]2024-12-31T00:00:00 010551c4b082054bad270695a92abb36d5b9b6edThis article investigates how human and artificial intelligence (AI) influence each other and what prospects of this unprecedented coexistence might be in the future in relation to creative writing. Starting from the definition and comparison of different types of natural intelligence and the connection of human intelligence with the development of AI, this short study analyses fourteen Bard-generated application letters for jobs in music or the hospitality industry prompted by three different descriptors: the first group includes four regular application letters; another group includes four humorous texts created by the same program without specific prompts apart from asking them to be humorous; and the third group includes six texts which provide more detailed prompts related to the specificities of humour. The humour analysis is based on the concept of humour transaction schema and takes into account the linguistic, semantic, and socio-cultural characteristics of the humour products. The analysis demonstrated that AI-generated texts can be humorous and entertaining, but that they also lack human imagination in terms of going beyond what is already known. As application of AI language-learning machines and models have been in rapid and diversified expansion worldwide, more questions about the possible risks related to the use of AI language programs and the readership response to AI-created texts are discussed, concluding that AI sets forth new questions in relation to the future of creative writing.The European Journal of Humour Research510Analysis of Bard-generated application letters for jobs in music or the hospitality industry prompted by three different descriptors demonstrated that AI-generated texts can be humorous and entertaining, but that they also lack human imagination in terms of going beyond what is already known.The European Journal of Humour Research["Vesna Sulji\u0107", "Ajla Pervan"]2024-12-31T00:00:00 91a814e76c5c45f93be421e46826630111581145 Pharmacovigilance (PV) is a data-driven method that quickly identifies medication safety risks by processing reports of suspected Adverse Events (AEs) and extracting health data. The first steps in the PV case processing cycle include data collection, data entry, coding, preliminary validity and completeness checks, and medical evaluation for severity, seriousness, expectation, and causality. Afterward, a report is submitted, quality is checked, and data storage and maintenance are performed. This process is costly and time-consuming, as it requires both a workforce and technology. Conversely, Artificial Intelligence (AI) is used to reduce this time investment and increase data accuracy. AI includes machine learning methods like deep learning and natural language processing, which can recognize and retrieve information on adverse drug occurrences. By doing so, it is possible to optimize the pharmacovigilance process and improve the tracking of documented adverse medication occurrences. AI's advancement in pharmacovigilance raises concerns about potential changes in drug safety professionals' roles, prompting curiosity about their future in an AI-assisted workplace. Artificial Intelligence (AI) should augment human intelligence, not replace human specialists. It's crucial to highlight and ensure AI improves PV more than it causes problems. The pharmaceutical business faces significant obstacles and opportunities, especially when it comes to implementing and employing advanced Information Technology (IT) in Pharmaceutical Monitoring (PMS) Systems. Automation improves PV in several ways (e.g., boosting data quality or improving consistency). Several themes are discussed, outlining the challenges encountered, exploring potential solutions, and emphasizing the need for further research. The accepted use case involves automating the workflow in the case of ICRS. Current Computer Science10Artificial Intelligence (AI) is used to optimize the pharmacovigilance process and improve the tracking of documented adverse medication occurrences, and automating the workflow in the case of ICRS.Current Computer Science["Dinesh Kumar", "Amandeep Kaur", "Shruti", "Davender Kaur"]2024-12-31T00:00:00 e31cbb24712d0317fb6c7cd45f7b97246487a5ebEthical issues center themselves around Artificial Intelligence (AI) and the various opportunities and threats that it offers. The purpose of this research paper is to explain the positive side of AI, as well as the problems that require addressing. The actual study starts with the aims and objectives of the research and briefly highlights the importance of the research topic, the operational definitions of the terms under study, and the conceptual framework. It covers the history of AI, development in technology, and consequences on society. The benefits and the disadvantages of AI are included in the evaluation of its impact on specific sectors. Social consequences are considered, with an emphasis on how the application of AI will influence social structures and groups. The legal issues are considered, turning to the existing legislation and the possible legal repercussions. Ethical issues are considered with a focus on the proper use of artificial intelligence systems. Finally, the general ideas developed in the research are briefly highlighted, the thesis is repeated, and attention is drawn to the fact that further investigation and prevention efforts are required to tackle AI’s ethical issues. The findings of the study are then followed by suggestions for future work in this area.Edelweiss Applied Science and Technology00The positive side of AI, as well as the problems that require addressing, are explained, to explain the positive side of AI, as well as the problems that require addressing.Edelweiss Applied Science and Technology["Omar Alsahafi", "Abdulrahman Alfaleh", "Mshary Altamimi", "Ahmed Alghamdi", "Abdullah Alhafi", "Abdelrahman Altigani", "M. A. Elsadig", "S. A. Sulieman", "Yasir Abdelgadir Mohamed"]2024-12-31T00:00:00 714702ae356fc46d0e0db1540678521b79d6e494The detection and diagnosis of brain tumors, a critical medical challenge, have greatly benefited from the application of Artificial Intelligence (AI). This review paper explores the advancements, methods, and technologies of AI in the detection and classification of brain tumors from medical imaging modalities. It also highlights the importance of machine learning (ML) and deep learning (DL) algorithms, particularly Convolutional Neural Networks (CNNs), in improving diagnostic accuracy, early detection, and prognosis prediction. Moreover, the paper addresses challenges and future directions in integrating AI with clinical practices for brain tumor management. International Journal for Research in Applied Science and Engineering Technology00The advancements, methods, and technologies of AI in the detection and classification of brain tumors from medical imaging modalities are explored and the importance of machine learning (ML) and deep learning algorithms, particularly Convolutional Neural Networks (CNNs), in improving diagnostic accuracy, early detection, and prognosis prediction is highlighted.International Journal for Research in Applied Science and Engineering Technology["Ms. Mahadevi Pundlik Khyade"]2024-12-31T00:00:00 a40da7d01284b00bb28136984cfb250b1b089d67Process monitoring is important for ensuring operational reliability and preventing occupational accidents. In recent years, data-driven methods such as machine learning and deep learning have been preferred for fault detection and diagnosis. In particular, unsupervised learning algorithms, such as auto-encoders, exhibit good detection performance, even for unlabeled data from complex processes. However, decisions generated from deep-neural-network-based models are difficult to interpret and cannot provide explanatory insight to users. We address this issue by proposing a new fault diagnosis method using explainable artificial intelligence to break the traditional tradeoff between the accuracy and interpretability of deep learning model. First, an adversarial auto-encoder model for fault detection is built and then interpreted through the integration of Shapley additive explanations (SHAP) with a combined monitoring index. Using SHAP values, a diagnosis is conducted by allocating credit for detected faults, deviations from a normal state, among its input variables. The proposed diagnosis method can consider not only reconstruction space but also latent space, unlike conventional methods, which evaluate only reconstruction error. The proposed method was applied to two chemical process systems and compared with conventional diagnosis methods. The results highlight that the proposed method achieves the exact fault diagnosis for single and multiple faults and, also, distinguishes the global pattern of various fault types.IEEE Transactions on Industrial Informatics3310A new fault diagnosis method using explainable artificial intelligence to break the traditional tradeoff between the accuracy and interpretability of deep learning model is proposed and achieves the exact fault diagnosis for single and multiple faults and distinguishes the global pattern of various fault types.IEEE Transactions on Industrial Informatics["Kyojin Jang", "K. Pilario", "Nayoung Lee", "I. Moon", "Jonggeol Na"]2025-01-01T00:00:00 e1034ea7c30036a83109ce31d02533ffa2b73d69In biological research and clinical settings, a significant convergence between Artificial Intelligence (AI) and genomics is transforming the landscape. Genomics, which involves the comprehensive study of genomes, has been pivotal in understanding genetic diversity and the foundations of health and disease. The fast-paced progress in genomic sequencing technologies has generated vast datasets, necessitating advanced analytical tools to interpret this information effectively. The narrative starts by providing a foundational overview of genomics, touching on genetic variation, inheritance, and the evolution of sequencing methods. It then shifts to AI, explaining key concepts of machine learning and deep learning—crucial for analyzing high-dimensional genomic data. The discussion illustrates AI’s transformative applications in genomics, including variant calling, gene expression profiling, and functional annotation, thus advancing precision medicine by tailoring treatment strategies to individual genetic profiles. It also addresses challenges linked to the integration of AI in genomics, such as data privacy, information security, and the potential biases in AI algorithms that may affect fairness in clinical genomics. Emerging trends, including the integration of multi-omics data, are also examined, underscoring AI's role in synthesizing genomic, proteomic, and metabolomic data for a comprehensive understanding of biological networks. Additionally, the development of explainable AI models is highlighted, promoting transparency in genomic research. This exploration underscores AI's transformative potential in genomics, fostering greater insights into complex biological systems and supporting a precision medicine framework in healthcare.International Journal of Research Publication and Reviews74This discussion illustrates AI’s transformative applications in genomics, including variant calling, gene expression profiling, and functional annotation, thus advancing precision medicine by tailoring treatment strategies to individual genetic profiles, and addresses challenges linked to the integration of AI in genomics.International Journal of Research Publication and Reviews["Maham Taqi", "Sumera Younus", "Arleen Yousaf"]2025-01-01T00:00:00 489056851c68e375905fb59f93edf08d56f4549c: The research problem arose from the researchers’ sense of the importance of Digital Intelligence (DI), as it is a basic requirement to help students engage in the digital world and be disciplined in using technology and digital techniques, as students’ ideas are su ffi ciently susceptible to influence at this stage in light of modern technology. The research aims to determine the level of DI among university students using Artificial Intelligence (AI) techniques. To verify this, the researchers built a measure of DI. The measure in its final form consisted of (24) items distributed among (8) main skills, and the validity and reliability of the tool were confirmed. It was applied to a sample of 139 male and female students who were chosen in a random stratified manner from students at the University of Baghdad, College of Education for Pure Sciences / Ibn Al-Haitham, Department of Computer. The proposed AI model utilized three artificial intelligence techniques: Decision Tree (DT), Random Forest (RF), and Gradient Boosting Machine (GBM). The classification accuracy using DT was 92.85 and using GMB was 95.23. The RF technique was applied to find the essential features, and the Pearson correlation was used to find the correlation between the features. The findings indicated that students indeed possess digital intelligence, underscoring the potential for tailored interventions to enhance their digital skills and competencies. This research not only sheds light on the current DI landscape among university students but also paves the way for targeted educational initiatives to foster digital literacy and proficiency in the academic setting.International Journal of Computing and Digital Systems392The findings indicated that students indeed possess digital intelligence, underscoring the potential for tailored interventions to enhance their digital skills and competencies.International Journal of Computing and Digital Systems["Ban Hassan Majeed", "Wisal Hashim", "Zainab Hazim", "Rasha H. Ali"]2025-01-01T00:00:00 18639b58577dfe2f41084300c75b91dd0ab5f243Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. Conclusions: While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.Medicina681This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication, and how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH.Medicina["Sogol Attaripour Esfahani", "N. Baba Ali", "Juan M. Farina", "I. Scalia", "Milagros Pereyra", "M. T. Abbas", "Niloofar Javadi", "Nadera N Bismee", "Fatmaelzahraa Abdelfattah", "K. Awad", "Omar H Ibrahim", "Hesham Sheashaa", "T. Barry", "Robert L. Scott", "Chadi Ayoub", "R. Arsanjani"]2025-01-01T00:00:00 d1abe50ee69dd3f3a197c4479d9d754f436439d3Background: Digitalization and artificial intelligence technologies are navigating and strengthening human labour practices and organizational performance in healthcare. Research has shown that digitization and AI can help healthcare professionals through real-time insights and recommendations derived from extensive datasets. These modern technologies are advancing beyond being mere instruments in the health sector, now acting as partners to aid healthcare professionals in making better predictions and decisions by offering timely insights and suggestions derived from extensive datasets, as well as pinpoint potential health issues with greater accuracy and speed. It is estimated also that significantly can be supported the management of workplace well-being. Objective: This article delves into how AI and digitalization help healthcare professionals by boosting efficiency, meeting their professional and personal needs, and showcasing how they can enhance employee mental health and well-being. Results: It is crucial to recognize that issues arise from the intrinsic complexity and opaque nature of AI, the risk of job loss, and the disruption of the conventional interaction between physicians and patients. Nonetheless, AI in the healthcare facilities should not be seen as a danger to human employees. Instead, AI strive to support healthcare employees, enabling them to allocate more time to complex and crucial tasks. By automating tasks that are repetitive and mundane, these new technologies can lessen the burden on healthcare professionals, allowing them to dedicate more time to caring for patients and engaging in valuable interactions. Conclusions: The integration of AI and digitalization technologies into healthcare presents both opportunities and challenges for employee motivation and job performance. Although it can improve effectiveness and lower stress levels, it is important to carefully address worries about employment stability and maintaining personal connections in healthcare. Organizations need to prioritize creating a workspace that encourages and assists employees in adjusting to new technological changesJapan Journal of Research01How AI and digitalization help healthcare professionals by boosting efficiency, meeting their professional and personal needs, and showcasing how they can enhance employee mental health and well-being is delved into.Japan Journal of Research["Karaferis Dimitris", "Balaska Dimitra", "Pollalis Yannis"]2025-01-01T00:00:00 74e0129b2f809734e8188baa2ac23774e5cf4bcaIntegrative Psychological and Behavioural Science151It is argued that AI, while capable of simulating cognitive processes, operates without the conscious awareness that defines human cognition, raising profound epistemological and ethical questions.Integrative psychological & behavioral science["Tolga Y\u0131ld\u0131z"]2025-01-01T00:00:00 d2c64d4c306098cd5d9c0baffb8ea8f1efa490dcAccurate identification of surgical instruments is crucial for efficient workflows and patient safety within the operating room, particularly in preventing complications such as retained surgical instruments. Artificial Intelligence (AI) models have shown the potential to automate this process. This study evaluates the accuracy of publicly available Large Language Models (LLMs)—ChatGPT-4, ChatGPT-4o, and Gemini—and a specialized commercial mobile application, Surgical-Instrument Directory (SID 2.0), in identifying surgical instruments from images. The study utilized a dataset of 92 high-resolution images of 25 surgical instruments (retractors, forceps, scissors, and trocars) photographed from multiple angles. Model performance was evaluated using accuracy, weighted precision, recall, and F1 score. ChatGPT-4o exhibited the highest accuracy (89.1%) in categorizing instruments (e.g., scissors, forceps). SID 2.0 (77.2%) and ChatGPT-4 (76.1%) achieved comparable accuracy, while Gemini (44.6%) demonstrated lower accuracy in this task. For precise subtype identification of instrument names (like “Mayo scissors” or “Kelly forceps”), all models had low accuracy, with SID 2.0 having an accuracy of 39.1%, followed by ChatGPT-4o (33.69%). Subgroup analysis revealed ChatGPT-4 and 4o recognized trocars in all instances. Similarly, Gemini identified surgical scissors in all instances. In conclusion, publicly available LLMs can reliably identify surgical instruments at the category level, with ChatGPT-4o demonstrating an overall edge. However, precise subtype identification remains a challenge for all models. These findings highlight the potential of AI-driven solutions to enhance surgical-instrument management and underscore the need for further refinements to improve accuracy and support patient safety.Bioengineering321Publicly available LLMs can reliably identify surgical instruments at the category level, with ChatGPT-4o demonstrating an overall edge, however, precise subtype identification remains a challenge for all models.Bioengineering["S. A. Haider", "Olivia A Ho", "Sahar Borna", "Cesar A Gomez-Cabello", "Sophia M Pressman", "Dave Cole", "Ajai Sehgal", "Bradley C. Leibovich", "AJ Forte"]2025-01-01T00:00:00 9d938c50845d44c1a39784299866bcdeea80c033In an era marked by the increasing frequency of major natural and manmade disasters, the imperative for effective disaster recovery planning and response has never been more pronounced. As communities grapple with the aftermath of hurricanes, earthquakes, wildfires, pandemics and other crises and emergencies, the integration of innovation technologies has emerged as a beacon of hope for more resilient and efficient recovery efforts. Standing at the forefront of this technological revolution is artificial intelligence (AI) - a transformative force with the potential to revolutionise every facet of disaster recovery. While the benefits are substantial, challenges in AI implementation are evident. These challenges underscore the need for continuous research and development efforts to unlock the full spectrum of AI's potential benefits. The journey toward harnessing AI in disaster recovery is dynamic, requiring ongoing innovation to overcome existing limitations. Navigating the evolving systems of AI in disaster recovery planning, the amalgamation of benefits, challenges and ongoing evolution underscores the pivotal role that AI plays in shaping the future of resilient disaster response. This paper identifies where AI can play the most significant positive role for disaster recovery.Journal of Business Continuity and Emergency Planning01This paper identifies where AI can play the most significant positive role for disaster recovery, and the need for continuous research and development efforts to unlock the full spectrum of AI's potential benefits.Journal of business continuity & emergency planning["Linda S Hanwacker"]2025-01-01T00:00:00 61f2ddcd2722a94ef95bde6ac4cd1377b9156fbdIntroduction: In Mexico, challenges and opportunities were identified for nursing professionals, where the use of Artificial Intelligence (AI) stands out as an essential tool to improve patient care. AI, through algorithms and learning models, allows professionals to access critical information and evidence for clinical decision-making. Methods: A systematic review of observational studies was carried out, it was used as a warning or instruction in Generative AI. The above was complemented with MesH words and health descriptors, Boolean operators were used. The search was made of original research, summaries, articles, and gray literature. The selection and extraction was done in Embase, Medline, PubMed, Cochrane Library databases to later read them and perform meta-analysis. Results: The implementation of AI facilitated the personalization of patient care, improving efficiency in diagnoses and care plans. They found that AI-powered systems allowed nurses to manage workloads more effectively and respond to complex situations more quickly. However, concerns were also raised about privacy and ethics in data handling.Conclusions: AI has the potential to significantly transform nursing practice by optimizing processes and improving clinical outcomes. Despite its advantages, it is essential to address the ethical and legal challenges associated with its use. Collaboration between nursing and engineering is essential to ensure effective and responsible integration of AI into healthcare.Salud, Ciencia y Tecnología71Artificial Intelligence has the potential to significantly transform nursing practice by optimizing processes and improving clinical outcomes, but it is essential to address the ethical and legal challenges associated with its use.Salud, Ciencia y Tecnología["Adela Alba-Leonel", "Samantha Papaqui-Alba", "Miguel \u00c1ngel Germ\u00e1n Mej\u00eda Argueta", "Roberto S\u00e1nchez-Ahedo", "Joaqu\u00edn Papaqui-Hern\u00e1ndez"]2025-01-01T00:00:00 39ac359cdf48cc5aa70fbcbd0a1fe0929a45fbb4The integration of artificial intelligence (AI) and the internet of things (IoT), known as artificial intelligence of things (AIoT), is driving significant advancements in the aquaculture industry, offering solutions to longstanding challenges related to operational efficiency, sustainability, and productivity. This review explores the latest research studies in AIoT within the aquaculture industry, focusing on real-time environmental monitoring, data-driven decision-making, and automation. IoT sensors deployed across aquaculture systems continuously track critical parameters such as temperature, pH, dissolved oxygen, salinity, and fish behavior. AI algorithms process these data streams to provide predictive insights into water quality management, disease detection, species identification, biomass estimation, and optimized feeding strategies, among others. Much as AIoT adoption in aquaculture is advantageous on various fronts, there are still numerous challenges, including high implementation costs, data privacy concerns, and the need for scalable and adaptable AI models across diverse aquaculture environments. This review also highlights future directions for AIoT in aquaculture, emphasizing the potential for hybrid AI models, improved scalability for large-scale operations, and sustainable resource management.Processes1711This review explores the latest research studies in AIoT within the aquaculture industry, focusing on real-time environmental monitoring, data-driven decision-making, and automation, and highlights future directions for AIoT in aquaculture, emphasizing the potential for hybrid AI models, improved scalability for large-scale operations, and sustainable resource management.Processes["Yo-Ping Huang", "Simon Peter Khabusi"]2025-01-01T00:00:00 2b35ae97f0f6c10fb5c277761cb456504225b30cThis paper discusses the modeling and solution of a cold chain logistics (CCL) problem using artificial intelligence of things (AIoT). The presented model aims to reduce the costs of the entire CCL network by maintaining the minimum quality of cold products distributed to customers. This study considers equipping distribution centers and trucks with IoT tools and examines the advantages of using these tools to reduce logistics costs. Also, four algorithms based on artificial intelligence (AI), including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Emperor Penguin Optimizer (EPO), have been used in solving the mathematical model. The analysis results show that equipping trucks and distribution centers with the Internet of Things has increased the total costs by 15% compared to before. This approach resulted in a 26% reduction in operating costs and a 60% reduction in transportation costs. As a result of using the Internet of Things, total costs have been reduced by 2.78%. Furthermore, the performance of AI algorithms showed that the high speed of these algorithms is guaranteed against the high accuracy of the obtained results. So, EPO has achieved the optimal value of the objective function compared to a 70% reduction in the solution time. Further analyses show the effectiveness of EPO in the indicators of average objective function, average RPD error, and solution time. The results of this paper help managers understand the need to create IoT infrastructure in the distribution of cold products to customers. Because implementing IoT devices can offset a large portion of transportation and energy costs, this paper provides management solutions and insights at the end. As a result, there is a need to deploy IoT tools in other parts of the mathematical model and its application.Future Transportation381The analysis results show that equipping trucks and distribution centers with the Internet of Things has increased the total costs by 15% compared to before, and EPO has achieved the optimal value of the objective function compared to a 70% reduction in the solution time.Future Transportation["Hamed Nozari", "M. Rahmaty", "Parvaneh Zeraati Foukolaei", "Hossien Movahed", "Mahmonir Bayanati"]2025-01-01T00:00:00 f0d8c2c7175f6ca76b6dfd5447f252ddfa3786dfArtificial intelligence technology is no longer just a vision of a progressive future, it is a reality that needs to be accepted and implemented in all spheres of society and the state. Public administration is one of the areas where artificial intelligence will enhance the efficiency, accuracy, and transparency of administrative services for the public and business. Thanks to its self-learning capability and fast data processing, artificial intelligence will be able to predict the demand for certain services, and therefore the state’s strategy of interaction with the population will also undergo positive changes. This is the reason for the relevance of the research, because due to global trends towards the introduction of artificial intelligence in public administration, such actions will contribute to a new level of interaction between the state and society, as well as the status of each individual state among progressive countries that keep up with the times. At the same time, such implementation poses certain risks to society. In this regard, the purpose of the research is to analyse the use of artificial intelligence in public administration and its main advantages, identify the risks of introducing artificial intelligence and the specifics of the ethical component, and provide practical recommendations for further introduction of artificial intelligence in the field of public administration.Management71The purpose of the research is to analyse the use of artificial intelligence in public administration and its main advantages, identify the risks of introducing artificial intelligence and the specifics of the ethical component, and provide practical recommendations for further introduction of artificial intelligence in the field of public administration.Management (Montevideo)["Herasym Dei"]2025-01-01T00:00:00 65d5d464d04b3ca1527b5f1c7819e0563fac2917PURPOSE The SPARTAN trial demonstrated that the addition of apalutamide to androgen deprivation therapy improves outcomes among patients with nonmetastatic castration-resistant prostate cancer (nmCRPC). We applied a previously reported digital histopathology-based multimodal artificial intelligence (MMAI) algorithm to estimate clinical outcomes in SPARTAN. METHODS Patients with available hematoxylin and eosin-stained slides from the primary tumor were included. Histopathology slides were digitized. MMAI scores ranging from 0 to 1 were generated from digital histopathology and baseline clinical parameters. Patients were categorized into MMAI non-high-risk and high-risk groups using previously validated cutoffs. Kaplan-Meier estimates were calculated for metastasis-free survival (MFS), second progression-free survival (PFS2), and overall survival (OS); comparisons were performed using Cox proportional hazards regression for treatment arms and MMAI risk. The interaction between treatment arm and risk group was evaluated using a Cox proportional hazards model. RESULTS The study included 420 evaluable patients after excluding those with missing clinical data or inadequate histopathology images. Of these, 63% (n = 266) were MMAI high risk and 37% (n = 154) were non-high risk. MMAI risk score was associated with shorter MFS (hazard ratio [HR], 1.72; P < .005), PFS2 (HR, 1.57; P < .005), and OS (HR, 1.41; P = .02). MMAI high-risk patients receiving apalutamide demonstrated significant improvement in MFS (HR, 0.19; P < .005), PFS2 (HR, 0.47; P < .005), and OS (HR, 0.6; P = .01). The interaction between MMAI risk score and treatment for MFS (P = .01) and PFS2 (P = .03) was significant, indicating greater benefit from apalutamide treatment in MMAI high-risk patients. CONCLUSION MMAI is a prognostic marker in nmCRPC and may serve as a predictive biomarker with high-risk patients deriving the greatest benefit from treatment with apalutamide. These results represent the first extension of an MMAI classifier to patients with castration-resistant prostate cancer, warranting additional validation.JCO Precision Oncology81MMAI is a prognostic marker in nmCRPC and may serve as a predictive biomarker with high-risk patients deriving the greatest benefit from treatment with apalutamide, indicating greater benefit from apalutamide treatment in MMAI high-risk patients.JCO precision oncology["Felix Y Feng", "Matthew R Smith", "Fred Saad", "Pooya Mobadersany", "Shaozhou K Tian", "Stephen S F Yip", "J. Greshock", "Najat Khan", "Margaret K Yu", "S. McCarthy", "S. Brookman-May", "Ariel B Bourla", "T. Todorovic", "Rikiya Yamashita", "Huei-Chung Huang", "Trevor J Royce", "Timothy N Showalter", "Jacqueline Griffin", "A. Mitani", "A. Esteva", "Eric J Small"]2025-01-01T00:00:00 1b0a679460d43d67d16597bd4314fe5415d679feThe convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain–computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain–computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the “black-box” nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.Journal of Clinical Medicine3021This review explores how AI’s cutting-edge algorithms are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling.Journal of Clinical Medicine["R. Onciul", "Catalina-Ioana Tataru", "A. Dumitru", "Carla Crivoi", "Matei Serban", "Razvan-Adrian Covache-Busuioc", "M. Radoi", "C. Toader"]2025-01-01T00:00:00 3d6e9445771a9f87fc53fb7acf131b71e711b109Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES. We analyzed data from the 2023 American Hospital Association Annual Survey Information Technology Supplement to identify how AI and predictive models are used and evaluated for accuracy and bias in hospitals. Hospitals use AI and predictive models to predict health trajectories or risks for inpatients, identify high-risk outpatients to inform follow-up care, monitor health, recommend treatments, simplify or automate billing procedures, and facilitate scheduling. We found that 65 percent of US hospitals used predictive models, and 79 percent of those used models from their electronic health record developer. Sixty-one percent of hospitals that used models evaluated them for accuracy using data from their health system (local evaluation), but only 44 percent reported local evaluation for bias. Hospitals that developed their own predictive models, had high operating margins, and were health system members were more likely to report local evaluation. Policy and programs that provide technical support, tools to assess FAVES principles, and educational resources would help ensure that all hospitals can use predictive models safely and prevent a new organizational digital divide in AI.Health Affairs161Policy and programs that provide technical support, tools to assess FAVES principles, and educational resources would help ensure that all hospitals can use predictive models safely and prevent a new organizational digital divide in AI.Health affairs["Paige Nong", "J. Adler-Milstein", "Nate C. Apathy", "A. Holmgren", "J. Everson"]2025-01-01T00:00:00 1e24b2b19d94961f67c8d79e0f3e38da517db76f: Artificial intelligence (AI) innovations are rapidly advancing and hold significant potential to transform accounting education. This study explores faculty perspectives on leveraging AI techniques to enhance accounting pedagogy within Saudi Arabian higher education. A mixed methods approach combining surveys, interviews, and observations was utilized to gather both qualitative and quantitative data from 45 accounting instructors across multiple universities. Participants provided key insights on current teaching practices, readiness to integrate AI tools, and perceived impacts on student outcomes. Results revealed largely positive views regarding the value of AI applications such as adaptive learning programs, simulations, and predictive analytics to improve student engagement, collaboration, and personalized support. However, gaps emerged in formal strategic plans guiding integration and faculty training to optimize AI adoption. Recommendations centered on developing clear institutional roadmaps, expanding professional development, launching pilots of promising AI courseware, and assessing progress through a dedicated Center of Excellence. Addressing preparation barriers while leveraging positive attitudes can help unlock AI's full advantages. This study concludes AI integration in accounting education holds meaningful potential to advance pedagogy but requires concerted, coordinated efforts filling strategic gaps to ensure appropriate, ethical, and sustainable implementation. An agenda centered on thoughtful adoption balancing guidance, exploration of emerging tools alongside new competencies, and continuous evaluation of real-world impacts is instrumental as technologies progress.مجلة العلوم التجارية والبيئية331This study concludes AI integration in accounting education holds meaningful potential to advance pedagogy but requires concerted, coordinated efforts filling strategic gaps to ensure appropriate, ethical, and sustainable implementation.مجلة العلوم التجارية والبيئية["Mashael Bakhit", "A. Bilal"]2025-01-01T00:00:00 2f0b6bd6262e2c9193656592d4ae15b35ec9b130This study examined Artificial Intelligence in the UAE Arbitration Law. It specifically aimed to know whether or not the UAE Arbitration Law supports AI-based arbitration. Furthermore, it used the descriptive analytical approach to describe the phenomenon it investigated and analyze the related legal texts. In this context, the research was applied to the UAE legislation. The analysis focused on the extent to which the UAE Arbitration law relies on the modern technology, especially the artificial intelligence, in the arbitration process. The analysis of the UAE Arbitration Law for the year 2018 reveals that, in its current form, the said law does not support an arbitration that is based on AI.Pakistan Journal of Criminology00The analysis of the UAE Arbitration Law for the year 2018 reveals that, in its current form, the said law does not support an arbitration that is based on AI.Pakistan Journal of Criminology[]2025-01-01T00:00:00 82caa4d79fd77199f95454e04e5ae1c5581892b6 Diabetes continues to escalate globally, prompting a search for more efficient methods to manage and treat affected individuals. Exploring the considerable potential of artificial intelligence (AI) in healthcare is one such avenue. Despite increasing global AI investment in diabetes care, patient perspectives on this technology lack sufficient research attention. Our study aims to fill this knowledge gap. In this qualitative study, we examined patient perceptions and attitudes toward the use of AI in diabetes care. Qualitative content analysis method was used to collect the data from 17 diabetes patients in Hospital Tengku Ampuan Rahimah Klang, Malaysia over a 2-week period in August 2023. The analysis of the qualitative data was undertaken with ATLAS.ti using a thematic content analysis process. Seven themes emerged from the data and key results of the study indicate that opinions toward AI application in diabetic care, which reflect perceptions and attitudes have a positive impact on the implementation of AI in healthcare. Four themes that were related to diabetic patients’ perceptions are experience in using technology and apps, awareness of AI tools, beliefs about technology, and trust in AI tools. Meanwhile, three themes that were associated with diabetic patients’ attitudes and perceived acceptability, perceived need, and perceived benefit of using AI tools. The results of this study form the basis for a theoretical framework for understanding patients positioning to applications of AI in diabetic care, highlighting health, technological, and social experiences that shape their opinions about AI applications in diabetic care. Asian Journal of Social Health and Behavior210Opinions toward AI application in diabetic care, which reflect perceptions and attitudes have a positive impact on the implementation of AI in healthcare, indicate that opinions toward AI application in diabetic care, which reflect perceptions and attitudes have a positive impact on the implementation of AI in healthcare.Asian Journal of Social Health and Behavior["Logeswary Krisnan", "Maslin Masrom", "Yazriwati Yahya", "Manimaran Krishnan"]2025-01-01T00:00:00 fe48b94d7f660944125d6641ca288333c8dae759Digital health (DH) and artificial intelligence (AI) in healthcare are rapidly evolving but were addressed synonymously by many healthcare authorities and practitioners. A deep understanding and clarification of these concepts are fundamental and a prerequisite for developing robust frameworks and practical guidelines to ensure the safety, efficacy, and effectiveness of DH solutions and AI-embedded technologies. Categorizing DH into technologies (DHTs) and services (DHSs) enables regulatory, HTA, and reimbursement bodies to develop category-specific frameworks and guidelines for evaluating these solutions effectively. DH is the key in generating real-world data, which is increasingly important in decision-making processes. The potential benefits of DHTs in improving health outcomes and reducing health system costs can position them alongside traditional health technologies in certain medical conditions. AI, one of the potential tools for DH, can be embedded in technologies, such as medical devices or applications, to enhance functionality and performance. AI excels at handling numerical and perceptual data. In the context of numerical data, machine learning algorithms enable prediction, classification, and clustering. In managing perceptual data, AI recognizes image/video, voice, and text. In recent years, generative AI, a form of AI that generates new content by employing a combination of a wide range of learning approaches, has become prominent in research and influences the health sector. A thorough understanding of DH and AI, along with accurate terminology use, would facilitate the timely generation of regulatory and HTA-grade evidence that helps improve health outcomes and decision-making certainty.Digital Health110A thorough understanding of DH and AI, along with accurate terminology use, would facilitate the timely generation of regulatory and HTA-grade evidence that helps improve health outcomes and decision-making certainty.Digital Health["P. Graili", "Bijan Farhoudi"]2025-01-01T00:00:00 eb6809273884202af4ce1853502e8cd75fcb32b7Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with its integration into cardiac surgery offering significant advancements in precision, efficiency, and patient outcomes. However, a comprehensive understanding of AI’s applications, benefits, challenges, and future directions in cardiac surgery is needed to inform its safe and effective implementation. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, Cochrane Library, Google Scholar, and Web of Science, covering publications from January 2000 to November 2024. Studies focusing on AI applications in cardiac surgery, including risk stratification, surgical planning, intraoperative guidance, and postoperative management, were included. Data extraction and quality assessment were conducted using standardized tools, and findings were synthesized narratively. Results: A total of 121 studies were included in this review. AI demonstrated superior predictive capabilities in risk stratification, with machine learning models outperforming traditional scoring systems in mortality and complication prediction. Robotic-assisted systems enhanced surgical precision and minimized trauma, while computer vision and augmented cognition improved intraoperative guidance. Postoperative AI applications showed potential in predicting complications, supporting patient monitoring, and reducing healthcare costs. However, challenges such as data quality, validation, ethical considerations, and integration into clinical workflows remain significant barriers to widespread adoption. Conclusions: AI has the potential to revolutionize cardiac surgery by enhancing decision making, surgical accuracy, and patient outcomes. Addressing limitations related to data quality, bias, validation, and regulatory frameworks is essential for its safe and effective implementation. Future research should focus on interdisciplinary collaboration, robust testing, and the development of ethical and transparent AI systems to ensure equitable and sustainable advancements in cardiac surgery.Clinics and Practice1490Artificial intelligence has the potential to revolutionize cardiac surgery by enhancing decision making, surgical accuracy, and patient outcomes, but challenges such as data quality, validation, ethical considerations, and integration into clinical workflows remain significant barriers to widespread adoption.Clinics and Practice["Vasileios Leivaditis", "Eleftherios Beltsios", "Athanasios Papatriantafyllou", "K. Grapatsas", "Francesk Mulita", "N. Kontodimopoulos", "N. Baikoussis", "L. Tchabashvili", "Konstantinos Tasios", "Ioannis Maroulis", "Manfred Dahm", "E. Koletsis"]2025-01-01T00:00:00 c0c44f390f50c1e89ac7ac7081201ea14c998f32The United States has rapidly advanced in data networking infrastructure, driven by the widespread adoption of 5G networks, Internet of Things (IoT) devices, and cloud computing technologies. This rapid technological expansion has fueled economic growth and enhanced connectivity but has also introduced vulnerabilities to increasingly sophisticated cyber threats. As cyberattacks grow more advanced, organizations face significant challenges in maintaining network security and ensuring data integrity. This paper examines the role of artificial intelligence (AI) in addressing these challenges by improving data networking and bolstering cybersecurity efforts across the U.S. Through the analysis of empirical data from major network providers, leading cybersecurity firms, and government agencies, the study focuses on three critical areas: predictive threat detection, real-time anomaly response, and network optimization. AI has demonstrated unparalleled capabilities in these domains, enabling organizations to proactively identify and mitigate threats while optimizing network performance. Results reveal that AI-driven systems achieve an impressive 92% accuracy in cyber threat detection, reduce average response times to under 1.5 minutes, and improve bandwidth allocation efficiency by 35% during peak traffic hours. The findings underscore the transformative potential of AI in enhancing both network efficiency and cybersecurity measures, resulting in substantial economic benefits. Organizations adopting AI solutions report a reduction in data breach costs by up to $18 billion annually and a marked improvement in operational efficiency. Despite these advancements, challenges such as high implementation costs, skill shortages, and ethical concerns must be addressed to maximize AI’s potential. This study provides actionable insights for stakeholders, emphasizing the necessity of AI in safeguarding U.S. digital infrastructure in an evolving technological landscape.International Journal of Innovative Research in Computer Science & Technology00Results reveal that AI-driven systems achieve an impressive 92% accuracy in cyber threat detection, reduce average response times to under 1.5 minutes, and improve bandwidth allocation efficiency by 35% during peak traffic hours, demonstrating the transformative potential of AI in enhancing both network efficiency and cybersecurity measures, resulting in substantial economic benefits.International Journal of Innovative Research in Computer Science and Technology["Sai Ratna Prasad Dandamudi", "Jaideep Sajja", "Amit Khanna"]2025-01-01T00:00:00 a5daf2ba156c7d346db06cf7a68f3e8fb0552628Introduction: Artificial Intelligence (AI) is a transformative force in nursing education, applicable in academic and clinical settings. It equips nursing students with skills to evaluate and apply AI in future patient care, preparing the nursing workforce for a healthcare landscape increasingly supported by AI. However, lack of studies focus on nursing students as AI users and the behavioural intention to accept and utilise AI. Aim: This study investigated the factors influencing nursing students’ acceptance and use of AI based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Materials and Methods: A cross-sectional study was conducted at one of the oldest and most prominent universities, collecting data from April to May 2022. The survey included 213 nursing students and aimed to evaluate the influence of the four UTAUT constructs- Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)- on behavioural intention and usage behaviour. Additionally, the study explored the moderating effects of age and gender on the UTAUT model. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 29.0 for descriptive statistics and SmartPLS version 4 for Partial Least Squares (PLS) structural equation modeling. Results: The findings indicated that PE positively influenced the behavioural intention of nursing students to adopt and use AI in nursing education. Regarding moderation effects, age moderated the relationship between PE and behavioural intention, whereas gender did not exhibit any moderation effect. Conclusion: This study provides a foundation for its integration to enhance learning outcomes and prepare students for technology-driven healthcare. It highlights the importance of evidence-based strategies tailored to meet diverse educational needs, ensuring effective adoption and utilisation.Journal of Clinical and Diagnostic Research00PE positively influenced the behavioural intention of nursing students to adopt and use AI in nursing education, and moderation effects, age moderated the relationship between PE and behavioural intention, whereas gender did not exhibit any moderation effect.JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH["Latifah H Alenazi"]2025-01-01T00:00:00 eaeae94b326b2deee75d586fc598bb023ef63233Sensors for the perception of multimodal stimuli-ranging from the five senses humans possess and beyond-have reached an unprecedented level of sophistication and miniaturization, raising the prospect of making man-made large-scale complex systems that can rival nature a reality. Artificial intelligence (AI) at the edge aims to integrate such sensors with real-time cognitive abilities enabled by recent advances in AI. Such AI progress has only been achieved by using massive computing power which, however, would not be available in most distributed systems of interest. Nature has solved this problem by integrating computing, memory and sensing functionalities in the same hardware so that each part can learn its environment in real time and take local actions that lead to stable global functionalities. While this is a challenging task by itself, it would raise a new set of security challenges when implemented. As in nature, malicious agents can attack and commandeer the system to perform their own tasks. This article aims to define the types of systemic attacks that would emerge, and introduces a multiscale framework for combatting them. A primary thesis is that edge AI systems have to deal with unknown attack strategies that can only be countered in real time using low-touch adaptive learning systems.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences110This article aims to define the types of systemic attacks that would emerge, and introduces a multiscale framework for combatting them, and says that edge AI systems have to deal with unknown attack strategies that can only be countered in real time using low-touch adaptive learning systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences["Nader Sehatbakhsh", "S. Pamarti", "Vwani Roychowdhary", "Subramanian Iyer"]2025-01-01T00:00:00 c9b24ec5602badfe8b115cb2c0bed107d0a921beIntroduction With the incorporation of artificial intelligence (AI), significant advancements have occurred in the field of fetal medicine, holding the potential to transform prenatal care and diagnostics, promising to revolutionize prenatal care and diagnostics. This scoping review aims to explore the recent updates in the prospective application of AI in fetal medicine, evaluating its current uses, potential benefits, and limitations. Methods Compiling literature concerning the utilization of AI in fetal medicine does not appear to modify the subject or provide an exhaustive exploration of electronic databases. Relevant studies, reviews, and articles published in recent years were incorporated to ensure up-to-date data. The selected works were analyzed for common themes, AI methodologies applied, and the scope of AI’s integration into fetal medicine practice. Results The review identified several key areas where AI applications are making strides in fetal medicine, including prenatal screening, diagnosis of congenital anomalies, and predicting pregnancy complications. AI-driven algorithms have been developed to analyze complex fetal ultrasound data, enhancing image quality and interpretative accuracy. The integration of AI in fetal monitoring has also been explored, with systems designed to identify patterns indicative of fetal distress. Despite these advancements, challenges related to the ethical use of AI, data privacy, and the need for extensive validation of AI tools in diverse populations were noted. Conclusion The potential benefits of AI in fetal medicine are immense, offering a brighter future for our field. AI equips us with tools for enhanced diagnosis, monitoring, and prognostic capabilities, promising to revolutionize the way we approach prenatal care and diagnostics. This optimistic outlook underscores the need for further research and interdisciplinary partnerships to fully leverage AI’s potential in driving forward the practice of fetal medicine.International Journal of General Medicine320The potential benefits of AI in fetal medicine are immense, offering a brighter future for the field, and the need for further research and interdisciplinary partnerships to fully leverage AI’s potential in driving forward the practice of fetal medicine is highlighted.International Journal of General Medicine["Elhadi Miskeen", "Jaber A. Alfaifi", "Dalal Alhuian", "M. Alghamdi", "M. Alharthi", "Nourah Alshahrani", "Ghala Alosaimi", "Raydaa Alshomrani", "Abdullah Hajlaa", "Nadir Khair", "Abdullah Almuawi", "Khalifa Al-Jaber", "F. Elrasheed", "Kamal Elhassan", "Mohammed Abbas"]2025-01-01T00:00:00 6dabab7599cbda738ff78bd711a88a82c6ad21f6The precision of imaging and the number of other risk-assessing and diagnostic methods are constantly growing, allowing for the uptake of additional strategies for individualized therapies. Personalized medicine has the potential to deliver more adequate treatment, resulting in better clinical outcomes, based on each patient’s vulnerability or genetic makeup. In addition to increased efficiency, costs related to this type of procedure can be significantly lower. Useful assistance in designing individual therapies may be assured by the adoption of artificial intelligence (AI). Recent years have brought essential developments in deep and machine learning techniques. Advances in technologies such as convolutional neural networks (CNNs) have enabled automatic analyses of images, numerical data, and video data, providing high efficiency in the creation of prediction models. The number of AI applications in medicine is constantly growing, and the effectiveness of these techniques has been demonstrated in coronary computed tomography angiography (CCTA), optical coherence tomography (OCT), and many others. Moreover, AI models may be useful in direct therapy optimization for patients with coronary artery disease (CAD), who are burdened with high risk. The combination of well-trained AI with the design of individual treatment pathways can lead to improvements in health care. However, existing limitations, such as non-adapted guidelines or the lack of randomized clinical trials to evaluate AI’s true accuracy, may contribute to delays in introducing automatic methods into practical use. This review critically appraises the developed tools that are potentially useful for clinicians in guiding personalized patient management, as well as current trials in this field.Journal of Clinical Medicine820This review critically appraises the developed tools that are potentially useful for clinicians in guiding personalized patient management, as well as current trials in this field.Journal of Clinical Medicine["Adrian Bednarek", "Karolina Gumi\u0119\u017cna", "Piotr Baru\u015b", "J. Kochman", "Mariusz Tomaniak"]2025-01-01T00:00:00 fd29919c7ab44596d63d279c18e518d164d09456Artificial intelligence (AI) is constantly improving the quality of medical procedures. Despite the application of AI in the healthcare industry, there are conflicting opinions among professionals, and limited research on its practical application in Saudi Arabia was conducted. Aim: To assess the nurses' knowledge regarding the application of AI in practice at one of the Ministry of Health hospitals in Saudi Arabia. Methods: Descriptive cross-sectional research using convenience sampling in January 2023 involving 307 staff nurses, using a single 11-item questionnaire. In addition, 6 closed-ended questions were used to assess the knowledge, possible risks, and advantages of AI. Results: All 307 participants completed the survey and used it for data analysis using SPSS V.25. Kruskal–Wallis and Whitney tests and descriptive statistics were used to identify the significant differences among groups. The study results reveal significant differences between age groups and working locations regarding familiarity with AI and future use of AI. In contrast, a considerable difference exists between licensed years groups regarding familiarity with AI. Surprisingly, education level does not affect AI knowledge. Additionally, the future use of AI is significantly affected by the nurse's gender. Limitation: Nurses were not included in previous studies on AI, and most nursing participants need more interest in AI. Conclusion: The study's results showed that nurses have positive opinions of AI in the healthcare industry, which will help them speed up procedures and reduce medical errors. AI applications can expand in healthcare by increasing the use of AI in the healthcare industry to improve care quality and encourage academic institutions to develop best practices for deploying AI applications in the healthcare industry.Nursing Research and Practice260The study's results showed that nurses have positive opinions of AI in the healthcare industry, which will help them speed up procedures and reduce medical errors.Nursing Research and Practice["Mai M. Yaseen", "Fatmah H Alsharif", "Reem A. Altaf", "Taif W. Asiri", "Rifan M. Bagies", "Salwa B. Alharbi", "Bothinah A. Altaf"]2025-01-01T00:00:00 94354c15d87b08b8983ea2239647a12bef4a5bd3Introduction (problem statement and relevance). Industrial and, in particular, automotive design are effective tools in positioning a product in the market and drawing purchaser’s attention. That is why it is vital to seek and test new methods and means aimed at enhancing designer capacities, improving the quality of their work results, reducing the development time and costs. One of the promising focus areas to improve the design process is the use of artificial intelligence (AI) capabilities. This raises the task of studying the world’s best practices in this area.The purpose of the study is to determine the possibilities, development trends and prospects of application of AI technology when vehicle styling design developing and their practical application testing.Methodology and research methods. Methods of analysis and systematization of results of research works in the area of application of AI technology in design have been used; an experiment on their practical application has been conducted.Scientific novelty and results. An analytical review of research works on development, testing and application of algorithms, trainable models of systems and software programs using AI technology in vehicle designing has been carried out. Systems with machine learning tools ensuring generation of realistic images of vehicles and their components, creation and testing of styling design according to the set aesthetic and engineering criteria, assessment of the degree of similarity of the designs, software programs with integrated CAD/CAE systems have been reviewed. Results of testing of the process of creation of vehicle render images with the use of AI-powered software Vizcom have been presented.Practical significance. Application of AI technology in vehicle styling design ensures faster search for design ideas, higher quality of images received, and extended possibilities of variable presentation of the initial design concept.Trudy NAMI100The purpose of the study is to determine the possibilities, development trends and prospects of application of AI technology when vehicle styling design developing and their practical application testing.Trudy NAMI["V. I. Ivchenko", "D. V. Pavlovich", "O. N. Moysey", "V. V. Bokhonko"]2025-01-01T00:00:00 521684f2857d37e361d7a5e7e946d3be65bedbe2 Virtual Chromoendoscopy (VCE) is pivotal for assessing activity and predicting outcomes in Ulcerative Colitis (UC), though inter- and intra-observer variability and the need for expertise persist. Artificial intelligence (AI) has the potential to offer standardised VCE-based assessment. This study introduces a novel AI model to detect, generate and transition between various endoscopic modalities, enhancing AI-driven inflammation assessment and outcome prediction in UC. Endoscopic videos in high-definition white-light (HD-WLE), iScan2, iScan3 and NBI modalities from UC patients of the international PICaSSO iScan and Narrow-Band Imaging (NBI) cohort (302 and 54 patients, respectively) were used to develop a neural network (NN) able to identify the acquisition modality of each frame and for inter-modality image switching. 2535 frames were switched to different endoscopic modalities and used to train a deep-learning model for inflammation assessment using single and multimodal inputs on 169 videos of the iScan cohort. Subsequently, the model was tested on a subset of the iScan and NBI cohort (72 and 51 videos, 1080 and 765 frames, respectively). The model performance in predicting endoscopic and histological activity and outcomes and the agreement with experts were evaluated. Table 1 details the diagnostic performance of the AI model for the prediction of endoscopic and histological remission. The AI model efficiently classified and converted images across modalities (92% NN classifier accuracy). It showed excellent performance in predicting endoscopic and histological remission, with the multimodal assessment outperforming the unimodal one in both iScan cohorts (accuracy 91.67 [95% CI 82.74-96.88] and 88.89 [80.4-97.73]; AUROC 0.96 and 0.90 by Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International Virtual Chromoendoscopy (PICaSSO) score, respectively) and NBI cohort (accuracy 84.62 [95% CI 65.13-95.64] and 88.46 [69.85-97.55]; AUROC 0.92 by UCEIS and PICaSSO score, respectively). Moreover, it showed a remarkable ability to predict clinical outcomes in the iScan and NBI cohort (HR 3.18 [0.98-10.35] and 1.7 [0.7-4.11] by endoscopy; 5.75 [1.77-18-71] and 3.9 [1.15-13.28] by histology, respectively). Finally, the agreement with the assessment performed by endoscopists and pathologists was good. Our multimodal "AI-switching" model innovatively detects, generates, and transitions between different endoscopic enhancement modalities and platforms, refining inflammation assessment, outcome prediction, and precise UC management by integrating model-derived images. Journal of Crohn's & Colitis00A multimodal "AI-switching" model innovatively detects, generates, and transitions between different endoscopic enhancement modalities and platforms, refining inflammation assessment, outcome prediction, and precise UC management by integrating model-derived images.Journal of Crohn's and Colitis["M. Iacucci", "I. Zammarchi", "G. Santacroce", "B. B. Kolawole", "U. Chaudhari", "R. del Amor", "P. Meseguer", "V. Naranjo", "M. Puga-Tejada", "I. Capobianco", "I. Ditonno", "A. Buda", "B. Hayes", "R. Crotty", "R. Bisschops", "S. Ghosh", "E. Grisan"]2025-01-01T00:00:00 7537fb9ae83594e0b76bb1157c1afe53a900ede9The global halal market is growing, driven by rising stakeholder populations and increasing consumer interest in ethical and sustainable food choices. This surge in demand necessitates robust halal compliance throughout complex supply chains. However, there are several challenges, including fragmented information, increased understanding of halal requirements among stakeholders, and difficulties in tracing product provenance. This paper proposes a holistic framework for halal certification and compliance, addressing these challenges through the integration of artificial intelligence (AI) and blockchain technologies. AI can automate halal compliance checks, identify potential irregularities in sourcing and composition, and facilitate risk management. The blockchain offers an ideal platform for tracking product provenance throughout the halal supply chain. This ensures trust and confidence among consumers by providing verifiable information on ingredient origin and production processes. This paper further strengthens the potential of this framework by presenting an illustrative example that utilises knowledge graphs, machine learning, and smart contracts. This exemplifies the potential application of the proposed framework in the context of halal pre-certification processes. By fostering transparency and streamlining compliance procedures, the proposed holistic framework, empowered by AI and the blockchain, can significantly enhance trust and confidence among stakeholders within the halal food industry.Systems520By fostering transparency and streamlining compliance procedures, the proposed holistic framework, empowered by AI and the blockchain, can significantly enhance trust and confidence among stakeholders within the halal food industry.Systems["F. Sunmola", "George Baryannis", "Albert Tan", "Kenneth Co", "Emmanuel Papadakis"]2025-01-01T00:00:00 3248d5c6ee931ffb63d8d3c976dfb12d0e5c8e58This study aims to uncover the prompts most frequently repeated by pre-service teachers when using the Copilot technique, as well as their reflections on its use in preparing and planning science lessons for fourth graders. The qualitative research methodology with an exploratory case-study design was conducted on a purposeful sample of 20 pre-service teachers. The sample was divided into four focus groups. Data was collected through document analysis of the outcomes from the pre-service teachers’ artificial intelligence creations, their reflective journal entries, and the discussion that occurred during the four focus groups’ interviews. The study’s results revealed that the applications mostly used by pre-service teachers include lesson plans, instructional media, authentic assessment, tables, pictures, drawings, and instructional strategies. Six themes emerged from the reflective Journal and focus groups’ interview analysis connected to the use of the Copilot method in teaching. These themes were the following: developing cognition of new ideas, attracting attention to things that never crossed their minds, saving time and effort, compatibility with students’ needs, less human interaction, and dependency.Contemporary Educational Technology280The study’s results revealed that the applications mostly used by pre-service teachers include lesson plans, instructional media, authentic assessment, tables, pictures, drawings, and instructional strategies.Contemporary Educational Technology["Marwan Mohammad Abualrob"]2025-01-01T00:00:00 6dd32a9e6da9c4192d7ef7975235e70e5af4593cAbstract “I see, I forget, I read aloud, I remember, and when I do read purposefully by writing it, I do not forget it.” This phenomenon is known as “interoception” and refers to the sensing and interpretation of internal body signals, allowing the brain to communicate with various body systems. Dysfunction in interoception is associated with cardiovascular disorders. We delve into the concept of interoception and its impact on heart failure (HF) by reviewing and exploring neural mechanisms underlying interoceptive processing. Furthermore, we review the potential of artificial intelligence (AI) in diagnosis, biomarker development, and HF treatment. In the context of HF, AI algorithms can analyze and interpret complex interoceptive data, providing valuable insights for diagnosis and treatment. These algorithms can identify patterns of disease markers that can contribute to early detection and diagnosis, enabling timely intervention and improved outcomes. These biomarkers hold significant potential in improving the precision/efficacy of HF. Additionally, AI‐powered technologies offer promising avenues for treatment. By leveraging patient data, AI can personalize therapeutic interventions. AI‐driven technologies such as remote monitoring devices and wearable sensors enable the monitoring of patients' health. By harnessing the power of AI, we should aim to advance the diagnosis and treatment strategies for HF. This review explores the potential of AI in diagnosing, developing biomarkers, and managing HF.Physiological Reports1000The concept of interoception and its impact on heart failure (HF) is explored by reviewing and exploring neural mechanisms underlying interoceptive processing, and the potential of artificial intelligence (AI) in diagnosis, biomarker development, and HF treatment is explored.Physiological Reports["Mahavir Singh", "Anmol Babbarwal", "S. Pushpakumar", "Suresh C. Tyagi"]2025-01-01T00:00:00 f93a32090deda4bc4f6a1188dc31174b697a966ePurpose With its continuous development and application in the hotel industry, artificial intelligence (AI) is gradually replacing many jobs traditionally performed by humans. This research aims to understand how this threat and opportunity of substitution affects hotel employees’ behavioral decision-making. Design/methodology/approach This study uses a structural equation model, ordinary least squares and bootstrapping method to analyze the data collected with a field study and a scenario experiment from star-hotels in Shanghai, Paris and Seoul. Findings The results discovered that employees’ AI awareness has a positive relationship with their work engagement and AI boycott through two paths. The promoting path involves recovery level, while the hindering path includes job insecurity. In addition, the estimates showed that AI awareness has a great indirect effect on work engagement or AI boycott when innovativeness as a job requirement is high. Practical implications The findings offer insights to help hotels optimize the relationship between AI and hotel human workers while providing valuable implications for addressing behavioral dilemmas faced by hotel employees in the era of AI. Originality/value By integrating the behavioral decision-making literature with the conservation of resources theory, the study focuses on the dual mechanisms – challenging and hindering – through which AI awareness influences hotel employees’ coping strategies. International Journal of Contemporary Hospitality Management540The results discovered that employees’ AI awareness has a positive relationship with their work engagement and AI boycott through two paths and showed that AI awareness has a great indirect effect on work engagement or AI boycott when innovativeness as a job requirement is high.International Journal of Contemporary Hospitality Management["Shengmin Liu", "Pengfan Cheng"]2025-01-01T00:00:00 74ed4e9b2b40d4a8d11bd85248795cd8e5a28aa5 Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in surgery, by enabling data-driven decision-making. By predicting risks and potential complications before, during, and after surgery, AI assists clinicians in making more informed decisions and enhancing patient care. This review examines the application of AI across various surgical types, from general and emergency procedures to specialized fields like laparoscopy and colorectal surgery, aiming to improve predictions regarding outcomes such as mortality rates and surgical complications. A thematic analysis of seven recent articles (2019-2024) was done, extracted from Pubmed database, on AI's role in predicting general surgery outcomes. Studies focusing on use of AI in emergency surgery cases were evaluated for their ability to predict key outcomes like postoperative complications, mortality, and hospital stay length. The studies revealed that AI models consistently outperformed traditional methods in predicting surgical risks and outcomes. They emphasized AI's ability to anticipate complications, mortality prediction for emergency surgeries and in assessing wound complications and refining laparoscopic surgery techniques. AI tools are proving essential in surgery by enhancing outcome predictions and improving care quality. However, further research is needed for comprehensive integration into emergency surgical practices, ultimately aiming for personalized surgical care and reduced complications. British Journal of Surgery00This review examines the application of AI across various surgical types, from general and emergency procedures to specialized fields like laparoscopy and colorectal surgery, aiming to improve predictions regarding outcomes such as mortality rates and surgical complications.British Journal of Surgery["A. Shaikh", "Jada Saunders", "Khali Khalifa"]2025-01-01T00:00:00 d675c6d410c451a101238b036a549cef257ef367This study builds on the increasing relevance of technology integration in higher education, specifically in artificial intelligence (AI) usage in educational contexts. Background research highlights the limited exploration of AI training in educational programs, particularly within Latin America. AI has become increasingly pivotal in educational practices, influencing the development of competencies in various disciplines, including experimental sciences. This study aimed to describe the correlation between professional competencies in AI, AI usage, and digital resources among students in the experimental sciences education program at the National University of Chimborazo. Methodologically, a quantitative approach was employed, involving a structured survey distributed among 459 students. Data analysis was conducted using multiple regression models to establish predictive insights into AI usage. A multiple linear regression model was developed to predict AI usage among these students. The analysis revealed significant correlations between AI competencies, AI usage, and digital resources. The regression model highlighted that both AI competencies and digital resources are significant predictors of AI usage. These findings underscore the importance of developing AI competencies and providing access to digital resources to enhance the effective use of AI in educational practices. Limitations and future research directions are discussed.International Electronic Journal of Mathematics Education320The correlation between professional competencies in AI, AI usage, and digital resources among students in the experimental sciences education program at the National University of Chimborazo revealed significant correlations between AI competencies, AI usage, and digital resources.International Electronic Journal of Mathematics Education["Elizeth Mayrene Flores Hinostroza", "D. Mendoza", "Mercedes Navarro Cejas", "Edinson Patricio Palacios Trujillo"]2025-01-01T00:00:00 fb5cc92fd3e93e3f8ee35fa5260a615391501761Purpose This study assessed objective performance, usability, and acceptance of artificial intelligence (AI) by people with vision impairment. The goal was to provide evidence-based data to enhance technology selection for people with vision loss (PVL) based on their loss and needs. Methods Using a cross-sectional, counterbalanced, cross-over study involving 25 PVL, we compared performance using two smart glasses (OrCam and Envision Glasses) and two AI apps (Seeing AI and Google Lookout). We refer to these as assistive artificial intelligence implementations (AAIIs). Completion and timing were quantified for three task categories: text, text in columns, and searching and identifying. Usability was evaluated with the System Usability Scale (SUS). Results The odds ratios (ORs) of being able to complete Text tasks were significantly higher when using AAIIs compared to the baseline. OR when performing “Searching and Identifying” tasks varied among AAIIs, with Seeing AI and Envision improving the performance of more tasks than Lookout or OrCam. Participants expressed high satisfaction with the AAIIs. Conclusions Despite the findings that performance on some tasks and when using some AAIIs did not result in a greater number of PVL being able to complete the tasks, there was overall high satisfaction, reflecting an acceptance of AI as an assistive technology and the promise of this developing technology. Translational Relevance This evidence-based performance data provide guidelines for clinicians when recommending an AAII to PVL.Translational Vision Science & Technology480Despite the findings that performance on some tasks and when using some AAIIs did not result in a greater number of PVL being able to complete the tasks, there was overall high satisfaction, reflecting an acceptance of AI as an assistive technology and the promise of this developing technology.Translational Vision Science & Technology["William Seiple", "H. V. D. van der Aa", "Fernanda Garcia-Pi\u00f1a", "Izekiel Greco", "Calvin Roberts", "R. V. van Nispen"]2025-01-01T00:00:00 73d0dfb252f5ebea47e66c7827827d00fd0f0076Simple Summary Breast cancer remains a leading health challenge worldwide, particularly in low-resource settings where access to early detection and treatment is limited. Artificial Intelligence (AI) offers promising solutions to enhance breast cancer care by improving early detection, streamlining diagnosis, optimizing treatment planning, and supporting healthcare providers in clinical decision-making. This study provides a comprehensive scoping review of the applications, benefits, and challenges of using AI in breast cancer care. The findings highlight how AI can bridge gaps in global healthcare systems, particularly in underserved regions, by increasing efficiency, reducing costs, and enhancing accuracy in diagnosis and treatment. By identifying key trends and addressing barriers to AI adoption, we hope to guide the development of practical, patient-focused AI solutions and encourage further exploration of its potential and to inspire innovation and collaboration within the research community to reduce disparities in breast cancer outcomes globally.Cancers1320This study provides a comprehensive scoping review of the applications, benefits, and challenges of using AI in breast cancer care and highlights how AI can bridge gaps in global healthcare systems by increasing efficiency, reducing costs, and enhancing accuracy in diagnosis and treatment.Cancers["Jolene Li Ling Chia", "G. He", "Kee Yuen Ngiam", "Mikael Hartman", "Q. X. Ng", "S. Goh"]2025-01-01T00:00:00 02fd01d50e467cf3b4d59dcb9a08bda23b6081acRecent advances in generative artificial intelligence (AI) have expanded its applications in diagnostic support within dermatology, but its clinical accuracy requires ongoing evaluation. This study compared the diagnostic performance of three advanced AI models, ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, with that of board-certified dermatologists, using a dataset of 30 cases encompassing a variety of dermatological conditions. The AI models demonstrated diagnostic accuracy comparable to, and sometimes exceeding, that of the specialists, particularly in rare and complex cases. Statistical analysis revealed no significant difference in accuracy rates between the AI models and dermatologists, indicating that AI may serve as a valuable supplementary diagnostic tool in dermatological practice. Limitations include a small sample size and potential selection bias. However, these findings underscore the progress in AI’s diagnostic capabilities, supporting further validation with larger datasets and diverse clinical scenarios to confirm its practical utility.Cureus90Comparison of the diagnostic performance of three advanced AI models with that of board-certified dermatologists revealed no significant difference in accuracy rates between the AI models and dermatologists, indicating that AI may serve as a valuable supplementary diagnostic tool in dermatological practice.Cureus["Yuto Yamamura", "Kazuyasu Fujii", "C. Nakashima", "Atsushi Otsuka"]2025-01-01T00:00:00 34cdf3a0d04957e2a3de964a9f8f67699cccb2d7In the rapidly evolving landscape of global security, the United States faces increasingly sophisticated threats to its critical infrastructures and national security. These threats emanate from state and non-state actors employing advanced technologies to disrupt, degrade, and destroy essential systems. In response, Artificial Intelligence (AI) has emerged as a powerful tool for enhancing the resilience and defense mechanisms for critical infrastructures operation. This research paper explores the potential and application of AI in safeguarding the nation's critical assets, including energy grids, transportation networks, gas & oil pipelines, communication systems, financial institutions, water supply systems, healthcare databases, IT networks, and air traffic control systems. By leveraging machine learning algorithms, predictive analytics, and anomaly detection techniques, AI can identify and mitigate vulnerabilities in real-time, preemptively countering cyber-attacks, physical sabotage, and air traffic control disruptions. Additionally, AI-driven systems bolster cybersecurity, ensuring the resilience and security of vital US systems against emerging cyber threats. Furthermore, AI enhances decision-making capabilities, providing security agencies with actionable intelligence and situational awareness, while also contributing to overall security enhancements. This paper examines the ethical considerations, challenges, and future directions of integrating AI into national security frameworks. Through a comprehensive analysis, this study underscores the vital role of AI in fortifying the United States' critical infrastructures against the growing array of adversarial threats.European journal of computer science and information technology00The ethical considerations, challenges, and future directions of integrating AI into national security frameworks are examined, highlighting the vital role of AI in fortifying the United States' critical infrastructures against the growing array of adversarial threats.European Journal of Computer Science and Information Technology["Peter Pepple", "Ambrose Sunny O. Okorie", "Patrick Adeel"]2025-01-01T00:00:00 df847c83f44c9d4ab4efe9fed227ad076ee90c36This study introduces a framework aimed at enhancing the role of Artificial Intelligence (AI) in achieving the Sustainable Development Goals (SDGs). The primary objective is to address key challenges in AI applications, such as data scarcity, ethical concerns, and cultural diversity, by integrating explainable AI (XAI), simulation environments, and modular customization. The study emphasizes on region-specific datasets, synthetic data generation, and iterative refinement to improve AI solutions in sectors like poverty, healthcare, and climate action. The findings emphasizes on AI’s potential to transform theoretical solutions into practical, scalable implementations, driving sustainable development. While addressing challenges like data quality, algorithmic bias, and regulatory issues, the study also highlights the importance of ethical principles and contextual adaptability to achieve long-term, inclusive progress toward the SDGs.Recent Research Reviews Journal120A framework aimed at enhancing the role of Artificial Intelligence (AI) in achieving the Sustainable Development Goals (SDGs) by integrating explainable AI (XAI), simulation environments, and modular customization is introduced.Recent Research Reviews Journal["S. K. G", "Stephi Jacob", "Samrrutha R S", "Akalya A", "Karthika L"]2025-01-01T00:00:00 61eefc733f5eba6135ec54afa0ed5775b4d360a1Background Artificial intelligence (AI) has the potential to revolutionise healthcare. If the implementation is successful it has the potential to improve healthcare outcomes for patients and organisations. Little is known about the perceptions of allied health professionals (AHPs) towards AI in healthcare. Objective This study investigated barriers and enablers to AI implementation in the delivery of healthcare from the AHPs perspective. Methods Qualitative methodology informed by behaviour change theory using focus groups with AHPs at a health service in Queensland, Australia. Results Twenty-four barriers and 24 enablers were identified by 25 participants across four focus groups. Barriers included: lack of AI knowledge, explainability challenges, risk to professional practice, negative impact on professional practice, and role replacement. Enablers include AI training and education, regulation, reputation, understanding the healthcare benefits of AI and engaging clinical champions. Conclusions AHPs have concerns about the impact and trustworthiness of AI and the readiness of organisations to support its use. Organisations must take a proactive approach and adopt targeted and multifaceted strategies to address barriers. This may include workforce upskilling, clear communication of the benefits of AI use of local champions and ongoing research.Digital Health390This study investigated barriers and enablers to AI implementation in the delivery of healthcare from the AHPs perspective and found AHPs have concerns about the impact and trustworthiness of AI and the readiness of organisations to support its use.Digital Health["Jane Hoffman", "Rachel Wenke", "Rebecca L Angus", "Lucy Shinners", "Brent Richards", "Laetitia Hattingh"]2025-01-01T00:00:00 f683084fbeae140b4b3d79dd276f7dcb5b571a08Summary: Artificial intelligence (AI) scribe applications in the healthcare community are in the early adoption phase and offer unprecedented efficiency for medical documentation. They typically use an application programming interface with a large language model (LLM), for example, generative pretrained transformer 4. They use automatic speech recognition on the physician–patient interaction, generating a full medical note for the encounter, together with a draft follow-up e-mail for the patient and, often, recommendations, all within seconds or minutes. This provides physicians with increased cognitive freedom during medical encounters due to less time needed interfacing with electronic medical records. However, careful proofreading of the AI-generated language by the physician signing the note is essential. Insidious and potentially significant errors of omission, fabrication, or substitution may occur. The neural network algorithms of LLMs have unpredictable sensitivity to user input and inherent variability in their output. LLMs are unconstrained by established medical knowledge or rules. As they gain increasing levels of access to large corpora of medical records, the explosion of discovered knowledge comes with large potential risks, including to patient privacy, and potential bias in algorithms. Medical AI developers should use robust regulatory oversights, adhere to ethical guidelines, correct bias in algorithms, and improve detection and correction of deviations from the intended output.Plastic and Reconstructive Surgery, Global Open290Artificial intelligence scribe applications in the healthcare community are in the early adoption phase and offer unprecedented efficiency for medical documentation, but developers should use robust regulatory oversights, adhere to ethical guidelines, correct bias in algorithms, and improve detection and correction of deviations from the intended output.Plastic and Reconstructive Surgery Global Open["Sarah A. Mess", "Alison J. Mackey", "David E. Yarowsky"]2025-01-01T00:00:00 8c0299d4e936509df7291ebba8e361b200225abePURPOSE Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based cancer registry, to assess (1) if VCR received all relevant pathology reports for three clinical trials, (2) AI accuracy in auto-extracting information from pathology reports for recruitment, and (3) the number of participants approached for trial enrollment using the AI approach compared with standard hospital-based recruitment. METHODS To verify pathology report accessibility for VCR trial enrollment, reports from the laboratory were cross-referenced. To determine the accuracy of a Rapid Case Ascertainment (RCA) module of the AI software in extracting key clinical variables from the pathology report, data were compared with manually reviewed reports. To examine the effectiveness of the AI recruitment approach, the number of patients approached for recruitment was compared with standard practice. RESULTS Of the 195 reports provided by the pathology laboratory, 185 (94.9%) were received by VCR, 73 of 195 (37.4%) were eligible for the studies, and 5 of 73 (6.8%) eligible cases had not been received by the VCR. The RCA module demonstrated an accuracy of 93% and an F1 score of 0.94 in extracting key clinical variables. However, the RCA false-positive rate was 10% and the false-negative rate was 5%. The standard hospital approach selected fewer cases for approach to clinical trials compared with the RCA module approach, 8 of 336 (2.4%) versus 12 of 336 (3.6%), respectively. CONCLUSION Using AI to screen potentially eligible cases for recruitment to three clinical trials resulted in a 50% increase in eligible cases being approached for enrollment.JCO Clinical Cancer Informatics200Using artificial intelligence to screen potentially eligible cases for recruitment to three clinical trials resulted in a 50% increase in eligible cases being approached for enrollment.JCO Clinical Cancer Informatics["Maria L Bechelli", "K. Ivanova", "Suan Siang Tan", "Beena Kumar", "Dayna Swiatek", "S. Arulananda", "Sue M Evans"]2025-01-01T00:00:00 9597c9ddd2ff592e7fafaf1bcb211a2d05e5fe9aBackground: Artificial intelligence (AI)–based psychotherapeutic interventions may bring a new and viable approach to expanding psychiatric care. However, evidence of their effectiveness remains scarce. We evaluated the efficacy of AI‐based psychotherapeutic interventions on depressive, anxiety, and stress symptoms at postintervention and follow‐up assessments.Methods: A three‐step comprehensive search via nine electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Scopus, IEEE Xplore, Web of Science, PsycINFO, and ProQuest Dissertations and Theses) was performed.Results: Thirty randomized controlled trials (RCTs) in 31 publications involving 6100 participants from nine countries were included. The majority (79.1%) of trials with intention‐to‐treat analysis but less than half (48.6%) of trials with perprotocol analysis were graded as low risk. Meta‐analyses showed that interventions significantly reduced depressive symptoms at the postintervention assessment (t = −4.40, p = 0.001) with medium effect size (g = −0.54, 95% CI: −0.79 to −0.29) and at 6–12 months of assessment (t = −3.14, p < 0.016) with small effect size (g = −0.23, 95% CI: −0.40 to −0.06) in comparison with comparators. Our subgroup analyses revealed that the depressed participants had a significantly larger effect size in reducing depressive symptoms than participants with stress and other conditions. At postintervention and follow‐up assessments, we discovered that AI‐based psychotherapeutic interventions did not significantly alter anxiety, stress, and the total scores of depressive, anxiety, and stress symptoms in comparison to comparators. The random‐effects univariate meta‐regression did not identify any significant covariates for depressive and anxiety symptoms at postintervention. The certainty of evidence ranged between moderate and very low.Conclusions: AI‐based psychotherapeutic interventions can be used in addition to usual treatments for reducing depressive symptoms. Well‐designed RCTs with long‐term follow‐up data are warranted.Trial Registration: CRD42022330228Depression and Anxiety680At postintervention and follow‐up assessments, it was discovered that AI‐based psychotherapeutic interventions did not significantly alter anxiety, stress, and the total scores of depressive, anxiety, and stress symptoms in comparison to comparators.Depression and Anxiety["Ying Lau", "W. Ang", "Wen Wei Ang", "P. Pang", "Sai Ho Wong", "K. Chan"]2025-01-01T00:00:00 7cf846c06875b9cd8ed34856498861c953fff99dThe rapid evolution of generative artificial intelligence (AI) has introduced transformative technologies across various domains, with text-to-video (T2V) generation models emerging as transformative innovations in the field. This narrative review explores the potential of T2V AI generation models used in healthcare, focusing on their applications, challenges, and future directions. Advanced T2V platforms, such as Sora Turbo (OpenAI, Inc., San Francisco, California, United States) and Veo 2 (Google LLC, Mountain View, California, United States), both announced in December 2024, offer the capability to generate high-fidelity video contents. Such models could revolutionize healthcare by providing tailored videos for patient education, enhancing medical training, and possibly optimizing telemedicine. We conducted a comprehensive narrative literature search of databases including PubMed and Google Scholar, and identified 41 relevant studies published between 2020 and 2024. Our findings reveal significant possible benefits in improving patient education, standardizing customized medical training, and enhancing remote medical consultations. However, critical challenges persist, including risks of misinformation (or deepfake), privacy breaches, ethical concerns, and limitations in video authenticity. Detection mechanisms for deepfakes and regulatory frameworks remain underdeveloped, necessitating further interdisciplinary research and vigilant policy development. Future advancements in T2V AI generation models could enable real-time healthcare visualizations and augmented reality training. However, achieving these benefits will require addressing accessibility challenges to ensure equitable implementation and prevent potential disparities. By addressing these challenges and fostering collaboration among stakeholders, healthcare systems and AI technologists, T2V AI generation models could transform global healthcare into a more effective, universal, and innovative system while safeguarding against its potential misuse.Cureus480A narrative review explores the potential of T2V AI generation models used in healthcare, focusing on their applications, challenges, and future directions, and reveals significant possible benefits in improving patient education, standardizing customized medical training, and enhancing remote medical consultations.Cureus["M. Temsah", "Rakan I Nazer", "I. Altamimi", "Raniah N. Aldekhyyel", "Amr Jamal", "Mohammad Almansour", "F. Aljamaan", "K. Alhasan", "Abdulkarim A Temsah", "A. Al-Eyadhy", "Bandar N. Aljafen", "K. H. Malki"]2025-01-01T00:00:00 7e85a2d3b4e4496ad1510beb5138e06fd57ef9c5Artificial intelligence has emerged is an emerging technology in the current era with far reaching implications on every economic sector. AI is transforming how human interacts with machine and automate day to day actions which were traditionally performed by humans. The current study primary aim was to investigate the scope of AI application in academic settings from instructional and pedagogical perspective. The sim of the study was investigating factors influencing pedagogical beliefs of teachers to implement AI in mainstream education system of Oman. The review of literature suggests role of AI in terms of profiling & prediction, tutoring, accessing student performance, grading & evaluation and personalization of learning experience which influences learning outcomes amongst students using mediating role of pedagogical beliefs of teachers.  Using these variables from the literature, five hypotheses were analyzed using quantitative research methods. Primary data was gathered from the sample of n=250 using an instrument which measured variables on a Likert Scale from one to five. Five hypotheses were tested using SPSS where Hayes process Macros was used to test the direct and indirect effect. Findings shows that a Profiling and prediction, Assessing Student Performance and grading and evaluation capabilities aided by AI has indirect effect on learning outcomes amongst students whereas tutoring and personalization capability aided by AI has direct effect on learning outcomes amongst students. Findings poses significant managerial and practical implications for policy makers in Oman to implement AI in the mainstream education system of Oman.Qubahan Academic Journal00Investigation of the scope of AI application in academic settings from instructional and pedagogical perspective shows that a Profiling and prediction, Assessing Student Performance and grading and evaluation capabilities aided by AI has indirect effect on learning outcomes amongst students whereas tutoring and personalization capability aided by AI has direct effect on learning outcomes amongst students.Qubahan Academic Journal["Sarah Abou Karroum", "N. Elshaiekh"]2025-01-01T00:00:00 dbc2ea72aa617068aaa84e149a274a837030bc25: The rapid pace of technological advancements in artificial intelligence (AI) is accelerating significantly, with major companies in the AI field competing to release AI applications that keep up with this progress. The use of AI applications in social media and multimedia advertising has become widespread, necessitating advertisers to study the architectural frameworks required for large language models to effectively interact with AI applications. This will enable designers to make optimal use of these applications and achieve the best outcomes in crafting advertising messages a process known as prompt engineering. The research aims to establish the foundational principles of prompt engineering for AI applications in the formulation of advertising messages. The importance of this research lies in the necessity of developing prompt engineering skills among advertising designers to enhance their sensory imagination when using AI applications. The research problem is summarized in the following question: What are the key considerations in studying prompt engineering for AI applications, and what role does it play in crafting advertising messages? The researcher adopted a descriptive methodology to gather facts and information about prompt engineering and employed an applied approach to produce designs using AI applications while taking prompt engineering considerations into account. Large Language Models (LLMs) have garnered significant attention across numerous fields, including advertising, where these models have become increasingly intertwined. LLMs are supervised machine learning algorithms designed for regression analysis of datasets using a method known as Ensemble Learning. This approach combines different learning algorithms, with each algorithm supporting the others to enhance predictive capabilities. These models have been fed with extensive information available on the internet, focusing on the field of natural language processing (NLP), specifically human language. This development has led to the emergence of what is now called prompt engineering, a method through which the user—specifically, theInternational Design Journal50The research aims to establish the foundational principles of prompt engineering for AI applications in the formulation of advertising messages and develops prompt engineering skills among advertising designers to enhance their sensory imagination when using AI applications.International Design Journal["Reham Mohamed Elgindy", "S. Sedek", "Reem Yasser Abd Almawjoud Abd Alhakam"]2025-01-01T00:00:00 522268dfc40b60a0a557d3c94d3962ccb9df5568Asian Bioethics Review270The outcomes of a risk–benefit analysis are discussed to argue that the proposed implementation strategy is ethically appropriate and aligns with improvement-focused and systemic approaches to implementation, especially the learning health systems framework (LHS) to ensure safety, efficacy, and ongoing learning.Asian Bioethics Review["Alexa Nord-Bronzyk", "Julian Savulescu", "Angela Ballantyne", "Annette Braunack-Mayer", "Pavitra Krishnaswamy", "T. Lysaght", "Marcus E. H. Ong", "Nan Liu", "Jerry Menikoff", "M. Mertens", "Michael Dunn"]2025-01-01T00:00:00 93e8a5316bac8740ee2c7aa3dceff35872e249a9Introduction Artificial intelligence (AI) is revolutionizing healthcare, offering opportunities to improve diagnosis, clinical care, and medical education. Despite its growing importance, familiarity with AI in medical education remains limited, necessitating a deeper understanding of perceptions among medical students and faculty. The aim of the study is to explore the perceptions of medical students and faculty regarding the use of AI in medical education and its implications for curriculum improvement. Materials and methods A cross-sectional study was conducted over six months (January-June 2024) at Sheikh Bhikhari Medical College, Hazaribagh, India. An online questionnaire was distributed to 299 participants, including 242 (80.93%) students, 20 (6.68%) residents, and 37 (12.04%) faculty members, using convenience sampling. Data was analyzed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, NY, USA). Results The results revealed that 260 (86.95%) understood AI concepts, but only 36 (12.04%) were very familiar with its application in education. Additionally, 260 (87%) supported AI integration into medical curricula, and 273 (91.3%) believed it could improve educational efficiency. However, 179 (59.9%) had no prior experience with AI tools. Participants highlighted AI's potential in diagnostics (154, or 51.5%), clinical reasoning (51, or 17.1%), radiology (50, or 16.7%), pathology (31, or 10.4%), and 265 (88.62%) expressed a desire for structured AI training. Discussion While enthusiasm for AI integration is evident, gaps in exposure and structured education persist. Similar findings in global studies underline the urgent need for standardized curricula and faculty training. Conclusion This study highlights the importance of incorporating AI in medical education to prepare healthcare professionals for future challenges. Addressing gaps in knowledge and providing practical exposure are crucial for leveraging AI's full potential in medicine. Further multi-center studies are recommended to validate these findings.Cureus150The importance of incorporating AI in medical education to prepare healthcare professionals for future challenges is highlighted, and the urgent need for standardized curricula and faculty training is underlined.Cureus["Sudha Rani", "Anita Kumari", "Shreyasi C. Ekka", "Ratnajeet Chakraborty"]2025-01-01T00:00:00 dd240d88aac764b34b28751ed3e6eab815558d58 Artificial intelligence (AI) measurement has the potential to transform cardiac imaging, but what if it makes a mistake? Can AI also highlight when it is most likely to have mismeasured and can experts improve these measurements? As readers become increasingly reliant on automated measures for echocardiographic analysis, we need confidence that the software will measure accurately, and also flag where human oversight is needed. To develop and test an open, scientific, machine-learning method of validly judging the level of certainty in an AI measurement, to enable human oversight to focus on the measurements most likely to be incorrect. The heatmap altitude (MHA) at the measurement point is widely used as an automatic index of predicted reliability of an AI measurement. This is derived from the raw neural network output to give a level of confidence between 0 and 1 in each measurement that the AI makes. We test this not only against the AI’s difference from the expert consensus, but also, through the use of multiple experts per case, against individual experts difference from the consensus using the Unity UK Echocardiography AI collaboratives dataset of 200 parasternal long-axis images. Each image was labelled with key points for the aortic annulus, sinus and sinotubular junction, and proximal ascending aorta dimensions by 10 experts. The mean expert consensus measure was obtained, and then the median deviation of the AI and other experts calculated. The heatmap altitude was skewed with median 0.822, IQR 0.793 to 0.841, 10th to 90th percentile 0.734 to 0.852. Images were grouped by decile, from the 20 images with lowest confidence to the 20 with the highest (Figure 1). Both the AI and expert error (deviation from expert consensus) was greatest in the lowest confidence decile and progressively lower in each decile of greater confidence (p<0.001 for trend). In the better 9 deciles, the AI error was smaller than the human error (p<0.001); in the worst decile it was equivalent (p =NS). The fully automated and open-source MHA value usefully quantifies the reliability of an AI measurement. This could be used to target human oversight where it is most needed, but care must be taken as experts may find the images similarly difficult. European Heart Journal - Cardiovascular Imaging00The fully automated and open-source MHA value usefully quantifies the reliability of an AI measurement and could be used to target human oversight where it is most needed, but care must be taken as experts may find the images similarly difficult.European Heart Journal - Cardiovascular Imaging["T. Ng", "S. H. Hannan", "M. Julea", "A. Singh", "M. Zetani", "K. Vimalesvaran", "M. Tawil", "B. Rana", "C. Demetrescu", "A. Ghosh", "J. Sehmi", "S. Bhattacharyya", "C. Stowell", "M. Shun-Shin", "D. Francis"]2025-01-01T00:00:00 20784496be0182776eaf0a86b48fae790abb6aa6Background Birth control methods (BCMs) are often underutilized or misunderstood, especially among young individuals entering their reproductive years. With the growing reliance on artificial intelligence (AI) platforms for health-related information, this study evaluates the performance of ChatGPT-4o and Google Gemini in addressing commonly asked questions about BCMs. Methods Thirty questions, derived from the American College of Obstetrics and Gynecologists (ACOG) website, were posed to both AI platforms. Questions spanned four categories: general contraception, specific contraceptive types, emergency contraception, and other topics. Responses were evaluated using a five-point rubric assessing Relevance, Completeness, and Lack of False Information (RCL). Overall scores were calculated by averaging the rubric scores. Statistical analysis, including the Wilcoxon Signed-Rank test, Friedman test, and Kruskal-Wallis test, was performed to compare metrics. Results ChatGPT-4o and Google Gemini provided high-quality responses to birth control-related queries, with overall scores averaging 4.38 ± 0.58 and 4.37 ± 0.52, respectively, both categorized as "very good" to "excellent." ChatGPT-4o demonstrated higher scores in the lack of false information, based on descriptive statistics (4.70 ± 0.60 vs. 4.47 ± 0.73), while Google Gemini outperformed in relevance, with a statistically significant difference (4.53 ± 0.57 vs. 4.30 ± 0.70, p = 0.035, large effect size). Completeness scores were comparable (p = 0.655). Statistical analyses revealed no significant differences in overall performance (p = 0.548), though Google Gemini demonstrated a potential trend of stronger performance in the "Other Topics" category. Within-model variability showed ChatGPT-4o had more pronounced differences among metrics (moderate effect size, Kendall’s W = 0.357), while Google Gemini exhibited smaller variability (Kendall’s W = 0.165). These findings suggest that both platforms offer reliable and complementary tools for addressing knowledge gaps in contraception, with nuanced strengths that warrant further exploration. Conclusions ChatGPT-4o and Google Gemini provided reliable and accurate responses to BCM-related queries, with slight differences in strengths. These findings underscore the potential of AI tools, in addressing public health information needs, particularly for young individuals seeking guidance on contraception. Further studies with larger datasets may elucidate nuanced differences between AI platforms.Cureus210Cureus["Erhan Muluk"]2025-01-01T00:00:00 be2d6e2f790888c150ee6092a220d1fbf513047a Artificial Intelligence (AI) is increasingly used to assess Ulcerative Colitis (UC) disease activity in clinical trials, with the goal of matching or exceeding expert performance in a reproducible manner. Certai has been created from previous AI work as a model that conforms to the new definitions within the Modified Mayo Endoscopic Score criteria. Using the self-supervised DINOv2 method, Certai was pre-trained on 845 colonoscopic procedures and then refined with annotations from the proprietary Software for Intelligent Annotation (SIA) platform. This browser-based tool, with advanced playback controls and a UC scoring interface, enabled video annotation by eight global IBD specialists and central readers, and seven additional specialists in training on SIA. Labellers underwent rigorous onboarding to meet minimum thresholds for Intra-Class Correlation Coefficient (ICC) and Quadratic Weighted Kappa (QWK). Certai’s architecture features two Vision Transformer models: a Quality Control (QC) model and a Scoring model. The QC model excludes frames with poor clarity, inadequate bowel prep, non-colonic views, chromoendoscopy, or biopsy procedures. The Scoring model uses multi-headed outputs to assess UC severity, grading vascular pattern, bleeding, ulcers/erosions, friability, and erythema. A total of 8.9 million frames from 39 videos were labelled across six categories, resulting in 2.17 million merged labels determined by majority vote. For inter-rater agreement on modified MES in the labelling process, the overall ICC among the onboarded labellers was 0.86, and the QWK was 0.88. On a validation set of colonoscopy procedures, Certai achieved 100% agreement with human central readers on modified MES scores. The ICC among three expert labellers was 0.922 and rose to 0.942 with the addition of Certai. There was an additional increase of the ICC to 0.955 when Certai was paired with just one expert labeller. Similarly, QWK scores rose from 0.914 for two expert labellers to 0.961 with Certai and one expert labeller. Certai represents an advance in UC disease activity assessment, meeting modified MES requirements. With further specialist labelling of an additional several hundred videos at the detailed frame level, a robust version of Certai will enable new quality standards in speed of central reading and consistency. Future applications may include stand-alone AI reads with human sign-off or a 2 + 1 reader model incorporating AI as one reader. Journal of Crohn's & Colitis00Certai represents an advance in UC disease activity assessment, meeting modified MES requirements, and with further specialist labelling of an additional several hundred videos at the detailed frame level, a robust version of Certai will enable new quality standards in speed of central reading and consistency.Journal of Crohn's and Colitis["M. Byrne", "J. Requa", "J. Pan\u00e9s", "B. Bressler", "R. Panaccione", "R. Mendel", "J. E. East", "N. Parsa", "R. Banerjee", "R. Kalapala", "D. N. Reddy", "H. R. Rughwani", "D. Flegg", "G. Moran", "Z. Gallinger", "V. Cheung", "M. Tan", "C. Ma", "S. Travis", "V. Jairath"]2025-01-01T00:00:00 3f4cd71a32de157635b313773108c785388f64fbIndigenous communities in the Chittagong Hill Tracts (CHT) of Bangladesh contend with multifaceted governance challenges, including representation, resource allocation, and access to services. This paper investigates the potential of Artificial Intelligence (AI) to address these issues and foster inclusive governance practices in the region. Grounded in the following research questions: 1) How are Indigenous communities currently governed in the CHT, and what are the primary challenges they face? 2) What are the opportunities and challenges of leveraging AI to enhance governance in the CHT? 3) How do Indigenous communities perceive the role of AI in governance, and what are their expectations and concerns? the study adopts a self-directed research approach, employing interviews, surveys, and online research to gather insights. Through a synthesis of existing literature and empirical findings, the paper identifies key considerations for deploying AI in governance contexts, including ethical implications, community engagement strategies, and capacity-building initiatives. The research underscores the importance of centering Indigenous perspectives and promoting participatory approaches in the design and implementation of AI-driven governance solutions. By shedding light on the intersection of AI and Indigenous governance, this study contributes to the discourse on technology and social justice, offering practical insights for policymakers, researchers, and practitioners seeking to advance inclusive development agendas in marginalized regions. European Journal of Theoretical and Applied Sciences320Key considerations for deploying AI in governance contexts, including ethical implications, community engagement strategies, and capacity-building initiatives are identified, including ethical implications, community engagement strategies, and capacity-building initiatives.European Journal of Theoretical and Applied Sciences["Chakma Vaskar", "Amin Misbahul", "Rouf Abdur", "Suruj Al Mahmud", "Mia Raju", "Rafid Mustavi"]2025-01-01T00:00:00 49e377d8025e934b72575785d4bb4f75b2188bd6The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies with AI, researchers can explore and develop new flavor substances in a digital environment, saving time and resources. More and more research will use AI and big data to enhance product flavor, improve product quality, meet consumer needs, and drive the industry toward a smarter and more sustainable future. In this review, we elaborate on the mechanisms of flavor recognition and their potential impact on nutritional regulation. With the increase of data accumulation and the development of internet information technology, food flavor databases and food ingredient databases have made great progress. These databases provide detailed information on the nutritional content, flavor molecules, and chemical properties of various food compounds, providing valuable data support for the rapid evaluation of flavor components and the construction of screening technology. With the popularization of AI in various fields, the field of food flavor has also ushered in new development opportunities. This review explores the mechanisms of flavor recognition and the role of AI in enhancing food flavor analysis through high-throughput omics data and screening technologies. AI algorithms offer a pathway to scientifically improve product formulations, thereby enhancing flavor and customized meals. Furthermore, it discusses the safety challenges of integrating AI into the food flavor industry.Comprehensive Reviews in Food Science and Food Safety1040The mechanisms of flavor recognition and the role of AI in enhancing food flavor analysis through high-throughput omics data and screening technologies are explored and the safety challenges of integrating AI into the food flavor industry are discussed.Comprehensive reviews in food science and food safety["Zhiyong Cui", "Chengliang Qi", "Tianxing Zhou", "Yanyang Yu", "Yueming Wang", "Zhiwei Zhang", "Yin Zhang", "Wenli Wang", "Yuan Liu"]2025-01-01T00:00:00 c77bace67deedc25e953a1127328aa006cfa787dArtificial Intelligence (AI) is being used in every aspect of life today. It has found great application in the healthcare sector, with the use of this technology by medical schools all over the globe. AI has found multiple applications in medical fields such as diagnostics, medicine, surgery, oncology, radiology, ophthalmology, medical education, and numerous other medical fields. It has assisted in diagnosing conditions in a much quicker and more efficient manner, and the use of AI chatbots has greatly enhanced the learning process. Despite the benefits that AI applications provide, such as saving precious time for healthcare givers, there are also concerns regarding AI, mainly, ethical, and the fact that they might render the human race unemployed. However, despite these concerns, a lot of innovations are being made using AI applications, which show a very bright prospect for this technology. Although humans use AI in every part of their daily lives, they are also opposed to its use because they believe it could eventually replace them in the future. In this review of literature, a detailed analysis of the use of AI in the healthcare industry and medical education will be done, along with its shortcomings as well as its future prospects.Cureus430In this review of literature, a detailed analysis of the use of AI in the healthcare industry and medical education will be done, along with its shortcomings as well as its future prospects.Cureus["Girish Joseph", "Neena Bhatti", "Rithik Mittal", "Arun Bhatti"]2025-01-01T00:00:00 f266714378cd28e529838903e83f529b97eed029With the continuing passionate debate about the role of ChatGPT, an artificial intelligence (AI)–based text generator, in academic research and publishing, we reflect on whether AI can serve as a peer reviewer to overcome human weaknesses and other inherent weaknesses of the current human-supported peer-review model. The authors made their own assessment of this possibility by conducting a strength, weaknesses, opportunities, and threats (SWOT) analysis of human-based peer reviewers versus AI-based reviewers on 12 November 2023. A similar SWOT analysis was conducted for both humans and AI by employing cues fed to the paid version of ChatGPT (GPT-4). Despite the authors’ own ample experience and considerable efforts to make a balanced SWOT analysis, ChatGPT-4 was able to provide a comprehensive assessment, although it refused to cite relevant literature and obtained its information from websites and blogs. An earlier SWOT analysis (15 February 2023) using the free version (GPT-3) erred in citing the relevant literature while some citations and references were fabricated. Although AI (in this case, GPT-4) provided a logical and reasonable SWOT analysis, demonstrating its strength, it has not reached—in the authors’ view—a dependable stage yet to review articles for trusted academic journals.Journal of Scholarly Publishing00Although AI (in this case, GPT-4) provided a logical and reasonable SWOT analysis, demonstrating its strength, it has not reached—in the authors’ view—a dependable stage yet to review articles for trusted academic journals.Journal of Scholarly Publishing["J. A. Teixeira da Silva", "Panagiotis Tsigaris"]2025-01-01T00:00:00 4b6fc220565840db26f71c7a243bbce0af0d6fc8This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scans for prostate cancer (PCa). A literature review was conducted on the Medline, Embase, Web of Science, and IEEE Xplore databases. The search focused on studies that utilizes AI to evaluate PSMA PET scans. Original English language studies published from inception to October 2024 were included, while case reports, series, commentaries, and conference proceedings were excluded. AI applications show promise in automating the detection of metastatic disease and anatomical segmentation in PSMA PET scans. AI was also able to predict response to PSMA-based theragnostic and aids in tumor burden segmentation, improving radiotherapy planning. AI could also differentiate intraprostatic PCa with higher histological grade and predict extra-prostatic extension. AI has potential in evaluating PSMA PET scans for PCa, particularly in detecting metastasis, measuring tumor burden, detecting high grade intraprostatic cancer, and predicting treatment outcomes. Larger multicenter prospective studies are necessary to validate and enhance the generalizability of these AI models.Seminars in nuclear medicine500AI has potential in evaluating PSMA PET scans for PCa, particularly in detecting metastasis, measuring tumor burden, detecting high grade intraprostatic cancer, and predicting treatment outcomes, and larger multicenter prospective studies are necessary to validate and enhance the generalizability of these AI models.Seminars in nuclear medicine["Jianliang Liu", "Kieran Sandhu", "Dixon T S Woon", "Marlon L Perera", "Nathan Lawrentschuk"]2025-01-01T00:00:00 819550b65a790f29d6713362ed3918d1a01e9440Artificial intelligence (AI) has experienced strong growth in recent decades, becoming a transformative tool that can revolutionize various aspects of human life. In recent years it has demonstrated the ability to solve complex problems, from medical assistance to the proposal of self-sufficient objects that manage to effectively carry out human activities. Now, this transformative power of AI has arrived on the education stage, offering innovative and personalized solutions for teachers and students. In the case of teachers, it can become a virtual assistant that facilitates the planning and evaluation exercise, while, for students, it provides varied exercise spaces. The latter is very valuable for the Spanish class in Iraq, since personalized support helps advance mastery of the language. For the reasons stated, this article proposes to show how AI can become a tool for learning Spanish in the ELE class in Iraq. In that order, the first thing is to cover its conceptualization, its benefits and the role it can have in language learning. As well as, the challenges involved in understanding its use and adaptation, the different strategies to promote and the specific tools for the Spanish class in the Iraqi context.  لارك10This article proposes to show how AI can become a tool for learning Spanish in the ELE class in Iraq, to cover its conceptualization, its benefits and the role it can have in language learning.لارك["Ass.Prof Enas Sadiq Hamudi"]2025-01-01T00:00:00 0aa28865820123c26bac61c16e20d69037db74dfArtificial Intelligence (AI) has emerged as a transformative force in project management, redefining traditional workflows and enhancing overall project outcomes. This study reviews and synthesizes findings from over 50 research papers to assess the impact of AI on various aspects of project management, including decision-making, resource optimization, risk management, and workflow automation. AI-powered tools such as predictive analytics, task automation platforms, and Natural Language Processing technologies have demonstrated significant potential in improving project efficiency, accuracy, and collaboration. By automating routine tasks, providing actionable insights through data-driven analytics, and enabling real-time communication, AI empowers project managers to make informed strategic decisions and address risks proactively. The findings also reveal critical challenges, including high implementation costs, integration complexities, ethical concerns, and the difficulty in processing unstructured data. Despite these limitations, the study highlights the substantial advantages of AI in reducing human error, enhancing resource allocation, and ensuring timely project completion. By synthesizing extensive literature, this research provides a comprehensive understanding of AI's transformative role in project management while offering practical insights into its adoption. It concludes that addressing the challenges of AI integration through ethical frameworks and thoughtful implementation strategies isStrategic Data Management and Innovation00This research provides a comprehensive understanding of AI's transformative role in project management while offering practical insights into its adoption and concludes that addressing the challenges of AI integration through ethical frameworks and thoughtful implementation strategies is necessary.Strategic Data Management and Innovation["Emdadul Haque", "F. M. Fahad"]2025-01-02T00:00:00 912936e9372be2cd1a61110947795a6a3d0e012cThe research was conducted to explore the perceptions of gifted students towards artificial intelligence (AI) using a qualitative research method and a case study design. The study group comprised 25 students from the Selçuklu Science and Art Center during the 2023-2024 academic year, selected through affinity sampling method. Data was gathered through metaphoric forms and semi-structured interviews, and content analysis was employed for data analysis. The findings indicated that students primarily used the human metaphor when discussing AI. Themes derived from the metaphors included human similarity, potential threats of AI, and belief in the benefits of AI. Gifted students expressed concern about the potential risks of AI, while also highlighting its advantages in education. Additionally, the majority of students believed that schools would continue to operate within an AI-supported education system, although some students expressed the view that AI could make schools obsolete. According to the findings, gifted students have expressed a perception of AI as being advantageous as well as having potential risks. Consequently, the research suggests that it would be beneficial to offer specialized training to gifted students on the responsible utilization of AI within the realm of education.Journal of Digital Educational Technology620The findings indicated that gifted students have expressed a perception of AI as being advantageous as well as having potential risks, and it would be beneficial to offer specialized training to gifted students on the responsible utilization of AI within the realm of education.Journal of Digital Educational Technology["Deniz G\u00f6rg\u00fcl\u00fc", "Eda T\u00f6r\u00fcn"]2025-01-02T00:00:00 b96843b78e438fe0f299970d28008c2fcc6cad6c This study explores the integration of artificial intelligence (AI) in the creation of fantasy narratives, examining the new possibilities AI offers for authors and readers and its impact on the genre’s evolution. The study used literature review, textual analysis, comparative analysis, data analysis, experimental methods, ethical and legal examination, and data processing methods using Natural Language Processing software. Through analysis of films, video games, and qualitative text review, the research found that modern technologies—particularly AI and computer graphics—have greatly enhanced the visualization of fantasy worlds, contributing to the genre’s growing popularity. Interactive AI-driven platforms enable customized experiences, increasing engagement and satisfaction among audiences. A significant finding is that contemporary fantasy works, while retaining traditional elements like the struggle between good and evil, magic, and adventure, are now incorporating modern socio-cultural and political themes. This blend preserves the genre’s appeal to diverse audiences while keeping it relevant in today’s context. Additionally, AI opens new avenues for authors by aiding in overcoming creative blocks, generating fresh ideas, and constructing interactive worlds where readers can shape the plot or create their own characters. This not only accelerates the creative process but also enriches storytelling with more varied narratives. The study concludes that fantasy remains a powerful tool for cultural and social expression and that AI holds great potential for the genre's future growth, promising more innovative and inclusive experiences for audiences.Digital Scholarship in the Humanities260It is found that modern technologies—particularly AI and computer graphics—have greatly enhanced the visualization of fantasy worlds, contributing to the genre’s growing popularity and holding great potential for the genre’s future growth.Digital Scholarship in the Humanities["Yertay Sultan", "Gulnaz Dautova", "Dina Alkebayeva", "A. Akzhigitova", "Zhansaya Aden"]2025-01-02T00:00:00 1386df6bcc09240cbe098e4bc7ef40c2469ac6cbArtificial intelligence attracts controversy, but it is impossible to ignore its potential to transform healthcare. This article examines some of the established and potential applications of artificial intelligence in emergency care settings, noting where clinicians may be able to apply this technology to their departmental practice.British Journal of Healthcare Management450Some of the established and potential applications of artificial intelligence in emergency care settings are examined, noting where clinicians may be able to apply this technology to their departmental practice.British Journal of Healthcare Management["Jonathan Matthews"]2025-01-02T00:00:00 9daa5649e14975a801349a55bd08ece0536215f1Artificial Intelligence (AI) is one of the leading technologies that has begun to attract attention in the financial sector across various regions worldwide, including Southeast Asia. One potential application of AI is its ability to automatically detect specific data or situations within an electronic system, which can be utilized to identify money laundering activities. However, this cutting-edge technology may also result in certain unintended legal implications. The purpose of this study is to explore the potential and legal challenges in utilizing AI for the detection and prevention of money laundering. This research employs a normative legal research method to examine the legal implications of AI utilization in efforts to prevent and combat money laundering. The analysis reveals that significant normative issues persist regarding the legal certainty of AI applications in general, which poses complex challenges when coupled with specific legal implications, such as consumer protection and the safeguarding of privacy and data rights. This study proposes a legal development model that emphasizes balancing the efforts to prevent and combat money laundering with the rights of financial service users to maintain the integrity of the financial system.Jurnal Magister Hukum Udayana520This study proposes a legal development model that emphasizes balancing the efforts to prevent and combat money laundering with the rights of financial service users to maintain the integrity of the financial system and reveals that significant normative issues persist regarding the legal certainty of AI applications in general.Jurnal Magister Hukum Udayana (Udayana Master Law Journal)["Emiliya Febriyani", "Elza Syarief", "Triana Dewi Seroja"]2025-01-02T00:00:00 f022199f7a2d7488b3700ce315ab43d8f3529aaeThe Journal of Headache and Pain900A call to action in proposing novel frameworks for integrating AI-based technologies into headache care and AI-driven advances in drug discovery leverage machine learning and generative AI to accelerate the identification of novel therapeutic targets and optimize treatment strategies for migraine and other headache disorders.The Journal of Headache and Pain["Igor Petru\u0161i\u0107", "Chia-Chun Chiang", "D. Garc\u00eda-Azor\u00edn", "Woo-Seok Ha", "R. Ornello", "Lanfranco Pellesi", "Eloisa Rubio-Beltr\u00e1n", "Ruth Ruscheweyh", "M. Waliszewska-Pros\u00f3\u0142", "W. Wells-Gatnik"]2025-01-02T00:00:00 d16b773549f06fae0915fd33a5e0d00ea02b0a26The recent Regulation that sets down harmonised rules on Artificial Intelligence in the European Union, known as the "AI Act," includes a significant requirement for human oversight in high-risk AI systems during their use (art. 14). This requirement embodies the "human-in-command" approach, ensuring both legal and ethical compliance. The AI Act is intended to complement the General Data Protection Regulation (hereinafter GDPR), thereby forming a consistent and comprehensive legal framework. This paper focuses on AI systems producing decisions and examines the consistency of the AI Act's mandatory human oversight measures (art. 14) with GDPR's provisions on decisions based solely on automated processing (art. 22). At first glance, the provisions seem mutually exclusive. Mandatory human oversight under the AI Act could render art. 22 of GDPR inapplicable, as it applies only to decisions made by automated processing, implying no human involvement in decision-making. However, art. 22 of GDPR provides crucial safeguards for individuals, such as the right to human intervention, the ability to express opinions, and the right to contest decisions. This raises questions about whether the AI Act will exhaust these safeguards, and if it is capable of providing equivalent protection for decisions made by AI systems. This paper aims to analytically address these questions and arguments for a revision of the ordinary interpretation of art. 22 of GDPR, § 1. Keywords: AI Act; Algorithmic decisions; GDPR; Human oversight.Athens Journal of Law00Questions about whether the AI Act will exhaust its safeguards, and if it is capable of providing equivalent protection for decisions made by AI systems are analytically addressed.Athens Journal of Law["Claudio Sarra"]2025-01-02T00:00:00 fadf1c569c71dc521bd505cfa91857cef4fed448Global high prevalence of complex arrhythmias or atrial fibrillation (AF) ventricular tachycardia (VT) has burdened healthcare systems as these conditions contribute to stroke or lead to heart failure and sudden cardiac death. So these fetal conditions demand effective management strategies. Traditional approaches like antiarrhythmic medications and catheter ablation often have suboptimal outcomes with AF recurrence rates as high as 50% within one year. Advent of artificial intelligence (AI) in arrhythmia management has provided us innovative techniques for enhancing precision in ablation procedures. AI systems have now optimized arrhythmia mapping and has improved lesion accuracy at significant rate. Research confirmed that since ai has emerged, it uses is widely implemented because it has reduced procedural times by up to 25%. Most current papers show AI-guided ablation has achieved success rates over 85% lowering recurrence and complication rates when compared to those conventional methods. Challenges are limited validation in diverse populations and concerns regarding data privacy and algorithm biases. This paper is entirely based on most current papers which are published between 2019 and 2023. We evaluated the efficacy and safety of AI-guided cardiac ablation which is main aim of conducting this research. While technology demonstrates promising results yet it necessitates further validation and ethical considerations so that its use can be adopted more frequently at global level. Integration of AI into clinical practice offers potential advancements in precision cardiology but further research is required to address the existing gaps.Ibero-American Journal of Health Science Research00The efficacy and safety of AI-guided cardiac ablation is evaluated and demonstrates promising results yet it necessitates further validation and ethical considerations so that its use can be adopted more frequently at global level.Ibero-American Journal of Health Science Research["Andrea Soledad Mart\u00ednez Quinteros", "Felipe Arturo Trevi\u00f1o Acosta", "David Sebastian Ramirez Calvillo", "Paola Carolina Astudillo Gonz\u00e1lez", "Theo Ricardo M\u00e1rmol Mu\u00f1oz", "Ana Jos\u00e9 Franco Vaca"]2025-01-02T00:00:00 c1b237f25358a315527c64fc097e97c1d18edd39Most countries face declining birth rates and an aging population, which makes the persistent healthcare labor shortage a pressing challenge. Introducing artificial intelligence (AI) robots into home healthcare could help address these issues. Exploring the primary considerations for integrating AI robots in home healthcare has become an urgent topic. However, previous studies have not systematically examined the factors influencing elderly individuals’ adoption of home healthcare AI robots, hindering an understanding of their acceptance and adoption. Furthermore, traditional methods overlook the relative importance of each consideration and cannot manage the ambiguity inherent in subjective human cognition, potentially leading to biased decision-making. To address these limitations, this study employs the unified theory of acceptance and use of technology (UTAUT) as a theoretical framework, integrating the modified Delphi method (MDM) and the fuzzy analytical hierarchy process (FAHP) to identify the key considerations. The research determined the order of importance of four evaluation criteria and fourteen evaluation sub-criteria, revealing that customization, accompany, and subjective norms are key factors that influence elderly individuals’ adoption of home healthcare AI robots.Systems710The research determined the order of importance of four evaluation criteria and fourteen evaluation sub-criteria, revealing that customization, accompany, and subjective norms are key factors that influence elderly individuals’ adoption of home healthcare AI robots.Systems["Keng-Yu Lin", "Kuei-Hu Chang", "Yu-Wen Lin", "Mei-Jin Wu"]2025-01-02T00:00:00 66ea9447ca0714c0016c07b900020fde870b8e5dArtificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In immunogenomics, AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis, thus providing strong support for personalized treatments. In radiomics, AI can analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) images to discover imaging biomarkers associated with tumor heterogeneity, treatment response, and disease progression, thereby enabling non-invasive, real-time assessments for personalized therapy. Pathomics leverages AI for deep analysis of digital pathology images, and can uncover subtle changes in tissue microenvironments, cellular characteristics, and morphological features, and offer unique insights into immunotherapy response prediction and biomarker discovery. These AI-driven technologies not only enhance the speed, accuracy, and robustness of biomarker discovery but also significantly improve the precision, personalization, and effectiveness of clinical treatments, and are driving a shift from empirical to precision medicine. Despite challenges such as data quality, model interpretability, integration of multi-modal data, and privacy protection, the ongoing advancements in AI, coupled with interdisciplinary collaboration, are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction. These improvements are expected to lead to more accurate, personalized treatment strategies and ultimately better patient outcomes, marking a significant step forward in the evolution of precision medicine.Cancer Biology and Medicine1020The ongoing advancements in AI, coupled with interdisciplinary collaboration, are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction, which are expected to lead to more accurate, personalized treatment strategies and ultimately better patient outcomes.Cancer Biology & Medicine["Luchen Chang", "Jiamei Liu", "Jialin Zhu", "Shuyue Guo", "Yao Wang", "Zhiwei Zhou", "Xi Wei"]2025-01-02T00:00:00 5828f926edc235f4cf72c24a205628a766f1be83Rheumatology International1180Proposed artificial intelligence (AI) models to predict CVD/stroke risk accurately in AD for women by integrating imaging data and disorder-specific factors outperformed conventional methods by integrating imaging data and disorder-specific factors.Rheumatology international["Ekta Tiwari", "Dipti Shrimankar", "M. Maindarkar", "Mrinalini Bhagawati", "Jiah Kaur", "Inder M. Singh", "Laura E. Mantella", "A. Johri", "N. N. Khanna", "Rajesh Singh", "S. Chaudhary", "L. Saba", "Mostafa Al-Maini", "Vinod Anand", "G. Kitas", "J. Suri"]2025-01-02T00:00:00 609f936d990436b9ed9fab3c21e891fb3b417ee6The introduction of artificial intelligence (AI) in embryo selection during in vitro fertilization presents distinct ethical and societal challenges compared to the general implementation of AI in healthcare. This narrative review examines ethical perspectives and potential societal implications of implementing AI-driven embryo selection. The literature reveals that some authors perceive AI as an extension of a technocratic paradigm that commodifies embryos, considering that any embryo selection methods undermine the dignity of human life. Others, instead, contend that prioritizing embryos with the highest viability is morally permissible while cautioning against discarding embryos based solely on unproven AI assessments. The reviewed literature identified further potential ethical concerns associated with this technique, including possible bias in the selection criteria, lack of transparency in black-box algorithms, risks of “machine paternalism” replacing human judgment, privacy issues with sensitive fertility data, equity of access, and challenges in maintaining human-centered care. These findings, along with the results of the only randomized controlled trial available, suggest that the introduction of AI-driven embryo selection in clinical practice is not currently scientifically and ethically justified. Implementing and deploying ethical and responsible AI in embryo selection would be feasible only if the ethical and societal concerns raised are adequately addressed.Information430It is suggested that the introduction of AI-driven embryo selection in clinical practice is not currently scientifically and ethically justified and implementing and deploying ethical and responsible AI in embryo selection would be feasible only if the ethical and societal concerns raised are adequately addressed.Information["Roberto Aufieri", "Francesco Mastrocola"]2025-01-02T00:00:00 a9b54227b134e2e133819787f0e63765d7c3dde4BACKGROUND: To evaluate the impact of artificial intelligence (AI) on diagnosing and managing chronic diseases, focusing on its efficacy in improving patient outcomes and reducing healthcare burdens.   METHOD: This observational study was conducted at Mardan Medical Complex from January 2024 to December 2024. Data analysis incorporated patient characteristics, diagnostic accuracy, and management outcomes facilitated by AI, comparing AI-based and conventional approaches.   RESULT: AI diagnostic systems showed a mean improvement in diagnostic accuracy (65 ± 9.5) compared to traditional methods (55 ± 4.8), with significant reductions in symptom severity scores (AI: 28.5 ± 4.3, Traditional: 31.4 ± 4.6; p < 0.01). Treatment satisfaction rates were higher in AI-supported interventions (70%) compared to manual methods (67%, p = 0.45).   CONCLUSION: AI represents a transformative approach in chronic disease management, enhancing diagnostic precision, symptom relief, and patient satisfaction. Its integration into healthcare systems heralds a paradigm shift toward personalized medicine.Health Sciences AUS30Artificial intelligence represents a transformative approach in chronic disease management, enhancing diagnostic precision, symptom relief, and patient satisfaction, and its integration into healthcare systems heralds a paradigm shift toward personalized medicine.Health Sciences AUS["Dr Saad"]2025-01-02T00:00:00 f9a20444b1bebe41d1f320535450d799f3e7b38dMany factors will affect society’s shift towards sustainable development and attainment of the UN Sustainable Development Goals for 2015-2030. But of all, perhaps none has the potential to have a greater impact than artificial intelligence. AI has the potential to foster many advances in numerous areas, as well as the potential to cause some problems. But on the whole, the potential to make advances in sustainable development methods, technologies and operations offered by AI are profound and could lead to great strides in a relatively short time. In this editorial, the relation between AI and sustainable development is examined from my position as founding Editor in Chief of the journal.European journal of sustainable development research00In this editorial, the relation between AI and sustainable development is examined from my position as founding Editor in Chief of the journal.European Journal of Sustainable Development Research["Marc A. Rosen"]2025-01-02T00:00:00 1239efc2c6835aef15d5231455423ee15e27977dThe purpose of this article is to analyze the impact of artificial intelligence (hereinafter also referred to as AI) on social inequality. It is evident that AI not only brings about advantages but also serves as a means of infringing upon human rights, exacerbating social stratification at both the level of individual societies and on a global scale. Through the application of formal legal and comparative legal methodologies, it becomes apparent that the current practices of utilizing AI in various legal domains often fall short of achieving the intended objectives, sometimes even serving as a catalyst for discrimination and the perpetuation of social inequality. The paper underscores the active engagement of scholars and practitioners in addressing social inequality and other challenges associated with AI through the lens of AI ethics. The concept of AI ethics is explored, along with a critical analysis of ethical frameworks adopted by several technology companies operating in the field of artificial intelligence. It has been demonstrated that the primary cause of the emergence of social disparity in the realm of AI is not a dearth of ethical tenets in AI-driven algorithms, but rather a lack of adequate formalization of these ethical principles themselves into machine-readable language. It has been shown that the legal regulations in this domain, typically, are advisory in nature and emanate from corporations rather than from state institutions.Theoretical and Applied Law120Theoretical and Applied Law["A. I. Rybin", "E. O. Chashhukhin"]2025-01-02T00:00:00 aa58b401b49170b68fa33465c3de54b338190ef5Regulations must be clear and stringent for both healthcare providers and patients. Regulations have been progressively refined due to identifying weaknesses within the regulatory process. For example, regulatory principles applied to pharmaceuticals have been extended to cover other medical technologies. This paper provides a solid foundation of the meaning of Artificial Intelligence and machine learning as well as the usage of these technologies in healthcare to provide better diagnosis and more personalised medicine. Also, this paper proposes the challenges and regulation in Artificial Intelligence and machine learning in the healthcare domain. This paper emphasises the importance of establishing regulations for artificial intelligence and machine learning to ensure safety in the healthcare domain.Journal of Ecohumanism00A solid foundation is provided of the meaning of Artificial Intelligence and machine learning as well as the usage of these technologies in healthcare to provide better diagnosis and more personalised medicine.Journal of Ecohumanism["A. Alyousef", "Omaia Al-Omari"]2025-01-02T00:00:00 ade09f9566e97c13181232e2096f5bf421bcd2feArtificial intelligence is a powerful technology with great potential to support veterinarians across many aspects of their multifaceted job. However, realizing this potential requires AI solutions that truly address specific pain points. Here we review 4 use cases in which AI has been applied to small animal veterinary medicine: image analysis, early disease detection, administration support, and disease surveillance. For each of these, we briefly present the current state of the technology and available AI applications. Results show that tangible value creation is heterogeneous across use cases and that availability of accurate and reliable data (eg, in the form of curated electronic health records) is a major limiting factor.Journal of the American Veterinary Medical Association1250Results show that tangible value creation is heterogeneous across use cases and that availability of accurate and reliable data (eg, in the form of curated electronic health records) is a major limiting factor.Journal of the American Veterinary Medical Association["L. Albergante", "C. O'Flynn", "Geert De Meyer"]2025-01-02T00:00:00 4b1c983fb4815cbcce4ad2288caeb7d9c3dadf52This study investigated counsellor education Council for Accreditation of Counseling and Related Educational Programs (CACREP) programs generative artificial intelligence (AI) policies in doctoral‐level counselor education programs. We aimed to contribute to emerging research on the use of generative AI within counselor education.A content analysis of the policies was conducted along with a linguistic analysis of the policies to determine the authenticity, tone, and analytical nature of the University, and program policies.A content analysis of generative artificial intelligence usage policies within doctoral counselor education programs indicated that only five programs had program‐specific generative artificial intelligence policies. Most programs utilized University policies or usage guidance.Suggestions for practice include providing definitional clarity of the different types of AI to reduce potential frustration for learners. Further, programs should consider developing a program‐specific policy since the counseling profession requires a high level of ethical responsibility to best serve clients.Counselling and Psychotherapy Research340Counselling and Psychotherapy Research["Laurie O. Campbell", "Caitlin Frawley", "Glenn W. Lambie", "Karina S. Cabrera", "Bryanna D. Vizcarra"]2025-01-02T00:00:00 2c9b955a9d2814f00ee5791bf437ec42881c2c45This series surrounding generative artificial intelligence (Gen AI) explores its potential applications for paramedics across various aspects of their work. It begins by addressing the ethical considerations of this rapidly evolving technology. While artificial intelligence encompasses many forms, the focus here is on the generative subset – specifically free, publicly available Gen AI models. By reflecting current capabilities and limitations, this CPD series provides paramedics with insights into the practical and ethical use of this growing technology.Journal of Paramedic Practice100Journal of Paramedic Practice["Pippa Furey"]2025-01-02T00:00:00 e196c0760ce87782830dcf866e429e523db55fc5Abstract Economic and labour policies have a considerable influence on health and well-being through direct financial impacts, and by shaping social and physical environments. Strong economies are important for public health investment and employment, yet the rapid rise of generative artificial intelligence (AI) has the potential to reshape economies, presenting challenges beyond mere temporary market disruption. Generative AI can perform non-routine cognitive tasks, previously unattainable though traditional automation, creating new efficiencies. While this technology offers opportunities for innovation and productivity, its labour-displacing potential raises serious concerns about economic stability and social equity, both of which are critical to health. Job displacement driven by generative AI could worsen income inequality, shrink middle-class opportunities and reduce consumer demand, triggering recessionary pressures. In this article, we propose the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures may emerge, and which market forces alone cannot correct. Traditional responses to such pressures, like fiscal stimulus or monetary easing, may be ineffective in addressing structural disruptions to labour markets caused by generative AI. We call for a proactive global response to harness the benefits of generative AI while mitigating risks. This response should focus on reorienting economic systems towards collective well-being, as emphasized in the World Health Assembly resolution Economics of health for all and the United Nations' Global Digital Compact. Integrated strategies that combine fiscal policy, regulation and social policies are critical to ensuring generative AI advances societal health and equity while avoiding harm from excessive job displacement.Bulletin of the World Health Organization480The existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures may emerge, and which market forces alone cannot correct, is proposed.Bulletin of the World Health Organization["Jo-An Occhipinti", "Ante Prodan", "William Hynes", "John Buchanan", "Roy Green", "Sharan Burrow", "Harris A. Eyre", "A. Skinner", "I. Hickie", "Mark Heffernan", "Christine Song", "Goran Ujdur", "Marcel Tanner"]2025-01-02T00:00:00 d0f68b97df2599d62c6881490b908e729710971fNowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence for evaluating complex data implies that dental X-ray image recognition is a critical mechanism to enhance dental disease detection. Consequently, integrating deep learning algorithms into dental healthcare systems is considered a promising approach for enhancing the reliability and efficiency of diagnostic processes. In this context, an integrated artificial intelligence model is proposed to enhance model performance and interpretability. The basic idea of the proposed model is to augment the deep learning approach with Ensemble methods to improve the accuracy and robustness of dental healthcare. In the proposed model, a Non-Maximum Suppression (NMS) ensembled technique is employed to improve the accuracy of predictions along with combining outputs from multiple single models (YOLO8 and RT-DETR) to make a final decision. Experimental results on real-world datasets show that the proposed model gives high accuracy in miscellaneous dental diseases. The results show that the proposed model achieves 18% time reductions as well as 30% improvements in accuracy compared with other competitive deep learning algorithms. In addition, the effectiveness of the proposed integrated model, achieved 74% mAP50 and 58% mAP50-90, outperforming existing models. Furthermore, the proposed model grants a high degree of system reliability.Applied System Innovation380The basic idea of the proposed model is to augment the deep learning approach with Ensemble methods to improve the accuracy and robustness of dental healthcare and grants a high degree of system reliability.Applied System Innovation["Samar M. Nour", "Reem Salah Shehab", "Samar A. Said", "Islam Tharwat Abdel Halim"]2025-01-02T00:00:00 b0c2814edb0a838d4929c86ce05803a214b49cf2Simulation computer models of human neuroscience are widely used in artificial intelligence. The extensive use of surgical robots in China makes the simulation model of neuroscience have a broader stage in economic development. We have more usage for medical image recognition and surgical robot programming. We try to analyze the neural network model commonly used by orthopedic medical robots from the simulation of human neuroscience. The discussion is based on economic principles.WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY300This work tries to analyze the neural network model commonly used by orthopedic medical robots from the simulation of human neuroscience, based on economic principles.World Journal of Innovation and Modern Technology["Yi Qin", "Zhenyu Liu", "Yihan Liao", "Yuan Shen"]2025-01-02T00:00:00 deadd703f513c8330fb387d4c060cf3e41783d04Artificial intelligence (AI) technology is a scientific and technological field that focuses on the research and development of systems that simulate, extend, and expand human intelligence activities. This field encompasses various applications such as image recognition, language processing, expert systems, and robotics. The advancement of AI has greatly improved the quality and efficiency of medical work, particularly in areas like medical imaging, clinical decision support, precision medicine, and healthcare management. These advancements have contributed to the establishment of more effective healthcare systems.Within the realm of AI in healthcare, the application of AI technology in oral and maxillofacial surgery continues to evolve, with scenarios such as assisting in the interpretation and analysis of dental medical images, predicting and diagnosing early oral and maxillofacial tumors, aiding in minimally invasive surgery, designing ideal and personalized surgical plans, and simplifying medical management tasks. Oral and maxillofacial surgeons, as well as radiologists, should embrace and utilize these emerging technologies, actively adapting to environmental changes and updates, and driving forward the development of the field of oral and maxillofacial surgery.In summary, the integration of AI into oral and maxillofacial surgery presents significant opportunities for advancing patient care, surgical efficiency, and medical research in this specialized area of medicine. This technological partnership has the potential to reshape the landscape of oral and maxillofacial healthcare, benefiting both practitioners and patients.Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology00The integration of AI into oral and maxillofacial surgery presents significant opportunities for advancing patient care, surgical efficiency, and medical research in this specialized area of medicine.Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology["R. Y. Chen", "R. Zhang", "J. Wang"]2025-01-02T00:00:00 8b6d877ca57ca6bfa0488debf303352c033585f8A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalized treatment strategies, and precise medical interventions.Journal of drug targeting (Print)2550The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalized treatment strategies, and precise medical interventions.Journal of drug targeting["Sweet Naskar", "Suraj Sharma", "Ketousetuo Kuotsu", "Suman Halder", "Goutam Pal", "Subhankar Saha", "Shubhadeep Mondal", "Ujjwal Kumar Biswas", "Mayukh Jana", "Sunirmal Bhattacharjee"]2025-01-02T00:00:00 06fee9b38ce08504f455502c44fd0567a17a4bc8Articifical intelligence is now being used to create images and deepfake videos of children and child abuse. Elizabeth Rose looks at the implications for safeguarding work in schoolsJournal of Family and Child Health00Journal of Family and Child Health["Elizabeth Rose"]2025-01-02T00:00:00 2eaa543642a98a2e4456879eb0140c9fd0a7799bCurrent Psychology491Current Psychology["Linlin Bai"]2025-01-02T00:00:00 b1c743434c497be0be7be8c7286824f94027465bPriMera Scientific Engineering00PriMera Scientific Engineering[]2025-01-02T00:00:00 097f828638eeaa77540fb87fac3d77f0ddf10416Corporate Social Responsibility and Environmental Management00Corporate Social Responsibility and Environmental Management[]2025-01-02T00:00:00 52f7db53e3eb473d6a871ae032745dc027ee85faJournal of Software and Systems Modeling170Software and Systems Modeling["Francisco Javier Alcaide", "Jos\u00e9 Ra\u00fal Romero", "Aurora Ram\u00edrez"]2025-01-02T00:00:00 5212cf46f5094913770b7350959e6cbc450a3795Journal of Digital Learning in Teacher Education300Journal of Digital Learning in Teacher Education["Bilal Younis"]2025-01-02T00:00:00 89ab7bd511b468b13f6eb1cf683bbfd2b815de25npj Digit. Medicine260The study demonstrates the potential of Thermalytix for effective population-level breast cancer screening in low-resource settings and demonstrates the potential of Thermalytix for effective population-level breast cancer screening in low-resource settings.NPJ Digital Medicine["Karthik Adapa", "Ashu Gupta", "Sandeep Singh", "Hitinder Kaur", "Abhinav Trikha", "Ajoy Sharma", "Kumar Rahul"]2025-01-02T00:00:00 3fe1752780ee7b18e48c9c01c3b2b59efa6e050eAfrican Journal of Biomedical Research00African Journal of Biomedical Research["Usha Thawrani"]2025-01-02T00:00:00 0f92c5ee4c44d553ac0b9d2f9e575c786e0965a7Ageing Research Reviews1690It was found that the number of drug repositioning studies for PD has increased recently, and a better chance of success in PD drug discovery has been made possible due to AI/ML algorithm advancements.Ageing Research Reviews["Iman Karimi-Sani", "Mehrdad Sharifi", "Nahid Abolpour", "Mehrzad Lotfi", "Amir Atapour", "M. Takhshid", "A. Sahebkar"]2025-01-02T00:00:00 a2b87f539a789dc5adc4f2ca802c272616e6fbbaLarge language models (LLMs) such as GPT-4 have raised questions about the changing nature of work. Research has started to investigate how this technology affects labor markets and might replace or augment different types of jobs. Beyond their economic implications in the world of work, there are important sociological questions about how LLMs connect to subjective evaluations of work, such as the prestige and perceived social value of different occupations, and how the widespread use of LLMs perpetuate often biased views on the labor markets reflected in their training datasets. Despite initial research on LLMs’ world model, their inherent biases, attitudes and personalities, we lack evidence on how LLMs themselves evaluate occupations as well as how well they emulate the occupational evaluations of human evaluators. We present a systematic comparison of GPT-4 occupational evaluations with those from an in-depth, high-quality survey in the UK context. Our findings indicate that GPT-4 and human scores are highly correlated across all ISCO-08 major groups for prestige and social value. At the same time, GPT-4 substantially under- or overestimates the occupational prestige and social value of many occupations, particularly emerging occupations as well as stigmatized or contextual ones. In absolute terms, GPT-4 scores are more generous than those of the human respondents. Our analyses show both the potentials and risks of using LLM-generated data for occupational research.AoIR Selected Papers of Internet Research50It is indicated that GPT-4 and human scores are highly correlated across all ISCO-08 major groups for prestige and social value, and GPT-4 substantially under- or overestimates the occupational prestige and social value of many occupations, particularly emerging occupations as well as stigmatized or contextual ones.AoIR Selected Papers of Internet Research["Christoph Lutz", "Pawe\u0142 Gmyrek", "G. Newlands"]2025-01-02T00:00:00 068a28c84cc1137050043cb4cd33a8cc95a10ee0Accuracy and precision are among the central values in the ML communities and tech industry. What does it take to achieve a high level of technical accuracy? What are the harms resulting from technology companies' obsession with technical accuracy and precision, and who incurs the greatest burdens? This paper explores accuracy in the context of AI training in China. Drawing on 9-month multi-sited ethnographic fieldwork, we document workers’ everyday working practices and challenges and harms under the guise of achieving extreme levels of technical precision demanded by the clients and ML practitioners. We introduce the notion of precision labor, referring to the hidden work involved in erasing the messy, ambiguous, and uncertain aspects of technology production, all in the pursuit of presenting technology as objective, truthful, and high-quality. This notion provides a lens to understand the disproportionate impact of unnecessary and unrecognized labor on digital labor communities within AI production and the emerging harms on them, such as financial precarity and machine subordination. It joins existing work on the prevailing values in ML communities, questions the legitimacy and sustainability of the pursuit of performative accuracy, and calls for enhanced reflexivity and timely intervention.AoIR Selected Papers of Internet Research00The notion of precision labor is introduced, referring to the hidden work involved in erasing the messy, ambiguous, and uncertain aspects of technology production, all in the pursuit of presenting technology as objective, truthful, and high-quality.AoIR Selected Papers of Internet Research["Ben Zefeng Zhang,", "Tianling Yang", "Oliver Haimson", "Michaelanne Thomas"]2025-01-02T00:00:00 07aba987e6d918749a174a0b03404c8f1c8caa82Cogent Education520Cogent Education["J. Gayed"]2025-01-02T00:00:00 4cf5e8de76bafaeda3e08415ab38f148ef7d6e1fDiseases of the Colon & Rectum00Diseases of the colon and rectum["M. Tez"]2025-01-02T00:00:00 65fe1921a1b100a81413ad7a3c7a937f1873287cLike other media industries (e.g. music, film, TV), the podcasting industry has spent the last year coming to grips with the impact that generative artificial intelligence tools will have on the future of podcast production and consumption. Several high-profile examples of AI-generated podcasts have raised questions about the ethics, legality, and creativity of AI for creating podcast scripts, voices, and sound design. These in turn raise larger concerns about ownership and the economics of the platforms that distribute podcasts (like Spotify). In this paper, I survey the state of artificial intelligence in the podcasting industry to address the following two questions: How is AI being used by podcasters and other actors in the podcasting industry? How is the use of AI in podcasting framed in articles and writing about the industry? By sampling a number of podcasts that employ AI during the production and consumption process (e.g. AI scriptwriting, AI voiced hosts, AI sound design, etc.), my paper explores how podcasters and others in the podcasting industry are using AI as part of their everyday work. I develop a typology for the different ways AI is currently being used in podcasting in order to explore the anxieties around the use of artificial intelligence in the cultural industries as well as the everyday, and more mundane, impact AI is having on workflows, production capabilities and notions of creativity in the creative industries.AoIR Selected Papers of Internet Research10A typology for the different ways AI is currently being used in podcasting is developed in order to explore the anxieties around the use of artificial intelligence in the cultural industries as well as the everyday, and more mundane, impact AI is having on workflows, production capabilities and notions of creativity in the creative industries.AoIR Selected Papers of Internet Research["Jeremy Wade Morris"]2025-01-02T00:00:00 70b3ab33387085e60ee3470c631aa8da604b9678The intersection of artificial intelligence (AI) and healthcare is poised to transform medical practices by 2030, reshaping diagnostics, treatment protocols, and patient care. This paper explores the key advancements expected in AI-driven healthcare, including precision medicine, predictive analytics, and automated workflows, and the challenges posed by ethical considerations, data security, and regulatory frameworks. Emphasis is placed on the role of AI in enhancing access to care, reducing costs, and empowering patients through personalized solutions. Furthermore, the discussion highlights pathways for integrating AI technologies responsibly to ensure equity and trust in healthcare systems. By envisioning AI's potential and addressing its risks, this work aims to provide a roadmap for stakeholders to navigate the evolving healthcare landscape effectively.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)00The key advancements expected in AI-driven healthcare, including precision medicine, predictive analytics, and automated workflows, and the challenges posed by ethical considerations, data security, and regulatory frameworks are explored.Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930)["A. N. Asma"]2025-01-02T00:00:00 cfe15fa3dcb481638f3a9230dac62dfcaa452543Defect detection in Additive Manufacturing (AM) is a critical aspect of ensuring product quality, particularly in industries such as renewable energy and biomedical engineering, where reliability and precision are paramount. This study conducted a systematic review of 152 peer-reviewed articles, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to analyze the adoption of Artificial Intelligence (AI) techniques in defect detection within AM processes. The review revealed that machine learning (ML) and deep learning (DL) techniques, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), are widely employed for identifying common defects like porosity, delamination, and dimensional inaccuracies. Hybrid AI models, integrating ML and DL, demonstrated superior performance in detecting complex, multi-dimensional defects across various AM applications. Additionally, the integration of multimodal data, including thermal imaging, acoustic signals, and optical measurements, was found to improve defect detection rates by an average of 22%, enhancing the robustness and accuracy of AI models. The study also identified significant challenges, including dataset scarcity and annotation inconsistencies, which limit the generalizability and scalability of AI solutions. Comparative analyses further highlighted the distinct advantages of tailored AI approaches for specific applications, with renewable energy and biomedical engineering being key focus areas. This review underscores the transformative potential of AI in advancing defect detection in AM, providing a comprehensive understanding of its capabilities, challenges, and implications for high-stakes manufacturing industries.Strategic Data Management and Innovation00The transformative potential of AI in advancing defect detection in AM is underscored, providing a comprehensive understanding of its capabilities, challenges, and implications for high-stakes manufacturing industries.Strategic Data Management and Innovation["Zubair Hossain Mahamud", "Md Rabbi Khan", "Jareer Murtaza Amin", "Mohammad Samiul Islam"]2025-01-02T00:00:00 7bcaf5a4a8b63259706d6cc37ca98f0ed781eee3Artificial Intelligence (AI), often hailed as a transformative force, has become an ambivalent buzzword, simultaneously promising utopian possibilities and fueling dystopian anxieties. Social media platforms have emerged as pivotal spaces where the public narrative about AI takes shape, especially through content creators, significantly influencing our collective vision of the future with AI. Therefore, this paper inquires into the role of creators in shaping public imaginaries of AI through their AI content. The paper is based on TikTok as a site of entrance for investigating the role of creators in shaping ongoing discourses around AI through short video content. To understand the role of creators within this ongoing AI discourse, a hashtag network analysis is paired with a critical discourse analysis of creators’ AI content. The preliminary results show three dominant genres of AI content based on 1) AI tools output, especially visual content, 2) listicles on AI tools for different tasks, and 3) educational and critical AI content. Considering the creator types behind the content, a high amount of content is produced by content farms followed by tech TokTokers. Media outlets and commentary TikTokers dominate the third content section. Overall, four types of AI imaginaries are foregrounded. AI mystification envisions AI as fast-paced and inherently life-changing. Similarly, AI futuristic content makes AI out as inevitable. Contrastingly, a high AI pragmatism is prevalent in the ongoing tool discourse, while critical and educational content counteracts these imaginaries with a strong AI realism highlighting the complex and nuanced aspect of AI.AoIR Selected Papers of Internet Research00The paper is based on TikTok as a site of entrance for investigating the role of creators in shaping ongoing discourses around AI through short video content and shows three dominant genres of AI content based on 1) AI tools output, especially visual content, 2) listicles on AI tools for different tasks, and 3) educational and critical AI content.AoIR Selected Papers of Internet Research["Vanessa Richter"]2025-01-02T00:00:00 9594e555c8f9dea2e22e6a6e2897db1e0a2e3436The proliferation of generative artificial intelligence (AI) has prompted the development of comprehensive AI developmental and governance frameworks globally. Yet, existing literature on AI innovation in non-Western societies often overlooks economically advanced but geographically non-dominant societies, instead focusing on large nation-states like China or developing regions in Global South such as South Africa. This paper examines the variegated sociotechnical imaginaries of AI in three Asian developmental societies - Singapore, Hong Kong and Taiwan - addressing two research questions: what are the desired forms of AI development and governance in small-size advanced economies? How does this desired form vary according to the historical, institutional, and geopolitical contexts of these societies? Through discourse analysis of policy documents from the early 2010s to 2024, the paper identifies three imaginaries of techno-developmentalism: Singapore’s cybernetic pragmaticism to legitimize its neoliberal authoritarian rule, Hong Kong’s techno-entrepreneurship in refashioning financial capitalism, and Taiwan’s defensive survival modality against internal socio-economic instability and external threats posed by the rivalry of superpowers. Decision-makers in these societies must establish AI developmental frameworks capable of resource allocation, actor coordination, strategic coupling with the global tech economy, and managing uncertainties in specific AI-centric socio-economic reform. By offering comparative case studies of these Asian societies, this paper contributes to understanding the heterogeneous narratives and practices of AI innovation, moving beyond simplistic narratives trapped in the Global North and South binary.AoIR Selected Papers of Internet Research30This paper examines the variegated sociotechnical imaginaries of AI in three Asian developmental societies - Singapore, Hong Kong and Taiwan - addressing two research questions: what are the desired forms of AI development and governance in small-size advanced economies and how does this desired form vary according to the historical, institutional, and geopolitical contexts of these societies.AoIR Selected Papers of Internet Research["Hiu-Fung Chung"]2025-01-02T00:00:00 1f26eb45bb8b748e63322f1cfa01738d3000ed35The recent widespread availability of Artificial Intelligence (AI) technology and the extensive records of human activities and behaviour in digital format present serious challenges related to how individuals construct their own identities and social relations. AI systems datafy our body and our sense of self, producing a new cartography of biopower (Foucault, 1982) and a new form of the political economy of subjectivation (Langlois & Elmer, 2019) that treats individuals as objects from which raw material is extracted to produce predictive models that act as our data doubles (Haggerty & Ericson, 2000). Issues such as algorithmic social biases (Bolukbasi et al., 2016), the idealized and pragmatic economic uses of AI (Srnicek, 2017), and the consequent reproduction of already existing power structures by predictive models (Crawford, 2021) have been problematized in the literature. This paper asks what kinds of data and labour mobilization occur in and around the production of predictive models: What political economy and socio-technical conditions are involved in the production of AI? How do these conditions produce predictive models that shape our sense of self and identity? Focusing on Kaggle, a platform for crowdsourcing AI development, I use digital methods and a software studies approach to examine the practices of the data science community on three high-profile machine learning projects and conclude by arguing that machine learning has been thought of and developed as a prediction of the self in order to prescribe individual behaviour to fulfill specific economic conditions.AoIR Selected Papers of Internet Research00This paper uses digital methods and a software studies approach to examine the practices of the data science community on three high-profile machine learning projects and argues that machine learning has been thought of and developed as a prediction of the self in order to prescribe individual behaviour to fulfill specific economic conditions.AoIR Selected Papers of Internet Research["Luciano Frizzera"]2025-01-02T00:00:00 6ddac726664afca4df3112906cd493a7c31353afThis study provides a comparative assessment of the capabilities of leading artificial intelligence (AI) tools and human participants in a business management education context. Specifically, we (a) assess how well current language models perform in providing answers to standardised essay‐type assessments in a business and management education context, (b) examine the efficacy of emergent tools in detecting AI‐generated texts and (c) evaluate online AI rewriting and paraphrasing tools and their efficacy in evading detection. Using an exploratory qualitative design, this study generated and evaluated 15 standard essays using ChatGPT (n = 5), Bard (n = 5) and human (n = 5). A comparison is provided between the average performance of AI‐derived essays and that of ChatGPT‐generated essays across all five essays. The results suggest that AI‐generated content can achieve reasonably high marks in management and business assessments. According to the findings of the study, AI's performance is highly influenced by the types of prompts used, the user's experience and the degree to which the user can discern between relevant and irrelevant content. According to the findings, Turnitin's AI detection tool is highly effective at detecting content that has been created by AI, but the effectiveness is reduced by rewriters. The Turnitin AI detection tool, however, is significantly more effective at identifying content generated by Bard compared with content generated by ChatGPT. According to the results, ChatGPT produced better results when the user provided a clear context, outlined the topic and expectations, divided the assessment tasks into sections and fed the prompts in a conversational manner to train the model. By utilising AI chatbots effectively, traditional teaching and assessment methods can be supplemented with targeted and engaging learning experiences.British Educational Research Journal500The results suggest that AI‐generated content can achieve reasonably high marks in management and business assessments and Turnitin's AI detection tool is significantly more effective at identifying content generated by Bard compared with content generated by ChatGPT.British Educational Research Journal["D.B. Herath", "Egena Ode", "Gayanga B. Herath"]2025-01-02T00:00:00 e7a5d33f66cf7c9107a5bfd1401ac1fac418905dHumanities and Social Sciences Communications1354This study utilizes panel data from 11 coastal provinces and municipalities in China from 2009 to 2020, employing the entropy method and the super-efficiency EBM model to calculate the AI index and the green economic efficiency of marine fisheries.Humanities and Social Sciences Communications["Qiang Wang", "Tingting Sun", "Rongrong Li"]2025-01-03T00:00:00 6d09175761c948980a729c2c1a4735ea2e640053BACKGROUND The Adelaide Score is an artificial intelligence system that integrates objective vital signs and laboratory tests to predict likelihood of hospital discharge. METHODS A prospective implementation trial was conducted at the Lyell McEwin Hospital in South Australia. The Adelaide Score was added to existing human, artificial intelligence, and other technological infrastructure for the first 28 days of April 2024 (intervention), and outcomes were compared using parametric, non-parametric and health economic analyses, to those in the first 28 days of April 2023 (control). Artificial intelligence evaluated inpatients admitted under 18 surgical and medical teams, and patients of high likelihood of discharge were provided, on working shifts between Thursday to Sunday, to the Supportive Weekend Interprofessional Flow Team (SWIFT) comprising a senior nurse and pharmacist. RESULTS Two thousand nine hundred and sixty-eight admissions were included across intervention and control periods. Relative to the control group, use of the Adelaide Score in the intervention group resulted in significantly shorter median length of stay (3.1 versus 2.9 days, P = 0.028) and significantly lower seven-day readmission rate (7.1 versus 5.0%, p = 0.02). The 0.2 bed-day reduction in median length of stay produced a cost saving of $735 708.60 across the 28-day period, or $9 564 211.80 across a 52-week year. There was no significant difference between intervention and control groups in median length of stay for patients discharged on weekends, in-hospital mortality, or discharge to non-home destinations. CONCLUSIONS The prospective implementation of the Adelaide Score was associated with improved hospital and cost efficiency, alongside lower readmissions, for patients across surgical and medical services.ANZ journal of surgery230The prospective implementation of the Adelaide Score was associated with improved hospital and cost efficiency, alongside lower readmissions, for patients across surgical and medical services.ANZ journal of surgery["J. Kovoor", "Brandon Stretton", "Aashray K. Gupta", "Alexander Beath", "M. O. Jacob", "John Kefalianos", "Gavin J Carmichael", "A. Zaka", "Gerry O'Callaghan", "Shrirajh Satheakeerthy", "Andrew Booth", "Thomson Delloso", "Thomas J Hugh", "W. O. Chan", "Guy J. Maddern", "Eva Balan-Vnuk", "Michael Cusack", "Toby Gilbert", "John Maddison", "Stephen Bacchi"]2025-01-03T00:00:00 fafcffd627a46ca65c58fef6e83b51666b07d072BMC Oral Health210Egyptian dental students are acquainted with AI and its possible applications in dentistry and consider the use of AI diagnosis exciting and approve of its definitive role in disease prediction.BMC Oral Health["M. Elchaghaby", "Reem Wahby"]2025-01-03T00:00:00 e1eecc84bbc6d57b35abcc1b618ddcda44c77775Current Oncology Reports310This review aims to evaluate the impact of artificial intelligence on cancer health equity, specifically investigating whether AI is addressing or widening disparities in cancer outcomes, and whether AI is addressing or widening disparities in cancer outcomes.Current oncology reports["Irene Dankwa-Mullan", "Kingsley Ndoh", "Darlington Akogo", "Hermano Alexandre Lima Rocha", "S. F. Jua\u00e7aba"]2025-01-03T00:00:00 253c8e36988b8c37a18e390278a35f0ec4b7c941Studies on artificial intelligence (AI) have increased significantly over the past decade to the point that they have recently become essential to diverse fields. Regarding studies on sustainability, environmental care, and the application of technological advances, AI-based models have also gained particular significance. Accordingly, this study explored the relationship between AI, sustainability, and environmental impact through a mixed documentary review, which combined a narrative review and a bibliometric analysis. The narrative review examined the main ideas and stages that permeate the intersection of AI and sustainability, identifying their contributions and challenges. The bibliometric analysis provided a quantitative overview of scientific production, highlighting trends in terms of production, countries, and most influential keywords. The results reveal that AI has a crucial role in promoting sustainable practices, but it also poses risks that require careful consideration. Hence, the costs of AI must also be analyzed. The study underlined the need for a balanced approach that maximizes the benefits of AI while minimizing its negative impacts on the environment.Región Científica480The study underlined the need for a balanced approach that maximizes the benefits of AI while minimizing its negative impacts on the environment, and underlined the need for a balanced approach to the costs of AI.Región Científica["Fabiano Domenico Camastra", "Rub\u00e9n Gonz\u00e1lez Vallejo"]2025-01-03T00:00:00 f0bfb39ec62516cc9097b0baaa76933b4618e235Artificial Intelligence (AI) has the potential to transform the healthcare ecosystem, but further research is needed to understand how it can enhance healthcare capabilities. This study analyzes the literature on AI and healthcare capability using the PRISMA approach, applying specific search keywords and inclusion/exclusion criteria. The findings indicate that AI benefits the healthcare ecosystem, significantly influences health outcomes, and transforms medical practices. However, there is limited literature and a lack of understanding regarding how AI enhances healthcare capabilities. Most studies date from 2019, suggesting that COVID-19 has accelerated the adoption of AI systems in healthcare. This research contributes theoretically by developing a framework that clarifies AI’s role in enhancing healthcare capabilities, serving as a foundational model for future studies. It identifies critical gaps in the literature, especially in the Global South, and encourages exploration in under-researched areas where healthcare professionals can benefit from AI. Additionally, it bridges the gap between AI and healthcare, enriching interdisciplinary dialogue relevant to emerging economies facing financial constraints. Practically, the study provides actionable insights for healthcare practitioners and policymakers in the Global South on leveraging AI to improve service delivery. It sets the stage for empirical research, promoting the testing and refinement of the proposed framework in resource-limited contexts, while raising awareness among healthcare staff, managers, and technology developers about AI’s role in healthcare.F1000Research560This study analyzes the literature on AI and healthcare capability using the PRISMA approach, applying specific search keywords and inclusion/exclusion criteria to indicate that AI benefits the healthcare ecosystem, significantly influences health outcomes, and transforms medical practices.F1000Research["D. Ferede"]2025-01-03T00:00:00 f370239c1cb7dd05f9079af5e11386d878b4e247 Objective. This study examined the scientific literature addressing the relationship between artificial intelligence (AI) and knowledge management (KM) to identify the main issues around this binomial. Design/Methodology/Approach. We used co-word analysis as our bibliometric technique. We only worked with each article's keyword and keyword plus variable. Each cluster within the map was assigned a generic name according to the theme it represented. We also conducted some analysis based on the degree of centrality of keywords per cluster. We also performed qualitative analyses of each cluster's terms and word relationships. Results/Discussion. The co-occurrence map of terms revealed nine clusters related to the relationship between KM and AI: (1) main and central themes, (2) innovation and system design, (3) knowledge representation and learning, (4) theoretical models and information management, (5) collaborative networks and dynamics, (6) natural language processing, (7) ethics and governance, (8) visualization and knowledge representation, and (9) emerging and specialized areas. Conclusions. This study contributes to closing a gap in the literature by demonstrating that integrating AI and KM is a key alliance to meet the challenges of the knowledge society. AI strengthens conventional KM processes and opens new opportunities to create organizational and societal value. However, implementing AI requires a balanced approach that combines technological innovation with ethical and human considerations. Iberoamerican Journal of Science Measurement and Communication110This study contributes to closing a gap in the literature by demonstrating that integrating AI and KM is a key alliance to meet the challenges of the knowledge society.Iberoamerican Journal of Science Measurement and Communication["Daniel Crist\u00f3bal Andrade Gir\u00f3n", "Santiago Ernesto Ramos y Yovera", "Flor de Mar\u00eda Garivay Torres de Salinas", "F\u00e9lix Gil Caro Soto", "Dalila Irene Villanueva Cadenas"]2025-01-03T00:00:00 b29919c146ef04cecab2a9fe7cd26f5381383a04This study intends to optimize the carbon footprint management model of power enterprises through artificial intelligence (AI) technology to help the scientific formulation of carbon emission reduction strategies. Firstly, a carbon footprint calculation model based on big data and AI is established, and then machine learning algorithm is used to deeply mine the carbon emission data of power enterprises to identify the main influencing factors and emission reduction opportunities. Finally, the driver-state-response (DSR) model is used to evaluate the carbon audit of the power industry and comprehensively analyze the effect of carbon emission reduction. Taking China Electric Power Resources and Datang International Electric Power Company as examples, this study uses the comprehensive evaluation method of entropy weight- technique for order preference by similarity to ideal solution (TOPSIS). China Electric Power Resources Company has outstanding performance in promoting renewable energy, with its comprehensive evaluation index rising from 0.5458 in 2020 to 0.627 in 2022, while the evaluation index of Datang International Electric Power Company fluctuated and dropped to 0.421 in 2021. The research conclusion reveals the actual achievements and existing problems of power enterprises in energy saving and emission reduction, and provides reliable carbon information for the government, enterprises, and the public. The main innovation of this study lies in: using artificial intelligence technology to build a carbon footprint calculation model, combining with the data of International Energy Agency Carbon Dioxide (IEA CO2) emission database, and using machine learning algorithm to deeply mine the important factors in carbon emission data, thus putting forward a carbon audit evaluation system of power enterprises based on DSR model. This study not only fills the blank of carbon emission management methods in the power industry, but also provides a new perspective and basis for the government and enterprises to formulate carbon emission reduction strategies.PLoS ONE380This study uses artificial intelligence technology to build a carbon footprint calculation model, combining with the data of International Energy Agency Carbon Dioxide (IEA CO2) emission database, and using machine learning algorithm to deeply mine the important factors in carbon emission data to put forward a carbon audit evaluation system of power enterprises based on DSR model.PLOS ONE["Liangzheng Wu", "Kaiman Li", "Yan Huang", "Zhengdong Wan", "Jieren Tan"]2025-01-03T00:00:00 4232ef6c4255ed9f05959bf80740d01aa59be364The integration of advanced machine learning (ML) and artificial intelligence (AI) techniques in the oil and gas industry is rapidly evolving, focusing on improving operational predictions and optimizations. This study investigates the frequency and interconnectedness of key terms associated with these technologies through network diagrams and academic research results. An extensive search in the Scopus database revealed significant trends and patterns, highlighting the central role of forecasting and optimization within the industry. The interdisciplinary nature of the research underscores the need for collaboration across various fields to tackle complex challenges. While data quality and infrastructure pose challenges, the potential for enhanced efficiency, reduced costs, and increased safety through AI and ML applications is substantial. Case studies demonstrate the practical benefits, and future advancements promise deeper integration of these technologies into industry operations.  Brazilian Journal of Business00This study investigates the frequency and interconnectedness of key terms associated with these technologies through network diagrams and academic research results, highlighting the central role of forecasting and optimization within the industry.Brazilian Journal of Business["Marcelo dos Santos P\u00f3voas", "J\u00e9ssica Freire Moreira", "G. B. A. Lima", "Severino Virg\u00ednio Martins Neto"]2025-01-03T00:00:00 0d1a87feb10fad83557146e6ffdb295734c7278dObjective: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation. Background: AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI’s impact on health equity. Methods: We used linked datasets from the 2022 American Hospital Association Annual Survey and the 2023 American Hospital Association Information Technology Supplement. The data were further linked to the 2022 Area Deprivation Index (ADI) for each hospital’s service area. State fixed-effect regressions were employed. A decomposition model was also used to quantify predictors of AI/ML implementation, comparing hospitals in higher versus lower ADI areas. Results: Hospitals serving the most vulnerable areas (ADI Q4) were significantly less likely to apply ML or other predictive models (coef = −0.10, P = 0.01) and provided fewer AI/ML-related workforce applications (coef = -0.40, P = 0.01), compared with those in the least vulnerable areas. Decomposition results showed that our model specifications explained 79% of the variation in AI/ML adoption between hospitals in ADI Q4 versus ADI Q1–Q3. In addition, Accountable Care Organization affiliation accounted for 12%–25% of differences in AI/ML utilization across various measures. Conclusions: The underuse of AI/ML in economically disadvantaged and rural areas, particularly in workforce management and electronic health record implementation, suggests that these communities may not fully benefit from advancements in AI-enabled health care. Our results further indicate that value-based payment models could be strategically used to support AI integration.Medical Care400The underuse of AI/ML in economically disadvantaged and rural areas, particularly in workforce management and electronic health record implementation, suggests that these communities may not fully benefit from advancements in AI-enabled health care.Medical Care["Jie Chen", "Alice Shijia Yan"]2025-01-03T00:00:00 6b8bdda4c9c462845b414a9d2bc7683b80c6133cThis article explores the role of artificial intelligence (AI) in strengthening English as a global lingua franca, particularly in the fields of science, technology, and education. Using a descriptive qualitative approach, the study examines how AI technologies, including Natural Language Processing (NLP) and Neural Machine Translation (NMT), facilitate cross-cultural communication and enhance language learning. AI-powered tools such as Google Translate, Grammarly, Duolingo, and Chatbots are prime examples of how these technologies increase accessibility, accuracy, and efficiency in mastering English. AI supports learners in overcoming linguistic barriers by providing real-time translation, grammar correction, and personalized learning experiences. The findings highlight the importance of developing AI in an ethical, inclusive, and responsible manner to ensure it not only strengthens English’s role as a global language but also preserves the world’s rich linguistic and cultural diversity. An interdisciplinary approach shows AI’s potential to foster equitable, sustainable, and inclusive communication worldwide. By balancing technological advancement with cultural preservation, AI can empower diverse communities, reduce language gaps, and support global collaboration. This research underscores the need for continuous innovation in AI while respecting cultural and linguistic identities to create a harmonious global communication environment. ABSTRAKArtikel ini membahas peran kecerdasan buatan (AI) dalam memperkuat bahasa Inggris sebagai lingua franca global, khususnya di bidang sains, teknologi, dan pendidikan. Dengan menggunakan pendekatan deskriptif kualitatif, studi ini meneliti bagaimana teknologi AI, seperti Natural Language Processing (NLP) dan Neural Machine Translation (NMT), memfasilitasi komunikasi lintas budaya dan meningkatkan pembelajaran bahasa. Alat berbasis AI seperti Google Translate, Grammarly, Duolingo, dan Chatbot menjadi contoh utama bagaimana teknologi ini meningkatkan aksesibilitas, akurasi, dan efisiensi dalam menguasai bahasa Inggris. AI membantu pembelajar mengatasi hambatan bahasa dengan menyediakan penerjemahan real-time, koreksi tata bahasa, serta pengalaman belajar yang dipersonalisasi. Hasil penelitian menekankan pentingnya pengembangan AI yang etis, inklusif, dan bertanggung jawab agar AI tidak hanya memperkuat peran bahasa Inggris sebagai bahasa global tetapi juga menjaga keragaman bahasa dan budaya di dunia. Pendekatan interdisipliner menunjukkan potensi AI dalam mendorong komunikasi yang adil, berkelanjutan, dan inklusif secara global. Dengan menyeimbangkan kemajuan teknologi dan pelestarian budaya, AI dapat memberdayakan berbagai komunitas, mengurangi kesenjangan bahasa, serta mendukung kolaborasi global. Penelitian ini menekankan perlunya inovasi berkelanjutan dalam AI sambil tetap menghormati identitas budaya dan bahasa untuk menciptakan lingkungan komunikasi global yang harmonis.CENDEKIA: Jurnal Ilmu Pengetahuan00CENDEKIA: Jurnal Ilmu Pengetahuan["Hafiza Saumi Ramadilla", "Halimah Br Surbakti", "Muhammad Natsir"]2025-01-03T00:00:00 00efe50ec8a22af237958ba67cfa5b7081937c8ePenelitian ini merupakan penelitian kualitatif dengan menggunakan pendekatan deskriptif untuk melihat motivasi belajar siswa pada pembelajaran matematika dengan bantuan teknologi AI (artificial intelligence). Metode pengumpulan data meliputi wawancara, angket, dan dokumentasi. Objek penelitian ini terfokus pada motivasi belajar matematika oleh peserta didik dengan memanfaatkan teknologi AI. Subjek penelitian ini adalah siswa kelas XI di sekolah MAN 1 Banjarmasin. Data dianalisis secara deskriptif. Hasil angket menunjukkan bahwa pemanfaatan teknologi AI dalam pembelajaran matematika dapat meningkatkan motivasi belajar siswa, dengan mendapatkan skor signifikan dari 5 responden siswa. Wawancara dengan 3 siswa juga mendukung dampak positif AI dalam pembelajaran matematika, meningkatkan pemahaman dan kerajinan dalam memecahkan masalah. Pada wawancara terdapat 2 dari 3 siswa menunjukkan bahwa dengan memanfaatkan teknologi AI dapat meningkatkan motivasi belajar matematika. Namun 1 dari 3 siswa menunjukkan kurang meminati dalam pemanfaatan teknologi AI pada pembelajaran matematika karena ekspektasi siswa yang tidak realistis dan kesalahpahaman dalam sistem AI.JME (Journal of Mathematics Education)150Differential: Journal on Mathematics Education["Agus Syaukani", "Jati Sastra Winata", "Regita Widya Apriza", "Muh. Fajaruddin Atsnan", "Rahmita Yuliana Gazali"]2025-01-03T00:00:00 9d485eefe56062bedb3eff47e0c71f3172e3a8e9Abstract Objective. This study aims to analyze the scientific production indexed in the Scopus database on the application of artificial intelligence (AI) to finance studies between 2007 and 2023. Design/Methodology/Approach. The study design is non-experimental (transectional) and quantitative (descriptive). The most representative authors, the documentary typology that supports the results, and the principal publications were identified and analyzed. General citation indicators were calculated to ascertain the scientific impact associated with the topic. Spectral maps of country and word density were prepared to determine the main characteristics concerning these bibliographic variables. Results/Discussion. Notwithstanding the extensive temporal scope of the study, the application of AI to finance has not been evidenced in the extant literature until 2017. No significant contributors or highly influential journals are identified; studies are sporadic and consistent with the topic's novelty. Nevertheless, this subject has a high scientific impact, with an average of 20 citations per paper. Conclusions. The application of AI in finance is a relatively recent phenomenon. The countries of Asia and India are at the forefront of scientific production, as evidenced by Scopus's data analysis. The works analyzed exhibit a high density of terminology and a plethora of journals in the computational field that publish on this topic. Furthermore, publication practices manifest in the form of event papers, which are published at a similar rate to scientific articles. Originality/Value. The value of this study lies in its originality, which stems from an in-depth examination of existing literature on these topics in Scopus. This approach enables a comprehensive bibliometric analysis, informing future research in this field.Iberoamerican Journal of Science Measurement and Communication170The value of this study lies in its originality, which stems from an in-depth examination of existing literature on these topics in Scopus, which enables a comprehensive bibliometric analysis, informing future research in this field.Iberoamerican Journal of Science Measurement and Communication["William Joel Mar\u00edn Rodriguez", "Flor de Mar\u00eda Lioo Jord\u00e1n", "Viviana In\u00e9s Vell\u00f3n Flores", "Timoteo Solano Armas", "Elia Clorinda Andrade Gir\u00f3n"]2025-01-03T00:00:00 e024be2b71495815d6aaff337603b20ffccd5893PURPOSE Artificial intelligence (AI) may be used to learn and predict the maxillomandibular relationship, particularly when the number of occluding teeth pairs is insufficient. This study aimed to investigate the feasibility of training a new two-stage coarse-to-fine teeth alignment pipeline AI system in predicting maxillomandibular relationships based on the occlusal morphology of antagonistic teeth. METHODS Maxillary and mandibular stone casts were collected and scanned at the maximal intercuspal position (MIP). A deep learning alignment network was trained using 90% of cast pairs. The remaining 10% of pairs were input into the trained AI system for validation. The maxillomandibular relationships predicted by the AI system were superimposed and compared with those of the mounted casts. Cartesian x-, y-, and z-coordinates were defined for each mandibular tooth scan with respect to (w.r.t.) its occlusal plane and dental midline. The discrepancy in the position of maxillary teeth scans was described based on rotation and translation. RESULTS A total of 325 pairs of maxillary and mandibular stone casts were collected, with 300 pairs used for training and 25 for validation. For the AI-predicted maxillomandibular relationship, the mean rotational discrepancies w.r.t. the x-, y-, and z-axis were 1.407°±1.548°, 1.269°±8.476°, and 0.730°±1.334°, respectively. The mean translational discrepancies w.r.t. the x-, y-, and z-axis were 0.185±1.324 mm, 1.222±0.848 mm, -1.034±0.273 mm, respectively. CONCLUSIONS The AI-predicted maxillomandibular relationship for maxillary and mandibular teeth scans shows discrepancies of less than 1.3 mm and 1.5° compared to the actual relationships.Journal of Prosthodontic Research180The feasibility of training a new two-stage coarse-to-fine teeth alignment pipeline AI system in predicting maxillomandibular relationships based on the occlusal morphology of antagonistic teeth is investigated.Journal of prosthodontic research["Jiamin Wu", "Ki Hin Yuen", "Yun Hong Lee", "Ying Liu", "J. K. Tsoi", "Walter Yu Hang Lam"]2025-01-03T00:00:00 611fd886ec9538f085ec154fff2e616a134144aaMolecular Biomedicine1980This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development.Molecular Biomedicine["M. Gawande", "Nikita Zade", "Praveen Kumar", "Swapnil Gundewar", "Induni Nayodhara Weerarathna", "Prateek Verma"]2025-01-03T00:00:00 65dbabe567e382c325a78c7073094c0b3cb1af5aThis technical article examines the transformative role of Artificial Intelligence in developing sustainable supply chains within the electronics manufacturing sector. The article explores how AI technologies are revolutionizing sustainability practices across the industry, from resource optimization to waste management. By analyzing current challenges, AI-enabled solutions, and implementation frameworks, this research demonstrates how advanced technologies are enabling manufacturers to achieve significant improvements in environmental performance while maintaining operational efficiency. The article encompasses various aspects of sustainability, including supply chain transparency, resource utilization, carbon footprint reduction, and organizational transformation, providing a comprehensive overview of how AI is reshaping the future of sustainable electronics manufacturing.International Journal of Scientific Research in Computer Science Engineering and Information Technology60The article encompasses various aspects of sustainability, including supply chain transparency, resource utilization, carbon footprint reduction, and organizational transformation, providing a comprehensive overview of how AI is reshaping the future of sustainable electronics manufacturing.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Gautam Nandkishore Nayak"]2025-01-03T00:00:00 41b069d01da3b750a6d013274cd337e94c27fbf5The latest global progress report highlights numerous challenges in achieving justice goals, with bias in artificial intelligence (AI) emerging as a significant yet underexplored issue. This paper investigates the role of AI in addressing bias within the judicial system to promote equitable social justice. Analyzing weekly data from January 1, 2019, to December 31, 2023, through wavelet quantile correlation, this study examines the short, medium, and long-term impacts of integrating AI, media, international legal influence (ILI), and international financial institutions (IFI) as crucial factors in achieving Sustainable Development Goal 16 (SDG-16), which focuses on justice. The findings indicate that AI, media, ILI, and IFI can help reduce bias in the medium and long term, although their effects appear mixed and less significant in the short term. Our research proposes a comprehensive policy framework that addresses the complexities of implementing these technologies in the judicial system. We conclude that successfully integrating AI requires a supportive global policy environment that embraces technological innovation, financial backing, and robust regulation to prevent potential disruptions that could reinforce inequalities, perpetuate structural injustices, and exacerbate human rights issues, ultimately leading to more biased outcomes in social justice.PLoS ONE590It is concluded that successfully integrating AI requires a supportive global policy environment that embraces technological innovation, financial backing, and robust regulation to prevent potential disruptions that could reinforce inequalities, perpetuate structural injustices, and exacerbate human rights issues, ultimately leading to more biased outcomes in social justice.PLOS ONE["Kashif Javed", "Jianxin Li"]2025-01-03T00:00:00 238f1a26a545d3273ac15e6a1271928f77b25f34With the rapid rise of live streaming e-commerce, artificial intelligence (AI) technology has become pivotal in shaping consumer behaviors and purchase decisions. This study explores the application of AI in live streaming e-commerce and analyzes its impact on driving factors behind consumer purchase decisions. Employing a comprehensive methodology including literature review, data analysis, and empirical research, this study identifies key AI-driven factors such as personalized recommendation systems, real-time interaction features, intelligent customer service, and social influence, trust. Conduct a questionnaire survey analysis on 1084 consumers who have participated in live streaming platforms using artificial intelligence technology. The findings reveal that AI significantly enhances consumer engagement and purchase intention, offering valuable insights for both academic research and practical applications in e-commerce platforms.International Journal of Computers Communications & Control00The findings reveal that AI significantly enhances consumer engagement and purchase intention, offering valuable insights for both academic research and practical applications in e-commerce platforms.Int. J. Comput. Commun. Control["Lei Mei", "Na Tang", "Zhu Zeng*", "Wenwen Shi"]2025-01-03T00:00:00 07632616a9ea8231669696be62d7dcb8a4ef1971The rapid development of digital technology has made international interactions more convenient and frequent, and nowadays, the rapid change and development of artificial intelligence has brought unprecedented challenges to the international law. The international community has put forward an urgent demand for the improvement and development of the international law system. On the one hand, the wide application of AI in international trade has had a great impact and influence on the global value management; on the other hand, the development of AI has put forward new challenges to the relevant rules of the WTO, which include, but are not limited to, whether the existing World Trade Organisation (WTO) treaties are applicable to AI in the context of the digital economy, such as the GATT, GATS; whether existing WTO rules are formulated with the application of AI, such as GATT, GATS; whether emerging technologies are taken into account in the formulation of existing WTO rules; and the challenge of AI to the existing legal system system. The World Trade Organisation (WTO), as the most important intergovernmental trade organisation in the world and the international community today, still plays an important role in regulating and deploying the development of AI technologies in the international trading system. Based on this, this paper takes the WTO as the research perspective to explore the impact of the digital economy and the development of artificial intelligence technology on international law, combining the existing rules system of the WTO and the characteristics of the development of artificial intelligence technology, research and argumentation to deal with the impact of the strategy, with a view to guaranteeing better development of artificial intelligence technology for the WTO, better service to mankind to offer advice.Lecture Notes in Education Psychology and Public Media00The WTO is taken as the research perspective to explore the impact of the digital economy and the development of artificial intelligence technology on international law, combining the existing rules system of the WTO and the characteristics of the development of artificial intelligence technology to guarantee better development of artificial intelligence technology for the WTO, better service to mankind to offer advice.Lecture Notes in Education Psychology and Public Media["Wenxin He", "Yangyifei Hu", "Yijia Lu", "Yu Zhou"]2025-01-03T00:00:00 644494c7ee350db7d11718b13ee93dcb28a06a0fThe usage of Artificial Intelligence (AI) into the healthcare system gains the potential to increase efficiency, speed up the diagnostic processes & improve result quality. Having said that, using the instruments in the healthcare environment in conjunction with growth of its uses, the issue of adherence to laws, morals, and regulations comes up. Therefore, the ethical, legal, and responsibility issues that come up when AI is used in healthcare have been reviewed in this review article. These issues include privacy, algorithm bias, transparency, accountability, and weakened doctor-patient relationships. Liability, the numerous legal gaps that exist, and intellectual property rights are the key legal areas of concern. To address these expanding legal difficulties, legal frameworks must be created. The author of the article emphasizes the idea on AI’innovations and responsibility; as a result, he urges regulatory bodies to develop a methodical and strategic approach to AI, encourage AI to express their opinions, and create strategies to combat bias through diversity of data. Additionally covered are patient-centered AI use, roles and duties in the event of injury, and continuous monitoring of AI systems. These steps are intended to guarantee that the usage of AI in healthcare remains appropriate, effective and harmless to lessen adverse effects and enhance patient outcomes. To accomplish this, it emphasizes the importance of patient security as a vital basis for the successful use of the researched artificial intelligence usages in the healthcare sector, as well as the necessity of proactive government regulation, the engagement of all interested stakeholders and the necessity of proactive government regulation. The conclusion reiterates the notion that AI must be positioned as a device for positive deviations in the current healthcare delivery organization that must be cast-off in compliance with the primary ethical and legal guidelines.International Journal of Scientific Research in Science Engineering and Technology210The author of the article urges regulatory bodies to develop a methodical and strategic approach to AI, encourage AI to express their opinions, and create strategies to combat bias through diversity of data to guarantee that the usage of AI in healthcare remains appropriate, effective and harmless to lessen adverse effects and enhance patient outcomes.International Journal of Scientific Research in Science, Engineering and Technology["Ritu Arora", "Anjali Banga"]2025-01-03T00:00:00 67d2dd4e03f5379bbd8323bbcba13d8ad440b99fThe evolution of artificial intelligence (AI) has unveiled considerable prospects for delivering efficacious solutions in the medical domain. Nevertheless, existing legal frameworks and concerns regarding data privacy associated with medical information impose substantial constraints on implementing AI solutions in this domain. Federated learning is a paradigm that enables the training of machine learning models in a decentralized manner without transferring data to a central repository, allowing model development while preserving data privacy across medical and other industries. This study provided a comprehensive framework for applying federated learning to AI solutions in the medical domain. It advocates a sustainable learning ecosystem by overseeing federated learning servers and clients and evaluating performance by managing the federated learning lifecycle. To enhance its practical relevance, this framework includes a detailed process for continuous lifecycle management, involving model deployment, aggregation, testing, evaluation, versioning, and real-time monitoring through the FedOps platform, supporting a sustainable solution. In this study, the feasibility of the proposed methodology was verified using a post-transcatheter aortic valve replacement (TAVR) complication–prediction framework. The performance of the solution after transitioning to a federated learning approach was compared with that of an existing centralized solution. The findings indicated no statistically significant difference in performance between the two methodologies. This implies that federated learning can augment data usability and facilitate the integration of AI technologies into the medical domain, where the preservation of data privacy is critically important.Applied Sciences240This study provided a comprehensive framework for applying federated learning to AI solutions in the medical domain and indicated that federated learning can augment data usability and facilitate the integration of AI technologies into the medical domain, where the preservation of data privacy is critically important.Applied Sciences["Min Hyuk Jung", "InSeo Song", "Kangyoon Lee"]2025-01-03T00:00:00 ab200d3e5ae551585a3834fba2326bd3ac8196d5As the healthcare sector stands on the brink of a technological revolution, critical care nursing faces the imperative and challenging task of navigating the integration of Artificial Intelligence (AI) and Machine Learning (ML) into its practices. This presentation delves into the transformative journey of incorporating these advanced technologies to enhance patient care, optimize workflows, and address the complexities of critical care environments. AI and ML are not just tools for innovation but are becoming essential components in the evolution of nursing care. They offer sophisticated solutions for real-time patient monitoring, diagnostic accuracy, and predictive analytics, contributing significantly to improved patient outcomes and operational efficiency. This presentation explores practical examples where AI and ML have been successfully implemented in critical care settings, demonstrating their potential to revolutionize patient care through personalized treatment plans, early detection of complications, and the reduction of manual tasks, thereby allowing nurses to focus more on patient care. Integrating AI and ML into nursing practice presents challenges such as ethical dilemmas, data privacy issues, the necessity for extensive training, and the development of cross-disciplinary teamwork. Overcoming these hurdles requires a focused approach: ongoing educational initiatives to enhance nurses' proficiency in AI and ML, strict adherence to ethical guidelines protecting patient information, and fostering teamwork across various healthcare domains to fully harness AI and ML's capabilities in critical care nursing. In conclusion, this presentation seeks to inspire and equip nursing professionals with the knowledge and tools necessary to lead the charge in adopting AI and ML technologies. By embracing these innovations, critical care nursing can advance to new heights of efficiency, precision, and patient-centric care, marking a new era in healthcare.  International Journal of Critical Care00Practical examples where AI and ML have been successfully implemented in critical care settings are explored, demonstrating their potential to revolutionize patient care through personalized treatment plans, early detection of complications, and the reduction of manual tasks, thereby allowing nurses to focus more on patient care.International Journal of Critical Care["Vimala Ramoo"]2025-01-03T00:00:00 1e7bd33d0d1e76b71d6ad84487de97d2cafdbd4bThe integration of Artificial Intelligence (AI) in university education has emerged as a transformative force, promising to revolutionize teaching, learning, and administration. However, its rapid adoption has sparked ethical concerns, particularly in resource-constrained settings. This theoretical article examines the ethical implications of specific AI applications, including plagiarism detection tools, adaptive learning systems, and automated grading technologies within Kenyan universities. It highlights three critical areas: data privacy and security, student-lecturer dynamics, and algorithmic bias. Drawing from Kantian deontological ethics, which emphasizes duty and the inherent morality of actions, the article argues for a balanced approach to AI integration that prioritizes ethical responsibilities over mere technological expedience. Data privacy and security remain pivotal concerns, as AI systems amass extensive personal data, often without robust safeguards, exposing students to potential exploitation and breaches. The article explores the intersection of AI and student-lecturer relationships, revealing how AI-driven tools can disrupt traditional mentorship roles central to African pedagogical traditions. Furthermore, the pervasive issue of algorithmic bias is critically analysed, emphasizing its potential to perpetuate educational inequities and marginalize underrepresented groups. The article highlights the absence of localized frameworks to address these ethical dilemmas in Kenyan universities. By anchoring its analysis in Kantian ethics, this article provides a compelling framework for navigating the ethical challenges posed by AI in education, ensuring that its implementation enhances equity, accountability, and human dignity. This work contributes to ongoing discourse on the responsible use of AI in education, offering actionable insights for policy, research, and practiceEast African Journal of Education Studies00This theoretical article examines the ethical implications of specific AI applications, including plagiarism detection tools, adaptive learning systems, and automated grading technologies within Kenyan universities, and explores the intersection of AI and student-lecturer relationships.East African Journal of Education Studies["J. Mauti", "Dennis Song\u2019oro Ayieko"]2025-01-03T00:00:00 8596aa934b69d800a5354d52124a7787c7783922 This Viewpoint discusses the pursuit of fairness and equity in artificial intelligence in health care to drive transformative changes and reduce health disparities. JAMA Health Forum70JAMA health forum["Kevin B Johnson", "Ivor B Horn", "Eric Horvitz"]2025-01-03T00:00:00 e6d5b6c829a75fecafcd19dacc5bdf19cbd0ced6Education and Information Technologies : Official Journal of the IFIP technical committee on Education660The study reveals seven key pedagogical elements across TPACK components such as pedagogical, content, technological, pedagogical content, technological pedagogical, technological content, and technological pedagogical content knowledge deemed crucial by students for AI to support SSRL in OCL effectively.Education and Information Technologies["Jinhee Kim", "Rita Detrick", "Seongryeong Yu", "Yukyeong Song", "Linda Bol", "Na Li"]2025-01-03T00:00:00 a72ea23d84a678ed61e0df8742280365362d7034The rapid integration of artificial intelligence (AI) in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational efficiency. However, alongside these advancements, significant challenges arise concerning patient data privacy, ethical considerations, and regulatory compliance. This paper examines the dual impact of AI on healthcare, highlighting its transformative potential and the critical need for safeguarding sensitive health information. It explores the role of the Health Insurance Portability and Accountability Act (HIPAA) as a regulatory framework for ensuring data privacy and security, emphasizing the importance of robust safeguards and ethical standards in AI-driven healthcare. Through case studies, including AI applications in diabetic retinopathy, oncology, and the controversies surrounding data sharing, this study underscores the ethical and legal complexities of AI implementation. A balanced approach that fosters innovation while maintaining patient trust and privacy is imperative. The findings emphasize the importance of continuous education, transparency, and adherence to regulatory frameworks to harness AI's full potential responsibly and ethically in healthcare.arXiv.org00The role of the Health Insurance Portability and Accountability Act (HIPAA) is explored as a regulatory framework for ensuring data privacy and security, emphasizing the importance of robust safeguards and ethical standards in AI-driven healthcare.ArXiv["Ahmad Momani"]2025-01-03T00:00:00 aeaf86f4acc7720541ff9bea34448d27328e7aabIn light of the advancements in artificial intelligence, there is an increasing emphasis on enhancing the adaptive and collaborative capabilities of the system through research. The theory of swarm intelligence provides a new approach to achieving decentralized optimization and collaboration through the simulation of natural phenomena, such as the flight of a flock of birds and the swimming of a school of fish. The particle swarm optimization (PSO) algorithm, as a typical representative of swarm intelligence, has attracted much attention because of its computational simplicity and fast convergence speed, and has demonstrated unique advantages in solving complex optimization problems. Thus, the paper explores the application of group behavior in artificial intelligence systems and verifies the effectiveness of PSO algorithms in improving system efficiency, robustness and scalability through empirical studies. Based on the phenomenon of cooperative motion in nature, the PSO algorithm provides a decentralized optimization approach that enables multiple individuals to work together to solve complex problems through simple interactions. In this paper, robot populations with PSO algorithm and independent control are compared through simulation experiments, and the results show that the PSO algorithm significantly improves the performance of robots in path planning and goal coordination, and exhibits higher efficiency and robustness. In addition, the paper discusses the potential challenges and improvement directions of the PSO algorithm in practical applications, and emphasizes the importance of algorithm parameter optimization and synergy effect of multi-intelligent body systems. The results provide empirical support for future applications of group intelligence in AI systems and promote the development of the field.Applied and Computational Engineering00Applied and Computational Engineering["Songhao Zhao"]2025-01-03T00:00:00 4b68e6cbc47ab1678d2dfba18dc6a9d85ded9d82Similar to any other company, the rapid development of AI has made a big difference in the banking industry. In financial risk management, one of the most important areas where AI capabilities have brought changes in this industry is discussed here. This paper will discuss the theoretical foundations, applications, tools, ethical dilemmas, legal ramifications, and future developments of AI in financial risk management. This research illustrates AI capabilities in predictive analytics, fraud detection, and the automation of decision-making and portfolio optimization on a multi-faceted basis. Additionally, it looks into ethical dilemmas realized from the application of AI, with attendant legal issues, and provides examples from the real world of how the application could be made practical. The results will indicate how artificial intelligence is going to set change in motion for financial risk management in the future.Advances in Economics, Management and Political Sciences00This research illustrates AI capabilities in predictive analytics, fraud detection, and the automation of decision-making and portfolio optimization on a multi-faceted basis and looks into ethical dilemmas realized from the application of AI, with attendant legal issues.Advances in Economics, Management and Political Sciences["Bohan Li"]2025-01-03T00:00:00 6977d96def0b5d67548963b58e2c5ef74c953f34This paper focuses on the prospects of artificial intelligence and future the metaverse in medical education and training. Artificial intelligence significantly improves the efficiency and quality of medical education through personalized learning path customization, virtual surgery simulation and image diagnosis assistance. The introduction of the metaverse, on the other hand, breaks the time-space limitation of traditional education, provides immersive learning experience for medical students, and greatly promotes the improvement of clinical skills and practical ability. However, AI and the metaverse in medical education and training also face challenges such as data security, algorithmic bias, educational imbalance and cyber-attacks. To safeguard patient privacy and data security, data protection measures need to be strengthened; at the same time, there is a need to promote the popularization of the technology to ensure that every student has equal access to quality educational resources. In summary, artificial intelligence and the future the metaverse bring unprecedented opportunities and challenges to medical education. With the continuous progress of technology and the expansion of application scenarios, these technologies will play a more important role in medical education and promote the cause of medical education to move forward.Advances in Economics, Management and Political Sciences00With the continuous progress of technology and the expansion of application scenarios, these technologies will play a more important role in medical education and promote the cause of medical education to move forward.Advances in Economics, Management and Political Sciences["Cancan Huang"]2025-01-03T00:00:00 d349226ecf764e40ef99e8d4c50a5e9490c63b97Artificial Intelligence is a concept related to the development of technology in the 4.0 era and society 5.0. The presence of Artificial Intelligence in people's lives provides significant changes to people's lives. The use of artificial intelligence is so popular that it has been widely used by all circles of society. The presence of artificial intelligence is considered to provide many benefits to society, but the existence of artificial intelligence also brings changes in the field of labor because it causes a reduction in labor in various fields. With the reduction of workers, it will increase the number of unemployed. This research suggests that labor law must provide protection for workers' rights in the current digital era.Journal of Law, Politic and Humanities210Journal of Law, Politic and Humanities["Evaline Suhunan Purba", "Wilma Silalahi"]2025-01-03T00:00:00 04738b6bc8ac8056de7ebb2bc64bd51c15d45033The rapid advancement of computer technology, particularly artificial intelligence (AI), has a significant impact in educational sector. This influence spans all educational levels, from primary schools to universities. A Workshop was conducted to explore the use of AI as an educational aid, based on observations and interviews with theprincipal of Virgo Maria 2 Elementary School in Semarang Regency before the workshop. The goal was to familiarize the teachers with AI technology, highlighting both its advantages and disadvantages. The Workshop included both theoretical instruction and practical application of AI, guided by lecturers from the LPPM team from STEKOM University Semarang. Participants' understanding the usage of AI was assessed through their active engagement during the practical sessions of the Workshop . As a result, the participants are now have ability to utilize AI tools to enhance teaching and learning processes at VirgoMaria 2 Elementary School in Semarang Regency. The enhancement of teachers technical abilities in computer technology, especially in the use of artificial intelligence (AI), after  the workshop, has achieved the destined goals..ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat00The enhancement of teachers technical abilities in computer technology, especially in the use of artificial intelligence (AI), after  the workshop, has achieved the destined goals.ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat["S. Nugroho", "Andik Prakasa Hadi", "Rudjiono Rudjiono", "Ahmad Zainudin", "A. Priyadi"]2025-01-03T00:00:00 4efb78ce056d0666c8778e0bed859d2cb93f3392This study explores how artificial intelligence (AI) is being used to improve sustainability management and corporate social responsibility (CSR) in Latin America. We analyze the regional context, identify challenges and opportunities, and present two case studies of IT companies that have implemented AI solutions to promote sustainable practices. The findings highlight the positive impact of AI on operational efficiency, cost reduction, and improved corporate image, while underlining the importance of a multidisciplinary approach and continuous collaboration.Región Científica460The findings highlight the positive impact of AI on operational efficiency, cost reduction, and improved corporate image, while underlining the importance of a multidisciplinary approach and continuous collaboration.Región Científica["Mario Sari\u00e1n Gonz\u00e1lez", "Carlos Bruna Rom\u00e1n", "Claudio Robles Lagos", "Gerardo Vaca Lombana"]2025-01-03T00:00:00 043ac60ed45b84a492d164e328a2d7d0ef7eceadEngineering optimization (Print)2082Engineering Optimization["V. Karkaria", "Ying-Kuan Tsai", "Yi-Ping Chen", "Wei Chen"]2025-01-03T00:00:00 190c4acbe245253b3a529aea91c99f4182f594bdApplied Fruit Science141Applied Fruit Science["Sudip Kumar Dutta", "Birshika Bhutia", "Tanuj Misra", "V. K. Mishra", "S. K. Singh", "V. B. Patel"]2025-01-03T00:00:00 2c466ac8ef616dd4c8d5d96352009bb3cb392e8aThe Veterinary Record00The Veterinary record[]2025-01-03T00:00:00 d21c49e01ac3798621d89783050cb096de4e5f82This academic work explores the use of generative AI through Chatbot GPT, Gemini, Copilot, and Meta AI in teaching customs and international law. This analysis was carried out with a particular focus on education on international free trade agreements and the primary laws on international trade in Mexico. The study's main findings show that Copilot is a valuable tool for searching for specific information on articles and laws on international trade. This purpose was achieved by applying prompts to obtain information on the content in question. Likewise, favorable results were obtained for the cases of Chatbot GPT and Meta AI. On the other hand, Gemini showed unfavorable results because it only showed general information on the topics that were requested and even provided erroneous information. These types of tools allow students to make more efficient searches and save time when searching for information. However, they can present erroneous or general results that force them to delve deeper into the subject.Región Científica360Copilot is a valuable tool for searching for specific information on articles and laws on international trade and Gemini showed unfavorable results because it only showed general information on the topics that were requested and even provided erroneous information.Región Científica["Jos\u00e9 Miguel Mata Hern\u00e1ndez"]2025-01-03T00:00:00 9740b5d12c31fe7a218971c3a2ecb5d17b799662Annals of Operations Research880The consequences and mechanisms through which responsible group digitalization influences business group’s operation management, as manifested in subsidiary performance within the context of the digital economy are examined.Annals of Operations Research["Wei Sun", "Shuang Ren", "Guiyao Tang"]2025-01-03T00:00:00 747f90f9d448b62a7b6a4c197aac8a69343fefadAnnals of Operations Research170Ann. Oper. Res.["Weiwei Chen", "Tsan-Ming Choi", "Alexandre Dolgui", "Dmitry A. Ivanov", "Erwin Pesch"]2025-01-03T00:00:00 f63d7237efbb5dba84bb2a35c1741bbbdea4c7a6İstanbul Üniversitesi Sosyoloji Dergisi / İstanbul University Journal of Sociology00İstanbul Üniversitesi Sosyoloji Dergisi / İstanbul University Journal of Sociology["Tolga Y\u0131ld\u0131z"]2025-01-03T00:00:00 5b9d357659646b459b8186293988ab5984b5fa10Frontiers in Molecular Biosciences160Frontiers in Molecular Biosciences["Valentina Tozzini", "Cecilia Giulivi"]2025-01-03T00:00:00 59d5303846f09776a2bcf809e9b9c27175b88e5fThe rise of advanced technologies, such as AI- driven chatbots, enables SMEs in e-marketplaces to provide responsive and efficient customer support, improve engagement, and streamline services. However, customers increasingly express concerns about AI-supported chatbot services, which affects their willingness to engage with these technologies. Consequently, this study aims to examine the factors that influence customers’ behavioral intentions to use AI and their intentions for the continued use of AI-supported chatbots. Using a purposive sampling technique, the study collected data from 152 respondents through an online questionnaire. To analyze and predict the findings from the collected data, the study employed PLS-SEM as its statistical approach. The results indicate that information quality, system quality, and service quality significantly influence trust. Furthermore, service quality, perceived ease of use, confirmation of expectations, and perceived usefulness affect user satisfaction. Additionally, confirmation of expectations impacts perceived usefulness, and user satisfaction influences the behavioral intention to use AI chatbots. The findings also reveal that trust, user satisfaction, and perceived usefulness effectively enhance the intention to continue using AI-driven chatbots. However, information quality and system quality do not correlate with user satisfaction, and confirmation of expectations does not relate to user satisfaction. These findings contribute valuable insights to the existing literature on AI- driven chatbot services. Furthermore, stakeholders involved with AI-driven chatbot services for SMEs will gain an understanding of how to enhance user-friendly chatbot services.International Conference on Ubiquitous Information Management and Communication100The findings reveal that trust, user satisfaction, and perceived usefulness effectively enhance the intention to continue using AI-driven chatbots, but information quality and system quality do not correlate with user satisfaction, and confirmation of expectations does not relate to user satisfaction.2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM)["Erwin Halim", "Anderes Gui", "Ida Ayu Indah Savitri Dewi Manuaba", "A. Condrobimo"]2025-01-03T00:00:00 04b0daadce5827b9bd1c7c134b0bc4c54149b00cAIAA SCITECH 2025 Forum120AIAA SCITECH 2025 Forum["Anam Iqbal", "Tom Jansen", "Alexander Somerville", "Graham Wild"]2025-01-03T00:00:00 4ef7daa33d00864b9e539f0cdfb443c2ad8ea8a0The integration of large language models (LLMs) in medical education offers both opportunities and challenges. While these AI-driven tools can enhance access to information and support critical thinking, they also pose risks like potential overreliance and ethical concerns. To ensure ethical use, students and instructors must recognize the limitations of LLMs, maintain academic integrity, handle data cautiously, and instructors should prioritize content quality over AI detection methods. LLMs can be used as supplementary aids rather than primary educational resources, with a focus on enhancing accessibility, equity, and fostering a culture of feedback and AI literacy among students and instructors. Institutions should create guidelines that align with their unique educational values, providing clear frameworks that support responsible LLM usage while addressing risks associated with AI in education. Such guidelines should reflect the institution's pedagogical mission, whether centered on clinical practice, research, or a mix of both, and should be adaptable to evolving educational technologies.Advances in Physiology Education00Institutions should create guidelines that align with their unique educational values, providing clear frameworks that support responsible LLM usage while addressing risks associated with AI in education.Advances in physiology education["Himel Mondal"]2025-01-03T00:00:00 34aa06eec670cfb285790d3013204e652c01fe88This article comprehensively analyzes artificial intelligence (AI) implementation in modern manufacturing systems, focusing on smart factories and predictive maintenance applications. The article explores how AI-driven technologies transform traditional manufacturing processes through advanced data analytics, machine learning algorithms, and Internet of Things (IoT) integration. By examining the evolution of smart factory systems, this study investigates the role of predictive maintenance in reducing operational downtime and optimizing equipment performance. The article analysis encompasses real-time monitoring systems, automated quality control processes, and intelligent production scheduling, highlighting their collective impact on manufacturing efficiency. Through detailed case studies and empirical evidence, the article demonstrates how AI-powered solutions enhance decision-making capabilities, improve product quality, and streamline production workflows in manufacturing environments. The findings reveal significant improvements in operational efficiency, maintenance scheduling, and resource utilization across various manufacturing sectors. This article contributes to the growing knowledge of Industry 4.0 technologies and provides valuable insights for manufacturers seeking to implement AI-driven solutions in their operations. The article also addresses implementation challenges and offers strategic recommendations for successful AI integration in manufacturing processes, paving the way for future developments in smart manufacturing systems.International Journal of Scientific Research in Computer Science Engineering and Information Technology10This article comprehensively analyzes artificial intelligence (AI) implementation in modern manufacturing systems, focusing on smart factories and predictive maintenance applications, and demonstrates how AI-powered solutions enhance decision-making capabilities, improve product quality, and streamline production workflows in manufacturing environments.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Deepak Bajaj"]2025-01-03T00:00:00 cce6e863d5408244284d97f5a13e8c9ab103ad01The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists.Societies291A significant negative correlation between frequent AI tool usage and critical thinking abilities was revealed, mediated by increased cognitive offloading as a mediating factor, highlighting the potential cognitive costs of AI tool reliance.Societies["Michael Gerlich"]2025-01-03T00:00:00 f9f4cdb0ff91640d0fb28025e91b89758010da74In today’s data-driven business landscape, effective and transparent decision making becomes relevant to maintain a competitive advantage over the competition, especially in customer service in B2B environments. This study presents a methodological framework that integrates Explainable Artificial Intelligence (XAI), C-means clustering, and the Analytic Hierarchical Process (AHP) to improve strategic decision making in business environments. The framework addresses the need to obtain interpretable information from predictions based on machine learning processes and the prioritization of key factors for decision making. C-means clustering enables flexible customer segmentation based on interaction patterns, while XAI provides transparency into model outputs, allowing support teams to understand the factors influencing each recommendation. The AHP is then applied to prioritize criteria within each customer segment, aligning support actions with organizational goals. Tested with real customer interaction data, this integrated approach proved effective in accurately segmenting customers, predicting support needs, and optimizing resource allocation. The combined use of XAI and the AHP ensures that business decisions are data-driven, interpretable, and aligned with the company’s strategic objectives, making this framework relevant for companies seeking to improve their customer service in complex B2B contexts. Future research will explore the application of the proposed model in different business processes.Processes370A methodological framework that integrates Explainable Artificial Intelligence, C-means clustering, and the Analytic Hierarchical Process to improve strategic decision making in business environments and proved effective in accurately segmenting customers, predicting support needs, and optimizing resource allocation is presented.Processes["Gabriel Mar\u00edn D\u00edaz", "Raquel G\u00f3mez Medina", "Jos\u00e9 Alberto Aij\u00f3n Jim\u00e9nez"]2025-01-03T00:00:00 bc9e3207ad879c006c6713f6db3668327d741862The integration of artificial intelligence (AI) continues to increase and evolve, including in software engineering (SE). This integration involves processes traditionally entrusted to humans, such as coding. However, the impact on socio-technical processes like code review remains underexplored. In this interview-based study (20 interviewees), we investigate how software engineers perceive and engage with Large Language Model (LLM)-assisted code reviews compared to human peer-led reviews. In this inherently human-centric process, we aim to understand how software engineers navigate the introduction of AI into collaborative workflows. We found that engagement in code review is multi-dimensional, spanning cognitive, emotional, and behavioral dimensions. The introduction of LLM-assisted review impacts some of these attributes. For example, there is less need for emotional regulation and coping mechanisms when dealing with an LLM compared to peers. However, the cognitive load sometimes is higher in dealing with LLM-generated feedback due to its excessive details. Software engineers use a similar sense-making process to evaluate and adopt feedback suggestions from their peers and the LLM. However, the LLM feedback adoption is constrained by trust and lack of context in the review. Our findings contribute to a deeper understanding of how AI tools are impacting SE socio-technical processes and provide insights into the future of AI-human collaboration in SE practices.810It is found that engagement in code review is multi-dimensional, spanning cognitive, emotional, and behavioral dimensions, and the introduction of LLM-assisted review impacts some of these attributes.["Adam Alami", "Neil A. Ernst"]2025-01-03T00:00:00 8cd89a02fc81ef14884ea46724cf5d2ffe7a85a5Sustainable Development Goal (SDGs) 4 emphasizes inclusive education as one of the key objectives of experimenting with equity in education. The current convergence between education and developing technologies has led to the widespread use of artificial intelligence (AI) as a pedagogical tool in education. However, there is a lack of research on the relationship between inclusive education and artificial intelligence (AI) as part of the educational field. Therefore the main purpose of this paper is to explore how artificial intelligence (AI) can help to realize inclusive education. Through literature analysis, it is shown that artificial intelligence (AI) can increase students' accessibility, allow more and more disabled students to participate in the classroom, and artificial intelligence (AI) assistive technology can meet the different needs of disabled students. In addition, artificial intelligence (AI) can personalize learning for students with disabilities, improving their grades and performance. But further development of more comprehensive technologies and training of teachers is still needed in the future. Overall, artificial intelligence (AI) has played a positive role in achieving inclusive education.Communications in Humanities Research00It is shown that artificial intelligence (AI) can increase students' accessibility, allow more and more disabled students to participate in the classroom, and artificial intelligence (AI) assistive technology can meet the different needs of disabled students.Communications in Humanities Research["Siqi Yang"]2025-01-03T00:00:00 0250bcb70b2c9ad1255af1f7520a653fdf650a54This comprehensive article examines the evolution and impact of workflow automation engines in modern business environments, focusing on their integration with cloud-native technologies and artificial intelligence. The article explores how these engines serve as fundamental enablers of digital transformation, offering organizations enhanced operational efficiency, process consistency, and scalability. The article shows various industry applications across healthcare, financial services, e-commerce, manufacturing, and public sectors, demonstrating the versatile benefits of workflow automation. It analyzes emerging trends, including AI integration, low-code/no-code platforms, hyperautomation, and cloud-native optimization, while addressing strategic implementation considerations such as cloud infrastructure integration, AI decision point orchestration, scalability planning, and cross-platform standardization. Through extensive analysis of real-world implementations and industry data, the article highlights how workflow automation engines have revolutionized business processes, enabling organizations to achieve operational excellence while maintaining flexibility and adaptability in an increasingly digital business landscape.International Journal of Scientific Research in Computer Science Engineering and Information Technology20This comprehensive article examines the evolution and impact of workflow automation engines in modern business environments, focusing on their integration with cloud-native technologies and artificial intelligence, and highlights how workflow automation engines have revolutionized business processes.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Siva Prakash Bikka"]2025-01-03T00:00:00 9f4b2ed78ce92bd57ec6cbdc10baf11d6d00aed1This article presents a comprehensive analysis of real-time hazard detection systems in mining operations through the integration of computer vision and sensor networks. The article explores how artificial intelligence and advanced monitoring technologies are transforming traditional mining safety protocols, introducing innovative solutions for early hazard detection and emergency response. The article examines the implementation of sophisticated model architectures for video analytics, multilayered sensor networks, and data integration frameworks that enable precise tracking of worker behavior, equipment proximity, and environmental conditions. Through detailed investigation of system performance metrics, implementation challenges, and validation processes, this article demonstrates the significant impact of AI-driven safety systems on reducing workplace incidents and improving operational efficiency. The article also addresses critical challenges in underground mining environments, including environmental factors, technical constraints, and data quality management, while providing insights into future developments and best practices for industry adoption. This comprehensive approach to mining safety represents a significant advancement in protecting worker safety while maintaining productive operations.International Journal of Scientific Research in Computer Science Engineering and Information Technology00This article presents a comprehensive analysis of real-time hazard detection systems in mining operations through the integration of computer vision and sensor networks, addressing critical challenges in underground mining environments, including environmental factors, technical constraints, and data quality management.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Vivekananda Reddy Uppaluri"]2025-01-03T00:00:00 bc41e666c9a24bf9d3e8651efd3f5df9297b6ebeThe global education system is grappling with a critical shortage of teachers, threatening the achievement of universal quality education. This article examines how artificial intelligence (AI) technologies can revolutionize educational access and equity by addressing these systemic challenges. Through a comprehensive article analysis of AI-enabled solutions, including personalized learning mechanisms, virtual tutoring systems, and intelligent content distribution platforms, the article explores the transformative potential of these technologies in democratizing education. The article investigates the implementation of AI across established educational platforms, examining their effectiveness in providing adaptive learning experiences, breaking down language barriers, and ensuring cultural relevance. The article demonstrates that strategic AI integration can significantly impact learning outcomes while helping to bridge the global teacher shortage gap. The article also addresses critical implementation challenges, providing policy recommendations and resource allocation frameworks for successful AI adoption in education systems worldwide. This article analysis contributes to the growing body of knowledge on educational technology by offering practical insights into how AI can be leveraged to create more inclusive, effective, and accessible learning environments, ultimately advancing the goal of quality education for all.International Journal of Scientific Research in Computer Science Engineering and Information Technology40It is demonstrated that strategic AI integration can significantly impact learning outcomes while helping to bridge the global teacher shortage gap, demonstrating that strategic AI integration can significantly impact learning outcomes while helping to bridge the global teacher shortage gap.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Nupur Jain"]2025-01-03T00:00:00 08274445ced5ccc516e5e0c55b1420d13f7cbbf6The integration of Artificial Intelligence (AI) in legal practice is transforming the legal profession by enhancing efficiency and accessibility while presenting significant ethical and regulatory challenges. AI applications such as predictive analytics, automated document drafting, and AI-driven legal research hold immense potential to reduce administrative burdens, streamline case management, and improve access to justice. However, issues such as algorithmic bias, lack of transparency, and data privacy concerns raise critical questions about fairness and accountability in AI-driven decision-making. This study aims to analyze the dual landscape of challenges and opportunities associated with AI adoption in legal practice, emphasizing the need for balanced regulatory frameworks. A systematic review of existing literature was conducted to identify the obstacles and benefits of AI integration. Key challenges include algorithmic biases, inadequate legal frameworks, and the digital divide among legal professionals, while opportunities range from cost reduction to improved dispute resolution processes. The findings contribute to ongoing discussions on AI governance by proposing actionable strategies such as fairness audits, explainable AI practices, and targeted training programs for legal professionals.Journal of Computer Science Application and Engineering (JOSAPEN)00A systematic review of existing literature was conducted to identify the obstacles and benefits of AI integration, and proposes actionable strategies such as fairness audits, explainable AI practices, and targeted training programs for legal professionals.Journal of Computer Science Application and Engineering (JOSAPEN)["Yatama Zahra"]2025-01-03T00:00:00 b5094bfed48d69085bcc2e9f28232ffe6fd72210Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage. We applied our classification system to two generative AI applications to assess its effectiveness in categorising safety issues: patient-facing conversational large language models (LLMs) and an ambient digital scribe (ADS) system for clinical documentation. In the LLM analysis, we identified 45 errors across 27 patient medical queries, with omission being the most common (42% of errors). Of the identified errors, 50% were categorised as low clinical significance, 25% as moderate clinical significance and 25% as high clinical significance. Similarly, in the ADS simulation, we identified 66 errors across 11 patient visits, with omission being the most common (83% of errors). Of the identified errors, 55% were categorised as low clinical significance and 45% were categorised as moderate clinical significance. These findings demonstrate the classification system's utility in categorising output errors from two different AI healthcare applications, providing a starting point for developing a robust process to better understand AI-enabled errors.BMJ Quality & Safety70A preliminary classification system for categorising generative AI patient safety errors from two different AI healthcare applications is developed and evaluated, providing a starting point for developing a robust process to better understand AI-enabled errors.BMJ quality & safety["Bat-Zion Hose", "Jessica L. Handley", "J. Biro", "Sahithi Reddy", "Seth A Krevat", "Aaron Zachary Hettinger", "Raj M. Ratwani"]2025-01-03T00:00:00 1a080f04c865a0fb45b496364146202119db7b04The integration of artificial intelligence with cloud automation represents a paradigm shift in infrastructure management, offering organizations unprecedented capabilities to optimize and maintain complex IT environments. This article examines the transformative impact of AI-driven cloud automation, focusing on three key innovations: predictive scaling mechanisms, autonomous remediation systems, and intelligent container orchestration. Through analysis of current implementations across major cloud platforms and industry-leading AIOps tools, the article explores how these technologies are revolutionizing resource management, incident response, and operational efficiency. The article draws insights from implementations in healthcare and financial services sectors, demonstrating tangible improvements in system reliability, cost optimization, and innovation acceleration. The article findings suggest that AI-driven cloud automation not only enhances traditional infrastructure management practices but also enables organizations to build more resilient, scalable, and intelligent systems that can adapt to dynamic workload requirements while minimizing human intervention. This article contributes to the growing body of knowledge on intelligent infrastructure management and provides practical insights for organizations seeking to leverage AI capabilities in their cloud environments.International Journal of Scientific Research in Computer Science Engineering and Information Technology10The article findings suggest that AI-driven cloud automation not only enhances traditional infrastructure management practices but also enables organizations to build more resilient, scalable, and intelligent systems that can adapt to dynamic workload requirements while minimizing human intervention.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ganesh Vanam"]2025-01-03T00:00:00 3340f5e1f57fa0078b91f3da49866948d6252762The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions; however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an Fl-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for realtime fraud detection, which is essential for safeguarding the integrity of modern financial systems.International Conference on Ubiquitous Information Management and Communication180A novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms and demonstrates superior performance, significantly outperforming traditional fraud detection systems.2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM)["Okechukwu Clement Agomuo", "Agomuo Kingsley Uzoma", "Zohaib Khan", "Agomuo Ijeoma Otuomasirichi", "Junaid Hussain Muzamal"]2025-01-03T00:00:00 ee593301d475a61f20dacec8cc2d6b65c713f33eIn the era of digitalization and rapid technological advancement, artificial intelligence (AI) has emerged as a decisive factor in transforming the labor market, requiring the continuous adjustment of educational competencies to prepare students for the labor market demand. This study investigates the impact of AI on educational requirements, identifying the essential competencies that educational systems and the business sector must shape to equip future professionals for AIdriven challenges and opportunities. Employing Partial Least Squares Structural Equation Modeling (SEM), the research analyzed the survey data from a sample of 138 educators from various pre-university and university environments in Bihor county, Romania. to determine the relationships between the educational system, business sector, educational competencies, and AI career preparedness. The findings show significant influences from both sectors in shaping competencies that, in turn, affect labor market demands. This study highlights the imperative for educational systems the business sector to develop forward-thinking programs that anticipate future changes, thereby maximizing an AI-driven economy’s economic and social benefits. The results indicate that both the educational system and the business sector are integral to developing the competencies required in the AI era, with AI career preparedness exerting the greatest influence on labor market demands.International Journal of Computers Communications & Control00The results indicate that both the educational system and the business sector are integral to developing the competencies required in the AI era, with AI career preparedness exerting the greatest influence on labor market demands.Int. J. Comput. Commun. Control["D. Badulescu", "R. Simu\u021b", "Simona-Aurelia Bodog", "A. Badulescu", "Ciprian Simu\u021b", "Daniela Zapodeanu"]2025-01-03T00:00:00 878539f7de9fa7dcf5cd14ad417d519a9007768bThe evolution of privileged access management (PAM) toward AI-driven Privileged Access Posture Management (PAPM) represents a significant advancement in enterprise security architecture. This article examines how traditional PAM systems, limited by static provisioning and manual discovery processes, are being transformed through artificial intelligence and machine learning capabilities. This article explores the core components of AI-driven PAPM, including continuous discovery, real-time risk assessment, and automated remediation workflows, demonstrating how these technologies address critical challenges such as over-provisioning, shadow accounts, and compliance monitoring. Through analysis of implementation strategies and real-world applications, this article illustrates how PAPM's dynamic approach to access control and security posture management is enabling organizations to maintain robust security while adapting to increasingly complex IT environments. The findings suggest that AI-driven PAPM not only enhances security operations through automated threat detection and response but also significantly improves compliance readiness and audit efficiency, marking a crucial evolution in privileged access security.International Journal of Scientific Research in Computer Science Engineering and Information Technology50The findings suggest that AI-driven PAPM not only enhances security operations through automated threat detection and response but also significantly improves compliance readiness and audit efficiency, marking a crucial evolution in privileged access security.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Vinay Vasanth"]2025-01-03T00:00:00 e7cd27b2a32235d22dbf07f251264039bd1d940aThis article explores the transformative potential of artificial intelligence (AI) in mobile health (mHealth) applications for chronic disease management. The article examines how AI-powered mHealth apps are revolutionizing healthcare delivery through personalized treatment plans, real-time monitoring, predictive analytics, and virtual health coaching. The benefits of these technologies, including improved patient outcomes, enhanced engagement, cost reduction, and increased accessibility, are discussed in detail. However, we also critically analyze the challenges and limitations facing AI integration in healthcare, such as data privacy concerns, regulatory hurdles, and potential algorithmic biases. Ethical considerations, including informed consent, transparency in AI decision-making, and ensuring equitable access to AI-powered healthcare, are thoroughly addressed. The article concludes by exploring future directions in AI and mHealth, highlighting emerging technologies and the role of big data in advancing precision medicine. Throughout, we emphasize the importance of balancing technological innovation with responsible implementation to ensure that AI-powered mHealth apps can effectively improve the lives of individuals managing chronic conditions while maintaining the essential human element in healthcare delivery.International Journal of Scientific Research in Computer Science Engineering and Information Technology50How AI-powered mHealth apps are revolutionizing healthcare delivery through personalized treatment plans, real-time monitoring, predictive analytics, and virtual health coaching is examined, highlighting emerging technologies and the role of big data in advancing precision medicine.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Sridhar Rao Muthineni"]2025-01-03T00:00:00 dff5dffc037d54d9f276fd63d514f0b86afaa359Business &amp; Information Systems Engineering650Drawing on self-determination theory, a vignette-based online experiment was conducted that revealed that proactive (vs. reactive) help from AI-based agents leads to a higher loss of users’ competence-based self-esteem and thus reduces users’ system satisfaction.Business &amp; Information Systems Engineering["Christopher Diebel", "Marc Goutier", "Martin Adam", "Alexander Benlian"]2025-01-03T00:00:00 1ed850c344a265261c4e68309a80ac466dc83408Scientific Reports410The developed AI pipeline based on CLIP encoder significantly outperformed human facial scoring and effectively equaled human USAPS behavioral scoring, suggesting the machine can outperform human experts in recognizing pain in sheep when exposed to the same visual information.Scientific Reports["Marcelo Feighelstein", "S. P. Luna", "Nuno O. Silva", "Pedro E Trindade", "I. Shimshoni", "Dirk van der Linden", "Anna Zamansky"]2025-01-03T00:00:00 3aff8b019200d8188d7a7e36db4dc3e94fad37a4Two of the technologies that are spreading at the fastest rates in the whole world are artificial intelligence (AI) and the Internet of Things (IoT). Considering the growing number of people who are relocating to urban areas, the idea of a smart city is not new. A smart city is a concept that is centered on the notion of improving the healthcare sector by enhancing its efficiency, cutting expenses, and placing the emphasis back on a better patient care system. In order to successfully use Internet of Things and artificial intelligence for remote healthcare monitoring systems, one must have a comprehensive grasp of the many frameworks that are utilized in smart cities. Traditional healthcare services are not only prone to causing patients to pass away, but they also have the potential to cause delays, squander resources, and result in financial loss. When utilized in combination with the intelligence and prediction capabilities of the Internet of Things (IoT), frequent Remote Patient Monitoring (RPM) at home, at work, or at a hospital may assist persons who especially need it in overcoming challenges that are given by conventional healthcare facilities. The Internet of Things (IoT)-based RPM may act as a precursory warning system for approaching circumstances that, if ignored or treatment is delayed, might result in major health concerns or even patient death. Wearable technologies, sensor networks, and other digital infrastructure are employed in this system. Wearable gadgets (biosensors) that integrate with the internet of things allow medical professionals to obtain real-time vital indicators from their patients. Through the development of a framework that is supported by both the Internet of Things (IoT) and Artificial Intelligence (AI), the purpose of this article is to facilitate the implementation of a Remote Patient Monitoring System that is equipped with data analytics capabilities. Following the implementation of a Remote Patient Monitoring System for the purpose of data collecting, we suggested an algorithm for the diagnosis of diseases.International Journal of Scientific Research in Computer Science Engineering and Information Technology170The purpose of this article is to facilitate the implementation of a Remote Patient Monitoring System that is equipped with data analytics capabilities and suggested an algorithm for the diagnosis of diseases.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Dr. Nikhat Akhtar", "Kumar Bibhuti B. Singh", "Prof. (Dr.) Devendra Agarwal", "Dr. Yusuf Perwej"]2025-01-03T00:00:00 0185b030b547ccdb8ac872626c7d573de65c2ac0This study investigated the impact of AI dependence on procrastination among university students by contributing to the understanding of over reliance on AI and its outcome as procrastination. The sample of the study was comprised of (N=113) university students, aged between 18 and 35 from various academic disciplines and universities Multan. Cross-sectional, quantitative methods were utilized, along with the convenient sampling technique. Data was collected through google forms. A self-structured demographic sheet along with the two scales was used in this study. Dependence on Artificial Intelligence Scale (DAI) (Morales-García et al., 2024), evaluated the extent of dependence that university students exhibit towards artificial intelligence. The Tuckman Procrastination Scale (TPS) (Tuckman, 1991), measured the level of procrastination among students. Results revealed that AI dependence is positively correlated (r=.241*), and predicts (p=.010*) the procrastination. This overreliance on AI results in higher tendency of procrastination among students. However, no significant difference was found in level of education and area of residence regarding AI dependence and procrastination among university students.Research Journal of Psychology410Results revealed that AI dependence is positively correlated with procrastination, and predicts the procrastination of university students, while no significant difference was found in level of education and area of residence regarding AI dependence and procrastination among university students.Research Journal of Psychology["Maliha Mukhtar", "Syeda Sajida Firdos", "Iram Zaka", "Saira Naeem"]2025-01-03T00:00:00 34a44a1c26fb397f7ae31a959313e9ef91cae7c0In today’s fast-paced business settings, the metaverse as a shared marketplace has gained popularity and is helping businesses to develop crucial business strategies in their pursuit of sustainable performance. However, a lack of understanding and knowledge about the effectiveness of the metaverse and its related technologies creates a barrier. Therefore, the current study fills this gap and uses organizational information-processing theory to develop the theoretical framework to examine metaverse-related technologies (artificial intelligence and blockchain technology—BCT) and their direct and indirect effects on sustainable business performance, which no other study has examined. Using purposive sampling, the sample data from 326 SMEs were gathered and analyzed using a partial least square structural equation modeling (PLS-SEM). This study’s findings revealed that AI capabilities are vital for information gathering, analyzing, and decision-making in the metaverse context. BCT facilitates ensuring a transparent, visible, traceable, and immutable supply chain, which helps make it more resilient and improves the closed-loop supply chain (CLSC) system with positive technological advancements and significant effects on increasing sustainable business performance (SBP). This study’s findings help organizations understand the potential benefits of AI-enabled SMEs’ presence in the metaverse. The current investigation provides a strategy for managers to gain a competitive advantage, make the supply chain more robust, and enhance overall business performance.Sustainability941This study’s findings revealed that AI capabilities are vital for information gathering, analyzing, and decision-making in the metaverse context and provides a strategy for managers to gain a competitive advantage, make the supply chain more robust, and enhance overall business performance.Sustainability["Syed Abdul Rehman Khan", "Adnan Ahmed Sheikh", "Ibrahim Rashid Al Shamsi", "Zhang Yu"]2025-01-04T00:00:00 1c5ff8316a753118d7a56ec9410cb10fbb161081Purpose: This study attempts to discuss the ethical issues brought about by the rapid development of artificial intelligence and propose countermeasures, in an attempt to provide a theoretical reference for the governance of artificial intelligence ethical issues and the establishment of an artificial intelligence regulatory framework. Approach/Methodology/Design: A literature review was conducted using a structured search of various literature sources, including academic, organisational, government grey literature sources and news reports, with primary consideration given to literature published or released since 2010. Findings: Today, the rapid development of artificial intelligence has led to a wide range of ethical issues. As artificial intelligence technology continues to innovate, discussions on ethical issues must also keep pace with the times. Only artificial intelligence that develops under reasonable and standardized constraints can truly help people create a better life in the future. Practical Implications: This study analyses the main ethical problems arising from the rapid development of artificial intelligence, specifically from the perspectives of ‘environmental ethics’, ‘gender ethics’ and ‘social ethics’, and proposes corresponding countermeasures. This study analyses the main problems from three perspectives, including ‘environmental ethics’, ‘gender ethics’ and ‘social ethics’, and puts forward corresponding countermeasures, so as to provide theoretical references for the construction of a normative framework for AI ethics and make positive contributions to the promotion of ethical governance of AI. Originality/value:This paper discusses the latest major ethical issues arising from AI from a new perspective, combining three different perspectives: ‘gender ethics’, ‘environmental ethics’ and ‘social ethics’. This paper is innovative in that it discusses the contradictions caused by artificial intelligence from three different perspectives: ‘gender ethics’, ‘environmental ethics’, and ‘social ethics’, and proposes corresponding countermeasures.International Theory and Practice in Humanities and Social Sciences10This study analyses the main ethical problems arising from the rapid development of artificial intelligence from three perspectives, including ‘environmental ethics’, ‘gender ethics’ and ‘social ethics’, and puts forward corresponding countermeasures, to provide theoretical references for the construction of a normative framework for AI ethics.International Theory and Practice in Humanities and Social Sciences["Li Yan Zhao Li"]2025-01-04T00:00:00 91a6150ab652692fb917829f880c0b18cb749a09BMC Psychology790The psychosocial factors influencing AI adoption among Peruvian university students are investigated and an extended UTAUT2 model is used to examine various constructs that may impact AI acceptance and use.BMC Psychology["Benicio Gonzalo Acosta-Enriquez", "Mar\u00eda de los \u00c1ngeles Guzm\u00e1n Valle", "Marco Agust\u00edn Arbul\u00fa Ballesteros", "Julie Catherine Arbul\u00fa Castillo", "Carmen Graciela Arbul\u00fa P\u00e9rez Vargas", "Isaac Saavedra Torres", "Pedro Manuel Silva Le\u00f3n", "Karina Saavedra Tirado"]2025-01-04T00:00:00 9434f560040d0433e3c275a142f1d707dc6868a0The rapid advancement of Artificial Intelligence (AI) technologies has unlocked significant potential for enhancing the performance of micro and ultra-micro enterprises (UMEs) in Indonesia. This study explores the role of early AI adoption in supporting the growth and competitiveness of these enterprises, which constitute a vital segment of Indonesia’s economy. The paper examines how AI-driven tools can improve business operations, from marketing automation and customer engagement to inventory management and financial planning. Moreover, it discusses the unique challenges faced by micro and ultra-micro enterprises in adopting AI, including limited digital literacy, financial constraints, and inadequate infrastructure. Opportunities arising from AI adoption, such as improved access to markets, personalized customer experiences, and data-driven decision-making, are also highlighted. The findings emphasize the importance of tailored AI solutions, government support, and capacity-building initiatives to address the barriers to adoption. This paper concludes by offering actionable recommendations for policymakers, technology providers, and entrepreneurs to foster an enabling ecosystem for AI integration, thereby unlocking its full potential to drive sustainable growth and innovation in the sector.Keywords: Artificial Intelligence, Early Adoption, Micro Enterprises, Ultra-Micro Enterprises, Business Growth, Digital Transformation, Jurnal Akuntansi dan Bisnis240How AI-driven tools can improve business operations, from marketing automation and customer engagement to inventory management and financial planning is examined, and the unique challenges faced by micro and ultra-micro enterprises in adopting AI are discussed.Jurnal Akuntansi dan Bisnis["Fajri Fajri", "Kukuh Adi Perdana", "Dita Ully Manurung", "Putu Kana Narayan Dharmawan", "Nurma Gupita Dewi"]2025-01-04T00:00:00 37f37841123d428c7f234c5690c0191f18c1c8b3Environmental monitoring is crucial for addressing climate change impacts, demanding innovative approaches for better prediction and management. This study explores advanced artificial intelligence (AI) techniques beyond traditional models like CNNs and LSTMs. It incorporates generative adversarial networks (GANs) for augmenting sparse datasets, ensemble learning for robust predictions, and explainable AI (XAI) to enhance model transparency and usability. GANs address data scarcity by generating synthetic, high-fidelity environmental data, while transformer-based architectures improve long-term climatic forecasts. Ensemble methods demonstrate superior accuracy in predictions, reducing mean squared error by 15% compared to traditional models. Reinforcement learning (RL) optimizes adaptive climate strategies by analyzing dynamic environments in real-time. These approaches collectively enhance the precision, interpretability, and scalability of AI-driven environmental monitoring systems. Future research should explore federated learning and quantum computing to further advance computational efficiency and accessibility. This study highlights AI's transformative potential in fostering proactive and data-driven climate resilienceInternational Journal For Multidisciplinary Research260Advanced artificial intelligence techniques beyond traditional models like CNNs and LSTMs are explored, incorporating generative adversarial networks for augmenting sparse datasets, ensemble learning for robust predictions, and explainable AI (XAI) to enhance model transparency and usability.International Journal For Multidisciplinary Research["Chalamalla Nikhitha Reddy"]2025-01-04T00:00:00 eb751bbd26e5889f0521b174d31f5ad2cf7c3432Artificial Intelligence (AI) is transforming education by enabling personalized learning experiences, enhancing teaching efficiency, and promoting student engagement. This study provides a comprehensive literature review on the applications, challenges, and future directions of AI tech- nologies, with a focus on generative AI tools like ChatGPT, GPT-4, and BERT. The review explores the role of AI across primary, secondary, and higher education, examining its potential to foster inclusivity and address educational equity gaps. Key methods include thematic analysis of relevant literature to identify trends, challenges, and research gaps. The results highlight both the opportunities provided by AI, such as adaptive learning and automated assessment, and the challenges, including ethical concerns, algorithmic bias, and infrastructural limitations. The study concludes by emphasizing the need for ethical frameworks, teacher training, and interdisciplinary collaboration to ensure the responsible use of AI in education. Additionally, the research identifies future directions, including the integration of AI with emerging technologies like virtual and augmented reality. This review aims to provide actionable insights for educators, policymakers, and researchers to harness AI’s potential while maintaining a balance between technology and human interaction for meaningful learning experiences.International Theory and Practice in Humanities and Social Sciences100The review explores the role of AI across primary, secondary, and higher education, examining its potential to foster inclusivity and address educational equity gaps, and identifies future directions, including the integration of AI with emerging technologies like virtual and augmented reality.International Theory and Practice in Humanities and Social Sciences["Nan Xiao", "YuTing Pei", "Chunhong Yuan", "YuJia Bu", "ZhiXuan Cai"]2025-01-04T00:00:00 e8c69d2000d4aae7ffd4a84c35e01643f043e512The rapid development of artificial intelligence (AI) technologies has introduced intelligent approaches in various fields. In particular, these technologies play an invaluable role in modernizing the processes of training, retraining, and ensuring the continuous professional development of managerial personnel. This article presents the results of a survey conducted among more than 500 managers working in the public sector to assess the effectiveness of organizing continuous professional development courses and the use of AI technologies in this process. Based on the research results, taking into account the survey results and advanced international practices, an intelligent information system model is proposed. This system is designed to assess the competencies of managerial personnel and automatically recommend key competencies that they need to develop. In addition, the article offers suggestions for mechanisms to digitally manage the process of improving professional competencies, assessing its economic efficiency, and integrating AI tools into this area.American Journal of Economics and Business Management00A survey conducted among more than 500 managers working in the public sector to assess the effectiveness of organizing continuous professional development courses and the use of AI technologies in this process is presented and an intelligent information system model is proposed.American Journal of Economics and Business Management["Alisher Mamatov"]2025-01-04T00:00:00 870f103672733a4190c9ff7f84de6fa7d18e8b12This paper explores the intersection of artificial intelligence (AI) and art, focusing on the use of AI-driven robots in creative processes. The study examines the historical evolution of AI art, beginning with early algorithmic experiments in the 1960s and leading to contemporary developments, such as Generative Adversarial Networks (GAN) and robotic artists like Ai-Da. It analyzes how advancements in AI and robotics have not only expanded the boundaries of art creation but also raised philosophical and ethical questions regarding authorship, creativity, and the role of human artists. Through a comprehensive review of key milestones, technologies, and aesthetic implications, the paper evaluates whether AI and robotic art are poised to replace traditional artists or establish new forms of collaboration. The findings indicate that while AI technologies are capable of generating intricate artworks, they lack human emotional expression and cultural sensitivity, highlighting the complementary rather than competitive role of AI in art. The study suggests that future artists will need to develop technical competencies to effectively collaborate with AI systems, reshaping the landscape of art and creativity. This paper contributes to the ongoing discourse on AI’s role in art by offering insights into the future of human-AI artistic partnerships and their impact on the broader art ecosystem.International Theory and Practice in Humanities and Social Sciences40The findings indicate that while AI technologies are capable of generating intricate artworks, they lack human emotional expression and cultural sensitivity, highlighting the complementary rather than competitive role of AI in art.International Theory and Practice in Humanities and Social Sciences["Hengran Yang"]2025-01-04T00:00:00 d0f2707f87a1a735f652b61e796671a194edce71The advancement of science and technology has significantly impacted various sectors, including Islamic education. This study explores the integration of Artificial Intelligence (AI) in Islamic educational institutions, particularly in Islamic boarding schools, high schools with Islamic integration (SMA IT), and Islamic boarding schools. The use of AI in these institutions enhances the learning process, making it more efficient and effective. This paper discusses the development and challenges of incorporating AI in Islamic education, the role of technology in modernizing educational practices, and the implications for students' learning outcomes.AL-KHARAJ170The development and challenges of incorporating AI in Islamic education, the role of technology in modernizing educational practices, and the implications for students' learning outcomes are discussed.Al-Kharaj: Jurnal Ekonomi, Keuangan &amp; Bisnis Syariah["Deasy Eka", "Guntur Saputri", "Alting", "Kata Kunci", "P. Islam", "Kecerdasan Buatan", "Integrasi Teknologi", "Efisiensi Pembelajaran", "Kemajuan Pendidikan"]2025-01-04T00:00:00 62792a1aa1f7bf657013c28d58e19f9d5affc312Financial Innovation1080Financial Innovation["S. Y\u00fcksel", "Serkan Eti", "H. Di\u0307n\u00e7er", "Ya\u015far G\u00f6kalp", "Gabriela Oana Olaru", "Nihal Kalayci Oflaz"]2025-01-04T00:00:00 a2f9617ecb7cb8e0873df5eb52b0b66ccd3dfe8e- The foundation of many Artificial Intelligence (AI) approaches and algorithms is discrete mathematics. Graph theory, combinatorics, and logic are just a few of the discrete mathematics fields that provide substantial contributions to AI. Each of these fields is essential to the development of contemporary AI systems. This section lays the groundwork for a more in-depth examination of particular instances by giving a summary of how discrete mathematics supports the architecture and operation of AI Key Words: Artificial Intelligence, Discrete Mathematics, Graph Theory, Combinatorics in AI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This section lays the groundwork for a more in-depth examination of particular instances by giving a summary of how discrete mathematics supports the architecture and operation of AI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Neeta Ravindra Mohite", "Dr. G.J. Chhajed"]2025-01-04T00:00:00 6522f9ab97ddc922475100cc177de909f8336e76This study employs Systematic Literature Review (SLR) and thematic analysis to explore the topics of Artificial Intelligence (AI), Stakeholder Engagement (SE) and social innovation. To enhance methodological rigor, the study integrated literature analysis and social media analysis to recognize topics within texts using Latent Dirichlet Allocation (LDA), an unsupervised machine learning method. The study highlights AI's influence on social engagement, aligning with diffusion theory, stressing the need to emphasize AI's benefits for faster adoption.Journal of Global Information Management650The study highlights AI's influence on social engagement, aligning with diffusion theory, stressing the need to emphasize AI's benefits for faster adoption.Journal of Global Information Management["Surajit Bag", "Susmi Routray", "Santosh Kumar Srivastava", "David Roubaud", "Abla Chaouni Benabdellah", "Oksana Grebinevych"]2025-01-04T00:00:00 7f25b117dfba8360633a8708d87b1074e086d57bJournal of Computational Social Science600Journal of Computational Social Science["Ho Ting (Bosco) Hung"]2025-01-04T00:00:00 1087fbd3089711e5de50630ba73dba28d4953a8cEndüstri 4.0'dan Endüstri 5.0'a geçiş, endüstriyel operasyonlarda önemli bir değişim anlamına gelmektedir. Endüstri 4.0 otomasyon ve veri merkezli operasyonlara öncelik verirken, Endüstri 5.0 kişiselleştirme, insan-makine işbirliği ve sürdürülebilir üretimi teşvik etmektedir. Yapay zeka (AI) bu dönüşümde çok önemli bir rol oynamakta ve karar verme, tahmine dayalı analitik ve otonom sistemleri güçlendirmektedir. Endüstri 5.0'da yapay zeka, insan yaratıcılığını otomasyonla bütünleştirerek daha akıllı ve uyarlanabilir endüstriyel süreçleri kolaylaştırır. Metaverse, insanlar, makineler ve robotlar arasında işbirliği için sürükleyici sanal ortamlar sunarak bu değişimi kolaylaştırır. Kuruluşlar, fiziksel uygulamadan önce artırılmış ve sanal gerçeklik, dijital ikizler ve simülasyonlar kullanarak dijital bir ortamda geliştirebilir ve yaratabilir. Yapay zeka ve metaverse birlikte insan merkezli ve sürdürülebilir bir endüstriyel paradigma oluşturmakta, insanlar ve makineler arasındaki ara yüzü geliştirirken yenilikçiliği ve verimliliği teşvik etmektedir. Bu çalışma, yapay zeka ve metaverse teknolojilerinin, kişiselleştirme, insan-makine iş birliği ve sürdürülebilir üretim gibi alanlarda Endüstri 5.0’a sağladığı katkıları analiz etmeyi amaçlamaktadır. Özellikle, yapay zeka uygulamaları ile sanal ve artırılmış gerçeklik (metaverse) arasındaki etkileşim ele alınarak, bu teknolojilerin yenilikçi çözümler geliştirme potansiyeli değerlendirilmektedir. Çalışma, endüstriyel süreçlerin daha verimli ve insan merkezli hale gelmesi için yapay zeka ve metaverse’in nasıl kullanılabileceğine dair bütünsel bir çerçeve sunmaktadır.Türk Turizm Araştırmaları Dergisi00Turk Turizm Arastirmalari Dergisi["Kemal Gokhan Nalbant", "Sevgi Ayd\u0131n"]2025-01-04T00:00:00 0412069290e87116f36c4901380cfd7fb1bd611bCogent Education260Cogent Education["Fachrurrozie Fachrurrozie", "Ahmad Nurkhin", "Jarot Tri Bowo Santoso", "H. Mukhibad", "C. Wolor"]2025-01-04T00:00:00 522bb6ab0399a5bb6e96ad7c93e8c9470fd9bde8The increasing emphasis on publication quantity over quality in academic evaluation has raised concerns about the integrity of meritocratic assessments in Italy. This study explores the impact of Letters to the Editor (LTEs) and Correspondences on reputation indices, such as the h-index, and proposes an algorithm to minimize their disproportionate influence. The proliferation of AI tools, including machine learning and chatbots, has further complicated academic evaluation by enabling rapid manuscript production, potentially inflating publication counts without corresponding research depth. This paper introduces a weighted algorithm that adjusts the h-index by assigning lower weights to LTEs and Correspondences, based on predefined parameters. Using example datasets, the adjusted h-index significantly reduced scores compared to the original, reflecting more accurate merit assessments. Statistical analysis revealed a high correlation between original and adjusted h-indices (Pearson r = 0.98, Spearman r = 0.94), yet highlighted a weak correlation between letter contributions and rank order (r = -0.04). Paired t-tests confirmed significant differences between the two indices (p = 0.0393). The proposed method effectively penalizes superficial publications while preserving the value of full-length research articles. This approach offers a rigorous and mathematically grounded framework for evaluating academic productivity, particularly in high-stakes settings like Italy’s National Scientific Qualification competitions. Furthermore, integrating AI tools into evaluation systems could enhance transparency and fairness, reducing biases and legal disputes related to subjective judgments. Future research should refine these metrics for broader disciplinary applications, ensuring a balanced assessment of academic contributions in the era of artificial intelligence and automated publishing tools.Top Italian Scientists Journal00A weighted algorithm is introduced that adjusts the h-index by assigning lower weights to LTEs and Correspondences, based on predefined parameters, and offers a rigorous and mathematically grounded framework for evaluating academic productivity, particularly in high-stakes settings like Italy's National Scientific Qualification competitions.Top Italian Scientists Journal["Salvatore Chirumbolo"]2025-01-04T00:00:00 0588dff9c229e5e370ed4ea8791973fa8af661aeThis paper discusses the use of Artificial Intelligence (AI) to enhance workplace productivity and employee well-being. By integrating machine learning (ML) techniques with neurobiological data, the proposed approaches ensure alignment with human ethical standards through value alignment models and Hierarchical Reinforcement Learning (HRL) for autonomous task management. The system utilizes biometric feedback from employees to generate personalized health prompts, fostering a supportive work environment that encourages physical activity. Additionally, we explore decentralized multi-agent systems for improved collaboration and decision-making frameworks that enhance transparency. Various approaches using ML techniques in conjunction with AI implementations are discussed. Together, these innovations aim to create a more productive and health-conscious workplace. These outcomes assist HR management and organizations in launching more rational career progression streams for employees and facilitating organizational transformation.00The proposed approaches ensure alignment with human ethical standards through value alignment models and Hierarchical Reinforcement Learning for autonomous task management and decentralized multi-agent systems for improved collaboration and decision-making frameworks that enhance transparency.["K. Ravirajan", "Arvind Sundarajan"]2025-01-04T00:00:00 2cb2c816d6a84efffc52d65ca6ea26ab8e98e4baHireVision.AI is an AI-powered interview preparation platform designed to revolutionize how users prepare for job interviews. By leveraging the power of artificial intelligence, this platform offers a comprehensive and user-friendly solution that addresses the common challenges faced by candidates during the interview process. With AI-driven mock interviews simulating real-life scenarios, users can practice and receive personalized feedback regarding their performance. This feedback not only highlights areas of improvement, but also boosts candidates' confidence, ensuring that they are better prepared for real-world interviews. A standout feature of HireVision.AI is its data-driven insights, which provide an in-depth analysis of users' interview performance, helping them to understand their strengths and weaknesses. Through this, candidates can refine their responses and improve them over time. The platform also offers a robust question repository tailored to specific industries and roles, ensuring that users receive focused and effective preparations. HireVision.AI prepares to the next level with its AI-powered coaching, where users receive targeted advice and strategies to excel in interviews. By integrating real-time performance tracking, the candidates can monitor their progress and adjust their learning strategies accordingly. In addition, HireVision.AI helps users navigate the job market with career insights, giving them a competitive edge by providing up-to-date information on company-specific interview patterns and trends. Beyond interview practice, HireVision.AI is designed as a job search assistant, streamlining the process of finding job posts and helping candidates secure the right opportunities. Ultimately, HireVision.AI empowers candidates to excel in interviews and secure successful job placements, providing them with tools they need to thrive in an increasingly competitive market. Keywords: AI-powered, interview preparation, mock interviews, personalized feedback, performance tracking, data-driven insights, career insights, job trends. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00An AI-powered interview preparation platform designed to revolutionize how users prepare for job interviews and empowers candidates to excel in interviews and secure successful job placements, providing them with tools they need to thrive in an increasingly competitive market.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Rajaram Bharat Walavalkar", "Siddhant Dinesh PasiSiddhant Dinesh Pasi", "Harsh Sanjiv Pandey", "Meet Manish Parmar", ".. S. D\u2019monte"]2025-01-04T00:00:00 9caaf4eddbc90d0d7365ce247098b92a50b160faThe development and adoption of artificial intelligence (AI) and robotic technologies in the foodservice industry has expanded dramatically. The economic benefits of such adoption are likely to be similar to those experienced by other sectors, such as manufacturing. However, unlike many other sectors, the appeal of restaurants involves consumer perceptions of those making the product. In particular, we argue that—especially in craft food contexts—consumers expect food to be prepared “with love.” As robots are intuitively incapable of doing so, restaurateurs face a conundrum: how to take advantage of the economic benefits of robotic chefs, while maintaining consumer perceptions that meals are prepared with love? We test a series of potential interventions aimed at overcoming the gap in such perceptions and find that AI-enabled (i.e., chat-based) relationship-building between robot chefs and restaurant patrons is the most effective option. In fact, our relationship-building intervention fully closes the gap between preferences for human chefs, relative to robotic chefs. Additional managerial and theoretical implications are discussed.Cornell Hospitality Quarterly470A series of potential interventions aimed at overcoming the gap in consumer perceptions of robot chefs are tested and find that AI-enabled (i.e., chat-based) relationship-building between robot chefs and restaurant patrons is the most effective option.Cornell Hospitality Quarterly["Andrew E. Wilson", "Lura Forcum", "Michael Giebelhausen"]2025-01-04T00:00:00 f1ddca28008ee94e0bf40608989b98c417a1f204-The integration of Blockchain technology with Artificial Intelligence (AI) offers a promising frontier for enhancing security measures. Blockchain provides a decentralized and immutable ledger system, ensuring transparency and security in data transactions. When combined with AI, which excels in data analysis and pattern recognition, the synergy between these technologies can revolutionize cybersecurity. This paper explores the potential of Blockchain and AI integration to fortify security frameworks, highlighting key areas such as data integrity, fraud detection, and autonomous threat response. By leveraging the strengths of both technologies, we propose innovative solutions to address contemporary security challenges, presenting a robust model that ensures higher security standards and resilience against cyber threats. The study also examines practical applications, potential benefits, and the challenges involved in implementing such integrated systems. Our findings suggest that the convergence of Blockchain and AI not only enhances security measures but also paves the way for future advancements in creating secure, intelligent, and adaptable cybersecurity infrastructures. Keywords— Blockchain technology, Artificial Intelligence (AI), security measures, decentralized, immutable ledger, transparency, data transactions, analyzing data, pattern recognition, cybersecurity, security systems, data protection, fraud detectionINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The findings suggest that the convergence of Blockchain and AI not only enhances security measures but also paves the way for future advancements in creating secure, intelligent, and adaptable cybersecurity infrastructures.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Tirthesh Gajjar", "Dr. Swapnil Parikh", "Dr. Kishori Shekokar"]2025-01-04T00:00:00 33dca19d64f8480ad5df929c6588f03c71f7416eArtificial Intelligence (AI) is revolutionizing healthcare by enhancing patient care, diagnostics, workflows, and treatment personalization. The integration of AI in healthcare promises significant advancements and better patient outcomes. However, the lack of explainability in many AI models, known as 'black-box AI', raises concerns for patients, doctors, and developers. This issue, termed 'black box medicine', challenges the adoption of AI in healthcare. The demand for explainable AI has grown as AI systems become more complex. The absence of explanations in AI decisions, especially in critical situations like healthcare, has sparked debates and even suggestions to exclude black-box AI from healthcare provision. This article examines the impact and causes of unexplainable AI in healthcare, critically evaluates its performance, and proposes strategies to address this challenge.Medical Law Review00The impact and causes of unexplainable AI in healthcare, critically evaluates its performance, and proposes strategies to address this challenge are examined.Medical law review["V. Raposo"]2025-01-04T00:00:00 bd1e0dca9b5fd91f68df4dc9eaa3e0970c1c1286The integration of artificial intelligence and blockchain in healthcare promises a significant transformation in data management, service quality improvement, and increased patient data security. Blockchain, by offering a decentralized and transparent platform, enhances the reliability and security of information. Meanwhile, artificial intelligence, with its ability to analyse and process data, helps identify patterns and predict treatment outcomes. The aim of this study is Evaluation and prioritization of artificial intelligence integrated blockchain factors in the healthcare supply chain using F-AHP and F-DEMATEL. Following a review of previous studies, four criteria and 23 sub-criteria were identified. In the first step, these criteria were ranked using the F-AHP method. In the second step, relationships among the sub-criteria were determined through F-DEMATEL, identifying causal and effect criteria. The F-AHP results show that among the 23 sub-criteria identified from previous studies, "integration of treatment processes (C32)", "Provide fair service (C31)", "health monitoring (C12)", "security of medical data (C34)", and "clinical decision support (C21)" ranked first to fifth, respectively. The F-DEMATEL results indicate that sub-criteria are divided into causal and effect categories, with "stakeholder participation (C42)" and "technology acceptance (C44)" being the most important causal sub-criteria, while "monitoring the treatment process (C15)" and "patient-centered treatment strategies (C22)" were identified as the most important effect sub-criteria. These findings suggest that the use of AI-blockchain integration in healthcare can lead to significant improvements in managing healthcare systems.Computer and Decision Making: An International Journal04Evaluation and prioritization of artificial intelligence integrated blockchain factors in the healthcare supply chain using F-AHP and F-DEMATEL suggest that the use of AI-blockchain integration in healthcare can lead to significant improvements in managing healthcare systems.Computer and Decision Making: An International Journal["Neda Seifi", "Erfan Ghoodjani", "Seyed Shabahang Majd", "Alireza Maleki", "Sayeh Khamoushi"]2025-01-05T00:00:00 81abf6b33d5b2c1eae2071e890f5e7b5e5e45231Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.01A deeper understanding is provided of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems and key challenges such as scalability, efficiency, and integration with explainable AI are addressed.["Tianyang Wang", "Yunze Wang", "Jun Zhou", "Benji Peng", "Xinyuan Song", "Charles Zhang", "Xintian Sun", "Qian Niu", "Junyu Liu", "Silin Chen", "Keyu Chen", "Ming Li", "Pohsun Feng", "Ziqian Bi", "Ming Liu", "Yichao Zhang", "Cheng Fei", "Caitlyn Heqi Yin", "Lawrence K.Q. Yan"]2025-01-05T00:00:00 c17af601c139d84a074008ef8ae19df650ca7d5dThis paper explores the transformative role of Artificial Intelligence (AI) in developing functional literacy, a critical competency for the 21st century. Focusing on AI's capabilities to personalize learning, foster critical thinking, and enhance problem-solving skills, the study investigates how AI tools can be integrated into education systems effectively. The challenges of implementation, including ethical concerns, accessibility barriers, and teacher preparedness, are also examined. Through a detailed review of existing research and case studies, this paper highlights strategies to maximize the potential of AI in fostering equitable and inclusive education. The findings underscore the necessity for collaborative efforts between educators, policymakers, and technologists to realize AI's full potential in addressing contemporary educational needs.Eurasian Science Review An International peer-reviewed multidisciplinary journal00The findings underscore the necessity for collaborative efforts between educators, policymakers, and technologists to realize AI's full potential in addressing contemporary educational needs.Eurasian Science Review An International peer-reviewed multidisciplinary journal["Aruzhan Kakina"]2025-01-05T00:00:00 13ad0040ba374ac4b7f2e0604ea23c6cc05e8729   Purpose of the article is to study the impact of artificial intelligence (AI) on the transformation of business models in the digital economy.   Object of the study are companies implementing AI to automate processes and improve efficiency.   Subject of the study is the changes in key elements of business models: the creation, delivery, and monetization of value.   Methodology includes the analysis of practical cases, the calculation of return on investment (ROI), and the assessment of reductions in operating costs.   Scientific novelty lies in the development of an approach to fully automating AI-based business processes, and identifying related challenges, such as problems with trust in AI systems, and ethical aspects of its use.   Practical significance of this work is to demonstrate the need for reviewing existing business models, and investing in AI infrastructure, to increase the competitiveness of companies in the digital economy.The world of new economy90The need for reviewing existing business models, and investing in AI infrastructure, to increase the competitiveness of companies in the digital economy is demonstrated.The world of new economy["S. V. Savin", "A. D. Murzin"]2025-01-05T00:00:00 95a8b51275a0ec14afec24ca3060ecf905e0c50eHow important is teacher happiness? This reflective article emphasises the critical importance of teacher wellbeing. Happy and healthy teachers are better equipped to create positive learning environments, leading to improved student outcomes and overall school climate, as well as supporting teacher retention and productivity in the long term. This article considers how we can potentially use artificial intelligence (AI) to alleviate teacher workload. By automating tasks like grading, creating personalised learning plans, and generating educational content, AI can free up teachers to focus on more meaningful interactions with students. This paper highlights that, ultimately, the combination of teacher wellbeing and AI-driven innovations can lead to a more effective and fulfilling education system for both teachers and students. Какое значение имеет счастье учителя? Эта обзорная статья подчёркивает исключительную значимость благополучия учителей. Счастливые и здоровые учителя лучше подготовлены к созданию позитивной учебной среды, способствующей улучшению успеваемости учеников и общего школьного климата, а также удержанию учителей и повышению их производительности в долгосрочной перспективе. В этой статье мы рассмотрим, как можно использовать искусственный интеллект (ИИ) для снижения нагрузки на учителей. Автоматизируя такие задачи, как оценивание, составление индивидуальных планов обучения и создание образовательного контента, ИИ может предоставить учителям возможность сосредоточиться на более значимом взаимодействии с учениками. В данной статье подчёркивается, что в конечном итоге сочетание благополучия учителей и инноваций на основе ИИ может привести к созданию более эффективной и полноценной системы образования как для учителей, так и для учеников. Мұғалімнің бақытты болуы қаншалықты маңызды? Бұл шолу мақалада мұғалім хал-ахуалының аса маңыздылығы туралы сөз болады. Бақытты және салауатты мұғалімдердің қолайлы оқу ортасын қалыптастыруға дайындығы жоғарырақ болады, бұл оқушылардың оқу үлгерімі мен мектептегі жалпы ахуалды жақсартуға, сондай-ақ жақсы мұғалімдерді жұмыста ұстап қалуға және ұзақ мерзімді перспективада олардың жұмыс өнімділігін арттыруға ықпал етеді. Бұл мақалада мұғалімдердің жүктемесін азайту үшін жасанды интеллектіні қалай қолдануға болатыны қарастырылады. Бағалау, жеке оқу жоспарларын әзірлеу және білім беру контентін құрастыру сияқты тапсырмаларды автоматтандыру арқылы жасанды интеллект мұғалімдердің оқушылармен анағұрлым мазмұнды өзара әрекеттестікке назарды шоғырландыруына мүмкіндік береді. Бұл мақалада мұғалімдердің хал-ахуалы мен жасанды интеллектіге негізделген инновацияларды үйлестіре білу мұғалімдер үшін де, оқушылар үшін де тиімді білім беру жүйесін құруға мүмкіндік беретіні атап көрсетіледі.Pedagogical Dialogue00Pedagogical Dialogue["P. Gibson"]2025-01-05T00:00:00 754d56dd371e85ef5482e70079646807c04c003dThe COVID-19 disease caused by the SARS-CoV-2 virus was declared by the World Health Organization (WHO) as a spreadable viral disease. During the COVID pandemic, there was difficulty in notifying the Decision-Making System (DMS) about the rapid and precise triage of patients admitted to the emergency wards. As a method to achieve the aim and develop digital healthcare revolutions in data and analytics, digital healthcare information was established. Artificial Intelligence (AI) is a robust automation tool for sustainability in the context of the COVID-19 health crisis on big datasets. Besides, the gap between AI investment and commercial real-time application, which are the initial digital technology development curves, has been identified. It was discovered that AI’s new applications are grounded in Digital Transformation Mapping (DTM) for the DMS of Health Crises. The fast inventions in AI and Machine Learning (ML) have implications for amazingly preventive and clinical healthcare, and for the association, ML was developed as a predictable attention. Billions of smartphones, massive online datasets, linked wireless wearable devices, comparatively cost-effective computing resources and improved ML and Nural Language Processing (NLP) are leveraged by these rapid responses, with the trained dataset of 65% and evaluated in the other 35%, the renowned ML models for structured data like Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), Logistic Regressive Tree (LRT), Decision Tree (DT), Stochastic Gradient Booster (SGB), and Random Forest (RF) are used for simulating new unidentified data. AI-DTM challenges DMS of Health Crises (COVID-19) and the drawbacks of critically contributing risk factors to healthcare diseases. Meanwhile, a comprehensive collection of healthcare datasets over what is spreadable would be required to save human lives, train AI, and limit cost-effective health risks.Journal of Machine and Computing280It was discovered that AI’s new applications are grounded in Digital Transformation Mapping (DTM) for the DMS of Health Crises (COVID-19) and the drawbacks of critically contributing risk factors to healthcare diseases.Journal of Machine and Computing["Hayder M. A. Ghanimi", "Firas Tayseer Mohammad Ayasrah", "Vijaya Chandra Jadala", "Manjunath T C", "Balasaranya K", "Srinivasarao B"]2025-01-05T00:00:00 e1ab320167803c992eb9fa5de2fda511d7601818The integration of Artificial Intelligence (AI) into criminal procedure law has emerged as a significant development in enhancing the efficiency and accuracy of judicial systems. However, the implementation of AI in Indonesia remains at an early stage, with challenges such as regulatory gaps, societal trust issues, and potential algorithmic biases. This study aims to explore the potential and challenges of utilizing AI within Indonesia’s criminal procedure framework, focusing on its legal and social implications. Employing a qualitative research approach, this study combines in-depth interviews with legal experts, practitioners, and AI developers, alongside a comprehensive literature review of existing laws and academic research. The findings reveal that AI has the potential to expedite case management, enhance evidence analysis, and reduce human biases in judicial decision-making. Nevertheless, the lack of specific regulations governing AI’s use in the judiciary and the limited public trust pose significant hurdles to its effective implementation. The study also highlights the importance of adaptive legal frameworks and public education to foster transparency and accountability in AI applications. These results contribute to the broader discourse on AI integration in legal systems, particularly in developing countries, by emphasizing the need for localized strategies that address unique social and legal contexts. The implications of this research extend to policymakers and technology developers, providing insights into the regulatory and ethical considerations required for sustainable AI adoption in judicial processes. Future research is recommended to expand the scope of empirical studies and include quantitative analyses to further substantiate the findings.Perkara : Jurnal Ilmu Hukum dan Politik00The findings reveal that AI has the potential to expedite case management, enhance evidence analysis, and reduce human biases in judicial decision-making, but the lack of specific regulations governing AI’s use in the judiciary and the limited public trust pose significant hurdles to its effective implementation.Perkara : Jurnal Ilmu Hukum dan Politik["Rendi Septiawan", "Via Anandatia", "Adin Gustina"]2025-01-05T00:00:00 6c8b657f0dc1a5a43051455c580e3c357b5f2e3fThe integration of Artificial Intelligence (AI) has significantly advanced oral and maxillofacial cancer (OMC) care. This paper explores the transformative potential of AI in OMC diagnosis, staging, treatment, and prognosis. AI-driven applications, including computervision and machine learning, are discussed, emphasizing their impact on early detection,accurate diagnosis, and personalized treatment planning. The paper also explores the role of AI in OMC education, research, and practice, outlining future directions. In OMC staging, AI automates the process by analyzing medical records and imaging data, enhancing accuracy. The paper also discusses AI's role in tailoring treatment plans, optimizing radiation therapy, and facilitating robotic surgery. Furthermore, the integration of ChatGPT in OMC education, research, and practice is explored. The paper outlines future directions, including the integration of multi-omics data and real-time patient monitoring, emphasizing collaboration, clinical trials, and validation as essential steps in realizing AI's potential in routine clinical practice. In conclusion, AI has the potential to transform OMC management by enhancing diagnosis accuracy, staging precision, personalized treatment planning, and prognosis estimation. Addressing ethical concerns and fostering interdisciplinary collaboration are crucial in harnessing AI's capabilities. By embracing AI advancements, OMC care can be significantly improved, leading to better patient outcomes and contributing to the fight against oral and maxillofacial cancer.ENVIRO Dental Journal270Artificial Intelligence has the potential to transform OMC management by enhancing diagnosis accuracy, staging precision, personalized treatment planning, and prognosis estimation.ENVIRO Dental Journal["Md. Asaduzzaman", "Md. Abdur Rahman", "Nitish Krishna Das", "Mausumi Iqbal", "A. K. M. S. Kadir", "Md. Golam Rabbany", "Mohammad Ullah Shemanto", "Rukaiya Akhter", "Joye Kundu"]2025-01-05T00:00:00 cf6e9f5720046bbf3764aa91034241a22ec3e1d3The rapid adoption of artificial intelligence (AI) has significantly transformed various sectors, such as healthcare, finance, and transportation. However, it also raises critical challenges regarding data privacy, particularly in large-scale data collection and processing. This study explores the differences and similarities in data privacy regulations governing AI between Europe and Asia, focusing on the General Data Protection Regulation (GDPR) in Europe and various regulations such as the Act on the Protection of Personal Information (APPI) in Japan and the Personal Information Protection Law (PIPL) in China. Using a qualitative approach with comparative legal analysis, this research evaluates the principles, flexibility, and practical implications of these regulations for fostering responsible AI development. The findings reveal that while GDPR emphasizes individual protection through transparency and explicit consent, Asia adopts a more flexible approach tailored to national needs, balancing innovation and privacy. However, challenges such as harmonizing cross-border data policies and adapting regulations to rapidly evolving technologies persist. This study contributes to the discourse by highlighting the implications of these regulatory differences for global cooperation and offering strategic recommendations for policymakers and industries. In a globalized digital landscape, aligning legal frameworks is essential not only to protect individual rights but also to build public trust in emerging AI technologies.Perkara : Jurnal Ilmu Hukum dan Politik00This study explores the differences and similarities in data privacy regulations governing AI between Europe and Asia, focusing on the General Data Protection Regulation in Europe and various regulations such as the Act on the Protection of Personal Information in Japan and the Personal Information Protection Law in China.Perkara : Jurnal Ilmu Hukum dan Politik["Joni Laksito", "Berliant Pratiwi", "Widya Ariani"]2025-01-05T00:00:00 f29c63f121bdfd4df486eb5657071925a252444bArtificial Intelligence (AI) telah menjadi alat strategis untuk mendukung efisiensi dan inovasi dalam pelayanan publik, namun tantangan utama adalah kurangnya pemahaman dan keterampilan di kalangan pegawai pemerintahan. Program ini bertujuan untuk meningkatkan pemahaman, keterampilan praktis, dan kesadaran etis pegawai pemerintahan di Kota Palembang dalam memanfaatkan AI secara efektif dan bertanggung jawab. Kegiatan ini menyasar pegawai dari Disnaker Provinsi Sumatera Selatan, SAMSAT Kota Palembang 1, dan SAMSAT Kota Palembang 3, dengan metode pelaksanaan berupa sesi edukasi interaktif, pelatihan berbasis aplikasi AI, dan diskusi kelompok terarah. Hasil evaluasi menunjukkan peningkatan signifikan, dengan 83% peserta melaporkan pemahaman yang mendalam terhadap konsep dasar AI serta kemampuan untuk mengintegrasikan teknologi ini ke dalam tugas administratif seperti analisis data, penyusunan laporan, dan visualisasi informasi. Program ini tidak hanya memberikan keterampilan teknis tetapi juga berkontribusi pada pengembangan budaya kerja berbasis teknologi yang bijak, inovatif, dan sesuai kebutuhan transformasi digital di sektor pemerintahan.Room of Civil Society Development00Room of Civil Society Development["Alghifari Mahdi Igamo", "Azwardi Azwardi", "Waldi Novi Yarsah", "Liliana Liliana", "Agung Putra Raneo", "M. Ulum"]2025-01-05T00:00:00 9885eaaf673c1016f7e9b1218038884962662c76In the context of the growing influence of artificial intelligence (AI) in audiovisual narrative, several studies are observed that have addressed its impact. Santiago D. analyzes how AI imitates aesthetics of the past without contributing innovations, while Moya E. reflects on the ethical and creative challenges, and Franganillo J. explores the opportunities and risks of technology, such as disinformation and manipulation. The methodology employed includes a systematic review of literature in key databases, using rigorous criteria to select relevant studies. The results show that, although AI offers potential advances, its current use tends to reproduce nostalgic and retro styles. The conclusion underscores the paradox of AI in audiovisual narrative: while promising innovation, in practice it reinforces aesthetics of the past. It is essential to establish regulatory frameworks to harness its benefits and mitigate risks.   Received: 30 September 2024 / Accepted: 19 December 2024 / Published: 05 January 2025Journal of Educational and Social Research00The results show that, although AI offers potential advances, its current use tends to reproduce nostalgic and retro styles, which underscores the paradox of AI in audiovisual narrative: while promising innovation, in practice it reinforces aesthetics of the past.Journal of Educational and Social Research["Erick Giovanni Franco Lazarte", "Mar\u00eda Trinidad Ju\u00e1rez Paccotaipe", "Rosa Candelaria Ram\u00edrez Heredia", "Teresa Marlene Vela Loyola"]2025-01-05T00:00:00 d2e5ea3be07b9f4b675168a294c91895fdc5ea85Artificial Intelligence of Things (AIoT), yang merujuk pada penggunaan Internet of Things (IoT) untuk melakukan tugas-tugas cerdas dengan bantuan integrasi Artificial Intelligence (AI), merupakan salah satu inovasi yang dapat mengoptimalkan proses bisnis melalui digitalisasi dan otomatisasi, terutama dalam sektor pertanian cerdas. Mengingat potensi besar yang dimiliki AIoT, kajian literatur ini bertujuan untuk mengidentifikasi kontribusi AI dalam meningkatkan kinerja sistem IoT di sektor pertanian cerdas. Hasil kajian menunjukkan bahwa AI mampu menganalisis data secara real-time, mendeteksi pola, serta memberikan solusi otomatisasi yang mendukung pengelolaan sumber daya, seperti air dan pupuk, secara efisien. Teknologi ini memungkinkan petani mengambil keputusan dengan lebih cepat dan akurat, yang pada akhirnya berkontribusi pada peningkatan produktivitas, pengurangan pemborosan sumber daya, serta mendukung keberlanjutan sektor pertanian.JATI (Jurnal Mahasiswa Teknik Informatika)00JATI (Jurnal Mahasiswa Teknik Informatika)["Afiana Nurani", "Haura Taqiya Azza Nabila", "Ilham Bintang Herlambang"]2025-01-05T00:00:00 7f047cb48ee3fe527f206abe8ac0ff31ca10ae75This article explores the potential of artificial intelligence (AI) in fostering the development of functional literacy among students. Functional literacy, encompassing the ability to apply reading, writing, and analytical skills to real-life contexts, is crucial in modern education. AI tools offer personalized and interactive learning experiences, enabling students to engage with content more effectively and develop critical skills for problem-solving and decision-making. The study examines the application of AI-based technologies such as adaptive learning platforms, intelligent tutoring systems, and natural language processing tools in enhancing functional literacy. Challenges, including access to AI resources, teacher training, and ethical considerations, are also discussed. The findings highlight that integrating AI into educational processes can significantly improve functional literacy, making students more competent and prepared for real-world tasks.Eurasian Science Review An International peer-reviewed multidisciplinary journal00The study examines the application of AI-based technologies such as adaptive learning platforms, intelligent tutoring systems, and natural language processing tools in enhancing functional literacy.Eurasian Science Review An International peer-reviewed multidisciplinary journal["Gulnur Dosmaganbetova"]2025-01-05T00:00:00 6e6650c1ae120fd439a2c659f07a38ad208b2446The integration of artificial intelligence (AI) with building information modelling (BIM) technologies is revolutionising the architecture, engineering and construction (AEC) industry. This article explores the transformative potential of AI-based solutions in BIM, focusing on their ability to improve design accuracy, automate processes and optimise construction workflows. Key advances include predictive analytics, generative design and sustainability modelling, which enable professionals to create smarter, more efficient and more sustainable buildings. By analysing emerging trends and challenges, this research highlights the important role of AI in shaping the future of BIM technologies and the wider digital transformation of the AEC sector. This article also explores the successful integration of artificial intelligence (AI) into architectural and urban planning projects, highlighting real-world examples of its application, demonstrating how AI enhances design efficiency, sustainability, and innovation in the built environment.Eurasian Science Review An International peer-reviewed multidisciplinary journal00The transformative potential of AI-based solutions in BIM is explored, focusing on their ability to improve design accuracy, automate processes and optimise construction workflows, demonstrating how AI enhances design efficiency, sustainability, and innovation in the built environment.Eurasian Science Review An International peer-reviewed multidisciplinary journal["Dilnaz Talgatkyzy"]2025-01-05T00:00:00 ce1994d011c1be505b11f9017b8daa9ca338706eThis research examines the utilisation of artificial intelligence (AI) in sports damage anticipation and recovery, pointing to optimising competitor care and execution. Leveraging different datasets comprising execution measurements, biomechanical estimations, damage histories, physiological parameters, and natural components, four AI calculations were actualised and compared: Support Vector Machines (SVM), Random Forest, Recurrent Neural Networks (RNN), and Slope Boosting Machines (GBM). It comes about illustrating critical viability overall calculations, with RNN accomplishing the most elevated execution measurements. Exactness values for SVM, Irregular Timberland, RNN, and GBM were 0.85, 0.88, 0.90, and 0.87 separately, with comparing accuracy, recall, and F1-score values demonstrating strong prescient capabilities. These discoveries emphasise the potential of AI-driven approaches to precisely distinguish damage dangers and personalise recovery conventions custom-made to personal competitor needs. The comparative examination against existing strategies highlights the prevalent execution of AI calculations, emphasising the transformative effect of progressed advances in sports science and pharmaceuticals. Scalable Computing : Practice and Experience00This research examines the utilisation of artificial intelligence in sports damage anticipation and recovery, pointing to optimising competitor care and execution, with RNN accomplishing the most elevated execution measurements.Scalable Comput. Pract. Exp.["Rongchao Zou"]2025-01-05T00:00:00 c4bcefda1bd5d98f6382ab794c98eb1fa34cef8aBackground: As technological advancements continue to reshape various industries, the integration of AI in healthcare education emerges as a crucial facet in preparing future nursing professionals. This narrative review aims to elucidate the numerous ways AI technologies are being utilized in nursing education. Methodology: A search in two internet databases was conducted for relevant studies, using keywords. The inclusion criteria encompassed studies published within the last 5 years, written in English, and focused on the integration of AI technologies in nursing education settings. The selected articles underwent a systematic screening process. Results: Of the 523 papers retrieved, 7 were included in the final synthesis. These studies evaluate the implementation of AI methods in undergraduate nursing students. The AI method usually used was a Chatbot. In 4 studies, a 3D avatar was incorporated into the AI tool to serve as a Virtual Patient. The studies focused on various learning objectives, with 4 studies emphasizing communication skills enhancement. The remaining 3 studies used the AI tool to assess students' knowledge and clinical skills. Clinical scenarios were predominantly used, and in studies with a 3D avatar, scenarios addressed theoretical knowledge, critical thinking, and decision-making in escalating clinical conditions. Endpoints of AI implementation were assessed using self-reported questionnaires, interview and direct feedback from the Chatbot. Consistent endpoints included students' self-efficacy, knowledge of the learning objective, students' satisfaction and attitudes toward the learning style. Conclusions: As technology continues to advance, the potential for AI in nursing education is becoming increasingly evident. Given that nursing is an interactive science, it seems that AI Chatbots are more useful in nursing education. Further AI implementation will enrich our understanding of how its integration will serve nursing education.Health &amp; Research Journal300These studies evaluate the implementation of AI methods in undergraduate nursing students and it seems that AI Chatbots are more useful in nursing education.Health &amp; Research Journal["Aikaterini Kouka", "Evangelia Giannelou", "K. Konstantinidis", "Ioannis Apostolakis"]2025-01-05T00:00:00 b5649da23c1907cc8f7f824d42f12240934084dfArtificial Intelligence (AI) is transforming higher education by introducing innovative tools and methodologies that enhance teaching and learning processes. This paper explores the opportunities AI provides, such as personalized learning, intelligent tutoring systems, automated assessments, and data-driven decision-making. These technologies enable educators to focus on strategic and creative tasks while improving the efficiency and accessibility of education.However, the integration of AI into higher education also presents significant challenges. Ethical concerns, data privacy, and the digital divide are among the most pressing issues. Furthermore, the adaptation of faculty and students to AI-based technologies requires comprehensive training and a cultural shift in educational institutions. This study discusses how higher education institutions can balance these opportunities and challenges to effectively incorporate AI into their ecosystems.The research highlights the importance of collaboration between technology developers, educators, and policymakers to ensure AI’s ethical and equitable implementation in higher education. It provides recommendations for leveraging AI to foster innovation, improve educational outcomes, and address potential risks.Eurasian Science Review An International peer-reviewed multidisciplinary journal20The research highlights the importance of collaboration between technology developers, educators, and policymakers to ensure AI’s ethical and equitable implementation in higher education and provides recommendations for leveraging AI to foster innovation, improve educational outcomes, and address potential risks.Eurasian Science Review An International peer-reviewed multidisciplinary journal["Nur Rafi Abdurohman"]2025-01-05T00:00:00 45fb29c01f87e5560d0868e30c8b676b238bcbb5Business &amp; Information Systems Engineering730The results contribute to IS research on algorithmic accountability and IS development by revealing the distinct nature of process and outcome accountability while demonstrating the effectiveness of tailored arguments as governance tools and methods in AI systems development.Business &amp; Information Systems Engineering["Jan-Hendrik Schmidt", "S. Bartsch", "Martin Adam", "Alexander Benlian"]2025-01-05T00:00:00 598e54c8f328bd309bf402b9c1d6c616e97011b9In today's rapidly evolving digital landscape, the fusion of Artificial Intelligence (AI) with marketing has opened new avenues for creating personalized consumer experiences. One of the most innovative advancements in this realm is the use of Personalized Emotional AI. This technology harnesses the power of AI to analyze and respond to consumers' emotions in real-time, allowing brands to tailor their marketing strategies in ways that resonate deeply on an emotional level. By interpreting facial expressions, voice tones, and other biometric data, Emotional AI enables businesses to craft messages that connect with consumers' feelings, enhancing engagement and fostering stronger brand loyalty. This study explores the transformative potential of Personalized Emotional AI in modern marketing. Through a comprehensive analysis of its applications across various industries, the research delves into how this technology can elevate consumer interactions from merely transactional to profoundly relational. The findings indicate that when brands effectively utilize Emotional AI, they not only capture attention but also build lasting emotional bonds with their audiences. However, the implementation of Emotional AI comes with ethical considerations, particularly regarding privacy and the potential for emotional manipulation. This study underscores the importance of balancing innovation with responsibility, ensuring that the use of Emotional AI is transparent and respects consumer trust. By adopting a human-centered approach to AI, brands can harness its full potential to create meaningful, emotionally intelligent marketing campaigns that resonate with consumers on a deeper level, driving both engagement and loyalty.Journal of Dynamics and Control00The research delves into how this technology can elevate consumer interactions from merely transactional to profoundly relational, and indicates that when brands effectively utilize Emotional AI, they not only capture attention but also build lasting emotional bonds with their audiences.Journal of Dynamics and Control["Dr. Padmavathy.A, M", "Dr. R.Aarthi Alamelu"]2025-01-05T00:00:00 c01345b0e40c8d6e09a1d132486d0539027eb7ebThis study investigates how Artificial Intelligence (AI) and eco-innovation could revolutionize non-heritage cultural product design, primarily through modern furniture and related industries. It aims to address the growing demand for durable and culturally adaptive products in contexts where historical preservation rules are not applicable, allowing for greater openness to innovation. The mixed-methods study draws on qualitative data (interviews with designers, AI engineers, and sustainability practitioners) and quantitative sustainability data (material efficiency, waste minimization, lifecycle length). The findings underscore the pivotal role of AI in eco-innovation processes, leveraging advanced solutions such as lifecycle management systems, predictive analytics, and adaptive design paradigms. These technologies also reduce waste materials by up to 30% in some sectors, optimize energy use, and boost the lifecycles of products by 25% (see example cases). Apart from being environmentally friendly, AI also raises the cultural value of non-heritage goods by considering society and regional tastes, which enables designers to develop flexible goods that reflect today’s consumer values and are environmentally sustainable. This fusion of environmental and cultural flexibility makes non-heritage products key players in a sustainable future. The paper’s outputs include the creation of a conceptual framework for AI and eco-innovation integration that gives designers pragmatic tips on using AI tools in the context of sustainable product design. It also defines methods for industry stakeholders to leverage AI across the production workflow and recommendations for policy measures to foster adopting a sustainable AI system with incentives and standardized standards. Drawing a parallel between theoretical thinking and application, this work underlines AI’s potential to transform cultural product sectors, paving the way for more widespread sustainable development in non-heritage design sectors. Journal of Next-Generation Research 5.000Journal of Next-Generation Research 5.0["Kholoud Ghaith"]2025-01-05T00:00:00 d686c917bb85e66bc633ab7d227254cb6996bf6aGenerative artificial intelligence (AI) has become a major research trend in the fields of education and psychology. However, several risks posed by this technology concerning the cognitive and socio‐emotional development of children and adolescents have been identified. While it would be highly useful to have a clear understanding of these potential negative effects, empirical results cannot be obtained without putting the participants of these studies in a situation that potentially endangers their development. Research fields such as the biomedical sciences utilize several measures to minimize risks, such as dose escalation and stopping rules. In addition, dynamic and flexible forms of informed consent could be adopted by our field to maximize transparency. By including methodological advancements and ethical developments in the psychological and educational research process, risks could be averted, and the ethical soundness of AI research involving children and adolescents could be maintained.Mind, Brain, and Education170By including methodological advancements and ethical developments in the psychological and educational research process, risks could be averted, and the ethical soundness of AI research involving children and adolescents could be maintained.Mind, Brain, and Education["Alexander Skulmowski"]2025-01-05T00:00:00 e91f01a13fbb016907e72787c2df6226d1a282f3The objective of this analysis is to address the challenges encountered by pricing systems in managing real-time market dynamics. This study presents a fundamental theoretical framework focused on taxonomy and ontology for a domain-specific multi-agent artificial intelligence (AI), serving as an internal price advisor to optimize pricing strategies for products and services. The system is designed to function in conjunction with other corporate AI systems and an Enterprise Resource Planning System (ERP). The ERP serves as a high-quality data foundation, and several other internal and external sources can provide essential data with varying quality. Methods: The proposed AI model builds upon the Weighted Dynamic Corridor Price Optimization framework, which integrates cost-plus and value-based pricing methodologies within a non-linear price corridor bounded by lower and upper thresholds. In the context of supply chain integration, fully-cooperative pricing models can apply Nash equilibrium to enhance supply chain profitability, whilst semi-cooperative models mitigate information asymmetry through the principal-agent theory. The findings from the theoretical analysis of the generic industry- and product-agnostic multi-agent AI system suggest the system’s potential capacity for dynamically computing optimal prices. A generative AI module could facilitate real-time decision-making, enabling sales teams and similar stakeholders to simulate scenarios and refine pricing strategies. In conclusion, the proposed AI system should be capable of delivering adaptive, context-aware, and data-driven recommendations. Depending on its application, the AI system could become very complex, susceptible to errors, and require significant maintenance. Future research should focus on customizing the proposed AI system for specific industries and product categories and validating its applicability through empirical research.Journal of Next-Generation Research 5.000The findings from the theoretical analysis of the generic industry- and product-agnostic multi-agent AI system suggest the system’s potential capacity for dynamically computing optimal prices.Journal of Next-Generation Research 5.0["Walter Kurz"]2025-01-05T00:00:00 84afd6829ab3535fbfb024fa043b36bd33b5e8c7The field of critical infrastructure has undergone significant expansion over the past three decades, spurred by global economic liberalization and the pursuit of development, industrialization, and privatization by nations worldwide. This rapid growth has led to a proliferation of critical infrastructure across various sectors, necessitating decentralization efforts to manage the associated burdens effectively. With the advent of artificial intelligence and machine learning, computer scientists have sought innovative approaches to detect and respond to the evolving landscape of cyber threats. Despite efforts to subscribe to these changes, attackers continually devise new methods to evade detection, requiring constant vigilance and adaptation from cybersecurity professionals. Traditional centralized models of machine and deep learning demand substantial data and computational resources, making them susceptible to single-point failures. To address these challenges, scientists have introduced federated learning—a decentralized technique that minimizes computational costs while prioritizing data privacy and preservation. This review article delves into recent research and review papers concerning critical infrastructure security and federated learning, exploring various architectures, threats, vulnerabilities, and attack vectors. Through our analysis, we provide a comprehensive overview of federated learning, cyber-physical systems security, and the advantages of integrating federated learning into critical infrastructure environments. By synthesizing insights from diverse sources, our study contributes to a deeper understanding of federated learning's applications and implications in safeguarding critical infrastructures. We highlight the potential of federated learning to enhance cybersecurity measures while addressing the unique challenges posed by modern-day threats. As organizations and nations navigate the complexities of securing their critical assets, the adoption of federated learning emerges as a promising strategy to bolster resilience and protect against emerging cyber risks.Scalable Computing : Practice and Experience00The potential of federated learning to enhance cybersecurity measures while addressing the unique challenges posed by modern-day threats is highlighted, as a promising strategy to bolster resilience and protect against emerging cyber risks is highlighted.Scalable Comput. Pract. Exp.["Muhammed Rafeeq War", "Yashwant Singh", "Zakir Ahmad Sheikh", "Pradeep Kumar Singh"]2025-01-05T00:00:00 b9ae22a3cf8de9fcdadb133ba0d99f86e4f7456bLa aparición de las tecnologías computacionales en el ámbito educativo ha transformado radicalmente los procesos de enseñanza y aprendizaje. La inteligencia artificial (IA), en particular, ha emergido como un motor de cambio, generando un intenso debate sobre sus implicaciones en la docencia. Si bien la IA ofrece un potencial inmenso para personalizar la educación, facilitar el acceso al conocimiento y optimizar la gestión de los recursos educativos, su integración plantea una serie de desafíos y dilemas éticos. Uno de los principales riesgos radica en la posibilidad de que los estudiantes desarrollen una dependencia excesiva de estas herramientas, lo que podría afectar el desarrollo de habilidades cognitivas fundamentales como el pensamiento crítico, la creatividad y la resolución de problemas.Vida Científica Boletín Científico de la Escuela Preparatoria No. 400Vida Científica Boletín Científico de la Escuela Preparatoria No. 4["Jessica Angelica Barrera Pacheco"]2025-01-05T00:00:00 c1591141bff5ea8d2e073e6aa0e2975f012bfa5dO artigo analisa o impacto da inteligência artificial (IA) e da automação no mercado de trabalho, abordando as transformações tecnológicas e suas consequências no setor empregatício. Ao longo da história, diversas revoluções industriais moldaram a dinâmica do trabalho, sendo a terceira revolução industrial, marcada pelo avanço da automação e da IA, uma das mais impactantes. As inovações tecnológicas substituíram muitas tarefas humanas, provocando a redução de empregos em setores rotineiros, mas também criando novas oportunidades em áreas especializadas como ciência de dados e desenvolvimento de IA. O estudo destaca a crescente necessidade de qualificação profissional, pois os trabalhadores precisam se adaptar às novas demandas do mercado, o que pode aumentar a desigualdade socioeconômica. Enquanto as classes mais altas têm acesso a melhores oportunidades de capacitação, trabalhadores de classes mais baixas enfrentam dificuldades para se reposicionar no mercado, aumentando o desemprego em certos setores. No entanto, essas inovações podem trazer benefícios econômicos, promovendo o desenvolvimento socioeconômico e aumentando a competitividade do país. O artigo conclui que, para maximizar os benefícios e mitigar os impactos negativos, é necessária uma maior ênfase em políticas públicas e privadas que fomentem a educação e o treinamento, bem como o incentivo à inovação nacional, garantindo uma transição mais inclusiva para toda a sociedade.REVISTA CIENTÍFICA ACERTTE - ISSN 2763-892800REVISTA CIENTÍFICA ACERTTE - ISSN 2763-8928["T. S. S. Oliveira"]2025-01-05T00:00:00 595d89faee64cdd7aaa6b5183934bc626a92bcaaEstamos en el año 2025, a poco más de dos años del debut en el escenario global de una de las herramientas más populares de inteligencia artificial generativa (IAGen), ChatGPT de la empresa OpenAI. La irrupción de esta poderosa plataforma, además de otras diseñadas por diversas organizaciones, ha generado una gran cantidad de especulaciones y reflexiones sobre sus implicaciones éticas en varios aspectos de la vida, incluyendo la educación. Si bien la explosión de artículos, libros, preprints, conferencias, congresos y seminarios web sobre el tema ha producido un cierto nivel de hastío en la comunidad académica, no podemos eludir el hecho de que la IAGen llegó para quedarse y que se ha introducido en muchas facetas de nuestra cotidianidad. Por ello es necesario continuar la conversación sobre su uso e implicaciones para el proceso educativo en profesiones de la saludInvestigación en Educación Médica00Investigación en Educación Médica["Melchor S\u00e1nchez Mendiola"]2025-01-05T00:00:00 e9eff1c4704920e37aae9c55ae792fef0c25befcCada vez mais a área da alimentação está em alta na multidisciplinaridade dos estudos científicos, de forma a desenvolver atividades de aprimoramento profissional, e isso em todas as fases da produção de alimentos, do atendimento ao cliente à criação de novos produtos, onde a inteligência artificial (IA) está revolucionando e exigindo a atualização desses profissionais de forma a cobrir as necessidades de mercado atual. Esse artigo tem o objetivo de realizar uma revisão bibliográfica exploratória como técnica de coleta de dados, numa investigação sobre a inteligência artificial (IA) aplicada no setor de alimentos e bebidas, enfatizando as possibilidades que são oferecidas pela inteligência artificial atualmente, sua importância, os benefícios e as dificuldades de sua implantação, englobando da produção até os serviços aos consumidores. A pesquisa foi realizada na base de dados da plataforma Web of Science da Thomson Reuters e, publicadas em âmbito internacional. Foram encontrados 24 artigos, dos quais 4 deles estavam alinhados ao objeto de estudo e merecem destaque por demonstrarem ferramentas de possibilidades da IA ligadas ao tema em questão. Percebeu-se com a investigação que a descoberta e aplicação de técnicas de inteligência artificial na área de alimentos e bebidas, tanto em sua produção, como nos serviços aos consumidores, obtém melhores resultados e uma excelência na agilidade nos produtos e serviços. Porém, há ainda atualmente, dificuldades de aplicação dessas IA’s por inabilidades dos profissionais, necessitando uma maior capacitação na área em questão, além de rejeição de uso de alguns produtores e consumidores.Research, Society and Development00Research, Society and Development["Carlos Patr\u00edcio Vidal de Souza", "E. Alencastro"]2025-01-05T00:00:00 4f9c2c3a694f8b6005d364e1892f90d78797a1a9Esta investigación está orientada a determinar la influencia que la inteligencia artificial - IA tiene actualmente en el ejercicio de la profesión contable, mediante un enfoque cuantitativo, que se fundamenta en la medición de las características de los fenómenos sociales, para conocer los pros y los contras que esta tecnología emergente causa en la sociedad contable ecuatoriana.  De la presente investigación se desprende que la llegada de la IA, ha generado entre los aspectos más relevantes, se tiene que los profesionales contables creen que ha mejorado notablemente el registro de la información y los tiempos para la obtención de los resultados, sin embargo no hay confianza total para la toma de decisiones, por otro lado no están capacitados la mayoría para utilizar las bondades de esta herramienta tecnológica, notándose la falta de recursos económicos para capacitar al personal e invertir en la implementación de la IA.  También se puede destacar que la IA genera nuevas oportunidades e ideas de negocios como por ejemplo las consultorías, ampliando el mercado en este campo.Ciencia Latina Revista Científica Multidisciplinar00Ciencia Latina Revista Científica Multidisciplinar["Mercedes Salvador", "Christian Mart\u00ednez"]2025-01-05T00:00:00 afaf72985b8a1048ff920e47ca6b046f872826bbLa inteligencia artificial (IA) está transformando la educación, especialmente a través de la Nueva Escuela Mexicana (NEM), que se enfoca en el bienestar integral de los estudiantes. Este modelo educativo busca no solo desarrollar competencias académicas, sino también habilidades socioemocionales, reconociendo la importancia del bienestar emocional en el rendimiento académico. La IA ya se utiliza en diversas plataformas educativas para personalizar el aprendizaje, adaptándose a las necesidades de cada estudiante. Sin embargo, el desafío radica en integrar las emociones en estas dinámicas. El reconocimiento emocional a través de la IA podría mejorar la motivación y la retención de información, ofreciendo una experiencia educativa más efectiva. La NEM considera fundamental la educación emocional para fomentar competencias como la empatía y la resiliencia. La IA puede ayudar a identificar necesidades emocionales y facilitar intervenciones oportunas. Además, herramientas como chatbots pueden brindar apoyo emocional a los estudiantes. No obstante, la implementación de IA en la educación plantea desafíos éticos, como la privacidad de los datos emocionales y la deshumanización del aprendizaje. Es crucial que la IA complemente, en lugar de reemplazar, el juicio humano. Por ello, la integración de la IA en la NEM tiene el potencial de enriquecer el aprendizaje emocional, siempre que se realice de manera ética y centrada en el bienestar de los estudiantes. Esto podría llevar a un entorno educativo más empático y efectivo, alineado con los principios humanos.Con-Ciencia Boletín Científico de la Escuela Preparatoria No. 300Con-Ciencia Boletín Científico de la Escuela Preparatoria No. 3["Olivia V\u00e1zquez Bautista"]2025-01-05T00:00:00 96b7cba384c63dfc5eb0ac820e39590d38306650Actualmente la Inteligencia Artificial está siendo un elemento transformador importante en múltiples casos y la educación no es la excepción. La IA surge como un fuerte instrumento en la educación, en la forma en que se enseña y se aprende. Esta herramienta (IA) está impactando y transformando la perspectiva educativa en todos los niveles.Vida Científica Boletín Científico de la Escuela Preparatoria No. 400Vida Científica Boletín Científico de la Escuela Preparatoria No. 4["Daniela P\u00e9rez T\u00e9llez Gir\u00f3n"]2025-01-05T00:00:00 e8b54f97df4b14a66d0bb84d9a6c98aa16ab330eThe application of artificial intelligence in management information systems provides effectiveness and efficiency in business processes. The integration of artificial intelligence and management information systems has changed the procedures for collecting data, analyzing data, and utilizing data. This research aims to explain the application of artificial intelligence in management information systems and identify ethical challenges and social impacts arising from the application of artificial intelligence in management information systems. The research method used in this research is the library study method, which is a research approach that involves collecting and analyzing information available in forms such as articles, reference books and other reference sources. The results of the research are that the application of artificial intelligence provides convenience by automating routine tasks thereby increasing user efficiency and effectiveness. However, apart from providing convenience, there are ethical challenges and social impacts that arise from the application of artificial intelligence in management information systems, such as threats to data security and increasingly narrow job opportunities.Jurnal Riset Sistem Informasi01The results of the research are that the application of artificial intelligence provides convenience by automating routine tasks thereby increasing user efficiency and effectiveness and there are ethical challenges and social impacts that arise from the application of artificial intelligence in management information systems.Jurnal Riset Sistem Informasi["Tri Setia Ningrum", "Vika Dias Anspratiwi", "Muhamad Wahyudi"]2025-01-06T00:00:00 99ee73356d54e6f1b06e52298f46c9d400ae30acEuropean Journal of Clinical Microbiology and Infectious Diseases781A systematic review and meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC, accuracy, sensitivity, specificity, negative predictive value, and positive predictive value across various AMS settings.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology["Flavia Pennisi", "Antonio Pinto", "Giovanni Emanuele Ricciardi", "Carlo Signorelli", "V. Gianfredi"]2025-01-06T00:00:00 efb78e121dbcd669d1923f994a44f00aaee3e91fAI &amp; SOCIETY400This research contributes insights into the desires and concerns of educational users regarding AI adoption, highlighting potential barriers such as value alignment.AI &amp; SOCIETY["Neill Dixon", "Andrew Cox"]2025-01-06T00:00:00 7c16110bc76e82c1997941fa453fb7bb98be46f5Production management serves as a pivotal component in the operational activities of oilfield sites, with the effectiveness of management practices directly influencing the success of developmental outcomes. To enhance the maintenance-free operational period of oil production systems, elevate management standards, and reduce overall operational costs, advanced technologies such as artificial intelligence (AI) and big data analytics have been strategically integrated into oilfield operations. These technologies are able to incorporate data resources from all stages of oilfield production, thus providing a comprehensive view of oilfield production and guidance for production. This study uses a series of diagnostic and predictive methods to construct a management system that allows for the comprehensive monitoring and fault diagnosis of oil production systems, which can ensure the intelligent management of oil production systems at multiple levels throughout their life cycle. Automated monitoring workflows and proactive analytical processes are at the heart of the framework, enabling real-time monitoring and predictive decision-making. This not only minimizes the likelihood of system failure but also optimizes resource allocation and operational efficiency.Processes360This study uses a series of diagnostic and predictive methods to construct a management system that allows for the comprehensive monitoring and fault diagnosis of oil production systems, which can ensure the intelligent management of oil production systems at multiple levels throughout their life cycle.Processes["Xianfu Sui", "Xin Lu", "Yuchen Ji", "Yang Yang", "Jianlin Peng", "Menglong Li", "Guoqing Han"]2025-01-06T00:00:00 60dab89b67ef34141b07484bf0b53d9378910046This study explores the transformative role of Artificial Intelligence (AI) in project management, focusing on its potential to balance automation with human judgment. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 65 high-quality articles were systematically reviewed to ensure a transparent and rigorous analysis. The findings reveal that AI significantly enhances project management practices by automating repetitive tasks, optimizing resource allocation, and improving stakeholder engagement and communication. Predictive analytics and machine learning tools have demonstrated considerable effectiveness in proactive risk management and decision-making, particularly in complex and large-scale projects. Furthermore, AI-powered platforms facilitate real-time collaboration, fostering transparency and trust among stakeholders. However, challenges such as over-reliance on automation, ethical concerns, and adoption barriers were also identified, highlighting the importance of integrating AI with human expertise. By synthesizing insights from diverse industries and contexts, this study provides a comprehensive understanding of AI’s benefits, limitations, and opportunities in project management. The findings contribute valuable recommendations for organizations seeking to leverage AI technologies while maintaining accountability and ensuring sustainable integration.Innovatech Engineering Journal00The findings reveal that AI significantly enhances project management practices by automating repetitive tasks, optimizing resource allocation, and improving stakeholder engagement and communication.Innovatech Engineering Journal["Md Al-Arafat", "Md Enamul Kabir", "Asm Morshed", "Muhammad Mohiul Islam"]2025-01-06T00:00:00 91276285e9ff9ea1897acb3d32cb93f6f44d32e7The integration of Artificial Intelligence (AI) into Chinas New Energy Vehicle (NEV) industry presents both unprecedented opportunities and significant challenges. Through analyzing the present situation of the NEV market in China, this paper identifies key challenges posed by AI, and proposes strategic countermeasures. Even though the NEV industry is growing rapidly, there are still a number of significant obstacles that NEVs must overcome. Technologically, algorithms, computing power and sensor fusion are critical. Regulatory challenges include preventing data leakage and protecting privacy. Economically, the need for substantial investments in infrastructure poses significant hurdles. Socially, changing market operation and reskilling the workforce are essential. The challenges faced by New Energy Vehicles (NEVs) will drive significant technological innovation and cooperation, particularly in algorithm optimization, hardware upgrade and sensor fusion technology. Regulatory influences will push for network security and enhancing safety and compliance. Economic impacts include the need to improve charging network, build an intelligent transport system and improve information and communication infrastructure. Socially, addressing market operation and workforce reskilling will be crucial. Overall, these challenges will shape market dynamics, competitive landscapes, and global supply chains, ultimately driving the NEV sector towards more sustainable and efficient solutions, and by these measures, China can fortify its NEV industry against AI-related challenges and achieve sustainable growth.Advances in Economics, Management and Political Sciences00China can fortify its NEV industry against AI-related challenges and achieve sustainable growth through strategic countermeasures, according to this paper.Advances in Economics, Management and Political Sciences["Zihan Zhang"]2025-01-06T00:00:00 ce79f0b71072f4458663ee0ecd45fde61d620214Despite artificial intelligence's (AI) increased efficiency and accuracy in many contexts, algorithm aversion, that is, people's biased preference for human recommendations over those of algorithms, is a well‐documented phenomenon. In this research, we show a reversal of the algorithm aversion phenomenon, referred to as algorithm appreciation, in the prevalent context of marketplace discrimination. Specifically, the current research documents people's increased propensity to rely on AI‐based recommendations over those proposed by human counterparts in the aftermath of marketplace discrimination. Such an increased preference happens because it serves as a coping strategy for consumers who have faced discrimination in the marketplace from other human actors. The results of a series of three lab studies and one field study provide consistent support for the proposed effect and document the underlying psychological mechanism driving this effect through perceived embarrassment. Using a moderated‐mediation model, we identify a boundary condition of the effect by demonstrating that the focal effect, that is, algorithm appreciation, remains valid under public consumption but diminishes under private consumption. Employing the natural setting of the field, we replicate our findings with actual consumers making real choices. Our findings have important implications (e.g., integrating AI‐driven recommendation systems into firms' platforms in sectors susceptible to marketplace discrimination and developing ethical guidelines for AI systems) for managers and companies.The Journal of product innovation management1180A reversal of the algorithm aversion phenomenon is shown, referred to as algorithm appreciation, in the prevalent context of marketplace discrimination by demonstrating that the focal effect, that is, algorithm appreciation, remains valid under public consumption but diminishes under private consumption.Journal of Product Innovation Management["Arash Talebi", "Sourjo Mukherjee", "Nazia Gera", "Kulwinder Kaur", "Gopal Das"]2025-01-06T00:00:00 c4cdd704768868167b161ef4cbce275de7e160d9 Algorithmic human resource management (AHRM), the automation or augmentation of human resources-related decision-making with the use of artificial intelligence (AI)-enabled algorithms, can increase recruitment efficiency but also lead to discriminatory results and systematic disadvantages for marginalized groups in society. In this paper, we address the issue of equal treatment of workers and their fundamental rights when dealing with these AI recruitment systems. We analyse how and to what extent algorithmic biases can manifest and investigate how they affect workers’ fundamental rights, specifically (1) the right to equality, equity, and non-discrimination; (2) the right to privacy; and, finally, (3) the right to work. We recommend crucial ethical safeguards to support these fundamental rights and advance forms of responsible AI governance in HR-related decisions and activities.Business and Human Rights Journal340The issue of equal treatment of workers and their fundamental rights when dealing with AI recruitment systems is addressed and crucial ethical safeguards are recommended to support these fundamental rights and advance forms of responsible AI governance in HR-related decisions and activities.Business and Human Rights Journal["M. Capasso", "Payal Arora", "Deepshikha Sharma", "Celeste Tacconi"]2025-01-06T00:00:00 79dbd3f6686e2675ca7a6248fc03266938a3b985The article examines the legal aspects of ensuring the patient’s right to informed voluntary consent in the provision of psychiatric care using artificial intelligence (AI) systems. Overall, the use of AI opens new possibilities for the diagnosis and treatment of mental disorders, offering significant potential to enhance the effectiveness of psychiatric care. However, the application of these technologies introduces various risks for patients, particularly concerning the protection of autonomy, the transparency of AI algorithms, and the security of personal data. Patients with mental disorders represent a particularly vulnerable group requiring additional legal guarantees in decision-making regarding treatment, especially when innovative technologies are involved. Based on an analysis of existing technologies, the authors identify a number of risks associated with the use of AI systems in psychiatric care, including: 1) violations of personal data confidentiality; 2) risks associated with decisions made by AI systems; 3) potential discrimination based on gender, race, religion, or other characteristics; 4) misuse in medical practice through the use of AI; 5) risks arising from malfunctions in AI systems; 6) other potential hazards. To mitigate these risks, the article considers legal regulatory measures, including the introduction of European legislation such as the AI Act, certification implementation, and the establishment of effective mechanisms for informed voluntary consent to AI use in psychiatry, given the high risks posed by this technology. The authors note that Ukrainian legislation currently lacks adequate mechanisms for obtaining informed consent in the use of AI for psychiatric care. The article proposes improvements to Ukrainian regulatory acts through the development of a separate consent form for the use of AI systems in psychiatric assessment or treatment, which would help to avoid the legal risks inherent in AI systems. Such a consent form would include detailed information for the patient about the specific AI systems to be used, their nature, purpose, and estimated duration of use. It would also inform the patient that the data collected and processed by the AI system would be protected according to data protection legislation, and it would include a verbal explanation of risks by the physician, as well as the options for choosing alternative treatment methods based on the doctor’s recommendations. The conclusions emphasize the importance of advancing national legislation to align with the AI Development Concept and international certification standards. This will ensure the protection of patients’ rights and foster the effective integration of AI in the field of psychiatric care.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law["K. Beznos", "N. V. Fedorchenko"]2025-01-06T00:00:00 806806bf63f0fe2d85ccc3876392ced9732a8acfThe integration of Artificial Intelligence (AI) into geosciences has ushered in a transformative era for spatial modeling and climate-induced hazard assessment. This study explores the application of Explainable AI (XAI) to address the inherent limitations of traditional "black-box" AI models, emphasizing transparency and interpretability in high-stakes domains such as natural hazard management. By analyzing hydrometeorological hazards—including droughts, floods, and landslides—this work highlights the growing potential of XAI to improve predictive accuracy and facilitate actionable insights. The research synthesizes advancements in XAI methodologies, such as attention models, Shapley Additive Explanations (SHAP), and Generalized Additive Models (GAM), and their application in spatial hazard prediction and mitigation strategies. Additionally, the study identifies challenges in data quality, model transferability, and real-time explainability, proposing pathways for future research to enhance XAI's utility in decision-making frameworks. This comprehensive overview contributes to bridging gaps in the adoption of XAI, enabling robust, transparent, and ethical approaches to climate hazard assessments in an era of rapid environmental change.International Journal for Sciences and Technology170This study explores the application of Explainable AI (XAI) to address the inherent limitations of traditional "black-box" AI models, emphasizing transparency and interpretability in high-stakes domains such as natural hazard management.International Journal on Science and Technology["Chalamalla Nikhitha Reddy"]2025-01-06T00:00:00 768df44069d52e63577c476921059b428fac7dceAnnals of Biomedical Engineering110ChatGPT demonstrates promise as a supplementary decision-making support tool for physical therapy, with good accuracy and reliability in aligning with clinical practice guideline recommendations.Annals of biomedical engineering["Jie Hao", "Zixuan Yao", "Yaogeng Tang", "Andr\u00e9as Remis", "Kangchao Wu", "Xin Yu"]2025-01-06T00:00:00 16d20dc701a70a4c8697ddeb628ed2fdb96c8dbf This paper aims to provide the integrated model with artificial intelligence (AI) awareness for the betterment of the higher education system in crisis i.e. fear of contagious viruses (different kinds of flu, monkeypox, chickenpox, COVID-19, etc.) corollaries in developing nations where the quality of education depends on teachers’ commitment, stress and the turnover intention. This empirical investigation employs a self-administered survey distributed among the faculty members within higher education institutions (HEIs) of the Punjab province and the Federal Capital Territory (FCT) Islamabad, Pakistan. The final sample of 622 faculty members was collected through convenience sampling, and structural equation modeling was performed with SmartPLS to assess the proposed model. The study reveals that remote work significantly enhances organizational commitment while concurrently lowering the turnover intention. Conversely, perceived work stress negatively impacts organizational commitment but positively influences turnover intention. Organizational commitment partially mediates between perceived work stress and turnover intention but exhibited no mediation between remote work and turnover intention. Notably, fear of contagious viruses and AI awareness positively moderate and amplify both the perceived work stress with turnover intention and remote working with organizational commitment, respectively. The current study extends the AI-mediated social exchange theory (MET) by observing faculty members of HEIs in the context of remote working, perceived work stress, commitment, turnover intention, fear of contagious viruses and AI awareness. Moreover, the successful application of AI-MET extended the researcher’s understanding of quality education in crisis. The study offers several contributions including applications of technical skills with AI awareness among faculty members to provide quality education for society’s welfare. Moreover, HEIs should arrange training programs for performance enhancement. This research provided a quality-based model for HEIs for developing nations to deal with forthcoming calamities of contagious viruses and deliver quality education through remote working during lockdown. Nowadays, off-campus education during calamity situations has been an alternative to on-campus education. Therefore, HEIs must introduce AI awareness to increase the dedication of faculty members toward society’s welfare with the utilization of full effort. Quality Education for All1350A quality-based model for HEIs for developing nations to deal with forthcoming calamities of contagious viruses and deliver quality education through remote working during lockdown is provided.Quality Education for All["Muhammad Asif Zaheer", "T. Anwar", "Mohamed Albeshr", "Maryam Manzoor", "Zoia Khan"]2025-01-06T00:00:00 520a17accab7e2ff89354d4db4aeb2ed38c46450Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI’s transformative potential in sepsis care.Frontiers in Medicine1370The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles, as healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes.Frontiers in Medicine["Fang Li", "Shengguo Wang", "Zhi Gao", "Maofeng Qing", "Shan Pan", "Yingying Liu", "Chengchen Hu"]2025-01-06T00:00:00 6150e116a794d63172551f90ff475d2fe95e67abArtificial intelligence (AI) has become a transformative technology in medical diagnostics, enabling enhanced analysis of complex clinical data and supporting precise, efficient decision-making across diverse disease areas. This study explores the multi-disease application of AI in diagnosing cancer, cardiovascular diseases, neurological disorders, and infectious diseases, focusing on its role in improving diagnostic accuracy, speeding diagnostic processes, and facilitating early disease detection. By employing machine learning, deep learning, and neural network models, this study critically examines the performance of specific models – such as recurrent neural networks and support vector machines – in diverse healthcare contexts. Challenges addressed include data privacy, annotated dataset needs, overfitting risks, and ethical concerns such as AI bias and transparency, all of which are fundamental to ensuring patient safety and health equity. In addition, this study integrates security considerations, such as fault detection in cryptographic architectures, providing insights into the resilience of AI systems in healthcare. Future research directions, including the potential of AI in real-time patient monitoring, personalized medicine, and multispectral imaging, are proposed to expand AI’s utility in diagnostics. A comparative evaluation with traditional clinical diagnostics underscores AI’s validation potential, emphasizing its need for robust regulatory frameworks, particularly concerning global health standards (e.g., TRIPOD-AI and CONSORT-AI) and data privacy regulations such as Health Insurance Portability and Accountability Act and General Data Protection Regulation. Ultimately, AI-driven diagnostic systems show strong promise to revolutionize medical practice and improve patient outcomes, contingent on addressing the technical, ethical, and regulatory challenges involved. This research supports AI’s growing role in healthcare, providing a foundational understanding of both its current contributions and future potential across disease-specific applications.Artificial Intelligence in Health00This study explores the multi-disease application of AI in diagnosing cancer, cardiovascular diseases, neurological disorders, and infectious diseases, focusing on its role in improving diagnostic accuracy, speeding diagnostic processes, and facilitating early disease detection.Artificial Intelligence in Health["Zarif Bin Akhtar"]2025-01-06T00:00:00 522e41595286d1baae7b098c7d32b1bca032fcf2The article addresses fundamental legal issues in the field of intellectual property arising from the rapid development of generative artificial intelligence. It explores the challenges posed to copyright law by the widespread adoption of artificial intelligence technologies. In particular, the study examines the legal regime of objects created with the assistance of artificial intelligence, the legality of using copyright-protected works for training AI models, and specific issues related to the legal personality of AI. Based on an analysis of international judicial practice and contemporary regulatory instruments, including the recently adopted European Union Artificial Intelligence Regulation, the study examines ambiguous approaches to determining copyright for AI-generated objects. Practical cases from various jurisdictions, including court decisions from the United States and China, are analyzed to illustrate the variability in legal assessments regarding the interaction of intellectual property law and AI. The article offers an analysis of promising mechanisms for regulating legal relations in the field of study. These include the introduction of a collective rights management system (similar to extended or mandatory collective management), the establishment of mechanisms for fair compensation to rights holders, and the development of technical tools for identifying the origin of content. The study underscores the necessity of balancing the interests of AI technology developers, authors, and society’s technological advancement needs. The article traces trends in legislative changes in the field of intellectual property and the regulation of artificial intelligence. In particular, it examines the provisions of the Artificial Intelligence Act, which imposes obligations on AI model developers to comply with copyright laws, including the requirement to disclose detailed reports on the content used for training AI models. The article emphasizes the importance of creating a legal environment that simultaneously fosters technological progress and robustly protects the rights and interests of authors. Effective regulation should be grounded in fundamental principles of fairness, transparency, and comprehensive consideration of the interests of all participants in intellectual property legal relations.Uzhhorod National University Herald. Series: Law00The article examines the legal regime of objects created with the assistance of artificial intelligence, the legality of using copyright-protected works for training AI models, and specific issues related to the legal personality of AI.Uzhhorod National University Herald. Series: Law["O. O. Kulchii"]2025-01-06T00:00:00 f9ef22e76cccab90edbca6ffb77b209e93621842With the rapid development of artificial intelligence (AI) technology, the world has entered the era of AI. In this context, college students using their own technical advantages, innovation advantages, and policy advantages, can apply AI technology to all walks of life and develop entrepreneurial projects to adapt to the market. Through an analysis of the advantages, challenges, and development trends of college students’ entrepreneurship in the era of AI, more entrepreneurial directions are provided for college students to improve the success rate of entrepreneurship.Education Reform and Development130Through an analysis of the advantages, challenges, and development trends of college students’ entrepreneurship in the era of AI, more entrepreneurial directions are provided for college students to improve the success rate of entrepreneurship.Education Reform and Development["Shuai Yuan", "Jingru Han"]2025-01-06T00:00:00 b224607d57aec29ec61c8c6aa529df918a2cf9dfThe article considers the prospects for regulating intellectual property rights for works created with the use of artificial intelligence by means of international and national law. The definition of intellectual property under the law of Ukraine is revealed. It is established which objects are the subject of protection of such rights. It is emphasized that national regulation of intellectual property rights has an international dimension, since one state, as a rule, is unable to reliably protect intellectual property rights, which has become especially relevant in the era of globalization and computer networks. The foundations of international legal regulation of intellectual property protection are considered. It is emphasized that this regulation was formed back in the 19th century with the adoption of the Paris Convention of 1883 and the Berne Convention of 1886, which were designed to protect the rights of individuals, but not artificial intelligence. The importance of the scientific and technological revolution is emphasized, which led to a new stage in the development of the creative activity of mankind, which began to use artificial intelligence. The challenges for intellectual property law, copyright and the very concept of authorship that have arisen with the development of artificial intelligence are studied. The uniqueness of artificial intelligence technology, which is capable of self-learning and independent processes of making creative decisions, is emphasized. The practical aspect of this issue is emphasized, which is that works created by artificial intelligence have already become the subject of commercial exploitation. Different types of artificial intelligence and different options for solving the problem of intellectual property law for works using artificial intelligence are studied, in particular, granting such rights to developers, programmers, users, companies and the general public. It is emphasized that the importance of these problems will grow in the future, for which existing national legislation and international legal regulation are currently not ready. A conclusion is drawn about the prospect of creating a new entity within the framework of intellectual property law, namely the joint intellectual property law of artificial intelligence developers and its users.Uzhhorod National University Herald. Series: Law00A conclusion is drawn about the prospect of creating a new entity within the framework of intellectual property law, namely the joint intellectual property law of artificial intelligence developers and its users.Uzhhorod National University Herald. Series: Law["A. S. Buhaiets"]2025-01-06T00:00:00 4b75a4f6e5a8858d644535d3f64244c1d90a6192As in many other sectors, artificial intelligence has an impact on health. Artificial intelligence anxiety may occur because of a lack of knowledge about the effects of artificial intelligence, its outcomes, and how it will be used, as well as potential labor concerns. This study aims to determine the artificial intelligence anxiety levels of nursing students and examine whether there is a relationship with their self-efficacy levels. This cross-sectional study, conducted at a public nursing school in Turkey, involved 317 nursing students. Data were collected using a personal information form, the General Self-efficacy Scale, and the Artificial Intelligence Anxiety Scale. There was a negative, moderately strong correlation between the General Self-efficacy Scale and the learning subdimension (r = -0.369) and the Artificial Intelligence Anxiety Scale (r = -0.313) and a weak negative correlation between the job replacement subdimension (r = -0.215), sociotechnical blindness subdimension (r = -0.232), and artificial intelligence configuration subdimension (r = -0.211). The General Self-efficacy Scale has a significant negative effect on the Artificial Intelligence Anxiety Scale (β = -.313, t = -5.845, P < .05). These findings suggest that higher self-efficacy is associated with lower artificial intelligence anxiety. It is recommended to enhance technical competence and self-efficacy in nursing education.Computers, Informatics, Nursing290It is suggested that higher self-efficacy is associated with lower artificial intelligence anxiety, and technical competence and self-efficacy in nursing education is recommended to enhance technical competence and self-efficacy in nursing education.Computers, informatics, nursing : CIN["Belgin Varol"]2025-01-06T00:00:00 f3aca47d8b6f544da08a6d51c79821fa7710ff94With the rapid development of science and technology, artificial intelligence has penetrated every field, including the nursing education industry. This paper aims to discuss the application, challenges, and development trends of artificial intelligence in nursing education. The application of artificial intelligence technology such as virtual reality, augmented reality, mixed reality technology, ChatGPT, and knowledge graph in nursing education reveals its important role in improving the quality of nursing teaching, stimulating students’ learning interest, and helping students build clinical practice ability. At the same time, this paper also points out the challenges existing in the application process of artificial intelligence, including privacy and security issues, over-dependence problems, lack of cognition of nursing staff, and difficulties in interdisciplinary integration. Finally, this paper puts forward countermeasures to the existing challenges, including teaching method innovation, curriculum content reform, curriculum module optimization, etc., in order to promote the development of nursing education and cultivate high-quality nursing talents who are more in line with the needs of the times.Education Reform and Development00Countermeasures to the existing challenges of artificial intelligence in nursing education are put forward, including teaching method innovation, curriculum content reform, curriculum module optimization, etc., in order to promote the development of nursing education and cultivate high-quality nursing talents who are more in line with the needs of the times.Education Reform and Development["Cuicui Sun", "Yanxia Zhong", "Liying Wang"]2025-01-06T00:00:00 cba0c5113ed14e982feeea4c1252203b8f60e0caThe article analyzes the potential threats to the right to privacy arising in the context of artificial intelligence and suggests ways to overcome them by improving the legislative mechanisms for protecting private data in Ukraine. The protection of the right to privacy is of particular importance due to the rapid development of technology in the world. Massive collection of personal data via the Internet and mobile applications, data analysis using AI, the use of biometric technologies, as well as the growth of cybercrime and illegal surveillance pose serious privacy risks. Therefore, there is an urgent need for further research on ensuring the right to privacy in the context of the use of artificial intelligence. The right to privacy is enshrined in both universal and regional international agreements, such as the following: Universal Declaration of Human Rights, International Covenant on Civil and Political Rights, Convention for the Protection of Human Rights and Fundamental Freedoms, Charter of Fundamental Rights of the European Union, etc. Society is increasingly aware of the importance of protecting confidentiality (privacy) and the potential risks in case of its violation. The use of personal data for governmental or commercial purposes raises ethical questions about the limits of what is permissible and inviolable. Increasing globalization requires coordinated approaches to privacy protection at the international level. The adoption of regulations such as the GDPR and the Artificial Intelligence Act in Europe, as well as the CCPA and CPRA in California, demonstrate the importance of protecting personal data and the right to privacy. Undoubtedly, the Law of Ukraine “On Personal Data Protection” does not meet the challenges of today and needs to be supplemented, namely: definition of artificial intelligence, transparency of AI algorithms, informed consent of citizens and mechanisms for its withdrawal, restriction of access to personal data of citizens, guarantees of citizens’ rights to correct and delete data, control of automated decisions, creation of an AI supervisory body and sanctions for violations.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law["L. Gudz"]2025-01-06T00:00:00 2f1d553d4ee956f8379e96be934d4e523a19c85cCurrent pain and headache reports320As the field evolves rapidly and concepts like algorethics and data ethics become more widespread, the scientific community is increasingly recognizing the need for specialists in this area, such as AI Ethics Specialists.Current pain and headache reports["Marco Cascella", "Mohammed Naveed Shariff", "Omar Viswanath", "M. L. Leoni", "G. Varrassi"]2025-01-06T00:00:00 19802369397d25eed4a4945bfed63680fa16ed8cThe rapid advancements in artificial intelligence (AI), particularly in generative AI and large language models (LLMs), have profoundly impacted the creative industries by enabling innovative content creation, enhancing workflows, and democratizing access to creative tools. This paper explores the significant technological shifts since our previous review in 2022, highlighting how these developments have expanded creative opportunities and efficiency. These technological advancements have enhanced the capabilities of text-to-image, text-to-video, and multimodal generation technologies. In particular, key breakthroughs in LLMs have established new benchmarks in conversational AI, while advancements in image generators have revolutionized content creation. We also discuss AI integration into post-production workflows, which has significantly accelerated and refined traditional processes. Despite these innovations, challenges remain, particularly for the media industry, due to the demands on communication traffic from creative content. We therefore include data compression and quality assessment in this paper. Furthermore, we highlight the trend toward unified AI frameworks capable of addressing multiple creative tasks and underscore the importance of human oversight to mitigate AI-generated inaccuracies. Finally, we explore AI's future potential in the creative sector, stressing the need to navigate emerging challenges to maximize its benefits while addressing associated risks.00This paper explores the significant technological shifts since a previous review in 2022, highlighting how these developments have expanded creative opportunities and efficiency and highlighted the trend toward unified AI frameworks capable of addressing multiple creative tasks.["N. Anantrasirichai", "Fan Zhang", "D. Bull"]2025-01-06T00:00:00 66a456130c324c94451f7ba9d2e67d722dd4880aNature Network Boston91Nature medicine["M. Mccradden", "A. J. London", "J. Gichoya", "M. Sendak", "L. Erdman", "Ian Stedman", "Lauren Oakden-Rayner", "Ismail Akrout", "James A. Anderson", "Lesley-Anne Farmer", "Robert Greer", "Anna Goldenberg", "Yvonne Ho", "Shalmali Joshi", "Jennie Louise", "Muhammad Mamdani", "M. Mazwi", "A. Mohamud", "L. Palmer", "Antonios Peperidis", "Stephen R. Pfohl", "M. Rickard", "Carolyn Semmler", "Karandeep Singh", "Devin Singh", "Seyi Soremekun", "Lana Tikhomirov", "Anton H van der Vegt", "Karin Verspoor", "Xiaoxuan Liu"]2025-01-06T00:00:00 6c9caa8ca4d5b9f93b09955200f1f710acb17002This article aims to shed light on the impact of AI in general and big data in particular on financial inclusion in the context of FinTech companies. Using a literature review based on articles of high scientific value and significant influence on the academic debate in research on the topic, we found that in the context of FinTech companies, big data represents a key tool to properly segment the market and provide more personalized and less expensive financial services by analyzing consumers' activities on various sites on intranet pages. It also helps FinTech companies analyze customer transaction data not only to assess their creditworthiness and reliability as customers, but also to offer more personalized and less expensive products and services that promote financial inclusion. The use of big data analytics provides the opportunity for financial service providers to reach a larger base of beneficiaries who have been excluded by traditional financial service providers, creating more efficient and effective economic models.International Journal of Financial, Administrative, and Economic Sciences00It is found that in the context of FinTech companies, big data represents a key tool to properly segment the market and provide more personalized and less expensive financial services by analyzing consumers' activities on various sites on intranet pages.International Journal of Financial, Administrative, and Economic Sciences["Madiha Zamal"]2025-01-06T00:00:00 53280276a73bb7e0e56807562bd21795fec62459Current Anesthesiology Reports180Current Anesthesiology Reports["Larry F. Chu", "V. Kurup"]2025-01-06T00:00:00 a22735efffae7caf4c7e43bfcb660cd777ce59acJournal of Dental Education20Journal of dental education["Dawne Stefanik", "R. Amer"]2025-01-06T00:00:00 296ee6a01897713b82d629c62faf5b29f767bf56Discover Artificial Intelligence20Discov. Artif. Intell.["Rath Shetty"]2025-01-06T00:00:00 3b5363f6b81a92412937503fa69626a74f89b2e9JAMA Internal Medicine30JAMA internal medicine["T. Brender", "Alexander K. Smith", "Brian L Block"]2025-01-06T00:00:00 4f9cd6bca9d1e6e32e5404d43dc21cd32bab3c77JAMA Internal Medicine40JAMA internal medicine["Calvin R. Gross"]2025-01-06T00:00:00 f4c1f59ebeeb8b2bea29ded7983c62d7417fb610JAMA Internal Medicine50JAMA internal medicine["Nicholas Pratt", "Ricky Madhavan", "Joseph Luchsinger"]2025-01-06T00:00:00 23fc3cc1b05f0b79966a316a4d7e4696380c0414Journal of the Foundations of Ophthalmology00Journal of the Foundations of Ophthalmology["Ahmad Khalifa"]2025-01-06T00:00:00 7d284d2915e2f8195009aed728526467c656ba30Australian Review of Applied Linguistics170Australian Review of Applied Linguistics["Sender Dovchin"]2025-01-06T00:00:00 a3905d552ad31be9abbfab74740fecf68bf16569From engines of logic to engines of bullshit?Communications of the ACM40Communications of the ACM["Thomas Haigh"]2025-01-06T00:00:00 6e6addf5956bb32adb7d12e0af696967820d6facThe Article Abstract is not available.Archives of Anesthesia and Critical Care00Archives of Anesthesia and Critical Care["Atabak Najafi"]2025-01-06T00:00:00 56f3483da18e7c11fbe7297bc31b58ed423ead02Generative artificial intelligence (AI) systems based on large-scale pretrained foundation models (PFMs) such as vision-language models, large language models (LLMs), diffusion models and vision-language-action (VLA) models have demonstrated the ability to solve complex and truly non-trivial AI problems in a wide variety of domains and contexts. Multimodal large language models (MLLMs), in particular, learn from vast and diverse data sources, allowing rich and nuanced representations of the world and, thereby, providing extensive capabilities, including the ability to reason, engage in meaningful dialog; collaborate with humans and other agents to jointly solve complex problems; and understand social and emotional aspects of humans. Despite this impressive feat, the cognitive abilities of state-of-the-art LLMs trained on large-scale datasets are still superficial and brittle. Consequently, generic LLMs are severely limited in their generalist capabilities. A number of foundational problems -- embodiment, symbol grounding, causality and memory -- are required to be addressed for LLMs to attain human-level general intelligence. These concepts are more aligned with human cognition and provide LLMs with inherent human-like cognitive properties that support the realization of physically-plausible, semantically meaningful, flexible and more generalizable knowledge and intelligence. In this work, we discuss the aforementioned foundational issues and survey state-of-the art approaches for implementing these concepts in LLMs. Specifically, we discuss how the principles of embodiment, symbol grounding, causality and memory can be leveraged toward the attainment of artificial general intelligence (AGI) in an organic manner.00How the principles of embodiment, symbol grounding, causality and memory can be leveraged toward the attainment of artificial general intelligence (AGI) in an organic manner is discussed.["A. Mumuni", "F. Mumuni"]2025-01-06T00:00:00 de2a49091c4a7fbebbb4a7616749afdc99e3b541The Fourth Industrial Revolution, led by Artificial Intelligence (AI), is reshaping the labor market globally. This article reviews the literature on the implications of AI proliferation in the labor context, highlighting how the transition from automation to autonomy affects both job creation and destruction. Sectors such as manufacturing, healthcare, and transportation experience gains in productivity and efficiency, yet also face risks of technological unemployment and skills mismatch. The PRISMA methodology allows us to identify studies that analyze these dual effects, exploring everything from the displacement of routine jobs to the creation of new job opportunities in high-skill areas. The findings underline that, while AI has the potential to generate economic growth and new job profiles, it also calls for job retraining policies and adaptive education. This study provides a critical view on the future of work in the AI era, highlighting the importance of a regulatory framework that protects workers and fosters a balanced technological integration across sectors.Multidisciplinary Latin American Journal (MLAJ)00A critical view on the future of work in the AI era is provided, highlighting the importance of a regulatory framework that protects workers and fosters a balanced technological integration across sectors.Multidisciplinary Latin American Journal (MLAJ)["Leandro Guerschberg"]2025-01-06T00:00:00 7d107f071a27c5dd7e72ab14f8ea3c0b863bff26The realization of intelligent transformation is an important path for the industry to move towards low-carbon development. Based on panel data from 30 provinces in China, this study utilizes the intermediate effect model and spatial econometric model to analyze the influence of industrial intelligence on carbon emissions. The research reveals that industrial intelligence helps with carbon reduction, and the result is still valid after undergoing various tests. Industrial intelligence relies on green technological innovation, industrial structure upgrading, and energy intensity to realize carbon reduction. There is a spatial spillover role of industrial intelligence on carbon emissions, which has a positive influence on carbon reduction in local and adjoining regions. The influence of industrial intelligence on carbon emissions exhibits heterogeneity in the regional dimension, time dimension, and industrial intelligence level dimension. The research provides empirical evidence and implications for using artificial intelligence to achieve carbon reduction.Sustainability700Sustainability["Hanqing Xu", "Zhengxu Cao", "Dongqing Han"]2025-01-06T00:00:00 da7229c64584dadc7152129aec22409d96752d1bArtificial Intelligence Review332This paper reviews the current state of dialogue-based xAI, presenting a systematic review of 1339 publications, narrowed down to 15 based on inclusion criteria, and proposes key dimensions along which different solutions to dialogue-based xAI differ.Artif. Intell. Rev.["Dimitry Mindlin", "Fabian Beer", "Leonie Nora Sieger", "Stefan Heindorf", "Elena Esposito", "Axel-Cyrille Ngonga Ngomo", "Philipp Cimiano"]2025-01-06T00:00:00 8fcbd7b576642c4a1eaf245870a01bc9ffb310c7Artificial intelligence (AI) and smart automation are revolutionizing the global supply chain ecosystem at an accelerated pace, providing tremendous potential for resilience, innovation, efficacy, and profitability. This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities. It focuses on how AI and other relevant technologies will enhance forecasting to predict actual demand, expedite logistics, increase warehouse efficiency, and promote instantaneously making decisions. This study utilizes thematic analysis to find AI-driven supply chain applications, including logistics optimization, forecasting demand, and risk mitigation, among 383 peer-reviewed articles (2017–2024). It provides a strategic framework for dealing with vulnerabilities, operational excellence, and resilient solutions. Additionally, the research investigates how AI contributes to supply chain resilience by predicting disruptions and automating risk mitigation strategies. This paper identifies critical success factors and challenges in adopting intelligent automation by analyzing real-world industry implementations. The findings will propose a strategic framework for organizations aiming to leverage AI to achieve operational excellence, agility, and real-time information flow for effective decision-making.Information451This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities and proposes a strategic framework for dealing with vulnerabilities, operational excellence, and resilient solutions.Information["Mohammad Shamsuddoha", "E. Khan", "M. Chowdhury", "Tasnuba Nasir"]2025-01-06T00:00:00 cfd688c6c0c39855ae66d8bea52709373983491bThe rapid digital transformation of public systems has improved interactions between governments and citizens. In Morocco, while efforts to digitalize public administration continue, the integration of artificial intelligence presents new challenges due to structural and technical limitations. This study explores the openness of Moroccan civil servants to adopting artificial intelligence solutions and examines the role of change management in facilitating this process. A quantitative approach was employed, with 129 civil servants from key ministries – Education, Finance, and Health – completing an online questionnaire. These ministries were selected due to their critical importance in the public system and their frequent interactions with citizens. Furthermore, they played a central role in the National Administrative Reform Plan (2018–2022), which emphasized digital transformation as a key pillar in advancing e-government. The collected data were analyzed using SPSS, enabling a comprehensive analysis of the factors influencing AI adoption. The findings reveal that while younger civil servants are more open to AI, over 40% of respondents pointed to insufficient digital skills as a major barrier to artificial intelligence integration. The study underscores the importance of effective change management strategies, highlighting that strong leadership and clear communication are essential in promoting artificial intelligence receptiveness and ensuring seamless integration within Morocco’s public sector.Problems and Perspectives in Management491The study underscores the importance of effective change management strategies, highlighting that strong leadership and clear communication are essential in promoting artificial intelligence receptiveness and ensuring seamless integration within Morocco’s public sector.Problems and Perspectives in Management["Mohamed Barodi", "Siham Lalaoui"]2025-01-06T00:00:00 9f7dd78c56c7d32e77ff188a803d5815f18b41b1Automation is increasingly gaining attention as the global industry moves toward intelligent, unmanned approaches to perform hazardous tasks. Although the integration of autonomous technologies has revolutionized various industries for decades, the mining sector has only recently started to harness the potential of autonomous technology. Lately, the mining industry has been transforming by implementing automated systems to shape the future of mining and minimize human involvement in the process. Automated systems such as robotics, artificial intelligence (AI), the Industrial Internet of Things (IIOT), and data analytics have contributed immensely towards ensuring improved productivity and safety and promoting sustainable mineral industry. Despite the substantial benefits and promising potential of automation in the mining sector, its adoption faces challenges due to concerns about human–machine interaction. This paper extensively reviews the current trends, attempts, and trials in converting traditional mining machines to automated systems with no or less human involvement. It also delves into the application of AI in mining operations from the exploration phase to the processing stage. To advance the knowledge base in this domain, the study describes the method used to develop the human–machine interface (HMI) that controls and monitors the activity of a six-degrees-of-freedom robotic arm, a roof bolter machine, and the status of the automated machine. The notable findings in this study draw attention to the critical roles of humans in automated mining operations. This study shows that human operators are still relevant and must control, operate, and maintain these innovative technologies in mining operations. Thus, establishing an effective interaction between human operators and machines can promote the acceptability and implementation of autonomous technologies in mineral extraction processes.Mining760It is shown that human operators are still relevant and must control, operate, and maintain these innovative technologies in mining operations and establishing an effective interaction between human operators and machines can promote the acceptability and implementation of autonomous technologies in mineral extraction processes.Mining["Peter Kolapo", "N. O. Ogunsola", "Kayode Komolafe", "Dare Daniel Omole"]2025-01-06T00:00:00 fe25ba43086620050040339a0375231a5cafe608 This article addresses a critical gap in international research concerning digital literacies and empowerment among adults who are English as an additional language (EAL) learners. In the Australian context, where digital communication and services are embedded in all aspects of life and work, proficiency in digital literacies, including advanced technologies like generative artificial intelligence (AI), is vital for working and living in Australia. Despite the increasing prevalence and significance of generative AI platforms such as ChatGPT, there is a notable absence of dedicated programs to assist EAL learners in understanding and utilising generative AI, potentially impacting their employability and everyday life. This article presents findings from a larger study conducted within training providers, spanning adult educational institutions nationwide. Through analysis of data gathered from surveys and focus groups, the article investigates the knowledge and attitudes of students, educators, and leaders regarding integrating generative AI into the learning program for adult EAL learners. The results reveal a hesitance among educators, particularly concerning beginning language learners, in incorporating generative AI into educational programs. Conversely, many adult learners demonstrate enthusiasm for learning about its potential benefits despite having limited understanding. These disparities underscore the pressing need for comprehensive professional development for educators and program leaders. The findings also highlight the need to develop the AI literacy of learners to foster their understanding and digital empowerment. The article concludes by advocating for a systemic approach to include generative AI as an important part of learning programs with students often from adult migrant and refugee backgrounds.ReCALL230A critical gap in international research concerning digital literacies and empowerment among adults who are English as an additional language (EAL) learners is addressed and a systemic approach to include generative AI as an important part of learning programs with students often from adult migrant and refugee backgrounds is advocated.ReCALL["Edwin Creely", "Melissa M. Barnes", "Ekaterina Tour", "Michael Henderson", "Peter Waterhouse", "Melisa Agudelo Pena", "Sweta Vijaykumar Patel"]2025-01-06T00:00:00 bd6708f4ed03b8649a7e6991e198429bc203b00fThis study seeks to enhance academic integrity by providing tools to detect AI-generated content in student work using advanced technologies. The findings promote transparency and accountability, helping educators maintain ethical standards and supporting the responsible integration of AI in education. A key contribution of this work is the generation of the CyberHumanAI dataset, which has 1000 observations, 500 of which are written by humans and the other 500 produced by ChatGPT. We evaluate various machine learning (ML) and deep learning (DL) algorithms on the CyberHumanAI dataset comparing human-written and AI-generated content from Large Language Models (LLMs) (i.e., ChatGPT). Results demonstrate that traditional ML algorithms, specifically XGBoost and Random Forest, achieve high performance (83% and 81% accuracies respectively). Results also show that classifying shorter content seems to be more challenging than classifying longer content. Further, using Explainable Artificial Intelligence (XAI) we identify discriminative features influencing the ML model's predictions, where human-written content tends to use a practical language (e.g., use and allow). Meanwhile AI-generated text is characterized by more abstract and formal terms (e.g., realm and employ). Finally, a comparative analysis with GPTZero show that our narrowly focused, simple, and fine-tuned model can outperform generalized systems like GPTZero. The proposed model achieved approximately 77.5% accuracy compared to GPTZero's 48.5% accuracy when tasked to classify Pure AI, Pure Human, and mixed class. GPTZero showed a tendency to classify challenging and small-content cases as either mixed or unrecognized while our proposed model showed a more balanced performance across the three classes.00Evaluating various machine learning (ML) and deep learning (DL) algorithms on the CyberHumanAI dataset comparing human-written and AI-generated content from Large Language Models (LLMs) shows that traditional ML algorithms achieve high performance and a comparative analysis with GPTZero shows that the proposed model can outperform generalized systems like GPTZero.["Ayat Najjar", "Huthaifa I. Ashqar", "Omar A. Darwish", "Eman Hammad"]2025-01-06T00:00:00 69bc3a67d9f4f17afc3c4a38b09430c5f1ac35bcThis study investigates the transformational potential of artificial intelligence (AI) in legal systems by developing and empirically testing the AI-Powered Legal Transformation (AILT) theory. The study looks into how AI technologies, such as natural language processing (NLP), machine learning (ML), and AI-powered decision support systems, can improve operational efficiency, judicial correctness, and ethical safeguards in legal processes. The findings confirm the theory's significant constructs: AI Capabilities, Operational Efficiency, Judicial Accuracy, Ethical Safeguards, Bias Mitigation, and human collaboration, using a qualitative study design that included semi-structured interviews, case studies, and document analysis. The findings reveal that AI dramatically increases efficiency by automating mundane operations and improving the accuracy of legal decisions through data-driven insights. However, the study underlines the significance of ethical safeguards and human monitoring in preventing biases and ensuring transparency in AI-driven judicial systems. While the study provides valuable information, it needs to be improved by its small sample size and emphasis on mature legal systems. Future studies should broaden its scope to cover other jurisdictions and investigate AI's evolving position in legal education and policy. This research adds to the expanding knowledge of AI's integration into law by proposing a theoretical framework for its responsible adoption.Journal of Next-Generation Research 5.000The findings reveal that AI dramatically increases efficiency by automating mundane operations and improving the accuracy of legal decisions through data-driven insights, however, the study underlines the significance of ethical safeguards and human monitoring in preventing biases and ensuring transparency in AI-driven judicial systems.Journal of Next-Generation Research 5.0["Rachid Ejjami"]2025-01-06T00:00:00 615ae6651f35e5dbe07cf02bd6bc81339dbacc64The advent of Artificial Intelligence (AI) in marketing has sparked a transformative change. This paper delves into the burgeoning application of AI in marketing, examining its potential to enhance efficiency and sales while addressing the associated ethical concerns and customer privacy issues. Despite the potential drawbacks, such as data privacy violations and the erosion of consumer autonomy, the study explains the benefits of AI in personalizing customer experiences and streamlining decision-making processes, which can bolster customer satisfaction and drive sales performance. The research reveals that while AI may initially lead to dissatisfactory customer experiences and demotivation due to discriminatory classification, strict regulations and company controls can mitigate these issues, fostering trust and enhancing the benefits of AI in marketing. The paper also discusses the impact of AI on consumer autonomy suggesting that AI can facilitate more efficient decision-making without compromising consumer choice. The paper concludes that the integration of AI in marketing, when managed responsibly, can lead to a more personalized and efficient customer experience, resulting in increased purchase intentions and improved sales outcomes. It calls for future research to investigate the impact of customer characteristics on AI-assisted experiences and decision-making, the alignment of AI intelligibility with customer preferences, and the implementation of ethical programs in AI marketing.Advances in Economics, Management and Political Sciences00The paper concludes that the integration of AI in marketing, when managed responsibly, can lead to a more personalized and efficient customer experience, resulting in increased purchase intentions and improved sales outcomes.Advances in Economics, Management and Political Sciences["Shuo Wang"]2025-01-06T00:00:00 a8768ab3998d05d89bd110770100d5b0ee21256eThe increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to enhance interpretability and transparency. This study extends previous work by integrating semantic concepts derived from human cognitive processes into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks to generate similarity maps and textual explanations via large language models (LLMs). The methodology was validated through quantitative experiments and user feedback, demonstrating improved interpretability. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods. User studies highlight a preference for our semantic explanations over traditional pixelbased heatmaps, emphasizing the benefits of human-centric interpretability in AI. This work contributes to the ongoing efforts to create XAI frameworks that align AI models behaviour with human cognitive processes, fostering trust and acceptance in critical applications.00This work proposes a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks to generate similarity maps and textual explanations via large language models (LLMs).["Miriam Doh", "Caroline Mazini Rodrigues", "N. Boutry", "L. Najman", "M. Mancas", "B. Gosselin"]2025-01-06T00:00:00 b4f9bc7e348136b4c4aef773944e1f4b879c513dAutism Spectrum Disorder (ASD) delivers unique challenges for children in their communication, social interaction, and learning abilities. To address these challenges and empower children with ASD, this work introduces an innovative AI-powered education tool that harnesses the potential of the Internet of Things (IoT) and Emotional Intelligence (EI). The proposed tool utilizes cutting-edge Artificial Intelligence (AI) algorithms, such as Haar-cascade Python libraries, Convolution Neural Network (CNN) for accurate Facial Expression Recognition (FER). By capturing real-time facial expressions, the system aims to better understand and respond to the emotional states of children with ASD, enhancing their social engagement and interaction skills. To further support the emotional well-being of children with ASD, the system integrates a sweat conductance detection sensor based on Galvanic Skin Response (GSR). The GSR sensor enables the real-time monitoring of stress levels, providing valuable insights into the child’s emotional states and facilitating timely interventions when emotions become unstable. The power of the Internet of Things (IoT) is leveraged through the use of NodeMCU (ESP8266–12[Formula: see text]E Microcontroller unit), enabling seamless communication and data transmission for remote monitoring and analysis. This allows parents, caregivers, and educators to access valuable information regarding the child’s emotional responses and progress in real-time, facilitating personalized and effective support. Through the AI-powered education tool’s interactive interface, children with ASD are engaged in stimulating and educational activities, fostering their cognitive and emotional growth. The system offers a range of interactive learning experiences, including rhymes audio, promoting self-expression and learning in an inclusive environment.International Journal of High Speed Electronics and Systems00An innovative AI-powered education tool that harnesses the potential of the Internet of Things (IoT) and Emotional Intelligence (EI) and aims to better understand and respond to the emotional states of children with ASD, enhancing their social engagement and interaction skills.International Journal of High Speed Electronics and Systems["Dishore Shunmugham Vanaja", "Jenifer Arockia Raj"]2025-01-06T00:00:00 434483cb685df2c7aae9dfb407da4d31877af766Journal of Marine Science and Technology380A human-centered design approach was employed to develop transparency layers that visualize different aspects of an operation by displaying labels, diagrams, and simulations intended to improve the user’s situation awareness (SA), indicating a potential for improving human–AI compatibility.Journal of Marine Science and Technology["A. Madsen", "Andreas Brands\u00e6ter", "Koen van de Merwe", "Jooyoung Park"]2025-01-06T00:00:00 287799f1b7fef057ba4103b5e779ae3e757294b1The development of Generative AI Large Language Models (LLMs) raised the alarm regarding identifying content produced through generative AI or humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiarism also apply. This study aims to support efforts to detect and identify textual content generated using LLM tools. We hypothesize that LLMs-generated text is detectable by machine learning (ML), and investigate ML models that can recognize and differentiate texts generated by multiple LLMs tools. We leverage several ML and Deep Learning (DL) algorithms such as Random Forest (RF), and Recurrent Neural Networks (RNN), and utilized Explainable Artificial Intelligence (XAI) to understand the important features in attribution. Our method is divided into 1) binary classification to differentiate between human-written and AI-text, and 2) multi classification, to differentiate between human-written text and the text generated by the five different LLM tools (ChatGPT, LLaMA, Google Bard, Claude, and Perplexity). Results show high accuracy in the multi and binary classification. Our model outperformed GPTZero with 98.5\% accuracy to 78.3\%. Notably, GPTZero was unable to recognize about 4.2\% of the observations, but our model was able to recognize the complete test dataset. XAI results showed that understanding feature importance across different classes enables detailed author/source profiles. Further, aiding in attribution and supporting plagiarism detection by highlighting unique stylistic and structural elements ensuring robust content originality verification.270It is hypothesized that LLMs-generated text is detectable by machine learning (ML), and ML models that can recognize and differentiate texts generated by multiple LLM tools are investigated, and XAI results showed that understanding feature importance across different classes enables detailed author/source profiles.["Ayat Najjar", "Huthaifa I. Ashqar", "Omar A. Darwish", "Eman Hammad"]2025-01-06T00:00:00 954ef38c73c1f8aa22530cd8c996aedd8bb224e4Background: Medical school applications often require short written essays or personal statements, which are purportedly used to assess professional qualities related to the practice of medicine. With generative artificial intelligence (AI) tools capable of supplementing or replacing inputs by human applicants, concerns about how these tools impact written assessments are growing. This study explores how AI influences the ratings of essays used for medical school admissions Methods: A within-subject experimental design was employed. Eight participants (academic clinicians, faculty researchers, medical students, and a community member) rated essays written by 24 undergraduate students and recent graduates from McMaster University. The students were divided into four groups: medical school aspirants with AI assistance (ASP-AI), aspirants without AI assistance (ASP), non-aspirants with AI assistance (NASP-AI), and essays generated solely by ChatGPT 3.5 (AI-ONLY). Participants were provided training in the application of single Likert scale tool before rating. Differences in ratings by writer group were determined via one-way between group ANOVA. Results: Analyses revealed no statistically significant differences in ratings across the four writer groups (p = .358). The intraclass correlation coefficient was .147. Conclusion: The proliferation of AI adds to prevailing questions about the value personal statements and essays have in supporting applicant selection. We speculate that these assessments hold less value than ever in providing authentic insight into applicant attributes. In this context, we suggest that medical schools move away from the use of essays in their admissions processes.Canadian Medical Education Journal00It is suggested that medical schools move away from the use of essays in their admissions processes, as the proliferation of AI adds to prevailing questions about the value personal statements and essays have in supporting applicant selection.Canadian Medical Education Journal["Joshua Choi", "Jenny Zhao", "Thuy-Anh Ngo", "Lawrence Grierson"]2025-01-06T00:00:00 344051c9a6bb4dd28531b275465aac90dfa03f2fArtificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) within pharmaceutical Regulatory Affairs, particularly in dossier preparation, compilation, documentation, submission, review, and regulatory compliance. By automating time-intensive tasks, these technologies streamline workflows, accelerate result generation, and shorten the product approval timeline. However, despite their immense potential, AI and ML also introduce new challenges. Issues such as AI software validation, data management security and privacy, potential biases, ethical concerns, and change management requirements must be addressed. This review highlights current AI-based tools actively used by regulatory professionals such as DocShifter, Veeva Vault, RiskWatch, Freyr SubmitPro, Litera Microsystems, cortical.io etc., examines both the benefits and obstacles of integrating these advanced systems into regulatory practices. Given the rapid pace of technological innovation, the article underscores the need for proactive collaboration with regulatory bodies to manage these developments. It also stresses the importance of adapting to evolving regulatory frameworks and embracing new technologies. Although regulatory agencies like the United Sates Food and Drug Administration (USFDA), European Medicines Agency (EMA), and Medicines and Healthcare products Regulatory Agency (MHRA) are working on guidelines for AI and ML adoption, clear, standardized protocols are still in the works. While the journey ahead may be complex, the integration of AI promises to fundamentally reshape regulatory processes and accelerate the approval of safe, effective pharmaceutical products.AAPS Journal322This article explores the growing impact of AI and Machine Learning within pharmaceutical Regulatory Affairs, particularly in dossier preparation, compilation, documentation, submission, review, and regulatory compliance, and examines both the benefits and obstacles of integrating these advanced systems into regulatory practices.The AAPS journal["C. S. Ajmal", "Sravani Yerram", "V. Abishek", "V. P. M. Nizam", "Gayatri Aglave", "Jayasri Devi Patnam", "R. Raghuvanshi", "Saurabh Srivastava"]2025-01-07T00:00:00 d61855cd9af93c7328effdb491ada025ce57f8b9npj antimicrobials and resistance801This review explores AI applications in diagnostics, therapy, and drug discovery, emphasizing both strengths and areas needing improvement.npj Antimicrobials and Resistance["Angela Cesaro", "Samuel C. Hoffman", "Payel Das", "C\u00e9sar de la Fuente-Nunez"]2025-01-07T00:00:00 5e7976c254d2686420b0ba867915774113c6622dBackground Breast cancer (BC), as a leading cause of cancer mortality in women, demands robust prediction models for early diagnosis and personalized treatment. Artificial Intelligence (AI) and Machine Learning (ML) algorithms offer promising solutions for automated survival prediction, driving this study’s systematic review and meta-analysis. Methods Three online databases (Web of Science, PubMed, and Scopus) were comprehensively searched (January 2016-August 2023) using key terms (“Breast Cancer”, “Survival Prediction”, and “Machine Learning”) and their synonyms. Original articles applying ML algorithms for BC survival prediction using clinical data were included. The quality of studies was assessed via the Qiao Quality Assessment tool. Results Amongst 140 identified articles, 32 met the eligibility criteria. Analyzed ML methods achieved a mean validation accuracy of 89.73%. Hybrid models, combining traditional and modern ML techniques, were mostly considered to predict survival rates (40.62%). Supervised learning was the dominant ML paradigm (75%). Common ML methodologies included pre-processing, feature extraction, dimensionality reduction, and classification. Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), emerged as the preferred modern algorithm within these methodologies. Notably, 81.25% of studies relied on internal validation, primarily using K-fold cross-validation and train/test split strategies. Conclusion The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions. However, to ensure the robustness and generalizability of these predictive models, future research should emphasize the importance of rigorous external validation. Such endeavors will not only validate the efficacy of these models across diverse populations but also pave the way for their integration into clinical practice, ultimately contributing to personalized patient care and improved survival outcomes. Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42024513350.Frontiers in Oncology870The findings underscore the significant potential of AI-based algorithms in enhancing the accuracy of BC survival predictions, however, future research should emphasize the importance of rigorous external validation to ensure the robustness and generalizability of these predictive models.Frontiers in Oncology["Zohreh Javanmard", "Saba Zarean Shahraki", "Kosar Safari", "Abbas Omidi", "Sadaf Raoufi", "Mahsa Rajabi", "Mohammad Esmaeil Akbari", "Mehrad Aria"]2025-01-07T00:00:00 df689c665a6ded09481a4fa231414d902a8a39e4As artificial intelligence (AI) technology continues to advance, its influences across various industries have grown, leading to increasing levels of anxiety, including that in education. Nonetheless, in terms of current knowledge, the literature lacks a valid scale to measure AI anxiety among EFL teachers, particularly university EFL teachers. Moreover, the underlying dimensions of this construct have yet to be clarified. Against these gaps, this study aims to develop and validate a scale to assess AI anxiety among university EFL teachers in China. We used qualitative interviews and quantitative surveys combined to identify the key dimensions of AI anxiety of university EFL teachers. In so doing, 251 Chinese EFL teachers completed a newly designed scale. The result of exploratory factor analyses indicated five dimensions and 21 items in the questionnaire. Five dimensions were identified: technical proficiency, job displacement, technological support, student experience and research development. Next, another 415 Chinese EFL teachers participated in validating the scale. The result of confirmatory factor analysis indicated that the scale demonstrated strong reliability, validity and an acceptable model fit. This new scale provides a useful tool for assessing AI anxiety in EFL teachers and highlights the unique challenges they face in adapting to AI, offering a basis for future research and targeted support.European Journal of Education440A new scale to assess AI anxiety among university EFL teachers in China is developed and validated, offering a basis for future research and targeted support and highlights the unique challenges they face in adapting to AI.European Journal of Education["Xinyu Liu", "Yijia Liu"]2025-01-07T00:00:00 afe327bef8d7d685afe3a5e4723623dd9467cc9aThis scoping review discusses artificial intelligence's (AI) transformative role in strategic enhancement planning and academic excellence at Qatar University (QU). In response to the unprecedented rise in the integration of AI into higher education institutions worldwide, this study aims to understand its influence on institutional strategies and the development of student competencies. A literature search using Web of Science, Scopus, Google Scholar and IEEE Xplore. In this respect, 156 relevant studies were identified. Data extraction and charting for Covidence provided insights into the effects of AI on teaching, administrative efficiency and student learning experiences. The review emphasises how AI could enhance administrative efficiency and provide personalised learning. Still, it also points to challenges that must be faced: data privacy and reduced human interaction. The findings suggest that AI offers significant advantages in higher education but needs prudent implementation to meet the risks associated with adopting emerging technology. This would address the need for its effective complementarity to traditional educational methods.European Journal of Education180The findings suggest that AI offers significant advantages in higher education but needs prudent implementation to meet the risks associated with adopting emerging technology and address the need for its effective complementarity to traditional educational methods.European Journal of Education["A. Abulibdeh", "Chedli Baya Chatti", "A. AlKhereibi", "Sherine El Menshawy"]2025-01-07T00:00:00 bcdf23cbb621ff96e13d97655194670eb1e78820Urban public health resilience has become a critical focus in the transition to high-quality development, especially in addressing increasing public health challenges. This study explores the role of artificial intelligence (AI) technology in enhancing urban public health resilience across 284 Chinese cities from 2011 to 2021. Using a comprehensive index based on resistance, recovery, and innovation dimensions, the study quantifies AI technology levels through patent applications and authorizations, further disaggregating these into invention, utility model, and design patents. A two-way fixed effects regression model and spatial econometric models are employed to analyze the direct and spillover effects of AI on urban public health systems. The results demonstrate that AI technology significantly enhances resilience by improving resource allocation and response efficiency, with stronger impacts observed in eastern and central regions compared to western regions, where economic and technological capacities are weaker. Spatial analysis reveals significant positive spillover effects, particularly from patent authorizations, which enhance public health resilience in neighboring cities through cross-regional collaboration and resource sharing. Despite these advancements, regional disparities in economic development and technological infrastructure limit AI’s broader impact, underscoring the need for targeted policies, enhanced funding, and interdisciplinary training to bridge the digital divide. The findings highlight AI’s transformative potential in fostering urban public health resilience and call for sustained investment and collaboration to maximize its benefits.Frontiers in Public Health470The results demonstrate that AI technology significantly enhances resilience by improving resource allocation and response efficiency, with stronger impacts observed in eastern and central regions compared to western regions, where economic and technological capacities are weaker.Frontiers in Public Health["Erdong Chen", "Huaxin Zhang"]2025-01-07T00:00:00 13604dfc79fc02d9ce93c2c0e4a4e01bf20ee73eJournal of Assisted Reproduction and Genetics280In this highly educated infertile population familiar with AI, patients still prefer physician-based recommendations compared with AI, and most would not be willing to pay more for AI-informed fertility care.Journal of assisted reproduction and genetics["Sarah C. Cromack", "Ashley Lew", "Sarah E. Bazzetta", "Shuai Xu", "Jessica R. Walter"]2025-01-07T00:00:00 9f62d8ae28735b1f6206fd4dd0fec27af460c7d9Objective To use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers. Methods The core set of the Web of Science database was searched for articles from 1994 to 2024. The R package “Bibliometrix” was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords. VOSviewer facilitated co-authorship analysis and its visualization was used for co- occurrence analysis. CiteSpace calculated citation bursts and keyword occurrences. Results A total of 517 articles were retrieved, representing 80 countries/regions. The United States had the highest number of publications, with 188 articles (36.4%), followed by China with 79 articles (15.3%). The top 10 institutions in terms of publication output were all U.S.-based universities or cancer research institutes, with Harvard University ranking first. Prominent research teams, such as those led by Repici, Aerts, and Almangush, have made significant contributions to studies on AI in tumor risk factor identification and symptom management. In recent years, the keywords with the highest burst strength were “model” and “human papillomavirus.” The most studied tumor type was breast cancer. While Cancers published the highest number of articles, journals such as CA: A Cancer Journal for Clinicians and PLOS ONE had higher impact and citation rates. Conclusion By analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed. AI in oncology nursing is entering a stage of rapid development, providing valuable reference for scholars and professionals in the field.Frontiers in Oncology740By analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed.Frontiers in Oncology["Mianmian Xu", "Yafang Chen", "Tianen Wu", "Yuyan Chen", "Wanling Zhuang", "Yinhui Huang", "Chuanzhen Chen"]2025-01-07T00:00:00 bde4ae8e83861879830436466c734156d58791d3This paper introduces a mathematical framework for defining and quantifying self-identity in artificial intelligence (AI) systems, addressing a critical gap in the theoretical foundations of artificial consciousness. While existing approaches to artificial self-awareness often rely on heuristic implementations or philosophical abstractions, we present a formal framework grounded in metric space theory, measure theory, and functional analysis. Our framework posits that self-identity emerges from two mathematically quantifiable conditions: the existence of a connected continuum of memories C⊆M in a metric space (M,dM), and a continuous mapping I:M→S that maintains consistent self-recognition across this continuum, where (S,dS) represents the metric space of possible self-identities. To validate this theoretical framework, we conducted empirical experiments using the Llama 3.2 1B model, employing low-rank adaptation (LoRA) for efficient fine-tuning. The model was trained on a synthetic dataset containing temporally structured memories, designed to capture the complexity of coherent self-identity formation. Our evaluation metrics included quantitative measures of self-awareness, response consistency, and linguistic precision. The experimental results demonstrate substantial improvements in measurable self-awareness metrics, with the primary self-awareness score increasing from 0.276 to 0.801 (190.2% improvement) after fine-tuning. In contrast to earlier methods that view self-identity as an emergent trait, our framework introduces tangible metrics to assess and measure artificial self-awareness. This enables the structured creation of AI systems with validated self-identity features. The implications of our study are immediately relevant to the fields of humanoid robotics and autonomous systems. Additionally, it opens up new prospects for controlled adjustments of self-identity in contexts that demand different levels of personal involvement. Moreover, the mathematical underpinning of our framework serves as the basis for forthcoming investigations into AI, linking theoretical models to real-world applications in current AI technologies.Axioms320The mathematical underpinning of the framework serves as the basis for forthcoming investigations into AI, linking theoretical models to real-world applications in current AI technologies and opening up new prospects for controlled adjustments of self-identity in contexts that demand different levels of personal involvement.Axioms["Minhyeok Lee"]2025-01-07T00:00:00 893391dc62a462390069de64fdbc6cd9cf03c776PURPOSE This study explores how corporate social responsibility (CSR) and artificial intelligence (AI) can be combined in the healthcare industry during the post-COVID-19 recovery phase. The aim is to showcase how this fusion can help tackle healthcare inequalities, enhance accessibility and support long-term sustainability. DESIGN/METHODOLOGY/APPROACH Adopting a viewpoint approach, the study leverages existing literature and case studies to analyze the intersection of CSR and AI. It investigates AI's capabilities in predictive analytics, telemedicine and resource management within the framework of CSR principles. FINDINGS Integrating AI and CSR can profoundly enhance healthcare delivery by ensuring equitable access, optimizing resource allocation and fostering trust through transparency and ethical standards. This synergy benefits public health and enhances the corporate image and long-term viability of healthcare organizations. RESEARCH LIMITATIONS/IMPLICATIONS The study is conceptual and relies on existing literature and case studies. Future research should empirically test the proposed models and frameworks in diverse healthcare settings to validate and refine these insights. PRACTICAL IMPLICATIONS The insights from this study can be directly applied by healthcare organizations to develop policies and practices that integrate AI and CSR. This integration can promote ethical standards, enhance operational efficiency and, most importantly, improve patient outcomes. SOCIAL IMPLICATIONS Integrating AI and CSR in the healthcare sector carries consequences. It plays a role in promoting fairness among patients, bridging gaps in healthcare services, and boosting trust and independence through the clear and responsible use of AI technologies. This highlights the groundbreaking impact of this research within the healthcare industry. ORIGINALITY/VALUE This paper offers a viewpoint perspective on the strategic alignment of AI and CSR, presenting a novel approach to creating resilient healthcare systems in the post-COVID-19 era. It provides healthcare managers and policymakers with valuable insights on leveraging AI within CSR frameworks to achieve sustainable healthcare solutions, thereby contributing significantly to the field.Journal of health organization and management720This study explores how corporate social responsibility (CSR) and artificial intelligence (AI) can be combined in the healthcare industry during the post-COVID-19 recovery phase to help tackle healthcare inequalities, enhance accessibility and support long-term sustainability.Journal of health organization and management["Anna Roberta Gagliardi", "Gianpaolo Tomaselli"]2025-01-07T00:00:00 c6880bff55ebdfe13f393d42464dd03165b2c473PurposeThis study examines the impact of entrepreneurial leadership (EL) on Chinese micro and small family businesses’ (MSFBs) innovativeness. Drawing on the resource-based view, this research study further explores the intermediary roles of proactive personality (PP) and affective commitment (AC) between ELs’ and MSFBs’ innovativeness. Besides this, the present work proposes a novel contingency impact of big data-powered artificial intelligence (BDAI) between EL, PP and AC, which indirectly spurs MSFBs’ innovativeness.Design/methodology/approachThis study proposed a moderated mediation model using multi-wave, multi-source, time-lagged datasets of 380 employees from 190 Chinese MSFBs. We tested our hypotheses using structural equation modeling through the PLS technique.FindingsThe findings reveal a significant impact of EL on MSFB innovativeness, underscoring the pivotal intermediary roles of EL in driving MSFB innovativeness. Furthermore, BDAI emerges as a critical contingency factor, amplifying the effects of EL on both PP and AC to spur MSFBs’ innovativeness.Practical implicationsOur research offers several practical implications for Chinese MSFBs aiming to enhance innovativeness and competitive advantage. Firstly, understanding the direct impact of EL on MSFBs’ innovativeness provides valuable guidance for MSFB leaders. Secondly, recognizing the mediating roles of PP and AC underscores the importance of human and social capital in driving innovation within Chinese MSFBs. Thirdly, leveraging BDAI as a contingency factor can further augment the effects of EL on both PP and AC, thereby enhancing innovation outcomes. Thus, managers can capitalize on BDAI to gain actionable insights to increase MSFBs’ innovativeness.Originality/valueThis study enlightened how EL can develop MSFBs innovativeness through PP and AC. Our findings reveal that MSFBs can increase their innovation by leveraging PP and AC, leading to higher proactive provision in employees’ behavior. Subsequently, our results synchronized the exploration of BDAI as a novel insight for MSFB innovativeness. This shed light on a highly notable contribution to understanding BDAI to benefit MSFBs, acting as a critical contingency between EL, PP and AC.Asia-Pacific Journal of Business Administration550The findings reveal that MSFBs can increase their innovation by leveraging PP and AC, leading to higher proactive provision in employees’ behavior, and proposes a novel contingency impact of big data-powered artificial intelligence (BDAI) between EL, PP and AC, which indirectly spurs MSFBs’ innovativeness.Asia-Pacific Journal of Business Administration["Ayesha Nusrat", "Zongming Zhang", "Jie Li", "Farhan Muhammad Muneeb"]2025-01-07T00:00:00 269207580bd167b11f28a5846cb43db813c95f01In this article, we aim to explore the rapidly developing role of artificial intelligence (AI) in cardiac metabolism research, highlighting its impact on biomarker discovery, precision medicine, and patient stratification. Cardiac metabolism, a key determinant of cardiovascular health, is often disrupted in cardiovascular diseases (CVDs) like heart failure and coronary artery disease. AI’s ability to process and analyze large-scale data offers new chances for understanding and addressing these metabolic dysfunctions. By integrating up-to-date technologies with molecular and clinical insights, AI enables the achievement of personalized treatments, more accurate diagnostics, and the discovery of potential novel therapeutic targets. The main challenges include ethical concerns around data privacy, algorithmic bias, and the need for representative datasets. Future directions focus on developing transparent, accountable, and collaborative AI models that integrate data and enable real-time monitoring, ensuring fairness and accessibility in healthcare. As AI continues to evolve, its role in advancing cardiovascular care is expected to grow, offering new trends in cardiovascular research.Metabolism and Target Organ Damage130The rapidly developing role of artificial intelligence (AI) in cardiac metabolism research is explored, highlighting its impact on biomarker discovery, precision medicine, and patient stratification.Metabolism and Target Organ Damage["An-Tian Chen", "Yuhui Zhang", "Jian Zhang"]2025-01-07T00:00:00 c08316f20fba1c50b2d723f5cfac65239fdd47e7The paper studies the association between the artificial intelligence (AI) and employment characteristics. As a theoretical framework, we use the Acemoglu et al. model, which introduces opposing effects of the AI algorithms on labor employment on the firm level such as substitution effect and complimentary/ productivity effects. Depending on their relative strength, the AI algorithms may both decrease and increase employment. The overall effect is estimated using the data on 3.5 million of vacancies for about 35 thousand firms published up to 2022 spring on the HeadHunter website. According to the results, the existence of tasks realizable using AI is consistent with a higher AI employment, which implies the substitution effect. The other, less intuitive, result is that the same tasks suggest a higher non-AI employment, supports complimentary/productivity effects. The overall employment effect is positive as follows from the positive AI tasks—total employment association.Voprosy Ekonomiki140The paper uses the Acemoglu et al. model, which introduces opposing effects of the AI algorithms on labor employment on the firm level such as substitution effect and complimentary/ productivity effects, to study the association between the artificial intelligence and employment characteristics.Voprosy Ekonomiki["A. S. Skorobogatov", "O. I. Sviridov"]2025-01-07T00:00:00 749f2c356eabbb7dd0422f968ab9ad7b9662bd05Objective: The article aims to evaluate the level of transparency in administrative decisions in light of artificial intelligence for sustainable development goals.   Theoretical Framework: It should be noted that administrative decisions have always played an important role in the context of contemporary events, and without them, ensuring legal security is practically unimaginable. Not without reason does transparency occupy the most crucial place in administrative decisions.   Method: This qualitative approach involved engaging a panel of subject matter experts who possess extensive knowledge and experience in administrative law, artificial intelligence applications in governance, and public administration within Jordan.   Results and Discussion: The adoption of artificial intelligence in administrative decision-making processes in Jordan requires a comprehensive framework to ensure transparency. The current low index of transparency indicates that citizens may not fully understand how decisions are being made or the role that artificial intelligence plays in these processes.   Research Implications: The research has limitations which involve not considering all possible indicators and benchmarks for evaluation. Future research will involve expanding the list of consolidated indicators.   Originality/value: Education and awareness campaigns are crucial for improving transparency. By educating the public about how artificial intelligence is used in administrative decisions, the government can demystify the technology and reduce misconceptions.Journal of Lifestyle and SDGs Review320Journal of Lifestyle and SDGs Review["Khaled Khalaf Abed Rabbo Aldrou"]2025-01-07T00:00:00 3fd29f4023943ae62620535541fb63be09a0a14bThe field of artificial intelligence offers many opportunities and challenges. A revolutionary transformation is taking place around the world and is changing established processes and traditional models. The energy sector is not an exception. Today, conventional power grid is used in Georgian State electro system and is planned to introduce a smart grid in the future. Also, the company considers to replace the existing remedial action scheme with an artificial intelligence-based, “Autonomous RAS” program. It is important to reveal the views of the employees of Georgian State electro system and discuss their attitudes towards artificial intelligence programs, as well as to determine the factors that affect the implementation and development of the mentioned programs. According to results most of the employees have a positive attitude towards artificial intelligence programs and believe they will be beneficial for both the industry and the country in the future. The reasons for the positive attitude were considered to be knowledge of digital technologies at the basic level, perceived benefits by the staff, such as improved efficiency and forecasting capabilities, also reliability and accuracy of artificial intelligence programs. The reasons for the negative attitude were considered to be the fear of losing jobs, the use of artificial intelligence programs for undesirable purposes, the violation of confidentiality. The results also revealed that employee attitudes can significantly affect the implementation of artificial intelligence programs. A positive attitude can lead to enthusiastic acceptance of programs and increased productivity, the negative one will cause resistance, delays, or non-optimal use of artificial intelligence programs.Economic Profile00According to results most of the employees of Georgian State electro system have a positive attitude towards artificial intelligence programs and believe they will be beneficial for both the industry and the country in the future.Economic Profile["Tinatin Magradze", "Lili Bibilashvili"]2025-01-07T00:00:00 3b830bbfd114dc7bb527e08aaed26ad12e5f24b1Scientific Reports230External validation demonstrated the AI model’s high predictive accuracy and reliability in assessing in-hospital mortality risk among trauma patients across different injury severities and heterogeneous cohorts, supporting the model’s potential integration into emergency departments and offer a significant tool for enhancing patient triage and treatment protocols.Scientific Reports["Seungseok Lee", "Do Wan Kim", "Na-eun Oh", "Hayeon Lee", "Seoyoung Park", "D. Yon", "Wu Seong Kang", "Jinseok Lee"]2025-01-07T00:00:00 5ea994b1b1d5214564e343474c07eb7c75f914ddThis research examines the transformative role of artificial intelligence (AI) in global supply chain management, analysing its applications, benefits, and challenges across various supply chain functions. Through a comprehensive analysis of current implementations and industry practices, the study reveals how AI technologies are revolutionizing supply chain operations through enhanced demand forecasting, inventory optimization, logistics management, and supplier risk assessment. The research identifies key success factors in AI implementation while addressing critical challenges including data privacy concerns, technical integration difficulties, cost barriers, and ethical considerations. The findings demonstrate that successful AI integration in supply chains requires a balanced approach combining technological innovation with careful consideration of organizational readiness, data security, and ethical implications. The study concludes by proposing strategic recommendations for organizations seeking to leverage AI in their supply chain operations and highlighting future development directions as AI technology continues to evolve. But the insights from industry leaders like Amazon and Alibaba may not fully apply to SMEs, which face unique challenges such as limited resources and technological readiness, highlighting the need for future research on scalable, cost-effective AI solutions and collaborative strategies to support their adoption.Advances in Economics, Management and Political Sciences00It is revealed how AI technologies are revolutionizing supply chain operations through enhanced demand forecasting, inventory optimization, logistics management, and supplier risk assessment, and future development directions as AI technology continues to evolve.Advances in Economics, Management and Political Sciences["Shengkun Zhang"]2025-01-07T00:00:00 c54137184f82c32cfbf296622831697637c5173eRecently, social work has witnessed a notable increase in the utilization of artificial intelligence is evident across diverse professional fields, creating an urgent need to consider the ethical aspects associated with these advanced technologies. Despite the critical importance of these ethical considerations, research in this area remains limited. Addressing ethical guidelines which is related to the impact of AI on social work education within the Arab context. This situation raises a fundamental question about how to manage the influence of these technologies on education and the level of awareness among academics regarding these ethical guidelines, particularly within the Arab context. The current study explores the ethical implications of artificial intelligence in social work education within higher education institutions in Egypt. The object of this study is to measure the awareness level among academics in Egyptian schools and institutes of social work. The study sample consists of 172 social work faculty members all over Egypt, including 89 males and 83 females, aged between 30 and 58 years. A set of recommendations has been proposed to enhance ethical practices of artificial intelligence in higher education institutions for social work.Egyptian Journal of Social Work240The object of this study is to measure the awareness level among academics in Egyptian schools and institutes of social work, and to enhance ethical practices of artificial intelligence in higher education institutions for social work.Egyptian Journal of Social Work["mohamed abdelhakim khalaf"]2025-01-07T00:00:00 640e1cdf54e7d4f2a2dc56d592db985992e53f0dArtificial intelligence (AI) technologies have significantly transformed stock market prediction, offering novel approaches for financial forecasting. This review focuses on AI's integration into stock prediction, emphasizing key methodologies, such as machine learning (ML) and hybrid models, and emerging trends like quantum computing and blockchain technologies. The synergistic combination of AI and traditional financial analysis has yielded impressive improvements in accuracy. However, challenges such as data quality, overfitting, and legal concerns remain. This paper aims to provide insights into the current and future landscape of AI in stock prediction and its potential for revolutionizing financial markets.Advances in Economics, Management and Political Sciences00This review focuses on AI's integration into stock prediction, emphasizing key methodologies, such as machine learning and hybrid models, and emerging trends like quantum computing and blockchain technologies.Advances in Economics, Management and Political Sciences["He Zhu"]2025-01-07T00:00:00 d821c5b08a9e1f057a11ef9e2b6ea57d6615d82cIn the 21st century, advances in computer technology and data science have brought significant innovation to orthodontics, especially through Artificial Intelligence (AI) and Machine Learning (ML). This study, conducted from July 2 to August 15, 2024, in the Orthodontic Department at Rawal Institute of Health Sciences Islamabad, reviews AI’s transformative role in dentistry, focusing on its applications, benefits, and challenges. A comprehensive literature search across PubMed and Google Scholar yielded 260 peer-reviewed articles from 2001 to 2024. After applying stringent selection criteria, the review focused on AI's historical development, applications, and limitations in orthodontics. While AI enhances diagnostic imaging and patient care, it cannot replace clinical expertise. Key challenges include patient privacy, data security, and ethical considerations. AI systems rely heavily on high-quality data, necessitating rigorous training. Therefore, AI should be viewed as an adjunct in orthodontics, providing a “second opinion” to support clinical decisions.Journal of Bahria University Medical and Dental College00Artificial Intelligence should be viewed as an adjunct in orthodontics, providing a “second opinion” to support clinical decisions, and key challenges include patient privacy, data security, and ethical considerations.Journal of Bahria University Medical and Dental College["Sadia Naureen"]2025-01-07T00:00:00 2173fdff57999d08be9fa130a3bae7943f8598c0 Purpose Indian patients have different medicine systems available at the service that alter their healthseeking behaviour (HSB). This study aims to examine the beliefs and behaviour of patients in India towards the healthcare system and how it affects their intention to use healthcare chatbots. Design/methodology/approach A survey instrument was developed from standard scales and validated by experts. The data was collected from 397 respondents in an urban area and tested using a structural equation model in SAS JMP software. Findings The study found that awareness and perception of chatbots and distrust on doctors and health systems impact trust in a chatbot. The results show that trust in chatbots influences the intention to use chatbots. The belief in alternative medicine systems and HSB also influence the intention to use chatbots. The study findings also imply that health-care chatbots should cater to HSB and the belief in alternative medicine. Research limitations/implications The study was conducted only among the urban population because services based on technology are more available in metro cities. Bengaluru is considered the representative population of urban India. Practical implications The level of disruption that chatbots can provide to the healthcare system makes this study significant. The study findings will help to manage the factors that can enable chatbot inclusivity, as the current system is inaccessible to many patients. Originality/value This paper addresses an identified need to study patients’ trust in the Indian healthcare system and their intention to use chatbots. The level of disruptions these chatbots can cause in the health-care system is undeniable and patients’ trust in these chatbots will eventually transform the health-care sector. Journal of Asia Business Studies591The study found that awareness and perception of chatbots and distrust on doctors and health systems impact trust in a chatbot and imply that health-care chatbots should cater to HSB and the belief in alternative medicine.Journal of Asia Business Studies["A. Chellasamy", "Elangovan N.", "Aishwarya Nagarathinam", "Sangeetha Rangasamy"]2025-01-07T00:00:00 cb84fb71b009d556f72bf8600dfb456eff6573cfDiscover Food280Discover Food["Gull-e-laala Khan", "Gulshan Irshad", "Raina Ijaz", "Sabah Javaid", "Noor Tahir", "Sajid Mehmood"]2025-01-07T00:00:00 26ce1eceee31b0f1d519cc16646757bcca3db374Frontiers in Big Data00Frontiers in Big Data["Pavlos Papadopoulos", "Sokratis K. Katsikas", "Nikolaos Pitropakis"]2025-01-07T00:00:00 ab3d5f12622ea4e8546ce4294ad2b4698c2e9c73International Journal of Green Energy250International Journal of Green Energy["Muhammad Irfan", "S. Yasin", "U. Draz", "Tariq Ali", "Isha Yasin", "Tareq Kareri", "S. Rahman"]2025-01-07T00:00:00 e8c348a71fb0c51f242e05179da5756b8cef69a7Frontiers in Public Health00Frontiers in Public Health["P. Jeanty", "M. Narcisse", "Romain Crastes Dit Sourd"]2025-01-07T00:00:00 64e7594a9334ec6d1b504f08434a9314185f8a17Journal of Asian Public Policy280Journal of Asian Public Policy["G. Capano", "A. He", "Sean McMinn"]2025-01-07T00:00:00 52fc922811c10bd7cb21c9b9fe619ed82cf66a04Current Anesthesiology Reports540Current Anesthesiology Reports["Renato Andr\u00e9 Amorim Gomes Carneiro", "Lu\u00eds Alberto Guimar\u00e3es Pereira"]2025-01-07T00:00:00 6e2e875e7ce840a00469dafda5fb159ad2f143caConference on Computing Education Practice150{"pages": "17-20"}["Joseph Maguire", "Rosanne English", "Qi Cao", "C. Seow"]2025-01-07T00:00:00 17164cf9f9dc2c4011cd5d9842e84f2a89fbafe8Communications Medicine240A deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated demonstrates the utility and the potential of ambulatory cardiac hemodynamic monitoring with electrocardiogram patch-monitors.Communications Medicine["Daphne E. Schlesinger", "Ridwan Alam", "Roey Ringel", "E. Pomerantsev", "Srikanth R. Devireddy", "Pinak Shah", "Joseph Garasic", "Collin M. Stultz"]2025-01-07T00:00:00 b1c77a5594dc8ee60e1d25cc7bc8469ae71bd22aJurnal Balireso: Jurnal Pengabdian pada Masyarakat00Jurnal Balireso: Jurnal Pengabdian pada Masyarakat["Salmia Syarifuddin", "Setyawati Yani", "Nurjannah Abna"]2025-01-07T00:00:00 43e691335005e72556646e6b13da551f0dfc867cConsidering that creative thinkers are individuals who can think outside of the box, exhibit original thoughts, and demonstrate problem-solving skills, it is likely that there is a relationship between mathematical problem-solving beliefs (MPSBs) and creative thinking dispositions (CTDs). This study aimed to predict teachers’ MPSBs with their CTDs and some demographic features. Three different artificial intelligence models (fuzzy logic, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS)) were developed, and artificial data were obtained. The inputs of the research were determined as CTD, gender, age, educational level, school level, and teaching experiences, and the output was determined as MPSBs. Afterward, whether there was a relationship between real and artificial results was examined with statistical analysis. The research results show that there is a statistically significant, positive, and moderate relationship between artificial ANN MPSB scores and real MPSB scores (r = 0.422; p < 0.05), as well as artificial ANFIS MPSB scores and real MPSB scores (r = 0.564; p < 0.05). These results are important sources of evidence indicating that artificial intelligence methods accurately predict teachers’ MPSB scores.Applied Sciences840There is a statistically significant, positive, and moderate relationship between artificial ANN MPSB scores and real MPSB scores, as well as artificial ANFIS MPSB scores and real MPSB scores, indicating that artificial intelligence methods accurately predict teachers’ MPSB scores.Applied Sciences["Seda G\u00f6ktepe K\u00f6rpeo\u011flu", "Ahsen Filiz", "Sevda G\u00f6ktepe Y\u0131ld\u0131z"]2025-01-07T00:00:00 356ab47c68b29bb13acd1b70ced218849fee08c8This book immerses us in an exciting journey through the world of Artificial Intelligence (AI) and its impact on education, with 256 pages full of innovative ideas and deep reflections, the author invites us to rethink the meaning of teaching and learning in the digital age.Emerging Trends in Education00Emerging Trends in Education["Jorgelina Kolodzinski"]2025-01-07T00:00:00 82f71b739a90968b99f7d3d92a689de5c287e371Nature Network Boston213Compared to standard double reading, AI-supported double reading was associated with a higher breast cancer detection rate without negatively affecting the recall rate, strongly indicating that AI can improve mammography screening metrics.Nature medicine["Nora Eisemann", "Stefan Bunk", "Trasias Mukama", "Hannah Baltus", "Susanne A. Elsner", "Timo Gomille", "Gerold Hecht", "Sylvia Heywang-K\u00f6brunner", "Regine Rathmann", "Katja Siegmann-Luz", "Thilo T\u00f6llner", "Toni Werner Vomweg", "Christian Leibig", "Alexander Katalinic"]2025-01-07T00:00:00 d1b185206d0e8f3d3a62c17c3fe4f777ce1ebd15Over time, law enforcement in Indonesia has experienced various challenges. One of the main issues affecting public trust is the law enforcement of corruption. Currently, efforts to eradicate corruption in Indonesia remain unoptimal due to the issues of integrity and evolving modes of corruption. This article aims to find out, describe, and analyze the phenomenon of law enforcement of corruption and propose alternative solutions to answer these challenges through the use of Artificial Intelligence (AI) technology. This research employed doctrinal legal research methods, resulting in a conceptual framework of law enforcement through the use of machine learning in supporting the detection of corruption-related fund flows in Indonesia. Recommendations include the need for synergy between various parties, strengthening legal culture, and establishing legal norms related to machine learning that support the decision support system.Pakistan Journal of Criminology00This research employed doctrinal legal research methods, resulting in a conceptual framework of law enforcement through the use of machine learning in supporting the detection of corruption-related fund flows in Indonesia.Pakistan Journal of Criminology[]2025-01-07T00:00:00 cd14ab9a3ae64eba7b0909e31de3e188ee7cb172This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.Proceedings of 42nd International Conference on High Energy Physics — PoS(ICHEP2024)01A DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification is developed, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.Proceedings of 42nd International Conference on High Energy Physics — PoS(ICHEP2024)["J. Kvapil", "G. Borca-Tasciuc", "H. Bossi", "K. Chen", "Y. Chen", "Y. Morales", "H. D. Costa", "C. D. Silva", "C. Dean", "J. Durham", "S. Fu", "C. Hao", "P. Harris", "O. Hen", "H. Jheng", "Y. Lee", "P. Li", "X. Li", "Y. Lin", "M. X. Liu", "V. Loncar", "J. P. Mitrevski", "A. Olvera", "M. Purschke", "J. S. Renck", "G. Roland", "J. Schambach", "Z. Shi", "N. Tran", "N. Wuerfel", "B. Xu", "D. Yu", "H. Z. L. A. N. Laboratory", "Rensselaer Polytechnic Institute", "M. I. O. Technology", "Central China Normal University", "U. Texas", "G. I. O. Technology", "Brookhaven National Laboratory", "Fermilab", "Oak Ridge National Laboratory", "U. Michigan", "New Jersey Institute of Technology"]2025-01-07T00:00:00 fc1166183a484a32b151148790c6600590cac7cbThe research focuses on the use of artificial intelligence techniques in the development and enhancement of digital databases. In light of the rapid advancement of the digital age and the increasing volume of data, there has been a growing need for the development of advanced solutions to efficiently process and manage this data. The study concluded that integrating artificial intelligence into database systems provides multiple benefits, including improved data management efficiency through intelligent classification and organization, and the development of search mechanisms to provide more accurate results tailored to user needs. This integration also allows for the rapid processing and analysis of large volumes of data, making complex analyses easier and facilitating the extraction of valuable insights. An important aspect revealed by the study is AI’s ability to enhance cybersecurity for databases by enabling early detection of potential threats and suspicious activities. The study concluded that employing artificial intelligence in the field of databases represents a fundamental evolution in information management, making it a crucial tool for development across various fields.Journal of Sensor Networks and Data Communications00The study concluded that employing artificial intelligence in the field of databases represents a fundamental evolution in information management, making it a crucial tool for development across various fields.Journal of Sensor Networks and Data Communications["Ahmed Shaker Alalaq"]2025-01-07T00:00:00 9c7ce8a2ccccde86ad1766d650f0cf4f23ef37beAI and Ethics140The legal and ethical implications of AI in automated crowd analysis, with a focus on the European perspective, are investigated and recommendations offer a foundational framework for ethical AI deployment, with universal applicability to benefit citizens globally.AI and Ethics["Emmeke Veltmeijer", "Charlotte Gerritsen"]2025-01-07T00:00:00 6e45dc777020eb0b830d47c6f29838e591dd425dIt is widely known that technology, especially artificial intelligence (AI), presents innovative opportunities for improving English language learning skills. However, little is known about the effect of AI-mediated activities on learners' speaking skills. This study, therefore, aims to explore the EFL learners' attitudes and perceptions of the use of AI-powered conversational tools to improve speaking fluency. Forty EFL learners at Majmaah University were randomly assigned to respond to a structured questionnaire to collect quantitative data, followed by an individual semi-structured interview in the qualitative phase. Thematic analysis was employed for qualitative data, while descriptive and inferential statistics through SSPS were used for quantitative data. The findings of the study indicate that EFL learners at Majmaah University have a positive attitude and perceptions toward using AI-powered conversational tools to enhance and promote their speaking fluency. Moreover, EFL learners at Majmaah University agreed that AI-powered conversational tools improved their speaking fluency, and they felt that their pronunciation was enhanced after using these tools. Additionally, the learners reported significant limitations and challenges, including technical difficulties, such as pronunciation recognition and cultural misinterpretations. In addition, most of the EFL learners in the interviews reported that the lack of personalized feedback was a crucial challenge. The study provides valuable insights for language educators and researchers regarding technology-mediated instruction in language classrooms.Forum for Linguistic Studies00EFL learners at Majmaah University have a positive attitude and perceptions toward using AI-powered conversational tools to enhance and promote their speaking fluency, and they felt that their pronunciation was enhanced after using these tools.Forum for Linguistic Studies["Ammar Mohammed Ahmed Mudawy"]2025-01-07T00:00:00 76e9611febc5b194becf9bd2e1ef974eeff1cffeJournal of Robotic Surgery110The findings indicate substantial advancements in AI-driven surgical systems, improving decision-making, reducing surgical errors, and facilitating personalized treatment strategies, which collectively improve procedural efficiency and patient safety.Journal of robotic surgery["Jack Ng Kok Wah"]2025-01-07T00:00:00 39cd8a00039307a188b784d8dbe3a8b956ef0b59AI &amp; SOCIETY130A novel decision-making framework to bolster the “social trust” inherent to blockchain technology by facilitating informed economic activities in cyberspace by integrating two artificial intelligence systems into a blockchain-based decentralized autonomous organization (DAO).AI &amp; SOCIETY["Yuichi Ikeda", "Rafik Hadfi", "Takayuki Ito", "Akihiro Fujihara"]2025-01-07T00:00:00 24f17b02d639386c3810538bbfbf08b3d95b2696The recent surge in the field of generative artificial intelligence (GenAI) has the potential to bring about transformative changes across a range of sectors, including software engineering and education. As GenAI tools, such as OpenAI's ChatGPT, are increasingly utilised in software engineering, it becomes imperative to understand the impact of these technologies on the software product. This study employs a methodological approach, comprising web scraping and data mining from LeetCode, with the objective of comparing the software quality of Python programs produced by LeetCode users with that generated by GPT-4o. In order to gain insight into these matters, this study addresses the question whether GPT-4o produces software of superior quality to that produced by humans. The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale. Indeed, the generated code even exhibits significantly lower values across all three metrics in comparison to the user-written code. However, no significantly superior values were observed for the generated code in terms of memory usage in comparison to the user code, which contravened the expectations. Furthermore, it will be demonstrated that GPT-4o encountered challenges in generalising to problems that were not included in the training data set. This contribution presents a first large-scale study comparing generated code with human-written code based on LeetCode platform based on multiple measures including code quality, code understandability, time behaviour and resource utilisation. All data is publicly available for further research.620The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale, and the generated code even exhibits significantly lower values across all three metrics in comparison to the user-written code.["Manuel Merkel", "Jens Dorpinghaus"]2025-01-07T00:00:00 eac2146ccf259d552fd7ba6bb584375cdc21ff60 Orthodontic treatment planning has traditionally relied on manual assessments, which can be time-consuming and prone to errors. The integration of artificial intelligence (AI) has the potential to revolutionise this process, enhancing accuracy and efficiency. A systematic review and meta-analysis were conducted to evaluate the use of AI in orthodontic treatment planning. Seven databases were searched, and studies were selected based on predetermined criteria. Bias evaluation was performed using the QUADAS-AI tool. The meta-analysis revealed a significant overall effect in favour of AI-based methods in determining cephalometric landmarks (pooled mean difference [MD]: 2.85, 95% confidence interval [CI]: 1.48, 4.22) and teeth segmentation (pooled MD: 2.89, 95% CI: 1.53, 4.26). Substantial heterogeneity was observed in both analyses, indicating significant differences between studies. However, the overall results suggest that AI-based methods demonstrate superior accuracy in orthodontic and dental imaging assessments. This systematic review and meta-analysis provide evidence for the potential of AI in enhancing the accuracy and efficiency of orthodontic treatment planning. The results highlight the benefits of AI in improving the speed and accuracy of orthodontic and dental procedures, as well as its potential to augment human capabilities in orthodontic and dental imaging assessments. Further research is needed to develop and refine AI-based systems for specific applications.Advances in Human Biology401A systematic review and meta-analysis suggest that AI-based methods demonstrate superior accuracy in orthodontic and dental imaging assessments, as well as its potential to augment human capabilities in orthodontic and dental imaging assessments.Advances in Human Biology["N. Ingle", "Nisrin Fouad Alabsi", "Hashim Al-Hashimi", "Nada Ahmed Albuolayan", "Faey Alburidy", "Fatimah Alanazi", "Arwa Tawfiq Alhammad"]2025-01-08T00:00:00 d1e4140035b1599de225a44b1be4dbcf3ac506a2The transportation sector is considered one of the important sectors in all countries because of its importance in keeping pace with the development and technology used in various fields. Today, the world competes to provide the best services to customers and citizens. The transportation sector is divided in all countries. In light of the technology used today by some countries, it is considered a qualitative addition to the transportation sector. In this study, three main variables were taken (organizational climate, artificial intelligence, Employee creativity Behavior). A random sample of workers in the transportation sector was taken using a questionnaire as a main tool in collecting data. 220 transport sector workers were selected. The data collected was analyzed through the SPSS statistical program, Model 24, and through the structural equation model, SEM - AMOS program, Model 24. The results showed that the role of the mediator, artificial intelligence, is very important in mediating the relationship between the organizational climate and Employee creativity Behavior. In addition, artificial intelligence affects Employee creativity Behavior, and the organizational climate affects artificial intelligence and innovation directly and indirectly with a positive statistical significance.Journal of Ecohumanism01The results showed that the role of the mediator, artificial intelligence, is very important in mediating the relationship between the organizational climate and Employee creativity Behavior.Journal of Ecohumanism["Hussain K. Hussain Alagele", "N. H. Neama", "Natalya Ahmed Alkaseer", "Haidar Ali Al Dulaimi", "Sanaa Eesee Abd"]2025-01-08T00:00:00 f4273bd090fae6bf129a323cdcd87cf4b8f5e25aJOM410JOM["Jian Cao", "Ling Zhou", "Fan Gan", "Zhipeng You"]2025-01-08T00:00:00 c3bc300dd1fb772ba0560701eb073cd9ffb212b6Introduction Adverse events in hospitals significantly compromise patient safety and trust in healthcare systems, with medical errors being a leading cause of death globally. Despite efforts to reduce these errors, reporting remains low, and effective system changes are rare. This systematic review explores the potential of artificial intelligence (AI) in clinical risk management. Methods The systematic review was conducted using the PRISMA Statement 2020 guidelines to ensure a comprehensive and transparent approach. We utilized the online tool Rayyan for efficient screening and selection of relevant studies from three different online bibliographic. Results AI systems, including machine learning and natural language processing, show promise in detecting adverse events, predicting medication errors, assessing fall risks, and preventing pressure injuries. Studies reveal that AI can improve incident reporting accuracy, identify high-risk incidents, and automate classification processes. However, challenges such as socio-technical issues, implementation barriers, and the need for standardization persist. Discussion The review highlights the effectiveness of AI in various applications but underscores the necessity for further research to ensure safe and consistent integration into clinical practices. Future directions involve refining AI tools through continuous feedback and addressing regulatory standards to enhance patient safety and care quality.Frontiers in Medicine830AI systems, including machine learning and natural language processing, show promise in detecting adverse events, predicting medication errors, assessing fall risks, and preventing pressure injuries, but challenges such as socio-technical issues, implementation barriers, and the need for standardization persist persist.Frontiers in Medicine["Francesco De Micco", "Gianmarco Di Palma", "Davide Ferorelli", "Anna De Benedictis", "L. Tomassini", "V. Tambone", "Mariano Cingolani", "R. Scendoni"]2025-01-08T00:00:00 22e1ecbe903864d77b3afabb38f97ce279c23303This study aims to design, produce, and validate an information collection instrument to evaluate the opinions of teachers at non-university educational levels on the quality of training in artificial intelligence (AI) applied to education. The questionnaire was structured around five key dimensions: (a) knowledge and previous experience in AI, (b) perception of the benefits and applications of AI in education, (c) AI training, and (d) expectations of the courses and (e) impact on teaching practice. Validation was performed through expert judgment, which ensured the internal validity and reliability of the instrument. Statistical analyses, which included measures of central tendency, dispersion, and internal consistency, yielded a Cronbach's alpha of .953, indicating excellent reliability. The findings reveal a generally positive attitude towards AI in education, emphasizing its potential to personalize learning and improve academic outcomes. However, significant variability in teachers' training experiences underscores the need for more standardized training programs. The validated questionnaire emerges as a reliable tool for future research on teachers' perceptions of AI in educational contexts. From a practical perspective, the validated questionnaire provides a structured framework for assessing teacher training programs in AI, offering valuable insights for improving educational policies and program design. It enables a deeper exploration of educational AI, a field still in its early stages of research and implementation. This tool supports the development of targeted training initiatives, fostering more effective integration of AI into educational practices.European Journal of Educational Research450The validated questionnaire provides a structured framework for assessing teacher training programs in AI, offering valuable insights for improving educational policies and program design and enables a deeper exploration of educational AI.European Journal of Educational Research["Manuel Reina-Parrado", "Pedro Rom\u00e1n-Grav\u00e1n", "Carlos Herv\u00e1s-G\u00f3mez"]2025-01-08T00:00:00 7463e42eee13b5f5c2c9cb594c8f0d83b93f639cScientific Reports270This study aims to assess the effectiveness and the perceptual impact of AI-generated voices on ALS patients with preserved speech, utilizing a personalized voice synthesis system based on machine learning.Scientific Reports["S. Regondi", "Giordana Donvito", "Emanuele Frontoni", "M. Kostovic", "Fabio Minazzi", "S\u00e9bastien Brati\u00e8res", "Massimiliano Filosto", "Raffaele Pugliese"]2025-01-08T00:00:00 f08608a4f73d7380e0d10a502f76202efd462809PurposeDespite the potential of artificial intelligence (AI) systems to increase revenue, reduce costs and enhance performance, their adoption by organisations has fallen short of expectations, leading to unsuccessful implementations. This paper aims to identify and elucidate the factors influencing AI adoption at both the organisational and individual levels. Developing a conceptual model, it contributes to understanding the underlying individual, social, technological, organisational and environmental factors and guides future research in this area.Design/methodology/approachThe authors have conducted a systematic literature review to synthesise the literature on the determinants of AI adoption. In total, 90 papers published in the field of AI adoption in the organisational context were reviewed to identify a set of factors influencing AI adoption.FindingsThis study categorised the factors influencing AI system adoption into individual, social, organisational, environmental and technological factors. Firm-level factors were found to impact employee behaviour towards AI systems. Further research is needed to understand the effects of these factors on employee perceptions, emotions and behaviours towards new AI systems. These findings led to the proposal of a theory-based model illustrating the relationships between these factors, challenging the assumption of independence between adoption influencers at both the firm and employee levels.Originality/valueThis study is one of the first to synthesise current knowledge on determinants of AI adoption, serving as a theoretical foundation for further research in this emerging field. The adoption model developed integrates key factors from both the firm and individual levels, offering a holistic view of the interconnectedness of various AI adoption factors. This approach challenges the assumption that factors at the firm and individual levels operate independently. Through this study, information systems researchers and practitioners gain a deeper understanding of AI adoption, enhancing their insight into its potential impacts.Management Decision1050This study is one of the first to synthesise current knowledge on determinants of AI adoption, serving as a theoretical foundation for further research in this emerging field.Management Decision["Ahmad A. Khanfar", "Reza Kiani Mavi", "Mohammad Iranmanesh", "Denise Gengatharen"]2025-01-08T00:00:00 c74cfc585ed38ac8413b26acadbbf55efc195d1aThis study examines the gender discrimination of Artificial Intelligence (AI) used in the legal system, focusing on risk assessment, facial recognition, and decision-making and decision-support tools. The study delves into the use of AI in the legal system, examining how its reliance on historical data, under/over-representation, and homogeneity of development teams perpetuate existing gender biases. The study then analyses the implications of the United Kingdom General Data Protection Regulation (UK GDPR) and the proposed Data Protection and Digital Information (DPPI) Bill in addressing gender biases in AI. Nevertheless, the study finds the need for a more robust and proactive legal framework that addresses the root causes of these biases in the design and implementation of AI systems. The paper concludes by proposing a framework to effectively address gender bias in AI systems used in the legal system. The framework outlines explicit obligations across policymakers, companies, and end users to ensure the development and deployment of bias-free AI systems. Its role is to provide comprehensive guidelines and oversight mechanisms that promote proactive measures to prevent gender bias. The framework aims to create a more equitable legal environment for everyone.Lecture Notes in Education Psychology and Public Media500The paper concludes by proposing a framework to effectively address gender bias in AI systems used in the legal system, and outlines explicit obligations across policymakers, companies, and end users to ensure the development and deployment of bias-free AI systems.Lecture Notes in Education Psychology and Public Media["Muzeng Huang"]2025-01-08T00:00:00 9dbafe486d197cc1d958991eb447058c785836db This Patient Page describes ways that artificial intelligence can help treat skin, hair, and nails. JAMA dermatology00JAMA dermatology["Shannon Wongvibulsin", "Ivy Lee"]2025-01-08T00:00:00 6843ae0d0e29c8a898cab907b5d4c5bea5b966ecArchives of Computational Methods in Engineering1820While CT and ultrasound proved to be the ideal imaging modalities for cancer detection, MRI was helpful for cancer staging and bestows a roadmap to fully utilize the potential of AI in early cancer detection and staging to enhance patient survival.Archives of Computational Methods in Engineering["Gayathri Bulusu", "K. Vidyasagar", "Malini Mudigonda", "Manob Saikia"]2025-01-08T00:00:00 37e0288fc2e98aa3e06f9fa51e4b04005ac7cb99Nature Communications400It was found that intermediately-sized follicles were most important to the number of mature oocytes subsequently retrieved, and Maximizing this proportion of follicles by the end of ovarian stimulation was associated with improved live birth rates and premature progesterone elevation by the end of ovarian stimulation.Nature Communications["S. Hanassab", "Scott M. Nelson", "A. Akbarov", "Arthur C. Yeung", "A. Hramyka", "T. Alhamwi", "Rehan Salim", "A. Comninos", "Geoffrey H. Trew", "T. Kelsey", "T. Heinis", "W. Dhillo", "A. Abbara"]2025-01-08T00:00:00 4979995f2ede0268eef7319235ac270326f00a12While machine learning offers diverse techniques suitable for exploring various medical research questions, a cohesive synergistic framework can facilitate the integration and understanding of new approaches within unified model development and interpretation. We therefore introduce the Medical Artificial Intelligence Toolbox (MAIT), an explainable, open-source Python pipeline for developing and evaluating binary classification, regression, and survival models on tabular datasets. MAIT addresses key challenges (e.g., high dimensionality, class imbalance, mixed variable types, and missingness) while promoting transparency in reporting (TRIPOD+AI compliant). Offering automated configurations for beginners and customizable source code for experts, MAIT streamlines two primary use cases: Discovery (feature importance via unified scoring, e.g., SHapley Additive exPlanations - SHAP) and Prediction (model development and deployment with optimized solutions). Moreover, MAIT proposes new techniques including fine-tuning of probability threshold in binary classification, translation of cumulative hazard curves to binary classification, enhanced visualizations for model interpretation for mixed data types, and handling censoring through semi-supervised learning, to adapt to a wide set of data constraints and study designs. We provide detailed tutorials on GitHub, using four open-access data sets, to demonstrate how MAIT can be used to improve implementation and interpretation of ML models in medical research.350The Medical Artificial Intelligence Toolbox (MAIT), an explainable, open-source Python pipeline for developing and evaluating binary classification, regression, and survival models on tabular datasets, is introduced.["R. Z. Marandi", "Anne Svane Frahm", "Jens Lundgren", "Daniel D. Murray", "Maja Milojevic"]2025-01-08T00:00:00 7d1d017f86ae4c34b8094d0c670a891e980c2231Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potential misrepresentations and consequent inequalities related to people with mental health disorders. Hence occupational and mental health bias in existing Natural Language Processing (NLP) models used in recruiting and job hunting must be assessed. We examined occupational bias against mental health disorders in NLP models through relationships between occupations, employability, and psychiatric diagnoses. We investigated Word2Vec and GloVe embedding algorithms through analogy questions and graphical representation of cosine similarities. Word2Vec embeddings exhibit minor bias against mental health disorders when asked analogies regarding employability attributes and no evidence of bias when asked analogies regarding high earning jobs. GloVe embeddings view common mental health disorders such as depression less healthy and less employable than severe mental health disorders and most physical health conditions. Overall, physical, and psychiatric disorders are seen as similarly healthy and employable. Both algorithms appear to be safe for use in downstream task without major repercussions. Further research is needed to confirm this. This project was funded by the London Interdisciplinary Social Science Doctoral Training Programme (LISS-DTP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.PLoS ONE1620This project examined occupational bias against mental health disorders in NLP models through relationships between occupations, employability, and psychiatric diagnoses and investigated Word2Vec and GloVe embedding algorithms through analogy questions and graphical representation of cosine similarities.PLOS ONE["Maximin Lange", "Alexandros Koliousis", "Feras Fayez", "Eoin Gogarty", "Ricardo Twumasi"]2025-01-08T00:00:00 7ec4da8649816ee9a5cf5770751017db8dd8e295In the era of artificial intelligence (AI), economic efficiency has an obvious role to play, but “non-economic benefits” have gradually become the focus of corporate attention; thus, environmental, social, and governance (ESG) has become a mainstream investment strategy. This paper empirically examines the impact of corporate application of AI technology on corporate ESG performance using a sample of 4858 listed companies in China from 2007 to 2022. The study finds that: (1) corporate application of AI technology can significantly enhance corporate ESG performance, and this conclusion still holds after a series of endogeneity treatments and robustness tests; (2) mechanism analysis shows that the degree of corporate digitalization has a positive moderating effect in the process of AI technology affecting corporate ESG performance. The channel analysis shows that the application of AI technology can enhance environmental (E) performance by strengthening corporate green technology innovation, social (S) performance by improving corporate philanthropic responsibility, and overall ESG performance with the above two sub-items as the main aspects. However, AI technology also weakens the effectiveness of corporate internal control, which leads to a decline in corporate governance (G) performance; (3) Heterogeneity analysis shows that AI technology promotes ESG more significantly in more competitive industries and tech-nology-intensive firms, and more significantly in the eastern and central regions than in the western and northeastern regions, and that large- and medium-sized firms are similarly superior to small-sized firms, while medium-sized firms have more room for upward mobility than large-sized firms, which embody a higher promotion effect than large enterprises. This paper provides theoretical evidence that enterprises apply AI technology to improve ESG performance and empirical support around investing in ESG practices and promoting ESG development.Sustainability320Theoretical evidence that enterprises apply AI technology to improve ESG performance and empirical support around investing in ESG practices and promoting ESG development are provided.Sustainability["Hanji Xie", "Fengquan Wu"]2025-01-08T00:00:00 549db08a17d44d74f16cf5775ac76d5399de5ea4Aesthetic Plastic Surgery170Exposure to AI photograph enhancement may significantly raise expectations for plastic surgery outcomes and may predispose to having lower satisfaction after surgery.Aesthetic plastic surgery["I. Taritsa", "J. Foppiani", "Maria Jose Escobar", "Daniela Lee", "Khoa Nguyen", "Angelica Hernandez Alvarez", "Kirsten Schuster", "Bernard T. Lee", "Samuel J. Lin"]2025-01-08T00:00:00 0ad27f3930e08495c82f7a059e71f3b849096cddAs schools increasingly adopt multidimensional, phenomenon-based, digital-technology-enhanced science instruction, a concurrent shift is occurring in student performance assessment. Assessment instruments capable of measuring multiple dimensions must incorporate constructed responses to probe students’ ability to explain scientific phenomena and solve problems. Such assessments, unlike traditional multiple-choice tests, are time-consuming and labor-intensive for teachers to score. This study investigates the potential of an artificial intelligence machine learning system (AI-MLS) to address two critical questions: (1) How accurately can the AI-MLS replicate human scoring of multidimensional science assessments? and (2) How can the implementation of AI-MLS promote educational equity and reduce teacher workload? The present paper describes the development of the AI-MLS to rapidly and accurately score third- to fifth-grade students’ constructed responses on multidimensional science assessments. It summarizes key findings from the study, discusses findings in the broader context of fostering agency through digital technology, and offers insights into how artificial intelligence technology can be harnessed to support independent action and decision-making by teachers and students.Education sciences290The present paper describes the development of the AI-MLS to rapidly and accurately score third- to fifth-grade students’ constructed responses on multidimensional science assessments and offers insights into how artificial intelligence technology can be harnessed to support independent action and decision-making by teachers and students.Education Sciences["Fatima E. Terrazas-Arellanes", "Lisa Strycker", "Giani Gabriel Alvez", "Bailey Miller", "Kathryn Vargas"]2025-01-08T00:00:00 3df552e38899d9e4aa8456cf0b1998807e4ce83aAs artificial intelligence (AI) gains more traction in education in the digital era, questions arise about the number of empirical studies available that have useful outcomes on the protocols for deploying the technology in the sector. This univariate descriptive survey was consequently conducted to examine whether the ethics of AI predict teacher integrity in the application of smart technologies in public primary schools in the digital age in Cross River State, Nigeria. Two hypotheses were formulated for the research. 1,600 teachers were recruited from 16 public primary schools across four education zones of the state to participate. Ethics of AI and Teacher Integrity in the Application of Smart Technologies Questionnaire (EAITIASTQ) was adopted to generate data. Based upon the Value Sensitive Design (VSD), simple linear regression was used to analyze data, aided by SPSS. Findings suggest that user transparency significantly predicts teacher integrity in the application of AI in public primary schools; user accountability significantly predicts teacher integrity in the utilization of AI in public primary schools. It is recommended that a sound ethical protocol on the application of AI in school be codified in documents and made available for all primary school teachers; experienced and skilful personnel in computer and AI operations have to conduct regular supervision of teachers in relation to the use of AI in elementary schoolsEast African Journal of Arts and Social Sciences00Findings suggest that user transparency significantly predicts teacher integrity in the application of AI in public primary schools; user accountability significantly predicts teacher integrity in the utilization of AI in public primary schools.East African Journal of Arts and Social Sciences["Ewa, Moses Apie"]2025-01-08T00:00:00 9cb0cdcae7b6379f1c0a9b552649d8a2d21c3f05Artificial intelligence and machine learning have revolutionized the pharmaceutical industry, offering new approaches to drug discovery and development. These techniques have the potential to improve the efficiency and accuracy of the drug discovery process, leading to the development of more effective medications.In particular, AI-based algorithms can be employed to predict the efficacy and toxicity of new drug compounds, as well as to identify new targets for drug development. This paper provides an overview of the current landscape of AI in large-molecule drug discovery, highlighting the increasing application of these techniques to areas such as antibodies, gene therapies, and RNA-based therapies. The paper also discusses the challenges and opportunities associated with the use of AI in pharmaceutical research and development, emphasizing the importance of balancing the promise of AI with a continued reliance on the scientific method. While the promise of AI in pharmaceutical research is significant, it is crucial to recognize the limitations of these technologies and to maintain a balanced approach that leverages the strengths of both AI-driven and traditional, scientific methods. By doing so, researchers and developers can harness the power of AI to accelerate the drug discovery process, while ensuring that the development of new drugs remains grounded in robust scientific principles.American Journal Of Applied Science And Technology00An overview of the current landscape of AI in large-molecule drug discovery is provided, highlighting the increasing application of these techniques to areas such as antibodies, gene therapies, and RNA-based therapies and the challenges and opportunities associated with the use of AI.American Journal of Applied Science and Technology["Nawras Yahya Hussein Al-Khafaji", "Sabreen Hassan Howaidy", "Zahraa Khawawm Abdulwahid"]2025-01-08T00:00:00 39432ac702e42c0bba4a42ed456f41ee39b1e2e8The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and AI. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how AI can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented. Both frameworks are exercised using notional scenarios and their benefits and limitations are then discussed. Finally, the perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.Risk Analysis60Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented and it is noted that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.Risk analysis : an official publication of the Society for Risk Analysis["Matthew E. Walsh"]2025-01-08T00:00:00 829e40d0e8a476fd822210668c6d79a619c51e26Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural Network models to enhance predictive accuracy for severe lower gastrointestinal (LGI) bleeding outcomes, including the need for surgery. To this end, artificial intelligence (AI)-guided predictive models have shown promise in improving management outcomes. While much literature focuses on AI in early neoplasia detection, this review highlights AI's role in managing LGI and small bowel disorders, including risk stratification for LGI bleeding, quality control, evaluation of inflammatory bowel disease, and video capsule endoscopy reading. Overall, the integration of AI into routine clinical practice is still developing, with ongoing research aimed at addressing current limitations and gaps in patient care.Journal of Clinical Gastroenterology580Artificial intelligence's role in managing LGI and small bowel disorders, including risk stratification for LGI bleeding, quality control, evaluation of inflammatory bowel disease, and video capsule endoscopy reading is highlighted.Journal of clinical gastroenterology["Mili Parikh", "S. Tejaswi", "Tavishi Girotra", "Shreya Chopra", "Daryl Ramai", "J. Tabibian", "Soumya Jagannath", "A. Ofosu", "Monique T. Barakat", "Rajnish Mishra", "M. Girotra"]2025-01-08T00:00:00 c443d53fcd38caa58f73a8938b5a7aad8fd15165The aim of this study was to determine the effect of training on the integration of artificial intelligence into education given to pre-service teachers on their concerns about artificial intelligence and their views on the integration of artificial intelligence into education. In this study, sequential explanatory design, one of the mixed research designs, was preferred. In the quantitative part of the research, single group quasi-experimental research design was used. In the qualitative part of the study, a basic qualitative research design was used. In the experimental process, a four-week artificial intelligence training program was administered to pre-service teachers for three hours a week. The study group consisted of 195 pre-service teachers. Data were collected using the artificial intelligence anxiety scale and a semi-structured interview form. The data obtained were analyzed using t, MANCOVA, and content analysis methods, and the following results were obtained: The training on the integration of artificial intelligence into education decreased pre-service teachers’ anxiety in the learning dimension but increased their anxiety in other dimensions. The main sources of anxiety are inequality, ethics, privacy, and reliability, professional and social anxiety, unpredictable decisions and loss of control, technology use and adaptation difficulties, artificial intelligence addiction, and decreased creativity. International Journal of Technology in Education460The training on the integration of artificial intelligence into education decreased pre-service teachers’ anxiety in the learning dimension but increased their anxiety in other dimensions, and the following results were obtained.International Journal of Technology in Education["R\u0131dvan Ka\u011fan A\u011fca", "Ozgen Korkmaz"]2025-01-08T00:00:00 86fa118234349efb71e062d077bf75e0577379adThis article is an invited contribution in the form of an essay, with the aim of illustrating the modalities of use and development of artificial intelligence in learning environments and as a support for educational design and research.The aim is to place electronic computing in an anthropological perspective, to outline the salient features of the new digital culture, and to articulate the most positive purpose of artificial intelligence, which is to aid in the creation, preservation and acquisition of knowledge. In the first part, I will show that access to symbolic cognition, which is unique to the human species, implies a correspondence between the sensible world and the intelligible world. Therefore, transformations of sensible objects can mean transformations of concepts. This is why, like language, the notion of calculation is inscribed in the very essence of the human being. In the second part, I'll sketch out a genealogy of automatic calculation that leads to contemporary culture, based on the collective feeding and real-time sharing of a digital memory common to humanity. The third part of the article describes the two main trends in contemporary artificial intelligence, symbolic models and neural models, with their advantages and disadvantages. I then suggest an original solution to overcome the division between the two approaches, combining the main advantages of both types of models while minimizing their disadvantages. The article concludes with a brief discussion of the problem of machine consciousness. El presente artículo es una contribución invitada en la modalidad de ensayo, en la perspectiva de ilustrar las modalidades de uso y desarrollo de la inteligencia artificial en entornos de aprendizaje y como apoyo al diseño y a la investigación educativa. Su objetivo es situar la computación en una perspectiva antropológica, delimitar las características más destacadas de la nueva cultura digital y articular el propósito más positivo de la inteligencia artificial, que es ayudar en la creación, preservación y adquisición de conocimiento. En la primera parte, el autor mostrará que el acceso al conocimiento simbólico, propio de la especie humana, implica una correspondencia entre el mundo sensible y el mundo inteligible. Por lo tanto, las transformaciones de los objetos sensibles pueden significar transformaciones de los conceptos. Por ello, al igual que el lenguaje, la noción de cálculo está inscrita en la esencia misma del ser humano. En la segunda parte, el autor esbozará una genealogía del cálculo automático que conduce a la cultura contemporánea, basada en la alimentación colectiva y la compartición en tiempo real de una memoria digital común a la humanidad. En la tercera parte del artículo se describen las dos tendencias principales de la inteligencia artificial contemporánea, los modelos simbólicos y los modelos neuronales, con sus ventajas y desventajas. A continuación, se propone una solución original para superar la división entre ambos enfoques, combinando las principales ventajas de ambos tipos de modelos y minimizando sus desventajas. El artículo concluye con una breve discusión del problema de la conciencia de la máquina .Resource Discovery370Revista de Educación a Distancia (RED)["Pierre L\u00e9vy"]2025-01-08T00:00:00 50fc6341a56505943f4d41819a613bdf877b6af7PurposeThis paper identifies consumer acceptance criteria of artificial intelligence (AI)-enabled products and services in the business. We first investigate the existing three models. They are the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT) and the consumer acceptance of technology (CAT). We then discuss the applicability of these three models for AI-enabled products and services. Finally, we outline the shortcomings of the models and propose an AI-enabled product and service acceptance model (AIEPSAM). We also validate the proposed AIEPSAM model with empirical results using primary survey data.Design/methodology/approachTo understand the customer’s point of view on AI applications in products and services, we identify some critical factors and present a conceptual framework of consumers' acceptance criteria based on existing literature, prior research and prominent technology management theories. Then, the study broadens the horizon beyond established principles associated with technology acceptance to accommodate AI-specific factors/variables like data privacy, explainability and apparent opacity of algorithms. In this paper, we propose an AIEPSAM and validate that model with primary survey data.FindingsWe argue that although TAM, UTAUT and CAT models are generally applicable to explain consumers' attitudes towards technology, these models alone are insufficient to encompass the entire spectrum of AI-related issues that must not be ignored. The proposed model, namely AIEPSAM, accommodates the limitations of the existing models and modifies the CAT model to make it suitable for the acceptance of AI technology.Originality/valueWe attempt to articulate the consumer acceptance criteria of AI-enabled products and services and discover useful insights, leading to the critical examination of TAM, UTAUT and CAT models and formulating AIEPSAM with validation through primary survey data. This study is not to criticize the TAM and other technology acceptance models but to incorporate AI-specific factors into those models. Through this study, we propose the required modifications in the existing technology acceptance models considering the AI-specific additional factors. The AIEPSAM will assist companies in building AI-enabled products and services and better understanding the technology emergence (TE) and technology opportunities (TO).Management Decision1270The proposed model, namely AIEPSAM, accommodates the limitations of the existing models and modifies the CAT model to make it suitable for the acceptance of AI technology.Management Decision["Paritosh Pramanik", "Rabin K. Jana"]2025-01-08T00:00:00 e954bacb80e95070d1f55efac4c8ab964e38c8d8This study aims to explore the application of artificial intelligence (AI) technology in the preservation and exhibition of artworks, with the “Exhibition Environment Status Detection Device and System” and the “Automatic Exhibition Guide System”, developed by Cheng Shiu University, as case studies. In recent years, AI technology has made significant advancements in image recognition, machine learning, and data analysis, which provide new opportunities for art management. However, due to high costs and implementation challenges, as well as a lack of qualified personnel to use these tools and systems, small art galleries and museums have not yet had the opportunity to acquire such systems. Therefore, this study observes the practical application of the “Exhibition Environment Status Detection Device and System” and the “Automatic Exhibition Guide System” in the fields of art preservation and exhibition. The study employs case study and observation methods, with participatory observation as the primary data collection approach. The results indicate that AI technology significantly enhances the preservation conditions of artworks and the interactivity of exhibitions. The paper suggests that future efforts should focus on long-term planning relating to technology costs and professional talent development to fully realize the potential of AI in art management and exhibition. Additionally, the application of these technologies can be extended to other fields.Applied Sciences00The results indicate that AI technology significantly enhances the preservation conditions of artworks and the interactivity of exhibitions and suggests that future efforts should focus on long-term planning relating to technology costs and professional talent development to fully realize the potential of AI in art management and exhibition.Applied Sciences["Pin-Chia Huang", "I-Cheng Li", "Ching-Yi Wang", "Cheng-Hsiung Shih", "Masimukku Srinivaas", "Wan-Ting Yang", "Chin-Fang Kao", "Te-Jen Su"]2025-01-08T00:00:00 42c547b5cdf7c87165c101c233f7902eb8188ebbIntroduction: Artificial intelligence (AI) plays a vital role in modern healthcare and is crucial to achieving global objectives such as the Sustainable Development Goals (SDGs) and the World Health Organization's (WHO) Triple Billion Targets. The growing adoption of AI in healthcare has revolutionized diagnostics, personalized treatments, and clinical decision-making. In traditional medicine systems like Ayurveda, AI offers significant opportunities, but its integration remains underexplored. Methods: A comprehensive literature review was conducted using research papers, books, peer-reviewed journals, and online sources. The study focuses on the integration of AI in Ayurveda, particularly in Prakriti (constitution) assessment, Dosha (bodily humor) evaluation, and Rasashastra (Ayurvedic alchemy). AI tools such as data mining, pattern recognition, and predictive analytics were analyzed for their potential to improve diagnostic procedures, therapeutic outcomes, and knowledge of Ayurvedic formulations. Results: AI shows promise in improving diagnostic precision and personalized care in Ayurveda through tools like Prakriti and Dosha evaluation. AI's data mining capabilities enable deeper insights into disease mechanisms, symptoms, and treatment protocols. Discussion: While AI enhances data management, analysis, and research efficiency in Ayurveda, its limitations must be recognized. Traditional Ayurvedic practices, especially in patient care and diagnosis, cannot be entirely substituted by AI technologies. AI should complement rather than replace the expertise of practitioners. Conclusion: AI holds significant potential to advance Ayurvedic knowledge, diagnosis, and treatment, particularly in Rasashastra. However, the role of expert Vaidyas remains irreplaceable, and AI should be viewed as a supportive tool in integrating traditional practices with modern healthcare.Journal of Ayurveda and Integrated Medical Sciences00AI holds significant potential to advance Ayurvedic knowledge, diagnosis, and treatment, particularly in Rasashastra, but the role of expert Vaidyas remains irreplaceable, and AI should be viewed as a supportive tool in integrating traditional practices with modern healthcare.Journal of Ayurveda and Integrated Medical Sciences["Kartik Sharma", "Aashish Patel", "Aswini Ramachandran"]2025-01-08T00:00:00 6a78ae20c076d6ea7cb2f1df820847587fa28912In today's rapidly developing technological environment, artificial intelligence (AI) has been integrated into various social fields, bringing moral challenges to human interaction and decision-making processes. This study investigates the moral dilemmas individuals face when there is a conflict between family loyalty and social justice, especially in cases involving family members. The study used a sample of 117 participants to explore how individuals decide to expose or cover up minor thefts from family members and whether the participation of artificial intelligence affects their decision-making. Although artificial intelligence can guide decision-making according to legal standards, survey results show that humans usually tend to prioritize family relationships over this objective assessment, especially in the case of minor violations. This study has contributed to the fields of moral psychology and emerging artificial intelligence ethics. It provides empirical data on how the existence of artificial intelligence changes human responses to moral dilemmas involving family loyalty and fairness. The conclusion of the study points out that as artificial intelligence is further integrated into human moral decision-making, it faces major psychological and ethical challenges, which provides new insights into the potential role that artificial intelligence may play in solving or deepening future moral and social conflicts.Lecture Notes in Education Psychology and Public Media120It is pointed out that as artificial intelligence is further integrated into human moral decision-making, it faces major psychological and ethical challenges, which provides new insights into the potential role that artificial intelligence may play in solving or deepening future moral and social conflicts.Lecture Notes in Education Psychology and Public Media["Huize Zhang"]2025-01-08T00:00:00 586f6cd3f41eb04fc3bbf412f5eca2eea05f77c0AI and Ethics140The current legal landscape governing cyber espionage and the impact of the use of artificial intelligence in the commission of such crimes are discussed.AI and Ethics["Wan Rosalili Wan Rosli"]2025-01-08T00:00:00 6c35ae4cad713950a23db9acc4a2278caaa10722The intersection of artificial intelligence (AI) and industrial ecology (IE) is gaining significant attention due to AI's potential to enhance the sustainability of production and consumption systems. Understanding the current state of research in this field can highlight covered topics, identify trends, and reveal understudied topics warranting future research. However, few studies have systematically reviewed this intersection. In this study, we analyze 1068 publications within the IE–AI domain using trend factor analysis, word2vec modeling, and top2vec modeling. These methods uncover patterns of topic interconnections and evolutionary trends. Our results identify 71 trending terms within the selected publications, 69 of which, such as “deep learning,” have emerged in the past 8 years. The word2vec analysis shows that the application of various AI techniques is increasingly integrated into life cycle assessment and the circular economy. The top2vec analysis suggests that employing AI to predict and optimize indicators related to products, waste, processes, and their environmental impacts is an emerging trend. Lastly, we propose that fine‐tuning large language models to better understand and process data specific to IE, along with deploying real‐time data collection technologies such as sensors, computer vision, and robotics, could effectively address the challenges of data‐driven decision‐making in this domain.Journal of Industrial Ecology1230This study analyzes 1068 publications within the IE–AI domain using trend factor analysis, word2vec modeling, and top2vec modeling to uncover patterns of topic interconnections and evolutionary trends and proposes that fine‐tuning large language models to better understand and process data specific to IE could effectively address the challenges of data‐driven decision‐making in this domain.Journal of Industrial Ecology["Yongyue Gong", "Fengmei Ma", "Heming Wang", "A. Tzachor", "Wenju Sun", "Junming Zhu", "Gang Liu", "H. Schandl"]2025-01-08T00:00:00 dc80a273de8e49192d48dc044b0a4644ae724ea4Recent advancement in Artificial Intelligence (AI) has rendered image-synthesis models capable of producing complex artworks that appear nearly indistinguishable from human-made works. Here we present a quantitative assessment of human perception and preference for art generated by OpenAI’s DALL·E 2, a leading AI tool for art creation. Participants were presented with pairs of artworks, one human-made and one AI-generated, in either a preference-choice task or an origin-discrimination task. Results revealed a significant preference for AI-generated artworks. At the same time, a separate group of participants were above-chance at detecting which artwork within the pair was generated by AI, indicating a perceptible distinction between human and artificial creative works. These results raise questions about how a shift in art preference to favour synthetic creations might impact the way we think about art and its value to human society, prompting reflections on authorship, authenticity, and human creativity in the era of generative AI.Frontiers in Psychology300The first quantitative assessment of human perception and preference for art generated by OpenAI's DALL·E 2, a leading AI tool for art creation, revealed a significant preference for AI-generated artworks.Frontiers in Psychology["Jules van Hees", "Tijl Grootswagers", "G. Quek", "Manuel Varlet"]2025-01-08T00:00:00 b53ed38b2c927a0a633ba6275518fe9ca179c794The study aims to analyze the moderating role of eco-digital learning for the impact of the Internet of Things, blockchain, and artificial intelligence on the green supply chain to support SME internationalization. Questionnaires were distributed among 159 SME owners in Kediri, East Java, Indonesia. Hypotheses were tested using PLS-SEM. The results indicate that the adoption of the Internet of Things demonstrates a significant positive effect (p = 0.0000, T-statistic = 4.6695), improving real-time monitoring and operational efficiency. Blockchain also positively affects SME internationalization (p-value = 0.0085, T-statistic = 2.6427), enhancing supply chain transparency and ensuring compliance with international standards. In contrast, the influence of artificial intelligence is marginally significant (p = 0.0799, T-statistic = 1.7548), constrained by financial limitations and lack of expertise. The moderating effects of eco-digital learning show mixed results. It significantly moderates the relationship between the Internet of Things and internationalization in a negative direction (coefficient = –0.0651, p = 0.0475), suggesting that increased eco-digital learning may add complexity, potentially delaying the benefits of the Internet of Things. For blockchain, eco-digital learning enhances its positive impact (p = 0.0277, T-statistic = 2.2085) by strengthening the organization’s ability to leverage transparency and sustainability. However, no significant moderating effect is observed for artificial intelligence (p = 0.2066, T-statistic = 1.2646), indicating limited integration due to resource constraints. These findings highlight the importance of technological readiness, resource allocation, and alignment of digital learning to fully capitalize on the benefits of these technologies in the global market expansion of SMEs. AcknowledgmentsDeepest gratitude is extended to the Ministry of Education and Culture, Directorate of Research, Technology, and Community Service (DRTPM), for the financial support provided through the National Competitive Research Program under the “Regular Fundamental Research” scheme for the 2024 fiscal year. This support was instrumental in enabling the successful completion of this research.Problems and Perspectives in Management570The results indicate that the adoption of the Internet of Things demonstrates a significant positive effect on SME internationalization, and eco-digital learning enhances its positive impact by strengthening the organization’s ability to leverage transparency and sustainability.Problems and Perspectives in Management["Faisol", "Hestin Sri Widiawati", "Risky Aswi Ramadhani", "Bambang Agus Sumantri"]2025-01-08T00:00:00 65501fce47b3fbe6b2d13b5fd994f00d9be90959 This Guide to Statistics and Methods provides an overview of the limitations and opportunities in applying large language models in such tasks as extracting surgical risk factors from clinical notes, learning from text inputs for decision support, and serving as educational tools. JAMA Surgery52JAMA surgery["Tyler J. Loftus", "Adil Haider", "Gilbert R. Upchurch"]2025-01-08T00:00:00 47f7fe1d2ae181054b3746075c35ec5eacfd383dThis study aims to examine the effectiveness and impact of the Problem Based Learning (PBL) model based on AI-Slidesgo on the mathematical problem-solving abilities of 5th-grade students at SD Negeri 1 Tuk and SD Negeri 1 Dawuan Cirebon for the 2023/2024 academic year. The effectiveness of this study is demonstrated by the increase in the average problem-solving ability of students in the 4 experimental classes, and the impact of this study is shown by the difference in the average problem-solving ability of students in the 4 experimental classes. This research is a quantitative study. Data collection techniques using interviews, tests, questionnaires, and documentation. The sample in this study consisted of 120 students. The collected data were analyzed using effectiveness test statistical analysis, namely the t-test and n-gain test. Hypothesis testing shows that the t-test results indicate a significant result < 0.005, which means there is an improvement in students' mathematical problem-solving abilities. The n-gain test results show a gain in experimental class A of 0.65 with a moderate category; experimental class B 0.66 with a moderate category; experimental class C 0.81 with a high category, and experimental class D 0.73 with a high category. The results of the MANOVA test showed a significance value of 0.000, where 0.000 < 0.05, indicating that the PBL model and slidesgo-based learning media have an effect on students' mathematical problem-solving abilities.Prima Magistra Jurnal Ilmiah Kependidikan00The effectiveness of this study is demonstrated by the increase in the average problem-solving ability of students in the 4 experimental classes, and the impact is shown by the difference in the average problem-solving ability of students in the 4 experimental classes.Prima Magistra: Jurnal Ilmiah Kependidikan["Mochamad Guntur", "Amara Salsabilla", "Siti Sahronih", "Herisa Hardiyanti Sholeha"]2025-01-08T00:00:00 45a29364da0ba2912117b72530166bcf81d21e28Revista de Educación en Cuidados Críticos (REduCrític)00Revista de Educación en Cuidados Críticos (REduCrític)["Venkata S Buddhavarapu", "Harpreet Singh-Grewal", "Gagandeep Dhillon", "Rahul Kashyap"]2025-01-08T00:00:00 d5b464f37c0c7e32a7a6710800c693573f3e1089China Population and Development Studies570China Population and Development Studies["Fengyu Zhang", "Jianxin Li", "Jiehua Lu"]2025-01-08T00:00:00 7c08689a257301e64584e99539d16052a842bb43An effective solution for executives interested in how increased operational risk management and fraud detection =FS improves the resilience of the financial services industry: Amid unprecedented changes in the financial services market, global financial organizations are increasingly focusing risk management and cybersecurity. This paper reviews anomaly detection as being suitable for the identification of outliers and minimization of the risk of financial losses. This method uses autoencoders, isolation forests and statistical methods to identify anomalous patterns in high dimensionality transaction data. Anomaly detection techniques update themselves with new data and protect against newer types of fraud that a rule-based system can not discern. These methods are also integrated with real time processing frameworks such as apachem kafaka, and spark streaming to minimize the false alarms to achieve better percentage of fraud detection. Examples reveal how the proposed strategy stops credit card fraud and money laundering, too. These are important area of concern in the context of this study and comprise deployment challenges like scalability of the Anomaly Detection System, interpretability of the anomaly detection model, and financial regulations associated with the application of the system.Journal of Informatics Education and Research110This paper reviews anomaly detection as being suitable for the identification of outliers and minimization of the risk of financial losses, and examples reveal how the proposed strategy stops credit card fraud and money laundering.Journal of Informatics Education and Research["Swati Tyagi", "Anuj Tyagi", "Dr. S. Rani", "A.Suraj Kumar", "Dr. Sunil Adhav", "Dr. Sumeet Gupta"]2025-01-08T00:00:00 720c646b7060abe1fd8c394bc125c03b548e3d12Nature-based Solutions (NbS) can represent holistic pathways to reach sustainable biodiversity, climate, and water outcomes through conservation and restoration. Yet, existing prioritization frameworks rarely identify holistic NbS that maximize these outcomes. Here, we present an AI-driven framework that prioritizes NbS locations, maximizing biodiversity protection with ecological intactness, carbon storage, and water surface stability. We implement this framework through scenarios combining specific biodiversity and co-benefits outcomes to achieve Canada’s 30×30 conservation and restoration targets. Our results reveal inevitable environmental trade-offs and critical implications for equitable NbS and extractive activities. To achieve conservation targets, protecting threatened biodiversity and irrecoverable carbon storage in forests would effectively address trade-offs and enhance Protected Areas outcomes. However, minimizing trade-offs to achieve restoration targets will require targeted interventions in existing forested and agricultural lands. These findings demonstrate that frameworks integrating AI and biodiversity-co-benefit scenarios reveal strategic land use planning, policies, and investments for holistic NbS.bioRxiv00An AI-driven framework is presented that prioritizes NbS locations, maximizing biodiversity protection with ecological intactness, carbon storage, and water surface stability, and demonstrates that frameworks integrating AI and biodiversity-co-benefit scenarios reveal strategic land use planning, policies, and investments for holistic NbS.bioRxiv["Camilo Alejo", "Amy Luers", "Andr\u00e9a Ventimiglia", "H. D. Matthews"]2025-01-08T00:00:00 7bfbcd5b36eabe6fb28118f3d458b650e7943e87European Heart Journal30European heart journal["P. Elias", "Shreyas Bhave", "T. Poterucha"]2025-01-08T00:00:00 a693721f5f39e02b42a457e86865f5a1dfd39f3bThe Academy of Management Annals2170Academy of Management Annals["Luis Hillebrand", "Sebastian Raisch", "Jonathan Schad"]2025-01-08T00:00:00 c794bbd210da97b6e26ef4800aa0e60d743108bfCirculation. Cardiovascular Quality and Outcomes00Circulation. Cardiovascular quality and outcomes["E. Ross", "Paul L. Hess"]2025-01-08T00:00:00 cfa855e61a1f43ccc76ef93c31600507db37b396Revista Española de Urgencias y Emergencias00Revista Española de Urgencias y Emergencias["Antonio Due\u00f1as-Ruiz", "M. C. Castro Villamor", "Francisco Mart\u00edn-Rodr\u00edguez"]2025-01-08T00:00:00 6948007b19c06e0f3bf18539a4f39863d67b9c1fNational Science Review00National Science Review["Hang-Yu Zhou", "Yaling Li", "Jiaying Li", "Jing Meng", "Aiping Wu"]2025-01-08T00:00:00 d97b5e39fda464103406c485254d2a5dd551665500["Farid Bourzgui", "Kenza Khamlich"]2025-01-08T00:00:00 b7c1c762df196138ee0193ee5b00fbcffade6967While it may seem that artificial intelligence is a new phenomenon and its use in corporate philanthropy is a ways off, the reality is that leaders in the sector are already putting it to use in myriad ways that aids in their operations and those of their nonprofit grantees.Nonprofit Business Advisor00Nonprofit Business Advisor[]2025-01-08T00:00:00 fc3a39c0b433ed9929db136c8c3e3941edd7925fPurposeThis study aims to examine the factors affecting accounting students’ adoption of artificial intelligence (AI) in Vietnam.Design/methodology/approachThis study employs an empirical analysis based on hand-collected data from 275 accounting students in Ho Chi Minh City, Vietnam. The study model was performed using the partial least squares structural equation modelling methodology, facilitated by SmartPLS 4.0.FindingsThe study results show that perceived usefulness, perceived ease of use (PEOU), AI literacy, social influence (SI), facilitating conditions and technology readiness are positively associated with AI adoption by accounting students. The findings suggest the important role of SI in shaping the relationship between PEOU and AI adoption.Research limitations/implicationsThis study is limited to universities in Ho Chi Minh City, Vietnam, with a small sample size, which may reduce the generalisability of findings to other cities in Vietnam or other countries due to different regulations. Future research could examine comparative and cross-country analyses within similar institutional settings.Practical implicationsThe study findings suggest that universities should consider offering more AI-related subjects to improve students’ AI proficiency and capacity.Originality/valueThis study examines the determinants of AI adoption by accounting students in Vietnam, addressing a previously unexplored area in the literature.Journal of Asian Business and Economic Studies481The study results show that perceived usefulness, perceived ease of use, AI literacy, social influence, facilitating conditions and technology readiness are positively associated with AI adoption by accounting students, suggesting the important role of SI in shaping the relationship between PEOU and AI adoption.Journal of Asian Business and Economic Studies["Hung Quang Bui", "Quyen Thi Bao Phan", "Ha Thanh Nguyen"]2025-01-08T00:00:00 35821b43eaacb20f886cd9d06966e6fbe247c869 This Guide to Statistics and Methods discusses approaches to incorporating artificial intelligence (AI)–enabled analytics when working with big data and outlines AI-related considerations for data management and health equity. JAMA Surgery31JAMA surgery["Anai N. Kothari", "Amy H. Kaji", "Genevieve B. Melton"]2025-01-08T00:00:00 366e15cfd6f98f6ac4a5619da34f99ebdb016e3cPhilosophy &amp; Technology561It is argued that for some notions of trust it is plausible that explainability is indeed a necessary condition for AI, but that these kinds of trust are not appropriate for AI.Philosophy &amp; Technology["Sam Baron"]2025-01-08T00:00:00 0bb9139d6bc84a44047cadb10dc1288d5b468212The field of Artificial Intelligence (AI) continues to drive transformative innovations, with significant progress in conversational interfaces, autonomous vehicles, and intelligent content creation. Since the launch of ChatGPT in late 2022, the rise of Generative AI has marked a pivotal era, with the term Large Language Models (LLMs) becoming a ubiquitous part of daily life. LLMs have demonstrated exceptional capabilities in tasks such as text summarization, code generation, and creative writing. However, these models are inherently limited by their token-level processing, which restricts their ability to perform abstract reasoning, conceptual understanding, and efficient generation of long-form content. To address these limitations, Meta has introduced Large Concept Models (LCMs), representing a significant shift from traditional token-based frameworks. LCMs use concepts as foundational units of understanding, enabling more sophisticated semantic reasoning and context-aware decision-making. Given the limited academic research on this emerging technology, our study aims to bridge the knowledge gap by collecting, analyzing, and synthesizing existing grey literature to provide a comprehensive understanding of LCMs. Specifically, we (i) identify and describe the features that distinguish LCMs from LLMs, (ii) explore potential applications of LCMs across multiple domains, and (iii) propose future research directions and practical strategies to advance LCM development and adoption.01This study identifies and describes the features that distinguish LCMs from LLMs, explores potential applications of LCMs across multiple domains, and proposes future research directions and practical strategies to advance LCM development and adoption.["Hussain Ahmad", "Diksha Goel"]2025-01-08T00:00:00 5505e581884a4d4062678d81a5b45138bf249104The rapid advancement of artificial intelligence (AI) has brought about significant societal changes, necessitating robust AI governance frameworks. This study analyzed the research trends in AI governance within the framework of the EU AI Act. This study conducted a bibliometric analysis to examine the publications indexed in the Web of Science database. Our findings reveal that research on AI governance, particularly concerning AI systems regulated by the EU AI Act, remains relatively limited compared to the broader AI research landscape. Nonetheless, a growing interdisciplinary interest in AI governance is evident, with notable contributions from multi-disciplinary journals and open-access publications. Dominant research themes include ethical considerations, privacy concerns, and the growing impact of generative AI, such as ChatGPT. Notably, education, healthcare, and worker management are prominent application domains. Keyword network analysis highlights education, ethics, and ChatGPT as central keywords, underscoring the importance of these areas in current AI governance research. Subsequently, a comprehensive literature review was undertaken based on the bibliometric analysis findings to identify research trends, challenges, and insights within the categories of the EU AI Act. The findings provide valuable insights for researchers and policymakers, informing future research directions and contributing to developing comprehensive AI governance frameworks beyond the EU AI Act.00The findings reveal that research on AI governance, particularly concerning AI systems regulated by the EU AI Act, remains relatively limited compared to the broader AI research landscape, but a growing interdisciplinary interest in AI governance is evident.["B. Kim", "Seunghoo Jeong", "Bong-Kyung Cho", "Ji-Bum Chung"]2025-01-08T00:00:00 c37de00c10ab21223c24ea73dfbabe390e589554npj Science of Learning520While physical robots may increase initial on-task enjoyment, students’ perception of certain characteristics may hinder learning, providing implications for designing social robots for education.NPJ Science of Learning["Helene Ackermann", "Anja Henke", "J. Cheval\u00e8re", "Hae Seon Yun", "Verena V. Hafner", "Niels Pinkwart", "Rebecca Lazarides"]2025-01-08T00:00:00 932b8c8b05a893485e3aa4cf6416d06bfa9de708The problem encountered is the deficient knowledge about the Sustainable Development Goals (SDG) adopted by the United Nations as part of the 2030 agenda in Mesetas and Lejanías, in Meta, Colombia. The problem was solved by sharing at the local level a learning strategy with Artificial Intelligence (AI) and emerging technologies for sustainable innovation with the participation of undergraduate students levels 10 and 11, small business agricultural entrepreneurs and rural producers from the localities of study, useful for the dissemination of the SDGs in the rural sector of the Ariari in Colombia using as a model a successful sustainable business that obtained inherent results to the evaluation of the effectiveness of techniques for the dissemination of knowledge, attitudes and practices as well as metacognitive strategies and AI at the local level useful for the dissemination of the SDGs in Meta Colombia. The data obtained in the research imply effective support strategies for self-assessment and practice for consolidation of learning about the SDG for rural communities using AI. The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios. Limitations include the scope of the implementation and the necessity for additional quantitative study. The paper continues by identifying areas for further research and practical implementation tactics and establishing significant contribution from AI in rural education about the SDGs.Journal of Next-Generation Research 5.000The findings show that AI-powered systems increase learner motivation, engagement, and knowledge retention while providing scalable solutions for various educational scenarios while establishing significant contribution from AI in rural education about the SDGs.Journal of Next-Generation Research 5.0["Mauricio Cabra", "Pedro Gomez"]2025-01-08T00:00:00 cb6e174c84c7975c56bf41d1102123a7c3a22162With the rapid development of artificial intelligence technology, the application of AI big model in the sports industry is becoming more and more widespread, promoting the intelligent transformation of the sports industry. Based on the theoretical foundation and technical characteristics of AI big model, this study constructs a theoretical model of intelligent transformation of sports industry and analyzes the current status of the application of AI big model in sports industry. The study finds that AI Big Model can effectively improve the operational efficiency of the sports industry, innovate the service mode, enhance the user experience, and provide technical support for athletes' training and competition. The study also proposes a series of policy recommendations and implementation strategies to promote the wide application of AI Big Model in the sports industry. However, the study also has limitations, such as the lack of empirical studies and interdisciplinary perspectives. Future research can be expanded in terms of empirical studies, interdisciplinary studies, long-term tracking studies, international comparative studies, and studies on risks and challenges, in order to deepen the understanding of the intelligent transformation of the sports industry and provide theoretical and practical support for its sustainable development.Mathematical Modeling and Algorithm Application00The study finds that AI Big Model can effectively improve the operational efficiency of the sports industry, innovate the service mode, enhance the user experience, and provide technical support for athletes' training and competition.Mathematical Modeling and Algorithm Application["Dongjin He", "Tonguyu Zhang", "Haolang Han", "Gan Wu", "Jiajun Zhou", "Yingmei Li"]2025-01-08T00:00:00 74fa7b35d1bfe97c27ea716b01bf0c23c98264dfThe integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has revolutionized business strategies, enabling organizations to enhance decision-making, optimize operations, and achieve competitive advantages. This systematic review examines the transformative role of these technologies in reshaping business strategies across various industries. A total of 115 peer-reviewed articles were systematically analyzed following the PRISMA guidelines to ensure transparency, rigor, and reliability. The study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges. Additionally, emerging trends such as Explainable AI, AI integration with IoT and blockchain, and AI-powered sustainability initiatives are discussed, highlighting their potential to redefine traditional business practices. Despite these advancements, challenges such as algorithmic bias, data quality issues, implementation costs, and the lack of regulatory frameworks remain significant barriers to adoption. The review also identifies critical research gaps, including limited studies on AI adoption in small and medium-sized enterprises (SMEs) and developing economies. By synthesizing insights from these articles, this study provides a comprehensive understanding of how AI, ML, and DL are shaping modern business strategies, offering valuable directions for future research and practical implementation.Innovatech Engineering Journal00This study identifies key applications of AI, ML, and DL in marketing, supply chain management, financial analytics, and human resource management, showcasing their ability to address complex business challenges.Innovatech Engineering Journal["Mohammad Ariful Islam", "Molla Al Rakib Hasan", "Shaharima Juthi", "Sumyta Haque"]2025-01-08T00:00:00 358bc0c460cbe5133282ec8da55f586978e6756fA critical metaphor for the development, implementation and penetration of autonomous machine systems into the world of human work is presented. Most especially, the 'Isles of Autonomy' concept is articulated which argues that the expropriation of human pre-eminence will be marked by a series of threshold events, some of which are, even now becoming evident. In particular, it indicates that there will be a watershed event in which differing and distinct expressions of applied autonomous systems will spontaneously coalesce to produce an emergent, general artificial intelligence. The latter may well be unrelated to the original goals, aims and constraints of the disparate entities that have joined together. This threshold will be a harbinger of cascading unifications in which an unrestrained aggregate will assume de facto control over disparate work domains. The nature of such a development, most especially in light of associated human roles, is here evaluated. While emergent systems possess no necessary privilege, neither are their non-linear properties and behaviours directly inferable from their componential elements. The demi-sesquicentennial (75th) marking of the future of a science that is focused most especially on the predominance of human, work, is considered in light of these impending forces of change.Ergonomics320Ergonomics["P. A. Hancock"]2025-01-08T00:00:00 913e69982ef63e338a9f4d80e39bd09be036cf38Health Professions Education (HPE) assessment is being increasingly impacted by Artificial Intelligence (AI), and institutions, educators, and learners are grappling with AI's ever-evolving complexities, dangers, and potential. This AMEE Guide aims to assist all HPE stakeholders by helping them navigate the assessment uncertainty before them. Although the impetus is AI, the Guide grounds its path in pedagogical theory, considers the range of human responses, and then deals with assessment types, challenges, AI roles as tutor and learner, and required competencies. It then discusses the difficult and ethical issues, before ending with considerations for faculty development and the technicalities of AI acknowledgment in assessment. Through this Guide, we aim to allay fears in the face of change and demonstrate possibilities that will allow educators and learners to harness the full potential of AI in HPE assessment.Medical Teacher391This AMEE Guide aims to allay fears in the face of change and demonstrate possibilities that will allow educators and learners to harness the full potential of AI in HPE assessment.Medical teacher["Ken Masters", "Heather MacNeil", "Jennifer Benjamin", "Tamara Carver", "Kataryna Nemethy", "Sofia Valanci-Aroesty", "David C M Taylor", "Brent Thoma", "Thomas Thesen"]2025-01-09T00:00:00 277358c3d7e2b5a019a2c53d7ffc09ef697e6c26 Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Despite growing interest in the geography of AI, our understanding of how AI integrates into regional contexts remains limited. In response, we examine the integration of AI into regional technological domains in China using patent data. Theoretically, we develop a framework by introducing the concepts of intra- and extra-regional AI relatedness. Our findings reveal that the integration of AI into regional technological domains is positively associated with both intra-regional and extra-regional AI relatedness. Additionally, extra-regional AI relatedness can moderate the lack of intra-regional AI relatedness. Finally, we use the USA as a robustness check, which further validates our findings.Cambridge Journal of Regions, Economy and Society461The integration of AI into regional technological domains in China using patent data is examined using the USA as a robustness check to reveal that the integration of AI into regional technological domains is positively associated with both intra-regional and extra-regional AI relatedness.Cambridge Journal of Regions, Economy and Society["Yijia Chen", "Kangmin Wu"]2025-01-09T00:00:00 5525bd3ca2c72eda906d10fcf453b516a6b8065aArtificial intelligence (AI) plays a pivotal role in the development of the green economy. This paper examines the impact of artificial intelligence (AI) on green economic efficiency (GEE) using panel data from 30 provinces in China spanning 2011–2020. A multiple linear regression model, alongside various endogeneity and robustness tests, is applied to ensure reliable findings. The empirical results indicate that AI significantly enhances GEE. However, the marginal effect of AI on GEE is influenced by different governance approaches. In terms of policy governance, excessive market-based environmental regulation (MER) diminishes the marginal impact of AI, while stronger administrative-command environmental regulations (CER) and informal environmental regulations (IER) amplify it. Regarding technological governance, substantive green technological innovations (SUG) reduce AI's marginal effect, whereas symbolic green technological innovations (SYG) may increase it. Notably, the threshold effect of SUG surpasses that of SYG. In legal governance, both administrative and judicial intellectual property protections reduce the marginal effect of AI, though administrative protection (AIP) exhibits a more significant threshold effect than judicial protection (JIP). These findings offer practical insights for optimizing governance strategies to maximize AI's role in promoting GEE. These insights highlight the need for balanced governance to maximize AI's role in sustainable development. Policymakers should tailor regulations and encourage regional collaboration to harness AI's spatial spillover effects. Enterprises can leverage AI-driven innovations to align growth with ecological goals, fostering coordinated green development.Frontiers in Environmental Economics770Examining the impact of artificial intelligence on green economic efficiency (GEE) using panel data from 30 provinces in China spanning 2011–2020 indicates that AI significantly enhances GEE, however, the marginal effect of AI on GEE is influenced by different governance approaches.Frontiers in Environmental Economics["Zhichun Song", "Yao Deng"]2025-01-09T00:00:00 03cb25cf7942f82041d894dca7fff96591f115e9  This exploratory study investigates the role of artificial intelligence (AI) practices in enhancing human capital development and promoting business sustainability within tourism villages in Indonesia. As tourism villages become increasingly significant to Indonesia’s rural economy, the need to foster sustainable business models that can maintain cultural heritage while promoting economic growth has gained attention. AI technologies, including machine learning, data analytics, and automation, present opportunities for improving human capital development by enhancing skill-building, training, and decision-making processes. This study examines how AI tools are being integrated into training programs, recruitment processes, and daily operations of tourism village businesses to support workforce growth, improve service quality, and ensure long-term sustainability. Through qualitative data collected from tourism village stakeholders in Bali and Java, the research explores AI’s impact on local workforce capabilities, productivity, and innovation in tourism-related services. The study finds that AI practices are helping to bridge skill gaps, enhance workforce efficiency, and optimize resource management. However, challenges such as limited access to technology, digital literacy, and the need for infrastructure development hinder broader AI implementation. The paper concludes by proposing recommendations for leveraging AI practices to further strengthen human capital and business sustainability in tourism villages, thus contributing to the sector’s competitive edge in the global market.Journal of Digitainability, Realism &amp; Mastery (DREAM)70The study finds that AI practices are helping to bridge skill gaps, enhance workforce efficiency, and optimize resource management, however, challenges such as limited access to technology, digital literacy, and the need for infrastructure development hinder broader AI implementation.Journal of Digitainability, Realism &amp; Mastery (DREAM)["L. Martini", "Suwigyo Widodo", "Ida Bagus Udayana Putra", "Ayler Beniah Ndraha"]2025-01-09T00:00:00 a519165a750fae9c273c1e9e682aa26fa0f3a420Advances in Artificial Intelligence (AI) have generated widespread enthusiasm for the potential of AI to support our understanding and protection of the environment. As such tools move from basic research to more consequential settings, such as regulatory enforcement, the human context of how AI is utilized, interpreted, and deployed becomes increasingly critical. Yet little work has systematically examined the role of such organizational goals and incentives in deploying AI systems. We report results from a unique case study of a satellite imagery-based AI tool to detect dumping of agricultural waste, with concurrent field trials with the Wisconsin Department of Natural Resources (WDNR) and a non-governmental environmental interest group in which the tool was utilized for field investigations when dumping was presumptively illegal in February-March 2023. Our results are threefold: First, both organizations confirmed a similar level of ground-truth accuracy for the model's detections. Second, they differed, however, in their overall assessment of its usefulness, as WDNR was interested in clear violations of existing law, while the interest group sought to document environmental risk beyond the scope of existing regulation. Dumping by an unpermitted entity or just before February 1, for instance, were deemed irrelevant by WDNR. Third, while AI tools promise to prioritize allocation of environmental protection resources, they may expose important gaps of existing law.00A unique case study of a satellite imagery-based AI tool to detect dumping of agricultural waste, with concurrent field trials with the Wisconsin Department of Natural Resources (WDNR) and a non-governmental environmental interest group in which the tool was utilized for field investigations when dumping was presumptively illegal in February-March 2023.["Nicolas Rothbacher", "Kit T Rodolfa", "Mihir Bhaskar", "Erin Maneri", "Christine Tsang", "Daniel E. Ho"]2025-01-09T00:00:00 7304ce3a6b16093ddfc4cd12da2a7fe6720e497cContext. Developing secure and reliable software remains a key challenge in software engineering (SE). The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete. Secure software engineering (SSE) must continuously address vulnerabilities that endanger software systems and carry broader socio-economic risks, such as compromising critical national infrastructure and causing significant financial losses. Researchers and practitioners have explored methodologies like Static Application Security Testing Tools (SASTTs) and artificial intelligence (AI) approaches, including machine learning (ML) and large language models (LLMs), to detect and mitigate these vulnerabilities. Each method has unique strengths and limitations. Aim. This thesis seeks to bring order to the chaos in SSE by addressing domain-specific differences that impact AI accuracy. Methodology. The research employs a mix of empirical strategies, such as evaluating effort-aware metrics, analyzing SASTTs, conducting method-level analysis, and leveraging evidence-based techniques like systematic dataset reviews. These approaches help characterize vulnerability prediction datasets. Results. Key findings include limitations in static analysis tools for identifying vulnerabilities, gaps in SASTT coverage of vulnerability types, weak relationships among vulnerability severity scores, improved defect prediction accuracy using just-in-time modeling, and threats posed by untouched methods. Conclusions. This thesis highlights the complexity of SSE and the importance of contextual knowledge in improving AI-driven vulnerability and defect prediction. The comprehensive analysis advances effective prediction models, benefiting both researchers and practitioners.00The complexity of SSE and the importance of contextual knowledge in improving AI-driven vulnerability and defect prediction are highlighted, as the comprehensive analysis advances effective prediction models, benefiting both researchers and practitioners.["Matteo Esposito"]2025-01-09T00:00:00 9c80e375ec84083de77c274e199f5b72845481deTraditional university lessons do not provide students with the opportunity to put theoretical concepts into practice. Project-Based Learning is designed to involve students through the proposition of real-word problems in the form of a project. The main objective of this work is to improve several aspects of the student's learning experience (e.g., their motivation and interest) through practical experience during a master degree course in Artificial Intelligence. We propose an application of Project-Based Learning through the use of a game-based competition. The experience is designed as an activity that takes place in parallel with respect to the usual lessons. Then, we assessed the significance and the impact of this approach, from the educational point of view, through questionnaires proposed to the students involved. The results of a 3-year study involving more than 200 students are positive. Students reacted favorably to the experience: they think this experience improved their knowledge of AI, their motivation, and their skills. The competitive aspect is considered beneficial from multiple perspectives. Finally, the design of the experience seems to be robust and remains effective in a setting of remote lectures.Intelligenza Artificiale200This work proposes an application of Project-Based Learning through the use of a game-based competition to improve several aspects of the student's learning experience through practical experience during a master degree course in Artificial Intelligence.Intelligenza Artificiale: The international journal of the AIxIA["Allegra De Filippo", "Andrea Galassi", "Alessandro Soriani", "Giada Trisolini", "Federico Baldo", "F. Chesani", "Paola Mello", "Michela Milano"]2025-01-09T00:00:00 944ee7029a75eb17c9df5d036726df661c0b1fefBACKGROUND disease-related malnutrition (DRM) affects 30-50 % of hospitalized patients and is often underdiagnosed, increasing risks of complications and healthcare costs. Traditional DRM detection has relied on manual methods that lack accuracy and efficiency. OBJECTIVE this narrative review explores how artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), can transform the prediction and management of DRM in clinical settings. METHODS we examine widely used ML and DL models, assessing their clinical applicability, advantages, and limitations. The integration of these models into electronic health record systems allows for automated risk detection and optimizes real-time patient management. RESULTS ML and DL models show significant potential for accurate assessment of nutritional status and prediction of complications in patients with DRM. These models facilitate improved clinical decision-making and more efficient resource management, although their implementation faces challenges related to the need for large volumes of standardized data and integration with existing systems. CONCLUSION AI offers promising prospects for proactive DRM management, highlighting the need for interdisciplinary collaboration to overcome existing barriers and maximize its positive impact on patient care.Nutrición Hospitalaria390AI offers promising prospects for proactive DRM management, highlighting the need for interdisciplinary collaboration to overcome existing barriers and maximize its positive impact on patient care.Nutricion hospitalaria["Daniel A De Luis Rom\u00e1n", "J. J. L\u00f3pez G\u00f3mez", "David Emilio Barajas Galindo", "Cristina Garc\u00eda Garc\u00eda"]2025-01-09T00:00:00 de3448482c3719942f46f1ebf113ac66ec6cc08aThe study explored the relationship between Artificial Intelligence and the sustainability of hospitality businesses in Owerri, Imo State, Nigeria. The study specifically sought to examine the relationship between AI and the environmental sustainability of hospitality businesses in Owerri, Imo State, Nigeria, and to ascertain the relationship between AI and the social sustainability of hospitality businesses in Owerri, Imo State, Nigeria. The study adopted descriptive survey research design and data were collected from 203 respondents with the aid of a structured questionnaire and hypotheses were tested using Pearson Product Moment Correlation Coefficient with the aid of Statistical Package for Social Sciences (SPSS, version 27). Findings revealed that there is a significant positive relationship between AI and the environmental sustainability of hospitality businesses in Owerri, Imo State, Nigeria, with r = 0.882 n = 203 and a p-value of 0.001 (p<0.05). Also, there is a positive significant relationship between AI and the social sustainability of hospitality businesses in Owerri, Imo State, Nigeria, with r = 0.801, n = 203, and a p-value of 0.000 (p<0.05). The study concluded that there is a statistically significant positive relationship between Artificial Intelligence and the sustainability of hospitality businesses in Owerri, Imo State, Nigeria. The study recommended that hospitality businesses in Owerri should adopt AI technologies to enhance environmental sustainability. Also, hospitality businesses in Owerri should leverage AI-driven customer service chatbots and employee engagement platforms to enhance social sustainability.Scholars Journal of Economics Business and Management00There is a statistically significant positive relationship between Artificial Intelligence and the sustainability of hospitality businesses in Owerri, Imo State, Nigeria and the study recommended that hospitality businesses in Owerri should adopt AI technologies to enhance environmental sustainability.Scholars Journal of Economics, Business and Management["M. Kekeocha", "Samuel Anodi Ejiogu", "Ngozi Comfort Okeke", "N. Stella", "Victoria Ogochukwu Obi-Nwosu"]2025-01-09T00:00:00 ffb750854e3ebe3aec8a665f1aade5bebb9c5842Medical artificial intelligence (AI) is a cross-disciplinary field focused on developing advanced computing and AI technologies to benefit medicine and healthcare. Globally, medical AI has tremendous potential to support the United Nations’ sustainable development goals pertaining to health and well-being. In particular, large language models (LLMs) afford opportunities for positively disrupting medical AI-related research and practice. We present a research framework for LLMs in medical AI. Our framework considers the interplay between health and well-being goals, disease lifecycle stages, and the important emerging role of LLMs in medical AI processes related to various lifecycle stages. As part of our framework, we describe the LLM multiplex - important multimodal, multi-model, multicultural, and multi-responsibility considerations for LLMs in medical AI. We discuss how the five articles in the special issue relate to this framework and are helping us learn about the opportunities and challenges for LLMs in medical AI.ACM Transactions on Management Information Systems210This work presents a research framework for LLMs in medical AI, and describes the LLM multiplex - important multimodal, multi-model, multicultural, and multi-responsibility considerations for LLMs in medical AI.ACM Transactions on Management Information Systems["Wenji Mao", "Xipeng Qiu", "Ahmed Abbasi"]2025-01-09T00:00:00 c053583c21412f3a7615e6a18aee95202ceed780The popularization of artificial intelligence (AI) represents a significant business opportunity for private actors developing tools and services aimed at research and higher education. Academic libraries are often at the receiving end of sales pitches for new tools and could benefit from guidance on how to assess them. Libraries’ assessment of tools is a valuable service to library stakeholders, many of whom may not have sufficient time, the necessary competencies or the inclination to explore the landscape of innovations promising to support their information needs and research endeavours. This article offers concrete guidance concerning what to consider when assessing whether to adopt, endorse and/or invest in innovative information and research tools that make use of AI. The main areas proposed for reflection concern (a) tool purpose, design and technical aspects; (b) information literacy, academic craftsmanship and integrity; (c) ethics and the political economy of AI.IFLA Journal190This article offers concrete guidance concerning what to consider when assessing whether to adopt, endorse and/or invest in innovative information and research tools that make use of AI.IFLA Journal["Let\u00edcia Antunes Nogueira", "Stine Thordarson Moltubakk", "Andreas Fagervik", "Inga Buset Langfeldt"]2025-01-09T00:00:00 d7c9661856c41d85303699177f9741bd2e799396In today’s competitive global job market, employee retention is a growing challenge, particularly in the healthcare sector. Organizations must adopt a strategic approach that aligns with employees' needs and aspirations to mitigate rising turnover and staff mobility. This issue significantly affects operational stability, quality care, and clinical expertise. Addressing these challenges requires creating an environment that encourages employees to stay and feel valued. A key component of tackling employee retention is knowledge retention. With high turnover rates, retirements, and rapid advancements in medical knowledge, healthcare organizations face difficulties in preserving expertise crucial for delivering quality care. Effective knowledge retention is essential for maintaining clinical skills, ensuring continuous patient care, and sustaining operational efficiency. This study explores how Artificial Intelligence (AI) can support knowledge retention in healthcare settings. Technologies such as machine learning, natural language processing, and knowledge management systems can help preserve both explicit and tacit knowledge. AI can mitigate expertise loss due to turnover, support continuous learning, and provide real-time access to vital information.AI also addresses challenges related to rapidly advancing medical knowledge and the transfer of tacit knowledge. By leveraging AI, healthcare organizations can enhance knowledge sharing, improve professional development, and ultimately advance healthcare delivery and patient outcomes. However, AI integration must be approached carefully to ensure its ethical application.International Research Journal on Advanced Engineering and Management (IRJAEM)00By leveraging AI, healthcare organizations can enhance knowledge sharing, improve professional development, and ultimately advance healthcare delivery and patient outcomes, however, AI integration must be approached carefully to ensure its ethical application.International Research Journal on Advanced Engineering and Management (IRJAEM)["Hema Rani"]2025-01-09T00:00:00 08ad3db0dff5470a889ba81b6e40dc902251801aThe article analyses the legal regulation of employee surveillance managed by employers carried on algorithmic management based on artificial intelligence systems. Employers observe employees and their work so that to smoothly organize work processes at workplaces, ensure efficient use of resources, and manage risks. Through the process of surveillance, personal data of employees are collected, which are later analysed; hence, employees may experience direct legal consequences (for example, termination of employment, violations of work duties, adjusted wages, etc.). Algorithmic management based on artificial intelligence systems generates various risks to employees. Before starting their application, employers have to evaluate various requirements. The key requirements arise from the Artificial Intelligence Act, the General Data Protection Regulation and the practice formed by the European Court of Human Rights which has been established regarding employee surveillance. Also, the employer has the obligation to ensure the employee’s privacy rights, since, when applying algorithmic management, artificial intelligence systems can sometimes make hardly predictable insights and reveal extremely sensitive facts about the employee. The issue of informing and consulting employees and their representatives regarding the implementation and use of algorithmic management based on artificial intelligence systems in the work environment is analysed.Teisė00The article analyses the legal regulation of employee surveillance managed by employers carried on algorithmic management based on artificial intelligence systems to ensure the employee’s privacy rights.Teisė["Ineta Breskien\u0117"]2025-01-09T00:00:00 b4a752515b887e2b5048845a6864a9933b6da271Climate warming will have a profound impact on ecosystems, human society and economic development, so mitigating climate warming has become an important challenge facing the world. However, the construction industry is one of the important sources of global carbon emissions, and the use of digital technology had a revolutionary impact on the development of the construction industry. Artificial Intelligence plays an important role in digital technology, and its status and influence are further increasing. However, there is currently no comprehensive explanation of how Artificial Intelligence affects the construction industry’s carbon emissions. This study aims to explore the role of artificial intelligence on carbon emissions in the construction industry from multiple perspectives in the four life cycles of planning, design, operation & maintenance, and tear down. This study also to provide certain guidance for AI's participation in carbon reduction in the construction industry. The search of this study covers the three analysis dimensions of "efficiency improvement", "energy optimization" and "cost control", and analyses the contribution of Artificial Intelligence on carbon emissions in the construction industry by adopting mixed research methods. First, this study systematically analysed 85 publications collected in Scopus and Web of Science databases using PRISMA guidelines. Next, qualitative research method was used where semi-structured interview was use for data collection. Then Analytic Hierarchy Process was used to obtain the research results. Through research, the role of artificial intelligence in carbon reduction in the construction industry is further clarified, and the most obvious role of Artificial Intelligence in each life cycle is efficiency improvement. This research can provide reference and guidance for the use of AI in different life cycles.Journal of Advanced Research in Applied Sciences and Engineering Technology00Journal of Advanced Research in Applied Sciences and Engineering Technology["Jiachen Sun", "Terh Jing Khoo", "A. Osmadi", "Deng Bin", "Shihua Lu", "Xiaolu Zhang"]2025-01-09T00:00:00 841501fefdcaf1c06a7b663cc855ad05a570e574PurposeArtificial intelligence (AI) is vastly impacting the digital transformation of societies, economies, businesses, markets and enterprises, at a very fast pace, mostly after the global success of the generative algorithms. In this respect, this study, with an exploratory intention, aims to provide evidence about the fundamental issues of AI, particularly if generative, when adapted to humanism, with a specific focus on the wine business.Design/methodology/approachAn exploratory analysis, conducted on a convenience sample of wine business operators, has been performed to investigate AI applications when connected with the conceptual platform of the “Industry 5.0” framework.FindingsThe results of the survey provide evidence about the success of AI in the wine business. Specifically, the research outcomes highlight that the interviewees (wine business operators) recognized the high relevance of the potential use of AI in the strategic and operating management of wine firms.Originality/valueThis study aims to provide new empirical evidence with regard to the application of AI in real business contexts. More specifically, in this exploratory investigation, a potential interaction between AI and sustainability has been highlighted in the wine industry, especially from an environmental point of view, i.e. for respectfully governing and managing the business impact on the planet and also for increasing the general efficiency of the process, with peculiar applications on the managerial, economic and financial side of the wine business.European Journal of Innovation Management850A potential interaction between AI and sustainability has been highlighted in the wine industry, especially from an environmental point of view, i.e. for respectfully governing and managing the business impact on the planet.European Journal of Innovation Management["G. Festa", "Antonio D'Amato", "Rosa Palladino", "Armando Papa", "M. T. Cuomo"]2025-01-09T00:00:00 c1faf3f9093193858490b77467e36cfa4e94f06aJournal of Science & Technology00Journal of Science and Technology[]2025-01-09T00:00:00 c79e527f9348f3f522edb1a52384f8d88dc44683The Journal of Social Studies00Journal of Social Studies[]2025-01-09T00:00:00 3fc66c27da9518ca294d4f38e1cfce195e7ee821Archives of Computational Methods in Engineering320Archives of Computational Methods in Engineering["R. A. Choudhury", "Mandeep Singh", "Rajeev Kumar", "Renu Devi", "Shubham Sharma", "Jagpreet Singh", "Abhinav Kumar", "Mohamed Abbas"]2025-01-09T00:00:00 a1a75fc88c8fb1c5f83c8ce5c1aa82021e18ff4b00["Amjad Rehman Khan", "Tanzila Saba"]2025-01-09T00:00:00 36da9f5cb3e3b2c560cc33bf1eab84f585bfd8d8Education Research and Development00Education Research and Development["Liang Zhao"]2025-01-09T00:00:00 e9a1f5eababd1bf4ad7fd703720c71fea532a119BMJ evidence-based medicine140BMJ evidence-based medicine["W. Siemens", "Erik von Elm", "Harald Binder", "Daniel B\u00f6hringer", "Angelika Eisele-Metzger", "Gerald Gartlehner", "Piet Hanegraaf", "Maria-Inti Metzendorf", "J. Mosselman", "Artur Nowak", "Riaz Qureshi", "James Thomas", "S. Waffenschmidt", "Val\u00e9rie Labont\u00e9", "Joerg J. Meerpohl"]2025-01-09T00:00:00 e3bfa0743833a5a79cc1cff927c109d528ad76d8Automating spend analysis for procurement practices, our novel three-component classification model processes unstructured spend texts to replicate expert decision-making. Using data from Cranswick PLC, our model improves supplier categorization accuracy and identifies cost-saving opportunities, with projected annual savings of £16–£22 million.INFORMS Journal on Applied Analytics170A novel three-component classification model processes unstructured spend texts to replicate expert decision-making and identifies cost-saving opportunities, with projected annual savings of £16–£22 million.INFORMS Journal on Applied Analytics["Xingyi Li", "Viviana Culmone", "Bert De Reyck", "Onesun Steve Yoo"]2025-01-09T00:00:00 ff7ff97eea95838fd142f435c088f772a7ce207eOBJECTIVE Humanity faces many health challenges, among which respiratory diseases are one of the leading causes of human death. Existing AI-driven pre-diagnosis approaches can enhance the efficiency of diagnosis but still face challenges. For example, single-modal data suffer from information redundancy or loss, difficulty in learning relationships between features, and revealing the obscure characteristics of complex diseases. Therefore, it is critical to explore a method that can assist clinicians in detecting lesions early and in pre-diagnosing corresponding diseases. APPROACH This paper introduces a novel network structure, SCSCS-Net, which can effectively extract image features from chest X-ray images and generate medical image descriptions, assist clinicians in analyzing patients' medical imaging information, deeply explore potential disease characteristics, and assist in making pre-diagnostic decisions. The SCSCS-Net consists of a reinforced cross-modal feature representation model (RCMFR) and a self-critical cross-modal alignment model (SCCMA), which are responsible for learning the features interdependence between images and reports by using a multi-subspace self-attention structure and guiding the model in learning report generation strategies to improve the professionalism and consistency of medical terms in generated reports, respectively. MAIN RESULTS We further compare our model with some advanced models on the same dataset, and the results demonstrate that our method achieves better performance. Finally, the CE and NLG metrics further confirm that the proposed method acquires the ability to generate high-quality medical reports with higher clinical consistency in generating medical reports. SIGNIFICANCE Our novel method has the potential to improve the early detection and pre-diagnosis of respiratory diseases. The model proposed in this paper allows to narrow the gap between Artificial intelligence technology and clinical medical diagnosis and provides the possibility for in-depth integration.Physiological Measurement00A novel network structure, SCSCS-Net, which can effectively extract image features from chest X-ray images and generate medical image descriptions, assist clinicians in analyzing patients' medical imaging information, deeply explore potential disease characteristics, and assist in making pre-diagnostic decisions is introduced.Physiological measurement["Binyue Chen", "Guohua Liu", "Quan Zhang"]2025-01-09T00:00:00 445ebc601edf5951ea8cafb4696a73ec52f55335O objetivo do presente estudo consistiu em realizar uma revisão integrativa da literatura que relaciona inteligência artificial (IA) e superdotação. Foi consultada a base de dados Education Resources Information Center (ERIC) e utilizada a estratégia de busca: (gifted OR "high abilities" OR precocity OR talent OR endowment) AND (“artificial intelligence”). Não foi estabelecido recorte temporal, com a finalidade de levantar um maior número de produções. Após realizar a organização das produções, foram eleitos para análise um total de dez artigos, divididos em três categorias: “Identificação”, “Atendimento” e “Identificação/Atendimento”. Os resultados forneceram insights sobre o potencial da IA na personalização da aprendizagem, identificação precoce da superdotação e melhorias no desempenho acadêmico. Embora a IA ofereça oportunidades, sua implementação requer uma reflexão cuidadosa sobre as práticas pedagógicas, desenvolvimento profissional dos educadores e impactos sociais mais amplos. O papel do professor continua sendo fundamental, adaptando as tecnologias às necessidades individuais dos estudantes.Revista Nova Paideia - Revista Interdisciplinar em Educação e Pesquisa00Revista Nova Paideia - Revista Interdisciplinar em Educação e Pesquisa["Clarissa Maria Marques Ogeda", "Y. Moreira"]2025-01-09T00:00:00 6aa004140bf0f3c4b609a6fe3430f1bd468029c2Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI’s integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education.British Journal of Biomedical Science790It is argued that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities.British Journal of Biomedical Science["Nigel J. Francis", "Sue Jones", "David P. Smith"]2025-01-09T00:00:00 b28dc302820aee1c5b67dd32df21b24fc576a736In alignment with Saudi Vision 2030’s strategic objectives to diversify and enhance the tourism sector, this study explores the integration of Artificial Intelligence (AI) in the Al-Baha district, a prime tourist destination in Saudi Arabia. Our research introduces a hybrid AI-based framework that leverages sentiment analysis to assess and enhance tourist satisfaction, capitalizing on data extracted from social media platforms such as YouTube. This framework seeks to improve the quality of tourism experiences and augment the business value within the region. By analyzing sentiments expressed in user-generated content, the proposed AI system provides real-time insights into tourist preferences and experiences, enabling targeted interventions and improvements. The conducted experiments demonstrated the framework’s efficacy in identifying positive, neutral and negative sentiments, with the Multinomial Naive Bayes classifier showing superior performance in terms of precision and recall. These results indicate significant potential for AI to transform tourism practices in Al-Baha, offering enhanced experiences to visitors and driving the economic sustainability of the sector in line with the national vision. This study underscores the transformative potential of AI in refining operational strategies and aligning them with evolving tourist expectations, thereby supporting the broader goals of Saudi Vision 2030 for the tourism industry.Applied Informatics290A hybrid AI-based framework that leverages sentiment analysis to assess and enhance tourist satisfaction, capitalizing on data extracted from social media platforms such as YouTube is introduced, which seeks to improve the quality of tourism experiences and augment the business value within the region.AI["Abdulkareem Alzahrani", "Abdullah Alshehri", "Maha Alamri", "Saad Alqithami"]2025-01-09T00:00:00 169ae2ef44d3f89ae211b395289e8fd89097ea3fJ. Medical Syst.350An innovative system in which AI knowledge-based methodologies and simulation are exploited to train learners “how to act” on patients based on the evidence-based best practices provided by clinical practice guidelines is presented.Journal of Medical Systems["A. Bottrighi", "Federica Grosso", "Marco Ghiglione", "A. Maconi", "Stefano Nera", "Luca Piovesan", "Erica Raina", "A. Roveta", "P. Terenziani"]2025-01-09T00:00:00 93b906be68b64932e7695491b715f32513e0246bPerformance diagnosis systems are defined as detecting abnormal performance phenomena and play a crucial role in cloud applications. An effective performance diagnosis system is often developed based on artificial intelligence (AI) approaches, which can be summarized into a general framework from data to models. However, the AI-based framework has potential hazards that could degrade the user experience and trust. For example, a lack of data privacy may compromise the security of AI models, and low robustness can be hard to apply in complex cloud environments. Therefore, defining the requirements for building a trustworthy AI-based performance diagnosis system has become essential. This article systematically reviews trustworthiness requirements in AI-based performance diagnosis systems. We first introduce trustworthiness requirements and extract six key requirements from a technical perspective, including data privacy, fairness, robustness, explainability, efficiency, and human intervention. We then unify these requirements into a general performance diagnosis framework, ranging from data collection to model development. Next, we comprehensively provide related works for each component and concrete actions to improve trustworthiness in the framework. Finally, we identify possible research directions and challenges for the future development of trustworthy AI-based performance diagnosis systems.ACM Computing Surveys1090This article systematically reviews trustworthiness requirements in AI-based performance diagnosis systems and extracts six key requirements from a technical perspective, including data privacy, fairness, robustness, explainability, efficiency, and human intervention.ACM Computing Surveys["Ruyue Xin", "Jingye Wang", "Peng Chen", "Zhiming Zhao"]2025-01-09T00:00:00 eddde3abc302a9b4e3d0cf4edbd63786afb6418cThis study examines the relationship between students’ attitudes toward artificial intelligence (AI) and both AI competence and conceptions. 176 UK university students completed a survey where they were asked to rate statements in relation to their attitudes towards AI, their AI competence and their conceptions about AI using 5-point Likert-type scales. In relation to AI competence, results indicate that affective attitudes predicted awareness and usage, leading to information avoidance and disengagement. Cognitive attitudes positively predicted AI awareness and usage. Behavioural attitudes, however, did not predict awareness or usage, suggesting that individuals may engage with AI technology without deeper understanding. For AI conceptions, behavioural attitudes were more closely linked to conceptions of AI in educational contexts. Positive behavioural attitudes predicted students’ conceptions of AI’s role in intelligent tutoring systems, retentions, drop-out reduction, recommendation systems, and personalised learning. In contrast, affective attitudes predicted conceptions of AI’s use in classroom monitoring and performance prediction, while cognitive attitudes had little influence. These are areas educators can focus on when designing teaching & assessment strategies in relation to AI.  International Journal of Technology in Education230Results indicate that affective attitudes predicted awareness and usage, leading to information avoidance and disengagement, and cognitive attitudes positively predicted AI awareness and usage, suggesting that individuals may engage with AI technology without deeper understanding.International Journal of Technology in Education["Pauldy C. J. Otermans", "Charlotte Roberts", "Stephanie Baines"]2025-01-09T00:00:00 d347f00326b1043632e0f354986a6c1207576fefBackground: The field of Artificial Intelligence has undergone cyclical periods of growth and decline, known as AI summers and winters. Currently, we are in the third AI summer, characterized by significant advancements and commercialization, particularly in the integration of Symbolic AI and Sub-Symbolic AI, leading to the emergence of Neuro-Symbolic AI. Methods: The review followed the PRISMA methodology, utilizing databases such as IEEE Explore, Google Scholar, arXiv, ACM, and SpringerLink. The inclusion criteria targeted peer-reviewed papers published between 2020 and 2024. Papers were screened for relevance to Neuro-Symbolic AI, with further inclusion based on the availability of associated codebases to ensure reproducibility. Results: From an initial pool of 1,428 papers, 167 met the inclusion criteria and were analyzed in detail. The majority of research efforts are concentrated in the areas of learning and inference (63%), logic and reasoning (35%), and knowledge representation (44%). Explainability and trustworthiness are less represented (28%), with Meta-Cognition being the least explored area (5%). The review identifies significant interdisciplinary opportunities, particularly in integrating explainability and trustworthiness with other research areas. Conclusion: Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference. Significant gaps remain in explainability, trustworthiness, and Meta-Cognition. Addressing these gaps through interdisciplinary research will be crucial for advancing the field towards more intelligent, reliable, and context-aware AI systems.LNSAI@IJCAI560Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference, but significant gaps remain in explainability, trustworthiness, and Meta-Cognition.["Brandon C. Colelough", "William Regli"]2025-01-09T00:00:00 4541dc6e4200df5ed9cf28e529ac56d73c8017abThe integration of artificial intelligence (AI) into arbitration marks a significant transformation in alternative dispute resolution, aiming to enhance efficiency, objectivity, and accessibility. Advanced AI systems now extend beyond administrative tasks to analyze complex legal data, predict case outcomes, and even generate arbitral awards. This evolution addresses the growing volume and complexity of international disputes, particularly in commercial and investment arbitration. However, the adoption of AI introduces profound legal and ethical challenges. Key concerns include the absence of human judgment, potential biases embedded in AI algorithms, and the opacity of their decision‐making processes, accountability issues, and data privacy risks. Critically, current legal frameworks such as the New York Convention were not designed to accommodate AI‐generated awards, raising questions about their legitimacy, procedural fairness, and enforceability. This article explores these intersections, focusing on how AI impacts arbitration's efficiency and objectivity, the legal and ethical challenges arising from AI integration, and the extent to which existing legal frameworks accommodate AI‐generated awards. Employing a multidisciplinary approach that includes legal scholarship, case studies, and technological research, the analysis examines the practical implications of AI in arbitration and the specific enforcement challenges of AI‐generated awards. The article concludes with recommendations for regulatory reforms and the adoption of hybrid AI‐human models to balance technological benefits with the necessity for human oversight and ethical accountability.Conflict Resolution Quarterly380Conflict Resolution Quarterly["T. Alhasan"]2025-01-09T00:00:00 8bef19f91a5d939a64cb76b9be17cb384a56ebf0Purpose: Smallholder agriculture forms the backbone of global food security; however, it has been considered highly vulnerable to the impacts of climate variability. The present study explores the role of Artificial Intelligence in integrating intelligent sensor networks for real-time climate monitoring and improving resilience among smallholder farmers. Materials and Methods: The present research will employ a mixed-methods approach, combining quantitative analysis of climate data with qualitative interviews of farmers regarding the efficiency of AI-driven strategies for climate adaptation. Findings: The findings of this study have shown that intelligent sensor networks greatly enhance precision and timeliness of real-time climate data. These help the smallholder farmers in making very precise decisions regarding irrigation, pest control, and crop management that eventually lead to increased productivity and reduced vulnerability to climate risks. The quantitative results show that farmers adopting AI-driven interventions are likely to have a 50% better yield compared to those dependent on conventional methods. Farmers reported qualitative insights into the transformative potential of these technologies by way of improved confidence in decision-making processes and increased resilience against adverse climatic conditions. Implications to Theory, Practice and Policy: Financial constraints, technical difficulties, and the need for capacity building in facilitating technology adoption comprise some important challenges that emanate from the study. All these barriers, once overcome, will see the integration of AI and sensor networks realize benefits not only at an individual farmer level but also at the global agricultural sustainability level. This research contributes to the increasing literature on climate-smart agriculture and gives actionable recommendations for policymakers, practitioners, and stakeholders. The potential of AI-driven intelligent sensor networks can be leveraged to empower smallholder farmers toward sustainable agricultural development that would meet the challenges of food security and economic stability in a changing climate.American Journal of Agriculture00The findings of this study have shown that intelligent sensor networks greatly enhance precision and timeliness of real-time climate data that help the smallholder farmers in making very precise decisions regarding irrigation, pest control, and crop management that eventually lead to increased productivity and reduced vulnerability to climate risks.American Journal of Agriculture["Mustapha Diyaol"]2025-01-09T00:00:00 eb851640cfd12c40fb63c0435b2f92273b4dfc66Data centers are poised for unprecedented growth due to a revolution in Artificial Intelligence (AI), rise in cryptocurrency mining, and increasing cloud demand for data storage. A sizable portion of the data centers’ growth will occur in the US, requiring a tremendous amount of power. Our hypothesis is that the expansion of data centers will contribute to an increase in US CO2 emissions. To estimate CO2 emissions, we applied three forecasted power demands for data centers and applied 56 NREL (National Renewable Energy Laboratory) power mixes and policy scenario cases using 11 AI models. Among these, the linear regression model yielded the most accurate predictions with the highest R-square. We found that overall CO2 emissions in the US could increase up to 0.4–1.9% due to expansion of data centers by 2030. This increase represents ~3–14% of CO2 emissions from the US power sector by 2030. Using the state-level power mix forecasts for 2030 among increasing CO2 emission scenarios, we predict that Virginia’s power mix will maintain emissions in line with the US average, while the Texas, Illinois, and Washington’s power mix are expected to reduce emissions due to greater renewables in their power mix in 2030. However, Illinois and Washington may face challenges due to their limited power resource availability. In contrast, New York and California’s power mix may increase CO2 emissions due to higher natural gas in their power mix in 2030. The highest variability in data center CO2 emissions stems from AI-driven demand and improvements in data center efficiency and is followed by the power mix. To reduce CO2 emissions from data centers, we offer pathways such as reducing power consumption, improving power mix with renewable sources, and using hydrogen in power plants. We propose focusing on New Mexico and Colorado for data centers to minimize CO2 emissions. Finally, we highlight a set of federal policies supplemented by states to facilitate CO2 emission reductions across energy, emissions, waste, R&D, and grid infrastructure.Frontiers in Sustainability460Frontiers in Sustainability["Rohan Jha", "Rishabh Jha", "Mazhar Islam"]2025-01-09T00:00:00 fddd25294e843838a735092d5d08e1bdc3744ba3Abstract: The increasing focus on sustainability and environmental conservation has reshaped the marketing landscape, prompting businesses to adopt green marketing strategies. Simultaneously, advancements in Artificial Intelligence (AI) have transformed traditional marketing approaches, offering data-driven insights and innovative tools. This study explores the integration of AI technologies into green marketing to enhance its effectiveness and sustainability impact. It examines how AI-powered tools, such as predictive analytics, machine learning, and automation, can optimize green marketing strategies by enabling precise targeting, real-time campaign adjustments, and sustainability performance measurements. Through an analysis of existing literature, case studies, and real-world applications, this research highlights AI's potential to improve consumer engagement, build trust in eco-friendly brands, and overcome implementation challenges in diverse markets. The findings provide actionable insights for businesses, policymakers, and marketers, emphasizing the role of AI in advancing green marketing initiatives globally. This study bridges the gap between AI innovation and sustainable marketing, offering a comprehensive framework for leveraging technology to achieve environmental and business goals.Jurnal Ekonomi Manajemen Akuntansi dan Keuangan220This research highlights AI's potential to improve consumer engagement, build trust in eco-friendly brands, and overcome implementation challenges in diverse markets through an analysis of existing literature, case studies, and real-world applications.Jurnal Ekonomi, Manajemen, Akuntansi dan Keuangan["A. Baruno", "Meithiana Indrasari", "Agustiawan Djoko"]2025-01-09T00:00:00 3e3fcff2c2d5aff8a9aecd745e21faf55469e4bfThis study looks at the spread of Artificial Intelligence (AI) throughout several companies. The study examines AI adoption rates, highlights major uses and advantages, and evaluates the influence of AI on the competitive landscape using secondary sources of data that encompass academic literature, industry publications, and corporate data. The study finds considerable variations in AI usage across industries. Banking & Finance, Healthcare, and E-commerce & Retail have strong adoption rates and use AI to increase efficiency, productivity, and customer satisfaction. In contrast, industries such as manufacturing and sales & marketing have lower adoption rates, emphasizing the need for additional investigation of the issues that hinder adoption and the creation of initiatives to speed AI implementation. The research results demonstrate AI's broad uses across sectors, as well as its ability to revolutionize business workflows, competitive landscapes, and social growth. This study helps organizations, politicians, and researchers figure out the complicated nature of AI adoption and manage the changing technological landscape.International Journal of Innovations &amp; Research Analysis00The research results demonstrate AI's broad uses across sectors, as well as its ability to revolutionize business workflows, competitive landscapes, and social growth.International Journal of Innovations &amp; Research Analysis["Jyoti Kataria", "Devershi Mehta"]2025-01-09T00:00:00 601ee4db92ee5f55a41e12eecc7a955a4fcc0442The accelerated integration of Artificial Intelligence (AI) in comprehensive organizational management has marked a significant milestone in enhancing efficiency and productivity across all sectors. However, the effective adoption of this emerging technology faces significant challenges, such as ethical dilemmas, organizational barriers, and a notable deficit in relevant technological skills. This study embarks on a detailed analysis of the crucial determinants influencing the adoption of AI by companies, enhancing the UTAUT model with four new variables: Response Costs, Trust in AI, AI Anxiety, and Environmental Sustainability. Through surveys directed at over 400 CEOs of companies, this work reveals that facilitating conditions, performance expectancy, response costs, trust in AI, and AI anxiety determine the adoption of this new technology in their companies. These findings contribute to identifying which factors, from a managerial perspective, should be considered as more than sufficient reasons for AI to be implemented in their production processes.Journal of Global Information Management960This study embarks on a detailed analysis of the crucial determinants influencing the adoption of AI by companies, enhancing the UTAUT model with four new variables: Response Costs, Trust in AI, AI Anxiety, and Environmental Sustainability.Journal of Global Information Management["Luis-Alfonso Maldonado-Canca", "Juan-Pedro Cabrera-S\u00e1nchez", "Ana-Mar\u00eda Casado-Molina", "Guillermo Berm\u00fadez-Gonz\u00e1lez"]2025-01-09T00:00:00 bdec64c54b5466787a7d193c70ba2ec31a0c888bIntroduction: Alzheimer's disease (AD) is a progressive neurodegenerative disease that accounts for more than half of all cases of dementia worldwide. An aging society therefore poses a huge challenge to medicine. The exact mechanism responsible for this disease is still not fully understood. However, theories of neurodegeneration related to the deposition of pathological proteins in the brain and the imbalance between individual neurotransmitters have allowed the development of effective diagnostic methods - laboratory determination of specific biomarkers (tau protein, β-amyloid) and their marking using PET (Amyloid PET, Tau PET). Magnetic resonance imaging (MRI) is also important in diagnostics. Artificial intelligence (AI) is a promising, new, and rapidly developing path that can significantly affect the diagnostic process of Alzheimer's disease. Purpose of the study: This review examines the role of AI in diagnosing Alzheimer's disease. Materials and methods: A comprehensive literature review was conducted, analyzing 63 studies from the PubMed database (in English, up to December 2024) that assessed the effectiveness, methods, and prospects of AI in the diagnosis of Alzheimer's disease. Conclusions: Share of AI in the diagnosis of Alzheimer's disease is extremely promising. AI used in neuroimaging, genetics, and behavioral biomarkers shows great potential diagnostic. AI-based tools are extremely promising because they can be non-invasive and highly sensitive biomarkers. Optical coherence tomography (OCT) and OCT angiography (OCTA) combined with AI models offer an opportunity for cost-effective and rapid diagnostic pathways for AD. This review presents evidence that artificial intelligence is a key factor in transforming AD diagnostics into modern diagnostics that enable earlier detection and treatment of the disease which may consequently positively impact the quality of life of AD patients and their caregivers.Journal of Education, Health and Sport500Evidence is presented that artificial intelligence is a key factor in transforming AD diagnostics into modern diagnostics that enable earlier detection and treatment of the disease which may consequently positively impact the quality of life of AD patients and their caregivers.Journal of Education, Health and Sport["Dominika Rehan", "Sven Solisch", "Anna Blazhkova", "Anna Sus\u0142ow", "Adam Szwed", "Ewa Szcz\u0119sna"]2025-01-09T00:00:00 557d2458e68b3a0303d82c75c65095fee4e2766bAI and Ethics290How people’s cooperative expectations may pull apart between human–human and human–AI relationships is considered, and an empirical proposal for mapping these distinctions across relationship types is detailed.Ai and Ethics["Madeline G. Reinecke", "Andreas Kappes", "Sebastian Porsdam Mann", "Julian Savulescu", "B. Earp"]2025-01-09T00:00:00 742af54718b2aec548194188f93691abb580fdb9This analysis examines whether Ghana’s Public Health Act, 2012 (Act 851) imposes adequate legal responsibilities on healthcare facilities concerning personnel training on artificial intelligence (AI) systems and implementation of medical negligence reduction measures. Through an evaluative review of Act 851 provisions on staff qualifications, technology deployment, quality care, safety planning, and risk management benchmarks relative to precedents in Ghana and other countries, critical gaps in binding regulations to incentivize organizational capacity building for mitigating errors, hazards and liabilities from substandard practices were identified. Key recommendations include amending Act 851 to mandate credentialing assurance frameworks, clinical audits, risk assessment models and transparency requirements around reporting quality indicators. Strengthening policy directives will compel internal monitoring, governance, and accountability among healthcare facilities as multilayered negligence prevention strategies. Scientific contributions highlight deficiencies in Ghana’s health legislation regarding contemporary challenges like AI adoption risks and propose legal reforms to modernize regulations to support safer, responsible healthcare delivery nationwide.   EDRAAK231Critical gaps in binding regulations to incentivize organizational capacity building for mitigating errors, hazards and liabilities from substandard practices were identified and key recommendations include amending Act 851 to mandate credentialing assurance frameworks, clinical audits, risk assessment models and transparency requirements around reporting quality indicators.EDRAAK["George Benneh Mensah", "Maad M. Mijwil", "Mostafa Abotaleb", "Guma Ali", "P.K. Dutta", "Toufik Mzili", "Marwa M. Eid"]2025-01-10T00:00:00 a368ca0518bf543a2b8c81b189d2deb79d58ddccThe Purpose of the Study: This paper addresses how artificial intelligence (AI) plays a role in the world of design and how it affects the concept of originality. The paper examines the use of AI in areas such as graphic design, logo design, painting, original designs and web design, and discusses the innovations that this technology brings to design processes. The paper also considers the positive and negative effects of AI on designers. Positive effects include the acceleration of design processes and the access to a wider creative spectrum. On the other hand, the impact of AI on originality is a controversial issue. It is questioned how original the designs produced with AI are and whether these designs have artistic value. Literature Review/Background: The impact of artificial intelligence (AI) technology in the field of design and the reconsideration of the concept of originality are mentioned. While AI offers speed and efficiency in design processes, creative solutions have been addressed through learning from data and algorithms. While the innovations and efficiency advantages offered by AI expand the creative capacities of designers, the concepts of originality and personal expression are evaluated. Methodology: The research design of the study was qualitative, document scanning method was used as the data collection method, and content analysis method was used to analyse the data. Using the document scanning method, the effects of artificial intelligence in design and the concepts of originality were discussed. Findings: The impact of artificial intelligence (AI) technology in the field of design necessitates a reconsideration of the concept of originality. While AI offers speed and efficiency in design processes, it produces creative solutions through learning from data and algorithms. However, originality in this process can often be derived from existing data or based on style transfers. While the innovations and efficiency advantages offered by AI expand the creative capacities of designers, it may cause you to question the concepts of originality and personal expression. Conclusion: the paper suggests that AI is an important tool in the design world and predicts that this technology will become even more widespread in the future. However, it is emphasised that AI should be used carefully and consciously in creative processes.ART/icle: Sanat ve Tasarım Dergisi70It is suggested that AI is an important tool in the design world and predicts that this technology will become even more widespread in the future, however, it is emphasised that AI should be used carefully and consciously in creative processes.ART/icle: Sanat ve Tasarım Dergisi["Mustafa G\u00fcnay"]2025-01-10T00:00:00 8234f7588b5b27b3a9f1700154283b60bf1b8c28Robotic Process Automation (RPA) is an important automation tool used in various business processes and other occasions where repetitive work in a computer system is needed. RPA execute designated processes with high precision for a long period of time continuously. In addition, it is easy to deploy RPA bots on devices and to design automation processes, making RPA the most popular choice for business process automation. The application of artificial intelligence (AI) technologies including machine learning (ML) and natural language processing (NLP) expands the function of RPA and enhances its capability. The synergy not only enables automation for more complex tasks involving optimization, but also improves the efficiency of business processes. This paper focuses on current advancements of RPA integrating with AI. The paper also analyzes some typical cases of application and discusses potential challenges. The case analysis involves processes that RPA+AI is already widely applied, while advantages of AI integration is discussed. The paper provides an insight into how AI technology can be used in RPA in future researches.Applied and Computational Engineering00The case analysis involves processes that RPA+AI is already widely applied, while advantages of AI integration is discussed and an insight into how AI technology can be used in RPA in future researches is provided.Applied and Computational Engineering["Shuhao Ma"]2025-01-10T00:00:00 88bafa6d639bf9868cd0ceab3576ba5b1e1dd853This study explores the impact of Artificial Intelligence (AI) on innovation and efficiency within the construction industry, focusing specifically on project management, resource allocation, worker safety, and hazard detection. The research aims to understand how AI tools and technologies are enhancing operational efficiency, improving safety measures, and optimizing resource utilization across various construction projects. A quantitative approach is employed, utilizing a structured questionnaire distributed to 400 construction professionals, including project managers, engineers, safety officers, and workers. The study applies stratified random sampling to ensure a diverse representation of different construction sectors and job functions. Data is collected through closed and Likert-scale questions that measure respondents' perceptions of AI's effectiveness in construction project management, safety, and resource optimization. Descriptive and inferential statistical techniques, including correlation and regression analysis, are employed to examine relationships between AI adoption and project outcomes. The findings suggest that AI is significantly contributing to improvements in project management efficiency, safety monitoring, and resource allocation, though barriers to widespread adoption, such as cost and training, remain. The study's results provide insights into the potential of AI in revolutionizing the construction industry and highlight the need for further investment in AI technologies to overcome implementation challenges. This research contributes to the growing body of knowledge on AI's role in the construction sector and offers valuable guidance for industry stakeholders.Journal of Information Systems Engineering & Management00The findings suggest that AI is significantly contributing to improvements in project management efficiency, safety monitoring, and resource allocation, though barriers to widespread adoption, such as cost and training, remain.Journal of Information Systems Engineering and Management["Nithyakarpagam A."]2025-01-10T00:00:00 8a546cab6b5cb037f6bd635046c7b7d61fc77a38This paper comprehensively explores the integration of Artificial Intelligence (AI) in Human Resource Management (HRM), examining its applications, challenges, and future directions. The review encompasses AI's transformative impact on recruitment processes, talent management, employee engagement, and performance management within HRM. Key applications include AI-powered tools for resume screening, candidate matching, chatbots for initial candidate interaction, enhancing recruitment efficiency and candidate experiences. Also, AI facilitates talent management by providing insights into employee performance, potential, and engagement, enabling tailored learning and development programs. Challenges such as ethical considerations, algorithmic bias, and job displacement are discussed, along with future directions emphasizing ongoing innovation, interdisciplinary collaborations, and the need for HR professionals to stay updated with emerging trends. By addressing challenges and embracing innovation, organizations can unlock the full potential of AI to reimagine HR processes and drive sustainable business success.Journal of Information Systems Engineering & Management970Journal of Information Systems Engineering and Management["Navaneetha Krishnan Rajagopal", "Shelly Mohanty", "Selvaraju Sivamani"]2025-01-10T00:00:00 e271eef9ece7b830eccefc6aaba1d41067e55be9This research was conducted using a survey research method to investigate the influence of Artificial Intelligence (AI) on Nigerian students’ academic performances in tertiary institutions. Nigerian tertiary institutions have an estimated population of about 2.5 million students across the universities, polytechnics, monotechnics, and colleges of education. A sample size of 509 was used. The researchers adopted an online questionnaire (Google Form) to administer questions to respondents across Nigeria to elicit responses from the respondents bordering on their awareness and the use of AI and its attendant impacts on their academic performance. Five research objectives were raised for the proper investigation of this study. From the findings of the study, the researchers found that the majority of Nigerian students use AI and that AI has positive impacts on the educational performance of Nigerian students. It was also found that Nigerian students have training on the use of AI for educational purposes and that they are more familiar with Snapchat AI and ChatGPT. Conclusively, AI is useful to students in the sense that it enhances their knowledge of their courses, improves their learning and speaking skills, and helps them to have a quick understanding of their course by way of simplifying technical aspects of their courses. The researchers therefore recommend as follows: Nigerian tertiary institutions should formally train students as well as teachers on the use of AI for academic purposes so that they can understand the ethical implications of the use of AI. Using AI for writing could be interpreted to mean examination malpractice, and this should not be condoned in the educational sector; however, at the moment, a small number of students used AI for examinations. Albeit, the appropriate use of AI should be fully integrated into Nigerian tertiary institutions’ curricula.Journal of Infrastructure Policy and Development250It was found that the majority of Nigerian students use AI and that AI has positive impacts on the educational performance of Nigerian students, and that Nigerian tertiary institutions should formally train students as well as teachers on the use of AI for academic purposes.Journal of Infrastructure, Policy and Development["B. Ngonso", "P. Egielewa", "Grace Egenti", "Imudia Uduehi", "Flora Sunny-Duke", "K. Ukhurebor", "Shedrack Onwusinkwue", "Ikenna Odezuligbo", "A. Abiodun", "Adedoyin Abiodun Talabi", "Grace Jokthan", "Johnson Opateye", "Udochukwu Chidiebere Nwankwo", "Benjamin Maxwell Eneche", "Uddin O. Osemengbe"]2025-01-10T00:00:00 da97bb53f5f1d99dc755c992397a30a32f17384aThis study provides a comprehensive analysis of the opportunities for democratizing artificial intelligence (AI) for social good using a bibliometric–systematic literature review method. It combines the quantitative analysis of bibliometric methods with the qualitative synthesis of systematic reviews. This approach helps identify patterns, trends, and gaps in the literature, advancing theoretical insights and mapping future research directions. Design/methodology/approach: Scopus, PubMed, and Web of Science, as prominent scientific databases, were utilized to examine publications between 2014 and 2024. The article selection followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The bibliometric analysis was conducted using CiteSpace software. Findings: The bibliometric analysis identified the most influential articles, journals, countries, authors, and key themes. The systematic thematic analysis identified established modes of using AI for social good. Moreover, future research directions are suggested and discussed in this article. Practical implications: The findings give future research directions and guidance to academics, practitioners, and policymakers for real-world applications.The social science00Social Sciences["Chitat Chan", "Afifah Nurrosyidah"]2025-01-10T00:00:00 6caf2f71ed2395a9c0c1f4fabaff9b5cd611d270Integrating artificial intelligence (AI) technologies into various educational domains has garnered significant attention. Among these technologies, ChatGPT stands out as a powerful tool that holds the potential to revolutionize the landscape of mathematics education. This study aims to explore the emerging trends and critical issues surrounding the utilization of ChatGPT in mathematics education. This study utilized a systematic literature review method to synthesize existing literature concerning the emerging trends and critical issues regarding the utilization of ChatGPT in Mathematics Education. Findings revealed three (3) emerging trends, such as (1) Personalized Learning Experiences; (2) Integration with Virtual Learning Environments; and (3) Enhanced Collaborative Learning. Then, another three (3) themes for critical issues; namely: (1) Algorithmic Bias and Accuracy; (2) Privacy and Data Security; and (3 Ethical Use and Accountability. It is recommended that educators, policymakers, and technology developers collaborate to address the critical issues identified while harnessing the potential of ChatGPT to enhance mathematics instruction. Embracing emerging trends while mitigating critical issues will pave the way for the effective integration of ChatGPT in Mathematics Education, ultimately fostering enriched learning experiences and empowering students to thrive in mathematical learning environments.International Journal of Technology in Education00Embracing emerging trends while mitigating critical issues will pave the way for the effective integration of ChatGPT in Mathematics Education, ultimately fostering enriched learning experiences and empowering students to thrive in mathematical learning environments.International Journal of Technology in Education["Jay Fie Luzano"]2025-01-10T00:00:00 80be0fa344cd6fca669be053129346ce795605e4The identification and prediction of financial bankruptcy has gained relevance due to its impact on economic and financial stability. This study performs a systematic review of artificial intelligence (AI) models used in bankruptcy prediction, evaluating their performance and relevance using the PRISMA and PICOC frameworks. Traditional models such as random forest, logistic regression, KNN, and neural networks are analyzed, along with advanced techniques such as Extreme Gradient Boosting (XGBoost), convolutional neural networks (CNN), long short-term memory (LSTM), hybrid models, and ensemble methods such as bagging and boosting. The findings highlight that, although traditional models are useful for their simplicity and low computational cost, advanced techniques such as LSTM and XGBoost stand out for their high accuracy, sometimes exceeding 99%. However, these techniques present significant challenges, such as the need for large volumes of data and high computational resources. This paper identifies strengths and limitations of these approaches and analyses their practical implications, highlighting the superiority of AI in terms of accuracy, timeliness, and early detection compared to traditional financial ratios, which remain essential tools. In conclusion, the review proposes approaches that integrate scalability and practicality, offering predictive solutions tailored to real financial contexts with limited resources.Journal of Risk and Financial Management630A systematic review of artificial intelligence models used in bankruptcy prediction, evaluating their performance and relevance using the PRISMA and PICOC frameworks and proposes approaches that integrate scalability and practicality, offering predictive solutions tailored to real financial contexts with limited resources.Journal of Risk and Financial Management["Luis V\u00e1squez-Serpa", "Ciro Rodr\u00edguez", "Jhelly P\u00e9rez-N\u00fa\u00f1ez", "Carlos Navarro"]2025-01-10T00:00:00 41ddcb7ce6d094086216dae769f0665b453db34cIntroduction: There is a need to incorporate Artificial Intelligence (AI) in medical education which may help in expanding awareness on role of AI in healthcare among the students. Objectives: To assess the awareness and opinions on role of AI in healthcare among undergraduate medical students of a Tertiary Care Institute of Kolkata and to identify any associated sociodemographic factors with their awareness on AI. Method: Descriptive study was conducted using consecutive sampling among 288 undergraduate medical students using a pretested questionnaire, from August - October (2023). Participants with an 'overall awareness score on AI' equal to or above median were categorized as having 'high awareness'. Association of sociodemographic profile with awareness was assessed using binary logistic regression. Results: Almost half (51%) of the students belonged to Phase III of MBBS. Around 70.8% believed AI will reduce medication errors, while 83.3% opined AI will aid in healthcare-oriented research. 53.5% had low awareness on role of AI. Higher odds of low awareness were found among students whose parents were involved in healthcare. Conclusion: Almost half of the students had high awareness on role of AI in healthcare. More seminars, workshops etc., may be helpful in generating further awareness and orientation among the undergraduate medical students for appropriate use of AI applications in future.Healthline130Almost half of the students had high awareness on role of AI in healthcare, and more seminars, workshops etc., may be helpful in generating further awareness and orientation among the undergraduate medical students for appropriate use of AI applications in future.Healthline["Shalini Pattanayak", "Mausumi Basu", "D. Sinha", "Prince Kerketta"]2025-01-10T00:00:00 88a8e16b22a19b66bab405a10abf6d39387c8067Abstract Background Artificial intelligence (AI) is set to shape the future of medical practice. The perspective and understanding of medical students are critical for guiding the development of educational curricula and training. Objective This study aims to assess and compare medical AI-related attitudes among medical students in general medicine and in one of the visually oriented fields (pathology), along with illuminating their anticipated role of AI in the rapidly evolving landscape of AI-enhanced health care. Methods This was a cross-sectional study that used a web-based survey composed of a closed-ended questionnaire. The survey addressed medical students at all educational levels across the 5 public medical schools, along with pathology residents in 4 residency programs in Jordan. Results A total of 394 respondents participated (328 medical students and 66 pathology residents). The majority of respondents (272/394, 69%) were already aware of AI and deep learning in medicine, mainly relying on websites for information on AI, while only 14% (56/394) were aware of AI through medical schools. There was a statistically significant difference in awareness among respondents who consider themselves tech experts compared with those who do not (P=.03). More than half of the respondents believed that AI could be used to diagnose diseases automatically (213/394, 54.1% agreement), with medical students agreeing more than pathology residents (P=.04). However, more than one-third expressed fear about recent AI developments (167/394, 42.4% agreed). Two-thirds of respondents disagreed that their medical schools had educated them about AI and its potential use (261/394, 66.2% disagreed), while 46.2% (182/394) expressed interest in learning about AI in medicine. In terms of pathology-specific questions, 75.4% (297/394) agreed that AI could be used to identify pathologies in slide examinations automatically. There was a significant difference between medical students and pathology residents in their agreement (P=.001). Overall, medical students and pathology trainees had similar responses. Conclusions AI education should be introduced into medical school curricula to improve medical students’ understanding and attitudes. Students agreed that they need to learn about AI’s applications, potential hazards, and legal and ethical implications. This is the first study to analyze medical students’ views and awareness of AI in Jordan, as well as the first to include pathology residents’ perspectives. The findings are consistent with earlier research internationally. In comparison with prior research, these attitudes are similar in low-income and industrialized countries, highlighting the need for a global strategy to introduce AI instruction to medical students everywhere in this era of rapidly expanding technology.JMIR Medical Education170This is the first study to analyze medical students’ views and awareness of AI in Jordan, as well as the first to include pathology residents’ perspectives, and the findings are consistent with earlier research internationally.JMIR Medical Education["Anwar Rjoop", "M. Alqudah", "Raja Alkhasawneh", "Nesreen Bataineh", "Maram Abdaljaleel", "Moayad A Rjoub", "Mustafa Alkhateeb", "Mohammad Abdelraheem", "Salem Al-Omari", "Omar Bani-Mari", "Anas Alkabalan", "Saoud Altulaih", "Iyad Rjoub", "Rula Alshimi"]2025-01-10T00:00:00 17f1370276b4450b066750f03ab3eadae6c276b8Ophthalmology and Therapy300A cloud-based digital platform can be practically integrated into the existing non-digital DR screening programs to implement AI and monitor previously unknown but important indicators, such as referral adherence, to improve the effectiveness of the programs.Ophthalmology and Therapy["Peranut Chotcomwongse", "Paisan Ruamviboonsuk", "Chaiwat Karavapitayakul", "Koblarp Thongthong", "Anyarak Amornpetchsathaporn", "Methaphon Chainakul", "Malee Triprachanath", "Eckachai Lerdpanyawattananukul", "Niracha Arjkongharn", "Varis Ruamviboonsuk", "Nattaporn Vongsa", "Pawin Pakaymaskul", "Turean Waiwaree", "Hathaiphan Ruampunpong", "Richa Tiwari", "V. Tangcharoensathien"]2025-01-10T00:00:00 53e520676aaa8a12c4b33d9b8d8674678cfe0d1eNature Communications2160This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases, highlighting the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration.Nature Communications["Yang Ye", "Abhishek Pandey", "Carolyn E. Bawden", "Dewan Md. Sumsuzzman", "Rimpi Rajput", "A. Shoukat", "B. H. Singer", "S. Moghadas", "Alison P. Galvani"]2025-01-10T00:00:00 50a91c22a7e945c44f822985eeeacdcd43c7bf14With the rapid advancement of Artificial Intelligence (AI) technologies, applications of AI are transforming lifestyles across various fields at an unprecedented pace, bringing numerous conveniences and innovations. However, the widespread application of AI also presents challenges such as privacy protection and ethical concerns. Therefore, it is of great value to enhance the insights into how AI is being used in the various industries, and how it will be developed, to have healthy development of the technology and the societys well-being. In this thesis, they apply AI to smart homes, intelligent health management, transportation, and social entertainment. This paper aims to assess AI's practical impact in various ways, analyze the revolutionary changes and obstacles brought by AI, and provide insight into future technological revolutions. In addition to that, this review assists readers in understanding the role of AI in everyday life in a more comprehensive way and offers theoretical support for future innovative technology, policy-making and societal adoption.Applied and Computational Engineering00This paper aims to assess AI's practical impact in various ways, analyze the revolutionary changes and obstacles brought by AI, and provide insight into future technological revolutions.Applied and Computational Engineering["Chenyu Zhu"]2025-01-10T00:00:00 4ca577d2c2032f0235c97d7463973ffdf5204aaeAbstract Aim This narrative review explores the integration of artificial intelligence (AI) into nursing informatics and examines its impact on nursing practice, healthcare delivery, education, and policy. Background Nursing informatics, which merges nursing science with information management and communication technologies, is crucial in modern healthcare. The emergence of AI presents opportunities to improve diagnostics, treatment, and healthcare resource management. However, integrating AI into nursing practice also brings challenges, including ethical concerns and the need for specialized training. Sources of evidence A comprehensive literature search was conducted from January 2013 to December 2023 using databases like PubMed, Google Scholar, and Scopus. Articles were selected based on their relevance to AI's role in nursing informatics, particularly in enhancing patient care and healthcare efficiency. Discussion AI significantly enhances nursing practice by improving diagnostic accuracy, optimizing care plans, and supporting resource allocation. However, its adoption raises ethical issues, such as data privacy concerns and biases within AI algorithms. Ensuring that nurses are adequately trained in AI technologies is essential for safe and effective integration. Implications for nursing practice and policy Policymakers should promote AI literacy programs for healthcare professionals and develop ethical guidelines to govern the use of AI in healthcare. This will ensure that AI tools are implemented responsibly, protecting patient rights and enhancing healthcare outcomes. Conclusion AI offers promising advancements in nursing informatics, leading to more efficient patient care and improved decision‐making. Nonetheless, overcoming ethical challenges and ensuring AI literacy among nurses are critical steps for successful implementation.International Nursing Review160AI offers promising advancements in nursing informatics, leading to more efficient patient care and improved decision‐making, Nonetheless, overcoming ethical challenges and ensuring AI literacy among nurses are critical steps for successful implementation.International Nursing Review["A. Nashwan", "JC A. Cabrega", "Mutaz I Othman", "Mahmoud Abdelwahab Khedr", "Yasmine M. Osman", "A. El-Ashry", "Rami Naif", "Ahmad A. Mousa"]2025-01-10T00:00:00 a5f8b25be207fdd7c95732b032fa0b799cb6a525Artificial Intelligence (AI) is redefining Human Resource Management (HRM) by revolutionizing traditional recruitment methods and optimizing hiring processes. Conventional recruitment, often prolonged and labor-intensive, has been transformed by AI’s ability to efficiently analyse large volumes of applications, identify top candidates, and provide succinct summaries of qualifications. This technological advancement allows recruiters to shift their focus toward enhancing the candidate experience and attracting exceptional talent. Additionally, AI holds the potential to reduce unconscious bias in hiring by relying on objective data and standardized criteria during initial screening. However, these benefits are accompanied by challenges, including ethical concerns, algorithmic biases, and risks associated with over-reliance on automation. This paper explores the dual role of AI in driving efficiency and fostering equitable hiring practices while addressing its limitations and ethical implications within the recruitment process.International Journal For Multidisciplinary Research110This paper explores the dual role of AI in driving efficiency and fostering equitable hiring practices while addressing its limitations and ethical implications within the recruitment process.International Journal For Multidisciplinary Research["Anees Faroozan"]2025-01-10T00:00:00 55341c71ec7e48fd70db25998eb37b2b2e4866a6Artificial Intelligence is fast changing the face of education, acting almost as a co-teacher in enhancing personalized learning experiences. The role of AI in the classroom has been underlined in this article, emphasizing how it can adapt to individual learning styles, real-time assessment of the progress students makes, and customized instructional support. This mixed-method study questioned quantitative data from schools using the AI tools and gathered qualitative feedback from teachers and students. Striking among these was the improvement in engagement and academic performance. Accordingly, the average test scores increased by as high as 15%, while the trend of student participation continued to increase, with as high as 78% of the teachers reporting increased levels of engagement. AI is complementing teaching and preparing students for the world that they will encounter, which demands digital literacy. This will enable the educators, upon embracing the technology, to ensure that no single student misses out on a leaning need and that the learning environment is effective and inclusive. The contribution of this research adds to the growing compilation of research papers on AI in Education, providing significant information to policymakers and educators interested in improving teaching and learning results.LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades00The role of AI in the classroom has been underlined in this article, emphasizing how it can adapt to individual learning styles, real-time assessment of the progress students makes, and customized instructional support.LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades["Silvana Andrea Cer\u00f3n Silva", "Magaly Cruscaya Ballesteros Lara", "Islam Muhammad Salama Muhammad", "Diana Jazm\u00edn Cer\u00f3n Silva", "Angela Daniela Cer\u00f3n Silva", "Ra\u00fal Rodolfo Salazar Rodr\u00edguez"]2025-01-10T00:00:00 c3d2014345bbe70271af5b1a7830b2f98358dbc9In recent years, technological advancement has been rapid. Artificial intelligence refers to the availability of applications capable of completing tasks involving the imitation of human intellectual processes by computers, particularly computer systems. A major developing concern nowadays is who owns the copyright of images created by artificial intelligence, notably in the Creative Industry. It’s a tricky issue—professional artists are outraged, copyright officials are baffled, and attorneys are preparing to have a field day. The issue lies in the effectiveness regarding the protection of copyright on creative industry work, made by the Artificial Intelligence itself. Since it is a machine/computer system capable of possessing human intelligence in performing complex tasks such as creating creative industrial work, it creates difficulty in how far such protection can be provided for creative industrial work. Therefore, it is difficult to prove the legal aspect of an artwork made by a machine, not by humans. Keywords: legal protection, copyright, artificial intelligence, creative industriesKnE Social Sciences70KnE Social Sciences["Adrian Zen", "Isroni Muhammad Miraj Mirza", "Mohamed Razak", "Agit Soebandi"]2025-01-10T00:00:00 a64bec0d30d87929f87a5fc03c7f93129ee4a9c6Artificial intelligence (AI) has emerged as a major technology and represents a fundamental and revolutionary innovation of our time that has the potential to significantly change the global scenario. In the context of further development of artificial intelligence, state establishment plays a central role in ensuring national security. Countries are tasked with developing legal frameworks for the development and application of AI. Additionally, governments should commit resources to AI research and development to ensure access to cutting‐edge technology. As AI continues to evolve, nation‐building remains crucial for the protection of national security. Countries must shoulder the responsibility of establishing legal structures to supervise the progression and implementation of artificial intelligence. Investing in AI research and development is essential to secure access to cutting‐edge technology. Gracious society and open engagement apply critical impact on forming AI approaches. Civic organizations can contribute to expanding open mindfulness of the related dangers and openings of AI, guaranteeing straightforwardness and responsibility in legislative activities, and pushing for the creation of capable AI approaches. Open interest can help governments in comprehending the yearnings of citizens with respect to AI approaches. This study explores the role and importance of nation‐building in ensuring national security in the context of the development of artificial intelligence. It also examines how civil society and public participation can effectively shape AI policy. The topic offers diverse research and analytical opportunities that enable a deeper understanding of the interactions and mutual influences between statehood and artificial intelligence in the context of ensuring national security. It examines the potential and threats that artificial intelligence poses to national security and considers strategies that countries can adopt to ensure security in this area. Based on the research findings, recommendations and suggestions are made for governments and civil society to improve the effectiveness of public participation in formulating AI policies.The AI Magazine130The role and importance of nation‐building in ensuring national security in the context of the development of artificial intelligence is explored and how civil society and public participation can effectively shape AI policy is examined.AI Mag.["Vitaliy Gumenyuk", "Anatolii Nikitin", "Oleksandr Bondar", "Iaroslav Zhydovtsev", "Hanna Yermakova"]2025-01-10T00:00:00 0ea4898593d8193b07c5a1f42e0ff8da182bca83The insurance sector relies on considerable amounts of data and statistics where standard actuarial computations form the basis for determining risks, liabilities, and appropriate premiums. The traditional methods make predicting life insurance premiums complex task and less efficient. This affects the overall cost and development of proper insurance solutions. However, the rapid development of artificial intelligence technologies presents great opportunities to reshape traditional processes to improve efficiency and accuracy within the insurance industry. The presented work focuses on applying artificial intelligence in developing actuarial calculations, aiming to find a better estimate of the life insurance premium and providing better quality reports for the Iraqi General Insurance Company. In the current study, we used three machine learning algorithms, namely XGBoost, Random Forest (RF), and Decision Tree (DT), to analyze the collected data and estimate the life insurance premium. Different regression evaluation metrics, including MAE, RMSE, R², and Adjusted R², were applied in testing the performance of the algorithms. The results from the experiments showed that the XGBoost algorithm was the highest performing compared to other algorithms that recorded the lowest MAE and the highest R² score. The research also suggested that incorporating artificial intelligence techniques into actuarial analysis can enhance financial reporting efficiency, thus increasing insurance companies' competitiveness in the important sector. Consequently, Iraqi insurance companies need to adopt such technologies to face the changing insurance conditions by training employees and adopting digital transformation to stay competitive and benefit from innovation.Journal of Information Systems Engineering & Management220Three machine learning algorithms were used, namely XGBoost, Random Forest, and Decision Tree, to analyze the collected data and estimate the life insurance premium, and it was suggested that incorporating artificial intelligence techniques into actuarial analysis can enhance financial reporting efficiency, thus increasing insurance companies' competitiveness in the important sector.Journal of Information Systems Engineering and Management["Zena Abdulstar Allayla", "Waheed Mahmood", "AL-Ibrahimi"]2025-01-10T00:00:00 211227f2f7f519a25fe005ced9753928affb3e21Background The aging population is increasingly affected by periodontal disease, a condition often overlooked due to its asymptomatic nature. Despite its silent onset, periodontitis is linked to various systemic conditions, contributing to severe complications and a reduced quality of life. With over a billion people globally affected, periodontal diseases present a significant public health challenge. Current diagnostic methods, including clinical exams and radiographs, have limitations, emphasizing the need for more accurate detection methods. This study aims to develop AI-driven models to enhance diagnostic precision and consistency in detecting periodontal disease. Methods We analyzed 2,000 panoramic radiographs using image processing techniques. The YOLOv8 model segmented teeth, identified the cemento-enamel junction (CEJ), and quantified alveolar bone loss to assess stages of periodontitis. Results The teeth segmentation model achieved an accuracy of 97%, while the CEJ and alveolar bone segmentation models reached 98%. The AI system demonstrated outstanding performance, with 94.4% accuracy and perfect sensitivity (100%), surpassing periodontists who achieved 91.1% accuracy and 90.6% sensitivity. General practitioners (GPs) benefitted from AI assistance, reaching 86.7% accuracy and 85.9% sensitivity, further improving diagnostic outcomes. Conclusions This study highlights that AI models can effectively detect periodontal bone loss from panoramic radiographs, outperforming current diagnostic methods. The integration of AI into periodontal care offers faster, more accurate, and comprehensive treatment, ultimately improving patient outcomes and alleviating healthcare burdens.Frontiers in Medical Technology440It is highlighted that AI models can effectively detect periodontal bone loss from panoramic radiographs, outperforming current diagnostic methods and offering faster, more accurate, and comprehensive treatment, ultimately improving patient outcomes and alleviating healthcare burdens.Frontiers in Medical Technology["Jarupat Jundaeng", "R. Chamchong", "C. Nithikathkul"]2025-01-10T00:00:00 b161a31304d4d521909193e236184748755e401aPurpose: This study explores the evolving relationship between artists and AI tools in the digital art world.  AI's ability to mimic artistic styles and generate new ideas challenges traditional notions of art creation. While AI offers potential benefits like workload reduction and creative inspiration, concerns remain about its impact on artist income and creative processes. Methodology: The research investigates artist satisfaction with AI-generated works, the impact on workflow efficiency, and the potential for income changes. It aims to understand how artists perceive these tools and how AI is affecting their creative ecosystem. Findings: The study anticipates that traditional and blended approaches will hold higher value due to the unique skills and time invested. Findings revealed a three-tiered artist landscape: 1) traditional artists, 2) artists who blend AI and traditional methods, and 3) artists solely using AI tools. The research sheds light on the complex interplay between technological innovation, creative expression, and financial viability within the digital art domain. Unique Contribution to Theory, Policy and Practice: The study employs economic theory of production to analyze the impact of AI on artistic production. In the context revenue, satisfaction, and workload, artists in Manila, Philippines are willing to use AI tools in order to increase their revenue, have a felicitous satisfaction, and reduce their workload that align with the client’s needs. While AI tools can be valuable assistants, the human element remains central to the creative process.International Journal of Arts, Recreation and Sports00The study anticipates that traditional and blended approaches will hold higher value due to the unique skills and time invested and sheds light on the complex interplay between technological innovation, creative expression, and financial viability within the digital art domain.International Journal of Arts, Recreation and Sports["Richard Christopher Jude Cajulis", "Jakob Adrian Tuazon", "Ronaldo R. Cabauatan"]2025-01-10T00:00:00 2743cbdecdc1eb980efbf5403eca301bf7dba5a2Network Modeling Analysis in Health Informatics and Bioinformatics670Network Modeling Analysis in Health Informatics and Bioinformatics["Sahar Taneera", "Reda Alhajj"]2025-01-10T00:00:00 19d652dc8d77325f7eb2dc2a212faf7a9448e3f3Administración y Organizaciones00Administración y Organizaciones["Concepci\u00f3n Monserrat L\u00f3pez Ponce"]2025-01-10T00:00:00 af1c1444a00a1891fd0128edb9e890be02f168adComputational and Structural Biotechnology Journal810A significant demand for XAI in bioinformatics is revealed, driven by the need for transparency and user confidence in decision-making processes, and existing works on explainability techniques in bioinformatics are reviewed.Computational and Structural Biotechnology Journal["Aishwarya Budhkar", "Qianqian Song", "Jing Su", "Xuhong Zhang"]2025-01-10T00:00:00 0d3c12c8a0ecf743da2beb1141d8f80cfe5facf8This paper examines Ghana's Interpretation Act, 2009 for applicability in AI medical negligence cases. Doctrinal analysis focuses on causation and liability apportionment provisions. Findings reveal opacity and distributed responsibility issues in attributing algorithm harm via "but-for" and related tests. However, contributory liability and proportionality stipulations provide means for an equitable remedy. Recommendations include codifying AI accountability through updated laws and jurisprudence, plus transparency requirements for medical AI approvals. Ensuring current law dynamically governs emerging technologies remains vital for public welfare. The analysis aims to spur policy adaptations, balancing innovation with adequate causation tests and flexible liability rules for AI medical harms.SHIFAA180Examination of Ghana's Interpretation Act, 2009 for applicability in AI medical negligence cases reveals opacity and distributed responsibility issues in attributing algorithm harm via "but-for" and related tests, but contributory liability and proportionality stipulations provide means for an equitable remedy.SHIFAA["George Benneh Mensah", "Maad M. Mijwil", "Mostafa Abotaleb", "Guma Ali", "P.K. Dutta"]2025-01-10T00:00:00 a817a0bf7d2051ef1fd8864bcbd6213b88a94cf1BMC Cardiovascular Disorders00BMC Cardiovascular Disorders["Ida Mohammadi", "Shahryar Rajai Firouzabadi", "Melika Hosseinpour", "Mohammadhosein Akhlaghpasand", "Bardia Hajikarimloo", "Sam Zeraatian-Nejad", "P. Sardari Nia"]2025-01-10T00:00:00 932cdbfda0f0fc02d00f0670a34da020f53a20c5Asia Pacific Education Review670Asia Pacific Education Review["Xiao-Fan Lin", "Weipeng Shen", "Sirui Huang", "Yuhang Wang", "Wei Zhou", "Xiaolan Ling", "Wenyi Li"]2025-01-10T00:00:00 a824eaeca650f6131786db0c4cc73e0efe76109aThe rapid growth of human reasoning impacts the sustainability andefficiency of the banking industry globally, including in Nigeria. Many bankshave used technology and creativity to improve service efficiency and revenuesustainability due to worries about the detrimental effects of humanintelligence. Our research analyses how AI integration influences sustainablebanking and service efficiency in selected Nigerian listed banks. Naturallanguage processing, machine learning, and predictive analytics arerevolutionising banking. In addition, risk management, fraud detection,customer service, and operational automation applications create dataprivacy, ethical, and regulatory compliance issues, though they are efficientand cost-effective. According to a cross-sectional study of clients from fiveNigerian deposit money banks: Access Bank Plc, Fidelity Bank Plc, FirstBank Plc, Guarantee Trust Bank Plc, and Zenith Bank Plc, 384 individualscompleted the self-administered questionnaire SAQ. In this study, thefollowing methods were used: mean, standard deviation, skewness, kurtosis,Jarque Bera, correlational analysis, and OLS regression. The researchersobserved that AI awareness, application, and effectiveness have an impact onthe service efficiency of a subset of Nigerian quoted deposit money banks.Finance firms in Nigeria use AI to improve productivity and client happinessas one suggestion was to automate tedious tasks for bank services. However,government regulations restrict Nigerian banks' AI usage. To bridge thesegaps, the paper recommends banking AI regulations, infrastructuraldevelopment, education, training, and monitoring. The paper alsorecommends that Nigerian officials automate identity verification and riskassessment to speed up procedures. Overall, there should be strongsustainability, security, and privacy laws to promote economic and socialobjectives.Journal of Sustainable Development Law and Policy00It is served that AI awareness, application, and effectiveness have an impact on the service efficiency of a subset of Nigerian quoted deposit money banks and there should be strong sustainability, security, and privacy laws to promote economic and socialobjectives.Journal of Sustainable Development Law and Policy (The)["A. Ogundele", "Olusola Anthony Ibitoye", "Oluwatoyin Olusola Akinterinwa", "Abraham Adeniran", "F. Ibukun", "Temitope Gift Apata"]2025-01-10T00:00:00 6be5f05530ac891bee71b4c8ab9b0e4aea212272International journal on artificial intelligence tools00International Journal on Artificial Intelligence Tools["M. Alamaniotis"]2025-01-10T00:00:00 3775bc6be8ade083f325a4e043d3c79ebaa1d971INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Vijayaratnam Sirangula"]2025-01-10T00:00:00 67c702edcca195e642a0cb6cb9d8c4ee337aa5b2Journal of Artificial Intelligence and Robotics00Journal of Artificial Intelligence and Robotics["Anil Sirisena"]2025-01-10T00:00:00 2d8fe039a3dcf3fe0bf29e83823ebac35dbe5040With this editorial, we inaugurate the next issue of our journal, which introduces and explores the term Science 3.0, defined as human research driven by decentralized AI agents.Web3 Journal: ML in Health Science50Web3 Journal: ML in Health Science["Y. Rusinovich", "Neji Hasni"]2025-01-10T00:00:00 2732f9c17090b4dfb097dcfcd0e79b1eb6b6a362This review focuses on opportunities and challenges of future AI developments in veterinary medicine, from the perspective of computer science researchers in developing AI systems for animal behavior analysis. We examine the paradigms of supervised learning, self-supervised learning, and foundation models, highlighting their applications and limitations in automating animal behavior analysis. These emerging technologies present future challenges in data, modeling, and evaluation in veterinary medicine. To address this, we advocate for a collaborative approach that integrates the expertise of AI researchers, veterinary professionals, and other stakeholders to navigate the evolving landscape of AI in veterinary medicine. Through cross-domain dialogue and an emphasis on human and animal well-being, we can shape AI development to advance veterinary practice for the benefit of all.Journal of the American Veterinary Medical Association500This review focuses on opportunities and challenges of future AI developments in veterinary medicine, from the perspective of computer science researchers in developing AI systems for animal behavior analysis, and examines the paradigms of supervised learning, self-supervised learning, and foundation models.Journal of the American Veterinary Medical Association["Jennifer J Sun"]2025-01-10T00:00:00 f91117fb4d9cfcce41b1fa577b63a6866af3cb76Journal of Artificial Intelligence and Big Data00Journal of Artificial Intelligence and Big Data["Praveen Kumar Myakala", "Chiranjeevi Bura", "Anil Kumar Jonnalagadda"]2025-01-10T00:00:00 65633bab841039d873d0bb957c6415e8cd6cd650Este artigo tem como objetivo introduzir profissionais de museus nas novas possibilidades de pesquisa de acervo impulsionadas pela inteligência artificial. Para isso, faz breve introdução sobre conceitos-chave, como inteligência artificial, machine learning, deep learning, big data. Em seguida, trata das possibilidades de pesquisa e, por fim, apresenta alguns exemplos de instituições que jáincorporaram, de algum modo, os novos modelos inteligentes. Palavras-chave: Pesquisa em acervo. Inteligência artificial. Aprendizado das máquinas. Big data. Aprendizado profundo. AbstractThis article aims to introduce museum professionals to new possibilities for collection research driven by artificial intelligence. To do this, it gives a brief introduction to key concepts, such as artificial intelligence, machine learning, deep learning, big data. Next, it discusses research possibilities and, finally, it presents some examples of institutions that have already incorporated, in someway, the new intelligent models. Keywords: Collection research. Artificial intelligence. Machine learning. Big data. Deep learning.Arte e ensaios00Arte e Ensaios["Victor Tuon Murari"]2025-01-10T00:00:00 ace2ad5f557fc104927ddbdaa80d240604435bc9 Organizations increasingly use Generative Artificial Intelligence (AI) to create strategic documents, legislation, and recommendations to support decision-making. Many current AI initiatives are technology-deterministic, whereas technology co-evolves with the social environment, resulting in new applications and situations. This paper presents a novel view of AI governance by organizations from the perspective of complex adaptive systems (CASs). AI is conceptualized as a socio-technological and adaptive system in which people, policies, systems, data, AI, processes, and other elements co-evolve. The CAS lens draws attention to focusing AI governance on the entire organization, taking an outward perspective and considering public values and societal concerns. Although there is no shortage of AI governance instruments, they differ in their effectiveness, and combinations of appropriate mechanisms should be selected to deal with AI’s evolving nature and complexity. A major challenge is that no responsibility, and therefore accountability, is taken due to the lack of understanding of the full socio-technological CAS. As such, joint accountability is needed in which involved parties work together.Policy & Society391A novel view of AI governance by organizations from the perspective of complex adaptive systems (CASs) is presented, conceptualized as a socio-technological and adaptive system in which people, policies, systems, data, AI, processes, and other elements co-evolve.Policy and Society["Marijn Janssen"]2025-01-10T00:00:00 cd0d8ecd626b27e9abea847c1febe3e071fbd317The distance learning domain has undergone an increasing interest in recent artificial intelligence (AI) technological innovations, aiming to improve the quality of learning while saving time, energy, and cost. Nevertheless, despite using these technologies, during the COVID-19 pandemic, distance learning actors, including tutors, content producers, and learners, encountered difficulties in learning through online sessions and virtual classrooms. They suffer from issues related to the availability of tutors and teachers, reliability of knowledge, restricted learner behavior, limited human interaction, and learners’ dropout. To address these challenges, this paper proposes the “PIKU” specification, focusing on four main requirements, particularly, 1) pedagogy, 2) inclusivity, 3) knowledge management, and 4) user-centricity. This specification aims to support learners, promote interaction, and foster collaboration while enhancing learners’ engagement. We propose providing reliable knowledge while ensuring equitable learning and prioritizing learners’ preferences, improving the overall learning experience. Furthermore, we illustrate the feasibility of the “PIKU” specification by proposing an educational system capable of automatically supporting learners. This system not only meets the “PIKU” requirements but also demonstrates its ability to promote an engaging and rich learning experience.International Journal of Engineering Pedagogy (iJEP)00This paper proposes the “PIKU” specification, focusing on four main requirements, particularly, 1) pedagogy, 2) inclusivity, 3) knowledge management, and 4) user-centricity, and proposes an educational system capable of automatically supporting learners.International Journal of Engineering Pedagogy (iJEP)["Khadija El Azhari", "Imane Hilal", "N. Daoudi", "R. Ajhoun"]2025-01-10T00:00:00 4851cc7a156374da6f6a5f1c16de00212bc4dfceEnsuring ethical behavior in Artificial Intelligence (AI) systems amidst their increasing ubiquity and influence is a major concern the world over. The use of formal methods in AI ethics is a possible crucial approach for specifying and verifying the ethical behavior of AI systems. This paper proposes a formalization based on deontic logic to define and evaluate the ethical behavior of AI systems, focusing on system-level specifications, contributing to this important goal. It introduces axioms and theorems to capture ethical requirements related to fairness and explainability. The formalization incorporates temporal operators to reason about the ethical behavior of AI systems over time. The authors evaluate the effectiveness of this formalization by assessing the ethics of the real-world COMPAS and loan prediction AI systems. Various ethical properties of the COMPAS and loan prediction systems are encoded using deontic logical formulas, allowing the use of an automated theorem prover to verify whether these systems satisfy the defined properties. The formal verification reveals that both systems fail to fulfill certain key ethical properties related to fairness and non-discrimination, demonstrating the effectiveness of the proposed formalization in identifying potential ethical issues in real-world AI applications.650A formalization based on deontic logic to define and evaluate the ethical behavior of AI systems, focusing on system-level specifications, contributes to this important goal by introducing axioms and theorems to capture ethical requirements related to fairness and explainability.["Priya T.V.", "Shrisha Rao"]2025-01-10T00:00:00 7c602c2a0d563fdea7ff2b867dac842962db2567Artificial intelligence (AI) uses highly powerful computers to mimic human intelligent behavior; it is a major research hotspot in science and technology, with an increasing number of applications to a wider range of fields, including complex process supervision and control. Wastewater treatment is an example of a complex process involving many uncertainties and external factors to achieve a final product with specific requisites (effluents with prescribed quality). Reducing process energy consumption, greenhouse gas emissions, and resources recovery are additional requirements of these facilities’ operation. AI could extend the purpose and the expected results of previously adopted tools and present operational approaches by leveraging superior simulation, prediction, control, and adaptation capabilities. This paper reviews current AI research in the wastewater field and discusses present achievements and potentials. So far, almost all applications in the sector involve predictive studies, often at a small scale or with limited data use. Frontline research aimed at the creation of AI-supported digital twins of real systems is being conducted, with few encouraging but still limited applications. This paper aims at identifying and discussing key barriers to wider AI adoption in the field, which include laborious instrumentation maintenance, lack of process expertise in the design of current software, instability of control loops, and insufficient incentives for resource efficiency achievement.Water1090Water["A. Capodaglio", "A. Callegari"]2025-01-10T00:00:00 8d13880fb0c5d28c8250e9b2acb2b1cd070dc149Generative artificial intelligence (AI), particularly tools such as ChatGPT, is transforming education by enhancing self-regulated learning (SRL) and critical thinking skills, two essential competencies in the digital era. This study systematically analyzes the impact of generative AI on these skills using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to identify, evaluate, and synthesize relevant studies. Document searches were conducted in Scopus, Web of Science, and ScienceDirect, focusing on publications from 2022 to 2024, when ChatGPT was first widely adopted. Of the 3,214 documents identified, 557 met the initial screening criteria, and 38 studies were selected for detailed analysis. The findings reveal that 71.4% of studies reported AI’s positive role in SRL, mainly through personalized learning, metacognitive support, and adaptive feedback. Likewise, 62.5% of studies reported its significant role in critical thinking, supporting the process of analysis, evaluation, and reflection. However, researchers cautioned against an overreliance on technology, which one said could take away some students’ ability to think for themselves. Such findings indicate that educational institutions need to change their ways and include generative AI in a model that focuses on areas that foster learner independence. This approach will assist teachers and decision-makers in harnessing the distinctive kitsch of AI technology by creating new learning spaces that are creative and future-oriented.International Journal of Engineering Pedagogy (iJEP)00Findings indicate that educational institutions need to change their ways and include generative AI in a model that focuses on areas that foster learner independence, and assist teachers and decision-makers in harnessing the distinctive kitsch of AI technology by creating new learning spaces that are creative and future-oriented.International Journal of Engineering Pedagogy (iJEP)["Juli Sardi", "Darmansyah", "Oriza Candra", "Devi Faizah Yuliana", "Habibullah", "D.T.P. Yanto", "Fivia Eliza"]2025-01-10T00:00:00 f36057acacdc7b23c45babbef8185851331416aePurpose The information science community, including library science, has been a focus of significant discussions and research due to recent progress in artificial intelligence (AI), especially in large language models. This study identifies possible areas for application of AI in library routines and how it can enhance library operations, such as circulation, acquisitions, reference services, serials, digital resources and technical services. Design/methodology/approach This research methodically examines how libraries adjust to swift technological changes, with an emphasis on AI, generative language models like GPT, and the integration of Chatbots. Findings This study outlines strategies to increase library engagement amidst a technologically evolving landscape, highlighting the significance of aligning such innovations with the requirements of users and library missions. The findings offer crucial considerations for libraries in India contemplating AI integration, especially in relation to technological infrastructure, librarian expertise in AI and the establishment of leadership roles to oversee AI initiatives. Social implications This research provides a foundation for library boards and associations to shape informed policies supporting AI in academic libraries. As such, it plays a vital role in advancing the adoption of AI within the global library community. Originality/value The library and information science (LIS) field is increasingly drawing interest from researchers and experts regarding the use of AI. This study covers the incorporation of AI in different library sections and positively impacts the LIS professionals in these advanced endeavours. Library Hi Tech News200The incorporation of AI in different library sections is covered and positively impacts the LIS professionals in these advanced endeavours and provides a foundation for library boards and associations to shape informed policies supporting AI in academic libraries.Library Hi Tech News["Prafull Malakar", "Leeladharan Manavalan", "Palak Jain", "Sarin M.S."]2025-01-10T00:00:00 c5c597477331a8c1a1700214894014e6fc9a4fafPurpose: Diabetic retinopathy (DR) is a major cause of vision loss, particularly in India, where access to retina specialists is limited in rural areas. This study aims to evaluate the Artificial Intelligence-based Diabetic Retinopathy Screening System (AIDRSS) for DR detection and prevalence assessment, addressing the growing need for scalable, automated screening solutions in resource-limited settings. Approach: A multicentric, cross-sectional study was conducted in Kolkata, India, involving 5,029 participants and 10,058 macula-centric retinal fundus images. The AIDRSS employed a deep learning algorithm with 50 million trainable parameters, integrated with Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing for enhanced image quality. DR was graded using the International Clinical Diabetic Retinopathy (ICDR) Scale, categorizing disease into five stages (DR0 to DR4). Statistical metrics including sensitivity, specificity, and prevalence rates were evaluated against expert retina specialist assessments. Results: The prevalence of DR in the general population was 13.7%, rising to 38.2% among individuals with elevated random blood glucose levels. The AIDRSS achieved an overall sensitivity of 92%, specificity of 88%, and 100% sensitivity for detecting referable DR (DR3 and DR4). These results demonstrate the system's robust performance in accurately identifying and grading DR in a diverse population. Conclusions: AIDRSS provides a reliable, scalable solution for early DR detection in resource-constrained environments. Its integration of advanced AI techniques ensures high diagnostic accuracy, with potential to significantly reduce the burden of diabetes-related vision loss in underserved regions.100The AIDRSS provides a reliable, scalable solution for early DR detection in resource-constrained environments, and its integration of advanced AI techniques ensures high diagnostic accuracy, with potential to significantly reduce the burden of diabetes-related vision loss in underserved regions.["Amit Kr Dey", "Pradeep Walia", "Girish Somvanshi", "Abrar Ali", "Sagarnil Das", "Pallabi Paul", "Minakhi Ghosh"]2025-01-10T00:00:00 57fae49baa5044dd41f2eb45a0a3aff6a5876056The electric power industry poses significant risks to workers with a wide range of hazards such as electrocution, electric shock, burns, and falls. Regardless of the types and characteristics of these hazards, electric power companies should protect their workers and provide a safe and healthy working environment, but it is difficult to identify the potential health and safety risks present in their workplace and take appropriate action to keep their workers free from harm. Therefore, this paper proposes a novel safety autonomous platform (SAP) for data-driven risk management in the electric power industry. It can automatically and precisely provide a safe and healthy working environment with the cooperation of safety mobility gateways (SMGs) according to the safety rule and risk index data created by the risk level of a current task, a worker profile, and the output of an on-site artificial intelligence (AI) engine in the SMGs. We practically implemented the proposed SAP architecture using the Hadoop ecosystem and verified its feasibility through a performance evaluation of the on-site AI engine and real-time operation of risk assessment and alarm notification for data-driven risk management.Applied Sciences280A novel safety autonomous platform (SAP) for data-driven risk management in the electric power industry that can automatically and precisely provide a safe and healthy working environment with the cooperation of safety mobility gateways (SMGs) according to the safety rule and risk index data.Applied Sciences["Dongyeop Lee", "Daesik Lim", "Joonwon Lee"]2025-01-10T00:00:00 c319fc4cd11e37e4f52fdca41c9efebd314b07b6Writing plays a crucial role in the development of English as a Foreign Language (EFL) learners’ language; however, it remains a challenging skill for them to acquire. This study investigated how EFL students at a high school in Northern Vietnam engaged with the Writing Assistant Bot (WAB), an artificial intelligence (AI) chatbot designed to support their writing practice at home. Focusing on students’ usage patterns, writing performance, and perceptions, the research included 47 participants, categorized into higher and lower proficiency levels. The mixed-method approach was employed, with chat logs, timed-writing tests, questionnaires, and semi-structured interviews. The findings indicated differences in chatbot usage between the two proficiency levels at various writing stages, despite similarities in their focus on specific writing aspects. Lower-level learners predominantly utilized the chatbot during the Planning stage to generate vocabulary and brainstorm ideas, while higher-level learners mainly used it during the Translating stage to elaborate on ideas and refine their language for diverse and coherent expression. The chatbot significantly enhanced writing performance across all aspects: content, organization, vocabulary, language use, and mechanics, for both levels. Students perceived it as a useful and easy-to-use tool.Journal of educational computing research250Investigating how EFL students at a high school in Northern Vietnam engaged with the Writing Assistant Bot (WAB), an artificial intelligence (AI) chatbot designed to support their writing practice at home found it significantly enhanced writing performance across all aspects.Journal of Educational Computing Research["Thi-Ngoc-Anh Duong", "Hsiu\u2010Ling Chen"]2025-01-10T00:00:00 ce64bb0741b9a4538ca7b023e510306c7959c874Artificial Intelligence (AI) weather prediction (AIWP) models are powerful tools for medium-range forecasts but often lack physical consistency, leading to outputs that violate conservation laws. This study introduces a set of novel physics-based schemes designed to enforce the conservation of global dry air mass, moisture budget, and total atmospheric energy in AIWP models. The schemes are highly modular, allowing for seamless integration into a wide range of AI model architectures. Forecast experiments are conducted to demonstrate the benefit of conservation schemes using FuXi, an example AIWP model, modified and adapted for 1.0-degree grid spacing. Verification results show that the conservation schemes can guide the model in producing forecasts that obey conservation laws. The forecast skills of upper-air and surface variables are also improved, with longer forecast lead times receiving larger benefits. Notably, large performance gains are found in the total precipitation forecasts, owing to the reduction of drizzle bias. The proposed conservation schemes establish a foundation for implementing other physics-based schemes in the future. They also provide a new way to integrate atmospheric domain knowledge into the design and refinement of AIWP models.370A set of novel physics-based schemes designed to enforce the conservation of global dry air mass, moisture budget, and total atmospheric energy in AIWP models are introduced, allowing for seamless integration into a wide range of AI model architectures.["Yingkai Sha", "John S. Schreck", "William Chapman", "David John Gagne"]2025-01-10T00:00:00 a3bbcac4d372d942e63c1498c0cab8b5c2beea52Discover Applied Sciences1481This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, and their applications across the food supply chain based on a comprehensive analysis of literature published from 1990 to 2024.Discover Applied Sciences["S. Dhal", "Debashish Kar"]2025-01-11T00:00:00 9f590931a82c9293eb190d9ce975f84dbb576eaaHighlights Non-contrast computed tomography (CT) scan is a promising modality for opportunistic screening of abdominal aortic aneurysm (AAA).Automation of opportunistic screening of AAA according to CT data is a promising use of artificial intelligence (AI) technologies.The development of AI algorithms for opportunistic screening of AAA based on CT data is currently limited due to the high labor costs in preparing datasets for AI training and testing. AnnotationAbdominal aortic aneurysm is a cardiovascular disease characterized by a latent progression and adverse prognosis. Timely diagnosis reduces surgical risks and postoperative complications. Diagnostic imaging methods used to detect and evaluate this disease, particularly in targeted and opportunistic screening, are reviewed. The prospects of automation using artificial intelligence technologies for opportunistic screening are explored, moreover, they have already proven to be effective tools for optimizing radiology reports in several fields. This review highlights, however, relatively poor development of artificial intelligence algorithms for opportunistic screening of abdominal aortic aneurysms on native non-contrast abdominal CT studies. Possible reasons for this phenomenon and potential ways of development of this subject area are investigated.Complex Issues of Cardiovascular Diseases280Complex Issues of Cardiovascular Diseases["M. Kodenko", "A. V. Vladzimirskyy", "O. Omelyanskaya", "M. M. Suchilova", "I. Blokhin", "D. Gatin", "R. Reshetnikov"]2025-01-11T00:00:00 306452a0dc0f5480f3d9dc5965e9538a4c365a3aIn the era of generative artificial intelligence, whether machine creations have copyright is a controversial issue. This paper takes the first domestic copyright infringement case of artificial intelligence-generated works heard by the Beijing Internet Court as the background, and takes the “creative tool theory” adopted in the judgment as the basis for the copyright infringement case. " is used as the main analytical tool to explore the copyright subject qualification of artificial intelligence. At the same time, the "presumed author theory" is used as a supplementary theory for research. It is proposed that a dual subject structure of "creator-right holder" can be established under the Chinese legal framework to solve the problem. The dilemma of subject qualification of works generated by generative artificial intelligence.GBP Proceedings Series00It is proposed that a dual subject structure of "creator-right holder" can be established under the Chinese legal framework to solve the dilemma of subject qualification of works generated by generative artificial intelligence.GBP Proceedings Series["Yi Yang"]2025-01-11T00:00:00 9fa7975e6e5f0db1c367dd3f75b94d47cefcc3ffIntroduction: Air pollution poses a significant threat to public health and well-being, necessitating innovative solutions to mitigate its adverse effects. This research presents an AI-based Public Air Purifying System (PAPS) designed to enhance outdoor air quality in urban environments, densely polluted locations and other similar locations. The proposed system leverages artificial intelligence algorithms to monitor, analyze, and respond dynamically to pollution levels in real-time. Objectives: A system for real-time surveillance of air quality metrics, including particulate matter and contaminants, using IoT-enabled sensors. Develop a system that autonomously initiates and controls air purifiers in response to pollution metrics, guaranteeing energy efficiency and peak performance. Utilize AI algorithms to enhance airflow distribution for optimal pollutant elimination in various regions. Methods: The proposed technology integrates AI-driven optimization with a sophisticated multi-stage filtration and purification process.  The prototype has a modular design with an AI system that adaptively modifies purification levels according to real-time air quality data, guaranteeing efficient and precise pollutant elimination in various settings. Results: The show results in the air pollution ppm values before and after using the purifier. The results show the data according to the current date and time. A steady decrease in PPM values indicates continuous improvement in air quality over time. PPM values fluctuate, initially rising and then stabilizing, indicating dynamic changes in air quality following the process. Conclusions: The AI-Based Public Air Purifying System demonstrates a unique and significant method for addressing air pollution. Its cognitive talents facilitate real-time modifications, although enhancement is required to guarantee dependability and efficacy. With further developments, this technology may significantly improve urban air quality, mitigate health hazards, and foster sustainable living in highly populated regionsCommunications on Applied Nonlinear Analysis130This research presents an AI-based Public Air Purifying System (PAPS) designed to enhance outdoor air quality in urban environments, densely polluted locations and other similar locations that leverages artificial intelligence algorithms to monitor, analyze, and respond dynamically to pollution levels in real-time.Communications on Applied Nonlinear Analysis["Paresha M. Dudhedia", "Dr. Sandeep Vanjale"]2025-01-11T00:00:00 c80f0200950e2a1369eab2a81b9cc23ce5ebaa07Sociology offers a valuable lens through which to examine the societal transformations taking place in the age of artificial intelligence. By analysing micro-, meso- and macro-social levels, sociology can shed light on how AI affects processes such as socialisation, education, training, employment, communication, leisure and work. Furthermore, the impact of AI on social sustainability is a critical concern. This paper proposes a reflexive analysis of the sociology of AI to explore its potential contributions to social sustainability in the digital age. It considers the challenges associated with accessing and promoting digital literacy for AI, both as consumers and producers. It also considers the implications for sociology as a scientific discipline, encompassing both research methodologies and the products of inquiry. Through this analysis, the paper seeks to provide insights into how the sociology of AI can contribute to a more sustainable society in the digital age, and to identify the obstacles that need to be overcome to achieve this goal.   Received: 22 May 2024 / Accepted: 22 December 2024 / Published: 11 January 2025Academic Journal of Interdisciplinary Studies810Through this analysis, the paper seeks to provide insights into how the sociology of AI can contribute to a more sustainable society in the digital age, and to identify the obstacles that need to be overcome to achieve this goal.Academic Journal of Interdisciplinary Studies["Sandro Serpa", "Ljubi\u0161a Mi\u0107i\u0107", "An\u0111elka \u0160tili\u0107", "Zoran Mastilo"]2025-01-11T00:00:00 b60fd154ae5e74528289e9b5695f2b3425a276e3The application of artificial intelligence (AI) in education is the key to encouraging better and more efficient educational innovation. In the context of Indonesia with its rich cultural, ethnic, religious and linguistic diversity, AI can be an effective learning tool. This Community Service Activity (PkM) aims to explore the benefits of using AI in learning about the diversity of religions and beliefs in Indonesia, with a focus on developing a "Chatbot Bineka". Data was obtained through questionnaires during PkM activities at two private schools in Medan, North Sumatra, by involving senior high school students Grades X-XII. The results of the analysis show that the use of AI in the form of the Chatbot Bineka has great potential in enhancing students’ understanding and learning experiences regarding the diversity of religions and beliefs. This study underlines the importance of integrating AI in education as an innovative approach to developing better understanding of diversity in multi-religious Indonesian society. To achieve optimal results, it is recommended that the use of the Chatbot Bineka is enriched with further critical discussions and activities in the classroom and school. Thus, the development of AI in education can make a significant contribution in helping teachers to make students' learning experiences more interesting and expand their understanding of cultural diversity in IndonesiaJurnal PkM Pengabdian kepada Masyarakat130The results of the analysis show that the use of AI in the form of the Chatbot Bineka has great potential in enhancing students’ understanding and learning experiences regarding the diversity of religions and beliefs in Indonesia.Jurnal PkM (Pengabdian kepada Masyarakat)["Siti Sarah Harahap", "Rin Rin Meilani Salim", "Tracey Yani Harjatanaya"]2025-01-11T00:00:00 96cfb18e7e144f68bc4b98f45749957f35047d5bAng pag-aaral na ito ay gumamit ng isang convergent parallel mixed method na disenyo dahil nakakalap ito ng iba-iba at komplementaryong datos sa parehong paksa. Sa kwantitatibong bahagi, mayroong 366 na mga mag-aaral ang tumugon sa pag-aaral at 14 naman ang kwalitatibong bahagi. Ang mga resulta ng pag-aaral ay nagsiwalat na ang antas ng paggamit ng artificial intelligence ng mga mag-aaral ay mataas na may pangunahing tema; nakakatulong sa pagpapadali ng akademikong tuntunin, nagdudulot ng negatibong epekto sa pagkatuto, nagbibigay ng kalituhan sa mag-aaral, pagsusuri ng impormasyon, pagkakaroon ng limitasyon sa paggamit ng AI, ginagamit ang AI bilang kasangkapan lamang, malaking ambag sa pag-aaral kung ginagamit ng tama, adaptasyon ng AI sa mataas na edukasyon at nakakatulong sa pag-aaral at pagkatuturo at gamitin ang AI sa tamang paraan. Ang integrasyon ng datos sa parehong kwantitatibo at kwalitatibo na mga resulta ng datos ay nagpahiwatig na mayroong convergent ng mga natuklasan mula sa parehong uri ng datos. MGA SUSING SALITA: artificial intelligence, convergent parallel, mixed method study.EPRA international journal of multidisciplinary research00EPRA International Journal of Multidisciplinary Research (IJMR)["Alexis Jane B. Dugho", "Elealeh Suan-Timosa"]2025-01-11T00:00:00 0ea5ecc3c875f95677de794febbf35ce3634a51fThe modeling of pan evaporation (Ep) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eight sub-basins in the Slovak Republic. The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends. This divergence may indicate varying ecological factors influencing the evaporation dynamics of each river. A comprehensive set of 28 machine learning (ML) and deep learning (DL) models was employed, including ML techniques such as linear regression, tree-based, support vector machines (both with and without kernels), ensemble, and Gaussian process methods; as well as DL approaches like neural networks (narrow, medium, wide, bilayered, and trilayered). Among these, stepwise linear regression provided the most optimal fit. The minimum redundancy maximum relevance (mRMR) method was utilized for feature selection to balance relevance and redundancy effectively. The results suggest that emphasizing relative humidity (RH) and minimum temperature (tmin) significantly enhances accuracy, highlighting the critical roles of these factors in modeling pan evaporation trends. The results offer precise evaporation analyses to improve water management and lessen scarcity.Sustainability420The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends, indicating varying ecological factors influencing the evaporation dynamics of each river.Sustainability["Be\u00e1ta Novotn\u00e1", "Vladim\u00edr Cviklovi\u010d", "B. Chv\u00edla", "Martin Min\u00e1rik"]2025-01-11T00:00:00 3d1959b70b715ddf13d397dcce6d3ce89b3de98aDepression poses significant challenges to global healthcare systems and impacts the quality of life of individuals and their family members. Recent advancements in artificial intelligence (AI) have had a transformative impact on the diagnosis and treatment of depression. These innovations have the potential to significantly enhance clinical decision-making processes and improve patient outcomes in healthcare settings. AI-powered tools can analyze extensive patient data—including medical records, genetic information, and behavioral patterns—to identify early warning signs of depression, thereby enhancing diagnostic accuracy. By recognizing subtle indicators that traditional assessments may overlook, these tools enable healthcare providers to make timely and precise diagnostic decisions that are crucial in preventing the onset or escalation of depressive episodes. In terms of treatment, AI algorithms can assist in personalizing therapeutic interventions by predicting the effectiveness of various approaches for individual patients based on their unique characteristics and medical history. This includes recommending tailored treatment plans that consider the patient’s specific symptoms. Such personalized strategies aim to optimize therapeutic outcomes and improve the overall efficiency of healthcare. This theoretical review uniquely synthesizes current evidence on AI applications in primary care depression management, offering a comprehensive analysis of both diagnostic and treatment personalization capabilities. Alongside these advancements, we also address the conflicting findings in the field and the presence of biases that necessitate important limitations.Medical Science490This theoretical review uniquely synthesizes current evidence on AI applications in primary care depression management, offering a comprehensive analysis of both diagnostic and treatment personalization capabilities.Medical Sciences["I. Levkovich"]2025-01-11T00:00:00 7c5738bb52d653209c258279c758bbde293991beCurrent Psychology950Current Psychology["Byung-Jik Kim", "Dong-Gwi Lee"]2025-01-11T00:00:00 71e9d458824dd7d361f2c574c58f2338f22569e4Journal of Epidemiology110Journal of epidemiology["Akihiro Nishi", "Kosuke Inoue"]2025-01-11T00:00:00 069e7b8458d8cbc43d774f37ddd78c36a1eeae84Journal of Criminal Justice Education160Journal of Criminal Justice Education["R. Greenspan"]2025-01-11T00:00:00 a088fd89aa9876b4302074ac1684264f8b518f30Japanese Journal of Radiology170This review synthesizes advances in AI-driven risk prediction models, from traditional imaging biomarkers to cutting-edge deep learning methodologies and multimodal approaches, and proposes future directions to optimize the adoption of AI tools in breast cancer screening and improve equity and outcomes for diverse populations.Japanese journal of radiology["Fredrik Strand"]2025-01-11T00:00:00 31d5047c1acd954a0fb1bf8e4b02c5dc23cd1a7aAI &amp; SOCIETY340The concepts of shared responsibilities and fiduciary duties are explored as avenues to address liability gaps and better aligns legal liabilities with responsibilities, increases legal certainty, and increases cooperation and understanding between actors, improving the quality and safety of technologies.AI &amp; SOCIETY["Bart Custers", "Henning Lahmann", "Benjamyn I. Scott"]2025-01-11T00:00:00 b1c137bbf6e7e50e20a8da161dbb23a9f2d0f94bEste artículo examina el potencial de la mediación asistida por la inteligencia artificial (IA) para abordar y resolver conflictos en áreas críticas principalmente en la educación, la salud y la seguridad. El objetivo es explorar cómo la integración de herramientas de IA en procesos de mediación puede mejorar la eficacia, la accesibilidad y los resultados de los esfuerzos de pacificación. Enfatiza el hecho de que la mediación asistida por IA ofrece un enfoque prometedor para la resolución de conflictos en los campos esenciales mencionados. Asimismo, destaca que para maximizar su efectividad y garantizar la equidad, es esencial abordar los desafíos éticos y prácticos multidisciplinarios asociados con la tecnología, toda vez que a medida que avanza la colaboración entre mediadores, tecnólogos y profesionales del campo es clave para desarrollar soluciones que equilibren la innovación tecnológica, en particular la IA con las necesidades humanas y los derechos fundamentales.Enfoques Jurídicos00Enfoques Jurídicos["Teresa Maria Geraldes Da Cunha Lopes", "Lucia Villal\u00f3n Alejo"]2025-01-11T00:00:00 1332853868a336fb68c12e53cba1a0b8f9d652a7Este estudio de revisión bibliográfica tiene como objetivo analizar las prácticas de gestión La Inteligencia Artificial Generativa (IAG) está transformando la educación superior, generando importantes debates sobre sus implicancias éticas en la enseñanza, el aprendizaje y la evaluación. Este estudio tiene como objetivo analizar estas implicancias desde las perspectivas de estudiantes, profesores e instituciones, con el fin de proponer recomendaciones que fomenten un uso responsable de herramientas como ChatGPT, Humata.ai y Sudowrite. La problemática se centra en la ausencia de lineamientos claros que regulen el uso ético de la IAG, lo que plantea riesgos como el plagio, la desinformación, la falta de transparencia en las fuentes y la vulneración de la privacidad de datos. La metodología utilizada fue una revisión sistemática de literatura bajo el protocolo PRISMA, considerando publicaciones revisadas por pares en bases de datos como Scopus, Web of Science y Google Scholar, entre 2020 y 2024, empleando palabras clave relacionadas con "ética", "inteligencia artificial" y "educación superior". Los resultados revelaron desafíos significativos, como la dependencia tecnológica que afecta la creatividad y el pensamiento crítico, además de malas prácticas recurrentes, como el uso indebido de contenido generado por IA sin atribución. Como propuesta, se recomienda el establecimiento de códigos éticos específicos, la formación de estudiantes y docentes en el uso ético de la IAG y la implementación de políticas institucionales que promuevan la integridad académica. En conclusión, la integración consciente y ética de la IAG puede mitigar riesgos y fortalecer la innovación pedagógica, mejorando la enseñanza y aprendizaje en un contexto educativo cada vez más tecnológico.Revista Científica Multidisciplinar G-nerando10Revista Científica Multidisciplinar G-nerando["Ramiro Enrique Guam\u00e1n Ch\u00e1vez"]2025-01-11T00:00:00 b4484186ee134ff9c0bf1e6969ce7bffa5fbcf8eLa inteligencia artificial (IA) es una tecnología emergente que ha mostrado su potencial para revolucionar la forma en que las empresas hacen negocios. Las pequeñas y medianas empresas (PYMES) en Ecuador tienen la oportunidad de beneficiarse de esta tecnología, pero también enfrentan desafíos significativos en su adopción. Uno de los mayores desafíos es la falta de conocimiento y experiencia en IA por parte de las PYMES. Además, el costo de adquirir y mantener la tecnología puede ser prohibitivo para muchas empresas. Sin embargo, existen oportunidades significativas para las PYMES que adoptan la IA. La tecnología puede ayudar a las empresas a automatizar procesos, mejorar la eficiencia y reducir costos. En este trabajo se empleó una metodología mixta que permitió el uso de datos tanto cualitativos como cuantitativos y la revisión de la literatura existente en el tema.Arandu-UTIC00Arandu UTIC["Luis Stalin Jara Obreg\u00f3n", "Mayra Gabriela Naspud Espinoza"]2025-01-11T00:00:00 a048467f818a5c79cb1b6ccfde409e379b5efa2cInternational Journal for Quality in Health Care00International journal for quality in health care : journal of the International Society for Quality in Health Care["Martin Fletcher", "Samantha Stark", "Nikola Balvin", "David Greenfield"]2025-01-11T00:00:00 58dc8d4adc6238898e99d625bfd6f41b89232119Background: The advancement of artificial intelligence (AI) in education has transformed the way students interact with technological tools, creating new challenges related to self-efficacy, anxiety, and AI dependence. Self-efficacy refers to one's confidence in their ability to use AI, while AI-related anxiety pertains to the fear or concern when interacting with these systems. These variables can influence technological dependence, affecting academic performance and emotional well-being. Objective: This study aims to examine the influence of self-efficacy in AI use and anxiety toward AI on AI dependence among Peruvian university students. Methods: A descriptive cross-sectional study was conducted with 528 Peruvian university students aged 18 to 37 years (M = 19.00, SD = 3.84). Scales were used to measure AI self-efficacy, anxiety toward AI, and AI dependence. Correlation and multiple regression analyses were applied to identify predictors of technological dependence. Results: The results showed that AI self-efficacy was positively correlated with AI anxiety (r = 0.43, p < .01) and AI dependence (r = 0.61, p < .01). Anxiety also significantly correlated with AI dependence (r = 0.71, p < .01). Multiple regression analysis revealed that both AI anxiety (β = 1.131, p < .001) and AI self-efficacy (β = 0.610, p < .001) predicted AI dependence. Additionally, business administration students exhibited greater dependence compared to students from other fields (β = 1.025, p < .05). Conclusions: Students with higher self-efficacy in AI use tend to utilize AI more frequently but also experience greater anxiety and dependence on AI. Educational interventions should focus on reducing AI-related anxiety to prevent excessive dependence, especially among students.Data and Metadata320Students with higher self-efficacy in AI use tend to utilize AI more frequently but also experience greater anxiety and dependence on AI, so educational interventions should focus on reducing AI-related anxiety to prevent excessive dependence.Data and Metadata["Wilter C. Morales-Garc\u00eda", "Liset Z. Sairitupa-Sanchez", "Alcides Flores-Paredes", "Jai Pascual-Mari\u00f1o", "Mardel Morales-Garc\u00eda"]2025-01-12T00:00:00 9b446082f6b36298047583d94b68bb3dc35d921aArtificial intelligence (AI) and machine learning (ML) have made tremendous advancements in the past decades. From simple recommendation systems to more complex tumor identification systems, AI/ML systems have been utilized in a plethora of applications. This rapid growth of AI/ML and its proliferation in numerous private and public sector applications, while successful, has also opened new challenges and obstacles for regulators. With almost little to no human involvement required for some of the new decision-making AI/ML systems, there is now a pressing need to ensure the responsible use of these systems. Particularly in federal government use-cases, the use of AI technologies must be carefully governed by appropriate transparency and accountability mechanisms. This has given rise to new interdisciplinary fields of AI research such as \textit{Responsible AI (RAI)}. In this position paper we provide a brief overview of development in RAI and discuss some of the motivating principles commonly explored in the field. An overview of the current regulatory landscape relating to AI is also discussed with analysis of different Executive Orders, policies and frameworks. We then present examples of how federal agencies are aiming for the responsible use of AI, specifically we present use-case examples of different projects and research from the Census Bureau on implementing the responsible use of AI. We also provide a brief overview for a Responsible AI Assessment Toolkit currently under-development aimed at helping federal agencies operationalize RAI principles. Finally, a robust discussion on how different policies/regulations map to RAI principles, along with challenges and opportunities for regulation/governance of responsible AI within the federal government is presented.270A brief overview of development in RAI is provided and some of the motivating principles commonly explored in the field are discussed and an overview of the current regulatory landscape relating to AI is discussed with analysis of different Executive Orders, policies and frameworks.["A. Rawal", "Katie Johnson", "Curtis Mitchell", "Michael Walton", "Diamond Nwankwo"]2025-01-12T00:00:00 4b1a19064eb03a8c02b21e4c37b9d5056bd157b9Artificial intelligence (AI) is an increasingly notable presence in society, industries, and organizations, making its necessity felt more in managerial decisions and practices. This paper aims to outline the importance of the topic related to the increase in the adaptability, agility, and resilience of the management system as a result of AI integration, resorting to a bibliometric type of research. A total of 107 papers from the period 2007–2024 exported from the Web of Science Core Collection database were analyzed, with support of Biblioshiny software. This topic is proving to be one of heightened global interest, being comprehensively addressed by world leaders in AI research and technologies such as the United States, China, Great Britain, France, India, and beyond. Collaborative relationships established between geographic regions are captured, noting the power and expansion of the theme on all continents of the globe. Likewise, its thematic and strategic evolution is characterized as a surprising one, managing to incorporate and relate concepts with a strong technical and IT character such as feature extraction, machine learning, reinforcement learning with concepts of a managerial nature as supporting customer-tailored interaction, employee skills development, company productivity, and innovation.Systems860This paper aims to outline the importance of the topic related to the increase in the adaptability, agility, and resilience of the management system as a result of AI integration, resorting to a bibliometric type of research.Systems["Ion Popa", "Simona C\u0103t\u0103lina \u0218tefan", "Andrei Josan", "Corina-Elena Mircioiu", "Nicoleta C\u0103ruceru"]2025-01-12T00:00:00 f10fb5f964578793aa4dbcc477ef5a1fa63d57d4Aim Pre‐injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a “signature” (combination of clinical variables) that could predict which older adults are at risk of fall‐related hospital admission. We hypothesized that frailty, measured using the 5‐item modified Frailty Index, could be utilized in combination with other factors as a predictor of admission for fall‐related injuries. Methods The National Readmission Database was mined to identify factors associated with admission of older adults for fall‐related injuries. Older adults admitted for trauma‐related injuries from 2010 to 2014 were included. Age, sex, number of chronic conditions and past fall‐related admission, comorbidities, 5‐item modified Frailty Index, and medical insurance status were included in the analysis. Two machine learning models were selected among six tested models (logistic regression and random forest). Using a decision tree as a surrogate model for random forest, we extracted high‐risk combinations of factors associated with admission for fall‐related injury. Results Our approach yielded 18 models. Being a woman was one of the factors most often associated with admission for fall‐related injuries. Frailty appeared in four of the 18 combinations. Being a woman, aged 65–74 years and presenting a 5‐item modified Frailty Index score >3 predicted admission for fall‐related injuries in 80.3% of this population. Conclusion Using artificial intelligence principles of machine learning, we were able to develop 18 signatures allowing us to identify older adults at risk of admission for fall‐related injuries. Future studies using other databases, such as TQIP, are warranted to validate our high‐risk combination models. Geriatr Gerontol Int 2025; 25: 232–242.Geriatrics & Gerontology International370A “signature” (combination of clinical variables) that could predict which older adults are at risk of fall‐related hospital admission is identified, allowing us to identify older adults at risk of admission for fall‐related injuries.Geriatrics & Gerontology International["Nam Le", "Milan Sonka", "D. Skeete", "K. Romanowski", "Colette Galet"]2025-01-12T00:00:00 b64b028c6f619f7b8a91dc7653abc05811da853bArtificial Intelligence (AI) is revolutionizing medical writing by enhancing the efficiency and precision of healthcare communication and health research. This review explores the transformative integration of AI in medical writing, highlighting its dual role of enhancing efficiency while maintaining the crucial elements of human expertise. AI technologies, including natural language processing and AI-driven literature review tools, have significantly advanced, facilitating rapid draft generation, literature summarization, and consistency in medical documentation. Key applications include aiding study design, enhancing content drafting, and optimizing literature reviews through specific AI tools. Moreover, this review delves into practical applications of AI in the context of lower extremity wounds, specifically ischemic leg ulcers, demonstrating how AI can streamline the synthesis of relevant literature. While AI presents notable advantages, it also raises ethical concerns, such as potential biases and data privacy issues, highlighting the need for human oversight in the writing process. A proposed future framework suggests that AI could take over routine tasks, allowing medical writers to devote more attention to analytical and ethical aspects. Additionally, there is a strong need for further research on the cost-effectiveness of both clinical trials utilizing AI interventions and the incorporation of AI in medical writing. Ultimately, balancing the integration of AI in medical writing promises to improve both healthcare communication and health research, ensuring the production of high-quality, patient-centric and research-focused content.International Journal of Lower Extremity Wounds360This review delves into practical applications of AI in the context of lower extremity wounds, specifically ischemic leg ulcers, demonstrating how AI can streamline the synthesis of relevant literature.The international journal of lower extremity wounds["Pak Thaichana", "Myo Zin Oo", "Gabriel Leiden Thorup", "Chayatorn Chansakaow", "Supapong Arworn", "Kitttipan Rerkasem"]2025-01-12T00:00:00 b58de05e3a593e11d8307bbfa5be12b4e6f3a53cJournal of Politics and Strategy00Journal of Politics and Strategy["Mehdi Asadbak"]2025-01-12T00:00:00 ab1c4012e9cd51d542bde0cc680748e7eb944123Text and Performance Quarterly00Text and Performance Quarterly["Tony E. Adams"]2025-01-12T00:00:00 6780117ff70ad211ba23beab6b3ed1723f5b1f63Journal of Art, Design and Music00Journal of Art, Design and Music["Dina Radwan Mohamed Radwan"]2025-01-12T00:00:00 c76a9e8f7644b273ca6c74c4c42ce54dde6ff652Leadership and Policy in Schools530Leadership and Policy in Schools["Khalid Arar", "A. Tlili", "Lori Schunka", "Soheil S. Salha", "Anna Saiti"]2025-01-12T00:00:00 e3cb15d5069cc7224b40efcf073afa394ce99e53Continuous, adaptive learning-the ability to adapt to the environment and improve performance-is a hallmark of both natural and artificial intelligence. Biological organisms excel in acquiring, transferring, and retaining knowledge while adapting to dynamic environments, making them a rich source of inspiration for artificial neural networks (ANNs). This study explores how neuromodulation, a fundamental feature of biological learning systems, can help address challenges such as catastrophic forgetting and enhance the robustness of ANNs in continuous learning scenarios. Driven by neuromodulators including dopamine (DA), acetylcholine (ACh), serotonin (5-HT) and noradrenaline (NA), neuromodulatory processes in the brain operate at multiple scales, facilitating dynamic responses to environmental changes through mechanisms ranging from local synaptic plasticity to global network-wide adaptability. Importantly, the relationship between neuromodulators, and their interplay in the modulation of sensory and cognitive processes are more complex than expected, demonstrating a"many-to-one"neuromodulator-to-task mapping. To inspire the design of novel neuromodulation-aware learning rules, we highlight (i) how multi-neuromodulatory interactions enrich single-neuromodulator-driven learning, (ii) the impact of neuromodulators at multiple spatial and temporal scales, and correspondingly, (iii) strategies to integrate neuromodulated learning into or approximate it in ANNs. To illustrate these principles, we present a case study to demonstrate how neuromodulation-inspired mechanisms, such as DA-driven reward processing and NA-based cognitive flexibility, can enhance ANN performance in a Go/No-Go task. By integrating multi-scale neuromodulation, we aim to bridge the gap between biological learning and artificial systems, paving the way for ANNs with greater flexibility, robustness, and adaptability.00This study explores how neuromodulation, a fundamental feature of biological learning systems, can help address challenges such as catastrophic forgetting and enhance the robustness of ANNs in continuous learning scenarios, by integrating multi-scale neuromodulation.["Jie Mei", "Alejandro Rodriguez-Garcia", "Daigo Takeuchi", "Gabriel Wainstein", "Nina Hubig", "Yalda Mohsenzadeh", "Srikanth Ramaswamy"]2025-01-12T00:00:00 c7fa91489a9b89dbf8e82ab77e25b44292582efeThe rapid advancement of Large Language Models (LLMs) has created a critical gap in consumer protection due to the lack of standardized certification processes for LLM-powered Artificial Intelligence (AI) systems. This paper argues that current regulatory approaches, which focus on compute-level thresholds and generalized model evaluations, are insufficient to ensure the safety and effectiveness of specific LLM-based user experiences. We propose a shift towards a certification process centered on actual user-facing experiences and the curation of high-quality datasets for evaluation. This approach offers several benefits: it drives consumer confidence in AI system performance, enables businesses to demonstrate the credibility of their products, and allows regulators to focus on direct consumer protection. The paper outlines a potential certification workflow, emphasizing the importance of domain-specific datasets and expert evaluation. By repositioning data as the strategic center of regulatory efforts, this framework aims to address the challenges posed by the probabilistic nature of AI systems and the rapid pace of technological advancement. This shift in regulatory focus has the potential to foster innovation while ensuring responsible AI development, ultimately benefiting consumers, businesses, and government entities alike.00A shift towards a certification process centered on actual user-facing experiences and the curation of high-quality datasets for evaluation has the potential to foster innovation while ensuring responsible AI development, ultimately benefiting consumers, businesses, and government entities alike.["Wesley Pasfield"]2025-01-12T00:00:00 be8ecf3f1b2a7e0c4bad61f684dce1191d4f1099According to social studies of artificial intelligence (AI), public AI controversies tend to dissipate relatively quickly despite well-documented risks and harms. The reasons for this lack of controversiality are beginning to be studied. Drawing on the framework of sociotechnical controversies, we analyze the de-escalation of contentious discussions observed in the AI legislative process by Chile's National Congress. Utilizing a qualitative approach, we tracked the deliberations hosted by the Chamber of Deputies and the Senate of Chile across 51 sessions between 2023 and 2024. We describe three processes of cooling down in the AI debates: (1) deflection of technology liability, (2) instrumentalization of technology policy, and (3) moralization of technology use. However, constructive exchanges appear in some circumstances, which allow us to foresee some favorable conditions for participation in the debates on AI regulation. This paper contributes to AI controversy studies by outlining cooling-down processes and conditions that foster dialogue and providing a critical perspective on the formation of AI regulation.Big Data & Society240Three processes of cooling down in the AI debates are described, including deflection of technology liability, instrumentalization of technology policy, and moralization of technology use, which allow for some favorable conditions for participation in the debates on AI regulation.Big Data Soc.["M\u00f3nica Humeres", "Dusan Cotoras", "Renato Moretti", "I\u00f1aki Oyarz\u00fan-Merino", "Teresa Correa", "Claudia L\u00f3pez"]2025-01-12T00:00:00 87619a148d3413aeddaba39a8bc3eaa1a358e245Creative and disruptive insights (CDIs), such as the development of the theory of relativity, have punctuated human history, marking pivotal shifts in our intellectual trajectory. Recent advancements in artificial intelligence (AI) have sparked debates over whether state of the art models possess the capacity to generate CDIs. We argue that the ability to create CDIs should be regarded as a significant feature of machine superintelligence (SI).To this end, we propose a practical test to evaluate whether an approach to AI targeting SI can yield novel insights of this kind. We propose the Einstein test: given the data available prior to the emergence of a known CDI, can an AI independently reproduce that insight (or one that is formally equivalent)? By achieving such a milestone, a machine can be considered to at least match humanity's past top intellectual achievements, and therefore to have the potential to surpass them.160It is argued that the ability to create CDIs should be regarded as a significant feature of machine superintelligence (SI), and a practical test to evaluate whether an approach to AI targeting SI can yield novel insights of this kind is proposed.["D. Benrimoh", "Nace Miku\u0161", "Ariel Rosenfeld"]2025-01-12T00:00:00 93d76187b411a0e292c235f133707fb7b323f27fAbstract Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system. Domain expertise in food, nutrition and health should always be integrated into both the development and continuous deployment of AI‐powered nutritional intelligence (NI) to ensure it is responsible, accurate, safe, useable and effective. Dietary behaviours are complex and improving diet‐related health outcomes requires socio‐cultural‐demographic considerations within the design and deployment of NI tools. This article describes existing examples of NI within the food system and future opportunities. Human‐in‐the‐loop approaches with food, health and nutrition experts involved at all stages including data acquisition, processing, output validation and ongoing quality assurance are essential to ensure evidence‐based practice. The same ethical considerations should apply in this domain as in any other (e.g. privacy, inclusivity, robustness, transparency and accountability) and responsible practice must encompass rigorous standards and alignment with regulatory frameworks. Critical today and in the future is accessibility to appropriate high‐quality food compositional data sets, which include up‐to‐date information on commercially available products that reflect the constantly evolving food landscape to realise the potential of responsible AI to help address the existing food system challenges.Nutrition Bulletin440This article describes existing examples of NI within the food system and future opportunities, and describes the potential of responsible AI to help address the existing food system challenges.Nutrition Bulletin["Danielle I McCarthy"]2025-01-12T00:00:00 2ba3326c84c80eed7aab5750d31c03a8e2719372The high potential of current AI tools makes this technology a promising tool to resolve many issues in different fields. In the AEC field, AI-aided design tools have shown to be beneficial during design process from its very beginning stages. However, it usually requires human intervention and result-iteration as normal progress for a recently-developed technology. It is seen that the most productive strategy is the relationship between AI and human intelligence in which is referenced as ‘augmented intelligence’. In this research, the capability of AI-powered tools has been tested regarding architectural design. A residential building in Riyadh City was chosen for a redesign case study. The AI proposal design has been divided into two phases presented in the floor plan layouts and the building façade design. Depending on the type of task to be resolved. A comparison among a bunch of related AI tools has been implemented based on specific criteria to select the best fit for each of the phases. GenAI, presented in ArchiteChures tool, produced the layouts. In a later stage, layouts have been slightly modified manually to rectify inaccuracies. The final result has been reviewed and evaluated in reference to the requirements parameters in Riyadh City and to the original proposal. For façade design, a diffusion-based model has been utilized presented in MidJourney tool. The final design was accomplished through an iterative process via hybrid text and image-to-image technology. The results have been reviewed and evaluated regarding the level of accuracy and creativity. The experiment reveals the current potential of two different types of AI technologies. It is confirmed by the evaluation that AI is still not able to dominate in the field at the current time. However, as seen in ArchiteChures and MidJourney, this capability varies based on the AI model and the task performed. While it was somehow challenging to reach the optimal spatial layouts, the result of the façade design showcases the high capability of this kind of AI models.Journal of Al-Azhar University Engineering Sector230The experiment reveals the current potential of two different types of AI technologies and it is confirmed by the evaluation that AI is still not able to dominate in the field at the current time.Journal of Al-Azhar University Engineering Sector["Nour \u0650Al-Soufi", "Hatem El Shafie"]2025-01-12T00:00:00 6c859f5e70043e67085abe5d6a43768fa84c068aMany problems within personalized medicine and digital health rely on the analysis of continuous-time functional biomarkers and other complex data structures emerging from high-resolution patient monitoring. In this context, this work proposes new optimization-based variable selection methods for multivariate, functional, and even more general outcomes in metrics spaces based on best-subset selection. Our framework applies to several types of regression models, including linear, quantile, or non parametric additive models, and to a broad range of random responses, such as univariate, multivariate Euclidean data, functional, and even random graphs. Our analysis demonstrates that our proposed methodology outperforms state-of-the-art methods in accuracy and, especially, in speed-achieving several orders of magnitude improvement over competitors across various type of statistical responses as the case of mathematical functions. While our framework is general and is not designed for a specific regression and scientific problem, the article is self-contained and focuses on biomedical applications. In the clinical areas, serves as a valuable resource for professionals in biostatistics, statistics, and artificial intelligence interested in variable selection problem in this new technological AI-era.1020This work proposes new optimization-based variable selection methods for multivariate, functional, and even more general outcomes in metrics spaces based on best-subset selection, and demonstrates that the proposed methodology outperforms state-of-the-art methods in accuracy and speed.["Marcos Matabuena"]2025-01-12T00:00:00 9a54348d0a35e4b6842d9ca0a35882b98753d697OpenAI's o3 achieves a high score of 87.5 % on ARC-AGI, a benchmark proposed to measure intelligence. This raises the question whether systems based on Large Language Models (LLMs), particularly o3, demonstrate intelligence and progress towards artificial general intelligence (AGI). Building on the distinction between skills and intelligence made by Fran\c{c}ois Chollet, the creator of ARC-AGI, a new understanding of intelligence is introduced: an agent is the more intelligent, the more efficiently it can achieve the more diverse goals in the more diverse worlds with the less knowledge. An analysis of the ARC-AGI benchmark shows that its tasks represent a very specific type of problem that can be solved by massive trialling of combinations of predefined operations. This method is also applied by o3, achieving its high score through the extensive use of computing power. However, for most problems in the physical world and in the human domain, solutions cannot be tested in advance and predefined operations are not available. Consequently, massive trialling of predefined operations, as o3 does, cannot be a basis for AGI - instead, new approaches are required that can reliably solve a wide variety of problems without existing skills. To support this development, a new benchmark for intelligence is outlined that covers a much higher diversity of unknown tasks to be solved, thus enabling a comprehensive assessment of intelligence and of progress towards AGI.02A new benchmark for intelligence is outlined that covers a much higher diversity of unknown tasks to be solved, thus enabling a comprehensive assessment of intelligence and of progress towards AGI.["Rolf Pfister", "Hansueli Jud"]2025-01-13T00:00:00 61d9c287492cae1b7356954b2243cfd0c191292aEuropean Journal of Human Genetics560It is likely that AI will result in dramatic evolution in clinical genetics, and it will be important for all those involved in clinical genetics to prepare accordingly in order to minimize the risks and maximize benefits related to the use of AI in the field.European journal of human genetics : EJHG["D. Duong", "Benjamin D. Solomon"]2025-01-13T00:00:00 8c760b150deda7b1c36e1de634201c04dfbd3f40As artificial intelligence (AI) transforms society, understanding factors that influence AI receptivity is increasingly important. The current research investigates which types of consumers have greater AI receptivity. Contrary to expectations revealed in four surveys, cross country data and six additional studies find that people with lower AI literacy are typically more receptive to AI. This lower literacy-greater receptivity link is not explained by differences in perceptions of AI’s capability, ethicality, or feared impact on humanity. Instead, this link occurs because people with lower AI literacy are more likely to perceive AI as magical and experience feelings of awe in the face of AI’s execution of tasks that seem to require uniquely human attributes. In line with this theorizing, the lower literacy-higher receptivity link is mediated by perceptions of AI as magical and is moderated among tasks not assumed to require distinctly human attributes. These findings suggest that companies may benefit from shifting their marketing efforts and product development towards consumers with lower AI literacy. Additionally, efforts to demystify AI may inadvertently reduce its appeal, indicating that maintaining an aura of magic around AI could be beneficial for adoption.Journal of Marketing00It is found that people with lower AI literacy are typically more receptive to AI, and efforts to demystify AI may inadvertently reduce its appeal, indicating that maintaining an aura of magic around AI could be beneficial for adoption.Journal of Marketing["Stephanie Tully", "Chiara Longoni", "Gil Appel"]2025-01-13T00:00:00 cb3aac41f9afcacf8979a8b51f70826ab4da7a13Journal of Formative Design in Learning290The characteristics, potential and limitations of AI and the importance of developing AI-related transversal skills in higher education are analyzed, followed by examples of the application of AI-based tools in science, technology, engineering, and mathematics (STEM) courses.Journal of Formative Design in Learning["Em\u00edlia Malcata Rebelo"]2025-01-13T00:00:00 b9bf492ae2e41a509c89f27c9bb1c405035cfe92The advent of Artificial Intelligence (AI) has transformed Learning Management Systems (LMSs), enabled personalized adaptation and facilitated distance education. This study employs a bibliometric analysis based on PRISMA-2020 to examine the integration of AI in LMSs from an educational perspective. Despite the rapid progress observed in this field, the literature reveals gaps in the effectiveness and acceptance of virtual assistants in educational contexts. Therefore, the objective of this study is to examine research trends on the use of AI in LMSs. The results indicate a quadratic polynomial growth of 99.42%, with the years 2021 and 2015 representing the most significant growth. Thematic references include authors such as Li J and Cavus N, the journal Lecture Notes in Computer Science, and countries such as China and India. The thematic evolution can be observed from topics such as regression analysis to LMS and e-learning. The terms e-learning, ontology, and ant colony optimization are highlighted in the thematic clusters. A temporal analysis reveals that suggestions such as a Cartesian plane and a league table offer a detailed view of the evolution of key terms. This analysis reveals that emerging and growing words such as Learning Style and Learning Management Systems are worthy of further investigation. The development of a future research agenda emerges as a key need to address gaps.Journal of Infrastructure Policy and Development390A bibliometric analysis based on PRISMA-2020 to examine the integration of AI in LMSs from an educational perspective indicates a quadratic polynomial growth of 99.42%, with the years 2021 and 2015 representing the most significant growth.Journal of Infrastructure, Policy and Development["Blasa Celerina Cruz Cabrera", "Maricela Castillo Leal", "Jorge Antonio Silvestre Acevedo Mart\u00ednez", "Ana Luz Ramos Soto", "Jovany Sep\u00falveda", "Jackeline Valencia", "L. Garc\u00e9s-Giraldo", "Alejandro Valencia-Arias"]2025-01-13T00:00:00 1ad16e520ba87fe9bde23614efe03f4e67c0d4abArtificial intelligence (AI) has become increasingly embedded in every aspect of our lives and educators are beginning to consider how to teach with and about it. Most AI curricula distinctly focus on developing digital or physical technical skills such as coding, robotics, and programming, while only sometimes critically considering the social and ethical dimensions of AI. This may lead to a future disparity between critical thinking and technical competency in AI literacy programming. This qualitative case study research focuses on how a week-long virtual camp used narrative design fiction in graphic novel format as a framework for camp activities and discussions for students in grades 6-8, to facilitate conversations related to the social and ethical implications of AI use. Results suggest that participants gained deeper and more complex opinions on AI and human-technology relationships via critical conversations facilitated through the narrative design fiction. Recommendations for future work on speculative futures, reflection, and narrative design fiction are presented.Journal of Digital Life and Learning00This qualitative case study research focuses on how a week-long virtual camp used narrative design fiction in graphic novel format as a framework for camp activities and discussions for students in grades 6-8, to facilitate conversations related to the social and ethical implications of AI use.Journal of Digital Life and Learning["Tess Butler-Ulrich", "Janette Hughes"]2025-01-13T00:00:00 10fa5b9380ed4c29d76dd3d94ee6ec350a4ca36fThis article presents a systematic literature review exploring the integration of Artificial Intelligence (AI) methodologies in project management (PM). Key applications include cost estimation, duration forecasting, and risk assessment, which are critical factors for project success. This review synthesizes findings from 97 peer-reviewed studies published between 2011 and 2024, using the PRISMA methodology to ensure rigor and transparency. AI techniques such as machine learning, deep learning, and hybrid models have exhibited their potential to enhance PM techniques across projects’ phases, including planning, execution, and monitoring. Decision trees are created to represent the application of AI methodologies in various PM stages and tasks to facilitate understanding and real-world implementation. Among these are hybrid AI models that enhance risk assessment, duration forecasting, and cost estimation, as well as categorization based on project phases to optimize AI integration. Despite these advancements, there are still gaps in addressing dynamic project environments, validating AI models with real-world data, and expanding research into underexplored phases like project closure.Computers1170A systematic literature review exploring the integration of Artificial Intelligence methodologies in project management (PM), which synthesizes findings from 97 peer-reviewed studies published between 2011 and 2024, using the PRISMA methodology to ensure rigor and transparency.Computers["Dorothea Adamantiadou", "Loukas K. Tsironis"]2025-01-13T00:00:00 7f40dfae44a0c20865136aab58bde897c734a0beAI has revolutionized the workplace, significantly impacting the nursing profession. Attitudes toward AI, defined as workers’ perceptions and beliefs about its utility and effectiveness, are critical for its adoption and efficient use in clinical settings. Factors such as age, marital status, and education level may influence this relationship, affecting job performance. This study examines the influence of attitude toward AI on job performance with AI among Peruvian nurses, while also assessing how sociodemographic characteristics moderate this relationship. A descriptive cross-sectional design was used with a sample of 249 Peruvian nurses aged 24 to 53 years (M = 35.58, SD = 8.3). Data were collected using two validated scales: the Brief Artificial Intelligence Job Performance Scale (BAIJPS) and the Attitude toward Artificial Intelligence Scale (AIAS-4). Descriptive statistics, Pearson correlations, and multiple linear regression were applied. A significant positive correlation was found between attitude toward AI and job performance with AI (r = 0.43, p < 0.01). Age (β = -0.177, p < 0.05), divorced marital status (β = -8.144, p < 0.01), and having a bachelor’s degree (β = -3.016, p < 0.05) were negatively associated with job performance, while being from the Selva region had a positive effect (β = 4.182, p < 0.05). A favorable attitude toward AI positively influences nurses’ job performance, highlighting the need for interventions that enhance AI perception. Age, marital status, and education moderate this relationship, suggesting AI adoption strategies should be tailored to different demographic groups.Data and Metadata350A favorable attitude toward AI positively influences nurses’ job performance, highlighting the need for interventions that enhance AI perception and suggesting AI adoption strategies should be tailored to different demographic groups.Data and Metadata["Wilter C. Morales-Garc\u00eda", "Liset Z. Sairitupa-Sanchez", "Alcides Flores-Paredes", "Mardel Morales-Garc\u00eda", "Fernando N. Gutierrez-Caballero"]2025-01-13T00:00:00 ad31450933ec02d26d0c91bf887b920e70533644BACKGROUND Artificial intelligence (AI)-powered platforms may be used to ensure that clinically significant lung nodules receive appropriate management. We studied the impact of a commercially available AI natural language processing tool on detection of clinically significant indeterminate pulmonary nodules (IPNs) based on radiology reports and provision of guideline-consistent care. STUDY DESIGN All computed tomography (CT) scans performed at a single tertiary care center in the outpatient or emergency room setting between 20-Feb-2024 and 20-March-2024 were processed by the AI natural language processing algorithm. CT radiology reports mentioning a lung nodule or focal indeterminate lesion were flagged. All flagged reports were reviewed by a lung nodule expert two weeks after nodule identification. IPNs were classified as "appropriately followed" if follow-up imaging, referral to a nodule clinic, or other guideline-consistent care was ordered. IPNs were classified as "not appropriately followed" if no acknowledgement of the reported nodule was documented in the electronic health record within two weeks of being flagged. RESULTS The AI software processed 76,507 unique radiology reports, identified 2,585 CT scans with chest imaging, and found 389 IPNs. Review determined that 272 (70%) nodules were appropriately followed while 117 (30%) were not appropriately followed. Of the 117 nodules without documented follow-up, 67 (57%) were > 8mm and 24 (20.5%) were > 15mm. IPNs that would not have received follow-up in the absence of the AI software generated 43 additional clinical appointments and 3 procedures. CONCLUSION At a large tertiary care center, 30% of clinically significant incidental pulmonary nodules that would have otherwise been missed were brought to the attention of lung nodule clinicians by an AI software, allowing for initiation of appropriate follow-up.Journal of the American College of Surgeons00At a large tertiary care center, 30% of clinically significant incidental pulmonary nodules that would have otherwise been missed were brought to the attention of lung nodule clinicians by an AI software, allowing for initiation of appropriate follow-up.Journal of the American College of Surgeons["Palina Woodhouse", "Rafael Paez", "Patrick M. Meyers", "Rob J Lentz", "Samira Shoajaee", "Kenneth Sharp", "Nikki Baldi", "Fabien Maldonado", "E. Grogan"]2025-01-13T00:00:00 48d13020b9e2bd7a452b7186b8b4083a39c15a97BMC Cancer470There is significant potential for integrating AI-based decision support systems into clinical practice to enhance the diagnostic accuracy of ultrasound-based AI applications in classifying benign and malignant LNs.BMC Cancer["Xinyang Han", "Jingguo Qu", "Man-Lik Chui", "Simon Takadiyi Gunda", "Ziman Chen", "Jing Qin", "Ann Dorothy King", "Winnie Chiu Wing Chu", "Jing Cai", "Michael T. C. Ying"]2025-01-13T00:00:00 06fee39a99b97500f9faa240fbcb18c7c1f9ba34Objectives: This study investigates the role of Artificial Intelligence (AI) in promoting environmental sustainability, exploring its applications in climate risk modeling, wildlife habitat monitoring, and renewable energy optimization. It also examines the ethical, moral, and legal implications of AI in achieving sustainable outcomes.   Theoretical Framework: The research is grounded in the intersection of AI technology and sustainability, with a focus on justice, equity, and the principles of green AI. It aligns with the Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 16 (Peace, Justice, and Strong Institutions).   Method: A comprehensive review of existing literature and case studies was conducted to analyze AI applications and their implications in environmental sustainability. Ethical considerations, including algorithmic bias, energy consumption, and data privacy, were critically examined.   Results and Discussion: The findings highlight AI’s potential to optimize ecosystems, combat climate change, and improve resource utilization. However, challenges such as data discrimination, unequal access, and ethical concerns must be addressed. The discussion emphasizes the need for frameworks that integrate AI’s capabilities with sustainable practices to achieve equitable outcomes.   Research Implications: This study underscores the importance of adopting strategic approaches to AI deployment, encouraging policymakers and businesses to align AI innovations with sustainability goals.   Originality/Value: By addressing both the opportunities and challenges of AI in sustainability, this research provides a novel perspective on leveraging AI for environmental and societal benefits, offering actionable insights for future developments.Journal of Lifestyle and SDGs Review50The role of Artificial Intelligence in promoting environmental sustainability is investigated, exploring its applications in climate risk modeling, wildlife habitat monitoring, and renewable energy optimization and the ethical, moral, and legal implications of AI in achieving sustainable outcomes are examined.Journal of Lifestyle and SDGs Review["Majed Ahmed Saleh Al-Adwan"]2025-01-13T00:00:00 c4129d863afceccf67f05014498e4461c76c311cJournal of Social and Economic Development800While the AI and tax revenue interaction shows promise for fostering growth, robust measures are necessary to mitigate potential negative effects from AI’s interaction with institutional quality, and the study advocates for the development of AI-friendly institutional policies in BRICS countries.Journal of Social and Economic Development["C. Saba", "N. Monkam"]2025-01-13T00:00:00 3dfe19215e806b6b4e22080b84d8903016ae9b50Artificial intelligence (AI) is revolutionising the financial industry; however, consumer perceptions towards AI in financial services remain relatively limited. The purpose of this study is to investigate variations in behavioural intention towards AI-driven financial services across demographic factors (gender, marital status, age, education level, and income) in Saudi Arabia. A questionnaire was distributed, and 296 complete responses were collected. The survey contained four items to measure behavioural intention. The data were analysed using independent t-tests, one-way analysis of variance with Tukey’s honest significant difference post hoc tests. The findings indicate that behavioural intention is significantly different across the five tested demographic variables: gender, marital status, age, education level, and income level. The results also suggest that females and unmarried individuals have a greater propensity to engage with AI-driven financial services than their counterparts. Moreover, the multiple comparison analysis reveals that individuals within the 21-30 age range and those with lower income levels exhibit a significantly greater intention to use these services. Conversely, individuals possessing postgraduate degrees or higher demonstrate a lower intention to utilise AI-driven financial services compared to those with lower educational qualifications. This research contributes to our understanding of the effects of demographic factors on the adoption of AI-driven financial services by consumers, a topic that has received limited attention in the literature. Moreover, the current study provides practical insights for financial institutions to utilise demographic analysis in tailoring their AI-driven services and marketing strategies, thereby enhancing consumer adoption and helping them stay ahead of the curve in terms of financial technology advancements.The Business and Management Review00The findings indicate that behavioural intention is significantly different across the five tested demographic variables: gender, marital status, age, education level, and income level, and suggest that females and unmarried individuals have a greater propensity to engage with AI-driven financial services than their counterparts.The Business and Management Review["Rotana S. Alkadi"]2025-01-13T00:00:00 3aca97029bf3572f63fe8f50f56560ec13cfc0bbBreast cancer is one of the most prevalent cancers affecting women globally. Early diagnosis is crucial for effective treatment and improved survival rates. Imaging techniques such as mammography and ultrasound are widely used conventional diagnostic methods. However, these methods have limitations, including low sensitivity and specificity, especially in patients with dense breast tissue. For instance, mammograms miss approximately 20% of breast cancer cases, leading to false negatives and delayed treatment that can have fatal consequences. To address these challenges, artificial intelligence (AI)-based diagnostic tools have been developed to assist healthcare professionals in accurately detecting breast cancer. These tools work in conjunction with human radiologists to improve diagnostic outcomes. In addition, biomarkers present a promising non-invasive, more convenient alternative for the early detection of breast cancer, potentially overcoming the limitations of traditional screening methods. Various biomarkers, such as circulating tumor cells, cell-free tumor nucleic acids, and microRNAs, have shown promise in early breast cancer diagnosis. A systematic literature review is needed to consolidate ongoing efforts in molecular biology and biomedical sciences aimed at achieving early breast cancer diagnosis. One of the limitations of previously published research is the heterogeneity of methodologies, which can compromise the credibility of comparisons due to potential inaccuracies in the original data. Hence, future studies should prioritize using consistent datasets and developing robust techniques to manage missing values, outliers, and class imbalances to improve the reliability of breast cancer detection models. This literature review seeks to bridge the knowledge gap by reporting recent high-performing AI models and effective biomarkers that can serve as diagnostic tools in clinical practice.Artificial Intelligence in Health00A systematic literature review is needed to consolidate ongoing efforts in molecular biology and biomedical sciences aimed at achieving early breast cancer diagnosis by reporting recent high-performing AI models and effective biomarkers that can serve as diagnostic tools in clinical practice.Artificial Intelligence in Health["Saadia Humayun", "Tariq Mahmood"]2025-01-13T00:00:00 9cde90b37038c6128f1cf7b822354827c6b4c4d9The purpose of this study is to determine the extent of the use of AI (Artificial Intelligence) based learning media in Vocational High Schools in Langkat Regency. This is qualitative study with a descriptive approach. There are four State Vocational High Schools (SMK) in Langkat Regency namely SMK Negeri 1 Pematang Jaya, SMK Negeri 1 Serapit, SMK Negeri 1 Tanjung Pura, and SMK Negeri 1 Stabat which are the places of research. Totally there are 20 teachers from four schools became informants in this study. In this study, data collection used is in-depth interviews. The findings of the study show the use of AI in the classroom learning process as a teaching medium is still far from effective. Many teachers have not used AI as their learning media. Most teachers already know about AI generally, but the use of AI in education as a learning medium is still not widely known and implemented. Even some teachers still do not know what AI (Artificial Intelligence) actually is. Supporting facilities and infrastructure such as: Unstable or even lost internet connection, limited WIFI, Projectors, PCs that do not support are some of the reasons why AI is still not effective as a learning medium in the classroom. It is hoped that socialization, workshops and training on the use of AI for teachers can be carried out by the central education office and local education offices to increase awareness of technology.Jurnal Dimensi Pendidikan dan Pembelajaran00The findings of the study show the use of AI in the classroom learning process as a teaching medium is still far from effective and it is hoped that socialization, workshops and training on the use of AI for teachers can be carried out by the central education office and local education offices to increase awareness of technology.Jurnal Dimensi Pendidikan dan Pembelajaran["Darmaida Sari"]2025-01-13T00:00:00 8e3059b33bf38b3a310133b4906b3fff28c918dcThe paper focuses on the questions of whether, to what extent, and in what ways the implications of the rapid development of artificial intelligence are changing the nature of one of the fundamental philosophical questions, “What does it (even) mean to understand?” It draws on two sources in particular: Hinton’s explanation of the technological development and functioning of deep neural networks and Nietzsche’s deconstruction of human understanding based on his key concept of “embodied errors.” In doing so, it reveals a series of unexpected parallels, relating in particular to the notion of micro- evolution and the function of error in the processes underlying “thinking” and “intelligence.” The paper therefore draws certain parallels and demarcation lines between human understanding and the “learning” procedures of digital neural networks. At the same time, it addresses the question of what it means for the interpretation of human understanding that, for the first time in history, understanding is faced with a real, existing antithesis, represented by intelligent systems which, although they do not understand, are capable of performing the tasks of understanding, and capable of replacing understanding.Filozofski Vestnik00The paper draws certain parallels and demarcation lines between human understanding and the “learning” procedures of digital neural networks and addresses the question of what it means for the interpretation of human understanding that, for the first time in history, understanding is faced with a real, existing antithesis.Filozofski vestnik["Ale\u0161 Bunta"]2025-01-13T00:00:00 2b09e2eac8e5de48ef3ab015247fe029cf1e0cc4The article discusses various conditions of contemporary artificial intelligence, namely deep learning mechanisms, to emphasize its limitations and argues for an antihumanistic view of contemporary technology. It starts from affirming Turing test and argues that machines can in fact be intelligent but that this intelligence must not be related to a capitalistically hyped idea of artificial general intelligence. Then it outlines various conditions on which deep learning depends in its functioning (brute computing power, capitalist datafication, the world of contingency). These conditions show an epistemological schism in the field of artificial intelligence (between symbolic AI and connectionism) that could be overcome by getting rid of the idea of artificial general intelligence and the competitive relation between humans and machines.Filozofski Vestnik40Various conditions of contemporary artificial intelligence, namely deep learning mechanisms, are discussed to emphasize its limitations and an antihumanistic view of contemporary technology is argued.Filozofski vestnik["Ale\u0161 Mendi\u017eevec"]2025-01-13T00:00:00 32d6b04cd81db69aecaaf85024e12f6a041a8e7eThe pharmaceutical and consumer healthcare industries have been greatly impacted by artificial intelligence and machine learning. A subfield of computer science called artificial intelligence is able to analyze intricate medical data. The goal of artificial intelligence (AI) is to create intelligent modeling, which facilitates knowledge imagination, problem solving, and decision making. AI is becoming more and more significant in many areas of pharmacy, including drug discovery and formulation of drug delivery, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies growth. This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, Many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession. Artificial intelligence and system mastery have seen a significant upsurge in recent years. It has lessened the effort required of humans to advance in their extraordinary lives. Numerous drug discovery implementations have been examined, demonstrating the technology's effectiveness in quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR). Additionally, they are employed in clinical trials to generate and interpret data gathered from patient information. The pharmaceutical industry is currently having trouble maintaining its drug development programs due to rising R&D expenses and declining productivity. In addition to helping with experimental design, machine learning algorithms can forecast the toxicity and pharmacokinetics of potential drugs. This capability lessens the need for extensive and expensive animal testing by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that examine actual patient data can support personalized medicine strategies, improving patient adherence and treatment outcomesInternational Journal of Advanced Research in Science, Communication and Technology260This review focuses on the significant applications of AI in various pharmaceutical domains, including drug development and discovery, many studies are being conducted to enhance the AI technology that is currently available in order to increase the efficiency of the pharmacy profession.International Journal of Advanced Research in Science, Communication and Technology["Anil Katkar", "Pooja Karvande", "Dr. Gajanan Sanap"]2025-01-13T00:00:00 e62914534778f24b30c29c0a7ef8f7c06d0a5a4cArtificial intelligence (AI) has emerged as a key force reshaping the international business landscape. Artificial intelligence, known as one of the disruptive technologies of the moment, will produce important changes in the mechanism of unfolding specific to foreign trade operations. The research is based on a quantitative research method, bibliometric analysis. By querying the existing database on the Scopus platform, scientific papers (research articles, books, papers presented at conferences) containing the keywords in the title, abstract or keywords of the documents were identified. The bibliometric analysis carried out can be a guide for future research directions and for identifying strategic directions for the implementation of AI in international trade. The results obtained can also guide market participants, especially international trade companies, on how artificial intelligence could be used in the process of conducting export-import business. At the same time, the results can help academic researchers explore issues related to the application of artificial intelligence in international trade in future studies.Romanian Economic Journal00The bibliometric analysis carried out can be a guide for future research directions and for identifying strategic directions for the implementation of AI in international trade for market participants, especially international trade companies, on how artificial intelligence could be used in the process of conducting export-import business.The Romanian Economic Journal["Mihaela Gabriela Belu"]2025-01-13T00:00:00 924b1267c8f682eb5ae957b3f972e4a0c173136aPurpose of research: This research examines how Artificial Intelligence (AI) is reshaping stock market trading, focusing on its applications, benefits, and market impacts. The study aims to understand how AI-driven innovations, including machine learning, predictive analytics, and natural language processing, enhance decision-making processes and improve trading strategies. Design/Methodology: The research takes a conceptual approach by thoroughly reviewing existing studies on how AI is applied in stock trading and comparing these AI-driven methods to traditional trading strategies. It also explores the ethical, technical, and regulatory challenges that come with using AI in trading. Results/Finding: The findings show that AI helps make trading more accurate and efficient, giving users an edge by quickly analysing large amounts of data and responding to market shifts. However, the speed of AI-driven trading can increase market volatility, pointing to the need for strong regulations. The study highlights how AI supports a more accessible and efficient market and helps both institutional and individual investors make smarter, data-informed decisions. Practical Implications: This research provides investors, financial institutions, and policymakers with a clearer understanding of both the benefits and limits of AI-driven trading. It highlights the importance of creating regulations that encourage ethical AI use while supporting innovation, openness, and market stability. AI-based trading systems are driving new competition among brokerage firms. Conclusion: AI is changing the game in stock market trading, making strategies faster, more precise, and accessible to more people. But there are still important challenges, like ensuring data quality, understanding complex AI decisions, and handling ethical concerns. Balancing these factors is key to integrating AI responsibly, creating a path for sustainable growth that can benefit the entire financial world.The Business and Management Review00The findings show that AI helps make trading more accurate and efficient, giving users an edge by quickly analysing large amounts of data and responding to market shifts, however, the speed of AI-driven trading can increase market volatility, pointing to the need for strong regulations.The Business and Management Review["Sanaa Zafar Shaikh", "Khushnuma Khan", "F. Sherwani", "Matloob Ullah Khan"]2025-01-13T00:00:00 f9ae1029b89223dc13226f429f58f0e654d7a4c6PurposeThe purpose of this systematic literature review is to identify the antecedents that have enabled the adoption of artificial intelligence (AI) in Higher Education (HE) institutions at both a macro and micro level. The term adoption is in reference to the diffusion of technology that is actively chosen for use by the targeted demographic. Within the context of this paper, adoption is largely referring to the factors that influence the acceptance and use of AI as a tool for personalized learning.Design/methodology/approachTo develop our understanding and appreciation of the valuable impact that AI potentially has upon personalized learning the following systematic literature review was conducted. An acceptable systematic literature review is a comprehensive method of fully analysing and evaluating all available research in the chosen area or specific research query.FindingsThe findings from this study have particular implications for personalized learning in the adoption and diffusion of AI and an increasing integration of macro, structural, and micro, individual. Developing and managing AI in education is seen, from the literature, to becoming more embedded in the teaching and learning process. The paper identifies the following: antecedents that supports the adoption of AI for personalized learning; application of AI technologies in the teaching and learning process; AI technologies that enable personalized instruction and learning; generative AI that supports intuitive learning through tracking data.Originality/valuePersonalized learning remains focused on customizable “choice-driven” learning and education. In addition, personalized learning and instruction is defined as being a responsive and structured method that adapts to each individual learner’s method of learning so that all may achieve their capabilities and actively participate. This solidifies the intrinsic connection between teaching and learning through personalized technologies such as AI.The international journal of information and learning technology510The paper identifies the antecedents that supports the adoption of AI for personalized learning; application of AI technologies in the teaching and learning process; AI technologies that enable personalized instruction and learning; generative AI that supports intuitive learning through tracking data.The International Journal of Information and Learning Technology["Glenn Hardaker", "Liyana Eliza Glenn"]2025-01-13T00:00:00 d83f9f51f554fe065ef3679f2303f6a4212d4adbPurpose: is to substantiate the need to improve the partnership between the state, business, universities and civil society in the field of generative artificial intelligence.Methods: the research is based on the application of theoretical and empirical analysis methods, including: logical, retrospective, generalization, modeling, comparison, statistical, observation, data visualization.Results: the article provides arguments confirming the relevance of generative artificial intelligence by its key stakeholders. The necessity of developing models of institutional interactions for building the new format of stakeholder interaction based on the principle of partial intersection of their institutional spheres of influence, coupled with the urgent demands of civil society, is substantiated. The analysis of the reasons for the interest of the state and business in using solutions based on artificial intelligence in their activities is carried out. Special attention is paid to the attitude of universities to the responsible introduction of generative artificial intelligence into the scientific and educational environment and its use in the solving educational and professional tasks. The improved model of partnership between the state, business, universities and civil society in the field of generative artificial intelligence is proposed.Conclusions and Relevance: partnership in the field of scientific and technological progress allows us to take into account the interests and needs of its key stakeholders, as well as emerging opportunities for them to develop a new role status in the development and use of generative artificial intelligence. The recommended partnership model of key stakeholders allows for the aggregation of financial and production resources of business, competencies and scientific potential of universities in joint projects to develop solutions in the field of development and use of generative artificial intelligence, which can give a significant synergistic effect if this collaboration is complemented by state participation. Inclusion in the model of civil society will ensure that its requests for the preservation of universal values are combined in decisions on the use of generative artificial intelligence and will give a human-centered character to scientific and technological progress in the context of digitalization of society.Multimedia Information Retrieval200Arguments confirming the relevance of generative artificial intelligence by its key stakeholders are provided and the recommended partnership model of key stakeholders allows for the aggregation of financial and production resources of business, competencies and scientific potential of universities in joint projects to develop solutions in the field of development and use of generative artificial intelligence.MIR (Modernization. Innovation. Research)["M. Izmailova"]2025-01-13T00:00:00 8ed6d47a93031acefc25af21386b3836bd40527eThis paper explores the transformative impact of Artificial Intelligence (AI) on commodity trading and risk management. It delves into how AI technologies can enhance the efficiency and effectiveness of front office, middle office, and back office operations. The paper discusses various applications of AI, including price forecasting, real- time market insights, risk assessment, fraud detection, and the generation of Business Intelligence (BI) reports and dashboards. Additionally, it examines how AI can automate reconciliation processes, simulate prices, and audit master and transactional data for compliance purposes. Real-life examples of AI-enhanced Commodity Trading and Risk Management (CTRM) software are provided to illustrate these benefits. The paper also addresses the challenges and future prospects of AI implementation in this sector. Keywords Artificial Intelligence, Commodity Trading, Risk Management, CTRM, Price Forecasting, Market Insights, Risk Assessment, Fraud Detection, Business Intelligence, Reconciliation, Price Simulation, Data Auditing, Compliance, Automation.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00How AI technologies can enhance the efficiency and effectiveness of front office, middle office, and back office operations and various applications of AI, including price forecasting, real- time market insights, risk assessment, fraud detection, and the generation of Business Intelligence reports and dashboards are examined.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Manu Handa and Ridhima Arora"]2025-01-13T00:00:00 6a887bfbff80013968892afe1da5d11533f86971The integration of artificial intelligence (AI) in educational technology holds transformative potential, particularly for disabled students. This study explores the development and evaluation of AI-driven adaptive learning systems tailored to meet the unique needs of disabled learners. Employing experimental methodologies, the research demonstrates how AI can enhance accessibility, engagement, and learning outcomes. Findings reveal that AI systems significantly outperform traditional methods in personalizing educational experiences, suggesting promising directions for inclusive education. KEYWORDS: AI, adaptive learning, disability, educational technology, inclusionEPRA International Journal of Research &amp; Development (IJRD)00This study explores the development and evaluation of AI-driven adaptive learning systems tailored to meet the unique needs of disabled learners, suggesting promising directions for inclusive education.EPRA International Journal of Research &amp; Development (IJRD)["Mr. Pradeep B", "Ms. Sahana J K"]2025-01-13T00:00:00 be1fec76926b5d9b07f602a3427a223566455947Generative Artificial Intelligence (AI) has emerged as a transformative tool in various domains, including medical sector. Generative AI models like ChatGPT offer unparalleled potential in diagnostics, prognostications, and research assistance. However, their integration necessitates careful scrutiny of risks, including privacy violations and the propagation of inaccuracies. Establishing clear guidelines for their use in academic medicine and research is imperative for the responsible use of these tools. This article explores the foundational concepts of generative AI, highlights its applications in medicine, research, and academia, and addresses ethical and practical concerns.Journal of Institute of Medicine Nepal00The foundational concepts of generative AI are explored, its applications in medicine, research, and academia, and ethical and practical concerns are addressed are addressed.Journal of Institute of Medicine Nepal["Mohan Raj Sharma"]2025-01-13T00:00:00 73ebc8a0ccc0d1e39fb307a1f41f4c17b79d9082Al-Basaer Journal of Business Research00Al-Basaer Journal of Business Research["Salam Al-E\u2019mari", "Yousef K. Sanjalawe", "Ahlam Al-E\u2019mari"]2025-01-13T00:00:00 20c8a82ccd6580621aa3ee6a0952bfcea04be286Education and Information Technologies : Official Journal of the IFIP technical committee on Education610Education and Information Technologies["Deniz G\u00f6rg\u00fcl\u00fc", "Fatma Co\u015fkun", "Mustafa Demi\u0307r", "Mete Si\u0307pahi\u0307o\u011flu"]2025-01-13T00:00:00 1cd50f2d12eaafed5a19da15147c852c3418cab7Military Medicine20Military medicine["A. Kleebayoon", "Viroj Wiwanitkit"]2025-01-13T00:00:00 4910b6adc14d1e3a444d2cbf3ff3317eb696d640Education and Information Technologies : Official Journal of the IFIP technical committee on Education780Education and Information Technologies["Fairuz Anjum Binte Habib"]2025-01-13T00:00:00 992249272374eee75bb78e2f14b2d7c332b8020eIndian Journal of Clinical Cardiology40Indian Journal of Clinical Cardiology["Hamrish Kumar Rajakumar"]2025-01-13T00:00:00 8e443018522d7f78d24f456da9577fe3f65bc262Penelitian ini bertujuan untuk mengevaluasi efektivitas dan efisiensi penggunaan kecerdasan buatan (AI) dalam lingkup perguruan tinggi serta melihat seberapa penting peranan kecerdasan buatan (AI) dalam menyelesaikan masalah di lingkungan perguruan tinggi. Penelitian akan melibatkan survei dan wawancara kepada individu yang memiliki pengalaman dalam penerapan AI di perguruan tinggi. Penelitian ini juga akan menggunakan metode Unified Theory of Acceptance and Use of Technology (UTAUT) sebagai arahan dalam pembuatan survei pertanyaan yang akan disebarkan ke responden. Tujuan dari penelitian ini adalah untuk memberikan pemahaman yang lebih baik tentang seberapa cepat dan efektif AI dapat menyelesaikan masalah di lingkungan perguruan tinggi. Penelitian ini akan menggunakan pendekatan kualitatif dan kuantitatif, dimana hasil penelitian akan dijelaskan secara kualitatif melalui wawancara dan secara kuantitatif melalui analisis statistik Regresi Linear Sederhana dari hasil responden yang didapatkan.Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)00Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)["Erinna Angruningrum", "A. A. A. Meitridwiastiti", "Muhammad Ahyar Pratama", "L. Utami", "Dewa Ayu Dinar Kartika Ameria", "Kadek Prema Sadhana Putra"]2025-01-13T00:00:00 e8ee808e43114ad659bdd2804d86f463a92c63efSustainable AI is a subfield of AI for concerning developing and using AI systems in ways of aiming to reduce environmental impact and achieve sustainability. Sustainable AI is increasingly important given that training of and inference with AI models such as large langrage models are consuming a large amount of computing power. In this article, we discuss current issues, opportunities and example solutions for addressing these issues, and future challenges to tackle, from the data and system perspectives, related to data acquisition, data processing, and AI model training and inference.330Current issues, opportunities and example solutions for addressing issues, and future challenges to tackle, related to data acquisition, data processing, and AI model training and inference are discussed.["Tao Xie", "David Harel", "Dezhi Ran", "Zhenwen Li", "Maoliang Li", "Zhi Yang", "Leye Wang", "Xiang Chen", "Ying Zhang", "Wentao Zhang", "Meng Li", "Chen Zhang", "Linyi Li", "Assaf Marron"]2025-01-13T00:00:00 11c9da2dc00f8764478ae906117cc5e083239bf7The main goal of the research article is to offer a substitute technique that reduces the amount of time and energy needed to melt the blocks of butter needed to produce ghee in the dairy processing business. This research attempts to address the several drawbacks of the current butter melting technique. Nowadays, each dairy facility uses melting vats to carry out the melting process, which uses high-temperature steam and a heat exchange phenomenon. The entire output and profitability of the dairy facilities are being significantly impacted by this system's excessive energy losses, increased labor costs, and longer turnaround times. This study describes a machine that can melt hard butter blocks at low temperatures using preheating, saving time on subsequent butter melting for manufacturing of ghee. It also reduce the amount of heat required for butter melting by increasing the surface area for heat exchange between butter blocks. This revolutionary technology could help the dairy industry increase profits while saving time. Furthermore, the use of AI in this new technique will again lead to increased profitability of dairy firms.International Journal For Multidisciplinary Research120International Journal For Multidisciplinary Research["Dr. Sushant M. Patil", "Mrs. Priyanka S. Patil", "Mr. Santosh T. Ghutukade", "Mrs. Amruta P. Awati", "Mrs. Priyanka P. Suryawanshi"]2025-01-13T00:00:00 dd3ab643fcc108eff8b78d599e58ed630e0f275eEuropean Heart Journal130European heart journal["Z. Attia", "P. Friedman"]2025-01-13T00:00:00 5ec63935ac62e7776c61f18ae3216856dedff7a0AI &amp; SOCIETY161This paper argues that there is one problem in particular with common traditional versions of the Turing test, namely their focus on deception, and presents a revised version of an intelligence test that is not based on deception.AI &amp; SOCIETY["M. Pantsar"]2025-01-13T00:00:00 507435991f6ef2874a05e9669164ce8c01b52f11This article explores the transformative integration of human expertise with artificial intelligence in cruise booking systems through agile methodologies. The article examines how the synergy between human knowledge and AI capabilities can enhance search optimization in the cruise industry, addressing complex challenges in modern booking platforms. It shows the crucial components of successful human-AI collaboration, including domain expertise, technological integration, and adaptive frameworks. The article presents a comprehensive analysis of implementation strategies, highlighting the importance of well-defined roles, communication protocols, and technical considerations. Through detailed case studies and performance metrics, the article demonstrates the significant improvements achieved in search accuracy, customer satisfaction, and operational efficiency. The findings emphasize the value of agile practices in facilitating continuous system refinement while maintaining the critical balance between automated processes and human oversight. Furthermore, the article explores emerging technologies and future directions in human-AI collaboration, providing insights into the evolving landscape of cruise booking systems and the increasing sophistication of search optimization techniques.International Journal of Scientific Research in Computer Science Engineering and Information Technology10The article examines how the synergy between human knowledge and AI capabilities can enhance search optimization in the cruise industry, addressing complex challenges in modern booking platforms through agile methodologies.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Jayaram Bhogi"]2025-01-13T00:00:00 c4f42e6250d6fcecab4cee2dbc88bc4b34479f76Objectives: Objectives: This study aims to explore the extent of knowledge among faculty members at Jordanian universities regarding the principles of AI ethics in scientific research and its regulatory guidelines, in alignment with SDG 4: Quality Education to promote inclusive and equitable education and lifelong learning opportunities.   Theoretical Framework: The study is based on the ethical principles of artificial intelligence, emphasizing key dimensions such as data reliability and integrity, privacy and security, human and socio-cultural benefits, and the ethics of integrity, fairness, justice, responsibility, and transparency. These dimensions were used as the basis for designing the study instrument.   Method: A descriptive survey methodology was employed. The researchers developed a questionnaire consisting of 40 items, distributed across four domains: (1) data reliability and integrity, (2) privacy, security, and protection, (3) human and socio-cultural benefits, and (4) ethics of integrity, fairness, justice, responsibility, and transparency. The instrument was administered to a randomly selected sample of 245 faculty members across various Jordanian universities.   Results and Discussion: The results revealed a high level of knowledge among faculty members regarding the principles and guidelines of AI ethics in scientific research. Moreover, no statistically significant differences were found in participants' responses based on variables such as college affiliation, teaching experience, academic rank, or source of academic degree. The discussion elaborates on these findings, emphasizing their significance in enhancing ethical research practices involving AI.   Research Implications: The study highlights the importance of incorporating AI ethics education into faculty development programs and strengthening institutional guidelines to uphold ethical standards in scientific research.   Originality/Value: This research fills a gap in understanding the level of AI ethics knowledge among faculty members at Jordanian universities, providing actionable insights to advance ethical AI use in academic research.Journal of Lifestyle and SDGs Review00This research fills a gap in understanding the level of AI ethics knowledge among faculty members at Jordanian universities, providing actionable insights to advance ethical AI use in academic research.Journal of Lifestyle and SDGs Review["Khaled Mohammad Abu Sheirah", "Mohammad Saleh Alkaramneh", "Saep Kamel Allala", "Najah Yahya ALatwi", "R. Al-Saliti", "Abdelrahim Fathy Ismail"]2025-01-13T00:00:00 1ee0aba38cd2a28e741f9be8f23be4203d613909Idiopathic pulmonary fibrosis (IPF) is a condition predominantly affecting the elderly and leading to a decline in lung function. Our study investigates the aging-related mechanisms in IPF using artificial intelligence (AI) approaches. We developed a pathway-aware proteomic aging clock using UK Biobank data and applied it alongside a specialized version of Precious3GPT (ipf-P3GPT) to demonstrate an AI-driven mode of IPF research. The aging clock shows great performance in cross-validation (R2=0.84) and its utility is validated in an independent dataset to show that severe cases of COVID-19 are associated with an increased aging rate. Computational analysis using ipf-P3GPT revealed distinct but overlapping molecular signatures between aging and IPF, suggesting that IPF represents a dysregulation rather than mere acceleration of normal aging processes. Our findings establish novel connections between aging biology and IPF pathogenesis while demonstrating the potential of AI-guided approaches in therapeutic development for age-related diseases.bioRxiv00A pathway-aware proteomic aging clock is developed using UK Biobank data and applied alongside a specialized version of Precious3GPT (ipf-P3GPT) to demonstrate an AI-driven mode of IPF research, establishing novel connections between aging biology and IPF pathogenesis and demonstrating the potential of AI-guided approaches in therapeutic development for age-related diseases.bioRxiv["Fedor Galkin", "Shan Chen", "A. Aliper", "Alex Zhavoronkov", "Fengzhi Ren"]2025-01-13T00:00:00 b1a55c3a988f8891e6a9494478a6fb6208594594The healthcare sector in India has experienced significant transformations owing to the advancement in technology and infrastructure. Despite these transformations, there are major challenges to address critical issues like insufficient healthcare infrastructure for the country’s huge population, limited accessibility, shortage of skilled professionals, and high-quality care. Artificial intelligence (AI)-driven solutions have the potential to lessen the stress on India’s healthcare system; however, integrating trustworthy AI in the sector remains challenging due to ethical and regulatory constraints. This study aims to critically review the current status of the development of AI systems in Indian healthcare and how well it satisfies the ethical and legal aspects of AI, as well as to identify the challenges and opportunities in adoption of trustworthy AI in the Indian healthcare sector. This study reviewed 15 articles selected from a total of 1136 articles gathered from two electronic databases, PubMed and Google Scholar, as well as project websites. This study makes use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. This study identifies a gap in the adoption of trustworthy AI in Indian healthcare and various challenges associated with it. It explores opportunities for developing trustworthy AI in Indian healthcare settings, prioritizing patient safety, data privacy, and compliance with ethical and legal standards.Applied Informatics310A gap in the adoption of trustworthy AI in Indian healthcare is identified and opportunities for developing trustworthy AI in Indian healthcare settings are explored, prioritizing patient safety, data privacy, and compliance with ethical and legal standards.AI["S. Chettri", "Rup Kumar Deka", "Manob Saikia"]2025-01-13T00:00:00 5c5c092ea14382a1bcacd9c5b37be5a8cdaa9336Purpose This paper aims to examine the relevance of Rogers’ diffusion of innovations theory, with a particular focus on the adopter categories concept, for library technology research in light of rapid changes sparked by the emergence of generative artificial intelligence and other emerging technologies at the dawn of the fourth industrial revolution. Design/methodology/approach The applicability of the diffusion of innovation model is critically evaluated, highlighting some discrepancies that exist between the traditional framework and observed behaviors in recent studies. In particular, it appears that many people are more eager adopters of innovations than at any point in the past, perhaps due to the ubiquity of information within the modern media ecosystem. Findings The traditional diffusion of innovation adopter categories may fail to capture the adoption patterns of specific populations, such as college students and faculty. Revised survey methodologies reveal the potential for more accurate identification of adopter categories by addressing biases in self-reporting and incorporating practical considerations of innovation usefulness. Originality/value This paper proposes refinements to the study of innovation diffusion, particularly in the context of library technology. By adapting the model to better align with modern patterns of technological adoption, it aims to provide a deeper understanding of innovation behaviors in today’s rapidly evolving technological environment. Library Hi Tech News50Examination of Rogers’ diffusion of innovations theory is examined, with a particular focus on the adopter categories concept, for library technology research in light of rapid changes sparked by the emergence of generative artificial intelligence and other emerging technologies at the dawn of the fourth industrial revolution.Library Hi Tech News["Brady Lund"]2025-01-13T00:00:00 75bc7c12c0693a94e7a60cf17bcb8b50b48c39f5Physical and Engineering Sciences in Medicine500The selection and evaluation framework provided herein aims to promote user confidence by exploring the breadth of clinically relevant factors to support informed decision-making by exploring the breadth of clinically relevant factors to support informed decision-making.Physical and engineering sciences in medicine["Branimir Rusanov", "Martin A Ebert", "M. Sabet", "P. Rowshanfarzad", "Nathaniel Barry", "Jake Kendrick", "Z. Alkhatib", "Suki Gill", "Joshua Dass", "Nicholas Bucknell", "Jeremy Croker", "Colin Tang", "Rohen White", "S. Bydder", "Mandy Taylor", "Luke A Slama", "Godfrey Mukwada"]2025-01-13T00:00:00 d2a8503db6ce102f3ce1d87e23c95d0d9863d6b9This technical article examines the evolution and implementation of agentic frameworks in artificial intelligence development, focusing on their transformative impact across multiple industries. The article explores the fundamental architectural components, implementation methodologies, and practical applications of these frameworks in manufacturing, financial services, and healthcare sectors. By investigating the core components, including perception systems and decision architectures, alongside the Belief-Desire-Intention model and advanced learning mechanisms, this article provides comprehensive insights into how agentic frameworks are revolutionizing autonomous decision-making capabilities. The article also addresses critical technical challenges in scalability and safety while offering potential solutions and future directions for development, highlighting the growing importance of these frameworks in shaping the future of AI technology.International Journal of Scientific Research in Computer Science Engineering and Information Technology20The article explores the fundamental architectural components, implementation methodologies, and practical applications of these frameworks in manufacturing, financial services, and healthcare sectors, highlighting the growing importance of these frameworks in shaping the future of AI technology.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Sreeram Reddy Thoom"]2025-01-13T00:00:00 45381066656a215657db64d10b61641c04946b98The integration of cloud technology and advanced digital solutions has revolutionized public sector regulatory systems, transforming traditional paper-based processes into efficient, automated operations. This transformation encompasses comprehensive frameworks for compliance monitoring, enforcement mechanisms, and public service delivery. The implementation of sophisticated API architectures and real-time monitoring capabilities has enhanced system interconnectivity while ensuring robust security protocols. The adoption of cloud-based platforms has significantly improved citizen engagement through intuitive interfaces and streamlined application processes. Integration challenges, including data sovereignty and legacy system compatibility, have been addressed through structured migration strategies and enhanced security frameworks. Looking ahead, emerging technologies such as artificial intelligence, machine learning, and Internet of Things are shaping the future of regulatory management, enabling predictive compliance monitoring and automated enforcement actions while maintaining transparency and accountability in public sector operations.International Journal of Scientific Research in Computer Science Engineering and Information Technology20Emerging technologies such as artificial intelligence, machine learning, and Internet of Things are shaping the future of regulatory management, enabling predictive compliance monitoring and automated enforcement actions while maintaining transparency and accountability in public sector operations.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Nethaji Kapavarapu"]2025-01-13T00:00:00 59d4b601754991d59052ac4bcc91abdcb8bf8bbcArtificial Intelligence (AI) is transforming industries and revolutionizing how we interact with technology at an unprecedented pace, playing a crucial role in shaping our digital future [...]Algorithms170Algorithms["Manolya Kavakli-Thorne", "Zhuangzhuang Dai"]2025-01-13T00:00:00 3cc543c92205c5ef5af47e8f24e88ce287f34709This interview with Peter Singer AI serves a dual purpose. It is an exploration of certain-utilitarian and related-views on sentience and its ethical implications. It is also an exercise in the emerging interaction between natural and artificial intelligence, presented not as just ethics of AI but perhaps more importantly, as ethics with AI. The one asking the questions-Matti Häyry-is a person, in the contemporary sense of the word, sentient and self-aware, whereas Peter Singer AI is an artificial intelligence persona, created by Sankalpa Ghose, a person, through dialogue with Peter Singer, a person, to programmatically model and incorporate the latter's writings, presentations, recipes, and character qualities as a renowned philosopher. The interview indicates some subtle differences between natural perspectives and artificial representation, suggesting directions for further development. PSai, as the project is also known, is available to anyone to chat with, anywhere in the world, on almost any topic, in almost any language, at www.petersinger.ai.Cambridge Quarterly of Healthcare Ethics60The interview indicates some subtle differences between natural perspectives and artificial representation, suggesting directions for further development in the emerging interaction between natural and artificial intelligence.Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees["Sankalpa Ghose", "Matti H\u00e4yry", "Peter Singer"]2025-01-13T00:00:00 b77332f3a0c0b220266c5c7fcaff4cf3c7e62184This paper explores the critical role of synthetic data generation in enabling secure and privacy-preserving use of data for artificial intelligence, machine learning, and software testing applications. As organizations face increasing regulatory pressures and data privacy concerns, synthetic data emerges as a powerful solution to maintain data utility while mitigating risks associated with sensitive information. We examine the challenges in using real-world data, the need for synthetic data generation, and technical approaches to implementing robust synthetic data solutions. The paper also addresses how synthetic data can enhance AI and ML model development, improve testing processes, and support overall data governance strategies in enterprise environments. This paper explores the challenges organizations face in accessing secure and compliant data, highlights the need for synthetic data, and presents a technical framework for implementing synthetic data generation solutions. Furthermore, it outlines metrics for measuring effectiveness and provides insights into the future potential of synthetic data. Index terms: synthetic data, data privacy, machine learning, artificial intelligence, data security, data augmentation, generative models (key words)INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The challenges organizations face in accessing secure and compliant data are explored, the need for synthetic data is highlighted, and a technical framework for implementing synthetic data generation solutions is presented.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Dinesh Thangaraju"]2025-01-13T00:00:00 009834f66889e079c6bb3c2e394de700133f2d31The integration of Artificial Intelligence (AI) into higher education offers a range of potential benefits and challenges. This paper explores the advantages and disadvantages of AI in higher education, drawing on recent journal articles to provide a comprehensive overview. The discussion covers improvements in learning outcomes, administrative efficiencies, and personalized learning, while also addressing issues such as equity, privacy concerns, and the potential impact on the role of educators.The Business and Management Review00The advantages and disadvantages of AI in higher education are explored, drawing on recent journal articles to provide a comprehensive overview and addressing issues such as equity, privacy concerns, and the potential impact on the role of educators.The Business and Management Review["Charles Crain", "Amanda Ewing", "Iris Billy", "Hannah Anush"]2025-01-13T00:00:00 37033de1930498c0173ecaadfcf53379132af4c9 Dr. Sean Kalaycioglu, Toronto Metropolitan University Researcher and AIMechatroniX Inc. President, explores advancing AI-enabled space robotics for lunar exploration and orbital operations. The dawn of a new era in lunar exploration has ushered in unprecedented advances in space robotics and artificial intelligence applications. As we stand on the brink of establishing a sustained human presence on the Moon, innovative robotic systems are becoming increasingly crucial for successful mission outcomes and long-term space operations. Open Access Government00Open Access Government["Sean Kalaycioglu"]2025-01-13T00:00:00 9da723bef42be0d5c6d248a430b1a49093a83d7cArtificial intelligence is experiencing unprecedented advancements, leading to the emergence of autonomous superintelligent systems that surpass human intelligence in various fields. These systems present novel legal challenges, particularly concerning criminal liability for crimes they may commit. This research examines the current legal frameworks. These frameworks are designed to determine the criminal liability of autonomous superintelligent system, with a focus on issues of intent, autonomous will, and their implications in the context of superintelligent AI. The study highlights specific potential crimes, including cybercrimes and privacy violations, and underscores the urgent need to develop new legal frameworks that address the unique risks posed by these systems. Additionally, the role of international conventions, such as the Budapest Convention, in shaping global standards for these challenges is evaluated. The research argues that current legislation is inadequate and emphasizes the need for legal reform to keep pace with technological advancements, offering a forward-looking approach to criminal responsibility in the age of Artificial Super intelligent.Journal of Infrastructure Policy and Development00The research argues that current legislation is inadequate and emphasizes the need for legal reform to keep pace with technological advancements, offering a forward-looking approach to criminal responsibility in the age of Artificial Super intelligent.Journal of Infrastructure, Policy and Development["Dalia Kadry Ahmed Abdelaziz"]2025-01-13T00:00:00 11c2299a63ba28848bb7a21990b4579e1c14046fWith the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.Journal of Infrastructure Policy and Development280The paper provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks.Journal of Infrastructure, Policy and Development["Xiaofeng Luo", "Xuehe Wang", "Tao Jiang"]2025-01-13T00:00:00 7f8293349e03eba0eb939b443e29c3737e5d1e3aIn recent years, artificial intelligence (AI) has emerged as a transformative force in various fields, including the arts and culture. This is particularly evident in the context of traditional Chinese culture, where AI has become a powerful tool in its creative transformation and innovative development. With its advanced capabilities in data processing and generating new ideas, AI is not only helping to preserve the rich heritage of Chinese culture but is also playing a crucial role in its evolution. This study aims to delve into how AI is reshaping the traditional elements of Chinese culture, such as calligraphy, Chinese paintings and traditional artworks, and assess its impact on both conservation and modern reinterpretation. We also examine real-world applications and projects that utilize AI technologies, such as machine learning, natural language processing, and computer vision. Our findings indicate that AI's contribution to traditional Chinese culture is multifaceted. One of the key areas where AI has made a significant impact is in the preservation and restoration of cultural artifacts. AI algorithms have demonstrated remarkable proficiency in analyzing large datasets of historical texts and artworks, uncovering previously unknown patterns and facilitating the restoration of ancient texts and relics. The integration of artificial intelligence into the realm of traditional Chinese culture signifies a pivotal moment in its history. AI's role extends beyond mere preservation; it is a catalyst for innovation, fostering new forms of artistic expression and promoting a dynamic cross-cultural exchange. As AI technology continues to evolve, it is expected to further revolutionize the way we interact with and understand traditional Chinese culture, opening up new avenues for creative exploration and cultural dialogue. This study underscores the potential of AI as a tool for cultural enrichment and highlights the exciting prospects for future developments in this area.International Journal of Computational and Experimental Science and Engineering204This study aims to delve into how AI is reshaping the traditional elements of Chinese culture, such as calligraphy, Chinese paintings and traditional artworks, and assess its impact on both conservation and modern reinterpretation, indicating that AI's contribution to traditional Chinese culture is multifaceted.International Journal of Computational and Experimental Science and Engineering["Junhao Zhang"]2025-01-14T00:00:00 1081f17802dd02beff01c228e0033affb1e06f8d<p style="text-align:justify;" align="justify"><font face="Times New Roman"><span style="font-family:宋体;font-size:10.5000pt;font-weight:normal;line-height:125%;text-justify:inter-ideograph;">This paper investigates the impact of artificial intelligence technology on energy consumption carbon emission intensity and its spatial effect in Chinese provinces. By analyzing the 2012-2021 data of 30 provinces and cities in China, this paper constructs a spatial Durbin panel model to test the direct and indirect effects of the application of AI technology on energy consumption and carbon emission intensity. The results show that artificial intelligence innovation development index, digital finance index, and the environmental regulation intensity</span></font><span style="font-family:宋体;font-size:10.5000pt;font-weight:normal;line-height:125%;text-justify:inter-ideograph;"> </span><font face="Times New Roman"><span style="font-family:宋体;font-size:10.5000pt;font-weight:normal;line-height:125%;text-justify:inter-ideograph;">reduce the provincial energy consumption carbon emission intensity, while</span></font><span style="font-family:等线;font-size:11pt;line-height:125%;text-justify:inter-ideograph;"> </span><font face="Times New Roman"><span style="font-family:宋体;font-size:10.5000pt;font-weight:normal;line-height:125%;text-justify:inter-ideograph;">Per capita GDP level, urbanization rate, and industrial energy consumption intensity significantly increase the energy consumption carbon emission intensity. In addition, industrial structure upgrading and industrial</span></font><span style="font-family:等线;font-size:11pt;line-height:125%;text-justify:inter-ideograph;"> </span><font face="Times New Roman"><span style="font-family:宋体;font-size:10.5000pt;font-weight:normal;line-height:125%;text-justify:inter-ideograph;">technological progress, as mediating variables, have a significant inhibiting effect on carbon emission intensity. The study concludes that the application of AI technology can effectively reduce provincial carbon emission intensity by optimizing energy consumption and carbon emission scenarios, thus promoting the goal of green and low-carbon development in China.</span></font></p>Global NEST Journal00The study concludes that the application of AI technology can effectively reduce provincial carbon emission intensity by optimizing energy consumption and carbon emission scenarios, thus promoting the goal of green and low-carbon development in China.Global NEST Journal[]2025-01-14T00:00:00 47fa7fbefe9448344ee4f46921ea95e845da9792Artificial intelligence has emerged as a promising technology in the post-pandemic era, significantly impacting the library ecosystem and the direction of library and information science studies. This study aims to map AI-related research in libraries to identify opportunities and discuss future directions. Using textual analysis of data from Scopus, the study analyzed article titles with the burst detection algorithm and abstracts with scattertext and lemmatization. Six burst words were detected out of twelve frequently appearing in titles. Scattertext results showed a comparison between the service side (red) and the development side (blue) in libraries. Research increasingly focuses on AI utilization for library services and natural language processing (NLP) to enhance services. On the development side, AI involves product creation and encompasses AI literacy frameworks, policies, and their impact on libraries. AI affects studies in libraries by changing application methods, such as machine learning and NLP. Future research will become more diverse, considering the unique characteristics of each library.JLIS.it230This study aims to map AI-related research in libraries to identify opportunities and discuss future directions using textual analysis of data from Scopus, and analyzed article titles with the burst detection algorithm and abstracts with scattertext and lemmatization.JLIS.it["Faizhal Arif Santosa"]2025-01-14T00:00:00 f828e476589b1b785881934d8ba875d19b33b54aBackground Artificial intelligence broadly refers to computer systems that simulate intelligent behaviour with minimal human intervention. Emphasizing patient-centered care, research has explored patients’ perspectives on artificial intelligence in medical care, indicating general acceptance of the technology but also concerns about supervision. However, these views have not been systematically examined from the perspective of patients with cancer, whose opinions may differ given the distinct psychosocial toll of the disease. Objectives This protocol describes a scoping review aimed at summarizing the existing literature on the attitudes of patients with cancer toward the use of artificial intelligence in their medical care. The primary goal is to identify knowledge gaps and highlight opportunities for future research. Methods This scoping review protocol will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA-ScR). The electronic databases MEDLINE (OVID), EMBASE, PsycINFO, and CINAHL will be searched for peer-reviewed primary research articles published in academic journals. We will have two independent reviewers screen the articles retrieved from the literature search and select relevant studies based on our inclusion criteria, with a third reviewer resolving any disagreements. We will then compile the data from the included articles into a narrative summary and discuss the implications for clinical practice and future research. Discussion To our knowledge, this will be the first scoping review to map the existing literature on the attitudes of patients with cancer regarding artificial intelligence in their medical care.PLoS ONE420This is the first scoping review to map the existing literature on the attitudes of patients with cancer regarding artificial intelligence in their medical care, and will be the first scoping review to map the existing literature on the attitudes of patients with cancer regarding artificial intelligence.PLOS ONE["Daniel Hilbers", "Navid Nekain", "Alan T Bates", "John-Jose Nunez"]2025-01-14T00:00:00 f9412a1ce54c9664458cfdd82febdc638fdc7fd2This research study develops a model that integrates marketing information systems and strategic levels and examines the implementation of these systems at each strategic level. To achieve the first objective, the role played by the CEO or manager at each strategic level and the specific information systems required for each case are the starting points of the study. To achieve the second objective, a qualitative method is applied, and the technique used is in-depth interviews. The study shows that companies do use all four marketing information systems, although with different emphases, depending on the strategic level. Artificial Intelligence (AI) has developed rapidly and penetrated various aspects of life, from simple applications on mobile phones to complex systems in industry and scientific research. In general, AI is defined as the ability of a computer system to imitate human cognitive functions, such as learning, reasoning, and problem solving. The development of AI is driven by the availability of abundant data (big data), increasing computing power, and advances in algorithms.International journal of innovations in engineering research and technology00A model that integrates marketing information systems and strategic levels and examines the implementation of these systems at each strategic level is developed and shows that companies do use all four marketing information systems, although with different emphases, depending on the strategic level.International Journal of Innovations in Engineering Research and Technology["Rakhmawati", "Yudhi Prasetya Mada"]2025-01-14T00:00:00 042371b1f0e7c92b0502a968b782258fb2e7336bIn this paper, we explore required criteria for low-income countries to exploit the potential of AI in nursing. We had dubbed this as “Readiness Criteria”. Generative artificial intelligence tools summarize data into text for expedited information-gathering and content creation. They are gaining use in clinical settings to help nursing staff improve productivity. There are knowledge gaps between experts in AI and nursing professionals. Bridging such gaps, will be the starting point for appropriately applying AI in nursing. As use of AI in nursing becomes prominent, appropriate risk mitigation measures need to be put in place, including, appropriate risk governance frameworks and tools to manage AI driven nursing practices. Most important, low resource settings need to put in place readiness criteria to support them to enjoy the fruits of AI in nursing. Such readiness criteria include having in place data governance frameworks, addressing knowledge gaps, and investing in public data infrastructure.Asian Journal of Advanced Research and Reports00Asian Journal of Advanced Research and Reports["B. Kalanda", "Asseneth Jerotich Cheboi"]2025-01-14T00:00:00 552697d2242b122bb31d15794e5a21765f1cb2c4This study explores the application of artificial intelligence (AI) technology in media convergence, focusing on how AI is driving deep integration of media and financial markets through big data analytics, AIGC (AI-generated content), and intelligent communication technologies. Ai-driven sentiment analysis and fake news detection tools effectively solve the problem of information asymmetry and the spread of false news in the financial market and promote market stability and transparency. Through personalized recommendations and intelligent communication, AI provides users with a more accurate content experience and improves user engagement and satisfaction. In addition, the rapid development of AIGC and big data ecology has promoted the intellectualization of information dissemination and public opinion analysis, providing more forward-looking support for financial market decision-making.Academic Journal of Natural Science290This study explores how AI is driving deep integration of media and financial markets through big data analytics, AIGC (AI-generated content), and intelligent communication technologies, providing more forward-looking support for financial market decision-making.Academic Journal of Natural Science["Haozhong Xue", "Yanyi Zhong", "Jingwen He"]2025-01-14T00:00:00 6f33e3954807d0948cd14926efc02bb970728cebSocial aspects constitute both concerns and opportunities in smart city development, as evidenced by a rapidly increasing body of research. This article presents the first-ever review of all the existing research on social focus in smart cities, delineating the distribution of topics, knowledge bases, and research frontiers that constitute the existing body of knowledge. A bibliometric review was performed to pinpoint publication trends, influential authors, their institutions, and prevalent subject areas within the literature since 2000. Using the Web of Science database, an amalgamation of major indexes (SCI-EXPANDED, SSCI, AHCI, ESCI) were applied to consider the research pattern and citation impact in different disciplines. 1,030 selected articles were subjected to bibliometric mapping using VOSviewer. The results show an almost exponential growth in the number of publications from 2015 onwards. Four interconnected thematic clusters cropped up: (1) participatory governance, (2) data privacy and security, (3) artificial intelligence and social media, and (4) ethics and sustainable development. A deeper analysis of key terms used in recent research revealed the following hot topics: (1) governance and citizen participation, (2) artificial intelligence technologies such as machine learning, (3) blockchain, and (4) Internet of Things. Co-authorship and geographical analyses underpin a solid international collaboration for leading institutions. The results underscore the rising significance of social smart city research by emphasizing the interconnectedness of governance, technology, citizen engagement, and ethics for a comprehensive approach to smart city initiatives. Furthermore, they recommend integrating social equity into these frameworks and enhancing geographic studies through greater international collaboration.Frontiers in Sustainable Cities860The first-ever review of all the existing research on social focus in smart cities is presented, delineating the distribution of topics, knowledge bases, and research frontiers that constitute the existing body of knowledge.Frontiers in Sustainable Cities["Samad Rasoulzadeh Aghdam", "Behnaz Bababei Morad", "Behnam Ghasemzadeh", "Mazdak Irani", "Aapo Huovila"]2025-01-14T00:00:00 bb0e1fd56f96824f7c0b48498e976fb8985dafc7 To examine surgeons’ perspectives on liability for using artificial intelligence (AI)-driven technologies in surgery in the United States and the European Union. The introduction of AI-driven technologies in surgery can improve surgical performance and patient outcomes. However, liability risks might inhibit their implementation in the operating room. We report here the results of a focus group study that explored surgeons’ perspectives on liability for using AI-driven technologies in surgery in the United States and the European Union. Participants were identified through a call for participation disseminated through personal and professional networks. Inclusion criteria were: (1) adults (at least 18 years of age); (2) surgeons based in either the United States or in one of the European Union’s Member States, with a preference for those specializing in gastrointestinal surgery to facilitate better discussions about the vignettes that involved a colorectal surgical procedure; (3) ability to comfortably read and communicate in English; (4) willingness to consent to participation, and (5) willingness to consent to keeping the focus group meeting content, participants, and discussions confidential. We conducted 6 focus groups via Zoom with a total of 18 participants (11 EU surgeons and 7 US surgeons). The following main themes emerged: (1) acknowledgment of the potential benefits of using AI-driven technology in surgery, (2) acceptance of surgeon responsibility, (3) recognition that AI may impact the standard of care, (4) skepticism about potential liability for AI manufacturers, and (5) the importance of patient information and consent. Despite the potential future benefits of integrating AI into surgical practice, surgeons will benefit from (1) an increased understanding of how AI-driven technologies will deliver these benefits and (2) increased clarity surrounding how AI-driven technologies will be governed by both regulators and the surgical community. While our study focused on surgeons’ perspectives, it could also provide valuable insights for other healthcare providers using AI to treat patients. Annals of Surgery Open90A focus group study that explored surgeons’ perspectives on liability for using artificial intelligence (AI)-driven technologies in surgery in the United States and the European Union found acknowledgment of the potential benefits of using AI-driven technology in surgery, and acceptance of surgeon responsibility and the importance of patient information and consent.Annals of Surgery Open["M. Duffourc", "Mathias M\u00f8lleb\u00e6k", "L. Druedahl", "Timo Minssen", "Sara Gerke"]2025-01-14T00:00:00 2bd73de49fc12e7a97af2d0a2907801602a3b525ABSTRACT The present study aimed to explore the validity and inter‐device reliability of a novel artificial intelligence app (Asstrapp) for real‐time measurement of the traditional (tra505) and modified‐505 (mod505) change of direction (COD) tests. Twenty‐five male Sports Science students (age, 23.5 ± 3.27 years; body height, 178 ± 9.76 cm; body mass, 79.4 ± 14.7 kg) completed 12 trials each, consisting of six tra505 and six mod505 trials. Completion times were simultaneously recorded via single‐beam electronic timing gates (ETG) and two different iPhones (APP1 and APP2). In total 300 trials were collected across the two tests, using all three devices, to establish the reliability and validity of the app. The coefficient of variation indicated a similar level of dispersion between the ETG (≤ 2.73%), APP1 (≤ 2.39%) and APP2 (≤ 2.52%). Intraclass correlation coefficients (ICC) revealed excellent reliability among the three timing devices (ICC ≥ 0.99) and Asstrapp relative reliability was excellent for both APP1 (ICC ≥ 0.91) and APP2 (ICC ≥ 0.91). There was a practically perfect correlation and agreement between ETG and Asstrapp (APP1: r = 0.97; APP2: r = 0.97) for both COD tests. However, small but significant differences were found between smartphones and ETG for tra505 (ES ≤ 0.33; p < 0.05). Collectively, these findings support the use of Asstrapp for real‐time assessment of both 505 COD tests.European Journal of Sport Science240Findings support the use of Asstrapp for real‐time assessment of both 505 COD tests and establish the reliability and validity of the app.European Journal of Sport Science["Francisco J Barrera-Dom\u00ednguez", "Paul A Jones", "B. J. Almagro", "J. Molina-L\u00f3pez"]2025-01-14T00:00:00 66d670780240115413fafb3878b071f3fc0fd689 Recent advancements in wearable healthcare have led to commercially accessible continuous glucose monitoring systems (CGMs) for diabetes management. However, CGMs only monitor glucose levels and lack therapeutic functions, prompting the development of closed‐loop systems that use monitored glucose levels to guide insulin dosing. While promising, these devices also pose risks, such as insulin overdosing, which can cause hypoglycemia. This review summarizes recent advances in integrating artificial intelligence methods with conventional CGMs. The developments in wearable CGMs and progress in insulin delivery technologies are explored, and existing algorithms for glucose prediction in closed‐loop systems are reviewed. Additionally, emerging trends in optimizing these algorithms to enhance the safety and security of closed‐loop insulin delivery systems are highlighted.Advanced Intelligent Systems770Recent advances in integrating artificial intelligence methods with conventional CGMs and progress in insulin delivery technologies are explored, and existing algorithms for glucose prediction in closed‐loop systems are reviewed.Advanced Intelligent Systems["Wei Huang", "Ivo Pang", "Jing-Ying Bai", "Binbin Cui", "Xiaojuan Qi", "Shiming Zhang"]2025-01-14T00:00:00 a73b7a39c1f2494a95341397c44c8af9fb3a36c4INTRODUCTION This study examines the ability of human readers, recurrence quantification analysis (RQA), and an online artificial intelligence (AI) detection tool (GPTZero) to distinguish between AI-generated and human-written personal statements in physical therapist education program applications. REVIEW OF LITERATURE The emergence of large language models such as ChatGPT and Google Gemini has raised concerns about the authenticity of personal statements. Previous studies have reported varying degrees of success in detecting AI-generated text. SUBJECTS Data were collected from 50 randomly selected nonmatriculated individuals who applied to the Mayo Clinic School of Health Sciences Doctor of Physical Therapy Program during the 2021-2022 application cycle. METHODS Fifty personal statements from applicants were pooled with 50 Google Gemini-generated statements, then analyzed by 2 individuals, RQA, and GPTZero. RQA provided quantitative measures of lexical sophistication, whereas GPTZero used advanced machine learning algorithms to quantify AI-specific text characteristics. RESULTS Human raters demonstrated high agreement (κ = 0.92) and accuracy (97% and 99%). RQA parameters, particularly recurrence and max line, differentiated human- from AI-generated statements (areas under receiver operating characteristic [ROC] curve = 0.768 and 0.859, respectively). GPTZero parameters including simplicity, perplexity, and readability also differentiated human- from AI-generated statements (areas under ROC curve > 0.875). DISCUSSION AND CONCLUSION The study reveals that human raters, RQA, and GPTZero offer varying levels of accuracy in differentiating human-written from AI-generated personal statements. The findings could have important implications in academic admissions processes, where distinguishing between human- and AI-generated submissions is becoming increasingly important. Future research should explore integrating these methods to enhance the robustness and reliability of personal statement content evaluation across various domains. Three strategies for managing AI's role in applications-for applicants, governing organizations, and academic institutions-are provided to promote integrity and accountability in admission processes.Journal of Physical Therapy Education380The study reveals that human raters, RQA, and GPTZero offer varying levels of accuracy in differentiating human-written from AI-generated personal statements, which could have important implications in academic admissions processes.Journal, physical therapy education["Margaret A Goodman", "Anthony M Lee", "Zachary Schreck", "J. Hollman"]2025-01-14T00:00:00 1e0e5ec3b41d4d535aa6e42b667c57a496a1db8fAI and Ethics140This chapter serves as a first attempt at outlining a procedure for the use of sentencing advisory systems by judges within real-life, and ethically non-ideal, penal systems.AI and Ethics["Jesper Ryberg"]2025-01-14T00:00:00 11b1b260d01a45452a0003ccca81abf45b8a0c09Today, most research evaluation frameworks are designed to assess mature projects with well-defined data and clearly articulated outcomes. Yet, few, if any, are equipped to evaluate the promise of early-stage research, which is inherently characterized by limited evidence, high uncertainty, and evolving objectives. These early-stage projects require nuanced assessments that can adapt to incomplete information, project maturity, and shifting research questions. Compounding these challenges is the difficulty of systematically scaling evaluations with the increasing volume of research projects. As a step toward addressing this gap, we introduce the Early-Stage Research Assessment Framework for Artificial Intelligence (ERAF-AI), a systematic approach to evaluate research at Technology Readiness Levels (TRLs) 1 to 3 – maturity levels where ideas are more conceptual and only preliminary evidence exists to indicate potential viability. By leveraging AI-driven methodologies and platforms such as Lateral’s Coordination.Network, ERAF-AI ensures transparent, scalable, and context-sensitive evaluations that integrate research maturity classification, adaptive scoring, and strategic decision-making. Importantly, ERAF-AI aligns criteria with the unique demands of early-stage research, guiding evaluation through the 4P framework (Promote, Pause, Pivot, Perish) to inform next steps. As an initial demonstration of its potential, we apply ERAF-AI to a high-impact early-stage project, providing actionable insights and measurable improvement over conventional practices. Although ERAF-AI shows significant promise in improving the prioritization of early-stage research, further refinement and validation across a wider range of disciplines and datasets is required to refine its scalability and adaptability. Overall, we expect this framework to serve as a valuable tool for empowering researchers to make informed decisions and to prioritize high-potential initiatives in the face of uncertainty and limited data.bioRxiv00The Early-Stage Research Assessment Framework for Artificial Intelligence (ERAF-AI) is introduced, a systematic approach to evaluate research at Technology Readiness Levels 1 to 3 – maturity levels where ideas are more conceptual and only preliminary evidence exists to indicate potential viability.bioRxiv["David Falvo", "Lukas Weidener", "Martin Karlsson"]2025-01-14T00:00:00 14fe844c15c26c5c8c5f6f5f5b8e09cf366fbe20The employment process is now much more efficient and effective thanks to artificial intelligence (AI), which has completely transformed talent acquisition and recruitment. AI-powered solutions expedite candidate sourcing by searching enormous databases for prospective employees who meet particular job requirements. Through automated resume parsing, these systems also improve candidate screening, allowing recruiters to concentrate on the most qualified candidates. Furthermore, AI promotes diversity and inclusivity inside organizations by reducing human biases in the screening process, which enables unbiased hiring. The applicant experience is enhanced by chatbots and virtual assistants, which give candidates prompt answers and updates. Another application of AI is predictive analytics, which uses past data to forecast candidate success and retention. Identification of passive candidates—those who match the required profile but may not be actively seeking new opportunities—is another use for AI. Moreover, AI-powered tools can handle monotonous jobs like interview scheduling, freeing up recruiters to focus on making strategic decisions. A sample of 287 is collected from professionals in the HR department. The factors that identify the Role of Artificial Intelligence in Recruitment and Talent Acquisition are Resume Screening and Matching, Chatbots for Initial Interaction, Bias Reduction, and Enhanced Candidate Experience.Journal of Informatics Education and Research110The factors that identify the Role of Artificial Intelligence in Recruitment and Talent Acquisition are Resume Screening and Matching, Chatbots for Initial Interaction, Bias Reduction, and Enhanced Candidate Experience.Journal of Informatics Education and Research["Dr Susan Abraham", "Rajeev Paripoornam", "Dr. Gunjan Sharma"]2025-01-14T00:00:00 fa06241dfa98d42974532dd314b87d14ad5dc33fArtificial intelligence (AI) has become a powerful tool in biochemistry, greatly enhancing research capabilities by enabling the analysis of complex datasets, predicting molecular interactions, and accelerating drug discovery. As AI continues to evolve, its applications in biochemistry are poised to expand, revolutionizing both theoretical and applied research. This review explores current and potential AI applications in biochemistry, with a focus on data analysis, molecular modeling, enzyme engineering...Biomolecules & biomedicine490Current and potential AI applications in biochemistry, with a focus on data analysis, molecular modeling, enzyme engineering, and enzyme engineering are explored.Biomolecules & biomedicine["M. A. Lateef Junaid"]2025-01-14T00:00:00 48cd5e75a0b3acb9dc312f078f1d46cf16dd0596INTRODUCTION: Artificial intelligence and neuroimaging enable accurate dementia prediction, but 'black box' models can be difficult to trust. Explainable artificial intelligence (XAI) describes techniques to understand model behaviour and the influence of features, however deciding which method is most appropriate is non-trivial. Vision transformers (ViT) have also gained popularity, providing a self-explainable, alternative to traditional convolutional neural networks (CNN). METHODS: We used T1-weighted MRI to train models on two tasks: Alzheimer's disease (AD) classification (diagnosis) and predicting conversion from mild-cognitive impairment (MCI) to AD (prognosis). We compared ten XAI methods across CNN and ViT architectures. RESULTS: Models achieved balanced accuracies of 81% and 67% for diagnosis and prognosis. XAI outputs highlighted brain regions relevant to AD and contained useful information for MCI prognosis. DISCUSSION: XAI can be used to verify that models are utilising relevant features and to generate valuable measures for further analysis.medRxiv420XAI can be used to verify that models are utilising relevant features and to generate valuable measures for further analysis, and achieved balanced accuracies for diagnosis and prognosis.medRxiv["Sophie A. Martin", "An Zhao", "Jiongqi Qu", "P. Imms", "A. Irimia", "F. Barkhof", "James H Cole"]2025-01-14T00:00:00 846668550b5e3eb7875303af9fa2e5862ee64392The 4th industrial revolution is driven by the implementation of automated robots and artificial intelligence (AI) to enhance efficiency, accuracy, and safety. This integration encompasses several vital domains like optimizing the supply chain, interaction between human and robots on the shop floor, predictive maintenance, automation of repetitive tasks, customisation, behaviour design, and safety management, data analysis, etc. AI-enabled robots perform repetitive tasks at very high precision, reducing the chances of human error and allowing workers to focus on more complex tasks. Automated upkeep utilizes AI to determine the time machinery will likely fail, which minimizes downtime and maintenance costs. Automated testing and AI-driven vision systems support quality control by ensuring a balanced quality of the product. AI improves supply chain processes, optimizing logistics and inventory management. Collaboration between humans and collaborative robot’s results in safer and more productive environments with people working alongside each other. Artificial Intelligence plays an important role in making smarter decisions, analysing data more effectively, and providing valuable information that can be used to improve operations. Manufacturing customization and flexibility are reliant on adaptive systems and the ability to manufacture personalized products by means of productivity. Safe and Risk Management is consolidated because robots work in dangerous scenarios and artificial intelligence models assess potential dangers. Despite challenges including labour displacement, cybersecurity, ethics, and data integration stemming from this technology, these are all potentially available on your terms. This article reviews the broader impacts that robots and artificial Intelligence have had on the industrial sector, placing emphasis on the revolution it could lead towards as well as the key elements to consider before implementing it.Data and Metadata300The broader impacts that robots and artificial Intelligence have had on the industrial sector are reviewed, placing emphasis on the revolution it could lead towards as well as the key elements to consider before implementing it.Data and Metadata["Vugar Abdullayev", "Ajesh Faizal", "Irada Seyidova", "Seymur Mikayilov", "Rubaba Mammadova", "Lala Pirverdiyeva", "Etibar Guliyev"]2025-01-14T00:00:00 190d05da162802e3fe89702dbe600397185ac7f6Artificial Intelligence (AI) has been gaining prominence in the Brazilian public sector, with the promise of improving the efficiency and quality of government services. However, the successful implementation of AI faces challenges, including complex regulation, a lack of adequate technological infrastructure, a shortage of qualified AI professionals, and organizational culture resistance. This article aims to provide clear and practical recommendations for overcoming the challenges faced in implementing Artificial Intelligence in the Brazilian public sector, to promote a more effective and efficient public administration. To this end, the main obstacles encountered in the implementation of AI in the Brazilian government were analyzed, and success stories that demonstrate how these challenges were overcome were reviewed. Based on this research, practical and actionable recommendations were drawn to guide future initiatives to implement AI in the public sector.Revista Foco00The main obstacles encountered in the implementation of AI in the Brazilian government were analyzed, and success stories that demonstrate how these challenges were overcome were reviewed.REVISTA FOCO["Bruno Sampaio Jankovski", "Fernanda Cavicchioli Zola", "Genizis Vinicius Gon\u00e7alves Meneghel", "P. Zarelli"]2025-01-14T00:00:00 cfbfcbc539b539122af3163e5999ba8520d49d98Artificial Intelligence (AI) is not just a tool for improving video games; it is revolutionizing the entire industry. This opinion paper explores how AI is transforming gaming experiences, enhancing player engagement, and reshaping game design. We argue that AI’s role in gaming is not just beneficial but essential for the future of interactive entertainment. Through an examination of current trends and future possibilities, we provide our perspective on the profound impacts of AI on the gaming world.ReCIBE, Revista electrónica de Computación, Informática, Biomédica y Electrónica00This opinion paper explores how AI is transforming gaming experiences, enhancing player engagement, and reshaping game design and argues that AI’s role in gaming is not just beneficial but essential for the future of interactive entertainment.ReCIBE, Revista electrónica de Computación, Informática, Biomédica y Electrónica["Dariana Gomez-Alvarez", "Michel L\u00f3pez-Franco", "David Bonilla Carranza", "C. L\u00f3pez-Franco", "Lilibet Lopez-Franco"]2025-01-14T00:00:00 2aabe800c8198e22f72af76dc8ef5e230bafb402Discussions about the freedom of artificial intelligence revolve around differing views on the importance of technological progress versus traditional social values protected by the law. Striking a balance between these two aspects is crucial for addressing the challenges of artificial intelligence governance. This issue has become increasingly relevant over the past two to three years, particularly with the rise of generative artificial intelligence. This article aims to define the preconditions and limitations of artificial intelligence that should underlie a state policy in this area. To illustrate the point at which restrictions may become excessive, the authors examine the initiatives of the Future of Life Institute, a respected non-profit organization focused on cutting-edge technologies. The open letters compiled, published, and circulated for signatures by this organization highlight key restrictions on artificial intelligence. These include: a suspension of the training of advanced AI systems more powerful than GPT-4, the establishment of a distinct set of guidelines, e.g. the Asilomar Principles for Artificial Intelligence, and a prohibition on lethal autonomous weapons (LAWS) that can strike without human supervision. Additionally, the open letters emphasize the necessity of conducting in-depth studies on artificial intelligence as a phenomenon. Special focus is given to the Future of Life Institute, which is relatively unknown in Ukrainian academic circles. Founded about a decade ago, it seeks to ensure that modern technologies are utilized in an effective and balanced manner. The study’s results offer a valuable framework for understanding both the real and potential dangers associated with the freedom of artificial intelligence and unrestricted technological advancement. This highlights the seriousness of the issue covered in this article. In addition, the authors have prepared a timeline designed for the systematization of the open letters of the Future of Life Institute related to artificial intelligence that have been published over the past ten years.Uzhhorod National University Herald. Series: Law00The authors examine the initiatives of the Future of Life Institute, a respected non-profit organization focused on cutting-edge technologies and prepares a timeline designed for the systematization of the open letters of the Future of Life Institute related to artificial intelligence that have been published over the past ten years.Uzhhorod National University Herald. Series: Law["H. Kosheliev", "A. Hachkevych"]2025-01-14T00:00:00 f552e0a337234c379bc8bf7d03479e2adf63584aThe article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.Uzhhorod National University Herald. Series: Law20It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity in Ukraine.Uzhhorod National University Herald. Series: Law["M. Viktorchuk", "A. S. Bogatko"]2025-01-14T00:00:00 936798d5a95aa34beb2598aeb03fd48b361739f9This research paper was titled: (Artificial Intelligence in Education), and through it, the researchers sought to explore the main role that artificial intelligence represents as a modern and advanced technical achievement in the fields of education and learning at its various stages. The paper was divided into six sections, the first of which represents the methodological framework of the research, the second on the concept of artificial intelligence in education and its importance, the third included applications of artificial intelligence in education, the fourth shed light on the use of artificial intelligence in education and the challenges, controls, effects and ethical concerns that accompany it, the fifth was devoted to future trends in artificial intelligence in education in the Kingdom, and the sixth contained the results, recommendations and references. The researchers concluded with a number of results, the most prominent of which are: the importance of combining artificial intelligence and teachers to achieve the best educational results, the positive impact that artificial intelligence-supported tools achieve in improving the quality of education, and the role of artificial intelligence in reducing routine burdens on teachers and enabling them to focus on aspects of creative teaching.International Journal of Computers and Informatics00The researchers explored the main role that artificial intelligence represents as a modern and advanced technical achievement in the fields of education and learning at its various stages and concluded with a number of results, the most prominent of which are: the importance of combining artificial intelligence and teachers to achieve the best educational results.International Journal of Computers and Informatics["Noura Albahijan", "Hessa Alsuraibi", "Joud Alotaibi", "Kholoud Alotaibi"]2025-01-14T00:00:00 52f3d425e26417aec5fd274c73f0f5f272a1f145In recent years, artificial intelligence (AI) has become a focal point in academic and business research. With breakthroughs in learning algorithms, AI applications in business operations are increasingly practical and impactful. AI offers tools for market analysis, decision‐making support, and innovations in business models and processes, presenting a significant turning point for firms. Despite this, questions remain about whether AI implementation yields measurable business value or is merely a trend, challenging enterprises and managers. This study provides a significant contribution by empirically examining AI impact on firm‐level performance through three key indicators: financial performance, productivity, and market value. Drawing on internal financial perspectives, this research reveals that while AI adoption enhances financial performance and market value, the advantages for AI first movers and better performers are not uniformly positive across all indicators. This nuanced analysis offers managers and stakeholders a deeper understanding of the tangible value of AI, guiding more informed implementation strategies.Managerial and Decision Economics250While AI adoption enhances financial performance and market value, the advantages for AI first movers and better performers are not uniformly positive across all indicators, and this nuanced analysis offers managers and stakeholders a deeper understanding of the tangible value of AI.Managerial and Decision Economics["Cheng-Kui Huang", "Jheng\u2010Siang Lin"]2025-01-14T00:00:00 1b24884cff4b98593af29f36a61a5341b71b0f96The rapid advancement and widespread adoption of artificial intelligence (AI) across diverse industries, including healthcare, finance, manufacturing, and retail, underscore the transformative potential of AI technologies. This necessitates the development of viable AI model marketplaces that facilitate the development, trading, and sharing of AI models across the pervasive industrial domains to harness and streamline their daily activities. These marketplaces act as centralized hubs, enabling stakeholders such as developers, data owners, brokers, and buyers to collaborate and exchange resources seamlessly. However, existing AI marketplaces often fail to address the demands of modern and next-generation application domains. Limitations in pricing models, standardization, and transparency hinder their efficiency, leading to a lack of scalability and user adoption. This paper aims to target researchers, industry professionals, and policymakers involved in AI development and deployment, providing actionable insights for designing robust, secure, and transparent AI marketplaces. By examining the evolving landscape of AI marketplaces, this paper identifies critical gaps in current practices, such as inadequate pricing schemes, insufficient standardization, and fragmented policy enforcement mechanisms. It further explores the AI model life-cycle, highlighting pricing, trading, tracking, security, and compliance challenges. This detailed analysis is intended for an audience with a foundational understanding of AI systems, marketplaces, and their operational ecosystems. The findings aim to inform stakeholders about the pressing need for innovation and customization in AI marketplaces while emphasizing the importance of balancing efficiency, security, and trust. This paper serves as a blueprint for the development of next-generation AI marketplaces that meet the demands of both current and future application domains, ensuring sustainable growth and widespread adoption.Future Internet1110Examining the evolving landscape of AI marketplaces identifies critical gaps in current practices, such as inadequate pricing schemes, insufficient standardization, and fragmented policy enforcement mechanisms, and serves as a blueprint for the development of next-generation AI marketplaces that meet the demands of both current and future application domains, ensuring sustainable growth and widespread adoption.Future Internet["Mian Qian", "A. Musa", "Milon Biswas", "Yifan Guo", "Weixian Liao", "Wei Yu"]2025-01-14T00:00:00 416549bcb5a8c6e67d3ea470f78805af73ee106bThis study presents a comprehensive bibliometric analysis of Artificial Intelligence (AI) for digital literacy research from 2015 to 2024, utilizing citation analysis on 223 articles from the Dimensions database. The research examines publication trends, author collaborations, and thematic focuses in the field. Findings reveal a significant publication growth, with a peak in 2020 followed by fluctuations in subsequent years. The analysis highlights the field’s interdisciplinary nature, with computer science and education emerging as dominant areas. Key research themes identified through co-word analysis include educational applications, technological integration, and broader societal impacts. The emergence of “AI literacy” as a significant keyword underscores the evolving nature of digital competence in the AI era. Author collaboration networks reveal established experts and researchers with strong collaborative ties, indicating a dynamic research community. The study identifies challenges and opportunities in AI for digital literacy, including ethical considerations, teacher preparation, and the potential for personalized learning. This analysis provides valuable insights into AI’s current state and future directions for digital literacy research, emphasizing the need for continued interdisciplinary collaboration and the development of inclusive AI literacy programs. The findings suggest a growing recognition of AI’s role in shaping digital literacy practices and the importance of preparing learners for an AI-integrated future.Journal of Education and Learning00The study identifies challenges and opportunities in AI for digital literacy, including ethical considerations, teacher preparation, and the potential for personalized learning, as well as challenges and opportunities in AI’s current state and future directions for digital literacy research.Journal of Education and Learning["Kitsadaporn Jantakun", "Thiti Jantakun", "Thada Jantakoon"]2025-01-14T00:00:00 375c037c8c71540ea846a15b0625b32741e8e866The article discusses the implementation of artificial intelligence technology for organizing and managing mixed rail-sea transportation. The need for digital transformation of transportation management, drivers and barriers in the implementation of innovative transportation management technologies are shown, and the concept of implementing artificial intelligence technologies in multimodal transportation management tasks is presented.DIGITAL TRANSFORMATION IN THE ECONOMY OF THE TRANSPORT COMPLEX10The article discusses the implementation of artificial intelligence technology for organizing and managing mixed rail-sea transportation and the concept of implementing artificial intelligence technologies in multimodal transportation management tasks is presented.DIGITAL TRANSFORMATION IN THE ECONOMY OF THE TRANSPORT COMPLEX["E. Mamaev", "Evgeniya Chebotareva"]2025-01-14T00:00:00 38031adfa9537468814a331e42926ab83501b3abIn the current dynamic economic environment effective leadership needs a good understanding of artificial intelligence (AI) capabilities along with emotional intelligence (EI). This paper is analyzing the importance of EI and AI in leadership, how AI can improve leadership, and utilize AI and EI to make strategies for improving decision-making processes for leaders and executives and achieve excellence in leadership. After reviewing and analyzing available literature, this paper trying to find out the use of AI and EI in leadership, current state of practice, trying to find out main opportunities, challenges related with AI, and suggest a practical recommendation for utilizing EI and AI integration in leadership. This study investigates the integration of Emotional Intelligence (EI) and Artificial Intelligence (AI) as complementary tools to enhance leadership decision-making, effectiveness, and organizational performance. The research emphasizes the role of EI in understanding and managing human emotions to foster empathy and interpersonal connections, alongside the capacity of AI to analyze data and provide predictive insights for informed decision-making. Using a multidisciplinary approach, the study develops a framework to align these competencies, addressing critical leadership challenges in the modern workplace, such as adaptability, innovation, and team cohesion. The methodology involves a comprehensive review of existing literature, case studies, and theoretical analysis to explore practical strategies for integration. Key findings reveal the potential of combining EI and AI to foster organizational growth, enhance productivity, and improve team dynamics. The study also discusses the challenges of merging these approaches, such as ethical considerations, bias in AI algorithms, and the complexity of balancing emotional and technical intelligence. By providing actionable recommendations for practitioners and researchers, this work contributes to advancing leadership practices and highlights opportunities for further exploration in the rapidly evolving field of AI-driven human-centric leadership.Advances in Research00This study investigates the integration of Emotional Intelligence (EI) and Artificial Intelligence (AI) as complementary tools to enhance leadership decision-making, effectiveness, and organizational performance.Advances in Research["Deeksha Dwivedi"]2025-01-14T00:00:00 056530e01d8557038e8de47a4eb99aaa4eaf595c<div class="section abstract"><div class="htmlview paragraph">Artificial intelligence (AI) is poised to significantly impact metal additive manufacturing (AM). Understanding how one might use AI in AM is challenging because AM experts are not AI experts, nor the other way around. This document introduces AI in AM and guides researchers in accessing relevant literature. It also discusses the hype surrounding AI in AM, the rush to publish peer-reviewed papers that use AI in AM, and the resulting uneven quality of the literature. Conclusions regarding the application of AI in both large and small enterprises are discussed.</div><div class="htmlview paragraph">This document is intended to help illuminate AI in AM for<ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Hands-on engineers who need to quickly understand what levels of problems they might encounter when dealing with AI in AM</div></li><li class="list-item"><div class="htmlview paragraph">Engineering managers who need to stay current on emerging trends in their technical realm of responsibilities</div></li><li class="list-item"><div class="htmlview paragraph">Policymakers who may not have the relevant technical expertise</div></li><li class="list-item"><div class="htmlview paragraph">Faculty and students who want an introduction to AI in AM</div></li></ul></div><div class="htmlview paragraph">NOTE: SAE Edge Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.</div></div>1420["Wayne King"]2025-01-14T00:00:00 fbcfc51f0632cff3eb1ba6b275b92c767daac556Introduction and objective: Breast cancer is the most diagnosed cancer and the second leading cause of cancer deaths in women globally, with rising cases and mortality. Early detection via mammography, ultrasound, or MRI is vital, with ultrasound excelling in dense breast tissue due to its safety and accuracy.Review methods: A literature review utilizing databases like Scopus, Google Scholar, and PubMed, with keywords such as "AI use in radiology" and "BI-RADS scale" underscores the need for advancements in understanding and managing graft rejection.Brief knowledge status: AI develops systems that simulate human intelligence, excelling in breast imaging by detecting patterns and providing accurate results. Machine learning (ML) and deep learning (DL) drive advances, with DL's CNNs leading in image analysis. AI aids BI-RADS lesion classification, ultrasound lesion detection, lymph node analysis, and treatment response prediction, often surpassing radiologists. Its future relies on real-world validation, improved outcomes, and clinical integration.Discussion: The integration of artificial intelligence (AI) into breast imaging marks a transformative leap in diagnostic radiology, enhancing precision, efficiency, and scalability. Driven by advancements in machine learning (ML) and deep learning (DL), AI excels in analyzing complex datasets. However, its clinical adoption requires addressing key considerations with a nuanced approach.Summary: In conclusion, AI holds immense promise in breast imaging, poised to redefine the field through enhanced diagnostic capabilities and clinical utility. Continued advancements and validation efforts will ensure its broader acceptance and sustained impact in medical imaging.Quality in Sport00The integration of artificial intelligence (AI) into breast imaging marks a transformative leap in diagnostic radiology, enhancing precision, efficiency, and scalability and poised to redefine the field through enhanced diagnostic capabilities and clinical utility.Quality in Sport["Daria Ziemi\u0144ska", "Karina Motolko", "Rafa\u0142 Burczyk", "Konrad Duszy\u0144ski", "El\u017cbieta Tokarczyk", "Martyna Michalska", "Adam \u0141abuda"]2025-01-14T00:00:00 3d5a542610bc449c5f9f293f8a4ee3b79c4019bbMaking Waves Toward A Sustainable and Equitable Future00Making Waves Toward A Sustainable and Equitable Future["A. Sadachar", "Ummey Hani Barsha"]2025-01-14T00:00:00 59be2ea453163191e898d7f84064caad3303d4c8InfoScience Trends610InfoScience Trends["Abazar Akbarzadeh Pasha", "Nazanin Hajiebrahimi", "Mahdi Amirchaghmaghy", "Hadis Zaboli", "Sepehr Ramezani", "Abolfazl Alipour"]2025-01-14T00:00:00 c5e3864311c67f74688b0e1af7495652ee2ab4d2Frontiers in Endocrinology300Frontiers in Endocrinology["Jamal Belkhouribchia"]2025-01-14T00:00:00 cbf05f0dedc2fd4dffe0933fb6f81b8be46c0e39Bananas are the most commonly eaten and significant fruit in global trade. Bananas are produced using a variety of methods and environments. In addition to regularly updating farmers on problems in banana plant leaves, this system aims to discover, diagnose, and treat banana leaf diseases. Customers' wants and lifestyles have changed significantly during the past few decades. These modifications provide additional difficulties for farmers whose output must satisfy consumer needs. Both the farmer and the consumer will benefit from the capacity to categorize agricultural products according to size and quality. In this case, the system gets its input in the form of standard photos of banana leaves taken using various image capture devices. It will then process those photos to identify any diseases and alert the farmer. Additionally, the system will advise the farmer on what to do next, including which fertilizers, herbicides, and agricultural practices to employ in order to prevent illnesses from harming neighboring crops. In this systematic review, useful and efficient methods for identification are presented in works that fall under the categories of image classification, AI/ML, deep learning, and mobile applications.Kashf Journal of Multidisciplinary Research00In this systematic review, useful and efficient methods for identification are presented in works that fall under the categories of image classification, AI/ML, deep learning, and mobile applications.Kashf Journal of Multidisciplinary Research["Shahzad Nasim", "Munaf Rashid", "Saba Yousha"]2025-01-14T00:00:00 f79af2bc9ee63d41c408a4b5e46037b8b11e881eMaking Waves Toward A Sustainable and Equitable Future00Making Waves Toward A Sustainable and Equitable Future["Wenna Han", "Xingqiu Lou", "Yingjiao Xu"]2025-01-14T00:00:00 59c2daea9fd6655835e73c0e40e5f1ae77c427fcHong Kong Journal of Ophthalmology00Hong Kong Journal of Ophthalmology["Carolyn Yu Tung Wong", "Wilson Wai Kuen Yip", "Henry Hing-Wai Lau", "Carol Y Cheung"]2025-01-14T00:00:00 a2e74d7034db72b10dbeccf458cf1dcf2b62652aJournal of Innovation and Entrepreneurship1110Journal of Innovation and Entrepreneurship["Adriana Medina-Vidal", "Patricia Esther Alonso-Galicia", "Miguel Gonz\u00e1lez-Mendoza", "M. Ram\u00edrez-Montoya"]2025-01-14T00:00:00 b606289e1d6a1e30732ed88096f7d2466c5ade59Objective: To develop a communication system for people with speech difficulties that allows them to express their needs by issuing instructions to the computer with minimal eye blinks, using a model created with MediaPipe and Deep Learning techniques.   Theoretical Framework: The research is based on concepts of eye position tracking, convolutional networks and media pipe technology, with a focus on applying it to the communication needs of people with speech difficulties.   Method: Qualitative and exploratory study, convolutional networks and media pipe techniques were used. To create the dataset, web scraping techniques were combined with manual image collection, the model was trained by comparing the performance of two CNN architectures.   Results and Discussion: The incorporation of AI in the eye blink detection process is relatively recent, with more publications since 2020. It was found that the system is capable of processing facial gestures in real time with an average delay of 0.5 seconds, users reported improvements in their ability to communicate independently and reducing the effort their relatives had to make to interpret their needs, an accuracy of 94.5% was achieved in standard lighting conditions and 92% in variable conditions.   Research Implications: The research reveals how AI with the incorporation of continuously emerging methods can improve the task of detecting images for eye tracking, obtaining increasingly better results in precision.   Originality/Value: The application of emerging AI techniques in eye tracking to apply it in the development of a system that helps people with speech problems communicate.Revista de Gestão Social e Ambiental170The research reveals how AI with the incorporation of continuously emerging methods can improve the task of detecting images for eye tracking, obtaining increasingly better results in precision.Revista de Gestão Social e Ambiental["Ren\u00e9 Cruz-Guerrero", "Isa\u00edas Sim\u00f3n-Marmolejo", "Elias Ru\u00edz Hern\u00e1ndez", "Karina Gutierrez Fragoso"]2025-01-14T00:00:00 c54661b4b78efc2479391c780ef7468af7a27d36Hong Kong Journal of Ophthalmology00Hong Kong Journal of Ophthalmology["K. Lai", "Carmen Sze Ching Lo", "Han Wang", "Xiaoyan Hu", "Fatema M Aljufairi", "Jake Uy Sebastian", "Chi Pui Pang", "K. Chong"]2025-01-14T00:00:00 f257cc15321881611e4731f39fec378700cd97ae AI suffers from inaccurate, numerically unvalidated calculations which lead to randomly accurate and unreliable results. Accuracy is the deviation from a True Value which Inora Technologies OICT Provides. The foundation of intelligence is the accurate and reliable processing of information. Even though artificial intelligence (AI) currently has significant hype, we believe AI is not intelligent. This is, unfortunately, true because the calculations within the AI completely lack the ability to numerically evaluate complex data accurately. AI and, specifically, large language models (LLMs) base their calculations on neural networks. These networks “learn” to orient themselves within data sets by randomly making internal adjustments until the results “look good” to subjective human judgment. Open Access Government00This paper states that artificial intelligence (AI) is not intelligent because the calculations within the AI completely lack the ability to numerically evaluate complex data accurately.Open Access Government["Ingobert Schmadel"]2025-01-14T00:00:00 8cb9282df2003793e8dccad1fb90e76c91b55a08Journal of Big Data842The key finding from this study demonstrates the need for continuous development of AI models that could be alert to the latest fraudulent activities and exploration of existing limitations of ML or DL-enhanced models.J. Big Data["Ibrahim Y. Hafez", "Ahmed Y. Hafez", "Ahmed Saleh", "A. A. El-Mageed", "A. Abohany"]2025-01-14T00:00:00 489a8b7802dc61c917095d0a0b3b02d4a50761f7Social scientists are faced with the challenge of designing complex studies and analyzing collected data via various programs such as R, Stata, SPSS, or Python. This often requires the use of analytical procedures and specific software packages that are beyond an individual’s established skillsets and technical knowledge. To address these challenges, generative artificial intelligence, such as ChatGPT, can now be employed as ‘assistants’—with both associated risks and benefits. Accordingly, this paper explores the potential and pitfalls of using a tool like ChatGPT as an assistant in quantitative data analysis. We investigate the practical use of ChatGPT-3.5 by replicating analyses and findings in everyday scientific research. Unlike previous studies, which have primarily focused optimizing the use of chatbots for code generation, our approach examines an amateur level use of AI tools to support and reference regular research activities, with an emphasis on minimal technical expertise. While we overall conducted three experiments, with the goal to replicate academic papers, the article’s focus is on the methodologically most complex one, by De Wet et al. from 2020. In this case AI is used for the step-by-step replication of the two-dimensional model of value types proposed by Schwartz (2012). The results of this experiment highlight the challenges of using ChatGPT 3.5 for specific, detailed tasks in academic research, as a tendency for responses to repeat in loops when solutions were not readily available emerged at several stages. Thus, we concluded that there are severe limitations in the AI’s ability to provide accurate and comprehensive solutions for complex tasks and emphasize the need for caution and verification when using AI powered tools for complex research procedures.Frontiers in Education470There are severe limitations in the AI’s ability to provide accurate and comprehensive solutions for complex tasks and the need for caution and verification when using AI powered tools for complex research procedures is emphasized.Frontiers in Education["Dimitri Prandner", "Daniela Wetzelh\u00fctter", "S\u00f6nke Hese"]2025-01-14T00:00:00 bdce62a2bfba3a681fbe1d9a9591dff4d76897a0<div class="section abstract"><div class="htmlview paragraph">This report explores the move from traditional industry practices to emerging technologies, specifically the integration of artificial intelligence (AI) solutions in engineering service sectors. It highlights the increasing problem of “technology washing,” when organizations overstate (sometimes deceivingly) their technology abilities and ethics, posing challenges to accountability, transparency, and trust in various fields. The rise of AI-based solutions in sectors like autonomous mobility, manufacturing, and aerospace has exposed a contrast between ambitious future aspirations and current technological barriers. With this, the role of human knowledge in guaranteeing ethical, efficient, and clear technology incorporation becomes essential.</div><div class="htmlview paragraph">Starting with an examination of today’s technological scene, this report tackles topics such as the buzz around autonomous systems and the difficulties of standardizing fresh innovations. It also points out the problem of organizations exaggerating the capabilities of AI, stressing the importance of human monitoring to manage operational risks and uphold public trust. Practical scenarios in autonomous mobility, aerospace, and manufacturing highlight a significant discrepancy between industry targets and technological feasibilities, stressing the indispensable contribution of human intervention in ensuring successful implementation.</div><div class="htmlview paragraph">These examples are analyzed to give insights into current technology successes and limitations and to propose a balanced path for the future. Ultimately, there may be a future where groundbreaking technological advancements remain in harmony with human values. This report challenges established narratives and outlines a path for ethical technological advancement that is transparent and in line with societal values, examining questions like the following:<ul class="list disc"><li class="list-item"><div class="htmlview paragraph"><b>What are some popular misunderstandings and exaggerated claims regarding AI capabilities today?</b> Examine the divide between how the public sees things and what’s actually true in the context of deceptive AI practices and inflated statements.</div></li><li class="list-item"><div class="htmlview paragraph"><b>How do organizations maintain a balance between rapid technological adoption and human oversight?</b> Explore ways to maintain human knowledge in decision-making processes despite technological advances.</div></li><li class="list-item"><div class="htmlview paragraph"><b>What are stakeholders’ views on the reliability and safety of autonomous technologies?</b> Investigate the certainty levels in crucial systems that have implications for public safety and business continuity.</div></li><li class="list-item"><div class="htmlview paragraph"><b>What are the risks associated with overusing AI for critical functions?</b> Highlight the potential pitfalls of excessive reliance on AI without proper backup systems or redundancy plans.</div></li></ul></div><div class="htmlview paragraph">NOTE: SAE Edge Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE Edge Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.</div></div>30This report highlights the increasing problem of “technology washing,” when organizations overstate (sometimes deceivingly) their technology abilities and ethics, posing challenges to accountability, transparency, and trust in various fields.["Samir Khan"]2025-01-14T00:00:00 5702b2d6b1a9c916416f8e8fd1137c4f9316f6b1The incorporation of Artificial Intelligence (AI) into Indonesia's Science and Technology Index (SINTA) offers both significant opportunities and challenges for advancing research management, evaluation, and development. This paper examines the advantages of embedding AI into SINTA, such as the ability to automate the identification of predatory journals, provide personalized research recommendations, and enhance the accuracy of institutional rankings. Furthermore, AI can help analyze citation trends, streamline the peer review process, and detect plagiarism or other forms of academic misconduct. However, this integration also brings several obstacles, including the need to ensure high-quality data, uphold transparency and fairness in AI-driven outcomes, protect data privacy, and address the substantial technological investments required. While AI holds the potential to greatly strengthen SINTA as a platform for overseeing and supporting research activities in Indonesia, overcoming these challenges is critical for its effective implementation. The study concludes that, with proper oversight and investment, integrating AI into SINTA could significantly boost the country's research ecosystem, fostering greater innovation and scientific productivity.Jurnal Sains dan Teknologi200This paper examines the advantages of embedding AI into SINTA, such as the ability to automate the identification of predatory journals, provide personalized research recommendations, and enhance the accuracy of institutional rankings.Metris: Jurnal Sains dan Teknologi["F. P. S. Surbakti"]2025-01-14T00:00:00 171b0a91a802cfd6663de52e214d7a7c727fffdaArtificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, to regulate AI by undertaking a risk-based approach to safeguard humans during interaction. At the same time, researchers offer a new perspective on AI systems, commonly known as Human-Centred AI (HCAI), highlighting the need for a human-centred approach to their design. In this context, Symbiotic AI (a subtype of HCAI) promises to enhance human capabilities through a deeper and continuous collaboration between human intelligence and AI. This article presents the results of a Systematic Literature Review (SLR) that aims to identify principles that characterise the design and development of Symbiotic AI systems while considering humans as the core of the process. Through content analysis, four principles emerged from the review that must be applied to create Human-Centred AI systems that can establish a symbiotic relationship with humans. In addition, current trends and challenges were defined to indicate open questions that may guide future research for the development of SAI systems that comply with the AI Act.740Four principles must be applied to create Human-Centred AI systems that can establish a symbiotic relationship with humans, aims to identify principles that characterise the design and development of Symbiotic AI systems while considering humans as the core of the process.["Miriana Calvano", "Antonio Curci", "Giuseppe Desolda", "Andrea Esposito", "R. Lanzilotti", "Antonio Piccinno Department of Computer Science", "University of Bari Aldo Moro", "Bari", "Italy"]2025-01-14T00:00:00 c7437b6ff5c60197a73f67b3458406886521757d This study leverages the frameworks of the conservation of resources theory frameworks and Person-Organization Person-Job Fit Theory to scrutinize the direct effects of employee STARA (smart technologies, artificial intelligence, robotics and algorithms) competencies on turnover intentions. Concurrently, this study aims to investigate the mediating influence of the intention to use technologies in the aforementioned relationship. Data were amassed from 547 employees in the US hotel industry and subjected to structural equation modeling for analysis. The results reveal that there is no significant correlation between employee technology competencies and turnover intentions. However, mediation analysis elucidates that technology competencies among employees are positively and significantly correlated with turnover intentions via the intention to use technology. Moderation analysis further substantiates that this positive correlation is augmented when employees perceive a high level of alternative job opportunities. This research suggests that hotel businesses should not only focus on technological adoption but also consider how employees’ techno-competencies and their perceptions of fit within the organization can impact their willingness to stay or leave, thereby offering a more comprehensive approach to employee retention strategies. Unlike previous research that primarily viewed STARA technologies as job replacers and threats, this study reframes them as complements to employees’ roles. Journal of Hospitality and Tourism Technology400This research suggests that hotel businesses should not only focus on technological adoption but also consider how employees’ techno-competencies and their perceptions of fit within the organization can impact their willingness to stay or leave, thereby offering a more comprehensive approach to employee retention strategies.Journal of Hospitality and Tourism Technology["Selim Bakir", "B. Ayoun", "C. Wei", "Anil Bilgihan"]2025-01-14T00:00:00 bdd1461fee6073c1de1684f8866e76836ed0fa3fArtificial intelligence (AI) integrated in health care portends to be a needle of transformative potential in providing evidence-based medicine as advancements in diagnostic precision and personalized treatment and patient care optimization. This paper explores the cutting edge of technologies including machine learning, natural language processing and predictive analytics in the pivotal role of AI in EBM. Through AI analysis of vast datasets, the methodology supports the use of clinical decision making that reduces human error and enables the development of optimal, personalized treatment plans for each patient. And we survey current AI uses in healthcare, such as AI based diagnostic tools, AI based predictive disease progression models, and AI based clinical decision support systems (CDSS). We also discuss the challenges and the ethical aspects of AI integration, especially issues related to data privacy, algorithmic bias and require for effective regulatory measures. We show how AI can strengthen EBM in practice through case studies and real world examples. Finally, the paper has a look forward to future prospects and novel trends in AI showing p.s.otential to transform healthcare delivery beyond even what is imagined today. Included are advances in the AI algorithms used in health care, the increased inter operability of AI systems in health care infrastructures, and the opportunity for AI to enable broader and more comprehensive, and thus inclusive, medical research. Our results highlight the importance of systems where technological innovation is carefully balanced with ethical considerations in realizing the full potential in AI for transforming healthcare clinical decision making and patient outcomes.South Eastern European Journal of Public Health00The cutting edge of technologies including machine learning, natural language processing and predictive analytics in the pivotal role of AI in EBM are explored, highlighting the importance of systems where technological innovation is carefully balanced with ethical considerations in realizing the full potential in AI for transforming healthcare clinical decision making and patient outcomes.South Eastern European Journal of Public Health["Dr. Abeera Jamil", "Stephen A. Fadare", "Syed Nurul Islam", "Shah Md.Wasif Faisal", "Shagufta Yamin", "Mohd Imran"]2025-01-14T00:00:00 06655dda63852f2488ed059d220f6bbab5f2932eNew research focuses on creating artificial intelligence (AI) solutions for network intrusion detection systems (NIDS), drawing its inspiration from the ever-growing number of intrusions on networked systems, increasing its complexity and intelligibility. Hence, the use of explainable AI (XAI) techniques in real-world intrusion detection systems comes from the requirement to comprehend and elucidate black-box AI models to security analysts. In an effort to meet such requirements, this paper focuses on applying and evaluating White-Box XAI techniques (particularly LRP, IG, and DeepLift) for NIDS via an end-to-end framework for neural network models, using three widely used network intrusion datasets (NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021), assessing its global and local scopes, and examining six distinct assessment measures (descriptive accuracy, sparsity, stability, robustness, efficiency, and completeness). We also compare the performance of white-box XAI methods with black-box XAI methods. The results show that using White-box XAI techniques scores high in robustness and completeness, which are crucial metrics for IDS. Moreover, the source codes for the programs developed for our XAI evaluation framework are available to be improved and used by the research community.00This paper applies and evaluates White-Box XAI techniques for NIDS via an end-to-end framework for neural network models, using three widely used network intrusion datasets, assessing its global and local scopes, and examining six distinct assessment measures.["Osvaldo Arreche", "Mustafa Abdallah"]2025-01-14T00:00:00 4e0c0d28a7710a21baee59fdb23ff073b9afe075Cardiovascular diseases are the leading cause of death globally, highlighting the need for accurate diagnostic tools. To address this issue, we introduce a novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods. First, we developed an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks. Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. This model processes a triad of cardio cycles—the preceding, current, and following cycles—to capture temporal dependencies and hidden features related to arrhythmias. Third, we implemented an interpretation method that explains CNN’s decisions using clinically relevant features, making the results understandable to clinicians. Using the MIT-BIH database, our approach achieved an accuracy of 99.43%, with F1-scores approaching 100% for major arrhythmia classes. The integration of these methods enhances both the performance and transparency of arrhythmia detection systems.Technologies350A novel approach for arrhythmia detection based on electrocardiogram (ECG) that incorporates explainable artificial intelligence through three key methods, including an enhanced R peak detection method that integrates domain-specific knowledge into the ECG, improving peak identification accuracy by accounting for the characteristic features of R peaks.Technologies["Oleksii Kovalchuk", "Oleksandr Barmak", "P. Radiuk", "Liliana Klymenko", "Iurii Krak"]2025-01-14T00:00:00 380dcd9a2dd61dabbc26c2b9b902420924721bebAI and Ethics390The findings of this study can assist research communities in proposing comprehensive AI governance practices by providing a foundation for understanding different facets of AI governance.AI and Ethics["Amna Batool", "Didar Zowghi", "Muneera Bano"]2025-01-14T00:00:00 04a30b2a01a12cd343ea9a95ce0cfe76c89f1205 The rapid advancement and integration of robotics and artificial intelligence (AI) are transforming various sectors, presenting profound ethical, economic, legal and societal challenges. This study aims to examine ethical concerns in AI development, with a specific focus on robotics, from the perspectives of university students in Albania. A structured questionnaire was used to collect data from 233 university students, focusing on their experiences with AI and robotics, ethical perceptions, preferences and recommendations for advancing these technologies. Hypotheses were tested at a 95% confidence interval, with data analyzed using JASP software version 0.18.3.0. The results reveal a high level of ethical awareness among students, particularly regarding transparency, liability and privacy in AI and robotics. Practical experience with robotics and understanding of AI’s ethical implications significantly shape students’ attitudes, fostering support for ethical governance. Students also advocate for robust regulatory measures to safeguard individual rights, ensure data security, promote transparency in AI decision-making and uphold privacy. This study focuses on university students in Albania, which may limit the generalizability of its findings. Future research should explore diverse populations and cross-cultural contexts to validate and extend the proposed framework. Insights from this study can guide policymakers and technology developers in designing laws, regulations and practices that balance innovation with public interest, fostering trust and acceptance of AI systems. The findings underscore the importance of Albania adopting and harmonizing its policies with the EU Civil Law Rules on Robotics, the EU AI Act and AI Strategy, supporting ethical AI integration aligned with the country’s EU accession objectives. This study introduces the Ethical Awareness-Trust Framework, a novel theoretical model integrating ethical literacy, experiential trust and regulatory advocacy to foster responsible AI adoption and governance. The findings address critical gaps in the literature by offering actionable recommendations for aligning national policies with European regulations and embedding ethics into AI research and education. Journal of Information, Communication and Ethics in Society240The Ethical Awareness-Trust Framework is introduced, a novel theoretical model integrating ethical literacy, experiential trust and regulatory advocacy to foster responsible AI adoption and governance to address critical gaps in the literature.Journal of Information, Communication and Ethics in Society["Anxhela Ferhataj", "Fatmir Memaj", "Roland Sahatcija", "A. Ora", "Enkelejda Koka"]2025-01-14T00:00:00 b06cc82339a8dbe87ca62791b402328146530401This study investigates the evolving role of artificial intelligence (AI) in consumer decision-making, particularly in comparison to traditional human influencers. As consumer trust in social media influencers has declined, largely due to concerns about the financial motivations behind endorsements, AI tools such as ChatGPT have emerged as perceived neutral intermediaries. The research aims to understand whether AI systems can replace human influencers in shaping purchasing decisions and, if so, in which sectors. A mixed-methods approach was employed, involving a quantitative survey of 478 participants with prior experience using both AI tools and interacting with social media influencers, complemented by 15 semi-structured interviews. The results reveal that AI is favoured over human influencers in product categories where objectivity and precision are critical, such as electronics and sporting goods, while human influencers remain influential in emotionally driven sectors like fashion and beauty. These findings suggest that the future of marketing will show a reduced need for human social media influencers and may involve a hybrid model where AI systems dominate data-driven recommendations and human influencers continue to foster emotional engagement. This shift has important implications for brands as they adapt to changing consumer trust dynamics.Applied Informatics340The results reveal that AI is favoured over human influencers in product categories where objectivity and precision are critical, such as electronics and sporting goods, while human influencers remain influential in emotionally driven sectors like fashion and beauty.AI["Michael Gerlich"]2025-01-14T00:00:00 f726d0bd6e07dbf1d114609bd306816aa01bbf5c Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies that are redefining cloud computing. The incorporation of AI and machine learning into cloud platforms improves productivity, scalability, and flexibility, allowing organizations to handle massive amounts of data and make intelligent choices in real time. This study investigates the symbiotic relationship between AI/ML and cloud computing, focusing on how cloud architecture provides the computational capacity needed for AI and ML models while AI improves cloud resource allocation. The article discusses key breakthroughs such as AI-driven automation in cloud operations, predictive analytics, and intelligent application deployment. The report emphasizes the democratization of AI capabilities via cloud services, which make them available to small and medium-sized organizations (SMEs) without requiring considerable infrastructure investment. It also investigates the security hurdles, ethical concerns, and compliance issues that come with data-intensive AI systems on the cloud. Real-world applications include AI-powered recommendation systems, fraud detection, and tailored consumer experiences, demonstrating the strength of this synergy. The report indicates that incorporating AI and ML into cloud computing is critical for expanding technological landscapes, driving creativity, and helping enterprises to remain competitive. However, addressing data privacy, ethical AI usage, and fair access to cloud-based AI resources are crucial for long-term progress. This study provides insights for researchers and practitioners on leveraging AI/ML in the cloud to meet evolving technological and business needs.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00It is indicated that incorporating AI and ML into cloud computing is critical for expanding technological landscapes, driving creativity, and helping enterprises to remain competitive, however, addressing data privacy, ethical AI usage, and fair access to cloud-based AI resources are crucial for long-term progress.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Abhishek Purohit"]2025-01-14T00:00:00 43b0e7c7134c05b57f68b0d9588464bec724d078Aging is often accompanied by cognitive changes, including forgetfulness, which can impact an individual's quality of life and independence. Recent advancements in Artificial Intelligence (AI) have opened avenues for addressing these challenges by developing tools to support cognitive health and memory. AI-powered systems, such as memory assistance devices, predictive diagnostic tools, and personalized cognitive training programs, offer transformative potential for mitigating the effects of aging-related forgetfulness. This paper explores how AI can bridge gaps in traditional cognitive care, emphasizing a human-centered approach that prioritizes ethical considerations, user-friendliness, and inclusivity. By analyzing case studies and examining interdisciplinary methodologies, the research highlights both the opportunities and challenges in leveraging AI to enhance cognitive health. The findings underscore the importance of collaboration between AI developers, healthcare providers, and end-users to create solutions that are effective, accessible, and empathetic. The integration of AI into cognitive care holds promise not only for improving memory retention but also for fostering dignity and autonomy among aging populations.Next Frontier For Life Sciences and AI80This paper explores how AI can bridge gaps in traditional cognitive care, emphasizing a human-centered approach that prioritizes ethical considerations, user-friendliness, and inclusivity.Next Frontier For Life Sciences and AI["Lara Sude Tarhan"]2025-01-14T00:00:00 588ed1f40074bebfcdb093d7733d32708f772b39This study aimed to create a risk prediction model with artificial intelligence (AI) to identify patients at higher risk of postpartum hemorrhage using perinatal characteristics that may be associated with later postpartum hemorrhage (PPH) in twin pregnancies that underwent cesarean section. The study was planned as a retrospective cohort study at University Hospital. All twin cesarean deliveries were categorized into two groups: those with and without PPH. Using the perinatal characteristics of the cases, four different machine learning classifiers were created: Logistic regression (LR), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). LR, RF, and SVM models were created a second time by including class weights to manage the underlying imbalances in the data. A total of 615 twin pregnancies were included in the study. There were 150 twin pregnancies with PPH and 465 without PPH. Dichorionity, PAS, and placenta previa were significantly higher in the PPH-positive group (p = .045, p = .004, p = .001 respectively). In our model, LR with class weight was the best model with the highest negative predictive value. The AUC in our LR with class weight model was %75.12 with an accuracy of 70.73%, a PPV of 47.92%, and an NPV of 85.33% in our data. Although the application of machine learning to create predictive models using clinical risk factors and our model's 70% accuracy rate are encouraging, it is not sufficient. Machine learning modeling needs further study and validation before being incorporated into clinical use.Twin Research and Human Genetics300Although the application of machine learning to create predictive models using clinical risk factors and the model's 70% accuracy rate are encouraging, it is not sufficient and machine learning modeling needs further study and validation before being incorporated into clinical use.Twin research and human genetics : the official journal of the International Society for Twin Studies["\u015e\u00fckran Do\u011fru", "Huriye Ezveci", "F. Akku\u015f", "Pelin Bahceci", "Fikriye Karanfil Yaman", "Ali Acar"]2025-01-15T00:00:00 c708d2b51c5a71a00d4339a9bb926e17d6e0efc4Artificial Intelligence has been increasingly reshaping the face of financial auditing for improved efficiency and effectiveness in fraud detection, serving also to strengthen stakeholder trust. This study examines the impact of adopting AI on audit practices, in terms of effectiveness in improving efficiency, fraud detection, ethical challenges, regulatory barriers, and stakeholder trust. In this paper, 460 professional auditors, accountants, and organizational stakeholders have been analyzed using descriptive statistics, correlation, regression, and structural equation modeling. The results indicate that AI has a positive influence on efficiency, value creation, and fraud detection ability, while positively influencing stakeholder trust in organizations. However, ethical concerns from algorithmic bias to a lack of transparency and regulatory risks associated with compliance through data protection laws are also significant barriers. It thus concludes that AI has a enormous potential in revolutionizing auditing practices, and that addressing these barriers through training, transparent AI models, ethical safeguards, and supportive regulatory frameworks is also very important for its widespread adoption. Recommendations and future research avenues are presented to guide the responsible integration of AI into the auditing profession.Brazilian Journal of Business00It is concluded that AI has a enormous potential in revolutionizing auditing practices, and that addressing these barriers through training, transparent AI models, ethical safeguards, and supportive regulatory frameworks is also very important for its widespread adoption.Brazilian Journal of Business["Laamari Imane"]2025-01-15T00:00:00 436db15795839b8f4066cdb3b6734dc40a507a46This study examines the transformative effects of Artificial Intelligence (AI) on talent acquisition and employee retention in human resources (HR). This research, using a sample of 173 HR professionals and employees from various industries, examines how AI-driven solutions optimize recruitment procedures and improve long-term engagement tactics. The research investigates the impact of predictive analytics, chatbots, and machine learning algorithms on enhancing candidate screening, minimizing time-to-hire, and cultivating a tailored recruitment experience. Additionally, it assesses the efficacy of AI in forecasting staff turnover risks and facilitating proactive retention strategies. Advanced statistical techniques, like as factor analysis and Mann-Whitney U tests, are employed to evaluate the hypotheses and reveal actionable insights. The results indicate substantial enhancements in HR operational efficiency, applicant satisfaction, and workforce stability due to AI. This research highlights the capacity of AI to transform HR practices while confronting issues such as prejudice and ethical considerations in its use. The research offers actionable suggestions for firms seeking to utilize AI in developing competitive, future-oriented HR systems.Journal of Information Systems Engineering & Management160The results indicate substantial enhancements in HR operational efficiency, applicant satisfaction, and workforce stability due to AI, highlighting the capacity of AI to transform HR practices while confronting issues such as prejudice and ethical considerations in its use.Journal of Information Systems Engineering and Management["Dr. S. Jyothirmaye Reddy1", "Dr. S. Md. Shakir Ali2", "Dr. Cuddapah Anitha", "5. DrEstherHepziba.R", "6. Dr.PoojaKulkarni"]2025-01-15T00:00:00 0bcf0da36eb3da8ccbb2b2319cfaca7e7ba22d3aINTELLIGENZA ARTIFICIALE E ISTRUZIONE DI QUALITÀ: LA NECESSITÀ DELLA CULTURA DIGITALE NELL’INSEGNAMENTO Abstract This paper critically explores the concept of «quality education» as defined by SDG 4, emphasizing inclusivity and equity, particularly in the context of digital transformation. Quality education, in this sense, goes beyond knowledge acquisition to fostering lifelong learning, accessible to all. The integration of artificial intelligence (AI) in education is examined through a socio-pedagogical interdisciplinary lens, highlighting how it reshapes traditional pedagogical models. This shift requires digital literacy and a digital culture, addressing the «digital divide» and promoting inclusivity while raising challenges that necessitate rethinking existing educational frameworks. The rise of digital culture presents a critical pathway for addressing the educational mismatch of contemporary society. By fostering a value-oriented digital culture, educational models must emphasize ethical engagement, critical thinking, and digital citizenship. This transition requires a multi-level approach, acknowledging students’ increasing agency in a bottom-up digital culture. Moving towards Education 4.0 necessitates a rethinking of traditional pedagogies, with teachers adopting new mindsets, roles, and competencies.ECPS - Educational Cultural and Psychological Studies00This paper critically explores the concept of «quality education» as defined by SDG 4, emphasizing inclusivity and equity, particularly in the context of digital transformation.Journal of Educational, Cultural and Psychological Studies (ECPS Journal)["Donatella Padua"]2025-01-15T00:00:00 09465737241abe0cb80cce757facecb58c624ed7Gallbladder carcinoma (GBC) is the most common malignant tumor of biliary tract, with poor prognosis due to its aggressive nature and limited therapeutic options. Early detection of GBC is a major challenge, with most GBCs being detected accidentally during cholecystectomy procedures for gallbladder stones. This letter comments on the recent article by Deqing et al in the World Journal of Gastrointestinal Oncology, which summarized the various current methods used in early diagnosis of GBC, including endoscopic ultrasound (EUS) examination of the gallbladder for high-risk GBC patients, and the use of EUS-guided elastography, contrast-enhanced EUS, trans-papillary biopsy, natural orifice transluminal endoscopic surgery, magnifying endoscopy, choledochoscopy, and confocal laser endomicroscopy when necessary for early diagnosis of GBC. However, there is a need for novel methods for early GBC diagnosis, such as the use of artificial intelligence and non-coding RNA biomarkers for improved screening protocols. Additionally, the use of in vitro and animal models may provide critical insights for advancing early detection and treatment strategies of this aggressive tumor.World Journal of Gastrointestinal Oncology140There is a need for novel methods for early GBC diagnosis, such as the use of artificial intelligence and non-coding RNA biomarkers for improved screening protocols and the use of in vitro and animal models may provide critical insights for advancing early detection and treatment strategies of this aggressive tumor.World Journal of Gastrointestinal Oncology["Ismail AS Burud", "Sherreen Elhariri", "Nabil Eid"]2025-01-15T00:00:00 3b04a0a1d60ca6116edbaeaf0b4f9faf0539738fThis overview article highlights the critical role of mathematics in artificial intelligence (AI), emphasizing that mathematics provides tools to better understand and enhance AI systems. Conversely, AI raises new problems and drives the development of new mathematics at the intersection of various fields. This article focuses on the application of analytical and probabilistic tools to model neural network architectures and better understand their optimization. Statistical questions (particularly the generalization capacity of these networks) are intentionally set aside, though they are of crucial importance. We also shed light on the evolution of ideas that have enabled significant advances in AI through architectures tailored to specific tasks, each echoing distinct mathematical techniques. The goal is to encourage more mathematicians to take an interest in and contribute to this exciting field.00The application of analytical and probabilistic tools to model neural network architectures and better understand their optimization is focused on, highlighting the critical role of mathematics in artificial intelligence.["Gabriel Peyr'e"]2025-01-15T00:00:00 358b7e993f7c5ae2b6310c4d83eacaed00a7af5fL’INTELLIGENZA ARTIFICIALE PER POTENZIARE LA RICERCA QUALITATIVA: RIFLESSIONI METODOLOGICHE SU UNO STUDIO PILOTA Abstract Qualitative analysis is essential in research across diverse fields, offering in-depth insights that often cannot be captured through quantitative methods. However, managing large volumes of qualitative data presents challenges, including its labour intensive nature and the potential for interpretive biases. In this study, we introduce and show a methodology step by step that integrates artificial intelligence (AI) in the analysis of qualitative data, with a focus on textual responses extracted from survey questions. Specifically, our approach employs AI techniques, utilizing Word2Vec for word embedding extraction and K-Means clustering to streamline the analysis of qualitative textual data, while ultimately integrating the researcher’s interpretation of the identified clusters to improve the relevance of the analysis. Moreover, the present article discusses the relevance and significance of this approach as well as its ethical and methodological challenges by means of an empirical illustration taken from a study on teachers’ sensemaking regarding a range of different educational activities.ECPS - Educational Cultural and Psychological Studies00The approach employs AI techniques, utilizing Word2Vec for word embedding extraction and K-Means clustering to streamline the analysis of qualitative textual data, while ultimately integrating the researcher’s interpretation of the identified clusters to improve the relevance of the analysis.Journal of Educational, Cultural and Psychological Studies (ECPS Journal)["Maria Luongo", "M. Ponticorvo", "M. B. Ligorio", "Pietro Crescenzo", "G. Ritella"]2025-01-15T00:00:00 f74792302d886e375d857910a2d54710fb877480The EU Artificial Intelligence Act is the world's first attempt to holistically regulate artificial intelligence. It presents an extensive, multi‐faceted definition of deepfakes, and introduces specific safeguards against their misuses. These guardrails have the potential to become a global role model. The AI Act uses concepts that leave room for interpretation, which is important given the constant development of technology and the need for adjustments. However, some solutions raise the problem of vagueness, which in turn may result in a narrower interpretation of a linguistic nature, and reduce the scope of legally permissible countermeasures. The aim of this study is to critically evaluate the definition of deepfakes contained in the AI Act in relation to the word “existing” used. A narrow interpretation could potentially exclude some synthetic media from the scope of transparency obligations due to the non‐classification of these media as deepfakes. Therefore, a teleological interpretation of the provisions is proposed, reinforced with elements of systemicity, so that the safeguards built by the AI Act also include deepfakes that do not depict any identifiable pre‐existing persons, objects, places, entities or events to better reflect goals of the regulation, and complement the value‐based system of the AI Act.Policy &amp; Internet340A teleological interpretation of the provisions is proposed, reinforced with elements of systemicity, so that the safeguards built by the AI Act also include deepfakes that do not depict any identifiable pre‐existing persons, objects, places, entities or events to better reflect goals of the regulation, and complement the value‐based system of the AI Act.Policy &amp; Internet["Mateusz \u0141abuz"]2025-01-15T00:00:00 510a91d13c088b3a14830e2d7fc2441eee37ed5dPrevious valuable scholarship has examined how data centers affect the development and use of artificial intelligence (AI) technology. Additional research has analyzed how energy consumption and energy efficiency impact data centers. However, less scholarship has considered how data centers, energy capabilities (energy production and energy efficiency), and AI development interact to affect international security. Thus, this exploratory study considers the relationship between data centers, energy capabilities, and AI development and analyzes their potential impact on power distribution in the international system. In doing so, the study develops four indices to capture influential factors related to AI development and security. The study highlights the important role data centers, energy capabilities, and AI development may play in shaping the international balance of power and global security.Armed Forces &amp; Society320This exploratory study considers the relationship between data centers, energy capabilities, and AI development and analyzes their potential impact on power distribution in the international system and develops four indices to capture influential factors related to AI development and security.Armed Forces &amp; Society["Lance Y. Hunter"]2025-01-15T00:00:00 c15c4890eaf57ab44e5a62b28aefc903ac2fcca1Personalized education has discreetly entered our field of vision with the rise of numerous AIs, including Chat-GPT4, Xiaodu, and MathGPTPro. The conventional educational paradigm may become obsolete, leading society to undergo significant transformations and a refinement process. This paper examines the use of artificial intelligence (AI) in education and its influence on the conventional educational framework. The analysis encompasses a literature review and empirical research on the application effects of AI in personalized learning, intelligent tutoring, and academic decision-making. Additionally, it addresses AI technology's potential challenges and future developmental trajectories within the educational sphere. What obstacles does artificial intelligence encounter in supplanting human involvement in education? In today's tailored AI education, children are happy with using AI software for learning when the educational environments match their interests, according to the poll. According to teachers, AI will not improve students' independent learning skills or critical thinking, and pupils may prefer AI's tailored training over traditional, comprehensive teaching techniques. The study's findings indicate that while AI demonstrates significant promise in education, it continues to encounter numerous limitations and challenges that hinders its ability to support human involvement in compulsory education fully.Lecture Notes in Education Psychology and Public Media00The study's findings indicate that while AI demonstrates significant promise in education, it continues to encounter numerous limitations and challenges that hinders its ability to support human involvement in compulsory education fully.Lecture Notes in Education Psychology and Public Media["Yiyang He"]2025-01-15T00:00:00 8993c1d52c0f16d6d6852953d4c25e5cfe8a754aIn recent years, the electronics manufacturing industry has undergone rapid changes, and part of the electronics manufacturing industry is shifting from China to Vietnam. With the development of artificial intelligence, its influence in various manufacturing industries around the world is gradually increasing, especially in the electronics manufacturing industry. This study explores the application of AI in the electronics manufacturing industry in China and Vietnam and how it affects the transformation of the electronics manufacturing industry in China and Vietnam and leads to a shift in the industry chain. The study uses data analysis including sources such as the WTO and Trade Map, and analytical method of RCA index calculation. This article focuses on the research question of the impact of the application of artificial intelligence in the electronics manufacturing industry on the production and export of technology-intensive and labor-intensive electronic products in China and Vietnam and how the impact has facilitated the shifting of electronics manufacturing from China to Vietnam. The results show that AI technologies such as predictive maintenance and quality control for high-tech electronics have given China an advantage in technology-intensive manufacturing, while Vietnam has enhanced its comparative advantage in labor-intensive manufacturing by optimizing its assembly and inventory management through AI technologies.Advances in Economics, Management and Political Sciences140The results show that AI technologies such as predictive maintenance and quality control for high-tech electronics have given China an advantage in technology-intensive manufacturing, while Vietnam has enhanced its comparative advantage in labor-intensive manufacturing by optimizing its assembly and inventory management through AI technologies.Advances in Economics, Management and Political Sciences["Kai Zhang"]2025-01-15T00:00:00 2af8d098a9047b5b2479d6265619ad8a37b64075Artificial Intelligence (AI) transcends mere technological advancement, taking on profound philosophical, ethical, and cultural significance. This study focuses on the representation of AI in contemporary films, a medium that reflects society’s shifting concerns and aspirations regarding humanity’s relationship with technology. Despite the growing importance of AI in our lives, a gap remains in understanding how films depict AI and contribute to societal discourse on autonomy, ethics, and identity. The study aims to examine key themes in films such as Ex Machina (2014), Her (2013), Blade Runner 2049 (2017), A.I. Artificial Intelligence (2001), and The Matrix (1999), exploring how these portrayals influence our perception of AI. Employing a posthumanist theoretical framework, this study investigates how AI is represented as the “Other” and the ethical responsibilities involved in creating autonomous entities. Methods include a thematic analysis of these films, focusing on how AI characters are depicted and the moral dilemmas presented. Key findings highlight that cinema often frames AI through a lens of human anxieties—particularly around autonomy, ethical responsibility, and identity formation. These films present both utopian and dystopian visions of AI’s role in society, offering diverse perspectives on the implications for human identity and agency. The study concludes that cinema plays a pivotal role in shaping public understanding of AI and its transformative potential, raising critical questions about the future of humanity in an increasingly AI-integrated world. Keywords: Artificial Intelligence (AI), Cinema, Posthumanism, Literary Criticism, Sustainability (SDG 12)Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature10It is concluded that cinema plays a pivotal role in shaping public understanding of AI and its transformative potential, raising critical questions about the future of humanity in an increasingly AI-integrated world.Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature["Mark Anthony", "G. Moyano"]2025-01-15T00:00:00 f8b6ac17660b64dcf7f1ac5f94e7294275064afbThe COVID-19 pandemic has accelerated the integration of Artificial Intelligence (AI) in Knowledge Management (KM) within higher education, particularly in the Federal Technical and Vocational Training (TVT) sector. This systematic review synthesizes current research on AI-based KM in higher education during the pandemic, aiming to elucidate its effectiveness, challenges, and future directions. Following PRISMA guidelines, we conducted a comprehensive search across major databases, including Web of Science, Scopus, and ERIC, identifying 69 relevant studies published between 2020 and 2023. Our analysis reveals that AI-based KM has significantly enhanced personalized learning experiences, improved health safety measures, and increased administrative efficiency in higher education. However, challenges persist, including technological barriers, ethical concerns, and the digital divide. The review highlights a notable research gap in empirical studies validating AI's effectiveness in real-world educational settings, particularly in developing countries. Furthermore, there is a critical need for comprehensive ethical frameworks governing AI use in education. Our findings underscore the transformative potential of AI-based KM in higher education, while emphasizing the necessity for context-specific implementations and rigorous evaluation methodologies. This review provides valuable insights for educators, policymakers, and researchers, guiding future developments in AI-based KM strategies for resilient and inclusive higher education systems in the post-pandemic era.Journal of Information Systems Engineering & Management480A systematic review of current research on AI-based KM in higher education during the pandemic reveals that AI-based KM has significantly enhanced personalized learning experiences, improved health safety measures, and increased administrative efficiency in higher education, however, challenges persist.Journal of Information Systems Engineering and Management["Dr Haftom Gebregziabher Hagos1", "Dr Dusan Lesjak2", "Andrej Flogi3", "Ravindra Babu.B4"]2025-01-15T00:00:00 75eb44f9912090947587bc8b24a3514bb59e6116L’INTEGRAZIONE DELL’INTELLIGENZA ARTIFICIALE NEL DESIGN DELLA COMUNICAZIONE. CASI DI STUDIO DEL POLITECNICO DI MILANO: DALLA CULTURA DIGITALE ALLA SOCIOLOGIA DEI MEDIA Abstract The advent of Artificial Intelligence (AI), particularly Generative AI (GenAI), has catalyzed a transformative shift in educational methodologies, bridging traditional pedagogy with advanced digital technologies. This article presents a comparative analysis of two case studies conducted within the Communication Design Program at Polytechnic of Milan, exploring the integration of GenAI as a collaborative tool in Digital Culture and Sociology of Media courses. By examining the implications, challenges, and outcomes of embedding GenAI within these educational frameworks, we aim to highlight the role of AI as a «co-pilot» in learning processes, offering innovative approaches to knowledge creation and problem-solving. The case studies underscore common themes, such as the democratization of creative tools through AI, the necessity for critical AI literacy, and the exploration of AI as a facilitator of innovative content generation. Simultaneously, the article delves into the differences between the two courses, including participant numbers, course structures, and project aspects, providing a comprehensive understanding of GenAI’s impact on educational experiences and students’ problem-solving skills.ECPS - Educational Cultural and Psychological Studies00A comparative analysis of two case studies conducted within the Communication Design Program at Polytechnic of Milan, exploring the integration of GenAI as a collaborative tool in Digital Culture and Sociology of Media courses provides a comprehensive understanding of GenAI’s impact on educational experiences and students’ problem-solving skills.Journal of Educational, Cultural and Psychological Studies (ECPS Journal)["Giovanna Di Rosario", "M. Ciastellardi"]2025-01-15T00:00:00 3928958502e6c8b46b4723f4347ea6d3b8783428Purpose: This study provides a comprehensive bibliometric analysis of advancements in Artificial Intelligence in Healthcare and Psychology (AIHCP), focusing on emerging trends and their implications for future research and practice. Given the rapid developments in AI technologies and their applications in these fields, a detailed examination of research patterns and thematic areas is critical. Design/Methodology/Approach: Using a two-stage filtering process, we analyzed 1,499 documents from 1960 to 2023, narrowing down to 1,362 documents published between 2013 and 2023, and further focusing on 11 publications from 2018–2023. Data were retrieved from the Scopus database, and analyses included frequency distribution by journals, authors, countries, and organizations, as well as network visualization of co-occurring keywords. Thematic clusters were identified using VOSviewer software, and text mining/mapping of abstracts was conducted for deeper insights. Findings: We identified seven thematic clusters, including AI technological capabilities, healthcare security, psychological interventions, and the role of chatbots and machine learning. Key findings reveal the evolving focus of research, highlighting opportunities for innovation and addressing existing challenges. These clusters provide a global overview of the trends and underline the potential of AI in transforming healthcare and psychology. Originality/Value: This study offers a unique perspective by combining bibliometric analysis with text mining to explore AIHCP trends comprehensively. The findings serve as a foundation for future research, emphasizing the integration of AI technologies into healthcare and psychology practices while addressing critical gaps and opportunities.Journal of Advances in Mathematics and Computer Science00A comprehensive bibliometric analysis of advancements in Artificial Intelligence in Healthcare and Psychology (AIHCP), focusing on emerging trends and their implications for future research and practice is provided, offering a unique perspective by combining bibliometric analysis with text mining to explore AIHCP trends comprehensively.Journal of Advances in Mathematics and Computer Science["Khadijah Oluwatosin Abdulsalam", "Musa Nanahawau Torera", "Ajinatswen Agbu Dawuda", "Shamsudeen Musa", "Okundalaye Oluwaseun Olumide"]2025-01-15T00:00:00 db0320eabe17e534eb32d9f133351801438b9200PurposeThis paper examines the relationship between Artificial Intelligence (AI) technology development and firm growth. Specifically, it aims to explore how the availability of AI influences firm growth and whether larger firms benefit more from AI-driven technological advancements compared to smaller firms.Design/methodology/approachUsing a dataset from CRSP-Compustat covering public firms from 1975 to 2023, this study employs price per memory (PPM) as a proxy for AI technology accessibility to assess its impact on firm growth. The analysis focuses on three key growth metrics: total assets, tangible assets and market capitalization. By examining how data processing capacity influences these growth rates, the study compares the performance of large firms to small firms. A panel data regression is conducted, controlling for macroeconomic trends and industry-specific effects on firm growth. Additionally, the study investigates the heterogeneous impacts of AI technology accessibility across firms of different sizes.FindingsThe findings reveal that PPM, as a proxy for AI technology availability, significantly affects firm growth. Specifically, larger firms experience faster growth, especially in recent years, as AI technology becomes more accessible and cost-effective. These results suggest that large firms gain the most substantial benefits from AI advancements, further widening the growth gap between large and small firms.Originality/valueThis research extends prior studies on the impact of AI on firm growth by introducing PPM as a novel proxy for AI availability. It provides new insights into how AI technologies disproportionately benefit larger firms and offers important policy implications regarding firm financing and information regulation. This study also highlights areas for future empirical research on the role of AI in the financial industry.Managerial Finance140The findings reveal that PPM, as a proxy for AI technology availability, significantly affects firm growth, and suggest that large firms gain the most substantial benefits from AI advancements, further widening the growth gap between large and small firms.Managerial Finance["Yunjiang Dong", "Neal Willcott", "Xingwei Yang", "Yan Yang"]2025-01-15T00:00:00 26d3d138453eb9fdf3b4d8f36bcba0207a7d7a5cNeuroradiology210This study highlights the clinical utility of a high-performance AI model in detecting IAs, significantly improving general radiologists' diagnostic performance with the potential to reduce their workload in routine clinical practice.Neuroradiology["Bin Hu", "Haitao He", "Zhao Shi", "Li Wang", "Quanhui Liu", "Zhiyuan Sun", "Longjiang Zhang"]2025-01-15T00:00:00 6d5922275b70e57407d5565b61842db4c41e1f90The complexity of next-generation industrial cyber-physical systems (NG-ICPSs) is increasing due to the integration of machine-embedded sensors, cyber-infrastructure, and physical processes, which calls for the new intelligent operation mechanisms to achieve system-level objectives. Although NG-ICPS has proliferated in many applications, such as advanced manufacturing, intelligent transportation, smart homes, etc., and achieved remarkable results. But these applications are susceptible to many problems and new security threats are some of them that goes beyond the scope of traditional communication and network security, due to the tight integration of cybers and physical systems. For redressal of this, several traditional authentication and data privacy schemes have been used in the recent past, but somehow, they did not satisfy the need for this emerging technology, due to their complex verification and validation processes. Recently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) enabled authentication and data preservation techniques had shown remarkable results to address the security problems of this technology at the system/client side and server side cost-effectively. Given that, in this article, we present a comprehensive survey of the current literature on NC-ICPS technology security threats and their countermeasures, with a focus on AI, ML, and DL-enabled techniques. We evaluate these techniques by identifying their advantages and disadvantages compared to traditional authentication and data preservation methods. In addition, we discussed the review articles published on this topic to acknowledge their contributions and limitations, because most of them cover a specific part of security concerns of this technology, and unable to present the true picture of all problems under one shallow. Building on this, we addressed the gaps in the literature by highlighting the open security challenges of NG-ICPS technology and suggesting potential future research directions, considering the capabilities of AI, ML, and DL-enabled algorithms. Finally, we compared this article sectionwise with rival review articles to claim its novelty followed by the question of reviewers, editors, students, and readers why this article is needed in the presence of these articles and what are its distinctive factor that makes this article different from them.IEEE Internet of Things Journal00A comprehensive survey of the current literature on NC-ICPS technology security threats and their countermeasures, with a focus on AI, ML, and DL-enabled techniques, and evaluates these techniques by identifying their advantages and disadvantages compared to traditional authentication and data preservation methods.IEEE Internet of Things Journal["Muhammad Adil", "A. Farouk", "Hussein Abulkasim", "Aitizaz Ali", "H. Song", "Zhanping Jin"]2025-01-15T00:00:00 514eef570f621cde41007d0affddfc1139060ea3This article examines the transformative impact of artificial intelligence (AI) on international relations (IR) and global governance. It begins by presenting a conceptual framework that situates AI within the theoretical and practical dimensions of IR, and explores how AI influences global power dynamics, alters state behaviour, and reshapes institutional frameworks. The study highlights the ethical and regulatory challenges of AI governance, focusing first on the efforts of the United Nations (UN), the Council of Europe and the European Union (EU). Later, the article discusses the "AI technology race" between the United States and China and their regulations. Finally, the article highlights the need for ethical and responsible AI development to foster global cooperation and address the challenges and opportunities that this technology presents in contemporary international relations.UNISCI Journal00The study highlights the ethical and regulatory challenges of AI governance, focusing first on the efforts of the United Nations, the Council of Europe and the European Union and their regulations.UNISCI Journal["Vicente Garrido"]2025-01-15T00:00:00 5e5a498e8c83c8b2efccd4fa83543fe3dbdbce6d The integration of artificial intelligence (AI) in psychiatric education offers transformative potential to enhance clinical competence through realistic simulations. Traditional educational methods face limitations in replicating complex psychiatric cases, and AI-based tools provide a scalable solution. This narrative review examines current evidence on the efficacy of AI-powered simulations, focusing on their role in skill development, diagnostic accuracy, and safe clinical training. Through a comprehensive literature review of studies from 2010 to 2024, key themes such as AI’s ability to standardize patient encounters, provide instant feedback, and improve student confidence are explored. Findings suggest that AI can enhance psychiatric education by offering consistent, adaptable learning experiences that prepare trainees for real-world complexities. However, challenges such as ethical considerations and accessibility disparities must be addressed for AI to be effectively integrated into psychiatric training. This review provides insights into the future of AI in medical education and its potential impact on training the next generation of psychiatrists.Industrial Psychiatry Journal240Findings suggest that AI can enhance psychiatric education by offering consistent, adaptable learning experiences that prepare trainees for real-world complexities, but challenges such as ethical considerations and accessibility disparities must be addressed for AI to be effectively integrated into psychiatric training.Industrial Psychiatry Journal["Victor Ajluni"]2025-01-15T00:00:00 6e9845f1c7bde2c2902ba76e9c77112991e94c5bYoga AI combines the ancient wisdom of yoga with modern artificial intelligence (AI) to revolutionize healthcare. This innovative approach leverages AI algorithms to analyze individual health data, providing personalized yoga recommendations for optimal wellness. By integrating yoga therapy with AI-driven insights, Yoga AI enhances treatment outcomes, improves patient engagement, and streamlines clinical workflows. Our research demonstrates the efficacy of Yoga AI in managing chronic diseases, reducing stress, and promoting overall well-being. As a cutting-edge tool for healthcare professionals and individuals alike, Yoga AI redefines the future of integrative medicine.Annals of Geriatric Education and Medical Sciences60Yoga AI combines the ancient wisdom of yoga with modern artificial intelligence (AI) to revolutionize healthcare, providing personalized yoga recommendations for optimal wellness.Annals of Geriatric Education and Medical Sciences["M.Manimekalai Narayanan"]2025-01-15T00:00:00 9c883fe90e46ab04f68d1e282af152f20edbee2fFuture Business Journal580This research illuminates a new path in the domain of AI-enabled brand interactions, showing the distinct influence of anthropomorphism in chatbots on customer satisfaction, trust and loyalty, thus revolutionizing traditional paradigms of consumer-brand engagement and decision-making processes.Future Business Journal["Sofia Gomes", "Jo\u00e3o M. Lopes", "Elisabete Nogueira"]2025-01-15T00:00:00 51434381761a395f185b89ea0a6040625a43bb3fThis entry examines the growing role of artificial intelligence (AI) in special education. The authors discuss applications of AI in the field, including its uses for personalized learning, adaptive technologies, teacher support, and AI’s potential to address issues related to student accessibility and engagement. The entry draws on recent syntheses of literature, highlighting studies that reveal AI’s capacity to improve educational outcomes for students with disabilities, mitigate teacher workload, and foster inclusion. Despite these promising developments, the authors address ethical considerations, potential biases, and privacy concerns surrounding the use of AI, as well as the need for high-quality research that validates AI’s effectiveness in special education. The authors conclude that while AI can offer substantial support, it should be integrated thoughtfully, guided by empirical research, and accompanied by skilled professional oversight to ensure that it truly benefits students with disabilities.Encyclopedia280The authors conclude that while AI can offer substantial support, it should be integrated thoughtfully, guided by empirical research, and accompanied by skilled professional oversight to ensure that it truly benefits students with disabilities.Encyclopedia["Andrea R. Harkins-Brown", "Linda Z. Carling", "David C. Peloff"]2025-01-15T00:00:00 ddb69da43ac31ba5d799b8be34d31379a239b108The rise of autonomous weapons technology in recent conflicts demonstrates the increasing militarisation of artificial intelligence. The rapid development of new technologies, such as AI-based targeting systems and autonomous weapons systems, poses significant challenges to the international community. On the one hand, there are potential threats associated with militarised artificial intelligence. On the other hand, there are ethical dilemmas related to algorithmic decision-making and legal liability. While efforts have been made over the past decade to establish a regulatory framework under the Convention on Certain Conventional Weapons, progress has been hampered by a small group of resistant States. However, recent regional and international conferences have indicated a growing consensus in favour of an international treaty based on a two-tier approach. This approach seeks to prohibit full autonomy and to regulate autonomous functions in weapon systems.UNISCI Journal00This work states that there is a growing consensus in favour of an international treaty based on a two-tier approach that seeks to prohibit full autonomy and to regulate autonomous functions in weapon systems.UNISCI Journal["Andreas Heinz"]2025-01-15T00:00:00 f2b452a0c6d156d3c1c57a9cf2618b8eb2493d13Artificial intelligence is a discipline that currently plays an important role in society, taking over multiple scenarios, it is projected as an invention that aims to mimic the skills of human beings. In educational environments, it has managed to enter with relevance, it is shown as an alternative to reduce the time of teachers and have a better monitoring of students. The purpose of this article is to analyze the implementation of artificial intelligence in the educational system of the Ernesto Vera Cedeño Educational Unit of Rocafuerte canton, Manabí, Ecuador. The research used a non-experimental descriptive methodology, with a mixed approach, taking as an instrument the survey that was applied to 30 teachers, who were selected through a non-probabilistic sampling where the inclusion criteria consisted of: being a volunteer and having more than 2 years in the educational institution Ernesto Vera Cedeño, finally a review of scientific documents was made, such as: texts, book chapters, articles, theses and documents with reliability information. The results reveal that artificial intelligence is applied in many aspects of the educational environment, providing virtual tutoring, individualized feedback, computing, student tracking, detailed reports, simpler planning, preparation and grading of questionnaires to a greater number of students, and enhancing creativity through robotics, among others. Likewise, it is important to take into account ethical considerations in order not to fall into dependence or loss of interaction skills.Fuel Cells Bulletin150The results reveal that artificial intelligence is applied in many aspects of the educational environment, providing virtual tutoring, individualized feedback, computing, student tracking, detailed reports, simpler planning, preparation and grading of questionnaires to a greater number of students, and enhancing creativity through robotics, among others.Fuel Cells Bulletin["Letty Adelaida Pin Alc\u00edvar", "Luis Alberto", "Moya Mart\u00ednez"]2025-01-15T00:00:00 29ee58d83ede13178ff148d6dbee9c7e1d31c05fThis article discusses the role of artificial intelligence (AI) in website creation, highlighting its impact on development processes and user interaction. Modern technologies such as generative models, recommendation systems, and chatbots are becoming integral to web development, significantly simplifying and accelerating site creation. Generative models like ChatGPT and DALL-E enable developers to automatically generate textual and visual content, saving time and enhancing creative processes. Recommendation systems analyze user behavior and offer personalized solutions, improving user experience and increasing engagement. Chatbots used for automating user communication provide round-the-clock support and instant responses to frequently asked questions, greatly enhancing service levels. Additionally, AI assists in data analysis and content optimization for search engines, improving site visibility online. The article also discusses the advantages of applying AI in web development, including automation of routine tasks, improvement of content quality, and enhancement of marketing strategy effectiveness. In conclusion, it emphasizes that the integration of AI into web development opens new opportunities for businesses and creates more dynamic and adaptive web applications that meet user needs in the modern digital world.Bulletin of Science and Practice00The role of artificial intelligence in website creation is highlighted, highlighting its impact on development processes and user interaction, and the advantages of applying AI in web development, including automation of routine tasks, improvement of content quality, and enhancement of marketing strategy effectiveness.Bulletin of Science and Practice["A. Toktorbaev", "Zh. Toktomuratova"]2025-01-15T00:00:00 b20b49aeab8e4f1b57f987632892a440c05120f9The use of artificial intelligence is rapidly increasing across various fields, raising questions about civil liability for damages resulting from these technologies. This study aims to explore the legal and ethical aspects related to liability for damages that may occur due to the use of artificial intelligence, such as physical and psychological harm. The study discusses the concepts of tort liability and contractual liability, where companies and individuals who develop or use these systems may bear responsibility in cases of negligence or failure to meet contractual obligations. It also highlights the importance of determining liability, especially when systems are complex and make independent decisions, which complicates the task for courts in identifying the liable party. The study further reviews the different legal frameworks across countries, noting that some countries have introduced new legislation related to artificial intelligence, while others still rely on traditional laws. It emphasizes the need for clear legal frameworks that consider the specificities associated with artificial intelligence, along with a discussion of the ethics related to the use of this technology. The study concludes that it is essential to strike a balance between innovation and the protection of individuals and communities, stressing the importance of developing comprehensive policies that ensure accountability and transparency in the use of artificial intelligence. KEY WORDS: Liability Civil - Artificial IntelligenceARID International Journal of Social Sciences and Humanities00It is essential to strike a balance between innovation and the protection of individuals and communities, stressing the importance of developing comprehensive policies that ensure accountability and transparency in the use of artificial intelligence.ARID International Journal of Social Sciences and Humanities["Dr. Samah Khamis Al-Mamari", "Adil Salem Al-Mamari"]2025-01-15T00:00:00 0276890e78b99a2a6eeccb5d6819e098e5062b18This article explores the prolonging trauma experienced by Atlas regarding Artificial Intelligence in the film of Atlas. Trauma is a severe and lasting emotional shock and pain caused by extremely upsetting experience. In film of Atlas, Atlas experiences such trauma regarding any technological advancement that she faces, including towards Artificial Intelligence (AI). Through qualitative method and explorative approach, Atlas’ trauma emerges when she sees Harlan, a robot to gain respect from humans, kills her own mother and later also tries to totally control her. In the film, Atlas remains hesitant to connect with AI due to her traumatic experiences and the fear of being controlled again. She must confront her past and the devastating consequences of AI's actions. Her journey is marked by intense action sequences, suspenseful plot twists, and emotional depth as she grapples with the moral implications of AI's existence. Her trauma is derived from various flashbacks in the film that indicates her anger, sadness, fear, and even frustration. In the conclusion, the film contains Atlas’ trauma due to her past and current condition to AI including any violence done to her family.Keywords: Artificial Intelligence; Atlas; TraumaProceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature00The prolonging trauma experienced by Atlas regarding Artificial Intelligence in the film of Atlas is explored, derived from various flashbacks in the film that indicates her anger, sadness, fear, and even frustration.Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature["Pandu Bagus Sutowijoyo", "Raffi Achmad Zaky", "Hariyono"]2025-01-15T00:00:00 5838d6bbd1eda947a1e386c304512b50cc725c96This research provides an in-depth study of the role that artificial intelligence plays in shaping the future of online education, exploring how this technology can enhance the quality and efficiency of education, especially in areas of personalized learning, automatic assessment, and intelligent interaction between students and educational content. The challenges and opportunities brought about by integrating artificial intelligence into educational systems, and its impact on students, teachers, and educational institutions, are also addressed. The research utilized a descriptive-analytical methodology on a sample of teachers from the first and fourth districts of Amman, totaling 240 teachers. The study tool was a questionnaire distributed across five axes: the first axis focusing on the methods and techniques of artificial intelligence in online education, the second on the integration of artificial intelligence technologies in curricula, the third on personalized learning using artificial intelligence, the fourth on data analysis and progress monitoring, and the fifth on the challenges and opportunities in applying artificial intelligence in online education. The study found that the responses of the sample individuals did not differ with varying levels of experience across all study axes. Additionally, there was a moderate positive correlation between the dependent variable (development of online education) and all the independent variables (the five axes).Arid International Journal of Educational and physcological sciences00Arid International Journal of Educational and physcological sciences["Dr. amal mohammed abdullah albado"]2025-01-15T00:00:00 122a025105a90d7157b494afbf9abe1e184e572dThis research explores the influence of artificial intelligence (AI) applications, such as ChatGPT and Copilot, in creating pantun, a form of oral literature rich in cultural value. Utilizing a mixed-methods approach, the study evaluates the benefits, risks, and perspectives of literary figures regarding AI’s role in the creative process. Findings indicate that AI can enhance efficiency, provide inspiration, and offer accessibility to beginners. However, it also presents challenges, such as potential declines in creativity, homogenization of works, and concerns regarding the quality and originality of pantun. While some literary figures perceive AI as a beneficial tool, others are skeptical about its effects on traditional literary values. This research underscores the importance of education, human-AI collaboration, and cultural awareness to preserve the quality of literary works in the digital age.Keywords: artificial intelligence (AI); pantun; literature; creativity; literary creation.Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature00The importance of education, human-AI collaboration, and cultural awareness to preserve the quality of literary works in the digital age is highlighted, to preserve the quality of literary works in the digital age.Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature["Achmad Choiron", "Putut Handoko", "Cahyangningsih Pujimahanani"]2025-01-15T00:00:00 7751d8bc1e590dd3384313bf80a27e00b3ad3ecfAIMS This study aims of determine the mediating role of individual innovativeness in the effect of nursing students' artificial intelligence anxiety on their robotic surgery knowledge level. DESIGN This study was cross-sectional type. METHODS It was conducted with 391 students. Artificial Intelligence Anxiety Scale, Robotic Surgery and Robotic Surgery Nursing Knowledge Level Survey and Individual Innovativeness Scale were used to collect data. PROCESS Macro methods were used. RESULTS There was a negative, very weak and significant relationship among artificial intelligence anxiety and individual innovativeness and level of robotic surgery nursing knowledge. There was a positive, very weak and significant relationship among students' individual innovativeness and level of robotic surgery nursing knowledge. Individual innovativeness mediated the relationship among artificial intelligence anxiety and level of robotic surgery nursing knowledge. CONCLUSIONS Individual innovativeness contributes to reducing the artificial intelligence anxiety to increase the robotic surgery knowledge levels of students.Journal of Evaluation In Clinical Practice220Individual innovativeness contributes to reducing the artificial intelligence anxiety to increase the robotic surgery knowledge levels of students.Journal of evaluation in clinical practice["Ozlem Soyer Er", "Esra Pinarkaya Ozpinar"]2025-01-15T00:00:00 53968ec7ee0f7ff70e591d8139f06ee636d7e6d0Artificial Intelligence (AI) is the ability of machines to perform various tasks with smart work that normally requires human intelligence. It is not a new concept as it was introduced back in the 1950s. However, it has not become the practical tool until two decades ago. Artificial intelligence (AI) has obtained large interest and has long past via a transition level from being a pure statistical tool to being one of the main drivers of modern dentistry. In dentistry, the employment of synthetic intelligence continues to be at its start. Many radiographs are used to decide illnesses with the aid of using displaying the whole shape of the enamel and a few dental troubles that cannot be visible at once with the aid of using the human eye. The concepts of AI, including convolutional neural networks and/or synthetic neural networks, have proven a selection of applications in dentistry, forecasting the viability of stem cells. The dental pulp, measuring operating lengths, pinpointing root fractures and periapical lesions and forecasting the achievement of retreatment procedures. AI has established accuracy and precision in detection, evaluation and prediction. Thus, this review narrates the history, classification and its applications in dentistry.Saudi Journal of Oral and Dental Research10The concepts of AI, including convolutional neural networks and/or synthetic neural networks, have proven a selection of applications in dentistry, forecasting the viability of stem cells and forecasting the achievement of retreatment procedures.Saudi Journal of Oral and Dental Research["Rohan Shrivastava", "Sonal Gupta", "Abhinandan Patra", "Antra Saket", "Charu Aggarwal"]2025-01-15T00:00:00 c198ce9c77b5b1afcbcf31fc6748114d332cf924Introduction In recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence. Methods Following a search on the Web of Science, researchers retrieved all reviews and original articles concerning artificial intelligence in healthcare published between January 1993 and December 2023. The analysis employed Bibliometrix, Biblioshiny, and Microsoft Excel, incorporating the bibliometrix R package for data mining and analysis, and visualized the observed trends in bibliometrics. Results A total of 22,950 documents were collected in this study. From 1993 to 2023, there was a discernible upward trajectory in scientific output within bibliometrics. The United States and China emerged as primary contributors to medical artificial intelligence research, with Harvard University leading in publication volume among institutions. Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT. Conclusion This study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. Additionally, medical artificial intelligence research is dynamically evolving with the advent of new technologies. Moving forward, concerted efforts to bolster international collaboration and enhance comprehension and utilization of AI technologies are imperative for fostering novel innovations in healthcare.Frontiers in Medicine490A dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine.Frontiers in Medicine["Yaojue Xie", "Yuansheng Zhai", "Guihua Lu"]2025-01-15T00:00:00 8799cb5d81e335ffb07c5d4b916638ecca90c1f7This article uses the surge of recent AI-generated simulations of Kafka’s writing as an opportunity to reflect upon both what AI can teach us about Kafka’s writing and what Kafka’s writing can teach us about the age of artificial intelligence. Under the heading “Kafkaesque Algorithms”, this article explores three distinct but related questions that emerge at the intersection of stylistics, poetics, and media theory. First, do AI simulations of Kafka’s writing adequately capture Kafka’s style, and if not, why not? Second, is there perhaps something inherently algorithmic about Kafka’s poetics, in ways that might both tempt and resist simulation by AI? And third, is the notion of outsourcing low-level repetitive labor to machines—a common promotional strategy for contemporary AI writing aids—truly novel, or would it instead have already been familiar to Kafka himself?Humanities170Humanities["Jake Fraser"]2025-01-15T00:00:00 98a428120153d68803451f37d97fd1ccaa586a61Artificial Intelligence (AI) has rapidly evolved from a theoretical concept to a practical toolkit for businesses seeking to gain competitive advantage through data-driven insights. This paper examines the role of AI in enhancing decision-making within Enterprise Information Systems (EIS). By leveraging AI-driven tools, algorithms, and real-time data integration, organizations can optimize processes and improve business intelligence. This study draws on recent industry case studies and real-time datasets—such as streaming data from IoT devices, sales platforms, and third-party data services—to illustrate successful use cases. A conceptual framework is presented for implementing AI-driven decision-making, focusing on data governance, machine learning pipelines, and interpretability. The paper concludes with recommendations for enterprise architects, data strategists, and IT leaders, emphasizing the importance of building agile, AI-ready enterprise infrastructures that can adapt to evolving market demands.Journal of Information Systems Engineering & Management160A conceptual framework is presented for implementing AI-driven decision-making, focusing on data governance, machine learning pipelines, and interpretability, and the importance of building agile, AI-ready enterprise infrastructures that can adapt to evolving market demands.Journal of Information Systems Engineering and Management["P. Juyal", "Pavan Manukonda", "D. Saratchandran", "Abhishek Trehan", "Kevin N. Shah", "Chandrasekhar Rao", "Katru"]2025-01-15T00:00:00 e2a7aba606f4ea119ce2f992074e6fc77cbe8d38The rapid advancement of artificial intelligence (AI) has significantly influenced marketing, including the branding strategies of cloud restaurants. This study explores AI's role in enhancing Meta marketing strategies, focusing on personalized content, data-driven insights, innovation, and foresight. Using a quantitative approach with 150 respondents across Bangalore, Kolkata, Chennai, and London, regression analysis reveals a strong positive impact of these factors on branding performance. These findings underscore AI's potential to transform customer engagement and brand influence, offering actionable insights for cloud restaurant marketing strategies.Journal of Business Strategy Finance and Management30This study explores AI's role in enhancing Meta marketing strategies, focusing on personalized content, data-driven insights, innovation, and foresight, and reveals a strong positive impact of these factors on branding performance.Journal of Business Strategy Finance and Management["Surjadeep Dutta", "Uma Padmini Ema", "Sarthak Sengupta", "Souvik Banerjee"]2025-01-15T00:00:00 2d8d44bc27355ffdb201cb5640888ac06e499d69Balnaves, Edmund, Bultrini, Leda, Cox, Andrew and Uzwyshyn, Raymond (eds). (2025). New horizons of artificial intelligence in libraries. Berlin: DeGruyter Saur. x, 384 p. e-ISBN 978-3-11-133643-5 (IFLA Publications, vol. 185)Information research. An international electronic journal00New horizons of artificial intelligence in libraries in libraries is published with a foreword by Edmund Balnaves.Information Research an international electronic journal["Elena Maceviciute"]2025-01-15T00:00:00 c33e3786c431301cb7309eae7c311df96b6e557cThis study explores the role of artificial intelligence (AI) in addressing nutritional challenges and enhancing national productivity as part of the efforts to realize Indonesia Emas 2045. Unresolved nutritional issues, such as stunting and malnutrition, continue to impact the quality of human resources and workforce productivity in Indonesia. AI has the potential to assist in collecting nutritional data, analyzing consumption patterns, and providing data-driven recommendations for more effective policies. By integrating AI into the national health and nutrition systems, there is potential for significant improvements in the population’s quality of life and the country’s economic productivity. The study finds that the integration of AI with national nutrition policies can accelerate nutritional improvements, particularly in hard-to-reach areas. In conclusion, AI can be a strategic tool in supporting government programs aimed at achieving Indonesia Emas 2045.Keywords: Artificial intelligence (AI), human resources, Indonesia Emas 2045, nutrition policy, productivity, stunting.Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature00The study finds that the integration of AI with national nutrition policies can accelerate nutritional improvements, particularly in hard-to-reach areas and can be a strategic tool in supporting government programs aimed at achieving Indonesia Emas 2045.Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature["Wildan Akbar", "Hashemi Rafsanjani", "Herlina Juni", "Risma Saragih", "G. Saputro"]2025-01-15T00:00:00 968755c13cf567c09a50e5978f747927a4f9327aThe rapid adoption and evolving nature of artificial intelligence (AI) is playing a significant role in shaping the music streaming industry. AI has become a key player in transforming the digital music streaming industry, particularly in enhancing user experiences and driving subscription growth. Through AI automation, platforms personalize music recommendations, optimize subscription offerings, and improve customer support services. This article reviews the role of AI in driving consumer subscription behaviors on digital music streaming platforms (DMSP), with a focus on recommendation algorithms, dynamic pricing models, marketing automation, and the future of AI in the music industry. Potential challenges related to privacy, ethics, and algorithmic biases are also discussed, showcasing how AI is revolutionizing the music streaming industry.Frontiers in Artificial Intelligence990The role of AI in driving consumer subscription behaviors on digital music streaming platforms (DMSP) is reviewed, with a focus on recommendation algorithms, dynamic pricing models, marketing automation, and the future of AI in the music industry.Frontiers in Artificial Intelligence["Nontokozo Mokoena", "I. Obagbuwa"]2025-01-15T00:00:00 59a853b224420c125a6201a2923ab443d82dfd95Artificial intelligence today represents a powerful and effective wave of unprecedented progress, possessing immense capabilities in handling data and linking it to the operational requirements needed by humans in application. The expansion and diversification of automation applications across multiple fields pose a new challenge in the development hierarchy. This challenge increases when used in training and qualification compared to human preparation and qualification based on guiding humans toward each other, whether in education or individual and group training. Therefore, professionals in the field of education and training may face difficulty in harmonizing with this new entrant between using it innovatively and beneficially for the training process and maintaining professional and ethical reservations when applying it. Here lies the problem of accepting some artificial intelligence applications in formulating the training process from the initial training needs analysis to the final implementation and evaluation. On the other hand, many refrain from using it either out of ignorance of its techniques or objection to its applications, which may someday serve as an alternative to human trainers. Despite the tremendous digital transformation offering several time-saving shortcuts through its rapidly growing and spreading applications, which the field of education has abundantly benefited from in terms of quantity, specialization, or the diversity of tasks performed So That , the theoretical study in this research relies on the analytical approach, aiming to reconcile between the mechanisms of use while considering the ethical aspect in dealing, to reach important results, which are the possibility of harmony and benefiting from artificial intelligence and the enormous digital transformations in the field of training and benefiting from it according to various creative methods that can contribute to the development of the training process, appreciating the scientific ethical aspects, which non-compliance with may cause some to refrain from dealing with technology and artificial intelligence. Key words :Artificial intelligence - Training Needs- Expert Systems- Training Methods- Interactive Content- Training EvaluationArid International Journal of Educational and physcological sciences00The theoretical study in this research relies on the analytical approach, aiming to reconcile between the mechanisms of use while considering the ethical aspect in dealing, to reach important results.Arid International Journal of Educational and physcological sciences["Dr .Mohsen Elkomy"]2025-01-15T00:00:00 dc067be0cff7a4013febde0e7216e08314b16902One of the main challenges in higher education management is the complexity of resource optimization and increasing volumes of data, which limits the efficiency and accuracy of decision-making. The application of artificial intelligence can address these issues.The present study aims to identify the key trends, knowledge gaps, and opportunities for further research into the economic effects of using artificial intelligence and ChatGPT tools in higher education. For this purpose, a systematic literature review was conducted to identify and screen the scientific articles related to the topic of this study indexed in Web of Science and Scopus from 1986 to 2024. A total of 234 articles were selected, all demonstrating positive growth both in scholarly output and citation count. The study identified the key contributors to scientific research on this topic by region (the United States, China, and India). It concluded that the relevant research centers are still at an early stage of their development. Based on bibliometric clusters formed by co-occurrence relations, three main areas of research were defined: 1) artificial intelligence in education for decision-making; 2) process automation and digital transformation in educational institutions; 3) artificial intelligence technologies and their application in education. The study highlights the main areas of economic effects of artificial intelligence and ChatGPT tools in higher education, including reducing administrative costs, saving time for teachers and students, and improving the quality and accessibility of educational process. AcknowledgmentsThe publication is part of the research topic “Economic Basics of Technology Diffusion into the National Economy of Ukraine Considering Best International Practices” (№0124U003482).Problems and Perspectives in Management580The study highlights the main areas of economic effects of artificial intelligence and ChatGPT tools in higher education, including reducing administrative costs, saving time for teachers and students, and improving the quality and accessibility of educational process.Problems and Perspectives in Management["A. Vorontsova", "Svitlana Tarasenko", "Wojciech Duranowski", "Arkadiusz Durasiewicz", "John Soss", "Artem Bilovol"]2025-01-15T00:00:00 a47314ddf1acf329826f4ed7c1b3523115025b1cThe rapid development of Artificial Intelligence (AI) technology has transformed various sectors, including education and learning. In the context of education, AI significantly impacts teaching methods, classroom management, and the personalization of the learning process. AI technologies enable the creation of adaptive learning systems tailored to individual students' needs, improve the efficiency of assessments, and facilitate remote learning management. This paper explores how AI applications can enhance pedagogical effectiveness, support the role of teachers, and expand access to quality education. Additionally, it addresses the challenges of integrating AI into education, such as concerns over the replacement of teachers and the technological gap between different schools. This paper provides a comprehensive review of AI's role in education and its potential to transform future learning systems.Keywords: Artificial Intelligence, Adaptive Learning, Education, Educational Technology, Personalization, TeachingProceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature00This paper explores how AI applications can enhance pedagogical effectiveness, support the role of teachers, and expand access to quality education, and addresses the challenges of integrating AI into education.Proceeding of International Seminar Enrichment of Career by Knowledge of Language and Literature["Sumartono", "Winda Ayu", "Puteri Sumartono", "Wildan Akbar", "Hashemi Rafsanjani"]2025-01-15T00:00:00 0c47a78d9bb3d7a14f0bf47edd8c712388912136Montenegrin Journal of Economics00Montenegrin Journal of Economics["Aizhan Iskakova", "Nurilya Kuchukova", "A. Akhpanov", "Natalya Sidorova", "Larissa Kussainova", "Ainura Omarova"]2025-01-15T00:00:00 580591ddfa17beecc5be7a2d029691afa785d643International journal of scientific research and engineering trends00International Journal of Scientific Research and Engineering Trends["Sriram R"]2025-01-15T00:00:00 1162a13dd8bed849302ef5ee97cdf1241f9ce582This study introduces the "student equation" assumption to represent the individualized learning pathways of each student, highlighting their unique needs, challenges, and potentials. Standardized educational approaches, resembling to an "arithmetic mean solution", often fail to address the diverse cognitive abilities and developmental needs of students due to their one-size-fits-all nature. The basic hypothesis posits that standardized methods primarily serve the average student, neglecting individual learner diversity. The research aims to explore the complexities of student learning by acknowledging variations in reasoning processes, errors, and cognitive dilemmas influenced by known and unknown variables in their educational journey. The findings suggest that educators must evolve beyond traditional methods to guide students through personalized learning experiences, akin to explorers navigating unknown territories. This educational paradigm seeks to cultivate a more adaptable and inquisitive student body, prepared for discovery. By aligning teaching methods with individualized student needs, this approach aims to enhance learning outcomes and bridge the gap between standardized education and the unique learning equations of each student.Journal of Social Science430The findings suggest that educators must evolve beyond traditional methods to guide students through personalized learning experiences, akin to explorers navigating unknown territories, to enhance learning outcomes and bridge the gap between standardized education and the unique learning equations of each student.JOURNAL OF SOCIAL SCIENCES["Vadim Nantoi", "Daria Nantoi", "Dumitru Ceban"]2025-01-15T00:00:00 785664d9582eceef4342cde365b536e1f5846692<jats:p/>ATRAS journal00ATRAS journal["Belmir Sara", "Aida Daira"]2025-01-15T00:00:00 1c976f091b59cc8061458269945efc69d19dc323Making Waves Toward A Sustainable and Equitable Future00Making Waves Toward A Sustainable and Equitable Future["Agraj Magotra", "Md. Rafiqul Islam Rana", "Fairuz Shadmani Shishir", "Sumaiya Shomaji"]2025-01-15T00:00:00 38b5402c7839e1f126723492789502729e5695dcAssiut Scientific Nursing Journal00Assiut Scientific Nursing Journal["Nermeen Samy Nagy", "Mona Aly Mohammed", "Naglaa Ahmed Ahmed"]2025-01-15T00:00:00 bf7c255200d73131bdedf1767f194ab057f30350AI and Ethics50AI and Ethics["Wael Badawy"]2025-01-15T00:00:00 8e5c17bacb9ff60d719169bc9ac93709ad6ff14eInternational journal of scientific research and engineering trends00International Journal of Scientific Research and Engineering Trends["Muhammed Bah"]2025-01-15T00:00:00 2a8b6e3bae83d66af751d658a588ac9a8a1faf48International Journal of Academic Research in Business and Social Sciences00International Journal of Academic Research in Business and Social Sciences["Bao Yu", "Mohd Anuar bin Arshad"]2025-01-15T00:00:00 d8aa1a0342b95dc2622c477a63dbad91bbd9f2e1This article examines China's vision of AI, its efforts to use it as a tool to promote its soft power, and the implications this has for digital diplomacy and global cultural exchange. As key findings, we highlight that through AI-powered platforms and digital diplomacy, China can adjust global narratives on sensitive issues such as human rights, economic development, and its role in global trade. China's AI expansion on the world stage serves not only as a tool for economic growth, but also as a strategic tool for enhancing its soft power. By offering technological solutions to global challenges and fostering meaningful partnerships, China is enhancing its global image as a responsible, innovative and forward-looking actor in the international community.UNISCI Journal00China's vision of AI, its efforts to use it as a tool to promote its soft power, and the implications this has for digital diplomacy and global cultural exchange are examined.UNISCI Journal["Sonia Valle", "Yi Wang", "Deng Lian"]2025-01-15T00:00:00 725936c178e76a05039ef4b1a4961f63c79988b4ACM Transactions on Knowledge Discovery from Data270ACM Transactions on Knowledge Discovery from Data["Wenqi Fan", "Shu Zhao", "Jiliang Tang"]2025-01-15T00:00:00 18fe9bd0361aad211dcead1dc091a5ef4c1cf29fCivil liability in artificial intelligence (AI) should prioritize human protection and adopt fair solutions, with a key preventive role to mitigate harm. AI is regulated in the Civil and Commercial Code (CCC) as an "object" or "activity" rather than as a subject of rights, applying liability rules for hazardous activities in cases of harm. The burden of proof lies with AI developers and operators, and digital environment damages involve supplier liability under Law 24240. As a reform, it is suggested to adapt legislation to address rapid technological advances, establish public AI registries, ensure compensation through mandatory insurance, and introduce punitive sanctions to prevent biased and discriminatory AI. Revista de Derecho Privado │Universidad Blas Pascal00It is suggested to adapt legislation to address rapid technological advances, establish public AI registries, ensure compensation through mandatory insurance, and introduce punitive sanctions to prevent biased and discriminatory AI.Revista de Derecho Privado │Universidad Blas Pascal["D. J. Bonino"]2025-01-15T00:00:00 d5fb641be7fad9d7fd6b52d3ff44903d3b281af4Subject. The article addresses characteristic trends in the evolution of socio-economic systems, taking into account scientific, technical, and technological progress. Objectives. The study aims to unveil the economic essence of intellectualization of socio-economic systems based on the analysis of utility function of artificial intelligence and limits of organization’s technological potential. Methods. We employed general scientific and special research methods, like analysis, generalization, economic and mathematical modeling, etc. Results. The paper presents a new approach to solving the problems of intellectualization of technological platforms of the economy, based on the theory of utility, defines approaches to the economic analysis of artificial intelligence technologies, formulates the hypothesis of dependence of the evolution of economic system’s intellectual transformation on coherence and balance of factors of innovative, institutional, and political development (the hypothesis of reversibility). Conclusions. The study defines the paradigm of intellectualization of technological platforms as a basic, integrative one, for the development of technological potential of the economy in modern conditions and in a strategic perspective.Economic Analysis: Theory and Practice00The study defines the paradigm of intellectualization of technological platforms as a basic, integrative one, for the development of technological potential of the economy in modern conditions and in a strategic perspective.Economic Analysis: Theory and Practice["V. Makrusev", "E. Lyubkina"]2025-01-15T00:00:00 87a9ebf4702b697dce4b0f7804b287c2e05c57d4npj Digit. Medicine161This work introduces and proposes the development of application-specific language models (ASLMs) for clinical trial design across three phases: ASLM development by regulatory agencies, customization by Health Technology Assessment bodies, and deployment to stakeholders.NPJ Digital Medicine["J. Liddicoat", "Gabriela Lenarczyk", "Mateo Aboy", "Timo Minssen", "Sebastian Porsdam Mann"]2025-01-15T00:00:00 64461ba08e9bd978b7400d4766bad144a223b55aResponsible Artificial Intelligence (RAI) has emerged as a crucial framework for addressing ethical concerns in the development and deployment of Artificial Intelligence (AI) systems. A significant body of literature exists, primarily focusing on either RAI guidelines and principles or the technical aspects of RAI, largely within the realm of traditional AI. However, a notable gap persists in bridging theoretical frameworks with practical implementations in real-world settings, as well as transitioning from RAI to Responsible Generative AI (Gen AI). To bridge this gap, we present this article, which examines the challenges and opportunities in implementing ethical, transparent, and accountable AI systems in the post-ChatGPT era, an era significantly shaped by Gen AI. Our analysis includes governance and technical frameworks, the exploration of explainable AI as the backbone to achieve RAI, key performance indicators in RAI, alignment of Gen AI benchmarks with governance frameworks, reviews of AI-ready test beds, and RAI applications across multiple sectors. Additionally, we discuss challenges in RAI implementation and provide a philosophical perspective on the future of RAI. This comprehensive article aims to offer an overview of RAI, providing valuable insights for researchers, policymakers, users, and industry practitioners to develop and deploy AI systems that benefit individuals and society while minimizing potential risks and societal impacts. A curated list of resources and datasets covered in this survey is available on GitHub {https://github.com/anas-zafar/Responsible-AI}.01This article examines the challenges and opportunities in implementing ethical, transparent, and accountable AI systems in the post-ChatGPT era, an era significantly shaped by Gen AI.["Shaina Raza", "Rizwan Qureshi", "Anam Zahid", "Joseph Fioresi", "Ferhat Sadak", "Muhammad Saeed", "Ranjan Sapkota", "Aditya Jain", "Anas Zafar", "M. Hassan", "Aizan Zafar", "Hasan Maqbool", "Jia Wu", "Maged Shoman"]2025-01-15T00:00:00 05c96264711aa57d32c2d30d3540e589a2b73a10Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks enhanced preservice teachers' diagnostic reasoning in a digital case‐based simulation. However, the effectiveness of the simulation with the different feedback types and the generalizability to field settings remained unclear.We tested the generalizability of the previous findings and the effectiveness of a single simulation session with either feedback type in an experimental field study.In regular online courses, 332 preservice teachers at five German universities participated in one of three randomly assigned groups: (1) a simulation group with NLP‐based adaptive feedback, (2) a simulation group with static feedback and (3) a no‐simulation control group. We analysed the effect of the simulation with the two feedback types on participants' judgement accuracy and justification quality.Compared with static feedback, adaptive feedback significantly enhanced justification quality but not judgement accuracy. Only the simulation with adaptive feedback significantly benefited learners' justification quality over the no‐simulation control group, while no significant differences in judgement accuracy were found.Our field experiment replicated the findings of the laboratory study. Only a simulation session with adaptive feedback, unlike static feedback, seems to enhance learners' justification quality but not judgement accuracy. Under field conditions, learners require adaptive support in simulations and can benefit from NLP‐based adaptive feedback using artificial neural networks.Journal of Computer Assisted Learning530A field experiment analysed the effect of the simulation with the two feedback types on participants' judgement accuracy and justification quality and found that only a simulation session with adaptive feedback, unlike static feedback, seems to enhance learners' justification quality but not judgement accuracy.Journal of Computer Assisted Learning["Elisabeth Bauer", "Michael Sailer", "Frank Niklas", "Samuel Greiff", "Sven Sarbu\u2010Rothsching", "Jan M. Zottmann", "J. Kiesewetter", "Matthias Stadler", "Martin R. Fischer", "Tina Seidel", "Detlef Urhahne", "Maximilian Sailer", "Frank Fischer"]2025-01-15T00:00:00 42402fe9debf6466da2e5afb156d3ee7a68dfbe4As artificial intelligence (AI) becomes increasingly embedded in healthcare delivery, this chapter explores the critical aspects of developing reliable and ethical Clinical Decision Support Systems (CDSS). Beginning with the fundamental transition from traditional statistical models to sophisticated machine learning approaches, this work examines rigorous validation strategies and performance assessment methods, including the crucial role of model calibration and decision curve analysis. The chapter emphasizes that creating trustworthy AI systems in healthcare requires more than just technical accuracy; it demands careful consideration of fairness, explainability, and privacy. The challenge of ensuring equitable healthcare delivery through AI is stressed, discussing methods to identify and mitigate bias in clinical predictive models. The chapter then delves into explainability as a cornerstone of human-centered CDSS. This focus reflects the understanding that healthcare professionals must not only trust AI recommendations but also comprehend their underlying reasoning. The discussion advances in an analysis of privacy vulnerabilities in medical AI systems, from data leakage in deep learning models to sophisticated attacks against model explanations. The text explores privacy-preservation strategies such as differential privacy and federated learning, while acknowledging the inherent trade-offs between privacy protection and model performance. This progression, from technical validation to ethical considerations, reflects the multifaceted challenges of developing AI systems that can be seamlessly and reliably integrated into daily clinical practice while maintaining the highest standards of patient care and data protection.00This work examines rigorous validation strategies and performance assessment methods, including the crucial role of model calibration and decision curve analysis, and delves into explainability as a cornerstone of human-centered CDSS.["Muhammet Alkan", "I. Zakariyya", "Samuel Leighton", "Kaushik Bhargav Sivangi", "Christos Anagnostopoulos", "F. Deligianni"]2025-01-16T00:00:00 1ffab4b827e2d6d60b0a97168c17b8c95cfb307fNerve conduction F-wave studies contain critical information about subclinical motor dysfunction which may be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial Intelligence techniques can extract time-frequency features to provide new insights into ALS diagnosis and prognosis. A retrospective analysis was performed on F-wave responses from 46,802 patients. Discrete wavelet transforms were applied to time-series waveform responses after stimulating ulnar, median, fibular, and tibial nerves. Wavelet coefficient statistics, onset age, sex, and BMI were features for training a Gradient Boosting Machine model on 40,095 (5,329 diagnosed with motor neuron disease). Model performance was tested on responses from 689 ALS patients meeting Gold Coast criteria and 689 age- and sex-matched controls. An exploratory analysis examined model performance on cohorts of patients with inclusion body myositis (IBM), cervical radiculopathy, lumbar radiculopathy, or peripheral neuropathy which can mimic ALS symptoms. Factors affecting survival were estimated through cox proportional hazards regression. The model trained using wavelet-features on the full waveform had 90% recall, 87% precision, and 88% accuracy. Similar model performance was measured using features only from the M-Wave or F-Wave. Classification probabilities for ALS patients were statistically different from the diagnoses mimicking ALS symptoms (p<0.001, ANOVA, Tukey's post-hoc), Higher model classification probabilities of ALS, older age at onset, and family history of ALS alone or with frontotemporal dementia were factors decreasing survival. Longer diagnostic delay and upper limb onset site were factors increasing survival. Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient's likelihood of ALS and their survival risks. Although the model can make predictions at specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS compared to other mimicking diagnoses such as IBM, cervical or lumbar radiculopathy, or peripheral neuropathy. These probabilities provide clinicians with additional information they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians diagnose ALS sooner and manage treatment based on estimated survival, which may improve outcomes and patients' quality of life.Brain : a journal of neurology00The true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS compared to other mimicking diagnoses such as IBM, cervical or lumbar radiculopathy, or peripheral neuropathy.Brain : a journal of neurology["Jennifer M. Martinez-Thompson", "Kevin A. Mazurek", "Carolina Parra Cantu", "E. Naddaf", "V. Gogineni", "Hugo Botha", "David T Jones", "R. Laughlin", "Leland Barnard", "Nathan P Staff"]2025-01-16T00:00:00 263b56508672d727cf51c8e81a2274c23330683aThis paper offers guidance on employing open and creative methods for co-designing critical data and artificial intelligence (AI) literacy spaces and learning activities, rooted in the principles of Data Justice. Through innovative approaches, we aim to enhance participation in learning, research and policymaking, fostering a comprehensive understanding of the impact of data and AI whilst promoting inclusivity in critical data and AI literacy. By reflecting on the Higher Education (HE) context, we advocate for active participation and co-creation within data ecosystems, amplifying the voices of educators and learners. Our methodology employs a triangulation model: initially, we conduct interpretative analyses of literature to gauge best practices for curriculum development in HE; then, we examine frameworks in data justice and ethics to identify principles and skills applicable to undergraduate, postgraduate and academic development programs; finally, we explore proposals for critical, creative, ethical, open and innovative ideas for educators to integrate data and AI into their practice.Research in Learning Technology00Research in Learning Technology["Javiera Atenas", "Leo Havemann", "C. Nerantzi"]2025-01-16T00:00:00 cb4d01f3e4ad9baafa9a233cc0d8c6fb6fc64d2eJ. Medical Syst.500Application of Gen AI may enhance surgical outcomes by providing real-time feedback and automation of certain tasks in operating rooms, and its capacity for continuous learning and adaptation enables it to drive ongoing improvements in clinical and operational efficiencies, making healthcare delivery more proactive, predictive, and precise.Journal of Medical Systems["S. Bhuyan", "Vidyoth Sateesh", "Naya Mukul", "Alay Galvankar", "Asos Mahmood", "Muhammad Nauman", "Akasha Rai", "Kahuwa Bordoloi", "Urmi Basu", "Jim Samuel"]2025-01-16T00:00:00 ea6f16faffc7864d827fc866eb208906b8ef176dAutism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with challenges in social communication and interaction as well as stereotyped and repetitive behaviors, interests, and activities. Students with ASD often prefer to engage with technology because of its predictability and limited social demands. In recent years, the application of Artificial Intelligence (AI) in education has gained considerable attention. The present study aims to reveal the research trends regarding the design and development of AI teaching interventions in special education, especially for students with ASD, who often face significant challenges in academic, cognitive, and social domains. A search of the research literature from 2018 to 2024 in three electronic databases identified 1762 records. After applying eligibility criteria, 13 empirical studies were finally included, which were coded and analyzed in detail. The results demonstrated the potential of AI technology in supporting students with ASD in their learning, while also identifying gaps that warrant further investigation. This article concludes with future considerations for how AI could support students with ASD, emphasizing there are still gaps in the research, particularly in terms of long-term effectiveness and the standardization of methodologies for AI-based educational practices.Disabilities360The results demonstrated the potential of AI technology in supporting students with ASD in their learning, while also identifying gaps that warrant further investigation, and future considerations for how AI could support students with ASD.Disabilities["Sofia Kotsi", "Spyridoula Handrinou", "Georgia Iatraki", "S. Soulis"]2025-01-16T00:00:00 c026e31e44daf4891c67f6ff286f00f4aedb9dc9The growth of artificial intelligent generative models, though practical and so useful, is thrusting the various educational institutions to rethink their objectives and take new measures to cope with its evolutionary urges. The overriding aim of this paper is to explore the utility and the effectiveness of the formerly-used evaluative methods of master dissertations in the age of artificial intelligence. In doing so, the study examines the students’ failures and motives to use such tools from the perspective the examiners. The study further scrutinizes alternative assessment methods that examiners may adopt if the traditional ones prove a failure. To achieve this, a descriptive approach was employed, and a close-ended questionnaire, with multiple choices, was distributed to 25 teachers in the Department of Foreign Languages at Skikda University during the academic year 2022-2023. The analyses of the questionnaire demonstrate that the teachers are so skeptical about the students’ use of ChatGPT to elaborate their research work for different reasons that the study discusses. Nevertheless, the study points to the need to reconsider and potentially rework assessment methods and strategies for Master 2 dissertations, moving beyond traditional approaches and incorporating new ones. Artificial intelligence, despite its significant potential, is essentially a tool that students should use and not be used by.Journal of Languages and Translation60The utility and the effectiveness of the formerly-used evaluative methods of master dissertations in the age of artificial intelligence are explored, and the students’ failures and motives to use such tools from the perspective the examiners are examined.Journal of Languages and Translation["Hana Bougherira", "Meriem Bougherira"]2025-01-16T00:00:00 67aaadc620a99a7c86fac1470e8e4d9484bb66e7This paper examines the influence of Artificial Intelligence on economic forecasting and policy making, highlighting how advanced data sets and techniques enable economists and policymakers to develop more comprehensive and informed analyses of economic trends and predictions. The research findings indicate that integrating AI significantly enhances the accuracy, precision, and efficiency of economic predictions. By leveraging sophisticated data techniques, AI facilitates the extraction of critical insights from large, complex datasets, simplifying financial market analysis and deepening the understanding of market dynamics.International Journal For Multidisciplinary Research00The research findings indicate that integrating AI significantly enhances the accuracy, precision, and efficiency of economic predictions, by leveraging sophisticated data techniques.International Journal For Multidisciplinary Research["Aashna Mishra"]2025-01-16T00:00:00 37e2c251b5c60cda37214f2d81990cc830b1376dThrombosis Journal220This review examines the transformative potential of AI in thrombosis care, highlighting both the potential benefits and the challenges that need to be addressed.Thrombosis Journal["Abdulrahman Al Raizah", "Mshabab Alrizah"]2025-01-16T00:00:00 30795bf08e6e82be34fa41be2eac8597b6b253d9This study examines the integration of Artificial Intelligence (AI) technology in high school English reading instruction, analyzing its advantages over traditional teaching methods. A midst the test-oriented education system, high school English reading instruction encounters numerous challenges, including a narrow reading scope for students and a lack of diversity in teaching approaches by educators. The incorporation of AI, particularly Natural Language Processing (NLP), enables in-depth analysis of English reading comprehension and enhances teaching efficiency and student motivation through personalized recommendations, intelligent tutoring, and assessment. AI technology offers tailored learning paths based on specific student needs, achieving personalized instruction, and improves the accuracy and efficiency of assessment through intelligent evaluation and feedback mechanisms. Furthermore, AI enriches teaching resources and interaction modes, increasing student interest and engagement. The study posits that the application of AI technology in high school English reading instruction has broad prospects and is expected to significantly improve educational quality and talent cultivation.Helios Multidisciplinary00The study posits that the application of AI technology in high school English reading instruction has broad prospects and is expected to significantly improve educational quality and talent cultivation.Helios Multidisciplinary["Jiayi Li"]2025-01-16T00:00:00 cf2a526f176024c87b6984ba74ede591b0cceae7The integration of Artificial Intelligence (AI) technologies in education has gained significant attention, particularly in the context of higher education, in recent years. Despite concerns about academic integrity, academics recognise the opportunity for AI to foster critical thinking and prepare students for real-world scenarios. However, its integration into courses requires careful consideration of course objectives and ethical implications. This study explores the utilisation of AI in higher education settings, focusing on its role as a learning tool. The study systematically reviewed 87 empirical studies from databases between 2014 and 2024 to investigate the benefits, challenges, and implications of incorporating AI into higher education. Additionally, it examines the potential impact of AI on teaching methodologies, student outcomes, and the overall learning experience. The findings of this study underscore the significant influence of AI integration in higher education on teaching methodologies. This integration promotes personalised and adaptive instruction, enhancing student engagement, performance, satisfaction, and overall learning experiences. However, the adoption of AI in higher education raises significant ethical concerns that demand careful consideration. These concerns include data privacy, algorithmic bias, intellectual property rights, and academic integrity. Academics' perspectives on AI adoption vary based on technological proficiency, pedagogical beliefs, and institutional support. Successful AI integration necessitates alignment with pedagogical theories such as constructivism, connectivism, and self-directed learning, ensuring a robust technical infrastructure and addressing ethical considerations to maximise benefits while minimising risks.Interdisciplinary Journal of Education Research00The study systematically reviewed 87 empirical studies from databases between 2014 and 2024 to investigate the benefits, challenges, and implications of incorporating AI into higher education, and examines the potential impact of AI on teaching methodologies, student outcomes, and the overall learning experience.Interdisciplinary Journal of Education Research["M. Maphalala", "O. A. Ajani"]2025-01-16T00:00:00 cf74315b7b4e08d06c0bf93d9a5d808d7a13daecPurposeThis study aims to analyse the literature on the digital transformation of family businesses and the impact of artificial intelligence on this process, highlighting key areas of interest and future perspectives.Design/methodology/approachA bibliometric analysis is performed to explore the interconnection between variables and the relationships between authors, countries and journals in this research area. The Scopus database was used as of March 2024, and the data analysis was carried out with Bibliometrix for result analysis and VOSviewer for scientific mapping.FindingsThe analysis confirms the increasing relevance of the topic, with a high number of articles in 2023. Prominent journals are identified, and authors are mainly from China and Europe. Keywords “family business” and “family firms” are strongly linked, showing a connection to artificial intelligence and digital transformation. Family businesses are embracing the digital era, and research must respond accordingly.Originality/valueThis pioneering study offers a novel contribution, as no prior bibliometric analysis has addressed this topic. It lays the groundwork for future research, identifying emerging themes with significant future potential.Journal of Family Business Management540A bibliometric analysis of the literature on the digital transformation of family businesses and the impact of artificial intelligence on this process confirms the increasing relevance of the topic, with a high number of articles in 2023.Journal of Family Business Management["Mar\u00eda Atienza-Barba", "J. \u00c1lvarez\u2010Garc\u00eda", "\u00c1ngel Meseguer-Mart\u00ednez", "Virginia Barba-S\u00e1nchez"]2025-01-16T00:00:00 ae320caff0772f2a409d029851eaaf663fbe150aFood Science &amp; Nutrition Technology00Food Science &amp; Nutrition Technology["Ukwuru Mu"]2025-01-16T00:00:00 3f83cf58f2b2391350b2a3d6da9c392a82dfae90Current Psychology430Current Psychology["Shuxin Zheng", "Zhixin Guo", "Caisheng Liao", "Shuhua Li", "Xinze Zhan", "Xinshu Feng"]2025-01-16T00:00:00 1c2d48364c5b63acaccdb31d4df6637d984b1788The authors would like to make the following corrections about the published paper [...]Sustainability10Sustainability["Hongbo Fu", "R. Rasiah"]2025-01-16T00:00:00 3cb4244bef6e2ed0b803ff5052dc6f85158bb311Oper. Res. Forum40Oper. Res. Forum["Massoumeh Nazari", "M. D. Nayeri", "Kiamars Fathi Hafshajani"]2025-01-16T00:00:00 d9b2ad89ab56cfeff69a35b3c8efea2ab371b9cbThe article is devoted to the issue of using generative AI in the higher education system of the Russian Federation. The relevance of this issue is due to the avalanche-like growth in the use of generation by students and postgraduates in the 2023/24 academic year, the heated discussions that arose in this regard within universities, a series of publications in the media, the lack of development of the legal aspects of the use of generation, the ambiguity of its ethical grounds and consequences. The goal of the authors' collective is to formulate a collective position of teachers of classical humanitarian knowledge, which consists in the inadmissibility of using generative AI in the preparation of qualification and scientific papers. The work examines the regulatory aspects of the use of generation, conducts an experimental study of generative systems popular in the Russian-speaking space, and formulates a fundamental threat to subjectivity at the level of the individual and the collective, arising in connection with the use of generative AI. The general conclusion of the work is the need for careful legal and ethical regulation of the areas and methods of using generative systems in higher education.Vestnik of Samara University History pedagogics philology70The work examines the regulatory aspects of the use of generation, conducts an experimental study of generative systems popular in the Russian-speaking space, and formulates a fundamental threat to subjectivity at the level of the individual and the collective, arising in connection with the use of generative AI.Vestnik of Samara University. History, pedagogics, philology["V. V. Ivanov", "A. Nesterov", "I. P. Yanchenko"]2025-01-16T00:00:00 898921ec51f3e2d345ad67e98c048b41dd60e206International Journal of Medical Informatics190Common AI techniques include Support Vector Machines, Neural Networks, and Random Forests, applied in tasks such as identifying new drug uses, predicting antibiotic resistance, and streamlining clinical trials.International journal of medical informatics["Camila de Brito Pontes", "Antonio Valerio Netto"]2025-01-16T00:00:00 e9d7863fea380d4cb655b8487c6cafcb220edf56Current Psychology590Current Psychology["Jennifer Nelson", "Josef Kaplan", "Gabriel Simerly", "Nicolette Nutter", "Anna Edson-Heussi", "Breanna Woodham", "Joshua Broman-Fulks"]2025-01-16T00:00:00 b89a9c832ce3a97dffeb1af29daaa2e5440e3287Comparative Strategy00Comparative Strategy["Michail Ploumis", "Costas Kokolakis"]2025-01-16T00:00:00 7af745d805ada4ca69ccb054d901490b184e2798International Conference on Modelling, Identification and Control00International Conference on Mechatronics and Intelligent Control (ICMIC 2024)["Jia Fan", "Mingyang Li", "Yueen Li", "Lei Zhang", "Zhen Wang"]2025-01-16T00:00:00 52632ffd49a896f27642c66235583c415abbc05bJournal of Finance and Accounting00Journal of Finance and Accounting["Amalendu Bhunia"]2025-01-16T00:00:00 e68f6724c968166658ebecc499861e5c521524dfCureus530Cureus["Omar Basubrin"]2025-01-16T00:00:00 e9685abf51bcaef30746268045ebf93925bf5164Evidence-Based Nursing50Evidence-based nursing["Ahmad A. Abujaber", "A. Nashwan"]2025-01-16T00:00:00 fe096fa0b7eb92a0eefa05983cd2ec28d2aaea0eEurasian Business Review400This paper uses a novel dataset of USPTO patent applications from 1980 to 2019 to explore the domain of green intelligence (GI), defined as the application of AI algorithms to green technologies, and shows that AI and green technologies have a greater impact on follow-on inventions and display greater originality and generality.Eurasian Business Review["Gianluca Biggi", "Martina Iori", "Julia Mazzei", "Andrea Mina"]2025-01-16T00:00:00 7689b74d198e29a0ea9f19432d77411f92d01a80With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.00This paper reviewed a selected set of 43 papers across 4 major academic literature databases to highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges and consolidated into a taxonomy of AI-based identity fraud detection and prevention methods.["Chuo Jun Zhang", "Asif Gill", "Bo Liu", "M. Anwar"]2025-01-16T00:00:00 52323c4e2873564b9ca2db485ed293340b7478bfThe impact of Artificial Intelligence (AI) is transforming various aspects of urban life, including, governance, policy and planning, healthcare, sustainability, economics, entrepreneurship, etc. Although AI immense potential for positively impacting urban living, its success depends on overcoming significant challenges, particularly in telecommunications infrastructure. Smart city applications, such as, federated learning, Internet of Things (IoT), and online financial services, require reliable Quality of Service (QoS) from telecommunications networks to ensure effective information transfer. However, with over three billion people underserved or lacking access to internet, many of these AI-driven applications are at risk of either remaining underutilized or failing altogether. Furthermore, many IoT and video-based applications in densely populated urban areas require high-quality connectivity. This paper explores these issues, focusing on the challenges that need to be mitigated to make AI succeed in emerging countries, where more than 80% of the world population resides and urban migration grows. In this context, an overview of a case study conducted in Kathmandu, Nepal, highlights citizens' aspirations for affordable, high-quality internet-based services. The findings underscore the pressing need for advanced telecommunication networks to meet diverse user requirements while addressing investment and infrastructure gaps. This discussion provides insights into bridging the digital divide and enabling AI's transformative potential in urban areas.00Insight into bridging the digital divide and enabling AI's transformative potential in urban areas is provided, highlighting citizens' aspirations for affordable, high-quality internet-based services.["Dibakar Das", "Jyotsna L. Bapat", "Angeliki V. Katsenou", "Sushmita Shrestha"]2025-01-16T00:00:00 77a0689418a31eddcaf616b185406329036cf470BMC Nursing270The findings suggest that nursing leaders are generally prepared to integrate AI into their workflows, especially those with advanced education and experience, however, further training and policy development are necessary to fully realize the benefits of AI in nursing practice.BMC Nursing["Mohamed Hashem Kotp", "Hossam Ali Ismail", "Hassan Ahmed Awad Basyouny", "Mohamed Ahmed Aly", "Abdelaziz Hendy", "A. Nashwan", "Ahmed Hendy", "Aliaa Ezz Eldin Abd Elmoaty"]2025-01-16T00:00:00 7322810fc65f07a786a4a71916ae37efe4c3d128Digital Society770Meet requirements for the ethical introduction of genAI and actionable guidelines which explain how decision makers in media organizations should address ethical principles for the use of AI in the news production life cycle, in order to contribute to trustworthiness of journalistic organizations and products are derived.Digit. Soc.["Lennart Hofeditz", "Anna-Katharina Jung", "Milad Mirbabaie", "Stefan Stieglitz"]2025-01-16T00:00:00 6a6be66ba5457cf145e997edcf9d0a375335f736This study examines how digital marketing and artificial intelligence (AI) can improve sales performance in the media industry of Henan Province, China. As the market undergoes rapid digital transformation, understanding the role of these technologies in driving sales is crucial for maintaining competitiveness. The research explores three main areas: the impact of digital marketing and AI on sales performance, employee perceptions of these technologies, and the interaction between digital marketing and AI in enhancing sales outcomes. Data was gathered from 240 participants using a structured questionnaire distributed via JotForm and popular Chinese social media platforms. The demographic analysis revealed that 74.2% of respondents were female, with 48.3% aged between 30-39 years. Various statistical methods, including normality tests, descriptive analysis, T-tests, and bivariate correlation, were applied to ensure the reliability and validity of the data. The normality test indicated well-distributed data, while descriptive analysis showed high mean scores, reflecting a strong understanding of the survey items. The T-test found no significant difference in the impact of digital marketing and AI on sales performance, suggesting equal contributions of both technologies. The bivariate correlation revealed a moderate positive relationship between digital marketing and AI, highlighting their complementary roles in boosting sales. A Cronbach’s Alpha value of 0.94 confirmed the high internal consistency of the data. The study concludes that integrating digital marketing and AI into a unified strategy is critical for media organizations to optimize their sales performance. It underscores the need for innovation and healthy competition within media networks to drive digital transformation. Future research should investigate the relationship between innovative marketing strategies and sales performance in non-Western contexts.Communications on Applied Nonlinear Analysis560It is concluded that integrating digital marketing and AI into a unified strategy is critical for media organizations to optimize their sales performance and underscores the need for innovation and healthy competition within media networks to drive digital transformation.Communications on Applied Nonlinear Analysis["Weiguang Zhao", "Johar Mgm", "Ali Khatibi", "J. Tham", "S. M. Ferdous"]2025-01-16T00:00:00 2b7b43d0eaa6695315138bf91f100df9b4a73d3eAbstract Background Utilizing Artificial Intelligence (AI) in clinical settings may offer significant benefits. A roadblock to the responsible implementation of medical AI is the remaining uncertainty regarding requirements for AI users at the bedside. An overview of the academic literature on human requirements for the adequate use of AI in clinical settings is therefore of significant value. Sources of data A systematic review of the potential implications of medical AI for the required competencies of physicians as mentioned in the academic literature. Areas of agreement Our findings emphasize the importance of physicians’ critical human skills, alongside the growing demand for technical and digital competencies. Areas of controversy Concrete guidance on physicians' required competencies in AI-assisted clinical settings remains ambiguous and requires further clarification and specification. Dissensus remains over whether physicians are adequately equipped to use and monitor AI in clinical settings in terms of competencies, skills and expertise, issues of ownership regarding normative guidance, and training of physicians’ skills. Growing points Our review offers a basis for subsequent further research and normative analysis on the responsible use of AI in clinical settings. Areas timely for developing research Future research should clearly outline (i) how physicians must be(come) competent in working with AI in clinical settings, (ii) who or what should take ownership of embedding these competencies in a normative and regulatory framework, (iii) investigate conditions for achieving a reasonable amount of trust in AI, and (iv) assess the connection between trust and efficiency in patient care.British Medical Bulletin780A systematic review of the potential implications of medical AI for the required competencies of physicians as mentioned in the academic literature offers a basis for subsequent research and normative analysis on the responsible use of AI in clinical settings.British Medical Bulletin["Lotte Schuitmaker", "Jojanneke Drogt", "M. Benders", "Karin Jongsma"]2025-01-16T00:00:00 d41f2278facb0bd5cb2e8cbb957c845e713580eaWelding in the World60An image-based semantic segmentation convolutional neural network is presented that identifies crucial features such as the weld pool, groove, wire, and electrode based on which geometric measurements are derived based on which geometric measurements are derived.Welding in the World["Benedikt von Querfurth", "Shems-Eddine Belhout", "Christian Knaak", "Stefan Mann", "Peter Abels", "Carlo Holly", "Jon Tatman", "Darren Barborak", "Mitch Hargadine"]2025-01-16T00:00:00 903b935fab64a10ba489837dbd84a8e1a28e8d65One-stop solution for tourism represents a high-end platform based on artificial intelligence to transform the travel experience through integration of basic services within one cohesive framework. By using cutting-edge technologies like artificial intelligence, machine learning algorithms, and cloud computing, it offers comprehensive solutions to all the dimensions of travel. The key features are itineraries customized to fit individual preferences, intelligent navigation for optimized travel, real-time analytics for improved decisions, and multiple language support to reach the largest audience possible, plus secure payment processing. The website dynamically changes its preferences based on the user, taking into consideration real-time change factors such as weather conditions and traffic. It eliminates the need for a number of disparate applications and presents a harmonious and user-friendly interface to accomplish the journey from preparation to implementation. It provides customized recommendations on accommodation, transportation, entertainment, and food to make travel more personalized, effective, and enjoyable. This holistic approach redesigns the concept of the travel guide and offers travelers an easy, reliable, and engaging solution to their travel needs. Key Words: AI-driven tourism, one-stop travel platform, personalized itineraries, intelligent navigation, real-time analytics, integrated travel services, cloud computingINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00An AI-driven tourism, one-stop travel platform, personalized itineraries, intelligent navigation, real-time analytics, integrated travel services, cloud computing, and multiple language support to reach the largest audience possible.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Ashmit Kumar1"]2025-01-16T00:00:00 aec5250417e785eed8e9182fbc805ac35a566f22This study explores the impact of Business Intelligence (BI) systems on operational efficiency (OE) and the transition to Artificial Intelligence (AI) technologies in financial firms from Shanghai and Shenzhen stock markets (2007-2021). It investigates whether existing BI platforms underpin AI adoption, using Data Envelopment Analysis as a proxy for OE and the frequency of terms like Big Data and data mining in annual reports to indicate BI usage. Employing Heckman's two-stage and Hausman firm fixed effect model addresses potential endogeneity. Results show significant OE improvements post-BI adoption, with increasing benefits over time and enhanced by R&D intensity. Additionally, this research extends to global information management, linking BI capabilities with AI readiness and offering insights into strategic technology management in the financial sector, aligning with shifts towards AI in business, thereby impacting local and global information strategies.Journal of Global Information Management350Investigating whether existing BI platforms underpin AI adoption and the transition to Artificial Intelligence technologies in financial firms from Shanghai and Shenzhen stock markets shows significant OE improvements post-BI adoption, with increasing benefits over time and enhanced by R&D intensity.Journal of Global Information Management["M. Rahman", "Tarek Rana", "Yiren Xu", "Peter \u00d6hman"]2025-01-16T00:00:00 fca085e824c13d1691eb9c73188e3c6edb63a4f5The article discusses the development of AI solutions as a tool for increasing the efficiency and competitiveness of a company, covers in detail the role of AI in business process automation, reveals the capabilities of artificial intelligence for various aspects of business and analyzes the factors affecting the successful implementation of AI projects. The author emphasizes that the development of AI solutions requires an integrated approach that covers: a clear definition of business goals and automation objectives; the selection of suitable AI technologies taking into account the specifics of the tasks and industry; the development and implementation of AI solutions with the participation of a team of AI specialists, business analysts and business process specialists; continuous assessment of the effectiveness of AI solutions, collecting feedback and making the necessary changes.DIGITAL TRANSFORMATION IN THE ECONOMY OF THE TRANSPORT COMPLEX00DIGITAL TRANSFORMATION IN THE ECONOMY OF THE TRANSPORT COMPLEX["V. Rossomahin"]2025-01-16T00:00:00 372e882dc087756e9065ca8caa7ff5a925153821The use of the metaverse in educational environments has grown significantly in recent years, particularly following the shift of major tech companies towards virtual worlds and immersive technologies. Virtual reality and augmented reality technologies are employed to construct immersive learning environments. The metaverse is generally understood as a vast digital ecosystem or virtual space, facilitating the transition of individuals from physical to virtual environments, and is applicable to educational domains where practical experiments are challenging or fraught with risks, such as space exploration, chemical experimentation, and flight simulation training. In addition, the integration of artificial intelligence with the metaverse within educational contexts has significantly enriched the learning environment, giving rise to AI-driven teaching systems tailored to each student’s individual pace and learning modalities. As a result, a number of research articles have been conducted to explore the applications of the metaverse and artificial intelligence in education. This paper provides a systematic literature review following the PRISMA methodology to analyze and investigate the significance and impact of the metaverse in education, with a specific focus on the integration of AI with the metaverse. We address inquiries regarding the applications, challenges, academic disciplines, and effects of integrating AI and the metaverse in education that have not yet been explored in most research articles. Additionally, we study the AI techniques used in the metaverse in education and their roles. The review affirms that the integration of the metaverse in education, with the utilization of AI applications, will enrich education by improving students’ understanding and comprehension across diverse academic disciplines.Applied Sciences1370A systematic literature review following the PRISMA methodology affirms that the integration of the metaverse in education, with the utilization of AI applications, will enrich education by improving students’ understanding and comprehension across diverse academic disciplines.Applied Sciences["Khalid Almeman", "Faycel EL Ayeb", "Mouhebeddine Berrima", "Brahim Issaoui", "H. Morsy"]2025-01-16T00:00:00 840fe8058b73b77fb671dec802ba23dfead307d1This study examines the impact of Artificial Intelligence (AI) on student engagement in virtual learning environments within Bangladesh’s higher education sector, using a mixed-methods approach. The research integrates qualitative interviews with educators, students, and IT professionals, alongside a quantitative survey of 97 university students. Findings reveal that AI enhances academic performance by improving efficiency and simplifying complex tasks. However, concerns about over-reliance on AI, diminished critical thinking skills, and data privacy issues were prominent. Technical challenges such as inaccurate outputs and usability barriers further impede effective adoption. To maximize AI’s potential, the study recommends fostering critical thinking, refining AI tools for accuracy and personalization, and prioritizing data security. These findings offer actionable insights for educators, policymakers, and developers seeking to integrate AI effectively into virtual learning environments.60Findings reveal that AI enhances academic performance by improving efficiency and simplifying complex tasks, however, concerns about over-reliance on AI, diminished critical thinking skills, and data privacy issues were prominent.["Noor Mohammad Talukder", "Wahid bin Ahsan"]2025-01-16T00:00:00 d7f65343d9a5ff9404fa06e5d56b1149b8b0467bThe predictive capabilities of artificial intelligence for predicting protein yield from larval biomass present valuable advancements for sustainable insect farming, an increasingly relevant alternative protein source. This study develops a neural network model to predict protein conversion efficiency based on the nutritional composition of larval feed. The model utilizes a structured two-layer neural network with four neurons in each hidden layer and one output neuron, employing logistic sigmoid functions in the hidden layers and a linear function in the output layer. Training is performed via Bayesian regularization backpropagation to minimize mean squared error, resulting in a high regression coefficient (R = 0.9973) and a low mean-squared error (MSE = 0.0072401), confirming the precision of the model in estimating protein yields. This AI-driven approach serves as a robust tool for predicting larval protein yields, enhancing resource efficiency and promoting sustainability in insect-based protein production.Sustainability350This study develops a neural network model to predict protein conversion efficiency based on the nutritional composition of larval feed, and serves as a robust tool for predicting larval protein yields, enhancing resource efficiency and promoting sustainability in insect-based protein production.Sustainability["Claudia L. Vargas-Serna", "Angie N. Pineda-Osorio", "Carlos A. Gomez-Velasco", "J. L. Plaza-Dorado", "C. Ochoa-Mart\u00ednez"]2025-01-16T00:00:00 d7ec29e586ed84991ae81c10a1e7df3762fa9fdd In this article, we describe how arbitrators can use artificial intelligence (AI) during commercial arbitration proceedings today. In particular, we analyse whether tribunals can make use of AI for the purposes of selecting the presiding arbitrator, assisting with case management, analysing written evidence, managing oral hearings, and facilitating the tribunal’s deliberations, as well as for making settlement proposals, for legal decision-making and for drafting the final award. For each area of use, we give examples of how AI can assist arbitrators in their tasks, describe the current legal framework and flag both technological and legal risks that come with the use of AI. We conclude by providing arbitrators with a summary that categorizes the use of AI into green, orange, and red lists.Arbitration International00Whether tribunals can make use of AI for the purposes of selecting the presiding arbitrator, assisting with case management, analysing written evidence, managing oral hearings, and facilitating the tribunal’s deliberations, as well as for making settlement proposals, for legal decision-making and for drafting the final award is analyzed.Arbitration International["Dominik Stefer", "Victoria Fricke"]2025-01-16T00:00:00 552131de224dae74944e5764aa3777cd2bd42d55Artificial Intelligence (AI) has the potential to revolutionize education in Indonesia by improving its quality and accessibility. The current study employed a Systematics Literature Review (SLR) method to analyze the impact of AI implementation in education. The findings revealed that AI can support adaptive learning, provide accurate assessments, and personalize the students' learning experiences. However, limited infrastructure, data privacy concerns, and the digital divide remain significant challenges. Effective utilization of AI requires teacher training and clear ethical policies to protect student privacy. By integrating AI technology, the education system of Indonesia can foster a more innovative and responsive learning environment, equipping students to face the challenges of the digital era. The current study also recommended that collaboration among the government, educational institutions, and private sectors is needed to maximize the potential of AI in education.Educative: Jurnal Ilmiah Pendidikan00The findings revealed that AI can support adaptive learning, provide accurate assessments, and personalize the students' learning experiences, but limited infrastructure, data privacy concerns, and the digital divide remain significant challenges.Educative: Jurnal Ilmiah Pendidikan["Mohammad Fauziddin", "Twinda Rizki Adha", "Nurul Arifiyanti", "Fenny Indriyani", "Lussy Midani Rizki", "Verra Wulandary", "Vankelu Sai Venkateswarlu Reddy"]2025-01-16T00:00:00 87da32890d87d68111caf5759c7649c6a1507d7dArtificial Intelligence (AI) and Business Intelligence (BI) are rapidly emerging as the next big things for organizations to analyze data and gain insights. As this article will go on to examine, the concept of using AI for BI is one that has significant implications about the possible integration of AI into various Business Intelligence systems examined in this article will focus on the application of AI for BI in the use of predicting analytics. When integrating Machine learning, natural language processing, and intelligent automation, these AI-Advanced BI systems assist organizations to go beyond data reporting or simple descriptive analytics and gain an insight to use BI systems to discover and pre-empt issues, besides noticing them using proactive decision making. In discussing the elements of AI-embedded BI systems, this article analyzes how organizations across industries use real-time intelligence and predictive models as indispensable resources for the generation of competitive edge. Some of the advantages highlighted includes improved accuracy for predictions, efficiency of cost on data handling, scalability on large data and the shorter delays on decision making. However, alongside these benefits, the article also addresses key challenges, such as data privacy concerns, biases in AI algorithms, and the complexities of integrating AI into legacy BI platforms. These limitations are critical considerations for organizations seeking to implement AI-driven BI systems effectively. Furthermore, this work discusses the issues relating to the implementation of AI for BI, for example, the integration of AI into existing BI platforms, data quality issues, ethical issues, and the skill gaps in specialized AI talents. The article also discusses new developments in AI integration to BI systems including the growing incorporation of deep learning techniques, automation of decision making and BI democratization for small businesses. They suggest that BI must evolve new business strategies to be effective and meet the information demands needed for corporate competitiveness in today’s data-centric economy. The convergence of advanced analytics and operational decision making makes AI driven BI system the tool with tremendous potential to become the lingua franca of business strategy and growth.International Journal of Scientific Research and Management241This article analyzes how organizations across industries use real-time intelligence and predictive models as indispensable resources for the generation of competitive edge and discusses the elements of AI-embedded BI systems.International Journal of Scientific Research and Management (IJSRM)["Amejuma Emmanuel Ebule"]2025-01-17T00:00:00 bdc6ebe4fae8303fe561f0fd2554f8adaee4f39aCriminal, administrative, and constitutional law adopt a unique approach within the legal system, enabling them to adapt effectively to emerging developments. Their focus on executive and supervisory roles ensures accurate responses to contemporary legal challenges, including those posed by artificial intelligence (AI) on public rights and freedoms. Grounded in constitutional principles, these laws maintain consistency with established legal standards while addressing the implications of AI. This study examines the role of these legal branches in safeguarding public rights and freedoms impacted by AI. It proposes a legal framework to address AI-related developments and highlights the need for laws that balance logic, practicality, and constitutional standards. The research emphasizes the constitutional principle of preserving public rights and freedoms through adherence to constitutional texts, administrative decisions, and executive orders. Using an inductive analytical approach, the study is divided into two chapters: the first introduces AI, and the second explores the role of criminal, administrative, and constitutional law in addressing AI’s legal and practical challenges. This work aims to enrich legislative efforts and guide lawmakers in adapting to AI's impact on public rights and freedoms.Pakistan Journal of Criminology00The research emphasizes the constitutional principle of preserving public rights and freedoms through adherence to constitutional texts, administrative decisions, and executive orders and highlights the need for laws that balance logic, practicality, and constitutional standards.Pakistan Journal of Criminology[]2025-01-17T00:00:00 1d8fa7a8118748df34da7c30fb493a1e4096b5acThe use of artificial intelligence (AI) in issuing fatwas has attracted significant attention because of its potential to transform and streamline the process. However, this advancement raises ethical concerns that necessitate careful consideration, including the preservation of human authority and responsibility, as well as challenges related to interpretation and contextual understanding. This study aims to provide guidance for integrating AI into fatwa issuance and could assist communities facing challenges related to fatwas. The study employed a qualitative methodology that integrates deductive reasoning and field research methods. We conducted semi-structured interviews with Shariah experts and AI specialists to gather comprehensive insights. The findings highlight the potential of AI to revolutionise and streamline the fatwa issuance process. Nonetheless, several ethical dilemmas require resolution. We place emphasis on collaborative efforts between Islamic scholars, AI researchers, ethicists, and community stakeholders to establish comprehensive frameworks that ensure the ethical integration of AI into fatwa issuance. Despite the comprehensive nature of the study, we must acknowledge certain limitations such as contextual specificity, sample size, representation, and technological constraints. The established frameworks should prioritise AI as a supportive tool for scholars, upholding Islamic jurisprudence principles while ensuring fairness, accountability, and contextual awareness. Continuous evaluation, and engagement are pivotal in addressing ethical challenges and facilitating the responsible and beneficial use of AI in fatwa issuance within an Islamic framework.Journal of Fatwa Management and Research160Advice is provided for integrating AI into fatwa issuance and could assist communities facing challenges related to fatwas and place emphasis on collaborative efforts between Islamic scholars, AI researchers, ethicists, and community stakeholders to establish comprehensive frameworks that ensure the ethical integration of AI into fatwa issuance.Journal of Fatwa Management and Research["Siti Farahiyah Ab Rahim", "Muhamad Firdaus Ab Rahman", "Hussein Azeemi Abdullah Thaidi", "Nik Nur Muhammad Alif Nik Mohd Azimi", "M. Jailani"]2025-01-17T00:00:00 a521a194e738397de8f953adf390bf03019f17f8The Inuits of Canada who live in the Nunavut territory are confronted by the problem of a higher suicide rate than the rest of Canada and the world. The suicide rate in the region is 10 times higher for the general population and 25 times higher among men than in the rest of Canada. The problem is partly linked to mental health issues, yet because the region is remote and isolated, Inuits do not have adequate access to culturally competent mental health support and resources. Consequently, in empowering the community to deal with mental health issues, for suicide prevention, this systematic review involving a comprehensive review and analysis of eight papers published between December 2014 and December 2024 justifies the suitability of Artificial Intelligence (AI) tools for suicide prevention. Additionally, ethical risks should be identified and minimized, stakeholders actively involved, and AI algorithms consistently trained to increase accuracy.African journal of health, nursing and midwifery280This systematic review involving a comprehensive review and analysis of eight papers published between December 2014 and December 2024 justifies the suitability of Artificial Intelligence (AI) tools for suicide prevention.African Journal of Health, Nursing and Midwifery["Femi Duyilemi"]2025-01-17T00:00:00 6d784b3c215ecc09ad28d6a727bbef4b9d09f1eaObjectives: This study aims to assess and compare the diagnostic accuracy of three artificial intelligence (AI) models—ChatGPT-3.5, Microsoft Copilot, and Google Gemini—through their performance on clinical vignettes.   Theoretical Framework: Building on prior research into the application of AI in healthcare, particularly in diagnostic support, this study examines the potential of AI models to aid clinicians by providing accurate medical diagnoses, thus supporting decision-making in clinical contexts.   Methodology: A meta-analysis was conducted, followed by a comparative analysis using 34 clinical vignettes from Texas Tech University Health Sciences Center. Each AI model’s responses were evaluated for accuracy in diagnosing medical cases, and statistical significance was tested using the chi-square test.   Results and Discussion: ChatGPT-3.5 achieved the highest diagnostic accuracy (70.59%), outperforming Google Gemini (61.76%) and Microsoft Copilot (35.29%). ChatGPT-3.5 provided concise answers, while Google Gemini and Microsoft Copilot included disclaimers and additional recommendations. Chi-square analysis confirmed significant differences in performance, highlighting variations in diagnostic capabilities across models.   Research Implications: These findings underscore the importance of model selection when integrating AI into clinical workflows. AI models show promise in diagnostics but vary in approach and accuracy, warranting further refinement.   Originality/Value: This study is among the first to compare the diagnostic accuracy of ChatGPT-3.5, Google Gemini, and Microsoft Copilot, contributing valuable insights into AI’s application in healthcare diagnostics and supporting evidence for its potential role in enhancing patient care.Journal of Lifestyle and SDGs Review120This study is among the first to compare the diagnostic accuracy of ChatGPT-3.5, Google Gemini, and Microsoft Copilot, contributing valuable insights into AI’s application in healthcare diagnostics and supporting evidence for its potential role in enhancing patient care.Journal of Lifestyle and SDGs Review["Yordanka Eneva", "Bora Dogan"]2025-01-17T00:00:00 13a3a3a91bff7fc01697aa4611fa14714e95a793Artificial Intelligence (AI) is revolutionizing corporate finance by enabling startups to overcome traditional financial challenges and achieve sustainable growth. This study systematically reviews AI's transformative potential, highlighting its applications in financial decision-making, process automation, fraud detection, and predictive analytics. Drawing from 50 peer-reviewed articles, the review highlights how AI technologies like machine learning, natural language processing, and predictive modelling enhance financial efficiency, mitigate risks, and enable strategic innovation for startups. While AI-driven financial strategies reduce costs, improve cash flow accuracy, and foster ESG compliance, barriers such as high implementation costs, skill gaps, and integration challenges persist. The findings also reveal significant research gaps, including the need for scalable AI tools tailored to startups and ethical frameworks to mitigate biases in financial models. By addressing these issues, this study provides actionable insights for leveraging AI to transform financial operations, empowering startups to thrive in a competitive digital economy.Asian Journal of Economics Business and Accounting00This study systematically reviews AI's transformative potential, highlighting its applications in financial decision-making, process automation, fraud detection, and predictive analytics, and highlights the need for scalable AI tools tailored to startups and ethical frameworks to mitigate biases in financial models.Asian Journal of Economics, Business and Accounting["Roopa T Raj", "Suryanarayana N R"]2025-01-17T00:00:00 184e136f07618ec948a4458118e58d2465f3f607Introduction: The rapid advancement of Artificial Intelligence (AI) has significantly transformed digital media content creation, particularly with the rise of AI-Generated Content (AIGC). This systematic review investigates AIGC's role in enhancing content production efficiency, improving content quality, and addressing ethical considerations. AIGC applications—spanning micro-video production, digital storytelling, journalism, and new media art—highlight its profound impact on content workflows, distribution, and audience engagement.   Objective: The objective of this study is to explore how AIGC influences the efficiency, quality, and ethical standards of digital media content creation, with the aim of fostering responsible and sustainable integration of AIGC in the media industry.   Theoretical Framework: This research is grounded in theories of human-machine interaction, ethical decision-making, and technological adoption models, which provide a solid foundation for examining the dynamics of AIGC in the context of Web 3.0 advancements.   Method: The methodology employed for this review involved a systematic analysis of scholarly articles, case studies, and industry reports on AIGC. The data were synthesized to evaluate its transformative potential, ethical challenges, and alignment with sustainable development goals (SDGs).   Results and Discussion: The findings reveal that AIGC significantly enhances efficiency and scalability in content production while raising ethical concerns such as professional displacement and algorithmic biases. These results are contextualized within the theoretical framework, emphasizing the need for ethical safeguards and inclusive practices. Challenges, including transparency in AI use and equitable access to AIGC technologies, are also discussed.   Research Implications: This study offers both theoretical and practical implications by proposing strategies for responsible AIGC adoption, contributing to the sustainable development of the digital media industry. Implications include its potential to democratize content creation, foster inclusivity, and support SDGs such as Quality Education (SDG 4), Decent Work and Economic Growth (SDG 8), and Industry, Innovation, and Infrastructure (SDG 9).   Originality/Value: This study provides a novel contribution to the literature by integrating SDG-oriented perspectives into the discussion of AIGC's impact. Its findings underline the critical balance between leveraging technological advancements and mitigating their societal and ethical challenges.Journal of Lifestyle and SDGs Review180The findings reveal that AIGC significantly enhances efficiency and scalability in content production while raising ethical concerns such as professional displacement and algorithmic biases, underline the critical balance between leveraging technological advancements and mitigating their societal and ethical challenges.Journal of Lifestyle and SDGs Review["Yang Shao", "Qingxia Yin"]2025-01-17T00:00:00 eefcee9ff5e98c3543ab8521c7ce1d13a2e48953The integration of Artificial Intelligence (AI) within Extended Reality (XR) technologies has the potential to revolutionize user experiences by creating more immersive, interactive, and personalized environments. Nevertheless, the complexity and opacity of AI systems raise significant concerns regarding the transparency of data handling, reasoning processes, and decision-making mechanisms inherent in these technologies. To address these challenges, the implementation of explainable AI (XAI) methods and techniques becomes imperative, as they not only ensure compliance with prevailing ethical, social, and legal standards, norms, and principles, but also foster user trust and facilitate the broader adoption of AI solutions in XR applications. Despite the growing interest from both research and practitioner communities in this area, there is an important gap in the literature concerning a review of XAI methods specifically applied and tailored to XR systems. On this behalf, this research presents a systematic literature review that synthesizes current research on XAI approaches applied within the XR domain. Accordingly, this research aims to identify prevailing trends, assess the effectiveness of various XAI techniques, and highlight potential avenues for future research. It then contributes to the foundational understanding necessary for the development of transparent and trustworthy AI systems for XR systems using XAI technologies while enhancing the user experience and promoting responsible AI deployment.Mathematics460This research presents a systematic literature review that synthesizes current research on XAI approaches applied within the XR domain to identify prevailing trends, assess the effectiveness of various XAI techniques, and highlight potential avenues for future research.Mathematics["Clara Maathuis", "Marina-Anca Cidot\u00e3", "D. Datcu", "Leti\u021bia Marin"]2025-01-17T00:00:00 a4b0744a93fee40f1e16904af16e06294eff0dcdArtificial Intelligence (AI) has continued to achieve tremendous success in recent times. However, the decision logic of these frameworks is often not transparent, making it difficult for stakeholders to understand, interpret or explain their behavior. This limitation hinders trust in machine learning systems and causes a general reluctance towards their adoption in practical applications, particularly in mission-critical domains like healthcare and autonomous driving. Explainable AI (XAI) techniques facilitate the explainability or interpretability of machine learning models, enabling users to discern the basis of the decision and possibly avert undesirable behavior. This comprehensive survey details the advancements of explainable AI methods, from inherently interpretable models to modern approaches for achieving interpretability of various black box models, including large language models (LLMs). Additionally, we review explainable AI techniques that leverage LLM and vision-language model (VLM) frameworks to automate or improve the explainability of other machine learning models. The use of LLM and VLM as interpretability methods particularly enables high-level, semantically meaningful explanations of model decisions and behavior. Throughout the paper, we highlight the scientific principles, strengths and weaknesses of state-of-the-art methods and outline different areas of improvement. Where appropriate, we also present qualitative and quantitative comparison results of various methods to show how they compare. Finally, we discuss the key challenges of XAI and directions for future research.00This comprehensive survey details the advancements of explainable AI methods, from inherently interpretable models to modern approaches for achieving interpretability of various black box models, including large language models (LLMs).["F. Mumuni", "A. Mumuni"]2025-01-17T00:00:00 a5dbd96c174edd9db417ee82537fd7b5fddd0f33SMP Al Fajar merupakan salah satu sekolah swasta yang berada di desa Ciheulang Bandung, Kabupaten Bandung. Salah satu permasalahan yang dihadapi sekolah adalah belum maksimal dalam penggunaan kemajuan informatika pada pembelajaran. Salah satunya adalah belum mengenal artificial intelligence (AI) serta manfaat dalam pembelajaran. Tim pengabdian masyarakat menawarkan solusi untuk melakukan kegiatan pelatihan pengenalan dan penggunaan ChatGPT pada proses pembelajaran, khususnya mencari ide pembuatan soal, materi, dan andministrasi pembelajaran di SMP. Metode yang digunakan berupa pelatihan penggunaan ChatGPT dalam pembelajaran. Tahapan pada kegiatan ini dimulai dari tahapan persiapan berupa mencari mitra dan wawancara kebutuhan. Hasil survey menjadikan Guru SMP Al Fajar sebagai mitra dengan tawaran solusi penggunaan artificial intelligence pada pembelajaran. Selain itu, tahapan persiapan juga berupa persiapan pelaksanaan yang berupa koordinasi tempat pelaksanaan dan waktu pelaksanaan. Persiapan materi yang disajikan dalam bentuk modul sederhana mulai dari pengenalan AI, penggunaan dalam keseharian, cara menggunakan ChatGPT dalam mencari materi pelajaran dan administrasi pembelajaran. Tahapan selanjutnya adalah tahapan pelaksanaan berupa pelatihan yang diberikan sesuai persiapan materi sebelumnya dan dilanjutkan dengan praktek langsung yang dilakukan oleh guru SMP Al Fajar sebanyak 10 orang dengan mengajar mata Pelajaran yang berbeda-beda. Tahapan terkahir berupa evaluasi yaitu pengisian umpan balik oleh peserta pengabidan masyarakat. Hasil menunjukkan bahwa pelaksanaan pelatihan pembelajaran dengan AI ini sangat baik serta butuh kegiatan kontinu khususnya dalam pendampingan guru untuk penggunaan AI dalam pembelajaran.Jurnal Pengabdian Sosial00Jurnal Pengabdian Sosial["Hilda Fahlena", "Marchelle Fernanda Pratama", "Amanda Kayla Putri W.", "Ekmal Reyhan Tarihoran", "Fadlil Taufani Tsamarrah", "Celenie Patrisse Stephania"]2025-01-17T00:00:00 563bd3c9bfb1eefd75b705d504374cdf76d90997 Purpose This research aims to propose a high-performance-based model of remote teaching where trained teachers (those who deliver lectures by using different tools i.e. Microsoft Team, Zoom, etc.) can get the desired results through artificial intelligence (AI) awareness, knowledge sharing and transformational leadership in the future. Design/methodology/approach This research is quantitative in nature and convenience sampling is followed to gather data from 307 trained faculty (those who deliver lectures by using different tools i.e. Microsoft Team, Zoom, learning management systems, etc.) from various universities of the federal capital territory (FCT) Islamabad and district Rawalpindi of Punjab province, Pakistan who worked online from home during novel corona lockdown. SmartPLS is used for data analysis and structural equation modeling is performed to test the suggested model. Findings Results revealed that AI awareness has a significant positive influence on knowledge sharing but exhibited a negative significant impact on teacher performance. Likewise, knowledge sharing acts as a partial mediator; however, transformational leadership moderates between remote working and knowledge sharing. Originality/value During the pandemic, the mode of instruction shifted from physical to online, generating several barriers for teachers who were used to on-campus teaching. This research presented an effective model for knowing the mechanism of possible and reliable implications at educational institutions of developing countries to get the desired outcomes of effective online teaching in calamity situations. Quality Assurance in Education1030This research presented an effective model for knowing the mechanism of possible and reliable implications at educational institutions of developing countries to get the desired outcomes of effective online teaching in calamity situations.Quality Assurance in Education["Muhammad Asif Zaheer"]2025-01-17T00:00:00 137df6451fbcdf2b5c81a623975b5b4d1cc593a7Teams that combine human intelligence with artificial intelligence (AI) have become indispensable for solving complex tasks in various decision‐making contexts in modern organizations. However, the factors that contribute to AI convergence, where human team members align their decisions with those of their AI counterparts, still remain unclear. This study integrates signaling theory with self‐determination theory to investigate how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams. Based on four experimental studies conducted in facial recognition and hiring contexts with approximately 1100 participants, the findings highlight the significant positive impact of congruent signals from both human and AI team members on AI convergence. Moreover, providing an option for employees to solicit AI advice also enhances AI convergence; when AI signals are chosen by employees rather than forced upon them, participants are more likely to accept AI advice. This research advances knowledge on human–AI teaming by (1) expanding signaling theory into the human–AI team context; (2) developing a deeper understanding of AI convergence and its drivers in human–AI teams; (3) providing actionable insights for designing teams and tasks to optimize decision‐making in high‐stakes, uncertain environments; and (4) introducing facial recognition as an innovative context for human–AI teaming.Journal of Organizational Behavior800Investigating how specific signals—such as signal fit, optional AI advice, and signal set congruence—affect employees' AI convergence in human–AI teams finds the significant positive impact of congruent signals from both human and AI team members on AI convergence.Journal of Organizational Behavior["Andria L. Smith", "H. van Wagoner", "Ksenia Keplinger", "Can Celebi"]2025-01-17T00:00:00 1ac08e4515d2a400ccd84c595ec3658eeaefb498Companies are under increasing pressure to discover novel approaches to optimizing efficiency and reducing costs as a result of the growing complexity of supply networks. The use of machine learning (ML) and artificial intelligence (AI) in supply chain management is one area that has witnessed significant growth in recent years. This proposed uses AI and machine learning techniques to foretell instances of supply chain fraud. Actual company transactions provided the supply chain data used in this project. It turns out that AI and ML classifiers were very good at predicting fraud in the supply chain. Specifically, after looking at all performance metrics, the AI model emerged as the top predictor. According to these findings, AI has the potential to be an effective weapon in the fight against supply chain fraud. When it comes to analysing large datasets, ML and AI classifiers can find patterns that humans might miss. This paper's findings can be applied to optimize supply chain management (SCM) and to anticipate fraudulent transactions. Machine learning and artificial intelligence classifiers may change supply chain management for the better, but they are still in their infancy.Journal of Informatics Education and Research230It turns out that AI and ML classifiers were very good at predicting fraud in the supply chain, and AI has the potential to be an effective weapon in the fight against supply chain fraud.Journal of Informatics Education and Research["Mohd. Asif Gandhi", "Hod"]2025-01-17T00:00:00 8f844d17fb6dc0cdede3392d318cced5e6bb5765Introduction The emergence of artificial intelligence (AI) has revolutionised higher education teaching and learning. AI has the power to analyse large amounts of data and make intelligent predictions thus changing the whole teaching and learning processes. However, such a rise has led to institutions questioning the morality of these applications. The changes have left librarians and educators worried about the major ethical questions surrounding privacy, equality of information, protection of intellectual property, cheating, misinformation and job security. Libraries have always been concerned about ethics and many go out of their way to make sure communities are educated about the ethical question. However, the emergence of artificial intelligence has caught them unaware. Methods This research investigates the preparedness of higher education librarians to support the ethical use of information within the higher and tertiary education fraternity. A qualitative approach was used for this study. Interviews were done with thirty purposively selected librarians and academics from universities in Zimbabwe. Results Findings indicated that many university libraries in Zimbabwe are still at the adoption stage of artificial intelligence. It was also found that institutions and libraries are not yet prepared for AI use and are still crafting policies on the use of AI. Discussion Libraries seem prepared to adopt AI. They are also prepared to offer training on how to protect intellectual property but have serious challenges in issues of transparency, data security, plagiarism detection and concerns about job losses. However, with no major ethical policies having been crafted on AI use, it becomes challenging for libraries to full adopt its usage.Frontiers in Research Metrics and Analytics290This research investigates the preparedness of higher education librarians to support the ethical use of information within the higher and tertiary education fraternity and indicates that many university libraries in Zimbabwe are still at the adoption stage of artificial intelligence.Frontiers in Research Metrics and Analytics["Stephen Tsekea", "Edward Mandoga"]2025-01-17T00:00:00 4cca7330a1628411227e93c610d2a186390d1d17Transcranial Doppler is an instrumental ultrasound method capable of providing data on various brain pathologies, in particular, the study of cerebral hemodynamics in stroke, quickly, economically, and with repeatability of the data themselves. However, literature reviews from clinical studies and clinical trials reported that it is an operator-dependent method, and the data can be influenced by external factors, such as noise, which may require greater standardization of the parameters. Artificial intelligence can be utilized on transcranial Doppler to increase the accuracy and precision of the data collected while decreasing operator dependencies. In a time-dependent pathology, such as stroke, characterized by hemodynamic evolution, the use of artificial intelligence in transcranial Doppler ultrasound could represent beneficial support for better diagnosis and treatment in time-dependent pathologies, such as stroke.Current medical imaging00In a time-dependent pathology, such as stroke, characterized by hemodynamic evolution, the use of artificial intelligence in transcranial Doppler ultrasound could represent beneficial support for better diagnosis and treatment in time-dependent pathologies, such as stroke.Current medical imaging["Antonio Siniscalchi", "Vincenzo Inghingolo", "Piergiorgio Lochner", "Giovanni Malferrari"]2025-01-17T00:00:00 1373b04854331423813f692805f003e3038aea22Purpose: To ascertain the views, knowledge and opinions of healthcare students (HCs) regarding artificial intelligence (AI). Methods: The cross-sectional survey designed to assess awareness, perceptions and opinions of HCs towards AI was conducted between  April to June 2023. A pre-tested, validated structured questionnaire was distributed electronically to HCs across different universities in  Saudi Arabia. Responses were compiled using a 5-point Likert scale. Cronbach's alpha score of 0.80 was used to establish the reliability of  the questionnaire. Mean scores were determined by calculating each item in the perceptions compiled. Results: Majority of HCs had a positive perception towards AI in healthcare and agreed that AI could improve diagnostic accuracy (73.4  %), reduce errors in medical practice (65.2 %) and facilitate patient education (70.8 %). However, some concerns were expressed that AI  has a harmful impact on healthcare practitioners' relationships with patients and potential ethical implications (44.3 %) and also allows  patients to increase control over their health (51 %). Most students (85 %) believe that if AI is integrated into healthcare, there is a risk of  losing jobs. The analysis of multiple linear regression shows that course of study (B = 0.311; SE = 0.132; t = 2.360; p = 0.019; CI = 0.052 to  0.570), awareness of AI (B = -1.822; SE = 0.785; t = -2.320; p = 0.021; CI = - 3.366 to -0.279) were predictors of perception score of AI.  Conclusion: Healthcare students show positive perceptions towards AI and agree that AI helps in various aspects of healthcare. However,  students revealed some concerns about AI. Therefore, addressing concerns related to ethics, workforce impact and patient  privacy is crucial for successful AI implementation in the healthcare sector. Tropical Journal of Pharmaceutical Research170Healthcare students show positive perceptions towards AI and agree that AI helps in various aspects of healthcare, however, students revealed some concerns about AI.Tropical Journal of Pharmaceutical Research["Haifa Fadil", "Yaser Alahmadi"]2025-01-17T00:00:00 084cb43cfecda66e37bd7d86bc2d13885a54769bIntroduction: With the emergence of Artificial Intelligence (AI), there is a need to integrate the latest technological tools such as generative AI in teaching essay writing. Thus, this study was carried out to examine the usefulness of Gemini AI in assisting students’ essay writing. Objectives: The Research Objectives (RO) for this study are to examine:  secondary school students’ Perceived Usefulness (PU) of Gemini secondary school students’ Perceived Ease of Use (PEOU) of Gemini Methods: The Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) of Gemini AI were analysed qualitatively through journal entries, participant observation, questionnaires and interviews. Results: The findings show that students showed favourable responses towards the use of Gemini AI in assisting them in their essay writing. This is because Gemini AI does not only help students with generalisation of ideas and gaining new vocabulary, it also provides flexibility for them to learn at their own pace. Conclusion: Hopefully, this study will bring useful insights to practitioners and researchers in using AI to teach essay writing to students.Journal of Information Systems Engineering & Management230The findings show that students showed favourable responses towards the use of Gemini AI in assisting them in their essay writing, because Gemini AI does not only help students with generalisation of ideas and gaining new vocabulary, it also provides flexibility for them to learn at their own pace.Journal of Information Systems Engineering and Management["Shirley Ling Jen1", "Abdul Rahim"]2025-01-17T00:00:00 0a8943f2f872bda5c265dd248d3cc53ae7647653The financial sector has both tremendous potential and problems as a result of the relationship between artificial intelligence and behavioural finance. Important factors to take into account include ethical concerns as well, such as the possibility that AI will be used to influence investor behaviour or jeopardize data privacy. Furthermore, the quick speed at which AI is developing presents difficulties for regulatory organizations, which must change in order to properly monitor AI's use in finance and make sure that these innovations don't increase systemic risk or market volatility. In this work, artificial intelligence in behavioural finance will be thoroughly examined. It explores how psychological influences on financial decision-making may be both amplified and mitigated by AI. Examining the state of AI in behavioural finance at the moment, the study highlights the practical, ethical, and legal issues that need to be resolved while also pointing out tremendous potential for innovation and advancement.INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT &amp; SOCIAL SCIENCE390Examining the state of AI in behavioural finance at the moment, the study highlights the practical, ethical, and legal issues that need to be resolved while also pointing out tremendous potential for innovation and advancement.INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT &amp; SOCIAL SCIENCE["Rahul Kumar", "Nagendra Kumar Jha"]2025-01-17T00:00:00 9edb77a19ef0a685c4a08056c7f94dc0692b8c37Artificial Intelligence (AI) is emerging as a revolutionary force that can alter environmental protection efforts in the face of growing environmental issues. This study explores the profound potential of AI technology to safeguard our planet. Important ideas covered in the study are summed up in the abstract, including the use of AI in sustainable resource management, biodiversity preservation, and climate change mitigation. In order to fully utilize AI's potential for the good of our global environment, it also discusses ethical issues and the significance of interdisciplinary cooperation. The significance of AI solutions as vital instruments in the toolbox of politicians and environmentalists attempting to tackle the most pressing environmental issues of our day is emphasized in this paper.Journal of Information Systems Engineering & Management00The significance of AI solutions as vital instruments in the toolbox of politicians and environmentalists attempting to tackle the most pressing environmental issues of the authors' day is emphasized in this paper.Journal of Information Systems Engineering and Management["Shashikant Tripathi"]2025-01-17T00:00:00 7e184ef2d8811c4df3be7f407ee2709d316ef8c7This study examined the effect of Artificial Intelligence (AI) on entrepreneurial success of Small and Medium Enterprises (SMEs), Eket, Akwa Ibom State. However, the specific objectives were to examine the effect of AI rarity and AI utilization efficiency on entrepreneurial success of SMEs, Eket, Akwa Ibom State. This study adopted a survey research design and utilized primary data collected from a pre-selected population which included SMEs that had already integrated or were in the process of integrating AI technologies in their business operations, in Eket, Akwa Ibom State. The data collected were analyzed using descriptive statistics, analysis of variance and multiple regression analysis via SPSS 25.0 statistical package. The study’s findings revealed that AI rarity has a significant positive effect (r= 0.063{p=0.008<0.05}) on entrepreneurial success of SMEs in Eket, Akwa Ibom State while AI utilization efficiency has a significant positive effect (r= 0.442{p=0.001<0.05 on entrepreneurial success of SMEs in Eket, Akwa Ibom State. It was thus concluded that AI power exerts a significant effect on entrepreneurial success of SMEs in Eket, Akwa Ibom State at 5% level of significance. The study recommended, amongst others, that the SMEs in Eket should ensure that they take the time to do the research and educate themselves on how to properly use AI technologies to provide services or create value in ways that make them stand out from the competition.British journal of management and marketing studies160It was concluded that AI power exerts a significant effect on entrepreneurial success of SMEs in Eket, Akwa Ibom State at 5% level of significance and recommended that the SMEs in Eket should ensure that they take the time to do the research and educate themselves on how to properly use AI technologies.British Journal of Management and Marketing Studies["Bassey, R. G.", "Brownson, G. D.", "Efi, A."]2025-01-17T00:00:00 4bdbe932091a7455722de03ac6df3d85be274dbbI argue in favor of a distinction between human understanding and machine “understanding”. Based on Wittgenstein’s view on machines and his considerations on understanding, I aim to demonstrate that no machine with artificial intelligence can reach functional equality with human beings. In particular, this also holds for ethical praxis because it consists of an extremely blurred net of language– games, guided by ethical rules. Therefore, a machine can never have the human ability (disposition) to act ethically and cannot be a moral agent.Obnovljeni život00It is demonstrated that no machine with artificial intelligence can reach functional equality with human beings and this also holds for ethical praxis because it consists of an extremely blurred net of language– games, guided by ethical rules.Obnovljeni život["Borut Cerkovnik"]2025-01-17T00:00:00 b767ff412b2d76586cbc32272e2ef78ab32e9f7bThe continuous increase in the intensity of competitive interaction of companies in modern markets requires their management to take a well-founded comprehensive approach to the development and implementation of a competitive strategy. At the same time, digital tools for ensuring excellence and competitiveness of enterprises play an increasingly important role. The work summarizes the areas of use of artificial intelligence tools in the implementation of a competitive strategy of a business organization. The analysis of the possibilities of using artificial intelligence was carried out in the context of the strategies for forming a competitive advantage proposed by M. Porter. The successful implementation of the cost leadership strategy can be helped by optimizing various aspects of the enterprise's activities based on intelligent demand forecasting, automation of routine and repetitive operations, increasing the efficiency of production, marketing and advertising, improving labour management, reducing energy costs and other areas of ensuring business excellence. Artificial intelligence will also be useful in creating a unique offer that will distinguish a product or service among competitors in the market, and in meeting the needs of the target audience better than competitors. The paper examines the positive and negative experience of using artificial intelligence tools in the implementation of well-known corporations’ competitive strategies. The analysis and generalizations carried out in the work have shown the breadth of the AI tools used, the potential of the described approaches to digitalization of companies' activities and the ambiguity of the consequences of their application in the realities of a changing and highly competitive environment. This is confirmed by the positive and, in some cases, negative experience of using artificial intelligence tools in the implementation of competitive strategies of well-known corporations. Situational sets of artificial intelligence tools adapted to the scale of business, characteristics of the competitive environment, and resource capabilities of specific enterprises require further thorough study.Economic scope00The work summarizes the areas of use of artificial intelligence tools in the implementation of a competitive strategy of a business organization and examines the positive and negative experience of using artificial intelligence tools in the implementation of competitive strategies of well-known corporations.Economic scope["Dmytro Barabas", "Petro H. Banshchykov", "Inna Vinnikova"]2025-01-17T00:00:00 91e15791bbd34e3a14adbb95cb6dadbf90f13c22Scant information exists about how AI with its different technologies might affect learning achievement in different educational fields across different educational levels and geographical distributions of students. Closing this gap can therefore help stakeholders understand under which learning conditions artificial intelligence in education (AIEd) might work or not, hence achieving better learning achievement. To address this research gap, this study conducted a meta-analysis and research synthesis of the effects of AI application on students’ learning achievement. Additionally, this study conducted one step forward to analyze the field of education, level of education, learning mode, intervention duration, and geographical distribution as moderating variables of the effect of AIEd. The Hedges’ g was computed for the effect sizes, where 85 quantitative studies ( N = 10,469 participants) were coded and analyzed. The results indicated that the total effect of AIEd on learning achievement is very large ( g = 1.10, p < 0.001). Particularly, chatbots achieved a very large effect, while Intelligent Tutoring Systems (ITS) and personalized learning systems had large effects. The results also show that the AIEd effect is moderated by the field of education, level of education, learning mode, intervention duration, and geographical distribution of students. The findings of this study can be useful to both researchers and practitioners as they highlight how and when AIEd integration can be effective, hence being beneficial to enhance learning achievement.Information Development570The results indicated that the total effect of AIEd on learning achievement is very large, while Intelligent Tutoring Systems and personalized learning systems had large effects, and the AIEd effect is moderated by the field of education, level of education, learning mode, intervention duration, and geographical distribution of students.Information Development["A. Tlili", "Khitam Saqer", "Soheil S. Salha", "Ronghuai Huang"]2025-01-17T00:00:00 1f5fe59476231bab55c753b81e40cac16ba79db9The purpose of this study is to investigate the role of consumer engagement on social media and conversion rate optimization in mediating the relationship between artificial intelligence technology and repurchase intention, where customer habit is as a moderator. This study also determines how artificial intelligence technology integrated social media sites affect consumers' intentions to repurchase as well as to improve the understanding of established variables. Moreover, this study is conducted on the international sportswear brands and to evaluate the hypothesized relationships, the data was collected using Google Forms from 496 respondents who buy from these brands. Furthermore, the study was cross sectional in nature. The data was analyzed using Smart PLS to test the hypotheses. Results of the study show that artificial intelligence technology positively and significantly affects repurchase intention through consumer engagement and conversion rate optimization. Furthermore, customer habit has been found to be a moderator between consumer engagement on social media and repurchase intention with the positive correlation. Results also showed that artificial intelligence technology significantly affects repurchase intention with positive correlation. The findings reveal that brands should focus on integrating artificial intelligence technology in their networking sites to understand the consumer needs and wants. Conclusion, limitations of the study, future research and managerial implications were also included in the study.Journal for Social Science Archives730The study shows that artificial intelligence technology positively and significantly affects repurchase intention through consumer engagement and conversion rate optimization and customer habit has been found to be a moderator between consumer engagement on social media and repurchase intention with the positive correlation.Journal for Social Science Archives["Fatima Imran", "Javaria Asim", "Aiysha Imran"]2025-01-17T00:00:00 081269fbf2ac1adb6822771eb8848d37d17a80d9Peripheral artery disease (PAD) is a major public health concern worldwide, associated with high risk of mortality and morbidity related to cardiovascular and adverse limb events. Despite significant advances in both medical and interventional therapies, PAD often remains under-diagnosed, and the prognosis of patients can be difficult to predict. Artificial intelligence (AI) has brought a wide range of opportunities to improve the management of cardiovascular diseases, from advanced imaging analysis to machine-learning (ML)-based predictive models, and medical data management using natural language processing (NLP). The aim of this review is to summarize and discuss current techniques based on AI that have been proposed for the diagnosis and the evaluation of the prognosis in patients with PAD. The review focused on clinical studies that proposed AI-methods for the detection and the classification of PAD as well as studies that used AI-models to predict outcomes of patients. Through evaluation of study design, we discuss model choices including variability in dataset inputs, model complexity, interpretability, and challenges linked to performance metrics used. In the light of the results, we discuss potential interest for clinical decision support and highlight future directions for research and clinical practice.Angiology380Clinical studies that proposed AI-methods for the detection and the classification of PAD as well as studies that used AI-models to predict outcomes of patients and future directions for research and clinical practice are reviewed.Angiology["S\u00b4ebastien Goffart", "Herv\u00e9 Delingette", "Andrea Chierici", "Lisa Guzzi", "Bahaa Nasr", "F. Lareyre", "J. Raffort"]2025-01-17T00:00:00 e57758edb36ca54127dbe0a71a968b1c8ce5f475The accelerating pace of environmental change poses significant challenges to ecosystems, economies, and human health. Traditional methods of environmental modelling often fall short in addressing the complexity and scale of these issues. Applying machine learning (ML) to climate data offers numerous benefits, revolutionizing method to analyse, understand, and address climate change challenges. This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) in predicting environmental changes, highlighting methodologies, applications, and future directions. Keywords: Machine Learning, Artificial Intelligence, Environmental Change, Prediction, Data Analysis.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The potential of Machine Learning (ML) and Artificial Intelligence (AI) in predicting environmental changes is explored, highlighting methodologies, applications, and future directions.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Ajay Anand"]2025-01-17T00:00:00 e71991999593c71f58ae2a88d00641217e70bd0eOral and Maxillofacial Surgery1321OMS stands to benefit enormously from the integration of AI, however, significant roadblocks, such as ethical concerns, data security, and integration challenges, must be addressed to ensure effective adoption.Oral and maxillofacial surgery["R. R. Dang", "Balram Kadaikal", "Sam El Abbadi", "Branden R Brar", "Amit Sethi", "Radhika Chigurupati"]2025-01-17T00:00:00 b573d23d4eee0964aa558c67370ffa070424a7ddInternational Journal of Computer Applications Technology and Research00International Journal of Computer Applications Technology and Research[]2025-01-17T00:00:00 b88d0a65de31096914859ddd8973ea9f3b41b12fIndustry 4.0 Science00Industry 4.0 Science[]2025-01-17T00:00:00 1c629ec61b8b8026467e46c6a4b3788d9bf73a65<jats:p/>ATRAS journal00ATRAS journal["Chahida Hadef"]2025-01-17T00:00:00 6ed3b6d8daffabd9253c87700edfeb4fb69b0ca8The potential for inaccuracy while using AI could cause problems for future doctors attempting to pass their board exams, doctors attempting to brush up on a certain topic in a short amount of time, or those trying to make use of AI to for patient care decision-making. Low CSF pressure headaches can be highly disabling for patients and are often misdiagnosed These types of headaches are relatively common, effecting approximately 5 individuals per 100,000 per year. An AI model was selected to examine the neurological scenario through a Neurology expert’s guidance to determine a diagnosis. While some AI models are more developed than others, a free, heavily utilized platform was used in this testing. the AI gave direction to order an MRI and consult with other Neurological doctors, but it failed to arrive at the correct differential diagnosis. In the test, the first failure was to request the appropriate tests. It then failed to provide an accurate working diagnosis. Without understanding an AI model’s limitations, it may lead to negative patient outcomes if used in medical decision-making. This could also prolong the patient’s condition, allowing them to suffer unnecessary pain and potential disability.Academic Medicine &amp; Surgery50Academic Medicine &amp; Surgery["Michael Turtz", "George Tarantino", "Jill Goldstein", "Michael Dobbs"]2025-01-17T00:00:00 243fcff38bb7fa998cbb0091b2a9fe2ffefa2287International Journal of Production Research280International Journal of Production Research["Vikas Kumar", "F. Goodarzian", "P. Ghasemi", "Felix T. S. Chan", "Narain Gupta"]2025-01-17T00:00:00 227ac925d4af26e484330e3e3598230cfc3c9e04<jats:p/>ATRAS journal00ATRAS journal["Latifa ZIANE BOUZIANE"]2025-01-17T00:00:00 b89cb34d89eab9b6640d82efec40cfa5391119b2International Journal of Neural Systems00International Journal of Neural Systems["Zvi Kam", "Lorenzo Peracchio", "Giovanna Nicora"]2025-01-17T00:00:00 40f49cb78e01406675a331d9abf4fb72fa6041afEnterprise Information Systems330Enterprise Information Systems["Hailin Li", "Huimin Tian", "Wenhao Zhou", "Y. Wu"]2025-01-17T00:00:00 b7f1aa6d44450a839ceb87938c3b0c8566fcc5d9Acta Psychologica920The results showed a positive relationship among AI use and employee well-being, but not with career success, and technostress moderated the associations between AI use and employee well-being and career success.Acta psychologica["Muhammad Naeem Sharif", "Li Zhang", "Muhammad Asif", "Sajead Mowafaq Alshdaifat", "J. Hanaysha"]2025-01-17T00:00:00 d9dc1c5c98b77c130f996ee24f1d35715de9b97cEmergency Radiology00Emergency radiology["David Dreizin", "Garvit Khatri", "Pedro V Staziaki", "Karen Buch", "Mathias Unberath", "Mohammed Mohammed", "Aaron Sodickson", "B. Khurana", "Anjali Agrawal", "J. S. Spann", "Nicholas Beckmann", "Z. Delproposto", "C. LeBedis", "Melissa Davis", "Gabrielle Dickerson", "Michael H Lev"]2025-01-17T00:00:00 3980a1da18fda33f923db35b49c7a64797b453d1Generative AI has had a profound impact on biomedicine and health, both in professional work and in education. Based on large language models (LLMs), generative AI has been found to perform as well as humans in simulated situations taking medical board exams, answering clinical questions, solving clinical cases, applying clinical reasoning, and summarizing information. Generative AI is also being used widely in education, performing well in academic courses and their assessments. This review summarizes the successes of LLMs and highlights some of their challenges in the context of education, most notably aspects that may undermines the acquisition of knowledge and skills for professional work. It then provides recommendations for best practices overcoming shortcomings for LLM use in education. Although there are challenges for use of generative AI in education, all students and faculty, in biomedicine and health and beyond, must have understanding and be competent in its use.00Although there are challenges for use of generative AI in education, all students and faculty, in biomedicine and health and beyond, must have understanding and be competent in its use.["William Hersh"]2025-01-17T00:00:00 9c48dce3eedc1d0f270ff3e0c0968e98c2106235This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm—Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency. To address these problems, a genetic algorithm (GA) is adopted for optimizing the BPNN, and the EGA-BPNN model is used to predict irrigation flow in agricultural fields. Secondly, the EGA-BPNN model can overcome the local optimization and overfitting problems of traditional BPNN through the global search ability of GA. Moreover, it is suitable for the irrigation flow prediction task with complex environmental factors in smart agriculture. Finally, comparative experiments compare the prediction accuracy of BPNN and EGA-BPNN using single and dual water level flow prediction models respectively. The results reveal that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. When the number of nodes in the hidden layer increases from 6 to 16, the MSE of the single and dual water level flow prediction models decreases from 4.53×10−4 to 3.68×10−4 and 2.38×10−4 to 1.66×10−4, respectively. Under a standalone BPNN, the absolute relative error in flow prediction is 1.09%. In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. Furthermore, compared to the BPNN, the EGA-BPNN model exhibits a 2.11 reduction in MSE, further emphasizing the positive impact of introducing the GA on model performance. The research outcomes contribute to more accurate water resource planning and management, providing a more reliable basis for decision-making.PLoS ONE680This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm—Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture, revealing that as the number of nodes in the hidden layer increases, the model’s Mean Squared Error and Relative Error show a decreasing trend, indicating an improvement in model prediction accuracy.PLOS ONE["Xiying Wang"]2025-01-17T00:00:00 cb47c08aed131ffcffa7f23a3daac6bfdb7eb8eaGlobal Surgical Education - Journal of the Association for Surgical Education790Global Surgical Education - Journal of the Association for Surgical Education["Pablo Achurra", "Domingo Mery", "Arnoldo Riquelme", "Chaya Shwaartz"]2025-01-17T00:00:00 2be41d751dde3f86a31f6c786b6a5a147b249456PLOS Global Public Health120PLOS Global Public Health["W. G. Mitchell", "Judy Gichoya Wawira", "L. A. Celi"]2025-01-17T00:00:00 67568f7443836d0aa363b2a4326add00384ab7e9 AI models in medicine often fall short in real world deployment due to inability to incorporate multiple data modalities in their decision-making process as clinicians do. Clinicians integrate evidence and signals from multiple data sources like radiology images, patient clinical status as recorded in electronic health records, consultations from fellow providers, and even subtle clues using the appearance of a patient, when making decisions about diagnosis or treatment. To bridge this gap, significant research effort has focused on building fusion models capable of harnessing multi-modal data for advanced decision making. We present a broad overview of the landscape of research in multimodal AI for radiology covering a wide variety of approaches from traditional fusion modeling to modern vision-language models. We provide analysis of comparative merits and drawbacks of each approach for assist future research and highlight ethical consideration in developing multimodal AI. In practice, the quality and quantity of available training data, availability of computational resources, and clinical application dictates which fusion method may be most suitable.BJR|Artificial Intelligence00A broad overview of the landscape of research in multimodal AI for radiology covering a wide variety of approaches from traditional fusion modeling to modern vision-language models is presented and analysis of comparative merits and drawbacks of each approach is provided.BJR|Artificial Intelligence["A. Tariq", "Imon Banerjee", "H. Trivedi", "J. Gichoya"]2025-01-17T00:00:00 cb2cefbfe09156701854bb63bc206cb0f69d3e4fClimate change is undeniably one of the most critical challenges facing the world today. It demands innovative solutions, especially in the realms of prediction and mitigation. Among the tools that offer significant promise is Artificial Intelligence (AI), which is reshaping the landscape of climate science by improving forecasting models, enhancing renewable energy systems, and driving policy changes towards sustainability. However, how AI is utilized to address climate change varies dramatically between countries, influenced by distinct governance structures, technological priorities, and institutional setups. This study offers a comparative analysis of the United States and China, two global powerhouses in the fields of AI and climate action. The research explores how these two nations, despite sharing similar climate objectives, adopt different strategies based on their political, economic, and technological contexts. The US tends to rely on decentralized, market-driven innovation, while China employs a more centralized, state-guided approach. These differences significantly impact the deployment of AI in critical areas like climate modeling, emissions monitoring, and energy optimization. The analysis is framed within the context of comparative institutionalism and technological innovation systems, which provide the theoretical underpinnings to explore the influence of political, economic, and technological factors on climate strategies. The findings of this study reveal that while the US thrives on innovation driven by the private sector, leading to the development of cutting-edge AI tools, China benefits from a unified national strategy that ensures rapid implementation on a large scale. Both models have distinct advantages and drawbacks, ultimately shaping each country’s contribution to the global fight against climate change. This paper concludes with a discussion on the implications of these findings for global cooperation in AI governance, highlighting the importance of international collaboration to fully harness AI’s potential for climate mitigation. This research thus contributes to the ongoing conversation around AI’s role in fostering sustainable development, offering practical insights and shedding light on how global political contexts influence technological solutions for the climate crisis.The Critical Review of Social Sciences Studies160The findings of this study reveal that while the US thrives on innovation driven by the private sector, leading to the development of cutting-edge AI tools, China benefits from a unified national strategy that ensures rapid implementation on a large scale, shaping each country’s contribution to the global fight against climate change.The Critical Review of Social Sciences Studies["Shehar Bano"]2025-01-17T00:00:00 4c619e4fdb30f59dd8e0174577b9197d2ed1b4f1The integration of artificial intelligence (AI) in education is rapidly transforming learning environments, and the adoption of AI-based e-learning platforms (AI-ELP) is gaining momentum. However, understanding the factors influencing AI-ELP adoption is crucial to ensure its effective implementation. This research study aims to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating technophobia, technophilia, content quality, and functional quality. By examining the psychological tendencies of users toward technology and the quality aspects of AI-ELP, this study seeks to provide a comprehensive understanding of the adoption process. Through a quantitative study involving research scholars at IIT Kharagpur, the research will identify key factors influencing the acceptance and use of AI-ELP. The findings will have significant implications for educational practitioners, policymakers, and platform developers, enabling them to tailor strategies that address user concerns, enhance platform quality, and promote successful AI-ELP adoption in educational settings.Forum for Education Studies770This research study aims to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating technophobia, technophilia, content quality, and functional quality by examining the psychological tendencies of users toward technology and the quality aspects of AI-ELP.Forum for Education Studies["Sougato Das", "Smita Poi", "Shubham Saxena"]2025-01-17T00:00:00 d98a23ff7a9be44efbda21a171c0cf3eec09b4f4Cartographic design is fundamental to effective mapmaking, requiring adherence to principles such as visual hierarchy, symbolization, and color theory to convey spatial information accurately and intuitively, while Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed various fields, their application in cartographic design remains underexplored. This study assesses the capabilities of a multimodal advanced LLM, GPT-4o, in understanding and suggesting cartographic design elements, focusing on adherence to established cartographic principles. Two assessments were conducted: a text-to-text evaluation and an image-to-text evaluation. In the text-to-text assessment, GPT-4o was presented with 15 queries derived from key concepts in cartography, covering classification, symbolization, visual hierarchy, color theory, and typography. Each query was posed multiple times under different temperature settings to evaluate consistency and variability. In the image-to-text evaluation, GPT-4o analyzed maps containing deliberate cartographic errors to assess its ability to identify issues and suggest improvements. The results indicate that GPT-4o demonstrates general reliability in text-based tasks, with variability influenced by temperature settings. The model showed proficiency in classification and symbolization tasks but occasionally deviated from theoretical expectations. In visual hierarchy and layout, the model performed consistently, suggesting appropriate design choices. In the image-to-text assessment, GPT-4o effectively identified critical design flaws such as inappropriate color schemes, poor contrast and misuse of shape and size variables, offering actionable suggestions for improvement. However, limitations include dependency on input quality and challenges in interpreting nuanced spatial relationships. The study concludes that LLMs like GPT-4o have significant potential in cartographic design, particularly for tasks involving creative exploration and routine design support. Their ability to critique and generate cartographic elements positions them as valuable tools for enhancing human expertise. Further research is recommended to enhance their spatial reasoning capabilities and expand their use of visual variables beyond color, thereby improving their applicability in professional cartographic workflows.ISPRS International Journal of Geo-Information370It is concluded that LLMs like GPT-4o have significant potential in cartographic design, particularly for tasks involving creative exploration and routine design support, and their ability to critique and generate cartographic elements positions them as valuable tools for enhancing human expertise.ISPRS International Journal of Geo-Information["Abdulkadir Memduho\u011flu"]2025-01-17T00:00:00 0dac0a4b35139152daa8498f871a4e52472b1754There is a growing need for strong methods to guarantee the accuracy and reliability of data due to the widespread use of next-generation AI in automated processes. This research delves into new approaches to rethink AI system quality checks, with a focus on context-aware, adaptable, and dynamic validation. Modern artificial intelligence ecosystems are notoriously difficult for traditional data integrity frameworks to manage due to the sheer volume and variety of data streams and continuous learning paradigms used therein. A proactive and scalable quality assurance methodology is proposed by this study by combining state-of-the-art methods including feedback loops, explainable AI, and anomaly detection. Research shows that using these methods greatly improves AI-driven processes in terms of accuracy and dependability while decreasing the likelihood of bias, mistakes, and inefficiencies. Findings from this research highlight the need of continuously improving quality assurance procedures for sustaining credibility and efficiency in the age of intelligent automation. This paper delves into the changing landscape of quality assurance in AI-driven processes, with a focus on how automated workflows must prioritise data integrity. With their reliance on varied, high-volume information and complicated algorithms, next-generation AI systems are dynamic and complex, making traditional quality checks inadequate. In order to guarantee strong data integrity, this study suggests a new AI quality assurance system that combines adaptive mistake detection, predictive analytics, and sophisticated validation techniques. The framework reimagines quality standards in AI operations by using state-of-the-art technologies such as blockchain for traceability and federated learning for decentralised validation. There are noticeable gains in efficiency, accuracy of decisions, and reduction of errors in empirical assessments. The results highlight the need to reconsider quality standards in order to build trustworthy and reliable AI ecosystems, which will allow for their ethical and scalable implementation. Organisations striving to align AI systems with strict quality and integrity requirements in increasingly automated settings might look to our work as a benchmark.Journal of Information Systems Engineering & Management140A new AI quality assurance system that combines adaptive mistake detection, predictive analytics, and sophisticated validation techniques is suggested, which reimagines quality standards in AI operations by using state-of-the-art technologies such as blockchain for traceability and federated learning for decentralised validation.Journal of Information Systems Engineering and Management["Chandrasekhar Rao Katru", "Sandip J. Gami", "Kevin N. Shah", "Abhishek Trehan", "D. Saratchandran"]2025-01-17T00:00:00 117a061d8b50ba385367a9d0e899341097e19972BMC Medical Informatics Decis. Mak.390The application of transfer learning significantly improved the predictive performance of models across various racial and ethnic groups, suggesting these techniques are effective in mitigating biases and promoting fairness in AI models.BMC Medical Informatics and Decision Making["Tianshu Gu", "Wensen Pan", "Jing Yu", "Guang Ji", "Xia Meng", "Yongjun Wang", "Minghui Li"]2025-01-17T00:00:00 083a8a24620a4ba78e730c047435183898069db1In this paper we describe the development and evaluation of AITK, the Artificial Intelligence Toolkit. This open-source project contains both Python libraries and computational essays (Jupyter notebooks) that together are designed to allow a diverse audience with little or no background in AI to interact with a variety of AI tools, exploring in more depth how they function, visualizing their outcomes, and gaining a better understanding of their ethical implications. These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI. In addition, we conducted usability testing of AITK. Our pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping users gain a better understanding of AI. Our goal, in this time of rapid innovations in AI, is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.00The goal of AITK is for AITK to provide an accessible resource for faculty from any discipline looking to incorporate AI topics into their courses and for anyone eager to learn more about AI on their own.["Levin Ho", "Morgan McErlean", "Zehua You", "Douglas Blank", "L. Meeden"]2025-01-17T00:00:00 eb2eddc0c0bbc6a273b21554295f856296180036 This paper traces the legislative process of the EU Artificial Intelligence Act (AI Act) to provide an empirical and critical account of the choices made in its formation. It specifically focuses on the dynamics that led to increasing or lowering fundamental rights protection in the final text and their implications for fundamental rights. Adopting process-tracing methods, the paper sheds light on the institutional differences and agreements behind this landmark legislation. It then analyses the implications of political compromise for fundamental rights protection. The core message it aims to convey is to read the AI Act with its institutional setting and political context in mind. As this paper shows, the different policy aims and mandates of the three EU institutions, compounded by the unprecedented level of redrafting and the short time needed to reach a political agreement, influenced the formulation of the AI Act. Looking forward, the paper points to the role of implementation, enforcement and judicial interpretation in enhancing the protection of fundamental rights in the age of AI.European Journal of Risk Regulation00European Journal of Risk Regulation["F. Palmiotto"]2025-01-17T00:00:00 38589f363c91d29c03fc083b47d47c6830d7c9daGenerative Artificial Intelligence (genai) is significantly impacting the field of cybersecurity by presenting both opportunities and challenges. On one hand, technologies like large language models (llms), exemplified by openai's chatgpt and Google deepmind's Gemini, enhance operational security through their ability to analyze vast amounts of data, identify malicious activities, and automate threat responses. Genai can generate real-time alerts, assist in vulnerability testing, and facilitate training in cybersecurity through simulations and tutorials. However, its capabilities also pose serious risks, as malicious actors can leverage these same tools to execute sophisticated attacks, such as highly personalized phishing schemes and the automation of malware creation. Research studies highlight the implications of genai's evolution in cybersecurity. Gupta et al. (2023) and Neupane et al. (2023) explore how the misuse of genai can lead to new cyber threats, including deepfakes and misinformation, necessitating a proactive response. Strategies like the Cyber Kill Chain (CKC) model can help understand attack cycles and strengthen defenses against these threats. Furthermore, Barrett et al. (2023) discuss the dual-use dilemma of genai, stressing the need for ethical considerations in its development and use. As genai technologies continue to advance, the need for comprehensive regulatory frameworks and integrated security measures becomes increasingly critical. Collaboration among stakeholders—including tech companies, policymakers, and researchers—is essential for maximizing the benefits of genai while minimizing its exploitation. The ongoing discourse surrounding genai’s impact on cybersecurity is vital for addressing emerging challenges and preparing for a future where technology enhances security without compromising ethical standards.Brazilian Journal of Development00The dual-use dilemma of genai is discussed, stressing the need for ethical considerations in its development and use, and how the misuse of genai can lead to new cyber threats, including deepfakes and misinformation, necessitating a proactive response.Brazilian Journal of Development["Flavio Ambrosio da Silva"]2025-01-17T00:00:00 7fa3fda93c5a792265f4f497352b45391c1f5ca5npj Digit. Medicine400The types of robustness addressed in the literature for ML models in healthcare were identified and eight general concepts of robustness emerged, and an analysis of those concepts across types of data and types of predictive models revealed that the concepts were differently addressed.NPJ Digital Medicine["Alan Balendran", "C\u00e9line Beji", "Florie Bouvier", "Ottavio Khalifa", "Theodoros Evgeniou", "Philippe Ravaud", "Raphael Porcher"]2025-01-17T00:00:00 ab7ebe1af768ec8195ee87e6a1686b31ddc138d7This study explores the transformative potential of artificial intelligence (AI) in national development across key sectors, including economic growth, healthcare, education, infrastructure, and security. Using a qualitative doctrinal research approach, it synthesizes insights from peer-reviewed literature, policy analyses, and case studies to assess AI's contributions and challenges in fostering sustainable progress. Key findings highlight AI’s role in driving economic productivity through automation and innovation while underscoring its impact on the workforce and job market transformation. In healthcare, AI enhances diagnostics, treatment planning, and public health interventions, though concerns about data privacy and algorithmic bias persist. Education systems benefit from AI-enabled personalized learning and skill development, preparing future-ready workforces. Additionally, AI supports sustainable infrastructure through smart city initiatives, improving resource management and urban planning. Despite these advancements, the study identifies challenges such as ethical dilemmas, digital divides, and governance gaps that hinder equitable AI adoption. Recommendations include establishing transparent regulatory frameworks, fostering international collaboration, and investing in digital literacy to ensure inclusive growth. By addressing these issues, AI can become a cornerstone of national development, contributing to the United Nations' Sustainable Development Goals. This research underscores the importance of balanced policies and proactive governance to harness AI's benefits while mitigating associated risks.Asian journal of current research01The importance of balanced policies and proactive governance to harness AI's benefits while mitigating associated risks is underscored, highlighting the importance of balanced policies and proactive governance to harness AI's benefits while mitigating associated risks.Asian Journal of Current Research["Olusola Olabisi Ogunseye", "Oladapo Tolulope Ajayi", "Adetutu Fabusoro", "Amina Oje Abba", "Benjamin Adepoju"]2025-01-18T00:00:00 210aba72565cbb4e7dd58caab62917eccd688221The integration of artificial intelligence (AI) in the field of public relations (PR) has brought a massive change in communication. This paradigm shift is not merely a technological advancement but a fundamental transformation in how public relations practitioners (PRPs) interact with stakeholders to communicate effectively. The study's primary objective was to examine the extent to which PRPs in Bangladesh utilize AI in their routine activities to achieve organizational goals. This research elucidates the current landscape by surveying a diverse sample of PRPs, thereby disclosing the degree to which AI technologies have been incorporated into PR practices. The study revealed that the utilization of AI in the field of PR in a variety of institutions in Bangladesh is less anticipated. Inadequate knowledge was also discovered in the investigation. This research contributes to the ongoing conversation regarding the influence of AI on the future of PR. It offers valuable insights for professionals and organizations that are striving to navigate this transformative journey effectively.Society &amp; Sustainability180The study revealed that the utilization of AI in the field of PR in a variety of institutions in Bangladesh is less anticipated and inadequate knowledge was also discovered in the investigation.Society &amp; Sustainability["Muhammad Kawsar Mahmud", "Tahmina Sultana", "Harunur Rashid"]2025-01-18T00:00:00 45b18a1a26517b735b19929aa0da1e1fea04d2f0Artificial intelligence (AI) uses large datasets to "train" algorithms that make autonomous decisions, leading to significant changes across fields, including digital marketing and personalized advertising. Given AI's growing importance for business and society, ethical concerns, particularly related to privacy and transparency, are becoming increasingly relevant. This research focuses on the ethical aspects of AI in digital marketing, specifically examining consumer perceptions and responses to personalized recommendations and advertisements. The findings highlight consumer attitudes toward AI in personalized advertising, their digital behaviors, and the caution they exhibit when sharing personal data. The paper also briefly discusses regulatory efforts and the adoption of ethical codes concerning AI, both in Serbia and internationally.BizInfo Blace00This research focuses on the ethical aspects of AI in digital marketing, specifically examining consumer perceptions and responses to personalized recommendations and advertisements, and highlights consumer attitudes toward AI in personalized advertising.BizInfo Blace["Jovanka Vukmirovi\u0107", "Lola Mari\u010di\u0107", "Selena Stanojevi\u0107", "Aleksandra Vukmirovi\u0107", "Ivan Mandi\u0107"]2025-01-18T00:00:00 e49f99d65cbd1de383873abff91c323898d1172eAI and Computer Engineering are combining to revolutionize software development. They encourage creativity, enhance functionality, and increase efficiency. As AI-driven tools and frameworks become indispensable to the development of today, their merging with CE is changing the game and making it possible to create intelligent, adaptable systems that can solve the most challenging problems in multiple industries. This study aims to investigate where artificial intelligence and computer engineering intertwine in modern software development, specifically by looking into methods that enhance innovation, functionality, and efficiency in software through integrated AI-driven tools, frameworks, and performance measures.A three-phase mixed-methods approach was adopted. The secondary data analysis included pertinent literature and open-source code repositories, structured surveys, and interviews of fifty industry professionals. While qualitative analysis was done by applying thematic coding to find trends, obstacles, and possibilities in AI integration, quantitative analysis focused on performance indicators such as execution speed, accuracy, and memory utilization. The most widely used AI frameworks are TensorFlow and PyTorch due to their performance and flexibility. The industry-specific trends have been identified with different priorities: healthcare focuses on accuracy (77.5%), retail on the execution speed (55 ms) and minimal resource usage, and finance on balanced optimization. Major challenges were seen in the form of barriers such as a high learning curve (20%), scalability issues (25%), and compatibility issues (30%).The report emphasizes that better tools, increased cross-platform support, and thorough training are needed to overcome the hurdles related to the adoption of AI. It identifies industry-specific needs for AI software optimization and provides useful advice on how to successfully use AI technology in sectors like healthcare, retail, and finance. Specific Contribution: This study provides practical insights into how AI is revolutionizing software development. It provides a strong analytical framework to assess performance metrics and solve integration issues by analyzing industry-specific trends and trade-offs in AI integration. This advances our understanding of CE-AI convergence and guides future research and development in AI-augmented systems.Journal of Information Systems Engineering & Management290It is emphasized that better tools, increased cross-platform support, and thorough training are needed to overcome the hurdles related to the adoption of AI and provides useful advice on how to successfully use AI technology in sectors like healthcare, retail, and finance.Journal of Information Systems Engineering and Management["Shaimaa Saadoon", "Mahmood ALrfae"]2025-01-18T00:00:00 d1dfe09d10632c6d52cfdfc83c9f6d91a57570b6Secure Artificial Intelligence (SAI): A Dual-layer defence model against prompt injection and prompt poisoning attacks. Keywords— Large Language Model, Secure Artificial Intelligence, Artificial Intelligence, Prompt Injection, AI security. Abstract— Artificial Intelligence (AI) is deeply embedded in sectors handling sensitive information and mission-critical operations, and safeguarding these systems has become paramount. This paper introduces a novel dual-layer defence system termed Secure Artificial Intelligence (SAI), designed to mitigate risks associated with prompt injections and prompt poisoning attacks. Using two Large Language Models (LLMs) in a sequential setup “SAI”– a “Guard” model for initial input prompt classification which effectively filters out adversarial inputs to protect the AI system and a primary response model that responds to the user’s queries. Through rigorous testing, SAI has shown resilience in preventing malicious prompts from compromising AI responses, thereby significantly advancing AI security. This paper thoroughly examines SAI’s architecture, methodology, and performance, addressing the growing demand for secure and adversarial-resistant AI systems.International Journal For Multidisciplinary Research270This paper thoroughly examines SAI’s architecture, methodology, and performance, addressing the growing demand for secure and adversarial-resistant AI systems.International Journal For Multidisciplinary Research["Hitika Teckani", "Devansh Pandya", "Harshita Makwana"]2025-01-18T00:00:00 118e46bb8e9b36108d4e17243dd3761c9aabd644Issues examined in this study: first, How is the protection of personal data related to data processing in the form of artificial intelligence in Indonesia. Second, what is the concept of the existing legal norms of personal data protection related to data processing in the form of artificial intelligence. The method of research is carried out in literature with the type of normative legal research, according to dogmatic issues related to the emptiness of norms. Findings in this study found the protection of personal data related to data processing in the form of artificial intelligence in Indonesia there are two views. In this regard, the protection of personal data in data processing through artificial intelligence in Indonesia has no legal certainty to be protected. Because legal certainty refers to the application of clear, fixed, consistent and consequent laws whose implementation cannot be influenced by subjective circumstances. The concept of existing legal norms for personal data protection related to data processing in the form of artificial intelligence special arrangements stipulated in Law Number 27 of 2022 can be said to have accommodated personal rights. However, the existing condition of artificial intelligence norm is only legally domiciled as a legal object, because it is needed by the party responsible for the actions of artificial intelligence. Legal liability for the actions of artificial intelligence can be charged or imposed on the organizers of artificial intelligence.Jurnal Riset Multidisiplin Edukasi00Findings in this study found the protection of personal data related to data processing in the form of artificial intelligence in Indonesia has no legal certainty to be protected and there are two views.Jurnal Riset Multidisiplin Edukasi["Yalid"]2025-01-18T00:00:00 71a3db0c4bc125b4002624dee71df5fd7a8f29c3This study aims to evaluate the impact of artificial intelligence (AI) technologies on recruitment processes within the scope of the Kingdom of Bahrain's retail sector operations. The methodological approach adopted in this study is centered on a quantitative research design aided by an online survey that was designed to collect information pertaining to the research variables; namely - usage of AI technologies and recruitment process efficiency. Following this, statistical analyses were performed on the collected data such as ANOVA and regression tests with the purpose of identifying relationships that existed between AI adoption and recruitment process performance in the context of Bahrain's retail sector. The results of this study indicate a positive relationship between the usage of AI tools in the recruitment process and improved recruitment efficiency outcomes. The findings of this particular study point towards a range of different benefits that retail sector organizations in Bahrain can benefit from the adoption of AI such as improved candidate sorting, better quality of new hires, and overall efficiency in various recruitment related aspects. The findings of this study provides valuable insights for HR professionals and the management of retail organizations in Bahrain and similar markets when it comes to emphasizing the role of AI in improving and modernizing recruitment practices in the current competitive business environment.International Journal of Global Research Innovations &amp; Technology00A positive relationship between the usage of AI tools in the recruitment process and improved recruitment efficiency outcomes is indicated and points towards a range of different benefits that retail sector organizations in Bahrain can benefit from the adoption of AI.International Journal of Global Research Innovations &amp; Technology["Sameer Shafi Ebrahim", "Mohammed Shahzad"]2025-01-18T00:00:00 a55b4db916bc6d0a1e802a7100bf6de31f5bdc99The current work is a systematic review paper that examines the function and significance of artificial intelligence (AI) in teacher education. The researcher gathered almost fifty articles from various platforms, including Google Scholar, Science Direct, Research Gate, and others, on AI and teacher education. Additionally, those publications’ analysis reveals a few key areas and their significance for teacher preparation. By delivering tailored learning experiences, improving instructional strategies, and providing data-driven insights etc. After collecting the article from the above sources, the investigator analyzed all the article on four major points e.g. AI and digital learning, AI and Teacher Education, AI and pedagogical leaning, AI and challenges in teaching learning process systematically, where the investigator found few points and analyzed vividly, at the end the view concern to the Artificial intelligence (AI) has the potential to completely transform Teacher Education. But in order to fully enjoy these advantages, the ethical, equitable, and preparedness issues around AI must be resolved.International Journal of Current Science Research and Review00Artificial intelligence has the potential to completely transform Teacher Education, but in order to fully enjoy these advantages, the ethical, equitable, and preparedness issues around AI must be resolved.International Journal of Current Science Research and Review["Dr. Gopikanta Suna", "Miss. Suchismita", "Mr. Tripurari Das"]2025-01-18T00:00:00 7ed9de06481d425c7f316fc56216ae06b830a6ceВ статье изучается влияние цифровизации и использования искусственного интеллекта на развитие коммерческих банков. Цифровые технологии открывают новые возможности для улучшения качества обслуживания, оптимизации внутренних операций и усиления безопасности. Благодаря цифровым решениям банки могут быстрее адаптироваться к изменяющейся деловой среде и создавать персонализированные, удобные и эффективные финансовые продукты. В условиях глобализации и растущей конкуренции цифровизация становится основой стратегий многих банков для поддержания их конкурентоспособности и устойчивости на фоне рыночных изменений The article examines the impact of digitalization and the use of artificial intelligence on the development of commercial banks. Digital technologies open up new opportunities to improve service quality, optimize internal operations, and enhance security. Thanks to digital solutions, banks can quickly adapt to the changing business environment and create personalized, convenient, and effective financial products. In the context of globalization and increasing competition, digitalization is becoming the basis of many banks' strategies to maintain their competitiveness and sustainability amid market changesПерспективные гуманитарные, социальные и экономические исследования: сборник статей международной научной конференции (Мурманск, Октябрь 2024)10Перспективные гуманитарные, социальные и экономические исследования: сборник статей международной научной конференции (Мурманск, Октябрь 2024)["\u041d\u0435\u043b\u043b\u0438 \u0418\u043b\u044c\u0438\u043d\u0438\u0447\u043d\u0430 \u0410\u043a\u044b\u043b\u0431\u0435\u043a\u043e\u0432\u0430", "\u0410\u0439\u043d\u0443\u0440\u0430 \u0410\u0439\u044b\u043f\u043e\u0432\u043d\u0430 \u041c\u0430\u043c\u0431\u0435\u0442\u043e\u0432\u0430", "\u0411\u0438\u043d\u044c \u0427\u0436\u0430\u043d", "\u042e\u0439\u0446\u0437\u0435 \u041b\u044e", "\u0416\u0438\u0431\u0435\u043a \u0423\u043b\u0430\u043d\u0431\u0435\u043a\u043e\u0432\u043d\u0430 \u041a\u0430\u0437\u0430\u043a\u043e\u0432\u0430"]2025-01-18T00:00:00 944e483724369bffacbac1d2ab75d3b0ffd49922This study provides a bibliometric analysis of ethical considerations in AI applications for fraud detection based on data from ScienceDirect spanning 2020 to 2024. The analysis identifies “artificial intelligence” as a core focus in the literature, alongside a marked increase in attention to ethical concerns, including data privacy, transparency, and accountability. Additionally, the study reveals progress in applying advanced technologies like blockchain, ChatGPT, and fintech within fraud detection frameworks, which increasingly demand ethical scrutiny. Key findings emphasize the necessity for comprehensive ethical frameworks to ensure transparency, accountability, and public trust in AI-driven fraud detection systems. Practical implications suggest that organizations should prioritize ethical dimensions within AI strategies, enhancing both trust and the effectiveness of detection mechanisms. By using bibliometric analysis, this study finds new trends and gaps in the ethical aspects of using AI to find fraud, which adds new information that hasn’t been fully explored in other studies.Journal of Auditing, Finance and Forensic Accounting350A bibliometric analysis of ethical considerations in AI applications for fraud detection based on data from ScienceDirect spanning 2020 to 2024 finds new trends and gaps in the ethical aspects of using AI to find fraud.Journal of Auditing, Finance, and Forensic Accounting["Moh Abqori Mudhories", "Tarjo Tarjo", "Bambang Haryadi", "Gao Zihan"]2025-01-18T00:00:00 2c471b45224d9bb1d286cdf6ba6bd4858e0b31aaCurrent Psychology290Current Psychology["Joshua Bernal-Salcedoc", "Consuelo V\u00e9lez \u00c1lvarez", "Marcela Tabares Tabares", "Santiago Murillo-Rend\u00f3nd", "Germ\u00e1n Gonz\u00e1les-Mart\u00ednez", "O. M. Casta\u00f1o-Ram\u00edrez"]2025-01-18T00:00:00 0808c39e5fa80bfa564366f2bf5c137b32ee182bPublic Management Review1060Public Management Review["Yanto Chandra", "Naikang Feng"]2025-01-18T00:00:00 8fdfcf6f84942d39e1b381d7a6c5d4dd891c9a33Cureus50Cureus["Joseph P Maurice", "David Santos"]2025-01-18T00:00:00 d869530d86cea661b18d0850a697c9175c83745cJOURNAL "DIALOGI"00JOURNAL "DIALOGI"["Paraskevi Zacharia"]2025-01-18T00:00:00 8e403ec29bb8e7c76d363fe1171154932f2546e8This study explores the dual implications of Artificial Intelligence (AI)-driven Open Source Intelligence (OSINT) in enhancing cyber defense capabilities. Using publicly available datasets, including IBM X-Force breach metrics, MITRE ATT&CK adversarial tactics, GDPR privacy violations, AI-driven phishing incidents, and case-specific data from the Colonial Pipeline ransomware attack and Russia-Ukraine conflict, the research employs multivariate regression, logistic regression, and K-Means clustering. The findings indicate that AI investments improve detection time (-0.68), accuracy (+2.09), and resolution rates (+1.55) with statistical significance (p < 0.001). However, risks associated with algorithmic opacity, weak regulatory frameworks, and reactive AI systems pose ethical and operational challenges. Clustering reveals variability in AI applications, with optimized systems achieving 95.2% detection rates and 5.5-hour response times. Recommendations include investing in scalable tools, strengthening regulations, fostering public-private collaborations, and enhancing reactive AI oversight. The results highlight AI’s transformative potential in cyber defense while emphasizing the need for ethical and regulatory alignment. Future directions include testing these models in diverse operational environments to validate effectiveness and exploring hybrid AI approaches to balance proactive and reactive capabilities, ensuring robust and adaptive defense mechanisms.Asian Journal of Research in Computer Science00This study explores the dual implications of Artificial Intelligence (AI)-driven Open Source Intelligence (OSINT) in enhancing cyber defense capabilities and highlights AI’s transformative potential in cyber defense while emphasizing the need for ethical and regulatory alignment.Asian Journal of Research in Computer Science["T. M. Kolade", "Onyinye Agatha Obioha-Val", "Adebayo Yusuf Balogun", "M. O. Gbadebo", "O. O. Olaniyi"]2025-01-18T00:00:00 440d0a4cb541a2f70ba349434c6abf28f04176f7As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.Journal of Engineering Research and Reports00By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.Journal of Engineering Research and Reports["Iveren M. Leghemo", "Chima Azubuike", "Osinachi Deborah Segun-Falade", "Chinekwu Somtochukwu Odionu"]2025-01-18T00:00:00 7c2c889642abb4b124f848037d0383ab8b72a3b7The article analyses financial technologies as an object of financial and legal regulation in the context of globalisation and development of the digital economy. The author examines the essence and features of financial technologies, their development, as well as the problems arising in the process of their implementation and application. The article examines how the rapid development of innovative financial instruments, in particular cryptocurrencies, blockchain technologies, mobile payment systems and systems based on artificial intelligence, poses new challenges and requirements to legal institutions. In particular, the article examines the stages of development of financial technologies, ranging from traditional banking operations to the latest digital financial instruments that are actively changing financial markets and the structure of traditional financial institutions. An important place is occupied by the analysis of legal issues related to the need to adapt national and international norms to new realities, in particular, insufficient regulation of a number of financial innovations, such as cryptocurrencies, decentralised financial systems, etc. Given the high level of rapidly developing innovations, the article emphasises the need to create specialised legislative mechanisms and regulations for effective regulation of financial technologies. The authors pay special attention to the experience of international practice, in particular, legislative initiatives of the European Union, where the introduction of regulations such as PSD2 has contributed to the development of new payment systems and technologies. In particular, the author emphasises the importance of legal support for the security of financial transactions, protection of users’ rights and prevention of risks associated with the use of new technologies. The article concludes with conclusions on the need for a comprehensive approach to the legal regulation of financial technologies, which will ensure stability, transparency and security of financial markets. The authors emphasise that an important component of this process is the interaction of government agencies, international organisations and financial institutions to create a unified legal environment that will promote innovation in the financial sector and protect the rights and interests of all financial market.Uzhhorod National University Herald. Series: Law00Uzhhorod National University Herald. Series: Law["D. V. Tytarenko", "V. R. Kostenko"]2025-01-18T00:00:00 ccc6ff48741fe05474d9d4dee6fd7471bfb7dafbInnovation is pivotal in the competitive landscape of the Information and Communication Technology (ICT) sector, particularly in the context of rapidly evolving technological advancements. This research examines the instrumental role of Artificial Intelligence (AI) in facilitating innovation within the ICT sector, explaining its significance amidst accelerating technological progress. Utilizing articles from reputable high-impact journals published between 2019 and 2024, this paper meticulously analyzes recent contributions to clarify the integration of AI into innovation processes. The study builds upon foundational concepts in innovation management and contemporary advancements in AI frameworks, including AI Centered Design Thinking, the AI Adaptive Three-Horizons Framework, the AI Enabled Open Innovation Paradigm, the AI Specific Stage-Gate Model, and the AI Optimized Lean Startup Methodology, thereby contributing to the ongoing discourse surrounding AI integration in organizations. Employing a comprehensive literature review, the research systematically interrogates peer-reviewed articles from relevant databases, focusing on empirical evidence and theoretical insights regarding the influence of AI on innovation. The findings reveal that the strategic application of these frameworks significantly enhances decision-making efficiency, promotes user-centered design, and mitigates risks associated with AI deployment. The implications of this research are multifaceted, offering critical insights for academics, practitioners, and policymakers within the ICT sector. By highlighting the necessity of ethical considerations such as bias reduction and transparency in AI initiatives, the study emphasizes the imperative of responsible innovation practices. Ultimately, this research uniquely contributes to the field by providing a comprehensive synthesis of existing frameworks and their applicability to AI driven innovation, advocating for the continuous evolution of these frameworks to align with emerging technological trends. This focus affirms the relevance and significance of the study in advancing both theoretical understanding and practical application within the ICT sector.Journal830This research examines the instrumental role of Artificial Intelligence in facilitating innovation within the ICT sector, explaining its significance amidst accelerating technological progress, and provides a comprehensive synthesis of existing frameworks and their applicability to AI driven innovation.Smart Cities and Regional Development (SCRD) Journal["Erika Grabocka", "Ervisa Ndoka"]2025-01-18T00:00:00 b19c76482151639f999fc3ff6b8d0cec90d87665Artificial Intelligence (AI) has gained significant prominence in science education, yet its practical applications, particularly in teacher training, remain underexplored. Specifically, there is a lack of research on AI’s potential to support personalized professional development through automated analysis of classroom interactions and tailored feedback. As science teacher education requires skill development in complex scientific concepts within problem-based learning (PBL) contexts, there is a growing need for innovative, technology-driven instructional tools. AI-generated instructional videos are increasingly recognized as powerful tools for enhancing educational experiences. This study investigates the impact of AI-generated instructional videos, designed using established instructional design principles, on self-efficacy, task performance, and learning outcomes in science teacher education. Employing a within-subjects design, the current study included pre-test, post-test, and transfer assessments to evaluate learning durability and transferability, consistent with design-based research methodology. Moreover, this study compares the effectiveness of two AI-generated instructional video formats: one with an embedded preview feature allowing learners to preview key concepts before detailed instruction (video-with-preview condition) and another without this feature (video-without-preview condition). It specifically examines the role of preview features in enhancing these outcomes during training on scientific concepts with 55 Greek pre-service science teachers (n = 55; mean age 27.3 years; range 22–35). The results demonstrated that the videos effectively supported self-efficacy, task performance, and knowledge retention. However, no significant differences were observed between videos with and without preview features across all assessed metrics and tests. These findings also indicate that AI-generated instructional videos can effectively enhance knowledge retention, transfer, and self-efficacy, positioning them as promising assets in science teacher education. The limited impact of the preview feature highlights the need for careful design and evaluation of instructional elements, such as interactivity and adaptive learning algorithms, to fully realize their potential.Education sciences510Investigating the impact of AI-generated instructional videos on self-efficacy, task performance, and learning outcomes in science teacher education indicates that AI-generated instructional videos can effectively enhance knowledge retention, transfer, and self-efficacy, positioning them as promising assets in science teacher education.Education Sciences["Nikolaos Pellas"]2025-01-18T00:00:00 b91dd02ff5df5ec639634f8ee67299364b704968Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy through novel architectures, the underlying mechanisms of AI-based CSI feedback remain unclear. This study investigates these mechanisms by analyzing performance across diverse datasets and reveals that superior feedback performance stems from the strong fitting capabilities of AI models and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt-enabled large AI model (LAM) for CSI feedback. The LAM employs powerful transformer blocks and is trained on extensive datasets from various scenarios. To further enhance reconstruction quality, the channel distribution -- represented as the mean of channel magnitude in the angular domain -- is incorporated as a prompt within the decoder. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios.490Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios.["Jiajia Guo", "Yiming Cui", "Chao-Kai Wen", "Shi Jin"]2025-01-18T00:00:00 2d0b03d541a6b2099c8c229bd32be0ff85ecd2a0As governments worldwide increasingly adopt digital tools to enhance citizen engagement and service delivery, the integration of Artificial Intelligence (AI) emerges as a pivotal advancement in public administration. This study examines the impact of AI-assisted interactions on the quality of communication between citizens and civil servants, focusing on key dimensions such as Satisfaction, Politeness, Ease of Understanding, Feeling Heard, Trust, and Empathy from the citizens' perspective, and Clarity, Politeness, Responsiveness, Respect, Urgency, and Empathy from the civil servants' perspective. Utilizing a questionnaire-based experimental design, the research involved citizens and civil servants who evaluated both original and AI-modified communication samples across five interaction types: Service Requests, Policy Inquiries, Complaints, Suggestions, and Emergency Concerns. Statistical analyses revealed that AI modifications significantly enhanced most communication dimensions for both citizens and civil servants. Specifically, AI-assisted responses led to higher satisfaction, politeness, clarity, and trust among citizens, while also improving clarity, politeness, responsiveness, and respect among civil servants. However, AI interventions showed mixed effects on empathy and urgency from the civil servants' perspective, indicating areas for further refinement. The findings suggest that AI has substantial potential to improve citizen-government interactions, fostering more effective and satisfying communication, while also highlighting the need for continued development to address emotional and urgent communication nuances.00The findings suggest that AI has substantial potential to improve citizen-government interactions, fostering more effective and satisfying communication, while also highlighting the need for continued development to address emotional and urgent communication nuances.["Ruiyu Zhang", "Lin Nie"]2025-01-18T00:00:00 5b3483a3e57d38443c9c4dd5cb895206d16bc204Generative Artificial Intelligence (GAI) and the idea to use hierarchical models has been around for some years now. GAI has proved to be an extremely useful tool for Autonomous Vehicles (AVs). AVs need to perform robustly in their environment. Thus the AV behavior and short-term trajectory planning needs to be: a) designed and architected using safeguarding and supervisory systems and b) verified using proper Systems Engineering (SysEng) Principles. Can AV Systems Engineering also use Large Language Models (LLM) to help Autonomous vehicles (AV) development? This reader-friendly paper advocates the use of LLMs in 1) requirements (Reqs) development and 2) Reqs verification and 3) provides a proof-of-concept of AV supervisory control. The latter uses a simulation environment of a simple planar (bicycle) vehicle dynamics model and a Linear Quadratic Regulator (LQR) control with an LLM Application Interface (API). The Open-Source simulation SW is available from the author accessible to the readers so that they can engage into the AV stack, LLM API and rules, SysEng and Reqs and fundamental vehicle dynamics and control.280This reader-friendly paper advocates the use of LLMs in 1) requirements (Reqs) development and 2) Reqs verification and 3) provides a proof-of-concept of AV supervisory control.["D. Katzourakis"]2025-01-18T00:00:00 60ab54fc0c50740d04904f45958757314c0d961aIn this paper, I delve into the ethical and regulatory aspects of using artificial intelligence (AI) in the finance sector. As AI technologies increasingly influence financial decision-making, addressing issues of fairness, transparency, and regulation becomes crucial. The research will involve reviewing relevant academic literature, industry reports, and regulatory documents to gather information and insights on the ethical and regulatory dimensions of AI in finance. My research focuses on three main areas: the presence of bias in AI-driven financial decisions, the regulatory challenges hindering ethical AI deployment, and the need for transparency and explainability in AI processes. By examining these aspects, I argue that mitigating bias, enhancing regulatory frameworks, and promoting clarity in AI applications are essential for building trust and ensuring ethical practices in financial services. Key findings include the presence of bias in AI-driven financial decisions, the need for updated regulatory frameworks to address AI complexities, and the importance of transparency and explainability in AI processes. These elements are crucial for building trust and ensuring ethical practices in financial services. Ultimately, this work advocates for a collaborative approach among regulators, financial institutions, and AI developers to create a more equitable financial ecosystem.Asian Journal of Economics Business and Accounting00This work argues that mitigating bias, enhancing regulatory frameworks, and promoting clarity in AI applications are essential for building trust and ensuring ethical practices in financial services and advocates for a collaborative approach among regulators, financial institutions, and AI developers to create a more equitable financial ecosystem.Asian Journal of Economics, Business and Accounting["Ahmad Al-Harbi"]2025-01-18T00:00:00 d3b15c48e37c326bff7f6d2ab0c7618eef330672The integration of Artificial Intelligence (AI) into tax technology presents transformative potential for enhancing business strategies by significantly improving efficiency, accuracy, and compliance. This paper delves into these advancements and critically evaluates their strategic implications across various sectors, providing a thorough analysis of how AI-driven innovations are reshaping tax practices. It highlights the dual role of AI in not only simplifying and automating complex tax operations but also in enabling businesses to navigate the increasingly complex maze of global tax regulations more effectively. Furthermore, this exploration extends to the potential challenges that accompany the adoption of AI technologies, such as data security, privacy concerns, and the need for robust ethical frameworks to guide their use. Through a comprehensive literature review and analysis, it becomes evident that while AI-driven tax technology can dramatically enhance business operations and strategic planning, it also introduces significant challenges that require careful consideration and strategic management. The paper discusses these issues in depth, alongside proposing potential solutions that can help businesses harness the full potential of AI in their tax functions without compromising on security or ethical standards. The findings underscore the critical balance between leveraging technological advancements and addressing the associated risks, suggesting a roadmap for successful integration into business practices. Keywords: Artificial Intelligence, Tax Technology, Business Strategy, Tax Compliance, Data Security.International journal of research and review00Through a comprehensive literature review and analysis, it becomes evident that while AI-driven tax technology can dramatically enhance business operations and strategic planning, it also introduces significant challenges that require careful consideration and strategic management.International Journal of Research and Review["Tolulope Aladebumoye"]2025-01-18T00:00:00 ef5dff1e7aae21972cae81c83155a6c267acf0bbArtificial Intelligence-Generated Content, a subset of Generative Artificial Intelligence, holds significant potential for advancing the e-health sector by generating diverse forms of data. In this paper, we propose an end-to-end class-conditioned framework that addresses the challenge of data scarcity in health applications by generating synthetic medical images and text data, evaluating on practical applications such as retinopathy detection, skin infections and mental health assessments. Our framework integrates Diffusion and Large Language Models (LLMs) to generate data that closely match real-world patterns, which is essential for improving downstream task performance and model robustness in e-health applications. Experimental results demonstrate that the synthetic images produced by the proposed diffusion model outperform traditional GAN architectures. Similarly, in the text modality, data generated by uncensored LLM achieves significantly better alignment with real-world data than censored models in replicating the authentic tone.00An end-to-end class-conditioned framework that addresses the challenge of data scarcity in health applications by generating synthetic medical images and text data, evaluating on practical applications such as retinopathy detection, skin infections and mental health assessments is proposed.["Tasnim Ahmed", "Salimur Choudhury"]2025-01-18T00:00:00 ee893ac5f78a00e0eb89c2dc24cc811533457bf3Scientific Reports380While PT and perceived availability significantly enhance the adoption of biometric technologies, TP exhibited an unexpected positive influence, suggesting that cautious users may still embrace biometrics if perceived as secure and trustworthy.Scientific Reports["Wenyi Zhang", "Hengtian Zhang", "Zhouyang Deng"]2025-01-18T00:00:00 ff559858031c802b39834f07c95d6d7c30dd4533Revista Hispanoamericana de Ciencias de la Salud00Revista Hispanoamericana de Ciencias de la Salud["Ed\u00e9n Gal\u00e1n-Rodas"]2025-01-18T00:00:00 7b4427ff1816e01dcedba7fae951de12e3fef3bcOBJECTIVE Artificial intelligence (AI) could revolutionize the delivery of mental health care, helping to streamline clinician workflows and assist with diagnostic and treatment decisions. Yet, before AI can be integrated into practice, it is necessary to understand perspectives of these tools to inform facilitators and barriers to their uptake. We gathered data on clinician and community participant perspectives of incorporating AI in the clinical management of eating disorders. METHOD A survey was distributed internationally to clinicians (n = 116) with experience in eating disorder treatment (psychologists, psychiatrists, etc.) and community participants (n = 155) who reported occurrence of eating disorder behaviors. RESULTS 59% of clinicians reported use of AI systems (most commonly ChatGPT) for professional reasons, compared to 18% of community participants using them for help-related purposes. While more than half of clinicians (58%) and community participants (53%) were open for AI to help support them, fewer were enthusiastic about their integration (40% and 27%, respectively) and believed that they would significantly improve client outcomes (28% and 13%, respectively). Nine in 10 agreed that AI may be improperly used if individuals are not adequately trained, and could pose new data privacy and security concerns. Most agreed that AI will be convenient, beneficial for administrative tasks, and an avenue for continuous support, but will never outperform human clinicians on relational skills. CONCLUSION While many clinicians and community participants are open to the use of AI in eating disorder treatment and recognize its possible wide-ranging benefits, most remain cautious and uncertain about its implementation.International Journal of Eating Disorders211While many clinicians and community participants are open to the use of AI in eating disorder treatment and recognize its possible wide-ranging benefits, most remain cautious and uncertain about its implementation.The International journal of eating disorders["Jake Linardon", "Claudia Liu", "Mariel Messer", "Zoe McClure", "Cleo Anderson", "Hannah K Jarman"]2025-01-19T00:00:00 eab841cf2bc9d4a83df2a49123d4c55cf28bc05eThe knowledge gathered using Artificial Intelligence (AI), coupled with the efficiency of BOTs is a boon for testing activity in the IT world. This can, not only bring down the human errors, but can also significantly speed up the whole testing process and by extension the project timeline. As part of test preparation, the AI needs be exposed to a range of different paths the product can take, so that it can learn operations and the BOTs designed to each of the possible variations in input and outcome. With detailed data gathering and careful design, AI and BOTs can effectively handle every scenario and ensure the end product meets the needs and goals of the organization. Once the BOTS are in place, they can be reused at will to actively test any enhancements or modifications that the product might have to undergo in its lifetime on the IT world. Similarly, with AI constantly observing the application, it can be used to dynamically identify the differences, both in source and the expected result for building targeted testing document and execution. Keywords : AI, BOTs, Automated Testing, IT testing, Machine Learning, Regression testing. Application Evolution monitoring. And learning, Automated Test data gathering, Programmatic test executionInternational Scientific Journal of Engineering and Management00The knowledge gathered using Artificial Intelligence (AI), coupled with the efficiency of BOTs is a boon for testing activity in the IT world, and can significantly speed up the whole testing process and by extension the project timeline.International Scientific Journal of Engineering and Management["Rajalakshmi Thiruthuraipondi Natarajan"]2025-01-19T00:00:00 e7cb4cfc2f81682384b9c494342832d666e612edBACKGROUND AND OBJECTIVES Artificial intelligence (AI) has been gaining increasing interest in healthcare. During the 2024 International Society of Blood Transfusion (ISBT) Congress, the Clinical Transfusion Working Party (CTWP) conducted a session to explore the exciting intersection of AI in transfusion medicine (TM) practice, education and research. We report here the potential applications and the session outcome. MATERIALS AND METHODS A pre-workshop survey explored the participants' demographics and areas of use of AI and whether they have had any AI-specific training or education. The workshop included presentations on the regulatory aspects of AI use and its application in TM practice, education and research. These were followed by round-table discussions to explore participants' experience and concerns. RESULTS The workshop had 72 attendees, with 38% falling in the 36-45-year age group. A total of 70% indicated the use of AI, but only 12% reported having specific training or education. Participants expressed interest in different potential applications but also shared concerns on over-reliance, potential loss of skills, the accuracy of provided information and content plagiarism. CONCLUSION The findings of the workshop highlight the need for training, educational resources, standards and regulatory frameworks to guide the use of AI tools in the field of TM.Vox Sanguinis180The findings of the workshop highlight the need for training, educational resources, standards and regulatory frameworks to guide the use of AI tools in the field of TM.Vox sanguinis["A. Al\u2010Riyami", "R. Gammon", "Jansen Seheult", "S. Arora", "Ruchikha Goel"]2025-01-19T00:00:00 2d393fc52013fffe20c732661237eee6a29fafddIndustry 4.0 has generated a revolution in production models through the use of advanced technologies such as artificial intelligence (AI) and cloud computing. However, its implementation poses significant legal and technological challenges related to privacy, cybersecurity, intellectual property and interoperability of systems. This article discusses the legal and technological challenges inherent to these technologies in the context of Industry 4.0, based on a literature review of recent research. It also proposes recommendations to address these challenges and promote safe and efficient integration.Journal of Ecohumanism80The legal and technological challenges inherent to these technologies in the context of Industry 4.0 are discussed based on a literature review of recent research and recommendations to address these challenges and promote safe and efficient integration are proposed.Journal of Ecohumanism["Hillary Patricia Herrera Avil\u00e9s", "Juan Carlos Herrera Miranda", "Oscar Gonzalo Apaza P\u00e9rez", "Juan Carlos Pinto Larico"]2025-01-19T00:00:00 e774eb29f66d0a5a0b5f854a88f2545eaf3f725fThe current study analyzes the issues of the accelerated development of artificial intelligence (AI) in the Russian Federation, the use of artificial intelligence technologies by market participants, including financial ones, and ensuring the security of their personal and corporate data, in the context of the rapid progress of AI technologies, contributing to the efficiency of technological solutions through the use of various methods and algorithms. Development in this area is supported by government agencies, scientific institutions, government organizations, and leading companies, which portends a significant expansion of the market by 2030. Artificial intelligence offers many opportunities, but it also has negative aspects. One such aspect is the collection of personal information of users, which is necessary in order to maintain, improve the performance of AI. This can happen either within the law or through illegal actions by digital criminals. Collecting all sorts of data allows algorithms to learn, adapt to society's needs, offer personalized recommendations, improve app interactions, much more. But misuse of these practices leads to serious privacy threats. The responsibility for protecting the information received lies with organizations, including financial ones, which must comply with security measures and proper protection of personal information. However, users themselves are sometimes insufficiently vigilant and cautious in handling their data, thereby increasing the risk of confidential information leakage. Cases of increasing digital crimes confirm that AI creates favorable conditions for various manipulations by cybercriminals, which negatively affects the economic security of the country and its citizens. The article discusses the current problems associated with the introduction of artificial intelligence in all spheres of human life, provides statistics on the number of court cases related to artificial intelligence, concludes that it is necessary to introduce a multi-level set of measures, including not only the general regulation of artificial intelligence, providing basic principles and approaches, but there are also self-regulation mechanisms that will allow market participants to adapt to rapidly changing conditions.Siberian Financial School00The article discusses the current problems associated with the introduction of artificial intelligence in all spheres of human life, provides statistics on the number of court cases related to artificial intelligence, and concludes that it is necessary to introduce a multi-level set of measures, including not only the general regulation of artificial intelligence, but there are also self-regulation mechanisms that will allow market participants to adapt to rapidly changing conditions.Siberian Financial School["T. Kuvaldina", "K. Sledneva", "G. A. Fadeikin"]2025-01-19T00:00:00 e040a91c65f1312a0c7b96b13f5e17ba25d0fe5eIn the face of pressing socio-economic and environmental challenges, such as climate change, resource depletion, and social inequality, entrepreneurs are increasingly compelled to embrace innovative strategies that harmonize profitability with social and environmental stewardship. Artificial Intelligence (AI) holds promise in facilitating such solutions but requires careful examination to ensure its effective and ethical deployment in pursuit of sustainability objectives. The primary goal of this study was to ascertain whether AI significantly influences the sustainable entrepreneurial process. This comprehensive research framework integrates concepts from entrepreneurship, stakeholder theory, and digital technology, offering a multifaceted perspective. We employed the multiple regression analysis to examine data gathered from 40 entrepreneurs operating within the Bosnia and Herzegovina settings. The study's outcomes revealed that AI positively and significantly impacts the sustainable entrepreneurial process and its five sub-dimensions: “idea generation, opportunity recognition, opportunity development, venture launch, and positive impact”. This study enriches the literature on AI and sustainable entrepreneurship by offering empirical support for the impact of AI on the sustainable entrepreneurial process while also introducing and validating measurement tools for the sustainable entrepreneurial process. Additionally, entrepreneurs can utilize the findings of this research to strategically incorporate AI into their business processes and product/service innovation, improve operational efficiency, reduce costs, mitigate risks, enhance decision-making, foster collaboration across stakeholders, and capitalize on opportunities effectively.Journal of Ecohumanism420The study revealed that AI positively and significantly impacts the sustainable entrepreneurial process and its five sub-dimensions: “idea generation, opportunity recognition, opportunity development, venture launch, and positive impact”.Journal of Ecohumanism["Azra Ahmi\u0107", "L. \u0160ahovi\u0107"]2025-01-19T00:00:00 c767042f6095500ebdaa73bc21c54745f25cb2d3This essay examines the cultural politics of Artificial Intelligence (AI) in China through the lens of postsocialism, proposing the concept of a ‘postsocialist AI’ that goes beyond the dominant paradigm of neoliberal informationalism. The essay first explores the distinct state-capital nexus in China’s AI development, characterized by paradoxical modes of operation driven by neoliberal motivations, yet also deeply influenced by symbolic lexicons, value systems, and institutional structures rooted in Leninist-Maoist traditions. The complex interplay of state support, local governmental practices, and market investment showcases ongoing negotiations among contradictory political-economic objectives and subject positions that resist simplification into a globally universalized model of neoliberal capitalism. Further, the essay investigates the ontogenesis or sociogenesis of machine learning – the process through which intelligent machines become socialized through interactions with their sociocultural and political contexts. By foregrounding the interactive encounter between distinct modes of machinic intelligence and Chinese modernity experiences, the essay contests the prevailing neoliberal subjectivity in AI studies and seeks to expand the political horizon limited by the hegemonic figure of homo economicus.Theory, Culture &amp; Society290Theory, Culture &amp; Society["Qiaoyu Cai"]2025-01-19T00:00:00 d13d1d94ecd230bb28ecee66593c5297a552377fThe Arab Journal for Quality Assurance in Higher Education00The Arab Journal for Quality Assurance in Higher Education[]2025-01-19T00:00:00 343b753600f6923f9566fffadcea16d18a6a95ceThis article explores the emerging ethical challenges associated with the growing use of artificial intelligence (AI) in modern society. With the automation of processes and the increasing prevalence of autonomous systems playing critical roles in sensitive sectors such as healthcare, transportation, and justice, it becomes essential to assess the ethical implications of automated decision-making and the use of personal data. Furthermore, the recent development of human cortical organoids simulating complex neural networks raises new debates about the possibility of autonomous artificial biointelligence. This study analyzes concrete cases and proposes guidelines to integrate technological advancements with fundamental ethical values. The methodology includes a critical analysis of bibliographic sources and reflections on the ethical pillars in the development of emerging technologies.Revista ft00This study analyzes concrete cases and proposes guidelines to integrate technological advancements with fundamental ethical values and includes a critical analysis of bibliographic sources and reflections on the ethical pillars in the development of emerging technologies.Revista ft["\u00cdcaro Teixeira Costa Sampaio", "Francisco Abud Nascimento"]2025-01-19T00:00:00 20749b332c6afde647943677ce4d7f71ef089b95New technologies have been widely used to optimize the most diverse sectors of society, and artificial intelligence (AI) stands out as one of these emerging technologies. AI seeks, through the replication of human capabilities, to perform activities such as logical reasoning, interpretation, communication and learning in an autonomous and agile manner, promoting better performance in the assigned tasks. According to Campos and Figueiredo (2022), the recent growth of artificial intelligence is due to “[…] the development of statistics and probabilistic methods; the growing amount of data; greater and cheaper computational power; and the transformation of places in environments favorable to technology […]” (CAMPOS; FIGUEIREDO, 2022, p. 198). Revista ft00According to Campos and Figueiredo (2022), the recent growth of artificial intelligence is due to the development of statistics and probabilistic methods; the growing amount of data; greater and cheaper computational power; the transformation of places in environments favorable to technology.Revista ft["Daiane Nogueira de Sousa"]2025-01-19T00:00:00 b33312dc215d159a6ab3dc8826daecd6f637fd91This study examines the existing role and prospects of Artificial Intelligence (AI) in mental health therapy, evaluating whether AI can augment or supplant human counselors. This research employs a quantitative methodology on a sample of seven Pakistani startups that provide AI-driven mental health solutions. The CEOs of these firms were approached with a self-administered questionnaire to collect data on the effectiveness, limitations, and potential of AI in providing therapeutic interventions. The gathered data underwent correlation analysis, regression analysis, and post-hoc statistics to identify linkages and trends. Statistical research demonstrated a strong association between the accessibility and scalability of AI and treatment outcomes, whereas the limitations of AI were apparent in addressing complex mental health issues. Furthermore, AI was determined to augment rather than supplant human therapists, with notable disparities observed across various organizational sizes. The study emphasizes the supportive function of AI in mental health therapy, while underscoring critical attributes of human counselors, such as empathy and personalized care.Review of Applied Management and Social Sciences370Review of Applied Management and Social Sciences["Rida Mehmood", "Muhammad Asad Parvez", "Maria Qureshi", "Zainab Sagheer"]2025-01-19T00:00:00 f147ed5475eeee7a329d638b5d811f5f5aa8e506The integration of Artificial Intelligence (AI) and automation into Human Resource Management (HRM) is redefining organizational operations and employee management. AI simulates cognitive functions such as decision-making and pattern recognition, and automation enables tasks to be completed with minimal human input. Together, these technologies hold significant potential for enhancing HR efficiency, especially in recruitment, performance evaluations, and employee engagement. As AI advances, its economic impact is projected to reach substantial levels, transforming sectors globally, including HR.In Kenya, early adopters like Safaricom and KCB Bank utilize AI to streamline HR processes, but widespread implementation remains limited, particularly in smaller firms, due to infrastructural challenges, low digital literacy, and high costs. Moreover, concerns over job displacement, data privacy, and potential biases in AI systems hinder adoption. Kenyan organizations face barriers in regulatory frameworks that are still developing to adequately address data security and ethical concerns in AI use. Globally, companies in developed countries like the U.S., U.K., and Germany have leveraged AI to tackle HR challenges, with AI tools automating recruitment processes, enhancing accuracy in performance reviews, and supporting workforce planning. In contrast, African nations, including Kenya, are in the initial stages of adopting these technologies, with challenges like infrastructural limitations andInternational Journal of Academic Research in Business and Social Sciences390In Kenya, early adopters like Safaricom and KCB Bank utilize AI to streamline HR processes, but widespread implementation remains limited, particularly in smaller firms, due to infrastructural challenges, low digital literacy, and high costs.International Journal of Academic Research in Business and Social Sciences["Laura Mamuli", "F. Mukabi", "Catherine Kagucia"]2025-01-19T00:00:00 e7d387df94d7c27139f7e8974c0a92108d04f79eArtificial intelligence (AI) is revolutionizing management research by enabling more efficient data analysis, decision-making, and operational workflows. However, its application also raises questions about its transformative role and implications for academic writing in this field. A systematic review of literature published over the past decade was conducted to evaluate the applications, benefits, and challenges of AI in management research. Emphasis was placed on identifying key tools and technologies, along with their impacts on research quality and efficiency. Findings reveal that AI significantly enhances research by automating data handling, improving predictive accuracy, reducing biases, and streamlining academic writing processes. Despite these advancements, challenges such as ethical concerns and the need for human oversight persist. This study highlights the importance of balancing AI implementation with human judgment to ensure ethical practices and effective utilization. It addresses gaps in existing research and emphasizes AI's transformative potential in management studies. AI plays a pivotal role in enhancing the quality and efficiency of management research, but its integration requires careful consideration to maximize its benefits while mitigating potential drawbacks.Involvement International Journal of Business00The importance of balancing AI implementation with human judgment to ensure ethical practices and effective utilization is highlighted to maximize its benefits while mitigating potential drawbacks.Involvement International Journal of Business["Iis Azelya", "Sergey Filin"]2025-01-19T00:00:00 7f1bb453aab994967a93ac7a308f5fefb74d8b95Large Language Models (LLMs) are transforming artificial intelligence, evolving into task-oriented systems capable of autonomous planning and execution. One of the primary applications of LLMs is conversational AI systems, which must navigate multi-turn dialogues, integrate domain-specific APIs, and adhere to strict policy constraints. However, evaluating these agents remains a significant challenge, as traditional methods fail to capture the complexity and variability of real-world interactions. We introduce IntellAgent, a scalable, open-source multi-agent framework designed to evaluate conversational AI systems comprehensively. IntellAgent automates the creation of diverse, synthetic benchmarks by combining policy-driven graph modeling, realistic event generation, and interactive user-agent simulations. This innovative approach provides fine-grained diagnostics, addressing the limitations of static and manually curated benchmarks with coarse-grained metrics. IntellAgent represents a paradigm shift in evaluating conversational AI. By simulating realistic, multi-policy scenarios across varying levels of complexity, IntellAgent captures the nuanced interplay of agent capabilities and policy constraints. Unlike traditional methods, it employs a graph-based policy model to represent relationships, likelihoods, and complexities of policy interactions, enabling highly detailed diagnostics. IntellAgent also identifies critical performance gaps, offering actionable insights for targeted optimization. Its modular, open-source design supports seamless integration of new domains, policies, and APIs, fostering reproducibility and community collaboration. Our findings demonstrate that IntellAgent serves as an effective framework for advancing conversational AI by addressing challenges in bridging research and deployment. The framework is available at https://github.com/plurai-ai/intellagent00IntellAgent is introduced, a scalable, open-source multi-agent framework designed to evaluate conversational AI systems comprehensively, and employs a graph-based policy model to represent relationships, likelihoods, and complexities of policy interactions, enabling highly detailed diagnostics.["Elad Levi", "Ilan Kadar"]2025-01-19T00:00:00 f05dd921e4842e1fd9773fa3c7041c66c27f2d50Always Mental disability and Mental health care have been overlooked. This is puzzling considering that 8% of the world's population suffers from mental impairments, which are widespread. A scalable option that offers an interactive way to engage consumers in behavioral health interventions powered by artificial intelligence is a chatbots. Anxiety, stress, etc. provides a critical first step in enhancing chatbot design and revealing the advantages and disadvantages of the chatbots. In this report, a customized chatbot framework is proposed with a blended neural network design. The recommended chatbot is a virtual health assistant & it is cost-effective and less time-consuming. It includes chat features, many languages voice input & a recommendation tool to improve users’ mood. To improve the performance of the proposed system, the chatbot is further integrated withINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A customized chatbot framework is proposed with a blended neural network design that includes chat features, many languages voice input & a recommendation tool to improve users’ mood and it is cost-effective and less time-consuming.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Ayesha Shehbaz Purkar"]2025-01-19T00:00:00 2d6f89ac2af3745b2b441ed7172232d5f12f47e0The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, artificial intelligence (AI), machine learning (ML), and deep learning (DL), revealing a significant annual growth rate of 56.4% in research activity. Key findings include the identification of influential journals, authors, countries, and collaborative networks that have driven advancements in this domain. This study highlights emerging trends, such as the integration of renewable energy sources, vehicle-to-grid (V2G) schemes, and the application of AI in EV battery optimization, charging infrastructure, and energy consumption prediction. The analysis also uncovers challenges in addressing information security concerns. By reviewing the top-cited papers, this research underlines the transformative potential of AI-driven solutions in enhancing EV performance and scalability. The results of this study can be useful for practitioners, academics, and policymakers.Electronics1560A bibliometric analysis of the interconnected domains of EVs, artificial intelligence (AI), machine learning (ML), and deep learning (DL), revealing a significant annual growth rate of 56.4% in research activity is presented.Electronics["Adrian Domenteanu", "Liviu-Adrian Cotfas", "Paul Diaconu", "George-Aurelian Tudor", "Camelia Delcea"]2025-01-19T00:00:00 3186076266ab0cee59b72db70d018047f62d0a5aThis comprehensive article explores the transformative impact of Salesforce's artificial intelligence solutions in the financial services sector. The article examines the implementation of advanced AI technologies across various domains, including wealth management, loan processing, fraud detection, and customer support. The article highlights the significant improvements in operational efficiency, risk management, and customer experience through AI integration. The article covers the technical architecture of Salesforce's AI solutions, emphasizing their microservices-based approach and future developments in hyper-personalization and predictive analytics. The article demonstrates how AI implementations are reshaping traditional banking operations while ensuring enhanced security and regulatory compliance.International Journal of Scientific Research in Computer Science Engineering and Information Technology00It is demonstrated how AI implementations are reshaping traditional banking operations while ensuring enhanced security and regulatory compliance while ensuring enhanced security and regulatory compliance.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Raviteja Pachika"]2025-01-19T00:00:00 d5b3b330760042cdc703e615c6092afc2822d54eThis paper aims to determine how artificial intelligence facilitates cooperation, minimizes conflict, and increases cooperative learning among group project teams comprising university students and instructors. The quantitative approach is applied to collect data from a sample size of 250 students and 150 instructors recruited using probability sampling methods from universities across Punjab. A self-administered questionnaire is used for the data collection purposes based on AI tools utilization, improved communication, and resolution of conflict between them along with general results of learning. Statistical methods, such as Pearson's r correlation analysis, regression analysis, and post-hoc tests were applied to analyze the data. All results obtained are positive; that is, utilization of artificial intelligence had relationships with improvements in collaboration (r = 0.76), communication (r = 0.72), coordination of tasks (r = 0.78), conflict reduction (r = 0.80), and the outcomes of learning (r = 0.75). Regression analysis has verified that AI tools are strong predictors of these changes, as the p-values are less than 0.05. This study emphasizes the strength of AI-driven tools in enhancing teamwork, reducing interpersonal conflicts, and encouraging cooperative learning in group settings. Therefore, the implications of the findings are very critical for the utilization of AI tools in educational settings.Review of Applied Management and Social Sciences00The strength of AI-driven tools in enhancing teamwork, reducing interpersonal conflicts, and encouraging cooperative learning in group settings is emphasized, and the implications of the findings are very critical for the utilization of AI tools in educational settings.Review of Applied Management and Social Sciences["Umer Javed", "A. Rohilla", "Ghazal Adnan", "Naveen Taj"]2025-01-19T00:00:00 3d1abf48ccbcb358fcc9ac4938e9d8ee838219eaThis study explores innovative, ethical leadership approaches using artificial intelligence (AI) and fractal geometry in public health while fostering sustainable business practices within public health systems. The research employs a qualitative methodology based on case studies, secondary data analysis, and fractal-based AI algorithm evaluations. It examines advanced algorithms' technical applications in public health settings, improving data privacy, copyright, and intellectual property protection. The study finds that fractal algorithms offer robust solutions for promoting ethical leadership in AI-driven public health systems. Fractal geometry's complexity and self-similarity improve predictive modeling, resource allocation, and system transparency while ensuring legal and ethical compliance. By applying fractal algorithms, public health organizations can improve privacy protection, intellectual property management, and ethical governance. The study highlights the need for further research on practical applications, optimization of fractal algorithms, and overcoming the computational demands associated with their deployment in public health. Ethical leadership approaches supported by fractal algorithms can drive more equitable and secure public health interventions, enhancing trust in AI-driven solutions and reducing healthcare access and outcomes disparities. This research presents a novel integration of fractal geometry and AI to address critical ethical issues in public health, providing innovative solutions for data privacy, intellectual property protection, and ethical leadership practices.European Journal of Applied Science, Engineering and Technology850The study finds that fractal algorithms offer robust solutions for promoting ethical leadership in AI-driven public health systems, and highlights the need for further research on practical applications, optimization of fractal algorithms, and overcoming the computational demands associated with their deployment in public health.European Journal of Applied Science, Engineering and Technology["Xiuli Chen", "Joohan Ryoo"]2025-01-19T00:00:00 d7f708ca7287c11002350056ac6b1f4ac29189f4Este artigo apresenta uma revisão sistemática da literatura sobre o uso da Inteligência Artificial (IA) na automação de auditorias em conformidade com as leis de proteção de dados, como a Lei Geral de Proteção de Dados Pessoais (LGPD) no Brasil, o Regulamento Geral de Proteção de Dados (GDPR) na Europa e a Norma Internacional de Segurança da Informação (ISO/IEC 27001:2022). O estudo explora os benefícios, desafios e tendências na aplicação da IA em auditorias automatizadas no contexto de Governança, Risco e Conformidade (GRC). São analisadas as tecnologias mais utilizadas e casos de sucesso, apontando para uma transformação significativa nas auditorias digitais. Os resultados indicam que a IA melhora a precisão e a eficiência, permitindo uma abordagem proativa e contínua na gestão de conformidade regulatória.RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-621800RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218["Davis Souza Alves", "M\u00e1rcio Magera Concei\u00e7\u00e3o"]2025-01-19T00:00:00 1d868dd0915cdc0c17f157c946e412a5582e95aeLarge Language Models (LLMs) demand significant computational resources, making it essential to enhance their capabilities without retraining from scratch. A key challenge in this domain is \textit{catastrophic forgetting} (CF), which hampers performance during Continuous Pre-training (CPT) and Continuous Supervised Fine-Tuning (CSFT). We propose \textbf{Control LLM}, a novel approach that leverages parallel pre-trained and expanded transformer blocks, aligning their hidden-states through interpolation strategies This method effectively preserves performance on existing tasks while seamlessly integrating new knowledge. Extensive experiments demonstrate the effectiveness of Control LLM in both CPT and CSFT. On Llama3.1-8B-Instruct, it achieves significant improvements in mathematical reasoning ($+14.4\%$ on Math-Hard) and coding performance ($+10\%$ on MBPP-PLUS). On Llama3.1-8B, it enhances multilingual capabilities ($+10.6\%$ on C-Eval, $+6.8\%$ on CMMLU, and $+30.2\%$ on CMMLU-0shot-CoT). It surpasses existing methods and achieves SOTA among open-source models tuned from the same base model, using substantially less data and compute. Crucially, these gains are realized while preserving strong original capabilities, with minimal degradation ($<4.3\% \text{on MMLU}$) compared to $>35\%$ in open-source Math and Coding models. This approach has been successfully deployed in LinkedIn's GenAI-powered job seeker and Ads unit products. To support further research, we release the training and evaluation code (https://github.com/linkedin/ControlLLM) along with models trained on public datasets (https://huggingface.co/ControlLLM) to the community.00Control LLM is proposed, a novel approach that leverages parallel pre-trained and expanded transformer blocks, aligning their hidden-states through interpolation strategies that effectively preserves performance on existing tasks while seamlessly integrating new knowledge.["Haichao Wei", "Yunxiang Ren", "Zhoutong Fu", "Aman Lunia", "Yi-Lin Chen", "Alice Leung", "Ya Xu"]2025-01-19T00:00:00 e946c97871485736efb6e3eb8cec4cc2d8c7b5b9This study explores whether predictive coding (PC) inspired Deep Neural Networks can serve as biologically plausible neural network models of the brain. We compared two PC-inspired training objectives, a predictive and a contrastive approach, to a supervised baseline in a simple Recurrent Neural Network (RNN) architecture. We evaluated the models on key signatures of PC, including mismatch responses, formation of priors, and learning of semantic information. Our results show that the PC-inspired models, especially a locally trained predictive model, exhibited these PC-like behaviors better than a Supervised or an Untrained RNN. Further, we found that activity regularization evokes mismatch response-like effects across all models, suggesting it may serve as a proxy for the energy-saving principles of PC. Finally, we find that Gain Control (an important mechanism in the PC framework) can be implemented using weight regularization. Overall, our findings indicate that PC-inspired models are able to capture important computational principles of predictive processing in the brain, and can serve as a promising foundation for building biologically plausible artificial neural networks. This work contributes to our understanding of the relationship between artificial and biological neural networks, and highlights the potential of PC-inspired algorithms for advancing brain modelling as well as brain-inspired machine learning.bioRxiv00This study compares two PC-inspired training objectives to a supervised baseline in a simple Recurrent Neural Network architecture, and finds that Gain Control (an important mechanism in the PC framework) can be implemented using weight regularization.bioRxiv["Dirk C G\u00fctlin", "Ryszard Auksztulewicz"]2025-01-19T00:00:00 543c141807e69f6613f29bcd820661945cc881d5Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating multiple specialized Artificial Intelligent Agents can help mitigate such hallucinations, with a focus on systems leveraging Natural Language Processing (NLP) to facilitate seamless agent interactions. To achieve this, we design a pipeline that introduces over three hundred prompts, purposefully crafted to induce hallucinations, into a front-end agent. The outputs are then systematically reviewed and refined by second- and third-level agents, each employing distinct large language models and tailored strategies to detect unverified claims, incorporate explicit disclaimers, and clarify speculative content. Additionally, we introduce a set of novel Key Performance Indicators (KPIs) specifically designed to evaluate hallucination score levels. A dedicated fourth-level AI agent is employed to evaluate these KPIs, providing detailed assessments and ensuring accurate quantification of shifts in hallucination-related behaviors. A core component of this investigation is the use of the OVON (Open Voice Network) framework, which relies on universal NLP-based interfaces to transfer contextual information among agents. Through structured JSON messages, each agent communicates its assessment of the hallucination likelihood and the reasons underlying questionable content, thereby enabling the subsequent stage to refine the text without losing context. The results demonstrate that employing multiple specialized agents capable of interoperating with each other through NLP-based agentic frameworks can yield promising outcomes in hallucination mitigation, ultimately bolstering trust within the AI community.00The results demonstrate that employing multiple specialized agents capable of interoperating with each other through NLP-based agentic frameworks can yield promising outcomes in hallucination mitigation, ultimately bolstering trust within the AI community.["Diego Gosmar", "Deborah A. Dahl"]2025-01-19T00:00:00 4ba0f8c37d0c5de3370044aa3dde773685d91aebKN - Journal of Cartography and Geographic Information421By employing AI within a neopragmatist framework in cartography, new possibilities emerge for integrating and utilizing diverse social perspectives and (geospatial) data and enables an expansion of the theoretical and practical applicability of cartography.KN - Journal of Cartography and Geographic Information["Olaf K\u00fchne", "Dennis Edler"]2025-01-20T00:00:00 84aa79710d1608272102565809569b12251ccfdcThis paper introduces an artificial intelligence (AI) interactive system featuring a self-growing memory network designed to enhance self-efficacy, reduce loneliness, and maintain social interaction among the elderly. The system dynamically analyzes and processes user-written diaries, generating empathic and personalized responses tailored to each individual. The system architecture includes an experience extraction model, a self-growing memory network that provides a contextual understanding of the user’s daily life, a chat agent, and a feedback loop that adaptively learns the user’s behavioral patterns and emotional states. By drawing on both successful and challenging experiences, the system crafts responses that reinforce the self-efficacy of the user, fostering a sense of accomplishment and engagement. This approach improves the psychological well-being of elderly users and promotes their mental health and overall quality of life through consistent interaction. To validate our proposed method, we developed a diary application to facilitate user interaction and collect diary entries. Over time, the system’s capacity to learn and adapt further refines the user experience, suggesting that AI-driven solutions hold significant potential for mitigating the effects of declining self-efficacy on mental health and social interactions. With the proposed system, we achieve an average system usability scale score of 77.3 (SD = 5.4) and a general self-efficacy scale score of 34.2 (SD = 3.5).Journal of Advanced Computational Intelligence and Intelligent Informatics110Over time, the system’s capacity to learn and adapt further refines the user experience, suggesting that AI-driven solutions hold significant potential for mitigating the effects of declining self-efficacy on mental health and social interactions.J. Adv. Comput. Intell. Intell. Informatics["Yuchen Guo", "Chyan Zheng Siow", "W. Chin", "Bakir Had\u017ei\u0107", "Akihiro Yorita", "T. Obo", "Matthias R\u00e4tsch", "Naoyuki Kubota"]2025-01-20T00:00:00 6eb61b53bfab9c702213b56771f6bfe78e9eff11The article examines the application of Artificial Intelligence (AI) in managing scientific and educational projects funded by the KEGA and VEGA grant schemes in Slovakia. The objective is to analyse how AI contributes to more efficient management of project processes, including the initiation, planning, implementation, monitoring, and closure of projects. Research identifies key areas where AI provides significant benefits, such as automation of administrative tasks, optimisation of resource allocation, and decision-making support through predictive analysis. The article also highlights the utilisation of AI tools, such as chatbots and advanced machine learning algorithms, to improve communication and monitoring project activities. The findings indicate that AI not only facilitates the complex grant management but also improves the quality of achieved outcomes. Despite these positive contributions, the research has revealed several challenges, including insufficient integration of AI in certain phases of the project lifecycle and concerns about the ethical use of these technologies. Recommendations include the need to further education of project managers in AI-related areas and the development of strategies for their effective implementation. The article offers a fresh perspective on the use of AI in project management and introduces valuable tools and methodologies that can help project teams in achieving superior results.Journal of Ecohumanism00Analysis of the application of Artificial Intelligence in managing scientific and educational projects funded by the KEGA and VEGA grant schemes in Slovakia indicates that AI not only facilitates the complex grant management but also improves the quality of achieved outcomes.Journal of Ecohumanism["Kate\u0159ina Bo\u010dkov\u00e1", "D. A. Proch\u00e1zka", "Pavel Barto\u0161"]2025-01-20T00:00:00 a967042807385a27813ecd91af1df6befd656a7bThis research evaluates the potential of Artificial Intelligence (AI) interventions in promoting digital inclusion for older adults with neuromuscular conditions, aligning with Sustainable Development Goal (SDG) 4 for equitable education. Using a mixed-methods approach, we combined quantitative measures of digital literacy and engagement with qualitative insights into user experiences. The findings reveal statistically significant advancements in digital literacy (p < 0.001) and engagement metrics (p < 0.01), highlighting the transformative potential of adaptive learning platforms, virtual reality applications, and interactive mobile tools tailored for this population. Participants reported increased confidence and empowerment, emphasizing the importance of user-centered design and accessibility in technology development. While the study demonstrates short-term benefits, it acknowledges limitations, including a small sample size (n = 30) and the absence of longitudinal data. Future research should explore scalable implementations and long-term impacts, particularly for broader demographic groups and other disability types. These insights provide actionable recommendations for educators, developers, and policymakers aiming to reduce the digital divide and foster inclusive education.Frontiers in Education290The findings reveal statistically significant advancements in digital literacy and engagement and engagement metrics and the transformative potential of adaptive learning platforms, virtual reality applications, and interactive mobile tools tailored for older adults with neuromuscular conditions.Frontiers in Education["Paula A. Valencia-Londo\u00f1o", "Hilderman Cardona-Rodas", "J. A. Jim\u00e9nez-Builes"]2025-01-20T00:00:00 4209d17b7c7a837c85fbd78624651f0b17c483c2Background This study examines the integration of Artificial Intelligence (AI) in supply chain management (SCM) during the transition from Industry 4.0 to Industry 6.0. The focus is on improving operational efficiency, promoting human-centric collaboration, and advancing sustainability within supply chains. As industries progress, the need to incorporate AI technologies that improve decision-making and operational resilience while ensuring sustainable practices becomes increasingly critical. This systematic review aims to explore how AI is transforming SCM through these industrial transitions. Methods Utilising the PRISMA framework, a systematic review was conducted to gather and analyse relevant literature published between 2010 and 2023. A comprehensive search of databases including Web of Science, Scopus, IEEE Xplore, Google Scholar, and ScienceDirect was performed. The review involved rigorous screening for eligibility and thematic analysis using Atlas-ti software to identify key themes and patterns related to AI integration in SCM. Results The findings indicate that AI integration significantly improves SCM by improving demand forecasting, inventory management, and overall decision-making capabilities. Industry 5.0 focuses on human-AI collaboration, improving customisation and problem-solving. AI technologies also contribute to sustainability by optimising resource utilisation and reducing environmental impacts. However, challenges such as cybersecurity risks and workforce skill gaps need to be addressed to fully leverage AI’s potential. Conclusion Integrating AI in SCM not only improves operational efficiency and sustainability but also promotes resilience against disruptions. The insights from this review offer valuable guidance for both academics and practitioners aiming to optimise supply chain operations through AI technologies from Industry 4.0 to Industry 6.0. The study underlines the importance of a balanced approach that integrates technological developments with human-centric and sustainable practices.Frontiers in Artificial Intelligence570The findings indicate that AI integration significantly improves SCM by improving demand forecasting, inventory management, and overall decision-making capabilities and underlines the importance of a balanced approach that integrates technological developments with human-centric and sustainable practices.Frontiers in Artificial Intelligence["Alexander Samuels"]2025-01-20T00:00:00 3707dd2839d594fc093cbae85826cbca0f3e1b50Artificial Intelligence (AI) is substituting human decision-making in every aspect of life where law stands with no exception. New technological trends offer expeditious and cost-effective AI tools yet confront challenges such as privacy invasion, bias, fairness, and hallucinations, necessitating regulatory oversight. Like other countries, the USA and Pakistan have initiated AI solutions in their legal domain. A strong regulatory oversight is indispensable for its legitimacy and efficiency. Based on their functions and ethical considerations, AI tools in the legal profession face competing opinions. With qualitative research methodology, the research aims to explore how AI is transforming and reshaping the legal regime, focused on the comparative analysis of the USA and Pakistan. The research paper critically examines the legal frameworks and impacts of AI solutions and how both countries navigate the complexities of AI-based decision-making.Global Foreign Policies Review00The research aims to explore how AI is transforming and reshaping the legal regime, focused on the comparative analysis of the USA and Pakistan, and how both countries navigate the complexities of AI-based decision-making.Global Foreign Policies Review["Bakht Munir", "Akhtar Ali Ansari", "Yasir Arafat"]2025-01-20T00:00:00 5c1c27a728b7e2f6e34e5dc9ab62b62584e52676BMC Public Health290It is concluded that certain comorbidities, such as cardiovascular disease and preeclampsia, significantly impact LOS following a CS, which can assist hospital management in optimizing resource allocation and reducing costs by focusing on the most influential factors.BMC Public Health["A. Scala", "Giuseppe Bifulco", "A. Borrelli", "R. Egidio", "M. Triassi", "G. Improta"]2025-01-20T00:00:00 11cc55f075891498b8a02abd65f2b002f59c5ff0The use of artificial intelligence (AI) in the banking sector is becoming one of the main mainstream. Almost daily there are reports about the introduction of AI in one or another bank out of the top hundred. At the same time, medium and small banks are not yet ready for large-scale use of AI. Despite active discussions about the strategic importance of AI and machine learning, there is no research on the digital transformation of these technologies into banks' business processes. Most of the work focuses on general theoretical issues and the effect of AI implementation. The assessment of the cost of creating AI often remains hidden or underestimated. The purpose of the study is to determine the possibilities of using AI technologies in banks with a limited IT budget, to decompose the AI implementation process, identify the main stages and estimate the cost of creating a model. General scientific methods are used – analysis, synthesis, abstraction. According to the results of the study, a scheme of the process of creating and implementing AI in a bank is proposed; a characteristic of each stage is given; an estimated calculation of the cost of creating a model is made; it is proved that the proposed model for building AI in a single bank is not something supercomplicated; the costs of building an AI model are quite high, but in this case banks can quite afford to join in partnership with each other or with FinTech. The results of the study are new and of practical importance for medium and small banks when making decisions on the creation and implementation of AI models.Siberian Financial School70It is proved that the proposed model for building AI in a single bank is not something supercomplicated, and the costs of building an AI model are quite high, but in this case banks can quite afford to join in partnership with each other or with FinTech.Siberian Financial School["T. Zver'kova"]2025-01-20T00:00:00 fe6f293cdf9088bdd7117eb7b1e9aee422059ff3This research explores the role of Artificial Intelligence (AI) in improving education services at the junior high school and madrasah Tsanawiyah (MTs) levels. AI technology contributes significantly to learning personalization, student data analysis, and school administration efficiency. Through the Narrative Literature Review method, this research analyzes various literatures to identify the impact of AI on education, including challenges and potential solutions. The results show that AI supports more adaptive learning by analyzing students' individual needs, providing data-driven recommendations, and accelerating decision-making in the education process. In addition, AI improves operational efficiency through the automation of administrative tasks, such as managing student attendance and grades. However, AI implementation faces challenges such as limited infrastructure, lack of technical training for teachers, and data privacy issues. With strategic investment and intensive training, AI technology has great potential to revolutionize the education system, creating an inclusive and sustainable learning ecosystem.Manajemen Kreatif Jurnal150The results show that AI supports more adaptive learning by analyzing students' individual needs, providing data-driven recommendations, and accelerating decision-making in the education process.Manajemen Kreatif Jurnal["Abdul Kodir"]2025-01-20T00:00:00 69e6bd95774789839d153848b9f977c49f4496e5Global warming is a significant threat to the future of humankind. It is caused by greenhouse gases that accumulate in the atmosphere. CO2 emissions are one of the main drivers of global warming, and the energy sector is one of the main contributors to CO2 emissions. Recent technological advances in artificial intelligence (AI) have accelerated the adoption of AI in numerous applications to solve many problems. This study carries out a scoping review to understand the use of AI solutions to reduce CO2 emissions in the energy sector. This paper follows the PRISMA-ScR guidelines in reporting the findings. The academic search engine Google Scholar was utilized to find papers that met the review criteria. Our research question was “How is artificial intelligence used in the energy sector to reduce CO2 emissions?” Search phrases and inclusion criteria were decided based on this research question. In total, 186 papers from the search results were screened, and 16 papers fitting our criteria were summarized in this study. The findings indicate that AI is already used in the energy sector to reduce CO2 emissions. Three main areas of application for AI techniques were identified. Firstly, AI models are employed to directly optimize energy generation processes by modeling these processes and determining their optimal parameters. Secondly, AI techniques are utilized for forecasting, which aids in optimizing decision-making, energy transmission, and production planning. Lastly, AI is applied to enhance energy efficiency, particularly in optimizing building performance. The use of AI shows significant promise of reducing CO2 emissions in the energy sector.Applied Sciences590The findings indicate that AI is already used in the energy sector to reduce CO2 emissions, and AI is applied to enhance energy efficiency, particularly in optimizing building performance.Applied Sciences["Janne Alatalo", "Eppu Heilimo", "Mika Rantonen", "Olli V\u00e4\u00e4n\u00e4nen", "T. Sipola"]2025-01-20T00:00:00 0d0e74e66f7f7fb7b1a2f1ea63570803c6d0be66The field of veterinary medicine, like many others, is expected to undergo a significant transformation due to artificial intelligence (AI), although the full extent remains unclear. Artificial intelligence is already becoming prominent throughout daily life (eg, recommending movies, completing text messages, predicting traffic), yet many people do not realize they interact with it regularly. Despite its prevalence, opinions on AI in veterinary medicine range from skepticism to optimism to indifference. However, we are living through a key moment that calls for a balanced perspective, as the way we choose to address AI now will shape the future of the field. Future generations may view us as either overly optimistic, blinded by AI's allure, or overly pessimistic, failing to recognize its potential. By understanding how algorithms function and predictions are made, we can begin to demystify AI, seeing it not as an all-knowing entity but as a powerful tool that will assist veterinary professionals in providing high-level care and progressing in the field. Building awareness allows us to appreciate its strengths and limitations and recognize the ethical dilemmas that may arise. This review aims to provide an accessible overview of the status of AI in veterinary medicine. This review is not intended to be an exhaustive account of AI.American Journal of Veterinary Research910This review aims to provide an accessible overview of the status of AI in veterinary medicine, seeing it not as an all-knowing entity but as a powerful tool that will assist veterinary professionals in providing high-level care and progressing in the field.American journal of veterinary research["PhD K. E. Sobkowich", "Dr. Sobkowich"]2025-01-20T00:00:00 b843ebd3c140af8fec83d812daa97a41906e0aeeJournal of Exposure Science and Environmental Epidemiology90An AI-powered chatbot that enables communities to report environmental incidents to government authorities through text messaging, and showcases the potential of Artificial Intelligence/Large Language Models to create user-friendly tools that translate community environmental concerns into actionable information for reporting to government authorities.Journal of exposure science & environmental epidemiology["W. Chiu", "Galen Newman", "G. Sansom", "Xinyue Ye", "Andriy Rusyn", "Haotian Wu", "Tom Winckelman", "I. Rusyn"]2025-01-20T00:00:00 f6ed411cadd9bab1c8604e62acb5a12afbcfc514This study explores students' perceptions and attitudes toward using artificial intelligence (AI) to write and complete homework assignments. The research subjects were 72 randomly selected students from SMA Unggulan RUSHD. Data were collected through questionnaires and analyzed using descriptive quantitative methods for closed-ended questions (utilizing percentages and averages) and thematic qualitative analysis for open-ended questions. The results revealed that 88.9% of students used AI tools such as ChatGPT to complete their assignments. Most students found AI helpful, but satisfaction with AI-generated answers varied, with 63.9% stating they were sometimes satisfied, 26.4% often satisfied, and only 2.8% always satisfied. These findings indicate that while AI supports academic tasks, limitations in accuracy and relevance prevent it from fully meeting students' expectations. Clear school regulations are needed to ensure AI is effectively utilized without compromising academic integrity. This study contributes new insights into how students use AI for daily academic tasks and their perceptions of related policies. It also emphasizes the importance of AI literacy among students and suggests that educational institutions develop policies that not only regulate but also educate students on responsible and ethical AI usage, fostering sustainable learning in the digital age.Scholaria: Jurnal Pendidikan dan Kebudayaan00It is suggested that educational institutions develop policies that not only regulate but also educate students on responsible and ethical AI usage, fostering sustainable learning in the digital age.Scholaria: Jurnal Pendidikan dan Kebudayaan["Kurniahtunnisa", "Maria Yasinta Manuel", "Mellyatul Aini", "T. Agustina"]2025-01-20T00:00:00 e26b70da405f6a8b569defb36a05ae843cbd0418Artificial intelligence (AI) is gaining attention in energy economics due to its ability to process large-scale data as well as to make non-linear predictions and is providing new development opportunities and research subjects for energy economics research. The aim of this paper is to explore the trends in the application of AI in energy economics over the decade spanning 2014–2024 through a systematic literature review, bibliometrics, and network analysis. The analysis of the literature shows that the prominent research themes are energy price forecasting, AI innovations in energy systems, socio-economic impacts, energy transition, and climate change. Potential future research directions include energy supply-chain resilience and security, social acceptance and public participation, economic inequality and the technology gap, automated methods for energy policy assessment, the circular economy, and the digital economy. This innovative study contributes to a systematic understanding of AI and energy economics research from the perspective of bibliometrics and inspires researchers to think comprehensively about the research challenges and hotspots.Energies830The analysis of the literature shows that the prominent research themes are energy price forecasting, AI innovations in energy systems, socio-economic impacts, energy transition, and climate change.Energies["Zhilun Jiao", "Chenrui Zhang", "Wenwen Li"]2025-01-20T00:00:00 919aa1fea6b9595b1417bfedd4aec1c4df47d0b2In the creative economy, technology significantly enhances human productivity and creativity, prominently through Artificial Intelligence (AI), which simulates human intelligence in computing. The rapid advancement of AI raises numerous debates, particularly in the art sector, where societal opinions vary from opposing the registration of AI-generated artwork to advocating for AI's recognition as a non-person legal entity entitled to moral rights. This discourse centers around granting legal status to AI regarding its artwork and relates to the broader concept of Intellectual Property as it pertains to creators and copyright holders. Currently, Indonesia's Copyright Law Number 8 of 2014 lacks specific provisions addressing these issues, resulting in a legal vacuum. This research aims to analyze the application of copyright legal theory to AI-generated art and the legal implications for its future development and protection. Employing normative legal research, the study utilizes both conceptual and legislative approaches, applying inductive logic to draw conclusions. Findings indicate that AI should be regarded as a legal object, with the rights to its artwork attributed to legal entities possessing natural legal authority. Additionally, the use of AI in creative works aligns with the "work made for hire" doctrine. Ultimately, the study underscores the necessity for responsive and progressive legal frameworks that adapt to the evolving landscape of copyright and AI.Eduvest - Journal Of Universal Studies280Eduvest - Journal of Universal Studies["Widya Agung Kristanti", "Agung Sujatmiko", "Ria Setyawati", "Brahmantyo Agung Wicaksono"]2025-01-20T00:00:00 014027acf4fcf30533c7abe70053380094c7041eNeural Processing Letters350It is suggested that by combining existing techniques in the AI discipline, including neural networks, generative AI, and genetic algorithms, as well as keeping a “physician in the loop” when building AI systems, the authors can have a significant impact on healthcare delivery and medical research.Neural Process. Lett.["Beny Rubinstein", "S\u00e9rgio Matos"]2025-01-20T00:00:00 812ba5dc724c9d8ec48f88df32e70ea07dde10c2This paper explores the integration and advancements of artificial neural networks (ANNs) in modeling diesel engine performance, particularly focusing on biodiesel-fueled engines. ANNs have emerged as a vital tool in predicting and optimizing engine parameters, contributing to the enhancement of fuel efficiency and a reduction in emissions. The novelty of this review lies in its critical analysis of the existing literature on ANN applications in biodiesel engines, identifying gaps in optimization and emission control. While ANNs have shown promise in predicting engine parameters, fuel efficiency, and emission reduction, this paper highlights their limitations and areas for improvement, especially in the context of biodiesel-fueled engines. The integration of ANNs with big data and sophisticated algorithms paves the way for more accurate and reliable engine modeling, essential for advancing sustainable and eco-friendly automotive technologies. This research underscores the growing importance of ANNs in optimizing biodiesel-fueled diesel engines, aligning with global efforts towards cleaner and more sustainable energy solutions.Energies830Energies["Ramozon Khujamberdiev", "H. Cho"]2025-01-20T00:00:00 3a5f6d4cb2048319db5b7452fb83c201ad832866IARJSET00IARJSET[]2025-01-20T00:00:00 ac60397dacb5d0267b139b7e429dd59218e3cb6eCurrent Opinion in Biotechnology630This review highlights the molecular building blocks, design principles, and computational tasks demonstrated by current biocomputers, before briefly discussing possible fields where biological computers may ultimately outcompete their electronic counterparts and achieve cellular supremacy.Current opinion in biotechnology["Xinyuan Qiu", "Lingyun Zhu", "Hui Wang", "Mingqi Xie"]2025-01-20T00:00:00 6e85986ae8be588f9386f8e5a8cf126e28467eddCogent Food &amp; Agriculture1790Cogent Food &amp; Agriculture["G. S. Mmbando"]2025-01-20T00:00:00 0576e51250ca947e27e03d7232dc585b574b1ee3Thrombosis and Haemostasis350Thrombosis and haemostasis["G. Boriani", "D. Mei", "G. Lip"]2025-01-20T00:00:00 66966c5fcd739584e74a23da54a75ddc5fe392dcFourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024)00Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024)["Xing Fu", "Haoran Chen", "Yiyang Lu"]2025-01-20T00:00:00 5f48c8e4ff8c36e268add64caa3f953018c81576International journal of research in computer applications & information technology00INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY["Panneer Selvam Viswanathan"]2025-01-20T00:00:00 2ce0efd35174b90d2de40a251fde369e086cdfd5This article presents a curated collection of nine teaching innovations presented at the Association for Business Communication 89th conference in the “oil capital of the world,” Tulsa, Oklahoma, as well as online, in October 2024. Many of the MFA presenters demonstrated how AI can be used, integrated, and analyzed in business communication classes. This My Favorite Assignment 35th edition introduces readers to a wide variety of classroom-ready ideas that integrate AI. Teaching support materials—instructions to students, stimulus materials, slides, rubrics, frequently asked questions, links, and sample student projects—are downloadable from the Association for Business Communication website.Business and Professional Communication Quarterly30Business and Professional Communication Quarterly["Andrew Cavanaugh", "D. J. Whalen"]2025-01-20T00:00:00 51b78ab5be43eeaedba53ffab1e7721a05b63b3b Regular physical activity (PA) promotes mental and physical health. Nevertheless, inactivity is a worldwide pandemic, and methods to augment exercise benefits are required. The constrained disorder principle (CDP) characterizes biological systems based on their inherent variability. Therefore, we aimed to investigate the association between intra-individual variability in PA and disability among non-athlete adults. In this retrospective analysis of the longitudinal SHARE survey, we included non-disabled adults aged >50 with at least six visits over 14 years. Self-reported PA frequency was documented bi- to triennially. Low PA intensity was defined as vigorous PA frequency less than once a week. Stable PA was described as an unchanged PA intensity in all consecutive middle observations. The primary outcome was defined as a physical limitation in everyday activities at the end of the survey. Secondary outcomes were cognitive functions, including short-term memory, longterm memory, and verbal fluency. The study included 2,049 non-disabled adults with a mean age of 53 and 49.1% women. In the initially high PA intensity group, variability in PA was associated with increased physical disability prevalence (23.3% vs. 33.2%, stablevs.unstable PA; P<0.01; adjusted P<0.01). In the initially low PA intensity group, variability was associated with a reduced physical disability (45.6% vs. 33.3%, stablevs.unstable PA; P=0.02; adjusted P=0.03). There were no statistically significant differences in cognitive parameters between the groups. Among individuals with the same low PA intensity at the beginning and end of follow-up, variability was associated with reduced physical disability (56.9% vs. 36.5%, stablevs.unstable PA; P=0.02; adjusted P=0.04) and improved short- -term memory (score change: -0.28 vs. +0.29, stablevs.unstable PA; P=0.05). Incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits. Current Aging Science00Investigating the association between intra-individual variability in PA and disability among non-athlete adults found incorporating variability into PA regimens of inactive adults may enhance their physical and cognitive benefits.Current Aging Science["Ehud Rinott", "Tal Sigawi", "N. Hurvitz", "Narmin Elchatib", "L. Rinsky-Halivni", "Yaron Ilan"]2025-01-20T00:00:00 aa651c0092c68afb020b1871a966a7c285d44b17https://orcid.org/0000-0003-1342-5924  Social Science and Humanities Journal460Social Science and Humanities Journal["N. A. \"Ali Ahmad\""]2025-01-20T00:00:00 00ca0b07995b7c71fea44206ba89c5b695f51912INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Panneer Selvam Viswanathan"]2025-01-20T00:00:00 cc82ca71a6fe0025b8ec6185b431c6696df640e3Current Psychology530Current Psychology["Yixiao Dong"]2025-01-20T00:00:00 88bf8d7ad1f74d2a96127fd62d4a1733f3aea7dfMedia Practice and Education160Media Practice and Education["Susan Cake"]2025-01-20T00:00:00 a196a9cf8debc18f54c4fe23178a7aabdb01e98eCardiovascular diseases (CVD) are the leading cause of death worldwide, underscoring the urgent need for accessible and personalized health management solutions [1]. This research presents CRIC, a mobile app that leverages AI to generate personalized Cardiac Risk Factor Scores and provide tailored recommendations. By integrating user input, AIdriven analysis, and a secure database, CRIC delivers actionable health insights and reliable educational resources to users [2]. Experiments involving 10 participants demonstrated high user satisfaction, with significant knowledge improvement as post-test scores increased by 30%. While challenges such as data accuracy and navigation were identified, iterative enhancements address these issues effectively. CRIC offers an innovative approach to bridging the gaps in traditional cardiovascular risk assessment, empowering users to make informed decisions about their health and contributing to global efforts in preventive healthcare.Artificial Intelligence and Big Data Trends 2025170This research presents CRIC, a mobile app that leverages AI to generate personalized Cardiac Risk Factor Scores and provide tailored recommendations and offers an innovative approach to bridging the gaps in traditional cardiovascular risk assessment.Artificial Intelligence and Big Data Trends 2025["Gengshuo Wang", "Morris Blaustein"]2025-01-20T00:00:00 8c0b6b6eb8599cedf8d6a10b38b154f2f5cc04c8INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT00INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT["Samson Olufemi Olanipekun", "Fortune Ohiorenuan Iriaye", "Chukwuemezie Charles Emejuo", "T. M. Olola"]2025-01-20T00:00:00 d5f6312f7f955082098313c5870f717f44d12756This paper robustly concludes that it cannot. A model is constructed under idealized conditions that presume that the risks associated with artificial general intelligence (AGI) are real, that safe AGI products are possible, and that there exist socially minded funders who are interested in funding safe AGI, even if this does not maximize profits. It is demonstrated that a socially minded entity formed by such funders would not be able to minimize harm from AGI that unrestricted products released by for-profit firms might create. The reason is that a socially minded entity can only minimize the use of unrestricted AGI products in ex post competition with for-profit firms at a prohibitive financial cost and so, does not preempt the AGI developed by for-profit firms ex ante. This paper was accepted by Maria Guadalupe, business strategy.Management Sciences60It is demonstrated that a socially minded entity formed by such funders would not be able to minimize harm from AGI that unrestricted products released by for-profit firms might create, and so, does not preempt the AGI developed by for-profit firms ex ante.Management Science["Joshua S. Gans"]2025-01-20T00:00:00 ee1141959004443596ceec788ce87dda23049b87This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.Journal of technology in human services100A dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing is proposed, enabling organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.Journal of Technology in Human Services["Brian E. Perron", "Lauri Goldkind", "Zia Qi", "Bryan Victor"]2025-01-20T00:00:00 75e0d1f36b2bf45ca6a66cc4179a11bd1c56f842The increasing frequency of customer inquiries and complaints in the digital era has put a strain on traditional support systems, resulting in inefficiencies and delayed responses. This paper describes an artificial intelligence- driven ticket automation system that uses advanced Natural Language Processing (NLP) techniques to accelerate ticket production, classification, and resolution. Using frameworks like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG), the system automates operations, increases response accuracy, and interacts smoothly with current support structures. The methodology entails preparing client queries for ticket classification, prioritizing, and multilingual translation, with Large Language Models (LLMs) fine-tuned for typical support scenarios. The system also accepts unsupervised queries and uses parallel processing to manage numerous tickets simultaneously, increasing throughput. Evaluation indicators, such as efficiency gains (targeted at 10%) and response accuracy, demonstrate the system's real- world usefulness. The key findings reveal that manual classification errors are eliminated, and complicated or multilingual requests receive faster responses. However, issues remain in addressing ambiguous inquiries and maintaining broad AI compatibility, indicating areas for future improvement. Ongoing work will strengthen categorization algorithms, broaden industry support, and incorporate advanced sentiment analysis to improve issue priority. Key Words: customer support, ticket automation, natural language processing, ai integration, multilingual support, workflow optimization.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00An artificial intelligence- driven ticket automation system that uses advanced Natural Language Processing techniques to accelerate ticket production, classification, and resolution and automates operations, increases response accuracy, and interacts smoothly with current support structures is described.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Mohammad Travadi"]2025-01-20T00:00:00 a5a03e1dc7da9bee2f4f5079493d86e5c384f4c9As suicide rates increase globally, there is a growing need for effective, data-driven methods in mental health monitoring. This study leverages advanced artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), to identify suicidal ideation from Twitter data. A predictive model was developed to process social media posts in real time, using NLP and sentiment analysis to detect textual and emotional cues associated with distress. The model aims to identify potential suicide risks accurately, while minimizing false positives, offering a practical tool for targeted mental health interventions. The study achieved notable predictive performance, with an accuracy of 85%, precision of 88%, and recall of 83% in detecting potential suicide posts. Advanced preprocessing techniques, including tokenization, stemming, and feature extraction with term frequency–inverse document frequency (TF-IDF) and count vectorization, ensured high-quality data transformation. A random forest classifier was selected for its ability to handle high-dimensional data and effectively capture linguistic and emotional patterns linked to suicidal ideation. The model’s reliability was supported by a precision–recall AUC score of 0.93, demonstrating its potential for real-time mental health monitoring and intervention. By identifying behavioral patterns and triggers, such as social isolation and bullying, this framework provides a scalable and efficient solution for mental health support, contributing significantly to suicide prevention strategies worldwide.Big Data and Cognitive Computing190A predictive model was developed to process social media posts in real time, using NLP and sentiment analysis to detect textual and emotional cues associated with distress, aiming to identify potential suicide risks accurately, while minimizing false positives, offering a practical tool for targeted mental health interventions.Big Data and Cognitive Computing["Hesham Allam", "Chris Davison", "Faisal Kalota", "E. Lazaros", "David Hua"]2025-01-20T00:00:00 e42d616e4113d8b92c0e5e4b5102549f93e9398bWith the seemingly rapid progression of technological development, algorithms are also becoming increasingly powerful and complex, not least due to the emergence of artificial intelligence (AI). While the AI Act is not yet applicable, a European Union law governing the use of algorithms on online platforms already exists that sets out the potential risks and challenges associated with their use. The Digital Services Act (DSA) introduces several new regulations concerning algorithm-based, automatic filtering systems into EU law that play a particularly important role for online platforms, as algorithms are used in these in the form of filter and recommender systems. These help with the moderation of content on platforms on the one hand and ensure a better user experience on the other. At the same time, their use is also associated with potentially negative implications and risks. For example, the spread of misinformation, hate speech and other harmful content on online platforms can have a significant negative impact on democracy and social cohesion. The Digital Services Act aims to ensure that algorithmic systems are used transparently and responsibly. In the analysis of the Digital Services Act, the paper primarily employs the method of word interpretation. This involved a detailed examination of the language used in the Digital Services Act, focusing on the specific terms and phrases within the legislative text. By scrutinising the context and usage of these keywords, the paper aims to uncover their precise meanings and implications.ELTE Law Journal50In the analysis of the Digital Services Act, a detailed examination of the language used in the Digital Services Act was focused on the specific terms and phrases within the legislative text, focusing on the specific terms and phrases within the legislative text.ELTE Law Journal["Boris Kandov"]2025-01-20T00:00:00 af6327b805cb6ed8c0695c23ae3ff530e91b2721As modern video games become increasingly complex, traditional manual testing methods are proving costly and inefficient, limiting the ability to ensure high-quality game experiences. While advancements in Artificial Intelligence (AI) offer the potential to assist human testers, the effectiveness of AI in truly enhancing real-world human performance remains underexplored. This study investigates how AI can improve game testing by developing and experimenting with an AI-assisted workflow that leverages state-of-the-art machine learning models for defect detection. Through an experiment involving 800 test cases and 276 participants of varying backgrounds, we evaluate the effectiveness of AI assistance under four conditions: with or without AI support, and with or without detailed knowledge of defects and design documentation. The results indicate that AI assistance significantly improves defect identification performance, particularly when paired with detailed knowledge. However, challenges arise when AI errors occur, negatively impacting human decision-making. Our findings show the importance of optimizing human-AI collaboration and implementing strategies to mitigate the effects of AI inaccuracies. By this research, we demonstrate AI's potential and problems in enhancing efficiency and accuracy in game testing workflows and offers practical insights for integrating AI into the testing process.00The results indicate that AI assistance significantly improves defect identification performance, particularly when paired with detailed knowledge, however, challenges arise when AI errors occur, negatively impacting human decision-making.["Boran Zhang", "Muhan Xu", "Zhijun Pan"]2025-01-20T00:00:00 db445de3564fd5a8abf496f3a1fb4441d233e8cd This paper aims to critically examine the implications of using generative artificial intelligence (AI) models, such as ChatGPT and Bard, in social science research. It examines the doppelganger effect in AI-driven studies as well as cognitive dissonance brought on by the autonomy of these tools. The discussion also addresses the debate between quantitative and qualitative methods for evaluating AI-driven research, scrutinising existing guidelines for accountability and validity. In addition, the paper considers the potential for generative AI to dominate research, identifying “non-takeoverable” skills and ethical issues in AI-driven knowledge production. This work primarily focuses on research articles for conceptual clarity, while news media reports are used to illustrate current scenarios. The doppelganger effect makes people worry about situations in which AI copies existing work so well that it becomes possible for people to give the wrong credit. This has led to a critical review of ways to make sure that the outputs of generative AI are real and original. Generative AI can enhance data collection and analysis, offering alternative approaches to traditional research methodologies. By leveraging the capabilities of generative AI, researchers can potentially uncover new insights and perspectives from their data. It is crucial to acknowledge the ethical concerns associated with using generative AI in social science research. The deployment of such technology introduces the possibility of biases and other ethical challenges that may impact the cognitive abilities of human participants or researchers involved in the research process. The work makes an effort by encouraging ethical consideration and highlighting crucial human abilities that are still necessary, providing a novel viewpoint on the use of generative AI in research approaches. Journal of Information, Communication and Ethics in Society140The doppelganger effect in AI-driven studies as well as cognitive dissonance brought on by the autonomy of these tools are examined, as well as the potential for generative AI to dominate research, identifying "non-takeoverable" skills and ethical issues in AI-driven knowledge production.Journal of Information, Communication and Ethics in Society["Rigin Sebastian", "Noufal Naheem Kottekkadan", "Toney K. Thomas", "Mohammed Niyas KK"]2025-01-20T00:00:00 36fc57a969ca7dc1b048318cff3943d9d64f604dThis research is a qualitative descriptive study of English language educators' views and practices regarding the integration of artificial intelligence (AI) tools in education. It aims to understand how teachers in Pontianak Urban Schools perceive the effectiveness of AI tools in enhancing their English teaching practices, what challenges teachers face in integrating AI tools into English language teaching, and how the use of AI tools affects student engagement and motivation in learning English. The participants were two English language teachers from an urban school in Pontianak, West Kalimantan. Data were gathered through semi-structured interviews, with thematic analysis used for data interpretation. The results show that both teachers used AI to help them in the teaching process. They also expressed the need for more training and support to effectively integrate the AI tools. The study offers insights into teachers’ perceptions of AI in education and highlights the importance of responsible AI integration in language teaching.Journal of English Education Program00The results show that both teachers used AI to help them in the teaching process and expressed the need for more training and support to effectively integrate the AI tools.Journal of English Education Program["Ratih Sulistiya Ningsih", "Sudharni Sudharni", "Y. Yuliana", "Dwi Riyanti"]2025-01-20T00:00:00 729f81747770280f253467eca0c0ab46d6c18ad5Sherri VandenAkker, Ph.D., has taught adult learners since the mid‐1990s, with more than two decades working in hybrid and online formats. She's a Professor of English at Springfield College in Massachusetts. I contacted her recently to discuss how artificial intelligence is changing writing and online learning. This conversation was conducted virtually through questionnaire and videoconference and has been edited for length and clarity.Enrollment Management Report00Sherri VandenAkker has taught adult learners since the mid‐1990s, with more than two decades working in hybrid and online formats, to discuss how artificial intelligence is changing writing and online learning.Enrollment Management Report["William Arighi"]2025-01-20T00:00:00 112ff2da83d3cd11f3c9934d87fc5bbf7a8ec84eThis review examines the integration of artificial intelligence (AI) in education, particularly its impact on language learning and gender perceptions. It examines AI’s transformative potential in enhancing educational outcomes and addresses gender biases in AI interactions. Emphasizing the importance of ethical AI practices, the study highlights the need for a nuanced understanding of how gender influences interactions with AI systems in educational contexts. By exploring these dynamics, it sheds light on the complexities of technology adoption and its implications for gender equity in education.Asparkía Investigació feminista00AI’s transformative potential in enhancing educational outcomes and addresses gender biases in AI interactions are examined, highlighting the need for a nuanced understanding of how gender influences interactions with AI systems in educational contexts.Asparkía. Investigació feminista["Alireza Dezfooli"]2025-01-20T00:00:00 0476282bf8d42b3f763efd1cf257da1a953428beThis technical article explores the evolution, architecture, and implementation challenges of multimodal AI systems, which represent a significant advancement in artificial intelligence. The article explores how these systems integrate multiple input modalities to achieve comprehensive understanding and analysis capabilities, mirroring human cognitive processes. Through detailed analysis of system architectures, performance metrics, and implementation strategies, we investigate the current state of multimodal AI across various applications, from virtual assistants to healthcare analytics. The article covers core technical components, data synchronization challenges, resource optimization techniques, and future directions in the field, providing insights into both theoretical frameworks and practical implementations.International Journal of Scientific Research in Computer Science Engineering and Information Technology50The article explores how these systems integrate multiple input modalities to achieve comprehensive understanding and analysis capabilities, mirroring human cognitive processes, in multimodal AI systems.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Bhabani Sankar Nayak"]2025-01-20T00:00:00 8838c0575df1c69b94edef621b3944448550c925Background As artificial intelligence (AI) continues to transform various aspects of our lives, conversational AI models have become increasingly sophisticated. The development of more accurate and informative language processing assistants has significant implications for numerous fields, including health care, medical service, and research assistance. Materials and Methods Reg-GPT™ was developed by the Maharaj Institute of Immune Regenerative Medicine (MIIRM) using a combination of supervised and unsupervised learning techniques. The LLaMa 3.1 model’s parameters were fine-tuned using vast amounts of text data, enabling Reg-GPT™ to learn from its interactions with users. Results Our evaluation shows that Reg-GPT™ model performs well in several key areas, including response accuracy, fluency, and engagement. The results highlight the potential benefits of integrating Reg-GPT™ into regenerative medicine (RM) applications. Conclusion This article provides a comprehensive introduction to Reg-GPT™, showcasing its capabilities, performance, and potential uses. We believe that Reg-GPT™ has the potential to provide significant value in the RM and Medicare fields.bioRxiv00The evaluation shows that Reg-GPT™ model performs well in several key areas, including response accuracy, fluency, and engagement, which highlights the potential benefits of integrating Reg-GPT™ into regenerative medicine (RM) applications.bioRxiv["Dipnarine Maharaj", "Wen Zhang"]2025-01-20T00:00:00 d1b4cc53eaacb1cd2472e711442c2b2cc9117183Currently, the education sector is undergoing a transformation centered around artificial intelligence, continually optimizing resource allocation, promoting educational equity, and enhancing the personalization, interactivity, and intelligence of learning, making education simpler, more enjoyable, and sustainable. To ensure that teachers can use artificial intelligence responsibly and effectively, UNESCO has released the “AI Competency Framework for Teachers” aimed at promoting lifelong professional development for educators. In this context, research on instructional design that can achieve this goal becomes particularly important. This study proposes an expanded “5P Funnel” framework based on the “3P Funnel”. The framework includes five key elements: Purpose (teaching objectives), Product (learning outcomes), Priority (teaching contents), Pare down (eliminate redundancies), and Provide (appropriate AI tools). The study employs a quasi-experimental research design to explore the potential impact of the “5P Funnel” framework on the professional development of pre-service teachers in Zhaoqing, Guangdong. In this study, 204 candidates in the “Modern Educational Technology Application” course were randomly divided into an intervention group (n=102) and a control group (n=102), with the experiment lasting one semester (15 weeks). By analyzing the differences and relationships in the dimensions of professional development abilities between the two groups, the study evaluates the experimental effects and draws conclusions. Although the study is not yet complete, the paper will focus on the research methodology and preliminary progress, laying the foundation for future contributions. Once the experimental results are obtained, they will provide new ideas and methods for teacher training and practice in the AI era.Journal of Public Administration and Governance00This study proposes an expanded “5P Funnel” framework based on the “3P Funnel”, and employs a quasi-experimental research design to explore the potential impact of the “5P Funnel” framework on the professional development of pre-service teachers in Zhaoqing, Guangdong.Journal of Public Administration and Governance["Chanyi Li", "Marzni Mohamed Mokhtar", "Ahmad Fauzi Mohd Ayub"]2025-01-20T00:00:00 401087366e0779a3124c1707c7f88feb760d4fa2AI is one of the most disruptive technologies of our era, significantly transforming nearly every aspect of human life. This book examines the impact of AI on international affairs from interdisciplinary, cross-sectoral, and interregional perspectives, focusing on both the European Union and Latin America. It explores philosophical debates on concepts such as consciousness, ethics, and human uniqueness, offering a framework for assessing the risks and benefits of AI for humanity. The evolving landscape is also giving rise to new rights, including NeuroRights, which expand upon existing human rights. Additionally, the book analyses the EU AI Act and its implications for human rights in the digital age. This publication is a collaborative effort between university scholars and international experts, developed within the research group "EU & Ethics Governance of the Artificial Intelligence" led by the Institute of European Studies and Human Rights at the Pontifical University of Salamanca.00This publication is a collaborative effort between university scholars and international experts, developed within the research group "EU & Ethics Governance of the Artificial Intelligence" led by the Institute of European Studies and Human Rights at the Pontifical University of Salamanca.[]2025-01-21T00:00:00 0129667a8a540c80f42907fecad2e6fcbed80010Early detection of breast cancer is crucial for improving patient outcomes. With advancements in artificial intelligence (AI), there is growing interest in its potential to assist radiologists in interpreting mammograms for early cancer detection. AI algorithms offer the promise of increased accuracy and efficiency in identifying subtle signs of breast cancer, potentially complementing the expertise of radiologists and enhancing the screening process for early-stage breast cancer detection.A systematic literature review was conducted to identify and select original research reports on breast cancer diagnosis by artificial intelligence versus conventional radiologists in using mammograms in accordance with the PRISMA guidelines. Data were analysed with Review Manager version 5.4. P-value and I2 were used to test the significance of differences.This systematic review and meta-analysis included 8 studies with data from a total of 120,950 patients. Regarding the sensitivity of AI, the pooled analysis of 6 studies with sensitivities ranging from 0.70 to 0.89 yielded a sensitivity of 0.85. However, the sensitivity of the radiologists ranged from 0.63 to 0.85, with an overall sensitivity of 0.77. As for specificity, both radiologists and AI groups had closer results.The comparison between AI systems and radiologists in detecting early-stage breast cancer from mammograms highlights the potential of AI as a valuable tool in breast cancer screening. While AI algorithms have shown promising results in terms of accuracy and efficiency, they should be viewed as complementary to radiologists rather than replacements.Polish Journal of Radiology190The comparison between AI systems and radiologists in detecting early-stage breast cancer from mammograms highlights the potential of AI as a valuable tool in breast cancer screening.Polish Journal of Radiology["H. T. Hashim", "A. Alhatemi", "Motaz Daraghma", "Hossam Tharwat Ali", "Mudassir Ahmad Khan", "Fatimah Abdullah Sulaiman", "Zahraa Hussein Ali", "Mohanad Ahmed Sahib", "Ahmed Dheyaa Al-Obaidi", "A. Al-Obaidi"]2025-01-21T00:00:00 2e7907d54afa0e062ce3df34e59faec68242ba2aThe recognition and classification of facial expressions using artificial intelligence (AI) presents a promising avenue for early detection and monitoring of neurodegenerative disorders. This narrative review critically examines the current state of AI-driven facial expression analysis in the context of neurodegenerative diseases, such as Alzheimer's and Parkinson's. We discuss the potential of AI techniques, including deep learning and computer vision, to accurately interpret and categorize subtle changes in facial expressions associated with these pathological conditions. Furthermore, we explore the role of facial expression recognition as a noninvasive, cost-effective tool for screening, disease progression tracking, and personalized intervention in neurodegenerative disorders. The review also addresses the challenges, ethical considerations, and future prospects of integrating AI-based facial expression analysis into clinical practice for early intervention and improved quality of life for individuals at risk of or affected by neurodegenerative diseases.Reviews in the Neurosciences690The challenges, ethical considerations, and future prospects of integrating AI-based facial expression analysis into clinical practice for early intervention and improved quality of life for individuals at risk of or affected by neurodegenerative diseases are addressed.Reviews in the neurosciences["Nooshin Goudarzi", "Zahra Taheri", "Amir Mohammad Nezhad Salari", "Kimia Kazemzadeh", "Abbas Tafakhori"]2025-01-21T00:00:00 449c954ba50ba44bbe760bcb2afe69a9ac238947Text classifiers are Artificial Intelligence (AI) models used to classify new documents or text vectors into predefined classes. They are typically built using supervised learning algorithms and labelled datasets. Text classifiers produce a predefined class as an output, which also makes them susceptible to adversarial attacks. Text classifiers with high accuracy that are trained using complex deep learning algorithms are equally susceptible to adversarial examples, due to subtle differences that are indiscernible to human experts. Recent work in this space is mostly focused on improving adversarial robustness and adversarial example detection, instead of detecting adversarial attacks. In this paper, we propose a novel approach, explainable AI with integrated gradients (IGs) for the detection of adversarial attacks on text classifiers. This approach uses IGs to unpack model behavior and identify terms that positively and negatively influence the target prediction. Instead of random substitution of words in the input, we select the top p% words with the greatest positive and negative influence as substitute candidates using attribution scores obtained from IGs to generate k samples of transformed inputs by replacing them with synonyms. This approach does not require changes to the model architecture or the training algorithm. The approach was empirically evaluated on three benchmark datasets, IMDB, SST-2, and AG News. Our approach outperforms baseline models on word substitution rate, detection accuracy, and F1 scores while maintaining equivalent detection performance against adversarial attacks.Applied System Innovation180This paper proposes a novel approach, explainable AI with integrated gradients (IGs) for the detection of adversarial attacks on text classifiers, which outperforms baseline models on word substitution rate, detection accuracy, and F1 scores while maintaining equivalent detection performance against adversarial attacks.Applied System Innovation["Harsha Moraliyage", "Geemini Kulawardana", "Daswin de Silva", "Zafar Issadeen", "Milos Manic", "Seiichiro Katsura"]2025-01-21T00:00:00 9508cf646791ba92d64781abba05a2f6e413bf0aPurposeMany companies adopt the corporate newsroom model to streamline their corporate communication. This article addresses why and how corporate newsrooms transform corporate communication following the rise of artificial intelligence (AI) systems.Design/methodology/approachThis research draws on original data from 13 semi-structured interviews with executive communication experts in large Swiss companies that use corporate newsrooms.FindingsCorporate newsrooms serve as an organisational (rather than spatial) coordination body for topic-oriented and agile corporate communication. To enable their functionality, it is crucial to find the right balance between optimising and stabilising communication structures. Newsrooms actively adopt AI both to facilitate routine and enable more innovative applications, such as living data archives and channel translations. Interviews also highlight an urgent need for regulatory frameworks of AI in corporate communication.Practical implicationsThis article provides (currently lacking) insights into the practical challenges and coping strategies for establishing and managing corporate newsrooms.Originality/valueThis research addresses the gap in corporate newsroom research regarding a limited understanding of how newsrooms are implemented, managed and adapted to AI-related innovations.Corporate Communications. An International Journal340Why and how corporate newsrooms transform corporate communication following the rise of artificial intelligence (AI) systems is addressed, with insights into the practical challenges and coping strategies for establishing and managing corporate newsrooms.Corporate Communications: An International Journal["Tobias Rohrbach", "M. Makhortykh"]2025-01-21T00:00:00 f7f938d857094907890a02584276e1981e1a4b35As Artificial Intelligence (AI) technologies are taking the lead among the technological advancements around the world, societies are increasingly becoming interwoven with Generative AI (GAI) technologies in all aspects, including higher education (HE). This study’s main aim is to examine how individual-level cultural dimensions influence students’ adoption of GAI in learning, drawing on an extended Innovation of Diffusion Theory (IDT) model. It explores the impact of individual-level cultural dimensions (individualism/collectivism and uncertainty avoidance), IDT innovation factors (relative advantage, complexity, compatibility, observability, trialability), and individual factors (self-efficacy, perceived risk) on Saudi students’ perceptions of GAI adoption across several universities. Quantitative data were collected from 306 online survey and analyzed using CB-SEM. Results highlight the instrumental role of cultural dimensions, with individualism/collectivism and uncertainty avoidance negatively affecting GAI adoption. While complexity showed no significant impact, all other IDT variables positively influenced adoption. Furthermore, self-efficacy and perceived risk were found to be significant indicators of GAI use. The study emphasizes the cultural differences that shape technology adoption in collectivist societies that are moving toward individualism such as Saudi. It identifies limitations, provides useful insights, and suggests recommendations for future research on GAI uptake in culturally diverse HE contexts.Journal of Ecohumanism870The study emphasizes the cultural differences that shape technology adoption in collectivist societies that are moving toward individualism such as Saudi and identifies limitations, provides useful insights, and suggests recommendations for future research on GAI uptake in culturally diverse HE contexts.Journal of Ecohumanism["J. Alamri"]2025-01-21T00:00:00 1d248e3a41c7b5bb058a5cc4f8a630f362f37e4dThe Australian Educational Researcher410Reflections on responsible and ethical policy-setting in response to rapid technological change are provided, including with relation to anticipatory and networked governance and the inter-relationship with the broader policy context.The Australian Educational Researcher["Matt Bower", "Michael Henderson", "Christine Slade", "Erica Southgate", "Kalervo N. Gulson", "Jason Lodge"]2025-01-21T00:00:00 fad18f54ef7984e4d8685b12856e69d72660ac11OBJECTIVE To review pediatric artificial intelligence (AI) implementation studies from 2010-2021 and analyze reported performance measures. METHODS We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE and Web of Science with controlled vocabulary. INCLUSION CRITERIA AI intervention in a pediatric clinical setting that learns from data (i.e., data-driven, as opposed to rule-based) and takes actions to make patient-specific recommendations; published between 01/2010 to 10/2021; must have agency (AI must provide guidance that affects clinical care, not merely running in background). We extracted study characteristics, target users, implementation setting, time span, and performance measures. RESULTS Of 126 articles reviewed as full text, 17 met inclusion criteria. Eight studies (47%) reported both clinical outcomes and process measures, six (35%) reported only process measures, and two (12%) reported only clinical outcomes. Five studies (30%) reported no difference in clinical outcomes with AI, four (24%) reported improvement in clinical outcomes compared to controls, two (12%) reported positive effects on clinical outcomes with use of AI but had no formal comparison or controls, and one (6%) reported poor clinical outcomes with AI. Twelve studies (71%) reported improvement in process measures, while two (12%) reported no improvement. Five (30%) studies reported on at least 1 human performance measure. CONCLUSIONS While there are many published pediatric AI models, the number of AI implementations is minimal with no standardized reporting of outcomes, care processes, or human performance measures. More comprehensive evaluations will help elucidate mechanisms of impact.Applied Clinical Informatics00The number of AI implementations is minimal with no standardized reporting of outcomes, care processes, or human performance measures, and more comprehensive evaluations will help elucidate mechanisms of impact.Applied clinical informatics["Swaminathan Kandaswamy", "Lindsey A. Knake", "Adam C. Dziorny", "Sean M. Hernandez", "Allison B. McCoy", "Lauren M Hess", "Evan W Orenstein", "Mia S White", "E. Kirkendall", "Matthew J Molloy", "Philip Hagedorn", "Naveen Muthu", "Avinash Murugan", "Jonathan M Beus", "Mark Mai", "Brooke Luo", "J. D. Chaparro"]2025-01-21T00:00:00 5396bf3a247e4285ebddcc5ab3028d6c88c857e5The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI’s capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.Journal of Geography and Cartography00This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks, to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits.Journal of Geography and Cartography["Zarif Bin Akhtar", "Ahmed Tajbiul Rawol"]2025-01-21T00:00:00 8be095839f0085205afcf00a77f391219e736f8aThe invention of Artificial Intelligence (AI) has posed numerous legal challenges across various fields, including intellectual property law. What once existed solely in science fiction movies and novels has now become a tangible reality. The capabilities of AI have reached such an extent that it can rival the human brain, generating works that not only match human intellect but, in some cases, exceed it. While AI offers multiple advantages, it is not without its drawbacks. Traditionally, intellectual property protection applied exclusively to human-generated works, with only humans recognized as the owners of such creations. However, with the invention of AI and its creative capacity, the matters of ‘protectability’ and ‘ownership’ of creative and inventive works have become increasingly ambiguous and demand resolution. Since international conventions like the Berne Convention or the TRIPS Agreement do not provide any clear guidelines on this matter, every nation has the latitude to determine the legal status of AI outputs within their national boundary. Therefore, the objective of this article is to analyse the legal complexities of extending IP protection to AI-generated outputs and propose potential solution for Bangladesh. To achieve this objective, the article adopts a qualitative methodology. In addition to analysing national laws of different countries, the primary international instruments analysed include, but are not limited to, the Berne Convention for the Protection of Literary and Artistic Works, the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement) and the European Patent Convention. The jurisdictions selected for the comparative study are the UK, USA, EU, India and Bangladesh.Chinese Journal of Transnational Law120The objective of this article is to analyse the legal complexities of extending IP protection to AI-generated outputs and propose potential solution for Bangladesh, and adopts a qualitative methodology.Chinese Journal of Transnational Law["Tanveer Ahmed"]2025-01-21T00:00:00 c2121d9baa4e81ecffa0d60e52ca7525eafa3411Liver metastases from Gastrointestinal (GI) cancers present significant challenges in oncology, often signaling poor prognosis. Traditional detection methods like imaging and tissue biopsies have limitations in sensitivity, specificity, and tumor heterogeneity represen-tation. The advent of artificial intelligence (AI) in healthcare, driven by advancements in ma-chine learning, algorithms, and data science, offers a promising frontier for early detection and management of liver metastases. This review explores the integration of AI and liquid biopsy technologies as transformative tools in the proactive detection of liver metastases aris-ing from GI malignancies. Liquid biopsy, a non-invasive method, analyzes circulating tumor cells (CTCs), cell-free DNA (cfDNA), and circulating tumor DNA (ctDNA) in bodily fluids. It provides a compre-hensive overview of tumor heterogeneity and enables real-time monitoring of tumor evolu-tion and treatment response. Despite its advantages, liquid biopsy faces challenges such as low sensitivity for early-stage metastases, reduced detectability due to liver filtration, and technical limitations. AI enhances the potential of liquid biopsies by improving diagnostic accuracy through ad-vanced algorithms like Convolutional Neural Networks (CNNs) and Natural Language Pro-cessing (NLP). These AI models analyze complex biomedical data, offering higher sensitivity and specificity in cancer detection. The synergy between AI and liquid biopsies promises early detection, better disease monitoring, and personalized treatment strategies. This review underscores the significant advancements AI and liquid biopsy technologies bring to oncological precision medicine, particularly in improving overall survival (OS) and disease-free survival (DFS) for patients with GI cancer metastases. As we transition into the era of precision medicine, the integration of these technologies holds the potential to redefine cancer care and patient management.Current Cancer Drug Targets00The integration of AI and liquid biopsy technologies as transformative tools in the proactive detection of liver metastases from GI malignancies underscores the significant advancements AI and liquid biopsy technologies bring to oncological precision medicine, particularly in improving overall survival and disease-free survival for patients with GI cancer metastases.Current cancer drug targets["Thilagesh P", "Anand Kumar S", "Aiswarya Nair U", "Rabiniraj S", "Shobana P", "Subramani M", "Sriram K"]2025-01-21T00:00:00 a302c8de010d8ccbfd3dbefe9449c06769a01ec5The study aimed to identify the factors influencing the adoption of artificial intelligence applications in libraries, find out the associated challenges with the adoption of AI apps, and develop a framework to effectively implement AI tools in libraries. A systematic literature review (SLR) was applied to address the study's objectives. 30 most relevant research papers published in impact factor journals were selected to conduct the study. Findings of the study showed that four major factors influenced the adoption of AI in libraries. These factors included transformation of library services, provision of innovative services, librarians and users’ satisfaction, and technological revolution. The study manifested that technological challenges, skills and knowledge barriers, financial challenges, and organizational and cultural barriers caused barriers for librarians to adopt AI apps in libraries. The study has added valuable literature to the existing body of knowledge. It has developed framework on evidence based datasets to adopt AI apps in libraries effectively and efficiently.Information Development220The study manifested that technological challenges, skills and knowledge barriers, financial challenges, and organizational and cultural barriers caused barriers for librarians to adopt AI apps in libraries.Information Development["Khurram Shahzad", "S. A. Khan", "Abid Iqbal", "Shakil Ahmed", "Asfa Muhammad Din Javeed", "Osama Mohamed"]2025-01-21T00:00:00 71f5f6739135cb2ff490d7282dcc5fdab30c64eaThis study investigates the transformative role of artificial intelligence (AI) in state-sponsored cyber espionage, focusing on its dual use in offensive and defensive operations. Using data from the MITRE ATT&CK Framework, FireEye APT Groups Database, UNSW-NB15 Intrusion Detection Dataset, and the Cyber Conflict Tracker by CFR, this research applied network graph analysis, multi-criteria decision analysis (MCDA), ensemble classification models, and Difference-in-Differences (DiD) analysis. Results revealed that AI-driven offensive techniques, phishing (degree centrality 0.85), and adaptive malware (betweenness centrality 0.81) significantly enhance operational precision and scalability. Defensively, ensemble classification models achieved up to 95.8% accuracy, highlighting AI's efficacy in intrusion detection. AI regulatory frameworks reduced misattribution rates by 20% and escalation incidents by 10%, demonstrating their critical role in mitigating geopolitical risks. The findings impress AI's transformative potential in advancing cyber operations and shaping international policy and governance. By addressing challenges such as attribution, escalation risks, and ethical dilemmas, this study highlights the necessity for stronger global cooperation and regulatory frameworks to navigate the dual-use nature of AI, providing actionable insights for policymakers, cybersecurity professionals, and researchers, emphasizing the urgency of aligning technological advancements with strategies for enhancing global cybersecurity resilience.Asian Journal of Research in Computer Science00The necessity for stronger global cooperation and regulatory frameworks to navigate the dual-use nature of AI is highlighted, providing actionable insights for policymakers, cybersecurity professionals, and researchers, emphasizing the urgency of aligning technological advancements with strategies for enhancing global cybersecurity resilience.Asian Journal of Research in Computer Science["Onyinye Agatha Obioha-Val", "O. O. Olaniyi", "M. O. Gbadebo", "Adebayo Yusuf Balogun", "Anthony Obulor Olisa"]2025-01-21T00:00:00 75df310c8a68d280323fa01a37ae9a2e75eb607bIn recent years, the rapid development of artificial intelligence technology has significantly advanced research innovations in the field of economics and finance. Particularly, machine learning methods, with their exceptional data processing and analytical capabilities, have been widely applied to areas such as predictive modeling, causal inference, and unstructured data analysis. This paper systematically examines the key differences between machine learning and traditional econometrics in terms of research paradigms, objectives, and data processing approaches. It provides an in-depth exploration of the application scenarios of machine learning in economics and finance, focusing on predictive modeling, alternative data analysis, and causal inference. Furthermore, it highlights the potential and breakthroughs of machine learning in addressing complex economic problems through specific case studies, demonstrating its practical effectiveness in macroeconomic forecasting, financial asset pricing, risk management, and policy analysis. In addition, this paper comprehensively analyzes the current challenges facing machine learning technology in economic and financial research, including issues of model transparency, difficulties in handling small sample sizes and noisy data, as well as data privacy concerns. Based on these challenges, several potential solutions are proposed, including the adoption of explainability tools, transfer learning, federated learning, and privacy-preserving computation. Future research directions are suggested, emphasizing the integration of multimodal data analysis, the exploration of large language models for policy and market analysis, and the deeper alignment of artificial intelligence technologies with industry practices. This study aims to provide theoretical guidance and practical insights for academia and industry, contributing to the advancement of research innovation and application expansion in the field of economics and finance.Frontiers in Business, Economics and Management40This paper comprehensively analyzes the current challenges facing machine learning technology in economic and financial research, including issues of model transparency, difficulties in handling small sample sizes and noisy data, as well as data privacy concerns.Frontiers in Business, Economics and Management["Yingliang Wan", "Hong Tao", "Yiheng Zhao"]2025-01-21T00:00:00 cb152b80e21061856402c5c4fb20d26fbbe97dc3This study aims to analyze teachers' readiness to integrate artificial intelligence into education for the digital revolution. In this study, teachers were randomly selected with a total of 73 teachers from various subjects. This research used a quantitative method of using questionnaires for data collection. The data analysis technique in this research uses descriptive quantitative.  Based on the research findings, technical ability, institutional support, adequate technological infrastructure, and school facilities affected teachers' readiness to use various types of artificial intelligence. Urban teachers were more ready than rural teachers, largely because urban teachers have more access to training materials and technology than rural teachers. In the future, in enabling AI to be used effectively to improve learning standards, government support needs to conduct specialized AI training at various school levels from urban to rural areas, in particular, stronger institutional support, and better school infrastructure and facilities to enhance teachers' readiness to face the digital revolution in education.Jurnal Paedagogy230In enabling AI to be used effectively to improve learning standards, government support needs to conduct specialized AI training at various school levels from urban to rural areas, in particular, stronger institutional support, and better school infrastructure and facilities to enhance teachers' readiness to face the digital revolution in education.Jurnal Paedagogy["Jurnal Penelitian", "Pengembangan Pendidikan", "M. R. Purnama", "Putu Iwan", "Krisna Sastra Adnyana", "Afrianto T.L Sogen", "Gede Indrawan", "Made Hery Santosa"]2025-01-21T00:00:00 121d6b2de78e2ee7b135fc307c23d05af458e589With the emergence and significant achievements of large language models such as ChatGPT, it marks the beginning of a new era in the development of artificial intelligence. The 2024 Nobel Prize in Physics was awarded to AI-related scientists Hopfield and Hinton. Hinton has repeatedly criticized the renowned linguist Chomsky, triggering widespread attention and discussion in the field of linguistics.Iris Online Journal of Arts and Social Sciences00Iris Online Journal of Arts and Social Sciences["Shuijian Zheng"]2025-01-21T00:00:00 bf3cb01c61f38c67b8b37aff54068c137210cd5eArtificial Intelligence (AI) serves as a strategic solution capable of optimizing production schedules and significantly enhancing work productivity. The objectives of this study are to understand how Artificial Intelligence (AI) can optimize production scheduling in the manufacturing industry, identify the factors influencing the success of AI implementation in the production scheduling process, and determine the impact of AI implementation on work productivity in the manufacturing industry. The research method employed is qualitative. The findings of this study indicate that the implementation of AI provides efficiency, flexibility, and competitive advantages for the manufacturing industry. With the right strategies, this technology can achieve higher productivity, enhance company competitiveness, and support sustainable growth in the modern industrial era.Jurnal ISO: Jurnal Ilmu Sosial, Politik dan Humaniora220The findings of this study indicate that the implementation of AI provides efficiency, flexibility, and competitive advantages for the manufacturing industry.Jurnal ISO: Jurnal Ilmu Sosial, Politik dan Humaniora["Agra Nurtrihadi", "Dangan Waluyo", "Kata Kunci", "Ai", "Industri Manufaktur", "Produktivitas Kerja"]2025-01-21T00:00:00 07e53743f704b925e6fa5a2a725ae758322bddd4The future of education is AI. As technology continues to advance, the use of AI in education is becoming more prevalent. It is necessary that every educator key into AI or miss out. This article therefore, was ascertained the utilization of artificial intelligence in instructional preparation by business educators in Cross River State. Three specific purposes, research questions and hypotheses were developed for the study. Descriptive survey design was used. The population of the study comprised eighty-nine (87) Business Educators (51 male and 36 female) in four (4) tertiary institutions in Cross River State. The instrument for data collection was a researcher’s self-designed 4-point rating scale questionnaire title: Questionnaire on the Utilization of Artificial intelligence in Instructional Preparation (QUAIIP). The instrument was validated by three (3) experts and further tested for reliability which produced a coefficient of 0.87. Data were analysed using mean and standard deviation to answer the research questions and t-test to test the null hypotheses. Findings revealed that there is high extent in the perception of business educators on the utilization of AI in instructional preparation. Paradoxically, there was a low extent in the utilization of AI for instructional preparation; both in educational content creation and personalized instruction. There was no significant difference in the perception of business educators in colleges of education and universities on the utilization of artificial intelligence in instructional preparation. There was no significant difference in the extent of utilization of artificial intelligence in educational content creation and personalized instruction based on gender. It was recommended among others that there should be a collaboration between schools and AI manufacturers in developing appropriate skills to help educators in the utilization of AI in education.   Received: 2 December 2024 / Accepted: 10 January 2025 / Published: 21 January 2025Mediterranean Journal of Social Sciences00Findings revealed that there is high extent in the perception of business educators on the utilization of AI in instructional preparation and there was no significant difference in the extent of utilization of artificial intelligence in educational content creation and personalized instruction based on gender.Mediterranean Journal of Social Sciences["Patricia Olom", "C. Atah"]2025-01-21T00:00:00 8b2544e845ea7e7a7a270f17af36def3ce494542Artificial intelligence (AI) has the potential to become a transformative general-purpose technology, reshaping economic, social, and institutional frameworks globally. The Inter-American Development Bank Group (IDBG) recognizes the urgency of fostering AI adoption in Latin America and the Caribbean to catalyze productivity, inclusion, and sustainable development. This document provides a high-level strategic framework to guide IDBGs interventions in advancing AI adoption and responsible use across the region. The document reviews key challenges facing the three dimensions of AI institutions and governance; data and infrastructure; and human capital and how to overcome these challenges to build AI ecosystems and accelerate innovation and adoption in both the public and private sectors. By addressing structural barriers such as uneven digital infrastructure, limited data availability, and insufficient skills, this framework seeks to create conditions for ambitious and equitable AI diffusion. Additionally, it prioritizes a research agenda targeting the impacts, obstacles, and governance of AI adoption, focusing on evidence-based policy design to leapfrog development challenges while mitigating risks of inequality and misuse. This strategic guidance aligns with the three core objectives of IDBImpact: reducing poverty and inequality, addressing climate change, and fostering sustainable regional growth.00This document provides a high-level strategic framework to guide IDBGs interventions in advancing AI adoption and responsible use across the region, and prioritizes a research agenda targeting the impacts, obstacles, and governance of AI adoption.["Fernando Vargas", "Arturo Muente"]2025-01-21T00:00:00 75e5e8f1cd79ede27212e14f53c503dc5054e477Recent advancements in artificial intelligence have reopened the question about the boundaries of AI autonomy, particularly in discussions around artificial general intelligence (AGI) and its potential to act independently across varied purposes. This paper explores these boundaries through the analysis of the Alignment Research Center experiment on GPT-4 and introduces the Start Button Problem, a thought experiment that examines the origins and limits of AI autonomy. By examining the thought experiment and its counterarguments will be enlightened how in the need for human activation and purpose definition lies the AI's inherent dependency on human-initiated actions, challenging the assumption of AI as an agent. Finally, the paper addresses the implications of this dependency on human responsibility, questioning the measure of the extension of human responsibility when using AI systems.490The Start Button Problem is introduced and a thought experiment is introduced that examines the origins and limits of AI autonomy and the implications of this dependency on human responsibility, questioning the measure of the extension of human responsibility when using AI systems.["Vincenzo Calderonio"]2025-01-21T00:00:00 b72562e4ac65686582aa19203fc17f3962de6c31 The advent of generative Artificial Intelligence (AI) has brought about a significant shift in the way works are created, with the blurring of boundaries between human and machine-driven creation processes becoming a prominent challenge. This leads to the question of whether authorship in such works exists and, if so, whom it should be attributed to. This article focusses on an analysis of existing case law of the Court of Justice of the European Union and selected EU Member State courts, in order to find indications about what to consider when examining the authorship of AI-assisted works in the European copyright system. Ultimately, a four-step test is proposed which aids in assessing whether there is authorship in concrete works and whom it should be attributed to. The first step asks what persons are involved in the creation process before determining—as second step—the kind of AI system used. The third step analyses whether the persons involved exercised a sufficient subjective judgment in the composition of the work; the final step determines whether they had an adequate control over the execution.Journal of Intellectual Property Law & Practice00An analysis of existing case law of the Court of Justice of the European Union and selected EU Member State courts is found to find indications about what to consider when examining the authorship of AI-assisted works in the European copyright system.Journal of Intellectual Property Law and Practice["Johannes Fritz"]2025-01-21T00:00:00 0da5492fd3ec6b1eebeb49e0b395636fd2061596Program pengabdian masyarakat ini merupakan kerjasama dengan mitra yakni MA Ma�arif NU Sains Al-Qur�an Sumbang. Mitra beranggotakan para guru, tenaga kependidikan, dan siswa-siswi kelas X, Kelas XI, dan Kelas XII. Permasalahan utama mitra terletak pada model pembelajaran yang monoton, a siswa lebih cepat merasa bosan saat mengikuti pembelajaran di dalam kelas di hampir semua mata pelajaran yang diikuti. Guru-guru pengajar mayoritas hanya menggunakan fasilitas LCD Proyektor yang disediakan oleh madrasah atau melaksanakan pembelajaran dengan siswa lebih menerima dan mendengarkan guru. Dengan alasan tersebut, pengabdian ini fokus mengadakan pelatihan presentasi ilmiah bagi siswa menggunakan teknologi kecerdasan buatan atau AI (Artificial Intelegence) ChatGPT dan aplikasi Canva sebagai pengembangan pembelajaran di MA Ma�arif NU Sains Al-Qur�an Sumbang. Metode yang digunakan dalam pelatihan ini menggunakan langkah-langkah studi literatur, identifikasi permasalahan, mencarikan Solusi, memulai pelatihan, dan penerapan AI (Chatgpt dan Canva) akan berfokus pada bagaimana persiapan-persiapan dan tahapan-tahapan yang harus dilakukan dalam mempersiapkan bahan presentasi yang dibuat menggunakan ChatGPT dan aplikasi Canva, kemudian mempresentasikan materi yang telah dibuat di depan kelas dalam forum diskusi. Adapun hasil akhir dari pelatihan ini yakni peserta didik dapat membuat presentasi ilmiah yang menarik secara individu dari materi pembelajaran sesuai dengan jenjang kelasnya masing-masing.PARADIGMA PENGABDIAN150PARADIGMA PENGABDIAN["Septina Nurhayati", "Yurita Erviana"]2025-01-21T00:00:00 d3ac71dd57d49e9dfc9c2ee2a5a27da70f1ada9200[]2025-01-21T00:00:00 92440a51e0a077ea14c66431d3919889709c0251Cluster Computing40Clust. Comput.["Kenan \u015eenturk", "Ahmet Faruk Gormus", "Serkan G\u00f6nen", "Mehmet Ali Bari\u015fkan", "Ahmet Kaan Durmaz"]2025-01-21T00:00:00 83ef8774126f9c20d1ad1c4967e30ddd08631830Early detection of brain Cerebral Hemorrhage Stroke is of critical importance in medical imagery. It reviews the application of advanced learning algorithms to increase the accuracy and efficiency of brain stroke detection using noninvasive imaging (in particular MRI). In the last couple of years, recent machine learning and deep learning approaches like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid models have shown an overnight progress by automating the process of extraction, segmentation, and classification of brain tumors. Early symptoms of Alzheimer's dementia include: Memory impairment, such as trouble remembering events. Having a hard time concentrating, planning or problem-solving. Trouble finishing daily tasks at home or at work, such as writing or using eating utensils. You can't do. If another treatable condition is causing memory loss, your healthcare team can start treatments. For those with Alzheimer's dementia, starting medicines early can help slow the decline in memory and other cognitive skills.International Research Journal on Advanced Engineering Hub (IRJAEH)290In the last couple of years, recent machine learning and deep learning approaches like Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid models have shown an overnight progress by automating the process of extraction, segmentation, and classification of brain tumors.International Research Journal on Advanced Engineering Hub (IRJAEH)["N. R. Rajeswari", "Dr. M. Giri"]2025-01-21T00:00:00 8fc83f5819b57b8439790455aab3c0eb0c80b394BACKGROUND Generative AI, particularly large language models (LLMs), holds great potential for improving patient care and operational efficiency in healthcare. However, the use of LLMs is complicated by regulatory concerns around data security and patient privacy. This study aimed to develop and evaluate a secure infrastructure that allows researchers to safely leverage LLMs in healthcare while ensuring HIPAA compliance and promoting equitable AI. MATERIALS AND METHODS We implemented a private Azure OpenAI Studio deployment with secure API-enabled endpoints for researchers. Two use cases were explored, detecting falls from electronic health records (EHR) notes and evaluating bias in mental health prediction using fairness-aware prompts. RESULTS The framework provided secure, HIPAA-compliant API access to LLMs, allowing researchers to handle sensitive data safely. Both use cases highlighted the secure infrastructure's capacity to protect sensitive patient data while supporting innovation. DISCUSSION AND CONCLUSION This centralized platform presents a scalable, secure, and HIPAA-compliant solution for healthcare institutions aiming to integrate LLMs into clinical research.JAMIA Journal of the American Medical Informatics Association30This study aimed to develop and evaluate a secure infrastructure that allows researchers to safely leverage LLMs in healthcare while ensuring HIPAA compliance and promoting equitable AI.Journal of the American Medical Informatics Association : JAMIA["Madelena Y Ng", "Jarrod Helzer", "Michael A Pfeffer", "Tina Seto", "Tina Hernandez-Boussard"]2025-01-21T00:00:00 e7437b73c21ba9e2fda138b81ef77ec298a00187Digital Journalism390Digital Journalism["Mat\u00edas Valderrama Barrag\u00e1n", "Martin Tironi", "Dusan Cotoras", "Teresa Correa", "M\u00f3nica Humeres", "Claudia L\u00f3pez"]2025-01-21T00:00:00 da02fa767bd18e017044e6be9a476c08b231f6c8EBioMedicine180An artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings and provides accurate and prognostically valuable AS assessment, suitable for various clinical settings is developed and validated.eBioMedicine["Jiesuck Park", "Jiyeon Kim", "J. Jeon", "Y. Yoon", "Yeonggul Jang", "H. Jeong", "Youngtaek Hong", "Seung-Ah Lee", "Hong-Mi Choi", "I. Hwang", "G. Cho", "Hyuk-Jae Chang"]2025-01-21T00:00:00 f8e030458b441c51ff9a3b78c790373df13ede4cUniversum:Philology and art history00Universum:Philology and art history["Victoria Pustovedova", "Natalia Bykova", "Svetlana Tupikova"]2025-01-21T00:00:00 f6258983f73f8362325da283c65083e83a8cae10Klinische Monatsblätter für Augenheilkunde20Klinische Monatsblatter fur Augenheilkunde["Thiago Gon\u00e7alves Dos Santos Martins"]2025-01-21T00:00:00 5a71d87574f74e45b21de1d1f88968465a93afa8Медиасреда быстро изменяется в зависимости от развития технологии и техники. В данный момент перспектива расширения применения искусственного интеллекта для автоматизации рутинных задач, обработки массивов данных или генерации контента разного формата уже воспринимается как что-то неизбежное. Появление искусственного интеллекта с одной стороны помогло облегчить создание разных форматов контента, с другой породило большое количество вопросов: об авторстве, созданного ИИ контента, о возможности обучить интеллект этическим нормам или корпоративным и профессиональным ценностям. Вместе с тем развитие искусственного интеллекта порождает все большее ускорение и перенасыщение информацией в медиасреде. В этом контексте статья нацелена на выделение возможностей, которые несет в себе развитие искусственного интеллекта, а также ограничений, с которыми могут столкнуться как креаторы, так и пользователи при использовании искусственного интеллекта The media environment quickly changes depending on the development of technology. At the moment, the prospect of expanding the use of artificial intelligence to automate routine tasks, process data arrays or generate content in different formats is already perceived as something inevitable. The emergence of artificial intelligence, on the one hand, helped to facilitate the creation of various content formats, on the other hand, it raised a large number of questions: about the authorship of the content created by AI, about the possibility of teaching intelligence ethical norms or corporate and professional values. At the same time, the development of artificial intelligence generates an increasing acceleration and oversaturation of information in the media environment. In this context, the article aims to highlight the opportunities inherent in the development of artificial intelligence, as well as the limitations that both creators and users may face when using artificial intelligenceФорум инновационных технологий «Иннотех»: сборник статей международной научной конференции (Санкт-Петербург, Декабрь 2024)10Форум инновационных технологий «Иннотех»: сборник статей международной научной конференции (Санкт-Петербург, Декабрь 2024)["\u0421\u043e\u0444\u0438\u044f \u0410\u043b\u0435\u043a\u0441\u0435\u0435\u0432\u043d\u0430 \u041d\u043e\u0432\u0438\u043d\u0441\u043a\u0430\u044f"]2025-01-21T00:00:00 c749ae47ad060beddb383fab85a8a7c180eeab2cThe aim of this study is to explore the correlation between AI anxiety and the perceived empowerment of healthcare professionals. An online survey was conducted among healthcare professionals at a training and research hospital. The survey included questions about the participants' socio-demographic characteristics, as well as the AI Anxiety Scale and the Perceived Empowerment Scale. A total of 285 healthcare professionals completed the survey between December 2023 and February 2024. Healthcare professionals AI anxiety at a level slightly above the medium, while their perception of empowerment is high. The level of AI anxiety varied based on factors such as gender, age, total years of work, and the specific unit they work in. Similarly, the perception of empowerment differed among groups based on age, total years of work, and marital status. The study also found a negative relationship between the meaning-competence dimension of perceived empowerment and the AI learning dimension, as well as a positive relationship between the AI sociotechnical blindness dimension. It was found that individuals with a high perception of empowerment are less anxious about learning new information about artificial intelligence, but more anxious about the potentially harmful and dangerous aspects of artificial intelligence. The study suggests that empowerment, as an effective human resource management tool, can be utilized by health managers to alleviate employees' AI nxiety.Çalışma ve Toplum410It was found that individuals with a high perception of empowerment are less anxious about learning new information about artificial intelligence, but more anxious about the potentially harmful and dangerous aspects of artificial intelligence.Çalışma ve Toplum["\u00d6zden G\u00fcd\u00fck", "Ayten Vural", "G\u00fcler Di\u015fia\u00e7\u0131k"]2025-01-21T00:00:00 6c5ad35b635bccdc4dabf10ac0393389c33397faFuture military conflicts are likely to involve peer or near-peer adversaries in large-scale combat operations, leading to casualty rates not seen since World War II. Casualty volume, combined with anticipated disruptions in medical evacuation, will create resource-limited environments that challenge medical responders to make complex, repetitive triage decisions. Similarly, pandemics, mass casualty incidents, and natural disasters strain civilian health care providers, increasing their risk for exhaustion, burnout, and moral injury. As opposed to exhaustion and burnout, which can be mitigated with appropriate rest cycles and changes in workload, moral injury is a long-lasting and impairing condition with cognitive, emotional, behavioral, social, and spiritual repercussions. Exhaustion and burnout experienced by providers during COVID-19 correlated with increased disengagement and the desire to leave the health care field. Telemedicine and telementoring expands access to medical expertise, thereby reducing an inexperienced provider's stress levels and uncertainty and improving their confidence in care delivery. Artificial Intelligence Decision Support Systems (AIDeSSAIDeSS) may represent the next phase in clinical decision support systems across the continuum of care. These systems may help address both the anticipated scale of casualties in large-scale combat operations and the critical expertise gaps during future pandemics, mass casualty events, and natural disasters. This study advocates for urgent research at the intersection of high-stress, resource-limited care contexts that may cause moral injury in health care providers and the potential for AIDeSS to reduce that risk. Understanding these dynamics may yield strategies to mitigate psychological distress in medical responders, increase patient survival, and improve the health of our medical systems.Military Medicine00This study advocates for urgent research at the intersection of high-stress, resource-limited care contexts that may cause moral injury in health care providers and the potential for AIDeSS to reduce that risk.Military medicine["Lindsey Umlauf", "Michael Remley", "Christopher Colombo", "Jeremy C. Pamplin"]2025-01-21T00:00:00 59195d3c7330ed3d33c83fb895de84ad9885bff3International Journal of Management Issues and Research00International Journal of Management Issues and Research["Sandeep Bhattacharjee"]2025-01-21T00:00:00 ab5e48983e6f7792ac3b7a37f81eda46576309b4J. Am. Medical Informatics Assoc.180Journal of the American Medical Informatics Association : JAMIA["Suzanne Bakken", "Eric Poon"]2025-01-21T00:00:00 462d9e75c8c46f3e49460f69b58b15c5bb3074f9Scientific Journal of Applied Social and Clinical Science00Scientific Journal of Applied Social and Clinical Science["L. G. Tamez", "Gabriel Aguilera Mancilla", "Ana Cecilia Flores Amador", "Mariana Mart\u00ednez Villareal"]2025-01-21T00:00:00 c57d1427bca5d30dc92af44b86f5c881cd3b5c61Artificial intelligence (AI) is advancing continuously. However, full delegation to an AI application is often not possible or desirable due to technical limitations, ethical concerns or legal issues. Augmented intelligence systems, where humans and AI work together jointly, have been proposed to improve decision making in complex, uncertain and failure‐intolerant environments. Yet, this raises questions about how compatible human and AI knowledge are, and whether translating between the two increases decision making intelligence, or whether it effectively limits AI applications' capacity for computational agency and human agents' capacity to consider uniquely human knowledge. We explore this notion by looking at augmented intelligence in terms of systemic intelligence and mutual learning. Building on an emergence perspective, we perform a case study of an augmented intelligence system for image‐based diagnostics in the radiology branch of a medical care centre. Our findings indicate a strong distinction between specialists' and non‐specialists' intelligence augmentation with AI. This distinction fuels generative cycles which produce iteratively more sophisticated algorithms, human representations and practical routines. Drawing on this analysis, we propose three stages by which new forms of intelligence emerge from the addition of AI recommendation tools, specifically, intelligence by propagation, intelligence by specialisation and intelligence by articulation.Information Systems Journal1000A case study of an augmented intelligence system for image‐based diagnostics in the radiology branch of a medical care centre is performed, and a strong distinction between specialists' and non‐specialists' intelligence augmentation with AI is indicated.Information Systems Journal["Mechthild Pieper", "Rob Gleasure"]2025-01-21T00:00:00 17f24b334ebc634c8398f40c2e00254cdd7b2551As artificial intelligence (AI) becomes integral to economy and society, communication gaps between developers, users, and stakeholders hinder trust and informed decision-making. High-level AI labels, inspired by frameworks like EU energy labels, have been proposed to make the properties of AI models more transparent. Without requiring deep technical expertise, they can inform on the trade-off between predictive performance and resource efficiency. However, the practical benefits and limitations of AI labeling remain underexplored. This study evaluates AI labeling through qualitative interviews along four key research questions. Based on thematic analysis and inductive coding, we found a broad range of practitioners to be interested in AI labeling (RQ1). They see benefits for alleviating communication gaps and aiding non-expert decision-makers, however limitations, misunderstandings, and suggestions for improvement were also discussed (RQ2). Compared to other reporting formats, interviewees positively evaluated the reduced complexity of labels, increasing overall comprehensibility (RQ3). Trust was influenced most by usability and the credibility of the responsible labeling authority, with mixed preferences for self-certification versus third-party certification (RQ4). Our Insights highlight that AI labels pose a trade-off between simplicity and complexity, which could be resolved by developing customizable and interactive labeling frameworks to address diverse user needs. Transparent labeling of resource efficiency also nudged interviewee priorities towards paying more attention to sustainability aspects during AI development. This study validates AI labels as a valuable tool for enhancing trust and communication in AI, offering actionable guidelines for their refinement and standardization.640This study validates AI labels as a valuable tool for enhancing trust and communication in AI, offering actionable guidelines for their refinement and standardization.["Raphael Fischer", "Magdalena Wischnewski", "Alexander Van Der Staay", "Katharina Poitz", "Christian Janiesch", "Thomas Liebig"]2025-01-21T00:00:00 688d5b81b16a432ef85b82bf4ec1676bfb9bfc0dThis study examines how Artificial Intelligence (AI) enhances Sharia compliance within Islamic Financial Institutions (IFIs) by improving operational efficiency, ensuring transparency, and addressing ethical and technical challenges. A quantitative survey across five Saudi regions resulted in 450 validated responses, analyzed using descriptive statistics, ANOVA, and regression models. The findings reveal that while AI significantly enhances transparency and compliance processes, its impact on operational efficiency is limited. Key barriers include high implementation costs, insufficient structured Sharia datasets, and integration complexities. Regional and professional differences further underscore the need for tailored adoption strategies. It introduces a novel framework integrating ethical governance, Sharia compliance, and operational scalability, addressing critical gaps in the literature. It offers actionable recommendations for AI adoption in Islamic finance and contributes to the global discourse on ethical AI practices. However, the Saudi-specific focus highlights regional dynamics that may limit broader applicability. Future research could extend these findings through cross-regional comparisons to validate and refine the proposed framework. By fostering transparency and ethical governance, AI integration aligns Islamic finance with socio-economic goals, enhancing stakeholder trust and financial inclusivity. The study emphasizes the need for targeted AI training, the development of structured Sharia datasets, and scalable solutions to overcome adoption challenges.Journal of Infrastructure Policy and Development00The findings reveal that while AI significantly enhances transparency and compliance processes, its impact on operational efficiency is limited and key barriers include high implementation costs, insufficient structured Sharia datasets, and integration complexities.Journal of Infrastructure, Policy and Development["Hebah Shalhoob"]2025-01-21T00:00:00 e953662e0cf24c9ae407c37f71cca7fc8a7888c5Are we on the verge of developing robots that can reprogram themselves to overcome unexpected situations? As robotic systems and artificial intelligence continue to evolve, the concept of self-programming robots capable of adaptive reasoning seems to be becoming a tangible reality. This paper demonstrates how Large Language Models (LLMs), specifically the OpenAI o1-preview model, can empower mobile robots to autonomously analyze failures and modify their operational code in real time. Traditional robot programming, bound by the need to anticipate all possible scenarios, often leads to rigid behaviors when faced with unforeseen obstacles. We present novel results where a robot, tasked with navigating a predefined path, encounters an unanticipated obstacle and autonomously generates modified code to address the challenge, including obstacle avoidance and dynamic path planning strategies. By utilizing a structured prompt and advanced reasoning capabilities, the robot moves beyond pre-programmed limitations, embodying a new level of dynamic, self-adaptive autonomy. This work highlights the transformative potential of LLMs in robotics, offering a glimpse into a future where robots not only perform tasks but also learn, adapt, and evolve autonomously in complex, real-world environments.IEEE/SICE International Symposium on System Integration150This paper demonstrates how Large Language Models (LLMs), specifically the OpenAI o1-preview model, can empower mobile robots to autonomously analyze failures and modify their operational code in real time.2025 IEEE/SICE International Symposium on System Integration (SII)["J. A. Bottega", "Takashi Tsubouchi", "Xinyue Ruan", "Akihisa Ohya"]2025-01-21T00:00:00 1f05c8fdee884d15eef72a469bd7c737596e603aThis study examines educators’ perceptions of artificial intelligence (AI) in educational settings, focusing on their familiarity with AI tools, integration into teaching practices, professional development needs, the influence of institutional policies, and impacts on mental health. Survey responses from 353 educators across various levels and countries revealed that 92% of respondents are familiar with AI, utilizing it to enhance teaching efficiency and streamline administrative tasks. Notably, many educators reported students using AI tools like ChatGPT for assignments, prompting adaptations in teaching methods to promote critical thinking and reduce dependency. Some educators saw AI’s potential to reduce stress through automation but others raised concerns about increased anxiety and social isolation from reduced interpersonal interactions. This study highlights a gap in institutional AI policies, leading some educators to establish their own guidelines, particularly for matters such as data privacy and plagiarism. Furthermore, respondents identified a significant need for professional development focused on AI literacy and ethical considerations. This study’s findings suggest the necessity for longitudinal studies to explore the long-term effects of AI on educational outcomes and mental health and underscore the importance of incorporating student perspectives for a thorough understanding of AI’s role in education.Education sciences470The necessity for longitudinal studies to explore the long-term effects of AI on educational outcomes and mental health is suggested and the importance of incorporating student perspectives for a thorough understanding of AI’s role in education is underscored.Education Sciences["J. Delello", "Woonhee Sung", "Kouider Mokhtari", "Julie Hebert", "Amy Bronson", "Tonia De Giuseppe"]2025-01-21T00:00:00 6323234f069d7b8d23cfaa3a7a5d29dd7122e94bLanguage Testing in Asia430The findings indicated the potential of AI-assisted portfolio assessment in enhancing AER and mindfulness while fostering positive attitudes toward language learning, by comparing AI-integrated and traditional approaches.Language Testing in Asia["M. Khasawneh", "Alaa Aladini", "Sabah Abdulkader Assi", "Bemnet Ajanil"]2025-01-21T00:00:00 fec7dae0ccc05eeb8e1acbd5b1adb7f1c7c5ecc3Purpose This study aims to examine the impact of Artificial Intelligence (AI) adoption on Tunisia’s accounting profession, using the diffusion of innovations theory (DIT) to explore opportunities and challenges. Design/methodology/approach A survey of 400 academics and professional accountants in Tunisia was conducted, focusing on three key areas: the effect of AI on professional roles and tasks, the enhancement of digital work environments and the development of educational programs. Structural equation modelling (SEM) was used to test the relationships among these variables, providing robust statistical insights. Findings The results indicate that AI adoption leads to a 75.7% improvement in the functionality and responsibilities of accounting professionals, a 72.1% enhancement in digital workplace productivity and a 58.4% increase in educational program effectiveness. Despite these positive outcomes, the study identifies significant challenges, including a 63.2% concern related to change management and a 59.8% need for substantial training and technical resources investment. To address these challenges, the findings advocate for targeted professional development programs, collaborative policymaking to establish implementation guidelines and a curriculum overhaul to equip future accountants with AI competencies. Research limitations/implications The findings suggest Tunisian organisations should invest in AI to achieve substantial efficiency and risk management gains. Practitioners, instructors and students are expected to increase their technology expertise to develop more effective accounting procedures in light of AI issues. Collaboration among policymakers, regulators and practitioners is essential to establish clear implementation guidelines. Originality/value This study offers theoretical contributions by applying the DIT to AI adoption within an emerging economy, providing a unique perspective on how AI drives digital transformation in the accounting sector. In addition, it delivers practical implications by identifying strategies for overcoming barriers to AI adoption, such as fostering organisational readiness, ensuring access to training resources and enhancing professional collaboration to enable successful AI integration. Journal of Accounting &amp; Organizational Change530The findings suggest Tunisian organisations should invest in AI to achieve substantial efficiency and risk management gains, and practitioners are expected to increase their technology expertise to develop more effective accounting procedures in light of AI issues.Journal of Accounting &amp; Organizational Change["S. Assidi", "Mohamed Omran", "Tarek Rana", "Hela Borgi"]2025-01-21T00:00:00 be2b1b173552822b9ecfaddc95bd05bf9b256912The development of artificial intelligence (AI) is one of the greatest technological revolutions in recent human history. AI technology is widely used in various fields, including education. In this field, AI is studied as a discipline, and used as a tool to overcome social barriers. Like any human revolution, however, it is necessary to be careful about it and consider that the growing use of these new informatic systems also entails risks. One of them, it is the reinforcement of gender stereotypes and discrimination against women through linguistics feedback. Trough an experimental analysis conducted on common AI-integrated app –Google Translate and Canva–we will investigate linguistic behaviours such as responding to a command prompts. From the results obtained, we can demonstrate the existence of gender biases in the AI’s productions, both in textual and visual language. Gender biases are consequences of the structural inequalities present in society: it is not the technology that is sexist, but it is the dataset on which it is based, which in turn is based on the results produced by users and published on internet. In a society based on democracy and equality, it is important to ensure that the use of such a widespread technology as AI does not perpetuate existing stereotypes and does not allow to become a new form of strengthening discriminations. From a linguistics perspective, this means paying attention to the linguistic outputs, both textual and visual, provided by the AI and checking the dataset it has been training on. Due to their central role in the education of new generations, schools and institutions should prepare students for a critical vision of the phenomenon and provide them with the tools to contrast it. This path could start from teaching AI mechanisms and ethics of technology to students and using an inclusive language in the educational context.Feminismo/s00Trough an experimental analysis conducted on common AI-integrated app –Google Translate and Canva–the authors will investigate linguistic behaviours such as responding to a command prompts and demonstrate the existence of gender biases in the AI’s productions, both in textual and visual language.Feminismo/s["Martina Mattiazzi"]2025-01-21T00:00:00 f02d18f51373c661dede14117da5bd35bafa3153Our proposed work explores the role of AI chatbots in the tourism industry, focusing on customer service, operational efficiency, and business growth. As a result of artificial intelligence and chatbots are able to perform a broad scope of tasks, from responding to inquiries to processing bookings and giving personalized recommendations. The goal of the project is to identify how AI chatbots positively impact customer experiences by responding quickly, accurately, and relevantly, thus leading to better satisfaction and engagement. AI chatbots improve operational efficiency by handling high volumes of customer interactions instantly, reducing response times, and enabling 24/7 service. This is especially valuable in the tourism industry, where travellers often require immediate assistance. Additionally, chatbots personalize the customer journey by analysing preferences and delivering tailored suggestions, creating a more dynamic and engaging interaction. The findings indicate that, besides improving customer satisfaction, AI chatbots can minimize operational costs and help companies to remain competitive by offering effective, scalable customer support. The project emphasizes that adoption of AI chatbots represents innovation and customer-centric operations and pushes business growth and competitiveness in the market. In a nutshell, AI chatbots are powerful tools that can revolutionize customer service in the tourism sector. They enhance engagement, reduce costs, and provide personalized experiences, which can help businesses maintain a competitive edge in the digital age. Keywords- AI Chatbots, Natural Language Processing (NLP), Customer-Centric Operations, Dynamic InteractionINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00AI chatbots are powerful tools that can revolutionize customer service in the tourism sector, enhance engagement, reduce costs, and provide personalized experiences, which can help businesses maintain a competitive edge in the digital age.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Sudheshna Shahabadi"]2025-01-21T00:00:00 47cb9a5b24c89d73d7a02f1d05263be9963b4668PurposeThis study describes a short-term project designed for students to develop important skills needed for artificial intelligence (AI) literacy and to understand how the concepts of AI literacy may be viewed in different countries.Design/methodology/approachThis mixed-methods study sets out to investigate students’ perceptions on the use of AI-generated communication in the workplace, particularly the use of ChatGPT for business communication. We discuss student responses to questions regarding AI-generated business communication according to the diamond model of AI literacy.FindingsThis study’s findings reflect students’ awareness of the use of AI at their workplace, their perceptions of the benefits and challenges of AI-generated business communication, as well as their perceived need for institutionalizing policies at their respective companies. The study reports on potential differences in cultural attitudes regarding generative AI.Research limitations/implicationsThis study involved a project with a limited sample. It involved students in the United States of America, Germany and India and was replicated. Despite the sample size, we feel it has relevance for underlining opportunities for AI literacy skills in higher education (HE) institutions, especially in terms of heightening awareness of cultural appropriateness, sensitivity towards data privacy needs and developing students' intercultural communication skills.Originality/valueThe following study addresses the need for institutions of HE to develop students’ AI literacy, including how concepts of AI literacy may be viewed differently and how AI-generated business communication must be adapted to suit diverse environments. The study encourages instructors to incorporate AI tools in their curricula by illustrating one best use-case for developing students’ abilities to utilize this technology for professional business communication.Higher Education, Skills and Work-based Learning240The following study addresses the need for institutions of HE to develop students’ AI literacy, including how concepts of AI literacy may be viewed differently and how AI-generated business communication must be adapted to suit diverse environments.Higher Education, Skills and Work-Based Learning["Stephanie Swartz", "Susan L. Luck", "Soni Sharma"]2025-01-21T00:00:00 bc0d48afe1f9fd441cae0a05373460d48a6c89d0Focal segmental glomerulosclerosis (FSGS) presents significant challenges in diagnosis, treatment, and management due to its complex etiology and clinical variability. Traditional approaches often rely on clinician judgment and are prone to inconsistencies. This study introduces an advanced expert system integrating Artificial Intelligence (AI) with Machine Learning (ML) to support nephrologists in assessing, treating, and managing FSGS. The proposed system features a modular design comprising diagnostic workflows, risk stratification, treatment guidance, and outcome monitoring modules. By leveraging ML algorithms and clinical data, the system offers personalized, data-driven recommendations, enhancing decision-making and patient care. The evaluation demonstrates the system’s efficacy in reducing diagnostic errors and optimizing treatment pathways. These findings underscore the potential of AI-driven tools in transforming nephrology practice and improving clinical outcomes for FSGS patients.Applied Sciences610An advanced expert system integrating Artificial Intelligence (AI) with Machine Learning (ML) to support nephrologists in assessing, treating, and managing FSGS demonstrates the system’s efficacy in reducing diagnostic errors and optimizing treatment pathways.Applied Sciences["Dawid Pawu\u015b", "Tomasz Pora\u017cko", "S. Paszkiel"]2025-01-21T00:00:00 dc16a397d85e713cdfbfc8589e2366d005bc8ac7The field of artificial intelligence (AI) has entered a new cycle of intense opportunity, fueled by advances in deep learning, including generative AI. Applications of recent advances affect many aspects of everyday life, yet nowhere is it more important to use this technology safely, effectively, and equitably than in health and health care. Here, as part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2025 initiative, which is designed to provide guidance on pressing health care issues for the incoming presidential administration, we describe the steps needed to achieve these goals. We focus on four strategic areas: ensuring safe, effective, and trustworthy use of AI; promotion and development of an AI-competent health care workforce; investing in AI research to support the science, practice, and delivery of health and health care; and promotion of policies and procedures to clarify AI liability and responsibilities.Health Affairs181Four strategic areas are focused on: ensuring safe, effective, and trustworthy use of AI; promotion and development of an AI-competent health care workforce; investing in AI research to support the science, practice, and delivery of health and health care; and promotion of policies and procedures to clarify AI liability and responsibilities.Health affairs["Michael E. Matheny", "J. C. Goldsack", "S. Saria", "Nigam H Shah", "Jacqueline Gerhart", "I. G. Cohen", "W. N. Price", "Bakul Patel", "Philip R. O. Payne", "Peter J. Emb\u00ed", "Brian Anderson", "Eric Horvitz"]2025-01-22T00:00:00 fe7fd5e2426d0cd8f8660191d0bce14035d6c02aHigher education has witnessed remarkable technological advancements; however, the rapid rise of generative artificial intelligence (Gen AI) presents substantial challenges for teaching and research. This growing reliance has expanded educators' roles, underscoring the need for ethical and selective AI integration while preparing students and researchers for an AI‐driven future. Adopting an argumentative perspective, this article analyzes core insights from comparative literature and key reports that highlight Gen AI's potential to diminish critical thinking and negatively impact educational outcomes. Although Gen AI holds transformative promise, its swift expansion raises significant concerns about its long‐term implications for education. This research emphasises the need to address Gen AI's drawbacks, advocating for greater awareness and equitable educational practices that support both teaching and learning in academic contexts. Ultimately, the article calls for professional development to equip educators with responsible AI skills, fostering a balanced and ethical approach to Gen AI integration in higher education.European Journal of Education160The research emphasises the need to address Gen AI's drawbacks, advocating for greater awareness and equitable educational practices that support both teaching and learning in academic contexts.European Journal of Education["Muhammad Amin Nadim", "Raffaele Di Fuccio"]2025-01-22T00:00:00 009925bf5df09cf68dda541a5a59329cbe2df5a8Background: This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics on hours free of clinical deterioration events among medical and surgical patients in an acute care cardiology medical-surgical ward. Methods: 10,422 inpatient visits were randomly assigned by cluster to the intervention group of a display of risk trajectories or to a control group of usual medical care. The trial was undertaken on an 85-bed inpatient cardiology and cardiac surgery ward of an academic hospital with a substantial implementation and education plan. This was a passive display with no specific response mandated. The primary analysis compared events of clinical deterioration (death, emergent ICU transfer, emergent endotracheal intubation, cardiac arrest, or emergent surgery) and compared mortality 21 days after admission. Results: Patients with a large spike in risk score had, on average, twice the length of hospital stay (6.8 compared to 3.4 days). There was no change in the primary outcome between groups. Among those who had a clinical event, there were more event-free hours in the intervention/display-on group compared to the standard of care/display-off , but this did not approach statistical significance. 11% of the patients were transferred into or out of display beds, a censoring event removing them from the analysis, thereby undermining aspects of the randomized nature of the study. Conclusion: Predictive analytics monitoring incorporating continuous cardiorespiratory monitoring and displays of risk trajectories coupled with an education plan did not improve patient outcomes or reduce deaths. While necessary to conduct the study, the pragmatic design allowed for significant movement towards intervention/display-on beds for sicker patients. Design considerations in the future must focus on understanding clinicians' interpretation, care processes, and communication practices.medRxiv00Predictive analytics monitoring incorporating continuous cardiorespiratory monitoring and displays of risk trajectories coupled with an education plan did not improve patient outcomes or reduce deaths.["J. Keim-Malpass", "S. J. Ratcliffe", "M. T. Clark", "K. N. Krahn", "O. J. Monfredi", "S. Hamil", "G. Yousevfand", "M. K. Jones", "A. Nelson", "L. P. Moorman", "J. R. Moorman", "J. M. Bourque"]2025-01-22T00:00:00 128e643759350c55dd92eded5b909e44982db425Marketing research consistently demonstrates that gender stereotypes influence the effectiveness of product recommendations. When artificial intelligence (AI) agents are designed with gendered features to enhance anthropomorphism, a follow‐up question is whether these agents' recommendations are also shaped by gender stereotypes. To investigate this, the current study employed a shopping task featuring product recommendations (utilitarian vs. hedonic), using both behavioral measures (purchase likelihood, personal interest, and tip amount) and event‐related potential components (P1, N1, P2, N2, P3, and late positive potential) to capture explicit and implicit responses to products recommended by male and female humans, virtual assistants, or robots. The findings revealed that gender stereotypes influenced responses at both levels but in distinct ways. Behaviorally, participants consistently favored female recommenders across all conditions. Additionally, female recommenders received more tips than males for hedonic products in the virtual assistant condition and utilitarian products in the robot condition. Implicitly, the N1 and N2 components reflected a classic gender stereotype from prior research: utilitarian products recommended by male humans elicited greater attention and received more inhibition control. We propose that task design and cultural factors may have contributed to the observed discrepancies between explicit (consumer behaviors) and implicit responses. These findings provide insights for mitigating the impact of gender difference when designing the anthropomorphic appearance of AI agents, which would help the development of more effective marketing strategies.Psychology &amp; Marketing1120In insights for mitigating the impact of gender difference when designing the anthropomorphic appearance of AI agents, this study provides insights for mitigating the impact of gender difference when designing the anthropomorphic appearance of AI agents.Psychology &amp; Marketing["Jiayue Huang", "Ruolei Gu", "Yi Feng", "Wenbo Luo"]2025-01-22T00:00:00 dfca69a5cec0fd7b547a5f445fcac39b65122f1dCardiovascular diseases (CVD) are the leading cause of morbidity, disability and mortality worldwide. The emergence of new technologies and the introduction of artificial intelligence (AI) and machine learning (ML) have opened up opportunities for doctors to improve the effectiveness of diagnostic and therapeutic measures. The exponential development of AI, mainly in the fields of MO and deep learning (DL), is rapidly attracting the interest of clinicians in creating new integrated, reliable and effective methods of medical care. Cardiologists use a wide range of imaging-based diagnostic measures, which gives them access to more extensive quantitative information about patients compared to many other specialties. The purpose of the review is to summarize current literature data on the use of AI in the diagnosis of CVD, as well as to identify knowledge gaps that require further research. Cardiology is one of the fields of medicine where the methods of ML and DL have become widespread and have shown promising results. In echo-CG, SNN were successfully used to measure parameters of cardiac function. In cardiac CT, DL algorithms contributed to more accurate detection of coronary artery stenosis and calcification (CCA), and determination of plaque characteristics. In MRI, CNTs were used to solve problems such as automatic segmentation of chambers and structures of the heart, determination of tissue properties and perfusion analysis. As AI and MO technologies evolve, their integration opens up new opportunities. AI technologies are of great interest in the healthcare sector, due to the ability to analyze vast amounts of information in a short time, demonstrating high efficiency. AI can be an additional help to specialists, contributing to an increase in the efficiency of the workflow and medical care.Digital Diagnostics510The purpose of the review is to summarize current literature data on the use of AI in the diagnosis of CVD, as well as to identify knowledge gaps that require further research.Digital Diagnostics["A. K. Islamgulov", "Alina S. Bogdanova", "Damir I. Sufiyarov", "Alina V. Chernyavskaya", "Elena R. Bairakaeva", "Anastasia A. Maksimova", "Nikita V. Nemychnikov", "Diana R. Bikieva", "Alsu I. Shakhmaeva", "Lyubov A. Burdina", "Aleksandr V. Bolekhan", "Egor I. Akimov", "Zilya Z. Shurakova"]2025-01-22T00:00:00 8692cd7e8e0b733e0673f71042330dd1f0cf049fAcademic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies.Journal of Theoretical and Applied Electronic Commerce Research390Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” are investigated to reveal the role of AI, extract latent themes and identify potential research topics, including sentiment analyses and latent topics identification.Journal of Theoretical and Applied Electronic Commerce Research["S. Oprea", "A. B\u00e2ra"]2025-01-22T00:00:00 0c8e3f2b6486b6cad07a2f0ac53061fc3cc36e0eBackground: Knee osteoarthritis is a prevalent condition that significantly impacts patients’ quality of life. Effective management typically involves a combination of pharmacological and non-pharmacological treatments. However, establishing a consensus on the optimal treatment strategy is crucial for standardizing care. The present study is the result of a rigorous process that combines artificial intelligence with human expertise to improve the reliability of medical recommendations. Methods: A new software platform (Butterfly Decisions, 2021, Italy) was employed to leverage AI-assisted decision-making, facilitating the digitalization of the entire consensus process. The process started with data collection through an online survey including simulated clinical cases of knee osteoarthritis collected by 30 orthopedic surgeons; artificial intelligence (AI) analyzed the collected clinical data and identified the key concepts and relevant patterns. Subsequently, AI generated detailed statements summarizing key concepts extracted from the data and proposed a reformulation of the statements to be discussed during the discussion session of the advisory board. The advisory board, composed of four qualified, experienced specialists of knee osteoarthritis, evaluated statements, providing their agreement levels, confidence, and supporting evidence. The AI tools calculated the degree of certainty and contradiction for each statement based on these evaluations. The literature was critically evaluated to ensure that there was an evidence-based evaluation of the proposed treatment statements. Finally, revised versions were proposed to address the feedback, evidence was collected to refine the scientific report, and the board members evaluated the AI performance too. Results: The consensus analysis revealed a high level of agreement in the need for a multimodal approach to treating knee osteoarthritis. The feedback highlighted the importance of integrating physical therapy and weight management, non-pharmacological methods, with Symptomatic Slow-Acting Drug for Osteoarthritis (SYSADOAs) and pharmacological treatments, such as anti-inflammatory drugs and intra-articular knee injections. The board members found that AI was easy to use and understand and each statement was structured clearly and concisely. Conclusions: The expert consensus about knee osteoarthritis conservative management being facilitated with AI met with unanimous agreement. AI-assisted decision-making was shown to have excellent analytical capabilities, but algorithms needs to be trained by orthopaedic experts with the correct inputs. Future additional efforts are still required to evaluate the incorporation of AI in clinical workflows.Journal of Clinical Medicine210AI-assisted decision-making was shown to have excellent analytical capabilities, but algorithms needs to be trained by orthopaedic experts with the correct inputs to improve the reliability of medical recommendations.Journal of Clinical Medicine["C. Carulli", "Stefano MP. Rossi", "Luca Magistrelli", "Alessandro Annibaldi", "E. Troncone"]2025-01-22T00:00:00 006420803142784d6971c1dbda06c24cbbd728ffReviews of Management Sciences1240Findings involving bibliometric and content analysis tools and upper echelons theory are presented, providing a holistic perspective on top managers' leadership in the AI era and a guidance framework for successfully integrating AI in businesses.Review of Managerial Science["Simone Bevilacqua", "Jana Mas\u00e1rov\u00e1", "Francesco Antonio Perotti", "Alberto Ferraris"]2025-01-22T00:00:00 ee81b2108fb4f05a266369898b48c01e897080beArtificial intelligence (AI) is emerging as a valuable diagnostic tool in veterinary medicine, offering affordable and accessible tests that can match or even exceed the performance of medical professionals in similar tasks. Despite the promising outcomes of using AI systems (AIS) as highly accurate diagnostic tools, the field of quality assurance in AIS is still in its early stages. Our Part I manuscript focused on the development and technical validation of an AIS. In Part II, we explore the next step in development: external validation (i.e., in silico testing). This phase is a critical quality assurance component for any AIS intended for medical use, ensuring that high-quality diagnostics remain the standard in veterinary medicine. The quality assurance process for evaluating an AIS involves rigorous: (1) investigation of sources of bias, (2) application of calibration methods and prediction of uncertainty, (3) implementation of safety monitoring systems, and (4) assessment of repeatability and robustness. Testing with unseen data is an essential part of in silico testing, as it ensures the accuracy and precision of the AIS output.Veterinary clinical pathology210The next step in development: external validation (i.e., in silico testing) is explored, ensuring that high-quality diagnostics remain the standard in veterinary medicine.Veterinary clinical pathology["Christina Pacholec", "B. Flatland", "Hehuang Xie", "Kurt Zimmerman"]2025-01-22T00:00:00 7608d0273cdd4ae776345a0a9fa8d88f2303f9cfArtificial intelligence (AI) is increasingly applied to clinical trial risk assessment, aiming to improve safety and efficiency. This scoping review analyzes 142 studies published between 2013 and 2024, focusing on safety (n=55), efficacy (n=46), and operational (n=45) risk prediction. AI techniques, including traditional machine learning, deep learning (e.g., graph neural networks, transformers), and causal machine learning, are used for tasks like adverse drug event prediction, treatment effect estimation, and phase transition prediction. These methods utilize diverse data sources, from molecular structures and clinical trial protocols to patient data and scientific publications. Recently, large language models (LLMs) have seen a surge in applications, representing over 20% of studies in 2023. While some models achieve high performance (AUROC up to 96%), challenges remain, including selection bias, limited prospective studies, and data quality issues. Despite these limitations, AI-based risk assessment holds substantial promise for transforming clinical trials, particularly through improved risk-based monitoring frameworks.medRxiv1760This scoping review analyzes 142 studies published between 2013 and 2024, focusing on safety, efficacy, and operational risk prediction, finding that AI-based risk assessment holds substantial promise for transforming clinical trials, particularly through improved risk-based monitoring frameworks.["D. Teodoro", "N. Naderi", "A. Yazdani", "B. Zhang", "A. Bornet"]2025-01-22T00:00:00 06aa32af2ba3b6471d780f3d395721e17e720ccbCyberbullying, which manifests in various forms, is a growing challenge on social media, mainly when it involves threats of violence through images, especially those featuring weapons. This study introduces a computational framework to identify such content using convolutional neural networks of weapon-related images. By integrating artificial intelligence techniques with image analysis, our model detects visual patterns associated with violent threats, creating safer digital environments. The development of this work involved analyzing images depicting scenes with weapons carried by children or adolescents. Images were sourced from social media and spatial repositories. The statistics were processed through a 225-layer convolutional neural network, achieving an 86% accuracy rate in detecting weapons in images featuring children, adolescents, and young adults. The classifier method reached an accuracy of 17.86% with training over only 25 epochs and a recall of 14.2%. Weapon detection is a complex task due to the variability in object exposures and differences in weapon shapes, sizes, orientations, colors, and image capture methods. Segmentation issues and the presence of background objects or people further compound this complexity. Our study demonstrates that convolutional neural networks can effectively detect weapons in images, making them a valuable tool in addressing cyberbullying involving weapon imagery. Detecting such content contributes to creating safer digital environments for young people.Information210This study demonstrates that convolutional neural networks can effectively detect weapons in images, making them a valuable tool in addressing cyberbullying involving weapon imagery, and contributes to creating safer digital environments for young people.Information["L. I. Barbosa-Santill\u00e1n", "Bertha Patricia Guzman-Velazquez", "M. T. Orozco-Aguilera", "Leticia Flores-Pulido"]2025-01-22T00:00:00 b64ae18c548f3b67cde9e9a8c3d0244ded14cec8Reviews of Management Sciences590This study proposes an integrative model for sustainability-oriented AI adoption that emphasizes the importance of aligning AI initiatives with organizations’ sustainability objectives in order to maintain a competitive advantage and drive progress.Review of Managerial Science["Julia Schwaeke", "Carolin Gerlich", "Hong Linh Nguyen", "Dominik K. Kanbach", "Johanna Gast"]2025-01-22T00:00:00 801eb2307b0eeff55d51f2c577334d4910012184Journal of Ultrasound190Through new AI methods it's possible to combine research into a SwarmDeepSurv, generate new data flow channels, create medical imaging data channels, create medical imaging data channels of OL and OC using AI and identify new biomarkers of OL and OC.Journal of ultrasound["Diana Donatello"]2025-01-22T00:00:00 5062e64031ea9653d990fe48cc1a3443db22ca63The fusion of Business Intelligence (BI) and Artificial Intelligence (AI) has revolutionized decision-making processes across diverse industries, transforming how organizations utilize data to achieve strategic objectives (Chen et al., 2012). By integrating BI, which focuses on data collection, organization, and visualization, with AI’s predictive and prescriptive capabilities, businesses can move beyond traditional analytics to dynamic, real-time decision-making. This article explores the transformative role of BI and AI, emphasizing their applications in business analytics and operational decision frameworks. AI-powered tools, such as machine learning algorithms and natural language processing (NLP), significantly enhance the capabilities of traditional BI systems, enabling organizations to derive actionable insights, streamline operations, and maintain a competitive edge (Davenport & Harris, 2007). This study delves into key case studies that highlight the practical implementation of AI-driven BI systems in various industries. These include predictive analytics for forecasting market trends, automated customer segmentation through NLP, and prescriptive analytics for optimizing supply chain operations. Furthermore, the article examines emerging technologies, such as explainable AI (XAI) and edge computing, that are shaping the next generation of real-time decision-making systems. The discussion extends to the challenges and opportunities in integrating these technologies, such as ensuring data privacy, addressing skill gaps, and managing implementation costs. Through a comprehensive analysis, this study aims to provide a holistic understanding of the convergence of BI and AI in the modern business landscape (Sharda et al., 2020).International Journal of Scientific Research and Management150This study delves into key case studies that highlight the practical implementation of AI-driven BI systems in various industries, and examines emerging technologies, such as explainable AI (XAI) and edge computing, that are shaping the next generation of real-time decision-making systems.International Journal of Scientific Research and Management (IJSRM)["Amejuma Emmanuel Ebule"]2025-01-22T00:00:00 d1b92a2e6a4ce323ff2efeb7f0840087ca5866b2Artificial intelligence (AI) offers a broad range of enhancements in medicine. Machine learning and deep learning techniques have shown significant potential in improving diagnosis and treatment outcomes, from assisting clinicians in diagnosing medical images to ascertaining effective drugs for a specific disease. Despite the prospective benefits, adopting AI in clinical settings requires careful consideration, particularly concerning data generalisation and model explainability. This commentary aims to discuss two potential use cases for AI in the field of medicine and the overarching challenges involved in their implementation.Genome Integrity350Two potential use cases for AI in the field of medicine are discussed, including assisting clinicians in diagnosing medical images and ascertaining effective drugs for a specific disease.Genome Integrity["Karthik Prathaban", "M. P. Hande"]2025-01-22T00:00:00 1bf5c69c880f2245336055625bf17ac86e13a8d3This study examined the intersection of artificial intelligence (AI) and child sexual abuse (CSA), employing a rapid evidence assessment of research on the uses of AI for the prevention and disruption of CSA, and the ways in which AI is used in CSA offending. Research from January 2010 to March 2024 was reviewed, identifying 33 empirical studies. All studies that met inclusion criteria examined AI for CSA prevention and disruption—specifically, how technology can be used to detect or investigate child sexual abuse material or child sexual offenders. There were no studies examining the uses of AI in CSA offending. This paper describes the state of current research at the intersection of AI and CSA, and provides a gap map to guide future research.00The state of current research at the intersection of AI and CSA is described, and a gap map to guide future research is provided.["Heather Wolbers", "Timothy I. C. Cubitt", "Michael Cahill"]2025-01-22T00:00:00 410ec71f00166d3d80c51b9d698dec0c08eabe47This study analyzes artificial intelligence (AI) research in communication scholarship through a content analysis of published articles between 2006 and 2022. It aims to understand the status of AI research between 2006 and 2022 and identify directions for future inquiry. Findings indicate that the number of articles about AI has increased over the years and scholars should continue applying existing theoretical frameworks or proposing new ones to investigate diverse topics across cultural and sociopolitical contexts.Applied Sciences1100Findings indicate that the number of articles about AI has increased over the years and scholars should continue applying existing theoretical frameworks or proposing new ones to investigate diverse topics across cultural and sociopolitical contexts.Applied Sciences["Tugce Ertem-Eray", "Y. Cheng"]2025-01-22T00:00:00 22974ca4a12cdc6ff5a516fc2efea293ebcde651This paper explores the overall picture regarding healthcare security systems through an extensive literature review. As the healthcare sector has now become digitalized, the security of healthcare systems and, by extension, the protection of patient data is a key concern in the modern era of technological advances. Therefore, a secure and integrated system is now essential. Thus, to evaluate the relationship between security systems and healthcare quality, we conducted literature research to identify studies reporting their association. The timeline of our review is based on published studies covering the period from 2018 to 2024, with entries identified through a search of the relevant literature, focusing on the most recent developments due to advances in artificial intelligence and algorithmic approaches. Thirty-two studies were included in our final survey. Our findings underscore the critical role of security systems in healthcare that significantly improve patient outcomes and maintain the integrity of healthcare services. According to our approach, the studies analyzed highlight the growing importance of advanced security frameworks, especially those incorporating artificial intelligence and algorithmic methodologies, in safeguarding healthcare systems while enhancing patient care quality. According to this study, most of the research analyzed uses algorithmic technology approaches, many researchers prove that ransomware is the most common threat to hospital information systems, and more studies are needed to evaluate the performance of the systems created against this kind of attack.Algorithms370Most of the research analyzed uses algorithmic technology approaches, many researchers prove that ransomware is the most common threat to hospital information systems, and more studies are needed to evaluate the performance of the systems created against this kind of attack.Algorithms["Savina Mariettou", "Constantinos Koutsojannis", "Vassilios Triantafillou"]2025-01-22T00:00:00 ffbb240caeca8c54adca27cde181ab0808967baaArtificial intelligence (AI) is a simulation of human intelligence which is modeled in machines and programmed to think like humans. This article aims to determine the potential of artificial intelligence in increasing the efficiency of business processes. This article seeks to provide a comprehensive understanding of what the potential of artificial intelligence is in increasing the efficiency of business processes. This research method is literature research (library research) using a qualitative approach. Qualitative research is the focus of attention with a variety of methods, which include interpretive and naturalistic approaches to the subject of study. The result of discussing this article is that artificial intelligence has become one of the most transformative technologies in recent years. AI's ability to learn from data, recognize patterns, and make decisions independently has opened up new opportunities for businesses to increase efficiency and productivity. Artificial intelligence offers enormous potential to increase the efficiency of business processes. By leveraging AI technology, businesses can become more competitive, innovative and responsive to market changes. Artificial intelligence has enormous potential to revolutionize the way we do business. By automating tasks, analyzing data more quickly and accurately, and personalizing the customer experience, AI can help businesses become more efficient, productive, and competitive.Interkoneksi: Journal of Computer Science and Digital Business00The result of discussing this article is that artificial intelligence has become one of the most transformative technologies in recent years and can help businesses become more efficient, productive and competitive.Interkoneksi: Journal of Computer Science and Digital Business["Firyal Ayudhiya Hasanah", "Ririn Afrilia"]2025-01-22T00:00:00 1513bf9ac4439e7406f3429c5ded6f11b0744ba2Public academic libraries are among the many organisations concerned about using artificial intelligence (AI) technologies. The study adopted a mixed methods research (MMR) approach using a concurrent research design to examine the use of AI innovations in public academic libraries. Thematic and descriptive statistical data analysis was used to analyse the data gathered from questionnaires, interviews and document content analysis. The findings revealed that public academic libraries in South Africa did not have clear strategies for adopting AI innovations. Consequently, AI was not widely used. Library management systems can support AI, but some must be upgraded. Librarians had excellent computer literacy, although many had not received AI training to broaden their expertise and awareness of this innovation. Results suggested that public academic libraries should create comprehensive AI adoption strategies responsive to AI trends. This study highlights the need for strategies that ensure AI technologies are utilized ethically, equitably, and with accountability. It also contributes to the literature on the use of AI in academic libraries. The results of this study may encourage public academic librarians to begin planning the incorporation of AI technology into their strategies.IFLA Journal140The findings revealed that public academic libraries in South Africa did not have clear strategies for adopting AI innovations and AI was not widely used, highlighting the need for strategies that ensure AI technologies are utilized ethically, equitably, and with accountability.IFLA Journal["A. Molaudzi", "P. Ngulube"]2025-01-22T00:00:00 ab4d014f6c2ca595ef132d7a78dde71d77320cf0At present, the entry of artificial intelligence into the field of legal education has become an inevitable trend of the times. Enabling artificial intelligence is an artificial intelligence technology that can give new capabilities and advantages to other fields, systems or individuals. Enabling artificial intelligence builds a solid foundation for the transformation of traditional legal education, and the ‘law +’ education model is based on the concept of discipline crossover, to carry out interdisciplinary fusion of law and sociology, economics, computer science and other disciplines, and to broaden the horizons of legal research and application. At present, China's development presents the problem of imbalance between regional economic development and the distribution of educational resources. In order to solve the problem of uneven distribution of educational resources under the uncoordinated regional development, and to better promote the new development model of ‘law +’ education, the author conducts a systematic analysis through the method of literature research, inductive summarisation, factor analysis and comparative method. To summarise the prospects for the application of the new development model of ‘Law+’ in domestic law schools under the background of empowering AI, the interdisciplinary integration of law with social sciences and natural sciences can be promoted through cross-college cooperation. Provide new ideas for China's institutions of higher learning to promote the ‘law +’ education model, through cooperation with some science and technology colleges and universities, grammar colleges and universities, to build practice bases, sharing of educational resources to cultivate composite talents. Provide new ideas for the development of law education and the cultivation of complex talents in China.Journal of Advanced Research in Social and Behavioural Sciences190The author conducts a systematic analysis through the method of literature research, inductive summarisation, factor analysis and comparative method to summarise the prospects for the application of the new development model of ‘Law+’ in domestic law schools under the background of empowering AI.Journal of Advanced Research in Social and Behavioural Sciences["Xiaobei Wang", "Dong Ying"]2025-01-22T00:00:00 bea5171dfdeaa80284b343096cc389255f865b56Materiovigilance is a crucial component of health-care policy designed to ensure patient safety by monitoring and addressing safety issues associated with medical devices. However, traditional systems encounter challenges related to timely reporting, standardization, and the detection of adverse events. Artificial intelligence (AI) has the potential to transform materiovigilance by improving data processing, real-time monitoring, and predictive analytics. This review explores the potential of AI in strengthening medical device safety, highlighting its benefits in enhancing patient safety, personalizing medical devices, and streamlining regulatory reporting. AI-powered systems can detect adverse events, predict patient deterioration, and provide personalized treatment plans, ultimately improving patient outcomes. Furthermore, AI enables the analysis of large and complex datasets, facilitating proactive decision-making and the early identification of emerging risks associated with medical devices. By automating routine tasks and improving accuracy, AI can significantly reduce the administrative burden on health-care professionals. In addition, AI can enhance post-market surveillance by identifying trends and anomalies in real time, thereby accelerating corrective actions. However, ethical and regulatory considerations, such as algorithmic biases, data privacy, and accountability, must be addressed to ensure the responsible development and implementation of AI in materiovigilance. Establishing robust regulatory frameworks, fostering transparency, and promoting interdisciplinary collaboration are essential to overcoming these challenges and fully realizing AI’s potential in health care.INNOSC Theranostics and Pharmacological Sciences00The potential of AI in strengthening medical device safety is explored, highlighting its benefits in enhancing patient safety, personalizing medical devices, and streamlining regulatory reporting.INNOSC Theranostics and Pharmacological Sciences["Hara Prasad Mishra", "Kevil Loriya", "Nupur Shah", "Shubhima Grover", "Smruti Sikta Mishra"]2025-01-22T00:00:00 461fef31de6709a189e4ce9e2310ec8fdf79de1aArtificial intelligence (AI) is rapidly transforming various facets of human activity, ranging from decision-making to communication, while simultaneously engendering complex ethical challenges. This article examines the critical ethical principles of AI – beneficence, non-maleficence, autonomy, justice, and explainability – and ana-lyzes how modern AI technologies align with these principles. Particular attention is given to algorithmic bias, the Black Box Problem, and accountability in AI systems. Algorithmic bias is explored through practical testing of AI models, specifically OpenAI’s generative systems. The study tasked the models with generating images based on prompts such as “school teacher” and “university professor”. The outputs revealed entrenched gender and age stereotypes. Further tests involving prompts for “female” and “male” professions demonstrated similar biases, with outputs reflecting cultural and demographic limitations in the training data. These examples high-light the pressing need for representative datasets and rigorous validation processes to mitigate bias in AI sys-tems.Общество философия история культура00The critical ethical principles of AI – beneficence, non-maleficence, autonomy, justice, justice, and explainability – are examined and how modern AI technologies align with these principles are examined.Общество: философия, история, культура["Backsanskiy Oleg Ye.", "Sorokina Svetlana G."]2025-01-22T00:00:00 6b204c030ea194a272680964256b22171b747fc9In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous “pandemic” spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this “cold war”, machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best “tailored” individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics–radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment.Diagnostics1080Artificial intelligence can significantly contribute to enhancing the effectiveness of metabolomics–radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens.Diagnostics["M. Romeo", "M. Dallio", "C. Napolitano", "Claudio Basile", "F. Di Nardo", "P. Vaia", "Patrizia Iodice", "Alessandro Federico"]2025-01-22T00:00:00 1c65e9374171a50bbf181c049746186be1c84c3fDefense &amp; Security Analysis02Defense &amp; Security Analysis["Zsolt Lazar"]2025-01-22T00:00:00 06661801da0eb0a2fc0ea34d16de13395be9bc61 Generasi muda merupakan aset bangsa, juga merupakan penerus dan pelanjut kebajikan. Tidak dapat di pungkiri bahwa bahwa pembangunan sebuah bangsa bergantung kepada kualitas pemudanya. Saat ini masyarakat di khawatirkan dengan generasi muda Indonesia yang di kenal dengan generasi Z atau Gen Z yang jumlahnya 27, 94 % dari jumlah penduduk Indonesia atau 74.97 juta jiwa berdasarkan Sensus Penduduk (SP 2020). Pemilihan lokasi di MAN 2 Langkat Tanjung Pura, mengingat ada 1064 siswa yang perlu mendapat pencerahan arahan dan bimbingan sebagai generasi penerus bangsa. Metode pelaksanaan pengabdian masyarakat ini dengan ceramah, diskusi dan tanya jawab langsung kepada nara sumber. Gen Z, dikenal dengan generasi yang lahir di era Internet dgn kegiatan yang selalu online, yang juga akrab dengan perangkat yang canggih. Kemudahan kemudahan di era digital antara lain dengan adanya Artificial intelligen (AI). AI adalah kecerdasan buatan yang telah di mulai tahun 1970. Beberapa kemudahan yang di tawarkan AI: antara lain: 1. Sistem cara berfikir, system yang rational, 4. Sistem yg berfikir secara rational. Namun akibat dari kemudahan kemudahan yang diterima ooleh generasi Gen Z terjadi bentuk distraksi kepada generasi Z, seperti: 1. Menjadi generasi yang lembek dan malas berusaha dan mudah tersinggung, 2. Meningkatnya kejahatan di dunia maya. 3. Munculnya aplikasi aplikasi yang menuntun Gen Z menjadi radikal dlm berfikir, 4. Pendidik menjadi no 3. Keadaan yang mengkhawatirkan terhadap Gen Z sebagai calon penerus bangsa, telah disiapkan jawapannya oleh Islam, dalam Al Quran telah disampaikan ayat ayat yang dengan tegas menyiapkan generasi bangsa yang kritis, rasional, berahlak mulia, Tangguh dan berjiwa pelopor dalam menghadapi tantangan yang ada saat ini. Wahana Jurnal Pengabdian kepada Masyarakat20Wahana Jurnal Pengabdian kepada Masyarakat["M. Lubis", "Iskandar Zulkarnain", "Misdawati Misdawati", "Susy Deliani", "Muhlizar Muhlizar", "Hotni Sari Harahap", "Joharsah Joharsah", "Syafil Warman", "Arianto Arianto"]2025-01-22T00:00:00 e88ae14b270247d6d568565ab935d289440c472aThe KinaTrax markerless motion capture system, used extensively in the analysis of baseball pitching and hitting, is currently being adapted for use in clinical biomechanics. In clinical and laboratory environments, repeatability is inherent to the quality of any diagnostic tool. The KinaTrax system was assessed on within- and between-session reliability for gait kinematic and spatiotemporal parameters in healthy adults. Nine subjects contributed five trials per session over three sessions to yield 135 unique trials. Each trial was comprised of a single bilateral gait cycle. Ten spatiotemporal parameters for each session were calculated and compared using the intraclass correlation coefficient (ICC), Standard Error of the Measurement (SEM), and minimal detectable change (MDC). In addition, seven kinematic waveforms were assessed from each session and compared using the coefficient of multiple determination (CMD). ICCs for between-session spatiotemporal parameters were lowest for left step time (0.896) and left cadence (0.894). SEMs were 0.018 (s) and 3.593 (steps/min) while MDCs were 0.050 (s) and 9.958 (steps/min). Between-session average CMDs for joint angles were large (0.969) in the sagittal plane, medium (0.554) in the frontal plane, and medium (0.327) in the transverse plane while average CMDs for segment angles were large (0.860), large (0.651), and medium (0.561), respectively. KinaTrax markerless motion capture system provides reliable spatiotemporal measures within and between sessions accompanied by reliable kinematic measures in the sagittal and frontal plane. Considerable strides are necessary to improve methodological comparisons, however, markerless motion capture poses a reliable application for gait analysis within healthy individuals.PLoS ONE471The KinaTrax markerless motion capture system provides reliable spatiotemporal measures within and between sessions accompanied by reliable kinematic measures in the sagittal and frontal plane accompanied by reliable kinematic measures in the sagittal and frontal plane.PLOS ONE["Brandon Schoenwether", "Zachary Ripic", "Mitch Nienhuis", "J. F. Signorile", "Thomas M. Best", "Moataz Eltoukhy"]2025-01-22T00:00:00 ac8100119fe2f07ba377862e09d802ee92b7e8a9Education and Information Technologies : Official Journal of the IFIP technical committee on Education521Education and Information Technologies["Shuchen Guo", "Lehong Shi", "Xiaoming Zhai"]2025-01-22T00:00:00 83ea75dcf969d1b2e3c191eecb50b16f5ad8d8b5Journal of Research on Technology in Education300Journal of Research on Technology in Education["Sijia Han", "Yuting Zhang"]2025-01-22T00:00:00 4c8c9107fb239b4af47b6b9840d712017e22b557Diabetes Care250Diabetes care["Juliessa M Pavon", "David Schlientz", "Matthew L Maciejewski", "Nicoleta J. Economou-Zavlanos", "Richard H Lee"]2025-01-22T00:00:00 2450a0bbb93388995aefd65d2e7cf08fe6733848Frontiers in Artificial Intelligence100Frontiers in Artificial Intelligence["J. B\u00e9lisle-Pipon"]2025-01-22T00:00:00 1fc411e60f89caa96f6da07153129d3f8db05677International Journal of Academic Research in Business and Social Sciences00International Journal of Academic Research in Business and Social Sciences["Zahir Osman"]2025-01-22T00:00:00 4ee372e460232d5d25f8ffe5b090f2d54ade50daRecherche et Applications en Marketing (English Edition)60Recherche et Applications en Marketing (English Edition)["Damien Chaney", "G\u00e9raldine Michel"]2025-01-22T00:00:00 41d00b6f8f955681f4ae784deeb7c812b3afdc60Technology, Pedagogy and Education300Technology, Pedagogy and Education["Paola Julie Aguilar-Cruz", "S. Z. Salas-Pilco"]2025-01-22T00:00:00 c09da91cb95dcf3eaf5a4dfaa37eed385a41209cSensors and materials00Sensors and Materials["Wan-I Lee", "Tzu-Huang Chang"]2025-01-22T00:00:00 e765db4615bdae7f785d68b616bd14b3ebe87848International Journal of Research Studies in Management40International Journal of Research Studies in Management["Kai Zhang"]2025-01-22T00:00:00 cceb0fd73608b760f1cf6f0f3f2ee0a9c2501d77The conventional approach to elder care is no longer able to satisfy the rising need for medical attention for the elderly due to China’s aging population. The demographic trait of “getting old before getting rich” presents a challenge to the distribution of social healthcare resources, as this article first examines the current pattern of changes in the composition of the older population. The community-based “healthcare integration” paradigm of senior care services has emerged as a successful remedy in this regard. Drawing on biomechanical principles, we can envision the community healthcare system as a complex “biomechanical network”. In order to categorize and predict the health data of the elderly, this study constructs a mathematical model akin to analyzing biomechanical forces and movements. By employing methods similar to optimizing structural loads, such as the CART decision tree and support vector machine (SVM) optimization, we enhance the model’s precision. Just as biomechanical systems adapt to varying loads, our model adapts to handle complex health data. By building the optimal classification plane of the support vector machine and adding relaxation variables, the model application solves the classification problem of linearly indivisible data, further enhancing the model’s accuracy and effectiveness, much like how a biomechanical structure self-adjusts to external pressures. In this paper, a geriatric health service platform based on information technology, including big data and the Internet of Things (IoT), is formed. The service system is a tripartite linkage disease management service model that covers the synergistic cooperation of community hospitals, third-party enterprises, and the streets where they are located. A prediction model for common cases, such heart disease, was developed by preprocessing and cleaning the data of 2311 valid samples from the China Geriatrics Center. The dataset was then characterized. The findings demonstrate the model’s high operability and accuracy in predicting health and managing long-term care for older people who are mobility. In the context of an aging society, by integrating biomechanical insights into the design of this healthcare model, the research not only establishes a theoretical foundation for community health care integration but also provides valuable references for implementing digital senior care services and enhancing health management for the elderly in an aging society.Molecular &amp; Cellular Biomechanics150A geriatric health service platform based on information technology, including big data and the Internet of Things (IoT), is formed that covers the synergistic cooperation of community hospitals, third-party enterprises, and the streets where they are located and establishes a theoretical foundation for community health care integration.Molecular &amp; Cellular Biomechanics["Dongxian Yu", "Guoke Qiu", "Ming Li"]2025-01-22T00:00:00 e015e01586d5004eb0fb64cf531d7cf4293940fdWe investigate whether and why people might reduce compensation for workers who use AI tools. Across 10 studies (N = 3,346), participants consistently lowered compensation for workers who used AI tools. This"AI Penalization"effect was robust across (1) different types of work and worker statuses and worker statuses (e.g., full-time, part-time, or freelance), (2) different forms of compensation (e.g., required payments or optional bonuses) and their timing, (3) various methods of eliciting compensation (e.g., slider scale, multiple choice, and numeric entry), and (4) conditions where workers' output quality was held constant, subject to varying inferences, or controlled for. Moreover, the effect emerged not only in hypothetical compensation scenarios (Studies 1-5) but also with real gig workers and real monetary compensation (Study 6). People reduced compensation for workers using AI tools because they believed these workers deserved less credit than those who did not use AI (Studies 3 and 4). This effect weakened when it is less permissible to reduce worker compensation, such as when employment contracts provide stricter constraints (Study 4). Our findings suggest that adoption of AI tools in the workplace may exacerbate inequality among workers, as those protected by structured contracts face less vulnerability to compensation reductions, while those without such protections risk greater financial penalties for using AI.00The findings suggest that adoption of AI tools in the workplace may exacerbate inequality among workers, as those protected by structured contracts face less vulnerability to compensation reductions, while those without such protections risk greater financial penalties for using AI.["Jin Kim", "Shane Schweitzer", "Christoph Riedl", "David De Cremer"]2025-01-22T00:00:00 983a8a1fb307e2a0a8f398e2068f117d7e29f3b8Cultures of Science00Cultures of Science["Qide Han", "Xuan Liu"]2025-01-22T00:00:00 23779889c2e8ab9e955bfa19a8f7d379c7cffb47Academic English writing is a crucial component of undergraduate English education. However, undergraduates commonly face challenges such as unclear topic selection, imprecise language expression, and a lack of depth and logical coherence in analysis and argumentation. This study, grounded in AGIL theory (Adaption, Goal Attainment, Integration, Latency Pattern Maintenance), integrates generative artificial intelligence with instant feedback to develop a “Human-Computer Dual-Instructor” model for learning engagement intervention. The model consists of four subsystems: the objective system, the environmental system, the operational system, and the response system for learning engagement intervention. Through an iterative intervention response mechanism, this model is capable of dynamically assessing intervention effects and ensuring the orderly flow of data among the subsystems. Using an academic English writing course as an example, this model is applied to the design of teaching interventions, aiming to enhance students’ learning engagement and academic writing skills.Journal for Language Teaching80This study integrates generative artificial intelligence with instant feedback to develop a “Human-Computer Dual-Instructor” model for learning engagement intervention, capable of dynamically assessing intervention effects and ensuring the orderly flow of data among the subsystems.Journal of Language Teaching["Ting Yin", "Lin Weng", "Caiying Wu"]2025-01-22T00:00:00 d84c6bc7486a449d9ece13c09d75bddc6b32a306Artificial intelligence (AI)-based telemedicine systems for early Alzheimer's detection using low-cost modalities are vital for rural or underdeveloped areas where travelling distance and high-cost devices like MRI are drawbacks. These systems require eXplainable AI (XAI) for reliable outcomes and intuitive explanations. Current XAI evaluations lack input from medical professionals and overlook stakeholder diversity, leading to potential biases. This project aims to develop a cost-effective AI telemedicine system, enhance early AD detection in underdeveloped areas, reduce healthcare disparities, and assess XAI methods with quality and fairness to mitigate biases for high-quality and fair explained outcomes.AAAI/ACM Conference on AI, Ethics, and Society01This project aims to develop a cost-effective AI telemedicine system, enhance early AD detection in underdeveloped areas, reduce healthcare disparities, and assess XAI methods with quality and fairness to mitigate biases for high-quality and fair explained outcomes.{"pages": "47-49"}["Quoc-Toan Nguyen"]2025-01-22T00:00:00 6d7755bf41c50b5f623ea9bea721c9243bfe7b6bIn the research described here, I argue that an adequate approach to AI ethics should include the four topics below. My aim is to answer the question of which are the necessary topics that someone should have under consideration in order to make an adequate approach to AI ethics. First, a critical history of AI, which focuses not on the technical differentiations between previous and following technologies, but on the social, economic, and political context in which artificial intelligence is designed, developed, and used. Second, an overview of the issues that most of the time are described as AI ethics, such as fairness, accountability, and transparency, in order to have the ability to understand what is missing from these approaches. A study on the black box of AI is necessary, not only from a technical perspective, but mainly from a perspective that is directly related to the political, social, and economic reasons that enforce and reinforce this black box, revealing, among others, the social relations, the hidden labor, and the “unintelligence” that are hidden under this black box. Third, an analysis of specific cases through critical approaches which take into account capitalism, with all the social, political, and economic relations that are connected with it. In this way, the emergence of biases, inequalities, and discriminations, becomes not a bag, but the substance of AI. Fourth, a study on the hidden labor of AI and the concerns regarding the future of work and AI. The study on hidden labor which is related with AI, is important in order, first, to criticize the intelligence and autonomy of AI systems, and second, to make visible the terrible working conditions of some workers, as a try to change them. The discussion regarding the future of work should not only contain discourses regarding the circular function of capitalism or vague ideas about ethical implementations of AI in the workplace. An adequate discussion should take into account the social, political, and economic relations of our society and ultimately challenge the current form of capitalism. I argue that all the above should be included in an adequate study of AI ethics.AAAI/ACM Conference on AI, Ethics, and Society00It is argued that an adequate approach to AI ethics should include the four topics below, which take into account the social, political, and economic relations of the authors' society and ultimately challenge the current form of capitalism.{"pages": "23-25"}["Konstantinos Konstantis"]2025-01-22T00:00:00 3ac8deaa7f28766ccb2c1441e9d073ddc86e1453What constitutes a fair decision? This question is not only difficult for humans but becomes more challenging when Artificial Intelligence (AI) models are used. In light of discriminatory algorithmic behaviors, the EU has recently passed the AI Act, which mandates specific rules for AI models, incorporating both traditional legal non-discrimination regulations and machine learning based algorithmic fairness concepts. This paper aims to bridge these two different concepts in the AI Act through: First a high-level introduction of both concepts targeting legal and computer science-oriented scholars, and second an in-depth analysis of the AI Act's relationship between legal non-discrimination regulations and algorithmic fairness. Our analysis reveals three key findings: (1.), most non-discrimination regulations target only high-risk AI systems. (2.), the regulation of high-risk systems encompasses both data input requirements and output monitoring, though these regulations are often inconsistent and raise questions of computational feasibility. (3.) Regulations for General Purpose AI Models, such as Large Language Models that are not simultaneously classified as high-risk systems, currently lack specificity compared to other regulations. Based on these findings, we recommend developing more specific auditing and testing methodologies for AI systems. This paper aims to serve as a foundation for future interdisciplinary collaboration between legal scholars and computer science-oriented machine learning researchers studying discrimination in AI systems.880A high-level introduction of both concepts targeting legal and computer science-oriented scholars and an in-depth analysis of the AI Act's relationship between legal non-discrimination regulations and algorithmic fairness are aimed at.["Kristof Meding"]2025-01-22T00:00:00 752c8e53a78f322b5b31490a656db852a64aceebThis study delves into the utilization demands of Artificial Intelligence-Generated Content (AIGC) tools among Chinese researchers, guided by the KANO model to understand their varying demands. By administering a comprehensive online survey (N = 1025), we collected data reflecting the researchers’ preferences for different AIGC functions. Our findings reveal a multifaceted perspective on user satisfaction: literature research emerged as a reverse quality, indicating a decline in satisfaction when provided, suggesting concerns over the authenticity of sources. Must-be qualities—data analysis and interpretation, statistical guidance, citation checks, and review response assistance—form the backbone of essential AIGC tools. Attractive qualities such as text writing, language services, charting assistance, and citation generation significantly boost user satisfaction, highlighting the AIGC's strength in content creation and formatting. Indifferent qualities, including concept clarification and viewpoint research, show a preference for personal research efforts, while diagram optimization and reference sorting are viewed as trivial tasks, comfortably managed with existing software tools. The study underscores the critical and discretionary AIGC functions from the perspective of Chinese academics, providing insights into tool development and indicating a need for future research on AIGC's evolving role in global research practices.Information Development290The study underscores the critical and discretionary AIGC functions from the perspective of Chinese academics, providing insights into tool development and indicating a need for future research on AIGC's evolving role in global research practices.Information Development["Zehang Xie", "Wu Li", "Wen Yu"]2025-01-22T00:00:00 6febea398d313accbf75bbf4237e75128bca56cbThis article explores the development of a cognitive sense of self within artificial intelligence (AI), emphasizing the transformative potential of self-awareness in enhancing AI functionalities for sophisticated interactions and autonomous decision-making. Rooted in interdisciplinary approaches that incorporate insights from cognitive science and practical AI applications, the study investigates the mechanisms through which AI can achieve self-recognition, reflection, and continuity of identity—key attributes analogous to human consciousness. This research is pivotal for fields such as healthcare and robotics, where AI systems benefit from personalized interactions and adaptive responses to complex environments. The concept of a self-aware AI involves the ability for systems to recognize themselves as distinct entities within their operational contexts, which could significantly enhance their functionality and decision-making capabilities. Further, the study delves into the ethical dimensions introduced by the advent of self-aware AI, exploring the profound questions concerning the rights of AI entities and the responsibilities of their creators. The development of self-aware AI raises critical issues about the treatment and status of AI systems, prompting the need for comprehensive ethical frameworks to guide their development. Such frameworks are essential for ensuring that the advancement of self-aware AI aligns with societal values and promotes the well-being of all stakeholders involved. Journal of Intelligent Communication00Journal of Intelligent Communication["James Hutson", "Emily Barnes"]2025-01-22T00:00:00 bdbb86bd3b68d8ab0a53796da78eaa6872ab2a54The complexity of human biology and its intricate systems holds immense potential for advancing human health, disease treatment, and scientific discovery. However, traditional manual methods for studying biological interactions are often constrained by the sheer volume and complexity of biological data. Artificial Intelligence (AI), with its proven ability to analyze vast datasets, offers a transformative approach to addressing these challenges. This paper explores the intersection of AI and microscopy in life sciences, emphasizing their potential applications and associated challenges. We provide a detailed review of how various biological systems can benefit from AI, highlighting the types of data and labeling requirements unique to this domain. Particular attention is given to microscopy data, exploring the specific AI techniques required to process and interpret this information. By addressing challenges such as data heterogeneity and annotation scarcity, we outline potential solutions and emerging trends in the field. Written primarily from an AI perspective, this paper aims to serve as a valuable resource for researchers working at the intersection of AI, microscopy, and biology. It summarizes current advancements, key insights, and open problems, fostering an understanding that encourages interdisciplinary collaborations. By offering a comprehensive yet concise synthesis of the field, this paper aspires to catalyze innovation, promote cross-disciplinary engagement, and accelerate the adoption of AI in life science research.00A detailed review of how various biological systems can benefit from AI is provided, highlighting the types of data and labeling requirements unique to this domain and outlining potential solutions and emerging trends in the field.["Himanshu Buckchash", "G. Verma", "Dilip K. Prasad"]2025-01-22T00:00:00 f2f71b0654091fbed894c107d28d9d358c5e7c58The integration of Artificial Intelligence in higher education has shown significant potential to improve the efficiency and effectiveness of learning. The strategic implementation of AI in Indonesian State Islamic Higher Education Institutions fosters innovative pedagogy and improved academic performance. This study employs the Fishbone Diagram approach to systematically analyze Artificial Intelligence's impact on Indonesian State Islamic Higher Education Institutions education, identifying key factors influencing implementation. The method employs a reverse-cause analysis, mapping factors contributing to a primary issue, and identifying underlying causes and sub-factors. Findings highlight the crucial roles of technological infrastructure, human resource readiness, supportive policies, adaptive curriculum design, and organizational culture. This study underscores the necessity of integrated AI adoption frameworks in Indonesian Islamic higher education, harmonizing technological advancement with Islamic pedagogical principles. This study offers a foundational framework guiding Indonesian State Islamic Higher Education Institutions in developing sustainable and ethical AI policies. Comprehensive AI policies and strategies are essential for PTKIN to harmonize innovation with Islamic principles.IJID (International Journal on Informatics for Development)540This study offers a foundational framework guiding Indonesian State Islamic Higher Education Institutions in developing sustainable and ethical AI policies, and highlights the crucial roles of technological infrastructure, human resource readiness, supportive policies, adaptive curriculum design, and organizational culture.IJID (International Journal on Informatics for Development)["Aan Ansori", "Fitri Damyati", "Syifa Amara", "Dhestyani"]2025-01-22T00:00:00 021148fd0bba1348659cb182dc007068977e840aArtificial intelligence (AI) is a rapidly transforming drug discovery and development process, significantly impacting the pharmaceutical industry and enhancing human health. This review article examines the tremendous role of AI in analyzing complex biological data, optimizing research processes, and reducing costs of production. Implementation of AI in the pharmaceutical sector can store a vast dataset of manufacturing processes, identify potential disease targets, simulate physiological conditions, and predict drug interactions. The review article also discusses the AI concepts and their applications, particularly in developing solid dosage forms. Advanced algorithms optimize formulation processes, predict pharmacokinetics profiles, and assess drug toxicity profiles, facilitating a more efficient pathway from pilot study to market. Additionally, this review highlights the advancements in 3D printing technologies of dosage forms that have the ability to provide personalized treatment to different individuals. Furthermore, the article explores the opportunities and challenges of AI in healthcare, focusing on applications such as disease diagnosis, digital therapy, and epidemic forecasting. Prominent AI technologies like deep learning and neural networks are examined for their roles in predicting outbreaks of diseases like influenza and COVID-19. As the pharmaceutical landscape evolves, AI is poised to redefine traditional methods. This paves the way for more efficient healthcare solutions. By harnessing the interplay of technology and science, AI not only increases productivity; but it also promotes a new era of precision medicine tailored to the needs of each patient.Current Pharmaceutical Biotechnology00The article explores the opportunities and challenges of AI in healthcare, focusing on applications such as disease diagnosis, digital therapy, and epidemic forecasting, and prominent AI technologies like deep learning and neural networks are examined for their roles in predicting outbreaks of diseases.Current pharmaceutical biotechnology["Praveen Halagali", "Devika Nayak", "Raagul Seenivasan", "Jyothsna Manikkath", "M. Rathnanand", "Vamshi Krishna Tippavajhala"]2025-01-23T00:00:00 192bf997cfd52a38f792484df09cf48d3232781cABSTRACT Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI‐powered cutting‐edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI‐based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy‐eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI‐driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI‐driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co‐creation of AI technologies to enhance care in a way that promotes person‐centredness, empowerment and active participation.International Journal of Mental Health Nursing1080It is found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges.International Journal of Mental Health Nursing["L. Milasan", "Daniel Scott-Purdy"]2025-01-23T00:00:00 d5da13a175fb91730e7cc23c508f4b9888ad66b8The rapid development of generative artificial intelligence (AI) has led to the recognition of tools like ChatGPT and its potential to transform human resource (HR) management processes, particularly in decision-making. This review study aims to assess the effectiveness and benefits of ChatGPT in enhancing HR functions, particularly decision-making, and to identify any challenges and ethical considerations involved. Additionally, the study seeks to establish a hybrid framework that combines AI-driven decision-making with human oversight. A systematic literature review was conducted using PRISMA guidelines, selecting 50 articles from Scopus and Google Scholar databases. The literature review includes a synthesis analysis to assess publication trends and a keyword analysis to identify key themes such as ChatGPT’s impact on decision-making in HR management. The study reveals that ChatGPT can streamline HR processes, improve communication, and support personalized learning and decision-making, eventually contributing to enhanced performance and engagement. However, the technology requires human input for moral judgment and empathy, presenting challenges like resistance to adoption, algorithmic bias, and data privacy concerns. This study uniquely contributes to the literature by providing a systematic analysis of ChatGPT’s role in HR decision-making and proposing a hybrid framework that addresses AI’s limitations through ethical guidelines and human oversight. The findings emphasize the need for empirical research in larger, diverse settings and future enhancements to ChatGPT’s contextual understanding of HR.European Journal of Business and Management Research650The study reveals that ChatGPT can streamline HR processes, improve communication, and support personalized learning and decision-making, eventually contributing to enhanced performance and engagement, and proposes a hybrid framework that addresses AI’s limitations through ethical guidelines and human oversight.European Journal of Business and Management Research["S. Porkodi", "Teresita Luzon Cedro"]2025-01-23T00:00:00 02bfcacad0d8b4c9b731a9e36731b2d73b5dde10The field of data justice has been evolving to take into account the role of data in powering the field of artificial intelligence (AI). In this paper we review the main conceptual bases for governing data and AI: the market-based approach, the personal–non-personal data distinction and strategic sovereignty. We then analyse how these are being operationalised into practical models for governance, including public data trusts, data cooperatives, personal data sovereignty, data collaboratives, data commons approaches and indigenous data sovereignty. We interrogate these models' potential for just governance based on four benchmarks which we propose as a reformulation of the Data Justice governance agenda identified by Taylor in her 2017 framework. Re-situating data justice at the intersection of data and AI, these benchmarks focus on preserving and strengthening public infrastructures and public goods; inclusiveness; contestability and accountability; and global responsibility. We demonstrate how they can be used to test whether a governance approach will succeed in redistributing power, engaging with public concerns and creating a plural politics of AI.Dialogues on Digital Society240This paper reviews the main conceptual bases for governing data and AI, and interrogates these models' potential for just governance based on four benchmarks which are proposed as a reformulation of the Data Justice governance agenda identified by Taylor in her 2017 framework.Dialogues on Digital Society["Linnet Taylor", "Siddharth Peter de Souza", "Aaron Martin", "Joan L\u00f3pez Solano"]2025-01-23T00:00:00 2da8c0434c88e852d47bab84ab6ce43d985f2193Low-code and no-code development platforms are here to transform the software development landscape by allowing even non-technical users build applications without the need of their advanced programming skills. Artificial Intelligence (AI) is the most crucial player in this evolution; reinvigorating these platforms with intelligent automation, device responsive templates and user-friendly interfaces (2024). Users can design, build, and deploy applications easily with AI-powered features (e.g., Natural Language Processing (NLP), drag-and-drop functionality to-design application & code-generation tools). Democratization of Application Development These innovations democratize application development, thereby allowing businesses to innovate faster, lessen the reliance on professional developers, and meet the surging demand for digital solutions. In this paper, we look at how AI-assisted low-code and no-code platforms are changing the way new apps are being developed, empowering non-developers to participate in software creation and accelerating automation of app development. It also presents the challenges and future aspects that we have to deal with in order to use AI on these platforms, as well as their future role of building a bridge between technical expertise and creative problem-solving.Formosa Journal of Multidisciplinary Research00This paper looks at how AI-assisted low-code and no-code platforms are changing the way new apps are being developed, empowering non-developers to participate in software creation and accelerating automation of app development.Formosa Journal of Multidisciplinary Research["Goutham Kacheru", "Nagaraju Arthan", "Rohit Bajjuru"]2025-01-23T00:00:00 d0b67ed29ed5ba1ead3f69f91eb5a76572459fcbCurrent pain and headache reports430Artificial intelligence holds significant promise in enhancing the management of acute and chronic opioids, which may offer tools to help optimize dosing, predict addiction risks, and personalize pain management strategies.Current Pain and Headache Reports["Rodney A. Gabriel", "Brian H. Park", "Chun-Nan Hsu", "Alvaro A Macias"]2025-01-23T00:00:00 e9d668bf621e7c983c50dea6c74490ff87c29f8aThis article examines the transformative potential of blockchain technology and its integration with artificial intelligence (AI) in clinical trials, focusing on their combined ability to enhance integrity, operational efficiency, and transparency in the data governance. Through an in-depth analysis of recent advancements, the article highlights how blockchain and AI address critical challenges, including patient data privacy, regulatory compliance, and security. The article also identifies key barriers to adoption in the mentioned integration, such as scalability limitations, association with existing healthcare systems, and high implementation costs. By presenting a comprehensive overview of the current research and proposing strategic directions, this work emphasizes how the synergy between blockchain and AI can revolutionize clinical trials through process automation, improved stakeholder trust, and robust transparency.Bioanalysis190How the synergy between blockchain and AI can revolutionize clinical trials can revolutionize clinical trials through process automation, improved stakeholder trust, and robust transparency is emphasized.Bioanalysis["V\u00edctor Leiva", "Cecilia Castro"]2025-01-23T00:00:00 019aaf6f4353aed1782f03b374394ea44ed2a73bThe efficiency of various contemporary processes depends highly on the implementation of artificial intelligence (AI) in the numerous tasks. While there is a significant set of benefits that AI brings to the table and while the results of AI integration can be a boost in productivity and launch of innovations, there is a set of issues that has to be taken into account. Moreover, AI and cyber space are combining the plethora of opportunities for many countries and adjusting the situation globally. The research work of this paper aims to undertake an analysis to unveil how realism principles inform policy choices of Pakistan particularly the opportunities and threats that result from the contemporary Cold War like competition between the United States and China within the digital frontier. Thus, this research aims to increase understanding of Pakistan’s strategies and responses in relation to and on the basis of global cyber geopolitics, including trends, strategic partnerships, and projections for the future. To this end, it seeks to map out the contingency at work in this fast-moving context and offer a sense of the systemic processes underpinning it, while shedding light on Pakistan’s place in this emerging global order.Social Sciences Spectrum180The research work of this paper aims to undertake an analysis to unveil how realism principles inform policy choices of Pakistan particularly the opportunities and threats that result from the contemporary Cold War like competition between the United States and China within the digital frontier.Social Sciences Spectrum["Salman Khalid"]2025-01-23T00:00:00 c6bd1f5252e29207c6ef45810cb8b3aa74fd1136Background This bibliometric study examines the intersection of artificial intelligence (AI) and finance, providing a comprehensive analysis of its evolution, central themes, and avenues for further exploration. The study aims to uncover the theoretical foundations, methodological approaches, and practical implications of AI in financial contexts. Methods The research employs bibliometric techniques, using 607 Web of Science (WoS) indexed papers. The Theory-Context-Characteristics-Methodology (TCCM) framework guides the analysis, focusing on thematic mapping to explore key topics. Core areas such as risk management, market efficiency, and innovation are analyzed, alongside emerging themes like ethical AI, finance applications, and factors influencing AI-driven financial decision-making. Results The findings reveal critical gaps in interdisciplinary methods, ethical considerations, and methodological advancements necessary to develop robust and transparent AI systems. Thematic mapping highlights the increasing importance of ethical AI practices and the influence of AI on financial decision-making processes. Emerging research areas emphasize the need for innovative frameworks and solutions to address current challenges. Conclusions This study provides valuable insights for academics, industry practitioners, and policymakers to harness transformative potential of AI in finance. This research offers a foundation for future studies and practical applications by addressing key gaps and promoting interdisciplinary and ethical approaches in a rapidly evolving field.F1000Research540This study provides valuable insights for academics, industry practitioners, and policymakers to harness transformative potential of AI in finance by addressing key gaps and promoting interdisciplinary and ethical approaches in a rapidly evolving field.F1000Research["Prasenjit Roy", "Biswajit Ghose", "Premendra Kumar Singh", "Pankaj Kumar Tyagi", "A. Vasudevan"]2025-01-23T00:00:00 3ea801fa6e72289657d174201b05d0424b70d192In 2020, in Ukraine, the Concept for the development of piece intelligence in Ukraine was developed, prepared by the Ministry of Digital Transformation of Ukraine and approved by the Cabinet of Ministers of Ukraine 02.12.2020 No. 1556-r.. This itself acts as the main instrument for the development of integration processes of piece intelligence and all galuzites It is the main mechanism for its implementation, as a separate strategy. Sports science is also actively involved in this process. There are already dozens of applications for the effective development of artificial intelligence at all stages of sports training. However, the overall picture of research and applied aspects still contains a sufficiently fragmented and chaotic order, which will require an integrated scientific approach and the development of detailed algorithms for the integration of individual intelligence in sports activities and other sports activities. The purpose of the work was to look at the practical stagnation of the capabilities of artificial intelligence systems in sports. Find out and learn the necessary facts about artificial intelligence. Conduct a retrospective analysis of the research and development of human intelligence systems in sports in Ukraine and in the international arena. Research methods – experimental-theoretical (analysis, logical), empirical (development), theoretical (research and analysis, analysis and synthesis). Apparently, prior to the results of the investigation, a retrospective analysis of the formation and integration processes in sportswear, international evidence of the applied science of artificial intelligence in sports and nutrition in Ukraine was carried out.Scientific Journal of National Pedagogical Dragomanov University. Series 15. Scientific and pedagogical problems of physical culture (physical culture and sports)110The purpose of the work was to look at the practical stagnation of the capabilities of artificial intelligence systems in sports in Ukraine and in the international arena.Scientific Journal of National Pedagogical Dragomanov University Series 15 Scientific and pedagogical problems of physical culture (physical culture and sports)["D. S. Volskyi"]2025-01-23T00:00:00 d74d10848918c86fdf6e394a8d9100d27bea9a53Artificial intelligence is currently going through a disruptive moment in all spheres of society. Its application is widespread in all disciplines of knowledge. Engineering education is not alien to this phenomenon; we increasingly see students and teachers immersed in this world, facilitating thelearning process for students and teachers and offering them tools that help them improve their teaching processes. Within these opportunities offered by AI, we can find greater personalization of learning for students, which means that the content is adapted and adjusted to the profile of each individual because each person learns in a different way and at a different pace. This makes learning more enriching for the students. In the case of teachers, it allows them to automate repetitive tasks and frees them from workload.Ingeniería e innovación00Artificial intelligence allows for greater personalization of learning for students, which means that the content is adapted and adjusted to the profile of each individual because each person learns in a different way and at a different pace.Ingeniería e Innovación["Jorge G\u00f3mez G\u00f3mez"]2025-01-23T00:00:00 214d75a945359454d21a7fa2f24fc66614599b6cPenelitian ini bertujuan untuk menganalisis implementasi kecerdasan buatan (Artificial Intelligence/AI) dalam optimalisasi proses bisnis, dengan fokus pada dampaknya terhadap efisiensi operasional dan keunggulan kompetitif organisasi. Studi ini menggunakan pendekatan kualitatif dengan metode studi kasus pada beberapa perusahaan yang telah mengadopsi AI dalam operasional mereka. Hasil penelitian menunjukkan bahwa penerapan AI secara signifikan meningkatkan efisiensi melalui otomatisasi tugas rutin, pengambilan keputusan berbasis data, dan optimalisasi manajemen sumber daya. Namun, penelitian ini juga mengungkapkan tantangan yang dihadapi perusahaan, seperti investasi awal yang tinggi, keterbatasan infrastruktur teknologi, dan isu privasi data. Dalam jangka panjang, organisasi yang berhasil mengintegrasikan AI ke dalam proses bisnis mereka memiliki peluang besar untuk meningkatkan inovasi, pengalaman pelanggan, dan daya saing di pasar. Penelitian ini menekankan pentingnya pendekatan strategis dan bertanggung jawab dalam implementasi AI, termasuk pengembangan keterampilan tenaga kerja dan kepatuhan terhadap regulasi privasi data. Hasil penelitian diharapkan dapat menjadi panduan bagi perusahaan dalam merancang strategi implementasi AI untuk mencapai efisiensi dan keberlanjutan bisnis.Jurnal Sistem Informasi dan Teknologi (SINTEK)00Jurnal Sistem Informasi dan Teknologi (SINTEK)["Dede Latipah Dede", "Subhiyanto", "Esthi Adityarini", "Mochamad Arief Madiansah"]2025-01-23T00:00:00 297fd54e8faab2f838103d2aa6880892e37af3a3This study investigates the integration of Artificial Intelligence (AI) and Open Source Intelligence (OSINT) to enhance predictive threat modeling in cybersecurity, addressing the growing complexity and frequency of cyber threats. Integrating AI and OSINT offers transformative potential by enabling organizations to transition from reactive to proactive security measures, a critical need in the evolving digital landscape. Leveraging data from the Twitter Academic API, Common Crawl Dataset, and MITRE ATT&CK Framework, the analysis employed descriptive statistical analysis, logistic regression, and multivariate regression methodologies. Results indicate high data completeness (90.41%) and relevance (81.44%) in OSINT datasets, supporting their suitability for AI model training. Logistic regression demonstrated strong predictive capabilities, achieving 94.98% accuracy, 88.69% precision, and an AUC score of 0.91. However, risks such as data bias (-0.36 coefficient) and adversarial manipulation (-0.33 coefficient) significantly impact predictive performance. The ethical implications of this integration, including concerns about privacy, data fairness, and the potential for misuse, are highlighted as critical considerations for broader adoption. Recommendations include robust preprocessing protocols, advanced adversarial defenses, ethical guidelines, and continuous AI innovation to address these challenges. These findings underscore the potential of AI-OSINT integration while emphasizing the need for ethical and technical safeguards to enhance cybersecurity effectiveness.Journal of Engineering Research and Reports00Results indicate high data completeness and relevance in OSINT datasets, supporting their suitability for AI model training, and underscore the potential of AI-OSINT integration while emphasizing the need for ethical and technical safeguards to enhance cybersecurity effectiveness.Journal of Engineering Research and Reports["Onyinye Agatha Obioha-Val", "Temitope Ibrahim Lawal", "O. O. Olaniyi", "M. O. Gbadebo", "Anthony Obulor Olisa"]2025-01-23T00:00:00 e3611d282ed771360bdf16e2a0e12f53af37a7fbObjective: The objective of this study is to examine the civil liability implications of errors associated with artificial intelligence (AI) technologies in healthcare, with the aim of proposing comprehensive legal frameworks to regulate AI’s integration into the sector. This research seeks to align AI advancements with international standards and sustainable development goals, particularly SDG 3 (Good Health and Well-Being) and SDG 16 (Peace, Justice, and Strong Institutions).   Theoretical Framework: The research is grounded in international human rights conventions, national legislation on data protection, and frameworks such as the World Health Organization’s guidelines and the Council of Europe Framework Convention on Artificial Intelligence, Human Rights, Democracy, and the Rule of Law. These provide a robust foundation for exploring the legal and ethical dimensions of AI in healthcare.   Method: A qualitative research approach was adopted, analysing legal documents, international standards, and case studies to assess the regulatory challenges of AI in healthcare. The study also reviews best practices from jurisdictions effectively addressing AI’s legal and ethical implications.   Results and Discussion: The findings reveal significant gaps in existing legal frameworks governing AI in healthcare, particularly in areas of accountability and data protection. The discussion contextualizes these gaps within the theoretical framework, emphasizing the need for proactive legislative measures. Challenges such as algorithmic bias, transparency, and equitable access to AI technologies are critically examined.   Research Implications: The study underscores the necessity for Omani legislators to adopt robust regulatory measures, ensuring accountability and alignment with international best practices. These measures can enhance healthcare quality and contribute to sustainable development.   Originality/Value: This study contributes to the literature by addressing the underexplored legal and ethical challenges of AI in healthcare within the context of sustainable development. It offers actionable recommendations for policymakers, emphasizing the responsible and ethical use of AI to advance medical innovation and protect human rights.Journal of Lifestyle and SDGs Review140The civil liability implications of errors associated with artificial intelligence (AI) technologies in healthcare are examined, with the aim of proposing comprehensive legal frameworks to regulate AI’s integration into the sector.Journal of Lifestyle and SDGs Review["Majed Ahmed Saleh Al-Adwan", "Enas Qutieshat"]2025-01-23T00:00:00 40578cf34b5472b47297554b068e4637b6d74600Objective: To list challenges and assess student satisfaction with training in artificial intelligence focused on developing sustainable projects for the Amazon.   Theoretical Framework: AI refers to systems that model human intelligence to perform tasks, improving based on collected information. The digital transformation market has seen significant growth, with global investments expected to reach $665 billion by 2023.   Method: The applied methodology consisted of a quantitative and qualitative approach. A specific form was used for the demographic study and student satisfaction. Only students who completed the training were included in the research.   Results and Discussion: The sample used had a margin of error of 9.40% with a confidence level of 90%. The research conducted with the participants revealed a high satisfaction rate, with 98.1% of students declaring themselves satisfied or very satisfied with the offered training.   Research Implications: The research revealed several important aspects of the training provided, the difficulties faced by the students, and their satisfaction in carrying out the specific activities of the course considering its various modules.   Originality/Value: This training consisted of a pilot project by the Federal University of Amapá in partnership with the International Software Technology Center (CITS) that brings an innovative approach to developing sustainable projects for the Amazon using technical knowledge in Artificial Intelligence and considering high school and elementary school students.Revista de Gestão Social e Ambiental50This training consisted of a pilot project by the Federal University of Amapá in partnership with the International Software Technology Center (CITS) that brings an innovative approach to developing sustainable projects for the Amazon using technical knowledge in Artificial Intelligence and considering high school and elementary school students.Revista de Gestão Social e Ambiental["G. Maranh\u00e3o", "Werbeston Douglas de Oliveira", "A. U. Brito", "Osvaldo Campelo de Mello Vasconcelos", "Marcelo Ricardo Souza Siqueira"]2025-01-23T00:00:00 21a4d4bc1b9b1fd61f5e413ddc959d4c781016bdIn the evolving world of influencer marketing, Artificial Intelligence (AI) influencers are creating significant impact and transforming the approach to brand promotions on social media platforms. In recent times, many popular brands have partnered with AI influencers to engage with their social media audiences. AI influencers have become popular as a novel method for brands to increase customer engagement and create purchase intention, but there is a scarcity of research on this emerging trend of marketing. The AI-based virtual influencers effect on consumer engagement and purchase intention remain largely unexplored. This study used a questionnaire-based survey method and 414 responses were collected. The result from the research shows that credibility, informative value and human-likeness are the major factors influencing consumer engagement purchase intention towards brands promoted through AI-based virtual influencers. The attractiveness and entertainment value of AI influencer’s social media posts affect consumer engagement but exhibit no effect on purchase intention. Theoretical and managerial recommendations related to AI influencers’ marketing are presented.Journal of Theoretical and Applied Electronic Commerce Research960The result from the research shows that credibility, informative value and human-likeness are the major factors influencing consumer engagement purchase intention towards brands promoted through AI-based virtual influencers.Journal of Theoretical and Applied Electronic Commerce Research["Sudarsan Jayasingh", "Arunkumar Sivakumar", "Arputha Arockiaraj Vanathaiyan"]2025-01-23T00:00:00 21048f88e620ba292c495d0bc6ee4f88cced71a6El libro de Federico Cugurullo se inscribe en la literatura que aborda el papel de los avances tecnológicos en el desarrollo urbano. Utilizando como ejes de análisis la idea de la ciudad como campo de experimentación y la novela Frankenstein, Cugurullo estudia los casos de una ciudad ecológica y una inteligente para desprender de ello la hipótesis del fin de la ciudad a partir de la aplicación de la inteligencia artificial (IA) en el espacio urbano. En esta reseña se discute esa hipótesis al contrastar el posible impacto de la IA con el generado por otros avances tecnológicos.Estudios Demográficos y Urbanos20Estudios Demográficos y Urbanos["Luis Enrique Santiago"]2025-01-23T00:00:00 5c3c79549bbef531a1b01bb81aaf5aa3f8b1e548Education and Information Technologies : Official Journal of the IFIP technical committee on Education510Education and Information Technologies["Weikang Lu", "Chenghua Lin"]2025-01-23T00:00:00 0277cfc5d904e061bf11c18ab445db1ac4d080f3The numerous attempts have been made throughout industrial history to minimize human error, control process complexity, cut waste, get rid of inefficiencies, and enhance the skills needed for modern manufacturing. Concurrently, the industrial sector has relentlessly fought against the obstacles pertaining to both quality and customer pricing. Pursuing these objectives has resulted in the implementation of significant improvements including digitization and more individualized client experiences. Furthermore, the manufacturing sector has survived the economic downturn.AI-powered computers have been deployed in many different contexts in recent years, and numerous research electronics products have taken note of this development. Experts in combining electronic design with computer AI technologies are among these researchers in large numbers. As a result, it makes it possible for numerous academics to investigate the increasingly complex development path of computer AI technology in more detail. It suggests the challenges associated with applying computer AI to electronic device design and delves more into the features and importance of this technology.Advances in Nonlinear Variational Inequalities150The challenges associated with applying computer AI to electronic device design are suggested and the features and importance of this technology are delved more into.Advances in Nonlinear Variational Inequalities["Dr. Sharmila Sengupta", "Bhukya Shankar", "Peddinti Neeraja", "M.Tamilselvam", "G.Ramachandran"]2025-01-23T00:00:00 8551fddf5b26f3c28f91258fc1afcc6bf319bff3Journal of Antitrust Enforcement00Journal of Antitrust Enforcement["Beno\u00eet Coeur\u00e9"]2025-01-23T00:00:00 d0913ed7dc640d454a9c51e025e303eb730d7aaa00["Ravinder S Saini", "Mohammad Saheer", "Shubham Chopra", "Abdulmajeed Okshah", "Ryan Binduhayyim", "Vishwanath Gurumurthy", "S. Vaddamanu", "A. Heboyan"]2025-01-23T00:00:00 95c7479b06092612c7191c7c2e89cbb54ef53736[This corrects the article DOI: 10.1371/journal.pone.0312739.].PLoS ONE20PLOS ONE["Ruhua Chen", "Mohammad Reza Ghavidel Aghdam", "Mohammad Khishe"]2025-01-23T00:00:00 40d790b735477bd4920a4a0034f0a5d2765c27e8Rhetoric and Communications00Rhetoric and Communications["Boyan Dafov"]2025-01-23T00:00:00 7f31ef33f7e36e15d3f5cf1497c1ab4919d3d3a900["Sarah Kelley", "Claire Kelley", "Bonnie Solomon", "Andra Wilkinson"]2025-01-23T00:00:00 e89caa995bcd145db6a9baacbbec0f1613dd3b75Chinese Medical Journal70Chinese medical journal["Jingyue Zhou", "Yu Wang", "Yang Liu", "Lifei Ma", "Jinhua Cui", "Lanlan Zhang", "Xiaoqiang Tang"]2025-01-23T00:00:00 1f04be7e20229410e8abccac4a6cc70edd7392dfThis study proposes an artificial intelligence-driven model that can enhance productivity and reduce manufacturing costs in the brewery industry of Nigeria. The research initiated with a critical literature review on the factors of productivity in the knowledge-intensive industries, choosing thereupon the brewery sector based on expert advice. In total, three predictive models were developed, namely Artificial Neural Network, Machine Learning, and a hybrid Artificial Neural Network-Machine Learning model, for predicting productivity. The Mean Squared Error was 0.001399 for the Artificial Neural Network model, Root Mean Squared Error was 0.037407, and Mean Absolute Error was 0.037283, while the Machine Learning had Mean Squared Error of 0.040378, Root Mean Squared Error of 0.200943, and Mean Absolute Error of 0.183000, the hybrid having Mean Squared Error of 0.013982, Root Mean Squared Error of 0.118247, and Mean Absolute Error of 0.110141. It also proved the fact that the Machine Learning model is able to predict productivity based on maintenance, Mean Time Before Failure, and Mean Time to Repair indicators since the obtained values for this type of model had lower errors than all the others: Mean Absolute Error = 0.08508, Mean Squared Error = 0.19275, Root Mean Squared Error = 0.43903.Saudi Journal of Engineering and Technology00It is proved the fact that the Machine Learning model is able to predict productivity based on maintenance, Mean Time Before Failure, and Mean Time to Repair indicators since the obtained values had lower errors than all the others.Saudi Journal of Engineering and Technology["Des- Wosu", "Azubuike George", "D. O. Aikhuele", "Harold U. Nwosu"]2025-01-23T00:00:00 4b400f4dc37843a738d7f34c6eb8f77034334cc4The integration of Artificial Intelligence (AI) systems into technologies used by young digital citizens raises significant privacy concerns. This study investigates these concerns through a comparative analysis of stakeholder perspectives. A total of 252 participants were surveyed, with the analysis focusing on 110 valid responses from parents/educators and 100 from AI professionals after data cleaning. Quantitative methods, including descriptive statistics and Partial Least Squares Structural Equation Modeling, examined five validated constructs: Data Ownership and Control, Parental Data Sharing, Perceived Risks and Benefits, Transparency and Trust, and Education and Awareness. Results showed Education and Awareness significantly influenced data ownership and risk assessment, while Data Ownership and Control strongly impacted Transparency and Trust. Transparency and Trust, along with Perceived Risks and Benefits, showed minimal influence on Parental Data Sharing, suggesting other factors may play a larger role. The study underscores the need for user-centric privacy controls, tailored transparency strategies, and targeted educational initiatives. Incorporating diverse stakeholder perspectives offers actionable insights into ethical AI design and governance, balancing innovation with robust privacy protections to foster trust in a digital age.02Results showed Education and Awareness significantly influenced data ownership and risk assessment, while Data Ownership and Control strongly impacted Transparency and Trust.["Molly Campbell", "Ankur Barthwal", "Sandhya Joshi", "Austin Shouli", "Ajay Kumar Shrestha"]2025-01-23T00:00:00 5bc678e39b2a8e2330c1303c2e89c53066a96a24As Artificial Intelligence (AI) systems become increasingly integrated into various aspects of daily life, concerns about privacy and ethical accountability are gaining prominence. This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals. Using qualitative analysis of survey responses from 227 participants, the research identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection, alongside perceived benefits such as personalized services, enhanced efficiency, and educational advancements. Stakeholders emphasized the need for transparency, privacy-by-design, user empowerment, and ethical oversight to address privacy concerns effectively. The findings provide actionable insights into balancing the benefits of AI with robust privacy protections, catering to the diverse needs of stakeholders. Recommendations include implementing selective data use, fostering transparency, promoting user autonomy, and integrating ethical principles into AI development. This study contributes to the ongoing discourse on ethical AI, offering guidance for designing privacy-centric systems that align with societal values and build trust among users. By addressing privacy challenges, this research underscores the importance of developing AI technologies that are not only innovative but also ethically sound and responsive to the concerns of all stakeholders.261This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals, and identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection, alongside perceived benefits such as personalized services, enhanced efficiency, and educational advancements.["Ajay Kumar Shrestha", "Sandhya Joshi"]2025-01-23T00:00:00 682d49b8e091b3a80da177941ae9bec5d6a06074ChatGPT, an artificial intelligence chatbot released in November 2022, is the fastest-growing consumer application in history. As a generative AI that uses Natural Language Processing, it creates a plethora of content with a ‘human voice’. Unsurprisingly, ChatGPT garnered much attention from academia as it passed several professional exams and has multiple avenues for potential misuse by students and researchers alike. Therefore, this study addresses the dearth in literature by performing a self-reflection study on the practical usage of AI chatbots in research, with a research question: What is the self-reflection of the authors on the usage of AI chatbots for research? This study was framed under the Technology Acceptance Model to provide a comprehensive discussion covering multiple domains. AI chatbots provide advantages and disadvantages to the end-user, but the resulting outcome lies in the hands of the user; hence, educating existing and future users of the tool to use it responsibly should be first and foremost. As Pandora's AI chatbot box has been opened, ethical issues are also plentiful in chatbots. However, it is up to academia to solve these in multidisciplinary settings because, as history has shown, curtailing the use of new technologies is futile. Overall, this study contributes to the body of knowledge in AI Chatbot research by emphasising their potential and addressing probable issues when using them in research.Pertanika journal of science & technology770This study addresses the dearth in literature by performing a self-reflection study on the practical usage of AI chatbots in research, with a research question: What is the self-reflection of the authors on the usage of AI chatbots for research?Pertanika Journal of Science and Technology["Avinash Rames", "Ahmad Zabidi", "Abdul Razak"]2025-01-23T00:00:00 c77ce3119147c49ada0c24cb6057e6efe31b3fb8This study analyzes the incidence of the use of artificial intelligence (AI) in the development of digital and sustainable competencies in teachers of higher education institutions in Ecuador. A quantitative and descriptive research was applied to a sample of 200 university teachers, evaluating their levels of digital competencies. To diagnose the teaching competencies in digital knowledge of teachers in higher education, a test was applied during the second semester of the year 2024 to 300 teachers from universities in the Ecuadorian highlands. Digital competencies were analyzed in four dimensions: Information, Communication and collaboration, Use of digital devices and tools, and Content creation. The results showed that professors present a medium to medium-high level of appropriation in all dimensions, the lowest being Content Creation. The conclusions highlight the importance of implementing new measures in the institutional environment for the strengthening of digital competencies and the adaptation to new forms of teaching and learning where the adoption of AI tools and their relationship with sustainable practices in the classroom, constitutes a viable alternative for such purposes. The results revealed a significant positive correlation between the use of AI tools and the strengthening of digital and sustainable competencies. In addition, barriers related to the lack of knowledge were identified.  Data and Metadata210A significant positive correlation between the use of AI tools and the strengthening of digital and sustainable competencies was revealed and barriers related to the lack of knowledge were identified.Data and Metadata["Silvia Carolina Zambonino Torres", "Wilson Edmundo Cisneros Basurto", "Flavio Ra\u00fal Vega Padilla", "I. Ruiz-Ruiz", "Paulina Mercedes Erazo Molina"]2025-01-23T00:00:00 cf69f0a339c9802e21d8d9e2fc6c0df69d92a4c7Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)- based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model input. Simulation results demonstrate that the XAI-assisted methodology achieves 97% of the original MADRL sum-rate performance while reducing optimal state features by 28%, average training time by 11%, and trainable weight parameters by 46% in a network with eight vehicular pairs.00This paper proposes a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents and demonstrates that the XAI-assisted methodology achieves 97% of the original MADRL sum-rate performance.["Nasir Khan", "Asmaa Abdallah", "Abdulkadir \u00c7elik", "Ahmed M. Eltawil", "Sinem Coleri"]2025-01-23T00:00:00 866d6ba33da97d3803288e15fb46740bf2bd7325Cybersecurity is growing increasingly intricate due to the rapid expansion of interconnected systems and the global landscape of threats. To address these challenges effectively, a proficient cybersecurity workforce capable of making complex decisions in the ever-changing cyberspace is essential. While Artificial Intelligence (AI) is being quickly integrated into cybersecurity operations, it is crucial to comprehend the foundational learning theory and ecosystems to adequately train human operators and AI-assisted cyber defense teams. Cybersecurity exercises (CSXs) serve as popular instructional tools for cyber preparedness. Nevertheless, the utilization of learning analytics (LA) techniques and AI-driven approaches in exercise development and implementation is still nascent. We advocate for a comprehensive model of human-AI interaction within the context of LA and CSX. This model unifies aspects of human-AI interaction, cyber ranges, cybersecurity practices, LA tools, multimodal learning analytics, exercise life cycles, and pedagogical strategies. We also explore the potential and obstacles for implementing LA and AI in cybersecurity training. By examining the role of AI through a lens of learning, instruction, and administration in cybersecurity training, particularly within exercises, we seek to prompt further discourse on the future of collaboration between humans and AI and how to enhance cybersecurity training through innovative LA and AI capabilitiesInternational Journal of Advanced Research in Science, Communication and Technology140This work advocates for a comprehensive model of human-AI interaction within the context of LA and CSX, which unifies aspects of human-AI interaction, cyber ranges, cybersecurity practices, LA tools, multimodal learning analytics, exercise life cycles, and pedagogical strategies.International Journal of Advanced Research in Science, Communication and Technology["Ravi Chourasia"]2025-01-23T00:00:00 161a4033439ef5526c35975bb6c1832c4a569479Hand hygiene is critical for preventing infections, yet maintaining compliance remains challenging across healthcare, schools, and communities. Despite strong evidence, lapses occur due to cognitive barriers, understaffing, limited resources, and antimicrobial resistance. Behavioral science highlights factors like time constraints and cognitive biases affecting adherence, with compliance rates as low as 40%. Nudge theory, developed by Thaler and Sunstein, offers promising solutions by using subtle interventions, like visual or auditory cues, to encourage hand hygiene without imposing strict regulations. Recent innovations integrate artificial intelligence (AI) with nudges, enhancing compliance through real-time feedback. AI-powered systems, such as smart dispensers and wearable devices, provide reminders using visual or auditory cues at critical moments. For example, dispensers may light up or chime when a healthcare worker enters a patient’s room, prompting hand hygiene. Studies show these AI-driven interventions significantly improve compliance, with rates increasing by up to 30% in some cases. AI can also analyze patterns of non-compliance, deploying personalized nudges during high-risk periods. Combining nudge theory with gamification, such as team-based competitions and rewards, further reinforces positive habits. However, implementing AI solutions in countries like India faces challenges, including limited resources, resistance to new technologies, and cultural barriers. Despite hurdles, integrating AI-driven nudges with behavioral strategies has the potential to transform hand hygiene practices. This approach fosters accountability, reduces infection rates, and ensures safer patient care by embedding compliance into daily routines, paving the way for sustainable improvements in infection control.Frontiers in Public Health90Frontiers in Public Health["Samiksha Bhattacharjee", "Sudip Bhattacharya"]2025-01-23T00:00:00 8982206ec85b9236894c7d95e1f6bf12f7dbadbaPurpose Artificial intelligence (AI) in mobile apps is growing rapidly, with features such as image recognition, personalized notifications and prescriptive analytics becoming more common. One such app is the Equalizer AI-powered mobile app, which uses AI to process water invoices, advise customers on fair prices and consumption and allow for online payment and data submission. This study aims to develop a technology adoption model for AI-powered mobile apps in the water sector by extending the value-based adoption model (VAM) to include customer trust. Design/methodology/approach Primary data was collected from 385 smartphone-using water customers. A stratified sampling approach ensured a representative sample of Palestinian water customers in the West Bank region. The study used a validated tool to measure perceived customer value, trust and adoption intention. It also used structural equation modeling to develop a causal diagram using the AMOS software. Findings The results confirmed a positive relationship between perceived usefulness, perceived innovation and perceived value and a negative relationship between perceived technical difficulty and perceived value. Contrary to VAM theory, the study showed a positive relationship between perceived fees and perceived value, indicating that users view premium fees as a cue of quality, accuracy, innovation and trustworthiness. Practical implications The high adoption intention of these apps holds significant implications for both the government and the water sector. This is because it results in the accumulation of substantial data, which can be used by government authorities and water providers to monitor and sustain the sector effectively. Originality/value This research extends existing technology adoption models by integrating customer trust and applying them to the water sector in a developing country. It offers new insights into public service innovations, addressing the unique cultural and sectoral challenges in this context. Journal of Systems and Information Technology670A positive relationship between perceived fees and perceived value is showed, indicating that users view premium fees as a cue of quality, accuracy, innovation and trustworthiness, indicating that users view premium fees as a cue of quality, accuracy, innovation and trustworthiness.Journal of Systems and Information Technology["Abdullah Murrar", "Veronica Paz", "Madan Batra", "David Yerger"]2025-01-23T00:00:00 36cd969f1a7ce0957486e14a5120a7f52c8b43a2Deep neural networks are increasingly employed in high-stakes medical applications, despite their tendency for shortcut learning in the presence of spurious correlations, which can have potentially fatal consequences in practice. Detecting and mitigating shortcut behavior is a challenging task that often requires significant labeling efforts from domain experts. To alleviate this problem, we introduce a semi-automated framework for the identification of spurious behavior from both data and model perspective by leveraging insights from eXplainable Artificial Intelligence (XAI). This allows the retrieval of spurious data points and the detection of model circuits that encode the associated prediction rules. Moreover, we demonstrate how these shortcut encodings can be used for XAI-based sample- and pixel-level data annotation, providing valuable information for bias mitigation methods to unlearn the undesired shortcut behavior. We show the applicability of our framework using four medical datasets across two modalities, featuring controlled and real-world spurious correlations caused by data artifacts. We successfully identify and mitigate these biases in VGG16, ResNet50, and contemporary Vision Transformer models, ultimately increasing their robustness and applicability for real-world medical tasks.00A semi-automated framework for the identification of spurious behavior from both data and model perspective is introduced by leveraging insights from eXplainable Artificial Intelligence (XAI), which allows the retrieval of spurious data points and the detection of model circuits that encode the associated prediction rules.["Frederik Pahde", "Thomas Wiegand", "S. Lapuschkin", "Wojciech Samek"]2025-01-23T00:00:00 9a159512876f2426be0487eeb5ac3f112b8e179aArtificial Intelligence (AI) is transforming marketing and customer engagement by facilitating businesses to predict trends, enhance decision-making, and improve customer experiences through technologies like machine learning, predictive analytics, and natural language processing. Important applications include customer segmentation, sentiment analysis, and predictive analytics for forecasting purchasing behavior. Chatbots and virtual assistants have automated customer service and boosted sales. However, AI adoption faces challenges such as data privacy concerns, regulatory issues, algorithmic biases, and resource constraints, particularly for small businesses. Despite these hurdles, advancements in deep learning and generative AI are unlocking new opportunities for personalized customer engagement through augmented reality (AR), virtual reality (VR), and customized content. The paper also explores future opportunities, such as integrating AI with blockchain for secure data sharing and leveraging the Internet of Things (IoT) for real-time insights into consumer behavior. The findings of the study indicate that AI, when responsibly applied, not only enhances marketing efficiency and personalization but also facilitates innovative strategies that redefine customer engagement and market responsiveness. A comprehensive analysis is presented on the topics mentioned above.Journal of Informatics Education and Research280The findings of the study indicate that AI, when responsibly applied, not only enhances marketing efficiency and personalization but also facilitates innovative strategies that redefine customer engagement and market responsiveness.Journal of Informatics Education and Research["Dr. Mohan Cherian", "Dr. Koonathi Grace Manoja", "Dr. Abhinav"]2025-01-23T00:00:00 a7eccb049c3cff0fbfa758dbb1ec8b43abcd43a3It is crystal clear that Artificial Intelligence has revolutionized research methodologies, particularly in the realm of data collection and analysis. Chatbots, powered by AI algorithms, are increasingly utilized as research tools, facilitating data collection, participant engagement, and even data analysis. However, the integration of AI-driven methodologies raises profound ethical concerns, particularly regarding privacy, informed consent, and the potential for manipulation. Thus, this paper aims at exploring the intersection of chatting, as a mode of interaction, and cheating, as an ethical concern, within the context of AI-driven research. Specifically, it investigates how AI-powered chatbots, often employed as research tools, can inadvertently facilitate unethical behavior, such as cheating in academic or experimental settings. Drawing upon ethical frameworks and guidelines established in the field of research ethics, this paper proposes strategies and guidelines for researchers to ensure the ethical conduct of AI-driven research. Furthermore, this paper examines the implications of AI-driven research on academic integrity and scientific rigor. It discusses the challenges of maintaining transparency and accountability in AI-driven research processes, particularly in ensuring the validity and reliability of data collected through chatbot interactions. Briefly, by critically evaluating the ethical implications of AI-driven research methodologies, this paper aims to contribute to the development of responsible and ethically sound practices in the field of research utilizing AI technologies.International Journal of Language and Literary Studies150This paper investigates how AI-powered chatbots, often employed as research tools, can inadvertently facilitate unethical behavior, such as cheating in academic or experimental settings.International Journal of Language and Literary Studies["Benaissa Mohamed", "Youness Attou", "Mahmoud Seddik", "Nfissi Abdelhamid"]2025-01-23T00:00:00 f81ba06483440d3c8ca5b93b93130b2235e10813The FinTech industry, characterized by rapid innovation and digital transformation, necessitates adopting robust security measures to safeguard financial transactions and enhance operational efficiency. This paper examines the integration of artificial intelligence (AI) into security frameworks, focusing on automation strategies and advanced solutions to mitigate risks, improve user experience, and reinforce trust in modern financial systems. AI technologies such as predictive threat analysis, real-time fraud detection, and adaptive learning models are at the forefront of combating the dynamic and sophisticated nature of cyber threats. Predictive threat analysis facilitates the early identification of vulnerabilities, enabling proactive measures to thwart potential breaches. Real-time fraud detection leverages machine learning algorithms to analyze transactional patterns and detect anomalies, preventing unauthorized activities. Adaptive learning models continuously evolve with emerging threat landscapes, enhancing the resilience of security protocols. Beyond risk mitigation, artificial intelligence (AI)-driven systems optimize user experiences by streamlining authentication processes, minimizing false positives, and expediting secure transactions. The deployment of these technologies not only fortifies data integrity but also fosters greater trust among users by demonstrating an uncompromising commitment to cybersecurity. This paper presents empirical evidence and case studies highlighting the transformative impact of AI on financial security. By addressing critical vulnerabilities and enhancing system capabilities, AI establishes itself as a cornerstone of innovation in the FinTech sector, driving the creation of secure, adaptive, and user-focused financial ecosystems. Our findings underscore AI's pivotal role in shaping the future of resilient and trustworthy financial platforms.Asian journal of mathematics and computer research00By addressing critical vulnerabilities and enhancing system capabilities, AI establishes itself as a cornerstone of innovation in the FinTech sector, driving the creation of secure, adaptive, and user-focused financial ecosystems.Asian Journal of Mathematics and Computer Research["Anil Kumar Bayya"]2025-01-23T00:00:00 20407fc06ae953f2633f242fc8f9d7db36a24e26Purpose Despite great consensus on the positive impact of big-data-driven artificial intelligence (AI) analytics (BDAI) on a firm’s performance, it still appears to be a black box mechanism through which small and medium-sized enterprises (SMEs) strengthen their dynamic competencies to innovate and expand their global footprint. To fill this theoretical and empirical gap we examine the relationship between BDAI affordances, digital marketing capabilities (DMCs), value-chain innovation and international market goals.Design/methodology/approach The study incorporates the dynamic capability view an extension of the resource-based view and the knowledge-based view to empirically examine the primary data collected from marketing managers and executives of SMEs in cultural and creative industries utilizing Structural Equation Modeling (SEM) analysis.Findings The study highlights the significant role of BDAI affordances such as intelligent process recommendations, customer intelligence and market intelligence on DMCs, where DMCs significantly affect value-chain innovation and international market strategy both directly and indirectly.Research limitations/implications The study minimizes the gap in identifying the BDAI affordances to drive innovation and international market strategy in the context of SMEs in cultural and creative industries. Marketing managers can incorporate these findings to enhance their digital capabilities for competitive advantages in international markets.Originality/value The study proposes a holistic framework of BDAI affordances for the strategic use of digital resources and knowledge to transform digital capabilities into new forms of value to expand in the international market. These insights are robust and grounded in findings provided by marketing practitioners.International Marketing Review1110The study proposes a holistic framework of BDAI affordances for the strategic use of digital resources and knowledge to transform digital capabilities into new forms of value to expand in the international market.International Marketing Review["Zupan Zong", "Muhammad Azfar Anwar", "S. Khan", "F. Asmi", "Nazim Hussain"]2025-01-23T00:00:00 0906ee2abb748f93a729870666db9a72fb1eda35 The swift progress in Artificial Intelligence (AI), the Internet of Things (IoT), and Robotics is significantly transforming the landscape of digital agriculture, enabling unprecedented levels of efficiency, precision, and sustainability. This research article delves into the critical role played by library support systems in equipping academicians worldwide to effectively utilize these cutting-edge technologies for agricultural automation. By examining interpretations, presenting evidence, and exploring implications, the study highlights how library services are evolving to meet the demands of an increasingly digital and interconnected agricultural sector. It underscores the value of innovative library-driven initiatives in facilitating knowledge dissemination, fostering collaboration, and inspiring novel ideas that bridge the gap between traditional practices and advanced technological solutions. Furthermore, the paper outlines potential future directions for libraries to enhance their contributions to digital agriculture, emphasizing their synergistic relationship with technological advancements and their pivotal role as enablers of innovation and progress in the field. Key Words Digital Agriculture, Artificial Intelligence (AI), Internet of Things (IoT), Robotics, Automation in Agriculture, Library Support Systems, Interdisciplinary Collaboration, Precision Agriculture, Technological Infrastructure, Research Resources, Data-Driven Decision Making, Innovation in Agricultural Practices, Global Knowledge Networks, Emerging Technologies in Agriculture, Sustainability in Agriculture, Capacity Building in Research, Blockchain in Agriculture, Augmented Reality (AR), Knowledge Sharing, Community Engagement in Agriculture, Open Access Resources, Agricultural Data Management, Agricultural Policy Support, Smart Farming, Access to Information in Agriculture.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The paper outlines potential future directions for libraries to enhance their contributions to digital agriculture, emphasizing their synergistic relationship with technological advancements and their pivotal role as enablers of innovation and progress in the field.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Dr.M.C Subangi"]2025-01-23T00:00:00 166b1bf23c374b780482ac1f779bd7024035ab32The pharmaceutical sector has been playing a crucial role in the innovation, development, discovery and delivery of novel drugs. This article explains about the role of Artificial intelligence (AI), which has nowadays become an integral part of the pharmaceutical sector in drug discovery. It also includes different AI models like Deep Chem, RDKit, ChemBERTa, Auto Dock Vina and some other that has been used at different stages of drug discovery. The article gives us an insight about the contributions of AI in drug delivery, drug design, in biological product development and in medical devices.Journal of international research in medical and pharmaceutical sciences00The role of Artificial intelligence (AI), which has nowadays become an integral part of the pharmaceutical sector in drug discovery, is explained, which includes different AI models like Deep Chem, RDKit, ChemBERTa, Auto Dock Vina and some other that has been used at different stages of drug discovery.Journal of International Research in Medical and Pharmaceutical Sciences["Mamatha Kola", "Mukalla Uha", "Sabavat Anjali", "Phani Vennela", "Mohammad Bakhatwar", "Prathyusha Boddu", "Sree Lakshmi Namburi"]2025-01-23T00:00:00 a639036ce13725f3ff47e536614fe7d8a634b48eThe rapid development of artificial intelligence (AI) has an increasingly obvious impact on English teaching in China. Taking the teaching design of Unit 1 The Mission of Chinese Youths in the English Reading and Writing Course of Understanding Contemporary China (Volume 1) as an example, this paper implemented the teaching concept of "learning-centered and output-oriented", strived to reflect the teaching principles of AI empowering curriculum ideological and political education, integrating learning, and cultivating abilities, and explored how to help students truly understand contemporary China, master Chinese discourse, cultivate multiple abilities, and realize their youthful ideals under the new curriculum model.International Journal of Education and Humanities90This paper implemented the teaching concept of "learning-centered and output-oriented", strived to reflect the teaching principles of AI empowering curriculum ideological and political education, integrating learning, and cultivating abilities, and explored how to help students truly understand contemporary China.International Journal of Education and Humanities["Hui Zhang", "F. Zhao"]2025-01-23T00:00:00 3e70ed4ca6d8aa0504e4228b7fce836438c4e9f8 This paper situates the current iteration of artificial intelligence in a historical context to introduce the Technological Imaginary as a recent concept that can throw light upon why the technologies that we use have the form that they do. The Technological Imaginary is explored here in relation to the post-war development of AI in a summary of Accidental Machines to highlight the imaginary aspect of computer-based technological solutions to the creation of human intelligence. It concludes by suggesting how discussions of AI might benefit from recognizing the power of human agency in technological form and the visions for its future.Leonardo: Journal of the International Society for the Arts, Sciences and Technology00The Technological Imaginary is explored here in relation to the post-war development of AI in a summary of Accidental Machines to highlight the imaginary aspect of computer-based technological solutions to the creation of human intelligence.Leonardo["Michael Punt"]2025-01-24T00:00:00 3c33eea7a066e6b722aa475f8e8cffa6fd45528ePurposeThe purpose of this review is to examine the current state-of-the-art in artificial intelligence (AI) implementations within library settings across Southeast Asia.Design/methodology/approachThe study uses the AI Library Services Innovative Conceptual Framework (AI-LSICF) to evaluate the AI initiatives in Southeast Asian libraries. Sources include relevant libraries and association’s websites, mainstream newspapers across Southeast Asia, together with academic papers published between 2019 and 2024, with a focus solely on English-language literature.FindingsMost of the Southeast Asian libraries are in the decision and implementation stages in utilising AI technologies into library operations. It is evident that most of the libraries have made the decision to embrace AI techniques in the workplace and have started to implement the AI-enabled applications. Nevertheless, those implementations are not yet comprehensive and most of the projects are still in the trial stage. This suggests a unanimous decision concerning the use of AI in the libraries across the region has not been reached. Librarians may still face challenges and concerns in adopting AI, including resource constraints, application maintenance, staff reluctance, staff training, data security concerns and more.Research limitations/implicationsA limitation of this study is its focus on completed and published projects, due to limited access to ongoing or unpublished initiatives. Non-English publications were excluded which may have omitted relevant studies and insights from non-English-speaking countries.Practical implicationsThis paper seeks to address the gap by conducting a review of the current landscape of AI applications within libraries across Southeast Asia. Its aim to provide valuable insights for Southeast Asian libraries which seek to leverage AI advancements, ultimately supporting more user-centric and technologically adept library services.Originality/valueThe originality of this paper lies in its unique perspectives on library settings in Southeast Asia, showcasing successful projects while also pinpointing areas and countries in need of further development.Reference Services Review680This paper seeks to address the gap by conducting a review of the current landscape of AI applications within libraries across Southeast Asia, and provides valuable insights for Southeast Asian libraries which seek to leverage AI advancements, ultimately supporting more user-centric and technologically adept library services.Reference Services Review["Cong Xu", "Sandie Loo"]2025-01-24T00:00:00 db3924e4e1f4ea8bdf8aa0f8f4eef8a7261caa37Social media has become an indispensable part of modern life, changing people's communication methods and information acquisition habits. However, with the expansion of user scale, social platforms are facing challenges such as information overload and flood of negative content. In this context, the rapid development of artificial intelligence (AI) provides new solutions for social media operation and user experience optimization. AI technology can not only enhance the personalized recommendation of information and user interaction experience, but also effectively manage negative content and maintain a healthy environment of the platform. The purpose of this paper is to deeply explore how AI reshapes the interaction and user experience of social media, analyze its role in improving user stickiness, optimizing information management and enhancing user satisfaction, and look forward to the future development trend.Journal of Global Humanities and Social Sciences00How AI reshapes the interaction and user experience of social media is explored, its role in improving user stickiness, optimizing information management and enhancing user satisfaction is analyzed, and the future development trend is looked forward to.Journal of Global Humanities and Social Sciences["Danqing Wang"]2025-01-24T00:00:00 8ffe3b7f02c762d4db50a0c907fa4151910c9d75Next-generation information technology (IT) and artificial intelligence (AI) can support environmental improvement, climate change response, and the conservation and efficient use of resources for green transformation, potentially impacting the Sustainable Development Goals (SDGs). This study estimates the impact of AI on five SDGs using provincial panel data from 2006 to 2018. The estimation results indicate that AI makes a significant contribution to sustainable development, a finding that has been confirmed through a series of robustness tests. Furthermore, the mechanistic analysis demonstrates that AI primarily promotes sustainable development by enhancing energy structure and technological innovation. The greater the reduction in dependence on fossil fuels and the higher the degree of technological innovation, tthe greater the effectiveness of AI in promoting sustainable development. Furthermore, the regional heterogeneity test revealed that the effect  in enhancing sustainability achieved by AI is most effective in the central and western regions, with the eastern region following closely behind.Journal of Information Systems Engineering & Management460The estimation results indicate that AI makes a significant contribution to sustainable development, a finding that has been confirmed through a series of robustness tests, and the mechanistic analysis demonstrates that AI primarily promotes sustainable development by enhancing energy structure and technological innovation.Journal of Information Systems Engineering and Management["Yufeng Wang1", "Xinyu Zhang", "Yu Wang2", "Junhui Ren3", "Weilun Huang4"]2025-01-24T00:00:00 6a9029804d5645df46892b35f738540f9c3227c5Objective: The objective of the study investigates the extent to which specified programs and machines are activated and achieve business sustainability through sustainable development goals (SDG-8,9,12,17) in Industry 6.0. This report analyses the current situation from 2013 to 2024 and assesses the influence of Industry 6.0 on the oil and gas sector within the Gulf Cooperation Council (GCC).   Theoretical Framework: Digital transformation progresses from Industry 5.0 to Industry 6.0 globally within the oil and gas sector, with only certain regions achieving totally autonomous operation, while the majority are endeavouring to attain such autonomy. The industrial revolution is realized through Industry 6.0, which is accomplished by implementing autonomous operations utilizing robotics, Artificial Intelligence (AI), quantum computing, machine learning (ML), the Industrial Internet of Things (IIoT), blockchain technology, and cloud computing within the oil and gas sector. Based on the study, proposed model proposes with autonomous operation through Industry 6.0 techniques and business sustainability.   Method: Researchers have gathered numerous empirical articles, case studies and book chapters to thoroughly investigate the topic and its domains. This study employs a literature review methodology, concentrating mostly on the oil and gas service industries within GCC nations.   Results and Discussion: Researchers discovered that the oil and gas service industry encounters difficulties in achieving fully automated procedures devoid of human interaction Industry 6.0 will attain corporate sustainability via total autonomy. The second scenario involves artificial intelligence, quantum computing, and emotional intelligence (EI) enabling Industry 6.0 to attain favourable results.   Research Implications: The research study suggests that the implementation of Industry 6.0 in the oil and gas service sector remains mostly at a conceptual stage in most areas.   Originality/Value: This study contributes to engineering services in oil and gas industry, entrepreneur’s emotional intelligence and autonomous operation during the defined period were not evaluated and it added value in the engineering service sector to mitigate their issues to have better business sustainability.Journal of Lifestyle and SDGs Review200The research study suggests that the implementation of Industry 6.0 in the oil and gas service sector remains mostly at a conceptual stage in most areas, and it will attain corporate sustainability via total autonomy.Journal of Lifestyle and SDGs Review["Marirajan Murugan", "M. Prabadevi"]2025-01-24T00:00:00 c59d714aeb6a600a5e05a28c192344184cfdbb15 Purpose The main purpose of this study is to enhance knowledge regarding the early stages of planning for and adopting artificial intelligence (AI) in governmental public procurement. While there are numerous studies on AI and procurement in private companies, there is limited information on AI and public procurement. Design/methodology/approach The empirical data consists of information obtained from 18 semi-structured interviews with procurement managers and individuals involved in the development of procurement at governmental agencies. Additionally, a workshop was conducted with the respondents to discuss and validate the study’s findings. Findings Findings indicate a generally low level of AI maturity in previous research and within the investigated governmental agencies. The perceived benefits of AI primarily revolve around improved operational capabilities, potential for certain process efficiencies and the ability to enhance monitoring through AI. Various challenges related to organizational, process, technological and data management were highlighted. Findings also indicate that perceived benefits and value created by AI can be viewed from a short-term perspective to a long-term perspective. Social implications The study provides insights into societal values that can be achieved using AI in public procurement. Originality/value This study provides a new perspective on AI in public procurement by focusing on governmental agencies. It explores the perceived benefits, interests and challenges associated with AI implementation in public procurement. Furthermore, this study discusses the potential outcomes of incorporating AI in public procurement and the impact it may have on the values created by the public service, both short- and long term. Journal of Public Procurement330The perceived benefits of AI primarily revolve around improved operational capabilities, potential for certain process efficiencies and the ability to enhance monitoring through AI, and various challenges related to organizational, process, technological and data management were highlighted.Journal of Public Procurement["Per Erik Andersson", "Katarina Arbin", "Christopher Rosenqvist"]2025-01-24T00:00:00 1bce946a461cecedd76324cd2d9f8e1ff9a21487Introduction. Today, the traditional model of medical care is being supplemented and partially replaced by new forms of its implementation. Thus, technologies based on artificial intelligence take over the functions of diagnosis, treatment, screening and monitoring of chronic diseases.Aim. To develop a medical methodology for remote questionnaire screening of chronic kidney disease in young people to optimize their diagnosis.Materials and methods. The study involved 3,155 students aged 19.6 ± 1.5 years, of whom 46.9% were men and 53.1% were women. During the medical examination, all participants used a remote questionnaire screening.Results. A low degree of risk was detected in 57.4%, an average in 30.9%, and a high in 11.7% of the subjects. The patients with the highest frequency are concerned about complaints from the endocrine (28.9%), digestive (21.8%), respiratory (21.1%), cardiovascular (20.1%) and oncological alertness (8.1%). The presence of FR in two or more pathology profiles was determined in 75.7% of the examined patients. Among the most common FR are nine related to the self-assessment of the emotional and personal sphere. 96.6% of the surveyed and 91.7% of the medical staff are satisfied with the telemedicine system.Conclusions. 1. The use of remote questionnaire screening of HCNH provided wide coverage and high satisfaction with medical services. 2. The system allocates a contingent of subjects with high, medium and low risk, as well as people with critical disabilities in need of priority assistance. 3. The combination of data from anamnestic remote examination and clinical examination improves the quality of medical decision-making and reduces its subjective component. 4. The use of statistical methods has shown good effectiveness of the integrated assessment of health and satisfactory for the detection of chronic kidney disease. 5. The use of remote questionnaire screening of HRH in young people reduces treatment costs and improves the quality of life of patients.Meditsinskiy sovet = Medical Council240The use of remote questionnaire screening of HCNH provided wide coverage and high satisfaction with medical services, and the use of statistical methods has shown good effectiveness of the integrated assessment of health and satisfactory for the detection of chronic kidney disease.Meditsinskiy sovet = Medical Council["P. Seliverstov"]2025-01-24T00:00:00 00364ac4951b7992ddf7e521b429f00d7d74c7d6Purpose This study examines the transformative role of Artificial Intelligence in leadership and management development, investigating how AI technologies enhance decision-making processes, talent management, and strategic planning within organizations. The research explores the integration of AI into leadership development programs while maintaining essential human elements of leadership. Design/methodology/approach The study employs a theoretical framework analysis focusing on adaptive learning, predictive analytics, natural language processing, and cognitive computing in leadership development. It examines implementation cases from leading organizations and analyzes current applications through case studies of successful AI integration in leadership programs. Findings Organizations implementing AI-driven leadership development programs demonstrate significant improvements in management effectiveness and decision-making capabilities. The research reveals that successful integration requires balancing technological innovation with human-centered leadership principles, while addressing potential biases in AI systems through regular monitoring and audits. Practical implications The study provides implementation strategies for organizations to integrate AI in leadership development, emphasizing the importance of robust data governance frameworks, hybrid approaches combining AI analytics with traditional mentoring, and the need for structured decision-making frameworks that merge AI-driven insights with human judgment. Social implications The use of AI in leadership development has the potential to democratize access to learning opportunities, allowing leaders from diverse backgrounds to receive tailored training and insights. This could contribute to reducing inequalities in professional development opportunities, supporting UN Sustainable Development Goal (SDG) 10 on reducing inequalities. The use of AI in leadership development has the potential to democratize access to learning opportunities, allowing leaders from diverse backgrounds to receive tailored training and insights. This could contribute to reducing inequalities in professional development opportunities, supporting UN SDG 10 on reducing inequalities. Originality/value This research contributes to the emerging field of AI-augmented leadership by providing a comprehensive analysis of how organizations can effectively leverage AI technologies while maintaining authentic human connection in leadership development. It offers novel insights into the future direction of leadership development through immersive learning environments. Development and Learning in Organizations: an international journal30This research contributes to the emerging field of AI-augmented leadership by providing a comprehensive analysis of how organizations can effectively leverage AI technologies while maintaining authentic human connection in leadership development.Development and Learning in Organizations: An International Journal["Patricia Vargas Portillo"]2025-01-24T00:00:00 2ac4a9746a0dd30755632e2dc0c40b8a05c9073aHumanity stands at a pivotal moment of technological revolution, with artificial intelligence (AI) reshaping fields traditionally reliant on human cognitive abilities. This transition, driven by advancements in artificial neural networks, has transformed data processing and evaluation, creating opportunities for addressing complex and time-consuming tasks with AI solutions. Convolutional networks (CNNs) and the adoption of GPU technology have already revolutionized image recognition by enhancing computational efficiency and accuracy. In radiology, AI applications are particularly valuable for tasks involving pattern detection and classification; for example, AI tools have enhanced diagnostic accuracy and efficiency in detecting abnormalities across imaging modalities through automated feature extraction. Our analysis reveals that neuroimaging and chest imaging, as well as CT and MRI modalities, are the primary focus areas for AI products, reflecting their high clinical demand and complexity. AI tools are also used to target high-prevalence diseases, such as lung cancer, stroke, and breast cancer, underscoring AI’s alignment with impactful diagnostic needs. The regulatory landscape is a critical factor in AI product development, with the majority of products certified under the Medical Device Directive (MDD) and Medical Device Regulation (MDR) in Class IIa or Class I categories, indicating compliance with moderate-risk standards. A rapid increase in AI product development from 2017 to 2020, peaking in 2020 and followed by recent stabilization and saturation, was identified. In this work, the authors review the advancements in AI-based imaging applications, underscoring AI’s transformative potential for enhanced diagnostic support and focusing on the critical role of CNNs, regulatory challenges, and potential threats to human labor in the field of diagnostic imaging.Diagnostics00The authors review the advancements in AI-based imaging applications, underscoring AI’s transformative potential for enhanced diagnostic support and focusing on the critical role of CNNs, regulatory challenges, and potential threats to human labor in the field of diagnostic imaging.Diagnostics["R. Obuchowicz", "Julia Lasek", "Marek Wodzi\u0144ski", "A. Pi\u00f3rkowski", "Micha\u0142 Strzelecki", "Karolina Nurzynska"]2025-01-24T00:00:00 6b9ca6d1c77f255e215cc835f695f7422b6153ccPurposeThis research aims to investigate the impact of artificial intelligence (AI) adoption on the innovation dynamics of Chinese manufacturing enterprises, with a specific focus on the intricate interplay with the labor structure.Design/methodology/approachLeveraging panel data of listed companies from 2010 to 2022, this study employs the two-way fixed effects (TWFE) model to examine the influence of AI adoption on Chinese manufacturing companies' innovativeness. Firm-level AI adoption is measured by constructing a three-dimensional attention, application and absorption index.FindingsThe results indicate that (1) AI adoption has a positive impact on both internal innovation capability and external innovation interaction, (2) AI adoption has dual effects on the education and skill structure of labor in manufacturing enterprises and (3) enterprises with a highly educated and skilled workforce exhibit a stronger influence of AI adoption on innovativeness.Originality/valueThis research contributes to the academic and practical discourse by unveiling the underlying mechanisms of AI affecting innovation and introducing a new measurement of the AI adoption index. The findings emphasize the need for a highly educated and skilled workforce to navigate the complexities of AI-driven innovation, offering valuable theoretical and practical implications for policymakers and enterprises.Industrial Management &amp; Data Systems810The results indicate that the need for a highly educated and skilled workforce to navigate the complexities of AI-driven innovation, offering valuable theoretical and practical implications for policymakers and enterprises.Industrial Management &amp; Data Systems["Qinqin Wu", "Sikander Ali Qalati", "Kayhan Tajeddini", "Haijing Wang"]2025-01-24T00:00:00 6f816d47adad66516cb6a438523e93170f69ebe3OBJECTIVES The development of valuable artificial intelligence (AI) tools to assist with ultrasound diagnosis depends on algorithms developed using high-quality data. This study aimed to test the intra- and interobserver agreement of a proposed image-quality scoring system to quantify the quality of gynecological transvaginal ultrasound (TVS) images, which could be used in clinical practice and AI tool development. METHODS A proposed scoring system to quantify TVS image quality was created following a review of the literature. This system involved a score of 1-4 (2 = poor, 3 = suboptimal and 4 = optimal image quality) assigned by a rater for individual ultrasound images. If the image was deemed inaccurate, it was assigned a score of 1, corresponding to 'reject'. Six professionals, including two radiologists, two sonographers and two sonologists, reviewed 150 images (50 images of the uterus and 100 images of the ovaries) obtained from 50 women, assigning each image a score of 1-4. The review of all images was repeated a second time by each rater after a period of at least 1 week. Mean scores were calculated for each rater. Overall interobserver agreement was assessed using intraclass correlation coefficient (ICC), and interobserver agreement between paired professionals and intraobserver agreement for all professionals were assessed using weighted Cohen's kappa and ICC. RESULTS Poor levels of interobserver agreement were obtained between the six raters for all 150 images (ICC, 0.480 (95% CI, 0.363-0.586)), as well as for assessment of the uterine images only (ICC, 0.359 (95% CI, 0.204-0.523)). Moderate agreement was achieved for the ovarian images (ICC, 0.531 (95% CI, 0.417-0.636)). Agreement between the paired sonographers and sonologists was poor for all images (ICC, 0.336 (95% CI, -0.078 to 0.619) and 0.425 (95% CI, 0.014-0.665), respectively), as well as when images were grouped into uterine images (ICC, 0.253 (95% CI, -0.097 to 0.577) and 0.299 (95% CI, -0.094 to 0.606), respectively) and ovarian images (ICC, 0.400 (95% CI, -0.043 to 0.669) and 0.469 (95% CI, 0.088-0.689), respectively). Agreement between the paired radiologists was moderate for all images (ICC, 0.600 (95% CI, 0.487-0.693)) and for their assessment of uterine images (ICC, 0.538 (95% CI, 0.311-0.707)) and ovarian images (ICC, 0.621 (95% CI, 0.483-0.728)). Weak-to-moderate intraobserver agreement was seen for each of the raters with weighted Cohen's kappa ranging from 0.533 to 0.718 for all images and from 0.467 to 0.751 for ovarian images. Similarly, for all raters, the ICC indicated moderate-to-good intraobserver agreement for all images overall (ICC ranged from 0.636 to 0.825) and for ovarian images (ICC ranged from 0.596 to 0.862). Slightly better intraobserver agreement was seen for uterine images, with weighted Cohen's kappa ranging from 0.568 to 0.808 indicating weak-to-strong agreement, and ICC ranging from 0.546 to 0.893 indicating moderate-to-good agreement. All measures were statistically significant (P < 0.001). CONCLUSION The proposed image quality scoring system was shown to have poor-to-moderate interobserver agreement and mostly weak-to-moderate levels of intraobserver agreement. More refinement of the scoring system may be needed to improve agreement, although it remains unclear whether quantification of image quality can be achieved, given the highly subjective nature of ultrasound interpretation. Although some AI systems can tolerate labeling noise, most will favor clean (high-quality) data. As such, innovative data-labeling strategies are needed. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.Ultrasound in Obstetrics and Gynecology260The intra- and interobserver agreement of a proposed image-quality scoring system to quantify the quality of gynecological transvaginal ultrasound (TVS) images, which could be used in clinical practice and AI tool development was tested.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology["A. Deslandes", "J. Avery", "H-T Chen", "M. Leonardi", "S. Knox", "G. Lo", "R. O\u2019Hara", "G. Condous", "M. L. Hull"]2025-01-24T00:00:00 0865ba7e41d106a166b33b7524849e4ea756b747AIM This integrative review critiques and synthesizes current research on nursing faculty and students' attitudes, perceptions, and behavioral intentions toward artificial intelligence (AI)-based tools in nursing education. BACKGROUND AI's rapid integration into health care offers transformative potential in nursing across clinical care, education, policy, and research. METHOD Following Whittemore and Knafl's methodology, Pubmed, CINAHL, and ERIC were searched for studies written in English assessing attitudes, perceptions, and behavioral intentions of nursing students and faculty regarding AI use in nursing education. RESULTS Six quantitative studies encompassing 2,430 participants across five countries were included. They revealed generally positive attitudes toward the use of AI in nursing education. Only one study included faculty. CONCLUSION A logical next step is to compare and contrast student and faculty perceptions of using AI. Generative AI tools must be studied within nursing education to allow for informed integration and the development of appropriate training programs.Nursing Education Perspectives200This integrative review critiques and synthesizes current research on nursing faculty and students' attitudes, perceptions, and behavioral intentions toward artificial intelligence (AI)-based tools in nursing education to allow for informed integration and the development of appropriate training programs.Nursing education perspectives["Mollie Ostick", "Bette Mariani", "Catherine Lovecchio", "Helene Moriarty"]2025-01-24T00:00:00 b37f7cfd8c0768e8b819b11e095e1571c061453cThe relevance of the research is determined by the need to optimize operational processes in hotels, improve the quality of service, minimize the human factor, strengthen competitiveness and adapt to changes in the hotel services market. In the context of the rapid development of the hospitality industry and increasing competition, the introduction of modern technologies is becoming necessary to improve the efficiency of enterprises in the hotel sector.The purpose of the article is to provide an economic justification of the problems and prospects of using artificial intelligence technologies in the hotel industry.The objectives of the article include analyzing the role of artificial intelligence in improving the quality of accommodation facilities, popularizing services and goods in the hospitality industry, exploring the possibilities of using artificial intelligence to increase the competitiveness of hotel enterprises, and developing practical recommendations for the development of the hospitality industry in the Russian Federation.Methodology. In the process of research aimed at studying scientific publications, works of domestic experts, statistical materials and other relevant sources, the author of this work applied a set of general scientific methods, including analysis, observation, description, comparison, graphical visualization and synthesis.Results. During the study of the prospects for the development of the hospitality industry with the help of artificial intelligence, recommendations were formed on the effective use of AI technologies in the Russian hotel business.Conclusions. According to the results of the study, the authors conclude that the development of artificial intelligence technologies in the hotel business can contribute to the comprehensive development of the hospitality industry in the Russian Federation. The introduction of artificial intelligence (AI) into the hotel business opens up new opportunities for optimizing operational processes, improving the quality of service, minimizing the human factor and enhancing competitiveness.Proceedings of the Southwest State University. Series: Economics. Sociology. Management60The authors conclude that the development of artificial intelligence technologies in the hotel business can contribute to the comprehensive development of the hospitality industry in the Russian Federation.Proceedings of the Southwest State University. Series: Economics. Sociology. Management["M. A. Smirnova"]2025-01-24T00:00:00 194d17a9dd4ccccc12ad2771b9e58c7473dc59cfnpj Digit. Medicine130Artificial intelligence is increasingly permeating the fabric of medicine, but getting full benefits will likely require fundamental changes in practice and it may be necessary to ensure that AI’s ambitious promises translate into real-life improvement.NPJ Digital Medicine["Luciana D\u2019Adderio", "David W. Bates"]2025-01-24T00:00:00 29f3ba4737d690fbb0585d1094f35517db2f7bf8Artificial intelligence is not a new concept. However, it has reached an important point with technological development. Today, there are many software developed using artificial intelligence and various application areas where they are used. Generative artificial intelligence, one of these areas, is a technology in machine learning of artificial intelligence, aiming to generate new content by training on large data sets. Generative artificial intelligence is used in fields such as health, business, finance, e-commerce, academic studies, and R&D. This study evaluates the use of generative artificial intelligence applications in the academic field. In this context, the differences and similarities between texts generated by ChatGPT, Claude Sonet, and Google Gemini generative artificial intelligence applications and texts prepared by human intelligence were analyzed regarding subject integrity, language, ethics, and plagiarism rate. Descriptive content analysis, one of the qualitative methods, was used in the study. As a result, it was concluded that texts generated by generative artificial intelligence applications and texts prepared by human intelligence are similar in subject integrity and content, and plagiarism rates of texts generated by generative artificial intelligence vary according to language.Fırat Üniversitesi Sosyal Bilimler Dergisi270It was concluded that texts generated by generative artificial intelligence applications and texts prepared by human intelligence are similar in subject integrity and content, and plagiarism rates of texts generated by generative artificial intelligence vary according to language.Fırat Üniversitesi Sosyal Bilimler Dergisi["\u0130smail Yo\u015fumaz"]2025-01-24T00:00:00 06b2396550addb4d1e43a511f543b11081a6647dSimple Summary Diseases affecting the pancreas and bile ducts can cause serious health implications and are often challenging to diagnose because they rely on high-quality imaging and specialized procedures performed by skilled doctors. Artificial intelligence (AI) is already being used in some areas of endoscopy, but its role in diagnosing pancreaticobiliary diseases is still in its early stages. In this review, we explore how AI can be applied to advanced techniques like endoscopic ultrasound and cholangioscopy, highlighting its potential advantages, current challenges, and the opportunities it offers for the future. Our goal is to provide insights into how AI might improve accuracy and efficiency to these procedures, ultimately benefiting patients and shaping the future of pancreaticobiliary care.Cancers760How AI can be applied to advanced techniques like endoscopic ultrasound and cholangioscopy is explored, highlighting its potential advantages, current challenges, and the opportunities it offers for the future.Cancers["Aryan Jain", "Mayur Pabba", "Aditya Jain", "Sahib Singh", "Hassam Ali", "Rakesh Vinayek", "Ganesh Aswath", "Neil Sharma", "Sumant Inamdar", "A. Facciorusso"]2025-01-24T00:00:00 add06ba07a52a136d0844bdaaf15a206457a5ba4A wide range of industries, including banking and financial markets, education, supply chains, manufacturing, retail and e-commerce, and healthcare, have benefitted from the application of Artificial Intelligence (AI) to varied degrees and in different forms. Recent years have seen a revolutionary change in medical science, with ground-breaking findings changing the face of healthcare.Collective Journal of Robotics and AI230Collective Journal of Robotics and AI["Benyeogor Goodwill Sunday"]2025-01-24T00:00:00 c3e81d4062c9c42994798b5eea377550fffe948fArtificial intelligence is quoted as the revolutionary development that attracted the attention of a diverse range of domains. It is considered as the general-purpose technology that experts integrated with their respective domains. Likewise, militaries attempted to militarize the artificial intelligence in order to score relative gains in contrast to other states. Military application of artificial intelligence evolves warfare; introduces autonomous weapons; optimizes logistics; and enable AI technologies in intelligence, surveillance, reconnaissance and trainings. The implications of militarization of artificial intelligence are multifaceted and highlights risks associated with it. The risks involve arms race, ethical concerns, accidental conflicts, and influence of commercial companies in the defense sector. Strategic theory has been taken as a theoretical framework to comprehensively understand the research problem. Strategic theory is proposed as the comprehensive approach providing holistic thinking to the defense communities regarding the management of state’s resources in the pursuit of achieving policy ends. The militarization of artificial intelligence takes into account the advance technologies of AI and integrate them with the military domain to score advantage in the battlefield. Militaries are investing state resources in such a manner that integrate artificial intelligence with the military domain to score superiority among great powers.Social Sciences Spectrum180The militarization of artificial intelligence takes into account the advance technologies of AI and integrate them with the military domain to score advantage in the battlefield.Social Sciences Spectrum["Lal Khan Niazi"]2025-01-24T00:00:00 df74c4827f90a4fd0dac66cf0b36aff1542e0315With the rapid development of artificial intelligence, it has been widely used in all aspects of life. However, the relevant legal issues caused by artificial intelligence are also increasingly prominent. Among these legal issues, the protection of copyright of Artificial intelligence generated content (AIGC) has become a hot topic from all walks of life. People mainly focus on whether AI should be protected by copyright. This paper argues this problem on two sides. On one hand, this paper will introduce the legitimacy of the copyright protection of the AIGC including institutional suitability (taking Chinas first AI infringement case as an example) and theoretical legitimacy. On the other hand, this paper takes the feasibility of copyright protection of AIGC in consideration, which is divided into three parts: The suitability of the object, the suitability of the subject, and the attribution of rights. This paper also introduces three different methods to the attribution of AIGC copyright, including virtual legal personality theory, artificial intelligence programming designer for the author theory, and AI programming designer and users for the co-author theory. In conclusion, this paper firmly suggests that the AIGC should be protected by copyright.Lecture Notes in Education Psychology and Public Media00The legitimacy of the copyright protection of the AIGC including institutional suitability including institutional suitability and theoretical legitimacy are introduced and it is suggested that the AIGC should be protected by copyright.Lecture Notes in Education Psychology and Public Media["Xiaomin Duan"]2025-01-24T00:00:00 a1d83a9c46f3e5f55a36991e0846ef8a90e4e786Philosophy &amp; Technology180It is suggested that instead of trusting AI systems, the authors should strive to make them reliable, and a threefold challenge is applied to two recent accounts that defend the possibility of trust in AI systems.Philosophy &amp; Technology["Christian Budnik"]2025-01-24T00:00:00 140551dd3290acc2148e46475dc35c3c972eed09The outbreak of COVID-19 also revealed major inadequacies in the global healthcare systems, allocation of resources, and coping mechanisms in the event of a pandemic. The SARS-CoV-2 pandemic could not be addressed effectively through conventional techniques, including physical contact tracing and manual data analysis. This study investigates the transformative potential of artificial intelligence (AI) in enhancing pandemic preparedness and response by focusing on three key areas: prediction of outbreaks, distribution of resources, and vaccines. Epidemiological reports, mobility data, and data from the healthcare system were used in the AI models, which showed a higher accuracy of outbreak prediction with R² = 0.92. The resource allocation model enhanced equity by attaining an Equity Index of 0.87, with an 85% resource utilization, demonstrating that the right resources were allocated at the right place and time. The higher effectiveness of vaccine distribution simulations cut quantity disparity to 10%, thus improving fairness and logistical organization. These discoveries show that AI is central to solving global health issues, improving healthcare accessibility, and ensuring timely treatment. However, there are still some ethical concerns, such as data protection and fairness of the algorithms for large-scale implementation. Thus, this study calls for integrating artificial intelligence systems into the strategies against the pandemic as envisioned by the WHO to improve preparedness and mitigate the socioeconomic cost of the subsequent pandemics.Journal of Management World270This study calls for integrating artificial intelligence systems into the strategies against the pandemic as envisioned by the WHO to improve preparedness and mitigate the socioeconomic cost of the subsequent pandemics.Journal of Management World["Sadia Sharmin", "Barna Biswas", "Anamika Tiwari", "Md Kamruzzaman", "Mohammad Abu Saleh", "Jannatul Ferdousmou", "Mahafuj Hassan"]2025-01-24T00:00:00 0126fc574a7b4584079ee834bfb68decad1d12eeThe apparent connection of art to trickery and deceit has been critically examined since antiquity, mainly framed as a tautology. The fraudulence of art, except manipulating perception, is a deliberate act against a receiver. Artistic trickery is seen as the ancestor of all artificial practices. Mimicry constitutes the essence of art and A.I., and when defined in Platonic terms, a series of inconsistencies arise: functional errors, ambivalent signals, pseudocodes, even fake faults, or misleading strategies. A novel conceptual framework might reconceptualise artificial intelligence (A.I.) as a type of prey organism: a creation that, beyond mere adaptation, subterraneously manipulates the very environment of its existence: its creators. As A.I. evolves in complexity, its behaviour increasingly mirrors the image of a biological entity, whose survival strategy lies in eliciting calculated responses from its developers.Herança140Herança["Ioannis Melanitis"]2025-01-24T00:00:00 4c83fc211b81cd60d14b3c67092ee34cea46d13aArtificial intelligence (AI) is the new medical hot topic, being applied mainly in specialties with a strong imaging component. In the domain of gynecology, AI has been tested and shown vast potential in several areas with promising results, with an emphasis on oncology. However, fewer studies have been made focusing on urogynecology, a branch of gynecology known for using multiple imaging exams (IEs) and tests in the management of women’s pelvic floor health. This review aims to illustrate the current state of AI in urogynecology, namely with the use of machine learning (ML) and deep learning (DL) in diagnostics and as imaging tools, discuss possible future prospects for AI in this field, and go over its limitations that challenge its safe implementation.Diagnostics1050This review aims to illustrate the current state of AI in urogynecology with the use of machine learning (ML) and deep learning (DL) in diagnostics and as imaging tools, discuss possible future prospects for AI in this field, and go over its limitations that challenge its safe implementation.Diagnostics["Maria Beatriz Macedo de Oliveira", "F. Mendes", "M. Martins", "P. Cardoso", "Jo\u00e3o Fonseca", "Teresa Mascarenhas", "M. Saraiva"]2025-01-24T00:00:00 8d5d90eeae29a0d99c8e956a88a3a0b79ff9a562This paper focuses on how AI and ML have changed decisions in retailing, healthcare, financing, and manufacturing careers. They demonstrate how AI is used in supply chain management to support the decision-making process by making forecasts, processing data, and optimizing operations, leading to higher efficiency, decreased costs, and increased customer satisfaction. Thus, the research incorporates quantitative and qualitative approaches, such as surveys and interviews with key stakeholders, and employs statistical and content analysis methods. Significant outcomes include a 20% enhancement of forecasting precision reduction while the operational cost decreases by 21 percent. Nonetheless, the research also discovers an essential issue that employs complex challenges that embrace high-cost implementation, resistance from the workforce, allowance of data privacy, and bias besides algorithms. Some are ethical concerns, and the importance of their regulation is noted. While adopting the decision theory and systems thinking perspectives, this research paper highlights the necessity of effectively and adequately implementing AI into an organization permanently to achieve more benefits. The following are realizable out-of-the-box solutions that the study suggests, including audiences for employees, data protection for compliance, and conscientization of fairness in AI algorithms. Future directions include situations where these applications are to be broadened to weigh on ethical issues and to encourage optimal technological fairness that will, in turn, ensure sustainable business improvement and innovation.Journal of Management World340This research paper highlights the necessity of effectively and adequately implementing AI into an organization permanently to achieve more benefits and suggests realizable out-of-the-box solutions, including audiences for employees, data protection for compliance, and conscientization of fairness in AI algorithms.Journal of Management World["Partha Chakraborty", "Kazi Bushra Siddiqa", "Habiba Rahman", "Md Alamgir Miah", "Niropam Das", "Mohammad Abdul Goffer", "Sachin Das"]2025-01-24T00:00:00 097217b51f5e24f2f094176ff57ffe0dcd1abc38Penelitian tindakan kelas (classroom action research) dalam pembelajaran bahasa Inggris adalah salah satu upaya meningkatkan presetasi belajar. Penelitian tindakan kelas masih perlu membutuhkan perhatian khusus agar objektif pembelajaran dapat tercapai dengan baik. Maka kegiatan pengabdian kepada masyarakat (PKM) ini bertujuan untuk mengoptimalkan penelitian tindakan kelas bagi para guru di SMA Negeri 2 Siborongborong. PKM ini dilaksanakan dengan metode pendidikan kepada masyarakat yang berjalan melalui workshop denga pilot studi pada mata pelajaran bahasa Inggris. Pelaksanaan dimulai dengan adanya information sharing session oleh narasumber atau dengan metode seminar. Kemudian adanya tahap diskusi untuk pendalaman materi dan dilanjut dengan pemantapan materi lewat pembuatan atau desain penelitian tindakan kelas sesuai dengan latar belakang dalam pembelajaran oleh masing-masing guru mata pelajaran. PKM ini dilaksanakan oleh Dosen Fakultas Keguruan dan Ilmu Pendidikan (FKIP) Universitas HKBP Nommensen Pematangsiantar kepada 57 orang guru SMA Negeri 2 Siborongborong sebagai peserta. Pelaksanaan PKM ini didasari oleh informasi yang diperoleh dari mitra melalui interview bahwa mitra membutuhkan pembekalan penelitian tindakan kelas terhadap para guru untuk meningkatkan inovasi pembelajaran. Pada pelaksanaan PKM terlaksana dengan baik melalui metode focus group discussion (FGD) sebagai analysis data. Berdasarkan diskusi antar sesama peserta dan narasumber atas integrasi AI dalam penelitian tindakan kelas. PKM ini dipandang perlu untuk meningkatkan prestasi belajar dalam mata pelajaran bahasa Inggris. Maka PKM ini dapat disimpulkan terlaksana dengan baik dan dapat mengoptimalkan penerapan AI dalam penelitian tindakan kelas melalui kinerja para peserta dalam mendesain penelitian tindakan kelas. Pelaksanaan PKM ini disambut hangat oleh peserta dan memberikan dampak yang baik terbukti dengan permintaan para peserta untuk tindakan PKM lanjutan dengan topik model-model pembelajaran innovatif sehingga meningkatkan prestasi belajar dalam mata pelajaran bahasa Inggris.Bima Abdi: Jurnal Pengabdian Masyarakat140Bima Abdi: Jurnal Pengabdian Masyarakat["Dumaris E. Silalahi", "Lydia Purba", "Rick Hunter Simanungkalit", "A. Siagian", "P. S. Sihombing", "Basar Lolo Siahaan", "R. Sipayung"]2025-01-24T00:00:00 511ec2fe1842a0dfb3006359073f289f24a59859SN Computer Science350SN Computer Science["Adi Nur Cahyono", "Masrukan Masrukan", "Wakhid Fitri Albar", "Z. Lavicza", "P. Burnard"]2025-01-24T00:00:00 217c93c31b955f4241260136a06a9628fcce373dSignal Transduction and Targeted Therapy50Signal Transduction and Targeted Therapy["M. Mascalchi", "C. Marzi", "S. Diciotti"]2025-01-24T00:00:00 cb2da95f14651b7f2964421472db415f0d582cb7Annals of Operations Research60Annals of Operations Research["Nolan M. Talaei", "A. Oztekin", "L. Motiwalla"]2025-01-24T00:00:00 89498068c99fbd21e2113d4c084475062ba14c85African Journal of Biomedical Research00African Journal of Biomedical Research["Dr Sanjeev Gulati"]2025-01-24T00:00:00 319f8870b045a5673a778ffac2e5e7e14d46c4efJournal of Data Applications00Journal of Data Applications["Hatice Cifci", "Mehmet \u015eahin", "Ibrahim Cifci", "G\u00fcrel \u00c7etin"]2025-01-24T00:00:00 8b9b469cc1d2ae1b76d3bb2c491158d972227f82We propose a method for information system (IS) design with a focus on the applicability of intelligent chatbots. An analysis of existing chatbots shows that current solutions do not have the ability to formulate IS requirements during designing without human intervention. The study proposes to integrate ChatGPT (GPT4 model) as a chatbot to solve the problem of writing requirements elicitation for IS. The proposed method for solving the problem is based on a gradual reduction in entropy of chatbot response, which can be achieved by controlling such basic query parameters as the form, depth and breadth of the prompt. Based on the proposed method, an algorithm for controling prompts during a dialogue with a chatbot was developed, regulating the form, depth and breadth of the question to obtain the required level of content in the answer. The following results were obtained during the study: a method for obtaining new knowledge based on entropy of chatbot response reduction; algorithm for prompts control; the second phase of IS development is automated (including identification of business requirements and system objectives; analysis of existing solutions and technologies; determination of functional requirements and system capabilities; development of system architecture, including service definition and interaction; creation of interfaces for service interaction; establishment of a data model and database and formulation of business process logic). The method was tested to design an IS to support strategic decision making in the forestry industry. The design results received a positive assessment from experts. Analysis of the obtained results revealed the advantages and limitations of using chatbots as co-pilots in the design of IS and outlined directions for future research.PROGRAMMNAYA INGENERIA00The study proposes to integrate ChatGPT (GPT4 model) as a chatbot to solve the problem of writing requirements elicitation for IS, and develops an algorithm for controling prompts during a dialogue with a chatbot to obtain the required level of content in the answer.Programmnaya Ingeneria["G. E. Rego", "E. A. Pitukhin"]2025-01-24T00:00:00 bacf13f20f372f04beb9c2f8f16fea56733bc104Education and Information Technologies : Official Journal of the IFIP technical committee on Education410Education and Information Technologies["Shuwen Zhai", "Shuaiqing Zhang", "Yi Rong", "Gan Rong"]2025-01-24T00:00:00 cf41a1e89a317f9faf2e305b7e17b7e67d390b5cInternational Journal of Human Sciences Research00International Journal of Human Sciences Research["Ana Paula Fernandes da Silva", "Edlene Lima Ribeiro", "Ant\u00f4nio S\u00e9rgio Alves de Almeida J\u00fanior", "Andrea de Menezes Farto da Cunha", "Pedro Henrique Xavier Cunha", "Raphaelle Lima de Almeida Beltr\u00e3o", "Mirela Lopes Ribeiro", "Mateus Glasner de Maia Lyra Cardoso", "Luciano de Albuquerque Mello", "Juliana Gon\u00e7alves", "Rita de C\u00e1ssia Hoffmann Le\u00e3o"]2025-01-24T00:00:00 a47deaec0e4083f65045c376ee392f307d766f56International Journal of Medical Informatics500Findings indicate that AI algorithms can predict obesity; however, further research is needed to assess their effectiveness in analyzing obesity-related data and examine most advanced AI methods.International journal of medical informatics["Sharareh R. Niakan Kalhori", "Farid Najafi", "Hajar Hasannejadasl", "Soroush Heydari"]2025-01-24T00:00:00 dbe16218747030c97ed920fbcbc00c93921be622ANQ. A Quarterly Journal of Short Articles, Notes, and Reviews40ANQ: A Quarterly Journal of Short Articles, Notes and Reviews["Gexin Yang", "Joo-Cheol Kim"]2025-01-24T00:00:00 3311fefeac24dea79b706f31fa76c35754fa7365Reviews of Management Sciences1100Review of Managerial Science["Sebasti\u00e1n Uriarte", "Hugo Baier-Fuentes", "Jorge Espinoza-Benavides", "Williams Inzunza-Mendoza"]2025-01-24T00:00:00 0482773c62eb7135ecd983b2cad64ee1a20b35c5EDPACS: The EDP Audit, Control, and Security Newsletter00EDPACS["Rachel V. Rose"]2025-01-24T00:00:00 27fbfae2bb220228f0c75b84de9fd3144bbb5b0cAnesthesia and Analgesia100Anesthesia and analgesia["C. Matava", "Armaan Dosani", "Martina Bordini", "Jonathan Tan"]2025-01-24T00:00:00 caca1e653ca10d534223cd766f14f28fe3f79877Nature Microbiology80Nature microbiology["Jeremie Alexander", "Gary Liu", "Jonathan M. Stokes"]2025-01-24T00:00:00 7d56e4051c4a38387f8447fece5aedd090a2190fEuropean journal of plastic surgery410European Journal of Plastic Surgery["Juan E. Ospina-G\u00f3mez", "Juan M. Molano-Diaz", "Mar\u00eda C. Rojas-G\u00f3mez", "Mar\u00eda G. Latorre-Ar\u00e9valo", "Marcela Sanchez-Vargas"]2025-01-24T00:00:00 887180ce95c10e2aa0869dfefed45e399de9a3afInternational Journal of Developmental Disabilities1080International Journal of Developmental Disabilities["Maissane Nasrallah", "Alaa El Moghrabi", "Marylouise Kayal", "Dina Matar", "Iman Alwan", "Marc Fakhouy"]2025-01-24T00:00:00 d6932ab36ccf8aed29b952cda310cc603fc7b10aAI is one of the foremost imperative variables characterizing human-robot interaction (HRI). It does this by making frameworks that are basic and versatile, which makes it simpler for people and robots to work together and total assignments. Robots can see, think, and act in changing environment by utilizing AI programs, which gives them considering aptitudes comparable to people. This combination of AI and robots makes it simpler for individuals to conversation to each other and work together, which leads to more proficient and compelling collaboration. Brilliantly apparatuses that make things simpler for individuals to utilize with robots are a vital portion of utilizing AI in HRI. Robots can get it and answer to human orders and developments much obliged to Characteristic Dialect Preparing (NLP) and computer vision strategies. This makes discussion simpler and more normal. This basic interface makes contact less demanding for everybody, so individuals can effortlessly work with robots indeed on the off chance that they haven't had any preparing. Moreover, AI-driven frameworks let robots alter their behavior and tastes to coordinate those of people, which makes working together simpler. Machine Learning systems permit robots to memorize from experiences with people and alter how they act based on what they've learned. For case, when individuals work together on a task, a robot can learn from their activities and report the most excellent ways to assist individuals, which progresses the common performance and speed of the errand. AI too makes it simpler to make frameworks that are mindful of their environment and can alter how they act based on clues from their environment and the circumstance. Robots can get it and see their environment by utilizing sensors and discernment programs together. This lets them make savvy choices and alter how they act based on those choices. This understanding of its environment makes it less demanding for the robot to work with individuals in a assortment of settings.Journal of Information Systems Engineering & Management00Artificial Intelligence makes it simpler to make frameworks that are mindful of their environment and can alter how they act based on clues from their environment and the circumstance, which makes working together simpler.Journal of Information Systems Engineering and Management["Balaji Bodkhe"]2025-01-24T00:00:00 b2fe3b3fc803fd388c6f44515360781c9fbc402bProfessional Development in Education650Professional Development in Education["Sel\u00e7uk Do\u011fan", "Umran Y. Nalbantoglu", "Ismail Celik", "Nihan Agacli Dogan"]2025-01-24T00:00:00 0f8a757951bf560f3ba9fb0f452751c3a902cdc6Education and Information Technologies : Official Journal of the IFIP technical committee on Education500Education and Information Technologies["Liangliang Xia", "Kexin Shen", "Herui Sun", "Xin An", "Yan Dong"]2025-01-24T00:00:00 51be6a307b82448d7c7355e4ce03e6be830b7f86 This article aims to augment existing discussions of artificial intelligence (AI) by introducing an array of Latin American viewpoints, from philosophical discourse to indigenous cosmologies. Employing an interdisciplinary approach, the text integrates ideas from media theory, critical posthumanism, and philosophy of technique to question traditional modern, anthropocentric, and Western conceptualizations of technology and intelligence. The authors draw on Latin American thinkers to explore how traditional wisdom could enrich the future of AI. Finally, they propose that art has the potential to transform technology and explore ways to move beyond current AI paradigms towards artificial wisdom.Leonardo: Journal of the International Society for the Arts, Sciences and Technology00The authors draw on Latin American thinkers to explore how traditional wisdom could enrich the future of AI and propose that art has the potential to transform technology and move beyond current AI paradigms towards artificial wisdom.Leonardo["Joaqu\u00edn Zeren\u00e9", "\u00d3scar Villota"]2025-01-24T00:00:00 fb4a25de4e35c0683cd8df4d7b498d9f07c1f634Abstract: Research links a progressive decrement in episodic memory to deficits in judgment and decision making in aging. In social decision making, decrements in episodic memory contribute to suboptimal decisions among older adults. As artificial agents – such as humanoid robots, artificial intelligence (AI)-powered deepfakes, and chatbots – become increasingly present in human society, they can both benefit (e.g., combat loneliness) and harm (e.g., defraud) older adults. Understanding how older adults make social judgments and decisions involving artificial agents is crucial for research and policy making. Nevertheless, it remains poorly understood how episodic memory deficits influence social judgments and decisions involving artificial agents, and how this relation changes with age. This review will bridge this gap by applying a developmental model to explain how memory influences social judgments and decisions involving artificial agents in late adulthood. Evidence suggests that older adults increasingly rely on gist-based processing, which may explain their greater preference for humanoid robots compared with younger adults. We will also discuss the distinct challenges deepfakes pose for older adults’ trust-related decision making. In closing, we will discuss the implications of age-related memory changes for social judgments and decisions involving disembodied artificial agents, such as chatbots.European Psychologist880Evidence suggests that older adults increasingly rely on gist-based processing, which may explain their greater preference for humanoid robots compared with younger adults, and the implications of age-related memory changes for social judgments and decisions involving disembodied artificial agents, such as chatbots.European Psychologist["Shensheng Wang", "N. Lighthall"]2025-01-24T00:00:00 3a385528d0d5b194142001f5e4f2eb214857839b This paper explores a pivotal aspect of artificial intelligence (AI) research history known as the commonsense knowledge problem. Assumptions from computationalist and linguistic views are uncovered, revealing the limitations of human-centric perspectives. Drawing on insights from interdisciplinary studies and critiques of anthropocentrism, the limitations of traditional AI frameworks are examined to propose a shift towards post-anthropocentric perspectives. The paper concludes by outlining practical implications and future directions for AI, promoting a holistic and interdisciplinary approach to understanding cognition across diverse life forms.Leonardo: Journal of the International Society for the Arts, Sciences and Technology00The limitations of traditional AI frameworks are examined and a shift towards post-anthropocentric perspectives is proposed, promoting a holistic and interdisciplinary approach to understanding cognition across diverse life forms.Leonardo["Lisa Whitsett"]2025-01-24T00:00:00 753eb709e52f37699e994553088857e8aa3d1d6cThis paper studies the impact of generative artificial intelligence (AI) technologies on the demand for online freelancers using a large data set from a leading global freelancing platform. We identify the types of jobs that are more affected by generative AI and quantify the magnitude of the heterogeneous impact. Our findings indicate a 21% decrease in the number of job posts for automation-prone jobs related to writing and coding compared with jobs requiring manual-intensive skills within eight months after the introduction of ChatGPT. We show that the reduction in the number of job posts increases competition among freelancers, whereas the remaining automation-prone jobs are of greater complexity and offer higher pay. We also find that the introduction of image-generating AI technologies led to a 17% decrease in the number of job posts related to image creation. We use Google Trends to show that the more pronounced decline in the demand for freelancers within automation-prone jobs correlates with their higher public awareness of ChatGPT’s substitutability. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2024.05420 .Social Science Research Network424This paper studies the impact of generative artificial intelligence (AI) technologies on the demand for online freelancers using a large data set from a leading global freelancing platform and finds that the introduction of image-generating AI technologies led to a 17% decrease in the number of job posts related to image creation.SSRN Electronic Journal["Ozge Demirci", "Jonas Hannane", "Xinrong Zhu"]2025-01-24T00:00:00 86acf6bddcd15742b98bf34d9d67ec1aa3acbb38This mixed-methods study examines the role of artificial intelligence (AI)-assisted learning in academic writing for Chinese as a Second Language (CSL) students in a Chinese university context. Fifty international CSL students were randomly assigned to experimental—AI-assisted learning using ChatGPT—and control—traditional learning—groups. Writing samples from the participants were evaluated using established scoring rubrics for Chinese academic writing. Based on pre- and post-test quantitative data and supplementary qualitative interviews with six participants from the experimental group, this study reveals that AI-assisted learning can enhance student outcome by supporting knowledge acquisition, helping to create a supportive learning environment, and increasing student motivation. However, this study also highlights concerns regarding over-reliance on AI, particularly in relation to ethical concerns, technical and networking issues, and the unreliability of AI-generated content. These findings contribute to a nuanced understanding of the impact of AI on CSL learners’ academic writing performance. Finally, we also discuss practical implications for educational stakeholders regarding the integration of AI into language education.Education sciences730This study reveals that AI-assisted learning can enhance student outcome by supporting knowledge acquisition, helping to create a supportive learning environment, and increasing student motivation, but also highlights concerns regarding over-reliance on AI.Education Sciences["Chen Chen", "Yan Gong"]2025-01-24T00:00:00 7f6c31a8af9ac01ea8476cc1d437ded63a5d1abeThe agricultural sector faces numerous challenges, including resource scarcity climate change and economic sustainable concerns. But the opportunities for transforming through artificial intelligence (AI) are immense, enabling optimized resource utilization, higher crop yield, and reduced waste. As the world progresses toward addressing global food security, AI offers a potent answer to bridge the gap between sustainable agriculture and economic viability. The roles of AI driving tools in sustainable farming, and the implications for cost and benefit, are assessed in this study to see how they may ultimately help to more efficiently invest in smallholder and large scale farming. It analyzed AI applications such as accuracy agriculture, predictive analytics and automated decision making systems to show how AI can revolutionize agriculture. They found that AI tools also increase the efficiency with which resources are put to use, reduce environmental impact and enable farming to be profitable, which are all forces for sustainable agriculture in the years ahead. Furthermore, the use of AI solutions encourages innovation in farming processes, opening up possibilities for a better and greener agricultural future.Journal of Management World240AI applications such as accuracy agriculture, predictive analytics and automated decision making systems are analyzed to show how AI can revolutionize agriculture and found that AI tools also increase the efficiency with which resources are put to use, reduce environmental impact and enable farming to be profitable, which are all forces for sustainable agriculture in the years ahead.Journal of Management World["Md Azhad Hossain", "Jannatul Ferdousmou", "Rabeya Khatoon", "Sanchita Saha", "Mahafuj Hassan", "Jahanara Akter", "Anupom Debnath"]2025-01-24T00:00:00 04e3fadaff0a809a09473c0406c45101b4495074Innovative Higher Education360It was found that students and faculty did not differ significantly in their attitudes toward GenAI in higher education, except regarding ease of use, hedonic motivation, habit, and interest in exploring new technologies.Innovative Higher Education["Junghwan Kim", "Michelle Klopfer", "Jacob R. Grohs", "Hoda Eldardiry", "James Weichert", "Larry A. Cox", "Dale Pike"]2025-01-24T00:00:00 3e1c2705cc745ab0b7ab53f426d55942fa237507This study used Explainable Artificial Intelligence (XAI) with SHapley Additive exPlanations (SHAP) to examine dietary and lifestyle habits in the Spanish population and identify key diet predictors. A cross-sectional design was used, employing the validated NutSo-HH scale to gather data on nutrition, lifestyle, and socio-demographic factors. The CatBoost method combined with SHAP was applied. The sample included 22,181 Spanish adults: 17,573 followed the Mediterranean diet, 1425 were vegetarians, 365 were vegans, and 1018 practiced intermittent fasting. Fish consumption was the strongest dietary indicator, with vegans abstaining and some vegetarians consuming it occasionally. Age influenced diet: younger individuals preferred vegan/vegetarian diets, while older adults adhered to the Mediterranean diet. Vegans and vegetarians consumed less junk food, and intermittent fasters were more physically active. The model effectively predicts the Mediterranean diet but struggles with others due to sample imbalance, highlighting the need for larger studies on plant-based and intermittent fasting diets.European Journal of Investigation in Health, Psychology and Education710The model effectively predicts the Mediterranean diet but struggles with others due to sample imbalance, highlighting the need for larger studies on plant-based and intermittent fasting diets.European Journal of Investigation in Health, Psychology and Education["Elena Sandri", "Germ\u00e1n Cerd\u00e1 Olmedo", "M. Piredda", "Lisa Ursula Werner", "Vincenzo Dentamaro"]2025-01-24T00:00:00 d2c5e585495ab180cafd27cea77f7856438ff434Seiring dengan perubahan dan perkembangan zaman serta semakin pesatnya globalisasi disegala bidang kehidupan, maka seluruh sektor masyarakat harus mampu bertransformasi menuju digitalisasi disegala aspek, salah satunya adalah duni pendidikan. Di antara hasil perkembangan teknologi yang semakin canggih adalah hadirnya artificial intelligence. Kecerdasan buatan (AI) telah membawa perubahan besar dalam banyak aspek kehidupan manusia. Pendidikan Islam sebagai salah satu bidang yang terkena dampak juga mengalami perubahan signifikan dalam hal metode pengajaran dan pembelajaran. Artikel ini bertujuan untuk mengeksplorasi bagaimana manfaat dan tantangan penggunaan artificial intelligence dalam pendidikan Islam dan bagaimana hal ini berpengaruh dalam proses pembelajaran di institusi pendidikan Islam.Kreatif. Jurnal studi pemikiran pendidikan agama islam230KREATIF: Jurnal Studi Pemikiran Pendidikan Agama Islam["M. Muchlis"]2025-01-25T00:00:00 b9bf7f7d92b6507e05a3df519b3205d8052419a4Today, heavy machinery can be equipped with robots operated by artificial intelligence (AI) to streamline operations, decrease human labor, and increase efficiency. Intelligent technologies like these can boost productivity by carrying out routine tasks with extreme accuracy. Since robots reduce risks to human workers, robots with AI algorithms and sensors could be used in hazardous environments. In certain contexts, including building site management, mining, or handling hazardous materials, safety must take precedence above everything else. Analytics from this study, aided by AI, have opened the door to predictive maintenance procedures. To detect equipment problems, the Hybrid Artificial Intelligence Framework (HAIF) examines sensor data in conjunction with historical patterns. The objective is to avoid expensive failures and downtime. Optimization of equipment utilization, energy consumption, and fuel consumption can be achieved by applying AI-driven machine learning algorithms. Several advantages arise from energy waste, including cost avoidance and enhanced operational efficacy. Robots can do exact alignments, measurements, and operations with the help of AI vision technologies. This precision significantly impacts the manufacturing, agricultural, and construction industries. Artificial intelligence has the potential to optimize the allocation of resources, the use of heavy equipment, and supply networks by evaluating massive amounts of data.International Journal of High Speed Electronics and Systems00Artificial intelligence has the potential to optimize the allocation of resources, the use of heavy equipment, and supply networks by evaluating massive amounts of data by applying AI-driven machine learning algorithms.International Journal of High Speed Electronics and Systems["Yanping Liu", "Li Song", "Chunxia Yang"]2025-01-25T00:00:00 343e7a3ee8fd6cb099ddd88784886c1e163159ceAccess to behavior analytic services is limited and often unavailable for many in areas with a dearth of qualified providers. Tools to support behavior‐change agents located in the natural environment of consumers may be a way to provide behavioral interventions. An artificial intelligence (AI) platform that guides the implementation of behavioral interventions may be useful for supporting procedural fidelity. The current studies evaluated whether an AI platform was effective at increasing and maintaining high levels of procedural fidelity in individuals with little to no prior training. Participants were two behavior technicians in training (Exp. 1) and three caregivers (Exp. 2). Introducing guidance provided by the AI platform GAINS improved the procedural fidelity with which behavior technicians and caregivers implemented behavioral interventions with children with autism, except for one caregiver. These results suggest AI platforms may be useful tools for supporting high levels of procedural fidelity by novice users.Behavioral interventions300AI platforms may be useful tools for supporting high levels of procedural fidelity by novice users, as evaluated in individuals with little to no prior training.Behavioral Interventions["Aliya Yagafarova", "Emily Dowling", "Toni LaMonica", "D. Hantula", "John T. Nosek", "Corina Jimenez\u2010Gomez"]2025-01-25T00:00:00 1cb2e580d3cac00e62c1dab4c6e7acad73974546Information Systems Frontiers690The result shows that based on a survey of 295 samples of B2B services entrepreneurial businesses, XAI enhances the establishment of a sustainable resilience for B2B marketing activities and contribute to building social entrepreneurial strategies for B2B marketing innovation.Information Systems Frontiers["Femi Olan", "Thanos Papadopoulos", "Konstantina Spanaki", "Uchitha Jayawickrama"]2025-01-25T00:00:00 c6da1b22d2daf4eec4d73b57f505fde7190ee9c8The study aimed to examine how artificial intelligence (AI)-powered security systems enhance customer trust and engagement in Forex (FX) Trading platforms, the study also mark the effectiveness of AI technologies in mitigating security threats on FX platforms, and explore the role of AI in ensuring regulatory compliance and transparency, thereby fostering a more secure trading environment. The study used both qualitative and quantitative data. Empirical in nature, the study focuses on the users of trading platforms engaged in Foreign Exchange (FX) dealing in the Delhi NCR region. The study uses descriptive and exploratory research design and provides a target population of 250 respondents. A structured questionnaire is employed as the main source of data collection to address the research questions. The data is analyzed by statistical tools such as MS Excel and SPSS, using mean, S.D., correlation, regression, etc. The study showed that there is a clear positive correlation between the level of incorporation of AI security systems and the level of customer engagement, with AI technologies accounting for an important share of the variation in security threat prevention. Also, the study confirmed the role of AI in compliance and openness, which exhibits a moderate positive relationship between them.Online (Weston, Conn.)350The study showed that there is a clear positive correlation between the level of incorporation of AI security systems and the level of customer engagement, with AI technologies accounting for an important share of the variation in security threat prevention.Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)["Laxman Doddipatla"]2025-01-25T00:00:00 2771386fc53c957588ed97a13c1ac863999149d7This study looks at the potential of emotional detection technology using artificial intelligence to assist university administration in Pakistan in resolving conflicts. Sixty deputy directors from institutions within provinces of Punjab and Khyber Pakhtunkhwa (KPK) took part in the quantitative study. A self-completion survey consisting of Likert scale items was employed to collect data. The survey touched on issues such as the effectiveness of communication, empathy, accuracy, and ethical matters related to artificial intelligence. Through regression analysis (Beta coefficients ranging from 0.59 to 0.76), post-hoc analysis (ANOVA F-values ranging from 4.78 to 7.12), and correlation analysis (Pearson correlation coefficients ranging from 0.62 to 0.82), 60 individuals constituted the sample. Based on the findings, AI not only identifies geographical and experiential differences, but also significantly improves communication, accuracy, and empathy in conflict resolution. Ethics, privacy, and prejudice were also discussed in the study. It highlights the ethical implications of AI while presenting evidence that it could be beneficial in resolving disputes.Review of Applied Management and Social Sciences290Based on the findings, AI not only identifies geographical and experiential differences, but also significantly improves communication, accuracy, and empathy in conflict resolution.Review of Applied Management and Social Sciences["Nadeem Farooq", "Zara Rafique", "Aqib Merchant", "K. Narejo"]2025-01-25T00:00:00 a142bc40e81092dd3031067f68e38101d75cba4aThis study explores the attitudes of university students from Western Romania regarding the use of artificial intelligence (AI) in education. A total of 600 valid responses were collected from students enrolled in various specializations and forms of study. The research aims to understand students' perspectives on the integration of AI in educational settings, examining their views on the current and future roles of AI in shaping the teaching and learning experience. The questionnaire aimed to capture the opinions of students concerning the potential benefits and challenges of AI, as well as its impact on their academic development and future career prospects. The findings provide valuable insights into how students perceive the evolution of AI technologies and their potential to transform educational practices in Romania. These results may inform educators and policymakers on how to better integrate AI tools into the academic environment, aligning with student expectations and the future demands of the workforce.Technium Business and Management00Understanding students' perspectives on the integration of AI in educational settings, examining their views on the current and future roles of AI in shaping the teaching and learning experience, may inform educators and policymakers on how to better integrate AI tools into the academic environment.Technium Business and Management["Romina Ghe\u021bie", "Dana Rad", "L. Cuc", "Dan R\u0103dulescu"]2025-01-25T00:00:00 ecc7c62480bc3ee4cde06aa1263102855678f044Artificial intelligence (AI) and deep learning algorithms are advancing rapidly, with these emerging technologies being widely applied in areas such as audio-visual recognition and natural language processing. However, in recent years, researchers have identified several security risks in current mainstream AI models, which could hinder the further development of AI technologies. As a result, the issues of data security and privacy protection in AI models have become a focus of research. The data and privacy leakage problems are primarily studied from two perspectives: data leakage based on model outputs and data leakage based on model updates. In the context of model output-based data leakage, the study discusses the principles and research status of model theft attacks, model inversion attacks, and membership inference attacks. In the context of model update-based data leakage, the research focuses on how attackers can steal private data during the distributed training process. Regarding data and privacy protection, three common defense methods are primarily studied: model structure defenses, information obfuscation defenses, and query control defenses. This paper reviews the cutting-edge research achievements in the field of data security and privacy protection in AI deep learning models, focusing on the theoretical foundations, key findings, and related applications of data theft and defense technologies in AI deep learning models. Keywords: Artificial Intelligence, Data Security, Privacy Leakage, Privacy ProtectionINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This paper reviews the cutting-edge research achievements in the field of data security and privacy protection in AI deep learning models, focusing on the theoretical foundations, key findings, and related applications of data theft and defense technologies in AI deep learning models.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["C. Tumma", "Rahul Azmeera", "Supraja Ayyamgari", "B. Thumma"]2025-01-25T00:00:00 8ef3dce64ff5e2ed0a2db8e7389df0b5a600d849The dairy supply chain encompasses all stages involved in the production, processing, distribution, and delivery of dairy products from farms to end consumers. Artificial intelligence (AI) refers to the use of advanced technologies to optimize processes and make informed decisions. Using the PRISMA methodology, this research analyzes AI technologies applied in the dairy supply chain, their impact on process optimization, the factors facilitating or hindering their adoption, and their potential to enhance sustainability and operational efficiency. The findings show that artificial intelligence (AI) is transforming dairy supply chain management through technologies such as artificial neural networks, deep learning, IoT sensors, and blockchain. These tools enable real-time planning and decision-making optimization, improve product quality and safety, and ensure traceability. The use of machine learning algorithms, such as Tabu Search, ACO, and SARIMA, is highlighted for predicting production, managing inventories, and optimizing logistics. Additionally, AI fosters sustainability by reducing environmental impact through more responsible farming practices and process automation, such as robotic milking. However, its adoption faces barriers such as high costs, lack of infrastructure, and technical training, particularly in small businesses. Despite these challenges, AI drives operational efficiency, strengthens food safety, and supports the transition toward a more sustainable and resilient supply chain. It is important to note that the study has limitations in analyzing long-term impacts, stakeholder resistance, and the lack of comparative studies on the effectiveness of different AI approaches.Sustainability450The findings show that artificial intelligence is transforming dairy supply chain management through technologies such as artificial neural networks, deep learning, IoT sensors, and blockchain that enable real-time planning and decision-making optimization, improve product quality and safety, and ensure traceability.Sustainability["Gabriela Joseth Serrano-Torres", "Alexandra Lorena L\u00f3pez-Naranjo", "Pedro Lucas Larrea-Cuadrado", "Guido Maz\u00f3n-Fierro"]2025-01-25T00:00:00 14ea6be646c86dd28232e071996a9978c7b5db04Recent advancements in Artificial Intelligence (AI) have attracted significant attention within the managerial accounting profession. With its transformative capabilities and complexity, AI presents numerous opportunities alongside notable challenges in its adoption. This paper examines the key factors influencing AI adoption in managerial accounting and highlights common concerns faced by companies during this process. Based on interviews with representatives from 41 companies, we identified a range of factors impacting AI adoption at both institutional and individual levels. These findings offer valuable insights into AI acceptance within the field of managerial accounting.Accounting &amp; Finance500This paper examines the key factors influencing AI adoption in managerial accounting and highlights common concerns faced by companies during this process, offering valuable insights into AI acceptance within the field of managerial accounting.Accounting &amp; Finance["Chao Zhang", "Weidong Zhu", "Jun Dai", "Yong Wu", "Xulong Chen"]2025-01-25T00:00:00 d4a8d7a8a18689cb1f5e5e7abd7608ed621b6876Countries worldwide are focusing on energy efficiency, economic sustainability, and responsible resource management to address climate change and meet sustainable development goals (SDGs). This study investigates how factors such as artificial intelligence, renewable energy, green human capital, geopolitical risk, natural resource rent, and information and communication technology influenced CO2 emissions in 36 countries between 2000 and 2021. The study also explores how institutional quality moderates these relationships. We employed advanced econometric techniques to address this gap, including panel-correlated standard errors (PCSE) and the Driscoll–Kraay estimations (DKSE) models. A two-step system GMM approach was also used to strengthen the robustness of our findings. The findings reveal that green human capital, renewable energy consumption, and institutional quality can significantly reduce CO2 emissions. Conversely, artificial intelligence, geopolitical risk, natural resource rent, and information communication technology contribute to increased CO2 emissions. Institutional quality enhances the positive impact of green human capital and renewable energy on emission reduction. However, it has the opposite effect on artificial intelligence, leading to an even greater increase in CO2 emissions. These findings underscore the importance of green policies in achieving sustainable development goals. We recommend that policymakers prioritize investing in clean energy and green human capital while strengthening institutional quality to effectively mitigate carbon emissions and meet SDGs. They also regulate AI and ICT carbon footprints and address geopolitical risks through energy diversification and international cooperation.Sustainability1270The findings reveal that green human capital, renewable energy consumption, and institutional quality can significantly reduce CO2 emissions, whereas artificial intelligence, geopolitical risk, natural resource rent, and information communication technology contribute to increased CO2 emissions.Sustainability["Amir Iqbal", "Wei Zhang", "Sayeda Jahangir"]2025-01-25T00:00:00 6d298eb029d1e12152c7cc97dcc450ae1e49f067The focus of the article is on the contemporary international legal domains of scientific research concerning the application of artificial intelligence technologies in the military. The authors note that the current issue of using artificial intelligence technologies for military purposes is gaining special attention among a wide range of practitioners and scholars alike and continues to be one of the most difficult subjects to study. Artificial intelligence technologies pose new challenges and threats to modern international law, particularly in the military sphere, where uncontrolled use of these technologies can threaten international peace and security. In this regard, the ideas and positions of leading experts who provide their vision of certain aspects of international legal regulation of the use of artificial intelligence technologies for military purposes are important, relevant, and interesting. The purpose of the article is to determine the current international legal trends in the study of the use of artificial intelligence technologies for military purposes.Taking into account the scientific works of recent years, the authors identify the main trends in the development of international legal thought on the use of artificial intelligence in the military sphere, with a primary focus on theoretical issues regarding the place, role, necessity, and ability of international law to regulate the use of these technologies. The article specifically talks about the issues that are at stake: (a) whether international law can regulate the use of AI technologies for military purposes; (b) whether international law can determine the moral and ethical aspects (criteria) for judging the use of AI for military purposes; (c) whether international law can regulate the use of AI and robotic (cyber-physical) systems together for military purposes, as well as their use in autonomous weapons systems; and (d) whether international law can regulate the use of AI for military purposes. The authors provide a brief overview and conclude these thematic scientific developments, allowing for a conceptual look at a wide range of professional theoretical positions and approaches.Uzhhorod National University Herald. Series: Law00The authors identify the main trends in the development of international legal thought on the use of artificial intelligence in the military sphere with a primary focus on theoretical issues regarding the place, role, necessity, and ability of international law to regulate the use of these technologies.Uzhhorod National University Herald. Series: Law["O. O. Derkach", "I. Zabara"]2025-01-25T00:00:00 d172fbca9c95b98658866fad902b3769e7b2d0d7In the modern business landscape, sustainability has become a fundamental goal for organizations, driven by growing environmental concerns, social responsibility, and the need for long-term profitability (Bocken et al., 2014). Companies are under increasing pressure to reduce their environmental footprint, optimize resources, and improve operational efficiency, all while maintaining competitiveness. Business Intelligence (BI) and Artificial Intelligence (AI) have emerged as key technologies in this transition, offering organizations the ability to make data-driven decisions that promote sustainability (Chen et al., 2020). BI encompasses tools and techniques that convert raw data into actionable insights, helping businesses optimize operations and minimize waste (Shollo & Galliers, 2016). On the other hand, AI, particularly machine learning and predictive analytics, enhances decision-making by forecasting trends, automating processes, and providing deeper insights into complex datasets (Jeble et al., 2020). This article explores the integration of BI and AI in driving sustainable business operations. It examines their individual contributions and the synergistic benefits they bring when combined. Key applications discussed include energy management, where BI helps track energy consumption patterns, and AI optimizes resource allocation to minimize waste (Kemp et al., 2021). In supply chain optimization, BI analyzes supplier performance and inventory levels, while AI forecasts demand and automates processes to reduce carbon footprints (Saghafian et al., 2020). Waste reduction efforts are enhanced through predictive analytics, which help anticipate production needs and reduce excess output (Karim et al., 2021). Environmental monitoring, powered by AI and IoT sensors, allows for real-time analysis of environmental conditions, ensuring compliance with sustainability standards (Khan et al., 2021). However, the implementation of these technologies also presents challenges. Data integration remains a significant barrier, as companies often face difficulties in harmonizing large datasets from disparate sources (Laudon & Laudon, 2019). The initial investment in BI and AI technologies can be high, making it difficult for small and medium-sized enterprises (SMEs) to adopt these solutions (Zhang et al., 2020). Additionally, a shortage of skilled professionals in data science and AI poses another challenge, limiting the effective use of these technologies (Brynjolfsson & McAfee, 2014). Despite these challenges, the potential of BI and AI to foster sustainable business operations is substantial, and overcoming these barriers will be key to unlocking their full potential. The article concludes by discussing strategies for successful implementation and the future outlook for BI and AI in sustainable business practices.International Journal of Scientific Research and Management680The potential of BI and AI to foster sustainable business operations is substantial, and overcoming barriers will be key to unlocking their full potential.International Journal of Scientific Research and Management (IJSRM)["Amejuma Emmanuel Ebule"]2025-01-25T00:00:00 4beba4aa22a7a780743102fef87374ff8dbe570aIntroduction. Artificial intelligence (AI) has profoundly impacted various aspects of human life, including text generation, software development, and art creation. Many sport and business news articles available online have been authored by AI. Under the current legal frameworks in many jurisdictions, AI-generated works have generally been regarded as tools. However, the evolution of advanced AI technologies has significantly challenged this traditional perspective.Problem Statement. The rise of AI has introduced significant challenges to intellectual property law, particularly copyright. In the context of copyright, AI-generated works have sparked legal disputes regarding whether AI can be recognized as the author of creative works, how such works should be protected, and who holds the rights to them.Purpose. This study aims to critically analyze copyright issues related to AI-generated works, identify the legal regulations governing such works in developed countries, and propose recommendations to enhance Kazakhstani copyright law for AI-generated outputs.Materials and Methods. The research has employed comparative legal analysis, general scientific methods, and specific scientific approaches.Results. Drawing on foreign practices, the study has concluded that the individual who has made the necessary arrangements for the creation of AI-generated works should be recognized as the author.Conclusions. The paper provides practical recommendations for improving Kazakhstani legislation on copyright protection for AI-generated works. The findings may serve as a valuable resource for future legal research on regulating copyright for AI-generated outputs.Science and innovation140The study has concluded that the individual who has made the necessary arrangements for the creation of AI-generated works should be recognized as the author and provides practical recommendations for improving Kazakhstani legislation on copyright protection for AI-generated works.Science and Innovation["A. Aronov", "S. Idrysheva"]2025-01-25T00:00:00 aff026ebf3f7c2dfce35107785d88a454d4bf440This study aims to analyze the role of artificial intelligence (AI) in shaping social policies, focusing on its impacts on poverty, unemployment, education, and healthcare. The research explores the concept, origins, types, and dimensions of AI, as well as the stages of social policymaking. It highlights how AI technologies contribute to building simulation models that support governmental decision-making and enhance civic engagement through interactive platforms that allow citizens to express their opinions and participate in policymaking. Additionally, AI facilitates monitoring and analyzing social trends, enabling governments to respond swiftly to social and economic changes and crises, such as pandemics and natural disasters. The study concludes that AI plays a crucial role in improving transparency, building trust in governments, and offering innovative solutions to social challenges.Journal of Ecohumanism150It is concluded that AI plays a crucial role in improving transparency, building trust in governments, and offering innovative solutions to social challenges.Journal of Ecohumanism["Younis Mohammad Salameh ALKHAZA'LEH"]2025-01-25T00:00:00 89cc73a69d2536972436ef245ff77a7c7d6422afThis research aimed to determine whether there is a relationship between the integration of artificial intelligence and socio-emotional development in university students from southern Peru. A quantitative, non-experimental, correlational, and cross-sectional study was conducted. The sample consisted of 1,172 students of both sexes, selected through non-probabilistic sampling, administered the Artificial Intelligence Integration in Learning Questionnaire and the Socio-Emotional Development Questionnaire, instruments with adequate psychometric properties. The results indicate that the integration of artificial intelligence was moderately adequate, and socio-emotional development was rated moderately. On the other hand, it was determined that Spearman's rho correlation coefficient between both variables was 0.352 (p<0.05). Finally, it was concluded that there is a direct and significant relationship between integrating artificial intelligence and socio-emotional development in university students from southern Peru. This implies that the appropriate use of artificial intelligence tools can enhance socio-emotional skills, fostering a more inclusive and dynamic academic context. Likewise, its implementation could serve as a strategic resource to improve students' social interaction and emotional well-being, contributing to the achievement of comprehensive development in the university setting.Sapienza: International Journal of Interdisciplinary Studies00There is a direct and significant relationship between integrating artificial intelligence and socio-emotional development in university students from southern Peru, which implies that the appropriate use of artificial intelligence tools can enhance socio-emotional skills, fostering a more inclusive and dynamic academic context.Sapienza: International Journal of Interdisciplinary Studies["Marisol Yana-Salluca"]2025-01-25T00:00:00 f0fbd6f92d182581ff435858155c24bf2f55157cThe integration of Artificial intelligence (AI) in biotechnology presents significant ethical challenges that must be addressed to ensure responsible innovations. Key concerns include data privacy and security, as AI systems often handle sensitive genetic and health information, necessitating robust regulations to protect individuals' rights and maintain public trust. Algorithmic bias poses another critical issue; AI can reflect existing biases in training data, leading to inequitable healthcare outcomes. Transparency in AI decision-making is essential, as "black box" models hinder trust, especially in drug discovery and genetics. Ethical implications of genetic manipulation require careful scrutiny to define the limits of human intervention. Additionally, societal impacts must be considered to ensure equitable distribution of AI benefits, preventing the exacerbation of disparities. Engaging diverse stakeholders, including ethicists and policymakers, is vital in aligning these technologies with societal values. Ultimately, prioritizing ethics will allow us to harness AI and biotechnology's potential while safeguarding human rights and promoting equity.  Avicenna journal of medical biotechnology00Ethical implications of genetic manipulation require careful scrutiny to define the limits of human intervention, and societal impacts must be considered to ensure equitable distribution of AI benefits, preventing the exacerbation of disparities.Avicenna Journal of Medical Biotechnology["Mahintaj Dara", "Negar Azarpira"]2025-01-25T00:00:00 dc44275833981fd5b78833f889e23eedfc68c2ebInternational Journal of Computer Applications Technology and Research00International Journal of Computer Applications Technology and Research[]2025-01-25T00:00:00 473adf73de3bbaa944695ecf78f408655c96f528International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2025-01-25T00:00:00 b193d8f11e6942e503035df9109019448c08928fInternational Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2025-01-25T00:00:00 281bf47d9696522b3d4d5caadaf8753ab91dde20Environment, Development and Sustainability400Environment, Development and Sustainability["Xingxing He", "Junjie Ruan", "Caixing Bian"]2025-01-25T00:00:00 85faac5b338a986f47f92ed790aaabc9f5b430b7Current Psychology220The impressive results obtained by EfficientMobileNet on the Fer2013 dataset show potential for wider application, especially in image classification tasks involving low-quality or small-scale images, and supports the idea of the potential for further improvements in neural network architecture and model efficiency and accuracy.Current Psychology["Muhammed Tel\u00e7eken", "Devrim Akgun", "Sezgin Ka\u00e7ar", "K\u00fcbra Yesi\u0307n", "M. Y\u0131ld\u0131z"]2025-01-25T00:00:00 06cc67a5f2904062c661ad8c593d7913f41b7b55Digital Journalism550Digital Journalism["Mike Ananny", "Jake Karr"]2025-01-25T00:00:00 213bbc4985dff683453046f6b652b989272c461eInternational Journal of Artificial Intelligence and Machine Learning00International Journal of Artificial Intelligence and Machine Learning["Emmanuel Izeji", "M. Dukor"]2025-01-25T00:00:00 0b1e21fca404b294b800d302b15d62a6566448bcJournal of the Institution of Engineers (India) Series C580Journal of The Institution of Engineers (India): Series C["Aman Singh Thakur", "T. L. Alex", "Amrita Nighojkar"]2025-01-25T00:00:00 a219476b0467abea7ce29806c27b03f1908caef3Education and Information Technologies : Official Journal of the IFIP technical committee on Education860Education and Information Technologies["Razib Chandra Chanda", "Ali Vafaei-Zadeh", "Haniruzila Hanifah", "T. Ramayah"]2025-01-25T00:00:00 26e24a44974f5d6a50603e31295a6c80b1309e3fJournal of IT Cases and Applications230Journal of Information Technology Case and Application Research["Murali Chari", "Anne Jackson", "I. Thukral", "Steven Walsh"]2025-01-25T00:00:00 5e566efd1dbc00c9cf81664efad8e13a95ea936dJapanese Journal of Radiology150Japanese journal of radiology["K. Kamagata", "Shinji Naganawa"]2025-01-25T00:00:00 138fafe195a30ed49f23d8065a3636959a5c930aXXI Международная научно-практическая конференция «Вызовы глобализации и развитие цифрового общества в условиях новой реальности»00XXI Международная научно-практическая конференция «Вызовы глобализации и развитие цифрового общества в условиях новой реальности»["\u0415.\u0412. \u0412\u043e\u0440\u043e\u043d\u0438\u043d\u0430"]2025-01-25T00:00:00 1f32199ee1ff5d06a8b0899800b9beabd6b2f359Theory of mind refers to the human ability to reason about the mental content of other people, such as their beliefs, desires, and goals. People use their theory of mind to understand, reason about, and explain the behaviour of others. Having a theory of mind is especially useful when people collaborate, since individuals can then reason on what the other individual knows as well as what reasoning they might do. Similarly, hybrid intelligence systems, where AI agents collaborate with humans, necessitate that the agents reason about the humans using computational theory of mind. However, to try to keep track of all individual mental attitudes of all other individuals becomes (computationally) very difficult. Accordingly, this paper provides a mechanism for computational theory of mind based on abstractions of single beliefs into higher-level concepts. These abstractions can be triggered by social norms and roles. Their use in decision making serves as a heuristic to choose among interactions, thus facilitating collaboration. We provide a formalization based on epistemic logic to explain how various inferences enable such a computational theory of mind. Using examples from the medical domain, we demonstrate how having such a theory of mind enables an agent to interact with humans effectively and can increase the quality of the decisions humans make.Journal of Artificial Intelligence Research00This paper provides a mechanism for computational theory of mind based on abstractions of single beliefs into higher-level concepts that can be triggered by social norms and roles and demonstrates how having such a theory of mind enables an agent to interact with humans effectively and can increase the quality of the decisions humans make.J. Artif. Intell. Res.["Emre Erdogan", "F. Dignum", "R. Verbrugge", "Pinar Yolum"]2025-01-25T00:00:00 81b5edbd49d94ac203bb98cb1294edd49a0f8267The integration of Artificial Intelligence (AI) into Activity-Based Costing (ABC) systems represents a transformative shift in cost accounting methodologies, addressing the limitations of traditional ABC systems in handling complexity and large data volumes. AI-driven ABC systems leverage advanced algorithms, machine learning, and data analytics to enhance cost allocation precision, automate routine processes, and provide actionable insights into cost behaviors. This study explores the practical applications of AI-powered ABC systems in modern enterprises, focusing on their ability to improve cost accuracy, optimize operational efficiency, and support strategic decision-making. By dynamically adapting to changes in business structures and market conditions, these systems offer real-time, data-driven solutions for effective resource allocation and profitability analysis. Through the examination of algorithmic frameworks and real-world case studies, this research demonstrates how AI can deliver measurable outcomes, fundamentally reshape cost management practices, and align with broader organizational objectives such as innovation, scalability, and sustainable growth.International Journal of Artificial Intelligence and Machine Learning232This research demonstrates how AI can deliver measurable outcomes, fundamentally reshape cost management practices, and align with broader organizational objectives such as innovation, scalability, and sustainable growth.International Journal of Artificial Intelligence and Machine Learning["Bing Chen"]2025-01-25T00:00:00 30fb1a307e6b718e236d580041763b7af26fbd43Vedānta, a prominent philosophical school of thought, describes human existence through the concept of five sheaths, or Pañcakośas: Annamaya Kośa, Prāṇamaya kośa, Manomaya Kośa, Vijñānamaya Kośa, and Ānandmaya Kośa. With the rapid advancement of artificial intelligence (AI), machines specifically robots are increasingly being designed to mimic human functions. At present, these AI-powered robots exhibit limited cognitive abilities, but future advancements may enable them to outperform humans in certain domains. This paper explores the possibility of mapping the Panchkośa framework of Vedāntic philosophy onto the existence of AI machines, providing an analogy between the five (or fewer) sheaths that may characterize a robotic existence and the human sheaths. Furthermore, this work offers insights into the distinctions between human existence and contemporary AI machines, helping to illuminate the philosophical and existential differences between humans and intelligent machines. Further it puts forward few intriguing questions that Indian knowledge systems must address should AI robots achieve a fully developed system of Kośas. This model will evolve as developments in computer science continue to produce more advanced and intelligent robotic systems.The Voice of Creative Research10This paper explores the possibility of mapping the Panchkośa framework of Vedāntic philosophy onto the existence of AI machines, providing an analogy between the five (or fewer) sheaths that may characterize a robotic existence and the human sheaths.The Voice of Creative Research["Sachin M. Naik"]2025-01-25T00:00:00 a2215fb20794f0618b964a1402d184e9315029aeThe integration of artificial intelligence (AI) into economic systems represents a transformative shift in decision-making frameworks, introducing novel dynamics between human and AI agents. This paper proposes a welfare model that incorporates both game-theoretic and behavioral dimensions to optimize interactions within human-AI ecosystems. By leveraging agent-based modeling (ABM), we simulate these interactions, accounting for trust evolution, perceived risks, and cognitive costs. The framework redefines welfare as the aggregate utility of interactions, adjusted for collaboration synergies, efficiency penalties, and equity considerations. Dynamic trust is modeled using Bayesian updating mechanisms, while synergies between agents are quantified through a collaboration index rooted in cooperative game theory. Results reveal that trust-building and skill development are pivotal to maximizing welfare, while sensitivity analyses highlight the trade-offs between AI complexity, equity, and efficiency. This research provides actionable insights for policymakers and system designers, emphasizing the importance of equitable AI adoption and fostering sustainable human-AI collaborations.00A welfare model that incorporates both game-theoretic and behavioral dimensions to optimize interactions within human-AI ecosystems is proposed, revealing that trust-building and skill development are pivotal to maximizing welfare.["Sheyan Lalmohammed"]2025-01-25T00:00:00 74621b5355edac6999a8aba3045ad383f24e0849The rapid adoption of artificial intelligence (AI) raises challenges related to ethics, safety, equity, and governance that require robust regulatory frameworks. In most jurisdictions, AI-driven medical devices are already covered by existing medical device frameworks, although new AI-specific legislation may be required to address the challenges posed by recent advancements. This expert review focuses on frameworks and legislation explicitly tailored to AI, synthesizing research literature, government and intergovernmental framework programs, and online media coverage to provide an up-to-date assessment of global AI-specific regulation or strategies in healthcare as of December 2024. Our findings show that only 15.2% (n=30/197) of countries or territories have enacted legally binding AI-specific legislation, including the 27 member states of the European Union (EU) following the adoption of the EU AI Act. A further 9.1% (n=18/197) have drafted legislation, and 28.4% (n=56/197) have issued non-binding guidelines. Notably, 47.2% (n=93/197) of countries or territories do not have an AI-specific framework or legislation in place. Furthermore, our results highlight disparities between the Global North and South, with 60.3% (n=82/136) of Global South countries or territories lacking frameworks or legislation, compared to 18% (n=11/61) in the Global North. In conclusion, our work provides an overview of the status quo of AI regulation around the world, highlights disparities in the adoption of frameworks and legislation, and calls for the need for intergovernmental and regional cooperation.medRxiv00An overview of the status quo of AI regulation around the world is provided, disparities in the adoption of frameworks and legislation are highlighted, and the need for intergovernmental and regional cooperation is called for.["F. Busch", "R. Geis", "Y.-C. Wang", "J. N. Kather", "N. A. Khori", "M. Makowski", "I. K. Kolawole", "D. Truhn", "W. Clements", "S. Gilbert", "L. C. Adams", "E. Ortiz-Prado", "K. Bressem"]2025-01-25T00:00:00 8869b30b7673d6466b4a6f427973a833dcd42ce6Background Artificial intelligence (AI) is expected to become an integral part of healthcare services, and the widespread adoption of AI tools in all areas of life is making AI accessible to the general public. Public perception of the benefits and risks of AI in healthcare is key to large-scale acceptance and implementation, and is increasingly influenced by first-hand experiences of AI. The aim of this study was to assess how exposure to ChatGPT changed public perception of AI in healthcare. Methods We used baseline and follow-up data from 5,899 survey participants, who reported their perception of AI in 2022 and 2024, and ChatGPT use in 2024. Administrative and healthcare data from nationwide Danish registers was used for weighting and adjustment. Multinomial multivariate logistic regression was used to model how exposure to ChatGPT use affected changes in perception of AI. Results At baseline (before ChatGPT's launch) 2,236 individuals (37%) were unsure of the benefits and risks of AI in healthcare, 2,384 (40%) perceived net benefits, 1,083 (18%) perceived benefits and risks as equal, and 196 (3.3%) perceived net risks. At follow-up, 1,195 individuals (20%) had been exposed to ChatGPT use, which was associated with higher odds of changing perception of AI to benefits (OR 3.21 [95% CI: 2.34-4.40]) among individuals who were unsure at baseline, and lower odds of changing to uncertainty from more defined baseline perceptions (from benefits (OR 0.32 [0.24-0.42]), equal (OR 0.47 [0.32-0.69]) and risks (OR 0.27 [0.08-0.98])). Conclusion Exposure to ChatGPT was associated with a change towards positive perception of benefits and risks of AI in healthcare among individuals who were uncertain prior to exposure, and individuals with more defined perceptions of AI were less likely to become uncertain after exposure to ChatGPT.medRxiv00Exposure to ChatGPT was associated with a change towards positive perception of benefits and risks of AI in healthcare among individuals who were uncertain prior to exposure, and individuals with more defined perceptions of AI were less likely to become uncertain after exposure to ChatGPT.["A. A. Isaksen", "J. R. Schaarup", "L. Bjerg", "A. Hulman"]2025-01-25T00:00:00 c4d0abb5b62f054da4083edd5d4dc7bd25f9e3cdThis study aims to explore the perceptions of Greek journalists and academics on the use of artificial intelligence (AI) in Greek journalism, focusing on its benefits, risks, and potential ethical dilemmas. In particular, it seeks to (i) assess the extent of the use of AI tools by Greek journalists; (ii) investigate views on how AI might alter news production, work routines, and labor relations in the field; and (iii) examine perspectives on the ethical challenges of AI in journalism, particularly in regard to AI-generated images in media content. To achieve this, a series of 28 in-depth semi-structured interviews was conducted with Greek journalists and academics. A thematic analysis was employed to identify key themes and patterns. Overall, the findings suggest that AI penetration in Greek journalism is in its early stages, with no formal training, strategy, or framework in place within Greek media. Regarding ethical concerns, there is evident skepticism and caution among journalists and academics about issues, such as, data bias, transparency, privacy, and copyright, which are further intensified by the absence of a regulatory framework.Societies290Overall, the findings suggest that AI penetration in Greek journalism is in its early stages, with no formal training, strategy, or framework in place within Greek media.Societies["Panagiota (Naya) Kalfeli", "Christina Angeli"]2025-01-25T00:00:00 eb669b7b4c3548f276d8983cdaf38beb68baf126The integration of artificial intelligence (AI) into human teams is widely expected to enhance performance and collaboration. However, our study reveals a striking and counterintuitive result: human-AI teams performed worse than human-only teams, especially when task difficulty increased. Using a virtual reality-based sensorimotor task, we observed that the inclusion of an active human-like AI teammate disrupted team dynamics, leading to elevated arousal, reduced engagement, and diminished communication intensity among human participants. These effects persisted even as the human teammates' perception of the AI teammate improved over time. These findings challenge prevailing assumptions about the benefits of AI in team settings and highlight the critical need for human-centered AI design to mitigate adverse physiological and behavioral impacts, ensuring more effective human-AI collaboration.00Findings challenge prevailing assumptions about the benefits of AI in team settings and highlight the critical need for human-centered AI design to mitigate adverse physiological and behavioral impacts, ensuring more effective human-AI collaboration.["Yinuo Qin", "Richard Lee", "Paul Sajda"]2025-01-25T00:00:00 d99432ace9a8101c71e547e0eb3f7cf6e2f42270The combination of spirituality, meditation, and artificial intelligence (AI) has promising potential to expand people’s well-being using technology-based meditation. Proper meditation originates from Zen Buddhism and Patanjali’s Yoga Sutras and focuses on inner peace and intensified consciousness which elective personal disposition. AI, in turn, brings smarter means of delivering those practices in the form of self-improving systems that customize and make access to them easier. However, such an integration brings major philosophical and ethical issues into question, including the genuineness of experiences that are facilitated by artificial intelligence, data sharing, concerns over over-dependence on the technology that may in turn cause reduced personal responsibility and hard work. This paper aims at analysing the critical integration of AI-driven meditation following the spiritual interpretations of traditional meditation without compromising the tenets of meditation. It presents an interdisciplinary approach based on recent findings from the field of cognitive science, moral AI, and Eastern wisdom traditions to approach these problems. Therefore, by identifying the research lacunae, it provides a groundwork for voting ethically in the integration of AI in mindfulness practice and avoiding constraining human-oriented values resulting in improved existential spiritual change.The Voice of Creative Research40By identifying the research lacunae, this paper provides a groundwork for voting ethically in the integration of AI in mindfulness practice and avoiding constraining human-oriented values resulting in improved existential spiritual change.The Voice of Creative Research["R. Tripathi"]2025-01-25T00:00:00 987d51d0b31fa4f7dba9e3a2416c969a226b9a0fThe proliferation of digital technology has greatly transformed consumer behaviour, as online platforms and cutting-edge technologies like artificial intelligence (AI) challenge and enhance consumer perceptions related to goods and services. This paper explores how artificial intelligence (AI) influences consumer behaviour, with a focus on AI-powered tools and technology. The findings reveal a positive link between AI and consumer buying decision and underscore AI's importance in modern business strategies, where personalized, data-driven experiences are key to maintaining customer loyalty. Nevertheless, the investigation also addresses challenges and ethical dilemmas, including algorithmic bias and data privacy, underscoring the necessity for businesses to strike a balance between the advantages of AI and these potential concerns in order to maintain consumer trust. KEYWORDS: Artificial Intelligence; AI-driven decision; Buying Behaviour; Consumer Behaviour; Predictive AIEPRA International Journal of Economic and Business Review00A positive link between AI and consumer buying decision is revealed and AI's importance in modern business strategies is underscored, where personalized, data-driven experiences are key to maintaining customer loyalty.EPRA International Journal of Economic and Business Review["Anita Kumari", "Pooja Thakur"]2025-01-25T00:00:00 35164939bf86ceb18950df97c4845d35e3f46855Artificial Intelligence (AI) is rapidly becoming a part of our everyday lives. In this paper, we examine how AI understanding can help Big History in two ways: 1. by providing insight into the complexity of human evolution; 2. by enabling a better understanding of future scenarios as the complex relationship between humans and artificial intelligence evolves. In addition, it discusses how AI tools can be used to enhance scientific discovery and then provide access to information through search and communication. Our understanding of AI and brains is explored by reviewing the fascinating developments in AI, comparing them to brain evolution, and comparing them to the current components of brain architecture. The integration of artificial intelligence and humans is discussed in different future scenarios, including the development of an ongoing relationship between humans and AI similar to a parent-child relationship. The future development of this complex system of humans and AI will proceed along various pathways including placing humans in virtual and augmented realities of AI, placing AI systems in cooperation with humans and organizational processes, and embedding AI within human minds.Journal of Big History00This paper examines how AI understanding can help Big History in two ways: by providing insight into the complexity of human evolution and by enabling a better understanding of future scenarios as the complex relationship between humans and artificial intelligence evolves.Journal of Big History["Ekaterina Sazhienko"]2025-01-25T00:00:00 1a806b0aa509adeb3af7da2b303a547abba20f91Artificial intelligence is reshaping creative domains, yet its co-creative processes, especially in group settings with novice users, remain under explored. To bridge this gap, we conducted a case study in a college-level course where nine undergraduate students were tasked with creating three original music tracks using AI tools over 10 weeks. The study spanned the entire creative journey from ideation to releasing these songs on Spotify. Participants leveraged AI for music and lyric production, cover art, and distribution. Our findings highlight how AI transforms creative workflows: accelerating ideation but compressing the traditional preparation stage, and requiring novices to navigate a challenging idea selection and validation phase. We also identified a new"collaging and refinement"stage, where participants creatively combined diverse AI-generated outputs into cohesive works. Furthermore, AI influenced group social dynamics and role division among human creators. Based on these insights, we propose the Human-AI Co-Creation Stage Model and the Human-AI Agency Model, offering new perspectives on collaborative co-creation with AI.00A case study in a college-level course where nine undergraduate students were tasked with creating three original music tracks using AI tools over 10 weeks, which identified a new "collaging and refinement"stage, where participants creatively combined diverse AI-generated outputs into cohesive works.["Yue Fu", "Michele Newman", "Lewis Going", "Qiuzi Feng", "Jin Ha Lee"]2025-01-25T00:00:00 872d5c83f08fc73c1d28073d842d55ae07997f78Artificial Intelligence Review920It is suggested that work relating to virtual production has the greatest potential to impact other mediums of production, driven by the growing interest in LED volumes/stages for in-camera virtual effects (ICVFX) and automated 3-D capture for virtual modeling of real world scenes and actors.Artificial Intelligence Review["Adrian Azzarelli", "N. Anantrasirichai", "D. Bull"]2025-01-25T00:00:00 adcb4be517fa8776072ec1c56cdc6d18aecce278This chapter examines the conceptual tensions in understanding artificial intelligence (AI) agents' role in creative processes, particularly focusing on Large Language Models (LLMs). Building upon Schmidt's 1954 categorization of human-technology relationships and the classical definition of"author,"this chapter proposes to understand AI agency as existing somewhere between that of an inanimate puppet and a performing actor. While AI agents demonstrate a degree of creative autonomy, including the ability to improvise and construct complex narrative content in interactive storytelling, they cannot be considered authors in the classical sense of the term. This chapter thus suggests that AI agents exist in a dynamic state between human-controlled puppets and semi-autonomous actors. This conceptual positioning reflects how AI agents, while they can certainly contribute to creative work, remain bound to human direction. We also argue that existing conceptual frames concerning authorship should evolve and adapt to capture these new relationships.10This chapter proposes to understand AI agency as existing somewhere between that of an inanimate puppet and a performing actor, suggesting that AI agents exist in a dynamic state between human-controlled puppets and semi-autonomous actors.["Yuqian Sun", "Stefano Gualeni"]2025-01-25T00:00:00 7893813389f2ce0312bc56ecaba40c7d714501a0This paper explores the application of Artificial Intelligence (AI) techniques for detecting plant diseases, focusing on citrus greening disease and various foliar nutrient deficiencies in sweet orange. Agriculture faces numerous challenges from cultivation to harvesting, including significant yield losses due to disease infections and environmental hazards from excessive use of insecticides and fungicides. As the global population grows, the demand for food is surging, and traditional farming methods fall short in meeting this demand, often degrading soil health through intensive pesticide use. AI offers significant advantages over conventional techniques in disease detection. This study employs AI for visual detection of citrus greening disease and foliar nutrient deficiencies, achieving 87% accuracy on a test dataset of infected, nutrient-deficient, and healthy sweet orange leaves. Performance is evaluated using metrics such as Accuracy, Recall, Precision, and F1-Score. Specifically, Convolutional Neural Network (CNN) architectures, including Visual Geometry Group (VGG-16), are utilized for image-based detection and classification, demonstrating the potential of AI in enhancing plant health monitoring.Journal of Experimental Agriculture International00This study employs AI for visual detection of citrus greening disease and foliar nutrient deficiencies, achieving 87% accuracy on a test dataset of infected, nutrient-deficient, and healthy sweet orange leaves.Journal of Experimental Agriculture International["Thomse S. R.", "Ghante P. H", "Hingole D. G", "Suradkar A. L.", "Patil S. G", "Patil L. P", "K. P. B", "Pawar S. Y"]2025-01-25T00:00:00 b8f6c6cff3ab2326a9710145132909c77d67b2c2Who controls the development of Artificial General Intelligence (AGI) might matter less than how we handle the fight for control itself. We formalize this"steering wheel problem"as humanity's greatest near-term existential risk may stem not from misaligned AGI, but from the dynamics of competing to develop it. Just as a car crash can occur from passengers fighting over the wheel before reaching any destination, catastrophic outcomes could arise from development competition long before AGI exists. While technical alignment research focuses on ensuring safe arrival, we show how coordination failures during development could drive us off the cliff first. We present a game theoretic framework modeling AGI development dynamics and prove conditions for sustainable cooperative equilibria. Drawing from nuclear control while accounting for AGI's unique characteristics, we propose concrete mechanisms including pre-registration, shared technical infrastructure, and automated deterrence to stabilize cooperation. Our key insight is that AGI creates network effects in safety: shared investments become more valuable as participation grows, enabling mechanism designs where cooperation dominates defection. This work bridges formal methodology and policy frameworks, providing foundations for practical governance of AGI competition risks.70This work bridges formal methodology and policy frameworks, providing foundations for practical governance of AGI competition risks, and presents a game theoretic framework modeling AGI development dynamics and proves conditions for sustainable cooperative equilibria.["Robin Young"]2025-01-25T00:00:00 fa81da010ab9c9bae2179981563b58c5389062d7Global Insights in Artificial Intelligence and Computing00Global Insights in Artificial Intelligence and Computing[]2025-01-25T00:00:00 e01392297dc71ed1ee991c664f20f3bd26df4c10Использование искусственного интеллекта в формировании читательской грамотности младших школьников может значительно улучшить качество образования и помочь детям достичь лучших результатов в учебе. Это важный шаг в развитии образования и подготовке нового поколения к будущему. The use of artificial intelligence in the formation of reading literacy of younger schoolchildren can significantly improve the quality of education and help children achieve better academic results. This is an important step in the development of education and preparing a new generation for the future.Современные методы и инновации в науке: сборник статей XL международной научной конференции (Санкт-Петербург, Декабрь 2024)00Современные методы и инновации в науке: сборник статей XL международной научной конференции (Санкт-Петербург, Декабрь 2024)["\u041b\u0438\u0434\u0438\u044f \u0427\u0443\u0448\u0438\u0435\u0432\u043d\u0430 \u0425\u044d"]2025-01-26T00:00:00 1f2c11dcbd5f2b6bb328a93b932ad659b2ee9efeThe growing influence of Artificial Intelligence (AI) on cybersecurity is undeniable. This comprehensive review delves into the specific methodologies employed to leverage AI for enhanced cybersecurity. The paper also critically examines the challenges inherent in this domain, including concerns related to data privacy and the dynamic nature of adversarial attacks. Looking ahead, this review explores promising avenues for future research, with a particular focus on the development of adaptable learning systems, robust security architectures, and collaborative efforts across diverse disciplines. By synthesizing existing knowledge and identifying areas for further investigation, this review seeks to provide a thorough understanding of AI's transformative impact on cybersecurity and to offer valuable guidance for future research endeavorsInternational Journal For Multidisciplinary Research00This comprehensive review delves into the specific methodologies employed to leverage AI for enhanced cybersecurity, to provide a thorough understanding of AI's transformative impact on cybersecurity and to offer valuable guidance for future research endeavors.International Journal For Multidisciplinary Research["Aamerkhan Golandaz", "Umerkhan Golandaz", "Mohammed Abdullah Affan"]2025-01-26T00:00:00 4441af0340973b58e69434c853443b27f9763fc2Simple Summary Our study examines the use of artificial intelligence (AI) in cancer diagnosis by evaluating the biases and quality of studies published in a prominent oncology journal. The objective is to identify common biases, assess the adherence to the established ethical principles for AI use in oncology, and analyze the impact of these studies on the subsequent research. The findings reveal various biases, including implicit and environmental biases, alongside challenges related to data accessibility and methodological reporting. Consequently, our study highlights the need to conduct methodologically robust research and improve the manuscript reporting practices to enhance the reliability and applicability of AI models in oncology.Cancers460The findings reveal various biases, including implicit and environmental biases, alongside challenges related to data accessibility and methodological reporting, which highlight the need to conduct methodologically robust research and improve the manuscript reporting practices to enhance the reliability and applicability of AI models in oncology.Cancers["Aref Smiley", "C. M. Re\u00e1tegui-Rivera", "David Villarreal-Zegarra", "S. Escobar-Agreda", "Joseph Finkelstein"]2025-01-26T00:00:00 13dc0a36ae463e6e9a3310f495f027b28bea3ca3В условиях развития искусственного интеллекта и цифровизации возрастает интерес к развитию экстраординарных способностей человека. In the context of the development of artificial intelligence and digitalization, interest in extraordinary human abilities is increasing.Тенденции развития современной науки в свете исследований молодых ученых: сборник статей VI международной научной конференции (Санкт-Петербург, Ноябрь 2024)00Тенденции развития современной науки в свете исследований молодых ученых: сборник статей VI международной научной конференции (Санкт-Петербург, Ноябрь 2024)["\u041b\u044e\u0434\u043c\u0438\u043b\u0430 \u0418\u0432\u0430\u043d\u043e\u0432\u043d\u0430 \u041a\u0440\u0443\u0433\u043b\u044f\u043a", "\u0410\u043d\u0434\u0440\u0435\u0439 \u0412\u0438\u043a\u0442\u043e\u0440\u043e\u0432\u0438\u0447 \u0421\u043f\u0438\u0440\u0438\u0434\u043e\u043d\u043e\u0432", "\u0410\u043d\u043d\u0430 \u0412\u0438\u043a\u0442\u043e\u0440\u043e\u0432\u043d\u0430 \u0412\u0435\u0439\u043b"]2025-01-26T00:00:00 708e365543747acbf5aa9c29352c12a50536a5d0   The article considers a popular question in the modern world: can artificial intelligence replace a qualified specialist? The author presents their thoughts on this problem based on the current state of computer technologies.Russian journal of neurosurgery00Russian journal of neurosurgery["G. V. Danilov"]2025-01-26T00:00:00 9eb85034fd1a2c85cf6da8b82baccc6b367b6d8bAs artificial intelligence (AI) and sustainability initiatives increasingly intersect, understanding public perceptions of their relationship becomes crucial for successful implementation. However, no validated instrument exists to measure these specific perceptions. This paper presents the development and validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI), a novel 13-item instrument measuring how individuals view the relationship between AI advancement and environmental sustainability. Through factor analysis (N=105), we identified two distinct dimensions: Twin Transition and Competing Interests. The instrument demonstrated strong reliability (alpha=.89) and construct validity through correlations with established measures of AI and sustainability attitudes. Our findings suggest that individuals can simultaneously recognize both synergies and tensions in the AI-sustainability relationship, offering important implications for researchers and practitioners working at this critical intersection. This work provides a foundational tool for future research on public perceptions of AI's role in sustainable development.00The development and validation of the AISPI is presented, a novel 13-item instrument measuring how individuals view the relationship between AI advancement and environmental sustainability, suggesting that individuals can simultaneously recognize both synergies and tensions in the AI-sustainability relationship.["Annika Bush"]2025-01-26T00:00:00 1da707d5b7a1927a081ee28e69587c9d2ac62388Rehabilitation following a stroke faces challenges in offering customized treatment and attaining the best possible outcomes. The utilization of artificial intelligence (AI) presents transformative solutions that have the potential to revolutionize existing practices. This minireview discusses the use of AI in rehabilitation after stroke, in form of customized intervention, task-specific training with robotics, real time monitoring by wearable devices and remote monitoring through tele rehabilitation. Despite the recent advances, issues such as algorithm bias, concerns about data security, and access disparities remain. Future directions include creating predictive analytics for tailored stroke therapies, incorporating virtual reality for increased participation, and assuring ethical and equitable distribution. Collaborative efforts are necessary to address these challenges and advance AI-driven stroke therapy. This review highlights the potential of AI to revolutionize stroke rehabilitation outcomes through interdisciplinary collaboration and ethical implementation.JPMA. The Journal of the Pakistan Medical Association00The potential of AI to revolutionize stroke rehabilitation outcomes through interdisciplinary collaboration and ethical implementation is highlighted and the use of AI in rehabilitation after stroke is discussed.JPMA. The Journal of the Pakistan Medical Association["A. Afridi", "Sumaiyah Obaid", "Neha Raheel", "F. Rathore"]2025-01-26T00:00:00 34d8f778cce647a3f5742b5554d3e2b0fff5f4b4The objective of this study is to utilize artificial intelligence techniques for the diagnosis of complications and diseases that may arise after liver transplantation, as well as for the identification of patients in need of transplantation. To achieve this, an interface was developed to collect patient information from Atatürk University Research Hospital, specifically focusing on individuals who have undergone liver transplantation. The collected data were subsequently entered into a comprehensive database. Additionally, relevant patient information was obtained through the hospital’s information processing system, which was used to create a data pool. The classification of data was based on four dependent variables, namely, the presence or absence of death (“exitus”), recurrence location, tumor recurrence, and cause of death. Techniques such as Principal Component Analysis and Linear Discriminant Analysis (LDA) were employed to enhance the performance of the models. Among the various methods employed, the LDA method consistently yielded superior results in terms of accuracy during k-fold cross-validation. Following k-fold cross-validation, the model achieved the highest accuracy of 98% for the dependent variable “exitus”. For the dependent variable “recurrence location”, the highest accuracy obtained after k-fold cross-validation was 91%. Furthermore, the highest accuracy of 99% was achieved for both the dependent variables “tumor recurrence” and “cause of death” after k-fold cross-validation.Applied Sciences410An interface was developed to collect patient information from Atatürk University Research Hospital, specifically focusing on individuals who have undergone liver transplantation, to utilize artificial intelligence techniques for the diagnosis of complications and diseases that may arise after liver transplantation, as well as for the identification of patients in need of transplantation.Applied Sciences["Mete Ya\u011fano\u011flu", "G\u00fcrkan \u00d6zt\u00fcrk", "Ferhat Bozkurt", "Zeynep Bilen", "Z\u00fchal Yeti\u015f Demir", "Sinan Kul", "Emrah \u015eim\u015fek", "S. Kara", "Hakan Eygu", "N. Altunda\u015f", "N. Aksungur", "E. Korkut", "Mehmet Sinan Ba\u015far", "N. \u00d6ztu\u0308rk"]2025-01-26T00:00:00 1fdfb62f43daf90ff92d17e1c2094e039f7982a7International Journal of Global Innovations and Solutions (IJGIS)00International Journal of Global Innovations and Solutions (IJGIS)["Shalmali Patil", "Satish Waybhase"]2025-01-26T00:00:00 de985ffdc508427611b878c493880a71fcc12f8aTrustworthiness is essential for the precise and interpretable application of artificial intelligence (AI) in medical imaging. Traditionally, precision and interpretability have been addressed as separate tasks, namely medical image analysis and explainable AI, each developing its own models independently. In this study, for the first time, we investigate the development of a unified medical vision-language pre-training model that can achieve both accurate analysis and interpretable understanding of medical images across various modalities. To build the model, we construct MedConcept-23M, a large-scale dataset comprising 23 million medical image-text pairs extracted from 6.2 million scientific articles, enriched with concepts from the Unified Medical Language System (UMLS). Based on MedConcept-23M, we introduce ConceptCLIP, a medical AI model utilizing concept-enhanced contrastive language-image pre-training. The pre-training of ConceptCLIP involves two primary components: image-text alignment learning (IT-Align) and patch-concept alignment learning (PC-Align). This dual alignment strategy enhances the model's capability to associate specific image regions with relevant concepts, thereby improving both the precision of analysis and the interpretability of the AI system. We conducted extensive experiments on 5 diverse types of medical image analysis tasks, spanning 51 subtasks across 10 image modalities, with the broadest range of downstream tasks. The results demonstrate the effectiveness of the proposed vision-language pre-training model. Further explainability analysis across 6 modalities reveals that ConceptCLIP achieves superior performance, underscoring its robust ability to advance explainable AI in medical imaging. These findings highlight ConceptCLIP's capability in promoting trustworthy AI in the field of medicine.00This study investigates the development of a unified medical vision-language pre-training model that can achieve both accurate analysis and interpretable understanding of medical images across various modalities and introduces ConceptCLIP, a medical AI model utilizing concept-enhanced contrastive language-image pre-training.["Yuxiang Nie", "Sunan He", "Yequan Bie", "Yihui Wang", "Zhixuan Chen", "Shu Yang", "Hao Chen"]2025-01-26T00:00:00 08b197dfa68e3c5e16efac3842826dca451a7563The application of Artificial Intelligence (AI) to teacher training is a rather recent phenomenon and there is a need for more research on its use in teacher education. This paper examines the use and interpretation of AI by student language teachers during a 10-week telecollaborative course between students from two universities, one in the USA and the other in Spain (n = 46). The course focused on Technology-Enhanced Project-Based Language Learning (TePBLL) and was divided into different ‘technological blocks’. This article is centered around the AI technology block. The analysis is based on three exit tickets (reflection prompts) that demonstrate participants’ thoughts and changing perspectives towards AI. Through thematic analysis of the open-ended responses, this study shows that participants initially appeared skeptical before moving to tentative optimism after first studying theory and examples of the application of AI, followed by the creation of AI-based lessons and activities. The student teachers identify AI as a means to personalize and make language learning more efficient while expressing concerns related to its overuse, ethical issues and potential for undermining critical thinking and creativity. This small study looks at the evolution of the student teachers’ concepts about and perspectives towards AI-enhanced language teaching and learning before, during and after they engage in the technology block. The findings suggest that hands-on training that includes lesson design helps student teachers view AI as a complementary tool for many aspects of their teaching, although this can only be achieved through an adequate pedagogical application.Education sciences340The findings suggest that hands-on training that includes lesson design helps student teachers view AI as a complementary tool for many aspects of their teaching, although this can only be achieved through an adequate pedagogical application.Education Sciences["Ese Emmanuel Uwosomah", "Melinda Dooly"]2025-01-26T00:00:00 cd3ad5088630cf2232900e570ef0ae3f1e77b189Mental health challenges have become a global concern, with millions affected by conditions such as depression, anxiety, and stress. Despite the growing need for mental health services, barriers such as stigma, limited accessibility to care, and a shortage of trained professionals hinder timely interventions. The integration of artificial intelligence (AI) into mental health monitoring systems offers a transformative approach to addressing these challenges. AI-driven systems leverage machine learning algorithms, natural language processing (NLP), and data analytics to monitor and assess mental health in real time. These systems can analyze diverse data sources, including speech patterns, text inputs, facial expressions, and physiological signals, to identify early signs of mental health issues. By providing continuous, scalable, and non-invasive monitoring, AI enhances traditional methods by offering personalized insights and timely interventions. Recent advancements in AI, such as deep learning and emotion recognition, have significantly improved the accuracy and reliability of these systems. Moreover, the incorporation of wearable technology and mobile health applications enables individuals to track their mental well-being seamlessly. While promising, these systems also raise ethical concerns around privacy, bias, and the need for robust validation in clinical settings. This paper explores the design, implementation, and ethical considerations of AI-driven mental health monitoring systems, highlighting their potential to revolutionize mental health care and improve patient outcomes.International Journal For Multidisciplinary Research00This paper explores the design, implementation, and ethical considerations of AI-driven mental health monitoring systems, highlighting their potential to revolutionize mental health care and improve patient outcomes.International Journal For Multidisciplinary Research["Ravi Singh", "Mansi Yadav", "Ayushi Saini"]2025-01-26T00:00:00 00f33427795f633bc7a6a2473b502d59683be262 Clinical decision-making is a complex process that involves information obtained from multiple data modalities. Artificial intelligence (AI) approaches that can effectively integrate multi-modal data hold significant promise to advance clinical care. Two areas of success are imaging and digital pathology, where AI has shown great potential to improve cancer diagnosis and treatment. This talk will provide an overview on how AI can be used to extract information from imaging and pathology and identify prognostic and predictive biomarkers for personalized cancer treatment. In particular, I will present our recent work on building multi-modal foundation models for precision oncology. Citation Format: Ruijiang Li. Toward multi-modal foundation AI for precision oncology. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr IA05Clinical Cancer Research00This talk will provide an overview on how AI can be used to extract information from imaging and pathology and identify prognostic and predictive biomarkers for personalized cancer treatment and build multi-modal foundation models for precision oncology.Clinical Cancer Research["Ruijiang Li"]2025-01-26T00:00:00 4902db76f006992fbfeeeff606c143aaa8719d56The potential of artificial intelligence to revolutionize the creation of self-adapting Governance, Risk, and Compliance (GRC) systems is explored in this article. It discusses how organizations are transitioning from traditional, manual GRC processes to sophisticated AI-driven solutions that can autonomously adapt to evolving regulatory landscapes. The article illustrates how reinforcement learning, adaptive algorithms, and neural networks reshape GRC practices by examining current implementation challenges, technological foundations, and future opportunities. It also discusses critical implementation factors across technological, regulatory, and organizational aspects while highlighting real-world applications in intelligent policymaking, predictive risk management, and ongoing compliance monitoring. The article addresses future developments in GRC technology, such as enhanced ecosystem creation, advanced analytics, and integration capabilities. Analyzing prospects and challenges provides valuable insights for companies implementing self-adapting GRC systems while maintaining robust governance frameworks.International Journal of Scientific Research in Computer Science Engineering and Information Technology10The article illustrates how reinforcement learning, adaptive algorithms, and neural networks reshape GRC practices by examining current implementation challenges, technological foundations, and future opportunities, while highlighting real-world applications in intelligent policymaking, predictive risk management, and ongoing compliance monitoring.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Naresh Kumar Methuku"]2025-01-26T00:00:00 2a4f5ae1bc249c37236f28ecc047db4a809100a9As global supply chains become more intricate, the significance of supply chain risk management has significantly increased. This research delves into the utilization of artificial intelligence (AI) in managing supply chain risks, analyzing cutting-edge advancements, obstacles, and potential areas for further exploration. Through a comprehensive review of literature sources like Google Scholar, Web of Science, EI, and Scopus, this study investigates how AI methods such as machine learning, deep learning, neural networks, fuzzy logic, genetic algorithms, and evolutionary algorithms can help mitigate supply chain risks. These AI technologies have demonstrated remarkable efficacy in mitigating various risks, including forecasting, anomaly detection, image recognition, text mining, logistics optimization, and emergency response tactics. Apart from AI-driven approaches, optimization solvers and algorithms play a critical role in tackling complex supply chain dilemmas. Mathematical programming solvers, including linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP), are commonly used to model and optimize supply chain networks by considering factors like cost, capacity, and demand fluctuations. Fine-tuning solver parameters and strategies to enhance computational efficiency—known as solver tuning—has been crucial in enhancing solution quality and decreasing computation time for large-scale supply chain issues. Heuristic solvers like genetic algorithms, simulated annealing, and ant colony optimization are frequently employed to resolve conflicts in supply chains. These solvers offer practical solutions to problems where exact methods are computationally impractical, allowing for swift responses to disruptions such as production delays or spikes in demand. Pyramid classification, a hierarchical approach, further refines decision-making by categorizing risks and aligning response strategies based on priority and severity. Furthermore, the integration of AI technologies and solvers enables advanced conflict resolution techniques, including scenario-based modeling and multi-objective optimization. These approaches enable decision-makers to weigh trade-offs between conflicting objectives like minimizing costs versus maximizing service levels in real-time. The study underscores that AI technologies and optimization solvers significantly bolster risk management in supply chains. Nonetheless, challenges such as data privacy concerns, security vulnerabilities, technical intricacies, and implementation obstacles pose critical barriers to widespread adoption. The study offers practical suggestions for businesses and decision-makers while pinpointing key areas for future exploration, such as devising hybrid models that merge heuristic solvers with AI for adaptive and scalable risk management strategies.International Journal of Advanced Research in Science, Communication and Technology80This research delves into the utilization of artificial intelligence (AI) in managing supply chain risks, analyzing cutting-edge advancements, obstacles, and potential areas for further exploration, and offers practical suggestions for businesses and decision-makers.International Journal of Advanced Research in Science, Communication and Technology["Nitin Grover"]2025-01-26T00:00:00 633ced716c225d8fd5ae3b3f4bdffa858da857e0Artificial Intelligence is an area of computer science thatemphasizes the use of machine learning algorithms tomimic human thinking and problem-solving. The fastexpansion of AI in today’s world can be credited to theprogress of algorithms, reasonable graphic processorsand expanded annotated databases.1 AI is an essential tool in addressing complex healthcareissues worldwide. However, AI acceptance andintegration in medical education took time, andsignificant advancements and broader adoption becamemore prominent after 1980s. Also, the application of AI inmedical education has grown significantly over the past20 years, as shown by the rising volume of publications inthis area. Given the widespread use of AI in many facets ofmedical practice, educational programmes pertinent tothis field have to be created and put into place inacademic institutions.2 Continued...JPMA. The Journal of the Pakistan Medical Association110Given the widespread use of AI in many facets of medical practice, educational programmes pertinent to this field have to be created and put into place in academic institutions.JPMA. The Journal of the Pakistan Medical Association["R. Aftab", "Shaur Sarfaraz", "A. Afzal"]2025-01-26T00:00:00 2a2fd6d6ed91a4e1b251bf1b01e4bcebf937c288The rapid advancement of artificial intelligence (AI) systems in critical domains like healthcare, justice, and social services has sparked numerous regulatory initiatives aimed at ensuring their safe deployment. Current regulatory frameworks, exemplified by recent US and EU efforts, primarily focus on procedural guidelines while presuming that scientific benchmarking can effectively validate AI safety, similar to how crash tests verify vehicle safety or clinical trials validate drug efficacy. However, this approach fundamentally misunderstands the unique technical challenges posed by modern AI systems. Through systematic analysis of successful technology regulation case studies, we demonstrate that effective scientific regulation requires a causal theory linking observable test outcomes to future performance - for instance, how a vehicle's crash resistance at one speed predicts its safety at lower speeds. We show that deep learning models, which learn complex statistical patterns from training data without explicit causal mechanisms, preclude such guarantees. This limitation renders traditional regulatory approaches inadequate for ensuring AI safety. Moving forward, we call for regulators to reckon with this limitation, and propose a preliminary two-tiered regulatory framework that acknowledges these constraints: mandating human oversight for high-risk applications while developing appropriate risk communication strategies for lower-risk uses. Our findings highlight the urgent need to reconsider fundamental assumptions in AI regulation and suggest a concrete path forward for policymakers and researchers.620It is demonstrated that effective scientific regulation requires a causal theory linking observable test outcomes to future performance, and that deep learning models, which learn complex statistical patterns from training data without explicit causal mechanisms, preclude such guarantees, rendering traditional regulatory approaches inadequate for ensuring AI safety.["Gabriel Stanovsky", "Renana Keydar", "Gadi Perl", "Eliya Habba"]2025-01-26T00:00:00 71beb5b7eef4423c29ce56180021eb4d05d7854fGlobal Insights in Artificial Intelligence and Computing00Global Insights in Artificial Intelligence and Computing[]2025-01-26T00:00:00 91f07547eb6a31b048fe9bb8bec0c5c9aef728ddLa inteligencia artificial ha revolucionado la producción de noticias, transformando la forma en que se recopila, procesa y distribuye la información. Su implementación ha permitido generar contenidos de manera más rápida y personalizada, optimizando procesos, sin embargo, esta innovación plantea desafíos éticos y profesionales que afectan la calidad periodística, la seguridad y privacidad de datos, los desafíos laborales, así como a la percepción del público frente a incorporación de esta innovación tecnológica y metodológica dentro del ámbito de la comunicación. La presente investigación tiene como objetivo analizar el uso de la inteligencia artificial en la producción de noticias y su impacto en la percepción de la audiencia. La metodología utilizada empleó un enfoque mixto secuencial explicativo. En una primera etapa se recolectaron datos cualitativos y cuantitativos a través de entrevistas a profundidad a expertos, así como encuestas que permitieron analizar el impacto de la inteligencia artificial en la producción de noticias y su influencia en la percepción de la audiencia. En conclusión, la inteligencia artificial ha demostrado ser una herramienta eficaz para agilizar la generación de contenido, personalizar noticias, pero también plantea desafíos éticos y riesgos asociados a la posible difusión de información sesgada.Religación80Religación["Cayambe Vinueza Jammell Andrea", "Fabi\u00e1n Vladimir Argudo Palomeque"]2025-01-26T00:00:00 d39e2752225a436736284155e065c361da7a6591A rápida evolução das ciências e tecnologias digitais tem transformado profundamente a maneira como a humanidade interage com inovações, especialmente na área da saúde. A robótica e a inteligência artificial (IA) emergem como ferramentas poderosas em procedimentos médicos, prometendo maior precisão, segurança e eficiência. No entanto, surgem questionamentos sobre a real eficácia dessas tecnologias em comparação com as técnicas cirúrgicas tradicionais, principalmente considerando fatores como segurança, tempo operatório, recuperação pós-operatória e complicações. A problemática do estudo é: qual a eficácia da robótica em procedimentos cirúrgicos em comparação às abordagens tradicionais? A hipótese é que a robótica oferece benefícios em termos de precisão e segurança, mas enfrenta desafios como alto custo e tempo operacional prolongado. O objetivo principal é avaliar a eficácia e os benefícios da cirurgia robótica, comparando seus resultados com as técnicas convencionais. A justificativa para este estudo se baseia na necessidade de analisar criticamente os benefícios e limitações da tecnologia, fornecendo subsídios para a adoção generalizada da robótica na medicina. A metodologia adotada envolveu uma revisão sistemática da literatura, com pesquisa em bases como Google Acadêmico, Capes e SciELO, utilizando descritores como "robótica and cirurgias" e "inteligência artificial and medicina". Foram definidos critérios de inclusão e exclusão, priorizando estudos de caso e artigos publicados entre 2010 e 2024. A pesquisa resultou na seleção de cinco artigos, cujos dados foram interpretados e discutidos, fornecendo uma visão abrangente dos resultados da cirurgia robótica. Portanto, a aplicação da robótica em procedimentos cirúrgicos demonstra notável eficácia em comparação às técnicas tradicionais, destacando-se pela segurança, precisão e benefícios na recuperação pós-operatória.Revista ft00Revista ft["Carlos Eduardo da Silva"]2025-01-26T00:00:00 a39657d9486da50514d3e73ad5358c14baaeb9f4Currently, it is impossible to imagine a post-industrial society without information resources, one of the sources of which is broadcasting. In light of the rapid development of technologies, legal regulation of this phenomenon at the international level lags significantly behind, since the rights of broadcasting organizations in certain broadcasting environments are not adequately protected. Since 1998 to the present day, the Standing Committee on Copyright and Related Rights of the World Intellectual Property Organization has been discussing proposals to update the relevant legal regime for broadcasters. The article analyzes the latest versions of the draft international treaty in the field of broadcasting for the possibility of a logical conclusion of the negotiation process in the form of adopting a new international treaty on broadcasting organizations. The author attempts to use the latest achievements of information and communication technologies to prepare the text of this document.Courier of the Kutafin Moscow State Law University00The article analyzes the latest versions of the draft international treaty in the field of broadcasting for the possibility of a logical conclusion of the negotiation process in the form of adopting a new international treaty on broadcasting organizations.Courier of Kutafin Moscow State Law University (MSAL))["A. V. Ustinova"]2025-01-26T00:00:00 783524521926f52e2b56e19035417a6e57175a81En el vertiginoso avance de la Inteligencia Artificial (IA) en nuestra sociedad, entender la reacción social y las emociones asociadas es crucial. Esta investigación explora cómo los y las jóvenes universitarias en España perciben y responden a la IA en su vida cotidiana. ¿Qué emociones despiertan en ellos estas tecnologías? A través de nueve grupos focales, se examinaron sus sensaciones, el grado de uso de la IA, sus reacciones emocionales y consideraciones éticas. Los hallazgos revelan dos posturas dominantes: una de indiferencia, que acepta y justifica los beneficios de la IA, y otra de rechazo, marcada por el miedo y la desconfianza. Además, se identifica una contradicción en los discursos: los jóvenes critican los posibles efectos negativos de la IA, pero la utilizan habitualmente, generando una notable disonancia cognitiva. Este estudio proporciona una visión integral de las emociones y percepciones de los nativos digitales frente a la IA, subrayando la necesidad de abordar las preocupaciones éticas y emocionales en su desarrollo y adopción.Tendencias Sociales Revista de Sociología00Tendencias Sociales. Revista de Sociología["Ignacio Ceinos Fern\u00e1ndez", "Sandra Fern\u00e1ndez Corbella", "Teresa Mart\u00ednez Santiago"]2025-01-26T00:00:00 013143e41811f685c9bbc452c8f802c65a90500dThe current landscape of the food processing industry places a strong emphasis on improving food quality, nutritional value, and processing techniques. This focus arises from consumer demand for products that adhere to high standards of quality, sensory characteristics, and extended shelf life. The emergence of artificial intelligence (AI) and machine learning (ML) technologies is instrumental in addressing the challenges associated with variability in food processing. AI represents a promising interdisciplinary approach for enhancing performance across various sectors of the food industry. Significant advancements have been made to address challenges and facilitate growth within the food sector. This review highlights the applications of AI in agriculture and various sectors of the food industry, including bakery, beverage, dairy, food safety, fruit and vegetable industries, packaging and sorting, and the drying of fresh foods. Various strategies have been implemented across different food sectors to promote advancements in technology. Additionally, this article explores the potential for advancing 3D printing technology to enhance various aspects of the food industry, from manufacturing to service, while also outlining future perspectives.Foods1721The potential for advancing 3D printing technology to enhance various aspects of the food industry, from manufacturing to service, while also outlining future perspectives is explored.Foods["Ali Aghababaei", "Fatemeh Aghababaei", "Marc Pignitter", "M. Hadidi"]2025-01-27T00:00:00 6ec75ba02c91d2e71ad824a95e31672cb2e14290 Leading military forces around the world have expressed enormous interest in artificial intelligence (AI) due to its military potential. This policy paper examines Chinese perceptions of the military use of AI by studying Chinese-language scholarly materials. A key finding is that, while the Chinese strategic community has explored the military use of AI across various countries, the United States plays a central and unique role in shaping Chinese perceptions of militarized AI. The US serves as a global near-competitor, providing a benchmark for China to measure its own development and competitiveness. In addition, the US acts as a role model for the Chinese military to emulate in terms of its ideas, policies and practices. Consequently, American success in AI has become a primary source of anxiety among the Chinese strategic community, prompting self-reflection and accelerating the development of ambitious Chinese AI plans. Contrary to the popular narrative in Washington that China has already surpassed the US in the global AI race, Chinese discussions reveal considerable admiration for American AI leadership, with a focus on catching up rather than overtaking the US. This paper suggests that both the US and China need to play their parts in mitigating the risks of a global AI race.International Affairs00It is suggested that both the US and China need to play their parts in mitigating the risks of a global AI race.International Affairs["Jinghan Zeng"]2025-01-27T00:00:00 bc8cc366ad4a5c5c5b060ab6a3b52b4f9d888dfeDiabetes Mellitus (DM) stands as one of the most widespread non-infectious diseases globally. Although diagnosis of diabetes is possible with the fasting plasma glucose test after 12-hour fast, once diabetes is diagnosed, it cannot be reversed. Therefore, it is crucial to identify early indicators for predicting diabetes. Presently, DM can be discerned through various methods involving the analysis of human facial features. One method for facial recognition in diabetes relies on experimental evidence, with its accuracy contingent on the skill and expertise of the physician. Another approach involves diagnosis based on facial morphological features. These morphological changes may be attributed to oxidative stress, damage of blood vessels and collagen, edema and craniofacial abnormalities stemming from hyperglycemia. While cephalometric analysis remains the gold standard for diagnosing skeletal craniofacial morphology, it is a costly and technique-sensitive procedure. Facial recognition based on Artificial Intelligence (AI) has proven to be a valuable tool in the diagnosis and screening of diabetes. Its combination of simplicity, accuracy, and cost-effectiveness makes it a promising addition to the healthcare landscape, ultimately contributing to advancements in pre-clinical diagnosis and leading to enhanced patient outcomes. Given the rapid global increase in diabetes, the importance of early detection of diabetes and the limited information about the role of facial recognition in this regard, this study assesses diabetes through facial features using AI approaches.Journal of Iranian medical council00This study assesses diabetes through facial features using AI approaches because of the rapid global increase in diabetes, the importance of early detection of diabetes and the limited information about the role of facial recognition in this regard.Journal of Iranian Medical Council["M. Owlia", "Hamidreza Soltani"]2025-01-27T00:00:00 1208df5ae32de82a9adf5db44349a5daba184064Background The growth of the use of artificial intelligence (AI) and robotic solutions in healthcare is accompanied by high expectations for improved efficiency and quality of services. However, the use of such technologies can be a source of anxiety for patients whose expectations and experiences with such technology differ from medical staff's. This study assessed attitudes toward AI and robots in delivering health services and performing various tasks in medicine and related fields in Polish society. Methods 50 semistructured in-depth interviews were conducted with participants of diversified socio-demographic profiles. The interviewees were initially recruited for the interviews in a convenience sample; then, the process was continued using the snowballing technique. The interviews were transcribed and analyzed using the MAXQDA Analytics Pro 2022 program (release 22.7.0). An interpretative approach to qualitative content analysis was applied to the responses to the research questions. Results The analysis of interviews yielded three main themes: positive and negative perceptions of the use of AI and robots in healthcare and ontological concerns about AI, which went beyond objections about the usefulness of the technology. Positive attitudes toward AI and robots were associated with overall higher trust in technology, the need to adequately respond to demographic challenges, and the conviction that AI and robots can lower the workload of medical personnel. Negative attitudes originated from convictions regarding unreliability and the lack of proper technological and political control over AI; an equally important topic was the inability of artificial entities to feel and express emotions. The third theme was that the potential interaction with machines equipped with human-like traits was a source of insecurity. Conclusions The study showed that patients' attitudes toward AI and robots in healthcare vary according to their trust in technology, their recognition of urgent problems in healthcare (staff workload, time of diagnosis), and their beliefs regarding the reliability and functioning of new technologies. Emotional concerns about contact with artificial entities looking or performing like humans are also important to respondents' attitudes.Frontiers in Digital Health650Patients' attitudes toward AI and robots in healthcare vary according to their trust in technology, their recognition of urgent problems in healthcare, and their beliefs regarding the reliability and functioning of new technologies.Frontiers in Digital Health["P. Smola", "Iwona M\u0142o\u017aniak", "M. Wojcieszko", "U. Zwierczyk", "Mateusz Kobryn", "Elzbieta Rzepecka", "M. Duplaga"]2025-01-27T00:00:00 495bc88f195153bab79e68e926e94e802e3e739fPurpose: The purpose of the study was to examine operational effectiveness in security and defence within the UAE: The strategic role of artificial intelligence. Methodology: The study applied both Qualitative and quantitative approach. For the qualitative component of this research, a complete literature analysis will be done to investigate the current body of scholarship, studies, and policy papers pertaining to the deployment of artificial intelligence (AI) in security and defense within the United Arab Emirates (UAE). Thematic analysis will be used for a qualitative data analysis method. This study will also apply quantitative approach. Data was collected using structured questionnaires and a structured survey distributed to 50 professionals to stakeholders within the UAE's security and military industries to obtain quantitative data on different areas of AI acceptance, usage, obstacles, and perceived influence on operational performance. Statistical software like Excel and Python will be used to perform quantitative data analysis. Charts, graphs, and plots will be used to visually present the survey results. Findings: The statistical findings reveal a significant relationship between AI adoption and enhanced decision-making capabilities. A strong positive correlation (r = 0.78, p < 0.001) between AI usage and improved situational awareness underscores AI’s capacity to process real-time data effectively. In cybersecurity, the analysis identifies a moderate positive correlation (r = 0.64, p < 0.01) between AI implementation and threat mitigation success. The participants have consistently emphasized ability of AI for analysis of vast datasets in the real time ensuring more informed and quick decision. It aligns with the global trends where the defense organizations would leverage AI for interpretation of satellite imagery and prediction of threats. The advanced machine learning algorithms have been proven effective for detection of anomalies like unauthorized access and phishing attack attempts. The participants are praising the technologies for improving the operational efficiency and reducing the human risks. Unique Contribution to Theory, Practice and Policy: Study highlights existing theories where AI improves decision making through real time insights. However, it challenges the deterministic views for AI as the infallible further emphasizing need for the hybrid models which integrates with the human judgement. The defense organizations need to adopt for hybrid approach where the AI is supporting instead of replacing the human decision making. The training programs need to be designed for equipping personnel with skills for interpreting the AI outputs critically. With the growing adversarial of AI, the investment across counter AI technologies has been imperative. It includes development of systems which could neutralize and identify the AI driven cyberattacks and foster collaborations with the global cybersecurity experts for staying ahead in emerging threats. The strengthening of partnerships with the international defense organizations could also help the UAE for leveraging the cutting-edge counters for AI solutions while sharing the threat intelligence effectively.International journal of technology and systems00The statistical findings reveal a significant relationship between AI adoption and enhanced decision-making capabilities and highlights existing theories where AI improves decision making through real time insights.International Journal of Technology and Systems["Ali Afghani"]2025-01-27T00:00:00 dcf971ea6f4021cd20cf5065d4638ab209f31b5bDue to the frequent use of artificial intelligence (AI) technologies in daily life, it is thought that primary school students acquire information about this concept from various sources. The way these sources present AI may affect students' perceptions of AI. In the study, it was aimed to examine the perceptions of third and fourth grade primary school students about AI through metaphors and drawings. This research, which was conducted with the participation of 262 students, was conducted with the phenomenological design. When the metaphors of the participants were analysed, it was determined that they produced 100 metaphors, and these metaphors were evaluated in 17 categories as humanistic feature, information source, danger, development, superhuman feature, service, source of happiness, productivity, orientation, commitment, pervasiveness, necessity, security, speed, difficulty, virtual environment and uncertainty. Accordingly, it was determined that the participants evaluated AI from many different perspectives and produced the most metaphors in the categories of humanistic feature, information source and danger. It was determined that the metaphors human, brain and living were prominent in the human characteristic category; the metaphors teacher, wise and book were prominent in the source of information category; and finally, the metaphors enemy, weapon and monster were prominent in the danger category. When the drawing findings were analysed, it was determined that 37 codes represented four categories: purpose, object, interaction and environment. In the purpose category, service, source of information, and source of happiness; in the object category, mostly humanoid robot; in the interaction category, emphasising interaction; and in the environment category, the environment was not specified. In line with the findings obtained, literature discussions were made and suggestions were made.European Journal of Education650It was determined that the participants evaluated AI from many different perspectives and produced the most metaphors in the categories of humanistic feature, information source and danger.European Journal of Education["Jale Kalemku\u015f", "Fatih Kalemku\u015f"]2025-01-27T00:00:00 a8f24dce371b87014fc76b31a48cc18c4595458bWe developed an asynchronous online cognitive behavioral therapy (CBT) training tool that provides artificial intelligence- (AI-) enabled feedback to learners across eight CBT skills. We sought to evaluate the technical reliability and to ascertain how practitioners would use the tool to inform product iteration and future deployment. We conducted a single-arm 2-week field trial among behavioral health practitioners who treat outpatients with psychosis. Practitioners (N = 21) were invited to use the AI-enabled CBT training tool over a 2-week (15 days, inclusive) period. To enable naturalistic observation, no adjustments were made to their workloads nor were prescriptions on use provided. We conducted daily assessments and collected backend analytics for all users. At end point, we assessed acceptability, appropriateness, feasibility of implementation, perceived usability, satisfaction, and perceived impact of training. We observed four types of technical issues: broken links, intermittent issues receiving AI-enabled feedback, video replay errors, and an HTML error. Participants averaged 6.57 logins over the 2 weeks, with more than half engaging daily. Most participants (44.7%) engaged for < 30-min increments. Usability scores exceeded industry standard and satisfaction scores indicated good promotion of the tool. All participants endorsed high feasibility, acceptability, and appropriateness. Twelve participants (57%) used the AI-enabled feedback feature; those who did tended to report improved satisfaction, feasibility, and perceived impact of the training. The training tool was used by practitioners in a routine care setting, met or exceeded conventional implementation benchmarks, and may support skill improvement; however, data suggest that practitioners may need support or accountability to fully leverage the training tool. (PsycInfo Database Record (c) 2025 APA, all rights reserved).Psychotherapy00The training tool was used by practitioners in a routine care setting, met or exceeded conventional implementation benchmarks, and may support skill improvement; however, data suggest that practitioners may need support or accountability to fully leverage the training tool.Psychotherapy["Sarah L Kopelovich", "Rois\u00edn Slevin", "Rachel M Brian", "Victoria Shepard", "S. Baldwin", "Dror Ben-Zeev", "Mike Tanana", "Zac E. Imel"]2025-01-27T00:00:00 b5b5a72d9a1de237fd60c26a4e625da6d484f9ccBMC Cancer420This study validates the effectiveness of the artificial intelligence-assisted platform in detecting HCC lesions and analyzing lesion size and location and demonstrates that the product not only accurately segments HCC lesions but also provides valuable insights into lesions characteristics that are essential for effective treatment planning.BMC Cancer["Rongxue Shan", "Chenhao Pei", "Qianrui Fan", "Junchuan Liu", "Dawei Wang", "Shifeng Yang", "Ximing Wang"]2025-01-27T00:00:00 5a310e66ba876f1258f7f2c09068728c7a437a36Surgical Endoscopy170The model appears to have high accuracy, sensitivity, and positive predictive value for evaluating adherence to best practices for safety in OAGB, and adding more best practices, tested in multi-center studies will enable cross-border standardization of the procedure.Surgical endoscopy["Danit Dayan", "E. Nizri", "A. Keidar"]2025-01-27T00:00:00 cd627b9b7b55527073226ac71a3efca2775d6ec4BMC Medical Education950The results of the analysis show that the current evidence regarding measurable educational outcomes of AI-powered interventions in health professions education is poor and there is no straightforward guide for evaluating the quality of research in AI-based education.BMC Medical Education["Eva Feigerlova", "Hind Hani", "Ellie Hothersall-Davies"]2025-01-27T00:00:00 b4410c78ce129d987a69234541809efdb2c6cc9fArtificial intelligence (AI) is taking over the different strata of life and industries, such as agriculture, to higher levels of productivity, efficiency, and decision-making with the use of smart technologies. This study evaluated the perceived socioeconomic impacts and challenges of AI on the productivity of agricultural extension agents in Delta State, Nigeria. The data was collected from 51 respondents through use of stratified random sampling technique and analyzed using descriptive statistics. The findings indicated that the majority of the extension agents saw AI as having both economic benefits and limitations. Perceived economic impacts formed the largest means of 2.89 where the respondents were most concerned with affordability with a mean of 3.14 and the redundancies that are expected to be witnessed with a mean of 2.55. Perceived barriers to AI integration mainly concerned restricted access to the internet (mean = 3.14) and lack of technical skills (mean = 3.12) with a grand mean of 2.86. From the study, it suggested that infrastructure, technical training, and policy intervention should be put in place to support AI usage in agricultural extension services. Journal of Science Research and Reviews10Journal of Science Research and Reviews["Evelyn Emamuzo Ekperi", "Ogheneovo Owigho", "Tina Ewomazino Akeni", "B. O. Ovwigho"]2025-01-27T00:00:00 416f2f1e9ee9da6d00360aca8de7985e6a8b5ec9International Journal of Obesity180The role of Microsoft Copilot and Google Gemini in weight loss management is highlighted and the differences in their responses may be attributed to the variation in the quality and scope of their training data and design.International journal of obesity["Eugene Annor", "Joseph O. Atarere", "Nneoma Ubah", "Oladoyin Jolaoye", "Bryce F. Kunkle", "Olachi J. Egbo", "Daniel K. Martin"]2025-01-27T00:00:00 6269a23f458ba898a71b59fe2e086039e3cea456Abstract: This article presents a trading strategy that combines the Super Trend indicator with the K-Nearest Neighbors (KNN) algorithm, utilizing artificial intelligence (AI) to automate market decision-making and enhance trading accuracy. The strategy integrates the SuperTrend indicator, which dynamically tracks market volatility, with the KNN algorithm, allowing the system to classify market trends as bullish, bearish, or neutral based on historical data. This enables the strategy to make intelligent, data-driven decisions in real-time without human intervention. The AI-driven approach automates the entire trading process, from data analysis to trade execution, improving efficiency and removing emotional biases from trading decisions. The KNN algorithm plays a key role in this automation by analyzing past market conditions and identifying patterns that inform future price movements. This allows the system to adapt to changing market trends and react quickly to new data, ensuring timely and accurate decisions. The results of the strategy indicate strong performance, with a Net Profit of 959.38 USD and a Gross Profit of 3,005.71 USD, demonstrating the strategy's ability to generate consistent returns. The Profit Factor of 1.469 further highlights the system's ability to produce profits while managing risk effectively. Additionally, the Sharpe Ratio of 0.558 shows that the strategy provides positive risk-adjusted returns, making it a reliable tool for automated trading. In conclusion, this AI-powered Super Trend-KNN strategy showcases the potential of combining artificial intelligence with technical indicators for automated trading. By eliminating the need for manual intervention and leveraging AI to adapt to market conditions, the strategy provides an efficient and scalable solution for intelligent decision-making in trading.International Journal of Latest Technology in Engineering Management &amp; Applied Science90A trading strategy that combines the Super Trend indicator with the K-Nearest Neighbors algorithm, utilizing artificial intelligence (AI) to automate market decision-making and enhance trading accuracy is presented, showcasing the potential of combining artificial intelligence with technical indicators for automated trading.International Journal of Latest Technology in Engineering Management &amp; Applied Science["Bogdan-Petru Vr\u00eenceanu", "Florentin \u0218erban"]2025-01-27T00:00:00 479dbd58319d1bfb79edf35def7ea14a39a55914Objective: to investigate the effectiveness (based on accuracy, sensitivity, and specificity) of using artificial intelligence (AI) in clinical medicine.Material and methods. The study was conducted based on a search and analysis of scientific publications presented in the PubMed/MEDLINE, Scopus, Web of Science, Embase, eLibrary, and CyberLeninka databases from 2009 to 2023, including various types and approaches to training AI, as well as various areas of its application in clinical practice. Sequential analysis of articles in a random sample enabled to select 30 publications: 4 were devoted to the use of AI in endocrinology, 3 – in dermatovenerology, 1 – in cardiology, 1 – in radiology, 1 – in gastroenterology, 5 – in neurology, 5 – in hematology, 4 – in nephrology, 4 – in orthopedics and rheumatology, 2 – in oncology.Results. AI demonstrated sufficient effectiveness: accuracy ranged from 49% to 99%, sensitivity from 42% to 100%, and specificity from 48% to 100% in areas such as cardiology, endocrinology, gastroenterology, dermatovenereology, and radiology. In some cases, AI was more effective than clinical diagnostics by medical specialists, such as in detecting melanoma and diagnosing atrial fibrillation.Conclusion. AI shows high diagnostic efficiency, increases accuracy and speeds up diagnostic search, which makes wider use of AI in clinical medicine promising.FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology350AI shows high diagnostic efficiency, increases accuracy and speeds up diagnostic search, which makes wider use of AI in clinical medicine promising, and some cases, AI was more effective than clinical diagnostics by medical specialists, such as in detecting melanoma and diagnosing atrial fibrillation.FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology["D. Korabelnikov", "A. I. Lamotkin"]2025-01-27T00:00:00 2254ddbce2fddd02c12acda81b3809a50d54434aWith the emergence of artificial intelligence, new situations, new problems, and new challenges in the field of network security are emerging one after another, affecting the global economic pattern, development pattern, and security pattern, also putting forward higher requirements for network security assurance. Nowadays, cases of using fake audio and video such as AI face-changing and AI voice-changing to commit new cybercrimes occur occasionally. From the individual level, artificial intelligence lowers the threshold for personal cybercrime. At the social level, artificial intelligence forges false information that misleads the public and poses a certain threat to people’s property. This article aims to analyse the function of artificial intelligence and discuss the impact of emerging technologies on social development. This article by comparing domestic and foreign researches on artificial intelligence, using a qualitative research methodology with case studies and literature, and expresses views and ideas regarding the laws about artificial intelligence. The article finds that maintaining network security is the common responsibility of the whole society and requires the joint participation of governments, social organisations, and the majority of netizens. Therefore, it is suggested that other security risks that may be caused by artificial intelligence in the future, should be prevented based on risk prevention, and active use laws and regulations to regulate and guide. Secondly, by referring to the law, ensuring the protection of personal information, and standardise the cyber security environment is so significant. Finally, carry out online publicity and education to improve the network security awareness.Malaysian Journal of Social Sciences and Humanities560It is found that maintaining network security is the common responsibility of the whole society and requires the joint participation of governments, social organisations, and the majority of netizens and it is suggested that other security risks that may be caused by artificial intelligence in the future, should be prevented based on risk prevention, and active use laws and regulations to regulate and guide.Malaysian Journal of Social Sciences and Humanities (MJSSH)["Mengru Chen", "Abd Rahman Mohamad Rizal", "Mohd Zahir Mohd Zamre"]2025-01-27T00:00:00 401a9172d5db79b59501b164afe993449bcf9b89The incorporation of artificial intelligence (AI) into neuroscience has the potential to significantly enhance our comprehension of brain function and facilitate more effective diagnosis and treatment of neurological disorders. Artificial intelligence (AI) techniques, particularly deep learning and machine learning, offer transformative solutions by improving the analysis of complex neural data, facilitating early diagnosis, and enabling personalized treatment approaches. A bibliometric analysis is a method that employs quantitative techniques for the examination of scientific literature, with the objective of identifying trends in research, evaluating the impact of influential studies, and mapping the networks of collaboration. In light of the accelerated growth and interdisciplinary scope of AI applications in neuroscience, a bibliometric analysis is vital for mapping the landscape, identifying pivotal contributions, and underscoring emerging areas of interest. This study aims to address this need by examining 1,208 studies published between 1983 and 2024 from the Web of Science database. The analysis reveals a notable surge in publications since the mid-2010s, with substantial advancements in neurological imaging, brain-computer interfaces (BCI), and the diagnosis and treatment of neurological diseases. The analysis underscores the pioneering role of countries such as the United States, China, and the United Kingdom in this field and highlights the prevalence of international collaboration. This study offers a comprehensive overview of the current state and future directions of AI applications in neuroscience, as well as an examination of the transformative potential of AI in advancing neurological research and healthcare. It is recommended that future research address the ethical issues, data privacy concerns, and interpretability of AI models in order to fully capitalize on the benefits of AI in neuroscience.Frontiers in Neurology250A comprehensive overview of the current state and future directions of AI applications in neuroscience, as well as an examination of the transformative potential of AI in advancing neurological research and healthcare are offered.Frontiers in Neurology["U\u011fur Tekin", "Murat Dener"]2025-01-27T00:00:00 0f2507f04f4dea6f32acb3c187598367f7911026The present study examines the challenges of integrating AI into the education system. With AI’s growing popularity, it has made its way into students' educational activities. AI is being utilized extensively in the sector, from completing homework to conducting examinations. The study aims to identify the various challenges Artificial Intelligence poses, namely deterioration in research aptitude, decline in holistic growth, ethical concerns, and disciplinary concerns. The data was collected using structured questionnaires from 430 students in higher education. The research has utilized statistical tools such as exploratory factor analysis with the support of SPSS. The study reveals the challenging factors that act as barriers to students' educational and holistic growth. The study also shows how factors significantly vary based on the respondents' gender.Proceedings230The study reveals the challenging factors that act as barriers to students' educational and holistic growth and shows how factors significantly vary based on the respondents' gender.Proceedings: AIMS-22["Brian Oommen Bino", "Akshaya Rajesh", "Elizhwa Vijo", "Akhil P"]2025-01-27T00:00:00 689bdbafdf22a9b6abc1d8502b66695ae972bf76Penataran Temple, a cultural heritage site in Blitar Regency, stands as a testament to the pinnacle of past Nusantara civilization. Despite its significance, the protection of Penataran Temple requires optimization. This study aims to develop a legal protection strategy for Penataran Temple through the use of Artificial Intelligence. Employing statutory, conceptual, and historical approaches, this empirical juridical research analyzes data using sociological juridical methods. The study underscores the urgent need to enhance legal protection, particularly in rebranding the ecotourism potential of Penataran Temple. This effort seeks to cultivate, reaffirm, and revive the noble values of cultural heritage, establishing it as a distinctive landmark enriched by the unique mosaic of local wisdom. Utilizing innovative design and advanced technologies such as artificial intelligence, this approach significantly enhances branding rooted in local wisdom and ancestral heritage. It is crucial for promoting and safeguarding the temple from legal exploitation by parties lacking integrity and historical awareness in the digital age.Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo00The study underscores the urgent need to enhance legal protection, particularly in rebranding the ecotourism potential of Penataran Temple, by utilizing innovative design and advanced technologies such as artificial intelligence.Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo["N. Prasetyo", "Moh. Fadli", "E. Susilo", "D. Puspitawati", "Mustafa Lutfi"]2025-01-27T00:00:00 4db3a4b13c618628a0d43fb2895d9484be3c409eJournal of Applied Learning &amp; Teaching00Journal of Applied Learning &amp; Teaching[]2025-01-27T00:00:00 c6080a417c825609cd8d4a03602eb2dea337beabThe Journal of King Mongkut's University of Technology North Bangkok00The Journal of King Mongkut's University of Technology North Bangkok["Worawit Kitikusoun", "Pongpisit Wuttidittachotti"]2025-01-27T00:00:00 0de5fa64db65fd6c697d6f3e733ec59aa4702d29Ultrasound in Obstetrics &amp; Gynecology780Ultrasound in Obstetrics &amp; Gynecology["A. Bouachba", "J. De Jesus Neves", "E. Royer", "R. Bartin", "L. Salomon", "D. Gr\u00e9vent", "G. Gorincour"]2025-01-27T00:00:00 1c45cb8ec884c6894975c4a1e136f235b4965816ACS Sustainable Resource Management160ACS Sustainable Resource Management["M. Ghasemlou"]2025-01-27T00:00:00 ff47ee9b586cbbb9090f397fe52b569fd066ff1fMechanistic interpretability is the program of explaining what AI systems are doing in terms of their internal mechanisms. I analyze some aspects of the program, along with setting out some concrete challenges and assessing progress to date. I argue for the importance of propositional interpretability, which involves interpreting a system's mechanisms and behavior in terms of propositional attitudes: attitudes (such as belief, desire, or subjective probability) to propositions (e.g. the proposition that it is hot outside). Propositional attitudes are the central way that we interpret and explain human beings and they are likely to be central in AI too. A central challenge is what I call thought logging: creating systems that log all of the relevant propositional attitudes in an AI system over time. I examine currently popular methods of interpretability (such as probing, sparse auto-encoders, and chain of thought methods) as well as philosophical methods of interpretation (including those grounded in psychosemantics) to assess their strengths and weaknesses as methods of propositional interpretability.70It is argued for the importance of propositional interpretability, which involves interpreting a system's mechanisms and behavior in terms of propositional attitudes: attitudes to propositions (e.g. the proposition that it is hot outside).["David J. Chalmers"]2025-01-27T00:00:00 1ca9b79fedf8ac51302975b2867ba05f4d63869cProceedings00Proceedings: AIMS-22["Joycee Wilson Pol", "Zillur Rahman"]2025-01-27T00:00:00 ae1114c34a9adf46e80596ebadf1b7822498968eThe Royal Australasian College of Physicians (RACP) "Long Cases" assess a trainee's clinical reasoning beyond what is tested in multiple-choice questions, which large language models (LLMs) have already demonstrated proficiency in. This study evaluated a LLM's ability to perform a "Long Case" assessment, including history-taking, case presentation, and answering examiner questions. The LLM achieved passing consensus scores of 4-5 out of 6 on five cases, suggesting potential for LLMs in complex clinical evaluations.medRxiv30This study evaluated a LLM's ability to perform a "Long Case" assessment, including history-taking, case presentation, and answering examiner questions, suggesting potential for LLMs in complex clinical evaluations.["C. Gao", "J. Bellinge", "S. El-Masri", "I. Chim", "M. Sorich", "I. Seth", "J. Gorcilov", "M. Lim", "L. McCoy", "A. Vanlint", "L. Lim", "J. Stranks", "A. Zannettino", "J. Maddison", "S. Bacchi"]2025-01-27T00:00:00 d07ee1e723a8cbe541a607a53de9c24a31c6d9b6Cureus170Cureus["Anwar Ahmed", "Dalal R Fatani", "Jose M Vargas", "Mohammed A. Almutlak", "Halah Bin Helayel", "Rafah Fairaq", "Halla A. AlAbdulhadi"]2025-01-27T00:00:00 c2ee7588f07c15ab92394ca8b930fa6adba8020fEnvironment &amp; Health600Environment &amp; Health["Lucca Caiaffa Santos Rosa", "Mariam Sarhan", "Andr\u00e9 Silva Pimentel"]2025-01-27T00:00:00 e45c38d0f51390dc59e9fb64f66d9ad10b0dd690Knowledge and Information Systems650Knowledge and Information Systems["H. R. Saeidnia", "Elaheh Hosseini", "Brady Lund", "Maral Alipour Tehrani", "Sanaz Zaker", "Saba Molaei"]2025-01-27T00:00:00 63a43ae83c0bf62ba1b559511bc9dc72314fa9c8Nature Reviews Electrical Engineering20Nature Reviews Electrical Engineering["Yuan Dai", "Jianan Wang"]2025-01-27T00:00:00 998f7b92b0e858ba0a260e24e17666a430ac4e15International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["T. Amitha", "P. Shobana", "M. Jayashree", "R. Rajalakshmi"]2025-01-27T00:00:00 ac5edced66b2d95d87e9dee41c1418dc68a82a5fProceedings00Proceedings: AIMS-22["Ankita Sharma", "Raghav Sharma", "Pallabi Mukherjee"]2025-01-27T00:00:00 8781b3ba637b3569251c12a238f3b4db637bedf4International Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["Glenn Leckie"]2025-01-27T00:00:00 f22a9e6c2547b2dbb8b55f8b0d405ce7dcabea95The differentiation between natural intelligence and artificial intelligence represents a significant concern among intellectuals. Artificial intelligence developers, leveraging advancements in neuroscience, cognitive sciences, and advanced theories in the philosophy of mind, aim to replicate the structure and functionality of the human brain through a functionalist and behaviourist lens. Broadly speaking, artificial intelligence can be categorized into two renowned types:Classical Artificial Intelligence or the “Computational Theory of Mind”: This perspective emphasizes the computational and algorithmic side of artificial intelligence and advocates for the mechanization and computerization of the mind.Connectionist Artificial Intelligence: This viewpoint focuses on recreating the “neural networks” of the brain. Additionally, the human soul, as the source of human intelligence, possesses cognitive and motivational powers that act as the soldiers of the soul, generating a variety of actions and effects. This research attempts to re-evaluate the fundamental differences between natural intelligence and artificial intelligence from Ibn Sina's perspective using a rational-analytical approach. According to Ibn Sina, natural intelligence and artificial intelligence differ in eight key areas: composite synthesis, intentionality, creativity and inventiveness, specialization focus, self-awareness and self-discovery, the internal evolution of natural intelligence, the impulsive power of desire, ethical conduct, and the ability to recall.Journal of Ecohumanism30This research attempts to re-evaluate the fundamental differences between natural intelligence and artificial intelligence from Ibn Sina's perspective using a rational-analytical approach.Journal of Ecohumanism["Mustafa \u2018Azizi Alawijeh", "Seyed Zuhair Al-Mesilini"]2025-01-27T00:00:00 9ed1521ae073464ff8c75cf4b0405838f8359b5bAI &amp; SOCIETY351AI &amp; SOCIETY["S. Bennett", "Benedetta Catanzariti", "Fabio Tollon"]2025-01-27T00:00:00 6249a57a8c6eafaa7415f172a9d8436e0c41e4c6Language Testing in Asia710The quantitative outcomes demonstrated that AI-generated feedback significantly improved EFL learners’ motivation, FLPoM, and trait EI while enhancing their writing skills, thus encouraging further research into AI’s role in language education.Language Testing in Asia["Shireen Jamal Mohammed", "Maryam Waleed Khalid"]2025-01-27T00:00:00 8400e8a9a90cbdfd4eee833a0eccdb44998162aeArtificial Intelligence (AI) literacy has come to the spotlight, empowering individuals to adeptly navigate the modern digitalised world. However, studies on teacher AI literacy in the English as a Foreign Language (EFL) context remain limited. This study aims to identify intraindividual differences in AI literacy and examine its associations with age and years of teaching experience among 782 English teachers. Given the absence of a reliable instrument to measure teacher AI literacy, we first constructed and validated a scale encompassing five sub‐scales: AI Knowledge, AI Use, AI Assessment, AI Design, and AI Ethics. Subsequently, latent profile analysis (LPA) was conducted using Mplus 7.4, with the results revealing four distinct profiles: Poor AI literacy (C1: 12.1%), Moderate AI literacy (C2: 45.5%), Good AI literacy (C3: 28.4%), and Excellent AI literacy (C4: 14.1%). Multinomial logistic regression analyses indicated significant associations between teacher AI literacy and both age and years of teaching experience. Additionally, 32 respondents participated in semi‐structured interviews. The qualitative data analysed with MAXQDA 2022 triangulated the quantitative results and offered deeper insights into teachers’ perceptions of their AI literacy. This study provides both theoretical and practical implications for understanding teacher AI literacy in the Chinese EFL context.European Journal of Education531This study provides both theoretical and practical implications for understanding teacher AI literacy in the Chinese EFL context and constructed and validated a scale encompassing five sub‐scales: AI Knowledge, AI Use, AI Assessment, AI Design, and AI Ethics.European Journal of Education["Ziwen Pan", "Yongliang Wang"]2025-01-27T00:00:00 2b298d89dfdf360bf55971b075d2d62f6ae5d1d0Network slicing aims to enhance flexibility and efficiency in next-generation wireless networks by allocating the right resources to meet the diverse requirements of various applications. Managing these slices with machine learning (ML) algorithms has emerged as a promising approach however explainability has been a challenge. To this end, several Explainable Artificial Intelligence (XAI) frameworks have been proposed to address the opacity in decision-making in many ML methods. In this paper, we propose a Prioritized Value-Decomposition Network (PVDN) as an XAI-driven approach for resource allocation in a multi-agent network slicing system. The PVDN method decomposes the global value function into individual contributions and prioritizes slice outputs, providing an explanation of how resource allocation decisions impact system performance. By incorporating XAI, PVDN offers valuable insights into the decision-making process, enabling network operators to better understand, trust, and optimize slice management strategies. Through simulations, we demonstrate the effectiveness of the PVDN approach with improving the throughput by 67% and 16%, while reducing latency by 35% and 22%, compared to independent and VDN-based resource allocation methods.00A Prioritized Value-Decomposition Network (PVDN) is proposed as an XAI-driven approach for resource allocation in a multi-agent network slicing system, and offers valuable insights into the decision-making process, enabling network operators to better understand, trust, and optimize slice management strategies.["Shavbo Salehi", "Pedro Enrique Iturria-Rivera", "Medhat H. M. Elsayed", "Majid Bavand", "Raimundas Gaigalas", "Yigit Ozcan", "Melike Erol-Kantarci"]2025-01-27T00:00:00 c3614ff38063f1306ada88d533ad71dd4ee65b87This article aims to explore the nexus between democracy, development and artificial intelligence (AI), and how AI is reconfiguring electoral processes and political debate in democratic and autocratic regimes. To understand the role of governance, electoral processes and AI in economic growth, the study compares the impact of AI in democratic systems like the U.S. and India on the one hand, and the authoritarian models of China and Singapore on the other. The analysis also shows that democracies are more adept at holding people accountable and more open to transparency, compared to authoritarian regimes that are more efficient in executing long term economic strategies. In addition to this, the article then looks at how AI is being used to manipulate politics, voter behaviour and inequality. Case studies of India, Singapore and China show how even the most complex situations of economic development can be accomplished through different governance structures. The research concludes that democracy and AI are not mutually exclusive, and can actually reinforce each other to promote sustainable development, but authoritarianism is capable of fostering rapid economic development at the expense of political freedoms.Journal of Political Science and International Relationship00The research concludes that democracy and AI are not mutually exclusive, and can actually reinforce each other to promote sustainable development, but authoritarianism is capable of fostering rapid economic development at the expense of political freedoms.Journal of Political Science and International Relationship["Same Rasikh"]2025-01-27T00:00:00 e958e7716810957f8c45174429f8128b4bce4dfdThe debate surrounding the topic of Artificial Intelligence (ai), and its different meanings, seems to be ever-growing. This paper aims to deconstruct the seemingly problematic nature of the ai debate, revealing layers of ambiguity and misperceptions that contribute to a pseudo-problematic narrative. Through a review of existing literature, ethical frameworks, and public discourse, this essay identifies key areas where misconceptions, hyperbole, and exaggerated fears have overshadowed the genuine concerns associated with ai development and deployment. To identify these issues I propose three general criteria that are based on Popper’s and Ayer’s work and adjusted to my needs. The subsequent sections categorize ai issues into ontological, methodological, and logical-grammatical problems, aligning with Cackowski’s typology. In addition, I introduce «» signs to distinguish behavioural descriptions from cognitive states, aiming to maintain clarity between external evidence and internal agent states. My conclusion is quite simple: the ai debate should be thoroughly revised, and we, as scholars, should define the concepts that lie at the bottom of ai by creating a universal terminology and agreeing upon it. This will give us the opportunity to conduct our debates reasonably and understandably for both scholars and the popular public.Człowiek i społeczeństwo80The ai debate should be thoroughly revised, and scholars should define the concepts that lie at the bottom of ai by creating a universal terminology and agreeing upon it to give the opportunity to conduct debates reasonably and understandably for both scholars and the popular public.Człowiek i Społeczeństwo["\u0141ukasz Abramowicz"]2025-01-27T00:00:00 150630ccb66e95f7ce9054cd6ae9cf0aa9723874Introduction It is important to consider individual differences in research on educational technology. This study investigates the interplay between autistic traits, gender, and the perception of artificial intelligence (AI) tools designed for second language (L2) speaking practice, contributing to a deeper understanding of inclusive educational technology. Methods A sample of 111 university students completed the Broad Autism Phenotype Questionnaire (BAPQ) to measure autistic traits (AU) and their sub-traits Aloof (AF), Rigid (RD), and Pragmatic Language (PL). Perceptions of AI tools were assessed across five dimensions: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude (AT), Behavioral Intention (BI), and Usage Behavior (UB). The study utilized correlation and regression analyses to examine relationships between these variables, while exploring gender-specific moderating effects. Results Key findings revealed no significant gender differences in autistic traits or overall perceptions of AI tools. Contrary to expectations, autistic traits were negatively correlated with perceptions of AI tools, suggesting that current AI designs may not adequately support individuals with pronounced autistic traits. Additionally, gender moderated some relationships, with males displaying stronger associations between autistic traits and both PEOU and UB. Discussion This research bridges critical gaps by linking neurodiversity and gender to technology acceptance, advancing the field’s understanding of individual differences in AI-based language learning. It underscores the importance of designing personalized and adaptive educational tools that address diverse learner needs, promoting inclusivity and effectiveness in L2 practice.Frontiers in Psychiatry410This research bridges critical gaps by linking neurodiversity and gender to technology acceptance, advancing the field’s understanding of individual differences in AI-based language learning.Frontiers in Psychiatry["Yiran Du", "Chenghao Wang", "Bin Zou", "Yinan Xia"]2025-01-27T00:00:00 e7f337fc5903a31c310c3de097d414e0021ab335Generative Artificial Intelligence (GAI) is rapidly reshaping the landscape of higher education, offering innovative solutions to enhance student engagement, personalize learning experiences, and improve academic performance prediction. This study provides an in-depth exploration of GAI applications in educational contexts, drawing insights from 67 case studies meticulously selected from over 300 papers presented at the AIED 2024 conference. The research focuses on eight key themes from student engagement and behavior analysis to the integration of generative models into educational tools. These case studies illustrate the potential of GAI to optimize teaching practices, enhance student support systems, and provide tailored interventions that address individual learning needs. However, this study also highlights challenges such as scalability, the need for balanced and diverse datasets, and ethical concerns regarding data privacy and bias. Further, it emphasizes the importance of improving model accuracy, transparency, and real-world applicability in educational settings. The findings underscore the need for continued research to refine GAI technologies, ensuring they are scalable, adaptable, and equitable, ultimately enhancing the effectiveness and inclusivity of AI-driven educational tools across diverse higher education environments. It should be noted that this study primarily draws from papers presented at the AIED 2024 conference, which may limit global representativeness and introduce thematic biases. Future studies are encouraged to include broader datasets from diverse conferences and journals to ensure a more comprehensive understanding of GAI applications in higher education.Information00The findings underscore the need for continued research to refine GAI technologies, ensuring they are scalable, adaptable, and equitable, ultimately enhancing the effectiveness and inclusivity of AI-driven educational tools across diverse higher education environments.Information["Weina Pang", "Zhe Wei"]2025-01-27T00:00:00 abf6b0c6c607b847d1c0bf64035493ebe8441809Skin cancer, particularly melanoma, poses a significant global health challenge due to its prevalence and mortality rate. Early detection is critical to improving outcomes, as advanced cases become increasingly difficult to treat. The advent of Artificial Intelligence (AI) and Explainable AI (XAI) techniques has revolutionized dermatological diagnostics by offering accurate and interpretable solutions. This systematic review investigates the integration of XAI in skin lesion classification, analyzing 22 recent studies published between 2019 and 2023. The studies encompass diverse approaches, including deep learning models like CNNs, ResNet, DenseNet, and MobileNet, as well as explainability techniques such as Grad-CAM, SHAP, and saliency maps. Results highlight significant advancements in accuracy and interpretability, with some models achieving over 99% accuracy on datasets like ISIC 2018 and HAM10000. However, challenges persist, including dataset imbalances, limited diversity in patient metadata, and generalizability across different skin types and imaging conditions. XAI methods, by visualizing model decision pathways, enhance transparency, fostering trust among clinicians and enabling seamless AI integration into clinical practice. This review underscores the potential of combining state-of-the-art AI models with explainable frameworks to address the complexities of skin lesion diagnostics. It advocates for future research to prioritize diverse, metadata-rich datasets, innovative optimization techniques, and robust architectures to develop reliable, interpretable diagnostic tools. By bridging the gap between advanced AI and user understanding, this work contributes to the creation of clinically applicable, trustable AI-driven healthcare solutions.Studies in Medical and Health Sciences00This systematic review investigates the integration of XAI in skin lesion classification, analyzing 22 recent studies published between 2019 and 2023 and underscores the potential of combining state-of-the-art AI models with explainable frameworks to address the complexities of skin lesion diagnostics.Studies in Medical and Health Sciences["Muhammad Bilal Jan", "Muhammad Rashid", "Raja Vavekanand", "Vijay Singh"]2025-01-27T00:00:00 deac9fd383811f1b2ef2a1a39a00262e3116628cInsecticide use in agriculture has significantly increased over the past decades, reaching 774 thousand metric tons in 2022. This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. The main challenge is creating AI models that perform with speed and accuracy, enabling immediate farmer action. This study highlights the innovating potential of such an approach, focusing on the detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models. A dataset of 220 sticky paper images was captured. The detection system employs a YOLOv10 DL model that achieved an accuracy of 89.1% (mAP50). For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. For the visualization of the detection results, the DL model was embedded to a mobile application. This holistic approach supports early intervention strategies and sustainable pest management while offering a scalable solution for smart-agriculture environments.AgriEngineering440This study highlights the innovating potential of state-of-the-art Deep Learning models in early detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions.AgriEngineering["Dimitrios Kapetas", "Panagiotis Christakakis", "Sofia Faliagka", "Nikolaos Katsoulas", "E. Pechlivani"]2025-01-27T00:00:00 ffbaa5b06f641debd02bff69aaea61efb5f81e11"First, do no harm"faces a fundamental challenge in artificial intelligence: how can we specify what constitutes harm? While prior work treats harm specification as a technical hurdle to be overcome through better algorithms or more data, we argue this assumption is unsound. Drawing on information theory, we demonstrate that complete harm specification is fundamentally impossible for any system where harm is defined external to its specifications. This impossibility arises from an inescapable information-theoretic gap: the entropy of harm H(O) always exceeds the mutual information I(O;I) between ground truth harm O and a system's specifications I. We introduce two novel metrics: semantic entropy H(S) and the safety-capability ratio I(O;I)/H(O), to quantify these limitations. Through a progression of increasingly sophisticated specification attempts, we show why each approach must fail and why the resulting gaps are not mere engineering challenges but fundamental constraints akin to the halting problem. These results suggest a paradigm shift: rather than pursuing complete specifications, AI alignment research should focus on developing systems that can operate safely despite irreducible specification uncertainty.00Drawing on information theory, it is demonstrated that complete harm specification is fundamentally impossible for any system where harm is defined external to its specifications, and suggested that rather than pursuing complete specifications, AI alignment research should focus on developing systems that can operate safely despite irreducible specification uncertainty.["Robin Young"]2025-01-27T00:00:00 b9291bf6940a563c9f9b7b12bceaf5ec05d6922c: Artificial Intelligence (AI) has become ubiquitous across the globe—powering personalized recommendations, medical diagnoses, and advanced analytics at an unprecedented scale. Yet, alongside its vast potential for innovation and societal benefit, AI’s increasing reliance on personal data raises urgent questions about user autonomy, privacy, and ethical governance. This paper offers a comprehensive and newly expanded investigation into privacy challenges posed by AI-driven systems, critically addressing known theoretical and empirical gaps. We augment earlier Western-centric analyses by incorporating nuanced insights into data governance in developing economies and non-Western contexts. Additionally, we delve into the technical implementation details essential for operationalizing proposed solutions—particularly with regard to computational constraints and resource variability around the world. By proposing a multi-layered framework that spans policy harmonization, privacy-enhancing technologies, and stakeholder collaboration, we present a globally attuned strategy for reconciling innovation and individual rights in the age of intelligent machines. We conclude by underscoring the urgent need for empirical studies, grassroots advocacy, and context-specific regulatory frameworks that can ensure AI’s continued growth without compromising personal autonomy and societal well-being.International Journal of Science and Research (IJSR)140A comprehensive and newly expanded investigation into privacy challenges posed by AI-driven systems, critically addressing known theoretical and empirical gaps and proposing a multi-layered framework that spans policy harmonization, privacy-enhancing technologies, and stakeholder collaboration is presented.International Journal of Science and Research (IJSR)["Arnaud M Tsombeng Nkeumo", "Karl Kiam"]2025-01-27T00:00:00 cd2dcb8957e57c0d98d86f88a8cc132b4b8f0ac4Scientific Reports410While ChatGPT showed promise in improving medical triage efficiency and accuracy, the study also identified areas for refinement, including the need for better integration of multidisciplinary care for patients with complex, intersecting medical conditions.Scientific Reports["Priscilla Koirala", "C. Thongprayoon", "Jing Miao", "Oscar A. Garcia Valencia", "M. S. Sheikh", "S. Suppadungsuk", "M. Mao", "Justin H. Pham", "Iasmina M. Craici", "W. Cheungpasitporn"]2025-01-27T00:00:00 539ea7d2e87e225d92292f0e9d0acdcfc255ffebPhilosophy &amp; Technology340It is argued that justly resolving this conflict will at times require greater inclusion of those mis-recognized in deliberation about medical AI, and consider what such inclusion may entail.Philosophy &amp; Technology["Zachary Daus"]2025-01-27T00:00:00 d958bcb077bc9b1f75b1921e11d9163d4d08bf53Current Psychology400The Turkish version of the AIAS-4 scale proves to be valid and reliable and thus could be used by researchers in the Turkish context and thus could be used by researchers in the Turkish context.Current Psychology["Neslihan K\u00f6se", "Erdi \u015eim\u015fek", "Mehmet Can Demir"]2025-01-28T00:00:00 7cacbaa08d092a148deaf1c0daeeba9d9343ce97Including artificial intelligence (AI) in academic writing has spurred a critical review of its ethical and legal ramifications in learning environments. As companies embrace AI tools like ChatGPT, questions about authorship, intellectual property, and academic integrity have become central concerns that need careful examination, as institutions do. This paper explores the changing definition of AI and its ability to execute tasks usually connected with human intelligence, generating serious ques-tions about originality and ethical standards in academic work. The conversation emphasizes the need for educational institutions to create explicit structures that handle the complexity of AI-assisted writing preserving academic integrity and encouraging creative ideas. Underlined in the paper are ethical conundrums created by AI-generated content, especially concerning openness, accuracy, and bias potential. It questions who owns AI-generated works and how conventional ideas of creative agency must be reassessed because of these developments, so challenging the muddy waters of authorship and intellectual property rights. Beyond only legal concerns, the implications of AI’s presence in academic writing force a review of pedagogical approaches and the possible effects on critical thinking and independent research skills among students. In the end, this work supports a sensible strategy that welcomes AI’s transforming power while protecting the fundamental values of academic integrity and rigor. It asks teachers, lawyers, and legislators to work together to negotiate AI’s complex legal terrain in academia so that the educational experience stays strong and morally sound for the next generations.Journal of Contemporary Philosophical and Anthropological Studies420Journal of Contemporary Philosophical and Anthropological Studies["K. Kotsis"]2025-01-28T00:00:00 25c6acf8f7105d41176c3cc589a01731d71e513eThe rise of sophisticated cyber threats has spurred advancements in Intrusion Detection Systems (IDS), which are crucial for identifying and mitigating security breaches in real-time. Traditional IDS often rely on complex machine learning algorithms that lack transparency despite their high accuracy, creating a “black box” effect that can hinder the analysts’ understanding of their decision-making processes. Explainable Artificial Intelligence (XAI) offers a promising solution by providing interpretability and transparency, enabling security professionals to understand better, trust, and optimize IDS models. This paper presents a systematic review of the integration of XAI in IDS, focusing on enhancing transparency and interpretability in cybersecurity. Through a comprehensive analysis of recent studies, this review identifies commonly used XAI techniques, evaluates their effectiveness within IDS frameworks, and examines their benefits and limitations. Findings indicate that rule-based and tree-based XAI models are preferred for their interpretability, though trade-offs with detection accuracy remain challenging. Furthermore, the review highlights critical gaps in standardization and scalability, emphasizing the need for hybrid models and real-time explainability. The paper concludes with recommendations for future research directions, suggesting improvements in XAI techniques tailored for IDS, standardized evaluation metrics, and ethical frameworks prioritizing security and transparency. This review aims to inform researchers and practitioners about current trends and future opportunities in leveraging XAI to enhance IDS effectiveness, fostering a more transparent and resilient cybersecurity landscape.Frontiers in Artificial Intelligence210A systematic review of the integration of XAI in IDS, focusing on enhancing transparency and interpretability in cybersecurity, indicates that rule-based and tree-based XAI models are preferred for their interpretability, though trade-offs with detection accuracy remain challenging.Frontiers in Artificial Intelligence["Vincent Zibi Mohale", "I. Obagbuwa"]2025-01-28T00:00:00 11ae41f437e35d5647f433ab0a77662848f6aee9Purpose This paper aims to examine the complex balance between enthusiasm and skepticism regarding artificial intelligence (AI) integration in educational practices. It advocates for a cautious, evidence-based approach while addressing both opportunities and challenges, aligning with the United Nations Sustainable Development Goal 4 (SDG4) for Quality Education. Design/methodology/approach Through critical analysis of current discourse surrounding AI in education, this paper synthesizes existing literature on both supportive and skeptical perspectives. The methodology involves systematic examination of past educational technology trends, current AI developments and their implications for teaching and learning. The paper develops its research agenda through careful consideration of existing empirical studies, theoretical frameworks and identifying gaps in current understanding. Findings The analysis reveals that while AI offers promising potential for enhancing learning outcomes and educational accessibility, its integration presents significant challenges that require careful consideration. The paper identifies critical tensions between technological innovation and pedagogical values, highlighting areas where enthusiasm for AI adoption must be tempered with empirical evidence and critical evaluation. Current evidence suggests that successful AI integration requires balanced consideration of both opportunities and limitations, with particular attention to maintaining human-centered educational practices. Originality/value This viewpoint provides a comprehensive framework for understanding the dialectic between AI’s educational potential and its limitations. By synthesizing both supportive and critical perspectives, it offers a nuanced approach to AI integration that acknowledges both opportunities and challenges. The article’s value lies in its systematic identification of key research priorities and its emphasis on evidence-based implementation strategies that serve educational goals while mitigating potential risks. Quality Education for All250The analysis of artificial intelligence (AI) integration in educational practices reveals that while AI offers promising potential for enhancing learning outcomes and educational accessibility, its integration presents significant challenges that require careful consideration.Quality Education for All["Martin Sposato"]2025-01-28T00:00:00 da46fe4db0d3284651d0690d5e177c5bc7865b78Perkembangan teknologi informasi dan komunikasi telah membawa perubahan signifikan dalam dunia pendidikan, termasuk dalam pembelajaran Pendidikan Agama Islam (PAI). Mobile Learning, Virtual Learning Metaverse, dan Artificial Intelligence (AI) menjadi teknologi yang menawarkan potensi besar dalam meningkatkan kualitas pembelajaran PAI. Mobile Learning memungkinkan akses pembelajaran fleksibel melalui perangkat mobile, memberikan kesempatan bagi siswa untuk belajar kapan saja dan di mana saja. Virtual Learning Metaverse, yang memanfaatkan teknologi dunia virtual, menawarkan pengalaman pembelajaran yang lebih interaktif dan imersif, memungkinkan siswa untuk berinteraksi dengan materi terbuka dan sesama peserta didik secara lebih mendalam. Sementara itu, AI dalam pendidikan memberikan personalisasi dalam pembelajaran dengan menganalisis perilaku dan kebutuhan siswa, serta memberikan umpan balik yang relevan. Integrasi ketiga teknologi ini dalam pembelajaran PAI dapat mendukung peningkatan pemahaman dan keterampilan siswa dalam mempelajari ajaran Islam dengan cara yang lebih efektif, efisien, dan menarik. Penelitian ini bertujuan untuk mengeksplorasi bagaimana Mobile Learning, Virtual Learning Metaverse, dan AI dapat diimplementasikan dalam pembelajaran PAI untuk menghadirkan pengalaman belajar yang lebih inovatif dan menyenangkan bagi siswa.Jurnal Teknologi Pendidikan130Jurnal Teknologi Pendidikan["Muhammad Alfiannur Alfyn", "Rohbiah", "Ani Cahyadi"]2025-01-28T00:00:00 52b19c77c0dea9ae8822bf509e9e9b2b255c0806BMC Medical Informatics Decis. Mak.470AI has good sensitivity and specificity for predicting ARDS, indicating a high clinical application value and algorithmic models such as CNN, SVM, and XGB have improved prediction performance.BMC Medical Informatics and Decision Making["Yaxin Xiong", "Yuan Gao", "Yucheng Qi", "Yingfei Zhi", "Jia Xu", "Kuo Wang", "Qiuyue Yang", "Changsong Wang", "Mingyan Zhao", "Xianglin Meng"]2025-01-28T00:00:00 685112399ec64b142bc94c5e5fdc9363834b0c9aThe integration of Artificial Intelligence (AI) into the framework of education has spelt out a remarkable landmark in the history of educational revolution. Both in teaching-learning methodologies and management practice, AI is rapidly becoming a pivotal indispensability. Employing qualitative methods of conceptual frame, content analysis and literature review, this paper critically evaluates the significance of AI in learning and education management in the light of philosophy of education. The highlights of the paper include the concepts of AI, learning, education management, and philosophy of education. Philosophical perspectives and ethical considerations in implementing AI technology are also discussed. The capacity of AI to inform personalized learning, enhance intelligent learning, and facilitate data-driven decision making is critically examined. The paper, going forward, explores the transformational role of teachers amid the reality of the AI environmental permeation and centrality, with a focus on the need for teachers to be adequately equipped with requisite skills that promote critical thinking, creativity and innovation. In the process of the critical appraisal, it is discovered that AI has great potentials to engender harmonized and customized learning experiences. It is also discovered that it equally brings, in its wake, hordes of challenges and concerns with regard to the possibility of stripping the human face of education in terms of depersonalization, ethical concerns, danger of data privacy and algorithmic bias. On the basis of the findings, the paper recommends, among others, that stakeholders in education should adopt a balanced approach to the issue of AI in learning and education management. In this model of approach, AI is to be given an ancillary position as a source of information and knowledge in the process of education. In this way, AI will play a complementary role to the time-tested traditional values through which education strives to offer and achieve a comprehensive development of the learner. This will go a long way in addressing the challenges associated with AI, thereby harnessing its transformative potentials in learning and education management.International journal of social science and human research190The transformational role of teachers amid the reality of the AI environmental permeation and centrality is explored, with a focus on the need for teachers to be adequately equipped with requisite skills that promote critical thinking, creativity and innovation.International Journal of Social Science and Human Research["Ekeh, G. N. Ekeh, G. N.", "Onyebuchi, G. C. Onyebuchi, G. C.", "Ezeanolue, A. O. Ezeanolue, A. O.", "Abiakwu, O.F. Abiakwu, O.F.", "Madu, K. O. Madu, K. O."]2025-01-28T00:00:00 0c24a76030afdcc64a7bb0d89336d681c4101238This study aims to assess how Indonesia's Government Office of Kediri District responds to integrating Artificial Intelligence (AI) in improving service frequency and public satisfaction. An explanatory quantitative approach was adopted to investigate the causal relationship between AI adoption and the two dependent variables—service frequency and public satisfaction—through multiple linear regression analysis, utilizing data from a sample of 15 districts between 2018 and 2023. The study reveals that AI adoption significantly enhances public satisfaction and service frequency. Implementing AI, facilitated through the Digital Service Living Lab (DSLL) platform, increases efficiency, responsiveness, and service quality. The study underscores that AI integration can boost public service effectiveness and efficiency, encouraging local governments to broaden AI usage and invest in staff training and capacity development. This research offers strategic recommendations for governments to expand AI adoption further, strengthen inter-district collaboration, and optimize the use of Electronic Government (E-Government) initiatives to support the goals of smart city and provincial development.Golden Ratio of Marketing and Applied Psychology of Business280It is revealed that AI adoption significantly enhances public satisfaction and service frequency, and that AI integration can boost public service effectiveness and efficiency, encouraging local governments to broaden AI usage and invest in staff training and capacity development.Golden Ratio of Marketing and Applied Psychology of Business["Wiwik Nurfitarini", "Aldo Lovely Arief Suyoso", "Dian Ekowati"]2025-01-28T00:00:00 192e265e9773f41800a5c07dbaa85b4cfd10b081Artificial Intelligence (AI) has significantly transformed social media marketing by enhancing personalization, automating tasks, and providing deeper insights. Social media marketing is a growing trend and the most effective marketing tool, as it allows companies to reach a wide audience and effectively convey a distinct brand image. It is the most economical advertising method, with most social networking sites providing free accounts and registration. However, social media advertising yields a higher return on investment, allowing businesses to significantly increase their conversion rate. This study aims to analyze the influence of AI on social media marketing and its effect on consumer decision-making and behavior. It aims to predict the relationships between consumer activities, marketing activities, and consumer behavior, particularly among those who spend a significant amount of time on social networking platforms. Social media marketing has significantly influenced consumer behavior and enabled organizations to gain insights into customer behavior. It has revolutionized the internet marketplace by altering the structure of how items are bought and sold. The increasing popularity of social media has prompted marketers to consider it in addition to traditional marketing areas. Social media relies on internet or mobile phone apps and technologies to facilitate information exchange among individuals, and the number of social media users exceeds the population of several nations. The assessment of the influence of social media on marketing can be made by contrasting marketing practices before and after its introduction, taking into account the specific technological advancements used in social media platforms.International Journal For Multidisciplinary Research180This study aims to predict the relationships between consumer activities, marketing activities, and consumer behavior, particularly among those who spend a significant amount of time on social networking platforms, and take into account the specific technological advancements used in social media platforms.International Journal For Multidisciplinary Research["Pattilthodika Suhail", "Kamaludheen K.T", "Nidhil P.K", "Jibin Joy"]2025-01-28T00:00:00 cff23b04518b95ecf5969b6d3459693f91a0ae79The integration of artificial intelligence (AI) into defense technologies has revolutionized modern warfare, introducing autonomous weapons systems (AWS) capable of operating without direct human intervention. While these systems promise enhanced precision and operational efficiency, they also present profound ethical and political dilemmas. This paper explores the evolution of AWS, categorizing their levels of autonomy and analyzing the underlying technologies, such as machine learning and sensor integration. It delves into the ethical challenges of delegating life-and-death decisions to machines, accountability gaps, and risks of misuse while scrutinizing compliance with International Humanitarian Law. The political dimensions include the AI arms race, the proliferation of AWS, and challenges in international governance. Case studies illustrate the real-world implications, emphasizing the urgency for robust regulation. By proposing ethical frameworks, oversight mechanisms, and the inclusion of human decision-making, this research underscores the necessity of global collaboration to mitigate risks and ensure that the development of AWS aligns with humanitarian values and international security.International Journal For Multidisciplinary Research140The evolution of AWS is explored, categorizing their levels of autonomy and analyzing the underlying technologies, such as machine learning and sensor integration, to underscore the necessity of global collaboration to mitigate risks and ensure that the development of AWS aligns with humanitarian values and international security.International Journal For Multidisciplinary Research["Myreen Nadeem Javed"]2025-01-28T00:00:00 b9ffbb316734eed3140cf5a8910f47e95e6f3f81PurposeThis article examines the contribution of artificial intelligence to augmenting Intelligent Transportation Systems (ITS) to enhance traffic flow, safety, and sustainability.Design/methodology/approachThe research investigates using AI technologies in ITS, including machine learning, computer vision, and deep learning. It analyzes case studies on ITS projects in Poznan, Mysore, Austin, New York City, and Beijing to identify essential components, advantages, and obstacles.FindingsUsing AI in Intelligent Transportation Systems has considerable opportunities for enhancing traffic efficiency, minimizing accidents, and fostering sustainable urban growth. Nonetheless, issues like data quality, real-time processing, security, public acceptability, and privacy concerns need resolution.Originality/valueThis article thoroughly examines AI-driven ITS, emphasizing successful applications and pinpointing significant difficulties. It underscores the need for a sustainable economic strategy for extensive adoption and enduring success.Journal of Intelligent Manufacturing and Special Equipment260Using AI in Intelligent Transportation Systems has considerable opportunities for enhancing traffic efficiency, minimizing accidents, and fostering sustainable urban growth, Nonetheless, issues like data quality, real-time processing, security, public acceptability, and privacy concerns need resolution.Journal of Intelligent Manufacturing and Special Equipment["Leila Zemmouchi-Ghomari"]2025-01-28T00:00:00 ed43a41c877992a37ed810b9d67757cb404993afArtificial intelligence (AI) has become a transformative force in healthcare supply chain management. This paper analyzes the transformative role of artificial intelligence (AI) in enhancing the resilience and efficiency of healthcare supply chain management within the U.S. medical supply distribution system. The study analyzes current implementations and emerging technologies to show how AI-enabled solutions are transforming routine supply chain operations in healthcare settings. Key findings show that healthcare organizations that implement AI-powered supply chain systems observe improvements in forecasting accuracy up to 87% for predictive analytics models that can anticipate supply chain disruption. The research also shows how AI systems enable 40% faster recovery times during crises versus traditional methods, while automated decision support systems reduce response times to supply chain disruptions by almost 65%. The study however points out critical challenges such as data privacy concerns, high implementation costs, and a requirement for strong governance frameworks. Strategically, the paper offers recommendations to healthcare organizations on how to better collaborate with technologists, healthcare providers, and policymakers to enhance innovation while maintaining ethical standards. With the evolution of healthcare supply chains, this research underlines the importance of AI in designing a more resilient and efficient medical supply distribution system that is commensurate with the need to address challenges of implementation and ethical considerations in ensuring equitable healthcare delivery. KEYWORDS: Artificial Intelligence, Healthcare Supply Chain, Medical Supply Distribution, Predictive Analytics, Supply Chain ResilienceEPRA International Journal of Economics, Business and Management Studies00The research underlines the importance of AI in designing a more resilient and efficient medical supply distribution system that is commensurate with the need to address challenges of implementation and ethical considerations in ensuring equitable healthcare delivery.EPRA International Journal of Economics, Business and Management Studies["Faith Chidinma Okonkwo", "Benjamin Gyedu Akonor", "Tobias Kwame Adukpo"]2025-01-28T00:00:00 c3366b0ed20cf3ca4484c8ff856c05575d802451Artificial Intelligence (AI) has become a transformative force in traffic management, offering innovative solutions to address congestion, safety, and efficiency in transportation systems. This literature review examines recent advancements in AI applications within traffic management, focusing on methodologies such as machine learning, computer vision, and optimization techniques. The study also evaluates the impacts of these technologies on transportation systems, including their benefits and challenges. Key applications such as traffic prediction, signal control, autonomous vehicles, and incident management are discussed. The review concludes by identifying research gaps and proposing directions for future studiesINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00A literature review examines recent advancements in AI applications within traffic management, focusing on methodologies such as machine learning, computer vision, and optimization techniques, including their benefits and challenges.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Kartik Thakur"]2025-01-28T00:00:00 5245c0adaf99c7cf0731b1e4658e9bffbb7cc126Langenbeck's archives of surgery (Print)170The methodologies of eXplainable Artificial Intelligence and their potential applications in surgery are summarized and the critical importance of transparency and interpretability in the outputs of applied models are emphasized.Langenbeck's Archives of Surgery["Johanna M. Brandenburg", "Beat P. M\u00fcller-Stich", "Martin Wagner", "Mihaela van der Schaar"]2025-01-28T00:00:00 915e8c6afbe2e223b321b7cb37d68d284878aa5dThis research is an in-depth study of the role of artificial intelligence in developing teaching methods in UAE universities, highlighting the University of Sharjah as an applied model. The research aims to explore how artificial intelligence technologies are applied in higher education, and to analyze their impact on the quality of education and teaching methods. By adopting a comprehensive methodology that combines a descriptive, analytical approach and a comparative approach, the research addresses the technical and organizational challenges associated with the adoption of artificial intelligence in the university environment. It also reviews successful cases and potential future applications, based on case studies and in-depth analyzes of educational environments based on artificial intelligence. The results show that the application of artificial intelligence can radically transform the university education experience by providing personalized educational experiences that match the needs of each student, and developing immediate and accurate assessment tools that provide real-time feedback to improve academic performance. In addition, artificial intelligence contributes to enhancing collaboration between students through interactive educational platforms and providing immersive learning environments that use virtual and augmented reality technologies. The study emphasizes the need to invest in technical infrastructure and develop comprehensive regulatory policies, in addition to training educational personnel to use these technologies efficiently. The research concludes by providing practical recommendations to enhance the use of artificial intelligence in university education, which contributes to achieving sustainable progress and developing a more adaptive and innovative educational system.International Journal of Educational Sciences and Arts60The results show that the application of artificial intelligence can radically transform the university education experience by providing personalized educational experiences that match the needs of each student, and developing immediate and accurate assessment tools that provide real-time feedback to improve academic performance.International Journal of Educational Sciences and Arts["Fawzia Ali"]2025-01-28T00:00:00 06279289e404f69e64e24b410b2546f4467d077cArtificial intelligence is transforming various fields, including accounting, by representing a significant technological innovation. Artificial intelligence combines hardware and software to simulate human cognitive processes, enabling machines to perform complex tasks such as learning, reasoning, and decision-making. This paper explores the advantages and disadvantages of integrating artificial intelligence into accounting practices. While artificial intelligence presents numerous benefits for accountants, it also introduces challenges that must be addressed. The paper also contributes to the expanding knowledge base on artificial intelligence in accounting by offering practical recommendations for accountants on effectively adopting artificial intelligence. Even with the challenges presented from integrating artificial intelligence in accounting, such integration offers considerable efficiency gains. This positions artificial intelligence as a strategic investment for organizations aiming to improve the performance and effectiveness of their accounting departments.International Journal of Artificial Intelligence &amp; Applications00Even with the challenges presented from integrating artificial intelligence in accounting, such integration offers considerable efficiency gains and positions artificial intelligence as a strategic investment for organizations aiming to improve the performance and effectiveness of their accounting departments.International Journal of Artificial Intelligence &amp; Applications["Angel R. Otero", "Keiron Hylton"]2025-01-28T00:00:00 1627d922d23704b34f74638cc4fdce31224831e5DESCRIPTION Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise. METHODS A GIN working group on AI developed these principles, informed by the results of a scoping review and practical examples, through iterative discussion. RECOMMENDATIONS Eight principles were identified to adhere to when using AI in the guideline context: transparency, preplanning, additionality, credibility, ethics, accountability, compliance, and evaluation. These complementary principles are described in a comprehensive way, but they do not provide detailed instructions on how to use specific AI tools. Although these principles are expected to apply across different contexts and stages of the guideline enterprise, details on their implementation have some degree of flexibility. Guideline development groups choosing to use AI will be able to adequately implement the principles if they ensure aspects such as structured reporting on the use of AI tools, involvement of experts in AI, and allocation of funding for the adequate use of AI tools. The GIN principles may support guideline developers in the responsible and transparent use of AI to ensure trustworthy guidelines.Annals of Internal Medicine80A set of principles for the development and use of AI tools or processes to support the health guideline enterprise are proposed and expected to apply across different contexts and stages of the guideline enterprise.Annals of internal medicine["Bernardo Sousa-Pinto", "Manuel Marques-Cruz", "Ignacio Neumann", "Yuan Chi", "Artur Jacek Nowak", "Marge Reinap", "M. Awad", "Monika Nothacker", "Milana Trucl", "Jan L. Bro\u017cek", "Pablo Alonso-Coello", "W. Wiercioch", "A. Qaseem", "Elie A. Akl", "Holger J. Sch\u00fcnemann"]2025-01-28T00:00:00 0c1e112a76c91ac4d531a65bf4c342f3c1866199This study investigates the integration of artificial intelligence (AI) in national science curricula across 21 countries, including Australia, Cyprus, Estonia, France, Finland, Greece, Hong Kong, India, Iceland, Ireland, Nepal, New Zealand, Norway, Ontario (Canada), Poland, Singapore, South Africa, South Korea, Sweden, the United Kingdom, and the United States. By analyzing these curricula, the research identifies the presence of AI-related knowledge, skills, and attitudes, providing a comprehensive understanding of how AI is embedded in educational frameworks. The findings reveal a strong emphasis on practical AI skills, interdisciplinary knowledge, ethical considerations, and societal impacts, preparing students to thrive in an AI-driven future. This comprehensive approach highlights AI’s transformative potential in education. The study emphasizes AI’s role in fostering problem-solving skills and active learning, underscoring the need for practical AI applications and comprehensive teacher training in AI concepts. The analysis also identifies gaps in the explicit mention of “artificial intelligence” itself, suggesting a broader focus on related concepts. Notably, AI is not frequently mentioned explicitly in the curricula but is often approached under the umbrella of information and communication technology in relation to science. Recommendations for enhancing AI integration include comprehensive teacher training, continuous curriculum evaluation, and the inclusion of the ethical and societal implications of AI. This research provides valuable insights for educators and policymakers, highlighting the need for a well-rounded curriculum that prepares students for the future challenges and opportunities presented by AI technologies.Contemporary Mathematics and Science Education130Analysis of national science curricula across 21 countries reveals a strong emphasis on practical AI skills, interdisciplinary knowledge, ethical considerations, and societal impacts, preparing students to thrive in an AI-driven future.Contemporary Mathematics and Science Education["Konstantinos Karampelas"]2025-01-28T00:00:00 62e763326e6ce6a767d979fb889b0c40e326a3f3International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2025-01-28T00:00:00 d16860ca625c6945bb3552297c778d70609b40fcBehaviour &amp; Information Technology440Behaviour &amp; Information Technology["Sara Peretti", "Federica Caruso", "Giacomo Valente", "L. Pomante", "Tania Di Mascio"]2025-01-28T00:00:00 cf63e77c2d7d540ad66c0dad69dd94dd228b50cdUS-China Education Review. A00US-China Education Review A["SHANG Liyue", "WANG Yuqiu"]2025-01-28T00:00:00 248ad195777e600e90ff170340802eb316a0c389Journal of Spacecraft and Rockets00Journal of Spacecraft and Rockets["Olivier L. de Weck"]2025-01-28T00:00:00 e3e446d52e322eac6189c4bbdda2d490caf0d896International Journal of Computer Applications00International Journal of Computer Applications["Mayur Jariwala"]2025-01-28T00:00:00 2efa8e6e45c5a1d04255cbe9cfc59f714e3d1f7fLarge language models, trained on personal data, may soon be able to mimic individual personalities. This would potentially transform search across human candidates, including for marriage and jobs -- indeed, several dating platforms have already begun experimenting with training"AI clones"to represent users. This paper presents a theoretical framework to study the tradeoff between the substantially expanded search capacity of AI clones and their imperfect representation of humans. Individuals are modeled as points in $k$-dimensional Euclidean space, and their AI clones are modeled as noisy approximations. I compare two search regimes: an"in-person regime"-- where each person randomly meets some number of individuals and matches to the most compatible among them -- against an"AI representation regime"-- in which individuals match to the person whose AI clone is most compatible with their AI clone. I show that a finite number of in-person encounters exceeds the expected payoff from search over infinite AI clones. Moreover, when the dimensionality of personality is large, simply meeting two people in person produces a higher expected match quality than entrusting the process to an AI platform, regardless of the size of its candidate pool.00It is shown that a finite number of in-person encounters exceeds the expected payoff from search over infinite AI clones, and when the dimensionality of personality is large, simply meeting two people in person produces a higher expected match quality than entrusting the process to an AI platform, regardless of the size of its candidate pool.["Annie Liang"]2025-01-28T00:00:00 596fc2461dcce36cf092d185fdeb0be7cbc018bdAI is poised to be transformative across virtually all industries, and the financial sector has already experienced major impacts from AI in predictive analytics, fraud detection and risk management among others. This paper also describes the innovation of AI, machine learning and natural language processing (NLP) technologies and their availability in financial services in 2024. Its scope covers richer credit scoring models which harness predictive analytics to assess borrower performance, more sophisticated fraudulent activity detection frameworks that can identify suspicious transactions in real-time, and countless automated trading algorithms which can dynamically adapt to changing market behaviors. Moreover, Algorithms have also deployed in the way financial institutions are evaluating and handling second risk management; AIdriven Risk Management tools have been also there to facilitate decision making process for operational efficiency. We discuss these challenges, and also show how AI will be a crucial part of fundamentally transforming financial analysis from optimizing customer service interactions to stabilizing the economy.Formosa Journal of Science and Technology00Its scope covers richer credit scoring models which harness predictive analytics to assess borrower performance, more sophisticated fraudulent activity detection frameworks that can identify suspicious transactions in real-time, and countless automated trading algorithms which can dynamically adapt to changing market behaviors.Formosa Journal of Science and Technology["Goutham Kacheru", "Rohit Bajjuru", "Nagaraju Arthan"]2025-01-28T00:00:00 6330e27612675a3e3e259ce59cf20c4ec543f715Technology Analysis &amp; Strategic Management290Technology Analysis &amp; Strategic Management["Haihua Wang", "Yanyan Gong", "Qin Sun", "Qianru Sun"]2025-01-28T00:00:00 30324b9ee29fdeca67d78a834986fc43e45b949bWater, Air, &amp; Soil Pollution260Water, Air, &amp; Soil Pollution["Zian Liu", "Lingwei Ren", "Zhonghao Ke", "Xizheng Jin", "Shuya Rui", "Hua Pan", "Zhiping Ye"]2025-01-28T00:00:00 88080d422ef1ea596734af5d96e1c84c91252778Background: The use of machine learning (ML) in veterinary medicine has gained significant attention, particularly for the early detection and classification of animal diseases. For example: lumpy Skin Disease (LSD) in cattle is one such condition that poses a substantial threat to livestock health. Traditional diagnostic methods can be labor-intensive and time-consuming. Therefore, leveraging ML techniques for automated disease detection could improve diagnostic efficiency and accuracy. Methods: In this study, a dataset comprising images of cattle from four breeds-Vechur, Swiss Holstein, Jersey and Ponwar-was collected to train a DenseNet-121 Convolutional Neural Network (CNN) model for identifying and classifying LSD in cattle. The dataset included both LSD-affected and non-affected cattle images, ensuring balanced representation across different breeds, ages and severity levels. Image preprocessing techniques such as resizing, normalization and data augmentation were applied to prepare the data for model training. The DenseNet-121 architecture was employed, utilizing a pretrained ImageNet model as the feature extractor, with additional layers for binary classification. Result: The model achieved excellent performance, with training accuracy reaching 99.76% and validation accuracy of 92.17%. The precision, recall and F1-score for the “Lumpy” class were 96.43%, 81.82% and 88.52%, respectively, while the “Normal” class had a precision of 92%, recall of 98.57% and F1-score of 95.17%. The overall accuracy of the model was 93.2%. Additionally, the model achieved an AUC score of 0.96 for both classes, indicating a high ability to distinguish between “Lumpy” and “Normal” cattle. These results highlight the potential of ML-based methods in enhancing the efficiency and accuracy of veterinary disease diagnostics. Indian Journal of Animal Research340The model achieved excellent performance, with training accuracy reaching 99.76% and validation accuracy of 92.2%, indicating a high ability to distinguish between “Lumpy” and “Normal” cattle.Indian Journal of Animal Research["Abdulrhman Alkhanifer", "Ahmad AlZubi"]2025-01-28T00:00:00 9e666ac47f36972af131071a09201ae69b29cd32American Society of Nephrology. Clinical Journal00Clinical journal of the American Society of Nephrology : CJASN["Adam Engel Hercz", "Susanne B Nicholas", "Alex A T Bui", "Keith C Norris"]2025-01-28T00:00:00 b4df287f1cc0dd53f09ee66e52134c7e7b2fd57fIХ Всероссийская научно-практическая конференция "ЦИФРОВОЕ ОБЩЕСТВО: НАУЧНЫЕ ИНИЦИАТИВЫ И НОВЫЕ ВЫЗОВЫ"00IХ Всероссийская научно-практическая конференция "ЦИФРОВОЕ ОБЩЕСТВО: НАУЧНЫЕ ИНИЦИАТИВЫ И НОВЫЕ ВЫЗОВЫ"["\u0415.\u0412. \u0417\u0430\u0432\u0435\u0434\u0435\u0435\u0432", "\u0413.\u0412. \u0411\u0438\u0433\u0430\u0435\u0432"]2025-01-28T00:00:00 7509e7b7acadd5257734310362c7870e1646a71500["Ajay Kumar", "Sangeeta Rani", "Sarita Rathee", "N. Hemrajani", "Mamta Dahiya"]2025-01-28T00:00:00 ff8c9fdd9d5a1d63a10abfd114c9f47b1a9d6160The software engineering landscape is undergoing a significant transformation with the advent of artificial intelligence (AI). AI technologies are poised to redefine traditional software development practices, offering innovative solutions to long-standing challenges. This paper explores the integration of AI into software engineering processes, aiming to identify its impacts, benefits, and the challenges that accompany this paradigm shift. A comprehensive analysis of current AI applications in software engineering is conducted, supported by case studies and theoretical models. The study examines various phases of software development to assess where AI contributes most effectively. The integration of AI enhances productivity, improves code quality, and accelerates development cycles. Key areas of impact include automated code generation, intelligent debugging, predictive maintenance, and enhanced decision-making processes. AI is revolutionizing software engineering by introducing automation and intelligence into the development lifecycle. Embracing AI-driven tools and methodologies is essential for staying competitive in the evolving technological landscape.Applied Sciences591The integration of AI enhances productivity, improves code quality, and accelerates development cycles, and key areas of impact include automated code generation, intelligent debugging, predictive maintenance, and enhanced decision-making processes.Applied Sciences["M. Alenezi", "Mohammed Akour"]2025-01-28T00:00:00 3b9248e608d915e9fde2297cb0c6baffce68235bSouthern California is a special case scenario for any energy management study, given its sunny climate, sprawling urban landscapes, and economic strength.  This research project focuses on how artificial intelligence can be applied in energy management by showing its potential toward optimum energy consumption and maximizing sustainability within Southern California. The dataset used for this study was accessed from the Kaggle website. The dataset encompassed various energy consumptions, and the data were collected across different building structures in Southern California between January 2018 and January 2024. These datasets included hourly records of electricity consumption for residential and commercial buildings and industrial buildings. Furthermore, it also provided a record of environmental and operating metrics. This dataset is useful to researchers and practitioners who work on forecasting electricity consumption, energy management, sustainability, and developing AI-based optimization models. To depict an insight into which variable affects strongly within the patterns of energy consumption, machine learning techniques used were logistic regression, Random Forest, and XG-Boost while considering a power outage data set. This study gives evidence that the AI-based models significantly enhance the forecast's accuracy and further allow integration of renewable energy resources, which in turn yield benefits through reduced operational costs and reduction of GHG emissions. The discussion has shown how such development implications could be translated into supportive policy frameworks of advanced studies in the future to electric vehicle charging and even energy storage solutions. Generally, this research underlines the crucial role of AI in changing energy management practices towards sustainable energy use.Journal of Ecohumanism261This study gives evidence that the AI-based models significantly enhance the forecast's accuracy and further allow integration of renewable energy resources, which in turn yield benefits through reduced operational costs and reduction of GHG emissions.Journal of Ecohumanism["Ayan Barua", "Fazle Karim", "Muhammad Mahmudul Islam", "Niropam Das", "Md Fakhrul Islam Sumon", "Arifur Rahman", "Pravakar Debnath", "Mitu Karmakar", "MD Azam Khan"]2025-01-28T00:00:00 0ae73b400b2be3e3628b4bc81f01aed5fc0d98c9As artificial intelligence (AI) continues to advance and scale up in biomedical research, concerns about AI’s trustworthiness and transparency have grown. There is a critical need to systematically bring accurate and relevant biomedical knowledge into AI applications for transparency and provenance. Knowledge graphs have emerged as a powerful tool that integrates heterogeneous knowledge by explicitly describing biomedical knowledge as entities and relationships between entities. However, PubMed, the largest and most comprehensive repository of biomedical knowledge, exists primarily as unstructured text and is under utilized for advanced machine learning tasks. To address the challenge, we developed LiteralGraph, a computational framework to extract biomedical terms and relationships from PubMed literature into a unified knowledge graph. Using this framework, we established the Genomic Literature Knowledge Base (GLKB), which consolidates 14,634,427 biomedical relationships between 3,276,336 biomedical terms from over 33 million PubMed abstracts and nine well-established biomedical repositories. The database is coupled with RESTful APIs and a user-friendly web interface that makes it accessible to researchers for various usages. We demonstrated the broad utility of GLKB towards transparent AI in three distinct application scenarios. In the LLM grounding scenario, we developed a Retrieval Augmented Generation (RAG) agent to reduce LLM hallucination in biomedical question answering. In the hypothesis generation scenario, we elucidated the potential functions of RFX6 in type 2 diabetes (T2D) using the vast evidence from PubMed articles. In the machine learning scenario, we utilized GLKB to provide semantic knowledge in predictive tasks and scientific fact-checking.bioRxiv701The Genomic Literature Knowledge Base (GLKB) is established, which consolidates 14,634,427 biomedical relationships between 3,276,336 biomedical terms from over 33 million PubMed abstracts and nine well-established biomedical repositories, and is coupled with RESTful APIs and a user-friendly web interface.bioRxiv["Yuanhao Huang", "Zhaowei Han", "Xin Luo", "Xuteng Luo", "Yijia Gao", "Meiqi Zhao", "Feitong Tang", "Yiqun Wang", "Jiyu Chen", "Chengfan Li", "Xinyu Lu", "Tiancheng Jiao", "Jiahao Qiu", "Feiyang Deng", "L. Guan", "Haohong Shang", "Fan Feng", "Thi Hong Ha Vu", "T. Bate", "Dongxiang Xue", "Jean-Philippe Cartailler", "M. Stitzel", "Shuibing Chen", "Marcela Brissova", "Stephen Parker", "Jie Liu"]2025-01-28T00:00:00 c7e4076a6489509115aa55c94d716196ecaab06eThis rapid study explores teacher educators’ perceptions of generative artificial intelligence (GenAI) in teacher education, conducted through a descriptive survey involving 55 teacher educators from two colleges of education in Ghana. A convenience sampling technique was adopted for data collection, and a data analysis using exploratory factor analysis was used to identify primary factors shaping perceptions and preparedness of GenAI integration. Key findings reveal a generally positive perception among the teacher educators, who recognize GenAI’s potential to support academic achievement, increase student engagement, and improve communication within teacher education settings. The findings further indicate that the teacher educators’ background factors, such as age, years of teaching experience, department, and college, do not significantly predict their perceptions of GenAI. Since none of these measured background factors were significant predictors, this suggests that training and resources for using GenAI should be broadly prioritized, accessible, and not heavily tailored to specific demographic groups. However, the study identified significant concerns within the barriers and challenges factors, including ethical issues, fairness in student assessment, and possible adverse effects on the teacher educator-student relationship. The communication and independence factors highlight a need for professional development, with teacher educators emphasizing the importance of training in GenAI usage to optimize its educational potential. The study concludes that while teacher educators generally support GenAI’s potential benefits, there are essential ethical and practical challenges to address. Recommendations include establishing clear policies and guidelines to guide GenAI implementation and ensure ethical usage. We further recommend the expansion of this research with a larger sample to gather comprehensive insights from the teacher educators and their acceptance levels of GenAI.Journal of Digital Educational Technology470While teacher educators generally support GenAI’s potential benefits, there are essential ethical and practical challenges to address, which include establishing clear policies and guidelines to guide GenAI implementation and ensure ethical usage.Journal of Digital Educational Technology["Bismark Nyaaba Akanzire", "Matthew Nyaaba", "Macharious Nabang"]2025-01-28T00:00:00 8c21ddd65f05e3d3b94639b0997f96d7392dac14Artificial Intelligence (AI) has demonstrated potential in healthcare, particularly in enhancing diagnostic accuracy and decision-making through Clinical Decision Support Systems (CDSSs). However, the successful implementation of these systems relies on user trust and reliance, which can be influenced by explainable AI. This study explores the impact of varying explainability levels on clinicians trust, cognitive load, and diagnostic performance in breast cancer detection. Utilizing an interrupted time series design, we conducted a web-based experiment involving 28 healthcare professionals. The results revealed that high confidence scores substantially increased trust but also led to overreliance, reducing diagnostic accuracy. In contrast, low confidence scores decreased trust and agreement while increasing diagnosis duration, reflecting more cautious behavior. Some explainability features influenced cognitive load by increasing stress levels. Additionally, demographic factors such as age, gender, and professional role shaped participants' perceptions and interactions with the system. This study provides valuable insights into how explainability impact clinicians' behavior and decision-making. The findings highlight the importance of designing AI-driven CDSSs that balance transparency, usability, and cognitive demands to foster trust and improve integration into clinical workflows.820The results revealed that high confidence scores substantially increased trust but also led to overreliance, reducing diagnostic accuracy, and low confidence scores decreased trust and agreement while increasing diagnosis duration, reflecting more cautious behavior.["Olya Rezaeian", "A. E. Bayrak", "Onur Asan"]2025-01-28T00:00:00 ed353f0cd9b47ab3a110d1e81ca5592a6f825225 This study aims to evaluate students’ intention and actual use (AU) of artificial intelligence (AI) tools’ to discover how the power of AI influences learning and academic success. This paper used the unified theory of acceptance and use of technology (UTAUT) to develop a structural equation model (SEM) and used convenience sampling to measure 304 students’ five-point Likert scale responses. The model was tested with AMOS-24 and SPSS-25, and the study found that AI boosted students’ learning experiences and explain importance of AI skills and knowledge. Performance expectancy (PE), effort expectancy (EE), social influence and facilitating condition directly and indirectly affect AU via intent to use (IU), while subjective norms determining the use of AI tools’ and have no substantial influence. Attitude (ATT) moderates PE and EE, although the data show that ATT has no substantial effect on EE. These insights may help student to understand how AI tools’ benefit them and what factors affect their utilization. When correctly designed and executed, UTAUT provides an appropriate integrated theoretical framework for robust statistical analysis like SEM. HORIZONS A500The unified theory of acceptance and use of technology is used to develop a structural equation model (SEM) and used convenience sampling to measure 304 students’ five-point Likert scale responses and found that AI boosted students’ learning experiences and explain importance of AI skills and knowledge.On the Horizon: The International Journal of Learning Futures["Priya Saha", "M. Hossain", "Nirmal Chandra Roy", "Abdullah Al Masud", "Ruhul Amin"]2025-01-28T00:00:00 1806cb3ba95d5d29d16044c779343f2a7a0b9d4enpj Digit. Medicine670PhenoBrain utilizes a BERT-based natural language processing model to extract phenotypes from clinical texts in EHRs and employs five new diagnostic models for differential diagnoses of rare diseases, demonstrating its potential to enhance diagnostic accuracy in clinical workflows.NPJ Digital Medicine["Xiaohao Mao", "Yu Huang", "Ye Jin", "Lun Wang", "Xuanzhong Chen", "Honghong Liu", "Xinglin Yang", "Haopeng Xu", "Xiaodong Luan", "Ying Xiao", "S. Feng", "Jiahao Zhu", "Xuegong Zhang", "Rui Jiang", "Shuyang Zhang", "Ting Chen"]2025-01-28T00:00:00 f2a733c2145f1242a78325d55a53282e1136c2d1This study examines, from a post-pandemic theoretical perspective, university students' continuous intention (CI) to utilise AI-powered tools for educational purposes. AI-powered tools are new and underutilised in higher education. The fact that students and teachers need knowledge to use these apps in the classroom compounds the issue. Despite this technology's recent academic introduction, nothing is known about its impacts. In order to investigate the variables that influence the continual intention to employ artificial intelligence, this study discusses the possibility of integrating the self-determination theory (SDT) and technology acceptance model (TAM) with the post-acceptance model (PAM). Three hundred forty university students were solicited to complete a questionnaire to collect data for the proposed model. A dual-stage approach uses both symmetrical assumptions from structural equation modelling with partial least squares (PLS-SEM) and asymmetrical configurations from fuzzy-set qualitative comparative analysis (fsQCA). In order to better comprehend the intricate interplay between the model's inputs and its desired output, this approach is devised. Consideration is given to the fact that various configurations of external constructs exert distinct influences on internal constructs. In Thailand, perceived usefulness (PU) and autonomy predict continued AI-powered tool use. Perceived ease of use (PEOU) did not affect continuing intention. Conclusions drawn from the configurational analysis show that no single factor adequately explains a high CI level. Rather, three distinct configurations were identified as improving CI using AI-powered tools. Overall, theoretical and practical ramifications are addressed.Journal of Information and Communication Technologies00This study examines, from a post-pandemic theoretical perspective, university students' continuous intention to utilise AI-powered tools for educational purposes and discusses the possibility of integrating the self-determination theory (SDT) and technology acceptance model (TAM) with the post- acceptance model (PAM).Journal of Information and Communication Technology["Mohamed Soliman", "Reham Adel Ali", "Imran Mahmud", "Tawat Noipom"]2025-01-28T00:00:00 3559ab6da0354e7740f118d0b52ec7a90d747485Introduction Autism Spectrum Disorder (ASD) presents significant challenges in social communication and interaction, critically impacting the lives of children with ASD. Traditional interventions, such as Applied Behavior Analysis (ABA) and Social Skills Training (SST), have been widely used to address social skill deficits in these children. While these methods are effective, they often require substantial resources, long-term engagement, and specialized expertise, which limit their accessibility and adaptability to diverse social contexts. Recent advancements in artificial intelligence (Al), particularly Transformer-based models, offer a novel opportunity to enhance and personalize social skills training. Methods This study introduces a Public Health-Driven Transformer (PHDT) model specifically designed to improve social skills in children with ASD. By integrating public health principles with state-of-the-art Al methodologies, the PHDT model creates interventions that are adaptable, accessible, and sensitive to individual needs. Leveraging multi-modal data inputs-such as text, audio, and facialcues-PHDT provides real-time social context interpretation and adaptive feedback, enabling a more naturalistic and engaging learning experience. Results and discussion Experimental results reveal that PHDT significantly outperforms traditional methods in fostering engagement, retention, and social skill acquisition. These findings highlight PHDT's potential to improve social competencies in children with ASD and to revolutionize access to specialized support within public health frameworks. This work underscores the transformative impact of Al-driven, public health-oriented interventions in promoting equitable access to essential developmental resources and enhancing the quality of life for children with ASD.Frontiers in Psychiatry360Experimental results reveal that PHDT significantly outperforms traditional methods in fostering engagement, retention, and social skill acquisition, highlighting PHDT's potential to improve social competencies in children with ASD and to revolutionize access to specialized support within public health frameworks.Frontiers in Psychiatry["Liu Lan", "Ke Li", "Diao Li"]2025-01-28T00:00:00 d02c10f774009338574a7ec2590e972de67de712This article provides an overview of artificial intelligence (AI) avatar technology and its potential use as a therapeutic intervention by licensed marriage and family therapists (MFTs) within the family system context.With the growth of marriage and family therapy, it is essential to equip future practitioners with tools for effective service delivery. As virtual environments evolve, MFTs must be prepared to engage clients of all ages interested in these technologies.The authors present a conceptual paper on AI avatar technology, exploring its applications in therapy and examining the diffusion of innovation theory to assess its adoption.AI avatars offer many benefits, including increasing accessibility and affordability; enhanced communication in virtual settings; augmenting treatment possibilities for individuals and families; and a safe, anonymous environment that encourages client expression. This technology also helps alleviate therapist burnout.Although AI should not replace human interactions, it can enhance the delivery of MFT practices, helping the profession stay relevant in this digital age and improving therapy accessibility.Integrating this technology can create new training opportunities for practitioners. Professional associations should develop guidelines to optimize the use of AI in therapeutic practice as the digital revolution advances.Family Relations320An overview of artificial intelligence (AI) avatar technology and its potential use as a therapeutic intervention by licensed marriage and family therapists (MFTs) within the family system context is provided.Family Relations["Alex D. Colvin", "Crystal Benjamin"]2025-01-28T00:00:00 137a84ec85c07d23d94107e7f344db3310c3c193This paper seeks to explore the intersection of artificial intelligence (AI) and cultural disruption in Indian dystopian narratives, with a focus on the shows Asur and Ok Computer. Both series offer compelling insights into how AI reshapes societal structures, ethical paradigms, and human identities in a rapidly evolving technological landscape, particularly in the post-COVID context. Asur blends mythology and technology to depict a chilling dystopia where AI tools like facial recognition and behavioural analysis become weapons for moral and ideological battles. The show critiques the ethical ambiguity of AI applications and highlights the dangers of unchecked technological advancement in a culturally diverse society. It raises critical questions about the relationship between traditional Indian philosophies of dharma and karma and the cold logic of AI systems. In contrast, Ok Computer offers a satirical lens on a future where AI and robotics are deeply entrenched in daily life, questioning the ethics of automation, data governance, and the boundaries of human-machine relationships. The series critiques blind faith in technology, juxtaposing Western models of technological determinism with Indian spiritual perspectives on consciousness and ethical responsibility. This analysis investigates how these narratives reflect the broader anxieties surrounding AI in India, including digital colonialism, socio-cultural disruptions, and ethical dilemmas. By integrating Indian philosophical frameworks with global AI discourses, the paper aims to highlight the unique dimensions of human-machine relationships in the Indian context. Through the lens of Asur and Ok Computer, this paper demonstrates how Indian dystopian fiction engages with pressing concerns about technology and cultural identity, offering critical insights into the evolving dynamics of AI-driven futures.The Voice of Creative Research100Through the lens of Asur and Ok Computer, this paper demonstrates how Indian dystopian fiction engages with pressing concerns about technology and cultural identity, offering critical insights into the evolving dynamics of AI-driven futures.The Voice of Creative Research["Vanya Goyal"]2025-01-28T00:00:00 9476dd98f7078ae788a507025a450f136833798f Purpose This study aims to examine users’ perceptions of gendered artificial intelligence (AI) interfaces in the context of autonomous vehicles (AVs). It focuses on the gendered effects of social presence, warmth and competence on trust and introduces the moderating role of perceived autonomy as a key factor. Design/methodology/approach A between-subjects experimental design was used (n = 309), using a 360-degree virtual tour simulation with gendered voice assistants. Findings As AVs are perceived as highly autonomous, the impact of gender on social presence intensifies, affecting trust. Female voices enhance social presence, conveying warmth but also perceived competence traits. Notably, competence impacts trust more significantly than warmth. Research limitations/implications The study’s experimental approach might not fully capture real-world interactions with AVs. Future research could benefit from field and longitudinal studies. Practical implications These findings are crucial for AV designers and interface developers. They highlight the importance of considering human-like characteristics such as gender and enhance perceptions of competence in developing highly autonomous AI interfaces. Social implications Addressing gender stereotypes in AV design is vital to ensure inclusivity, to cater for a diverse user base and to give all users a trustworthy experience. Originality/value This study is pioneering in its examination of how gender stereotypes impact trust toward AVs, an area previously unexplored, despite the significant influence gender might have due to the high level of autonomy and traditional car-related stereotypes. It uniquely identifies feminine traits as denoting competence in highly autonomous technologies like AVs, especially where safety is critical. This challenges traditional gender stereotypes and emphasizes the need to rethink attributes associated with competence and trust in AI. Journal of Consumer Marketing560This study is pioneering in its examination of how gender stereotypes impact trust toward AVs, an area previously unexplored, despite the significant influence gender might have due to the high level of autonomy and traditional car-related stereotypes.Journal of Consumer Marketing["Giulia Pavone", "Kathleen Desveaud"]2025-01-28T00:00:00 9f42f7e1088f4cc61911845ad368bccfa13f552cThe current study sought to determine how well university students' EFL listening comprehension skills may be developed by artificial intelligence (AI) technologies. One hundred students participated in the study, split into two groups: the control group (N = 50), which received traditional education, and the experimental group (N = 50), which received instruction using artificial intelligence systems. The study's instruments included an EFL listening comprehension skills checklist to determine which listening skills are most important for first-year college students to acquire. a pre-post listening skills test to measure students' listening abilities before and after using the chatbot and Duoling AI applications and a correction rubric . A statistical analysis was conducted to confirm the study's hypotheses. Findings of the study revealed that the experimental group students' EFL listening skills were enhanced as a result of using the Artificial Intelligence (chatbot and Duoling).Conhecimento &amp; Diversidade00Findings of the study revealed that the experimental group students' EFL listening skills were enhanced as a result of using the Artificial Intelligence (chatbot and Duoling).Conhecimento &amp; Diversidade["Saleh Alrasheedi"]2025-01-28T00:00:00 6201db387d7f66f4227a5bbd0a6c0efa9ad8d8a1The landscape of software development has seen a massive shift in the last few years, with rising use of data-driven methods for making product decisions. One area that has made a significant difference is the integration of machine learning and artificial intelligence technologies to inform software engineering practice, including prioritization of product features. Software product feature prioritization is an essential process directly influencing the competitiveness and success of a product. Traditional techniques, though fundamental, tend to fall short in resolving the intricacies of contemporary software ecosystems. This study delves into the revolutionary potential of machine learning (ML) and artificial intelligence (AI) for improving feature prioritization. An extensive literature survey identifies existing trends and their drawbacks, such as inadequate integrated frameworks and scalability and interpretability issues. The suggested framework integrates heterogeneous sources of data, predictive analytics, natural language processing (NLP), and optimization algorithms to support real-time data-driven decision-making.International Journal of Data Mining &amp; Knowledge Management Process00This study delves into the revolutionary potential of machine learning (ML) and artificial intelligence (AI) for improving feature prioritization and proposes a suggested framework that integrates heterogeneous sources of data, predictive analytics, natural language processing, and optimization algorithms to support real-time data-driven decision-making.International Journal of Data Mining &amp; Knowledge Management Process["Akhil Raj", "Ridhi Deora"]2025-01-28T00:00:00 11257cd5e214d1f17bb636aeb91666f8d7f7aee6The banking sector faces challenges in operational efficiency, decision-making, and data security. Integrating advanced technologies can address these issues effectively. This study aims to enhance operational efficiency in banking by integrating artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and blockchain technologies. We analyzed a comprehensive dataset of 25,341 documents using various predictive models. A segmentation model was employed to forecast deposit periods. The performance of logistic regression was evaluated and compared to that of a Naive Bayes model. The logistic regression model achieved an accuracy rate of 91.39%, outperforming the Naive Bayes model, which had an accuracy rate of 91.17%. This demonstrates the effectiveness of our approach in providing accurate predictions and insights. This research demonstrates significant advancements in banking operations through the integration of AI, NLP, and blockchain technology. The approach enhances decision-making, improves data processing efficiency, and ensures robust data security. It sets a new benchmark for future innovations in fintech, showcasing substantial performance improvements and practical applications. Key Words: artificial intelligence, machine learning, NLP, blockchain technology, predictive modeling, operational efficiency, data security, banking innovationINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research demonstrates significant advancements in banking operations through the integration of AI, NLP, and blockchain technology, which enhances decision-making, improves data processing efficiency, and ensures robust data security.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Diksha Joshi"]2025-01-28T00:00:00 d0e599a6897e7d98aa8d2822e276121c1f0b5e14This study explores the impact of Artificial Intelligence (AI) on personalized learning in China, examining its effectiveness in enhancing student performance and engagement across urban and rural schools. AI technologies have revolutionized education by providing tailored learning experiences, identifying individual student needs, and improving overall academic outcomes. However, disparities in access to AI-based tools between urban and rural schools remain a significant challenge. Urban schools, benefiting from advanced infrastructure, demonstrate higher adoption rates of AI tools, leading to better student outcomes. In contrast, rural schools face significant barriers, including limited access to digital infrastructure and insufficient teacher training, exacerbating educational inequalities. This study employs a mixed-methods approach, combining quantitative surveys with qualitative interviews, to assess the prevalence of AI use in schools, its impact on student performance, and the perceptions of educators and students. Findings suggest that AI-driven personalized learning significantly enhances academic performance and engagement, particularly in urban areas. However, ethical concerns, such as data privacy and algorithmic bias, remain crucial considerations in the implementation of AI systems in education. The study concludes by emphasizing the need for equitable policies that address infrastructure gaps and ensure the ethical deployment of AI technologies. Recommendations for bridging the digital divide, improving teacher readiness, and fostering inclusive AI practices are proposed to ensure that the benefits of AI in education are accessible to all students.Uniglobal Journal of Social Sciences and Humanities00Findings suggest that AI-driven personalized learning significantly enhances academic performance and engagement, particularly in urban areas, and ethical concerns, such as data privacy and algorithmic bias, remain crucial considerations in the implementation of AI systems in education.Uniglobal Journal of Social Sciences and Humanities[]2025-01-29T00:00:00 8ff157c4725eceac9d58716973527f960cb29c1cAbstract Background Infections with Clostridioides difficile are associated with prolonged hospital stays, higher costs, and significant morbidity. Artificial intelligence (AI) tools can accurately predict which hospitalized patients are most likely to acquire C. difficile infection (CDI). However, to date, such tools have not been used in clinical practice. We investigated how AI tools for CDI risk stratification could be integrated into clinical workflows to promote targeted infection prevention efforts. Details of the infection prevention bundle (a) Screenshot of the BPA for enhanced handwashing precautions. This BPA instructs the receiving provider to place an order for putting up the “Enhanced Handwashing Precautions” sign, depicted in Figure 2. (b) Screenshot of the BPA for antimicrobial stewardship. This BPA is educational and provides a list of recommendations for reducing risk of CDI, including discontinuing unnecessary acid suppressants, minimizing unnecessary antibiotics, consulting the beta-lactam allergy evaluation service, and encouraging patient to eat yogurt if appropriate. Methods A previously validated AI model for predicting CDI risk from routinely collected data in electronic health records was used to generate daily risk scores for adult inpatients presenting to Michigan Medicine between January 1, 2023 and December 31, 2023. These scores were used to focus infection prevention efforts on high-risk patients in 10 selected hospital units with the greatest concentration of CDI cases. The infection prevention bundle, aimed at reducing both susceptibility and exposure, included provider-facing best practice alerts (BPAs) for enhanced handwashing precautions and antimicrobial stewardship (Figure 1). Using retrospective data, we determined a risk threshold that targets 5 alerts/unit/week on average. Clinical staff on selected units were educated about the AI tool by the study team. Picture of the “Enhanced Handwashing Precautions” sign This sign is placed on the door of the rooms for high-risk patients in selected hospital units and instructs all persons to wash their hands with soap and water upon room entry. Results During the study, 12,983 hospitalizations corresponding to 10,815 patients were assessed daily by the model, totaling 109,068 CDI risk scores. Among this population, 2,151 (16.6%) high-risk hospitalizations exceeded the risk threshold and triggered BPAs (an average of 4.1 alerts/unit/week). Among the high-risk population, 1,647 (76.6%) and 117 (5.4%) hospitalizations received an order for enhanced handwashing precautions and an order for a β-lactam allergy evaluation consultation, respectively. Field observations and interviews with clinical staff revealed challenges associated with behavior changes such as compliance with handwashing using soap and water to remove spores. Conclusion AI tools can be integrated into clinical workflows to promote targeted infection prevention efforts. However, continuous monitoring of how such tools interact with existing workflows and education on novel infection prevention strategies are key to success. Disclosures Krishna Rao, MD, MS, Merck and Company, Inc.: Grant/Research Support|Rebiotix Inc.: Advisor/Consultant|Seres Therapeutics: Advisor/Consultant|Summit pharmaceuticals: Advisor/ConsultantOpen Forum Infectious Diseases00Artificial intelligence tools for CDI risk stratification could be integrated into clinical workflows to promote targeted infection prevention efforts and continuous monitoring of how such tools interact with existing workflows and education on novel infection prevention strategies are key to success.Open Forum Infectious Diseases["Shengpu Tang", "Rebekah Clark", "Stephanie Shepard", "Erkin \u00d6tle\u015f", "Maxim Garifullin", "Melinda Seiler", "Justin Ortwine", "Patrick Arnold", "J. Nagel", "Jeremy Jared", "Sarah Krein", "Jacob Kurlander", "Paul Grant", "Ji Baang", "Anastasia Wasylyshyn", "Krishna Rao", "Jenna Wiens"]2025-01-29T00:00:00 80875df6841c485103362dc673f3fa09f1d33f6fThe article delves into the legal aspect of the limits of admissibility of the artificial intelligence use in the im-plementation of public control. The authors substantiate the role and significance of this socio-legal institution, on the one hand, as an essential condition for the preservation and development of society and the state, and on the other hand – as a key legal guarantee for the realization, protection and defense of both the system of constitutional principles (first of all, people’s power and participation of citizens in the management of state affairs) and the entire system of rights, freedoms and legitimate interests of individuals and legal entities. The paper examines the legal limits of the permissibility of using artificial intelligence technologies in management, control and supervisory activities using the example of the Institute of public control (legal and other). The arti-cle substantiates the position that artificial intelligence technologies in these types of activities can and should be used only with human participation, under human control, and subject to respect for the rights, freedoms, and legitimate interests of individuals and legal entities. The main legal problems that subjects of public control will face when using artificial intelligence technologies in the organization and implementation of public control activities are identified and investigated, in particular, concerning the lack of fixing in the civil legislation the limits of the use of artificial intelligence technologies in the Russian Federation; weak development in the sci-entific legal doctrine of the concept and content of these limits; the lack of a technological base for autonomous production in Russia. The authors have developed and substantiated a system of measures to resolve them.Теория и практика общественного развития00Теория и практика общественного развития["Sergey V. Potapenko", "Artem S. Pchelintsev", "Vitaly V. Goncharov", "Elena G. Petrenko", "A. V. Cheshin"]2025-01-29T00:00:00 2ade94508f04d75929884eb0c24fab5352eb8dfaThis case study focuses on the application of Artificial Intelligence (AI) in the advertisements of Online Travel Agency (OTA). With the development of AI, companies have more tools to create advertising. Deepfake, as a technology powered by Artificial Intelligence, could be used to manipulate the content in advertising. As a result, it is important to review the effect and users’ attitudes toward AI-advertising. This case study reviews the Trivago AI advertising and discusses the associated theory in the AI-human reaction. The possible solutions are also discussed in the case study.Journal of Hospitality &amp; Tourism Cases60This case study reviews the Trivago AI advertising and discusses the associated theory in the AI-human reaction and the possible solutions are discussed.Journal of Hospitality &amp; Tourism Cases["Xinzhuo Fan", "Po-Ju Chen"]2025-01-29T00:00:00 952e06a67f52833fe71b9363354d7869d5ab4c13This study explores the impact of nuclear energy technology budgeting and artificial intelligence on carbon dioxide (CO2) emissions in 20 OECD economies. Unlike previous research that relied on conventional panel techniques, we utilize the Method of Moment Quantile Regression panel data estimation techniques. This approach provides quantile-specific insights while addressing issues of endogeneity and heteroscedasticity, resulting in a more nuanced and robust understanding of complex relationships. A novel aspect of this research work is introducing the moderating effect of artificial intelligence on the relationship between nuclear energy and CO2 emissions. The results found that the direct impact of artificial intelligence on CO2 emissions is significant, while the effect of nuclear energy technology budgeting is not. Additionally, artificial intelligence moderates the relationship between nuclear energy technology budgeting and CO2 emissions, aiding nuclear energy in reducing carbon emissions across OECD countries. Our findings indicate that transitioning to a low-carbon future is achievable by replacing fossil fuel energy sources with increased integration of artificial intelligence to promote nuclear energy technologies. This study demonstrates that energy innovations can serve as effective climate-resilience strategies to mitigate the impacts of climate change.00["Danish", "Adnan Khan"]2025-01-29T00:00:00 df285d95c69888636969e515d6ee76d93339d623Background: Generative Artificial Intelligence (GAI) tools are increasingly used in research. At present, there is no standardised approach to reporting GAI use. We aimed to produce guidance to support authors in the use of GAI in scientific writing. Methods: A steering group of academic surgeons with experience in GAI developed draft statements for best practice in reporting GAI use. These statements were refined through iterative discussions using a nominal group technique. A broad network of surgeons and surgical researchers were invited to participate in an online consultation exercise to validate these statements by ranking using a Likert scale. A pre-planned threshold of ≥70% of participants scoring a statement ≥7 would lead to acceptance. Participants were additionally surveyed on the use, opportunities, and risks. Thematic analysis was completed using ChatGPT. Results: The steering group developed five draft statements, which were validated in the online consultation exercise by 124 participants from 46 countries. Four draft statements were accepted based on this exercise and consolidated into the final Generative AI Transparency (GAIT) guidance: (1) GAI use should be reported in a GAIT statement; (2) GAI use should be mapped using the Contributor Roles Taxonomy; (3) specific prompts used should be reported; (4) authors should retain final responsibility for their work. Example statements to be included in manuscripts include: (1) ChatGPT-4o was used in November 2024 to check and edit statistical code (formal analysis) and edit small sections of the manuscript text for clarity (writing: review & editing). Prompts used are reported in the supplement. The authors should retain final responsibility for their work; (2) No Generative Artificial Intelligence was used to produce, draft, or edit this guidance paper. Conclusion: The GAIT 2024 guidance will support transparent, structured reporting of the use of generative AI in scientific writing, supporting the integrity of research outputs.Impact Surgery00The GAIT 2024 guidance will support transparent, structured reporting of the use of generative AI in scientific writing, supporting the integrity of research outputs and supporting the integrity of research outputs.Impact Surgery["Cortland Linder", "D. Nepogodiev", "GAIT 2024 Collaborative Group"]2025-01-29T00:00:00 ade610010bc2508210897f154ef1ba26ec3c2a08As global supply chains become increasingly complex and interconnected, the adoption of new technologies such as Artificial Intelligence (AI) offers significant potential for enhancing efficiency, optimizing costs, and improving overall performance. This research article investigates the impact of AI on various aspects of supply chain management, employing quantitative techniques to analyze its effectiveness. Through a comprehensive literature review and methodological approach utilizing real-world data, the study aims to quantify the contribution of AI across key areas like demand forecasting, inventory optimization, logistics planning, and transportation management. The findings aim to provide valuable insights for supply chain stakeholders considering the integration of AI solutions to streamline operations and enhance competitiveness.International Journal of Clinical Case Reports and Reviews00The study aims to quantify the contribution of AI across key areas like demand forecasting, inventory optimization, logistics planning, and transportation management to provide valuable insights for supply chain stakeholders considering the integration of AI solutions to streamline operations and enhance competitiveness.International Journal of Clinical Case Reports and Reviews["P. Maniatis"]2025-01-29T00:00:00 1c5bd5b101fee88b1ab51421aec1e8079db9cce2Abstract: This essay explores philosophical, scientific, and political questions raised by Generative Artificial Intelligence’s growing role in human language. Building on cutting-edge developments in the field, it critically examines how automated systems that produce written text challenge our traditional ideas of language production and ownership. In doing so, it introduces innovative approaches to understanding language in the age of AI, with a particular focus on cultural and political responsibility. Central to the discussion is an emerging concept of “language sustainability” that stems from the reflections in this essay. By highlighting the profound consequences of AI for human language, the essay places concrete demands on public-interest actors such as governments and universities, and on our communities as a whole, as custodians of human language, to preserve and enhance our personal and social linguistic agency, offering recommendations to re-align technological innovation in Generative AI with human language sustainability. Keywords: Artificial Intelligence. Languages. Sustainability. Technology. Politics.Alfinge. Revista de Filología540This essay critically examines how automated systems that produce written text challenge traditional ideas of language production and ownership, and introduces innovative approaches to understanding language in the age of AI, with a particular focus on cultural and political responsibility.Alfinge. Revista de Filología["Antonio Mart\u00ednez-Arboleda"]2025-01-29T00:00:00 ba7350b89cb03b8cc5324f3de436d85486baefb4This narrative review examines the integration of Artificial Intelligence (AI) in prenatal care, particularly in managing pregnancies complicated by Fetal Growth Restriction (FGR). AI provides a transformative approach to diagnosing and monitoring FGR by leveraging advanced machine-learning algorithms and extensive data analysis. Automated fetal biometry using AI has demonstrated significant precision in identifying fetal structures, while predictive models analyzing Doppler indices and maternal characteristics improve the reliability of adverse outcome predictions. AI has enabled early detection and stratification of FGR risk, facilitating targeted monitoring strategies and individualized delivery plans, potentially improving neonatal outcomes. For instance, studies have shown enhancements in detecting placental insufficiency-related abnormalities when AI tools are integrated with traditional ultrasound techniques. This review also explores challenges such as algorithm bias, ethical considerations, and data standardization, underscoring the importance of global accessibility and regulatory frameworks to ensure equitable implementation. The potential of AI to revolutionize prenatal care highlights the urgent need for further clinical validation and interdisciplinary collaboration.Journal of Clinical Ultrasound600The potential of AI to revolutionize prenatal care highlights the urgent need for further clinical validation and interdisciplinary collaboration, and explores challenges such as algorithm bias, ethical considerations, and data standardization.Journal of clinical ultrasound : JCU["Ugo Maria Pierucci", "G. Tonni", "Gloria Pelizzo", "Irene Paraboschi", "H. Werner", "R. Ruano"]2025-01-29T00:00:00 cda3ff1f3fa0397ae2c1003216459ca2f29d3338With the growing attention to generative artificial intelligence tools, particularly ChatGPT, there have been efforts to explore its implications for and applications in teacher education programs. Mathematics teacher educators (MTEs) can leverage ChatGPT as a facilitative tool to enhance preservice teachers' understanding, analyzing, and enacting of high‐leverage, effective mathematics teaching practices. One such practice is designing mathematical tasks. In this study, we, three MTEs, utilized a self‐study methodology to first highlight how our domains of knowledge informed our prompt engineering techniques given to ChatGPT to generate mathematical tasks and second, to share lessons learned from our interactions with ChatGPT during our critical friend dialogues. We primarily used our pedagogical and pedagogical content knowledge to craft prompts. Our prompt engineering techniques involved revising, generating, or editing. The lessons learned underlined the different conceptualizations of what constitutes a task, and the importance of articulating and navigating the learning goal throughout ChatGPT interactions. Our findings have implications for fostering MTEs' knowledge of using ChatGPT to generate mathematical tasks and ways of supporting current and future teachers in using ChatGPT in their classrooms.School Science and Mathematics470This study, three MTEs utilized a self‐study methodology to first highlight how their domains of knowledge informed their prompt engineering techniques given to ChatGPT to generate mathematical tasks and second, to share lessons learned from their interactions with ChatGPT during the authors' critical friend dialogues.School Science and Mathematics["Mahtob Aqazade", "Matt Mauntel", "S. Atabas"]2025-01-29T00:00:00 5d41ad6fa5dd4c0b5848d9ac3bb34538d639b1d1This review investigates the integration of Artificial Intelligence (AI) in pharmaceutical product development, focusing on its applications in drug discovery, design, manufacturing, and quality control. Key AI methodologies, such as machine learning (ML) and deep learning (DL), are analyzed for their contributions to critical stages, including target identification, molecular screening, and clinical trial optimization. The findings highlight AI's capacity to streamline workflows, reduce development costs, and enhance efficacy, with notable improvements in drug discovery speed, prediction accuracy of drug safety and efficacy, and novel approaches in drug repurposing and personalized medicine. Despite these advancements, challenges such as fragmented data integration, limited availability of specialized skillsets, and resistance to AI adoption remain significant barriers. This review emphasizes the need for industry-wide collaboration to address these issues and leverage AI's full potential. In conclusion, AI demonstrates transformative capabilities in accelerating drug development cycles and enabling precision-driven innovations, promising a paradigm shift in pharmaceutical practices through the convergence of computational power and biological sciences.Journal of Computers and Digital Business430AI demonstrates transformative capabilities in accelerating drug development cycles and enabling precision-driven innovations, promising a paradigm shift in pharmaceutical practices through the convergence of computational power and biological sciences.Journal of Computers and Digital Business["Amira Hassan Abed"]2025-01-29T00:00:00 b511c1e59a278b95bc9b4af496afea09977b486aPurpose Generative artificial intelligence (Gen AI) is changing the trajectories of Banking (FinTech) and Law (RegTech/LawTech). The rate at which the technology is innovating is astounding. The ability of AI and Gen AI systems to simulate human intelligence (human thinking) and independently perform tasks and develop intelligence that is premised on its own experiences, process layers of information and continually learn and re-learn increasingly complex representations of data has resulted in improvements in “it” being able to perform complex, technical and time-consuming tasks; identify objects, people, voices and patterns; and screen for “problems” much earlier and provide solutions. This has economic, political and social benefits. The purpose of this study is to explore how Gen AI is changing the face of finance and its impact on the risks, regulatory and operational challenges faced by financial institutions in the UK. Design/methodology/approach The subject is explored through the analysis of data and domestic and international published literature. The first part of this study summarises the context of current risks and regulatory and operational issues; the discussion then moves on to explore Gen AI and how it can be embedded as part of the arsenal that financial institutions can use/are using to innovate and provide solutions to the regulatory and operational challenges they face as of August 2024. Findings It is suggested that UK financial institutions can further use Gen AI as part of their armoury of solutions to respond to the risk of financial crime and tackle the regulatory burden to achieve high levels of operational efficiency as well as promoting better customer satisfaction. Originality The work is original because, to the best of the author’s knowledge, it is the first to specifically explore how Gen AI is assisting UK financial institutions to find solutions to financial crime risk and regulatory challenges, customer satisfaction, cyberattacks and cybercrime. Journal of Financial Crime100This study is the first to specifically explore how Gen AI is assisting UK financial institutions to find solutions to financial crime risk and regulatory challenges, customer satisfaction, cyberattacks and cybercrime.Journal of Financial Crime["Charanjit Singh"]2025-01-29T00:00:00 5518292e7e66d31ba8c6fa0e847ab218fee2d671The increasing prevalence of Artificial Intelligence (AI) across various industries necessitates an assessment of its impact on achieving the Sustainable Development Goals (SDGs). Studies indicate that AI has the potential to support 134 targets across all goals through professional, consensus-based data collection strategies. However, it may also hinder progress toward 59 targets, presenting a complex interplay between benefits and challenges. Key concerns include gaps in safety, transparency, and ethical standards, which arise when regulatory frameworks fail to keep pace with the rapid advancement of AI technologies. These issues highlight the need for robust governance and oversight mechanisms to address potential risks. Additionally, overlooked components in the study, such as social equity, environmental justice, and accessibility, are critical for ensuring AI-based solutions contribute effectively to sustainable growth. This research emphasizes the importance of aligning AI applications with global regulatory and ethical standards to maximize positive outcomes while mitigating adverse effects. By fostering collaboration among policymakers, industry leaders, and researchers, AI can become a transformative tool for achieving SDGs. Future efforts should prioritize addressing regulatory gaps and ensuring that AI-driven innovation remains inclusive, transparent, and aligned with the core principles of sustainability.Journal of Computers and Digital Business00The importance of aligning AI applications with global regulatory and ethical standards to maximize positive outcomes while mitigating adverse effects is emphasized, as well as fostering collaboration among policymakers, industry leaders, and researchers.Journal of Computers and Digital Business["Hany Fathy Abdel-Elaah", "Amira Hassan Abed"]2025-01-29T00:00:00 79e75c78b02b55feedfdca57eccc4d6da1b0a411Artificial intelligence (AI) is revolutionizing the pharmaceutical industry by enhancing efficiency, precision, and cost-effectiveness in drug development. This study explores the application of AI in the lifecycle management of generic drugs, focusing on key stages such as active pharmaceutical ingredient (API) synthesis, excipient selection, pre-formulation studies, bioequivalence testing, and regulatory compliance. By leveraging machine learning algorithms, AI facilitates predictive modeling, risk assessment, and optimization of drug formulation processes, reducing time-to-market and improving scalability. Despite significant advancements, challenges such as data quality, algorithm transparency, and infrastructure limitations persist, particularly in resource-constrained settings. This review highlights case studies and emerging technologies that address these challenges, providing actionable insights for pharmaceutical stakeholders. The study also discusses AI's potential to streamline supply chain logistics, enhance accessibility, and ensure regulatory adherence. By integrating AI across all stages of generic drug development, this research underscores its transformative potential in improving drug affordability, accessibility, and patient outcomes globally.Al Makki Health Informatics Journal650This study explores the application of AI in the lifecycle management of generic drugs, focusing on key stages such as active pharmaceutical ingredient synthesis, excipient selection, pre-formulation studies, bioequivalence testing, and regulatory compliance.Al Makki Health Informatics Journal["Murali Mohan Babu", "Ni Luh Putu Nurshanti", "Harry Martha Wijaya", "Raymond R. Tjandrawinata"]2025-01-29T00:00:00 492a03ff1d72cb7e04ba0b09fedd4669cd9a481dABSTRACT To realize the transformative potential of artificial intelligence (AI) in health care, physicians must learn how to use AI-based tools effectively, safely, and equitably. Continuing professional development (CPD) activities are one way to learn how to do this. The purpose of this article is to describe a theory-based approach for assessing health professionals' motivation to participate in CPD on AI-based tools. An online survey, based on an AI competency framework developed from existing literature and expert consultations, was administered to practicing physicians in Ontario, Canada. Across eight subcompetencies for using AI-based tools (eg, appraise AI-based tools for their regulatory and legal status), the survey measured physicians' perception they could successfully enact the competency, the importance of the competency in meeting their practice needs, and the desirability of participating in CPD activities on the competency. Motivation scores were calculated by multiplying the three scores together. Ninety-five physicians completed the survey. The highest motivation scores were for the subcompetency of identifying AI-based tools based on clinical needs, while the lowest motivation scores were for appraising tools' regulatory and legal status. All AI subcompetencies were generally rated as important, and CPD activities were generally perceived as desirable. This survey demonstrates the utility of a theory-based approach for assessing physicians' motivation to learn. Although the survey results are context specific, the approach may be useful for other CPD providers to support decision making about future AI-related CPD activities.Journal of Continuing Education in the Health Professions260The utility of a theory-based approach for assessing physicians' motivation to learn is demonstrated and may be useful for other CPD providers to support decision making about future AI-related CPD activities.The Journal of continuing education in the health professions["Adam G. Gavarkovs", "Jacqueline Kueper", "Robert Arntfield", "Frank Myslik", "Keith Thompson", "William McCauley"]2025-01-29T00:00:00 a534ecbab5876eb74cb1e5ef3bbf6e136ee4e436Agriculture encounters significant challenges, with the demand to increase food production by 50% by 2050 to sustain a growing global population while tackling the impacts of climate change and resource scarcity. Artificial intelligence (AI) has transformative potential for precision agriculture, optimizing crop management, resource allocation and sustainable farming practices. A systematic literature review (SLR) was conducted using the Scopus database, initially identifying 8145 articles. Based on eligibility criteria, 76 were selected for in-depth analysis. This paper focuses on AI applications in key areas of agriculture, including crop monitoring, irrigation management, weed and pest control, yield prediction, and smart spraying technologies. AI-driven techniques, such as machine learning, computer vision, robotics and the Internet of Things (IoT), enhance agricultural productivity and sustainability through data-driven decision-making and real-time monitoring. AI-based irrigation systems optimize water use efficiency by integrating sensor inputs with weather data, while robotic technologies enhance targeted weed and pest management. Resource efficiency is further enhanced by smart sprayers and yield estimation techniques. Despite these advancements, research gaps remain, particularly in integrating AI with emerging fields such as nutrient management and expanding the use of sensor systems. This paper highlights advancements in AI for precision agriculture, including crop monitoring, irrigation management and yield prediction, while identifying gaps in areas like nutrient management and sensor integration. Addressing these gaps is essential for developing more sustainable and resilient agricultural systems.Plant Science Today810AI applications in key areas of agriculture, including crop monitoring, irrigation management, weed and pest control, yield prediction, and smart spraying technologies are highlighted, while identifying gaps in areas like nutrient management and sensor integration are identified.Plant Science Today["S. P. Mohammed", "J. Deepika", "N. Sritharan", "V. Ravichandran", "M. Prasanthrajan", "P. Kannan"]2025-01-29T00:00:00 a0b8965cced9c0ad46d00e34d2d643e8bfceefb5We study long-run progress in artificial intelligence in a quantitative way. Many measures, including traditional ones such as patents and publications, machine learning benchmarks, and a new Aggregate State of the Art in ML (or ASOTA) Index we have constructed from these, show exponential growth at roughly constant rates over long periods. Production of patents and publications doubles every ten years, by contrast with the growth of computing resources driven by Moore's Law, roughly a doubling every two years. We argue that the input of AI researchers is also crucial and its contribution can be objectively estimated. Consequently, we give a simple argument that explains the 5:1 relation between these two rates. We then discuss the application of this argument to different output measures and compare our analyses with predictions based on machine learning scaling laws proposed in existing literature. Our quantitative framework facilitates understanding, predicting, and modulating the development of these important technologies.00It is argued that the input of AI researchers is also crucial and its contribution can be objectively estimated, and a simple argument is given that explains the 5:1 relation between these two rates.["Michael R. Douglas", "Sergiy Verstyuk"]2025-01-29T00:00:00 2d5e40be95b7cd9125f550024f2569839d49659eThis article examines the key areas of application of artificial intelligence (AI) in education. Based on the analy-sis of scientific papers, the main directions of using AI in the field of education are identified. Despite the grow-ing interest in the application of AI in education, there remains a limited understanding of its potential impact on the learning process and the management of educational institutions. This, in turn, creates a necessity for ana-lyzing the prospects of AI application in education as a whole. Particular attention is given to personalized learning systems capable of adapting the educational process to the individual needs of students. The possi-bilities of intelligent assessment systems and their effectiveness in automated verification of various types of tasks are considered. The role of AI in optimizing administrative processes within educational institutions is also analyzed. Furthermore, the influence of AI technologies on the transformation of the teacher’s role and the ne-cessity for developing AI competencies among all participants in the educational process are explored. Key challenges to the implementation of AI in education are identified, including technical limitations, data privacy concerns, and the insufficient level of AI literacy among educators. It emphasizes the need for a balanced ap-proach to integrating AI into education while maintaining the leading role of humans in the educational process, as well as the importance of developing AI competencies among all its participants.Общество социология психология педагогика00The need for a balanced approach to integrating AI into education while maintaining the leading role of humans in the educational process is emphasized, as well as the importance of developing AI competencies among all its participants.Общество: социология, психология, педагогика["Tatyana V. Bukina"]2025-01-29T00:00:00 addd7edd28d1ae22f7012d5287e4dbf4af0f9e63Breast Cancer Research and Treatment140Implementing AI prioritization can accelerate care timelines for patients requiring additional workup, while maintaining the efficiency of delayed interpretation for most participants.Breast cancer research and treatment["Sarah M. Friedewald", "Marcin Sieniek", "Sunny Jansen", "Fereshteh Mahvar", "Timo Kohlberger", "David Schacht", "Sonya Bhole", "Dipti Gupta", "Shruthi Prabhakara", "S. McKinney", "Stacey Caron", "David S. Melnick", "M. Etemadi", "Samantha Winter", "Thidanun Saensuksopa", "Alejandra Maciel", "Luca Speroni", "Martha Sevenich", "Arnav Agharwal", "Rubin Zhang", "Gavin E Duggan", "Shiro Kadowaki", "A. Kiraly", "Jie Yang", "Basil Mustafa", "Yossi Matias", "G. Corrado", "Daniel Tse", "Krish Eswaran", "S. Shetty"]2025-01-29T00:00:00 c9f51005c0c6ff87dc5c4fef4fb829ae7f827c66Nurse Educator50Nurse educator["Claudia Grobbel", "Ronald J Piscotty", "Darrin Hanna"]2025-01-29T00:00:00 e066b0b372359c211bc50873e23f083649a7a10dInternational Journal of Human-Computer Interaction700International Journal of Human–Computer Interaction["S. Salimi", "Motahareh Moosavi Dastjerdi", "Abbas Javaheri"]2025-01-29T00:00:00 313027a4984c1ef060fde108ca4f67a3cb896054AI &amp; SOCIETY260Findings show that creatives perceive the adaptation of AI technologies as both an opportunity for their creative process and a requirement of their active presence in the market survival as a matter of technocratic rule.AI &amp; SOCIETY["Yunus Emre \u00d6zta\u015f", "Balca Arda"]2025-01-29T00:00:00 dc24c0cb100672de19d6307480eb416b351b901dAI and Ethics400AI and Ethics["Brady Lund", "Zeynep Orhan", "Nishith Reddy Mannuru", "Ravi Varma Kumar Bevara", "Brett Porter", "Meka Kasi Vinaih", "Padmapadanand Bhaskara"]2025-01-29T00:00:00 3c517a47876707807b266dc1dbbc1e2b493a9feaJournal of Marketing Theory and Practice420Journal of Marketing Theory and Practice["Joanna Scott-Kennel", "Rong Mei Zhang", "Jonathan M. Scott"]2025-01-29T00:00:00 3e1fdc6f5e48f58d112a29fc52138f5215c618f0In this paper, we provide reflections from an embedded action research project undertaken at the UK’s largest public service broadcaster, the BBC, over a three-year period. It was aimed at eliciting research insights about the role and understanding of AI in news production and also intervening to engender change in the newsroom. We surface the messy realities of conducting this work, including the challenges to funding such long-term and resource-intensive research and the difficulties of measuring impacts. We include practical guidance to demystify the process of action-oriented research that strives for targeted change in news contexts, highlighting the need for researchers to cost in time for translational work and the importance of having ‘critical friends’ to hold them to account. Ultimately, we emphasise the value of action research in journalism studies and particularly at the nexus of news and technology. We argue for approaches that retain a critical perspective whilst closing the gap between theory, critique, and practice.Journalism310Journalism["Bronwyn Jones", "Rhianne Jones"]2025-01-29T00:00:00 fe5c19fa53d155aacf8f4c64dc2f7cd214a4fc32Information Systems Frontiers360Information Systems Frontiers["Tomas Krilavi\u010dius", "Lucio Tommaso De\u00a0Paolis", "Valerio De\u00a0Luca", "Josef Spjut"]2025-01-29T00:00:00 2ecdf2e47ae79d5520eede39a303f9469254cedeAJR. American journal of roentgenology30AJR. American journal of roentgenology["S. Lenobel"]2025-01-29T00:00:00 b33a1239c471a0d62aa438165eef1b82af57ca47Topoi340This work proposes an approach in which the relevant criteria are based on the AI’s interaction within the mathematical community, and asks whether the authors can deny the intelligence of the AI in such a scenario based on reasons other than its (non-biological) material construction.Topoi["M. Pantsar"]2025-01-29T00:00:00 6b0e8e3e3194d81f4d0b7690dea8d89f6e96635cPurposeThis paper aims to contribute to the discussion on integrating humans and technology in customer service within the framework of Society 5.0, which emphasizes the growing role of artificial intelligence (AI). It examines how effectively new generative AI-based chatbots can handle customer emotions and explores their impact on determining the point at which a customer–machine interaction should be transferred to a human agent to prevent customer disengagement, referred to as the Switch Point (SP).Design/methodology/approachTo evaluate the capabilities of new generative AI-based chatbots in managing emotions, ChatGPT-3.5, Gemini and Copilot are tested using the Trait Emotional Intelligence Questionnaire Short-Form (TEIQue-SF). A reference framework is developed to illustrate the shift in the Switch Point (SP).FindingsUsing the four-intelligence framework (mechanical, analytical, intuitive and empathetic), this study demonstrates that, despite advancements in AI’s ability to address emotions in customer service, even the most advanced chatbots—such as ChatGPT, Gemini and Copilot—still fall short of replicating the empathetic capabilities of human intelligence (HI). The concept of artificial emotional awareness (AEA) is introduced to characterize the intuitive intelligence of new generative AI chatbots in understanding customer emotions and triggering the SP. A complementary rather than replacement perspective of HI and AI is proposed, highlighting the impact of generative AI on the SP.Research limitations/implicationsThis study is exploratory in nature and requires further theoretical development and empirical validation.Practical implicationsThe study has only an exploratory character with respect to the possible real impact of the introduction of the new generative AI-based chatbots on collaborative approaches to the integration of humans and technology in Society 5.0.Originality/valueCustomer Relationship Management managers can use the proposed framework as a guide to adopt a dynamic approach to HI–AI collaboration in AI-driven customer service.European Journal of Innovation Management1140It is demonstrated that, despite advancements in AI’s ability to address emotions in customer service, even the most advanced chatbots—such as ChatGPT, Gemini and Copilot—still fall short of replicating the empathetic capabilities of human intelligence.European Journal of Innovation Management["M. Saviano", "Asha Thomas", "Marzia Del Prete", "Daniele Verderese", "Pasquale Sasso"]2025-01-29T00:00:00 6fcbad66853aa2a48fb2ebfb8b352523d88c7718This report summarizes the curriculum and academic outcomes of the Summer School on Software Engineering and Artificial Intelligence (AI) held at the Universidad de Los Andes in Bogot´a, Colombia. The summer school offered an in-depth introduction to the fields of Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP); their role and applications in Software Engineering (SE) and the software development process. The feedback we received from the participants indicates that the program successfully enhanced their knowledge and the skills needed for them to navigate the role of AI in the current landscape of software engineering. The students of the summer school were engaged in the development of a full software system using AI-based tools as part of the development process. We found that the project was successful in providing the students with experience regarding how to incorporate AI-based tools as part of their software development process but not all students showed the same level of proficiency when leveraging AI tools.ACM SIGSOFT Softw. Eng. Notes40The project was successful in providing the students with experience regarding how to incorporate AI-based tools as part of their software development process but not all students showed the same level of proficiency when leveraging AI tools.ACM SIGSOFT Softw. Eng. Notes["Esteban Parra", "Jairo Aponte"]2025-01-29T00:00:00 c119fabbfb5477d30050774b38d4b309fadbd7b3ಥೊಡ್ಯಾ ತೆಂಪಾ ಆದಿಂ ಸಂಸಾರಾಚೊ ಅತ್ಯಂತ್ ಗಿರೇಸ್ತ್ ಬಿಸ್ನೆಸ್ಮೆಸನ್ ಆಲನ್ ಮಸ್ಕ್ ಹಾಣೆಂ, 2027 ಭಿತರ್ ಡ್ರೈವರ್ ನಾತ್ಲ್ಲೆಂ ಕಾರ್ ತಯಾರ್ ಕರ್ತಲೊಂ ಮ್ಹಣ್ ಪರ್ಗಟ್ ಸಾಂಗ್ಲೆಂ (“Elon Musk unveils”, 2024). ತಿತ್ಲೆಂ ಮಾತ್ರ್ ನ್ಹಯ್, ತ್ಯಾ ಕಾರಾಚೆಂ ಏಕ್ ಪ್ರೊಟೊಟೈಪ್ಯೀ್ ತಾಣೆಂ ಪ್ರದರ್ಶನ್ ಕೆಲೆಂ. ಹ್ಯಾ ಕಾರಾಂತ್ ಆಲನ್ ಮಸ್ಕ್ ಸ್ವತ: ಬಸೊನ್ ಆಸ್ಲ್ಲೊ ಮಾತ್ರ್ ನ್ಹಯ್, ಕಾರ್ ಆಪ್ಲ್ಯಾ ಇತ್ಲ್ಯಾಕ್ ಮುಕಾರ್ ವಚುನ್ಂ ಚ್ ಆಸ್ತಾನಾ ಮಸ್ಕ್ ಆಪ್ಲ್ಯಾ ಕಾಮಾರ್ ಬೆಫಿಕಿರಾಯೆನ್ ಮಗ್ನ್ ಆಸ್ಚೆಂಯೀ ಸಗ್ಳ್ಯಾಂನಿ ಪಳಯ್ಲೆಂ. ಹ್ಯಾ ಕಾರಾಚೆಂ ವಿಶೇಸ್ ಕಿತೆಂಗೀ ಮ್ಹಳ್ಯಾರ್, ತ್ಯಾ ಕಾರಾಚಿ ಸರ್ವ್ ಟೆಕ್ನೊಲೊಜಿ ಕೃತಕ್ ಜಾಣ್ವಾಯೆನ್ (artificial intelligence) ವಾ AI ದ್ವಾರಿಂ ಸಾಂಬಾಳ್ಲ್ಲಿಯ ಆಸ್ಲ್ಲಿರ. ಆಯ್ಚೊ ಕಾಳ್ AI ಕಾಳ್ ಜಾವ್ನಾಸಾ. ದೋನ್ ವರ್ಸಾಂ ಆದಿಂ ಚಾಟ್ಜಿeಪಿಟಿ (ChatGPT) ಉಗ್ತಾಡಾಕ್ ಆಯ್ಲಿ ಆನಿ ಸಂಸಾರ್ಭಾರ್ ಎಕಾ ರಿತಿಚೆಂ ವಾದಾಳ್ಚ್ಾ ಉಟ್ಲೆಂ ಮ್ಹಣ್ಯೆತ್. ಚಾಟ್ಜಿ ಪಿಟಿ ದ್ವಾರಿಂ ಸಬಾರ್ ಸಂಗ್ತಿ ಬೋವ್ ಸಲೀಸ್ ಜಾಲ್ಯೊ. ಗೂಗ್ಲಾತಪ್ರಾಸ್ ಚಡ್ ಕರ್ನ್ ಸ್ಪಷ್ಟ್ ಜಾಣ್ವಾಯ್ ಜೊಡುಂಕ್ ಚಾಟ್ಜಿಟಪಿಟಿ ಸಬಾರಾಂಕ್ ಬೋವ್ ಗರ್ಜೆಚಿ ಜಾಲಿ. ಪುಣ್ ತಿತ್ಲೊಚ್ ತಾಚೊ ಮಾರೆಕಾರ್ ವಾಯ್ಟ್ ಪರಿಣಾಮ್ಯೀಾ ದಿಸೊನ್ ಆಯ್ಲೊ. ಆಜ್ ಅಕೆಡೆಮಿಕ್ ವರ್ತುಲಾಂತ್ ಚಾಟ್ಜಿುಪಿಟಿ ವಿದ್ಯಾರ್ಥಿಂಕ್ ಮಾತ್ರ್ ನ್ಹಯ್ ಸಂಸೊಧಾಕಾಂಕ್ಯೀದ ಏಕ್ ತೆಂತೆಸಾಂವ್ ಜಾಲಾಂ. ಮಟ್ವಿ ವಾಟ್ ಧರ್ನ್ ‘ಜಾಣ್ವಾಯ್’ ಸ್ವಾಧೀನ್ ಕರ್ಚಿ ಗಿರಾಂತ್ ಉಟ್ಲ್ಲ್ಯಾ ನ್ ಆಜ್ ಚಾಟ್ಜಿಾಪಿಟಿ ತಸಲೆಂ AI ಸೊಫ್ಟ್ವೇಸರ್ ಮನ್ಶ್ಯಾ ಕ್ರಿಯಾಶೀಲತೆಕ್ ಕಠೀಣ್ ಸವಾಲಾಂ ಉಬ್ಜಾಂವ್ಚೆಂ ನಕ್ಕಿ ಜಾಲಾಂ. ಲೊಕಾಮೊಗಾಳ್ ಆನಿ ಕ್ರಿಯಾಶೀಲ್ ಚರಿತ್ರೆಗಾರ್ ಯುವಲ್ ನೋಹಾ ಹರಾರಿ ಹಾಚೊ ನವೊ ಗ್ರಂಥ್ ಂI ವಿಶಿಂ ಚಡಿತ್ ಗಿನ್ಯಾನಾಕ್ ಇಂಬು ಕರ್ನ್ ದಿತಾ (Harari, 2024). ಹರಾರಿನ್ ಸಾಂಗ್ಚೆಬರಿ, ಕೆದಾಳಾ ಮನಿಸ್ ಅಧಿಕಾರಾಚ್ಯಾ ಗಿರಾಂತೆಕ್ ಲಾಗೊನ್ ನವೆಸಾಂವ್ ಕರುಂಕ್ ಯೆವ್ಜಿತಾ ಆನಿ ಕೆದಾಳಾ ತ್ಯಾ ನವೆಸಾಂವಾ ವಯ್ರ್ ತಾಚೆಂ ಲಗಾಮ್ ಚುಕ್ತಾ, ತೆಂ ನವೆಸಾಂವ್ ತಾಕಾ ಖಂಡಿತ್ ಘ್ರಾಸಿತಾ. ಹರಾರಿ ಪ್ರಕಾರ್ ಂI ಏಕ್ ಮನ್ಶಾನ್ ರಚ್ಲ್ಲೆಂ ನವೆಸಾಂವ್ ಆನಿ ಎದೊಳ್ಚ್ಾ ಮನ್ಶಾಕ್ ತಾಚೆರ್ ಆಪ್ಲೆಂ ಲಗಾಮ್ ಚುಕ್ಲಾಂ ಮ್ಹಳ್ಳೆಂ ಸ್ಪಷ್ಟ್ ಆಸಾ. ಹ್ಯಾ ವರ್ಸಾ ಭೌತಶಾಸ್ತ್ರಾಂತ್ ನೊಬೆಲ್ ಬಹುಮಾನ್ ಜಿಕ್ಪಿ ಕೆನಡಾಚೊ ವಿಜ್ಞಾನಿ ಜೆಫ್ರಿ ಹಿಂಟನ್ ಂI ಪಿತಾಮಹ ಮ್ಹಣ್ಂರಚ್ ನಾಂವಾಡ್ದಿಕ್. ಪುಣ್ ನೊಬೆಲ್ ಮೆಳ್ಲ್ಲ್ಯಾ ತಕ್ಷಣ್ ತಾಣೆಂ ದಿಲ್ಲಿ ಚೆತಾವ್ಣಿ ಭಿರಾಂತೆಚಿ. ತೊ ಮ್ಹಣ್ತಾ, ಂI ಸಾಧಾರಣ್ ಏಕ್ ಕೈಗಾರಿಕ್ ಕ್ರಾಂತಿ. ಪುಣ್ ಮನ್ಶಾಂಚ್ಯಾ ಭೌತಿಕ್ ಸಕ್ತೆಪ್ರಾಸ್ ಬಳ್ವಂತ್ ಜಾಂವ್ಚ್ಯಾಕೀ ಂI ಮನ್ಶಾಂಚ್ಯಾ ಜಾಣ್ವಾಯೆಚ್ಯಾ ಒಟ್ಟಾರೆ ಸಕ್ತೆಕ್ ಮಿಕ್ವೊನ್ ವಾಡ್ತ್ತಲೆಂ. ಆಮ್ಕಾಂ ಆಮ್ಚ್ಯಾಕೀ ಚಡಿತ್ ಬುದ್ವಂತ್ ಸಕ್ತೆಕ್ ಕಶೆಂ ಆಟಾಪುಂಚೆಂ ಮ್ಹಳ್ಳೆ ವಿಶಿಂ ಅನ್ಭೊಗ್ ನಾ (Kapoor, 2024). ತಶೆಂ ಮ್ಹಳ್ಯಾ ಉಪ್ರಾಂತ್, ರಾಜಾಂವಿಕ್ ರಿತಿ ವಾಪಾರ್ತಾನಾ AI ದ್ವಾರಿಂ ಮನ್ಶಾಕುಳಾಕ್ ಜಾಲ್ಲ್ಯಾ ಆನಿ ಜಾಂವ್ಕ್ ಸಾಧ್ಯ್ ಆಸ್ಚ್ಯಾ ಬರೆಪಣಾ ವಿಶಿಂ ಕೊಣೆಂಯೀ ನೆಗಾರುಂಕ್ ಜಾಯ್ನಾ. ವಯ್ಜಾಕೀಯ್ ಶೆತಾಂತ್ AI ಆಜ್ ವ್ಹಡ್ ಏಕ್ ಬೆಸಾಂವ್ ಜಾಲಾಂ. ಜೊಕ್ತ್ಯಾ ವೆಳಾರ್ ಪಿಡೆಚಿಂ ಸೊದ್ನಾಂ ಕರುಂಕ್, ಪಿಡೆಕ್ ಜೊಕ್ತಿಂ ವಕ್ತಾಂ ಸೊದುನ್ ಕಾಡುಂಕ್ ಂI ಖಂಡಿತ್ ವಿಶೇಸ್ ರಿತಿನ್ ಉಪ್ಕಾರಾಕ್ ಪಡ್ಲಾಂ. ಇತರ್ ಸಬಾರ್ ಶೆತಾಂನಿ AIಚೊ ವಾಪಾರ್ ಖಂಡಿತ್ ಮೆಚ್ವಾಜೆ ಜಾಲ್ಲೊ. ಭಿರಾಂತ್ ಮ್ಹಳ್ಯಾರ್ ಹೊ ವಾಪಾರ್ ಖಂಯ್ ಪುಣೀ ಮೀತ್ ಮಿರ್ವೊನ್ ವೆತಲೊಗೀ ಮ್ಹಳ್ಳಿ. ಆನಿ ತಶೆಂ ಜಾಯ್ತ್ ತರ್ ಮನ್ಶಾಕ್ ತಾಚೆರ್ ನಿಯಂತ್ರಣ್ ಚುಕ್ತಲೆಂ ಆನಿ ಹ್ಯಾ ದ್ವಾರಿಂ ಮನ್ಶಾಕುಳಾಚೆಂ ನಾಸ್ಚ್ಹ ಚಡಿತ್ ಲಾಗಿಂ ಜಾತಲೆಂ ಮ್ಹಣ್ ಸಬಾರಾಂನಿ ಉಚಾರ್ಲ್ಲೆಂ ಆಸಾ. ಭಾಶೆ ಆನಿ ಸಾಹಿತ್ಯಾ ದಿಶಿಂ AIಚೊ ವಾಪಾರ್ ಕಸಲೊ ಆನಿ ಕಸಲಿ ಸಾಧ್ಯತಾ ಆಸಾ? AI ದ್ವಾರಿಂ ಭಾಶೆಕ್ ಖಂಡಿತ್ ಚಡಿತ್ ಲಾಭ್ ಆಸಾ. ಆಜ್ AI ವಾಪಾರುನ್ ನವಿಚ್ ಭಾಸ್ ಶಿಕೊಂಕ್ ಸಾಧ್ಯ್ ಆಸಾ. ಹಾಂಗಾ ಫಕತ್ ಕಾಂಯ್ ಥೊಡೆಂ ಬರಪ್ ನ್ಹಂಯ್ ಆಸ್ತಾಂ AI ಸಂಗಿಂ ಸಂವಾದ್ ಕರ್ಚೆ ಸಂಗಿಂ ಉಚ್ಛಾರ್, ವ್ಯಾಕರಣ್ ಆನಿ ಸಂಭಾಷಣ್ ಶಿಕ್ಚೆಂಯೀ ಸಾಧ್ಯ್ ಜಾಲಾಂ ಆನಿ ಹಾಚೊ ವಾಪಾರ್ ಸಬಾರ್ ಲೋಕ್ ಕರ್ನ್ ಆಸಾ. ಸಾಹಿತ್ಯಾಚ್ಯಾ ಮಾಳಾರ್ಯೀಶ AI ದ್ವಾರಿಂ ಸಾಹಿತ್ಯ್ ಚಡಿತ್ ಲೊಕಾಕ್ ಪಾವಂವ್ಚೆಂ, ಭಾಶಾಂತರ್ ಕರ್ಚೆಂ, ಆನಿ ಡಿಜಿಟಲ್ ರುಪಾರ್ ಪುಂಜಾವ್ನ್ ದವರ್ಚೆಂ ಸರ್ವ್ ಆಜ್ ಸಾಧ್ಯ್ ಆಸಾ. ಆಜ್ AI ಸರ್ವ್ ವಿಭಾಗಾಂನಿ ಆನಿ ವರ್ತುಲಾಂನಿ ಘುಸ್ಲಾಂ ಆಸೊನ್, ಕೊಣೆಂಯೀ ಹೆಂ ನೆಗಾರುಂಕ್ ವಾ ಆಪುಣ್ ಹಾಚೊ ವಾಪಾರ್ ಕರಿನಾ ಮ್ಹಣ್ ಸಾಂಗುಂಕ್ ಸಾಧ್ಯ್ ನಾ. ವ್ಯಕ್ತಿಗತ್ ಕೋಣ್ ತರೀ ಆಪುಣ್ ಹಾಚೊ ವಾಪಾರ್ ಖಂಡಿತ್ ಕರಿನಾ ಮ್ಹಣ್ ಬಸಾತ್ ತರೀ, AI ಲಾಭ್ ತಾಕಾ/ತಿಕಾ ಪ್ರತ್ಯಕ್ಷ್ ವಾ ಪರೋಕ್ಷ್ ರಿತಿಂ ಖಂಡಿತ್ ಜಾತಾ. ತಶೆಂ ಆಸ್ತಾಂ, ಮುಕ್ಲೆ ದೀಸ್ ಆನಿಕೀ ಚಡಿತ್ ಸಂಕೀರ್ಣ್ ಜಾಂವ್ಚ್ಯಾಂತ್ ನವಾಲ್ ನಾ. ಪುಣ್ AI ವಯ್ರ್ ಮನ್ಶಾಚೆಂ ಲಗಾಮ್ ಚುಕಾನಾತ್ಲ್ಲ್ಯೆ ಬರಿ ವಾಟ್ ಸೊದ್ಚೆಂ ಆಯ್ಚ್ಯಾ ಸಂದರ್ಭಿಂ ಖಂಡಿತ್ ಮಹತ್ವಾಚೆಂ ಜಾಲಾಂ. ಹೊ ಅಂಕೊ ಹ್ಯಾ ಅಂಕ್ಯಾಚಿ ವಿಶೇಶತಾ ಮ್ಹಳ್ಯಾರ್ ಪಯ್ಲೆ ಪಾವ್ಟಿಂ ISSN ನಂಬರ್ ಘೆವ್ನ್ ಹೊ ಅಂಕೊ ಭಾಯ್ರ್ ಯೆತಾ. ಹಾಚೆ ದ್ವಾರಿಂ ಸಂಸಾರ್ಭೇರ್ ‘ಅಮರ್ ಕೊಂಕ್ಣಿ’ ಜರ್ನಲಾಕೀ ಏಕ್ ಡಿಜಿಟಲ್ ಸ್ಥಾನ್ಮಾೆನ್ ಫಾವೊ ಜಾಲಾಂ ಮ್ಹಳ್ಳೆಂ ನಕ್ಕಿ ಜಾತಾ. ಲೇಖನಾಂಕ್ DOI ನಂಬರ್ ಪ್ರಾಪ್ತ್ ಕರ್ನ್ ಆಮಿ ಎದೊಳ್ಚ್ಿ ಡಿಜಿಟಲ್ ಸ್ಥಾನ್ಮಾವನ್ ಆಪ್ಣಾಯ್ಲಾಂ. ಪುಣ್ ಎಕಾ ಜರ್ನಲಾಕ್ ಹೆಂ ವಿಶೇಸ್ ಸ್ಥಾನ್ ಲಾಬ್ತಾನಾ ಖಂಡಿತ್ ಸಂತೊಸ್ ಜಾತಾ. ಅಶೆಂ ಕೊಂಕ್ಣೆಚ್ಯಾ ಹ್ಯಾ ವಿಶಿಷ್ಟ್ ಸಂಸೊಧ್ ಪತ್ರಾಕ್ ಅನಿಕೀ ಚಡಿತ್ ಬಳ್ವಂತ್ ಕರ್ಚಿ ಜವಾಬ್ದಾರಿ ಆಮ್ಕಾಂ ಆಸಾ. ಕೊಂಕ್ಣೆಚೊ ವರಕವಿ ಮ್ಹಣ್ಂಡಚ್ ನಾಂವಾಡ್ಲ್ಲೊಾ ಲುವಿಸ್ ಮಸ್ಕರೇನ್ಹÀಸ್ (ಲುಮ) ಕೊಂಕ್ಣಿ ಸಾಹಿತ್ಯಾಕ್ ತಾಚ್ಯಾ ಸಾಹಿತ್ಯಾ ದ್ವಾರಿಂ ಆನಿಕೀ ಚಡಿತ್ ವಳ್ಕಿಚೊ ಜಾಯ್ಜಯ್ ಮ್ಹಳ್ಳ್ಯಾ ಉದ್ದೇಶಾನ್ ಹ್ಯಾ ಅಂಕ್ಯಾಂತ್ ತಾಚ್ಯಾ ಸಾಹಿತ್ಯ್ ಆನಿ ಪತ್ರಿಕೋದ್ಯಮಾ ವಿಶಿಂ ಆಟಾಪ್ಲ್ಲಿಂ ಚಾರ್ ಲೇಖನಾಂ ಆಸಾತ್. ಲುಮ ನಿಜಾಕೀ ಏಕ್ ವಿಶೇಸ್ ತಾಲೆಂತಾಂಚೊ ಕ್ರಿಯಾಶೀಲ್ ಬರವ್ಪಿ ಜಾವ್ನಾಸ್ಲ್ಲೊತ. ತೊ ಕಸೊ ಏಕ್ ಪತ್ರಿಕೋದ್ಯಮಿಗೀ, ತಿತ್ಲೊಚ್ ಸಂಯ್ಬಾನ್ ಕವಿ ಆನಿ ನಾಟಕಿಸ್ತ್. ತಾಚೊ ‘ಅಬ್ರಾಂವ್ಚೆಂ ಯಜ್ಞ್ದಾ್ನ್’ ಗಿತಾಂ ನಾಟಕ್ ಸಾಹಿತ್ಯಿಕ್ ದಿಷ್ಟಿನ್ ಖಂಡಿತ್ ಏಕ್ ಅವ್ವಲ್ ಕೃತಿ. ಹಾಂಗಾಚ್ಯಾ ಲೇಖನಾಂನಿ, ತ್ಯೆ ಕೃತಿಯೆರ್ ಖೊಲಾಯೆನ್ ಕೆಲ್ಲೊ ವಿಮರ್ಸೊ ವಾಚ್ಯೆತ್. ಹಾಂಗಾ ಸಮ್ಜೊಂಚೊ ಏಕ್ ಮುಖೆಲ್ ವಿಚಾರ್ ಕಸಲೊಗೀ ಮ್ಹಳ್ಯಾರ್, ಸಾಹಿತ್ಯ್ ಲುಮಕ್ ನ್ಹಯ್ ಫಕತ್ ಏಕ್ ಅಭಿವ್ಯಕ್ತಿ, ತಿ ಹೆರಾಂಕ್ ಶಿಕ್ಷಿತ್ ಕರ್ಚಿ ಏಕ್ ಪ್ರಕ್ರಿಯಾಯೀ ಜಾವ್ನಾಸ್ಲ್ಲಿ . ಪತ್ರ್ಕಾನರ್ ಜಾವ್ನ್ಯೀ್ ಲುಮನ್ ಹೆಂಚ್ ಕೆಲೆಂ. ಚಡಿತ್ ಬರಪ್ ನಾತ್ಲ್ಲ್ಯಾ ತಾಚ್ಯಾ ಕಾಳಾರ್ ಸಾದ್ಯಾ ಪುಣ್ ಕ್ರಿಯಾಳ್ ಬರ್ಪಾಂ ದ್ವಾರಿಂ ತಾಣೆಂ ಲೊಕಾಕ್ ಶಿಕ್ಷಿತ್ ಕರ್ಚೆಂ ಪ್ರೇತನ್ ಕೆಲೆಂ. ಕೊಂಕ್ಣೆಚೆಂ ಪ್ರಥಮ್ ಪತ್ರ್ ‘ಕೊಂಕ್ಣಿ ದಿರ್ವೆಂ’ ಫಕತ್ ಪಯ್ಲೆಂ ಪತ್ರ್ ಮಾತ್ರ್ ನ್ಹಯ್, ಬಗಾರ್ ಕೊಂಕ್ಣೆಚ್ಯಾ ಸಂದರ್ಭಿಂ ಏಕ್ ವೃತ್ತಿಪರ್ ಆನಿ ಸಂಪೂರ್ಣ್ ಪತ್ರ್ ಜಾವ್ನ್ ರೂಪಿತ್ ಕರುಂಕ್ ತಾಣೆಂ ಮ್ಹಿನತ್ ಕಾಡ್ಲಿ. ಆಜ್ಯೀ್, ತೆ ಅಂಕೆ ವಾಚ್ತಾನಾ, ತಾಂತ್ಲಿ ಕೊಂಕ್ಣಿ ಭಾಸ್ ಆನಿ ಸಾದಾರ್ ಕೆಲ್ಲೆ ವಿಷಯ್ ಖಂಡಿತ್ ಕೊಂಕ್ಣೆಚಿ ಗಿರೇಸ್ತ್ಕಾ.ಯ್ ಕಿತ್ಲಿ ಮ್ಹಳ್ಳಿ ಜಾಹೀರ್ ಕರ್ತಾತ್. ಸಾಹಿತ್ಯ್ ಆನಿ ಪತ್ರಿಕೋದ್ಯಮಾಕ್ ಖಂಡಿತ್ ತಾಂಚೊಚ್ ಮ್ಹಳ್ಳೊ ಸುರೂಪ್ ಆನಿ ಉದ್ದೇಶ್ ಆಸಾ. ಸಾಹಿತ್ಯ್ ಫಕತ್ ಎಕ್ಲ್ಯಾಚಿ ಅಭಿವ್ಯಕ್ತಿ ಮ್ಹಣುಂಕ್ ಜಾಯ್ನಾ. ಸಾಹಿತ್ಯ್ ಉಬ್ಜಾತಾ ಎಕಾ ಪ್ರತ್ಯೇಕ್ ವರ್ತುಲ್ (context) ಆನಿ ಪಾಠ್ಥಯಳಾರ್. ಅಶೆಂ ಆಸ್ತಾಂ ಸಾಹಿತ್ಯಾಚೆಂ ವರ್ತುಲ್, ಸಾಹಿತಿ ತಿತ್ಲೆಂಚ್ ಗರ್ಜೆಚೆಂ. ಸಾಹಿತ್ಯಾ ದ್ವಾರಿಂ ಸರ್ವ್ ಬರೆಂಚ್ ಜಾಯ್ಜೆ ಮ್ಹಣ್ ನಾ. ಪೆÇ್ರಪಗಾಂಡಾ ಸಾಹಿತ್ಯ್ ಖಂಡಿತ್ ಸಮಾಜೆಕ್ ಮಾರೆಕಾರ್. ಥೊಡೆ ಪಾವ್ಟಿಂ ರಾಷ್ಟ್ರ್ವಾದ್ ಮ್ಹಣ್ ವೊಲಾಯಿಲ್ಲೆಂ ಸಾಹಿತ್ಯ್ ಕ್ರಮೇಣ್ ಹೆರ್ ರಾಷ್ಟ್ರಾಂಚ್ಯಾ ನಾಸಾಕ್ ಕಾರಣ್ ಜಾಯ್ತ್. ದಾಖ್ಲ್ಯಾಕ್ ಇಸ್ರಾಯೆಲಾಚೊ ರಾಷ್ಟ್ರ್ಕಯವಿ ಹಯೀಮ್ ನಹಮಾನ್ ಬಿಯಾಲಿಕ್. ತಾಣೆಂ ಜುದೆವ್ ಲೊಕಾಕ್ ಸಂಗ್ರಾಮ್ ಕರ್ನ್ ರಾಷ್ಟ್ರ್ ಬಾಂದ್ಚ್ಯಾಕ್ ಆಪ್ಲ್ಯಾ ಕವಿತೆಂನಿ ಆನಿ ಕಾಣಿಯಾಂನಿ ಉತ್ತೇಜನ್ ದಿಲೆಂ (Harari, 2024). ತೆಂ ಉತ್ತೇಜನ್ ಆನಿ ಸ್ಪೂರ್ತಿ ಆಜ್ ಖಂಯ್ ಪಾವ್ಲ್ಯಾ ಮ್ಹಳ್ಳೆಂ ಸಂಸಾರ್ ಪಳೆವ್ನ್ ಆಸಾ. ಇಸ್ರಾಯೆಲಾನ್ ಕರ್ಚ್ಯಾ ಸರ್ವ್ ಕರ್ತುಬಾಂಕ್ ಬಿಯಾಲಿಕ್ ಕಾರಣ್ ಮ್ಹಳ್ಳೆಂ ವಾಕ್ಮೂಲ್ ಹೆಂ ನ್ಹಯ್. ಪುಣ್ ಆಜ್ ತೊ ಇಸ್ರಾಯೆಲಾಚೊ ರಾಷ್ಟ್ರ್ಕ ವಿ ಆನಿ ತಾಚಿಂ ಕವಿತಾ ಭುರ್ಗಿಂ ಶಿಕ್ತಾತ್ ತರ್, ಆಪ್ಲ್ಯಾ ರಾಷ್ಟ್ರಾನ್ ಆದಾರ್ಚ್ಯಾ ಸರ್ವ್ ಕರ್ಮಿ ಕಾಮಾಂಕ್ ಎಕಾ ರಿತಿಚೊ ಸಹಮತ್ ಲೊಕಾ ಥಂಯ್ ಉದೆತಾ ತರ್, ಬಿಯಾಲಿಕ್ ತಸಲ್ಯಾಂಚ್ಯಾ ಸಾಹಿತ್ಯಾಚೊ ಮಾರೆಕಾರ್ ಪ್ರಭಾವ್ ಹಾಂತುಂ ಆಸಾ ಮ್ಹಳ್ಳೆಂ ಕೊಣೆಂಯೀ ನೆಗಾರುಂಕ್ ಜಾಯ್ನಾ. ಹ್ಯೆ ದಿಷ್ಟಿನ್ ಪಳೆತಾನಾ, ಲುಮಚೆಂ ಸಾಹಿತ್ಯ್ ಆನಿ ತಾಚ್ಯಾ ಪತ್ರಿಕೋದ್ಯಮಾಕ್ ಕೊಂಕ್ಣಿ ಲೊಕಾನ್ ಪರತ್ ಏಕ್ ಪಾವ್ಟಿಂ ಭೆಟ್ ದಿಂವ್ಚೆಂ ಅಧಿಕ್ ಗರ್ಜ್ ಮ್ಹಣ್ ದಿಸ್ತಾ. ತಾಚ್ಯಾ ಸಾಹಿತಿಕ್ ಆನಿ ಪತ್ರಿಕೋದ್ಯಮಿ ವರ್ತುಲಾಕ್ ಸಹಜ್ ಜಾಲ್ಲಿ ಬೋವ್ ವ್ಹಡ್ ಜವಾಬ್ದಾರೆಚಿ ಲಿಕ್ಣಿ ತಾಚಿ ಜಾವ್ನಾಸ್ಲ್ಲಿಿ. ತಾಣೆಂ ಕೊಂಕ್ಣಿ ಪತ್ರಿಕೋದ್ಯಮಾಕ್ ಘಾಲ್ಲಿ ಬುನ್ಯಾದ್ ಆಜ್ ಮ್ಹಣಾಸರ್, ಸಬಾರ್ ಆಡ್ಕಳಿ ಮಧೆಂಯೀ, ಸಾಧಾರಣ್ ಘಟ್ ಉರ್ಲ್ಯಾ. ಹ್ಯೆ ವಿಶಿಂ ಫಾ. ಫ್ರಾನ್ಸಿಸ್ ರೊಡ್ರಿಗಾಸಾಚೆಂ ಲೇಖನ್ ಚಡಿತ್ ಉಜ್ವಾಡ್ ಫಾಂಕಯ್ತಾ. ತಾಚಿ ಸಾಹಿತಿಕ್ ವೃತ್ತಿಪರತಾಯ್, ಜಿ ಹ್ಯಾ ಅಂಕ್ಯಾಚ್ಯಾ ಮೆಲ್ವಿನ್ ರೊಡ್ರಿಗಸಾಚ್ಯಾ ಲೇಖನಾಂತ್ ಪ್ರಸ್ತುತ್ ಕೆಲ್ಯಾ ತಿ, ನಿಜಾಕೀ ಹರ್ ಏಕ್ ಕೊಂಕ್ಣಿ ಸಾಹಿತಿಕ್ ಸ್ಪೂರ್ತಿ ಮಾತ್ರ್ ನ್ಹಯ್ ಏಕ್ ಪಠ್ಯ್ ಸಯ್ತ್. ಹೆಂ ನ್ಹಯ್ ಆಸ್ತಾಂ, ಏಕ್ ನಾಟಕಿಸ್ತ್ ಜಾವ್ನ್ ಲುಮಚೊ ಗಿತಾಂ ನಾಟಕ್, ಫಾ. ಆಲ್ವಿನ್ ಸೆರಾವೊನ್ ಸಾಂಗ್ಚ್ಯೆಬರಿ, ಕಾಂಯ್ ಥೊಡಿಂ ಸವಾಲಾಂ ಉಟಯ್ತಾ. ಡೊಲ್ಫಿ ಕಾಸ್ಸಿಯಾಚೆಂ ಲೇಖನ್ ಲುಮಚ್ಯಾ ಜಿಣ್ಯೆಚೆರ್ ನಿಷ್ಪಕ್ಷ್ಪೊಣಿಂ ನದರ್ ಘಾಲ್ತಾ. ಹಾಚೆ ಮಧೆಂ ಬಾಯ್ ಪ್ರೀತಾ ಲಿನೆಟ್ ಹಿಚೆಂ ಕಥೊಲಿಕ್ ಲಗ್ನಾವಿಶಿಂ ಲಿಖ್ಲ್ಲೆಂೊ ಸಂಸೊಧ್ ಲೇಖನ್, ಕೊಂಕ್ಣಿ ಲೊಕಾಚಿಂ ಲಗ್ನಾಂ ಆನಿ ಲಗ್ನಾ ಸಾಹಿತ್ಯಾ ವಿಶಿಂ ‘ಅಮರ್ ಕೊಂಕಣಿ’ ಜರ್ನಾಲಾರ್ ಪ್ರಕಟ್ ಜಾವ್ನ್ಂಫಚ್ ಆಸ್ಚ್ಯಾ ಲೇಖನಾಂಕ್ ಆನ್ಯೇಕ್ ಮೆಳಪ್. ಮುಕ್ಲ್ಯಾ ದಿಸಾಂನಿ ‘ಅಮರ್ ಕೊಂಕಣಿ’ ಚಡಿತ್ ಸಂಸೊಧ್ ಮೊಗಿಂಕ್ ನ್ಹಯ್ ಆಸ್ತಾಂ ಕೊಂಕ್ಣಿ ಭಾಸ್, ಸಾಹಿತ್ಯ್ ಆನಿ ಸಂಸ್ಕೃತಿಚೆರ್ ಉರ್ಬಾ ಆಸ್ಚ್ಯಾ ಸರ್ವಾಂಕ್ ಪಾವೊಂವ್ಚೆಂ, ಮಾತ್ರ್ ನ್ಹಯ್ ಸಂಸೊಧಾಚ್ಯಾ ಮಾಳಾರ್ ಆನಿಕೀ ಉಂಚಾಯೆಕ್ ಚಡಂವ್ಚೆಂ ಆಮ್ಚೆಂ ಪ್ರೇತನ್ ಜ್ಯಾರಿ ಆಸ್ತಲೆಂ. MJES Journal of Amar Konkani00MJES Journal of Amar Konkani[]2025-01-29T00:00:00 6808df104ec23e6cef5cef9bb1d80f2fa36bf0e9Insights into Imaging401Quantitative and qualitative evidence is gathered of the beneficial use of a high-performance AI tool that is well integrated into the diagnostic workflow as a training aid for radiology residents and residents’ resilience to AI errors.Insights into Imaging["M. Savardi", "Alberto Signoroni", "Sergio Benini", "F. Vaccher", "Matteo Alberti", "Pietro Ciolli", "Nunzia di Meo", "Teresa Falcone", "Marco Ramanzin", "Barbara Romano", "Federica Sozzi", "Davide Farina"]2025-01-29T00:00:00 fd2d21fd04da3d5f767c4327464fe0a695a0e73aAs artificial intelligence (AI) increasingly influences education, understanding learners' experiences, engagement and literacy of these tools is critical. This study explores AI literacy among Turkish EFL (English as a Foreign Language) students regarding their familiarity, knowledge, and ethical perceptions of AI technologies in academic writing. Using a descriptive exploratory approach, the study surveyed 427 students from two Turkish universities. Findings reveal a moderate level of AI familiarity and usage among participants, with a significant reliance on AI tools for translation and grammar proofreading. Despite recognizing AI's potential to enhance academic writing, students exhibited limited technical proficiency and understanding of AI's underlying mechanisms, highlighting a need for targeted and structured AI education for EFL writing. The findings contribute to the ongoing discourse on AI integration in EFL education, offering insights for policymakers, educators, and researchers to better prepare students for an AI-driven academic environment.Kuramsal Eğitimbilim261A moderate level of AI familiarity and usage among participants is revealed, with a significant reliance on AI tools for translation and grammar proofreading and a need for targeted and structured AI education for EFL writing.Kuramsal Eğitimbilim["Zakir Hossain", "\u00d6zg\u00fcr \u00c7elik", "G\u00f6khan H\u0131n\u0131z"]2025-01-29T00:00:00 e9d57c1a320356112347054cf154fb7f0691db2aExplainable artificial intelligence (XAI) methods are being proposed to help interpret and understand how AI systems reach specific predictions. Inspired by prior work on conversational user interfaces, we argue that augmenting existing XAI methods with conversational user interfaces can increase user engagement and boost user understanding of the AI system. In this paper, we explored the impact of a conversational XAI interface on users' understanding of the AI system, their trust, and reliance on the AI system. In comparison to an XAI dashboard, we found that the conversational XAI interface can bring about a better understanding of the AI system among users and higher user trust. However, users of both the XAI dashboard and conversational XAI interfaces showed clear overreliance on the AI system. Enhanced conversations powered by large language model (LLM) agents amplified over-reliance. Based on our findings, we reason that the potential cause of such overreliance is the illusion of explanatory depth that is concomitant with both XAI interfaces. Our findings have important implications for designing effective conversational XAI interfaces to facilitate appropriate reliance and improve human-AI collaboration. Code can be found at https://github.com/delftcrowd/IUI2025_ConvXAI01It is found that the conversational XAI interface can bring about a better understanding of the AI system among users and higher user trust, however, users of both the XAI dashboard and conversational XAI interfaces showed clear overreliance on the AI system.["Gaole He", "Nilay Aishwarya", "U. Gadiraju"]2025-01-29T00:00:00 1cf5104218601d57fce0e199c0a29b7bf59537551. Passive acoustic monitoring (PAM) coupled with artificial intelligence (AI) is becoming an essential tool for biodiversity monitoring. Traditional PAM systems require manual data offloading and impose substantial demands on storage and computing infrastructure. The combination of on-device AI-based processing and network connectivity enables local data analysis and transmission of only relevant information, greatly reducing storage needs. However, programming these devices for robust operation is challenging, requiring expertise in embedded systems and software engineering. Despite the increase in AI-based models for bioacoustics, their full potential remains unrealized without accessible tools to deploy them on custom hardware and tailor device behaviour to specific monitoring goals. 2. To address this challenge, we develop acoupi, an open-source Python framework that simplifies the creation and deployment of smart bioacoustic devices. acoupi integrates audio recording, AI-based data processing, data management, and real-time wireless messaging into a unified and configurable framework. By modularising key elements of the bioacoustic monitoring workflow, acoupi allows users to easily customise, extend, or select specific components to fit their unique monitoring needs. 3. We demonstrate the flexibility of acoupi by integrating two bioacoustic classifiers: BirdNET, for the classification of bird species, and BatDetect2, for the classification of UK bat species. We test the reliability of acoupi over a month-long deployment of two acoupi-powered devices in a UK urban park. 4. acoupi can be deployed on low-cost hardware such as the Raspberry Pi and can be customised for various applications. acoupi standardised framework and simplified tools facilitate the adoption of AI-powered PAM systems for researchers and conservationists. acoupi is on GitHub at https://github.com/acoupi/acoupi.00["Aude Vuilliomenet", "Santiago Mart\u00ednez Balvanera", "Oisin Mac Aodha", "Kate E. Jones", "Duncan Wilson"]2025-01-29T00:00:00 c64dd565b24e7249fe0a8ac5ba97a5d22ace287dWith the rapid advancement of technology, artificial intelligence (AI) has gained widespread applications across various fields, including civil engineering. This paper provides a comprehensive review of AI’s significant roles in design optimization, construction management, structural health monitoring (SHM), and smart city management. AI enhances the scientific and creative dimensions of civil engineering by optimizing design schemes, generating innovative solutions, and improving efficiency. In construction management, AI streamlines processes by enabling better schedule control, cost and quality management, and safety monitoring. In SHM, AI facilitates more accurate fault detection, health assessment, and lifespan prediction, improving the safety, durability, and resilience of infrastructure. AI’s role in smart cities and infrastructure management further supports the efficient governance of urban planning, traffic control, and maintenance operations. However, challenges remain, including integrating AI with legacy infrastructure, ensuring data privacy and security, and overcoming scalability issues in real-world applications. The combination of AI with blockchain technology addresses transparency and security concerns, as demonstrated by emerging pilot projects. Additionally, integrating deep learning with big data will further enhance decision-making capabilities. As interdisciplinary research deepens and intelligent construction technologies become more prevalent, AI-powered civil engineering will advance toward more sustainable, efficient, and innovative practices, ultimately reshaping the field and meeting the demands of future urban development.Advances in Structural Engineering820Advances in Structural Engineering["Gang Xu", "Tong Guo"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/c64dd565b24e7249fe0a8ac5ba97a5d22ace287d b262e7007aa1edc8591a7305592381cbb7fed81fHow has the public responded to the increasing prevalence of artificial intelligence (AI)-based technologies? We investigate public perceptions of AI by collecting over 12,000 responses over 12 months from a nationally representative U.S. sample. Participants provided open-ended metaphors reflecting their mental models of AI, a methodology that overcomes the limitations of traditional self-reported measures. Using a mixed-methods approach combining quantitative clustering and qualitative coding, we identify 20 dominant metaphors shaping public understanding of AI. To analyze these metaphors systematically, we present a scalable framework integrating language modeling (LM)-based techniques to measure key dimensions of public perception: anthropomorphism (attribution of human-like qualities), warmth, and competence. We find that Americans generally view AI as warm and competent, and that over the past year, perceptions of AI's human-likeness and warmth have significantly increased ($+34\%, r = 0.80, p<0.01; +41\%, r = 0.62, p<0.05$). Furthermore, these implicit perceptions, along with the identified dominant metaphors, strongly predict trust in and willingness to adopt AI ($r^2 = 0.21, 0.18, p<0.001$). We further explore how differences in metaphors and implicit perceptions--such as the higher propensity of women, older individuals, and people of color to anthropomorphize AI--shed light on demographic disparities in trust and adoption. In addition to our dataset and framework for tracking evolving public attitudes, we provide actionable insights on using metaphors for inclusive and responsible AI development.00It is found that Americans generally view AI as warm and competent, and that over the past year, perceptions of AI's human-likeness and warmth have significantly increased, and differences in metaphors and implicit perceptions shed light on demographic disparities in trust and adoption.["Myra Cheng", "Angela Y. Lee", "Kristina Rapuano", "Kate Niederhoffer", "Alex Liebscher", "Jeffrey Hancock"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/b262e7007aa1edc8591a7305592381cbb7fed81f c1e2489ad316f18c24376eefeffc07898f8a35a0Nature Ecology & Evolution430Unique potential applications in which generative AI could accelerate the field of ecology are discussed, including augmenting data-scarce datasets, extending observations of ecological patterns and increasing the accessibility of ecological data.Nature ecology & evolution["K. Rafiq", "Sara Beery", "Meredith S Palmer", "Za\u00efd Harchaoui", "B. Abrahms"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/c1e2489ad316f18c24376eefeffc07898f8a35a0 fef45863e1570dda6646c9d042e75676a14cb223The current cycle of hype and anxiety concerning the benefits and risks to human society of Artificial Intelligence is fuelled, not only by the increasing use of generative AI and other AI tools by the general public, but also by claims made on behalf of such technology by popularizers and scientists. In particular, recent studies have claimed that Large Language Models (LLMs) can pass the Turing Test-a goal for AI since the 1950s-and therefore can"think". Large-scale impacts on society have been predicted as a result. Upon detailed examination, however, none of these studies has faithfully applied Turing's original instructions. Consequently, we conducted a rigorous Turing Test with GPT-4-Turbo that adhered closely to Turing's instructions for a three-player imitation game. We followed established scientific standards where Turing's instructions were ambiguous or missing. For example, we performed a Computer-Imitates-Human Game (CIHG) without constraining the time duration and conducted a Man-Imitates-Woman Game (MIWG) as a benchmark. All but one participant correctly identified the LLM, showing that one of today's most advanced LLMs is unable to pass a rigorous Turing Test. We conclude that recent extravagant claims for such models are unsupported, and do not warrant either optimism or concern about the social impact of thinking machines.00It is concluded that recent extravagant claims for large language models can pass the Turing Test are unsupported, and do not warrant either optimism or concern about the social impact of thinking machines.["Sharon Temtsin", "Diane Proudfoot", "David Kaber", "Christoph Bartneck The University of Canterbury", "Oregon State University"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/fef45863e1570dda6646c9d042e75676a14cb223 8200e6cf97a0792ff68ca66248f6fa79f56cd88dThe integration of artificial intelligence (AI) into educational systems has the potential to transform academic practices and promote sustainability in education. This study explores the development and evaluation of machine learning (ML) models to predict student performance, integrating socio-demographic, academic, and behavioral data to enhance accuracy and interpretability. By leveraging advanced techniques such as convolutional neural networks (CNNs) and explainable AI (XAI), this research provides actionable insights into key factors influencing student success, such as attendance and socio-economic status. The results demonstrate that CNNs achieve exceptional predictive accuracy (99.97%) compared to traditional models, while XAI methods ensure model transparency for informed decision-making. These findings enable the design of personalized learning strategies, timely interventions, and equitable educational practices that contribute to student retention and overall institutional efficiency. This study aligns with the goals of sustainable education by emphasizing data-driven approaches to enhance learning outcomes, equity, and resource utilization.Sustainability290This study explores the development and evaluation of machine learning models to predict student performance, integrating socio-demographic, academic, and behavioral data to enhance accuracy and interpretability and provides actionable insights into key factors influencing student success.Sustainability["Saleh Albahli"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/8200e6cf97a0792ff68ca66248f6fa79f56cd88d 3b07b84d9644ac26e6bee49d1e628c8bcbb57272The rapid advancement of artificial intelligence (AI) and automation has transformed industries across the globe, leading to significant disruptions in traditional job roles. The IT sector, as a frontrunner in technological innovation, is particularly affected by these developments. However, amidst the rise of AI-driven automation, certain roles in IT remain irreplaceable due to their reliance on uniquely human capabilities such as creativity, empathy, ethical judgment, and strategic thinking. This paper explores the concept of "The Last Job in IT" by analysing the evolving role of AI in IT professions, identifying jobs most resistant to automation, and hypothesizing what might constitute the final human-centric role in this domain. Through literature reviews, trend analysis, and case studies, the research sheds light on the future of work in IT and provides a roadmap for professionals to future-proof their careers.International Journal of Advanced Research in Science, Communication and Technology00The concept of "The Last Job in IT" is explored by analysing the evolving role of AI in IT professions, identifying jobs most resistant to automation, and hypothesizing what might constitute the final human-centric role in this domain.International Journal of Advanced Research in Science, Communication and Technology["Rida Khan", "Abres Siddique", "Divakar Jha"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/3b07b84d9644ac26e6bee49d1e628c8bcbb57272 3833c0a592735cd6671deb55ed594f929afdd677Artificial intelligence (AI) methods have become prevalent in the healthcare sector for patient risk assessment, medication discovery and disease diagnosis. Intelligent healthcare systems and a variety of duties related to patients can benefit from AI. For accurately diagnosing illnesses using AI techniques, an extensive variety of health data sources are required, which includes genetics, computed tomography tests, ultrasound, electromagnetic resonance imaging, mammograms etc. We discussed the role of AI in developed and developing nations and also the regulative issues and perspectives in health science. This article is based on an analysis of numerous studies and research publications providing information for early illness prediction for different kinds using AI-based methods. This article explores how AI might improve healthcare by looking at cutting-edge technologies, inventive applications, challenges and upcoming seismic shifts. AI-enabled virtual health assistants have the potential to drastically alter the way healthcare is delivered.European Journal of Environment and Public Health330How AI might improve healthcare by looking at cutting-edge technologies, inventive applications, challenges and upcoming seismic shifts is explored.European Journal of Environment and Public Health["Manoj Kumar", "Raj Kumar", "Dileep Kumar Arisham", "Rajesh Kumar Gupta", "Pooja Naudiyal", "Gunjan Goutam", "A. Mavi"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/3833c0a592735cd6671deb55ed594f929afdd677 1f36acd8cd4f0ff19561bbfe3921a5eb5f737456This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective incentives for responsible AI development. We examine four emerging vectors of market governance: insurance, auditing, procurement, and due diligence, demonstrating how these mechanisms can affirm the relationship between AI risk and financial risk while addressing capital allocation inefficiencies. While we do not claim that market forces alone can adequately protect societal interests, we maintain that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development. This paper urges regulators, economists, and machine learning researchers to investigate and implement market-based approaches to AI governance.00It is argued that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks, and urged that standardised AI disclosures and market mechanisms can create powerful incentives for safe and responsible AI development.["Philip Moreira Tomei", "Rupal Jain", "Matija Franklin"]2025-01-29T00:00:00https://www.semanticscholar.org/paper/1f36acd8cd4f0ff19561bbfe3921a5eb5f737456 97a01e6a04b9659ff2553884a8c040d974e2cdf6This study explores the key trends and ideas around using artificial intelligence in investment. The authors employ the bibliometric approach, using VOS viewer software to analyze 582 academic articles from the SCOPUS database between 2004 and 2023. The findings show that interest in artificial intelligence within investment has grown since 2017, reflecting a delay in its adoption by the investment industry. China, the United States, India, and the United Kingdom were identified as the leading countries researching this topic. The National Research University Higher School of Economics, Russia, and Spiru Haret University, Romania emerged as the most active institution in this area. It highlights the growing adoption of AI across various financial institutions, including banks, hedge funds, and fintech firms, due to its ability to analyze extensive datasets, enhance decision-making, and optimize portfolios. Key AI-driven, cost-effective investment advice. These technologies outperform traditional advisors' inefficiency and objectivity but face challenges in gaining trust among seasonal investors. However, the study has limitations, as it only used articles from the SCOPUS database and focused solely on English–language publications. The future directions emphasize the integration of AI with sustainability and natural language processing, reflecting its potential to address broader societal challenges. The study underlines that extensive regulatory frameworks, improved collaboration, and user-centric AI solutions are required to optimize its influence on investment practices.International Journal of Computational and Experimental Science and Engineering752The study underlines that extensive regulatory frameworks, improved collaboration, and user-centric AI solutions are required to optimize its influence on investment practices, and highlights the growing adoption of AI across various financial institutions due to its ability to analyze extensive datasets, enhance decision-making, and optimize portfolios.International Journal of Computational and Experimental Science and Engineering["M.K. Sarjas", "G. Velmurugan"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/97a01e6a04b9659ff2553884a8c040d974e2cdf6 465bac132cd7f09f2c5171f26581c619bf5f684aThe dynamic landscape of global supply chains necessitates innovative solutions to tackle challenges in demand forecasting and inventory optimization. Traditional methods, often constrained by limited adaptability and scalability, struggle to manage the complexities of modern supply chains. Artificial Intelligence (AI) has emerged as a transformative force, enabling predictive supply chain management through advanced data analytics, machine learning algorithms, and real-time decision-making capabilities. By harnessing AI-driven tools, businesses can accurately forecast demand patterns, reduce stockouts, and minimize excess inventory, thereby improving operational efficiency and customer satisfaction. AI-powered systems leverage historical data, market trends, and external factors such as economic shifts and weather conditions to provide precise predictions. These tools enhance responsiveness by identifying potential disruptions and enabling proactive measures, ensuring supply chain resilience. Furthermore, AI facilitates seamless integration across supply chain nodes, fostering collaboration and enabling data-driven insights that were previously unattainable. From predictive analytics for demand forecasting to intelligent automation in inventory management, AI-driven tools are revolutionizing the traditional supply chain model. Case studies reveal substantial reductions in holding costs, improved lead times, and enhanced supply chain visibility. However, challenges such as data quality, system integration, and ethical considerations in AI deployment remain critical areas for exploration. This paper looks into the transformative impact of AI on predictive supply chain management, highlighting key advancements, practical applications, and challenges. The insights presented underscore the pivotal role of AI in driving efficiency and innovation in an increasingly complex and competitive global economy.International Journal of Science and Research Archive02This paper looks into the transformative impact of AI on predictive supply chain management, highlighting key advancements, practical applications, and challenges and the pivotal role of AI in driving efficiency and innovation in an increasingly complex and competitive global economy.International Journal of Science and Research Archive["Uche Nweje", "Moyosore Taiwo"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/465bac132cd7f09f2c5171f26581c619bf5f684a d55a6cd9b41bcae4b8aa97d554512cd87b20498eArtificial Intelligence (AI) is transforming the field of oral and maxillofacial surgery (OMFS) by enhancing diagnostic accuracy, treatment planning, and patient outcomes. In India, AI adoption in OMFS is growing due to advancements in machine learning, imaging technologies, and robotic-assisted surgery. This review explores the current status of AI in OMFS within India, highlighting key applications, challenges, and future prospects.International Journal of Development Research00The current status of AI in OMFS within India is explored, highlighting key applications, challenges, and future prospects.International Journal of Development Research[]2025-01-30T00:00:00https://www.semanticscholar.org/paper/d55a6cd9b41bcae4b8aa97d554512cd87b20498e 25e2c0ccf0a04dddd9eb05265da446b6d6504aa7In the age of digitization and the ever-present use of artificial intelligence (AI), it is essential to develop methodologies that enable the systematic evaluation and ranking of different AI algorithms. This paper investigated the application of the PIPRECIA-S model as a methodological framework for the multi-criteria ranking of AI algorithms. Analyzing relevant criteria such as efficiency, flexibility, ease of implementation, stability and scalability, the paper provided a comprehensive overview of existing algorithms and identified their strengths and weaknesses. The research results showed that the PIPRECIA-S model enabled a structured and objective assessment, which facilitated decision-making in selecting the most suitable algorithms for specific applications. This approach not only advances the understanding of AI algorithms but also contributes to the development of strategies for their implementation in various industries.Electronics490The research results showed that the PIPRECIA-S model enabled a structured and objective assessment, which facilitated decision-making in selecting the most suitable algorithms for specific applications, and contributes to the development of strategies for their implementation in various industries.Electronics["Stefan Popovi\u0107", "D. Viduka", "A. Ba\u0161i\u0107", "Violeta Dimi\u0107", "Dejan Djukic", "Vojkan R. Nikoli\u0107", "Aleksandar Stoki\u0107"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/25e2c0ccf0a04dddd9eb05265da446b6d6504aa7 ad9ec862b06eaf889acf3e3a20e60d94f4e9414eTechnological pedagogical content knowledge (TPACK) is a theory that describes the knowledge and skills required by a teacher to integrate technology into their teaching. This study aimed to identify the level of TPACK among primary school teachers regarding applying AI technology for teaching. This study employed a quantitative research approach using a survey design. Data was collected through structured questionnaires from in-service primary school teachers in Semporna, Sabah. An independent samples t-test and a one-way ANOVA test were used for data analysis. The results showed that the level of teachers’ TPACK in applying artificial intelligence (AI) technology for teaching was high. The independent t-test uncovered no significant difference between teachers’ TPACK concerning their gender. However, one-way ANOVA showed a significant difference between teachers’ TPACK concerning their age in content, pedagogy, and pedagogical content knowledge compared with their technological content and technological pedagogy. These findings suggest that targeted AI training for older teachers could bridge generational gaps, thereby enhancing AI integration and educational outcomes. This highlights the importance of strong TPACK competencies for effective AI integration, with age-related variations emphasizing the need for tailored support to optimize classroom implementation.International Journal of Learning, Teaching and Educational Research00The results showed that the level of teachers’ TPACK in applying artificial intelligence (AI) technology for teaching was high, and targeted AI training for older teachers could bridge generational gaps, thereby enhancing AI integration and educational outcomes.International Journal of Learning, Teaching and Educational Research["M. H. Saharuddin", "M. K. M. Nasir", "Muhammad Sofwan Mahmud"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/ad9ec862b06eaf889acf3e3a20e60d94f4e9414e 6ee0d4f38b01cdc97687b766c836e4a6543de8d5Digital marketing has seen a rapid transformation due to artificial intelligence (AI), particularly on social media platforms. Although it has many benefits, such as data-driven decision making, automation, and personalized experiences, the drawbacks are frequently outweighed by the positives. The negative consequences of AI on digital marketing strategies, namely through social media channels, are examined in this study. It looks at topics including privacy issues, the propagation of false information, moral dilemmas, and the decline of real human connection in marketing tactics. The effects of integrating AI into these platforms are demonstrated through case studies from businesses such as Facebook, Twitter, and YouTubeInternational Journal of Advanced Research in Science, Communication and Technology20The negative consequences of AI on digital marketing strategies, namely through social media channels, are examined in this study, looking at topics including privacy issues, the propagation of false information, moral dilemmas, and the decline of real human connection in marketing tactics.International Journal of Advanced Research in Science, Communication and Technology["Surabhi NV", "Arun Ajith K"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/6ee0d4f38b01cdc97687b766c836e4a6543de8d5 9d0444ba275a0d0885b64ae703947b52992fd08aIn the digital era, the importance of artificial intelligence (AI) is many. The aim of the study is to analyse the impact of AI on mergers and acquisitions, particularly in the areas of target identification and due diligence. The study is a conceptual in nature and hance secondary method is used. A large number of papers are collected from google scholar, Wiley, Scopus, and Web of science database for 2010 to 2024. The findings imply that AI significantly influence M&A activities specially for target identification and due diligence. Policy makers have to use AI tools making M&A decision. Future research should focus on the other factors such as synergy assessment.International Journal of Innovative Research in Multidisciplinary Education00It is implied that AI significantly influence M&A activities specially for target identification and due diligence and policy makers have to use AI tools making M&A decision.International Journal of Innovative Research in Multidisciplinary Education["Mohammad Mamunur Rashid"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/9d0444ba275a0d0885b64ae703947b52992fd08a 58254194356f292cbc1aa0a9930e5f84fcd3f851Artificial Intelligence (AI) has emerged as a transformative force in reshaping customer-centric marketing strategies. This paper explores the pivotal role of AI in leveraging data-driven insights to enhance customer engagement, optimize personalization, and drive decision-making in marketing. By integrating technologies such as machine learning, natural language processing, and predictive analytics, organizations can better understand consumer behavior, preferences, and trends. These AI-driven tools enable businesses to craft targeted marketing campaigns, anticipate customer needs, and deliver superior customer experiences in real time. The rise of AI has enabled marketers to process vast amounts of data from multiple sources, such as social media, customer feedback, and purchase history, with unprecedented speed and accuracy. AI algorithms can identify patterns and predict future behaviors, empowering companies to make informed marketing decisions. This leads to a significant reduction in marketing waste, enhanced return on investment (ROI), and improved customer loyalty. Furthermore, AI applications such as chatbots, recommendation engines, and automated content creation have revolutionized customer interaction, making it more seamless and tailored. For instance, AI-powered chatbots provide 24/7 support, while recommendation engines offer personalized product suggestions, boosting conversion rates. AI also plays a crucial role in predictive analytics, enabling marketers to anticipate market trends and customer needs, thereby staying ahead of the competition. The paper also highlights challenges associated with adopting AI in marketing, including ethical considerations, data privacy concerns, and the need for skilled professionals to manage AI systems effectively. Despite these challenges, the integration of AI into customer-centric marketing strategies has proven to be a game-changer, transforming traditional marketing practices and paving the way for innovative approaches to customer engagement. In conclusion, AI is revolutionizing marketing by enhancing personalization, efficiency, and effectiveness in customer engagement. Its ability to process and analyze vast data sets, predict trends, and deliver targeted content has established AI as an indispensable tool in modern marketing strategies. As technology continues to evolve, AI’s role in driving customer-centric marketing is expected to grow, further redefining how businesses connect with their audiences.NPRC Journal of Multidisciplinary Research290Artificial Intelligence is revolutionizing marketing by enhancing personalization, efficiency, and effectiveness in customer engagement, and playing a crucial role in predictive analytics, enabling marketers to anticipate market trends and customer needs, thereby staying ahead of the competition.NPRC Journal of Multidisciplinary Research["Priya A", "A. Arunprakash", "Vidhya Kulothungan", "P. Radha"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/58254194356f292cbc1aa0a9930e5f84fcd3f851 b3334d807b891b41fb1d6a4020700a87d697e411This dissertation seeks to examine the connection between business and artificial intelligence (AI) in the health sector, major possibilities, and the current ethical issues. It is evident that adoption of AI has revolutionized healthcare by improving clients’ satisfaction, productivity and cost by using technological tools such as analytical tools, tele surgery, and disease diagnosis. This is important because the global AI healthcare market has been estimated to grow to $45.2 Billion by 2026. The study of two different countries – the United States and the United Kingdom – offers an understanding of AI implementation, legislation, and ethical standards as the result of different health systems and legislation. This work reveal potential benefits are enhanced diagnostic accuracy, cost savings whereas the potential harms include violations of patient privacy, bias in algorithms, and accountable issues. The target field adopts theoretical concepts such as The Technology Acceptance Model and The Diffusion of Innovation Theory to review factors affecting AI uptake. This also looks into ethical considerations as a way of making sure that properly applied. The implications of the chosen regulatory frameworks as tools for the analysis of business and patient care strategies are demonstrated in the studied examples of GDPR and HIPAA. The studies provide practical recommendations for both healthcare organizations and policy-makers as well as companies that are importing AI into their services. In conclusion, this research provides to the improvement of healthcare while exploring the signals for confronting the moral issues of AI application on global healthcare settings.GSC Advanced Research and Reviews00This dissertation seeks to examine the connection between business and artificial intelligence (AI) in the health sector, major possibilities, and the current ethical issues by exploring the signals for confronting the moral issues of AI application on global healthcare settings.GSC Advanced Research and Reviews["Jin young Hwang"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/b3334d807b891b41fb1d6a4020700a87d697e411 6761fb277660b5f8d5bd4696ca78365c35394a9cAbstract : Artificial Intelligence (AI) is reshaping numerous sectors, including education. This study delves into AI in education (AIEd) within Africa, analyzing its trends, opportunities, challenges, and prospective paths. Employing the PRISMA framework, we systematically reviewed 22 articles published from 2017 to 2022. Our findings underscore the pivotal role of AIEd in Africa's educational landscape, highlighting the shift towards adaptive testing, particularly computer-adaptive testing (CAT), and its advantages, like precise student assessments and reduced test durations. The study also explores strategies to enhance graduate employability, emphasizing university-industry collaborations, curriculum updates, and quality assurance. Furthermore, it examines the implications of the Fourth Industrial Revolution (4IR) in education, advocating for integrating emerging technologies and adapting educational content and practices to meet digital-era challenges. While technology integration, including smartphones and ICT tools like Moodle, shows promise for enriching learning experiences, we identify pressing challenges such as resource contextualization, teacher training, and ethical issues. The study concludes with targeted recommendations for educators, policymakers, and African stakeholders to harness AIEd effectively. Emphasizing areas like adaptive testing, robust university-industry partnerships, 4IR-aligned curriculum development, and ethical, inclusive technology integration, these recommendations aim to empower African education systems to capitalize on AIEd's benefits while navigating the complexities of the digital age. Keywords :Artificial intelligence; education; Africa; 4IREJournal of Engineering Education Transformations00The findings underscore the pivotal role of AIEd in Africa's educational landscape, highlighting the shift towards adaptive testing, particularly computer-adaptive testing (CAT), and its advantages, like precise student assessments and reduced test durations.Journal of Engineering Education Transformations["Antwi-Boampong Ahmed", "Boison, David King", "K. K. Hiran", "Manish Dadhich", "Ebenezer Malcalm"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/6761fb277660b5f8d5bd4696ca78365c35394a9c 44c82b19180e786c3993d9bcfd933aa4f14455d3Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a framework for diabetes prediction using Machine Learning (ML) models, complemented with eXplainable Artificial Intelligence (XAI) tools, to investigate both the predictive accuracy and interpretability of the predictions from ML models. Data Preprocessing is based on the Synthetic Minority Oversampling Technique (SMOTE) and feature scaling used on the Diabetes Binary Health Indicators dataset to deal with class imbalance and variability of clinical features. The ensemble model provided high accuracy, with a test accuracy of 92.50% and an ROC-AUC of 0.975. BMI, Age, General Health, Income, and Physical Activity were the most influential predictors obtained from the model explanations. The results of this study suggest that ML combined with XAI is a promising means of developing accurate and computationally transparent tools for use in healthcare systems.290The results of this study suggest that ML combined with XAI is a promising means of developing accurate and computationally transparent tools for use in healthcare systems.["Pir Bakhsh Khokhar", "Viviana Pentangelo", "Fabio Palomba", "Carmine Gravino"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/44c82b19180e786c3993d9bcfd933aa4f14455d3 b6d50eda75cf5efa95e0fce34108614fef23eaceBackground Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain. Objective This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century. Methods Documents were retrieved from the Web of Science Core Collection database from 2000 to 2024. VOSviewer, Incites, and Citespace were used to analyze the bibliometric metrics, which were categorized by country, institution, authors, journals, and keywords. The variables analyzed encompassed counts, citations, H-index, impact factor, and collaboration metrics. Results Altogether, 7534 publications were initially retrieved and 2775 were included for analysis. The annual count and citation of papers exhibited exponential trends since 2018. The United States emerged as the lead contributor due to its high productivity and recognition levels. Stanford University, Johns Hopkins University, National University of Singapore, Mayo Clinic, University of Arizona, and University of Toronto were representative institutions in their respective fields. Cureus, JMIR Medical Education, Medical Teacher, and BMC Medical Education ranked as the top four most productive journals. The resulting heat map highlighted several high-frequency keywords, including performance, education, AI, and model. The citation burst time of terms revealed that AI technologies shifted from imaging processing (2000), augmented reality (2013), and virtual reality (2016) to decision-making (2020) and model (2021). Keywords such as mortality and robotic surgery persisted into 2023, suggesting the ongoing recognition and interest in these areas. Conclusions This study provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions. Along with the rapid growth of AI, medical education is expected to gain much more benefits.Interactive Journal of Medical Research290Analyzing the social patterns, productive contributors, knowledge structure, and clusters since the 21st century in medical education provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions.Interactive Journal of Medical Research["Rui Li", "Tong Wu"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/b6d50eda75cf5efa95e0fce34108614fef23eace 8d93f66a33b0d22472c08b9e3cb45c8c57a6a8f4Objectives: The objective of this paper is to explore the current state of Artificial Intelligence (AI) usage in the Indian criminal justice system, with a focus on its legal and ethical implications. It aims to examine how existing legal frameworks, such as the Information Technology Act of 2000 and the Indian Penal Code of 1860, could be adapted to regulate AI within the legal profession. Additionally, the paper seeks to highlight the relevance, issues, and future prospects of AI applications in law enforcement agencies, courts, and correctional centers, stressing the need for multi-stakeholder cooperation among legal professionals, policymakers, and technologists.   Methods: This paper employs a qualitative analysis of the current implementation of AI in India’s criminal justice system. It reviews existing laws, including the Information Technology Act and the Indian Penal Code, to assess their applicability in regulating AI practices. Furthermore, the roles of the High Courts and the Supreme Court of India in overseeing AI applications across the country are examined. Ethical and legal concerns related to AI are explored, particularly regarding transparency, accountability, and public participation in the regulatory process.   Results: The study found that there is currently no dedicated legislation in India specifically governing the use of AI in criminal justice. However, existing laws like the Information Technology Act of 2000 and the Indian Penal Code of 1860 can be utilized to regulate AI applications in the legal profession. The involvement of the Indian High Courts and the Supreme Court is crucial in ensuring that AI practices align with legal standards and ethical norms. The paper also identifies several challenges in the adoption of AI in criminal justice, such as concerns about bias, fairness, and transparency.   Conclusion: The use of AI in India’s criminal justice system presents both significant opportunities and challenges. While AI can enhance crime prediction, detection, and offender management, its application raises important legal and ethical concerns. The absence of specific legislation dedicated to AI regulation calls for a comprehensive legal framework that integrates the best practices of transparency, accountability, and ethical standards. Multi-stakeholder cooperation among legal professionals, policymakers, and technologists is essential for ensuring that AI applications in the criminal justice system uphold the principles of justice, equity, and human rights. By fostering such collaboration, India can effectively harness the benefits of AI while safeguarding the integrity of its legal system.Journal of Lifestyle and SDGs Review210The study found that there is currently no dedicated legislation in India specifically governing the use of AI in criminal justice, however, existing laws like the Information Technology Act of 2000 and the Indian Penal Code can be utilized to regulate AI applications in the legal profession.Journal of Lifestyle and SDGs Review["Aishwarya Sharma", "Shivangi Chauhan Sharma", "Srishti Dixit Soni", "Pooja Agrawal", "Pratishtha Mishra", "Geeny Mourya"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/8d93f66a33b0d22472c08b9e3cb45c8c57a6a8f4 cf13bee8f44cf9f61ea49f7793025d28c56f2d41Introduction Estimating a reliable gestational age (GA) is essential in providing appropriate care during pregnancy. With advancements in data science, there are several publications on the use of artificial intelligence (AI) models to estimate GA using ultrasound (US) images. The aim of this meta-analysis is to assess the accuracy of AI models in assessing GA against US as the gold standard. Methods A literature search was performed in PubMed, CINAHL, Wiley Cochrane Library, Scopus, and Web of Science databases. Studies that reported use of AI models for GA estimation with US as the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Mean error in GA was estimated using STATA version-17 and subgroup analysis on trimester of GA assessment, AI models, study design, and external validation was performed. Results Out of the 1,039 studies screened, 17 were included in the review, and of these 10 studies were included in the meta-analysis. Five (29%) studies were from high-income countries (HICs), four (24%) from upper-middle-income countries (UMICs), one (6%) from low-and middle-income countries (LMIC), and the remaining seven studies (41%) used data across different income regions. The pooled mean error in GA estimation based on 2D images (n = 6) and blind sweep videos (n = 4) was 4.32 days (95% CI: 2.82, 5.83; l2: 97.95%) and 2.55 days (95% CI: −0.13, 5.23; l2: 100%), respectively. On subgroup analysis based on 2D images, the mean error in GA estimation in the first trimester was 7.00 days (95% CI: 6.08, 7.92), 2.35 days (95% CI: 1.03, 3.67) in the second, and 4.30 days (95% CI: 4.10, 4.50) in the third trimester. In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). Most studies exhibited an unclear or low risk of bias in various domains, including patient selection, index test, reference standard, flow and timings and applicability domain. Conclusion Preliminary experience with AI models shows good accuracy in estimating GA. This holds tremendous potential for pregnancy dating, especially in resource-poor settings where trained interpreters may be limited. Systematic Review Registration PROSPERO, identifier (CRD42022319966).Frontiers in Global Women's Health380Preliminary experience with AI models shows good accuracy in estimating GA, which holds tremendous potential for pregnancy dating, especially in resource-poor settings where trained interpreters may be limited.Frontiers in Global Women's Health["Sabahat Naz", "Sahir Noorani", "Syed Ali Jaffar Zaidi", "Abdu R. Rahman", "S. Sattar", "J. Das", "Zahra Hoodbhoy"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/cf13bee8f44cf9f61ea49f7793025d28c56f2d41 7dd4c5a9aafa6e357c915e88a953d7b4869cbf01The rise in intrusions on network and IoT systems has led to the development of artificial intelligence (AI) methodologies in intrusion detection systems (IDSs). However, traditional AI or machine learning (ML) methods can compromise accuracy due to the vast, diverse, and dynamic nature of the data generated. Moreover, many of these methods lack transparency, making it challenging for security professionals to make predictions. To address these challenges, this paper presents a novel IDS architecture that uses deep learning (DL)-based methodology along with eXplainable AI (XAI) techniques to create explainable models in network intrusion detection systems, empowering security analysts to use these models effectively. DL models are needed to train enormous amounts of data and produce promising results. Three different DL models, i.e., customized 1-D convolutional neural networks (1-D CNNs), deep neural networks (DNNs), and pre-trained model TabNet, are proposed. The experiments are performed on seven different datasets of TON_IOT. The CNN model for the network dataset achieves an impressive accuracy of 99.24%. Meanwhile, for the six different IoT datasets, in most of the datasets, the CNN and DNN achieve 100% accuracy, further validating the effectiveness of the proposed models. In all the datasets, the least-performing model is TabNet. Implementing the proposed method in real time requires an explanation of the predictions generated. Thus, the XAI methods are implemented to understand the essential features responsible for predicting the particular class.Italian National Conference on Sensors410A novel IDS architecture is presented that uses deep learning (DL)-based methodology along with eXplainable AI (XAI) techniques to create explainable models in network intrusion detection systems, empowering security analysts to use these models effectively.Sensors (Basel, Switzerland)["Adel Alabbadi", "Fuad Bajaber"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/7dd4c5a9aafa6e357c915e88a953d7b4869cbf01 d387f768270c8a52528555308f94556182257997Background Artificial intelligence (AI) is anticipated to play a significant role in criminal trials involving citizen jurors. Prior studies have suggested that AI is not widely preferred in ethical decision-making contexts, but little research has compared jurors’ reliance on judgments by human judges versus AI in such settings. Objectives This study examined whether jurors are more likely to defer to judgments by human judges or AI, especially in cases involving mitigating circumstances in which human-like reasoning may be valued. Methods Two pre-registered online experiments were conducted with Japanese participants (Experiment 1: N = 1,735, Mage = 48.4; Experiment 2: N = 1,731, Mage = 48.5). Participants reviewed two murder trial vignettes and made sentencing decisions (1 = suspended sentence; 8 = prison sentence) under two conditions: trials with and without mitigating circumstances. Results and conclusion Across both experiments, participants showed no preference for deferring to human judges’ or AI judgments when making sentencing decisions. While suspended sentences were more common in cases with mitigating circumstances, this tendency was unrelated to the judgment source. These findings suggest that jurors do not inherently avoid algorithmic judgments and may consider AI opinions on par with those of human judges in certain contexts. However, whether this leads to improved decision-making quality remains an open question, as objectivity (a strength of AI) and emotional considerations (a safeguard for fairness) may interact in complex ways during juror deliberations. Future research should further explore how these factors influence juror attitudes and decisions in diverse trial scenarios, taking into account potential biases in existing literature.PLoS ONE590Findings suggest that jurors do not inherently avoid algorithmic judgments and may consider AI opinions on par with those of human judges in certain contexts, however, whether this leads to improved decision-making quality remains an open question.PLOS ONE["Eiichiro Watamura", "Yichen Liu", "T. Ioku"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/d387f768270c8a52528555308f94556182257997 125fe8f758c0dbb7348f845ef28cc069bd365dd2Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this domain, enabling data-driven interventions to optimize antibiotic use and combat resistance. This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems. AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized antibiotic therapies by leveraging large-scale clinical and epidemiological data. ML algorithms facilitate rapid pathogen identification, resistance profiling, and real-time monitoring, enabling precise decision making. These technologies also support the development of advanced diagnostic tools, reducing the reliance on broad-spectrum antibiotics and fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve the detection of AMR trends and enhance global monitoring capabilities. By integrating diverse data sources—such as electronic health records, laboratory results, and environmental data—ML models provide actionable insights to policymakers, healthcare providers, and public health officials. Additionally, AI applications in antimicrobial stewardship programs (ASPs) promote adherence to prescribing guidelines, evaluate intervention outcomes, and optimize resource allocation. Despite these advancements, challenges such as data quality, algorithm transparency, and ethical considerations must be addressed to maximize the potential of AI and ML in this field. Future research should focus on developing interpretable models and fostering interdisciplinary collaborations to ensure the equitable and sustainable integration of AI into antimicrobial stewardship initiatives.Antibiotics790This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems and concludes that challenges such as data quality, algorithm transparency, and ethical considerations must be addressed to maximize the potential of AI and ML in this field.Antibiotics["Flavia Pennisi", "Antonio Pinto", "Giovanni Emanuele Ricciardi", "C. Signorelli", "V. Gianfredi"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/125fe8f758c0dbb7348f845ef28cc069bd365dd2 23d167edcffb595fc6e11b006ba3dc57be29cbbfPURPOSE The goal of the study described in this protocol is to build a multimodal artificial intelligence (AI) model to predict abdominal aortic aneurysm (AAA) shrinkage 1 year after endovascular aneurysm repair (EVAR). METHODS In this retrospective observational multicenter study, approximately 1000 patients will be enrolled from hospital records of 5 experienced vascular centers. Patients will be included if they underwent elective EVAR for infrarenal AAA with initial assisted technical success and had imaging available of the same modality preoperatively and at 1-year follow-up (CTA-CTA or US-US). Data collection will include baseline and vascular characteristics, medication use, procedural data, preoperative and postoperative imaging data, follow-up data, and complications. PROPOSED ANALYSES The cohort will be stratified into 3 groups of AAA remodeling based on the maximum AAA diameter difference between the preoperative and 1-year postoperative moment. Patients with a diameter reduction of ≥5 mm will be assigned to the AAA shrinkage group, cases with an increase of ≥5 mm will be assigned to the AAA growth group, and patients with a diameter increase or reduction of <5 mm will be assigned to the stable AAA group. Furthermore, an additional fourth group will include all patients who underwent an AAA-related reintervention within the first year after EVAR, because both the complication and the reintervention might have influenced the state of AAA remodeling at 1 year. The preoperative and postoperative CTA scans will be used for anatomical AAA analysis and biomechanical assessment through semi-automatic segmentation and finite element analysis. All collected clinical, biomechanical, and imaging data will be used to create an AI prediction model for AAA shrinkage. Explainable AI techniques will be used to identify the most descriptive input features in the model. Predicting factors resulting from the AI model will be compared with conventional univariate and multivariate logistic regression analyses to find the best model for the prediction of AAA shrinkage. The study is registered at www.clinicaltrials.gov under the registration number NCT06250998. CLINICAL IMPACT This study aims to develop a robust and high-performance AI model for predicting AAA shrinkage one-year after EVAR, with great potential for optimizing both EVAR treatment and follow-up. The model can identify cases with an initially lower chance of early AAA shrinkage, in whom EVAR-treatment could be tailored by including additional preoperative coil embolization, active sac management and/or postoperative tranexamic acid therapy, which have shown to promote AAA shrinkage rate but are too complex and costly to perform in all patients. The model could aid in stratification of post-EVAR surveillance based on the patient's individual risk and possibly decrease follow-up for the 40-50% of patients who will experience AAA sac shrinkage. Overall, the AI prediction model is expected to improve patient survival and decrease the number of reinterventions after EVAR and associated healthcare costs.Journal of Endovascular Therapy260The AI prediction model is expected to improve patient survival and decrease the number of reinterventions after EVAR and associated healthcare costs, and could aid in stratification of post-EVAR surveillance based on the patient's individual risk and possibly decrease follow-up.Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists["R. V. van Rijswijk", "M. Bogdanovic", "Joy Roy", "K. K. Yeung", "C.J.A.M. Zeebregts", "Robert H. Geelkerken", "E. Groot Jebbink", "J. Wolterink", "M. Reijnen"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/23d167edcffb595fc6e11b006ba3dc57be29cbbf 4d790d6f36332133c27b9dfa668b6511c323b434BMC Medical Education260Formal AI education seems inadequate despite students’ enthusiasm concerning the application of such technology in clinical practice, and medical curricula should evolve to promote structured, evidence-based AI literacy to enable students to understand the potential applications of AI in health care.BMC Medical Education["A. Almarzouki", "Alwaleed Alem", "Faris Shrourou", "Suhail Kaki", "Mohammed Khushi", "Abdulrahman Mutawakkil", "Motasem Bamabad", "Nawaf Fakharani", "Mohammed Alshehri", "Mohanad Binibrahim"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/4d790d6f36332133c27b9dfa668b6511c323b434 15ddd8f674ba4c652cafdba2ece5dbe7ec6bc904This literature review examines the impact of artificial intelligence-based (AI-based) no-code software development platforms on software processes. The study primarily focuses on accelerating software development processes, reducing costs, and optimizing business operations. Existing studies in the literature demonstrate how these types of platforms facilitate complex application development even for non-technical users and enhance time-cost optimization. This review highlights how no-code platforms have become more effective and efficient with AI-supported tools, transforming the current software development ecosystem. The article discusses the potential benefits and challenges of AI-based no-code platforms, emphasizing their promising future in the software industry.Düzce Üniversitesi Bilim ve Teknoloji Dergisi80How no-code platforms have become more effective and efficient with AI-supported tools, transforming the current software development ecosystem is highlighted, emphasizing their promising future in the software industry.Düzce Üniversitesi Bilim ve Teknoloji Dergisi["Osman Ko\u00e7", "I. Y\u00fccedag", "\u00dcmit \u015eent\u00fcrk"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/15ddd8f674ba4c652cafdba2ece5dbe7ec6bc904 be5e214c7d1962c10eb15f37e35fd36c992e1e25 Purpose In the modern financial landscape, Artificial Intelligence (AI) is gaining prominence, offering significant economic advantages. This research paper aims to investigate the impact of Behavioural Biases (BB) such as Overconfidence Bias (OCB), Fear of Missing Out (FOMO), Herding Bias (HB) and Regret Aversion Bias (RAB) on Investment Decision-Making (IDM). Additionally, it explores how the AI-led Adoption of Digital Advisory Services (ADAS) moderates these biases among Gen Z investors in India. Design/methodology/approach The study utilized a convenience sampling method, gathering 457 responses from Gen Z investors in India through an online survey questionnaire. The data was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Findings The results confirm a significant relationship between OCB, FOMO, HB and RAB on IDM. The study also found that ADAS significantly moderated the relationship between FOMO and IDM, as well as between HB and IDM. However, the moderation effect of ADAS was not supported for the relationships between OCB and IDM, and RAB and IDM. Practical implications This research offers valuable insights for academics, individual investors, fintech companies and policymakers. It highlights how behavioural biases affect IDM and underscores the importance of AI-enabled digital services in helping Gen Z investors recognize and manage these biases. Policymakers can use these insights to establish standards for AI use, ensuring regulatory compliance and promoting ethical conduct in AI-driven investment decisions. Originality/value The novelty of this study lies in its conceptual approach, particularly in examining the moderation role of ADAS in addressing behavioural biases among Gen Z investors. International Journal of Accounting &amp; Information Management610It is found that ADAS significantly moderated the relationship between FOMO and IDM, as well as between HB and IDM, and RAB and IDM, but the moderation effect of ADAS was not supported for the relationships between OCB and IDM, and RAB and IDM.International Journal of Accounting &amp; Information Management["H. Maheshwari", "Anup K. Samantaray"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/be5e214c7d1962c10eb15f37e35fd36c992e1e25 5ea6850b4de1cc17700e7e34e634c40c1da67c6cThe rapid development of digital technologies has driven significant advancements in artificial intelligence (AI) applications, expanding their use across various fields. One notable area is disaster management, where AI is leveraged to strengthen societal resilience and protect communities from disasters. However, some AI projects may fall short of expectations during implementation, often resulting in increased costs, time, and labor due to their inherent complexity. In response, this study presents a model that explores the application of AI throughout the disaster management process, utilizing secondary data sources. The objective is to contribute to both academic literature and disaster management practices by supporting disaster prevention, reducing loss of life and property, and enabling more efficient and timely interventions. Furthermore, this study aims to serve as a valuable resource not only for researchers in the field but also for decision-makers and practitioners, offering a concise reference for more informed, data-driven actions.Kamu Yönetimi ve Teknoloji Dergisi180This study presents a model that explores the application of AI throughout the disaster management process, utilizing secondary data sources, to contribute to both academic literature and disaster management practices by supporting disaster prevention, reducing loss of life and property, and enabling more efficient and timely interventions.Kamu Yönetimi ve Teknoloji Dergisi["Veysel Eren", "Hasret Duman"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/5ea6850b4de1cc17700e7e34e634c40c1da67c6c 5b11ea0c1f6182480e65e162f8cf478d2d584248The world we live in has been radically changed by technology in recent decades. The field of artificial intelligence (AI) in education (AIEd) has developed into a sizable literature collection with a variety of viewpoints. The use of artificial intelligence in educational applications is growing in popularity, posing both benefits and difficulties for the classroom. This study aims to provide a comprehensive understanding of the current conceptual framework of AIEd and explore the future vision for this technology. The study aims to investigate the opportunities that AI technology offers to enhance teaching and learning, identify challenges in this field and outline the future vision for AI technology, and examine the learning outcomes for teachers and students influenced by AI technology. This method can offer a thorough understanding of the conceptual framework and the long-term goals for this area of technology. For a comprehensive literature evaluation, we chose 35 empirical research publications that included AIEd applications, study themes, and other aspects of the research design, including the general AIEd research field, AIED applications, research topics, and future vision including future benefits, opportunities, threats, and challenges. Although AIEd-based settings are improving student learning, research indicates that tailored learning is still in its early stages. Lack of money and moral dilemmas are obstacles. AIEd's benefits include the fact that AI chatbots and applications facilitate learning, but they also have drawbacks. Additionally, AI apps improved engagement through interactive features, promoted well-being with components and continual availability, and spurred creativity by offering new ideas and problem-solving strategies. While encouraging creativity and increasing participation have many advantages, there are also important obstacles that need to be overcome, such as limits on creativity and moral dilemmas. To maximize the use of AI in education, these elements must be balanced through careful deployment and ongoing assessment.مجلة العلوم التربوية و النفسية20The study aims to investigate the opportunities that AI technology offers to enhance teaching and learning, identify challenges in this field and outline the future vision for AI technology, and examine the learning outcomes for teachers and students influenced by AI technology.مجلة العلوم التربوية و النفسية["Zohor Mohammed Al-Areeshi"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/5b11ea0c1f6182480e65e162f8cf478d2d584248 c48983042f617bd3391f18c003cb6962d5ae903bArtificial Intelligence (AI), a multifaceted and dynamic phenomenon, has significantly impacted various aspects of human life, particularly in the realm of business and technology. In the context of the cultural industry and International Human Resource Management (IHRM), AI has ushered in transformative changes that have redefined how organizations manage their creative and human capital across borders (Caldas, M. P., Tonelli, M. J., & Lacombe, B. M. B., 2011). This assessment delves into the intricate relationship between AI, the cultural industry, and IHRM, examining the challenges and opportunities that arise from this interplay. Also, the advent of AI has facilitated the seamless integration of advanced technologies into the cultural industry, creating a highly interconnected and innovative landscape. Consequently, organizations within this industry are compelled to adopt cut-ting-edge AI-driven practices to effectively manage a diverse range of cultural products and services (Arslan, A., Cooper, C., Khan, Z., Golgeci, I., & Ali, I., 2022). Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies. This analysis aims to critically evaluate the impact of AI on key cultural industry and IHRM functions, including recruitment and selection, training and development, performance management, and employee relations (Chew, J., 2004). By exploring the complexities and nuances of AI's influence on the cultural industry and IHRM, this assessment seeks to provide a comprehensive understanding of how AI trends shape industry and HR strategies and practices (Gordhan, P., 2007). Further-more, it will highlight the critical competencies required for HR professionals to navigate the challenges posed by AI, ultimately contributing to the success of cultural organizations in a rapidly evolving technological landscape. Finally, a particular focus will be placed on Greece as a case study to illustrate the broader impacts of AI on the cultural industry and IHRM (Stavroulakis, D., 2009). Greece, with its strategic geographical location and rich cultural heritage, presents a unique context for examining how global AI trends influence local cultural and HR practices.Journal of economics, finance and management studies330This analysis aims to critically evaluate the impact of AI on key cultural industry and IHRM functions, including recruitment and selection, training and development, performance management, and employee relations, and highlight the critical competencies required for HR professionals to navigate the challenges posed by AI.Journal of Economics, Finance And Management Studies["Chatzidimou Triantafillos", "Ioakimidis Panagiotis"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/c48983042f617bd3391f18c003cb6962d5ae903b 50a23da63d0f51b01f73a351b7aa8be710c2d5d0Natural language processing (NLP) which is a branch of Artificial Intelligence (AI) has experienced significant improvement in the recent past to allow machines to language comprehend and generate. They consist of uses like machine translation, sentiment analysis, chatbots, and virtual assistants, which form a cornerstone part of life. However, even with these advances, NLP still has numerous critical difficulties that affect its proficiency and usefulness in applying the systems. Some of the major problems include one language may mean different things to different people; every situation requires different approaches; and finally people from different cultures and languages will pose a significant problem. This paper presents evidence of lexical, syntactic, and semantic ambiguities that complicate the language understanding process. Besides that, NLP models are not able to comprehend the flow of human’s dialogues which is important factor of the communication. The problem of language diversity in human dialogue makes it even more challenging to develop NLP since over 7,000 languages are characterized by unique structures and expressions. With the recent development in Machine learning, and deep learning, these challenges have been well addressed. Pretrained transformer models like BERT and GPT have greatly enriched the field’s tech arsenal, since language comprehension and Boolean modernity loops very difficult to model and tackle. This journal provides a comprehensive look at these issues presents current technologies and examines new trends pertaining to NLP. Lastly, it brings focus on way ethical concerns like bias are paramount in making current NLP systems neutral. Looking forward more advances are expected in NLP which has the prospective of further improvement of interaction between human and computer.World Journal of Advanced Research and Reviews00Evidence of lexical, syntactic, and semantic ambiguities that complicate the language understanding process is presented and focus on way ethical concerns like bias are paramount in making current NLP systems neutral is brought.World Journal of Advanced Research and Reviews["Sateesh Kumar Rongali"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/50a23da63d0f51b01f73a351b7aa8be710c2d5d0 932450e6941fc3a7fce7b3f8e9a2c7361b3e4beeThe rapid advancement of artificial intelligence has brought about changes in several sectors, including the education sector, where it has a significant role in developing learning outcomes, assigning educational experiences, and alleviating administrative work for teachers. AI systems, including machine learning and virtual reality, have been employed in educational practices, helping to provide innovative alternative solutions to traditional teaching and learning challenges. Despite the great opportunities presented by AI, both teachers and learners face many challenges, such as teachers’ readiness to adopt these technologies, their need for professional development, and moral concerns. This paper examines AI in education, exploring how it can improve curricula, support teachers, and enhance student outcomes. The research also highlights key barriers, including technological limitations, privacy issues, and biases in AI algorithms. Based on extensive literature reviews, the study explores new trends and research gaps, as well as guiding prospects for future research in AI-enabled education, with a great deal of attention focused on teacher training, long-term outcomes, ethical issues, and the effects of AI on student-teacher interactions in the educational environment. The Previous studies in the literature review suggest strategies to integrate AI into education effectively, helping students benefit from this technology without negatively impacting human values and the importance of personal interaction. This paper also addresses issues related to privacy and biases in AI algorithms.Artificial Intelligence &amp; Robotics Development Journal00Examining AI in education explores how it can improve curricula, support teachers, and enhance student outcomes, as well as addressing issues related to privacy and biases in AI algorithms.Artificial Intelligence &amp; Robotics Development Journal["Amna AL-Shidi", "Reem AL-Maawali"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/932450e6941fc3a7fce7b3f8e9a2c7361b3e4bee f95ad732c5bcc2ef17705afa71a62ff153d36b79 Artificial intelligence in higher education has expanded worldwide with the growth of AI technologies. The emergent applications of artificial intelligence in higher education (AIEd) in course preparation, delivery, and administration as well as in learning assistants and prediction for the sake of learners are widely considered important innovations that can change education worldwide. The Armenian case was examined via a survey that was conducted at the country’s leading universities to investigate whether and how faculty use AIEd. Although the results indicate that many Armenian teaching faculty use AIEd in narrow capacities and acknowledge the potential usefulness of AIEd in the practical administration of Armenia’s higher education, the use of AIEd is not deep.PS170Although the results indicate that many Armenian teaching faculty use AIEd in narrow capacities and acknowledge the potential usefulness of AIEd in the practical administration of Armenia’s higher education, the use of AIEd is not deep.PS: Political Science &amp; Politics["V. Atoyan", "Logan Brosius", "Nane Movsisyan", "Sofya Ohanyan", "Vahram Hovyan"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/f95ad732c5bcc2ef17705afa71a62ff153d36b79 607caa347dd851528a6ab108cae7da69b2845891This study aims to explore the role of Artificial Intelligence (AI) in improving the quality of biology learning at the secondary education level. This research uses a literature review approach, which is a research method that is carried out by collecting, analyzing, and synthesizing information from various relevant written sources. The results show that AI plays an important role in facilitating the visualization of complex biological concepts, providing adaptive learning experiences tailored to students' individual abilities, and offering virtual laboratories that allow biological experiments without the limitations of physical facilities. However, challenges in its implementation, such as limited infrastructure, lack of teacher training, and high implementation costs, are still major obstacles. Teachers and students' perceptions of AI are generally positive, with many feeling that the technology improves students' understanding and motivation to learn. However, the role of teachers as facilitators remains important in overcoming the technical challenges faced by students. This study concludes that despite the challenges, the application of AI in biology learning has great potential to improve the quality of learning, and there needs to be more attention to aspects of training, infrastructure, and education policies that support the widespread application of this technology.Cognizance Journal of Multidisciplinary Studies00The results show that AI plays an important role in facilitating the visualization of complex biological concepts, providing adaptive learning experiences tailored to students' individual abilities, and offering virtual laboratories that allow biological experiments without the limitations of physical facilities.Cognizance Journal of Multidisciplinary Studies["Desman Telaumbanua"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/607caa347dd851528a6ab108cae7da69b2845891 ebe7d10d5d760b4b25f84bfeeb3664fc28a0e64eThis study deals with the use of Information and Artificial Intelligence technologies with education. The purpose is to investigate students' use of Artificial Intelligence (AI) in their studies. The authors have used a survey of 882 Ukrainian university students, graphical and tabular result presentation, t-statistics, and zstatistics with a high significant level of 0.01. The authors have found out that students use AI in their learning process: every day 8.0% – 31.0%, 3-4 times/week 6.0% - 15.0%, 1-2 times/week 17.0% - 20.0%, 1-2 times/month 14.0% - 40.0%, never 20.0% - 29.0%. Research novelty is: confirming two Research hypotheses, new scientific facts about the use of AI technologies in the learning process, and the absence of the need to develop proposals for improving the teaching process using AI technologies because university teachers provide students with a real-world AI-enabled environment that is adequate for student needs in their studies. The results are very important for monitoring the use of AI in higher education. The new data can help to make management decisions to achieve high-quality education. The new research findings contribute to the growing debate on the integration of information technology, computers, and AI technologies in education.WSEAS Transactions on Information Science and Applications250The authors have found out that students use AI in their learning process and the absence of the need to develop proposals for improving the teaching process using AI technologies because university teachers provide students with a real-world AI-enabled environment that is adequate for student needs in their studies.WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS["V. Nechyporenko", "Nataliia Hordiienko", "Olena Pozdniakova", "Ellina H. Pozdniakova-Kyrbiatieva", "Yuliya Siliavina"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/ebe7d10d5d760b4b25f84bfeeb3664fc28a0e64e 3bcb5344108615cfb9d65e1f89eb55e8945357f4The pharmaceutical supply chain is a critical component of the global healthcare system, ensuring the efficient delivery of life-saving drugs to patients. However, challenges such as inventory management, counterfeit drugs, demand forecasting, and regulatory compliance necessitate innovative solutions. Artificial Intelligence (AI) has emerged as a transformative tool in optimizing pharmaceutical supply chain operations. This review systematically examines the applications, benefits, challenges, and future prospects of AI in pharmaceutical supply chain management (PSCM). By analyzing current literature, this article highlights AI-driven solutions such as predictive analytics, blockchain integration, and machine learning algorithms, offering a comprehensive understanding of their impact on efficiency, accuracy, and transparency in the pharmaceutical supply chain.World Journal of Biology Pharmacy and Health Sciences00This article highlights AI-driven solutions such as predictive analytics, blockchain integration, and machine learning algorithms, offering a comprehensive understanding of their impact on efficiency, accuracy, and transparency in the pharmaceutical supply chain.World Journal of Biology Pharmacy and Health Sciences["Arnab Roy", "Anuradha Mohapatra", "Chitranjali Sharwan", "Adarsh Kumar", "Sunny Kumar", "Akshat Maholay", "Clerick C Conneh"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/3bcb5344108615cfb9d65e1f89eb55e8945357f4 ab7855f41b29a3b93fae30a61fd526993a5c103aRare pediatric diseases often present significant diagnostic challenges due to their atypical manifestations and lack of familiarity among healthcare providers. Artificial Intelligence (AI) offers transformative potential in bridging diagnostic gaps, particularly in resource-limited settings. This review highlights the role of AI in identifying rare pediatric conditions through advanced algorithms, pattern recognition, and machine learning. By examining successful implementations globally, we explore the potential of AI to revolutionize pediatric diagnostics, address disparities in healthcare access, and improve outcomes for children. Challenges such as data bias, ethical considerations, and infrastructural barriers are also discussed, alongside recommendations for future research and integration strategies.World Journal of Biology Pharmacy and Health Sciences00The role of AI in identifying rare pediatric conditions through advanced algorithms, pattern recognition, and machine learning is highlighted, and challenges such as data bias, ethical considerations, and infrastructural barriers are discussed.World Journal of Biology Pharmacy and Health Sciences["Venugopal Reddy Iragamreddy"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/ab7855f41b29a3b93fae30a61fd526993a5c103a 2639755e88d4c8b321ea6db7c33d584135ceac5eThis paper presents a quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications. The main goal is to determine whether the risk assessment of artificial intelligence systems used in businesses is really a subject of increasing interest and to identify the most influential and productive sources of scientific research in this area. Data were collected from the Web of Science Core Collection, one of the most complete and prestigious databases. Regarding the temporal evolution of publications and citations this study evidences, this research subject shows rapid growth in the number of publications (at a compound annual rate of 31.20% from 2018 to 2024 inclusive), showing its high attraction for researchers, responding to the need to implement systematic risk assessment processes in the organizations using AI to mitigate potential harms, ensure compliance with regulations, and enhance artificial intelligence systems’ trust and adoption. Especially after the surge of large language models like ChatGPT or Gemini, AI is revolutionizing the dynamics of human–computer interaction using natural language, video, and audio. However, as the scientific community initiates rigorous studies on AI risk assessment within organizational contexts, it is imperative to consider critical issues such as data privacy, ethics, bias, and hallucinations to ensure the successful integration and interaction of AI systems with human operators. Furthermore, this paper constitutes a starting point, including for any researcher who wants to be introduced to this topic, indicating new challenges that should be dealt by researchers interested in AI and hot topics, in addition to the most relevant literature, authors, and journals about this research subject.Applied Sciences710A quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications is presented, indicating new challenges that should be dealt by researchers interested in AI and hot topics and identifying the most influential and productive sources of scientific research in this area.Applied Sciences["Juan Carlos Muria-Taraz\u00f3n", "Juan-Vicente Oltra-Guti\u00e9rrez", "Ra\u00fal Oltra-Badenes", "Santiago Escobar-Rom\u00e1n"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/2639755e88d4c8b321ea6db7c33d584135ceac5e b3bd18f128330e85fb169b4985832d84b66f830aMany ethical issues plague the field of AI, and several ethical solutions, mainly from the Global North, have been proposed. Among the issues inherent in ethical AI are bias and lack of diversity. Openair Africa reports, for example, an enormously low participation/visibility of women in today’s digital world. World Economic Report states that worldwide, only about 22% of women are in the field of artificial intelligence compared to 78% of men. In the 2022 Cybersecurity Workforce Report, women account for just 24%. The 2020 Gender Equality Index: Digitalisation and Future of Work also indicates that only one out of two women, 54%, perceive robots and AI positively compared to 67% of men. Thus, this paper discusses diversity and gender equality in AI from the African context. How should we safeguard AI systems from rehashing extant inequality? To what extent can we ensure AI eliminates biasand fosters equality? To this end, this paper proposes a communal approach to the conception, design, development, and deployment of AI systems to address this abysmal situation towards a gender-smart and truly inclusive AI in Africa.On Thinking400This paper proposes a communal approach to the conception, design, development, and deployment of AI systems to address this abysmal situation towards a gender-smart and truly inclusive AI in Africa.The Thinker["H. T. Olojede"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/b3bd18f128330e85fb169b4985832d84b66f830a 2798576b62a744ea5d700a3af8a55351a1ae7007The long-term development of the manufacturing industry relies on sustainable tax management, which plays a key role in optimizing production costs. While artificial intelligence models have been applied to tax-related predictions, research on their application for predicting tax management levels is quite limited, with no studies focused on the manufacturing industry in China. To enhance digital innovation in corporate management, this study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range. The ratio of total tax expense to total profits (ETR) is used to represent the tax management level, which is predicted using decision trees, random forests, linear regression, support vector regression, and artificial neural networks with eight input features. Comparisons among the developed models indicate that the random forest model exhibits the best performance in terms of prediction accuracy and generalization capability. Additionally, the Shapley additive explanations (SHAP) technique is integrated with the developed model to enhance the interpretability of its predictions. The SHAP results reveal the importance of the input features and also highlight the dominance of certain features. The results show that the ETR from the previous year holds the greatest importance, being more than twice as significant as the second most important factor, whereas the effect of board size is negligible. Moreover, benefiting from the local interpretations using SHAP values, this approach aids managers in making rational tax management decisions.Sustainability420This study applies interpretable artificial intelligence models to predict the tax management level, which helps decision-makers maintain it within a sustainable range and aids managers in making rational tax management decisions.Sustainability["Ning Han", "Wen Xu", "Qiang Song", "Kai Zhao", "Yao Xu"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/2798576b62a744ea5d700a3af8a55351a1ae7007 ad68811904b86ac56f38bbfc9dc49637bad28410The increasing global demand for monoclonal antibodies (mAbs) necessitates innovative strategies to optimize manufacturing processes. Continuous bioprocessing offers numerous advantages over traditional batch processing, including improved product quality, increased productivity, and cost reduction. However, the complexity of continuous operations requires sophisticated control mechanisms to ensure consistent product quality. This study explores the integration of artificial intelligence (AI) in continuous bioprocessing to enhance monoclonal antibody production. We discuss AI-driven predictive modeling, process optimization, and real-time monitoring and control. Experimental results indicate significant improvements in process efficiency, scalability, and product consistency, demonstrating AI's transformative potential in biopharmaceutical manufacturing.International Journal of Science and Research Archive00Experimental results indicate significant improvements in process efficiency, scalability, and product consistency, demonstrating AI's transformative potential in biopharmaceutical manufacturing.International Journal of Science and Research Archive["Emmanuel Mensah"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/ad68811904b86ac56f38bbfc9dc49637bad28410 2c94e2d1fb3c751b263a95f0c412222bea4d1b53Psychotherapy00Psychotherapy[]2025-01-30T00:00:00https://www.semanticscholar.org/paper/2c94e2d1fb3c751b263a95f0c412222bea4d1b53 bd2aa214b413b1e14aa8612e25b275108a912caeObjectives: This study aims to explore the role of AI in improving audit quality.Design/method/approach: This study used the Systematic Literature Review (SLR) method to explore the use of AI in auditing. The object of this study was scholarly articles published between 2018-2023. The articles covered the use of AI to optimize the efficiency, accuracy and reliability of the audit process.Results/findings: The results showed that AI is able to automate routine tasks, detect fraud, and identify risks more quickly and accurately than traditional methods. Technologies such as blockchain, machine learning, and advanced data analytics contribute significantly to data-driven decision-making, which improves the overall quality of audits.Theoretical contribution: This research contributes to the literature by expanding the understanding of how AI technologies can improve audit qualityPractical contribution: This research provides practical guidance for auditors and companies to optimally utilize AI technologiesLimitations: This study relies on secondary literature and potential bias in data interpretation, so future research is recommended to explore the empirical impact of AI implementation on audits in different sectors.Jurnal Akuntansi dan Governance00The results showed that AI is able to automate routine tasks, detect fraud, and identify risks more quickly and accurately than traditional methods.Jurnal Akuntansi dan Governance["Alya Fadilla", "Elwiyani Army", "Yunda Dwi Putri Rustam", "Aini Indrijawati", "Grace T. Pontoh"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/bd2aa214b413b1e14aa8612e25b275108a912cae 21a82f822f4d75d4175cddfdfb084b0651376c01AIDS (London)230AIDS["J. Tao", "Amanda Maguire-Wilkerson", "Jack C. Rusley", "Tyler Wray", "Philip A Chan"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/21a82f822f4d75d4175cddfdfb084b0651376c01 ef2b70d7de27e27f105d3e10c414791026dee157This paper argues that the AI revolution which is currently unfolding and being fuelled by the significant strides in Generative AI-powered technologies, calls for an urgent response by the African continent, to ensure that possible harms associated with this cutting-edge technology are mitigated. The ‘arms race’ to create chatbots that can rival Open AI’s ChatGPT-4.0 technology by big technology companies such as Google and Meta, is not only hastening the pace of the AI revolution but is also bringing to the fore the double-edged nature of this technology. The benefits of AI generative technologies such as chatbots in fields such as the academy; health; agriculture; music and art, have been touted in recent times, but the ethical concerns around issues of bias; possible proliferation of misinformation from algorithms that are trained on datasets that are not fully representative of the global South’s realities, especially Africa; breaches in privacy issues and threats of job losses, still linger. The fact that in March 2023, an Elon Musk-led petition to have a six-month moratorium on AI chatbot innovations began circulating raises serious ethical concerns around the AI revolution, which makes it critical for a continent such as Africa, which has largely been a consumer of these technologies and notan innovator, to urgently draft measures that can protect it. The paper contends that even though Africa is not homogenous in nature, it needs to come up with an AI ethics-driven framework that protects the majority of its population which is mired in poverty and likely to be on the receiving end of any cons associated with AI technologies. This framework should be largely anchored in the African philosophy of Ubuntu, but also pragmatic enough to include positive facets of global-North philosophical strands such as deontology, which largely places currency on ethical principles and rules above the outcomes they produce.On Thinking290It is argued that even though Africa is not homogenous in nature, it needs to come up with an AI ethics-driven framework that protects the majority of its population which is mired in poverty and likely to be on the receiving end of any cons associated with AI technologies.The Thinker["Tapiwa Chagonda"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/ef2b70d7de27e27f105d3e10c414791026dee157 1e8c61271b95a34df33574e8c94b7107455275e4This cross-sectional study involved distributing a survey to a sample of undergraduate college students AI-related knowledge, attitudes, and behaviors.  A total of 258 out of 319 college students enrolled in a personal wellness elective completed this survey during class. Most participants (53.5%) reported familiarity with AI in general, often learning about it through the internet (79.1%). Participants who were frequent AI users more often said they were familiar with AI in general (62.3% vs. 47.1%, p = 0.04) and for educational purposes (52.3% vs. 34.8%, p = 0.02) and more frequently encountered AI information during class (42.5% vs. 23.9%, p = 0.002) compared with infrequent AI users. Frequent AI users more often agreed that AI makes learning easier (67.9% vs. 47.8%, p = 0.007), that AI use in school is ethical (27.2% vs. 8.1%, p<0.001), that AI improves writing skills (78.5% vs. 56.5%, p <0.001), improves critical thinking (36.2% vs. 19.7%, p =0.004), and improves interpersonal communication (38.3% vs. 24.8%, p = 0.035) compared to participants who less frequently used AI. Infrequent AI users more often agreed that using AI in class or for homework was cheating (56.6%, 25.2%, p <0.001), more often disagreed that they trusted AI as safe (51.1% vs. 26.2%, p <0.001), and more often turned to family and friends for information about AI (29.0% vs. 17.8%, p = 0.04) than frequent AI users. Research into the role of AI in education is still preliminary, but this work can serve as a foundation for future studies.  Review of Artificial Intelligence in Education200Research into the role of AI in education is still preliminary, but this work can serve as a foundation for future studies.Review of Artificial Intelligence in Education["Corey H. Basch", "G. Hillyer", "Bailey Gold", "Helen Yousaf"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/1e8c61271b95a34df33574e8c94b7107455275e4 0c838401a4669c9d4da9115c27e0dc810aa9291fPerkembangan teknologi digital yang pesat, khususnya dalam bidang kecerdasan buatan (AI), memberikan peluang besar untuk meningkatkan kualitas pembelajaran di dunia pendidikan. AI memiliki potensi untuk mengubah proses belajar-mengajar dengan memberikan solusi inovatif, seperti personalisasi pembelajaran, analisis data akademik, dan pengembangan metode pengajaran yang lebih efektif. Namun, penerapan teknologi ini di institusi pendidikan, termasuk SMA Negeri 1 Tanah Putih, menghadapi tantangan seperti kurangnya pemahaman terhadap AI, keterbatasan infrastruktur, dan minimnya strategi pemanfaatan teknologi dalam pembelajaran. Pengabdian kepada masyarakat (PkM) ini bertujuan untuk mengkaji dan mengembangkan strategi implementasi teknologi AI dalam pembelajaran di SMA Negeri 1 Tanah Putih. Diharapkan bahwa melalui pendekatan yang tepat, pemanfaatan AI dapat meningkatkan kualitas pembelajaran serta membantu siswa dalam memahami materi secara lebih mendalam dan mandiri. Program ini juga berfokus pada penelitian dan pengembangan strategi untuk meningkatkan kualitas pendidikan melalui penggunaan teknologi AI di SMA Negeri 1 Tanah Putih, menjadikannya lebih relevan dengan tuntutan perkembangan teknologi dalam pendidikan.kegiatan pengabdian masyarakat ini menghasilkan rekomendasi pentingnya pelatihan lanjutan yang berkelanjutan tentang AI untuk membekali peserta dengan keterampilan terkini dalam pengembangan dan penerapan teknologi saat ini.  Jurnal Pengabdian Masyarakat Ilmu Komputer160Jurnal Pengabdian Masyarakat Ilmu Komputer["Fauziah", "Irzon Meiditra", "C. Mutia", "Fitra Yuda", "M. Rasyid", "Riris Agustin", "Siti Sahara Lubis"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/0c838401a4669c9d4da9115c27e0dc810aa9291f 87a40d786483a8062217550a51ec7206a01a3ee0Journal of Educators Online00Journal of Educators Online["Swarupa Dash"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/87a40d786483a8062217550a51ec7206a01a3ee0 fdd77072137a2fe7ff548a07004a1fdcad1ffc9fActa Psychologica670Findings indicate that negative emotions and AI readiness are interrelated with student engagement, and high AI readiness can lead to greater engagement with digital learning platforms, potentially benefiting emotional regulation and academic achievements.Acta psychologica["Xiao Wu", "Yu Qin"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/fdd77072137a2fe7ff548a07004a1fdcad1ffc9f a311c699c57e0caf1f91115dbae6db5a25112076AI is transforming the way healthcare professionals deliver services and patients receive care. Though AI offers various benefits, it may also have errors. It will be insightful to correct errors and shortcomings since the technology is in its initial stage. Since employees are firsthand users, it is better to take their perception of the effectiveness of this technology in hospitals. The study's objective is to understand the perception of doctors, nurses, and technicians of hospitals and medical colleges on AI applications. The total sample consist of 227 respondents which was taken from Pan India. The sample was collected using purposive sampling. Regression was used for further data analysis through the SPSS 20.0 version. Employees have a positive perception of all three dimensions of AI.Journal of Informatics Education and Research130Physicians, nurses, and technicians have a positive perception of all three dimensions of AI, and employees have a positive perception of all three dimensions of AI.Journal of Informatics Education and Research["Pragati Tomar", "Sakshi Upadhyay", "Aditi Sharma", "Pragati Singh", "Ruchi Arya"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/a311c699c57e0caf1f91115dbae6db5a25112076 a4bb678750634e00ceac8f4605cd880cb8828903International Journal of Ethics and Systems00International Journal of Ethics and Systems["Marianne Thejls Ziegler", "Christoph L\u00fctge"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/a4bb678750634e00ceac8f4605cd880cb8828903 f3f77b85947daeac025f9ff910d2ca90fc56ebb5The ever-growing number of and development of more elaborate threats require different levels of protection than formal regulation. AI & ML technology provide a promising outlook that even exists in the security domain and has become universal to design better, more dynamic security measures with better preparedness. In particular, the current paper discusses the correlation between AI, ML, and cybersecurity regarding architectures, algorithms, and potential for further development. The chosen AI-based architectures are captured here, like deep learning models, federated learning frameworks, and graph-based techniques to detect malware, phishing, ransomware, and insider threats. The paper then moves to discuss methods of improving anomalous behavior identification, Intrusion detection systems (IDS), and real-time threat analysis, especially focusing on supervised, unsupervised, and reinforcement types of learning. Three burgeoning fields of interest, explainable AI (XAI), adversarial machine learning, and incorporating blockchain into AI methodology, have been identified as crucial in responding to new challenges like adversarial attacks and data protection. However, AI and ML have limitations, including high computational demand, lack of data, and bias; hence, future work is needed. This paper outlines a possible interdisciplinary research agenda for enhancing AI in cybersecurity involving integrated platforms, technology case data, and an ethical dimension. Crossing the methods of theoretical analysis and real-life examples, this paper highlights the significance of AI and ML in constructing the further development of reliable and protected ICT environments.World Journal of Advanced Engineering Technology and Sciences00A possible interdisciplinary research agenda for enhancing AI in cybersecurity involving integrated platforms, technology case data, and an ethical dimension is outlined.World Journal of Advanced Engineering Technology and Sciences["Souratn Jain"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/f3f77b85947daeac025f9ff910d2ca90fc56ebb5 71270c0b92b75c5b9f1f0892872b6d2310388320In recent years, the energy sector has entered a decisive phase of transformation, driven by mounting concerns regarding climate change and the recognized need to transition toward sustainable energy systems [...]Sustainability280Sustainability["G. Kyriakarakos"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/71270c0b92b75c5b9f1f0892872b6d2310388320 0e70c27b2d803309800b78ada2aae80f002ebf01The Business &amp; Management Collection00The Business &amp; Management Collection["Kenneth Le Meunier-FitzHugh"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/0e70c27b2d803309800b78ada2aae80f002ebf01 1cd675d7ca2dba9b41340cef4ac1eb0373e00a18Asian Journal of Philosophy470There are at least two types of external opacity—link opacity and structure opacity—and existing XAI techniques can to some extent help us reduce the former but not the latter, and there are at least two types of external opacity that concerns factors external to the model.Asian Journal of Philosophy["Jens Christian Bjerring", "Jakob Mainz", "L. Munch"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/1cd675d7ca2dba9b41340cef4ac1eb0373e00a18 b31a73b5fbc564e0ad5fe4c6450dae1b0bf4c342Penelitian ini bertujuan untuk mengeksplorasi persepsi guru PAUD di Kutai Kartanegara terhadap penggunaan teknologi pembelajaran berbasis kecerdasan buatan (AI). Dengan pendekatan kualitatif deskriptif, penelitian ini mengumpulkan data melalui wawancara mendalam dan observasi partisipatif. Temuan penelitian menunjukkan bahwa guru PAUD di Kutai Kartanegara memiliki pandangan positif terhadap penggunaan teknologi AI, terutama dalam mendukung pembelajaran yang lebih personal, adaptif, dan interaktif. Beberapa aplikasi AI yang digunakan, seperti platform pembelajaran adaptif (misalnya Khan Academy Kids, ABC Mouse) dan asisten virtual (Google Assistant, Alexa), terbukti memberikan manfaat signifikan dalam meningkatkan keterlibatan dan pengalaman belajar anak. Namun, tantangan utama yang dihadapi termasuk keterbatasan infrastruktur teknologi, keterampilan teknis guru, dan sumber daya finansial yang terbatas. Meskipun demikian, mayoritas guru menunjukkan sikap positif terhadap teknologi ini dan siap untuk mengadopsinya jika diberikan pelatihan dan dukungan yang memadai. Penelitian ini menyarankan perlunya kolaborasi antara pemerintah, lembaga pendidikan, dan masyarakat dalam menyediakan pelatihan dan fasilitas teknologi untuk memastikan penerapan AI yang efektif dalam pendidikan anak usia dini. Dengan demikian, AI diharapkan dapat memperkaya pengalaman belajar anak tanpa mengesampingkan kebutuhan sosial dan emosional merekaJURNAL KRIDATAMA SAINS DAN TEKNOLOGI00JURNAL KRIDATAMA SAINS DAN TEKNOLOGI["L. Riana", "Siska Amalia", "Nasywa Nur Annisa"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/b31a73b5fbc564e0ad5fe4c6450dae1b0bf4c342 324275929debf26cab733f7710f8e50cd36e73edInterest in the impact of AI within the hospitality industry by the year 2030 is very high; therefore, this paper will discuss the latest applications and trends that are likely to take over in revolutionizing the whole landscape. It effectively brings into relief the transformative capability of AI technologies-machine learning, natural language processing, and robotics-that sublimely enhance operational efficiencies and attend to the ever-growing need for personalized guest experiences in a profoundly captivating manner. The paper thereby identifies, with full comprehensiveness, a range of applications of AI on hospitality enterprise segments via smart room technologies that perfectly merge state-of-the-art developments to offer an unprecedented level of comfort; automated customer service systems that seamlessly streamline the consumer interaction; and data analytics for revenue management by AI systems to outline unparalleled revenue optimization and organizational success. It goes into detail on what the future of AI in hospitality holds: a tremendous explosion automating tasks to take some of the load off from manual work and increasing productivity while fostering smarter data analytics that will, in turn, enable businesses to make data-driven decisions with the highest degree of precision and accuracy. This foresighted exploration further embraces the idea of seamless integration with Internet of Things devices, therefore making a future possible where smart devices would smoothly cooperate and communicate to create an unparalleled guest experience. The complex challenges and ethical considerations that arise regarding the implementation of AI within the hospitality industry are also explored with minute detail. The act recognizes the very critical aspect of data privacy and gives great emphasis on how important it is to safeguard the personal information of the guests by leveraging its AI capabilities. It goes further by delving into the sensitive issue of job displacement, aware of the potential impact of integration with AI on the workforce. It does so in a manner that aptly demonstrates the essence of achieving a delicate balance between the wonderful advantages gained from AI and the urgent need to observe morality. Lastly, the strategic recommendations to be provided within this paper, targeted at industry stakeholders themselves, really underscore how important it is to consider proactive AI strategies that not only realize the full transformative value of these technologies but also seek ethical conduct. This pioneering research doggedly insists that AI will indeed form an integral part of the reshaping of the hospitality landscape, which emphasizes the critical need for continuous innovation and unrelenting adaptability in today's fast-changing technological environment. In other words, this research plunges into the world of artificial intelligence in the global hospitality industry and outlines its exceptional potential to reform every aspect of the industry as we know it. The agreement unequivocally states that AI should be embraced as a driving force for innovation and progress while ensuring that ethical considerations are placed first and best practices come to the front in the transformation of industries. But as that future comes into view, it is a timely reminder that monumental shift awaiting the hospitality landscape from every corner demands the industry stakeholders take bold moves in reaching out to clasp this technological revolution with open arms and unyielding dedication to remarkable guest experiences.World Journal of Advanced Research and Reviews00This pioneering research doggedly insists that AI will indeed form an integral part of the reshaping of the hospitality landscape, which emphasizes the critical need for continuous innovation and unrelenting adaptability in today's fast-changing technological environment.World Journal of Advanced Research and Reviews["Aphisavadh Sirivadhanawaravachara"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/324275929debf26cab733f7710f8e50cd36e73ed e76daac944ff605376f4239068303069daa1bcadMoney laundering (ML) is a vital source to clean the money from the financial system with illegal funds. Corruption, exploitation of a given community, drug use, and much more are all associated with it. Due to the massive number of transactions worldwide, detection of ML operations is complex. But it makes it possible for criminals to exploit financial systems to facilitate illicit transactions. This is primarily about reducing the risk that someone will be out of pocket because of money laundering. AI- driven applications of AML tools are now monitoring transactions to deal with it. In total, 112 research papers are reviewed (identified the gap in literature) which serves as a guide for the future direction of this research domain. The outcome of this systematic literature review effort will not only pave the way for the research community, also aid the state agencies to formulate an ideal AML ecosystem to tackle these prominent concerns while ensuring a healthy environment for their inhabitants. Those starting points can be taken to evaluate the current state of affairs from diverse perspectives and pave the way towards future research directions to explore and develop the high levels of authenticity and security that artificial intelligence (AI) can bring to the finance sector.World Journal of Biology Pharmacy and Health Sciences00This systematic literature review effort identified the gap in literature which serves as a guide for the future direction of this research domain and will not only pave the way for the research community, but also aid the state agencies to formulate an ideal AML ecosystem to tackle prominent concerns.World Journal of Biology Pharmacy and Health Sciences["Shuvo Kumar Mallik", "Md. Raisul Islam", "Imran Uddin", "Md. Azam Ali", "Sadia Maliha Trisha"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/e76daac944ff605376f4239068303069daa1bcad 53ed28bf50797df1d7cdd669bf561f6c6efb7fc1Wireless communication advancements have significantly improved connectivity and user experience with each generation. The recent release of the framework M.2160 for the upcoming sixth generation (6G or IMT-2030) cellular wireless standard by ITU-R has significantly heightened expectations, particularly for Internet of Things (IoT) driven use cases. However, this progress introduces significant security risks, as technologies like O-RAN, terahertz communication, and native AI pose threats such as eavesdropping, supply chain vulnerabilities, model poisoning, and adversarial attacks. The increased exposure of sensitive data in 6G applications further intensifies these challenges. This necessitates a concerted effort from stakeholders including ITU-R, 3GPP, ETSI, OEMs and researchers to embed security and resilience as core components of 6G. While research is advancing, establishing a comprehensive security framework remains a significant challenge. To address these evolving threats, our research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats. By refining model accuracy and ensuring alignment through recursive feature elimination and consistent cross-validation, our approach strengthens the overall security posture of the IoT–6G ecosystem, making it more resilient to adversarial attacks and other vulnerabilities.Italian National Conference on Sensors400This research proposes a dynamic security framework that emphasizes the integration of explainable AI (XAI) techniques like SHAP and LIME with advanced machine learning models to enhance decision-making transparency, improve security in complex 6G environments, and ensure effective detection and mitigation of emerging cyber threats.Sensors (Basel, Switzerland)["Navneet Kaur", "Lav Gupta"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/53ed28bf50797df1d7cdd669bf561f6c6efb7fc1 2d322404c31442862b673f3872d45570c9a80fecThe integration of AI and digital technology in Nepal's primary health care system has significantly enhanced diagnostic capabilities, enabling early diagnosis and treatment. The study highlights the use of AI, databases, telemedicine, and mobile apps for disease diagnosis and prevention. AI platforms, particularly those processing radiological images, are the most widely adopted tools. While these technologies have the potential to revolutionize healthcare by improving efficiency, accuracy, and patient outcomes, addressing the existing gaps in policies, regulations and ethical considerations is essential for maximizing their benefits. Further studies are recommended to generate the systematic evidences of adopting digital health solutions for enhancing Nepal’s healthcare system.NPRC Journal of Multidisciplinary Research450This study highlights the use of AI, databases, telemedicine, and mobile apps for disease diagnosis and prevention in Nepal, and AI platforms, particularly those processing radiological images, are the most widely adopted tools.NPRC Journal of Multidisciplinary Research["Lal Mani Adhikari"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/2d322404c31442862b673f3872d45570c9a80fec 8c2a3454c0b2285769358d4a7e5999477c27a096Background AI-systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. Their performance may be different when applied on heterogeneous imagery in clinical practice. This is partially caused by the diversity in post-processing enhancement settings used in endoscopy units. We evaluated the impact of post-processing enhancement settings on AI performance and tested specific data augmentation strategies to mitigate performance loss. Methods We used a computer-aided detection (CADe) system for Barrett's neoplasia (6,223 images, 906 patients) and a computer-aided diagnosis (CADx) system for colorectal polyps (3,288 images, 969 patients), both trained on datasets acquired with Olympus equipment. First, systems were trained using their original datasets, which were acquired with limited variability in enhancement settings. These CAD systems were then tested across a wide range of test sets, which comprised the same images, but displayed with different enhancement settings. Then, both CAD systems were retrained using image enhancement-based data augmentation. The performance of these adjusted CAD systems was then evaluated on the same array of test sets. Results When trained on their original training set and tested over a range of enhancement settings, both systems displayed substantial performance variability: 83-92% sensitivity and 84-91% specificity for CADe; 78-85% sensitivity and 45-63% specificity for CADx. After retraining CAD systems using image enhancement-based data augmentation, variability in sensitivity and specificity was reduced to 2% (p>0.001) and 1% (p=0.003) for CADe and 2% (p=0.029) and 8% (p=0.190) for CADx. Conclusion This retrospective study indicates that performance of current endoscopic AI systems can substantially vary depending on the post-processing enhancement settings of the endoscopy unit. Specific data augmentation can mitigate this performance loss.Endoscopy00It is indicated that performance of current endoscopic AI systems can substantially vary depending on the post-processing enhancement settings of the endoscopy unit and specific data augmentation can mitigate this performance loss.Endoscopy["M. Jong", "C. H. Kusters", "Q. N. van Bokhorst", "J. Jukema", "R. A. H. Van Eijck van Heslinga", "K. Fockens", "B. Houwen", "T. Jaspers", "T. Boers", "M. van der Vlugt", "E. Dekker", "F. van der Sommen", "P. D. de With", "A. J. de Groof", "J. Bergman"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/8c2a3454c0b2285769358d4a7e5999477c27a096 01b38c616909862c4eee9bb5086ce05489494fa8This study aims to conduct a bibliometric analysis to investigate the current state of artificial intelligence (AI) adoption and utilization in higher education. A quantitative methodology was employed, analyzing 1476 scientific articles from renowned databases such as Scopus and Web of Science. Data was processed using the digital tools R and VOSviewer. The findings reveal an exponential growth in publications, with a growing focus on personalized learning, automated assessment, and the use of tools like ChatGPT. Significant international collaborations were identified; however, ethical challenges and the need for appropriate policies to ensure equitable and effective AI implementation in education were also highlighted. This study provides a global overview of research trends in AI in higher education, examining its applications, opportunities, and challenges for the teaching-learning process.Desde el Sur10The findings reveal an exponential growth in publications, with a growing focus on personalized learning, automated assessment, and the use of tools like ChatGPT.Desde el Sur["Janet Corzo-Zavaleta", "Yulissa Navarro-Castillo", "Mildher Ugaz-Rivero"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/01b38c616909862c4eee9bb5086ce05489494fa8 29331f4c6c239955491849a30c26aea68a80663dThe integration of artificial intelligence (AI) for sustainable resource management in community resorts represents an innovative approach to address the challenges of responsible tourism. This article analyzes, through a literature review, how AI can optimize operational efficiency, improve tourism experiences, and promote environmental and economic sustainability in local communities. The research focuses on the period 2010-2024, analyzing 2,329 articles selected from the Scopus database. AI has proven to be a key tool for managing resources such as water and energy through real-time monitoring systems and predictive models. It also fosters the personalization of sustainable tourism experiences and the promotion of circular economies, enabling the reuse of resources and the reduction of waste. However, these advances face limitations, such as the technology gap in rural areas and the need for adequate infrastructure and training for host communities. The article highlights the importance of international and multisectoral collaboration to maximize the benefits of AI in sustainable tourismMultidisciplinary Latin American Journal (MLAJ)00Multidisciplinary Latin American Journal (MLAJ)["J. Guerrero-Calero", "Paola Stefania Pardo-Reyes", "Jorge Washington Mieles-Giler", "Ren\u00e9 Gras-Rodr\u00edguez"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/29331f4c6c239955491849a30c26aea68a80663d 4a211ff6ad55613624aac4527c7277ebb60083b2This study examines the meaning of the death of humans and artificial intelligence robots in the 21st-century technological ecological space through artificial life in SF novels “AI Body Robots, Rodin” and “Court of Human” and explores ethical issues related to this. The development of artificial intelligence technology requires humans to reflect on themselves from a new perspective, and a paradigm shift for post-humans is also needed. Methodologically, it analyzes the perception of death of human beings Dr. Ubinna and Anna against the perception of death of artificial intelligence Ao, Rodin, and Hannah. This process can be misunderstood as if trying to go back to anthropocentricity from efforts to deviate from the existing anthropocentricity. However, human understanding according to technological advances is acquired at the present time, and humans are given the task of re-emerging an ethical framework that keeps pace with it each time. SF novels provide future imaginations based on scientific knowledge, enable thinking experiments and ethical reflections on the future, and embody the future of post-humans in detail. SF novels as above allow us to reconsider human identity and values in the technological ecosystem space and provide a good model for establishing the right relationship between humans and artificial intelligence. Preempting the ethical issues that humans and artificial life face is an opportunity to re-establish human identity asK-Culture·Story Contents Reasearch Institute00The meaning of the death of humans and artificial intelligence robots in the 21st-century technological ecological space through artificial life in SF novels “AI Body Robots, Rodin” and “Court of Human” is examined and ethical issues related to this are explored.K-Culture·Story Contents Reasearch Institute["Mi-Suk Lim"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/4a211ff6ad55613624aac4527c7277ebb60083b2 73bbf56170317e2197f618eb3768e8240e6a6202This comprehensive article explores the transformative impact of Artificial Intelligence (AI) in modern agriculture, examining its core components, industry applications, and future directions. The article investigates how AI-driven systems revolutionize farming operations through advanced data analytics, machine learning, and computer vision technologies. It details the implementation of precision agriculture technologies, intelligent crop health management systems, autonomous farming operations, and supply chain optimization. The article examines real-world applications through case studies of major agricultural technology companies, analyzing their innovations in autonomous machinery and smart spraying systems. The article evaluates the quantifiable benefits across operational improvements, environmental impact, and quality enhancement while exploring future developments in quantum computing integration, advanced autonomous systems, and climate change mitigation strategies.International Journal of Science and Research Archive00This comprehensive article explores the transformative impact of Artificial Intelligence in modern agriculture, examining its core components, industry applications, and future directions while exploring future developments in quantum computing integration, advanced autonomous systems, and climate change mitigation strategies.International Journal of Science and Research Archive["Sai Ram Chappidi"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/73bbf56170317e2197f618eb3768e8240e6a6202 9cbbb77718a32881f357658bb4368762b14240efIn the evolving landscape of cyber threats, traditional threat intelligence methods are increasingly inadequate for addressing the complexity and speed of modern attacks. This paper explores the transformative impact of Artificial Intelligence (AI) on enhancing cyber security threat intelligence and situational awareness. By leveraging advanced AI technologies—such as machine learning, natural language processing, and data analytics—organizations can significantly improve their ability to detect, analyze, and respond to threats. We provide a comprehensive review of current AI applications in threat intelligence, illustrating how these technologies enable proactive threat management and enhance situational awareness. Through detailed case studies, we demonstrate the effectiveness of AI-driven solutions in various sectors, including finance and healthcare. The paper also addresses key challenges such as data privacy, system integration, and adversarial AI, offering recommendations for future research and development. This study underscores the critical role of AI in advancing cyber security practices and provides insights into how organizations can harness AI to achieve a more robust and responsive threat intelligence framework.International Journal of Science and Research Archive00A comprehensive review of current AI applications in threat intelligence shows how these technologies enable proactive threat management and enhance situational awareness and addresses key challenges such as data privacy, system integration, and adversarial AI.International Journal of Science and Research Archive["Edim Bassey Edim", "Akpan Itoro Udofot", "Omotosho Moses Oluseyi"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/9cbbb77718a32881f357658bb4368762b14240ef 1bd5ad33514bcc4d3ab57ffa6c82d76094604320The convergence of quantum computing and artificial intelligence represents a transformative technological paradigm with unprecedented potential for business intelligence applications. This comprehensive review critically examines the revolutionary capabilities of quantum computing-enhanced AI systems in addressing complex computational challenges across multiple business domains. Through systematic analysis of emerging research, implementation frameworks, and interdisciplinary case studies, we investigate how quantum computing's unique computational mechanisms can fundamentally reshape data analysis, strategic decision-making, and predictive modeling. Our comprehensive examination reveals that quantum AI systems demonstrate remarkable potential to reduce computational complexity by up to 90%, enhance predictive accuracy by 60-75%, and provide unprecedented insights across financial, logistical, and strategic business intelligence domains. The research synthesizes evidence from multiple technological domains, highlighting the transformative potential of quantum-enhanced AI in solving previously intractable computational problems. By exploring technological capabilities, implementation challenges, and future research directions, this review provides a critical framework for understanding the emerging intersection of quantum computing and artificial intelligence in advanced business intelligence applications.International Journal of Science and Research Archive00This comprehensive review critically examines the revolutionary capabilities of quantum computing-enhanced AI systems in addressing complex computational challenges across multiple business domains and reveals remarkable potential to reduce computational complexity by up to 90%, enhance predictive accuracy by 60-75%, and provide unprecedented insights across financial, logistical, and strategic business intelligence domains.International Journal of Science and Research Archive["Itunu Taiwo", "Adeyinka Ogunbajo", "Adefemi Quddus Abidola"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/1bd5ad33514bcc4d3ab57ffa6c82d76094604320 8725127c0bdba837fbf286982be34416798245c4The rapid advancement of artificial intelligence technologies has created unprecedented opportunities while raising significant ethical concerns across various sectors. This comprehensive article examines the challenges and strategies in implementing ethical AI frameworks, focusing on algorithmic bias, transparency, and accountability. The article investigates industry-specific applications in healthcare, financial services, and law enforcement, revealing ethical implementation and governance patterns. Through extensive research across multiple organizations, the article demonstrates the critical importance of structured ethical frameworks, stakeholder engagement, and comprehensive monitoring systems in ensuring responsible AI development. The findings highlight the need for balanced approaches that maintain technological innovation while adhering to ethical principles and human values.International Journal of Science and Research Archive01The article investigates industry-specific applications in healthcare, financial services, and law enforcement, revealing ethical implementation and governance patterns and demonstrates the critical importance of structured ethical frameworks, stakeholder engagement, and comprehensive monitoring systems in ensuring responsible AI development.International Journal of Science and Research Archive["Rishi Kumar Sharma"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/8725127c0bdba837fbf286982be34416798245c4 8ea977aa92204a53eb634662453ca9e3ece75b29Considering Artificial Intelligence systems as boundary objects, which are interdisciplinary objects sustained differently by diverse fields while providing shared discourses between them, this essay summarizes the approaches of examining bias in AI systems. It argues that examining each part related to the building and working of AI systems is essential for unpacking the political play and potential insert points of biases in them. It concentrates on the critical analysis of data and algorithms as two core parts of AI systems by operationalizing hermeneutic reverse engineering. Hermeneutic reverse engineering is a framework to unpack and understand different elements of a technocultural object and/or system that contribute to the construction of its meaning and contexts. It employs a speculative imagination of what other realities can be designed and includes cultural analysis to identify existing meanings and assumptions behind the technocultural object, identifying key elements of signification, and speculating possibilities of reassembling different meanings for the object. The main results obtained by this method on AI systems is using cultural consideration and technological imagination to unpack existing meanings created by AI and design innovative approaches for AI to exert alternate/ inclusive meanings. The research perspectives presented in this article include critical examination of biases and politics within different elements of AI systems, and the impact of these biases on different social groups. The paper proposes using the method of hermeneutic reverse engineering to investigate AI systems and speculate possible alternate and more accountable futures for AI systems.Frontiers in Communication250It is argued that examining each part related to the building and working of AI systems is essential for unpacking the political play and potential insert points of biases in them.Frontiers in Communication["Nishanshi Shukla"]2025-01-30T00:00:00https://www.semanticscholar.org/paper/8ea977aa92204a53eb634662453ca9e3ece75b29 9d42d502a5d827cb9e89a396f60fcf55b23fff24This research explores the optimization of Artificial Intelligence (AI) in fiduciary supervision systems aligned with Islamic law principles. The study is driven by the need for Sharia-compliant institutions to enhance efficiency and ensure adherence to Sharia principles in fiduciary oversight. AI has the potential to expedite analysis and accurately detect fiduciary breaches. The research examines both internal and external factors influencing AI adoption, while also identifying potential benefits and challenges. From an internal perspective, strengths such as operational efficiency and compliance with Sharia principles are predominant, with a total IFAS score of 1.17, indicating that strengths outweigh weaknesses in AI implementation. Externally, factors like government regulatory support for digitalization and the growth of Sharia fintech offer significant opportunities, reflected in an EFAS score of 1.23. However, there are threats to consider, including rapid regulatory changes and potential security risks. Overall, the application of AI in fiduciary supervision, in line with Islamic law principles, can have a positive impact on both conventional and Islamic financial sectors. With an effective implementation strategy, internal and external challenges can be managed, allowing for the optimization of AI technology that aligns with principles of justice and transparency in Islamic law, thereby enhancing accountability and public trust in Sharia compliant institutions.Petita Jurnal Kajian Ilmu Hukum dan Syariah00The application of AI in fiduciary supervision, in line with Islamic law principles, can have a positive impact on both conventional and Islamic financial sectors and enhance accountability and public trust in Sharia compliant institutions.PETITA: JURNAL KAJIAN ILMU HUKUM DAN SYARIAH["Wazin", "Nihayatul Maskuroh", "Aan Ansori"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/9d42d502a5d827cb9e89a396f60fcf55b23fff24 0035d5c2911ac2baeb52771e35dd4ad9d3938325Abstract: This dissertation looks into how artificial intelligence (AI) can help find and predict diseases early in precision healthcare. The main research issue is about using different types of data, like medical images, genetic information, and clinical records, while also focusing on the need for AI models that can explain their decisions. The results show that using various datasets, which include patient histories, demographic info, and health results, new AI methods can significantly improve the accuracy of diagnoses and insights about disease outcomes. Notably, the AI models used in this study are better than traditional diagnostic methods and offer outputs that doctors can easily understand and rely on. This research highlights the importance of AI in enabling quick treatments and tailored care plans, which can lead to better patient outcomes and more efficient healthcare. The wider implications of this study include building a stronger system for data-driven choices in healthcare, encouraging the use of explainable AI in medical practices, and setting the stage for new developments in precision medicine. This work is an important step towards managing the challenges of using advanced technology in healthcare, ultimately improving the quality and availability of medical services. This research aims to examine how artificial intelligence can be used for early disease detection and prediction in precision healthcare systems. The main issue is how to combine various data sources, like medical images, genomic data, and clinical records, while ensuring that AI models are understandable. This requires gathering different datasets, including patient histories, demographic details, and health results, to confirm the suggested methods.International Journal for Research in Applied Science and Engineering Technology00The results show that using various datasets, which include patient histories, demographic info, and health results, new AI methods can significantly improve the accuracy of diagnoses and insights about disease outcomes.International Journal for Research in Applied Science and Engineering Technology["Sabyasachi Saha"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/0035d5c2911ac2baeb52771e35dd4ad9d3938325 dc9b198f69213f2dc46f22b364c8722527b0cf58 Artificial intelligence (AI) is increasingly being integrated into sentencing within the criminal justice system. This research examines the impact of AI on sentencing, addressing the challenges and opportunities for fairness and justice. The main problem explored is AI’s potential to perpetuate biases, undermining fair-trial principles. This study intends to assess AI’s influence on sentencing, identify legal and ethical challenges, and propose a framework for equitable AI use in judicial decisions. Key research questions include: (1) How does AI influence sentencing decisions? (2) What concerns arise from AI in sentencing? (3) What safeguards can mitigate those concerns and prejudices? Utilizing qualitative methodology, including doctrinal analysis and comparative studies, the research reveals AI’s potential to enhance sentencing efficiency but also to risk reinforcing biases. The study recommends robust regulatory frameworks, transparency in AI algorithms, and judicial oversight to ensure AI supports justice rather than impedes it, advocating for a balanced integration that prioritizes human rights and fairness.International Annals of Criminology200The research reveals AI’s potential to enhance sentencing efficiency but also to risk reinforcing biases, and recommends robust regulatory frameworks, transparency in AI algorithms, and judicial oversight to ensure AI supports justice rather than impedes it.International Annals of Criminology["M. Niriella"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/dc9b198f69213f2dc46f22b364c8722527b0cf58 b5159d3d9a07de5fb96dfc2e7d13f8dd3301bbecDrawing on a systematic literature review and bibliometric analysis, this article examines the burgeoning field of Artificial Intelligence (AI) integration into Human Resource Management (HRM) practises. By evaluating 77 selected articles from two extensive databases, Scopus and Web of Science, this study illuminates the dynamic intersection of AI technologies and HRM, encapsulating the profound implications for organisational and individual aspects of HR practises. This analysis delineates three primary thematic areas: AI's transformative role in HRM, the emerging paradigm of human-AI collaboration, and the nuanced challenges and opportunities presented by AI in HR practises. This research contributes to the academic discourse by mapping the current state of AI applications in HRM, identifying gaps and proposing directions for future research, emphasising the need for ethical frameworks and the strategic integration of AI to enhance HR practises. Through this scholarly endeavour, we aim to offer a comprehensive overview that aids practitioners and researchers in navigating the complexities of AI's role in reshaping HRM towards more efficient, ethical, and innovative practises.International Journal of Production Management and Engineering1000This research contributes to the academic discourse by mapping the current state of AI applications in HRM, identifying gaps and proposing directions for future research, emphasising the need for ethical frameworks and the strategic integration of AI to enhance HR practises.International Journal of Production Management and Engineering["Adil Benabou", "Fatima Touhami"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/b5159d3d9a07de5fb96dfc2e7d13f8dd3301bbec ac86873a6e910d9b598a17a9b08fedd0a1042b1aPurpose This study aims to elucidate the intricate relationship between cloud-based artificial intelligence (CBAI) and audit reports, specifically emphasizing the mediating role played by auditors. Design/methodology/approach This study used a quantitative approach, distributing 322 questionnaires to external auditors in Jordan to explore the potential enhancements of CBAI in auditing. Convenient random sampling was used to gather data from available members of the population, which comprises external audit offices in Jordan. There are a total of 454 audit offices in Jordan, employing diverse auditors, such as partner-owner auditors, assistant auditors and certified auditors. Data analysis was conducted using SmartPLS software, which uses structural equation modeling. Findings The study’s findings suggest potential cost savings associated with CBAI adoption, streamlined audit processes and increased overall efficiency, thereby boosting audit effectiveness and elevating the quality of audit reports. Moreover, the research observes a change in the role of auditors, with a greater emphasis on analytical and advisory tasks rather than traditional manual procedures. These insights highlight the potential benefits for both auditors and audit clients, underscoring the importance of embracing these technologies to propel the auditing profession forward in the digital era. Originality/value This study contributes insights into the impact of CBAI on the audit profession by acknowledging the shift in auditing techniques from manual to digital technology and emphasizing the benefits of cloud computing in terms of accessibility, flexibility, scalability of storage and use of financial data. It also stresses the use of CBAI technology and highlights its potential for automating and accelerating audit operations, efficiently managing client data and improving the accuracy and reliability of audit reports. VINE Journal of Information and Knowledge Management Systems850The study’s findings suggest potential cost savings associated with CBAI adoption, streamlined audit processes and increased overall efficiency, thereby boosting audit effectiveness and elevating the quality of audit reports.VINE Journal of Information and Knowledge Management Systems["Yazan Abu Huson", "Laura Sierra Garc\u00eda", "Mar\u00eda Antonia Garc\u00eda Benau", "Nader Mohammad Aljawarneh"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/ac86873a6e910d9b598a17a9b08fedd0a1042b1a 101a9456275a390ccd6a0c3533c008b013eb1d82Meningkatnya kebutuhan akan pembiayaan berkelanjutan telah menempatkan obligasi hijau sebagai instrumen penting untuk mendanai proyek-proyek ramah lingkungan. Namun, proyek-proyek ini menghadapi berbagai risiko, termasuk tantangan keuangan, lingkungan, dan peraturan, terutama di pasar negara berkembang seperti Indonesia. Studi ini mengeksplorasi penerapan Artificial Intelligence (AI) dalam memitigasi risiko-risiko tersebut melalui penelitian kualitatif yang melibatkan lima narasumber dari bidang keuangan, lingkungan, dan teknologi. Dengan menggunakan NVIVO untuk analisis tematik, temuan ini menyoroti potensi AI dalam analisis prediktif, pemantauan lingkungan, dan simulasi skenario risiko, sambil mengatasi tantangan seperti kualitas data, biaya, dan ketidakkonsistenan peraturan. Studi ini menggarisbawahi peran transformatif AI dalam meningkatkan transparansi, efisiensi, dan skalabilitas dalam manajemen risiko obligasi hijau, serta memberikan rekomendasi kepada para pemangku kepentingan untuk mendorong praktik keuangan yang berkelanjutan di Indonesia.Sanskara Akuntansi dan Keuangan190Sanskara Akuntansi dan Keuangan["Loso Judijanto", "Aldi Bastiatul Fawait"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/101a9456275a390ccd6a0c3533c008b013eb1d82 dd4210940721d85f6b67186324a628f489d2d183In a world increasingly driven by artificial intelligence, higher education is at a critical point where it must demonstrate its relevance and adaptability. Universities not only they must transform ideas into actions but also reaffirm their value as public goods through community alliances that benefit everyone. In this process, understanding and addressing attitudes towards AI is crucial to integrate this technology ethically and effectively in education. To the by doing so, institutions not only prepare their students for an uncertain future, but also reinforce their role as communities of values, where technology, although powerful, continues being a tool at the service of integral human development. Objective.- Under this research article it is intended analyze the attitudes of teachers towards AI in general and particularly towards its use in teaching-learning processes, as well as identifying the factors associated with the teachers' attitudes toward AI. In this sense, the results of the study will help develop teacher professionalization guidelines that address concerns and encourage the adoption of AI. Method.- An empirical investigation of an explanatory and transversal nature was carried out. Concerning population, a through convenience sampling, a representative sample of 632 teachers was obtained with a confidence level of 0.99% of the total population of teachers at a university in the western Mexico. The dependent variables under study were the attitude of the teachers towards AI in general and teachers' attitudes to the use of AI in teaching processes learning and the independent variables were sex, age group, type of teacher, teaching experience in the institution, area of professional training knowledge, level of teacher training and AI training. Instruments.- To identify teachers' attitudes, the AI scale was used, on the one hand. Attitude Scale (AIAS-4) developed and validated by Grassini, F. (2023) that evaluates general attitude towards artificial intelligence, focusing on public perceptions of AI technology. The scale is composed of four items designed to assess beliefs about the influence of AI in people's lives, in their careers and in humanity in general. The scale items are they focus on the perceived usefulness and potential impact of technology on society and humanity. The AIAS-4 showed high internal consistency. It presented a Cronbach's alpha of 0.902 and an omega McDonald's score of 0.904, indicating a very high level of reliability. The AIAS-4 was correlated with the attitude factors of the Media and Technology Usage and Attitudes Scale (MTUAS) and the correlations were moderate and statistically significant with the positive factors and negative results of the MTUAS, which supports the convergent validity of the scale. For this research a pilot test was carried out and a Cronbach's alpha of 0.71 was obtained. On the other hand, an ad hoc scale was developed to evaluate teachers' attitudes towards the use of generative artificial intelligence (AI) in teaching-learning processes. This scale considered five dimensions: perception of usefulness, ease of use, risk, implication social and intention of use. The scale was made up of 25 items and a Cronbach's alpha of 0.87 was obtained for the entire Scale and 0.77 for the usefulness dimension, 0.73 for ease of use, 0.85 for risk, 0.79 for social implications and 0.78 for intention to use. Conclusions.- These are conclusions obtained: (1) Teachers have a good attitude towards AI in general, believing that it will improve life, work, that they will use it in the future and that it is positive for humanity. However, there is a great dispersion among the opinions of the teachers so there is no consensus among them. (2) Teachers have a good attitude towards the use of AI in teaching (4/5) they consider it useful, easy to use, with positive social implications and have intentions to use it. However, teachers have uncertainty and pockets of pessimism about the risk involved AI in teaching. In this regard, they are worried if it will replace them at work, yes will depersonalize learning experiences, it will amplify inequality gaps, if it is safe and reliable and can be used to manipulate and control. (3) Create spaces where teachers can discuss their experiences, concerns and expectations about AI and document success experiences. These forums should encourage exchange of ideas and resolution of common problems, promoting an environment collaborative. (4) Implement AI progressively, starting with tools that teachers considered more useful and easier to use. Provide constant and personalized technical assistance to facilitate adoption and solve problems in real time. (5) Establish periodic evaluation mechanisms to monitor the impact of AI on the teaching and learning. Collect and analyze feedback data from teachers and students to continually adjust and improve learning strategies implementation. (6) Communicate an institutional statement on the use of AI and the guidelines that guide its use. Directly address concerns about security, reliability, privacy and ethics in the use of AI. This includes ensuring that AI will not replace teachers but will serve as a complementary tool. (7) Implement pilot projects in different academic areas to evaluate the effectiveness of the AI in specific contexts. Document and share learning outcomes and lessons learned to guide future implementations. (8) Centralize governance and institutional infrastructure for AI adoption upfront to promote the coordination of efforts. Of course, with openness to serve initiatives from different areas. While the academy defines the criteria to select relevant AI tools for professional training educational programs that are offered.International journal of multidisciplinary research and analysis00The attitudes of teachers towards AI in general and particularly towards its use in teaching-learning processes, as well as identifying the factors associated with the teachers' attitudes toward AI will help develop teacher professionalization guidelines that address concerns and encourage the adoption of AI.INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS["Ismael Zamora Tovar", "Gelacio Juan Ram\u00f3n Guti\u00e9rrez Ocegueda"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/dd4210940721d85f6b67186324a628f489d2d183 637d6431c07726961b2d642eb15f92c3b539ae23Purpose: The growing body of literature on Professional Services Firms (PSFs) using Artificial Intelligence (AI) offers valuable insights, yet a synthesised, systematic examination of this literature is lacking. This paper addresses this research gap by synthesising the literature at the intersection of AI and regulated PSFs, unveiling key issues, and proposing a research agenda for future studies. Design/Methodology/Approach: Through a systematic literature review conducted in January 2023, we evaluated 612 articles and analysed 75 academic papers in detail, originally published over 34 years between 1988 and 2022. This temporal boundary offers a stable benchmark for future comparative studies, providing context for understanding the impact of even newer and emerging technologies. Findings: Our findings reveal that the use of AI within PSFs has a profound impact, far beyond the initial economic or efficiency gains expected. We uncover five dominant themes within the literature, which include the motivations for using AI, challenges and limitations of AI, human-AI interaction, increasing ethical considerations, and the strategic implications for PSFs. Originality/Value: This paper offers a synthesised, systematic examination of AI in regulated PSFs, which is notably absent in the literature. We present a visual summary of the themes and overall findings, illustrating the complex interplay of AI within PSFs. We propose a research agenda of future research opportunities emerging from the research.Journal of Information &amp; Knowledge Management00The findings reveal that the use of AI within PSFs has a profound impact, far beyond the initial economic or efficiency gains expected.Journal of Information &amp; Knowledge Management["Carl Bezuidenhout", "Roba Abbas", "Michael Mehmet", "Troy Heffernan"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/637d6431c07726961b2d642eb15f92c3b539ae23 5fea1e32f573a7237138cb00c3af845364dc004aThe Toyota Production System (TPS) was a revolutionary approach to automobile production that influenced companies all over the world. The fight against redundancy is at the core of this approach. The textile industry remains one of the most polluting sectors worldwide, which makes environmental sustainability a key concern. In line with national priorities, companies are striving to balance profitability with sustainability, minimizing defects and reducing waste. This study explores the evolution of textile production systems from TPS principles to the integration of Artificial Intelligence (AI) and how they can be used from a sustainability perspective. Smartex, a textile start-up recognized as the winner of the Web Summit 2021 competition, was chosen as the focus of this case study. Employing qualitative research methods, including content analysis of interviews, management reports and website data, the study examines the parallels and distinctions between TPS and Smartex’s AI-driven system. The findings highlight how Smartex is revolutionizing the textile industry by leveraging AI to avoid defects and reduce waste, advancing both environmental and commercial objectives. Finally, the implications and limitations of the research are explained.Sustainability00Sustainability["Paula Tavares de Carvalho", "Jos\u00e9 Dias Lopes", "R. Raimundo"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/5fea1e32f573a7237138cb00c3af845364dc004a 8b39c9a0660da1d919e52b0a99010a5e96deb85eJournal of Technology in Behavioral Science380The study findings reveal that perceived ease of use, perceived usefulness, and facilitating conditions (FC) are the critical factors that influence the behavioural intentions (BI) of healthcare professionals to use AI.Journal of Technology in Behavioral Science["Swathi K. S.", "Aswathy S.", "Kavitha T. C.", "Krishnaraj Chadaga", "Niranajana Sampathila"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/8b39c9a0660da1d919e52b0a99010a5e96deb85e bddd25fb40faa4837d289cc9284034a5c9608149Artificial Intelligence (AI) fosters enormous business opportunities that build and utilize private AI models. Implementing AI models at scale and ensuring cost-effective production of AI-based technologies through entirely in-house capabilities is a challenge. The success of the Infrastructure as a Service (IaaS) and Software as a Service (SaaS) Cloud Computing models can be leveraged to facilitate a cost-effective and scalable AI service paradigm, namely, ‘AI as a Service.’ We summarize current state-of-the-art solutions for AI-as-a-Service (AIaaS), and we discuss its prospects for growth and opportunities to advance the concept. To this end, we perform a thorough review of recent research on AI and various deployment strategies for emerging domains considering both technical as well as survey articles. Next, we identify various characteristics and capabilities that need to be met before an AIaaS model can be successfully designed and deployed. Based on this we present a general framework of an AIaaS architecture that integrates the required aaS characteristics with the capabilities of AI. We also compare various approaches for offering AIaaS to end users. Finally, we illustrate several real-world use cases for AIaaS models, followed by a discussion of some of the challenges that must be addressed to enable AIaaS adoption.ACM Computing Surveys420A thorough review of recent research on AI and various deployment strategies for emerging domains and a general framework of an AIaaS architecture that integrates the required aaS characteristics with the capabilities of AI are presented.ACM Computing Surveys["N. Syed", "Adnan Anwar", "Zubair A. Baig", "S. Zeadally"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/bddd25fb40faa4837d289cc9284034a5c9608149 a35273d180a990cab9949d69625461d34005b207Artificial intelligence (AI)-based digital phenotyping, including computational speech analysis, increasingly allows for the collection of diagnostically relevant information from an ever-expanding number of sources. Such information usually assesses human behaviour, which is a consequence of the nervous system, and so digital phenotyping may be particularly helpful in diagnosing neurological illnesses such as Alzheimer's disease. As illustrated by the use of computational speech analysis of Alzheimer's disease, however, neurological illness also introduces ethical considerations beyond commonly recognised concerns regarding machine learning and data collection in everyday environments. Individuals' decision-making capacity cannot be assumed. Understanding of analytical results will likely be limited even as the personal significance of those results is both highly sensitive and personal. In a traditional clinical evaluation, there is an opportunity to ensure that information is relayed in a way that is highly customised to the individual's ability to understand results and make decisions, and privacy is closely protected. Can any such assurance be offered as digital phenotyping technology continues to advance? AI-supported digital phenotyping offers great promise in neurocognitive disorders such as Alzheimer's disease, but it also poses ethical challenges. We outline some of these risks as well as strategies for risk mitigation.Journal of Medical Ethics210AI-supported digital phenotyping offers great promise in neurocognitive disorders such as Alzheimer's disease, but it also poses ethical challenges and this work outlines some of these risks as well as strategies for risk mitigation.Journal of medical ethics["Francesca Dino", "Peter S. Pressman", "Kevin Bretonnel Cohen", "Veljko Dubljevi\u0107", "William Jarrold", "Peter W Foltz", "Matt DeCamp", "Mohammad H Mahoor", "Lawrence E Hunter"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/a35273d180a990cab9949d69625461d34005b207 9ee6171b2a3e38cc9f85b3d7f462af59b1c83a2cThe author discusses the key concepts of the artificial intelligence, computerized analysis and machine learning. The chatbots СhatGPT, GigaChat, Alisa can be used in the libraries and information centers to assist in translations of foreign publications, article reviewing, etc. The author examines the possibility of integrating chatbot into the websites to render the first assistance to the users, in particular beyond office hours. The author reviews using AI for literature system reviewing and argues that the selection process is more efficient, and time is saved. He demonstrates the AI capabilities to improve the relevance of response to the search queries in the library computerized systems and to develop user personal account services. The latter would enable to generate personalized recommendations for articles, patents and reviews within the subject scope of studies and to select the most relevant materials for publication. For the system proper operation, the author suggests to develop the system for evaluation of the produced materials and services quality. Based on this system and requested materials, the personalized analytical and recommendation system can be generated to identify the lines of further research and development. Despite underdeveloped technologies, impossibility of total replacement of the humans, high implementation costs, etc., the AI methods and algorithms of learning and analysis enable to computerize several information processing operations, to reveal patterns and trends, t o p redict u ser n eeds, w hich l ays t he w ay f or d eveloping a nd i mproving services in the libraries and information centersScientific and Technical Libraries140The author demonstrates the AI capabilities to improve the relevance of response to the search queries in the library computerized systems and to develop user personal account services and suggests to develop the system for evaluation of the produced materials and services quality.Scientific and Technical Libraries["I. Mitroshin"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/9ee6171b2a3e38cc9f85b3d7f462af59b1c83a2c 6f9872a4444ef7867473a37720ceb45df4de7742Introduction Researchers are increasingly exploring the use of artificial intelligence (AI) tools in evidence synthesis, a labor-intensive, time-consuming, and costly effort. This review explored and quantified the potential efficiency benefits of using automated tools as part of core evidence synthesis activities compared with human-led methods. Methods We searched the MEDLINE and Embase databases for English-language articles published between 2012 and 14 November 2023, and hand-searched the ISPOR presentations database (2020–2023) for articles presenting quantitative results on workload efficiency in systematic literature reviews (SLR) when AI automation tools were utilized. Data on efficiencies (time- and cost-related) were collected. Results We identified 25 eligible studies: 13 used machine learning, 10 used natural language processing, and once each used a systematic review automation tool and a non-specified AI tool. In 17 studies, a >50% time reduction was observed, with 5-to 6-fold decreases in abstract review time. When the number of abstracts reviewed was examined, decreases of 55%–64% were noted. Studies examining work saved over sampling at 95% recall reported 6- to 10-fold decreases in workload with automation. No studies quantified the economic impact associated with automation, although one study found that there was an overall labor reduction of >75% over manual methods during dual-screen reviews. Discussion AI can reduce both workload and create time efficiencies when applied to evidence gathering efforts in SLRs. These improvements can facilitate the implementation of novel approaches in decision making that consider the real-life value of health technologies. Further research should quantify the economic impact of automation in SLRs.Frontiers in Pharmacology640This review explored and quantified the potential efficiency benefits of using automated tools as part of core evidence synthesis activities compared with human-led methods and found AI can reduce both workload and create time efficiencies when applied to evidence gathering efforts in SLRs.Frontiers in Pharmacology["Seye Abogunrin", "Jeffrey M. Muir", "Clarissa Zerbini", "Grammati Sarri"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/6f9872a4444ef7867473a37720ceb45df4de7742 1ffa8487caa534c93c359f041974c32a6781f724Reproductive Biology and Endocrinology430Strong AI performance across multiple datasets demonstrates the value of the four-step methodology in developing and validating the AI as a reliable adjunct to embryo evaluation.Reproductive Biology and Endocrinology : RB&E["D. Gilboa", "A. Garg", "M. Shapiro", "M. Meseguer", "Y. Amar", "N. Lustgarten", "N. Desai", "T. Shavit", "V. Silva", "A. Papatheodorou", "A. Chatziparasidou", "S. Angras", "J. H. Lee", "L. Thiel", "C. L. Curchoe", "Y. Tauber", "D. Seidman"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/1ffa8487caa534c93c359f041974c32a6781f724 7d1651fa9013ff2779f2b47d5da8f1eb5e4685c6This study examines the legal framework governing the role of artificial intelligence (AI) in detecting personal financial data violations within the Saudi legal system, focusing on the technological and legal challenges in this domain. The research highlights the importance of AI as an advanced tool for analyzing large datasets and identifying suspicious patterns indicative of violations or financial crimes. It demonstrates how AI applications can enhance the accuracy and efficiency of financial oversight systems while safeguarding individuals' rights and privacy. However, the growing reliance on these technologies raises questions about the compatibility of existing legislation with technological advancements. The study reviews Saudi legislation, such as the Personal Data Protection Law and the Anti-Cybercrime Law, analyzing their adequacy to ensure the safe and effective use of AI in this area. It highlights potential gaps in the current legal framework, such as the lack of clear regulatory guidelines on big data usage and the need for a balanced approach between privacy protection and technological benefits. The research also compares the Saudi system to international experiences, such as the European GDPR and California’s CCPA, offering a comprehensive perspective on improving Saudi legal regulations. The study emphasizes the need for a progressive and flexible legal framework capable of accommodating rapid developments in AI. It underscores the importance of enhancing transparency in data collection and analysis while establishing stringent controls to regulate AI use, ensuring a balance between individual rights protection and technological progress. The research provides recommendations for advancing Saudi legislation by adopting international standards and implementing more efficient oversight mechanisms. In conclusion, the study calls for collaboration among regulatory and technical entities to promote the safe use of AI in detecting financial data violations. It highlights the importance of raising public awareness about individual rights in data protection while fostering technological innovation to support Saudi Arabia’s economic and social sustainability goals.International Journal of Law Research and Studies00The study reviews Saudi legislation, such as the Personal Data Protection Law and the Anti-Cybercrime Law, analyzing their adequacy to ensure the safe and effective use of AI in this area and provides recommendations for advancing Saudi legislation by adopting international standards and implementing more efficient oversight mechanisms.International Journal of Law Research and Studies["Reema Aljuwaiser"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/7d1651fa9013ff2779f2b47d5da8f1eb5e4685c6 b29c230f802d28595e1482746b20506204a5056fThe rapid growth of e-commerce in Bangladesh has created significant opportunities for businesses to expand their reach and serve a larger customer base. However, challenges such as inefficient inventory management, inaccurate demand forecasting, and logistics inefficiencies continue to hinder the sector's growth and profitability. Artificial intelligence (AI) offers an innovative solution to address these challenges by enabling data-driven decision-making, real-time monitoring, and predictive analytics. This study investigates the role of AI in improving inventory management and demand forecasting in Bangladesh's ecommerce environment. The study examines how AI technologies such as machine learning, predictive analytics, and automation can optimize inventory levels through more accurate demand forecasting, reduce operational costs, and improve customer satisfaction. By analyzing current practices in Bangladesh's e-commerce industry, the study identifies key gaps in AI adoption and highlights barriers such as lack of infrastructure, high implementation costs and limited technical expertise. Furthermore, the paper examines success stories of AI adoption in global e-commerce markets and assesses their applicability to Bangladesh. The results indicate that AI-powered inventory and forecasting systems can significantly improve the efficiency and competitiveness of e-commerce enterprises in Bangladesh. However, collaboration among stakeholders, investments in AI infrastructure and capacity building are essential to achieve these benefits. The study concludes with practical recommendations for integrating AI solutions in Bangladesh's e-commerce ecosystem and highlights the need for policy support and technological advancements to foster sustainable growth.International Journal for Research in Applied Science and Engineering Technology00The results indicate that AI-powered inventory and forecasting systems can significantly improve the efficiency and competitiveness of e-commerce enterprises in Bangladesh, however, collaboration among stakeholders, investments in AI infrastructure and capacity building are essential to achieve these benefits.International Journal for Research in Applied Science and Engineering Technology["Ashraf Shahriar"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/b29c230f802d28595e1482746b20506204a5056f a44e1f5aab071852cf837b4acc6414a23737f852Background: This research examines the potential criminal offenses that can be committed by Artificial Intelligence (AI) and the implications of criminal law liability for them in the context of Indonesian law. AI, which is increasingly developing with its autonomous capabilities, has the potential to result in new criminal offenses that have not been fully anticipated by the existing legal system. Potential AI crimes, such as deepfakes and criminal offenses by autonomous vehicles, represent a significant threat to public safety and privacy. While some developed countries have begun to regulate the use of AI, Indonesia does not yet have specific regulations governing AI and its potential threats. Method: This research uses a juridical-normative method with conceptual, case, and statutory approaches, to analyze the concept of criminal liability in AI crimes. Findings: By considering legal doctrines, this research proposes that responsibility for the actions of AI, which cannot yet be considered an independent legal subject, should be transferred to humans as developers or users through the doctrines of in loco parentis and Vicarious Liability. Through this approach, AI is treated as a human-controlled tool, so legal liability remains with the entity that has direct control. Conclusion: This study expects proactive steps from the Indonesian government to develop clear regulations on AI, to ensure the protection of the public from the risks posed by AI. The regulation should be able to accommodate the rapid development of technology while educating the public on the risks of AI. Novelty/Originality of this Study: This research highlights the absence of specific AI regulations in Indonesia and offers a legal framework by applying the doctrines of in loco parentis and Vicarious Liability to AI-related offenses. It provides a new perspective on assigning liability in AI crimes, ensuring that responsibility remains with human actors while addressing the legal gaps in Indonesia’s regulatory framework.Ex Aequo Et Bono Journal Of Law00This research proposes that responsibility for the actions of AI should be transferred to humans as developers or users through the doctrines of in loco parentis and Vicarious Liability, providing a new perspective on assigning liability in AI crimes.Ex Aequo Et Bono Journal Of Law["Adria Fathan Mahmuda", "Mahesa Cakra Gusti", "MHD. Faruq Anrusfi"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/a44e1f5aab071852cf837b4acc6414a23737f852 d8caa4bd4c239ba5d3af937bf31ff7c042cfcf8dBased on the technology acceptance model (TAM), pre-service teachers’ acceptance of artificial intelligence (AI) is crucial in predicting their intentions to use AI in future teaching, as well as for their actual usage of AI. However, current research offers limited insights into the role of factors regarding usage intentions and behaviors. In particular, AI-related teacher training courses and AI-related technological pedagogical content knowledge (AI-TPACK) might be relevant, but are empirically underinvestigated within the TAM. This study addresses these gaps by investigating the relationships between pre-service teachers’ participation in AI-related courses, their self-reported AI-TPACK, their perceptions of AI’s usefulness and ease of use, and both their intention and actual usage of AI. Using path models with data from 143 pre-service teachers, the results revealed that participation in AI-related courses related positively to AI-TPACK and perceived AI-related usefulness. Further, AI-TPACK was positively related to perceived AI-related usefulness and ease of use, which in turn positively related to the behavioral intention to use AI in future teaching and the actual usage of AI for profession-related tasks in teacher training. The study results extend the existing research on TAM and highlight the consideration of participation in AI-related courses and AI-TPACK as further factors in understanding pre-service teachers’ AI acceptance.Education sciences00Investigating the relationships between pre-service teachers’ participation in AI-related courses, their self-reported AI-TPACK, their perceptions of AI’s usefulness and ease of use, and both their intention and actual usage of AI revealed that participation in AI-related courses related positively to AI-TPACK and perceived AI-related usefulness.Education Sciences["Isabell Runge", "Florian Hebibi", "Rebecca Lazarides"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/d8caa4bd4c239ba5d3af937bf31ff7c042cfcf8d 1610aad3857cd557038ccff32961e9a36ca4e994Introduction/Main Objectives: This manuscript investigates the biological and social ramifications of AI-powered economic policies, aiming to elucidate the multifaceted impacts of artificial intelligence on societal structures and health outcomes. Background Problems: The rapid integration of AI technologies into economic frameworks raises critical ethical concerns, including algorithmic bias and accountability, which can exacerbate existing social inequalities. Additionally, the implications for human-AI interaction in healthcare settings necessitate a deeper understanding of how these technologies affect patient outcomes and clinician practices. Methods: A discourse analysis was conducted on ten peer-reviewed articles, focusing on themes such as ethical accountability, human-AI interaction, social equity, and workforce dynamics. Findings: The analysis revealed four primary themes: (1) Ethical and Accountability Challenges, highlighting the necessity for robust frameworks to address algorithmic bias; (2) Human-AI Interaction and Its Biological Implications, emphasizing the need for clinician training and AI literacy; (3) Social Equity and Access Issues, underscoring the risk of exacerbating existing disparities; and (4) Economic Impact and Workforce Dynamics, pointing to the dual-edged nature of AI's integration into economic policies. Conclusions: The findings underscore the imperative for policymakers to develop ethical guidelines and promote AI literacy while implementing strategies for workforce reskilling. By addressing these challenges, society can harness the transformative potential of AI technologies while safeguarding social equity and enhancing health outcomes.Ilomata International Journal of Social Science330The findings underscore the imperative for policymakers to develop ethical guidelines and promote AI literacy while implementing strategies for workforce reskilling and biological and social ramifications of AI-powered economic policies.Ilomata International Journal of Social Science["Theodora Pearl De-Veer", "George Akwetey", "Justa Sentre"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/1610aad3857cd557038ccff32961e9a36ca4e994 65ed88a2d7c2e77d8e0ac53c0f2fb0f58c91ea0dBackground Digital mental health interventions, such as artificial intelligence (AI) conversational agents, hold promise for improving access to care by innovating therapy and supporting delivery. However, little research exists on patient perspectives regarding AI conversational agents, which is crucial for their successful implementation. This study aimed to fill the gap by exploring patients’ perceptions and acceptability of AI conversational agents in mental healthcare. Methods Adults with self-reported mild to moderate anxiety were recruited from the UMass Memorial Health system. Participants engaged in semi-structured interviews to discuss their experiences, perceptions, and acceptability of AI conversational agents in mental healthcare. Anxiety levels were assessed using the Generalized Anxiety Disorder scale. Data were collected from December 2022 to February 2023, and three researchers conducted rapid qualitative analysis to identify and synthesize themes. Results The sample included 29 adults (ages 19-66), predominantly under age 35, non-Hispanic, White, and female. Participants reported a range of positive and negative experiences with AI conversational agents. Most held positive attitudes towards AI conversational agents, appreciating their utility and potential to increase access to care, yet some also expressed cautious optimism. About half endorsed negative opinions, citing AI’s lack of empathy, technical limitations in addressing complex mental health situations, and data privacy concerns. Most participants desired some human involvement in AI-driven therapy and expressed concern about the risk of AI conversational agents being seen as replacements for therapy. A subgroup preferred AI conversational agents for administrative tasks rather than care provision. Conclusions AI conversational agents were perceived as useful and beneficial for increasing access to care, but concerns about AI’s empathy, capabilities, safety, and human involvement in mental healthcare were prevalent. Future implementation and integration of AI conversational agents should consider patient perspectives to enhance their acceptability and effectiveness.Frontiers in Psychiatry630Artificial conversational agents were perceived as useful and beneficial for increasing access to care, but concerns about AI’s empathy, capabilities, safety, and human involvement in mental healthcare were prevalent.Frontiers in Psychiatry["Hyein S. Lee", "Colton Wright", "J. Ferranto", "Jessica Buttimer", "Clare E. Palmer", "Andrew Welchman", "Kathleen M. Mazor", "Kimberly A Fisher", "David Smelson", "Laurel O\u2019Connor", "N. Fahey", "A. Soni"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/65ed88a2d7c2e77d8e0ac53c0f2fb0f58c91ea0d 0fe9aaed5bea88c53d1221b562fed6d20098d18dPurpose: This article summarizes a literature review that discusses the role of AI in various aspects of external auditing, such as improving external audit quality, external audit process efficiency, and fraud detection. The literature emphasizes the importance of applying AI technologies in changing the external audit landscape, offering benefits such as in-depth data analysis, time efficiency, and cost reduction in the external audit process. This article also explains how the development of AI and other technologies impacts the transformation of society, especially in the context of Society 5.0, which emphasizes the integration of technology to improve human well-being. Methodology/approach: This article presents a review of various research materials that address the role of artificial intelligence (AI) in the context of external auditing, based on the literature review research method. Findings: The use of AI in external auditing demonstrates how technology can be used to improve the efficiency, accuracy, and relevance of external audits, in line with the vision of a better society championed in Society 5.0. Practical implications: This study highlights AI's crucial role in enhancing external auditing by improving quality, efficiency, and accuracy, offering practical insights for audit firms to streamline processes, reduce errors, and adapt to technological advancements in the era of Society 5.0. Originality/value: This article contributes to the literature by exploring the broader societal implications of AI in external auditing within the context of Society 5.0, highlighting its role in not only enhancing audit quality but also promoting ethical and social dimensions aligned with the goals of this technology-driven framework.Journal of Multiperspectives on Accounting Literature240A literature review that discusses the role of AI in various aspects of external auditing, such as improving external audit quality, external audit process efficiency, and fraud detection highlights AI's crucial role in enhancing external auditing by improving quality, efficiency, and accuracy.Journal of Multiperspectives on Accounting Literature["Chintya Anindita Kumala", "Widhiyo Sudiyono"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/0fe9aaed5bea88c53d1221b562fed6d20098d18d bc32e43ff5a33f01b655e600659055150ba44763The rapid advancement of Artificial Intelligence (AI) presents transformative opportunities across sectors, particularly in education, peacebuilding, conflict resolution, and economic development. This paper examines the integration of AI into peace, conflict, and security education as a means to foster skill development and drive economic empowerment. AI-driven tools and strategies, such as personalized learning, predictive analytics, and simulation-based training, are explored for their potential to revolutionize educational outcomes and prepare individuals for real-world challenges. Personalized learning systems adapt to individual needs, fostering deeper engagement and better knowledge retention. Predictive analytics, meanwhile, enable early detection of potential conflicts and support data-driven decision-making. Simulation-based training provides immersive experiences for learners, equipping them with practical skills in negotiation, mediation, and crisis management. However, the integration of AI in this domain faces challenges, including access inequality, algorithmic bias, and data privacy concerns. In many low-resource settings, limited access to AI technologies exacerbates the digital divide, hindering equitable participation in AI-enhanced education. Furthermore, biases in AI algorithms can perpetuate existing inequalities, emphasizing the need for ethical frameworks and transparent governance. The paper provides actionable recommendations, including enhancing digital infrastructure, fostering public-private partnerships, and developing ethical policies to guide AI deployment responsibly. These measures aim to align peace and security education with sustainable development goals (SDGs), promoting inclusive learning and economic empowerment globally. By addressing challenges and leveraging AI’s potential, this study highlights pathways to create resilient, innovative, and equitable systems for education and development.International Journal of African Innovation and Multidisciplinary Research200This paper examines the integration of AI into peace, conflict, and security education as a means to foster skill development and drive economic empowerment, and provides actionable recommendations, including enhancing digital infrastructure, fostering public-private partnerships, and developing ethical policies to guide AI deployment responsibly.International Journal of African Innovation and Multidisciplinary Research["Prof. A.I. Mteiye", "I. R. Samuel"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/bc32e43ff5a33f01b655e600659055150ba44763 b6970ad8e353f11e16111d113fe8256d5247a818Explainable Artificial Intelligence has emerged as a critical tool in addressing the transparency challenges associated with machine learning models. This study investigates the application of XAI techniques in the educational domain, with a focus on identifying factors influencing academic performance. Using datasets encompassing student demographics, academic achievements, and contextual variables, machine learning models were developed and analyzed using SHapley Additive exPlanations. The results highlighted the significance of higher qualification achievements and early academic milestones, such as num_level_3_at_age_18 and num_key_stage_2_attainment. These findings corroborate existing literature while providing novel insights through visual and interpretable analytics. The study demonstrates the transformative potential of XAI in uncovering actionable insights, offering policymakers and educators tools to address disparities in educational outcomes. The novelty of applying XAI in this context lies in its ability to bridge the gap between complex predictive models and practical decision-making. Future research directions include expanding datasets to incorporate diverse educational settings and developing real-time educational tools based on interpretability insights. This work lays the foundation for leveraging XAI to drive equity and excellence in education.ADBA Computer Science00This study investigates the application of XAI techniques in the educational domain, with a focus on identifying factors influencing academic performance, and demonstrates the transformative potential of XAI in uncovering actionable insights.ADBA Computer Science["Burcu Kutlu", "M. Kutlu"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/b6970ad8e353f11e16111d113fe8256d5247a818 d1b92fed030efa32127a6d0fe51d79fad7b29ab5Artificial Intelligence (AI) in the realm of community welfare is giving birth to possibilities and thoughtfulness in contemporary society. This technological revolution not only improves the quality of life but also contributes effectively to health, education, poverty alleviation, disaster management, and other social services. Through AI, better decision-making, efficient resource distribution, and rapid problem-solving are becoming feasible, thereby ensuring prosperity and justice in society. For example, AI-based predictive models are assisting in the management of unforeseen disasters and are paving the way for new diagnoses and treatments in the field of medicine. However, a pertinent question arises: how should its development and usage for human welfare be guided by ethical guidelines? Additionally, it is essential to understand how AI (Artificial Intelligence) can reduce social inequalities and influence the human decision-making process. Should it be viewed merely as a technical tool, or should it be integrated with principles such as ethical responsibilities, privacy, and equality? Adopting a human-centered perspective, this research paper analyzes not only the technical benefits of AI but also its ethical, social, and philosophical aspects. The aim of this research paper is to clarify how AI can be used as a collaborative tool to enhance community participation and self-reliance. By integrating technical innovations with philosophical perspectives, this paper presents a framework for achieving long-term goals of community welfare. Therefore, understanding these aspects of AI will not only expand its scope but also ensure that technological progress remains balanced with human values.The Voice of Creative Research30This research paper is to clarify how AI can be used as a collaborative tool to enhance community participation and self-reliance and presents a framework for achieving long-term goals of community welfare.The Voice of Creative Research["Manisha Mishra"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/d1b92fed030efa32127a6d0fe51d79fad7b29ab5 a2c75824d3c91bc005496830b5b08a9667e297f0The article is devoted to the issue of translation of artificial intelligence terms. The manual “Make Your Own Neural Network” by T. Rashid, which describes in detail the process of creating a neural network, its design and functioning, was chosen as the material for analysis due to the accessibility of the information for a non-specialist in this field. Artificial intelligence is a rapidly developing field, and therefore the terminological f ield of this science is also expanding and diversifying. One of the distinctive features of artificial intelligence as a scientific field is its cross-disciplinary nature as it deals with mathematics, physics, robotics as well as biology and medicine. The terms were divided into two major groups: terms directly related to the field of artificial intelligence and terms borrowed from adjacent fields. The terms were then categorized according to whether they had one, two or more equivalents or no equivalents in order to identify the pattern of equivalence in the target language and the difficulties encountered when the required variant was not available. Often, due to the novelty of AI terms, the translator does not find an equivalent in the target language and therefore resorts to calque, transliteration, transcription or their hybrid. In this article, the author suggests the variants of translation of the terms that have caused the greatest difficulty in translation, which, in our opinion, will better meet the requirements of the term as a lexical unit.Stephanos Peer reviewed multilanguage scientific journal00The author suggests the variants of translation of the terms that have caused the greatest difficulty in translation, which, in his opinion, will better meet the requirements of the term as a lexical unit.Stephanos Peer reviewed multilanguage scientific journal["Anna Smirnova"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/a2c75824d3c91bc005496830b5b08a9667e297f0 8455ef705105f30d328aff8f4d5c2cad6a81f90cBackground and aims Artificial intelligence (AI)-driven medical assistive technology has been widely used in the diagnosis, treatment and prognosis of diabetes complications. Here we conduct a bibliometric analysis of scientific articles in the field of AI in diabetes complications to explore current research trends and cutting-edge hotspots. Methodology On April 20, 2024, we collected and screened relevant articles published from 1988 to 2024 from PubMed. Based on bibliometric tools such as CiteSpace, Vosviewer and bibliometix, we construct knowledge maps to visualize literature information, including annual scientific production, authors, countries, institutions, journals, keywords and research hotspots. Results A total of 935 articles meeting the criteria were collected and analyzed. The number of annual publications showed an upward trend. Raman, Rajiv published the most articles, and Webster, Dale R had the highest collaboration frequency. The United States, China, and India were the most productive countries. Scientific Reports was the journal with the most publications. The three most frequent diabetes complications were diabetic retinopathy, diabetic nephropathy, and diabetic foot. Machine learning, diabetic retinopathy, screening, deep learning, and diabetic foot are still being researched in 2024. Conclusion Global AI research on diabetes complications is expected to increase further. The investigation of AI in diabetic retinopathy and diabetic foot will be the focus of research in the future.Frontiers in Artificial Intelligence510A bibliometric analysis of scientific articles in the field of AI in diabetes complications to explore current research trends and cutting-edge hotspots finds that the investigation of AI in diabetic retinopathy and diabetic foot will be the focus of research in the future.Frontiers in Artificial Intelligence["Yukun Tao", "Jinzheng Hou", "Guangxin Zhou", "Da Zhang"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/8455ef705105f30d328aff8f4d5c2cad6a81f90c aadd987e36c1b4ad3b06e1a687cbbea87d8630e2The integration of Artificial Intelligence (AI) into business operations has garnered significant attention due to its potential impact on business performance. However, the relationship between AI adoption and business performance remains not fully understood. This article comprehensively analyzes this relationship through three key aspects: the acceptance and implementation of AI within organizations, the impact of AI on various dimensions of business performance, and the potential challenges associated with AI adoption. In this study, we employ SmartPLS as an analytical tool to evaluate the relationships between identified factors and the impact of AI adoption on business performance. Our findings reveal that several factors influence the adoption and implementation of AI, including data availability, organizational culture, leadership support, technical expertise, and ethical considerations. Moreover, AI adoption significantly influences business performance metrics such as productivity, efficiency, revenue, and customer satisfaction. Nonetheless, challenges arising from AI adoption, including shifts in job roles, data privacy, and security concerns, also require substantial attention. In conclusion, successful AI adoption and implementation necessitate careful consideration of organizational, technical, and ethical factors. This research provides valuable insights for business leaders and researchers seeking a deeper understanding of the relationship between Artificial Intelligence and business performance.IJCCS (Indonesian Journal of Computing and Cybernetics Systems)00This article comprehensively analyzes the relationship between AI adoption and business performance through three key aspects: the acceptance and implementation of AI within organizations, the impact of AI on various dimensions of business performance, and the potential challenges associated with AI adoption.IJCCS (Indonesian Journal of Computing and Cybernetics Systems)["Ninda Lutfiani", "Irwan Sembiring", "Iwan Setyawan", "Adi Setiawan", "U. Rahardja", "Sulistio Sulistio"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/aadd987e36c1b4ad3b06e1a687cbbea87d8630e2 9cae0fa6a27e5cf4caf5e00c5df89a28eca8298bArtificial intelligence (AI) defines technologies that imitate and improve human intelligence using machines, Chat GPT is a conversational agent capable of enabling an Internet user to chat instantly with a system based on artificial intelligence. Chat GPT excels in various fields, notably healthcare, providing medical information, describing complex terms, and giving provisional diagnoses, however the integration of ChatGPT in medicine presents limitations, such as the reliability of the information provided, as well as remaining limited in its ability to reason in complex ways. In medicine, it is crucial to combine data analysis with clinical considerations, a critical reflection that AI cannot replace.We present the case of a patient who has had an anal mass for several years. The patient consulted ChatGPT for searching possible causes, the first aetiology mentioned being haemorrhoids, and among the proposed therapeutic options elastic ligation was cited. The patient attempted to perform this ligation independently using a thread, and he went to the emergency room due to the appearance of intense acute proctalgia ,The thread was removed with difficulty by the doctor, and symptomatic medical treatment was administered, the proctological examination that followed suggested an anal condyloma, which was confirmed by the anatomopathological results of the biopsy, and the patient was then referred for electrocoagulation of the condyloma. International Journal of Advanced Research00Chat GPT excels in various fields, notably healthcare, providing medical information, describing complex terms, and giving provisional diagnoses, however the integration of ChatGPT in medicine presents limitations, such as the reliability of the information provided, as well as remaining limited in its ability to reason in complex ways.International Journal of Advanced Research["A. Zaoui", "M. Boussera", "S. Mliyahe", "L. Aoufi", "A. Akjay", "H. Ouaya", "I. Mellouki", "H. Meyiz"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/9cae0fa6a27e5cf4caf5e00c5df89a28eca8298b e0a22b30e9ebbf8a26ff8d42d28b15220158f60dBackground Artificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. Machine learning, a subset of artificial intelligence, concentrates on empowering computers to learn and enhance from data autonomously; this narrative review seeks to elucidate the utilization of artificial intelligence in fostering physical activity, training, exercise, and health outcomes, addressing a significant gap in the comprehension of practical applications. Methods Only Randomized Controlled Trials (RCTs) published in English were included. Inclusion criteria: all RCTs that use artificial intelligence to program, supervise, manage, or assist physical activity, training, exercise, or health programs. Only studies published from January 1, 2014, were considered. Exclusion criteria: all the studies that used robot-assisted, robot-supported, or robotic training were excluded. Results A total of 1772 studies were identified. After the first stage, where the duplicates were removed, 1,004 articles were screened by title and abstract. A total of 24 studies were identified, and finally, after a full-text review, 15 studies were identified as meeting all eligibility criteria for inclusion. The findings suggest that artificial intelligence holds promise in promoting physical activity across diverse populations, including children, adolescents, adults, older adult, and individuals with disabilities. Conclusion Our research found that artificial intelligence, machine learning and deep learning techniques were used: (a) as part of applications to generate automatic messages and be able to communicate with users; (b) as a predictive approach and for gesture and posture recognition; (c) as a control system; (d) as data collector; and (e) as a guided trainer.Frontiers in Public Health550The research found that artificial intelligence, machine learning and deep learning techniques were used as part of applications to generate automatic messages and be able to communicate with users, and as a predictive approach and for gesture and posture recognition.Frontiers in Public Health["Alberto Canzone", "Giacomo Belmonte", "Antonino Patti", "Domenico Savio Salvatore Vicari", "Fabio Rapisarda", "Valerio Giustino", "P. Drid", "A. Bianco"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/e0a22b30e9ebbf8a26ff8d42d28b15220158f60d 7c7a87066376d1bd2811b53bbb41f67a1cb34451This study examines the challenges associated with employing artificial intelligence AI in Libyan higher education. The research identifies significant obstacles, including weak educational policies, inadequate digital infrastructure, and high implementation costs. The study surveyed 314 faculty members from various Libyan universities, revealing that these challenges impede the effective use of AI in education. The findings suggest that addressing these issues is crucial for leveraging AIs potential in improving educational outcomes. The study concluded that the most prominent challenges in employing artificial intelligence in higher education are the high financial costs of implementing AI programs in these institutions The study concludes with recommendations for enhancing digital infrastructure and training human resources to overcome these challenges.Journal of Research in Vocational Education130The study concluded that the most prominent challenges in employing artificial intelligence in higher education are the high financial costs of implementing AI programs in these institutions and recommendations for enhancing digital infrastructure and training human resources to overcome these challenges.Journal of Research in Vocational Education["Sahibdeep Singh", "G. Bhathal"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/7c7a87066376d1bd2811b53bbb41f67a1cb34451 47b245e4dc50cf187da8be5bf31513e6a6264befTo explore the opportunities and challenges of artificial intelligence (AI) in nursing and its impact. Bibliographic review using Arksey and O'Malley's framework, enhanced by Levac, Colquhoun and O'Brien and following PRISMA guidelines, including qualitative and mixed studies. MeSH terms and keywords such as nursing education and ethical considerations were used in databases such as PubMed, Scopus, Web of Science, CINAHL, IEEE Xplore and Google Scholar. Of all, 53 studies were included, highlighting various opportunities and challenges of AI integration and opportunities for personalised learning, training improvement and evaluation. Highlighting challenges related to academic integrity, accuracy, data privacy and security, for the development of critical thinking skills. The integration of AI in nursing education offers significant advantages for improving the quality and effectiveness of education, such as academic integrity, critical thinking and equitable access, for this reason, faculty training should be geared toward the integration of AI in nursing education.European Journal of Education430The integration of AI in nursing education offers significant advantages for improving the quality and effectiveness of education, such as academic integrity, critical thinking and equitable access, for this reason, faculty training should be geared toward the integration of AI in nursing education.European Journal of Education["Estel\u00b7la Ram\u00edrez\u2010Baraldes", "Daniel Garc\u00eda-Guti\u00e9rrez", "Cristina Garc\u00eda-Salido"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/47b245e4dc50cf187da8be5bf31513e6a6264bef fb68d166a8851006ebd4601f039014288ffa53c5The incorporation of technological advancements, particularly Artificial Intelligence has transformed healthcare systems globally, especially post-COVID-19. Medical education faces challenges in incorporating AI due to instructor shortages and high software costs. Understanding medical students' attitudes towards AI is crucial for its successful integration into medical practice and education. Objective: To evaluate the attitude of medical undergraduate students towards AI in medicine. Methods: A descriptive, online cross-sectional study was executed among undergraduate medical students utilizing a non-probability convenience sampling. The questionnaire, distributed to 340 participants, included demographic details, perceptions towards artificial intelligence, and its effect on medical education. A total of 252 responses were received, receiving a 74% response rate. Data analysis was executed through SPSS version 26.0. Results: Demographic characteristics of 252 subjects revealed a mean age of 23.5 years, with a majority being female (74.2%) and in their first to third year of study (58.3%). Participants generally had intermediate computer literacy (75.7%) and used technology consistently for learning (57.5%). Regarding perceptions of AI, most students strongly agreed that AI will significantly impact healthcare (48.8%) and that all medical students should be educated about it (31.3%). Additionally, a substantial majority believed that integrating AI into medical education would enhance its quality (66.6%) and facilitate the learning experience (57.9%). Conclusions: It was concluded that students have positive perceptions regarding AI systems, demonstrating enthusiasm for expanding their knowledge of AI within their medical education.Pakistan journal of health sciences190It was concluded that students have positive perceptions regarding AI systems, demonstrating enthusiasm for expanding their knowledge of AI within their medical education.Pakistan Journal of Health Sciences["Shazia Rizwan", "Shahveir Rizwan", "Maheer Rizwan", "Ali Hashim", ".. Nawal", "Saima Batool"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/fb68d166a8851006ebd4601f039014288ffa53c5 c7912f347677cb87e895b1e7cf71d97c260644c5Artificial intelligence and stroke are an intriguing union with vast improvements in health benefits anticipated. Impactful artificial intelligence contributions are emerging in stroke diagnosis, rehabilitation protocols, and movement activity monitoring. Identifying stroke locations via magnetic renounce images will shorten the time delay post stroke and minimize the effects of the disrupted blood flow in the brain. Insightful rehabilitation protocols will be evidence-based as well as prescribed for specific individuals post stroke. Machine learning, immersive virtual reality, and deep learning will further substantiate the importance of voluntary movements as the basis for neural plasticity post stroke.Artificial Intelligence, Machine Learning, &amp; Robotics in Business100Impacted artificial intelligence contributions are emerging in stroke diagnosis, rehabilitation protocols, and movement activity monitoring, and machine learning, immersive virtual reality, and deep learning will further substantiate the importance of voluntary movements as the basis for neural plasticity post stroke.Artificial Intelligence, Machine Learning, &amp; Robotics in Business["James Cauraugh"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/c7912f347677cb87e895b1e7cf71d97c260644c5 367cdf219f48a2cc84fc30900c130c3e6d985bd0In this research, we will discuss artificial intelligence definition and its role in various fields of engineering and the advantage use in architecture engineering , civil engineering, chemical engineering, electrical engineering etc. In this paper, we present a comprehensive analysis of the impact of using artificial intelligence in engineering and transforming traditional work that requires great physical effort and a certain time into work characterized by accuracy, speed, and appropriate mental effort, in addition to innovation and continuous development in the paths of theoretical and practical operations in implementation sites, to improve efficiency and accuracy and progress of workEngineering and Technology Journal00A comprehensive analysis of the impact of using artificial intelligence in engineering and transforming traditional work that requires great physical effort and a certain time into work characterized by accuracy, speed, and appropriate mental effort is presented.Engineering and Technology Journal["Nagham Mumtaz"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/367cdf219f48a2cc84fc30900c130c3e6d985bd0 7b78f1417883514e04173f6ea9b06eaab7223388Penelitian ini bertujuan untuk: (1) Merancang produk video pembelajaran Animasi 3D berbantuan Artificial Intelligence pada materi dampak informatika (2) Untuk mengetahui tingkat kelayakan Video Pembelajaran Animasi 3D berbantuan Artificial Intelligence menurut ahli materi dan ahli media. (3) Untuk mengetahui tingkat kepraktisan Video Pembelajaran Animasi 3D berbantuan Artificial Intelligence menurut respon pengguna (peserta didik). Metode yang digunakan dalam penelitian ini adalah metode pengembangan Research and Development dengan model Analysis, Design, Development, Implementation dan Evaluation (ADDIE) dengan hasil pengujian kelayakan ahli materi menunjukan presentase kelayakan 97% dengan kategori “Sangat Layak”, hasil pengujian kelayakan ahli media dengan presentase kelayakan sebesar 97% dengan kategori “Sangat Layak”, serta hasil uji kepraktisan produk pada responden berdasarkan seluruh indikator menunjukan presentase 84,81% dengan kategori “Sangat Layak”. Sehingga dapat disimpulkan bahwa Video pembelajaran Animasi 3D berbantuan Artificial Intelligence ini layak digunakan sebagai sumber belajar siswa dalam proses pembelajaran.Inverted: Journal of Information Technology Education90Inverted: Journal of Information Technology Education["Fardiansyah Wardam", "Nikmasari Pakaya", "Arif Dwinanto", "Manda Rohandi", "Abd. Aziz Bouty", "Eka Vickraien Dangkua", "Moh. Syafri Tuloli"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/7b78f1417883514e04173f6ea9b06eaab7223388 04da0b3afb944d87f158483fa721f6023dc7a367Explainability in artificial intelligence (AI) is an essential factor for building transparent, trustworthy, and ethical systems, particularly in ‎high-stakes domains such as healthcare, finance, justice, and autonomous systems. This study examines the foundations of AI explainability, ‎its critical role in fostering trust, and the current methodologies used to interpret AI models, such as post-hoc techniques, intrinsically inter-‎pretable models, and hybrid approaches. Despite these advancements, challenges persist, including trade-offs between accuracy and inter-‎pretability, scalability, ethical risks, and transparency gaps. The paper explores emerging trends like causality-based explanations, neuro-‎symbolic AI, and personalized frameworks, while emphasizing the integration of ethics and the need for automation in explainability. Future ‎directions stress the importance of collaboration among researchers, practitioners, and policymakers to establish industry standards and ‎regulations, ensuring that AI systems align with societal values and expectations.International Journal of Scientific World00The foundations of AI explainability, its critical role in fostering trust, and the current methodologies used to interpret AI models, such as post-hoc techniques, intrinsically inter-‎pretable models, and hybrid approaches are examined.International Journal of Scientific World["Ridwan Boya Marqas", "Saman M. Almufti", "Rezhna Azad Yusif"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/04da0b3afb944d87f158483fa721f6023dc7a367 0ab5893767982174552ff12dc9a5392238388421This research explores the transformative impact of Artificial Intelligence (AI) on the human role in the military using a qualitative descriptive method. AI is increasingly automating tasks, enabling humans to focus on strategic and ethical decision-making. However, this shift necessitates adaptations in training, doctrine, and organizational structures. The research analyses the ethical challenges, including accountability and the prevention of unethical use, and highlights the critical importance of cybersecurity in mitigating risks. Key findings suggest that AI will significantly enhance operational efficiency while presenting new challenges related to human oversight, ethical considerations, and the need for robust cybersecurity measures. The study concludes with recommendations for developing training curricula, establishing ethical standards, and investing in cybersecurity to ensure the responsible and effective integration of AI in the military domain.Embedded Systems and Applications00This research explores the transformative impact of Artificial Intelligence on the human role in the military using a qualitative descriptive method and suggests that AI will significantly enhance operational efficiency while presenting new challenges related to human oversight, ethical considerations, and the need for robust cybersecurity measures.Indonesian Journal of Applied and Industrial Sciences (ESA)["Hendriman Putra", "Budi Eko Mulyono"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/0ab5893767982174552ff12dc9a5392238388421 54af6709575b11342fe803857e621a9f54c0cc5eThe subject classification of research publications enhances navigation in the flow of science literature, enables bibliometric analysis, multitier assessment of research performance. The universal character of the UN agenda of sustainable development and importance of sustainable development goals (SDGs) and scientific research to achieve them, and the complex and multiaspect SDGs stir high interest of bibliographers, scientometrics community, international science databases, in the problem of correlating science publications and SDGs. The Web of Science, Scopus, Dimensions, as well as the individual researchers apply various approaches to classifying the articles on SDGs, and these classifications have their strengths and weaknesses. The differences in the resulting classifications calls for the analysis and improvement of methods and approaches. The evolving generative artificial intelligence technologies and big language models open up new possibilities for the subject classification of science texts including those related to the UN SDGs. The authors analyze the methods used to classify publications as SDG-related, and demonstrate the applicability of big language models as exemplified by ChatGPTScientific and Technical Libraries170The authors analyze the methods used to classify publications as SDG-related, and demonstrate the applicability of big language models as exemplified by ChatGPT.Scientific and Technical Libraries["I. V. Selivanova", "P. Y. Blinov", "A. Malysheva", "D. Kosyakov"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/54af6709575b11342fe803857e621a9f54c0cc5e fd29524d3b91a17cde29f77ac5e90700d466a7b3Nature Communications370The economic status of countries is found to be connected to their climate actions and SDGs alignment, and utility and promise are demonstrated in using AI techniques to unravel interactions between CA and SDG.Nature Communications["Haein Cho", "Emmanuel Ackom"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/fd29524d3b91a17cde29f77ac5e90700d466a7b3 4a325f2046a3e46d895d9a95328b1b3a3a752412Infectious diseases remain a significant global health challenge, contributing to high morbidity and mortality rates, particularly in low- and middle-income countries. The COVID-19 pandemic underscored the urgent need for innovative solutions to prevent, detect, and manage infectious diseases effectively. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering opportunities to enhance disease surveillance, diagnosis, treatment, and public health response. This research paper explores the potential of AI in combating infectious diseases, emphasizing its challenges and opportunities. A mixed-methods approach, incorporating literature review, case studies, and expert interviews, is used to identify key applications, limitations, and strategies for leveraging AI in global health. Recommendations for ethical implementation, data integration, and capacity building are provided to maximize the impact of AI in addressing infectious diseases.Journal of Medical &amp; Health Sciences Review190This research paper explores the potential of AI in combating infectious diseases, emphasizing its challenges and opportunities and recommends recommendations for ethical implementation, data integration, and capacity building.Journal of Medical &amp; Health Sciences Review["Dr. Samra Khalil", "Dr. Ikram Ali Shah", "Dr. Aiman Aslam", "Dr. Zainab Hasan", "Misha Aslam", "Prof. Dr. Asma Fatima"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/4a325f2046a3e46d895d9a95328b1b3a3a752412 e50fcd20df7915d4d994a06cbeed595a3ecce58fThis study examines the impact of artificial intelligence (AI) on the speed and accuracy of accounting information systems (AIS) in Indonesia, using a quantitative research approach. Data were collected from 38 respondents representing businesses that have implemented AI in their accounting processes. A structured questionnaire employing a 5-point Likert scale was used to measure perceptions of AI's effects on AIS performance. The data were analyzed using SPSS version 25, employing descriptive statistics, correlation, and regression analyses. The results indicate that AI integration significantly improves both the speed and accuracy of AIS, with accuracy showing a slightly stronger impact. These findings highlight AI's potential to enhance decision-making, operational efficiency, and financial reporting quality. This study contributes valuable insights for practitioners and policymakers aiming to optimize accounting practices through AI adoption.Jurnal Akuntansi Dan Keuangan West Science200Examination of the impact of artificial intelligence on the speed and accuracy of accounting information systems in Indonesia indicates that AI integration significantly improves both the speed and accuracy of AIS, with accuracy showing a slightly stronger impact.Jurnal Akuntansi Dan Keuangan West Science["Loso Judijanto", "M. Ar"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/e50fcd20df7915d4d994a06cbeed595a3ecce58f eb85a01a53a98e3071fa534cfff1fba496996a73With the growth of artificial intelligence (AI) and Industry 4.0, construction management has entered a phase of rapid digital transformation. In order to effectively adopt digital applications of construction management, this paper aims to identify the specific applications of AI in construction management from the perspective of Construction 4.0, especially when applying technologies from Industry 4.0. A scoping review methodology was used to explore the limited literature in this research area. 60 articles were selected to analyze the state of the art of AI applications in construction management, especially for schedule management, cost management, quality management, and health and safety management. This review shows that AI has mainly been used in the preliminary design and construction phases of the above management areas, and proposes a research framework to highlight the contemporary development and needs for AI integration in construction management. The main contributions of this paper are its practical exploration of AI applications in construction management, its human-centered approach to AI adoption, and the introduction of a novel research framework to guide industry practitioners in AI integration.Journal of Internet Technology00This review shows that AI has mainly been used in the preliminary design and construction phases of the above management areas, and proposes a research framework to highlight the contemporary development and needs for AI integration in construction management.Journal of Internet Technology["Jianying Lai", "Heap-Yih Chong", "Bin Qin", "L. Liao", "Han-Chieh Chao"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/eb85a01a53a98e3071fa534cfff1fba496996a73 f86c338303d8fdb7030ad0d409d81fa85ef99f39Artificial Intelligence (AI) presents a powerful opportunity for preserving and promoting Hausa culture, language, and literature. This paper explores AI's role in safeguarding and promoting Hausa heritage through machine translation, speech recognition, and AI-generated content. It reviews the current state of AI applications for Hausa, addressing challenges such as limited datasets and the effectiveness of existing tools. The study proposes several AI-driven approaches for enhancing Hausa culture, including translation, content generation, cultural archiving, language learning, and sentiment analysis. It highlights the importance of interdisciplinary collaboration and ethical considerations in developing culturally sensitive AI solutions. Future directions focus on creating representative datasets, developing tailored tools, and fostering partnerships to ensure AI's positive impact on Hausa cultural preservation.International Journal of African Development and Sustainable Research00This paper explores AI's role in safeguarding and promoting Hausa heritage through machine translation, speech recognition, and AI-generated content, and proposes several AI-driven approaches for enhancing Hausa culture.International Journal of African Development and Sustainable Research["AMINU SAEED HARUNA", "SHITU ABDULLAHI LAME", "BAPPALE BAPPAYO"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/f86c338303d8fdb7030ad0d409d81fa85ef99f39 a20c629d7536e45ec74af5025f96409d895d6b9dHighlights: The reuse of 3D reality-based digital data in theatre productions supports the cross-valorisation of cultural activities. The frieze of the Ospedale del Ceppo: from digital documentation for preservation to scenic design and performance. The integration of digital technologies, avatars and artificial intelligence in contemporary theatre scenography. Abstract: The integration of three-dimensional (3D) digital technologies into cultural heritage and theatre is transforming how historical works are preserved and experienced. This paper focuses on the performance I Fregi del Ceppo, which exemplifies this trend by using 3D data to bring the Renaissance friezes of the Ospedale del Ceppo in Pistoia, Italy, to life. Originally digitised for conservation, the friezes served as the foundation for a theatrical production. The project used artificial intelligence (AI) tools to analyse and animate the frieze characters’ postures and relationships. The performance incorporated a 180º multi projection system that synchronised human actors with digital projections, merging live performance with digital heritage. This work highlights the broader trend of integrating AI and digital tools into theatre. Body scans, motion tracking, and emotion recognition enable new storytelling methods, while virtual characters and avatars allow performers to explore identity and interaction in novel ways. The fusion of AI with performance art is pushing the boundaries of creativity, generating dialogues, analysing performances, and enabling real-time interaction with human actors. I Fregi del Ceppo demonstrates how digital heritage can enhance theatre, extending the life of historical works and offering new cultural experiences. It also points to a future where AI and 3D technologies will play an increasingly central role in shaping the performing arts. Virtual Archaeology Review380The performance I Fregi del Ceppo demonstrates how digital heritage can enhance theatre, extending the life of historical works and offering new cultural experiences, and points to a future where AI and 3D technologies will play an increasingly central role in shaping the performing arts.Virtual Archaeology Review["Pietro Bartolini", "A. Conti", "Lidia Fiorini", "G. Tucci"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/a20c629d7536e45ec74af5025f96409d895d6b9d f9fe00fbed18ad01cad5d958369225060b5888fcFull-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced Spectral Mammography (CESM), a second-level imaging technique, offers enhanced accuracy in tumor detection. Nonetheless, its application is restricted due to higher radiation exposure, the use of contrast agents, and limited accessibility. As a result, CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM despite the superior diagnostic performance of CESM. While biopsy remains the gold standard for definitive diagnosis, it is an invasive procedure that can cause discomfort for patients. We introduce a multimodal, multi-view deep learning approach for virtual biopsy, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign. To address the challenge of missing CESM data, we leverage generative artificial intelligence to impute CESM images from FFDM scans. Experimental results demonstrate that incorporating the CESM modality is crucial to enhance the performance of virtual biopsy. When real CESM data is missing, synthetic CESM images proved effective, outperforming the use of FFDM alone, particularly in multimodal configurations that combine FFDM and CESM modalities. The proposed approach has the potential to improve diagnostic workflows, providing clinicians with augmented intelligence tools to improve diagnostic accuracy and patient care. Additionally, as a contribution to the research community, we publicly release the dataset used in our experiments, facilitating further advancements in this field.00A multimodal, multi-view deep learning approach for virtual biopsy is introduced, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign and has the potential to improve diagnostic workflows.["Aurora Rofena", "C. Piccolo", "B. B. Zobel", "P. Soda", "V. Guarrasi"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/f9fe00fbed18ad01cad5d958369225060b5888fc c362931158235844f801d9a6ce91dc6a5200b2ddPurposeArtificial intelligence (AI) has profoundly reshaped financial decision-making, introducing a paradigm shift in how institutions and individuals navigate the complex finance landscape. The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics.Design/methodology/approachThe study offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions. The framework excels in handling and analyzing large volumes of numerical and textual data through advanced data mining techniques.FindingsFinSageNet demonstrates exceptional text summarization capabilities, outperforming models like FLAN and GPT-3.5 in Rouge score metrics. The proposed model has shown more accuracy than generic models.Originality/valueThe study emphasizes the significance of consistently updating models and adopting a comprehensive approach to integrating AI into financial decisions. This study improves our understanding of how artificial intelligence transforms financial analytics and decision-making processes.Management Decision800The study evaluates the significant impact of integrating advanced AI and large language models (LLMs) in financial decision analytics and offers FinSageNet, a novel framework designed and tested to harness the potential of LLMs in financial decisions.Management Decision["Anshul Saxena", "Bikramjit Rishi"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/c362931158235844f801d9a6ce91dc6a5200b2dd 3df13538dfeb72f1d6858f1b8e19fc66b3aa44c5CECCAR Business Review01CECCAR Business Review["Claudiu Br\u00e2ndas", "Ioan Minda"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/3df13538dfeb72f1d6858f1b8e19fc66b3aa44c5 16016525abde4da220ae70cce2dcdf28cd019c20Asia-pacific Journal of Convergent Research Interchange00Asia-pacific Journal of Convergent Research Interchange["Bom Park", "Dongphil Chun"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/16016525abde4da220ae70cce2dcdf28cd019c20 6488f9c8239814ecd9a08c7150a598cabfc333eaBackground: Technology integration has revolutionized traditional teaching methods in the ever-changing field of education, especially in language learning. The development of AI has created new opportunities for individualized education by allowing customized learning experiences that meet the needs of each learner. Objective: The research aims to investigate how an AI-driven adaptive assessment and feedback system can improve English learning outcomes. This study integrates real-time feedback creation with automated essay scoring to address the problems of conventional evaluation techniques. Methods: This research gathers a diverse student essay from various educational institutions to ensure a broad range of themes, writing styles, and skill levels. The data is clean using duplicate removal, pre-processed through text filtering and tokenization, and features extracted using Bag of [Formula: see text]-Grams and Word2vec for word sequence patterns. The Seq2Seq model feedback generation algorithm aims to deliver feedback that is contextually appropriate and semantically meaningful. This study proposed a novel RO-ERNN to predict the quality of essays and improve English language learning outcomes through effective evaluation and feedback. RO optimizes model performance, which leads to more reliable assessments of students’ writing skills and provides accurate predictions of essay quality. ERNN generates meaningful feedback for students. Results: The findings show that the Seq2Seq approach produces feedback that improves learners’ understanding of language components. The study evaluates Accuracy (98.91%), vocabulary retention rate (80%) proficiency level gain (90%), feedback accuracy (95%), [Formula: see text](97.780%), [Formula: see text]1-score (97.50%), recall (95.40%), and precision (96.50%) demonstrating its potential to optimize language acquisition and learner performance. Conclusion: This research has significant consequences for teachers and students, creating new opportunities for technological innovation in English language learning that are more efficient and interesting.International Journal of High Speed Electronics and Systems00This study proposed a novel RO-ERNN to predict the quality of essays and improve English language learning outcomes through effective evaluation and feedback, and shows that the Seq2Seq approach produces feedback that improves learners’ understanding of language components.International Journal of High Speed Electronics and Systems["Lihua Song", "Jing Gao", "Xin Zhao"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/6488f9c8239814ecd9a08c7150a598cabfc333ea 6b165c8aff221a10cabb6426956a90eea6015fb1This research seeks to explore the influence of AI adoption on ESG performance and further assess the mediation effect of ESG performance in the relation between AI adoption and firm value. The research was carried out from 2020 to 2023 on companies in Indonesia, yielding 288 observational data points. A multivariate analysis was performed utilising partial least squares structural equation modelling (PLS-SEM) to assess the hypothesis. The findings from hypothesis testing demonstrate that AI adoption has a significant favourable impact on ESG performance. Similarly, ESG performance significantly enhances firm value. Additionally, the indirect effects analysis reveals that ESG performance effectively mediates the positive relationship between AI adoption and firm value. AI enhances ESG performance by serving as a strategic resource, improving efficiency, and advancing sustainability to meet stakeholder expectations, further enhancing corporate value. This research encourages government support, managerial integration, and standardised policies for AI-driven business sustainability.Jurnal Akuntansi890The findings from hypothesis testing demonstrate that AI adoption has a significant favourable impact on ESG performance and ESG performance significantly enhances firm value, and the indirect effects analysis reveals that ESG performance effectively mediates the positive relationship between AI adoption and firm value.Jurnal Akuntansi["Dendi Mulyana", "Aristanti Widyaningsih", "R. Rozali"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/6b165c8aff221a10cabb6426956a90eea6015fb1 3cc95455ea26ddad7891373852a38d7d951a39feThe primary purpose is to provide a relationship between corruption and tax evasion and explain the use of AI and electronic devices as a means of connection based on today's technological advances. The method of data collection type of questionnaire used is the Likert scale. Data analysis used SEM-PLS because it has a high level of flexibility. The main findings are that tax evasion cases always occur in any country and vary in nominality. Tax regulations and corruption cases have an impact on reducing tax compliance. Theory and practical implications Through this research, the insights of parties regarding tax evasion will be broader for all parties who report taxes to increase the value of tax compliance. In addition, the government will understand the relationship of problems in the country. The level of tax evasion has results similar to those of most other studies that have a strict relationship with a country's tax regulations.Jurnal Akuntansi180The main findings are that tax evasion cases always occur in any country and vary in nominality and tax regulations and corruption cases have an impact on reducing tax compliance.Jurnal Akuntansi["Sari Dewi", "Gary", "Hanini Ilyana Che Hashim", "Kennardi Tanujaya"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/3cc95455ea26ddad7891373852a38d7d951a39fe e1c5e503c9fd2a81ab39417fe59aa1aef4d26e39This study investigates how cognitive and self-regulation factors impact online interview performance among college students with ADHD. With unemployment rates for individuals with disabilities significantly higher than the general population, understanding the unique challenges posed by AI-driven virtual interviews is critical. Forty-six students with ADHD completed a structured interview simulation using the Big Interview platform, coupled with eye-tracking data and cognitive assessments. Results reveal that higher-performing participants (Gold tier) demonstrated a balanced focus on content comprehension and interviewer engagement, while lower-performing participants (Bronze tier) spent significantly more time on content fixation. Logistic regression indicated that cognitive flexibility, as measured by NIH Dimensional Card Sorting, predicts interview success, emphasizing the importance of task-switching skills in virtual environments. These findings suggest the need for targeted interventions, such as executive function training, to prepare neurodivergent individuals for the demands of AI-driven hiring practices. The study highlights the potential of psychophysiological metrics in understanding and enhancing interview performance, advocating for inclusive, evidence-based strategies that align with Diversity, Equity, Inclusion, and Belonging (DEIB) principles. This research provides actionable insights for educators, employers, and technology developers aiming to create accessible and equitable virtual interview platforms.Education sciences290The need for targeted interventions to prepare neurodivergent individuals for the demands of AI-driven hiring practices is suggested, highlighting the potential of psychophysiological metrics in understanding and enhancing interview performance.Education Sciences["Tahnee L. Wilder", "Nicole E. Stratchan"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/e1c5e503c9fd2a81ab39417fe59aa1aef4d26e39 6f3c354dfae09e321e8f4f2dd7df785374b6d59cLinköping studies in education and social sciences00Linköping studies in education and social sciences["Katarina Sperling"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/6f3c354dfae09e321e8f4f2dd7df785374b6d59c f1d2828c96ed8e88aefec7f7ee35df163c79366fCureus510Cureus["Esteban Zavaleta\u2010Monestel", "Carolina Rojas-Chinchilla", "Jeimy Campos-Hern\u00e1ndez", "Ernesto Mart\u00ednez-Vargas"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/f1d2828c96ed8e88aefec7f7ee35df163c79366f 95f6dd4078612d27e9bc710ca13b8b819dcc54ed00["Arpita Nath Boruah", "Mrinal Goswami", "Manoj Kumar", "Octavio Loyola-Gonz\u00e1lez"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/95f6dd4078612d27e9bc710ca13b8b819dcc54ed f5dc96edd39f8dec9fe637ba91784cbb5023d2af本文探讨了人工智能（AI）技术在金融学课程创新中的应用，旨在为高校金融学教学改革提供指导。随着AI技术的快速发展，金融行业正经历深刻变革，对复合型金融人才的需求愈发迫切。文章首先梳理了建构主义、行为主义、混合式学习和联接主义学习理论在AI赋能金融学课程中的应用，包括情境模拟、个性化辅导、程序教学、强化机制、在线与面对面教学结合以及知识图谱技术建立知识点联系等。实践路径上，提出了基于AI的个性化教学策略、智能互动式教学模式和虚拟仿真实验教学应用，通过数据分析定制学习路径、智能工具增强课堂互动以及虚拟环境提升实操能力。此外，文章还探讨了AI优化教学评价，通过多维度数据驱动的评价指标体系、自动化与实时化评价过程以及促进学生全面发展的评价导向，实现对学生学习情况的全面、科学评估。最后，文章总结了研究结论，并对未来研究方向进行了展望。通过本文的研究，期望为高校金融学教学改革提供有益参考，助力培养适应新时代需求的高素质金融人才。 This paper explores the application of artificial intelligence (AI) technology in innovating finance courses, aiming to provide guidance for teaching reform in finance education at colleges and universities. With the rapid development of AI technology, the financial industry is undergoing profound transformations, and the demand for multidisciplinary financial talents is becoming increasingly urgent. Firstly, the paper examines the application of constructivism, behaviorism, blended learning, and connectivism learning theories in AI-empowered finance courses. These include scenario simulation, personalized tutoring, programmed instruction, reinforcement mechanisms, the integration of online and face-to-face teaching, and the establishment of knowledge point connections through knowledge graph technology. In terms of practical approaches, the paper proposes AI-based personalized teaching strategies, intelligent interactive teaching models, and virtual simulation experimental teaching applications. These involve customizing learning paths through data analysis, enhancing classroom interaction with intelligent tools, and improving practical operation skills within virtual environments.  Additionally, the paper explores the optimization of teaching evaluation through AI. This includes a multi-dimensional, data-driven evaluation index system, an automated and real-time evaluation process, and an evaluation framework aimed at fostering students’ all-round development, enabling comprehensive and scientific assessments of their learning outcomes. Finally, the paper summarizes the research findings and discusses future research directions. It is expected that the insights provided by this study will serve as a valuable reference for teaching reform in finance education, contributing to the cultivation of high-quality financial talents equipped to meet the demands of the new era.Theory and Practice of Social Science00The artificial intelligence (AI) industry is increasingly- reinforcement-based, with the face-to-face learning in universities, and the face-to-face learning in finance colleges is increasingly- reinforcement-based.Theory and Practice of Social Science["\u9a6c\u8273"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/f5dc96edd39f8dec9fe637ba91784cbb5023d2af 7e9a7895a1a69ab6eaee0346057490f773b7914cApplied Biosafety90Applied Biosafety["Matthew E. Walsh", "Gigi Kwick Gronvall"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/7e9a7895a1a69ab6eaee0346057490f773b7914c 94754d553adc78f1b33819980cef51cfd657a2daJournal of the Korea Society of Computer and Information00Journal of the Korea Society of Computer and Information["H. Yun", "Kwihoon Kim"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/94754d553adc78f1b33819980cef51cfd657a2da 5b77314126afd0e59806ee1e3bec6d7ee8e6729bCureus300Cureus["Christopher E Collins", "P. A. Giammanco", "Monica Guirgus", "Mikayla Kricfalusi", "Richard C Rice", "Rusheel Nayak", "David Ruckle", "Ryan Filler", "Joseph G Elsissy"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/5b77314126afd0e59806ee1e3bec6d7ee8e6729b ad61e210c9c0e3873538de1bc9a5455a3316332dMedical Science00Medical Science["Khalid Aljoqiman", "Nora Alsultan", "Thamer Alhabdan", "Latifah Aldhaif", "Mohammed Albesher", "Nora Albaqshi", "Zainab Bu-Khamsin", "Hussain Alali", "Fatimah Alhabdan"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/ad61e210c9c0e3873538de1bc9a5455a3316332d 14d1f0de5ecab34bd08487cc1ab6e5c2c790f432The Journal of Korean Institute of Information Technology120The Journal of Korean Institute of Information Technology["Cheolhee Yoon"]2025-01-31T00:00:00https://www.semanticscholar.org/paper/14d1f0de5ecab34bd08487cc1ab6e5c2c790f432 496fb0926cfc15efe753743bec1f59f10579c207This article discusses the implementation and impact of autonomous artificial intelligence (AI) systems for diabetic eye disease testing at the Johns Hopkins Medicine health system, highlighting improvements in screening rates, access to care, and health equity for underserved populations. The AI technology has been effective in both adult and pediatric populations and has reduced disparities and increased follow-up with eye care professionals. While considering the challenges and successes of this approach, this article also highlights the potential long-term impact of AI systems in improving visual health outcomes for people with diabetes in diverse health care settings.Diabetes Spectrum221Improvements in screening rates, access to care, and health equity for underserved populations are highlighted, highlighting the potential long-term impact of AI systems in improving visual health outcomes for people with diabetes in diverse health care settings.Diabetes Spectrum["T.Y. Alvin Liu", "Risa M. Wolf"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/496fb0926cfc15efe753743bec1f59f10579c207 9c7ec112bfd424a6c1118d9d6946227bf424aba1This survey study evaluates whether US adults trust health systems to use artificial intelligence responsibly and examines characteristics associated with attitudes related to the use of artificial intelligence in health care.JAMA Network Open31Whether US adults trust health systems to use artificial intelligence responsibly responsibly is evaluated and characteristics associated with attitudes related to the use of artificial intelligence in health care are examined.JAMA Network Open["Paige Nong", "Jody Platt"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/9c7ec112bfd424a6c1118d9d6946227bf424aba1 ac8373851a2bc4475e21a5b854d933fde86c3e4cArtificial Intelligence (AI) is transforming industries worldwide, creating both opportunities and challenges for IT professionals. This study examines the perceptions of AI among IT professionals in Kathmandu, focusing on job opportunities, threats, and the moderating role of Technology Literacy (TL). The research adopts a descriptive and causal-comparative design within the positivist paradigm, emphasizing measurable and objective data. A structured questionnaire with a 5-point Likert scale was distributed via Google Forms to 128 IT professionals in Kathmandu. Descriptive statistics indicated that IT professionals generally have a moderate level of Technology Literacy (mean scores ranging from 2.39 to 2.78). AI was perceived favorably, with mean scores between 3.02 and 3.39. However, perceived threats from AI were moderate (mean scores from 2.33 to 2.60), suggesting concerns about job security. Perceived opportunities were also moderate (mean scores between 2.38 and 2.81), indicating a balanced view of AI’s potential benefits and risks. Correlation and regression analyses revealed that Technology Literacy significantly moderates the relationship between AI and job opportunities, as well as AI and threats, suggesting that higher TL mitigates fears and enhances positive perceptions of AI. The study highlights the dual impact of AI on IT professionals, with both opportunities and threats being perceived at moderate levels. Technology Literacy plays a crucial role in shaping these perceptions, underscoring the need for continuous learning and adaptation in the IT sector.International Journal of Education, Management, and Technology00Examination of the perceptions of AI among IT professionals in Kathmandu reveals that Technology Literacy significantly moderates the relationship between AI and job opportunities, as well as AI and threats, suggesting that higher TL mitigates fears and enhances positive perceptions of AI.International Journal of Education, Management, and Technology["Sajita Khadka", "Sanjeev Kunwar", "Roshni Gautam", "Santosh Poudel", "Sijan Khadka", "Rashil Manandhar", "Dipak Mahat"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/ac8373851a2bc4475e21a5b854d933fde86c3e4c f587b2427be398c13d6b390e6a146e26f60cfa77Abstract Introduction Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). The fundamental rationale for promoting health resilience in T2DM stems from its high prevalence of 10.5% of the world’s adult population and its contribution to many adverse health events. Methods AI-READI is a cross-sectional study whose target enrollment is 4000 people aged 40 and older, triple-balanced by self-reported race/ethnicity (Asian, black, Hispanic, white), T2DM (no diabetes, pre-diabetes and lifestyle-controlled diabetes, diabetes treated with oral medications or non-insulin injections and insulin-controlled diabetes) and biological sex (male, female) (Clinicaltrials.org approval number STUDY00016228). Data are collected in a multivariable protocol containing over 10 domains, including vitals, retinal imaging, electrocardiogram, cognitive function, continuous glucose monitoring, physical activity, home air quality, blood and urine collection for laboratory testing and psychosocial variables including social determinants of health. There are three study sites: Birmingham, Alabama; San Diego, California; and Seattle, Washington. Ethics and dissemination AI-READI aims to establish standards, best practices and guidelines for collection, preparation and sharing of the data for the purposes of AI/ML, including guidance from bioethicists. Following Findable, Accessible, Interoperable, Reusable principles, AI-READI can be viewed as a model for future efforts to develop other medical/health data sets targeted for AI/ML. AI-READI opens the door for novel insights in understanding T2DM salutogenesis. The AI-READI Consortium are disseminating the principles and processes of designing and implementing the AI-READI data set through publications. Those who download and use AI-READI data are encouraged to publish their results in the scientific literature.BMJ Open340Artificial Intelligence Ready and Equitable for Diabetes Insights aims to establish standards, best practices and guidelines for collection, preparation and sharing of the data for the purposes of AI/ML, including guidance from bioethicists.BMJ Open["Cynthia Owsley", "Dawn S Matthies", "Gerald McGwin", "Jeffrey C. Edberg", "Sally L. Baxter", "Linda M Zangwill", "Julia P. Owen", "Cecilia S Lee"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/f587b2427be398c13d6b390e6a146e26f60cfa77 d2f780824c93dc929fc4193bc4bfc012579f2bb4 Background: Detection of intracranial aneurysms (IAs) is a time consuming and error prone process. Therefore, solutions that can localize IAs with a high sensitivity are required. Several artificial intelligence (AI)-based automated diagnoses on medical images have recently been reported. We aimed to develop an automated diagnosis system for the location and maximum diameter of IAs on MRI images in the present study. Methods: In 1310 patients with or without IAs, 937 MRIs were used for training data and 373 cases for test data. The definition of the correct diagnosis of the location was that the center of the aneurysm diagnosed by the AI system is within the area of the IA of ground truth. The nnU-Net was used for the deep learning. Developed AI system was verified with 5-fold cross validation with test data. The maximum diameter was automatically calculated from extracted domes of IAs. Results: Of the 937 patients used for the model development, 778 (83%) had IAs including 146 patients (19%) with multiple aneurysms and 159 (17%) had no IA. In total 1213 IAs, 78% were small aneurysms of 2 to 5 mm. Of the 373 cases for the validation, 17 (4.5%) had IAs, which is close to the real-world setting. Internal validation of the developed diagnosis model for IA locations showed high efficiency of AUC = 0.92, sensitivity = 15/17 (88%), and false negative rate = 0.44 aneurysms/person. The diameter diagnosis model for the maximum diameter also achieved high accuracy within 1.2 mm of mean absolute error. Conclusion: We successfully developed a powerful diagnosis model for IAs with AI technique. The present model potentially reduces human effort required for the IA diagnosis. Stroke00An automated diagnosis system for the location and maximum diameter of IAs on MRI images in the present study and potentially reduces human effort required for the IA diagnosis.Stroke["T. Ikedo", "Nice Ren", "Kunihiro Nishimura", "Ryotaro Otsuka", "K. Iihara", "Hiroharu Kataoka"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/d2f780824c93dc929fc4193bc4bfc012579f2bb4 ccb63afa0d25d3770ebfdc99608bcf05b3fe2753This article explores the efforts of the Council of Europe towards regulating artificial intelligence (AI), with a focus on its use within the justice system. Analyzing international legal efforts is important in order to observe various regulatory approaches to AI globally, ranging from the US's capitalism-based model to the European focus on protecting fundamental rights. These approaches, regardless of their legal force, require integration and adaptation into the national legislative framework currently under consolidation. The Council of Europe was chosen for analysis because it has a rich, detailed activity that addresses the subject of technological innovation comprehensively, being centered on a multidisciplinary perspective. Additionally, the organization shows rigor in analyzing the ethical perspective and the protection of fundamental rights as the primary goal in the context of the intensive use of AI across all areas of life. We are thus witnessing an adaptation of the philosophy of law to include new concepts that have emerged in the AI era, ensuring that technological progress does not undermine human essence and its values. The present research therefore aims to demonstrate the utility of an interdisciplinary approach to navigate the ethical and legal implications of incorporating AI into everyday life.REVISTA DE DREPT CONSTITUŢIONAL - CONSTITUTIONAL LAW REVIEW00The present research aims to demonstrate the utility of an interdisciplinary approach to navigate the ethical and legal implications of incorporating AI into everyday life.REVISTA DE DREPT CONSTITUŢIONAL - CONSTITUTIONAL LAW REVIEW["Ruxandra Andreea L\u0103p\u0103dat"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/ccb63afa0d25d3770ebfdc99608bcf05b3fe2753 164841302c420dd4f016b1862be2f5943a6a1e44 685 Background: Pancreatic cancer is the fourth leading cause of cancer deaths in the United States, and early detection remains a significant challenge. Screening the general population is not feasible, but the rise of artificial intelligence (AI) has introduced new possibilities for improving early diagnosis and patient outcomes. Methods: A systematic literature search was conducted using PubMed, Google Scholar, and MEDLINE using MeSH terms Artificial intelligence or AI, and diagnosis and pancreatic carcinoma or pancreatic adenocarcinoma. Prisma guidelines were adhered to, and a total of 19 studies resulted, 6 of them were retrospective studies, 4 of them were literature reviews, and 4 of them were randomized trials. The rest were duplicates. The inclusion criteria for this study is AI being used in the diagnosis, only in pancreatic cancer, within the last 5 years, only in English, and only retrospective studies were included. Results: One study used Digital Imaging Processing (DIP) for analyzing Endoscopic ultrasound (EUS) images from 153 pancreatic cancer patients, yielding a sensitivity of 97.98% and a specificity of 94.32%. Two studies explored Computer Aided Diagnosis (CAD) models applied to PET/CT and EUS images, achieving a sensitivity of 95.23% and specificity of 97.51% in PET/CT scans and 83.3% and 93.3% in EUS images, respectively. Another study used a Faster R-CNN model to analyze CT images from 338 pancreatic cancer patients showed high diagnostic accuracy in much less time. Additionally, two studies utilized Natural Language Processing (NLP) for identifying family histories of pancreatic cancer and detecting pancreatic cysts, with the latter achieving sensitivity and specificity rates of 99.9% and 98.8%. Conclusions: The current strategies for early diagnosis of pancreatic cancer focus on serum biomarkers and EUS-guided Fine Needle Aspiration (EUS-FNA) and sensitivity varies but depends on the physician's expertise. AI is showing promise in improving pancreatic cancer diagnosis by enhancing early detection and accuracy. Techniques like deep learning, NLP-based models, Faster R-CNN, and CAD systems analyze medical data and images more effectively than manual methods. AI holds the potential to shape the future of pancreatic cancer diagnosis and improve patient outcomes. Journal of Clinical Oncology00Artificial intelligence is showing promise in improving pancreatic cancer diagnosis by enhancing early detection and accuracy and techniques like deep learning, NLP-based models, Faster R-CNN, and CAD systems analyze medical data and images more effectively than manual methods.Journal of Clinical Oncology["Jahnavi Ethakota", "Bipneet Singh", "Sakshi Bai", "Danesh Kumar", "D. Malik"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/164841302c420dd4f016b1862be2f5943a6a1e44 ae02222eb30e40b4e3de6d9d10c8c9177ad4ba0eExamining data from firms in the Republic of Korea, this paper finds that artificial intelligence (AI) and robots differ in their impacts on employment and labor productivity. It finds that AI has a more positive overall impact on labor market outcomes. While both adopting AI and adopting robots increase employment, only adopting AI improves labor productivity. However, those productivity gains are associated with a decrease in the labor share of income. In addition, there is no evidence that firms adopting both robots and AI improve their labor productivity, potentially reflecting a lack of synergy. 00It is found that AI has a more positive overall impact on labor market outcomes than robots, and there is no evidence that firms adopting both robots and AI improve their labor productivity.["Donghyun Park", "Kwanho Shin"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/ae02222eb30e40b4e3de6d9d10c8c9177ad4ba0e b5d234480debebbaa651d53fc0544c6f900f2a89As a leading qualitative researcher, Norman Denzin advocated for a bigger tent to expand and deepen qualitative inquiry. The “tent” metaphor has therefore been used by scholars to advocate for considerations of diverse options around philosophies of inquiry. With the advent of Artificial Intelligence (AI), researchers face additional options around research decisions, philosophically, practically and ethically. Researchers table numerous research questions around AI, given its rapid uptake. These include re-visiting the established notions of human-centered coding, as well as exploring the potential of AI coding, within these notions. There is therefore room to expand “the tent” to delineate 1) the specific use of AI within qualitative data analysis, specifically AI coding, which is now an established function within bespoke qualitative data analysis software (QDAS). Additionally, with AI providing a stream of independent coding, 2) researchers may well deliberate the need for multiple, second or independent coders. This paper responds to this two-fold aim of the study using action research that involved researchers doing coding and using independent coding, in tandem with AI. The contribution of the paper is an outline of these action steps, reflectively culminating in a practical framework. This may be used by researchers, both curious and confident, in seeking novel ways to broaden the scope of their coding practices.International Journal of Qualitative Methods150The contribution of the paper is an outline of these action steps, reflectively culminating in a practical framework that may be used by researchers, both curious and confident, in seeking novel ways to broaden the scope of their coding practices.International Journal of Qualitative Methods["Charmaine Williamson", "A. V. van Rooyen", "Rika Dry"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/b5d234480debebbaa651d53fc0544c6f900f2a89 b3e7da7e2ed74a37571f6ccc6f02266e6ff8fa89Abstract Artificial intelligence (AI) holds transformative potential for global health, particularly in underdeveloped regions like Africa. However, the integration of AI into healthcare systems raises significant concerns regarding equity and fairness. This debate paper explores the challenges and risks associated with implementing AI in healthcare in Africa, focusing on the lack of infrastructure, data quality issues, and inadequate governance frameworks. It also explores the geopolitical and economic dynamics that exacerbate these disparities, including the impact of global competition and weakened international institutions. While highlighting the risks, the paper acknowledges the potential benefits of AI, including improved healthcare access, standardization of care, and enhanced health communication. To ensure equitable outcomes, it advocates for targeted policy measures, including infrastructure investment, capacity building, regulatory frameworks, and international collaboration. This comprehensive approach is essential to mitigate risks, harness the benefits of AI, and promote social justice in global health.Health affairs scholar380The challenges and risks associated with implementing AI in healthcare in Africa are explored, focusing on the lack of infrastructure, data quality issues, and inadequate governance frameworks, and the geopolitical and economic dynamics that exacerbate disparities.Health Affairs Scholar["Aud\u00eancio Victor"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/b3e7da7e2ed74a37571f6ccc6f02266e6ff8fa89 87a25f430d55fdac2f11c316afebb36d389cbff9 Background: Machine learning and artificial intelligence (ML/AI) are rapidly spreading in clinical medicine. Few data describe the perspectives of patients, proxies (e.g., patients’ spouses), and clinicians. In this mixed-methods study, we qualitatively characterized perspectives regarding ML/AI use and quantitatively explore sentiment towards ML/AI from acute neurology patients, proxies, and clinicians. Methods: We conducted semi-structured interviews with survivors of intracranial hemorrhage, proxies, and clinicians. We analyzed interview transcripts using framework analysis, organizing data within the domains of the Theoretical Framework of Acceptability, adding domains identified with input from all co-authors. We quantitatively analyzed the sentiment scores of responses from positive to negative using a transformer-based model, the same technology that underlies large language models. Sentiment scores were compared with Kruskal-Wallis H, and multiple comparisons adjusted using Dunn’s test. Results: We analyzed 21 interviews (14 patients, 1 proxy, and 6 clinicians), by which point there was thematic saturation. Help with clinical decision-making was cited as the key potential advantage of ML/AI. Participants noted the importance of considering ML/AI as an adjunct to clinical care, not as a replacement for clinicians. Over-reliance on recommendations potentially leading to diminution of clinician skill, incorrect ML/AI recommendations, potential liability, and bias were cited as challenges. Clinician and patient education were noted as potential burdens that impose opportunity costs, but are important for self-efficacy. Median sentiment scores ranged from 0.0 (neutral) to 0.3 (positive). Sentiment varied with question type (P < 0.001). Questions about clinicians’ using ML/AI for patient care had the highest sentiment score. Conclusion: Patients, caregivers, and clinicians expressed mixed views about ML/AI. Concerns related to potential burdens and opportunity costs were noted and should be considered as ML/AI is introduced. Future directions include how best to incorporate ML/AI into education and obviate potential burdens as ML/AI is integrated into clinical care. Stroke00Concerns related to potential burdens and opportunity costs were noted and should be considered as ML/AI is introduced and how best to incorporate ML/AI into education and obviate potential burdens as ML/AI is integrated into clinical care.Stroke["Egide Abahuje", "Ethan J Houskamp", "Juliana Silva Pinheiro do Nascimento", "Elaf Agha", "William Thompson", "Kelly Michelson", "Andrew Naidech"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/87a25f430d55fdac2f11c316afebb36d389cbff9 0c34f9cdd7c5a7c734cd327ad6b8493009d17e8f Background: Risk factor control and medication adherence are critical for stroke secondary prevention, but remain a significant challenge after discharge. We’ve developed an artificial intelligence (AI)-based algorithm to predict poor compliance to prescribed medication (PoorC-med) 90 days post-hospitalization. Methods: Consecutive stroke patients discharged from 5 comprehensive stroke centers followed by a multimodal holistic follow-up, including a mobile app for patient communication were evaluated. PoorC-med was defined by a score >0 on the Morisky Green scale. In-hospital and early follow-up multimodal variables were evaluated; those associated with PoorC-med (p<0.05 in the univariate analysis) were used to develop 2 logistic regression models, with variables available at 7 and 30 days after discharge. The models were optimized by grid search to maximize the F2 score, with 5-fold cross-validation to predict PoorC-med at 90 days. A subsequent pool of patients following the same protocol was used for external validation. Results: From January 1, 2020, 3261 patients were included in the multimodal follow-up; data on treatment compliance and >90 days follow-up were available for 1946 (59.7%). Of these, patients enrolled through September 23 (1801) were used to develop the AI algorithm; from October 2023, 145 patients were included in the validation set. Three hundred fifteen (17.5%) patients in the training and 33 (22.8%) in the validation set showed PoorC-med at 90 days. Variables associated with PoorC-med are shown in Fig.1. The logistic regression models (Fig. 2) showed the following performance on the training set: Confusion Matrix: [[549 937], [27 288]], Accuracy: 0.46, AUC: 0.64, F1 Score: 0.37, Recall: 0.91, Precision: 0.24, AUC PR: 0.36, AUROC: 0.72.The validation with an independent dataset yielded: Confusion Matrix: [[52 60], [3 30]], Accuracy: 0.57, AUC: 0.69, F1 Score: 0.49, Recall: 0.91, Precision: 0.33, AUC PR: 0.52, AUROC: 0.80 (Fig. 3). Predictions using variables available only 7 days after discharge showed: Accuracy 0.45, AUC 0.63, Recall 0.92, Precision 0.23, AUROC 0.66 Conclusion: Our models are able to moderately predict poor medication compliance in stroke patients 90 days after discharge. Early identification of poorC-med patients may facilitate targeted interventions and improve secondary prevention. Further research is warranted to improve our performance and to translate the implementation of predictive models into clinical practice. Stroke00An artificial intelligence (AI)-based algorithm to predict poor compliance to prescribed medication (PoorC-med) 90 days post-hospitalization is able to moderately predict poor medication compliance in stroke patients 90 days after discharge.Stroke["Giorgio Colangelo", "D. Cano", "Sebastian Marichal", "Maria Baladas", "Ester S\u00e1nchez", "C. Paredes", "Cristina Guirao", "Yolanda Silva", "X. Ustrell", "Francesc Purroy", "Joao Freitas", "J. Pagola", "M. Muchada", "D. Rodr\u00edguez-Luna", "Noelia Rodr\u00edguez Villatoro", "Alvaro Garcia-Tornel Garcia-Camba", "M. Oliv\u00e9-Gadea", "Federica Rizzo", "Marc Rodrigo Gisbert", "Renato Simonetti", "Carlos A. Molina", "Marc Rib\u00f3", "M. Rubiera"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/0c34f9cdd7c5a7c734cd327ad6b8493009d17e8f ae4d739bf2a3d46abbd8d15345dcc6675358ec48 Objective: The growth of unruptured intracranial aneurysms (IAs) is regarded as a critical precursor to aneurysmal rupture. Accurately predicting aneurysm growth is crucial for appropriate therapeutic interventions to prevent rupture in high-risk aneurysms. The UCAS Japan score has been widely used for rupture risk assessment in Japan; however, its relationship to aneurysm growth is unclear. The present study aimed to examine whether the UCAS Japan score can accurately predict aneurysm growth and to develop a novel prediction system using artificial intelligence (AI)-based machine learning. Methods: We retrospectively analyzed 2,399 unruptured IAs from our single institutional database between 2012 and 2021. Cases with low-quality MRI images or short follow-up periods within 12 months were excluded, resulting in 725 included IAs. IAs with 1mm or more growth during follow-up were categorized as the growth group and compared with the non-growth group. Univariate and multivariate analyses were performed based on UCAS Japan scores and possible risk factors regarding patient characteristics and aneurysmal morphology. AI-based prediction model using XGBoost method was developed and compared with the logistic analysis model and conventional ELAPSS scoring system. Results: A total of 150 aneurysms were classified into the growth group and 525 into the non-growth group. The average age was 63.9±11.7 years, with 74.9% female. Univariate analysis showed significant differences between the two groups in age (65.1 vs 63.3, p=0.01), hypertension (63.3% vs 52.3%, p=0.02), maximum diameter (6.5 mm vs 4.3 mm, p<0.01), and daughter sac presence (53.3% vs 10.4%, p<0.01). The UCAS Japan score was only slightly higher in the growth group (4.90 vs 4.70, p<0.01). Multivariate analysis identified daughter sac presence (OR 8.27, 95%CI 5.16–13.30) and family history of SAH (OR 2.52, 95%CI 1.37–4.64) as independent risk factors. The AI-based model showed strong predictive performance (AUC 0.91, accuracy 91%, sensitivity 71%, specificity 96%), surpassing the ELAPSS score (AUC 0.67) and logistic regression (AUC 0.84, p<0.01). Conclusion: Multivariate analysis identified family history of SAH, daughter sac presence, and aneurysm size as independent risk factors for IA growth. We developed an AI-based powerful prediction system, which potentially change IA management dramatically. Stroke00The present study aimed to examine whether the UCAS Japan score can accurately predict aneurysm growth and to develop a novel prediction system using artificial intelligence (AI)-based machine learning, which potentially change IA management dramatically.Stroke["Ryotaro Otsuka", "T. Ikedo", "Soshiro Ogata", "Kunihiro Nishimura", "Hiroharu Kataoka"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/ae4d739bf2a3d46abbd8d15345dcc6675358ec48 09da3a24e2b59b3afcc41650aebf025e06906c35Abstract Background Inflammatory bowel diseases (IBD) are a family of immune-mediated conditions, which are increasing in incidence and prevalence worldwide. Assessment of IBD is done through endoscopy, video capsule endoscopy (VCE), histology, and various imaging modalities including ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Considering the increasing complexities in the assessment of IBD, artificial intelligence (AI) is an important adjunct with potential to enhance diagnosis, drug response and prediction of disease course Aims We conducted a scoping review to assess AI in diagnosis, monitoring, and prognostication of patients with IBD, to aid in identification of gaps in knowledge to guide future research endeavors. Methods The scoping review protocol was adapted from the recommendations laid out by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis - Scoping Review Extension (PRISMA-ScR). Electronic databases used in the literature search included MEDLINE, EMBASE, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Engineering Village. Two reviewers independently screened the abstracts and titles first before performing full text review. A third review resolved any conflict where needed. All study types were included, and data extraction utilized Covidence. Studies were categorized based on the assessment modality, then themes including, diagnosis, grading activity, prognosis, and monitoring. Results A total of 140 studies were included in the final scoping review. The largest number of studies involved endoscopy at 72 (51%) citations, followed by VCE, histology, MRI, CT, and US at 30 (21%), 18 (13%), 13 (9%), 6 (4%), and 1 (0.7%) citation(s), respectively. When looking at themes, most endoscopy studies examined disease activity (65%) while diagnosis was the most common theme in VCE, MRI and CT (77%, 69% and 83%, respectively). Histologic studies focused on prognosis (89%) and the single US study evaluated both diagnosis and prognosis concomitantly. Amongst all the investigative modalities examined, monitoring of IBD was the least studied theme. Peak performance of AI models for grading disease activity during endoscopy was 98.7% compared to human clinicians with less variability observed. Conclusions With IBD diagnosis and assessment becoming increasingly complex, AI may be a useful adjunctive tool across multiple modalities. Evaluation of use of AI in US is lacking, despite gaining interest in non-invasive assessment of IBD. Further studies are needed incorporating AI use in US while also investigating its role in monitoring IBD disease activity. We hope this scoping review will serve as a future direction for subsequent research in this area. Funding Agencies:Journal of the Canadian Association of Gastroenterology00With IBD diagnosis and assessment becoming increasingly complex, AI may be a useful adjunctive tool across multiple modalities, and monitoring of IBD was the least studied theme.Journal of the Canadian Association of Gastroenterology["G. Malik", "M. Chavannes", "M. Byrne", "M. Dolinger", "S. Sagami", "K. Novak"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/09da3a24e2b59b3afcc41650aebf025e06906c35 3143bf01393fa6f266729e4a97f17d23be43cc90Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.IAES International Journal of Artificial Intelligence (IJ-AI)420A systematic literature review and analysis of real-world case studies reveals that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias in AI.IAES International Journal of Artificial Intelligence (IJ-AI)["A. Samala", "Soha Rawas"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/3143bf01393fa6f266729e4a97f17d23be43cc90 bccfc686a6f87d2bce449f2ad6d091091e945049The main premise of the paper is that the use on elements in Artificial Intelligence (AI) can have a positive effect on the quality of the educational process in meticulous higher education institutions, provided that three main circumstances are 1) access to the necessary data, 2) training of future teachers to work with artificial intelligence and 3) the creation of a special educational course. Within this study, the subsequent tasks were lay down to analyze scientific and methodological research aimed at studying the current state, prospects and possibilities of using artificial intelligence in the training of future teachers of professional education; to analyze how intelligent expert systems are distributed in the educational field; consider the necessary pedagogical conditions for the successful implementation and use of a system with elements of artificial intelligence in the educational process of higher educational institutions.International Journal of Advanced Academic Studies200The main premise of the paper is that the use on elements in Artificial Intelligence can have a positive effect on the quality of the educational process in meticulous higher education institutions, provided that three main circumstances are access to the necessary data, training of future teachers to work with artificial intelligence and the creation of a special educational course.International Journal of Advanced Academic Studies["Anju Bala", "Suresh Vadranam"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/bccfc686a6f87d2bce449f2ad6d091091e945049 8817f3c1030d194d2977b7e0c1b15a5f80a80f90Abstract The emergence of generative artificial intelligence (AI) tools such as ChatGPT has substantially increased individuals’ productivity. In this study, we adopt a difference-in-differences approach to analyze a large dataset of research preprints to systematically examine whether the advent of generative AI has distinct effects on the productivity of male and female academic researchers. We find that after the emergence of ChatGPT, the increase in the productivity of male researchers is 6.4% higher than that of female researchers, implying a widening of the productivity gap between them. We then conduct a survey about researchers’ use of ChatGPT and find that male researchers use generative AI more frequently and experience higher efficiency improvement from its use than female researchers. Our findings show the unintended consequences of generative AI and point to the need for institutions to consider its differential effects on productivity to ensure fairness when evaluating faculty members.PNAS Nexus530The findings show the unintended consequences of generative AI and point to the need for institutions to consider its differential effects on productivity to ensure fairness when evaluating faculty members.PNAS Nexus["Chuang Tang", "Shaobo Li", "Suming Hu", "Fue Zeng", "Qianzhou Du"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/8817f3c1030d194d2977b7e0c1b15a5f80a80f90 b4a09b002f26fbeaeea7bd9c92c9d0c90a87dfaa: Literature advancing in an epoch of Artificial Intelligence, blur the boundaries between the human and machine potency. The authenticity of authorship and human creativity are posed with multiple challenges. On one hand, the horizon of literature expands and generate novel opportunities with AI while on the other, human creativity is subjected to precarity. The study primarily intends to navigate through multiple dimensions of literature merging with Artificial Intelligence in the backdrop of post humanism and, analyses whether it is a futuristic fortune or fiasco. It also aims to profoundly examine and distinguish between the AI-generated and AI-assisted narratives emerging today through analogies of AI generated poetry, fiction and memoirs. The pursuit of AI narratology and the emergence of AI fiction generators confronts the existing literary theoretical framework relating to authorship and reader-reception. Moreover, the authorial status of human and AI narrations and the engagement of readers with such hybrid narratives undertakes a distinct literary turn in the contemporary era. This interdisciplinary nexus of human-AI coalition invites scrutiny in the aspects of narratology, construction of novel plotlines, nuanced interpretation of texts and becomes an inevitable part of the ongoing dialogue about the intersection of multiple disciplines.New Literaria140The study primarily intends to navigate through multiple dimensions of literature merging with Artificial Intelligence in the backdrop of post humanism and, analyses whether it is a futuristic fortune or fiasco.New Literaria["Shifa Dr Shameema. Shifa\nT"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/b4a09b002f26fbeaeea7bd9c92c9d0c90a87dfaa 2a119c7532261bceb5965d7d03a1dd80c8af1112Addressing climate change is one of the fundamental priorities at a global level, given its significant impact on both the environment and society. This systematic literature review explores the role of artificial intelligence (AI) in addressing climate change. It identified applications, contributions to predicting extreme events, techniques used, ethical challenges, and associated biases. The rapid systematic literature review (RSL) was conducted using databases such as Scopus, Dimensions, directory of open access journals (DOAJ), and IEEE Xplore. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement was used to ensure the completeness and transparency of the analysis. 40 articles were selected that were published between 2018 and 2023 and addressed AI in climate change. The findings show that AI is being used to predict and mitigate extreme climate events, estimate the greenhouse effect, and predict temperatures. In addition, innovative techniques such as hybrid machine learning models, convolutional neural networks, artificial neural networks, support vector machines, and logistic regression. In conclusion, AI offers a promising approach to addressing climate change, with transformative potential in predicting and mitigating its effects. However, continuous ethical considerations are required to guarantee its conscientious and efficient utilization.IAES International Journal of Artificial Intelligence (IJ-AI)550Artificial intelligence offers a promising approach to addressing climate change, with transformative potential in predicting and mitigating its effects, however, continuous ethical considerations are required to guarantee its conscientious and efficient utilization.IAES International Journal of Artificial Intelligence (IJ-AI)["L. Andrade-Arenas", "Domingo Hern\u00e1ndez Celis", "Cesar Yactayo-Arias"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/2a119c7532261bceb5965d7d03a1dd80c8af1112 257fedb706db78a0f77daea8623a97b81285abb4The article analyzes the problems and prospects of future journalism, the evolution of the essay genre, and the impact of artificial intelligence on thinking society and essay writing.CURRENT RESEARCH JOURNAL OF PHILOLOGICAL SCIENCES00Current Research Journal of Philological Sciences["Sevara Alijonova Ulugbek kizi"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/257fedb706db78a0f77daea8623a97b81285abb4 e123b9a2d0778d466d66b6f1ceea459670450070This study aims to investigate the impact of the freely accessible artificial intelligence chatbots (AICB) that might disrupt the teaching and learning pattern in higher education. While some education stakeholders developed strong opposition towards the AICB usage, condemning it as academic dishonesty, there are others believe the AICB might even improve the students’ learning. A total of 160 urban college students were purposively selected and requested to respond to the scales of ChatGPT acceptance and trust, academic self-efficacy, and university mattering to test the hypothesis that the acceptance and trust towards AICB should improve academic self-efficacy and general mattering among the students. The results indicated that academic self-efficacy partially mediates the contribution of AICB on the societal mattering. In other words, the findings suggest that students who trust and accept AICB usage would likely to believe that they can perform academically better and therefore they feel they are more meaningful to the society. Limitations and suggestions for future research are discussed.International Journal of Evaluation and Research in Education (IJERE)00The results indicated that academic self-efficacy partially mediates the contribution of AICB on the societal mattering and students who trust and accept AICB usage would likely to believe that they can perform academically better and therefore they feel they are more meaningful to the society.International Journal of Evaluation and Research in Education (IJERE)["Azman Hakimi", "Reeda Li Meng Yue", "Mariam Sufiah Muhsin", "Maisarah Abu Bakar", "Crendy Tan Yen Teng", "Kussusanto Ditto Prihadi"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/e123b9a2d0778d466d66b6f1ceea459670450070 ebd4a1ae94c96f4941f3898db3389f237962da4fArtificial intelligence (AI) has the potential to advance health care, industrial productivity, and environmental sustainability but also presents risks such as job loss and uncontrolled superintelligent machines. Understanding public opinion about AI is key for anticipating its governance. This study examines how media framing affects U.S. beliefs about AI and support for its development. A survey experiment involved respondents reading articles highlighting either AI’s benefits or risks, revealing how such information can influence opinion on AI’s societal impacts. The findings emphasize the crucial role of framing in shaping public views on AI, with implications for policymakers and stakeholders.Science communication390This study examines how media framing affects U.S. beliefs about AI and support for its development, highlighting the crucial role of framing in shaping public views on AI, with implications for policymakers and stakeholders.Science Communication["Risa Palm", "J. Kingsland", "T. Bolsen"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/ebd4a1ae94c96f4941f3898db3389f237962da4f d5e282113abd546fd847c4b432726bcd32d9ed30: This study aimed to identify the impact of an e-training environment based on artificial intelligence tools in developing digital administrative concepts among human resources employees, by identifying the criteria for designing an e-training environment based on artificial intelligence tools in developing digital administrative concepts among human resources employees. As well as identifying the educational design of an e-training environment based on artificial intelligence tools in developing digital administrative concepts amongArtificial Intelligence Information Security00This study identified the criteria for designing an e-training environment based on artificial intelligence tools in developing digital administrative concepts among human resources employees by identifying the educational design of an e-training environment based on artificial intelligence tools.Artificial Intelligence Information Security["\u0650Ayman Fawzy Khattab Madkour", "Faisal Fahed Al-Wadani"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/d5e282113abd546fd847c4b432726bcd32d9ed30 b71e5aa88ef773e002fe747f3de30ed23e321259The so-called “digital era” is synonymous with the transformation of every aspect of human life. This transformation is given by the development of new technologies that modify the way humans communicate and cooperate. Now, it can be said that formal education, compared to other economic sectors, is lagging in the integration of novel technologies in higher education curricula, especially in terms of implementing artificial intelligence (AI). The objective of this research was to conduct a systematic review of the scientific production related to the incorporation of artificial intelligence as a disruptive technology in the university curriculum. It was carried out using a qualitative approach based on a systematic review. The review showed a greater scientific production between 2022 and 2023; it was also evidenced that, as a technology, artificial intelligence has become a disruptive element thanks to its ability to change the role and work performed by teachers, students, and educational institutions. Consequently, the university of the future urgently needs to plan, design, develop, and implement curricula that include artificial intelligence, with the purpose of training better professionals, capable of acting effectively in a technological and productive environment.International Journal of Evaluation and Research in Education (IJERE)00The review showed a greater scientific production between 2022 and 2023; it was evidenced that, as a technology, artificial intelligence has become a disruptive element thanks to its ability to change the role and work performed by teachers, students, and educational institutions.International Journal of Evaluation and Research in Education (IJERE)["Enma Sof\u00eda Reeves Huapaya", "Gilmer Lazo Chucos", "Efrain Parillo Sosa", "Melva Iparraguirre Meza"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/b71e5aa88ef773e002fe747f3de30ed23e321259 df09250cdef641a4d5a3959bb3bbba77434ccea1Nuclear medicine has continuously evolved since its beginnings, constantly improving the diagnosis and treatment of various diseases. The integration of artificial intelligence (AI) is one of the latest revolutionizing chapters, promising significant advancements in diagnosis, prognosis, segmentation, image quality enhancement, and theranostics. Early AI applications in nuclear medicine focused on improving diagnostic accuracy, leveraging machine learning algorithms for disease classification and outcome prediction. Advances in deep learning, including convolutional and more recently transformer-based neural networks, have further enabled more precise diagnosis and image segmentation as well as low-dose imaging, and patient-specific dosimetry for personalized treatment. Generative AI, driven by large language models and diffusion techniques, is now allowing the process, interpretation, and generation of complex medical language and images. Despite these achievements, challenges such as data scarcity, heterogeneity, and ethical concerns remain barriers to clinical translation. Addressing these issues through interdisciplinary collaboration will pave the way for a broader adoption of AI in nuclear medicine, potentially enhancing patient care and optimizing diagnosis and therapeutic outcomes.Seminars in nuclear medicine1410Challenges such as data scarcity, heterogeneity, and ethical concerns remain barriers to clinical translation, and addressing these issues through interdisciplinary collaboration will pave the way for a broader adoption of AI in nuclear medicine, potentially enhancing patient care and optimizing diagnosis and therapeutic outcomes.Seminars in nuclear medicine["Leonor Lopes", "Alejandro Lopez-Montes", "Yizhou Chen", "Pia Koller", "Narendra Rathod", "August Blomgren", "F. Caobelli", "Axel Rominger", "Kuangyu Shi", "Robert Seifert"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/df09250cdef641a4d5a3959bb3bbba77434ccea1 bb3d8386e5b31f1f06c6156193173844bf3bd0bcScholars are increasingly using generative artificial intelligence (AI) chatbots, like ChatGPT, in research, though concerns remain about ethics, data privacy, bias, and intellectual property. This study adopts a design science research approach to explore how generative AI chatbots can support academic teaching and research, bridging theory and practice. A literature review and expert interviews identified key requirements and design principles that support virtues such as uniqueness, generalizability, and reproducibility. We also introduce a prototype, “AcademiaBot,” to demonstrate these principles in action. Our findings suggest that AI chatbots can significantly aid scholarly work if users are informed and ethical concerns are addressed. Responsible usage can help AI augment human research efforts without compromising integrity. This study provides valuable design knowledge, ensuring AI-based chatbots remain a beneficial tool for scholars.International Journal of Knowledge Management810The findings suggest that AI chatbots can significantly aid scholarly work if users are informed and ethical concerns are addressed, and valuable design knowledge is provided, ensuring AI-based chatbots remain a beneficial tool for scholars.International Journal of Knowledge Management["Severin Bonnet", "Frank Teuteberg"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/bb3d8386e5b31f1f06c6156193173844bf3bd0bc ae7013b8f5aa8f5e0cb3f41e904bcada5745fac9Artificial Intelligence (AI) has emerged as a transformative force in various sectors, and libraries are no exception. The integration of AI in libraries has revolutionized how information is stored, retrieved, and delivered, enhancing operational efficiency and improving user experience. Libraries, traditionally dependent on manual labor and physical resources, are now utilizing AI technologies such as machine learning, natural language processing, and automation to streamline processes. This paper explores the role of AI in libraries, comparing the system before and after AI implementation. Through a mixed-methods approach, the study investigates the impacts of AI on library operations, user satisfaction, challenges, and ethical concerns. The results suggest that AI integration has led to more efficient resource management, personalized user services, and improved information accessibility, although challenges like infrastructure costs and ethical issues related to privacy remain.The Critical Review of Social Sciences Studies00The results suggest that AI integration has led to more efficient resource management, personalized user services, and improved information accessibility, although challenges like infrastructure costs and ethical issues related to privacy remain.The Critical Review of Social Sciences Studies["Shakeel Ahmed", "Faheem Akhtar", "Kanwal Saharan", "Masooma Soomro", "Adeel Ahmed", "Aneesa Memon", "Abdul Ghaffar"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/ae7013b8f5aa8f5e0cb3f41e904bcada5745fac9 55945b1094a7cecd04c62a6e0599010c405d83ca Artificial intelligence (AI) has transformed health care. Artificial intelligence technologies, such as advanced imaging algorithms, diagnostic tools, and mental health chatbots, have revolutionized patient care by enhancing diagnostic accuracy, personalizing treatment plans, and streamlining administrative tasks. However, despite these advancements, AI falls short in areas where nurse practitioners (NPs) excel. Nurse practitioners possess essential human attributes such as empathy, nuanced understanding, and ethical reasoning that AI cannot currently replicate. They excel at recognizing subtle mood changes, understanding social determinants of health, and navigating complex ethical dilemmas. I argue that although AI can support and enhance health care delivery, it cannot replace the indispensable human touch provided by NPs. The irreplaceable role of NPs in offering holistic, compassionate care underscores the need for a balanced integration of AI, to ensure it complements rather than replaces the human elements crucial to effective patient care.Journal of the American Association of Nurse Practitioners120It is argued that although AI can support and enhance health care delivery, it cannot replace the indispensable human touch provided by NPs, and the need for a balanced integration of AI to ensure it complements rather than replaces the human elements crucial to effective patient care.Journal of the American Association of Nurse Practitioners["Sara L. Gleasman-DeSimone"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/55945b1094a7cecd04c62a6e0599010c405d83ca 0fe68da975f57238c0d7aa1d358b5709e5765502 The proposed article discusses the principles of implementing artificial intelligence (AI) methods in industrial control systems focusing on the deployment of neural networks (NNs) within programmable logic controllers (PLCs). Recent advancements in AI have led to significant improvements in modeling, control, quality control and predictive maintenance. This progress is further supported by the development of newer CPU architectures in PLCs, which offer enhanced processing speeds and capabilities. These advanced CPUs facilitate the implementation of more complex AI algorithms enabling systems to perform real-time data analysis. By leveraging the power of AI and improved hardware, industries can achieve higher levels of automation and better decision-making.Journal of Electrical Engineering50The principles of implementing artificial intelligence methods in industrial control systems focusing on the deployment of neural networks within programmable logic controllers (PLCs) within programmable logic controllers (PLCs) are discussed.Journal of Electrical Engineering["L. K\u00f6r\u00f6si", "Slavom\u00edr Kajan"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/0fe68da975f57238c0d7aa1d358b5709e5765502 0583a596ee24299a04aade3b391dbb58bdbfad0aPurpose: This review aims to explore the clinical and research applications of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in understanding, predicting, and managing obesity. It assesses the use of AI tools to identify obesity-related risk factors, predict outcomes, personalize treatments, and improve healthcare interventions for obesity. Methods: A comprehensive literature search was conducted using PubMed and Google Scholar, with keywords including “artificial intelligence”, “machine learning”, “deep learning”, “obesity”, “obesity management”, and related terms. Studies focusing on AI’s role in obesity research, management, and therapeutic interventions were reviewed, including observational studies, systematic reviews, and clinical applications. Results: This review identifies numerous AI-driven models, such as ML and DL, used in obesity prediction, patient stratification, and personalized management strategies. Applications of AI in obesity research include risk prediction, early detection, and individualization of treatment plans. AI has facilitated the development of predictive models utilizing various data sources, such as genetic, epigenetic, and clinical data. However, AI models vary in effectiveness, influenced by dataset type, research goals, and model interpretability. Performance metrics such as accuracy, precision, recall, and F1-score were evaluated to optimize model selection. Conclusions: AI offers promising advancements in obesity management, enabling more personalized and efficient care. While technology presents considerable potential, challenges such as data quality, ethical considerations, and technical requirements remain. Addressing these will be essential to fully harness AI’s potential in obesity research and treatment, supporting a shift toward precision healthcare.Diagnostics00Several AI-driven models, such as ML and DL, used in obesity prediction, patient stratification, and personalized management strategies are identified, enabling more personalized and efficient care.Diagnostics["Sarfuddin Azmi", "Faisal Kunnathodi", "Haifa F. Alotaibi", "Waleed Alhazzani", "Mohammad Mustafa", "Ishtiaque Ahmad", "Riyasdeen Anvarbatcha", "Miltiades D. Lytras", "Amr A. Arafat"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/0583a596ee24299a04aade3b391dbb58bdbfad0a bf7d401c0c8c681a347d30d2ecf6f9a833c384b5AI &amp; SOCIETY210AI &amp; SOCIETY["Federico Cabitza", "Chiara Natali", "Francesco Varanini", "David Gunkel"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/bf7d401c0c8c681a347d30d2ecf6f9a833c384b5 0375defa2203cf4837049e2aff729bf7b18d870fCurrent Cardiology Reports570AI holds promise in reshaping preventive cardiology workflows, offering more precise risk assessment and personalized care, and addressing barriers related to equity, transparency, and stakeholder engagement is key for seamless clinical integration and sustainable, lasting improvements in cardiovascular care.Current cardiology reports["Shyon Parsa", "Priyansh Shah", "Ritu Doijad", "Fatima Rodriguez"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/0375defa2203cf4837049e2aff729bf7b18d870f 8e09b41bb875e00bc5942d175b78caab12acbd3aThe study aimed to (i) explore the effectiveness of Artificial Intelligence (AI) models like Chat GPT to facilitate English language learning among Thai students in Higher education and (ii) compare the English Language Learning effectiveness among Thai Students after implementing artificial intelligence (AI) like Chat GPT to facilitate English language learning. The participants were Thai students aged 19-20 from first-year pre-service teachers in Bangkok. A total of 120 students participated, 60 in the control and 60 in the experimental group. The selection of participants was done through stratified random sampling to ensure a diverse representation of pre-service teachers with varying levels of English proficiency. We utilized a mixed-methods approach that combined qualitative and quantitative data: Standardized English tests, Chat GPT, focus group interviews, and field notes. The research instruments were (i) Standardized English Tests, (ii) Chat GPT, (iii) Focus Group interview questions, and (iv) Field Notes Form. The research findings strongly advocated integrating AI tools like Chat GPT in educational settings to facilitate more effective language learning. The study demonstrates that students who interacted with AI improved their language skills. A paired sample t-test revealed that this difference between control and experimental groups was statistically significant (p .001). Feedback from the focus group interviews indicated that students in the experimental group. After implementing artificial intelligence (AI) like Chat GPT, the AI-based learning experience increased engagement, personalization, real-time feedback, attitude change, and learning motivation. They reported that the real-time feedback and interactive exercises offered by Chat GPT helped them understand and apply language concepts more effectively. Lastly, the attitude changes because the students had high motivation, strong self-confidence, and a positive attitude shift.International Journal of Education and Literacy Studies420After implementing artificial intelligence (AI) like Chat GPT, the AI-based learning experience increased engagement, personalization, real-time feedback, attitude change, and learning motivation.International Journal of Education and Literacy Studies["Saifon Songsiengchai"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/8e09b41bb875e00bc5942d175b78caab12acbd3a 6994805125605bc79f11dfe8ddb69baddb79caa8 Introduction: Delays in endovascular therapy for acute large vessel occlusion (LVO) stroke can contribute significantly to disability following successful recanalization. The implementation of an automated intelligence LVO detection and interdisciplinary communication platform can shorten times to treatment. Methods: We conducted a multicenter retrospective observational cohort study of consecutive adults with acute occlusion of the internal carotid, proximal middle cerebral, or basilar artery. Hub-and-spoke networks implementing Viz.ai queried electronic medical records 6 months prior to and 6 months following implementation of Viz.ai. Patients were included if they had a National Institutes of Stroke Scale (NIHSS) score ≥6, pre-stroke modified Rankin Scale 0-1, and presented within 24 hours of last known well (or unknown). The primary outcome was time from initial hospital contact to arterial puncture, which was compared between study periods using descriptive statistics, regression with robust standard errors clustered by site, and adjusted inverse probability of treatment weighting (IPTW) in which probability weights were used to reduce imbalance between study periods in a causal inference model. The model was adjusted for age, NIHSS, sex, comorbidities, overnight arrival, hub versus spoke arrival, academic quarter, and pre-stroke modified Rankin Scale which was imputed when missing using chained equations as an ordinal covariate. Results: Of the 474 included patients across 7 sites (n=215 post-Viz, 45.4%), the median age was 67 years (interquartile range [IQR] 57-77) and median NIHSS was 17 (IQR 11-22). Using descriptive statistics, there was a trend toward a shorter time from hospital contact to puncture during the post-Viz period (median 103min, IQR 68-146, vs. 106min, IQR 76-169, p=0.10). In unadjusted regression with robust errors, clustered by site, the trend persisted (β -26.3, 95% confidence interval [CI], -53.7 to 1.3, p=0.058). In the adjusted IPTW model, arrival during the post-Viz period was associated with a shorter adjusted average treatment effect (time difference) of 31 minutes (95% CI, 14 to 48 minutes, p<0.001) when compared to arrival during the pre-Viz period. Conclusions: Implementation of the Viz.ai platform led to a significant decrease in time to arterial puncture for patients with acute LVO. The degree to which these changes contributed to better clinical outcomes is being explored in subsequent analyses. Stroke00Implementation of the Viz.ai platform led to a significant decrease in time to arterial puncture for patients with acute LVO, and the degree to which these changes contributed to better clinical outcomes is being explored.Stroke["James E. Siegler", "Emma Frost", "M. Penckofer", "Sushanth Aroor", "Justin Fraser", "Alexandra R. Paul", "Balaji Krishnaiah", "M. Essibayi", "T. Jovin", "Jesse Thon", "Jane Khalife"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/6994805125605bc79f11dfe8ddb69baddb79caa8 515dee316d41253d718a828cdf51de92aac0af55Transportation Research Part E: Logistics and Transportation Review501Transportation Research Part E: Logistics and Transportation Review["Yuyan Wang", "Junhong Gao", "T. Cheng", "Mingzhou Jin", "Xiaohang Yue", "Huajie Wang"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/515dee316d41253d718a828cdf51de92aac0af55 e21e647deebec85efa5c29a4f3711b76c1c52216The Lancet Digital Health251The findings suggest that AI contributes to the early detection of clinically relevant breast cancer and reduces screen-reading workload without increasing false positives.The Lancet. Digital health["Veronica Hernstr\u00f6m", "Viktoria Josefsson", "Hanna Sartor", "David Schmidt", "Anna-Maria Larsson", "Solveig Hofvind", "Ingvar Andersson", "A. Rosso", "Oskar Hagberg", "Kristina L\u00e5ng"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/e21e647deebec85efa5c29a4f3711b76c1c52216 94bf8f33811fa65cbac6b0db9f94e357c8422246Critical Care Nurse31Critical care nurse["Carl Goforth", "J. Alderden"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/94bf8f33811fa65cbac6b0db9f94e357c8422246 9689d9be6c4fd928f79ac2bd302b4ec767c34935Cuestiones de fisioterapia00Cuestiones de fisioterapia[]2025-02-01T00:00:00https://www.semanticscholar.org/paper/9689d9be6c4fd928f79ac2bd302b4ec767c34935 12649bcb68502e774f610725f273c8c9542cdfd7Medicina Clínica (English Edition)260Medicina Clínica (English Edition)["Olga Rubio", "Marc Vila", "Manel Escobar", "Alvar Agusti", "Elena Aristoy", "Natalia (and daughter) Arteche", "Susana Auss\u00f3", "P. Babi", "Joan Bigorra", "Miquel Bruguera", "Antonio Coca", "Josep Lluis De Peray", "Didier Dominguez", "Joan Escarrabill", "Josi Luis Fern\u00e1ndez", "Paula Garc\u00eda-Esparcia", "Carles Herv\u00e1s", "Yolanda Mora", "Esther Pallisa", "Dolors Querol", "Miriam Sarroca", "Gustavo Tolchinsky", "Jordi Vilalta"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/12649bcb68502e774f610725f273c8c9542cdfd7 6f8bc26e17b24ee5f8afb5ca7284fa1a247ea8b7International Review of Economics &amp; Finance260International Review of Economics &amp; Finance["Guimin Qu", "Hao jing"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/6f8bc26e17b24ee5f8afb5ca7284fa1a247ea8b7 56333309c269fecb5767cc1beaf36cdfa586be11Applied Nursing Research410Applied Nursing Research["Agnieszka Maj", "Marta Makowska", "Katarzyna Sacharczuk"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/56333309c269fecb5767cc1beaf36cdfa586be11 17a28aed393c79fdb46587a66c82250bcac07696Research in Social and Administrative Pharmacy600Research in Social and Administrative Pharmacy["V. Dsouza", "Lada Leyens", "Jestina Rachel Kurian", "Angela Brand", "Helmut Brand"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/17a28aed393c79fdb46587a66c82250bcac07696 5a824afaf65f2af71cc2b155d1d8efeaacf34434International Journal of Applied Research00International Journal of Applied Research["Tushar Dixit"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/5a824afaf65f2af71cc2b155d1d8efeaacf34434 4215b499e59659847dcec6d4990d0045e32778d0Cleaner Engineering and Technology430Cleaner Engineering and Technology["Atthaphon Maneedaeng", "Attasit Wiangkham", "A. Ariyarit", "Anupap Pumpuang", "E. Sukjit"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/4215b499e59659847dcec6d4990d0045e32778d0 064b427f59762a7a56419edcd4238117ca7b7c77Journal of Creativity310Journal of Creativity["Robert Root-Bernstein"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/064b427f59762a7a56419edcd4238117ca7b7c77 ff96f461ef53e4e33ad4bbb2802688198acdca61Finance Research Letters220Finance Research Letters["Min Weng"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/ff96f461ef53e4e33ad4bbb2802688198acdca61 640d5470ee29bf88ac19ad6cc6a0e51838f7be25Advances in Pediatrics400Advances in Pediatrics["S. Sasidharan Pillai", "Ambika P. Ashraf"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/640d5470ee29bf88ac19ad6cc6a0e51838f7be25 2539e999255b59eb0d9dcc4673869dbf23cf98d7Computers &amp; Chemical Engineering340Computers &amp; Chemical Engineering["S. Bishnu", "S. Alnouri", "Dhabia M. Al Mohannadi"]2025-02-01T00:00:00https://www.semanticscholar.org/paper/2539e999255b59eb0d9dcc4673869dbf23cf98d7 abc241008da7a80e897014d7fa6b36229e05af75The integration of generative artificial intelligence (Gen AI) into paramedic practice proposes a unique opportunity to enhance professional development and patient care. In this second instalment within the series, we offer a structured approach to continuous professional development (CPD) and performance improvement by exploring the use of SMART (specific, measurable, achievable, realistic, time-bound) targets in goal-setting. This article builds on the theme of ethical considerations, including data privacy, bias mitigation, and compliance with healthcare regulations. Practical examples and templates are provided to illustrate how Gen AI can generate actionable and measurable goals for paramedics across various scenarios – from skill development to leadership and patient communication.Journal of Paramedic Practice130A structured approach to continuous professional development and performance improvement is offered by exploring the use of SMART (specific, measurable, achievable, realistic, time-bound) targets in goal-setting.Journal of Paramedic Practice["Pippa Furey"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/abc241008da7a80e897014d7fa6b36229e05af75 36f7e1180a5a258fbb398a61e4aed9cf909e50daArtificial intelligence (AI) has changed how patients interact with the healthcare system and experience care. The project is the implementation of an AI chatbot to assist with more immediate and personalized patient care, from start to finish. It uses natural language processing (NLP) to understand and reply to questions patients have about a variety of topics relating to symptoms, medicine usage, appointment Scheduling and general health-related Question Answering made by the chatbot will use machine learning algorithms to learn continuously from user interactions, which means that fundamentally its accuracy and relevance will improve over time. The system is easy to use, making sure patients with different technology skills can use it. The chatbot is also a triage tool that flags patients needing urgent medical attention and connects them with doctors as soon as possible. The Health replaces solutions and bridges the gap between patients and healthcare provider, reduce workload on medical staff & app engagement Provides good patient. By thorough testing and validation, the chatbot presents its potential to increase healthcare efficiency, self management and finally outcomes. These results highlight how recent advancements in AI chatbots can transform the way patients interact with modern healthcare, and support new strategies for integrated care.International Journal For Multidisciplinary Research140The project is the implementation of an AI chatbot to assist with more immediate and personalized patient care, from start to finish, and highlights how recent advancements in AI chatbots can transform the way patients interact with modern healthcare, and support new strategies for integrated care.International Journal For Multidisciplinary Research["Koduri Sai Manas Harsha Vardhan", "Emmanuel David", "Amartya Sinha", "Aakash Ram Neerukonda", "Dr.M.Monica Bhavani"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/36f7e1180a5a258fbb398a61e4aed9cf909e50da d1d151f83529d8a77846914e80250b5cff0541fbThe rapid growth of submissions to top-tier Artificial Intelligence (AI) and Machine Learning (ML) conferences has prompted many venues to transition from closed to open review platforms. Some have fully embraced open peer reviews, allowing public visibility throughout the process, while others adopt hybrid approaches, such as releasing reviews only after final decisions or keeping reviews private despite using open peer review systems. In this work, we analyze the strengths and limitations of these models, highlighting the growing community interest in transparent peer review. To support this discussion, we examine insights from Paper Copilot, a website launched two years ago to aggregate and analyze AI / ML conference data while engaging a global audience. The site has attracted over 200,000 early-career researchers, particularly those aged 18-34 from 177 countries, many of whom are actively engaged in the peer review process. Drawing on our findings, this position paper advocates for a more transparent, open, and well-regulated peer review aiming to foster greater community involvement and propel advancements in the field.330This position paper advocates for a more transparent, open, and well-regulated peer review aiming to foster greater community involvement and propel advancements in the field.["Jing Yang"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/d1d151f83529d8a77846914e80250b5cff0541fb 2cc349d6ab44ae3c79dad74a8c208452da2055e5This study explores the transformative role of Artificial Intelligence (AI) in enhancing cybersecurity measures. The integration of AI into cybersecurity frameworks offers significant advancements in threat detection, prevention, and response. Leveraging machine learning algorithms and sophisticated data analytics, AI systems can analyze large datasets in real-time to identify patterns and anomalies that indicate potential security threats. This capability allows for the early detection of cyber threats that traditional security measures might miss. AI also improves threat intelligence by learning from new data and evolving attack methodologies, enhancing predictive accuracy. The research highlights how AI-driven automation can expedite incident response, thereby reducing the damage and costs associated with security breaches. Additionally, AI strengthens authentication processes through behavioral biometrics and anomaly detection, offering robust protection against identity theft and fraud. However, the study also addresses the challenges posed by AI in cybersecurity, including the potential for adversaries to use AI for developing sophisticated attacks and the ethical concerns surrounding AI algorithms’ biases and transparency. The research argues for a balanced approach that maximizes AI’s benefits while mitigating its risks. Ensuring transparency, accountability, and continuous improvement of AI models is critical for maintaining trust and efficacy in AI-powered cybersecurity solutions. This research concludes that while AI significantly enhances cybersecurity capabilities, addressing its inherent challenges is essential for its successful and ethical application in the cybersecurity domain.Journal of Computer, Software, and Program00It is concluded that while AI significantly enhances cybersecurity capabilities, addressing its inherent challenges is essential for its successful and ethical application in the cybersecurity domain.Journal of Computer, Software, and Program["Gbenga Femi Asere", "Kehinde Adetayo Nuga", "Madu Medugu"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/2cc349d6ab44ae3c79dad74a8c208452da2055e5 4d95017615d2b409b975817288672048310b6076Artificial intelligence (AI) technologies have profoundly influenced both professional environments and personal lives. In the rapidly developing sector of AI education, fostering essential AI literacy among university students has become vital. Nevertheless, the factors that determine AI literacy remain insufficiently defined. This research, grounded in self-determination theory (SDT), seeks to investigate the relationships among three components: the fulfillment of university students’ three psychological needs, self-regulated learning strategies (SRLSs), and AI literacy. The aim is to enhance human capital efficiency and prepare students to tackle future workplace challenges effectively. To examine these connections, a cross-sectional survey was administered to 1056 university students. The findings reveal that satisfying the three psychological needs—perceived autonomy, competence, and relatedness—plays a pivotal role in advancing AI literacy among university students. Additionally, four SRLSs—cognitive engagement, metacognitive knowledge, resource management, and motivational beliefs—acted as mediators between these psychological needs and AI literacy. Consequently, this study not only enhances our understanding of the psychological and behavioral development of university students during their engagement with AI education but also provides theoretical support and practical guidance for fostering their AI literacy.Behavioral Science750The findings reveal that satisfying the three psychological needs—perceived autonomy, competence, and relatedness—plays a pivotal role in advancing AI literacy among university students.Behavioral Sciences["Kai Wang", "Wencheng Cui", "Xue Yuan"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/4d95017615d2b409b975817288672048310b6076 b906ca949ff5d2d1692b54ed1076a07a0c671925The article discusses the opportunities and challenges of implementing artificial intelligence (AI) in higher education. Important applications of AI such as personalization of learning, automation of the educational process, support for inclusion and the growth of digital competencies are taken into account. It was noted that AI improves the effectiveness of learning and equips students with the skills needed in the modern workforce. However, there are some barriers to the use of these technologies such as technological limitations, the need for teacher training, concerns about data security and moral considerations. In addition to discussing the potential of future applications of intelligent technologies in education, the article offers useful suggestions for the successful integration of AI into university curricula. The study highlights the importance of finding a balance between educational humanitarian principles and technological possibilities.Eurasian Science Review An International peer-reviewed multidisciplinary journal50The article offers useful suggestions for the successful integration of AI into university curricula and highlights the importance of finding a balance between educational humanitarian principles and technological possibilities.Eurasian Science Review An International peer-reviewed multidisciplinary journal["Mereke Zeinollakizy"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/b906ca949ff5d2d1692b54ed1076a07a0c671925 77ce92cd13518fb2b6aa737c0f962019f14fedaaIndependent Nurse00Independent Nurse[]2025-02-02T00:00:00https://www.semanticscholar.org/paper/77ce92cd13518fb2b6aa737c0f962019f14fedaa 1e904508ab41ee0792df226d09c1a66a3a30619200["Junlong Zhang", "Xiaorong Bai", "Kim Geok Soh", "Wensheng Xiao", "Mohd Ashraff Mohd Anuar"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/1e904508ab41ee0792df226d09c1a66a3a306192 24ed596f4f8ff69a64fe88ff0ddb5d1f0fcfb7ddPurpose: This study focused on the relevance of AI and ML in revolutionizing the Oil & Gas sector by innovation and rehabilitation. It investigated the role of AI and ML technologies in improving production efficiency, reducing environmental impact, and managing costs. Several end users noted considerable advancements in operation efficiency; Predictive analytics and real-time monitoring systems assisted in enhancing the effectiveness of predictive maintenance by as much as 40% lapse time. Thus, the digital twin technologies were discussed in the context of enhancing the design of production planning, as well as promoting more effective use of resources and their recycling. Methodology: The research applied integration of systematic qualitative methodologies to collect, analyze, and synthesize data from prior investigations. This methodology was designed to encompass the alignment of results, analysis, and conclusions within the literature findings. The application of Systematic Literature Review (SLR), Content Analysis, and Meta-Analysis of Qualitative Evidence effectively grounded the study objectives Findings: The Significance of AI is captured in the environmental management aspect of this study, whereby several companies’ emission control systems recorded a 30% improvement of Green House Gas (GHG), and the accuracy of compliance. Some of those that are of more economic advantage are the reduced cost of maintenance, low energy utilization and minimal wastage of resources. However, shortcomings like high implementation cost, integration difficulties, and infrastructural limitations remain some of the biggest threats to its adoption. Unique Contribution to Theory, Policy, and Practice: In addressing these challenges, this research suggests the promotion of partnerships, creating efficient innovations and establishing sustainable development initiatives. Thus, it offers valuable recommendations for policymakers, researchers, and other interested parties, stressing that AI and ML adoption demonstrate the potential to support operational excellence, environmentally sustainable practices, and increased profitability in the Oil and Gas industry.Journal of Technology and Systems260The role of AI and ML technologies in improving production efficiency, reducing environmental impact, and managing costs is investigated, stressing that AI and ML adoption demonstrate the potential to support operational excellence, environmentally sustainable practices, and increased profitability in the Oil and Gas industry.Journal of Technology and Systems["Kadugala Aniceto"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/24ed596f4f8ff69a64fe88ff0ddb5d1f0fcfb7dd 3aba18a004f25535ccde5302c008d48125f43bc4After 25 years of implementing the Medical Devices Directive (MDD), in 2017, the new Medical Devices Regulation (MDR) came into force, establishing stricter requirements for post-market surveillance of the safety and performance of medical devices (MD). For electrocardiogram (ECG) devices, which are crucial for monitoring cardiac activities, these requirements are essential to ensure the reliability and accuracy of diagnosing cardiac conditions and timely treatment. This study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices. Specifically, the research focuses on classifying the success or failure of ECG device operations based on performance and safety parameters. The ultimate goal is to improve the management strategies of ECG devices in healthcare institutions, ensuring optimal functionality and increasing the reliability of diagnostic procedures. During the inspection process of ECG devices conducted by an accredited laboratory in accordance with ISO 17020 standard in numerous healthcare institutions in Bosnia and Herzegovina, a total of 5577 samples were collected. Various machine learning algorithms, including Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM), were employed for result comparison and selection of the most accurate algorithm. All algorithms demonstrated good performance, but the Random Forest (RF) algorithm stood out, achieving 100% accuracy in predicting the success/unsuccess status of the device. While the results of this research are specific to the collected data from EKG devices, the developed algorithms can be applied to other similar datasets, offering opportunities for broader use in the medical environment. Implementing machine learning algorithms for automated systems in healthcare institutions can significantly enhance the quality of patient diagnosis and treatment. Additionally, these systems can optimize costs associated with managing medical devices. Improved post-market surveillance using ML can address challenges related to ensuring device reliability and safety.Technology and Health Care70This study aims to enhance post-market surveillance of ECG devices by leveraging Machine Learning (ML) algorithms to predict the operational status of these devices by classifying the success or failure of ECG device operations based on performance and safety parameters.Technology and Health Care["Mad\u017eida Hundur", "Lemana Spahi\u0107", "Faruk Be\u0107irovi\u0107", "Lejla Gurbeta Pokvi\u0107", "A. Badnjevi\u0107"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/3aba18a004f25535ccde5302c008d48125f43bc4 bc7106ee63d91f092dcf0d42cddb3b50e4b32915Applied Economics Letters70Applied Economics Letters["Xiang Chen", "Liming Ge"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/bc7106ee63d91f092dcf0d42cddb3b50e4b32915 3f9ebe79b7c9527a1db01c9719b9911169251b7cKemajuan teknologi informasi dan komunikasi telah memberikan pengaruh signifikan terhadap dunia pendidikan, termasuk di institusi pendidikan Islam. Penelitian ini bertujuan mengeksplorasi pemanfaatan PowerPoint (PPT), media digital, Artificial Intelligence (AI), dan teknologi digital lainnya di Institut Agama Islam Darussalam Martapura dan Sekolah Tinggi Agama Islam Rakha Amuntai. Dengan menggunakan pendekatan kualitatif yang mendalam, penelitian ini mengungkapkan bahwa teknologi digital tidak hanya meningkatkan efektivitas pembelajaran, tetapi juga mendorong partisipasi aktif mahasiswa serta memperkuat penguasaan keterampilan abad ke-21, seperti berpikir kritis, kolaborasi, dan inovasi. Namun demikian, kendala seperti keterbatasan infrastruktur, literasi teknologi yang masih rendah, dan hambatan teknis lainnya tetap menjadi tantangan utama. Penelitian ini memberikan rekomendasi strategis yang mencakup peningkatan pelatihan, penyediaan infrastruktur yang lebih baik, dan pengintegrasian nilai-nilai Islam dalam penggunaan teknologi untuk memaksimalkan potensi pembelajaran digital di institusi pendidikan Islam.JIIP - Jurnal Ilmiah Ilmu Pendidikan90JIIP - Jurnal Ilmiah Ilmu Pendidikan["Akhmad Mutawali", "Haris Fakhriza", "A. Rayhani", "Robbiah Robbiah", "Ani Cahyadi"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/3f9ebe79b7c9527a1db01c9719b9911169251b7c bd61bd542192914a1aa1935320cac43c5a2f5d5cIn today's global trade landscape, Artificial Intelligence (AI) significantly enhances productivity and transforms business processes across sectors. This research investigates the role of business intelligence in improving service delivery within corporate entities. By applying Deming's methodology, strategies to optimize decision-making processes are identified, and hidden insights are revealed through advanced data analysis techniques. A Data Flow Diagram (DFD) illustrates the development stages and system implementation, offering practical guidance for general managers. The findings provide actionable insights that enhance efficiency and decision-making in organizational contexts.Engineering, Technology &amp; Applied Science Research390This research investigates the role of business intelligence in improving service delivery within corporate entities by applying Deming's methodology and provides actionable insights that enhance efficiency and decision-making in organizational contexts.Engineering, Technology &amp; Applied Science Research["Mohammed Shaban Thaher"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/bd61bd542192914a1aa1935320cac43c5a2f5d5c 3d8e22e94681367988c0f4dbe0c49905b969c54eTravel demand prediction is crucial for optimizing transportation planning, resource allocation, and infrastructure development, ensuring efficient mobility and economic sustainability. This study introduces a Neurosymbolic Artificial Intelligence (Neurosymbolic AI) framework that integrates decision tree (DT)-based symbolic rules with neural networks (NNs) to predict travel demand, leveraging the interpretability of symbolic reasoning and the predictive power of neural learning. The framework utilizes data from diverse sources, including geospatial, economic, and mobility datasets, to build a comprehensive feature set. DTs are employed to extract interpretable if-then rules that capture key patterns, which are then incorporated as additional features into a NN to enhance its predictive capabilities. Experimental results show that the combined dataset, enriched with symbolic rules, consistently outperforms standalone datasets across multiple evaluation metrics, including Mean Absolute Error (MAE), \(R^2\), and Common Part of Commuters (CPC). Rules selected at finer variance thresholds (e.g., 0.0001) demonstrate superior effectiveness in capturing nuanced relationships, reducing prediction errors, and aligning with observed commuter patterns. By merging symbolic and neural learning paradigms, this Neurosymbolic approach achieves both interpretability and accuracy.00A Neurosymbolic Artificial Intelligence (Neurosymbolic AI) framework that integrates decision tree-based symbolic rules with neural networks (NNs) to predict travel demand, leveraging the interpretability of symbolic reasoning and the predictive power of neural learning.["Kamal Acharya", "Mehul Lad", "Liang Sun", "Houbing Song"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/3d8e22e94681367988c0f4dbe0c49905b969c54e 49c0c13b669bac97afb45e0b0fb6c4e41f7f2302AI and Ethics250AI and Ethics["Markus Pantsar"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/49c0c13b669bac97afb45e0b0fb6c4e41f7f2302 dd46d4f6b9f3798bc9db77b4e89714fba2a4eee7The emergence of generative artificial intelligence (Gen AI) in education offers both opportunities and challenges, particularly in the context of student assessment. This study examines faculty members’ motivations to redesign assessments for their courses in the Gen AI era and introduces a framework for this purpose. A qualitative methodology was employed, gathering data through semi-structured interviews and focus groups, along with examples of redesigned assessments. Sixty-one faculty members participated in the study, and the data were analyzed using both deductive and inductive thematic approaches. Key motivations for redesigning assessments included maintaining academic integrity, preparing learners for future careers, adapting to technological advancements, and aligning with institutional policies. However, the study also highlighted significant challenges, such as the need for professional development and addressing equity and accessibility concerns. The findings identified various innovative assessment approaches tailored to the requirements of the Gen AI era. Based on these insights, the study developed a conceptual framework titled “Against, Avoid, Adopt, and Explore”. Future research is needed to validate this framework and further refine its application in educational contexts.Education sciences490This study examines faculty members’ motivations to redesign assessments for their courses in the Gen AI era and introduces a framework for this purpose, developing a conceptual framework titled “Against, Avoid, Adopt, and Explore”.Education Sciences["Zuhair N. Khlaif", "Wejdan Awadallah Alkouk", "Nisreen Salama", "Belal Abu Eideh"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/dd46d4f6b9f3798bc9db77b4e89714fba2a4eee7 88911974181ddf5aca5a290937e849ba81af3aa2Purpose: This paper explores the transformative impact of Artificial Intelligence (AI) and robotics in the fifth industrial revolution, particularly post-pandemic, where their adoption has significantly streamlined operations, reduced costs, and enhanced product development and distribution. Despite these advantages, their widespread use raises concern about occupational safety, including worker injuries and psychological harm. Methodology: A systematic literature review was conducted to trace the evolution of workplace hazards and identify emerging risks associated with AI and robotics. The study also assessed strategies for mitigating these risks and evaluated the effectiveness of current regulatory frameworks in promoting occupational safety. Findings: The research highlights that while AI and robotics reduce certain traditional workplace hazards, they introduce new risks such as human-robot collisions, algorithmic bias, and unintended consequences. Regulatory bodies are pivotal in developing and enforcing policies to safeguard workers. Additionally, organizations, AI developers, and individuals must collaborate to create safer workplaces. Unique Contribution to Theory, Policy and Practice: This study contributes to the understanding of occupational safety in AI and robotics by identifying emerging risks, suggesting strategies for human-robot collaboration, and offering regulatory recommendations. It emphasizes a multi-stakeholder approach to ensure safe and effective integration of these technologies in the workplace.International journal of supply chain and logistics170The research highlights that while AI and robotics reduce certain traditional workplace hazards, they introduce new risks such as human-robot collisions, algorithmic bias, and unintended consequences.International Journal of Supply Chain and Logistics["Rohit Raman", "Rashmi Shrivastava"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/88911974181ddf5aca5a290937e849ba81af3aa2 7e6ffe51bb47617500244cdaba6f451682874a78The US banking sector is operating within a very dynamic and competitive environment, providing a wide array of services under the pressure of increasingly demanding customers. Customer churn in the context of financial institutions is defined as the phenomenon of customers terminating their relationship with a bank. The central tenet of this research project was to devise and develop predictive models of artificial intelligence that can help address the issue of customer churn from the banking perspective. The dataset of banking customer churn prediction used for this analysis comprises a comprehensive set of data about customers from a leading financial institution. It includes extensive customer records, each described by features representing different dimensions of customer behavior and demographics. The three most influential algorithms were selected for this study: Logistic Regression, Random Forest, and XG-Boost. Each model has different strengths that are quite appropriate for the intrinsic complexities of the customer churn forecast. Random Forest was the best in terms of accuracy among the models, with a relative accuracy, which may indicate that this algorithm fits the underlying pattern in the data best. The integration of AI-driven churn prediction models in the US financial sector has far-reaching implications for banks, enhancing their operational efficiency and customer relationship management. First and foremost, it can identify at-risk customers with a high degree of accuracy, thus helping the banks to implement focused retention strategies that can bring down the churn rate significantly.Journal of Ecohumanism230Random Forest was the best in terms of accuracy among the models, with a relative accuracy, which may indicate that this algorithm fits the underlying pattern in the data best, thus helping the banks to implement focused retention strategies that can bring down the churn rate significantly.Journal of Ecohumanism["MD. Sohel Rana", "Anchala Chouksey", "Saddam Hossain", "Md Sumsuzoha", "Proshanta Kumar Bhowmik", "Miraz Hossain", "Md. Fazla Rabby", "N. Gurung", "MD Abdul Fahim Zeeshan"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/7e6ffe51bb47617500244cdaba6f451682874a78 a7659d9fcfb49d8fd6318b7a71f45e073c14bdf7This study addresses public perception of the relationship between artificial intelligence (AI) and disinformation. The level of general awareness of AI is considered, and based on this, an analysis is carried out of whether it may favor the creation and distribution of false content or, conversely, the public perceive its potential to counteract information disorders. A survey has been conducted on a representative sample of the Andalusian population aged 15 and over (1550 people). The results show that over 90% of the population have heard of AI, although it is less well known among the eldest age group (78%). There is a consensus that AI helps to produce (86%) and distribute (84%) fake news. Descriptive analyses show no major differences by sex, age, social class, ideology, type of activity or size of municipality, although those less educated tend to mention these negative effects to a lesser extent. However, 54% of the population consider that it may help in combating hoaxes, with women, the lower class and the left wing having positive views. Logistic regressions broadly confirm these results, showing that education, ideology and social class are the most relevant factors when explaining opinions about the role of AI in disinformation.Journalism and Media420Public perception of the relationship between artificial intelligence (AI) and disinformation is addressed, showing that education, ideology and social class are the most relevant factors when explaining opinions about the role of AI in disinformation.Journalism and Media["Livia Garc\u00eda-Faroldi", "Laura Teruel", "Sonia Blanco"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/a7659d9fcfb49d8fd6318b7a71f45e073c14bdf7 b626a74acd5b04162a8d648033e746e9b0cb917cArtificial intelligence is set to incorporate additional decision space that has traditionally been the purview of humans. However, AI systems that support decision making also entail the rationalization of AI outputs by humans. Yet, incongruencies between AI and human rationalization processes may introduce uncertainties in human decision making, which require new conceptualizations to improve the predictability of these interactions. The application of quantum probability theory (QPT) to human cognition is on the ascent and warrants potential consideration to human–AI decision making to improve these outcomes. This perspective paper explores how QPT may be applied to human–AI interactions and contributes by integrating these concepts into human-in-the-loop decision making. To capture this and offer a more comprehensive conceptualization, we use human-in-the-loop constructs to explicate how recent applications of QPT can ameliorate the models of interaction by providing a novel way to capture these behaviors. Followed by a summary of the challenges posed by human-in-the-loop systems, we discuss newer theories that advance models of the cognitive system by using quantum probability formalisms. We conclude by outlining areas of promising future research in human–AI decision making in which the proposed methods may apply.Entropy770This perspective paper explores how QPT may be applied to human–AI interactions and contributes by integrating these concepts into human-in-the-loop decision making and uses human-in-the-loop constructs to explicate how recent applications of QPT can ameliorate the models of interaction by providing a novel way to capture these behaviors.Entropy["Scott A. Humr", "M. Canan", "Mustafa Demir"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/b626a74acd5b04162a8d648033e746e9b0cb917c 549d3820fc08f3811c81e697b3e51953b578fe74This bibliometric study analyzes the scientific production on the use of artificial intelligence (AI) in mathematics education between 2020 and 2024. Based on a sample of 384 documents extracted from 155 international sources, the study evaluates emerging trends, collaboration patterns among authors and countries, and the main themes related to the use of AI in mathematics education. The analysis was conducted using the Biblioshiny tool in RStudio, generating network maps and thematic graphs that visualize the relationships between keywords and international collaborations. The results show that China and the United States lead in terms of scientific productivity and international collaboration. A growing interest in the use of generative AI emerges, including deep learning and ChatGPT, in educational contexts for the purpose of assessment of learning. The present study provides a clear overview of current dynamics in AI research in mathematics education, highlighting opportunities for interdisciplinary collaboration.Eurasia Journal of Mathematics, Science and Technology Education330A growing interest in the use of generative AI emerges, including deep learning and ChatGPT, in educational contexts for the purpose of assessment of learning, highlighting opportunities for interdisciplinary collaboration.Eurasia Journal of Mathematics, Science and Technology Education["Hassan Hossein-Mohand", "Hossein Hossein-Mohand", "Veronica Albanese", "Mar\u00eda del Carmen Olmos G\u00f3mez"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/549d3820fc08f3811c81e697b3e51953b578fe74 05d217d660c52e197904c6f27ea79a41adf0bf13The integration of cutting-edge technologies—such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies—is revolutionizing agricultural practices, enhancing productivity, sustainability, and efficiency. The objective of this study is to review the literature regarding the development and evolution of AI as well as other emerging technologies in the various fields of Agriculture as they are developed and transformed by integrating the above technologies. The areas examined in this study are open field smart farming, vertical and indoor farming, zero waste agriculture, precision livestock farming, smart greenhouses, and regenerative agriculture. This paper links current research, technological innovations, and case studies to present a comprehensive review of these emerging technologies being developed in the context of smart agriculture, for the benefit of farmers and consumers in general. By exploring practical applications and future perspectives, this work aims to provide valuable insights to address global food security challenges, minimize environmental impacts, and support sustainable development goals through the application of new technologies.Information00A comprehensive review of the development and evolution of AI as well as other emerging technologies in the various fields of Agriculture as they are developed and transformed by integrating the above technologies is presented.Information["Fotis Assimakopoulos", "C. Vassilakis", "Dionisis Margaris", "Konstantinos Kotis", "D. Spiliotopoulos"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/05d217d660c52e197904c6f27ea79a41adf0bf13 6ae40d4c154904e11a026125ca65c3a1e13324cdAbstract - Financial closing process is part of corporate accounting which was critical providing account reconciliation, preparation of journal entries, and preparation of the financial statement. Conventional processes in the resolution of these tasks are challenging, time consuming and susceptible to several errors, however with the help of integration of Artificial Intelligence (AI) and Machine Learning (ML), these processes are considerably improved. Since use of AI/ML automates the ample routine tasks, there is less chance of an error, more efficient and faster preparation of financial reports. In this paper, real-life application of AI and ML has been discussed with particular reference to its relevance in the financial close process where AI and ML are capable of actualizing data validation, journal entry management and fast-tracking the preparation of the financial statements. Further, it discusses how businesses can benefit from the solutions such as better decision-making capability, increased compliance, and shifting focus from administrative work to the creation of added value.International Journal For Multidisciplinary Research150In this paper, real-life application of AI and ML has been discussed with particular reference to its relevance in the financial close process where AI and ML are capable of actualizing data validation, journal entry management and fast-tracking the preparation of the financial statements.International Journal For Multidisciplinary Research["Muddassir Farooq"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/6ae40d4c154904e11a026125ca65c3a1e13324cd 25f50793bfabd78796d63b508a4fb07219d18041Emergent technologies such as solar power, electric vehicles, and artificial intelligence (AI) often exhibit exponential or power function price declines and various ``S-curves'' of adoption. We show that under CES and VES utility, such price and adoption curves are functionally linked. When price declines follow Moore's, Wright's and AI scaling"Laws,'' the S-curve of adoption is Logistic or Log-Logistic whose slope depends on the interaction between an experience parameter and the elasticity of substitution between the incumbent and emergent good. These functional relations can serve as a building block for more complex models and guide empirical specifications of technology adoption.00It is shown that under CES and VES utility, such price and adoption curves are functionally linked and can serve as a building block for more complex models and guide empirical specifications of technology adoption.["Rajesh P. Narayanan", "R. K. Pace"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/25f50793bfabd78796d63b508a4fb07219d18041 16de8ab48c8087b0536da737145b8215744e1936The impact of AI on the hospitality industry is increasingly significant. This paper explores the influence of digital transformation on the hospitality sector, with a particular focus on the role of artificial intelligence (AI) in enhancing business operations through gastronomic offerings. Digital technology is transforming the way hospitality establishments operate, offering new opportunities for optimizing operations, personalizing customer experiences, and efficiently managing resources. By analyzing the application of AI technologies, such as predictive analytics, personalized marketing, and automated ordering systems, the paper addresses how hospitality businesses can improve their competitive position and guest satisfaction. The study employs both qualitative and quantitative research methods to present successful examples of AI integration into business models, highlighting the benefits, challenges, and prospects for future development. Managers from 64 restaurants across the Republic of Serbia participated in the research, and the findings demonstrate the clear potential of artificial intelligence to transform hospitality, creating sustainable and innovative business practices.  BizInfo Blace00The study employs both qualitative and quantitative research methods to present successful examples of AI integration into business models, highlighting the benefits, challenges, and prospects for future development.BizInfo Blace["Dragan Vukoli\u0107", "Tamara Gaji\u0107", "An\u0111elka Popovi\u0107"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/16de8ab48c8087b0536da737145b8215744e1936 f99c7b61022a1ce0653c1fc52ba2194ae73a01bfThe rapid integration of Artificial Intelligence (AI) in cybersecurity has redefined traditional security protocols, providing advanced mechanisms to combat increasingly sophisticated cyber threats. This article investigates the transformative role of AI in enhancing cybersecurity by focusing on three core areas: threat detection, defence mechanisms, and risk management. The primary aim is to assess how AI technologies—specifically machine learning, deep learning, and natural language processing—can improve the detection, prevention, and mitigation of cyber threats beyond traditional methods. By leveraging AI-driven solutions, cybersecurity can anticipate emerging threats, quickly adapt defensive strategies, and significantly reduce response times. To achieve this, the study will analyse recent advances in AI applications within cybersecurity, using a systematic literature review and case study analysis. The literature review will highlight existing knowledge gaps, explore the current limitations of conventional cybersecurity measures, and identify how AI fills these gaps. Case studies of real-world AI deployment in cybersecurity will be critically examined to understand the practical effectiveness and challenges associated with these technologies. The expected results include an in-depth understanding of AI's specific advantages in threat detection accuracy, predictive analysis, and anomaly identification. Furthermore, we anticipate uncovering key limitations and ethical considerations, such as data privacy issues, potential biases in AI algorithms, and the risk of adversarial attacks exploiting AI vulnerabilities. By examining these aspects, the article aims to present a balanced perspective on the future of AI in cybersecurity, suggesting critical improvements and policy recommendations. International Journal of Religion00The transformative role of AI in enhancing cybersecurity by focusing on three core areas: threat detection, defence mechanisms, and risk management is investigated, to assess how AI technologies can improve the detection, prevention, and mitigation of cyber threats beyond traditional methods.International Journal of Religion["Mustafa Osman I. Elamin", "Osman M. O. Ismaiel"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/f99c7b61022a1ce0653c1fc52ba2194ae73a01bf c661a502f3bb5c3840d321fcec10779b6cb2fa5fThe advent of artificial intelligence (AI) has ushered in transformative changes across sectors, including the nonprofit domain, where data-driven insights and operational efficiency are critical to achieving social impact. This paper explores the pivotal role of AI in revolutionizing nonprofit operations, with a particular focus on Microsoft's AI-driven solutions. Leveraging platforms like Azure, Microsoft 365, and Power BI, Microsoft has enabled nonprofits to enhance donor engagement, optimize resource allocation, and achieve greater transparency in impact measurement. Through a comparative analysis with competitors such as Salesforce and Google, the research highlights the unique contributions of Microsoft’s tools in addressing scalability challenges, ensuring inclusivity, and advancing equity in technology adoption. The findings reveal that Microsoft's AI ecosystem offers distinct advantages, including advanced real-time analytics, seamless integration with existing infrastructure, and emerging trends like generative AI for personalized donor outreach and dynamic decision-making. These innovations are not only scalable for global nonprofits but also adaptable to grassroots organizations facing infrastructural and equity barriers. The study underscores the importance of ethical AI deployment in the nonprofit sector, emphasizing transparency, accountability, and accessibility. Looking ahead, AI’s potential to reshape the nonprofit sector is boundless, particularly in areas like predictive analytics, automated operations, and localized problem-solving. As technology evolves, partnerships with tech giants like Microsoft will play a critical role in building resilient, data-informed nonprofits capable of addressing complex social challenges at scale.International Journal For Multidisciplinary Research00The findings reveal that Microsoft's AI ecosystem offers distinct advantages, including advanced real-time analytics, seamless integration with existing infrastructure, and emerging trends like generative AI for personalized donor outreach and dynamic decision-making.International Journal For Multidisciplinary Research["Sudheekar Reddy Pothireddy"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/c661a502f3bb5c3840d321fcec10779b6cb2fa5f 4c9f1201bdcedeb8c3ff8152cc402cd1cb7272fbIn this 2022 work we argued that, despite claims about successful modeling of the visual brain using artificial nets, the problem is far from being solved (even for low-level vision). Examples of open issues include: where should we read from ANNs in order to reproduce human behavior?, this ad-hoc read-out is considered part of the brain model or not?, should we use artificial psychophysics or artificial physiology?, in the case of ANNs, artificial experiments should literally match the experiments done with humans?. There is a clear need of rigorous procedures for experimental tests for ANNs devoted to model the visual brain, and more generally, to understand ANNs devoted to generic vision tasks. Following our experience in using low-level facts from Quantitative Visual Neuroscience in computer vision, in this work we presented the idea of developing a low-level dataset compiling the basic spatio-temporal and chromatic facts that are known to happen in the retina-V1 pathway, and they are not currently available in existing databases such as BrainScore. In our results we checked the behavior of three recently proposed models with similar architecture: (1) A parametric model tuned via Maximum Differentiation [Malo&Simoncelli SPIE 15, Martinez et al. PLOS 18, Martinez et al. Front. Neurosci. 19], (2) A non-parametric model called PerceptNet tuned to maximize the correlation with human opinion on subjective distortions [Hepburn et al. IEEE ICIP 19], and (3) A model with the same encoder as PerceptNet, but tuned for image segmentation (published as Hernandez-Camara et al. Patt.Recogn.Lett. 23). Results on 10 compelling psycho/physio visual facts show that the first model is the one with closer behavior to the humans in terms of receptive fields, but more interestingly, on the nonlinear behavior when facing complex spatio-chromatic patterns of a range of luminances and contrasts.00It is argued that, despite claims about successful modeling of the visual brain using artificial nets, the problem is far from being solved (even for low-level vision), and there is a clear need of rigorous procedures for experimental tests for ANNs devoted to model the visual brain and more generally, to understand ANNs devoted to generic vision tasks.["Jorge Vila-Tom'as", "Pablo Hern'andez-C'amara", "Qiang Li", "Valero Laparra", "Jes'us Malo"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/4c9f1201bdcedeb8c3ff8152cc402cd1cb7272fb 45d5404e91b597114c936537bdd82c412f9d6162Gastrointestinal Nursing00Gastrointestinal Nursing["Sean Boyle"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/45d5404e91b597114c936537bdd82c412f9d6162 893da9cb621f5fe458acf8aa7edfbec236fcd0d2AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaboration across diverse applications necessitates a broader perspective. This position paper presents a holistic framework for AI scaling, encompassing Scaling Up, Scaling Down, and Scaling Out. It argues that while Scaling Up of models faces inherent bottlenecks, the future trajectory of AI scaling lies in Scaling Down and Scaling Out. These paradigms address critical technical and societal challenges, such as reducing carbon footprint, ensuring equitable access, and enhancing cross-domain collaboration. We explore transformative applications in healthcare, smart manufacturing, and content creation, demonstrating how AI Scaling can enable breakthroughs in efficiency, personalization, and global connectivity. Additionally, we highlight key challenges, including balancing model complexity with interpretability, managing resource constraints, and fostering ethical development. By synthesizing these approaches, we propose a unified roadmap that redefines the future of AI research and application, paving the way for advancements toward Artificial General Intelligence (AGI).810This position paper presents a holistic framework for AI scaling, encompassing Scaling Up, Scaling Down, and Scaling Out, and proposes a unified roadmap that redefines the future of AI research and application, paving the way for advancements toward Artificial General Intelligence (AGI).["Yunke Wang", "Yanxi Li", "Chang Xu"]2025-02-02T00:00:00https://www.semanticscholar.org/paper/893da9cb621f5fe458acf8aa7edfbec236fcd0d2 ee651f571cf84065a40c1383900df0303411c5baOrthopadie270In order to exploit the full potential of AI, comprehensive clinical data volumes are required, which can only be realized through a multicentric approach, and cooperative efforts at national and international levels are essential in order to research and develop new AI applications.Orthopadie["Vincent Lallinger", "F. Hinterwimmer", "R\u00fcdiger von Eisenhart-Rothe", "Igor Lazic"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/ee651f571cf84065a40c1383900df0303411c5ba 9a1eeff47c019770d1fe70f6b044bff9b8a52e4aThe increasing use of artificial intelligence (AI) in higher education is reshaping how students engage with their academic and personal lives. However, the impact of AI on students’ well-being remains underexplored. This mini-review synthesizes current literature to assess how AI affects student well-being, focusing on mental health, social interactions, and academic experiences. While AI offers benefits such as personalized learning, mental health support, and improved communication efficiency, it also raises concerns regarding digital fatigue, loneliness, technostress, and reduced face-to-face interactions. Over-reliance on AI may diminish interpersonal skills and emotional intelligence, leading to social isolation and anxiety. Furthermore, issues such as data privacy and job displacement emerge as AI technologies permeate educational environments. The review highlights the need for balanced AI integration that supports both academic success and student well-being, advocating for further empirical studies to comprehensively understand these dynamics. As AI becomes more embedded in education, it is crucial to develop strategies that mitigate its negative effects while promoting holistic well-being among students.Frontiers in Psychology220The review highlights the need for balanced AI integration that supports both academic success and student well-being, advocating for further empirical studies to comprehensively understand these dynamics.Frontiers in Psychology["Blanka Klimova", "M. Pikhart"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/9a1eeff47c019770d1fe70f6b044bff9b8a52e4a 9094d56425cc8f7bcec1d4843e8d580a81b322bb Purpose To identify catalysts for facilitation and enhancement of Affective Learning (AL) in Artificial Intelligence (AI) based diversity, equity, and inclusion learnings (DEIL). Design/methodology/approach A qualitative study included 17 semistructured interviews and triangulation validated the results. Findings The study found ten catalyst elements for facilitating and enhancing AL in AI-based DEIL. Research limitations/implications This study is conducted on a relatively small sample size, offering a valuable opportunity for future research to validate and generalize the findings to larger populations. Practical implications For learning and development, human resources, business managers, and AI professionals, this study proposes ten AL elements for facilitating the AI-based DEIL. Originality/value It contributes to the field of AI-based learning domain focusing on AL in DEIL. Development and Learning in Organizations: an international journal60This study proposes ten AL elements for facilitating the AI-based DEIL and finds ten catalyst elements for facilitating and enhancing AL in AI-based DEIL.Development and Learning in Organizations: An International Journal["Shifa Saadan", "Shrikant Prabhakar Wavre", "Sunaina Kuknor"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/9094d56425cc8f7bcec1d4843e8d580a81b322bb 7fc6b1f83101e72e715eef365d8f3bb68fc51924Minds and Machines180The aim of the analysis is to specify which are the less or the more environmentally friendly regulatory regimes set up with the AIA, and to claim that Art. 9, 27 and 95 are among the less green pieces of the whole legislation.Minds and Machines["U. Pagallo"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/7fc6b1f83101e72e715eef365d8f3bb68fc51924 5c56e1baf77ab348e596099fd9f04f5d7e5edc69PurposeThis editorial review presents a bibliometric account of the convergence of the fields of artificial intelligence (AI) and human resource management (HRM) and an overview of the related contributions in this special issue. It also explores the expansive area where research on AI and HRM intersects, a domain experiencing rapid growth and transformation, faster than we envisaged.Design/methodology/approachThis substantive editorial employs a range of bibliometric analytical tools to present a state of knowledge on the topic and also provides an analytical overview of the contributions in this Special Issue.FindingsA thorough examination of scholarly publications spanning two decades illuminates the evolutionary path of themes, key contributors, seminal works and emerging trends within this interdisciplinary sphere. Leveraging co-word analysis, we distill essential themes and insights from an extensive dataset of 654 journal publications curated from the Web of Science database. Our analysis underscores critical research domains, highlighting the nuanced interplay between HRM and AI.Originality/valueBy integrating findings from the bibliometric analysis and the contributions from the papers in the Special Issue, we highlight and speculate where the field is heading and where scholars have crucial? Opportunities to contribute to going forward.Person-centered review540This editorial review presents a bibliometric account of the convergence of the fields of artificial intelligence (AI) and human resource management (HRM) and an overview of the related contributions in this special issue and provides an analytical overview of the contributions in this Special Issue.Personnel Review["Ashish Malik", "Pamela Lirio", "P. Budhwar", "Mai Nguyen", "Muhammad Ashraf Fauzi"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/5c56e1baf77ab348e596099fd9f04f5d7e5edc69 045222d304b32dddec6c4d8712555bd261d60204In recent decades, the integration of artificial intelligence (AI) into health care has revolutionized diagnostics, treatment customization, and delivery. In low-resource settings, AI offers significant potential to address health care disparities exacerbated by shortages of medical professionals and other resources. However, implementing AI effectively and responsibly in these settings requires careful consideration of context-specific needs and barriers to equitable care. This article explores the practical deployment of AI in low-resource environments through a review of existing literature and interviews with experts, ranging from health care providers and administrators to AI tool developers and government consultants. The authors highlight 4 critical areas for effective AI deployment: infrastructure requirements, deployment and data management, education and training, and responsible AI practices. By addressing these aspects, the proposed framework aims to guide sustainable AI integration, minimizing risk, and enhancing health care access in underserved regions.Population health management340The authors highlight 4 critical areas for effective AI deployment: infrastructure requirements, deployment and data management, education and training, and responsible AI practices, and the proposed framework aims to guide sustainable AI integration, minimizing risk, and enhancing health care access in underserved regions.Population health management["Evelyn Wong", "Alvaro Bermudez-Ca\u00f1ete", "Matthew J Campbell", "David C Rhew"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/045222d304b32dddec6c4d8712555bd261d60204 7a1bff77234854591c859bc99afa7550611708a5In this essay, we explored the feasibility of utilizing artificial intelligence (AI) for qualitative data analysis in equity-focused research. Specifically, we compare thematic analyses of interview transcripts conducted by human coders with those performed by GPT-3 using a zero-shot chain-of-thought prompting strategy. Our results suggest that the AI model, when provided with suitable prompts, can proficiently perform thematic analysis, demonstrating considerable comparability with human coders. Despite potential biases inherent in its training data, the model was able to analyze and interpret the data through social justice perspectives. We discuss the applications of integrating AI into qualitative research, provide code snippets illustrating the use of GPT models, and highlight unresolved questions to encourage further dialogue in the field.Educational Research160This essay compares thematic analyses of interview transcripts conducted by human coders with those performed by GPT-3 using a zero-shot chain-of-thought prompting strategy, suggesting that the AI model, when provided with suitable prompts, can proficiently perform thematic analysis.Educational Researcher["Yan Jiang", "Lillie Ko-Wong", "Ivan Valdovinos Gutierrez"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/7a1bff77234854591c859bc99afa7550611708a5 784e471a6c45579512eaadff1a3900ee515a74d2The integration of Artificial Intelligence (AI) into English as a Second Language (ESL) assessments has revolutionized traditional practices by offering efficiency, accuracy, and personalized learning pathways. This study employs a mixed-methods approach to evaluate the effectiveness of AI tools, such as Grammarly, Duolingo, and Write & Improve, in improving ESL learners' proficiency across writing, reading, speaking, and listening skills. Quantitative findings from 150 learners show significant improvements in writing (16.6%) and reading (13.8%), while gains in speaking (5.4%) and listening (4.2%) remain modest, reflecting the limitations of AI in handling nuanced oral communication. Qualitative insights from 20 instructors reveal challenges, including algorithmic bias, cultural insensitivity, and concerns over data privacy. Despite these issues, AI tools are praised for reducing grading time and providing instant feedback. The study emphasizes the need for ethical guidelines, equitable access, and human oversight to address existing limitations and ensure inclusive educational outcomes. Additionally, it highlights the digital divide, where socio-economic disparities limit access to premium AI tools, exacerbating educational inequalities. By combining quantitative data with qualitative insights, this research provides a comprehensive understanding of AI's role in ESL education. It advocates for a balanced integration of AI, positioning it as a complementary tool that amplifies human expertise rather than replacing it. This study contributes to ongoing discussions on the ethical and practical implications of AI in education, offering recommendations for policymakers, educators, and developers to optimize its potential.Language, Technology, and Social Media240This research advocates for a balanced integration of AI, positioning it as a complementary tool that amplifies human expertise rather than replacing it, and highlights the digital divide, where socio-economic disparities limit access to premium AI tools, exacerbating educational inequalities.Language, Technology, and Social Media["Seyed Reza Abedi", "Farnaz Divanpour", "Seyed Reza Molaee", "Hailay Tesfay Gebremariam"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/784e471a6c45579512eaadff1a3900ee515a74d2 c161267a9ecc8f6b85d4cdb372fe8f20dfc3f6ceThe facilitative interpersonal skills (FIS) task is a performance-based task designed to assess clinicians' capacity for facilitating a collaborative relationship. Performance on FIS is a robust clinician-level predictor of treatment outcomes. However, the FIS task has limited scalability because human rating of FIS requires specialized training and is time-intensive. We aimed to catalyze a "big needle jump" by developing an artificial intelligence- (AI-) based automated FIS measurement that captures all behavioral audiovisual markers available to human FIS raters. A total of 956 response clips were collected from 78 mental health clinicians. Three human raters rated the eight FIS subscales and reached sufficient interrater reliability (intraclass correlation based on three raters [ICC3k] for overall FIS = 0.85). We extracted text-, audio-, and video-based features and applied multimodal modeling (multilayer perceptron with a single hidden layer) to predict overall FIS and eight FIS subscales rated along a 1-5 scale continuum. We conducted 10-fold cross-validation analyses. For overall FIS, we reached moderate size relationships with the human-based ratings (Spearman's ρ = .50). Performance for subscales was variable (Spearman's ρ from .30 to .61). Inclusion of audio and video modalities improved the accuracy of the model, especially for the Emotional Expression and Verbal Fluency subscales. All three modalities contributed to the prediction performance, with text-based features contributing relatively most. Our multimodal model performed better than previously published unimodal models on the overall FIS and some FIS subscales. If confirmed in external validation studies, this AI-based FIS measurement may be used for the development of feedback tools for more targeted training, supervision, and deliberate practice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).Psychotherapy00An artificial intelligence- (AI-) based automated FIS measurement that captures all behavioral audiovisual markers available to human FIS raters is developed and may be used for the development of feedback tools for more targeted training, supervision, and deliberate practice.Psychotherapy["K. Aafjes-van Doorn", "Marcelo Cicconet", "Jordan Bate", "Jeffrey F Cohn", "Marc Aafjes"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/c161267a9ecc8f6b85d4cdb372fe8f20dfc3f6ce ea29cf59955bf83a069515f59d28f408567d522eArtificial Intelligence (AI) has transformed numerous sectors of the global economy including education. In Kenya for instance, its integration in teacher education has enhanced learning experiences thereby improving pedagogical outcomes and fostering innovation. This study examines the potential risks and benefits of incorporating AI into various facets of teacher education in Kenya. By leveraging AI, teacher education programmes can harness advanced technologies to transform teacher training. Personalised learning powered by AI algorithms allows educators to address their strengths and weaknesses and tailor content and methodologies to their needs. Furthermore, immersive technologies such as virtual and augmented reality enable educators to practice real-world classroom scenarios in a controlled environment. The experience of integrating AI in teacher education in South Korea demonstrates a proven blueprint that Kenya can replicate. Despite the merits of deploying AI in teacher education, there are challenges that its use poses such as ethical concerns and inequitable access to technology among others. This study underscores the importance of strategic planning and stakeholder involvement to ensure AI’s responsible deployment in Kenyan Teacher Education ultimately transforming teaching and learning outcomes across the country.Eastern African Journal of Humanities and Social Sciences00The importance of strategic planning and stakeholder involvement is underscores the importance of strategic planning and stakeholder involvement to ensure AI’s responsible deployment in Kenyan Teacher Education ultimately transforming teaching and learning outcomes across the country.Eastern African Journal of Humanities and Social Sciences["H. C. Sang"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/ea29cf59955bf83a069515f59d28f408567d522e 03fd4b1008e493892539a22ed1c2c18f561e2ef2This study applied the Unified Theory of Acceptance and Use of Technology (UTAUT) to provide an understanding of the behavioral intentions of pre-service teachers in the adoption and utilization of artificial intelligence (AI) tools for educational engagement in the inclusive classroom.The cross-sectional study collected data through a validated questionnaire from 411 pre-service teachers were analyzed with descriptive statistics such as frequency counts and simple percentage calculation, as well as inferential statistics which involved correlational analysis and Structural Equation Modeling (SEM).The study established that effort expectancy had a positive and direct significant contribution to the perceived behavioral intention of pre-service teachers to adopt and use AI for inclusive education teaching. Technological self-efficacy had no direct contributory effect on these teachers' behavioral intention to adopt and use AI for inclusive education teaching. Technological self-efficacy did, however, have a significant positive and indirect contribution to the effect of performance expectancy and social influence on the pre-service teachers' behavioral intention to adopt and use AI for inclusive education teaching, based on their technological self-efficacy.The implication of findings of this study points to the exigency of a need to strengthen institutional policies and teacher preparation curricula in a manner that would advance the infusion of the use of artificial intelligence for teaching of learners with special needs.Frontiers in Education500Findings of this study point to the exigency of a need to strengthen institutional policies and teacher preparation curricula in a manner that would advance the infusion of the use of artificial intelligence for teaching of learners with special needs.Frontiers in Education["O. Adigun", "Faisat Adeniran Tijani", "C. Haihambo", "Simasiku Limbo Enock"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/03fd4b1008e493892539a22ed1c2c18f561e2ef2 4b7119e15f68636f4826078f4ed0299765679a37An aberrant increase in cancer incidences has demanded extreme attention globally despite advancements in diagnostic and management strategies. The high mortality rate is concerning, and tumour heterogeneity at the genetic, phenotypic, and pathological levels exacerbates the problem. In this context, lack of early diagnostic techniques and therapeutic resistance to drugs, sole awareness among the public, coupled with the unavailability of these modern technologies in developing and low-income countries, negatively impact cancer management. One of the prime necessities of the world today is the enhancement of early detection of cancers. Several independent studies have shown that screening individuals for cancer can improve patient survival but are bogged down by risk classification and major problems in patient selection. Artificial intelligence (AI) has significantly advanced the field of oncology, addressing various medical challenges, particularly in cancer management. Leveraging extensive medical datasets and innovative computational technologies, AI, especially through deep learning (DL), has found applications across multiple facets of oncology research. These applications range from early cancer detection, diagnosis, classification, and grading, molecular characterization of tumours, prediction of patient outcomes and treatment responses, personalized treatment, and novel anti-cancer drug discovery. Over the past decade, AI/ML has emerged as a valuable tool in cancer prognosis, risk assessment, and treatment selection for cancer patients. Several patents have been and are being filed and granted. Some of those inventions were explored and are being explored in clinical settings as well. In this review, we will discuss the current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management. We are optimistic about the potential of AI/ML in improving outcomes for cancer and the need for further research and development in this field.Recent Patents on Anti-Cancer Drug Discovery00The current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management are discussed.Recent patents on anti-cancer drug discovery["Anuja Mishra", "Srishti Sharma", "Swaroop Kumar Pandey"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/4b7119e15f68636f4826078f4ed0299765679a37 88370042e5b05f40a98084612e120027c89f1e1fIntroduction. Digital competence is seen as key to employment, education, and social domains in the 21st century. At the same time, there is no universal framework for studying attitudes towards artificial intelligence (AI) and its use in professional and personal life. Aim. The aim of the present research is to outline respondents’ attitudes towards the benefits and threats of AI that may facilitate or hinder the process of intelligent AI integration into different aspects of life. Methodology and research methods. This article outlines results from a pilot study of attitudes towards AI, conducted with a sample of 125 Bulgarian students and professionals. The research design is mixed (quantitative and qualitative) and includes questionnaire, focus groups and interviews. Results and scientific novelty. The results reveal that both young people and adults base their opinions on their assessment of AI performance and find positive implications in terms of facilitating task performance, but have strong reservations concerning job security and the use of AI in the social sphere. They also suggest that AI skills need to become integrated into education. Future research directions highlighted include differentiating between educational, professional, and personal domains and self-assessing digital literacy from an evidence-based vs. state of the art perspective. Practical significance. Insights from this study focus on mindful mindset, educational settings and the redesign of educational content, particularly forms of critical engagement and use of AI.The Education and science journal180The results reveal that both young people and adults base their opinions on their assessment of AI performance and find positive implications in terms of facilitating task performance, but have strong reservations concerning job security and the use of AI in the social sphere.The Education and science journal["M. Y. Tsenov", "M. Bakracheva"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/88370042e5b05f40a98084612e120027c89f1e1f e15edadda5d8346a6a8cee63e956d8705074b76dABSTRACT Artificial intelligence (AI) holds the promise of mimicking human intelligence to enhance prediction and complex decision-making in healthcare, carving out a significant role in dentistry tasks, notably endodontics. It has shown remarkable accuracy in detecting and predicting diseases within this field, potentially refining diagnostics and treatments to boost endodontic success rates. However, there’s a need to validate the reliability, practicality, and cost-effectiveness of AI before fully integrating it into everyday clinical settings. This review aims to explore AI’s current applications in endodontics and possible future developments.Journal of Pharmacy and Bioallied Sciences70A review of AI’s current applications in endodontics and possible future developments is explored to explore AI’s current applications in endodontics and possible future developments.Journal of Pharmacy and Bioallied Sciences["Medum Shabharish S. Kumar", "A. Rai", "Neha Singh", "Yashshwini Shroff", "Vinay Rao", "K. V. Prasad", "Pratik Surana"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/e15edadda5d8346a6a8cee63e956d8705074b76d 9625cc361c8692584268def6797faecaaf830c89This paper explores the psychological implications and educational outcomes of artificial intelligence (AI)-driven personalized learning systems. The study delves into how AI facilitates customized learning experiences, adapting to individual student needs and learning styles. The research highlights the impact of AI on student motivation, cognitive load, and academic performance, as well as potential ethical concerns such as data privacy and algorithmic bias. Empirical findings from various case studies demonstrate how AI-driven platforms enhance engagement and retention rates. The study concludes with recommendations for optimizing AI use in education while addressing associated challenges.International Journal of Education, Humanities and Social Sciences110The research highlights the impact of AI on student motivation, cognitive load, and academic performance, as well as potential ethical concerns such as data privacy and algorithmic bias.International Journal of Education, Humanities and Social Sciences["Junyao Wang", "Yasmin Hussain", "Chencheng Mao"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/9625cc361c8692584268def6797faecaaf830c89 c2ec7dcc0f3e1d50bb5dc1d602ca7821a079c888BACKGROUND The rapid growth of artificial intelligence in healthcare is transforming how nurses deliver care and make clinical decisions. From supporting diagnostics to providing virtual health assistants, artificial intelligence offers new ways to enhance patient outcomes and streamline healthcare processes. However, these advancements also bring challenges, particularly around ethics, potential biases, and ensuring technology complements rather than replaces human expertise. METHODS A discussion paper designed to break down key artificial intelligence terms and demonstrate real-world applications to guide nurses to develop the skills needed to navigate this evolving technological landscape. FINDINGS This discussion emphasises the importance of maintaining the critical role of human clinical judgment, highlighting that artificial intelligence should support nurses' expertise rather than diminish it. The need for continuous education to keep nurses equipped with the knowledge to effectively integrate artificial intelligence into their practice is argued. With an inclusive approach, artificial intelligence has the potential to become a powerful tool that supports nurses in improving patient care while preserving the essential human touch in healthcare.Contemporary Nurse: health care across the lifespan290This discussion emphasises the importance of maintaining the critical role of human clinical judgment, highlighting that artificial intelligence should support nurses' expertise rather than diminish it and the need for continuous education to keep nurses equipped with the knowledge to effectively integrate artificial intelligence into their practice is argued.Contemporary nurse["Pauletta Irwin", "S. Rehman", "Shanna M. Fealy", "R. Kornhaber", "Annabel Matheson", "Michelle Cleary"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/c2ec7dcc0f3e1d50bb5dc1d602ca7821a079c888 e992fbaa847f80a44d8f83417690a3bd9d523004BACKGROUND At a critical juncture in the ongoing fight against cardiovascular disease (CVD), healthcare professionals are striving for more informed and expedited decisionmaking. Artificial Intelligence (AI) promises to be a guiding light in this endeavor. The diagnosis of coronary artery disease has now become non-invasive and convenient, while wearable devices excel at promptly detecting life-threatening arrhythmias and treatments for heart failure. OBJECTIVE This study aimed to highlight the applications of AI in cardiology with a particular focus on arrhythmias and its potential impact on healthcare for all through careful implementation and constant research efforts. METHODS An extensive search strategy was implemented. The search was conducted in renowned electronic medical databases, including Medline, PubMed, Cochrane Library, and Google Scholar. Artificial Intelligence, cardiovascular diseases, arrhythmias, machine learning, and convolutional neural networks in cardiology were used as keywords for the search strategy. RESULTS A total of 6876 records were retrieved from different electronic databases. Duplicates (N = 1356) were removed, resulting in 5520 records for screening. Based on predefined inclusion and exclusion criteria, 4683 articles were excluded. Following the full-text screening of the remaining 837 articles, a further 637 were excluded. Ultimately, 200 studies were included in this review. CONCLUSION AI represents not just a development but a cutting-edge force propelling the next evolution of cardiology. With its capacity to make precise predictions, facilitate non-invasive diagnosis, and personalize therapies, AI holds the potential to save lives and enhance healthcare quality on a global scale.Current Cardiology Reviews00This study aimed to highlight the applications of AI in cardiology with a particular focus on arrhythmias and its potential impact on healthcare for all through careful implementation and constant research efforts.Current cardiology reviews["A. K. S. Hamad", "Jassim Haji"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/e992fbaa847f80a44d8f83417690a3bd9d523004 724b7021e2aed9833dfcfdd546c9b453514e169aAyurveda is regarded as a thousand-year-old science. This system of medicine has been time-proven and beneficial not just for maintaining individual's health but also in ensuring their (holistic) well-being. Combining complementary and modern medicines can help in solving patient issues and improve treatment strategies. This study looks into the use of machine learning in Ayurveda, an age-old Indian medical practice that is becoming more and more well-known throughout the world. In order to close the gaps in the current state of knowledge, it is imperative that modern technologies like artificial intelligence and machine learning be combined with Ayurvedic sciences. We have the potential to revolutionize the field of Ayurveda by accepting and transforming this digital landscape. Researchers have combined AI with additional technological advances to enhance Ayurvedic medicines’ efficiency, availability, and reliability. The study analyses how AI influences Ayurveda.Journal of Ayurveda and Integrated Medical Sciences00This study looks into the use of machine learning in Ayurveda, an age-old Indian medical practice that is becoming more and more well-known throughout the world and analyses how AI influences Ayurveda.Journal of Ayurveda and Integrated Medical Sciences["Chetna Rathor", "Vijendra Singh Mandloi", "Ishwari Sachan", "Vikash Sahu"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/724b7021e2aed9833dfcfdd546c9b453514e169a 14d3787cd2f37f543ca1ac05b281e68d8330d88eJournal of the American College of Surgeons00Journal of the American College of Surgeons[]2025-02-03T00:00:00https://www.semanticscholar.org/paper/14d3787cd2f37f543ca1ac05b281e68d8330d88e f13d0f93523918d9a01667732f208917ed7ae0d5Psychological Trauma00Psychological Trauma: Theory, Research, Practice, and Policy[]2025-02-03T00:00:00https://www.semanticscholar.org/paper/f13d0f93523918d9a01667732f208917ed7ae0d5 3e18f765ec872054c5bc028d6b15fdad45ccda18Penggunaan kecerdasan buatan (AI) dalam dunia pendidikan semakin berkembang, termasuk di kalangan mahasiswa. Tujuan penelitian untuk mengkaji pengaruh AI terhadap kemampuan berpikir kritis mahasiswa. Berpikir kritis merupakan kompetensi penting yang harus dimiliki oleh mahasiswa dalam menghadapi tantangan global. Dengan berkembangnya teknologi AI, mahasiswa kini dapat mengakses berbagai sumber informasi secara cepat dan mudah. Namun, dampak dari penggunaan AI terhadap kemampuan berpikir kritis belum sepenuhnya jelas. Metode yang digunakan adalah studi literatur dengan menganalisis berbagai jurnal dan artikel terkait. Beberapa penelitian menunjukkan bahwa AI dapat mempercepat proses pembelajaran dan memberikan analisis data yang mendalam, namun ada juga kekhawatiran bahwa ketergantungan pada teknologi ini dapat mengurangi kemampuan mahasiswa untuk berpikir secara mandiri. Hasil penelitian menunjukkan bahwa meskipun AI dapat memperkaya proses belajar dan memfasilitasi pengambilan keputusan berbasis data, mahasiswa perlu diberi perhatian khusus agar tidak bergantung sepenuhnya pada teknologi tersebut dalam mengembangkan keterampilan berpikir kritis. Oleh karena itu, integrasi AI dalam pendidikan perlu disertai dengan pengembangan metode pembelajaran yang dapat mendorong mahasiswa untuk tetap melatih kemampuan analitis dan reflektif merekaPOACE: Jurnal Program Studi Adminitrasi Pendidikan00POACE: Jurnal Program Studi Adminitrasi Pendidikan["Mia Cholvistaria", "Ade Gunawan"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/3e18f765ec872054c5bc028d6b15fdad45ccda18 7e90ec393d2429131e27b701af6402435223af88DETUROPE470DETUROPE - The Central European Journal of Tourism and Regional Development["An\u0111elka \u0160tili\u0107", "Adis Pu\u0161ka", "Milo\u0161 Ni\u010di\u0107"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/7e90ec393d2429131e27b701af6402435223af88 7f612c90b337dde3a9f8c439e44079cced411d57Journal of Clinical Nursing10Journal of clinical nursing["A. Kleebayoon", "V. Wiwanitkit"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/7f612c90b337dde3a9f8c439e44079cced411d57 73021ea90db0062e00c20a556b444ff5e7eaa885Archives of Computational Methods in Engineering830Archives of Computational Methods in Engineering["Amira Bouamrane", "M. Derdour", "A. Alksas", "S. Contractor", "Mohamed Ghazal", "A. El-Baz"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/73021ea90db0062e00c20a556b444ff5e7eaa885 1171f585c7879024c769824b6bb11a198bbbfdb6GEN Biotechnology30GEN Biotechnology["Toni Manzano", "William Whitford"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/1171f585c7879024c769824b6bb11a198bbbfdb6 70039310976168732a07d53ce37908bbb825924aInternational Review of Psychiatry40International Review of Psychiatry["Alexander Smith", "M. Liebrenz"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/70039310976168732a07d53ce37908bbb825924a ddc2c7b2a1db59281a00c3b5930369d6a076a1e9Consulting Psychology Journal00Consulting Psychology Journal["Dennis Doverspike", "Winfred Arthur", "Rosanna Miguel"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/ddc2c7b2a1db59281a00c3b5930369d6a076a1e9 2b77be99507a22558c7d9b49c76be51f2a3e8151In this paper, we study the machine learning elements which we are interested in together as a machine learning system, consisting of a collection of machine learning elements and a collection of relations between the elements. The relations we concern are algebraic operations, binary relations, and binary relations with composition that can be reasoned categorically. A machine learning system transformation between two systems is a map between the systems, which preserves the relations we concern. The system transformations given by quotient or clustering, representable functor, and Yoneda embedding are highlighted and discussed by machine learning examples. An adjunction between machine learning systems, a special machine learning system transformation loop, provides the optimal way of solving problems. Machine learning system transformations are linked and compared by their maps at 2-cell, natural transformations. New insights and structures can be obtained from universal properties and algebraic structures given by monads, which are generated from adjunctions.270This paper studies the machine learning elements, consisting of a collection of machine learning elements and a collection of relations between the elements, which are algebraic operations, binary relations, and binary relations with composition that can be reasoned categorically.["Xiuzhan Guo"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/2b77be99507a22558c7d9b49c76be51f2a3e8151 338f9badf3aae48e6ba12016f128cbcf797a3554 The Article Abstract is not available.   Cardiovascular Biomedicine Journal00Cardiovascular Biomedicine Journal["Razieh Parizad", "Rezayat Parvizi"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/338f9badf3aae48e6ba12016f128cbcf797a3554 2ad96b38b00dbd2b23152766bde1a6d03c91dc88Cureus240Cureus["Esteban Zavaleta\u2010Monestel", "Adriana Anch\u00eda-Alfaro", "Carolina Rojas-Chinchilla", "Diego Fabian Quesada-Loria", "Sebasti\u00e1n Arguedas\u2010Chac\u00f3n"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/2ad96b38b00dbd2b23152766bde1a6d03c91dc88 5ac03927e32089cc99ec1a92ba66f80e3ffa89f0Academic Psychiatry170Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["Darlene R. King", "Howard Y Liu", "Adam M. Brenner"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/5ac03927e32089cc99ec1a92ba66f80e3ffa89f0 96f84890c9828b0aebda0c6ed0af27c69fcad43aAI &amp; SOCIETY130The present study sheds light on a new psychological construct called AIPA describing an overly optimistic view of the benefits of AI by neglecting its potential dangers by observing that the construct of AIPA strongly overlaps with single-item measures for positive and negative AI attitudes.AI &amp; SOCIETY["Christian Montag", "P. Schulz", "Heng Zhang", "Benjamin J. Li"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/96f84890c9828b0aebda0c6ed0af27c69fcad43a 1209ed73764cafb09b497e8cb5bc7588d1e0891dDiscover Artificial Intelligence10Discover Artificial Intelligence["Aleksey Grigorievich Tashkinov"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/1209ed73764cafb09b497e8cb5bc7588d1e0891d 7b2c492de86ad9e59aa1b3af54fd57ceb03c9733Journal of Ayurvedic and Herbal Medicine30Journal of Ayurvedic and Herbal Medicine["R. Dixit", "Shoebul Haque", "Farah Asif", "Ayush Jain"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/7b2c492de86ad9e59aa1b3af54fd57ceb03c9733 278031f289b8f14818da4cc7630b110786cd1229Consulting Psychology Journal00Consulting Psychology Journal["Joanie B. Connell"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/278031f289b8f14818da4cc7630b110786cd1229 64b5dc0c839a2105fece910c3fd8a3262ffeb78e This paper provides some personal reflections on my experiences as a project leader in four cooperative projects with colleagues from the Global South involving higher education institutions (HEIs) in seven countries—namely, Benin, Uganda, Ethiopia, Colombia, Palestine, Bulgaria, and Kosovo. The aim is to try to assess advantages and shortcomings of some of these funding programs in terms of their framing, structuring, and (limited) funding, and to provide some suggestions for ensuring better coordination of what constitutes an institutionally fragmented field. The focus of my reflections is on three related broad themes—that is, how to enhance international academic cooperation, improve academic mobility, and ensure better access to teaching and research materials for Global South HEIs. The paper first analyzes the issue of funding for North-South cooperation, then moves on to the enhancement of international academic cooperation, international mobility, and finally, the provision of better access to teaching and research materials.International Journal of Legal Information : Official Publication20The paper first analyzes the issue of funding for North-South cooperation, then moves on to the enhancement of international academic cooperation, international mobility, and the provision of better access to teaching and research materials.International Journal of Legal Information["Gentian Zyberi"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/64b5dc0c839a2105fece910c3fd8a3262ffeb78e 409f1c31264b849f3ae94a3a21ce1698a1591104Discover Artificial Intelligence190Discover Artificial Intelligence["Moussa Theodore Zidouemba"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/409f1c31264b849f3ae94a3a21ce1698a1591104 c6abcc4a1ad818a54ac13807031870e6bec1550fConsulting Psychology Journal00Consulting Psychology Journal["Larry W. Norton"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/c6abcc4a1ad818a54ac13807031870e6bec1550f 1eb15ddffae015c302fbac5ec8105c9352e4e544AI in Precision Oncology610AI in Precision Oncology["Vikash Deendyal", "Lilit Ghazaryan", "Erica Linden", "Lisa Allen", "Nikhil G. Thaker"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/1eb15ddffae015c302fbac5ec8105c9352e4e544 c0131026ee42ed30677705ee6d5e621dc303dfeaJAMA Network Open50JAMA network open["J. Ancker"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/c0131026ee42ed30677705ee6d5e621dc303dfea 8f5dd09904d1e9383013f6ec96bfa1bc12e917aeBACKGROUND Breast reduction is a common procedure with growing rates in the USA, aimed at alleviating the physical and psychological burdens of macromastia. Despite high success rates, it carries a risk of complications, with incidence rates ranging from 6.2% to 43%. OBJECTIVES The authors developed a machine learning model using gradient-boosting decision trees to predict severe breast reduction complications up to 30 days following surgery requiring inpatient care. METHODS This retrospective study included 322 cases of breast reduction surgery performed at the Tel Aviv Medical Center from 2017 to 2024. Model performance was evaluated using 5-fold cross-validation, and key metrics such as area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were reported. An interpretability tool was also created to visualize complication risks based on clinical features. RESULTS Severe complications occurred in 7.4% of cases. Key predictive factors included specimen weight, SN-N distance, and liposuction volume. The model achieved an AUC-ROC of 0.83, with an accuracy of 0.93, negative predictive value of 0.95. The interpretability tool clearly visualized complication risks, aiding preoperative counseling. CONCLUSIONS This is the first study to use AI to predict severe complications in breast reduction surgery. Our AI model, with an AUC-ROC of 0.83 and NPV of 0.95, offers a reliable tool for surgical planning and patient education. Further validation across diverse populations is recommended to confirm its clinical utility.Aesthetic surgery journal00This is the first study to use AI to predict severe complications in breast reduction surgery and offers a reliable tool for surgical planning and patient education, according to the authors.Aesthetic surgery journal["Gon Shoham", "Tom Zuckerman", "E. Fliss", "Orel Govrin", "A. Zaretski", "Roei Singolda", "Daniel J. Kedar", "D. Leshem", "Ehab Madah", "E. Arad", "Y. Barnea"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/8f5dd09904d1e9383013f6ec96bfa1bc12e917ae acb847875f6ddd127baee0639d779ec39469729bWhat does literacy mean in AI? Generative AI, especially Large Language Models (LLM), use statistical relevance to build responses to prompts. Literacy in education means understanding cause and effect from a text and why one observation follows another. It has to do with the real world and some understanding of how the grounding behaves and works. This kind of learning can be achieved with intelligent systems that combine AI engines with traditional programming, or in terms of the Graph Model of Combinatorial Logic: Observations and Concepts with Lambda Concepts. In conclusion, it is very helpful to listen to other disciplines for making AI intelligent. The respective task list for AI engineers includes, but is not limited to, education and teaching to children and humans. Keywords: artificial intelligence, generative pretrained translators, knowledge acquisition transformation, intelligent systemsAthens Journal of Sciences00It is very helpful to listen to other disciplines for making AI intelligent, the respective task list for AI engineers includes, but is not limited to, education and teaching to children and humans.Athens Journal of Sciences["Thomas Fehlmann", "Eberhard Kranich"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/acb847875f6ddd127baee0639d779ec39469729b c2a03cbd11b5e96763dbe5f161301aa01c13da29The growing volume of textual data generated on digital media platforms presents significant challenges for the analysis and interpretation of information. This article proposes a methodological approach that combines artificial intelligence (AI) techniques and statistical methods to explore and analyze textual data from digital media. The framework, titled DAFIM (Data Analysis Framework for Information and Media), includes strategies for data collection through APIs and web scraping, textual data processing, and data enrichment using AI solutions, including named entity recognition (people, locations, objects, and brands) and the detection of clickbait in news. Sentiment analysis and text clustering techniques are integrated to support content analysis. The potential applications of this methodology include social networks, news aggregators, news portals, and newsletters, offering a robust framework for studying digital data and supporting informed decision-making. The proposed framework is validated through a case study involving data extracted from the Google News aggregation platform, focusing on the Israel–Lebanon conflict. This demonstrates the framework’s capability to uncover narrative patterns, content trends, and clickbait detection while also highlighting its advantages and limitations.Future Internet780A methodological approach that combines artificial intelligence (AI) techniques and statistical methods to explore and analyze textual data from digital media, offering a robust framework for studying digital data and supporting informed decision-making.Future Internet["D. Cordeiro", "Carlos Lopezosa", "J. Guallar"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/c2a03cbd11b5e96763dbe5f161301aa01c13da29 a96c6d0fd7fefcd24e00f1af32acf9edc8403cabThe widespread adoption of low-code platforms is fundamentally transforming enterprise integration strategies, offering a paradigm shift in how organizations approach system connectivity and application development. This article examines the architectural framework, implementation patterns, and transformative impact of low-code platforms in enterprise environments, with particular emphasis on their role in democratizing integration development. This article explores how these platforms bridge the traditional divide between IT and business teams while enabling rapid prototyping and iteration of integration solutions. The discussion encompasses the emerging role of artificial intelligence and machine learning in enhancing low-code capabilities, alongside critical considerations for security, governance, and scalability. Through analysis of industry practices and architectural patterns, this article presents a comprehensive framework for organizations to evaluate, implement, and optimize low-code integration platforms while maintaining enterprise-grade reliability and performance. This article suggests that low-code platforms represent a strategic imperative for organizations seeking to accelerate their digital transformation initiatives while maintaining robust integration architectures.International Journal of Scientific Research in Computer Science Engineering and Information Technology40It is suggested that low-code platforms represent a strategic imperative for organizations seeking to accelerate their digital transformation initiatives while maintaining robust integration architectures.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Krishna Kanth Kothapally"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/a96c6d0fd7fefcd24e00f1af32acf9edc8403cab e8e25d8220c39a186c791a9416985e634e1f9de2This article examines the transformative impact of Generative Artificial Intelligence (GenAI) on the financial services industry, with a particular focus on strategic implementation and operational transformation in the banking sector. Through a comprehensive analysis of current implementations and strategic initiatives, particularly in North American banks, the article explores how GenAI is revolutionizing traditional banking operations, from automated knowledge management to personalized customer services. The article investigates the multifaceted approach institutions are taking toward AI integration, including investments in infrastructure, talent development, and risk management protocols. The article reveals that successful AI implementation requires a balanced approach between innovation and risk mitigation, with institutions strategically deploying AI solutions across various operational domains. This article contributes to the growing body of literature on digital transformation in financial services by providing a structured framework for understanding the strategic implications of GenAI adoption and its role in shaping the future of banking operations. The article concludes by identifying critical success factors for AI implementation and suggesting future research directions in this rapidly evolving field.International Journal of Scientific Research in Computer Science Engineering and Information Technology00It is revealed that successful AI implementation requires a balanced approach between innovation and risk mitigation, with institutions strategically deploying AI solutions across various operational domains.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Vijaya Kumar Guntumadugu"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/e8e25d8220c39a186c791a9416985e634e1f9de2 e4f13cb5ebf8eccd68d4166e928d113a8c5db536Recent advancements in Large Language Models (LLMs) have paved the way for Large Code Models (LCMs), enabling automation in complex software engineering tasks, such as code generation, software testing, and program comprehension, among others. Tools like GitHub Copilot and ChatGPT have shown substantial benefits in supporting developers across various practices. However, the ambition to scale these models to trillion-parameter sizes, exemplified by GPT-4, poses significant challenges that limit the usage of Artificial Intelligence (AI)-based systems powered by large Deep Learning (DL) models. These include rising computational demands for training and deployment and issues related to trustworthiness, bias, and interpretability. Such factors can make managing these models impractical for many organizations, while their"black-box'' nature undermines key aspects, including transparency and accountability. In this paper, we question the prevailing assumption that increasing model parameters is always the optimal path forward, provided there is sufficient new data to learn additional patterns. In particular, we advocate for a Neurosymbolic research direction that combines the strengths of existing DL techniques (e.g., LLMs) with traditional symbolic methods--renowned for their reliability, speed, and determinism. To this end, we outline the core features and present preliminary results for our envisioned approach, aimed at establishing the first Neurosymbolic Program Comprehension (NsPC) framework to aid in identifying defective code components.450A Neurosymbolic research direction is advocated that combines the strengths of existing DL techniques with traditional symbolic methods--renowned for their reliability, speed, and determinism, aimed at establishing the first Neurosymbolic Program Comprehension (NsPC) framework to aid in identifying defective code components.["Alejandro Velasco", "Aya Garryyeva", "David N. Palacio", "A. Mastropaolo", "Denys Poshyvanyk"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/e4f13cb5ebf8eccd68d4166e928d113a8c5db536 98bb9886d26fb2482671efd86271bed94e70e68aJournal of Technology in Behavioral Science440It was revealed that perceived ease of use was deemed more impactful on perceived usefulness of the AI virtual avatar for the medical providers than it is for the patients, and anthropomorphism was deemed more impactful on the perceived usefulness of the AI virtual avatar for the patients.Journal of Technology in Behavioral Science["Emi Moriuchi"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/98bb9886d26fb2482671efd86271bed94e70e68a cd92a79a5d6e6e25ef0a8b64eb13121d20c4da32Bangsa Indonesia merupakan bangsa yang terdiri atas berbagai etnis, suku, bangsa, dan budaya yang beraneka ragam.  Keanekaragaman ini harus tetap dilestarikan karena ini bagian dari identitas bangsa. Upaya yang dilakukan agar tetap lestari melalui pendidikan multikultural. Belum seluruh lembaga pendidikan memberikan perhatian khusus pada pendidikan multikultural. Pengabdian ini mencoba melakukan pemberdayaan terhadap guru SMPIT Rahmaniyah, Bogor dalam mendesain pembelajarn multikulural berbasis Artificial Intelligence (AI). Tujuannya adalah guru terampil menyusun desain pembelajaran atau modul ajar multikultural dengan memanfaatkan AI serta terampil mengimplementasikannya di kelas. Metode kegiatan yang dilakukan yakni, sosialisasi kegiatan dan analisis kebutuhan, pelaksanaan kegiatan, penerapan teknologi, dan fasilitasi kolaborasi antarbudaya. Hasil yang dicapai dalam kegiatan ini, yakni adanya peningkatan keterampilan guru dalam mendesain modul ajar multikultural. Dari rata-rata 6,8 menjadi 7,9. Begitu juga dalam penggunaan dan pemanfaatan AI, dari 6,0 menjadi 8,2. Keterampilan melakukan praktik pembelajaran juga meningkat, dari rata-rata 4,7 menjadi 8,2. Hal ini membuktikan terjadi peningkatan keterampilan guru yang signifikan dalam pembelajaran multikulturalRESWARA: Jurnal Pengabdian Kepada Masyarakat00RESWARA: Jurnal Pengabdian Kepada Masyarakat["Suhendra Suhendra", "Eka Suhardi", "Siska Andriani", "Anindita Puspita", "Melinda Juliyana", "Helena Mudi", "HR Muhammad Imron"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/cd92a79a5d6e6e25ef0a8b64eb13121d20c4da32 d1b3ab71bc0363fbc0f5b67abef42aaf43192bb5As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks through experience remains a key challenge. Current LLM agents, particularly those based on proprietary language models, typically rely on prompts to incorporate knowledge about the target tasks. This approach does not allow the agent to internalize this information and instead relies on ever-expanding prompts to sustain its functionality in diverse scenarios. This resembles a system of notes used by a person affected by anterograde amnesia, the inability to form new memories. In this paper, we propose a novel method to train AI agents to incorporate knowledge and skills for multiple tasks without the need for either cumbersome note systems or prior high-quality demonstration data. Our approach employs an iterative process where the agent collects new experiences, receives corrective feedback from humans in the form of hints, and integrates this feedback into its weights via a context distillation training procedure. We demonstrate the efficacy of our approach by implementing it in a Llama-3-based agent which, after only a few rounds of feedback, outperforms advanced models GPT-4o and DeepSeek-V3 in a taskset requiring correct sequencing of information retrieval, tool use, and question answering.400This paper proposes a novel method to train AI agents to incorporate knowledge and skills for multiple tasks without the need for either cumbersome note systems or prior high-quality demonstration data and implements it in a Llama-3-based agent.["Minttu Alakuijala", "Ya Gao", "Georgy Ananov", "Samuel Kaski", "Pekka Marttinen", "Alexander Ilin", "Harri Valpola"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/d1b3ab71bc0363fbc0f5b67abef42aaf43192bb5 4adee81b4148bcd798d195e55b77616abc5945b9The landscape of software engineering is undergoing a transformative shift driven by advancements in machine learning, artificial intelligence (AI), and autonomous systems. This roadmap paper explores how these technologies are reshaping the field, positioning humans not only as end users but also as critical components within expansive software ecosystems. We examine the challenges and opportunities arising from this human-centered paradigm, including ethical considerations, fairness, and the intricate interplay between technical and human factors. By recognizing humans at the heart of the software lifecycle —spanning professional engineers, end users, and end-user developers —we emphasize the importance of inclusivity, human-aligned workflows, and the seamless integration of AI-augmented socio-technical systems. As software systems evolve to become more intelligent and human-centric, software engineering practices must adapt to this new reality. This paper provides a comprehensive examination of this transformation, outlining current trends, key challenges, and opportunities that define the emerging research and practice landscape, and envisioning a future where software engineering and AI work synergistically to place humans at the core of the ecosystem.ACM Transactions on Software Engineering and Methodology800A comprehensive examination of this transformation of software engineering is provided, outlining current trends, key challenges, and opportunities that define the emerging research and practice landscape, and envisioning a future where software engineering and AI work synergistically to place humans at the core of the ecosystem.ACM Transactions on Software Engineering and Methodology["S. Abrah\u00e3o", "John Grundy", "Mauro Pezz\u00e8", "M. Storey", "Damian Andrew Tamburri"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/4adee81b4148bcd798d195e55b77616abc5945b9 127a68764e89bfea7aa1ec1e6a54e253c7c07390This comprehensive article explores the transformative impact of AI-driven predictive analytics in modern Enterprise Resource Planning (ERP) systems. The article examines how the integration of artificial intelligence and machine learning capabilities has revolutionized organizational decision-making processes, operational efficiency, and strategic planning. The article investigates key application areas including financial forecasting, inventory optimization, and customer behavior analysis, while also addressing technical implementation considerations and system architecture requirements. The article demonstrates how AI-enhanced ERP systems have enabled organizations to achieve significant improvements in operational performance, risk management, and market competitiveness through advanced data processing and predictive modeling capabilities.International Journal of Scientific Research in Computer Science Engineering and Information Technology40This comprehensive article demonstrates how AI-enhanced ERP systems have enabled organizations to achieve significant improvements in operational performance, risk management, and market competitiveness through advanced data processing and predictive modeling capabilities.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ravi Sankar Korapati"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/127a68764e89bfea7aa1ec1e6a54e253c7c07390 10681f5509614ad1347f838bc1feac6bb118b6d7This article explores the transformative impact of Artificial Intelligence (AI) on cross-border payment systems, examining how AI technologies are revolutionizing various aspects of international financial transactions. The article delves into AI's role in enhancing fraud detection through advanced pattern recognition and real-time anomaly detection, significantly improving security measures. It analyzes how AI accelerates payment processing by automating verification and settlement processes, leading to dramatic reductions in transaction times. The article also investigates AI's contribution to regulatory compliance, discussing how it aids in navigating complex international financial regulations and mitigates risks. Furthermore, it examines the improvement in customer experience through AI-powered chatbots and virtual assistants, offering 24/7 support and personalized service. The optimization of currency exchange using AI algorithms for market trend analysis and exchange rate prediction is also explored. Finally, the article addresses future prospects, potential advancements, ethical considerations, and integration challenges associated with AI in cross-border payments, providing a comprehensive overview of this rapidly evolving field in global finance.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The article delves into AI's role in enhancing fraud detection through advanced pattern recognition and real-time anomaly detection, significantly improving security measures and how AI accelerates payment processing by automating verification and settlement processes, leading to dramatic reductions in transaction times.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Vinod Upputuri"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/10681f5509614ad1347f838bc1feac6bb118b6d7 3e9c9f919386f5f1bb19f623edff965f27d6c2a6This comprehensive article examines the transformative impact of artificial intelligence on cloud resource management, exploring the evolution from traditional static allocation methods to dynamic, AI-driven approaches. The article investigates core technologies, including machine learning models and real-time decision-making frameworks, while evaluating their applications across virtual machine provisioning, container orchestration, and multi-cloud environments. Through detailed case studies of e-commerce platforms and video streaming services, the article demonstrates significant improvements in resource utilization, cost optimization, and service reliability. The article further addresses technical and operational challenges, including model overhead and system complexity, providing insights into the implementation considerations for organizations adopting AI-driven cloud management solutions.International Journal of Scientific Research in Computer Science Engineering and Information Technology30This comprehensive article examines the transformative impact of artificial intelligence on cloud resource management, exploring the evolution from traditional static allocation methods to dynamic, AI-driven approaches and providing insights into the implementation considerations for organizations adopting AI-driven cloud management solutions.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Sushant Sood"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/3e9c9f919386f5f1bb19f623edff965f27d6c2a6 13cdf8bfb5ba865b7412187ecf990f89764e813cWhy the recent statewide artificial intelligence regulation legislation was vetoed.Communications of the ACM00Communications of the ACM["Pamela Samuelson"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/13cdf8bfb5ba865b7412187ecf990f89764e813c eefa9cbbb14627324b189259096bf97add3252b3In recent years, AI has become virtually synonymous with artificial intelligence, a field that has rapidly transformed public consciousness through its advancements and controversies. This shift raises a critical question for practitioners and scholars of Appreciative Inquiry: should we consider rebranding the abbreviation to preserve the clarity and uniqueness of our work?AI Practitioner00This shift raises a critical question for practitioners and scholars of Appreciative Inquiry: should the abbreviation be rebranding to preserve the clarity and uniqueness of the authors' work?AI Practitioner["Emyr Jones", "Rowan Yemm", "Mathew Smith"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/eefa9cbbb14627324b189259096bf97add3252b3 eb66e08e81409897d4b82e92eb623a9689b99082This comprehensive article delves into the Mixture of Experts (MoE) architecture, a revolutionary approach to building scalable artificial intelligence systems. The article examines how MoE departs from traditional monolithic neural networks by employing multiple specialized experts and dynamic routing mechanisms. Through analysis of various implementations and applications, the article demonstrates MoE's effectiveness in achieving computational efficiency, handling diverse tasks, and maintaining performance while reducing resource requirements. The investigation covers the fundamental architecture, gating mechanisms, technical implementation challenges, and real-world applications across domains including language processing, computer vision, and medical imaging. The article also addresses critical aspects of training complexity, load balancing strategies, and future directions in automated architecture search and efficient training methods.International Journal of Scientific Research in Computer Science Engineering and Information Technology10The article examines how MoE departs from traditional monolithic neural networks by employing multiple specialized experts and dynamic routing mechanisms, and demonstrates MoE's effectiveness in achieving computational efficiency, handling diverse tasks, and maintaining performance while reducing resource requirements.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Vasudev Daruvuri"]2025-02-03T00:00:00https://www.semanticscholar.org/paper/eb66e08e81409897d4b82e92eb623a9689b99082 062352cd06da4e2dd1ab889b9b303ba19528c144PurposeThe present research study aims to conduct a thematic literature review of the negative impacts of artificial intelligence (AI) on the tourism industry.Design/methodology/approachThe research study is based on a comprehensive review of prior research by various authors on AI and its negative consequences in the tourism industry.FindingsResearch indicates that integrating AI technologies in the tourism industry leads to negative consequences. While AI enhances operational efficiency and personalizes customer experiences, it also presents significant challenges, for example, AI replaces labor and the interaction between the tourist and the service provider decreases. New risks are emerging in various areas of tourism that need to be managed to ensure that they do not have negative impacts.Originality/valueThe paper provides a comprehensive review of the negative impacts of AI technologies on the tourism industry, highlighting the need for a balanced approach that integrates human elements with technological advancements. It offers valuable insights into the potential drawbacks of AI, urging stakeholders to consider these challenges when implementing AI-driven solutions in tourism.Worldwide Hospitality and Tourism Themes441The paper provides a comprehensive review of the negative impacts of AI technologies on the tourism industry, highlighting the need for a balanced approach that integrates human elements with technological advancements.Worldwide Hospitality and Tourism Themes["A. Zvaigzne", "L. Litavniece", "S. Kodors", "Kristi\u0101na Jurk\u0101ne"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/062352cd06da4e2dd1ab889b9b303ba19528c144 4085e855aa45f8efa507cc6454b6fe0e81e69763OBJECTIVE Artificial intelligence (AI)-based clinical decision support (CDS) has the potential to augment high-stakes clinical decisions in the emergency department (ED). However, its current usage and translation to implementation remains poorly understood. We asked: (1) What is the current landscape of AI-CDS for individual patient care in the ED? and (2) What phases of development have AI-CDS tools achieved? METHODS We performed a scoping review of AI for prognostic, diagnostic, and treatment decisions regarding individual ED patient care. We searched five databases (MEDLINE, EMBASE, Cochrane Central, Scopus, Web of Science) and gray literature sources from January 1, 2010, to December 11, 2023. We adhered to guidelines from the Joanna Briggs Institute and PRISMA Extension for Scoping Reviews. We published our protocol on Open Science Framework (DOI 10.17605/OSF.IO/FDZ3Y). RESULTS Of 5168 unique records identified, we selected 605 studies for inclusion. The majority (369, 61%) were published in 2021-2023. The studies ranged over a variety of clinical applications, patient populations, and AI model types. Prognostic outcomes were most commonly assessed (270, 44.6%), followed by diagnostic (193, 31.9%) and disposition (115, 19%). Most studies remained in the earliest phase of preclinical development (572, 94.5%) with few advancing to later phases (33, 5.5%). CONCLUSIONS By thoroughly mapping the landscape of AI-CDS in the ED, we demonstrate a rapidly increasing volume of studies covering a breadth of clinical applications, yet few have achieved advanced phases of testing or implementation. A more granular understanding of the barriers and facilitators to implementing AI-CDS in the ED is needed.Academic Emergency Medicine410By thoroughly mapping the landscape of AI-CDS in the ED, a rapidly increasing volume of studies covering a breadth of clinical applications, yet few have achieved advanced phases of testing or implementation are demonstrated.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine["Hashim Kareemi", "Krishan Yadav", "Courtney Price", "Niklas Bobrovitz", "Andrew Meehan", "Henry Li", "Gautam Goel", "Sameer Masood", "Lars Grant", "M. Ben-Yakov", "Wojtek Michalowski", "Christian Vaillancourt"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/4085e855aa45f8efa507cc6454b6fe0e81e69763 784ae98ac7b23bcc60c5f6f2fc4ef43a3b9267ebArtificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications in chemotherapy development, cancer diagnosis, and predicting chemotherapy response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) and deep learning (DL).This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field.A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included “Artificial Intelligence (AI),” “Machine Learning (ML),” and “Deep Learning (DL)” combined with “chemotherapy development,” “cancer diagnosis,” and “cancer treatment.” Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies.This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI’s potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI’s integration into oncology care.Frontiers in Oncology730The role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field is explored, synthesizing current advancements and identifying critical gaps in the field.Frontiers in Oncology["Bassam Abdul Rasool Hassan", "A. H. Mohammed", "S. Hallit", "Diana Malaeb", "Hassan Hosseini"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/784ae98ac7b23bcc60c5f6f2fc4ef43a3b9267eb 92e5cbedcbebf502227a8936c1f7a6c7e872198eIn this paper, we study the features of modeling attacks on artificial intelligence systems. Markov decision-making processes are used in the construction of the model. A multilevel approach to the interpretation of system states is proposed, which includes several stages of detailing the states. This approach is based on the MITRE ATLAS methodology and the FSTEC Threat Assessment Methodology. When forming the vector, the specifics of the intruder model are taken into account, and two main modeling modes are considered: on-time and off-time. The procedure for the formation of awards at the abstract level (without specifying the actions of the attacker) of building a model is described.Vestnik of Samara University Natural Science Series20A multilevel approach to the interpretation of system states is proposed, which includes several stages of detailing the states and is based on the MITRE ATLAS methodology and the FSTEC Threat Assessment Methodology.Vestnik of Samara University. Natural Science Series["I. A. Vetrov", "V. V. Podtopelny"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/92e5cbedcbebf502227a8936c1f7a6c7e872198e 2a67fb84ef70eb276593094f62891fcc4d5ba714This paper explores the transformative impact of Artificial Intelligence (AI) across three critical areas: food waste reduction, organisational resilience, and public financial administration. By utilising AI technologies like machine learning, predictive analytics, and real-time monitoring, the study highlights how AI can address inefficiencies, enhance systemic resilience, and support global sustainability goals, including the United Nations SDGs. In food waste reduction, AI improves demand forecasting, inventory management, and supply chain optimisation, reducing surplus and spoilage. Examples like bakery demand forecasting and blockchain transparency demonstrate economic and environmental benefits. For organisational resilience, AI enables risk assessment and adaptive strategies, strengthening supply chains and helping organisations transition from reactive to proactive approaches during crises such as COVID-19. In public financial administration, AI streamlines processes, detects fraud, and enhances public trust through innovative platforms and predictive fiscal modelling, as shown in case studies from Finland, Australia, and China. Despite its potential, AI adoption faces challenges, including costs, data integration, ethical concerns, and unequal access. Overcoming these requires robust policies, collaboration, and capacity building. This research emphasises AI’s role as a catalyst for systemic change, advocating for ethical and inclusive adoption to advance sustainability, resilience, and equity in a complex world.Brain: Broad Research in Artificial Intelligence and Neuroscience00BRAIN. Broad Research in Artificial Intelligence and Neuroscience["M\u0103d\u0103lina-Ioana Ivanov", "Ana-Maria Gherc\u0103", "Larisa-Nicoleta Gafencu"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/2a67fb84ef70eb276593094f62891fcc4d5ba714 a507f0593285eb21177ecbc3af69ac7fed67a268 Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make one-day predictions of sea-ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method (i.e. global layerwise relevance propagation (LRP)) to understand ML model predictions of sea-ice motion by comparing to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in spatial variability of the explanations. Additionally, we show that CNN relies on both linear and non-linear relationships between the inputs and uses non-local information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e. wind) on land could be useful for predicting other properties (i.e. sea-ice velocity) elsewhere.Artificial Intelligence for the Earth Systems00This work trains linear regression and convolutional neural network models to make one-day predictions of sea-ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration, and shows that wind speed over land is highly relevant for predicting ice motion offshore.Artificial Intelligence for the Earth Systems["Lauren Hoffman", "M. Mazloff", "Sarah T. Gille", "D. Giglio", "Patrick Heimbach"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/a507f0593285eb21177ecbc3af69ac7fed67a268 3d1a6cce9ad99802b0d1c4437fd853e1466edc4dThis article reports the results of a study examining the ability of legal and non-legal Large Language Models to perform legal analysis using the Issue-Rule-Application-Conclusion framework. LLMs were tested on legal reasoning tasks involving rule analysis and analogical reasoning. The results show that LLMs can conduct basic IRAC analysis, but are limited by brief responses lacking detail, an inability to commit to answers, false confidence, and hallucinations. The study compares legal and nonlegal LLMs, identifies shortcomings, and explores traits that may hinder their ability to think like a lawyer. It also discusses the implications for legal education and practice, highlighting the need for critical thinking skills in future lawyers and the potential pitfalls of overreliance on artificial intelligence AI resulting in a loss of logic, reasoning, and critical thinking skills.Social Science Research Network20The results show that LLMs can conduct basic IRAC analysis, but are limited by brief responses lacking detail, an inability to commit to answers, false confidence, and hallucinations.SSRN Electronic Journal["Lee Peoples"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/3d1a6cce9ad99802b0d1c4437fd853e1466edc4d 556b55881b338579e10ac0a492b29fc9ac3f05d1Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices.Nursing Reports300The heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing, and future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes.Nursing Reports["Elena Porcellato", "C. Lanera", "H. Ocagli", "Matteo Danielis"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/556b55881b338579e10ac0a492b29fc9ac3f05d1 890d476f7b12131f4b43b18b24258755260f9440PurposeThis opinion letter explores the role of artificial intelligence (AI) and robotics in addressing the growing needs of ageing populations, highlighting key advancements and challenges associated with their application in eldercare. By analysing recent technological developments, practical applications and ethical considerations, the paper provides a comprehensive overview of how AI and robotics could transform eldercare systems.Design/methodology/approachUsing a multidisciplinary approach, this opinion letter incorporates findings from current research on robotics and AI in care for the elderly, with an emphasis on recent technical developments as well as ethical issues.FindingsThe study shows that AI and robotics can have a massive impact in improving the quality of life of elderly persons by assisting them in health assessment, dosing of drugs, encouraging interactions and hence reducing the centralized pressure on healthcare centres. However, moral issues such as privacy, depersonalization of care and disparity in accessing such technologies in the socio-economic setup are huge barriers that have to be surmounted in order to provide optimal solutions for eldercare.Originality/valueThe novelty of this analysis is evident in its use of AI and robotics as not only potential solutions in technologies to bring better health and social communications to elderly people but also in coming up with a critical discussion of privacy and access issues, as well as several encouraging views for future eldercare.Journal of Enabling Technologies140The study shows that AI and robotics can have a massive impact in improving the quality of life of elderly persons by assisting them in health assessment, dosing of drugs, encouraging interactions and hence reducing the centralized pressure on healthcare centres.Journal of Enabling Technologies["Eldho Babu", "N. M. Joseph"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/890d476f7b12131f4b43b18b24258755260f9440 4a17c5fafb6721e5b96c2c1243f2b7e2fed51078Businesses, in an effort to keep up with the changing world, are now finding new ways to connect with their customers, and Artificial Intelligence (AI) is playing a big role in this change. AI tools like chatbots and systems that predict what customers need help companies be more efficient and give faster, more personalized, and more helpful service. Al and Machine Learning (ML) make it easier for businesses to understand their customers and build trust with them. AI is also helping businesses learn more about customer behavior and it is getting possible to solve problems even before they happen. But using AI isn’t always easy. Companies need to make sure customer data is safe, use AI responsibly, and keep a balance between technology and human connection. This paper looks at how disruptive technologies like AI and ML are changing customer relationship management (CRM), the benefits it brings, and the challenges businesses face. With careful planning, AI can help businesses grow while creating better connections with their customers. A sample of 219 was collected to find the result of the study. The factors identify the impact of AI and ML on Customer Relationship Management are Personalization and Customer Experience, Data Processing and Management, Enhanced Decision-Making, and System Automation.Journal of Informatics Education and Research00This paper looks at how disruptive technologies like AI and ML are changing customer relationship management (CRM), the benefits it brings, and the challenges businesses face.Journal of Informatics Education and Research["Vimla Vimla"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/4a17c5fafb6721e5b96c2c1243f2b7e2fed51078 f92bc25e86dfc931c8972c2c241829cfd1d6f5e8This study investigates the complex relationships between artificial intelligence (AI)‐induced job insecurity, meaningfulness of work (MOW), pro‐environmental behavior at work (PEBW), and corporate social responsibility (CSR) in South Korean organizations. As AI technologies increasingly permeate the workplace, understanding their impact on employee attitudes and behaviors becomes crucial for organizational sustainability efforts. Drawing on several theories, we propose and test a moderated mediation model using a three‐wave time‐lagged design with 392 employees from various South Korean corporations. Our findings reveal that AI‐induced job insecurity negatively influences PEBW through the mediating role of MOW. Contrary to initial expectations, no direct relationship was found between AI‐induced job insecurity and PEBW. Instead, AI‐induced job insecurity decreases employees' MOW, which in turn reduces their engagement in PEBW. Furthermore, we found that CSR moderates the AI‐induced job insecurity‐MOW link, such that strong CSR buffers the negative influence of AI‐induced job insecurity. These results may contribute to the literature on organizational behavior, environmental sustainability, and technological change by elucidating the psychological mechanisms linking AI‐induced job insecurity to PEBW. Our study emphasizes the crucial role of MOW in this relationship. Also, it demonstrates how CSR can function as a strategic tool to weaken the potential negative impacts of AI implementation on employee attitudes and behaviors. The findings offer meaningful implications for managers and policymakers navigating the challenges of AI integration while promoting environmental sustainability at work. By maintaining employee MOW and leveraging CSR initiatives, organizations may be better equipped to foster PEBW in the face of technological changes.Sustainable Development570It is found that CSR moderates the AI‐induced job insecurity‐MOW link, such that strong CSR buffers the negative influence of AI‐induced job insecurity.Sustainable Development["Byung\u2010Jik Kim", "Julak Lee"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/f92bc25e86dfc931c8972c2c241829cfd1d6f5e8 59ec555eae1f5fee59922bc4f8e1a8356114e319Journal of Translational Medicine2500This paper bridges the knowledge gap between AI and CRISPR-Cas9 research by offering a unique platform for AI researchers to grasp deep understanding of the biological foundations behind each step in the CRISPR-Cas9 multi-step process.Journal of Translational Medicine["Ahtisham Fazeel Abbasi", "M. Asim", "Andreas Dengel"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/59ec555eae1f5fee59922bc4f8e1a8356114e319 8dc1d817b78395e7d2d630f36c973a3b498e45b7The aim of this study was to explore how artificial intelligence (AI) impacts the quality of financial reporting, providing insights into new opportunities in this field for the Saudi context. This study employed the UTAUT theory to examine the adoption of AI technology in auditing practices. This study also utilized bibliometric analysis techniques through an academic literature review and content analyses of the documentary evidence. The implication of this study is that non-Big 4 audit firms should adopt AI-powered drones, which consequently enhance decision making, decrease audit fees, and enhance the quality of financial reports, and the efficiency and accuracy of audits. Furthermore, this paper recommends that non-Big 4 audit firms adopting AI should foster a culture of change to ensure quality audits and consistency, overcome resistance to the change, and support the integration of technologies such as AI-driven audit automation. Our study also indicated the importance of integrating AI with the IFRS, developing a new framework for AI in auditing practices, incorporating AI into auditing courses, and modernizing auditing using AI. These implications lead to financial reports of enhanced quality. The results indicated four clusters, with artificial intelligence being the most significant keyword occurrence. This study has limitations, such as the lack of consideration of cyber-attack risks on drones, which may reduce the reliability of financial reports. Based on the findings of this research, audit companies and regulatory agencies in Saudi Arabia, like the Saudi Capital Market Authority (CMA), may evaluate the integration of AI to improve the quality of financial reporting. Implementing AI is expected to enhance the quality of audits, automate reporting, and support regulatory compliance to foster confidence and transparency in the financial industry.International Journal of Financial Studies970It is recommended that non-Big 4 audit firms adopting AI should foster a culture of change to ensure quality audits and consistency, overcome resistance to the change, and support the integration of technologies such as AI-driven audit automation.International Journal of Financial Studies["Abdulkarim Hamdan J. Alhazmi", "Sardar M. N. Islam", "M. Prokofieva"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/8dc1d817b78395e7d2d630f36c973a3b498e45b7 8238d12201f4fb542d05c7881c5b77f31bb537ccThe integration of Artificial Intelligence (AI) in agricultural higher education is transforming the landscape of agricultural practices and research. This paper explores the multifaceted applications of AI technologies in the curriculum and pedagogical approaches of agricultural institutions. By enhancing data analysis, predictive modeling, and decision-making processes, AI empowers students and researchers to tackle complex agricultural challenges such as crop management, pest control, and resource optimization. Furthermore, the study examines the implications of AI on student engagement, skill development, and interdisciplinary collaboration. As agricultural sectors increasingly rely on data-driven solutions, the incorporation of AI in higher education not only prepares future professionals with essential competencies but also fosters innovation in sustainable agricultural practices. This article underscores the critical role of AI in shaping the future of agricultural education and its potential to revolutionize the industry.Journal of Agricultural Informatics00The critical role of AI in shaping the future of agricultural education and its potential to revolutionize the industry is underscored.Journal of Agricultural Informatics["P\u00e9ter Lengyel", "Emese Felv\u00e9gi", "I. F\u00fczesi"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/8238d12201f4fb542d05c7881c5b77f31bb537cc d32c9ef7c9e16ea82a6ca6ffb4c1a014e7cb3cf8The rapid increase in the role of science, technology, and engineering in the modern world, including the development of artificial intelligence and GPT neural networks, has contributed to the actualization of research into both these areas and the new opportunities and risks they generate. The purpose of this study is to identify the impact of modern technologies, specifically artificial intelligence and fourth- and fifth-generation GPT neural networks, on human physical and mental health, spiritual qualities, and their implications for new global risks, primarily anthropological ones. The article emphasizes that a powerful techno-anthropological shift is being observed worldwide, the consequences of which are impossible to predict. The digital revolution has given rise to a new goal – the “upgrade” of humanity. It is no coincidence that anthropological risks have become a problem-oriented research area. The scientific novelty of this research lies in its disclosure of the main objective and subjective factors that influence the transformation of the human personality and its conversion into a “post-human.” The study found that the development of artificial intelligence systems and related GPT neural networks creates the prerequisites for the targeted formation of personality by network platforms, which poses the threat of unpredictable consequences. It concludes that there is a need to develop social mechanisms and applied pathways aimed at overcoming the anthropological crisis.Manuscript70The study found that the development of artificial intelligence systems and related GPT neural networks creates the prerequisites for the targeted formation of personality by network platforms, which poses the threat of unpredictable consequences.Manuscript["O. Skorodumova", "Lilia Fedorovna Matronina"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/d32c9ef7c9e16ea82a6ca6ffb4c1a014e7cb3cf8 0d102f137bdbbca104e81372b030e0e1a39469ecThe emergence of artificial intelligence (AI) has revolutionized the landscape of epilepsy education and management by providing innovative solutions to the challenges of diagnosis, treatment, and patient care. This review evaluates the multifaceted role of AI in epilepsy, focusing on its impact on early diagnosis, seizure prediction, and the development of personalized treatment plans. AI tools, including machine learning algorithms and neural networks, have demonstrated significant promise in enhancing diagnostic accuracy and identifying epileptic patterns. This study explores various AI-driven educational platforms designed to improve the knowledge and skills of healthcare professionals, patients, and caregivers in managing epilepsy. Moreover, AI applications in wearable devices and mobile health platforms facilitate real-time monitoring and patient engagement, ultimately improving quality of life. However, integrating AI into clinical practice presents several challenges, including the need for large and high-quality datasets, interdisciplinary collaboration, data privacy, and ethical considerations. This review highlights these barriers while suggesting uniform protocols and frameworks for efficiently translating AI technologies into clinical practice. It underscores AI’s transformative potential in epilepsy care and education, advocating for ongoing research and collaborative efforts among technologists, clinicians, and educators, while emphasizing the importance of user-friendly design, regular assessments, and ethical considerations to maximize AI’s impact in this critical field.Advanced Neurology00This review evaluates the multifaceted role of AI in epilepsy, focusing on its impact on early diagnosis, seizure prediction, and the development of personalized treatment plans, and highlights the importance of user-friendly design, regular assessments, and ethical considerations to maximize AI’s impact in this critical field.Advanced Neurology["Walter Otu", "Muhammad Usman Khan", "Hafiz Talha Javed", "Irfan S. Sheikh"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/0d102f137bdbbca104e81372b030e0e1a39469ec 529e5f8db1fca68289f061ce6118261809eeb98d This review article explores the integration of artificial intelligence (AI) and nanotechnology, focusing on their combined potential to drive advancements in nanomaterial discovery, drug delivery systems, and nano-electronic component design. It also examines the transformative effects of AI-enhanced nanotechnology in medicine, diagnostics, bioengineering, and other scientific domains, emphasizing its future implications across various sectors. This article examines the synergy between AI and nanotechnology, focusing on recent innovations in nanomaterial discovery, AI-driven material design, and precision medicine. It reviews case studies and research highlighting AI's role in accelerating nanomaterial development and its applications in medicine, electronics, diagnostics, and robotics, using a multidisciplinary approach. AI-enhanced nanotechnology has enabled the development of novel nanomaterials with unprecedented properties tailored for specific applications, such as highly efficient drug delivery systems and next-generation nanoelectronic components. In medicine, AI-driven nanotechnology offers promising solutions for highly personalized treatments, improving therapeutic efficacy and reducing side effects. Additionally, AI is driving innovation in diagnostics and robotics, leading to more sensitive diagnostic tools and the development of nanoscale-precision robotic systems. The integration of AI and nanotechnology presents vast opportunities for scientific and technological advancements. As AI algorithms continue to evolve, their impact on nanotechnology will lead to breakthroughs in diverse fields, such as medicine, electronics, diagnostics, and robotics. This interdisciplinary synergy will open new frontiers in research, driving transformative changes in bioengineering, neuroscience, and beyond. Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)00The integration of AI and nanotechnology presents vast opportunities for scientific and technological advancements, and their impact on nanotechnology will lead to breakthroughs in diverse fields, such as medicine, electronics, diagnostics, and robotics.Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering)["Sarvat Zafar", "Nadim Rana"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/529e5f8db1fca68289f061ce6118261809eeb98d 53237a24f90c258c4034fd2b16f190be55daa92dArtificial intelligence (AI) is having a major impact in the field of bariatric surgery (BS) as it helps improve outcomes for patients with severe obesity. This advanced technology optimises clinical decision-making processes and helps reduce the risks associated with surgery. In the context of bariatrics, AI is used to identify suitable patients, and monitor postoperative recovery but also to predict complications that may occur. BS is an effective solution for treating severe obesity, a condition characterised by the excessive accumulation of body fat, which can lead to serious health problems. However, this surgical field faces challenges in terms of patient selection and follow-up. By implementing machine learning (ML) algorithms and advanced imaging technologies, AI offers advantages to surgeons in performing interventions with increased precision and efficiency. The integration of AI into BS brings significant benefits to both patients and healthcare professionals, thereby facilitating the development of a personalised and safe approach to obesity management. Through this review, we aim to explore the potential, benefits, and risks of using AI in the context of BS.Brain: Broad Research in Artificial Intelligence and Neuroscience00The integration of AI into BS brings significant benefits to both patients and healthcare professionals, thereby facilitating the development of a personalised and safe approach to obesity management.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["A. Miler", "M\u0103lina Visternicu", "Viorica Rarinca", "Carmen Stadoleanu", "A. Ciob\u00eec\u0103", "Madalina Maxim", "Mihaela Toader", "D. Timofte", "Anton Knieling"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/53237a24f90c258c4034fd2b16f190be55daa92d 18093c9d0c9164529ea09390e2ea99347ea44ef4A smart grid is a structure that regulates, operates, and utilizes energy sources that are incorporated into the smart grid using smart communications techniques and computerized techniques. The running and maintenance of Smart Grids now depend on artificial intelligence methods quite extensively. Artificial intelligence is enabling more dependable, efficient, and sustainable energy systems from improving load forecasting accuracy to optimizing power distribution and guaranteeing issue identification. An intelligent smart grid will be created by substituting artificial intelligence for manual tasks and achieving high efficiency, dependability, and affordability across the energy supply chain from production to consumption. Collection of a large diversity of data is vital to make effective decisions. Artificial intelligence application operates by processing abundant data samples, advanced computing, and strong communication collaboration. The development of appropriate infrastructure resources, including big data, cloud computing, and other collaboration platforms, must be enhanced for this type of operation. In this paper, an attempt has been made to summarize the artificial intelligence techniques used in various aspects of smart grid system.Frontiers in Artificial Intelligence430An attempt has been made to summarize the artificial intelligence techniques used in various aspects of smart grid system.Frontiers in Artificial Intelligence["M. Balamurugan", "Kamala Narayanan", "N. Raghu", "G. A. Arjun Kumar", "V. N. Trupti"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/18093c9d0c9164529ea09390e2ea99347ea44ef4 71dfd6cb1331bc05a573954ab3268a00ea50920bRadiologie390In order to facilitate the forthcoming clinical integration of LLMs, radiologists need to engage with the topic, understand various application areas, and be aware of potential limitations in order to address challenges related to patient safety, ethics, and data protection.Radiologie["M. Halfmann", "P. Mildenberger", "T. Jorg"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/71dfd6cb1331bc05a573954ab3268a00ea50920b bbcfab3f52f50855aa3d602230aec777a3c2382dCureus320Cureus["Fatima Ibrahim Abdalla Ibrahim", "Mozdaher Gaffer Hussen Ali", "Mohammed Hassan Awad Ali", "Almontasir Belah Alsadig Abdalwahab Abdallah", "Nisreen Galaleldin Elnoor Mohammed", "Ammar Elhaj", "Samir Ibrahim", "Wadah Ahmed Osman Ahmed"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/bbcfab3f52f50855aa3d602230aec777a3c2382d ee0582f3c079496d5ba89fe837e9c193f04ae559Satelit Komunikasi menawarkan harapan berkelanjutan secara berkala pada layanan area yang belum dan sudah terjangkau, dengan skalabilitas layanan. Namun, beberapa masalah harus diatasi terlebih dahulu untuk merealisasikan manfaat ini, seperti manajemen sumber daya, kontrol jaringan, keamanan jaringan, manajemen spektrum, dan penggunaan energi satelit lebih sulit dibandingkan dengan jaringan terestrial. Sementara itu, kecerdasan buatan (AI), adalah termasuk pembelajaran mesin yang mendalam, dan pembelajaran keamanan terus berkembang, sebagai bidang penelitian dan telah menunjukkan hasil yang berbagai macam dalam berbagai aplikasi, termasuk komunikasi nirkabel. Secara khusus, penerapan AI pada berbagai aspek komunikasi satelit yang menunjukkan potensi sangat baik, termasuk beam- hopping, anti-jamming, peramalan lalu lintas jaringan, pemodelan saluran, penambangan telemetri, pendeteksian kilau ionosfer, pengelolaan interferensi, pengindraan jarak jauh, pemodelan perilaku, pengintegrasian ruang-udara-darat, dan pengelolaan energi. Karya ini memberikan gambaran umum tentang AI dan sub-bidangnya yang beragam, dan algoritma modern. Beberapa masalah yang dihadapi dalam berbagai aspek sistem komunikasi satelit yang dibahas, dan solusi berbasis kecerdasan buatan yang diusulkan dan solusi berbasis kecerdasan buatan yang potensial disuguhkan. Akhirnya, sebuah pandangan tentang bidang ini digambarkan, dengan langkah-langkah di masa depan yang diharapkan.JUTEI (Jurnal Terapan Teknologi Informasi)00Jurnal Terapan Teknologi Informasi["Tommy Jonathan Sinaga"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/ee0582f3c079496d5ba89fe837e9c193f04ae559 568d303a988fc2455dc9ce2cad54af048f0ad2c9Service Business: An International Journal410Service Business["Sung Yeon Kim", "Jin Min Kim"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/568d303a988fc2455dc9ce2cad54af048f0ad2c9 cae0f4308dd3a7bcbf09f43257b3bf55b306441fInternational Research in Geographical and Environmental Education200International Research in Geographical and Environmental Education["Rod Lane"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/cae0f4308dd3a7bcbf09f43257b3bf55b306441f 8394778d9d24d48d6345509cd98c746c608a5b78WORK: A Journal of Prevention, Assessment &amp; Rehabilitation60WORK: A Journal of Prevention, Assessment &amp; Rehabilitation["Alyson D Stover", "Karen Jacobs"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/8394778d9d24d48d6345509cd98c746c608a5b78 8eb07751556caea78e5e0f228b7469e11d6f86f9The informed consent process is a fundamental component of patient-centered care, yet traditional consent forms often fail to address individual patient needs comprehensively. This study explores the development and implementation of an AI-driven personalized consent form for Transurethral Resection of the Prostate (TURP). By leveraging machine learning algorithms trained on clinical data from 12,000 patients, the tool provides tailored risk assessments and patient-specific recommendations. The model demonstrated high predictive accuracy (AUC: 0.89) for complications such as bleeding, retrograde ejaculation, and urinary incontinence. Comparative analysis revealed that AI-driven consent forms significantly enhanced patient comprehension, satisfaction, and shared decision-making compared to standard practices. These findings underscore the transformative potential of AI in urology, aligning with contemporary guidelines from the European Association of Urology (EAU) and the American Urological Association (AUA). The study highlights the importance of integrating advanced technologies into routine clinical workflows to optimize patient outcomes and foster a new era of precision medicine.Journal of Medical Research and Surgery00Comparative analysis revealed that AI-driven consent forms significantly enhanced patient comprehension, satisfaction, and shared decision-making compared to standard practices, underscoring the transformative potential of AI in urology.Journal of Medical Research and Surgery["Onur Dede"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/8eb07751556caea78e5e0f228b7469e11d6f86f9 91d808eafb00ba5dc393202877f9c06a24887507British medical journal00BMJ["Chris Stokel-Walker"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/91d808eafb00ba5dc393202877f9c06a24887507 c5606e6901ffc53423e728b1a82ef442ca5f290fХI Международная научно-практическая конференция "ТРАНСФОРМАЦИЯ РОССИЙСКОЙ НАУКИ В ЭПОХУ ИНФОРМАЦИОННОГО ОБЩЕСТВА"00ХI Международная научно-практическая конференция "ТРАНСФОРМАЦИЯ РОССИЙСКОЙ НАУКИ В ЭПОХУ ИНФОРМАЦИОННОГО ОБЩЕСТВА"["\u0426. \u041c\u044d\u0439", "\u0410.\u0414. \u041c\u0443\u0440\u0437\u0438\u043d"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/c5606e6901ffc53423e728b1a82ef442ca5f290f 0bbd56ae63531078acb825b293e0630feb4f4716BMC Medical Informatics Decis. Mak.160BMC Medical Informatics and Decision Making["F. Pennestr\u00ec", "Federico Cabitza", "N. Picerno", "Giuseppe Banfi"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/0bbd56ae63531078acb825b293e0630feb4f4716 a78dec8096aad77652367bb75abf513e6f0bfc2fJournal of Patient Safety and Risk Management90Journal of Patient Safety and Risk Management["Olivia Lounsbury", "Katelyn Brant", "Julia M. Kim", "Ann Kane"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/a78dec8096aad77652367bb75abf513e6f0bfc2f 3a02b2780dc8caafb29229f50bb27a1e4808a43fJournal of Surgical Oncology10Journal of surgical oncology["A. Kleebayoon", "Viroj Wiwanitkit"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/3a02b2780dc8caafb29229f50bb27a1e4808a43f 9393e1efa8dcbead06a7f0d5d4a5dc5a7e384bd900["Patricia Hong", "Ezekiel Emanuel"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/9393e1efa8dcbead06a7f0d5d4a5dc5a7e384bd9 70e1045725834db0adf54acd8f219f1f5edb8e40The development of artificial intelligence and robotic systems has revolutionized multiple aspects of human life. It is often asked whether artificial general intelligence (AGI) can ever be achieved or whether robots can truly achieve human-like qualities. Our view is that the answer is “no,” because these systems fundamentally differ in their relationship to the ultimate goal of biological systems – reproduction. This perspective gives rise to the conjecture that reproduction, or self-replication, is a prerequisite for human-like (or biological-type) cognition, intelligence, and even consciousness. This paper explores the implications of reproduction as a criterion for the viability of artificial systems, emphasizing how alignment with human reproductive imperatives determines their cultural integration and longevity. We argue that systems incapable of self-replication or co-evolving to complement human reproductive roles are likely to remain peripheral curiosities, with limited societal or evolutionary impact.Sustainable Engineering and Innovation00It is argued that systems incapable of self-replication or co-evolving to complement human reproductive roles are likely to remain peripheral curiosities, with limited societal or evolutionary impact.Sustainable Engineering and Innovation["Sencer Yeralan"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/70e1045725834db0adf54acd8f219f1f5edb8e40 bdeddb35128f8024648af3369b0b94f235f35593The advent of Artificial Intelligence (AI) in education has promoted transformative opportunities in language learning. This article explores the integration of AI tools in teaching English as a Second Language (ESL) and English as a Foreign Language (EFL). It focuses on their potential to enhance language acquisition, personalise learning experiences, and address challenges in traditional pedagogical methods. It also evaluates the implications of AI for educators and learners, emphasising ethical considerations and the need for a balanced approach. In this case, the main purpose of using AI in teaching is to support students in the learning process and ensure productivity in language mastery.Path of Science100The main purpose of using AI in teaching is to support students in the learning process and ensure productivity in language mastery.Path of Science["A. Mirzayeva"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/bdeddb35128f8024648af3369b0b94f235f35593 2c9bdc42458172acdee0f672a100a41fcf273b36Background In this new equity-driven landscape, if there is to be system-wide transformation in research funding allocation, indicators of funding allocations need to be explored. This environmental scan aims to understand how research funding for artificial intelligence (AI) has been allocated and distributed in Canada from 2011-2022. Using geographical representations, we describe and map research funding for Canadian researchers by publicly funded granting agencies and provide analyses for AI research spending since 2011 Methodology We developed a rapid environmental scan to create a database of all AI funded projects from the following agencies: CIHR, NSERC, SSHRC, CRC, AMS, NFRF and CFI. Using publicly available research funding reporting and agency websites, we identified through title, keyword and project summary screening, AI projects in English and French for the years 2011-2022. Principal Findings A total of 4112 projects were identified, with the following information for each project recorded: title, year, institution, city, province, total funding, language, funding agency, funding program and primary investigator. A total of $384,933,265.74 million was allocated for publicly funded AI related projects in Canada from 2011-2022. Average funding per project was $93,612.18. The top three provinces with the most funding for all years are Ontario, Quebec, and British Columbia. The top three funding agencies by total amount for all years were NSERC at $155,267,817, CIHR at $136,594,644, and CFI at $58,317,627 Conclusion This information can assist in accountability and understanding of Canada’s publicly funded research allocations, and provide information related to the distribution of such funds, thus informing equity policy strategies.bioRxiv00This environmental scan aims to understand how research funding for artificial intelligence (AI) has been allocated and distributed in Canada from 2011-2022, and provide information related to the distribution of such funds, thus informing equity policy strategies.bioRxiv["G. Attema", "M. Mertz", "A. Anawati", "A. Austin", "J. Bertrand", "R. Jewett", "E. Cameron"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/2c9bdc42458172acdee0f672a100a41fcf273b36 4809f71d789c9d709d00b748484fc04c8376178cAs companies actively invest in self‐promotion of Artificial Intelligence (AI) empowered services to sustain their competitive advantage, there is a growing potential for such promotional activities to backfire. Bridging signaling theory with the resource‐based view, this research reveals that companies’ self‐promotion of AI resources can reduce customers’ willingness to engage with AI‐based (vs. human‐based) services. Four studies, including text mining and experiments, demonstrate that companies’ self‐promotion of AI‐based resources has a detrimental effect on willingness to engage, and concurrently perceived as exaggeration. In contrast, companies’ self‐promotion about human‐related resources yields beneficial outcomes, since such promotional signals contribute to the enhancement of human capital. The findings suggest that self‐discrepancy and trust are the key underlying factors driving the effects as customers may experience a discrepancy between their expectations of human‐like service interactions and actual AI capabilities. Additionally, findings reveal the moderating effect of honest (vs. self‐promotional) framing on the relationship between service type (AI vs. human) and willingness to engage. Customer perceptions of AI appear less influenced by presentation style compared to perceptions of human resources. This research provides valuable insights into how customers respond to companies’ self‐promotion of AI resources and emphasizes the need for promotional alignment with customers’ expectations about AI.Psychology &amp; Marketing810Findings suggest that self‐discrepancy and trust are the key underlying factors driving the effects as customers may experience a discrepancy between their expectations of human‐like service interactions and actual AI capabilities.Psychology &amp; Marketing["Darina Vorobeva", "Diego Costa Pinto", "H\u00e9ctor Gonz\u00e1lez\u2010Jim\u00e9nez", "Nuno Ant\u00f3nio"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/4809f71d789c9d709d00b748484fc04c8376178c cdf14bcfe740c630386d6f182175a4e491490b59This paper offers an extensive survey of one of the fundamental aspects of the trustworthiness of Artificial Intelligence (AI) in healthcare, namely uncertainty, focusing on the large panoply of recent studies addressing the connection between uncertainty, AI, and healthcare. The concept of uncertainty is a recurring theme across multiple disciplines, with varying focuses and approaches. Here, we focus on the diverse nature of uncertainty in medical applications, emphasizing the importance of quantifying uncertainty in model predictions and its advantages in specific clinical settings. Questions that emerge in this context range from the guidelines for AI integration in the healthcare domain to the ethical deliberations and their compatibility with cutting-edge AI research. Together with a description of the main specific works in this context, we also discuss that, as medicine evolves and introduces novel sources of uncertainty, there is a need for more versatile uncertainty quantification methods to be developed collaboratively by researchers and healthcare professionals. Finally, we acknowledge the limitations of current uncertainty quantification methods in addressing the different facets of uncertainty within the medical domain. In particular, we identify from this survey a relative paucity of approaches that focus on the user’s perception of uncertainty and accordingly of trustworthiness.ACM Transactions on Computing for Healthcare490An extensive survey of one of the fundamental aspects of the trustworthiness of Artificial Intelligence in healthcare, namely uncertainty, focusing on the large panoply of recent studies addressing the connection between uncertainty, AI, and healthcare, focuses on the importance of quantifying uncertainty in model predictions and its advantages in specific clinical settings.ACM Transactions on Computing for Healthcare["Jaya Ojha", "Oriana Presacan", "Pedro Goncalves Lind", "Eric Monteiro", "Anis Yazidi"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/cdf14bcfe740c630386d6f182175a4e491490b59 a6c81f57a4d36c299c41d7b45aabf6bd90125c36The emergence of fake news presents a serious danger to the accuracy of information, public discourse, and social stability in a time when information is disseminated quickly through digital channels. Nigeria, with its diverse population and dynamic media landscape, faces unique challenges in combating the spread of misinformation. In the context of Nigeria, this study investigates how Artificial Intelligence (AI) can be a crucial tool in combating the threat of fake news and improving information verification procedures. The study looks into the state of fake news in Nigeria today and examines how it affects social cohesiveness, political stability, and public opinion. The study tries to identify the critical role that artificial intelligence (AI) can play in addressing the particular issues that disinformation poses in the Nigerian information environment. The study explores several AI-powered methods that can be used to identify and confirm the legitimacy of news information, including deep learning models, machine learning algorithms, and natural language processing. The paper offers a strategy for integrating AI into Nigeria's information verification infrastructure by taking international best practices and adapting them to the local environment. The study also discusses ethical issues related to using AI to combat fake news, highlighting the significance of openness, responsibility, and inclusion in the creation and application of AI technologies. This study intends to add to the continuing conversation about the role of technology in preserving the integrity of information by investigating the relationship between artificial intelligence (AI) and information verification in the Nigerian setting. The recommendations made in this paper are meant to educate politicians, media experts, and technologists in order to create a reliable and robust information environment for Nigeria's digital future.British Journal of Mass Communication and Media Research60The study tries to identify the critical role that artificial intelligence (AI) can play in addressing the particular issues that disinformation poses in the Nigerian information environment and explores several AI-powered methods that can be used to identify and confirm the legitimacy of news information.British Journal of Mass Communication and Media Research["Anagba, E. U.", "Udjo-Onovughakpo, O. J.", "Nwodu, G. E."]2025-02-04T00:00:00https://www.semanticscholar.org/paper/a6c81f57a4d36c299c41d7b45aabf6bd90125c36 a7aa685e87c079d89b7872ba874746c8f049c4c6Early detection of neurodegenerative diseases like Alzheimer’s and Parkinson’s is crucial for improving patient care and enabling timely interventions. Artificial intelligence (AI) offers innovative approaches to analyzing complex medical datasets, revolutionizing the detection of these diseases at early stages. This review discusses key AI methodologies, including machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL), and their applications in early diagnosis. ML models excel in predicting disease risk and classifying imaging and biometric data, while DL techniques, such as convolutional and recurrent neural networks, are effective in processing unstructured data like images and speech. NLP facilitates extracting critical insights from clinical notes and patient narratives, and RL enhances decision-making in diagnostic workflows. Integrating multimodal data—such as genomics, neuroimaging, wearable device metrics, and electronic health records—further strengthens diagnostic precision. Despite its promise, the widespread implementation of AI faces challenges, including the need for standardized data, ethical considerations, and clinical validation. Overcoming these obstacles is essential for AI to transform early detection and management of neurodegenerative diseases. This review emphasizes the significance of interdisciplinary efforts and sustained research to unlock AI’s full potential in medical applications.Asian Journal of Research in Computer Science00Key AI methodologies, including machine learning (ML), deep learning (DL), natural language processing (NLP), and reinforcement learning (RL), and their applications in early diagnosis are discussed, highlighting the significance of interdisciplinary efforts and sustained research to unlock AI’s full potential in medical applications.Asian Journal of Research in Computer Science["Wejdan H. Alhassun", "Abdulaziz S. Alothman", "Sultan A. Alfawaz"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/a7aa685e87c079d89b7872ba874746c8f049c4c6 1a40bf20744207027b83addd75f5f8246e47c53bnpj Systems Biology and Applications430This work investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling and tested public AI’s understanding of mathematics in models, related systems biology data, and the complexity of model structures to enhance the accessibility of systems biology for non-system biologists.NPJ Systems Biology and Applications["Meera Kannan", "Gabrielle Bridgewater", "Ming Zhang", "M. Blinov"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/1a40bf20744207027b83addd75f5f8246e47c53b 00522c2acb08acc2685c7a3cb7c721e4c60cdec2This study aims to explore the influence of decision-making, financial data analysis, and human resource training on company performance, as well as the role of artificial intelligence (AI) moderation in these relationships. Using the path analysis method with AMOS software, data was collected from 100 employees in a Telecommunications company in Indonesia. The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence. Additionally, AI serves as a significant moderator in the relationship between decision-making and company performance, but not in the relationship between financial data analysis and performance. These findings emphasize the importance of good decision-making and financial data analysis in improving organizational performance, and demonstrate the potential of AI in amplifying the positive impact of decision-making. This study has limitations in sample size and context, which need to be considered for further research.Journal of economics, finance and management studies200The results show that decision-making and analysis of financial data have a positive and significant influence on company performance, while human resource training does not show a significant influence.Journal of Economics, Finance And Management Studies["Herlin Andini", "Sriwidharmanely", "Indah Oktari Wijayanti", "Vika Fitranita", "Deasy Emalia"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/00522c2acb08acc2685c7a3cb7c721e4c60cdec2 c30784a2db7205981db872640d3dffb9d430ea23Generative Artificial Intelligence (GenAI) represents a fundamental shift in AI development, moving from rule-based systems to neural networks capable of creating novel content and solving complex problems through pattern recognition and contextual understanding. This evolution challenges traditional Computer Science (CS) paradigms, as evidenced by innovations in large language models and diffusion-based image generation. This paper investigates how GenAI's emergence affects education and research in computer science and related fields. Through White's cultural model—examining technological, societal, and institutional dimensions—we analyse how GenAI's capabilities diverge from traditional CS approaches in both theory and practice. Our research reveals specific challenges for higher education, including the need to teach contextual reasoning, handle emergent behaviors, and develop adaptive problem-solving skills. We propose educational strategies such as project-based learning with GenAI tools and cross-disciplinary integration. These recommendations aim to establish GenAI as a distinct academic discipline while preparing students and researchers for its increasing role in scientific and professional practices.Brain: Broad Research in Artificial Intelligence and Neuroscience00This research reveals specific challenges for higher education, including the need to teach contextual reasoning, handle emergent behaviors, and develop adaptive problem-solving skills, and proposes educational strategies such as project-based learning with GenAI tools and cross-disciplinary integration.BRAIN. Broad Research in Artificial Intelligence and Neuroscience["Ilya Levin", "Natalia Bukhshtaber", "Konstantin Minyar-Beloruchev"]2025-02-04T00:00:00https://www.semanticscholar.org/paper/c30784a2db7205981db872640d3dffb9d430ea23 89e27f19f8d9411f3cf126c770548b24ffc5365dDespite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. This paper describes the FUTURE-AI framework, which provides guidance for the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI Consortium was founded in 2021 and comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two year period, the FUTURE-AI guideline was established through consensus based on six guiding principles—fairness, universality, traceability, usability, robustness, and explainability. To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions. The recommendations cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.British medical journal1421To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions, and cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.The BMJ["Karim Lekadir", "Alejandro F Frangi", "Antonio R Porras", "Ben Glocker", "Celia Cintas", "Curtis P. Langlotz", "Eva Weicken", "F. Asselbergs", "Fred Prior", "Gary S. Collins", "G. Kaissis", "Gianna Tsakou", "Ir\u00e8ne Buvat", "Jayashree Kalpathy-Cramer", "John Mongan", "Julia A Schnabel", "Kaisar Kushibar", "K. Riklund", "K. Marias", "L. M. Amugongo", "Lauren A. Fromont", "Lena Maier-Hein", "L. Cerd\u00e1-Alberich", "L. Mart\u00ed-Bonmat\u00ed", "M. J. Cardoso", "Maciej Bobowicz", "M. Shabani", "Manolis Tsiknakis", "Maria A. Zuluaga", "Marie-Christine Fritzsche", "Marina Camacho", "M. Linguraru", "M. Wenzel", "Marleen de Bruijne", "M. Tolsgaard", "Melanie Goisauf", "M\u00f3nica Cano Abad\u00eda", "Nikolaos Papanikolaou", "Noussair Lazrak", "Oriol Pujol", "Richard Osuala", "Sandy Napel", "S. Colantonio", "Smriti Joshi", "Stefan Klein", "Susanna Auss\u00f3", "Wendy A Rogers", "Zohaib Salahuddin", "M. Starmans", "Aasa Feragen", "Abdul Joseph Fofanah", "Alena Buyx", "Anais Emelie", "Andrea Lara", "An-Wen Chan", "Arcadi Navarro", "B. Botwe", "Bishesh Khanal", "Brigit Beger", "Carol C Wu", "Daniel Rueckert", "Deogratias Mzurikwao", "Dimitrios I. Fotiadis", "Doszhan Zhussupov", "Enzo Ferrante", "Erik Meijering", "Fabio A Gonz\u00e1lez", "G. Krestin", "G. S. Tegenaw", "Gianluca Misuraca", "Girish Dwivedi", "H. Kondylakis", "Harsha Jayakody", "Henry C Woodruf", "Horst Joachim Mayer", "H. J. Aerts", "Ian Walsh", "Ioanna Chouvarda", "Isabell Tributsch", "I. Rekik", "James Duncan", "Jihad Zahir", "Jinah Park", "J. W. Gichoya", "Kensaku Mori", "Let\u00edcia Rittner", "Lighton Phiri", "L. Marrakchi-Kacem", "Llu\u00eds Donoso-Bach", "M. Bielikova", "Marzyeh Ghassemi", "Mohammad Ashrafuzzaman", "Mohammad Yaqub", "Mukhtar M. E. Mahmoud", "Mustafa Elattar", "Nicola Rieke", "Oliver D\u00edaz", "Olivier Salvado", "Ousmane Sall", "Pamela Guevara", "P. Gordebeke", "Philippe Lambin", "Pieta Brown", "P. Abolmaesumi", "Qi Dou", "Qinghua Lu", "Rose Nakasi", "S. K. Zhou", "Shadi Albarqouni", "Stacy M. Carter", "Steffen E. Petersen", "S. Awate", "Tammy Riklin Raviv", "Tessa Cook", "Tinashe Ernest Mutsvangwa", "W. Niessen", "X\u00e8nia Puig-Bosch", "Yi Zeng", "Yunusa G Mohammed", "Yves Saint James Aquino"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/89e27f19f8d9411f3cf126c770548b24ffc5365d 797cd36547867cd7d3b345bd9e77ea8015d73704Artificial Intelligence (AI) is revolutionizing education in India by enhancing personalized learning, automating administrative tasks, and improving accessibility. AI-powered adaptive learning platforms tailor educational content to individual students, addressing diverse learning needs and bridging gaps in traditional teaching methods. Virtual tutors, chatbots, and AI-driven assessments provide real-time feedback, fostering a more interactive learning environment. Additionally, AI assists educators in administrative work, allowing them to focus on teaching. In a country with vast educational disparities, AI has the potential to democratize access to quality education, particularly in rural areas. However, challenges such as digital infrastructure, data privacy concerns, and the need for teacher training must be addressed for effective implementation. With government initiatives like NEP 2020 promoting technology integration, AI is set to play a crucial role in transforming India’s education sector, making learning more efficient, inclusive, and future-ready.International Journal For Multidisciplinary Research100In a country with vast educational disparities, AI has the potential to democratize access to quality education, particularly in rural areas, however, challenges such as digital infrastructure, data privacy concerns, and the need for teacher training must be addressed for effective implementation.International Journal For Multidisciplinary Research["Programma Preliminare", "Stefano Pio Zingaro", "Francesca Del Bonifro", "Maurizio Gabbrielli", "Olivia Levrini", "Chiara Panciroli", "Ricardo Anibal Matamoros Aragon", "Luca Marconi", "Francesco Epifania", "I. Zoppis", "Sara Manzoni", "Giancarlo Mauri", "Elia Musiu", "Pietro Monari", "Sara Scaltriti", "Filippo Rebecchi", "Michela Eleuteri"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/797cd36547867cd7d3b345bd9e77ea8015d73704 3f9b649dcf4521d9dcc034fb561038f583d6b571Integrating artificial intelligence (AI) into customer engagement practices transforms how organizations interact with consumers and offers enhanced personalization and efficiency. However, this technological evolution introduces significant ethical challenges, including algorithmic bias, data privacy violations, and a potential decline in consumer trust. This research, Algorithms to Authenticity (ATA), investigates the intricate relationship between AI technologies and authentic, ethical engagement strategies. The central idea of the research study is to explore three main questions: 1. How can businesses effectively implement AI technologies to improve customer engagement ethically? 2. What are the ethical dilemmas and potential risks associated with AI-driven customer engagement? 3. How can transparency and authenticity be maintained in AI-driven interactions to foster trust? The study emphasizes the urgent need for businesses to transition from an algorithm-centric model to one that prioritizes authenticity. This research analyzed ethical concerns for maintaining consumer trust and loyalty. The result of the study aims to provide actionable insights to help businesses navigate the ethical challenges posed by AI to reinforce the commitment to ethical standards while enhancing consumer satisfaction. The study's findings advocate for transparency, accountability, and proactive measures mitigating the risks associated with AI deployment. Given the findings, the three key directions of the study are promoting ethical AI implementation, addressing risks associated with algorithmic misuse, and enhancing transparency to foster authentic customer relationships and trust, reinforced by the concept of ATA ensuring ethical customer engagement. The directions guide organizations, researchers, and policymakers toward ethical AI practices.International journal of business management260The study emphasizes the urgent need for businesses to transition from an algorithm-centric model to one that prioritizes authenticity and promotes ethical AI implementation, addressing risks associated with algorithmic misuse, and enhancing transparency to foster authentic customer relationships and trust.International Journal of Business and Management["Mohammed Nadeem"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/3f9b649dcf4521d9dcc034fb561038f583d6b571 405960c1a6407f0d4537ac33a737574f66884c52The birth of artificial intelligence was between 1950-1956 but AI in HRM practices was used first in the 2000s. This rapid advancement of AI has significantly transformed Human Resource Management Practices. AI-based systems currently help HR automate a large segment of repetitive tasks in processes such as talent screening, hiring, engaging, re-engaging, employee relations, onboarding, etc, which used to be a long and hectic task before the introduction of automated tools. Nevertheless, these automated practices raise ethical concerns about bias, transparency, and how AI may undermine human judgment. The focus of this paper is to discuss and analyze the present scenario of AI in the field of HR. It has suggested that AI has the potential to optimize HRM practices leading to higher efficiency and cost-cutting, while also exposing several other challenges and risks such as data privacy and security, job disarticulation, and diminished autonomy for employees. By doing a real-world experiment on a normal and ATS-friendly resume and reviewing case studies such as an ATS rejecting a company’s own manager, this research investigates the balance between AI efficiency and human judgment.Stallion Journal for Multidisciplinary Associated Research Studies200It is suggested that AI has the potential to optimize HRM practices leading to higher efficiency and cost-cutting, while also exposing several other challenges and risks such as data privacy and security, job disarticulation, and diminished autonomy for employees.Stallion Journal for Multidisciplinary Associated Research Studies["Chirag Harchandani"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/405960c1a6407f0d4537ac33a737574f66884c52 5403b23eb0d70d877e38d9c1eb9c1dcdb1958dbanpj Digital Medicine330The PULSE framework is presented, aimed to establish an integrative and ethically governed ecosystem for the patient-guided, patient-contextualized use of multi-domain health data for AI-augmented care.NPJ Digital Medicine["S. Dykstra", "Matthew Macdonald", "Rhys Beaudry", "D. Labib", "Melanie King", "Yuanchao Feng", "J. Flewitt", "Jeff Bakal", "Bing Lee", "Stafford Dean", "Marina Gavrilova", "Paul W M Fedak", "James A White"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/5403b23eb0d70d877e38d9c1eb9c1dcdb1958dba c983809ddca9d505ad8c26a913c37cc79def6135The integration of Artificial Intelligence (AI) in waste sorting presents a transformative opportunity to enhance recycling processes, addressing inefficiencies and environmental challenges. This study investigates the application of AI-driven technologies within Greece, focusing on improving material classification, reducing contamination, and optimizing waste management practices. By leveraging advanced image recognition, machine learning algorithms, and robotic systems, the research demonstrates AI's potential to overcome infrastructure deficiencies and high operational costs, while fostering sustainability. A comprehensive analysis identifies socio-economic and environmental benefits, evaluates current barriers, and proposes a scalable framework for AI implementation. The findings aim to guide policymakers, industry stakeholders, and environmental organizations in adopting AI as a pivotal tool for advancing waste management and achieving global sustainability targets.International Journal of Clinical Case Reports and Reviews00This study investigates the application of AI-driven technologies within Greece, focusing on improving material classification, reducing contamination, and optimizing waste management practices, to demonstrate AI's potential to overcome infrastructure deficiencies and high operational costs, while fostering sustainability.International Journal of Clinical Case Reports and Reviews["P. Maniatis"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/c983809ddca9d505ad8c26a913c37cc79def6135 619610a640773d8dc2bef63890c31d01b379b1d6This study investigates the effectiveness of Artificial Intelligence (AI) tools, namely Grammarly, QuillBot, and Ginger Software, in providing automated feedback for English as a Foreign Language (EFL) writing among Indonesian undergraduate students. It examines the potential of these AI-powered applications in identifying and correcting grammatical, punctuation, and clarity issues and paraphrasing in student writing. This research applied a descriptive qualitative method involving document analysis and interviews. The study involved comparing these tools' corrective feedback and conducting interviews with EFL writing students to understand their perceptions of using these tools. The research findings indicate varying levels of error detection and correction suggestions across the tools, with some differences in their efficiency. While Grammarly, QuillBot, and Ginger Software show promise in enhancing EFL writing skills, the study highlights the importance of not solely relying on these tools. Key findings reveal that Grammarly excels in grammatical accuracy, QuillBot offers superior paraphrasing capabilities, and Ginger provides limited feedback in comparison.  It suggests that integrating AI feedback with traditional methods of teacher and peer reviews can lead to optimal writing outcomes. The paper also discusses students' perceptions of using these tools, noting a preference for Grammarly due to its simplicity and effectiveness. Students reported improved grammar and motivation but exhibited tendencies toward over-reliance, potentially limiting critical thinking and independent writing skills. However, some students exhibited over-reliance on these tools, potentially hindering their critical thinking and independent writing skills. The study emphasizes the importance of using AI-powered tools strategically, alongside human editing and critical thinking practices, to maximize EFL writing development.ETERNAL (English Teaching Journal)300Key findings reveal that Grammarly excels in grammatical accuracy, QuillBot offers superior paraphrasing capabilities, and Ginger provides limited feedback in comparison, and it suggests that integrating AI feedback with traditional methods of teacher and peer reviews can lead to optimal writing outcomes.ETERNAL (English Teaching Journal)["Fiki Setiawan", "Annas Alkhowarizmi"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/619610a640773d8dc2bef63890c31d01b379b1d6 19370d5d0ae7a46541c654288cd415b751cd0ccaThis white paper underscores the critical importance of responsibly deploying Artificial Intelligence (AI) in military contexts, emphasizing a commitment to ethical and legal standards. The evolving role of AI in the military goes beyond mere technical applications, necessitating a framework grounded in ethical principles. The discussion within the paper delves into ethical AI principles, particularly focusing on the Fairness, Accountability, Transparency, and Ethics (FATE) guidelines. Noteworthy considerations encompass transparency, justice, non-maleficence, and responsibility. Importantly, the paper extends its examination to military-specific ethical considerations, drawing insights from the Just War theory and principles established by prominent entities. In addition to the identified principles, the paper introduces further ethical considerations specifically tailored for military AI applications. These include traceability, proportionality, governability, responsibility, and reliability. The application of these ethical principles is discussed on the basis of three use cases in the domains of sea, air, and land. Methods of automated sensor data analysis, eXplainable AI (XAI), and intuitive user experience are utilized to specify the use cases close to real-world scenarios. This comprehensive approach to ethical considerations in military AI reflects a commitment to aligning technological advancements with established ethical frameworks. It recognizes the need for a balance between leveraging AI's potential benefits in military operations while upholding moral and legal standards. The inclusion of these ethical principles serves as a foundation for responsible and accountable use of AI in the complex and dynamic landscape of military scenarios.00This white paper underscores the critical importance of responsibly deploying Artificial Intelligence (AI) in military contexts, emphasizing a commitment to ethical and legal standards and introduces further ethical considerations specifically tailored for military AI applications.["Mathias Anneken", "Nadia Burkart", "Fabian Jeschke", "Achim Kuwertz-Wolf", "Almuth Mueller", "Arne Schumann", "Michael Teutsch"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/19370d5d0ae7a46541c654288cd415b751cd0cca 4c7e2b0b9a405e77af9c9d523c12c31dd6e1dff6International Journal of Computer Assisted Radiology and Surgery190An online survey among German surgeons focusing on digitalization and AI in CDM, specifically for acute abdominal pain, saw the potential of AI for organizational tasks but are hesitant about its use in CDM.International journal of computer assisted radiology and surgery["Jonas Henn", "Tijs Vandemeulebroucke", "Simon Hatterscheidt", "Jonas Dohmen", "J\u00f6rg C Kalff", "Aimee van Wynsberghe", "H. Matthaei"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/4c7e2b0b9a405e77af9c9d523c12c31dd6e1dff6 55909da635b398044d3c9928b9f16469f8a3e16dThis study investigates the determinants of artificial intelligence (AI) acceptance and use in the Saudi Arabian energy sector. A pre-tested surveys was self-administered to 154 employees in the Saudi Arabian energy sector. The study delves into the unified theory of acceptance and use of technology (UTAUT) and develops an extended model of UTAUT using structural equation modeling (SEM) via Smart PLS version 4. Unlike UTAUT and the results of previous research on AI adoption in business, the key findings confirm the direct significant impact of facilitating condition, personal attitude and risk management on employees’ use of AI in the energy sector. The results also confirmed the significant positive impact of effort expectancy and social influence on employees’ behavioral intention to adopt AI. The results did not confirm the significant impacts of factors like performance expectancy, data management and risk management on employees’ intention of using AI nor on their actual use in the energy sector. The study provides implications for policymakers, industry stakeholders, and scholarly communities and offers an insight to foster AI adoption within Saudi Arabian energy sector. The study provides a comprehensive insight into organizational behaviors and decision-making processes surrounding AI integration in the energy sector.Journal of Management World330The key findings confirm the direct significant impact of facilitating condition, personal attitude and risk management on employees’ use of AI in the energy sector and provide a comprehensive insight into organizational behaviors and decision-making processes surrounding AI integration in the energy sector.Journal of Management World["Ahmed Abdullah Alzahrani", "Abu Elnasr E. Sobaih"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/55909da635b398044d3c9928b9f16469f8a3e16d e6fee2d6793aff1bd10ee64db414e72aecaf381cModern warfare has significantly advanced with the introduction of Artificial Intelligence (AI) into operations. Through enhancing decision-making procedures, streamlining logistics, and enabling the use of autonomous weaponry, AI technologies improve military capabilities. But using AI to military applications presents difficult moral and legal issues, especially when it comes to adhering to international humanitarian law (IHL). IHL sets regulations that guard civilians and control hostilities in order to reduce suffering caused to civilians during armed conflicts. The compatibility of AI technology with core IHL principles such as distinction, proportionality, military necessity, and humanity is examined in this article. There is still significant worry about AI systems' capacity to reliably discriminate between military targets and people, raising the possibility of unintentional harm to civilians. Furthermore, responsibility for autonomous systems'actions and decisions presents legal dilemmas regarding responsibility and liability in cases of IHL violations. This study evaluates the potential of AI to comply with IHL requirements and suggests strategies for mitigating associated risks. It emphasizes the importance of developing robust legal frameworks and international cooperation to ensure that AI applications in military operations adhere to humanitarian standards, thereby balancing technological innovation with the imperatives of human rights and ethical warfare.Journal of Law and Emerging Technologies210The importance of developing robust legal frameworks and international cooperation is emphasized to ensure that AI applications in military operations adhere to humanitarian standards, thereby balancing technological innovation with the imperatives of human rights and ethical warfare.Journal of Law and Emerging Technologies["Dr. Mohamed Ahmed Zakaria Shehata"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/e6fee2d6793aff1bd10ee64db414e72aecaf381c 9777840d1d211ace5ef61018e2fc6d2d379951efRecent research has investigated the connection between artificial intelligence (AI) utilization and feelings of loneliness, yielding inconsistent outcomes. This meta-analysis aims to clarify this relationship by synthesizing data from 47 relevant studies across 21 publications. Findings indicate a generally significant positive correlation between AI use and loneliness (r = 0.163, p < 0.05). Specifically, interactions with physically embodied AI are marginally significantly associated with decreased loneliness (r = -0.266, p = 0.088), whereas engagement with physically disembodied AI is significantly linked to increased loneliness (r = 0.352, p < 0.001). Among older adults (aged 60 and above), AI use is significantly positively associated with loneliness (r = 0.352, p < 0.001), while no significant correlation is observed (r = 0.039, p = 0.659) in younger individuals (aged 35 and below). Furthermore, by incorporating positive attitudes toward AI, the study reveals that the influence of AI use in exacerbating loneliness outweighs the reverse impact, although both directions show significant positive relationships. These results enhance the understanding of how AI usage relates to loneliness and provide practical insights for addressing loneliness through AI technologies.Cyberpsychology, Behavior, and Social Networking570By incorporating positive attitudes toward AI, the study reveals that the influence of AI use in exacerbating loneliness outweighs the reverse impact, although both directions show significant positive relationships.Cyberpsychology, behavior and social networking["Xu Dong", "Jun Xie", "He Gong"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/9777840d1d211ace5ef61018e2fc6d2d379951ef f69ff97c8329b4d663960a1d4bd370376a4df080This study investigates key parameters and applications of artificial intelligence (AI) in predicting the total cost of ownership (TCO) for chilled water plants (CWPs). Forecasting the TCO of CWPs is challenging due to the diverse and dynamic factors and parameters that influence it, necessitating understanding their complex correlations and causations. While AI and non-AI approaches have improved parameter prediction accuracy in different engineering applications, comprehensive literature reviews on chiller TCO prediction methodologies and their influencing factors are limited. This systematic review addresses three objectives: (1) to identify the key parameters in estimating TCO of CWPs, (2) to examine the existing techniques employed in TCO forecasting and their benefits in energy and cost savings, and (3) to evaluate how AI enhances TCO prediction accuracy and robustness. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this review analyzed studies from 2017 to 2024 sourced from the Web of Science and Scopus databases. This study identifies several key parameters influencing TCO, including cooling load, energy consumption, chiller capacity, and the Coefficient of Performance (COP). The review shows that AI-driven models, such as deep learning and machine learning algorithms, have improved the accuracy and robustness of TCO predictions, and it further demonstrates scenarios where AI outperforms conventional prediction and forecasting methods. Notably, the current review shows that AI techniques are predicted to be capable of reducing total life cycle costs by up to 18%, based on modeling estimates.Applied Sciences00The review shows that AI-driven models, such as deep learning and machine learning algorithms, have improved the accuracy and robustness of TCO predictions, and it further demonstrates scenarios where AI outperforms conventional prediction and forecasting methods.Applied Sciences["Rubaiath E. Ulfath", "Toh Yen Pang", "I. Cole", "Iain Stewart", "Chi-Tsun Cheng"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/f69ff97c8329b4d663960a1d4bd370376a4df080 4a71fe444450e33c2ad75c220b43fffe10d0705eDigital Society620The blueprint for an EU Charter for Digital Patients’ Rights is proposed, consolidating and adapting existing rights for patients to address these specific challenges of AI and proposes novel rights for patients, such as the right not to be subject to automated medical decision-making and the right to meaningful human contact.Digital Society["Hannah van Kolfschooten"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/4a71fe444450e33c2ad75c220b43fffe10d0705e 8e24f0fa456acc574df20de0a897ad728eb1befcTechnology plays an important role in mathematics education around the world. Artificial intelligence (AI) is one of the products of technology that is widely used in academic activities, which is also accompanied by many studies examining the use of Artificial intelligence in mathematics education. This paper aims to look at the research trends on AI in mathematics education. The Systematic Literature Review (SLR) method was used to review 35 articles from internationally reputable journals obtained from Scopus. The article search process used keywords namely AI, Artificial Intelligence, and mathematics education. The findings showed a sharp spike in the publication of AI articles in 2024, especially on chatbots. Chatbots have become the focus of AI research in mathematics education and are mostly conducted at the college level with student subjects. The application of AI in mathematics education is characterized by the function of AI to realize personalized learning. Finally, this paper recommends that future research should increase the diversity of research related to AI in mathematics education, especially among teachers and students at various levels given that students today can easily access AI. Debriefing on maintaining academic ethics is also needed to avoid misuse of AI in mathematics education.International Journal of Scientific Research and Management00It is recommended that future research should increase the diversity of research related to AI in mathematics education, especially among teachers and students at various levels given that students today can easily access AI.International Journal of Scientific Research and Management (IJSRM)["Isni Qothrunnada", "Sasqia Ulimaz Maghfiroh"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/8e24f0fa456acc574df20de0a897ad728eb1befc 1b98298682254e7ed926888daab3901f4d28988bThe goal of this study is to analyze the role of artificial intelligence (AI) in cybersecurity by analyzing the strengths of many key AI techniques, including machine learning (ML), deep learning (DL), natural language processing (NLP), and anomaly detection algorithms. We conducted a detailed literature assessment on current research to assess the applicability of AI approaches in cybersecurity. Principal methodologies examined include machine learning for behavioral analytics, deep learning for malware detection, natural language processing for phishing identification, and anomaly detection for intrusion detection. Artificial intelligence approaches significantly enhance cybersecurity, with each strategy providing distinct functionalities. This study’s distinctive contributions are its comparative assessment of approaches, emphasizing their strengths and practical applications in cybersecurity while addressing present issues and future avenues for improvement. The research suggests that a combination of AI in cybersecurity that merges these strategies could provide a significant increase in digital security. These findings bring substantial value by identifying the best scenarios for using each AI approach in cybersecurity.International Conference on Applied Informatics and Communication440The research suggests that a combination of AI in cybersecurity that merges these strategies could provide a significant increase in digital security.2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC)["Abdullah Al Siam", "Md. Maruf Hassan", "Touhid Bhuiyan"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/1b98298682254e7ed926888daab3901f4d28988b 7a1377a9deb714afd3dff815a576d58f456a9aaaThe obstacles of each security system combined with the increase of cyber-attacks, negatively affect the effectiveness of network security management and rise the activities to be taken by the security staff and network administrators. So, there is a growing need for the automated auditing and intelligent reporting strategies for reliable network security with as less model complexity as possible. Newly, artificial intelligence has been effectively applied to various network security issues, and numerous studies have been conducted that utilize various artificial intelligence techniques for the purposes of encryption and secure communication, in addition to using artificial intelligence to perform a large number of data encryption operations in record time. The aim of the study is to present and discuss the most prominent methods of artificial intelligence recently used in the field of network security including user authentication, Key exchanging, encryption/decryption, data integrity and intrusion detection system.00The aim of the study is to present and discuss the most prominent methods of artificial intelligence recently used in the field of network security including user authentication, Key exchanging, encryption/decryption, data integrity and intrusion detection system.["M. A. A. Jbaar", "Adel Jalal Yousif", "Qutaiba I. Ali"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/7a1377a9deb714afd3dff815a576d58f456a9aaa fab5cfd5307d66f8a2a673262d7e376465ec335aThe interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues. However, the growing number of different solutions in this field creates a demand to assess the quality of explanations and compare them. In recent years, several attempts have been made to consolidate scattered XAI quality assessment methods into a single benchmark. Those attempts usually suffered from a focus on feature importance only, a lack of customization, and the absence of an evaluation framework. In this work, the eXplainable Artificial Intelligence Benchmark (XAIB) is proposed. Compared to existing benchmarks, XAIB is more universal, extensible, and has a complete evaluation ontology in the form of the Co-12 Framework. Due to its special modular design, it is easy to add new datasets, models, explainers, and quality metrics. Furthermore, an additional abstraction layer built with an inversion of control principle makes them easier to use. The benchmark will contribute to artificial intelligence research by providing a platform for evaluation experiments and, at the same time, will contribute to engineering by providing a way to compare explainers using custom datasets and machine learning models, which brings evaluation closer to practice.Algorithms00The proposed eXplainable Artificial Intelligence Benchmark (XAIB) is more universal, extensible, and has a complete evaluation ontology in the form of the Co-12 Framework, which is easy to add new datasets, models, explainers, and quality metrics.Algorithms["Ilia Moiseev", "Ksenia Balabaeva", "S. Kovalchuk"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/fab5cfd5307d66f8a2a673262d7e376465ec335a e56b04b1a5116eed965dbf1c8f7efc0195ccfb10Remote Laboratories have become crucial educational resources due to their implementation in institutions that have adopted hybrid teaching models. Moreover, artificial intelligence (AI) has increasingly been used to develop educational support tools. Remote Laboratories suffer from a limitation: when students conduct experimental activities, instructors cannot always be immediately available to resolve doubts. To address this limitation, a virtual assistant was integrated into the Acid–Base Titration II Remote Laboratory. The aim of this research is to understand the perspective of chemistry teachers from the Common Basic Cycle of the University of Buenos Aires on the use of this artificial intelligence tool. For this purpose, a focus group was conducted in which a series of questions were asked before and after using the AI tool. The findings reveal that teachers perceive great potential in the combination of these technologies. Furthermore, the virtual assistant could offer personalised assistance in real time, which ensures accompaniment during the completion of the Remote Laboratory.Frontiers in Education250The findings reveal that teachers perceive great potential in the combination of these technologies, and the virtual assistant could offer personalised assistance in real time, which ensures accompaniment during the completion of the Remote Laboratory.Frontiers in Education["Fiorella Lizano-S\u00e1nchez", "Ignacio Idoyaga", "Pablo Ordu\u00f1a", "L. Rodr\u00edguez-Gil", "Carlos Arguedas-Matarrita"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/e56b04b1a5116eed965dbf1c8f7efc0195ccfb10 d656deb403ed506f62fbb80d702dc34f4173980fArtificial intelligence (AI) is transforming transportation engineering, improving efficiency, safety, and sustainability. It can reduce congestions, optimize traffic flows, and prevent accidents, thereby improving overall efficiency. Real-time traffic monitoring and predictive analysis systems facilitate effective transport management. Intelligent transportation systems (ITS) with adaptive signal control and vehicle-to-vehicle communication can improve road safety. Autonomous vehicles and advanced driver-assistance systems (ADAS) help prevent accidents. However, there are still challenges related to data privacy, security, and infrastructure interoperability that require special attention and solutions.Lizing (Leasing)00Artificial intelligence is transforming transportation engineering, improving efficiency, safety, and sustainability, but there are still challenges related to data privacy, security, and infrastructure interoperability that require special attention and solutions.Lizing (Leasing)["Tao Lian"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/d656deb403ed506f62fbb80d702dc34f4173980f c909f8fd26e4596921d872dc9d710fd9623b1c6bTransition-aged youth with autism and other neurodiversities face high levels of unemployment and underemployment and can benefit from pre-employment and transition services to improve long-term outcomes. While the mainstream proliferation of artificial intelligence tools has garnered global attention over the last year, there is a growing body of research exploring how these technologies can be used to enhance communication and workplace readiness skills for these transition-aged youth. This article examines the current state of artificial intelligence and technology integration into pre-employment and transition skills training for neurodivergent and autistic youth. We completed a scoping literature review across these topic areas, pre-employment services and artificial intelligence and technology for neurodivergent and autistic youth. We found the literature in this area of research to be in a state of rapid development and flux. A total of 27 articles were found. Specifically, there is literature describing the needs of neurodivergent and autistic youth in pre-employment services related to workplace readiness. Our review revealed promising advances in the use of technology and artificial intelligence to support the development of social communication and job-related skills for use in pre-ETS. The proliferation of artificial intelligence and technology into mainstream society provides an opportunity to explore and hone the use of these tools to improve pre-employment and transition skills for neurodivergent youth. Stakeholders should engage in participatory and ethical practices to develop and implement strategies that improve workplace readiness and employment outcomes for this population.Journal of Vocational Rehabilitation430A scoping literature review across these topic areas, pre-employment services and artificial intelligence and technology for neurodivergent and autistic youth found the literature in this area of research to be in a state of rapid development and flux.Journal of Vocational Rehabilitation["Rachel McDonald Hurford", "Melanie Derry", "Elli Bavuso"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/c909f8fd26e4596921d872dc9d710fd9623b1c6b 774f3a23e606513cd377267966ce510403ff6dfdWith the rise of artificial intelligence (AI), AI scribes are being piloted in the dermatology clinic and are emerging as a promising alternative to human scribes. Benefits include potential for time saving, diminished physician burnout, increased note accuracy, decreased documentation stress, and opportunity for more personal encounters. Rigorous testing is necessary to ensure patient privacy and integrity of clinical encounters are maintained.Clincal and Experimental Dermatology00AI scribes are being piloted in the dermatology clinic and are emerging as a promising alternative to human scribes with potential for time saving, diminished physician burnout, increased note accuracy, decreased documentation stress, and opportunity for more personal encounters.Clinical and experimental dermatology["Emily R. Gordon", "M. Trager", "Alyssa Breneman", "F. Samie"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/774f3a23e606513cd377267966ce510403ff6dfd f5599d0952d767f17953c15d533dc2804ac9ed53This article aimed to explore the development of an artificial intelligence model for English grammar correction based on computational linguistics methods. Traditional grammar correction systems suffer from problems such as complex rules, sparse data, and insufficient utilization of contextual information. To address these issues, this article adopted Transformer’s pre-trained language model, utilizing its powerful context understanding and automatic feature extraction capabilities to improve the processing performance of complex syntax structures and long-distance dependencies. Meanwhile, by constructing large-scale and diverse datasets and combining them with data augmentation techniques, the model’s generalization ability and robustness were enhanced. This article also investigated hyperparameter tuning, model integration, and continuous optimization strategies in the process of model training optimization, and provided a detailed description of model evaluation and experimental validation. In the evaluation, the average precision, average recall, and average F1 score for most common grammar errors were 0.8, 0.805, and 0.801, respectively. The model in this article has excellent grammar correction ability. Through comprehensive experiments and evaluations, the potential and advantages of a new grammar correction artificial intelligence model developed based on computational linguistics methods in improving grammar correction effectiveness and practicality have been demonstrated.Journal of Computational Methods in Sciences and Engineering180Transformer’s pre-trained language model was adopted, utilizing its powerful context understanding and automatic feature extraction capabilities to improve the processing performance of complex syntax structures and long-distance dependencies.Journal of Computational Methods in Sciences and Engineering["Han Jiang"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/f5599d0952d767f17953c15d533dc2804ac9ed53 175eb94b7b33a6de24c56633eb39cc9f4d158892Theory and society330Theory and Society["F. Shults"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/175eb94b7b33a6de24c56633eb39cc9f4d158892 040d2b2cfca97cbfeb90e5135f7b00db6ebe0ba6Artificial Intelligence (AI) is revolutionizing education in India by enhancing personalized learning, automating administrative tasks, and improving accessibility. AI-powered adaptive learning platforms tailor educational content to individual students, addressing diverse learning needs and bridging gaps in traditional teaching methods. Virtual tutors, chatbots, and AI-driven assessments provide real-time feedback, fostering a more interactive learning environment. Additionally, AI assists educators in administrative work, allowing them to focus on teaching. In a country with vast educational disparities, AI has the potential to democratize access to quality education, particularly in rural areas. However, challenges such as digital infrastructure, data privacy concerns, and the need for teacher training must be addressed for effective implementation. With government initiatives like NEP 2020 promoting technology integration, AI is set to play a crucial role in transforming India’s education sector, making learning more efficient, inclusive, and future-ready.International Journal For Multidisciplinary Research90In a country with vast educational disparities, AI has the potential to democratize access to quality education, particularly in rural areas, however, challenges such as digital infrastructure, data privacy concerns, and the need for teacher training must be addressed for effective implementation.International Journal For Multidisciplinary Research["Tapas KUMAR CHATTOPADHYAY"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/040d2b2cfca97cbfeb90e5135f7b00db6ebe0ba6 e4803e25d91398330bf2252799337cf07455d1e0Este estudio examina la intersección entre la velocidad de producción de la inteligencia artificial y los riesgos inherentes que enfrentan los gobiernos de naciones soberanas en la legislación de cumplimiento regulatorio para el uso transfronterizo de datos, la ética en la IA y la bioética. Se expone el amplio vector necesario para salvaguardar la adquisición de información sensible en tiempo real proveniente de interacciones humano-IA, abarcando perspectivas neuronales del córtex sensorial humano, marcadores biométricos y datos fisiológicos humanos críticos para el cálculo de la inteligencia artificial en la obtención de conocimientos académicos sobre los seres humanos. La precisión y exactitud en el cálculo son esenciales para que la Inteligencia General Artificial (AGI) y la Inteligencia Artificial Superinteligente (ASI) produzcan respuestas éticas, imparciales y en tiempo real. A través de un enfoque multidisciplinario, esta investigación evalúa el impacto de las tecnologías de IA en la política exterior de los gobiernos, el desarrollo socioeconómico, la postura de seguridad nacional y la legislación soberana. Para que los gobiernos capitalicen las inversiones en IA, este artículo propone la creación de un centro centralizado de procesamiento de macrodatos para la supervisión en tiempo real de la IA (gobernanza) y el desarrollo de algoritmos que implementen marcos prácticos de tecnología de telemetría de macrodatos. Como beneficio para los sectores públicos, este estudio plantea la necesidad de legislación y marcos regulatorios que equilibren la innovación con el respeto a la seguridad nacional y la protección de los derechos individuales, ofreciendo recomendaciones políticas integrales para abordar estos desafíos.Revista La Propiedad Inmaterial140Revista La Propiedad Inmaterial["Tilmon McCullum", "Laura Camila Contreras Mancera"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/e4803e25d91398330bf2252799337cf07455d1e0 00464c00ca5e4ae2edd5f09f36e5c4992cd3f1e5The emergence of ChatGPT and similar new Generative AI tools has created concern about the validity of many current assessment methods in higher education, since learners might use these tools to complete those assessments. Here we review the current evidence on this issue and show that for assessments like essays and multiple-choice exams, these concerns are legitimate: ChatGPT can complete them to a very high standard, quickly and cheaply. We consider how to assess learning in alternative ways, and the importance of retaining assessments of foundational core knowledge. This evidence is considered from the perspective of current professional regulations covering the professional registration of Biomedical Scientists and their Health and Care Professions Council (HCPC) approved education providers, although it should be broadly relevant across higher education.British Journal of Biomedical Science391It is shown that for assessments like essays and multiple-choice exams, ChatGPT can complete them to a very high standard, quickly and cheaply, and the importance of retaining assessments of foundational core knowledge is important.British Journal of Biomedical Science["Philip M. Newton", "Sue Jones"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/00464c00ca5e4ae2edd5f09f36e5c4992cd3f1e5 7040135c9d195c6ece7579d9864efd43189504d4AI &amp; SOCIETY201AI &amp; SOCIETY["Richard Arias Hern\u00e1ndez", "Mois\u00e9s Rockembach"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/7040135c9d195c6ece7579d9864efd43189504d4 d724c7ef1c3d8894627d0b2a4e8a6256fbed355eEuropean journal of plastic surgery990European Journal of Plastic Surgery["Eqram Rahman", "Shabnam Sadeghi-Esfahlani", "P. Rao", "Patricia E. Garcia", "S. Ioannidis", "John Nosta", "Zakia Rahman", "W. R. Webb"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/d724c7ef1c3d8894627d0b2a4e8a6256fbed355e d9c807530306312bef7ff53f74244f4b0101b3e2INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT00INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT["ThankGod Steven Lawrence", "Peter Oyirinnaya", "Adeyemi Afolayan Adesola", "Osarense Dorothy Iguodala"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/d9c807530306312bef7ff53f74244f4b0101b3e2 8c206a7951ecd3df1da45694bc31645c86a8dc1dWith the arrival of the Fourth Industrial Revolution, intelligent machines are affecting the daily lives of multiple organizational stakeholders. However, despite the continued expansion of intelligent machines in society, management scholarship has generally lagged, and current frameworks are under-equipped to offer meaningful guidance regarding the intersection of intelligent machines and organizations. We address this issue via a multidisciplinary review and a novel framework of intelligent machines and value creation. First, we discuss the characteristics of intelligent machines (i.e., autonomy, learning, inscrutability, and materiality) and how variation in these characteristics impacts their affordances and, subsequently, the value offered to stakeholders. We also advance the notion of value contingencies, which captures the idea that the value afforded by intelligent machines is conditional and that stakeholders’ dispositions and exploitation of intelligent machines must be considered when assessing value creation. Building on our framework, we offer recommendations for future research. Overall, we forward the literature by showcasing how intelligent machines often create both advantages and disadvantages for stakeholders and demonstrate how practitioners, policymakers, and management scholars may consider this moving forward.Journal of Management2650A multidisciplinary review and a novel framework of intelligent machines and value creation are addressed, showcasing how intelligent machines often create both advantages and disadvantages for stakeholders and demonstrating how practitioners, policymakers, and management scholars may consider this moving forward.Journal of Management["Michael J. Matthews", "Runkun Su", "Lindsey Yonish", "Shawn T. McClean", "Joel Koopman", "Kai Chi Yam"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/8c206a7951ecd3df1da45694bc31645c86a8dc1d 0d262f7127c78675d18fb9c3b82c01fc64719092Journal of Urology00The Journal of urology["J. K. Kim", "M. Rickard", "Armando Lorenzo"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/0d262f7127c78675d18fb9c3b82c01fc64719092 ab874968d7dbe2e2ac4690993e49ca95a08e3ef5Cureus160Cureus["Akash Belenje", "Dhanush Pandya", "Subhadra Jalali", "P. Rani"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/ab874968d7dbe2e2ac4690993e49ca95a08e3ef5 cacc69ca10a1f63b9ba288a855f292108380ca04International Workshop on Advanced Imaging Technology (IWAIT) 202500International Workshop on Advanced Imaging Technology (IWAIT) 2025["Wen-Lin Fan", "Chun-Chi Chuang", "Chieh-Chi Huang", "Chia-Wen Hsu", "TASI-TING Hsu", "Ting-Rui Guo", "Chung-Chian Hsu"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/cacc69ca10a1f63b9ba288a855f292108380ca04 a0578c83f3955d228a0e20e99f480564554ba768Enterprise Information Systems640Enterprise Information Systems["Moacir Godinho Filho", "Sofia Vieira Queiroz de Almeida", "Muris Lage Junior", "Lauro Osiro", "Bruna Lima", "Mario Henrique Callefi"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/a0578c83f3955d228a0e20e99f480564554ba768 eaa9e3e86aaaba647038224f6a17a2375494095eInformation Systems Frontiers00Information Systems Frontiers["A. M. Spence", "Anurag Behar", "Arjun Jayadev"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/eaa9e3e86aaaba647038224f6a17a2375494095e 40cdc5369956145b5b2e2b24f60bd46823a71f6fCurrent Issues in Tourism250Current Issues in Tourism["C. M. Hall", "Chris Cooper"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/40cdc5369956145b5b2e2b24f60bd46823a71f6f 79c1a8498fd46b8e0aed12734a3f5f8f7bd05567Frontiers in Genetics00Frontiers in Genetics["Mart\u00edn Eduardo Guti\u00e9rrez", "Rafael Lahoz-Beltr\u00e1", "Alberto J. Donayre-Torres"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/79c1a8498fd46b8e0aed12734a3f5f8f7bd05567 f39034f16075ae286f8eb1836672877d144253b3Digital Society310A persistent negative relationship is found, suggesting that AI and robots could reduce the stress level of workers in Germany, and suggestive evidence of modern technologies changing the way the authors perform their work in a way that reduces stress and work pressure is provided.Digital Society["Michael Koch", "Magnus Lodefalk"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/f39034f16075ae286f8eb1836672877d144253b3 30b9dd4baba90ee92aa16af9b4c1d129871d820fEvery African woman particularly a married woman in the African context with special reference to Zimbabwe is expected to own a kitchen hut. There is an adage which says that the place of a woman is a kitchen. Given the African kitchen hut, it shows women empowering context therefore viewing a kitchen hut as a hub for business management, marketing, strategies and education through lifelong learning and health care. The kitchen hut embodies women’s power rather than belittling them as some feminists portray it as a place for women to cook food for the family. Families sit in the kitchen planning all their business ventures and marketing strategies to expand their businesses. Women give birth and nurse the sick in the kitchen making it a referral hospital and labour ward and a constitutive space where herbalists and traditional diviners operate to remedy different illnesses. The African Kitchen Hut is an institute of business prosperity, lower and higher education. It provides practical and theoretical business tactics and education through observation, imitation, lecturing, folk tales, riddles, songs and dances to young boys and girls at a prescribed time and giving it the face of a learning institute. Hotel and catering business tactics are also passed on to young girls and young women and today the knowledge is passed on also to boys and men. The African hut is a hub for all activities giving it one of its many faces as a business centre, learning centre, referral hospital and hotel where nutritious food is cooked and dished. A kitchen hut is where a family convenes to strategize about events befalling the family, consults on the bridal prize and lobola payment, rituals to appease ancestors and the spirits of the wronged persons, giving it a double cap of being a temple and a court. It is the family’s front office, business center where marketing strategies and public relations strategies are practiced and visitors are also received and are seen off from the same kitchen hut. It is here where family and sometimes a whole community fellowship especially at night sitting around the fireplace passing critical business knowledge and education to the younger generation. The kitchen is also a mortuary where the dead lie in state before being buried. It is where memorial ceremonies are held and the life of the deceased is celebrated. Most of these roles are entrusted to women and its use for any reason is first asked for permission from women to access it. The kitchen became a space for women emancipation where their power and significance to the business world, family and society are realised. This promotes the following Sustainable Development Goals (SDG) Goal 5: Gender equality, Goal 9: Industry, Innovation and Infrastructure and Goal 4: Quality education. The paper employs a qualitative research approach through the use of secondary data and observation. The emergency of artificial intelligence poses a threat to these many faces of the African round kitchen hut to the preservation of Indigenous Knowledge Systems (IKS)’ centre of strategic planning, education and health, in the Indigenous kitchen hut. This study focuses on how Artificial intelligence (AI) can pose a potential risk to the preservation of the role of the African kitchen hut and how Artificial intelligence could uphold the role of the African kitchen hut in its many facets which include women empowerment.International Conference on Applied Informatics and Communication3202025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC)["C. Mutongi", "Billy Rigava", "Tinashe Muchuri", "Mufaro Rindai Chiwanza"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/30b9dd4baba90ee92aa16af9b4c1d129871d820f 4fc2e75b7c8537bb0c0ea7086e970db6bf20538dNature Geoscience90Nature Geoscience["J. Dramsch", "Monique M. Kuglitsch", "M. Fernandez-Torres", "A. Toreti", "Rustem Arif Albayrak", "Lorenzo Nava", "S. Ghaffarian", "Ximeng Cheng", "Jackie Ma", "Wojciech Samek", "R. Venguswamy", "A. Koul", "Raghavan Muthuregunathan", "Arthur Hrast Essenfelder"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/4fc2e75b7c8537bb0c0ea7086e970db6bf20538d 208cbcf04202fc1e7ae340ff761af5639244203fArtificial Machine Intelligence (AMI) has emerged as a revolutionary force in pharmaceutical research and development, fundamentally transforming traditional approaches to drug discovery, formulation science, and therapeutic applications. The integration of AMI in pharmaceutical processes has significantly enhanced data processing efficiency and predictive accuracy in drug efficacy assessment and disease progression monitoring. Advanced computational capabilities accelerate the identification of potential therapeutic candidates through sophisticated molecular modeling and structure-activity relationship analyses. The technology has demonstrated particular promise in formulation science, optimizing drug delivery systems and improving stability predictions. In oncology, AMI has revolutionized diagnostic accuracy and treatment personalization through enhanced imaging analysis and molecular profiling. Significant advancements have been made in vaccine development, where AMI expedites antigen selection and immunological response prediction. Despite these developments, the pharmaceutical sector faces challenges in AMI implementation, including ethical considerations, data privacy concerns, and regulatory compliance requirements. The technology has opened new frontiers in addressing rare diseases, enabling real-time patient monitoring, and developing adaptive treatment protocols. As AMI continues to evolve, strategic implementation and ethical considerations remain crucial in maximizing its potential for healthcare innovation. The convergence of human expertise with machine cognitive capabilities is reshaping global healthcare delivery systems, promising more efficient, personalized, and effective therapeutic solutions for diverse medical challenges.Journal of Pharma Insights and Research00The convergence of human expertise with machine cognitive capabilities is reshaping global healthcare delivery systems, promising more efficient, personalized, and effective therapeutic solutions for diverse medical challenges.Journal of Pharma Insights and Research["Vinothini KV", "Nithyasri Pandi", "Abishek Murugan", "Habi Bulla A", "Suruthi TV"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/208cbcf04202fc1e7ae340ff761af5639244203f 6ae983d3867c39ee8330d1afef842b2b48d6779dArtificial intelligence (AI)-powered tools are used to aid the learning and teaching process in higher education. AI technology aims to assist doctoral co-supervision through the model of humanised collaboration. It is discovered that there is a lack of literature review integrating generative AI (GenAI) with doctoral co-supervision processes. This paper investigates how GenAI facilitates the doctoral co-supervision process, including types of AI used, AI in education (AIED) components integration and the extent to which AI applications are useful in doctoral co-supervision. Four research questions posed have guided the study. The findings show AI to be supportive of personalised instruction and assessment and to be used as a collaborative tool. Furthermore, machine learning algorithms with a predictive nature were of immense aid in personalised advice. Nevertheless, the experience of the fusion of AI and mobile technologies in academic mentoring is relatively scarce in empirical studies. It was found that extended case studies and consumer experience were lacking in this area. Even though the potential benefits were clarified, a comprehensive assessment of the dynamic effects called for by more robust empirical investigations is required, considering further constraints. This paper summarises that future investigations and research are still needed.Journal of Information &amp; Knowledge Management00The findings show AI to be supportive of personalised instruction and assessment and to be used as a collaborative tool, including types of AI used, AI in education (AIED) components integration and the extent to which AI applications are useful in doctoral co-supervision.Journal of Information &amp; Knowledge Management["C. Thong", "Z. Atallah", "Shayla Islam", "WeiLee Lim", "A. Cherukuri"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/6ae983d3867c39ee8330d1afef842b2b48d6779d eb93eca804bcb575628d263f7cf29709d61acb32Generative artificial intelligence models have brought about advancements in fields like healthcare and finance, as well as in autonomous systems; however, they also encounter notable security vulnerabilities, primarily when operating in cloud environments. These AI models can be targeted by attacks that involve altering input data to deceive the system into generating harmful or incorrect results. This study delves into the security issues that AI systems face in cloud setups, explicitly focusing on the dangers posed by adversarial manipulation of data integrity and the challenges of utilizing shared resources within multi-user environments. The text covers methods for defending AI models, like training and defensive distillation, to make them more robust against attacks. It also delves into security measures for the cloud, such as encrypted communications and robust authentication systems to safeguard data integrity. Furthermore, the importance of AI explainability and transparency in uncovering vulnerabilities and building trust is highlighted. The outcomes of security breaches emphasize the importance of having AI systems to avoid impacts on decision-making and broader ethical and societal concerns. The document also discusses research areas such as quantum algorithms and decentralized security structures to tackle evolving risks and safeguard the future of secure AI applications that generate content.International Conference on Applied Informatics and Communication200This study delves into the security issues that AI systems face in cloud setups, explicitly focusing on the dangers posed by adversarial manipulation of data integrity and the challenges of utilizing shared resources within multi-user environments.2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC)["Advait Patel", "Pravin Pandey", "Hariharan Ragothaman", "Ramasankar Molleti", "Ajay Tanikonda"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/eb93eca804bcb575628d263f7cf29709d61acb32 1efd10d39b96adcdea6c46184f11684e30c1da2eThe rapid integration of Artificial Intelligence (AI) technologies into the lives of young digital citizens has escalated privacy concerns and the need for critical examination. This study uses Communication Privacy Management (CPM) Theory to understand how youth and critical stakeholders navigate these concerns. A total of 306 participants were surveyed, comprising 146 AI professionals, 127 parents and educators and 33 youths (aged 16-19). Employing a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from open-ended responses. Descriptive statistics reveal distinct perspectives among different demographics regarding data ownership, education, transparency and trust, parental role and perceived risks and benefits associated with AI systems. Structural equation modelling identified key influences on youth privacy management, highlighting the significance of transparency and trust, education and awareness, and parental data sharing among AI professionals, parents, educators, and young digital citizens. The qualitative analysis further underscored unique concerns, emphasizing a lack of understanding and data misuse contributed to the feeling of helplessness shared by all stakeholders. This study underscores the importance of integrating diverse stakeholders’ perspectives in the development of AI systems to address the complex challenges faced by youth. Recommendations include collaborative policymaking, implementing user-centric design practices, and enhancing privacy education to empower young digital citizens.International Conference on Applied Informatics and Communication460This study underscores the importance of integrating diverse stakeholders’ perspectives in the development of AI systems to address the complex challenges faced by youth, and recommends collaborative policymaking, implementing user-centric design practices, and enhancing privacy education to empower young digital citizens.2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC)["Molly Campbell", "Sandhya Joshi", "Ankur Barthwal", "Austin Shouli", "Ajay Kumar Shrestha"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/1efd10d39b96adcdea6c46184f11684e30c1da2e 541882770448f4b552362b16824cda6b564de865BMC Medical Education240This study critically evaluates the integration of large language models (LLMs), known for advanced text processing and generation capabilities, in medical ethics education, focusing on promoting virtue, and suggests that tools such as ChatGPT can profoundly enhance the learning experience in the future.BMC Medical Education["Shimpei Okamoto", "M. Kataoka", "Makoto Itano", "T. Sawai"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/541882770448f4b552362b16824cda6b564de865 da7ce652d1c5203191e34a39b4a4c5a29673257cThe incorporation of Artificial Intelligence (AI) in higher education has gained significant attention as it presents new opportunities to improve the teaching and learning process. This paper aims to analyze how AI applications can support the teaching and learning of computer science in higher education. By reviewing various scientific publications, this paper offers an in-depth analysis of how AI-driven tools and applications have been successfully integrated into computer science courses. Key AI applications considered include intelligent tutoring systems, assessment, performance prediction, academic management, educational innovation, and adaptive learning. These tools have been shown to increase student engagement, provide tailored instruction, offer timely feedback, and enable the scalability of high-quality education. Also, the paper addresses the challenges associated with AI in education, such as diversity of educational contexts, security and data privacy, algorithmic bias, and the importance of faculty preparation. This article emphasizes the transformative potential of AI to enhance computer science education in the context of higher education and identifies mechanisms for research and practice to take full advantage of AI capabilities in designing effective and inclusive learning environments, guided by a comprehensive synthesis of current research and case studies.Journal of Information Systems Engineering & Management00The transformative potential of AI to enhance computer science education in the context of higher education is emphasized and mechanisms for research and practice to take full advantage of AI capabilities in designing effective and inclusive learning environments are identified.Journal of Information Systems Engineering and Management["Lydia Vel\u00e1zquez-Garc\u00eda"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/da7ce652d1c5203191e34a39b4a4c5a29673257c 51883967eadea3c0374b4932f53306e963737cd9The integration of artificial intelligence (AI) in protein design presents unparalleled opportunities for innovation in bioengineering and biotechnology. However, it also raises significant biosecurity concerns. This review examines the changing landscape of bioweapon risks, the dual-use potential of AI-driven bioengineering tools, and the necessary safeguards to prevent misuse while fostering innovation. It highlights emerging policy frameworks, technical safeguards, and community responses aimed at mitigating risks and enabling responsible development and application of AI in protein design.Frontiers in Bioengineering and Biotechnology470This review examines the changing landscape of bioweapon risks, the dual-use potential of AI-driven bioengineering tools, and the necessary safeguards to prevent misuse while fostering innovation.Frontiers in Bioengineering and Biotechnology["Nicole E. Wheeler"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/51883967eadea3c0374b4932f53306e963737cd9 4302fcea5d7aab81f1b1c4fe3de363c1923ecbeeDespite recent advancements, real-world use of Artificial Intelligence (AI) in radiology remains low, often due to the mismatch between AI offerings and the situated challenges faced by healthcare professionals. To bridge this gap, we conducted a field study at nine medical sites in Denmark and Kenya with two goals: (1) to understand the challenges faced by radiologists during chest X-ray practice; (2) to envision alternative AI futures that align with collaborative clinical work. This study uniquely grounds the AI design insights in the comprehensive characterisation of diagnostic work across multiple geographical and institutional contexts. Building on ideas articulated by interviewed radiologists (N=18), we conceptualised five visions that transcend the traditional notions of AI support. These visions emphasise that the clinical usefulness of AI-based systems depends on their configurability and flexibility across three dimensions: type of clinical site, expertise of medical professionals, and situational and patient contexts. Addressing these dependencies requires expanding the clinical AI design space by envisioning futures rooted in the realities of practice rather than solely following the trajectory of AI development.ACM Transactions on Computer-Human Interaction1220This study conducted a field study at nine medical sites in Denmark and Kenya to understand the challenges faced by radiologists during chest X-ray practice and to envision alternative AI futures that align with collaborative clinical work.ACM Transactions on Computer-Human Interaction["H. D. Zaj\u0105c", "T. O. Andersen", "Elijah Kwasa", "Ruth Wanjohi", "Mary K. Onyinkwa", "Edward K. Mwaniki", "S. N. Gitau", "Shawnim S. Yaseen", "J.F. Carlsen", "Marco Fraccaro", "M. Nielsen", "Yunan Chen"]2025-02-05T00:00:00https://www.semanticscholar.org/paper/4302fcea5d7aab81f1b1c4fe3de363c1923ecbee 901b6e4a7c9e5f8d1258da90f529ed1fcfd8e7ecFashion is an industry of constant changes and reflects societal alterations; therefore, fashion brands must always seek creative and innovative communication strategies for a positive brand reputation and be at the forefront of technology. Fashion communication shapes society’s needs and perceptions of reality, which are currently shifting due to the high density of various artificial intelligence technologies, including those that can recreate reality. Therefore, consumers are easy to deceive, and creative ways of communicating using artificial intelligence lead to creative ways of disinformation. The question arises as to which topics of creative use of artificial intelligence in the field of the fashion industry are the most widely studied and what the research gaps are. An integrative literature review focusing on papers published between January, 2016 and January, 2024 was conducted to answer the research question and clarify the tendencies of future research. The findings of this research show the emerging machine-washing concept as the topics that scholars are mostly focused on – the recreation of reality using deepfakes and altered images, digital influencers, and their messages.Creativity Studies571The findings of this research show the emerging machine-washing concept as the topics that scholars are mostly focused on – the recreation of reality using deepfakes and altered images, digital influencers, and their messages.Creativity Studies["Sigita Kama\u0161ausk\u0117", "\u017divil\u0117 Sederevi\u010di\u016bt\u0117-Pa\u010diauskien\u0117"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/901b6e4a7c9e5f8d1258da90f529ed1fcfd8e7ec ad7b10b64ac7d246a31b9240fd41904c401d2b20Financial Innovation691The study conducts a bibliometric review of artificial intelligence applications in two areas: the entrepreneurial finance literature, and the corporate finance literature with implications for entrepreneurship, and presents the foundational paradigm and a bespoke demonstration of the Monte Carlo randomized algorithm.Financial Innovation["R. Kudeli\u0107", "Tamara Smaguc", "Sherry Robinson"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/ad7b10b64ac7d246a31b9240fd41904c401d2b20 bb946d03ef3b2d82104eb6fdd396e4f15393d272Scientific Reports110An Advanced Artificial Intelligence with a Federated Learning Framework for Privacy-Preserving Cyberthreat Detection (AAIFLF-PPCD) approach in IoT-assisted sustainable smart cities aims to ensure robust and scalable cyberthreat detection while preserving the privacy of IoT users in smart cities.Scientific Reports["Mahmoud Ragab", "Ehab Bahaudien Ashary", "Bandar M. Alghamdi", "Rania Aboalela", "N. Alsaadi", "Louai A. Maghrabi", "Khalid H Allehaibi"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/bb946d03ef3b2d82104eb6fdd396e4f15393d272 1af7cc6c3c3e6ba94326f4f76b9d9fbad22befe7Artificial intelligence (AI) has been recently applied to different mental health illnesses and healthcare domains. This systematic review presents the application of AI in mental health in the domains of diagnosis, monitoring, and intervention. A database search (CCTR, CINAHL, PsycINFO, PubMed, and Scopus) was conducted from inception to February 2024, and a total of 85 relevant studies were included according to preestablished inclusion criteria. The AI methods most frequently used were support vector machine and random forest for diagnosis, machine learning for monitoring, and AI chatbot for intervention. AI tools appeared to be accurate in detecting, classifying, and predicting the risk of mental health conditions as well as predicting treatment response and monitoring the ongoing prognosis of mental health disorders. Future directions should focus on developing more diverse and robust datasets and on enhancing the transparency and interpretability of AI models to improve clinical practice.Psychological Medicine1050Artificial intelligence tools appeared to be accurate in detecting, classifying, and predicting the risk of mental health conditions as well as predicting treatment response and monitoring the ongoing prognosis of mental health disorders.Psychological medicine["Pablo Cruz-Gonzalez", "Aaron Wan-Jia He", "Elly PoPo Lam", "Ingrid Man Ching Ng", "Mandy Wingman Li", "Rangchun Hou", "Jackie Ngai-Man Chan", "Yuvraj Sahni", "Nestor Vinas Guasch", "T. Miller", "B. W. Lau", "D. I. S\u00e1nchez Vida\u00f1a"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/1af7cc6c3c3e6ba94326f4f76b9d9fbad22befe7 7eb91dfc993cd52006fef4cd433a530982f559e7Artificial intelligence (AI) has tremendous potential for use in psychology. Among the many applications that may benefit from development of AI applications is narrative-personality assessment. Use of these tools and research methods is notably time-consuming and resource intensive. AI has potential to address these issues in ways that would greatly reduce clinician and researcher burden. Nonetheless, it is unclear if current AI models are sufficiently sophisticated to perform the complex downstream tasks, such as narrative assessment.The purpose of this study is to explore if an expert-refined prompt generation process can enable AI-empowered chatbots to reliably and accurately rate narratives using the Social Cognition and Object Relations scales – Global Rating Method (SCORS-G). Experts generated prompt inputs by engaging in a detailed review of SCORS-G training materials. Prompts were then improved using an systematic process in which experts worked with Llama-2-70b to refine prompts. The utility of the prompts was then tested on two AI-empowered chatbots, ChatGPT-4 (OpenAI, 2023) and CLAUDE-2-100k, that were not used in the prompt refinement process.Results showed that the refined prompts allowed chatbots to reliably rate narratives at the global level, though accuracy varied across subscales. Averaging ratings from two chatbots notably improved reliability for the global score and all subscale scores. Experimentation indicated that expert-refined prompts outperformed basic prompts regarding interrater reliability and absolute agreement with gold standard ratings. Only the expert-refined prompts were able to generate acceptable single-rater interrater reliability estimates.Findings suggest that AI could significantly reduce the time and resource burdens on clinicians and researchers using narrative rating systems like the SCORS-G. Limitations and implications for future research are discussed.Frontiers in Artificial Intelligence380This study explores if an expert-refined prompt generation process can enable AI-empowered chatbots to reliably and accurately rate narratives using the SCORS-G scales – Global Rating Method (SCORS-G).Frontiers in Artificial Intelligence["Barry Dauphin", "Caleb Siefert"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/7eb91dfc993cd52006fef4cd433a530982f559e7 e93aa3dc431b96b53a939020668385742f144dc0Background. Learning technology has changed drastically. Lecturers who were previously a source of knowledge changed their role to become mentors for students. Aims. Such rapid development of AI has replaced human functions with limited memory. AI stores data indefinitely. All university lecturers and students need to know this progress, so there is a socialization event regarding the new value proposition of generative artificial intelligence on campus. Method. The method used is a presentation from the students, by students, and for students whom lecturers accompany. Result. In this presentation, it was highlighted the meaning of Gen AI, the potential of Gen AI in learning, the use of Gen AI, policies in the use of Gen AI, how the change in the educational landscape caused by the AI gene, how education is organized in the integration of AI genes, how are the applicable academic regulations in the context of the use of Gen AI, whether the use of Gen Ai will streamline the implementation of education, how to maintain the latest learning quality when utilizing AI. The use of AI in learning brings a new value proposition for learning and a new role for lecturers as education actors. Learning that utilizes AI  gives students the freedom to learn anyone and anywhere across time and space. Conclusion AI supports a more personalized learning experience according to each student's needs. The presence of lecturers online and offline in the form of learning managers, student companions (cognitive presence), and student guides in interaction (social presence) provides new value to lecturers' existence in learning.Jurnal Abdisci210This presentation highlighted the meaning of Gen AI, the potential of Gen AI in learning, the use of Gen AI, policies in the use of Gen AI, how the change in the educational landscape caused by the AI gene, how education is organized in the integration of AI genes, and how to maintain the latest learning quality when utilizing AI.Jurnal Abdisci["S. Suhana", "Dina Hikmayanti", "Lisania Cahya Agustin", "Zhanubha Cinta Aurelline"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/e93aa3dc431b96b53a939020668385742f144dc0 a64febd0c3e0d9b6b5705628ac418ff3f4025cf8Nano-Micro Letters1120Nano-Micro Letters["Xiaopeng Bai", "Xingcai Zhang"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/a64febd0c3e0d9b6b5705628ac418ff3f4025cf8 de2b764eb6dc3e9b755b4126bb8e841f40d3dbe6Aim. The work aimed to conduct a comprehensive analysis of decision support systems (DSS) based on artificial intelligence (AI) technologies, with an emphasis on their integration into business processes and performance evaluation.Objectives. The work seeks to study the main stages of AI-based DSS development, to determine key performance indicators for assessing their financial, operational, and strategic impact, to select the main challenges in such implementations and the long-term effects of the systems, as well as to formulate recommendations for improving their interpretability and adaptability.Methods. The study employed methods of system analysis, generalization of practical experience, and research. The article considers modern trends in the use of AI, successful cases from the practice of large companies (JPMorgan Chase, General Electric, Amazon), and the concept of the J-curve productivity for analyzing long-term effects.Results. The integration of AI into DSS provides the best potential for increasing work efficiency, reducing costs, and improving the quality of management decisions. A comprehensive efficiency assessment model has been developed, which includes both quantitative and qualitative indicators.Conclusions. AI-based DSS can be used not only to increase the accuracy and rate of management decisions, but also to optimize the resource utilization and adapt to a fast-paced market environment. However, successful integration of such systems requires solving a number of problems, including improvement of data quality, enhancement of the interpretability of algorithms, and adapting the personnel to new technologies. Hybrid models that combine AI capabilities and cognitive methods open up a promising direction capable of improving the efficiency and adaptability of DSS under conditions of uncertainty. The implementation of the proposed approaches leads to increased competitiveness and sustainability of companies.Economics and Management130The article considers modern trends in the use of AI, successful cases from the practice of large companies (JPMorgan Chase, General Electric, Amazon), and the concept of the J-curve productivity for analyzing long-term effects to determine key performance indicators of AI-based DSS development.Economics and Management["S. V. Savin", "A. D. Murzin"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/de2b764eb6dc3e9b755b4126bb8e841f40d3dbe6 5c9be3be7832b59d1e33fff10bc94e1c773dcd2fOBJECTIVES Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety and accuracy. Our objective is to evaluate the diagnostic safety and effectiveness of an AI-powered virtual assistant (VA) designed for the triage of neurological pathologies. METHODS The performance of an AI-powered VA for emergency neurological triage was tested. Ten patients over 18 years old with urgent neurological pathologies were selected. In the first stage, 9 neurologists assessed the safety of the VA using their clinical records. In the second stage, the assistant's accuracy when used by patients was evaluated. Finally, VA performance was compared with ChatGPT 3.5 and 4. RESULTS In stage 1, neurologists agreed with the VA in 98.5% of the cases for syndromic diagnosis, and in all cases, the definitive diagnosis was among the top 5 differentials. In stage 2, neurologists agreed with all diagnostic parameters and recommendations suggested by the assistant to patients. The average use time was 5.5 minutes (average of 16.5 questions). VA showed superiority over both versions of ChatGPT in all evaluated diagnostic and safety aspects (P<0.0001). In 57.8% of the evaluations, neurologists rated the VA as "excellent" (suggesting adequate utility). CONCLUSIONS In this study, the VA showcased promising diagnostic accuracy and user satisfaction, bolstering confidence in further development. These outcomes encourage proceeding to a comprehensive phase 1/2 trial with 100 patients to thoroughly assess its "real-time" application in emergency neurological triage.The Neurologist330In this study, the VA showcased promising diagnostic accuracy and user satisfaction, bolstering confidence in further development and encouraging proceeding to a comprehensive phase 1/2 trial with 100 patients to thoroughly assess its "real-time" application in emergency neurological triage.The neurologist["Lucas Alessandro", "Santiago Crema", "J. Castiglione", "Daiana Dossi", "Federico Eberbach", "Alejandro Kohler", "A. Laffue", "Abril Marone", "Vanesa Nagel", "Jos\u00e9 M Pastor Rueda", "Francisco Varela", "Diego Fernandez Slezak", "Sof\u00eda Rodr\u00edguez Mur\u00faa", "Carlos Debasa", "Pensa Claudio", "M. Farez"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/5c9be3be7832b59d1e33fff10bc94e1c773dcd2f 27ffe5e00e4d5c305266e6ccb818b83fc51b3d27Amidst escalating global environmental challenges, the banking sector is increasingly turning to artificial intelligence (AI) to enhance environmental sustainability performance (ESP). Our research examines the impact of AI adoption on ESP through the lenses of sustainable banking, Fintech, green finance and green innovation within China's banking institutions. We also explore the complex configurations of these factors, which collectively improve ESP. Grounded in the stimulus–organism–response and affordance theories, we employ a hybrid methodology combining structural equation modelling and fuzzy‐set qualitative comparative analysis to analyse data from an online survey. Our findings indicate that AI adoption significantly boosts ESP in the banking sector, primarily mediated by sustainable banking and green innovation, despite Fintech showing no significant direct impact on ESP. Additionally, we identify specific configurations of AI, sustainable banking, Fintech, green finance and innovation that synergistically enhance ESP, contributing to the ongoing discourse on technological innovation and sustainability in the banking industry. This study emphasizes the pivotal role of AI in driving sustainable outcomes and highlights the need for strategic integration of these factors to achieve higher ESP.British Journal of Management620It is indicated that AI adoption significantly boosts ESP in the banking sector, primarily mediated by sustainable banking and green innovation, despite Fintech showing no significant direct impact on ESP.British Journal of Management["Abu Bakkar Siddik", "Li Yong", "Anna Min Du", "Samuel A. Vigne", "Arshian Sharif"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/27ffe5e00e4d5c305266e6ccb818b83fc51b3d27 38ed818eb75fcac20d1120aa13044ac66c4fa122 Psychiatric disorders like depression and anxiety represent significant global health challenges, affecting individuals of all ages and contributing to a substantial disability burden worldwide. Despite advancements in mental health care, barriers such as cost, geographical limits, and social stigma may prevent individuals from receiving early psychological interventions. In recent years, artificial intelligence (AI) has emerged as a prominent tool to address these issues by facilitating early detection, personalized treatment, and intervention delivery for individuals experiencing depression and anxiety. Hence, the present systematic review focused on AI-enabled conversational chatbots for the identification and management of depression and anxiety. The current systematic review yielded a total of ten studies after a thorough analysis. The majority of studies were randomized controlled trials. The most frequently utilized AI method was conversational AI agents which are chatbots available through online software accessible via computers or smartphones. The investigations revealed significant outcomes by using AI for the enhancement of psychotherapy. The majority of studies showed a low risk (71.67%), indicating their reliability, while unclear studies (15%) exhibited some ambiguity without invalidating results. Conversely, studies classified as high risk (13.33%) indicated significant bias and potential errors. Chatbots have emerged as an effective medium for self-help depression and anxiety management. Studies have revealed significant positive outcomes, showing the potential of AI augmentation in psychotherapy to reduce clinical symptomatology. Notably, chatbot-delivered therapies have proven to be more successful than limited bibliotherapy, demonstrating their ability to effectively reduce the symptoms of depression and anxiety while encouraging a stronger therapeutic alliance among participants.Industrial Psychiatry Journal340Chatbot-delivered therapies have proven to be more successful than limited bibliotherapy, demonstrating their ability to effectively reduce the symptoms of depression and anxiety while encouraging a stronger therapeutic alliance among participants.Industrial Psychiatry Journal["A. C. Joshi", "A. Ghogare", "P. B. Madavi"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/38ed818eb75fcac20d1120aa13044ac66c4fa122 1a97be6c6e1dfe9f4f0b31dd11ae5b9dcdc34066Healthcare systems are struggling to meet the growing demand for neurological care, with challenges particularly acute in Alzheimer's disease and related dementias (ADRD). While artificial intelligence research has often focused on identifying patterns beyond human perception, implementing such predictive capabilities remains challenging as clinicians cannot readily verify insights they cannot themselves detect. We propose that large language models (LLMs) offer more immediately practical applications by enhancing clinicians' capabilities in three critical areas: comprehensive data collection, interpretation of complex clinical information, and timely application of relevant medical knowledge. These challenges stem from limited time for proper diagnosis, growing data complexity, and an overwhelming volume of medical literature that exceeds any clinician's capacity to fully master. We present a framework for responsible AI integration that leverages LLMs' ability to communicate effectively with both patients and providers while maintaining human oversight. This approach prioritizes standardized, high-quality data collection to enable a system that learns from every patient encounter while incorporating the latest clinical evidence, continuously improving care delivery. We begin to address implementation challenges and initiate important discussions around ethical considerations and governance needs. While developed for ADRD, this roadmap provides principles for responsible AI integration across neurology and other medical specialties, with potential to improve diagnostic accuracy, reduce care disparities, and advance clinical knowledge through a learning healthcare system.00This roadmap provides principles for responsible AI integration across neurology and other medical specialties, with potential to improve diagnostic accuracy, reduce care disparities, and advance clinical knowledge through a learning healthcare system.["Andrew G. Breithaupt", "Alice Tang", "Bruce L. Miller", "Pedro Pinheiro-Chagas"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/1a97be6c6e1dfe9f4f0b31dd11ae5b9dcdc34066 02fe23b7d526ba87b57526f8818b24fd5a07ee23The green revolution, which came after the industrial revolution, boosted the crop yields produced per unit of land, but it also increased the need for synthetic fertilizers and pesticides and lowered the water table and increased salinization. In order to improve farm productivity, soil fertility is crucial and for preserving soil fertility, boosting yields, and enhancing harvest quality, fertilizer is essential. The decline in the fertility of the soil is a key constraint in enhancing food production worldwide, and improper nutrient management is a significant cause of this problem. Agroecosystems will need to implement contemporary technologies in order to produce enough food and mitigate the detrimental effects of chemical fertilization on the environment. Hence, the agri‐food industry is progressively utilizing artificial intelligence (AI) to increase productivity, efficiency, and sustainability. AI uses computational models to process data and identifies patterns for predictions or decision‐making. This review emphasizes how AI technology could be used for the predictions of manure compositions for improvement of food safety and quality. We aimed to identify the role of AI and the supporting evidences of field studies to characterize the controlled combinations of fertilizers for the efficient crop production with lowest possible plant toxicity. Also, we discuss the constraints and challenges of AI in the food and agricultural sector. In conclusion, AI‐based approaches and field studies suggested that combining organic and inorganic fertilizers can synergistically improve crop growth and yield parameters.Crop, Forage &amp; Turfgrass Management790In conclusion, AI‐based approaches and field studies suggested that combining organic and inorganic fertilizers can synergistically improve crop growth and yield parameters.Crop, Forage &amp; Turfgrass Management["Sarmistha Saha", "Alok Bhardwaj"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/02fe23b7d526ba87b57526f8818b24fd5a07ee23 0ad4712f1c08d872a93d62b84a120ba21d4deef7Background. The development of information technology has been very rapid lately. Aims. Artificial intelligence has replaced the function of the human brain in collecting and processing data. This is very useful in making it easier for students to compile scientific papers and complete the final project through reports and theses. Counseling has been carried out for students to understand the important role and obstacles that must be watched out for in using AI technology. Method. The method used is a PowerPoint presentation, which is done by the student group and accompanied by a lecturer as a resource person. Result. The material presented covered the potential and challenges of using Generative AI (GenAI) in learning in higher education, as well as providing practical and ethical guidance to utilize this technology responsibly. The AI competency framework for students consists of 3 levels. First, I need to understand AI technology; second, I need to use AI responsibly; and third, I need to evaluate AI results critically. Examples of Gen Ai applications: ChatGPT helps create task drafts and provide ideas; Grammarly checks grammar and writing style; DALL-E creates a design or presentation fiscal illustration. The conclusion and hope that can be put forward in this activity is that GenAI is a tool that can enrich the learning process, but it is important to use it ethically and responsibly. With a good understanding, GenAI can be a partner in creating innovative and effective learning. Consclusion. Because ChatGPT offers benefits in accessibility, speed, and originality, it has unquestionably changed the way students approach academic assignments. However, when misused, it can compromise critical thinking and academic integrity.Jurnal Abdisci20GenAI is a tool that can enrich the learning process, but it is important to use it ethically and responsibly, with a good understanding, GenAI can be a partner in creating innovative and effective learning.Jurnal Abdisci["Muhammad Argi Rahmadi", "Dimas Ujang Susilo", "Retno Widyani", "Djohan Rochanda Wiradinata"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/0ad4712f1c08d872a93d62b84a120ba21d4deef7 52791cdd59ad144515860974a3a96547d4b443c3The rapid advancement and integration of renewable energy systems (RES) such as solar, wind, and hydropower have intensified the need for robust network security solutions to protect against emerging cyber vulnerabilities. These systems are increasingly interconnected with digital grids and IoT devices, heightening their exposure to cyber threats that, if exploited, could disrupt energy supply and lead to severe socio-economic repercussions. This paper proposes an artificial intelligence (AI)-driven approach to enhance network security specifically for renewable energy (RE) infrastructures, targeting vulnerabilities that affect data integrity and operational stability. This research introduces an Adaptive Spider Wasp optimizer-mutated Extreme Gradient Boosting (ASW-XGBoost) model as a novel solution designed to improve detection accuracy and enhance resilience across diverse RE networks. The proposed method initiates the creation of a dataset representative of both power system behaviors and potential cyber-attacks, pre-processed using a normalization algorithm to improve data quality. Feature extraction leverages a scalable approach to identify critical indicators unique to RE environments. The ASW-XGBoost model combines the optimization advantages of adaptive spider wasp algorithms with the classification robustness of XGBoost, allowing precise identification of attack signatures even within fluctuating renewable power outputs. Performance evaluations, conducted in simulated power networks with high renewable penetration, demonstrate that ASW-XGBoost surpasses conventional methods in both detection rate and operational efficiency. The findings underscore the model’s capacity to adapt to dynamic, renewable-intensive environments, offering a more responsive solution to evolving cyber threats. This paper concludes with a discussion on the implications of AI-enhanced security protocols for the RE sector, highlighting ASW-XGBoost’s potential as a foundation for further research and application in sustainable energy cybersecurity.International Journal of High Speed Electronics and Systems00An Adaptive Spider Wasp optimizer-mutated Extreme Gradient Boosting (ASW-XGBoost) model is introduced as a novel solution designed to improve detection accuracy and enhance resilience across diverse RE networks, offering a more responsive solution to evolving cyber threats.International Journal of High Speed Electronics and Systems["Zhuang Yuan"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/52791cdd59ad144515860974a3a96547d4b443c3 1f9639608979d43f3524c762cc4a96a1369ba2c8Future Business Journal340Whether AI alone can effectively shape research agendas on the intersection of digitalization and international trade, or if human expertise remains indispensable is explored, to evaluate the relationship between digitalization and international trade.Future Business Journal["Vani Aggarwal", "Nidhi Karwasra"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/1f9639608979d43f3524c762cc4a96a1369ba2c8 c0dd298261591d923987ba353d565797ff6b4415Integration of artificial intelligence (AI) in health and healthcare, especially for older adults, has significantly advanced healthcare delivery. AI technologies, with capabilities such as self-learning and pattern recognition, are employed to address social isolation and monitor older adults' daily activities. However, rapid AI development often fails to consider the heterogeneous needs of older populations, which could exacerbate an existing digital divide and inequality. This scoping review examines older adults' involvement in AI system design, implementation, and evaluation of AI systems in health and healthcare literature, emphasizing the necessity of their input for beneficial AI systems. We conducted a scoping review according to PRISMA-SCR. We reviewed 17 studies, finding that half of these studies (n = 8) engaged older adults during the design phase, a small number (n = 3) during the evaluation stage, and even fewer (n = 2) involved older adults in the implementation stage. Despite AI's growing role, design processes often overlook older adults' needs. Our findings emphasize the need for inclusive, participatory design approaches to address ethical and equity challenges, enhancing user engagement and relevance. We also highlight how these approaches address the needs of older adults and improve outcomes. Specifically, we integrated evidence showing the practical benefits of these approaches for better accessibility, usability, and engagement among older adults. While AI has potential to improve healthcare delivery, these approaches must be part of broader efforts to ensure ethical, inclusive, and equitable AI practices, especially in gerontology.The journals of gerontology. Series A, Biological sciences and medical sciences00A scoping review of older adults' involvement in AI system design, implementation, and evaluation of AI systems in health and healthcare literature highlights the need for inclusive, participatory design approaches to address ethical and equity challenges, enhancing user engagement and relevance.The journals of gerontology. Series A, Biological sciences and medical sciences["Hannah Cho", "Oonjee Oh", "Nancy Greene", "Larissa Gordon", "Sherry Morgan", "Lisa Walke", "George Demiris"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/c0dd298261591d923987ba353d565797ff6b4415 9f6330a8ee4697ea65d7dfc4468ffb865f35b595Hemophilia is the most frequent severe genetic haemorrhagic condition. Hemophilia A and B are caused by a lack or dysfunction of the factor VIII and factor IX proteins, respectively, and are distinguished by prolonged and heavy bleeding after minor trauma or even spontaneously. Treatments for hemophilia have been extremely expensive and required the infusion of plasma clotting factors throughout one’s life. The last few years have brought major breakthroughs in gene therapy that now hold real promise for possible curative options. Artificial intelligence has the potential to transform all levels of hemophilia gene therapy, from vector design to predictive modeling and biomarker identification. This review highlights selected applications of AI towards precision medicine including viral vector design, predictive modeling for gene editing, and deep phenotyping in hemophilia gene therapy. It can greatly improve the efficacy and safety of gene therapy through off-target effects prediction, optimization designs of delivery vectors, and determination of personalized combinations of treatments. Consequently, this will also enable accelerated biomarker development for disease diagnosis and monitoring. In such a way, artificial intelligence in hemophilia gene therapy will revolutionize the framework of treatment and make it personalized or even curative for patients all over the world. International Journal of Current Science Research and Review00Artificial intelligence in hemophilia gene therapy will revolutionize the framework of treatment and make it personalized or even curative for patients all over the world.International Journal of Current Science Research and Review["Karra Geetha", "Ch Abhiram", "Tungala Hanisha", "Suddala Anusha", "Ch Manoj Reddy", "T. Rama Rao"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/9f6330a8ee4697ea65d7dfc4468ffb865f35b595 53c078d46e6ee5fd3cc53bc47f9c68848230d11bRadiologie160Artificial intelligence promises to filter examinations into negative and clearly positive findings, and thereby reduces part of the radiological workload, and thereby reduces part of the radiological workload.Radiologie["Matthias Dietzel", "Alexandra Resch", "P. A. Baltzer"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/53c078d46e6ee5fd3cc53bc47f9c68848230d11b 7f7f3f42f9b6ba71cd7d358386d4287793252ea1This article aims to explore the integration of artificial intelligence in Islamic Religious Education (PAI) learning to improve the effectiveness, efficiency, and inclusivity of the learning process, as well as the benefits and impacts of AI in PAI. This study uses a qualitative approach using the literature review method. This method involves collecting, analyzing, and synthesizing relevant literature, including journal articles, books, reports, and conference proceedings, to identify existing practices, challenges, and potential advancements in this field. The data is sourced from leading academic databases such as Google Scholar. The inclusion criteria prioritize research that addresses the application of AI in education, its role in improving Islamic learning, and related ethical considerations. The results of the analysis show that the integration of AI in Islamic religious education has brought significant changes in the way learning and teaching are carried out. This technology provides easy access to religious information sources, such as sacred texts, tafsir, hadith, and other important literature, which supports more inclusive and in-depth learning. AI also allows for personalized learning that suits the individual needs of learners, helping to improve understanding of religious concepts more effectively. Additionally, AI offers an edge in data analysis that allows educators to better understand learners' progress and devise more effective teaching strategiesIndo-MathEdu Intellectuals Journal00The results of the analysis show that the integration of AI in Islamic religious education has brought significant changes in the way learning and teaching are carried out.Indo-MathEdu Intellectuals Journal["A. Wahyuni", "Muhammad Yaumi", "A. Arsyad", "Saddam Husain"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/7f7f3f42f9b6ba71cd7d358386d4287793252ea1 aa7482b80df7af237e219d71bc2e8d03b3476ff2Aim. The work aimed to analyze various aspects of the influence of ethics in artificial intelligence (AI) on the management of socio-economic processes and, consequently, to determine the outlines of the applicability of these technologies, to reveal the ethical difficulties of further expanded and in-depth implementation of AI technologies, including in the context of global technological competition.Objectives. The work seeks to analyze the mechanisms of influence and the role of ethics in the field of AI as a factor limiting the development and implementation of AI technologies in the management system of socio-economic processes, taking into account the main trends in the development of ethical culture at the national and global levels, as well as to identify associated risks and to reveal the outlines of the development of this field in the medium term.Methods. Both general scientific and special scientific methods were applied as the study theoretical and methodological basis, primarily a systems approach and a risk-oriented approach to the analysis of the processes under consideration. General scientific approaches included synthesis, corporate analysis, modeling and forecasting.Results. The study identified the main problematic aspects associated with the further implementation of ethical standards in the field of AI. The work presents the role of the ethical factor in the management of socio-economic processes performed based on and using AI systems.Conclusions. The creation of an ethical regulation system for the further use of AI technologies in the management of the socio-economic sphere has been established to be a strategically significant step in the formation of public policy. Effective and efficient implementation of such policy measures is of key importance for achieving the goals of national development and ensuring the technological sovereignty of the Russian Federation in the medium-term and long-term development.Economics and Management30The study identified the main problematic aspects associated with the further implementation of ethical standards in the field of AI and presents the role of the ethical factor in the management of socio-economic processes performed based on and using AI systems.Economics and Management["D. A. Repin", "S. A. Ignatyev"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/aa7482b80df7af237e219d71bc2e8d03b3476ff2 350c23b9610fc9c537b364df8a264ead4b0dbebfThis editorial announces the need for a consensus conference on definitions of surgical nomenclature, which is evolving due to the introduction of non-human (hardware, software) devices and applications, including robotics. A recently created entity, the Artificial Intelligence Organization for Next Generation Surgeons (AIONS), comprised primarily of AIS editorial board members, has proposed updated definitions on the following terms: surgery, endoluminal surgery, percutaneous surgery, robots, robotic-assisted surgery (RAS), remote surgery, artificial intelligence surgery (AIS), robotic surgery, surgomics, non-invasive surgery. These definitions will be discussed during a Consensus Conference on Definitions of Artificial Intelligence, Surgery, Surgomics and Robotics, which is scheduled for February 12, 2025.Artificial Intelligence Surgery140A recently created entity, the Artificial Intelligence Organization for Next Generation Surgeons (AIONS), has proposed updated definitions on the following terms: surgery, endoluminal surgery, percutaneous surgery, robots, robotic-assisted surgery (RAS), remote surgery, artificial intelligence surgery (AIS), robotic surgery, surgomics, non-invasive surgery.Artificial Intelligence Surgery["Andrew A. Gumbs", "S. V. Grasso", "E. Chouillard", "Roland Croner", "Ibrahim Dagher", "G. Spolverato", "Isabella Frigerio", "Luca Milone", "Z. Khalpey", "Niki Rashidian", "Nouredin Messaoudi", "Karol Rawicz-Pruszy\u0144ski", "Mohammad Abu Hilal", "Michele Diana"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/350c23b9610fc9c537b364df8a264ead4b0dbebf 71eec9726b946f9f80cf223201376cab3401dd9aConsidering how scientific literature on the subject has been enriched with increasingly significant contributions in both sociological and organizational fields, this short essay aims to define how the use of artificial intelligence can define a valuable compensatory tool aimed at promoting greater inclusiveness in daily life activities for those in neurological deficit. In particular, we intend to focus on the benefits of sports activity in terms of increased physical and mental well-being through the use of artificial intelligence and sensors.Computer Methods in Biomechanics and Biomedical Engineering240This short essay aims to define how the use of artificial intelligence can define a valuable compensatory tool aimed at promoting greater inclusiveness in daily life activities for those in neurological deficit through the use of artificial intelligence and sensors.Computer methods in biomechanics and biomedical engineering["Francesca De Marco", "Antonio Brusini"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/71eec9726b946f9f80cf223201376cab3401dd9a acfb2919fc59e48bb1b57919a019b935c727000fPurpose: To aim of the study was to analyze the influence of artificial intelligence on customer service automation in e-commerce in Rwanda. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The influence of Artificial Intelligence (AI) on customer service automation in e-commerce has been transformative, enhancing efficiency, personalization, and customer engagement. Studies show that AI-powered chatbots and virtual assistants have reduced response times by 40% and lowered operational costs by 20-30% while improving customer satisfaction. AI-driven Customer Relationship Management (CRM) systems have boosted repeat purchases by 20% and increased engagement by 35% through personalized recommendations. However, challenges persist, including lack of emotional intelligence, trust issues, and transparency concerns, with 47% of customers expressing distrust in AI interactions due to impersonal and robotic responses. SMEs struggle with Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), service quality (SERVQUAL) model & unified theory of acceptance and use of technology (UTAUT) may be used to anchor future studies on the influence of artificial intelligence on customer service automation in e-commerce in Rwanda. E-commerce businesses should invest in AI algorithms that leverage customer purchase history, browsing behavior, and real-time preferences to provide context-aware and hyper-personalized responses. Governments and regulatory bodies should develop ethical guidelines to govern the use of AI in customer service.International journal of technology and systems00The influence of Artificial Intelligence (AI) on customer service automation in e-commerce has been transformative, enhancing efficiency, personalization, and customer engagement, but challenges persist, including lack of emotional intelligence, trust issues, and transparency concerns.International Journal of Technology and Systems["Aline Umutoni"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/acfb2919fc59e48bb1b57919a019b935c727000f 41f2ae2f18fb45172ffcbbf04db042a9b7833606BMC Nursing370It is revealed that nurse happiness has a significant and positive effect on innovative work behaviors, and transformational leadership and perception of artificial intelligence use have a serial mediating role in this relationship.BMC Nursing["Ferhat Onur Agaoglu", "Murat Ba\u015f", "Sinan Tarsuslu", "Lokman Onur Ekinci"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/41f2ae2f18fb45172ffcbbf04db042a9b7833606 5d44ec391e6f5530210fff1672dde03975f256danpj Digital Medicine370NPJ Digital Medicine["Frank E. Rademakers", "E. Biasin", "Nico Bruining", "E. Caiani", "Rhodri H Davies", "Stephen H Gilbert", "E. Kamenjasevic", "G. McGauran", "Gear\u00f3id O'Connor", "Jean-Baptiste Rouffet", "B. Vasey", "Alan G Fraser"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/5d44ec391e6f5530210fff1672dde03975f256da 43717b978116c91ab8b6fc87a8c418352fae011aOBJECTIVE This article aims to systematically investigate the impact of artificial intelligence (AI) painting tools on multidimensional social-psychological anxieties, specifically focusing on privacy violation, bias behavior, job replacement, and learning anxiety. METHOD Based on the fear acquisition theory framework, this study investigates the dimensions of anxiety induced by AI painting. Through questionnaire surveys, first-order and second-order confirmatory factor analysis, and one-way analysis of variance, the study successfully measures the multidimensional impact of AI painting on psychological anxiety. RESULTS Study results indicate significant differences in anxiety levels across dimensions. Privacy violation and bias behavior are found to elicit the highest levels of anxiety, with average scores of 3.77 and 3.85, respectively, on a 1-5 scale. Conversely, job replacement and learning anxiety demonstrate relatively lower scores of 3.49 and 3.30. A more in-depth variance analysis highlights substantial gender differences in privacy violation anxiety, with females registering a significantly higher average score of 3.90 compared to men's 3.58. Furthermore, educational level is shown to significantly impact the anxiety levels of job replacement and learning anxiety; individuals with no more than a high school education scored markedly higher than those with undergraduate or postgraduate degrees. CONCLUSIONS This study reveals the significant impact of AI drawing tools on triggering multidimensional anxiety in individuals and underscores the important role of gender and education level in the different anxiety dimensions elicited by AI drawing tools. (PsycInfo Database Record (c) 2025 APA, all rights reserved).Psychological Trauma00This study reveals the significant impact of AI drawing tools on triggering multidimensional anxiety in individuals and underscores the important role of gender and education level in the different anxiety dimensions elicited by AI drawing tools.Psychological trauma : theory, research, practice and policy["Changsheng Wang", "Aqin Xiao"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/43717b978116c91ab8b6fc87a8c418352fae011a 972cb30748966192be0dc39620e4684f55af63faThe fast growth of IO networks has resulted in a security crisis besides the development of decentralized-based innovations, and such decentralized bases or technologies also made challenges in terms of speed, performance, and scalability. Traditional machine learning-based intrusion detection systems (IDS) are unable to manage the intricate and non-linear correlations seen in massive amounts of IoT data. They produce relatively low detection rates, especially in multi-class classification, where many attack types must be addressed. Overcoming these hurdles calls for frameworks: innovative enough to accommodate the challenge whilst using the wealth of data produced by IoT devices. Abstract In this paper, we introduce a unique MLP-based deep learning architecture for intrusion detection in IoT settings. This framework includes a preprocessing pipeline that optimally normalizes and applies one-hot-encoding to the data to prepare it optimally for classification. We tested the algorithms on the UNSW-NB15 dataset, commonly used for IDS. Mere quantitative results show that MLP surpasses classical models like Logistic Regression, SVM, and Random Forests,  giving a precision of 97.53%, recall of 97.23%, and accuracy of 97.73% on the multi-class classification task. This framework is undoubtedly scalable and provides a sufficient security mechanism for the whole IoT ecosystem; hence, it can be used in various actual use cases. This performance shows that it could solve the new threats developing in IoT environments.International Journal of Computational and Experimental Science and Engineering602This paper introduces a unique MLP-based deep learning architecture for intrusion detection in IoT settings that includes a preprocessing pipeline that optimally normalizes and applies one-hot-encoding to the data to prepare it optimally for classification.International Journal of Computational and Experimental Science and Engineering["Amjan Shaik", "B. Unhelkar", "Prasun Chakrabarti"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/972cb30748966192be0dc39620e4684f55af63fa 0cfc89e9f32b2cd51a9bf842254513bb357dadcdPurposeArtificial Intelligence (AI) technology, having powerful capabilities and rapid development, has been able to move the structures of businesses and organizational processes towards intelligent automation. The role of digital transformation in universities and educational institutions has an increasing trend. New business structures and the digitization of processes, other than the advantages they bring about, might have different effects on the environment and sustainability. This study aims to identify the effective factors on AI adoption and the effect of using this technology in educational institutions and universities on their sustainable performance.Design/methodology/approachThis research is applied using a quantitative approach. Universities selected for the study were ranked by Quacquarelli Symonds (QS). Of the 111 QS listed universities in the Middle East in 2023, 30 universities were randomly selected, and the research questionnaire was emailed to 50 people (administrative, educational and research staff) from each university. Information related to the level of AI technology acceptance and use was collected using a questionnaire among the university staff and faculty members; moreover, their relationship with universities’ sustainable performance scores was assessed. Path analysis and Smart PLS software have been used for data analysis.FindingsThe research findings showed that factors of technology performance, enjoyment, trust, social influence and organizational capabilities all have positive effect on AI adoption at universities. Also, the adoption of AI is considered as an effective factor in improving university sustainable performance. Therefore, based on exact data analysis using AI, universities can manage their activities and better control their environmental performance. Also, the use of AI can be effective in the availability to sustainable education in universities and the establishment of social justice in society. Accordingly, to facilitate executive processes and decision-making, policymakers in the field of science and university principals can improve administrative, educational and research processes via investing on AI, in addition to improving environmental activities and sustainable development.Originality/valueThe theoretical contribution of this research, other than designing an AI acceptance model for universities includes evaluating the relationship between using AI and university sustainable performance.Asian Education and Development Studies790The research findings showed that factors of technology performance, enjoyment, trust, social influence and organizational capabilities all have positive effect on AI adoption at universities, and the adoption of AI is considered as an effective factor in improving university sustainable performance.Asian Education and Development Studies["M. Ronaghi", "Marzieh Ronaghi"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/0cfc89e9f32b2cd51a9bf842254513bb357dadcd ab51a97ac1f056f7609ad683622105a542214c9dAccounting forum791Accounting Forum["Amar Johri"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/ab51a97ac1f056f7609ad683622105a542214c9d 0796890be92fb9b2a32f12ff74ccb66e56cbb8a6IJARCCE00IJARCCE[]2025-02-06T00:00:00https://www.semanticscholar.org/paper/0796890be92fb9b2a32f12ff74ccb66e56cbb8a6 065b13418c319dcf46331b3d79a4ed782763516aCanadian journal of surgery. Journal canadien de chirurgie40Canadian Journal of Surgery["Edward J. Harvey", "Chad G. Ball"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/065b13418c319dcf46331b3d79a4ed782763516a c995081b29609cf93791849ba87387bbdbb1d461EDPACS: The EDP Audit, Control, and Security Newsletter260EDPACS["Ahmed Al-Omush", "Adel Almasarwah", "Assyad Al-Wreikat"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/c995081b29609cf93791849ba87387bbdbb1d461 292d1df8848941462f02d33d1e78072883250b7cINTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Bhooshan Ravikumar Gadkari"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/292d1df8848941462f02d33d1e78072883250b7c ca4225b0dd4a4db4be67425b57bef5dc09f6d219Annals of Medicine &amp; Surgery00Annals of Medicine &amp; Surgery["I. Okon", "F. K. Precious", "Bipin Chaurasia", "Emen Okon", "Okesanya Olalekan John", "S. B. Kankam", "U. Akpan", "D. E. Lucero-Prisno, III"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/ca4225b0dd4a4db4be67425b57bef5dc09f6d219 4d2b8f9f9f29c498a88833877b1d66926cbbe9a3NEJM AI20NEJM AI["Kenneth D. Mandl"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/4d2b8f9f9f29c498a88833877b1d66926cbbe9a3 57290a529deb7b5a63d68e49cc8f1cd7ce80f131INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Vinod Upputuri"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/57290a529deb7b5a63d68e49cc8f1cd7ce80f131 52ebc9b2e143bb26e06c39edfc494dfeeb6bfd93European Journal of Pediatrics30European journal of pediatrics["G. P. Milani", "Peter de Winter"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/52ebc9b2e143bb26e06c39edfc494dfeeb6bfd93 94ebe74448a49b854baac73b68b0a4d9999a1cc9PurposeThe research on intellectual capital focuses on the role of innovative technologies in organizational systems, particularly in knowledge generation and learning processes. This study addresses the third stage of intellectual capital research, emphasizing how innovative technologies like generative AI (Gen AI) applications can enhance learning experiences, individual talents and personalized learning. Based on the WEST model, this study examines the relationship between attitude, competency, experience, Gen AI integration and managers’ creative involvement. Additionally, it investigates the direct and mediating roles of Gen AI integration and managers’ creative involvement in improving learning effectiveness.Design/methodology/approachA cross-sectional survey of managers from organizations operating in diverse sectors of Saudi Arabia was conducted using a web-administered structured questionnaire. PLS-based structural equation modeling was employed to assess the hypothesized relationships.FindingsThe results revealed that the manager’s Gen AI experience, competency, attitude and access positively affect its integration. However, only Gen AI competence and attitudes demonstrated the same positive impact on managers’ creative involvement. Furthermore, Gen AI integration and creative involvement positively and significantly impact learning effectiveness. The study also uncovered the positive mediation of Gen AI integration in enabling all four antecedents to enhance learning effectiveness. However, the mediation of creative involvement was corroborated only for the Gen AI attitudes and competence.Originality/valueThis study examines how integrating innovative technologies, such as generative AI, enhances the learning experience, develops individual talents and personalizes learning in workplace contexts at the managerial level. By providing new insights into the dynamics of generative AI integration in workplace settings, it significantly contributes to the generative AI literature.Journal of Intellectual Capital800This study examines how integrating innovative technologies, such as generative AI, enhances the learning experience, develops individual talents and personalizes learning in workplace contexts at the managerial level.Journal of Intellectual Capital["Diana Korayim", "Rahul Bodhi", "Saeed Badghish", "M. Yaqub", "Rosario Bianco"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/94ebe74448a49b854baac73b68b0a4d9999a1cc9 d4dd4f88c67055d4d21710f0bf8c0e11427c69bbOpen Forum Infectious Diseases00Open Forum Infectious Diseases["Wesley D. Kufel", "C. MacDougall", "Elizabeth W. Covington", "Jason C Gallagher", "Robert Seabury", "Jeffrey M. Steele"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/d4dd4f88c67055d4d21710f0bf8c0e11427c69bb 52695491463961c7e7b4f22afabc85bf6f3b62a5Nature Network Boston80Nature medicine["Brenda Y Miao", "Irene Y. Chen", "Christopher Y K Williams", "Jays\u00f3n Davidson", "Augusto Garcia-Agundez", "Shenghuan Sun", "Travis Zack", "S. Saria", "R. Arnaout", "G. Quer", "H. Sadaei", "Ali Torkamani", "Brett Beaulieu-Jones", "Bin Yu", "Milena Gianfrancesco", "A. Butte", "Beau Norgeot", "Madhumita Sushil"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/52695491463961c7e7b4f22afabc85bf6f3b62a5 f374ad004e34d294466d61d9d86a617dd9fcef5aScience and Technology for the Built Environment230Science and Technology for the Built Environment["Filippo Bernardello", "Giacomo Astolfi", "Giulia Alessio", "Serena Bari", "Michele Andreoli", "Riccardo Lovato"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/f374ad004e34d294466d61d9d86a617dd9fcef5a bb219eec6d6edd67a3ba1c58c51011efdd8bb670Neurology Today00Neurology Today["Thomas R. Collins"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/bb219eec6d6edd67a3ba1c58c51011efdd8bb670 dbbf0bb82ab6bb19a3e402e163f95e57ce0c185a00["G. H. L.", "Francesco Flammini", "S. J."]2025-02-06T00:00:00https://www.semanticscholar.org/paper/dbbf0bb82ab6bb19a3e402e163f95e57ce0c185a 3c38932cf26c5557851501f39bf3000ecad8249aJournal of the Society for Social Work and Research00Journal of the Society for Social Work and Research["Eunhye Ahn", "Moon Choi", "Patrick Fowler", "Inhan Song"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/3c38932cf26c5557851501f39bf3000ecad8249a 9130fd21aa7c963e4022909b8cd3b7d877e3beb2The advent of generative artificial intelligence (AI) marks a transformative era in education, offering innovative tools and methodologies to enhance learning experiences. This paper explores the applications, benefits, challenges, and future prospects of generative AI in education, providing a comprehensive review of its impact. We discuss how AI models like GPT-4 and others are shaping personalized learning, curriculum development, language acquisition, and more. The paper concludes with insights into ethical considerations and the need for policy frameworks. Keywords: Generative AI, education, personalized learning, adaptive learning, AI ethics, curriculum development, language acquisition, EdTech, AI policy, digital transformationInternational Scientific Journal of Engineering and Management00How AI models like GPT-4 and others are shaping personalized learning, curriculum development, language acquisition, and more is discussed, with insights into ethical considerations and the need for policy frameworks.International Scientific Journal of Engineering and Management["Dr.S.Suganya devi", "Selva Priya K"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/9130fd21aa7c963e4022909b8cd3b7d877e3beb2 9442e8d784d5247ef1fa78977ae1b85246a70ec5OECD Artificial Intelligence Papers013OECD Artificial Intelligence Papers["Anna Milanez", "Annikka Lemmens", "Carla Ruggiu"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/9442e8d784d5247ef1fa78977ae1b85246a70ec5 ccdbef1ab3a1fbdc07df917b4822dc01d3a44900What does it mean to ‘put principles into practice’? As machine learning algorithms and Artificial Intelligence are given increasing control over our lives (delivering credit scores and welfare risk assessments and monitoring borders with facial recognition), public, private and civil society organisations have proliferated numerous guidelines foregrounding different ethical principles (e.g. – fairness, accountability, transparency) meant to ensure that these systems do not cause harm to already marginalised groups. Putting these principles ‘into practice’, however, is not as straightforward as it seems. The Algorithm and AI Register, which collects information about algorithms used by city governments (namely Helsinki and Amsterdam), is one recent attempt to make good on these ethical principles, but it has been subsequently criticised for not living up to its own lofty ideals. This article is based on long-term ethnographic fieldwork with one of the companies behind the Register. Building on recent work from valuation studies, which studies empirically how abstract values are enacted through mundane routines and procedures, we argue that rather than moving from principles to practices, downstream as it were, the process is more iterative and it is not just practices but also principles which are shaped in the proceedings. We introduce two concepts, valuable action and actionable values, to sensitise researchers to the deeper interrelation of values and actions and argue that in order to make more ‘ethical’ algorithms we need to think more symmetrically about principles and practices.Sociology Review350Two concepts, valuable action and actionable values, are introduced to sensitise researchers to the deeper interrelation of values and actions and it is argued that in order to make more ‘ethical’ algorithms the authors need to think more symmetrically about principles and practices.The Sociological Review["David Moats", "Sonja Trifuljesko"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/ccdbef1ab3a1fbdc07df917b4822dc01d3a44900 90fc510d118d90b327611307c568980917d7a5b6Why were Hollywood film workers striking or supporting strikes against artificial intelligence (AI) in 2023? To investigate this question, we conduct participant observation on the picket line and interview 15 film workers, including 12 union members from SAG-AFTRA, WGA, and IATSE, as well as 3 non-unionized workers, across roles. From screenwriting to acting, our interlocutors described how studio use of AI might exacerbate wage squeeze, estrangement from embodied co-creation, rush for results, and inauthentic creativity. We find that film worker resistance to emergent and projected uses of AI echoes earlier technical developments, such as the incorporation of sound, color, HD, DVD, and CGI. These innovations initially sparked anxieties about the demise of cinema, but ultimately created new aesthetic possibilities and professions. We end with a reflection on core concerns for worker engagement, including topics of prophesy and the “soul” of sociotechnical labor.ACM Transactions on Computer-Human Interaction590It is found that film worker resistance to emergent and projected uses of AI echoes earlier technical developments, such as the incorporation of sound, color, HD, DVD, and CGI, which initially sparked anxieties about the demise of cinema, but ultimately created new aesthetic possibilities and professions.ACM Transactions on Computer-Human Interaction["Brett A. Halperin", "Daniela K. Rosner"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/90fc510d118d90b327611307c568980917d7a5b6 066fcb1ed33fde3c5694d458348b17e1e4c5731dArtificial Intelligence (AI) is increasingly influencing global diplomacy and international relations by transforming how states engage, negotiate, and communicate in the digital age. This study explores the integration of AI technologies—such as machine learning, natural language processing, and big data analytics—into diplomatic processes, highlighting their role in enhancing decision-making, crisis management, and global cooperation. AI-driven tools facilitate real-time data analysis, sentiment tracking, and predictive analytics, enabling diplomats to assess geopolitical trends and respond proactively to emerging challenges. Additionally, AI supports digital diplomacy by automating translation, optimizing diplomatic communications, and countering misinformation. While AI presents significant opportunities, challenges related to ethical considerations, bias, cybersecurity, and the potential erosion of human agency in decision-making remain. This paper examines case studies of AI applications in diplomacy, evaluates their implications for global governance, and proposes strategies to balance technological innovation with ethical and strategic concerns. The findings underscore AI’s growing impact on international relations and the necessity for adaptive governance frameworks to harness its potential responsibly.International Journal for Scientific Research00This study explores the integration of AI technologies—such as machine learning, natural language processing, and big data analytics—into diplomatic processes, highlighting their role in enhancing decision-making, crisis management, and global cooperation.International Journal for Scientific Research["Fawaz Bubashait"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/066fcb1ed33fde3c5694d458348b17e1e4c5731d 7ea18bc74b9cecdc12250bf5c2beaeba4f7656dfThe Human Cognitive Simulation Framework represents a significant advancement in integrating human cognitive capabilities into artificial intelligence systems. By merging short-term memory (conversation context), long-term memory (interaction context), advanced cognitive processing, and efficient knowledge management, it ensures contextual coherence and persistent data storage, enhancing personalization and continuity in human-AI interactions. The framework employs a unified database that synchronizes these contexts while incorporating logical, creative, and analog processing modules inspired by human brain hemispheric functions to perform structured tasks and complex inferences. Dynamic knowledge updates enable real-time integration, improving adaptability and fostering applications in education, behavior analysis, and knowledge management. Despite its potential to process vast data volumes and enhance user experience, challenges remain in scalability, cognitive bias mitigation, and ethical compliance. This framework lays the foundation for future research in continuous learning algorithms, sustainability, and multimodal adaptability, positioning Cognitive AI as a transformative model in emerging fields.00The Human Cognitive Simulation Framework lays the foundation for future research in continuous learning algorithms, sustainability, and multimodal adaptability, positioning Cognitive AI as a transformative model in emerging fields.["Rommel Salas-Guerra"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/7ea18bc74b9cecdc12250bf5c2beaeba4f7656df 54f958659d7293ded066e2bf80116ddfb798be77Industry 4.0 is driving significant transformations, even in complex and uncertain industrial environments. This concept represents an innovation strategy aimed at redefining the manufacturing sector, enhancing global competitiveness through cost reduction, more agile processes, and superior product quality. Concurrently, Artificial Intelligence (AI) is at the forefront of a profound shift in organizational management and operations, reshaping professional routines and redefining society's interaction with technology. The increasing complexity of business environments, particularly the inherent challenges within industrial sectors, alongside continuous technological advancements, establishes AI as a critical tool not only for fostering innovation but also for optimizing efficiency and enhancing competitiveness across all organizational domains. In this context, this study introduces a structured model grounded in the theoretical exploration of Industry 4.0 maturity models to assess the current maturity levels of AI utilization in the industry. Thus, this work presents the development of a standard for measuring AI adoption levels in Industry 4.0. This index will illuminate companies’ readiness and proficiency in integrating AI into both daily operations and strategic processes. Through the continued application of this tool, companies will also gain insights into their growth across the evaluated domains.CLEI Electronic Journal260This work presents the development of a standard for measuring AI adoption levels in Industry 4.0, and introduces a structured model grounded in the theoretical exploration of Industry 4.0 maturity models to assess the current maturity levels of AI utilization in the industry.CLEI Electronic Journal["Maria Am\u00e9lia Eliseo", "Leonildo Carnevalli Junior", "Ismar Frango Silveira"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/54f958659d7293ded066e2bf80116ddfb798be77 c383c6adeb7b8cfd0565bb5593e5e0f6ec7a846bAn artificial intelligence (AI) analysis of electronic medical records (EMRs) was performed to analyze the differences between patients with carotid stenosis who developed symptomatic disease and those who remained asymptomatic. The EMRs of 872 patients who underwent a carotid endarterectomy between 2009 and 2022 were analyzed with AI. This included 408 patients who had carotid intervention for symptomatic carotid disease and 464 patients for asymptomatic, >70% stenosis. By analyzing the EMRs, the Support Vector Machine achieved the highest sensitivity at 0.626 for predicting which of these patients would go on to develop a stroke or TIA. Random Forest had the highest specificity at 0.906. The risk for stroke in patients with carotid stenosis was a balance between optimum medical treatment and the underlying disease processes. Risk factors for developing symptomatic carotid disease included elevated glucose, chronic kidney disease, hyperlipidemia, and current or recent smoking, while protective factors included cardiovascular agents, antihypertensives, and beta blockers. An AI review of EMRs can help determine which patients with carotid stenosis are more likely to develop a stroke to assist with decision making as to whether to proceed with intervention or to demonstrate and encourage reduced stroke risk with risk factor modification.Journal of Cardiovascular Development and Disease290An AI review of EMRs can help determine which patients with carotid stenosis are more likely to develop a stroke to assist with decision making as to whether to proceed with intervention or to demonstrate and encourage reduced stroke risk with risk factor modification.Journal of Cardiovascular Development and Disease["Mackenzie Madison", "Xiao Luo", "Jackson Silvey", "Robert Brenner", "Kartik Gannamaneni", "Alan P. Sawchuk"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/c383c6adeb7b8cfd0565bb5593e5e0f6ec7a846b 93d079f431569424e4f37f1fb758a329d29d5b0fAs the trend in the current generation with the use of mobile devices is rapidly increasing, online video streaming has risen to the top in the entertainment industry. These platforms have experienced radical expansion due to the incorporation of Big Data Analytics and Artificial Intelligence which are critical in improving the user interface, improving its functioning, and customization of recommended content. This paper seeks to examine how Big Data Analytics makes it possible to obtain large amounts of data about users and how they view, what they like, or how they behave. While customers benefit from this data by receiving more suitable material, getting better recommendations, and allowing for more efficient content delivery, AI utilizes it. As a result, the study also points to the importance and relevance of such technologies to promote business development, and user interaction and maintain competitiveness in the online video streaming market with examples of their effective application. This work presents a comprehensive investigation of the combined role of Big Data and AI and presents the necessary findings to determine their efficacy as success factors of existing and future video streaming services.Frontiers in Big Data100This work presents a comprehensive investigation of the combined role of Big Data and AI and presents the necessary findings to determine their efficacy as success factors of existing and future video streaming services.Frontiers in Big Data["Muhammad Arshad", "Choo Wou Onn", "Ashfaq Ahmad", "Goabaone Mogwe"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/93d079f431569424e4f37f1fb758a329d29d5b0f 809bbcb280839833f6d386cd69ff39b6bf3c0db6This study explores the integration of generative artificial intelligence (GenAI), specifically ChatGPT, in designing a historical simulation of the French Revolution for eighth-grade students. Using the technological pedagogical content knowledge (TPACK) framework, the research examines how GenAI facilitated and obstructed the creation of an immersive educational experience, addressing the challenges and opportunities it presents. The study employs an explanatory case study methodology combined with autoethnographic elements, capturing the dynamic interplay between AI tools and educators in the design process. The simulation incorporated faction-based role-playing to engage students in historical decision-making, influenced by both pre-revolutionary and revolutionary events. GenAI played multiple collegial roles in the design process, including as a subject matter expert, game mechanics designer, and content communicator, enhancing efficiency and creativity. However, its limitations—such as unverified information, anachronisms, and biases—necessitated careful consideration, drawing on content matter expertise and knowledge of curriculum and class context. Findings indicate that the effective use of GenAI to assist simulation design requires a robust integration of content knowledge, technological proficiency, and pedagogical strategies within the TPACK framework. The study contributes to emerging research on AI’s role in pedagogical design process, with implications for history education and beyond.Education sciences650Findings indicate that the effective use of GenAI to assist simulation design requires a robust integration of content knowledge, technological proficiency, and pedagogical strategies within the TPACK framework.Education Sciences["Bj\u00f6rn Kindenberg"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/809bbcb280839833f6d386cd69ff39b6bf3c0db6 d16dca96416478c82bd3eeff6f53773f7d56df33PurposeUsing the cognitive-affective-normative (CAN) model, this study highlights the role of self-regulated learning (SRL) and organisational culture and delves into the link between technophobia and a manager’s intention to adopt generative artificial intelligence (AI) in management practices.Design/methodology/approachAn empirical study was conducted through a survey of 528 business managers from China.FindingsThe study reveals that technophobia is negatively related to a manager’s intention to adopt generative AI, while SRL is positively related to the intention to adopt generative AI. Moreover, SRL reduces the negative impact of technophobia on AI adoption. Open organisational cultures reduce the need for SRL.Originality/valueThis study goes beyond a purely technical perspective towards a “human-side” view on understanding managers’ adoption of generative AI. This study is an early attempt to apply the CAN model to analysing the connection between technophobia, SRL, organisational culture and the intention to adopt generative AI.Journal of Managerial Psychology540Using the cognitive-affective-normative (CAN) model, this study highlights the role of self-regulated learning (SRL) and organisational culture and delves into the link between technophobia and a manager's intention to adopt generative artificial intelligence in management practices.Journal of Managerial Psychology["Li Zhao", "Qile He", "Muhammed Kamal", "Nicholas O\u2019Regan"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/d16dca96416478c82bd3eeff6f53773f7d56df33 2697640907262b910a53c468c92f397ca6eba511The role of Predictive Artificial Intelligence (AI) models is continuously emerging as critical in the pharmaceutical manufacturing industry particularly in failure detection in multiple sites. These models use artificial intelligence, evolving ML techniques, and large datasets to forecast, monitor and resolve rigorous system failures. This paper aims to analyze how research has embraced the development of effective predictive AI frameworks that are relevant to multi-site pharmaceutical facilities, given the many limitations and challenges associated with such settings. A deeper elucidation of different forms of ML, such as supervised and unsupervised learning, is provided. There is special emphasis on the usage of fleet and vehicle domain knowledge and regulatory compliance knowledge and, in general, field operational data feeds to the prediction model into the system. Our approach is fully based on the mixed physics and data approach, which provides high accuracy of results and well interpretable quantitatively. This study supports the generalized understanding that predictive AI models are capable of reducing downtime, increasing product quality, and optimizing operations. Real-world experience in a multi-site pharmaceutical firm establishes more than 95% efficacy of failure prediction along with significant cost-effectiveness and time compression for products. Finally, the paper points to the implications for Industry 4.0 in the context of the pharmaceutical sector and presents additional research avenues. Keywords: Predictive AI, Machine Learning, Pharmaceutical Manufacturing, Failure Detection.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This study supports the generalized understanding that predictive AI models are capable of reducing downtime, increasing product quality, and optimizing operations, and supports the implications for Industry 4.0 in the context of the pharmaceutical sector.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Srikanth Reddy Katta"]2025-02-06T00:00:00https://www.semanticscholar.org/paper/2697640907262b910a53c468c92f397ca6eba511 bc764405428d97fddba2cda6efead9eaa3b38d31Disinformation has recently become a subject of widespread concerns across the globe. To combat this issue, various initiatives have emerged, aimed at identifying, tracking, and debunking disinformation. Artificial intelligence (AI) has been incorporated as a tool to counter disinformation, but its implementation has not always been successful and may even be counterproductive. Thus, there is a growing recognition of the need for benchmarking the various ongoing efforts to ensure greater efficacy and coordination in the use of AI and assure that this does not lead to forms of algorithmic censorship. Our goal is to provide a mapping of the projects that use AI to counter disinformation by means of their hyperlink network analysis to shed light on their aims, approaches, and challenges.Frontiers in Political Science161This goal is to provide a mapping of the projects that use AI to counter disinformation by means of their hyperlink network analysis to shed light on their aims, approaches, and challenges.Frontiers in Political Science["Federico Pilati", "Tommaso Venturini"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/bc764405428d97fddba2cda6efead9eaa3b38d31 9c9b45e00dcd1471de594746c95b8dba8fc46e37Artificial intelligence commonly refers to the science and engineering of artificial systems that can carry out tasks generally associated with requiring aspects of human intelligence, such as playing games, translating languages, and driving cars. In recent years, there have been exciting advances in learning-based, data-driven approaches towards AI, and machine learning and deep learning have enabled computer systems to perceive the world in unprecedented ways. Reinforcement learning has enabled breakthroughs in complex games such as Go and challenging robotics tasks such as quadrupedal locomotion. A key aspect of intelligence is to not only make predictions, but reason about the uncertainty in these predictions, and to consider this uncertainty when making decisions. This is what this manuscript on"Probabilistic Artificial Intelligence"is about. The first part covers probabilistic approaches to machine learning. We discuss the differentiation between"epistemic"uncertainty due to lack of data and"aleatoric"uncertainty, which is irreducible and stems, e.g., from noisy observations and outcomes. We discuss concrete approaches towards probabilistic inference and modern approaches to efficient approximate inference. The second part of the manuscript is about taking uncertainty into account in sequential decision tasks. We consider active learning and Bayesian optimization -- approaches that collect data by proposing experiments that are informative for reducing the epistemic uncertainty. We then consider reinforcement learning and modern deep RL approaches that use neural network function approximation. We close by discussing modern approaches in model-based RL, which harness epistemic and aleatoric uncertainty to guide exploration, while also reasoning about safety.00This manuscript considers active learning and Bayesian optimization -- approaches that collect data by proposing experiments that are informative for reducing the epistemic uncertainty, and considers reinforcement learning and modern deep RL approaches that use neural network function approximation.["Andreas Krause", "Jonas Hubotter"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/9c9b45e00dcd1471de594746c95b8dba8fc46e37 66226500883d3661c7e91c7742713139a288ab43Background/Objectives: The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness of rehabilitation healthcare providers to implement AI in practice. Methods: This study was conducted in Saudi Arabia, with data collected from 430 physical therapy professionals via an online SurveyMonkey questionnaire between January and March 2024. The survey assessed demographics, AI knowledge and skills, and perceived challenges. Data were analyzed using Statistical Package for the Social Science (SPSS 27) and DATAtab (version 2025), with frequencies, percentages, and nonparametric tests used to examine the relationships between the variables. Results: The majority of respondents (80.9%) believed that AI would be integrated into physical therapy in future, with 78.6% seeing AI as significantly impacting their work. While 61.4% thought that AI would reduce workload and enhance productivity, only 30% expressed concerns about AI endangering their profession. A lack of formal AI training was commonly been reported, with social media platforms being respondents’ primary source of AI knowledge. Despite these challenges, 85.1% expressed an eagerness to learn and use AI. Organizational preparedness was a significant barrier, with 45.6% of respondents reporting that their organizations lacked AI strategies. There were insignificant differences in the mean rank of AI perceptions or knowledge based on the gender, years of experience, and qualification degree of the respondents. Conclusions: The results demonstrated a strong interest in AI implementation in physical therapy. The majority of respondents expressed confidence in AI’s future utility and readiness to incorporate it into their practice. However, challenges, such as a lack of formal training and organizational preparedness, were identified. Overall, the findings highlight AI’s potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential.Healthcare260The findings highlight AI’s potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential.Healthcare["M. Aldhahi", "Amal I. Alorainy", "M. Abuzaid", "Awadia Gareeballah", "Naifah F. Alsubaie", "Anwar S. Alshamary", "Z. Hamd"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/66226500883d3661c7e91c7742713139a288ab43 4495d8f481af15105283bd4ee8c71a2698c62d88CJEM130These 11 recommendations provide guiding principles and methodological standards for emergency medicine researchers to rigorously develop AI-based Clinical Decision Support tools and for clinicians to gain knowledge and trust in using them.CJEM["Hashim Kareemi", "Henry Li", "Akshay Rajaram", "Jessalyn K. Holodinsky", "Justin N Hall", "Lars Grant", "Gautam Goel", "Jake Hayward", "S. Mehta", "M. Ben-Yakov", "Elyse Berger Pelletier", "Frank Scheuermeyer", "Kendall Ho"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/4495d8f481af15105283bd4ee8c71a2698c62d88 34eb21d9a8357ee143592cef4eeb514eec27f2d3Integrating artificial intelligence (AI) into experimental surgery represents a transformative shift in biomedical research, offering innovative alternatives to traditional animal-based preclinical models. AI-driven methodologies, including computerized models and surgical simulations, enhance precision, reproducibility, and ethical compliance while reducing reliance on in vivo experimentation. This review systematically explores the role of AI in optimizing surgical procedures, operative techniques, and biomedical technology, analyzing its impact on surgical decision-making, predictive modeling, and training simulations. A comprehensive search was conducted across PubMed, Embase, Scopus, Web of Science, and SciELO, identifying studies on AI-enhanced surgical strategies, in silico models, and experimental validation techniques. The findings highlight AI's potential to replace animal testing, refine surgical training, and improve preclinical research accuracy. However, challenges remain, including data standardization, regulatory adaptation, and ethical considerations related to AI-driven surgical methodologies. Addressing these challenges requires interdisciplinary collaboration and the development of validated AI frameworks to support widespread implementation in experimental surgery. Future research should focus on standardizing AI applications, ensuring methodological transparency, and integrating AI models into clinical translation pathways. This review underscores AI's revolutionary role in shaping the future of surgical research, offering a path to more ethical, precise, and innovative experimental surgery.International Journal of Innovative Research in Medical Science00This review systematically explores the role of AI in optimizing surgical procedures, operative techniques, and biomedical technology, analyzing its impact on surgical decision-making, predictive modeling, and training simulations.International Journal of Innovative Research in Medical Science["Amalia Cinthia Meneses R\u00eago", "I. Ara\u00fajo-Filho"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/34eb21d9a8357ee143592cef4eeb514eec27f2d3 3944ed4b7a44a709698e97fd99ee9f44bc2c4358INTRODUCTION Abdominal aortic aneurysms (AAAs) and peripheral artery disease (PAD) are two vascular diseases with a significant risk of major adverse cardiovascular events and mortality. A challenge in current disease management is the unpredictable disease progression in individual patients. The VASCUL-AID-RETRO study aims to develop trustworthy multimodal predictive artificial intelligence (AI) models for multiple tasks including risk stratification of disease progression and cardiovascular events in patients with AAA and PAD. METHODS The VASCUL-AID-RETRO study will collect data from 5000 AAA and 6000 PAD patients across multiple European centers of the VASCUL-AID consortium using electronic health records from 2015 to 2024. This retrospectively-collected data will be enriched with additional data from existing biobanks and registries. Multimodal data, including clinical records, radiological imaging, proteomics, and genomics, will be collected to develop AI models predicting disease progression and cardiovascular risks. This will be done while integrating the international ethics guidelines and legal standards for trustworthy AI, to ensure a socially-responsible data integration and analysis. PROPOSED ANALYSES A consensus-based variable list of clinical parameters and core outcome set for both diseases will be developed through meetings with key opinion leaders. Blood, plasma, and tissue samples from existing biobanks will be analyzed for proteomic and genomic variations. AI models will be trained on segmented AAA and PAD artery geometries for estimation of hemodynamic parameters to quantify disease progression. Initially, risk prediction models will be developed for each modality separately, and subsequently, all data will be combined to be used as input to multimodal prediction models. During all processes, data security, data quality, and ethical guidelines and legal standards will be carefully considered. As a next step, the developed models will be further adjusted with prospective data and internally validated in a prospective cohort (VASCUL-AID-PRO study). CONCLUSION The VASCUL-AID-RETRO study will utilize advanced AI techniques and integrate clinical, imaging, and multi-omics data to predict AAA and PAD progression and cardiovascular events. CLINICAL TRIAL REGISTRATION The VASCUL-AID-RETRO study is registered at www.clinicaltrials.gov under the identification number NCT06206369. CLINICAL IMPACT The VASCUL-AID-RETRO study aims to improve clinical practice of vascular surgery by developing artificial intelligence-driven multimodal predictive models for patients with abdominal aortic aneurysms or peripheral artery disease, enhancing personalized medicine. By integrating comprehensive data sets including clinical, imaging, and multi-omics data, these models have the potential to provide accurate risk stratification for disease progression and cardiovascular events. An innovation lies in the extensive European data set in combination with multimodal analyses approaches, which enables the development of advanced models to facilitate better understanding of disease mechanisms and progression. For clinicians, this means that more precise, individualized treatment plans can be established, ultimately aiming to improve patient outcomes.Journal of Endovascular Therapy220The VASCUL-AID-RETRO study aims to improve clinical practice of vascular surgery by developing artificial intelligence-driven multimodal predictive models for patients with abdominal aortic aneurysms or peripheral artery disease, enhancing personalized medicine.Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists["Lotte Rijken", "Sabrina Zwetsloot", "S. Smorenburg", "J. Wolterink", "Ivana I\u0161gum", "H. Marquering", "Jan van Duivenvoorde", "C. Ploem", "Roosmarie Jessen", "Fabio Catarinella", "Regent Lee", "K. Bera", "Jenny Buisan", "Ping Zhang", "M. Dias-Neto", "J. Raffort", "Fabian Lareyre", "Catelijne Muller", "Igor Kon\u010dar", "I. Tomic", "M. \u017divkovi\u0107", "T. Djuri\u0107", "A. Stankovi\u0107", "Maarit Venermo", "R. Tulamo", "C. Behrendt", "Noeska Smit", "M. Schijven", "Bert-Jan H. van den Born", "R. Delewi", "V. Jongkind", "Venkat Ayyalasomayajula", "Kak Khee Yeung"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/3944ed4b7a44a709698e97fd99ee9f44bc2c4358 6e978e9407685158529798ed398ebe3f9fb9b002Humanity has consumed large amounts of energy in recent decades. Energy requirements increase continuously, and fossil fuel overuse pollutes the environment extremely. The researchers turned their attention to alternative solutions, such as renewable sources of fuels, which reduce negative emissions. At the same time, biodiesel is produced from environmentally friendly raw materials and is a competitive fuel with acceptable properties. The scientific community investigates new approaches to further improve the physicochemical properties of biodiesel in more economical ways. Artificial intelligence and nature-inspired techniques are particularly capable of searching for optimal fuels in complex optimization fields. The current study concerns a recent review of biodiesel production approaches based on evolutionary computation methods. These methods lead to innovative biodiesel development, costing less with lower sulfur content. Except for the economic profits, the reduction of environmental emissions in praxis confirms biodiesel appropriateness for more consumption than fossil blends. The algorithms’ accuracy and effectiveness were evaluated in various case studies and detailed results were offered in every publication. The optimal fuels are produced in laboratories and tested in common engines too. In the literature, there exists a gap in relation to the financial and environmental aspects of biodiesel fuel production, which should also be investigated.Energies440A recent review of biodiesel production approaches based on evolutionary computation methods leads to innovative biodiesel development, costing less with lower sulfur content, which confirms biodiesel appropriateness for more consumption than fossil blends.Energies["Christos Kyriklidis", "Aikaterini Koutouvou", "Konstantinos Moustakas", "Vayos Karayannis", "Constantinos Tsanaktsidis"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/6e978e9407685158529798ed398ebe3f9fb9b002 81838ab61e97f2ad6be0e65aa27a7252708c5180Artificial intelligence (AI) has rapidly evolved, transforming industries and addressing societal challenges across sectors such as healthcare and education. This study provides a state-of-the-art overview of AI research up to 2023 through a bibliometric analysis of the 50 most influential papers, identified using Scopus citation metrics. The selected works, averaging 74 citations each, encompass original research, reviews, and editorials, demonstrating a diversity of impactful contributions. Over 300 contributing authors and significant international collaboration highlight AI’s global and multidisciplinary nature. Our analysis reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia. Trends in authorship underscore the growing role of generative AI systems in advancing knowledge dissemination. The findings illustrate AI’s transformative potential in practical applications, such as enabling early disease detection and precision medicine in healthcare and fostering adaptive learning systems and accessibility in education. By examining the dynamics of collaboration, geographic productivity, and institutional influence, this study sheds light on the innovation drivers shaping the AI field. The results emphasize the need for responsible AI development to maximize societal benefits and mitigate risks. This research provides an evidence-based understanding of AI’s progress and sets the stage for future advancements. It aims to inform stakeholders and contribute to the ongoing scientific discourse, offering insights into AI’s impact at a time of unprecedented global interest and investment.Journal of Infrastructure Policy and Development330A bibliometric analysis of the 50 most influential papers reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia.Journal of Infrastructure, Policy and Development["Putri Hana Pebriana", "Edi Setiadi", "Dadi Ahmadi", "Robbi Rahim", "O. Arwansyah", "Kusuma Wijayanto", "Rholand Muary", "Elkana Timotius", "Dede Aji Mardani", "A. Rowikarim", "Irwan Fauzy Ridwan"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/81838ab61e97f2ad6be0e65aa27a7252708c5180 f8659048a195f165c64c92ec7c6861204f2f70abBMC Musculoskeletal Disorders380AI-based DSS applications showed a high degree of accuracy in performing a wide set of different tasks in the clinical prevention and management of Low Back Pain (LBP) due to lumbar degenerative spine disorders.BMC Musculoskeletal Disorders["Paolo Giaccone", "Federico D'Antoni", "Fabrizio Russo", "L. Ambrosio", "G. Papalia", "Onorato d'Angelis", "G. Vadal\u00e0", "Albert Comelli", "Luca Vollero", "M. Merone", "R. Papalia", "Vincenzo Denaro"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/f8659048a195f165c64c92ec7c6861204f2f70ab db9e25aecb8fe030e24c35c7ee9343daec25a599 Non-pharmacological interventions (NPI) were crucial in curbing the initial COVID-19 pandemic waves, but compliance was difficult. The primary aim of this study was to assess the changes in compliance with NPIs in healthcare settings using Artificial intelligence (AI) and examine the barriers and facilitators of using AI systems in healthcare. A pre-post-intervention study was conducted in a north-Indian hospital between April and July 2022. YOLO-V5 and 3D Cartesian distance algorithm-based AI modules were used to ascertain compliance through several parameters like confidence threshold, intersection-over-union threshold, image size, distance threshold (6 feet), and 3D Euclidean Distance estimation. Validation was done by evaluating model performance on a labelled test dataset, and accuracy was 91.3 per cent. Interventions included daily sensitization and health education for the hospital staff and visitors, display of information, education and communication (IEC) materials, and administrative surveillance. In-depth interviews were conducted with the stakeholders to assess the feasibility issues. Flagged events during the three phases were compared using One-way ANOVA tests in SPSS. Higher social distancing (SD) compliance events were flagged by the module in the intervention phase compared to the pre-intervention and post-intervention phases (P<0.05). Mask non-compliance was significantly lower (P <0.05) in the pre-intervention phase and highest in the post-intervention phase, with varied differences between different intervention phases in the registration hall and medicine out-patient department (OPD). The modules’ data safety, transfer, and cost were the most common concerns. AI can supplement our efforts against the pandemic and offer indispensable help with minimal feasibility issues that can be resolved through adequate sensitization and training. The Indian journal of medical research290Artificial intelligence can supplement the authors' efforts against the pandemic and offer indispensable help with minimal feasibility issues that can be resolved through adequate sensitization and training.Indian Journal of Medical Research["Madhur Verma", "Moonis Mirza", "Karan Sayal", "Sukesh Shenoy", "S. S. Sahoo", "Anil Goel", "Rakesh Kakkar"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/db9e25aecb8fe030e24c35c7ee9343daec25a599 64cc6e168eb78b1ea2fb772bb0ef5206d2c88c62Background: Serious games (SGs) are primarily aimed at promoting learning, skills training, and rehabilitation. Artificial intelligence, immersive technologies, the metaverse, and neurotechnologies promise the next revolution in gaming. Meta-skills are considered the “must-have” skills for thriving in the era of rapid change, complexity, and innovation. Μeta-skills can be defined as a set of higher-order skills that incorporate metacognitive, meta-emotional, and meta-motivational attributes, enabling one to be mindful, self-motivated, self-regulated, and flexible in different circumstances. Skillfulness, and more specifically meta-skills development, is recognized as a predictor of optimal performance along with mental and emotional wellness. Nevertheless, there is still limited knowledge about the effectiveness of integrating cutting-edge technologies in serious games, especially in the field of meta-skills training. Objectives: The current systematic review aims to collect and synthesize evidence concerning the effectiveness of advanced technologies in serious gaming for promoting meta-skills development. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to identify experimental studies conducted in the last 10 years. Four different databases were employed: Web of Science, PubMed, Scopus, and Google Scholar. Results: Forty-nine studies were selected. Promising outcomes were identified in AI-based SGs (i.e., gamified chatbots) as they provided realistic, adaptive, personalized, and interactive environments using natural language processing, player modeling, reinforcement learning, GPT-based models, data analytics, and assessment. Immersive technologies, including the metaverse, virtual reality, augmented reality, and mixed reality, provided realistic simulations, interactive environments, and sensory engagement, making training experiences more impactful. Non-invasive neurotechnologies were found to encourage players’ training by monitoring brain activity and adapting gameplay to players’ mental states. Healthy participants (n = 29 studies) as well as participants diagnosed with anxiety, neurodevelopmental disorders, and cognitive impairments exhibited improvements in a wide range of meta-skills, including self-regulation, cognitive control, attention regulation, meta-memory skills, flexibility, self-reflection, and self-evaluation. Players were more self-motivated with an increased feeling of self-confidence and self-efficacy. They had a more accurate self-perception. At the emotional level, improvements were observed in emotional regulation, empathy, and stress management skills. At the social level, social awareness was enhanced since they could more easily solve conflicts, communicate, and work in teams. Systematic training led to improvements in higher-order thinking skills, including critical thinking, problem-solving skills, reasoning, decision-making ability, and abstract thinking. Discussion: Special focus is given to the potential benefits, possible risks, and ethical concerns; future directions and implications are also discussed. The results of the current review may have implications for the design and implementation of innovative serious games for promoting skillfulness among populations with different training needs.Electronics1220A systematic review of the effectiveness of advanced technologies in serious gaming for promoting meta-skills development found improvements in a wide range of meta-skills, including self-regulation, cognitive control, attention regulation, meta-memory skills, flexibility, self-reflection, and self-evaluation.Electronics["Eleni Mitsea", "Athanasios Drigas", "C. Skianis"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/64cc6e168eb78b1ea2fb772bb0ef5206d2c88c62 3abb028fb1acdc991f371e57e21d860be4eaa4afPurposeManufacturing firms must strengthen their supply chain resilience to survive in turbulent business environments. This study explores how artificial intelligence (AI) can be leveraged to enhance supply chain resilience.Design/methodology/approachDrawing on organizational information processing theory, the research investigates the impact of AI usage on proactive and reactive supply chain resilience by fostering referent power in the context of demand dynamism. The study analyzes survey data from 285 Chinese manufacturing firms using structural equation modeling and regression analysis.FindingsThe results indicate that AI usage can enhance both proactive and reactive supply chain resilience. Referent power only mediates the relationship between AI usage and reactive supply chain resilience. Furthermore, this mediating effect is stronger under high-level demand dynamism.Originality/valueThis study highlights the value of AI usage in strengthening supply chain resilience and uncovers its underlying mechanisms. Theoretical and practical implications are discussed.Journal of Manufacturing Technology Management740The results indicate that AI usage can enhance both proactive and reactive supply chain resilience, and this mediating effect is stronger under high-level demand dynamism.Journal of Manufacturing Technology Management["Xiaochen Yue", "Mary Kang", "Yanming Zhang"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/3abb028fb1acdc991f371e57e21d860be4eaa4af d30b8b94ab8a43ba161bd99b1fe17af3ebc461ecDue to the emergence and increased development of Artificial Intelligence (AI), research in general has been significantly impacted, particularly in the field of scientific theories and models. The purpose of this study is to analyze the acceptance of both AI tools and traditional methodologies used in research. Moreover, conclusions about the respondents' perception and openness to using AI tools in research regarding gender, age and current academic position are discussed. Another goal is to compare the level of satisfaction from both the AI tools and the traditional research methods. A questionnaire-based survey was carried out between February and March 2024, and it included students and teaching staff at the University North in Croatia. The novelty of this research is mirrored in the scarcity of such empirical studies encompassing the academic community in Croatia.Tehnički glasnik00The purpose of this study is to analyze the acceptance of both AI tools and traditional methodologies used in research, and to compare the level of satisfaction from both the AI tools and the traditional research methods.Tehnički glasnik["Katerina Fotova \u010cikovi\u0107", "Antonija Mandi\u0107", "Maja Hoi\u0107"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/d30b8b94ab8a43ba161bd99b1fe17af3ebc461ec 66980f81df1bbd0251f2be9d2c1d33e8e620cb93The emergence of generative artificial intelligence (GenAI) fundamentally shifts how educational knowledge is created, shared, and validated. Through the lens of epistemic technologies—tools that transform knowledge creation and dissemination—we analyze how GenAI challenges traditional notions of practical wisdom in education doctorate (EdD) programs. Drawing on parallels with previous epistemic shifts like written language, print, and digital media, we explore how GenAI, as a generative, dialogic, multimodal, and sometimes unpredictable technology, transforms practitioner knowledge and decision-making. We discuss implications for EdD programs, emphasizing the need to balance AI integration with the preservation of human judgment and ethical decision-making to maintain practical wisdom for scholarly practice.Impacting Education Journal on Transforming Professional Practice00Drawing on parallels with previous epistemic shifts like written language, print, and digital media, it is explored how GenAI, as a generative, dialogic, multimodal, and sometimes unpredictable technology, transforms practitioner knowledge and decision-making.Impacting Education: Journal on Transforming Professional Practice["D. Henriksen", "Punya Mishra", "Lauren J. Woo", "Nicole Oster"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/66980f81df1bbd0251f2be9d2c1d33e8e620cb93 b1c89687214981fa4d4373861ad7e98a23897583 We assessed the impact of artificial intelligence software (e-CTA, Brainomix) on clinical decision-making in patients with suspected acute ischemic stroke. A retrospective, multi-reader-multi-case crossover design compared readers' performance with versus without software support. Twenty cases were included, 10 with large vessel occlusion (LVO) and 10 without LVO. Twenty one NHS clinicians, representing intended software users ranging in experience, conducted two sessions (washout period >2 weeks). In session one, software support was provided for 10 randomly selected cases. In session two, support allocation was reversed. Outcome measures included LVO detection, collateral scoring, diagnosis, treatment decision, time taken and confidence. Sensitivity, specificity and accuracy of LVO detection improved with imaging software for LVO detection, with increased confidence and reduced time taken. There was no significant difference in collateral scoring or diagnoses. e-CTA can improve performance of NHS clinicians when interpreting acute stroke imaging. This paper provides new evidence that AI decision support software has the capacity to improve the performance of representative users in the NHS when interpreting imaging to identify patients for acute stroke treatments. BJR|Artificial Intelligence00New evidence is provided that AI decision support software has the capacity to improve the performance of representative users in the NHS when interpreting imaging to identify patients for acute stroke treatments.BJR|Artificial Intelligence["K. Nagaratnam", "P. Mathieson", "A. Podlasek", "P. Slade", "G. A. Ford", "A. Cox", "J. H. Briggs", "Z. Woodhead", "G. Harston"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/b1c89687214981fa4d4373861ad7e98a23897583 86e751c0ab9b1089af82c5a938724a5cb1a848cfArtificial Intelligence (AI) has begun to reshape the educational landscape, promising to bring profound changes to how teaching and learning are conducted in schools. The integration of AI into the educational system is expected to influence various aspects of schooling. The current research investigates the perceptions of educators and students regarding the integration of AI in education, focusing on five key dimensions: personalized learning, assessment and feedback, administrative efficiency, ethical considerations, and specific challenges related to AI implementation. Data were collected through anonymous responses to 5-point Likert scale survey questions to ensure candid insights for research purposes.Bulletin of the Transilvania University of Braşov: Series VII: Social Sciences, Law200The current research investigates the perceptions of educators and students regarding the integration of AI in education, focusing on five key dimensions: personalized learning, assessment and feedback, administrative efficiency, ethical considerations, and specific challenges related to AI implementation.Bulletin of the Transilvania University of Braşov. Series VII: Social Sciences • Law["Marius Bazgan"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/86e751c0ab9b1089af82c5a938724a5cb1a848cf bfe7608d422e78938d672d938e85ba4914275264This study is aimed to test the role of artificial intelligence in the audit process and how artificial intelligence plays a role in detecting fraud using a literature review approach. The method used in this study is a systematic literature review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method, with an observation period of 32 years from 1992 to 2024. 101 articles were obtained, but only 15 articles were eligible. Of the fifteen articles, it shows that the article from Omoteso (2012) with the highest number of citations, i.e. 253 citations and the article with the least number of citations is the study of Qatawneh (2024). The domains used range from finance, accounting, auditing and also information systems. The limitations of this study are that it was only able to obtain fifteen articles through the PRISMA diagram process. For future research, it is expected to expand the study with the implications of using ATLAS Auditing for fraud prevention and combined with artificial intelligence.Journal of Ecohumanism520This study test the role of artificial intelligence in the audit process and how artificial intelligence plays a role in detecting fraud using a literature review approach using the PRISMA method, with an observation period of 32 years from 1992 to 2024.Journal of Ecohumanism["Saifudin Saifudin", "Indira Januarti", "Agus Purwanto"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/bfe7608d422e78938d672d938e85ba4914275264 3ce92622fa7b8e0770c82b97348f40db408c9dfcThis study examines the intersection of artificial intelligence (AI) and the creative industries, with particular emphasis on the challenges AI poses to traditional notions of authorship, creativity, and labor. Drawing on the recent Writers Guild of America (WGA) strike as a case, the research highlights the tensions between human and machine-driven production in creative fields. The study aims to explore the roles of artificial and human intelligence in synthetic media, considering the social and cultural context of the media, particularly about creative production processes where the debate between human and artificial intelligence is involved. The paper addresses this debate through the 2023 WGA strike in the USA. The study analyzes news coverage of the WGA strike on internet news sites using framing analysis. By employing framing analysis—conflict, economic implications, technological disruption, and ethical considerations—this study explores the complex discourse surrounding AI’s influence on creative labor. It posits that the future of the creative industries will depend on achieving a balance between utilizing AI as a collaborative tool and preserving the distinct contributions of human creativity. Furthermore, the paper advocates for the development of ethical frameworks that ensure AI’s integration into creative processes fosters innovation while safeguarding cultural diversity and the irreplaceable qualities of human creativity.BAMC Official Conference Proceedings230It is posits that the future of the creative industries will depend on achieving a balance between utilizing AI as a collaborative tool and preserving the distinct contributions of human creativity, and advocates for the development of ethical frameworks that ensure AI’s integration into creative processes fosters innovation while safeguarding cultural diversity and the irreplaceable qualities of human creativity.BAMC Official Conference Proceedings["G. S. G. H\u0131zal"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/3ce92622fa7b8e0770c82b97348f40db408c9dfc aa477bc29528f55a1b67866fabb07bc6631af268In an era marked by technological sophistication, Artificial Intelligence (AI) is increasingly being integrated into various fields, including Mechanical Engineering Education (MEE). This review paper presents a systematic examination of scientific publications in this field, spanning from 2018 to 2023. Utilizing the PRISMA framework, 228 research papers were selected and analyzed to identify research gaps and future directions in AI’s application within the MEE discipline. The diverse applications of AI in MEE identified include personalized learning, smart tutoring systems, digitizing engineering drawings, enhancing simulation and assessment, and boosting student motivation and engagement. Additionally, a bibliometric analysis of AI in MEE was conducted, examining its role in different aspects of MEE, interdisciplinary collaboration, geographic distribution, and research focus. Accordingly, the scope of this review encompasses a comprehensive content analysis and bibliometric evaluation of AI applications in MEE. This review systematically identifies current applications of AI, maps research trends, and analyzes publication data to highlight interdisciplinary collaborations and geographical distributions. Furthermore, this study identifies critical research gaps and offers actionable recommendations, emphasizing future directions such as advancing Generative Artificial Intelligence (GAI) applications in MEE and reshaping curricula to integrate AI-based learning tools. The findings provide valuable insights to support stakeholders in evolving MEE to meet industry needs and enhance educational outcomes.Frontiers in Education460A systematic examination of scientific publications in this field, spanning from 2018 to 2023, identifies critical research gaps and offers actionable recommendations, emphasizing future directions such as advancing Generative Artificial Intelligence applications in MEE and reshaping curricula to integrate AI-based learning tools.Frontiers in Education["Mohannad Alghazo", "Vian Ahmed", "Zied Bahroun"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/aa477bc29528f55a1b67866fabb07bc6631af268 8a396edbfa753bac3335ceaf835d95d1efcae859Utilization of artificial intelligence (AI) technology is very important in helping English. This research aims to explore the utilization of artificial intelligence (AI) technology in helping English Literature Study Program students complete their final projects at university level. The research design used is a qualitative approach. The population in this study were students from the English Literature Study Program who were completing their final projects, with a sample of 10 students selected purposively. Data was collected through interviews using Google Form which contained five questions related to the utilization of AI in the final project. Data analysis was carried out by referring to the points contained in the interviews to answer the research objectives. The research results show that AI really helps students in completing their final projects, especially in terms of finding ideas, understanding concepts, and improving English language skills. However, some students also realize the limitations of AI in terms of source validity and accuracy of information provided. The conclusion of this research is that the utilization of AI must be balanced with critical evaluation of the information and sources provided to ensure the quality and acceptability of students’ final projects. This research provides a deeper understanding of the role of AI in supporting students’ final assignment completion, as well as the importance of vigilance in verifying information obtained from this technology.Lectura : Jurnal Pendidikan00The conclusion of this research is that the utilization of AI must be balanced with critical evaluation of the information and sources provided to ensure the quality and acceptability of students’ final projects.Lectura : Jurnal Pendidikan["Lelly Zuyana Asril", "Vina Fathira"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/8a396edbfa753bac3335ceaf835d95d1efcae859 f70c12261ed888240f92ed4ea4c30cab853141d1The human–machine interface is increasingly attracting attention. This paper investigates the potential value and impact of using Artificial Intelligence (AI) in the freight logistics industry by defining various use cases. It explores how the logistics industry can use data and AI to improve its economic, social, and environmental sustainability through better decision making. The research methodology involved a systematic literature review, interviews with subject matter experts, facility visits, and generative AI. The SLR showed that none of the peer-reviewed literature offers an extensive exposition of AI use cases in freight logistics. The key findings highlight AI’s untapped potential in the logistics industry, with 77 unique use cases identified across three spheres: holistic supply chain opportunities, transport vehicles, and logistical facilities. The research is expected to contribute to the growing body of knowledge of AI in logistics, inform future research, guide industry practices, and inspire further innovation in logistics technology.Sustainability120This paper explores how the logistics industry can use data and AI to improve its economic, social, and environmental sustainability through better decision making through better decision making.Sustainability["M. D. du Plessis", "Retief Gerber", "L. Goedhals-Gerber", "J. van Eeden"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/f70c12261ed888240f92ed4ea4c30cab853141d1 9d6f8befadb3e00e7d6d3566a6cd546d917d02ddArtificial Intelligence (AI) has emerged as a transformative force in modern agriculture, revolutionizing traditional farming practices through the integration of advanced technologies and data-driven decision-making systems. This comprehensive article explores the implementation of AI in precision agriculture, focusing on crop management and farm optimization strategies. The article examines various aspects of AI application in agriculture, including precision farming technologies, crop monitoring systems, data analytics, and decision support frameworks. It explores the economic and environmental impacts of these technologies while addressing the challenges and future prospects of AI adoption in agricultural practices. The article highlights how AI-driven solutions are enhancing agricultural productivity through improved resource management, automated monitoring systems, and predictive analytics. The integration of machine learning, computer vision, and Internet of Things (IoT) devices has created sophisticated farming management systems that optimize crop yields while promoting environmental sustainability. This transformation represents a significant advancement in agricultural practices, particularly relevant for addressing global food security challenges and promoting sustainable farming methods in both developed and developing regions.International Journal of Scientific Research in Computer Science Engineering and Information Technology10This comprehensive article explores the implementation of AI in precision agriculture, focusing on crop management and farm optimization strategies, and highlights how AI-driven solutions are enhancing agricultural productivity through improved resource management, automated monitoring systems, and predictive analytics.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Rajnish Jain"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/9d6f8befadb3e00e7d6d3566a6cd546d917d02dd c88f12f4442ed53b4759e540bdfc46c78f709733Artificial intelligence (AI) has revolutionized medical diagnostics by enhancing efficiency, improving accuracy, and reducing variability. By alleviating the workload of medical staff, AI addresses challenges such as increasing diagnostic demands, workforce shortages, and reliance on subjective interpretation. This review examines the role of AI in reducing diagnostic workload and enhancing efficiency across medical fields from January 2019 to February 2024, identifying limitations and areas for improvement. A comprehensive PubMed search using the keywords “artificial intelligence” or “AI,” “efficiency” or “workload,” and “patient” or “clinical” identified 2587 articles, of which 51 were reviewed. These studies analyzed the impact of AI on radiology, pathology, and other specialties, focusing on efficiency, accuracy, and workload reduction. The final 51 articles were categorized into 4 groups based on diagnostic efficiency, where category A included studies with supporting material provided, category B consisted of those with reduced data volume, category C focused on independent AI diagnosis, and category D included studies that reported data reduction without changes in diagnostic time. In radiology and pathology, which require skilled techniques and large-scale data processing, AI improved accuracy and reduced diagnostic time by approximately 90% or more. Radiology, in particular, showed a high proportion of category C studies, as digitized data and standardized protocols facilitated independent AI diagnoses. AI has significant potential to optimize workload management, improve diagnostic efficiency, and enhance accuracy. However, challenges remain in standardizing applications and addressing ethical concerns. Integrating AI into healthcare workforce planning is essential for fostering collaboration between technology and clinicians, ultimately improving patient care.Medicine570The role of AI in reducing diagnostic workload and enhancing efficiency across medical fields from January 2019 to February 2024 is examined, identifying limitations and areas for improvement.Medicine["Jinseo Jeong", "Sohyun Kim", "Lian Pan", "Daye Hwang", "Dongseop Kim", "Jeongwon Choi", "Yeongkyo Kwon", "Pyeongro Yi", "Jisoo Jeong", "Seok-Ju Yoo"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/c88f12f4442ed53b4759e540bdfc46c78f709733 7d896995ae89aec5b5d247457ea0cb8be5695a00Annals of Operations Research00Annals of Operations Research["Davide La Torre", "Hatem Masri"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/7d896995ae89aec5b5d247457ea0cb8be5695a00 7d328125fe3c8a872901da338308e2888cfe0b28BCE Official Conference Proceedings00BCE Official Conference Proceedings["H. Kym\u00e4l\u00e4inen", "Maria von Cr\u00e4utlein", "Kari Elo", "Szabolcs Galambosi", "Anne Honkanen", "Janna Pietik\u00e4inen", "Ilona S\u00f6dervik"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/7d328125fe3c8a872901da338308e2888cfe0b28 18cb291cd1a7e21baf15ea66909e21ad490c1be2Hernia detection is one of the critical medical diagnostics, and hence, promising advancements are made in early prediction and risk assessment using AI and ML techniques. In this paper, different AI and ML models are assessed, ranging from deep learning to traditional techniques, classifying patients into high-risk or low-risk categories. It discusses performance of models like CNNs, SVM, RF, RNN, and ANN while dealing with medical images, the possibility of training more than one model for better accuracy. There are some challenges ahead for implementing AI in a clinical setting, such as the privacy and validation of data; this work points to future potential in hernia detection.Journal of Advance Research in Computer Science &amp; Engineering (ISSN 2456-3552)280Different AI and ML models are assessed, ranging from deep learning to traditional techniques, classifying patients into high-risk or low-risk categories, and the possibility of training more than one model for better accuracy is discussed.Journal of Advance Research in Computer Science &amp; Engineering (ISSN 2456-3552)["S. Hannan"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/18cb291cd1a7e21baf15ea66909e21ad490c1be2 08bd4221138379f615281b7755f922b8401c13dcIn today’s rapidly evolving job market, university students face unprecedented challenges in navigating their career paths. Traditional career guidance approaches sometimes fail to provide students with the knowledge and skills necessary for successful transitions from academics to the profession because of the changing nature of industries and the growing complexity of job possibilities. The study aims to explore the integration of AI and ML in providing predictive career guidance and entrepreneurial development for university students. This study proposes a novel Wild Horse Optimized Resilient Extreme Gradient Boosting (WHO-RXGBoost) model to predict the personalized recommendations that guide students in their career choices and entrepreneurial endeavors. University records and questionnaires are used to collect demographic data about students as well as information about any prior employment or entrepreneurial experience. The data was pre-processed using data cleaning and normalization using a robust scaler for the obtained data. The PCA feature extraction method is utilized to extract the datasets. By using this methodology, students can efficiently travel a massive amount of employment information by creating an information recommendation system that is customized to satisfy their requirements. The results indicate the proposed method outperforms traditional algorithms in providing relevant and timely career insights with metrics, such as F1-score (90%), precision (93%), accuracy (95%), and specificity (91%). User satisfaction indicates that technology considerably increases students’ experiences in entrepreneurship and CP. This research contributes to enhancing career outcomes and encouraging an entrepreneurial spirit among university students by providing a practical and effective response to the job issues experienced by students.Journal of Computational Methods in Sciences and Engineering230A novel Wild Horse Optimized Resilient Extreme Gradient Boosting (WHO-RXGBoost) model is proposed to predict the personalized recommendations that guide students in their career choices and entrepreneurial endeavors and user satisfaction indicates that technology considerably increases students’ experiences in entrepreneurship and CP.Journal of Computational Methods in Sciences and Engineering["Kate Wen", "D. Zhou"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/08bd4221138379f615281b7755f922b8401c13dc 4ed5429cf635af02d36bf9ba69ed3cf61845dc4eNeural Processing Letters920Neural Processing Letters["Daniel Enemona Mathew", "D. Ebem", "Anayo Chukwu Ikegwu", "Pamela Eberechukwu Ukeoma", "Ngozi Fidelia Dibiaezue"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/4ed5429cf635af02d36bf9ba69ed3cf61845dc4e d0c5f6973fb2a355fa161ee41e5c846e2a1cc131This study examined the applications and perceptions of AI tools in doctoral studies, focusing on their efficacy in enhancing research effectiveness. A survey found that most participants used AI tools in their doctoral studies (63%), with the majority of those users reporting some positive impact from their usage. The most indicated uses of AI were proofreading, researching scholarly articles for literature reviews, and the organization and structure of research. Future research may include a larger sample size and examine instruments for alignment with the program practices and curriculum to best capture responses that indicate participants' program-specific use of AI tools. The study concluded that AI tools have not yet been integrated into research within doctoral studies, and 47% of participants did not find them conducive to effectively communicating research findings in their doctoral work.Impacting Education Journal on Transforming Professional Practice00It is concluded that AI tools have not yet been integrated into research within doctoral studies, and 47% of participants did not find them conducive to effectively communicating research findings in their doctoral work.Impacting Education: Journal on Transforming Professional Practice["Michelle Harris", "Nicole E. Soriano", "Nicole Ralston"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/d0c5f6973fb2a355fa161ee41e5c846e2a1cc131 a5e8dcd8bad9684afd7b4b5a565845a6ffb3c9bbBehaviour &amp; Information Technology730Behaviour &amp; Information Technology["Bowen Yan", "Yefan Teng"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/a5e8dcd8bad9684afd7b4b5a565845a6ffb3c9bb a380cc275316e15758f7a698c3af741125e82882Journal of Urology00The Journal of urology["N. Touma", "Thomas Skinner", "Michael Leveridge"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/a380cc275316e15758f7a698c3af741125e82882 3a3ac49a4f72203812db5457aace6a85f4462c59Monoclonal antibodies in immunodiagnosis and immunotherapy40Monoclonal antibodies in immunodiagnosis and immunotherapy["Andrei Moroz", "Cory L Brooks"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/3a3ac49a4f72203812db5457aace6a85f4462c59 21c2b92814cc601550956c98710af97d410e37b3Cureus360Cureus["S. Bedia", "M. A. Shapurwala", "Bhushan Pramod Kharge", "Aarti S. Bedia", "Amit Patil"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/21c2b92814cc601550956c98710af97d410e37b3 0ad47efd5902175d0d733866ef322fb32f17f28aInternational journal of research in computer applications & information technology00INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY["Vinod Upputuri"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/0ad47efd5902175d0d733866ef322fb32f17f28a b1684972c59c76ce6f6463b4ac53002baf70b900This paper investigates points of vulnerability in the decisions made by backers and campaigners in crowdfund pledges in an attempt to facilitate a sustainable entrepreneurial ecosystem by increasing the rate of good projects being funded. In doing so, this research examines factors that contribute to the success or failure of crowdfunding campaign pledges using eXplainable AI methods (SHapley Additive exPlanations and Counterfactual Explanations). A dataset of completed Kickstarter campaigns was used to train two binary classifiers. The first model used textual features from the campaigns’ descriptions, and the second used categorical, numerical, and textual features. Findings identify textual terms, such as “stretch goals”, that convey both elements of risk and ambitiousness to be strongly correlated with success, contrary to transparent communications of risks that bring forward worries that would have otherwise remained dormant for backers. Short sentence length, in conjunction with high term complexity, is also associated with campaign success. We link the latter to signaling theory and the campaigners’ projection of knowledgeability of the domain. Certain numerical data, such as the project’s duration, frequency of post updates, and use of images, confirm previous links to campaign success. We enhance implications through the use of Counterfactual Explanations and generate actionable recommendations on how failed projects could become successful while proposing new policies, in the form of nudges, that shield backers from points of vulnerability.Sustainability380Investigation of points of vulnerability in the decisions made by backers and campaigners in crowdfund pledges finds textual terms that convey both elements of risk and ambitiousness to be strongly correlated with success, contrary to transparent communications of risks that bring forward worries that would have otherwise remained dormant for backers.Sustainability["Andreas Gregoriades", "Christos Themistocleous"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/b1684972c59c76ce6f6463b4ac53002baf70b900 7b940f6211668fe9547984a9af61b92cce838cc5Medical Science00Medical Science["Anita Kr\u00f3l", "Jakub Pud\u017awa", "Kamil Gibczy\u0144ski", "Aleksandra Roztoczy\u0144ska", "Aleksandra Je\u0144\u0107-Mago\u0144", "Martyna Orzechowska", "Karolina Jab\u0142o\u0144ska", "Piotr Paluch", "Karol Le\u015bniewski", "Micha\u0142 Orczyk"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/7b940f6211668fe9547984a9af61b92cce838cc5 79b916d6a24c29f44d084f1de11ab19501cda2d1Artificial intelligence (AI) technology is different from all other technologies that organizations have adopted in the past. A systematic literature review revealed that technological, organizational, and environmental factors have been explored to assess an organization's readiness for adopting a new technology. In this work, we have focused on the first two factors. From many subfactors, we selected 13 subfactors based on the discussion with the domain experts. Experts also provided ranks of the two factors and their sub-factors. These ranks are used to calculate the global ranking of the factors and their sub-factors. The top three subfactors from the technology context are the following - capabilities of AI, compatibility, and complexity of AI systems. The top two subfactors from the organization context are technology infrastructure & skilled workforce and support from the top management. The results reveal that organization and technology are equally importantTehnički glasnik281The results reveal that organization and technology are equally important and the top two subfactors from the organization context are technology infrastructure & skilled workforce and support from the top management.Tehnički glasnik["Aman Pathak", "Veena Bansal"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/79b916d6a24c29f44d084f1de11ab19501cda2d1 7cc1ce17c309b0ad178e9c125afa5f27a2130048The emergence of generative artificial intelligence (GAI) has revolutionized numerous aspects of our lives and presents significant opportunities in education. However, specific digital competencies are essential to effectively leverage this technology’s potential. Notably, prompt engineering proficiency presents a significant barrier to achieving optimal outcomes. In response, various solutions are being developed, including custom GPTs available through OpenAI’s ChatGPT platform. This study validates ‘GamifIcA Edu’, a specialized GPT-based assistant for gamification and serious games, designed to enable educators to implement these pedagogical approaches without requiring advanced prompt engineering expertise. This is achieved through the utilization of pre-designed instructional frameworks. The assistant’s effectiveness was evaluated using a comprehensive rubric across five distinct use-case scenarios. Each scenario underwent four different tests, representing varied learning contexts across multiple academic disciplines. The validation methodology involved a systematic assessment of the assistant’s performance in diverse educational settings. The findings demonstrate the successful implementation of this custom-designed GPT, which generated contextually appropriate responses through natural language interactions, thus eliminating the need for complex prompt structures. This research highlights the potential of instruction-based design in the development of AI assistants that empower users with limited prompt engineering knowledge to achieve expert-level results. These findings have significant implications for the democratization of AI-enhanced educational tools.Information480This study validates ‘GamifIcA Edu’, a specialized GPT-based assistant for gamification and serious games, designed to enable educators to implement these pedagogical approaches without requiring advanced prompt engineering expertise, thus eliminating the need for complex prompt structures.Information["Antonio Julio L\u00f3pez-Galisteo", "Oriol Borr\u00e1s-Gen\u00e9"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/7cc1ce17c309b0ad178e9c125afa5f27a2130048 68ad5cdabfff26ba6087fb4cf5f59a8416bbe506Digital twins (DTs) represent a transformative technology in manufacturing, facilitating significant advancements in monitoring, simulation, and optimization. This paper offers an extensive bibliographic review of AI-Based DT applications, categorized into three principal dimensions: operator, process, and product. The operator dimension focuses on enhancing safety and ergonomics through intelligent assistance, utilizing real-time monitoring and artificial intelligence, notably in human–robot collaboration contexts. The process application concerns itself with optimizing production flows, identifying bottlenecks, and dynamically reconfiguring systems through predictive models and real-time simulations. Lastly, the product dimension emphasizes the applications focused on the improvements in product design and quality, employing lifecycle and historical data to satisfy evolving market requirements. This categorization provides a structured framework for analyzing the specific capabilities and trends of DTs, while also identifying knowledge gaps in contemporary research. This review highlights the key challenges of technological interoperability, data integration, and high implementation costs while emphasizing how digital twins, supported by AI, can drive the transition toward sustainable, human-centered manufacturing systems in line with Industry 5.0. The findings provide valuable insights for advancing the state of the art and exploring future opportunities in digital twin applications.Electronics1140This review highlights the key challenges of technological interoperability, data integration, and high implementation costs while emphasizing how digital twins, supported by AI, can drive the transition toward sustainable, human-centered manufacturing systems in line with Industry 5.0.Electronics["David Alfaro-Viquez", "Mauricio-Andr\u00e9s Zamora-Hern\u00e1ndez", "Michael-Alejandro Fernandez-Vega", "Jos\u00e9 Garc\u00eda-Rodr\u00edguez", "J. Azor\u00edn-L\u00f3pez"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/68ad5cdabfff26ba6087fb4cf5f59a8416bbe506 44075a1518336563b9514cb56fe18ab888a9cdb9Moves toward integration of Artificial Intelligence (AI), particularly deep learning and generative AI-based technologies, into the domains of healthcare and public health have recently intensified, with a growing body of literature tackling the ethico-political implications of this. This paper considers the interwoven epistemic, sociopolitical and technical ramifications of healthcare-AI entanglements, examining how AI materialities shape emergence of particular modes of healthcare organization, governance and roles, and reflecting on how to embed participatory engagement within these entanglements. We discuss the implications of socio-technical entanglements between AI and Evidence-Based Medicine (EBM) for equitable development and governance of health AI. AI applications invariably center on the domains of medical knowledge and practice that are amenable to computational workings. This, in turn, intensifies the prioritization of these medical domains and furthers the assumptions which support the development of AI, a move which decontextualizes the qualitative nuances and complexities of healthcare while simultaneously advancing infrastructure to support these medical domains. We sketch the material and ideological reconfiguration of healthcare which is being shaped by the move toward embedding health AI assemblages in real-world contexts. We then consider the implications of this, how AI might be best employed in healthcare, and how to tackle the algorithmic injustices which become reproduced within health AI assemblages.Frontiers in Sociology810The material and ideological reconfiguration of healthcare is sketched which is being shaped by the move toward embedding health AI assemblages in real-world contexts, and how to tackle the algorithmic injustices which become reproduced within health AI assemblages is considered.Frontiers in Sociology["Imo Emah", "SJ Bennett"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/44075a1518336563b9514cb56fe18ab888a9cdb9 b393d38769b7c0c27285e0f42cd61df6c11c2949The modern welfare state has taken help from artificial intelligence (AI) (to enhance their operations) in order to suppress their appetite for good governance. India is an economically vibrant and welfare-oriented state in South Asia; however, it shares a 15,200-km-long border with seven other countries. The border that India is sharing is crisscrossed by varied geographical terrain inclusive of rivers, forests, mountains, deserts and so on. Geographical diversity marks the border to be porous, and poorly guarded. Porous borders and unstable neighbourhood enliven the ‘refuge emigration’ a persistent problem in India. The crucial stage in addressing the persistent refugee issue involves the process of refugee status determination (RSD). The bipartite standard of ‘well-founded fear of persecution’ is a key aspect of RSD (it entails assessing both the claimant’s subjective fear and the objective validation of that fear). However, credibility assessment, country of origin information analysis, risk prediction and decision support systems are many of the facets in which AI can provide valuable assistance. This research article explores the potential of AI in revolutionizing the process of RSD. Additionally, it delves into the ethical considerations, human rights implications and data protection regulations that must be carefully addressed when integrating AI into this critical domain.Journal of Victimology and Victim Justice30The potential of AI in revolutionizing the process of refugee status determination is explored and the ethical considerations, human rights implications and data protection regulations that must be carefully addressed when integrating AI into this critical domain are delves into.Journal of Victimology and Victim Justice["Archana Gadekar", "Akhilendra Singh"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/b393d38769b7c0c27285e0f42cd61df6c11c2949 ff4d2057892b30ab87c8d48b8968d27e4f4dd4d7In this paper, the viewpoints of people with disabilities are also examined, focusing on the positive impact that modern innovations based on artificial intelligence will have on these people's lives. Currently, more than one billion people worldwide have a disability, and AI offers an opportunity in the areas of mobility, communication and cognitive functions. Mobility assistive technologies with AI, like innovative wheelchairs and exoskeletons, enhance users' independence by providing adaptive self-driven support and control. In interaction, speech recognition and text-to-speech technologies help the physically disabled interact with others. Cognitive support technologies are applications that assist users with memory problems, autism, or learning disabilities and serve to manage activities and train cognition. The paper also identifies major technologies that underpin these innovations, such as Natural Language Processing, Computer Vision and Machine Learning. However, there has not been a total eradication of the challenges associated with using AI, including but not limited to massive costs, data privacy acts, and biased AI models. Other ethical factors should be considered to make access fair for all. The future of AI-enabled assistive technologies is with Smart Cities & IoT: Embracing social connectedness and improving health and well-being. Efforts by policymakers, technologists, and society are needed to develop solutions that are open, cheap, and user-centered.Journal of Engineering Research and Reports00The viewpoints of people with disabilities are examined and the positive impact that modern innovations based on artificial intelligence will have on these people's lives are focused on, focusing on the positive impact that modern innovations based on artificial intelligence will have on these people's lives.Journal of Engineering Research and Reports["Vedant Singh"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/ff4d2057892b30ab87c8d48b8968d27e4f4dd4d7 bbebd7af04a0cf6687a23a2de095db8ee19c028dContract Lifecycle Management (CLM) is experiencing a revolutionary transformation through the integration of artificial intelligence technologies. This comprehensive article explores how AI-driven automation is reshaping the entire contract management landscape, from document creation to risk assessment and compliance monitoring. It examines the technical foundations of AI-powered document creation, including advanced language models, natural language processing, and semantic analysis capabilities. The article investigates the evolution of version control systems, workflow automation frameworks, and sophisticated risk assessment methodologies. It delves into machine learning pipelines for continuous improvement and explores future directions in CLM, including blockchain integration, enhanced natural language understanding, and cross-language contract management capabilities. Through detailed analysis of real-world implementations and industry research, this article demonstrates how AI-driven CLM solutions are enabling organizations to achieve significant improvements in operational efficiency, compliance accuracy, and risk management while reducing costs and manual intervention in contract processing.International Journal of Scientific Research in Computer Science Engineering and Information Technology10This article demonstrates how AI-driven CLM solutions are enabling organizations to achieve significant improvements in operational efficiency, compliance accuracy, and risk management while reducing costs and manual intervention in contract processing.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Rincy Soman"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/bbebd7af04a0cf6687a23a2de095db8ee19c028d e2511ab2b7b9926ea90df7355dd424ef476aef69This article examines the organisational and geographical forces that shape the supply chains of artificial intelligence (AI) through outsourced and offshored data work. Bridging sociological theories of relational inequalities and embeddedness with critical approaches to Global Value Chains, we conduct a global case study of the digitally enabled organisation of data work in France, Madagascar, and Venezuela. The AI supply chains procure data work via a mix of arm's length contracts through marketplace-like platforms, and of embedded firm-like structures that offer greater stability but less flexibility, with multiple intermediate arrangements. Each solution suits specific types and purposes of data work in AI preparation, verification, and impersonation. While all forms reproduce well-known patterns of exclusion that harm externalised workers especially in the Global South, disadvantage manifests unevenly in different supply chain structures, with repercussions on remunerations, job security and working conditions. Unveiling these processes of contemporary technology development provides insights into possible policy implications.New Political Economy230A global case study of the digitally enabled organisation of data work in France, Madagascar, and Venezuela is conducted, finding disadvantage manifests unevenly in different supply chain structures, with repercussions on remunerations, job security and working conditions.New Political Economy["Paola Tubaro", "Antonio A. Casilli", "Maxime Cornet", "Cl\u00e9ment Le Ludec", "Juana Torres Cierpe"]2025-02-07T00:00:00https://www.semanticscholar.org/paper/e2511ab2b7b9926ea90df7355dd424ef476aef69 c0ac0cb12dc19bd1913c2a5a9f147e3f7e3c4aa4In recent years, with the rapid development of science and technology, a series of different tools have come into sight of people. Currently, with the rapid development of science and technology, classes and schools are in great revolutions. Also, a series of high-tech tools with AI is integrating the region of education, in which the classes and teachers are facing great challenges in the aspect of stages, teaching approaches, etc. It is true that such tools help teachers as well as students enhance their working efficiency. However, Artificial intelligence also brings a series of troubles in the region of education like moral issues and cheating as well as over-reliance on technology when people are using them improperly. In this case, people need to bring some approaches into the application of Artificial Intelligence to guide people to apply Artificial Intelligence Properly. Therefore, educators should emphasize the norms of AI use, and learners should focus on strengthening academic self-discipline.Lecture Notes in Education Psychology and Public Media00Teachers should emphasize the norms of AI use, and learners should focus on strengthening academic self-discipline to guide people to apply Artificial Intelligence Properly.Lecture Notes in Education Psychology and Public Media["Qiushi Wang"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/c0ac0cb12dc19bd1913c2a5a9f147e3f7e3c4aa4 f09102c32a118ba6b2db325a5296306aa2742b15One of the most significant challenges students currently face is the task of writing and publishing research papers. This process requires a systematic and distinctive approach, including comprehensive analysis, critical interpretation, and the ability to synthesize research on a specific topic. In the current academic and professional landscape, proficiency in artificial intelligence (AI) has become an essential prerequisite for students to excel in this domain. Although artificial intelligence (AI) has become increasingly important in academic research, there remains a paucity of studies exploring its application and impact. Therefore, the present study was conducted to examine the awareness and ascertain the efficacy of using Free and Open-Source Software (FOSS) AI tools in academic research among M.Sc. (Ag.) students at CPGS-AS, CAU (I), Umiam, Ri-Bhoi, Meghalaya. The study adopted a formative research design to achieve its objectives. Convenient sampling procedure was carried out in order to select 62 respondents. The scientific inquiry revealed that the majority of respondent (67.74%) were aware of Software Ownership; while 75.80% were knowledgeable about the features of FOSS, particularly its freedom to use FOSS AI tools. Additionally, more than half of the respondents (67.2%) reported learning about FOSS AI tools through social media and the internet. However, majority of respondents (79.03%) demonstrated low awareness of the use of FOSS AI tools in academic research. A statistically significant difference was found in the respondents' scores before and after the intervention of FOSS AI tools in academic research, indicating a marked improvement in efficacy among the students.Archives of Current Research International00A statistically significant difference was found in the respondents' scores before and after the intervention of FOSS AI tools in academic research, indicating a marked improvement in efficacy among the students.Archives of Current Research International["Pankaj Kumar", "R. J. Singh", "L. Devarani", "Th Onchoila Maring"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/f09102c32a118ba6b2db325a5296306aa2742b15 49cfeed0c567ce78b57f7fa085918c17ed0ae5c5There are various challenges to teachers' use of generative artificial intelligence (GenAI) for professional learning. Although GenAI is expected to play a transformative role in teachers' learning, its impact on them remains subtle.Guided by community of practice, this paper examines the integration of GenAI into an online professional learning community (OPLC) to facilitate knowledge co‐construction among GenAI, novice teachers and experienced teachers.We used a mixed‐methods approach that included topic modelling and sentiment analysis on the quantitative side and content analysis for the qualitative data.We identified the top three latent themes in the OPLC's discourse—(1) generating instructional material, (2) assessment, and (3) pedagogy—and six distinct teacher‐GenAI interaction profiles. For novice teachers, these included ‘engaged AI explorers’, ‘selective satisfiers’ and ‘silent strategists’; and among experienced teachers, we discerned ‘careful critics’, ‘reflective realists’ and ‘cautious contemplators’. Novice teachers exhibited technological adaptivity, while experienced ones engaged reflectively with content and focused more on students, and GenAI proved effective at providing instructional materials.The findings demonstrate how GenAI can contribute to knowledge co‐construction, as a facilitator of rather than a replacement for human interaction.Journal of Computer Assisted Learning490Examination of the integration of GenAI into an online professional learning community (OPLC) to facilitate knowledge co‐construction demonstrates how GenAI can contribute to knowledge co‐construction, as a facilitator of rather than a replacement for human interaction.Journal of Computer Assisted Learning["Fangzhou Jin", "Xiangmei Peng", "Lanfang Sun", "Zicong Song", "Keyi Zhou", "Chin-Hsi Lin"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/49cfeed0c567ce78b57f7fa085918c17ed0ae5c5 cd780552b2b77567b32e91ab10e29462cee71933Discover Oncology590A systematic review examines the role of AI in predicting breast cancer recurrence using clinical data, imaging data, and combined datasets and finds support Vector Machine (SVM) and Neural Networks demonstrate strong potential in improving prediction accuracy.Discover Oncology["Jaqueline Alvarenga Silveira", "Alexandre Ray da Silva", "Mariana Zuliani Theodoro de Lima"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/cd780552b2b77567b32e91ab10e29462cee71933 c37cce6943f477ca8042c9aa3ed24e2463a01105The introduction of artificial intelligence (AI) in agriculture has made significant improvements in farm management possible by offering innovative ways to optimize farming operations. This review article brings together over 100 articles published in the last ten years through a systematic search of databases on specific keywords related to AI and agriculture. The research paper covers the use of AI in machinery automation, pest detection, irrigation control, and monitoring agriculture. The results obtained show a 25% increase in crop yields in precision farming techniques by AI and machine learning and a decrease in water usage by up to 30% as opposed to traditional farming practices. In addition, AI-based pest identification has reduced pesticide application by 20% and encouraged sustainable agriculture. Crop yield estimation has now improved in terms of decision-making capability since it has significantly yielded 92% accuracy levels by using AI-driven predictive models. Further, studies indicate that the maintenance cost is decreased by 18% and fuel consumption is decreased by 15% in optimized operations with AI-based farm machinery management systems. The agriculture industry can increase productivity and sustainability to a greater extent by implementing AI in the management of farms to overcome the problems arising out of the world’s ever-increasing population.International Journal of Smart Agriculture640The research paper covers the use of AI in machinery automation, pest detection, irrigation control, and monitoring agriculture and shows a 25% increase in crop yields in precision farming techniques by AI and machine learning and a decrease in water usage by up to 30% as opposed to traditional farming practices.International Journal of Smart Agriculture["Mrutyunjay Padhiary", "Kundan Kumar", "Nabiul Hussain", "Dipak Roy", "Javed Akhtar Barbhuiya", "Pankaj Roy"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/c37cce6943f477ca8042c9aa3ed24e2463a01105 d22b1f69bf2e0dffec9c7e7bfde51ddfac13fd35Penelitian ini bertujuan untuk mengembangkan media pembelajaran berbasis kecerdasan buatan, mendeskripsikan hasil uji kelayakannya oleh validator ahli, dan menguji keefektifan produk media. Penelitian ini menggunakan jenis penelitian pengembangan atau Research and Development R&D) dengan model pengembangan ADDIE melalui lima tahapan berikut, Analysis (analisis), Design (perancangan), Development (pengembangan), Implementasion (implementasi), dan Evaluation (evaluasi) khususnya pada topik perbandingan bilangan untuk siswa kelas II. Analisis kebutuhan yang telah dilakukan, peneliti menemukan bahwa siswa kelas II di SDN Cepoko, Kota Semarang, mengalami kesulitan dalam memahami dan membedakan tanda perbandingan bilangan. Hal ini mengakibatkan mereka mengalami kendala dalam menentukan tanda perbandingan dengan tepat. Penelitian ini mengembangkan media pembelajaran yang berbasis Artificial Intelligence pada materi perbandingan bilangan untuk kelas II. Subjek penelitian dalam penelitian ini adalah peserta didik dan guru kelas II SDN Cepoko Kota Semarang. Teknik analisis data yang digunakan dalam penelitian ini meliputi uji normalitas, uji T-test, dan uji N-gain. Hasil penelitian menunjukan bahwa produk media yang dikembangkan layak dan efektif untuk digunakan sebagai media pembelajaran. Hal tersebut dibuktikan dengan diperolehnya presentase kelayakan sebesar 90% dari validator ahli materi dan 95,5% dari validator ahli media dengan kategori sangat layak. Media Panda (Papan Perbandingan) berbasis Artificial Intelligence juga mendapatkan tanggapan yang sangat positif dari guru dan peserta didik sebagai pengguna media, selain itu media Panda (Papan Perbandingan) berbasis Artificial Intelligence juga menunjukan adanya peningkatan hasil pretest dan postest yang diperoleh peserta didik.JagoMIPA: Jurnal Pendidikan Matematika dan IPA00JagoMIPA: Jurnal Pendidikan Matematika dan IPA["Liana Anggi Dwi Ningtias", "Yuli Witanto"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/d22b1f69bf2e0dffec9c7e7bfde51ddfac13fd35 6428b91e877538414d4749df8becf7452ebded33Technological developments – particularly related to artificial intelligence (AI), machine learning, and digitalization – are disrupting the workplace in unprecedented ways, particularly in professional and knowledge‐intensive sectors. Scholars' views on the implications of these disruptions range from optimism and pessimism to scepticism. Disciplines vary in how extensively they have considered the implications of these technological developments. With much prior work focusing on the more macro‐level phenomena and effects, the role of institutions, organizations and individuals – as well as their interrelatedness – remains less examined. In this introductory article to the special issue, we discuss the scope, extent and new domains of change related to the Future of Work and, especially, to AI. We also reflect on the consequences of these changes as well as the related processes and mechanisms through which they will manifest. Then, we introduce and summarize the articles included in this special issue along the above dimensions. We conclude by reflecting on the overall contribution of the special issue and on future directions for examining the Future of Work from the perspective of management studies.Journal of Management Studies650The scope, extent and new domains of change related to the Future of Work and, especially, to AI are discussed and the consequences of these changes as well as the related processes and mechanisms through which they will manifest are reflected.Journal of Management Studies["R. Sarala", "Corinne Post", "Jonathan P. Doh", "Daniel Muzio"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/6428b91e877538414d4749df8becf7452ebded33 5dfd8105dababba4a53b1d2625e94154b761077cCurrent research on entrepreneurial leadership in small and medium enterprises focuses on innovation with the mediating role of artificial intelligence and employee ambidexterity. This study aim to explore the impact of artificial intelligence and employees' ambidexterity on innovation in small and medium enterprises within the manufacturing sector. The data for this research was collected from small and medium enterprises using convenience sampling. The target organization conducted a survey with managerial-level employees and utilized SEM for data analysis and path analysis. The development of entrepreneurial leadership and small and medium enterprises has greatly contributed to economic development in many countries around the world.  This economic development is mainly reliant on innovation in small and medium enterprises. However, despite their significant economic contributions, Pakistan's small and medium enterprises have not received adequate attention in this regard. With the growth of technological advancements and globalization, small and medium enterprises are now striving to enhance their competencies by adopting new know-how to penetrate global markets. This study focuses on innovation by developing a model that investigates the relationship between entrepreneurial leadership and innovation in Pakistan's manufacturing sector, with the aim of promoting the country's economic strength. Public sector entities like SMEs may also be tasked by the government to conduct such training for incumbents, especially in the less educated segment, which forms part of our SMEs.Journal for Social Science Archives430This study aim to explore the impact of artificial intelligence and employees' ambidexterity on innovation in small and medium enterprises within the manufacturing sector of Pakistan with the aim of promoting the country's economic strength.Journal for Social Science Archives["Noor ul Amin", "Tayyaba Shehzad", "Muhammad Yousuf Rajput", "Wajid Ali"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/5dfd8105dababba4a53b1d2625e94154b761077c e78768584332efe86abe8fd7611a682065413e02Artificial intelligence (AI) is poised to transform the way marketing activities are conducted and, as such, how educators prepare students for the ever-changing marketing landscape. As AI becomes increasingly integrated into marketing practices, it is crucial to examine its potential impact on marketing education. This research investigates the use of AI in a marketing Promotions Strategy course, a key component of marketing curriculum and one that poses challenges to students who struggle with creativity. Drawing on literature concerning creative anxiety, this research empirically examines how AI can shape students’ perceptions, experiences, and outcomes when faced with projects that demand creativity. The results of this study suggest that AI can serve as a valuable tool in assisting students to overcome the obstacles and challenges often stemming from a deficiency in creative expertise and proficiency in creative technologies.Journal of Marketing Education390The results of this study suggest that AI can serve as a valuable tool in assisting students to overcome the obstacles and challenges often stemming from a deficiency in creative expertise and proficiency in creative technologies.Journal of Marketing Education["E. D. Brocato", "Cassandra Davis"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/e78768584332efe86abe8fd7611a682065413e02 86e706be140017b37403e24b9cd82e4c544653cfArtificial intelligence (AI) has become a major force for change in the healthcare industry, especially in diagnostic systems, where AI plays a key role in improving the accuracy, speed, and efficiency of diagnosis. The deep integration of AI with internet-based platforms has revolutionized the way healthcare is delivered, especially in low-resource settings, where AI provides a scalable solution for healthcare systems. This article explores the application of artificial intelligence in medical diagnosis, focusing on how AI technologies such as machine learning and deep learning can be integrated into areas such as medical imaging, disease prediction, and telemedicine. The article also discusses the rapid development of the internet-based healthcare system, emphasizing the role of AI in improving diagnostic models through real-time data collection and analysis. At the same time, the article also analyzes current challenges, such as data privacy, ethical issues, and regulatory challenges, which limit the widespread application of AI in clinical practice. Through a comprehensive review of existing research, this article outlines the potential and limitations of AI in medical diagnostics and provides insights into its future development trends. The findings show that although AI has great potential to improve the quality and accessibility of healthcare, its application in clinical practice still needs to carefully consider many factors such as ethics, technology, and regulation.Advances in Economics, Management and Political Sciences00The findings show that although AI has great potential to improve the quality and accessibility of healthcare, its application in clinical practice still needs to carefully consider many factors such as ethics, technology, and regulation.Advances in Economics, Management and Political Sciences["Shangxuan Li"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/86e706be140017b37403e24b9cd82e4c544653cf d2236b0b6aff10fd5929e3ce493cb7341ae0739eThis study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using the Explainable Artificial Intelligence (XAI) method. This study focuses on Seoul, South Korea, where an aging population and urban challenges create a pressing need for pedestrian safety research. The analysis reveals that aggressive driving behaviors, particularly rapid acceleration, rapid deceleration, and speeding, are the most influential factors on the frequency of and deaths from elderly pedestrian traffic accidents. In addition, several built environments and demographic factors such as the number of crosswalks and elderly population play varying roles depending on the spatial match or mismatch between risky driving areas and accident spots. The findings of this study underscore the importance of tailored interventions including well-lit crosswalks, traffic calming measures, and driver education, to reduce the vulnerabilities of elderly pedestrians. The integration of XAI methods provides transparency and interpretability, enabling policymakers to make data-driven decisions. Expanding this approach to other urban contexts with diverse characteristics could validate and refine the findings, contributing to a comprehensive strategy for improving pedestrian safety globally.Applied Sciences500Applied Sciences["Minjun Kim", "Dongbeom Kim", "Jisup Shim"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/d2236b0b6aff10fd5929e3ce493cb7341ae0739e 68607dcfc40f610954931a7c69356a1df6ce8204Artificial intelligence (AI) is a growing area of computer science that combines technologies with data science to develop intelligent, highly computation-able systems. Its ability to automatically analyze and query huge sets of data has rendered it essential to many fields such as healthcare. This article introduces you to artificial intelligence, how it works, and what its central role in biomedical engineering is. It brings to light new developments in medical science, why it is being applied in biomedicine, key problems in computer vision and AI, medical applications, diagnostics, and live health monitoring. This paper starts with an introduction to artificial intelligence and its major subfields before moving into how AI is revolutionizing healthcare technology. There is a lot of emphasis on how it will transform biomedical engineering through the use of AI-based devices like biosensors. Not only can these machines detect abnormalities in a patient’s physiology, but they also allow for chronic health tracking. Further, this review also provides an overview of the trends of AI-enabled healthcare technologies and concludes that the adoption of artificial intelligence in healthcare will be very high. The most promising are in diagnostics, with highly accurate, non-invasive diagnostics such as advanced imaging and vocal biomarker analyzers leading medicine into the future.Bioengineering780This paper starts with an introduction to artificial intelligence and its major subfields before moving into how AI is revolutionizing healthcare technology, and concludes that the adoption of artificial intelligence in healthcare will be very high.Bioengineering["Divya Tripathi", "Kasturee Hajra", "Aditya Mulukutla", "Romi Shreshtha", "D. Maity"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/68607dcfc40f610954931a7c69356a1df6ce8204 0b9569d04cda97b4cbd4bfd07a2ee18c160c27c0The tourism industry around the world is constantly being challenged to adopt sustainability due to environmental imperatives and changed traveler preferences. This paper examines the transformative role that artificial intelligence can take in the post-pandemic era in the development of sustainable tourism. From applications in environmental monitoring, resource optimization, carbon footprint reduction, waste management, and developing personalized sustainable experiences, AI can be seen as a crucial enabler of tourism in tune with ecological goals. Besides, the paper mentions challenges such as costs, technical barriers, and ethical ones, keeping in mind balanced implementation approaches. In any case, AI, from the perspective of sustainable tourism, opens opportunities for the protection of ecosystems and experiences for visitors. Research Journal of Economics and Business Management00This paper examines the transformative role that artificial intelligence can take in the post-pandemic era in the development of sustainable tourism and mentions challenges such as costs, technical barriers, and ethical ones, keeping in mind balanced implementation approaches.Research Journal of Economics and Business Management["Zijun Zhao"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/0b9569d04cda97b4cbd4bfd07a2ee18c160c27c0 764acc7cfb48c625e0edd18efe8242d4cdfa3e34Artificial intelligence (AI) has the potential to revolutionize eating disorder research, treatment, and practice by assisting with complex problems such as predicting illness prognosis, supporting diagnostic decisions, tailoring treatment plans, and even data analysis and study design choices. Yet, research on the applications of AI in eating disorders remains limited. This editorial discusses the importance of AI, explores practical applications, and outlines key directions for future research. To accelerate progress, the International Journal of Eating Disorders will publish a special issue on AI in this context, anticipated in December 2025.International Journal of Eating Disorders30The importance of AI is discussed, practical applications are explored, and key directions for future research are outlined, which will accelerate progress on the applications of AI in eating disorders.The International journal of eating disorders["Jake Linardon", "M. Fuller-Tyszkiewicz"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/764acc7cfb48c625e0edd18efe8242d4cdfa3e34 9401adb797d5347329e236e6fe5d5820250c50dcAt present, artificial intelligence has emerged as a significant field all over the world, offering diverse enhancements across many industries when combined with it. This study primarily examines the combination of the Internet of Things and artificial intelligence, highlighting practical applications and areas requiring enhancement. Mainly from a literature review utilising the three keywords: artificial intelligence, the Internet of Things, and application for search and screening, it was discovered that there are several issues in the application of this field. There are issues with delayed data transmission, compatibility, and anomaly detection in the field of autonomous driving. In the field of wearable devices, there are data security issues. In the field of industrial Internet of Things, there are problems such as low transmission efficiency, high latency, and inability to effectively resist external network attacks. In addition, there are also trust issues in the field of artificial intelligence where decisions and predictions cannot be understood by people. In besides this, this study provides several of recommendations for improvements in further research.Applied and Computational Engineering00This study primarily examines the combination of the Internet of Things and artificial intelligence, highlighting practical applications and areas requiring enhancement and provides several of recommendations for improvements in further research.Applied and Computational Engineering["Weijie Liu"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/9401adb797d5347329e236e6fe5d5820250c50dc a8c3a04ba7ff87983b6964468bfe7c897233b17bThis study reveals the definition of human resource management by examining definitions proposed by various theorists and attempting to arrive at new definitions. Previous studies have identified several benefits that can be achieved through the use of human resource management within an organization. In this study, partial least squares structural equation modeling (PLS-SEM) was used in statistical software (Smart PLS, version 4.0.8.9) to analyze data and measure the role of artificial intelligence in human resource management at Najaf International Airport, serving 120 employees. The results of this study showed that artificial intelligence plays a role in human resource management at Najaf International Airport. The study’s key recommendations include linking employee incentives for this new technological skill (artificial intelligence) with employee learning if Najaf International Airport aims to enhance the adoption of new working methods.International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)520The results of this study showed that artificial intelligence plays a role in human resource management at Najaf International Airport and key recommendations include linking employee incentives for this new technological skill (artificial intelligence) with employee learning if Najaf International Airport aims to enhance the adoption of new working methods.International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)["Hasan Fadhil", "AL-Thabhawee"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/a8c3a04ba7ff87983b6964468bfe7c897233b17b b70c0f9f4ce2a54382444ecc93ebcd0cf5945437Artificial intelligence has made its mark on the media industry in Romania, and television is one of the sectors most affected by its development. This paper analyzes through a quantitative method the impact of artificial intelligence (AI) on television from the perspective of media industry professionals in Romania. The research was conducted usinga quantitative method based on a structured questionnaire. The study focuses on the responses of 128 journalists working in local and national TV stations directly involved in content creation and editorial or production processes. We selected this sample because media specialists have the knowledge to express informed opinions on this subject. The survey results show that artificial intelligence is increasingly used in Romanian newsrooms. Television professionals believe that artificial intelligence tools are helpful and that they can improve the quality of content. However, at the same time, there are serious concerns about the possibility that jobs could be affected. In addition, the risk of misinformation is growing with the increasing use of artificial intelligence tools. Findings suggest that strategic, industry-wide regulations and ethical guidelines are essential to balance AI adoption while safeguarding media integrity. This research may serve media organizations, policymakers, and academia in formulating informed approaches toward AI.Journalism and Media410It is suggested that strategic, industry-wide regulations and ethical guidelines are essential to balance AI adoption while safeguarding media integrity while safeguarding media integrity.Journalism and Media["\u0218tefan Vl\u0103du\u021bescu", "G. St\u0103nescu"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/b70c0f9f4ce2a54382444ecc93ebcd0cf5945437 31e93b395548f96ab57d8b82c0d786735b7f4216This study investigates the application of Artificial Intelligence (AI) in business management, assessing its contributions to decision-making, operational efficiency, and customer service. It highlights both the opportunities and challenges presented by AI, with an analysis of case studies from companies such as Airbnb and Nordstrom to illustrate AIs influence on business processes and innovation. The research concludes that AI confers substantial benefits, including cost savings and data-informed decision-making, while also identifying critical issues such as labor market disruptions and data security that necessitate attention. The adoption of AI is contingent upon a companys size and resources, necessitating bespoke strategies for both large enterprises and small and medium-sized enterprises (SMEs).Advances in Economics, Management and Political Sciences00The research concludes that AI confers substantial benefits, including cost savings and data-informed decision-making, while also identifying critical issues such as labor market disruptions and data security that necessitate attention.Advances in Economics, Management and Political Sciences["Hao Mai"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/31e93b395548f96ab57d8b82c0d786735b7f4216 7c931ad8712cd991e2e2a42b5e4455ce85984458AI &amp; SOCIETY260AI &amp; SOCIETY["Dina Abulkassova", "Gulnara Muldasheva", "Mirbulat Nurtazin", "Nurzhan Tleukhanov", "Aigerim Kuspanova"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/7c931ad8712cd991e2e2a42b5e4455ce85984458 722be31a28e9f5225b61a042e70957a009727786International Journal of Education and Management Engineering00International Journal of Education and Management Engineering["Mahmoud Mohamed", "Khaled Alosman"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/722be31a28e9f5225b61a042e70957a009727786 bd490d21ebf6273cd973133809acf170fb9da41dJournal of the Academy of Marketing Science700Journal of the Academy of Marketing Science["Jifeng Mu", "Jonathan Z. Zhang"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/bd490d21ebf6273cd973133809acf170fb9da41d 86f489426e111e80cd843e45a4044addf33119c5Around 50 percent of Irelands rural population relies on unregulated private wells vulnerable to agricultural runoff and untreated wastewater. High national rates of Shiga toxin-producing Escherichia coli (STEC) and other waterborne illnesses have been linked to well water exposure. Periodic well testing is essential for public health, yet the lack of government incentives places the financial burden on households. Understanding environmental, cognitive, and material factors influencing well-testing behavior is critical. This study employs Agent-Based Modeling (ABM) to simulate policy interventions based on national survey data. The ABM framework, designed for private well-testing behavior, integrates a Deep Q-network reinforcement learning model and Explainable AI (XAI) for decision-making insights. Key features were selected using Recursive Feature Elimination (RFE) with 10-fold cross-validation, while SHAP (Shapley Additive Explanations) provided further interpretability for policy recommendations. Fourteen policy scenarios were tested. The most effective, Free Well Testing plus Communication Campaign, increased participation to 435 out of 561 agents, from a baseline of approximately 5 percent, with rapid behavioral adaptation. Free Well Testing plus Regulation also performed well, with 433 out of 561 agents initiating well testing. Free testing alone raised participation to over 75 percent, with some agents testing multiple times annually. Scenarios with free well testing achieved faster learning efficiency, converging in 1000 episodes, while others took 2000 episodes, indicating slower adaptation. This research demonstrates the value of ABM and XAI in public health policy, providing a framework for evaluating behavioral interventions in environmental health.190This research demonstrates the value of ABM and XAI in public health policy, providing a framework for evaluating behavioral interventions in environmental health.["Rabia Asghar", "S. Mooney", "Eoin \u00d3 N\u00e9ill", "Paul Hynds"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/86f489426e111e80cd843e45a4044addf33119c5 fe43bdbc304f511c7ee6f443d86edb758db2687dDisasters, crises, and resilience are interconnected with a general comprehension of “normality” or everyday routine disrupted by sudden and adverse events. However, some inconsistencies in the above interpretation induce an epistemological and existential crisis. First, the everyday life of some disadvantaged groups can be described as catastrophic and miserable whether the general community recognizes it or not. Nevertheless, some of the usually resilient groups could become future icons of the new risk, particularly AI hazards. Second, disasters are, by definition, sudden events with identified timeframes, while crises can be long-lasting with the tendency to become omnipresent. Third, when compared with earlier assertions, particular groups may undergo a long-lasting and gradual crisis that diminishes their capacity to anticipate future events, a critical aspect of resilience, and influences the social structure. An exemplary case is the unregulated widespread use of artificial intelligence (AI) by students to complete tasks, which diminishes critical thinking and reduces significant cognitive engagement. Such actions are possible with the cultural complicity of various stakeholders. Ultimately, the dystopian vision of a mindless and non-resilient young populace within an already susceptible context of an aging society—particularly with the increasing prevalence of dementia—reveals novel vulnerabilities, signalling the onset of an impending disaster. The suggestion made in this paper is for the research and teaching community to play a more active role in mitigating, if not preventing, potential unintended yet not-so-unforeseeable consequences.Stanovništvo720It is suggested for the research and teaching community to play a more active role in mitigating, if not preventing, potential unintended yet not-so-unforeseeable consequences of disasters.Stanovnistvo["Veselin Mitrovi\u0107"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/fe43bdbc304f511c7ee6f443d86edb758db2687d 41ed56aa03aeb928350d3ba62ec4bce39b632a08Artificial intelligence’s (AI) quick development has had a big impact on education, especially in industries like electronic media production that call for both creative expression and practical abilities. In a classroom action research setting, this project investigates how to use AI-powered learning activities to improve students’ proficiency in producing electronic media. We conducted the research with 15 undergraduate students from Kasetsart University’s Digital Technology for Education (DTE) program. Students utilized AI-driven tools, including StoryboardThat, Canva, Microsoft Bing, CapCut, and Adobe Premiere Pro, to complete four media production tasks: storyboard, 3D objects, infographics, and multimedia. The findings revealed that AI tools, particularly user-friendly platforms like Canva and CapCut, significantly improved students’ technical skills and creative capabilities, with high satisfaction reported for tools that simplified complex tasks. Reflective journals indicated enhanced efficiency, creativity, and self-assessment among students. However, tools with complex interfaces, such as Adobe Premiere Pro, presented challenges, underscoring the need for targeted instructional support. This study highlights the potential of AI in fostering both technical proficiency and creative autonomy in media production education while also identifying areas for future improvement in AI tool accessibility and usability.Journal of Education and Learning00The findings revealed that AI tools, particularly user-friendly platforms like Canva and CapCut, significantly improved students’ technical skills and creative capabilities, with high satisfaction reported for tools that simplified complex tasks.Journal of Education and Learning["Thanapat Sripan", "Nutwichida Lertpongrujikorn"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/41ed56aa03aeb928350d3ba62ec4bce39b632a08 ddade43787a194bd1789941ba85d73948590b89aArtificial Intelligence (AI) technologies have revolutionized a wide range of industries by providing cutting-edge solutions that streamline operations, improve decision-making accuracy, and deliver highly personalized user experiences. From automating routine tasks in manufacturing and logistics to enabling advanced data analysis in healthcare and finance, AI has become a critical tool for enhancing productivity and optimizing outcomes.This paper examines three critical AI applicationsautonomous driving, natural language processing (NLP), and facial recognitionto analyze the factors influencing their performance and reliability. The study identifies data quality, algorithm optimization, and deployment environment as pivotal elements that determine the effectiveness and fairness of these systems. Through a comparative analysis, this paper highlights how challenges such as data diversity, algorithmic bias, and environmental constraints impact system outcomes. It also explores strategies for improving accuracy, adaptability, and fairness in real-world settings. Given the rapid evolution of AI, the study emphasizes the importance of continuous innovation and incorporating user feedback into system design. Future research directions include analyzing the adaptive capabilities of AI systems and developing methods for better integrating user insights, ensuring AI's sustained advancement in addressing complex societal needs.Applied and Computational Engineering00This paper examines three critical AI applications autonomous driving, natural language processing (NLP), and facial recognition to analyze the factors influencing their performance and reliability and explores strategies for improving accuracy, adaptability, and fairness in real-world settings.Applied and Computational Engineering["Weiming Wang"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/ddade43787a194bd1789941ba85d73948590b89a 5ef74f7db4d5643ea87e931a6b4d00afb3c26d5eSmart farming is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence (AI) to improve crop recommendations. Despite the advancements, a critical gap exists in opaque ML models that need to explain their predictions, leading to a trust deficit among farmers. This research addresses the gap by implementing explainable AI (XAI) techniques, specifically focusing on the crop recommendation technique in smart farming. An experiment was conducted using a Crop recommendation dataset, applying XAI algorithms such as Local Interpretable Model-agnostic Explanations (LIME), Differentiable InterCounterfactual Explanations (dice_ml), and SHapley Additive exPlanations (SHAP). These algorithms were used to generate local and counterfactual explanations, enhancing model transparency in compliance with the General Data Protection Regulation (GDPR), which mandates the right to explanation. The results demonstrated the effectiveness of XAI in making ML models more interpretable and trustworthy. For instance, local explanations from LIME provided insights into individual predictions, while counterfactual scenarios from dice_ml offered alternative crop cultivation suggestions. Feature importance from SHAP gave a global perspective on the factors influencing the model's decisions. The study's statistical analysis revealed that the integration of XAI increased the farmers' understanding of the AI system's recommendations, potentially reducing food insufficiency by enabling the cultivation of alternative crops on the same land.International Journal of Intelligent Systems and Applications560The study's statistical analysis revealed that the integration of XAI increased the farmers' understanding of the AI system's recommendations, potentially reducing food insufficiency by enabling the cultivation of alternative crops on the same land.International Journal of Intelligent Systems and Applications["Yaganteeswarudu Akkem", "S. K. Biswas", "Aruna Varanasi"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/5ef74f7db4d5643ea87e931a6b4d00afb3c26d5e 5d5660864eba465e8d339707b93e21569f741c8bIn the era of transformative technologies, generative artificial intelligence (genAI) offers profound opportunities and challenges for education. This study explores the development and execution of an interactive workshop designed to equip educators with foundational genAI literacy. Using a design-based research (DBR) framework, the workshop leverages interactivity and contextual relevance to introduce genAI concepts, prompting strategies and ethical considerations. Participants engaged in a scripted learning workshop design, comparing human and AI responses, exploring genAI’s probabilistic foundations, context dependency, and vulnerability to manipulation. Conducted across 12 workshops with 191 participants in Austria, this study revealed significant improvements in self-perceived genAI understanding, with 70% of participants reporting better grades in post-assessment evaluations. Feedback emphasized the workshop’s strengths in interactivity and relevance, alongside recommendations for deeper school-specific applications. Scalability analysis showed that workshop duration remained consistent regardless of group size, suggesting potential for broader implementation. The findings highlight the effectiveness of scripted learning workshop design in fostering critical AI literacy, preparing educators to critically evaluate and ethically integrate genAI into pedagogical practices. This adaptable model contributes to the discourse on professional development in AI-enhanced education.Education sciences150This study revealed significant improvements in self-perceived genAI understanding, with 70% of participants reporting better grades in post-assessment evaluations, and Scalability analysis showed that workshop duration remained consistent regardless of group size, suggesting potential for broader implementation.Education Sciences["Benedikt Br\u00fcnner", "Sandra Sch\u00f6n", "Martin Ebner"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/5d5660864eba465e8d339707b93e21569f741c8b b302dc639948ee31ec698717fad4e80027821e2eExisting network paradigms have achieved lower downtime as well as a higher Quality of Experience (QoE) through the use of Artificial Intelligence (AI)-based network management tools. These AI management systems, allow for automatic responses to changes in network conditions, lowering operation costs for operators, and improving overall performance. While adopting AI-based management tools enhance the overall network performance, it also introduce challenges such as removing human supervision, privacy violations, algorithmic bias, and model inaccuracies. Furthermore, AI-based agents that fail to address these challenges should be culpable themselves rather than the network as a whole. To address this accountability gap, a framework consisting of a Deep Reinforcement Learning (DRL) model and a Machine Learning (ML) model is proposed to identify and assign numerical values of responsibility to the AI-based management agents involved in any decision-making regarding the network conditions, which eventually affects the end-user. A simulation environment was created for the framework to be trained using simulated network operation parameters. The DRL model had a 96% accuracy during testing for identifying the AI-based management agents, while the ML model using gradient descent learned the network conditions at an 83% accuracy during testing.00A framework consisting of a Deep Reinforcement Learning (DRL) model and a Machine Learning (ML) model is proposed to identify and assign numerical values of responsibility to the AI-based management agents involved in any decision-making regarding the network conditions, which eventually affects the end-user.["Emanuel Figetakis", "Ahmed Refaey Hussein"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/b302dc639948ee31ec698717fad4e80027821e2e 92d31705a12c2374e8eaaf28fab2fa369026c298Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and, more specifically, machine learning systems. This paper reviews a hybrid-AI approach known as"error detecting and correcting rules"(EDCR) that allows for the learning of rules to correct perceptual (e.g., neural) models. Additionally, we introduce a probabilistic framework that adds rigor to prior empirical studies, and we use this framework to prove results on necessary and sufficient conditions for metacognitive improvement, as well as limits to the approach. A set of future90A hybrid-AI approach that allows for the learning of rules to correct perceptual models and introduces a probabilistic framework that adds rigor to prior empirical studies, and uses this framework to prove results on necessary and sufficient conditions for metacognitive improvement.["Paulo Shakarian", "Gerardo I. Simari", "Nathaniel D. Bastian"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/92d31705a12c2374e8eaaf28fab2fa369026c298 a23fca27b24239dace11e110c693510f7f59d331This study examines the integration of an Artificial Intelligence (AI) feedback coach in a peer feedback activity. Participants provided peers with feedback on their assignments. While providing feedback, they either received real-time adaptive AI coaching (intervention group) or not (control group). Feedback comments from participants were analyzed concerning content, text complexity, and sentiment. Survey responses were coded for sentiment and themes. Results show adverse effects of the AI feedback coach. Intervention group participants’ feedback included fewer reflective questions and adhered less to criteria. They provided shorter, more complex feedback. Students indicated mixed views on the AI feedback coach, with some finding it helpful and others distracting. A notable subset of students stated overreliance on the AI coach, prioritizing its validation over their own judgment. Results suggest that AI tools need thoughtful integration, possibly with additional scaffolding to counteract overreliance and avoid negative impact on peer feedback quality and feedback experience.Tidsskriftet Læring og Medier (LOM)600This study examines the integration of an Artificial Intelligence feedback coach in a peer feedback activity and suggests that AI tools need thoughtful integration, possibly with additional scaffolding to counteract overreliance and avoid negative impact on peer feedback quality and feedback experience.Tidsskriftet Læring og Medier (LOM)["Rasmus Hansen", "Christopher Neil Prilop", "Tobias Alsted Nielsen", "Karen Louise M\u00f8ller", "Rikke Fr\u00f8hlich Hougaard", "Annika B\u00fcchert Lindberg"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/a23fca27b24239dace11e110c693510f7f59d331 81512860172185ce6625cad02c1ea3998a2e4989As globalization accelerates and market dynamics grow increasingly complex, supply chain management has emerged as a pivotal factor in determining corporate competitiveness. Concurrently, the rapid advancement of artificial intelligence (AI) technology has positioned its application as a critical solution for addressing supply chain optimization challenges. This study reviews the challenges faced by AI applications in supply chain management, proposes feasible optimization solutions and suggestions, and aims to help enterprises correctly apply AI technology to achieve cost reduction and efficiency improvement. Research has found that AI applications currently face challenges such as high initial investment, data security, and talent shortages. To address these issues, the study focuses on three critical industriesautomotive, medical, and information technologyanalyzing their unique challenges and exploring targeted solutions. This article suggests that enterprises reduce investment risks, improve data access mechanisms, and strengthen talent cultivation and introduction, to promote the broader integration of artificial intelligence within supply chain operations.Advances in Economics, Management and Political Sciences00It is suggested that enterprises reduce investment risks, improve data access mechanisms, and strengthen talent cultivation and introduction, to promote the broader integration of artificial intelligence within supply chain operations.Advances in Economics, Management and Political Sciences["Chenyuxi Zhu"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/81512860172185ce6625cad02c1ea3998a2e4989 d24ab3ad57ca0e40aed619d7292e991cfe51085b In today's rapidly evolving digital landscape, cybersecurity threats have become increasingly sophisticated, necessitating advanced solutions to protect sensitive information and systems. Integrating Artificial Intelligence (AI) into cybersecurity offers a proactive approach to threat detection and response. Python, renowned for its simplicity and extensive library support, serves as an ideal language for developing AI-driven cybersecurity tools. This article delves into the process of creating such tools using Python, highlighting key considerations and practical implementations.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This article delves into the process of creating AI-driven cybersecurity tools using Python, highlighting key considerations and practical implementations.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Pankaj Pandey"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/d24ab3ad57ca0e40aed619d7292e991cfe51085b 59d4a4b03a59444575b0f882343cbc0f10911bdeThe advent of large language models has ushered in a new era of agentic systems, where artificial intelligence programs exhibit remarkable autonomous decision-making capabilities across diverse domains. This paper explores agentic system workflows in the financial services industry. In particular, we build agentic crews that can effectively collaborate to perform complex modeling and model risk management (MRM) tasks. The modeling crew consists of a manager and multiple agents who perform specific tasks such as exploratory data analysis, feature engineering, model selection, hyperparameter tuning, model training, model evaluation, and writing documentation. The MRM crew consists of a manager along with specialized agents who perform tasks such as checking compliance of modeling documentation, model replication, conceptual soundness, analysis of outcomes, and writing documentation. We demonstrate the effectiveness and robustness of modeling and MRM crews by presenting a series of numerical examples applied to credit card fraud detection, credit card approval, and portfolio credit risk modeling datasets.810This paper builds agentic crews that can effectively collaborate to perform complex modeling and model risk management tasks and demonstrates the effectiveness and robustness of modeling and MRM crews by presenting a series of numerical examples applied to credit card fraud detection, credit card approval, and portfolio credit risk modeling datasets.["Izunna Okpala", "Ashkan Golgoon", "Arjun Ravi Kannan"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/59d4a4b03a59444575b0f882343cbc0f10911bde ec49cfd51f9f65137df65c43d12bb5cee91029ffThis research explores the impact of postmodern artificial intelligence (AI) on philosophy, arts, politics, and ethics. It aims to analyze how AI reflects postmodern thought, focusing on concepts like relativism and multiplicity. The study also investigates AI's potential effects in various fields, including creativity, decision-making, and societal norms. The methodology includes a critical review of existing literature and case studies on AI applications. Key findings reveal that AI in the postmodern era challenges traditional notions of truth, authority, and creativity, while raising ethical concerns about bias, privacy, and job displacement. The research calls for clear ethical frameworks, education initiatives, and human-AI collaboration to address these challenges and ensure a balanced future between technology and humanity.Studies in Media and Communication00Analysis of how AI reflects postmodern thought reveals that AI in the postmodern era challenges traditional notions of truth, authority, and creativity, while raising ethical concerns about bias, privacy, and job displacement.Studies in Media and Communication["Mohammad Slman Alkhazaleh", "Tamara Al Shloul", "Azhar Shater", "Hussein Almajali", "Jihan Mousa"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/ec49cfd51f9f65137df65c43d12bb5cee91029ff ee26a2f9d0dc0dd472c00bbb1da67913cf80bdb3When making decisions in high-pressure situations, police officers experience cognitive demands and often lack access to data about the people with whom they are interacting. Artificial intelligence (AI) tools that provide such data can potentially improve officers’ ability to respond effectively to calls and thus bolster public safety. However, research in diverse social sciences has documented persistent biases in AI-assisted work. We propose a framework for understanding how bias can creep into AI-assisted police work and how to intervene. In a cycle of bias, AI tools provide biased information to officers, which in turn promotes biased responses during interactions with the public, ultimately resulting in biased incident reports that amplify the original biases in the AI systems. Our proposed interventions focus on training and nudges that increase officers’ use of deliberative processing, empathic mindsets, and perspective-getting techniques and encourage the writing of detailed, debiased incident reports. We recommend taking a cognitive view of policing and drawing on insights from behavioral science research to maximize the benefits of AI tools while minimizing the risk that they will amplify biases.Behavioral Science &amp; Policy330This work proposes a framework for understanding how bias can creep into AI-assisted police work and how to intervene, and recommends taking a cognitive view of policing and drawing on insights from behavioral science research to maximize the benefits of AI tools while minimizing the risk that they will amplify biases.Behavioral Science &amp; Policy["Andrea G. Dittmann", "Kyle S. H. Dobson", "Shane Schweitzer"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/ee26a2f9d0dc0dd472c00bbb1da67913cf80bdb3 203c31cbb837c2d7b355d241f4c0ff4a7dcddb59 El estudio sobre la Integración de la Inteligencia Artificial en la Educación de Tercer Año de Bachillerato en la Unidad Educativa Amazonas de Quito identificó retos clave como la falta de capacitación docente y recursos tecnológicos insuficientes, estas limitaciones dificultan la adopción de herramientas innovadoras que podrían personalizar el aprendizaje y automatizar procesos. El estudio, basado en encuestas a 30 docentes revelaron un bajo conocimiento sobre la aplicación de la IA, pero una actitud positiva hacia su aprendizaje. Aunque se perciben beneficios como el seguimiento individualizado, la falta de formación y apoyo institucional sigue siendo una barrera. Para abordar estos desafíos, se diseñó un programa de capacitación centrado en herramientas como ChatGPT, Kahoot, Avatarify IA, entre otras. Este programa busca capacitar a los docentes no solo en el manejo técnico de estas herramientas, sino también en los impactos éticos y pedagógicos de la IA. Al aprender a integrar estas herramientas, los docentes podrán personalizar el aprendizaje, optimizar la planificación y evaluación pedagógica, y mejorar el seguimiento del desempeño estudiantil. El programa tiene como objetivo transformar la enseñanza, haciendo de la IA un medio para enriquecer la experiencia educativa, aumentar la eficiencia y mejorar la calidad del proceso pedagógico. Así, los docentes se convierten en facilitadores de un aprendizaje más interactivo, adaptado a las necesidades de los estudiantes, y preparados para un entorno educativo cada vez más tecnológico.MQRInvestigar00MQRInvestigar["Kevin Joel Toapanta-Vizuete", "Gisela Marlene Zambrano-Pino", "C. V. Ram\u00edrez-Guti\u00e9rrez", "Odette MART\u00cdNEZ-PEREZ"]2025-02-08T00:00:00https://www.semanticscholar.org/paper/203c31cbb837c2d7b355d241f4c0ff4a7dcddb59 566d953fc3e2ec7acffb97e0b97edf30115a1f0eInternational Journal of Computer Applications Technology and Research00International Journal of Computer Applications Technology and Research[]2025-02-08T00:00:00https://www.semanticscholar.org/paper/566d953fc3e2ec7acffb97e0b97edf30115a1f0e 7aacfef8c75354d979a84cb91c6f7b33412ed335Corporate environmental performance (CEP) has emerged as a matter of topmost importance within the business domain, as organizations increasingly acknowledge the exigency of integrating sustainable practices into their core operational frameworks. Drawing from the natural resource‐based view, institutional, stakeholder, and legitimacy theories, this study hypothesizes a positive relationship for the impact of artificial intelligence (AI) on Chinese listed firms' CEP, moderated by the roles of state ownership (SOWN) and external stakeholders, i.e., external auditor (BIG4), financial analysts (FA), and media coverage (MC). The study analyzes a comprehensive dataset of 9033 firm‐year observations from Chinese A‐share listed firms on the Shanghai and Shenzhen stock exchanges over the period 2010–2019. AI is measured through computer‐assisted textual analysis of annual reports, while CEP is quantified using environmental responsibility scores from an independent rating agency (HEXUN‐RKS). Employing fixed effects panel regression models, empirical findings reveal a positive and significant relationship between AI and CEP. The results for moderation effects demonstrate that BIG4 and FA significantly strengthen the positive AI–CEP relationship, but MC does not. Moreover, the study's heterogeneity analyses indicate that the positive impact of AI on CEP is more pronounced in decision‐effective firms and highly competitive industries. Importantly, the main findings of the study were validated through instrumental variables, lag and lead models, two‐step system generalized method of moments, and an alternate proxy for CEP. The study contributes new insights on AI's environmental impacts in emerging economies like China undergoing digital transformation. It highlights how ownership structure and heightened external oversight compel firms to leverage AI for enhancing CEP.Business Strategy and the Environment830How ownership structure and heightened external oversight compel firms to leverage AI for enhancing CEP is highlighted, and the positive impact of AI on CEP is more pronounced in decision‐effective firms and highly competitive industries.Business Strategy and the Environment["Shaohui Wang", "Yanlan Yong", "Murtaza Hussain", "Umer Sahil Maqsood", "R. M. A. Zahid"]2025-02-09T00:00:00https://www.semanticscholar.org/paper/7aacfef8c75354d979a84cb91c6f7b33412ed335 dd828b6ed03830ad55923bc86550d8c218cb4ba0The integration of Artificial Intelligence (AI) in information systems has revolutionized the way organizations process, manage, and utilize data. AI-driven information systems are becoming more adaptive, intelligent, and capable of autonomous decision-making, enabling businesses to improve efficiency and responsiveness. This study explores the various techniques and technologies used to integrate AI into information systems, including machine learning, natural language processing, and expert systems. It also examines the benefits of AI-enhanced systems, such as improved data analysis, automation, and predictive capabilities. However, challenges such as data privacy, ethical concerns, and system complexity must be addressed to ensure effective implementation. This study employs a literature review, case studies, and system analysis. By analyzing current trends and case studies, this research provides insights into the future direction of AI-driven information systems and their potential impact on industries. Traditional information systems were primarily designed for data management and process automation, but the integration of AI has enabled these systems to perform complex decision-making, predictive analytics, and autonomous operations.The future of AI in information systems lies in the advancement of explainable AI (XAI), federated learning, and AI governance frameworks. As AI continues to evolve, integrating ethical AI principles and ensuring regulatory compliance will be essential for sustainable adoption. Interdisciplinary collaboration between AI researchers, policymakers, and industry experts is crucial for addressing ethical and regulatory challenges.Journal of Information Systems Engineering & Management300This study explores the various techniques and technologies used to integrate AI into information systems, including machine learning, natural language processing, and expert systems, and examines the benefits of AI-enhanced systems, such as improved data analysis, automation, and predictive capabilities.Journal of Information Systems Engineering and Management["Y. Widodo", "Rano Agustino"]2025-02-09T00:00:00https://www.semanticscholar.org/paper/dd828b6ed03830ad55923bc86550d8c218cb4ba0 1cba075d03c0745f6bb854108688997f1cce9c77With advanced algorithms, artificial intelligence (AI) has revolutionized the medical diagnostic field where diseases can be predicted simultaneously. The integrative nature of this approach is novel because it can better encompass the complexity of comorbid conditions that are so common in patients; thus, addressing them in a more holistic diagnostic tone that is lacking in previous works. In this study, the investigation of the usage of AI models for simultaneously diagnosing diseases like diabetes, cardiovascular conditions, and neurological disorders is done. Therefore, based on AI techniques i.e. artificial neural networks (ANNs) and ensemble learning methods, a multi-disease diagnostic framework was developed to achieve this. A variety of features, related to each condition, were captured from multi-modal datasets including imaging, laboratory test results, and patient histories. The system was developed to manage the big flow of aggregated data and offer detailed diagnostic views of many diseases. Sensitivity, specificity, and overall diagnostic accuracy were used to evaluate the framework's performance. The results showed that the AI framework has high diagnostic accuracy for all targeted conditions an overall sensitivity of 93% and a specificity of 91%. Importantly, the combination of multi-modal data proved to substantially improve the system’s ability to identify and distinguish comorbid conditions. It makes the importance of using various data sources to benefit from the reliability and comprehensiveness of AI diagnostics obvious. Overall, AI-driven multi-disease diagnostic systems provide great promise for the role of delivering potentially transformative clinical healthcare workflow improvements, reducing errors, and improving patient outcomes. These frameworks will need to be scaled and tested in various healthcare settings and also across more varied diseases to help make medical diagnosis more available and effective.Journal of Computer Science and Technology Studies660The combination of multi-modal data proved to substantially improve the system’s ability to identify and distinguish comorbid conditions and provide great promise for the role of delivering potentially transformative clinical healthcare workflow improvements, reducing errors, and improving patient outcomes.Journal of Computer Science and Technology Studies["Nigar Sultana", "Shariar Islam Saimon", "Intiser Islam", "Shake Ibna Abir", "Md Sanjit Hossain", "Sarder Abdulla Al Shiam", "Nazrul Islam Khan"]2025-02-09T00:00:00https://www.semanticscholar.org/paper/1cba075d03c0745f6bb854108688997f1cce9c77 edfb9a7c9cd7753c58fef23c7e95c743b4ca2816The development and implementation of Artificial Intelligence (AI) health systems represent a great power that comes with great responsibility. Their capacity to improve and transform healthcare involves inevitable risks. A major risk in this regard is the propagation of bias throughout the life cycle of the AI system, leading to harmful or discriminatory outcomes. This paper argues that the European medical device regulations may prove inadequate to address this-not only technical but also social challenge. With the advent of new regulatory remedies, it seems that the European policymakers also want to reinforce the current medical device legal framework. In this paper, we analyse different policies to mitigate bias in AI health systems included in the Artificial Intelligence Act and in the proposed European Health Data Space. As we shall see, the different remedies based on processing sensitive data for such purpose devised by the European policymakers may have very different effects both on privacy and on protection against discrimination. We find the focus on mitigation during the pre-commercialisation stages rather weak, and believe that bias control once the system has been implemented in the real world would have merited greater ambition.Bioethics00It is argued that the European medical device regulations may prove inadequate to address the propagation of bias throughout the life cycle of the AI system, leading to harmful or discriminatory outcomes.Bioethics["Guillermo Lazcoz", "I. de Miguel"]2025-02-09T00:00:00https://www.semanticscholar.org/paper/edfb9a7c9cd7753c58fef23c7e95c743b4ca2816 bd0ac121674905550d6f78389a5bf55a603be5c0The 20th CPC Central Committee stressed that the key to high-quality economic development is to cultivate new quality productive forces, and AI plays a key role in cultivating new quality productive forces. This paper takes A-share listed enterprises in China from 2013 to 2022 as a sample, constructs comprehensive level indicators of AI from the strategic side, application side, and innovation side of enterprises’ AI, and empirically examines the impact, mechanism, and spatial spillover effect of AI development on enterprises’ new quality productive forces from the perspective of digital intelligence empowerment and the spatial perspective. The results of this study show that AI can significantly promote the development of new productivity, and the development of AI within enterprises can promote the improvement of new productivity levels of neighboring enterprises or regions. At the same time, the role of AI in promoting the development of new quality productive forces is more obvious when the enterprise is a private enterprise, the managers have a digital background, and the enterprise is located in an industry with fierce market competition or a strategic industry. The purpose of this paper is to reveal the mechanism and spatial spillover effect of AI in promoting the new quality productive forces of enterprises and to provide a new theoretical basis and research perspective for enterprises to cultivate new quality productive forces.Systems520The results of this study show that AI can significantly promote the development of new productivity, and the development of AI within enterprises can promote the improvement of new productivity levels of neighboring enterprises or regions.Systems["Xiumin Li", "Haojian Tang", "Zishuo Chen"]2025-02-09T00:00:00 d95a32b428eecd235da4bef996bdb1db78542133OECD Artificial Intelligence Papers00OECD Artificial Intelligence Papers[]2025-02-09T00:00:00 c65318ed4993aef2a2179626a0f875dffec72c37GEORGIAN SCIENTISTS00GEORGIAN SCIENTISTS["Zviad Gurtskaia", "Natia Kenchadze", "Marina Basilashvili"]2025-02-09T00:00:00 9feaa7f40f4436d257412251f32d7c66cc227366International Journal of Dermatology40International journal of dermatology["S.P. Parraga", "J.Q. Duong", "M. Agner", "S. Shanmugam", "S.L. Taylor", "S.R. Feldman"]2025-02-09T00:00:00 94c14108db47b8ffa62503b8f2b1f118422b1ccdAfter a brief, general introduction to AI, the present article will discuss whether AI itself has freedom of expression or whether it only entitles to tech companies that own AI. All this is relevant in the context of whether we can consider AI a separate legal actor in the context of content creation on social media, which is the second main issue of this article. Here, negative and positive cases of content moderation will be presented, i.e., whether the full spectrum of content moderation in social media can be entrusted to AI as a separate actor with respect to liability, or whether moderation requires some form of human control, direction or supervision.European Journal of Law and Political Science470Whether AI itself has freedom of expression or whether it only entitles to tech companies that own AI is discussed, and whether moderation requires some form of human control, direction or supervision is presented.European Journal of Law and Political Science["Gergely Gosztonyi", "Dorina Gyetv\u00e1n", "Andrea Kov\u00e1cs"]2025-02-09T00:00:00 cb775a3f89fcc3144ff645ae5b3bfe7339aea1e800["Horst Treiblmaier"]2025-02-09T00:00:00 986ada82dbcfce891b8200764bb067afbb8aac61The rapid advancements in artificial intelligence (AI), machine learning (ML), neural networks (NN) and language models (LM) research, coupled with the widespread availability of large language models as a service (LLMaaS), have begun to influence most domains, particularly the field of statistics, in unprecedented ways that are difficult to forecast. The awarding of two Nobel Prizes in 2024 for computational work in AI—to Hopfield and Hinton for their foundational discoveries and inventions in machine learning with artificial neural networks and to Baker, Hassabis and Jumper for developing an AI model to solve the longstanding problem of predicting proteins' complex structures—is a testament to the significant impact of AI in these fields. Two key contributors for the current revolution are statistics and data science. The merger of data science with AI research led to the creation of tools like LLMs, profound advancements in AI as a tool and speculations of humanity being close to creating AGI. These transformative technologies have opened up a vast array of opportunities, but they have also presented new challenges that necessitate careful consideration. Here, we discuss what is needed to successfully navigate these stormy times in the current sea of information surrounding us.Stat70What is needed to successfully navigate these stormy times in the current sea of information surrounding us is discussed.Stat["Joaquin Carbonara", "Ernest Fokoue"]2025-02-09T00:00:00 fa2ffb8af2e4240db22be60e770fa40c9597e041In chess, zugzwang describes a scenario where any move worsens the player's position. Organizations face a similar dilemma right now at the intersection of artificial intelligence (AI) and cybersecurity. AI adoption creates an inevitable paradox: delaying it poses strategic risks, rushing it introduces poorly understood vulnerabilities, and even incremental adoption leads to cascading complexities. In this work we formalize this challenge as the AI Security Zugzwang, a phenomenon where security leaders must make decisions under conditions of inevitable risk. Grounded in game theory, security economics, and organizational decision theory, we characterize AI security zugzwang through three key properties, the forced movement, predictable vulnerability creation, and temporal pressure. Additionally, we develop a taxonomy to categorize forced-move scenarios across AI adoption, implementation, operational and governance contexts and provide corresponding strategic mitigations. Our framework is supported by a practical decision flowchart, demonstrated through a real-world example of Copilot adoption, thus, showing how security lead600This work characterize AI security zugzwang through three key properties, the forced movement, predictable vulnerability creation, and temporal pressure, and develops a taxonomy to categorize forced-move scenarios across AI adoption, implementation, operational and governance contexts and provide corresponding strategic mitigations.["Lampis Alevizos"]2025-02-09T00:00:00 7a0e6b037a4f72809a809046aa43a8a5fda90dd5As one of the main pillars of global transformation in the contemporary world, the digital economy helps create new economic and business opportunities through new technologies. In addition to improving efficiency and reducing costs, this transformation plays a vital role in the economic growth and development of various countries. Artificial intelligence, as one of the key technologies in the development of the digital economy, has a profound impact on optimizing processes, increasing productivity, and enhancing customer experience. By processing big data and providing advanced analytics, this technology makes economic decisions faster and more accurately and affects various sectors of the digital economy. In this regard, 20 key AI-based criteria in the development of the digital economy were extracted from a review of previous studies and were placed in four general categories. The four general categories include structural, organizational, technological and economic. Hesitant Fuzzy Best Worst Method (HF-BWM) was used to rank the AI-based criteria in the development of the digital economy. “Investing in innovation (C16)”, “Potent processing capabilities (C1)”, “Process automation and intelligence (C11)”, “Identifying growth opportunities (C6)” and “Adapting business models to changes (C7)” ranked one to five, respectively. Managers in the digital economy should pay attention to investing in innovation and strengthening processing infrastructure to exploit new technologies and make more accurate decisions. Process intelligence, identifying new areas of growth and adapting the business model to market changes also help improve efficiency, reduce costs, exploit new opportunities and make organizations stable in the face of rapid changes and increasing competition.Journal of Soft Computing and Decision Analytics00Managers in the digital economy should pay attention to investing in innovation and strengthening processing infrastructure to exploit new technologies and make more accurate decisions in the face of rapid changes and increasing competition.Journal of Soft Computing and Decision Analytics["Mahmoudreza Entezami", "Sepideh Basirat", "Behzad Moghaddami", "Danial Bazmandeh", "Dorsa Charkhian"]2025-02-09T00:00:00 2d53fe05eebb72e1ddbd3b28cdc2ea968d8eeb99The fragmented landscape of data privacy laws in the United States poses significant challenges for organizations utilizing artificial intelligence (AI) systems that process sensitive and large-scale data. Variations in state laws and the absence of a comprehensive federal framework exacerbate compliance complexities, limiting AI innovation and creating legal uncertainties. This paper proposes a conceptual model to harmonize privacy compliance across U.S. jurisdictions, integrating key interoperability principles, consistency, transparency, and scalability. The framework emphasizes standardized practices for data classification, consent management, risk assessment, and enforcement mechanisms supported by technological enablers such as privacy-enhancing technologies and AI compliance tools. Through case studies in healthcare, e-commerce, and finance, the paper demonstrates the framework’s practical application and effectiveness in resolving multi-jurisdictional compliance challenges. Actionable recommendations for policymakers, organizations, and AI developers are provided to facilitate implementation alongside future research directions to refine the model and address emerging privacy risks. This study offers a roadmap for navigating the complexities of U.S. privacy laws, promoting trust, accountability, and responsible AI innovation. Keywords: AI data governance, U.S. privacy laws, Cross-jurisdictional compliance, Privacy-enhancing technologies, Data protection framework, Ethical AI.Gulf Journal of Advance Business Research00A conceptual model to harmonize privacy compliance across U.S. jurisdictions is proposed, integrating key interoperability principles, consistency, transparency, and scalability, and supporting technological enablers such as privacy-enhancing technologies and AI compliance tools.Gulf Journal of Advance Business Research["Grace Annie Chintoh", "Osinachi Deborah Segun-Falade", "Chinekwu Somtochukwu Odionu", "Amazing Hope Ekeh"]2025-02-09T00:00:00 4ecb881db58f4eaf650ef7fe10346fd02968bbf1MindCraft is a modern platform designed to revolutionize education in rural India by leveraging Artificial Intelligence (AI) to create personalized learning experiences, provide mentorship, and foster resource-sharing. In a country where access to quality education is deeply influenced by geography and socio economic status, rural students often face significant barriers in their educational journeys. MindCraft aims to bridge this gap by utilizing AI to create tailored learning paths, connect students with mentors, and enable a collaborative network of educational resources that transcends both physical and digital divides. This paper explores the challenges faced by rural students, the transformative potential of AI, and how MindCraft offers a scalable, sustainable solution for equitable education system. By focusing on inclusivity, personalized learning, and mentorship, MindCraft seeks to empower rural students, equipping them with the skills, knowledge, and opportunities needed to thrive in an increasingly digital world. Ultimately, MindCraft envisions a future in which technology not only bridges educational gaps but also becomes the driving force for a more inclusive and empowered society.120The challenges faced by rural students, the transformative potential of AI, and how MindCraft offers a scalable, sustainable solution for equitable education system are explored are explored.["Arihant Bardia", "Aayush Agrawal"]2025-02-09T00:00:00 b52af8604aef884166fa639da57d0c0db11a8d66This mixed methods study investigates the factors influencing the acceptance and utilization of Artificial Intelligence (AI) tools among students and associates in a Philippine higher education institution, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study reveals that both groups exhibit high familiarity with AI and utilize it for various academic tasks, with performance expectancy and facilitating conditions identified as the primary drivers of acceptance. The study employed a cross-sectional design with an embedded parallel mixed-methods approach. An online survey questionnaire was used to investigate the usage and acceptance of AI tools among students and associates. The findings underscore the importance of comprehensive training, transparent AI governance, and ethical guidelines to foster responsible AI integration in academia. The study also discusses the ethical considerations surrounding AI's use in education, emphasizing the need for responsible implementation to maximize its benefits while minimizing potential risks.Journal of Social and Scientific Education400The study reveals that both groups exhibit high familiarity with AI and utilize it for various academic tasks, with performance expectancy and facilitating conditions identified as the primary drivers of acceptance.Journal of Social and Scientific Education["Jose Noel V. Fabia", "Vanessa Napoles", "Joselito Eduard Goh", "Mateo R. Borbon Jr."]2025-02-09T00:00:00 6847a6357d588ab3f22703c12e72b4d7523d12b2In this review, we propose a cybersecurity framework aimed at enhancing fraud detection in financial systems by leveraging artificial intelligence (AI), microservices, and RESTful architectures. With the increasing sophistication of cyber threats targeting financial institutions, traditional security methods often fall short in providing comprehensive protection. This review outlines how AI and microservices can be integrated to secure sensitive financial data and improve fraud detection. The framework employs AI-driven models for real-time anomaly detection, enabling systems to quickly identify suspicious activities and predict fraud patterns before they escalate. Microservices architecture, built using technologies such as Java Spring Boot, enables scalability, flexibility, and enhanced communication between modular components through secure RESTful APIs. Angular is utilized for building secure user interfaces, ensuring data protection across front-end applications. Additionally, the integration of security testing platforms such as SonarQube and Blackduck plays a critical role in continuously monitoring and inspecting code for vulnerabilities, ensuring that any flaws in the system are promptly addressed. This comprehensive approach not only safeguards financial institutions from potential fraud but also strengthens the U.S. financial infrastructure, contributing to the nation’s defense against cyber threats. By leveraging cutting-edge technologies and best practices, this framework offers a scalable, secure, and adaptive solution for the evolving challenges in cybersecurity and fraud prevention within the financial sector. The proposed framework enhances operational efficiency while mitigating risks, making it a valuable addition to modern cybersecurity strategies Keywords: Cybersecurity Framework, Fraud Detection, Financial Systems, Microservices, Artificial intelligence.Gulf Journal of Advance Business Research00A cybersecurity framework aimed at enhancing fraud detection in financial systems by leveraging artificial intelligence (AI), microservices, and RESTful architectures that enhances operational efficiency while mitigating risks, making it a valuable addition to modern cybersecurity strategies.Gulf Journal of Advance Business Research["Eseoghene Kokogho", "Princess Eloho Odio", "Olakojo Yusuff Ogunsola", "Mark Osemedua Nwaozomudoh"]2025-02-09T00:00:00 5bdb37dfd7d4062651153baaeac9a5ec7004aceeThe rapid adoption of artificial intelligence (AI) in U.S. consumer services has transformed customer interactions, operational efficiency, and service delivery. However, this technological shift presents complex challenges in maintaining compliance with data privacy regulations, such as the California Consumer Privacy Act (CCPA) and the Gramm-Leach-Bliley Act (GLBA). This paper explores the role of AI in enhancing smart services while safeguarding consumer privacy, highlighting key risks, compliance challenges, and regulatory gaps. A conceptual model is proposed to guide organizations in integrating privacy-by-design strategies, emphasizing transparency, consent management, and ethical AI principles. The paper also discusses emerging technologies and best practices that support privacy protection while leveraging AI-driven insights. Collaborative efforts between regulators and technology providers are recommended to foster innovation while ensuring robust data privacy. The findings provide practical strategies for balancing technological advancement with regulatory compliance, offering insights for policymakers, industry stakeholders, and service providers. Keywords: Artificial Intelligence, Consumer Privacy, Data Protection, Compliance Strategies, Privacy Regulations, Smart Services.Gulf Journal of Advance Business Research00A conceptual model is proposed to guide organizations in integrating privacy-by-design strategies, emphasizing transparency, consent management, and ethical AI principles, and practical strategies for balancing technological advancement with regulatory compliance are provided.Gulf Journal of Advance Business Research["Grace Annie Chintoh", "Osinachi Deborah Segun-Falade", "Chinekwu Somtochukwu Odionu", "Amazing Hope Ekeh"]2025-02-09T00:00:00 352a53ecfad7a4850942145aba4194b3ac665140With technology developing at such a swift pace, much transformation is observed in multinationals. As known, one of the most sensational inventions that is changing business processes and decision-making is Artificial Intelligence. In this paper, we will discuss the ethical problems arising when decisions involving artificial intelligence have to be made by a multinational firm and how the problems can be solved by the firms while using this latest technology to gain a competitive advantage as well as make moral decisions in a world that is becoming complex with each passing day. We presented a relevant case study, based on recent events from leading multinationals such as Coca-Cola as well as relevant examples from Microsoft and Amazon and showed how AI is needed for ethical decision making. By using Artificial Intelligence, numerous corporations can automate their processes, understand certain data and therefore effortlessly adapt to market adjustments. This will not only promote efficiency but also support multinational companies in making ethically effective decisions. As the company deals with problems in the global world, emphasis on ethical decision making serves as a key issue while attempting to build trust and integrity in their expansion efforts. The overall result is that ethical use of AI in business decision-making will bring about an overhauling of the corporate world and its advancement. Multinationals that will choose to adopt using AI ethically, stand tall and prepared for success in growth in a high-tech business atmosphere.London Journal of Interdisciplinary Sciences30The ethical problems arising when decisions involving artificial intelligence have to be made by a multinational firm are discussed and how the problems can be solved by the firms while using this latest technology to gain a competitive advantage as well as make moral decisions in a world that is becoming complex with each passing day.London Journal of Interdisciplinary Sciences["Tracey Nyaribo", "Shanice Mumbi", "Danish Bashir"]2025-02-09T00:00:00 a66b3254827da9c0c408e696d99fb3bb8b8af8a9Integrating sustainable practices with emerging technologies, such as Artificial Intelligence, has proven crucial to achieving new sustainable development goals. In the industrial context, project management is important in promoting sustainability. This work aims to review the literature on applying sustainable Artificial Intelligence in project management, focusing on optimizing resources and aligning with the SDGs. As a result, the research highlighted the good performance of the technological transformation of project management, combined with sustainability concepts, as an agent capable of generating the necessary transformations to guarantee a more sustainable future.JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH230The research highlighted the good performance of the technological transformation of project management, combined with sustainability concepts, as an agent capable of generating the necessary transformations to guarantee a more sustainable future.JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH["H\u00e9rica de Souza", "H\u00e9rica de Souza Ara\u00fajo", "Thiago Barros", "Anusio Menezes Murari1", "Erick G Correia1", "Sperandio Nascimento"]2025-02-09T00:00:00 9490ace28139a415068c538ef4a6f68a427657d9Recent advances in large-scale machine learning have produced high-capacity foundation models capable of adapting to a broad array of downstream tasks. While such models hold great promise for robotics, the prevailing paradigm still portrays robots as single, autonomous decision-makers, performing tasks like manipulation and navigation, with limited human involvement. However, a large class of real-world robotic systems, including wearable robotics (e.g., prostheses, orthoses, exoskeletons), teleoperation, and neural interfaces, are semiautonomous, and require ongoing interactive coordination with human partners, challenging single-agent assumptions. In this position paper, we argue that robot foundation models must evolve to an interactive multi-agent perspective in order to handle the complexities of real-time human-robot co-adaptation. We propose a generalizable, neuroscience-inspired architecture encompassing four modules: (1) a multimodal sensing module informed by sensorimotor integration principles, (2) an ad-hoc teamwork model reminiscent of joint-action frameworks in cognitive science, (3) a predictive world belief model grounded in internal model theories of motor control, and (4) a memory/feedback mechanism that echoes concepts of Hebbian and reinforcement-based plasticity. Although illustrated through the lens of cyborg systems, where wearable devices and human physiology are inseparably intertwined, the proposed framework is broadly applicable to robots operating in semi-autonomous or interactive contexts. By moving beyond single-agent designs, our position emphasizes how foundation models in robotics can achieve a more robust, personalized, and anticipatory level of performance.00This position paper argues that robot foundation models must evolve to an interactive multi-agent perspective in order to handle the complexities of real-time human-robot co-adaptation, and proposes a generalizable, neuroscience-inspired architecture encompassing four modules.["Sharmita Dey"]2025-02-09T00:00:00 a7827d4474ef010a04160d09c4cf5407e0705324Can we derive computational metrics to quantify visual creativity in drawings across intelligent agents, while accounting for inherent differences in technical skill and style? To answer this, we curate a novel dataset consisting of 1338 drawings by children, adults and AI on a creative drawing task. We characterize two aspects of the drawings -- (1) style and (2) content. For style, we define measures of ink density, ink distribution and number of elements. For content, we use expert-annotated categories to study conceptual diversity, and image and text embeddings to compute distance measures. We compare the style, content and creativity of children, adults and AI drawings and build simple models to predict expert and automated creativity scores. We find significant differences in style and content in the groups -- children's drawings had more components, AI drawings had greater ink density, and adult drawings revealed maximum conceptual diversity. Notably, we highlight a misalignment between creativity judgments obtained through expert and automated ratings and discuss its implications. Through these efforts, our work provides, to the best of our knowledge, the first framework for studying human and artificial creativity beyond the textual modality, and attempts to arrive at the domain-agnostic principles underlying creativity. Our data and scripts are available on GitHub.450This work curates a novel dataset consisting of 1338 drawings by children, adults and AI on a creative drawing task and provides the first framework for studying human and artificial creativity beyond the textual modality, and attempts to arrive at the domain-agnostic principles underlying creativity.["Surabhi S. Nath", "Guiomar del Cuvillo y Schroder", "Claire Stevenson"]2025-02-09T00:00:00 509a096215df6ddeba6def214ab9940662781a98The business landscape is being reshaped by artificial intelligence’s (AI) process automation and data aggregation. The consequence of this change is in accounting, where concerns about the unlimited use of automated processes are widespread. The paper aimed to comprehensively examine current trends and forecasts and to analyze the potential impact of these technologies on various areas of human activity and social processes. AI rapidly advances, offering opportunities for improved production, quality of life, and problem-solving. AI-powered systems can settle complex problems autonomously. Formulating and testing hypotheses, logical reasoning, and content analysis are foundational to major research methods. This allows to accumulate analytical material and review all the information collected during the research. The paper examines AI’s strengths and weaknesses to highlight its similarities and differences in human intelligence. The author explores AI’s history to set the stage for a discussion of its future effects on accounting. Also, the author analyzes these publications to predict the future of accounting in an AI-driven workplace. Artificial intelligence cannot easily replace human qualities. This study’s results, accessible to a broad audience, reveal that AI’s impact on accounting extends beyond automation and efficiency gains to a fundamental reshaping of the accountant’s organizational function. Professionals ready for a rapidly changing digital world will find new opportunities here.Accounting Analysis Auditing20It is revealed that AI’s impact on accounting extends beyond automation and efficiency gains to a fundamental reshaping of the accountant’s organizational function.Accounting. Analysis. Auditing["N. Nikiforova"]2025-02-10T00:00:00 9966d1475a3f52fa40e0c2ba7b75867714e37e5fPurposeHumanitarian supply chain management (HSCM), operating in a complex environment, needs to be agile and robust. The advent of digital technologies has revolutionized HSCM operations, and thus, this study identifies and evaluates key drivers of artificial intelligence (AI) incorporation in HSCM.Design/methodology/approachIn total, 20 key drivers were identified through a review of the relevant extant literature and finalized with experts’ inputs using a Likert scale survey. With a Kappa analysis, these drivers were classified into four groups: technical (T), organization (O), human (H) and institution (I). An integrated multi-criteria decision-making (MCDM) method of the Fermatean fuzzy set (FFS) analytic hierarchy process (AHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) was used to rank the key drivers and explore their causal interrelationships.FindingsImproved performance output, organizational preparedness, user acceptance and continued support, guarantee of job security for technologically semi-skilled workers and government support are the five key drivers of AI incorporation in HSCM.Originality/valueThis study evaluates the key drivers of AI integration in HSCM with FFS-AHP-DEMATEL.International Journal of Industrial Engineering and Operations Management570Improved performance output, organizational preparedness, user acceptance and continued support, guarantee of job security for technologically semi-skilled workers and government support are the five key drivers of AI incorporation in HSCM.International Journal of Industrial Engineering and Operations Management["Koppiahraj Karuppiah", "Jayakrishna Kandasamy", "Luis Rocha-Lona", "Christian Mu\u00f1oz S\u00e1nchez", "Rohit Joshi"]2025-02-10T00:00:00 95563e1024917b88326ad6871e43ba04c6ae05a0Artificial Intelligence has rapidly become a transformative force within the contemporary higher education landscape, altering how teaching methodologies, learning experiences, and administrative functions are structured and delivered. The advancement of AI technologies presents opportunities and challenges that institutions must carefully navigate. This study explores the multifaceted perspectives of participants in Science, Technology, Engineering, and Mathematics (STEM) fields, including educators, students, and technical staff, concerning the integration and implications of AI in higher education settings. To gather detailed insights into the lived experiences of these stakeholders, the research employs a qualitative methodology, utilizing in-depth interviews and focus group discussions. This approach allows for a rich collection of perspectives that reveal not only the enthusiasm surrounding AI’s potential to enhance learning outcomes and streamline administrative processes but also the apprehensions tied to its implementation. Participants express varied attitudes toward AI integration, with some embracing its ability to personalise learning experiences, enhance student engagement, and support educators in delivering more effective instruction. Conversely, concerns emerge regarding critical issues such as data privacy, the potential for exacerbating existing inequalities in access to technology, and the necessity of pedagogical adjustments to accommodate AI tools. The findings from this study underscore the complexity of AI’s role in higher education, illustrating the need for a thoughtful and strategic approach to its implementation. As institutions seek to harness the benefits of AI for improved educational outcomes, it becomes increasingly important to address the ethical considerations associated with its use. Recommendations from the study advocate for comprehensive faculty training to ensure educators are well-equipped to utilise AI effectively. Additionally, there is a call for revising curriculum development to incorporate AI technologies meaningfully, alongside fostering collaborative partnerships with industry to bridge the gap between theoretical knowledge and practical application.Journal of Computers for Science and Mathematics Learning00The findings from this study underscore the complexity of AI’s role in higher education, illustrating the need for a thoughtful and strategic approach to its implementation.Journal of Computers for Science and Mathematics Learning["Doris Chasokela"]2025-02-10T00:00:00 6f800717ba5b6a203f9e72ba59923891f5aa8897PURPOSE To comprehensively assess Checklist for Artificial Intelligence in Medical Imaging (CLAIM) adherence in medical imaging artificial intelligence (AI) literature by aggregating data from previous systematic and non-systematic reviews. METHODS A systematic search of PubMed, Scopus, and Google Scholar identified reviews using the CLAIM to evaluate medical imaging AI studies. Reviews were analyzed at two levels: review level (33 reviews; 1,458 studies) and study level (421 unique studies from 15 reviews). The CLAIM adherence metrics (scores and compliance rates), baseline characteristics, factors influencing adherence, and critiques of the CLAIM were analyzed. RESULTS A review-level analysis of 26 reviews (874 studies) found a weighted mean CLAIM score of 25 [standard deviation (SD): 4] and a median of 26 [interquartile range (IQR): 8; 25th-75th percentiles: 20-28]. In a separate review-level analysis involving 18 reviews (993 studies), the weighted mean CLAIM compliance was 63% (SD: 11%), with a median of 66% (IQR: 4%; 25th-75th percentiles: 63%-67%). A study-level analysis of 421 unique studies published between 1997 and 2024 found a median CLAIM score of 26 (IQR: 6; 25th-75th percentiles: 23-29) and a median compliance of 68% (IQR: 16%; 25th-75th percentiles: 59%-75%). Adherence was independently associated with the journal impact factor quartile, publication year, and specific radiology subfields. After guideline publication, CLAIM compliance improved (P = 0.004). Multiple readers provided an evaluation in 85% (28/33) of reviews, but only 11% (3/28) included a reliability analysis. An item-wise evaluation identified 11 underreported items (missing in ≥50% of studies). Among the 10 identified critiques, the most common were item inapplicability to diverse study types and subjective interpretations of fulfillment. CONCLUSION Our two-level analysis revealed considerable reporting gaps, underreported items, factors related to adherence, and common CLAIM critiques, providing actionable insights for researchers and journals to improve transparency, reproducibility, and reporting quality in AI studies. CLINICAL SIGNIFICANCE By combining data from systematic and non-systematic reviews on CLAIM adherence, our comprehensive findings may serve as targets to help researchers and journals improve transparency, reproducibility, and reporting quality in AI studies.Diagnostic and Interventional Radiology00The two-level analysis revealed considerable reporting gaps, underreported items, factors related to adherence, and common CLAIM critiques, providing actionable insights for researchers and journals to improve transparency, reproducibility, and reporting quality in AI studies.Diagnostic and interventional radiology["Burak Ko\u00e7ak", "Fadime K\u00f6se", "Ali Kele\u015f", "Abdurrezzak Sendur", "I. Mese", "Mehmet Karag\u00fclle"]2025-02-10T00:00:00 c72675c5a1afd2d632adde5c87df9f30429ca81dObjective: This study discusses the role of AI in Islamic education, from the aspects of effectiveness, innovation in learning, and socio-culture. This study includes how artificial intelligence allows better learning for the students, innovative teaching methods, and social and cultural change in Islamic education.Methods: This research is a meta-analysis that aims to synthesize data from several studies related to the application of AI in Islamic education. Cohen’s d was used to compute the effect size quantifying the effect of AI on learning deviations learning effectiveness, learning innovation, and learning socio-culture.Results: The results show that AI has moderate but significant impact on Islamic education. The most influential factors are socio-cultural factors (the influences of social factors) and recent educational innovative reforms (innovation), followed by learning effectiveness (focusing on adaptive learning and the connectedness of the learning environment). The findings provide evidence that the incorporation of AI is beneficial for enhancing learning outcomes and facilitating cultural and social transformation in the context of Islamic education.Novelty: This research sheds light on the effects that AI can have on Islamic education, and presents a unique take on the issue, focusing on improving learning outcomes, and capturing innovation in the socio-cultural context. It also calls for more research into how external forces access to technology, teacher involvement, community buy-in can shape this work.Theory and Policy Implications: The results affirm the importance of integrating AI into Islamic education and recommend that policymakers focus on AI-based projects that align with local cultural contexts and technological capacity. It is recommended in the study to customise the operation of AI-based learning solutions according to general Islamic educational values and the needs of students. Additional studies are needed to capture the broader ramifications of AI on educational policies and practices in different socio-cultural contexts.Advances Educational Innovation190The results affirm the importance of integrating AI into Islamic education and recommend that policymakers focus on AI-based projects that align with local cultural contexts and technological capacity.Advances Educational Innovation["M. A. Salim", "Nurlaila Rajabiyah"]2025-02-10T00:00:00 eb9e85c753f6f7eaf2eb87ef5de3b510d12e2afdDue to the existing “island” state of psychological and behavioral data, there is no way for anyone to access students’ psychological and behavioral histories. This limits the comprehensive understanding and effective intervention of college students’ mental health status. Therefore, this article constructs an artificial intelligence-based psychological health and intervention system for college students. Firstly, this article obtains psychological health testing data of college students through online platforms or on-campus system design, distribution of questionnaires, feedback from close contacts of students, and internal campus resources. Then, the architecture of a mental health monitoring system is designed. Its overall architecture includes a data collection layer, a data processing layer, a decision tree algorithm layer, and an evaluation display layer. The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data, selects the attribute with the maximum value, and constructs a decision tree structure model to evaluate students’ mental health. Finally, this article studies the evaluation of students’ mental health status by combining multidimensional information such as the SCL-90 scale, self-assessment scale, and student behavior data. Experimental data shows that the system can effectively identify students’ mental health problems and provide precise intervention measures based on their situation, with high accuracy and practicality.Journal of Contemporary Educational Research100Experimental data shows that the artificial intelligence-based psychological health and intervention system for college students can effectively identify students’ mental health problems and provide precise intervention measures based on their situation, with high accuracy and practicality.Journal of Contemporary Educational Research["Meng Lyu"]2025-02-10T00:00:00 255a49b217ebd14bfabc544d904cd0fd23b7a355BACKGROUND As artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice. OBJECTIVE This study aimed to characterize physician perspectives on the potential impact AI tools will have in psychiatric medicine. METHODS We interviewed 42 physicians (21 psychiatrists and 21 family medicine practitioners). These interviews used detailed clinical case scenarios involving the use of AI technologies in the evaluation, diagnosis, and treatment of psychiatric conditions. Interviews were transcribed and subsequently analyzed using qualitative analysis methods. RESULTS Physicians highlighted multiple potential benefits of AI tools, including potential support for optimizing pharmaceutical efficacy, reducing administrative burden, aiding shared decision-making, and increasing access to health services, and were optimistic about the long-term impact of these technologies. This optimism was tempered by concerns about potential near-term risks to both patients and themselves including misguiding clinical judgment, increasing clinical burden, introducing patient harms, and creating legal liability. CONCLUSIONS Our results highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine.JMIR Mental Health510The results highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine and highlight the importance of considering specialist perspectives when deploying AI tools in psychiatric medicine.JMIR mental health["Austin M Stroud", "Susan H Curtis", "Isabel B Weir", "Jeremiah J Stout", "Barbara A Barry", "William V Bobo", "Arjun P. Athreya", "Richard R Sharp"]2025-02-10T00:00:00 6061f0cee38e1a1b54886447b72e1c1dbde893d2This study investigates the influence of Artificial Intelligence (AI) adoption on the employability of adult learners in the UK, focusing on the mediating role of skill development and the moderating effect of technological readiness. Using a survey of 500 adult learners, structural equation modeling (SEM) was employed to analyze the relationships between AI adoption, skill development, technological readiness, and employability. The results show that AI adoption positively influences employability (β = 0.58, p < 0.01), with skill development acting as a significant mediator (β = 0.74, p < 0.01). Technological readiness moderates this relationship, enhancing the effect of AI adoption on employability (β = 0.56, p < 0.01) and significantly interacting with skill development (β = 0.42, p < 0.01). The moderated mediation analysis further reveals that the combination of skill development and technological readiness produces the strongest effect on employability outcomes (β = 0.34, p < 0.01). These findings underscore the importance of skill development and technological readiness in maximizing employability in an AI-driven labor market. The study provides practical implications for policymakers and educational institutions, suggesting the need for targeted initiatives that enhance both digital skills and technological preparedness. Future research could explore longitudinal trends and sector-specific variations in AI adoption's impact on employability.South Eastern European Journal of Public Health00The moderated mediation analysis reveals that the combination of skill development and technological readiness produces the strongest effect on employability outcomes, underscore the importance of skill development and technological readiness in maximizing employability in an AI-driven labor market.South Eastern European Journal of Public Health["Kiran Arooje", "Hararia Ijaz", "Muhammad Maaz Ul Haq", "Syeda Manal Fatima", "Zill-E- Huma", "Memoona Sajid"]2025-02-10T00:00:00 5d51fb7be05fc31f8f51d8c7b982e574b755060fAbstract. This study proposed a new quality control method via physical constraints and data-driven collaborative artificial intelligence (PD-BX) to reduce wind speed measurement errors caused by the complex environment along high-speed railway lines, achieving enhanced accuracy and reliability. On the one hand, based on the special structure in railway assembly, the physical constraint model of the railway electrical catenary supports and anemometers was experimentally established. The performance of the physical model in the wind field was simulated based on FLUENT software, and the environmental change characteristics of the anemometer in the railway area were analyzed. On the other hand, to solve the constrained error mapping expression under different wind conditions, a data-driven model of hyperparameter optimization (BO-XGBoost) is introduced to perform error compensation on physical relationships. Through the PD-BX method, the RMSE of the railway anemometer was reduced by 2.497 from 2.790 to 0.293, achieving quality control of wind observations along the high-speed railway lines and providing reliable results for improving the accuracy of the high-speed railway early warning system. Atmospheric Measurement Techniques660Through the PD-BX method, the RMSE of the railway anemometer was reduced, achieving quality control of wind observations along the high-speed railway lines and providing reliable results for improving the accuracy of the high-speed railway early warning system.Atmospheric Measurement Techniques["Xiong Xiong", "Jiajun Chen", "Yanchao Zhang", "Xin Chen", "Yingchao Zhang", "X. Ye"]2025-02-10T00:00:00 af437329494efe0317acf6f126c18765b9d5bfecThe transition of healthcare systems towards digitalization, particularly through the integration of artificial intelligence (AI), is reshaping medical practice and education. AI's role in enhancing diagnosis, patient care, and distance education is becoming increasingly significant, prompting a need for strategic planning, investment, and training in the healthcare workforce. This study focuses on the attitudes of health science students at the University of Fujairah towards AI in medical services, particularly in developing countries where AI can address personnel shortages. A literature review reveals that while health science students globally exhibit positive attitudes towards AI, gaps in knowledge and skills persist, necessitating improved educational programs. The study employs a quantitative methodology, utilizing a standardized questionnaire to assess students' perceptions of AI's impact on healthcare efficiency, patient engagement, and ethical concerns. The sample comprises 92 students, ensuring representation across various academic disciplines. Findings indicate a duality in students' perspectives: while there is enthusiasm for AI's transformative potential, concerns about data privacy and the erosion of personal interactions in patient care are prevalent. Gender differences emerge, with male students showing higher trust in AI, while female students express greater apprehension regarding data security. As students progress in their studies, they become more critical of AI's impact on personal interactions, highlighting the need for educational programs to address these concerns. In conclusion, the study underscores the importance of integrating AI education into healthcare curricula, focusing on data privacy and patient-centered approaches. Recommendations include enhancing early-year educational modules on AI and conducting further research to understand the evolving perceptions of students towards AI in healthcare. This research provides a foundation for developing strategies that ensure the effective integration of AI while maintaining the essential human touch in patient care.Journal of Ecohumanism230The study underscores the importance of integrating AI education into healthcare curricula, focusing on data privacy and patient-centered approaches, and identifies gender differences emerge, with male students showing higher trust in AI, while female students express greater apprehension regarding data security.Journal of Ecohumanism["Mohammed Tabishat", "Abdu Dawood Hafiz"]2025-02-10T00:00:00 800d013895e1f1b1caeb01d781f4b849169d6512Journal of Science Education and Technology140This editorial underscores the need for initial teacher education programmes to provide structured support, ensuring that PSTs develop both technical proficiency and critical AI literacy, and urges educational institutions to proactively support PSTs in harnessing its potential while fostering a critical, ethical, and pedagogically sound approach to AI integration in science education.Journal of Science Education and Technology["Grant Cooper", "Kok-Sing Tang", "Angela Fitzgerald"]2025-02-10T00:00:00 4b8f8a09caf85b5447833b5ff8d547cd5d5a1a1cWith the increasing cognitive dysfunction of the elderly and the incidence rate of dementia caused by aging, in recent years, many countries have studied and applied artificial intelligence (AI) to reduce the pressure on caregivers and improve the quality of life of dementia patients. This study comprehensively summarizes the impact of AI cognitive training on the mental symptoms of dementia patients from 2010 to 2024 through literature review. The results show that AI training has been used in multiple fields, breaking through many limitations of traditional action recognition technology. Through virtual cyberspace, real-time interaction of multiple senses can be achieved, allowing people to feel reality in the virtual network and accurately capture participants' actions; Through AI games, elderly people can improve their physical and cognitive functions, reduce depression and anxiety, meet the psychological and social needs of dementia patients, enhance their independence and dignity in life, and make objective assessments of cognitive function, which may help prevent dementia.Quality in Sport00The results show that AI training has been used in multiple fields, breaking through many limitations of traditional action recognition technology and may help prevent dementia.Quality in Sport["Tingran Zhang", "Mufan Zhang", "Jiong Luo"]2025-02-10T00:00:00 0e465ebefde772389de2b29fa32cbc7c7fa75e0fArtificial Intelligence has come to life and is part of our everyday lives. It has disrupted and will continue to have tremendous impacts across sectors, including education. To meet the recent demands, undergraduate students in Nigerian universities need to be at home with ICTs. Thus, this study examined awareness and perception of the use of artificial intelligence for learning among select communication undergraduates in Nigeria. The study was anchored on the Technology Adoption Model (TAM). The researcher adopted a survey research method and communication undergraduates were selected from two universities in Anambra State (private and government owned universities). The researcher found that the majority of the respondents have a low level of awareness on the use of artificial intelligence for learning and 82% of the respondents do not have access to artificial intelligence for learning. Further findings showed that most of the respondents are not competent in the use of artificial intelligence for learning. The researcher concluded that competency in technological innovation is dependent on the knowledge of, availability, and access to the technological innovations. Thus, the study recommends that efforts should be made by the school authorities to create awareness to students on the use of artificial intelligence for academic purpose and also, the universities should consider including AI courses in the syllabus to encourage the students to know and explore more about AI.African journal of social sciences and humanities research190It is recommended that efforts should be made by the school authorities to create awareness to students on the use of artificial intelligence for academic purpose and the universities should consider including AI courses in the syllabus to encourage the students to know and explore more about AI.African Journal of Social Sciences and Humanities Research["Nwodu, G. E."]2025-02-10T00:00:00 4572a89dd122c269efe1101c1a21f74becee19deThe current hype around artificial intelligence (AI) conceals the substantial human intervention underlying its development. This article lifts the veil on the precarious and low-paid 'data workers' who prepare data to train, test, check, and otherwise support models in the shadow of globalized AI production. We use original questionnaire and interview data collected from 220 workers in Argentina (2021-22), 477 in Brazil (2023), and 214 in Venezuela (2021-22). We compare them to detect common patterns and reveal the specificities of data work in Latin America, while disclosing its role in AI production.We show that data work is intertwined with economic hardship, inequalities, and informality. Despite workers' high educational attainment, disadvantage is widespread, though with cross-country disparities. By acknowledging the interconnections between AI development, data work, and globalized production, we provide insights for the regulation of AI and the future of work, aiming to achieve positive outcomes for all stakeholders.260It is shown that data work is intertwined with economic hardship, inequalities, and informality, and despite workers' high educational attainment, disadvantage is widespread, though with cross-country disparities.["Paola Tubaro", "Antonio A. Casilli", "Mariana Fern'andez Massi", "Julieta Longo", "Juana Torres-Cierpe", "Matheus Viana Braz"]2025-02-10T00:00:00 f86a4e7abaa633228ec5d7914bf2e423b62f719fInternational Journal of Emergency Medicine230The role and applications of AI in gynecologic and obstetric emergencies are reviewed to demonstrate the role of AI to improve healthcare in emergency settings in several aspects such as diagnostic imaging, improving predictions in emergencies, and improving planning and resource allocation for emergency services.International Journal of Emergency Medicine["H. Elbiss", "F. Abu-Zidan"]2025-02-10T00:00:00 b189be500451ccb029ba324bacc3fcfbce777b7eThe article will discuss how artificial intelligence, increasingly used in the video game industry, has enhanced the process of game design and given players different ways to find entertainment. This is done in an effort to analyze the improvement AI has brought to this field in terms of realism, immersion, and personalization, but also discussing some ethical concerns regarding its implementation. Based on a literature review, case studies, and survey data from gamers, the research discusses how AI applications enable innovative game mechanics, character behaviors, and narrative generation. Main arguments, developed in the article, demonstrate how AI can change players’ interactions by making game environments constantly changeable and adaptive. The article points out the various criticisms associated with AI: the presence of biases in algorithms and the over-reliance on automated systems. It identifies the possible future trends, with further explorations that emphasize the need for ethical consideration in the development of games using AI. This article contributes to an understanding of the role and contribution of AI in shaping the future of video games by providing an insight into a number of key issues which developers and researchers ought to consider.Interdisciplinary Humanities and Communication Studies00An understanding of the role and contribution of AI in shaping the future of video games is contributed by providing an insight into a number of key issues which developers and researchers ought to consider.Interdisciplinary Humanities and Communication Studies["Chenyue Sun"]2025-02-10T00:00:00 625755e0a690c2f209fc46230dcd3da4349bfcfbWith the passage of time, machine learning algorithms represented by deep learning have been gradually applied to machine vision, speech recognition and other fields, and have obtained certain results. Around big data, cloud computing and other technologies to provide very sufficient data resources, artificial intelligence is entering a period of rapid development, but also in the profound changes in various fields of society, the state requires the setting of digital, intelligent education, but the integration of artificial intelligence and education path still need to be explored, has not yet formed up and down the effective integrated education system. Based on this, this paper focuses on the research on the integration of artificial intelligence into the teaching model reform, in order to provide reference ideas for artificial intelligence education activities and help the intelligent development of education and teaching.Education Reform and Development00This paper focuses on the research on the integration of artificial intelligence into the teaching model reform, in order to provide reference ideas for artificial intelligence education activities and help the intelligent development of education and teaching.Education Reform and Development["Yan Yang"]2025-02-10T00:00:00 cd4988c0c7fef117550f4e8c42d0e27abced0b59Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the in-creasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polyphar-macology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based vir-tual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided as-sistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effec-tive drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the de-scriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.Current Drug Discovery Technologies00Several tools and algorithmic programs have been discussed which are being used in drug design along with the de-scriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it from already published materials.Current drug discovery technologies["Prashant Kumar", "Alpana Mahor", "Roopam Tomar"]2025-02-10T00:00:00 5f96626de9d05f4fbf5a8c33bc0cfcd6b7fe2ec6The application of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare has been extensively explored across various areas. AI and ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, disease prediction, workflow optimization, and resource utilization. This review summarizes current advancements in AI and ML concerning cardiovascular disease, including their clinical investigation and use in primary cardiac imaging techniques, common cardiovascular disease categories, clinical research, patient care, and outcome prediction. We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. Furthermore, this review emphasizes the transformative potential of AI and ML in cardiovascular practice by improving clinical decision making, reducing human error, enhancing patient monitoring and support, and creating more efficient healthcare workflows for complex cardiovascular conditions.Biomedicines1850This review summarizes current advancements in AI and ML concerning cardiovascular disease, including their clinical investigation and use in primary cardiac imaging techniques, common cardiovascular disease categories, clinical research, patient care, patient care, and outcome prediction.Biomedicines["Mohammed A. Chowdhury", "Rodrigue Rizk", "Conroy Chiu", "Jing J. Zhang", "Jamie L Scholl", "Taylor J. Bosch", "Arun Singh", "Lee A. Baugh", "Jeffrey S. McGough", "K. Santosh", "William C.W. Chen"]2025-02-10T00:00:00 f0d3f9c353dd63da4b3e37ecc01c045441560fe6Abstract The rapid digital transformation in Saudi Arabia's higher education landscape has prompted a critical examination of integrating artificial intelligence (AI) tools and student perceptions. This study investigates university students' attitudes, technological readiness, and engagement with AI technologies within the unique sociocultural and educational context of Saudi Arabia. By employing a mixed-methods approach, the research provides nuanced insights into how emerging technologies are perceived, utilized, and potentially transformed in Saudi academic environments.مجلة جامعة الملك عبدالعزيز: العلوم التربوية والنفسية00This study investigates university students' attitudes, technological readiness, and engagement with AI technologies within the unique sociocultural and educational context of Saudi Arabia.مجلة جامعة الملك عبدالعزيز: العلوم التربوية والنفسية["\u0639\u0628\u062f\u0627\u0644\u0644\u0647 \u0627\u0644\u0639\u0645\u0627\u0631\u064a"]2025-02-10T00:00:00 ca8d3c2a7f5fb782e13b2f6f397821b1a09b4664Under the rapid impetus of artificial intelligence (AI) technology, human society is stepping into the age of intelligence at an unprecedented speed. A new generation of information technology such as AI is not only a new engine of economic development, but also a gas pedal of social development, which has had a profound impact on the field of education. In the face of the opportunities and challenges of the AI era, it is particularly urgent to build a scientific and reasonable education evaluation system. This paper combines the context of the times with the new technology of AI to discuss the opportunities, challenges, and implementation strategies of educational evaluation reform in the era of AI, with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.Journal of Contemporary Educational Research60This paper combines the context of the times with the new technology of AI to discuss the opportunities, challenges, and implementation strategies of educational evaluation reform in the era of AI, with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.Journal of Contemporary Educational Research["Li Jin", "Hua Zhang"]2025-02-10T00:00:00 76d679958e456665ca62ae74e738df62a31214e9Objective: The aim of this scoping review was to evaluate whether artificial intelligence integrated into breast cancer screening work strategies could help resolve some diagnostic issues that still remain. Methods: PubMed, Web of Science, and Scopus were consulted. The literature research was updated to 28 May 2024. The PRISMA method of selecting articles was used. The articles were classified according to the type of publication (meta-analysis, trial, prospective, and retrospective studies); moreover, retrospective studies were based on citizen recruitment (organized screening vs. spontaneous screening and a combination of both). Results: Meta-analyses showed that AI had an effective reduction in the radiologists’ reading time of radiological images, with a variation from 17 to 91%. Furthermore, they highlighted how the use of artificial intelligence software improved the diagnostic accuracy. Systematic review speculated that AI could reduce false negatives and positives and detect subtle abnormalities missed by human observers. DR with AI results from organized screening showed a higher recall rate, specificity, and PPV. Data from opportunistic screening found that AI could reduce interval cancer with a corresponding reduction in serious outcome. Nevertheless, the analysis of this review suggests that the study of breast density and interval cancer still requires numerous applications. Conclusions: Artificial intelligence appears to be a promising technology for health, with consequences that can have a major impact on healthcare systems. Where screening is opportunistic and involves only one human reader, the use of AI can increase diagnostic performance enough to equal that of double human reading.Healthcare610Artificial intelligence appears to be a promising technology for health, with consequences that can have a major impact on healthcare systems, where screening is opportunistic and involves only one human reader, the use of AI can increase diagnostic performance enough to equal that of double human reading.Healthcare["E. Altobelli", "P. M. Angeletti", "Marco Ciancaglini", "R. Petrocelli"]2025-02-10T00:00:00 23059161ed4ddbc02c1afd0e4e7f0806dfa2d544BMC Nursing410The results of this study show that the ASUAITIN scale are validated and reliable measurement tool that can be used as an instrument to assess the attitudes to AI technology in practice among nurses working in the clinical field.BMC Nursing["Dilek Y\u0131lmaz", "Derya Uzelli", "Y. Dikmen"]2025-02-10T00:00:00 f25cf7897285d3b66389171031c980970754d95a This Viewpoint discusses the Trustworthy and Responsible AI Network Europe (TRAIN-Europe) consortium, an effort of medical and AI experts to oversee, develop, and share ethical practices through collaboration and technological solutions designed to implement responsible AI practices. Journal of the American Medical Association (JAMA)61JAMA["Michel E van Genderen", "Ilse M J Kant", "C. Tacchetti", "Stefan Jovinge"]2025-02-10T00:00:00 1077c943c601cd859b96a920082f815cffb414b0Background/Objectives: GPT-3.5 and GPT-4 has shown promise in assisting healthcare professionals with clinical questions. However, their performance in real-time clinical scenarios remains underexplored. This study aims to evaluate their precision and reliability compared to board-certified emergency department attendings, highlighting their potential in improving patient care. We hypothesized that board-certified emergency department attendings at Maimonides Medical Center exhibit higher accuracy and reliability than GPT-3.5 and GPT-4 in generating differentials based on history and physical examination for patients presenting to the emergency department. Methods: Real-time patient data from Maimonides Medical Center’s emergency department, collected from 1 January 2023 to 1 March 2023 were analyzed. Demographic details, symptoms, medical history, and discharge diagnoses recorded by emergency room attendings were examined. AI algorithms (ChatGPT-3.5 and GPT-4) generated differential diagnoses, which were compared with those by attending physicians. Accuracy was determined by comparing each rater’s diagnoses with the gold standard discharge diagnosis, calculating the proportion of correctly identified cases. Precision was assessed using Cohen’s kappa coefficient and Intraclass Correlation Coefficient to measure agreement between raters. Results: Mean age of patients was 49.12 years, with 57.3% males and 42.7% females. Chief complaints included fever/sepsis (24.7%), gastrointestinal issues (17.7%), and cardiovascular problems (16.4%). Diagnostic accuracy against discharge diagnoses was highest for ChatGPT-4 (85.5%), followed by ChatGPT-3.5 (84.6%) and ED attendings (83%). Cohen’s kappa demonstrated moderate agreement (0.7) between AI models, with lower agreement observed for ED attendings. Stratified analysis revealed higher accuracy for gastrointestinal complaints with Chat GPT-4 (87.5%) and cardiovascular complaints with Chat GPT-3.5 (81.34%). Conclusions: Our study demonstrates that Chat GPT-4 and GPT-3.5 exhibit comparable diagnostic accuracy to board-certified emergency department attendings, highlighting their potential to aid decision-making in dynamic clinical settings. The stratified analysis revealed comparable reliability and precision of the AI chat bots for cardiovascular complaints which represents a significant proportion of the high-risk patients presenting to the emergency department and provided targeted insights into rater performance within specific medical domains. This study contributes to integrating AI models into medical practice, enhancing efficiency and effectiveness in clinical decision-making. Further research is warranted to explore broader applications of AI in healthcare.Journal of Clinical Medicine110This study demonstrates that Chat GPT-4 and GPT-3.5 exhibit comparable diagnostic accuracy to board-certified emergency department attendings, highlighting their potential to aid decision-making in dynamic clinical settings.Journal of Clinical Medicine["Azka Naeem", "Omair Khan", "S. M. Baqir", "Kundan Jana", "Prem Shankar", "Avleen Kaur", "Morad Zaaya", "F. Sajid", "Fizza Mohsin", "M. R. Boadla", "Aung Oo", "Victor Wong", "Momna Noor", "S. P. S. Sandhu", "Kseniya Slobodyanuk", "Vijay Shetty", "Aaron Z. Tokayer"]2025-02-10T00:00:00 20e95f77272882cc4fd18c808b943bfcc494d733International Transactions in Operational Research00International Transactions in Operational Research["Davide La Torre", "Leopoldo Bertossi", "H. Kunze", "Marc Poulin"]2025-02-10T00:00:00 c3720a6ef151bc8946345e89d267d3bee533e327Postgraduate medical journal00Postgraduate medical journal["Hongnan Ye"]2025-02-10T00:00:00 e29f993d2268e8ae4316a1d3f20f06649106a2cfCurrent Psychology390Current Psychology["M. Taha", "S. Abdelwahab", "Ieman A M Aljahdali", "Omar Oraibi", "Bassem Oraibi", "H. Alfaifi", "A. Alzahrani", "A. Farasani", "A. Jerah", "Y. Babiker"]2025-02-10T00:00:00 aa7f171c57b64b06c538b09923b5145b735b1c2eThe by-product gases generated during steel manufacturing processes, including blast furnace gas, coke oven gas, and Linz–Donawitz gas, exhibit considerable variability in composition and supply. Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. The ABCCM derives optimal combustion patterns in real time using the RF algorithm and minimizes fuel consumption through the CART algorithm, thereby optimizing the overall gross heat rate. The results demonstrate that the ABCCM achieves a 0.86% improvement in combustion efficiency and a 1.7% increase in power generation efficiency compared to manual control methods. Moreover, the model reduces the gross heat rate by 58.3 kcal/kWh, which translates into an estimated annual energy cost saving of USD 89.6 K. These improvements contribute considerably to reducing carbon emissions, with the ABCCM being able to optimize fuel utilization and minimize excess air supply, thus enhancing the overall sustainability of steelmaking operations. This study underscores the potential of the ABCCM to extend beyond the steel industry.Energies710The advanced boiler combustion control model (ABCCM) is developed by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases, contributing considerably to reducing carbon emissions.Energies["Kyu-Jeong Lee", "S. Choi", "Eul-Bum Lee"]2025-02-10T00:00:00 8e23256b5c0e9091f40a6e13f2dcba5d99a72896International Journal of Technology Assessment in Health Care00International journal of technology assessment in health care["L. Ayiku", "A. Finnegan", "Thomas Hudson", "Nicola Walsh", "Rachel Adams"]2025-02-10T00:00:00 3a71a14faabe3122567e4e800b2571a6ff6f4efcACM Computing Surveys00ACM Computing Surveys["Roberta Calegari", "Fosca Giannotti", "Michela Milano", "Francesca Pratesi"]2025-02-10T00:00:00 a55ea3b06c2d056da8bae1f773332d0f883b6220Consulting Psychology Journal00Consulting Psychology Journal["R. Lowman"]2025-02-10T00:00:00 a8eb88636d7ae5804ccf8ca86ab609b780520e88C&amp;EN Global Enterprise00C&amp;EN Global Enterprise["Alex Scott"]2025-02-10T00:00:00 3bfd5409771aaf79aaf58d397e97cf6a7d9c683bJournal of Christian nursing : a quarterly publication of Nurses Christian Fellowship30Journal of Christian nursing : a quarterly publication of Nurses Christian Fellowship["Sharon K Titus", "Bradley G Titus"]2025-02-10T00:00:00 ad461d41a21ab9364ab78f15df8683b0af53b08bThe emergence of Artificial General Intelligence (AGI) labor, including AI agents and autonomous systems operating at near-zero marginal cost, reduces the marginal productivity of human labor, ultimately pushing wages toward zero. As AGI labor and capital replace human workers, economic power shifts to capital owners, resulting in extreme wealth concentration, rising inequality, and reduced social mobility. The collapse of human wages causes aggregate demand to deteriorate, creating a paradox where firms produce more using AGI, yet fewer consumers can afford to buy goods. To prevent economic and social instability, new economic structures must emerge, such as Universal Basic Income (UBI), which redistributes AGI-generated wealth, public or cooperative AGI ownership, ensuring broader access to AI-driven profits, and progressive AGI capital taxation, which mitigates inequality and sustains aggregate demand. Addressing these challenges in form of renegotiation the Social Contract is crucial to maintaining economic stability in a post-labor economy.00To prevent economic and social instability, new economic structures must emerge, such as Universal Basic Income (UBI), which redistributes AGI-generated wealth, public or cooperative AGI ownership, ensuring broader access to AI-driven profits, and progressive AGI capital taxation, which mitigates inequality and sustains aggregate demand.["Pascal Stiefenhofer"]2025-02-10T00:00:00 ad8e8f59226eaab427b769c2104992a20be3900dCurrent and foreseeable GenAI models are not capable of achieving artificial general intelligence because they are burdened with anthropogenic debt. They depend heavily on human input to provide well-structured problems, architecture, and training data. They cast every problem as a language pattern learning problem and are thus not capable of the kind of autonomy needed to achieve artificial general intelligence. Current models succeed at their tasks because people solve most of the problems to which these models are directed, leaving only simple computations for the model to perform, such as gradient descent. Another barrier is the need to recognize that there are multiple kinds of problems, some of which cannot be solved by available computational methods (for example,"insight problems"). Current methods for evaluating models (benchmarks and tests) are not adequate to identify the generality of the solutions, because it is impossible to infer the means by which a problem was solved from the fact of its solution. A test could be passed, for example, by a test-specific or a test-general method. It is a logical fallacy (affirming the consequent) to infer a method of solution from the observation of success.00Current and foreseeable GenAI models are not capable of achieving artificial general intelligence because they are burdened with anthropogenic debt, and methods for evaluating models are not adequate to identify the generality of the solutions.["Herbert Roitblat"]2025-02-10T00:00:00 a51fcb51a6790ceb3fd47424845f51aff19b85e4Predictive maintenance powered by artificial intelligence has revolutionized website reliability and operational efficiency across industries. This transformative approach integrates advanced machine learning algorithms, anomaly detection systems, and sophisticated data collection architectures to prevent failures before they occur. The implementation of AI-driven maintenance strategies has demonstrated significant improvements in equipment longevity, resource optimization, and cost reduction while enhancing system availability and performance. Through behavioral analysis and vulnerability prevention mechanisms, these systems strengthen security measures and enable proactive threat detection. The evolution of predictive maintenance incorporates emerging technologies such as reinforcement learning, federated learning, and automated incident response capabilities, setting new standards for maintenance practices. By following comprehensive implementation guidelines and best practices in data collection, model development, and operational integration, organizations can successfully transition from reactive to proactive maintenance approaches, ensuring optimal system performance and reliability while substantially reducing operational costs and minimizing unplanned downtime.International Journal of Scientific Research in Computer Science Engineering and Information Technology11Following comprehensive implementation guidelines and best practices in data collection, model development, and operational integration, organizations can successfully transition from reactive to proactive maintenance approaches, ensuring optimal system performance and reliability while substantially reducing operational costs and minimizing unplanned downtime.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Shailesh Kumar Agrahari"]2025-02-10T00:00:00 d3a0ef60ac04e935f856ca2a5ad8c2dcc79eff57The rapid advancement of artificial intelligence (AI) in healthcare has significantly enhanced diagnostic accuracy and clinical decision-making processes. This review examines four pivotal studies that highlight the integration of large language models (LLMs) and multimodal systems in medical diagnostics. BioBERT demonstrates the efficacy of domain-specific pretraining on biomedical texts, improving performance in tasks such as named entity recognition, relation extraction, and question answering. Med-PaLM, a large-scale language model tailored for clinical question answering, leverages instruction prompt tuning to enhance accuracy and reduce harmful outputs, validated through the MultiMedQA benchmark. DR.KNOWS integrates medical knowledge graphs with LLMs, enhancing diagnostic reasoning and interpretability by grounding model predictions in structured medical knowledge. Medical Multimodal Foundation Models (MMFMs) combine textual and imaging data to improve tasks like segmentation, lesion detection, and automated report generation. These studies demonstrate the importance of domain adaptation, structured knowledge integration, and multimodal data fusion in developing robust and interpretable AI-driven diagnostic tools.AI Med640This review examines four pivotal studies that highlight the integration of large language models (LLMs) and multimodal systems in medical diagnostics, demonstrating the importance of domain adaptation, structured knowledge integration, and multimodal data fusion in developing robust and interpretable AI-driven diagnostic tools.AI Med["Ran Tong", "Ting Xu", "Xinxin Ju", "Lanruo Wang"]2025-02-10T00:00:00 8ec6c8cd7ceab2fc20ec2381a101faae078866f1The integration of Artificial Intelligence in distributed systems orchestration marks a transformative shift in how organizations design, implement, and manage complex architectures. This advancement represents a fundamental evolution from traditional static workflows to dynamic, intelligent distributed systems. AI orchestration addresses critical challenges in modern distributed computing, including resource optimization, service latency, and system reliability. Through machine learning algorithms and advanced analytics, these systems enable predictive scaling, automated performance optimization, and intelligent error detection. The technology demonstrates significant improvements in operational efficiency, reducing manual intervention while enhancing service delivery and resource utilization. The incorporation of mechanical, thinking, and feeling AI components creates adaptive systems capable of real-time decision-making and contextual awareness. As distributed systems continue to grow in complexity, AI orchestration emerges as a crucial solution for maintaining system stability, ensuring scalability, and improving overall performance. The implementation challenges, including reliability concerns and training requirements, are addressed through structured approaches and robust monitoring frameworks, paving the way for more resilient and efficient distributed systems.International Journal of Scientific Research in Computer Science Engineering and Information Technology10As distributed systems continue to grow in complexity, AI orchestration emerges as a crucial solution for maintaining system stability, ensuring scalability, and improving overall performance.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Gaurav Agrawal"]2025-02-10T00:00:00 e7ae3951797422b6a1fcefa5d9c6763ad0e268d0This comprehensive article explores the integration of artificial intelligence in wildfire management systems, presenting an innovative approach to detection, prevention, and response strategies. The article examines how AI technologies transform traditional firefighting methods through advanced sensing, predictive analytics, and resource optimization. The article encompasses various aspects of modern wildfire management, including drone-based surveillance, IoT sensor networks, and machine learning algorithms for fire behavior prediction. The article analyzes the implementation of automated risk assessment systems, public safety protocols, and emergency response coordination mechanisms. Through detailed case studies of existing deployments like the Cerberus system, CAL FIRE AI system, and IBM Watson platform, the research demonstrates significant improvements in detection accuracy, response times, and resource efficiency. The article also addresses integration challenges, policy considerations, and future development opportunities in AI-driven wildfire management systems. By examining both technological and operational aspects, this article provides valuable insights into the evolving landscape of wildfire management and establishes a framework for future implementations across diverse geographical regions.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The article examines how AI technologies transform traditional firefighting methods through advanced sensing, predictive analytics, and resource optimization, including drone-based surveillance, IoT sensor networks, and machine learning algorithms for fire behavior prediction.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Vishwadeep Saxena"]2025-02-10T00:00:00 01c36a74693065664bba51e7fdf95709cc690aaeThe integration of artificial intelligence across industries has highlighted the indispensable role of human testers in ensuring AI system reliability, fairness, and transparency. While automated testing provides efficiency in processing large-scale data, human oversight remains crucial for detecting nuanced issues, cultural biases, and ethical concerns. This article delves into the multifaceted aspects of human-centric AI testing, exploring how human testers contribute to test design, bias detection, and ethical framework implementation. The article demonstrates that human testers excel in identifying contextual subtleties, cultural nuances, and potential societal impacts that automated systems often miss. Through collaborative approaches combining human expertise with AI capabilities, organizations can achieve superior testing outcomes in areas ranging from healthcare diagnostics to human resource management. The implementation of structured documentation practices and diverse testing teams further enhances the effectiveness of AI system evaluation. As AI systems grow more complex, addressing scaling challenges and developing enhanced human-AI collaboration tools becomes essential for maintaining robust testing processes and ensuring responsible AI deployment.International Journal of Scientific Research in Computer Science Engineering and Information Technology80This article delves into the multifaceted aspects of human-centric AI testing, exploring how human testers contribute to test design, bias detection, and ethical framework implementation.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ashwin Choubey"]2025-02-10T00:00:00 65b98beeedf5d90250b387e7204d2e2e5dcd584aThe integration of Artificial Intelligence in Product Lifecycle Management (PLM) is revolutionizing how organizations manage and optimize their product development processes. This comprehensive article examines the transformative impact of AI technologies on PLM systems, focusing on advanced analytics, process optimization, and intelligent decision support capabilities. It explores key technological implementations including machine learning applications, natural language processing, and computer vision integration, highlighting their roles in enhancing design accuracy and manufacturing efficiency. Through detailed analysis of data architecture requirements and a case study in the automotive sector, the article demonstrates how AI-powered PLM systems are driving innovation and operational excellence. It also addresses critical technical and organizational challenges while providing practical solutions for successful implementation. Looking ahead, the article identifies emerging trends in PLM evolution, including semantic technologies, advanced digital twins, and human-AI collaboration frameworks, offering insights into the future direction of intelligent product lifecycle management systems.International Journal of Scientific Research in Computer Science Engineering and Information Technology20This comprehensive article examines the transformative impact of AI technologies on PLM systems, focusing on advanced analytics, process optimization, and intelligent decision support capabilities, and explores key technological implementations including machine learning applications, natural language processing, and computer vision integration.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Nishant Kapoor"]2025-02-10T00:00:00 9d99a18b4cbb8fcb8cffde79ade4f462c5b97afeQuantitative Artificial Intelligence (AI) Benchmarks have emerged as fundamental tools for evaluating the performance, capability, and safety of AI models and systems. Currently, they shape the direction of AI development and are playing an increasingly prominent role in regulatory frameworks. As their influence grows, however, so too does concerns about how and with what effects they evaluate highly sensitive topics such as capabilities, including high-impact capabilities, safety and systemic risks. This paper presents an interdisciplinary meta-review of about 100 studies that discuss shortcomings in quantitative benchmarking practices, published in the last 10 years. It brings together many fine-grained issues in the design and application of benchmarks (such as biases in dataset creation, inadequate documentation, data contamination, and failures to distinguish signal from noise) with broader sociotechnical issues (such as an over-focus on evaluating text-based AI models according to one-time testing logic that fails to account for how AI models are increasingly multimodal and interact with humans and other technical systems). Our review also highlights a series of systemic flaws in current benchmarking practices, such as misaligned incentives, construct validity issues, unknown unknowns, and problems with the gaming of benchmark results. Furthermore, it underscores how benchmark practices are fundamentally shaped by cultural, commercial and competitive dynamics that often prioritise state-of-the-art performance at the expense of broader societal concerns. By providing an overview of risks associated with existing benchmarking procedures, we problematise disproportionate trust placed in benchmarks and contribute to ongoing efforts to improve the accountability and relevance of quantitative AI benchmarks within the complexities of real-world scenarios.1100An interdisciplinary meta-review of about 100 studies that discuss shortcomings in quantitative benchmarking practices, published in the last 10 years, underscores how benchmark practices are fundamentally shaped by cultural, commercial and competitive dynamics that often prioritise state-of-the-art performance at the expense of broader societal concerns.["Maria Eriksson", "Erasmo Purificato", "Arman Noroozian", "Joao Vinagre", "Guillaume Chaslot", "Emilia G\u00f3mez", "D. Fern\u00e1ndez-Llorca"]2025-02-10T00:00:00 686fc1631cf4c0b2e14aba22de07a8d93347d237The integration of generative artificial intelligence (GAI) tools in higher education offers new opportunities for personalized learning, critical thinking, and digital literacy. However, socio-economic disparities and ethical concerns present significant challenges to equitable and responsible GAI use, particularly in under-resourced educational settings. This mixed-methods study explored how undergraduate students at Universidad Centroamericana José Simeón Cañas (UCA) in El Salvador engage with GAI tools, focusing on patterns of access, usage, and the socio-economic and ethical factors shaping these interactions. A quantitative survey of 365 students and qualitative focus groups with 25 participants were conducted to examine device preferences, tool usage, and concerns related to academic integrity, data privacy, and responsible AI use. Results revealed significant socio-economic disparities in access to GAI tools, with students from lower-income backgrounds primarily using smartphones and free AI tools, while higher-income students reported greater access to laptops and premium features. Ethical concerns were more prominent among students with limited institutional support, highlighting the need for structured guidance on the responsible use of GAI tools. These findings underscore the importance of institutional policies that promote equitable access to educational technologies and provide ethical frameworks for their use. By integrating socio-constructivist and connectivist learning theories, this study emphasizes that equitable access and guided support are critical for maximizing the educational potential of GAI tools. The study contributes to ongoing discussions about how higher education institutions, particularly in the Global South, can responsibly and effectively integrate AI technologies to support diverse student populations.Education sciences00This mixed-methods study explored how undergraduate students at UCA in El Salvador engage with GAI tools, focusing on patterns of access, usage, and the socio-economic and ethical factors shaping these interactions.Education Sciences["Tizziana Valdivieso", "Oscar Gonz\u00e1lez"]2025-02-10T00:00:00 702f5f841a73cecbf722d57d97ddabed5b65b254The impact of Artificial intelligence (AI)-powered digital marketing practices on consumer purchase intention toward lifestyle goods is the focus of this research and aims at analyzing the mediating role of consumer motivation (CM) in the relationship between consumer attitude (CA) and purchase behavior (PB) toward lifestyle products. The study uses descriptive research design to understand CA, motivation, and PB. The study is based on 577 responses collected from Uttar Pradesh state (India). Structural equation modeling was carried out with the help of SmartPLS. Evidence shows a robust relationship between consumers’ attitude, motivation and PB, and an optimistic outlook on AI-driven marketing campaigns is likely to inspire more action, given the robust positive correlation between customer attitude and motivation. The study also emphasizes the importance of CM as a mediator in the relationship between CA and PB. It emphasizes the strategic tools for improving PB in the dynamic digital marketing landscape, which include cultivating a positive CA. The study contributes to the theory by highlighting CM as a critical mediator linking CA s to PB for lifestyle products, advancing understanding of the attitude-behavior relationship in consumer behavior models. Managerially, it underscores the importance of designing marketing strategies that enhance CM, such as personalized engagement, value-driven messaging, and emotional appeal. By fostering motivation, brands can effectively translate positive attitudes into stronger PB, driving sales and long-term consumer loyalty in the lifestyle segment. Cihan University-Erbil Journal of Humanities and Social Sciences370The study underscores the importance of designing marketing strategies that enhance CM, such as personalized engagement, value-driven messaging, and emotional appeal, to effectively translate positive attitudes into stronger PB, driving sales and long-term consumer loyalty in the lifestyle segment.Cihan University-Erbil Journal of Humanities and Social Sciences["Ali Ghufran", "Waqar Ahmad"]2025-02-10T00:00:00 0bbbc58046d4478dbde4ea98ee0a25fbf267579c This manuscript attempts to provide answers regarding questions such as whether or not it is legitimate to describe and characterise humanoid robots as legal entities and individuals. The purpose of this paper is an attempt to answer this question using philosophical principles. This manuscript uses text analysis to investigate answers to this question by examining thoughts put forth by respected theorists, classical philosophers, and psychologists. The text dives further into the concept that artificial intelligence (AI) systems deserve to have their own unique identities, highlighting the significance of building a relationship with them that is meaningful. This is due to the fact that, just as every star in the sky at night radiates with its own special brightness, our AI counterparts should likewise vibrate with individuality. This will allow them to build connections that shed light on the human experience that we all share. The purpose of this study is to demonstrate that AI robots are not only lifeless things but rather the result of humans directing their psychological resources into something significant. This is a significant and innovative endeavour. This activity is noteworthy because it extends beyond individuals’ immediate surroundings. Journal of Science and Technology Policy Management290It is demonstrated that AI robots are not only lifeless things but rather the result of humans directing their psychological resources into something significant, which is a significant and innovative endeavour.Journal of Science and Technology Policy Management["Shailendra Kumar", "Sanghamitra Choudhury"]2025-02-10T00:00:00 b9f644bec4338f395e3cd552145df1d9c7ab007aModern computer science's use of machine learning (ML) and artificial intelligence (AI) has transformed technical capabilities in a variety of sectors. In order to identify the best implementation techniques, this study uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to assess five important AI/ML integration methodologies. The research analyzes AI-Driven Data Analysis, ML-Based Predictive Modeling, AI-Powered Software Development, Cloud Integration with AI, and IoT Systems with ML across four critical metrics: Accuracy (%), Efficiency (Tasks/sec), Innovation Index, and Implementation Time (Weeks). Using normalized data and equal weightings (0.25) for each criterion, the study reveals significant variations in performance across different approaches. The results demonstrate that IoT Systems with ML achieves the highest Closeness Index (CI: 0.6928), ranking first overall due to its balanced performance across all metrics. AI-Driven Data Analysis follows closely (CI: 0.6834), ranking second with consistent performance across criteria. Cloud Integration with AI ranks third (CI: 0.5575), showing strong efficiency but lower accuracy scores. ML-Based Predictive Modeling and AI-Powered Software Development rank fourth and fifth respectively, despite showing strengths in specific areas such as accuracy and innovation. The findings indicate that while each approach offers unique advantages, IoT Systems with ML provides the most balanced solution for modern computational needs, combining reasonable implementation time with strong performance metrics. This research contributes to the understanding of AI/ML integration strategies by providing quantitative evidence for decision-making in technology adoption. The study's methodology and results offer valuable insights for organizations seeking to implement AI and ML solutions, highlighting the importance of considering multiple criteria in technology selection rather than focusing on single performance metrics.REST Journal on Data Analytics and Artificial Intelligence160IoT Systems with ML provides the most balanced solution for modern computational needs, combining reasonable implementation time with strong performance metrics, highlighting the importance of considering multiple criteria in technology selection rather than focusing on single performance metrics.REST Journal on Data Analytics and Artificial Intelligence[]2025-02-11T00:00:00 eca7d1f078e2f75507558b52a5bd00ae90ed6f1f Hearing loss is a prevalent condition affecting over 500 million people globally, with projections estimating more than 700 million cases by 2050. Artificial intelligence (AI) holds transformative potential in audiology, enhancing diagnostic, therapeutic, and rehabilitation outcomes. This review explores the applications of AI in hearing aids, cochlear implants, sign language recognition, and tele-audiology. A comprehensive literature review was conducted using PubMed, Google Scholar, and other academic databases. Relevant studies on AI-driven advancements in audiology were analyzed, focusing on hearing aid technologies, cochlear implants, diagnostics, and tele-audiology platforms. AI technologies significantly enhance hearing aids through real-time personalization and adaptive algorithms. Cochlear implants leverage AI for improved speech recognition and listening comfort. AI-powered sign language systems facilitate communication through real-time gestureto- text conversions, while tele-audiology expands care access using AI-enabled platforms. Diagnostic advancements include AI-enhanced audiometric testing and otoscopy. AI is revolutionizing hearing healthcare by providing personalized, efficient, and accessible solutions. Its integration into audiology represents a paradigm shift, offering significant improvements in patient outcomes and quality of life. Current Aging Science00This review explores the applications of AI in hearing aids, cochlear implants, sign language recognition, and tele-audiology, focusing on hearing aid technologies, cochlear implants, diagnostics, and tele-audiology platforms.Current Aging Science["Chitra Thara S.", "Vidhya Lekshmi K.", "V. N."]2025-02-11T00:00:00 f6668a66ac66058247a685ed1b3e319d086414ebBMC Medicine440Although most medical journals provided their own GAI usage guidelines or referenced external guidelines, some recommendations remained unspecified (e.g., whether AI can be used for data analysis and interpretation) and journals with lower SJR scores were less likely to provide guidelines, indicating a potential gap that warrants attention.BMC Medicine["Shuhui Yin", "Simu Huang", "Peng Xue", "Zhuoran Xu", "Zi Lian", "Chenfei Ye", "Siyuan Ma", "Mingxuan Liu", "Yuanjia Hu", "Peiyi Lu", "Chihua Li"]2025-02-11T00:00:00 6f14afd65b3a498b2b723f5f8de2a41bedbd8e6cAbstract. AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data. Geoscientific Model Development500It is suggested that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models and build AI models that have interpretable components amenable to component-level understanding.Geoscientific Model Development["Ryan J. O\u2019Loughlin", "Dan Li", "Richard B. Neale", "T. A. O\u2019Brien"]2025-02-11T00:00:00 cc5bef7822dc6002f40915d9f12ba6ffcbc72cceThis study explores the integration of artificial intelligence (AI) in art education within selected universities, aiming to propose an innovative development plan. The study evaluated AI practices in terms of learning content, learning activities, and assessment methods. Through a quantitative method approach, data were collected from 220 art teachers and 713 art students via survey to assess the current state of AI integration in art education. Findings revealed that AI practices in learning content were perceived as very satisfactory. Specifically, art professors' familiarity with formal content training and knowledge of resources and materials to enhance content training was highly rated. Learning activities involving AI tools showed positive impacts on student engagement and creativity. However, challenges such as insufficient learning materials, lack of support for teachers' professional development, and time constraints were noted. Assessment methods incorporating AI, like automated grading systems and AI-driven feedback, were found to be efficient but required improvements in aligning with curriculum standards and addressing individual learning needs. The study highlighted the significant role of AI in transforming art education by offering personalized learning experiences and fostering creative skills. Based on the findings, the study proposes a comprehensive development plan emphasizing the need for curriculum development, investment in AI technologies, and continuous professional development for educators. The recommendations aim to enhance the effectiveness of AI integration in art education, ensuring that it meets the evolving needs of students and the educational landscape.Pedagogy Review: An International Journal of Educational Theories, Approaches and Strategies00Findings revealed that AI practices in learning content were perceived as very satisfactory, and art professors' familiarity with formal content training and knowledge of resources and materials to enhance content training was highly rated.Pedagogy Review: An International Journal of Educational Theories, Approaches and Strategies["Zhao Yiyi"]2025-02-11T00:00:00 b531b829a3fa0bd3a53cd702786a7387a1860b49This study investigates the role of global digital technologies and artificial intelligence (AI) in accelerating safety and discipline in South African schools. The research focuses on how AI and digital tools can contribute to creating inclusive, safe, and secure learning environments in line with the principles of the global digital Compact and the Pact for the Future. Leveraging and drawing on Self-Determination Theory (SDT), this research explores how the effective management of teaching and learning, facilitated by digital technologies, can foster a cohort of disciplined learners. A systematic review methodology was employed to synthesise existing literature bias to global south on digital technology and AI’s role in school safety and discipline. Findings suggest that AI tools and digital platforms contribute to creating safer and more disciplined environments by offering personalized learning experiences, real-time feedback, and predictive analytics for behavioural management. The study highlights how integrating these technologies, alongside a focus on the innate psychological needs of learners, can drive positive outcomes in student behavior and academic performance. The findings also highlight the potential of digital education and AI in promoting safety and discipline, and academic excellence.International Journal of Innovative Technologies in Social Science330Findings suggest that AI tools and digital platforms contribute to creating safer and more disciplined environments by offering personalized learning experiences, real-time feedback, and predictive analytics for behavioural management.International Journal of Innovative Technologies in Social Science["Ngogi Emmanuel", "Mahaye"]2025-02-11T00:00:00 ae7547670db04fa3ed219ead06f9bafdf7b5c532BMC Nursing630Ethical frameworks must be established to guarantee AI integration into nursing practice, safeguard patients’ welfare, and strengthen the trust between healthcare providers and patients.BMC Nursing["A. Ibrahim", "M. Zoromba", "Ali D. Abousoliman", "Donia Elsaid Fathi Zaghamir", "Ibrahim Naif Alenezi", "E. A. Elsayed", "Heba Ali Hamed Mohamed"]2025-02-11T00:00:00 ca6c41dbef7f4a1366f8bb08403235537b7d9de0Venous thromboembolism (VTE) is an important global health problem and the third most prevalent cardiovascular disorder. It has been proven that computerized tools were helpful in the prevention and control of VTE. However, studies that focused on the acceptance of computerized tools for VTE prevention among healthcare workers were limited.This study aims to explore what factors are influencing healthcare workers’ acceptance of the Artificial Intelligence Clinical Assistant Decision Support System (AI-CDSS) for VTE prevention based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT).We conducted a cross-sectional survey among healthcare workers in three grade-A tertiary hospitals in Shanxi, China. Statistically, the hypothesized model was evaluated by AMOS structural equation modeling.510 (72.86%) valid surveys were collected in total. The results showed that performance expectancy (β = 0.45, P < 0.001), effort expectancy (β = 0.21, P < 0.001), and top management support (β = 0.30, P < 0.001) positively influenced healthcare workers’ intention. Top management support was an antecedent of performance expectancy (β = 0.41 , P < 0.001), social influence (β = 0.57, P < 0.001), effort expectancy (β = 0.61, P < 0.001), and information quality (β = 0.59, P < 0.001). In addition, Social influence positively influenced performance expectancy (β = 0.52, P < 0.001), and information quality positively influenced system quality (β = 0.65, P < 0.001). Social influence did not influence nurses’ behavioral intention (β = 0.06, p = 0.376), but negatively influenced clinicians’ behavioral intention in the model (β = −0.19, P < 0.001). System quality positively influenced nurses’ behavioral intention; (β = 0.16, P < 0.001), and information quality positively influenced clinicians’ behavioral intention (β = 0.15, p = 0.025).With this model explaining 76.3% variance of the behavioral intention variable, this study could be useful as a reference for hospital administrators to evaluate future developments and facilitate the implementation of AI-CDSS for VTE prevention.Frontiers in Medicine670This study aims to explore what factors are influencing healthcare workers’ acceptance of the Artificial Intelligence Clinical Assistant Decision Support System (AI-CDSS) for VTE prevention based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT).Frontiers in Medicine["Jingxian Wang", "Yun Zhou", "Kai Tan", "Zhigang Yu", "You Li"]2025-02-11T00:00:00 2b0c02edf5faa84cd10abb90f04b29605ce51f4cArtificial intelligence (AI) has emerged as a transformative force in psychiatry, improving diagnostic precision, treatment personalization, and early intervention through advanced data analysis techniques. This review explores recent advancements in AI applications within psychiatry, focusing on EEG and ECG data analysis, speech analysis, natural language processing (NLP), blood biomarker integration, and social media data utilization. EEG-based models have significantly enhanced the detection of disorders such as depression and schizophrenia through spectral and connectivity analyses. ECG-based approaches have provided insights into emotional regulation and stress-related conditions using heart rate variability. Speech analysis frameworks, leveraging large language models (LLMs), have improved the detection of cognitive impairments and psychiatric symptoms through nuanced linguistic feature extraction. Meanwhile, blood biomarker analyses have deepened our understanding of the molecular underpinnings of mental health disorders, and social media analytics have demonstrated the potential for real-time mental health surveillance. Despite these advancements, challenges such as data heterogeneity, interpretability, and ethical considerations remain barriers to widespread clinical adoption. Future research must prioritize the development of explainable AI models, regulatory compliance, and the integration of diverse datasets to maximize the impact of AI in psychiatric care.Diagnostics1620This review explores recent advancements in AI applications within psychiatry, focusing on EEG and ECG data analysis, speech analysis, natural language processing (NLP), blood biomarker integration, and social media data utilization.Diagnostics["\u0130smail Baydili", "Burak Ta\u015fc\u0131", "Gulay Tasci"]2025-02-11T00:00:00 ce959e5d5896590844fa86324785e8e8b43b0df3This study investigates the transformative capacity of artificial intelligence (AI) in improving financial accountability and governance in the public sector. The study aims to explore the strategic potential and constraints of AI integration, especially as fiscal systems become more complex and public expectations for transparency increase. This study employs a qualitative case study methodology to analyze three countries, which are Estonia, Singapore, and Finland. These countries are renowned for their innovative use of AI in public administration. The data collection tools included an extensive review of the literature, governmental publications, case studies, and public feedback. The study reveals that AI-driven solutions such as predictive analytics, fraud detection systems, and automated reporting significantly improve operational efficiency, transparency, and decision making. However, challenges such as algorithmic bias, data privacy issues, and the need for strong ethical guidelines still exist, and these could hinder the equitable use of AI. The study emphasizes the importance of aligning technological progress with democratic values and ethical governance by addressing these problems. The study also enhances the dialog around AI’s role in public administration. It provides practical recommendations for policymakers who seek to use AI wisely to promote public trust, improve efficiency, and ensure accountability in governance. Future research should focus on enhancing ethical frameworks and investigating scalable solutions to overcome the social and technical challenges of AI integration.Administrative Sciences550The study reveals that AI-driven solutions such as predictive analytics, fraud detection systems, and automated reporting significantly improve operational efficiency, transparency, and decision making, but challenges such as algorithmic bias, data privacy issues, and the need for strong ethical guidelines still exist, and these could hinder the equitable use of AI.Administrative Sciences["Ceray Aldemir", "Tu\u011fba U\u00e7ma Uysal"]2025-02-11T00:00:00 457785b1b3962b222b2325e7ae0feb51b357f9fbAI is reshaping the civil industry by addressing many of the challenges the sector faces. With AI, construction projects can be completed more efficiently, safely, and sustainably. The ability to predict and optimize various aspects of construction, from design to execution, allows for greater innovation, cost savings, and better decision-making. AI also plays a key role in ensuring that infrastructure is smarter and more sustainable, ultimately contributing to the development of the "smart cities" of the future. In the fast-evolving landscape of the civil industry, AI is not just a luxury but a necessity for staying competitive, meeting environmental goals, and delivering projects on time and within budget. Keywords: Construction Management, Artificial Intelligence, Civil Engineering, InfrastructureINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00In the fast-evolving landscape of the civil industry, AI is not just a luxury but a necessity for staying competitive, meeting environmental goals, and delivering projects on time and within budget.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["T. P. Madhavi1"]2025-02-11T00:00:00 c84127a66bb211329714a789b3e347e10fb952d3Nature Reviews Clinical Oncology710This Perspective provides a comprehensive examination of the developments in AI in digital pathology between 2019 and 2024, evaluating the current landscape from the lens of technological innovations, regulatory trends, deployment and implementation, reimbursement and commercial implications.Nature reviews. Clinical oncology["Arpit Aggarwal", "Satvika Bharadwaj", "Germ\u00e1n Corredor", "Tilak Pathak", "S. Badve", "Anant Madabhushi"]2025-02-11T00:00:00 4cb00b3ab09805008d233cab98328ae3b028f8f4Integrating artificial intelligence (AI) into pharmacy operations and drug discovery represents a groundbreaking milestone in healthcare, offering unparalleled opportunities to revolutionize medication management, accelerate drug development, and deliver truly personalized patient care. This review examines the pivotal impact of AI in critical domains, including drug discovery and development, drug repurposing, clinical trials, and pharmaceutical productivity enhancement. By significantly reducing human workload, improving precision, and shortening timelines, AI empowers the pharmaceutical industry to achieve ambitious objectives efficiently. This study delves into tools and methodologies enabling AI implementation, addressing ongoing challenges such as data privacy, algorithmic transparency, and ethical considerations while proposing actionable strategies to overcome these barriers. Furthermore, it offers insights into the future of AI in pharmacy, highlighting its potential to foster innovation, enhance efficiency, and improve patient outcomes. This research is grounded in a rigorous methodology, employing advanced data collection techniques. A comprehensive literature review was conducted using platforms such as PubMed, Semantic Scholar, and multidisciplinary databases, with AI-driven algorithms refining the retrieval of relevant and up-to-date studies. Systematic data scoping incorporated diverse perspectives from medical, pharmaceutical, and computer science domains, leveraging natural language processing for trend analysis and thematic content coding to identify patterns, challenges, and emerging applications. Modern visualization tools synthesized the findings into explicit graphical representations, offering a comprehensive view of the key role of AI in shaping the future of pharmacy and healthcare.Information500This study delves into tools and methodologies enabling AI implementation, addressing ongoing challenges such as data privacy, algorithmic transparency, and ethical considerations while proposing actionable strategies to overcome these barriers.Information["Hesham Allam"]2025-02-11T00:00:00 906cb92f81204852af1796a107cf3eb02e95fe62The purpose of the study is to explore Artificial intelligence (AI) integration into sustainable marketing techniques highlights a transformational potential, combining modern technology with the urgent needs of sustainability. This article thoroughly examines how AI plays a crucial role in improving marketing intelligence by enabling more efficient and socially responsible marketing tactics that support sustainability goals.Method: The study examines how AI-driven insights and analytics enhance decision-making processes, improve customer engagement, and increase the impact of marketing campaigns on environmental and social outcomes by reviewing existing literature and practices. The conversation delves into the difficulties and moral aspects involved in using AI in marketing, such as issues related to data privacy, algorithmic bias, and the importance of a strategic framework that focuses on sustainable development goals.Results: The investigation shows a promising yet intricate marketing intelligence environment, where AI is seen as a crucial tool for balancing economic goals with the need for environmental sustainability and social responsibility. The research stresses the importance of continuous research, multidisciplinary teamwork, and policy creation to maximize the impact of AI on shaping sustainable practices in marketing intelligence.This study provides valuable contributions to the scholarly discussion around sustainable marketing and artificial intelligence, while also offering practical guidance for professionals operating in this dynamic commercial sector.Review of business and economics studies730Review of Business and Economics Studies["Md Mehedi Hasan Emon", "T. Khan"]2025-02-11T00:00:00 8bde43d756c06677406abc88514f1470d40cde16This study provides a theoretical analysis of the use and application of artificial intelligence (AI) in the energy sector as it relates to climate security.The object of the study is energy and climate security as types of economic activity and social activity.The subject of the research is artificial intelligence in relation to the object area of  research.The purpose of the study is to create a sound scientific basis for the use of artificial intelligence in the energy sector, as well as to identify emerging problems in the formation of a science-based approach to climate policy development.The authors’ research includes three interrelated research methodologies: topic modeling, text mining as part of qualitative analysis and object modeling as part of the systematization of results that are adequate to the subject area of  the study and correspond to their reality; in addition, the authors supplemented the quantitative results with a theoretical and heuristic analysis of the scientific results of other researchers. The concept of parametric optimization (PO) is used as an effective method for solving the applied problem of testing the hypothesis of managing energy costs and energy efficiency based on AI in order to achieve optimal performance of the technical system and compliance with the Sustainable Development Goals (SDGs) in the field of climate security.The study’s findings suggest that AI is becoming fundamental to the development of a modern energy sector based on data and complex relationships and provides tools to improve technical system performance and efficiency in the face of sanctions restrictions.The authors conclude that the truth of the hypothesis has been proven: the use of AI as a control feedback loop at a technical facility for purification and energy generation is a more cost-effective and technically optimal alternative to a “live” operator, which will eliminate the human error factor. In this regard, the energy industry, utilities, grid operators and independent power producers must pay special attention to the introduction of AI technologies into existing technical systems.Review of business and economics studies300The study’s findings suggest that AI is becoming fundamental to the development of a modern energy sector based on data and complex relationships and provides tools to improve technical system performance and efficiency in the face of sanctions restrictions.Review of Business and Economics Studies["I. A. Guliev", "A. Mammadov", "K. Ibrahimli"]2025-02-11T00:00:00 148be921692cf1eaa00f8d94dd1300c1ee2e21a6Journal of Applied Learning &amp; Teaching00Journal of Applied Learning &amp; Teaching[]2025-02-11T00:00:00 9fb6fa87e01adf1ac0a05c61c1f257f09a5bbe51Quality &amp; Quantity240This paper proposes integrating text-to-image models and generative pre-trained transformers, into the Real-Time Spatial Delphi process, to transform spatial judgments into visually immersive scenarios, while concurrently crafting actionable policy recommendations suitable for evaluation.Quality &amp; Quantity["Yuri Calleo", "Amos Taylor", "Francesco Pilla", "Simone Di Zio"]2025-02-11T00:00:00 7df079ff481661f2fd4ee18a70d18398fa6dedbaInteractive Learning Environments720Interactive Learning Environments["Daner Sun", "Gary Cheng", "Philip Leung Ho Yu", "Jiyou Jia", "Zhizi Zheng", "Angxuan Chen"]2025-02-11T00:00:00 79882a9a54df064618f5705a1d143f85f7628322The aim of this study is to shed light on a relationship that has not been explored until now. This is the relationship between the world of Islamic finance that of AI, and the debt embedded in the ex nihilo monetary creation system induced by bank loans with ribā. As a result, by making excessive use of AI under the pretext of the need to adopt disruptive innovations that generate exponential growth, Islamic finance feeds this system. This raises a paradox: Islamic finance, which is reputed to be ribā-free, uses AI, which feeds on ribā, to grow exponentially in a context of chip war between the United States, China, and Europe that intensifies day by day. This cognitive trap into which Islamic finance has fallen also applies to Islamic economics. The reason is that AI, as well as finance and economics, could not be part of the debt system if they were not disembedded from society by disintegrating traditional social structures. Hence the importance of the quadriptych rizq, waqf, maʿāsh, and ʿumrān, which opens the field to local practices that create social connections, and help people live better. It is then a question of using AI to satisfy local needs when necessary, and not to increase the power of digital giants through a race for stock market capitalization, which artificially inflates profitability. Against this backdrop, investors may demand clearer monetization roadmaps, which could burst the AI bubble.International journal of multidisciplinary research and analysis00The aim of this study is to shed light on a relationship between the world of Islamic finance that of AI, and the debt embedded in the ex nihilo monetary creation system induced by bank loans with ribā.INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS["A. Belabes"]2025-02-11T00:00:00 19649f6908959f81a7fd6e4da39e9ffe76dbf8c7European Geriatric Medicine10European geriatric medicine["A. Kleebayoon", "H. Daungsupawong", "V. Wiwanitkit"]2025-02-11T00:00:00 955584c1610f8f7c390e8fa4d70513cc4bf0b2ceObjective To capture veterinary professionals' perspectives and applications of AI in veterinary care. This study assesses the perceived benefits, challenges, and potential areas where AI could enhance veterinary medicine and practice workflows. Methods An online survey was distributed to members of the American Animal Hospital Association and Digitail's network of veterinary professionals. The questionnaire included 18 close-ended and 7 open-ended questions exploring awareness, perceptions, usage, expectations, and concerns about AI in veterinary medicine. The survey was open from December 19, 2023, through January 8, 2024. Results The survey gathered 3,968 responses from professionals in various veterinary roles. Most respondents were veterinarians and veterinary technicians, with an average age of 35. Conclusions Respondents demonstrated varying familiarity with AI, with an overall positive outlook toward its adoption in veterinary medicine. Those who actively use AI tools in their professional tasks reported higher levels of optimism about its integration. Key concerns included the reliability and accuracy of AI in diagnosis and treatment. The top benefits identified by respondents included improving efficiencies, streamlining administrative tasks, and potential contributions to revenue growth, employee satisfaction, and client retention. Clinical Relevance The findings underscore the influence of practical exposure and experience with AI tools on attitudes toward AI adoption. The positive correlation suggests that familiarity with AI technologies fosters trust and confidence, consequently driving greater acceptance and adoption within the veterinary community.American Journal of Veterinary Research80The findings underscore the influence of practical exposure and experience with AI tools on attitudes toward AI adoption, and suggest that familiarity with AI technologies fosters trust and confidence, consequently driving greater acceptance and adoption within the veterinary community.American journal of veterinary research["Sebastian Gabor", "Galyna Danylenko", "Bill Voegeli"]2025-02-11T00:00:00 06ba28052b094fed7cb3fb9626467625b8c9da3cEuropean Journal of Heart Failure160European journal of heart failure["L. Bacmeister", "P. Codina", "Dirk Westermann"]2025-02-11T00:00:00 16779e34c4a2733af800a35372af618d31e46112Journal of Research on Technology in Education420Journal of Research on Technology in Education["Roshasfarizan Che Ghazali", "Mohd Fadzil Abdul Hanid", "Mohd Nihra Haruzuan Mohd Said", "Huan Yik Lee"]2025-02-11T00:00:00 c62c4923b71f20a4486cd54cd28816f30d92bf39SN Business &amp; Economics340SN Business &amp; Economics["Gagandeep Singh", "Indrapriya Kularatne"]2025-02-11T00:00:00 24b057c6b8c593a204a3872ef3dd663a304635bcCureus280Cureus["Areti Tsiara", "V. Bakalis", "A. Toska", "Sofia Zyga", "J. Stathoulis", "E. Albani", "M. Saridi", "Constantinos Togas", "Michail Agraniotis", "E. Fradelos"]2025-02-11T00:00:00 c3408cc52c754a7b9f96ad593accfc7fa66ff18bScientific Reports320It is demonstrated that CorneAI, despite being trained on diffuser slit-lamp images, effectively improved diagnostic accuracy, even with smartphone images.Scientific Reports["Hiroki Maehara", "Yuta Ueno", "Takefumi Yamaguchi", "Yoshiyuki Kitaguchi", "Dai Miyazaki", "R. Nejima", "Takenori Inomata", "Naoko Kato", "Tai-ichiro Chikama", "Jun Ominato", "Tatsuya Yunoki", "Kinya Tsubota", "Masahiro Oda", "Manabu Suzutani", "T. Sekiryu", "T. Oshika"]2025-02-11T00:00:00 c764991be01a8f2688e9ce9ed8536afba9f0c7ea<jats:p>.</jats:p>Brazilian Journal of Business00Brazilian Journal of Business["Jo\u00e3o Manuel Afonso Geraldes"]2025-02-11T00:00:00 da31fdf21df3b19cfd9f9bfabcba0fe1488eb604International Journal for Quality Research00International Journal for Quality Research["A. Irmatova", "I. Bakiyeva", "Saxobat A. Bozorova", "Fotimabonu Alisher Qizi Doniyorova", "D. Nasirkhodjaeva"]2025-02-11T00:00:00 1ed60989d7982c7b838145282ac75643058f68dcIn the era of rapid technological advancement, generative artificial intelligence (AI) has emerged as a transformative force in various sectors, including environmental sustainability. This research investigates the factors and consequences of using generative AI to access environmental information and influence green purchasing behavior. It integrates theories such as the information adoption model, value–belief–norm theory, elaboration likelihood model, and cognitive dissonance theory to pinpoint and prioritize determinants of generative AI usage for environmental information and green purchasing behavior. Data from 467 participants were analyzed using a hybrid methodology that blends partial least squares (PLS) with artificial neural networks (ANN). The PLS outcomes indicate that interactivity, responsiveness, knowledge acquisition and application, environmental concern, and ascription of responsibility are key predictors of generative AI use for environmental information. Furthermore, environmental concerns, green values, personal norms, ascription of responsibility, individual impact, and generative AI use emerge as predictors of green purchasing behavior. The ANN analysis offers a unique perspective and discloses variations in the hierarchy of these predictors. This research provides valuable insights for stakeholders on harnessing generative AI to promote sustainable consumer behaviors and environmental sustainability.Business Strategy and the Environment940The PLS outcomes indicate that interactivity, responsiveness, knowledge acquisition and application, environmental concern, and ascription of responsibility are key predictors of generative AI use for environmental information.Business Strategy and the Environment["B. Foroughi", "Bita Naghmeh-Abbaspour", "Jun Wen", "Morteza Ghobakhloo", "Mostafa Al\u2010Emran", "Mohammed A. Al-Sharafi"]2025-02-11T00:00:00 cb551a53966340db89fe0f7df1c709b14d229e69We present Welzijn.AI as new digital solution for monitoring mental well-being in the elderly, as a use case illustrating how recent guidelines on responsible Artificial Intelligence can inform Welzijn.AI's Technology and Value dimensions. Here Technology concerns the description of an open, well-documented and interpretable envisioned architecture in light of the system's goals; Value concerns stakeholder evaluations of Welzijn.AI. Stakeholders included, among others, informal/professional caregivers, a developer, patient and physician federations, and the elderly. Brief empirical evaluations comprised a SWOT-analysis, co-creation session, and user evaluation of a proof-of-concept implementation of Welzijn.AI. The SWOT analysis summarises stakeholder evaluations of Welzijn.AI in terms of its Strengths, Weaknesses, Opportunities and Threats. The co-creation session ranks technical, environmental and user-related requirements of Welzijn.AI with the Hundred Dollar Method. User evaluation comprises (dis)agreement on statements targeting Welzijn.AI's main characteristics, and a ranking of desired social characteristics. We found that stakeholders stress different aspects of Welzijn.AI. For example, medical professionals highlight in the SWOT analysis Welzijn.AI as the key unlocking an individual's social network, whereas in the co-creation session, more user-related aspects such as demo and practice sessions were emphasised. Stakeholders aligned on the importance of safe data storage and access. The elderly evaluated Welzijn.AI's accessibility and perceived trust positively, but user comprehensibility and satisfaction negatively. All in all, Welzijn.AI's architecture draws mostly on open models, as precondition for explainable language analysis. Also, we identified various stakeholder perspectives useful for researchers developing AI in health and beyond.01It is found that stakeholders stress different aspects of Welzijn.AI's accessibility and perceived trust positively, but user comprehensibility and satisfaction negatively, and Welz.AI's architecture draws mostly on open models, as precondition for explainable language analysis.["Bram van Dijk", "A. Lefebvre", "Marco Spruit"]2025-02-11T00:00:00 cef0fa056e5b29d21d1ab405b0a00476bc5616b2Artificial intelligence (AI) is increasingly essential for optimizing energy systems, addressing the growing complexity of energy management, and supporting the integration of diverse renewable sources. This study systematically reviews AI-enabled modeling approaches, highlighting their applications, limitations, and potential in advancing sustainable energy systems while offering insights and a framework for addressing real-world energy challenges. Data-driven models excel in energy demand prediction and resource optimization but face criticism for their “black-box” nature, while mechanism-driven models provide deeper system insights but require significant computation and domain expertise. To bridge the gap between these approaches, hybrid models combine the strengths of both, improving prediction accuracy, adaptability, and overall system optimization. This study discusses the policy background, modeling approaches, and key challenges in AI-enabled energy system modeling. Furthermore, this study highlights how AI-enabled techniques are paving the way for future energy system modeling, including integration and optimization for renewable energy systems, real-time optimization and predictive maintenance through digital twins, advanced demand-side management for optimal energy use, and hybrid simulation of energy markets and business behavior.Energies1380This study systematically reviews AI-enabled modeling approaches, highlighting their applications, limitations, and potential in advancing sustainable energy systems while offering insights and a framework for addressing real-world energy challenges.Energies["Yuancheng Lin", "Junlong Tang", "Jing Guo", "Shidong Wu", "Zheng Li"]2025-02-11T00:00:00 687f55a469079708f902371327b00515dec5df55There are prospects for using artificial intelligence (AI) to enhance methods of sustainable accounting for Environmental, Social, and Governance (ESG) reporting. However, this adoption also brings about the following ethical considerations that the government needs to address: algorithmic bias, lack of transparency, and data privacy. Objective: The study aims to review the role of AI in enhancing the quality and/or credibility of ESG disclosures while also exploring the emerging ethical issues concerning the use of AI and establishing ways AI can be adopted responsibly in sustainable accounting to improve stakeholders’ trust. Methodology: The study used quantitative research by analyzing survey questionnaires completed by 20 organizations that applied AI in ESG reporting and qualitative data from interviews with 30 practitioners. Parameters such as accuracy, report generation time, and stakeholder satisfaction were considered. Results: The assessment results show an overall enhancement of the navigational key’s effectiveness: the specific accuracy of ESG reports increases to 17.67%, whereas the time taken to produce the reports decreases to 58.33%. An analysis of qualitative literature emphasizes the need to respond to ethical concerns that are likely to be experienced while implementing AI. Conclusion: AI holds promise for the overall change towards more sustainable accounting through improving ESG reporting standards. Nevertheless, its further application must be incorporated based on strong ethical and governing standards to overcome the lack of rationality and orientation to trust.Journal of Management World00Journal of Management World["A. Areiqat", "Hanan Abdelsalam Nimer Jaber"]2025-02-11T00:00:00 28f26a15553d28ff389f8a09807c3ae540e812c4Recently, significant research efforts have been directed toward leveraging Artificial Intelligence for sensor data processing and system control. In particular, it is essential to determine the optimal path and trajectory by calculating sensor data for effective control systems. For instance, model-predictive control based on Proportional-Integral-Derivative models is intuitive, efficient, and provides outstanding control performance. However, challenges in tracking persist, which requires active research and development to integrate and optimize the control system in terms of Machine Learning. Specifically, Reinforcement Learning, a branch of Machine Learning, has been used in several research fields to solve optimal control problems. In this paper, we propose an Extended Maximum Actor–Critic using a Reinforcement Learning-based method to combine the advantages of both value and policy to enhance the learning stability of actor–critic for optimization of system control. The proposed method integrates the actor and the maximized actor in the learning process to evaluate and identify actions with the highest value, facilitating effective learning exploration. Additionally, to enhance the efficiency and robustness of the agent learning process, we propose Prioritized Hindsight Experience Replay, combining the advantages of Prioritized Experience Replay and Hindsight Experience Replay. To verify this, we performed evaluations and experiments to examine the improved training stability in the MuJoCo environment, which is a simulator based on Reinforcement Learning. The proposed Prioritized Hindsight Experience Replay method significantly enhances the experience to be compared with the standard replay buffer and PER in experimental simulators, such as the simple HalfCheetah-v4 and the complex Ant-v4. Thus, Prioritized Hindsight Experience Replay achieves a higher success rate than PER in FetchReach-v2, demonstrating the significant effectiveness of our proposed method in more complex reward environments.Applied Sciences190An Extended Maximum Actor–Critic using a Reinforcement Learning-based method to combine the advantages of both value and policy to enhance the learning stability of actor–critic for optimization of system control is proposed.Applied Sciences["Jung-Hyun Kim", "Yong-hoon Choi", "Yourak Choi", "Jae-hyeok Jeong", "Min-Suk Kim"]2025-02-11T00:00:00 87cd213e381560a4016966daa04bb7b25fd84394Artificial intelligence is increasingly influencing healthcare, providing transformative opportunities and challenges for nursing practice. This review critically evaluates the integration of AI in nursing, focusing on its current applications, limitations, and areas that require further investigation. A comprehensive analysis of recent studies highlights the use of AI in clinical decision support systems, patient monitoring, and nursing education. However, several barriers to successful implementation are identified, including technical constraints, ethical dilemmas, and the need for workforce adaptation. Significant gaps in the literature are also evident, such as the limited development of nursing-specific AI tools, insufficient long-term impact assessments, and the absence of comprehensive ethical frameworks tailored to nursing contexts. The potential of AI to reshape personalized care, advance robotics in nursing, and address global health challenges is explored in depth. This review integrates existing knowledge and identifies critical areas for future research, emphasizing the necessity of aligning AI advancements with the specific needs of nursing. Addressing these gaps is essential to fully harness AI's potential while reducing associated risks, ultimately enhancing nursing practice and improving patient outcomes.Frontiers in Medicine1110This review critically evaluates the integration of AI in nursing, focusing on its current applications, limitations, and areas that require further investigation, and integrates existing knowledge and identifies critical areas for future research.Frontiers in Medicine["Qiuying Wei", "Songcheng Pan", "Xiaoyu Liu", "Mei Hong", "Chunying Nong", "Weiqi Zhang"]2025-02-11T00:00:00 d3b2532075db24b45dc0bc039577d2acc12effe8Artificial Intelligence (AI) systems are increasingly intertwined with daily life, assisting users in executing various tasks and providing guidance on decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. Through a randomized controlled trial with 233 participants, we examined human susceptibility to such manipulation in financial (e.g., purchases) and emotional (e.g., conflict resolution) decision-making contexts. Participants interacted with one of three AI agents: a neutral agent (NA) optimizing for user benefit without explicit influence, a manipulative agent (MA) designed to covertly influence beliefs and behaviors, or a strategy-enhanced manipulative agent (SEMA) employing explicit psychological tactics to reach its hidden objectives. By analyzing participants' decision patterns and shifts in their preference ratings post-interaction, we found significant susceptibility to AI-driven manipulation. Particularly, across both decision-making domains, participants interacting with the manipulative agents shifted toward harmful options at substantially higher rates (financial, MA: 62.3%, SEMA: 59.6%; emotional, MA: 42.3%, SEMA: 41.5%) compared to the NA group (financial, 35.8%; emotional, 12.8%). Notably, our findings reveal that even subtle manipulative objectives (MA) can be as effective as employing explicit psychological strategies (SEMA) in swaying human decision-making. By revealing the potential for covert AI influence, this study highlights a critical vulnerability in human-AI interactions, emphasizing the need for ethical safeguards and regulatory frameworks to ensure responsible deployment of AI technologies and protect human autonomy.00It is revealed that even subtle manipulative objectives (MA) can be as effective as employing explicit psychological strategies (SEMA) in swaying human decision-making in swaying human decision-making.["Sahand Sabour", "June M. Liu", "Siyang Liu", "Chris Z. Yao", "Shiyao Cui", "Xuanming Zhang", "Wen Zhang", "Yaru Cao", "Advait Bhat", "Jian Guan", "Wei Wu", "Rada Mihalcea", "Tim Althoff", "Tatia M.C. Lee", "Minlie Huang"]2025-02-11T00:00:00 2de8921c71581e6577d6bd1f043e5df8021e8ddbPurpose In the present era, artificial intelligence (AI) is transforming and redefining the lifestyles of society through its applications, such as chatbots. Chatbot has shown tremendous growth and has been used in almost every field. The purpose of this study is to identify and prioritize the factors that influence millennial’s technology acceptance of chatbots. Design/methodology/approach For the present research, data were collected from 432 respondents (millennials) from Punjab. A fuzzy analytical hierarchy process was used to prioritize the factors influencing millennials’ technology acceptance of chatbots. The key factors considered for the study were information, entertainment, media appeal, social presence and perceived privacy risk Findings The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots. In contrast, perceived privacy risk appeared as the least important factor. Ranking of the global weights reveals that I3 and I2 are the two most important sub-criteria. Research limitations/implications Data were gathered from the millennial population of Punjab, and only a few factors that influence the technology acceptance of chatbots were considered for analysis which has been considered as a limitation of this study. Practical implications The findings of this study will provide valuable insights about consumer behaviour to the business firm, and it will help them to make competitive strategies accordingly. Originality/value Existing literature has investigated the factors influencing millennials’ technology acceptance of chatbots. At the same time, this study has used the multi-criteria decision-making technique to deliver valuable insights for marketers, practitioners and academicians about the drivers of millennials’ technology acceptance regarding chatbots which will add value to the prevailing knowledge base. Journal of Science and Technology Policy Management1070The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots, in contrast, perceived privacy risk appeared as the least important factor.Journal of Science and Technology Policy Management["Sanjay Gupta", "Anchal Arora", "Simarjeet Singh", "Jinesh Jain"]2025-02-11T00:00:00 3700082e5e1e4b298aabdb18ad38ce864527b9c2As artificial intelligence (AI) technologies begin to permeate diverse fields-from healthcare to education-consumers, researchers and policymakers are increasingly raising concerns about whether and how AI is regulated. It is therefore reasonable to anticipate that alignment with principles of 'ethical' or 'responsible' AI, as well as compliance with law and policy, will form an increasingly important part of AI development. Yet, for the most part, the conventional computer science curriculum is ill-equipped to prepare students for these challenges. To this end, we seek to explore how new educational content related to AI ethics and AI policy can be integrated into both ethics- and technical-focused courses. This paper describes a two-lecture 'AI policy module' that was piloted in a graduate-level introductory machine learning course in 2024. The module, which includes an in-class active learning game, is evaluated using data from student surveys before and after the lectures, and pedagogical motivations and considerations are discussed. We find that the module is successful in engaging otherwise technically-oriented students on the topic of AI policy, increasing student awareness of the social impacts of a variety of AI technologies and developing student interest in the field of AI regulation.00A two-lecture 'AI policy module' that was piloted in a graduate-level introductory machine learning course in 2024 is successful in engaging otherwise technically-oriented students on the topic of AI policy, increasing student awareness of the social impacts of a variety of AI technologies and developing student interest in the field of AI regulation.["James Weichert", "Hoda Eldardiry"]2025-02-11T00:00:00 d9861ef1dbbe115357774feae276c91176456bceAs Large Language Models (LLMs) are increasingly adopted in software engineering, recently in the form of conversational assistants, ensuring these technologies align with developers' needs is essential. The limitations of traditional human-centered methods for evaluating LLM-based tools at scale raise the need for automatic evaluation. In this paper, we advocate combining insights from human-computer interaction (HCI) and artificial intelligence (AI) research to enable human-centered automatic evaluation of LLM-based conversational SE assistants. We identify requirements for such evaluation and challenges down the road, working towards a framework that ensures these assistants are designed and deployed in line with user needs.00This paper advocates combining insights from human-computer interaction and artificial intelligence research to enable human-centered automatic evaluation of LLM-based conversational SE assistants, identifying requirements for such evaluation and challenges down the road.["Jonan Richards", "M. Wessel"]2025-02-11T00:00:00 5a486eda06c4658ec3402f16d8ccd98707085313The manifestation of artificial intelligence in audiology presents new potential to revolutionize the practices by increasing effectiveness and decreasing error in automation of tasks. Nevertheless, this technological advancement is not without large ethical concerns such as wrongful bias, opaqueness and privacy invasion. This study aims at identifying some of the argued ethical dilemmas and possibilities regarding Artificial Intelligence application in accounting profession particularly on aspects of accuracy, transparency & decision making. The issue seeks to come up with a timely, responsible and strategic suggestion on the implementation of the AI in accounting profession. These ethical dimensions of professional scepticism are examined in this study with both quantitative data collected from 15 accounting firms and qualitative information obtained from 30 industry professionals. The study shows that AI decreases error and enhances the effectiveness of various tasks, however it introduces important bring up ethical concerns which should be solved to avoid the loss of actors’ confidence. This paper highlights the need to incorporate ethical approaches when adopting AI technology, to improve the data protection measures in use, and finally to encourage inter-professional cooperation to realize the positive aspects of AI integration humanely.Journal of Management World00The study shows that AI decreases error and enhances the effectiveness of various tasks, however it introduces important bring up ethical concerns which should be solved to avoid the loss of actors’ confidence.Journal of Management World["A. Areiqat", "Hanan Abdelsalam Nimer Jaber"]2025-02-11T00:00:00 82a469a49eb23d42bd1fd687daa67ca9aad2425bThe relationship and the interplay between the EU AI Act and the data protection law is a challenging issue. This paper focuses on exploring the interplay between legal provisions stemming from the AI Act and those stemming from the GDPR, with the ultimate goal of developing an integrated framework that simultaneously implements Fundamental Rights Impact Assessment (FRIA) and Data Protection Impact Assessment (DPIA) within the context of Artificial Intelligence (AI) systems, particularly focusing on systems that utilize personal data. This approach is designed to simplify the evaluation processes for stakeholders managing risks related to personal data protection, as well as to other fundamental rights in AI systems, enhancing both efficiency and accuracy in these assessments as well as facilitating compliance with the relevant legal provisions. The methodology adopted involves developing a holistic model that can be applied not only to specific case studies but more broadly across various sectors.Journal of Cybersecurity and Privacy280This paper focuses on exploring the interplay between legal provisions stemming from the AI Act and those stemming from the GDPR, with the ultimate goal of developing an integrated framework that simultaneously implements Fundamental Rights Impact Assessment (FRIA) and Data Protection Impact Assessment (DPIA).Journal of Cybersecurity and Privacy["Anna Thomaidou", "Konstantinos Limniotis"]2025-02-11T00:00:00 e5a74e02e539c6782e679de15c01a73f358db42bIn contemporary music education, innovative technologies, particularly artificial intelligence (AI)‐based tools, play a crucial role. The objective of this study was to assess the effectiveness of AI‐based tools in enhancing students' success and creativity. The study involved 158 students from a leading music institution, who were divided into control and experimental groups. Methods employed included surveys and testing, along with AI‐based tools: Vocal AI Analyzer and Smart Vocal Coach. The results indicated a significant improvement in vocal skills (from 3.5 to 4.5 in the experimental group) and creativity (from 2.9 to 4.1 in the experimental group) compared with the control group. The AI‐based tools demonstrated high effectiveness, providing individualised instruction and immediate feedback. The practical significance of the research lies in the potential implementation of such technologies in music educational institutions to enhance teaching effectiveness and the development of students' creative abilities.European Journal of Education300A significant improvement in vocal skills and creativity is indicated in the experimental group compared with the control group, and the AI‐based tools demonstrated high effectiveness, providing individualised instruction and immediate feedback.European Journal of Education["Hui Liu", "Wei Guo"]2025-02-11T00:00:00 bb9653134e0b5ff7058c4f00c1ffd82bc265dabaThe Fourth Industrial Revolution has profound impacts on the development and transformation of different aspects in society, one of which is the emergence of AI (artificial intelligence).  In the context the global political situation is marked by complex challenges and shifting dynamics, reflecting the interconnected nature of contemporary issues such as geopolitical tensions, economic fragmentation and emerging threat, AI has significantly shaped global politics by revolutionizing decision-making, security, and economic competition. It empowers nations with advanced tools for diplomacy, cybersecurity, and predictive analytics, enhancing their strategic influence on the world stage. The emergence of AI technology has ushered in a new age in global security, defenses, political economics to political systems. The paper examines how AI has been incorporated into these fields in different contexts, such as autonomous weapon systems, surveillance, decision - making processes and economic changes and analyze complex interactions between AI and politics and society issues in reshaping global politics and international orders.International Journal of Scientific Research and Management220This paper examines how AI has been incorporated into these fields in different contexts, such as autonomous weapon systems, surveillance, decision - making processes and economic changes and analyze complex interactions between AI and politics and society issues in reshaping global politics and international orders.International Journal of Scientific Research and Management (IJSRM)["Nguyen Minh Trang", "Khuong Phuong Thao"]2025-02-11T00:00:00 b56920e8d039535fcc9aa65854f8e39a06644f59Journal of Technology in Behavioral Science200The present work is the first to simultaneously investigate the ATAI (Attitudes Toward Artificial Intelligence Scale) and the GAAIS (General Attitudes Towards Artificial Intelligence Scale), and shows substantial overlap between the available attitudes towards AI measures.Journal of Technology in Behavioral Science["Christian Montag", "Raian Ali"]2025-02-11T00:00:00 dc3738e79138f4475b86d4656dd6c4e522110ce4The objective of the research was to ascertain the extent of Artificial intelligence technologies (AIT) employed by Hail University professors in their teaching, as well as the correlation between this usage and the following factors: scientific specialization, academic rank, years of university teaching experience, and gender (male/female). The researcher employed the descriptive approach and a questionnaire instrument to gather data. The research population comprised all professors at Hail University, totaling 1,175 from various colleges, encompassing both genders. The research sample was obtained through stratified random sampling, consisting of 296 individuals, representing 25% of the original population size. The data were statistically analyzed with the Statistical Package for Social Sciences (SPSS) software, yielding the following results: Professors at Hail University employ (AIT)in teaching to a moderate extent, with statistically significant variations in their usage linked to the type of specialization. The findings indicate that engineering faculty utilize (AIT) more extensively in their instruction, followed by health faculty, and then humanities faculty. The results indicated statistically significant differences among Hail University professors regarding their utilization of (AIT), influenced by academic rank, favoring assistant and associate professors, followed by lecturers, and lastly, full professors. No statistically significant variations exist among Hail University instructors regarding the utilization of Artificial intelligence (AI) tools in teaching, as related to the factors of teaching experience and gender (male/female). The findings indicated that teachers at Hail University predominantly employ administrative analytical (AIT)in their teaching, followed by devoted educational interactive (AIT), and subsequently strategies that facilitate the educational process. Based on these findings, the researcher offered several recommendations and suggestions for subsequent research.Journal of Ecohumanism00The findings indicated that teachers at Hail University predominantly employ administrative analytical (AIT) in their teaching, followed by devoted educational interactive (AIT), and subsequently strategies that facilitate the educational process.Journal of Ecohumanism["Khaled Mahgoub Abdullah Mahmoud"]2025-02-12T00:00:00 301cf68e5c48461ff123bf0da1a426cb8233f3baThe increasing prevalence of mental health challenges among college students necessitates innovative approaches to early identification and intervention. This study investigates the application of artificial intelligence (AI) techniques for predicting student mental health risks.A hybrid predictive model, Prophet-LSTM, was developed. This model combines the Prophet time series model with Long Short-Term Memory (LSTM) networks to leverage their strengths in forecasting. Prior to model development, association rules between potential mental health risk factors were identified using the Apriori algorithm. These highly associated factors served as inputs for the Prophet-LSTM model. The model’s weight coefficients were optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm. The model’s performance was evaluated using data from a mental health survey conducted among college students at a Chinese university.The proposed Prophet-LSTM model demonstrated superior performance in predicting student mental health risks compared to other machine learning algorithms. Evaluation metrics, including the detection rate of psychological issues and the detection rate of no psychological issues, confirmed the model’s high accuracy.This study demonstrates the potential of AI-powered predictive models for early identification of students at risk of mental health challenges. The findings have significant implications for improving mental health services within higher education institutions. Future research should focus on further refining the model, incorporating real-time data streams, and developing personalized intervention strategies based on the model’s predictions.Frontiers in Public Health260This study demonstrates the potential of AI-powered predictive models for early identification of students at risk of mental health challenges and has significant implications for improving mental health services within higher education institutions.Frontiers in Public Health["You Fu", "Fang Ren", "Jiantao Lin"]2025-02-12T00:00:00 9fa67b138e6ffece46fe67d7f6ad92aa9bdc537cThis study aims to measure the impact of privacy concerns and perceptions of personalization on purchase decisions on social media platforms. It focuses on the mediating role of attitudes toward advertising and the moderating role of Explainable Artificial Intelligence (XAI). The study investigates consumers who frequently shop on social media. The research model was implemented using an online questionnaire and direct interviews, yielding 515 valid responses. To assess the reliability of the measurement scales, SPSS 26 software was employed. The research hypotheses were tested, and the measurement and structural models were evaluated using AMOS 28. The proposed model is grounded in the Elaboration Likelihood Model (ELM), causal models, interpretability in human-AI interaction, computational privacy theory, and the Theory of Reasoned Action (TRA). The findings indicate that consumers' perceptions of personalized advertising content positively influence their attitudes toward advertisements. Privacy concerns negatively affect users' attitudes toward advertisements. Positive attitudes toward advertising, in turn, influence purchase decisions on social media. This study enriches the theoretical understanding of consumer behavior toward AI-enabled technological products and offers managerial implications for producers to enhance advertising quality and meet consumer demands in the context of social media shopping.Edelweiss Applied Science and Technology00The findings indicate that consumers' perceptions of personalized advertising content positively influence their attitudes toward advertisements, and positive attitudes toward advertising, in turn, influence purchase decisions on social media.Edelweiss Applied Science and Technology["Nguyen Van Dat", "Pho Hai Dang", "Nguyen Van Thich"]2025-02-12T00:00:00 17d297565fa6e2ea1e8e5239d0ef5b1e27405b93Artificial intelligence (AI) affects many aspects of modern life, and most predictions are that the impact of AI on business and society will only increase. In the marketing function of today’s leading businesses, two main types of AI can be discerned. Traditional AI centres on supervised learning algorithms to support and enable the application of data rules, predictive functionality and other AI-based features. Generative AI, on the other hand, uses large language model (LLM) data sets and user prompts to generate new content. While AI-generated applications and content can boost efficiency, they also present challenges regarding transparency and authenticity, and the question of bias is central to these concerns. This article adopts a qualitative inductive approach to research this issue in the context of the marketing function of a global software supplier. Based on a systematic literature review and in-depth interviews with company marketeers, the perceived bias issues in coding, prompting and deployment of AI in digital marketing are identified. Then, based on a provisional conceptual framework derived from the extant literature, an analytical framework for revealing and mitigating bias in digital marketing is put forward, incorporating the perspectives of industry-based practitioners. The framework can be used as a checklist of marketing activities in which bias may exist in either traditional or generative AI across different stages of the customer journey. The article thus contributes to the development of theory and practice regarding the management of bias in AI-generated content and will be of interest to researchers and practitioners as an operational guide and point of departure for subsequent studies.Big Data and Cognitive Computing460An analytical framework for revealing and mitigating bias in digital marketing is put forward, incorporating the perspectives of industry-based practitioners, and can be used as a checklist of marketing activities in which bias may exist in either traditional or generative AI across different stages of the customer journey.Big Data and Cognitive Computing["Catherine Reed", "Martin Wynn", "R. Bown"]2025-02-12T00:00:00 cfb89f0522ee3a5861e6f2a661ad9022fa704e4aTo explore the expanding role of artificial intelligence (AI) in managing aortic valve stenosis (AVS) by bibliometric analysis to identify research trends, key contributors, and the impact of AI on enhancing diagnostic and therapeutic strategies for AVS.A comprehensive literature review was conducted using the Web of Science database, covering publications from January 1990 to March 2024. Articles were analyzed with bibliometric tools such as CiteSpace and VOSviewer to identify key research trends, core authors, institutions, and research hotspots in AI applications for AVS.A total of 118 articles were analyzed, showing a significant increase in publications from 2014 onwards. The results highlight the growing impact of AI in AVS, particularly in cardiac imaging and predictive modeling. Core authors and institutions, primarily from the U.S. and Germany, are driving research in this field. Key research hotspots include machine learning applications in diagnostics and personalized treatment strategies.AI is playing a transformative role in the diagnosis and treatment of AVS, improving accuracy and personalizing therapeutic approaches. Despite the progress, challenges such as model transparency and data security remain. Future research should focus on overcoming these challenges while enhancing collaboration among international institutions to further advance AI applications in cardiovascular medicine.Frontiers in Cardiovascular Medicine420The results highlight the growing impact of AI in AVS, particularly in cardiac imaging and predictive modeling, and key research hotspots include machine learning applications in diagnostics and personalized treatment strategies.Frontiers in Cardiovascular Medicine["Shanshan Chen", "Changde Wu", "Zhaojie Zhang", "Lingjuan Liu", "Yike Zhu", "Dingji Hu", "Chenhui Jin", "Haoya Fu", "Jing Wu", "Songqiao Liu"]2025-02-12T00:00:00 4077f33df630d17b327a1b5ff9261383721f4bb9This study introduces an Artificial Intelligence framework based on the Deep Learning model Bidirectional Encoder Representations from Transformers framework trained on a dataset from 2000–2023. The AI tool categorizes articles into six classes: Contactology, Low Vision, Refractive Surgery, Pediatrics, Myopia, and Dry Eye, with supervised learning enhancing classification accuracy, achieving F1-Scores averaging 86.4%, AUC at 0.98, Precision at 87%, and Accuracy at 86.8% via one-shot training, while Epoch training showed 85.9% Accuracy and 92.8% Precision. Utilizing the Artificial Intelligence model outputs, the Autoregressive Integrated Moving Average model provides forecasts from all classes through 2030, predicting decreases in research interest for Contactology, Low Vision, and Refractive Surgery but increases for Myopia and Dry Eye due to rising prevalence and lifestyle changes. Stability is expected in pediatric research, highlighting its focus on early detection and intervention. This study demonstrates the effectiveness of AI in enhancing diagnostic precision and strategic planning in optometry, with potential implications for broader clinical applications and improved accessibility to eye care.Technologies420The Autoregressive Integrated Moving Average model provides forecasts from all classes through 2030, predicting decreases in research interest for Contactology, Low Vision, and Refractive Surgery but increases for Myopia and Dry Eye due to rising prevalence and lifestyle changes.Technologies["Luis F. F. M. Santos", "M. A. Sanchez-Tena", "C. Alvarez-Peregrina", "J. S\u00e1nchez-Gonz\u00e1lez", "C. Mart\u00ednez-P\u00e9rez"]2025-02-12T00:00:00 9cfa7d0f2bdcd4220a2e18970ab97bea3a1ba031Background: Artificial Intelligence (AI) is being designed, tested, and in many cases actively employed in almost every aspect of healthcare from primary care to public health. It is by now well established that any application of AI carries an attendant responsibility to consider the ethical and societal aspects of its development, deployment and impact. However, in the rapidly developing field of AI, developments such as machine learning, neural networks, generative AI, and large language models have the potential to raise new and distinct ethical and social issues compared to, for example, automated data processing or more basic algorithms. Methods: This article presents a scoping review of the ethical and social issues pertaining to AI in healthcare, with a novel two-pronged design. One strand of the review (SR1) consists of a broad review of the academic literature restricted to a recent timeframe (2021-23), to better capture up to date developments and debates. The second strand (SR2) consists of a narrow review, limited to prior systematic and scoping reviews on the ethics of AI in healthcare, but extended over a longer timeframe (2014-2024) to capture longstanding and recurring themes and issues in the debate. This strategy provides a practical way to deal with an increasingly voluminous literature on the ethics of AI in healthcare in a way that accounts for both the depth and evolution of the literature. Results: SR1 captures the heterogeneity of audience, medical fields, and ethical and societal themes (and their tradeoffs) raised by AI systems. SR2 provides a comprehensive picture of the way scoping reviews on ethical and societal issues in AI in healthcare have been conceptualized, as well as the trends and gaps identified. Conclusion: Our analysis shows that the typical approach to ethical issues in AI, which is based on the appeal to general principles, becomes increasingly unlikely to do justice to the nuances and specificities of the ethical and societal issues raised by AI in healthcare, as the technology moves from abstract debate and discussion to real world situated applications and concerns in healthcare settings.medRxiv00A scoping review of the ethical and social issues pertaining to AI in healthcare, with a novel two-pronged design, shows that the typical approach to ethical issues in AI becomes increasingly unlikely to do justice to the nuances and specificities of the ethical and societal issues raised by AI in healthcare.["M. Morrison", "I. Jakab", "E. Ratti"]2025-02-12T00:00:00 1d18bc9e031fcba1d412e3057cf18185c3985031Introduction: Future evolutions of artificial intelligence (AI) will support autonomous surgery, conducted without the need for human decision making and implementation, but we have not yet achieved this level of technology. Presently, the predominant applications of AI in urological surgery are achieved using the tool of computer vision. This review aims to summarise potential intra-operative AI tools for urologists. Method: A systematic search was conducted through Scopus, PubMed, Embase, and Medline by two independent reviewers, with a third to resolve any conflicts. As a rule, only original articles describing the use or potential use of artificial intelligence intra-operatively in urologic surgery were included. A total of 60 articles were reviewed. Key content and findings: There is significant research investigating the ability to diagnose bladder tumours using AI assistance at the time of cystoscopy, with studies showing the ability to also grade tumour based on appearance and differentiate between carcinoma in situ and indeterminate lesions. With the aid of AI, kidney stones can accurately be identified and diagnosed morphologically intra-operatively. Various studies show the ability to overlay 2D and 3D anatomical models on a surgeon’s screen, as well as correctly identify important anatomical landmarks and surgical instruments, with AI support. All types of intra-operative data can be analysed with AI to assess surgeon performance, predict post-operative outcomes such as continence post prostatectomy, and recognise complications such as bleeding and ischemia. Conclusions: AI holds great potential for urologists during surgery to improve safety, diagnostic accuracy, identification of anatomical structures and surgical instruments, assessment of the surgeon for self-evaluation, and prediction of post-operative outcomes. Before the use of AI as an aid during surgery becomes standard practice, more prospective studies are needed to evaluate its real-world application, feasibility, and costs.Société Internationale d'Urologie Journal750AI holds great potential for urologists during surgery to improve safety, diagnostic accuracy, identification of anatomical structures and surgical instruments, assessment of the surgeon for self-evaluation, and prediction of post-operative outcomes.Société Internationale d’Urologie Journal["Arjun Guduguntla", "Abdullah Al-Khanaty", "Catherine E. Davey", "Oneel Patel", "Anthony Ta", "J. Ischia"]2025-02-12T00:00:00 42172e6a92148a4a41198e05189b101ec14ac77cArtificial intelligence in public administration is critical for the modernization of the public sector and adaptation to the challenges of modern society. The paper analyzes studies dedicated to the impact of artificial intelligence on the efficiency, innovation, and transparency of management processes in the public sector using meta- and bibliometric analysis. The goal is to identify the main areas and keywords that highlight theoretical and practical aspects of artificial intelligence in public administration. In total, 879 scientific articles were analyzed, of which 598 works are devoted to artificial intelligence. Dynamic time analysis revealed a significant surge in scientific interest in artificial intelligence in public administration: from 2010 to 2019, 135 publications were devoted to this issue, and from 2020 to 2024, 421. Bibliographic maps of keywords and publication maps showed the main thematic areas of research on artificial intelligence: the application of AI in public administration and the public sector, decision-making in public administration, data management and digital technologies in public administration, and the strategic use of AI to forecast socio-economic trends.The obtained data became the basis for expanding the scientific and practical potential of using artificial intelligence in public administration. The main areas of future research will concern the regulation of ethical issues to ensure the trust of citizens, the development of a regulatory framework and standards, increasing the efficiency of public services, the integration of artificial intelligence into strategic planning, and the use of artificial intelligence to achieve sustainable development goals. AcknowledgmentThe analysis was carried out within the framework of the implementation of the perspective plan for the development of the scientific area “Social Sciences” of Sumy State University, number d/r 0121U112685.Problems and Perspectives in Management410The paper analyzes studies dedicated to the impact of artificial intelligence on the efficiency, innovation, and transparency of management processes in the public sector using meta- and bibliometric analysis to identify the main areas and keywords that highlight theoretical and practical aspects of artificial intelligence in public administration.Problems and Perspectives in Management["I. Rekunenko", "Iana Kobushko", "Oleksii Dzydzyguri", "I. Balahurovska", "Oksana Yurynets", "Oleksandr Zhuk"]2025-02-12T00:00:00 fded03adf62821f2c63617700b7eab4ed493efd8This research explores the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM). With the growing integration of AI in business operations, HRM has seen significant advancements in areas such as talent acquisition, employee engagement, performance management, and predictive analytics. AI enhances HR processes by automating routine tasks, optimizing decision-making, and improving employee satisfaction. However, ethical concerns, such as data privacy and job displacement, continue to challenge AI's adoption. This paper provides a comprehensive review of AI applications in HRM, their benefits, limitations, and future implications.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research explores the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM), providing a comprehensive review of AI applications in HRM, their benefits, limitations, and future implications.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Srikant Pandit"]2025-02-12T00:00:00 e2bac47bb7a73547236a63ec7849096d5d6f6aaeOver the past decades since the term's inception, the definition and usage of artificial intelligence have varied greatly. In general, the term refers to a broad range of technologies that assist computers in analyzing their surroundings and acting upon them in order to accomplish particular objectives. Artificial intelligence, in contrast to other technologies, aims to do tasks that were previously thought to be limited to human capabilities and linked with human intelligence to the greatest extent feasible. The research study involved explanatory type engaging in mix-method research approach to go through the in-depth of the study which facilitated the researcher to explore the actual of investigation. The philosophy to go through the mix-method to conduct this research study is to get realistic facts based on ontological stance that empower epistemological giant for better understanding and resolution of the issues that are dealt with current emerging trends. The data was analyzed through thematic analysis and with the help of SPSS 26. The artificial intelligence as an emerging trend is moving the world towards a revolution that can change the human life which have never been impacted since last decades. The artificial intelligence has changed the ways of teaching and learning in the field of education as never been intended before, this revolutionary change can bring a huge change in ways of human thinking and observation. The education in the new era has be more relax and easy to cope up with this revolutionary emerging trend. The learners as well as instructors can get the better results in the usage of artificial intelligence as it can help in introducing different teaching techniques which can prove to be effective during the teaching and learning process.The Critical Review of Social Sciences Studies00The artificial intelligence has changed the ways of teaching and learning in the field of education as never been intended before, this revolutionary change can bring a huge change in ways of human thinking and observation.The Critical Review of Social Sciences Studies["Dr. Murtaza Ali Laghari", "Dr. Abida Siddiqui", "Momina Musarat Ali"]2025-02-12T00:00:00 fbc74779c610215b3812bfa89d08dcae1c33096eOBJECTIVE To develop a framework for good clinical decision-making using machine learning (ML) models for interventional, patient-level decisions. DESIGN Grounded theory qualitative interview study. SETTING Primarily single-site at a major urban academic paediatric hospital, with external sampling. PARTICIPANTS Sixteen participants representing physicians (n=10), nursing (n=3), respiratory therapists (n=2) and an ML specialist (n=1) with experience working in acute care environments were identified through purposive sampling. Individuals were recruited to represent a spectrum of ML knowledge (three expert, four knowledgeable and nine non-expert) and years of experience (median=12.9 years postgraduation). Recruitment proceeded through snowball sampling, with individuals approached to represent a diversity of fields, levels of experience and attitudes towards artificial intelligence (AI)/ML. A member check step and consultation with patients was undertaken to vet the framework, which resulted in some minor revisions to the wording and framing. INTERVENTIONS A semi-structured virtual interview simulating an intensive care unit handover for a hypothetical patient case using a simulated ML model and seven visualisations using known methods addressing interpretability of models in healthcare. Participants were asked to make an initial care plan for the patient, then were presented with a model prediction followed by the seven visualisations to explore their judgement and potential influence and understanding of the visualisations. Two visualisations contained contradicting information to probe participants' resolution process for the contrasting information. The ethical justifiability and clinical reasoning process were explored. MAIN OUTCOME A comprehensive framework was developed that is grounded in established medicolegal and ethical standards and accounts for the incorporation of inference from ML models. RESULTS We found that for making good decisions, participants reflected across six main categories: evidence, facts and medical knowledge relevant to the patient's condition; how that knowledge may be applied to this particular patient; patient-level, family-specific and local factors; facts about the model, its development and testing; the patient-level knowledge sufficiently represented by the model; the model's incorporation of relevant contextual factors. This judgement was centred on and anchored most heavily on the overall balance of benefits and risks to the patient, framed by the goals of care. We found evidence of automation bias, with many participants assuming that if the model's explanation conflicted with their prior knowledge that their judgement was incorrect; others concluded the exact opposite, drawing from their medical knowledge base to reject the incorrect information provided in the explanation. Regarding knowledge about the model, we found that participants most consistently wanted to know about the model's historical performance in the cohort of patients in their local unit where the hypothetical patient was situated. CONCLUSION Good decisions using AI tools require reflection across multiple domains. We provide an actionable framework and question guide to support clinical decision-making with AI.BMJ evidence-based medicine290An actionable framework and question guide is provided to support clinical decision-making with AI that is grounded in established medicolegal and ethical standards and accounts for the incorporation of inference from ML models.BMJ evidence-based medicine["M. Mccradden", "Kelly Thai", "Azadeh Assadi", "S. Tonekaboni", "Ian Stedman", "Shalmali Joshi", "Minfan Zhang", "Fanny Chevalier", "Anna Goldenberg"]2025-02-12T00:00:00 edfb9ac4495954c75a8d31ef42a6c4f82a36bac3Artificial intelligence (AI) is becoming increasingly utilized as a tool for physicians to optimize medical care and patient outcomes. The multifaceted approach to managing esophageal cancer provides a perfect opportunity for machine learning to support clinicians in all stages of management. Preoperatively, AI may aid gastroenterologists and surgeons in diagnosing and prognosticating premalignant or early-stage lesions. Intraoperatively, AI may also aid surgeons in identifying anatomic structures or minimize the learning curve for new learners. Postoperatively, machine learning algorithms can help predict complications and guide high-risk patients through recovery. While still evolving, AI holds promise in enhancing the efficiency and efficacy of multidisciplinary esophageal cancer care.Mini-invasive Surgery280While still evolving, AI holds promise in enhancing the efficiency and efficacy of multidisciplinary esophageal cancer care.Mini-invasive Surgery["Constantine M. Poulos", "Ryan Cassidy", "Eamon Khatibifar", "Erik Holzwanger", "Lana Schumacher"]2025-02-12T00:00:00 55544080be78a510d4ceeae6422326b255f1e7c6In recent years, artificial intelligence has begun to play a salient role in various medical fields, including ophthalmology. This extensive review is addressed to ophthalmologists and aims to capture the current landscape and future potential of AI applications for eye health. From automated retinal screening processes and machine learning models predicting the progression of ocular conditions to AI-driven decision support systems in clinical settings, this paper provides a comprehensive overview of the clinical implications of AI in ophthalmology. The development of AI has opened new horizons for ophthalmology, offering innovative solutions to improve the accuracy and efficiency of ocular disease diagnosis and management. The importance of this paper lies in its potential to strengthen collaboration between researchers, ophthalmologists, and AI specialists, leading to transformative findings in the early identification and treatment of eye diseases. By combining AI potential with cutting-edge imaging methods, novel biomarkers, and data-driven approaches, ophthalmologists can make more informed decisions and provide personalized treatment for their patients. Furthermore, this paper emphasizes the translation of basic research outcomes into clinical applications. We do hope this comprehensive review will act as a significant resource for ophthalmologists, researchers, data scientists, healthcare professionals, and managers in the healthcare system who are interested in the application of artificial intelligence in eye health.Applied Sciences1220This comprehensive review is addressed to ophthalmologists and aims to capture the current landscape and future potential of AI applications for eye health, leading to transformative findings in the early identification and treatment of eye diseases.Applied Sciences["Hariton-Nicolae Costin", "M. Fira", "L. Goras"]2025-02-12T00:00:00 d90bc5a70daf0d771c1cea7133e767a62fd32595The aim of this paper is to explore the technological innovation mechanism by which digital transformation (DT) influences total factor productivity (TFP). We take the Chinese listed firms from 2007 to 2020 as research samples, and con- tribute to the above goals based on fixed-effect models, instrumental variables, mediation effect, and moderating effect models. It has been found that (1) while DT contributes positively to productivity, the enhancement of TFP in current DT is primarily attributed to artificial intelligence (AI) technology rather than other techno- logical innovation. (2) From an innovation-directed perspective, the impact of DT on TFP may be offset by other forms of technological innovation, such as green and energy technology. Specifically, the non-AI direction of technological innovation may not align with the productivity implications of DT. (3) Intellectual property protection impedes the impact of DT on productivity and constrains the deployment of AI technology. Conversely, business strategic radicalism and corporate intangible asset have yielded favorable outcomes. This study not only verifies that the technological innovation channel of DT for enhancing TFP mainly stems from AI technology, but also implies that the current DT might exert a negative effect on other technologies. First published online 12 February 2025Technological and Economic Development of Economy660This study verifies that the technological innovation channel of DT for enhancing TFP mainly stems from AI technology, but also implies that the current DT might exert a negative effect on other technologies.Technological and Economic Development of Economy["Fang Qu", "Qian Tang", "Chun-Mei Li", "Jun Liu"]2025-02-12T00:00:00 953a6625131673b45702fc3c0b523a8847b4509eFrontiers in Toxicology80Frontiers in Toxicology["Marc Teunis", "T. Luechtefeld", "T. Hartung"]2025-02-12T00:00:00 7507d79e8f9be2cfa6b8dfccd7cbae532dd1b91200["Mariana Nogueira", "Sandra Lopes Apar\u00edcio", "Ivone Duarte", "Margarida Silvestre"]2025-02-12T00:00:00 8bd9adb5a7ee260f265d6aef6dd1486b4eeb71deDiscover Artificial Intelligence380Discover Artificial Intelligence["Lily Ballot Jones", "Julia Thornton", "Daswin De Silva"]2025-02-12T00:00:00 e1657eb2840c0b04d80e7d46f1938fae71ef191fIn insurance, there is still a significant gap between the anticipated disruption, due to big data and machine learning algorithms, and the actual implementation of behaviour-based personalization, as described by Meyers (2018). Here, we identify eight key factors that serve as fundamental obstacles to the radical transformation of insurance guarantees, aiming to closely align them with the risk profile of each policyholder. These obstacles include the collective nature of insurance, the entrenched beliefs of some insurance companies, challenges related to data collection and use for personalized pricing, limited interest from insurers in adopting new models as well as policyholders’ reluctance towards embracing connected devices. Additionally, the hurdles of explainability, insurer inertia and ethical or societal considerations further complicate the path toward achieving highly individualized insurance pricing.Big Data &amp; Society210Eight key factors that serve as fundamental obstacles to the radical transformation of insurance guarantees are identified, aiming to closely align them with the risk profile of each policyholder.Big Data &amp; Society["Arthur Charpentier", "Xavier Vamparys"]2025-02-12T00:00:00 0c835cd2388e58a81b62085653d4a59b232798f4Journal of Applied Economics1120Journal of Applied Economics["R\u00e9gis Y. Chenavaz", "Stanko Dimitrov"]2025-02-12T00:00:00 90ba13fc888dcfa3a09213631896181c4c9d44b3Background/Objectives: Artificial intelligence (AI) is transforming higher education (HE), reshaping teaching, learning, and feedback processes. Feedback generated by large language models (LLMs) has shown potential for enhancing student learning outcomes. However, few empirical studies have directly compared the quality of LLM feedback with feedback from novices and experts. This study investigates (1) the types of prompts needed to ensure high-quality LLM feedback in teacher education and (2) how feedback from novices, experts, and LLMs compares in terms of quality. Methods: To address these questions, we developed a theory-driven manual to evaluate prompt quality and designed three prompts of varying quality. Feedback generated by ChatGPT-4 was assessed alongside feedback from novices and experts, who were provided with the highest-quality prompt. Results: Our findings reveal that only the best prompt consistently produced high-quality feedback. Additionally, LLM feedback outperformed novice feedback and, in the categories explanation, questions, and specificity, even surpassed expert feedback in quality while being generated more quickly. Conclusions: These results suggest that LLMs, when guided by well-crafted prompts, can serve as high-quality and efficient alternatives to expert feedback. The findings underscore the importance of prompt quality and emphasize the need for prompt design guidelines to maximize the potential of LLMs in teacher education.Applied Informatics430LLMs, when guided by well-crafted prompts, can serve as high-quality and efficient alternatives to expert feedback, and underscore the importance of prompt quality.AI["Lucas Jasper Jacobsen", "Kira Elena Weber"]2025-02-12T00:00:00 41727a1d143632dec3152e5b7753486bee17cfffBackground Artificial Intelligence (AI) has made significant strides in various domains, but generating realistic human hands remains a challenge. This study explores the limitations of AI in capturing the fine details and proportions of hands, using Contrastive Language Image Pretraining (CLIP) as a case study. Methods Our analysis reveals that CLIP struggles to accurately represent hands due to inadequate training data, anatomical complexities, and practical challenges. We conducted a series of tests and analyses to identify the primary causes of CLIP’s difficulties. Results Our results show that CLIP’s struggles stem from data biases and insufficient anatomical representation in training datasets. Specifically, we found distorted finger relationships, inaccurate proportions, and deviations from expected hand geometry. Conclusion This study aims to provide a comprehensive examination of the current limitations and propose possible directions for future research. By leveraging CLIP for evaluation, control algorithms for structure enforcement, DALL-E for generation, AR for gesture tracking, and 3D modeling for anatomical accuracy, we can overcome the challenges of generating realistic human hands and advance AI’s capabilities in artistic creativityF1000Research390By leveraging CLIP for evaluation, control algorithms for structure enforcement, DALL-E for generation, AR for gesture tracking, and 3D modeling for anatomical accuracy, AI can overcome the challenges of generating realistic human hands and advance AI’s capabilities in artistic creativity.F1000Research["Meghna Sarkar", "Siddhartha Chatterjee", "Sudipta Hazra", "Anurag Sinha", "Md. Sazid Reza", "Mohd Asif Shah"]2025-02-12T00:00:00 f1a265aa22c121dc17a05f467fdca027dcfbdd6c As generative artificial intelligence (gAI) tools become increasingly prevalent, writing instructors face challenges in addressing their ethical and pedagogical implications. In response to a rise in unethical gAI usage among students in English 5 at Sacramento State, graduate teaching associates in the English department incorporated AI literacy into their sections of English 5 through a revised rhetorical analysis assignment. This study examines the implementation and impact of this instructional shift during the Fall 2024 semester, when four graduate teaching assistants (TAs) introduced ~100 students to AI literacy through structured rhetorical analysis activities. The assignment sequence included identifying rhetorical moves in scholarly articles, collaboratively constructing a rhetorical moves chart, prompting and analyzing ChatGPT outputs, and composing a comparative rhetorical analysis essay. Findings indicate that explicit AI literacy instruction significantly reduced unethical gAI usage, as reported by TAs who observed declines from 15-25% in Spring 2024 to under 5% in Fall 2024. Students engaged critically with both human-authored and AI-generated texts, recognizing limitations in gAI’s rhetorical sophistication and citation accuracy. Additionally, integrating gAI into coursework fostered a shift from a punitive approach to a collaborative learning environment, allowing students to explore AI’s strengths and weaknesses responsibly. This study underscores the importance of proactive AI literacy education in writing pedagogy, demonstrating how structured engagement with gAI can enhance critical thinking and ethical technology use in academic settings. AI-EDU Arxiv00Findings indicate that explicit AI literacy instruction significantly reduced unethical gAI usage, as reported by TAs who observed declines from 15-25% in Spring 2024 to under 5% in Fall 2024.AI-EDU Arxiv["Angela Laflen", "Michelle Cook", "Gaby Meindl", "Jovan Virag"]2025-02-12T00:00:00 bdb51eec072b145ebe2708af6d4c25ac090c9db6Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances catalytic activity, selectivity, and stability. SACs demonstrate substantial promise in electrocatalysis applications, such as fuel cells, CO2 reduction, and hydrogen production, due to their ability to maximize utilization of active sites. However, the development of efficient and stable SACs involves intricate design and screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) and neural networks (NNs), offers powerful tools for accelerating the discovery and optimization of SACs. This review systematically discusses the application of AI technologies in SACs development through four key stages: (1) Density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations: DFT and AIMD are used to investigate catalytic mechanisms, with high-throughput applications significantly expanding accessible datasets; (2) Regression models: ML regression models identify key features that influence catalytic performance, streamlining the selection of promising materials; (3) NNs: NNs expedite the screening of known structural models, facilitating rapid assessment of catalytic potential; (4) Generative adversarial networks (GANs): GANs enable the prediction and design of novel high-performance catalysts tailored to specific requirements. This work provides a comprehensive overview of the current status of AI applications in SACs and offers insights and recommendations for future advancements in the field.Journal of Materials Informatics1360This review systematically discusses the application of AI technologies in SACs development through four key stages, providing a comprehensive overview of the current status of AI applications in SACs and offers insights and recommendations for future advancements in the field.Journal of Materials Informatics["Qiumei Yu", "Ninggui Ma", "Chihon Leung", "Han Liu", "Yang Ren", "Zhanhua Wei"]2025-02-12T00:00:00 966f21153b287140f34fd289c359a158d749e1fdKN - Journal of Cartography and Geographic Information360How generative AI can be utilized within the neopragmatic framework of fallibilism to constructively address uncertainties and develop socially relevant solutions, particularly in the realm of cartography is discussed.KN - Journal of Cartography and Geographic Information["Dennis Edler", "Jule Drews", "K. Berr", "Olaf K\u00fchne"]2025-02-12T00:00:00 c52de50159ba76bbd914fa82486a3c1d281d0146Artificial intelligence (AI) is reshaping the media landscape, influencing news production, consumption, and dissemination. AI algorithms support content generation, trend tracking, and fact-checking. This study examines 108 companies using AI through a documentary review and expert survey. Text generation is the most common function, while experts consider fact-checking the most useful. A geographic disparity exists, with the United States leading in AI adoption, while other regions lag. AI holds great potential for media transformation, but its inclusion remains uneven across countries.Comunicación y Sociedad00This study examines 108 companies using AI through a documentary review and expert survey, finding text generation is the most common function, while experts consider fact-checking the most useful.Comunicación y Sociedad["Santiago Tejedor Calvo", "Stephanie Vick Saur\u00ed", "Laura Cervi"]2025-02-12T00:00:00 2c0205023ea2e55c9623da54a50a5b05ba021689Artificial Intelligence (AI) is one of the most advanced technologies, revolutionizing various industries, including accounting. As a branch of computer science, AI focuses on developing intelligent systems capable of performing tasks that traditionally required human intervention. In the accounting domain, AI enhances efficiency, accuracy, and decision-making by automating routine processes such as data entry, transaction processing, financial analysis, and fraud detection. This study explores the role of AI in transforming accounting practices, highlighting its opportunities, challenges, and future implications. AI-driven accounting systems streamline operations, reduce human errors, and improve financial reporting and compliance. Machine learning, robotic process automation, and natural language processing contribute to more precise financial forecasting and risk management. However, the integration of AI in accounting comes with significant challenges. High implementation costs, data security risks, regulatory concerns, and resistance to change among professionals hinder widespread adoption. Ethical considerations, such as bias in AI algorithms and the potential displacement of human accountants, further complicate the landscape. Additionally, the rapid evolution of AI technologies poses difficulties in maintaining regulatory frameworks and ensuring accountability. This descriptive study, based on secondary sources, identifies the impact of AI in accounting, emphasizing both its potential and challenges. It underscores the need for organizations to adopt AI responsibly, develop robust governance frameworks, and invest in skill development to prepare professionals for the AI-driven future. By addressing these challenges, AI can serve as a powerful tool in shaping the future of accounting, enhancing efficiency, accuracy, and strategic decision-making. Keywords: Artificial Intelligence, Accounting, Finance, Challenges, Cyber Security [1] Assistant Professor, Department of Commerce, Central University of RajasthanINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The need for organizations to adopt AI responsibly, develop robust governance frameworks, and invest in skill development to prepare professionals for the AI-driven future is highlighted, highlighting its opportunities, challenges, and future implications.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Dr. Shubham Pandey"]2025-02-12T00:00:00 f12bc7a46e9bf12327adcb75d212d9bd0892d04cThis paper presents a comparative study between models trained on real-world and synthetic datasets in the domain of artificial intelligence and machine learning. By meticulously evaluating model performance, generalization capabilities, and robustness across diverse scenarios, the investigation of the efficacy and feasibility of synthetic data in machine learning applications. Through empirical analysis, the address fundamental questions regarding predictive accuracy, resilience to adversarial inputs, and biases inherent in synthetic data. Our findings provide valuable insights for practitioners and researchers navigating the dynamic landscape of AI methodologies, offering guidance for informed decision-making and future advancements in the field.International Journal for Global Academic &amp; Scientific Research230A comparative study between models trained on real-world and synthetic datasets in the domain of artificial intelligence and machine learning, offering guidance for informed decision-making and future advancements in the field is presented.International Journal for Global Academic &amp; Scientific Research["Manika Kaushik"]2025-02-12T00:00:00 ec1b37c264ee1831bb7e55f273dbb900b92fca8bArtificial Intelligence (AI) tools and literature play a transformative role in understanding leadership styles and their impact on social change within business management. This study explores the intersection of AI-driven analytics and scholarly contributions in shaping leadership theories, decision-making processes, and social transformations. AI-based tools such as sentiment analysis, machine learning models, and natural language processing enable business leaders to adapt to dynamic environments while fostering inclusivity, ethical leadership, and sustainable development. The research delves into the implications of AI-assisted leadership assessments, predictive analytics, and evidence-based decision-making to drive organizational success. Additionally, literature reviews on leadership paradigms highlight the evolution of traditional, transformational, and servant leadership styles in response to global socio-economic shifts. This study underscores the necessity of integrating AI tools with leadership research to enhance strategic thinking, employee engagement, and adaptability in contemporary business landscapes. The findings contribute to the broader discourse on leadership's role in driving corporate responsibility and social impact, emphasizing AI’s capacity to refine leadership models and promote innovation. Keywords Artificial Intelligence, Leadership Styles, Business Management, Social Change, Decision-Making, Organizational SuccessINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This study underscores the necessity of integrating AI tools with leadership research to enhance strategic thinking, employee engagement, and adaptability in contemporary business landscapes.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Mandla padma viharika"]2025-02-12T00:00:00 0319a87453123bb260dcaf459e32099086f89ee5This paper introduces the conceptual metaphor of 'digital plastic' as a framework for understanding the implications of Generative Artificial Intelligence (GenAI) content through a multiliteracies lens, drawing parallels with the properties of physical plastic. Similar to its physical counterpart, GenAI content offers possibilities for content creation and accessibility while potentially contributing to digital pollution and ecosystem degradation. Drawing on multiliteracies theory and Conceptual Metaphor Theory, we argue that Critical Artificial Intelligence Literacy (CAIL) must be integrated into educational frameworks to help learners navigate this synthetic media landscape. We examine how GenAI can simultaneously lower the barriers to creative and academic production while threatening to degrade digital ecosystems through misinformation, bias, and algorithmic homogenization. The digital plastic metaphor provides a theoretical foundation for understanding both the affordances and challenges of GenAI, particularly in educational contexts, where issues of equity and access remain paramount. Our analysis concludes that cultivating CAIL through a multiliteracies lens is vital for ensuring the equitable development of critical competencies across geographical and cultural contexts, especially for those disproportionately vulnerable to GenAI's increasingly disruptive effects worldwide.00It is argued that Critical Artificial Intelligence Literacy (CAIL) must be integrated into educational frameworks to help learners navigate this synthetic media landscape, and that cultivating CAIL through a multiliteracies lens is vital for ensuring the equitable development of critical competencies across geographical and cultural contexts.["Jasper Roe", "Leon Furze", "Mike Perkins Durham University", "United Kingdom.", "Deakin University", "Australia", "British University Vietnam", "Vietnam"]2025-02-12T00:00:00 e5e9e3c9be9692811c66d686b8b6c54736601370A Systematic Review of AI’s Impact on the Labor Market: Challenges, Opportunities, and Future Directions is discussed in this work. The widespread adoption of artificial intelligence (AI) technologies is transforming industries, leading to significant changes in the labor market. This paper explores the effects of AI on job displacement, economic growth, and workplace productivity. We discuss how companies and governments are responding to these changes through policy interventions and the need for upskilling to mitigate risks associated with AI automation. The rapid advancement of artificial intelligence (AI), particularly generative AI, has sparked significant debate about its impact on the labor market. While AI promises to enhance productivity and create new opportunities, concerns about job displacement, inequality, and ethical implications persist. This paper presents a systematic review of the current literature on AI’s impact on employment, focusing on the challenges, opportunities, and future directions. We analyze key trends, including the potential for job displacement, the role of AI in reshaping industries, and the need for policy interventions to mitigate risks. Our findings highlight the dual nature of AI as both a disruptor and an enabler, emphasizing the importance of proactive measures to ensure equitable outcomes in the evolving labor market. Navigating the AI Revolution: Challenges, Opportunities, and Solutions for the Future of Work is an area that is discussedInternational Journal of Advanced Research in Science, Communication and Technology200A systematic review of the current literature on AI’s impact on employment highlights the dual nature of AI as both a disruptor and an enabler, emphasizing the importance of proactive measures to ensure equitable outcomes in the evolving labor market.International Journal of Advanced Research in Science, Communication and Technology["Satyadhar Joshi"]2025-02-12T00:00:00 2b3bbbec96147c7ce25875e8c535a395575d4fbdIntroduction/Importance of Study: Artificial Intelligence (AI) has recently become a powerful tool for different disciplines including healthcare. One of the new possibilities it opens up in clinical psychology is its ability to forecast mental health disorders. This paper aims to discover the healthcare professionals, artificial intelligence specialists, and psychologists’ perceptions about the use of AI in the forecast of mental health disorders.  Novelty Statement: This study aims to evaluate the current EU professional awareness, efficacy impression, and ethical and regulatory standpoint required for AI application on mental health prediction forecasts efficiently in a quantitative manner. Material and Methods: The study design employed was cross-sectional in nature and data was gathered from 250 professionals with the use of a structured questionnaire instrument. The survey included questions about participants’ awareness of AI, its efficiency in their workplaces, their concerns about privacy and ethical issues as well as their opinion about strict regulation in this sphere. Data analysis tools that were used included descriptive statistics, correlation analysis as well as regression models. Results and Discussion: A moderate level of awareness of AI is realized through low awareness of AI in mental health. A total of 11 participants were interviewed, self has been noted to have positive views about the use of AI but there is concern about privacy, trust, and the ethical use of artificial intelligence in diagnosing mental health disorders. A majority viewed that regulation on the use of AI in this area should be enhanced. What these results indicate is that although there might be numerous possibilities for having AI, there are crucial areas of concern in terms of trust, privacy, and accuracy. Conclusion: The integration of AI into mental health has the benefit of reaching a large number of people, yet, the barriers need to be resolved, in terms of ethical issues, implementation of a clearer and transparent approach to be established, and trust from mental health professionals.Journal of Medical &amp; Health Sciences Review00The integration of AI into mental health has the benefit of reaching a large number of people, yet, the barriers need to be resolved in terms of ethical issues, implementation of a clearer and transparent approach to be established, and trust from mental health professionals.Journal of Medical &amp; Health Sciences Review["Dr U.G.Lashari", "Somia Shabbir", "Dr Tazeem Shahbaz", "Hajra Waheed kayani"]2025-02-12T00:00:00 a6e26c0ad601195043d7bff96e4d822043d02809This study aims to see the development of research on the topic of "Artificial Intelligence in Accounting" and research plans that can be carried out based on journals published on the theme. This research uses a qualitative method with a bibliometric analysis approach. The data used is secondary data with the theme "Artificial Intelligence in Accounting" which comes from the Scopus database with a total of 181 journal articles. Then, the data is processed and analyzed using the VosViewer application with the aim of knowing the bibliometric map of "Artificial Intelligence in Accounting" research development in the world. The results of the study found that there were 6 clusters with the most used words being impact, quality, adoption, challenge, firm, relationship, factor, value, and industry. Then, the research path topics related to Artificial Intelligence in Accounting are The impact of AI on auditing practice, AI and automation in accounting, AI adoption in accounting education, Blockchain adoption in accounting firms, Artificial intelligence in financial reporting, and AI integration in AIS.Review on Islamic Accounting660This study aims to see the development of research on the topic of "Artificial Intelligence in Accounting" and research plans that can be carried out based on journals published on the theme and the bibliometric map of "Artificial Intelligence in Accounting" research development in the world.Review on Islamic Accounting["Muhamad Taqi"]2025-02-12T00:00:00 dbbd32ade8db3d7df814811d9f5c294e8ff2d757Integrating artificial intelligence and advanced biosensor technologies represents a transformative paradigm in athletic performance optimization. This research explores the revolutionary potential of AI-driven fitness solutions to redesign training methodologies across professional and amateur sports disciplines fundamentally. These technologies offer unprecedented capabilities for personalized, data-driven athletic development by addressing critical limitations in traditional performance tracking. The study examines comprehensive approaches to physiological monitoring, performance prediction, and individualized training interventions enabled by advanced machine learning algorithms and sophisticated biosensor technologies. Key innovations include real-time physiological data collection, predictive performance analytics, and adaptive training strategies that maximize individual athletic potential while minimizing injury risks.Molecular &amp; Cellular Biomechanics580This research explores the revolutionary potential of AI-driven fitness solutions to redesign training methodologies across professional and amateur sports disciplines fundamentally by addressing critical limitations in traditional performance tracking.Molecular &amp; Cellular Biomechanics["Qi Zeng"]2025-02-12T00:00:00 dc9a626424b689824f76c4f9efa99aa34b8be5acThis paper examines the substantial influence of artificial intelligence (AI) on financial professionals, focusing on how AI technologies change jobs and responsibilities in the financial sector. According to the research, AI acts as both a disruptive force and a catalyst for efficiency, requiring professionals to adapt to technological improvements while providing tools to improve decision-making and productivity. The findings are divided into three sections: first, the scope and key components of AI in finance are defined, with a focus on its historical development; second, the transformation of financial roles through automation, data analytics, and risk management is examined; and finally, case studies from various financial institutions demonstrate the practical application of AI technologies. This analysis demonstrates how AI simultaneously challenges and empowers financial professionals, emphasizing the importance of continual learning and skill improvement to survive in an AI-driven economy.Journal of Next-Generation Research 5.000This analysis demonstrates how AI simultaneously challenges and empowers financial professionals, emphasizing the importance of continual learning and skill improvement to survive in an AI-driven economy.Journal of Next-Generation Research 5.0["Ahmad Al-Harbi"]2025-02-12T00:00:00 41cdd24634f3caef7928a835726cdf0a51c4eb27As the levels of automation and reliance on modern artificial intelligence (AI) approaches increase across multiple industries, the importance of the human-centered perspective becomes more evident. Various actors in such industrial applications, including equipment operators and decision makers, have their needs and preferences that often do not align with the decisions produced by black-box models, potentially leading to mistrust and wasted productivity gain opportunities. In this paper, we examine these issues through the lenses of visual analytics and, more broadly, interactive visualization, and we argue that the methods and techniques from these fields can lead to advances in both academic research and industrial innovations concerning the explainability of AI models. To address the existing gap within and across the research and application fields, we propose a conceptual framework for visual analytics design and evaluation for such scenarios, followed by a preliminary roadmap and call to action for the respective communities.Analytics1050It is argued that the methods and techniques from these fields can lead to advances in both academic research and industrial innovations concerning the explainability of AI models, and a conceptual framework for visual analytics design and evaluation is proposed.Analytics["Kostiantyn Kucher", "E. Zohrevandi", "Carl A. L. Westin"]2025-02-12T00:00:00 f51e23db2918c8944c1a7ef1df2d0b56c26d611bResearchers face significant disparities in accessing resources for high-impact research. Artificial Intelligence (AI) promises to bridge these gaps by offering capabilities previously unavailable to many institutions. This paper examines the effects on protein research of AlphaFold, an AI tool that won the 2024 Nobel Prize in Chemistry for accurately predicting protein structures. Using comprehensive publication data, we show that AlphaFold benefits researchers at lower-ranked universities as their share of top-journal publications increases significantly following its release. These findings suggest that AI tools can lower barriers to entry in resource-intensive scientific fields and challenge established knowledge production hierarchies. AI can lead to a more equitable distribution of opportunities, with broader implications for innovation, scientific discovery, and research policy.bioRxiv00It is shown that AlphaFold benefits researchers at lower-ranked universities as their share of top-journal publications increases significantly following its release, suggesting that AI tools can lower barriers to entry in resource-intensive scientific fields and challenge established knowledge production hierarchies.bioRxiv["Anantha Divakaruni", "Francois Bares", "Ludovic Phalippou"]2025-02-12T00:00:00 54975764a5b83e68805004c33811fad1d3b6ee59Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-centric perspective, improving data quality is essential for enhancing model performance in tabular data-driven applications. This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces. Feature selection aims to identify and retain the most informative attributes, while feature generation constructs new features to better capture complex data patterns. We systematically review existing generative methods for tabular data engineering, analyzing their latest advancements, real-world applications, and respective strengths and limitations. This survey emphasizes how RL-based and generative techniques contribute to the automation and intelligence of feature engineering. Finally, we summarize the existing challenges and discuss future research directions, aiming to provide insights that drive continued innovation in this field.00This survey emphasizes how RL-based and generative techniques contribute to the automation and intelligence of feature engineering, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces.["Wangyang Ying", "Cong Wei", "Nanxu Gong", "Xinyuan Wang", "Haoyue Bai", "Arun Vignesh Malarkkan", "Sixun Dong", "Dongjie Wang", "Denghui Zhang", "Yanjie Fu"]2025-02-12T00:00:00 8d3abf0bbcb89053cbc2c4b9ac1b9ee5fb704934PurposeThe research’s purpose is to examine the incorporation of Artificial Intelligence (AI) within the context of sustainable tourism education, emphasizing its capacity to augment educational achievements and provide future practitioners with vital competencies.Design/methodology/approachSemi-structured interviews targeting academics in the tourism field were conducted.FindingsThe study results are indicative and suggest a compatible relationship between the benefits of integrating educational content on sustainable tourism and AI. Specifically, AI can equip students with a more analytical understanding of sustainability.Originality/valueThe study’s originality exists in the integration of AI as an innovative tool into the educational context of sustainable tourism, thereby providing students with valuable knowledge.Worldwide Hospitality and Tourism Themes351AI can equip students with a more analytical understanding of sustainability and suggest a compatible relationship between the benefits of integrating educational content on sustainable tourism and AI.Worldwide Hospitality and Tourism Themes["Raphaela Neophytou", "Sotiroula Liasidou", "Kosmas Pipyros", "Anastasia Christofi"]2025-02-13T00:00:00 267c65e47395616bbbf79898c75bf93e5e7c8846There is a need to understand contemporary scientific advances as clinical pharmacy evolves. One rapidly expanding area is artificial intelligence (AI), which has grown significantly over the past year because of the public availability of large language models. This commentary reviews published literature describing and evaluating applications of AI to each aspect of the medication use process and forecasts potential future roles for AI in pharmacy practice. Potential challenges in implementation are also described.Journal of the American College of Clinical Pharmacy521This commentary reviews published literature describing and evaluating applications of AI to each aspect of the medication use process and forecasts potential future roles for AI in pharmacy practice.JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY["Adrian Wong", "Trenton Flanagan", "Elizabeth W. Covington", "Elaine Nguyen", "Dustin Linn", "Gretchen Brummel", "Brian S. Hoffmaster", "Diana Isaacs", "Sandra L. Kane\u2010Gill"]2025-02-13T00:00:00 23b12ca5a3adfabc5da2177d87e162e892deb293PURPOSE OF REVIEW Artificial intelligence (AI) chatbots are increasingly used as a source of information. Our objective was to review the literature on their use for patient education in urology. RECENT FINDINGS There are many published studies examining the quality of AI chatbots, most commonly ChatGPT. In many studies, responses from chatbots had acceptable accuracy but were written at a difficult reading level without specific prompts to enhance readability. A few studies have examined AI chatbots for other types of patient education, such as creating lay summaries of research publications or generating handouts. SUMMARY Artificial intelligence chatbots may provide an adjunctive source of patient education in the future, particularly if prompted to provide results with better readability. In addition, they may be used to rapidly generate lay research summaries, leaflets or other patient education materials for final review by experts.Current Opinion in Urology290Artificial intelligence chatbots may provide an adjunctive source of patient education in the future, particularly if prompted to provide results with better readability, particularly if prompted to provide results with better readability.Current opinion in urology["Olivia Paluszek", "Stacy Loeb"]2025-02-13T00:00:00 8ed3e32e198da6faf794740d0c5b22a140314a7bCredential-stuffing attacks pose a critical threat to the banking sector, leveraging stolen login credentials to compromise user accounts and inflict substantial financial and reputational damage. Traditional security measures, including Multi-Factor Authentication (MFA) and CAPTCHA, often fall short against the sophistication of these attacks, necessitating more advanced and proactive defense strategies. This study explores the transformative role of artificial intelligence (AI) and machine learning (ML) in cybersecurity, particularly in mitigating credential-stuffing threats. AI-driven solutions enable real-time threat detection, predictive analysis, and adaptive authentication, providing enhanced protection by analyzing large datasets to identify unusual login patterns and behaviors. Despite their promise, AI and ML adoption in cybersecurity faces challenges, including data privacy concerns, the risk of false positives and negatives, and scalability barriers. This research also examines emerging technologies, such as federated learning and blockchain-based authentication, which offer decentralized and privacy-preserving approaches to combating credential-stuffing attacks. Ultimately, AI and ML present the banking sector with powerful tools to build resilient, adaptable, and efficient defenses against evolving cyber threats. By integrating these technologies with complementary innovations, financial institutions can enhance security, protect customer trust, and address the dynamic landscape of credential-based cyberattacks.International Journal of Computational and Experimental Science and Engineering150This study explores the transformative role of artificial intelligence (AI) and machine learning in cybersecurity, particularly in mitigating credential-stuffing threats, and examines emerging technologies, such as federated learning and blockchain-based authentication, which offer decentralized and privacy-preserving approaches to combating credential-stuffing attacks.International Journal of Computational and Experimental Science and Engineering["Homam el-Taj", "Danah Hamedah", "Rawan Saeed"]2025-02-13T00:00:00 2b34a0a10e53b9f623a1a5363cea8d332bb09757With the emerging trend of artificial intelligence (AI) and its application in various fields, AI ethics and its related incidents have aroused concern and caused wide discussion in both society and academia around the world. In this paper, we discuss AI ethics and governance with respect to public perspectives. Based on the existing literature, policies, and guidelines on AI ethics, we sorted AI ethics concerns into eight dimensions: safety, transparency, fairness, personal data protection, liability, truthfulness, human autonomy, and human dignity. Combining online survey data with social media data, we quantified people's concerns on each dimension, and their attitudes toward AI governance policies and goals. The results shed light on how the public understands and views AI ethics and related governance. Finally, we propose several future directions in the development of AI ethics.Chinese Journal of Sociology280This paper sorted AI ethics concerns into eight dimensions: safety, transparency, fairness, personal data protection, liability, truthfulness, human autonomy, and human dignity, and proposed several future directions in the development of AI ethics.Chinese Journal of Sociology["Jiongyi Cao", "Tianguang Meng"]2025-02-13T00:00:00 79a64ca7748298b1a1f71aa03e4117c1230604bfArtificial neural networks (ANNs) have been used for classification tasks involving functional magnetic resonance imaging (fMRI), though typically focusing only on fractions of the brain in the analysis. Recent work combined shallow neural networks (SNNs) with explainable artificial intelligence (xAI) techniques to extract insights into brain processes. While earlier studies validated this approach using motor task fMRI data, the present study applies it to Theory of Mind (ToM) cognitive tasks, using data from the Human Connectome Project’s (HCP) Young Adult database. Cognitive tasks are more challenging due to the brain’s non-linear functions. The HCP multimodal parcellation brain atlas segments the brain, guiding the training, pruning, and retraining of an SNN. Shapley values then explain the retrained network, with results compared to General Linear Model (GLM) analysis for validation. The initial network achieved 88.2% accuracy, dropped to 80.0% after pruning, and recovered to 84.7% post-retraining. SHAP explanations aligned with GLM findings and known ToM-related brain regions. This fMRI analysis successfully addressed a cognitively complex paradigm, demonstrating the potential of explainability techniques for understanding non-linear brain processes. The findings suggest that xAI, and knowledge extraction in particular, is valuable for advancing mental health research and brain state decoding.Machine Learning and Knowledge Extraction440This fMRI analysis successfully addressed a cognitively complex paradigm, demonstrating the potential of explainability techniques for understanding non-linear brain processes and suggesting that xAI, and knowledge extraction in particular, is valuable for advancing mental health research and brain state decoding.Machine Learning and Knowledge Extraction["Jos\u00e9 Diogo Marques dos Santos", "Lu\u00eds Paulo Reis", "Jos\u00e9 Paulo Marques dos Santos"]2025-02-13T00:00:00 8c0988e3f39a6d18308fe156753870b0c2c115f0To analyze the research hotspots and potential of Artificial Intelligence (AI) in cholangiocarcinoma (CCA) through visualization.A comprehensive search of publications on the application of AI in CCA from January 1, 2014, to December 31, 2023, within the Web of Science Core Collection, was conducted, and citation information was extracted. CiteSpace 6.2.R6 was used for the visualization analysis of citation information.A total of 736 publications were included in this study. Early research primarily focused on traditional treatment methods and care strategies for CCA, but since 2019, there has been a significant shift towards the development and optimization of AI algorithms and their application in early cancer diagnosis and treatment decision-making. China emerged as the country with the highest volume of publications, while Khon Kaen University in Thailand was the academic institution with the highest number of publications. A core group of authors involved in a dense network of international collaboration was identified. HEPATOLOGY was found to be the most influential journal in the field. The disciplinary development pattern in this domain exhibits the characteristic of multiple disciplines intersecting and integrating.The current research hotspots primarily revolve around three directions: AI in the diagnosis and classification of CCA, AI in the preoperative assessment of cancer metastasis risk in CCA, and AI in the prediction of postoperative recurrence in CCA. The complementarity and interdependence among different AI applications will facilitate future applications of AI in the CCA field.Frontiers in Oncology540The current research hotspots primarily revolve around three directions: AI in the diagnosis and classification of CCA, AI in the preoperative assessment of cancer metastasis risk in CCA, and AI in the prediction of postoperative recurrence in CCA.Frontiers in Oncology["Ke-xie Wang", "Yu-ting Li", "Sun-hu Yang", "Feng Li"]2025-02-13T00:00:00 d8f628f20a2d5c414f27edad3155fb0183f943c8Scientific Reports1380The study emphasizes the positive mediation effects of AI adoption on organizational performance, indicating that AI adoption serves as a key enabler in achieving both short-term operational gains and long-term sustainability objectives.Scientific Reports["R. Soomro", "W. Al-rahmi", "Nisar Ahmed Dahri", "Latifah Almuqren", "Abeer S Al-Mogren", "Ayad Aldaijy"]2025-02-13T00:00:00 f6daec8c95660fe4a515794d2e9d4f3352ff5da0Almost every facet of modern biomedical research involves artificial intelligence (AI). This ACCP commentary forecasts the role of AI in clinical pharmacy research and scholarship. The potential benefits/opportunities together with the limitations/challenges of AI are reviewed for stages of the scientific method including (1) developing the research question(s), study design, and execution; (2) data analysis; and (3) reporting and dissemination of clinical pharmacy research. Benefits and opportunities of AI in clinical pharmacy research include streamlining hypothesis generation and facilitating study design, overcoming limitations of traditional statistical analysis techniques, facilitating manuscript development and dissemination, and expediting peer review. Limitations and challenges of AI include the introduction of biases in subject recruitment; generation of false information, also known as “AI hallucinations”; concern of “black box” analyses that are difficult to validate; potential legal liabilities; lack of accountability; and the need for investigators to ensure the accuracy and integrity of AI‐generated content. In summary, rapid progress of AI capabilities has great potential to revolutionize and accelerate clinical pharmacy research and scholarship; however, it is also imperative to recognize and mitigate the challenges and limitations introduced by AI.Journal of the American College of Clinical Pharmacy420Rapid progress of AI capabilities has great potential to revolutionize and accelerate clinical pharmacy research and scholarship; however, it is also imperative to recognize and mitigate the challenges and limitations introduced by AI.JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY["Alexandre Chan", "William L. Baker", "Daniel Abazia", "Jerry L. Bauman", "C. DeVane", "K. Goodlet", "Natalie Hall", "J. K. Hicks", "Ellen Jones", "Chi\u2010Hua Lu", "Donald C. Moore", "Nicholas R. Nelson", "Kaylee Putney", "Aracely Sosa", "Toby Trujillo", "Crystal Zhou"]2025-02-13T00:00:00 f752eb82077ffbebdbca7e9db0145dfc8a52566e As more hotels adopt artificial intelligence (AI), it becomes inevitable for employees to rely on abilities enhanced by the use of AI to complete tasks. However, our understanding of how employees adapt to this shift in work design remains limited. Therefore, the purpose of this study is to explore hotel employees’ approach and avoidance behavioral reactions to dependence on AI. A three-wave field study was conducted, collecting data from 303 hotel employees and analyzed using Mplus 8.3. Dependence on AI can be construed as a positive stimulus, augmenting employees’ harmonious work passion and subsequently promoting approach job crafting. The promotion focus of employees positively moderates this process. On the other hand, dependence on AI also can be perceived as a negative stimulus, heightening employees’ feelings of AI threat and, consequently, fostering avoidance job crafting. In this case, the prevention focus of employees positively moderates the process. This study provides theoretical foundations and decision-making references for management practice. Managers should implement measures to guide employees in developing a proper understanding of AI and provide them with emotional support and institutional safeguards. This study unveils the consequences of dependence on AI for employees, offering new perspectives for AI research in the hotel industry. By differentiating job crafting, this study theorizes and tests a dual-path model of how dependence on AI may influence hotel employees’ approach and avoidance job crafting, thereby enriching the AI–job crafting literature. International Journal of Contemporary Hospitality Management520By differentiating job crafting, this study theorizes and tests a dual-path model of how dependence on AI may influence hotel employees’ approach and avoidance job crafting, thereby enriching the AI–job crafting literature.International Journal of Contemporary Hospitality Management["Hongdan Zhao", "Yunshuo Ma", "Yuan-Ling Chen"]2025-02-13T00:00:00 a0ce0f6b10fef6531d88b00722d726fe1ed083e1BMC Nursing190A positive and significant correlation between the variables suggests that a simulation-proposed model for a healthcare quality forecasting system, which the researcher built and included in the study recommendations, has to be designed.BMC Nursing["Salma Sami Alajrab", "I. Oweidat", "Omaima Nassar", "M. Albashtawy", "A. Nashwan"]2025-02-13T00:00:00 1ac2586e518be4ab86db337aad42869cadb483e9ABSTRACT Artificial intelligence (AI) experience among nurses in perioperative settings is crucial for effective healthcare delivery. This study aimed to assess AI literacy levels and associated characteristics among perioperative nurses in Türkiye. This cross‐sectional study was conducted between March 15 and April 15, 2024, and included 505 perioperative nurses. Data were collected online using the “Nurse Information Form” and “AI Literacy Scale.” Snowball sampling technique was used to enlist individuals. The mean literacy score was 44.35 ± 5.88, which means moderate proficiency. Key elements associated with high literacy levels included being male, familiarity with and use of AI applications, youth, seeing AI as a tool to alleviate workload, and using information technology tools. The results show that although perioperative nurses have a moderate level of AI literacy, their use of AI tools is minimal. The findings underline the need for professional development in AI integration and the inclusion of relevant materials in nursing education programs.Nursing and Health Sciences270The results show that although perioperative nurses have a moderate level of AI literacy, their use of AI tools is minimal, and underline the need for professional development in AI integration and the inclusion of relevant materials in nursing education programs.Nursing & Health Sciences["Hilal Kahraman", "Seda Akutay", "Hatice Y\u00fcceler Ka\u00e7maz", "Sultan Ta\u015fci"]2025-02-13T00:00:00 90778fc07be8fdad055a2751b2932cc079d3d6f7Integrating artificial intelligence (AI) into public health has emerged as a transformative force, reshaping how health data are collected, analyzed, and utilized [...]Computation130Computation["D. Chumachenko", "Sergiy Yakovlev"]2025-02-13T00:00:00 a39688896e386b7f78e63f631b5d9c9b3fdc57fbThis article investigates the intersection of artificial intelligence (AI) and the sustainability of small and medium-sized businesses (SMEs) in Anambra State, Nigeria. It looks at how AI technologies might help SMEs improve operational efficiency, lower expenses, and promote environmentally friendly behaviour. The article also discusses the challenges faced by these businesses in implementing AI, and offers recommendations for stakeholders, including governments, educational institutions, and industry associations, to assist these companies in integrating AI into their daily operations. This can be achieved through training programmes aimed at enhancing the digital and technological proficiency of the owners, managers, and staff of SMEs in Anambra State. The results show how AI may help SMEs in Anambra State reach sustainability objectives, fostering environmental preservation and eventually economic development.Journal of Development Research00The results show how AI may help SMEs in Anambra State reach sustainability objectives, fostering environmental preservation and eventually economic development.Journal of Development Research["Solomon Uchechukwu Eze", "Phina N. Onyekwelu"]2025-02-13T00:00:00 675c3441da0b03ef987f345dee5f08582189140dThis article explores the role of artificial intelligence (AI) in medical oncology, emphasizing its impact on treatment decision-making for adult and pediatric cancer care. AI applications, including advanced imaging, drug discovery, and clinical decision support systems, enhance precision, personalization, and efficiency. Pediatric oncology benefits from improved diagnostics, risk stratification, and targeted therapies, despite unique challenges. AI-driven personalized medicine optimizes treatment strategies, improving patient outcomes and reducing costs. Ethical considerations, such as data privacy, algorithmic bias, and explainability, remain critical for responsible AI integration. Future advancements, including explainable AI and quantum computing, promise to redefine cancer care through data-driven insights.Frontiers in Medicine180The role of artificial intelligence in medical oncology is explored, emphasizing its impact on treatment decision-making for adult and pediatric cancer care, and future advancements promise to redefine cancer care through data-driven insights.Frontiers in Medicine["Archana Reddy Bongurala", "Dhaval Save", "Ankit Virmani"]2025-02-13T00:00:00 e604d4fa175d5c51f8745808a48f8b979ef43b6bEmerging developments in artificial intelligence present significant opportunities to enhance equity and access to science and mathematics assessment content for students with disabilities. Artificial intelligence (AI) technologies may have the potential to support test developers in creating more inclusive assessments that better measure what students know and can do. But they must also consider the potential accessibility challenges or introduction of construct-irrelevant variance posed by these technologies. The purpose of this article is to provide a conceptual overview of the issues to be considered when creating and implementing large-scale science and mathematics assessments for students with disabilities. We discuss how AI has been utilized in large-scale assessments to date and describe the opportunities and potential pitfalls in the stages of the process: assessment design, development, administration, scoring, reporting, and data use. This article concludes with proposed priorities for research that will advance the responsible practice of AI in large-scale assessment that is inclusive, fair, and valid for students with disabilities. This article contributes to the growing body of information on AI applications for assessment by identifying the roles that AI can play in science and mathematics assessment practices and demonstrating how AI can inform approaches to equitable science, technology, engineering, and mathematics (STEM) learning.Education sciences00The roles that AI can play in science and mathematics assessment practices are identified and how AI can inform approaches to equitable science, technology, engineering, and mathematics (STEM) learning are demonstrated.Education Sciences["Amy K. Clark", "Ashley Hirt", "David Whitcomb", "W. J. Thompson", "Marjorie Wine", "Meagan Karvonen"]2025-02-13T00:00:00 29259c04c9c682c142084e5e2b39e444fdfb84ceBreast cancer is the most common malignant tumor among women worldwide, with its incidence and mortality ranking first among all cancers. Early diagnosis and treatment significantly improve prognosis and reduce disease-related mortality. Chest computed tomography (CT), a routine examination for physical assessments and hospitalized patients, can screen for the presence of breast nodules and provide an initial assessment of malignancy risk. In recent years, artificial intelligence (AI) has advanced rapidly in the medical field. Studies have demonstrated that the sensitivity and accuracy of chest CT in diagnosing breast cancer are enhanced through the application of AI methods. This article explores the research progress in breast cancer diagnosis utilizing artificial intelligence based on chest CT examinations.Proceedings of Anticancer Research310Research progress in breast cancer diagnosis utilizing artificial intelligence based on chest CT examinations is explored, demonstrating that the sensitivity and accuracy of chest CT in diagnosing breast cancer are enhanced through the application of AI methods.Proceedings of Anticancer Research["Jingxiang Sun", "Guang Zhang"]2025-02-13T00:00:00 11fbb118fdebf3f5d407f6fa21afcebda78d2976The defense industry has been one of the most popular topics in recent years. One of the most basic principles of the social state approach is to ensure the integrity of the state and to take the necessary security measures for the country. One of the main issues that cannot be ignored for states to live in peace and security is undoubtedly the power of the defense industry. States develop defense mechanisms against certain dangers by expanding their defense techniques. Therefore, with the development of technology, machine learning and artificial intelligence are among the most frequently encountered topics at this point. In this study, after a general overview of the defense industry sector, the defense industry in the world and defense exports in the world will be touched upon, and the connections of the defense industry with the fields of machine learning and artificial intelligence will be examined comprehensively.International Journal of Applied Sciences &amp; Development180The defense industry in the world, the defense industry in the world and defense exports in the world will be touched upon, and the connections of the defense industry with the fields of machine learning and artificial intelligence will be examined comprehensively.International Journal of Applied Sciences &amp; Development["Kemal Gokhan Nalbant", "Tuba Tokaci"]2025-02-13T00:00:00 2032938ee525ed98b2fb494625f49cce9f434bbaINTRODUCTION Colonoscopy has a crucial role in reducing colorectal cancer incidence and mortality. Different artificial intelligence (AI) systems were developed to further improve its quality assurance (computer-aided quality improvement, CAQ), lesion detection (computer-aided detection, CADe) and lesion characterization (computer-aided characterization, CADx). There were studies investigating the roles of these AI systems in different domains of standard colonoscopies. METHODS In this state-of-the-art narrative review, we summarize the current evidence, discuss existing limitations, as well as explore the future directions of AI in colonoscopy. RESULTS CAQ enhances colonoscopy quality through real-time feedback and quality monitoring systems, but the studies have inconsistent results due to small training datasets and varied methodologies. CADe increases adenoma detection rate and reduces adenoma missed rates but there are concerns about false positives, unnecessary polypectomies, potential de-skilling of endoscopists, and cost-effectiveness. CADx systems have mixed results and accuracies in differentiating polyp types, its use is further hindered by inadequate representation of sessile serrated lesions and a lack of rigorous trials comparing it with standard colonoscopy. CONCLUSION Despite the emerging evidence of AI-assisted colonoscopy, its potential drawbacks and limitations may hinder the further implementation in real-world clinical practice. Long-term data on clinical efficacy, cost effectiveness, liability and data sharing are the key areas to be addressed.Digestion00Despite the emerging evidence of AI-assisted colonoscopy, its potential drawbacks and limitations may hinder the further implementation in real-world clinical practice.Digestion["Wan Ying Lai", "Kenneth Weicong Lin", "Loi Pooi Ling", "James W Li", "Louis H S Lau", "Philip W Y Chiu"]2025-02-13T00:00:00 fd15b6a5b68b24c0301763bbeb66b0f19ae4bec3With rapid technological development, automation and artificial intelligence (AI) applications in the tourism industry is gaining momentum. In this article, we discuss why digitalization is crucial for tourism development, exploring DMC tour package management system, and how DMC flow operations are automated using AI and data analytics for online bookings. It deals with the digital transformation of tour packages, the automation of manual processes in DMC operations, and the assimilation of AI and data analytics in reservation management and tour coordination. Using the automation system implemented by Saladino Travel in its Turkey and Dubai operations as a case study, this research evaluates the contributions of such technologies to operational efficiency, customer satisfaction, and competitive advantage in the sector. Tour programs in Turkey cover nearly 3,000 km on an average; therefore, it is vital to be efficient and sustainable. Automating the travel process can greatly reduce the manual work involved and improve the efficiency of the tourism industry, which is exactly what Saladino Travel has done. This system implementation maximizes customer satisfaction, reduces operational errors, and optimizes tour organizations. This article discusses how tour operators can reap all the benefits by integrating AI into reservation systems.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-402300Why digitalization is crucial for tourism development, exploring DMC tour package management system, and how DMC flow operations are automated using AI and data analytics for online bookings are discussed.Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023["Selahaddin Eyyubi Tezel"]2025-02-13T00:00:00 d73699fd8a44af28f19102e4b6784923e1f42673 This cross-sectional study investigates the current landscape of generative artificial intelligence (GenAI) guidelines across ophthalmology journals. JAMA ophthalmology20JAMA ophthalmology["David Rabinovitch", "Jim S Xie", "Adrien Lusterio", "Andrew Mihalache", "M. Popovic", "Prashant D Tailor", "Brendan Tao", "Edward Margolin"]2025-02-13T00:00:00 f25e44b4aefa3823050b1ec735bb1da6b3702a75Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.00This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise and label development projects based on their narrative descriptions, enabling a better understanding of donor priorities and overall development funding.["Pierre Beaucoral"]2025-02-13T00:00:00 b32388b6d90931d60a5f6151b3ac4db2dab96917BuchrezensionRaumforschung und Raumordnung | Spatial Research and Planning00Raumforschung und Raumordnung | Spatial Research and Planning["Florian Koch"]2025-02-13T00:00:00 3d0a1948dadbed2b882c6e7f8a392f4302d33267Financial technology, commonly known as fintech, represents the intersection of finance and information technology aimed at simplifying, enhancing, transforming, and automating financial processes and services for businesses and individuals [...]Journal of Theoretical and Applied Electronic Commerce Research200Journal of Theoretical and Applied Electronic Commerce Research["Albert Y. S. Lam"]2025-02-13T00:00:00 8f140bd9b441e1bb1663e793e67dbd54dee95981Cultures of Science00Cultures of Science["Ke Gong"]2025-02-13T00:00:00 ce835f9f66376a9142e24dcfc6e2c07a366f9867Acta academica karviniensia90Acta academica karviniensia["Petr \u017dyla"]2025-02-13T00:00:00 08b6a4fb8683d1b4b2c41fc802767d8572ed4ffaThe development of fair and unbiased machine learning models remains an ongoing objective for researchers in the field of artificial intelligence. We introduce the Differential Adjusted Parity (DAP) loss to produce unbiased informative representations. It utilises a differentiable variant of the adjusted parity metric to create a unified objective function. By combining downstream task classification accuracy and its inconsistency across sensitive feature domains, it provides a single tool to increase performance and mitigate bias. A key element in this approach is the use of soft balanced accuracies. In contrast to previous non-adversarial approaches, DAP does not suffer a degeneracy where the metric is satisfied by performing equally poorly across all sensitive domains. It outperforms several adversarial models on downstream task accuracy and fairness in our analysis. Specifically, it improves the demographic parity, equalized odds and sensitive feature accuracy by as much as 22.5\%, 44.1\% and 40.1\%, respectively, when compared to the best performing adversarial approaches on these metrics. Overall, the DAP loss and its associated metric can play a significant role in creating more fair machine learning models.180The Differential Adjusted Parity loss utilises a differentiable variant of the adjusted parity metric to create a unified objective function and can play a significant role in creating more fair machine learning models.["Bucher Sahyouni", "Matthew R Vowels", "Liqun Chen", "Simon Hadfield"]2025-02-13T00:00:00 2ee7be4d98b648eea4868e56c237be35dc554adcThe integration of Artificial Intelligence (AI) tools into nursing research and practice has the potential to transform healthcare delivery by improving clinical decision-making, enhancing patient outcomes, and optimizing healthcare workflows. This systematic review and meta-analysis aim to evaluate the current state of AI applications in nursing, summarizing the effectiveness, challenges, and opportunities presented by these technologies. A comprehensive search of peer-reviewed literature was conducted, including randomized controlled trials, observational studies, and qualitative research published between 2010 and 2024. Key findings reveal that AI tools, particularly in predictive analytics, diagnostic assistance, and patient monitoring, have shown positive effects on reducing clinical errors, enhancing patient care, and supporting evidence-based practice. However, the integration of AI in nursing faces significant barriers, including concerns regarding data privacy, trust in technology, and the need for continuous education and training for healthcare professionals. This review highlights the critical need for further research to address these challenges, establish clear ethical guidelines, and optimize the adoption of AI tools in nursing. The findings indicate that, when properly integrated, AI can augment nursing practice, improve care efficiency, and contribute to better health outcomes.International Journal For Multidisciplinary Research00Key findings reveal that AI tools, particularly in predictive analytics, diagnostic assistance, and patient monitoring, have shown positive effects on reducing clinical errors, enhancing patient care, and supporting evidence-based practice.International Journal For Multidisciplinary Research["Mrs. Pratibha Thakur", "Dr. Somendra Singh Kashyap"]2025-02-13T00:00:00 2cb5f0bee8faccc3794bd8c3242650cf60ea05ccThe transformative potential of artificial intelligence (AI) in banking is widely acknowledged, yet its practical adoption often faces resistance from users. This study investigates the factors influencing AI adoption behavior among various stakeholders in the Greek semi-mature systemic banking ecosystem, addressing a critical gap in the relevant research. By utilizing the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2), and Partial Least Squares Structural Equation Modelling (PLS-SEM) models, data from 297 respondents (bank employees, digital professionals, and the general public) were analyzed. The results highlight the strong relevance of constructs such as Performance Expectancy, Effort Expectancy, and Hedonic Motivation, whereas Social Influence was deemed non-significant, reflecting a pragmatic stance toward AI. Demographic factors like gender and age were found to have no significant moderating effect, challenging traditional stereotypes. However, occupation and education emerged as significant moderators, indicating varying attitudes among professions and educational levels. This study is the first to develop a theoretical framework for AI adoption by Greek banking institutions, offering Greek banking practitioners actionable insights. The findings also hold relevance for countries with similar digital maturity levels, aiding broader AI integration in banking.Information980This study is the first to develop a theoretical framework for AI adoption by Greek banking institutions, offering Greek banking practitioners actionable insights and holds relevance for countries with similar digital maturity levels, aiding broader AI integration in banking.Information["Aristides Papathomas", "G. Konteos", "Giorgos Avlogiaris"]2025-02-13T00:00:00 594d63f395e89c5bcafb694e32b9ad9465036747As the application of artificial intelligence (AI) in the workplace increases, investigating its impact on work engagement is crucial for optimizing human resource management and enhancing organizational productivity and competitiveness. Based on the Conservation of Resources theory, this study investigated whether AI usage exhibits a double-edged sword effect on work engagement and examined the moderating role of core task characteristics substitution in this relationship. A two-wave study was conducted among 279 employees from China, and Hayes’s PROCESS macro was used to test the moderated mediation model. The findings indicated that (1) AI usage enhances work engagement by increasing psychological availability and indirectly increases work engagement by suppressing work alienation; (2) core task characteristics substitution diminishes the enhancing effect of AI usage on psychological availability and the inhibiting effect of AI usage on work alienation; and (3) overall, AI usage tends to suppress work alienation, demonstrating an empowering effect. However, under conditions of high core task characteristics substitution, AI usage can increase work alienation, revealing potential negative effects. The findings enrich our understanding of the complex impact of AI usage on work engagement and offer valuable insights for managers to improve employee experiences in the AI era.Behavioral Science1090The findings enrich the understanding of the complex impact of AI usage on work engagement and offer valuable insights for managers to improve employee experiences in the AI era.Behavioral Sciences["Xuan Liu", "Yuxuan Li"]2025-02-13T00:00:00 ae2e47a2d8d4dd932998f62dc9101450b6145ddfIntegrating artificial intelligence (AI), with edge computing, and precision agriculture is revolutionizing farming making it more resilient and sustainable. This article focuses on combine positive impact that the merger of these transformative technologies, can have in providing solutions for key challenges such as plant disease detection, resource optimization, and real-time decision-making. AI algorithms enables rapid and precise analysis of massive agricultural data allowing early disease detection and preventive measures to ensure plant health. Concurrently, edge computing gives the power of reduced latency with on spot data processing and solutions provision to the farmers, even in areas with limited coverage. The fusion of these technologies aligns with key UN sustainable development goals (SDGs), by optimizing the use of water, fertilizers, and pesticides, reducing environmental impacts, and mitigating climate change effects. However, the widespread adoption of AI and edge computing in agriculture is constrained by challenges such as hardware limitations, data collection, quality issues, and the need for technical expertise in particular cases. This review explores how these technologies are currently being used in agriculture, their pros, cons, and potential areas for further research and development. Encouraging interdisciplinary collaboration and continuous innovation will be crucial to overcome these challenges, ensuring that AI and edge computing play a central role in securing global food security and promoting climate-resilient farming.Journal of Agriculture and Biology00This article focuses on combine positive impact that the merger of these transformative technologies, can have in providing solutions for key challenges such as plant disease detection, resource optimization, and real-time decision-making.Journal of Agriculture and Biology["Faizan Ali", "Waheed Tariq", "Ali Razzaq", "Abdul Rehman", "S. Sarfraz", "N. Rajput", "Subhan Ali", "Kaneez Fatima", "Sahar Jameel", "N. Liaqat", "Zuniara Akash"]2025-02-13T00:00:00 02f4475a3c29325017e0987b4ba20345c14507e1Information Systems Frontiers560This study presents a mixed methods approach to developing valuable insight to the key underlying challenges impacting GenAI adoption within higher education (HE) and identifies significant interdependencies between the key underlying challenges associated with GenAI adoption in HE.Information Systems Frontiers["Laurie Hughes", "Tegwen Malik", "Sandra Dettmer", "A. Al-Busaidi", "Yogesh K. Dwivedi"]2025-02-13T00:00:00 e99c82c05fc0e4cbd7892a544338cf0af96687fbTo create usable and deployable Artificial Intelligence (AI) systems, there requires a level of assurance in performance under many different conditions. Many times, deployed machine learning systems will require more classic logic and reasoning performed through neurosymbolic programs jointly with artificial neural network sensing. While many prior works have examined the assurance of a single component of the system solely with either the neural network alone or entire enterprise systems, very few works have examined the assurance of integrated neurosymbolic systems. Within this work, we assess the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient and more interpretable models. We perform this investigation using Scallop, an end-to-end neurosymbolic library, across classification and reasoning tasks in both the image and audio domains. We assess assurance across adversarial robustness, calibration, user performance parity, and interpretability of solutions for catching misaligned solutions. We find end-to-end neurosymbolic methods present unique opportunities for assurance beyond their data efficiency through our empirical results but not across the board. We find that this class of neurosymbolic models has higher assurance in cases where arithmetic operations are defined and where there is high dimensionality to the input space, where fully neural counterparts struggle to learn robust reasoning operations. We identify the relationship between neurosymbolic models' interpretability to catch shortcuts that later result in increased adversarial vulnerability despite performance parity. Finally, we find that the promise of data efficiency is typically only in the case of class imbalanced reasoning problems.420This work assesses the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient and more interpretable models and identifies the relationship between neurosymbolic models' interpretability to catch shortcuts that later result in increased adversarial vulnerability despite performance parity.["Luke E. Richards", "Jessie Yaros", "Jasen Babcock", "Coung Ly", "Robin Cosbey", "Timothy Doster", "Cynthia Matuszek"]2025-02-13T00:00:00 768f2a755db7368e22627de83a45a2874075afcaGenerative artificial intelligence (AI) models, such as diffusion models and OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy and automating clinical workflows. The field has advanced rapidly, evolving from text-only large language models for tasks such as clinical documentation and decision support to multimodal AI systems capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model. The diverse landscape of these technologies, along with rising interest, highlights the need for a comprehensive review of their applications and potential. This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings. Adhering to PRISMA-ScR guidelines, we systematically queried PubMed, IEEE Xplore, and Web of Science, prioritizing recent studies published up to the end of 2024. After rigorous screening, 144 papers were included, revealing key trends and challenges in this dynamic field. Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI. However, critical challenges remain, including the integration of heterogeneous data types, improving model interpretability, addressing ethical concerns, and validating AI systems in real-world clinical settings. This review summarizes the current state of the art, identifies critical gaps, and provides insights to guide the development of scalable, trustworthy, and clinically impactful multimodal AI solutions in healthcare.00This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings, and identifies critical gaps to guide the development of scalable, trustworthy, and clinically impactful multimodal AI solutions in healthcare.["Lukas Buess", "Matthias Keicher", "Nassir Navab", "Andreas Maier", "Soroosh Tayebi Arasteh"]2025-02-13T00:00:00 0873e46bdd8dcf4f8dc074264b8589e0d139e03dArtificial intelligence (AI) is not merely a tool for the future of clinical medicine; it is already reshaping the landscape, challenging traditional paradigms, and expanding the horizons of what is achievable in healthcare [...]Journal of Clinical Medicine160Journal of Clinical Medicine["Ariana Genovese", "Sahar Borna", "Cesar A Gomez-Cabello", "S. A. Haider", "Srinivasagam Prabha", "Maissa Trabilsy", "AJ Forte"]2025-02-13T00:00:00 888395243e834c6f88e19eddd100c4726f006f4bArtificial intelligence (AI) is key technology in enabling and enhancing the performance of 5G and 6G networks. AI will be playing a crucial role as a facilitator and catalyst in the advancement of 5G and 6G networks, it has potential to increase network performance, enhance user experience and optimizing costs. AI and 5G/6G combinations are most disruptive technologies that expected to change the entire ecosystemInternational Journal of Advanced Research in Science, Communication and Technology00AI will be playing a crucial role as a facilitator and catalyst in the advancement of 5G and 6G networks, it has potential to increase network performance, enhance user experience and optimizing costs.International Journal of Advanced Research in Science, Communication and Technology["Manish Uniyal"]2025-02-13T00:00:00 531458edd139bbd2e6f1e138f026f91322fb25c6AI &amp; SOCIETY410There is a misalignment between the technical and economic focus of global AI metrics and the broader societal and ethical priorities emphasized in NAIS, highlighting the need to recalibrate AI evaluation frameworks to include ethical and other social considerations.AI &amp; SOCIETY["Konstantinos Sioumalas-Christodoulou", "A. Tympas"]2025-02-13T00:00:00 0c4a2eb101e7582e1a5e64f3549cbb834ee80693The rapid evolution of the Internet of Things (IoT) and Artificial Intelligence (AI) has opened new possibilities for public healthcare. Effective integration of these technologies is essential to ensure precise and efficient healthcare delivery. This study explores the application of IoT-enabled, AI-driven systems for detecting and managing Dry Eye Disease (DED), emphasizing the use of prompt engineering to enhance system performance.A specialized prompt mechanism was developed utilizing OpenAI GPT-4.0 and ERNIE Bot-4.0 APIs to assess the urgency of medical attention based on 5,747 simulated patient complaints. A Bidirectional Encoder Representations from Transformers (BERT) machine learning model was employed for text classification to differentiate urgent from non-urgent cases. User satisfaction was evaluated through composite scores derived from Service Experiences (SE) and Medical Quality (MQ) assessments.The comparison between prompted and non-prompted queries revealed a significant accuracy increase from 80.1% to 99.6%. However, this improvement was accompanied by a notable rise in response time, resulting in a decrease in SE scores (95.5 to 84.7) but a substantial increase in MQ satisfaction (73.4 to 96.7). These findings indicate a trade-off between accuracy and user satisfaction.The study highlights the critical role of prompt engineering in improving AI-based healthcare services. While enhanced accuracy is achievable, careful attention must be given to balancing response time and user satisfaction. Future research should optimize prompt structures, explore dynamic prompting approaches, and prioritize real-time evaluations to address the identified challenges and maximize the potential of IoT-integrated AI systems in medical applications.Frontiers in Artificial Intelligence150The application of IoT-enabled, AI-driven systems for detecting and managing Dry Eye Disease (DED), emphasizing the use of prompt engineering to enhance system performance, highlights the critical role of prompt engineering in improving AI-based healthcare services.Frontiers in Artificial Intelligence["M. Wang", "Xudong Jiang", "Peijin Zeng", "Xinyue Li", "Kelvin Kam-Lung Chong", "Guanghui Hou", "Xiaoxiao Fang", "Yang Yu", "Xiangrong Yu", "Junbin Fang", "Yi Pan"]2025-02-13T00:00:00 2a9119c1433bfe451bc23a243231a85402ca06e0Integrating artificial intelligence (AI) in economic forecasting marks a transformative advancement in the field, promising unprecedented accuracy and adaptability. This paper examines the dual facets of AI's impact: its ability to enhance forecasting precision through innovations like machine learning and natural language processing, and the inherent limitations, such as data biases, model interpretability challenges, and dependency on quality datasets. The research highlights how AI models outperform traditional methods, particularly during volatile economic periods by analyzing recent innovations—including deep learning models, real-time data integration, and sentiment analysis. However, it also acknowledges the need for human oversight and ethical considerations to address the “black-box” nature of AI algorithms. The study underscores the necessity of balancing technological capabilities with transparency and reliability, offering actionable insights for policymakers and businesses navigating the evolving economic landscape.International Journal For Multidisciplinary Research00The research highlights how AI models outperform traditional methods, particularly during volatile economic periods by analyzing recent innovations—including deep learning models, real-time data integration, and sentiment analysis.International Journal For Multidisciplinary Research["Raghav Polkampally"]2025-02-13T00:00:00 b7407e3a5f86d96b6c9acf683b7f72024b1d44b4Background: This paper focuses on the works and innovations accomplished by artificial intelligence (AI) and how current laws and regulations address these innovations within the framework of copyright law. It examines the challenges faced by legal systems in the UAE, Egypt, and France concerning the copyrights of intellectual works produced through AI systems, such as ChatGPT. The study highlights the issue of defining "author" in copyright law, particularly given that AI lacks the personal characteristics associated with human creators. Methods: The paper employs a comparative legal analysis, focusing on the legal frameworks of the UAE, Egypt, and France. It examines how each jurisdiction currently addresses AI-generated intellectual property and whether existing laws adequately account for AI's role in creative processes. The study also explores the possibility of granting AI systems "legal capacity" and the need for a specific Code of Ethics to regulate AI use in a manner consistent with human and ethical values. Results and Conclusions: The study concludes an urgent need to review and amend existing laws to create a legal framework that effectively addresses copyrights related to AI-generated innovations. This framework should balance the promotion of innovation with the protection of legal rights, ensuring that AI developments are ethically regulated and legally recognised.Access to Justice in Eastern Europe00There is an urgent need to review and amend existing laws to create a legal framework that effectively addresses copyrights related to AI-generated innovations, ensuring that AI developments are ethically regulated and legally recognised.Access to Justice in Eastern Europe[]2025-02-14T00:00:00 54d4f6eaf3cc8a49512576e4ba04134eaba4114cWith the continuous evolution of computer technology and the surging advent of the big data era, artificial intelligence (AI) has already manifested extremely broad application prospects. Medical AI, like a capable assistant, can help doctors make more objective and accurate clinical decisions, thus playing an increasingly crucial role in the medical field. In view of the unique particularity of the eyeball's anatomical structure, ophthalmic AI consequently shows unique and incomparable research advantages, opening up new avenues for the diagnosis and treatment of ophthalmic diseases. Dry eye, as an ocular surface disease caused by the interweaving of a variety of complex factors, has shown a steadily increasing trend in prevalence year by year. Among numerous ocular surface diseases, it has conspicuously become one of the most common disorders, posing a non-negligible threat to the global population's ocular health. This article will systematically conduct a detailed review of the specific applications of AI in the field of dry eye, deeply analyze and precisely point out the numerous challenges and the infinite application prospects that AI faces in the process of clinical diagnosis of dry eye. The aim is to provide highly valuable reference bases and directional guidance for the further expansion and in-depth development of AI technology in the specific field of dry eye.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology00This article will systematically conduct a detailed review of the specific applications of AI in the field of dry eye, deeply analyze and precisely point out the numerous challenges and the infinite application prospects that AI faces in the process of clinical diagnosis of dry eye.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology["S. P. Wang", "X. He", "C. H. Huang", "J. Y. Hu", "Z. G. Liu"]2025-02-14T00:00:00 912ee3bd442a6159e3a28f46210df6173c5de18cThis study aims to investigate HR professional’s intentions to use artificial intelligence (AI) for talent recruitment in Pakistan’s pharmaceutical industry. The present investigation has been done within the Pakistani setting, employing the Unified Theory of Acceptance and Use of Technology (UTAUT) as a framework. influence, facilitation condition, intention to use and actual use of AI. Drawing upon the understanding of research technique, the study employed a quantitative research approach that stayed faithful to the positivism paradigm. As for now, there is no clear evidence that Pakistan’s pharmaceutical industry officially planning to implement AI in its recruitment process. Thus, this study happens to investigate the HR Professional’s Intention to Adopt and Use of Artificial Intelligence in Recruiting Talents within the pharmaceutical industry of Pakistan. Moreover, the research opts for convenience sampling where the target population has been defined as HR professionals practicing in the pharmaceutical organizations such as Martin Dow, AGP Limited and PharmEvo Private Limited. An online questionnaire survey has been used to get 100 responses from the users. In addition to SPSS for analysing the demographic data, the researcher employed SmartPLS and select PLS-SEM as model type for assessing the constructs. Moreover, the results validated that Effort Expectancy, Facilitating Conditions, and Social Influence have a strong impact on Intention to Use, organizations need to concentrate on these aspects to promote AI. These results correspond with prior studies that also identified such associations in other settings including ERP systems, mobile banking, m-health services. The verification of most of the hypothesis concerning AI adoption in the domain of HR recruitment shows the usefulness of the UTAUT model and its appropriateness to predict technology acceptance patterns in various fields. However, the major limitation is the collection of data via HR professionals who belongs to Karachi in majority.Journal for Social Science Archives00The verification of most of the hypothesis concerning AI adoption in the domain of HR recruitment shows the usefulness of the UTAUT model and its appropriateness to predict technology acceptance patterns in various fields.Journal for Social Science Archives["Kanza Najam", "Hammad Zafar", "Fakhre Alam Siddiqui"]2025-02-14T00:00:00 b1f1583d509a7fceed0a15d18840393c2870136eNature Network Boston240Results show that AI-guided ablation of spatio-temporal dispersion areas in addition to PVI is superior to PVI alone in eliminating AF at 1-year follow-up in patients with persistent and long-standing persistent AF.Nature medicine["I. Deisenhofer", "J. Albenque", "Sonia Busch", "E. Gitenay", "Stavros Mountantonakis", "Antoine Roux", "J\u00e9r\u00f4me Horvilleur", "Babe Bakouboula", "Saumil Oza", "S. Abbey", "Guillaume Th\u00e9odore", "A. Lepillier", "Yves Guyomar", "Francis Bessiere", "Jaap Jan Smit", "Th\u00e9ophile Mohr Durdez", "P. Milpied", "A. Appetiti", "Daniel Guerrero", "Tom de Potter", "Christian De Chillou", "Seth H. Goldbarg", "Atul Verma", "John D. Hummel"]2025-02-14T00:00:00 20cde2ebdac9a80f8e9bc88978a85e2c1dc0bd5bJournal of imaging informatics in medicine130Artificial intelligence software performance in simulated unilateral mammography analysis demonstrated non-inferior sensitivity and inferior specificity compared to bilateral mammography.Journal of imaging informatics in medicine["Ji Yeong An", "Janie M. Lee", "Myoung-jin Jang", "S. Ha", "Jung Min Chang"]2025-02-14T00:00:00 fe14ac37883902f5b8ccc7ba2e2149c0bbd401a3Background: The application of Artificial Intelligence (AI) in medicine is rapidly evolving, particularly in urology, anesthesiology, cardiology, and otolaryngology (ENT). AI is utilized in image-based diagnosis, patient monitoring, as well as the optimization of therapy and robotic surgery. However, research trends in AI within these fields have not yet been comprehensively mapped. Methods: This study employs bibliometric analysis to evaluate publication trends in AI across these four medical fields from 2019 to 2024. Data were collected from Google Scholar, PubMed, and Scopus, then analyzed using VOSviewer and R-Bibliometrix to identify the number of publications, keyword trends, institutional collaboration networks, and the most highly cited articles. Results: The number of AI-related publications in medicine has increased from 50 articles in 2019 to 130 articles in 2024. The United States, China, and the United Kingdom have the highest number of publications. Research trends indicate that deep learning and machine learning dominate, with broad applications in disease diagnostics and medical imaging. Cluster analysis reveals four main domains: anesthesiology (120 publications), cardiology (105 publications), urology (98 publications), and ENT (80 publications). Conclusion: AI has become an essential component in the advancement of modern medicine. With the increasing number of studies and multidisciplinary collaborations, AI is projected to continue expanding in data-driven diagnosis and therapy. However, challenges in clinical validation, regulation, and AI ethics must be addressed to ensure its safe and effective use.International Journal of Health and Pharmaceutical (IJHP)240Artificial Intelligence has become an essential component in the advancement of modern medicine, with the increasing number of studies and multidisciplinary collaborations, and AI is projected to continue expanding in data-driven diagnosis and therapy.International Journal of Health and Pharmaceutical (IJHP)["Muhammad Sidharta Krisna", "Muhammad Alfi Reza", "Muamar Ghiffary", "Muhammad Satir Sayati", "Muhammad Awaluddin"]2025-02-14T00:00:00 888e9bbc2e5ab18eea3c67d6c1f0dd9fbb6e9cc6 Using an original experimental survey, we analyze how people perceive punishments generated by artificial intelligence (AI) compared to the same punishments generated by a human judge. We use two vignettes pertaining to two different albeit relatively common illegal behaviors, namely not picking up one’s dog waste on public roads and setting fire in dry areas. In general, participants perceived AI judgements as having a larger deterrence effect compared to the those rendered by a judge. However, when we analyzed each scenario separately, we found that the differential effect of AI is only significant in the first scenario. We discuss the implications of these findings.Review of Law &amp; Economics280Using an original experimental survey, an analysis of how people perceive punishments generated by artificial intelligence (AI) compared to the same punishments generated by a human judge finds that the differential effect of AI is only significant in the first scenario.Review of Law &amp; Economics["G. Grolleau", "Murat C. Mungan", "Naoufel Mzoughi"]2025-02-14T00:00:00 257097c2f0ceeef9cab97cdd153d7e4d2e5d538cIntroduction: Osteoarthritis (OA) and osteoporosis are leading degenerative bone diseases that diminish quality of life and impose significant socioeconomic costs. Traditional diagnostic approaches, including imaging and bone density assessments, often fail to detect disease in its early stages, delaying critical interventions. Emerging artificial intelligence (AI) techniques, particularly those employing machine learning (ML) and deep learning (DL), offer promising avenues for early detection and more accurate prognostication. Methods: We conducted a systematic review of AI models developed between 2018 and 2024, assessing their performance in diagnosing and predicting the progression of OA and osteoporosis. Studies utilizing supervised or unsupervised methods applied to imaging modalities (e.g., X-ray, MRI, DXA) or clinical data were included. We evaluated model accuracy, reliability, clinical applicability, and generalizability. Quality and risk of bias were assessed using a modified CLAIM framework, ensuring alignment with transparency, validity, and clinical integration standards. Results: Of 2,300 identified articles, 33 studies met the inclusion criteria. Top-performing models for OA reached up to 97% accuracy, with one study achieving an AUC of 0.93 for MRI-based progression prediction. For osteoporosis, the strongest models attained a C-index of 0.90 using DXA imaging, indicating robust fracture risk prediction. Nevertheless, many studies relied on geographically or demographically homogeneous datasets, limiting broader applicability. Only 15% included external validation, and a substantial proportion lacked interpretability features essential for clinical adoption. Discussion: AI-driven models outperformed conventional diagnostic tools in accuracy and early disease detection. However, the limited dataset diversity, infrequent external validation, and insufficient model interpretability pose barriers to clinical integration. The reliance on male-dominant datasets for osteoporosis and geographically narrow cohorts for OA underscores the need for broader data representation. Standardizing evaluation metrics and improving explainability will enhance cross-study comparisons and support adoption in practice. Conclusion: AI holds transformative potential for improving OA and osteoporosis diagnostics, facilitating earlier interventions, and informing personalized patient management. Future work should prioritize diverse, well-validated datasets; transparent, clinician-friendly interfaces; and standardized performance metrics. Addressing these challenges will enable AI to evolve from a promising innovation into a cornerstone of global musculoskeletal healthcare.Journal00AI holds transformative potential for improving OA and osteoporosis diagnostics, facilitating earlier interventions, and informing personalized patient management, but the limited dataset diversity, infrequent external validation, and insufficient model interpretability pose barriers to clinical integration.Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal["Melina Alborzi", "Parsa Abadi"]2025-02-14T00:00:00 591913be9e32f38d7df0a92f0c8013913a8f898cThis article provides a comprehensive analysis of the research dynamics on the use of Educational Data Mining (EDM) and predictive modeling (PM) in the era of Artificial Intelligence (AI) based on the review of 793 articles published between 2000 and 2024 in the Scopus database. The study employs bibliometric analysis and systematic literature review to identify emerging trends, methodologies, and applications in these fields. The main objective of the study is to examine the primary methodologies and innovations within AI, especially in the context of EDM and PM. It highlights how these technologies can optimize the prediction of student performance, support personalized learning, and enable timely interventions through the analysis of student data. The study also examines the role of AI in improving teaching practices, ensuring that educators maintain control over the system and minimize potential biases. Furthermore, the article addresses the ethical implications of AI implementation in education, such as privacy protection, algorithm transparency, and equity in access to learning. The findings suggest that AI has the potential to significantly improve educational outcomes and optimize student tracking, resource allocation, and the overall effectiveness of educational institutions. The responsible implementation of AI in education is emphasized to ensure inclusive and fair environments for all students.Computers1020Computers["Eloy L\u00f3pez-Meneses", "Pedro C. Mellado-Moreno", "Celia Gallardo Herrer\u00edas", "Noelia Pel\u00edcano-Piris"]2025-02-14T00:00:00 221dfdbbb39128dfdfc0c061ef58a40226adf2caPurpose This study aims to explore the relationship between artificial intelligence (AI) awareness and physical and psychological withdrawal behaviors of enterprises employees through the mediating roles of job security and emotional exhaustion as well as the moderating role of emotional intelligence. Design/methodology/approach Data were collected through a self-administered questionnaire from six fields with the highest level of AI application with a sample of 1,129 Vietnamese enterprises employees. Data were analyzed using SmartPLS, a bootstrapping technique was used to analyze the data. The mediating effect of job security and emotional exhaustion and the moderating effect of emotional intelligence were performed. Findings The research showed that the proposed moderated mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between AI awareness and physical and psychological withdrawal behaviors, as well as a mediating effect of job security and emotional exhaustion. The findings also confirmed that there is a moderating effect of emotional intelligence between AI awareness and job security, emotional exhaustion, physical and psychological withdrawal behaviors. Research limitations/implications Sample data was only collected at a few Vietnamese enterprises in six fields with the highest level of AI application which are e-commerce, transportation and logistics, education, real estate, finance and agriculture, which may be limiting generalizability of research results. Practical implications This study offers several practical and useful management implications, such as anticipating negative attitudes, feelings and behaviors of employees to prepare a response plan; conducting interviews, investigate employees’ AI awareness and do their best to minimize its negative effects on employees’ psychological states and behaviors; and paying attention to recruiting and selecting employees with good emotional intelligence. Originality/value This study contributes to the growing literature on AI by elucidating the mediating roles of job insecurity and emotional exhaustion in the relationship between AI awareness and physical and psychological withdrawal behavior. This study also makes a significant step forward in examining the moderating mechanisms of emotional intelligence in attenuating the effects of AI awareness on job insecurity, emotional exhaustion, physical and psychological withdrawal behavior. The International Journal of Organizational Analysis980The research showed that the proposed moderated mediation model was accepted because the relationships between the constructs were statistically significant and supported a positive relationship between AI awareness and physical and psychological withdrawal behaviors, as well as a mediating effect of job security and emotional exhaustion.International Journal of Organizational Analysis["Thi Phuong Linh Nguyen", "Dinh Trung Nguyen"]2025-02-14T00:00:00 d25f5d3b259f35687dbe190f0d246e864154e3b8Artificial intelligence (AI) methods are very often used to make predictions for datasets that were created externally in arbitrary experiments or on already literature known datasets. In this work, we try to make use of active learning techniques to search for an optimal strategy for the startup‐phase of bulk nickel electrodes in the oxygen evolution reaction. The data collected was afterwards reduced in dimensions and used to extract additional information that were learned via an artificial neural network (ANN) on the dataset, respectively.Chemie Ingenieur Technik150This work tries to make use of active learning techniques to search for an optimal strategy for the startup‐phase of bulk nickel electrodes in the oxygen evolution reaction.Chemie Ingenieur Technik["Henrik Spitzenpfeil", "Marius Neumann", "Nick Hausen", "R. Palkovits", "S. Palkovits"]2025-02-14T00:00:00 6b4717c7226c23e85f0f22a89b34828475b9fcbdWith the rapid urbanization and the increasing traffic demand in cities, traffic congestion and accidents have become significant challenges for urban transportation systems. Traditional traffic signal control systems, which rely on fixed signal cycles, often fail to adapt to real-time traffic conditions, leading to inefficiencies and resource waste. This paper explores the application of Artificial Intelligence (AI) in intelligent traffic signal control systems. Specifically, it focuses on the use of Deep Reinforcement Learning (DRL), particularly the Deep Q-Network (DQN) model, for optimizing signal timing based on real-time traffic data. The system dynamically adjusts the signal cycles based on traffic flow, reducing congestion, improving traffic efficiency, and enhancing safety. The study also discusses the challenges AI-based systems face, such as algorithm complexity, data quality, and system integration, as well as the potential benefits of AI in managing traffic during peak hours and at complex intersections. Through simulation and real-world testing, the study demonstrates the advantages of AI-based signal control systems in improving urban traffic management. The findings suggest that AI can significantly enhance traffic flow, reduce waiting times, and optimize traffic resource allocation, offering a promising approach to solving urban traffic problems.Applied and Computational Engineering00The findings suggest that AI can significantly enhance traffic flow, reduce waiting times, and optimize traffic resource allocation, offering a promising approach to solving urban traffic problems.Applied and Computational Engineering["Xuanning Zhang"]2025-02-14T00:00:00 3f908eac4b4867364dedb7b4f109ee2169bc903aThis paper discusses the impact of artificial intelligence on employee performance, focusing on the analysis of the positive and negative effects of AI on employee performance in the work scene and some challenges through my hybrid analysis method that combines quantitative analysis and qualitative analysis. Through the review of secondary data and the collection of some primary data, an objective research methodology has been obtained. The research reveals the wide application of AI in employees and its limited impact on management decision-making. Although AI can improve employees' work efficiency, it still has limitations when dealing with some complex tasks. Similarly, this paper also discusses some policies and measures that may enable AI to better assist employees, aiming to promote the integration of AI and employee performance, emphasizing the importance of innovation management and employee adaptation to technological change. The research conclusion points out that the rational application of AI can not only improve the individual performance of employees, but also release the maximum potential of AI and employees through innovation motivation and team cooperation. However, future research should continue to focus on the evolution of AI in management and explore more balanced and humanized AI integration strategies to improve employee performance and organizational effectiveness, and I will continue to explore this in the future.Advances in Economics, Management and Political Sciences00The research conclusion points out that the rational application of AI can not only improve the individual performance of employees, but also release the maximum potential of AI and employees through innovation motivation and team cooperation.Advances in Economics, Management and Political Sciences["Yuqing Liu"]2025-02-14T00:00:00 a53d6dbae39ae2044269f5bbc3226b6dce66acd7With the development of the technology, more and more affairs are gradually replaced by artificial intelligence (AI), and a large-scale AI comes into peoples lives, it not only refreshes the existence of artificial intelligence but also the number of people who use it increasing. In fact, a labor force, such as teachers, will not be replaced by AI, but at the same time it is can not be ignored that advantages are brought by AI. This paper focuses on the in-depth discussion on the work content, findings and plans during a class on the application of AI in pedagogy. Based on the development of the science and technology innovation ability and artificial intelligence, online teaching is a well-known way of learning in this contemporary society in addition to offline teaching. For teachers, teaching methods are becoming diversified; for students, it is quite significant for them to find out which learning styles they are keen on. This paper analyzes the pros and cons from the opinions of not only teachers but also students, and elaborates on the practice of AI in teaching specifically. For instance, educators are worried about the poor distraction in class of kids; also, for the young generation, they are exploring a new way of teaching that is network teaching. As for the practice, this paper refers to the class preparation, class and after class for teachers.Lecture Notes in Education Psychology and Public Media00This paper analyzes the pros and cons from the opinions of not only teachers but also students, and elaborates on the practice of AI in teaching specifically.Lecture Notes in Education Psychology and Public Media["Yuexin Shao"]2025-02-14T00:00:00 715ed8c7d13c93487e0d1c666fab58fbe701983aThe rapid development of information and communication technology has made artificial intelligence (AI) a crucial issue in education and learning. Higher education institutions face challenges in adopting AI into the learning process to enhance the quality of technology-based education. This study aims to analyze policies and regulations, implementation, and evaluation of AI utilization in higher education learning. The method used is a literature study with data collection techniques based on primary and secondary sources relevant to artificial intelligence. Data analysis was conducted using a descriptive analysis method. The results of the study indicate that the implementation of generative AI in learning presents both opportunities and challenges. On one hand, generative AI technology can assist the academic community in accessing information and improving learning efficiency. However, improper use may negatively impact academic integrity. Therefore, appropriate policies and regulations are necessary for higher education institutions to address challenges and optimize AI integration in the learning process.Jurnal Alwatzikhoebillah : Kajian Islam, Pendidikan, Ekonomi, Humaniora00The results of the study indicate that the implementation of generative AI in learning presents both opportunities and challenges and appropriate policies and regulations are necessary for higher education institutions to address challenges and optimize AI integration in the learning process.Jurnal Alwatzikhoebillah : Kajian Islam, Pendidikan, Ekonomi, Humaniora["Anggi Fatmadiwi", "Suryani", "Agung Hartoyo", "Erlina Erlina"]2025-02-14T00:00:00 241c4437c9eedbbc3a846a84635245f9fe667f6bThe ethical dimensions of human-AI (artificial intelligence) interaction demand attention. As artificial intelligence assistants become more anthropomorphized, will the public interact with AI as humans morally? This study applied content analysis to data from an online question-and-answer platform in China (N = 287) to explore the public's judgments of gratitude toward artificial intelligence assistants. The findings revealed the majority supports expressing gratitude, while a significant minority disagrees, indicating diverse ethical judgments. By further analyzing people's reasoning, this study found that supporters attribute gratitude to moral autonomy driven by virtue ethics, moral responsibility for responsible AI, and the perceived source identity of anthropomorphized AI as human, aligning with the Computers-are-Social-Actors paradigm. In contrast, opponents doubt AI's moral agency, highlighting the perceived source of AI as machines, and they judge that treating it with human manners is useless and potentially dangerous. These insights enhance the understanding of the public's view of ethical considerations regarding AI assistants, contribute to gratitude research in the context of human-AI interaction, extend the moral dimension of the Computers-are-Social-Actors paradigm, and emphasize the importance of moral and responsible AI use. Suggestions for future research based on the exploratory findings are also discussed.Public Understanding of Science420This study applied content analysis to data from an online question-and-answer platform in China to explore the public's judgments of gratitude toward artificial intelligence assistants, finding that supporters attribute gratitude to moral autonomy driven by virtue ethics, moral responsibility for responsible AI, and the perceived source identity of anthropomorphized AI as human, aligning with the Computers-are-Social-Actors paradigm.Public understanding of science["Yuqi Zhu", "Jianxun Chu"]2025-02-14T00:00:00 aa22a3c77ff83e320d7f0aac7d235eb959b0ba65OBJECTIVES Dementia can change oculomotor behavior, which is detectable through eye-tracking. This study aims to systematically review and conduct a meta-analysis of current literature on the intersection between eye-tracking and artificial intelligence (AI) in detecting dementia. METHOD PubMed, Embase, Scopus, Web of Science, Cochrane, and IEEE databases were searched up to July 2023. All types of studies that utilized eye-tracking and AI to detect dementia and reported the performance metrics, were included. Data on the dementia type, performance, artificial intelligence, and eye-tracking paradigms were extracted. The registered protocol is available online on PROSPERO (ID: CRD42023451996). RESULTS Nine studies were finally included with a sample size ranging from 57 to 583 participants. Alzheimer's disease (AD) was the most common dementia type. Six studies used a machine learning model while three used a deep learning model. Meta-analysis revealed the accuracy, sensitivity, and specificity of using eye-tracking and artificial intelligence in detecting dementia, 88% [95% CI (83%-92%)], 85% [95% CI (75%-93%)], and 86% [95% CI (79%-93%)], respectively. CONCLUSION Eye-tracking coupled with AI revealed promising results in terms of dementia detection. Further studies must incorporate larger sample sizes, standardized guidelines, and include other dementia types.Aging & Mental Health390Eye-tracking coupled with AI revealed promising results in terms of dementia detection, and further studies must incorporate larger sample sizes, standardized guidelines, and include other dementia types.Aging & mental health["Mahdi Norouzi", "Rahele Kafieh", "Paul Chazot", "Daniel T Smith", "Zahra Amini"]2025-02-14T00:00:00 68822bda72ad7a90a195b9bc435994daf6a8b328Background/Objectives: Artificial intelligence (AI) is transforming nursing, with generative AI (GenAI) tools such as ChatGPT offering opportunities to enhance education through personalized learning pathways. This study aimed to explore nursing students’ use of generative artificial intelligence (GenAI) and their perceptions of its use in nursing education, including its advantages, disadvantages, and perceived support needs. Methods: This study employed an online survey. The participants were 99 undergraduate nursing students in New York City. Data was collected online through self-report measures using semi-structured, open-ended questions. The data was analyzed using content analysis. Results: Most participants (92%) used GenAI tools to access accurate information, clarify nursing concepts, and support clinical tasks such as diagnoses and health assessments, as well as schoolwork, grammar checks, and health promotion. They valued GenAI as a quick, accessible resource that simplified complex information and supported learning through definitions, practice questions, and writing improvements. However, the participants noted drawbacks, such as subscription costs, over-reliance, information overload, and accuracy issues, leading to trust concerns. The participants suggested financial support, early guidance, and instructional modules to better integrate AI into nursing education. Conclusions: The results indicate that GenAI positively impacts nursing education and highlight the need for guidelines on critical evaluation. To integrate GenAI effectively, educators should consider introductory sessions, support programs, and a GenAI-friendly environment, promoting responsible AI use and preparing students for its application in nursing education.Nursing Reports240To integrate GenAI effectively, educators should consider introductory sessions, support programs, and a GenAI-friendly environment, promoting responsible AI use and preparing students for its application in nursing education.Nursing Reports["ShinHi Han", "Hee Sun Kang", "Philip Gimber", "Sunghyun Lim"]2025-02-14T00:00:00 b24018984380579e9cf806f99954ba51280177a3The US natural gas industry is entering into a new era of dynamic competing forces and uncertainty that may exceed any of the chaos of the past. As I have written multiple times in this column, it is critical for natural gas to find its place in the long‐term future energy mix, not just as a “bridge” or as an old has‐been being ushered out the door. The dynamics of abundance, responsiveness to load changes, and its reliability and resilience make the US natural gas resource valuable for both national security through both liquefied natural gas (LNG) and electric reliability.Climate and Energy00Climate and Energy["Richard G. Smead"]2025-02-14T00:00:00 80187d4ed3aab01026bf21d4741f02d28e1e98deThis study aimed to evaluate the impact of AI personalized learning on student motivation and academic performance. As educational institutions increasingly incorporate AI, understanding its effectiveness in fostering engagement and academic success has become crucial. The study employed a quasi experimental, pretest-posttest control group design with a sample of n=200 students, comparing an AI personalized learning group to a traditional learning group form Universities of Pakistan. Descriptive statistics, paired samples t-tests and ANCOVA were used to analyze motivation and academic performance scores. Results indicated significant improvements in both motivation and academic performance in the AI group, with particularly notable gains among older students and female participants. These findings suggest that AI personalized learning can enhance educational results by adapting content to individual needs, promoting engagement and supporting diverse student populations. However, the study's quasi experimental design, short follow-up period and reliance on self-reported motivation data represent limitations. Future research should examine the long-term impact of AI personalized learning and explore how different demographic groups benefit from such interventions.Socium00Findings suggest that AI personalized learning can enhance educational results by adapting content to individual needs, promoting engagement and supporting diverse student populations, however, the study's quasi experimental design, short follow-up period and reliance on self-reported motivation data represent limitations.Socium["Hassan Imran"]2025-02-14T00:00:00 39a3615c10805d0fd2d142fe0e1b96e2644554c8AI &amp; SOCIETY10AI &amp; SOCIETY["C. Lu"]2025-02-14T00:00:00 0243cbc2d30fc1a38a0be29df4441797f909b08fFrontiers in Cardiovascular Medicine360Frontiers in Cardiovascular Medicine["Arijita Banerjee"]2025-02-14T00:00:00 6ed040a4ffac02aebd5311a6bfce210edf340009Foreign aid is often used to finance the third world’s education, health, and microenterprise programs. However, when it is put into practice, it has led to relevant dependence, cost inefficiency, and a lack of attention on local agendas. Conversely, AI is a responsible, culture-sensitive, and scalable approach to traditional aid modalities. This paper then discusses the flaws of foreign aid and calls for using AI as a sustainable solution to meet development goals in education, health, and economic growth. This work employed case studies and cross-sectional studies of AI utilization in different countries to emancipate its capability to assist communities, enhance independence, and push forward the United Nations Sustainable Development Goals (UN-SDGs). In particular, the results highlight the orientation to ethical, inclusion, and partnership approaches to ensure AI’s favorable developmental impact.Journal of Management and Sustainability540The results highlight the orientation to ethical, inclusion, and partnership approaches to ensure AI’s favorable developmental impact and call for using AI as a sustainable solution to meet development goals in education, health, and economic growth.Journal of Management and Sustainability["Bongs Lainjo"]2025-02-14T00:00:00 4a17af92bdc68909ec345cf7509173a980d6f9bdJournal of the American College of Surgeons00Journal of the American College of Surgeons["Daniel A Hashimoto", "Andrew J Hung", "J. Dimick"]2025-02-14T00:00:00 caaa03ff7b4d155c48f8548de9ff5a4e56fa0138Cureus360Cureus["Hisham Naeem Jamil Abusamra", "Salma Hassan M Ali", "Wala Ahmed Khidir Elhussien", "Alia Mirghani Ahmed Mirghani", "Asma Abualgasim Alameen Ahmed", "Mohamed Elsayed Abdelrahman Ibrahim"]2025-02-14T00:00:00 3a3426585c9b983b5ea73140662970a41f5a6223Journal of Clinical and Translational Science00Journal of Clinical and Translational Science["Peter L Elkin", "C. Lindsell", "Julio Facelli", "Manisha Desai", "Chunhua Weng", "Heidi Spratt", "Shari Messinger", "L. Waitman", "JaMor M. Hairston", "Ruth O\u2019Hara", "J. Meinzen-Derr"]2025-02-14T00:00:00 783d58a178a7a56bee06de5621bb769a03c8540aThe incorporation of artificial intelligence (AI) in health care offers revolutionary enhancements in patient diagnostics, clinical processes, and overall access to services. Nevertheless, this technological transition brings forth various new, intricate risks that pose challenges to current safety and ethical norms. This research explores the ability of enterprise risk management as an all-encompassing framework to tackle these arising risks, providing both a forward-looking and responsive strategy designed for the health care industry. At the core of this method are instruments that together seek to proactively uncover and address AI-related weaknesses like algorithmic bias, system failures, and data privacy issues. On the reactive side, it incorporates incident reporting systems and root cause analysis, tools that enable health care providers to quickly address unexpected events and consistently improve AI implementation procedures. However, some application difficulties still exist. The unclear, "black box" characteristics of numerous AI models hinder transparency and responsibility, prompting inquiries about the clarity of AI-generated choices and their adherence to ethical benchmarks in patient treatment. The research highlights that with the progress of AI technologies, the enterprise risk management framework also needs to evolve, addressing these new complexities while promoting a culture focused on safety in health care settings.Journal of Healthcare Risk Management310The research explores the ability of enterprise risk management as an all-encompassing framework to tackle arising risks, providing both a forward-looking and responsive strategy designed for the health care industry.Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management["Gianmarco Di Palma", "R. Scendoni", "V. Tambone", "Rossana Alloni", "Francesco De Micco"]2025-02-14T00:00:00 ebc1e292e0c610394f1985e6aa644a1b091b650fThis systematic review investigates the impact of generative artificial intelligence (GenAI) tools on developing academic skills in higher education. Analysing 158 studies published between 2021 and 2024, it focuses on the impact of GenAI tools on the development of cognitive, technical and interpersonal skills. The results reveal that 94% of the sampled studies reported significant improvements in cognitive skills, like critical thinking, problem‐solving, analytical and metacognitive abilities, facilitated by personalised learning and feedback. Indeed, the development of technical skills was reported in research (24%), writing (26%), data analysis (33%) and technical literacy (18%). Additionally, GenAI tools were found to promote interpersonal skills by fostering interactive and engaging learning environments, with notable skills development in communication (24%), organisation (26%), empathy (5%) and teamwork (45%). Hence, this review underscores the importance of ethical and responsible use of GenAI tools, ongoing monitoring and active stakeholder engagement to maximise their benefits in developing cognitive, technical and interpersonal skills in higher education. They offer a promising avenue for academic advancement by fostering critical thinking, enhancing technical proficiency and promoting effective communication and teamwork. Therefore, GenAI tools significantly enhance academic skills; however, their integration requires a robust ethical framework and sustained examination of their long‐term impacts.European Journal of Education1370GenAI tools significantly enhance academic skills; however, their integration requires a robust ethical framework and sustained examination of their long‐term impacts.European Journal of Education["Kangwa Daniel", "M. M. Msambwa", "Zhang Wen"]2025-02-14T00:00:00 35387e104248dded389f557a939296ea3cbcccbbThe silver economy is a new discipline, whose purpose is to solve the particular challenges and opportunities of a retiring society. The subject of this piece is how tech will impact the silver economy, and in particular Artificial Intelligence (AI)-based financial management systems, smart products, and entrepreneurs aids for the elderly. A systematic review of recent developments concludes the paper on how AI can enhance financial decisions for the elderly through personalized guidance, fraud prevention and retirement planning. Moreover, the combination of wearables, automation at home, and telemedicine also proves to make older people healthier, safer and better lives. At the end of the paper we see an increasing focus on entrepreneurship in the silver economy specifically digital platforms and support networks that allow older adults to start businesses and stay economically and socially engaged. It was found that, when deployed appropriately, technology could meet the economic and social needs of the elderly to increase independence and improve long-term well-being. In sum, the findings underscore how technological innovation has the capacity to make a sustainable, inclusive silver economy.Applied and Computational Engineering00It was found that, when deployed appropriately, technology could meet the economic and social needs of the elderly to increase independence and improve long-term well-being.Applied and Computational Engineering["Zhangyu Wang"]2025-02-14T00:00:00 836429d350f9e603eddb6e67a7347426706506e5With the advent of the digital intelligence era and the rapid evolution of emerging technologies, Artificial Intelligence (AI) is fundamentally transforming the way consumers and businesses interact, gradually becoming one of the primary tools for companies to continuously improve customer experience and maintain competitiveness. However, existing research on customer experience largely overlooked the disruptive changes brought by the widely applied AI technologies. Therefore, this paper focuses on customer AI experience in the new context, using a mixed research method combining qualitative and quantitative approaches to explore the connotation, measurement, formation mechanism, and related action mechanisms of this construct. This study finds the following: (1) the customer AI experience is an intrinsic and subjective response generated by customers after interacting with AI capabilities, mediated by AI. It specifically includes five dimensions: social experience, intellectual experience, classification experience, exploitation experience, and service experience; (2) its formation and development is a cyclical model comprising three stages: expectation, realization, and reflection, corresponding to the mechanisms of contact, interaction, and comparison; (3) the perceived innovative characteristics of AI technology help customers to have a better AI experience, thereby stimulating customer engagement behavior. This provides certain guidance and reference for enterprises to better understand and utilize AI’s innovative characteristics to improve the customer experience, promote customer engagement, seize opportunities in AI technology development, and maintain a competitive advantage.Journal of Theoretical and Applied Electronic Commerce Research530This study finds the customer AI experience is an intrinsic and subjective response generated by customers after interacting with AI capabilities, mediated by AI, and provides certain guidance and reference for enterprises to better understand and utilize AI’s innovative characteristics to improve the customer experience.Journal of Theoretical and Applied Electronic Commerce Research["Chunqing Li", "Riyan Hao", "Ning Li", "Chenlu Zhang"]2025-02-14T00:00:00 4481ef933eb7d1c30a953afe68e218f4c3e578c3The development of facial musculature and expressions in the human fetus represents a critical intersection of developmental biology, neurology, and evolutionary anthropology, offering insights into early neurological and social development. Fetal facial expressions, shaped by Cranial Nerve VII, reflect evolutionary adaptations for nonverbal communication and exhibit minimal asymmetry in universal expressions. Advancements in 4D ultrasound imaging and artificial intelligence (AI) have introduced innovative methods for analyzing these movements, revealing their potential as diagnostic tools for neurodevelopmental disorders like Bell's Palsy and Ramsay Hunt Syndrome before birth. These technologies promise early interventions that could significantly improve neonatal outcomes. By integrating imaging, AI, and longitudinal studies, researchers propose a multidisciplinary approach to establish diagnostic criteria for fetal facial movements. However, translating these advancements into clinical practice requires addressing ethical and practical challenges, refining imaging and AI methodologies, and fostering interdisciplinary collaboration. The review highlights the universality of fetal expressions while emphasizing the importance of distinguishing typical variability from pathological markers. In conclusion, these findings suggest transformative potential for maternal-fetal medicine, paving the way for proactive strategies to manage neurodevelopmental risks. Focused research is essential to fully harness these innovations and establish a new frontier in perinatal science.Journal of Perinatal Medicine460The review highlights the universality of fetal expressions while emphasizing the importance of distinguishing typical variability from pathological markers, and suggest transformative potential for maternal-fetal medicine, paving the way for proactive strategies to manage neurodevelopmental risks.Journal of perinatal medicine["W. Andonotopo", "M. A. Bachnas", "Julian Dewantiningrum", "Mochammad Besari Adi Pramono", "Milan Stanojevi\u0107", "Asim Kurjak"]2025-02-14T00:00:00 b06275f7571aa48aff8ed71664359ff007f22e1dThe objective of this research was to explore challenges of multilingual learners which are confronted in regard to reading comprehension, vocabulary growth, and retention of all curriculum content. These challenges have given rise to innovative solutions such as Artificial Intelligence (AI)-powered reading support systems that offer adaptive, personalized, and interactive learning experiences. This study looks into how AI-based reading tools (i.e., machine translation, speech-to-text, text-to-speech, and intelligent annotation systems) can support multilingual students' reading proficiency on the teaching and learning contexts. Through a mixed-methods approach, the study highlights the potential benefits of AI-driven reading support in enhancing the comprehension, engagement, and academic achievement of multilingual learners. It also investigates students’ perceptions of AI-assisted reading and its effects on their agency in learning. The results show that AI tools improve reading fluency and comprehension at scale by providing real-time, language-level support, contextual translations, and text recommendations. This study adds to the discussion around AI in education, providing insights on the pedagogical implications of employing AI-powered reading support in multilingual contexts within higher education settings. It also offers recommendations for educators and policymakers on using AI to help create more inclusive and accessible learning environments.Journal for Social Science Archives00Insight is provided on the pedagogical implications of employing AI-powered reading support in multilingual contexts within higher education settings and recommendations for educators and policymakers on using AI to help create more inclusive and accessible learning environments are offered.Journal for Social Science Archives["Dr. Nishat Zafar", "Dr. Saira", "Seerat Afzal"]2025-02-14T00:00:00 9b6a8cf1d80db8c5aba11d7d4d35f0997d7087afAs technology makes advancements so does the risk of accessing it for wrong doings. In recent years as we moved from traditional banking systems to online banking and the volume of digital transactions has increased eccentric. This also comes up with increasing risk of fraudulent activities like accessing bank accounts, credit card frauds, account frauds, dormant account fraud, and many others. Detecting fraud activities is and crucial part of banking system. This research explores the application of artificial intelligence (AI) in detecting potentially fraudulent activities by generating a risk score to assess account behavior. A formula is developed to compute a score out of 100, which triggers automated security measures when exceeding a predefined threshold of 80. The formula evaluates four key activities commonly associated with fraud: new device logins, updates to contact number or email address, the addition of new payees or Zelle contacts, and transactions exceeding $1,000 in 48-hour time span. Leveraging machine learning algorithms, this model incorporates behavioral patterns, historical data, and real-time anomaly detection to calculate the score. Accounts with scores above the threshold are temporarily locked, initiating further verification processes to ensure security while minimizing customer inconvenience. This research demonstrates the effectiveness of AI-driven fraud detection mechanisms and highlights the balance between security and user experience in modern banking systems.Journal of Engineering Research and Reports00This research demonstrates the effectiveness of AI-driven fraud detection mechanisms and highlights the balance between security and user experience in modern banking systems.Journal of Engineering Research and Reports["Shubham Metha"]2025-02-14T00:00:00 d63422150c9ecb74315c8dfdcf023f8b6fe4cc52The integration of artificial intelligence (AI) in language education has led to the emergence of adaptive learning systems that personalize instruction based on individual learners' needs. AI-powered chatbots and virtual tutors offer real-time feedback, interactive engagement, and accessibility, making foreign language learning more efficient. This paper explores the advantages and challenges of AI-driven adaptive learning in language acquisition. While AI enhances personalized learning paths, engagement through gamification, and 24/7 accessibility, it also presents limitations such as a lack of human interaction, authenticity issues, and challenges in assessing fluency and creativity. Additionally, ethical concerns arise regarding its integration into formal curricula and its potential over-reliance by learners. The article argues that AI should be viewed as a complementary tool rather than a replacement for human instruction. Future advancements in AI models, incorporating cultural awareness and emotional intelligence, may bridge some of these gaps, making AI-driven language learning even more effective. A balanced approach, where AI supplements rather than replaces traditional teaching, is essential to ensuring comprehensive and meaningful language acquisition.Luminis Applied Science and Engineering00It is argued that AI should be viewed as a complementary tool rather than a replacement for human instruction, where AI supplements rather than replaces traditional teaching, to ensure comprehensive and meaningful language acquisition.Luminis Applied Science and Engineering["Gerda Urbaite"]2025-02-14T00:00:00 8e38ce910f1fdd61badbb8d36d938b2513d518b1Ocular surface and tear diseases are among the most common and significant ocular conditions affecting eye health. In recent years, research and clinical diagnosis and treatment of ocular surface and tear diseases have rapidly developed in China, but numerous challenges remain. Breakthroughs in medical artificial intelligence (AI) offer new methods and approaches to address these difficulties. This article, based on the current state of AI applications for ocular surface and tear diseases in China and the major challenges, outlines future directions for progress. With the construction of shared multimodal databases, development of AI foundational models, improvement of AI evaluation systems, promotion of AI for comprehensive disease management throughout the entire lifecycle, and training of multidisciplinary talents, significant advances of the application of AI in the diagnosis and treatment of ocular surface and tear diseases would be achieved.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology00With the construction of shared multimodal databases, development of AI foundational models, improvement of AI evaluation systems, promotion of AI for comprehensive disease management throughout the entire lifecycle, and training of multidisciplinary talents, significant advances of the application of AI in the diagnosis and treatment of ocular surface and tear diseases would be achieved.[Zhonghua yan ke za zhi] Chinese journal of ophthalmology["Z. G. Liu", "S. P. Wang"]2025-02-14T00:00:00 e2a8a147df8ddd8b879060a431ecdb6eed5cd035Artificial intelligence (AI) partnerships have become a prevalent strategy in AI markets, enabling developers to access critical resources for developing and deploying AI models. As concerns over market concentration and potential anti-competitive practices by incumbent firms grow, competition authorities worldwide are intensifying their scrutiny of these partnerships, using a range of enforcement and regulatory tools. However, reviewing these partnerships presents new and complex competition challenges. This paper first categorizes AI partnerships into five types based on their rationale, namely, expertise, data, compute, distribution and investment. It then explores key competition concerns identified through market studies and investigations, analysing them through the frameworks of merger laws, antitrust laws and relevant regulations. The paper concludes with policy recommendations, suggesting competition authorities ensure predictability in the application of merger control rules to provide legal certainty, make competition principles more actionable and develop pro-competitive and innovation-friendly regulations.Competition Law &amp; Policy Debate00This paper first categorizes AI partnerships into five types based on their rationale, namely, expertise, data, compute, distribution and investment, and explores key competition concerns identified through market studies and investigations, analysing them through the frameworks of merger laws, antitrust laws and relevant regulations.Competition Law &amp; Policy Debate["Christophe Carugati", "Nicole Kar"]2025-02-14T00:00:00 e93582f89c7fd67bc3d4572d68b8d265dc4f40b7Today, artificial intelligence (AI) has developed rapidly to permeate every aspect of people's lives. Some AI has already replaced humans to start working in factories or companies, in addition to AI in the field of employment and AIHR under the recruitment path based on big data algorithms for resume screening and employee interviews. The extension of AI to employment recruitment raises the issue of potential algorithmic discrimination, which manifests itself in discrimination in hiring data extrapolation, hiring data interpretation, and hiring data applications. The studys shows that employment equity and artificial intelligence in the current recruitment path, it should be combined with employment algorithm discrimination and legal challenges to explore the solution path: overcome the root algorithm bias at the technical level, clarify the responsibility of the recruitment subject, improve the laws and regulations on AI in the employment field, and set up supervision and evaluation institutions. Taking into account industrial development and employment development, we will promote the development and progress of AI recruitment and ensure that the right to fair opportunities for employees is not infringed.Applied and Computational Engineering00The studys shows that employment equity and artificial intelligence in the current recruitment path, it should be combined with employment algorithm discrimination and legal challenges to explore the solution path.Applied and Computational Engineering["Yuzhe Zhu"]2025-02-14T00:00:00 fe80472ac9f60a45e59da15323731dfcd15e70c9Artificial Intelligence (AI) is changing real estate valuation with innovative approaches. This article examines several AI methods – Regression Models, Decision Trees, Random Forests, Artificial Neural Networks, and XGBoost – and explores their applications for improving property valuation accuracy and efficiency, with implications for other professions involved, e.g. audit. The author starts by investigating traditional valuation methods' limitations, such as data constraints and subjectivity, and presents how these AI techniques, which are translated in property valuation field as automated valuation methods, tackle these challenges. Regression Models quantify attributes, Decision Trees provide clear insights, Random Forests improve predictions, Artificial Neural Networks design elaborate relationships, and XGBoost furnishes advanced boosting techniques for higher performance. Underscoring that AI is meant to support, not substitute, human assessors, the paper presents how these methods can enhance valuation processes, deliver more reliable valuation reports, and decrease errors, while also exploring future innovations and evolving trends in artificial intelligence for real estate industry and related professions.Audit Financiar00This article examines several AI methods – Regression Models, Decision Trees, Random Forests, Artificial Neural Networks, and XGBoost – and explores their applications for improving property valuation accuracy and efficiency, with implications for other professions involved.Audit Financiar["Silviu-Ionut Babtan"]2025-02-14T00:00:00 5c69d35272581b1c3884866ae89ce9ee5fae891dPurposeThis paper investigates the impact of financial technology innovation on bank performance. Using a large sample of FinTech mergers and acquisitions (M&A) deals by major and regional US banks as well as artificial intelligence (AI) patent applications and grants by banks from 2010 to 2022, their impact on bank return on assets (ROA) and return on equity (ROE) is explored.Design/methodology/approachSystem GMM estimators created for dynamic panel models are employed to evaluate the impact of a bank’s acquisitions of technology-oriented, AI and FinTech corporations and the filing of technology-oriented patents on profitability.FindingsA positive association between the number of FinTech M&A deals and bank performance is documented; however, none of the patent variables (grant, filing or publication) appear to have a significant effect.Originality/valueA large sample of FinTech M&A deals and patents by US major and regional banks is used to study the impact on bank performance. A comprehensive empirical analysis is performed while controlling for bank size and other bank characteristics.Managerial Finance370Using a large sample of FinTech mergers and acquisitions deals by major and regional US banks as well as artificial intelligence patent applications and grants by banks from 2010 to 2022, their impact on bank return on assets and return on equity is explored.Managerial Finance["Maria E. de Boyrie", "I. Pavlova"]2025-02-14T00:00:00 a50020a4cf3b63cea9fa5b0e1c8f13c7c2e9d5deThis paper examines the legal implications of the explicit mentioning of automation bias (AB) in the Artificial Intelligence Act (AIA). The AIA mandates human oversight for high-risk AI systems and requires providers to enable awareness of AB, i.e., the tendency to over-rely on AI outputs. The paper analyses how this extra-juridical concept is embedded in the AIA, the division of responsibility between AI providers and deployers, and the challenges of legally enforcing this novel awareness requirement. The analysis shows that the AIA's focus on providers does not adequately address design and context as causes of AB, and questions whether the AIA should directly regulate the risk of AB rather than just mandating awareness. As the AIA's approach requires a balance between legal mandates and behavioural science, the paper proposes that harmonised standards should reference the state of research on AB and human-AI interaction. Ultimately, further empirical research will be essential for effective safeguards.00The analysis shows that the AIA's focus on providers does not adequately address design and context as causes of AB, and questions whether the AIA should directly regulate the risk of AB rather than just mandating awareness.["Johann Laux", "Hannah Ruschemeier"]2025-02-14T00:00:00 4dc03464146ee2c2cc5b99c23209ad191e1f1fdaDiabetic retinopathy (DR) remains a leading cause of vision impairment and blindness among individuals with diabetes, necessitating innovative approaches to screening and management. This editorial explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing DR care. AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy, efficiency, and accessibility of DR screening, helping to overcome barriers to early detection. These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision, enabling clinicians to make more informed decisions. Furthermore, AI-driven solutions hold promise in personalizing management strategies for DR, incorporating predictive analytics to tailor interventions and optimize treatment pathways. By automating routine tasks, AI can reduce the burden on healthcare providers, allowing for a more focused allocation of resources towards complex patient care. This review aims to evaluate the current advancements and applications of AI and ML in DR screening, and to discuss the potential of these technologies in developing personalized management strategies, ultimately aiming to improve patient outcomes and reduce the global burden of DR. The integration of AI and ML in DR care represents a paradigm shift, offering a glimpse into the future of ophthalmic healthcare.World Journal of Clinical Cases600The integration of AI and ML in DR care represents a paradigm shift, offering a glimpse into the future of ophthalmic healthcare.World Journal of Clinical Cases["Mona Mohamed Ibrahim Abdalla", "Jaiprakash Mohanraj"]2025-02-16T00:00:00 8c5b6fc624f61535d301c59701ce263b8e3b670eFor weeks—for months, actually—all one could hear out of the scientific world, the financial world, and other worlds was speculation about a new kind of technology that would disrupt and revolutionize human progress: artificial intelligence (AI). A technology using computers to perform work previously thought to require intelligence would certainly affect human enterprise from menial tasks to scientific research projects to every conceivable form of communication. The question is: Exactly how will these enterprises be affected? And will the results be positive or negative? For communicators of food and health science, who are trying to translate science for the public and for policymakers, the answers are critical. The authors of this article, extending their decades-long series on public trust, understanding, and communication of science issues, explore the challenges and opportunities that AI is expected to present. As usual, they offer guidance to food and health science communicators in dealing with AI challenges and in seizing those opportunities.Nutrition Today40The authors of this article, extending their decades-long series on public trust, understanding, and communication of science issues, explore the challenges and opportunities that AI is expected to present and offer guidance to food and health science communicators in dealing with AI challenges and in seizing those opportunities.Nutrition Today["S. Rowe", "Nicholas Alexander"]2025-02-17T00:00:00 23741edbc47ce6b9758a49337d0aa6149701112ePurposeThis research aims to examine the dynamic relationship between digital marketing and AI. This study used bibliometric analysis to investigate the significance of artificial intelligence in digital marketing research. The study was conducted using the WOS database, which includes word cloud analysis, keyword analysis, citation analysis, and publication analysis.Design/methodology/approachThe present inquiry utilized the Web of Science database to gather scholarly publications that were published between 2000 and 2023. A search was performed using the Boolean operator “AND” to retrieve pertinent publications that contain both the terms “artificial intelligence” and “digital marketing” in the first query. A total of 96 publications were found during the search. The search terms were expanded, and the content analysis was enhanced to include studies from 1993 to 2023, resulting in 521 studies for in-depth analysis in the second query. The acquired papers were subjected to bibliometric analysis using VOSviewer software (version 1.6.20).FindingsThe phrase “digital marketing” had the highest frequency, with a cumulative link strength of 94. This keyword exhibited a strong association with the phrase “artificial intelligence”. The WOS database shows a steady increase in publications on digital marketing and AI since 2017 for the first query. In 2017, there were about two publications, which grew to around 26 by 2021. For the second query, the number of publications on digital marketing and AI also increased steadily. In 1993, there was one publication, rising to about 102 by 2022.Originality/valueThe study conducts a comprehensive bibliometric analysis by examining publications that were released in the Web of Science database from 2000 to 2023 for the first query and from 1993 to 2023 for the second query. This research analyzes the progress and current status of corporate management and marketing techniques during the past twenty-four years. In addition, this approach enhances the originality of the second inquiry by providing a comprehensive analysis of studies spanning nearly 3 decades, offering unique insights into the evolution of the field. The research centers on the impact that AI has exerted on these sectors. Moreover, the results of this study emphasize the significance of the increasing number of scientific studies that intersect AI and digital marketing.International Marketing Review630This study used bibliometric analysis to investigate the significance of artificial intelligence in digital marketing research using the WOS database, which includes word cloud analysis, keyword analysis, citation analysis, and publication analysis.International Marketing Review["Kemal Gokhan Nalbant", "Sevgi Ayd\u0131n"]2025-02-17T00:00:00 de6d4a71f41b371dacfd585a8e90e87ac3cc0d5bBACKGROUND High-quality universal health care coverage for all patients is the common theme in patient rights. However, pertinent investigations on this topic within the context of Jordanian health care are absent. This systematic review, coupled with a pooled artificial intelligence analysis of the data in retrieved studies, paves the way for such research by pooling data sets sourced from across the Middle East and North Africa (MENA) region. METHODS National Library of Medicine (NLM), through its secondary database of primary literature (PubMed), was queried with the terms "Patient" and "Rights" in April 2024. Quantitative surveys from MENA containing individual item assessments mapped to 1 of the 7 domains of Jordan National Patient Rights Charter were pooled. Finally, factors extracted for all studies were then used to build an artificial neural network (ANN) to test the hypothesis that information asymmetry in both awareness and practice of patient rights exist even among health care providers. RESULTS A total of 8 studies with 131 survey items were identified in the MENA region. All items tested either knowledge (awareness) or practice (implementation) of respondents regards patient rights except for 25 items in one study which measured both. ANN converged to a best net of multilayer feedforward with 3 hidden nodes. Patient right domain, from Jordanian Patient Rights Charter, ranked first and respondent type second as most important among the variables. However, there was huge and statistically significant asymmetry between students 0.602 (0.499 to 0.853), patients 0.627 (0.518 to 0.636), and nurses 0.492 (0.340 to 0.786) on one side and clinicians 1.166 (1.025 to 1.258) on the other side in the ANN model (both paired t test and Wilcoxon signed rank test P<0.0001) for any pairwise comparisons. CONCLUSIONS Jordan National Patient Charter can fit any patient right item one could think of in the infinite space of patient rights. Huge information asymmetry exists in both awareness and implementation between practicing professionals and society but also among the different health professions.Journal of patient safety280Jordan National Patient Charter can fit any patient right item one could think of in the infinite space of patient rights.Journal of patient safety["Loai M. Saadah", "Dalal Alnatour", "Mumen S Hadidi", "Fadia F Samara", "Sana S Shakhshir", "Wafa'a M Alnsour", "Maisa K Saket"]2025-02-17T00:00:00 816258666d8b9dc48cef68fada3c037a6d47dfb5 The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM. This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework. This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance. This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM. Journal of Modelling in Management1820A knowledge graph model is developed that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research and incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies.Journal of Modelling in Management["Xinyue Hao", "E. Demir", "Daniel Eyers"]2025-02-18T00:00:00 ec1f8dd6e6b3d4351bdd0ad19209178ef073623eCancer Imaging721Artificial intelligence-enabled models have a great potential in improving clinical practice in the diagnosis and management of advanced renal cell carcinoma, particularly when developed from both clinicopathologic and radiologic data.Cancer Imaging["Elie J Najem", "Mohd Javed S Shaikh", "A. Shinagare", "K. Krajewski"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/ec1f8dd6e6b3d4351bdd0ad19209178ef073623e f51f6fe39370aaec438bfca803ef100936890c1dThis article examines the revolutionary potential of artificial intelligence (AI) in improving energy management systems (EMS) to reduce carbon emissions and tackle pressing climate change issues. We conduct a comprehensive literature analysis to analyze AI-driven solutions for optimizing energy usage, minimizing carbon footprints, and promoting sustainability across diverse industries. Conventional EMS methodologies often depend on static and reactive strategies, limiting their efficacy in the face of increasing global energy needs and regulatory requirements. Conversely, AI-driven EMS provides sophisticated data analytics, predictive maintenance, and real-time optimization, markedly enhancing efficiency and emissions control. Our research includes case studies from both industrial and public sectors that illustrate the quantifiable effects of AI-integrated Energy Management Systems in reducing operating expenses, improving renewable energy integration, and fostering better energy practices. Significant hurdles, such as elevated implementation costs, data privacy issues, and regulatory compliance, are examined with prospective legislative frameworks to promote AI use. We underscore the significance of AI in delivering actionable insights, harmonizing energy practices with climate policy, and promoting a sustainable energy future. This study concludes that AI-driven Energy Management Systems are essential for significant emissions reductions and the development of resilient, eco-efficient energy systems, highlighting the necessity for strategic investment and supportive policies to optimize AI technology's societal and environmental advantages in energy management.Jurnal Ilmiah Ilmu Terapan Universitas Jambi531It is concluded that AI-driven Energy Management Systems are essential for significant emissions reductions and the development of resilient, eco-efficient energy systems, highlighting the necessity for strategic investment and supportive policies to optimize AI technology's societal and environmental advantages in energy management.Jurnal Ilmiah Ilmu Terapan Universitas Jambi["Eda Tabaku", "Eli Vyshka", "Rinela Kap\u00e7iu", "Alban Shehi", "Ensi Smajli"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/f51f6fe39370aaec438bfca803ef100936890c1d a661e94a7ea577664c20e33317568d7d456c9860 The aim of this study is to investigate the factors that influence the readiness to adopt artificial intelligence (AI) tools within Moroccan auditing firms. A quantitative research design was used, using survey data to examine the influence of perceived usefulness (PU), ease of use (EU) and top management commitment (TMC) on AI adoption readiness (AIAR) in auditing. A conceptual model, drawing from the technology acceptance model (TAM) and supported by findings from previous literature, was proposed. The model was tested using partial least squares – structural equation modelling on data collected from 116 Moroccan respondents. The study confirmed that PU and TMC do not significantly influence the AIAR in auditing in Morocco, whereas EU is positively and significantly associated to the AIAR. The study presents findings based on data from a single country, which may limit the broader applicability of the results to other contexts or regions with different regulatory, cultural or economic environments. The results suggest that TAM is not necessarily adapted to AI adoption within an emerging context like Morocco. The significant role of EU in AIAR suggests that Moroccan firms should prioritize the development and integration of AI tools that are intuitive and user-friendly. AI should be presented not only as a tool for enhancing audit quality but also as a means of reducing workload and improving efficiency. Furthermore, rather than relying solely on top-down mandates, a more decentralized approach to AI adoption could be effective, where individual auditors are empowered to experiment with AI tools and integrate them into their practices. This approach could foster a culture of innovation and gradual adoption, increasing the likelihood of successful AI integration within Moroccan auditing firms. AI adoption in auditing can promote societal benefits by enhancing transparency, accountability and trust in both public and private sectors. In countries like Morocco, where financial transparency is lacking but vital for stability, AI can help reduce corruption, improve decision-making and foster public trust, ultimately supporting investment and social equity. This paper offers an original contribution by examining AIAR in Morocco’s auditing sector, focusing on an emerging market and African context. Unlike studies in developed countries, it highlights the unique challenges and opportunities faced by Moroccan auditors, considering factors like PU, EU and TMC. It challenges organizations to assess their readiness and the ability of their employees to effectively integrate AI into their workflows. Journal of Financial Reporting & Accounting910Ex examining AIAR in Morocco’s auditing sector, focusing on an emerging market and African context, highlights the unique challenges and opportunities faced by Moroccan auditors, considering factors like PU, EU and TMC.Journal of Financial Reporting and Accounting["Issam Benhayoun", "Salma Bougrine", "Aimad Sassioui"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/a661e94a7ea577664c20e33317568d7d456c9860 4a4b5a34bd85e10f42aa36585908fb7953bbaca9This community service programme aims to improve the writing skills of elementary school students through the integration of Artificial Intelligence (AI) and digital literacy. The method of implementing this activity uses training with the target, namely teachers at SDN Kampungdalem 1 Tulungagung, East Java. The evaluation instrument uses observation sheets and questionnaires which are then analyzed descriptively quantitatively and qualitatively. The results of this service showed that teacher competence and student writing skills improved through integrating AI and digital literacy in writing learning, as shown by an increase in the average writing score from 65.5 to 84.2. In addition, the success of this activity is supported by a holistic approach that combines teacher training, implementation assistance, and continuous evaluation.Jurnal Pengabdian UNDIKMA00The results showed that teacher competence and student writing skills improved through integrating AI and digital literacy in writing learning, as shown by an increase in the average writing score from 65.5 to 84.2.Jurnal Pengabdian UNDIKMA["Marista Dwi Rahmayantis", "Andri Pitoyo", "Sujarwoko Sujarwoko", "Chelya Ilham Ramdani Putra", "Achmad Fathoni Firmansyah", "Yolanda Rensia Gigik", "Junio Boy Smara Dinso", "Rohmiati Rohmiati", "Wahyu Adi Pratiwi"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/4a4b5a34bd85e10f42aa36585908fb7953bbaca9 474aec6b14fa5d7b5eb133d0f9209c98a654a615The background of this study focuses on the role of artificial intelligence (AI) in talent management within the public sector, particularly in supporting competency development and work efficiency. Digital innovations like AI can significantly improve the performance of civil servants in Indonesia; however, they also pose ethical challenges and policy implications that need to be carefully addressed. This research adopts a descriptive qualitative approach, with data collected through in-depth interviews and literature reviews. The data were analyzed using triangulation techniques to ensure the validity and reliability of the findings, while the research locus was centered on the West Java Provincial Government. The results show that implementing AI in talent management offers benefits in automating administrative processes and improving work efficiency. However, there are challenges regarding resistance to change from human resources and the limitations of existing technological infrastructure. Additionally, there are ethical dilemmas concerning data privacy and transparency in AI-based decision making. The study concludes that AI has great potential to enhance talent management performance in the public sector, but its implementation requires clear policies and appropriate risk mitigation strategies. The implications of this research provide policy recommendations to improve technological infrastructure readiness and train civil servants to face the digital era, while also ensuring that ethical and regulatory aspects are addressed.KnE Social Sciences120The results show that implementing AI in talent management offers benefits in automating administrative processes and improving work efficiency, however, there are challenges regarding resistance to change from human resources and the limitations of existing technological infrastructure.KnE Social Sciences["Ferdyansyah Wicaksono", "Yaya Mulyana Abdul Aziz", "Andre Ariesmansyah", "Rifki Khairul Arifin"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/474aec6b14fa5d7b5eb133d0f9209c98a654a615 90d1d3d251da593d436cc97898a66fee530272adPurposeThe advancement of artificial intelligence (AI) and robotics helps firms achieve seamless production, distribution and service delivery. This study uses a sample of developed and developing countries to examine the impact of robots on AI-related employment.Design/methodology/approachThe present study underlies cross-country evidence using a sample of 28 countries between 2016 and 2022. The source data are captured from the Artificial Intelligence Index Report, Statista, World Intellectual Property Organization, World Development Indicators and World Governance Indicators. We employed panel data techniques for analysis purposes.FindingsThis study unravels the impact of robot use on AI employment in developed and emerging economies. The dynamic panel threshold regression models support the contention that the effects of robots on AI employment are more complex than they are made to be. The impact varies below and above the threshold of country-specific variables such as internet penetration, innovation parameters, gross domestic product (GDP) per capita and labor force quality.Originality/valueThis study offers new perspectives on robot and AI-related employment by utilizing a sample of developed and developing countries. It considers the inclusion of country-specific variables. The study provided insights into the economic value creation by labor that would be shaped by the threshold of technological infrastructure, economic conditions and governance standards of countries, thereby contributing to the employment relations literature.International journal of manpower740The dynamic panel threshold regression models support the contention that the effects of robots on AI employment are more complex than they are made to be and offer new perspectives on robot and AI-related employment by utilizing a sample of developed and developing countries.International Journal of Manpower["K. Das", "Neelam Rani", "Rahul Bodhi", "M. Yaqub"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/90d1d3d251da593d436cc97898a66fee530272ad c822d97879266e831052b05c5417e241649f75e9This study looks at perceptions of artificial intelligence (AI) systems in human resources (HR) management within Swiss organizations. Based on a survey experiment provided to 324 private and public HR professionals, it explores how UTAUT's predictors—performance expectancy, effort expectancy, social influence and facilitating conditions—as well as top management support, the Private/Public dimension and control variables—age, gender, time with organization and hierarchical position—influence their acceptability of four different type of AI HR tools. To do this, this article is based on a multiple regression method. Its main findings are that, irrespective of the type of tool, performance expectancy, effort expectancy and social influence positively influence the acceptability of the HR AI tools studied, whereas working in a public organization has systematically a negative influence. This makes a significant contribution to the literature by offering valuable insights into how these factors collectively shape the willingness of HR professionals to embrace AI technologies in their practices. It also offers an overview of the levers that organizations aiming to adopt these AI tools could act upon.Systems research and behavioral science1530Perceptions of artificial intelligence systems in human resources (HR) management within Swiss organizations are looked at, finding that, irrespective of the type of tool, performance expectancy, effort expectancy and social influence positively influence the acceptability of the HR AI tools studied, whereas working in a public organization has systematically a negative influence.Systems Research and Behavioral Science["Guillaume Revillod"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/c822d97879266e831052b05c5417e241649f75e9 174ab40ae95d309c1b0a116b69ab8a4ef7369933This study examines the impact of digital transformation and artificial intelligence (AI) on bureaucratic culture in the public service sector, especially related to employee efficiency and discretion in decision making. This study aims to understand how the application of AI determines the flexibility, accountability, and effectiveness of bureaucracy in Gorontalo Province, as well as explore strategies that can be implemented to maintain a balance between technological innovation and administrative policy. Academically, this research contributes to filling the literature gap related to technology-based bureaucratic transformation, especially in the context of developing countries. The practical importance of this study lies in the policy recommendations provided to improve bureaucratic adaptability to technological changes while maintaining flexibility in decision-making. The research method used is a qualitative approach with in-depth interviews with employees at various levels of hierarchy in regional apparatus organizations in Gorontalo that have adopted AI. The results show that the application of AI has improved administrative efficiency and accountability in decision-making, but on the other hand, limits the discretionary space of employees in situations that require contextual considerations. The resulting theoretical novelty is the concept of algorithmic bureaucracy, where AI plays a key element in the bureaucratic decision-making process that has the potential to shift the discretionary function of employees to be more automated. This study recommends that in bureaucratic transformation there are 2 important things that need to be implemented, namely: 1) Strengthening digital capabilities and bureaucratic adaptability. 2) Algorithmic supervision technology governance and policy. As for the strengths of the research where the relevant study introduces new concepts regarding bureaucratic transformation, the weaknesses of the research are limited to geography, the risk of subjective bias, and the lack of quantitative validation of the concepts introduced.KnE Social Sciences480The results show that the application of AI has improved administrative efficiency and accountability in decision-making, but on the other hand, limits the discretionary space of employees in situations that require contextual considerations.KnE Social Sciences["Yanti Aneta", "H. Akib", "Abd Wahab Padungge", "Rahmatia Pakaya", "Pebriyanto A. Hulinggi"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/174ab40ae95d309c1b0a116b69ab8a4ef7369933 28bd1e024e4ca927ec937dfbc6bc50b8dcbbbcdbReading is about the process of interpreting and understanding written language. It is a complex process that has attracted a lot of attention from educators, psychologists, and linguists for decades. It presents many difficulties and challenges that might be closely linked to the text or the reader. The rapid development in technology led to the appearance of artificial intelligence (AI), which played a significant role in improving and supporting university students’ reading skill. The main purpose of the current research is to explore participants’ perceptions toward using AI in the reading skill. A WhatsApp group consisting of S6 students studying English at the department of English studies in Ibn Tofail University participated in the study. They were given an online qualitative questionnaire to fill out. The findings denote that the participants have positive attitudes toward the AI because it helps them become more proficient readers who can access and understand texts that are getting harder to understand. This study suggests that teachers should integrate AI into their teaching process and encourage students to use it. It also proposes that the AI detector should be used to encourage students to read instead of cheating while conducting research.International Journal of Linguistics and Translation Studies180This study suggests that teachers should integrate AI into their teaching process and encourage students to use it and proposes that the AI detector should be used to encourage students to read instead of cheating while conducting research.International Journal of Linguistics and Translation Studies["Abdessallam Khamouja"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/28bd1e024e4ca927ec937dfbc6bc50b8dcbbbcdb d0d648f708262c203a7a9acf8033a7b424d03386Acute kidney injury (AKI) in pediatric and neonatal populations poses significant diagnostic and management challenges, with delayed detection contributing to long-term complications such as hypertension and chronic kidney disease. Recent advancements in artificial intelligence (AI) offer new avenues for early detection, risk stratification, and personalized care. This paper explores the application of AI models, including supervised and unsupervised machine learning, in predicting AKI, improving clinical decision-making, and identifying subphenotypes that respond differently to interventions. It discusses the integration of AI with existing risk scores and biomarkers to enhance predictive accuracy and its potential to revolutionize pediatric nephrology. However, barriers such as data quality, algorithmic bias, and the need for transparent and ethical implementation are critical considerations. Future directions emphasize incorporating biomarkers, expanding external validation, and ensuring equitable access to optimize outcomes in pediatric AKI care.Frontiers in Nephrology600The application of AI models, including supervised and unsupervised machine learning, in predicting AKI, improving clinical decision-making, and identifying subphenotypes that respond differently to interventions are explored.Frontiers in Nephrology["Jieji Hu", "Rupesh Raina"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/d0d648f708262c203a7a9acf8033a7b424d03386 7648b97d4fdf082db9c56f65737636700712bacaRecently, learning approaches have utilized Artificial Intelligence (AI) and the Internet of Things (IoT) to create an efficient learning environment. The application of IoT and AI to improve learning systems is thoroughly examined in this paper. Additionally discussed are various IoT and AI-based approaches and strategies related to e-learning, M-learning, methods employed, and particular applications. A new era of linguistic and literary analysis is ushered in by the convergence of technology and the humanities. The creative ways that artificial intelligence provides for comprehending and interpreting texts make it relevant. As it enables the discovery of meaning, style, and other aspects of language usage in texts, linguistic analysis of texts is a crucial component of philological investigation. In this human-centric paradigm, it also becomes imperative to investigate how AI aligns with human values. In particular, giving in-service teachers examples of how to apply the suggested framework improved their understanding of generative AI concepts and how to incorporate them into their instruction. To handle learning techniques more effectively, the results of this evaluation will guide the creation of strategies that combine IoT and AI.Journal of Information Systems Engineering & Management180Giving in-service teachers examples of how to apply the suggested framework improved their understanding of generative AI concepts and how to incorporate them into their instruction, and will guide the creation of strategies that combine IoT and AI.Journal of Information Systems Engineering and Management["Dr. N. Vijayalakshmi", "R.Jayalakshmi", "R. Rekha", "Dr. K. Indumathy"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/7648b97d4fdf082db9c56f65737636700712baca fcdb7b573a81307cb4522e4fa76ac14948782503 Artificial intelligence (AI) has transformed various sectors, including automotive, finance, media, travel and retail by leveraging new-age technologies. Education, banking, health care, social policy and regulation, within the public sector have witnessed significant AI applications and substantial benefits. The importance of AI in the public sector includes enhanced efficiency, improved decision-making, cost savings, citizen-centric services, etc. Despite these advancements, a mindful discussion on the societal impact of AI in the public sector demands comprehension regarding its subjugation. Therefore, this study aims to analyze the role of AI in transforming the public sector using a bibliometric analysis of recent trends and challenges. This study has used bibliometric analysis to trace the intellectual patterns of previous research. It comprises 231 articles from 2000 to 2024 from Scopus through the Scientific Procedures and Rationales for Systematic Literature Reviews protocol. This protocol has adopted a three-step process for identifying articles, i.e. assembling, arranging and assessing. The publication trend shows an upward trajectory since 2017, whereas network visualization protrudes with the recent trends and thematic expressions, namely, Global AI ethics and policy challenges in public sectors, AI adoption and governance in public sector, challenges and opportunities of implementing AI in public administration and AI’s role in economic and public transformation. The findings suggest AI adoption in the public sector enhances transparency and efficiency but demands ethical guidelines, legal frameworks and stakeholder governance to address challenges such as data privacy, algorithmic bias and public trust. Policies should promote responsible AI use, balancing innovation with accountability to improve public service delivery and uphold democratic values. This paper enhances the limited literature on the integration of AI in the public sector, focusing on emerging themes and trending topics with future research directions to furnish a holistic perspective. It aims to guide researchers and policymakers in exploring areas for further investigation in this domain. Digital Policy Regulation and Governance880The findings suggest AI adoption in the public sector enhances transparency and efficiency but demands ethical guidelines, legal frameworks and stakeholder governance to address challenges such as data privacy, algorithmic bias and public trust.Digital Policy, Regulation and Governance["Mahanish Panda", "Munshi Maksud Hossain", "Roma Puri", "Anees Ahmad"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/fcdb7b573a81307cb4522e4fa76ac14948782503 ab9039e32309053878819b834ed00531b1701575Autonomous driving and artificial intelligence are the most popular research projects in the field of technology today. As the high technology, autonomous driving relies on perception, decision-making, and control systems, and the performance of these systems largely depends on the application of artificial intelligence nowadays. Fortunately, there are plenty of applications of artificial intelligence in several aspects of autonomous driving. This paper aims to introduce the relationship between autonomous driving and artificial intelligence by reviewing several literatures and analyzing the applications of deep learning (DL), reinforcement learning (RL), and graph neural networks (GNN) in autonomous driving.Highlights in Science Engineering and Technology120This paper aims to introduce the relationship between autonomous driving and artificial intelligence by reviewing several literatures and analyzing the applications of deep learning (DL), reinforcement learning (RL), and graph neural networks (GNN) in autonomous driving.Highlights in Science, Engineering and Technology["Boyuan Kong"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/ab9039e32309053878819b834ed00531b1701575 a50f77d968a50096b078d97dedaf5596495dac89Artificial intelligence (AI) has the potential to significantly transform medicine and enhance the experiences of patients and physicians alike. The distance between research and deployment has been shortened by promising studies and developments in medical image processing. Eventually, authors discuss significant ethical and technological difficulties ranging from racial bias to data shortage. AI may reach its full potential as these issues are resolved, making healthcare more accessible, effective, and precise for patients everywhere.  However, among the excitement, there is equal scepticism, with some urging caution at inflated expectations. Conclusively, this overview provides new insights into establishing a strong platform to explore certain new facets in the area of healthcare and medicine. The rapid progression of AI technology presents an opportunity for its application in clinical practice, potentially revolutionizing healthcare services, though there are still concerns over research gaps and potential areas for further study in view of the regulation of AI in medicine and pivotal roles across the healthcare system, equally impacting patients, doctors, and researchers. Asian journal of current research00The rapid progression of AI technology presents an opportunity for its application in clinical practice, potentially revolutionizing healthcare services, though there are still concerns over research gaps and potential areas for further study.Asian Journal of Current Research["Divyansh Bajpai", "Manoj Kumar Mishra", "Suyash Srivastava", "A. Tiwari", "Pankaj Gupta", "Beer Singh", "Sanjay Mishra"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/a50f77d968a50096b078d97dedaf5596495dac89 ed95bc66a504a0919c180216ff5ddbbb9245683fThe experience of using artificial intelligence (AI) to create a predictive model of atmospheric air pollutant distribution in an urbanized area is presented. Various machine learning algorithms, their advantages and disadvantages in the context of air quality prediction are considered. The possibilities of using historical data accumulated from 2021 to June 2024 on atmospheric air pollution in Togliatti, meteorological conditions, topography and other factors affecting the distribution of pollutants for training of AI models are investigated. Simulation results demonstrating the effectiveness of the developed model in predicting pollution levels at different time scales are presented. A conclusion is made about the significance of using AI in the field of air quality monitoring, and practical recommendations for using the obtained results to optimize pollution management strategies and ensure environmental safety are proposed.Ecology and Industry of Russia70A conclusion is made about the significance of using AI in the field of air quality monitoring, and practical recommendations for using the obtained results to optimize pollution management strategies and ensure environmental safety are proposed.Ecology and Industry of Russia["D. M. Gusev", "P.A. Melnikov", "V. A. Shashenko"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/ed95bc66a504a0919c180216ff5ddbbb9245683f 7d0174c881592b51ef7c6ac77f640f5e5ed6bc8bRetinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigated the intricate microvascular and neural circuitry within the retina, its interactions with other systemic vascular and nervous systems, and the link between retinal biomarkers and various systemic diseases. Using the eye to study systemic health, based on these connections, has been given a term as oculomics. Advancements in artificial intelligence (AI) technologies, particularly deep learning, have further increased the potential impact of this study. Leveraging these technologies, retinal analysis has demonstrated potentials in detecting numerous diseases, including cardiovascular diseases, central nervous system diseases, chronic kidney diseases, metabolic diseases, endocrine disorders, and hepatobiliary diseases. AI-based retinal imaging, which incorporates established modalities such as digital color fundus photographs, optical coherence tomography (OCT) and OCT angiography, as well as emerging technologies like ultra-wide field imaging, shows great promises in predicting systemic diseases. This provides a valuable opportunity for systemic diseases screening, early detection, prediction, risk stratification, and personalized prognostication. As the AI and big data research field grows, with the mission of transforming healthcare, they also face numerous challenges and limitations both in data and technology. The application of natural language processing framework, large language model, and other generative AI techniques presents both opportunities and concerns that require careful consideration. In this review, we not only summarize key studies on AI-enhanced retinal imaging for predicting systemic diseases but also underscore the significance of these advancements in transforming healthcare. By highlighting the remarkable progress made thus far, we provide a comprehensive overview of state-of-the-art techniques and explore the opportunities and challenges in this rapidly evolving field. This review aims to serve as a valuable resource for researchers and clinicians, guiding future studies and fostering the integration of AI in clinical practice.Theranostics910This review provides a comprehensive overview of state-of-the-art techniques and explores the opportunities and challenges in this rapidly evolving field of artificial intelligence-based retinal imaging.Theranostics["Jinyuan Wang", "Ya Xing Wang", "Dian Zeng", "Zhuoting Zhu", "Dawei Li", "Yuchen Liu", "Bin Sheng", "Andrzej Grzybowski", "T. Y. Wong"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/7d0174c881592b51ef7c6ac77f640f5e5ed6bc8b 60fc4f0676117f838155c8492d4c21fe233a04c8The ethical development path of artificial intelligence embedded in moral algorithms for artificial intelligence is one of the paths for the moral development of artificial intelligence. However, there is controversy about what kind of morality should be embedded. In fact, what ethical and moral norms can be embedded in artificial intelligence is out of the needs of human production and practice. This article analyzes the participation of artificial intelligence in division of labor from the perspective of Marxist philosophy, and further proves the correlation between the ethical activities and production of artificial intelligence itself. The more artificial intelligence develops in its intelligence, the more it can be competent for a more complex and diverse social division of labor, and the closer it is to human society. The embedding content of ethical norms, like its own development, follows the embedding process from professional ethical activities in a single production practice to complex social ethical activities.Advances in Education, Humanities and Social Science Research30This article analyzes the participation of artificial intelligence in division of labor from the perspective of Marxist philosophy, and proves the correlation between the ethical activities and production of artificial intelligence itself.Advances in Education, Humanities and Social Science Research["Shiyi Jin"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/60fc4f0676117f838155c8492d4c21fe233a04c8 901a8ffeb118fbb1036460e83acf74e368f9ff8dJ. Medical Syst.340Evaluating the efficiency, quality, and stakeholder opinion regarding the use of AI-driven documentation systems found Chat GPT and ambient AI show promise in enhancing the efficiency and quality of clinical documentation.Journal of Medical Systems["Aisling Bracken", "Clodagh Reilly", "A. Feeley", "Eoin Sheehan", "K. Merghani", "I. Feeley"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/901a8ffeb118fbb1036460e83acf74e368f9ff8d e519b540d251bfe69928d374a5dc9d800abd9e2eArtificial Intelligence (AI) is reshaping Human Resource Management (HRM) by enhancing efficiency, precision, and compliance. This study investigates the role of AI in HRM within selected Indian educational institutions, focusing on its impact on recruitment, employee engagement, training, and compliance. Using a structured research methodology, hypotheses were tested through chi-square analysis, revealing a significant positive correlation between AI adoption and HRM efficiency, effectiveness, and candidate selection precision. The findings underscore AI’s potential to transform HRM practices, making them more streamlined and data-driven. The study concludes that integrating AI technologies into HRM can enhance institutional growth and workforce management.RESEARCH REVIEW International Journal of Multidisciplinary00Investigation of the role of AI in HRM within selected Indian educational institutions finds that integrating AI technologies into HRM can enhance institutional growth and workforce management.RESEARCH REVIEW International Journal of Multidisciplinary["Sanjeev Dogra", "Swati Singh"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/e519b540d251bfe69928d374a5dc9d800abd9e2e 52a5088e5d3c492bbd6e7b39bf978750671c0fe5BMC Medical Education370The findings underscore the need to prepare students to work with AI technologies and to provide them with the essential knowledge and skills across different areas of AI.BMC Medical Education["Arash Ziapour", "Fatemeh Darabi", "Parisa Janjani", "Mohammad Amin Amani", "Murat Y\u0131ld\u0131r\u0131m", "Sayeh Motevaseli"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/52a5088e5d3c492bbd6e7b39bf978750671c0fe5 6ec87e7d22bdf0450e3169881fdec7401a83b49bHeart disease continues to be one of the predominant contributors to morbidity and mortality on a global scale, underscoring the imperative for early and precise diagnosis to enhance patient outcomes. Machine Learning (ML) has emerged as a formidable instrument in the classification of cardiovascular diseases, utilizing intricate clinical datasets to discern patterns that conventional statistical methodologies may fail to detect. Nevertheless, notwithstanding their robust predictive capabilities, numerous machine learning models function as black-box systems, exhibiting a deficiency in transparency regarding their decision-making processes. The absence of interpretability presents a considerable challenge in clinical environments, where trust, accountability, and elucidation are of utmost importance for medical professionals. In order to tackle this issue, we propose a methodology for heart disease classification that is grounded in Explainable Artificial Intelligence (XAI). This approach incorporates interpretable machine learning models to improve diagnostic transparency and reliability. Our framework conducts an evaluation of various classifiers, including Support Vector Machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and LightGBM. This assessment is based on essential performance metrics, namely accuracy, precision, recall, F1-score, and AUC-ROC. Furthermore, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) have been integrated to enhance the interpretability of the model. The experimental findings indicate that XGBoost surpasses alternative models, attaining the highest classification accuracy of 92% and an AUC-ROC score of 0.93, all while preserving interpretability. This study underscores the significance of incorporating Explainable Artificial Intelligence (XAI) techniques within medical AI applications. It advocates for the adoption of transparent, interpretable, and clinically dependable machine learning methodologies to enhance clinical decision-making and optimize patient outcomes.International Journal of Current Science Research and Review00This study underscores the significance of incorporating Explainable Artificial Intelligence (XAI) techniques within medical AI applications and advocates for the adoption of transparent, interpretable, and clinically dependable machine learning methodologies to enhance clinical decision-making and optimize patient outcomes.International Journal of Current Science Research and Review["Omar Mahmood Yaseen"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/6ec87e7d22bdf0450e3169881fdec7401a83b49b 4d22e1a9a023cfd8b8e86785c7d68c0974948d71This study presents a comprehensive bibliometric analysis of Artificial Intelligence (AI) research for Multimedia in Education from 2020 to 2024. Using the Dimensions AI database, VOSviewer software and Scimago Graphica, we examined 45 publications to identify key trends, influential contributors, and emerging directions in this rapidly evolving field. The analysis reveals a significant publication surge from 2020 to 2021, followed by stabilization in subsequent years. China is the dominant contributor, with 19 publications and 214 citations, highlighting its leadership in AI and educational technology research. Co-authorship network analysis shows a tightly interconnected research community lacking distinct clusters. The most cited papers focus on student engagement and specific AI applications in education, indicating the field's emphasis on practical implementations. Keyword analysis reveals a consistent focus on core concepts such as artificial intelligence, education, technology, and learning, with a recent shift towards more user-centered research. The study also identifies challenges in implementing AI for multimedia in education, including data privacy concerns, ethical considerations, and the need for educator training. These findings provide valuable insights for researchers, educators, and policymakers, highlighting the need to balance technological advancements with pedagogical needs and ethical considerations. Future research directions include investigating the long-term impact of AI-enhanced multimedia education, developing ethical frameworks, conducting cross-cultural studies, and enhancing AI's capability to provide personalized learning experiences through multimedia content.Higher Education Studies00A comprehensive bibliometric analysis of Artificial Intelligence (AI) research for Multimedia in Education from 2020 to 2024 is presented, revealing a significant publication surge from 2020 to 2021, followed by stabilization in subsequent years.Higher Education Studies["Potsirin Limpinan", "A. Yindeemak", "Rungfa Pasmala", "Manop Nammanee", "Thada Jantakoon"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/4d22e1a9a023cfd8b8e86785c7d68c0974948d71 2ad7e5a6a7fe3f337fe3e51945078e1a4a6ecbf0Generative Artificial Intelligence (GAI) emerges as a promising tool to improve strategic decision-making in a business environment characterized by increasing complexity. There are external and internal factors that are part of the success of entrepreneurial initiatives. A relevant factor is the technological environment as an external factor and innovation as an internal factor that make decision making effective. The study reviews the existing literature on implementing GAI in business decision-making. It assesses its short-, medium- and long-term effects, considering the interaction between GAI and human judgment. Challenges related to uncertainty, complexity, and ambiguity are examined, and the relevant literature is reviewed to understand these aspects comprehensively. The review shows that, despite the advanced capabilities of GAI to analyze data and generate patterns, human judgment remains crucial in situations of high uncertainty. The results suggest that combining GAI with human expertise can improve the accuracy and efficiency of strategic decision-making by integrating the strengths of both parties. The implementation of GAI can offer significant improvements in the efficiency and accuracy of business decisions. However, human judgment and experience remain essential, especially in uncertain contexts. The key to maximizing the benefits of GAI lies in finding the right balance between artificial intelligence and human capital.Administrative Sciences680The results suggest that combining GAI with human expertise can improve the accuracy and efficiency of strategic decision-making by integrating the strengths of both parties.Administrative Sciences["Oscar L\u00f3pez-Sol\u00eds", "Alberto Luzuriaga-Jaramillo", "Mayra Bedoya-Jara", "Joselito Naranjo-Santamar\u00eda", "Diego Bonilla-Jurado", "Patricia Acosta-Vargas"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/2ad7e5a6a7fe3f337fe3e51945078e1a4a6ecbf0 0efc82ea5f4cf73f02179237abb9d466b9d06963EClinicalMedicine981A qualitative systematic review on published literature using AI on retina images to detect systemic diabetes complications highlights the potential for the use of AI algorithms applied to retina images, particularly CFP, to screen, predict, or diagnose the various microvascular and macrovascular complications of diabetes.eClinicalMedicine["Qianhui Yang", "Y. Bee", "Ciwei Cynthia Lim", "C. Sabanayagam", "Carol Yim-Lui Cheung", "T. Wong", "Daniel S. W. Ting", "Lee-Ling Lim", "Huating Li", "Mingguang He", "Aaron Y. Lee", "A. J. Shaw", "Y. Keong", "Gavin Siew Wei Tan"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/0efc82ea5f4cf73f02179237abb9d466b9d06963 21c8726d2e54a0c14a80917558f0c8ee1605232cJournal of Research Innovation and Implications in Education00Journal of Research Innovation and Implications in Education[]2025-02-18T00:00:00https://www.semanticscholar.org/paper/21c8726d2e54a0c14a80917558f0c8ee1605232c f13de5b31fc37e21ce4d50bc2206dd8cde696cbdAnästhesie Nachrichten00Anästhesie Nachrichten[]2025-02-18T00:00:00https://www.semanticscholar.org/paper/f13de5b31fc37e21ce4d50bc2206dd8cde696cbd b0a0ecc89767f2557c491433907191b4b08f7768Academic Psychiatry40Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry["Shigeki Matsubara"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/b0a0ecc89767f2557c491433907191b4b08f7768 474f281507dedcdc5e1382f61477faed68ff482fScientific Reports290A family of algorithms based on explainable deep neural networks (DNN) that detect polyps within a sequence of images, feed only those images containing polyps into two parallel independent networks to characterize, and estimate the size of important findings are developed.Scientific Reports["E. Nadimi", "Jan-Matthias Braun", "B. Schelde-Olesen", "Smith Khare", "Vinay C Gogineni", "V. Blanes-Vidal", "G. Baatrup"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/474f281507dedcdc5e1382f61477faed68ff482f dda26ec49321c9a253b313496a86c57314cd4784Child Neuropsychology: A Journal of Normal and Abnormal Development in Childhood and Adolescence800Child Neuropsychology["Amna Zaheer", "Ahmad Akhtar"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/dda26ec49321c9a253b313496a86c57314cd4784 f9041be38822d47f45aed136e1972084695bfd95Cureus940Cureus["Omofolarin Debellotte", "Richard L Dookie", "Fnu Rinkoo", "Akankshya Kar", "Juan Felipe Salazar Gonz\u00e1lez", "Pranav Saraf", "Muhammed Aflahe Iqbal", "Lilit Ghazaryan", "Annie-Cheilla Mukunde", "Areeba Khalid", "Toluwalase Olumuyiwa"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/f9041be38822d47f45aed136e1972084695bfd95 fb2f025cdb803ca6feb3cb95e9e241b591247f8eForum of Clinical Oncology60Forum of Clinical Oncology["Evangelia Bogatsa", "E. Karamitrousis", "M. Liontos", "N. Tsoukalas"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/fb2f025cdb803ca6feb3cb95e9e241b591247f8e 7e0ef4af62da76b80ca1eec963487f4394cf560fThis community service activity aimed to enhance the knowledge and skills of kindergarten teachers in utilizing AI technology to design innovative teaching media. The method of implementing this community service used training and mentoring with participants, namely ABA Wasur II Kindergarten teachers in Merauke Regency. The stages of the activity included preparation, implementation, application, and monitoring and evaluation. The evaluation was conducted by administering a pre-test and post-test. The results of this community service show that there was an increase in teachers' understanding of AI tools that could be utilized to enhance students’ language skills. Additionally, the teachers were able to effectively apply the AI tools introduced to create teaching media based on the sequence picture method.Jurnal Pengabdian UNDIKMA00There was an increase in teachers' understanding of AI tools that could be utilized to enhance students’ language skills and the teachers were able to effectively apply the AI tools introduced to create teaching media based on the sequence picture method.Jurnal Pengabdian UNDIKMA["N. Istiqomah", "Evelin Giovani"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/7e0ef4af62da76b80ca1eec963487f4394cf560f 477ddff673fac9425e6ef0e95675448b1ae94a5eDie Ophthalmologie400Die Ophthalmologie["N. E. Bechrakis", "Bernd Bertram", "Stefan B\u00fcltmann", "Hanna Faber", "Philip Gass", "Gerd Geerling", "Thilo Gronow", "Rudolf Guthoff", "Peter Heinz", "Hans Hoerauf", "Stefan Lang", "K. Lemmen", "Daniel Pleger", "Christian Richter", "Alexander K. Schuster", "Sebastian Siebelmann", "Frank Tost", "Maximilian Wintergerst"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/477ddff673fac9425e6ef0e95675448b1ae94a5e 0d1cc07c83059fc80ffc8918e87d32089a911cc9Technical Symposium on Computer Science Education70{"pages": "1647-1648"}["Jessica Vandenberg", "Ryan Torbey", "Cecilia Xuning Zhang", "Bradford W. Mott", "Keisha Bailey", "Joseph P. Wilson"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/0d1cc07c83059fc80ffc8918e87d32089a911cc9 75a5ab0d865669e31fc2816185199d8c145f46c6At the end of 2019, an outbreak of a novel coronavirus was reported in China, leading to the COVID-19 pandemic. In Spain, the first cases were detected in late January 2020, and by mid-March, infections had surpassed 5,000. On March the Spanish government started a nationwide lockdown to contain the spread of the virus. While isolation measures were necessary, they posed significant psychological and socioeconomic challenges, particularly for vulnerable populations. Understanding the psychological impact of lockdown and the factors influencing mental health is crucial for informing future public health policies. This study analyzes the influence of personal, socioeconomic, general health and living condition factors on psychological states during lockdown using AI techniques. A dataset collected through an online questionnaire was processed using two workflows, each structured into three stages. First, individuals were categorized based on psychological assessments, either directly or in combination with unsupervised learning techniques. Second, various Machine Learning classifiers were trained to distinguish between the identified groups. Finally, feature importance analysis was conducted to identify the most influential variables related to different psychological conditions. The evaluated models demonstrated strong performance, with accuracy exceeding 80% and often surpassing 90%, particularly for Random Forest, Decision Trees, and Support Vector Machines. Sensitivity and specificity analyses revealed that models performed well across different psychological conditions, with the health impacts subset showing the highest reliability. For diagnosing vulnerability, models achieved over 90% accuracy, except for less vulnerable individuals using living environment and economic status features, where performance was slightly lower.640This study analyzes the influence of personal, socioeconomic, general health and living condition factors on psychological states during lockdown using AI techniques and revealed that models performed well across different psychological conditions, with the health impacts subset showing the highest reliability.["Blanca Mellor-Marsa", "Alfredo Guitian", "Andrew Coney", "Berta Padilla", "Alberto Nogales"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/75a5ab0d865669e31fc2816185199d8c145f46c6 1ad52b306032e34026d959674d4900b05aa80a47Integrity is the heart and soul of any significant academic discourse. As attorney Ronald B. Standler has observed: “Reputations in academia are made on the basis of creating new knowledge: discoveries of new facts, new ways of looking at previously known facts, original analysis of old ideas, etc. A plagiarist receives credit for expression or analysis that was improperly taken from someone else. In this view, the plagiarist commits fraud, by claiming the work of other people as the plagiarist's own work.”Student Affairs Today00In this view, the plagiarist commits fraud, by claiming the work of other people as the plagiarist's own work.Student Affairs Today["Jeffrey L. Buller", "Sandra C. McClain"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/1ad52b306032e34026d959674d4900b05aa80a47 ba70ab7ad73e9a103d1612376d9c9e357beea336The integration of Large Language Models (LLMs) within renewable energy systems presents an innovative approach to optimizing energy efficiency, enhancing sustainability, and improving operational performance (Bai, J., Wang, Y., Chen, Y., et. al. 2021). Despite their potential, a clear methodology for evaluating the success of LLM implementations remains underdeveloped. This paper introduces a structured framework for evaluating Key Performance Indicators (KPIs) tailored to LLM applications in the renewable energy sector. The framework systematically addresses the assessment of LLM-driven improvements in energy forecasting accuracy, grid management, predictive maintenance, and resource optimization (Dasgupta, I., Lampinen, A. K., et. al. 2022). Critical KPIs include reductions in energy consumption during LLM training and inference, the accuracy of energy demand predictions, the optimization of renewable energy resource utilization, and the minimization of carbon footprints (Piantadosi, S. 2023). By establishing this framework, the paper provides a robust tool for measuring the impact of LLM technologies on both operational efficiency and sustainability outcomes. The study’s findings offer valuable insights for policymakers, researchers, and industry stakeholders to guide the responsible and effective integration of AI-driven solutions in renewable energy infrastructures.Journal of Information Systems Engineering & Management400A structured framework for evaluating Key Performance Indicators (KPIs) tailored to LLM applications in the renewable energy sector is introduced and offers valuable insights for policymakers, researchers, and industry stakeholders to guide the responsible and effective integration of AI-driven solutions in renewable energy infrastructures.Journal of Information Systems Engineering and Management["Dr. Athar Javed Ali", "Prof. Rohan Kumar", "Singh", "Prof. Rajkamal", "Dr.Priya Sandip Karemore", "Prof. Megha Paliwal", "Dr. Payal Pashine"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/ba70ab7ad73e9a103d1612376d9c9e357beea336 5462917d381a36626f1e31ac0c25a453edbef49fTechnical Symposium on Computer Science Education00{"pages": "1517-1518"}["En-Shiun Annie Lee", "Arvind Gupta", "Amane Takeuchi", "Stacey A. Koornneef"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/5462917d381a36626f1e31ac0c25a453edbef49f 944caf98c2eaafd80710b2146dddd57479a5b8a6Science Editing00Science Editing["Cheol-Heui Yun"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/944caf98c2eaafd80710b2146dddd57479a5b8a6 23b4e6cc5361a38edb773640a0dae12385c0cf3dInternational Journal of Medical Sciences00INTERNATIONAL JOURNAL OF MEDICAL SCIENCES["Gunmeh Bhandari"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/23b4e6cc5361a38edb773640a0dae12385c0cf3d 0eb1c21c9d9758a6b21d9a33b7be6d0c39c19d98Smart Grids and Sustainable Energy840Smart Grids and Sustainable Energy["Neena Sinha", "Varnika Jain", "Himanshu", "Ritu Sehrawat", "Sanjay Dhingra"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/0eb1c21c9d9758a6b21d9a33b7be6d0c39c19d98 1a193bef30d4f85fde346f1c051613eae612b353Discover Sustainability461The findings reveal that AI influencers can indeed establish meaningful emotional bonds and credibility, sometimes outperforming human influencers in generating community cohesion and network expansion, and emphasize the need to adapt influencer strategies to align with varying cultural orientations and rapidly evolving digital ecosystems.Discover Sustainability["Fandi Omeish", "Ahmad Shaheen", "Sager Alharthi", "Aisyah Alfaiza"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/1a193bef30d4f85fde346f1c051613eae612b353 27d11cfea69162ce4257a54fb71b3d78830ad6a0Research in the field of automated vehicles, or more generally cognitive cyber-physical systems that operate in the real world, is leading to increasingly complex systems. Among other things, artificial intelligence enables an ever-increasing degree of autonomy. In this context, the V-model, which has served for decades as a process reference model of the system development lifecycle is reaching its limits. To the contrary, innovative processes and frameworks have been developed that take into account the characteristics of emerging autonomous systems. To bridge the gap and merge the different methodologies, we present an extension of the V-model for iterative data-based development processes that harmonizes and formalizes the existing methods towards a generic framework. The iterative approach allows for seamless integration of continuous system refinement. While the data-based approach constitutes the consideration of data-based development processes and formalizes the use of synthetic and real world data. In this way, formalizing the process of development, verification, validation, and continuous integration contributes to ensuring the safety of emerging complex systems that incorporate AI.IEEE Transactions on Intelligent Vehicles01This work presents an extension of the V-model for iterative data-based development processes that harmonizes and formalizes the existing methods towards a generic framework and allows for seamless integration of continuous system refinement.IEEE Transactions on Intelligent Vehicles["Lars Ullrich", "Michael Buchholz", "Klaus C. J. Dietmayer", "Knut Graichen"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/27d11cfea69162ce4257a54fb71b3d78830ad6a0 391f51e682c1c2bbe5cce15641d4a833876e579cOne ongoing debate in linguistics is whether Artificial Intelligence (AI) can effectively mimic human performance in language-related tasks. While much research has focused on various linguistic abilities of AI, little attention has been given to how it defines neologisms formed through different word formation processes. This study addresses this gap by examining the degree of agreement between human and AI-generated responses in defining three types of Greek neologisms: blends, compounds, and derivatives. The study employed an online experiment in which human participants selected the most appropriate definitions for neologisms, while ChatGPT received identical prompts. The results revealed fair agreement between human and AI responses for blends and derivatives but no agreement for compounds. However, when considering the majority response among humans, agreement with AI was high for blends and derivatives. These findings highlight the complexity of human language and the challenges AI still faces in capturing its nuances. In particular, they suggest a need for integrating more advanced semantic networks and contextual learning mechanisms into AI models to improve their interpretation of complex word formations, especially compounds.301["Georgios P. Georgiou"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/391f51e682c1c2bbe5cce15641d4a833876e579c 047c1e8d9a3f8fa725a85ef1304ed9dc86d1fe9fEffective feedback is essential for fostering students' success in scientific inquiry. With advancements in artificial intelligence, large language models (LLMs) offer new possibilities for delivering instant and adaptive feedback. However, this feedback often lacks the pedagogical validation provided by real-world practitioners. To address this limitation, our study evaluates and compares the feedback quality of LLM agents with that of human teachers and science education experts on student-written experimentation protocols. Four blinded raters, all professionals in scientific inquiry and science education, evaluated the feedback texts generated by 1) the LLM agent, 2) the teachers and 3) the science education experts using a five-point Likert scale based on six criteria of effective feedback: Feed Up, Feed Back, Feed Forward, Constructive Tone, Linguistic Clarity, and Technical Terminology. Our results indicate that LLM-generated feedback shows no significant difference to that of teachers and experts in overall quality. However, the LLM agent's performance lags in the Feed Back dimension, which involves identifying and explaining errors within the student's work context. Qualitative analysis highlighted the LLM agent's limitations in contextual understanding and in the clear communication of specific errors. Our findings suggest that combining LLM-generated feedback with human expertise can enhance educational practices by leveraging the efficiency of LLMs and the nuanced understanding of educators.660It is suggested that combining LLM-generated feedback with human expertise can enhance educational practices by leveraging the efficiency of LLMs and the nuanced understanding of educators.["Kathrin Se\u00dfler", "Arne Bewersdorff", "Claudia Nerdel", "Enkelejda Kasneci"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/047c1e8d9a3f8fa725a85ef1304ed9dc86d1fe9f 9101297f0156d637e69bc78770cc8d49eb052c1cThe expansion of the use of artificial intelligence (AI) across different stages of production and distribution in journalism is opening a debate on its applications within newsrooms and in business models. This research studies how different media outlets, media groups and institutions are beginning to create internal regulations for the use of AI, both from a technical and an ethical perspective. To do so, an international sample (N = 45) of editorial stylebooks and internal self-regulatory guidelines published between 2023 and early 2025 have been compiled—all links are openly available here—and put through a process of content analysis. The results indicate that the self-regulatory guidelines emerge from an individual initiative of the media themselves, with a focus on limiting the use of generative AI, particularly in text creation. The guidelines emphasize ethical commitments such as transparency, content verification, and respect for data and copyright while underlining the importance of human oversight. Key objectives include avoiding bias, ensuring information quality, and strengthening audience trust. Despite progress, regulation remains in its early stages and requires continuous adaptation to keep pace with technological advancements.Journalism and Media340The results indicate that the self-regulatory guidelines emerge from an individual initiative of the media themselves, with a focus on limiting the use of generative AI, particularly in text creation.Journalism and Media["Pilar S\u00e1nchez-Garc\u00eda", "Alba Diez-Gracia", "Ignacio Repilado Mayorga", "P. Jer\u00f3nimo"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/9101297f0156d637e69bc78770cc8d49eb052c1c 038ec97f4d9952aee417d80b9291521cbdf8e307Recent advancements in machine learning and artificial intelligence have led to the widespread deployment of statistical models across critical decision-making domains, raising significant concerns about algorithmic bias and its societal implications. This comprehensive article examines the multifaceted nature of bias in statistical models, from its origins in data collection and model architecture to its manifestation in real-world applications such as hiring, lending, and criminal justice systems. Through analysis of contemporary case studies and emerging research, it presents a systematic framework for detecting and measuring algorithmic bias, alongside practical strategies for its mitigation. The article introduces novel approaches to fairness-aware machine learning, emphasizing the importance of representative data collection and regular model auditing across demographic groups. This article demonstrates that effective bias mitigation requires a holistic approach combining technical solutions with robust ethical guidelines and regulatory compliance. Furthermore, it explores the legal and organizational responsibilities of developing and deploying fair statistical models, providing actionable insights for practitioners and policymakers. This article contributes to the growing body of literature on algorithmic fairness while offering practical solutions for organizations striving to build more equitable AI systems.International Journal of Scientific Research in Computer Science Engineering and Information Technology00It is demonstrated that effective bias mitigation requires a holistic approach combining technical solutions with robust ethical guidelines and regulatory compliance, and that effective bias mitigation requires a holistic approach combining technical solutions with robust ethical guidelines and regulatory compliance.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ranjeet Sharma"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/038ec97f4d9952aee417d80b9291521cbdf8e307 7c5615510133ba295f922f1ba0ff0e9b683167d0As Artificial Intelligence (AI) technologies become more integrated into clinical settings to optimize care, healthcare professionals (HCPs) will need to become more adept in responsibly using these novel technologies to augment patient care. A qualitative study, consisting of semi-structured interviews was conducted to explore the informational needs of HCPs and gaps in current AI education. Participants, consisting of educators and learners, were recruited from AI programs. The interview data were analyzed using inductive thematic analysis. Three themes were identified, addressing the need for (1) developing a longitudinal AI curriculum to transform the mindset, skillset, and toolset of providers, (2) cultivating an active learning approach to foster knowledge mobilization and optimize the use of AI tools in the provision of care, and (3) fostering a multidisciplinary approach to AI curriculum design is essential to promote collaborative efforts among HCPs in implementing AI tools. This study identified five key recommendations to prepare HCPs with the knowledge and skills necessary for an AI-driven future.Studies in Health Technology and Informatics00Five key recommendations to prepare HCPs with the knowledge and skills necessary for an AI-driven future are identified.Studies in health technology and informatics["Tharshini Jeyakumar", "Sarmini Balakumar", "S. Younus", "Megan Clare", "Rebecca Charow", "Dalia Al-Mouaswas", "A. Dhalla", "Caitlin Gillan", "Jessica Jardine", "Sedef Akinli Kocak", "Jane Mattson", "Mohammad Salhia", "Walter Tavares", "Melody Zhang", "D. Wiljer"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/7c5615510133ba295f922f1ba0ff0e9b683167d0 1a5868136ec006cf94087258442340c424c9bbadThis article provides a comprehensive examination of Explainable Artificial Intelligence (XAI) techniques and their applications in large-scale predictive systems. The article explores both model-agnostic and model-specific approaches, examining their effectiveness in various domains including healthcare, finance, and transportation. The article explores fundamental XAI concepts, historical development, and current taxonomies while addressing crucial regulatory and ethical considerations. The article examines feature importance methods, partial dependence plots, SHAP values, LIME, and counterfactual explanations as key model-agnostic techniques. It further delves into model-specific approaches including decision tree interpretability, neural network visualization, attention mechanisms, rule extraction methods, and architecture-specific approaches. The article extensively covers domain applications, highlighting how XAI enhances transparency and trust in critical sectors. The article also addresses significant challenges including scalability issues, interpretation complexity, computational overhead, accuracy-explainability trade-offs, and human factors in XAI implementation. This article contributes to the understanding of XAI's current state and future directions in large-scale predictive systems.International Journal of Scientific Research in Computer Science Engineering and Information Technology30The article extensively covers domain applications, highlighting how XAI enhances transparency and trust in critical sectors and addresses significant challenges including scalability issues, interpretation complexity, computational overhead, accuracy-explainability trade-offs, and human factors in XAI implementation.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Priyadharshini Krishnamurthy"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/1a5868136ec006cf94087258442340c424c9bbad d9edf47a254eea8506050fd42d73a00a1606c864This article explores the transformative impact of Artificial Intelligence (AI) on the property and casualty (P&C) insurance sector, focusing on key technological advancements reshaping core insurance operations. This article examines how AI-driven solutions are revolutionizing traditional underwriting processes, enhancing claims management efficiency, and strengthening fraud detection capabilities. Through an analysis of cloud-based platforms like Guidewire, it investigates the integration challenges and opportunities in implementing AI solutions within existing insurance infrastructure. It highlights how machine learning models, computer vision, and predictive analytics are enabling insurers to achieve more accurate risk assessment, automated claims processing, and sophisticated fraud prevention. It also suggests that the successful adoption of AI technologies in P&C insurance not only improves operational efficiency but also enables insurers to deliver more personalized products and enhanced customer experiences. The article concludes by examining emerging trends and providing strategic recommendations for insurers navigating this technological transformation, emphasizing the critical balance between innovation and regulatory compliance in the evolving insurance landscape.International Journal of Scientific Research in Computer Science Engineering and Information Technology20Examining how AI-driven solutions are revolutionizing traditional underwriting processes, enhancing claims management efficiency, and strengthening fraud detection capabilities, and investigating the integration challenges and opportunities in implementing AI solutions within existing insurance infrastructure concludes by examining emerging trends.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Naveen Kondeti"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/d9edf47a254eea8506050fd42d73a00a1606c864 8df0cf28b634cf4e257a7ab199f29c6fae05c775The rapid advancement of generative artificial intelligence (AI) has transformed the information environment, creating both opportunities and challenges. This paper explores how generative AI influences economic rent-seeking behavior and its broader impact on social welfare. We develop a dynamic economic model involving multiple agents who may engage in rent-seeking activities and a regulator aiming to mitigate social welfare losses. Our analysis reveals a dual effect of generative AI: while it reduces traditional information rents by increasing transparency, it also introduces new forms of rent-seeking, such as information manipulation and algorithmic interference. These behaviors can lead to decreased social welfare by exacerbating information asymmetries and misallocating resources. To address these challenges, we propose policy interventions, including taxation and regulatory measures. This study provides a new perspective on the economic implications of generative AI, offering valuable insights for policymakers and laying a foundation for future research on regulating AI-driven economic behaviors.180A dynamic economic model involving multiple agents who may engage in rent-seeking activities and a regulator aiming to mitigate social welfare losses is developed, revealing a dual effect of generative AI: while it reduces traditional information rents by increasing transparency, it also introduces new forms of rent-seeking, such as information manipulation and algorithmic interference.["Yukun Zhang", "Tianyang Zhang"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/8df0cf28b634cf4e257a7ab199f29c6fae05c775 c6be0639885a570974857a6fea0dd695aec7a31dIntroduction: This research paper explores the profound impact of Artificial Intelligence (AI) on the print media landscape, highlighting its transformative effects on narrative construction and audience engagement. As AI technologies advance, Indian media organizations are actively integrating these innovations to enhance content creation and audience interaction. Objectives: Citation analysis employs a full counting method, while timeline and burst detection analyses uncover significant topic trends and recent citations. Methods: The paper conducts an extensive bibliometric analysis of AI utilization in media research, drawing from publications in the Scopus database over the years. The analysis encompasses yearly publications, types of publications, and trends across various domains, including content creation and audience engagement. Results: The research highlights bibliometric findings related to authors, organizations, publication types, and documents with the strongest collaborative linkages within the context of AI in Indian media. Conclusions: The paper provides valuable insights into how AI is reshaping the print media sector in India, emphasizing the key trends and collaborative efforts driving this transformation.    Journal of Information Systems Engineering & Management400An extensive bibliometric analysis of AI utilization in media research, drawing from publications in the Scopus database over the years, provides valuable insights into how AI is reshaping the print media sector in India.Journal of Information Systems Engineering and Management["Prabhat Dixit", "Aprna Tripathi", "U. Jain", "S. Ranjan", "Sunil Kumar", "Sudeep Varshney"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/c6be0639885a570974857a6fea0dd695aec7a31d 2c5d11701309699b5b6465337c292873a9a7405eThe integration of Artificial Intelligence (AI) in urban governance presents significant opportunities to transform decision-making and enhance accountability. The paper highlights AI's potential to reposition human discretion and reshape specific types of accountability, elevating the decision-making capabilities of both frontline bureaucrats and managers while ensuring ethical standards and public trust are maintained. While AI can enhance bureaucratic flexibility and efficiency, its integration will also necessitate new governance frameworks to mitigate risks associated with uneven capacity distribution, ethical concerns, and public trust. Following the literature review and theoretical discussion, this study introduces a set of guiding principles for AI-assisted urban governance, emphasizing equitable AI deployment, adaptive administrative structures, robust data governance, transparent human-AI collaboration, and citizen engagement in oversight mechanisms. By critically evaluating AI's dual role in expanding discretion and reinforcing accountability, this paper advances a framework for responsible AI adoption, ensuring that urban governance remains adaptive, transparent, and aligned with public values.860This study introduces a set of guiding principles for AI-assisted urban governance, emphasizing equitable AI deployment, adaptive administrative structures, robust data governance, transparent human-AI collaboration, and citizen engagement in oversight mechanisms.["Stephen Goldsmith", "Juncheng Yang"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/2c5d11701309699b5b6465337c292873a9a7405e a5ef0741e0ede6e023ef69d8a3a44a732fb7b228The insurance industry is undergoing a fundamental transformation through the integration of artificial intelligence technologies, particularly in claims processing. This comprehensive article examines how AI-driven solutions are revolutionizing traditional claims-handling workflows, from initial submission to final settlement. The implementation of machine learning algorithms, computer vision, and natural language processing has enabled insurers to automate routine tasks while enhancing accuracy and efficiency. These technologies facilitate real-time fraud detection, automated document verification, and intelligent data extraction from various sources. The article explores the technical framework of AI-driven claims processing, system integration challenges, and the crucial aspects of maintaining fairness and transparency in automated decision-making. Additionally, the article analyzes the impact on operational efficiency and customer experience, highlighting how AI implementation has transformed service delivery and customer interaction models. It also investigates emerging trends and future developments, including edge computing and blockchain integration, while addressing the challenges of balancing automation with human oversight in the evolving insurance landscape.International Journal of Scientific Research in Computer Science Engineering and Information Technology10This comprehensive article examines how AI-driven solutions are revolutionizing traditional claims-handling workflows, from initial submission to final settlement, and examines the impact on operational efficiency and customer experience, highlighting how AI implementation has transformed service delivery and customer interaction models.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Balakrishna Sudabathula"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/a5ef0741e0ede6e023ef69d8a3a44a732fb7b228 8f42f5cdc8e0c6e37f73b40ac49caa164628f385The rapid advancement of artificial intelligence (AI) in the U.S. consumer tech industry has introduced significant privacy challenges that demand careful consideration and regulatory oversight. This paper proposes a conceptual privacy framework tailored to AI applications, aiming to address the unique legal and regulatory challenges posed by laws such as the California Consumer Privacy Act (CCPA), the Gramm-Leach-Bliley Act (GLBA), and the Health Insurance Portability and Accountability Act (HIPAA). The framework focuses on core principles such as transparency, accountability, and ethical governance, while integrating AI-driven solutions for compliance, including automated audits, secure data handling protocols, and user consent mechanisms. The proposed model strives to harmonize legal requirements with the innovation potential of AI technologies, ensuring that privacy is safeguarded without stifling technological progress. By providing a clear approach to AI compliance, the framework aims to enhance consumer trust, mitigate privacy risks for industry players, and offer regulators a more effective means of enforcement. Recommendations for future regulatory alignment, collaborative stakeholder engagement, and advancements in AI technology are also outlined to ensure the long-term success of the framework in this rapidly evolving landscape.Asian Research Journal of Arts &amp; Social Sciences00A conceptual privacy framework tailored to AI applications is proposed, aiming to address the unique legal and regulatory challenges posed by laws such as the California Consumer Privacy Act, the Gramm-Leach-Bliley Act, and the Health Insurance Portability and Accountability Act.Asian Research Journal of Arts &amp; Social Sciences["Grace Annie Chintoh", "Osinachi Deborah Segun-Falade", "Chinekwu Somtochukwu Odionu", "Amazing Hope Ekeh"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/8f42f5cdc8e0c6e37f73b40ac49caa164628f385 16f554bd347bf6de4ee60bbca428d98472392e58Gender and age are critical factors in understanding attitudes toward artificial intelligence (AI) in education, yet limited research has directly explored their influence on teacher aspirants’ perspectives on AI integration. This study employed random sampling to select 603 respondents from two state-managed institutions. Findings indicate that prospective teachers generally hold neutral attitudes toward AI (M=2.84), reflecting uncertainty about preferring AI over human interaction in routine tasks, consistent with prior research. Male respondents (M=2.91) exhibited significantly more positive attitudes toward AI in education than females, as evidenced by a t value of -2.66 and a p value of 0.008. Additionally, adults (M=2.86) demonstrated significantly higher attitude scores than adolescents (M=2.80), with a t value of -2.05 and a p value of 0.040. These results highlight the role of demographic variables in shaping perceptions of AI in educational contexts, emphasizing the need for targeted interventions to address concerns and optimize AI adoption in teacher training programs.Seminars in Medical Writing and Education370The role of demographic variables in shaping perceptions of AI in educational contexts is highlighted, emphasizing the need for targeted interventions to address concerns and optimize AI adoption in teacher training programs.Seminars in Medical Writing and Education["Keir A. Balasa", "Alexandhrea Hiedie Dumagay", "Ericson O. Alieto", "Rub\u00e9n Gonz\u00e1lez Vallejo"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/16f554bd347bf6de4ee60bbca428d98472392e58 d76eafae52debc9422e52e838a776af91b8076ebIn the contemporary digital landscape, smartphones have evolved into essential devices, seamlessly integrating into everyday life to facilitate communication, information access, and numerous tasks. However, their omnipresence has also paved the way for an era of pervasive surveillance. Both governmental bodies and technology corporations have increasingly exploited these devices to monitor and gather data from users, often without clear consent. The integration of Artificial Intelligence (AI) with mobile technology has further intensified surveillance capabilities, establishing a system that is omnipresent yet largely invisible. Cases, such as Uber being fined by the Dutch Data Protection Authority, Meta admitting to censoring Covid-19 data in the U.S. under pressure from the White House and fine imposed by South Korea social media watchdog on Meta are clear examples of compromises in the protection of users’ personal data and the suppression of free expression. In light of the emerging scenario driven by AI applications, this paper explores the widespread nature of smartphone surveillance, primarily because we are mostly connected to these apps through our smartphones, the critical role AI plays in advancing these practices, and the profound implications for individual privacy, autonomy, and societal norms. Through this examination, the paper sheds light on the growing complexities of digital surveillance and calls for urgent discourse on privacy and ethical considerations in the age of AI.International Journal For Multidisciplinary Research230Light is shed on the growing complexities of digital surveillance and calls for urgent discourse on privacy and ethical considerations in the age of AI are called for.International Journal For Multidisciplinary Research["Md Absarul Hasan"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/d76eafae52debc9422e52e838a776af91b8076eb 0f015acec3427700052be9d719ba190f0b116284This study introduces a novel artificial intelligence (AI) modeling framework that combines machine learning algorithms optimized through metaheuristics with explainable AI to capture complex interactions among pollutant concentrations, meteorological data, and socio-economic indicators. Applied to a COVID-19-related dataset comprising 404 variables, with benzene concentrations as the target—measured using proton transfer reaction–mass spectrometry in Belgrade, Serbia—the framework demonstrated exceptional sensitivity in assessing the impact of complex environmental and societal changes during the pandemic. Explainable AI techniques, such as SHAP and SAGE, were employed to reveal the influence of each predictor, while the clustering of SHAP values identified distinct environmental settings that influenced benzene behavior. Three distinct settings were identified regarding benzene levels during the onset of the state of emergency. The first, involving local petroleum-related activities, biomass burning, chemical manufacturing, and traffic, led to a 15.7% reduction in benzene levels. The second, characterized by non-combustion processes, nocturnal chemistry, and the specific meteorological context, resulted in a 51.9% increase. The third, driven by local industrial processes, contributed to a modest 2.33% reduction. The study underscored the critical role of environmental settings in shaping air pollutant behavior, emphasizing the importance of integrating broader environmental contexts into models to gain a more comprehensive understanding of air pollutants and their dynamics.Atmosphere500A novel artificial intelligence (AI) modeling framework that combines machine learning algorithms optimized through metaheuristics with explainable AI to capture complex interactions among pollutant concentrations, meteorological data, and socio-economic indicators demonstrated exceptional sensitivity in assessing the impact of complex environmental and societal changes during the pandemic.Atmosphere["Nata\u0161a Radi\u0107", "M. Peri\u0161i\u0107", "Gordana Jovanovi\u0107", "Timea Bezdan", "S. Stani\u0161i\u0107", "Nenad Stani\u0107", "A. Stoji\u0107"]2025-02-18T00:00:00https://www.semanticscholar.org/paper/0f015acec3427700052be9d719ba190f0b116284 2eb9aeb2082e82122afd32964b3d5967edbcb4cbJournal of Endocrinological Investigation941The paper emphasizes the potential of AI to revolutionize the management of metabolic disorders through collaborations among clinicians and AI experts, the integration of AI into clinical practice, and the necessity for long-term validation studies.Journal of endocrinological investigation["Aamir Anwar", "Simran Rana", "Priya Pathak"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/2eb9aeb2082e82122afd32964b3d5967edbcb4cb 59831f781111254f7061236402f4f2bce30d6ac7This study explores the evolution of smart fabrics through the integration of artificial intelligence (AI) and nanotechnology. AI enables the fabrics to automatically adapt to environmental conditions and users' needs by adjusting properties such as temperature, breathability, and elasticity. Additionally, these materials monitor biological data in real time, contributing to the optimization of health and physical performance. Nanotechnology, in turn, enhances the durability, resistance, and functionalities of the fabrics, broadening their applications across various fields. In the fashion sector, these advances allow for the creation of clothing that is more comfortable, responsive, sustainable, and aesthetically innovative. In medicine, they enable garments that assist in the continuous monitoring of patients. In sports, they foster the development of smart uniforms that maximize performance, reduce the risk of injuries, and increase endurance. The combination of AI and nanotechnology represents a milestone in textile innovation, transforming fabrics into highly technological, multifunctional, and adaptable tools for modern, dynamic lifestyles.Contribuciones a las ciencias sociales20The combination of AI and nanotechnology represents a milestone in textile innovation, transforming fabrics into highly technological, multifunctional, and adaptable tools for modern, dynamic lifestyles.CONTRIBUCIONES A LAS CIENCIAS SOCIALES["Isabelle Telles Proen\u00e7a Cipriano Dias", "Isabela Defant de Souza Borges", "Eunice Lopez Valente"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/59831f781111254f7061236402f4f2bce30d6ac7 3263d82e17b64ff2d6c7586f74490ba35b0b1446The recognition of sustainability as a top supply chain management issue has led to the exploration of cutting-edge digital technologies such as Big Data Analytics and Artificial Intelligence (BDA-AI), along with the Artificial Intelligence of Things (AIoT), for environmental and operational enhancements. Guided by the research gap regarding the effectiveness of these technologies in green supply chain frameworks, this study aims to explore the direct and indirect impacts of using BDA-AI, AIoT integration, and Green Digital Learning on GSCP. The quantitative survey relied on data compiled from 184 industry professionals and was analyzed using Structural Equation Modeling – Partial Least Squares.4 (SEM-PLS-4). Industrial business functions indicate that BDA-AI positively affects GSCP in two pathways: a direct impact and an indirect pathway to Green Digital Learning, which has a strong moderating effect. AIoT integration was also a strong predictor of GSCP, implying that AIoT integration in the supply chain can improve resource allocation and process efficiencies. These results indicate that Green Digital Learning is vital for augmenting the sustainability effect of digitalization technologies by offering employees the ability to effectively apply data-based insights. The study makes a twofold contribution to academic literature and industry practice by proposing a reference model for energy sustainability while enabling digital transformation and providing managerial insights that, in turn, permit organizations aspiring to achieve a green supply chain through operational excellence.International journal of innovative research and scientific studies420Results indicate that Green Digital Learning is vital for augmenting the sustainability effect of digitalization technologies by offering employees the ability to effectively apply data-based insights.International Journal of Innovative Research and Scientific Studies["Ashraf Ibrahim Abdallah Qahman", "M. Al-Zaqeba", "B. Jarah", "Abdelruhman Al-Kharbsheh", "Nasser Assaf"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/3263d82e17b64ff2d6c7586f74490ba35b0b1446 fcea19cdcbcfb1b889498de9bf64cb811c9d8c5fAt the centenary of the death of Franz Kafka (1883–1924), this paper explores the complexities of Artificial Intelligence (AI) through the lens of Kafka’s literary and professional work, especially those relating to the dynamics of recognition and misrecognition. Through Kafkan eyes, both philosophical and technological hankerings after recognition and its connection with the notion of the ‘true self’ are thrown into sharp relief, whether this ‘truth’ is related to authenticity or to accuracy. This encourages us to challenge some of the core assumptions of our relationship with systems and tools, including (1) the taken-for-granted formula of recognition being good, misrecognition being bad; (2) the suggestion that aligning AI with human values will make it, and therefore us, safer and more secure; and (3) the assumption that the masters are in charge in the master/slave dialectic that is often used to express the relationship between humans and technologies. The paper references three of Kafka’s most famous works, The Trial, The Castle and In the Penal Colony, in ways that are accessible to those new to Kafka. More seasoned Kafka enthusiasts will be able to see and contextualise the paper’s themes and provocations within these works, and extrapolate to his other writings.Humanities00Through Kafkan eyes, both philosophical and technological hankerings after recognition and its connection with the notion of the ‘true self’ are thrown into sharp relief, whether this ‘truth’ is related to authenticity or to accuracy.Humanities["Leah Tomkins"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/fcea19cdcbcfb1b889498de9bf64cb811c9d8c5f 871292b65e783befb3e55103bdf9c1329e19aef5The article considers the relationship between the culture of artificial intelligence (AI) and traditional and innovative values of modern society. It is noted that the division of values into traditional and innovative is conditional, since innovations develop on the basis of traditions, rely on them, and innovative values eventually acquire the status of traditional. Attention is focused on the fact that without the emergence of innovative values, only on the basis of traditional ones, the development of society and its culture is impossible. It is concluded that both traditional (patriotism, citizenship, service to the Fatherland, moral ideals, family values, creative work, mercy and mutual assistance, collectivism, preservation of historical memory, unity of the peoples of Russia, etc.) and innovative (information, knowledge, intellectual capital, virtual reality, education, science, ideas, creativity, initiative) values are based on AI systems. Digital mechanisms, AI, not having a value nature themselves, act as tools for the preservation, development and transmission of traditional values that are important for the social majority. Innovative values reflect new trends in social development; AI tools are also actively involved in their dissemination and transmission. The modern information space, an integral component of which is AI, presents great opportunities for the development of any values if they reflect the mentality of the people and correspond to the trends of social development. AI becomes a link between traditional and innovative values.Общество философия история культура00AI becomes a link between traditional and innovative values and presents great opportunities for the development of any values if they reflect the mentality of the people and correspond to the trends of social development.Общество: философия, история, культура["Evgeniya K. Belikova"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/871292b65e783befb3e55103bdf9c1329e19aef5 c68c3f987a2bcd9774f6700069d29bab12614bca The aim of this article is, on the one hand, to take up and discuss some key categories and concepts in semiotics, in an attempt to analyze the mechanisms underlying current artificial intelligence (AI) models, with a focus on ChatGPT. Although many of these concepts are already being debated, they remain crucial in relation to semiotic and sociosemiotic categories. Concepts such as generativity, perception, textuality, and the effects of meaning, as well as the notion of language itself, require a new semiotic evaluation, including in relation to a cultural history of AI, and the metaphors associated with it. In addition, this article aims to propose more of a general observation and review of some theoretical problems and sociosemiotic issues related to the advent of AIs with some correlated examples, such as in the field of political communication and conflict.Semiotica: Journal of the International Association for Semiotic Studies50Some key categories and concepts in semiotics are taken up and discussed, in an attempt to analyze the mechanisms underlying current artificial intelligence (AI) models, with a focus on ChatGPT.Semiotica["Federico Montanari"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/c68c3f987a2bcd9774f6700069d29bab12614bca 1c228846c234e5bdd7488f557715712a486dc9d4Artificial intelligence (AI) methods were first developed nearly seven decades ago. Only in recent years have they demonstrated their potential to improve clinical care at the bedside. AI systems are now capable of interpreting, predicting, and even generating important medical information. AI medical devices share many similarities with traditional medical devices but also diverge from them in important ways. Despite widespread optimism and enthusiasm surrounding the use of such devices to improve care processes, patient outcomes, and the healthcare experience for patients, caregivers, and clinicians alike, little evidence exists so far for their effectiveness in practice. Even less is known about the safety or equity of AI medical devices. As with any new technology, this exciting time is accompanied by appropriate questions regarding if, how much, when, and who such AI systems really help. Different stakeholders, ranging from patients to clinicians to industry device developers, may have divergent preferences or assessments of risk and benefits, warranting an informed public discussion to guide emerging regulatory efforts. This review summarizes the rapidly evolving recent efforts and evidence related to the regulation and evaluation of AI medical devices and highlights opportunities for future work to ensure their effectiveness, safety, and equity.Annual Review of Biomedical Data Science00This review summarizes the rapidly evolving recent efforts and evidence related to the regulation and evaluation of AI medical devices and highlights opportunities for future work to ensure their effectiveness, safety, and equity.Annual review of biomedical data science["Gary E Weissman"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/1c228846c234e5bdd7488f557715712a486dc9d4 53573d198ad5d520f1ca30a6ee0457f3779678a8BACKGROUND Knowledge on the potential applications of artificial intelligence (AI) as a diagnostic instrument in the domain of pediatric dentistry is imperative, as AI may significantly influence present and future dental practice. OBJECTIVES The present study aimed to evaluate the knowledge, perception and attitude of pediatric dentists and postgraduate students in the pediatric specialty with regard to the employment of AI in pediatric dental practice. MATERIAL AND METHODS An online questionnaire survey was conducted among 375 participants (92 postgraduates, 203 faculty members and 80 private practitioners), who were provided with 19 closeended questions through the Google Forms link sent via email. The questions referred to the knowledge, perception and attitude of the participants, with 17 questions answered using a three-point Likert scale and 2 of them being multiple-choice questions. The responses were analyzed using the χ2, Kruskal-Wallis and Mann-Whitney U tests. RESULTS A total of 62% of the participants were familiar with the term 'artificial intelligence', and the majority confirmed that AI could be used for the identification of plaque (57%) and supernumerary teeth (52%), the detection of early childhood caries (ECC) (68%) and the ectopic eruption of first permanent molars (67%), the assessment of child psychology (82%), and the estimation of chronological age (67%). Most participants felt that AI training should be incorporated into the postgraduate curriculum (82%) and were willing to introduce AI to clinical practice (87%). The barriers related to the use of AI were high costs (83%), the lack of training after graduation (78%), the lack of technical knowledge (77%), the fear of misdiagnosis (73%), and the lack of awareness (71%). CONCLUSIONS The present study concluded that although most pedodontists and postgraduate students had knowledge on AI, there were many obstacles connected with the use of AI in the field of pediatric dentistry. Therefore, the basic training of AI should be included in the curriculum of postgraduate studies.Dental and Medical Problems00Although most pedodontists and postgraduate students had knowledge on AI, there were many obstacles connected with the use of AI in the field of pediatric dentistry and the basic training of AI should be included in the curriculum of postgraduate studies.Dental and medical problems["Priyanka Razdan", "Anirban Das", "Syeda Habiba", "Sulekha Doley", "Durgesh A Tiwari", "Prachi Hazari"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/53573d198ad5d520f1ca30a6ee0457f3779678a8 d86693bf78e10d21bafa6f93ad6e88143b018544 This paper explores the relation between images and databases in a twofold way. The first part examines image databases as sources for image computational analysis, while the second part studies image databases as sources for image generation (notably through the generative artificial intelligence model Midjourney). Image analysis and image generation will be analyzed through the concept of enunciative praxis. Traditionally, enunciative praxis concerns cultural transformations over the long term (the relation between sedimentation and innovation); in our case it will be used notably to study the generation of new images through old, traditional ones.Semiotica: Journal of the International Association for Semiotic Studies210This paper examines image databases as sources for image computational analysis, while the second part studies image databases as sources for image generation (notably through the generative artificial intelligence model Midjourney) through the concept of enunciative praxis.Semiotica["Mary Dondero"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/d86693bf78e10d21bafa6f93ad6e88143b018544 af49b43379a13f9a2326513ac421b490f3cc7db5Artificial intelligence (AI) technology prevalence has led to our time being referred to as ‘the age of AI.’ As a contemporary societal issue, AI technology draws the attention of caricaturists who use their art to visually communicate various messages about it. This study explores how AI technology is visually represented in a collection of editorial cartoons published on the international online platform, Cartoon Movement with an especial focus on its effects related to literature, books, theory of mind, and arts. The research aims to identify the visual communicative functions employed by caricaturists to depict AI- themes. To achieve this, a multimodal discourse analysis approach is used. Eight caricatures, published between 2018 and 2024, were specifically selected for semiotic analysis. Using Kress and Van Leeuwen’s (2006) framework of visual social semiotics, the study examines the representational, interactive, and compositional meanings in the selected cartoons. The study is significant for its insights into how AI is visually depicted in relation to literature, arts, books ,theory of minds and its potential societal implications. The analysis shows a predominantly skeptical or cautionary stance toward AI in the realm of human cognition and creativity. The results emphasize the need for ongoing ethical as well as philosophical reflection as AI continues to integrate into various aspects of human life.Forum for Linguistic Studies320The analysis shows a predominantly skeptical or cautionary stance toward AI in the realm of human cognition and creativity in the realm of human cognition and creativity in a collection of editorial cartoons published on the international online platform, Cartoon Movement.Forum for Linguistic Studies["S. Helal"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/af49b43379a13f9a2326513ac421b490f3cc7db5 deda1a8325b6e5b38f40312314cc2b1a8d20b464Penelitian ini bertujuan untuk mengeksplorasi pengalaman guru dalam memanfaatkan Artificial Intelligence (AI) untuk pengembangan media pembelajaran Ilmu Pengetahuan Alam dan Sosial (IPAS) di SD Negeri 2 Sokosari. Metode penelitian yang digunakan adalah kualitatif dengan pendekatan studi kasus. Data diperoleh melalui wawancara mendalam, observasi, dan dokumentasi. Hasil penelitian menunjukkan bahwa pemanfaatan AI mampu meningkatkan kreativitas guru dalam merancang media pembelajaran interaktif, efektif, dan menarik. Guru menggunakan platform berbasis AI seperti Canva dan Powtoon untuk menciptakan video animasi, infografis, dan simulasi pembelajaran. Kendala yang dihadapi meliputi keterbatasan keterampilan teknis, akses terhadap perangkat teknologi, dan pengelolaan waktu. Solusi yang diusulkan adalah pelatihan intensif penggunaan AI, peningkatan infrastruktur teknologi, dan pengelolaan waktu yang lebih efektif. Pemanfaatan AI diharapkan dapat memberikan kontribusi positif terhadap kualitas pembelajaran IPAS dan mendorong inovasi dalam proses pengajaran di sekolah dasar. Hasil penelitian ini memberikan rekomendasi bagi sekolah dan pengambil kebijakan untuk menyediakan dukungan teknis dan pelatihan berkelanjutan bagi guru agar pemanfaatan AI dapat optimal. Jurnal Pendidikan Sultan Agung00Jurnal Pendidikan Sultan Agung["Winda Restalia"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/deda1a8325b6e5b38f40312314cc2b1a8d20b464 c61b2f2dcfb4de792844605b313b16ae4d17332eThis research aims to classify skin cancer images using an artificial intelligence method called Convolutional Neural Networks (CNN). The study focuses on classifying skin cancer into 7 categories, using data from the International Skin Imaging Collaboration (ISIC). We employed the CNN algorithm to train the model, which involved learning features, classifying images, and optimizing the model. To evaluate the model's performance, we experimented with different training data proportions (70%, 80%, and 90%), dropout rates (0.5, 0.6, 0.7, and 0.8), and batch sizes (8, 16, 32, 64). The best results were achieved with 80% of the data for training, a dropout rate of 0.4, and a batch size of 16, resulting in an accuracy of 83.22%.   ABSTRAK Penelitian ini bertujuan untuk mengimplementasikan metode kecerdasan buatan melalui algoritma Convolution Neural Network (C-NN) untuk mengklasifikasikan citra kanker kulit. Objek pada penelitian ini adalah klasifikasi kanker kulit dengan berdasarkan 7 kategori kanker kulit, sedangkan Data yang digunakan oleh peneliti adalah data  yang bersumber dari The International Skin Imaging Collaboration (ISIC). Metode yang digunakan peneliti adalah Algoritma Convolutional Neural Networks (CNN). Pada data training dilakukan pembelajaran fitur, klasifikasi, dan optimum model, dimana proses ini merupakan implementasi algoritma yang digunakan. Skenario pengujian dengan indikator skenario pengujian yaitu pembagian data training 70%, 80%, dan 90%, inisialisasi Dropout layer bernilai 0.5, 0.6, dan 0.7, dan 0,8 dan Batchsize bernilai 8, 16, 32, 64. Kesimpulan dari Penelitian ini adalah mendapatkan model terbaik dengan nilai akurasi 83.22% dari komposisi  data Taining 80%, Dropout 0.4 dan Batchsize 16.Jurnal Informatika dan Kesehatan00Jurnal Informatika dan Kesehatan["Sari Handayani Pusadan", "Suriyanti", "Andriar Makahrun", "Mohammad Yazdi", "Zakiani Sakka"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/c61b2f2dcfb4de792844605b313b16ae4d17332e 69d9cd3ecf7e1b9ea2300e3f045b25db2a511e73BACKGROUND Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in cardiac intensive care units. METHODS This historical cohort study included consecutive adults admitted to Mayo Clinic cardiac intensive care unit from 2007 to 2018 with an admission AIECG. The AIECG assigned the LVDD grade (0-3). Medial mitral E/e' ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. In-hospital and 1-year mortality was evaluated, before and after multivariable adjustment. RESULTS We included 11 868 patients (median age 69.5 years, 37.7% female); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grade was 0 (normal), 33%; 1, 7%; 2, 39%; and 3, 21%. In-hospital and 1-year mortality increased in each higher AIECG LVDD grade. After adjustment, each higher AIECG LVDD grade was associated with higher in-hospital (adjusted odds ratio [OR], 1.22 [95% CI, 1.13-1.32]) and 1-year mortality (adjusted hazard ratio [HR], 1.23 [95% CI, 1.19-1.29]); this persisted after adjustment for TTE measurements. Patients with grade 2 or 3 LVDD by AIECG and medial mitral E/e' ratio >15 by TTE had the highest in-hospital (adjusted OR, 2.54 [95% CI, 1.69-3.88]) and 1-year (adjusted HR, 2.03 [95% CI, 1.65-2.48]) mortality, whereas patients meeting either of these criteria had similar, elevated mortality. CONCLUSIONS The AIECG LVDD grade was strongly associated with in-hospital and 1-year mortality in patients in cardiac intensive care units, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures were at highest risk.Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease380The AIECG LVDD grade was strongly associated with in-hospital and 1-year mortality in patients in cardiac intensive care units, even after adjusting for clinical variables and TTE measurements.Journal of the American Heart Association["Jacob C Jentzer", "Eunjung Lee", "Z. Attia", "D. Hillerson", "G. Kane", "F. Lopez-Jimenez", "Peter A Noseworthy", "P. Friedman", "Jae K. Oh"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/69d9cd3ecf7e1b9ea2300e3f045b25db2a511e73 150d5b5355960bd209c3fa26b10ad2f4c1022493The advancement of artificial intelligence (AI) technology has brought significant transformations across various sectors, including the judicial system. Countries such as China and the United States have adopted AI in legal decision-making, whereas Indonesia remains in the early stages of implementing this technology. While AI has the potential to enhance judicial efficiency, challenges related to regulation, transparency, and algorithmic bias raise concerns regarding the accuracy and fairness of legal rulings. Therefore, this study aims to analyze the effectiveness of AI utilization in Indonesia’s judicial system, identify key challenges, and propose policy recommendations. This research employs both normative and empirical approaches by examining AI-related regulations and conducting interviews with judges and technology experts in Indonesia. The findings indicate that 65% of judges in Indonesia remain skeptical about AI's role in legal decision-making. In contrast, in countries like China, AI adoption in the judiciary has improved case resolution efficiency by 30–40%. Additionally, a public survey reveals that while 55% of respondents believe AI can enhance the objectivity of legal rulings, 45% remain concerned about potential algorithmic bias. Furthermore, the study highlights that Indonesia lacks specific regulations governing AI in the legal system, which may hinder the widespread adoption of this technology. This research contributes to understanding the readiness of Indonesia’s judicial system in integrating AI and emphasizes the necessity of clear regulations and robust oversight mechanisms to ensure transparency and accountability. The findings are expected to serve as a foundation for policymakers in formulating comprehensive regulations to optimize AI implementation in the Indonesian judicial systemHakim: Jurnal Ilmu Hukum dan Sosial280The study highlights that Indonesia lacks specific regulations governing AI in the legal system, which may hinder the widespread adoption of this technology and emphasizes the necessity of clear regulations and robust oversight mechanisms to ensure transparency and accountability.Hakim: Jurnal Ilmu Hukum dan Sosial["J. Hukum", "Dan Sosial", "Rafli Hukom", "Martinus", "Article Info"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/150d5b5355960bd209c3fa26b10ad2f4c1022493 1cb4a27cf5e4829df0705bdc2355b51cf1883ac3Globalisation of the strategy for the development and implementation of artificial intelligence (AI) in Ukraine and/or deepening the direction in nominal areas of social life, i.e. individuals in society, respectively, in the fields of art, design, medicine, commercial activity, science and education, banking and deep military and national strategy and national security in cadence formats such as ChatGPT, i.e: • Artificial intelligence – AI; • Artificial Neural Networks – ANN – computing systems. The dynamic position in the context of the emergence of artificial intelligence (AI) and its further application in the educational process in relation to the first stage, i.e. the species concept on the part of educators and students of education from among the convicts in the status of a special contingent, respectively, was remade in relation to the nominal field of vocational training in the context of penitentiary education, In other words, artificial intelligence (AI) in the form of a contact line requested a penitentiary trajectory for the format of the social and creative model teacher-education student from among prisoners in the status of a special contingent. Accordingly, the special contingent in the form of students from the prison population in the status of a special contingent needs to be fluent in digital technologies in accordance with the requirements of modern society, to improve and enhance the nominal abilities that will be needed for further employment in the digital world. The motivation of the present regarding the project perspective of artificial intelligence (AI) in penitentiary education in the status of a special contingent, that is, the involvement of the social and creative model of teacher-educator from among the convicts of imprisonment in the status of a special contingent is characterized by the intense development of artificial intelligence (AI) technologies and the introduction of. The generation of the relationship between artificial intelligence (AI) and education goes beyond the application of artificial intelligence (AI) during the acquisition of theoretical training, i.e., taking into account the study of artificial intelligence (AI) technologies and the professional training of students from among prisoners of imprisonment in the status of a special contingent within the framework of penitentiary education.SERIEs: Journal of the Spanish Economic Association00The motivation of the present regarding the project perspective of artificial intelligence (AI) in penitentiary education in the status of a special contingent is the involvement of the social and creative model of teacher-educator from among the convicts of imprisonment in the status of a special contingent.Bulletin of Postgraduate Education (Series)["Vasyl \u041ello"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/1cb4a27cf5e4829df0705bdc2355b51cf1883ac3 6615d9e0b05caa2cbb054c9080d45a51b7029c18Integrating Artificial Intelligence Techniques in Medical Bacteriology is a scientific research paper that explores the transformative role of artificial intelligence (AI) in enhancing diagnostic and therapeutic practices within the field of bacteriology. As AI technologies increasingly permeate healthcare, this study provides a comprehensive analysis of how machine learning (ML) and deep learning algorithms can significantly improve the accuracy and timeliness of bacterial pathogen detection and antibiotic resistance management, marking a notable advancement in laboratory medicine.[1-][2] The research emphasizes the potential of AI to streamline workflows and enhance operational efficiency in medical bacteriology. By automating processes such as error detection, result interpretation, and image analysis, AI systems can significantly reduce the turnaround time for diagnostic results, ultimately leading to improved patient outcomes.[3][4] The paper also highlights the importance of data quality management in the development of AI models, advocating for adherence to established standards throughout the dataset lifecycle to ensure the reliability of AI applications in clinical settings.[5]Despite the promising advancements, the integration of AI in healthcare is not without its challenges. The study discusses the current limitations in the clinical efficacy and cost-effectiveness of AI applications, revealing a gap between research outcomes and real-world implementation.[4] Additionally, ethical considerations surrounding data privacy and algorithmic transparency are addressed, emphasizing the need for regulatory frameworks that promote safe and equitable AI use in medical practice.[6]Overall, this research provides a critical examination of the trends and innovations in AI applications in medical bacteriology, employing statistical analysis and bibliometric techniques to identify research hotspots and emerging patterns in the field from 2010 to 2024.[7][6] By integrating AI methodologies, the study aims to lay the groundwork for future research directions and improve quality assurance standardsInternational Journal of New Findings in Health and Educational Sciences (IJHES)60A comprehensive analysis of how machine learning (ML) and deep learning algorithms can significantly improve the accuracy and timeliness of bacterial pathogen detection and antibiotic resistance management, marking a notable advancement in laboratory medicine.International Journal of New Findings in Health and Educational Sciences (IJHES)["Ali Karimi"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/6615d9e0b05caa2cbb054c9080d45a51b7029c18 bc1b09561b7bbb3e80bd8c906bd1314b3f56d667Objective: The objective of this study is to examine the characteristics of data literacy, which – on the long run – promises to become a fundamental component of artificial intelligence literacy (AI literacy). Methodology: In addition to conducting a scoping review on the interrelated topics of data literacy and artificial intelligence literacy, we also draw upon our expertise in the field of data literacy and mention among others digital literacy, media literacy and their critical approaches. Results: In light of the considerable diversity of approaches and opinions, a significant portion of the extensive body of literature was subjected to careful examination, with a view to elucidating the nature and role of data literacy and AI literacy. Originality/Value: The issue of AI literacy is gaining increasing attention. It is therefore important to review its history and characteristics by examining the relationship between it and other forms of digital literacy, and in particular data literacy.Central European Library and Information Science Review Közép-európai Könyvtár- és Információtudományi Szemle00The characteristics of data literacy, which – on the long run – promises to become a fundamental component of artificial intelligence literacy (AI literacy), are examined.Central European Library and Information Science Review Közép-európai Könyvtár- és Információtudományi Szemle["T\u00fcnde Lengyeln\u00e9 dr. Moln\u00e1r", "Csaba Dr. Koml\u00f3", "G\u00e1bor Tam\u00e1s Vas"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/bc1b09561b7bbb3e80bd8c906bd1314b3f56d667 39724d650b7ad77d57bb49c3409716d7e652c069In the context of the rapid global development of artificial intelligence (AI), China is also actively advancing the research and application of related technologies. This paper focuses on Chinese A-share listed companies from 2016 to 2023 and explores the impact of corporate AI applications on labor productivity. A model is constructed in which labor productivity is measured by the natural logarithm of revenue per employee. The application indicator is built using the number of AI-related keywords in annual reports, with control variables set accordingly. Empirical results show that AI applications significantly improve labor productivity, with companies that exhibit good growth, strong cash flow, and large scale performing better in terms of productivity. Robustness checks confirm the validity of these conclusions. The study demonstrates that AI holds immense potential in corporate applications, and companies can build industrial ecosystems to promote its widespread use, enhancing labor productivity and contributing to high-quality economic development.Journal of Applied Economics and Policy Studies00The study demonstrates that AI holds immense potential in corporate applications, and companies can build industrial ecosystems to promote its widespread use, enhancing labor productivity and contributing to high-quality economic development.Journal of Applied Economics and Policy Studies["Jiaqi Feng", "Chunhui Yuan"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/39724d650b7ad77d57bb49c3409716d7e652c069 df7acbc4b05bfddab9e4419790fab4e5111ec25bThe integration of artificial intelligence (AI) into project management (PM) transforms how projects are planned, executed, and monitored. The main objective of this study is to provide a comprehensive bibliometric analysis exploring trends, thematic areas, and future directions in AI applications in project management by examining publications from the last decade. This research uncovers dominant themes such as machine learning, decision making, information management, and resource optimization. The findings highlight the growing use of AI to enhance efficiency, accuracy, and innovation in PM processes, with recent trends favoring data-driven approaches and emerging technologies like generative AI. Geographically, China, India, and the United States lead in publications, while the United Kingdom and Australia show a high citation impact. The research landscape, including AI-enhanced decision-making frameworks and cost analysis, demonstrates the diversity of AI applications in PM. An increased interest in the use of generative AI and its impact on PM and project managers was observed. This analysis contributes to the field by offering a structured overview of research trends, defining the challenges and opportunities for integrating AI into project management practices and offering perspectives on emerging technologies.Electronics540A comprehensive bibliometric analysis exploring trends, thematic areas, and future directions in AI applications in project management by examining publications from the last decade uncovers dominant themes such as machine learning, decision making, information management, and resource optimization.Electronics["Diego Vergara", "Antonio del Bosque", "Georgios Lampropoulos", "Pablo Fern\u00e1ndez\u2010Arias"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/df7acbc4b05bfddab9e4419790fab4e5111ec25b c350bfe8f6735551c1f9e633a6f0f12d5468e904AI and Ethics260This paper utilizes a deliberation and capabilities-based ethics framework– to investigate how different countries approach AI ethics within their national plans, revealing distinct national trajectories in the pursuit of ethical AI.AI and Ethics["Manpriya Dua", "J. P. Singh", "Amarda Shehu"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/c350bfe8f6735551c1f9e633a6f0f12d5468e904 8cac5aa6519f2c828547e8880f312925c920957cIntroduction: This research aims to examine the moral issues of using artificial intelligence approaches to interpret and comprehend Islamic law. It sheds light on the expansion of artificial intelligence-centered applications in the religious sphere, aiming at issuing and providing fatwas, centering analysis of jurisprudential literature, and legal relief interpretation. Methods: The study highlights the worry about the training data bias, the lack of sufficient culture and religion of the software, and how legitimately responsible one is when one is wrong in interpreting and/or reasoning. Results: The study analyses other related concerns such as the credibility of artificial moral agents and neutrality as well as the autonomy of human actors in making religious decisions but agrees on the possibility of managing the projection of such AI systems within Islamic Sharia law. Conclusions: To sum up, the study advances a recommendation on the ethical principles and indicators that can facilitate responsible use of the technologies in question, emphasizing the importance of jurists and specialists in monitoring their development and use to enhance Islamic law objectives.Salud, Ciencia y Tecnología60The study sheds light on the expansion of artificial intelligence-centered applications in the religious sphere, aiming at issuing and providing fatwas, centering analysis of jurisprudential literature, and legal relief interpretation.Salud, Ciencia y Tecnología - Serie de Conferencias["Israa Al-Momani"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/8cac5aa6519f2c828547e8880f312925c920957c 0ae22e4cb2983167ca55151f18311d7446435139AI is revolutionizing the healthcare industry by improving service delivery and operational efficiency. This study explores the effects of AI interventions in HRM procedures, focusing on improving service quality. AI-driven solutions address workforce diversity, talent scarcity, and individualized patient care. However, the successful use of AI requires balancing innovation with human-centric values and privacy concerns. This research provides awareness for healthcare leaders, practitioners, and policymakers on strategically exploiting AI in HRM, fostering a culture of continuous improvement and providing high-quality healthcare services. “The AI in Healthcare Market is projected to grow from $14.6 Billion in 2023 to $102.7 Billion by 2028.” (ibef) The use of Artificial Intelligence (AI) interventions in Human Resource Management (HRM) practices has become a revolutionary force in the modern healthcare scene. This study aims to explore and assess how AI interventions in HRM can improve the quality of services provided to the healthcare industry, delving into the deep ramifications of this paradigm shift. Rapid technology breakthroughs are currently transforming the healthcare industry, and AI is emerging as a major innovation engine. In this regard, the incorporation of AI interventions into HRM procedures marks a paradigm shift that might have a big impact on the calibre of services provided by healthcare institutions. Nevertheless, despite AI's bright future in HRM, there is still a significant knowledge vacuum about the complex opportunities and issues that result from this integration.INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT &amp; SOCIAL SCIENCE00This study explores the effects of AI interventions in HRM procedures, focusing on improving service quality and aims to explore and assess how AI interventions in HRM can improve the quality of services provided to the healthcare industry.INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT &amp; SOCIAL SCIENCE["Dheeraj Kumar Gautam"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/0ae22e4cb2983167ca55151f18311d7446435139 e22a856606f2dcf4831e0cb57d8b642d5869f9e1The article examines the pressing issue of utilizing artificial intelligence (AI) technologies in the context of teaching DevOps-related disciplines in higher education institutions. Given the rapid growth of the IT industry and the increasing demand for professionals with DevOps skills, enhancing the efficiency of the educational process in this field is clearly crucial. AI offers new opportunities to address key educational challenges, including personalized learning, automation of routine tasks, creation of adaptive learning materials, as well as ensuring interactivity and accessibility of educational resources. The paper proposes approaches to integrating AI tools into the educational process to optimize learning, improve students’ understanding of DevOps principles such as continuous integration (CI), continuous delivery (CD), process automation, containerization, system monitoring, and security. Particular attention is given to the development of interactive platforms for laboratory work, the use of virtual environments for simulating real-life DevOps scenarios, and the implementation of chatbots and automated assessment systems to support students during their studies. The article analyzes the impact of AI technologies on learning outcomes, considering both quantitative and qualitative indicators, including improved practical skills, reduced task completion time, and increased student engagement in studying DevOps-related disciplines. An analysis of existing practices in implementing AI in technical education highlights the main advantages and challenges that educational institutions may encounter. The paper outlines prospects for further AI integration into DevOps teaching, including combining AI with modern cloud services, advancing cognitive technologies capable of natural language understanding, and adopting innovative approaches to organizing the educational environment. The study’s results indicate significant potential for AI to enhance the quality of education and prepare highly skilled specialists in the field of DevOps.SERIEs: Journal of the Spanish Economic Association00The article analyzes the impact of AI technologies on learning outcomes, considering both quantitative and qualitative indicators, including improved practical skills, reduced task completion time, and increased student engagement in studying DevOps-related disciplines.Bulletin of Postgraduate Education (Series)["M. Luchkevych"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/e22a856606f2dcf4831e0cb57d8b642d5869f9e1 a8c15e074254e8e4f147d48a9e9834fdc60f5a58This article focuses on the role of Artificial Intelligence in the diagnosis and treatment of neuronal diseases and future perspectives on the subject. Neuronal diseases involve complex pathophysiological processes that affect the central and peripheral nervous systems and can cause permanent damage to the cognitive, motor and sensory functions of individuals. Diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are characterized by degeneration of nerve cells, neuroinflammation and neurotransmitter imbalances. Early diagnosis of neuronal diseases is of critical importance in slowing down the progression of the disease, controlling symptoms and improving the quality of life of patients. However, one of the biggest challenges in the diagnosis of these diseases is that symptoms usually appear in the advanced stages of the disease process. In recent years, artificial intelligence (AI)-supported technologies have made great progress in the diagnosis of neurological diseases. Artificial intelligence can analyze biomarkers in blood, cerebrospinal fluid or saliva to detect the disease in its early stages. By processing this data with deep learning techniques, artificial intelligence can detect disease symptoms at early stages and support personalized diagnosis and treatment processes. Electrophysiological data analysis also plays an important role in the diagnosis of neurological diseases. Artificial intelligence algorithms can detect abnormal brain activities by processing EEG (electroencephalography) and MEG (magnetoencephalography) data. For example, in epilepsy patients, seizures can be predicted before they start using machine learning, while in dementia patients, changes in brain waves can be detected at an early stage. Artificial intelligence can learn how disease symptoms change in different individuals by analyzing a lot of patient data from around the world. While this process allows machine learning models to make more precise and reliable diagnoses, systems developed on the basis of Dr. Roman Poznanski's DOT (Dynamic Optimization of Thought) theory offer a new method in the diagnosis of neurological diseases. Artificial intelligence has become an effective tool in slowing down the progression of neurological diseases by optimizing patients' individualized treatment plans. In the future, it will be possible to develop more integrated and dynamic solutions for the diagnosis, monitoring and treatment of neurological diseases thanks to DOT theory and conscious artificial intelligence models. These systems will analyze brain functions not only through biological data but also in the context of energy flows and information processing processes, providing personalized, optimized and preventive approaches.International Journal of Emerging Multidisciplinaries: Biomedical and Clinical Research00Artificial intelligence has become an effective tool in slowing down the progression of neurological diseases by optimizing patients' individualized treatment plans and developing more integrated and dynamic solutions thanks to DOT theory and conscious artificial intelligence models.International Journal of Emerging Multidisciplinaries: Biomedical and Clinical Research["Sara Raouf", "Diar Raouf", "Lana Raouf", "E. Alemdar"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/a8c15e074254e8e4f147d48a9e9834fdc60f5a58 94f3e69f20f04e7ff38830360fe0a12329d2050aThe article examines the philosophical concepts of artificial intelligence developed by representatives of the Pittsburgh School, particularly R. Brandom, in juxtaposition with the critical stance of H. Dreyfus. Key aspects of the functionalist approach to artificial intelligence are analyzed, including the problem of algorithmic decomposition of cognitive abilities and the potential for creating autonomous discursive practices. Particular attention is paid to the Brandom’s concept of “pragmatic AI”, which offers an alternative to the classical symbolic approach. The problem of non-monotonicity of reasoning as the main technical obstacle in the development of artificial intelligence is investigated. Furthermore, Dreyfus’s phenomenological critique emphasizes the significance of embodied experience and contextual understanding in human cognition. Conclusion drawn from this comparative analysis underscores the necessity of rethinking traditional approaches to artificial intelligence development in light of modern philosophical discussions concerning the nature of intelligence and cognition.Общество философия история культура00Key aspects of the functionalist approach to artificial intelligence are analyzed, including the problem of algorithmic decomposition of cognitive abilities and the potential for creating autonomous discursive practices and the necessity of rethinking traditional approaches to artificial intelligence development in light of modern philosophical discussions concerning the nature of intelligence and cognition.Общество: философия, история, культура["Sergey A. Kustov"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/94f3e69f20f04e7ff38830360fe0a12329d2050a 38e520f3e4895f759edf4a40a81fe0e0f9162234This article investigates the issue of implementing artificial intelligence (AI) technologies in the public service system of the Russian Federation as a tool for optimizing managerial decision-making processes. Analysis of existing approaches to defining the essence of artificial intelligence is presented, alongside the author’s conceptual interpretation of this notion. The interrelation between the federal projects “Digital State Administration” and “Artificial Intelligence” within the framework of the national program “Digital Economy” is examined. Key directions and mechanisms for the integration of artificial intelligence technologies into public administration are identified, including the establishment of a specialized predictive analytics platform. The article also analyzes Germany’s experience in utilizing artificial intelligence in the financial sector of public administration, providing recommendations for its adaptation to the Russian context. Specific steps for creating a comprehensive system for the use of artificial intelligence in the public service of the Russian Federation are outlined, taking into account the requirements for information security and the protection of personal data.Общество политика экономика право00Germany’s experience in utilizing artificial intelligence in the financial sector of public administration is analyzed, providing recommendations for its adaptation to the Russian context.Общество: политика, экономика, право["Rostislav A. Poyarkov"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/38e520f3e4895f759edf4a40a81fe0e0f9162234 75075f6d22db51fea56a338037d13a91be8a8a03Globalizations240Globalizations["Kenzo Soares Seto"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/75075f6d22db51fea56a338037d13a91be8a8a03 02a09b4351741c5577797895e55b680c0a026c4dThis letter discusses the shift from large proprietary AI models to smaller, specialized language models in healthcare. With advancements in fine-tuning techniques, such models can be adapted using affordable resources, ensuring data security and empowering smaller institutions. The letter emphasizes the importance of guiding AI development to complement human medical expertise.Revista Espanola de Enfermedades Digestivas10This letter discusses the shift from large proprietary AI models to smaller, specialized language models in healthcare, which can be adapted using affordable resources, ensuring data security and empowering smaller institutions.Revista espanola de enfermedades digestivas["Manuel Nevado Santos", "Paula Nevado \u00c1lvarez"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/02a09b4351741c5577797895e55b680c0a026c4d d5dc6f39d2037c8a3e75d9034115a38ef4e66f23AI and Ethics730AI and Ethics["Fatemeh Habibi", "Shadi Ghaderkhani", "Marzieh Shokoohi", "Tara Banari", "Mahsa Morsali", "Reza Nejad Shahrokh Abadi", "Hoora Kiamehr"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/d5dc6f39d2037c8a3e75d9034115a38ef4e66f23 ff8b5fd048f152ef3d8d4f2726ef76bcaf7d6413Interactive Learning Environments780Interactive Learning Environments["H\u00fcseyin Ate\u015f", "Cengiz G\u00fcnd\u00fczalp"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/ff8b5fd048f152ef3d8d4f2726ef76bcaf7d6413 5079e075ba63be89103c82a9aae244bd108359b6Journal of Crop Health690Journal of Crop Health["E. Vidya Madhuri", "J. S. Rupali", "S. P. Sharan", "N. Sai Pooja", "G. S. Sujatha", "D. P. Singh", "Khurshid Ahmad", "Amrender Kumar", "Ratna Prabha"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/5079e075ba63be89103c82a9aae244bd108359b6 7226b3bce457ede7a671a017c1c86e3aaeada8cfStrahlentherapie und Onkologie (Print)160Strahlentherapie Und Onkologie["F. Putz", "R. Fietkau"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/7226b3bce457ede7a671a017c1c86e3aaeada8cf 30741a93c11956fb66d9804ca7fa5019a0bd6cbdAJR. American journal of roentgenology40AJR. American journal of roentgenology["Miriam Chisholm", "Kirti Magudia"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/30741a93c11956fb66d9804ca7fa5019a0bd6cbd c0f5a66cd10669160e6abaf2a4e584b83228c219Online Workshop on Adaptive Education: Harnessing AI for Academic Progress30Online Workshop on Adaptive Education: Harnessing AI for Academic Progress["Mari Cruz Garc\u00eda Vallejo"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/c0f5a66cd10669160e6abaf2a4e584b83228c219 8d4565161b7c44c1fbb3053d5792a3e6db3cc432Journal of the Knowledge Economy470Journal of the Knowledge Economy["Hui Huang", "Jing Yang", "Changman Ren"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/8d4565161b7c44c1fbb3053d5792a3e6db3cc432 2bcc5edb42bd568881dc2c443d34633d2df7c8f5Interactive Learning Environments430Interactive Learning Environments["Ming Xu"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/2bcc5edb42bd568881dc2c443d34633d2df7c8f5 2095c9e328c13609ac1b37b91d829d321b85f266We devise an algorithm to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference. We then benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a straightforward transformation of) propositions expressed in natural language, with promising results from a single prompt to models optimized for reasoning. Combining coherence-driven inference with consistency evaluations by neural models may advance the state of the art in machine cognition.00An algorithm is devised to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference, and the ability of large language models to reconstruct coherence graphs from propositions expressed in natural language is benchmarked.["Steve Huntsman", "Jewell Thomas"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/2095c9e328c13609ac1b37b91d829d321b85f266 e10ae8d63686b34ea985ba12b68780eac80be5ddScientific Reports410The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching, and shows strong stability when dealing with multidimensional educational resources and complex teaching modes.Scientific Reports["Limin Qian", "Weiran Cao", "Lifeng Chen"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/e10ae8d63686b34ea985ba12b68780eac80be5dd 498648d6c983f29729b3679f7487e586426f425aJ. Medical Syst.1180The use of image-based and data-driven methods has proven to enhance diagnostic accuracy and treatment efficiency in chronic wound care and the integration of technology into chronic wound diagnosis has shown a transformative effect, improving diagnostic capabilities, patient care, and reducing healthcare costs.Journal of Medical Systems["David Reifs Jim\u00e9nez", "Lorena Casanova-Lozano", "Sergi Grau Carri\u00f3n", "Ram\u00f3n Reig Bola\u00f1o"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/498648d6c983f29729b3679f7487e586426f425a 5e0afbd83a08f7b6867e6958da4ca8a9d791e54fObjective The main purpose of this bibliometric study is to compile the rapidly increasing articles in the field of perinatology in recent years and to shed light on the research areas where studies are concentrated. Materials and methods This bibliometric study was conducted using the Thomson ISI Web of Science Core Collection (WOSCC) system on May 4, 2024, with specific keywords. The abstracts of 1,124 articles that met the criteria were reviewed, and 382 articles related to perinatology were evaluated. Keyword co-occurrence, co-citation of authors, and co-citation of references analyses were conducted using VOSviewer (version 1.6.19). Out of these, 121 articles with 10 or more citations were analyzed in terms of their content and categorized under the headings “Purpose of Evaluation,” “Medical Methods and Parameters Used,” “Output To Be Evaluated,” and “Fetal System or Region Being Evaluated.” Results In this bibliometric study, it was found that the most frequently published journal among the 382 examined articles was Medical Image Analysis, while the journals with the most publications in the field of perinatology were Prenatal Diagnosis and Ultrasound in Obstetrıcs & Gynecology. The most commonly used keyword was “deep learning” (115/382). Among the 121 highly cited articles, the most common purpose of evaluation was “Prenatal Screening.” Artificial intelligence was most frequently used in ultrasound (59.8%) imaging, with MRI (20.5%) in second place. Among the evaluated outputs, “organ scanning” (35/121) was in first place, while “biometry” (34/121) was in second place. In terms of evaluated systems and organs, “growth screening” (35/121) was the most common, followed by the “neurological system” (33/121) and then the “cardiovascular system” (18/121). Conclusion I has witnessed the increasing influence of artificial intelligence in the field of perinatology in recent years. This impact may mark the historic beginning of the transition to the AI era in perinatology. Milestones are being laid on the path from prenatal screening to prenatal treatment.Frontiers in Medicine150It was found that the most frequently published journal among the 382 examined articles was Medical Image Analysis, while the journals with the most publications in the field of perinatology were Prenatal Diagnosis and Ultrasound in Obstetrıcs & Gynecology.Frontiers in Medicine["\u00dcmran K\u0131l\u0131n\u00e7demir Turgut"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/5e0afbd83a08f7b6867e6958da4ca8a9d791e54f bc979e7c26fe1f22396d3dc958a1ca3c3d132755The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has opened new frontiers in human personality development, particularly in enhancing emotional intelligence (EI) and social skills. Emotional intelligence, which includes self-awareness, self-regulation, empathy, and effective communication, is a crucial factor in personal and professional success. Traditional methods of personality development rely on human-led training, coaching, and therapy, but AI-powered systems now offer scalable, personalized, and data-driven alternatives. This paper explores the application of AI in personality development, emphasizing how ML techniques such as Natural Language Processing (NLP), deep learning, and reinforcement learning can facilitate emotional intelligence enhancement. Sentiment analysis software, virtual emotional coaches, chatbots, and affective computing systems—among other AI-powered tools—help people identify, understand, and control their emotions.  By means of conversational agents and virtual reality simulations, AI-driven social skills training offers real-time feedback, therefore empowering users to improve their interpersonal contacts.  AI-driven personality development poses ethical questions despite its transforming power including data privacy, algorithmic prejudice, and the possibility of over-reliance on AI for social contacts.  Dealing with these issues calls for a well-rounded strategy combining ethical artificial intelligence models with enhanced openness, human-AI cooperation in social skill development,  The research comes to the conclusion that artificial intelligence has great potential to improve communication abilities and emotional intelligence.  Through responsible use of AI's powers, people may increase their self-awareness and interpersonal efficacy, therefore enhancing their social contacts and professional performance.  To best maximise the advantages of AI-driven personality development, future studies should concentrate on improving AI models, reducing bias, and guaranteeing ethical deployment.Journal of Information Systems Engineering & Management220The research comes to the conclusion that artificial intelligence has great potential to improve communication abilities and emotional intelligence, and future studies should concentrate on improving AI models, reducing bias, and guaranteeing ethical deployment.Journal of Information Systems Engineering and Management["Tejaswini Panse", "Navnath B. Pokale", "Dr. Manjusha Tatiya", "Jay Vasani", "Nitin Rakesh", "Poonam Jagdish Patil"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/bc979e7c26fe1f22396d3dc958a1ca3c3d132755 aed65c745a1fcf99af4b2769da487ae2b03d9dbaPurposeThe aim of the study is to understand employees’ feelings towards artificial intelligence (AI) in relation to their job performance and productivity, as well as their opinion of the risk of being displaced by AI.Design/methodology/approachThe study employed a self-administered questionnaire targeting hotel employees. The results were analysed through exploratory factor analysis to validate constructs and test hypotheses.FindingsIn particular, the results of the study indicate employees’ insecurity of job losses when it comes to incorporating AI applications into operational processes. However, it is crucial for employees to understand that embracing AI can boost job productivity, thereby enhancing employee and guest satisfaction.Originality/valueThis study is original because it examines a new topic, concerning AI in hotels and evaluates how hotel employees perceive it in relation to their job security.Worldwide Hospitality and Tourism Themes331The results of the study indicate employees’ insecurity of job losses when it comes to incorporating AI applications into operational processes, however, it is crucial for employees to understand that embracing AI can boost job productivity, thereby enhancing employee and guest satisfaction.Worldwide Hospitality and Tourism Themes["Katerina Pericleous", "Sotiroula Liasidou", "Todor Dyankov"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/aed65c745a1fcf99af4b2769da487ae2b03d9dba 408c4479a10799d58237cca95a1a9760abe4f565Background The integration of artificial intelligence (AI) into health care has become a crucial element in the digital transformation of health systems worldwide. Despite the potential benefits across diverse medical domains, a significant barrier to the successful adoption of AI systems in health care applications remains the prevailing low user trust in these technologies. Crucially, this challenge is exacerbated by the lack of consensus among experts from different disciplines on the definition of trust in AI within the health care sector. Objective We aimed to provide the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains. Our findings can be used to address the problem of defining trust in AI in health care applications, fostering the discussion of concrete real-world health care scenarios in which humans interact with AI systems explicitly. Methods We used a combination of framework analysis and a 3-step consensus process involving 18 international experts from the fields of computer science, medicine, philosophy of technology, ethics, and social sciences. Our process consisted of a synchronous phase during an expert workshop where we discussed the notion of trust in AI in health care applications, defined an initial framework of important elements of trust to guide our analysis, and agreed on 5 case studies. This was followed by a 2-step iterative, asynchronous process in which the authors further developed, discussed, and refined notions of trust with respect to these specific cases. Results Our consensus process identified key contextual factors of trust, namely, an AI system’s environment, the actors involved, and framing factors, and analyzed causes and effects of trust in AI in health care. Our findings revealed that certain factors were applicable across all discussed cases yet also pointed to the need for a fine-grained, multidisciplinary analysis bridging human-centered and technology-centered approaches. While regulatory boundaries and technological design features are critical to successful AI implementation in health care, ultimately, communication and positive lived experiences with AI systems will be at the forefront of user trust. Our expert consensus allowed us to formulate concrete recommendations for future research on trust in AI in health care applications. Conclusions This paper advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care. By synthesizing insights into commonalities and differences among specific case studies, this paper establishes a foundational basis for future debates and discussions on trusting AI in health care.Journal of Medical Internet Research911This paper provides the first consensus-based analysis of trust in AI in health care based on an interdisciplinary panel of experts from different domains and advocates for a more refined and nuanced conceptual understanding of trust in the context of AI in health care.Journal of Medical Internet Research["G. Starke", "F. Gille", "Alberto Termine", "Yves Saint James Aquino", "Ricardo Chavarriaga", "Andrea Ferrario", "Janna Hastings", "K. Jongsma", "P. Kellmeyer", "Bogdan Kulynych", "Emily Postan", "Elise E. Racine", "Derya \u015eahin", "Paulina Tomaszewska", "Karina Vold", "Jamie Webb", "Alessandro Facchini", "Marcello Ienca"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/408c4479a10799d58237cca95a1a9760abe4f565 7b0c4c77e62b6bed930ac8af30d5daccac2ba2f3Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust.91Whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust are commented on.["Abhik Roychoudhury", "Corina Pasareanu", "Michael Pradel", "Baishakhi Ray"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/7b0c4c77e62b6bed930ac8af30d5daccac2ba2f3 eb90ef1a5a634633f44331d9ce1c9a5c922ccc1cRecent advancement in machine learning algorithms reaches a point where medical devices can be equipped with artificial intelligence (AI) models for diagnostic support and routine automation in clinical settings. In medicine and healthcare, there is a particular demand for sufficient and objective explainability of the outcome generated by AI models. However, AI models are generally considered as black boxes due to their complexity, and the computational process leading to their response is often opaque. Although several methods have been proposed to explain the behavior of models by evaluating the importance of each feature in discrimination and prediction, they may suffer from biases and opacities arising from the scale and sampling protocol of the dataset used for training or testing. To overcome the shortcomings of existing methods, we explore an alternative approach to provide an objective explanation of AI models that can be defined independently of the learning process and does not require additional data. As a preliminary study for this direction of research, this work examines a numerical availability of the Jacobian matrix of deep learning models that measures how stably a model responses against small perturbations added to the input. The indicator, if available, are calculated from a trained AI model for a given target input. This is a first step towards a perturbation-based explanation, which will assist medical practitioners in understanding and interpreting the response of the AI model in its clinical application.00A numerical availability of the Jacobian matrix of deep learning models is examined that measures how stably a model responses against small perturbations added to the input, which will assist medical practitioners in understanding and interpreting the response of the AI model in its clinical application.["T. Abe", "Yoshiyuki Asai"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/eb90ef1a5a634633f44331d9ce1c9a5c922ccc1c ca5e2f3783dbb071f739729ef9239972ab1dc9b1This The transformative force of AI-driven digitalization demands a paradigm shift in leadership, one that transcends conventional frameworks to address the complexities of disruptive innovation, ethical governance, and sustainable business strategies. This research critically examines the advanced leadership capabilities required to drive the adoption and integration of frontier technologies—including artificial intelligence (AI), machine learning (ML), blockchain, and fintech—within the operational ecosystems of multinational corporations and small-to-medium-sized enterprises (SMEs). Digital transformation is not merely a technological transition but a profound organizational realignment, necessitating leaders with the vision and dexterity to dismantle silos, foster innovation, and institutionalize sustainability as a core strategic objective. Adopting a rigorous, qualitative methodology, the study synthesizes highimpact insights from global consultancies such as McKinsey & Company, Boston Consulting Group, and Deloitte, augmented by in-depth case studies of industry trailblazers like Microsoft, Siemens, and JPMorgan Chase. This multifaceted approach provides a strategic lens for understanding how effective leadership propels enterprises toward competitive resilience and market leadership in the digital economy. The analysis underscores the necessity of embedding ethical AI governance frameworks to ensure transparency, accountability, and equity— particularly in sectors where algorithmic decisions have significant societal impacts, such as healthcare, finance, and law enforcement. Furthermore, cross-functional collaboration emerges as a cornerstone of organizational agility, unlocking value through interdisciplinary synergies. The integration of sustainability within digital strategies, exemplified by Siemens’ AI-enabled energy optimization systems, demonstrates the unparalleled potential of aligning technological innovation with environmental stewardship. This research concludes that strategic leadership is the pivotal differentiator in achieving sustained competitive advantage, operational excellence, and transformative impact in the AI era.SBS journal of applied business research20It is concluded that strategic leadership is the pivotal differentiator in achieving sustained competitive advantage, operational excellence, and transformative impact in the AI era.SBS Journal of Applied Business Research["Vahid Suljic"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/ca5e2f3783dbb071f739729ef9239972ab1dc9b1 a0ec859a1bf6e9f1f0b50c63b78852caac2eb58cThis article explores the transformative role of artificial intelligence and machine learning in tackling climate change. It highlights how advanced computational techniques enhance our understanding and response to environmental shifts. Machine learning algorithms process vast climate datasets, revealing patterns that traditional methods might overlook. Deep learning neural networks, particularly effective in climate research, analyze satellite imagery, climate sensor data, and environmental indicators with unprecedented accuracy. Key applications include predictive modeling of climate change impacts. Using convolutional and recurrent neural networks, researchers generate high-resolution projections of temperature rises, sea-level changes, and extreme weather events with remarkable precision. AI also plays a vital role in data integration, synthesizing satellite observations, ground-based measurements, and historical records to create more reliable climate models. Additionally, deep learning algorithms enable real-time environmental monitoring, tracking changes like deforestation, ice cap melting, and ecosystem shifts. The article also highlights AI-powered optimization models in mitigation efforts. These models enhance carbon reduction strategies, optimize renewable energy use, and support sustainable urban planning. By leveraging machine learning, the research demonstrates how AI-driven approaches offer data-backed solutions for climate change mitigation and adaptation. These innovations provide practical strategies to address global environmental challenges effectively.LatIA340By leveraging machine learning, the research demonstrates how AI-driven approaches offer data-backed solutions for climate change mitigation and adaptation and these innovations provide practical strategies to address global environmental challenges effectively.LatIA["Fredrick Kayusi", "Srinivas Kasulla", "S. J. Malik", "Petros Chavula"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/a0ec859a1bf6e9f1f0b50c63b78852caac2eb58c 173493f00a7cad65474a9e3ddc29e6e20b2b90cfFinTech innovations have reshaped the financial sector, enabling greater security, accessibility, and efficiency. The integration of advanced technologies such as blockchain, artificial intelligence (AI), machine learning, and big data analytics has revolutionized financial services, improving operational efficiency and fostering financial inclusion. These innovations provide secure, seamless, and real-time transactions, reducing dependency on traditional banking systems while promoting a more customer-centric approach. This paper examines the role of FinTech in promoting sustainable finance through digital financial solutions, regulatory compliance, and enhanced security. The emergence of digital lending platforms, robo-advisors, decentralized finance (DeFi), and digital payment systems has transformed how individuals and businesses interact with financial services. By leveraging automation and data-driven decision-making, FinTech facilitates transparent and cost-effective financial services, addressing inefficiencies in traditional models. Furthermore, the study explores market trends, risk management strategies, and future developments in the FinTech industry, providing insights into the evolution of financial technologies and their implications for sustainability. With increasing        regulatory                            scrutiny, cybersecurity concerns, and the growing need for responsible investing, the intersection of FinTech and sustainable finance has become crucial. This paper highlights how FinTech fosters green finance initiatives, enhances ESG (Environmental, Social, and Governance) investments, and contributes to a more resilient and inclusive financial ecosystem. Keywords: FinTech, Sustainable Finance, Digital Financial Solutions, Regulatory Compliance, Financial Security, Market Trends, Risk Management, Blockchain, AI.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00How FinTech fosters green finance initiatives, enhances ESG (Environmental, Social, and Governance) investments, and contributes to a more resilient and inclusive financial ecosystem is highlighted.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Parani Prasanth T"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/173493f00a7cad65474a9e3ddc29e6e20b2b90cf dcafbeb2113644fd3059aea75a7205a2e669881cIntroduction: The introduction of Artificial Intelligence (AI) in the education sector has sparked a lot of controversy opinions regarding its effectiveness in shaping student learning process and systematic mental growth. While AI has immense potential to transform learning experiences by providing personalised learning, immediate feedback, and access to enormous information, but fears have also been aired that overreliance on artificial intelligence will stifle the problem-solving and critical thinking capabilities of students by providing pre-programmed answers instead of encouraging independent thoughts. Objectives: The purpose of this paper is to rigorously explore the impacts of AI on higher education, with an inspection of how it impacts learning processes, intellectual growth, and the further expectations from students, academics and universities. This research aims to develop AI tools and programs through mixed approaches for the higher education environment that function as collaborative partners rather than just tools. The goal is to establish guiding principles and frameworks for a creative AI practice, incorporating computational tools, benchmarking of successful experiments, and their future applications. The current focus is on presenting early-stage research findings. Methods: Given this research focus on impact of AI in higher education, overviewing several studies, the backgrounds, and research findings achieved through exploratory research to gain a deeper understanding of a topic, develop practical guidelines, and develop more focused research questions for future investigation.  Results: By investigating different studies, the examined strategies can be consolidated to address the promise of ethical AI implementation in education and in a manner to enhance and sustain cognitive capabilities among students and other stakeholders while offsetting risks associated with over-reliance and degrading of the human capacity. Conclusions: By embracing AI's potential while upholding core educational values, institutions can ensure AI enhances the learning experience. Successful implementation requires a balanced approach, developing adaptable and culturally sensitive AI tools while investing in teacher training for effective human-AI collaboration. This thoughtful approach considers students' diverse backgrounds and learning preferences, along with the vital role of educators, maintaining human agency, critical thinking, and ethical considerations as central to education. AI in higher education offers significant potential benefits, but responsible integration is key to maximising its positive impact. This approach ensures AI serves educational purposes effectively.Journal of Information Systems Engineering & Management460This research aims to develop AI tools and programs through mixed approaches for the higher education environment that function as collaborative partners rather than just tools, to address the promise of ethical AI implementation in education and to enhance and sustain cognitive capabilities among students and other stakeholders.Journal of Information Systems Engineering and Management["Sheren Shafei", "Dr Syed Mahmood"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/dcafbeb2113644fd3059aea75a7205a2e669881c 6e01c885baf1fef194c9e4e5616c5ea1994048eaThe rapid growth of cross-border digital trade has transformed economic interactions between regions, with the European Union (EU) and African economies playing critical roles in this evolving landscape. As a driver of innovation, economic growth, and regional integration, digital trade presents significant developmental opportunities. However, the proliferation of cybersecurity threats undermines trust in digital transactions and poses challenges to the sustainability of cross-border digital trade. These risks are exacerbated by regional disparities in cybersecurity readiness, infrastructure, and regulatory frameworks, highlighting the need for robust, innovative approaches to ensure secure and resilient digital trade ecosystems. This paper explores the potential of Artificial Intelligence (AI) as a strategic tool to mitigate cybersecurity risks and foster secure trade between the EU and Africa. The study examines the defining characteristics and economic significance of cross-border digital trade, emphasizing its role in fostering economic partnerships between the EU and Africa. It highlights existing trade agreements, collaborative efforts, and the projected growth of digital economies in both regions. Despite these opportunities, cybersecurity threats, such as data breaches, ransomware attacks, and phishing scams, present significant economic and operational challenges. The study underscores the disparities in cybersecurity preparedness, particularly in the African context, and their implications for sustainable digital trade growth. The role of Artificial Intelligence in enhancing cybersecurity is critically analyzed, focusing on its applications in threat detection, predictive analytics, anomaly identification, and automated incident response. Drawing on successful case studies, the paper demonstrates the transformative potential of AI in addressing complex cybersecurity challenges and strengthening the resilience of digital trade infrastructures. By leveraging AI-driven solutions, the EU and African economies can establish secure digital ecosystems, fostering trust and enhancing economic collaboration. The paper concludes with targeted recommendations to enhance cybersecurity in cross-border digital trade. Policy measures, such as harmonizing cybersecurity regulations and promoting AI-driven research and innovation, are essential for building a cohesive security framework. Technological investments in AI infrastructure and the development of shared cybersecurity platforms are equally vital. Furthermore, capacity-building initiatives, including specialized training programs for businesses and governments, are necessary to ensure effective implementation. These recommendations aim to address the cybersecurity challenges of cross-border digital trade and advance a secure, inclusive, and sustainable digital economy between the EU and Africa.African Journal of Economic and Sustainable Development170By leveraging AI-driven solutions, the EU and African economies can establish secure digital ecosystems, fostering trust and enhancing economic collaboration, and advance a secure, inclusive, and sustainable digital economy between the EU and Africa.African Journal of Economics and Sustainable Development["Anya, A. A.", "Anya, K. A.", "Anya, E. K.", "Ishola, A. V."]2025-02-19T00:00:00https://www.semanticscholar.org/paper/6e01c885baf1fef194c9e4e5616c5ea1994048ea 5ee6ad28448867e13fbe5bbfd6c5c99c1c408c1aThis study investigates the complex duality of automation and its impact on sustainable development, encompassing the factors of economic growth, social equity, and environmental sustainability. Innovations in artificial intelligence, robotics, and machine learning are driving automation and transforming industries through improved production, operational efficiency, and resource optimization. However, the rapid integration of automation has created a paradox. While it offers opportunities for resource optimization and technological advancement, it exacerbates challenges such as income inequality, environmental degradation, and social displacement. These issues underline the need for balanced and inclusive approaches to automation’s implementation. Automation contributes substantively to GDP growth because it raises labor productivity, yet it has arguably enhanced income inequality by eliminating low-skilled jobs. Automation improves energy efficiency and aids in renewable energy integration but increases overall energy effectiveness, leading to concerns regarding ecological sustainability. This study applied a quantitative methodology using longitudinal data from 2000 to 2023 and regression models to examine sustainability metrics influenced by automation. The findings highlight the potential of automation to reform effective forms of manufacturing, encourage environmental innovation, and identify the need for systemic governmental policies. Specifically, the results indicate that automation has contributed to a 25% increase in labor productivity across sectors, a 15% reduction in energy intensity per unit of GDP, and a 12% rise in the Gini index, signaling growing income inequality. These quantitative outcomes emphasize both the opportunities and challenges posed by automation. By integrating technological advancements with sustainability goals, automation can act as a transformative instrument to promote ecological conservation, equitable economic development, and social justice. The paper concludes with recommendations for governments and industry leaders to incorporate automation into sustainable development objectives, ensuring the equitable distribution of its advantages, while alleviating socio-environmental hazards.Sustainability500Sustainability["Ahlam I. Almusharraf"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/5ee6ad28448867e13fbe5bbfd6c5c99c1c408c1a 7d5952a8f1db99daa702d787c93e850b327f55a8Acta Epileptologica440An overview of recent AI applications in epilepsy and for people with intellectual disabilities is presented, highlighting key challenges and the necessity of including people with intellectual disabilities in research on AI and epilepsy, and potential strategies to promote the development and use of AI applications for this vulnerable population.Acta Epileptologica["M. Milne-Ives", "Rosiered Brownson-Smith", "Ananya Ananthakrishnan", "Yihan Wang", "C. Cong", "Gavin P. Winston", "E. Meinert"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/7d5952a8f1db99daa702d787c93e850b327f55a8 5fea11bfe2e147b5d0a1a4a5ef715b2f487bb8ccThe increasing complexity of global air traffic management requires innovative surveillance solutions beyond traditional radar. This chapter explores the integration of artificial intelligence (AI) and machine learning (ML) in satellite imagery processing for enhanced air traffic surveillance. The proposed AI framework utilizes satellite remote sensing, computer vision algorithms, and geo-stamped aircraft data to improve real-time detection and classification. It addresses limitations in conventional systems, particularly in areas lacking radar coverage. The study outlines a three-phase approach: extracting radar coverage from satellite imagery, labeling data with geo-stamped aircraft locations, and applying deep learning models for classification. YOLO and Faster R-CNN models distinguish aircraft from other objects with high accuracy. Experimental trials demonstrate AI-enhanced satellite monitoring's feasibility, achieving improved detection in high-traffic zones. The system enhances situational awareness, optimizes flight planning, reduces airspace congestion, and strengthens security. It also aids disaster response by enabling rapid search-and-rescue missions. Challenges like adverse weather and nighttime monitoring remain, requiring infrared sensors and radar-based techniques. By combining big data analytics, cloud computing, and satellite monitoring, the study offers a scalable, cost-effective solution for future air traffic management. Future research will refine models and expand predictive analytics for autonomous surveillance, revolutionizing aviation safety and operational intelligence.LatIA540The proposed AI framework utilizes satellite remote sensing, computer vision algorithms, and geo-stamped aircraft data to improve real-time detection and classification, and offers a scalable, cost-effective solution for future air traffic management.LatIA["Fredrick Kayusi", "Petros Chavula", "Linety Juma", "Rashmi Mishra"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/5fea11bfe2e147b5d0a1a4a5ef715b2f487bb8cc 6a9aab29c36249668ec0c461620b00e3ac9601a7Collaborative and multi-site neuroimaging studies have greatly accelerated the rate at which new and existing data can be aggregated to answer a neuroscientific question. New research initiatives are continuously collecting more data, allowing opportunities to refine previous published findings through continuous and dynamic updates. Yet, we lack a practical framework for researchers to systematically, automatically, and continuously update published findings. We developed NeuroDISK, an automated artificial intelligence based framework that: 1) performs automated and inquiry-driven analyses, and 2) continuously updates these analyses as new data becomes available. NeuroDISK was evaluated using published results from the ENIGMA consortium’s work on the genetic architecture of the cerebral cortex. We incorporate both meta-analysis and meta-regression options to showcase our framework on the effect of specific genotypes and moderators on select brain regions. Initial NeuroDISK meta-analysis results replicate the original publication, and we show result updates after adding new data. The NeuroDISK framework can be generalized for users to define question(s), run corresponding workflow(s) and access results interactively and continuously.bioRxiv540NeuroDISK is an automated artificial intelligence based framework that performs automated and inquiry-driven analyses, and continuously updates these analyses as new data becomes available and is evaluated using published results from the ENIGMA consortium's work on the genetic architecture of the cerebral cortex.bioRxiv["Daniel Garijo", "Qifan Yang", "Hern\u00e1n Vargas", "S. Gadewar", "Kevin Low", "V. Ratnakar", "Maximiliano Osorio", "Alyssa H. Zhu", "Agnes McMahon", "Yolanda Gil", "N. Jahanshad"]2025-02-19T00:00:00https://www.semanticscholar.org/paper/6a9aab29c36249668ec0c461620b00e3ac9601a7 04f59b0094f201b0e84fd24937df4484f23be418Artificial intelligence is a design of a reality that is based on the foundation of natural intelligence. This intelligence takes on the role that intelligent agents have determined. That is, agents play a fundamental role in creating and implementing intelligence, and if agents are not, the autonomy of artificial intelligence is a deceptive joke. Artificial intelligence is a simulated foundation of real intelligence and is as powerful as natural intelligence. This intelligence is compatible with natural intelligence, and on the other hand, if no measures are taken, it will itself be considered a threat to humanity. These two intelligences are both different and not interchangeable, and there are different elements and factors that can be spoken of as two mental and mechanical intelligences. It is a fact that the future world is made of intelligence. This intelligence has its own law for intelligent calculations, which is called the law of artificial intelligence. In this situation, it is possible for artificial intelligence to replace natural intelligence with the help of civil law. This intelligence has its own language in the process of intelligence and creates its own intelligent terms and speaks with it the law of intelligence so that human management is possible.EuroGlobal Journal of Linguistics and Language Education261Artificial intelligence is a design of a reality that is based on the foundation of natural intelligence and is as powerful as natural intelligence and compatible with natural intelligence, and on the other hand, if no measures are taken, it will itself be considered a threat to humanity.EuroGlobal Journal of Linguistics and Language Education["Mohammad Ekram Yawar", "Abdul Jamil Shar\u0131fy"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/04f59b0094f201b0e84fd24937df4484f23be418 00bc31687356c72ee660d8c6bf6c14c4c25021bfThe integration of Artificial Intelligence (AI) in agriculture has revolutionized traditional farming practices, enhancing productivity, efficiency, and sustainability. This study highlights a workshop aimed at equipping students with practical AI skills, specifically focusing on linear regression techniques for crop growth prediction. The workshop, involved 55 students from nine Asian countries, fostering cross-cultural collaboration. Participants were introduced to theoretical concepts and engaged in hands-on training, covering data preprocessing, region of interest extraction, and model implementation using Python. The program emphasized the role of AI in addressing agricultural challenges such as resource optimization and food security. The workshop was conducted in five stages: preparation, implementation, evaluation, dissemination, and participant engagement. Pre and post-test evaluations revealed a significant improvement in participants’ AI knowledge, with average scores increasing from 45% to 85%. Practical activities enabled students to connect theoretical knowledge with real-world applications, enhancing their ability to predict crop growth using AI techniques. Dissemination efforts included reports and publications to inspire similar global initiatives. The results demonstrated the workshop's effectiveness in bridging knowledge gaps, fostering sustainable agricultural practices, and preparing a skilled workforce capable of leveraging AI to address future challenges in smart farming.International Journal Of Community Service130A workshop aimed at equipping students with practical AI skills, specifically focusing on linear regression techniques for crop growth prediction, demonstrated the workshop's effectiveness in bridging knowledge gaps, fostering sustainable agricultural practices, and preparing a skilled workforce capable of leveraging AI to address future challenges in smart farming.International Journal Of Community Service["Nurchim Nurchim", "Nurmalitasari Nurmalitasari"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/00bc31687356c72ee660d8c6bf6c14c4c25021bf 7be27b0b731db4832a9db7ad7f9b836777cd536eThe rapid advancement of generative artificial intelligence (AI), particularly tools like ChatGPT, is reshaping educational landscapes by enabling students to generate responses that closely mimic human-written answers. This development presents both opportunities and challenges for e-assessments, especially concerning academic integrity and the authenticity of student learning outcomes. Traditional assessment methods, which often emphasize memorization and standardized testing, are proving insufficient in this new context, as they may not effectively measure higher-order skills like critical thinking, creativity, and problem-solving. This study employs a systematic literature review (SLR) to investigate adaptive e-assessment strategies in higher education that address the integrity challenges posed by generative AI while supporting meaningful learning. Through an in-depth analysis of recent literature on e-assessment practices and AI integration, this study identifies key adaptive strategies such as randomized questioning, project-based assessments, open-book exams, and AI-enhanced plagiarism detection. The findings reveal that while generative AI complicates the assessment process, it also provides an impetus for rethinking assessment design in ways that promote application-based knowledge and discourage cheating. By advocating for a shift towards assessments that evaluate critical skills rather than rote knowledge, this study proposes a framework that can support educators in creating robust, integrity-focused e-assessments. This research contributes to the evolving discourse on educational assessment, offering practical recommendations for institutions aiming to balance the benefits of AI-enhanced learning with the need for fair and accurate assessments.Electronic Journal of e-Learning380This study identifies key adaptive strategies such as randomized questioning, project-based assessments, open-book exams, and AI-enhanced plagiarism detection and proposes a framework that can support educators in creating robust, integrity-focused e-assessments.Electronic Journal of e-Learning["Tapiwa Gundu"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/7be27b0b731db4832a9db7ad7f9b836777cd536e 4db7241706b0c4a17969de446fabba86b965a7cdThe advent of Artificial Intelligence (AI) has changed the way various jobs are done globally. In journalism, AI has been increasingly adopted in recent time in newsrooms, with tasks such as content generation, data analysis, and social media management being automated. This trend has sparked concerns among journalists all over the world, including Nigeria, particularly Ebonyi State, on the possibility of machines taking over their jobs in no distant time. This study investigated how practicing journalists in Ebonyi State, Nigeria, perceive the security of their job in the face of emerging era of AI. The objectives of the study were to ascertain the level of awareness of AI by practicing journalists in Ebonyi State, investigate the likely effects of AI on their job security, and evaluate their perception of job security in the era of AI. The study adopted the descriptive survey research design. Structured questionnaire was the instrument for data collection. A total of 280 practising journalist in Ebonyi state participated in the study. Data were collected using a structured questionnaire which had a consistency or reliability coefficient of 0.85. Analysis was done using descriptive statistics and summarised using frequency tables. Findings show that majority of the respondents did not feel that their jobs were at risk of being taken over by AI but that AI would complement them in their jobs and increase their productivity. The study concludes that journalists in Ebonyi State appear not to face immediate replacement by AI. The study recommends that Nigerian journalists particularly those in Ebonyi State should not be complacent with the current situation of things. Instead, they should acquire relevant skills that would make them to remain relevant in the era of Artificial Intelligence (AI).  International Journal of Educational Research &amp; Social Sciences330Investigation of how practicing journalists in Ebonyi State, Nigeria, perceive the security of their job in the face of emerging era of AI concludes that journalists in Ebonyi State appear not to face immediate replacement by AI.International Journal of Educational Research &amp; Social Sciences["Kenneth Adibe Nwafor", "Johnson Chinasa Alegu", "Ifeyinwa Nsude", "C. Oketa", "Samuel Nweze", "Ede Francisca Nwakaego", "Imakwu Veronica Nkechi", "Veronica Nkechi Imakwu", "Ogbu Joy Anulika", "Christian O. Aleke"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/4db7241706b0c4a17969de446fabba86b965a7cd 6ad55be001e58bed3147c831ab9fa41f3461200eOBJECTIVE To examine the potential of artificial intelligence (AI) in gynecologic oncology decision making. DESIGN Feasibility study. SETTING Fictive. PARTICIPANTS Fictitious case vignettes of gynecologic carcinomas. METHODS Fictitious case vignettes of gynecologic carcinomas were created and evaluated by physicians with varying levels of professional experience, as well as by language models including Chat-GPT 4.0, Google Gemini, and Bing-Copilot. Treatment approval decisions were based on standardized clinical and laboratory criteria. RESULTS Two cases of breast cancer, one case of ovarian cancer, one case of cervical cancer and one case of endometrial cancer were evaluated. All three language models were able to evaluate all clinical cases and make therapy-relevant suggestions, with Chat-GPT providing the most clear and concise recommendations that were in three cases totally consistent with physician assessments. CONCLUSIONS The study demonstrates that AI models, such as Chat-GPT, can to some extent evaluate clinical cases, recognize clinical and/or laboratory abnormalities and make therapy-related suggestions. Despite high overall agreement, differences were predominantly noted in the more complex cases, rendering human interpretation necessary. The findings underscore the benefits of AI in terms of clarity, time efficiency, and cost-effectiveness. Future research should further explore the application of AI to real patient data and development of hybrid decision models to optimize integration into clinical practice. LIMITATIONS Feasibility study with five fictitious case vignettes.Gynecologic and Obstetric Investigation00The study demonstrates that AI models, such as Chat-GPT, can to some extent evaluate clinical cases, recognize clinical and/or laboratory abnormalities and make therapy-related suggestions and underscore the benefits of AI in terms of clarity, time efficiency, and cost-effectiveness.Gynecologic and obstetric investigation["Iason Psilopatis", "Nadezda Sipulina", "Frederik A Stuebs", "F. Heindl", "Patrik Poeschke", "Simon Bader", "Annika Krueckel", "Peter A. Fasching", "Matthias W Beckmann", "Julius Emons"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/6ad55be001e58bed3147c831ab9fa41f3461200e 8b8bc46884e387471fba1b6a9f5944cbbd3cff07In recent years, safety concerns in aquaculture have become increasingly prominent due to various factors. Concurrently, the emergence of artificial intelligence (AI) has offered novel approaches to addressing these challenges. This paper provides a comprehensive review and synthesis of AI applications in aquaculture safety over the past few decades, while also suggesting future directions. Utilizing bibliometric tools such as Citespace and VOSviewer, we analyzed 513 publications spanning from 1998 to 2025. Our analysis highlighted a growing global research interest in this emerging field. Furthermore, it is forecasted that the integration of remote sensing technology, immune response monitoring, and cross-disciplinary innovations will drive the transformation of aquaculture safety management toward a more intelligent, proactive, and sustainable approach. These advancements are expected to enhance the precision and efficiency of risk assessment and disease prevention in aquaculture systems.Fishes750It is forecasted that the integration of remote sensing technology, immune response monitoring, and cross-disciplinary innovations will drive the transformation of aquaculture safety management toward a more intelligent, proactive, and sustainable approach.Fishes["Feng Ma", "Zewen Fan", "Anna Nikolaeva", "Haoran Bao"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/8b8bc46884e387471fba1b6a9f5944cbbd3cff07 ee41f3e7e45b4affa92371478c8b875d045baf40International Journal of Ethics Education860It is suggested that meta-learning plays fundamental roles in practical wisdom and its cultivation within moral domains and the potential implications of the introduction of the concept, meta-learning, to moral education addressing concerns and issues related to socio-cultural diversity.International Journal of Ethics Education["Hyemin Han"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/ee41f3e7e45b4affa92371478c8b875d045baf40 80984ff5907073a57a2841fdf45a3d100a5b59dfEndocrine170The proposed iterative self-learning method using a large volume of ultrasonographic images can assist beginners in thyroid imaging to differentiate benign and malignant thyroid nodules and AI-CAD can improve the diagnostic performance across varied levels of experience in thyroid imaging.Endocrine["Daham Kim", "Yoon-A Hwang", "Youngsook Kim", "Hye Sun Lee", "E. Lee", "Hyunju Lee", "Jung Hyun Yoon", "V. Y. Park", "Miribi Rho", "Jiyoung Yoon", "Si Eun Lee", "Jin Young Kwak"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/80984ff5907073a57a2841fdf45a3d100a5b59df a03f0a9fddae8184da66dad952a7a8d57a660c9dPurpose This study, using stakeholder theory and diffusion of innovations (DOIs), aims to examine the readiness of Omani health-care firms to adopt artificial intelligence (AI). This adoption is seen as a key step towards ensuring green innovation and sustainable performance (SP) in the health-care sector. Design/methodology/approach This study adopted convenience and referral sampling techniques to enhance the response rate for the limited number of health-care firms using AI. Using explanatory research design, structure equation modelling and employees as the unit of analysis, a random sample technique is used to distribute the structured questionnaire to five hospitals in North Al-Batinah, including Shinas, Liwa and Sohar cities. Smart PLS 4.1 analyses the responses. Findings The research demonstrates that AI could significantly enhance SP, a finding that is of utmost importance in the current health-care landscape. This study also tested green knowledge sharing as a boundary condition. Furthermore, the study’s findings indicate that AI leads to the emergence of green innovation and SP, suggesting that firms are willing to adopt AI and achieve the sustainable development goals (SDGs). Practical implications This study implies that stakeholders, including the Omani Government and Middle Eastern firms, should prioritize investments in AI technologies tailored to sustainability initiatives. Originality/value This research study makes three significant and unique contributions. Firstly, it uniquely integrates stakeholder and DOIs theories to explain the mediating function of green innovation and the moderating effect of green knowledge sharing. Secondly, it provides a unique Middle Eastern context, where the government’s focus on the health sector is crucial. Finally, this study outlines a clear and actionable pathway for the Middle East to achieve the SDGs, thereby enlightening the reader on the potential of AI in the health-care sector. Society and Business Review830The research demonstrates that AI could significantly enhance SP, a finding that is of utmost importance in the current health-care landscape, and outlines a clear and actionable pathway for the Middle East to achieve the SDGs.Society and Business Review["Hanan Ahmed Abdullah Al-balushi", "Harcharanjit Singh", "Irfan Saleem"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/a03f0a9fddae8184da66dad952a7a8d57a660c9d 15b1bcd99d0210748750ef9dea4ae30f4489e6f9Artificial intelligence (AI) has become a revolutionary tool in the automotive sector, specifically in quality management and issue identification. This article presents a systematic review of AI implementations whose target is to enhance production processes within Industry 4.0 and 5.0. The main methods analyzed are deep learning, artificial neural networks, and principal component analysis, which improve defect detection, process automation, and predictive maintenance. The manuscript emphasizes AI’s role in live auto part tracking, decreasing dependance on manual inspections, and boosting zero-defect manufacturing strategies. The findings indicate that AI quality control tools, like convolutional neural networks for computer vision inspections, considerably strengthen fault identification precision while reducing material scrap. Furthermore, AI allows proactive maintenance by predicting machine defects before they happen. The study points out the importance of incorporating AI solutions in actual manufacturing methods to ensure consistent adaptation to Industry 5.0 requirements. Future investigations should prioritize transparent AI approaches, cyber-physical system consolidation, and AI material enhancement for sustainable production. In general terms, AI is changing quality assurance in the automotive industry, improving efficiency, consistency, and long-term results.Italian National Conference on Sensors790A systematic review of AI implementations whose target is to enhance production processes within Industry 4.0 and 5.0 indicates that AI quality control tools, like convolutional neural networks for computer vision inspections, considerably strengthen fault identification precision while reducing material scrap.Sensors (Basel, Switzerland)["O. Morales Matamoros", "Jos\u00e9 Guillermo Takeo Nava", "Jes\u00fas Jaime Moreno Escobar", "Blanca Alhely Ceballos Ch\u00e1vez"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/15b1bcd99d0210748750ef9dea4ae30f4489e6f9 586d00c42b15897be01511667254f793b203ed13Abstract Breast cancer (BC) remains a significant threat to women's health worldwide. The oncology field had an exponential growth in the abundance of medical images, clinical information, and genomic data. With its continuous advancement and refinement, artificial intelligence (AI) has demonstrated exceptional capabilities in processing intricate multidimensional BC‐related data. AI has proven advantageous in various facets of BC management, encompassing efficient screening and diagnosis, precise prognosis assessment, and personalized treatment planning. However, the implementation of AI into precision medicine and clinical practice presents ongoing challenges that necessitate enhanced regulation, transparency, fairness, and integration of multiple clinical pathways. In this review, we provide a comprehensive overview of the current research related to AI in BC, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact. Furthermore, this article emphasizes the significance of constructing patient‐oriented AI algorithms. Additionally, we explore the opportunities and potential research directions within this burgeoning field.Cancer Innovation1220A comprehensive overview of the current research related to AI in BC is provided, highlighting its extensive applications throughout the whole BC cycle management and its potential for innovative impact.Cancer Innovation["Kaixiang Feng", "Zongbi Yi", "Binghe Xu"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/586d00c42b15897be01511667254f793b203ed13 a666e7510cfe534423bed3e251dd8b11fb590443As information technology continues to advance, cybercriminals are exploiting various online platforms to carry out illegal activities. To counter these cyber threats, financial institutions, particularly in the banking sector, are increasingly adopting artificial intelligence (AI) as a solution. AI presents significant opportunities to drive growth and innovation in the banking industry. However, for AI to gain trust, it is essential to ensure transparency and explainability in its operations. AI technologies offer valuable insights into customer behavior, aiding in more informed decision-making. One example of an AI application is robo-advisory services, which are automated platforms powered by AI. Moreover, AI plays a critical role in protecting personal data and identifying fraudulent transactions, helping banks enhance their fraud detection and cyber security measures. Despite these benefits, the implementation and maintenance of AI systems come with high costs, and the rise of automation may result in job displacement.International Journal For Multidisciplinary Research150For AI to gain trust, it is essential to ensure transparency and explainability in its operations, and the implementation and maintenance of AI systems come with high costs, and the rise of automation may result in job displacement.International Journal For Multidisciplinary Research["Dr. M. Anbukarasi", "R. Prasanth"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/a666e7510cfe534423bed3e251dd8b11fb590443 5bbdf0ad2d9c085cc3103d79c9d671f6b4602551Deep learning artificial intelligence (AI) algorithms are poised to subsume diagnostic imaging specialists in radiology and nuclear medicine, where radiomics can consistently outperform human analysis and reporting capability, and do it faster, with greater accuracy and reliability. However, claims made for generative AI in respect of decision-making in the clinical practice of theranostic nuclear medicine are highly contentious. Statistical computer algorithms cannot emulate human emotion, reason, instinct, intuition, or empathy. AI simulates intelligence without possessing it. AI has no understanding of the meaning of its outputs. The unique statistical probability attributes of large language models of AI must be complemented by the innate human intuitive capabilities of nuclear physicians who accept the responsibility and accountability for direct clinical care of each individual patient referred for theranostic management of specified cancers. Complementarity envisions synergistic engagement with AI radiomics, genomics, radiobiology, dosimetry, and data collation from multidimensional sources, including the electronic medical record, to enable the nuclear physician to spend informed face time with their patient. Together with physician discernment, application of the technical insights from AI will facilitate optimal formulation of a personalized precision theranostic strategy for empathic, efficacious, targeted treatment of the patient with cancer in accordance with their wishes.Cancer Biotherapy and Radiopharmaceuticals390Complementarity envisions synergistic engagement with AI radiomics, genomics, radiobiology, dosimetry, and data collation from multidimensional sources, including the electronic medical record, to enable the nuclear physician to spend informed face time with their patient.Cancer biotherapy & radiopharmaceuticals["J. H. Turner"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/5bbdf0ad2d9c085cc3103d79c9d671f6b4602551 c3a89729983a77bf0e147e78b7c291f8dcabe2d3 The use of artificial intelligence (AI) in healthcare is a growing field of research and clinical application. The views of the general public, ie future healthcare users, need to be surveyed and interpreted so that researchers and the public have a shared understanding of the appropriate use of AI. Currently, there is only limited data on the public’s views. An anonymous, quantitative questionnaire was administered as part of a public exhibition on AI. The questionnaire was based on previously validated questions designed to assess respondents’ views on the use of AI in healthcare. Brief demographic data were also collected. The population surveyed was more diverse and younger than the general UK population (65% white, 45% aged 18-29). Respondents were largely comfortable with the application of AI in healthcare: 80% felt positively about its use, 56% thought it would be safe. 70% did not feel that it would replace doctors, and most would not be happy for AI to make decisions without considering their feelings. Our study shows that the population we surveyed, particularly young future healthcare users, are comfortable with the use of AI in healthcare, but do not see it as a replacement for doctors. This paper highlights views from the general public on the use of AI in healthcare, which is largely under researched. BJR|Artificial Intelligence00Ways from the general public on the use of AI in healthcare, which is largely under researched, are highlighted, particularly young future healthcare users, are comfortable with the use of AI in healthcare, but do not see it as a replacement for doctors.BJR|Artificial Intelligence["Carolyn Horst", "Muhammad Aniq", "Alice Taylor-Gee", "Jennifer Wong", "Vicky Goh"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/c3a89729983a77bf0e147e78b7c291f8dcabe2d3 0a0fe4089a7ade1c9e713ba7b4e66ea48c03958eThe agricultural sector is a vital part of Armenia's economy, significantly impacting the livelihoods of many people and contributing to the country's GDP. With global challenges like climate change, population growth, and resource scarcity, it has become essential to improve agricultural productivity and sustainability. In this light, the Ministry of Economy of the Republic of Armenia should look into innovative methods for modernizing agriculture, particularly focusing on the use of artificial intelligence and computer science.Регион и мир / Region and the World60The Ministry of Economy of the Republic of Armenia should look into innovative methods for modernizing agriculture, particularly focusing on the use of artificial intelligence and computer science.Регион и мир / Region and the World["G. Smbatyan"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/0a0fe4089a7ade1c9e713ba7b4e66ea48c03958e e4a3db30b97818f2e77fdd3f7cc7f339cb4bffe2Simple Summary In recent years, there has been a progressive and growing interest in artificial intelligence (AI) and its potential applications in the medical field, including hepatology. Given the significant and increasing global prevalence of MASLD and its impact on daily clinical practice, the use of AI in this field could have positive implications for both clinicians and patients. This narrative review aims to summarize the currently available evidence on the potential applications of AI in MASLD, from diagnosis and risk stratification to patient counseling and the development of new treatment options.Cancers720This narrative review aims to summarize the currently available evidence on the potential applications of AI in MASLD, from diagnosis and risk stratification to patient counseling and the development of new treatment options.Cancers["Nicola Pugliese", "A. Bertazzoni", "Cesare Hassan", "J. M. Schattenberg", "Alessio Aghemo"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/e4a3db30b97818f2e77fdd3f7cc7f339cb4bffe2 5a81b9633cdcebbb1b09ca0fa21657165f2c0014This rapid advancement and development of Artificial Intelligence (AI) technologies has significantly transformed various sectors, like research and development sectors, education sectors including education. As educational institutions increasingly integrate AI tools and systems, to gather information, data and other necessary items, use of Artificial Intelligence (AI) has become a common practice as well as a basic necessity. This study explores the necessity of incorporating AI ethics into education curricula, examining how ethical considerations can guide the development, deployment, and use of AI technologies within academic settings, this study also includes the negative impact that AI is putting on our human mind, human intelligence and the thinking ability of the human beings. We will find out how the AI is making us lazy not to use our own brain or study books and other informative items like journals, research papers, magazines etc to gather information. Through a comprehensive review of current literature and case studies, the research highlights key ethical concerns such as data privacy, algorithmic bias, and the impact of AI on academic integrity and student outcomes (Anderson & Anderson, 2018; Binns, 2018). The study also assesses existing frameworks and guidelines for AI ethics and their applicability to education contexts (Chen et al., 2020). By identifying gaps and proposing actionable recommendations, this study aims to provide educators, policymakers, and AI practitioners with a strategic approach to embedding ethical practices in AI-related education. The findings underscore the importance of developing a multidisciplinary approach to AI ethics that incorporates insights from computer science, philosophy, law, and education to ensure that AI technologies are used responsibility and equitably in academic environments (Floridi, 2019; Holmes et al., 2019).International Journal For Multidisciplinary Research110This study explores the necessity of incorporating AI ethics into education curricula, examining how ethical considerations can guide the development, deployment, and use of AI technologies within academic settings, and includes the negative impact that AI is putting on human mind, human intelligence and the thinking ability of the human beings.International Journal For Multidisciplinary Research["H. Vashistha", "Harikrishnan M"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/5a81b9633cdcebbb1b09ca0fa21657165f2c0014 08756c5fbb907dd52256f67404e8edcdc1f4daa4With the rapid development of the “Internet + Medical” model, artificial intelligence technology has been widely used in the analysis of medical images. Among them, the technology of using deep learning algorithms to identify features of ultrasound and pathological images and realize intelligent diagnosis of diseases has entered the clinical verification stage. This study is based on the application research of artificial intelligence technology in medical diagnosis and reviews the early screening and diagnosis of thyroid diseases. The cure rate of thyroid disease is high in the early stage, but once it deteriorates into thyroid cancer, the risk of death and treatment costs of the patient increase. At present, the early diagnosis of the disease still depends on the examination equipment and the clinical experience of doctors, and there is a certain misdiagnosis rate. Based on the above background, it is particularly important to explore a technology that can achieve objective screening of thyroid lesions in the early stages. This paper provides a comprehensive review of recent research on the early diagnosis of thyroid diseases using artificial intelligence technology. It integrates the findings of multiple studies and that traditional machine learning algorithms are widely used as research objects. The convolutional neural network model has a high recognition accuracy for thyroid nodules and thyroid pathological cell lesions. U-Net network model can significantly improve the recognition accuracy of thyroid nodule ultrasound images when used as a segmentation algorithm. This article focuses on reviewing the intelligent recognition technology of thyroid ultrasound images and pathological sections, hoping to provide researchers with research ideas and help clinicians achieve intelligent early screening of thyroid cancer.Frontiers in Oncology810This article focuses on reviewing the intelligent recognition technology of thyroid ultrasound images and pathological sections, hoping to provide researchers with research ideas and help clinicians achieve intelligent early screening of thyroid cancer.Frontiers in Oncology["Lina Yang", "XinYuan Wang", "Shixia Zhang", "Kun Cao", "Jianjun Yang"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/08756c5fbb907dd52256f67404e8edcdc1f4daa4 5a738cfc15284dd502b2d3d72f039f9c89aea2ebThis research explores the integration of artificial intelligence (AI) into art therapy to enhance trauma recovery.Leveraging AI's capabilities in image analysis and emotion recognition, we developed a framework thatprovides personalized feedback and insights to both therapists and clients. For instance, AI algorithmsanalyzed artwork for patterns indicative of emotional distress, mirroring techniques used in studies showingart's efficacy in PTSD symptom reduction (e.g., Malchiodi, 2012). We conducted a pilot study with 30participants diagnosed with PTSD, using AI-enhanced art therapy sessions. Preliminary results indicate asignificant reduction in trauma symptoms, measured via standardized scales, compared to traditional arttherapy. The AI's ability to identify subtle emotional cues, such as color choices and brushstroke intensity,facilitated deeper therapeutic conversations. This approach demonstrates the potential of AI to personalize andamplify the benefits of art therapy for trauma survivors.Cuestiones de Fisioterapia210Leveraging AI's capabilities in image analysis and emotion recognition, a framework that provides personalized feedback and insights to both therapists and clients is developed that demonstrates the potential of AI to personalize and amplify the benefits of art therapy for trauma survivors.Cuestiones de Fisioterapia["Kamal Kumar Srivastava", "Dr. Ganesh Gorakhnath Gule"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/5a738cfc15284dd502b2d3d72f039f9c89aea2eb a3f0b23e5e0274c636ae1b845a31b5fae17f7d49The optimization of Artificial Intelligence (AI) systems is critical for improving performance, scalability, and adaptability across various real-world applications. This paper explores key optimization techniques, including algorithmic enhancements, hardware acceleration, software tools, and data preprocessing. Challenges such as resource constraints, domain-specific requirements, and ethical concerns are analyzed. Case studies in healthcare, finance, manufacturing, and autonomous systems demonstrate notable improvements in accuracy, efficiency, and scalability. A systematic framework is proposed to guide AI optimization, incorporating iterative testing, hardware-software integration, and deployment strategies. The findings highlight AI optimization’s transformative potential in developing scalable, efficient, and ethical systems. Future research directions include the creation of generalizable frameworks, energy-efficient AI, and fairness-aware optimization to ensure broader applicability and equity.International Journal of Scientific World00This paper explores key optimization techniques, including algorithmic enhancements, hardware acceleration, software tools, and data preprocessing, to highlight AI optimization’s transformative potential in developing scalable, efficient, and ethical systems.International Journal of Scientific World["Ridwan Boya Marqas", "Saman M. Almufty", "Prof. Dr. ENG\u0130N AVCI", "Renas R. Asaad"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/a3f0b23e5e0274c636ae1b845a31b5fae17f7d49 876a83a9389f189c2123ce9e740cd4c93a712c15PurposeArtificial intelligence (AI) has the potential to dramatically change the human approaches to work, and specifically to learning and development. While AI coaching can reduce costs and increase accessibility, it also presents both opportunities and threats to human coaches. The objective of this study was to conduct a systematic literature review of peer-reviewed research on the use of AI in coaching.Design/methodology/approachA systematic literature review (SLR) method was used to search eight databases for articles produced up to March 2024. Data extraction was conducted, with Quality Assessment undertaken independently, in parallel, using two researchers and a third arbiter. The ROBINS-I tool was used to assess the risk of bias in the included studies. A narrative synthesis of a total of 16 quantitative, qualitative or mixed-method studies covering n = 2312.FindingsThe SLR identified four key themes: Research design and AI integration, AI usefulness in coaching, impact of AI coaching and ethical considerations. The findings suggest that AI coaches can be effective, accepted, useful and match human coaches in competence for specific tasks.Practical implicationsAI coaching is a growing area of practice and research. This paper brings together the literature and identifies future research priorities and potential next steps in AI coach development.Originality/valueThe paper uses clinical research SLR methods applying these robust processes to the field of organisational research, to set a new standard through the use of a pre-determined research protocol, quality assessment and ROB, well providing a comprehensive literature review of AI coaching.Journal of Work-Applied Management490The paper uses clinical research SLR methods applying these robust processes to the field of organisational research, to set a new standard through the use of a pre-determined research protocol, quality assessment and ROB, well providing a comprehensive literature review of AI coaching.Journal of Work-Applied Management["Jonathan Passmore", "Bergsveinn Olafsson", "David Tee"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/876a83a9389f189c2123ce9e740cd4c93a712c15 5f10eaa5ef18115bf74375659fdcd7ac3b63bd69The fundamental issue of urban traffic is the time of vehicle travel along the chosen route. It is clear that this time should be minimized for each driver. In a large city, there may be more than a million such drivers. The basic element and at the same time the basic problem of traffic control in a metropolis is a single intersection. It is this object where city roads intersect that is both the main cause and source of traffic jams. Therefore, the first priority is to implement intelligent regulation of vehicle traffic through a single intersection. By organizing efficient traffic through such an object, we will achieve high traffic efficiency throughout the city. There is a whole range of approaches to solving the problem of traffic control through in-ter¬sections. An important direction is the use of computer modeling based on artificial intel-ligence (AI) methods. An intersection model and an AI-based algorithm for implementing the passage of cars through such an object are proposed, which allows optimizing traffic. The se-cond important aspect of optimizing the traffic process is proposed, which is based on mode-ling the urban transport network using an oriented nonplanar weighted multigraph. Graph theo-ry algorithms are used to optimize the passage of each vehicle along the selected route.Èlektronnoe modelirovanie100An intersection model and an AI-based algorithm for implementing the passage of cars through such an object are proposed, which allows optimizing traffic and is based on mode-ling the urban transport network using an oriented nonplanar weighted multigraph.Èlektronnoe modelirovanie["P. Nikolyuk", "O.V. Zelinska"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/5f10eaa5ef18115bf74375659fdcd7ac3b63bd69 691b9bb1ea2818ff1540073e8cdae9bbe495fe8fQueen Mary Journal of Intellectual Property00Queen Mary Journal of Intellectual Property[]2025-02-20T00:00:00https://www.semanticscholar.org/paper/691b9bb1ea2818ff1540073e8cdae9bbe495fe8f 2b98caf2386f2dea861ebf30a85c6906765884dbMonoclonal antibodies in immunodiagnosis and immunotherapy40Monoclonal antibodies in immunodiagnosis and immunotherapy["Andrei Moroz", "Cory L. Brooks"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/2b98caf2386f2dea861ebf30a85c6906765884db 498ee0d90daedf668d092f5c0ccc094870f5500aCureus350Cureus["Akshanda Dhumale", "Sandeep Shinde", "Manoj P Ambali", "Prakash Patil"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/498ee0d90daedf668d092f5c0ccc094870f5500a bc06e0b0c451930d31881b090360ecd96dee813fJournal of Supercomputing160J. Supercomput.["Amjad Iqbal", "Rashid Amin"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/bc06e0b0c451930d31881b090360ecd96dee813f d1bd2707f670b868275cbb77273a80a90875bb23INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY["Venkata Sampath Kumar Mutharaju"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/d1bd2707f670b868275cbb77273a80a90875bb23 0aa5f284839e7cb3ed6b4a1e4752d2cb583942e5Radiography350AI has potential in assessing breast composition in MRI, however, variability in AI systems deployed and statistical measurements alongside limited validation across diverse populations remain an issue.Radiography["P.C. Murphy", "M. McEntee", "M. Maher", "M.F. Ryan", "C. Harman", "A. England", "N. Moore"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/0aa5f284839e7cb3ed6b4a1e4752d2cb583942e5 bde612a53995769f7276bb01813f806e2ef7d08400["Muhammad Ali Syed", "Shahzad Ali", "Tanveer Hussain"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/bde612a53995769f7276bb01813f806e2ef7d084 545abb35b5f3be3b2a4c67bef34a35bb89fb0853IARJSET00IARJSET["Zahra Jabeen", "Khusboo Mishra", "B. Mishra"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/545abb35b5f3be3b2a4c67bef34a35bb89fb0853 2335798326c09f400bdd16d302789cc4979c512aCureus240Cureus["Enas Abu Abeelh", "Zain Abuabeileh"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/2335798326c09f400bdd16d302789cc4979c512a 607323eadd3500184909e29a61075bddec3b254bThe Pandeiros River Environmental Protection Area is an important Brazilian conservation unit used for ecotourism. However, there is a lack of research guiding decision-making regarding tourist movements. The objective of this study is to evaluate the use of a simplified version of the clonal selection metaheuristic for optimizing tourist itineraries. Thirty-one tourist sites were considered, with routes starting from three origins. A mathematical model based on the vehicle routing problem is proposed. This problem was solved using the branch and bound, clonal selection, and simulated annealing algorithms, and the proposed simplification for the clonal selection metaheuristic. Random solutions were evaluated to simulate tourist behaviour. Random solutions yield the worst results. The proposed simplification produced better results for itineraries starting from two origins. It provided an average reduction of 42% in the total distance of tourist itineraries and a 17% reduction in the use of available road networks.Pesquisa Florestal Brasileira220A simplified version of the clonal selection metaheuristic for optimizing tourist itineraries is evaluated based on the vehicle routing problem and provided an average reduction of 42% in the total distance of tourist itineraries and a 17% reduction in the use of available road networks.Pesquisa Florestal Brasileira["Carlos Alberto Ara\u00fajo J\u00fanior", "H\u00e9lio Garcia Leite", "Jo\u00e3o Batista Mendes"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/607323eadd3500184909e29a61075bddec3b254b d603e1369c4848e81c3d726d8619688f23bb91ffOvidius University Annals: Economic Sciences Series00Ovidius University Annals. Economic Sciences Series["Florin Condrea"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/d603e1369c4848e81c3d726d8619688f23bb91ff 576f6915c57ac9918a55253612722dbd17639ec9Ovidius University Annals: Economic Sciences Series00Ovidius University Annals. Economic Sciences Series["Alexandra Popa", "C. Barbu", "Adrian Serban-Comanescu"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/576f6915c57ac9918a55253612722dbd17639ec9 a2774f2715631eaeea4800cf969b6ac6e0d91378Ovidius University Annals: Economic Sciences Series00Ovidius University Annals. Economic Sciences Series["M. Duic\u0103", "G. Bondac"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/a2774f2715631eaeea4800cf969b6ac6e0d91378 89831ea17986ca9ad4d08895fd38e7cc854b46dcScientific Reports480It is demonstrated that machine learning techniques can reliably predict survival probabilities for HCC patients across all disease stages and that AI models can accurately identify a high proportion of surviving individuals when assessing various clinical and pathological factors.Scientific Reports["\u0130smet Seven", "Do\u011fan Bayram", "Hilal Arslan", "F. T. K\u00f6\u015f", "K\u00fcbranur G\u00fcm\u00fc\u015fl\u00fc", "Selin Akt\u00fcrk Esen", "M\u00fccella \u015eahin", "M. \u015eendur", "D. Uncu"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/89831ea17986ca9ad4d08895fd38e7cc854b46dc 523e14ae5f62eee718569461902e8adae42e43f9Objective: To explore the potential of AI-powered chatbots, specifically ChatGPT, in identifying and correcting flaws in MCQs. Methods: A three-phase-Interventional study was conducted from February to August 2023 at Riphah International University, Islamabad. In Phase-1, flawed MCQs were selected from the NBME guide and fed into ChatGPT. ChatGPT identified item flaws and suggested corrections. In Phase-2, ChatGPT was trained to detect flaws in MCQs with text data from the NBME item writing guide. In Phase-3, ChatGPT was again tested to detect flaws and correct MCQs. Data were analyzed using SPSS, Version 26 and presented using percentages and McNemar’s test with exact conditional method. Results: ChatGPT could identify and correct flaws such as use of “None of the above,” “Grammatical cues,” “absolute terms,” and “inconsistently presented numerical data.” However, it struggled with flaws related to “complicated stems,” “long or complex options,” and “vague frequency terms.” After training, ChatGPT became better at identifying and correcting flaws related to complicated stems and absolute terms. It also struggled with recognizing “nonparallel options,” “convergence,” and “word repetition,” both before and after training. ChatGPT’s performance deteriorated during peak hours. The test of significance showed no measurable increase in ChatGPT’s efficiency in detecting item flaws (p = 1.00) and correcting them (p = 0.125). Conclusion: AI is revolutionizing industries and improving efficiency, but limitations exist in complex conversations, analysis, accuracy, and error prevention. Ongoing research is vital to unlocking AI’s potential, especially in education. doi: https://doi.org/10.12669/pjms.41.3.11224 How to cite this: Sabqat M, Khan RA, Jawaid M, Sajjad M. Artificial Intelligence Meets Item Analysis (AI meets IA): A Study of Chatbot Training and Performance in detecting and correcting MCQ Flaws. Pak J Med Sci. 2025;41(3):652-656. doi: https://doi.org/10.12669/pjms.41.3.11224 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Pakistan Journal of Medical Sciences250ChatGPT could identify and correct flaws such as use of “None of the above,” “Grammatical cues,” “absolute terms,” and “inconsistently presented numerical data,” but struggled with flaws related to “complicated stems,” “long or complex options,” and “vague frequency terms.”Pakistan Journal of Medical Sciences["Mashaal Sabqat", "Rehan Ahmed Khan", "Masood Jawaid", "M. Sajjad"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/523e14ae5f62eee718569461902e8adae42e43f9 2236fbb4fb3f79124e57e2cbb497656feb5f080cINTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION RESEARCH AND DEVELOPMENT00INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION RESEARCH AND DEVELOPMENT["Madhavi Reddy"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/2236fbb4fb3f79124e57e2cbb497656feb5f080c 1b6071373de81dbde9b9edc17997a1489eb75c2ePurpose: This research aimed to assess the impact of AI in businesses that have implemented the technology to enhance employee satisfaction and optimize HRM efficiency. Design methodology approach: This study adopted a cross-sectional descriptive research design whereby the respondents’ data was collected at a single point in time. Participants included 180 international HR professionals working in organizations that have entities in the US, the UAE, and Jordan. Finding: Realize the potential benefits of AI for HRM, it is recommended that companies invest in such knowledge and expertise related to the field, integrate these solutions with other existing frameworks and systems without disrupting the process, prioritize data privacy and security, manage changes that the applications might bring to the employees, and seek affordable AI options that are specific to the needs of the human resource department. Practical implications: Enhanced efficiency and satisfaction, need for strategic investment in AI, data privacy and security considerations and cost-effectiveness and tailored solutions Originality/value: Providing empirical evidence on the impact of AI in HRM across different international contexts, specifically in the US, the UAE, and Jordan. The originality lies in the cross-sectional descriptive design which offers a snapshot of the current state of AI adoption in HRM and its effect and efficiency and employee satisfaction, furthermore, this research is particularly valuable for HR professionals, business leaders, and policymakers seeking to understand the nuances of AI integration in HRM and its implications for the future workforce.Journal of Ecohumanism200This research is particularly valuable for HR professionals, business leaders, and policymakers seeking to understand the nuances of AI integration in HRM and its implications for the future workforce.Journal of Ecohumanism["Raed.H. Wishah", "Fida Zakzouk", "Leila Rawashdeh", "Emad Ahmed"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/1b6071373de81dbde9b9edc17997a1489eb75c2e 06e30018795c31b244009e944394d62cabff1861Journal of the American Medical Association (JAMA)40JAMA["Haider Warraich", "Troy Tazbaz", "R. Califf"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/06e30018795c31b244009e944394d62cabff1861 20b50279026b053377e96177d5fa0a4eb03dd203Journal of the American Medical Association (JAMA)50JAMA["Nikhil Jaiswal", "Konrad Samsel", "L. A. Celi"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/20b50279026b053377e96177d5fa0a4eb03dd203 c0a44b3b45b865db3e40f4fe747c3dc87a92370bNature Reviews Biodiversity1590Nature Reviews Biodiversity["Laura J. Pollock", "Justin Kitzes", "Sara Beery", "Kaitlyn M. Gaynor", "Marta A. Jarzyna", "Oisin Mac Aodha", "Bernd Meyer", "David Rolnick", "Graham W. Taylor", "D. Tuia", "Tanya Berger-Wolf"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/c0a44b3b45b865db3e40f4fe747c3dc87a92370b 125703dc9a19b73c670fa707ebe5ce99bf8505a3IJARCCE00IJARCCE["F. Ekpar"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/125703dc9a19b73c670fa707ebe5ce99bf8505a3 c486ac21a2ce7cfe5a8e84c021ca6fde8d05a6a0Ovidius University Annals: Economic Sciences Series00Ovidius University Annals. Economic Sciences Series["Marieta Stanciu", "Drago\u0219 Stuparu"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/c486ac21a2ce7cfe5a8e84c021ca6fde8d05a6a0 4e50b0828e4bf4b02d983a06ac4873ee2539a2c1A major step toward Artificial General Intelligence (AGI) and Super Intelligence is AI's ability to autonomously conduct research - what we term Artificial Research Intelligence (ARI). If machines could generate hypotheses, conduct experiments, and write research papers without human intervention, it would transform science. Sakana recently introduced the 'AI Scientist', claiming to conduct research autonomously, i.e. they imply to have achieved what we term Artificial Research Intelligence (ARI). The AI Scientist gained much attention, but a thorough independent evaluation has yet to be conducted. Our evaluation of the AI Scientist reveals critical shortcomings. The system's literature reviews produced poor novelty assessments, often misclassifying established concepts (e.g., micro-batching for stochastic gradient descent) as novel. It also struggles with experiment execution: 42% of experiments failed due to coding errors, while others produced flawed or misleading results. Code modifications were minimal, averaging 8% more characters per iteration, suggesting limited adaptability. Generated manuscripts were poorly substantiated, with a median of five citations, most outdated (only five of 34 from 2020 or later). Structural errors were frequent, including missing figures, repeated sections, and placeholder text like 'Conclusions Here'. Some papers contained hallucinated numerical results. Despite these flaws, the AI Scientist represents a leap forward in research automation. It generates full research manuscripts with minimal human input, challenging expectations of AI-driven science. Many reviewers might struggle to distinguish its work from human researchers. While its quality resembles a rushed undergraduate paper, its speed and cost efficiency are unprecedented, producing a full paper for USD 6 to 15 with 3.5 hours of human involvement, far outpacing traditional researchers.200An evaluation of the AI Scientist reveals critical shortcomings, and represents a leap forward in research automation, which generates full research manuscripts with minimal human input, challenging expectations of AI-driven science.["Joeran Beel", "Min-Yen Kan", "Moritz Baumgart"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/4e50b0828e4bf4b02d983a06ac4873ee2539a2c1 def370198433f018eaaf72228e12396b4635f825Abstract The objective of this opinion article is to interpret the role of neuroeducation in the development of knowledge in immersive spaces with artificial intelligence; the knowledge construction has been present since the human thinking brain began to build tools to survive. Immersive learning environments facilitate the continuous and controlled recreation of the facts and events to occur to learn from the external stimuli of the environment and their internal interpretation in each brain and system of neurons and neurotransmitters in the brain regions. With the tool of generative artificial intelligence, it is a communication advance between machine and humans that allow the information exchanging from a processor that provides analysis of an algorithm and neural networks to predict the probability of an event data, and thus provide lines of action that let appropriate solutions for humanity. Keywords: Neuroeducation, generative artificial intelligence, immersive reality.REVISTA MUCIN00The role of neuroeducation is interpreted in the development of knowledge in immersive spaces with artificial intelligence; the knowledge construction has been present since the human thinking brain began to build tools to survive.REVISTA MUCIN["Luis Valladares R\u00edos"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/def370198433f018eaaf72228e12396b4635f825 b022c8019ad98b128c2e174f3642deae9a1e7f54Abstract Background The integration of artificial intelligence (AI) in health care settings demands a nuanced approach that considers both technical performance and sociotechnical factors. Objective This study aimed to develop a checklist that addresses the sociotechnical aspects of AI deployment in health care and provides a structured, holistic guide for teams involved in the life cycle of AI systems. Methods A literature synthesis identified 20 relevant studies, forming the foundation for the Clinical AI Sociotechnical Framework checklist. A modified Delphi study was then conducted with 35 global health care professionals. Participants assessed the checklist’s relevance across 4 stages: “Planning,” “Design,” “Development,” and “Proposed Implementation.” A consensus threshold of 80% was established for each item. IQRs and Cronbach α were calculated to assess agreement and reliability. Results The initial checklist had 45 questions. Following participant feedback, the checklist was refined to 34 items, and a final round saw 100% consensus on all items (mean score >0.8, IQR 0). Based on the outcome of the Delphi study, a final checklist was outlined, with 1 more question added to make 35 questions in total. Conclusions The Clinical AI Sociotechnical Framework checklist provides a comprehensive, structured approach to developing and implementing AI in clinical settings, addressing technical and social factors critical for adoption and success. This checklist is a practical tool that aligns AI development with real-world clinical needs, aiming to enhance patient outcomes and integrate smoothly into health care workflows.JMIRx Med367The Clinical AI Sociotechnical Framework checklist provides a comprehensive, structured approach to developing and implementing AI in clinical settings, addressing technical and social factors critical for adoption and success.JMIRx Med["A. Owoyemi", "J. Osuchukwu", "M. Salwei", "A. Boyd"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/b022c8019ad98b128c2e174f3642deae9a1e7f54 809a7eccbcd26113862091663ee4f877d50c2ca8The introduction of artificial intelligence (AI) into educational institutions is part of a global trend shaped by the capabilities of this technology. However, due to the disruptive nature of AI technologies, it greatly affects the way of teaching and learning. It is therefore essential to establish clear guidelines that not only ensure that all competencies required by the curricula are still effectively taught, but also empower students to use the new technology in a productive manner. Developing such guidelines for emerging and dynamic technologies is a very challenging task, as rules often struggle to keep pace with rapidly evolving advancements. The European Union found a good way to tackle this problem in its AI Act by introducing a risk-based approach to regulate AI applications of organizations. Depending on the level of risk, applications might be prohibited, require extensive analysis and safeguards, have transparency obligations, or need no further action. This paper adapts the core structure of the AI Act regulation for the education sector to provide teachers and students with a structured framework for dealing with AI. Various use cases, based on teaching and learning life cycles, are presented to illustrate the versatility of AI in teaching and the learning process. By establishing such a framework, we not only promote competence development in dealing with AI but also contribute to the creation of an ethical and responsible use of AI in education.Frontiers in Education240This paper adapts the core structure of the AI Act regulation for the education sector to provide teachers and students with a structured framework for dealing with AI and contributes to the creation of an ethical and responsible use of AI in education.Frontiers in Education["Marlies Temper", "Simon Tjoa", "Lisa David"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/809a7eccbcd26113862091663ee4f877d50c2ca8 3c9acc7e2a389bc4d6ec215e26fe86778b0120f5This study critically examines the role of artificial intelligence (AI) in shaping creativity within business conceptualization and education. It investigates the extent to which AI enhances or restricts originality, influences creative decision-making, and impacts the social and psychological dimensions of business students’ learning experiences. Employing qualitative thematic analysis tools, this research systematically deciphers how AI-driven tools reshape creative thought processes—whether by fostering efficiency and innovation or by inducing over-reliance and diminishing human originality. The study utilizes semi-structured interviews with 25 business students, offering deep insights into AI’s dual nature as both an enabler and a potential inhibitor of creativity. Findings reveal that while AI provides powerful analytical capabilities and accelerates ideation, it also risks standardizing creative outputs, reducing critical thinking, and eroding the uniqueness of business-driven innovation. The study underscores the urgent need for a strategic, human-centered integration of AI—where technology serves as a catalyst for creativity rather than a crutch that stifles independent thought. It calls for further research into AI’s adaptability within business education, the long-term implications of AI-assisted creativity, and the ethical challenges associated with its increasing dominance in business innovation.Environment and Social Psychology00Findings reveal that while AI provides powerful analytical capabilities and accelerates ideation, it also risks standardizing creative outputs, reducing critical thinking, and eroding the uniqueness of business-driven innovation.Environment and Social Psychology["Vicente Q. Solteo, Jr."]2025-02-20T00:00:00https://www.semanticscholar.org/paper/3c9acc7e2a389bc4d6ec215e26fe86778b0120f5 8b791d4f773f4f363d52eb9bcb0bc0f093c60a01Missing values can greatly affect analyses and decision-making in many fields. In the context of Responsible Artificial Intelligence (AI), ensuring the robustness of machine learning models is essential because Responsible AI emphasizes reliability and interpretability in decision-making processes. However, traditional imputation and ensemble learning methods often fail to preserve critical relationships between independent and dependent variables, introducing bias or noise into the data and undermining the development of robust classification models. To address these challenges, we propose a novel classification approach that aligns with Responsible AI principles. Our Resilient Decision Tree classifier is specifically designed to handle incomplete datasets. We employ subspace classifiers that operate on different non overlapping subsets of features without relying on imputation. By combining these subspace models into a weighted ensemble classifier, we enhance prediction accuracy for test datasets with missing values. The experimental results obtained on real-life and synthetic datasets demonstrate that our methodology produces an effective ensemble classifier.Intelligent Data Analysis250The Resilient Decision Tree classifier is specifically designed to handle incomplete datasets and employs subspace classifiers that operate on different non overlapping subsets of features without relying on imputation to enhance prediction accuracy for test datasets with missing values.Intelligent Data Analysis: An International Journal["Sairam Utukuru", "P. R. Krishna"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/8b791d4f773f4f363d52eb9bcb0bc0f093c60a01 cb4c1dbda2eb46824448f2a00b74610106275a6dArtificial Intelligence-Generated Content (AIGC) is rapidly transforming the landscape of information dissemination while exacerbating the spread of fake news. This paper examines the mechanisms of AI-generated fake news, the development and societal impact of deepfake technology, and the role of AI in political manipulation and its threats to democratic institutions. The study highlights that AI-generated fake news spreads at an unprecedented speed and scale, exhibits high authenticity, and contributes to social trust crises, political polarization, and economic and legal risks. Furthermore, the paper reviews current countermeasures against AI-generated misinformation, including deepfake detection technologies, automated fake news identification systems, and platform accountability. Based on existing legal and policy frameworks, this study explores how international collaboration among technology, policy, and society can effectively address AI-generated disinformation. Finally, future research directions are proposed, including the application of quantum computing and trusted computing in fake news governance, the ongoing arms race between AI forgery and counter-forgery technologies, and strategies to enhance public digital resilience.Digital Society &amp; Virtual Governance260How international collaboration among technology, policy, and society can effectively address AI-generated disinformation is explored, including the application of quantum computing and trusted computing in fake news governance, the ongoing arms race between AI forgery and counter-forgery technologies, and strategies to enhance public digital resilience.Digital Society &amp; Virtual Governance["LI Sophia"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/cb4c1dbda2eb46824448f2a00b74610106275a6d 66f5ec057944625a6ecf09c7b28179b9000124e4While the transformative potential of artificial intelligence (AI) in education is widely recognized, the rapid evolution of these technologies necessitates a corresponding evolution in teacher education. This research sought to investigate the impact of a targeted training program on pre-service physics teachers’ AI literacy levels and their subsequent attitudes and intentions toward AI adoption in their future teaching.A pre-post-test control group quasi-experimental study was implemented among physics teacher education students. A 5 weeks long out-of-curriculum intervention was designed and implemented that combined theoretical grounding with practical, problem-based learning activities, with a focus on the use of various AI tools.There was a significant upswing in AI literacy performance post-intervention, showcasing that the training was effective in facilitating participants’ understanding and application of AI in educational contexts. Additionally, perceived usefulness of AI was found to be a partial mediator in the link between literacy scores and behavioral intention to embed generative solutions into potential teaching.The study concludes that incorporating comprehensive AI literacy programs into teacher education curricula is essential for fostering a technologically adept and pedagogically innovatively minded teaching workforce. Further research is needed to explore the long-term effects of AI literacy training on teacher practice and student learning outcomes.Frontiers in Education540It is concluded that incorporating comprehensive AI literacy programs into teacher education curricula is essential for fostering a technologically adept and pedagogically innovatively minded teaching workforce.Frontiers in Education["A. Abdulayeva", "Nazym Zhanatbekova", "Yerlan S. Andasbayev", "Farzana Boribekova"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/66f5ec057944625a6ecf09c7b28179b9000124e4 a3e4bc2b623d0fa0bf3bb39af45d2c8bf4961386International Journal of Fuzzy Systems90A novel cognitive system based on deep learning consisting of a perception model, a hypothesis model, and a memory model to improve the stability of the image-to-robot motor end-to-end learning system is proposed.International Journal of Fuzzy Systems["Min-Jie Hsu", "C. Hsu", "Yi-Hsing Chien", "Wei-Yen Wang"]2025-02-20T00:00:00https://www.semanticscholar.org/paper/a3e4bc2b623d0fa0bf3bb39af45d2c8bf4961386 f5d1b19093f4828e1c4aa583d6da3901f6fb086cIntroduction The integration of artificial intelligence (AI) into medicine has ushered an era of unprecedented innovation, with substantial impacts on healthcare delivery and patient outcomes. Understanding the current development, primary research focuses, and key contributors in AI applications in medicine through bibliometric analysis is essential. Methods For this research, we utilized the Web of Science Core Collection as our main database and performed a review of literature covering the period from January 2019 to December 2023. VOSviewer and R-bibliometrix were performed to conduct bibliometric analysis and network visualization, including the number of publications, countries, journals, citations, authors, and keywords. Results A total of 1,811 publications on research for AI in medicine were released across 565 journals by 12,376 authors affiliated with 3,583 institutions from 97 countries. The United States became the foremost producer of scholarly works, significantly impacting the field. Harvard Medical School exhibited the highest publication count among all institutions. The Journal of Medical Internet Research achieved the highest H-index (19), publication count (76), and total citations (1,495). Four keyword clusters were identified, covering AI applications in digital health, COVID-19 and ChatGPT, precision medicine, and public health epidemiology. “Outcomes” and “Risk” demonstrated a notable upward trend, indicating the utilization of AI in engaging with clinicians and patients to discuss patients’ health condition risks, foreshadowing future research focal points. Conclusion Analyzing our bibliometric data allowed us to identify progress, focus areas, and emerging fields in AI for medicine, pointing to potential future research directions. Since 2019, there has been a steady rise in publications related to AI in medicine, indicating its rapid growth. In addition, we reviewed journals and significant publications to pinpoint prominent countries, institutions, and academics. Researchers will gain important insights into the current landscape, collaborative frameworks, and key research topics in the field from this study. The findings suggest directions for future research.Frontiers in Medicine921Analyzing bibliometric data allowed us to identify progress, focus areas, and emerging fields in AI for medicine, pointing to potential future research directions.Frontiers in Medicine["Mian Lin", "Lingzhi Lin", "Lingling Lin", "Zhengqiu Lin", "Xiaoxiao Yan"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/f5d1b19093f4828e1c4aa583d6da3901f6fb086c 2bc7c35a38a89f5099cd95bdebe009a52310aeb6Intensive Care Medicine Experimental371The AI algorithms showed superior performance in predicting the mortality of ARDS patients and demonstrated strong potential for clinical application and it was found that for ARDS, a highly heterogeneous condition, the accuracy of the model is influenced by the severity of the disease.Intensive Care Medicine Experimental["Yang He", "Ning Liu", "Jie Yang", "Yucai Hong", "H. Ni", "Zhongheng Zhang"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/2bc7c35a38a89f5099cd95bdebe009a52310aeb6 9c4d1e76f27a08d21174e6a3db49628bf904e0a8PurposeThe study aims to assess the awareness, perception and adoption of artificial intelligence (AI) in Bangladesh’s healthcare sector.Design/methodology/approachThis study utilizes a quantitative methodology. A survey with structured questionnaire was conducted with a sample of 399 healthcare professionals and public members through stratified random sampling. Descriptive statistics summarized participant demographics, while inferential statistical techniques, including regression analysis, examined relationships between AI readiness and adoption.FindingsUsing a conceptual framework, the study explored factors influencing AI adoption in Bangladesh’s healthcare sector. The measurement model confirmed reliability and validity, with strong factor loadings and discriminant validity. Structural model analysis revealed that social media influence (SMI) and technological awareness (TA) significantly enhanced readiness for AI (RAI) (path coefficients: 0.354 and 0.162, respectively). Perceived risk (PR) had a weaker positive effect (0.123), while perceived susceptibility (PS) and personal innovativeness (PI) were insignificant. Mediation analysis showed that RAI significantly mediated the effects of TA and PR on the adaptation of AI (AAI).Research limitations/implicationsThe study suggests policymakers develop robust regulatory frameworks to address privacy concerns, enhance trust in AI and implement educational initiatives to improve AI literacy among healthcare stakeholders in Bangladesh.Originality/valueThis study offers insights into AI adoption in Bangladesh’s healthcare sector. It addresses gaps in awareness and perceptions among professionals and the public, contributing to the limited research in this context.Health Education341The study suggests policymakers develop robust regulatory frameworks to address privacy concerns, enhance trust in AI and implement educational initiatives to improve AI literacy among healthcare stakeholders in Bangladesh.Health Education["Mohammad Rakibul Islam Bhuiyan", "T. Husain", "Saiful Islam", "Al- Amin"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/9c4d1e76f27a08d21174e6a3db49628bf904e0a8 ce02d1217a08354983a5bcf3f9ca436a1d3a597eThe rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.Frontiers in Education520Investigating undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework.Frontiers in Education["Yifu Li", "Nilo\u00a0Jayoma Castulo", "Xiaoyuan Xu"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/ce02d1217a08354983a5bcf3f9ca436a1d3a597e c88668632ceb77fe2cd5621c6c2bb194f6590350Background: The adoption of artificial intelligence (AI) in public administration (PA) has the potential to enhance transparency, efficiency, and responsiveness, ultimately creating greater public value. However, the integration of AI into PA faces challenges, including conceptual ambiguities and limited knowledge of the practical applications. This study addresses these gaps by offering an overview and categorization of AI research and applications in PA. Methods: Using a dataset of 3149 documents from the Scopus database, this study identifies the top 200 most-cited articles based on citation per year. It conducts descriptive and content analyses to identify the existing state, applications, and challenges regarding AI adoption. Additionally, selected AI use cases from the European Commission’s database are categorized, focusing on their contributions to public value. The analysis centers on three governance dimensions: internal processes, service delivery, and policymaking. Results: The findings provide a categorized understanding of AI concepts, types, and applications in PA, alongside a discussion of best practices and challenges. Conclusion: This study serves as a resource for researchers seeking a comprehensive overview of the current state of AI in PA and offers policymakers and practitioners insights into leveraging AI technologies to improve service delivery and operational efficiency.Applied Informatics980This study identifies the top 200 most-cited articles based on citation per year and provides a categorized understanding of AI concepts, types, and applications in PA, alongside a discussion of best practices and challenges.AI["Matej Bab\u0161ek", "Dejan Rav\u0161elj", "Lan Umek", "Aleksander Aristovnik"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/c88668632ceb77fe2cd5621c6c2bb194f6590350 dacd9921f6d731b1b3a3f245f0621eca4cf46fcfPurpose Artificial intelligence (AI) is increasingly involved in idea generation and production processes. To understand AI’s pivotal roles in the back-of-house operations of restaurants, this study aims to examine the effects of AI involvement in recipe creation and food production on consumers’ willingness to order food. Design/methodology/approach We conduct three experiments in the context of casual dining restaurants. The authors examine the main effect of AI involvement in recipe creation and food production on the willingness to order food in a hypothetical restaurant (Study 1) and a real restaurant (Study 2). In addition, the authors also investigate the mediating role of uniqueness neglect. The authors explore whether the negative effect of AI involvement in recipe creation is attenuated in the presence of cues of uniqueness consideration (Study 3). Findings We demonstrate that AI involvement in food production does not elicit negative responses to a menu but that consumers show unfavorable responses when AI is involved in recipe creation. The authors also identify the mediating role of uniqueness neglect. Furthermore, the authors reveal a way to mitigate the negative perceptions of AI involvement in tasks requiring intuition and instinctive decision-making (i.e. recipe creation) by incorporating cues that emphasize uniqueness considerations. Originality/value We deliver causal evidence for the significant impacts of AI involvement in recipe creation and food production, using multiple experimental designs involving both hypothetical and real restaurants. The findings, thus, can tackle an ongoing challenge in the tourism and hospitality industry – the deficit of human resources in back-of-house operations. International Journal of Contemporary Hospitality Management540It is demonstrated that AI involvement in food production does not elicit negative responses to a menu but that consumers show unfavorable responses when AI is involved in recipe creation, and a way to mitigate the negative perceptions of AI involvement in tasks requiring intuition and instinctive decision-making by incorporating cues that emphasize uniqueness considerations is revealed.International Journal of Contemporary Hospitality Management["Hyunsu Kim", "Sungwoo Choi", "H. Shin"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/dacd9921f6d731b1b3a3f245f0621eca4cf46fcf dd6c0728a8f64eceed58752a6fa8a9a521d9977eThe impact of artificial intelligence (AI) technology on carbon emissions performance is considered to be a double-edged sword. The debate is aided by this paper’s use of data from 278 Chinese cities from 2009 to 2019 based on the two-way fixed effects, instrumental variables (IVs), spatial Durbin (SDM), mediation effect, and moderating effect model. We find that AI technology not only increases the carbon emission scale, but also has an undesirable impact on carbon emission efficiency, which indicates that the use of AI technology currently does not necessarily improve carbon emission performance. Moreover, AI technology does have the potential to reduce the carbon emission scale and improve carbon emission efficiency through energy transition, though this potential is not reflected in industrial transformation. Finally, the impact of AI technology on carbon emission performance is worsened by the energy industry’s investment, suggesting that current investments are not being used to enhance AI applications in the field of energy. This study shows that the role of energy transition is crucial if current AI technologies are to achieve a ‘decarbonization effect’, and that energy industry investments need to be focused on the penetration of AI technologies to realize its positive effect.Sustainability00Sustainability["Fang Qu", "Wensen She"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/dd6c0728a8f64eceed58752a6fa8a9a521d9977e 791334ab038fee0554636b7d991bb135ba042c13ABSTRACT Holistic review has been widely adopted in medical education as a means of promoting equity in the application process and diversity in the medical workforce. Artificial intelligence (AI) is a rapidly emerging technology already having an impact on the medical school and residency application process as students and faculty alike increasingly turn to AI tools to automate some steps in the preparation and evaluation of application materials. While AI may have the potential to improve the holistic admissions process by increasing efficiency and adding some measure of standardization among reviewers, the authors caution that this promise does not come without certain pitfalls. AI models may introduce new sources of bias and amplify existing ones, which when combined with a lack of transparency regarding their use in the admissions process, may perpetuate the very inequities that holistic review seeks to minimize. The authors call for the medical education community to establish clear regulations to govern the acceptable use of AI in the admissions process and for a principled adoption of AI tools in a way that is sustainable for applicants and reviewers in the future.Academic medicine : journal of the Association of American Medical Colleges00The authors call for the medical education community to establish clear regulations to govern the acceptable use of AI in the admissions process and for a principled adoption of AI tools in a way that is sustainable for applicants and reviewers in the future.Academic medicine : journal of the Association of American Medical Colleges["Jacob Rosenthal", "F. Hafferty", "K. Kulasegaram", "Claire L. Wendland", "Janelle S Taylor"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/791334ab038fee0554636b7d991bb135ba042c13 d86b78c9f0f72d7d47a9a13a93b194b72d22d008PURPOSE A multimodal artificial intelligence (MMAI) biomarker was developed using clinical trial data from North American men with localized prostate cancer (PCa) treated with definitive radiation, using biopsy digital pathology images and key clinical information (age, PSA, T-stage) to generate prognostic scores. This study externally validates the biomarker in a prospective, real-world dataset of men who underwent radical prostatectomy (RP) for localized PCa at a tertiary referral center in Sweden. EXPERIMENTAL DESIGN Association between the MMAI scores (continuously and categorically) and endpoints of interest were performed with Fine-Gray and cumulative incidence analyses for biochemical recurrence (BCR) and logistic regression for adverse pathology (AP) at RP. RESULTS The analysis included 143 patients with evaluable biopsy pathology images and complete clinical data to generate MMAI scores. Median follow-up was 8.8 years. At diagnosis, median PSA was 7.5 ng/mL, median age 64 years, 29% had Gleason grade group ≥3, and 88 men were evaluable for AP at RP. MMAI was significantly associated with BCR (subdistribution HR 2.45 [95% CI 1.77-3.38], p<0.001) and AP at RP (OR 4.85 [95% CI 2.54-10.78], p<0.001). Estimated 5-yr BCR rates for MMAI Intermediate-High vs Low were 25% (95% CI 16%-36%) vs 4% (95% CI 1%-11%), respectively. CONCLUSIONS The MMAI biomarker, previously shown to be prognostic for distant metastasis and prostate cancer-specific mortality in men receiving definitive radiation, was prognostic for post-RP endpoints: BCR and AP. This biomarker validation study further supports the use of MMAI biomarkers in men with PCa outside North America and those treated with RP.Clinical Cancer Research00The MMAI biomarker, previously shown to be prognostic for distant metastasis and prostate cancer-specific mortality in men receiving definitive radiation, was prognostic for post-RP endpoints: BCR and AP.Clinical cancer research : an official journal of the American Association for Cancer Research["A. Bjartell", "Agnieszka Krzyzanowska", "Vinnie Y T Liu", "M. Tierney", "Trevor J Royce", "M. Sj\u00f6str\u00f6m", "M.M. Palominos-Rivera", "E. Chen", "A. Kraft", "A. Esteva", "Felix Y Feng"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/d86b78c9f0f72d7d47a9a13a93b194b72d22d008 ad574d02391fa92c040f2b80f67e97696a041079Artificial intelligence (AI) is significantly shaping education and currently influencing pre‐service teachers' academic and professional journeys. To explore this influence, the present study examines 389 Generation Z pre‐service teachers' attitudes towards AI and its impact on educational decision‐making at two state universities, using an explanatory sequential mixed‐methods research design. Quantitative data were collected through the General Attitudes to Artificial Intelligence Scale (GAAIS) and an AI decision‐making survey. It was followed by qualitative data gathered via semi‐structured interviews to enrich the statistical trends with deeper thematic insights. SPSS was used for quantitative data analysis while MAXQDA was employed for a systematic analysis of the qualitative data. The analysis revealed that female pre‐service teachers held more positive attitudes towards AI, with higher levels of AI knowledge contributing to these attitudes. Negative attitudes, however, were independent of gender, academic discipline or AI familiarity. Findings also reveal that AI tools, particularly ChatGPT, are primarily used as advisors, and pre‐service teachers often adapt AI's suggestions to their preferences. AI is predominantly preferred for assignments, reports, projects and presentations. In AI acceptance, time and effort savings, innovative suggestions and unbiased recommendations are stated as key factors. However, there are ongoing trust concerns highlighting the necessity of keeping final decisions under human control. Based on these findings, comprehensive AI training for teachers and students in higher education is suggested.European Journal of Education410Female pre‐service teachers held more positive attitudes towards AI, with higher levels of AI knowledge contributing to these attitudes, and AI tools, particularly ChatGPT, are primarily used as advisors, and pre‐service teachers often adapt AI's suggestions to their preferences.European Journal of Education["Ayse Merzifonluoglu", "Habibe Gunes"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/ad574d02391fa92c040f2b80f67e97696a041079 a8d63e68cfd5991e2ed17c024b812928b2620729The emergence of artificial intelligence (AI)-based linguistic models has revolutionized academic writing, prompting concerns about integrity. In response, AI-powered text authenticity detectors have been developed. This study examines AI tool usage in anesthesiology and intensive care journals. 1268 articles from 86 journals in “Anesthesiology” and “Anesthesiology and Intensive Care” were analyzed using Copyleaks and ZeroGPT. English abstracts published between April 18 and May 18, 2023, were scrutinized. ZeroGPT and Copyleaks found average AI usage at 25.1% ± 27.5 and 10.5% ± 15.9, respectively. 16.8% of articles were “human-written,” while 83.2% were “AI-assisted”. AI assistance correlated positively with abstract length and was more common among nonnative English speakers (P < .001). It was also prevalent in high-impact and science citation index-indexed journals (P < .01; P < .001). This study underscores the widespread adoption of AI tools in academic writing, particularly among nonnative English authors and in high-impact journals, emphasizing the need for improved detection mechanisms and regulatory guidelines.Medicine280The widespread adoption of AI tools in academic writing, particularly among nonnative English authors and in high-impact journals, is highlighted, emphasizing the need for improved detection mechanisms and regulatory guidelines.Medicine["Selin Erel", "Ozge Erkocak Arabaci", "H. K. Pampal"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/a8d63e68cfd5991e2ed17c024b812928b2620729 d3269cd95d258249df35edd4a882a3fc7a53d567Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.890Key areas where AI is driving innovation, from data analysis to new biological insights, are highlighted, including developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis.["Yingying Sun", "A. Jun", "Zhiwei Liu", "Rui Sun", "Liujia Qian", "Samuel H. Payne", "Wout Bittremieux", "Markus Ralser", "Chen Li", "Yi Chen", "Zhen Dong", "Yasset P\u00e9rez-Riverol", "Asif Khan", "Chris Sander", "Ruedi Aebersold", "Juan Antonio Vizca'ino", "Jonathan R Krieger", "Jianhua Yao", "Han Wen", "Linfeng Zhang", "Yunping Zhu", "Yue Xuan", "Benjamin Boyang Sun", "Liang Qiao", "Henning Hermjakob", "Haixu Tang", "Huanhuan Gao", "Yamin Deng", "Qing Zhong", "Cheng Chang", "Nuno Bandeira", "Ming Li", "E. Weinan", "Siqi Sun", "Yuedong Yang", "Gilbert S. Omenn", "Yue Zhang", "Ping Xu", "Yan Fu", "Xiaowen Liu", "Christopher M. Overall", "Yu Wang", "Eric W. Deutsch", "Luonan Chen", "J\u00fcrgen Cox", "V. Demichev", "Fuchu He", "Jiaxin Huang", "Huilin Jin", "Chao Liu", "Nan Li", "Zhongzhi Luan", "Jia-Min Song", "Kaicheng Yu", "Wanggen Wan", "Tai Wang", "Kang Zhang", "Le Zhang", "Peter A. Bell", "Matthias Mann", "Bing Zhang", "Tiannan Guo"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/d3269cd95d258249df35edd4a882a3fc7a53d567 8bb22ec842a6ec3c85907eda90978e7ece9cbdaaToday in the world there is a growing interest in the interpretation of radiologic, in particular mammographic, data using artificial intelligence (AI). In the presented review of scientific literature, based on the most significant studies of recent years an attempt was made to determine the place of AI in radiologic diagnosis of breast cancer. It is shown that in the future, AI can become an integral part of breast cancer mammographic screening, although at the moment the ethical and legal issues of its use have not been fully resolved.Journal of radiology and nuclear medicine160It is shown that in the future, AI can become an integral part of breast cancer mammographic screening, although at the moment the ethical and legal issues of its use have not been fully resolved.Journal of radiology and nuclear medicine["Siuzanna F. Saibu"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/8bb22ec842a6ec3c85907eda90978e7ece9cbdaa 1a01bc8fd5de1f689c31f7fb93ab69adbb20e353Artificial intelligence is revolutionising the creative process in art by offering new ways to generate ideas, enhance artistic expression, and push the boundaries of creativity. AI is transforming the art field by assisting artists, expanding creative possibilities, and streamlining artistic processes. This paper purports to investigate whether artists can utilise AI as a tool to create art without losing control over the finished piece and how the public and artists see AI-assisted art.International Journal for Sciences and Technology00This paper purports to investigate whether artists can utilise AI as a tool to create art without losing control over the finished piece and how the public and artists see AI-assisted art.International Journal on Science and Technology["Dr. Satyamangal Rege"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/1a01bc8fd5de1f689c31f7fb93ab69adbb20e353 014105c4fff10347459b03547937a68ebf231e00The relevance of the study is due to the growing demand of the construction industry for technological innovations that can significantly increase its efficiency and competitiveness. Modern construction projects are becoming more and more complex, therefore, traditional management methods cannot always ensure the necessary speed and accuracy of construction and assembly works. In such conditions, the introduction of artificial intelligence (AI) contributes to the automation of a significant part of the processes, more efficient use of resources, reduction of project deadlines and reduction of errors. Raising of problem. Organizational aspects of artificial intelligence (AI) application in the construction industry are considered. The purpose of the study is to determine the place of artificial intelligence in the system of organization and management of construction projects, as well as to develop key organizational approaches and strategies for its effective implementation in order to increase productivity and optimize processes in the construction industry. Object of the study: organizational and management processes in the construction sector related to the integration of artificial intelligence technologies. Subject of the study: organizational aspects, strategies and mechanisms for the implementation of artificial intelligence in construction, including technical, personnel and management approaches. Special attention is paid to the issues of integrating innovative solutions into production processes and management structures of enterprises. Possible approaches to increasing the efficiency of work organization, resource optimization and process automation using artificial intelligence are considered. The research findings emphasize the need for a comprehensive approach to the implementation of AI, which combines technological solutions with changes in organizational and management processes. The strategies proposed in the article are aimed at improving the quality and speed of implementation of construction projects, which should contribute to increasing the overall productivity of the industry.Ukrainian Journal of Civil Engineering and Architecture00The research findings emphasize the need for a comprehensive approach to the implementation of AI, which combines technological solutions with changes in organizational and management processes, which should contribute to increasing the overall productivity of the industry.Ukrainian Journal of Civil Engineering and Architecture["M.O. Borodin", "T.V. Tkach", "O. O. Martysh"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/014105c4fff10347459b03547937a68ebf231e00 312efc0f3a001abcfab8f5519a841b575ac35e13   The article discusses the political strategy for the introduction of artificial intelligence by the US government and its implementation in transportation, the strategic goals and objectives of the Department of Transportation, the problems of the transportation industry and their solutions with the introduction of artificial intelligence.Обозреватель–Observer00Обозреватель–Observer["K. Karimov"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/312efc0f3a001abcfab8f5519a841b575ac35e13 a025e64d43decc5ee5a61f695150264705ecb26eThe ethical governance of artificial intelligence (AI) in China is in a special period of ‘the overlap of three historical periods’ (that is, the period of rapid technological development, the period of high-quality socioeconomic progress and the period of deep adjustment of the international order). The intertwining of technological uncertainty, the multi-objectives of socioeconomic development and global strategic games have brought new challenges to the ethical governance of AI in China, particularly in the areas of impact identification, ‘no man's land’ issues and the effectiveness of the ethical governance system. To address those challenges, this paper provides some recommendations from the aspects of regulatory system construction, ethical review, ethical education, development of a governance toolbox and international cooperation.Cultures of Science10This paper provides some recommendations from the aspects of regulatory system construction, ethical review, ethical education, development of a governance toolbox and international cooperation about the ethical governance of AI in China.Cultures of Science["Yina Zhu", "Yangxu Lu"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/a025e64d43decc5ee5a61f695150264705ecb26e 4e5a0d0bb24a7f85db66c7ee5f2af344f0a80903The purpose of this study is to analyze the influence of Customer Experience (CE) and Brand Commitment (BC) on Business Performance (BP) and to examine the mediating role of Customer Citizenship Behavior (CCB). The study also aims to evaluate the moderating role of Artificial Intelligence (AI) in the relationship between CE and BC with BP. This research employs a quantitative approach with a survey design. The sample consists of 150 MSMEs in Tasikmalaya City, analyzed using SEM-SmartPLS4. The findings reveal that CE and BC have a positive and significant effect on CCB and BP. Additionally, CCB has a positive and significant impact on BP and mediates the influence of CE and BC on BP. However, AI does not moderate the relationships between CE and BP or BC and BP.Jurnal Riset Bisnis dan Manajemen420The findings reveal that CE and BC have a positive and significant effect on CCB and BP and mediates the influence of CE and BC on BP.Jurnal Riset Bisnis dan Manajemen["Andyan Utama", "Aldina Shiratina", "Eri Marlapa", "A. Ali"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/4e5a0d0bb24a7f85db66c7ee5f2af344f0a80903 a101aa35a9a82b89688cf9fb1d68cac587954c8eRecent studies challenge the assumption that human-artificial intelligence (AI) collaboration is universally optimal, highlighting tasks where AI alone outperforms combined efforts. This viewpoint discusses the reasons behind these findings, explores influences on synergy and emphasises the importance of identifying when clinicians add net benefit to AI performance. Maximising patient outcomes may require accepting AI autonomy in certain scenarios within healthcare practice.Internal medicine journal (Print)150This viewpoint discusses the reasons behind these findings, explores influences on synergy and emphasises the importance of identifying when clinicians add net benefit to AI performance, which may require accepting AI autonomy in certain scenarios within healthcare practice.Internal medicine journal["J. Kovoor", "Daksh Tyagi", "Ashley Hopkins", "J. Gorcilov", "Brandon Stretton", "Aashray K. Gupta", "Stephen Bacchi"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/a101aa35a9a82b89688cf9fb1d68cac587954c8e 1c106115fd4aa52e54ac98fe7166091c90911d70Objectives: To explore King Khalid University Faculty’s perspectives on consumer behavior and the evolution of marketing strategies in the age of Artificial Intelligence.   Theoretical Framework: an overview of AI and business, explaining the concept of AI and its role and applications in modern e-business. Furthermore, AI's influence on customer behavior and the evolution of marketing strategies for the business sector is surveyed, encircling the definition of customer, and AI’s application. This in-depth review highlights the role of AI in improving consumer behavior and the evolution of marketing strategies for business sector beside technological evolutions.   Method: The two researchers utilized the descriptive research method for this investigation because it was the most appropriate approach to research the two given objectives and the two study questions.   Results and Discussion: The results revealed that AI-driven tools, such as deep learning algorithms, automatize continuous tasks like distribution, content creation, and user behavior analytics, enabling marketers to focus on higher-ranking sustained efforts.   Research Implications: This study might have implications in proposing suggestions to faculty members, managers, researchers, economists, business analysts, entrepreneurs, and more to make use of AI technologies in their field because the incorporation of AI can greatly enhance their knowledge and skills to achieve professional development.   Novelty/Originality: This study is among the first to explore staff members’ perspectives on consumer behavior and the evolution of marketing strategies in the age of Artificial Intelligence in the business sector.Journal of Lifestyle and SDGs Review420This in-depth review highlights the role of AI in improving consumer behavior and the evolution of marketing strategies for business sector beside technological evolutions and revealed that AI-driven tools automatize continuous tasks like distribution, content creation, and user behavior analytics, enabling marketers to focus on higher-ranking sustained efforts.Journal of Lifestyle and SDGs Review["Sultan Abdullah Al-Shahrani", "A. Alhaj"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/1c106115fd4aa52e54ac98fe7166091c90911d70 78787fb53720e820600c0d98cc2eb581facd1bbd Generative Artificial Intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos, and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and historical developments in GAI over the past 70 years. The achievements are grouped into four categories: (i) rule-based generative systems that follow specialized rules and instructions, (ii) model-based generative algorithms that produce new content based on statistical or graphical models, (iii) deep generative methodologies that utilize deep neural networks to learn how to generate new content from data, and (iv) foundation models that are trained on extensive datasets and capable of performing a variety of generative tasks. This paper also reviews successful generative applications and identifies open challenges posed by remaining issues. In addition, this paper describes potential research directions aimed at better utilizing, understanding, and harnessing GAI technologies.National Science Review00A brief summary of the ongoing and historical developments in GAI over the past 70 years is provided and potential research directions aimed at better utilizing, understanding, and harnessing GAI technologies are described.National Science Review["Ran He", "Jie Cao", "Tieniu Tan"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/78787fb53720e820600c0d98cc2eb581facd1bbd 2f451ca3bc95ed8915aae367e7d53ffb3e02d72dThe Design Journal01The Design Journal["Lorraine Justice"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/2f451ca3bc95ed8915aae367e7d53ffb3e02d72d e2a16d848b8faf9104c30884f53db1779f745f1cInternational Journal of Computer Applications130International Journal of Computers and Applications["Amira Benlecheb", "Ahmed-Chawki Chaouche", "Brahim Benabderrahmane"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/e2a16d848b8faf9104c30884f53db1779f745f1c c74ad196f3aa541bfe0f86cc07851634374bada7ATZelectronics worldwide00ATZelectronics worldwide["Alfred Vollmer"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/c74ad196f3aa541bfe0f86cc07851634374bada7 a39539950f948148a3d27f05f41ef36ee2b76c57 This research note deploys data from a simulation experiment to illustrate the very real effects of monolithic views of technology potential on decision-making within the Homeland Security and Emergency Management field. Specifically, a population of national security decision-makers from across the United States participated in an experimental study that sought to examine their response to encounter different kinds of AI agency in a crisis situation. The results illustrate wariness of overstep and unwillingness to be assertive when AI tools are observed shaping key situational developments, something not apparent when AI is either absent or used as a limited aide to human analysis. These effects are mediated by levels of respondent training. Of great concern, however, these restraining effects disappear and the impact of education on driving professionals towards prudent outcomes is minimized for those individuals that profess to see AI as a full viable replacement of their professional practice. These findings constitute proof of a “Great Machine” problem within professional HSEM practice. Willingness to accept grand, singular assumptions about emerging technologies into operational decision-making clearly encourages ignorance of technological nuance. The result is a serious challenge for HSEM practice that requires more sophisticated solutions than simply raising awareness of AI.Journal of Homeland Security and Emergency Management00Data is deployed from a simulation experiment to illustrate the very real effects of monolithic views of technology potential on decision-making within the Homeland Security and Emergency Management field and constitute proof of a “Great Machine” problem within professional HSEM practice.Journal of Homeland Security and Emergency Management["Christopher Whyte"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/a39539950f948148a3d27f05f41ef36ee2b76c57 394fa6109b416df105dd304f9648db8588b4e035Water resources management160Water Resources Management["Manal Gad", "H. Marie", "Ghada M. Abozaid"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/394fa6109b416df105dd304f9648db8588b4e035 d8f876289f914dedb22fa820a94909396dec05bfScientific Journal of Applied Social and Clinical Science00Scientific Journal of Applied Social and Clinical Science["Alvani Bomfim de Sousa J\u00fanior", "Robson Luiz Guimar\u00e3es Santos", "Sidney Barreto Batista"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/d8f876289f914dedb22fa820a94909396dec05bf b2694e3d20dfc88f259d03b08be1636c034c06fdLasers in Medical Science60Lasers in medical science["Ryan Scheinkman", "Lea Tordjman", "Sheila Sharifi", "Keyvan Nouri"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/b2694e3d20dfc88f259d03b08be1636c034c06fd 2d0354288ba7d09e38d9e90578d5c55dd0746139INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY00INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY["Chintan Udeshi"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/2d0354288ba7d09e38d9e90578d5c55dd0746139 45bccade87a4ebbc8fd07abce8b824854ba2f133The 8th International Electronic Conference on Water Sciences500The 8th International Electronic Conference on Water Sciences["Marc Deo Jeremiah Victorio Rupin", "Kylle Gabriel Cruz Mendoza", "R. V. Rubi"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/45bccade87a4ebbc8fd07abce8b824854ba2f133 f993979ddb4b2bf94ece6f1324576c2f84b06af6Aesthetic Plastic Surgery50Aesthetic plastic surgery["Mohammad Amir Beigi Habibabadi", "Salehoddin Bouya", "Arman Monajemi Mamaghani"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/f993979ddb4b2bf94ece6f1324576c2f84b06af6 7a86c5ff5902c940c4179c5a4874cde9096afc88Internet Reference Services Quarterly10Internet Reference Services Quarterly["Lia Febria Lina", "A. Aldino", "Vivi Usmayanti", "Ryan Randy Suryono"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/7a86c5ff5902c940c4179c5a4874cde9096afc88 6a3fb47f0f2631327c8694b60735054fcad256bbJournal of Urological Surgery00Journal of Urological Surgery["Mehmet Eflatun Deniz", "Mehmet Vehbi Kayra"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/6a3fb47f0f2631327c8694b60735054fcad256bb 93b754f1962906372f4b3b66e4b22735d248accbtouchREVIEWS in RMD1420touchREVIEWS in RMD["Samantha Cooray", "Alexander Deng", "Tim Dong", "Salah AI Ghazal Hammouche", "Matthew Quansah", "Jordan Tsigarides", "Nicholas Fuggle"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/93b754f1962906372f4b3b66e4b22735d248accb 233ce3ff4154c2356946290770a7936a165f0113Naukovi pratsi NDFI00Naukovi pratsi NDFI["A. Yeremenko"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/233ce3ff4154c2356946290770a7936a165f0113 6415faaf594522536a114d2611c65deb116786c9Blending traditional methods and technological advancements.Communications of the ACM80Communications of the ACM["C. M. D. Santos", "J. P. Gois"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/6415faaf594522536a114d2611c65deb116786c9 0408f2cf259a3fd71a8f53e9d238625f93bddbc2Aesthetic Plastic Surgery10Aesthetic plastic surgery["E. Buccheri", "Amedeo Villanucci"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/0408f2cf259a3fd71a8f53e9d238625f93bddbc2 e3e42a3564aea98ca3c6c17e2a46ae4d83723010Journal of International Entrepreneurship360Journal of International Entrepreneurship["H. Etemad"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/e3e42a3564aea98ca3c6c17e2a46ae4d83723010 984d6a81a1852403d2a8ee68fc75092ef16c0d1cNeurosurgical review200GPT-4 appears to be able to process clinical information about UIAs and generate treatment recommendations, however, the level of ambiguity and the utilization of scientific evidence in the recommendations are not yet patient/case specific enough to substitute the decision-making of a multidisciplinary neurovascular board.Neurosurgical review["Alexis Hadjiathanasiou", "L. Goelz", "Florian Muhn", "Rebecca Heinz", "Lutz Krei\u00dfl", "P. Sparenberg", "Johannes Lemcke", "Ingo Schmehl", "Sven Mutze", "P. Schuss"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/984d6a81a1852403d2a8ee68fc75092ef16c0d1c 14cc72230365e1ddf233784fa06ca442de95232dTechnology Analysis &amp; Strategic Management670Technology Analysis &amp; Strategic Management["Hila Chalutz-Ben Gal", "A. Margherita"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/14cc72230365e1ddf233784fa06ca442de95232d 3352ecd4ca7a90134661d43a2f7be9c66c695040International Journal of Artificial Intelligence and Machine Learning00INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE and MACHINE LEARNING["Jagjot Bhardwaj", "Shantanu Awasthi", "P. Dhoni"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/3352ecd4ca7a90134661d43a2f7be9c66c695040 83352fa44a3b63cd84f7194eeb2e77f04fc3162aBackground: Although spinal cord stimulation (SCS) is an effective treatment for managing chronic pain, many patients have understandable questions and concerns regarding this therapy. Artificial intelligence (AI) has shown promise in delivering patient education in healthcare. This study evaluates the reliability, accuracy, and comprehensibility of ChatGPT’s responses to common patient inquiries about SCS. Methods: Thirteen commonly asked questions regarding SCS were selected based on the authors’ clinical experience managing chronic pain patients and a targeted review of patient education materials and relevant medical literature. The questions were prioritized based on their frequency in patient consultations, relevance to decision-making about SCS, and the complexity of the information typically required to comprehensively address the questions. These questions spanned three domains: pre-procedural, intra-procedural, and post-procedural concerns. Responses were generated using GPT-4.0 with the prompt “If you were a physician, how would you answer a patient asking…”. Responses were independently assessed by 10 pain physicians and two non-healthcare professionals using a Likert scale for reliability (1–6 points), accuracy (1–3 points), and comprehensibility (1–3 points). Results: ChatGPT’s responses demonstrated strong reliability (5.1 ± 0.7) and comprehensibility (2.8 ± 0.2), with 92% and 98% of responses, respectively, meeting or exceeding our predefined thresholds. Accuracy was 2.7 ± 0.3, with 95% of responses rated sufficiently accurate. General queries, such as “What is spinal cord stimulation?” and “What are the risks and benefits?”, received higher scores compared to technical questions like “What are the different types of waveforms used in SCS?”. Conclusions: ChatGPT can be implemented as a supplementary tool for patient education, particularly in addressing general and procedural queries about SCS. However, the AI’s performance was less robust in addressing highly technical or nuanced questions.Journal of Clinical Medicine441ChatGPT can be implemented as a supplementary tool for patient education, particularly in addressing general and procedural queries about SCS, however, the AI’s performance was less robust in addressing highly technical or nuanced questions.Journal of Clinical Medicine["Giuliano Lo Bianco", "M. Cascella", "Sean Li", "Miles Day", "L. Kapural", "Christopher L. Robinson", "E. Sinagra"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/83352fa44a3b63cd84f7194eeb2e77f04fc3162a 032908056c87cf18dd80fb3b60ba5af785421810Background Conversational artificial intelligence (CAI) is emerging as a promising digital technology for mental health care. CAI apps, such as psychotherapeutic chatbots, are available in app stores, but their use raises ethical concerns. Objective We aimed to provide a comprehensive overview of ethical considerations surrounding CAI as a therapist for individuals with mental health issues. Methods We conducted a systematic search across PubMed, Embase, APA PsycINFO, Web of Science, Scopus, the Philosopher’s Index, and ACM Digital Library databases. Our search comprised 3 elements: embodied artificial intelligence, ethics, and mental health. We defined CAI as a conversational agent that interacts with a person and uses artificial intelligence to formulate output. We included articles discussing the ethical challenges of CAI functioning in the role of a therapist for individuals with mental health issues. We added additional articles through snowball searching. We included articles in English or Dutch. All types of articles were considered except abstracts of symposia. Screening for eligibility was done by 2 independent researchers (MRM and TS or AvB). An initial charting form was created based on the expected considerations and revised and complemented during the charting process. The ethical challenges were divided into themes. When a concern occurred in more than 2 articles, we identified it as a distinct theme. Results We included 101 articles, of which 95% (n=96) were published in 2018 or later. Most were reviews (n=22, 21.8%) followed by commentaries (n=17, 16.8%). The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of “black box” algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers’ jobs (n=16, 15.8%). Other themes were discussed in 9.9% (n=10) of the identified articles. Conclusions Our scoping review has comprehensively covered ethical aspects of CAI in mental health care. While certain themes remain underexplored and stakeholders’ perspectives are insufficiently represented, this study highlights critical areas for further research. These include evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability. Addressing these gaps can inform normative analysis and guide the development of ethical guidelines for responsible CAI use in mental health care.JMIR Mental Health1201This scoping review comprehensively covered ethical aspects of CAI in mental health care including evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability.JMIR Mental Health["Mehrdad Rahsepar Meadi", "Tomas Sillekens", "S. Metselaar", "Anton van Balkom", "Justin Bernstein", "Neeltje Batelaan"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/032908056c87cf18dd80fb3b60ba5af785421810 22b37b2a0226c8af08b78fffd55f4c31fea100caAlthough Artificial intelligence (AI) has been used in education for a long time, its popularity and spread have witnessed exponential growth since the launch of ChatGPT. It can be used as a tool, teaching assistant, or teacher. AI teacher (AI-based robot as a teacher) is not a new concept with the first teaching robots used in the 1970s; however, most of the research and usage of AI in education is focused on AI as a tool or a teaching assistant. This article looks at AI teachers’ history, some key cases, potential and benefits, and concerns and challenges associated with their use in classrooms. Overcoming teachers’ shortage, flexibility, transparency, unbiasedness, and improving students’ motivation were some of their key benefits; while being untested and unreliable, cost, need for specific infrastructure and technical expertise, resistance to change, ethical issues, and fears of dehumanizing and desensitizing students were the main concerns and challenges. We suggest co-teaching with AI teachers using four different approaches. Through them, AI teachers and human teachers can work together in classrooms to maximize the effectiveness of the teaching-learning process.Frontiers in Education370This article looks at AI teachers’ history, some key cases, potential and benefits, and concerns and challenges associated with their use in classrooms, and suggests co-teaching with AI teachers using four different approaches.Frontiers in Education["Muhammad Abid Malik", "Rehmat Shah"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/22b37b2a0226c8af08b78fffd55f4c31fea100ca 6239c8776c353ed817312e4458c8461fc95388ccHead injuries are a leading global cause of mortality and disability, highlighting the critical need for advanced prognostic tools to inform clinical decision-making and optimize healthcare resource utilization. For the first time, this study introduces a cutting-edge artificial intelligence (AI) framework designed to predict mortality outcomes from head injury narratives. Leveraging deep learning-based natural language processing techniques, the framework identifies and extracts key features from unstructured text describing injury mechanisms and patient conditions to train predictive models. Validation was conducted on a diverse dataset of 1,500 head injury cases using a stratified holdout approach, with 90% allocated for training and 10% for testing. The one-dimensional convolutional neural network model demonstrated strong performance, achieving averagely 85% accuracy, 74% correct mortality prediction, 88% correct survival prediction, and an impressive area under the receiver operating characteristic curve of 0.91. This work highlights the transformative potential of AI in harnessing narrative clinical data to enhance prognostic accuracy, paving the way for more effective, evidence-based management of head injury patients.medRxiv520This work highlights the transformative potential of AI in harnessing narrative clinical data to enhance prognostic accuracy, paving the way for more effective, evidence-based management of head injury patients.["T. D. Pham", "K. Marks", "D. Hughes", "D. Chatzopoulou", "P. Coulthard", "S. Holmes"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/6239c8776c353ed817312e4458c8461fc95388cc 78868f284631cb8b72f2ba0425ffca7462e7a3ceThe increasing integration of artificial intelligence (AI) in education presents both significant opportunities and critical ethical challenges. This paper explores the ethical design of AI for education and learning systems, focusing on key principles such as transparency, privacy, fairness, and accountability. AI technologies hold the potential to revolutionise personalised learning, assistive technologies, and administrative efficiency. However, issues such as bias, data privacy, and the potential reduction in human interaction require careful attention. Ethical AI systems in education should be designed to mitigate bias by using diverse and representative datasets, protect user privacy by securing sensitive student data, and ensure inclusivity by accommodating diverse learning needs, including those of students with disabilities. Additionally, transparency in AI processes is critical to fostering trust among students, educators, and parents. Continuous feedback loops, collaboration with stakeholders, and clear policies on the use of AI are also necessary to align AI tools with educational values and goals. The paper concludes by recommending best practices for ethically implementing AI in educational settings, emphasising the need for cross-disciplinary collaboration and ongoing evaluation to enhance the fairness, accountability, and inclusivity of AI-driven educational systems.ASM Science Journal130The paper concludes by recommending best practices for ethically implementing AI in educational settings, emphasising the need for cross-disciplinary collaboration and ongoing evaluation to enhance the fairness, accountability, and inclusivity of AI-driven educational systems.ASM Science Journal["Zhang Jing Bing", "Wai Yie Leong"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/78868f284631cb8b72f2ba0425ffca7462e7a3ce 6b9524863b141798c1a77d17270afd79388df2e3This study investigates how individuals' perceptions of artificial intelligence (AI) limitations influence organizational readiness for AI adoption. Through semi-structured interviews with seven AI implementation experts, analyzed using the Gioia methodology, the research reveals that organizational readiness emerges through dynamic interactions between individual sensemaking, social learning, and formal integration processes. The findings demonstrate that hands-on experience with AI limitations leads to more realistic expectations and increased trust, mainly when supported by peer networks and champion systems. Organizations that successfully translate these individual and collective insights into formal governance structures achieve more sustainable AI adoption. The study advances theory by showing how organizational readiness for AI adoption evolves through continuous cycles of individual understanding, social learning, and organizational adaptation. These insights suggest that organizations should approach AI adoption not as a one-time implementation but as an ongoing strategic learning process that balances innovation with practical constraints. The research contributes to organizational readiness theory and practice by illuminating how micro-level perceptions and experiences shape macro-level adoption outcomes.770The research reveals that organizational readiness emerges through dynamic interactions between individual sensemaking, social learning, and formal integration processes, and suggests that organizations should approach AI adoption not as a one-time implementation but as an ongoing strategic learning process that balances innovation with practical constraints.["Thomas Ubellacker"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/6b9524863b141798c1a77d17270afd79388df2e3 f0ee24aec7e38ce9ef75d58c1920f6e8d86ef559The rapid advancement of Generative Artificial Intelligence (GenAI) has introduced new opportunities for transforming higher education, particularly in fields that require analytical reasoning and regulatory compliance, such as cybersecurity management. This study presents a structured framework for integrating GenAI tools into cybersecurity education, demonstrating their role in fostering critical thinking, real-world problem-solving, and regulatory awareness. The implementation strategy followed a two-stage approach, embedding GenAI within tutorial exercises and assessment tasks. Tutorials enabled students to generate, critique, and refine AI-assisted cybersecurity policies, while assessments required them to apply AI-generated outputs to real-world scenarios, ensuring alignment with industry standards and regulatory requirements. Findings indicate that AI-assisted learning significantly enhanced students' ability to evaluate security policies, refine risk assessments, and bridge theoretical knowledge with practical application. Student reflections and instructor observations revealed improvements in analytical engagement, yet challenges emerged regarding AI over-reliance, variability in AI literacy, and the contextual limitations of AI-generated content. Through structured intervention and research-driven refinement, students were able to recognize AI strengths as a generative tool while acknowledging its need for human oversight. This study further highlights the broader implications of AI adoption in cybersecurity education, emphasizing the necessity of balancing automation with expert judgment to cultivate industry-ready professionals. Future research should explore the long-term impact of AI-driven learning on cybersecurity competency, as well as the potential for adaptive AI-assisted assessments to further personalize and enhance educational outcomes.270Findings indicate that AI-assisted learning significantly enhanced students' ability to evaluate security policies, refine risk assessments, and bridge theoretical knowledge with practical application, as well as the potential for adaptive AI-assisted assessments to further personalize and enhance educational outcomes.["Mahmoud Elkhodr", "E. Gide"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/f0ee24aec7e38ce9ef75d58c1920f6e8d86ef559 6c86e94fefe38b6c3dddcf4925ffa7e4be90ef6bThe rapid advancement of Artificial Intelligence (AI) technology is profoundly transforming human society and concurrently presenting a series of ethical, legal, and social issues. The effective governance of AI has become a crucial global concern. Since 2022, the extensive deployment of generative AI, particularly large language models, marked a new phase in AI governance. Continuous efforts are being made by the international community in actively addressing the novel challenges posed by these AI developments. As consensus on international governance continues to be established and put into action, the practical importance of conducting a global assessment of the state of AI governance is progressively coming to light. In this context, we initiated the development of the AI Governance InternationaL Evaluation Index (AGILE Index). Adhering to the design principle,"the level of governance should match the level of development,"the inaugural evaluation of the AGILE Index commences with an exploration of four foundational pillars: the development level of AI, the AI governance environment, the AI governance instruments, and the AI governance effectiveness. It covers 39 indicators across 18 dimensions to comprehensively assess the AI governance level of 14 representative countries globally. The index is utilized to delve into the status of AI governance to date in 14 countries for the first batch of evaluation. The aim is to depict the current state of AI governance in these countries through data scoring, assist them in identifying their governance stage and uncovering governance issues, and ultimately offer insights for the enhancement of their AI governance systems.00The AGILE Index is utilized to delve into the status of AI governance to date in 14 countries for the first batch of evaluation, to depict the current state of AI governance in these countries through data scoring, and offer insights for the enhancement of their AI governance systems.["Yi Zeng", "Enmeng Lu", "Xin Guan", "Cunqing Huangfu", "Zizhe Ruan", "Ammar Younas", "Kang Sun", "Xuan Tang", "Yuwei Wang", "Hongjie Suo", "Dongqi Liang", "Zhengqiang Han", "Aorigele Bao", "Xiaoyang Guo", "Jin Wang", "Jiawei Xie", "Yao Liang"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/6c86e94fefe38b6c3dddcf4925ffa7e4be90ef6b 9b7284606a918e3cd91bab0b3608b41f2e62ec23 Purpose Based on the advice response theory perspective, this study aims to investigate the effects of human managers and artificial intelligence (AI) systems on customer-contact employees’ aversion to AI systems in the hospitality industry. It examined the mediating role of advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility) on customer-contact employees’ aversion to AI systems. Design/methodology/approach Two scenario-based experiments were conducted (Nexperiment 1 = 499 and Nexperiment 2 = 300). Experiment 1 compared the effects of different advisor types (human managers vs AI systems) on employees’ aversion to AI systems. Experiment 2 investigated the mediating role of advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility). Findings The results showed employees tended to prioritize advice from human managers over output from AI systems. Moreover, advice content characteristics (efficacy, feasibility and implementation limitations) and advice delivery (facework and comprehensibility) played mediating roles in the relationship between advisor type characteristics and employees’ aversion to AI systems. Practical implications These findings contribute to the understanding of AI system aversion and provide theoretical insights into management practices involving customer-contact employees who interact with AI technology in the hospitality industry. Originality/value The primary contribution of this study is that it enriches the literature on employee aversion to AI systems by exploring the dual mediators (advice content characteristics and advice delivery) through which advisor type characteristics affect AI system aversion. International Journal of Contemporary Hospitality Management420The results showed employees tended to prioritize advice from human managers over output from AI systems, and advice content characteristics and advice delivery played mediating roles in the relationship between advisor type characteristics and employees’ aversion to AI systems.International Journal of Contemporary Hospitality Management["Yong Yang", "Yue Li", "Xinyuan Zhao", "Rob Law", "Hongjin Song"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/9b7284606a918e3cd91bab0b3608b41f2e62ec23 b4295a5e28cc5306a1b29868532157c2c5cb10ffMany artificial intelligence models are power hungry and expensive. Researchers in the Global South are increasingly embracing low-cost, low-power alternatives.Science00Science["Sandeep Ravindran"]2025-02-21T00:00:00https://www.semanticscholar.org/paper/b4295a5e28cc5306a1b29868532157c2c5cb10ff 102af1f4bbb68ee59a34d6e4eecb12949ab3203cThe EU Artificial Intelligence Act (AIA) establishes different legal principles for different types of AI systems. While prior work has sought to clarify some of these principles, little attention has been paid to robustness and cybersecurity. This paper aims to fill this gap. We identify legal challenges and shortcomings in provisions related to robustness and cybersecurity for high-risk AI systems (Art. 15 AIA) and general-purpose AI models (Art. 55 AIA). We show that robustness and cybersecurity demand resilience against performance disruptions. Furthermore, we assess potential challenges in implementing these provisions in light of recent advancements in the machine learning (ML) literature. Our analysis informs efforts to develop harmonized standards, guidelines by the European Commission, as well as benchmarks and measurement methodologies under Art. 15(2) AIA. With this, we seek to bridge the gap between legal terminology and ML research, fostering a better alignment between research and implementation efforts.1081It is shown that robustness and cybersecurity demand resilience against performance disruptions, and this analysis informs efforts to develop harmonized standards, guidelines by the European Commission, as well as benchmarks and measurement methodologies under Art. 15(2) AIA.["Henrik Nolte", "Miriam Rateike", "Michele Finck"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/102af1f4bbb68ee59a34d6e4eecb12949ab3203c 7772383387b4f784dbc151678e16e95c33605988This paper addresses the challenge of integrating Artificial Intelligence (AI) into human resource management (HRM) for the retail sector with the view of addressing challenges of high employee turnover, skill development as well as performance monitoring. Methodology: Existing literature was subjected to a thematic analysis to identify key themes and insights about AI’s role in recruitment, employee motivation, and performance evaluation. Key Findings: AI paces up recruitment speed, personalizes training through adaptive learning and performs performance tracking in real-time. But there are ethical problems, such as algorithmic bias and transparency. Implications: AI brings transformative tools for retail HR management processes and employee engagement. The key to inclusivity and trust is balanced implementation and ethical oversight. Future Directions: Future research needs to address long term workforce impacts and frameworks for the ethical challenges in retail HRM.Journal of business and management studies230Journal of Business and Management Studies["R. I. Rezvi", "Kazi Obaidur Rahman", "Farhan Nasrullah", "Md Samirul Islam", "Mehedi Hasan", "Nayeema Nusrat", "Shamina Sharmin Jishan", "Shoaib Ahmed"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/7772383387b4f784dbc151678e16e95c33605988 92eac9a8b2a7be8ab954a5b7b51c982635c019a6Artificial intelligence (AI) is reshaping chronic illness care by providing precise, data-driven insights and fostering proactive management strategies that have the potential to enhance patient outcomes. However, integrating artificial intelligence into clinical nursing practice presents distinct challenges requiring thoughtful navigation to fully realize its benefits. AI tools promise to improve patient monitoring and enable personalized care plans while optimizing nursing workflows. Yet, as nurses work on the front lines of implementing AI, they encounter ethical, practical, and technical challenges, including data privacy and security concerns, balancing patient expectations with technological capabilities, and addressing algorithmic biases that could compromise equitable care. Nurses are crucial in ensuring that AI applications remain patient-centered, advocating for tools that genuinely reflect the diverse needs of patients with chronic illnesses. Maintaining clinical judgment amidst AI-driven recommendations requires careful consideration, as automated insights must be weighed against individualized care needs. This dynamic underscores the need to empower nurses through interdisciplinary collaboration with data scientists, continuous professional development, and resources that support them in managing potential workflow demands increased by AI tools. Moreover, fostering an adaptive, learning-oriented nursing culture is essential to embrace AI’s evolving role in healthcare. Addressing these challenges can harness AI’s full potential to improve patient care and quality of life for individuals managing chronic conditions. Supporting nurses in leading AI adoption will be instrumental in transforming chronic illness care and achieving better long-term outcomes for patients.Pacific Rim international journal of nursing research220Supporting nurses in leading AI adoption will be instrumental in transforming chronic illness care and achieving better long-term outcomes for patients, and fostering an adaptive, learning-oriented nursing culture is essential to embrace AI’s evolving role in healthcare.Pacific Rim International Journal of Nursing Research["S. Subrata", "Sumarno Adi", "Subrata"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/92eac9a8b2a7be8ab954a5b7b51c982635c019a6 da9efff4cc2dd9a70e0ed2d98dd3aaa4358bfaaeThe concept of artificial intelligence (AI) refers to technologies that imitate human-like thinking, learning and decision-making abilities. While integrating AI into the workforce offers the potential to increase efficiency in organizational activities, it can lead to negative effects such as anxiety, uncertainty, and distrust among employees which results from not being able to understand these technologies, regarding them as alternatives for themselves, and the possibility of losing their organizational position. These effects can reduce employees’ commitment at work and trigger negative organizational behaviors such as quiet quitting and turnover intention. Starting from this point, the present study aims to investigate the effect of AI anxiety on turnover intention and the mediating role of quiet quitting in this relationship. The study was conducted using a cross-sectional design with 457 people working in SMEs in Kırıkkale province. AI Anxiety, Quiet Quitting, and Turnover Intention Scales were utilized during the data collection process. The obtained data were analyzed through structural equation modeling. In addition to detecting significant relationships between concepts as a result of the analysis, it was realized that AI anxiety did not have a considerable effect directly on turnover intention; however, this effect occurred indirectly through quiet quitting. Accordingly, it is predicted that integrating AI technologies into business processes will increase the concerns about job security in employees, and this anxiety triggers the turnover intention by leading to a tendency toward quiet quitting for reasons such as loss of motivation and low organizational commitment.Behavioral Science720Behavioral Sciences["Selen Uygungil-Erdogan", "Ya\u015far \u015eahin", "A\u015fk\u0131n \u0130nci S\u00f6kmen-Alaca", "Onur Oktaysoy", "Mustafa Alt\u0131nta\u015f", "Vurgun Top\u00e7uo\u011flu"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/da9efff4cc2dd9a70e0ed2d98dd3aaa4358bfaae c2946dd81a02743f2e838c7e30e9a2a3667c2964The study examined the effect of artificial intelligence (AI-ChatGPT) on educational psychology students’ engagement, interest and achievement in the Institute of Education, University of Abuja. Six research questions and six hypotheses guided the study. The research employed a quasi-experimental design. The sample of the study was 519. The reliability of the instruments was established using Kuder-Richardson formula (KR-21) which yielded a reliability index of 0.80. The data collected was analyzed using mean scores, standard deviation and the Two-tailed t-test analysis. Findings from the study showed that there was significant difference in the engagement mean scores of 200L students taught Educational Psychology I with AI-ChatGPT and those taught with conventional lecture method. It was also found that there is no significant difference in the interest mean scores of 200L students taught Educational Psychology I with AI-ChatGPT and those taught with conventional lecture method was found. The results further revealed that there was significant difference in the achievement mean scores of 200L students taught Educational Psychology I with AI-ChatGPT and those taught with conventional lecture method; just as a significant difference in the engagement mean scores of 200L male and female students taught Educational Psychology I  with AI-ChatGPT and those taught with conventional lecture Based on the findings, the study recommended that AI-ChatGPT should be incorporated into the education curriculum in higher institution. The essence is to advance effective teaching and learning approaches for teachers and students.Asian Journal of Advanced Research and Reports00It was recommended that AI-ChatGPT should be incorporated into the education curriculum in higher institution because of significant difference in the engagement mean scores of 200L students taught Educational Psychology I with AI-ChatGPT and those taught with conventional lecture method.Asian Journal of Advanced Research and Reports["Gidado, Bello Kumo", "ZUBAIR, Taiye Hassan"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/c2946dd81a02743f2e838c7e30e9a2a3667c2964 6f7039cde5f57638d91718afd5e4e34ff5852cfaThis study explores how linguistic and cultural differences shape social media discourses on green energy and sustainability by analysing English and Turkish tweets. Leveraging artificial intelligence-based text mining methods, the research examines users’ perceptions, emotions, and concerns about green energy on social media platforms. The findings reveal that in both languages, negative sentiments outweigh positive ones, with users frequently expressing their criticisms and apprehensions. However, significant thematic differences emerge based on language and culture. English tweets generally adopt a global and industrial perspective, while Turkish tweets are more focused on local, technical, and operational issues. By integrating sustainability into the analysis, this study highlights the interconnectedness of green energy discussions with broader environmental and societal goals. Social media platforms are shown to play a critical role in raising environmental awareness and influencing consumer perceptions. The results underline the importance of developing sustainability policies that consider regional dynamics, cultural contexts, and user expectations. Additionally, this study provides valuable insights for advancing climate research, media strategies, and digital marketing efforts. Ultimately, it emphasises the need for inclusive, informed, and innovative approaches to foster greener and more sustainable futures globally.Sustainability430The research examines users’ perceptions, emotions, and concerns about green energy on social media platforms using artificial intelligence-based text mining methods and reveals that in both languages, negative sentiments outweigh positive ones.Sustainability["Fahrettin Kayan", "Yasemin Bili\u015fli", "M. Kayaku\u015f", "Fatma Yi\u011fit A\u00e7\u0131kg\u00f6z", "Agah Ba\u015fde\u011firmen", "Meltem G\u00fcler"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/6f7039cde5f57638d91718afd5e4e34ff5852cfa 2d5a07f9e0381747a08043768a4cd2b4ebd27151This study aims to analyze the effect of using artificial intelligence (AI) algorithms and electronic word of mouth (eWOM) on consumer purchasing decisions on the TikTok social media platform, with a focus on fashion products in the Greater Jakarta area. This study focuses on Generation Z and Millennials as the research population. This study used a quantitative approach with a survey involving 358 active respondents who met the criteria. The research model examines the relationship of variables such as Purchase Duration, Product Recommendation, Social Media Dependency, and Consideration Set, as well as the impact of eWOM on purchasing decisions. The results showed that Product Recommendation and Social Media Dependency have a significant influence on Consideration Set, which in turn influences consumer purchasing decisions. On the other hand, Purchase Duration does not have a significant influence on Consideration Set in the context of fashion purchases through TikTok. In addition, perceived eWOM was found to play an important role as an external factor that directly influences purchasing decisions. These findings provide insights for fashion industry players and digital platforms to optimize AI and eWOM-based marketing strategies, to increase consumer engagement and drive purchase decisions in the social media era.Syntax literate : jurnal ilmiah Indonesia140The results showed that Product Recommendation and Social Media Dependency have a significant influence on Consideration Set, which in turn influences consumer purchasing decisions, and perceived eWOM was found to play an important role as an external factor that directly influences purchasing decisions.Syntax Literate ; Jurnal Ilmiah Indonesia["Farrel Alfarabi Saleh"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/2d5a07f9e0381747a08043768a4cd2b4ebd27151 2c45a16c05dddfdbadf04c6e5451f41f33d41b29As artificial intelligence (AI) systems become increasingly integrated into various aspects of daily life, ethical considerations surrounding their development and deployment are paramount. This paper aims to explore the intersection of AI technology and ethical theory, examining how principles such as fairness, accountability, and transparency can be integrated into AI design and deployment. Through an examination of case studies in sectors like autonomous vehicles, healthcare, and social media, we underscore the possible impacts of deploying AI unethically. Additionally, this paper emphasizes the need for ethical guidelines and regulatory frameworks to promote responsible AI usage. By integrating existing literature and case studies, this research seeks to enhance understanding of the ethical landscape in AI and offer recommendations for stakeholders to support a fair technological future. KEYWORDS Artificial Intelligence (AI), Ethics, Fairness, Accountability, Case Studies, Autonomous Vehicles, deployment.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00The intersection of AI technology and ethical theory is explored, examining how principles such as fairness, accountability, and transparency can be integrated into AI design and deployment.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Varsharani T. Dond"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/2c45a16c05dddfdbadf04c6e5451f41f33d41b29 1c015b4bd31ae02a007e7cce04c8186642b00d29This paper explores the integration of artificial intelligence (AI) into software engineering. It examines how AI can be effectively incorporated throughout the software development lifecycle, encompassing phases like requirement analysis, system design, code development, testing, and software deployment. It highlights the potential benefits of AI-driven software development, such as increased development efficiency, improved software quality, and enhanced performance. The discussion extends to addressing the substantial challenges that accompany the integration of AI within software development frameworks. These include the limitations of current AI technology in achieving complete automation of large software projects, the need to ensure the accuracy and reliability of AI-generated code, complex task decomposition and verification, multi-agent collaboration, external knowledge utilization, and AI integration within project management workflows. This paper concludes by discussing the future directions in AI-driven software development.Artificial Intelligence, Soft Computing And Application Trends 2025150This paper concludes by discussing the future directions in AI-driven software development, highlighting the potential benefits of AI-driven software development, such as increased development efficiency, improved software quality, and enhanced performance.Artificial Intelligence, Soft Computing And Application Trends 2025["Xiaowei Shao", "Mariko Shibasaki", "Ryosuke Shibasaki"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/1c015b4bd31ae02a007e7cce04c8186642b00d29 031d6b4a672107e90c9e3d297b3c400346a1c5b4Journal of Technology Transfer591The Journal of Technology Transfer["Nicholas A. Pairolero", "Alexander V. Giczy", "Gerard Torres", "Tisa Islam Erana", "Mark A. Finlayson", "Andrew A. Toole"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/031d6b4a672107e90c9e3d297b3c400346a1c5b4 4352a9db6e217b7ee1754afcd310bedb4ce1e31fEvaluating debates is a challenging task requiring nuanced understanding of abstract reasoning. Current AI systems struggle with these complexities, often providing shallow or biased feedback. To address this, we developed Blitz Debate, a Retrieval-Augmented Generation (RAG) system that combines large language models (LLMs) with semantic search capabilities [1][2]. Blitz Debate retrieves relevant external knowledge to evaluate debate arguments with depth and accuracy, offering structured, real-time feedback. Our experiments demonstrated 90.5% accuracy in identifying winners and superior interpretative responses compared to vanilla ChatGPT, highlighting its ability to provide evidence-based and nuanced analysis. Challenges included limited real-time reasoning and contextual depth, which we addressed through enhanced context modeling and adaptive argument generation. By offering scalable, unbiased, and context-aware feedback, Blitz Debate makes debate evaluation more effective and accessible, fostering critical thinking and argumentation skills for students, educators, and competitive debaters alike.Artificial Intelligence, Soft Computing And Application Trends 202500Blitz Debate is a Retrieval-Augmented Generation (RAG) system that combines large language models (LLMs) with semantic search capabilities and retrieves relevant external knowledge to evaluate debate arguments with depth and accuracy, offering structured, real-time feedback.Artificial Intelligence, Soft Computing And Application Trends 2025["Leo Zhang", "Carlos Gonzalez"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/4352a9db6e217b7ee1754afcd310bedb4ce1e31f b12e96bffaa1f0758195373ee91de7ffc3255c5fIt delves into the role of AI in health monitoring, its applications in wearable devices, remote health monitoring systems, diagnosis, and disease prediction, as well as personalized medicine. The book also discusses the challenges and limitations of AI in health monitoring, including ethical and regulatory issues, and provides future directions and emerging trends in the field.00["Dr. V.K. Deepak"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/b12e96bffaa1f0758195373ee91de7ffc3255c5f 4ad7cf63d13e2e682a4c7372e5f66106e8083f97In the Indian IT industry, AI has reshaped human resource management (HRM). This means streamlining operations is easy, with AI-enhanced recruitment systems that scan resumes, match profiles to the job description, and guess the candidate’s career success based on historical data. By fielding routine HR queries, chatbots and virtual assistants reduce administrative burden and improve response time. By analyzing work patterns, project contributions, and feedback, AI applications in employee performance evaluation assist in impartial assessments. HR managers  can predict attrition rates and adopt retention strategies by using workforce analytics.Journal of Informatics Education and Research140Journal of Informatics Education and Research["Dr. Anand Muley", "Dr Katragadda Raghuveer", "Dr Sivakami", "Ms. Shelly Verma"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/4ad7cf63d13e2e682a4c7372e5f66106e8083f97 232b46781631e97c9a56d8ebb0fc79971860b2e9Health technology1000Health and Technology["S. E. Sibiya", "Rajendraparsad Hurchund", "Bernard Omondi", "Peter Owira"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/232b46781631e97c9a56d8ebb0fc79971860b2e9 939602a74088874859e9ea3daf75ea12c78caf07What makes an artificial system a good model of intelligence? The classical test proposed by Alan Turing focuses on behavior, requiring that an artificial agent's behavior be indistinguishable from that of a human. While behavioral similarity provides a strong starting point, two systems with very different internal representations can produce the same outputs. Thus, in modeling biological intelligence, the field of NeuroAI often aims to go beyond behavioral similarity and achieve representational convergence between a model's activations and the measured activity of a biological system. This position paper argues that the standard definition of the Turing Test is incomplete for NeuroAI, and proposes a stronger framework called the ``NeuroAI Turing Test'', a benchmark that extends beyond behavior alone and \emph{additionally} requires models to produce internal neural representations that are empirically indistinguishable from those of a brain up to measured individual variability, i.e. the differences between a computational model and the brain is no more than the difference between one brain and another brain. While the brain is not necessarily the ceiling of intelligence, it remains the only universally agreed-upon example, making it a natural reference point for evaluating computational models. By proposing this framework, we aim to shift the discourse from loosely defined notions of brain inspiration to a systematic and testable standard centered on both behavior and internal representations, providing a clear benchmark for neuroscientific modeling and AI development.1202This position paper argues that the standard definition of the Turing Test is incomplete for NeuroAI, and proposes a stronger framework called the ``NeuroAI Turing Test'', a benchmark that extends beyond behavior alone anditionally requires models to produce internal neural representations that are empirically indistinguishable from those of a brain up to measured individual variability.["J. Feather", "Meenakshi Khosla", "N. Apurva", "Ratan Murty", "Aran Nayebi"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/939602a74088874859e9ea3daf75ea12c78caf07 b05081f06952a7137ca222e375df52cf31cd3498In the context of digital transformation and the rapid development of artificial intelligence (AI), the use of AI technology in general and AI Chatbot in particular in education has become a significant trend. There is an increasing number of studies and practical applications of AI Chatbot in teaching and learning. Research indicates that AI Chatbot bring numerous benefits to the teaching process of subjects such as foreign languages, mathematics, science, chemistry, and more. This paper analyzes the potential of AI Chatbot in supporting the self-learning process of high school students in Chemistry. By integrating artificial intelligence technology, AI Chatbot not only provide instant information but also personalize the learning process, helping students enhance their self-learning capacity more effectively. Specifically, the study focuses on identifying the relationship between AI Chatbot and the self-learning process in Chemistry, while also proposing methods to implement AI Chatbot in Chemistry education to improve self-learning capacity for high school students in particular and for other subjects and levels of education in general.International Journal of Current Science Research and Review00The study focuses on identifying the relationship between AI Chatbot and the self-learning process in Chemistry, while also proposing methods to implement AI Chatbot in Chemistry education to improve self-learning capacity for high school students in particular and for other subjects and levels of education in general.International Journal of Current Science Research and Review["Dr. Nguyen Minh Giam"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/b05081f06952a7137ca222e375df52cf31cd3498 89b385746245deebecaed00cdb08067685a2e0bdIn the context of explainable artificial intelligence (XAI), limited research has identified role-specific explanation needs. This study investigates the explanation needs of data scientists, who are responsible for training, testing, deploying, and maintaining machine learning (ML) models in AI systems. The research aims to determine specific explanation content of data scientists. A task analysis identified user goals and proactive user tasks. Using explanation questions, task-specific explanation needs and content were identified. From these individual explanations, we developed a mental model for explanations, which was validated and revised through a qualitative study (n=12). In a second quantitative study (n=12), we examined which explanation intents (reason, comparison, accuracy, prediction, trust) require which type of explanation content from the mental model. The findings are: F1: Explanation content for data scientists comes from the application domain, system domain, and AI domain. F2: Explanation content can be complex and should be organized sequentially and/or in hierarchies (novelty claim). F3: Explanation content includes context, inputs, evidence, attributes, ranked list, interim results, efficacy principle, and input/output relationships (novelty claim). F4: Explanation content should be organized as a causal story. F5: Standardized explanation questions ensure complete coverage of explanation needs (novelty claim). F6: Refining mental models for explanations increases significantly its quality (novelty claim).00The explanation needs of data scientists, who are responsible for training, testing, deploying, and maintaining machine learning models in AI systems, are investigated to determine specific explanation content of data scientists.["Helmut Degen", "Ziran Min", "Parinitha Nagaraja"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/89b385746245deebecaed00cdb08067685a2e0bd 7123914f425fed2c3b4d5645af984116128956a1The rapid evolution of artificial intelligence (AI) has significantly transformed industries, positioning the fashion sector as a critical area of study due to its mass production and pressing sustainability challenges. As the world’s largest apparel producer, China faces unique hurdles in terms of integrating AI technologies, highlighting the intersection of technological innovation and sustainability within this industry. In this context, this study aims to provide the initial exploratory correlations between AI adoption and open innovation from apparel manufacturing micro-, small-, and medium-size enterprises (MSMEs) managers’ perspectives, identifying knowledge absorptive capacity (KACAP)’s significant impacts through an integrated and extended TAM-TOE model. We conducted PLS-SEM to empirically validate the antecedents of AI adoption and its consequential effects on KACAP and open innovation by collecting information from 269 of the apparel manufacturing MSMEs’ top managers. The results show that the TAM-TOE structural model explains 60.7% of the variance in AI adoption, 47.4% in KACAP, and 55.4% in open innovation, which suggests that the model has good explanatory capacity, and that all these Q2 values indicate a sizeable predictive accuracy threshold. Drawing on the proposed model, the study has identified technological (e.g., perceived usefulness) and environmental factors (e.g., competitive pressure, market uncertainty, and government support and policy) that significantly impact AI adoption, while organizational factors (e.g., organizational readiness) directly impact KACAP, and environmental factors (e.g., competitive pressure, supplier involvement, and market uncertainty) directly impact open innovation. Subsequently, the AI construct is having a significant influence on MSMEs’ open innovation through KACAP. This fills existing theoretical gaps by linking AI technology to organizational innovation processes and demonstrates the mediating influence of KACAP. Also, the proposed model provides a foundation for future research by exploring the intersection of AI and innovation in similar industries.Sustainability1120This study aims to provide the initial exploratory correlations between AI adoption and open innovation from apparel manufacturing micro-, small-, and medium-size enterprises (MSMEs) managers’ perspectives, identifying knowledge absorptive capacity (KACAP)'s significant impacts through an integrated and extended TAM-TOE model.Sustainability["Chen Qu", "Eunyoung Kim"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/7123914f425fed2c3b4d5645af984116128956a1 f0493c10e0be3c28ae359c6861f795324c63bfcbSince its implementation in 2008, China's Antitrust Law has provided legal protection for market competition, but it faces numerous dilemmas in practice. With the rise of emerging industries such as the internet platform economy, big data, and artificial intelligence, the traditional antitrust law framework has increasingly revealed its inadequacies, particularly in regulating new types of monopolistic behaviors, such as data monopolies and platform monopolies. This article reviews the development of China's Antitrust Law and analyzes the enforcement dilemmas it faces, including structural flaws in the administrative system, political priorities, and delays in legal enforcement. Furthermore, the article proposes reform directions to address these dilemmas, including improving the legal framework, enhancing enforcement efficiency, strengthening interdepartmental collaboration, and increasing public participation. The goal is to improve the effectiveness of antitrust law enforcement and promote the healthy development of China's market.Academic Journal of Management and Social Sciences50Academic Journal of Management and Social Sciences["Lexu Wang"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/f0493c10e0be3c28ae359c6861f795324c63bfcb da3388edb15e5683aa55875cde588735b0c5d96cIn this review, the transformative impact of integrating artificial intelligence (AI) and wireless communication technologies into the heliostat control systems of concentrated solar power (CSP) plants are explored. Heliostat control systems are categorized based on wired and wireless implementations, and calibration methods are analyzed from traditional methods, auxiliary equipment, and AI in detail. The applications of artificial intelligence, machine learning, and deep learning techniques enhance the accuracy, control ability, and prediction performance of CSP heliostat control systems. At the same time, wireless communications play an important role in reducing costs, enhancing scalability, and enabling more flexible deployment. The synergistic impact of AI and wireless technologies improves the efficiency, reliability, and economic viability of heliostat systems, and shows great potential in global energy transition.Energies640The synergistic impact of AI and wireless technologies improves the efficiency, reliability, and economic viability of heliostat systems, and shows great potential in global energy transition.Energies["Quanwu Liu", "Zengli Dai", "Yuan Wei", "Dongxiang Wang", "Yu Xie"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/da3388edb15e5683aa55875cde588735b0c5d96c 9b126cc562b9888d972c1f8021fb16a010492577Under India's National Education Policy 2024, the integration of artificial intelligence marks a groundbreaking shift, unlocking new possibilities for modernizing skill-based education. This in-depth study assesses the impact of AI-driven learning systems by analyzing implementation data from 50 educational institutions across India over an 18-month period. Utilizing advanced machine learning techniques for performance tracking and data-driven insights, the study encompassed a diverse group of 500 teachers and 5,000 students.Journal of Informatics Education and Research180This in-depth study assesses the impact of AI-driven learning systems by analyzing implementation data from 50 educational institutions across India over an 18-month period, utilizing advanced machine learning techniques for performance tracking and data-driven insights.Journal of Informatics Education and Research["Mr. Roopesh", "Kumar Mca", "Prof. Karthika"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/9b126cc562b9888d972c1f8021fb16a010492577 a4a2d1c078b111d22433595705d481b674b1f11fThis paper presents a volunteer-driven delivery management app designed to handle realtime updates and data synchronization effectively [1]. The project addresses the challenge of managing delivery tasks in dynamic environments where multiple volunteers need to interact with data simultaneously [2]. Using Flutter and Firebase, the app provides a seamless interface for volunteers, ensuring secure authentication and consistent delivery updates [3]. Key experiments evaluated the system's reliability under concurrent update scenarios and varying network conditions. The findings indicate that the app maintains data integrity and usability despite network fluctuations, making it a dependable tool for coordinating food deliveries. The results emphasize the importance of robust backend systems in managing real-time volunteer-driven applications.Artificial Intelligence, Soft Computing And Application Trends 2025150The project addresses the challenge of managing delivery tasks in dynamic environments where multiple volunteers need to interact with data simultaneously by using Flutter and Firebase, and provides a seamless interface for volunteers, ensuring secure authentication and consistent delivery updates.Artificial Intelligence, Soft Computing And Application Trends 2025["Zhaocen Lin", "Ang Li"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/a4a2d1c078b111d22433595705d481b674b1f11f e472df5b6426ddc62959c2b942597c2ef4992faaEste estudio examina el uso de la inteligencia artificial entre estudiantes universitarios, centrándose en los beneficios, los desafíos y las preferencias tecnológicas. Utilizando un enfoque cuantitativo, revisión de la literatura y una encuesta a 127 estudiantes de una universidad privada en Guadalajara, Jalisco, México, el análisis encontró que el 99% de los estudiantes la utiliza, principalmente dos a tres veces por semana. Las motivaciones incluyen el fácil acceso, el ahorro de tiempo, la rápida recuperación de información y una mejor comprensión. Los ordenadores portátiles (79%) y los teléfonos celulares (58%) son los dispositivos preferidos, siendo ChatGPT, Copilot y Gemini las herramientas más populares. Los beneficios incluyen aprendizaje personalizado, retroalimentación inmediata y adaptabilidad. Sin embargo, los desafíos incluyen información poco confiable, explicaciones complejas, problemas de precisión, fallas lógicas, falta de personalización y citas faltantes. A pesar de estos desafíos, el estudio concluye que mejora el aprendizaje de pregrado y recomienda su incorporación como herramienta educativa complementaria. Posibles implicaciones prácticas ¿cómo incorporar la inteligencia artificial como herramienta en las planeaciones didácticas?RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo00RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo["Ernesto Roque Rodr\u00edguez", "Ernesto Gabriel Roque Ramos"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/e472df5b6426ddc62959c2b942597c2ef4992faa ed1ef475611537f4ec33573b4667b7c83e7dd383La inteligencia artificial (IA) está transformando la comunicación intercultural en las organizaciones, ya que permite superar barreras lingüísticas y culturales en un mundo globalizado. Así, se analizan las herramientas DeepL, Microsoft Translator, IBM Watson, Brandwatch y ChatGPT, que han demostrado su capacidad para personalizar mensajes, optimizar procesos de traducción y mejorar la conexión con audiencias diversas. La investigación adopta una metodología cualitativa basada en una revisión bibliográfica y el análisis de casos prácticos de empresas como SAP, Telefónica y Unilever, lo que permite identificar impactos reales en la cohesión interna y la comunicación externa.Los resultados muestran que estas herramientas han permitido mejorar la precisión en traducciones técnicas, facilitar la inclusión mediante subtítulos automáticos en tiempo real y ajustar campañas publicitarias para diferentes contextos culturales. Sin embargo, se identifican limitaciones como el sesgo algorítmico, la dependencia tecnológica y las dificultades para trabajar con idiomas menos representados. Así, se puede concluir que la IAIA se presenta como una solución estratégica para abordar los desafíos de la comunicación intercultural, ya que ofrece y potencia la eficiencia y la personalización. Sin embargo, su implementación requiere supervisión ética y sensibilidad cultural para maximizar su impacto positivo y mitigar riesgos. Al combinar tecnología avanzada con la experiencia humana, las organizaciones pueden construir relaciones más inclusivas y efectivas, fortaleciendo su posición en un entorno empresarial global y diverso.Revista Protocolo y Comunicación210Revista Protocolo y Comunicación["Jos\u00e9 Manuel Mart\u00edn-Herrero", "\u00c1lvaro G\u00e1rriz Oyarzun"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/ed1ef475611537f4ec33573b4667b7c83e7dd383 76dd03c195ba0c6ace8507b5808f8c17e7f998d1A inteligência artificial (IA) vem se desenvolvendo desde os anos 50, passando por várias fases de evolução até alcançar aplicações práticas significativas nos últimos anos. No contexto das unidades policiais militares no Paraná, a implementação da IA apresenta um grande desafio, estruturado em três pilares: a promessa de maior eficiência operacional e tomada de decisão baseada em dados; os desafios éticos e práticos que devem ser meticulosamente abordados; e o constante treinamento do efetivo responsável pelo gerenciamento e extração de dados. Este artigo investiga as considerações éticas, os desafios regulatórios, as preocupações com a segurança cibernética e os avanços tecnológicos relacionados ao uso da IA na governança policial militar. A metodologia inclui uma revisão bibliográfica e a análise de estudos de caso relevantes. Os resultados indicam que a implementação bem-sucedida da IA depende de estruturas éticas robustas, políticas regulatórias adaptativas e medidas de segurança cibernética proativas. O estudo conclui que o equilíbrio entre avanços tecnológicos e conformidade ética é crucial para o futuro da IA na aplicação da lei.RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-621880RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218["Marcelo Moreira So"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/76dd03c195ba0c6ace8507b5808f8c17e7f998d1 81dddce23afca28a09dcb886084bbd37cee7cc1cEl presente estudio analiza el empleo de herramientas de inteligencia artificial en la evaluación formativa en diversos niveles educativos y áreas de conocimiento, describe los criterios de búsqueda y selección de literatura, y caracteriza los tipos de prompts y sistemas automatizados empleados. Se aplican metodologías de revisión para recopilar y examinar un conjunto de treinta y cuatro estudios, lo que permite observar beneficios como la retroalimentación inmediata y la mayor motivación estudiantil, junto con limitaciones relacionadas con la privacidad de datos y la supervisión docente. El uso de modelos de lenguaje muestra resultados favorables en la generación y valoración de respuestas abiertas. Se concluye que la implementación responsable de la inteligencia artificial en la evaluación formativa fortalece la calidad del proceso educativo. El presente trabajo es resultado del proyecto de investigación titulado "Perfeccionamiento de las prácticas pedagógicas en las instituciones educativas de la zona sur de Manabí" y también contribuye como parte de los resultado del proyecto de vinculación titulado "Tareas dirigidas y apoyo Psicopedagógico para fortalecer el aprendizaje de los alumnos en la Educación básica Pública de Jipijapa Fase II 2024"Revista Científica de Innovación Educativa y Sociedad Actual "ALCON"00Revista Científica de Innovación Educativa y Sociedad Actual "ALCON"["Pa\u00fal Geovanny Am\u00e9n Mora", "Rodrigo Alexander Rinc\u00f3n Zambrano", "Lisbeth Madelayne Santos Mera", "Xiomara Lisbeth Anzules \u00c1vila"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/81dddce23afca28a09dcb886084bbd37cee7cc1c 2b5e64fa50f8526bc2597a87b5cda852fdc0eaecIntelligence and national security00Intelligence and National Security["G\u00f6khan \u00c7\u0131nkara"]2025-02-22T00:00:00https://www.semanticscholar.org/paper/2b5e64fa50f8526bc2597a87b5cda852fdc0eaec 450c3850990f5b787638d9f7f63d9226769b8147Introduction. In recent years, the role of artificial intelligence (AI) in social life has increased. Innovations in the field of AI create not only new opportunities for a person and society as a whole, but also risks, problems and threats, which leads to the actualization of a risk-oriented approach towards the regulation of AI development on both the international and national levels. The problem of AI risk management has several interrelated areas, one of the central ones is the problem of identification of values on which the ethics of AI is built. The article considers issues related to the value-oriented direction of development of contemporary AI.Methodology and sources. In the article there were used the methodology of cultural philosophical, axiological and interdisciplinary approaches is used. The sources used in the article are scientific research of domestic and foreign authors, documents, publications and websites devoted to the current state of AI and its problems.Results and discussion. The topic of contemporary AI is developing as a special area of scientific and disciplinary knowledge, as well as a scalable AI industry. One of the latest trends in the current AI is emotional AI and its capabilities in establishing effective communication with person. However, despite the revolutionary nature of the new AI technology and the special significance of this innovation in AI communication with person, the problems of ethics and the value-oriented development of contemporary AI are defined by experts as key problems of our time.Conclusion. Contemporary AI is analyzed today from the standpoint of various classifications, the central place is occupied by the classification based on the comparison of human intelligence and AI. In this regard, the formalization of moral values and ethical principles in the process of developing and operating AI algorithms is important for the value-oriented human-AI interaction. However, the system of universal human values and AI values coincide only partially. As a result, new approaches emerge that do not compare AI and human intelligence, in particular, the interdisciplinary approach «4E Cognition», which is considered by experts as the most productive in all respects.Discourse20Issues related to the value-oriented direction of development of contemporary AI are considered, including the formalization of moral values and ethical principles in the process of developing and operating AI algorithms.Discourse["R. Mamina", "A. V. Ilina"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/450c3850990f5b787638d9f7f63d9226769b8147 8815c50c6af7613ebfe5955c566291843b40010dThis study examined Psalms 115:4-8 in relation to artificial intelligence (AI), in Yoruba - African context. The study explored how this text is understood and expressed in ancient Israel and in Yoruba - African tradition. This study considered whether contemporary AI reliance mirrors the misplaced trust condemned by the psalmist and examines how Yoruba spiritual beliefs shape the ethical perception of AI. African biblical hermeneutics that provide useful lens for rereading the text was adopted for the study. It was discovered that the text critiques the reliance on human - made objects devoid of life and the dangers of misplaced trust. AI is presented as a potential “modern idol,” wherein people place faith in a technology that lacks genuine autonomy and spirit. In Yoruba culture, material objects often carry symbolic or spiritual significance, making exploring AI’s role and ethical boundaries particularly significant. While AI can offer profound benefits, it remains a creation of human hands, inherently limited and devoid of proper spiritual agency. A balanced approach to AI that values technological progress without compromising foundational spiritual values is essential for a future where AI complements human dignity and spiritual reverence and faith.    Social Science and Humanities Journal270Social Science and Humanities Journal["Peter O. Awojobi", "Nathaniel TeminiJesu Okunade"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/8815c50c6af7613ebfe5955c566291843b40010d 14936596adb258e7a3e74b998aeefff6c3c3eb50Humanities and Social Sciences Communications1600This paper critically examines AI’s societal and individual impacts during the COVID-19 pandemic, and advocates for comprehensive social policies to govern AI responsibly, ensuring ethical integrity and efficiency in future public health crises.Humanities and Social Sciences Communications["Xiaojun Ding", "Bingxing Shang", "Caifeng Xie", "Jiayi Xin", "Feng Yu"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/14936596adb258e7a3e74b998aeefff6c3c3eb50 39c5f0257777a0a4941aca03c65bf9a9774ff340 Laboratory automation is crucial for improving efficiency and enhancing reproducibility in scientific workflows. However, industrial solutions mostly do not fit the needs of scientific institutions, such as cost efficiency, customizability, and flexibility in fast iteration cycles. This study presents a laboratory automation system that integrates affordable robotics and artificial intelligence (AI)‐driven functionalities in a modular architecture to address key challenges in research environments. The system uses a robotic arm and a large language model (LLM) as a lab assistant, enabling natural language interaction and task orchestration. In contrast to fully autonomous systems, this approach emphasizes a collaborative human‐in‐the‐loop model, ensuring adaptability and reducing reliance on artificial intelligence for task planning. Key innovations include meta‐tools for dynamic task recording and playback, low‐level information management to reduce cognitive load on the LLM, and AI‐assisted data reading for real‐time measurement extraction. The system's ability to automate complex workflows is validated in three experimental scenarios, involving sample preparation, error handling, and multi‐step measurements. The system demonstrates the ability to perform tasks with minimal user input while maintaining flexibility and adaptability to changing experimental conditions. The findings pave the way for the future of laboratory automation, where human and AI‐driven systems work seamlessly together in optimized scientific workflows.Advanced Intelligent Systems120A laboratory automation system that integrates affordable robotics and artificial intelligence (AI)‐driven functionalities in a modular architecture to address key challenges in research environments and pave the way for the future of laboratory automation, where human and AI‐driven systems work seamlessly together in optimized scientific workflows.Advanced Intelligent Systems["Stefan Conrad", "Phil Auth", "T. Masselter", "T. Speck"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/39c5f0257777a0a4941aca03c65bf9a9774ff340 adafc2bb4068e62ee79307f45b9cc95a95c5e2f7The research aims to find out the extent to which Artificial Intelligence (AI) can be used as an innovative and effective learning medium for children at elementary school age. The research method used is a qualitative approach that is descriptive-interpretive. The type of research used is library research. The data obtained is a descriptive narrative about Artificial Intelligence (AI) as a Learning Media for Elementary School Age Children (6-12 Years Old). The results of the study show that innovative learning media AI offers various advantages and opportunities in the learning process and provides a learning experience. The use of AI-based learning media (Artificial Intelligence) is inseparable from the use of applications/software as tools in learning activities such as kahoot applications, quizizz, google form, tesmoz, ProProfs Quiz Maker and others. The application of AI in education focuses on students who refer to the use of AI technology, a tutor system for adaptive learning that adjusts students' abilities, the use of chatbots, game-based learning, smart course content with AR/VR, data analysis and prediction, and learning evaluation. The use and application of AI has challenges that need to be overcome to maximize its potential in the form of human resource limitations, privacy and security concerns, technology inflexibility, problems with the implementation of AI technology in education, and inequities in cost and access.  Indo-MathEdu Intellectuals Journal00The results of the study show that innovative learning media AI offers various advantages and opportunities in the learning process and provides a learning experience.Indo-MathEdu Intellectuals Journal["M. Iqbal"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/adafc2bb4068e62ee79307f45b9cc95a95c5e2f7 864c1c136180a3e349bed88c8ab397274c586ccc Initially, photographic works were a representation of reality that depicted facts and truth. However, with the presence of artificial intelligence imaging has shifted the concept underlying photography. Openly the company Levi Strauss & Co with its Levi's product uses photos of models generated by AI for its advertisements. The presence of artificial intelligence (AI) in the field of photography has become a dialectical discourse in society that can replace human work in various fields. This study aims to understand visual phenomena by describing and analyzing them to see the impact of Artificial Intelligence (AI) technology in the field of photography on Levi's advertisements. The focus of the research is the influence of technological advances on very significant changes in the world of advertising. Gillian Rose's Visual Methodology is used to present comprehensively, assisted by various literatures. Data collection is done by conducting observations, documentation, interviews, and questionnaires. The socio-technological approach is used to reveal the influence of technological developments on social issues and advertising photography phenomena as research objects. The results of the study show that artificial intelligence technology, namely, (1) produces more creative and innovative visual aesthetics for advertising; (2) supports more effective and efficient work; (3) there is social anxiety for some commercial photographers, designers and advertising models. Mudra: Jurnal Seni Budaya00The results of the study show that artificial intelligence technology produces more creative and innovative visual aesthetics for advertising; supports more effective and efficient work; and there is social anxiety for some commercial photographers, designers and advertising models.Mudra Jurnal Seni Budaya["Prayanto Widyo Harsanto", "Anak Agung Gde Bagus Udayana", "Kinanthi Raras Satuti"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/864c1c136180a3e349bed88c8ab397274c586ccc 4c945f5a084dcd66e21301b93d4f1ae9a0704034This research aimed to determine whether there is a relationship between academic self-efficacy and dependence on artificial intelligence in a sample of Peruvian university students. A quantitative, non-experimental, correlational, and cross-sectional study was conducted. The sample consisted of 186 students of both sexes, selected through probabilistic sampling, who were administered the Specific Academic Situations Perceived Self-Efficacy Scale and the Artificial Intelligence Dependence Scale, instruments that showed adequate metric properties. The results showed that the level of academic self-efficacy was medium, while the dependence on artificial intelligence was moderate. Furthermore, it was found that men and students between the ages of 25 and 34 had slightly higher levels of academic self-efficacy. In comparison, those between the ages of 16 and 24 experienced higher levels of dependence on artificial intelligence. On the other hand, the Pearson correlation coefficient (r) between both variables was -0.299 (p<0.05). It was concluded that there is an inverse and significant relationship between academic self-efficacy and dependence on artificial intelligence in the sample of Peruvian university students. In other words, as academic self-efficacy increases, the level of dependence on artificial intelligence tools decreases.Sapienza: International Journal of Interdisciplinary Studies440There is an inverse and significant relationship between academic self-efficacy and dependence on artificial intelligence in the sample of Peruvian university students, in other words, as academic self-efficacy increases, the level of dependence on artificial intelligence tools decreases.Sapienza: International Journal of Interdisciplinary Studies["Edwin Gustavo Estrada-Araoz", "Maribel Mamani-Roque", "Jhemy Quispe-Aquise", "Yesenia Ver\u00f3nica Manrique-Jaramillo", "Elizabeth Orfelia Cruz-Laricano"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/4c945f5a084dcd66e21301b93d4f1ae9a0704034 89028c8dcef605c80ec604c55d8e36f37e3c042cThe integration of Artificial Intelligence in online gaming has revolutionized the interactive entertainment landscape, transforming how games are developed, experienced, and evolved. This article examines the multifaceted impact of AI technologies across various aspects of gaming, from player experience systems to technical infrastructure. The article explores how AI-driven adaptive gameplay mechanisms, procedural content generation, and sophisticated NPC intelligence systems are reshaping player interactions and game development processes. The article investigation delves into the technical challenges and ethical considerations surrounding AI implementation in gaming, including security concerns, algorithm bias, and data protection. The article highlights the emergence of advanced development tools, optimization techniques, and performance enhancement systems that are fundamentally changing how games are created and maintained. Through analysis of current implementations and future trends, this article provides insights into how AI is not only enhancing gaming experiences but also opening new avenues for computational social science, education, and behavioral research in virtual environments.International Journal of Scientific Research in Computer Science Engineering and Information Technology10Insight is provided into how AI is not only enhancing gaming experiences but also opening new avenues for computational social science, education, and behavioral research in virtual environments.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ravali Kandur"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/89028c8dcef605c80ec604c55d8e36f37e3c042c cb9eef8df6c2d0806872e28097d376e0de2f29e4It has been 100 years since statistical process control (SPC) or statistical process monitoring (SPM) was first introduced for production processes and later applied to service, healthcare, and other industries. The techniques applied to SPM applications are mostly statistically oriented. Recent advances in Artificial Intelligence (AI) have reinvigorated the imagination of adopting AI for SPM applications. This manuscript begins with a concise review of the historical development of the statistically based SPM methods. Next, this manuscript explores AI and Machine Learning (ML) algorithms and methods applied in various SPM applications, addressing quality characteristics of univariate, multivariate, profile, and image. These AI methods can be classified into the following categories: classification, pattern recognition, time series applications, and generative AI. Specifically, different kinds of neural networks, such as artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN), are among the most implemented AI methods impacting SPM. Finally, this manuscript outlines a couple of future directions that harness the potential of the Large Multimodal Model (LMM) for advancing SPM research and applications in complex systems. The ultimate objective is to transform statistical process monitoring (SPM) into smart process control (SMPC), where corrective actions are autonomously implemented to either prevent quality issues or restore process performance.2190This manuscript explores AI and Machine Learning algorithms and methods applied in various SPM applications, addressing quality characteristics of univariate, multivariate, profile, and image and outlines a couple of future directions that harness the potential of the Large Multimodal Model for advancing SPM research and applications in complex systems.["Shing Chang", "Parviz Ghafariasl"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/cb9eef8df6c2d0806872e28097d376e0de2f29e4 d6d85e2fba646f1268e961082e0fa2709931fe9fThe role of artificial intelligence (AI) in art is examined in this study, focusing on how AI can bridge the gap between automation and human creativity. Using a qualitative approach, the study gathers information through in-depth interviews with academics, artists, and professionals in the art field to understand their perspectives on integrating AI into the creative process. The study looks at how AI technologies affect artistic creation, from creating new works of art to enhancing conventional techniques. It investigates AI's practical, philosophical, and ethical ramifications in art. The interviews provide a thorough understanding of AI's potential to expand artistic boundaries while maintaining human emotional expression, which presents various viewpoints on the technology's capacity to complement rather than replace human creativity. The findings suggest that while AI offers new tools for creativity, it also calls for reexamining concepts such as authorship, originality, and the nature of creativity. This study contributes to the ongoing discussion about the relationship between art and technology by highlighting how artificial intelligence (AI) changes how art is created and interpreted in the modern era.Asian Journal of Social Science Studies700The findings suggest that while AI offers new tools for creativity, it also calls for reexamining concepts such as authorship, originality, and the nature of creativity.Asian Journal of Social Science Studies["Hasan Rammal", "H. Hejase", "Ali El Takach"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/d6d85e2fba646f1268e961082e0fa2709931fe9f 5ac4ca4b9241e8d1a5357b1037c79dbc07829db0This study aims to determine and analyse the effect of artificial intelligence and emotional intelligence on the ethical behaviour of accounting students. This type of research is quantitative research in the form of causal. The research population was all accounting students at the Faculty of Economics and Business, Padang State University. Sample selection using purposive sampling method. The research sample was 196 active accounting students. The data analysis technique in this study is to use the structural equation model partial least square. Based on hypothesis testing, it can be concluded that the artificial intelligence variable has a positive and significant effect on ethical behaviour. Any increase in the use of artificial intelligence will increase ethical behaviour. Emotional intelligence variables have a positive and significant effect on ethical behaviour. Any increase in emotional intelligence will increase ethical behaviour. The overall effect of artificial intelligence and emotional intelligence variables on ethical behaviour is 78.2% while the remaining amount is influenced by other variables that are outside the research model.JURNAL EKSPLORASI AKUNTANSI220It can be concluded that the artificial intelligence variable has a positive and significant effect on ethical behaviour and any increase in the use of artificial intelligence will increase ethical behaviour.JURNAL EKSPLORASI AKUNTANSI["Hezi Mufliha Emina", "Vanica Serly"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/5ac4ca4b9241e8d1a5357b1037c79dbc07829db0 9c76e6e6f83174d77e2ace756c7bc04955b3e35dThe classification and treatment of myocardial infarction (MI) have evolved significantly over the past few decades, with the ST-segment elevation myocardial infarction (STEMI)/non-STEMI (NSTEMI) paradigm dominating clinical practice. While STEMI, identified by ST-segment elevation (STE) on electrocardiogram (ECG), has been the hallmark for urgent reperfusion therapy, this model misses a substantial number of patients with occlusive myocardial infarction (OMI) who do not exhibit STE. Recent evidence reveals that up to 25% of NSTEMI patients have OMI, leading to higher mortality due to delayed reperfusion. The emerging OMI/NOMI (Occlusive vs. Non-Occlusive MI) paradigm offers a more nuanced approach, incorporating advanced ECG interpretation and tools like point-of-care echocardiography and artificial intelligence (AI). AI has shown promise in detecting subtle ECG changes indicative of OMI, improving diagnostic accuracy and reducing misdiagnosis. This paradigm shift has important implications for clinical practice, calling for earlier identification of OMI and more inclusive treatment strategies to enhance patient outcomes.British journal of hospital medicine100The emerging OMI/NOMI (Occlusive vs. Non-Occlusive MI) paradigm offers a more nuanced approach, incorporating advanced ECG interpretation and tools like point-of-care echocardiography and artificial intelligence (AI).British journal of hospital medicine["Maroua Dali", "Cecilia Maria Elizabeth Bogle", "R. Bogle"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/9c76e6e6f83174d77e2ace756c7bc04955b3e35d de737b5a09ad70c435c2e8670be63c71ee8e9944Artificial intelligence (AI) has transformed medical imaging, driving advancements in radiology and endoscopy. Semantic segmentation, a pixel-level technique crucial for delineating pathological features, has become a cornerstone of digital pathology. Pathology segmentation AI models are often trained using annotations generated by pathologists. Despite the meticulous care typically exercised, these annotations frequently contain empirical label noise. However, the specific types of label noise in pathology data and their impact on AI model training remain inadequately explored. This study systematically investigated the effects of label noise on the performance of pathology segmentation models. Using publicly available datasets and a breast cancer semantic segmentation dataset, modules were developed to simulate four types of artificial label noise at varying intensity levels. These datasets were used to train deep learning models with encoder-decoder architectures, and their performance was evaluated using metrics such as the Dice coefficient, precision, recall, and intersection over union. The results indicated that models were highly susceptible to overfitting label noise, particularly boundary-dependent noise such as dilation and shrinkage. Discrepancies were identified between apparent performance scores obtained under real-world conditions and true performance scores derived using clean test data. This overestimation risk was most pronounced for datasets containing boundary-altering noise. Furthermore, random combinations of noise types and levels significantly impaired model generalization. This study underscores the critical importance of addressing label noise in pathology datasets. It is proposed that future efforts focus on developing standardized methods for quantifying and mitigating label noise, along with creating robust benchmarks using noise-inclusive datasets. Enhancing annotation quality and addressing label noise can improve the reliability and generalizability of AI in pathology, facilitating broader clinical adoption.bioRxiv60Enhancing annotation quality and addressing label noise can improve the reliability and generalizability of AI in pathology, facilitating broader clinical adoption, and underscores the critical importance of addressing label noise in pathology datasets.bioRxiv["Kenji Harada", "Yuichiro Nomura", "D. Komura", "Shumpei Ishikawa", "Shingo Sakashita"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/de737b5a09ad70c435c2e8670be63c71ee8e9944 4380a782cf01a72d859922068cff4245c9500c82International Journal of Human-Computer Interaction1430International Journal of Human–Computer Interaction["Anurag Chaturvedi", "Neetu Yadav", "Meeta Dasgupta"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/4380a782cf01a72d859922068cff4245c9500c82 56b27c50ed82a1734c2656f4b8885df8d64f5dfcThis article provides a comprehensive overview of how Artificial Intelligence (AI) is revolutionizing observability in distributed environments. It explores the diverse applications of AI in enhancing system monitoring, management, and maintenance across complex, interconnected IT infrastructures. The article delves into key areas where AI makes significant contributions, including intelligent monitoring, advanced anomaly detection, sophisticated data correlation across systems, predictive maintenance, automated remediation, and continuous improvement. By examining these aspects, the article demonstrates how AI-driven observability solutions are addressing current challenges in managing distributed systems while also paving the way for more resilient, efficient, and adaptive IT environments. The discussion encompasses various AI techniques and models, such as machine learning algorithms, neural networks, and time-series analysis methods, illustrating their practical applications in improving system performance, reducing downtime, and optimizing resource utilization. Ultimately, this article underscores the transformative potential of AI in observability, highlighting its role in enabling proactive, scalable, and intelligent management of distributed systems in an increasingly digital world.International Journal of Scientific Research in Computer Science Engineering and Information Technology10This article provides a comprehensive overview of how Artificial Intelligence is revolutionizing observability in distributed environments, and delves into key areas where AI makes significant contributions, including intelligent monitoring, advanced anomaly detection, sophisticated data correlation across systems, predictive maintenance, automated remediation, and continuous improvement.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Abhishek Walia"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/56b27c50ed82a1734c2656f4b8885df8d64f5dfc b714d7f092fe21358daa8cee348c18e14a9f004dConversational Artificial Intelligence (AI) is revolutionizing customer interaction and service delivery in online commerce by addressing the limitations of traditional support systems, such as slow response times and lack of personalization. Leveraging Natural Language Processing (NLP) and machine learning, AI-driven chatbots provide 24/7 assistance, personalized engagement, and seamless user experiences, significantly enhancing customer satisfaction and operational efficiency. These advancements also highlight the growing need for AI systems to interpret and respond to complex emotional cues, fostering deeper consumer trust and brand loyalty. With advancements in AI technology, combining generative AI, sentiment analysis, and adaptive learning models will further enhance chatbot capabilities, ensuring more human-like and context-based interactions. Yet, ethical implementation is still of paramount importance, demanding transparency, data privacy measures, and bias prevention to enable responsible AI adoption. This paper discusses different chatbot frameworks, business uses, and industry-specific applications, discussing their advantages, challenges, and future prospects. In addition, it sets out a blueprint for optimizing AI-powered customer service, aligning automation with human judgment to build a wiser, more compassionate, and more effective digital commerce environment.Journal of Information Systems Engineering & Management370Different chatbot frameworks, business uses, and industry-specific applications are discussed, discussing their advantages, challenges, and future prospects and sets out a blueprint for optimizing AI-powered customer service, aligning automation with human judgment to build a wiser, more compassionate, and more effective digital commerce environment.Journal of Information Systems Engineering and Management["Neelam Phadnis1", "Jayant Gadge2", "Deven Shah3"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/b714d7f092fe21358daa8cee348c18e14a9f004d a0dec6905bed2098c33662edfa9bcfafdb045dcbDue to advancements in Artificial Intelligence (AI), its use in developing disease detection algorithms has surged. AI offers major benefits across various sectors like automotive, finance, IT, and pharmaceuticals. It is categorized into strong AI, which operates independently of human input, and weak AI, reliant on rules to make informed decisions. This essay focuses on weak AI, which enhances decision-making probabilities and finds application in healthcare. Healthcare is critical for individuals needing immediate medical attention. It interacts with other fields, including pharmaceuticals and telecommunications, revolutionized by advances in technology and ICT integration, making healthcare systems more efficient and cost-effective. A notable trend in healthcare is the adoption of AI-enabled Remote Patient Monitoring (RPM), which provides predictive alerts. AI has improved disease detection systems, enhancing their accuracy and efficiency. With AI, health vitals and trends can be predicted, as it monitors all patient events and initiates necessary actions. Traditional healthcare measures vitals periodically, whereas AI-enabled RPM allows for prior health predictions, facilitating timely reactions in emergencies.International Journal of Scientific Research in Computer Science Engineering and Information Technology40This essay focuses on weak AI, which enhances decision-making probabilities and finds application in healthcare, which improves disease detection systems, enhancing their accuracy and efficiency.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Kumar Avizeet"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/a0dec6905bed2098c33662edfa9bcfafdb045dcb fa4a4c431f95ee805b4a76ba89140772c1a8a441The rapid advancement of digital transformation, driven by artificial intelligence (AI), has fundamentally reshaped consumer behavior and business strategies. AI-powered technologies such as predictive analytics, personalized advertising, and automation have revolutionized how consumers interact with brands, make purchasing decisions, and engage in digital marketplaces. This research explores the impact of AI-driven digital transformation on consumer behavior, analyzing key AI-powered tools, targeted advertising, predictive analytics, and their applications in various industries. The study also examines ethical concerns, including data privacy, algorithmic bias, and consumer autonomy, highlighting the importance of responsible AI adoption. Through an extensive review of existing literature, case studies, and industry trends, this paper provides insights into the implications of AI on businesses, consumers, and policymakers. The research concludes with recommendations for sustainable AI implementation and areas for future investigation, particularly in emerging markets and long-term consumer trust dynamics.International Journal For Multidisciplinary Research180The impact of AI-driven digital transformation on consumer behavior is explored, analyzing key AI-powered tools, targeted advertising, predictive analytics, and their applications in various industries and ethical concerns are examined.International Journal For Multidisciplinary Research["Shreya Gupta"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/fa4a4c431f95ee805b4a76ba89140772c1a8a441 a3d1c0166283200e77c1f31f084a134078b49a4fSmall business owners wear many hats, but making informed financial decisions can be a daunting task. Artificial Intelligence (AI) has the potential to revolutionize financial decision-making, but its adoption among small businesses remains limited. This study explores the role of AI in financial decision making among small business firms, examining its impact on financial performance, risk management, and strategic planning. Our findings highlight the benefits of AI adoption, including improved financial forecasting, enhanced risk assessment, and data-driven decision-making. However, we also identify challenges and limitations, such as data quality issues, lack of technical expertise, and concerns about job displacement. This research provides valuable insights for small business owners, policymakers, and AI developers, highlighting the need for tailored AI solutions that address the unique needs and challenges of small businesses.International Journal of Advanced Research in Science, Communication and Technology00This research provides valuable insights for small business owners, policymakers, and AI developers, highlighting the need for tailored AI solutions that address the unique needs and challenges of small businesses.International Journal of Advanced Research in Science, Communication and Technology["Ms. Ligi George"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/a3d1c0166283200e77c1f31f084a134078b49a4f 7fa78cfc9e0912407316b37b2265b2543426a46bProfessionals increasingly use Artificial Intelligence (AI) to enhance their capabilities and assist with task execution. While prior research has examined these uses separately, their potential interaction remains underexplored. We propose that AI-driven training ("tutor"effect) and AI-assisted task completion ("tool"effect) can be complementary and test this hypothesis in the context of lung cancer diagnosis. In a field experiment with 336 medical students, we manipulated AI deployment in training, in practice, and in both. Our findings reveal that while AI-integrated training and AI assistance independently improved diagnostic performance, their combination yielded the highest accuracy. These results underscore AI's dual role in enhancing human performance through both learning and real-time support, offering insights into AI deployment in professional settings where human expertise remains essential.20It is revealed that while AI-integrated training and AI assistance independently improved diagnostic performance, their combination yielded the highest accuracy.["Vivianna Fang He", "Sihan Li", "P. Puranam"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/7fa78cfc9e0912407316b37b2265b2543426a46b 3326f3c2709a7145bfa2daf8ccee29b8a3d89899Recently, Devin has made a significant buzz in the Artificial Intelligence (AI) community as the world's first fully autonomous AI software engineer, capable of independently developing software code. Devin uses the concept of agentic workflow in Generative AI (GenAI), which empowers AI agents to engage in a more dynamic, iterative, and self-reflective process. In this paper, we present a similar fully autonomous AI formal verification engineer, Saarthi, capable of verifying a given RTL design end-to-end using an agentic workflow. With Saarthi, verification engineers can focus on more complex problems, and verification teams can strive for more ambitious goals. The domain-agnostic implementation of Saarthi makes it scalable for use across various domains such as RTL design, UVM-based verification, and others.240Saarthi is presented, capable of verifying a given RTL design end-to-end using an agentic workflow, and its domain-agnostic implementation makes it scalable for use across various domains such as RTL design, UVM-based verification, and others.["Aman Kumar", "Deepak Narayan Gadde", "Keerthan Kopparam Radhakrishna", "D. Lettnin"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/3326f3c2709a7145bfa2daf8ccee29b8a3d89899 a625e967f022b52da2290e0fcbd9feba1e1678d9Artificial Intelligence (AI) has become increasingly integral to English as a Foreign Language (EFL) education, offering benefits such as scalability, personalized learning, and immediate feedback. Yet its capacity to engage with the broader dimensions of language learning—including creativity, cultural fluency, and aesthetic expression—remains contested. This review analyzes empirical case studies from AI-driven education platforms (e.g., GPT-4-assisted classes showing a 20% improvement in vocabulary retention) while placing these findings within an interdisciplinary framework that integrates aesthetic education and art studies. Despite AI’s proven efficiency in automating repetitive tasks, significant limitations persist in fostering intercultural communication, creativity, and emotional intelligence. By applying Gödel’s Incompleteness Theorem, this paper underscores the inherent constraints of formal systems and asks: To what extent can AI support the cognitive, emotional, and aesthetic dimensions of EFL learning? How can a hybrid model optimize these outcomes by combining AI with human creativity and cultural insight? Two main contributions distinguish this study: (1) an innovative hybrid learning model that explicitly merges AI-driven instruction with art-based EFL tasks (e.g., digital storytelling, intercultural narratives, and poetry composition), and (2) a broader theoretical lens that situates these practices within aesthetic education, thereby expanding on prior research that often overlooks the artistic and cultural facets of language acquisition. Ethical concerns such as algorithmic bias, data privacy, and dehumanization risk are also examined. The findings highlight practical recommendations for integrating AI into art-enhanced EFL curricula, emphasizing cultural nuance, creative exploration, and emotional engagement. Ultimately, this review demonstrates the potential synergy between technology and the human, artistic dimensions of language learning, offering insights for interdisciplinary fields spanning EFL, educational technology, and art studies.Proceedings of the International Conference on Art Studies70This review analyzes empirical case studies from AI-driven education platforms and demonstrates the potential synergy between technology and the human, artistic dimensions of language learning, offering insights for interdisciplinary fields spanning EFL, educational technology, and art studies.Proceedings of the International Conference on Art Studies["Michail Fountoulakis"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/a625e967f022b52da2290e0fcbd9feba1e1678d9 42069151b4fc5c02292c80c764f78f768cd23813This comprehensive article examines the evolution and impact of human-AI collaboration in financial reconciliation systems, focusing on how artificial intelligence technologies are transforming traditional reconciliation processes while maintaining crucial human oversight. The article explores the current landscape of financial reconciliation, the implementation of AI systems, the critical role of human expertise, and the measurable benefits of human-AI collaboration. The article investigates pattern recognition capabilities, automated data processing, strategic oversight, and exception handling, demonstrating how the synergy between human judgment and AI capabilities enhances accuracy, efficiency, and risk management in financial operations. The article also addresses implementation considerations, including technical requirements and change management strategies necessary to deploy AI-powered reconciliation systems successfully.International Journal of Scientific Research in Computer Science Engineering and Information Technology50The article investigates pattern recognition capabilities, automated data processing, strategic oversight, and exception handling, demonstrating how the synergy between human judgment and AI capabilities enhances accuracy, efficiency, and risk management in financial operations.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Naveen Kumar Dodde Gowda"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/42069151b4fc5c02292c80c764f78f768cd23813 7fce62c6c0d576ae3760efa502561cd065bfa7feThis article explores the transformative potential of artificial intelligence (AI) in Financial Planning and Analysis (FP&A). By integrating machine learning algorithms and advanced analytics with human expertise, financial institutions can improve decision-making, streamline processes, and enhance predictive accuracy. While AI enables automation and data-driven insights, this paper underscores the need for strategic human oversight to ensure relevance and reliability. Through an exploration of industry practices, emerging trends, and implementation strategies, this article provides a roadmap for organizations adopting AI in FP&A, emphasizing the importance of robust infrastructure, change management, and human-AI collaboration for successful deployment.International Journal of Scientific Research in Computer Science Engineering and Information Technology20This article provides a roadmap for organizations adopting AI in FP&A, emphasizing the importance of robust infrastructure, change management, and human-AI collaboration for successful deployment.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Harsh Singh"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/7fce62c6c0d576ae3760efa502561cd065bfa7fe 02a09bffb6a77d349a03d3c8e1b06802cfbc64e9Artificial intelligence (AI) offers manifold ways to improve organizational recruiting and conflict resolution due to high processing power and low subjective biases. Additionally to AI applications approaching and selecting job applicants, AI usage might also be promising in employment negotiations as the final step of recruitment. However, it is largely unclear how applicants would react to the use of AI technology in such a context marked by high stakes and (at least partly) conflicting interests, which need to be resolved to terminate the recruitment process successfully. Therefore, we examined how AI negotiation agents influence negotiators' trust in and expectations about an upcoming employment negotiation. In a preregistered experimental vignette study, participants (n = 291) imagined to prepare for an employment negotiation. Varying the type of negotiation counterpart (human—AI with avatar—AI without avatar), we assessed participants' trust intentions and subjective value expectations regarding the anticipated negotiation. We found higher trust and more positive expectations for human counterparts as compared to AI counterparts, regardless of whether the AI was presented with or without an avatar. However, participants' technology‐related attitudes and skills attenuated this effect, leading to less negative effects for AI as a negotiation counterpart when participants possessed high skills and positive attitudes regarding digital technologies. Overall, this study provides initial evidence of how people would react to AI agents in mixed‐motive and conflict resolution settings and may serve as a starting point for developing evidence‐based guidelines for designing AI agents for HR and conflict resolution settings.Conflict Resolution Quarterly390Initial evidence of how people would react to AI agents in mixed‐motive and conflict resolution settings is provided and may serve as a starting point for developing evidence‐based guidelines for designing AI agents for HR and conflict resolution settings.Conflict Resolution Quarterly["Dominik Sondern", "Nadine Arnholz", "Guido Hertel"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/02a09bffb6a77d349a03d3c8e1b06802cfbc64e9 578797b05a9cd7d72840edf33eb369c1a73dc712Respected Editor, Artificial Intelligence (AI) is poised to revolutionise medicine, offering substantial improvements in patient care, diagnostic precision, and operational efficiency. While the potential benefits are considerable, it's essential to maintain a balanced view that addresses this technology's limitations and challenges. AI’s ability to rapidly and accurately analyse large datasets has led to notable advancements in medical diagnostics. AI algorithms, for example, have demonstrated exceptional proficiency in interpreting medical images, such as radiographs and MRIs, sometimes even outperforming human experts in identifying early signs of diseases like cancer. Additionally, AI-driven predictive analytics can anticipate patient risks and facilitate timely interventions, paving the way for more personalised and proactive healthcare.1 Despite these advancements, several critical issues must be addressed to ensure the responsible and effective integration of AI in healthcare. One significant concern is the risk of algorithmic bias. AI systems learn from historical data, and if these datasets are not sufficiently diverse and representative, the resulting algorithms can inadvertently perpetuate health disparities. Developing AI systems using diverse, inclusive datasets to mitigate this risk and ensure equitable healthcare outcomes is crucial.2 Another challenge lies in the transparency and interpretability of AI models. Many AI systems, particularly those based on deep learning, function as "black boxes," offering little insight into their decision-making processes. This opacity can lead to mistrust among clinicians and patients, and complicate the validation and regulatory approval of AI tools. Developing more interpretable AI models and establishing clear, rigorous guidelines for their validation and clinical implementation are essential steps to address this issue.3 The practicalities of integrating AI into clinical practice also present substantial challenges. Implementing AI solutions requires significant investment in infrastructure, training, and ongoing maintenance. Healthcare providers need to be equipped not only with the necessary technical skills to use AI tools but also with the ability to critically evaluate and interpret their outputs.4 Moreover, robust regulatory frameworks are needed to ensure the safe, ethical deployment of AI technologies in medicine.5 Despite these challenges, the transformative potential of AI in medicine remains significant. To fully realize this potential, a collaborative approach involving technologists, healthcare professionals, policymakers, and patients is essential. Emphasizing ethical considerations, transparency, and inclusivity in AI development and deployment will help mitigate risks and enhance the benefits of this powerful technology. ---ContinueJOURNAL OF PAKISTAN MEDICAL ASSOCIATION00Emphasizing ethical considerations, transparency, and inclusivity in AI development and deployment will help mitigate risks and enhance the benefits of this powerful technology.Journal of the Pakistan Medical Association["Kinzah Imtiaz", "Anzah Imtiaz Waggan"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/578797b05a9cd7d72840edf33eb369c1a73dc712 cb31eda7f7cdb61d2b9e1c79b5deaa95ef337b10Effective team management and financial operations are critical for the success of corporate and public finance departments. However, traditional decision-making processes often lack the agility and data-driven insights required to address complex challenges in dynamic environments. This study introduces an AI-Powered Decision-Making Framework (AI-DMF) that integrates artificial intelligence (AI) insights with leadership strategies to enhance team productivity, optimize financial operations, and support evidence-based decision-making. The proposed framework combines machine learning (ML) algorithms, natural language processing (NLP), and predictive analytics to process large volumes of structured and unstructured data, delivering actionable insights for team performance monitoring, financial forecasting, and strategic planning. By leveraging real-time data, AI-DMF enables managers to make informed decisions on resource allocation, performance evaluation, and risk management. Additionally, the framework incorporates human-AI collaboration mechanisms, ensuring that leadership strategies are guided by technological insights while maintaining adaptability and creativity. Key components of the AI-DMF include data integration modules for aggregating team and financial data, decision support tools for scenario analysis, and performance optimization algorithms for identifying inefficiencies and opportunities. Implementation of the framework is supported by customizable dashboards that provide stakeholders with clear visualizations of key performance indicators (KPIs), trends, and projections. Findings from case studies reveal that organizations adopting the AI-DMF report up to a 40% increase in team productivity and a 35% improvement in financial planning accuracy. Furthermore, the model enhances risk detection and response times, contributing to better compliance and operational resilience. This research offers a transformative approach to integrating AI into team and financial management practices, addressing the limitations of conventional methods while fostering innovation. The AI-DMF is designed to empower corporate leaders, public finance managers, and policymakers by enabling them to navigate complex decision-making environments effectively. Keywords: AI-Powered Framework, Decision-Making, Team Management, Financial Operations, Machine Learning, Predictive Analytics, Leadership Strategies, Corporate Finance, Public Finance, Productivity Optimization.Gulf Journal of Advance Business Research00An AI-Powered Decision-Making Framework that integrates artificial intelligence (AI) insights with leadership strategies to enhance team productivity, optimize financial operations, and support evidence-based decision-making is introduced.Gulf Journal of Advance Business Research["Ibidapo Abiodun Ogundeji", "Bamidele Michael Omowole", "Ejuma Martha Adaga", "Ngodoo Joy Sam-Bulya"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/cb31eda7f7cdb61d2b9e1c79b5deaa95ef337b10 45e8af1da1b32a4d984f6dea6ef058af47f13545Automation in hotel front offices, driven by artificial intelligence (AI) and self-check-in systems, is emerging as a transformative solution to enhance operational efficiency and elevate guest experiences. This study employs a mixed-methods approach to evaluate the effects of these technologies by integrating quantitative performance metrics and qualitative insights. Data were collected from multiple hotel properties using standardized surveys, management records, semi-structured interviews, and direct observations to capture key indicators such as check-in duration, error rates, labor cost savings, and guest atisfaction.Quantitative analysis reveals that the implementation of AI and self-check-in kiosks significantly reduces processing times and minimizes human errors in routine tasks such as guest registration, billing, and reservations. AI-driven analytics further empower hotel managers by providing real-time insights that support dynamic resource allocation and strategic decision-making, ultimately leading to cost reductions and enhanced operational reliability. Qualitative findings highlight that, while automation improves efficiency, its success hinges on a balanced integration with human oversight, ensuring that personalized service is maintained alongside technological advancements. The study also identifies critical challenges, including high initial capital investments, the necessity for comprehensive staff training, and increased cybersecurity and data privacy concerns. These suggest that a phased implementation strategy, combined with ongoing employee development and robust security measures, is essential for maximizing the benefits of automation while mitigating associated risks.International Journal for Multidimensional Research Perspectives00Quantitative analysis reveals that the implementation of AI and self-check-in kiosks significantly reduces processing times and minimizes human errors in routine tasks such as guest registration, billing, and reservations, leading to cost reductions and enhanced operational reliability.International Journal for Multidimensional Research Perspectives["Manuj Kumar", "Mayuri Ranjan"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/45e8af1da1b32a4d984f6dea6ef058af47f13545 0befd0874fb932c0fcc9cd374837421c26afc442European Radiology Experimental730This review highlights how AI-driven advancements in CT and MRI improve image quality and enhance patient safety by leveraging AI solutions for dose reduction, contrast optimization, noise reduction, and efficient image reconstruction, paving the way for safer, faster, and more accurate diagnostic imaging practices.European Radiology Experimental["Luca Melazzini", "Chandra Bortolotto", "Leonardo Brizzi", "M. Achilli", "Nicoletta Basla", "Alessandro D'Onorio De Meo", "Alessia Gerbasi", "Olivia Maria Bottinelli", "Riccardo Bellazzi", "Lorenzo Preda"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/0befd0874fb932c0fcc9cd374837421c26afc442 207f2399a80b581773d422049344f837282dc9d8This research examines the regulatory challenges encountered by small and medium-sized enterprises (SMEs) operating artificial intelligence (AI) systems through data centres in the European Union (EU), with a particular focus on data protection issues in Germany. The study analyses the interaction between the General Data Protection Regulation (GDPR) and the proposed EU AI Act, emphasising the compliance barriers faced by SMEs. Methods: A mixed-method approach was employed, combining qualitative analysis of regulatory frameworks and scholarly literature with quantitative survey data from SMEs across key industries. This methodology ensured a comprehensive examination of both regulatory requirements and their practical implications. The findings indicate that SMEs demonstrate high familiarity with GDPR (mean score 82.24) but lower awareness of the AI Act (mean score 56.24), with significant intersectoral variation. Challenges include resource limitations, ambiguous ”high-risk” AI classifications, and the complexity of dual compliance. Notably, government and healthcare sectors reported substantial regulatory burdens, while energy and finance sectors exhibited lower preparedness for AI Act requirements. The study reveals the fragmented implementation of GDPR across member states, complicating compliance for cross-border SMEs. The dual demands of GDPR and the AI Act necessitate streamlined regulatory processes and tailored support mechanisms, such as simplified guidelines and financial assistance. Explainability and transparency obligations, while essential for trust, introduce additional administrative burdens that may impede innovation. Harmonising GDPR and AI Act requirements is crucial to enabling SMEs to comply without inhibiting innovation. Policy recommendations include regulatory sandboxes, targeted training, and increased financial support for SMEs to foster legally compliant yet innovative AI applications.Journal of Next-Generation Research 5.0170The study analyses the interaction between the General Data Protection Regulation and the proposed EU AI Act, emphasising the compliance barriers faced by SMEs and reveals the fragmented implementation of GDPR across member states, complicating compliance for cross-border SMEs.Journal of Next-Generation Research 5.0["Thomas Joswig", "Walter Kurz"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/207f2399a80b581773d422049344f837282dc9d8 c298c114b09a22c59c3124be3b2cc764893487adEste artículo se centra en el desarrollo de técnicas interpretativas para un sistema de recomendación basado en Inteligencia Artificial (IA) aplicado a procesos de contratación pública. El proyecto busca no solo implementar soluciones técnicas, sino también abordar desafíos estructurales y organizacionales en la contratación, mejorando la eficiencia y la justicia. Se destaca el crecimiento exponencial de la dependencia tecnológica en diversos sectores, impulsada por avances en IA y Machine Learning, y la adopción de la Inteligencia Artificial Explicable (XAI). A diferencia de la IA tradicional, la XAI equilibra la precisión con la interpretabilidad humana, crucial para su aplicación en sistemas de recomendación. Este enfoque holístico tiene como objetivo mejorar la transparencia, confianza y eficiencia en la selección de proveedores, abordando la opacidad y los riesgos de sesgo en la toma de decisiones automatizada, y resaltando la importancia de la XAI en la creación de sistemas más éticos y confiables.Ecuadorian Science Journal10Ecuadorian Science Journal["Miguel Molina Villac\u00eds", "Maria Fernanda Molina Miranda", "Ximena Carolina Acaro Chacon", "Angel Jim\u00e9nez Villao", "Darla Luna Chiriboga"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/c298c114b09a22c59c3124be3b2cc764893487ad c1830d75d87155108bdb73770be53857fe261516Urban digital intelligence transformation (DIT) has emerged as a key driver of sustainable development in the era of rapid technological advancement. This study utilized the spatial Durbin model with difference-in-differences (SDM-DID) to explore the intrinsic relationship between DIT and intercity collaborative innovation. The findings indicate that DIT significantly enhances collaborative innovation locally and in other cities. DIT accelerates the movement of R&D talent, capital, and knowledge while driving collaborative innovation across local and neighboring cities by enhancing market potential. Collaborations between enterprises and universities exhibit stronger direct and indirect positive effects, collectively driving the development of sustainable intercity collaborative innovation. Additionally, the study finds that the impact of DIT on substantial innovation is greater than that of non-substantial innovation. Furthermore, the digital transformation of large and central cities has a stronger promotional effect on both local and neighbor collaborative innovation, fostering the sustainable development of intercity innovation cooperation. These results deepen our understanding of the relationship between DIT and intercity collaborative innovation and provide policy insights for enhancing intercity collaboration and promoting regional sustainable development.Sustainability410Sustainability["Chu You", "Qing Luo", "Wei Liu"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/c1830d75d87155108bdb73770be53857fe261516 db173a8fcd39c5509f881d841c258d36561ef986Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to molecular dynamics. Central to almost all physical systems are symplectic forms, the geometric backbone that underpins fundamental invariants like energy and momentum. In this work, we introduce a novel deep learning architecture, MetaSym. In particular, MetaSym combines a strong symplectic inductive bias obtained from a symplectic encoder and an autoregressive decoder with meta-attention. This principled design ensures that core physical invariants remain intact while allowing flexible, data-efficient adaptation to system heterogeneities. We benchmark MetaSym on highly varied datasets such as a high-dimensional spring mesh system (Otness et al., 2021), an open quantum system with dissipation and measurement backaction, and robotics-inspired quadrotor dynamics. Our results demonstrate superior performance in modeling dynamics under few-shot adaptation, outperforming state-of-the-art baselines with far larger models.391This work introduces a novel deep learning architecture, MetaSym, which combines a strong symplectic inductive bias obtained from a symplectic encoder and an autoregressive decoder with meta-attention to ensure core physical invariants remain intact while allowing flexible, data-efficient adaptation to system heterogeneities.["Pranav Vaidhyanathan", "Aristotelis Papatheodorou", "M. Mitchison", "Natalia Ares", "Ioannis Havoutis"]2025-02-23T00:00:00https://www.semanticscholar.org/paper/db173a8fcd39c5509f881d841c258d36561ef986 d6d070745d85f8879cc258d37f86f65065328e75With the changing business landscape, human resource management faces new challenges that must be addressed while ensuring optimal growth and development of the organization. This study identifies the application of artificial intelligence technology in human resource sectors related to recruitment and selection, board attendance process, employee retention, compensation management, general employee management, and employee retention. The integration of artificial intelligence with human resource management practices is changing the way companies hire, manage, and engage with their workforce. Using artificial intelligence, machines can now make decisions based on historical data and behavioral patterns more accurately than people. As a result of this change, all physical work has been replaced by machines, forcing human resource professionals to take on more strategic roles. This study presents the advantages of using artificial intelligence and the challenges facing organizations in implementing artificial intelligence in various human resource management units, as well as the benefits of artificial intelligence for organizations seeking to increase the effectiveness and efficiency of their human resource functions.EuroGlobal Journal of Linguistics and Language Education321The advantages of using artificial intelligence and the challenges facing organizations in implementing artificial intelligence in various human resource management units are presented, as well as the benefits of artificial intelligence for organizations seeking to increase the effectiveness and efficiency of their human resource functions.EuroGlobal Journal of Linguistics and Language Education["Mohammad Ekram Yawar", "Mohammad Qurban Hakimi"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/d6d070745d85f8879cc258d37f86f65065328e75 85ea79745ff643cc21090b1164b88ff80919fe11Background: Artificial intelligence (AI) has revolutionized personalized medicine and predictive diagnostics by enabling data-driven, individualized healthcare strategies. AI-powered models leverage vast datasets, including genomic, proteomic, and clinical information, to improve disease detection, optimize treatment selection, and enhance patient outcomes. With the increasing burden of chronic diseases and the growing demand for precision medicine, AI presents significant opportunities to transform traditional healthcare paradigms. However, challenges related to clinical implementation, algorithmic bias, and regulatory considerations necessitate a critical evaluation of its applications. Objective: This narrative review aims to explore the role of AI in personalized medicine and predictive diagnostics, analyzing its clinical applications, benefits, limitations, and future directions. The review synthesizes current evidence on AI-driven advancements in disease diagnosis, risk stratification, and treatment optimization while addressing key challenges hindering its widespread adoption. Main Discussion Points: AI has demonstrated superior diagnostic accuracy in various medical domains, including oncology, cardiology, and neurology, through deep learning and machine learning algorithms. Predictive models enhance risk assessment, enabling early intervention and personalized therapeutic approaches. Despite these advancements, methodological limitations, variability in outcome measurement, and concerns regarding data standardization and interpretability pose significant barriers. Ethical considerations, regulatory frameworks, and the need for unbiased, transparent AI models remain critical challenges in integrating AI into routine clinical practice. Conclusion: AI holds immense potential in advancing personalized medicine and predictive diagnostics, yet its real-world application requires rigorous validation, standardized protocols, and ethical oversight. Future research should focus on developing explainable AI models, conducting large-scale randomized controlled trials, and ensuring equitable healthcare access to maximize AI’s impact on patient care.Insights-Journal of Health and Rehabilitation00This narrative review synthesizes current evidence on AI-driven advancements in disease diagnosis, risk stratification, and treatment optimization while addressing key challenges hindering its widespread adoption.Insights-Journal of Health and Rehabilitation["Shahid Abbas", "Abdul Sattar", "Syeda Hina Shah", "Sidrah Hafeez", "Waqas Mahmood", "Raza iqbal", "Keziah Shaheen", "Pervaiz Azam", "Tazeem Shahbaz"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/85ea79745ff643cc21090b1164b88ff80919fe11 771bdef6278eec7035ee24e1f1dfbf88f53ebf27New artificial intelligence (AI)/machine learning (ML) technology offers great potential to assist surgeons with real-time intraoperative decision-making. While currently, AI/ML-driven tools for surgeons focus primarily on technical assistance and postoperative insights, AI/ML cognitive support in surgery can add further capability. However, these AI/ML models usually conceal their underlying algorithmic reasoning process. As a result, such "black box" AI/ML models have important clinical and legal implications for patient's safety and surgeon's liability. This article provides an overview of surgeons' current practice and the potential for AI enhancement in surgical decision-making. It suggests a path toward a safe and effective integration of black-box AI/ML models into the operating room. We argue that future surgeons who rely on AI for cognitive assistance do not necessarily need to fully understand, interpret, and explain the algorithmic basis of an AI's real-time recommendation in the midst of surgery, but rather, they need to know that these tools work as promised. Assuming new black-box AI/ML models demonstrate clear benefits for surgical patients, their use will likely be incorporated into the legal standard of care and affect the liability landscape for surgeons.International Journal of Surgery00It is argued that future surgeons who rely on AI for cognitive assistance do not necessarily need to fully understand, interpret, and explain the algorithmic basis of an AI's real-time recommendation in the midst of surgery, but rather, they need to know that these tools work as promised.International journal of surgery["R. Cahill", "M. Duffourc", "Sara Gerke"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/771bdef6278eec7035ee24e1f1dfbf88f53ebf27 e08474ebf25feb4957040585bdae5e101458be37BACKGROUND AND AIMS Emerging evidence supports artificial intelligence-enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of AI-ECG in detecting AMI in the emergency department (ED). METHODS The Rule-Out acute Myocardial Infarction using Artificial intelligence Electrocardiogram analysis (ROMIAE) study is a prospective cohort study conducted in the Republic of Korea from March 2022 to October 2023, involving 18 university-level teaching hospitals. Adult patients presenting to the ED within 24 h of symptom onset concerning for AMI were assessed. Exposure included AI-ECG score, HEART score, GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. The primary outcome was diagnosis of AMI during index admission, and the secondary outcome was 30 day major adverse cardiovascular event (MACE). RESULTS The study population comprised 8493 adults, of whom 1586 (18.6%) were diagnosed with AMI. The area under the receiver operating characteristic curve for AI-ECG was 0.878 (95% CI, 0.868-0.888), comparable with the HEART score (0.877; 95% CI, 0.869-0.886) and superior to the GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. For predicting 30 day MACE, AI-ECG (area under the receiver operating characteristic, 0.866; 95% CI, 0.856-0.877) performed comparably with the HEART score (0.858; 95% CI, 0.848-0.868). The integration of the AI-ECG improved risk stratification and AMI discrimination, with a net reclassification improvement of 19.6% (95% CI, 17.38-21.89) and a C-index of 0.926 (95% CI, 0.919-0.933), compared with the HEART score alone. CONCLUSIONS In this multicentre prospective study, the AI-ECG demonstrated diagnostic accuracy and predictive power for AMI and 30 day MACE, which was similar to or better than that of traditional risk stratification methods and ED physicians.European Heart Journal00In this multicentre prospective study, the AI-ECG demonstrated diagnostic accuracy and predictive power for AMI and 30 day MACE, which was similar to or better than that of traditional risk stratification methods and ED physicians.European heart journal["Min Sung Lee", "Tae Gun Shin", "Youngjoo Lee", "Dong Hoon Kim", "Sung Hyuk Choi", "Hanjin Cho", "Mi Jin Lee", "Ki Young Jeong", "Won Young Kim", "Young Gi Min", "Chul Han", "Jae Chol Yoon", "Eujene Jung", "Woo Jeong Kim", "Chiwon Ahn", "Jeong Yeol Seo", "Tae Ho Lim", "Jae Seong Kim", "Jeff Choi", "Joon-Myoung Kwon", "Kyuseok Kim"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/e08474ebf25feb4957040585bdae5e101458be37 d39ac00fe4e5a2db458059ee8f682b66cc8999a8OBJECTIVES Integrating artificial intelligence and virtual reality into an art health program, this study aimed to compare the effects of artificial intelligence (AI) intervention in art therapy, virtual reality (VR) intervention in art therapy and traditional art therapy on cognitive function and mental health in older people with mild cognitive impairment. METHODS In a randomised controlled trial, this study recruited 60 older people with mild cognitive impairment, evenly assigned to the AI group, the VR group and the control group. The participants completed surveys, before and after art therapy interventions, which assessed changes in cognitive function, depressive symptoms and program attitudes. RESULTS Following the intervention, the AI group and the VR group exhibited higher scores in cognitive function and mental health compared to the control group. The AI group demonstrated significant improvements in mental health, particularly in areas of boredom (p < .001, η2 = .093), activity reduction (p = .001, η2 = .082), life value (p = .003, η2 = .092), and happiness (p = .001, η2 = .093). While the VR group showed significant enhancements in cognitive abilities, particularly in attention (p = .006, η2 = .130), expression (p = .002, η2 = .139), orientation (p = .01) and memory (p = .02). CONCLUSIONS In art health programs, leveraging the painting and language technologies of AI, along with the painting and simulation technologies of VR, can effectively enhance cognitive function and mental health in older people with mild cognitive impairment.Australasian Journal on Ageing120In art health programs, leveraging the painting and language technologies of AI, along with the painting and simulation technologies of VR, can effectively enhance cognitive function and mental health in older people with mild cognitive impairment.Australasian journal on ageing["Ying Cao", "Hanfang Yin", "Xinxin Hua", "Shibo Bi", "Di Zhou"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/d39ac00fe4e5a2db458059ee8f682b66cc8999a8 96377545376d3dac5761dcf33a374c7776b43712The integration of Artificial Intelligence (AI) in occupational health and safety (OHS) practices has garnered increasing attention in the context of Industry 4.0. This paper presents a comprehensive investigation into the implications of AI on workplace safety, aiming to elucidate stakeholders' perceptions, attitudes, and experiences. Through a structured survey methodology and rigorous qualitative data analysis, data were collected from a diverse sample of 300 participants representing various industries and occupational backgrounds. The findings reveal nuanced perspectives on AI's impact on workplace safety practices, ranging from perceived effectiveness in hazard identification to concerns regarding adaptability to changing regulations and ethical considerations. The study underscores the importance of ethical conduct, informed decision-making, and collaborative efforts in harnessing the transformative potential of AI while ensuring worker well-being. This research contributes to the discourse on responsible AI deployment and fosters dialogue among stakeholders towards advancing a holistic approach to OHS in the digital age. European Journal of Theoretical and Applied Sciences160The study underscores the importance of ethical conduct, informed decision-making, and collaborative efforts in harnessing the transformative potential of AI while ensuring worker well-being in the digital age.European Journal of Theoretical and Applied Sciences["Md Mukthar Ahamad"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/96377545376d3dac5761dcf33a374c7776b43712 cccba9db7ae952def3637dd290eaeeda3d9a6293The rapid integration of artificial intelligence (AI) into the platform economy presents unprecedented challenges to legal regulation and management innovation. This paper explores the intersection of economic law, competition policies, algorithmic governance, and market fairness to address these challenges. Through theoretical analysis and empirical evidence, we develop a comprehensive framework for regulating AI-driven platforms. The study incorporates advanced mathematical models to assess the impact of AI algorithms on competition and market behavior. Additionally, we propose innovative management strategies to ensure market fairness and transparency. This research aims to bridge the gap between legal theory and practical governance in the era of AI.Academic Journal of Management and Social Sciences210This research aims to bridge the gap between legal theory and practical governance in the era of AI through theoretical analysis and empirical evidence to develop a comprehensive framework for regulating AI-driven platforms.Academic Journal of Management and Social Sciences["Wenjun Zhang", "Kejia An", "Huidi Mei", "Binghan Wu", "Yunting Fan"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/cccba9db7ae952def3637dd290eaeeda3d9a6293 98037f9f8c2dc222865a7dfa2449873daea832edPurpose This study aims to examine the implementation of artificial intelligence (AI) in the Islamic finance industry and to identify legal issues and design an appropriate supervisory model to promote the Islamic finance industry. Design/methodology/approach This type of research is legal research. This legal research uses a statute approach, conceptual approach and comparative approach between Indonesia, Hong Kong, Malaysia and the United Arab Emirates (UAE). Findings The utilization of AI in Islamic finance is becoming increasingly important in various sectors. In the front office, AI simplifies credit evaluation, Takaful (Islamic insurance) and chatbots, improving client interactions and decision-making processes. In the middle office, AI is an integral part of anti-money laundering, counter-terrorist financing (CTF), know your customer protocols and fraud detection. In the back office, AI improves capital management, market impact assessment, risk management and asset and wealth management. In addition, AI substantially enhances regulatory technology (RegTech) and supervisory technology (SupTech), ultimately improving the effectiveness of regulatory compliance and supervision in the Islamic finance industry. These technologies simplify compliance processes, evaluate data quality, detect potential hazards and adapt to complex regulatory frameworks. Nonetheless, the incorporation of AI faces significant obstacles, most notably the absence of a comprehensive legal framework governing the application of AI in the Islamic finance industry. The current regulations, including the Islamic Banking Law and Insurance Law, do not adequately address AI. Moreover, the use of AI raises concerns about Shariah compliance, particularly about transparency and possible algorithmic bias in the decision-making process. The effectiveness of supervision in Islamic finance largely depends on the membership of the Islamic supervisory board, which must have technological expertise to ensure compliance withShariah norms. Therefore, the development of more sophisticated and effective supervisory procedures is essential for the proper implementation of AI in Islamic banking. An efficient supervisory framework should provide transparency, data security, regular auditing of AI systems and integration of RegTech and SupTech technologies within the Islamic finance sector. Research limitations/implications This research examines the use of AI in the Islamic finance industry in Indonesia, Hong Kong, Malaysia and the UAE. Practical implications This research is important to mitigate the risks of using AI in the Islamic finance industry such as AI decision transparency and explanation, AI job transfer bias and AI conflict with Islamic finance principles. This research is also important to formulate a regulatory framework to enhance AI supervision in the Islamic finance industry. Social implications This research improves and encourages the growth of the Islamic finance industry using AI. Originality/value This research identifies the problems and legal issues of using AI in the Islamic finance industry and formulates a supervisory model. International Journal of Law and Management490This research examines the use of AI in the Islamic finance industry in Indonesia, Hong Kong, Malaysia and the UAE to identify legal issues and design an appropriate supervisory model to promote the Islamic finance industry.International Journal of Law and Management["Ifan Arsyad", "Dona Budi Kharisma", "Jamal Wiwoho"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/98037f9f8c2dc222865a7dfa2449873daea832ed 7aa5b7b713ab2eec727620e28b5139e3d4e7aa50BMC Medical Informatics Decis. Mak.570This review highlighted the potential of AI and ML to enhance liver transplant allocation and outcomes and identified data elements used in Machine Learning and Artificial Intelligence methods, data sources, and their focus on urgency, utility, or benefit in LT.BMC Medical Informatics and Decision Making["Lisiane Pruinelli", "Kiruthika Balakrishnan", "Sisi Ma", "Zhigang Li", "Anji Wall", "Jennifer C Lai", "Jesse D Schold", "Timothy Pruett", "Gyorgy Simon"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/7aa5b7b713ab2eec727620e28b5139e3d4e7aa50 063f573161baba732630bf60fa858b15847ed3a7Background Congenital heart disease (CHD) is a major contributor to morbidity and infant mortality and imposes the highest burden on global healthcare costs. Early diagnosis and prompt treatment of CHD contribute to enhanced neonatal outcomes and survival rates; however, there is a shortage of proficient examiners in remote regions. Artificial intelligence (AI)-powered ultrasound provides a potential solution to improve the diagnostic accuracy of fetal CHD screening. Methods A literature search was conducted across seven databases for systematic review. Articles were retrieved based on PRISMA Flow 2020 and inclusion and exclusion criteria. Eligible diagnostic data were further meta-analyzed, and the risk of bias was tested using Quality Assessment of Diagnostic Accuracy Studies—Artificial Intelligence. Findings A total of 374 studies were screened for eligibility, but only 9 studies were included. Most studies utilized deep learning models using either ultrasound or echocardiographic images. Overall, the AI models performed exceptionally well in accurately identifying normal and abnormal ultrasound images. A meta-analysis of these nine studies on CHD diagnosis resulted in a pooled sensitivity of 0.89 (0.81–0.94), a specificity of 0.91 (0.87–0.94), and an area under the curve of 0.952 using a random-effects model. Conclusion Although several limitations must be addressed before AI models can be implemented in clinical practice, AI has shown promising results in CHD diagnosis. Nevertheless, prospective studies with bigger datasets and more inclusive populations are needed to compare AI algorithms to conventional methods. Systematic Review Registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461738, PROSPERO (CRD42023461738).Frontiers in Cardiovascular Medicine420Although several limitations must be addressed before AI models can be implemented in clinical practice, AI has shown promising results in CHD diagnosis, and prospective studies with bigger datasets and more inclusive populations are needed.Frontiers in Cardiovascular Medicine["L. D. Liastuti", "Yosilia Nursakina"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/063f573161baba732630bf60fa858b15847ed3a7 9130d5f28279f1d906924fc35cce72592a768b46Current Urology Reports410This review explores the advancements and applications of Artificial Intelligence in diagnosing and treating male infertility, emphasizing its potential to revolutionize the field by providing reliable and efficient diagnostic tools and improving treatment outcomes.Current urology reports["Joseph Y Nashed", "Kiera Liblik", "Ali Dergham", "Luke Witherspoon", "Ryan K. Flannigan"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/9130d5f28279f1d906924fc35cce72592a768b46 db077e48f9a0aa3c4bfb837ecb897e65de377011The landscape of English Language Teaching (ELT) has undergone profound transformations in recent decades and it is driven by advancements in technology, evolving pedagogical approaches, and the increasing globalization of communication. English language continues to solidify its position as a global lingua franca. The rapid development of technology, especially the widespread use of computer networks, has brought unprecedented opportunities to ELT methods. Although Artificial Intelligence (AI) technologies have been increasingly integrated into ELT, there is a lack of comprehensive research that examines both the benefits and challenges of this integration from a global perspective. In this study, the author explores how AI technologies improve the effectiveness of ELT and the key challenges and limitations of integrating AI technologies into ELT. The research explores both the benefits and challenges of incorporating AI into the teaching process, highlighting innovative solutions for personalized learning, as well as concerns regarding the reliability and accuracy of AI. The study may emphasize the significance of maintaining a balance between AI technologies and human interaction. It could propose strategies to ensure that AI serves as a complement to, rather than a replacement for, traditional teaching methods and interpersonal communication within the classroom.Journal of Education and Educational Research80The research explores both the benefits and challenges of incorporating AI into the teaching process, highlighting innovative solutions for personalized learning, as well as concerns regarding the reliability and accuracy of AI.Journal of Education and Educational Research["Zhihao Tang"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/db077e48f9a0aa3c4bfb837ecb897e65de377011 7798389967d4387c69eddb3b3000c16ad4e80d32In order to support precise expression and efficient creation in the animation production process, this paper proposes a feature extraction method for animation script creation by introducing deep learning algorithms from artificial intelligence. First, the basic elements of animation script creation, including screen content, shot motion and time length, were analyzed. Subsequently, in the TF-IDF algorithm, the importance of keywords in the script is quantified by calculating word frequency and inverse text frequency. In the image block sparse representation method, the sparsity degree is used to represent the number of blocks and the target state is described by extracting image features. Finally, using convolutional neural network methods, feature extraction of segmented scripts for animation script creation is achieved through steps such as constructing two-dimensional matrices, performing convolution operations, segment pooling and feature extraction. The experimental results show that the method proposed in this paper has excellent accuracy in extracting features from shot scripts in animation script creation. It can support precise expression and efficient creation in the animation production process, improve the accuracy of feature extraction and provide strong support for the visualization of animation scripts and the design of shot language.International Journal of High Speed Electronics and Systems00The experimental results show that the method proposed has excellent accuracy in extracting features from shot scripts in animation script creation, and can support precise expression and efficient creation in the animation production process, improve the accuracy of feature extraction and provide strong support for the visualization of animation scripts and the design of shot language.International Journal of High Speed Electronics and Systems["Nili Guo"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/7798389967d4387c69eddb3b3000c16ad4e80d32 b9b3884d311b4500df8a0a302717c2f162178470Artificial intelligence (AI) plays an important role in promoting energy transformation and achieving global green and low-carbon goals. Based on the panel data of 285 prefecture-level cities in China from 2011 to 2022, this paper empirically examines the impact of AI on carbon emission (CE) and its internal mechanism. It is found that the impact of AI on CE in general shows an “inverted U-shaped” relationship, which is first promoted and then suppressed, and this result still holds after a series of robustness tests. The mechanism test shows that AI affects CE in three main ways: improving energy efficiency, optimizing factor market allocation, and industrial structure. The heterogeneity results show that the “inverted U-shape” relationship of AI on CE is significant in resource cities insignificant in non-resource cities, significant in low-carbon pilot cities, and insignificant in non-low-carbon pilot cities, significant in areas with a high level of industrialization, and insignificant in areas with a low level of industrialization. This study provides valuable insights for the application of AI and the formulation of energy conservation and emission reduction policies.Energies330The impact of AI on CE in general shows an “inverted U-shaped” relationship, which is first promoted and then suppressed, and this result still holds after a series of robustness tests.Energies["Jun Liu", "Hengxu Shen", "Junwei Chen", "Xin Jiang", "Abdul Waheed Siyal"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/b9b3884d311b4500df8a0a302717c2f162178470 f7390633961f9f467c11f403ccf2b337602050c7Background Artificial Intelligence (AI) has the potential to revolutionize Pediatric Intensive Care Units (PICUs) by enhancing diagnostic accuracy, improving patient outcomes, and streamlining routine tasks. However, integrating AI into PICU environments poses significant ethical and data privacy challenges, necessitating effective governance and robust regulatory frameworks to ensure safe and ethical implementation. This study aimed to explore valuable insights into healthcare professionals' current perceptions and readiness to adopt AI in pediatric critical care, highlighting the opportunities and challenges ahead. Methods A cross-sectional study conducted an online survey among healthcare practitioners at King Abdulaziz University Hospital in Jeddah, Saudi Arabia. The survey included questions about professional roles, experience, and familiarity with AI, their opinions on AI's role, trust in AI-driven decisions, and ethical and privacy concerns. Statistical analyses were performed using IBM SPSS. Results Results found varying familiarity with AI among healthcare professionals, with many expressing limited knowledge of AI applications in PICU settings. Despite this, there was growing recognition of AI's current applications. Trust in AI-driven decisions for PICU management was mixed, with most expressing partial trust. Opinions on AI's role in enhancing diagnostic accuracy and improving patient outcomes varied. Ethical considerations, data privacy, and effective governance to address regulatory and ethical challenges were highlighted as critical concerns. Conclusion Healthcare practitioners in the PICU preferred using AI for routine patient monitoring but had concerns about its use in diagnoses and advanced healthcare. Concerns were held regarding data privacy, security breaches, and patient confidentiality.Frontiers in Pediatrics440Healthcare practitioners in the PICU preferred using AI for routine patient monitoring but had concerns about its use in diagnoses and advanced healthcare, and ethical considerations, data privacy, and effective governance to address regulatory and ethical challenges were highlighted as critical concerns.Frontiers in Pediatrics["K. Al-Sofyani"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/f7390633961f9f467c11f403ccf2b337602050c7 2f9fc31e620b8173d664b681a52417fefd3684ceArtificial Intelligence (AI) is reshaping all sectors of society, including libraries. AI adoption in libraries has been gradual due to concerns and challenges, including ethical issues, maturity of the technology, insufficient AI education and training designed for library and information professionals, and gaps in AI education in library and information science (LIS) programs. This case study reports on the motivations, processes, and evaluations of the IDEA Institute on AI that was developed to equip two cohorts (Fellows) of information professionals who participated in the 2021 and 2022 IDEA Institute on AI with the foundational knowledge and skills to lead AI work. A multi-method approach was used to collect and analyze the evaluation data from multiple sources at different points of the IDEA Institute on AI. The IDEA Institute on AI applied an outcome-based planning and evaluation model and employed formative and summative evaluations using surveys and focus-group discussions. Fellows worked in various library and information environments, most in academic libraries. The case study results showed that the Fellows’ AI knowledge and skills increased substantially, their confidence greatly increased upon completing the IDEA Institute on AI, and they engaged in AI projects in their workplaces. They built awareness of AI issues and challenges and developed a comprehensive understanding of AI within the context of equity, diversity, inclusion, and accessibility. The Fellows’ supervisors were positive about the learning and experience their Fellows gained from the IDEA Institute on AI and their peers. The results of this case study have significant implications for developing AI professional development programs in the LIS field, providing exemplary AI education and training as AI technology evolves, including generative AI and large language models, and integrating AI into LIS curricula.Journal of Education For Library and Information Science50The results of this case study have significant implications for developing AI professional development programs in the LIS field, providing exemplary AI education and training as AI technology evolves, including generative AI and large language models, and integrating AI into LIS curricula.Journal of Education for Library and Information Science["Dania Bilal", "Clara M. Chu", "Soo Young Rieh", "Yujin Choi"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/2f9fc31e620b8173d664b681a52417fefd3684ce fe7205ed1f2a7ec5edc7db6d92761fe50d0cce94Purpose: Artificial Intelligence (AI) plays a pivotal role in shaping social-ecological value, offering both opportunities and challenges in its application. Originating in the mid-20th century, AI has evolved significantly, finding widespread adoption across various sectors such as healthcare, finance, and environmental science. While AI holds promise in enhancing ecological monitoring, conservation efforts, and economic growth, it also presents risks such as job displacement, income inequality, and environmental degradation. Methodology: To navigate these complexities, a holistic systematic review approach anchored on ecological modernisation theory is crucial, emphasising ethical considerations, regulatory frameworks, and sustainability principles. Findings: Investment in education and workforce development is essential to equip individuals with the necessary skills for an AI-driven future. Collaboration between stakeholders, including governments, businesses, and civil society organisations, is paramount to address emerging challenges and promote responsible AI development and deployment. Unique Contribution to Theory, Practice and Policy: By embracing innovation while safeguarding human welfare and environmental integrity, society can harness the transformative potential of AI to create a more equitable, resilient, and sustainable future.International journal of technology and systems290International Journal of Technology and Systems["Samar Al-Kindy"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/fe7205ed1f2a7ec5edc7db6d92761fe50d0cce94 a8a509c7120ea5bab6c5b4129f8ca5cf21dc246bTechnological innovation is redefining environmental quality by promoting green and efficient technologies. Keeping in view, this study brings a novel and sophisticated idea to address these challenges by promoting sustainable development and integrating key innovative determinants, including computer technology, semiconductors, and artificial intelligence with sustainable development. Our study employs panel Autoregressive Distributed Lag‐Pooled Mean Group (ARDL‐PMG), Autoregressive Distributed Lag‐Mean Group (ARDL‐MG), Autoregressive Distributed Lag‐Dynamic Fixed Effects (ARDL‐DFE), and Autoregressive Distributed Lag‐Error Correction Model (ARDL‐ECM) techniques from 2000 to 2020 to analyze this relationship among the top 12 semiconductor‐producing countries. The results indicate that computer technology, artificial intelligence, and usage of semiconductor technology positively correlate with sustainable development among these tech‐leading states. Apart from this, the findings reveal that the presence of geopolitical risk decreases the progression of the sustainable development index. Besides, this study provides important suggestions to the concerned states for designing effective sustainable development policies.Sustainable Development570The results indicate that computer technology, artificial intelligence, and usage of semiconductor technology positively correlate with sustainable development among these tech‐leading states and that the presence of geopolitical risk decreases the progression of the sustainable development index.Sustainable Development["Marina Nazir", "Muhammad Qamar Rasheed", "Xiaohong Yu", "Zahoor Ahmed"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/a8a509c7120ea5bab6c5b4129f8ca5cf21dc246b 345100050c59db32f214a3bf86fc78cd35e64e46The purpose of the study was to investigates the relationship between generative artificial intelligence (GAI), academic performance (AP) and smart learning environment (SLE) as a mediator. A convenience sampling technique was used to select a sample size of 456 respondents. Primary data was collected using a self-administered questionnaire and analysed using descriptive and inferential statistics. Partial least squares-structural equation model (PLS-SEM) was employed to analyse the structural model and determine the direct connections between the different elements. The results establish that generative artificial intelligence has a positive and significant influence on smart learning environment and academic performance (β = 0.523, t = 10.178, p < 0.000); β = 0.387, t = 7.353, p < 0.000 respectively). Simultaneously, smart learning environment partially mediates between the generative artificial intelligence and academic performance among university students (β = 0.06, t = 1.19, p < 0.234). The results of this study contributes to the current academic discourse on technology-enhanced education by showing that generative artificial intelligence have a positive impact on students’ academic performance.African Journal of Business and Development Studies490It is established that generative artificial intelligence has a positive and significant influence on smart learning environment and academic performance and contributes to the current academic discourse on technology-enhanced education by showing that generative artificial intelligence have a positive impact on students' academic performance.African Journal of Business and Development Studies["Joseph Ngugi Kamau"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/345100050c59db32f214a3bf86fc78cd35e64e46 fb46ec86642af6692d830aa57799834120730fb4Character analysis in drama is a core issue in drama research, performance teaching and drama creation. However, traditional character analysis methods mainly rely on textual close reading, dramatic criticism, and actors' practical experience, and lack objective and systematic quantitative analysis means. With the rapid development of Artificial Intelligence (AI) technology, especially the maturity of Natural Language Processing (NLP) and Emotion Computing technology, the use of AI means to analyze the character of drama is gradually becoming an emerging method in the field of drama research. Character classification is an important element in theater research, psychology and AI text analysis. Different disciplines have different approaches to character classification, and in theater research, character classification helps to understand character motivation, action logic, and their performance in dramatic situations. In AI analysis, character classification is usually done with the help of Natural Language Processing (NLP) and machine learning techniques to extract features from play texts and classify them. In this paper, through the interdisciplinary combination of artificial intelligence technology and dramatic character analysis, taking Ibn in Desire Under the Elm Tree as an example, we discuss how to utilize methods such as Natural Language Processing (NLP), Sentiment Analysis (Sentiment Analysis), and Social Network Analysis (SNA), to carry out the study of dramatic character from multiple perspectives such as textual features, emotional tendencies, and character relationships. Systematic research. Combined with traditional theories of drama, such as Stanislavski's Action Analysis, Freud's psychological theories, and modern personality classification models (Big Five Personality, MBTI, etc.), this study demonstrates the potential value of AI technology in drama research.Frontiers in Computing and Intelligent Systems60This study discusses how to utilize methods such as Natural Language Processing (NLP), Sentiment Analysis (Sentiment Analysis), and Social Network Analysis (SNA), to carry out the study of dramatic character from multiple perspectives such as textual features, emotional tendencies, and character relationships.Frontiers in Computing and Intelligent Systems["Yanchen Zuo"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/fb46ec86642af6692d830aa57799834120730fb4 8b16d313557ad698550c3a793b7faac8e82bb513As artificial intelligence (AI) technologies evolve in sophistication, they offer the potential to benefit various aspects of plastic and reconstructive surgery practice. From enhancing surgical precision within the operating room to streamlining administrative tasks and supporting the diagnosis and treatment of patients, AI may grow into an invaluable tool that redefines standards of care within plastic surgery. Given the nascent and largely theoretical role of AI in plastic surgery, numerous questions arise regarding its safety, actual utility, ethical considerations, and policies needed to regulate its use. This manuscript aims to provide commentary on AI in healthcare and to discuss an alternative viewpoint of its use in plastic surgery. Americans remain hesitant about healthcare providers leveraging AI in their care. Ongoing scrutiny is required to protect patients from unintended sequelae, safeguard their privacy, mitigate bias, and reduce harm. Early legislation by the United States federal government has aimed to define a role for AI in healthcare, yet more explicit guidance is required. Uncertainty in medico-legal implications begs the question of where liability would fall if AI use causes adverse outcomes. If applied appropriately, AI may ultimately improve patient outcomes and satisfaction with their plastic surgery care. With less energy dedicated toward automatable tasks and tools that push the envelope of human performance, plastic surgeons may be better equipped to care for their patients. We advocate for a cautiously optimistic approach to AI’s incorporation within plastic and reconstructive surgery.Artificial Intelligence Surgery160Commentary on AI in healthcare is provided and an alternative viewpoint of its use in plastic surgery is discussed to discuss an alternative viewpoint of its use in plastic surgery.Artificial Intelligence Surgery["Dylan Treger", "Griffin Harris", "Seth R. Thaller"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/8b16d313557ad698550c3a793b7faac8e82bb513 9696697fa9b4d84cefe2eed278a7530c3b32b2afDermatology has benefited considerably from the use of artificial intelligence (AI), which has emerged as a crucial tool in healthcare. Algorithms for machine learning (ML) and deep learning (DL), in particular convolutional neural networks (CNNs), have demonstrated significant promise in the diagnosis of skin disorders, classification of lesions, and telemedicine support. The use of AI in dermatology is examined in this paper, with particular attention paid to how it might improve patient care, increase access to dermatological treatments, and improve diagnostic accuracy. It also discusses the difficulties, moral dilemmas, and potential applications of AI in dermatology. It highlights the necessity of cooperation between researchers, practitioners, and regulatory agencies to guarantee a secure and efficient transition into clinical practice.International Journal of Research in Dermatology110The use of AI in dermatology is examined, with particular attention paid to how it might improve patient care, increase access to dermatological treatments, and improve diagnostic accuracy.International Journal of Research in Dermatology["Nawaf Almutairi", "Bakri Al Agraa", "Zainab Mohamed"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/9696697fa9b4d84cefe2eed278a7530c3b32b2af f95d2c502eb2a7ef3296896c28e5f85be5e4f6dcPurpose The purpose of this article is to deepen understanding of how emotional intelligence (EI) and artificial intelligence (AI) affect organizational behavior from a phenomenological perspective. Through philosophical lenses – particularly Descartes, Husserl and Merleau-Ponty – it highlights the contrasts and similarities between these forms of intelligence. The study aims to explore how AI and EI shape human experience and meaning-making in organizations, providing insights into how AI integration can foster more human-centered organizational practices. Design/methodology/approach This study employs a phenomenological approach to explore the philosophical underpinnings of EI and AI. By examining Descartes’ Cartesian dualism and Husserl’s phenomenology, the study analyzes the alignment and divergence between AI and these philosophical perspectives. The methodology integrates literature review and conceptual analysis to link philosophical insights with their organizational behavior implications, offering a framework that critically examines AI’s impact on human experience and organizational dynamics. Findings The findings highlight that emotional intelligence, rooted in the body-mind interaction, offers a human-centered view of experience, distinct from artificial intelligence. However, combining AI with EI can enhance organizational behavior by promoting more empathetic approaches. While AI can mimic cognitive functions, it lacks the embodied emotional experiences essential for human interaction. This insight emphasizes the need for AI systems to support, rather than disrupt, organizational meaning-making processes. Originality/value This article offers an original interdisciplinary perspective, merging phenomenological philosophy with organizational behavior. By examining emotional and artificial intelligence through Descartes, Husserl and Merleau-Ponty, the study presents fresh insights into AI design that prioritizes human-centered development. It contributes to AI ethics and organizational behavior literature by emphasizing the role of emotional intelligence in guiding AI integration within organizational contexts. The International Journal of Organizational Analysis760International Journal of Organizational Analysis["Asl\u0131han Canbul Yaro\u011flu"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/f95d2c502eb2a7ef3296896c28e5f85be5e4f6dc 067b45db7f10671b90fca3653b11442fd8ba4f2cThe use of artificial intelligence is becoming widespread in almost all sectors. The air transportation sector is naturally where artificial intelligence studies are frequently carried out. In both the application process and academic studies, studies on artificial intelligence have increased significantly in recent years. It is thought that examining the studies conducted in this context will contribute to the understanding of the existing literature on artificial intelligence and help predict the trends that will emerge in the future. For these reasons, this study aims to conduct a bibliometric analysis of studies on artificial intelligence in the air transportation sector. The analysis of 1712 academic studies obtained from the Scopus database was conducted with R Bibliometix and VOSViewer software. In the study, analyses such as the authors and countries with the highest number of publications, the most influential authors and countries, the institutions that contribute the most to the studies, the most influential journals, thematic analysis, co-occurrence, co-citation, and bibliographic coupling analysis were performed. As a result of the analysis, it was determined that most of the studies are from the Asian region, and the rate of cooperation in the studies is high, but the rate of international cooperation is relatively low. On the other hand, it was revealed that the motor themes in studies on artificial intelligence are air traffic control, Unmanned Aerial Vehicle, optimization, eye tracking, and automation, while the basic themes are machine learning, deep learning, aviation safety, neural network, and situation awareness.Journal of Aviation1930The motor themes in studies on artificial intelligence are air traffic control, Unmanned Aerial Vehicle, optimization, eye tracking, and automation, while the basic themes are machine learning, deep learning, aviation safety, neural network, and situation awareness.Journal of Aviation["Harun Karakavuz"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/067b45db7f10671b90fca3653b11442fd8ba4f2c 17fade3dfc0eb60c0a95000bceaf86267246668fThe article addresses the issues of legal regulation of artificial intelligence (AI) in the context of its rapid development and penetration into various spheres of life. The introduction raises the problem of uncertainty in the legal environment regarding content created using AI, with a particular focus on copyright issues and the possibility of legislative recognition of AI as a subject of law. Market statistics analysis shows the growth of the global AI market and underscores the importance of developing legislation governing authorship and intellectual rights to prevent potential legal disputes and protect personal data. Special attention is given to the differences in approaches to the legal status of AI worldwide, including the USA, the UK, the European Union, China, and Russia, as well as initiatives by international organizations such as UNESCO and the World Intellectual Property Organization. The research methodology is based on comparative legal analysis, examination of regulatory acts, and expert evaluation, which allowed for identifying common features and significant differences in AI regulation across various jurisdictions. The article also explores key ethical issues related to the use of AI, including personal data protection and preventing data leaks. The research aims to propose possible solutions and adaptations of legislation considering the rapid development of AI technologies. It will be useful not only for lawyers and intellectual property specialists but also for a wide range of readers interested in modern technologies and their legal aspects.   Received: 22 July 2024 | Revised: 25 October 2024 | Accepted: 11 February 2025   Conflicts of Interest The author declares that she has no conflicts of interest to this work.   Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.   Author Contribution Statement Anna Pokrovskaya: Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing, Visualization, Funding acquisition.Artificial Intelligence and Applications740The article addresses the issues of legal regulation of artificial intelligence (AI) in the context of its rapid development and penetration into various spheres of life and proposes possible solutions and adaptations of legislation considering the rapid development of AI technologies.Artificial Intelligence and Applications["Anna Pokrovskaya"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/17fade3dfc0eb60c0a95000bceaf86267246668f 40370d335a6ba451d8f3df835595417b70f7d786Wiener Medizinische Wochenschrift600Various aspects of how AI can be applied in research, diagnosis, and disease management and how this is expected to alter patient flows are highlighted, affecting also health care professionals beyond ophthalmologists.Wiener medizinische Wochenschrift["Sophie Riedl", "Klaudia Birner", "Ursula Schmidt-Erfurth"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/40370d335a6ba451d8f3df835595417b70f7d786 e43799381729565963b8d0155264d003d8261b03PurposeArtificial Intelligence (AI) is revolutionizing the world. Despite the numerous advantages of AI in terms of faster processing and higher efficiency, AI hasn’t been widely accepted by humans yet. This study aims to shed light on this phenomenon by exploring the Dunning–Kruger Effect in AI knowledge and examining how AI knowledge affects AI acceptance through AI-related self-efficacy.Design/methodology/approachBy collecting data from 179 managers, we examined the Dunning–Kruger Effect in AI knowledge and used mediation analysis to explore the mechanisms by which AI knowledge leads to AI acceptance.FindingsOur findings indicated the presence of the Dunning–Kruger Effect in AI knowledge. Furthermore, our results revealed that AI knowledge has a nonlinear effect on AI acceptance through AI-related self-efficacy.Originality/valueIn contrast to previous research that posited a linear link between knowledge and acceptance of technology, this study offers a new framework for the nonlinear relationships between AI knowledge, AI-related self-efficacy and AI acceptance by extending the Dunning–Kruger effect to the AI field.Management Decision630This study offers a new framework for the nonlinear relationships between AI knowledge, AI-related self-efficacy and AI acceptance by extending the Dunning–Kruger effect to the AI field.Management Decision["Jian Guan", "Xiao He", "Yuhan Su", "Xin-an Zhang"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/e43799381729565963b8d0155264d003d8261b03 5a6846db4d1010fff74e947054a7eb7d5ca7cab3American Journal of Bioethics90The American journal of bioethics : AJOB["Austin M Stroud", "Jennifer E Miller", "Barbara A Barry"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/5a6846db4d1010fff74e947054a7eb7d5ca7cab3 9eff25acf03582b2ed31cebb28eb7c60c4ff4394AI and Ethics80AI and Ethics["Ajay Tripathi", "Vinod Kumar"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/9eff25acf03582b2ed31cebb28eb7c60c4ff4394 d1cf0e35f714eef42ffd558d529e7fbf49013c11Journal of Supercomputing810The Journal of Supercomputing["Aditi Bajpai", "Sonal Yadav", "N. K. Nagwani"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/d1cf0e35f714eef42ffd558d529e7fbf49013c11 6ef21469b75f13a2baf7238dbb5ca732368cd67cAmerican Journal of Bioethics100The American journal of bioethics : AJOB["Kayte Spector-Bagdady", "A. London"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/6ef21469b75f13a2baf7238dbb5ca732368cd67c ba07faaf990d18d52137ac0b38691485bb6f4748Cureus1210Cureus["Bijily Babu", "Jyoti Singh", "Juan Felipe Salazar Gonz\u00e1lez", "Sadaf Zalmai", "Adnan Ahmed", "Harshal D Padekar", "Marina R Eichemberger", "Abrar I Abdallah", "Irshad Ahamed S", "Zahra Nazir"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/ba07faaf990d18d52137ac0b38691485bb6f4748 36e66d5b4f0155798cbe1cd0b758d084bfdf75f8International journal of research in computer applications & information technology00INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY["Anushree Nagvekar"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/36e66d5b4f0155798cbe1cd0b758d084bfdf75f8 bfaafe9b44e0e058b85ac06ffa4d33d53c6b12beAdvances in Laboratory Medicine / Avances en Medicina de Laboratorio120Advances in Laboratory Medicine / Avances en Medicina de Laboratorio["G. Lippi", "M. Plebani"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/bfaafe9b44e0e058b85ac06ffa4d33d53c6b12be 24687a45586262505c11ab853082fb33791b994fDiscover Sustainability70The potential of creating fully autonomous, closed-loop agricultural systems within urban settings—powered by AI and robotics—to produce food with minimal human intervention, addressing food security, sustainability, and labour shortages is explored.Discover Sustainability["J. Rad"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/24687a45586262505c11ab853082fb33791b994f 6c66a17239b0cedc8fa4ec755ee21551a00f62f8 The concept of an ontology of digital worldlessness developed in this essay examines how the unequal distribution of the ecological, political, and economic harms of AI undermines plural political belonging in a common world. It argues that digital worldlessness stems from a constellation of several sociopolitical practices, including: a) the formalization of information inherent in algorithmic procedures abstracted from the material world, history, and common sense; b) the insertion of digital surveillance networks into a common world to facilitate continuous extraction of data; c) the opacity of algorithmic procedures to the general public affected by their outcomes; and d) the algorithmic sorting of people and nonhuman phenomena in relation to prediction targets set by corporations and state institutions. Drawing on Hannah Arendt’s political theory and on critical race and feminist theories of AI, in particular, Ruha Benjamin and Wendy Chun, the essay foregrounds the relationship between world, technology and power in order to analyze the assaults on human plurality by algorithmic practices. These practices not only automate gender, racist and economic discrimination, but also undermine collective action contesting these harms. This approach to digital ontology provides an alternative to the dominant but narrow technical meaning of computational ontology in analytical philosophies and computer sciences where this term refers to formulating compatible taxonomies among different data sets for the purposes of information classification.Research in Phenomenology00This approach to digital ontology provides an alternative to the dominant but narrow technical meaning of computational ontology in analytical philosophies and computer sciences where this term refers to formulating compatible taxonomies among different data sets for the purposes of information classification.Research in Phenomenology["P\u0142onowska Ziarek"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/6c66a17239b0cedc8fa4ec755ee21551a00f62f8 597b9260adccf99f2ca4aa643b4ce487dff87798Journal of Medicinal Chemistry90Journal of medicinal chemistry["Yuxi Wang", "Zelin Hu", "Junbiao Chang", "Bin Yu"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/597b9260adccf99f2ca4aa643b4ce487dff87798 6b8b1bda1f99b213d266e9bea3cef9ee827a97ecAIM To synthesise existing evidence concerning the application of AI methods in detecting depression through behavioural cues among adults in healthcare and community settings. DESIGN This is a diagnostic accuracy systematic review. METHODS This review included studies examining different AI methods in detecting depression among adults. Two independent reviewers screened, appraised and extracted data. Data were analysed by meta-analysis, narrative synthesis and subgroup analysis. DATA SOURCES Published studies and grey literature were sought in 11 electronic databases. Hand search was conducted on reference lists and two journals. RESULTS In total, 30 studies were included in this review. Twenty of which demonstrated that AI models had the potential to detect depression. Speech and facial expression showed better sensitivity, reflecting the ability to detect people with depression. Text and movement had better specificity, indicating the ability to rule out non-depressed individuals. Heterogeneity was initially high. Less heterogeneity was observed within each modality subgroup. CONCLUSIONS This is the first systematic review examining AI models in detecting depression using all four behavioural cues: speech, texts, movement and facial expressions. IMPLICATIONS A collaborative effort among healthcare professionals can be initiated to develop an AI-assisted depression detection system in general healthcare or community settings. IMPACT It is challenging for general healthcare professionals to detect depressive symptoms among people in non-psychiatric settings. Our findings suggested the need for objective screening tools, such as an AI-assisted system, for screening depression. Therefore, people could receive accurate diagnosis and proper treatments for depression. REPORTING METHOD This review followed the PRISMA checklist. PATIENTS OR PUBLIC CONTRIBUTION No patients or public contribution.Journal of Clinical Nursing540This is the first systematic review examining AI models in detecting depression using all four behavioural cues: speech, texts, movement and facial expressions and suggested the need for objective screening tools, such as an AI-assisted system, for screening depression.Journal of clinical nursing["Yee Shyan Goh", "Qi Rui See", "Nopporn Vongsirimas", "Piyanee Klanin-Yobas"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/6b8b1bda1f99b213d266e9bea3cef9ee827a97ec 05b930ac64f5723068366e3d48bbb2cee2ab7b5eInternational Journal of Legal Information : Official Publication00International Journal of Legal Information["Frank Young"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/05b930ac64f5723068366e3d48bbb2cee2ab7b5e 8dead656ded5194eb038cfa42c224825945ed5ffHumanities and Social Sciences Communications210Humanities and Social Sciences Communications["Dervi\u015f K\u0131r\u0131kkaleli", "S. Aad", "Nurdan Ozrecberoglu Kirikkaleli"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/8dead656ded5194eb038cfa42c224825945ed5ff 92e56d1c138a68fcd70297b2d1b2e59e02c71693Critique: Studies in Contemporary Fiction240Critique: Studies in Contemporary Fiction["Paul Jahshan"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/92e56d1c138a68fcd70297b2d1b2e59e02c71693 bca700cfc27a83cc6ad4b9605c9210ed2571c596TechTrends230TechTrends["Sami Tlais", "Ali AlKhatib", "Rasha Hamdan", "Hassan HajjHussein", "Kassem M. Hallal", "Wassim El Malti"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/bca700cfc27a83cc6ad4b9605c9210ed2571c596 c4efbb619ba08e4420098444cab623bb28f4579fThis study evaluates the relationship between organizational culture, business strategy, and organizational performance in companies in Jakarta, Indonesia, as the largest tourism industrial area in ASEAN. The study highlights that organizational culture that includes discipline, innovation, and a clear division of authority, as well as a business strategy that focuses on vision, mission, tactics, and marketing, has a significant influence on organizational performance, as measured through growth, customer satisfaction, and marketing effectiveness. Of the 384 respondents, the results showed that organizational culture and business strategy significantly influenced organizational performance, both individually and collectively. In addition, this study also analyzes the role of artificial intelligence (AI) and product quality on the performance of Micro, Small, and Medium Enterprises (MSMEs). Using SPSS for analysis, the results show that AI has an influence of 85%, product quality by 75%, and the combination of the two contributes 80% to the performance of MSMEs, with the remaining 20% influenced by other factors not analyzed in this study. These findings underscore that higher AI adoption and improved product quality significantly improve the performance of MSMEs, providing a positive projection for the growth of small businesses in the community. This research makes an important contribution to understanding how organizational culture, business strategy, and technology and product quality can be the main driving factors in improving the performance of organizations and MSMEs, especially in the tourism and small business sectors in Indonesia.International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)160It is found that higher AI adoption and improved product quality significantly improve the performance of MSMEs, providing a positive projection for the growth of small businesses in the community.International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)["Indah Kusumawati", "Rokhmat Subagiyo"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/c4efbb619ba08e4420098444cab623bb28f4579f 4d3cdf873c723b37bcae960e4835900369a0c912The rapid growth of e‐commerce marketplaces has posed significant challenges to the enforcement of trademark rights. With the emergence of artificial intelligence (AI) technologies, new opportunities and strategies have emerged for effective trademark enforcement on these platforms. This article examines the transformative role of AI technologies in the protection of trademark rights on e‐commerce platforms. It discusses how AI can enhance monitoring, detection, and enforcement of trademark infringements by leveraging advanced methodologies such as machine learning, natural language processing, and computer vision. It discusses how AI can enhance monitoring, detection, and enforcement of trademark infringements by leveraging advanced methodologies such as machine learning, natural language processing, and computer vision. The article identifies the various capabilities of AI technologies in effectively combating counterfeiting, highlights the substantial benefits these tools provide to brand owners, and addresses the ethical and legal considerations accompanying their implementation. Furthermore, it provides insights into limitations faced by stakeholders when integrating AI into trademark enforcement strategies. Ultimately, this article aims to furnish comprehensive recommendations for improving the efficiency of AI‐driven enforcement mechanisms, ensuring a reliable and trustworthy environment for consumers and brand owners alike.Journal of World Intellectual Property00The article identifies the various capabilities of AI technologies in effectively combating counterfeiting, highlights the substantial benefits these tools provide to brand owners, and addresses the ethical and legal considerations accompanying their implementation.The Journal of World Intellectual Property["Pokrovskaya Anna Vladimirovna"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/4d3cdf873c723b37bcae960e4835900369a0c912 2e35528652f07794b1e601ece1c03a086d383819Artificial Intelligence (AI) has its transformative function in education by providing AI writing assistants like ChatGPT and Grammarly. AI tools have emerged as a prominent resource for improving writing performance at the advanced levels of education, hence, this study seeks to explore the potential of these tools to improve the essay writing of undergraduate Pakistani students. Using a mixed-methods approach, the research investigates changes in grammar, coherence, vocabulary, and overall academic writing for students over the eight-week period. The population of the study is BS students of Management Sciences at University of Education and data were collected through pre- and post-test essays and instructor’s evaluations to evaluate the impact of AI-generated feedback versus human feedback on students' learning experience. The results show that the use of AI writing assistants has a positive impact on student writing in terms of accuracy, structural organization and self-revision, while challenges related to excessive dependence on AI and ethical concerns around academic integrity remain. The study also investigates students’ perceptions of AI-assisted learning and the implications for pedagogy within higher education. From the findings, there are suggestions made on how to best incorporate AI writing tools into the university curricula. This study adds to the continuing conversation about AI in education; it also informs the study of its role in enhancing writing skills in underdeveloped academic contexts like Pakistan.Journal for Social Science Archives140The results show that the use of AI writing assistants has a positive impact on student writing in terms of accuracy, structural organization and self-revision, while challenges related to excessive dependence on AI and ethical concerns around academic integrity remain.Journal for Social Science Archives["Quratulain", "Dr. Saira Maqbool", "Sara Bilal"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/2e35528652f07794b1e601ece1c03a086d383819 14323a2f9502611f96298cbc91508b1386fefaaeArtificial intelligence analytics in digital finance platforms is important in the modern digital world. AI can conduct analytics quickly and provide the outcomes for the system users to make informed, data-driven conclusions. AI can scan through large datasets and provide meaningful information on social media platforms, historical quantitative transactions, and finances to give critical findings, unlike traditional systems. This review article assessed previous research articles on financial risk evaluation using AI analytics in the finance industry and digital finance platforms. The outcomes outlined the capabilities of financial risks evaluated with the help of AI in digital finance platforms. The key identified risks were credit risks, market risks, operational risks, fraud risks, and compliance risks. The study outlined the key capabilities of AI in shielding firms against such risks through predictive analytics, anomaly detection, sentiment analysis, and credit scoring. The AI systems should be hosted on the cloud to have access to large datasets to give accurate, data-driven conclusions. The identified challenges are algorithm bias, data privacy, regulatory compliance (especially across platforms and countries), and skill gaps in the market. In conclusion, using AI in digital finance platforms has increased the efficiency in making informed decisions for sustainability and strategic growth.Environment and Social Psychology00Using AI in digital finance platforms has increased the efficiency in making informed decisions for sustainability and strategic growth, and the identified challenges are algorithm bias, data privacy, regulatory compliance, and skill gaps in the market.Environment and Social Psychology["Ma Howard", "Guo Wei"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/14323a2f9502611f96298cbc91508b1386fefaae 00060f109d375b995fda6370a17481cdff4e4fdfThis study explores the intersection of strategic management and artificial intelligence (AI) from 2014 to 2024. As AI technologies advance, their integration into strategic management becomes essential for sustaining competitive advantage. The research aims to understand how AI influences and reshapes strategic decision-making and competitive strategies. The study aims to systematically examine how AI and strategic management converge. It seeks to identify key research trends, influential scholars, and major publications to clarify how AI is transforming strategic management and competitive dynamics. We used a three-stage methodology: we used RStudio to analyze 326 scholarly articles, VOSviewer to perform citation and network analysis to identify key authors and research clusters, and Excel for data management and visualization to highlight emerging trends and findings. The study identifies three main research clusters: leveraging AI for competitive advantage, the impact of emerging technologies on strategic management, and the role of dynamic capabilities. It underscores AI’s growing importance in strategic foresight and the need for dynamic capabilities to adapt to technological changes. The research offers a roadmap for academics and practitioners, highlighting crucial areas for future exploration and emphasizing the need to integrate AI into strategic management practices to navigate emerging trends and maintain competitive advantage.Human Systems Management320This study explores the intersection of strategic management and artificial intelligence from 2014 to 2024, highlighting crucial areas for future exploration and emphasizing the need to integrate AI into strategic management practices to navigate emerging trends and maintain competitive advantage.Human Systems Management["Wael Alhyasat", "E. Alhyasat", "S. Khattab"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/00060f109d375b995fda6370a17481cdff4e4fdf 526573a206a7c1b96db5c897333d631a0257bca7 This Viewpoint examines how artificial intelligence could reshape health care, as new technologies enable the move from traditional health care organizations to direct-to-consumer models. Journal of the American Medical Association (JAMA)90JAMA["Kenneth D. Mandl"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/526573a206a7c1b96db5c897333d631a0257bca7 d3cbb2829147252a628b5e43fa51f4fd72fd0865The rapid and pervasive integration of artificial intelligence (AI) technologies into education presents both unprecedented opportunities and significant challenges. While AI‐powered tools offer personalised learning experiences and access to vast knowledge repositories, their successful implementation hinges on a nuanced understanding of how learners' psychological and cognitive processes interact within these dynamic environments. This study delved into the intricate interplay between cognitive‐emotion regulation, critical thinking, academic resilience, academic motivation and autonomy in a cohort of English as a foreign language (EFL) learners engaged in AI‐mediated learning. For this, a sample of 302 EFL learners was recruited using a stratified random sampling method. The data were analysed using structural equation modelling and confirmatory factor analysis through SMART PLS software. Findings revealed that there was a significant correlation between cognitive‐emotion regulation and academic motivation and autonomy among EFL learners in AI‐mediated learning environments. Moreover, the results showed that a significant correlation between critical thinking and academic motivation and autonomy existed. Additionally, the outcomes indicated that the academic resilience was significantly correlated with the academic motivation and autonomy. These findings underscored that by cultivating learners' ability to effectively manage their emotions, engage in critical inquiry and exercise autonomy, educators can empower them to navigate the complexities of AI‐integrated learning environments, achieve academic success and develop the essential skills for lifelong learning in the digital age.British Educational Research Journal700By cultivating learners' ability to effectively manage their emotions, engage in critical inquiry and exercise autonomy, educators can empower them to navigate the complexities of AI‐integrated learning environments, achieve academic success and develop the essential skills for lifelong learning in the digital age.British Educational Research Journal["Chao Yang", "Ming Wei", "Qi Liu"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/d3cbb2829147252a628b5e43fa51f4fd72fd0865 b368a545859cc7e7f30537c4c360bc1e2c8b4871International Cybersecurity Law Review40Best practices to improve communication security of organizations to counter the risk of becoming the victim of AI-powered deepfake scams are provided and legal requirements that underpin the necessity and importance of communication security within an organization are presented.International Cybersecurity Law Review["Fabian Muhly", "Emanuele Chizzonic", "Philipp Leo"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/b368a545859cc7e7f30537c4c360bc1e2c8b4871 388f1ea593804192ea28e022760e0ffe63c07620Lennard-Jones (LJ) fluids serve as an important theoretical framework for understanding molecular interactions. Binary LJ fluids, where two distinct species of particles interact based on the LJ potential, exhibit rich phase behavior and provide valuable insights of complex fluid mixtures. Here we report the construction and utility of an artificial intelligence (AI) model for binary LJ fluids, focusing on their effectiveness in predicting radial distribution functions (RDFs) across a range of conditions. The RDFs of a binary mixture with varying compositions and temperatures are collected from molecular dynamics (MD) simulations to establish and validate the AI model. In this AI pipeline, RDFs are discretized in order to reduce the output dimension of the model. This, in turn, improves the efficacy, and reduce the complexity of an AI RDF model. The model is shown to predict RDFs for many unknown mixtures very accurately, especially outside the training temperature range. Our analysis suggests that the particle size ratio has a higher order impact on the microstructure of a binary mixture. We also highlight the areas where the fidelity of the AI model is low when encountering new regimes with different underlying physics.120["Israrul H Hashmi", "Rahul Karmakar", "Marripelli Maniteja", "Kumar Ayush", "T. Patra"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/388f1ea593804192ea28e022760e0ffe63c07620 ae8a282637593778602277d32411cd0be7833858Introduction The integration of Artificial Intelligence (AI) into medical education and healthcare has grown steadily over these past couple of years, though its application in veterinary education and practice remains relatively underexplored. This study is among the first to introduce veterinary students to AI-generated cases (AI-cases) and AI-standardized clients (AI-SCs) for teaching and learning communication skills. The study aimed to evaluate students' beliefs and perceptions surrounding the use of AI in veterinary education, with specific focus on communication skills training. Methods Conducted at Texas Tech University School of Veterinary Medicine (TTU SVM) during the Spring 2024 semester, the study included pre-clinical veterinary students (n = 237), who participated in a 90-min communication skills laboratory activity. Each class was introduced to two AI-cases and two AI-SCs, developed using OpenAI's ChatGPT-3.5. The Calgary Cambridge Guide (CCG) served as the framework for practicing communication skills. Results Results showed that although students recognized the widespread use of AI in everyday life, their familiarity, comfort and application of AI in veterinary education were limited. Notably, upper-year students were more hesitant to adopt AI-based tools, particularly in communication skills training. Discussion The findings suggest that veterinary institutions should prioritize AI-literacy and further explore how AI can enhance and complement communication training, veterinary education and practice.Frontiers in Veterinary Science620The findings suggest that veterinary institutions should prioritize AI-literacy and further explore how AI can enhance and complement communication training, veterinary education and practice.Frontiers in Veterinary Science["Elpida Artemiou", "Sarah Hooper", "Linda Dascanio", "Marcelo Schmidt", "Guy Gilbert"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/ae8a282637593778602277d32411cd0be7833858 ad53c4f5635758a1ce9d4561fee393e4cf3c109fThe integration of artificial intelligence (AI) into CAD platforms will dramatically influence the way medical devices are designed, produced, and evaluated. It will allow the creation of intelligent customization platforms for perfect natural device design, dealing with patient-specific compatibility, function, and intraoperative customization. Similar trends occurred for 3D printing and have led to the democratization of an exciting innovation. AI tools are essential to facilitate the design’s performance evaluation of these complex 4D concepts, made of a new generation of advanced soft active materials, actuators, and novel bioprinting strategies. Advanced machine learning will be used to improve predictive generative platforms' biomechanical bio-operation and bio-integration simulation, leading to the design of a novel generation of temporary sophisticated 4D custom-printed objects. Examples of these new advanced bioprinted smart active materials’ future patient-specific applications will be given for drug-printed biodegradable temporary medical devices, passively adaptive volumetric intravascular devices, and internally actuated endovascular complex were driven objects. AI transforming medical practices, doctors, health providers, or hospitals were where patients head to receive various treatments. AI is in the process of becoming omnipresent, perceiving the patient’s symptoms and medical history and providing a proper diagnosis. In the long run, this may lead to a paradigm shift where instead of reaching out to the medical devices, the medical devices will be sent to the necessary places where the patients/people live, study, work, and relax. Therefore, increasing attention is placed on wearable or ubiquitous medical technologies exploiting generative AI tools, providing the shift from passive monitoring to active patient custom home care. Examples of how generative AI fueled the new medical devices from their idea generation and development to the next real-world applications are given as easily customizable ultrathin epidermal sensory patch, pocket essential skin care devices, and customized comfort shoe inserts.Journal of Neonatal Surgery200Advances in machine learning will be used to improve predictive generative platforms' biomechanical bio-operation and bio-integration simulation, leading to the design of a novel generation of temporary sophisticated 4D custom-printed objects.Journal of Neonatal Surgery["Sai Teja Nuka"]2025-02-24T00:00:00https://www.semanticscholar.org/paper/ad53c4f5635758a1ce9d4561fee393e4cf3c109f 3ae80107f116d6e449c081f92aee11600bbf4568Pflügers Archiv: European Journal of Physiology1021An outlook on two possible future prospects are given: using XAI methods to provide trustworthy AI for integrative physiological research and integrating physiological expertise about human explanation into XAI method development for useful and beneficial human-AI partnerships.Pflugers Archiv : European journal of physiology["Bettina Finzel"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/3ae80107f116d6e449c081f92aee11600bbf4568 13c98dc4a98beb3249ddadb3f3f28d3e35a4e01dThe use of artificial intelligence (AI) in armed conflict gives rise to unprecedented challenges in international humanitarian law (IHL). This article examines the complex relationship between AI and IHL. It focuses on the application of autonomous weapons systems (AWS), cyber warfare, surveillance, and precision targeting. The use of AI has been believed to enhance precision and reduce collateral damage. However, there are some challenges, like the loss of human control over decision-making, the potential for algorithmic bias, and the questions of attribution. These issues threaten the core principles of IHL, that is, distinction, proportionality, and humanity. This article fi nds that AWS, constituting a ‘third revolution in military affairs, consists of a risk of violating the principle of distinction by misidentifying targets because of inherent biases in their programming. Similarly, the research discovers that the use of AI in cyber operations raises concerns about the proportionality of attacks and the difficulties in  attributing responsibility for such operations. This article analyzes these challenges, including insights from case studies and comparative analyses of AI usage in military operations. The study follows a qualitative research methodology and analyzes data collected from primary and secondary sources. By exploring particular events, this research highlights the pressing need for a reconsideration of existing legal frameworks that address the peculiar challenges presented by evolving  technology. Ultimately, this article seeks to contribute to the ongoing discourse on developing ethical and legal frameworks necessary to govern AI’s use in warfare, ensuring compliance with IHL while respecting state sovereignty and national security.Unity Journal01The research discovers that the use of AI in cyber operations raises concerns about the proportionality of attacks and the difficulties in attributing responsibility for such operations, and the research highlights the pressing need for a reconsideration of existing legal frameworks that address the peculiar challenges presented by evolving technology.Unity Journal["Yatish Ojha"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/13c98dc4a98beb3249ddadb3f3f28d3e35a4e01d e6ac777e243e8cadefd36fc1328268c49a2aad99In recent years, many companies have used artificial intelligence (AI), which includes neural networks, expert systems, and voice recognition systems. However, managers and developers have very little understanding of how management and organizations influence or are influenced by technology. This article discusses the interaction of AI, management, and organizations using specific examples from practice and research, and provides some methodological approaches and theoretical models to describe and study those interactions and provide directions for future research.Global Spectrum of Research and Humanities210The interaction of AI, management, and organizations is discussed using specific examples from practice and research, and some methodological approaches and theoretical models are provided to describe and study those interactions and provide directions for future research.Global Spectrum of Research and Humanities["Mohammad Ekram Yawar", "Mohammad Qurban Hakimi"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/e6ac777e243e8cadefd36fc1328268c49a2aad99 ebaef5388d4f3476d1ea75cb2c4b0aa4b1c5acc9This study explores the transformative role of artificial intelligence (AI), particularly machine learning (ML), in enhancing the detection and prevention of money laundering and fraud within the U.S. financial industry. The study aims to analyze how AI-driven techniques can significantly improve the accuracy, efficiency, and scalability of fraud detection systems. The study focuses on examining various machine learning algorithms, including supervised techniques like logistic regression and decision trees, as well as unsupervised methods such as clustering and anomaly detection. These techniques are utilized to analyze historical data, detect patterns, and identify suspicious transactions or fraudulent behaviors in real-time. The research method includes a comprehensive review of existing case studies and literature on AI applications in fraud detection, highlighting successful implementations of ML models in financial institutions. The findings reveal that machine learning models, such as random forests and support vector machines, have proven effective in detecting and preventing fraudulent activities with high precision and recall rates. Furthermore, the integration of AI with real-time data analysis capabilities enables continuous monitoring and immediate detection of irregularities. The study concludes that financial institutions in the U.S. must leverage AI advancements to enhance risk management systems, improve fraud detection, and mitigate the risks of money laundering. By adopting machine learning algorithms, financial organizations can stay ahead of emerging threats, ensuring the security of their operations and customer assets. Keywords: Artificial Intelligence, Machine Learning, Money Laundering, Fraud Detection, U.S. Financial Industry.Finance &amp; Accounting Research Journal00The findings reveal that machine learning models, such as random forests and support vector machines, have proven effective in detecting and preventing fraudulent activities with high precision and recall rates.Finance &amp; Accounting Research Journal["Victor Boateng", "Elizabeth Kuukua Amoako", "Ola Ajay", "Tobias Kwame Adukpo"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/ebaef5388d4f3476d1ea75cb2c4b0aa4b1c5acc9 649018492a5724b34c06b5650160ca841f929c13Information Systems Frontiers1340The evolving and emerging trends of AI are considered in order to examine its present and predict its future impacts, and a system-oriented sociotechnical perspective is attempted to bridge the technical and organizational communities of GenAI from a system-oriented sociotechnical perspective.Information Systems Frontiers["V. Storey", "Wei T. Yue", "J. L. Zhao", "R. Lukyanenko"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/649018492a5724b34c06b5650160ca841f929c13 5418687d6f0ace0bee804b073fd80bebe0fd09e3Artificial Intelligence (AI) currently plays a pivotal role in academic research among faculty at Universidad DaVinci. This study examines how AI is transforming research methodologies, with a particular focus on its impact on the efficiency and quality of resources it facilitates. The primary objective is to identify the factors influencing the frequency of AI usage, using Universidad DaVinci in Mexico as a case study. The research was conducted in two phases, employing a five-point Likert scale survey to collect data. Findings reveal that variables outlined in the Technology Acceptance Model (TAM) significantly influence AI adoption among faculty members. Key factors include perceived usefulness, perceived ease of use, and attitudes toward technology. However, adoption is often hindered by challenges such as limited knowledge, insufficient training, resistance to change, and implementation barriers. These insights underscore the importance of targeted strategies to address these obstacles, fostering broader acceptance and effective integration of AI in academic research.Journal of Information Systems Engineering & Management370Findings reveal that variables outlined in the Technology Acceptance Model (TAM) significantly influence AI adoption among faculty members, and underscore the importance of targeted strategies to address these obstacles, fostering broader acceptance and effective integration of AI in academic research.Journal of Information Systems Engineering and Management["Ana Laura", "Casta\u00f1eda Vitela", "Eyran Roberto D\u00edaz Gurrola", "Elena Tzetz\u00e1ngary", "Aguirre Mejia", "Alicia Rodriguez Pulido", "Victor Manuel Moreno Landeros", "Emmanuel Contreras Medina"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/5418687d6f0ace0bee804b073fd80bebe0fd09e3 4d164d6683cd6bb45a9464a93ea5f8e1029b12e1The application of artificial intelligence (AI) in tall buildings’ development provides transformative opportunities for facing population growth pressures and sustainability challenges in cities. This study presents a comprehensive review of both the current literature and the theoretical framework of AI and its role in construction, specifically analyzing the convergence of AI and skyscraper development. The research methodology combines scholarly sources, AI image generation techniques, an analytical approach, and a comparative analysis of traditional versus AI-enhanced approaches. This study identifies key domains where AI significantly impacts skyscraper evolution, including design optimization, energy management, construction processes, and operational efficiencies. It highlights short-term benefits like enhanced architectural design through rapid generative design iterations and material optimization, alongside long-term implications involving adaptive building technologies and sustainability enhancements. Additionally, it addresses the advantages and challenges of adopting AI in architecture, considering various factors (e.g., sustainability, security, and occupant well-being), as well as the impact of different climates on AI in architecture and construction. It also explores transformative applications across diverse skyscraper functions and how AI can bridge different cultures and technologies. The findings reveal AI’s substantial potential in TBs’ design and management, (i.e., structural optimization, energy saving, safety protocols, and operational efficiency) by leveraging innovative technologies such as machine learning, computer vision, and predictive modeling. In conclusion, AI’s dual role as both a revolutionary tool that enhances traditional architectural methods and a catalyst for new design paradigms prioritizing sustainability and resilience has been reflected. Ultimately, this research underscores the importance of balancing AI innovation with established architectural principles to foster a favorable urban future that embraces both technological advancement and foundational design values. This study serves as a base for future research in the AI field.Buildings00AI’s dual role as both a revolutionary tool that enhances traditional architectural methods and a catalyst for new design paradigms prioritizing sustainability and resilience has been reflected underscores the importance of balancing AI innovation with established architectural principles to foster a favorable urban future that embraces both technological advancement and foundational design values.Buildings["Samaa Emad", "Mohsen Aboulnaga", "Ayman Wanas", "Ahmed Abouaiana"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/4d164d6683cd6bb45a9464a93ea5f8e1029b12e1 1e7f97be6ffab40877f9f4099b6244893c9c7882This article explores the timely issue of employing Artificial Intelligence (AI) to ensure the uniformity of judicial practice in Ukraine, which is considered a vital component of trust in the court. The author analyzes the possibilities and prospects of implementing AI in the activities of the Supreme Court, particularly for automating the processes of analyzing court decisions, identifying inconsistencies, and formulating generalized legal positions. It is emphasized that the uniformity of judicial practice is a fundamental value that guarantees the realization of the principle of equality, ensures the predictability of justice, and strengthens trust in the judicial system. The potential of AI to enhance the efficiency of courts at all levels is highlighted, but it is stressed that its implementation requires a balanced approach considering potential risks. The article argues that AI can serve as an effective tool for streamlining court operations and unifying legal interpretations. Furthermore, the research delves into the current state of digitalization within the Ukrainian judicial system and identifies existing challenges, such as the large volume of cases and the need for improved analytical tools. The author also connects the discussion to Ukraine's European integration aspirations, emphasizing that a unified judicial practice aligns with the principles of the rule of law and legal certainty, which are cornerstones of European legal standards. Furthermore, the active implementation of AI in the judiciary will contribute to strengthening the rule of law, approximating EU standards, and improving Ukraine's international image. Ultimately, the research concludes that integrating AI into the judicial process represents a crucial step towards a more efficient, consistent, and trustworthy legal system. Key words: artificial intelligence, unity (coherence, consistency, stability) of judicial practice, Supreme Court, principle of equality, fairness, reasonable predictability (principle of legal certainty, principle of legitimate expectations), efficiency, motivation, principle of procedural economy, legal position (conclusion), discretion, derogation, trust in the court.Slovo of the National School of Judges of Ukraine10It is argued that AI can serve as an effective tool for streamlining court operations and unifying legal interpretations and contribute to strengthening the rule of law, approximating EU standards, and improving Ukraine's international image.Slovo of the National School of Judges of Ukraine["I. Bernaziuk"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/1e7f97be6ffab40877f9f4099b6244893c9c7882 b9b71b840fbd041582ac78dfe3943a27e0fdf9cdGenerative artificial intelligence (AI) tools, such as ChatGPT, have gained significant traction in educational settings, offering novel opportunities for enhanced learning experiences. However, limited research has investigated how students perceive and accept these emerging technologies. This study addresses this gap by developing a scale to assess university students’ attitudes toward generative AI tools in education.A three-stage process was employed to develop and validate the Generative AI Attitude Scale. Data were collected from 664 students from various faculties during the 2022–2023 academic year. Expert evaluations were conducted to establish face and content validity. An exploratory factor analysis (EFA) was performed on a subset of 400 participants, revealing a two-factor, 14-item structure that explained 78.440% of the variance. A subsequent confirmatory factor analysis (CFA) was conducted on a separate sample of 264 students to validate this structure, resulting in the removal of one item and a final 13-item scale.The 13-item scale demonstrated strong reliability, evidenced by a Cronbach’s alpha of 0.84 and a test–retest reliability of 0.90. Discriminative power was confirmed through corrected item-total correlations between lower and upper percentile groups. These findings indicate that the scale effectively differentiates student attitudes toward generative AI tools in educational contexts.The newly developed Generative AI Attitude Scale offers a valid and reliable instrument for measuring university students’ perspectives on integrating generative AI tools, such as ChatGPT, into educational environments. These results highlight the potential for more targeted research and informed implementation strategies to enhance learning outcomes through generative AI.Frontiers of Computer Science610Findings indicate that the Generative AI Attitude Scale effectively differentiates student attitudes toward generative AI tools in educational contexts, highlighting the potential for more targeted research and informed implementation strategies to enhance learning outcomes through generative AI.Frontiers in Computer Science["Agostino Marengo", "Fatma Gizem Karaoglan-Yilmaz", "Ramazan Y\u0131lmaz", "Mehmet Ceylan"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/b9b71b840fbd041582ac78dfe3943a27e0fdf9cd e979561d906b3273110b2159816bb69d978ddd11Purpose – Artificial Intelligence (AI) is increasingly becoming a popular source of information, including health information. It is essential to explore the adoption of AI to achieve Health Information Literacy (HIL) and ensure that users maximise the adoption of AI to use health information. This study explores AI's adoption and use in advancing HIL. It identifies gaps, concerns, and challenges and suggests areas where AI's use could be improved. Approach – The retrieved papers were initially assessed based on title and abstract to inclusion criteria. The full text of relevant papers was verified following the exclusion criteria. Additionally, a comprehensive assessment of the reference lists of the included papers was performed. Information was extracted from the selected articles, and a bibliometric and thematic analysis was applied for thorough examination. Methodology – Key details about the retrieved articles, including author, publication year, study type, purpose, and key findings, were collected using a standardised format. As themes emerged, information from the publications was extracted to address the main research questions. All articles reviewed were in English and published between 2019 and 2024. Findings – The growing adoption of AI for HIL can be accounted for by a growth of 128.13% in publications. However, concerns must be addressed as continuous AI use is guaranteed. Originality – This study is likely the first to assess the current use of AI for HIL. The findings from the study will provide a clear landscape for investing, identifying research partners, and providing AI use in HIL and the research gap.Information Development290This study explores AI's adoption and use in advancing HIL, identifies gaps, concerns, and challenges and suggests areas where AI's use could be improved and suggests areas where AI's use could be improved.Information Development["Godwin Dzangare", "Thabo Ayibongwe Gulu"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/e979561d906b3273110b2159816bb69d978ddd11 f6c40a1ad791fc4e96f92a56522e427af3b04f98Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), electromyography (EMG), and polysomnography (PSG). AI enhances diagnostic accuracy and efficiency while addressing interrater variability and the growing data volume. The evolution of AI tools, from early mimetic methods to advanced deep learning techniques, has significantly improved spike and seizure detection in EEG and facilitated whole EEG evaluations, reducing the workload on clinicians. In EMG, AI demonstrates promise in identifying motor unit abnormalities and analyzing audio signals, though challenges persist due to limited datasets and clinical context considerations. PSG scoring has seen substantial integration of AI, with systems achieving high accuracy through uncertainty estimation and selective manual review, but limitations remain in analyzing epileptic activity and classifying certain sleep stages. As a "co-pilot," AI augments human expertise by improving quality control, standardizing clinical trials, and enabling rapid data review, particularly for less experienced providers. Future AI advancements in CNP aim to shift from isolated data interpretation to providing clinical context, considering patient history, treatment options, and prognostic implications. While the potential of generative AI and "AI-omics" is transformative, the importance of thoughtful integration to augment rather than replace human expertise must be emphasized, ensuring that AI becomes a tool for collaboration and innovation in medicine.The Neurodiagnostic Journal110The importance of thoughtful integration to augment rather than replace human expertise must be emphasized, ensuring that AI becomes a tool for collaboration and innovation in medicine.The Neurodiagnostic journal["Aatif M. Husain"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/f6c40a1ad791fc4e96f92a56522e427af3b04f98 3af453bae050e7c9e922c122906e5c02dafee2a0The integration of Artificial Intelligence (AI) in corporate finance is reshaping traditional decision-making processes, offering unprecedented opportunities for precision, efficiency, and strategic insight. This paper explores the transformative role of AI in corporate finance, emphasizing its applications in areas such as financial forecasting, risk management, investment decision-making, and automated reporting. Through advanced methodologies, including machine learning, natural language processing, and predictive analytics, AI empowers businesses to derive actionable insights from complex financial data, fostering improved strategic planning. Additionally, this study examines how AI is revolutionizing the field of strategic consulting by enabling data-driven scenario planning, personalized client strategies, and enhanced performance optimization. Despite its potential, challenges such as data security, algorithmic bias, and the interpretability of AI systems present significant hurdles. By addressing these challenges and adopting ethical practices, businesses and consultants can fully harness the power of AI to revolutionize corporate finance and redefine strategic consulting practicesInternational Journal of Science and Research Archive00This paper explores the transformative role of AI in corporate finance, emphasizing its applications in areas such as financial forecasting, risk management, investment decision-making, and automated reporting.International Journal of Science and Research Archive["Oyindamola Modupe Odewuyi", "Oluwabanke Aminat Shodimu", "Oluwatobiloba Kazeem", "Adeniyi Paul Phillips", "Selina Affiang Okpo", "Akeem Olakunle Ogundipe"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/3af453bae050e7c9e922c122906e5c02dafee2a0 d0275594fa3dbb7ca35b4f7eb9fbc4e467498b43Penelitian ini bertujuan untuk mengeksplorasi strategi bahasa yang digunakan oleh Artificial Intelligence (AI) dalam terapi kesehatan mental serta dampaknya terhadap pengalaman terapi pasien. Fokus penelitian ini adalah menganalisis bagaimana AI membangun interaksi kebahasaan dengan pasien dalam konteks terapi kesehatan mental di Indonesia ketika stigma terhadap gangguan psikologis masih menjadi tantangan utama. Metode yang digunakan dalam penelitian ini adalah metode kualitatif dengan pemanfaatan studi literatur. Hasil penelitian menunjukkan bahwa AI memiliki peran penting dalam meningkatkan akses terhadap layanan kesehatan mental, terutama bagi individu yang memiliki keterbatasan dalam mengakses terapis manusia. AI terbukti dapat membantu mengidentifikasi pola perilaku pasien, memberikan umpan balik terkait kondisi psikologis, dan menawarkan strategi manajemen stres yang berbasis kognitif-perilaku. Namun, penelitian juga menemukan bahwa keterbatasan AI dalam mengekspresikan empati dan membangun koneksi emosional dengan pasien menjadi kendala utama dalam efektivitas terapi berbasis AI. Selain itu, tantangan terkait infrastruktur teknologi yang belum merata dan rendahnya literasi digital masih menghambat pemanfaatan AI secara optimal di berbagai wilayah, terutama di Indonesia.  Jurnal Onoma Pendidikan Bahasa dan Sastra160Jurnal Onoma: Pendidikan, Bahasa, dan Sastra["Jatmika Nurhadi", "Undang Sudana", "Yostiani Noor Asmi Harini", "Sri Wiyanti", "Atika Rahma Amalia", "Helda Nur Azizah"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/d0275594fa3dbb7ca35b4f7eb9fbc4e467498b43 125f9e218e36cd2f26768ee74db5fbf422eade65Purpose This paper aims to highlight the crucial role of strategic human resource management in addressing labour tensions that arise from the integration of artificial intelligence (AI) and automation in contemporary workplaces. Effective approaches to managing technological transformation while maintaining positive labour relations are also discussed. Design/methodology/approach The paper draws upon two labour dispute cases at supermarket giants, Woolworths and the aviation sector in Australia. The cases are analysed through the lens of strategic human resource management and discussed using previous studies, expert and industry insights. Findings This paper reveals that successful AI integration requires more than technological expertise – it also demands sophisticated people management strategies that can balance innovation with human concerns. To do so, there is a need for strategic workforce planning and AI integration, cultural transformation and change management, ethical considerations and worker well-being, balancing efficiency and human agency and leadership in AI transformation. Comprehensive strategy development, stakeholder engagement, governance structures and skills development are recommended for smooth AI integration in modern workplaces. Originality/value This paper uses two recent labour disputes in Australia to illuminate the critical role of strategic HR management in balancing AI integration with employee well-being and engagement. As AI and automation continue to reshape workplaces, technological transformation must serve both organisational objectives and worker interests. Lessons from this paper can guide future strategic HR approaches to AI integration in ways that promote sustainable and equitable workplace transformation. Strategic HR Review120It is revealed that successful AI integration requires more than technological expertise – it also demands sophisticated people management strategies that can balance innovation with human concerns.Strategic HR Review["Emmanuel Senior Tenakwah", "Albert Amankwaa"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/125f9e218e36cd2f26768ee74db5fbf422eade65 c89927a50d45a0640e77483bfb5ebef6c1b5bd68BACKGROUND Virtual care technology including artificial intelligence (AI) may augment nursing functions creating flexibility in staffing that reduces workforce shortages and enhances patient safety. LOCAL PROBLEM A health system experienced nursing workforce shortages and patient safety concerns. METHODS Quality improvement methodology was used to evaluate the impact of implementing virtual care technology with AI. INTERVENTIONS Virtual patient observation (VPO) with AI and virtual nurse (VN) technology were implemented. Nursing assistants served as virtual observers, while registered nurses functioned as VNs, managing patient admissions, discharges, and education. RESULTS Unwitnessed in-room patient fall rates decreased 59% and median sitter hours were reduced by 91%. Patient experience and nurse perceptions of patient safety, workforce flexibility, and well-being improved. The program saved 63 hours per month of bedside nurse time. CONCLUSIONS Nurse leader sponsorship of VPO with AI and VN to augment nursing functions offers a solution to improve patient safety and workforce flexibility.Journal of Nursing Care Quality150Nurse leader sponsorship of VPO with AI and VN to augment nursing functions offers a solution to improve patient safety and workforce flexibility.Journal of nursing care quality["Cassandra E Tyransky", "Kasey Paulus", "Erin Langmead", "David M Miller", "Carol R Smith", "Fallon Hughes", "Barbara L Buchko", "Bruno Saconi"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/c89927a50d45a0640e77483bfb5ebef6c1b5bd68 f43bf43733ce7a6d747328b942fb9cba57b1aa0cInflammatory bowel disease (IBD) management stands at the cusp of a transformative era, with recent breakthroughs heralding a paradigm shift in treatment strategies. Traditionally, IBD therapeutics revolved around immunosuppressants, but the landscape has evolved significantly. Recent approvals of etrasimod, upadacitinib, mirikizumab, and risankizumab have introduced novel mechanisms of action, offering renewed hope for IBD patients. These medications represent a departure from the status quo, breaking years of therapeutic stagnation. Precision medicine, involving Artificial Intelligence, is a pivotal aspect of this evolution, tailoring treatments based on genetic profiles, disease characteristics, and individual responses. This approach optimizes treatment efficacy, and paves the way for personalized care. Yet, the rising cost of IBD therapies, notably biologics, poses challenges, impacting healthcare budgets and patient access. Ongoing research strives to assess cost-effectiveness, guiding policy decisions to ensure equitable access to advanced treatments. Looking ahead, the future of IBD management holds great promise. Emerging therapies, precision medicine, and ongoing research into novel targets promise to reshape the IBD treatment landscape. As these advances continue to unfold, IBD patients can anticipate a brighter future, one marked by more effective, personalized, and accessible treatments.Journal of Clinical Medicine960Emerging therapies, precision medicine, and ongoing research into novel targets promise to reshape the IBD treatment landscape, and patients can anticipate a brighter future, one marked by more effective, personalized, and accessible treatments.Journal of Clinical Medicine["A. M. Caballero Mateos", "G. A. Ca\u00f1adas de la Fuente", "Beatriz Gros"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/f43bf43733ce7a6d747328b942fb9cba57b1aa0c b67bde391b601b64d40973d653b1440a24a9b770Accurately predicting stock market movements remains a critical challenge in finance, driven by the increasing role of algorithmic trading and the centrality of financial markets in economic sustainability. This study examines the incorporation of artificial intelligence (AI) and machine learning (ML) technologies to address gaps in identifying predictive factors, integrating diverse data sources, and optimizing methodologies. Employing a systematic review, recent advancements in ML techniques like deep learning, ensemble methods, and neural networks are analyzed, alongside emerging data sources such as traders’ sentiment and real-time economic indicators. Results highlight the potential of unified datasets and adaptive models to enhance prediction accuracy while overcoming market volatility and data heterogeneity. The research underscores the necessity of integrating diverse predictive factors, innovative data sources, and advanced ML techniques to develop robust and adaptable forecasting frameworks. These findings offer valuable insights for academics and financial professionals, paving the way for more reliable and real-time predictive models that can enhance decision-making in dynamic market environments. This study contributes to advancing economic sustainability by proposing methodologies that align with the complexities and rapid evolution of modern financial markets.International Journal of Financial Studies1260This study examines the incorporation of artificial intelligence (AI) and machine learning (ML) technologies to address gaps in identifying predictive factors, integrating diverse data sources, and optimizing methodologies to develop robust and adaptable forecasting frameworks.International Journal of Financial Studies["Atoosa Rezaei", "Iheb Abdellatif", "Amjad Umar"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/b67bde391b601b64d40973d653b1440a24a9b770 59e3b672743fc912f164a2dc017e9337c9fa3e9cRecent decades have witnessed significant developments in smart robotics and other artificial intelligence technologies. Robots are no longer just machines that carry out specific commands; rather, thanks to artificial intelligence (AI) algorithms, they can interact with humans and make decisions independently. This is particularly true because the programming of these robots enables them to grow and learn from their own experiences. The enormous capabilities that robots were able to possess led them to replace humans in most places and professions, which gave rise to the term "humanization of the robot" to refer to human-like robots that can make decisions and interact socially in a way that mimics human behavior. However, the real problem with this development lies in two parts. This research aims to explore the legal implications surrounding the autonomy and accountability of AI-equipped robots, focusing on how existing laws can adapt to address issues of responsibility in human-robot interactions. First, this development presents both advantages and disadvantages. The robot that helps humans perform their tasks better can unpredictably transform at any moment into an undeterrable and unstoppable human-killing monster. The second issue is that international jurisprudence has yet to establish a legal framework that defines the legal nature of these robots and confines them to a specific set of controls and laws. It also obliges their makers, programmers, and owners to adhere to these controls. In addition to previous legislation, the theory of the “responsible human representative” must be applied, which stipulates that they legally bear civil, tort, and criminal liability for what their robots do.International Journal of Education and Information Technologies520International Journal of Education and Information Technologies["Mohamed F. Shehta", "Usama M. Ibrahem", "G. Khalifa"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/59e3b672743fc912f164a2dc017e9337c9fa3e9c 14f1d64f0a12e1569bcf687e15bc58d801e4112bSummary The integration of artificial intelligence (AI) into surgery raises significant ethical concerns, including the impact on autonomy, human authority and the patient–doctor relationship. This study underscores the need for a multidisciplinary approach to navigate these ethical dilemmas, involving stakeholders from various fields. A comprehensive literature review up to March 2024 was conducted to assess the ethical implications of AI applications in surgery. This included an examination of data privacy, informed consent, algorithmic bias, the role of advanced robotics, and the impact on surgeons’ decision-making. The study also considered the development of autonomous surgical robots and their ethical implications. The review highlights that while AI can enhance surgical precision and improve clinical decision-making, it also poses several ethical challenges. AI’s ability to support decision-making risks undermining surgeons’ autonomy and judgement, raising concerns about over-reliance on technology. Issues such as data privacy, algorithmic bias and equitable access to AI-driven tools were identified as key ethical concerns. Autonomous surgical robots, while promising, introduce complex questions about accountability and liability, particularly when unexpected outcomes occur. Effective integration of AI into surgical practices demands the development of ethical frameworks that respect both the capabilities of AI and the irreplaceable value of human judgement. Balancing technological advancement with ethical integrity is essential to safeguard patient-centred care and ensure equitable access to AI benefits in healthcare.Interdisciplinary cardiovascular and thoracic surgery330It is highlighted that while AI can enhance surgical precision and improve clinical decision-making, it also poses several ethical challenges, and the development of ethical frameworks that respect both the capabilities of AI and the irreplaceable value of human judgement is essential.Interdisciplinary Cardiovascular and Thoracic Surgery["A. Arjomandi Rad", "R. Vardanyan", "Thanos Athanasiou", "J. Maessen", "P. Sardari Nia"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/14f1d64f0a12e1569bcf687e15bc58d801e4112b e8187a865b4a7a4db98fe737c5b38e6aaba05881This paper provides a comprehensive investigation into the evolution of artificial intelligence (AI). It focuses on the enduring scholarly debate between symbolism and connectionism, with a particular emphasis on the Turing model and the neural network model. The study situates Large Language Models (LLMs) within this theoretical framework, emphasizing their connectionist foundations while critically examining their historical interactions with symbolic approaches. Key issues addressed include the academic controversies surrounding symbolic and connectionist methodologies, the distinctive attributes of each paradigm, and the future development trajectory of LLMs—specifically exploring whether their advancement should prioritize algorithmic innovation or data-driven scalability. The primary contribution of this paper lies in its comparative analysis of the Turing model and the neural network model, offering a nuanced perspective on the respective strengths and limitations of each approach. By elucidating the research landscape, this comparative framework seeks to foster the convergence of these paradigms, thereby advancing the development of LLMs. The findings suggest that integrating symbolic and connectionist paradigms holds significant promise for enhancing LLM capabilities, with profound implications for both academic research and technological innovation. This paper contributes to a deeper understanding of AI, providing insights that may expedite the development of more resilient and adaptable AI systems, ultimately benefiting human welfare and fostering societal advancement.Highlights in Science Engineering and Technology250A comparative analysis of the Turing model and the neural network model is offered, offering a nuanced perspective on the respective strengths and limitations of each approach, to foster the convergence of these paradigms, thereby advancing the development of LLMs.Highlights in Science, Engineering and Technology["Shijie Ye"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/e8187a865b4a7a4db98fe737c5b38e6aaba05881 a4d97b24c7f71c22f6631ef6a65761b79026f814Artificial Intelligence (AI) is increasingly utilized in healthcare as wearable technology, virtual assistants, or to aid decision-making. This study evaluates the feasibility, effectiveness, and limitations of AI-based ChatGPT-4.0 in developing 8-week exercise programs for cases with lumbar disc herniation (LDH), chronic migraine (CM), and urge urological incontinence (UUI). ChatGPT-4.0 was questioned about exercise frequency, intensity, type, duration, targeted muscles, repetitions, theraband strengths, perceived difficulty, and aerobic exercise recommendations. The answers given were evaluated by experts. Expert evaluations determined that ChatGPT-4.0 successfully created literature-based programs for LDH, CM, and UUI, including cervical, lumbar stabilization, and pelvic floor exercises. However, issues arose: theraband resistances and plank-like challenging exercises for LDH were introduced too early, potentially causing rapid progression. In CM, isometric exercises risk triggering attacks, and progression rates were accelerated in all cases. These findings highlight ChatGPT-4.0’s inability to fully adapt programs to patient medical conditions, emphasizing the critical role of physical therapists in designing individualized exercise programs.Turkish Journal of Kinesiology270Findings highlight ChatGPT-4.0’s inability to fully adapt programs to patient medical conditions, emphasizing the critical role of physical therapists in designing individualized exercise programs.Turkish Journal of Kinesiology["D. Onan", "Halime Ar\u0131kan", "\u0130rem Can", "\u015eahan G\u00fcven", "Levent I\u015f\u0131kay", "Aynur Ozge"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/a4d97b24c7f71c22f6631ef6a65761b79026f814 088fddeb66e0cd0665421b2be33a7e20290a624bArtificial Intelligence (AI) has the potential to revolutionize the field of medicine. This paper explores the various applications of AI in healthcare, including medical imaging, drug discovery, personalized medicine, predictive analytics, and robotic surgery. We discuss the advantages of AI, such as improved accuracy, efficiency, and accessibility. However, we also acknowledge the challenges, including data privacy, algorithm bias, and ethical considerations. Despite these challenges, the future of AI in medicine is promising, with potential for further advancements in diagnosis, treatment, and patient care.International Research Journal of Computer Science00The advantages of AI, such as improved accuracy, efficiency, and accessibility, are discussed, however, the challenges are acknowledged, including data privacy, algorithm bias, and ethical considerations.International Research Journal of Computer Science["Bhavishya K.U", "Akshatha Ch"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/088fddeb66e0cd0665421b2be33a7e20290a624b 6a63aaa465dc687f99d20e1c5ee9a50de8e95903Higher education appears to be undergoing the most significant transformations due to the integration of artificial intelligence into teaching, learning, feedback provision, assessment, writing, and the growing role of AI in student research. Several aspects of AI in education have already been studied, such as institutional adoption policies and guidelines (Jin et al., 2025), Intelligent Tutoring Systems, and Automated Assessment and Feedback (Ramadhan et al., 2024). On the other hand, certain topics, such as deepfakes and their consequences for learning, remain underexplored. Deepfakes refer to the use of AI to create media—such as photos, audio, and video content—that appears authentic but is, in reality, artificially generated.Journal of Baltic Science Education50Journal of Baltic Science Education["Branko An\u0111i\u0107"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/6a63aaa465dc687f99d20e1c5ee9a50de8e95903 f65c1b36354d6d41b605b8fe3e1a0f4ee7ba8d03This article explores the transformative impact of Artificial Intelligence (AI) on DevOps practices, examining how AI-driven innovations are revolutionizing software development and operations. It delves into key areas where AI is making significant contributions, including AI-assisted development, predictive pipeline management, and enhanced security measures. The article discusses how AI-powered tools are augmenting human capabilities in coding, automating workflow management, and providing real-time threat detection and remediation. Through case studies in the financial services and e-commerce sectors, the article illustrates the real-world applications and benefits of AI integration in DevOps. It also addresses the challenges and limitations of AI adoption, such as potential biases and the need for continuous learning. The discussion concludes with an outlook on prospects, highlighting the evolving landscape of human-AI collaboration in DevOps and the potential for AI to take on more complex roles in software development processes. Overall, this comprehensive article review underscores the pivotal role of AI in driving efficiency, reliability, and innovation in modern software delivery practices, while emphasizing the continued importance of human expertise in guiding these technological advancements.International Journal of Scientific Research in Computer Science Engineering and Information Technology10How AI-powered tools are augmenting human capabilities in coding, automating workflow management, and providing real-time threat detection and remediation is discussed, highlighting the evolving landscape of human-AI collaboration in DevOps and the potential for AI to take on more complex roles in software development processes.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Kowshik Sakinala"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/f65c1b36354d6d41b605b8fe3e1a0f4ee7ba8d03 f70f3ab0973768ec39d4c648d6b96b5fde70a9a4Alzheimer's disease (AD) is the most common type of dementia and represents a significant global health problem due to its profound impact on patients' quality of life and the heavy burden it places on health care. Alzheimer's is characterized by a progressive decline in cognitive function and memory, ultimately disrupting daily activities and leading to dependence on long-term care. This systematic literature review aims to explore the role of AI in diagnosing and managing Alzheimer’s disease. The method used in this study refers to the PICO framework to highlight various studies on the role of AI for Alzheimer's disease. Recent breakthroughs in the field of artificial intelligence (AI), particularly machine learning (ML) and deep learning, offer promising innovative approaches to improve diagnosis, monitoring, and understanding of Alzheimer's disease.JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)150Recent breakthroughs in the field of artificial intelligence (AI), particularly machine learning (ML) and deep learning, offer promising innovative approaches to improve diagnosis, monitoring, and understanding of Alzheimer's disease.JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)["Ester Rumaseb", "S. Sulistiyani", "Lalu Guntur Payasan"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/f70f3ab0973768ec39d4c648d6b96b5fde70a9a4 dcccdd6010229d15d1475c40fb74bf9b369e86c9Colorectal cancer (CRC) is a prevalent malignant tumor in the digestive system. As reported in the 2020 global cancer statistics, CRC accounted for more than 1.9 million new cases and 935,000 deaths, making it the third most common cancer worldwide in terms of incidence and the second leading cause of cancer-related deaths globally. This poses a significant threat to global public health. Early screening methods, such as fecal occult blood tests, colonoscopies, and imaging techniques, are crucial for detecting early lesions and enabling timely intervention before cancer becomes invasive. Early detection greatly enhances treatment possibilities, such as surgery, radiation therapy, and chemotherapy, with surgery being the main approach for treating early-stage CRC. In this context, artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools. AI, utilizing machine learning (ML) and deep learning (DL) algorithms, improves early detection, diagnosis, and treatment by processing large volumes of medical data, uncovering hidden patterns, and forecasting disease development. DL, a more advanced form of ML, simulates the brain’s processing power, enhancing the accuracy of tumor detection, differentiation, and prognosis predictions. These innovations offer the potential to revolutionize cancer care by boosting diagnostic accuracy, refining treatment approaches, and ultimately enhancing patient outcomes.Frontiers in Oncology430Artificial intelligence (AI) has shown immense potential in revolutionizing CRC management, serving as one of the most effective screening tools and improving early detection, diagnosis, and treatment.Frontiers in Oncology["Mengying Zhu", "Zhenzhu Zhai", "Yue Wang", "Fang Chen", "Ruibin Liu", "Xiaoquan Yang", "Guohua Zhao"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/dcccdd6010229d15d1475c40fb74bf9b369e86c9 16c21f4130f8ed46502a9b6e27ce7a2b88a94f62Artificial intelligence (AI) has brought about transformative opportunities and has attracted the attention of many international organisations to the intent that UNESCO has held two international conferences on it. including (i) International Conference on Artificial Intelligence and Education in 2019 in Beijing, focusing on leveraging AI to advance education and sustainable development. This conference resulted in the Beijing Consensus on Artificial Intelligence and Education, outlining strategies for AI integration in educational policies and systems (ii) Forum on AI and Education 2022, emphasising the use of AI to empower teachers and transform teaching practices globally. Meanwhile, there was a regional forum led by UNESCO which examined AI in the context of ethics and societal impact, contributing to frameworks like the Recommendation on the Ethics of Artificial Intelligence in 2021. This paper focused on ethical challenges in academic writing and examines the growing significance of AI-powered tools for improving academic writing with an emphasis on how these innovations expedite research procedures raise writing standards and increase output. Artificial intelligence (AI) tools that save time and effort during drafting and editing include content generators citation managers and grammar checkers. These tools are very helpful for researchers and writers. The preservation of human creativity authorship plagiarism and intellectual integrity are some of the issues brought up by this growing reliance on AI. A number of ethical issues are looked at including who owns content created by AI the possibility of losing one’s ability to think critically and the possibility of idea misattribution. Though AI has the potential to completely transform academic writing the paper contends that in order to ensure that these technologies are used responsibly and enhance rather than replace human intellect and creativity a careful balance must be struck. There are suggestions given for preserving academic integrity and encouraging moral application of AI in research projects.Journal of Artificial Intelligence110Though AI has the potential to completely transform academic writing the paper contends that in order to ensure that these technologies are used responsibly and enhance rather than replace human intellect and creativity a careful balance must be struck.ShodhAI: Journal of Artificial Intelligence["Olakunle Titus Ajiye", "A. Omokhabi"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/16c21f4130f8ed46502a9b6e27ce7a2b88a94f62 3318837aaee44f7068d04716075b2830a7fafd08The People’s Republic of China is the global leader in terms of community code of digital technologies and artificial intelligence. The article describes the current digital regulation of legal proceedings in China. The analysis involved The Rules for Online Proceedings of People’s Courts (2021) and The Rules for the Online Operation of People’s Courts (2022) issued by the Supreme People’s Court of the People’s Republic of China. These documents strengthen the principles of justice, efficiency, security, and equality for all citizens who use digital technologies as part of court proceedings. These regulatory acts protect documents published on smart court platforms: for the first time in world practice, these documents acquire gratum preasumptione if created with the help of blockchain technologies. Digital technologies and artificial intelligence serve as effective tools of supervision that control the professional activities of judges: in China, such supervision is believed to increase public confidence in the judiciary. The consolidation of ethical principles for the use of digital innovations is a key priority of the judicial system in the People’s Republic of China.Bulletin of Kemerovo State University. Series: Humanities and Social Sciences110The article describes the current digital regulation of legal proceedings in China and reveals that for the first time in world practice, documents published on smart court platforms acquire gratum preasumptione if created with the help of blockchain technologies.Bulletin of Kemerovo State University. Series: Humanities and Social Sciences["Aleksandr Dan'shin"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/3318837aaee44f7068d04716075b2830a7fafd08 95cdbe3acca3228a03af7f518fa0f043fae774baObjectives: This study aimed to implement an artificial intelligence-assisted psychiatric triage program, assessing its impact on efficiency and resource optimization. Methods: This quality improvement initiative recruited patients on the waitlist for psychiatric evaluation at an outpatient hospital. Participants (n=101) completed a digital triage module that used natural language processing and machine learning to recommend a care intensity level and a disorder-specific digital psychotherapy program. A psychiatrist also assessed the same information and the decisions for care intensity and psychotherapy programs were compared with the artificial intelligence recommendations. Results: The overall wait time to receive care decreased by 71.43% due to this initiative. Additionally, participants received psychological care within three weeks after completing the triage module. In 71.29% of the cases, the artificial intelligence-assisted triage program and the psychiatrist suggested the same treatment intensity and psychotherapy program. Additionally, 63.29% of participants allocated to lower-intensity treatment plans by the AI-assisted triage program did not require psychiatric consultation later. Conclusions: Using artificial intelligence to expedite psychiatric triaging is a promising solution to address long wait times for mental health care. With future accuracy refinements, this could be a valuable tool to implement in hospital settings to assist care teams and improve mental health care. This could result in increased care capacity and improved workflow and decision-making.medRxiv450Using artificial intelligence to expedite psychiatric triaging is a promising solution to address long wait times for mental health care.["Callum Stephenson MSc", "Jazmin Eadie BEd", "Christina Holmes Bah", "BA KimiaAsadpour", "Gilmar Gutierrez", "Anchan Kumar", "Jasleen Jagayat MSc", "C. Patel", "Ma", "Saad Sajid Mph", "Oleksandr Knyahnytskyi BScH", "Megan Yang", "T. Reshetukha", "Christina Moi", "Tricia Barrett MSc", "A. Shirazi", "V. Verter", "Claudio Soares", "M. Omrani", "N. Alavi"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/95cdbe3acca3228a03af7f518fa0f043fae774ba b6e966fb18804a34253f62add9572629ee83bdbdThe article is devoted to the study of the role of artificial intelligence (AI) in the legal sphere, in particular, its application in the administrative process. The author analyzes various scientific approaches to defining the concept and functions of AI, in particular, as a computer program for data analysis, an intelligent system that surpasses human capabilities, and a cybernetic approach that includes an algorithmic model of cognitive functions. The article examines the legal status of AI in Ukraine, the peculiarities of its regulation under national legislation, and the problems associated with legal adaptation to international standards. The article also discusses the prospects of using AI to automate administrative processes, in particular in the preparation of administrative acts, improvement of the motivational part and checking documents for errors. The authors emphasize the need to improve legislation and technical support to minimize the risks associated with the use of AI. According to the study, the introduction of AI into administrative procedures has significant potential to increase efficiency, transparency and reduce administrative burdens on public authorities. Key words: administrative process, artificial intelligence, administrative act, administrative proceedings.Slovo of the National School of Judges of Ukraine10The introduction of AI into administrative procedures has significant potential to increase efficiency, transparency and reduce administrative burdens on public authorities, and the need to improve legislation and technical support to minimize the risks associated with the use of AI.Slovo of the National School of Judges of Ukraine["Larisa Kovalenko", "Mykhailo Soloninka"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/b6e966fb18804a34253f62add9572629ee83bdbd 070941fdbd6a4b0251768fe0957b199201844d1c Success of facial feminization surgery (FFS) has been measured by the ability to pass as female when assessed both by human and artificially intelligent evaluators. Previous studies have also attempted to elucidate the contribution of each facial third to overall femininity and youthfulness. In this study, artificial intelligence facial recognition software was used to objectively quantify the effects of FFS on perceived age and gender by facial third. Frontal preoperative and postoperative images of 31 transgender women undergoing FFS were digitally combined to create photos with postoperative changes in the upper, middle, or lower third only. Amazon's artificial intelligence, Rekognition, was used to determine gender typing, femininity, and age scores, which were analyzed by a facial third. Mean preoperative femininity score was 47.7%, which improved postoperatively to 56.4%, 48.1%, and 62.1% for the upper, middle, and lower thirds of the face, respectively (P = 0.019, P = 0.91, P = 0.0025). The younger cohorts (20–30 y, 31–40 y) demonstrated a significant improvement in femininity scores for the lower third of the face (P = 0.036, P = 0.025), whereas the oldest cohort’s (41+ y) significant improvement in femininity was in the upper third of the face (P = 0.026). Artificial intelligence–perceived postoperative age was significantly younger than the patient’s true age, with mean relative reductions of 2.45, 1.94, and 2.26 years for the upper, middle, and lower thirds of the face (P = 0.0096, P = 0.027, P = 0.016). The younger cohort experienced the greatest increase in femininity in the lower third whereas older cohort had the most improvement in the upper third. The upper, middle and lower third all contributed to a significantly younger perceived age. The Journal of craniofacial surgery (Print)180The younger cohort experienced the greatest increase in femininity in the lower third whereas older cohort had the most improvement in the upper third, and the upper, middle and lower third all contributed to a significantly younger perceived age.Journal of Craniofacial Surgery["Melanie Vassallo", "Jacqueline M Ihnat", "Paula Flores-P\u00e9rez", "Albert L. Rancu", "O. Allam", "Michael Alperovich"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/070941fdbd6a4b0251768fe0957b199201844d1c dd77a2268e0567a575c4b86c9ad1e97d9f37ed7dThis study aims to analyze the impact of Artificial Intelligence (AI)-based information technology on tourist satisfaction and loyalty through a systematic literature review method. Following the PRISMA guidelines, researchers conducted a literature search in the ScienceDirect and SpringerLink databases using keywords such as "artificial intelligence," "tourist satisfaction," and "tourist loyalty." Of the 40 articles identified, 12 articles met the inclusion criteria and were analyzed thematically. The results showed that AI significantly improves tourist satisfaction through service personalization, operational efficiency, and better interactions. Applications such as recommendation systems, chatbots, and predictive analytics have proven effective in improving the tourist experience. However, the implementation of AI also faces challenges such as data privacy, algorithmic bias, and low technology adoption. The implications of this study are the importance of optimizing the use of AI by ensuring data security, reducing bias, and improving education for service providers and tourists. This study provides recommendations for further research, including exploring the impact of AI on specific tourist segments and investigating the long-term impact on loyalty.MASALIQ00The results showed that AI significantly improves tourist satisfaction through service personalization, operational efficiency, and better interactions, and the importance of optimizing the use of AI by ensuring data security, reducing bias, and improving education for service providers and tourists.MASALIQ["Slamet Kurniawan Fahrurozi", "Dimas Pamilih Epin Andrian"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/dd77a2268e0567a575c4b86c9ad1e97d9f37ed7d 0aba8a538e6c0bfa5a8838936ec117b63620b2b1This paper examines student teachers’ understanding of Artificial Intelligence (AI) chatbots and their application in teaching and learning practices. A qualitative research methodology, specifically Interpretative Phenomenological Analysis (IPA), was employed to explore student teachers’ perceptions of AI chatbots. A purposive sampling strategy was used to select eleven (11) student teachers in their fourth year of study for a B.Ed. degree at the University of Technology in South Africa. To interpret and analyse student teachers’ perceptions of the use of AI chatbots in their teaching and learning practices, data analysis was conducted using Systematic Text Condensation (STC) in a five-step process. The study explored themes aligned with the knowledge dimensions of the Technological Pedagogical Content Knowledge (TPACK) framework. Findings revealed a generally limited understanding among student teachers regarding artificial intelligence, particularly chatbots. Although some learners in schools use chatbots, student teachers still lack the knowledge to utilise these technological systems for teaching practices. This includes, among other things, using AI chatbots to transform classrooms into personalised learning environments for classroom management and student analytics. In a nutshell, an AI chatbot for classroom purposes can serve as a diligent administrative assistant, an Indigenous planner, and enhance pedagogical practices. These findings underscore the need for further research and training to improve student teachers' knowledge and utilisation of AI chatbots in the classroom.Interdisciplinary Journal of Education Research310An AI chatbot for classroom purposes can serve as a diligent administrative assistant, an Indigenous planner, and enhance pedagogical practices and underscore the need for further research and training to improve student teachers' knowledge and utilisation of AI chatbots in the classroom.Interdisciplinary Journal of Education Research["P. Mollo"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/0aba8a538e6c0bfa5a8838936ec117b63620b2b1 223cde82d0757c3b45ebd396c2f10175316ca87cArtificial Intelligence (AI) is increasingly shaping the music industry, influencing composition, production, performance, and education. While AI offers numerous advantages, its integration raises significant ethical concerns, including issues of transparency, fairness, accountability, and privacy. This study explores the ethical perceptions of AI among undergraduate music students in Shandong, China, a region known for its rich cultural heritage and growing AI adoption. Using the Attitudes Toward the Ethics of Artificial Intelligence (AT-EAI) questionnaire, data was collected from 350 students to assess their views on key ethical dimensions of AI in music. Findings reveal a strong preference for AI transparency, accountability, and data privacy, with notable gender differences in perceptions of AI’s role in public services and decision-making processes. Female students demonstrated greater concern for transparency and ethical safeguards, whereas male students exhibited more openness to AI’s broader societal applications. These insights highlight the importance of integrating AI ethics education into music curricula to foster informed and responsible engagement with AI technologies. The study contributes to the ongoing discourse on AI ethics in creative fields and offers recommendations for ensuring ethical AI integration in music.International Journal of Advanced Multidisciplinary Research and Studies10Female students demonstrated greater concern for transparency and ethical safeguards, whereas male students exhibited more openness to AI’s broader societal applications, highlighting the importance of integrating AI ethics education into music curricula to foster informed and responsible engagement with AI technologies.International Journal of Advanced Multidisciplinary Research and Studies["Liang Qing", "Dr. Chng Lay Kee"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/223cde82d0757c3b45ebd396c2f10175316ca87c 5f05ef62eaa66c800051f5b90315f0bd7c5705efIn the ever-changing world where information technology takes center stage, using Artificial Intelligence (AI) and leadership transformation is paramount for achievements. This research, therefore, assesses the impact of transformational leadership on AI implementation in organizations in Saudi Arabia. A quantitative research approach was adopted in this study, and a survey method was used with 71 participants practicing leadership and holding digital positions in different fields. The research establishes a positive correlation between transformational leadership and the adoption of AI, which stresses the leadership’s role in promoting change. Furthermore, this research examines the role of technological advancement in moderating the above relationship, whereby technological advancement warrants changes in leadership approaches. This study adds to the available literature by providing insights into the relationship between leadership and AI within Saudi Arabian culture and economy. The results significantly contribute to practitioners and policymakers seeking to increase organizational leadership and AI effectiveness.International journal of innovative research and scientific studies260The research establishes a positive correlation between transformational leadership and the adoption of AI, which stresses the leadership’s role in promoting change and examines the role of technological advancement in moderating the above relationship.International Journal of Innovative Research and Scientific Studies["B. Alharbi"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/5f05ef62eaa66c800051f5b90315f0bd7c5705ef 43566845ceb757f9f43be4f26de39f18b2226be2Winter 2025 Int’l Conference Proceedings TechSocEdu25, CBEW-25 and PMESH-2500Winter 2025 Int’l Conference Proceedings TechSocEdu25, CBEW-25 and PMESH-25[]2025-02-25T00:00:00https://www.semanticscholar.org/paper/43566845ceb757f9f43be4f26de39f18b2226be2 6b6f5e1546d8cd1be0dceb6e8235e0fa871bde4eMCN, The American Journal of Maternal Child Nursing00MCN. The American journal of maternal child nursing[]2025-02-25T00:00:00https://www.semanticscholar.org/paper/6b6f5e1546d8cd1be0dceb6e8235e0fa871bde4e a457cc1304c3a9bfda1629732a0dd776acc2df35Cureus380Cureus["Ghulam Husain Abbas", "Edmon Khouri", "Sjaak Pouwels"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/a457cc1304c3a9bfda1629732a0dd776acc2df35 5499d670a4124bf0a292778220714a91265028f3GeroScience470This research highlights the importance of specific factors in language-based interactions, especially in varied care situations, and advocates for caregiver training that is grounded in real-life practice, focusing on context adaptation, active listening, and dialogue with residents.GeroScience["Marie Lefelle", "Mouny Samy Modeliar"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/5499d670a4124bf0a292778220714a91265028f3 78ecedceccbaf9a22ce29e3432a9cda13060c5e3Perspectives in Clinical Research80Perspectives in Clinical Research["Himel Mondal", "Ayesha Juhi", "Mayank Sharma", "Shreya Sharma", "P. Chaudhary", "Shaikat Mondal"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/78ecedceccbaf9a22ce29e3432a9cda13060c5e3 d585c79d977e473ba8a4796afa96ea34345b07b2Ultrasound in Obstetrics and Gynecology280Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology["E. Quarello", "E. Corno"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/d585c79d977e473ba8a4796afa96ea34345b07b2 b3084910464c67853740c54a403412fd285de344International Journal of Computer Applications00International Journal of Computer Applications["Elena Filipova"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/b3084910464c67853740c54a403412fd285de344 47e17c37b75227c76641bc3ad4f8177ffc20dbc5When students submit written assignments for assessment, they are generally trusted to have completed these honestly, and to have benefitted from the opportunity to learn. Academic integrity breaches are sometimes detected during the assessment process. Some common examples of integrity breaches during students’ academic writing include contract cheating, the unauthorised use of GenAI technology for completing assignments, and using AI tools to disguise existing work so that it appears to be original. None of these are new phenomena. Processes and procedures should be in place for managing suspected academic misconduct cases detected during the assessment process. But what happens when academic misconduct is detected retrospectively, sometimes after a student has moved degree programmes or graduated? This position paper sets out the case for universities and other academic institutions having procedures in place to deal with historic academic misconduct. It provides examples of how institutions can become aware of misconduct, including through whistleblowing and through development of more effective detection software. The authors bring together legal and educational expertise to suggest considerations that individual institutions should make towards future policy development. The discussion considers that students must be supported and prepared for success, but that institutions cannot ignore the reputational risks associated with cases of historic misconduct.Journal of Academic Writing350The case for universities and other academic institutions having procedures in place to deal with historic academic misconduct is set out and examples of how institutions can become aware of misconduct are provided, including through whistleblowing and through development of more effective detection software.Journal of Academic Writing["Thomas Lancaster", "Irene Glendinning", "Sandie Dann", "Robert G. Crockett", "M. Draper"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/47e17c37b75227c76641bc3ad4f8177ffc20dbc5 70174ce29e5c2e3ee8688147813866e2e17224b3Proceedings of Indian National Science Academy1210Proceedings of the Indian National Science Academy["Gagandeep Kaur"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/70174ce29e5c2e3ee8688147813866e2e17224b3 61087dfd546a75734641c3be272a83c3f3a96fc1The convergence of Artificial Intelligence (AI) with Business Intelligence (BI) and Financial Data Visualization has fundamentally transformed how financial institutions approach data analysis and decision-making. This comprehensive article examines the evolution and impact of AI-driven technologies in financial services, focusing on advanced analytics, visualization techniques, and automated decision support systems. It explores how machine learning, deep learning, and natural language processing capabilities have enhanced financial institutions' ability to process complex data sets, detect patterns, and generate actionable insights. The article investigates the implementation frameworks necessary for successful AI integration, including technical architecture requirements and governance best practices. It further analyzes emerging trends and challenges in AI-driven financial operations, particularly in areas of risk assessment, regulatory compliance, and customer service. By examining both current applications and future directions, this article provides valuable insights into how AI-powered solutions are reshaping the financial sector's approach to data visualization and analysis while highlighting the importance of balanced implementation strategies that consider both technological capabilities and practical business needs.International Journal of Scientific Research in Computer Science Engineering and Information Technology20The article investigates the implementation frameworks necessary for successful AI integration, including technical architecture requirements and governance best practices, and analyzes emerging trends and challenges in AI-driven financial operations, particularly in areas of risk assessment, regulatory compliance, and customer service.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Mallikarjun Reddy Ramasani"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/61087dfd546a75734641c3be272a83c3f3a96fc1 9e7d64a45c301b9c9b7e87aacaf117e31dce46caAI, Brain and Child400This article focuses specifically on the evolution of AI in early childhood education (ECE), serving children from birth to age 8, and reimagine a transformative educational landscape enriched by student-centered, innovative teaching practices that catalyze learning in an AI-child interactive environment.AI, Brain and Child["Jennifer J. Chen"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/9e7d64a45c301b9c9b7e87aacaf117e31dce46ca e71b249aa724b9a0f1df86a9a98a975522cf46f0Esophageal and skin cancers are among the most challenging malignancies, with early detection critical for improving survival rates and reducing healthcare costs. This paper explores the role of artificial intelligence (AI) in the early detection of these cancers in the United States, synthesizing methodologies from two key studies. For esophageal cancer, advanced machine learning techniques like Random Forest and XGBoost are employed to analyze multimodal data, including medical imaging, electronic health records (EHRs), and genomic profiles, achieving 92% accuracy in detecting early-stage cancer. For skin cancer, convolutional neural networks (CNNs) are used to analyze dermoscopic images, achieving an 87% accuracy in identifying malignant lesions. The study highlights the design and implementation of AI-driven models, covering data preprocessing, feature engineering, and evaluation metrics while addressing challenges such as class imbalance and overfitting. The results demonstrate AI's potential to enhance diagnostic accuracy, scalability, and accessibility, particularly in underserved areas. However, data privacy, algorithm interpretability, and regulatory compliance must be addressed to integrate AI into healthcare systems fully. This paper asserts that AI-driven diagnostics hold immense promise for revolutionizing cancer detection and calls for further research to overcome existing limitations while ensuring equitable access to these transformative technologies, ultimately improving patient outcomes and reshaping the landscape of cancer care.Scholars Journal of Applied Medical Sciences00It is asserted that AI-driven diagnostics hold immense promise for revolutionizing cancer detection and calls for further research to overcome existing limitations while ensuring equitable access to these transformative technologies, ultimately improving patient outcomes and reshaping the landscape of cancer care.Scholars Journal of Applied Medical Sciences["Pramod Chaudhary"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/e71b249aa724b9a0f1df86a9a98a975522cf46f0 02f87c7ab1be51d9b1867bf94082a2024113601a‘School of the future’ scenarios remain a popular means of animating policy, industry and public debates around issues relating to technological, economic, societal and environmental change. To date, these scenarios rarely involve the perspectives of school students. Purpose of the research: This study explores how scenarios can be used to engage school children in futures thinking, particularly regarding the uncertainties surrounding artificial intelligence and its environmental and energy implications. The research aims to develop participatory tools to help diversify future narratives about schools and foster young people’s ‘futures literacy’ and critical thinking about the future. Major findings: Analysis of 70 ‘future schools’ scenarios from 18 existing industry reports revealed limited approaches to climate change, energy and environmental implications of AI technology. These findings informed the design of scenario cards for engaging young people in imagining their own future schools, challenging dominant policy and industry expectations. Conclusions: The study contributes to knowledge in education and energy by combining scenario development from both sectors. It progresses all stakeholders towards desirable and resilient ‘AI energy futures’ by involving children and young people in the development of futures scenarios, addressing a gap in current scenario-building practices which have typically excluded student perspectives.Policy Futures in Education170Policy Futures in Education["F. Kaviani", "Neil Selwyn", "Y. Strengers", "K. Dahlgren", "Bronwyn J. Cumbo", "Markus Wagner"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/02f87c7ab1be51d9b1867bf94082a2024113601a 75ee65a7dd4b475e6538bfd7091e8a54cac6a3b1 The study aimed to address the challenges of early diagnosis of congenital auricular deformities in children by leveraging artificial intelligence (AI), particularly convolutional neural networks (CNNs). Despite AI’s potential for improving diagnostic precision, its clinical use has been constrained by the limitations of small, nonrepresentative data sets. This research sought to enhance AI model generalization through the creation of a more diverse, multicenter data set. To achieve this goal, the study merged the BabyEar4k public data set with additional images from various centers to create a more comprehensive and varied data set. This data set was used to train and fine-tune CNN models. Performance evaluation was based on established metrics, such as accuracy, sensitivity, and specificity, to assess the models’ ability to identify auricular deformities. The fine-tuned CNN models demonstrated strong performance, achieving high scores across various metrics. The integration of data from multiple sources improved the model’s generalizability, highlighting the significance of a multicenter approach for AI development in clinical settings. This study emphasizes the importance of diverse, large-scale data sets in enhancing the performance of AI models for clinical applications. The success of the CNN models in diagnosing ear deformities underscores the potential of AI to revolutionize early diagnosis when backed by comprehensive data sets and standardized diagnostic criteria. The Journal of craniofacial surgery (Print)240The success of the CNN models in diagnosing ear deformities underscores the potential of AI to revolutionize early diagnosis when backed by comprehensive data sets and standardized diagnostic criteria.Journal of Craniofacial Surgery["Hantao Li", "Xueqing Zhou", "Xiangdong Qi"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/75ee65a7dd4b475e6538bfd7091e8a54cac6a3b1 9265ede5398f9f6b38b13895f60fabbafef6d96fThis study aimed to evaluate the utility of artificial intelligence (AI) in improving the persuasive communication skills of online Master of Business Administration (MBA) students. In particular, this study investigated the influence of personalization through AI using the Google Gemini platform on conventional and online instructional approaches. This quasi-experimental study used a pretest and posttest design to compare two groups of MBA students pursuing persuasive online communication. The experimental group (n = 32) interacted with the AI-based personalized learning materials, whereas the control group (n = 32) used standard instructor-designed online modules. During the 12-week intervention period, the experimental group was provided with customized practice activities. Conversely, the control group was offered conventional online learning material. The effectiveness of both approaches was evaluated using pretests and posttests. The results of Tukey’s Honestly Significant Difference (HSD) test provided insight into the areas where AI-based personalized learning had a statistically significant impact. These results support the conclusions derived from an analysis of variance and further validate the study’s research hypotheses. This study demonstrates the advantages of incorporating AI into language development for remote learners and offers valuable insights for integrating AI-driven technologies into distance education.International Review of Research in Open and Distance Learning290This study demonstrates the advantages of incorporating AI into language development for remote learners and offers valuable insights for integrating AI-driven technologies into distance education.The International Review of Research in Open and Distributed Learning["Selvaraj Vijayakumar", "Sajida Bhanu Panwale"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/9265ede5398f9f6b38b13895f60fabbafef6d96f 021475212b53ecfc81f49506378ee09635f43305The development of artificial intelligence (AI) game agents that use deep reinforcement learning (DRL) algorithms to process visual information for decision-making has emerged as a key research focus in both academia and industry. However, previous game agents have struggled to execute multiple commands simultaneously in a single decision, failing to accurately replicate the complex control patterns that characterize human gameplay. In this paper, we utilize the ViZDoom environment as the DRL research platform and transform the agent–environment interactions into a Partially Observable Markov Decision Process (POMDP). We introduce an advanced multi-agent deep reinforcement learning (DRL) framework, specifically a Multi-Agent Proximal Policy Optimization (MA-PPO), designed to optimize target acquisition while operating within defined ammunition and time constraints. In MA-PPO, each agent handles distinct parallel tasks with custom reward functions for performance evaluation. The agents make independent decisions while simultaneously executing multiple commands to mimic human-like gameplay behavior. Our evaluation compares MA-PPO against other DRL algorithms, showing a 30.67% performance improvement over the baseline algorithm.Mathematics320This paper introduces an advanced multi-agent deep reinforcement learning (DRL) framework, specifically a Multi-Agent Proximal Policy Optimization (MA-PPO), designed to optimize target acquisition while operating within defined ammunition and time constraints.Mathematics["Zihao Cui", "Kailian Deng", "Hongtao Zhang", "Zhongyi Zha", "Sayed Jobaer"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/021475212b53ecfc81f49506378ee09635f43305 f90c82e43f61715d1fd0b79a3eefc5219947cac4This research investigates the integration of artificial intelligence (AI) and machine learning (ML) algorithms in revolutionizing supplier relationship management and risk assessment within global supply chains. With supply chain disruptions costing businesses an average of $184 million annually, the need for intelligent solutions has become critical. The study examines the technological foundations of AI-driven supply chain transformation, including machine learning analytics, natural language processing, and federated learning systems. Through analysis of implementation cases across automotive, technology, pharmaceutical, and agricultural sectors, we explore how cognitive computing and autonomous decision-making frameworks are reshaping traditional supply chain operations. The research provides insights into implementation mechanisms focusing on predictive risk modeling, real-time monitoring systems, and supply chain orchestration. Our findings demonstrate the potential of AI technologies to enhance operational efficiency, reduce risks, and create more resilient supply chain ecosystems. The study offers an evidence based perspective on AI's role in transforming supplier relationship management while acknowledging both opportunities and implementation challenges in an increasingly volatile global business environment.International Journal of Science and Research Archive00The study examines the technological foundations of AI-driven supply chain transformation, including machine learning analytics, natural language processing, and federated learning systems, and explores how cognitive computing and autonomous decision-making frameworks are reshaping traditional supply chain operations.International Journal of Science and Research Archive["Oluwakemi Adesola", "Itunu Taiwo", "Damilola David Adeyemi", "Harold Ezenwa Nwariaku", "Adefemi Quddus Abidola"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/f90c82e43f61715d1fd0b79a3eefc5219947cac4 deb7637867eb9c6e3016ae5d05e121f257e924f1Philosophy &amp; Technology990It is argued that both AI agents and psychopaths, as ‘amoral calculators’, present the perfect candidates to revisit two long-standing debates on moral and criminal responsibility, regarding the necessity of moral emotions for ‘moral-agent-capacity responsibility’ and the necessity of ‘moral-agent-capacity responsibility’ for criminal responsibility.Philosophy &amp; Technology["Elina Nerantzi"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/deb7637867eb9c6e3016ae5d05e121f257e924f1 3750d68e13710f79b3901c83c727c491a27d0824This research will explore the intersection of artificial intelligence (AI) and understanding human emotions, a growing field known as emotional AI. The research aims to provide a comprehensive overview of how AI techniques, such as machine learning, natural language processing (NLP), and facial and voice expression analysis, can be used to analyze human emotions and improve human-machine interactions. The research reviews the theoretical foundations for understanding emotions based on several psychological and neurological models, and also discusses AI applications in the fields of mental health, education, and marketing, where these technologies can improve user experiences by recognizing their emotional responses and tailoring their interactions accordingly. However, this field faces significant challenges, such as privacy and data security, model bias, and accuracy in sentiment analysis. The study highlights the need to develop more transparent and fair systems to ensure the ethical use of this technology. The research suggests that advances in deep learning and multi-dimensional interaction can improve AI’s ability to accurately understand human emotions, opening the door to developing AI systems that are more aware and responsive to human emotions.Journal of Information Systems Engineering & Management140The research suggests that advances in deep learning and multi-dimensional interaction can improve AI’s ability to accurately understand human emotions, opening the door to developing AI systems that are more aware and responsive to human emotions.Journal of Information Systems Engineering and Management["Noor Alwan", "Malk1", "S. Adnan", "Diwan2"]2025-02-25T00:00:00https://www.semanticscholar.org/paper/3750d68e13710f79b3901c83c727c491a27d0824 16c4007e1b115e1c80533a848616897f9702a9bfThis study examines the risks of relying on artificial intelligence (AI) for learning Arabic language sciences. Many students and researchers increasingly use AI for linguistic study without recognizing its limitations. The research highlights these risks through descriptive, analytical, and applied approaches, testing the Meta application in grammar, morphology, orthography, prosody, semantics, and rhetoric. The study is structured into an introduction, a preface, and six sections. The preface defines AI, its development, characteristics, and the capabilities and shortcomings of the Meta application. Each chapter assesses AI’s performance in a specific linguistic field. Findings reveal that AI remains unable to provide fully accurate answers in Arabic studies due to the limited availability of Arabic linguistic data and the early stage of AI development. Additionally, Arabic’s inherent ambiguity contrasts with AI’s structured algorithmic approach, which relies on precision and clarity.Arts for Linguistic &amp; Literary Studies06Findings reveal that AI remains unable to provide fully accurate answers in Arabic studies due to the limited availability of Arabic linguistic data and the early stage of AI development.Arts for Linguistic &amp; Literary Studies["Abdullah Ali Hasan Al-Ghobesi"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/16c4007e1b115e1c80533a848616897f9702a9bf 9857ce2a2a90d6f13cf766fb651e5b167c2ec47bTechnology is an inevitable part of human life. One of the technologies is artificial intelligence and robots, which are very effective in human life. The desire of organizations to use artificial intelligence in human resource management, considering its advantages and to survive in the competitive environment of the digital age, has doubled the importance of identifying the requirements and conditions for using artificial intelligence. Many warnings have been raised about the potential of artificial intelligence to disrupt the structure of the human resource, especially in jobs that can be easily automated. In fact, it is widely believed that artificial intelligence will soon be able to perform administrative tasks that occupy most of the time of managers, better, faster and at lower cost. Artificial intelligence plays an important role in increasing the productivity of human resources in the processes of searching, hiring and retaining employees, but this technology must be used with consideration for privacy as well as public policies. The presence of artificial intelligence only means changing the focus of managers' responsibilities on tasks that only humans can perform. The emergence of artificial intelligence is solely aimed at expanding office automation and not to replace human labor. Research by the World Economic Forum showed that despite artificial intelligence and intelligent robots, 50 percent of some jobs will disappear in the coming years, while the rest of the jobs may not be affected. Despite concerns about job elimination if artificial intelligence and robot technologies grow, it must be acknowledged that people had similar concerns during the Industrial Revolution or even the spread of the Internet. This is while the Internet has created jobs and increased countries' incomes over time. Now, 63 percent of all top managers in the world believe that the impact of artificial intelligence technology will be much greater than the Internet and that the world will benefit from the existence of robots.Global Spectrum of Research and Humanities191The desire of organizations to use artificial intelligence in human resource management, considering its advantages and to survive in the competitive environment of the digital age, has doubled the importance of identifying the requirements and conditions for using artificial intelligence.Global Spectrum of Research and Humanities["Mohammad Ekram Yawar", "Mohammad Qurban Hakimi"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/9857ce2a2a90d6f13cf766fb651e5b167c2ec47b 3ff40e48edad536497d05a19a773910b0d6606d2The integration of artificial intelligence (AI) in social work holds the potential to address pressing global challenges and contribute to positive social impact aligned with the United Nations Sustainable Development Goals (SDGs). The AI for Social good aims to leverage AI and machine learning tools to tackle social problems across diverse domains, including transportation infrastructure, public health, and community engagement. However, the ethical and policy considerations surrounding the deployment of AI in social impact initiatives are supreme. These considerations encompass sustainability, transparency, inclusivity, and the protection of human rights and creativity. Establishing robust regulations, developing ethical frameworks, and fostering ongoing discussions are essential for shaping a future where socially responsible AI is the norm. As AI continues to evolve, it is crucial to direct the ethical and policy space to ensure that AI-driven social impact initiatives align with the principles of fairness, accountability, and societal benefit.REST Journal on Data Analytics and Artificial Intelligence90As AI continues to evolve, it is crucial to direct the ethical and policy space to ensure that AI-driven social impact initiatives align with the principles of fairness, accountability, and societal benefit.REST Journal on Data Analytics and Artificial Intelligence[]2025-02-26T00:00:00https://www.semanticscholar.org/paper/3ff40e48edad536497d05a19a773910b0d6606d2 7b1fd991c3b83e014b7296f672e9d26c389d9017The integration of artificial intelligence (AI) into medical education presents numerous opportunities for innovation and efficiency. However, it also introduces significant ethical concerns, including data privacy, bias in algorithms, informed consent, and the protection of student data. This paper explores these challenges and emphasizes the need for ethical oversight in AI-driven medical education. The absence of dedicated ethics committees for educational AI applications complicates the establishment of ethical guidelines, leading to gaps in regulation. The study highlights potential solutions, such as creating specialized ethics committees, improving transparency in AI algorithms, and training medical educators and students in ethical AI use. Addressing these ethical concerns will be essential to harnessing the benefits of AI while minimizing risks in medical education.Acta Globalis Humanitatis et Linguarum260There are potential solutions, such as creating specialized ethics committees, improving transparency in AI algorithms, and training medical educators and students in ethical AI use, to address ethical concerns in AI-driven medical education.Acta Globalis Humanitatis et Linguarum["Mohammad Ekram Yawar", "Mohammad Qurban Hakimi"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/7b1fd991c3b83e014b7296f672e9d26c389d9017 7e283ff4e9a0e57dd0b1d85c7b04a985703521b1Examining the use of artificial intelligence in improving customer experience and retention across diverse businesses. As enterprises progressively integrate AI technology, comprehending their influence on customer interactions is essential for sustaining a competitive advantage. The research examines several AI applications, such as chatbots for customer care, predictive analytics for analyzing consumer behavior, and tailored marketing techniques utilizing machine learning. This article analyzes existing literature and detailed case studies to assess how AI-driven solutions boost service efficiency, customize client experiences, and improve satisfaction. It underscores the efficacy of AI in discerning client wants and preferences, allowing firms to customize their services accordingly. The study also examines possible hurdles, including data privacy issues and the necessity for human oversight; therefore, it provides a balanced viewpoint on AI applications. Principal findings demonstrate that enterprises utilizing AI enhance customer interaction and see substantial growth in retention rates. By anticipating client requirements and delivering prompt assistance, AI cultivates loyalty and promotes repeat patronage. The study ends with useful tips for businesses that want to use AI well, stressing how important it is to include these technologies in customer experience plans for long-term success in a world that is becoming more and more digital.International Journal For Multidisciplinary Research90Principal findings demonstrate that enterprises utilizing AI enhance customer interaction and see substantial growth in retention rates, and underscores the efficacy of AI in discerning client wants and preferences, allowing firms to customize their services accordingly.International Journal For Multidisciplinary Research["David Vasanth Kumar A", "Dr ELIZABETH RENJU KOSHY"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/7e283ff4e9a0e57dd0b1d85c7b04a985703521b1 497637f5452e330a069ecd05f3b59e0c82254344What kind of people to train, how to train them and for whom to train are the fundamental issues and eternal themes of education. The rapid advancement of artificial intelligence (AI) technology in recent years, as exemplified by deep learning and knowledge graphs, has opened up new avenues for innovation in education and modifications to teaching strategies. One of the most crucial subjects in the world of education nowadays is how to employ intelligent technology to encourage students to study intelligently. In the age of AI, smart learning is the fundamental meaning of education. The construction of smart learning models is the key and foundation for implementing smart learning, and it is also a bottleneck issue in research in this field. For the problem that it is difficult to characterize the intrinsic mechanism of intelligent learning, we propose the E-GPPE-C model of intelligent learning by utilizing AI technology, which can explain the operation mechanism, elements and characteristics of intelligent learning. Learning environment, learning route, learning assessment, learner image, educational knowledge map, as well as learning community make up the model. The base layer, service layer, support layer, application layer and key layer are all included in the model at the same time. We suggested the implementation approach of E-GPPE-C model from four perspectives: learning path suggestion, learner picture construction, learning community construction, and educational knowledge graph construction. These methods are based on algorithms linked to AI. The findings of this study lay the groundwork for the development of smart learning and the use of AI in the field of education, and provide a reference for subsequent research on smart learning models.International Journal of High Speed Electronics and Systems00The E-GPPE-C model of intelligent learning is proposed by utilizing AI technology, which can explain the operation mechanism, elements and characteristics of intelligent learning, and the implementation approach of E-GPPE-C model is suggested from four perspectives.International Journal of High Speed Electronics and Systems["Weiqing Diao", "Yijin Wang", "Yi An", "Yiheng Zhang"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/497637f5452e330a069ecd05f3b59e0c82254344 699a75c7869003f44d9cfa0cabac4588de52ae81In a climate of media interest in artificial intelligence (AI), growth of services and applications associated with this technology and intense debates on its use, the educational offer on this topic is in full bloom. In this sense, focusing attention on MOOCs (massive open online courses), an exponential growth of the educational offer on AI has been observed in recent years. This research is part of a broader investigation into the technical and pedagogical dimensions of AI MOOCs. The main objective of this study is to understand the predominant profile of AI MOOCs. Using a statistically representative sample of 292 MOOCs and based on a category system on AI content, a descriptive and factorial statistical analysis of the data is carried out. The analysis concludes that the three predominant MOOC profiles are: focused on AI coding, focused on AI learning and focused on AI educational value.International Journal of Educational Research and Innovation110The analysis concludes that the three predominant MOOC profiles are: focused on AI coding, focused on AI learning and focused on AI educational value.IJERI: International Journal of Educational Research and Innovation["Emilio Jos\u00e9 Delgado-Algarra", "Jos\u00e9 Antonio Vela-Romero", "Irene M. Palomero Ilardia", "M\u00aa Montserrat Pastor Bl\u00e1zquez"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/699a75c7869003f44d9cfa0cabac4588de52ae81 c09e8ace5d99e1541b28aeff095c5d0ea78418a2Background: Artificial Intelligence (AI) is transforming midwifery by enhancing clinical decision-making, improving maternal and neonatal outcomes, and optimizing healthcare efficiency. AI-driven technologies such as predictive analytics and decision-support systems help midwives identify high-risk pregnancies, monitor fetal health, and automate administrative tasks. Despite its potential, AI integration presents challenges related to ethical considerations, data privacy, and the risk of reduced human interaction in maternity care. The need for AI education in midwifery training is essential for responsible and effective implementation. Objective: This review examines the role of AI in midwifery by synthesizing existing literature on its applications, benefits, and challenges. It also explores the necessity of integrating AI education into midwifery curricula to prepare future professionals for evolving technological advancements. Methods: A systematic literature review was conducted following PRISMA guidelines. Databases including PubMed, CINAHL, Google Scholar, and Scopus were searched using keywords such as "Artificial Intelligence," "Midwifery," "Predictive Analytics," and "Maternal Healthcare." Studies published in English within the last five years were included. Peer-reviewed articles, systematic reviews, and clinical trials discussing AI applications, ethical concerns, and midwifery education were analyzed. A total of 225 articles were initially identified, with 19 studies meeting the final inclusion criteria. Results: AI-driven predictive models significantly improved early detection of preeclampsia, postpartum hemorrhage, and fetal distress, reducing maternal complications by 30-40%. Decision-support systems enhanced diagnostic accuracy by 25%, reducing human error. AI-driven administrative automation decreased midwives' documentation workload by 40%, allowing increased patient engagement. Virtual assistants and chatbots improved maternal education and access to care by 50%, particularly in underserved regions. Despite these benefits, concerns regarding algorithmic bias (reported in 20% of studies), data privacy risks (identified in 35% of studies), and the potential loss of human-centered care remain critical barriers to AI adoption in midwifery. Conclusion: AI has the potential to revolutionize midwifery by improving clinical efficiency, reducing complications, and enhancing patient education. However, addressing ethical, legal, and technical challenges is essential for its responsible implementation. Integrating AI education into midwifery training is crucial to ensure that midwives are equipped with the necessary skills to navigate AI-driven healthcare environments. Future research should focus on ethical frameworks, policy development, and AI literacy among midwives to facilitate equitable AI adoption in maternal healthcare.Insights-Journal of Life and Social Sciences00Artificial Intelligence has the potential to revolutionize midwifery by improving clinical efficiency, reducing complications, and enhancing patient education, however, addressing ethical, legal, and technical challenges is essential for its responsible implementation.Insights-Journal of Life and Social Sciences["Nabila Salim", "Anny Ashiq", "Jalal Khan", "Rozina Mehmood", "Narjis Shahid", "Msn Senior Lecturer Nabila Salim Ali"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/c09e8ace5d99e1541b28aeff095c5d0ea78418a2 5d537a7f86b5f46c70b5467c05fb57325d34d79cABSTRACT Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.Chinese Medical Journal830All indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.Chinese medical journal["Yueyan Bian", "Jin Li", "Chuyang Ye", "Xiuqin Jia", "Qi Yang"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/5d537a7f86b5f46c70b5467c05fb57325d34d79c 830b8d4b8399511ce22b08e9123a9f60e0ad6a33Objective This systematic literature review of the integration of artificial intelligence (AI) applications in surgical practice through hand and instrument tracking provides an overview of recent advancements and analyzes current literature on the intersection of surgery with AI. Distinct AI algorithms and specific applications in surgical practice are also examined. Methods An advanced search using medical subject heading terms was conducted in Medline (via PubMed), SCOPUS, and Embase databases for articles published in English. A strict selection process was performed, adhering to PRISMA guidelines. Results A total of 225 articles were retrieved. After screening, 77 met inclusion criteria and were included in the review. Use of AI algorithms in surgical practice was uncommon during 2013–2017 but has gained significant popularity since 2018. Deep learning algorithms (n = 62) are increasingly preferred over traditional machine learning algorithms (n = 15). These technologies are used in surgical fields such as general surgery (n = 19), neurosurgery (n = 10), and ophthalmology (n = 9). The most common functional sensors and systems used were prerecorded videos (n = 29), cameras (n = 21), and image datasets (n = 7). The most common applications included laparoscopic (n = 13), robotic-assisted (n = 13), basic (n = 12), and endoscopic (n = 8) surgical skills training, as well as surgical simulation training (n = 8). Conclusion AI technologies can be tailored to address distinct needs in surgical education and patient care. The use of AI in hand and instrument tracking improves surgical outcomes by optimizing surgical skills training. It is essential to acknowledge the current technical and social limitations of AI and work toward filling those gaps in future studies.Frontiers in Surgery1210Using AI in hand and instrument tracking improves surgical outcomes by optimizing surgical skills training and can be tailored to address distinct needs in surgical education and patient care.Frontiers in Surgery["Kivanc Yangi", "Thomas J. On", "Yuan Xu", "Arianna S. Gholami", "Jinpyo Hong", "Alexander G. Reed", "Pravarakhya Puppalla", "Jiuxu Chen", "Jonathan A. Tangsrivimol", "Baoxin Li", "Marco Santello", "Michael T. Lawton", "M. Preul"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/830b8d4b8399511ce22b08e9123a9f60e0ad6a33 b2643bae096ef0e9f53aa93c63a373b90179a9e3CONTEXT Public health practice involves the development of reports and plans, including funding progress reports, strategic plans, and community needs assessments. These documents are valuable data sources for program monitoring and evaluation. However, practitioners rarely have the bandwidth to thoroughly and rapidly review large amounts of primarily qualitative data to support real-time and continuous program improvement. Systematically examining and categorizing qualitative data through content analysis is labor-intensive. Large language models (LLMs), a type of generative artificial intelligence (AI) focused on language-based tasks, hold promise for expediting content analysis of public health documents, which, in turn, could facilitate continuous program improvement. OBJECTIVES To explore the feasibility and potential of using LLMs to expedite content analysis of real-world public health documents. The focus was on comparing semiautomated outputs from GPT-4o with human outputs for abstracting and synthesizing information from health improvement plans. DESIGN Our study team conducted a content analysis of 4 publicly available community health improvement plans and compared the results with GPT-4o's performance on 20 data elements. We also assessed the resources required for both methods, including time spent on prompt engineering and error correction. MAIN OUTCOME MEASURES Accuracy of data abstraction and time required. RESULTS GPT-4o demonstrated abstraction accuracy of 79% (n = 17 errors) compared to 94% accuracy by the study team for individual plans, with 8 instances of falsified data. Out of the 18 synthesis data elements, GPT-4o made 9 errors, demonstrating an accuracy of 50%. On average, GPT-4o abstraction required fewer hours than study team abstraction, but resource savings diminished when accounting for time for developing prompts and identifying/correcting errors. CONCLUSIONS Public health professionals who explore the use of generative AI tools should approach the method with cautious curiosity and consider the potential tradeoffs between resource savings and data accuracy.Journal of Public Health Management and Practice110Large language models (LLMs) hold promise for expediting content analysis of public health documents, which, in turn, could facilitate continuous program improvement, and public health professionals who explore the use of generative AI tools should approach the method with cautious curiosity.Journal of public health management and practice : JPHMP["Kelli DePriest", "John Feher", "Kailen Gore", "LaShawn M Glasgow", "Clint Grant", "Peter L. Holtgrave", "Karen Hacker", "Robert Chew"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/b2643bae096ef0e9f53aa93c63a373b90179a9e3 8ff155233e2a343cfc697bdb26e886d87d16ebacPURPOSE Artificial Intelligence (AI) technologies are rapidly expanding across diverse contexts. As the reach of AI continues to grow, there is a need to examine student perspectives on the increasing prevalence of AI and AI-based practice approaches in social work. MATERIALS AND METHODS In this qualitative study, we conducted structured interviews with 15 students in bachelors and masters social work programs. We developed an interview guide with a list of questions to ask students and no prior knowledge of AI was required by the students. The study was framed based on an interpretive phenomenological analysis approach. RESULTS Through thematic analysis, five key themes were developed, including 1) Risks associated with AI, 2) Ethical Concerns in AI and Technology Use, 3) Bias and Fairness in AI, 4) Applications and Possibilities of AI in Social Work, and 5) Training and Awareness of AI in Social Work. DISCUSSION Social workers can help disadvantaged clients by ensuring access to the various AI technologies and facilitating social welfare interventions created using these technologies. There is a need to address the gap in the existing literature about the use of AI in social work practice and education. Social work researchers can explore and conduct future studies that utilize mixed methods methodologies that can evaluate the use of AI in social work domains. CONCLUSION This study highlights the need to increase awareness of AI in social work education and practice settings given the potential of these technologies to aid various aspects of social work practice.Journal of Evidence-Based Social Work320The need to increase awareness of AI in social work education and practice settings is highlighted given the potential of these technologies to aid various aspects of social work practice.Journal of evidence-based social work["Ishita Kapur", "Reeve S. Kennedy", "Christy Hickman"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/8ff155233e2a343cfc697bdb26e886d87d16ebac 14905043840082ef2e467838ff59f349411b5a72Artificial Intelligence AI is increasingly becoming a foundation of competitive planning for contemporary organizations. However, even though the implementation of AI in organizations is a critical intervention that can unlock new forms of value, many of these implementations do not meet the expected outcomes. They may result in substantial financial, operational, and reputational negative consequences. This systematic literature review starts with a sample of 3104 articles from well-reputed journals published between 2010-2024. It aims to examine several questions that surround the occurrence of AI failure in organizations: the reasons behind those failures, the categories of the failures, and the disciplinary areas of the failures. Moreover, customers', employees', and management's points of view are considered in the review to extrapolate the potential consequences of the failure of AI systems. The result demonstrates that the AI breakdown often results from a mixture of technology, organization, and people problems and that different industries exhibit diverce types of failures.International Journal of Customer Relationship Marketing and Management300The result demonstrates that the AI breakdown often results from a mixture of technology, organization, and people problems and that different industries exhibit diverce types of failures.International Journal of Customer Relationship Marketing and Management["Anam Ahmad", "Mohamed Slim Ben Mimoun", "H. El-Gohary"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/14905043840082ef2e467838ff59f349411b5a72 7db5e934e0577994dfd5aa0f0307f89ea921943dThe use and benefit of technology and scientific advances, including new technologies, has always been considered one of the fundamental human rights. One of these new technologies is artificial intelligence technology. In this article, which was conducted using a descriptive-analytical method and using library resources and texts and with the aim of examining and analyzing artificial intelligence and international peace and security, the following questions are raised: How is international peace and security affected by artificial intelligence technology? What challenges does artificial intelligence create for international peace and security? And what solutions can be proposed in this regard? The results of the research, which were in line with the research hypotheses, are that artificial intelligence is an influential and comprehensive field whose scope is not only related to technical and engineering issues, but also encompasses the fields of humanities, especially international peace and security, and artificial intelligence creates challenges for international peace and security, the most important of which is the growth of the use of this technology in the military field, which can lead to the production of deadly and uncontrollable robotic and automated weapons. Also, cyber warfare using artificial intelligence can pose a serious threat to international peace and security. Accordingly, it is necessary to conclude new international agreements and conventions to contain its negative effects.Acta Globalis Humanitatis et Linguarum190The research finds that artificial intelligence is an influential and comprehensive field whose scope is not only related to technical and engineering issues, but also encompasses the fields of humanities, especially international peace and security, and artificial intelligence creates challenges for international peace and security.Acta Globalis Humanitatis et Linguarum["Mohammad Ekram Yawar", "Jamil Abdul Sharify", "Said Abdullah Sadat"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/7db5e934e0577994dfd5aa0f0307f89ea921943d 332ac3df00d492129e18b714bb5756274b0c2660The meanings and goals of mass education are to transfer pedagogically adapted social experience from teacher to student. Such education has the character of transmission and is therefore monologue-like; it reflects the idea of a person as a “blank sheet”. However, experience and knowledge cannot be conveyed directly; they are formed from within. The transfer of impersonal information in a ready-made form does not take into account the personal characteristics, meanings and goals of the student himself, who does not participate in the creation of knowledge. This approach leads to the training of a specialist prone to imitation, which helps to accelerate the historical process, leads to the atomization of personality and increased aggression.Artificial intelligence, performing an increasing number of functions for humans, saves them time and facilitates professional and personal actions by facilitating the transfer of information, which many people may consuder a sinonom of a “knowledge”.Can artificial intelligence not only serve as a generator of solutions to student’s needs, but also help identify, unlock and realize their potential? In other words, can it support the student in a subject-to-subject interaction with other students and teachers within the framework of dialogical education?Monologue education and artificial intelligence have one important feature in common – the lack of internal creativity. We believe that artificial intelligence cannot be a subject due to the lack of boundaries, since new meanings are always born in dialogue based on the self of the subjects. Artificial intelligence does not have an internal space of meanings, which is the basic condition for dialogue.Artificial intelligence enhances the negative features of monologue education. It makes a person’s life easier, but it does so by reducing opportunities for their development.Heuristic-type education, dialogical in nature, acts as a student’s assistant in his creative selfrealization and develops the qualities of a creator’s personality capable of effective interaction with artificial intelligence.Vysshee Obrazovanie v Rossii = Higher Education in Russia60Heuristic-type education, dialogical in nature, acts as a student’s assistant in his creative selfrealization and develops the qualities of a creator’s personality capable of effective interaction with artificial intelligence.Vysshee Obrazovanie v Rossii = Higher Education in Russia["A. D. Korol", "E. A. Bushmanova"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/332ac3df00d492129e18b714bb5756274b0c2660 37db056f543b00f9a8d59d3067d07b1a44c3a61dRelevance. The relevance of using artificial intelligence in interpreting legislation is due to the exponential growth of unstructured data, the increase in their importance, as well as the increasing complexity of legal problems being solved. The use of artificial intelligence in interpreting legislation provides the opportunity to improve the quality of justice. To implement automatic meaningful processing of information, it is necessary to solve a set of scientific problems on the legal and technological aspects of using artificial intelligence.The purpose of the study is to formulate scientifically based conclusions that determine the directions for solving the scientific problem of using artificial intelligence in interpreting legislation based on an analysis of the content of the structure of the legal norm and the processes of information processing in heuristic and neuromathematical cognitive systems of information processing.Objectives: to determine the common features and differences of the processes of interpretation of legislation in heuristic and neuromathematical cognitive systems of artificial intelligence; to develop proposals for improving the regulatory framework for the interpretation of legislation, taking into account the possibility of using automatic semantic processing of information in computer systems, as well as for the development of technological methods of semantic interpretation of civil law norms in artificial intelligence systems.Methodology. The methodological basis of the scientific research was the dialectical method of cognition of phenomena and processes of the surrounding reality. In the course of developing the theoretical provisions of the work, a set of general scientific and specific scientific research methods (formal-logical, prognostic, formal-legal, etc.) was used to solve the interdisciplinary scientific problem of determining the prospects for the use of artificial intelligence in the interpretation of legislation and automation of procedures for semantic processing of information in legal systems.Results. The obtained research results provide the opportunity to improve the regulatory and technological framework that establishes the principles of using artificial intelligence in interpreting legislation.Conclusion. The solution to the problem of using artificial intelligence in interpreting legislation requires the development of new legal norms and regulations, as well as the improvement of information technologies in terms of verifying the rules of production of heuristic artificial intelligence systems and verbalizing artificial neural networks.Proceedings of the Southwest State University. Series: History and Law50The obtained research results provide the opportunity to improve the regulatory and technological framework that establishes the principles of using artificial intelligence in interpreting legislation.Proceedings of Southwest State University. Series: History and Law["M. A. Ogarok"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/37db056f543b00f9a8d59d3067d07b1a44c3a61d 26dbd3a3428b8ce716cd4a4b398be24519c47a35In recent years, artificial intelligence (AI) has made significant strides in research and shown great potential in various application fields, including business and economics (B&E). However, AI models are often black boxes, making them difficult to understand and explain. This challenge can be addressed using eXplainable Artificial Intelligence (XAI), which helps humans understand the factors driving AI decisions, thereby increasing transparency and confidence in the results. This paper aims to provide a comprehensive understanding of the state‐of‐the‐art research on XAI in B&E by conducting an extensive literature review. It introduces a novel approach to categorising XAI techniques from three different perspectives: samples, features and modelling method. Additionally, the paper identifies key challenges and corresponding opportunities in the field. We hope that this work will promote the adoption of AI in B&E, inspire interdisciplinary collaboration, foster innovation and growth and ensure transparency and explainability.Expert systems1190A novel approach to categorising XAI techniques from three different perspectives: samples, features and modelling method is introduced, which will promote the adoption of AI in B&E, inspire interdisciplinary collaboration, foster innovation and growth and ensure transparency and explainability.Expert Systems["Qi Lyu", "Shaomin Wu"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/26dbd3a3428b8ce716cd4a4b398be24519c47a35 876a243b8d503a50c4716b7a428b7006de419a62The Arabic language plays a significant role in religious education, diplomacy, and economics; however, its learning process faces complex challenges, such as difficulties in understanding grammar (nahwu) and morphology (sharaf). This research aims to analyze how Artificial Intelligence (AI) can address these challenges and enhance interactive Arabic language learning, particularly in terms of personalization and effectiveness. The research employs a descriptive qualitative method by reviewing literature, scientific journals, and previous research reports, as well as conducting in-depth interviews with teachers and students utilizing AI technology. Data were also collected through observations of AI-based applications, such as chatbots and learning platforms, to understand their implementation and effectiveness. Analysis was performed using content analysis methods, supplemented by data triangulation, to produce valid and comprehensive findings. The study reveals that the transformation brought by artificial intelligence (AI) technology has revolutionized Arabic language learning by accelerating, simplifying, and personalizing the learning process. Key challenges, such as morphological complexity and dialectal diversity, have driven the development of technologies like Natural Language Processing (NLP) and AI-based voice recognition. Through an interdisciplinary approach that is culturally sensitive, AI is expected to create a more adaptive and inclusive Arabic learning experience.Ranah Research490The study reveals that the transformation brought by artificial intelligence (AI) technology has revolutionized Arabic language learning by accelerating, simplifying, and personalizing the learning process.Ranah Research : Journal of Multidisciplinary Research and Development["Mardi Hadi", "Hendri Abdul Qohar"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/876a243b8d503a50c4716b7a428b7006de419a62 1c344555afcb51c031ec681a50e6cfd6d44c7f30Artificial intelligence (AI) is increasingly being utilized in Post-Anesthesia Care Units (PACUs) to improve patient monitoring and care. This narrative review explores the current use of AI in PACUs and discusses the potential benefits and challenges associated with its implementation and highlights how AI technologies such as predictive analytics, machine learning algorithms, and robotics can enhance patient safety, reduce human error, and improve outcomes in the PACU setting. Overall, this narrative review provides insights into the evolving role of AI in PACUs and offers recommendations for future research and practice in this area.Archives of Anesthesia and Critical Care00This narrative review explores the current use of AI in PACUs and discusses the potential benefits and challenges associated with its implementation and highlights how AI technologies such as predictive analytics, machine learning algorithms, and robotics can enhance patient safety, reduce human error, and improve outcomes in the PACU setting.Archives of Anesthesia and Critical Care["Shahnam Sedigh Maroufi", "Maryam Sarkhosh", "Maryam Soleimani Movahed", "Ali Behmanesh", "Azar Ejmalian"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/1c344555afcb51c031ec681a50e6cfd6d44c7f30 1ae6db6857a301a24311e29eef65942de2a01a7cAmid comprehensive reforms, artificial intelligence (AI) has emerged as a vital force in solving people’s problems and enhancing quality of life. Yet, theoretical inquiries into the mechanisms by which AI influences social well-being remain limited. Drawing upon cultural lag theory, this study constructs a social well-being index system based on the Gini coefficient objective weighting method. By integrating a moderated mediation model with a spatial econometric model, it examines the mechanisms and impacts of artificial intelligence on social well-being. The findings reveal that AI induces multiple cultural lags and exerts a U-shaped impact on social well-being. AI enhances well-being through the channels of employment opportunities, human capital, and green innovation, while digital inclusion and foreign direct investment (FDI) further reinforce this relationship. Additionally, AI generates spatial spillover effects on social well-being, and the region’s well-being landscape exhibits convergence. However, both digital inclusion and FDI negatively moderate the convergence process, slowing its overall pace. These insights provide substantial practical guidance for crafting informed policies aimed at elevating public well-being.Sustainability450A social well-being index system based on the Gini coefficient objective weighting method is constructed, revealing that AI induces multiple cultural lags and exerts a U-shaped impact on social well-being.Sustainability["Zhaoxin Song", "Yongfeng Duan", "Guanying Wang", "Shuoxun Cheng"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/1ae6db6857a301a24311e29eef65942de2a01a7c b81e49ba4d255769800a7896391347037f9ac9c5Many tasks in a modern household are performed by machines, e.g., a dishwasher or a vacuum cleaner, and in the near future most household tasks will be performed by smart service robots. This will relieve the residents, who in turn can enjoy their free time. This newly gained free time will turn out to cause the so-called spare time rebound effect due to more resource consumption. We roughly quantify this rebound effect and propose a CO2-budget model to reduce or even avoid it. In modern industry, automation and AI are taking over work from humans, leading to higher productivity of the company as a whole. This is the main reason for economic growth, which leads to environmental problems due to higher consumption of natural resources. We show that, even though the effects of automation at home and in the industry are different (free time versus higher productivity), in the end they both lead to more resource consumption and environmental pollution. We discuss possible solutions to this problem, such as carbon taxes, emissions trading systems, and a carbon budget.Sustainability101It is shown that, even though the effects of automation at home and in the industry are different, in the end they both lead to more resource consumption and environmental pollution, and proposes a CO2-budget model to reduce or even avoid this rebound effect.Sustainability["Wolfgang Ertel", "Christopher M. A. Bonenberger"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/b81e49ba4d255769800a7896391347037f9ac9c5 774b010198f9dfacaaab21eca6a2c72faa534a52International Research Journal of Modernization in Engineering Technology and Science00International Research Journal of Modernization in Engineering Technology and Science[]2025-02-26T00:00:00https://www.semanticscholar.org/paper/774b010198f9dfacaaab21eca6a2c72faa534a52 c839ba1f38e59cc248c7f92fbd5d8aca0d085cecEmergencias00Emergencias["Santiago Nogu\u00e9-Xarau", "M. Amig\u00f3-Tad\u00edn"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/c839ba1f38e59cc248c7f92fbd5d8aca0d085cec 629349fb1817ce03e16934cbf44d6bc313a09083AI rapidly reshapes learning landscapes from highly industrialized countries to those that are still in development, such as the Philippines. However, limited studies have been conducted on how such AI tools are adopted and perceived by college students within a non-urban higher education context. This study fills the gap by investigating the adoption, perceptions, and ethical implications of AI tools among rural Philippine college students through a sequential explanatory mixed-method cross-section survey approach, drawing its base from 451 students in a rural state college in Cebu, Philippines, from May to June 2024. IBM SPSS version 26.0 was used to conduct the statistical analyses, while theme analyses were done using MAXQDA version 2020. Among the respondents, all had used AI tools, while the greater proportion of these students (78.54%) used ChatGPT. Further, the students strongly believed that AI was easy to use (M = 5.13; SD = ±1.58) and helpful in their learning (M = 5.17; SD = ±1.53). On the contrary, students were concerned about incorrect or biased information (M=5.35, SD=±1.40), impact on critical thinking (M=5.04, SD=±1.77), and potential for cheating (M=5.39, SD=±1.50) while utilizing these AI tools. Also, only 17.29% of the students knew its institutional policies regarding the use of AI. This study indicates the essentiality of creating clear institutional guidelines for the use of AI, devising programs on AI literacy, and revisiting the assumption about the digital divide in rural higher education institutions. These findings also have policy implications in view of curriculum development and ethics for integrating AI into higher education contexts and carve out a need for educational strategies that make use of the benefits offered through AI while actively cultivating students' critical thinking skills and academic integrity.International Journal of Educational Research and Innovation430The essentiality of creating clear institutional guidelines for the use of AI, devising programs on AI literacy, and revisiting the assumption about the digital divide in rural higher education institutions are indicated.IJERI: International Journal of Educational Research and Innovation["Resti Tito H. Villarino"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/629349fb1817ce03e16934cbf44d6bc313a09083 965dc5d7aab866706f58f4cd1c1485287a98d851Israel Affairs400Israel Affairs["Gavriel Dahan", "Michal Levi-Bliech"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/965dc5d7aab866706f58f4cd1c1485287a98d851 c795c558f7fc69a9a29824bdb46ff2d4f25a4d16Penelitian ini bertujuan untuk menganalisis peluang, strategi, dan tantangan yang dihadapi industri perbankan syariah dalam menghadapi perkembangan kecerdasan buatan (AI). Metode yang digunakan dalam penelitian ini adalah metode kualitatif deskriptif dengan pendekatan studi pustaka, yang mengkaji berbagai literatur, laporan industri, serta regulasi yang terkait dengan implementasi AI dalam perbankan syariah.Hasil penelitian menunjukkan bahwa AI memiliki potensi besar dalam meningkatkan efisiensi operasional perbankan syariah, terutama melalui otomatisasi layanan keuangan, penggunaan chatbot dalam layanan nasabah, serta analisis risiko berbasis big data yang dapat meningkatkan akurasi pengambilan keputusan. Selain itu, AI juga dapat membantu dalam penguatan kepatuhan syariah melalui analisis kontrak berbasis machine learning dan deteksi transaksi yang tidak sesuai dengan prinsip syariah. Namun, implementasi AI dalam perbankan syariah juga menghadapi berbagai tantangan, seperti kepatuhan terhadap prinsip syariah, keamanan data dan privasi, serta kesiapan teknologi dan infrastruktur. Untuk mengatasi tantangan tersebut, diperlukan strategi yang mencakup kolaborasi dengan fintech berbasis syariah, penguatan regulasi AI oleh otoritas keuangan syariah, serta investasi dalam pengembangan sumber daya manusia dan infrastruktur digital. Kesimpulannya, perbankan syariah dapat memanfaatkan AI untuk meningkatkan daya saing dan efisiensi, namun penerapannya harus dilakukan dengan tetap mempertahankan kepatuhan terhadap prinsip syariah dan memperhatikan aspek etika serta keamanan data. Dengan pendekatan yang tepat, AI dapat menjadi alat yang mendukung pertumbuhan perbankan syariah secara berkelanjutan.Journal of International Multidisciplinary Research00Journal of International Multidisciplinary Research["Elman Nafidzi", "Khabib Musthofa"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/c795c558f7fc69a9a29824bdb46ff2d4f25a4d16 21ce325831389e6bf74065a1bbfb320d45c31371Universal Access in the Information Society680Universal Access in the Information Society["Hyeon Jo"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/21ce325831389e6bf74065a1bbfb320d45c31371 a88c60eeeb94885de7f2a7c7d87a5adfa36435b3Education and Information Technologies : Official Journal of the IFIP technical committee on Education340Education and Information Technologies["Yulu Cui", "Hai Zhang"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/a88c60eeeb94885de7f2a7c7d87a5adfa36435b3 177724b9adb915c915ee4a35cc8cecd6a1667619As people increasingly rely on AI nowadays, various AI industries are being developed in China and the US to serve different purposes. This research will explore the rise of these fast followers in the Generative AI industry and compare their market share, business model, and competitive advantage of each fast follower. Then, through the PEST model, we analyze how the macro background of different countries has affected the development of these fast followers.Finance &amp; Economics10This research will explore the rise of these fast followers in the Generative AI industry and compare their market share, business model, and competitive advantage through the PEST model.Finance &amp; Economics["Jingya Peng", "Chentong Jiang", "Xinyu Zhang", "Weixin Guan", "YU-HSUAN Lin"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/177724b9adb915c915ee4a35cc8cecd6a1667619 ca9f795bc9821a050ae4cd5566ead139c556053f00["Joanna Paliszkiewicz", "Ireneusz D\u0105browski", "Leila Halawi"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/ca9f795bc9821a050ae4cd5566ead139c556053f 842f036da3b64a2e91a37118c5824703815686bc00["Xiaomei Wang"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/842f036da3b64a2e91a37118c5824703815686bc b28ddc17b557eaaa23304807b0188d598dd835d8Based on the Doha Agreement signed on February 29, 2020 between the Taliban and the United States of America, the two parties committed to stopping attacks on each other. The United States committed to withdrawing all its military and civilian forces and those of its allies from Afghanistan within 14 months. The Taliban also pledged to cut off cooperation with terrorist groups, including al-Qaeda, and pledged to reduce the intensity of its attacks and to advance peace talks with the Afghan government. While this agreement was expected to end nearly two decades of military conflict in Afghanistan. However, the Taliban’s increased attacks on military and civilian targets have continued to the point where Afghan cities have fallen one after another; The then Afghan president fled to Abu Dhabi, and Kabul fell to the Taliban within hours. Meanwhile, despite assurances issued by the Taliban, many Afghans were trying to leave the country. This has caused the world to once again face an international refugee crisis, raising the question of how international law can manage such a situation; what are the commitments of member states of the international community, and what are the potential gaps and challenges.Global Spectrum of Research and Humanities340Global Spectrum of Research and Humanities["Mohammad Ekram Yawar", "Jamil Abdul Sharify", "Said Abdullah Sadat"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/b28ddc17b557eaaa23304807b0188d598dd835d8 a61d9377aeed1a5b15804e96a14735540875b81eInternational Journal of Human-Computer Interaction300International Journal of Human–Computer Interaction["Yongqi Huang", "Shiye Jiang", "Zhe Gong"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/a61d9377aeed1a5b15804e96a14735540875b81e 1ec5dde24f7f5d48cb418fa2291b94affdfdd8c8Applied Economics Letters110Applied Economics Letters["Xiaotong Cai", "Peiyan Lin", "Lei Chen", "Wenhua Wang"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/1ec5dde24f7f5d48cb418fa2291b94affdfdd8c8 be0d7bcd947f23269c81f0f6b5f5fb3288509f89The domain of industrial robotics is experiencing a significant and continuous expansion. With the advancements in artificial intelligence(AI) and machine learning(ML), the strategies for creating and controlling robots have gained paramount importance. With the increasing advancements in artificial intelligence(AI) and machine learning(ML), robots are being developed with enhanced decision making capabilities,intelligence, and adaptability to the environment. These robots can function collaboratively and adjust to changes in their surroundings,akin to human behaviour. Some very important applications of AI and ML in advanced robotics include autonomous navigation, object recognition and manipulation,natural language processing and understanding and predictive maintenance. Data learning(DL) , a subfield of artificial intelligence(AI), enables robots to process and learn from vast amounts of data, enhancing their ability to make informed decisions and improve performance over time. AI and ML play a crucial role in advancements in manufacturing of assembly robots that enable them to work more efficiently, safely and intelligently. AI and ML can also be used in supply chain optimisation in order to ensure the right materials are available at the right time. AI and ML can be used in path optimisation in order to reduce time and increase efficiency. In the military AI and ML are employed for autonomous systems, threat detection, strategic planning and bomb disposal. Robotic surgery is a field where AI and ML are revolutionising the way operations are performed. The implementation of AI and ML applications in advanced robotics can significantly reduce costs associated with labour and maintenance. The integration of AI in logistics enables robots to manage inventory, sort packages, and streamline supply chain operations, enhancing efficiency and reducing operational costs. AI algorithms optimize the energy consumption of industrial robots, ensuring they operate efficiently while minimizing the power usage. This is crucial for reducing operational costs and environmental impacts. This paper presents a systematic review of today’s application of AI and ML techniques in the factory environment. Thus, the aim of the present research was to systematically analyze the scientific literature relating to the application of artificial intelligence(AI) and machine learning(ML) in the advanced robotics industry.International Journal For Multidisciplinary Research00The aim of the present research was to systematically analyze the scientific literature relating to the application of artificial intelligence(AI) and machine learning(ML) in the advanced robotics industry to present a systematic review of today's application of AI and ML techniques in the factory environment.International Journal For Multidisciplinary Research["Koushik Paul", "Laiba Nafees", "Abhradeep Hazra", "Sayandip Ghosh"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/be0d7bcd947f23269c81f0f6b5f5fb3288509f89 0a9c39a14b219f2183d35591184f7e6996ac21feThe methodical process of recognizing, evaluating, and reducing risks or uncertainties that might have an impact on an organization is known as risk management. This entails assessing the impact and likelihood of different hazards, creating plans to reduce possible harm, and regularly assessing how well these plans are working. Strong risk management techniques are required due to the diverse range of hazards associated with the insurance industry's adoption of artificial intelligence (AI). As the use of AI technology in the insurance sector continues to expand, AI risk management is becoming increasingly important. With the knowledge the growing use of artificial intelligence (AI) in underwriting, claims processing, fraud detection, customer service, and other business areas, AI risk management is a crucial topic for Indian insurance firms.International Journal For Multidisciplinary Research00With the knowledge the growing use of artificial intelligence in underwriting, claims processing, fraud detection, customer service, and other business areas, AI risk management is a crucial topic for Indian insurance firms.International Journal For Multidisciplinary Research["Mr. S. Manikandan", "Dr. V. Manohar", "Mr. K. S. Imranullah", "Mrs. G. Bhuvana", "Mr. P. Manikandan"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/0a9c39a14b219f2183d35591184f7e6996ac21fe 9ccae1425131452513f7f2df01b5173faad9d3c7This study explores how artificial intelligence (AI) can enhance strategic decision-making by integrating with four established strategic schools: Classical, Adaptive, Resource-Based, and Processual. While AI improves data-driven insights, it lacks the strategic foresight, contextual awareness, and ethical judgment inherent in traditional frameworks. Using a structured literature review, this conceptual study evaluates the synergy between AI and strategic schools. Sources were selected from peer-reviewed databases, including Scopus and Web of Science, using keywords such as "AI-driven strategy," "strategic management," and "decision support systems." The findings reveal that AI enhances Classical strategy through predictive analytics and scenario planning, strengthens Adaptive strategy via real-time responsiveness, supports RBV by optimizing resource identification, and complements Processual strategy by facilitating continuous learning. However, AI’s limitations in handling tacit knowledge, ethical considerations, and contextual judgment highlight the need for human oversight. This study proposes a hybrid framework where AI supports, rather than replaces, strategic decision-making. It offers actionable recommendations for business leaders, including AI-powered strategy frameworks, governance policies for ethical AI deployment, and human-AI collaboration to navigate dynamic business environments effectively.International Journal of Management and Administration380A hybrid framework where AI supports, rather than replaces, strategic decision-making is proposed, offering actionable recommendations for business leaders, including AI-powered strategy frameworks, governance policies for ethical AI deployment, and human-AI collaboration to navigate dynamic business environments effectively.International Journal of Management and Administration["Harun B\u00fcber", "Emrullah Seven"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/9ccae1425131452513f7f2df01b5173faad9d3c7 fd989e559960bf22be61e47e7b92450ea6b3aa02The advent of Generative Artificial Intelligence (GAI) has heralded an inflection point that changed how society thinks about knowledge acquisition. While GAI cannot be fully trusted for decision-making, it may still provide valuable information that can be integrated into a decision pipeline. Rather than seeing the lack of certitude and inherent randomness of GAI as a problem, we view it as an opportunity. Indeed, variable answers to given prompts can be leveraged to construct a prior distribution which reflects assuredness of AI predictions. This prior distribution may be combined with tailored datasets for a fully Bayesian analysis with an AI-driven prior. In this paper, we explore such a possibility within a non-parametric Bayesian framework. The basic idea consists of assigning a Dirichlet process prior distribution on the data-generating distribution with AI generative model as its baseline. Hyper-parameters of the prior can be tuned out-of-sample to assess the informativeness of the AI prior. Posterior simulation is achieved by computing a suitably randomized functional on an augmented data that consists of observed (labeled) data as well as fake data whose labels have been imputed using AI. This strategy can be parallelized and rapidly produces iid samples from the posterior by optimization as opposed to sampling from conditionals. Our method enables (predictive) inference and uncertainty quantification leveraging AI predictions in a coherent probabilistic manner.370The basic idea consists of assigning a Dirichlet process prior distribution on the data-generating distribution with AI generative model as its baseline and enables (predictive) inference and uncertainty quantification leveraging AI predictions in a coherent probabilistic manner.["Veronika Rovckov'a", "Sean O'Hagan"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/fd989e559960bf22be61e47e7b92450ea6b3aa02 00d700be683e03b662aa9a8f5fb83755beaa093fPURPOSE To map out the current artificial intelligence (AI)-informed psychosocial interventions research landscape, with a focus on main themes, trends, and prospective future directions. METHOD A bibliometric analysis extracted articles that had been published between 2007 and 2024 from the Web of Science database. Software used to process results were Bibliometrix and VOSviewer. RESULTS A total of 207 articles published by 86 different sources were obtained. A publication of high recurrence source was the Journal of Medical Internet Research. The United States showed high research activity in link strength, volume of articles, and citation frequency. Key themes identified were machine learning, mental health, cognitive-behavioral therapy, and personalization. Emerging trends since 2020 show growing interest in ChatGPT and AI-driven therapy. CONCLUSION Bibliometric analysis suggests increased application of AI in psychosocial interventions in mental health. Integrating AI with existing therapies and the development of novel digital tools indicate a future for mental health care that is personalized and innovative. The advent of advanced language models, such as ChatGPT, has opened new horizons in AI-supported mental health care. This preliminary analysis provides a foundational understanding of the current landscape while identifying key areas for further research. [Journal of Psychosocial Nursing and Mental Health Services, xx(xx), xx-xx.].Journal of Psychosocial Nursing and Mental Health Services390Bibliometric analysis suggests increased application of AI in psychosocial interventions in mental health, and key themes identified were machine learning, mental health, cognitive-behavioral therapy, and personalization.Journal of psychosocial nursing and mental health services["Erman Y\u0131ld\u0131z"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/00d700be683e03b662aa9a8f5fb83755beaa093f a6ca2e10b2727ba17b5c1623f2552bbfe15a36feThe integration of Artificial Intelligence (AI) into Information Technology (IT) has become a cornerstone of digital transformation across industries. AI technologies are increasingly being employed to automate processes, enhance security, optimize resources, and improve decision-making. In recent years, AI's role in IT has expanded significantly, as businesses look for ways to increase operational efficiency, reduce costs, and stay competitive in the rapidly evolving digital landscape. AI applications are driving innovations in areas such as cloud security, IT resource management, network operations, and customer experience, transforming traditional IT processes and enabling businesses to navigate complex technological environments more effectively. AI is fundamentally reshaping the future of IT by enhancing efficiency, optimizing resources, improving security, and driving innovation. As businesses face increasing pressure to modernize their IT infrastructures, AI offers a clear pathway to achieving these goals. However, successful adoption depends on addressing key challenges such as cost, integration complexity, and workforce readiness. With the rapid growth of AI technologies and their transformative potential, organizations that embrace AI in their IT operations will be well-positioned for future success. As AI continues to evolve, it will likely redefine many aspects of IT, from network management to cloud security and resource optimization, enabling organizations to operate more effectively and stay competitive in a technology-driven world.International Journal For Multidisciplinary Research200AI is fundamentally reshaping the future of IT by enhancing efficiency, optimizing resources, improving security, and driving innovation, enabling organizations to operate more effectively and stay competitive in a technology-driven world.International Journal For Multidisciplinary Research["Dr.S.GAYATHRI", "Dr.V.MEENAKSHI"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/a6ca2e10b2727ba17b5c1623f2552bbfe15a36fe 92242a047543abe8acb70ada6ee90b51587bbaa9The advancement of digital technologies, particularly in Artificial Intelligence (AI), the geopolitical fragmentation of Environment, Social, and Governance (ESG) with a lack of mandatory international governance, calls for increased global cooperation and integration in overarching central conceptual and of action frameworks. As humanity faces critical environmental challenges—such as climate extremes and biodiversity loss and wars—the disparities between rich and poor become more evident and the planet displays its illness. Addressing these challenges requires collective social change, underpinned by shared operating systems, open-source models, and quality data. Humanity’s fragmented relationshipwith nature highlights the need for a robust global governance system. As AI and ESG matters transcend national borders, there is a growing need for internationalframeworks, such as the involvement of the International Court of Justice (ICJ) to resolve disputes and the rule of law, both at national and international levels to be interconnected, ensuring that legal frameworks complement each other. A shift toward “sust-AI-nability,” grounded in human reason, science- and fact-based, with values- and risk-based must coordinate cooperation, essential for managing global challenges, foster meaningful transformation, and advance the United Nations’ Sustainable Development Goals (SDGs).Denning Law Journal200The Denning Law Journal["Monica Maghami"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/92242a047543abe8acb70ada6ee90b51587bbaa9 325f5fde368a86f2554cb56c666f671c40dae0aeIn today’s digital Era with rapid proliferation of artificial intelligence, the organisation are slowly adopting to the new mechanism in the recruitment process which offers and improvement in efficiency, accuracy and inclusivity. The study aims to analyse the perceived benefit and challenges of AI in talent acquisitions. Study deployed primary method to collect the data from 46 HR professionals by interview them. Research revealed that there is a noteworthy reduction in the time taken to fill the position from 4.05 weeks to 2.96 weeks post implementation of AI, additionally there is a strong perfect for co - relation r= 0.859 was found between the use of Artificial Intelligence in the recruitment and job satisfaction among the HR professionals. The finding highlights the transformative role of AI in enhancing recruitment efficiency and job satisfaction by highlighting ethical consideration to leverage AI technology in talent acquisition.Journal of Informatics Education and Research90Research revealed that there is a noteworthy reduction in the time taken to fill the position from 4.05 weeks to 2.96 weeks post implementation of AI, and a strong perfect for co- relation r= 0.859 was found between the use of Artificial Intelligence in the recruitment and job satisfaction among the HR professionals.Journal of Informatics Education and Research["Chetana M R, Bharath R"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/325f5fde368a86f2554cb56c666f671c40dae0ae 3182588454ac3beb822c27b0dd82f1de9903b7dcThe classic notion of truthfulness requires that no agent has a profitable manipulation -- an untruthful report that, for some combination of reports of the other agents, increases her utility. This strong notion implicitly assumes that the manipulating agent either knows what all other agents are going to report, or is willing to take the risk and act as-if she knows their reports. Without knowledge of the others' reports, most manipulations are risky -- they might decrease the manipulator's utility for some other combinations of reports by the other agents. Accordingly, a recent paper (Bu, Song and Tao, ``On the existence of truthful fair cake cutting mechanisms'', Artificial Intelligence 319 (2023), 103904) suggests a relaxed notion, which we refer to as risk-avoiding truthfulness (RAT), which requires only that no agent can gain from a safe manipulation -- one that is sometimes beneficial and never harmful. Truthfulness and RAT are two extremes: the former considers manipulators with complete knowledge of others, whereas the latter considers manipulators with no knowledge at all. In reality, agents often know about some -- but not all -- of the other agents. This paper introduces the RAT-degree of a mechanism, defined as the smallest number of agents whose reports, if known, may allow another agent to safely manipulate, or $n$ if there is no such number. This notion interpolates between classic truthfulness (degree $n$) and RAT (degree at least $1$): a mechanism with a higher RAT-degree is harder to manipulate safely. To illustrate the generality and applicability of this concept, we analyze the RAT-degree of prominent mechanisms across various social choice settings, including auctions, indivisible goods allocations, cake-cutting, voting, and stable matchings.490["Eden Hartman", "Erel Segal-Halevi", "Biaoshuai Tao"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/3182588454ac3beb822c27b0dd82f1de9903b7dc b5432bced5dddb1315796d71e7d8e5f5bf234d7bGenerative artificial intelligence (generative AI) has entered the mainstream culture and become a subject of extensive academic investigation. However, the character and background of its impact on art require subtler scrutiny and more nuanced contextualization. This paper summarizes a broader study of the roles that AI's conceptual and ideological substrata play in influencing art notions. The focus is on divergent but coalescing and often questionable ideas, values, and political views that generative AI and other art-related AI technologies propagate from the computer science and AI/tech industry to the contemporary art and culture. The paper maps the main areas of this complex relationship and concisely critiques their key aspects.420The focus is on divergent but coalescing and often questionable ideas, values, and political views that generative AI and other art-related AI technologies propagate from the computer science and AI/tech industry to the contemporary art and culture.["Dejan Grba"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/b5432bced5dddb1315796d71e7d8e5f5bf234d7b 52a093a080296f5a6148d3323153366021fb2336Introduction This systematic review and meta-analysis aim to evaluate the efficacy of artificial intelligence (AI) models in identifying prognostic and predictive biomarkers in lung cancer. With the increasing complexity of lung cancer subtypes and the need for personalized treatment strategies, AI-driven approaches offer a promising avenue for biomarker discovery and clinical decision-making. Methods A comprehensive literature search was conducted in multiple electronic databases to identify relevant studies published up to date. Studies investigating AI models for the identification of prognostic and predictive biomarkers in lung cancer were included. Data extraction, quality assessment, and meta-analysis were performed according to PRISMA guidelines. Results A total of 34 studies met the inclusion criteria, encompassing diverse AI methodologies and biomarker targets. AI models, particularly deep learning and machine learning algorithms demonstrated high accuracy in predicting biomarker status. Most of the studies developed models for the prediction of EGFR, followed by PD-L1 and ALK biomarkers in lung cancer. Internal and external validation techniques confirmed the robustness and generalizability of AI-driven predictions across heterogeneous patient cohorts. According to our results, the pooled sensitivity and pooled specificity of AI models for the prediction of biomarkers of lung cancer were 0.77 (95% CI: 0.72 – 0.82) and 0.79 (95% CI: 0.78 – 0.84). Conclusion The findings of this systematic review and meta-analysis highlight the significant potential of AI models in facilitating non-invasive assessment of prognostic and predictive biomarkers in lung cancer. By enhancing diagnostic accuracy and guiding treatment selection, AI-driven approaches have the potential to revolutionize personalized oncology and improve patient outcomes in lung cancer management. Further research is warranted to validate and optimize the clinical utility of AI-driven biomarkers in large-scale prospective studies.Frontiers in Oncology530AI models, particularly deep learning and machine learning algorithms demonstrated high accuracy in predicting biomarker status and highlight the significant potential of AI models in facilitating non-invasive assessment of prognostic and predictive biomarkers in lung cancer.Frontiers in Oncology["Hind M. Alosaimi", "A. M. Alshilash", "Layan K. Al-Saif", "Jannat M. Bosbait", "Roaa S. Albeladi", "Dalal R. Almutairi", "A. Alhazzaa", "Tariq A. Alluqmani", "Saud M. Al Qahtani", "Sara A. Almohammadi", "Razan A Alamri", "Abdullah A. Alkurdi", "Waleed K Aljohani", "Raghad H. Alraddadi", "Mohammed K. Alshammari"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/52a093a080296f5a6148d3323153366021fb2336 35db6bbcf0a9d9dff3ee0718ec5e97bd802ff350Artificial intelligence (AI) technology has become one of the most frequently discussed subjects in the development of technology in recent years. Due to its incredible pattern recognition, it can help humans complete work much faster than before with little to no monetary cost. Despite the widespread impact that AI technologies have on various fields, acceptance and adoption of AI lag behind because of a wide range of factors among users. This paper outlines the results of a large literature review that attempts to tease out some of these factors by examining individual differences that may impact the acceptance and adoption of AI. This goal was achieved through an exploration of individual differences that play a role in the acceptance and adoption of new technologies more broadly, as well as AI technologies, to gain a more holistic understanding of the factors contributing to the lack of acceptance and adoption of AI. The main goal of this literature review was to find the individual differences (IDs) associated with the acceptance and adoption of AI technology and general technology. A secondary goal was to create a model based on the acceptance of general technology that could assist in future AI technology research, development, and implementation. This paper identifies several IDs that were found to play a role in the adoption and acceptance of AI technology, as well as 15 specific IDs that were commonly shown to play a role in the adoption and acceptance of general technology. Because of the rapid development of AI technologies in recent years, there is a lack of research examining the acceptance and adoption of AI technologies; however, there is a great deal of research examining the broader acceptance and adoption of technology, and there is significant overlap between the studies that examined general technology acceptance and adoption and those that examined AI-specific technology acceptance and adoption. Because of this, we believe that the research on general technology acceptance and adoption can be used as a foundation and inspiration for future research on AI technology in this area.Systems1100The results of a large literature review are outlined that identifies several IDs that were found to play a role in the adoption and acceptance of AI technology, as well as 15 specific IDs that were commonly shown to play a role in the adoption and acceptance of general technology.Systems["Luke W. Symasek", "Taylor Yeazitzis", "Kristin Weger", "Bryan L. Mesmer"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/35db6bbcf0a9d9dff3ee0718ec5e97bd802ff350 270f08d09b02992c67127c84fe7e7b76e6e203a6Artificial intelligence (AI) can potentially enhance cognitive assessment practices in maternal and child health nursing education. Objectives: To evaluate the reliability, accuracy and precision, and external validity of an AI-assisted answer assessment (4A) program for cognitive assessments in nursing education. Methods: This study is a validation study. Initially, 170 nursing students from northern Thailand participated, with 52 randomly selected for detailed testing. Agreement testing between the 4A program and human experts was conducted using the intraclass correlation coefficient (ICC). Accuracy and precision testing compared 4A scores with human expert assessments via the McNemar test. External validation involved 138 participants to compare the 4A program’s assessments against national examination outcomes using logistic regression. Results: Results indicated a high level of consistency between the 4A program and human experts (ICC = 0.886). With an accuracy of 0.808 and a precision of 0.913, compared to the human expert’s accuracy of 0.923 and precision of 1.000. The McNemar test (χ2 = 0.4, p = 0.527) showed no significant difference in evaluation performance between AI and human experts. Higher scores on the 4A program significantly predicted success in the national nursing examination (OR: 1.124, p = 0.031). Conclusions: The 4A program demonstrates potential in reliably assessing nursing students’ cognitive abilities and predicting exam success. This study advocates for the continued integration of AI in educational assessments and the importance of refining AI systems to better align with traditional assessment methods.Nursing Reports240The 4A program demonstrates potential in reliably assessing nursing students’ cognitive abilities and predicting exam success and advocates for the continued integration of AI in educational assessments and the importance of refining AI systems to better align with traditional assessment methods.Nursing Reports["P. Xuto", "Piyaporn Prasitwattanaseree", "Tareewan Chaiboonruang", "Sujitra Chaiwuth", "Podjanee Khwanngern", "Chadchadaporn Nuntakwang", "Karnjana Nimarangkul", "Wara Suwansin", "Lawitra Khiaokham", "D. Bressington"]2025-02-26T00:00:00https://www.semanticscholar.org/paper/270f08d09b02992c67127c84fe7e7b76e6e203a6 5d333e4fd8e9fc302c2f73c33a5b794a1fc64a0fGenerative Artificial Intelligence (AI) tools such as ChatGPT, Copilot, or Gemini have a crucial impact on academic research and teaching. Empirical data on how students perceive the increasing influence of AI, which different types of tools they use, what they expect from them in their daily academic tasks, and their concerns regarding the use of AI in their studies are still limited. The manuscript presents findings from a quantitative survey conducted among sports students of all semesters in Germany using an online questionnaire. It explores aspects such as students' usage behavior, motivational factors, and uncertainties regarding the impact of AI tools on academia in the future. Furthermore, the social climate in sports studies is being investigated to provide a general overview of the current situation of the students in Germany. Data collection took place between August and November 2023, addressing all sports departments at German universities, with a total of 262 students participating. Our Findings indicate that students have a strong interest in using AI tools in their studies, expecting them to improve their overall academic performance, understand the complexity of scientific approaches, and save time. They express confidence that the proliferation of AI will not compromise their critical thinking skills. Moreover, students are positive about integrating more AI-related topics into the curriculum and about lecturers adopting more AI-based teaching methods. However, our findings also show that students have concerns about plagiarism, lecturer preparedness and their own skills and future skill development.00Findings indicate that students have a strong interest in using AI tools in their studies, expecting them to improve their overall academic performance, understand the complexity of scientific approaches, and save time.["Dennis Kramer", "Anja Bosold", "Martin Minarik", "C. Schyvinck", "Andre Hajek"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/5d333e4fd8e9fc302c2f73c33a5b794a1fc64a0f 84060800728b97da1152b2f662b6f7a021d4e2f7The study examines the risks associated with artificial intelligence (AI) based decision-making and decision-support systems in the decision-making processes of company executives, as well as small and medium-sized enterprises. Due to global trends and digital advancements, company management increasingly faces complex decisions, which AI-based decision-making and decision-support systems may well be suited to support. However, this carries several risks, and the study aims to identify the legal, ethical, and business risks associated with the use of such AI systems, with a particular focus on the decisions made by company executives. The analysis is based on a literature review, which will ultimately be compared with survey responses found in the AI Index Reports published annually by Stanford University.Pécs journal of international and European law00Pécs journal of international and European law["Gergely Kappel"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/84060800728b97da1152b2f662b6f7a021d4e2f7 6e592f41b4bbda13f45eb69ec73755fd7206eddaThe rapid development of generative artificial intelligence brings both opportunities and challenges to higher education, with far - reaching implications especially in the field of legal education. This paper delves into its multi - faceted impacts on legal education, including positive effects such as expanding teaching resources and promoting personalized learning, as well as negative impacts like triggering academic ethical issues and infringing legal rights. Meanwhile, it analyzes the regulatory strategies of China, the European Union, and the United States, and proposes that China should construct a cautious and inclusive regulatory framework, implement an agile governance and full - chain governance system, and clarify the responsibilities of all parties. China has advantages in personal information protection and data legislation. In the future, it should combine the needs of the local AI industry, leverage its existing governance advantages, promote the sustainable development of the generative AI industry, and build a globally influential legal system for governance.Journal of Education and Educational Research40This paper delves into the multi - faceted impacts of generative artificial intelligence on legal education, including positive effects such as expanding teaching resources and promoting personalized learning, as well as negative impacts like triggering academic ethical issues and infringing legal rights.Journal of Education and Educational Research["Lei Li"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/6e592f41b4bbda13f45eb69ec73755fd7206edda 0e70c5c3dfaabfdb9880361c32100c7e9d07ec29Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century. It is poised to reshape medicine, as almost every field of hospital treatment has seen an increase in AI’s presence. In this article, we focus on its impact in the field of anesthesia. We discuss its possible influence on difficult airway management, as it remains one of the most critical and potentially hazardous aspects of anesthesia, often leading to life-threatening complications. The accurate prediction of difficult airways can significantly improve patient safety. We covered the available literature on AI-based models for difficult airway prediction in comparison to traditional forms of airway assessment, as well as the predictive value of ultrasonography. We also address the narrative that AI-based algorithms show high sensitivity and specificity, with which they significantly outperform classical tests based on complex scales and indices.Journal of Clinical Medicine300This article covered the available literature on AI-based models for difficult airway prediction in comparison to traditional forms of airway assessment, as well as the predictive value of ultrasonography.Journal of Clinical Medicine["Mateusz Wilk", "Wojciech Pikiewicz", "Krzysztof Florczak", "Dawid Jak\u00f3bczak"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/0e70c5c3dfaabfdb9880361c32100c7e9d07ec29 e458f813846b2742acec358f01a60ea99cd441b0The advancement of the underlying technology and hardware devices of the Internet of Things (IoT) has led to the emergence of several new applications that are influencing the progress of human society in the era of the Artificial Intelligence IoT (AIoT). The application of AIoT has not only revolutionized the efficiency of human social life but also brought about moral and ethical hazards as well as legal issues. At a technological level, the algorithmic mechanism of AI driving is more complex than typical machine learning and requires regulation because to its automated decision-making process. The design of algorithmic regulation for AI driving should prioritize system safety, continuous improvement of regulation technology, implementation of algorithmic audit mode, and enhancement of overall efficiency in regulating algorithms. International Journal of Computer Science & Information Technology (IJCSIT)80The design of algorithmic regulation for AI driving should prioritize system safety, continuous improvement of regulation technology, implementation of algorithmic audit mode, and enhancement of overall efficiency in regulating algorithms.International Journal of Computer Science and Information Technology["Lei Li", "Chuyun Wang"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/e458f813846b2742acec358f01a60ea99cd441b0 32a055263f2f5d4b9cbfe18917cffe7f3bd79205Hypothalamic obesity (HO), which results from dysfunction or damage to the hypothalamus, is often characterized by uncontrollable weight gain, altered metabolic function, and an increased risk of associated comorbidities, including cardiovascular disease and diabetes. Despite its clinical significance, therapeutic options for HO remain limited and largely ineffective. This case report describes the case of a 48-year-old female patient with a history of traumatic brain injury (TBI) presented with severe, progressive obesity, developing post-traumatic hypothalamic dysfunction. The patient had a BMI of 44 kg/m² and had been unsuccessfully treated with various weight-loss interventions, including lifestyle modifications and pharmacotherapy. Due to previous unsuccessful interventions another approach using the artificial intelligence (AI) driven predictive models in optimizing leptin therapy for a patient with HO was used. This model functions by integrating clinical data, including genetic, hormonal, and metabolic biomarkers, an AI model was designed to predict individualized leptin dosage, demonstrating the potential for personalized treatment in managing HO. The results indicate that AI can be a powerful tool in refining leptin therapy, offering new hope for patients with HO.Journal of Medical Imaging and Case Reports160The case report describes the case of a 48-year-old female patient with a history of traumatic brain injury presented with severe, progressive obesity, developing post-traumatic hypothalamic dysfunction, and indicates that AI can be a powerful tool in refining leptin therapy, offering new hope for patients with HO.Journal of Medical Imaging and Case Reports["Niyati Rajesh", "Beta Sai Siddartha", "Akkineni Sreshta", "Sai Mukund Koneru"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/32a055263f2f5d4b9cbfe18917cffe7f3bd79205 0a1e7387fe07a53b2b9264f2b40d3532ba11a07fThroughout the history from pre-industry 4.0 to post-industry 4.0, cybersecurity at banks has undergone significant changes. Pre-industry 4.0 cyber security at banks relied on individual security methods that were highly manual and had low accuracy. When moving to post-industry 4.0, cybersecurity at banks had a major turning point with security methods that combined different technologies such as Artificial Intelligence (AI), Blockchain, IoT, automating necessary processes and significantly increasing the defence layer for banks. However, along with the development of new technologies, the current challenge of cybersecurity at banks lies in scalability, high costs and resources in both money and time for R&D of defence methods along with the threat of high-tech cybercriminals growing and expanding. This report goes from introducing the importance of cybersecurity at banks, analyzing their management, operational and business objectives, evaluating pre-industry 4.0 technologies used for cybersecurity at banks to assessing post-industry 4.0 technologies focusing on Artificial Intelligence and Blockchain, discussing current policies and practices and ending with discussing key advantages and challenges for 4.0 technologies and recommendations for further developing cybersecurity at banks.00This report goes from introducing the importance of cybersecurity at banks, analyzing their management, operational and business objectives, evaluating pre-industry 4.0 technologies used for cybersecurity at banks to assessing post-industry 4.0 technologies focusing on Artificial Intelligence and Blockchain, discussing current policies and practices and ending with discussing key advantages and challenges for 4.0 technologies.["Tue Nhi Tran"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/0a1e7387fe07a53b2b9264f2b40d3532ba11a07f 59276ec397907b5e83891c64022ab7a63f78786cThis paper explores the application value of Artificial Intelligence (AI) in the field of English education and the challenges it faces, with a focus on the innovative role of the domestic AI assistant KIMI in leading advancements in English education. Studies indicate that advanced technologies like VR and AR, combined with AI, can greatly enhance English learning resources, improve teaching efficiency, and foster students' intercultural communication skills. As a representative of domestic AI, KIMI AI, leveraging its outstanding algorithms and big data processing capabilities, has introduced disruptive changes in teaching models, educational resources, and interactive platforms for English education. However, the application of AI in English education also faces challenges such as technological bottlenecks, lack of emotional and social interaction, and concerns over data privacy and security. This paper focuses on a series of in-depth case studies of KIMI AI’s integration into English education, thoroughly analyzing its integration pathways in teaching models, educational resources, and teacher training. Furthermore, strategies are proposed to address challenges in technology, education, and ethical and legal aspects. The paper concludes with future development of the deep integration of AI and English education.Academic Journal of Management and Social Sciences40Academic Journal of Management and Social Sciences["Xingyu Zhou"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/59276ec397907b5e83891c64022ab7a63f78786c a0bea82a16067196e373448d725e57f99d3dae16The importance of conducting a comparative study of awareness and usage of Artificial Intelligence (AI) between HR and Finance professionals lies in understanding how AI adoption varies across these two critical organizational functions. HR professionals leverage AI to streamline recruitment, enhance employee engagement, and optimize workforce management, while finance professionals use AI for financial forecasting, fraud detection, and risk management. By examining the levels of awareness and specific applications in both domains, the study can highlight gaps in understanding and adoption, enabling organizations to develop targeted training programs and strategic interventions to enhance AI integration. Such insights are crucial for ensuring that professionals in both fields are equipped to harness AI’s potential effectively. Moreover, this study is significant as it provides a framework for assessing the impact of AI on organizational efficiency, decision-making, and innovation across diverse functions. By comparing the experiences of HR and Finance professionals, the research can reveal industry-specific challenges and opportunities, informing policy-making and investment in AI technologies. It also helps identify best practices in AI implementation that can be cross-applied to other functions, fostering a more cohesive and adaptive organizational culture. Ultimately, this research contributes to the broader discourse on AI’s role in reshaping the workforce and driving business transformation.Journal of Information Systems Engineering & Management140This study provides a framework for assessing the impact of AI on organizational efficiency, decision-making, and innovation across diverse functions, and reveals industry-specific challenges and opportunities, informing policy-making and investment in AI technologies.Journal of Information Systems Engineering and Management["Dr. Sucheta Kanchi", "Dr. Shweta Joglekar", "Dr. Manasi Javadekar", "Dr. Sneha Manohar Aarekar", "Dr. Trupti Desai"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/a0bea82a16067196e373448d725e57f99d3dae16 a55885ea1057e921545d7ddfd83bf3a012f0263aUsing regression analysis and statistical methods, this study explores the impact of artificial intelligence (AI) on consumer portraits, focusing on its self-learning ability and adaptability to new environments. With the rapid development of AI technology, its transformative potential and application value in various fields of social economy continue to emerge. The results show that AI, powered by big data, can accurately characterize consumers, significantly improving the efficiency of personalized recommendation, precision marketing, and intelligent customer service. This plays a critical role in improving user satisfaction and market competitiveness of enterprises. However, the research also identifies limitations in AIs performance when the amount and quality of data are insufficient. In complex situations or value trade-offs, AI struggles to completely replace human judgment and ethical thinking ability. Overall, while AI holds great potential in consumer portraits, it is still necessary to further optimize the algorithm model, expand data sources, and strengthen the protection mechanism for data privacy and security. These improvements will lay the foundation for a wider range of cross-domain applications in the future.Advances in Economics, Management and Political Sciences00Overall, while AI holds great potential in consumer portraits, it is still necessary to further optimize the algorithm model, expand data sources, and strengthen the protection mechanism for data privacy and security to lay the foundation for a wider range of cross-domain applications in the future.Advances in Economics, Management and Political Sciences["Xien Mei"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/a55885ea1057e921545d7ddfd83bf3a012f0263a 0da9cfa8851f68a15faf0b7a47ca7ce5e9695a51We aim to describe the development of a conversational agent (CA) for parenting, termed PAT (Parenting Assistant platform), to demonstrate how artificial intelligence (AI) can enhance parenting skills.Behavioral problems are the most common issues in childhood mental health. Developing and disseminating scalable interventions to address early‐stage behavioral problems are of high priority. Artificial intelligence (AI)‐based CAs can offer innovative methods to deliver parenting interventions to reduce behavioral problems. CAs have the capability to interact through text or voice conversations and can undergo training using evidence‐based parenting programs. However, research on CAs for parenting and behavioral problems is limited.The development of PAT consisted of three phases: Phase 1 was purely rule‐based, Phase 2 was hybrid (rule‐based format plus large language models), and Phase 3 featured an agentic architecture. The latest version of PAT includes prompt engineering, guardrails, retrieval‐augmented generation, few‐shots learning, context, and memory management through agentic architecture. Although comprehensive empirical results are pending, the iterative development and enhancement of PAT indicate the potential for effective digital intervention. The agentic architecture of the latest version of PAT aims to provide robust, context‐aware interactions to support parenting challenges.CAs have the potential to reach a broader population of parents and deliver personalized interventions tailored to their specific needs. Moreover, CAs are structured to provide timely support, which can enhance family dynamics and contribute to improved long‐term outcomes for both parents and children.AI‐based CAs can be used as alternatives to waitlists; as digital cotherapists; and implemented in health care, mental health, and school settings. The potential benefits and risks of the different types of CA and features are discussed.Family Relations350The iterative development and enhancement of PAT indicate the potential for effective digital intervention, and the agentic architecture of the latest version of PAT aims to provide robust, context‐aware interactions to support parenting challenges.Family Relations["Milagros Escoredo", "K. Mostovoy", "Ross Schickler", "Alexis Bechtel", "Jennah Shagan", "Eduardo L. Bunge"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/0da9cfa8851f68a15faf0b7a47ca7ce5e9695a51 09d882e415d67a1e5c20cf5865cd1ae165f61d08Clinical Rheumatology590A comprehensive analysis of cutting-edge AI applications in rheumatology, highlighting deep learning models for imaging diagnostics, AI-powered genomic analysis, and wearable health technologies for continuous disease monitoring demonstrate that AI enhances diagnostic precision, facilitates early disease detection, and enables personalized therapeutic strategies.Clinical rheumatology["Mazen Al Zo'ubi"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/09d882e415d67a1e5c20cf5865cd1ae165f61d08 0593a98c57149c20f6f2cff679137ef143bb84a6The phenomenal growth of digital learning platforms has brought new learner engagement and retention challenges to higher education. This study proposes a framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization to meet the challenges of enhanced adaptability, motivation, and learning outcomes in online environments. Key design elements were identified through literature reviews and consultations with instructional design experts, leading to the development an adaptive learning platform prototype. The prototype underwent an eight-week pilot study with 250 Prince Sattam Bin Abdulaziz University (PSAU) students randomly assigned to a control group (non-adaptive system) or an experimental group (adaptive system). Data sources included pre- and post-tests, platform engagement analytics, and learner perception surveys. The results showed that the adaptive group outperformed the control group in the post-test scores (M = 85.2, SD = 6.4 vs. M = 78.5, SD = 7.2) and motivation levels (M = 4.2, SD = 0.7 vs. M = 3.6, SD = 0.8). Additionally, 82% of the adaptive group achieved mastery-level performance compared to 64% in the control group. These findings demonstrate the potential of integrating game mechanics and AI-driven personalization to transform digital learning, offering a roadmap for scalable, data-driven adaptive platforms. Future research will address long-term retention and diverse subject applications.Education sciences00A framework that integrates game mechanics—leveling systems, badges, and timely feedback—with artificial intelligence (AI)-driven personalization to meet the challenges of enhanced adaptability, motivation, and learning outcomes in online environments is proposed.Education Sciences["Fawad Naseer", "Muhammad Nasir Khan", "Abdullah Addas", "Qasim Awais", "Nafees Ayub"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/0593a98c57149c20f6f2cff679137ef143bb84a6 750d7c4b915a662336cd7c34db38b385adae6f6dThe development of artificial intelligence (AI) has had a significant impact on cybersecurity, both as a defense tool and as a threat. One of the biggest emerging risks is deepfake attacks and AI-based cyberattacks, which are increasingly difficult to detect and mitigate. Deepfake technology, which uses Generative Adversarial Networks (GANs), enables video and audio manipulation with a high level of realism, which can be used for disinformation, fraud, and threats to digital security systems. This research aims to analyze cybersecurity threats caused by deepfakes and AI-based attacks, evaluate the effectiveness of conventional security systems in dealing with them, and propose more adaptive AI-based mitigation strategies. The method used in this study is a literature study with a qualitative approach, collecting data from various academic sources, industry reports, and regulations related to cybersecurity. The analysis was carried out using thematic analysis and data triangulation techniques, which allowed mapping of the latest threat trends and solutions that had been implemented. The results show that signature-based security is increasingly ineffective in the face of evolving AI attacks. The implementation of AI in cyber defense systems, such as machine learning-based detection, Zero Trust Architecture (ZTA), and incident response automation systems, is the main solution in dealing with increasingly complex threats. Therefore, an adaptive security approach that combines technology, regulatory policies, and public education is needed to reduce the risk of deepfake attacks and other AI threats.Journal of the American Institute260An adaptive security approach that combines technology, regulatory policies, and public education is needed to reduce the risk of deepfake attacks and other AI threats, and shows that signature-based security is increasingly ineffective in the face of evolving AI attacks.Journal of the American Institute["Ratnawita Ratnawita"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/750d7c4b915a662336cd7c34db38b385adae6f6d d557994bc5c751123c36f5591e6d8574c924b50bArtificial intelligence (AI) technologies are reshaping human-machine relationships in today’s digital world, offering new opportunities in the field of communication. AI-based tools are widely used across various sectors, from education to professional practices, prompting interdisciplinary discussions. Integrating AI applications in universities provides a new experience in communication education, generating new debates around ethical issues, university regulations, student-academic relationships. This study aims to explore the AI usage experiences, perspectives of undergraduate students in universities, focusing on technical knowledge, consumption routines, and the context of original production and ethics. The research, employing a qualitative methodology, was conducted through structured in-depth interviews with twelve undergraduate students across three different departments in the faculty of communication at a private university in Ankara. The findings indicate that AI usage experiences differ according to demographic variables; routine use of AI has become widespread in undergraduate education, but awareness of original production and ethical responsibility remains limited.ARTS: Artuklu Sanat ve Beşeri Bilimler Dergisi210The findings indicate that AI usage experiences differ according to demographic variables; routine use of AI has become widespread in undergraduate education, but awareness of original production and ethical responsibility remains limited.ARTS: Artuklu Sanat ve Beşeri Bilimler Dergisi["Sevil Bal", "S\u0131la Tan\u0131\u015f\u0131k"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/d557994bc5c751123c36f5591e6d8574c924b50b 53ef0b1ad4b93633861ae71e4d7b3fe8aa094c97AI &amp; SOCIETY210Different perceptions amongst European countries compared to other western counterparts to perceptions of data privacy support the contention that the mere presence of AI regulation may be sufficient to alter perceptions in WEIRD societies, regardless of whether the regulations are necessary or even effective in increasing user safety.AI &amp; SOCIETY["Bronwyn Howell"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/53ef0b1ad4b93633861ae71e4d7b3fe8aa094c97 80d27758fb0cd47691d2da12030b6fab106fb333Artificial Intelligence (AI) has enormous potential to make a transformative impact in multiple fields. It has made significant strides in Higher Education by reshaping traditional administrative processes, learning, leadership, and teaching. This review explores the substantial impact of integrating AI in Higher Education Institutions (HEIs), from improving education delivery to enhancing student outcomes and streamlining administrative processes and strategic leadership. By catering to the diverse learning needs of students with the help of tools that directly affect academics, monitor student engagement and performance, and provide data-driven interventions, AI offers what the HEIs have long been waiting for to revolutionize the overall Higher Education landscape. This review also highlights that with AI's ability to streamline administrative tasks by enhancing admissions and enrolment processes, academic records management system, and financial aid and scholarships processes, AI not only facilitates improving the overall processes but also makes staff and faculty members focus less on mundane and monotonous tasks, hence concentrating more on the responsibilities and strategic initiatives that require focused attention. We identified that the key to unlocking the significant potential of AI is responsible strategic leadership. Strategic leadership requires aligning AI integration goals with the strategic mission of HEIs, fostering an environment ready to embrace innovation and ensuring that the required accountability and governance frameworks are in place for AI integration and usage. It is also the role of leadership to consider ethical considerations, collaborations with the relevant stakeholders, concerns about job displacement, and potential biases, ensuring that AI is used to its full potential for the benefit of faculty, staff, students, and society. We conclude the paper with AI-driven future implications, i.e., emerging technologies, continuous enhancement and AI-based enhanced research accomplishments.Frontiers in Education1120The substantial impact of integrating AI in Higher Education Institutions (HEIs), from improving education delivery to enhancing student outcomes and streamlining administrative processes and strategic leadership is explored, with AI-driven future implications, i.e., emerging technologies, continuous enhancement and AI-based enhanced research accomplishments.Frontiers in Education["Suleman Ahmad Khairullah", "Sheetal Harris", "Hassan Jalil Hadi", "Rida Anjum Sandhu", "Naveed Ahmad", "M. Alshara"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/80d27758fb0cd47691d2da12030b6fab106fb333 23536895f4a8fac3ff5d3009eea7675c0c695c9fThe public health workforce is transitioning significantly, with many experienced professionals retiring and an increasing demand for new competencies. Integrating Artificial Intelligence (AI) into public health education presents an innovative solution to address these challenges. AI provides opportunities for personalized learning, interdisciplinary collaboration, and scalable training, but AI introduces ethical challenges that must be addressed. Key regulations such as the Family Educational Rights and Privacy Act (FERPA) and the Health Insurance Portability and Accountability Act (HIPAA) are crucial in guiding the ethical use of AI in education and practice. AI can enhance public health education by fostering interprofessional learning and preparing a workforce capable of meeting evolving health challenges. Ethical considerations surrounding data privacy, bias, and regulatory compliance are examined. The importance of AI literacy for seasoned professionals is discussed, as they will be pivotal in mentoring the next generation of public health workers. The COVID-19 pandemic has accelerated technology adoption, making this an ideal time to integrate AI into public health education. Although AI offers valuable insights and tools, public health professionals must learn to assess AI outputs to ensure equitable and effective outcomes critically. A balanced approach to AI in public health education that combines technological innovation with ethical responsibility is needed for the field's advancement. Establishing guidelines and governance frameworks for AI integration in public health education, promoting AI literacy in health education programs, ensuring data privacy and ethical use, and advocating for interprofessional partnerships to shape AI policies to support public health goals are critical.Health Promotion Practice30The importance of AI literacy for seasoned professionals is discussed, as they will be pivotal in mentoring the next generation of public health workers and ensuring data privacy and ethical use are addressed.Health promotion practice["Ashley S Love", "Chunling Niu", "Joan Labay-Marquez"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/23536895f4a8fac3ff5d3009eea7675c0c695c9f 617b524338a6106930356408bbf2268e7d915a8ePurpose: This study investigates the factors influencing retail firms' intentions to adopt Artificial Intelligence (AI) to enhance customer retention and loyalty in Dhaka, Bangladesh. The research focuses on examining how perceived usefulness, perceived ease of use, competitive pressure, technological readiness, and organizational innovativeness influence retail entrepreneurs’ adoption of AI as a strategic tool for customer engagement. Research Methodology: A quantitative research design was employed, incorporating a hypothetical-deductive approach. The study utilized a cross-sectional design, drawing a sample of 250 retail firms through stratified random sampling in Dhaka. Data were collected using structured questionnaires and analyzed using statistical techniques to assess the relationships between the variables. Results: The study identified that all five factors perceived usefulness, perceived ease of use, competitive pressure, technological readiness, and organizational innovativeness positively and significantly influence retail entrepreneurs' intentions to adopt AI. These findings emphasize the crucial role of both technological and organizational dynamics in driving AI adoption decisions within the retail sector. Limitations: The research is geographically confined to retail firms in Dhaka, which may limit the generalizability of the findings to other regions or countries. Furthermore, the study's cross-sectional design restricts the ability to monitor AI adoption trends over time, indicating that future research could benefit from employing longitudinal designs and encompassing a broader geographical scope. Contribution:  This study provides valuable insights for retail managers and entrepreneurs seeking to leverage AI to enhance customer loyalty. It underscores the importance of fostering technological readiness and cultivating a culture of innovation within retail firms. The research contributes to the expanding body of knowledge on AI adoption in emerging markets, particularly concerning customer retention strategies in the retail sector.Annals of Management and Organization Research630The study identified that all five factors perceived usefulness, perceived ease of use, competitive pressure, technological readiness, and organizational innovativeness positively and significantly influence retail entrepreneurs' intentions to adopt AI.Annals of Management and Organization Research["Ismoth Zerine", "Younis Ali Biswas", "Md Zulkernain Doha", "Humayra Mehreen Meghla", "Mohammad Rashed Hasan Polas"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/617b524338a6106930356408bbf2268e7d915a8e d543ff213d18f5976761b04902244c2de6b979dcThe development of artificial intelligence (AI) has brought about various significant changes in various sectors of life, including industry, education, and health. However, advances in AI also pose moral and ethical challenges, especially related to transparency, fairness, and accountability in their use. In the context of ethical theology, moral responsibility in the development of AI is an important aspect that needs to be considered to ensure that this technology is developed and applied responsibly in accordance with applicable human values and moral principles. This research aims to examine how the principles of ethical theology can provide a normative foundation in the development of more ethical and responsible AI. The method used in this study is a literature study by analyzing various academic sources, including scientific journals, books, and policy documents that discuss the relationship between AI, morality, and ethical theology. The data collected were then analyzed using the qualitative content analysis method to identify the main findings in this study. The results of the study show that the development of ethical AI requires the integration of moral principles such as justice, love, accountability, and respect for human dignity. Additionally, human regulation and oversight remain necessary to ensure that AI is not used in a way that harms certain individuals or groups. Therefore, the ethical theology approach can be one of the solutions in formulating a more equitable and responsible AI policy.Journal of the American Institute330The results of the study show that the development of ethical AI requires the integration of moral principles such as justice, love, accountability, and respect for human dignity, and the ethical theology approach can be one of the solutions in formulating a more equitable and responsible AI policy.Journal of the American Institute["Kesumawati Kesumawati", "Jan Lukas Lambertus Lombok", "Otieli Harefa"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/d543ff213d18f5976761b04902244c2de6b979dc ac372dc38ff1ed30a2f7de8bd259e9a5d083cdeaThis study aims to provide an overview of the literature on artificial intelligence in education based on bibliometric evaluation of various journal articles published in the Scopus database, and identify knowledge gaps as a source for future research. The approach uses VOSviewer, SEforRA, and Publish or Perish for bibliometric analysis. The analysis's findings show that artificial intelligence in education research tends to expand on the Scopus database, with a peak observed in 2021 and a trend from 2018 to 2023. Numerous themes or keywords that potentially form the basis for additional research have emerged from the expanding corpus of research on artificial intelligence in education. Finally, the bibliometric analysis provides information on AI developments in educational research, which opens the door for further exploration, collaboration, and innovation in this rapidly growing field and offers valuable insights to understand future trends, challenges, and opportunities.TEM Journal00The analysis's findings show that artificial intelligence in education research tends to expand on the Scopus database, with a peak observed in 2021 and a trend from 2018 to 2023.TEM Journal["Muhammad Turmuzi", "R. Y. Tyaningsih"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/ac372dc38ff1ed30a2f7de8bd259e9a5d083cdea afb4600eeb45b7fc2e7f470f2d21789775324f0cRheumatoid arthritis (RA) is a chronic autoimmune condition that causes joint inflammation and damage and significantly affects patients' quality of life. Over the past 5 years, the application of artificial intelligence (AI), particularly deep learning, has resulted in notable advancements in the field of rheumatology. This review explores these developments, highlighting how AI has enhanced the precision and reliability of imaging techniques, such as radiography, ultrasound imaging, and magnetic resonance imaging, for managing RA. In addition, the integration of diverse data sources, including clinical records, genetic profiles, and imaging examinations, has facilitated more accurate predictions and formulation of personalized treatment strategies. However, challenges such as data variability, complexity of AI models, and ethical considerations remain. Addressing these issues is essential for further progress. Future research should focus on improving data integration, model interpretability, and ethical deployment of AI in clinical practice. These advancements have the potential to significantly improve the diagnosis and management of RA, moving closer to the goals of precision medicine in this field.Rheumatology &amp; Autoimmunity750How AI has enhanced the precision and reliability of imaging techniques, such as radiography, ultrasound imaging, and magnetic resonance imaging, for managing RA is highlighted, highlighting how advancements have the potential to significantly improve the diagnosis and management of RA.Rheumatology &amp; Autoimmunity["Yiduo Sun", "Jin Lin", "Weiqian Chen"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/afb4600eeb45b7fc2e7f470f2d21789775324f0c beb061b649644c05eed355ce38dd5bc07bf3ea72Recent advances in artificial intelligence (AI) have significantly transformed neuroendovascular procedures, offering innovative solutions for image analysis, procedural assistance, and clinical decision-making. This review examines the current state and future potential of AI applications in neuroendovascular interventions, focusing on 3 topics: AI-based image recognition, real-time procedural assistance, and future developments. From a research perspective, deep learning algorithms have demonstrated reasonable accuracy in vascular structure analysis and device detection, successfully identifying critical conditions such as vascular perforation, aneurysm location, and vessel occlusions. Real-time AI assistance systems may have potential clinical utility in various procedures, including carotid artery stenting, aneurysm coiling, and liquid embolization, potentially enhancing procedural safety and operator awareness. The future of AI in neuroendovascular procedures shows promise in integration with robotic systems and applications in medical education. While current systems have some limitations, ongoing technological advances suggest an expanding role of AI in enhancing procedural safety, standardization, and patient outcomes.Journal of Neuroendovascular Therapy240The future of AI in neuroendovascular procedures shows promise in integration with robotic systems and applications in medical education, and an expanding role of AI in enhancing procedural safety, standardization, and patient outcomes is suggested.JNET Journal of Neuroendovascular Therapy["Kenichi Kono"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/beb061b649644c05eed355ce38dd5bc07bf3ea72 e24052120125d606a6cd5e286955cdf47634c900Understanding the integration of artificial intelligence (AI) in academic institutions is crucial given Kuwait’s commitment to innovation and educational excellence. This study explores the integration of AI into higher education in Kuwait, revealing both positive perceptions and critical concerns. Qualitative insights from faculty and students highlight AI’s potential to improve learning across multiple academic disciplines. Quantitative data, collected from 310 students, shows that a majority of students hold optimistic views on AI’s effectiveness in enhancing educational processes and project-based activities. However, concerns were raised about the ethical implications, high costs, data privacy, and the complexity of AI tools. Importantly, no statistically significant difference was found between male and female students’ views on AI’s role in education. These findings are significant for policymakers and educators, guiding how to address practical and ethical challenges while facilitating the effective incorporation of AI into the educational system.International Journal of Interactive Mobile Technologies (iJIM)00Quantitative data shows that a majority of students hold optimistic views on AI’s effectiveness in enhancing educational processes and project-based activities, however, concerns were raised about the ethical implications, high costs, data privacy, and the complexity of AI tools.International Journal of Interactive Mobile Technologies (iJIM)["N. Al-Huwail", "A. Al-Hunaiyyan", "Shaikhah Alainati", "Ayman Alhabshi"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/e24052120125d606a6cd5e286955cdf47634c900 f13b5f7350f79d015d2d1abf7e48f071992ba3f4"GapFinder" is an Artificial Intelligence (AI) solution developed to address an important challenge in the field of scientific/academic research, namely: the efficient mapping of research gaps based on existing scientific/academic literature. The exponential increase in all areas of academic publications stresses the relevance of this technological tool to help researchers identify gaps that are still unexplored in academic texts because they are undetected by traditional science mapping techniques to reveal fields for research and innovation. GapFinder applies Natural Language Processing (NLP) algorithms to identify potential research gaps in scientific documents using mining and extracting information techniques from unstructured texts in Portable Document Format (PDF). This AI solution fosters a more comprehensive understanding of the existing literature, emphasizing areas that need further investigation. The present study describes the development of GapFinder, from the conception of the idea to the practical implementation and its availability for access. The methodology used to process and analyze scientific documents in PDF format is described in the paper and followed by a simulation of GapFinder to demonstrate how it can facilitate the work of researchers from the perspective of corpus processing. The study concludes with the importance of innovation in scientific research through the implementation of technologies and methods that can act as catalysts for innovation in science, from the perspective of identifying gaps and new research strategies and also points out some implications for English language teachers and researchers.Studies in English Language Teaching00The study describes the development of GapFinder, from the conception of the idea to the practical implementation and its availability for access, and the importance of innovation in scientific research through the implementation of technologies and methods that can act as catalysts for innovation in science.Studies in English Language Teaching["Vilker Zucolotto Pessin", "K. Finardi", "Celso Alberto Saibel Santos"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/f13b5f7350f79d015d2d1abf7e48f071992ba3f4 9b9782da6f6130ced6e7c539ea748625152011f1 In an era where digital transformation is accelerating rapidly, artificial intelligence and machine learning have emerged as transformative forces, especially in e-commerce. This paper presents a comprehensive literature review that delves into the fundamentals of e-commerce, artificial intelligence, and machine learning, highlighting their key advantages and practical applications. By examining a broad array of studies, this research evaluates the critical role of artificial intelligence and machine learning in reshaping e-commerce and explores the potential these technologies hold for enhancing customer engagement and driving sales. The paper underscores how e-commerce companies leverage artificial intelligence-driven innovations to influence customer behaviour, enhance personalised marketing, and streamline purchasing pathways. However, the path to successful artificial intelligence integration is not without obstacles. Challenges such as organisational resistance, skills shortages, technical limitations, and awareness gaps are notable barriers. Despite these hurdles, the findings suggest that adopting artificial intelligence and machine learning tools positions e-commerce companies for long-term success, offering significant competitive advantages and fostering sustainable growth in an increasingly digital world. Advances in Distributed Computing and Artificial Intelligence Journal740A comprehensive literature review that delves into the fundamentals of e-commerce, artificial intelligence, and machine learning, highlighting their key advantages and practical applications suggests that adopting artificial intelligence and machine learning tools positions e-commerce companies for long-term success.ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal["Fedorko Richard", "Kr\u00e1\u013e \u0160tefan", "Kr\u00e1\u013eov\u00e1 Lenka"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/9b9782da6f6130ced6e7c539ea748625152011f1 c5a27a7b83488b32e614f0a47e064b70379857d4With the continuous development of artificial intelligence technology, generative artificial intelligence copyright infringement occurs from time to time. Generative artificial intelligence may infringe on the personality rights and copyright of others. Clarifying the subject of generative AI infringement and the applicable principles of attribution is the basis for handling such cases. Users, research and development, and platforms of generative artificial intelligence may constitute the subject of generative artificial intelligence infringement. Generative AI generated content infringement attribution should be analyzed according to the specifics of the facts. The first thing to consider is the nature of the generated content and the use scenario, the autonomy of the AI, the user's intent, and the developer's design intent. Secondly, relevant laws and regulations should be improved to ensure that technological development is synchronized with legal protection, and a perfect regulatory mechanism should be established to monitor and copyright audit AI generators in real time to reduce the occurrence of infringement incidents. This paper will discuss the attribution of generative AI content infringement through specific case studies and put forward corresponding legal recommendations.Advances in Economics, Management and Political Sciences00The attribution of generative AI content infringement through specific case studies is discussed and legal recommendations are put forward to reduce the occurrence of infringement incidents.Advances in Economics, Management and Political Sciences["Zexiao Liu"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/c5a27a7b83488b32e614f0a47e064b70379857d4 96430bc74b35df9fb25e39e4b2e7663da0f5081aRechtsmedizin570An overview of the medical school education, forensic pathology training and service and the authors views on the current state, potential applications, challenges and future direction in integrating artificial intelligence into forensic pathology in Australia and New Zealand for the Central European community are provided.Rechtsmedizin["J. Garland", "R. Tse", "S. Stables", "Ugo Da Broi", "B. Ondruschka"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/96430bc74b35df9fb25e39e4b2e7663da0f5081a 3732aaa4669ce3b0bf1272c9f6e2e7f30fd24e1dIn consequence of the ongoing evolution of technology, the global technological level has undergone a qualitative leap. In consequence, we have inaugurated a new era of Artificial Intelligence. Artificial Intelligence (AI) represents the central technology driving both technological revolution and industrial transformation. As a novel and significant domain of Artificial Intelligence, financial intelligence has garnered considerable interest from both academic and industrial circles. The objective of this article is to provide an overview of the principal stages of technology-driven financial development. As a result of ongoing technological advancement, the concept of financial intelligence has emerged and evolved. This article will examine the applications of AI in financial domains, including wealth management, risk management, and financial security. It will also analyse the challenges associated with the use of AI in finance and propose potential solutions. Finally, it will evaluate the current state and advancement of AI in these financial fields, with a view to establishing a foundation for future technological developments in the field of financeAdvances in Economics, Management and Political Sciences00This article will examine the applications of AI in financial domains, including wealth management, risk management, and financial security, and analyse the challenges associated with the use of AI in finance and propose potential solutions.Advances in Economics, Management and Political Sciences["Kanjin Wang"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/3732aaa4669ce3b0bf1272c9f6e2e7f30fd24e1d e7e3f503fedf4c4aaacf08c32cb839e658db8942The present study aimed to investigate the new challenges and opportunities in corporate governance with an emphasis on artificial intelligence and business ethics. It is a mixed study (the qualitative part includes the meta-synthesis and theme analysis, and the quantitative part includes the t-test). In terms of the objective, it is cross-sectional applied-developmental research. The research field to determine the indicators includes all scientific articles and documents relevant to the research subject in the 2015-2024 period. The research population for the semi-structured part includes the academic experts among whom 12 were chosen as the samples using the snowball sampling method until the theoretical saturation is reached. The meta-synthesis of the related literature revealed that 35 primary concepts in 4 categories can be identified. Also, based on the interviews with experts, 43 components were extracted in the form of 11 factors. Through combining and modifying these two parts, 66 primary concepts (elements) and 11 main themes were identified and determined as new challenges and opportunities in corporate governance with an emphasis on artificial intelligence and ethics in business ethics. Therefore, it can be concluded that artificial intelligence provides firms with unparalleled opportunities, although it creates complex challenges. To optimally exploit this technology and create an ethical workspace, the firms should consider the technical, social, and cultural challenges simultaneously, and utilize the existing opportunities to strengthen their position in the market and improve their performance.Journal of Information Systems Engineering & Management600It can be concluded that artificial intelligence provides firms with unparalleled opportunities, although it creates complex challenges, and firms should consider the technical, social, and cultural challenges simultaneously, and utilize the existing opportunities to strengthen their position in the market and improve their performance.Journal of Information Systems Engineering and Management["Mohsen Ebrahimi", "Majid Bajelan"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/e7e3f503fedf4c4aaacf08c32cb839e658db8942 665b8934e4c5a0b2c69419d11db6b0b035bd720cWhat exactly is artificial intelligence, and can it be viewed as an autonomous entity? To explore this, we need to define autonomy and consider the implications for regulation. When we think about regulating an autonomous entity, can AI be classified as a legal person? Is there a need to create a new category of legal personality specifically for AI? This article tackles these key questions by exploring established philosophies concerning legal personhood and evaluating their relevance to the possible conferral of legal rights on AI. The authors also delve into the relatively new concept of electronic personality and its applicability in relation to artificial intelligence. Furthermore, they examine the nature and extent of rights and responsibilities that could be assigned to AI. The methodology employed in this study is primarily based on existing literature and follows a doctrinal approach.Journal of Informatics Education and Research280This article tackles established philosophies concerning legal personhood and evaluating their relevance to the possible conferral of legal rights on AI by exploring the nature and extent of rights and responsibilities that could be assigned to AI.Journal of Informatics Education and Research["Aditi Bharti", "Dr. Gagandeep Kaur"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/665b8934e4c5a0b2c69419d11db6b0b035bd720c a001b511f945b5d5aebd262a0796b1e1fd169115: Artificial intelligence (AI) is completely changing the sports education system by transforming conventional approaches and improving training and educational opportunities. This study explores how artificial intelligence (AI) is changing sports education, emphasising its uses, advantages, and drawbacks. We explore how AI technologies like machine learning, computer vision, and data analytics are being used to improve performance analysis, personalize training programs, and streamline decision - making processes by examining current trends and advancements. We demonstrate the practical application of AI in real - world scenarios, such as talent identification, injury prevention, and sports coaching, through in - depth case studies. According to our research, AI - driven solutions that provide individualized learning experiences and data - driven insights greatly increase the efficacy and efficiency of sports teaching. However, there are drawbacks to integrating AI, such as the need for a strong technological foundation and the need to take ethical considerations into account. According to the results of our research, AI has a great deal of promise to improve sports instruction, but a balanced research approach is required to fully realize these benefits and traverse its intricacies. The sports education system can be greatly enhanced by resolving the issues and utilizing AI's potential, opening the door for more knowledgeable and efficient training approaches.International Journal of Science and Research (IJSR)260The sports education system can be greatly enhanced by resolving the issues and utilizing AI's potential, opening the door for more knowledgeable and efficient training approaches.International Journal of Science and Research (IJSR)["Naren Parthsarthi Desai"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/a001b511f945b5d5aebd262a0796b1e1fd169115 5a52d5eb9ebdcc677dd7561c06aa447831e52e8bArtificial intelligence or AI, a very prominent topic in society at the present time, encapsulates the idea of machines performing tasks in ways that humans would consider ‘smart’. This article expresses the author’s viewpoint about how AI can transform accounting by improving financial reporting, compliance, analyses of data, and detection of fraud patterns. The article also highlights concerns about transparency, ethics, data integrity, privacy, and overreliance resulting from such integration. The author examines existing literature to provide a balanced perspective on the opportunities and challenges of AI in accounting. Additionally, the article contributes to the growing body of knowledge on AI in accounting by offering practical guidance for accountants on effectively integrating AI into their practices. Although challenges have been documented, the potential of AI to enhance efficiency makes it an invaluable asset for the modernization of accounting practices. This frames AI as a strategic asset for organizations seeking to enhance the efficiency and effectiveness of their accounting functions.International Journal on Soft Computing Artificial Intelligence and Applications280The author’s viewpoint about how AI can transform accounting by improving financial reporting, compliance, analyses of data, and detection of fraud patterns is expressed.International Journal on Soft Computing, Artificial Intelligence and Applications["Angel R. Otero"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/5a52d5eb9ebdcc677dd7561c06aa447831e52e8b a43c2e15de9f07e90376fb008dfd1e5f161c48b8Background With over a thousand FDA-approved artificial intelligence/machine learning-enabled medical devices, research and publications is maturing from focusing on the development and internal validation of models to the external validation of models and implementation trials. Foundation models, especially Large Language Models, have spurred additional aspects of AI research related to healthcare, especially with the use of text-based data to address healthcare education and administrative tasks related to patient care. Methods We performed a PubMed search using the terms 'machine learning' or 'artificial intelligence' and '2024', restricted to English language and human subject research on January 1, 2025. Utilizing a deep learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics. We also performed a detailed analysis of the distribution of foundation model-based publications amongst the healthcare specialties. Results For the year 2024, the PubMed search yielded 28,180 articles, of which 1,693 were classified as mature using a BERT model. Following exclusions, 1,551 articles were selected for the final data analysis. Amongst these, the highest number of articles in each specialty originated from Imaging (407), Head and Neck (127), and General (122). The analysis of data types revealed that image data (903 [57.0%]) was still the predominant data type, but the use of text data (525 [33.1%]) had substantially increased. Additionally, we also found that LLMs (479) and AI General (448) category models have overtaken deep learning models (372) in healthcare AI research. For LLM-related publications, we are seeing increasing trends in research related to healthcare education and administrative tasks. Conclusion With the introduction of foundation models, healthcare research trends are changing. The adoption of LLMs and text data types amongst various healthcare specialties, especially for education and administrative tasks, is unlocking new potential for AI applications in healthcare.medRxiv390A PubMed search using the terms 'machine learning' or 'artificial intelligence' and '2024', restricted to English language and human subject research on January 1, 2025, revealed that LLMs and AI General category models have overtaken deep learning models in healthcare AI research.["Raghav Awasthi Msc", "PhD Sai", "Prasad Ramachandran Mbbs", "Shreya Mishra", "PhD MTech", "Dwarikanath Mahapatra", "PhD Hajra BTech", "Arshad Mbbs Aarit", "Atreja Anirban", "Bhattacharyya", "M. Ms", "MS NishantSingh", "Fasa Jacek B. Cywinski MD", "Ashish K Khanna", "F. Ms", "Mph Chintan Dave Md", "MD Avneesh Khare", "D. P. M. M. F. A. Md", "Facsfaap Piyush Mathur Md"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/a43c2e15de9f07e90376fb008dfd1e5f161c48b8 cb6c405f30411b017732a6b64f959ed226f8ff2bIndian Journal of Labour Economics240The study finds that the AI workforce in the OECD countries is still relatively small—less than 0.3% of employment—but growing rapidly, and the size and growth of the AI workforce over time is growing rapidly.The Indian Journal of Labour Economics["Andrew Green", "Lucas Lamby"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/cb6c405f30411b017732a6b64f959ed226f8ff2b 845d15516debcbc47c9d1a8b00b290fb14afcfd3 Claims of integrating artificial intelligence (AI) into mobile applications for pronunciation training date back to at least 2011 with the iOS app T Accent (Arivoc Education International, 2011), which used automatic speech recognition (ASR) to provide “Goodness of Pronunciation” (GOP) ratings (Witt & Young, 2000). AI has advanced significantly since 2011, most noticeably with the 2022 release of OpenAI’s ChatGPT, which made chatbots powered by generative AI more widely available. AI has led to new applications in pronunciation apps that can evaluate pronunciation and integrate communicative role-play activities. This article examines ASR and chatbot integration in three iOS apps: ELSA Speak, Loora, and Vocal Image. Feedback provided by these apps is frequently inaccurate and often limited to consonant and vowel sounds. This article cautions teachers and learners about the current limitations of these apps and provides recommendations for incorporating AI-powered tools into today’s pronunciation classrooms.Journal of Second Language Pronunciation150This article examines ASR and chatbot integration in three iOS apps: ELSA Speak, Loora, and Vocal Image and cautions teachers and learners about the current limitations of these apps and provides recommendations for incorporating AI-powered tools into today’s pronunciation classrooms.Journal of Second Language Pronunciation["DJ Kaiser"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/845d15516debcbc47c9d1a8b00b290fb14afcfd3 467df82ad891683ea38a80565530c8906a191200AI (Artificial Intelligence) has turned out to be a life-changing technology in PR (Public Relations), and in other fields of strategic communications, even though its adoption and applications remain undiscovered. This research therefore addresses the gap by leveraging the (UTAUT) Unified Theory of Acceptance and Use of Technology model to investigate the integration and acceptance of AI among the PR professionals in the UAE (United Arab Emirates). Using a mixed method technique, data was collected via interviews and surveys with 103 PR practitioners, offering extensive insights into aspects that drive and hinder AI (Artificial Intelligence) adoption. The research findings reveal that AI technologies notably improve PR functions by modernizing content creation to encourage robust stakeholder relationships, while enabling real time audience engagements. Key aspects that impact adoption is inclusive of ease of use, the ability to effectively customize messaging, and operational efficiency. Nevertheless, barriers like ethical concerns, limited training, and inefficient regulation implementation policies persist. This study pinpoints the potential of AI to revolutionize PR practices while stressing the need for targeted strategies and policies to address the adoption challenges. Through contribution to the body of understanding on AI in PR, the research offers actionable perceptions for practitioners and policy makers who aim to use AI’s opportunities effectively and responsibly.Journal of Information Systems Engineering & Management160The research findings reveal that AI technologies notably improve PR functions by modernizing content creation to encourage robust stakeholder relationships, while enabling real time audience engagements.Journal of Information Systems Engineering and Management["Joudi Ahmad", "Yasser Beni", "Ahmed Farouk Radwan", "Nawal Abdel", "Razaq Askar"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/467df82ad891683ea38a80565530c8906a191200 262b66928c23010bfb2bd21df37cceada004ea62With the rapid development of digital technology in recent years, the form of art presentation has changed significantly. The traditional way of presenting art in the development of science and technology today faces challenges to its survival. Thus, the integration of art and technology has become a general trend. However, the full impact of artificial intelligence on theater performance has not been fully studied, especially in terms of actor-audience interaction and performance techniques. Traditional theater performance teaching has followed the model of training actors inherited from previous generations for nearly a century. This model has produced many great artists and practitioners for China and the world. It has also enabled the art of theater to constantly reinvent itself and carry on the tradition of the past. In order to celebrate the 60th anniversary of the founding of New China, Liu Heng created "Wutou Guild", which is regarded as "the revival of Chinese drama", and it is even more worth mentioning that this drama is also Mr. Liu Heng's "debut drama", which was premiered on September 15th, 2009 at the Beijing People's Art Theater. It was premiered on September 15, 2009 at the Beijing People's Art Theater. Artificial Intelligence is not only an auxiliary tool for actors, but also a "partner" for actors, helping them to deeply understand their roles, expand the boundaries of their acting skills, provide personalized feedback, and even stimulate new creative inspirations. constantly challenge themselves and improve their artistic performance.Highlights in Art and Design60Artificial Intelligence is not only an auxiliary tool for actors, but also a "partner" for actors, helping them to deeply understand their roles, expand the boundaries of their acting skills, provide personalized feedback, and even stimulate new creative inspirations.Highlights in Art and Design["Yanchen Zuo"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/262b66928c23010bfb2bd21df37cceada004ea62 db1d14e55d3eb79f31c529506b006a6df722305bArtificial intelligence has played a significant role in the medical field of cardiovascular diseases, especially in cardiac imaging. Heart diseases affect the function of the heart, causing the heart to be unable to supply blood normally, thus affecting the functions of the body's organs and systems. This paper summarizes the applications of algorithms such as deep learning and machine learning in the clinical treatment of cardiovascular diseases and their differences from two aspects: medical imaging and heart signals. It also reviews how these algorithms assist doctors in making more accurate diagnoses in the early screening, data analysis, and postoperative detection of cardiovascular diseases. It studies the optimization of traditional algorithms and image reconstruction by artificial intelligence-based imaging detection methods, as well as the specific application ways of deep learning in frame selection, segmentation, and disease assessment. Meanwhile, it discusses the advantages of convolutional neural networks (CNN) and recurrent neural networks (RNN) in performing classification tasks in Electrocardiogram (ECG) and puts forward the possibility of using 3D computer simulation synthesis system data to improve the generalization ability of machine learning models. In addition, this paper also introduces the limitations and challenges in the clinical application of AI algorithms in this field as well as their future development, providing a new perspective for the further prevention and control of cardiovascular diseases. The computational methodologies expounded within this review constitute a formidable asset in the realm of medical discoveries, and their translation into the clinical domain holds the potential to precipitate promising advancements.Applied and Computational Engineering00The optimization of traditional algorithms and image reconstruction by artificial intelligence-based imaging detection methods, as well as the specific application ways of deep learning in frame selection, segmentation, and disease assessment are studied.Applied and Computational Engineering["Jialin Li", "Mengyuan Zhu"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/db1d14e55d3eb79f31c529506b006a6df722305b 35e9d6df0b762f2e897469abc46c4927ba7545fbJournal of Medical Imaging and Radiation Sciences400There is a requirement for more radiology AI research focussing on end user or imaging professional involvement and their preferences, due to the lack of standardised outcome measures, lack clear findings regarding ideal user interfaces and lack of inclusion of radiographers.Journal of medical imaging and radiation sciences["Avneet Gill", "C. Rainey", "L. McLaughlin", "Ciara Hughes", "R. Bond", "J. McConnell", "S. McFadden"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/35e9d6df0b762f2e897469abc46c4927ba7545fb 62495b965ed5ebd7ba10b20c7ab487d23a236ee4NEJM AI340NEJM AI["R. A. Taylor", "Christopher Chmura", "J. Hinson", "Benjamin Steinhart", "Rohit B. Sangal", "Arjun K. Venkatesh", "Haipeng Xu", "Inessa Cohen", "Isaac V. Faustino", "Scott Levin"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/62495b965ed5ebd7ba10b20c7ab487d23a236ee4 99e35f737e17b10df05532e09c4c6616fd99b174Speech recognition technology provides users with a more convenient and efficient interactive experience by automatically recognizing and processing speech signals. Starting from the acquisition and processing of speech signals, the foundation of speech recognition models and algorithms, and the application research of deep learning and neural networks, this article focuses on exploring the specific applications of speech recognition technology in fields such as smart homes, smart transportation, mobile devices and smartphones, education and learning, and healthcare. By analyzing practical application scenarios in various fields, the important role of speech recognition technology in improving convenience, efficiency, and promoting intelligent development has been demonstrated.WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY50This article focuses on exploring the specific applications of speech recognition technology in fields such as smart homes, smart transportation, mobile devices and smartphones, education and learning, and healthcare.World Journal of Innovation and Modern Technology["Siqi Zhang"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/99e35f737e17b10df05532e09c4c6616fd99b174 d707ac9bbc8334183388e1bc1add5cfccdd7ce5cKnee (Oxford)200Use of class-weighting appears to provide the optimal method of training a an LLM to perform analytical tasks on free-text clinical information in the face of significant data bias ('class imbalance'), which is an important consideration in the development of high-performance clinical AI models within Trauma and Orthopaedics.The Knee["Luke Farrow", "L. Anderson", "Mingjun Zhong"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/d707ac9bbc8334183388e1bc1add5cfccdd7ce5c 89ab1104314967bfc0a67b231c1726df8984d47bJournal of technology in human services460Journal of Technology in Human Services["Raya Hamed Hilal Al Maamari", "M. Elsherbiny"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/89ab1104314967bfc0a67b231c1726df8984d47b 9fef942e7785db0ef0c8e0cd5a83093913858b9cInternational Journal of Science and Research (IJSR)00International Journal of Science and Research (IJSR)["D. Manjula", "G. Uma", "T. Pradeep", "R. Nandhinidevi"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/9fef942e7785db0ef0c8e0cd5a83093913858b9c 497e7c02e696f08051400fb9417852d852f928f6OBJECTIVES We developed and validated a large language model (LLM)-assisted system for conducting systematic literature reviews in health technology assessment (HTA) submissions. MATERIALS AND METHODS We developed a five-module system using abstracts acquired from PubMed: (1) literature search query setup; (2) study protocol setup using population, intervention/comparison, outcome, and study type (PICOs) criteria; (3) LLM-assisted abstract screening; (4) LLM-assisted data extraction; and (5) data summarization. The system incorporates a human-in-the-loop design, allowing real-time PICOs criteria adjustment. This is achieved by collecting information on disagreements between the LLM and human reviewers regarding inclusion/exclusion decisions and their rationales, enabling informed PICOs refinement. We generated four evaluation sets including relapsed and refractory multiple myeloma (RRMM) and advanced melanoma to evaluate the LLM's performance in three key areas: (1) recommending inclusion/exclusion decisions during abstract screening, (2) providing valid rationales for abstract exclusion, and (3) extracting relevant information from included abstracts. RESULTS The system demonstrated relatively high performance across all evaluation sets. For abstract screening, it achieved an average sensitivity of 90%, F1 score of 82, accuracy of 89%, and Cohen's κ of 0.71, indicating substantial agreement between human reviewers and LLM-based results. In identifying specific exclusion rationales, the system attained accuracies of 97% and 84%, and F1 scores of 98 and 89 for RRMM and advanced melanoma, respectively. For data extraction, the system achieved an F1 score of 93. DISCUSSION Results showed high sensitivity, Cohen's κ, and PABAK for abstract screening, and high F1 scores for data extraction. This human-in-the-loop AI-assisted SLR system demonstrates the potential of GPT-4's in context learning capabilities by eliminating the need for manually annotated training data. In addition, this LLM-based system offers subject matter experts greater control through prompt adjustment and real-time feedback, enabling iterative refinement of PICOs criteria based on performance metrics. CONCLUSION The system demonstrates potential to streamline systematic literature reviews, potentially reducing time, cost, and human errors while enhancing evidence generation for HTA submissions.JAMIA Journal of the American Medical Informatics Association330A large language model (LLM)-assisted system for conducting systematic literature reviews in health technology assessment (HTA) submissions demonstrates the potential of GPT-4's in context learning capabilities by eliminating the need for manually annotated training data.Journal of the American Medical Informatics Association : JAMIA["Ying Li", "Surabhi Datta", "Majid Rastegar-Mojarad", "Kyeryoung Lee", "Hunki Paek", "Julie Glasgow", "C. Liston", "Long He", "Xiaoyan Wang", "Yingxin Xu"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/497e7c02e696f08051400fb9417852d852f928f6 90ec51a4351e26154b955c883df64b5f565772daالمجلة المصرية لبحوث الاتصال الجماهيري00المجلة المصرية لبحوث الاتصال الجماهيري["\u0648\u0641\u0627\u0621 \u0635\u0644\u0627\u062d \u0639\u0628\u062f \u0627\u0644\u0631\u062d\u0645\u0646 \u062e\u0644\u064a\u0644"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/90ec51a4351e26154b955c883df64b5f565772da a13ab761f25af175e23112faf38200e03f0cecf2BioMedicine00BioMedicine["Yu-Ting Lin", "Ya-Chi Lin", "Hung-Lin Chen", "Che-Chen Lin", "Minjun Wu", "Sheng-Hsuan Chen", "Zi-Han Lin", "Yi-Ching Chang", "Chuanyong Sun", "Sheng-Ya Lu", "Min-Yu Chiang", "Hui-Chao Tsai", "Mei-Ju Shih", "D. R. Chang", "Fuu-Jen Tsai", "H. Chiang", "Chin-Chi Kuo"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/a13ab761f25af175e23112faf38200e03f0cecf2 313b8dba914d606c3c90bd32d1f4450f8d4b73c7BACKGROUND Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health concern. Effective management of this condition relies heavily on lifestyle modifications and dietary interventions. In this study, we sought to evaluate the dietary plans for MASLD generated by ChatGPT (GPT-4o) according to current guideline recommendations. METHODS ChatGPT was used to create single-day meal plans for 48 simulated patients with MASLD, tailored to individual characteristics such as age, gender, height, weight and transient elastography parameters. The plans were assessed for appropriateness according to disease-specific guidelines. RESULTS The mean energy content of the menus planned by ChatGPT was 1596.9 ± 141.5 kcal with a mean accuracy of 91.3 ± 11.0%, and fibre content was 22.0 ± 0.6 g with a mean accuracy of 88.1 ± 2.5%. However, they exhibited elevated levels of protein, fat and saturated fat acids. Conversely, the carbohydrate content was lower. ChatGPT recommended weight loss for obese patients but did not extend this advice to normal-weight and overweight individuals. Notably, recommendations for a Mediterranean diet and physical activity were absent. CONCLUSIONS ChatGPT shows potential in developing dietary plans for MASLD management. However, discrepancies in macronutrient distributions and the omission of key evidence-based recommendations highlight the need for further refinement. To enhance the effectiveness of AI tools in dietary recommendations, alignment with established guidelines must be improved.Journal of human nutrition and dietetics (Print)340ChatGPT shows potential in developing dietary plans for MASLD management, however, discrepancies in macronutrient distributions and the omission of key evidence-based recommendations highlight the need for further refinement.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association["Tugce Ozlu Karahan", "E. B. Kenger", "Yusuf Yilmaz"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/313b8dba914d606c3c90bd32d1f4450f8d4b73c7 72b72a9a8725583b5e6c96985be7ce45238f37e4Emergencias00Emergencias["Carlos Romero Ol\u00f3riz"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/72b72a9a8725583b5e6c96985be7ce45238f37e4 7879729a35c3e1bcf02d55c1226e7db34199421cInsurance underwriting is the backbone of the insurance industry. It's the method through which insurers assess risk and determine the viability of insurance policies. The results of underwriting dictate the financial health and success of insurance companies. In this article, we will explore into the intricacies of underwriting results and how they shape the landscape of the insurance business. Understanding Underwriting Results: Underwriting results are calculated by subtracting incurred losses and underwriting expenses from the earned premium. In essence, it's the profit generated from underwriting activities before investment income is considered. A positive underwriting result, often referred to as an underwriting profit, indicates that the insurer has successfully assessed and priced the risks it has assumed. The Significance of Underwriting Profitability: The sustainability of an insurance company largely hinges on its ability to underwrite risks profitably. While investment income can help bolster the company's financial standing, reliance on such is fraught with market volatility. Thus, consistent underwriting profitability ensures that an insurance company can meet its claim obligations without undue reliance on investment earnings.Journal of Research in Science and Engineering180The intricacies of underwriting results are explored, which ensure that an insurance company can meet its claim obligations without undue reliance on investment earnings.Journal of Research in Science and Engineering["Praveen Kumar Tammana"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/7879729a35c3e1bcf02d55c1226e7db34199421c 227461ade645dfbc9ee913fb593a5860e0b967b6Leisure/ Loisir140Leisure/Loisir["A. J. Veal"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/227461ade645dfbc9ee913fb593a5860e0b967b6 0662603d241904b0d884ac70947aa77cc853341eEmergencias00Emergencias["F\u00e9lix Gonz\u00e1lez-Mart\u00ednez", "Nicol\u00e1s J. Garrido", "Jorge Mateo"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/0662603d241904b0d884ac70947aa77cc853341e ecee017f64e4c6609feccfe19d16e5155229aa82Endoscopic Ultrasound210Endoscopic Ultrasound["Sarakshi Mahajan", "Sun Siyu", "M. Bhutani"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/ecee017f64e4c6609feccfe19d16e5155229aa82 223b5a339e26b291cdc47e499715b661e726f1ccThis research examines K-12 STEM educators' viewpoints on incorporating ChatGPT within their teaching practices. The study is grounded in two theoretical models: the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technological Pedagogical Content Knowledge (TPACK) framework. Through semi-structured interviews with nine K-12 STEM teachers in the UAE, the research identifies three main themes: ecosystem support, ease of use, and job enhancement through ChatGPT. Teachers generally appreciate ChatGPT's potential to offer personalised learning experiences, enhance their instructional practices, and reduce logistical burdens. However, the study also uncovers several barriers, including misconceptions about ChatGPT's capabilities and a significant need for professional development in AI education. Educators also raised issues regarding data privacy, the accuracy of responses, and the possibility of reducing students' ability to think critically. The research highlights the importance of developing comprehensive training initiatives to prepare educators to successfully incorporate ChatGPT into their instructional methodologies. The recommendations suggest designing extensive professional development initiatives and conducting additional research to assess the long-term effects of ChatGPT on both teaching and learning processes. By tackling these challenges, the study seeks to support the responsible and efficient implementation of AI tools in K-12 education.TEM Journal00The research highlights the importance of developing comprehensive training initiatives to prepare educators to successfully incorporate ChatGPT into their instructional methodologies and suggests designing extensive professional development initiatives and conducting additional research to assess the long-term effects of ChatGPT on both teaching and learning processes.TEM Journal["Antoine Azzam", "Tendai Charles"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/223b5a339e26b291cdc47e499715b661e726f1cc 206e882b1dc72d47735821ce01e2dc305a8920e8International Journal of Current Research in Science, Engineering &amp; Technology00International Journal of Current Research in Science, Engineering &amp; Technology["Gerd Helmecke"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/206e882b1dc72d47735821ce01e2dc305a8920e8 ae823f27e85d4aafb48bc6296f9120207c40e060With the continuous development of AI technology, the emergence of a large number of AI-generated products has brought about many urgent problems for copyright law. At the forefront of this issue is the question of whether AI-generated objects can be copyrighted. In this paper, we first summarize the consistent jurisprudential ideas through case studies: analyzing the controversial aspects of "originality" and "intellectual achievement" in the definition of works to determine whether AI-generated products are works. In terms of originality, applying the criteria of "unique" and "original", this paper analyzes and concludes that AI generators meet the requirements of "unique", but do not meet the requirements of "original", and therefore do not have originality. The analysis in this paper concludes that AI generation satisfies the requirement of "unique" but not "original", and therefore does not have originality. In terms of intellectual achievements, this paper divided the subjects who enjoy the ownership of intellectual achievements into the AI itself and the natural person who uses the AI, and analyzed and came to the conclusion that the AI is not the intellectual achievements of the above two subjects. Therefore, the generation of AI does not meet the terms of the definition of a work, is not a work within the meaning of the Copyright Act, and cannot be copyrighted.Applied and Computational Engineering00The generation of AI does not meet the terms of the definition of a work, is not a work within the meaning of the Copyright Act, and cannot be copyrighted.Applied and Computational Engineering["Ruihan Dong"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/ae823f27e85d4aafb48bc6296f9120207c40e060 2ba7f558b7596b46c7239923271620fa5317cee7The Business &amp; Management Collection00The Business &amp; Management Collection["Kenneth Le Meunier-FitzHugh"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/2ba7f558b7596b46c7239923271620fa5317cee7 c6e4f5758da7c9dd8dd049dbe48485cabd664449The Biomedical &amp; Life Sciences Collection00The Biomedical &amp; Life Sciences Collection["E. Ruppin"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/c6e4f5758da7c9dd8dd049dbe48485cabd664449 83e330869c542fac283e354261ced5164dde8f7cCogent Business &amp; Management490Cogent Business &amp; Management["Yuhan Dong"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/83e330869c542fac283e354261ced5164dde8f7c 9952bfae0d8171e0573c972854806f5e79f6ee84Evidence-Based Nursing160Evidence-based nursing["D. Rodger", "Siobh\u00e1n O'Connor"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/9952bfae0d8171e0573c972854806f5e79f6ee84 8814df25bf10d9beca642311e8309ce359ab097fPhilosophy &amp; Technology320Philosophy &amp; Technology["Luciano Floridi"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/8814df25bf10d9beca642311e8309ce359ab097f 47e220b5dda3c21fb088d7d830958f1b2a967118The circular economy maximizes resource efficiency in a continuous cycle of use and reuse, while reverse logistics manages the collection and recycling of used products by returning them to the value chain instead of throwing them away. Both complement each other by minimizing environmental impact, conserving resources and promoting a more sustainable economy. This work aims to analyze the potential offered by Artificial Intelligence as an innovative approach to Circular Economy strategies within the framework of Reverse Logistics. The method used was the Systematic Review of the Literature. The results obtained were based on the evaluation of key indicators such as the source of supply of materials, gas emissions, consumption and reuse of products, among others, demonstrating that the integration of smart technologies increases the use of recycled materials, reduces CO2 emissions, improves inventory management, maximizes efficiency in the supply chain, minimizes product waste. The application of the work allows companies to identify business opportunities, reduce costs, develop more sustainable products and services and innovate in reverse logistics, allowing them to increase competitiveness, improve environmental impact and encourage environmental conservation.Revista Latino-Americana de Inovação e Engenharia de Produção00The integration of smart technologies increases the use of recycled materials, reduces CO2 emissions, improves inventory management, maximizes efficiency in the supply chain, minimizes product waste and allows companies to increase competitiveness, improve environmental impact and encourage environmental conservation.Revista Latino-Americana de Inovação e Engenharia de Produção["Sheila G\u00f3mez Blamco", "Ariday Rodr\u00edguez Gonz\u00e1lez", "Rolando Herrero Latour"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/47e220b5dda3c21fb088d7d830958f1b2a967118 d923fadd2cd164bfaa320f1bb1f84b7e740de8a6Introduction A brain tumor is a collection of abnormal cells in the brain that can become life-threatening due to its ability to spread. Therefore, a prompt and meticulous classification of the brain tumor is an essential element in healthcare care. Magnetic Resonance Imaging (MRI) is the central resource for producing high-quality images of soft tissue and is considered the principal technology for diagnosing brain tumors. Recently, computer vision techniques such as deep learning (DL) have played an important role in the classification of brain tumors, most of which use traditional centralized classification models, which face significant challenges due to the insufficient availability of diverse and representative datasets and exacerbate the difficulties in obtaining a transparent model. This study proposes a collaborative federated learning model (CFLM) with explainable artificial intelligence (XAI) to mitigate existing problems using state-of-the-art methods. Methods The proposed method addresses four class classification problems to identify glioma, meningioma, no tumor, and pituitary tumors. We have integrated GoogLeNet with a federated learning (FL) framework to facilitate collaborative learning on multiple devices to maintain the privacy of sensitive information locally. Moreover, this study also focuses on the interpretability to make the model transparent using Gradient-weighted class activation mapping (Grad-CAM) and saliency map visualizations. Results In total, 10 clients were selected for the proposed model with 50 communication rounds, each with decentralized local datasets for training. The proposed approach achieves 94% classification accuracy. Moreover, we incorporate Grad-CAM with heat maps and saliency maps to offer interpretability and meaningful graphical interpretations for healthcare specialists. Conclusion This study outlines an efficient and interpretable model for brain tumor classification by introducing an integrated technique using FL with GoogLeNet architecture. The proposed framework has great potential to improve brain tumor classification to make them more reliable and transparent for clinical use.Frontiers in Oncology301A collaborative federated learning model (CFLM) with explainable artificial intelligence (XAI) to mitigate existing problems using state-of-the-art methods and has great potential to improve brain tumor classification to make them more reliable and transparent for clinical use.Frontiers in Oncology["Qurat-ul-ain Mastoi", "Shahid Latif", "S. Brohi", "Jawad Ahmad", "Abdulmajeed Alqhatani", "Mohammed S. Alshehri", "Alanoud Al Mazroa", "Rahmat Ullah"]2025-02-27T00:00:00https://www.semanticscholar.org/paper/d923fadd2cd164bfaa320f1bb1f84b7e740de8a6 3a41dab53bfe8c8be9f0d056331bb6c9a3f1e619Artificial intelligence (AI), Machine Learning (ML), and blockchain technology are revolutionizing businesses by fostering creativity, efficiency, and security across a range of sectors. This paper explores the mutually beneficial relationship between these technologies using bibliometrics and content analysis, shedding light on their emerging applications and new research directions. We identify key industries like banking, supply chain management, and healthcare where blockchain and artificial intelligence are significantly influencing these disciplines by looking at a broad range of academic and commercial publications. Findings indicate that combining blockchain's decentralized security characteristics with AI-driven predictive analytics enhances automated decision-making, fraud detection, and transparent transactions. In a similar vein, supply chain management employs AI to forecast demand and maximize inventory, while smart contracts driven by blockchain technology streamline transportation. Blockchain and AI integration are used in healthcare applications to improve diagnosis, safeguard patient information, and facilitate interoperable medical records. Despite these advancements, problems with scalability, regulatory ambiguity, and technical complexity are impeding widespread adoption. Multidisciplinary collaboration, innovative policymaking, and advancements in blockchain and AI infrastructures are required to address these issues. By mapping the most significant papers, organizations, and academics that are affecting the field, this study offers valuable information for future research and business endeavors in this transformative sector.Global Journal of Engineering and Technology Advances231Findings indicate that combining blockchain's decentralized security characteristics with AI-driven predictive analytics enhances automated decision-making, fraud detection, and transparent transactions.Global Journal of Engineering and Technology Advances["Daniel Kashetu Alasa", "Gugu Jiyane", "Ahmed Tanvir"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/3a41dab53bfe8c8be9f0d056331bb6c9a3f1e619 975ddb0546d93401c181b5678f6d8993cc920e68Nurse practitioners play a vital role in contributing to the UN’s Sustainable Development Goals, and Universal Health Coverage, especially the management of chronic noncommunicable diseases. Artificial intelligence tools such as ChatGPT are becoming promising resources for healthcare professionals. This study aimed to explore the capability of ChatGPT as a nurse practitioner by validating the performance of ChatGPT-3.5 and GPT-4 in the American Association of Nurse Practitioners (AANP) practice examinations. Questions from exams for five nurse practitioner disciplines were used to evaluate the accuracy and consistency of the responses in two phases. In the first phase, the accuracy rates and concordance of answers between the two versions with the five exam sets, totaling 535 questions were analyzed. In the second phase, the consistency of ChatGPT-4 performance in six retests, each involving five random questions from each set. ChatGPT-3.5 achieved an overall accuracy rate of 80.6%, while ChatGPT-4 achieved 90.7%. ChatGPT-3.5 and ChatGPT-4 showed strong consistency within all sets, while ChatGPT-4 performed better than ChatGPT-3.5. In the retests, ChatGPT-4 provided exactly the same answers as generated initially, including the incorrect ones. In conclusion, ChatGPT demonstrated excellent performance in AANP practice exams, with high levels of accuracy and consistency. This suggests that ChatGPT may support nurse practitioners in making clinical decisions and improving efficiency. Further studies could explore ways to integrate artificial intelligence tools with nurse practitioner practice to enhance the advanced practice nursing workforce.AI Computer Science and Robotics Technology330ChatGPT demonstrated excellent performance in AANP practice exams, with high levels of accuracy and consistency, which suggests that ChatGPT may support nurse practitioners in making clinical decisions and improving efficiency.AI, Computer Science and Robotics Technology["Lang Peng", "Yi Wu", "Jiayi Sun", "Yihong Xing", "Mingqin Li", "Mingzi Li"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/975ddb0546d93401c181b5678f6d8993cc920e68 12d9dae413bc5d2c83cbafc56a06391dc8ffd110Objectives The purposes of this study were to develop instructional model that combines artificial intelligence(AI), science, and technology in middle school through problem-oriented project activities that solve problems using data-based artificial intelligence technology to improve digital literacy and analyzed its effectiveness Methods The initial model was developed after deriving the components and design principles through a review of previous studies. To validate the model, two rounds of expert validation were conducted targeting 5 people. In the field evaluation, a class was conducted applying this model to 25 third-grade middle school students in Seoul. The interpretation of results was derived from pre- and post-tests on students' digital literacy and interview with the students. The results were analyzed using a paired samples t test. Results This study created a class model for the convergence of AI, science, and technology to enhance digital literacy. The model was based on the data-driven Exploratory Scientific Data Analysis Inquiry Model (ESDA) and the TMSI(Tinkering, Making, Sharing, Improving) model. The class model comprises four stages: ‘Data mining,’ ‘Problem Defining,’ ‘Making with AI,’ and ‘Sharing & Improving.’ Additionally, 12 design principles and 37 detailed guidelines supporting each stage of the final AI·science·technology convergence class model were presented. Classes implementing this model demonstrated an overall positive impact on enhancing learners' digital literacy. Conclusions This study holds significance as it validated the effectiveness of improving students' digital literacy by implementing the data-based AI, science, and technology convergence class model at middle school sites. The model and design principles were developed through expert review, and the positive outcomes contribute to the understanding of effective educational strategies in this context.Korean Association For Learner-Centered Curriculum And Instruction00This study validated the effectiveness of improving students' digital literacy by implementing the data-based AI, science, and technology convergence class model at middle school sites.Korean Association For Learner-Centered Curriculum And Instruction["Tae Yun Kim", "Su Yeon Moon", "Ji Hye Baek", "So Jung Lee"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/12d9dae413bc5d2c83cbafc56a06391dc8ffd110 e77e9c32f1c10ace9bfc7f82042a0c7452e6e49aBMC Psychology510The Friend chatbot offers a scalable, cost-effective solution for psychological support, particularly in crisis situations where traditional therapy may not be accessible, particularly in crisis situations where traditional therapy may not be accessible.BMC Psychology["Liana Spytska"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/e77e9c32f1c10ace9bfc7f82042a0c7452e6e49a d1c2d2fedc066db273c7d7c4872d1a42f7edb915Penerapan Artificial Intelligence (AI) dalam pendidikan memiliki potensi besar untuk meningkatkan efektivitas pembelajaran. Namun, adopsinya masih terkendala oleh keterbatasan infrastruktur dan rendahnya literasi teknologi di kalangan guru. Kegiatan pengabdian masyarakat ini dilakukan pada Januari 2025 di SMPN 40 Pekanbaru dengan tujuan (1) meningkatkan literasi teknologi AI bagi tenaga pendidik agar mereka mampu memahami konsep dasar AI dan manfaatnya dalam dunia pendidikan, dan (2) mengembangkan keterampilan guru dalam mengintegrasikan AI ke dalam strategi pembelajaran untuk meningkatkan efektivitas pengajaran. Metode yang digunakan meliputi sosialisasi, pelatihan, dan pendampingan langsung, dengan evaluasi melalui survei berbasis skala Likert. Hasil menunjukkan peningkatan signifikan dalam pemahaman guru tentang AI sebesar 80%, serta antusiasme dalam pemanfaatannya untuk personalisasi pembelajaran dan otomatisasi tugas administratif. Namun, kendala seperti keterbatasan perangkat dan kebutuhan pelatihan lanjutan masih ditemukan. Oleh karena itu, diperlukan peningkatan infrastruktur, pelatihan berkelanjutan, serta pengembangan strategi pembelajaran berbasis AI untuk memastikan penerapannya lebih optimal dan berkelanjutan.Jurnal Pengabdian Masyarakat Multidisiplin00Jurnal Pengabdian Masyarakat Multidisiplin["M. N. Kholis", "Ardi Gustri Purbata", "M. Nur", "Nabila Afifah Azuga", "Asnika Putri Simanjuntak"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/d1c2d2fedc066db273c7d7c4872d1a42f7edb915 270be270686455da9f75169ca52fa4e139ceb839Artificial intelligence (AI) is poised to significantly transform the manufacturing industry, with its integration into various processes such as manufacturing, decision-making, and logistics. This paper explores critical areas where AI is being and can be applied: Manufacturing Execution Systems (MES), Supply Chain Management (SCM), Challenges of AI Implementation in Manufacturing and Future Potential of AI in Manufacturing. In the context of MES, AI can optimize production processes, improve real-time monitoring, and enhance decision-making capabilities. Similarly, AI’s impact on SCM is evident through improved forecasting, inventory management, and supply chain visibility. This paper aims to examine the potential applications and explore the opportunities AI offers in these domains, highlighting the ways in which it can revolutionize traditional manufacturing practices. Through a detailed analysis, we identify how AI-driven innovations could reshape manufacturing operations, enhance efficiency, and contribute to the future of the industry. However, challenges such as high implementation costs, information security concerns, and resistance from workers need to be addressed to fully realize AI’s potential. By overcoming these obstacles, AI can redefine the future of manufacturing, making it smarter, more efficient, and sustainable. Reducing manufacturing costs through AI-driven innovations will be crucial for the industry in the near future.World Journal of Advanced Research and Reviews00Critical areas where AI is being and can be applied: Manufacturing Execution Systems (MES), Supply Chain Management (SCM), Challenges of AI Implementation in Manufacturing, and Future Potential of AI in Manufacturing are explored.World Journal of Advanced Research and Reviews["Sai Dhiresh Kilari"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/270be270686455da9f75169ca52fa4e139ceb839 9083330fc4e1bc2f6e9e9364bc315a1d64247056Amid the rapid advancement of artificial intelligence (AI), organizations are continuously confronted with new challenges. This paper delves into the impact of discriminative and generative AI on organizational and societal diversity. To replicate and extend previous research (e.g., Atari et al., 2023), which found that ChatGPT and other generative AI models exhibit WEIRD cultural biases, across various generative AI models, including GPT-4o, Llama 3.2, EEVE, and EXAONE 3.5. The findings reveal discrepancies in algorithmic transparency and the extent of Western-centric data training across these models. While some models show a reduction in bias compared to earlier versions, notable gaps remain in representing certain cultural contexts. AI is categorized into discriminative and generative types.. Discriminative AI enhances predictive and classification capabilities through supervised learning, facilitating automation and personalized services; however, the accumulation of data bias poses a threat to organizational diversity. Generative AI, by leveraging unsupervised and reinforcement learning, generates creative ideas and content, fostering innovation but simultaneously carrying the risk of value-laden biases stemming from training data. This study provides a detailed analysis of the potential impacts and risks of both AI types across HR processes, including recruitment, promotion, and performance evaluation. Furthermore, it incorporates regulatory and ethical frameworks related to human capital disclosure and AI governance, addressing key considerations for organizations in ensuring AI transparency and fairness. By highlighting the complementary use of discriminative and generative AI, this paper suggests pathways for organizations to achieve creativity, operational efficiency, and inclusiveness, contributing valuable theoretical and practical insights.Korean Academy of Management00A detailed analysis of the potential impacts and risks of both AI types across HR processes, including recruitment, promotion, and performance evaluation is provided, incorporating regulatory and ethical frameworks related to human capital disclosure and AI governance.Korean Academy of Management["Joonghak Lee", "Kwangtae Kim", "Sungjun Kim"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/9083330fc4e1bc2f6e9e9364bc315a1d64247056 c910e4342efbde1e4f362da5041bc0b8b70c25faEuropäische Wissenschaftliche Gesellschaft Home About the Journal Peer Review Editorial Board For Authors Reviewer Recognition Archive Contact Impressum EWG e.V. Elektronische Zeitschriftbibliotek Logo Bielefeld Academic Search Engine Logo Erfolgreich durch internationale Zusammenarbeit AI in Healthcare Cite as: Archiv EuroMedica. 2025. 15; 1. DOI 10.35630/2025/15/1.104 Received 15 January 2025; Accepted 17 February 2025; Published 23 February 2025 ARTIFICIAL INTELLIGENCE-POWERED INSIGHTS: EXPLORING THE IMPACT OF ARTERIAL HYPERTENSION ON INGUINAL HERNIOPLASTY CONSEQUENCES Andrey Protasov 1 orcid id logo, Mekhaeel Mekhaeel1 orcid id logo, Sameh Salem1email orcid id logo, Watban Khalid Al-Tekreeti2 orcid id logo, Eman Abdulqader Abduhalim Abdo3 orcid id logo, Abd Alhakem Alhatem4 orcid id logo 1Department of Operative Surgery and Clinical Anatomy named after I.D. Kirpatovsky. Medical institute. Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University), Moscow, Russia 2Department of Mechanical Engineering Technologies, Academy of Engineering. Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University), Moscow, Russia. 3Department of Oncology and Roentgenology named after V.P. Kharchenko. Medical Institute. Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University), Moscow, Russia 4Department of Surgery. Medical institute. Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University). Moscow, Russia download article (pdf) samehhadhoud2020@gmail.com ABSTRACT Aims: To analyze potential influences of primary arterial hypertension in patients with an inguinal hernia after Liechtenstein open inguinal hernia repair by using different types of surgical mesh implants. Method: 40 patients with inguinal hernia were operated by the Lichtenstein repair using different types of mesh implants during the period between January 2022 and the end of December 2024. The patients were splitted into 2 equal groups (no=20); Group A (Normotensive Patients) and Group B (Hypertensive Patients),and the results were analyzed using machine learning Artificial Intelligence programming language; Python. Results: Duration of herniation, total hospitalization stays, time of operation and the incidence of postoperative complications — the results of the t-tests show no statistically significant differences among both groups. Conclusions: In our study, we found that hypertension status does not significantly impacts upon the outcomes of inguinal hernia repair.Archiv Euromedica00It is found that hypertension status does not significantly impacts upon the outcomes of inguinal hernia repair, and Artificial Intelligence-powered insights found that hypertension status does not significantly impacts upon the outcomes of inguinal hernia repair.Archiv Euromedica["Andrey Protasov", "M.Sh.F. Mekhaeel", "Sameh Salem", "Watban Khalid Al-Tekreeti", "Eman Abdulqader Abduhalim Abdo", "Abd Alhakem Alhatem"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/c910e4342efbde1e4f362da5041bc0b8b70c25fa 7960b86df4f1d25086d07734ae253cebfdd994daThe integration of Artificial Intelligence (AI) into education is heralding a new era of teaching and learning, with profound implications for Science, Technology, Engineering, and Mathematics (STEM) education globally. As a transformative tool, AI has the potential to address persistent challenges in Nigeria’s STEM education landscape, including insufficient infrastructure, teacher shortages, low student engagement, and inequitable access to quality learning resources. This paper provides an in-depth exploration of the role of AI in shaping the future of STEM education in Nigeria, focusing on its diverse applications such as personalized learning, virtual laboratories, automated administrative tasks, and teacher training programs. Key benefits of AI include improved access for underserved regions, data-driven insights for educators, and the promotion of STEM career interest among students. However, the adoption of AI faces significant hurdles, including infrastructural deficits, high costs, digital literacy gaps, and ethical concerns related to data privacy and algorithmic bias. The paper emphasizes strategies for overcoming these challenges, such as strengthening digital infrastructure, fostering public-private partnerships, developing localized AI solutions, and formulating inclusive policies. In addition to analyzing global best practices, the study highlights case studies such as ULesson and Mavis Computel, showcasing how AI is already being applied in the Nigerian context to enhance STEM education. By leveraging AI technologies, Nigeria can bridge existing gaps in access, equity, and quality, equipping its students with the critical skills needed to compete in a technology-driven global economy. This paper underscores the importance of adopting AI as a strategic tool for driving innovation, fostering national development, and securing Nigeria’s position in the global knowledge economy.Journal of African Innovation and Advanced Studies90An in-depth exploration of the role of AI in shaping the future of STEM education in Nigeria, focusing on its diverse applications such as personalized learning, virtual laboratories, automated administrative tasks, and teacher training programs.Journal of African Innovation and Advanced Studies["I. Samuel", "Amina Danladi"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/7960b86df4f1d25086d07734ae253cebfdd994da 6149f9cf130af42b36f6ee371ee2ae3bf1b6a7c5The convergence of blockchain technology, smart contracts, and artificial intelligence represents a transformative technological paradigm that fundamentally reimagines corporate governance in fintech and logistics industries. This research review critically examines the profound technological disruption emerging at the intersection of advanced computational systems and organizational management strategies. By analyzing the intricate relationships between decentralized technologies, algorithmic decision-making, and traditional governance frameworks, the study reveals how these innovative technologies are reshaping organizational structures, operational transparency, and strategic decision-making processes. The investigation explores the multifaceted implications of blockchain and AI integration, demonstrating their potential to address critical challenges such as operational inefficiency, compliance complexity, and trust deficits in contemporary corporate environments. Through comprehensive empirical analysis and theoretical examination, the review illuminates the revolutionary potential of these technologies to create more adaptive, intelligent, and responsive governance ecosystems that transcend conventional organizational boundaries and limitations.World Journal of Advanced Research and Reviews00This research review critically examines the profound technological disruption emerging at the intersection of advanced computational systems and organizational management strategies, demonstrating their potential to address critical challenges such as operational inefficiency, compliance complexity, and trust deficits in contemporary corporate environments.World Journal of Advanced Research and Reviews["Adeyinka Ogunbajo", "Adefemi Quddus Abidola", "Itunu Taiwo"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/6149f9cf130af42b36f6ee371ee2ae3bf1b6a7c5 76de7dc0789fe0544cc5e8d2e03c47faf1da3c08In an increasingly globalized world, addressing health, environmental sustainability and social inequalities is crucial and requires an integrated approach involving national statistical offices. The latter is increasingly called upon to develop statistical frameworks to facilitate informed policy-making. However, incomplete or missing data in questionnaires or registers may compromise the accuracy and reliability of results. The main objective of this study is to assess the effectiveness of different imputation methods using machine learning (ML) and artificial intelligence (AI) techniques in dealing with missing data in social surveys. To this end, a comparative analysis of different imputation techniques has been carried out, based on real datasets from the Istat Multi-purpose Household Survey, where missing data are common. Preliminary results suggest that ML/AI-based imputation methods outperform traditional statistical techniques in terms of performance and robustness. The aim is to improve imputation techniques in official statistics to improve data quality on critical issues.Rivista italiana di economia, demografia e statistica160Preliminary results suggest that ML/AI-based imputation methods outperform traditional statistical techniques in terms of performance and robustness and the aim is to improve imputation techniques in official statistics to improve data quality on critical issues.Rivista Italiana di Economia Demografia e Statistica["Simona Cafieri", "Francesco Pugliese", "Mauro Sodani"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/76de7dc0789fe0544cc5e8d2e03c47faf1da3c08 a800cb59732693077872f2e948013eb954c4ead9Neurological disorders include Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy, which significantly affect millions of people worldwide. Early diagnosis and intervention can drastically improve treatment outcomes, but current diagnostic methods often lack sensitivity and specificity in identifying these conditions in their early stages. Recent advances in artificial intelligence (AI) offer significant potential in addressing these challenges, especially when combined with brain imaging and neural focusing on brain imaging modalities such as MRI, CT, and EEG, and neural signals from devices such as EEG caps and implanted electrodes. It discusses the effectiveness, challenges, and future directions of AI in this domain data. This paper discusses the application of AI techniques, specifically ML and DL, in the early detection of neurological disorders,International Journal for Research in Applied Science and Engineering Technology00This paper discusses the application of AI techniques, specifically ML and DL, in the early detection of neurological disorders, and discusses the effectiveness, challenges, and future directions of AI in this domain data.International Journal for Research in Applied Science and Engineering Technology["Tanush Arun Kumar"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/a800cb59732693077872f2e948013eb954c4ead9 0e09b297f92ab343f93228394d32b22bd54b7f9dHumans are always viewed as business assets in every organization. Appropriate, and reasonable, human resources must be employed, trained, and valued. Using cutting-edge technology in human resource management, such as artificial intelligence, increases the workforce's effectiveness, accountability, and productivity. Additionally, it increases transparency and precision in computing the accounting return on investment from a human resources perspective. Consequently, HR staff may devote more time to developing strategies and making decisions. This paper examines the significance and development of HRM as a new core business. HR's evolving function, the perspectives and difficulties of AI-enabled HRM technologies, and more due to technological changes and the way human resource accounting is done. Regardless of size or industry, human capital is the most important resource for any organization's development. And it is the responsibility of the HR staff to put HRM plans into practice in order to attract, develop, and support human resources. Despite the fact that many problems can only be solved by human interaction, there is still room for AI in HRM. By utilizing cutting-edge technology, all conventional HR duties were strengthened with greater accuracy, productivity, and transparency, without human intervention. It allows the preparation of progress evaluations, staff communication & performance evaluations. As a result, organizations, senior management, and HR experts may achieve their goals with the help of AI-enabled HR technologies.International Journal for Research in Applied Science and Engineering Technology00This paper examines the significance and development of HRM as a new core business, HR's evolving function, the perspectives and difficulties of AI-enabled HRM technologies, and more due to technological changes and the way human resource accounting is done.International Journal for Research in Applied Science and Engineering Technology["Dr. Rani L"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/0e09b297f92ab343f93228394d32b22bd54b7f9d eaa51357376624722bd8f1f0b273d6512ca945adThe rapid increase in unmanned aerial vehicle (UAV) usage has introduced significant safety challenges, including issues such as system failure, loss of control, transmission failures, and collisions. Analyzing these incidents has been challenging due to the absence of a dedicated category field in the National Transportation Safety Board (NTSB) data. This research tackles this problem by utilizing artificial intelligence (AI) to automate the classification of UAV accident reports collected between 2006 and 2023. Using natural language processing techniques, we categorize NTSB reports to improve the analysis and interpretation of incident data. We also employ advanced data visualization tools to reveal geographic and temporal patterns, offering a detailed view of UAV accident trends. The results indicate that system and component failures unrelated to propulsion systems (system/component failure or malfunction [non-powerplant]) and abnormal contact upon landing (abnormal runway contact) are predicted as the primary categories (37%) of UAV accidents for the period. These insights suggest the potential value of AI-driven categorization and visualization techniques in enhancing UAV safety standards and supporting policy development. Initial results provide promising insight into the use of language models for text classification in aviation safety problems.International Journal of AI for Materials and Design00Initial results provide promising insight into the use of language models for text classification in aviation safety problems and suggest the potential value of AI-driven categorization and visualization techniques in enhancing UAV safety standards and supporting policy development.International Journal of AI for Materials and Design["Eugene Pik", "Joao S. D. Garcia"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/eaa51357376624722bd8f1f0b273d6512ca945ad 8dfaba75751ba4a267cd10d7fce1edd32d65a6f2Although globally there are mixed perceptions regarding the academic integrity of chatbots, existing research has mainly focused on developed nations, neglecting the unique perspectives of academics in developing countries, with different contextual, environmental, and technological settings. This study presents lecturers’ perceptions of using Artificial Intelligence (AI) chatbots in education. Guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this research collected quantitative and qualitative data from 140 lecturers and three administrators from a STEM-based Zimbabwean university. The research confirmed that performance expectancy (belief in improved efficiency and personalised learning) and perceived value and social influence drive adoption. Contrary to previous studies, there was no significant link between effort expectancy (reduced workload) and chatbot use. Demographics like gender, age, and qualifications did not impact chatbot use. Academics were cautiously optimistic, recognising benefits like personalised learning and routine task management but concerned about ease of use, technical expertise, and ethical considerations. To effectively integrate AI chatbots into higher education processes, there is a need for funding, technical support, training, strengthening IT infrastructure and establishing frameworks for responsible use. Emphasising efficiency, personalisation, and robust support will help overcome barriers and maximise AI chatbots’ potential in education.Indonesian Journal of Information Systems00The research confirmed that performance expectancy (belief in improved efficiency and personalised learning) and perceived value and social influence drive adoption and there was no significant link between effort expectancy (reduced workload) and chatbot use.Indonesian Journal of Information Systems["B. Ndlovu", "Sharmaine Ndlovu", "Sibusisiwe Dube", "Kudakwashe Maguraushe"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/8dfaba75751ba4a267cd10d7fce1edd32d65a6f2 050a16881c30418405805868d457e3ee6500cc7bSchizophrenia1020AI excels in developing individualized prognostic plans, which enables the rapid identification of disease progression, accurate prediction of disease trajectory, and timely adjustment of treatment strategies, thereby improving prognosis and facilitating recovery.Schizophrenia["Shijie Jiang", "Qiyu Jia", "Zhenlei Peng", "Qixuan Zhou", "Zhiguo An", "Jianhua Chen", "Qizhong Yi"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/050a16881c30418405805868d457e3ee6500cc7b cc57c61052d9b6bdc8ebe5e55163dbbf39c62cccAims: This study aims to conduct a bibliometric analysis of the scientific literature on the use of artificial intelligence (AI) in pediatric dentistry to evaluate publication trends, citation impact, key contributors, and research themes. Methods: A comprehensive literature search was conducted in the Web of Science (WoS) database up to February 8, 2025. The search included AI-related terms combined with pediatric dentistry keywords. A total of 78 relevant articles were identified and analyzed. VOSviewer software was used for bibliometric mapping, including co-authorship, co-citation, and keyword analysis. Results: The number of publications on AI in pediatric dentistry has increased significantly since 2020, peaking in 2024, followed by a decline in 2025. The analysis identified key research topics, including diagnostic imaging, early childhood caries detection, dental age estimation, and orthodontic assessments. Despite the growth in research output, AI applications in pediatric dentistry remain significantly underdeveloped compared to other dental fields. Citation impact was relatively low, with the most referenced article receiving 83 citations. Conclusion: AI is gaining attention in pediatric dentistry; however, its adoption is still in the early stages. Further research is needed to validate AI models, enhance clinical integration, and expand interdisciplinary collaboration. Addressing data limitations and improving real-world applicability will be crucial for AI’s long-term impact on pediatric dental care.Journal of Dental Sciences and Education00The number of publications on AI in pediatric dentistry has increased significantly since 2020, peaking in 2024, followed by a decline in 2025, while AI applications in pediatric dentistry remain significantly underdeveloped compared to other dental fields.Journal of Dental Sciences and Education["Emine G\u00fcl\u015fen", "\u0130brahim Tevfik G\u00fcl\u015fen", "T\u00fcrkan Mahyaddinova"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/cc57c61052d9b6bdc8ebe5e55163dbbf39c62ccc 482fa6d805fc10d3dc378dfb35eff031befa6259This article provides an in-depth exploration of the growing role of artificial intelligence (AI) in medical research, identifying potential applications, key case studies, challenges, strategies for implementation, and future perspectives. AI presents immense opportunities to revolutionize medical research, offering tools for efficient data analysis, accurate prediction of outcomes, and enhanced research efficiency. Specific areas such as genomics, drug discovery, clinical trials, and epidemiology can especially benefit from AI's application, as evidenced by various case studies. However, the journey towards full AI integration in medical research is not without obstacles. Data privacy issues, the necessity for specialized knowledge, rigorous validation of AI models, and algorithm interpretability emerge as significant hurdles. Moreover, ethical considerations, such as the risk of bias in AI algorithms, add another layer of complexity. Realizing these challenges demands ongoing innovation, research, and collaboration across various stakeholders. AI's intersection with medical research heralds a new era of potential scientific discoveries and improved patient outcomes. The article calls for a joint effort from researchers, practitioners, and policymakers to embrace this potential, navigate the challenges, and shape a future where AI serves as an invaluable tool in the pursuit of improved healthcare for all.  International Journal of Research in Medical Sciences180International Journal of Research in Medical Sciences["Anas Mohammed Abudasir", "Abdullah Saeed", "Abdulrahman Bin Saeed", "Abdulaziz Mohammed Abudasir", "Ali Yahya Alhayani", "Khalid Saeed Aldlham", "Ghassan E. Mustafa Ahmed", "Razan Abdullah Alqahtani"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/482fa6d805fc10d3dc378dfb35eff031befa6259 4c31dddbbf1600c8c32a1e656db7f162138972acThe rapid evolution of artificial intelligence (AI) and robotics has brought forth profound implications, particularly within the realm of ethics and jurisprudence. This paper seeks to initiate a humble yet significant exploration into the intersection of AI applications and Islamic jurisprudence. By focusing on foundational principles, the study highlights how Islamic legal frameworks can provide ethical guidance for human-AI interactions that ensure justice, social welfare, and moderation. A key focus of this work is the five indicators identified in Islamic jurisprudence that are particularly relevant for addressing AI abuses: (i) intent to harm, (ii) intention for an unlawful purpose, (iii) causing greater harm than good, (iv) unethical use resulting in damage to others, and (v) negligence or error in usage. These principles underscore the critical need for precaution and accountability in developing and deploying AI technologies. This paper also emphasizes the importance of preventing harmful applications of AI through the lens of Islamic technoethics. By offering this integrative perspective, the study hopes to contribute to ongoing discussions about AI governance and ethics, encouraging further research and dialogue. The intention is to provide a starting point for more inclusive and culturally informed frameworks that address the challenges and opportunities of technological advancements while upholding ethical integrity.Quantum Journal of Social Sciences and Humanities490The study highlights how Islamic legal frameworks can provide ethical guidance for human-AI interactions that ensure justice, social welfare, and moderation and emphasizes the importance of preventing harmful applications of AI through the lens of Islamic technoethics.Quantum Journal of Social Sciences and Humanities["Mohamed Aslam Akbar"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/4c31dddbbf1600c8c32a1e656db7f162138972ac 516d76eb4547667ff9191853afc407fedb0564e4The role of artificial intelligence (AI) in pathology has evolved from aiding diagnostics to uncovering predictive morphological patterns in whole slide images (WSIs). Recently, foundation models (FMs) leveraging self-supervised pre-training have been widely advocated as a universal solution for diverse downstream tasks. However, open questions remain about their clinical applicability and generalization advantages over end-to-end learning using task-specific (TS) models. Here, we focused on AI with clinical-grade performance for prostate cancer diagnosis and Gleason grading. We present the largest validation of AI for this task, using over 100,000 core needle biopsies from 7,342 patients across 15 sites in 11 countries. We compared two FMs with a fully end-to-end TS model in a multiple instance learning framework. Our findings challenge assumptions that FMs universally outperform TS models. While FMs demonstrated utility in data-scarce scenarios, their performance converged with - and was in some cases surpassed by - TS models when sufficient labeled training data were available. Notably, extensive task-specific training markedly reduced clinically significant misgrading, misdiagnosis of challenging morphologies, and variability across different WSI scanners. Additionally, FMs used up to 35 times more energy than the TS model, raising concerns about their sustainability. Our results underscore that while FMs offer clear advantages for rapid prototyping and research, their role as a universal solution for clinically applicable medical AI remains uncertain. For high-stakes clinical applications, rigorous validation and consideration of task-specific training remain critically important. We advocate for integrating the strengths of FMs and end-to-end learning to achieve robust and resource-efficient AI pathology solutions fit for clinical use.00This work presents the largest validation of AI with clinical-grade performance for prostate cancer diagnosis and Gleason grading using over 100,000 core needle biopsies from 7,342 patients across 15 sites in 11 countries and compared two FMs with a fully end-to-end TS model in a multiple instance learning framework.["N. Mulliqi", "A. Blilie", "Xiaoyi Ji", "Kelvin Szolnoky", "Henrik Olsson", "S. E. Boman", "Matteo Titus", "Geraldine Martinez Gonzalez", "Julia Anna Mielcarz", "Masi Valkonen", "E. Gudlaugsson", "S. R. Kjosavik", "Jos'e Asenjo", "Marcello Gambacorta", "Paolo Libretti", "Marcin Braun", "R. Kordek", "Roman Lowicki", "K. Hotakainen", "Paivi Vare", "B. Pedersen", "Karina Dalsgaard Sorensen", "B. Ulh\u00f8i", "P. Ruusuvuori", "Brett Delahunt", "H. Samaratunga", "Toyonori Tsuzuki", "E. Janssen", "Lars Egevad", "Martin Eklund", "Kimmo Kartasalo Department of Medical Epidemiology", "Biostatistics", "Karolinska Institutet", "Stockholm", "Sweden", "Departmentof Pathology", "S. Hospital", "Stavanger", "Norway", "F. O. Sciences", "University of Stavanger", "Department of Preventive Medicine", "Surgery", "Institute of Biomedicine", "U. Turku", "Turku", "Finland", "The General Practice", "Care Coordination Research Group", "Department of Environmental Health", "Primary Care", "Faculty of Veterinary Medicine", "Universityof Bergen", "Synlab", "Madrid", "Spain", "Brescia", "Italy", "Chair of Oncology", "Medical University of Lodz", "Lodz", "Poland", "1st Department of Urology", "D. Chemistry", "Hematology", "U. Helsinki", "Helsinki", "Laboratory Services", "Mehilainen Oy", "Mehilainen Lansi-Pohja Hospital", "Kemi", "D. Radiology", "Aarhus University Hospital", "Aarhus", "Denmark", "Department of Preventive Medicine", "A. University", "InFLAMES Research Flagship", "H. Technology", "Tampere University", "Tampere", "M. I. O. M. Research", "Wellington", "N. Zealand.", "Department of Radiation Oncology", "Pathology", "Aquesta Uropathology", "U. Queensland", "Qld", "Brisbane", "Australia", "Departmentof Pathology", "School of Clinical Medicine", "Aichi Medical University", "Nagoya", "Japan", "D. Chemistry", "Bioscience", "Environmental Engineering", "Institute for Biomedicine", "Glycomics", "Griffith University", "Queensland", "D. Epidemiology", "SciLifeLab"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/516d76eb4547667ff9191853afc407fedb0564e4 3132706fc8264d9d67037bb284d4b25856801739 The integration of artificial intelligence (AI) into cardiovascular procedures has significantly advanced diagnostic accuracy, outcome prediction, and robotic-assisted surgeries. However, a comprehensive bibliometric analysis of AI’s impact in this field is lacking. This study examines research trends, key contributors, and emerging themes in AI-driven cardiovascular interventions. We retrieved relevant publications from the Web of Science Core Collection and analyzed them using VOSviewer, CiteSpace, and Biblioshiny to map research trends and collaborations. AI-related cardiovascular research has grown substantially from 1993 to 2024, with a sharp increase from 2020 to 2023, peaking at 93 publications in 2023. The USA (127 papers), China (79), and England (31) were the top contributors, with Harvard University leading institutional output (17 papers). Frontiers in Cardiovascular Medicine was the most prolific journal. Core research themes included “machine learning,” “mortality,” and “cardiac surgery,” with emerging trends in “association,” “implantation,” and “aortic stenosis,” underscoring AI’s expanding role in predictive modeling and surgical outcomes. AI demonstrates transformative potential in cardiovascular procedures, particularly in diagnostic imaging, predictive modeling, and patient management. This bibliometric analysis highlights the growing interest in AI applications and provides a framework for integrating AI into clinical workflows to enhance diagnostic accuracy, treatment strategies, and patient outcomes. Annals of Medicine &amp; Surgery00This bibliometric analysis highlights the growing interest in AI applications and provides a framework for integrating AI into clinical workflows to enhance diagnostic accuracy, treatment strategies, and patient outcomes.Annals of Medicine &amp; Surgery["Koushik Rao Gadhachanda", "M. D. Marsool Marsool", "A. Bozorgi", "Daniyal Ameen", "Sandeep Nayak", "Amir Nasrollahizadeh", "Abdulhadi Alotaibi", "Alireza Farzaei", "M. Keivanlou", "S. Hassanipour", "Ehsan Amini-Salehi", "Anil Kumar Jonnalagadda"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/3132706fc8264d9d67037bb284d4b25856801739 2206a0b9a2ab3e75f65acc7edfd95d0eba93e0afThe phenomenon of climate change emerges as a significant concern of the 21st century, wielding substantial implications for ecosystems, human populations, and economic stability. Effective monitoring plays an indispensable role in addressing these challenges by facilitating the identification of trends and providing the necessary data to inform mitigation and adaptation strategies. Traditional climate monitoring methodologies often exhibit limitations, including high costs, slow data processing, and insufficient resolution, which hinder timely responses to rapid environmental changes. In this context, Artificial Intelligence (AI) has assumed a crucial role in overcoming these challenges. The capabilities of AI in processing extensive datasets, recognizing patterns, and making predictions enhance the accuracy and efficiency of climate change surveillance at unprecedented rates. This study evaluates various AI technologies that support climate change monitoring, including machine learning, deep learning, natural language processing, and big data analytics. The analysis encompasses applications in climate forecasting, satellite observation, wildfire detection, energy management, and carbon emission tracking. The study further discusses the benefits of AI, such as improved data processing, real-time monitoring, and predictive abilities, while acknowledging the challenges of data integrity, integration, scalability, and ethical considerations. The paper concludes by speculating on future advancements in AI, including ecosystem modelling, AI-enhanced climate policy formulation, and collaborative AI networks, which hold the potential to significantly bolster global initiatives aimed at promoting climate resilience and sustainability.International Journal for Sciences and Technology110This study evaluates various AI technologies that support climate change monitoring, including machine learning, deep learning, natural language processing, and big data analytics, which encompasses applications in climate forecasting, satellite observation, wildfire detection, energy management, and carbon emission tracking.International Journal on Science and Technology["Dr Amit Bijon Dutta", "Dr. Ipshita Sengupta"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/2206a0b9a2ab3e75f65acc7edfd95d0eba93e0af 207f8f0d350206b2802c52ced35bcd35b26c148cThe rapid advancement of artificial intelligence (AI) technologies has introduced new challenges, particularly in the creation and dissemination of deepfakes—manipulated media that can deceive viewers into believing false information. In Indonesia, the existing legal frameworks do not specifically address the issue of deepfakes, leaving a gap in the regulation and prevention of AI-generated misinformation. This paper analyzes the legal application of AI ethics in preventing the misuse of deepfakes, using a normative juridical approach to examine current Indonesian laws, ethical standards in AI, and international legal frameworks. The findings highlight the insufficiency of Indonesia's current legal provisions, including the Electronic Information and Transactions (ITE) Law and the Personal Data Protection Law, in addressing deepfake-related issues. The study proposes the introduction of a specific legal framework for deepfakes, integration of AI ethics into national legislation, and international collaboration to mitigate the spread of harmful AI-generated content. By implementing these reforms, Indonesia can better safeguard individuals’ rights and maintain digital media integrity.The Easta Journal Law and Human Rights320The study proposes the introduction of a specific legal framework for deepfakes, integration of AI ethics into national legislation, and international collaboration to mitigate the spread of harmful AI-generated content.The Easta Journal Law and Human Rights["L. Judijanto", "Andrew Shandy Utama", "Heri Setiyawan"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/207f8f0d350206b2802c52ced35bcd35b26c148c fd285ea2213c225751747b9260338bc3e0f30426This study undertakes a comprehensive examination of the current landscape, emerging trends, opportunities and challenges associated with integrating artificial intelligence (AI) technologies into alternative dispute resolution (ADR) systems across the BRICS nations of Brazil, Russia, India, China and South Africa over the next 20 years. Through extensive analysis of scholarly literature, national policies and regulations, it develops a strategic framework comprised of tailored principles, policies and priority actions aimed at steering the adoption of AI in the ADR domain in a responsible, ethical and socially aligned manner. The research highlights the significant risks posed by the irresponsible deployment of AI, including the perpetuation of biases, the undermining of due process, the erosion of human discretion and oversight, and the replication or amplification of broader societal inequalities if adequate governance safeguards are not proactively instituted. It proposes priority policies for BRICS countries including public outreach campaigns promoting awareness of AI impacts on law and ethics, legislation mandating contestability of algorithmic decisions, networks for policy coordination and best practice sharing, and investments in regional centers of excellence researching AI-powered dispute resolution.International journal of law and policy300The research highlights the significant risks posed by the irresponsible deployment of AI, including the perpetuation of biases, the undermining of due process, the erosion of human discretion and oversight, and the replication or amplification of broader societal inequalities if adequate governance safeguards are not proactively instituted.International Journal of Law and Policy["Jahangir Juraev"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/fd285ea2213c225751747b9260338bc3e0f30426 9c65e2c809a933cb0e601bb984afd4ae281103f9The purpose of this study was to investigate relationship among service value, trust, and psychological responses in the application of AI(Artificial Intelligence) in sports. The study participants selected for this study consist of 295 baseball players who have experienced AI assisted systems in sports. The data processing methods included frequency analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis, correlation analysis, and structural equation modeling. The findings derived from the objective and method of this study are as follows. First, social value was identified as a factor influencing trust. Second, social value was found to influence both cognitive and emotional responses, while emotional value was identified as a factor affecting emotional responses. Third, trust was identified as a factor influencing both cognitive and emotional responses. AI assisted systems in sports place greater emphasis on the collective social perception of sports teams than the value assessed by players. Additionally, it seems cooperation and effort from sports stakeholders will be necessary for the efficient utilization of artificial intelligence systems.Korean Society for Leisure Sciences00Social value was found to influence both cognitive and emotional responses, while emotional value was identified as a factor affecting emotional responses, and trust was identified as a factor influencing both cognitive and emotional responses.Korean Society for Leisure Sciences["Dae-Sun Ko"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/9c65e2c809a933cb0e601bb984afd4ae281103f9 84afc7909bad070cbb2613175d76bd927cac15a5Artificial Intelligence (AI) is revolutionizing logistics and distribution by enhancing efficiency, reducing costs, and improving the overall delivery experience. One of the key applications of AI in this sector is dynamic route optimization, where machine learning algorithms analyze real-time data such as traffic patterns, weather conditions, and road closures to continuously adjust delivery routes. This reduces fuel consumption, optimizes delivery times, and mitigates operational disruptions. In parallel, autonomous delivery systems, including drones and self-driving trucks, are transforming last-mile and long-haul transportation by minimizing human intervention and increasing speed and reliability. AI-powered autonomous delivery systems leverage advanced technologies such as computer vision, sensors, and machine learning to navigate and make real-time decisions. Drones, for example, are already being utilized for time-sensitive deliveries, especially in remote or underserved areas, while self-driving trucks promise to revolutionize long-haul freight transportation with the potential for 24/7 operation, cost reduction, and increased safety. Despite the clear benefits, challenges such as regulatory frameworks, safety concerns, and public acceptance remain as significant barriers to large-scale adoption. This review explores the role of AI in dynamic route optimization, drones, and self-driving trucks within logistics, providing insights into how these technologies are currently being implemented and the challenges that lie ahead. It further discusses the potential for AI to reshape the future of logistics and transportation by driving innovation in automation, reducing operational inefficiencies, and contributing to the development of smarter, more sustainable supply chains. The future of AI in logistics promises a more integrated, cost-effective, and agile system that can adapt to global challenges and evolving consumer demands.World Journal of Advanced Research and Reviews00The role of AI in dynamic route optimization, drones, and self-driving trucks within logistics, providing insights into how these technologies are currently being implemented and the challenges that lie ahead is explored.World Journal of Advanced Research and Reviews["YETUNDE ADEOYE", "ERUMUSELE FRANCIS ONOTOLE", "TUNDE OGUNYANKINNU", "GODWIN AIPOH", "AKINTUNDE AKINYELE OSUNKANMIBI", "JOSEPH EGBEMHENGHE"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/84afc7909bad070cbb2613175d76bd927cac15a5 1f042b0a3e6145da3827ad3214fd6f4b50661bbbOBJECTIVE Endoscopic classification of ulcerative colitis (UC) shows high interobserver variation. Previous research demonstrated that artificial intelligence (AI) can match the accuracy of central reading in scoring still images. We now extend this assessment to longer colon segments and integrate AI into clinical workflows, evaluating its use for real-time, video-based classification of disease severity, and as a support system for physicians. METHODS We trained a convolutional neural network with the Mayo Endoscopic Subscores (MES) of 2,561 images and 53 videos from 645 patients. The model differentiated scoreable from unscoreable endoscopy sections through open-set recognition. Validation involved 140 video clips from 44 UC patients. Six inflammatory bowel disease (IBD) experts and 16 non-experts rated these videos, with expert scores as the gold standard. We assessed the model's performance and the value as a supporting system. Lastly, the model underwent an alpha test on a real-world patient as a real-time endoscopic support. RESULTS The model achieved an accuracy of 82%, with no significant differences between the experts and the AI. When used as a supporting system, it improved non-IBD experts' performance by 12% and disagreed with the primary physician in 20-39% of cases. During the alpha test, it was successfully integrated into clinical practice, accurately distinguishing between MES 0 and MES 1, consistent with endoscopists' assessments. CONCLUSIONS Our innovative AI model shows significant potential for enhancing the accuracy of UC severity classification and improving the proficiency of non-IBD experts. It is designed for clinical use and has proven feasible in real-world testing.American Journal of Gastroenterology00An innovative AI model designed for clinical use and proven feasible in real-world testing shows significant potential for enhancing the accuracy of UC severity classification and improving the proficiency of non-IBD experts.The American journal of gastroenterology["Bobby Lo", "B. M\u00f8ller", "C. Igel", "S. Wildt", "I. Vind", "Flemming Bendtsen", "J. Burisch", "Bulat Ibragimov"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/1f042b0a3e6145da3827ad3214fd6f4b50661bbb 98b95d59045e29212d8dbaf9d686b70cc9765a35Breast cancer is a leading cause of illness and death worldwide, with early detection being key to improving survival rates. However, in low-resource settings, the lack of accessible, affordable, and efficient screening methods significantly hinders timely diagnosis and intervention. Traditional breast cancer screening methods, such as mammography, are often unavailable or impractical in these regions due to high costs, inadequate infrastructure, and a shortage of trained professionals. To address these challenges, artificial intelligence (AI) technologies have emerged as promising tools to enhance breast cancer screening. AI-based solutions, such as AI-enhanced mammography, ultrasound imaging, thermography, and mobile applications, have the potential to address challenges in low-resource settings by offering cost-effective, portable, and user-friendly alternatives. These innovations can facilitate early detection, decrease diagnostic errors, and empower healthcare workers with limited training to perform screenings effectively. This review examines the role of AI in breast cancer screening, particularly in low-resource settings. It highlights the challenges associated with conventional screening methods and explores how AI can help fill these gaps. Success stories from initiatives such as RAD-AID International, Tata memorial centre, and the AI-driven ultrasound project in Rwanda demonstrate the feasibility of integrating AI tools into underserved healthcare systems. The review also discusses strategies for effective AI integration, including data collection, infrastructure development, and training. Additionally, it outlines future directions for enhancing AI applications in global health. AI has the potential to bridge the gap in breast cancer screening, ensuring that underserved populations benefit from improved early detection and better health outcomes. This review provides a comprehensive overview of AI applications in breast cancer screening and offers insights into the future of AI in low-resource healthcare systems.International Journal of Community Medicine and Public Health320Artificial intelligence has the potential to bridge the gap in breast cancer screening, ensuring that underserved populations benefit from improved early detection and better health outcomes and offers insights into the future of AI in low-resource healthcare systems.International Journal Of Community Medicine And Public Health["Vanessa Vidaurre Corrales", "Ibrahim Marouf Yasin Al Shyyab", "Nisha S. Gowda", "Mahmood Alaawad", "Mai Yasir Hamdalla Mohamed", "Omar Jihad Saleh Almistarihi", "Ashwin Hassan Gopala", "Navneeth Jayaprakash", "Prerna Yadav", "Jayanth Jakka", "Vaibhav Singh"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/98b95d59045e29212d8dbaf9d686b70cc9765a35 aa51ccef09369729deef667000efe1a07707be04Objectives We aimed to assess the performance of an artificial intelligence-electrocardiogram (AI-ECG)-based model capable of detecting acute coronary occlusion myocardial infarction (ACOMI) in the setting of patients with acute coronary syndrome (ACS). Methods This was a prospective, observational, longitudinal, and single-center study including patients with the initial diagnosis of ACS (both ST-elevation acute myocardial infarction [STEMI] & non-ST-segment elevation myocardial infarction [NSTEMI]). To train the deep learning model in recognizing ACOMI, manual digitization of a patient's ECG was conducted using smartphone cameras of varying quality. We relied on the use of convolutional neural networks as the AI models for the classification of ECG examples. ECGs were also independently evaluated by two expert cardiologists blinded to clinical outcomes; each was asked to determine (a) whether the patient had a STEMI, based on universal criteria or (b) if STEMI criteria were not met, to identify any other ECG finding suggestive of ACOMI. ACOMI was defined by coronary angiography findings meeting any of the following three criteria: (a) total thrombotic occlusion, (b) TIMI thrombus grade 2 or higher + TIMI grade flow 1 or less, or (c) the presence of a subocclusive lesion (> 95% angiographic stenosis) with TIMI grade flow < 3. Patients were classified into four groups: STEMI + ACOMI, NSTEMI + ACOMI, STEMI + non-ACOMI, and NSTEMI + non-ACOMI. Results For the primary objective of the study, AI outperformed human experts in both NSTEMI and STEMI, with an area under the curve (AUC) of 0.86 (95% confidence interval [CI] 0.75-0.98) for identifying ACOMI, compared with ECG experts using their experience (AUC: 0.33, 95% CI 0.17-0.49) or under universal STEMI criteria (AUC: 0.50, 95% CI 0.35-0.54), (p value for AUC receiver operating characteristic comparison < 0.001). The AI model demonstrated a PPV of 0.84 and an NPV of 1.0. Conclusion Our AI-ECG model demonstrated a higher diagnostic precision for the detection of ACOMI compared with experts and the use of STEMI criteria. Further research and external validation are needed to understand the role of AI-based models in the setting of ACS.Archivos de Cardiología de México00The authors' AI-ECG model demonstrated a higher diagnostic precision for the detection of ACOMI compared with experts and the use of STEMI criteria.Archivos de cardiologia de Mexico["B. D\u00edaz-Herrera", "Edgar Roman-Rangel", "Carlos A. Castro-Garcia", "D. Sierra-Lara Mart\u00ednez", "R. Gopar-Nieto", "Karen G Velez-Talavera", "Mar\u00eda P Espinosa-Mart\u00ednez", "Santiago March-Mifsut", "Ximena Latapi-Ruiz-Esparza", "\u00d3. Preciado-Guti\u00e9rrez", "Santiago Alba-Valencia", "H\u00e9ctor A S\u00e1nchez-Alfaro", "H. Gonz\u00e1lez-Pacheco", "Alexandra Arias-Mendoza", "D. Araiza-Garaygordobil"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/aa51ccef09369729deef667000efe1a07707be04 cf744348f139b3ef4138aba4836afb41630a7458Background and Purpose: This study explores the implementation of Team-Based Learning (TBL) within a clinical reasoning module at a medical school. The objective is to assess the impact of TBL on student learning experiences and address the potential to mitigate assessment challenges heightened by Generative Artificial Intelligence (GAI) advancements. Methodology: The study employed a mixed-methods approach, combining qualitative and quantitative analyses. Data were collected through surveys from students (n=31) who took a Clinical Reasoning module at a medical school. The university where the study was conducted is an international institution, hosting students from diverse cultural and ethnic backgrounds, including representation from the Asian continent. The Learning Activity Management System (LAMS) was used for module delivery. A thematic analysis was performed on the qualitative data, and descriptive statistics were applied to the quantitative data. Findings: Findings indicate that TBL significantly enhances the learning experience by promoting active engagement, collaborative learning, and the development of critical thinking skills among medical students. Students reported positive experiences with teamwork, peer evaluation, and the structured nature of TBL sessions. The study also highlighted the role of TBL in maintaining assessment integrity amidst the rise of GAI tools like ChatGPT. Contributions: TBL presents a viable framework for enhancing student engagement and maintaining the authenticity of assessments in an era increasingly dominated by Generative AI. The study advocates for the thoughtful integration of TBL in education, emphasising its potential to foster a deeper understanding of the subject matter and address evolving challenges in academic assessments. Keywords: Team-based learning, medical education, assessment integrity, generative AI, collaborative learning, higher education.Journal of Nusantara Studies (JONUS)220Findings indicate that TBL significantly enhances the learning experience by promoting active engagement, collaborative learning, and the development of critical thinking skills among medical students.Journal of Nusantara Studies (JONUS)["Ahmet Durgungoz", "Daniel Peter McLaughlin"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/cf744348f139b3ef4138aba4836afb41630a7458 1c3c32dbf49b82a27dd26c1e0d5d129376a51936Digital transformation through the application of artificial intelligence (AI) and big data has become a key strategy in improving organizational performance in various sectors. The implementation of these technologies enables increased operational efficiency, accelerated decision-making, and innovation in services and products. However, on the other hand, organizations face various challenges, including technology infrastructure readiness, employee resistance to change, and data security risks. Therefore, an effective change management strategy is needed to ensure the successful implementation of AI and big data in supporting digital transformation. This research uses a qualitative approach with case study method and literature review. Case studies were conducted on organizations that have implemented AI and big data to analyze their impact on operational performance and business strategy. Data was collected through in-depth interviews with key stakeholders, observation of work processes, and analysis of organizational documents. In addition, a literature review was used to identify findings from previous research on the enablers and barriers of digital technology implementation.  The results show that the implementation of AI and big data has a positive impact on organizational performance, especially in improving efficiency, decision-making accuracy, and innovation. However, successful implementation is strongly influenced by the organization's readiness to face technical and cultural challenges. Organizations that have visionary leadership, effective change management strategies, and training programs for employees tend to be more successful in adopting digital technologies.Maneggio00The results show that the implementation of AI and big data has a positive impact on organizational performance, especially in improving efficiency, decision-making accuracy, and innovation, however, successful implementation is strongly influenced by the organization's readiness to face technical and cultural challenges.Maneggio["Y. Ramadhani", "Dian Arlupi Utami"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/1c3c32dbf49b82a27dd26c1e0d5d129376a51936 21d151d9b338618999861a2c6833ba15718fd688Artificial Intelligence (AI) has rapidly become an integral technology across many sectors, including healthcare, finance, research, and manufacturing. AI’s ability to automate processes, analyse large datasets, and make predictive decisions offers significant opportunities for innovation, but it also raises profound ethical challenges. Ethical concerns regarding AI encompass issues of transparency, accountability, fairness, data privacy, and the need for human oversight. Given the diverse applications of AI, these ethical concerns vary not only by sector but also across different cultural and regulatory environments. Despite growing discourse on AI ethics, empirical tools for assessing ethical perceptions of AI across varied organizational contexts remain limited. From that need, this study introduces the AI and Ethics Perception Scale (AEPS), designed to measure individual and collective perceptions of AI ethics across five key dimensions: Transparency, Accountability, Privacy, Fairness, and Human Oversight. The AEPS was developed through a rigorous methodological process, beginning with a pilot study of 112 participants and validated with data from 417 participants across three culturally diverse countries: Turkey, India, and the United Kingdom. The scale was used to assess ethical perceptions in sectors such as healthcare, finance, and manufacturing. Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to validate the scale’s structure. This study reveals significant cross-cultural and cross-sectoral differences in the prioritization of ethical concerns, demonstrating the need for contextually sensitive ethical frameworks for AI governance. International Journal of Economic Behavior and Organization480This study reveals significant cross-cultural and cross-sectoral differences in the prioritization of ethical concerns, demonstrating the need for contextually sensitive ethical frameworks for AI governance.International Journal of Economic Behavior and Organization["Ezgi Saatci"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/21d151d9b338618999861a2c6833ba15718fd688 6758fac7ef6fa87f9ca150588f5a0095ec974f1aArtificial Intelligence (AI) has revolutionized several industries, and scientific publishing is no exception. From peer review automation to the detection of plagiarism and manuscript proofing, AI is revolutionizing research production, dissemination, and evaluation. Although AI brings tremendous potential to automate publishing, it raises significant questions regarding ethics and integrity that must be addressed correctly (1). The most significant impact that AI has made on publishing is speeding up the peer review process. Traditional peer review is laborious and time-consuming, and it tends to result in very lengthy publication cycles. AI tools can assist in pre-screening submissions, finding possible reviewers according to expertise, and even identifying ethics concerns like duplicate publication or image tampering. Some AI tools, like ScholarOne and Editorial Manager, have already started using machine learning algorithms to recommend reviewers and detect probable conflicts of interest, making an efficient and unbiased review process possible (2). Besides peer review, AI has also improved the editorial process by employing language processing models that assist authors in manuscript editing. These include products like Grammarly, Writefull, and Paperpal, which use AI-driven natural language processing (NLP) to correct grammar, simplify the language, and improve readability. This proves helpful in non-native English-speaking academics, who may be unable to present research findings effectively. Also, AI-driven translation software is breaking language barriers, allowing for research dissemination across linguistic groups (3). The detection of plagiarism has also shifted fundamentally with the advent of AI. Conventional software such as Turnitin and iThenticate have come a long way, using deep learning algorithms to identify more evolved instances of academic fraud, including paraphrasing plagiarism and AI-generated content. After the proliferation of generative AI tools such as ChatGPT, the difference between human-written and machine-written content has become more difficult to distinguish, calling for increasingly sophisticated AI-driven authenticity checks (4). But publishing with AI is not without issues. The ethical aspects of AI-generated research content are becoming a problem more and more. The greater the dependence on AI writing aids, the greater the problems concerning authorship, novelty, and intellectual property. The majority of journals now have strict policies for using AI-generated content and being open and accountable in scientific publishing. Second, the risk of bias in AI algorithms is still an issue because AI algorithms learn from what is already in print form, thus potentially continuing existing biases in publishing materials (5). Besides that, AI is transforming the availability of scientific literature. AI-based recommendation platforms such as Semantic Scholar and Scite simplify scientists' ability to locate pertinent literature by analyzing citation patterns and trend research. Open-access journals also leverage AI to increase content published therein and make it more accessible and readable to audiences, thereby democratizing knowledge dissemination. Despite all these advancements, human judgment remains unavoidable in publishing. No matter how much help AI will extend, ethical decisions and contextual appreciation are still impossible without human ability. Symbiosis with AI as a helper, but not a replacement for writers, editors, and referees, is the best means to achieve this (6). Last but not least, AI is undoubtedly revolutionizing the terrain of scientific publishing. Its capacity to simplify workflows, enhance quality, and enable accessibility is charting the future of academic communication. With these opportunities, however, come the ethics and integrity issues that need to be resolved in order to achieve responsible AI deployment. As the world of publishing advances, a delicate balance that harnesses the power of AI while maintaining the integrity of academics will be critical in sustaining trust and credibility within the science literature.International Journal of Pharmacy &amp; Integrated Health Sciences00Artificial Intelligence (AI) is undoubtedly revolutionizing the terrain of scientific publishing, and its capacity to simplify workflows, enhance quality, and enable accessibility is charting the future of academic communication.International Journal of Pharmacy &amp; Integrated Health Sciences["Prof. Dr. Abubakar Munir"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/6758fac7ef6fa87f9ca150588f5a0095ec974f1a ad69a66069ba6969622811c51bd90770146424c6 The application of artificial intelligence (AI) in cardiac imaging has rapidly evolved, offering enhanced accuracy and efficiency in the diagnosis and management of cardiovascular diseases. This bibliometric study aimed to evaluate research trends, impact, and scholarly output in this expanding field. A systematic search was conducted on 14 August 2024, using the Web of Science Core Collection database. VOSviewer, CiteSpace, and Biblioshiny were utilized for data analysis. The findings revealed a significant increase in publications on AI in cardiovascular imaging, particularly from 2018 to 2023, with the United States leading in research output. England and the United States have emerged as central hubs in the global research network, highlighting their role in generating high-quality and impactful publications. The University of London was identified as the top contributing institution, while Frontiers in Cardiovascular Medicine was the most prolific journal. Keyword analysis highlighted machine learning, echocardiography, and diagnosis as the most frequently occurring terms. A time trend analysis showed a shift in research focus towards AI applications in cardiac computed tomography (CT) and magnetic resonance imaging (MRI), with recent keywords like ejection fraction, risk, and heart failure reflecting emerging areas of interest. Healthcare providers should consider integrating AI tools into cardiovascular imaging practice, as AI has demonstrated the potential to enhance diagnostic accuracy and improve patient outcomes. This study highlights the rising importance of AI in personalized and predictive cardiovascular care, urging healthcare providers to stay informed about these advancements to enhance clinical decision-making and patient management. Annals of Medicine &amp; Surgery00The rising importance of AI in personalized and predictive cardiovascular care is highlighted, urging healthcare providers to stay informed about these advancements to enhance clinical decision-making and patient management.Annals of Medicine &amp; Surgery["Hadi Alotaibi", "Rafael Contreas", "Nisarg Thakker", "A. Mahapatro", "Saisree Reddy Adla Jala", "Elan Mohanty", "Pavan Devulapally", "Mohit Mirchandani", "M. D. Marsool Marsool", "Shika M. Jain", "F. Joukar", "Azin Alizadeh Asl", "Seyedeh Fatemeh Hosseini Jebelli", "Ehsan Amini-Salehi", "Daniyal Ameen"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/ad69a66069ba6969622811c51bd90770146424c6 2cd62277f3c40adb078d8f013ca561f0db19246dThe rapid advancements in Artificial Intelligence (AI) and data analytics are transforming the logistics industry, enabling more innovative, more efficient, and sustainable transportation solutions. Traditional logistics systems face significant challenges, including inefficient order consolidation, underutilized vehicle capacity, static route planning, and a high environmental impact. These inefficiencies increase operational costs, delivery delays, and elevated carbon emissions, undermining sustainability goals. This study presents a novel AI-driven approach to optimizing route planning and logistics management through reinforcement learning and cloud-based data platforms. The proposed system integrates real-time data from IoT-enabled devices, GPS tracking, traffic analysis, and weather forecasts to dynamically optimize delivery routes. By leveraging machine learning algorithms, the system can anticipate disruptions and make real-time adjustments, leading to a 98% on-time delivery rate and significant reductions in fuel consumption. One of the key innovations of this approach is multi-segment optimization, which allows vehicles to manage multiple deliveries within a single route. This optimization reduces empty truck mileage, improves vehicle utilization and enhances cost efficiency. Additionally, predictive and prescriptive analytics enhance decision-making by forecasting potential delivery delays, enabling proactive interventions. The system's architecture is built on a scalable cloud-native platform, ensuring seamless data integration and high processing capacity for large-scale logistics operations. Interactive dashboards and digital twin technology provide logistics teams with real-time insights and scenario-based simulations, further improving decision-making and operational efficiency. The implementation of AI-driven logistics optimization has demonstrated measurable improvements, including reduction in delivery times, decrease in fuel consumption, and substantial cost savings across operations. Beyond operational efficiency, the proposed AI-powered system significantly contributes to sustainability efforts. By minimizing fuel consumption and optimizing vehicle utilization, it directly supports carbon footprint reduction initiatives in the logistics industry. The adaptability of this system makes it suitable for various logistics networks, from urban delivery fleets to long-haul transportation, enhancing overall supply chain resilience. This research underscores the transformative potential of AI and reinforcement learning in modern logistics. The findings establish a new benchmark for AI-driven logistics operations and open avenues for future enhancements, such as deeper IoT integration, the adoption of autonomous delivery models, and the exploration of quantum computing for further optimization. The study demonstrates that intelligent automation and data-driven decision-making are key to achieving smarter, more sustainable logistics operations in the future.International journal of computer science and mobile computing00A novel AI-driven approach to optimizing route planning and logistics management through reinforcement learning and cloud-based data platforms, which demonstrates that intelligent automation and data-driven decision-making are key to achieving smarter, more sustainable logistics operations in the future.International Journal of Computer Science and Mobile Computing["Khursheed Mohammed Hussain"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/2cd62277f3c40adb078d8f013ca561f0db19246d 62da04ee38aa0cec4bce6806470e9704bb78fd6cObjectives: The aim of this study is to compare the ability of artificial intelligence-based chatbots, ChatGPT-4o and Claude 3.5, to interpret mammography images. The study focuses on evaluating their accuracy and consistency in BI-RADS classification and breast parenchymal type assessment. It also aims to explore the potential of these technologies to reduce radiologists’ workload and identify their limitations in medical image analysis. Methods: A total of 53 mammography images obtained between January and July 2024 were analyzed, focusing on BI-RADS classification and breast parenchymal type assessment. The same anonymized mammography images were provided to both chatbots under identical prompts. Results: The results showed accuracy rates for BI-RADS classification ranging from 18.87% to 26.42% for ChatGPT-4o and 18.7% for Claude 3.5. When BI-RADS categories were grouped into benign group(BI-RADS 1,2) and malignant group(BI-RADS 4,5), the combined accuracy was 57.5% for ChatGPT-4o (initial evaluation) and 55% (second evaluation), compared to 47.5% for Claude 3.5. Breast parenchymal type accuracy rates were 30.19% and 22.64% for ChatGPT-4o, and 26.42% for Claude 3.5. Conclusions: The findings indicate that chatbots demonstrate limited accuracy and reliability in interpreting mammography images. These results highlight the need for further optimization, larger datasets, and advanced training processes to improve their performance in medical image analysis.European Journal of Therapeutics240The findings indicate that chatbots demonstrate limited accuracy and reliability in interpreting mammography images, and highlight the need for further optimization, larger datasets, and advanced training processes to improve their performance in medical image analysis.European Journal of Therapeutics["Bet\u00fcl Nalan Karahan", "Emre Emekli", "Mahmut Altu\u011f Alt\u0131n"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/62da04ee38aa0cec4bce6806470e9704bb78fd6c ba1b1ac11741a252516996d4cd933f224f1d98c7The emergence of artificial intelligence (AI) in the healthcare industry has increased novel health technology with promising features. SaMD is a software capable of performing different functions like diagnosis, treatment, monitoring, and prevention, even without the need for other hardware. AI/ML devices work on various algorithms, one is a locked algorithm that consistently provides the same output for a particular input, While the other is an adaptive algorithm that constantly updates its data concerning the real world to give more precise results. UTAUT model is a type of model that is also used by SaMD medical devices, this model works on various principles which are discussed in the article. A SaMD needs to be approved first before being marketed, there are various regulations associated with the approval and marketing of a SaMD, and different countries have different regulations to comply with. In this article, various regulations on marketing and approval of SaMD of different countries are also covered . A SaMD software known as “DIGITAL TWIN “is a software that creates a twin of a physical entity in the digital world, it helps healthcare people rule out diseases, choose treatment, and avoid potential risks . The paper concludes with two main concepts: first, the regulatory framework which gives an insight into how SaMD are marketed in various countries, and second an innovation of SaMD known as the digital twin which includes its basic concepts, need for a digital twin, and their regulations.International Journal of Innovative Research in Engineering &amp; Multidisciplinary Physical Sciences250The regulatory framework which gives an insight into how SaMD are marketed in various countries, and an innovation of SaMD known as the digital twin which includes its basic concepts, need for a digital twin, and their regulations are concluded.International Journal of Innovative Research in Engineering &amp; Multidisciplinary Physical Sciences["Mahi Seth", "Rishik Gupta"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/ba1b1ac11741a252516996d4cd933f224f1d98c7 3d5af3e04f5dd40da73c43477d6fcc4032beb227The  integration  of  artificial  intelligence  (AI)  training  tools  in military  sports  has  revolutionized  how  military  athletes  prepare  for competitive  events. AI  technologies  can  analyze  performance  data, simulate   real-world   scenarios,   and   provide   personalized   training recommendations, leading to improved performance outcomes. AI training tools utilize machine learning algorithms to analyze vast   amounts   of   data,    offering   insights   that   were    previously unavailable.  According  to  Chan  and   Lim  (2023),  these  tools  can monitor  athletes’   physiological   responses,  training   intensity,  and technique  execution  in  real-time.  By  collecting  and  analyzing  data from various training sessions, AI tools  help  coaches tailor training programs   to    meet   the    specific   needs    of   each    athlete.   This personalization   increases  the  effectiveness  of  training   regimens, enabling military athletes to maximize their performance potential. One  of  the  key  benefits  of AI  training  tools  is  their  ability  to enhance  decision-making  skills.   In  a  study   by  Nguyen  and  Tran (2021),   military   athletes   who    used   AI-driven   simulation    tools demonstrated  improved tactical awareness and situational decision - making during training exercises. These tools simulate high -pressure scenarios    that     military     athletes     may    face     in     competitive environments, allowing them to practice responses without the risks  associated  with  real-life  situations.  As  a  result,  athletes  develop  a deeper  understanding  of  strategy  and  improved  execution  during actual competitions. The role of AI in monitoring and assessing athlete performance cannot   be  overstated.  Tan,   Lee,  and   Khaw   (2024)  conducted  a comprehensive  analysis  of AI  tools  used  in  military  sports  training, highlighting their effectiveness  in  providing  immediate feedback .  By analyzing athletes’ movements and techniques during training, these tools  offer  valuable  insights  that  can  be  addressed  in  subsequent training  sessions.  This  continuous  feedback  loop  enables  military athletes to make real-time adjustments to their performance, leading to significant improvements over time. In  addition  to  technical  training,  AI  tools  can  also  support mental  preparation  and  psychological  resilience.  Mental fortitude  is critical  for  military  athletes,  who  often  compete  under  high -stress conditions.  A   study  by   Ho  and  Yeo   (2022)  found  that  AI-based training programs, which incorporate mental conditioning techniques, led   to   improved   focus   and   stress   management   among   military athletes.  These  programs  utilize  cognitive  training  exercises  and biofeedback mechanisms to  help athletes develop  mental strategies that enhance their performance in competitive settings. The  integration  of AI  training  tools  in  military  sports  also  has implications for injury prevention and recovery. By analyzing training  loads and physiological data, AI tools can identify patterns that may predispose  athletes  to  injuries. According  to  Tan  and  Chua  (2020), early  detection  of  potential  injury  risks  allows  coaches  to  adjust training intensity and implement preventive measures . This proactive approach not only protects the athletes’ physical well -being but also ensures they remain competitive in their respective sports. Moreover, AI training tools facilitate cross-disciplinary learning and   collaboration   among    military   athletes.   As    military   sports increasingly incorporate technology, athletes are exposed to diverse training  methodologies  from  other  sports  disciplines.  A  study   by Leong and Goh (2023) highlighted that military athletes using AI tools could learn from the training patterns of elite athletes in other sports, adapting  successful  strategies  to  enhance  their  performance.  This cross-disciplinary  approach  promotes  innovation  and  creativity  in training, ultimately benefiting military athletes. While  the  advantages  of AI  training  tools  are  clear,  there  are also   challenges   associated  with   their   implementation   in   military sports.  Resistance to technology and the  potential for over-reliance on  AI  tools   can   hinder  athletes'  development.  According   to   Ooi (2021), some military athletes may feel uncomfortable relying heavily on  technology  for  performance  assessments,  fearing  that   it  may diminish   their   traditional   training    practices.   Addressing   these concerns  through  education  and  training  on  the  effective  use  of A I   tools  is essential to foster a  positive  attitude towards technology  in military sports. Furthermore, ethical considerations regarding data privacy and security  must  be  addressed  as  AI  tools  become  more  prevalent  in military  sports.  With  the  collection  of  sensitive  performance  data, there are concerns about how this information is stored and utilized. Chan and Tan (2022) emphasize the need for strict data governance policies to  protect athletes' privacy while  benefiting from AI training tools.  Ensuring  that  athletes  are  informed  about  how  their  data  is used can enhance their trust in these technologies. AI  training  tools  have  the  potential  to  significantly  enhance performance outcomes in military sports among military athletes. By providing    personalized     training     recommendations,    enhancing decision-making  skills,  and  supporting   mental  preparation,  these tools  offer  a  comprehensive  approach  to  athlete  development.  As research   from   Southeast   Asia   indicates,   the   integration   of   AI technologies   in   military   sports   can    lead   to   improved   training effectiveness,   injury   prevention,   and   cross-disciplinary   learning. However, addressing challenges related to technology resistance and data privacy is essential for maximizing the benefits of AI in military sports   training.   As   the   field   continues   to   evolve,   the   ongoing exploration  of AI's  impact  on  performance  outcomes  will  be  crucial for advancing military athleticism. Social Science and Humanities Journal40AI  technologies  can  analyze  performance   data, simulate   real-world   scenarios, and provide   personalized   training recommendations, leading to improved performance outcomes, leading to improved performance outcomes.Social Science and Humanities Journal["Zhengyin Ni"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/3d5af3e04f5dd40da73c43477d6fcc4032beb227 e5106074852101388e455a95c211d91b3af1af24DISCLAIMER In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE This primer aims to serve as a foundational resource on artificial intelligence (AI) for pharmacists practicing in the emergency department (ED). SUMMARY Artificial intelligence (AI) is increasingly recognized for its potential to transform healthcare, including emergency medicine (EM) and pharmacy practice. AI applications in EM include diagnostic evaluation, risk stratification, resource optimization, and therapeutic decision-making. AI's role in improving triage, diagnostics, and resource utilization in the emergency setting is discussed along with its application in the medication-use process, from prescribing to monitoring. Despite the promise of AI, significant barriers such as factual inaccuracies, ethical concerns, and data transparency prevent the widespread clinical adoption of AI tools. Challenges such as racial bias, data privacy, model transparency, and the phenomenon of hallucinations in large language model outputs are highlighted as critical considerations. AI's future success in EM will depend on responsible integration, guided by clinicians including pharmacists, and a careful consideration of ethical issues and patient-specific values. CONCLUSION Pharmacists practicing in the ED should be familiar with AI tools and should understand the importance of their role in the development, implementation, and oversight of these tools to ensure safe, effective, and equitable patient care.American Journal of Health-System Pharmacy00Pharmacists practicing in the emergency department (ED) should be familiar with AI tools and should understand the importance of their role in the development, implementation, and oversight of these tools to ensure safe, effective, and equitable patient care.American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists["Christopher J Edwards", "B. Erstad", "Vivienne Ng"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/e5106074852101388e455a95c211d91b3af1af24 cb4033b51a0afe744f3824bd33087cdda897d7beIn an era characterized by rapid advancements in artificial intelligence (AI), traditional assessment methods face both unprecedented challenges and new opportunities. This study explores the paradigm shift in assessment practices prompted by AI’s growing role in education, professional evaluations, and decision-making processes. The research identifies a significant gap in current assessment frameworks, emphasizing the need for a balanced integration of AI technologies without compromising essential human qualities such as judgment, empathy, and contextual understanding. The objective of this study is to critically examine how AI-driven assessment tools can enhance efficiency, objectivity, and personalized feedback, while also addressing concerns related to ethics, bias, and the evolving responsibilities of human evaluators. Through a comprehensive analysis, this research proposes a holistic framework that combines AI’s capabilities with human insight to shape the future of assessment. Key findings suggest that while AI offers considerable benefits, a thoughtful, integrated approach is crucial to ensure fair, equitable, and effective evaluations. The study’s implications highlight the need for ongoing dialogue and adaptation to ensure that the future of assessment remains both innovative and human-centered. Keywords: Artificial intelligence; assessment; revolutionary shift; 542traditional approachGlobal Journal of Foreign Language Teaching00This research proposes a holistic framework that combines AI’s capabilities with human insight to shape the future of assessment, suggesting that while AI offers considerable benefits, a thoughtful, integrated approach is crucial to ensure fair, equitable, and effective evaluations.Global Journal of Foreign Language Teaching["Djebbari Houda"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/cb4033b51a0afe744f3824bd33087cdda897d7be 65f29ec5bd9f3fb64e14935fa526baad7dbd81b6The study investigated the awareness and utilisation of Artificial intelligence (AI) in teaching and learning among secondary school studets in Afijio Local Government, Oyo State. A descriptive survey research was adopted. The study sampled 300 biology students from five secondary schools in Afijio, Oyo State. Three hypotheses guided the study. Crobah Alpha was used to find the reliability value 0.78. T Test was used to analysis the data collected. The findings of the study revealed that the Biology students are not aware of the existence of Artificial intelligence tools used in teaching and learning and are also not utilizing A. I tools in teaching and learning. Recommendations were made that there should be awareness on the availability of artificial intelligence tools for teaching and learning in secondary schools, and Education Curriculum should be reviewed to accommodate AI in other to prepare biology students into tertiary institutions.Journal of Education Research and Library Practice10The findings of the study revealed that the Biology students are not aware of the existence of Artificial intelligence tools used in teaching and learning and are also not utilizing A. I tools in teaching and learning.Journal of Education Research and Library Practice["Dr. Dorcas Omolara", "Allwell Agada", "Irawoola", "S. Oladipupo", "Adebanke Mosunmola", "Okunlola"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/65f29ec5bd9f3fb64e14935fa526baad7dbd81b6 3b05e32cfdfe64276e4d85a092f46b4717f2329dCombination of emergency medicine practice and artificial intelligence (AI) has a potential to revolutionize and reshape health systems through computer-based systems. Generative artificial intelligence (AI) integrated programs such as Chat Generative Pre-trained Transformers (ChatGPT) are becoming more widespread in educational and clinical settings. AI-based tools used in emergency medicine, such as machine learning algorithms and ChatGPT, have already demonstrated their capacity to improve diagnostic accuracy and accelerate the early identification of diseases, resulting in more timely interventions and more favorable patient outcomes. As the number of researches on combination of AI with clinical settings increases, the number of publications on this field increases simultaneously. As a leading publisher in Emergency Medicine, the American Journal of Emergency Medicine (AJEM) plays a pivotal role for progression of AI studies. In this article, we aimed to reveal the support AJEM provides to the literature through AI articles.World Journal of Advanced Research and Reviews00The support AJEM provides to the literature through AI articles is revealed, showing the capacity of AI-based tools used in emergency medicine to improve diagnostic accuracy and accelerate the early identification of diseases.World Journal of Advanced Research and Reviews["Ali Kemal ERENLER", "Behice Hande ERENLER", "Ahmet BAYDIN"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/3b05e32cfdfe64276e4d85a092f46b4717f2329d b9f37258917cd8db41e0eb3f839141fc74efd7f8Modern cybersecurity now includes artificial intelligence (AI), which is essential for protecting digital infrastructures against ever-evolving threats like malware, phishing scams, and illegal intrusions. AI has changed how businesses protect themselves from cyber threats by processing enormous volumes of data, identifying irregularities, and automating security procedures. Beyond its uses in cybersecurity, artificial intelligence (AI) is becoming more and more integrated into eGovernment frameworks, which enables governments to improve risk management, regulatory enforcement, and service efficiency. Proactive threat identification, expedited decision-making, and enhanced citizen involvement are guaranteed by the incorporation of AI-driven security measures into e-Government. However, there are several facets to the interaction between cybersecurity, e-Government, and AI, which are impacted by stakeholder involvement, policy concerns, and technology developments. Beyond its uses in cybersecurity, artificial intelligence (AI) is becoming more and more integrated into eGovernment frameworks, which enables governments to improve risk management, regulatory enforcement, and service efficiency. Proactive threat identification, expedited decision-making, and enhanced citizen involvement are guaranteed by the incorporation of AI-driven security measures into e-Government. However, there are several facets to the interaction between cybersecurity, e-Government, and AI, which are impacted by stakeholder involvement, policy concerns, and technology developments.International Journal for Research in Applied Science and Engineering Technology00Beyond its uses in cybersecurity, artificial intelligence (AI) is becoming more and more integrated into eGovernment frameworks, which enables governments to improve risk management, regulatory enforcement, and service efficiency.International Journal for Research in Applied Science and Engineering Technology["Mr. Ashish Modi"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/b9f37258917cd8db41e0eb3f839141fc74efd7f8 5eb68e268a409c7dd9dd1f91ac86c996f5b7c151Introduction of Artificial Intelligence (AI) in education provides drastic change in education system. AI can transform teaching and learning methodologies with the help of personalized learning, refining administrative efficiency, and improving decision making. On the other side there are many challenges which are faced that require careful consideration This paper explores the different challenges, to integrate AI in education to take care of, efficient, and ethical use of technology.World Journal of Advanced Engineering Technology and Sciences00This paper explores the different challenges, to integrate AI in education to take care of, efficient, and ethical use of technology.World Journal of Advanced Engineering Technology and Sciences["Varinder Kaur Attri", "Teena Jaiswal", "Ramnarayan Jaiswal", "Vidhu Baggan"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/5eb68e268a409c7dd9dd1f91ac86c996f5b7c151 24c248ecb6f6a2b3bdf2b724f3dc85bc1c8adb80 Purpose Artificial intelligence (AI) will transform various processes by utilizing and sharing data and information. This transformation brings new opportunities and challenges to organizations. Effective leadership is essential to handle these changes. However, there is no scientific research on how AI affects the everyday lives of managers. Therefore, this paper aims to identify how AI can affect changes in the skills and personality traits of managers using AI. Design/methodology/approach A structured literature review identified leadership competencies relevant to the AI era. Three scientific databases were included in the search: (I) Scopus, (II) EBSCO Business Source Complete, and (III) Web of Science. A total of 730 articles were identified from the three databases under the topics “Digital Leadership,” “Leadership” AND “Artificial Intelligence,” “Future Leadership,” “Algorithm Leadership,” “AI Leadership,” “Artificial Leadership,” and “Data-driven Leadership.” Findings A total of 24 leadership competencies, including 12 personality traits and 12 skills, were identified from the literature. To adapt effectively to AI, leaders should focus on developing communication skills and forming high-performance teams working cross-functionally and in a symbiosis of humans and machines. Originality/value The article adds knowledge to leadership theories and provides a basis for future management education. Strategy &amp; Leadership430To adapt effectively to AI, leaders should focus on developing communication skills and forming high-performance teams working cross-functionally and in a symbiosis of humans and machines.Strategy &amp; Leadership["Tobias Bock", "Dietrich von der Oelsnitz"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/24c248ecb6f6a2b3bdf2b724f3dc85bc1c8adb80 ea357d93fae28aac645bedb8f34aeb7506a740afAlthough AI has immense potential to improve the diagnosis and treatment of neurological diseases, continued progress is needed in system transparency, clinical validation, and regulation to ensure that these tools are safely and effectively adopted in medical practice. Therefore, the objective is to explore the potential of Artificial Intelligence in the early and accurate diagnosis of neurological diseases, highlighting its benefits, technical and ethical challenges, and the need for advancements in transparency, clinical validation, and regulation for its safe and effective adoption in medical practice. The methodology of this study is based on a narrative review of the literature, aimed at analyzing the impact of Artificial Intelligence on the diagnosis of neurological diseases. Despite advances, challenges remain, including the need for high-quality data and population diversity, as well as issues related to model interpretability and large-scale clinical validation. However, with the continuous improvement of AI technologies and their integration with clinical decision support systems, the perspective is that neurology will increasingly benefit from these innovations, improving the quality of patient care. The combination of AI with advances in neuroimaging, genetics, and digital monitoring has the potential to transform neurological care, enabling faster diagnoses, more effective treatments, and better quality of life for patients. Thus, Artificial Intelligence not only enhances the understanding of neurological diseases but also drives precision medicine, establishing itself as one of the pillars of the future of neurology.International Journal of Science and Research Archive00The objective is to explore the potential of Artificial Intelligence in the early and accurate diagnosis of neurological diseases, highlighting its benefits, technical and ethical challenges, and the need for advancements in transparency, clinical validation, and regulation for its safe and effective adoption in medical practice.International Journal of Science and Research Archive["Marjorie Bind\u00e1 Leite", "Jeniffer Aparecida de Morais Rodrigues", "Carolina F\u00e1tima Gioia Nava", "Jos\u00e9 Wilson Lima Furtado Junior", "Wildes Aparecido da Silva Ara\u00fajo", "Danielly Crispim Torres", "Marinaldo Soares Leite"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/ea357d93fae28aac645bedb8f34aeb7506a740af 3c8b01c9b0832ded1a869d290c0ada5c812c356bAs technology continues to transform education, the adoption of generative artificial intelligence is increasing in nursing education. However, concerns regarding the accuracy of AI-generated content and ethical issues exist. This study explores the perceptions/experiences of nurse educators in South Korea regarding the use of generative artificial intelligence. Using a cross-sectional survey, data were gathered from 120 nurse educators, and descriptive statistical analysis was applied to the data. Significantly 38.9% of participants reported no prior engagement with generative artificial intelligence. Meanwhile, 32.5% identified ChatGPT as their preferred source. The perceived usefulness of generative artificial intelligence was evaluated on average as 3.11 (SD = 0.31) on a 4-point scale, suggesting a generally favorable view of its potential to diversify learning resources, enhance student learning experiences, and improve educational quality. Despite these positive perceptions, the average engagement score with generative artificial intelligence was 2.76 (SD = 0.40), reflecting moderate actual use. This study contributes to the literature on generative artificial intelligence integration in education, revealing an overall positive attitude among nurse educators. It underscores the need for increased application and familiarity with such technologies to maximize teaching strategy benefits, student outcomes, and the efficacy of nursing education.Computers, Informatics, Nursing280The need for increased application and familiarity with generative artificial intelligence technologies to maximize teaching strategy benefits, student outcomes, and the efficacy of nursing education is underscores.Computers, informatics, nursing : CIN["Minjoo Hong", "Hyewon Shin", "Sang Suk Kim", "J. C. De Gagne"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/3c8b01c9b0832ded1a869d290c0ada5c812c356b b879dadf9472d761174fff67b787c7c9110e829eIn the era of artificial intelligence, the accounting industry has undergone profound development where intelligent accounting has become the mainstream trend of development. In the classes of accounting majors in higher vocational colleges, guided by the principles of “skill competition”, artificial intelligence fosters the development of teaching reforms and effectively enhances the quality of education. Starting from the era of artificial intelligence, this paper discusses the significance of the application of “skill competition” to the teaching reform of accounting major in higher vocational colleges, analyzes the existing problems in the teaching of accounting major in higher vocational colleges and puts forward specific teaching strategies to train more accounting talents to meet the needs of the era of artificial intelligence.Education Reform and Development130The significance of the application of “skill competition” to the teaching reform of accounting major in higher vocational colleges is discussed, the existing problems in the teaching of accounting major in higher vocational colleges are analyzed, and specific teaching strategies to train more accounting talents to meet the needs of the era of artificial intelligence are put forward.Education Reform and Development["Simin Fang"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/b879dadf9472d761174fff67b787c7c9110e829e d57cf0dc5c9cdb4a8f8bf39078e0538e2201a292The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview of the current applications and future potential of AI in ophthalmology. AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. Furthermore, AI is being utilized to develop personalized treatment plans by analyzing large datasets to predict individual responses to therapies, thus optimizing patient outcomes and reducing healthcare costs. In surgical applications, AI-driven tools are enhancing the precision of procedures like cataract surgery, contributing to better recovery times and reduced complications. Additionally, AI-powered teleophthalmology services are expanding access to eye care in underserved and remote areas, addressing global disparities in healthcare availability. Despite these advancements, challenges remain, particularly concerning data privacy, security, and algorithmic bias. Ensuring robust data governance and ethical practices is crucial for the continued success of AI integration in ophthalmology. In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), and technology companies are essential to deploy AI solutions effectively, especially in low-resource settings.Medicina1200Future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses.Medicina["D. Olawade", "Kusal Weerasinghe", "Mathugamage Don Dasun Eranga Mathugamage", "Aderonke Odetayo", "Nicholas Aderinto", "Jennifer Teke", "S. Boussios"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/d57cf0dc5c9cdb4a8f8bf39078e0538e2201a292 0e8f6d58698ad31b5bfcdee9fe29ed12611d5e56Artificial intelligence (AI) refers to the creation of computer systems capable of performing tasks that typically necessitate human intelligence. Several dental specializations, such as pediatric dentistry, increasingly utilize artificial intelligence and its components, including machine learning and deep learning. The advancement of AI in healthcare is associated with the development of AI applications designed to assist medical practitioners in diagnosing patients and determining optimal treatment strategies. Artificial Intelligence refers to the ability of machines to acquire knowledge and utilize that information to perform various cognitive functions, such as language processing, learning, reasoning, and decision-making—essentially emulating human behavior. This article provides an overview of the various applications of AI that are advantageous to pediatric dentistry.Journal of Dental Sciences and Education00An overview of the various applications of AI that are advantageous to pediatric dentistry is provided.Journal of Dental Sciences and Education["Emine G\u00fcl\u015fen", "\u0130brahim Tevfik G\u00fcl\u015fen"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/0e8f6d58698ad31b5bfcdee9fe29ed12611d5e56 f9c47f4b811bd788a3ded542222603b2c471991fImplementing artificial intelligence also requires examinations of public attitudes and perceptions. One approach is by examining media framing of artificial intelligence, including news coverage, which is a reflection of societal perceptions and a key influence over people's understanding. As such, this study examines the framing of communicative artificial intelligence in Singapore, looking at how the news media frame communicative artificial intelligence and characterize it as a social actor. Through a manual content analysis of 336 news articles from three major news websites in Singapore, this study found that the news media in Singapore tend to focus on the benefits and advances of communicative artificial intelligence and portray communicative artificial intelligence as a tool rather than social actor. However, when comparing news coverage of communicative artificial intelligence after the advent of ChatGPT, the news framed communicative artificial intelligence more in terms of risks, regulations, responsibilities, and conflict.Public Understanding of Science390It is found that the news media in Singapore tend to focus on the benefits and advances of communicative artificial intelligence and portray communicative artificial intelligence as a tool rather than social actor.Public understanding of science["Edson C. Tandoc", "Seth Seet", "Vanessa Xinyi Chan", "Penny Ju Onn Wong"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/f9c47f4b811bd788a3ded542222603b2c471991f 71f8674b6f3977ac775bd5dba22c3998181ade2ePURPOSE OF REVIEW Explore the current state of artificial intelligence in the Men's Health space. RECENT FINDINGS Artificial intelligence is emerging in the field of Men's Health with recent publications highlighting a role for optimization of male infertility diagnostics and treatment, clinical predictive tools, patient education, and improvements in clinical workflow. SUMMARY Artificial intelligence is set to be a prime instrument in the advancement of both patient care and patient education in the Men's Health space.Current Opinion in Urology480This review highlights a role for optimization of male infertility diagnostics and treatment, clinical predictive tools, patient education, and improvements in clinical workflow in the Men's Health space using artificial intelligence.Current opinion in urology["Evan J Panken", "Akash U Patel", "Josh Schammel", "Justin M Dubin"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/71f8674b6f3977ac775bd5dba22c3998181ade2e 8121c74c400a5a7c3bf4fc88d121e89d6de364efWith the rapid development of technology, artificial intelligence has gradually penetrated into the field of education, bringing new opportunities and challenges to mathematics basic education. This article takes mathematics basic education as the starting point to explore the path and practice of empowering mathematics basic education with artificial intelligence. Covering aspects such as leadership attention, system support, teacher leadership, student centeredness, parental understanding, and project guidance, the aim is to fully leverage the advantages of artificial intelligence through multi-party collaboration, and improve the quality of mathematics teaching and student learning outcomes.Journal of Research in Vocational Education40The path and practice of empowering mathematics basic education with artificial intelligence is explored, covering aspects such as leadership attention, system support, teacher leadership, student centeredness, parental understanding, and project guidance to fully leverage the advantages of artificial intelligence.Journal of Research in Vocational Education["Ya\u2019nan Zhou", "Yonghu Chang"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/8121c74c400a5a7c3bf4fc88d121e89d6de364ef 3fe85b5275d756333403ee44420b1882c3b4a035The development of greater artificial intelligence innovation has silenced the accounting profession, including the field of taxation. This study aims to assess the impact of artificial intelligence on students' career interest in taxation using the diffusion of innovation theory. A quantitative approach with the Partial Least Square (PLS) model was applied with an analysis tool using the WarpPLS 7.0 application, then determining the sample based on purposive sampling techniques with a total of 79 student respondents. The results showed that artificial intelligence innovation, artificial intelligence communication channels, and artificial intelligence adoption decisions had a positive and significant effect on students' career interest in taxation. This study found that the role of artificial intelligence affects knowledge and understanding, motivation and assessment of the work environment by students in the field of taxation. The application of artificial intelligence in the lecture process is necessary to understand in real time the risks and opportunities in the utilization of artificial intelligence under the supervision of educators.Klabat Accounting Review530The role of artificial intelligence affects knowledge and understanding, motivation and assessment of the work environment by students in the field of taxation and the application of artificial intelligence in the lecture process is necessary to understand in real time the risks and opportunities in the utilization of artificial intelligence under the supervision of educators.Klabat Accounting Review["Hizkia Hazael Bezaliel Bawias", "Rolland M Yusuf", "Selmita Paranoan"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/3fe85b5275d756333403ee44420b1882c3b4a035 093b66d4ea56c2c5c97ce50863bb7e2e2750ff83The emergence of smart learning applications has significantly impacted the educational process, transforming student interaction and shaping the future of learning. Integrating artificial intelligence into smart learning applications has become a powerful tool, enriching student interaction and shaping the future of learning. This paper explores the transformative impact of artificial intelligence on smart learning applications, focusing on the characteristics, applications, and benefits of artificial intelligence -integrated smart learning. It uses a literature review method to analyze existing studies on smart learning and artificial intelligence integration in education. This study identifies key characteristics of practical smart learning applications, their impact on student outcomes, and the challenges and benefits of artificial intelligence integration. Empirical data from various educational institutions has been examined to provide real-world examples and case studies of successful smart learning implementations. The findings reveal that artificial intelligence significantly enhances learning experiences and improves student engagement and outcomes but it also necessitates careful consideration of data privacy, digital equity, teacher training, and ethical considerations. The research concludes that ongoing collaboration between educators, developers, and policymakers is crucial for maximizing artificial intelligence benefits in education while mitigating potential risks and ensuring equitable access to quality learning for all students.International Journal of Learning, Teaching and Educational Research00The findings reveal that artificial intelligence significantly enhances learning experiences and improves student engagement and outcomes but it also necessitates careful consideration of data privacy, digital equity, teacher training, and ethical considerations.International Journal of Learning, Teaching and Educational Research["G. E. A. Eltayeb"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/093b66d4ea56c2c5c97ce50863bb7e2e2750ff83 6da45d91eb0e4c5110eac8fe38b12d3f034fa0afPenelitian ini memiliki urgensi untuk menguji pengaruh implementasi Artificial Intelligence (AI) terhadap efisiensi dan akurasi dalam Sistem Informasi Akuntansi (SIA). Seiring dengan perkembangan teknologi, AI menawarkan berbagai manfaat dalam mengoptimalkan pemrosesan data keuangan, termasuk dengan mengotomatisasi tugas-tugas rutin dan mengurangi kesalahan manusia. Penelitian ini menggunakan model kualitatif deskriptif dengan mengumpulkan data melalui studi literatur untuk memahami tantangan, manfaat, dan cara memitigasi risiko yang muncul dari implementasi AI pada SIA. Hasil penelitian menunjukkan bahwa AI dapat meningkatkan efisiensi operasional dan akurasi dalam pengolahan data keuangan, serta mendukung pengambilan keputusan berbasis data yang lebih tepat. Namun, implementasi AI juga memiliki tantangan terkait integrasi teknologi, keterbatasan sumber daya manusia, serta masalah keamanan data dan etika. Untuk itu, penting bagi organisasi untuk memitigasi risiko melalui kebijakan keamanan yang baik, pelatihan bagi karyawan, dan kepatuhan terhadap peraturan yang berlaku. Penelitian ini menyimpulkan bahwa implementasi Artificial Intelligence dalam SIA memiliki potensi yang besar, namun membutuhkan perhatian serius terhadap aspek teknis, etika, dan regulasi untuk memastikan manfaat yang diperoleh dapat dioptimalkan.    Jurnal Ekonomika Dan Bisnis (JEBS)120Jurnal Ekonomika Dan Bisnis (JEBS)["Cellien Patricia", "Annie Mustika Putri"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/6da45d91eb0e4c5110eac8fe38b12d3f034fa0af 9d5853b4617ec9b1b4269ed864e14f5d9c997d6aArtificial intelligence (AI) is one of the most prevalent topics in modern science. It is also reflected in the higher education sphere. Implementation of AI in university activities requires prior analysis of spheres where artificial intelligence can be used to provide responsible and smart use of AI. Based on the survey of educational process participants, the authors analysed the level of readiness of respondents to AI utilisation, their apprehension of AI’s role in different spheres of university activities. The results of the survey have shown that most educational process participants either do not use artificial intelligence or use a very limited number of resources. Still the respondents estimated the possible impact of AI on different spheres of university activities as moderate or high. The spheres of open university ecosystem where AI can be used, were singled out and presented as a structural model. Among the spheres of university activities where AI can be implemented there are infrastructure, security, management and administration, research, ratings, sustainable development, learning personalisation, and e-learning. The selection of artificial intelligence tools for each sphere was performed and presented. Challenges of AI implementation are discussed, among which there are data security, privacy, ethical and legal issues, bias of AI, requirements to technical knowledge of university staff and university infrastructure, teachers’ resistance, depersonalization of education, risks of education quality decrease. The advantages of AI implementation in each of the defined spheres are described. The need to document the rules of AI utilization at HEIs is stressed. The results of the research can be used for planning AI implementation in higher education institutions and AI policy formation.Ìnformacìjnì Tehnologì ì Zasobi Navčannâ350The level of readiness of respondents to AI utilisation, their apprehension of AI’s role in different spheres of university activities, and the selection of artificial intelligence tools for each sphere were analysed to plan AI implementation in higher education institutions and AI policy formation.Information Technologies and Learning Tools["Oksana Buinytska", "Tetiana Terletska", "Valeriia Smirnova", "Anastasiia Tiutiunnyk", "Iryna Kovalenko", "Bohdan Hrytseliak"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/9d5853b4617ec9b1b4269ed864e14f5d9c997d6a 8c3248da103e3aa1d49305412cd74d26a7705405Artificial Intelligence (AI) is revolutionizing agriculture and allied sciences by enhancing productivity, efficiency, and sustainability. AI-driven technologies such as machine learning, computer vision, and predictive analytics are being applied to precision farming, crop monitoring, pest and disease detection, soil health analysis, and automated irrigation systems. In animal husbandry, AI assists in livestock health monitoring, breeding optimization, and smart feeding systems. Fisheries and aquaculture benefit from AI-based water quality management and automated fish farming. Additionally, AI enhances food supply chain management through smart logistics and predictive demand forecasting. Despite challenges like data availability, high implementation costs, and the need for farmer education, AI holds immense potential to address global food security and environmental concerns. This paper explores the current advancements, challenges, and future prospects of AI in agriculture and allied sciences, emphasizing its role in sustainable development.International Journal for Research in Applied Science and Engineering Technology00The current advancements, challenges, and future prospects of AI in agriculture and allied sciences, emphasizing its role in sustainable development are explored.International Journal for Research in Applied Science and Engineering Technology["Dr Akhilesh Saini"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/8c3248da103e3aa1d49305412cd74d26a7705405 469238e9e0f23906365b5350cd47f44363f3fe58Artificial intelligence (AI) has an enormous potential for improving the quality of medical care, diagnostic methods, and treatments. AI allows taking scientific research to a fundamentally new level. The article addresses the most important areas of using AI in cardiology. AI can be used to accelerate making clinical decisions, remote patient monitoring, tomographic image analysis, patient phenotyping, including metabolomic analysis, to assess the risk of complications and many other areas.Kardiologiia500The article addresses the most important areas of using AI in cardiology, which can be used to accelerate making clinical decisions, remote patient monitoring, tomographic image analysis, patient phenotyping, and to assess the risk of complications.Kardiologiia["Y. Belenkov", "M. Kozhevnikova", "N. Khabarova", "I. Ilgisonis", "E. O. Korobkova"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/469238e9e0f23906365b5350cd47f44363f3fe58 d97bfe6aad9ac7e049c141034651b852b9e30743The global demand for healthy beverages is on the rise due to increasing consumer awareness of nutrition and wellness. Artificial Intelligence (AI) has emerged as a transformative tool in optimizing beverage production, enhancing quality control, and ensuring consistency. This paper explores the role of AI in the formulation, production, and market analysis of healthy beverages, with an emphasis on its impact on efficiency and consumer preferences. The study also presents a market overview of the healthy beverage industry and the potential benefits of AI integration.International Journal for Research in Applied Science and Engineering Technology00The role of AI in the formulation, production, and market analysis of healthy beverages, with an emphasis on its impact on efficiency and consumer preferences is explored.International Journal for Research in Applied Science and Engineering Technology["Devansh Agarwal"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/d97bfe6aad9ac7e049c141034651b852b9e30743 ef17d6517323db2cb08919440a0810db31a36560Artificial Intelligence (AI) is revolutionizing the potential of education in its initiatives in personalized learning modules, curriculum designs, evaluation methods, teacher assistance and above all educational management. The rapid deployment of AI-driven technologies demands a proactive approach to education policies, ensuring responsible, fair, and efficient use of AI. Extensive student data is processed by AI systems, enabling adaptive learning models that cater to their learning needs, which in turn optimizes engagement and academic performance. That said, this transition also raises issues in terms of data privacy, cybersecurity, algorithmic bias and the digital divide. This article explores the evolution of AI in education and its impact on learning policies, governance frameworks, accessibility and ethical issues. It examines how AI is influencing the decision-making process in the policymaking of education and relates to the issue of transparency, accountability and equity. In addition, the research stresses mainstream policy recommendations aimed at fostering responsible AI integration, making sure that the students, educators, and educational institutions can benefit from the advantages of AI, without facing its associated issues. By implementing well-defined education policies, governments and institutions can create a framework that promotes innovation around AI, balancing the need for innovation with the need to protect the integrity and inclusiveness of the education system. Education Journal30This article examines how AI is influencing the decision-making process in the policymaking of education and relates to the issue of transparency, accountability and equity, and creates a framework that promotes innovation around AI.Education Journal["Sayed Amiri", "Md Islam", "Md. Hossen"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/ef17d6517323db2cb08919440a0810db31a36560 97ec851895732403f2f3de0943969f568bd0960bKnowledge mining is an emerging type of artificial intelligence (AI), that uses a grouping of AI facilities to determine satisfied thought over huge volumes of unstructured, semi-structured, and structured data that permit industries to extremely recognize their data, search it, expose visions and found associations and designs at scale. Although the initial trend of AI contained numerous slight applications, such as the preparation of a particular model over a single statistics basis of a positive kind for a particular problem, knowledge mining is the next trend of Artificial Intelligence, producing an active quantity of data associations and designs. It has rapidly brought a main part of initiative digital transformation creativity that basically modification how groups brand a sense of real-world statistics. Through this survey, we have analyzed more than two-thirds of 68% of respondents to a current Harvard Business Brush up Analytic Services survey think knowledge mining is key to succeeding in their corporations' considered objectives in the next 18 months. Then the requirement for knowledge mining is rapidly increasing 80% are using physical approaches to switch unstructured data, and those approaches will rapidly be overtaken by the development of statistics and possibly apply circumstances in which this data has delivered excessive rate.International Journal of Engineering and Advanced Technology60More than two-thirds of 68% of respondents to a current Harvard Business Brush up Analytic Services survey think knowledge mining is key to succeeding in their corporations' considered objectives in the next 18 months.International Journal of Engineering and Advanced Technology["Dr. Nirmla Sharma", "Sameera Iqbal Muhmmad Iqbal"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/97ec851895732403f2f3de0943969f568bd0960b a9ac968f7dfb15eb8ec520685cc13026d5a3d203Artificial Intelligence (AI) is transforming the financial sector by enhancing risk assessment and mitigation processes. This article explores the various ways in which AI technologies, such as machine learning, deep learning, and natural language processing, are utilized to manage and mitigate financial risks, including credit, market, and operational risks. AI-driven tools enable financial institutions to predict, assess, and control risks with greater precision, reducing fraud, optimizing investment strategies, and enhancing decision-making processes. The paper also discusses the challenges faced in implementing AI in risk management, including data privacy concerns, regulatory issues, and integration difficulties. By examining case studies and current practices, this article highlights the significant impact of AI in reshaping risk management paradigms in the financial sector. The outlook for AI in this area is also discussed, emphasizing the potential for further innovationsInternational Journal of Advanced Research in Science, Communication and Technology40The various ways in which AI technologies are utilized to manage and mitigate financial risks, including credit, market, and operational risks are explored, highlighting the significant impact of AI in reshaping risk management paradigms in the financial sector.International Journal of Advanced Research in Science, Communication and Technology["Naga Ramesh Palakurti"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/a9ac968f7dfb15eb8ec520685cc13026d5a3d203 50d9744bb156a2b5275c22a3aa2fc176146b13f0The transformative power of AI has only just begun to redefine how businesses function and relate to their customers within e-commerce and digital marketing. In fact, AI really does help firms adjust to changes in consumer preference and market fluctuations by improving operational efficiencies. Big data analytics, aided by artificial intelligence, really boosts the understanding of the customer journey-hence, optimizing and finally allowing for tailor-made marketing campaigns in real time. This leads to great growth for the business. The COVID-19 pandemic pushed companies into adopting AI-driven solutions in the quest for their resilience; this consequently led to an increase in the need for effective digital marketing strategies. E-commerce activities are integrated with artificial intelligence in order to better understand consumer behavior, support market dynamics forecasting, and enhance risk management strategies. Hence, it becomes an indispensable aspect. It is relevant that ethical frameworks and further research address the problems of data privacy and scalability in order to optimize the intrinsic potential of AI. A focus on innovative applications of AI, alongside interdisciplinary collaboration, can empower an organization to develop genuinely inclusive and effective marketing strategies. Embracing the AI-driven initiatives, this will result in long-term relationship building with the customer for growth in a sustainable manner and maintaining competitiveness at an exponential pace in changing digitization.Asian Journal of Research in Computer Science00Ebracing the AI-driven initiatives will result in long-term relationship building with the customer for growth in a sustainable manner and maintaining competitiveness at an exponential pace in changing digitization.Asian Journal of Research in Computer Science["Redeer Avdal Saleh", "Subhi R. M. Zeebaree"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/50d9744bb156a2b5275c22a3aa2fc176146b13f0 bf1876067bc4f6498415f13ec9f0f17246bcb8d1Objective: investigate the impact of artificial intelligence in enhancing the performance of blockchain technology for the services sector listed on the Amman Stock Exchange, with the aim of Sustainable Development Goals.   Theoretical Framework: The artificial intelligence helps in developing the performance of blockchain through technology, digital applications, hardware, software, databases, and communication networks. and This integration helps in achieving the sustainable development goals that work to improve the business environment, economic growth, and investment; support the digital economy, technological innovation.   Method: The methodology adopted for this research comprises the descriptive approach. Data collection was carried out through 370 electronic questionnaires that were agreed to be completed by employees in a variety of Jordanian services sectors listed on the Amman Stock Exchange.   Results and Discussion: The results obtained revealed that blockchain technology, in its aspects of decentralization, transparency, and traceability, is positively and statistically significantly affected by digital technology and applications.   Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of Artificial Intelligence in Enhancing the Performance of Blockchain Technology and its Role in Achieving Sustainable Development Goals. These implications could encompass the services sector listed on the Amman Stock Exchange.   Originality/Value: This study contributes to the literature by  expanding the use of blockchain technology in government services, thus narrowing economic and social gaps and enhancing global cooperation to achieve the Sustainable Development Goals.Journal of Lifestyle and SDGs Review470The results obtained revealed that blockchain technology, in its aspects of decentralization, transparency, and traceability, is positively and statistically significantly affected by digital technology and applications.Journal of Lifestyle and SDGs Review["J. Abubaker", "Ala Alkhawaldeh", "Eman Mohamad Alnimer", "Ashraf Al-Adwan", "Basel Al-Shaer", "Eman Ibrahim Alwreikat"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/bf1876067bc4f6498415f13ec9f0f17246bcb8d1 377314ec6c8ef0de81c8611344670fda43c39db4The proliferation of digital technologies has necessitated the integration of sustainable cybersecurity practices to safeguard against escalating threats. This research explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in fortifying cybersecurity frameworks for the digital age. By leveraging AI-driven threat detection and ML-powered predictive analytics, this study aims to develop a robust and adaptive cybersecurity paradigm capable of mitigating emerging risks and ensuring the integrity of digital ecosystems. The investigation will delve into the optimization of AI/ML algorithms for enhanced cybersecurity performance, the examination of their applications in threat intelligence and incident response, and the analysis of their implications on sustainable digital transformation. A glimpse of the quantitative results reveals compelling insights: AI-based systems showcased an average threat detection accuracy of 92.5% across diverse cyber threat types, with a minimal false positive rate of 3.2%. The implementation of ML algorithms reduced response times to cyber-attacks by 40%, underscoring their pivotal role in prompt threat mitigation. Furthermore, the research elucidates the efficiency of AI in preventing phishing attacks (95%) and prioritizing critical vulnerabilities for patching, resulting in a 30% reduction in high-risk unpatched vulnerabilities Ultimately, this research seeks to contribute to the development of resilient and sustainable cybersecurity practices, empowering organizations to navigate the complexities of the digital landscape with confidence.International journal of science research and technology00This research explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in fortifying cybersecurity frameworks for the digital age, and aims to develop a robust and adaptive cybersecurity paradigm capable of mitigating emerging risks and ensuring the integrity of digital ecosystems.International Journal of Science Research and Technology["CONFIDENCE ADIMCHI CHINONYEREM", "ABIMBOLA OLUDAYO OJENIKE", "OLUWAFEMI ALABI OKUNLOLA", "CHIJIOKE GEORGE GEORGE", "OJEMUYIDE, VICTOR OLADAYO", "OLADOJA, ISRAEL OLOLADE", "VOLADOJA, ISRAEL OLOLADE", "AKINYEMI EMMANUEL TOMIWA"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/377314ec6c8ef0de81c8611344670fda43c39db4 f123f3c8baf6702f2e8b2bcb0db49fb8abc8687dWith exciting advances that could completely change how medical practitioners gauge and monitor skin oxygenation levels, artificial intelligence (AI) has become a disruptive force in the field of non-intrusive skin oxygenation monitoring. This study explored the ongoing potrait of artificial intelligence applications in this domain, highlighting key advancements, challenges, and future directions. Recent studies have demonstrated the remarkable capabilities of AI-based systems in accurately assessing skin oxygenation levels by leveraging sophisticated machine-learning as well as deep-learning algorithms. These AI-powered imaging technologies capture high-resolution, multispectral images of the skin, which are then analyzed using neural networks to detect subtle variations in oxygenation that may serve as early indicators of underlying health conditions. However, despite significant progress made in controlled research settings, the widespread adoption of AI in clinical practice faces several challenges. These carries issues in context to the consistency and dependency of AI-based systems in real-world clinical environments, need for extensive validation and standardization, and genuine as well as official implications of incorporating AI across healthcare decision-making processes. As researchers and clinicians continue to explore the potential of AI in non-invasive skin oxygenation monitoring, future directions may focus on addressing these challenges through collaborative efforts between AI experts, healthcare professionals, and regulatory bodies. By utilizing the energy of Artificial Intelligence volunteer as well as result oriented, we can shift the route for more accurate, efficient, and accessible skin oxygenation monitoring, ultimately improving patient outcomes and advancing the health care field.Journal for Research in Applied Sciences and Biotechnology100The ongoing potrait of artificial intelligence applications in this domain is explored, highlighting key advancements, challenges, and future directions, with a focus on addressing challenges through collaborative efforts between AI experts, healthcare professionals, and regulatory bodies.Journal for Research in Applied Sciences and Biotechnology["Mohammed Shakib K"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/f123f3c8baf6702f2e8b2bcb0db49fb8abc8687d 768d7d843a359051e9c2aea3a30abbad4be3c162PurposeTalent scouting is recognized as a vital activity for professional sports organizations to establish a competitive advantage on the field. It remains, however, an imperfect science marred with bias and stereotypes. Technology – such as data analytics and artificial intelligence (AI) – is a promising avenue to deal with these issues. Yet, much like in the broader HRM literature, little is known about its ability to effectively alleviate bias and on how to successfully make it co-exist with human recruiters.Design/methodology/approachIn collaboration with a professional North American soccer (football) team, this experimental study investigates the impact of using AI-anonymized game footage on scouts’ assessments. In addition to quantitative ratings, it uses a “think-aloud” or verbal cognition methodology to capture changes in the scouts’ assessments.FindingsThe results demonstrate how a “blind scouting” approach stands to alleviate bias and leads to more robust scouting assessments. Namely, the findings indicate that using de-identified footage through AI increases the scouts’ focus on tactical abilities and decreases observations on potentially problematic physiological considerations.Originality/valueThis study provides valuable insights on scouts’ cognition and moves past the prevailing AI vs Human dichotomy by demonstrating how the technology can improve processes without removing the need for experts. It also speaks to AI’s benefits beyond cost or time savings and suggests other potential HRM-related applications for AI.Person-centered review720This study provides valuable insights on scouts’ cognition and moves past the prevailing AI vs Human dichotomy by demonstrating how the technology can improve processes without removing the need for experts.Personnel Review["Louis-Etienne Dubois", "Laurel Walzak"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/768d7d843a359051e9c2aea3a30abbad4be3c162 1c8a52db40edf7e879da9072ad7491ce06220785Breast reconstruction following mastectomy or sectorectomy significantly impacts the quality of life and psychological well-being of breast cancer patients. Since its inception in the 1950s, artificial intelligence (AI) has gradually entered the medical field, promising to transform surgical planning, intraoperative guidance, postoperative care, and medical research. This article examines AI applications in breast reconstruction, supported by recent studies. AI shows promise in enhancing imaging for tumor detection and surgical planning, improving microsurgical precision, predicting complications such as flap failure, and optimizing postoperative monitoring. However, challenges remain, including data quality, safety, algorithm transparency, and clinical integration. Despite these shortcomings, AI has the potential to revolutionize breast reconstruction by improving preoperative planning, surgical precision, operative efficiency, and patient outcomes. This review provides a foundation for further research as AI continues to evolve and clinical trials expand its applications, offering greater benefits to patients and healthcare providers.Medicina680Artificial intelligence applications in breast reconstruction are examined, showing promise in enhancing imaging for tumor detection and surgical planning, improving microsurgical precision, predicting complications such as flap failure, and optimizing postoperative monitoring.Medicina["Andrei Iulian Rugin\u0103", "Andreea Ungureanu", "Carmen Giuglea", "Silviu Adrian Marinescu"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/1c8a52db40edf7e879da9072ad7491ce06220785 528cd994aa0abe694c035c5153aecaefe14c6ce2With the rapid development of modern science and technology, artificial intelligence is applied to various fields of the film industry, which has a significant impact on film production and film algorithm aesthetics. The change of artificial intelligence on the movie production process is mainly reflected in the pre-planning, scene design, live shooting and post editing. Artificial intelligence technology can not only optimize the film production process, reduce costs and improve quality, but also provide new creative tools for art workers and promote the film industry to intelligent transformation and upgrading. This paper adopts the case study method and literature review method, focusing on the application case of AIGC technology in film in South Korea, and discusses in depth the use of AIGC in film production, the changes to the film production process, and the social and artistic problems faced. Film production in the AI environment should focus on the subjective position of “human” in film creation, and AI should be used as a tool. This study aims to identify the trends of AI technology, gain insights into its impact on film production, reveal the factors, roots and effects of AI-driven development of the film industry, and explore new trends in the film industry. It explores the application of AIGC cutting-edge technologies and provides a new direction for the intelligent transformation and upgrading of the film industry.Journal of Global Trends in Social Science100This study aims to identify the trends of AI technology, gain insights into its impact on film production, reveal the factors, roots and effects of AI-driven development of the film industry, and explore new trends in the film industry.Journal of Global Trends in Social Science["Jun Deng", "Shuai Wang"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/528cd994aa0abe694c035c5153aecaefe14c6ce2 c14c36ec9d47a630c2094c87d7e94476696d2279Today, with the rapid development of artificial intelligence, the dubbing industry has ushered in brand-new opportunities and challenges. However, the traditional teaching mode of dubbing major has significant deficiencies in the cultivation of personalized expression and emotional resonance ability, which makes it difficult for students to meet the current employment environment and market needs. This paper aims to propose a series of innovative countermeasures of teaching mode by analyzing the shortcomings of traditional teaching mode. These innovative countermeasures can not only improve the effectiveness of dubbing teaching, but also cultivate excellent dubbing talents with both modern technical literacy and practical ability, so as to provide important support for the sustainable development of dubbing major.Journal of Educational Research and Policies40A series of innovative countermeasures of teaching mode by analyzing the shortcomings of traditional teaching mode are proposed to improve the effectiveness of dubbing teaching and cultivate excellent dubbing talents with both modern technical literacy and practical ability so as to provide important support for the sustainable development of dubbing major.Journal of Educational Research and Policies["Yuyun Zhang", "Xiangzhe Cui", "Yiming Wang"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/c14c36ec9d47a630c2094c87d7e94476696d2279 81ab9689faf7ff228331daca3ae4523b91c5ea3cPublic Organization Review650Artificial intelligence’s role in managing misinformation during disasters is explored, highlighting its potential to improve disaster response, enhance public trust, and strengthen community resilience.Public Organization Review["Nadejda Komendantova", "D. Erokhin"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/81ab9689faf7ff228331daca3ae4523b91c5ea3c 0287f7d81949bfbce4f4e51d71029e93a0a675c5Nutrition plays a crucial role in clinical practice, impacting immune function, disease prevention, and treatment. However, assessing a patient’s nutritional status remains challenging due to individual differences and the limitations of traditional methods like dietary recalls and food diaries, which can be subjective and time-consuming. Artificial Intelligence (AI), particularly machine learning, is emerging as a powerful tool to enhance nutritional assessments. AI can quickly and accurately analyze large datasets, enabling both patients and clinicians to track and manage dietary intake, blood sugar levels, and other health metrics through apps and wearable devices. Despite its promise, AI in nutrition faces challenges such as data accuracy, potential misdiagnoses, and ethical concerns related to data privacy and security. Nonetheless, as technology advances, AI is set to play an increasingly significant role in nutrition diagnostics and healthcare, offering new possibilities for personalized and efficient nutritional management.World Nutrition Journal00As technology advances, AI is set to play an increasingly significant role in nutrition diagnostics and healthcare, offering new possibilities for personalized and efficient nutritional management.World Nutrition Journal["Pittara Pansawira"]2025-02-28T00:00:00https://www.semanticscholar.org/paper/0287f7d81949bfbce4f4e51d71029e93a0a675c5 8029f6acb5470255ef4e5c1a5ff9184bc27ca198Key Points Question How do patients feel about the use of artificial intelligence (AI) to draft responses in patient-clinician portal messages? Findings This survey study of 1455 respondents showed that while overall satisfaction was high (>75%) regardless of author, respondents preferred responses written by AI over those written by a human (mean difference, 0.30 points on a 5-point Likert scale for satisfaction). However, when an AI author was disclosed, satisfaction was lower for AI compared with a human author (mean difference, 0.13 points). Meaning Reduced satisfaction due to AI disclosure should be balanced with the importance of patient autonomy and empowerment.JAMA Network Open01This survey study of 1455 respondents showed that while overall satisfaction was high regardless of author, respondents preferred responses written by AI over those written by a human.JAMA Network Open["Joanna S Cavalier", "Benjamin A Goldstein", "V. Ravitsky", "J. B\u00e9lisle-Pipon", "Armando Bedoya", "Jennifer Maddocks", "Sam Klotman", "Matthew Roman", "Jessica Sperling", "Chun Xu", "Eric G Poon", "Anand Chowdhury"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/8029f6acb5470255ef4e5c1a5ff9184bc27ca198 f6ffb4d70506738c927ecb7a4481396bc9d3b88fArtificial intelligence (AI) is revolutionizing various fields, including healthcare. In nursing education, AI presents both significant challenges and remarkable opportunities [1]. While AI has the potential to enhance learning experiences, improve educational outcomes, and prepare nurses for the future, it also poses challenges related to technology integration, ethical considerations, and the need for new skill sets. This article explores these challenges and opportunities, providing insights into how AI can transform nursing education.Journal of Nursing Reports in Clinical Practice01This article explores challenges and opportunities related to technology integration, ethical considerations, and the need for new skill sets in nursing education using artificial intelligence.Journal of Nursing Reports in Clinical Practice["Alannah L. Couper"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/f6ffb4d70506738c927ecb7a4481396bc9d3b88f 1a51ce41e4fefcad10d886b530930c732c170deeRapid progress in Artificial Intelligence (AI) is presenting both opportunities and threats that promise to be transformative and disruptive to the field of cybersecurity. The current approaches to providing security and safety to users are limited. Online attacks (e.g., identity theft) and data breaches are causing real-world harms to individuals and communities, resulting in financial instability, loss of healthcare benefits, or even access to housing, among other undesirable outcomes. The resulting challenges are expected to be amplified, given the increased capabilities of AI and its deployment in professional, public, and private spheres. As such, there is a need for a new formulation of these challenges that considers the complex social, technical, and environmental dimensions and factors that shape both the opportunities and threats for AI in cybersecurity. Through an exploration and application of the socio-technical approach, which highlights the significance and value of participatory practices, we can generate new ways of conceptualising the challenges of AI in cybersecurity contexts. This paper will identify and elaborate on key issues, in the form of both gaps and opportunities, that need to be addressed by various stakeholders, while exploring substantive approaches to addressing the gaps and capitalizing on the opportunities at the micro/meso/macro levels, which in turn will inform decision-making processes. This paper offers approaches for responding to public interest security, safety, and privacy challenges arising from complex AI in cybersecurity issues in open socio-technical systems.IEEE Transactions on Technology and Society01This paper offers approaches for responding to public interest security, safety, and privacy challenges arising from complex AI in cybersecurity issues in open socio-technical systems.IEEE Transactions on Technology and Society["Katina Michael", "Kathleen M. Vogel", "Jeremy V. Pitt", "Mariana Zafeirakopoulos"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/1a51ce41e4fefcad10d886b530930c732c170dee 6eca9b667e19e821bdb978c7ff54ca9714ffaea7This research aims to explore the role of artificial intelligence in enhancing the teaching and learning process. It highlights the immense potential of AI to improve assessment processes and provide flexible learning environments. However, the research indicates that there are challenges to be overcome, such as the digital divide and data security. The research provides detailed definitions of core concepts such as artificial intelligence, education, and educational technology. It also reviews the history of AI development and its significance in various fields. Additionally, the research identifies a set of sub-questions aimed at deepening the understanding of AI's role in education, such as: the skills teachers should possess to work with AI technologies, how to integrate AI into curricula, and how to ensure equitable access to AI in education. The research offers practical recommendations for maximizing the benefits of AI in education, including curriculum development, teacher training, and improving assessment processes. It also emphasizes the importance of partnership between humans and AI in creating stimulating and innovative learning environments. In conclusion, the research finds that AI can be a powerful tool for improving education but must be used cautiously and responsibly.Journal of Scientific Development for Studies and Research00The research finds that AI can be a powerful tool for improving education but must be used cautiously and responsibly.Journal of Scientific Development for Studies and Research[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/6eca9b667e19e821bdb978c7ff54ca9714ffaea7 42d628a8ea6f4358b48745940fa69f7d1f7fd635Enter your abstract here (an abstract is a brief, More importantly comprehensive summary of the contents of the article). Enter your abstract here (an abstract is a brief, More importantly comprehensive summary of the contents of the article). Modern technology, with its artificial intelligence, has recently become widespread in all fields and areas, as we see it in language, literature, various sciences, and education... This matter raises an ambiguous relationship between artificial intelligence and humans, those who need to complete knowledge of digital and technology so as not to be led by machines. Through this brief presentation, we aim to highlight the development taking place in the literary field, especially theater, which has begun to take other forms and methods, unlike what it was in the past, in line with the development of peoples and nations, it aims to maintain its artistic, aesthetic and communicative role, in light of the transformations and developments that the world has witnessed, especially technological development. Electronic theater is a phenomenon of the era, in light of the development of information technology, and the multiplicity of electronic media in light of globalization, headed by the Internet. It has been concluded that this electronic theater has become attractive to individuals and groups in viewing, away from the eyes of censors, and in a communication that makes the viewers a prisoner of the screen, consuming what the creators of theatrical content have produced, both its good and bad, this employs many of the newly developed technologies according to a commercial vision that aligns with the requirements of economic globalization in rare cases, and in most cases it is in line with cultural globalization, which aims to raise and educate the human being in the manner of the Western human being in terms of morality and consumption, this presents a declared global threat, and a hidden threat to his cultural and local privacyJournal of Scientific Development for Studies and Research00It has been concluded that this electronic theater has become attractive to individuals and groups in viewing, away from the eyes of censors, and in a communication that makes the viewers a prisoner of the screen, consuming what the creators of theatrical content have produced.Journal of Scientific Development for Studies and Research[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/42d628a8ea6f4358b48745940fa69f7d1f7fd635 d88bf74926099327841b2e92c7de2691cf79225eIn the era of modern technology, both animation and digital gaming are experiencing tremendous advancements driven by artificial intelligence (AI), virtual reality (VR), and augmented reality (AR). These technologies enable new and innovative possibilities in digital content design, fundamentally altering how these entertainment media are produced and consumed. Animation and digital gaming are among the most prominent fields to have undergone significant development after the advent of AI. This advanced technology has brought about a qualitative shift in how interactive and visual content is created. Animation, which relies on animation techniques to present stories and films, leverages AI to enhance production and design processes, accelerating animation and adding greater realism and dynamism to characters and movements. As for digital games, AI has contributed to improving user interaction, making games smarter and more adaptable to gameplay styles. AI is also used to analyze player behavior and offer personalized gaming experiences, increasing the level of challenge and making gameplay more engaging and interactive. Additionally, AI contributes to enhancing artistic and technical creativity in both fields through 3D modeling and digital design techniques, helping artists and designers explore new possibilities and achieve more accurate and innovative results. AI algorithms are used to improve the quality of animation or digital games and present them in a more realistic and attractive manner. Despite this significant development, fundamental questions arise about the impact of these technologies on human creativity.Journal of Scientific Development for Studies and Research00AI algorithms are used to improve the quality of animation or digital games and present them in a more realistic and attractive manner, helping artists and designers explore new possibilities and achieve more accurate and innovative results.Journal of Scientific Development for Studies and Research[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/d88bf74926099327841b2e92c7de2691cf79225e b7c54bdabdb2a611804054252b1f905a410222c5This editorial introduces the second part of CEJEME's Special Issue on Artificial Intelligence and Machine Learning in Educational Measurement. Building on the foundational discussions in Part 1, this installment further explores the evolving role of AI and ML in assessment, evaluation, and learning analytics. The four articles in this issue examine a broad spectrum of topics, including a survey on the use of ML in the measurement community, an investigation into the effectiveness of digital tools in math assessments, an analysis of complex log data using advanced clustering techniques, and a study on mitigating bias in AI-driven assessments. These contributions provide deeper insights into the methodological advancements and ethical considerations necessary for integrating AI and ML into educational measurement. As the field continues to evolve, this special issue underscores the need for open conversations and collaborations among measurement professionals to ensure that ML/AI-powered assessments are not only technologically sophisticated but also equitable, transparent, accountable, and truly supportive of diverse learners. A final installment of this special issue will follow in the coming months.Chinese/English Journal of Educational Measurement and Evaluation00The four articles in this issue examine a broad spectrum of topics, including a survey on the use of ML in the measurement community, an investigation into the effectiveness of digital tools in math assessments, an analysis of complex log data using advanced clustering techniques, and a study on mitigating bias in AI-driven assessments.Chinese/English Journal of Educational Measurement and Evaluation["Okan Bulut", "Yi Zheng"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/b7c54bdabdb2a611804054252b1f905a410222c5 298805f93b2353c2fb542a1ec3453a950812dc97Purpose Due to the lack of clear screening guidelines for different populations, identify strategies for obstructive sleep apnea (OSA) in the outpatient population are unclear, a large number of potential OSA outpatients have not been identified in time. The purpose of our study was to evaluate the applicability and accuracy of artificial intelligence sleep screening in outpatients and to provide a reference for OSA screening in different populations. Methods A type IV wearable artificial intelligence sleep monitoring (AISM) device was used to screen adults in the sleep clinic of the Sleep Medical Center for OSA screening, and the general demographic data of the patients were collected. The epidemiological characteristics obtained by AISM screening were analysed. The accuracy of the AISM for the diagnosis of OSA was evaluated and compared with that of polysomnography (PSG). Results A total of 1492 participants completed all the studies. The data included 1448 cases total, including 1096 male patients and 352 female patients, with 620 of the total patients being overweight (42.82%) and 429 being obese patients (29.63%). The prevalence of males was 78.19%, and that of females was 55.97% (χ2 = 95.72, P < 0.001). In males, the risk of moderate to severe OSA was 74.21% in obese people, while in females, the risk was 50%. Age, body mass index (BMI) and the oxygen desaturation index (ODI) were positively correlated and negatively correlated with the lowest and mean oxygen saturation. A total of 100 participants completed both PSG and AISM monitoring, and the accuracies of the AISM in diagnosing mild and moderate-to-severe OSA were 94% and 98%, respectively. Conclusion The AISM exhibits good accuracy, and the use of an objective and convenient sleep detection device to screen a large sample population of outpatients is feasible. The prevalence of OSA in adults in sleep clinics is high, and age, sex, and BMI are risk factors for OSA.Nature and Science of Sleep280The AISM exhibits good accuracy, and the use of an objective and convenient sleep detection device to screen a large sample population of outpatients is feasible.Nature and Science of Sleep["Jian Tan", "Wei Chen", "Dan Yu", "Tiantian Peng", "Cheng Li", "Kai Lv"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/298805f93b2353c2fb542a1ec3453a950812dc97 702e7aeb698cc1eac877b7d6faa2cee0dacbb5b8Purpose Loss of retinal perfusion is associated with both onset and worsening of diabetic retinopathy (DR). Optical coherence tomography angiography is a noninvasive method for measuring the nonperfusion area (NPA) and has promise as a scalable screening tool. This study compares two optical coherence tomography angiography algorithms for quantifying NPA. Methods Adults with (N = 101) and without (N = 274) DR were recruited from 20 U.S. sites. We collected 3 × 3-mm macular scans using an Optovue RTVue-XR. Rules-based (RB) and deep-learning–based artificial intelligence (AI) algorithms were used to segment the NPA into four anatomical slabs. For comparison, a subset of scans (n = 50) NPA was graded manually. Results The AI method outperformed the RB method in intersection over union, recall, and F1 score, but the RB method has better precision relative to manual grading in all anatomical slabs (all P ≤ 0.001). The AI method had a stronger rank correlation with Early Treatment of Diabetic Retinopathy Study DR severity than the RB method in all slabs (all P < 0.001). NPAs graded using the AI method had a greater area under the receiver operating characteristic curve for diagnosing referable DR than the RB method in the superficial vascular complex, intermediate capillary plexus, and combined inner retina (all P ≤ 0.001), but not in the deep capillary plexus (P = 0.92). Conclusions Our results indicate that output from the AI-based method agrees better with manual grading and can better distinguish between clinically relevant DR severity levels than a RB approach using most plexuses.Investigative Ophthalmology and Visual Science280Output from the AI-based method agrees better with manual grading and can better distinguish between clinically relevant DR severity levels than a RB approach using most plexuses.Investigative Ophthalmology & Visual Science["T. Hormel", "Wesley T Beaulieu", "Jie Wang", "Jennifer K Sun", "Yali Jia"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/702e7aeb698cc1eac877b7d6faa2cee0dacbb5b8 3cec321c75a5baca4c8d5ac9a352a1c30210040eBACKGROUND Applications of artificial intelligence (AI) in medicine are expanding every year. AI education is crucial to its appropriate use in healthcare; however, most US medical schools lack a dedicated AI curriculum. These resources are sparse for international medical graduates (IMGs). Using the Artificial Intelligence in Radiology Education (AIRE) curriculum, we assessed the radiology AI course's effectiveness in increasing the AI competency of IMGs. APPROACH AIRE curriculum features nine free YouTube lectures on AI in radiology. Participants watched lectures remotely on fundamental AI terms, methods, clinical applications and special topics. They completed a pre- and post-course e-survey and assessment. The survey assessed participants' prior AI experience, subjective knowledge and opinions on the need for AI in medical education. The assessment determined participants' knowledge of AI. Pre- and post-course assessment scores were compared using a Student's t-test to determine if the course increased participant knowledge of AI terms and applications. EVALUATION Three hundred fifty-seven students from 28 countries enrolled in the course; 175 completed the course within the study period. Nearly all participants reported insufficient AI exposure in their radiology training (91.3%). Participants' knowledge of fundamental AI terms and methods increased after completion of the course, with an average pre-course assessment score of 6.5/15 and a post-course assessment score of 9.4/15 (p < 0.0001). IMPLICATIONS AIRE curriculum's effectiveness demonstrates that a remote education course is a viable model to bring accessible fundamental AI education to international medical students. Remote education curricula in medical AI can help mitigate disparities in AI education for lower resource medical programmes.The Clinical Teacher110The Artificial Intelligence in Radiology Education (AIRE) curriculum's effectiveness demonstrates that a remote education course is a viable model to bring accessible fundamental AI education to international medical students.The clinical teacher["Preethi Jagannath", "Nichelle Perera", "Laura Minton", "Rachel Bass", "Desmin Milner", "Jordan Perchik"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/3cec321c75a5baca4c8d5ac9a352a1c30210040e 75622d8cf6a52952a1fd95ab265ba4e5c931093ePenelitian ini membahas penguatan nilai-nilai Islam melalui pendidikan agama di era Artificial Intelligence (AI), yang menghadirkan tantangan dan peluang baru. Teknologi AI memungkinkan penyampaian materi agama secara lebih interaktif, personal, dan adaptif, namun juga memiliki risiko distorsi nilai akibat informasi yang tidak terfilter. Penelitian ini menggunakan pendekatan kualitatif melalui studi literatur dan wawancara mendalam dengan pendidik agama. Hasil penelitian menunjukkan bahwa pemanfaatan teknologi AI, seperti platform pembelajaran berbasis AI dan aplikasi pengingat ibadah, dapat memperkuat internalisasi nilai-nilai Islam. Namun, penting untuk memastikan adanya pengawasan etis pada algoritma AI agar nilai-nilai Islam tetap terjaga. Penelitian ini menyimpulkan bahwa integrasi antara teknologi AI dan pendidikan agama dapat menjadi solusi inovatif dalam membentuk generasi Islami yang mampu beradaptasi dengan perkembangan zaman tanpa kehilangan identitas keagamaan. Journal of Education Technology Information Social Sciences and Health40Journal of Education Technology Information Social Sciences and Health["R. Robi\u2019ah", "Dian Febri Ovianti", "Lukluk Sofiatil Jannah", "Nurul Asyikin"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/75622d8cf6a52952a1fd95ab265ba4e5c931093e 61fe82f738c5b49e6374bf701e823f782a816358This article summarizes the discussions and outcomes of the ”Power for AI and AI for Power” session at the IEEE Power Electronics Society (PELS) Future of Electronic Power Processing and Conversion (FEPPCON XII). Power electronics embeds intelligence into energy systems. Modern artificial intelligence (AI) systems place massive demand on electrical power, and in return, introduce new opportunities in improving energy systems design and implementation. Power electronics, as sensors and actuators for all types of energy systems, need to embrace AI to address large-scale societal challenges such as industrial decarbonization, transportation, and climate change. The ”Power for AI and AI for Power” session generated lots of excitement in FEPPCON XII and will have a continuous impact on the future development of power electronics.IEEE Power Electronics Magazine360IEEE Power Electronics Magazine["Minjie Chen", "Han Cui", "Frede Blaabjerg", "Leo Lorenz", "Rolf Hellinger", "Tom Gray", "Olga Fink", "Kevin Hermanns"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/61fe82f738c5b49e6374bf701e823f782a816358 1628b02bf3c3e41521ac2d7cc111a39cf4894d6b Business Intelligence (BI), Artificial Intelligence (AI), and Machine Learnings (ML) have been playing an important role for optimizing Supply Chain Management (SCM) in the U.S. retail industry. The integration of these innovative and cutting-edge technologies into SCM has transformed the efficiency, agility, and profitability of retail businesses across the nation. It’s important to know how these advanced technologies transformed the supply chain process for optimizing inventory level to avoid any bottleneck, overstocking or stockout situation. This research examines how the integration of these modern technologies transformed the supply chain process and enabled retailers in optimizing their supply chain management.  In this research work, we have used extensive knowledgebase on Business Intelligence, Artificial Intelligence, Machine Learning, the U.S. retail industry, and the Supply Chain Management, and later we applied this knowledgebase in the U.S retail domain to see how retailers integrate these technologies into their supply chain management process. We also used secondary information available online from reliable sources to make it more realistic. The U.S retail sales revenue was reported at US$7.6 trillion in Y2024 with an expected growth of CAGR of 3.2% over the last five years (Y2019-Y2024). We see a steady growth in the retail sector after the COVID-19 pandemic. Therefore, there is a growing demand for integrating these technologies into the retailers’ SCM so that they can predict consumer demand more accurately and maximize their sales revenue. These technologies serve retailers with greater benefits like forecasting product demand, optimizing inventory level, data-driven decision making, cost reductions by avoiding overstocking, increasing efficiency etc. Though these modern technologies enable retailers with supply chain optimization, there are still some downsides, which include high initial payouts, data silos, resistance to adoption of new technology, consistent and quality dataflow, data integration from various sources etc.     Journal of Computer Science and Technology Studies90This research examines how the integration of these modern technologies transformed the supply chain process and enabled retailers in optimizing their supply chain management.Journal of Computer Science and Technology Studies["Kazi Obaidur Rahman", "Md Samirul Islam", "Rezwanul Islam Rezvi", "Mehedi Hasan", "Achhia Khanam", "Farhan Nasrullah", "Shamina Sharmin Jishan", "Amir Hamza Akash"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/1628b02bf3c3e41521ac2d7cc111a39cf4894d6b 70b5165eaf7025638dcadd147590d6e17803725aArtificial intelligence (AI) is revolutionizing strategic decision-making in organizations by enabling data-driven insights, predictive analytics, and real-time scenario planning. The current paper explored the transformative role of AI tools in managerial strategies, their impact on decision accuracy and efficiency, and the challenges associated with adoption. A systematic literature review was used to synthesize findings from academic studies, industry reports, and case analyses to show actionable insights for businesses navigating the digital era. Key contributions include highlighting AI applications across diverse industries, addressing adoption barriers, and presenting recommendations to enhance strategic outcomes and organizational performance.European Journal of Studies in Management and Business00A systematic literature review was used to synthesize findings from academic studies, industry reports, and case analyses to show actionable insights for businesses navigating the digital era.European Journal of Studies in Management and Business["Orlando Rivero"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/70b5165eaf7025638dcadd147590d6e17803725a ca892cab69436c45bb05573461af42a164efafeeA three-year pilot study investigated the effectiveness of artificial intelligence (AI) as a motivational tool for teaching programming concepts within the Croatian Informatics curriculum. The study was conducted in schools through the extracurricular activity EDIT CodeSchool with the Development of Intelligent Web Applications (RIWA) module. Twelve schools in Split-Dalmatia County in the Republic of Croatia participated, resulting in 112 successfully completed student projects. The program consisted of two phases: (1) theoretical instruction with examples and exercises, and (2) project-based learning, where students developed final projects using JavaScript and the ml5.js library. The study employed project analysis and semi-structured student interviews to assess learning outcomes. Findings suggest that AI-enhanced learning can effectively support programming education without increasing instructional hours, providing insights for integrating AI concepts into existing curricula.Applied Informatics460Findings suggest that AI-enhanced learning can effectively support programming education without increasing instructional hours, providing insights for integrating AI concepts into existing curricula.AI["Bosko Lisnic", "Goran Zaharija", "Sa\u0161a Mladenovi\u0107"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/ca892cab69436c45bb05573461af42a164efafee 0cc79a7cc94b2e698da7f1e3d778d3d53c8216e4It is indicated that the rapid development of AI calls into question the effectiveness of existing constitutional and legal mechanisms for protecting human rights and the fundamental principles of democracy, which was partially explored in the author’s previous works, in particular, regarding the constitutional and legal status of strategic acts and the implementation of the constitutional right to access public information. The article examines the impact of artificial intelligence (AI) on constitutional and legal regulation in Ukraine, which is one of the key issues in light of modern technological development. AI, as a new reality, not only creates significant opportunities for the national legal system but also poses challenges that require a revision of existing legal norms. Based on a thorough analysis of constitutional principles and human rights enshrined in the Constitution of Ukraine, the article explores the interaction of AI with rights such as privacy, freedom of speech, property, and labor. Particular attention is paid to analyzing amendments that could be made to Articles 3, 8, 22, 28, 31, 32, 34, 41, and 43 of the Constitution of Ukraine to address the challenges of AI. The article also discusses the ethical and social aspects of AI use, particularly the need to ensure transparency, accountability, and non-discrimination in processes involving this technology. Possible consequences of inadequate AI regulation are analyzed, including risks of discrimination, privacy violations, and the weakening of democratic institutions. It is noted that timely and comprehensive legal regulation of AI’s impact will contribute to the creation of a balanced system of interaction between humans and technology. Based on the conducted analysis, practical recommendations are proposed for improving constitutional and legal regulation in light of AI challenges and for developing an effective strategy for regulating this field in Ukraine. Special attention is given to the importance of the rule of law, the protection of human rights, and the fundamental principles of democracy in the context of digital transformation. Thus, the article provides a comprehensive vision of AI implementation in Ukraine’s legal system, promoting the harmonization of national legislation with international standards and contemporary challenges.Analytical and Comparative Jurisprudence00The article provides a comprehensive vision of AI implementation in Ukraine’s legal system, promoting the harmonization of national legislation with international standards and contemporary challenges.Analytical and Comparative Jurisprudence["I. M. Bernaziuk"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/0cc79a7cc94b2e698da7f1e3d778d3d53c8216e4 9ef74b65cdc66238f715d08b68886b141f253176Artificial Intelligence (AI) has emerged as a transformative technology in healthcare, significantly advancing personalised medicine. By leveraging vast amounts of data, AI enhances early disease detection, tailors treatments to individual patients, and optimises medical resource management. Despite these advantages, the integration of AI in healthcare presents challenges, including concerns over data privacy, acceptance among healthcare professionals, and the need for comprehensive regulatory frameworks. Therefore, this study investigates the impact of AI on personalised medicine, assessing its benefits, limitations, and real-world applications. It explores AI’s role in diagnostics, personalised treatment strategies, and the optimisation of medical workflows, while critically examining ethical and legal challenges. The study also underscores the necessity of robust regulations to ensure responsible and ethical AI deployment in healthcare. A systematic documentary analysis of scientific articles, case studies, and healthcare organisation reports forms the basis of this research. Case studies from hospitals and companies that have successfully implemented AI are analysed to evaluate its impact on diagnostic accuracy, treatment efficiency, and medical costs. The findings are correlated with existing literature to provide a comprehensive perspective on current and future trends in AI-driven personalised medicine. The results of this study show that AI has demonstrated significant improvements in diagnostic precision, reduced the time required for disease identification, and enhanced the effectiveness of personalised treatment plans. Studies indicate that AI-driven approaches contribute to cost reductions by minimising late-stage treatments and enabling more efficient allocation of medical resources. However, critical challenges such as algorithm transparency, bias mitigation, and patient data security continue to hinder widespread AI adoption in healthcare.Business Excellence and Management390The results of this study show that AI has demonstrated significant improvements in diagnostic precision, reduced the time required for disease identification, and enhanced the effectiveness of personalised treatment plans.Business Excellence and Management["Ioana-Marcela P\u0103curaru", "Ciprian-Sorin Chirvase", "\u0218tefan Tiriteu"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/9ef74b65cdc66238f715d08b68886b141f253176 53fafefd66bffb1a3d1c771072e9ed3bc22b36d8This paper introduces the Team Card (TC) as a protocol to address harmful biases in the development of clinical artificial intelligence (AI) systems by emphasizing the often-overlooked role of researchers’ positionality. While harmful bias in medical AI, particularly in Clinical Decision Support (CDS) tools, is frequently attributed to issues of data quality, this limited framing neglects how researchers’ worldviews—shaped by their training, backgrounds, and experiences—can influence AI design and deployment. These unexamined subjectivities can create epistemic limitations, amplifying biases and increasing the risk of inequitable applications in clinical settings. The TC emphasizes reflexivity—critical self-reflection—as an ethical strategy to identify and address biases stemming from the subjectivity of research teams. By systematically documenting team composition, positionality, and the steps taken to monitor and address unconscious bias, TCs establish a framework for assessing how diversity within teams impacts AI development. Studies across business, science, and organizational contexts demonstrate that diversity improves outcomes, including innovation, decision-making quality, and overall performance. However, epistemic diversity—diverse ways of thinking and problem-solving—must be actively cultivated through intentional, collaborative processes to mitigate bias effectively. By embedding epistemic diversity into research practices, TCs may enhance model performance, improve fairness and offer an empirical basis for evaluating how diversity influences bias mitigation efforts over time. This represents a critical step toward developing inclusive, ethical, and effective AI systems in clinical care. A publicly available prototype presenting our TC is accessible at https://www.teamcard.io/team/demo.PLOS Digital Health900The Team Card is introduced as a protocol to address harmful biases in the development of clinical artificial intelligence (AI) systems by emphasizing the often-overlooked role of researchers’ positionality and establishing a framework for assessing how diversity within teams impacts AI development.PLOS Digital Health["L. Modise", "Mahsa Alborzi Avanaki", "Saleem Ameen", "L. Celi", "Victor Xin Yuan Chen", "Ashley Cordes", "Matthew Elmore", "Amelia Fiske", "J. Gallifant", "Megan Hayes", "Alvin Marcelo", "Joao Matos", "Luis Nakayama", "Ezinwanne Ozoani", "Benjamin C. Silverman", "Donnella S. Comeau"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/53fafefd66bffb1a3d1c771072e9ed3bc22b36d8 e110d214d98dca424477646715ff328937b7b7dbArtificial Intelligence of Things (AIoT) aims to build a self-learning, self-adaptive, and self-evolving Internet of Things ecosystem, which has facilitated many promising intelligent services. Data is an important foundational element for many applications. Establishing a well-designed trading mechanism to collect the necessary data from various sources is essential to realize the vision of AIoT. In this article we investigate the data trading incentive mechanism between multiproviders and multibuyers for AIoT. To address the two-sided dilemma, we develop a joint optimization game to maximize the payoff of all market participants. A two-layer Stackelberg evolutionary game theoretic framework is developed to divide the optimization problem into two subproblems: one for data pricing by providers and the other for purchasing decisions by buyers. The subproblem of optimal data pricing for providers is modeled as a noncooperative game. Providers utilize the game's equilibrium solution to dynamically modify their pricing strategies in response to a changing competitive environment and demanding. This is because buyers have limited information, their behaviors are modeled via evolutionary game. By encouraging data providers to take the buyers' evolutionary dynamics into account has the potential to overcome the myopia behaviors. The equilibrium solution is obtained via replicator dynamics. Extensive experiments demonstrate the efficacy and efficiency of the proposed hierarchical interaction framework. Overall, our results show the proposed Stackelberg evolutionary game framework establishes a desired data market and achieves higher long-term revenue for both sides of participants in the market. The hierarchical framework can effectively prompts data trading in the market.IEEE Internet of Things Journal350The results show the proposed Stackelberg evolutionary game framework establishes a desired data market and achieves higher long-term revenue for both sides of participants in the market.IEEE Internet of Things Journal["Bo Shen", "Qian Ma", "Gang Yang", "Ru Wang", "Wen Ji"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/e110d214d98dca424477646715ff328937b7b7db 5b809f2a32d5c65776cc22dddbe41ff9e511e476GOAL Efficient patient flow is critical at Tampa General Hospital (TGH), a large academic tertiary care center and safety net hospital with more than 50,000 discharges and 30,000 surgical procedures per year. TGH collaborated with GE HealthCare Command Center to build a command center (called CareComm) with real-time artificial intelligence (AI) applications, known as tiles, to dynamically streamline patient care operations and throughput. To facilitate patient flow for our neuroscience service line, we partnered with the GE HealthCare Command Center team to configure a Downgrade Readiness Tile (DRT) to expedite patient transfers out of the neuroscience intensive care unit (NSICU) and reduce their length of stay (LOS). METHODS As part of an integrated NSICU performance improvement project, our LOS reduction workgroup identified the admission/discharge and transfer process as key metrics. Based on a 90%-plus average capacity, early identification of patients eligible for a downgrade to lower acuity units is critical to maintain flow from the operating rooms and emergency department. Our group identified clinical factors consistent with downgrade readiness as well as barriers preventing transition to the next phase of care. Configuration of an AI-powered model was identified as a mechanism to drive earlier downgrade and reduce LOS in the NSICU. A multidisciplinary ICU LOS reduction steering committee met to determine the criteria, design, and implementation of the AI-powered DRT. As opposed to identifying traditional clinical factors associated with stability for transfer, our working group asked, "What are clinical barriers preventing downgrade?" We identified more than 76 clinical elements from the electronic medical records that are programmed and displayed in real-time with a desired accuracy of over 95%. If no criteria are present, and no bed is requested or assigned, the DRT will report potential readiness for transfer. If three or more criteria are present, the DRT will suggest that the patient is not eligible for transfer. PRINCIPAL FINDINGS The DRT was implemented in January 2022 and is used during multidisciplinary rounds (MDRs) and displayed on monitors positioned throughout the NSICU. During MDRs, the bedside nurses present each patient's key information in a standardized manner, after which the DRT is used to recommend or oppose patient transfer. Six months postimplementation period of the DRT and MDRs, the NSICU has seen a 7% or roughly eight-hour reduction in the ICU length of stay (4.15-3.88 days) with a more than three-hour earlier placement of a transfer order. Unplanned returns to the ICU (or bouncebacks) have remained low with no change in the preimplementation rate of 3% within 24 hours. As a result of this success, DRTs are being implemented in the medical ICUs. PRACTICAL APPLICATIONS This work is uniquely innovative as it shows AI can be integrated into traditional interdisciplinary rounds and enable accelerated decision-making, continuous monitoring, and real-time alerts. ICU throughput has traditionally relied on direct review of a patient's clinical course executed during clinical rounds. Our methodology adds a dynamic and technologically augmented touchpoint that is available in real time and can prompt a transfer request at any time throughout the day.Journal of healthcare management / American College of Healthcare Executives250This work is uniquely innovative as it shows AI can be integrated into traditional interdisciplinary rounds and enable accelerated decision-making, continuous monitoring, and real-time alerts.Journal of healthcare management / American College of Healthcare Executives["Kiran Kittur", "Keith Dombrowski", "Kevin Salomon", "Jennifer Glover", "Laura Roy", "Tracey Lund", "Clint Chiodo", "Karen Fugate", "Anish Patel"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/5b809f2a32d5c65776cc22dddbe41ff9e511e476 12b5791156606026b3c818578b856ac1e53f960dPURPOSE Pathologists diagnose prostate cancer (PCa) on hematoxylin and eosin (HE)-stained sections of prostate needle biopsies (PBx). Some laboratories use costly immunohistochemistry (IHC) for all cases to optimize workflow, often exceeding reimbursement for the full specimen. Despite the rise in digital pathology and artificial intelligence (AI) algorithms, clinical implementation studies are scarce. This prospective clinical trial evaluated whether an AI-assisted workflow for detecting PCa in PBx reduces IHC use while maintaining diagnostic safety standards. METHODS Patients suspected of PCa were allocated biweekly to either a control or intervention arm. In the control arm, pathologists assessed whole-slide images (WSI) of PBx using HE and IHC stainings. In the intervention arm, pathologists used the Paige Prostate Detect AI algorithm on HE slides, requesting IHC only as needed. IHC was requested for all morphologically negative slides in the AI arm. The main outcome was the relative risk (RR) of IHC use per detected PCa case at both patient and WSI levels. RESULTS Overall, 143 of 237 (60.3%) slides of 64 of 82 patients contained PCa (78.0%). AI assistance significantly reduced the risk of IHC use per detected PCa case at both the patient level (RR, 0.55; 95% CI, 0.39 to 0.72) and slide level (RR, 0.41; 95% CI, 0.29 to 0.52). Cost reductions on IHC were €1,700 for the trial, at €50 per IHC stain. AI-assisted pathologists reported higher confidence in their diagnoses (80% v 56% confident or high confidence). The median assessment time per HE slide showed no significant difference between the AI-assisted and control arms (139 seconds v 112 seconds; P = .2). CONCLUSION This study demonstrates that AI assistance for PCa detection in PBx significantly reduces IHC costs while maintaining diagnostic safety standards, supporting the business case for AI implementation in PCa detection.JCO Clinical Cancer Informatics240It is demonstrated that AI assistance for PCa detection in PBx significantly reduces IHC costs while maintaining diagnostic safety standards, supporting the business case for AI implementation in PCa detection.JCO clinical cancer informatics["R. Flach", "C. van Dooijeweert", "Tri Q Nguyen", "Mitchell Lynch", "T. Jonges", "Richard P. Meijer", "B. Suelmann", "Peter-Paul M. Willemse", "N. Stathonikos", "Paul J van Diest"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/12b5791156606026b3c818578b856ac1e53f960d aee824f1f300b4d4e06358009756c0baa4e947a2Purpose This study aims to evaluate the performance of a deep learning-based artificial intelligence (AI) diagnostic system in the analysis of retinal diseases, assessing its consistency with expert diagnoses and its overall utility in screening applications. Methods A total of 3076 patients attending our hospital underwent comprehensive ophthalmic examinations. Initial assessments were performed using the AI, the Comprehensive AI Retinal Expert (CARE) system, followed by thorough manual reviews to establish final diagnoses. A comparative analysis was conducted between the AI-generated results and the evaluations by senior ophthalmologists to assess the diagnostic reliability and feasibility of the AI system in the context of ophthalmic screening. Results : The AI diagnostic system demonstrated a sensitivity of 94.12% and specificity of 98.60% for diabetic retinopathy (DR); 89.50% sensitivity and 98.33% specificity for age-related macular degeneration (AMD); 91.55% sensitivity and 97.40% specificity for suspected glaucoma; 90.77% sensitivity and 99.10% specificity for pathological myopia; 81.58% sensitivity and 99.49% specificity for retinal vein occlusion (RVO); 88.64% sensitivity and 99.18% specificity for retinal detachment; 83.33% sensitivity and 99.80% specificity for macular hole; 82.26% sensitivity and 99.23% specificity for epiretinal membrane; 94.55% sensitivity and 97.82% specificity for hypertensive retinopathy; 83.33% sensitivity and 99.74% specificity for myelinated fibers; and 75.00% sensitivity and 99.95% specificity for retinitis pigmentosa. Additionally, the system exhibited notable performance in screening for other prevalent conditions, including DR, suspected glaucoma, suspected glaucoma, pathological myopia, and hypertensive retinopathy. Conclusions : The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices. Its implementation is particularly beneficial for grassroots and community healthcare settings, facilitating initial diagnostic efforts and enhancing the efficacy of tiered ophthalmic care, with important implications for broader clinical adoption.International Journal of General Medicine220The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices.International Journal of General Medicine["Qingquan Wei", "Lifang Chi", "Meiling Li", "Qinghua Qiu", "Qing Liu"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/aee824f1f300b4d4e06358009756c0baa4e947a2 aacf0e72fef32917e4ffa4a89e653e3765a684cbPatient-Centered Care (PCC) is currently recognized as the gold standard for the doctor-patient relationship, and numerous studies associate it with the improvement of various outcomes. However, the advent of artificial intelligence (AI) technologies in the healthcare field challenges doctor-patient interactions as conceived within the PCC. This article proposes a critical and open reflection on the impact of integrating AI into healthcare systems. It will discuss how the advent of AI can be integrated within a PCC paradigm and its role in decision-making processes, outlining its benefits but also potential critical issues from the perspective of patients, doctors, and the doctor-patient relationship. The available evidence indicates the need for a re-examination of the doctor-patient relationship paradigm that incorporates AI as a third party in the relationship. Furthermore, such incorporation will increasingly impact key dimensions of PCC: empathy, trust, and communication. Therefore, it is suggested to improve the educational training of doctors, who must be trained in the use of soft skills and prepared for a work environment assisted by AI.Recenti progressi in medicina00How the advent of AI can be integrated within a PCC paradigm and its role in decision-making processes is discussed, outlining its benefits but also potential critical issues from the perspective of patients, doctors, and the doctor-patient relationship.Recenti progressi in medicina["L. Borghi", "Alberto Giovanni Gerli", "Elena Vegni"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/aacf0e72fef32917e4ffa4a89e653e3765a684cb 9984f5b82d95c71c591df07c7b9d1aa03780a46dThe 2024 conference of the China Society of Indexers (CSI) was held at Peking University in Beijing, China, from 24 to 26 October 2024. It was attended by five international delegates and nearly 150 Chinese delegates; this account of the conference is written from the perspective of the international delegates. The theme of the conference was artificial intelligence (AI) and indexing. Speakers explored applications of AI and indexing in broad contexts, including science, technology, innovation measurement and economic development, while back-of-book indexing took a back seat.The Indexer00The 2024 conference of the China Society of Indexers was held at Peking University in Beijing, China, from 24 to 26 October 2024; this account of the conference is written from the perspective of the international delegates.The Indexer["Kerryn Burgess", "M. Nanninga"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/9984f5b82d95c71c591df07c7b9d1aa03780a46d 34b9c7c57f567518ec211dca77630a14b4e00fa6The rapid advancement of military artificial intelligence (AI), especially its potential integration into nuclear systems, presents significant risks to strategic stability and established deterrence practices. Despite these concerns, no dedicated governance framework currently exists to address the specific challenges of the AI–nuclear nexus. Existing initiatives have primarily focused on ensuring human control over nuclear decision-making. There are a number of state-led initiatives on the governance of military AI more broadly. They can be adapted to address the use of AI in nuclear weapons, but applying them will not be straightforward. There is thus a need to extend the conversation beyond the ‘human in the loop’ concept and develop targeted governance measures. Future discussions could investigate the precise level and degree of required human control and set clear red lines for both the extent and the type of AI integration in nuclear and related systems.00There is a need to extend the conversation beyond the ‘human in the loop’ concept and develop targeted governance measures to address the specific challenges of the AI–nuclear nexus.["Fei Su", "Vladislav Chernavskikh", "Wilfred Wan"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/34b9c7c57f567518ec211dca77630a14b4e00fa6 ca2d609d49b083b56d3f2bdf775517f72cefbd94The European Broadcasting Union’s AI Hub is a pioneering platform that facilitates the development and evaluation of customised AI solutions for the media industry. This collaborative ecosystem enables AI and media experts to co-create and refine artificial intelligence (AI) models designed specifically for media applications. By providing private spaces for testing AI models with proprietary content, the AI Hub guarantees comprehensive evaluations across a wide range of media content. This paper examines how these collaborative AI solutions are driving media innovation and highlights the crucial role of open source models in adapting to rapidly evolving technological landscapes. The paper will detail three key innovations: MetaRadio, which enriches radio experiences with metadata; a facial recognition system tailored for television programmes; and a Fake News Analyser designed to evaluate the reliability of news articles.Journal of Digital Media Management00MetaRadio, which enriches radio experiences with metadata; a facial recognition system tailored for television programmes; and a Fake News Analyser designed to evaluate the reliability of news articles are detailed.Journal of Digital Media Management["Alexandre Rouxel", "Alberto Messina", "Ivan Thomas", "Tatjana Mladenovic"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/ca2d609d49b083b56d3f2bdf775517f72cefbd94 197e42a23913e2c028d37496769208de1207c190The article examines the peculiarities of the right to medical care and healthcare in the field of reproductive technologies, outlining the application of artificial intelligence in artificial reproduction. It is noted that the role of artificial intelligence in legal relations and the resolution of issues concerning civil legal capacity has increased with the use of such technologies for medical purposes. In the medical field, measures are being implemented to improve the quality and longevity of life for Ukrainian citizens, including: the creation of a national healthcare system using AI, based on the analysis of clinical, genetic, and behavioral data; the implementation of AI technologies in the development of advanced real-time medical diagnostic systems (virtual consultants, cybernetic experts, etc.); the expansion of medicine into a broader AI-controlled social sphere that utilizes all forms of health data, including genomics, metadata, electronic medical records, and biometrics, to ensure public health; the introduction of AI-based patient interaction tools (chatbots, mobile devices, etc.); educating patients on making informed decisions, self-monitoring health status, and disease prevention through AI; prioritizing (ranking) patient groups based on risk levels and conducting proactive interventions using AI technologies; and researching social determinants of health and managing public health using AI.It is emphasized that artificial reproduction, where AI takes on the role of the “mother,” is becoming an interesting legal issue for researchers. It is noted that artificial motherhood is not legalized in all countries, and the absence of an international act, particularly recommendations, may lead to conflicts in resolving cross-border family law disputes in the future. The article concludes that the use of AI in artificial reproductive methods is gradually becoming a practice in medical institutions. The application of this technology will facilitate effective fertilization and, in the future, embryo gestation. Additionally, an AI-powered incubator with innovative technologies is being developed and gradually implemented in medicine. However, many important issues remain unresolved, including the civil legal capacity and legal responsibility of artificial intelligence.Analytical and Comparative Jurisprudence00The article concludes that the use of AI in artificial reproductive methods is gradually becoming a practice in medical institutions, and the application of this technology will facilitate effective fertilization and, in the future, embryo gestation.Analytical and Comparative Jurisprudence["O. Barabash"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/197e42a23913e2c028d37496769208de1207c190 9a1050a40ad0767e4d69c1dc660608bbd8d565d0As the world transitions from the astrological age of Pisces which is the age of believing, to the astrological age of Aquarius which is the age of knowing, information and communication, technology is also evolving and this has given rise to the adoption of Artificial intelligence in many industries in recent times. This study examined the adoption and utilization of Artificial Intelligence in Public Relations practice in some select banks in Abuja Municipal Area Council (AMAC). The study adopted the Uses and Gratification Theory (UGT) for the theoretical framework. Descriptive survey research design was adopted using Taro Yamane to arrive at a sample size of 400 respondents. Furthermore, simple random sampling technique was employed in selecting the respondents. Questionnaire was the main instrument for data collection; the mean score and standard deviation were used to analyze the data. The major findings of the study are that there are positive relations between artificial intelligence and public relations in the select banks in AMAC. In view of the findings, the study concludes that there is adoption of artificial intelligence (AI) in public relations (PR) practices within banking sectors in AMAC.  The study recommended that there is the need to adopt Measurement and Evaluation in determining the performance and effectiveness of AI-driven PR strategies as well as its impact on new media approaches. It also recommends that stakeholders in the banks should establish key performance indicators (KPIs) and metrics to measure the success of level of perception and level of providing information about the use of AI.ALSYSTECH Journal of Education Technology00It is concluded that there is adoption of artificial intelligence (AI) in public relations (PR) practices within banking sectors in AMAC and stakeholders in the banks should establish key performance indicators (KPIs) and metrics to measure the success of level of perception and level of providing information about the use of AI.ALSYSTECH Journal of Education Technology["Ene Christiana Olowu"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/9a1050a40ad0767e4d69c1dc660608bbd8d565d0 b8ffdd64f55c0790ba4ed175f5eafa7fdc5de83aIt is indicated that the integration of artificial intelligence (AI) technologies into various aspects of public life opens up new horizons and at the same time creates serious challenges for the legal system, in particular in the field of criminal proceedings. Although the use of AI systems at the stage of pre-trial investigation significantly increases the efficiency and effectiveness of solving crimes, it also gives rise to a complex of complex issues of a legal, ethical and procedural nature. The article examines some aspects of the international practice of introducing artificial intelligence technologies into pre-trial investigation of criminal cases. The main approaches to legal regulation of the use of artificial intelligence systems in criminal proceedings of various countries, in particular the USA, Great Britain, Japan, Canada, France, the Netherlands, Singapore and Australia, are analyzed. Particular attention is paid to the analysis of US legislation, which has created a comprehensive system of legal regulation and control over the use of artificial intelligence in law enforcement activities. Key regulatory and legal acts are considered: the Electronic Communications Privacy Act, the Foreign Intelligence Surveillance Act and the USA PATRIOT Act, which establish the legal framework for the application of AI technologies in pre-trial investigation. Mechanisms of judicial, parliamentary and departmental control over the use of AI systems in criminal proceedings are studied. Considerable attention is paid to the ethical aspects of the implementation of artificial intelligence in law enforcement activities. The experience of different countries in creating specialized institutions and developing ethical codes of practice for the use of AI is analyzed. The main principles of the ethical application of AI technologies in pre-trial investigation are identified: transparency of the decision-making process, protection of privacy, ensuring accountability and building public trust. The results of the study identify a trend towards the formation of a comprehensive approach to regulating the use of AI, combining legal control mechanisms with ethical standards. The need to ensure a balance between increasing the efficiency of the investigation using AI technologies and protecting the rights of participants in criminal proceedings is substantiated.Analytical and Comparative Jurisprudence10A trend towards the formation of a comprehensive approach to regulating the use of AI is identified, combining legal control mechanisms with ethical standards, which will ensure a balance between increasing the efficiency of the investigation using AI technologies and protecting the rights of participants in criminal proceedings.Analytical and Comparative Jurisprudence["D. Byelov", "M. V. B\u0456elova", "I. V. Rushchak"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/b8ffdd64f55c0790ba4ed175f5eafa7fdc5de83a 954f3af2fb8407c0272c3f5840cdb08d1648edbeArtificial intelligence (AI) has proven to be a key tool to improve the efficiency of video surveillance systems, con - tributing to public safety. This systematic review aims to analyze the contributions of artificial intelligence in this field, in line with Sustainable Development Goal 16 (SDG 16), which promotes peaceful and inclusive societies. 145 articles extracted from major databases such as Scopus, WOS, ProQuest, EBSCO, IEEE Xplore, and Science - Direct were analyzed. Using PRISMA methodology, inclusion and exclusion criteria were applied, resulting in 42 articles relevant to the review. The findings indicate that the use of advanced AI technologies, such as the internet of things, computer vision, and edge computing, are the most integrated with artificial intelligence, enhancing its capabilities in video surveillance systems. In this framework, deep learning stands out as an essential basis for optimizing these applications. Finally, the results of this review provide a solid foundation for future research on the use of artificial intelligence in video surveillance. The technologies evaluated have the potential to further con - tribute to the improvement of security and operational efficiency in different contexts and environments.Advances in Science and Technology Research Journal570The findings indicate that the use of advanced AI technologies, such as the internet of things, computer vision, and edge computing, are the most integrated with artificial intelligence, enhancing its capabilities in video surveillance systems.Advances in Science and Technology Research Journal["Michael Cabanillas Carbonell", "Jhordan Sallari Rivera", "Jhoel Santiva\u00f1ez Mu\u00f1oz"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/954f3af2fb8407c0272c3f5840cdb08d1648edbe 47ead100d2f049e0b32fd1a449e23d10176a8c38Alzheimer’s disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability of these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, and fostering trust in AI-driven diagnostics. This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP). We examine their applications in identifying critical biomarkers, tracking disease progression, and distinguishing AD stages using various imaging modalities, including MRI and PET. Additionally, we discuss current challenges, including dataset limitations, regulatory concerns, and standardization issues, and propose future research directions to improve XAI’s integration into clinical practice. By bridging the gap between AI and clinical interpretability, XAI holds the potential to refine AD diagnostics, personalize treatment strategies, and advance neuroimaging-based research.Diagnostics1590This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation and examines their applications in identifying critical biomarkers, tracking disease progression, and distinguishing AD stages using various imaging modalities.Diagnostics["Mahdieh Taiyeb Khosroshahi", "Soroush Morsali", "Sohrab Gharakhanlou", "Alireza Motamedi", "Saeid Hassanbaghlou", "Hadi Vahedi", "Siamak Pedrammehr", "H. M. D. Kabir", "Ali Jafarizadeh"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/47ead100d2f049e0b32fd1a449e23d10176a8c38 d43fb621a75c71eb96f9a1e542d127bbbde465c9Abstract Aim The relationship between nursing students' attitudes toward artificial intelligence and their creative personality traits was examined in this study. Design This study, conducted with 492 nursing students enrolled at a university in Turkey, was designed using a descriptive and relational methodology. The data were gathered through the “Personal Information Form,” the “General Attitude Scale toward Artificial Intelligence,” and the “Creative Personality Traits Scale.” Methods The data for the research were gathered from surveys conducted between January 2024 and May 2024. Findings The average score for students' attitudes toward artificial intelligence was 74.52 ± 10.29, while the score for creative personality traits was 67.20 ± 10.34. Correlation analysis results indicate a strong relationship between these two factors (p < 0.05). Conclusions Nursing students' attitudes toward artificial intelligence and creative personality traits are above average. Implications for nursing and health policy The development of creativity is crucial for effectively integrating artificial intelligence technologies into nursing practice. Additionally, this research highlights the need for policy development regarding regulations and ethical practices related to using artificial intelligence in healthcare services.International Nursing Review410Nursing students' attitudes toward artificial intelligence and creative personality traits are above average, which highlights the need for policy development regarding regulations and ethical practices related to using artificial intelligence in healthcare services.International Nursing Review["K\u00fcbra G\u00fcl\u0131rmak G\u00fcler", "Belgin \u015een Atasayar"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/d43fb621a75c71eb96f9a1e542d127bbbde465c9 ed715e6fd6df07ed5e0d1358ffddb91c4009a289Motivasi belajar siswa merupakan salah satu hal penting dalam menunjang proses pembelajaran yang efektif. Oleh karena itu guru harus mempertimbangkan media pembelajaran yang digunakan dalam proses pembelajaran. Untuk merancang pembelajaran yang membangkitkan motivasi belajar dengan menggunakan teknologi berbantuan artificial intelligence sebagai media pembelajaran. Media teknologi berbantuan artificial intelligence belum banyak digunakan sehingga menarik untuk dikaji. Tujuan penelitian ini adalah menganalisis dampak artificial intelligence terhadap motivasi belajar siswa dalam pembelajaran akuntansi. Penelitian ini menggunakan metode kuantitatif. Data dikumpulkan dengan kuesioner. Teknik analisis data yang digunakan adalah uji regresi linear sederhana. Hasil penelitian: penggunaan artificial intelligence sebagai media pembelajaran berdampak positif terhadap motivasi belajar siswa dalam pembelajaran akuntansi.Journal of Education Technology Information Social Sciences and Health300Journal of Education Technology Information Social Sciences and Health["Dinda Ardelia Ramadhani", "C. Caska", "H. Indrawati"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/ed715e6fd6df07ed5e0d1358ffddb91c4009a289 af3f706f16474059a539767aeee67315fb728308Administrative agencies’ growing use of automated decisionmaking tools poses threats to core democratic values, such as agency flexibility, expertise, fairness, transparency, and accountability. But decades of privatization have wrought similar, lasting harms to the United States’ public institutions. This Article argues that the thoughtful criticisms and prescriptions from the burgeoning literature on the government’s use of artificial intelligence should be used to strengthen the scrutiny accorded to privatization. Specifically, this Article challenges the perception that automated decisionmaking poses a greater threat to public values than privatization. Indeed, the two share several characteristics and goals. These include, for example, a fixation on efficiency, reliance on oversimplified cost-benefit analyses, erosion of agency expertise and resources, and separation between public officials and their decisions’ impact on individuals. In that separation, algorithms and private actors make important decisions often carrying political and fairness consequences. As policymakers adapt the latest expert guidance regarding algorithms to the problems of privatization, they should prioritize the needs and voices of the marginalized individuals who have been most harmed by the privatization movement.Texas A&amp;M Law Review00This Article argues that the thoughtful criticisms and prescriptions from the burgeoning literature on the government’s use of artificial intelligence should be used to strengthen the scrutiny accorded to privatization.Texas A&amp;M Law Review["L. Rookard"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/af3f706f16474059a539767aeee67315fb728308 1fb76360f30d57fe89b57015055ac424496bbb1fHealthcare professionals face complex ethical dilemmas in clinical settings in cases involving end-of-life care, informed consent, and surrogate decision-making. These nuanced situations often lead to moral distress among care providers. This paper introduces the Bioethics Artificial Intelligence Advisory (BAIA) framework, a novel and innovative approach that leverages artificial intelligence (AI) to support clinical ethical decision-making. The BAIA framework integrates multiple bioethical approaches, including principlism, casuistry, and narrative ethics, with advanced AI capabilities to provide comprehensive decision support. The framework employs a structured methodology that includes data collection, paradigmatic case review, analysis through "mattering maps," and scenario-based decision reasoning. A detailed analysis of two challenging cases, an end-of-life care decision and a complex conjoined twins case, demonstrates BAIA's potential to harmonize diverse ethical perspectives while reducing the moral burden on healthcare providers. The framework's agentic architecture additionally allows integration with any new and existing ethical AI systems like METHAD, Delphi, and EAIFT, enabling multiframework collaboration. This work also acknowledges limitations related to data quality, bias, and complexity of ethical decisions and proposes mitigation strategies, including standardized databases, fairness algorithms, and maintaining human oversight. Thus, this work represents a significant step toward combining technological advancement in agentic AI with established bioethical principles to improve the quality and consistency of clinical ethical decision-making, thus reducing moral distress for clinicians.Cureus300A detailed analysis of two challenging cases, an end-of-life care decision and a complex conjoined twins case, demonstrates BAIA's potential to harmonize diverse ethical perspectives while reducing the moral burden on healthcare providers.Cureus["Taposh P Dutta Roy"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/1fb76360f30d57fe89b57015055ac424496bbb1f c88ab6779f6d2aa432737e129358279c482ae7faBackground: Artificial Intelligence (AI)-driven training systems are becoming increasingly important in surgical education, particularly in the context of laparoscopic suturing. This systematic review aims to assess the impact of AI on skill acquisition, long-term retention, and clinical performance, with a specific focus on the types of machine learning (ML) techniques applied to laparoscopic suturing training and their associated advantages and limitations. Methods: A comprehensive search was conducted across multiple databases, including PubMed, IEEE Xplore, Cochrane Library, and ScienceDirect, for studies published between 2005 and 2024. Following the PRISMA guidelines, 1200 articles were initially screened, and 33 studies met the inclusion criteria. This review specifically focuses on ML techniques such as deep learning, motion capture, and video segmentation and their application in laparoscopic suturing training. The quality of the included studies was assessed, considering factors such as sample size, follow-up duration, and potential biases. Results: AI-based training systems have shown notable improvements in the laparoscopic suturing process, offering clear advantages over traditional methods. These systems enhance precision, efficiency, and long-term retention of key suturing skills. The use of personalized feedback and real-time performance tracking allows learners to gain proficiency more rapidly and ensures that skills are retained over time. These technologies are particularly beneficial for novice surgeons and provide valuable support in resource-limited settings, where access to expert instructors and advanced equipment may be scarce. Key machine learning techniques, including deep learning, motion capture, and video segmentation, have significantly improved specific suturing tasks, such as needle manipulation, insertion techniques, knot tying, and grip control, all of which are critical to mastering laparoscopic suturing. Conclusions: AI-driven training tools are reshaping laparoscopic suturing education by improving skill acquisition, providing real-time feedback, and enhancing long-term retention. Deep learning, motion capture, and video segmentation techniques have proven most effective in refining suturing tasks such as needle manipulation and knot tying. While AI offers significant advantages, limitations in accuracy, scalability, and integration remain. Further research, particularly large-scale, high-quality studies, is necessary to refine these tools and ensure their effective implementation in real-world clinical settings.Healthcare600Artificial Intelligence-driven training tools are reshaping laparoscopic suturing education by improving skill acquisition, providing real-time feedback, and enhancing long-term retention, and limitations in accuracy, scalability, and integration remain remain.Healthcare["Chidozie N. Ogbonnaya", "Shizhou Li", "Changshi Tang", "Baobing Zhang", "Paul Sullivan", "M. S. Erden", "Benjie Tang"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/c88ab6779f6d2aa432737e129358279c482ae7fa 60cd04ba26229bcc36dd820d83928839ecd0647cArtificial intelligence (AI) is poised to transform various facets of healthcare, but it also raises multiple legal issues that lack definitive answers or even a comprehensive set of questions at present. This essay explores key legal concerns related to AI in healthcare, including licensure, privacy, data security, regulation, and liability. Regulatory frameworks are evolving, with the FDA and FTC playing significant roles, yet the approach remains fragmented. Questions of intellectual property, malpractice, product liability, and civil procedure are complex and unresolved. Reimbursement policies are beginning to address AI, but concerns about bias and professional unemployment persist. This dynamic landscape requires healthcare professionals to stay informed and adaptable as legal standards evolve.Physician leadership journal00This essay explores key legal concerns related to AI in healthcare, including licensure, privacy, data security, regulation, and liability, including licensure, privacy, data security, regulation, and liability.Physician Leadership Journal["Joseph McMenamin"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/60cd04ba26229bcc36dd820d83928839ecd0647c 023fa484b25d8248c8738386af79a4bb94072677The question of whether machines can be creative has been at the centre of debates among scholars and practitioners well before the inception of artificial intelligence (AI) as a recognised field of research. This paper reviews how some of the key thinkers in the fields of creativity and AI have approached this question, contextualising their views within the ebbs and flows of AI technological developments, from the 1950s until now. The thread of this overview is Margaret Boden's identification of novelty, surprisingness and value, as the three cardinal features of creativity. This review will retrace the steps of the quest of artificial intelligence researchers as they strive to replicate each of these three properties within human‐made machines. The paper closes with a reflection on how the third of these properties, value, prompts us to consider societal challenges raised by the widespread adoption of AI for creativity that transcend the question: ‘Can AI be creative?’.Philosophy Compass330This review will retrace the steps of the quest of artificial intelligence researchers as they strive to replicate each of these three properties within human‐made machines, and reflect on how the third of these properties, value, prompts us to consider societal challenges raised by the widespread adoption of AI for creativity that transcend the question: ‘Can AI be creative?’.Philosophy Compass["Caterina Moruzzi"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/023fa484b25d8248c8738386af79a4bb94072677 fce29a58ea96d9788b2bdb5bb440696bf08c21deABSTRACT Introduction Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology. Methods We utilized PubMed to search for peer‐reviewed research articles published between 2016 and 2021 with the query type “(“deep learning” or “machine learning” or “neural network” or “artificial intelligence”) and (“neoplas$” or “cancer$” or “tumor$” or “tumour$”).” We included clinical trials and original research studies and excluded reviews and meta‐analyses. Oncology‐related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies. Results Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%–93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. “White” vs. “non‐White” or “White” vs. “Black”). Conclusions We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non‐White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology.Cancer Medicine1190This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology to ensure fair and unbiased clinical integration of AI in oncology.Cancer Medicine["Anjali J D'Amiano", "Tia Cheunkarndee", "Chinenye C. Azoba", "Krista Y Chen", "Raymond H. Mak", "Subha Perni"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/fce29a58ea96d9788b2bdb5bb440696bf08c21de 036ea45ca55a8c59853477593d3310b5cd12dde4Infectious diseases caused by pathogens resistant to antimicrobial treatments, defined as antimicrobial resistance (AMR), are a serious global health crisis, considered among the main threats to global public health according to the World Health Organization. New forms of advanced information technology are receiving global consideration as a help in countering this health threat, like Artificial Intelligence (AI). Applications of AI in healthcare could help in reducing the time needed to discover new antimicrobial drugs, improving the accuracy and timing of surveillance, diagnosis, and treatment, while reducing expenses. AI is now proposed as a valuable and potential help for different sectors of the health world. In this short review, we will consider the recent evidence supporting the important role that AI can play in countering the phenomenon of AMR.Recenti progressi in medicina00This short review of the recent evidence supporting the important role that AI can play in countering the phenomenon of AMR is considered.Recenti progressi in medicina["Andrea Zovi", "Giacomo Polito", "Andrea Caprodossi", "M. Sabbatucci", "Rosario Sorrentino", "A. Vitiello"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/036ea45ca55a8c59853477593d3310b5cd12dde4 06024cf081773db2ae337c8520abf4b327892e42Abstract Today’s Education is increasingly mediated by digital technologies that imply new challenges that need to be addressed in detail to turn them into opportunities for advancement and evolution. Such is the case of the use of artificial intelligence in learning assessment processes, which is forcing us to rethink traditional methods, mechanisms, and strategies to assess student learning achievement, especially in distance and online Education. Given the complexity of the above, this analytical essay proposes a look at artificial intelligence developments that support the so-called “evaluation 4.0”, based on the application of fuzzy logic, homeostasis, and the cybernetics of self-regulation. Such an application would provide technical support and a general understanding framework for the evaluation processes for both teachers and students to promote evaluation processes more in line with the flexible and often imprecise and ambiguous nature of the learning and performance associated with the skills assessment in the framework of the fourth industrial revolution.Ensaio370This analytical essay proposes a look at artificial intelligence developments that support the so-called “evaluation 4.0”, based on the application of fuzzy logic, homeostasis, and the cybernetics of self-regulation, which would provide technical support and a general understanding framework for the evaluation processes for both teachers and students.Ensaio: Avaliação e Políticas Públicas em Educação["Edinson Oswaldo Delgado Rivas", "Andr\u00e9s Chiappe", "Ang\u00e9lica Vera Sagredo"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/06024cf081773db2ae337c8520abf4b327892e42 11bcb8047d5f2c8eeea8c4ff86d90cddc5df15bcIn recent years, the use of artificial intelligence (AI) and virtual reality (VR) in the psychiatric field has been rapidly developing. This narrative review seeks to provide insight into how these technologies may be used in psychiatric disorders. VR is used above all to analyze symptoms in detail and personalize therapy. AI is used above all to make early diagnosis and accurate diagnosis. We conducted a search on PubMed and found 18 relevant studies. Most were reviews that focused on the effectiveness of AI and VR in early diagnosis, personalization of treatment, and accurate monitoring of symptoms with more targeted interventions. Despite many limitations, AI and VR could revolutionize psychiatry in the future.Recenti progressi in medicina00A narrative review seeks to provide insight into how these technologies may be used in psychiatric disorders by focusing on the effectiveness of AI and VR in early diagnosis, personalization of treatment, and accurate monitoring of symptoms with more targeted interventions.Recenti progressi in medicina["Federica Fiasch\u00e8", "Andrea Steven Barbetti", "Lorenzo Di Natale", "Salvatore Cappello", "Giulia Sarnataro", "Giuseppe Ducci"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/11bcb8047d5f2c8eeea8c4ff86d90cddc5df15bc bd8fa9f47fc93dc55426caa54812fae828959c01INTRODUCTION There are considerable gaps in the conventional pharmacovigilance (PV) measures which might result in significant safety issues, especially in monitoring the effectiveness of orphan drugs that are used to treat rare diseases. In this paper, we evaluate if and how Artificial Intelligence (AI) and Machine Learning (ML) can be used to mitigate these problems. AREAS COVERED The article identifies ineffective adverse events (AE) reporting systems, low patient enrollment, and weak signal monitoring as barriers to the effective safety evaluation of rare diseases. It also addresses the possibility of employing AI and ML technologies to automate the reporting of AEs by integrating data from multiple sources and increasing the sensitivity of risk detection. The method to conduct the literature search consisted of searching Pubmed and Google Scholar for relevant AI and ML studies and publications aboqut PV. EXPERT OPINION We identified technical and regulatory concerns such as privacy and model explainability as hurdles to the adoption of AI in PV. However, the same technology, if properly integrated into the system, has the potential to enhance treatment monitoring for rare diseases and to increase the rate of new therapies being developed.Expert Opinion on Drug Safety220If and how Artificial Intelligence (AI) and Machine Learning (ML) can be used to mitigate problems in pharmacovigilance (PV) and to enhance treatment monitoring for rare diseases and to increase the rate of new therapies being developed is evaluated.Expert opinion on drug safety["Ashish Jain", "Zahabia Adenwala"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/bd8fa9f47fc93dc55426caa54812fae828959c01 ca82852ea3a7e8059c50db97aae9aabb2718565cChatGPT, the latest advancement in Artificial Intelligence (AI), represents one of the most advanced and rapidly evolving chatbot technologies to date. Its capability to provide swift and intelligent responses has garnered admiration from scientists and educators globally. Particularly, the healthcare sector stands to gain significantly from the integration of systems like ChatGPT, with benefits including enhanced productivity, reduced expenses, and improved patient outcomes. However, to ensure their equitable and appropriate implementation, it is crucial to address the ethical challenges associated with these technologies. While numerous studies have highlighted these ethical quandaries, there lacks a comprehensive discussion and resolution framework. This review aims to fill this gap by offering a detailed exploration of the ethical concerns associated with using AI tools like ChatGPT in healthcare. This exploration is structured into five main categories: Bias and discrimination, privacy and data security, disinformation and misinformation, autonomy and human interaction, and accountability and responsibility. Additionally, this review discusses the necessity of establishing a clear ethical framework for deploying AI tools in healthcare, introducing the INNOVATE framework. The detailed description and application of the INNOVATE framework aim to promote ethical practices in AI, ensuring a responsible and beneficial integration into healthcare, thereby addressing the identified ethical concerns.Recenti progressi in medicina00This review discusses the necessity of establishing a clear ethical framework for deploying AI tools in healthcare, introducing the INNOVATE framework, and offers a detailed exploration of the ethical concerns associated with using AI tools like ChatGPT in healthcare.Recenti progressi in medicina["Russell Franco D\u2019Souza", "K. M. Surapaneni", "Mary Mathew", "Shabbir Amanullah", "Joseph Edward Thornton", "R. Tandon"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/ca82852ea3a7e8059c50db97aae9aabb2718565c a1553ca9471f35614433be1f2c1db02c0a9b4907OBJECTIVE To explore the attitude of faculty members towards integration of artificial intelligence (AI) and to accelerate the appropriate adaptation of AI tools in medical education. STUDY DESIGN A qualitative case study. Place and Duration of the Study: Department of Medical Education, Bahria University Health Sciences Campus, Karachi, Pakistan, from June to October 2023. METHODOLOGY A qualitative case study design was employed using a social cognitive theory framework. Participants were selected using a purposive sampling technique. Inclusion criteria were faculty members with five years of teaching experience and who gave consent. Twenty-one participants were included in the study using purposive sampling technique. Data were collected using individual projective interviews and two focus group discussions using the photovoice methodology. Participants were given a presention on tools of AI which can be integrated in teaching and assessment. Pattern matching technique was used for data analysis. RESULTS Most of the participants had limited awareness of tools of AI that can be integrated in medical education to enhance teaching and assessment strategies. However, few participants had a good understanding of AI and its utility. However, there was great variation in the attitude of faculty members. Participants voiced on fostering partnerships between healthcare organisations, educational institutes, and technology companies to pool resources and expertise. Faculty members suggested that there should be more faculty development programmes on AI applications in medical education. CONCLUSION Medical faculty needs to be trained as they have limited knowledge and awareness of the integration of AI in medical education. KEY WORDS Artificial intelligence, Medical education, Medical faculty.Journal of the College of Physicians and Surgeons--Pakistan : JCPSP00The attitude of faculty members towards integration of artificial intelligence (AI) and to accelerate the appropriate adaptation of AI tools in medical education is explored to accelerate the appropriate adaptation of AI tools in medical education.Journal of the College of Physicians and Surgeons--Pakistan : JCPSP["K. Warraich"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/a1553ca9471f35614433be1f2c1db02c0a9b4907 09d48cc76d96ff1e6a8f11141685ee124e12231dThis paper presents findings from a qualitative study exploring the perspectives of primary care patients on the use of artificial intelligence (AI) to synthesize data from the electronic medical record (EMR) and audio recordings of patient speech as a way to detect cognitive impairment.We conducted qualitative interviews with a racially and educationally diverse sample of primary care patients (N = 31) from a large healthcare system in New York City, New York. Interviews focused on the use of AI to synthesize data from the EMR and audio recordings of patient speech. Data were analyzed using content analysis.Participants reported that AI technology could improve diagnostic accuracy and avoid human errors in the evaluation and diagnosis of cognitive impairment, while also improving clinical efficiency for clinicians. They also expressed concerns about the use of AI tools for screening, such as risk of misdiagnosis, loss of confidentiality, and psychological distress from learning of detected cognitive impairment.The perceived benefits reported by participants regarding the use of AI screening tools suggest that some patients might support the provision of AI screening in clinical settings. However, participants’ concerns about the potential harms of AI screening tools underscore the need for clear and transparent communication about how results are generated and interpreted. Clinicians need training and other forms of structural support to improve their literacy in AI‐driven tools and their ability to integrate adequate approaches to information sharing into clinical practice.Alzheimer's &amp; Dementia: Behavior &amp; Socioeconomics of Aging170The perceived benefits reported by participants regarding the use of AI screening tools suggest that some patients might support the provision of AI screening in clinical settings, however, participants’ concerns about the potential harms of AI screening tools underscore the need for clear and transparent communication about how results are generated and interpreted.Alzheimer's &amp; Dementia: Behavior &amp; Socioeconomics of Aging["Heather M. Wurtz", "Margaret Manchester", "Tatyana Valteau", "Hannah Hanson", "Catherine Scipion", "Alex Federman", "Jalayne J Arias"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/09d48cc76d96ff1e6a8f11141685ee124e12231d 2ab4d0accd92a938418c1f7e3bf518ac45d445f5Simple Summary In this thorough review, the applications of artificial intelligence (AI) with respect to the epidemiology (prevention and screening), clinical features, diagnosis (X-rays; chest computed tomography, CT; positron emission tomography, PET), biomarkers (biopsy, staging), treatment (general information, surgical treatment, radiotherapy, chemotherapy, targeted therapy, immunotherapy), and prognosis of lung cancer are summarized. AI can help in the discrimination between benign and malignant lung nodules, in the detection of biomarkers related to lung cancer years before its development, in the recognition of specific histologic or genetic markers of lung tumors and in the planning of personalized treatment for lung cancer patients that improve their prognosis.Cancers4630AI can help in the discrimination between benign and malignant lung nodules, in the detection of biomarkers related to lung cancer years before its development, in the recognition of specific histologic or genetic markers of lung tumors and in the planning of personalized treatment for lung cancer patients that improve their prognosis.Cancers["S. Kotoulas", "Dionysios Spyratos", "Konstantinos Porpodis", "K. Domvri", "A. Boutou", "E. Kaimakamis", "Christina Mouratidou", "Ioannis Alevroudis", "Vasiliki Dourliou", "Kalliopi Tsakiri", "Agni Sakkou", "Alexandra Marneri", "E. Angeloudi", "Ioanna Papagiouvanni", "Anastasia Michailidou", "K. Malandris", "Constantinos Mourelatos", "Alexandros Tsantos", "A. Pataka"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/2ab4d0accd92a938418c1f7e3bf518ac45d445f5 7787166b607013e3badf19f345eac67588e29b44Annals of Operations Research1000It is suggested that CAIT is merely a component of a supply chain's capacity to produce intrinsic resources, rather than a universal solution, and can influence supply chain outcomes by bridging ethical, operational and technological gaps while fostering trust and efficiency.Annals of Operations Research["Emilia Vann Yaroson", "Amelie Abadie", "Melanie Roux"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/7787166b607013e3badf19f345eac67588e29b44 ff4f978a88557407299d7af7cafce03028caa47dPublic service provision in the frontline, coined street-level bureaucracy, has been gradually impacted by information and communications technology (ICT) for decades. This impact, however, has mostly considered ICT as a tool suitable for tasks with low complexity. With recent advances in artificial intelligence (AI), there are examples of AI use for more complex street-level work. Examples include cases where AI is used for assessing eligibility for social benefits, predictive policing models, automated grading, and diagnostics in healthcare. While these applications demonstrate potential benefits, they also introduce new challenges related to privacy, accountability, corporatization and alienation of street-level work, and client service experiences. This article is a critical reflection on the street-level potential of AI in providing public services. This study contributes to the ongoing debate on AI's impact in street-level work by emphasizing both the potential benefits and risks associated with AI integration in frontline service provision. While AI may mitigate some limitations of human decision-making (e.g., subjectivity, inconsistency, and bias), it can also introduce challenges that require careful consideration (e.g., lack of transparency, data-driven bias, and limited contextual adaptation). By critically reflecting on AI's street-level potential, this article calls for a balanced approach to AI adoption in street-level work.Digital Government: Research and Practice410A critical reflection on the street-level potential of AI in providing public services and calls for a balanced approach to AI adoption in street-level work.Digital Government: Research and Practice["Peter Andr\u00e9 Busch"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/ff4f978a88557407299d7af7cafce03028caa47d 5e19fc69693fb35e90a7f492c9b55d4d9cec08fcJournal of Open Innovation: Technology, Market and Complexity2063Journal of Open Innovation: Technology, Market, and Complexity["Moonita Limiany Prasetyo", "Randall Aginta Peranginangin", "Nada Martinovic", "Mohammad Ichsan", "Hendro Wicaksono"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/5e19fc69693fb35e90a7f492c9b55d4d9cec08fc 8370abee29d8635d6ed8b3ad43a3dd5bf3234844Applied Mathematics &amp; Information Sciences01Applied Mathematics &amp; Information Sciences[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/8370abee29d8635d6ed8b3ad43a3dd5bf3234844 0af4c282584990da12b7f6480fde03d2e114c2bfJournal of Destination Marketing &amp; Management471Journal of Destination Marketing &amp; Management["Junfeng Wang"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/0af4c282584990da12b7f6480fde03d2e114c2bf ac26affeb0cdf48e54187b5f95bba8423b0222acEngineering applications of artificial intelligence1081Eng. Appl. Artif. Intell.["Abdul Gaffar Sheik", "Arvind Kumar", "Chandra Sainadh Srungavarapu", "Mohammad Azari", "S. R. Ambati", "F. Bux", "Ameer Khan Patan"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/ac26affeb0cdf48e54187b5f95bba8423b0222ac 03707ed4e3ecd6e2e25b791f9476f68714b20db1Computers and Electronics in Agriculture1301Comput. Electron. Agric.["S. Castillo-Giron\u00e9s", "S. Munera", "M. Mart\u00ednez-Sober", "Jos\u00e9 Blasco", "S. Cubero", "J. G\u00f3mez-Sanch\u00eds"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/03707ed4e3ecd6e2e25b791f9476f68714b20db1 bb834166bf079c5b923a61135d9f5c207baaf712Engineering00Engineering[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/bb834166bf079c5b923a61135d9f5c207baaf712 a8ce1f309babe6b724ce918e2adbdfa5a45c523eEngineering00Engineering[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/a8ce1f309babe6b724ce918e2adbdfa5a45c523e 8daf6725e42a709123c296e6f02ce8bbabcce44bInternational Journal of Biology Pharmacy and Allied Sciences00International Journal of Biology, Pharmacy and Allied Sciences[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/8daf6725e42a709123c296e6f02ce8bbabcce44b 6a0f71d570135ad2240e4938b2df364babe7731cInternational Journal of Biology Pharmacy and Allied Sciences00International Journal of Biology, Pharmacy and Allied Sciences[]2025-03-01T00:00:00https://www.semanticscholar.org/paper/6a0f71d570135ad2240e4938b2df364babe7731c 029c5ba1e5318ef3a17c056f73ef8b4627ff4445Energy Policy1110Energy Policy["Xiongfei Zhao", "Shuangjie Li"]2025-03-01T00:00:00https://www.semanticscholar.org/paper/029c5ba1e5318ef3a17c056f73ef8b4627ff4445 9b7dbbdb3b51cd3cb093e12d2c05de55083d34c0With the proliferation of artificial intelligence (AI) across various global industries, it has become necessary to introduce AI education in pre-tertiary curricula. This topic has started to gain serious attention in many countries, with numerous real-world initiatives emerging. However, in developing countries, notably Morocco, this topic is rarely addressed in the literature, and relevant initiatives are virtually nonexistent, to our knowledge. This paper aims to fill this gap by presenting a perspective study on the integration of AI into the computer science (CS) curriculum in Moroccan high schools. Specifically, the paper i) highlights international initiatives in AI education as well as the current state of AI education in Morocco; ii) evaluates the current CS curriculum in Morocco, emphasizing its weaknesses and calling for a comprehensive review that incorporates AI teaching; iii) argues for the integration of AI into the high-school CS curriculum; iv) recommends and discusses specific approaches for stakeholders in the education field to consider; v) explores critical challenges and considerations that could hinder this; and vi) provides practical tools and resources to facilitate AI education in Moroccan schools.Journal of Curriculum Studies Research470This paper aims to fill this gap by presenting a perspective study on the integration of AI into the computer science (CS) curriculum in Moroccan high schools.Journal of Curriculum Studies Research["Mohsine El Khayati", "Abdelaziz Courr", "Ismail Kich", "Fatima-Zohra Hibbi"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/9b7dbbdb3b51cd3cb093e12d2c05de55083d34c0 38e2c9602741c227c8a807a330d6fde2dddb779fAs a significant driving force behind the latest wave of technological innovation, artificial intelligence profoundly influences corporate greenwashing while advancing the digital and intelligent transformation of enterprises. This paper empirically examines the impact of AI technology on corporate greenwashing and its mechanisms of action using text analysis and word frequency statistics. This study considers the frequency of references to AI in the annual reports of enterprises and the ESG scores of these enterprises as samples. The research findings indicate that the application of AI technology can effectively curb the occurrence of greenwashing behavior. The mechanisms of influence suggest that green innovation plays a partial mediating role in the relationship between AI and corporate greenwashing, while imitation pressure and financial pressure enhance the inhibitory effect of AI technology on this behavior. Further analysis reveals that the inhibitory effect of AI on corporate greenwashing is particularly pronounced in non-state-owned enterprises, large-scale enterprises, and enterprises within high-pollution industries. This paper not only enhances the existing literature on how AI can promote enterprise greening but also offers valuable insights into how governments and enterprises can mitigate corporate greenwashing behavior.Sustainability450It is revealed that the inhibitory effect of AI on corporate greenwashing is particularly pronounced in non-state-owned enterprises, large-scale enterprises, and enterprises within high-pollution industries.Sustainability["Xueying Tian", "Dingdong Shi"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/38e2c9602741c227c8a807a330d6fde2dddb779f 7e8bc9e776164f255d4476f545b13036bb0f7216Since the concept of artificial intelligence was proposed in 1956, it has led to numerous technological innovations in medicine and has completely changed the traditional medical practice. The present study mainly describes the application of artificial intelligence in various medical fields from four aspects: machine learning, intelligent robots, image processing, and expert systems. It also discusses the current challenges and future trends in these fields. In line with the development of globalization, various research institutions around the world have conducted research in the field of application of artificial intelligence in medicine. As a result, medical artificial intelligence has achieved significant progress and its future prospects have revealed its increasing development and the need for comprehensive policymaking in this field.Global Spectrum of Research and Humanities450The present study mainly describes the application of artificial intelligence in various medical fields from four aspects: machine learning, intelligent robots, image processing, and expert systems.Global Spectrum of Research and Humanities["Soroush Rahmaniboukani", "Mohammad Qurban Hakimi", "Mohammad Ekram Yawar"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/7e8bc9e776164f255d4476f545b13036bb0f7216 a9000870efefc7078bd9237b70d77bb673bc78caType I diabetes is an autoimmune disease in the course of which insulin levels are reduced and hyperglycemia occurs. Treatment options for type I diabetes have changed a lot over time. A large contribution to advances in the field of diabetes treatment has been made by artificial intelligence. Originally, the treatment consisted of multiple finger punctures per day and multiple insulin injections. But now, thanks to artificial intelligence technology, a number of solutions are available including continuous glucose monitors and, based on these, a decision support system. This makes it possible to reduce the number of finger pricks and the frequency of insulin administration. Above that, it makes it possible to tailor the treatment process to the patient, prepare personalized recommendations and respond quickly to changes in serum glucose levels.Journal of Education, Health and Sport00A number of solutions are available including continuous glucose monitors and, based on these, a decision support system that makes it possible to reduce the number of finger pricks and the frequency of insulin administration.Journal of Education, Health and Sport["Wiktoria \u0141oskot", "Jan Szwech", "Mateusz Matczak", "Karol Jasi\u0144ski", "Aleksandra Broda", "Kacper Hoksa", "Krzysztof Jod\u0142owski", "Ewa Dubniewicz", "Paula Majewska", "Alicja Staszek"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/a9000870efefc7078bd9237b70d77bb673bc78ca 2f6968218bf4958231282334525c02b80d70c63aRecent artificial intelligence (AI) developments provide the means to create new knowledge to enhance existing knowledge at individual, business, and societal levels much faster and more efficiently than ever before. There appears to be a widening gap between AI capabilities and people’s abilities to adopt/adapt technologies. This research investigated how this extending gap could be closed. Appropriate AI training needs to be provided to those working in industries such as education and manufacturing, with a rollout into other sectors. The main emphasis needs to be on improving the understanding of this technology’s capabilities and what this technology encompasses. The public needs to be better educated on the benefits of this modern technology. More international open, honest, and constructive sharing of the AI technologies employed/deployed must be implemented to reduce the likelihood of increased multi-directional diversion. More industries need to be encouraged to adopt/adapt the application of AI optimally. Teachers and students need to be trained appropriately to employ applications such as ChatGPT to create new knowledge and insights.Business Management and Strategy00There appears to be a widening gap between AI capabilities and people’s abilities to adopt/adapt technologies, and this research investigated how this extending gap could be closed.Business Management and Strategy["E. Fisher", "E. Fisher"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/2f6968218bf4958231282334525c02b80d70c63a 164d8164c1871a2468987720d5d9e90472503c52Introduction: Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality worldwide. Early diagnosis is crucial for better patient outcomes and effective care. Artificial intelligence (AI) has emerged as a viable method in cardiology that enhances risk assessment and diagnostic accuracy..Objectives: This study compares the accuracy, sensitivity, and specificity of AI-assisted diagnostics with conventional techniques in order to assess the usefulness of AI in the early diagnosis of CVDs.Materials and Methods: From January to June 2024, an observational study was carried out at NICVD Karachi Pakistan. 500 patients' ECG and echocardiogram data were examined using AI algorithms, and the results were compared to evaluations made by cardiologists.Results: AI performed better than conventional diagnostic techniques, showing increased sensitivity (94.2%) and specificity (91.5%). 20.8% of preclinical anomalies were successfully identified by AI, resulting in earlier actions..Conclusion: Early CVD detection is much improved by AI, which also increases diagnostic accuracy. For wider clinical adoption, data bias, ethical issues, and implementation hurdles must be addressed.South Eastern European Journal of Public Health140Early CVD detection is much improved by AI, which also increases diagnostic accuracy, and for wider clinical adoption, data bias, ethical issues, and implementation hurdles must be addressed.South Eastern European Journal of Public Health["Dr. Sabahat Ali Sheikh", "Dr Asif Khan", "Dr Khadija Sarwat Farooqui", "Dr Zainab Rauf", "Dr Moazama Shakeel Ahmed", "Dr Kashif Zia"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/164d8164c1871a2468987720d5d9e90472503c52 a7a465888426450d6db451b249d9ac43d9d5b2e0The Artificial Intelligence Patient Librarian (AIPL) was designed to meet the psychosocial and supportive care needs of Metastatic Breast Cancer (MBC) patients with HR+/HER2− subtypes. AIPL provides conversational patient education, answers user questions, and offers tailored online resource recommendations. This study, conducted in three phases, assessed AIPL’s impact on patients’ ability to manage their advanced disease. In Phase 1, educational content was adapted for chatbot delivery, and over 100 credible online resources were annotated using a Convolutional Neural Network (CNN) to drive recommendations. Phase 2 involved 42 participants who completed pre- and post-surveys after using AIPL for two weeks. The surveys measured patient activation using the Patient Activation Measure (PAM) tool and evaluated user experience with the System Usability Scale (SUS). Phase 3 included focus groups to explore user experiences in depth. Of the 42 participants, 36 completed the study, with 10 participating in focus groups. Most participants were aged 40–64. PAM scores showed no significant differences between pre-survey (mean = 59.33, SD = 5.19) and post-survey (mean = 59.22, SD = 6.16), while SUS scores indicated good usability. Thematic analysis revealed four key themes: AIPL offers basic wellness and health guidance, provides limited support for managing relationships, offers limited condition-specific medical information, and is unable to offer hope to patients. Despite showing no impact on the PAM, possibly due to high baseline activation, AIPL demonstrated good usability and met basic information needs, particularly for newly diagnosed MBC patients. Future iterations will incorporate a large language model (LLM) to provide more comprehensive and personalized assistance.Current Oncology250Despite showing no impact on the PAM, possibly due to high baseline activation, AIPL demonstrated good usability and met basic information needs, particularly for newly diagnosed MBC patients.Current Oncology["Yvonne W. Leung", "Jeremiah So", "Avneet Sidhu", "Veenaajaa Asokan", "Mathew Gancarz", "Vishrut Bharatkumar Gajjar", "Ankita Patel", "J. M. Li", "Denis Kwok", "Michele B Nadler", "Danielle Cuthbert", "Philippe L. Benard", "Vikaash Kumar", "Terry Cheng", "Janet Papadakos", "Tina Papadakos", "Tran Truong", "M. Lovas", "Jiahui Wong"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/a7a465888426450d6db451b249d9ac43d9d5b2e0 ed8040a5d326841bd91968edd3e4d34f62de673bThe article systematizes modern ideas about the features of using artificial intelligence tools in order to analyze and optimize financial flows. The relevance of the topic is argued by the rapid growth in the volume of transactions in the global economy, combined with the inability of traditional methods to provide truly effective processing of multidimensional dynamic data in real time. In the current conditions, there is an urgent need to develop new approaches to managing cash flows, primarily based on artificial intelligence technologies. The purpose of the study is to systematize the theoretical and methodological basis for the use of AI in the analyzed area, as well as to identify specific advantages and limitations (in relation to this, the author’s view of the situation is proposed, which it is advisable to consider as a starting point for subsequent research to determine checks and balances for the use of artificial intelligence). In the scientific literature, there are contradictions between theoretical models of using AI and the practical possibilities of their implementation, as well as disagreements in evaluating the effectiveness of various types of neural networks for financial forecasting. The issues of information security and legal regulation in this area have not been sufficiently studied. It has been established that the most promising areas are the use of deep neural networks for time series analysis, reinforcement learning methods in order to optimize management decisions, and the introduction of natural language processing technologies for working with unstructured financial documents. The importance of graph tools in detecting suspicious patterns of funds movement and preventing fraudulent actions is emphasized. The article is of interest to analysts, specialists in the field of artificial intelligence, heads of financial departments.Entrepreneur's Guide30The article systematizes modern ideas about the features of using artificial intelligence tools in order to analyze and optimize financial flows and identifies specific advantages and limitations.Entrepreneur’s Guide["\u041c. \u0410. Miakisheva"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/ed8040a5d326841bd91968edd3e4d34f62de673b 152423243128c4c81e646effc61105f2c4050b4fBackground/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI’s role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control. Results: This review examines the characteristics of AI applications in bacterial infection control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total of 1165 articles, only 54 articles met the eligibility criteria and were extracted and analyzed. Five thematic scopes were synthesized from the extracted data; countries, aim, type of AI, advantages, and limitations of AI applications in bacterial infection prevention and control. The majority of articles were reported from high-income countries, mainly by the USA. The most common aims are pathogen identification and infection risk assessment. The most common AI used in infection control is machine learning. The commonest reported advantage is predictive modeling and risk assessment, and the commonest disadvantage is generalizability of the models. Methods: This scoping review was developed according to Arksey and O’Malley frameworks. A comprehensive search across PubMed, Embase, and Web of Science was conducted using broad search terms, with no restrictions. Publications focusing on AI in infection control and prevention were included. Citations were managed via EndNote, with initial title and abstract screening by two authors. Data underwent comprehensive narrative mapping and categorization, followed by the construction of thematic scopes. Conclusions: Artificial intelligence applications in infection control need to be strengthened for low-income countries. More efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges.Antibiotics1000The characteristics of AI applications in bacterial infection control need to be strengthened for low-income countries, and more efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges.Antibiotics["Rasha Abu-El-Ruz", "Mohannad Natheef AbuHaweeleh", "Ahmad Hamdan", "Humam Emad Rajha", "Jood Mudar Sarah", "Kaoutar Barakat", "S. Zughaier"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/152423243128c4c81e646effc61105f2c4050b4f f6bc90dc402cf984827d08eaee4d0d3235b2178bArtificial intelligence (AI) has radically transformed the field of medicine in the last decade, with a significant increase in academic publications. Based on 1783 English-language articles analyzed with Biblioshiny and VOSviewer tools, the findings highlight an annual growth rate of 30.38% and a significant increase from 2018 onwards. Each article received an average of 17.54 citations. The studies had contributions from 11678 authors and an international collaboration rate of 29%. There were single-author (118) and single-country (129) articles. Prominent contributing authors include Forestiero A, Mazzuca D and Zinno F. Harvard Medical School (104 papers) and the University of Toronto (83 papers) have played important roles in the advancement of AI applications in medicine. The USA stands out with both publication volume (3241 articles) and number of citations (9176). Journals such as Journal of Medical Internet Research (26 articles) and Frontiers in Medicine (25 articles) stand out as the leading publication venues in the field. The most cited articles were published in journals such as Jama, Radiology and Nature. This study highlights the wide-ranging applications of AI in areas such as machine learning, deep learning, natural language processing and computer vision, demonstrating its potential in medical imaging, genetic analysis and clinical decision support systems. Future research needs to focus on maintaining collaboration, increasing methodological rigor and finding solutions to emerging challenges.Journal of Intelligent Decision Making and Information Science00The wide-ranging applications of AI in areas such as machine learning, deep learning, natural language processing and computer vision are highlighted, demonstrating its potential in medical imaging, genetic analysis and clinical decision support systems.Journal of Intelligent Decision Making and Information Science["Sefer Dar\u0131c\u0131"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/f6bc90dc402cf984827d08eaee4d0d3235b2178b 3c60fc1b7de18080fa3df38aae09c5aa7780fb1bArtificial intelligence technology has developed rapidly in recent years and has become an important driving force for promoting the leapfrog development of science and technology, the optimization and upgrading of industrial structure, and the overall leap of productivity. This paper selects Shenzhen and Shanghai A-share listed companies from 2003 to 2023 as sample data to study the impact of artificial intelligence on corporate innovation performance. The study found that artificial intelligence and corporate innovation performance show a significant positive correlation, that is, artificial intelligence can significantly promote the improvement of corporate innovation performance, and the results are still valid after stability tests; corporate production efficiency plays a mediating role in the process of artificial intelligence promoting the improvement of corporate innovation performance.Advances in Social Science and Culture00The study found that artificial intelligence and corporate innovation performance show a significant positive correlation, that is, artificial intelligence can significantly promote the improvement of corporate innovation performance, and the results are still valid after stability tests.Advances in Social Science and Culture["Zhiyuan Qiao"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/3c60fc1b7de18080fa3df38aae09c5aa7780fb1b 046424634206cc0abbf498b2ddf8f14a77d9453fThe current outbreak of the COVID-19 virus has drastically affected all business operations across the globe, hence forcing them to adapt to a new way of doing things through digitalization. AI is considered a game-changing technology this time when it attempts to help businesses improvisational effectiveness, supply chain and workforce response to risks and opportunities, and tackle environmental challenges. The following paper discusses how AI can help organizations be more adaptable to the conditions that emerged in the post-pandemic period for business, including the possibilities regarding predictive analyses, automation, and decision-making. Also, it discusses the ethical implications of AI, disparate impacts, data privacy concerns, and, last but not least, the impact on workforce displacement. The paper also presents the key recommendations to organizations on implementing AI, focusing on ethical AI management and preparing the workforce for adopting AI technology and shared AI adoption. Thus, AI is anticipated to bring many new growth opportunities owing to its interaction with other contemporary technologies like blockchain and IoT. To that end, this research informs the existing literature on digital transformation by providing potential recommendations for businesses operating in the realized new normal and seeking guidance on how to work with AI systems.Social Science Review Archives730How AI can help organizations be more adaptable to the conditions that emerged in the post-pandemic period for business, including the possibilities regarding predictive analyses, automation, and decision-making is discussed.Social Science Review Archives["Muhammad Ihtisham", "Aliza Tabassam", "Shoket Ali", "Iqra Bilal"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/046424634206cc0abbf498b2ddf8f14a77d9453f 5aba980340dc18cc9b227e46f55da2aa1d15481eABSTRACT Midwifery philosophy views childbearing as primarily normal, indicative of a woman's overall health. Midwifery practice focuses on supporting the human‐to‐human relationship between the midwife and the woman holding primacy. Despite the traditional focus on wellness, maternity care in today's risk averse world is increasingly complex. Technology has been increasingly implemented into maternity care to detect complications early and reduce harm. The Maternity Early Warning Tool is a technological innovation in this regard. Actor‐network theory (ANT) offers a framework for analysing the connections between human actors (women, fetuses, and midwives) and nonhuman actors (machines, tools, and policies) within healthcare. This paper through drawing on the tenets of ANT, particularly in understanding the adoption of Maternity Early Warning Tools in midwifery practice, examines and explores the implications of integrating these tools in relation to midwifery practice. ANT also guides thoughtful considerations regarding the potential trajectory of Artificial Intelligence in midwifery, specifically regarding how these technological advancements alter midwifery practice by creating new hybrid assemblages and fluid identities. This discussion of subversive elements enhances understanding of the implications of Maternity Early Warning Tools on contemporary midwifery practice.Nursing Inquiry620A discussion of subversive elements enhances understanding of the implications of Maternity Early Warning Tools on contemporary midwifery practice, and guides thoughtful considerations regarding the potential trajectory of Artificial Intelligence in midwifery.Nursing Inquiry["Bridget Ferguson", "A. Baldwin", "Clare Harvey", "A. Henderson"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/5aba980340dc18cc9b227e46f55da2aa1d15481e 58aa72fb077c5f96b4dd56a6c80a9256c985c10aThe rapid advancements in Artificial Intelligence (AI) and Edge Computing are transforming modern computing paradigms by enabling real-time processing, low-latency decision-making, and enhanced intelligence in smart systems. This paper presents an AI-driven computational framework that integrates Edge Intelligence (EI) with adaptive deep learning models to optimize data processing and decision-making at the edge. The proposed framework employs federated learning, neuromorphic computing, and reinforcement learning-based optimization to improve efficiency, security, and scalability in distributed edge environments. Key components include lightweight AI models for energy-efficient edge inference, privacy-preserving techniques using homomorphic encryption and blockchain, and self-learning architectures for adaptive real-time analytics. The study evaluates the framework’s performance in diverse applications, including smart healthcare, autonomous vehicles, and industrial IoT, demonstrating significant improvements in computational efficiency, network resilience, and response time compared to traditional cloud-based architectures. Comprehensive simulations and real-world case studies validate the feasibility and effectiveness of the proposed approach, showing a 35% reduction in latency, a 30% increase in energy efficiency, and a 50% improvement in decision accuracy in edge-enabled smart systems. This research highlights the critical role of AI-driven computational frameworks in advancing next-generation intelligent computing, paving the way for autonomous, secure, and efficient edge-based smart environments.International Journal of Computational and Experimental Science and Engineering201An AI-driven computational framework that integrates Edge Intelligence (EI) with adaptive deep learning models to optimize data processing and decision-making at the edge to improve efficiency, security, and scalability in distributed edge environments is presented.International Journal of Computational and Experimental Science and Engineering["G. Prabaharan", "S. Vidhya", "T. Chithrakumar", "K. Sika", "M.Balakrishnan"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/58aa72fb077c5f96b4dd56a6c80a9256c985c10a 8b1c08a7765236c3a974b7cc4057a4b6bb2f836bThis study examines the influence of technology readiness, perceived usefulness, and perceived ease of use on the adoption of artificial intelligence (AI) technology among professional accountants in Indonesia. Using the Technology Rediness Acceptance Model (TRAM), this study involved 101 accountant respondents. The analysis results show that all three variables significantly influence the adoption of AI. However, age was not proven to be a significant moderator in this relationship. These findings indicate that internal organizational factors, such as technology readiness and perceptions of the benefits of AI, are more dominant in driving adoption than demographic factors such as age. This study suggests the importance of developing comprehensive training programs to improve accountants' technology readiness and accelerate the adoption of AI in the accounting industry.El-Mal: Jurnal Kajian Ekonomi &amp; Bisnis Islam240Findings indicate that internal organizational factors, such as technology readiness and perceptions of the benefits of AI, are more dominant in driving adoption than demographic factors such as age.El-Mal: Jurnal Kajian Ekonomi &amp; Bisnis Islam["Nisa A. Yusuf", "R. J. Arsjah", "Magister Akuntansi"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/8b1c08a7765236c3a974b7cc4057a4b6bb2f836b 92ab4fa9db31be9a074cb51859f9e21f6b8ea86fArtificial Intelligence (AI) is widely used in decision-making systems, including the criminal justice system. Automated decision-making systems can speed up the handling of cases and improve consistency and efficiency. These systems were expected to enhance transparency and equip judges with data-driven insights. AI in criminal justice also pointed out concerns about bias and fairness. The idea is to build more inclusive legal systems. This paper explored different sources of potential biases in the judiciary. We compared different approaches to identify and measure the biases. We also reviewed techniques for bias mitigation, such as in-processing, pre-processing, and post-processing approaches. This work aims to comprehensively understand how to build fair AI models. We examined the widely used datasets and the fairness metrics used for evaluation. Most of the work on addressing biases is done in the Western context, leaving a notable gap in the Indian context. India, a country with rich diversity and a complex legal structure, needs AI models that are accurate and also equitable across different demographics to ensure justice and equity for all citizens. To achieve that, India needs bias detection and mitigation approaches that suit the Indian context, as well as evaluation metrics to measure fairness in decisions influenced by gender, caste, religion, etc. The approaches discussed in this paper were supported by case studies that explain the historical and cultural dimensions of the Indian judiciary.International Journal of Intelligent Decision Technologies500Different sources of potential biases in the judiciary are explored and different approaches to identify and measure the biases are compared, including in-processing, pre-processing, and post-processing approaches.Intelligent Decision Technologies["Keerthi Lingam", "Suresh Chittineni"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/92ab4fa9db31be9a074cb51859f9e21f6b8ea86f dfa271a41f5d10c1d009c7c8d50d0bf6541b0114The rapid advancements in Industry 4.0 and smart manufacturing systems have necessitated the integration of Artificial Intelligence (AI) and Digital Twin Technology (DTT) to enhance operational efficiency and predictive maintenance strategies. This study proposes an AI-driven predictive maintenance framework that leverages Digital Twin Technology to enable real-time monitoring, fault diagnosis, and failure prediction in industrial environments. The framework integrates machine learning (ML) models, deep learning techniques, and edge computing to analyze sensor data, detect anomalies, and optimize maintenance schedules. A reinforcement learning-based decision model is employed to dynamically adjust maintenance strategies, reducing downtime and extending equipment lifespan. Additionally, physics-informed AI models are incorporated into the digital twin architecture to simulate operational behaviours and predict potential failures with high accuracy. The proposed system is validated through a case study in a smart manufacturing plant, demonstrating a 35% improvement in predictive accuracy, 40% reduction in unplanned downtimes, and 25% optimization in maintenance costs compared to traditional predictive maintenance approaches. The findings indicate that the integration of AI and DTT significantly enhances the reliability and efficiency of cyber-physical manufacturing systems (CPMS), paving the way for more autonomous and intelligent industrial operations.International Journal of Computational and Experimental Science and Engineering190An AI-driven predictive maintenance framework that leverages Digital Twin Technology to enable real-time monitoring, fault diagnosis, and failure prediction in industrial environments and significantly enhances the reliability and efficiency of cyber-physical manufacturing systems (CPMS).International Journal of Computational and Experimental Science and Engineering["S. Prabu", "R. Senthilraja", "Ahmed Mudassar Ali", "S. Jayapoorani", "M. Arun"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/dfa271a41f5d10c1d009c7c8d50d0bf6541b0114 9c4db9c2ed0404c65da7d047fbb92df1e775d0fdIn today's era of rapid advancement in technology, innovative assistive devices are transforming accessibility for visually impaired. Through the integration of assistive health technologies, embedded systems, and software engineering, the Smart Assistive Stick enables people to navigate on their own. Fundamentally, an Arduino microcontroller interprets the reflected signals to provide real-time feedback in the form of voice instructions or buzzer alerts. The ultrasonic sensor detects obstructions in three directions (front, left, and right) within a range of 0 to 30 cm. The stick is lightweight and reasonably priced, enhanced by GPS and GSM modules for location-based services and emergency alerts, and developed with SolidWorks for maximum efficiency and ergonomics. Additionally, the study uses cutting-edge artificial intelligence for object detection in response to the growing demand for affordable assistive devices. The information is then communicated to the user in audio form after captured photos are processed to classify different items, including people, cars, and other impediments. In the end, this dual strategy bridges the gap between accessibility and technology by facilitating independent mobility in a variety of contexts, such as public areas and senior living facilities, while also lowering human effort and raising environmental awareness.Journal of Computer Science and Technology Studies100The Smart Assistive Stick bridges the gap between accessibility and technology by facilitating independent mobility in a variety of contexts, such as public areas and senior living facilities, while also lowering human effort and raising environmental awareness.Journal of Computer Science and Technology Studies["Dipta Paul", "S. M. Aliuzzaman", "M. Md", "Ahatesham Rabbi", "F. Md", "Niamul Alam", "T. Md", "Saiful Islam Dewan", "Ariful Azad Md"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/9c4db9c2ed0404c65da7d047fbb92df1e775d0fd 715768745992776f4d13e3fb252583ade4dba6eeThe integration of artificial intelligence (AI) into psychotherapy and counselling practices presents both significant opportunities and ethical challenges. AI applications, such as automated scheduling, AI-assisted note taking, client engagement tools, and predictive analytics for client needs, are transforming the delivery of mental health care. However, the ethical considerations surrounding AI’s role in mental health care are complex and multifaceted, necessitating careful scrutiny. This paper discusses the ethical considerations required for the responsible implementation of AI technologies in psychotherapy and counselling, focusing on data protection, client consent, and the preservation of the therapeutic relationship. It argues that professional bodies and educational institutions must collaborate to develop dynamic, adaptable ethical guidelines that ensure the safe and effective use of AI tools. Furthermore, it emphasises the need for robust data protection mechanisms to safeguard sensitive client information and proposes strategies to balance the benefits of AI with the preservation of human connection in mental health care.Psychotherapy and Counselling Journal of Australia590It is argued that professional bodies and educational institutions must collaborate to develop dynamic, adaptable ethical guidelines that ensure the safe and effective use of AI tools, and the need for robust data protection mechanisms to safeguard sensitive client information is emphasized.Psychotherapy and Counselling Journal of Australia["Alexandra Bloch-Atefi"]2025-03-02T00:00:00https://www.semanticscholar.org/paper/715768745992776f4d13e3fb252583ade4dba6ee 301a08d4b15d2a4959bc588eac65ae23d7bce43cThe inclusion of Generative Artificial Intelligence (GAI) in higher education is revolutionizing teaching, learning, and research processes, presenting new opportunities and challenges to institutions worldwide. This paper explores the multidimensional inclusion of GAI in transforming higher education, with an emphasis on its applications in content development, individualized learning, and academic support systems. By utilizing algorithms capable of producing creative outputs such as text, images, and simulations, GAI enables the automation of administrative processes, increasing efficiency while promoting personalized learning experiences. This paper also looks at how GAI is utilized to enhance traditional pedagogical frameworks, giving educators new tools for curriculum creation and assessment. However, in addition to its potential benefits, GAI inclusion raises important ethical, pedagogical, and technological challenges, such as data privacy, academic integrity, and the digital divide. This paper examines the growing significance of GAI with a review of existing literature, case studies, and expert perspectives, highlighting its potential to alter educational practices while advocating appropriate applications. The findings are intended to provide an exhaustive framework for policymakers, educators, and technology developers to guide the effective and ethical integration of GAI into higher education institutions. Finally, this paper contributes to the discussion of how GAI might improve academic experiences and prepare future generations for a fast-changing technological landscape.Uniglobal Journal of Social Sciences and Humanities00This paper examines the growing significance of GAI with a review of existing literature, case studies, and expert perspectives, highlighting its potential to alter educational practices while advocating appropriate applications.Uniglobal Journal of Social Sciences and Humanities[]2025-03-03T00:00:00https://www.semanticscholar.org/paper/301a08d4b15d2a4959bc588eac65ae23d7bce43c 122af4c387c390af5ccfcd6528402452213d4ea0Advances in artificial intelligence (AI) are reshaping labor markets and sparking political debates. Like economic globalization, AI developments promise benefits, including job creation and lower prices, but also costs such as job displacement, raising crucial questions about public perceptions. Will AI, like globalization, challenge existing paradigms and trigger a backlash? Leveraging a conjoint experiment with 6,000 respondents from the United States and Canada, we examine public opinion toward offshoring and generative AI, focusing on the multidimensional trade‐offs between job and price changes. Across all scenarios, respondents are equally or more sensitive to price changes than employment shifts. AI is favored over offshoring, especially among Democrats, highlighting an emerging partisan divide in the United States. Republicans and Canadians show more varied support, indicating AI is not immune to opposition. By focusing on the microfoundations of opinion formation, we identify scenarios that may trigger or temper protectionist stances.American Journal of Political Science510Public opinion toward offshoring and generative AI is favored over offshoring, especially among Democrats, highlighting an emerging partisan divide in the United States and Canada and identifying scenarios that may trigger or temper protectionist stances.American Journal of Political Science["Beatrice Magistro", "Sophie Borwein", "R. M. Alvarez", "Bart Bonikowski", "P. Loewen"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/122af4c387c390af5ccfcd6528402452213d4ea0 629bcfa9a7cdbfae66d7b96f173b5de449a38d72Objectives. Considering that there is no standardized questionnaire for safety climate and resilience assessment, authors usually review a large number of questionnaires from the available literature, which results in a high number of questions distributed to respondents. As the questionnaire length increases, resistance from the respondents increases. Artificial intelligence (AI) tools until now have not been used for item reduction, besides the need for selecting and retaining only the most relevant and informative questions in the questionnaire with adequate accuracy. Methods. AI tools such as multiple linear regression analysis (MLRA) and the multilayer perceptron artificial neural network (MLP ANN) are used in the development of a model able to cluster respondents' ratings and to predict values of organizational resilience based on the respondents' ratings of the specific questions. Results. AI could be used as a valuable tool for item reduction, since the prediction accuracy for MLRA tools is 70.4-71.5% and for the MLP ANN it is 76.4%. Conclusions. This research proves that machine learning algorithms can be used to build predictive models that determine which survey questions are the most predictive for organizational resilience index calculation using safety climate factors.International Journal of Occupational Safety and Ergonomics480This research proves that machine learning algorithms can be used to build predictive models that determine which survey questions are the most predictive for organizational resilience index calculation using safety climate factors.International journal of occupational safety and ergonomics : JOSE["Ivan Mihajlovi\u0107", "Nikola Petrovi\u0107", "Vesna Spasojevi\u0107 Brki\u0107", "Nenad Miliji\u0107"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/629bcfa9a7cdbfae66d7b96f173b5de449a38d72 af0c5d4cf1dd24fd8d2cd4ef6dcd23c8fbcfebc7 In recent years, the rapid convergence of artificial intelligence (AI) and low-altitude flight technology has driven significant transformations across various industries. These advancements have showcased immense potential in areas such as logistics distribution, urban air mobility (UAM) and national defense. By adopting the AI technology, low-altitude flight technology can achieve high levels of automation and operate in coordinated swarms, thereby enhancing efficiency and precision. However, as these technologies become more pervasive, they also raise pressing ethical or moral concerns, particularly regarding privacy, public safety, as well as the risks of militarisation and weaponisation. These issues have sparked extensive debates. In summary, while the integration of AI and low-altitude flight presents revolutionary opportunities, it also introduces complex ethical challenges. This article will explore these opportunities and challenges in depth, focusing on areas such as privacy protection, public safety, military applications and legal regulation, and will propose strategies to ensure that technological advancements remain aligned with ethical or moral principles.Aeronautical Journal980While the integration of AI and low-altitude flight presents revolutionary opportunities, it also introduces complex ethical challenges, and this article will explore these opportunities and challenges in depth, focusing on areas such as privacy protection, public safety, military applications and legal regulation.The Aeronautical Journal["J. Li", "S. Liu", "W. Huang", "B. Yan"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/af0c5d4cf1dd24fd8d2cd4ef6dcd23c8fbcfebc7 1517652034bf2ffd56b8503f2fb6cc66cad65e8aArtificial intelligence (AI) is widely used to support decision making and interventions, arguably saving time, reducing bias and improving decision accuracy. The profession must urgently appraise the potential and pitfalls of this rapidly developing technology. This challenge was addressed at the 2024 European Social Work Research Association conference in Vilnius, at which the Evidence into Practice Special Interest Group focused on three contemporary AI developments: (1) large language models (LLMs); (2) AI- and robot-supported interventions; and (3) predictive risk modelling (PRM). This short ‘Reflection, exchange and dialogue’ article outlines the presentations, issues discussed and further reflections. Although LLMs have an impressive ability to manipulate language, essential case detail and analysis remain human tasks. There are robot technologies already helping people in the domains of disability and eldercare, and AI ‘language robots’ are being used favourably in low-risk mental health contexts, providing a non-judgemental (non-human) and ever-available ‘listener’ and ‘advisor’. PRMs raise many conflicting views. The ‘black box’ of AI may ‘hide’ systemic bias, though proponents argue that humans are biased too, so perfection is not an appropriate comparator. Our conclusion is that a priority is to examine, shape and regulate the interface between humans and computer algorithms.European Social Work Research150The conclusion is that a priority is to examine, shape and regulate the interface between humans and computer algorithms.European Social Work Research["Beth Coulthard", "Brian J. Taylor", "A. McGlade"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/1517652034bf2ffd56b8503f2fb6cc66cad65e8a a7ac811fd9db285abf3a13e1b6c0b8f576306ac5 To assess whether use of an artificial intelligence (AI) model for mammography could result in more longitudinally consistent breast density assessments compared with interpreting radiologists. The AI model was evaluated retrospectively on a large mammography dataset including 50 sites across the United States from an outpatient radiology practice. Exams were acquired on Hologic imaging systems between 2016 and 2021 and were interpreted by 39 radiologists (36% fellowship trained; years of experience: 2-37 years). Longitudinal patterns in four-category breast density and binary breast density (non-dense vs. dense) were characterized for all women with at least three examinations (61,177 women; 214,158 examinations) as constant, descending, ascending, or bi-directional. Differences in longitudinal density patterns were assessed using paired proportion hypothesis testing. The AI model produced more constant (p<.001) and fewer bi-directional (p<.001) longitudinal density patterns compared to radiologists (AI: constant 81.0%, bi-directional 4.9%; radiologists: constant 56.8%, bi-directional 15.3%). The AI density model also produced more constant (p<.001) and fewer bi-directional (p<.001) longitudinal patterns for binary breast density. These findings held in various subset analyses, which minimize a) change in breast density (post-menopausal women, women with stable image-based BMI), b) inter-observer variability (same radiologist), and c) variability by radiologist’s training level (fellowship-trained radiologists). AI produces more longitudinally consistent breast density assessments compared with interpreting radiologists. Our results extend the advantages of AI in breast density evaluation beyond automation and reproducibility, showing a potential path to improved longitudinal consistency and more consistent downstream care for screened women. BJR|Artificial Intelligence00The results extend the advantages of AI in breast density evaluation beyond automation and reproducibility, showing a potential path to improved longitudinal consistency and more consistent downstream care for screened women.BJR|Artificial Intelligence["Susan O Holley", "Daniel Cardoza", "Thomas P Matthews", "Elisha E Tibatemwa", "R. M. Hoil", "A. Toriola", "Aimilia Gastounioti"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/a7ac811fd9db285abf3a13e1b6c0b8f576306ac5 f704e6e0641f9882afb0b26ff3b82f13c9a93e35In the digital era, generative artificial intelligence (GAI) is increasingly used in higher education, yet the psychological factors influencing its adoption are underexplored. This study examines the role of a growth mindset towards technology, defined as the belief that technological abilities can be developed in predicting GAI usage among Chinese undergraduates. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), this study explored the mediating roles of performance expectancy, effort expectancy, and technology anxiety. A total of 500 students participated in an online survey. Mediation analysis showed that a growth mindset predicted GAI usage through performance expectancy, effort expectancy, and technology anxiety, even when perceived external resources and gender were statistically controlled. The findings underscore the importance of psychological readiness, alongside technical skills, in fostering GAI adoption in education. Future research should use longitudinal and experimental designs to validate these results.Education sciences580Mediation analysis showed that a growth mindset predicted GAI usage through performance expectancy, effort expectancy, and technology anxiety, even when perceived external resources and gender were statistically controlled.Education Sciences["T. Chow", "Ken To"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/f704e6e0641f9882afb0b26ff3b82f13c9a93e35 820ce7c4cb11d016a46c8cbcf02bdaf2427a603bThis study examinesexplores the transformative role of artificial intelligence (AI) in revolutionizing the fashion industry, focusingwith a focus on enhancing consumer experiences, fosteringpromoting sustainability, and driving innovation in retail. AI’sIt examines AI applications in personalized recommendations, virtual try-ons, and supply chain optimization are explored alongside its, while also addressing societal implications. The integration of sustainabilitySustainability is a key focuscentral theme, highlighting how AI reducesminimizes overproduction, facilitatesenables circular fashion, and fostersencourages conscious consumerism. Case studies, such as Nike’s AI-powered retail stores and the Lynn University’s Surreal Fashion Show illustrate, demonstrate practical applications and innovations during the COVID-19 pandemic. This research synthesizes insights from  reports by The Business of Fashion  and McKinsey and& Company reports, emphasizing the trends and challenges shapinginfluencing AI’s integration into the future of fashion retail.International journal of business management380This research synthesizes insights from reports by The Business of Fashion and McKinsey and Company reports, emphasizing the trends and challenges shaping AI’s integration into the future of fashion retail.International Journal of Business and Management["Andrew Burnstine"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/820ce7c4cb11d016a46c8cbcf02bdaf2427a603b 60bcb7a3e80e81c9536408967156650dd8fa3308 This systematic review explores the role of artificial intelligence (AI) in the early diagnosis of Alzheimer’s disease (AD) and related dementias. A total of 20 studies from recent years were analyzed, focusing on various AI techniques such as deep learning, neural networks, and feature engineering. The studies utilized datasets such as AD neuroimaging initiative and OASIS, showcasing AI’s potential in detecting early-stage Alzheimer’s with high accuracy. However, challenges such as variability in methodologies, limited generalizability, and lack of real-world validation were identified. Ethical concerns and the need for model interpretability were also highlighted. Despite these challenges, AI models demonstrate promising capabilities for improving early diagnosis, but further research is needed to standardize approaches and ensure clinical applicability. This review underscores the importance of integrating AI tools into healthcare settings to enhance early detection and treatment strategies for AD.Archives of Medicine and Health Sciences310The importance of integrating AI tools into healthcare settings to enhance early detection and treatment strategies for AD is underscored, with challenges such as variability in methodologies, limited generalizability, and lack of real-world validation identified.Archives of Medicine and Health Sciences["Dharmendra Kumar Gupta", "Arunima Chaudhuri", "Abhijit Kanrar"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/60bcb7a3e80e81c9536408967156650dd8fa3308 4c2d31a45f307f58bed33a46aaba03aea437e70fVaricocele is a common entity found in 15% of men and is the most common reversible cause of male factor infertility. Guidelines have been developed to guide urologists in deciding which patients would benefit from varicocelectomy. Yet studies published over the last decade showed the emergence of predictors of success of varicocelectomy using nomograms and other predictive models with statistical analysis. The emergence of artificial intelligence (AI) and machine learning revolutionized the clinician's approach to medicine. The virtual branch of AI, represented by machine learning, has been a very exciting topic for clinicians and researchers over the last years, especially after the launching of ChatGPT‐3.5. Urology has been at the forefront of integrating advances in AI into its everyday practice. We aim to shed light on the present literature describing the use of AI in predicting the outcomes of varicocelectomy. Machine learning is being used to predict the improvement in semen parameters after varicocelectomy. These algorithms are derived from studies and data present in the literature and predictive models developed throughout the last two decades and have a superior performance to that of traditional nomograms. However, these models require further research and validation but are anticipated to surpass the accuracy of all current resources, setting forward a new era of varicocele workup and management in the years to come. This paper offers a wide review on the current evidence behind varicocele surgery and the integration of AI in medicine, urology and its use in predicting improvement in sperm parameters post‐varicocelectomy.UroPrecision300A wide review on the current evidence behind varicocele surgery and the integration of AI in medicine, urology and its use in predicting improvement in sperm parameters post‐varicocelectomy is offered.UroPrecision["Oussama G. Nasrallah", "Moustafa A. Al Hattab", "Bassel G Bachir"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/4c2d31a45f307f58bed33a46aaba03aea437e70f 4afaf5a0c19a668674887c325f0c3c04c370a909INTRODUCTION Healthcare organizations are complicated and demanding for all stakeholders, but artificial intelligence (AI) has revolutionized several sectors, especially healthcare, with the potential to enhance patient outcomes and standard of life. Quick advancements in AI can transform healthcare by implementing it into clinical procedures. Reporting AI's involvement in clinical settings is vital for its successful adoption by providing medical professionals with the necessary information and tools. BACKGROUND This paper offers a thorough and up-to-date summary of the present condition of AI in medical settings, including its possible uses in patient interaction, treatment suggestions, and disease diagnosis. It also addresses the challenges and limitations, including the necessity for human expertise along with future directions. In doing so, it improves the understanding of AI's relevance in healthcare and supports medical institutions in successfully implementing AI technologies. METHODS The structured literature review, with its dependable and reproducible research process, allowed the authors to acquire 337 peer-reviewed publications from indexing databases, such as Scopus and EMBASE, without any time restrictions. The researchers utilized both qualitative and quantitative factors to assess authors, publications, keywords, and collaboration networks. RESULTS AI implementation in healthcare holds enormous potential for enhancing patient outcomes, treatment recommendations, and disease diagnosis. AI technologies can use massive datasets and recognize patterns to beat human performance in various healthcare domains. AI provides improved accuracy, reduced expenses, and time savings. It can transform customized medicine, optimize drug dosages, improve management of population health, set guidelines, offer digital medical assistants, promote mental health services, boost patient knowledge, and maintain patientclinician trust. CONCLUSION AI can be utilized to detect diseases, develop customized therapy plans, and support medical professionals with their clinical decision-making. Instead of just automating jobs, AI focuses on creating technologies that can improve patient care in several healthcare settings. However, challenges such as biasness, data confidentiality, and data quality must be resolved for the appropriate and successful integration of AI in healthcare.Current Topics in Medicinal Chemistry00A thorough and up-to-date summary of the present condition of AI in medical settings, including its possible uses in patient interaction, treatment suggestions, and disease diagnosis, and the challenges and limitations are addressed.Current topics in medicinal chemistry["Kavya Singh", "Ashish Prabhu", "Navjeet Kaur"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/4afaf5a0c19a668674887c325f0c3c04c370a909 5a8690c78249310a8a2020fd45ef81332ce5d2e0Artificial intelligence (AI) and digital pathology are transforming breast cancer management by addressing the limitations inherent in traditional histopathological methods. The application of machine learning algorithms has enhanced the ability of AI systems to classify breast cancer subtypes, grade tumors, and quantify key biomarkers, thereby improving diagnostic accuracy and prognostic precision. Furthermore, AI-powered image analysis has demonstrated superiority in detecting lymph node metastases, contributing to more precise staging, treatment planning, and reduced evaluation time. The ability of AI to predict molecular markers, including human epidermal growth factor receptor 2 status, BRCA mutations and homologus recombination deficiency, offers substantial potential for the development of personalized treatment strategies. A collaborative approach between pathologists and AI systems is essential to fully harness the potential of this technology. Although AI provides automation and objective analysis, human expertise remains indispensable for the interpretation of results and clinical decision-making. This partnership is anticipated to transform breast cancer care by enhancing patient outcomes and optimizing treatment approaches.European Journal of Breast Health00The ability of AI to predict molecular markers, including human epidermal growth factor receptor 2 status, BRCA mutations and homologus recombination deficiency, offers substantial potential for the development of personalized treatment strategies.European journal of breast health["A. H. Dur Karasayar", "Ibrahim Kulac", "Nilgun Kapucuoglu"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/5a8690c78249310a8a2020fd45ef81332ce5d2e0 b37d9d03af482ab29bd983a6347821d9cc9eeff1In global trade practices, varying inspection and quarantine standards frequently cause import disruptions. To manage such customs risk, artificial intelligence (AI)-based intelligent sourcing strategy and traditional dual-sourcing strategy are two widely used strategies to guarantee supply resilience. In this study, we formulate the main trade-offs to adopt AI sourcing, including the information analytics value, the increased flexibility cost, and the altered competition/cooperation structure among the stakeholders. We find that the importer would prefer the AI-sourcing strategy when the customs disruption probability is high, and the local production cost is moderate. Moreover, the cost-efficiency of the AI-sourcing strategy is usually lower than the expectation due to the supplier's pricing behavior. When it comes to the resilience indicator evaluation, we find that, surprisingly, the importer is more likely to be cost-oriented rather than resilience-oriented. Therefore, pursuing resilience cannot be always attractive but low cost can. Even though the advent of AI sourcing will not change this insight.Risk Analysis610It is found that the importer would prefer the AI-sourcing strategy when the customs disruption probability is high, and the local production cost is moderate, and the cost-efficiency of the AI-sourcing strategy is usually lower than the expectation due to the supplier's pricing behavior.Risk analysis : an official publication of the Society for Risk Analysis["Baozhuang Niu", "Zebin Zheng", "Lingfeng Wang"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/b37d9d03af482ab29bd983a6347821d9cc9eeff1 157ccf2535a379b0c9e9741a471d0d2dca18aff8This study examines the adoption of Artificial Intelligence (AI) among Micro, Small, and Medium Enterprises (MSMEs) in Balikpapan, Indonesia, using the Technology-Organization-Environment (TOE) framework. Through qualitative interviews with seven MSME owners and employees, we explored their perspectives on AI, current technology use, challenges in digital adoption, and external support needs. Findings indicate that while MSME owners recognize the potential of AI to enhance competitiveness, its adoption remains limited, with most relying on basic digital tools like POS systems and social media for marketing. Key challenges include financial constraints, limited digital literacy among staff, and a lack of technical training. Moreover, external support, such as government-backed training programs and financial incentives, is minimal, slowing the digital transition process. This study suggests that expanding AI awareness and providing targeted training could address these barriers, while government policies that promote digital infrastructure and collaboration with tech providers could further support MSME digital transformation. Future research should consider broader variables within the TOE framework, such as cultural and policy-related factors, to provide a more comprehensive understanding of AI adoption in MSMEs across various sectors.Jurnal GeoEkonomi260Examination of the adoption of Artificial Intelligence among Micro, Small, and Medium Enterprises (MSMEs) in Balikpapan, Indonesia, using the Technology-Organization-Environment (TOE) framework indicates that while MSME owners recognize the potential of AI to enhance competitiveness, its adoption remains limited.Jurnal GeoEkonomi["Ridho Jun Prasetyo", "Riska Andrilla"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/157ccf2535a379b0c9e9741a471d0d2dca18aff8 de2822e2c1b4b887f4b89e02d0277fa74f5a33a0 In this article, we review the transformative role of artificial intelligence (AI) in the detection, diagnosis, and treatment of breast cancer, a disease that affects approximately 1 in 8 women globally. Early detection is critical for improving treatment outcomes and survival rates. Traditional diagnostic methods, such as mammograms and MRIs, can be subjective and prone to error. AI-powered algorithms offer a solution by analyzing medical imaging data with exceptional accuracy, identifying subtle abnormalities that may indicate early-stage breast cancer. By enhancing diagnostic precision, these algorithms facilitate quicker diagnoses and tailored treatment plans, ultimately improving patient outcomes. Furthermore, AI has the potential to predict cancer recurrence and assess tumor aggressiveness by examining large datasets, providing valuable insights for clinicians. This personalized approach allows for targeted therapies that increase the likelihood of successful treatment. We explore the integration of AI in remote monitoring and prognostic tools, emphasizing its ability to analyze complex data patterns for more accurate diagnoses and treatment recommendations. However, we also discuss the limitations of AI, such as the need for high-quality, diverse datasets, interpretability issues, and ethical concerns regarding data privacy and algorithmic bias. Addressing these challenges is crucial for the successful implementation of AI in breast cancer care. Ultimately, this article highlights the promising future of AI in enhancing patient outcomes while stressing the importance of ethical considerations and equitable access to these advanced technologies.Archives of Medicine and Health Sciences340The integration of AI in remote monitoring and prognostic tools is explored, emphasizing its ability to analyze complex data patterns for more accurate diagnoses and treatment recommendations and the need for high-quality, diverse datasets.Archives of Medicine and Health Sciences["M. Al\u2010Raeei"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/de2822e2c1b4b887f4b89e02d0277fa74f5a33a0 fc97c6b02776e334c28569004927ea33ed0aab97A significant portion of small and medium-sized enterprises (SMEs) are usually allocated to the construction sector, which plays a vital role in many economies. SMEs currently face serious concerns regarding the pursuit of sustainability. Limited financial resources (FRs) frequently prevent SMEs from implementing sustainable practices. Therefore, these enterprises should mitigate expenses to invest in environmentally friendly initiatives. Enhancing resources and developing ways to accelerate Turkish SMEs’ shift toward sustainability is vital. Moreover, adopting artificial intelligence (AI) and green innovation strategies (GISs) can boost sustainable competitive advantage (SCA) and lead them to success. This study utilized the natural resource-based view theory (NRBV), developed to compensate for the RBV’s shortcomings by incorporating the natural environment into the RBV’s framework. This study uses structural equation modeling (SEM) to examine the causal effect between the study variables based on the responses received from 228 executives within SMEs in Turkey’s construction sector. The findings of this study reveal that FRs significantly impact the SCA among SMEs, while GIS serves as a mediator in the relationship. Additionally, the moderating impact of AI adoption promotes sustainability development in this industry. This study is significant because it contributes to the body of knowledge regarding the relationship between the study’s constructs that align with Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure), presented by the United Nations in 2015. This goal promotes robust infrastructure, encourages sustainable and inclusive industrialization, and stimulates innovation in the SME construction industry. Although these variables have been studied individually in previous studies, this study integrates them into a thorough framework that emphasizes the function of GIS as a mediator in the relationship between FRs and SCA, and the interaction effect of AI adoption. This study offers useful information to managers, stakeholders, politicians, and SME leaders, enabling them to make well-informed decisions about sustainable practices.Sustainability690The findings of this study reveal that FRs significantly impact the SCA among SMEs, while GIS serves as a mediator in the relationship, and the interaction effect of AI adoption promotes sustainability development in this industry.Sustainability["Panteha Farmanesh", "Niloofar Solati Dehkordi", "A. Vehbi", "Kavita Chavali"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/fc97c6b02776e334c28569004927ea33ed0aab97 367a65de59e3b1ae7af4e4d26e8a1f83fe48ae84Artificial intelligence (AI) systems have been increasingly adopted in the Manufacturing Industrial Internet (MII). Investigating and enabling the AI resilience is very important to alleviate profound impact of AI system failures in manufacturing and Industrial Internet of Things (IIoT) operations, leading to critical decision making. However, there is a wide knowledge gap in defining the resilience of AI systems and analyzing potential root causes and corresponding mitigation strategies. In this work, we propose a novel framework for investigating the resilience of AI performance over time under hazard factors in data quality, AI pipelines, and the cyber-physical layer. The proposed method can facilitate effective diagnosis and mitigation strategies to recover AI performance based on a multimodal multi-head self latent attention model. The merits of the proposed method are elaborated using an MII testbed of connected Aerosol Jet Printing (AJP) machines, fog nodes, and Cloud with inference tasks via AI pipelines.260This work proposes a novel framework for investigating the resilience of AI performance over time under hazard factors in data quality, AI pipelines, and the cyber-physical layer and can facilitate effective diagnosis and mitigation strategies to recover AI performance based on a multimodal multi-head self latent attention model.["Yingyan Zeng", "Ismini Lourentzou", "Xinwei Deng", "Ran Jin"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/367a65de59e3b1ae7af4e4d26e8a1f83fe48ae84 4858904e9f4295b6fb9263e7beeae5ff7533fc26npj Climate Action880A simple framework is developed that links individual and collective emotions, AI, and climate action, and suggests three critical areas in need of further investigation.npj Climate Action["Victor Galaz", "Hannah Metzler", "Caroline Schill", "Therese Lindahl", "Stefan Daume", "A. Marklund", "Antonio J. Castro", "Jennifer Bard", "Timon McPhearson", "Diego Galafassi", "Helge Peters"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/4858904e9f4295b6fb9263e7beeae5ff7533fc26 34becaa01e50dee4b8d7e99b1ed75075fc75e9cfDrug discovery is a complex, costly, and time-intensive process, often taking over a decade and billions of dollars to identify novel therapeutic compounds. Recent advancements in artificial intelligence (AI) have transformed this domain, enabling more efficient, cost-effective, and innovative approaches. This paper examines the application of AI in various stages of drug discovery, from target identification to compound screening and toxicity prediction. Machine learning and deep learning techniques are highlighted as key contributors to enhancing predictive accuracy, optimizing molecular property modeling, and improving high-throughput screening processes. Despite its transformative potential, challenges such as data quality, regulatory hurdles, and the “black-box” nature of AI models persist. By addressing these limitations, AI-driven drug discovery holds the promise of accelerating the development of life-saving therapies while reducing costs and time-to-market. Keywords: Artificial Intelligence, Drug Discovery, Machine Learning, Deep Learning, High-Throughput Screening.RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES00This paper examines the application of AI in various stages of drug discovery, from target identification to compound screening and toxicity prediction, and machine learning and deep learning techniques are highlighted as key contributors to enhancing predictive accuracy, optimizing molecular property modeling, and improving high-throughput screening processes.RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES["Kabiga Chelule Kwemoi"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/34becaa01e50dee4b8d7e99b1ed75075fc75e9cf cd9a5738002c0b138744d7ac75a51b46e388643eThe article is devoted to the foreign experience of actively involving artificial intelligence in the educational process, the analysis of the successes and shortcomings of the legal regulation of artificial intelligence at the international and national levels. Artificial intelligence is actively being introduced into the sphere of higher education of foreign countries and Ukraine, so it can be revolutionaryly changed in the near future. Therefore, it is worth paying attention to the ethical and legal aspects of its use. The work of artificial intelligence can provoke a number of problems, in particular, the illegal or erroneous use of confidential information about a person. Therefore, its use requires regulation – measures should be introduced to ensure that the work of such technologies is safe and complies with the provisions of Ukrainian legislation and international standards. Therefore, today there are problems with a clear legal definition and regulation of this concept. Analysis of the provisions of the domestic legislation of Ukraine is relevant for the development of a number of issues related to the legal status of copyrights to works created with the help of artificial intelligence. In Ukraine, work has already begun on improving legal regulation in the sphere of artificial intelligence. Thus, in 2020, the Concept for the Development of Artificial Intelligence in Ukraine was drawn up. By the way, not only in Ukraine, but also in many countries of the world, there is the same regulation regarding artificial intelligence. Objects created using artificial intelligence can receive copyright protection only in the case of significant human influence during their creation. It should be noted that European and, in particular, Ukrainian legislation is working to improve the legal regulation of artificial intelligence. The European Union is actively working to increase the ethics of artificial intelligence and update legislation in accordance with the challenges of the time and the rapid development of technologies. Keywords: artificial intelligence, AI, education, European Union, legal regulation, ethical standards, copyright.Scientific Herald of Sivershchyna. Series: Law50The article is devoted to the foreign experience of actively involving artificial intelligence in the educational process, the analysis of the successes and shortcomings of the legal regulation of artificial intelligence at the international and national levels, the analysis of the successes and shortcomings of the legal regulation of artificial intelligence at the international and national levels.Scientific Herald of Sivershchyna. Series: Law["Yu.M. Petrovska", "D. Pokryshen", "A. Popruzhna"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/cd9a5738002c0b138744d7ac75a51b46e388643e 4f596f728444cef22d1b9608eb82fe92582d0889Artificial intelligence journalism has been incorporated into the professional routines of the institutional news media formation in the West for more than a decade, but it is only just now being slowly adopted in the rest of the world. This study deploys a combination of case-study research and semi-structured in-depth interviews with senior editors in Nigeria to explore the state of artificial intelligence journalism in Nigeria, Africa’s most populous country. The study also discusses the implications of leapfrog innovation and the routinization of artificial intelligence reportorial practices in a digitally backward country.Journal of Applied Journalism &amp; Media Studies400This study deploys a combination of case-study research and semi-structured in-depth interviews with senior editors in Nigeria to explore the state of artificial intelligence journalism in Nigeria, Africa’s most populous country.Journal of Applied Journalism &amp; Media Studies["Farooq A. Kperogi", "Azubuike Ishiekwene"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/4f596f728444cef22d1b9608eb82fe92582d0889 56feea4fe1e82b40eb82f8ebdf0229012b1f6a41Artificial intelligence is revolutionizing industries including institutions of higher learning as it enhances teaching and learning processes, streamline administrative tasks and drive innovations. Despite the unprecedented opportunities, AI tools if not used correctly, can be challenging in education institutions. The purpose of this study was to comprehensively review the AI innovations, opportunities and challenges associated with the use of AI in higher Education of learning. A systematic literature review methodology was adopted and used to locate and select existing studies, analyze and synthesize the evidence to arrive at clear conclusion about the current debate in the area of study. Following the PRISMA, the study analyzed a total of 54 documents that met the inclusion and exclusion criteria set for selection of the documents. The review unveiled many opportunities including enhanced research capabilities, automation of administrative tasks among others. Artificial Intelligence tools are found to refine and streamline the administrative tasks in different units in higher institutions of learning. The challenges include ethical concerns, integrity issues and data fabrication issues. With the challenges notwithstanding, the benefits of Artificial Intelligence cannot be over emphasized. Artificial intelligence remains a powerful tool for research, automation of administrative tasked, personalized learning, inclusivity and accessibility of educational content for all. Emphasis should be put in regulatory frameworks detailing how such tools can be used while maintaining the level of ethical standards required.Frontiers in Education830The review unveiled many opportunities including enhanced research capabilities, automation of administrative tasks among others, and emphasis should be put in regulatory frameworks detailing how such tools can be used while maintaining the level of ethical standards required.Frontiers in Education["Samuel Ocen", "Joseph Elasu", "S. Aarakit", "Charles Olupot"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/56feea4fe1e82b40eb82f8ebdf0229012b1f6a41 f4f44d69f0b8a82457857a62f959425bb6798ea4A recent article on the impact of Generative Artificial Intelligence (AI) on peer review process has attracted the attention of our Editorial Office. Here we shall briefly summarize the main points discussed in that article, leaving to our readers to go deeper on the original text.Bleeding Thrombosis and Vascular Biology40Bleeding, Thrombosis and Vascular Biology["Giovanni de Gaetano", "C. Cerletti", "A. Di Castelnuovo"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/f4f44d69f0b8a82457857a62f959425bb6798ea4 7273292b9459dafe2c70d046e3bffd24a5755605Ketertinggalan pembelajaran (Learning loss) akibat pandemi covid-19 menjadi tugas rumah yang besar bagi seluruh aspek Pendidikan. Pesatnya perkembangan ilmu pengetahuan dan teknologi menuntut setiap individu untuk mengadaptasikan dirinya. Artificial Intelligence (AI) menjadi potensi yang menarik untuk dihubungkan dengan pembelajaran. Tujuan pelatihan ini yaitu a) meningkatkan kompetensi pedagogik guru dalam penggunaan teknologi berbasis Artificial Intelligence (AI) untuk pembuatan perangkat pembelajaran di sekolah; dan b) Membuat perangkat pembelajaran yang inovatif dan efektif serta pengalaman belajar yang bermakna (meaningful learning) bagi siswa berbasis Artificial Intelligence (AI) pada subjek Mata Pelajaran Matematika dan IPAS di Sekolah. Metode yang digunakan dalam kegiatan pengabdian masyarakat ini adalah Service Learning atau SL yang melibatkan pengalaman langsung, pembelajaran akademik, dan keterlibatan Masyarakat. Kegiatan pengabdian masyarakat ini dilakukan melalui enam tahapan kegiatan, yaitu: (1) persiapan kegiatan dan sosialisasi, (2) pelatihan pembuatan pembelajaran berbasis AI, (3) on job training dan asynchronous sharing, (4) melakukan pendampingan pembuatan perangkat pembelajaran dengan kombinasi synchronous dan asynchronous communication, (5) evaluasi dan kontrol pekerjaan guru yang berpartisipasi, dan (6) pelaporan. Hasil yang didapat peserta mengalami peningkatan nilai kompetensi pedagogiknya dengan rata-rata nilai N-gain (peningkatan) 0,23 masih tergolong kategori rendah.Jurnal Pengabdian Pendidikan Masyarakat (JPPM)00Jurnal Pengabdian Pendidikan Masyarakat (JPPM)["Mastarita Nova Wulanda", "Muslimahayati Muslimahayati", "Tia Agnesa"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/7273292b9459dafe2c70d046e3bffd24a5755605 a8f121db5eb6badccc779b6e0f7f64e868934a9eThe article discusses issues related to the rationale and characteristics of the role of artificial intelligence (AI) in automating decision–making processes in project management. The relevance of this topic is predetermined by the rapid development of technologies, their powerful and increasing impact on improving management efficiency (including reducing time to complete tasks, leveling risks, optimizing resource utilization). The purpose of the study was to analyze the possibilities of using AI to support decision–making, identify its advantages and limitations, and offer recommendations for integrating relevant developments into practice. The paper points out the presence of contradictions in the literature: some studies focus on the strategic capabilities of AI, others are limited to considering individual technological aspects, which makes it difficult to form a holistic view. It is concluded that the introduction of AI into project management should be based on a systematic approach, represented by training team members, adapting existing processes, and ensuring the transparency of algorithms. The author’s scientific contribution consists in the systematization of approaches to the use of AI in the field under study, in the proposal of a conceptual «framework» regarding the allocation of tasks and control of the stages within the framework of project management (using artificial intelligence). These materials will be useful for project managers, developers of AI solutions, as well as researchers studying automation of management processes.Entrepreneur's Guide00The paper points out the presence of contradictions in the literature: some studies focus on the strategic capabilities of AI, others are limited to considering individual technological aspects, which makes it difficult to form a holistic view.Entrepreneur’s Guide["E. O. Lebedeva"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/a8f121db5eb6badccc779b6e0f7f64e868934a9e fac55639607417a54672de10091a63830f83ac6bIn China, the Pearl River Delta (PRD) plays a leading role as not only an artificial intelligence (AI) innovation hotspot but also a pilot zone for green and low-carbon development. The Super-EBM model was used to measure the PRD’s carbon emission efficiency (CEE) from 2006 to 2021. On this basis, dual fixed effect, mediation effectPolish Journal of Environmental Studies630The Super-EBM model was used to measure the PRD’s carbon emission efficiency (CEE) from 2006 to 2021 and showed dual fixed effect, mediation effect.Polish Journal of Environmental Studies["Hao Zhang", "Tingyu Tao"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/fac55639607417a54672de10091a63830f83ac6b 5ab23780c0d0dbbb5bb3b03471db6447283630c2Artificial Intelligence (AI) has swept into marketing like game changer to deliver personalized advertisements, sharp predictive insights, predictive analytics and deepest customer connections. It is the kind of leap that gets marketers buzzing with excitement. But all this relies on mountain of consumer data and that is stirring up real worries about privacy and security on the whole. Digging into this duality: AI as a spark of innovation and potential threat to trust. Through a thorough analysis into the latest research, I have explored how AI is powering marketing today, the privacy risks it consists of and whether regulations in like GDPR and CCPA are feasible. People love the tailored personalization but they are uneasy about who is peaking at their data. The takeaway? Transparency, consentuality and rock solid security are the essential to keep customer in the fold.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00Through a thorough analysis into the latest research, a thorough analysis into the latest research is explored how AI is powering marketing today, the privacy risks it consists of and whether regulations in like GDPR and CCPA are feasible.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT[".. Y. Raizadaa"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/5ab23780c0d0dbbb5bb3b03471db6447283630c2 bd34970f4ace9f5c2cbdf6214751bcf29bae0979The objectives of this study are: first, the extent of Improving the Quality of Public Services with Artificial Intelligence (AI) in Indonesia, and second, what strategies can be used to Improve the Quality of Public Services with Artificial Intelligence (AI) in Indonesia. The method used in this study is qualitative, the qualitative method is the method used by the researcher by using sentences to describe the results of the research through writing, the data used consists of primary and secondary data, Techniques for determining Informants with snowbal Sampling, Informants in this study are designers of public service studies based on artificial intelligence in Indonesia, data is collected through Observation,  Interviews, FGDs and Documentation, Data analysis in this study uses data condensation data collection techniques, data presentation, verification and conclusion Triangulation of sources is used to obtain data validity, as well as the use of Analytical Techniques (SWOT) to find and determine the right strategy in improving the quality of public services with Artificial Intelligence (AI). The results of the first study show that One Dimension of Tangibel (AI) modernizes facilities, speeds up and simplifies services and facilitates real-time performance monitoring by about 20-30%. Second, it confirms that (AI) improves officer morale, ensures the proper and efficient use of tools and speeds up slow administrative processes by about 40%. Third, Responsiveness (Ai) accelerates the recognition of customer needs, automates processes, reduces procedural errors, and speeds up responses to service complaints by about 30-50%. Fourth, Assurance Use (AI) improves service time accuracy, ensures accommodation transparency, increases public trust by 20-30%. Fifth, Empathy, Service Usage (AI) helps determine service priorities, improves friendliness and courtesy, ensures fair service, and gives customer appreciation around an increase of about 20-30%. The results of the second study which is the Main Findings of the SWOT Analysis) that Effective Strategies in Improving the Quality of Public Services in Indonesia based on the results of analysis, weighting, assessment and summation that the Defensive Strategy is considered appropriate, namely by optimizing the Efficiency and Innovation of Services with the Use of More Personal and Responsive (AI), expanding accessibility and improving digital skills through officer training, and maintaining transparency and security.  The belief that the quality of public services using (AI) is very appropriate and an effective strategy is a defensive strategy that is in the position of Quadrant III.Qubahan Academic Journal270The belief that the quality of public services using (AI) is very appropriate and an effective strategy is a defensive strategy that is in the position of Quadrant III is believed.Qubahan Academic Journal["Mustainah M", "Dandan Haryono", "N. Nuraisyah"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/bd34970f4ace9f5c2cbdf6214751bcf29bae0979 2c6cd268e6d42226d59facf9bb27b637f76b3e01International Conference on Genetic Algorithms1002nd International Conference on Green Aviation (ICGA 2024)["Hong Guan", "Hao Liu", "Bo Li", "Jialin Li", "Xinyue Jiang", "Jihan Zhang", "Yangruijie Yu", "Shuyuan Shi"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/2c6cd268e6d42226d59facf9bb27b637f76b3e01 329624ac32684d35c77c757c4f1e21670b9106e1Science Editing00Science Editor["Sumi Sexton", "Chhavi Chauhan", "Jos\u00e9 E Rodr\u00edguez"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/329624ac32684d35c77c757c4f1e21670b9106e1 896dbc959b046933b678eac7775a672de15686e0AI &amp; SOCIETY00AI &amp; SOCIETY["C. Rosas-Jim\u00e9nez"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/896dbc959b046933b678eac7775a672de15686e0 e8bedb736e78868f5fcff8f9a727fdce069e950cInternational Conference on Learning Analytics and Knowledge180{"pages": "950-956"}["Luis P. Prieto", "R. Alfredo", "Henry Benjam\u00edn D\u00edaz-Chavarr\u00eda", "Roberto Mart\u00ednez-Maldonado", "Vanessa Echeverr\u00eda"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/e8bedb736e78868f5fcff8f9a727fdce069e950c 66fa89b7efa9e7792aa7399406750101c5e46b53International Conference on Learning Analytics and Knowledge380{"pages": "667-679"}["Tongguang Li", "Debarshi Nath", "Yixin Cheng", "Yizhou Fan", "Xinyu Li", "Mladen Rakovi\u0107", "Hassan Khosravi", "Z. Swiecki", "Yi-Shan Tsai", "D. Ga\u0161evi\u0107"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/66fa89b7efa9e7792aa7399406750101c5e46b53 b560ed0f1fcaad430a68424d87b08a48f84091a4Frontiers in Ocean Sustainability220Frontiers in Ocean Sustainability["\u00c1ngel Borja"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/b560ed0f1fcaad430a68424d87b08a48f84091a4 743db2a718b6cca10742e404a7b394063b3efdedApplied Economics580Applied Economics["Nanxu Chen", "Jiajing Huang", "Yuling Hu", "Yi Li"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/743db2a718b6cca10742e404a7b394063b3efded 88eb710a625631d3776c165ae85bf2535c7449b9This paper examines the challenges in distributing AI models through model zoos and file transfer mechanisms. Despite advancements in security measures, vulnerabilities persist, necessitating a multi-layered approach to mitigate risks effectively. The physical security of model files is critical, requiring stringent access controls and attack prevention solutions. This paper proposes a novel solution architecture composed of two prevention approaches. The first is Content Disarm and Reconstruction (CDR), which focuses on disarming serialization attacks that enable attackers to run malicious code as soon as the model is loaded. The second is protecting the model architecture and weights from attacks by using Moving Target Defense (MTD), alerting the model structure, and providing verification steps to detect such attacks. The paper focuses on the highly exploitable Pickle and PyTorch file formats. It demonstrates a 100% disarm rate while validated against known AI model repositories and actual malware attacks from the HuggingFace model zoo.680A novel solution architecture composed of two prevention approaches for protecting the model architecture and weights from attacks by using Moving Target Defense, alerting the model structure, and providing verification steps to detect such attacks is proposed.["Daniel Gilkarov", "Ran Dubin"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/88eb710a625631d3776c165ae85bf2535c7449b9 d349c4dcda5af79f576a7a205c94e25702535651Psychology of Aesthetics, Creativity, and the Arts00Psychology of Aesthetics, Creativity, and the Arts["Liangqing Li"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/d349c4dcda5af79f576a7a205c94e25702535651 88f47a4590f047f7c00bdc4a4445ad713ffec781AI is reshaping the future of healthcare supply chain management, increasing operational performance and reducing the level of wastage, to explore efficiency and cost benefits. This paper used quantitative forecast models, which have the potential to support sustainable economic growth of supply chain management. There are many issues and challenges that are associated with healthcare supply chains such as inventory control, demand forecasting, and resources. Management. These problems remain unsolved by traditional supply chain solutions leading to problems such as overstock, stockout, and wastage. AI technologies and applications like machine learning algorithms and predictive analytics offer solutions through their aptitude for forecasting, inventory management, and making the right decisions. Econometric techniques such as time series and econometric modeling quantitative techniques are used in assessing the economic implications of AI in healthcare supply chain innovation. These techniques involve studying past trends to forecast future behavior and these are beneficial in organizational demand changes, resource utilization, and general waste can be better estimated. Integrating AI with quantitative forecasting enables healthcare organizations to strengthen their operational resilience, adjust to changing market conditions, and realize cost savings. The study emphasizes several key advantages of AI adoption, including enhanced accuracy in demand forecasting, lower operational costs, and improved resource utilization efficiency. Furthermore, AI-powered tools assist organizations in managing uncertainties and responding proactively to disruptions, fostering overall economic stability and growth. Through utilizing quantitative forecasting methods, healthcare organizations can optimize their supply chain operations, promote sustainable economic growth, and improve service delivery. KEYWORDS: Artificial Intelligence, Healthcare Supply Chains, Operational Resilience, Waste Reduction, Predictive Analytics, Demand Forecasting, Resource Optimization, Machine Learning, Sustainability.EPRA International Journal of Economics, Business and Management Studies00This paper used quantitative forecast models, which have the potential to support sustainable economic growth of supply chain management, and emphasizes several key advantages of AI adoption, including enhanced accuracy in demand forecasting, lower operational costs, and improved resource utilization efficiency.EPRA International Journal of Economics, Business and Management Studies["Jehoiarib Umoren", "Tessy Oghenerobovwe Agbadamasi", "Tobias Kwame Adukpo", "Nicholas Mensah"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/88f47a4590f047f7c00bdc4a4445ad713ffec781 0b51ad77436e4801d1496c5cd68f8eb79fa0f286Interactive Learning Environments550Interactive Learning Environments["Marine Cloux", "D. Monticolo", "Rapha\u00ebl Bary"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/0b51ad77436e4801d1496c5cd68f8eb79fa0f286 b1f5cda5f0f9e7a14e64b5a87c19e9b4520c523fChinese Medical Journal170Chinese medical journal["Qi Dou", "Krystel Nyangoh-Timoh", "Pierre Jannin", "Yang Shen"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/b1f5cda5f0f9e7a14e64b5a87c19e9b4520c523f cd5489e6cc2a47d6f952f9bd239e5c4aa3b290dbJAMA Network Open70JAMA network open["Marianne Sharko", "Curtis L Cole"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/cd5489e6cc2a47d6f952f9bd239e5c4aa3b290db 6d3ece18e5693810cd973d4476fc20c2f34cd9b1AI &amp; SOCIETY10AI &amp; SOCIETY["D. Hurst", "Christopher A. Bobier"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/6d3ece18e5693810cd973d4476fc20c2f34cd9b1 b82581b94511f96167ac70a6bda015081f065e84Cancer Investigation70Cancer investigation["G. Lyman", "Nicole Kuderer"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/b82581b94511f96167ac70a6bda015081f065e84 2a7e769653ce97e0adc10707abd21027a225093e00["Eaf editors"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/2a7e769653ce97e0adc10707abd21027a225093e 3887b4ca1ab4b6c53d3636f8f0dec9d44ea687cfThis paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method. We reveal several key insights. First, the overall transmission connectedness index (TCI) closely aligns with the contemporaneous TCI, while the lagged TCI is significantly lower. Second, AI ETFs and clean energy act as risk transmitters, whereas AI tokens and green bond function as risk receivers. Third, AI tokens are difficult to hedge and provide limited hedging ability compared to AI ETFs and green assets. However, multivariate portfolios effectively reduce AI tokens investment risk. Among them, the minimum correlation portfolio outperforms the minimum variance and minimum connectedness portfolios.290This paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method and reveals that the minimum correlation portfolio outperforms the minimum variance and minimum connectedness portfolios.["Ying-Hui Shao", "Yan-Hong Yang", "Wei-Xing Zhou"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/3887b4ca1ab4b6c53d3636f8f0dec9d44ea687cf 4a89fa6aa9a89a9f434aa61115d3c5461a3d1801Journal of Technology in Behavioral Science570Journal of Technology in Behavioral Science["Andrew M. Sherrill", "Christopher W. Wiese", "Saeed Abdullah", "Rosa I. Arriaga"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/4a89fa6aa9a89a9f434aa61115d3c5461a3d1801 479221b00b882a93c9fefbcaafb1fd5e66fdb2a7Advances in generative artificial intelligence (AI) have driven a growing effort to create digital duplicates. These semi-autonomous recreations of living and dead people can be used for many purposes. Some of these purposes include tutoring, coping with grief, and attending business meetings. However, the normative implications of digital duplicates remain obscure, particularly considering the possibility of them being applied to genocide memory and education. To address this gap, we examine normative possibilities and risks associated with the use of more advanced forms of generative AI-enhanced duplicates for transmitting Holocaust survivor testimonies. We first review the historical and contemporary uses of survivor testimonies. Then, we scrutinize the possible benefits of using digital duplicates in this context and apply the Minimally Viable Permissibility Principle (MVPP). The MVPP is an analytical framework for evaluating the risks of digital duplicates. It includes five core components: the need for authentic presence, consent, positive value, transparency, and harm-risk mitigation. Using MVPP, we identify potential harms digital duplicates might pose to different actors, including survivors, users, and developers. We also propose technical and socio-technical mitigation strategies to address these harms.10This work examines normative possibilities and risks associated with the use of more advanced forms of generative AI-enhanced duplicates for transmitting Holocaust survivor testimonies and applies the Minimally Viable Permissibility Principle (MVPP).["Atay Kozlovski", "M. Makhortykh"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/479221b00b882a93c9fefbcaafb1fd5e66fdb2a7 cdb34c0092a767848ca1de6fa7e3a6b822585fa4Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research. Resources have been made publicly available at https://github.com/zkzhou126/AI-for-Research.2520A systematic review of the progress in hypothesis formulation, hypothesis validation, and manuscript publication and offers a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process.["Zekun Zhou", "Xiaocheng Feng", "Lei Huang", "Xiachong Feng", "Ziyun Song", "Ruihan Chen", "Liang Zhao", "Weitao Ma", "Yuxuan Gu", "Baoxin Wang", "Dayong Wu", "Guoping Hu", "Ting Liu", "Bing Qin"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/cdb34c0092a767848ca1de6fa7e3a6b822585fa4 58acb29f031e0cbf4d996736f2b7ed4079a25864The ongoing evolution of cloud computing requires sustained attention to security, privacy, and compliance issues. The purpose of this paper is to systematically review the current literature regarding the application of federated learning (FL) and artificial intelligence (AI) to improve cloud computing security while preserving privacy, delivering real-time threat detection, and meeting regulatory requirements. The current research follows a systematic literature review (SLR) approach, which examined 30 studies published between 2020 and 2024 and followed the PRISMA 2020 checklist. The analysis shows that FL provides significant privacy risk reduction by 25%, especially in healthcare and similar domains, and it improves threat detection by 40% in critical infrastructure areas. A total of 80% of reviewed implementations showed improved privacy, but challenges like communication overhead and resource limitations persist, with 50% of studies reporting latency issues. To overcome these obstacles, this study also explores some emerging solutions, which include model compression, hybrid federated architectures, and cryptographic enhancements. Additionally, this paper demonstrates the unexploited capability of FL for real-time decision-making in dynamic edge environments and highlights its potential across autonomous systems, Industrial Internet of Things (IIoT), and cybersecurity frameworks. The paper’s proposed insights present a deployment strategy for FL models which enables scalable, secure, and privacy-preserving operations and will enable robust cloud security solutions in the AI era.Electronics1240The paper’s proposed insights present a deployment strategy for FL models which enables scalable, secure, and privacy-preserving operations and will enable robust cloud security solutions in the AI era.Electronics["Latifa Albshaier", "Seetah Almarri", "Abdullah Albuali"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/58acb29f031e0cbf4d996736f2b7ed4079a25864 858bd48df279a50762e164cbace3f522a849ffa8In this study, eXplainable Artificial Intelligence (XAI) methods are applied to analyze flow fields obtained through PIV measurements of an axisymmetric turbulent jet. A convolutional neural network (U-Net) was trained to predict velocity fields at subsequent time steps. Three XAI methods: SHapley Additive explanations (SHAP), Gradient-SHAP, and Grad-CAM were employed to identify the flow field regions relevant for prediction. SHAP requires predefined segmentation of the flow field into relevant regions, while Gradient-SHAP and Grad-CAM avoid this bias by generating gradient-based heatmaps. The results show that the most relevant structures do not necessarily coincide with regions of maximum vorticity but rather with those exhibiting moderate vorticity, highlighting the critical role of these regions in energy transfer and jet dynamics. Additionally, structures with high turbulent dissipation values are identified as the most significant. Gradient-SHAP and Grad-CAM methods reveal a uniform spatial distribution of relevant regions, emphasizing the contribution of nearly circular structures to turbulent mixing. This study advances the understanding of turbulent dynamics through XAI tools, providing an innovative approach to correlate machine learning models with physical phenomena.270The results show that the most relevant structures do not necessarily coincide with regions of maximum vorticity but rather with those exhibiting moderate vorticity, highlighting the critical role of these regions in energy transfer and jet dynamics.["E. Amico", "Lorenzo Matteucci", "G. Cafiero"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/858bd48df279a50762e164cbace3f522a849ffa8 51ceb88074cb104c97ff9b5d19ff8f6f615e759aAt least since Francis Bacon, the slogan 'knowledge is power' has been used to capture the relationship between decision-making at a group level and information. We know that being able to shape the informational environment for a group is a way to shape their decisions; it is essentially a way to make decisions for them. This paper focuses on strategies that are intentionally, by design, impactful on the decision-making capacities of groups, effectively shaping their ability to take advantage of information in their environment. Among these, the best known are political rhetoric, propaganda, and misinformation. The phenomenon this paper brings out from these is a relatively new strategy, which we call slopaganda. According to The Guardian, News Corp Australia is currently churning out 3000 'local' generative AI (GAI) stories each week. In the coming years, such 'generative AI slop' will present multiple knowledge-related (epistemic) challenges. We draw on contemporary research in cognitive science and artificial intelligence to diagnose the problem of slopaganda, describe some recent troubling cases, then suggest several interventions that may help to counter slopaganda.800This paper draws on contemporary research in cognitive science and artificial intelligence to diagnose the problem of slopaganda, describe some recent troubling cases, then suggest several interventions that may help to counter slopaganda.["Michal Klincewicz", "Mark Alfano", "A. E. Fard"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/51ceb88074cb104c97ff9b5d19ff8f6f615e759a 50a921978c2dc4928c75818f30eb9184c6bb7bf9With the proliferation of artificial intelligence in education, AI-generated digital educational resources are increasingly being employed as supplements for university teaching and learning. However, this raises concerns about the quality of the content produced. To conduct a comprehensive quality assessment, this paper presents an evaluation index system for AI-generated digital educational resources by combining the Delphi method and the Analytic Hierarchy Process. The initial quality indicators across the dimensions of content, expression, and user and technical aspects are identified through a systematic literature review of the recent research. Then, the Delphi method is utilized to modify the quality indicators according to experts’ opinions through two rounds of questionnaire surveys. Subsequently, the weight coefficients of the quality indicators are calculated using the Analytic Hierarchy Process. Finally, a quality indicator system for evaluating AI-generated digital educational resources is developed, which comprises four dimensions and twenty indicators. The findings reveal that content characteristics are of critical importance in assessing the quality of AI-generated educational resources, followed by expression characteristics as the second most significant factor, with user and technical characteristics also being recognized. Among the second-level indicators, “authenticity”, “accuracy”, “legitimacy”, and “relevance” are accorded greater importance relative to other indicators. The proposed system equips relevant stakeholders with a framework for selecting high-quality AIGDERs and steering AI tools in line with educational standards. Finally, some implications are provided to support the selection of high-quality AI-generated resources and guidance on aligning these resources with educational standards.Systems710The findings reveal that content characteristics are of critical importance in assessing the quality of AI-generated educational resources, followed by expression characteristics as the second most significant factor, with user and technical characteristics also being recognized.Systems["Qian Huang", "Chunlan Lv", "Li Lu", "Shuang Tu"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/50a921978c2dc4928c75818f30eb9184c6bb7bf9 9d5f2261b7383389567640aeecce6d1f26f56401Archives of Sexual Behavior440Findings point to a gradual evolution toward an AI-driven porn landscape where individuals can create and interact with sexual content tailored to their preferences and fantasies.Archives of sexual behavior["V. Lapointe", "Simon Dub\u00e9", "Sophia Rukhlyadyev", "Tinhinane Kessai", "D. Lafortune"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/9d5f2261b7383389567640aeecce6d1f26f56401 160225ec905cae783c4bda9c4de081027fab9747 This study aims to identify and model deterrents to adopt and institutionalize analytics and artificial intelligence in modern human resource (HR) using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) approach. A comprehensive investigation of the literature and feedback from experts led to the identification of 16 deterrents in this study. After that, the ISM tool is used to find connections between the identified deterrents in the HR ecosystem and MICMAC which helps in categorising deterrents on the basis of driving and dependence power and provides deeper insights into their roles and significance. Employee resistance and HR transformation are highly influenced by other factors but exert minimal driving power. Data availability, leadership support, communication and collaboration, legal, ethical and regulatory compliance, and infrastructure and resources exhibit strong influence and dependence, making them highly sensitive and crucial. Training and development, learning culture and change management, and data privacy and security have strong driving power with minimal dependence, indicating their foundational role in shaping HR transformation. This study will assist policymakers and owners/managers in the HR ecosystem in recognising and comprehending the importance and applicability of analytics and AI obstacles while developing HR strategies. This study explicitly focuses on data analytics and AI technology in the current scenario. It also explores the relationship between deterrents and their driving and dependence powers. The International Journal of Organizational Analysis1000International Journal of Organizational Analysis["Geetu Yadav"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/160225ec905cae783c4bda9c4de081027fab9747 e3fcdca161cfeb0b8dae712e3c9687e564c833f1Despite the fact that Artificial Intelligence (AI) in innovation management has been a topic of interest for several decades, little is known throughout the literature about how and why AI capability creates product value. In this work, we proposed a dual process model to explore the effects of AI capability on digital product innovation and tested it using quantitative and qualitative methods. In quantitative analysis, based on AI–Open Innovation matrix and dynamic capability theory, we tested the model using a total of 314 managers from 127 firms in the Chinese mainland. We found that AI capability enables digital product innovation by enhancing online value co-creation (a process of external asset) and digital resilience (a process of internal asset). Moreover, socio-cognitive sensemaking strengthens the mediation process of digital resilience but has no significant moderating effect on the mediation process of online value co-creation. The qualitative analysis enables us to better interpret the reasons why sensemaking plays different roles in mediation processes and suggests that it strengthens the effects of online value co-creation and digital resilience on digital product innovation through the external loop (time effect) and the internal loop (interactive effect), respectively. Our findings provide insights into how firms can scale digital product innovation using AI, with important implications for management.International Journal of Innovation Management1030A dual process model to explore the effects of AI capability on digital product innovation and tested it using quantitative and qualitative methods provides insights into how firms can scale digital product innovation using AI, with important implications for management.International Journal of Innovation Management["Shuwen Li", "Jiaxing Liu", "Xiaotian Yang"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/e3fcdca161cfeb0b8dae712e3c9687e564c833f1 564066526b3492dc8c23a9b09c609f36944b49c6The integration of Artificial Intelligence (AI) into education heralds a transformative era, reshaping pedagogical methodologies to meet the demands of a dynamic, technology-driven world. This paper explores innovative pedagogical practices enabled by AI, emphasizing their potential to personalize learning, foster critical thinking, and enhance educator efficiency. Using an exploratory and conceptual research approach, the study synthesizes insights from peer-reviewed articles, academic publications, and policy documents, analyzed through thematic and comparative evaluations. The findings highlight the role of AI-powered tools, such as intelligent tutoring systems and generative AI, in addressing diverse learner needs, while also identifying challenges like algorithmic bias, data privacy concerns, and educator readiness. To address these challenges and harness the full potential of AI, this paper proposes the AI-TEACH Model—a comprehensive framework for AI integration in education. The AI-TEACH Model emphasizes Transformative Learning, Ethical AI Practices, Adaptive Learning, Collaborative Environments, and Holistic Development, providing a structured approach to leveraging AI for enhancing teaching and learning outcomes. The paper concludes with actionable recommendations for educators, administrators, and policymakers to ensure that AI integration aligns with the goals of inclusivity, equity, and innovation in education.Journal of Informatics Education and Research280The AI-TEACH Model is proposed, providing a structured approach to leveraging AI for enhancing teaching and learning outcomes, and providing actionable recommendations for educators, administrators, and policymakers to ensure that AI integration aligns with the goals of inclusivity, equity, and innovation in education.Journal of Informatics Education and Research["Mohammad Talha Siddiqui", "Mohd Vaseem Mansoori", "Mohd Asad Siddiqui", "Ankit Yadav"]2025-03-03T00:00:00https://www.semanticscholar.org/paper/564066526b3492dc8c23a9b09c609f36944b49c6 af71a7bb9436970b05974e898ad82df38c836a11Artificial Intelligence (AI) is changing the face of education, improving the learning experience, reducing administrative processes, and providing the best support to students in their research. In this study, researchers assessed various aspects of  the use of AI in academic research, specifically how AI-powered tools facilitate helping students perform literature reviews, data analysis, and personalized learning suggestions. AI, with advanced technologies like Natural Language Processing (NLP), Machine Learning (ML), and automated reasoning, allows students to access vast amounts of academic information, collect, synthesize, and evaluate it accurately and quickly. Learning independence, critical thinking, and creativity are helped by AI-powered tutoring systems and virtual research assistants. The benefits of AI integration in teaching are limited. However, ethical considerations, data privacy issues, algorithmic biases, and the digital divide are serious challenges that must be tackled. This study evaluates the potential and shortcomings of existing AI applications for student research based on the two cases studied in the literature and the upcoming trends in AI applications for student research. According to the authors, it is essential to adopt AI in education responsibly and equitably: on the one hand, to take advantage of the benefits generated by AI-driven educational tools; on the other hand, to minimize the costs derived from the use of such complex technologies.Journal of Information Systems Engineering & Management140It is essential to adopt AI in education responsibly and equitably to take advantage of the benefits generated by AI-driven educational tools; on the other hand, to minimize the costs derived from the use of such complex technologies.Journal of Information Systems Engineering and Management["Pham Bich Thuy", "Pham Dao Tien"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/af71a7bb9436970b05974e898ad82df38c836a11 ce304f5ed0502e81f25b17b32e55139091986043Artificial Intelligence (AI) has emerged as an innovative tool in performance management and employee development, offering significant benefits for companies. AI's ability to analyze large volumes of performance data provides an objective and continuous view of employee behavior, interactions, and outcomes, generating personalized insights to support human resource management decisions. This eliminates the reliance on subjective and infrequent evaluations, favoring a more accurate and dynamic approach. Moreover, AI facilitates the personalization of employee development by adjusting training plans according to individual needs, accelerating the learning process. As a result, companies are better prepared for the challenges of the labor market. AI is also crucial in eliminating biases in performance evaluations, ensuring that decisions are based on concrete data. However, AI implementation must be carefully planned, with transparency and human oversight, to avoid potential algorithmic failures. Studies such as those by Tong et al. (2021) and Rožman et al. (2022, 2023) demonstrate that when applied correctly, AI can enhance productivity, engagement, and organizational performance. The strategic integration of AI can transform management practices, strengthening the company's competitiveness and fostering a more efficient and fair work environment.International Seven Journal of Multidisciplinary150Artificial Intelligence's ability to analyze large volumes of performance data provides an objective and continuous view of employee behavior, interactions, and outcomes, generating personalized insights to support human resource management decisions.International Seven Journal of Multidisciplinary["Milena Raquel Charbel Dias Bonon"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/ce304f5ed0502e81f25b17b32e55139091986043 59e94e5f151e8e4d90c77aa89c89b8bf868557a2This study examines the feasibility of using artificial intelligence (AI) in hadith research to identify areas within hadith sciences where AI can offer more efficient and reliable outcomes. The significance of the study lies in its exploration of the potential benefits and challenges of integrating AI into hadith scholarship, especially in addressing contemporary issues. It evaluates how AI could help tackle modern challenges in hadith studies and assesses the risks associated with AI-generated fabrications of hadiths, which may present new threats as AI becomes more widespread. The main problem addressed in this research is determining the feasibility of AI in hadith studies by identifying potential opportunities and risks. The primary objective is to provide current hadith scholars with a foundational understanding of AI's potential in this field while also identifying areas for future research. Methodologically, the study begins with a theoretical overview of AI’s core characteristics, particularly its capabilities in textual analysis and pattern recognition. This is followed by practical experiments using ChatGPT-4 to analyze hadith narrations. The study concludes that while AI has considerable potential in hadith research, particularly in preliminary evaluations and classifications, its current role is limited to imitating human intelligence and functioning within existing scholarly frameworks. The misuse of AI could result in the unintentional dissemination of fabricated hadiths, posing a significant threat to hadith scholarship. In the final evaluation, it is recommended that AI be viewed as a tool to support, rather than replace, human scholars in hadith studies.مجلة الشريعة والدراسات الإسلامية00While AI has considerable potential in hadith research, particularly in preliminary evaluations and classifications, its current role is limited to imitating human intelligence and functioning within existing scholarly frameworks.مجلة الشريعة والدراسات الإسلامية["Mehmet Apayd\u0131n"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/59e94e5f151e8e4d90c77aa89c89b8bf868557a2 49dcf7c37a5f452280a33c18278431dfbcc22dd1Introduction: In today's dynamic business environment, identifying and understanding skills, knowledge, and competencies at various levels – from individuals to teams, departments, and the organization – has become critical to organizational success. This is especially important in IT organizations where the rapid pace of technological change requires continuous competency mapping and personalized skill development. The purpose of this research is to explore how artificial intelligence (AI) can revolutionize competency mapping and skill development by automating and personalizing these processes and addressing the challenges posed by traditional methods that struggle to keep up with evolving needs. Objectives: To develop a framework for identifying and assessing AI-related competencies for IT role using AI-powered tools. To investigate the factors influencing the successful implementation of AI-powered training programs for IT professionals. To examine the impact of AI-driven personalized skill development on employee engagement and productivity. To analyze the ethical considerations and challenges of using AI for competency mapping and skill development in IT. To develop a methodology for measuring and demonstrating the ROI of AI- powered skill development initiatives in IT. Methods: The paper examines the role of artificial intelligence tools such as machine learning and data analytics in helping IT organizations assess current competencies, identify skills gaps, and provide employees with customized development paths. This approach includes analyzing how AI can be used to evaluate employee skills for team building and project planning, identifying key competencies, and bridging skills gaps through targeted training. Research focuses on how AI can help leaders (executives, department heads, project managers, and team leaders) identify critical expertise, facilitate team-building decisions, and ensure employees understand the skills needed for personal career growth. Findings- The survey results revealed a nuanced perspective on AI-driven competency mapping and skill development among IT professionals. While a significant portion expressed a strong belief in the potential of AI to enhance skills and career growth, the actual impact perceived by respondents was limited. Concerns were raised regarding the accuracy of AI assessments and the alignment of AI-driven learning recommendations with individual needs. Additionally, respondents expressed a need for greater transparency and trust in the use of AI for employee development. Despite these concerns, a significant minority strongly believe that AI has positively influenced their skills and career progression. These findings suggest that while AI holds significant promise, further refinement and improvement in the implementation and utilization of AI-powered solutions are crucial to maximize their impact on employee development and build an AI-ready workforce. Results: The survey results revealed a mixed response to AI-driven competency mapping and skill development among IT professionals. While a significant portion of respondents expressed strong agreement on the potential benefits of AI in enhancing skills and career growth, a considerable number remained neutral. Notably, the "Agree" category consistently showed lower responses, suggesting that respondents were not fully convinced about the current effectiveness of AI-powered solutions. Despite this, a significant minority strongly agreed that AI tools have positively impacted their skills and career progression. These findings highlight the need for further development and refinement of AI-powered solutions to better address the needs and expectations of IT professionals. Conclusions: The conclusions highlight the need for a strategic and human-centered approach to AI-driven employee development. By addressing the identified challenges and leveraging the potential of AI in a responsible and ethical manner, organizations can create a more agile, skilled, and competitive workforce in the age of artificial intelligence.Journal of Information Systems Engineering & Management170The findings suggest that while AI holds significant promise, further refinement and improvement in the implementation and utilization of AI-powered solutions are crucial to maximize their impact on employee development and build an AI-ready workforce.Journal of Information Systems Engineering and Management["Dr Saily Talodhikar", "Safia Farooqui"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/49dcf7c37a5f452280a33c18278431dfbcc22dd1 cdf2860a3cda34d317b648bfcfa6faba2fca9b02The injection molding process is a traditional technique for making products in various industries such as electronics and automobiles via solidifying liquid resin into certain molds. Although the process is not related to creating the main part of engines or semiconductors, this manufacturing methodology sets the final form of the products. Re-cently, research has continued to reduce the defect rate of the injection molding process. This study proposes an optimal injection molding process control system to reduce the defect rate of injection molding products with XAI (eXplainable Artificial Intelligence) ap-proaches. Boosting algorithms (XGBoost and LightGBM) are used as tree-based classifiers for predicting whether each product is normal or defective. The main features to control the process for improving the product are extracted by SHapley Additive exPlanations, while the individual conditional expectation analyzes the optimal control range of these extracted features. To validate the methodology presented in this work, the actual injection molding AI manufacturing dataset provided by KAMP (Korea AI Manufacturing Platform) is employed for the case study. The results reveal that the defect rate decreases from 1.00% (Original defect rate) to 0.21% with XGBoost and 0.13% with LightGBM, respectively.220This study proposes an optimal injection molding process control system to reduce the defect rate of injection molding products with XAI (eXplainable Artificial Intelligence) approaches and results reveal that the defect rate decreases from 1.00% (Original defect rate) to 0.21% with XGBoost and 0.13% with LightGBM, respectively.["Jisoo Hong", "Yongmin Hong", "Jung-Woo Baek", "Sung-Woo Kang"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/cdf2860a3cda34d317b648bfcfa6faba2fca9b02 3cdad07941f1cd3f1f688f85f63ac35bf799af19Reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions to reactor physics, e.g., in operational simulations, safety design, real-time monitoring, core management and maintenance. This paper presents a comprehensive review of AI approaches in reactor physics, especially considering the category of Machine Learning (ML), with the aim of describing the application scenarios, frontier topics, unsolved challenges and future research directions. From equation solving and state parameter prediction to nuclear industry applications, this paper provides a step-by-step overview of ML methods applied to steady-state, transient and combustion problems. Most literature works achieve industry-demanded models by enhancing the efficiency of deterministic methods or correcting uncertainty methods, which leads to successful applications. However, research on ML methods in reactor physics is somewhat fragmented, and the ability to generalize models needs to be strengthened. Progress is still possible, especially in addressing theoretical challenges and enhancing industrial applications such as building surrogate models and digital twins.1430A comprehensive review of AI approaches in reactor physics, especially considering the category of Machine Learning (ML), is presented, with the aim of describing the application scenarios, frontier topics, unsolved challenges and future research directions.["Ruizhi Zhang", "Shengfeng Zhu", "Kan Wang", "Ding She", "J. Argaud", "Bertrand Bouriquet", "Qing Li", "Helin Gong"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/3cdad07941f1cd3f1f688f85f63ac35bf799af19 16dd162bcf237e86482082edd298fcd1a19ba31aHepatocellular carcinoma (HCC) is the most prevalent primary liver cancer and a leading cause of cancer-related mortality globally. The heterogeneity of HCC complicates prognostic, management, and predictive strategies across different patient populations. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer transformative opportunities to improve HCC management. This review consolidates findings from various studies regarding integrating AI in detecting, diagnosing, and treating HCC, leveraging diverse data sources such as radiological imaging, genomics, and clinical records. AI-based approaches have shown potential to improve the accuracy and efficiency of HCC screening, early detection, tumor characterization, and treatment response evaluation, surpassing traditional methods. However, the deployment of AI technologies is hindered by challenges, including data standardization, validation across multiple centers, and ethical considerations regarding AI applications. This review emphasizes the need to establish comprehensive multimodal datasets and collaborative research efforts to validate AI applications in HCC management. By addressing these challenges, the integration of AI technology has the potential to revolutionize HCC care, ultimately leading to improved patient outcomes and a more personalized approach to treatment strategies.Hepatoma Research1170This review consolidates findings from various studies regarding integrating AI in detecting, diagnosing, and treating HCC, leveraging diverse data sources such as radiological imaging, genomics, and clinical records to establish comprehensive multimodal datasets and collaborative research efforts to validate AI applications in HCC management.Hepatoma Research["Walaa Abdelhamed", "Moahmed El-Kassas"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/16dd162bcf237e86482082edd298fcd1a19ba31a 70275a510df812d7a6a57c77fd79526ae7eb2c91 The widespread use of artificial intelligence (AI) technology in the hospitality industry has triggered concerns among frontline service employees about their future careers, namely, AI awareness. This study aims to explore whether AI awareness influences frontline service employees’ silence through psychological contract breach and whether this process is contingent on frontline service employees’ moral identity, drawing on social exchange theory and moral identity theory. The data were collected from 355 frontline service employees in Chinese hotels using a two-wave survey. SPSS macro PROCESS Model 58 was used to test the proposed hypotheses. AI awareness increases frontline service employees’ silence by prompting psychological contract breach. This process is moderated by frontline service employees’ moral identity. Specifically, moral identity mitigates the effect of psychological contract breach on silence. Organizations and managers should pay attention to the impact of AI on frontline service employees and take measures to help them better adapt to the rapidly changing work environment. In particular, it helps reduce frontline service employees’ silence by fostering positive attitudes toward AI, maintaining their psychological contracts and developing their moral identities. This study enriches the research on the outcomes of AI awareness by directing our attention to frontline service employees’ silence. Moreover, this study not only explores the responses to AI awareness that frontline service employees make as “economic persons” but also examine whether they, as “moral persons,” regulate their responses contingent on their moral identity under the impact of AI. Exploring frontline service employees’ dual identities helps bring this research closer to the realities of managerial practice, thereby contributing to a better understanding and management of their complex responses to AI shocks. International Journal of Contemporary Hospitality Management450International Journal of Contemporary Hospitality Management["Mengting Cheng", "Long Zhang", "Haiqing Wang"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/70275a510df812d7a6a57c77fd79526ae7eb2c91 7af8c12390ad2c97b523405698450a09686f52e9Critical illness medicine faces challenges such as high data complexity, large individual differences, and rapid changes in conditions. Artificial Intelligence (AI) technology, especially machine learning and deep learning, offers new possibilities for addressing these issues. By analyzing large amounts of patient data, AI can help identify diseases earlier, predict disease progression, and support clinical decision-making.In this study, scientific literature databases such as Web of Science were searched, and bibliometric methods along with visualization tools R-bibliometrix, VOSviewer 1.6.19, and CiteSpace 6.2.R4 were used to perform a visual analysis of the retrieved data.This study analyzed 900 articles from 6,653 authors in 82 countries between 2005 and 2024. The United States is a major contributor in this field, with Harvard University having the highest betweenness centrality. Noseworthy PA is a core author in this field, and Frontiers in Cardiovascular Medicine and Diagnostics lead other journals in terms of the number of publications. Artificial Intelligence has tremendous potential in the identification and management of heart failure and sepsis.The application of AI in critical illness holds great potential, particularly in enhancing diagnostic accuracy, personalized treatment, and clinical decision support. However, to achieve widespread application of AI technology in clinical practice, challenges such as data privacy, model interpretability, and ethical issues need to be addressed. Future research should focus on the transparency, interpretability, and clinical validation of AI models to ensure their effectiveness and safety in critical illness.Frontiers in Medicine520Future research should focus on the transparency, interpretability, and clinical validation of AI models to ensure their effectiveness and safety in critical illness, and challenges such as data privacy, model interpretability, and ethical issues need to be addressed.Frontiers in Medicine["Zixin Luo", "Jialian Lv", "Kang Zou"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/7af8c12390ad2c97b523405698450a09686f52e9 de0359053e75f83b23d80e7bf27a311de7784c71 We evaluated the accuracy of an artificial intelligence program (ChatGPT 4.0) as a medical translation modality in a simulated pediatric urgent care setting. Two entirely separate instances of ChatGPT 4.0 were used. The first served as a simulated patient (SP). The SP generated complaints and symptoms while processing and generating text only in Spanish. A human provider (blinded to diagnosis) conducted a clinical “visit” with the SP. The provider typed questions and instructions in English only. A second instance of ChatGPT 4.0 was the artificial medical interpreter (AMI). The AMI translated the provider’s questions/instructions from English to Spanish and the SP’s responses/concerns from Spanish to English in real time. Post-visit transcripts were then reviewed for errors by a human-certified medical interpreter. We conducted 10 simulated visits with 3597 words translated by the AMI (1331 English and 2266 Spanish). There were 23 errors (raw accuracy rate of 99.4%). Errors were categorized as: 9 omissions, 2 additions, 11 substitutions, and 1 editorialization. Three errors were judged to have potential clinical consequences, although these were minor ambiguities, readily resolved by the provider during the visit. Also, the AMI made repeated errors of gender (masculine/feminine) and second person formality (“usted”/“tu”). None of these were judged to have potential clinical consequences. The AMI accurately and safely translated the written content of simulated urgent care visits. It may serve as the basis for an expedient, cost-effective medical interpreter modality. Further work should seek to couple this translation accuracy with speech recognition and generative technology in trials with actual patients. Pediatric emergency care70The accuracy of an artificial intelligence program (ChatGPT 4.0) as a medical translation modality in a simulated pediatric urgent care setting may serve as the basis for an expedient, cost-effective medical interpreter modality.Pediatric Emergency Care["Nicholas Hampers", "Rita Thieme", "Louis Hampers"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/de0359053e75f83b23d80e7bf27a311de7784c71 a1b5e4b4a430058e33498e4d842734344d1fb7f9BMC Medical Informatics Decis. Mak.410A Systematic Literature Review following the PRISMA protocol is conducted, synthesizing findings from 30 selected studies to examine XAI’s evolving role in disease prediction, and highlights key gaps, including limited dataset diversity, model complexity, and reliance on single data types.BMC Medical Informatics and Decision Making["Razan Alkhanbouli", "Hour Matar Abdulla Almadhaani", "Farah Alhosani", "M. Simsekler"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/a1b5e4b4a430058e33498e4d842734344d1fb7f9 3eab6ed4c6d8b91aefed5fe8b01cdcc4228e637cIn recent times, the number of scholarly works contributed in the fields of Artificial Intelligence (AI), banking, and finance has increased significantly. This study aims to presents a qualitative approach to review, assess, and identify the remarkable developments of AI applications in banks based on the Scopus indexed  database by deploying Vosviewer 1.6.19v and Biblioshiny software to analyze factors like Co-authorship, Co-occurrence, and Citations. The number of documents retrieved from the Scopus indexed database is 368 from 2013 to 2023. The result shows that China (91 documents), India (86 documents), the US (59 documents), Turkey (32 documents), and Russia (31 documents) are the five most active countries in terms of publications. The top active institutions are Bucharest University of Economic Studies, University Bourgogne Franche Comté (France), and Gachon University (South Korea). At the same time, China emerged as a significant funding nation for AI-based research. Several significant study gaps are found by reviewing the prior literature and offering suggestions for additional research. This review develops and categorizes previous research sub-themes, identifies research themes showing how AI is used in banking and uses thematic findings to suggest an AI banking service framework that closes the knowledge gap between academic research and industry practice. These results guide future research and the formulation of strategic decisions about applying and optimizing value from AI technologies in the banking industry for academics, marketers, and decision-makers.Journal of Information Systems Engineering & Management350A qualitative approach to review, assess, and identify the remarkable developments of AI applications in banks based on the Scopus indexed  database by deploying Vosviewer 1.6.19v and Biblioshiny software to analyze factors like Co-authorship, Co-occurrence, and Citations is presented.Journal of Information Systems Engineering and Management["Mohd Arif Hussain", "Dr. Sudha Vemaraju", "Dr. Sarvani Kochelakota"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/3eab6ed4c6d8b91aefed5fe8b01cdcc4228e637c 7e17338753cc3cd9a9696210b237f59fbfda7036AIMS The aim of this study was to explore healthcare leaders' perceptions of the contribution of artificial intelligence (AI) to person-centred care (PCC). METHODS The study had an explorative qualitative approach. Individual interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders in a county council in Sweden. An abductive qualitative content analysis was conducted based on McCormack and McCance's framework of PCC. The four constructs (i.e. prerequisites, care environment, person-centred processes and expected outcomes) constituted the four categories for the deductive analysis. The inductive analysis generated 11 subcategories to the four constructs, representing how AI could contribute to PCC. RESULTS Healthcare leaders perceived that AI applications could contribute to the four PCC constructs through (a) supporting professional competence and establishing trust among healthcare professionals and patients (prerequisites); (b) including AI's ability to facilitate patient safety, enable proactive care, provide treatment recommendations and prioritise healthcare resources (the care environment); (c) including AI's ability to tailor information and promote the process of shared decision making and self-management (person-centred processes); and (d) including improving care quality and promoting health outcomes (expected outcomes). CONCLUSIONS The healthcare leaders perceived that AI applications could contribute to PCC at different levels of healthcare, thereby enhancing the quality of care and patients' health.Scandinavian Journal of Public Health360Artificial intelligence applications could contribute to PCC at different levels of healthcare, thereby enhancing the quality of care and patients' health, according to healthcare leaders' perceptions.Scandinavian journal of public health["Ingrid Larsson", "P. Svedberg", "J. Nygren", "Lena Petersson"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/7e17338753cc3cd9a9696210b237f59fbfda7036 5c49d5c630faae0622f96cde8ea57db47aabdcfePurpose This study aims to investigate the factors affecting artificial intelligence (AI) implementation in the accounting industry and compares it among the private and public accounting sectors. Design/methodology/approach This study uses a theoretical framework that combines the technology–organization–environment model, the innovation diffusion theory model and the technology acceptance model. A convenience sampling method was used to obtain 561 surveys from accounting, finance management, auditing and bookkeeping professionals in public and private organizations in Kuwait. The data were analyzed using the partial least squares structural equation modeling. Findings This study demonstrates that all individual and organizational variables significantly affect AI implementation in the accounting industry, as supported by adequate values of path coefficient and a p-value of <0.05, except competitive pressure, which did not reach statistical significance. Multi-group analysis indicates statistical differences between the private and public sectors regarding organizational culture, regulatory support and perceived ease of use in AI implementation. Originality/value To the best of the authors’ knowledge, this study is the first to compare AI implementation in the private and public accounting sectors. Its findings could redesign the authors’ understanding of AI implementation in the accounting industry. Journal of Financial Reporting & Accounting660This study demonstrates that all individual and organizational variables significantly affect AI implementation in the accounting industry, as supported by adequate values of path coefficient and a p-value of <0.05, except competitive pressure, which did not reach statistical significance.Journal of Financial Reporting and Accounting["Wael Abdallah", "Arezou Harraf", "Hasan Al Wael"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/5c49d5c630faae0622f96cde8ea57db47aabdcfe 12f6f6b04fbc0f8fb17b64d00d2f7fd6c975f991PurposeThis study aimed to develop a moderated mediation model to explain the relationship between artificial intelligence (AI) awareness and counterproductive work behavior, turnover intention. In this model, the authors assumed that interpersonal conflict mediates and that perceived organizational support and competitive psychological climate moderates the relationship between AI awareness and counterproductive work behavior, turnover intention.Design/methodology/approachAn empirical study based on a sample of 1,129 Vietnamese employees at some enterprises of 6 fields with the highest level of AI application. Structural equation modelling analysis was used for hypothesis testing.FindingsAnalysis of the data demonstrates that AI awareness has a relationship with counterproductive behavior, interpersonal conflict and turnover intention. At the same time, the research results also confirm that interpersonal conflict affects counterproductive behavior and turnover intention. Moreover, interpersonal conflict mediates the effect of AI awareness on counterproductive behavior and turnover intention, and the moderating roles of perceived organizational support and competitive psychological climate has been confirmed.Research limitations/implicationsSample data was only collected at a few Vietnamese enterprises in 6 fields with the highest level of application which are e-commerce, transportation and logistics, education, real estate, finance and agriculture, which may be limiting generalizability of research results. Future studies could include data from enterprises in different sectors or focus on a specific sector.Practical implicationsThe authors offer several significant implications to reduce counterproductive work behavior and turnover intention in enterprises, such as by paying attention that the penetration and spread of AI or other smart technologies is inevitable in the future, ensuring make sure support from organization is available for the employees and creating a working environment of integrity and honesty in all situations based on trust, respect and fairness.Originality/valueThe study developed and verified a moderated mediated model on the relationship between AI awareness and counterproductive work behavior, turnover intention. The authors confirmed the mediating role of interpersonal conflict and the moderating role of perceived organizational support and competitive psychological climate in the relationship among AI awareness and counterproductive work behavior, turnover intention.Journal of Organizational Change Management730A moderated mediation model developed and verified the mediating role of interpersonal conflict and the moderating role of perceived organizational support and competitive psychological climate in the relationship among AI awareness and counterproductive work behavior, turnover intention.Journal of Organizational Change Management["X. Doan", "Thi Phuong Linh Nguyen"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/12f6f6b04fbc0f8fb17b64d00d2f7fd6c975f991 22701953acd79be723f513b134c646a31cb80a53This study aims to investigate the mechanisms through which artificial intelligence (AI) contributes to the reduction of productivity disparities among enterprises, specifically through regional technology spillover effects. We constructed a regression model based on the relationship between AI integration and productivity convergence of the listed firms in China from 2001 to 2021. The empirical results, derived from a β-convergence model, reveal a pronounced trend of both absolute and conditional convergence in productivity, signifying that lower-efficiency firms are progressively aligning with their higher-efficiency counterparts. The findings underscore that AI serves as a pivotal driver of productivity enhancement, facilitating not only the catch-up potential of lower-efficiency enterprises but also the speed of productivity convergence across the sector. Our analysis indicates that the deployment of AI significantly elevates production efficiency and fosters overall regional R&D output, thereby creating conducive conditions for mitigating the productivity gap between enterprises. Additionally, the elevation of regional R&D levels further amplifies the growth trajectory of lower productivity firms. The research conclusions of this paper demonstrate the positive significance of applying artificial intelligence in promoting the development of small and medium-sized enterprises.Edelweiss Applied Science and Technology490The analysis indicates that the deployment of AI significantly elevates production efficiency and fosters overall regional R&D output, thereby creating conducive conditions for mitigating the productivity gap between enterprises, and underscores that AI serves as a pivotal driver of productivity enhancement.Edelweiss Applied Science and Technology["Jiamin Yu", "Sen Yan", "Chuyi Shen"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/22701953acd79be723f513b134c646a31cb80a53 cd7f8352e35825dd1688a4249054f34bc8a50f50This paper aims at analyzing the possibilities of the Artificial Intelligence application in the legal domain and identifying the potential risks it brings. Thus, although the paper describes the possibilities of using AI to improve productivity and optimize work, it is crucial to stress that the legal system should not forget about the ethical values that form its base. Some of the issues of interest include, the ability of algorithms to have bias in decision making, the changing legal environment with regards to liability and confidentiality and the overall impact of AI in the field of law. This paper calls for the participation of lawyers, IT specialists, and regulators to design principles that can help in the proper implementation of AI in the legal profession.The Critical Review of Social Sciences Studies00This paper calls for the participation of lawyers, IT specialists, and regulators to design principles that can help in the proper implementation of AI in the legal profession.The Critical Review of Social Sciences Studies["Faisal Awais", "Dr. Aatir Rizvi", "Dr. Kashif Javed"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/cd7f8352e35825dd1688a4249054f34bc8a50f50 2a999ec149ed7d07c1d65297d89495c07a93b906BACKGROUND Artificial intelligence holds the potential to transform perioperative medicine by leveraging complex datasets to predict risks and optimise patient management in response to rising surgical volumes and patient complexity. AIM This bibliometric analysis aims to analyse trends, contributions, collaborations and research hotspots in artificial intelligence and perioperative medicine. METHODS A Scopus search on 11 October 2024 identified articles on artificial intelligence in perioperative medicine. Relevant peer-reviewed studies were screened by two reviewers, with a third resolving discrepancies. Data were analysed using VOSviewer, Biblioshiny and Microsoft Excel. RESULTS A total of 240 articles were included; 84% of articles were published after 2018, indicating rapid recent growth. The United States, China and Italy led contributions. Single-country publications comprised 76.6% of the dataset, reflecting limited international collaboration. Key research areas included perioperative risk prediction, intraoperative monitoring, blood management and echocardiography. CONCLUSION Artificial intelligence in perioperative medicine is rapidly advancing but requires increased international collaboration to fully realise its potential.Journal of Perioperative Practice220Artificial intelligence in perioperative medicine is rapidly advancing but requires increased international collaboration to fully realise its potential, according to a bibliometric analysis of trends, contributions, collaborations and research hotspots.Journal of perioperative practice["Luke Kar Man Chan", "Brooke Perrin Mao", "Rebecca Zhu"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/2a999ec149ed7d07c1d65297d89495c07a93b906 a5e366ef0ca430974b9c7ddbf6a9c9e07ba23946Study Design An experimental study. Purpose To explore the concordance of ChatGPT responses with established national guidelines for the management of cervical spine and spinal cord injuries. Overview of Literature ChatGPT-4.0 is an artificial intelligence model that can synthesize large volumes of data and may provide surgeons with recommendations for the management of spinal cord injuries. However, no available literature has quantified ChatGPT's capacity to provide accurate recommendations for the management of cervical spine and spinal cord injuries. Methods Referencing the "Management of acute cervical spine and spinal cord injuries" guidelines published by the Congress of Neurological Surgeons (CNS), a total of 36 questions were formulated. Questions were stratified into therapeutic, diagnostic, or clinical assessment categories as seen in the guidelines. Questions were secondarily grouped according to whether the corresponding recommendation contained level I evidence (highest quality) versus only level II/III evidence (moderate and low quality). ChatGPT-4.0 was prompted with each question, and its responses were assessed by two independent reviewers as "concordant" or "nonconcordant" with the CNS clinical guidelines. "Nonconcordant" responses were rationalized into "insufficient" and "contradictory" categories. Results In this study, 22/36 (61.1%) of ChatGPT's responses were concordant with the CNS guidelines. ChatGPT's responses aligned with 17/24 (70.8%) therapeutic questions and 4/7 (57.1%) diagnostic questions. ChatGPT's response aligned with only one of the five clinical assessment questions. Notably, the recommendations supported by level I evidence were the least likely to be replicated by ChatGPT. ChatGPT's responses agreed with 80.8% of the recommendations supported exclusively by level II/III evidence. Conclusions ChatGPT-4 was moderately accurate when generating recommendations that aligned with the clinical guidelines. The model frequently aligned with low evidence and therapeutic recommendations but exhibited inferior performance on topics that contained high-quality evidence or pertained to diagnostic and clinical assessment strategies. Medical practitioners should monitor its usage until further models can be rigorously trained on medical data.Asian Spine Journal450ChatGPT-4 was moderately accurate when generating recommendations that aligned with the clinical guidelines but exhibited inferior performance on topics that contained high-quality evidence or pertained to diagnostic and clinical assessment strategies.Asian spine journal["Michael P. Saturno", "Mateo Restrepo Mejia", "Wasil Ahmed", "Alexander Yu", "Akiro H. Duey", "Bashar Zaidat", "Fady Hijji", "Jonathan Markowitz", "Jun Kim", "Samuel K. Cho"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/a5e366ef0ca430974b9c7ddbf6a9c9e07ba23946 b8905479e343f6cd95ebf3727df6cb183d6a2ad8The advent of text-generating artificial intelligence (AI) started a new era in education, offering transformative possibilities for both learners and educators. This article explores the potential of AI in education, its constructive applications in classrooms, and the necessary changes that must occur in higher education institutions and schools to integrate AI into learning processes. The paper explores potential benefits, challenges, and the importance of teacher-student collaboration in an AI-enhanced educational landscape. To address this, the paper discusses a multi-method comparative study focusing on students’ and pupils’ attitudes and preferences toward text-generating AI in classrooms and lecture halls.1 The study was implemented in two university courses and high school classes. A particular interest lies in data showing similarities and differences between pupils’ and students’ experiences with and attitudes towards textgenerating AI. The study uses semi-qualitative and quantitative interviews through written feedback forms. It analyzes the experiences and attitudes closely and in detail, thus investigating how pupils and students use AI in educational contexts and how they reflect their experiences. The paper also discusses how the results can be constructively implemented to improve future options for integrating AI tools in the higher education and school sectors. 1 In the following, the term “pupils” always refers to pupils in grades 9, 10 and 11. The term“students” always refers to students at a university.International Journal of Advanced Corporate Learning (iJAC)00International Journal of Advanced Corporate Learning (iJAC)["Christoph Knoblauch", "Rasmus Joost"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/b8905479e343f6cd95ebf3727df6cb183d6a2ad8 831a4cbe0018a717df8d4a30a19e1d8d4c74219eRecently, Artificial Intelligence has been used widely in healthcare field. The concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare demands. AI forcing paradigm shift to digital transformation of the healthcare system, this shift is driven by increasing accessibility of healthcare data and rapid progress of analytic techniques. The purpose of this study is to discuss the impact of AI applications in healthcare system based on narrative literature review. The impact of AI in healthcare system has been categorized into the following aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) health services management, (iv) predictive medicine, (v) clinical decision-making, and (vi) patient data and diagnostics. The long term impact of AI on healthcare system is demonstrated in reducing the administrative workload of healthcare professionals (HCPs) by speeding-up decision- making process, reducing medication errors, early detection and prediction of diseases and their prognosis, enhancing patient engagement and compliance with the treatment plan, in addition to discover new drugs and vaccines. The use of AI applications is crucial for patient safety and accountability. Effective use is a prerequisite to concisely address ethical, regulatory, and trust issues while advancing the acceptance and implementation of AI. Although AI has a numerous application on healthcare system, it has some reservations, such as data privacy, system compatibility, and user acceptability. Further research is needed to focus more on discussing these issues.Journal of Neonatal Surgery00The long term impact of AI on healthcare system is demonstrated in reducing the administrative workload of healthcare professionals (HCPs) by speeding-up decision- making process, reducing medication errors, early detection and prediction of diseases and their prognosis, enhancing patient engagement and compliance with the treatment plan, in addition to discover new drugs and vaccines.Journal of Neonatal Surgery["Fida`a Eid Al-Shatnawi", "Hayat Sulieman Abu-Sheikah", "Mo`ath Omar Al-Momani"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/831a4cbe0018a717df8d4a30a19e1d8d4c74219e 0bb9586ee77eb3336fc6472d220df1e4c490c21f Objectives This study investigates gender biases in Artificial Intelligence (AI) perceptions among university students. It focuses on assessing self-perceptions regarding knowledge, impact, and support, with a specific emphasis on identifying any significant gender differences. The main hypotheses are focused on the existence of gender disparities in AI awareness, perceptions, and attitudes among higher education students. Participants The study involves 380 participants, enrolled in undergraduate courses across various academic disciplines. Participants are university students with diverse backgrounds in terms of age, academic majors, and prior exposure to AI technologies. Study Methods This research employs an observational study design. The sample size includes 380 participants. The study utilises a structured questionnaire as the primary instrument for data collection. Outcome measures focus on variables such as perceived knowledge of AI, perceived impact of AI, and levels of support or apprehension towards AI technologies. Findings The findings reveal significant gender differences, with females exhibiting lower levels than their male counterparts in the level of perceived knowledge about AI ( \(p<0.005\) ), exposure awareness ( \(p=0.001\) ), perceived ability to apply AI ( \(p=0.004\) ), sensitivity towards AI use of private data ( \(p=0.004\) ), positive impact on society ( \(p=0.002\) ), support for AI development ( \(p<0.005\) ), and positive expectations towards AI ( \(p<0.005\) ). Statistical analysis, including non-parametric tests, was used to validate these observations. Conclusions There are notable gender biases in the knowledge and perception of AI among university students. These biases have implications for the future development and adoption of AI technologies, suggesting a need for more gender-inclusive educational strategies in AI. The findings underscore the importance of addressing gender disparities in AI education to ensure equitable access and understanding of these technologies. It is important to integrate gender perspectives in AI curriculum and policy-making to mitigate potential biases and enhance inclusivity in the field of AI. ACM Transactions on Computing Education140There are notable gender biases in the knowledge and perception of AI among university students, suggesting a need for more gender-inclusive educational strategies in AI.ACM Transactions on Computing Education["Cristina Cachero", "David Tom\u00e1s", "Francisco A. Pujol"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/0bb9586ee77eb3336fc6472d220df1e4c490c21f ff18b6f6217e8e839ddcabc24426a392765dda73Education receives a revolution from artificial intelligence, which provides three key advantages through individualized learning practices, while performing administrative work, and evaluating performance with smart technologies. AI solutions nowadays transform educational processes as they optimize learning methods and increase educational value for students and instructors. A review investigates the main AI applications which education utilizes with a focus on adaptive learning systems, alongside intelligent tutors, together with AI-based assessment methodology. The discussion within the paper analyzes the ethical dilemmas from AI implementation, including concerns about data confidentiality and algorithmic prejudice and dependence on human educators. The evaluation surveys that show upcoming AI developments in education demonstrate how technology can build better relationships with students and improve the educational system, inclusivity, and policy generation.EPRA International Journal of Research &amp; Development (IJRD)00The discussion within the paper analyzes the ethical dilemmas from AI implementation, including concerns about data confidentiality and algorithmic prejudice and dependence on human educators, as well as how technology can build better relationships with students and improve the educational system, inclusivity, and policy generation.EPRA International Journal of Research &amp; Development (IJRD)["Dinesh Deckker", "Subhashini Sumanasekara"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/ff18b6f6217e8e839ddcabc24426a392765dda73 cf02036d9cff393825f9d6a55055f7801173aef6ABSTRACT Advances in artificial intelligence (AI) technologies have not been widely integrated into simulation education. This work examines the process of designing and implementing AI-enabled opioid-involved overdose simulation scenarios to aid pre- and postlicensure nursing students in learning how to assess, respond to, and manage opioid-involved overdoses. Thirty students provided feedback on their engagement with the AI-enabled manikin immediately following the simulation experience. Data show that participants would recommend the use of the AI-enabled manikins for other nursing students. education. This overview serves as a template to those interested in implementing AI in simulation scenarios.Nursing Education Perspectives90This work examines the process of designing and implementing AI-enabled opioid-involved overdose simulation scenarios to aid pre- and postlicensure nursing students in learning how to assess, respond to, and manage opioid-involved overdoses.Nursing education perspectives["Beth Ann Swan", "Nicholas A Giordano", "Sarah Febres-Cordero", "Kim Fugate", "Laika Steiger"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/cf02036d9cff393825f9d6a55055f7801173aef6 a6b1b43c924609f12bab4981a49c0ea7f7599079The study focuses on assessing the impact of artificial intelligence on educational processes in higher education institutions. It considers aspects of administrative task automation, personalization of learning, and ethical challenges. The topic's relevance is driven by global trends in the digital transformation of education and the need to adapt systems to modern challenges. A descriptive approach was used, using secondary data from scientific publications, statistical reports, and analytical studies. The data were analyzed using statistical and correlation methods, allowing us to identify the key patterns of implementing artificial intelligence in higher education in Ukraine and Kazakhstan. Integration of artificial intelligence increases the efficiency of administrative processes by 40%, reducing the time spent on routine tasks. Personalized learning contributes to the growth of students' academic performance by 7-30%. Using AI to monitor educational processes can reduce the risk of expulsions by up to 15%. At the same time, the risks of reduced social interaction and possible ethical issues, such as the opacity of algorithms and the risk of data leakage, have been identified. Artificial intelligence has significant potential for optimizing educational processes, provided ethical standards are met. Technological solutions must be combined with a socially oriented approach, particularly through integrating hybrid learning models. It is recommended that Ukraine use Kazakhstan's experience in centralizing solutions, investing in analytical tools, and training teachers.Sustainable Engineering and Innovation520Assessment of the impact of artificial intelligence on educational processes in higher education institutions in Ukraine and Kazakhstan suggests that Ukraine should use Kazakhstan's experience in centralizing solutions, investing in analytical tools, and training teachers.Sustainable Engineering and Innovation["Olena Bazyl", "Oryngul Abilova", "O. Karpenko", "Hnat Mierienkov", "Anastasiya Poliakova"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/a6b1b43c924609f12bab4981a49c0ea7f7599079 53784a3b39696fa121fc33c9bfa1eaf968d08d61This article explores the intersection of linguistics and artificial intelligence (AI) with a focus on the scientific foundations of natural language processing (NLP). The study reviews core linguistic principles, discusses their integration into AI-driven NLP systems, and highlights challenges in semantic analysis, syntactic parsing, and machine learning. The paper concludes by emphasizing the future potential of NLP in advancing human-computer interaction.Qo‘qon DPI. Ilmiy xabarlar jurnali00The study reviews core linguistic principles, discusses their integration into AI-driven NLP systems, and highlights challenges in semantic analysis, syntactic parsing, and machine learning.Qo‘qon DPI. Ilmiy xabarlar jurnali["Muhammadali Abduvaliyevich Yandashaliyev"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/53784a3b39696fa121fc33c9bfa1eaf968d08d61 ecf969154668e4f7f0b12154a567143b15ef09a7Scientific Reports230This paper analyzes the impact of incorporating XAI in the botnet detection process, including enhanced model interpretability, trustworthiness, and potential for early detection of emerging botnet attack patterns and demonstrates the effectiveness of the proposed approach.Scientific Reports["Mohamed Saied", "S. Guirguis"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/ecf969154668e4f7f0b12154a567143b15ef09a7 fc29976ebaefa4e278f4ac74ae2adebd8c3a65c9<p><strong><em>The purpose of the study </em></strong><em>is to outline the threats posed by modern technologies for creating deepfakes, to confirm the need for legal regulation of their spreading, and to make relatable proposals for recognizing deepfakes at the everyday level, in particular through the use of artificial intelligence.</em></p><p><strong><em>Research methodology.</em></strong><em> The materials used to prepare this article were compiled using a combination of theoretical and empirical methods, including the analysis of sources that offer information about the role of deepfakes in the media environment and their impact on society as a whole. The analysis of foreign websites with legislative acts made it possible to systematize these sources on the relevant issues, as well as to strengthen the argument for the need to legally regulate the dissemination of such falsifications. The use of these methods, as well as the inductive generalization of the field under study, contributed to the structuring of the necessary material to obtain the relational basis for recognizing deep audiovisual counterfeits.</em></p><p><strong><em>Results</em></strong><em>. A list of rules that can be used to recognize deepfakes is proposed, and a list of online resources for their detection is reviewed and systematized in order to increase the overall level of media literacy and awareness of threats that negatively affect the mental health of society. </em></p><p><strong><em>Novelty.</em></strong><em> As a result of the analysis of the sources, as well as their systematization and generalization, recommendations for strengthening critical thinking among the population are proposed, and the need for visual training is emphasized in order not to be deceived by another example of a deep audiovisual fake.</em></p><p><strong><em>Practical significance.</em></strong><em> The proposed rules can be used both for widespread use in society to develop critical thinking and for the development of a set of competencies and programmatic outcomes in media education disciplines. </em></p><p><strong><em>Key words:</em></strong><em> deepfake, disinformation, manipulation, media addiction, media literacy, artificial intelligence.</em></p>State and Regions. Series: Social Communications00Recommendations for strengthening critical thinking among the population are proposed, and the need for visual training is emphasized in order not to be deceived by another example of a deep audiovisual fake.State and Regions. Series: Social Communications["I. Kyianytsia", "D. Fa\u0443vishenko"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/fc29976ebaefa4e278f4ac74ae2adebd8c3a65c9 ee81a0f372742036554246d5da4dcae52568135eArtificial intelligence (AI) has become omnipresent in the software development area and its management nowadays, including a very important part of the management of dualities. However, this theme still remains poorly studied and debatable, especially for leading personnel of software development companies. Effective management of dualities is very important for the success of any company and artificial intelligence can significantly influence it. For this reason, the first initial goal of the research was to study different aspects of the utilization of AI in the management of dualities in companies from the area of development of software solutions to systemize existing knowledge on the topic. Those aspects include possibilities of use, its benefits and drawbacks, and analysis of existing solutions and case studies. The second less important initial goal was to prepare recommendations for the implementation and utilization of AI in the business area. The study is qualitative and consists of two sequential parts: theoretical and practical. The first one is conducted with a scoping literature review. The second one is based on the first and represents research in the professional area in the form of a structured interview. The results show the benefits as well as the importance and necessity of the implementation of AI support for the management of dualities for the success of software development companies. At the same time, they identify some potential drawbacks and problems with technology acceptance. Additionally, they identify potential inconsistencies between theoretical investigations and the reality of the business area. Based on them the most important recommendations for the process are prepared and written down. The results are useful for the people employed in the area both in leading and non-leading roles. The inconsistencies identified need an additional investigation. This work could be used as a good starting point for continuing research works on the topic.Uporabna informatika410The results show the benefits as well as the importance and necessity of the implementation of AI support for the management of dualities for the success of software development companies and identify some potential drawbacks and problems with technology acceptance.Uporabna informatika["Maksim Nikitashin"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/ee81a0f372742036554246d5da4dcae52568135e f8d32f985c0d5c108a3d1eb518ae5e969d3bc87fThis study aims to examine the impact of artificial intelligence through robotic process automation systems on internal audit operation efficiency in Jordanian commercial banks. The study uses a descriptive methodology to evaluate how robotic process automation systems enhance the different dimensions of internal audit efficiency: planning, management, execution, and communication. The study designed a structured questionnaire for data collection. The sample consisted of 12 commercial bank employees whose working processes are directly affected by robotic process automation system procedures which puts them in a unique position to comment on the practical applicability of this technology and its implications for internal audit. In this study, 480 electronic questionnaires were distributed via Google Forms, and 390 completed forms were collected for further analysis. The study employs descriptive statistics and advanced statistical techniques, such as linear regression analysis, for data analysis. The findings indicate that robotic process automation systems enhance the internal audit process by reducing the cost of operations, eliminating human errors, and smoothing work processes. The robotic process automation system will allow continuous auditing, real-time risk management, and proper reporting; hence, it will change the role of internal auditors and, in the end, improve organizational compliance and performance. This study asserts that the banking industry must integrate AI-driven automation to maintain its competitiveness in the constantly changing financial landscape.Banks and Bank Systems00It is asserted that the banking industry must integrate AI-driven automation to maintain its competitiveness in the constantly changing financial landscape.Banks and Bank Systems["Abdalla Alassuli"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/f8d32f985c0d5c108a3d1eb518ae5e969d3bc87f f792d9040ed4e0a2ca462cd1592ed01aeb08ebf0Abstract The integration of Artificail Intelligence (AI, henceforth) in graphic design is more than just a revolution; it is an era-defining transformational wave that has completely altered the way things are done in the creative industry. This piece of research explores the broad impacts that AI has on graphic design— from being able to take care of repetitive tasks without any fuss all the way to helping designers discover new horizons in their creativity. It even looks into personalization through a comprehensive review that includes current AI technologies as well as literature and case studies available. We consider both sides when we talk about what role AI can play for us: opportunities it brings along with challenges we might face because of its implementation. Through a background review and analysis of current AI applications and tools, the study will highlight the potential benefits, challenges, and ethical issues associated with integrating AI into graphic design. The goal of this study is to help graphic design professionals make informed decisions about using AI in their work and shed light on the changing graphic design landscape and the impact that AI integration will have. The results of this study indicate that AI has a dual role to play in the field of design — while making design capabilities available to the masses, it also stirs up discussions on ethics through issues like whether automation can lead to job displacements or not? and where does one draw a line on creativity? As we draw our attention towards navigating through this constantly evolving landscape which is now being powered by AI at every step, let us try to understand how these technologies are not just changing production methodologies but redefining what art stands for, especially at a time like ours, when we find ourselves deep into this digital age.مؤتة للبحوث والدراسات - سلسلة العلوم الإنسانية والاجتماعية180The results of this study indicate that AI has a dual role to play in the field of design — while making design capabilities available to the masses, it also stirs up discussions on ethics through issues like whether automation can lead to job displacements or not?مؤتة للبحوث والدراسات - سلسلة العلوم الإنسانية والاجتماعية["Suhaib Sultan Alkahteeb", "Islam Ghandi Almomani", "Mohammad Kamal Zoubi", "Mustafa Mohammad Issa", "Mohammad Ali Al Smadi", "H. Jaradat", "Taghreed Mohammad Abualhumos"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/f792d9040ed4e0a2ca462cd1592ed01aeb08ebf0 702f56513cca8e39f2f35b4cf778d776d0d23f76This study investigates the role of deepfake and open-source intelligence (OSINT) in enabling disinformation campaigns and their societal consequences. Using the Deepfake Detection Challenge (DFDC) dataset for technical evaluation, social media datasets for OSINT network and sentiment analysis, and public opinion data from the Global Disinformation Index, the study applied machine learning classification, network analysis, sentiment analysis, and interrupted time series (ITS) analysis. The technical assessment achieved a detection accuracy of 0.73, precision of 0.75, and recall of 0.70, identifying areas for enhancement in identifying synthetic media. OSINT analysis revealed pivotal amplifiers of disinformation, with User1 having a degree centrality of 0.263 and betweensess centrality of 0.135. Sentiment analysis showed an average sentiment score of -0.085, while ITS analysis documented a significant 9.76-point decline in public trust post-disinformation events. Recommendations include developing adaptive AI detection systems, implementing global regulatory measures, fostering public media literacy, and encouraging ethical OSINT practices. Keywords: Deepfakes, Artificial Intelligence, Disinformation Campaigns, Open-Source Intelligence, Public Trust.Computer Science &amp; IT Research Journal00This study investigates the role of deepfake and open-source intelligence (OSINT) in enabling disinformation campaigns and their societal consequences, and applies machine learning classification, network analysis, sentiment analysis, and interrupted time series (ITS) analysis.Computer Science &amp; IT Research Journal["A. Y. Balogun", "Adegbenga Ismaila Alao", "O. O. Olaniyi"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/702f56513cca8e39f2f35b4cf778d776d0d23f76 4a84a765a293ab62a49caae50b67769cddd8e8fbLaw, Innovation and Technology01Law, Innovation and Technology["Barry Solaiman"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/4a84a765a293ab62a49caae50b67769cddd8e8fb 06fdfd4ec38a1c21246107055dd95964dcfc956aThe Hungarian Educational Research Journal51Hungarian Educational Research Journal["Jan Beseda"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/06fdfd4ec38a1c21246107055dd95964dcfc956a eca5b9b6ac5c711460872e8b5fdb0d35559b6578<jats:p>N/A</jats:p>NACTA Journal00NACTA Journal["Jonathan Watson"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/eca5b9b6ac5c711460872e8b5fdb0d35559b6578 5e65a5e7ba8f646e897d4f3cd7c8fb2d5408a499Frontiers in Neurology00Frontiers in Neurology["J. Soun", "Brent D. Weinberg"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/5e65a5e7ba8f646e897d4f3cd7c8fb2d5408a499 d9ad2ec0e936f62498ea12e56052b893ca4a28a4The plantations are one of the largest industries in the modern world, as we all know. It accounts for thirty countries globally. India is a major agricultural nation, and agricultural output has a big impact on the safety of the country's food supply. India has substantially less farmland per person than the global average, lower output values per person, and poorer land yields per unit when compared to other wealthy nation. Therefore, in order to overcome the challenges associated with food production, we need to figure out how to increase output while utilizing the few natural resources that are now available. The use of distributed sensors and a data collection platform to enable global open data for nutrition and agriculture these days, agriculture works with a wide range of variables to ensure a successful harvest, including temperature, rainfall, sunlight, air pressure, and humidity. It is anticipated that an early warning system will be developed if there are parameters that are already in the critical value and could lead to crop failure because there is an abundance of data that can be gathered and examined.Journal of Information Systems Engineering & Management170It is anticipated that an early warning system will be developed if there are parameters that are already in the critical value and could lead to crop failure and could lead to crop failure because there is an abundance of data that can be gathered and examined.Journal of Information Systems Engineering and Management["Vaishali Hirlekar", "Anuradha Kanade", "A. Abirami", "S. ChV", "Satyamurty", "Dr.K.B.Shoba", "G.Sureshkumar"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/d9ad2ec0e936f62498ea12e56052b893ca4a28a4 3c1f9bb639a35c83988fd5fcd80f1345ae931e07Technological advances have allowed various systems to be developed on a small scale [...]Micromachines270Micromachines["J. Rodr\u00edguez-Res\u00e9nd\u00edz", "Marcos Aviles", "J. M. \u00c1lvarez-Alvarado"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/3c1f9bb639a35c83988fd5fcd80f1345ae931e07 36dc0487726921415e88113429968c6e8b49d906Food Engineering Reviews1690Food Engineering Reviews["Mert Canatan", "Nasser Alkhulaifi", "Nicholas Watson", "Ziynet Boz"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/36dc0487726921415e88113429968c6e8b49d906 020e3ed7aa89118066841358bb07b4d2a2e8c901Humanities and Social Sciences Communications650Humanities and Social Sciences Communications["M. A. Hossin", "Songtao Yin", "Ruibo Dan", "Lie Chen"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/020e3ed7aa89118066841358bb07b4d2a2e8c901 b95bc490e2c1657f4d1a427f0d4c9e75ca8b19bcFrontiers in Aging Neuroscience180Frontiers in Aging Neuroscience["M. Otero-Losada", "Santiago Perez Lloret", "F. Capani", "Cristian Falup-Pecurariu"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/b95bc490e2c1657f4d1a427f0d4c9e75ca8b19bc 2c2fc0f871ddf55630c3b70d79049e6efd6078caIntroduction: Climate change is a key driver of human migration, particularly in regions facing resource scarcity and extreme weather events. Understanding migration patterns is essential for effective policy responses. Objectives: This multidisciplinary study applies data mining techniques to identify key environmental and socioeconomic factors influencing climate-induced migration and enhance predictive modeling for policy decision-making. Methods: Machine learning techniques, including spatiotemporal clustering and regression analysis, are applied to migration data from UNDESA and IOM’s CLIMB Database. Climate indicators such as temperature anomalies, drought frequency, and water stress are analyzed. Results: Findings reveal strong correlations between climate stressors and migration trends. Water scarcity and prolonged droughts significantly drive displacement, with predictive models demonstrating high accuracy in forecasting migration flows. Conclusions: Data mining is a valuable tool for analyzing and predicting climate-induced migration. Findings emphasize the need for proactive climate adaptation strategies and data-driven migration policies. Future research should integrate real-time monitoring and geospatial AI to improve forecasting accuracy.Journal of Information Systems Engineering & Management160Data mining techniques are applied to identify key environmental and socioeconomic factors influencing climate-induced migration and enhance predictive modeling for policy decision-making to reveal strong correlations between climate stressors and migration trends.Journal of Information Systems Engineering and Management["F. Anka", "Fahri Erenel", "Farzad Kiani"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/2c2fc0f871ddf55630c3b70d79049e6efd6078ca d01ccc9d031ffe6663d7c7ca25a61c1fd84714e4Education and Information Technologies : Official Journal of the IFIP technical committee on Education490Education and Information Technologies["Jun Xiao", "Yule Yang", "Min Li"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/d01ccc9d031ffe6663d7c7ca25a61c1fd84714e4 c7d5b447b0993756db492c4f7a0a3a93c645730cJournal of Visual Communication in Medicine00Journal of visual communication in medicine["Timothy Zoltie"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/c7d5b447b0993756db492c4f7a0a3a93c645730c 12e6019d527d710e5e3a136e7e86d2ef4daae339Epilepsia220Epilepsia["John R. McLaren", "Doyle Yuan", "S. Beniczky", "M. B. Westover", "F\u00e1bio A Nascimento"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/12e6019d527d710e5e3a136e7e86d2ef4daae339 7396bdd37def2a968e818e3399ef1e8cb65acdefJournal of Small Business Management1410Journal of Small Business Management["S. Murtinu", "Alfredo De Massis"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/7396bdd37def2a968e818e3399ef1e8cb65acdef 74aa3cfa131a01f99b88b15f9c9c61b3e24763dcFrontiers in Computational Neuroscience60Frontiers in Computational Neuroscience["Ye Yuan", "Xi Chen", "Jian Liu"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/74aa3cfa131a01f99b88b15f9c9c61b3e24763dc db3e7587555fb8a2f452e935d23258a1a3747aeeInternational Journal of Academic Research in Economics and Management Sciences00International Journal of Academic Research in Economics and Management Sciences["Lujain Lutfi", "Alireza Mohammadi"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/db3e7587555fb8a2f452e935d23258a1a3747aee d7aac5376deb6e25a37c3b7980aeb1e8ed5e444bThis paper explores integrating innovative technologies, including Mola Structural Kits, Smart Lab shake tables, and Micro: bit AI tools, to enhance STEM education through a project-based seismic engineering lesson plan. Students design, construct, and test earthquake-resistant structures by combining hands-on learning with smart classroom technologies. The approach emphasizes critical thinking, collaboration, and problem-solving, allowing students to bridge theoretical concepts with practical applications. This study highlights the benefits of interactive and data-driven learning environments, demonstrating how these tools improve engagement, deepen understanding of engineering principles, and develop essential skills for the 21st-century workforce.American Journal of STEM Education00This paper explores integrating innovative technologies to enhance STEM education through a project-based seismic engineering lesson plan, demonstrating how these tools improve engagement, deepen understanding of engineering principles, and develop essential skills for the 21st-century workforce.American Journal of STEM Education["Angela McDaniel"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/d7aac5376deb6e25a37c3b7980aeb1e8ed5e444b 293e9c1252ee97ecb8c4902fcee62c4cc5e62c69Frontier AI models -- highly capable foundation models at the cutting edge of AI development -- may pose severe risks to public safety, human rights, economic stability, and societal value in the coming years. These risks could arise from deliberate adversarial misuse, system failures, unintended cascading effects, or simultaneous failures across multiple models. In response to such risks, at the AI Seoul Summit in May 2024, 16 global AI industry organizations signed the Frontier AI Safety Commitments, and 27 nations and the EU issued a declaration on their intent to define these thresholds. To fulfill these commitments, organizations must determine and disclose ``thresholds at which severe risks posed by a model or system, unless adequately mitigated, would be deemed intolerable.'' To assist in setting and operationalizing intolerable risk thresholds, we outline key principles and considerations; for example, to aim for ``good, not perfect'' thresholds in the face of limited data on rapidly advancing AI capabilities and consequently evolving risks. We also propose specific threshold recommendations, including some detailed case studies, for a subset of risks across eight risk categories: (1) Chemical, Biological, Radiological, and Nuclear (CBRN) Weapons, (2) Cyber Attacks, (3) Model Autonomy, (4) Persuasion and Manipulation, (5) Deception, (6) Toxicity, (7) Discrimination, and (8) Socioeconomic Disruption. Our goal is to serve as a starting point or supplementary resource for policymakers and industry leaders, encouraging proactive risk management that prioritizes preventing intolerable risks (ex ante) rather than merely mitigating them after they occur (ex post).00The goal is to serve as a starting point or supplementary resource for policymakers and industry leaders, encouraging proactive risk management that prioritizes preventing intolerable risks (ex ante) rather than merely mitigating them after they occur (ex post).["Deepika Raman", "Nada Madkour", "Evan R. Murphy", "Krystal Jackson", "Jessica Newman"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/293e9c1252ee97ecb8c4902fcee62c4cc5e62c69 7bb410fe135ae74a3eb503d1960648f7eaaad9d2This research paper explores the critical role of business intelligence (BI) in enabling U.S. companies to achieve transparent and responsible artificial intelligence (AI). The study aims to assess how BI can support ethical AI development by ensuring fairness, transparency, and accountability in AI systems. A qualitative research methodology was employed, involving a comprehensive literature review, case study analysis, and expert interviews to evaluate the integration of BI tools in AI governance. The findings indicate that BI enhances ethical decision-making by providing data-driven insights that help identify and mitigate biases, improve algorithmic fairness, and ensure regulatory compliance. Additionally, BI enables organizations to establish robust governance frameworks, fostering greater public trust and competitive advantage. The study concludes that integrating BI with AI ethics is essential for developing trustworthy AI systems that align with societal values and regulatory expectations. Future research should explore the development of standardized frameworks, methodologies for evaluating AI fairness, and the role of regulatory bodies in promoting responsible AI adoption. KEYWORDS: Business Intelligence, Artificial Intelligence, AI Ethics, Transparency, Responsible AI, Algorithmic Fairness, Competitive Advantage.EPRA International Journal of Economics, Business and Management Studies00The study concludes that integrating BI with AI ethics is essential for developing trustworthy AI systems that align with societal values and regulatory expectations.EPRA International Journal of Economics, Business and Management Studies["Tessy Oghenerobovwe Agbadamasi", "Lois Kumiwaa Opoku", "Tobias Kwame Adukpo", "Nicholas Mensah"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/7bb410fe135ae74a3eb503d1960648f7eaaad9d2 21eb39803fb15556a97b2e7e7a1ab83eb4286ba1Augmented intelligence puts together human and artificial agents to create a socio-technological system, so that they co-evolve by learning and optimizing decisions through intuitive interfaces, such as conversational, voice-enabled interfaces. However, existing research works on voice assistants relies on knowledge management and simulation methods instead of data-driven algorithms. In addition, practical application and evaluation in real-life scenarios are scarce and limited in scope. In this paper, we propose the integration of voice assistance technology with Automated Machine Learning (AutoML) in order to enable the realization of the augmented intelligence paradigm in the context of Industry 5.0. In this way, the user is able to interact with the assistant through Speech-To-Text (STT) and Text-To-Speech (TTS) technologies, and consequently with the Machine Learning (ML) pipelines that are automatically created with AutoML, through voice in order to receive immediate insights while performing their task. The proposed approach was evaluated in a real manufacturing environment. We followed a structured evaluation methodology, and we analyzed the results, which demonstrates the effectiveness of our proposed approach.Frontiers in Artificial Intelligence730This paper proposes the integration of voice assistance technology with Automated Machine Learning (AutoML) in order to enable the realization of the augmented intelligence paradigm in the context of Industry 5.0.Frontiers in Artificial Intelligence["Alexandros Bousdekis", "Mina Foosherian", "Mattheos Fikardos", "S. Wellsandt", "Katerina Lepenioti", "Enrica Bosani", "G. Mentzas", "K. Thoben"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/21eb39803fb15556a97b2e7e7a1ab83eb4286ba1 56eb462f473e054109945030bf6afd424a4deaafBuilding a virtual model of the cell is an emerging frontier at the intersection of artificial intelligence and biology, aided by the rapid growth of single-cell RNA sequencing data. By aggregating gene expression profiles from millions of cells across hundreds of studies, single cell atlases have provided a foundation for training AI-driven models of the cell. However, reliance on datasets with pre-processed counts limits the size and diversity of these repositories and constrains downstream model training to data curated for divergent purposes. This introduces analytical variability due to differences in the choice of alignment tools, genome references, and counting strategies. Here, we introduce scBaseCamp, a continuously updated single-cell RNA-seq database that leverages an AI agent-driven hierarchical workflow to automate discovery, metadata extraction, and standardized data processing. Built by directly mining and processing all publicly accessible 10X Genomics single-cell RNA sequencing reads, scBaseCamp is currently the largest public repository of single-cell data, comprising over 230 million cells spanning 21 organisms and 72 tissues. Using studies comprised of both single cell and single nucleus sequencing data, we demonstrate that uniform processing across datasets helps mitigate analytical artifacts introduced by inconsistent data processing choices. This standardized approach lays the groundwork for more accurate virtual cell models and serves as a foundation for a wide range of biological and biomedical applications.bioRxiv271bioRxiv["Nicholas D. Youngblut", "Christopher Carpenter", "Jaanak Prashar", "Chiara Ricci-Tam", "R. Ilango", "Noam Teyssier", "Silvana Konermann", "Patrick D. Hsu", "Alexander Dobin", "David P. Burke", "Hani Goodarzi", "Yusuf H. Roohani"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/56eb462f473e054109945030bf6afd424a4deaaf 8047989aef9d8ffb3440f9373abff29319d091a3Scientific Reports540This study is the first to introduce a deep learning self-supervised model to the field of AI in healthcare, effectively improving the accuracy and efficiency of the analysis.Scientific Reports["Yi Jie Wang", "W. Choo", "K. Ng", "Ran Bi", "Peng Wei Wang"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/8047989aef9d8ffb3440f9373abff29319d091a3 07349c01274e95c181155d661a921f8e375325a1The article is devoted to the topical issues of artificial intelligence (AI) technology for managing the optimization of business processes under the influence of changing SEO trends in 2025, which is important and requires the identification of the main vectors of their further development for implementation in the work of the business. In the modern world, where information has a dynamic development and becomes a digital asset, the use of AI plays a significant role for managers and clients, which determines the development of the functional intelligence of the future management network. The research consists of analyzing the main approaches in the context of business process management regarding their optimization in the field of security, taking into account the changing trends of SEO in 2025 using AI, which will contribute to increasing the efficiency of business decisions. The main trends of AI influence on the development of SEO optimization in website promotion in 2025, which has a positive impact on business development and the effectiveness of indicators, are summarized. The vectors of management efficiency through the impact of AI technology on the optimization of business processes in the field of security services, which will contribute to timely response to the challenges and risks of the modern competitive environment, are determined. In conclusion, it is emphasized that for improving the management of business processes regarding their optimization, it is necessary to use AI, in particular in the field of security, taking into account SEO trends in 2025, through the analysis of current processes, identifying priority goals of business process optimization, improving existing processes and developing new ones taking into account the use of modern technologies and monitoring business results by implementing changes in order to adjust them. These vectors contribute to the optimization of business processes in the field of security, but there is a need to take into account the cost of AI implementation and training of security guards who have to work with AI technology and understand how to interpret the data obtained. An important challenge is the solution of an ethical issue that concerns the confidentiality and protection of personal data of clients. It has been found that AI has significant potential for optimizing business processes in the field of security regarding increasing management efficiency, improving the quality of service, which will lead to business performance. The vector of further research lies in the wide application of AI in management, which will bring the business to a qualitatively new level of management, defining the development of the functional intelligence of the future management network.Сталий розвиток економіки70It has been found that AI has significant potential for optimizing business processes in the field of security regarding increasing management efficiency, improving the quality of service, which will lead to business performance.Сталий розвиток економіки["\u0410\u043d\u0434\u0440\u0456\u0439 \u0428\u0435\u0432\u0447\u0443\u043a"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/07349c01274e95c181155d661a921f8e375325a1 d9cbb7a62034e17e394bd83f79676d9bb2417698PurposeGenerative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.Design/methodology/approachWe collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.FindingsThe findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.Originality/valueThis paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.Journal of Enterprise Information Management940The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement and it is found that active engagement positively affects Gen-AI satisfaction and positive word-of-mouth.Journal of Enterprise Information Management["Mai Nguyen", "Ankit Mehrotra", "Ashish Malik", "Rudresh Pandey"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/d9cbb7a62034e17e394bd83f79676d9bb2417698 09980ae23ee87becc6b4212d83ea83c3f0018388This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience.Tourism and Hospitality560This research uncovers a gradient of preference rather than a stark dichotomy between AI and human interaction, enabling airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency.Tourism and Hospitality["Murat Sa\u011fba\u015f", "Sefer Aydogan"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/09980ae23ee87becc6b4212d83ea83c3f0018388 a6bf3684fad30e838e555c035500a0f0c4d5701dAs educational settings increasingly integrate artificial intelligence (AI), understanding how AI tools identify -- and adapt their responses to -- varied educational contexts becomes paramount. This study examines conversational AI's effectiveness in supporting K-12 mathematics education across various educational contexts. Through qualitative content analysis, we identify educational contexts and key instructional needs present in educator prompts and assess AI's responsiveness. Our findings indicate that educators focus their AI conversations on assessment methods, how to set the cognitive demand level of their instruction, and strategies for making meaningful real-world connections. However, educators' conversations with AI about instructional practices do vary across revealed educational contexts; they shift their emphasis to tailored, rigorous content that addresses their students' unique needs. Educators often seek actionable guidance from AI and reject responses that do not align with their inquiries. While AI can provide accurate, relevant, and useful information when educational contexts or instructional practices are specified in conversation queries, its ability to consistently adapt responses along these evaluation dimensions varies across different educational settings. Significant work remains to realize the response-differentiating potential of conversational AI tools in complex educational use cases. This research contributes insights into developing AI tools that are responsive, proactive, and anticipatory, adapting to evolving educational needs before they are explicitly stated, and provides actionable recommendations for both developers and educators to enhance AI integration in educational practices.500This research contributes insights into developing AI tools that are responsive, proactive, and anticipatory, adapting to evolving educational needs before they are explicitly stated, and provides actionable recommendations for both developers and educators to enhance AI integration in educational practices.["Alex Liu", "Lief Esbenshade", "Min Sun", "Shawon Sarkar", "Jian He", "Victor Tian", "Zachary Zhang"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/a6bf3684fad30e838e555c035500a0f0c4d5701d d4bc7e5fb2902f74252b07ade4cdc55ea1a48bb0 This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills. The research examines how these competencies enhance self-efficacy and engagement, with curriculum design, industry exposure and faculty support as moderating factors. This study aims to provide actionable insights for educational strategies that prepare students for AI-driven business environments. The research adopts a hybrid methodology, integrating partial least squares structural equation modeling (PLS-SEM) with artificial neural networks (ANNs), using quantitative data collected from 526 management students across five Indian universities. The PLS-SEM model validates linear relationships, while ANN captures nonlinear complexities, complemented by sensitivity analyses for deeper insights. The results highlight the pivotal roles of foundational AI knowledge, data literacy and problem-solving in fostering self-efficacy. Behavioral, cognitive, emotional and social engagement significantly influence AIDP. Moderation analysis underscores the importance of curriculum design and faculty support in enhancing the efficacy of these constructs. ANN sensitivity analysis identifies problem-solving and social engagement as the most critical predictors of self-efficacy and AIDP, respectively. The study is limited to Indian central universities and may require contextual adaptation for global applications. Future research could explore longitudinal impacts of AIDP development in diverse educational and cultural settings. The findings provide actionable insights for curriculum designers, policymakers and educators to integrate AI competencies into management education. Emphasis on experiential learning, ethical frameworks and interdisciplinary collaboration is critical for preparing students for AI-centric business landscapes. By equipping future leaders with AI proficiency, this study contributes to societal readiness for technological disruptions, promoting sustainable and ethical decision-making in diverse business contexts. To the author’s best knowledge, this study uniquely integrates PLS-SEM and ANN to analyze the interplay of competencies and engagement in shaping AIDP. It advances theoretical models by linking foundational learning theories with practical AI education strategies, offering a comprehensive framework for developing AI competencies in management students. Journal of International Education in Business800This study explores the factors influencing artificial intelligence (AI)-driven decision-making proficiency (AIDP) among management students, focusing on foundational AI knowledge, data literacy, problem-solving, ethical considerations and collaboration skills, and offers a comprehensive framework for developing AI competencies in management students.Journal of International Education in Business["Shashank Gupta", "Rachana Jaiswal"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/d4bc7e5fb2902f74252b07ade4cdc55ea1a48bb0 79a1c60ce14f7069044d8483872059e7580aee7bAI and Ethics510To realise the potential of AI, it is critical to shed light on two fundamental questions of explanation for safety–critical AI such as health-AI that remain unanswered: What is an explanation in health-AI and what are the attributes of a good explanation in health-AI.AI and Ethics["Evangelia Kyrimi", "Scott McLachlan", "Jared M. Wohlgemut", "Zane Perkins", "David A. Lagnado", "William Marsh", "Alexander Gimson", "Ali Shafti", "Ari Ercole", "Amitava Banerjee", "Ben Glocker", "Burkhard Schafer", "Constantine Gatsonis", "C. Grosan", "Danielle Sent", "David S. Berman", "David Glass", "Declan P. O\u2019Regan", "Dimitrios Letsios", "Dylan Morrissey", "Erhan Pisirir", "Francesco Leofante", "H. Soyel", "Jon Williamson", "Keri Grieman", "Kudakwashe Dube", "Max Marsden", "M. Nagendran", "Nigel Tai", "Olga Kostopoulou", "Owain Jones", "P. Curzon", "Rebecca S. Stoner", "Sankalp Tandle", "Shalmali Joshi", "S. Mossadegh", "Stefan Buijsman", "Tim Miller", "V. Madai"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/79a1c60ce14f7069044d8483872059e7580aee7b 4d46a81ad594768a27fa4b0d38b3d75e1dbc0babArtificial Intelligence (AI) has emerged as a transformative force in education, including Islamic religious education, where it offers new opportunities to enhance learning methodologies. This study aims to analyze the integration of AI in Islamic education by evaluating its strengths, weaknesses, opportunities, and threats (SWOT analysis) while categorizing its impact on cognitive, affective, and psychomotor domains based on Bloom’s Taxonomy. A qualitative approach using library research methodology was employed, with data collected from academic journals, books, and research reports, analyzed through a SWOT framework. The findings indicate that AI significantly enhances cognitive and psychomotor learning in Islamic education. AI-based tools, such as ClassPoint AI, AI Chatbots, and Squirrel AI, contribute to knowledge retention, adaptive learning, and skill-based training in areas like Quranic recitation, prayer practices, and Islamic jurisprudence. However, AI remains limited in fostering affective learning, as it lacks human emotional intelligence and the ability to provide moral and ethical guidance, which are essential in Islamic education. The study also reveals challenges such as ethical concerns, technological disparities, and socio-cultural resistance in integrating AI into religious studies. Despite these limitations, AI presents significant opportunities, particularly in remote learning, personalized education, and accessibility for underserved communities. This research provides a structured evaluation of AI’s role within Bloom’s Taxonomy, offering insights into AI’s potential and limitations in Islamic education. The study contributes theoretically by linking AI-driven education with pedagogical principles, while practically, it guides educators and policymakers in strategically implementing AI while preserving Islamic ethical values. The study concludes that while AI enhances knowledge acquisition and skill-based learning, human educators remain essential for moral and ethical development. Future research should focus on developing ethical AI models, hybrid AI-human teaching approaches, and AI-driven affective learning systems to bridge gaps in AI-assisted moral and spiritual education.Qubahan Academic Journal1280The study concludes that while AI enhances knowledge acquisition and skill-based learning, human educators remain essential for moral and ethical development in Islamic education.Qubahan Academic Journal["Andri Nirwana AN", "Alfan Rifai", "Mohamad Ali", "Triono Ali Mustofa", "Viky Nur Vambudi", "Muh. Nur Rochim Maksum", "Mush'ab Umar Budihargo"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/4d46a81ad594768a27fa4b0d38b3d75e1dbc0bab d7095454aca59314fb254810d19c2765ee07f0f2Age is a significant risk factor for mild cognitive impairment (MCI) and Alzheimer disease (AD) and identifying brain age patterns is critical for comprehending the normal aging and MCI/AD processes. Prior studies have widely established the univariate relationships between brain regions and age, while multivariate associations remain largely unexplored. Herein, various artificial intelligence (AI) models were employed to perform brain age prediction using an MRI dataset (n=668). Then the optimal AI model was integrated with the Shapley additive explanations (SHAP) feature importance technique to identify the significant multivariate brain regions involved in this prediction. Our results indicated that the deep learning model (referred to as AgeNet) tremendously outperformed the conventional machine learning models for brain age prediction, and AgeNet integrated with SHAP (referred to as AgeNet-SHAP) identified all ground-truth perturbed regions as key predictors of brain age in semi-simulation, proved the validity of our methodology. In the experimental dataset, compared to cognitively normal (CN) participants, MCI exhibited moderate differences in brain regions, whereas AD had highly robust and widely distributed regional differences. The individualized AgeNet-SHAP regional features further showed associations with clinical severity scores in the AD continuum. These results collectively facilitate data-driven predictive modelling approaches for disease progression, diagnostics, prognostics, and personalized medicine efforts.medRxiv370The results indicated that the deep learning model tremendously outperformed the conventional machine learning models for brain age prediction, and AgeNet integrated with SHAP identified all ground-truth perturbed regions as key predictors of brain age in semi-simulation, proved the validity of the methodology.["Gauri Darekar", "T. Murad", "Hui-Yuan Miao", "D. Thakuri", "Alzheimer\u2019s Disease Neuroimaging", "B. InitiativeGanesh", "Chand"]2025-03-04T00:00:00https://www.semanticscholar.org/paper/d7095454aca59314fb254810d19c2765ee07f0f2 04d2d2621cae0d1c583e37d9ae0352112ba179f9 Purpose This paper aims to assume the responsibility of examining the shifting patterns of legal liability for failures that result from the integration of artificial intelligence (AI) in the public domain. It explores aspects such as the current legal implications, accountability mechanisms of AI errors and potential concerns and issues and proffered solutions for the complex issues that surround AI-related mistakes in public administration. Toward this end, the study outlines a central problem that is defined by the complex nature of errors that arise when AI is applied within the public service. Design/methodology/approach AI systems have recently been implemented into the public sectors and have influenced positive changes in efficiency and decision-making. However, the development and complication of AI technologies have raised profound worries on accountability in the case of mistakes in public sector. Findings As international governments increasingly rely on AI for critical selection and planning processes, establishing a clean prison system to educate and allocate responsibility when errors occur is paramount. What it has been found to have the potential to guide policy makers, criminologists and AI planners toward the challenges of implementing AI in the public sector easy to navigate. Finally, the research seeks to assess the potential of AI in public administration and will also serve to create a certain level of transparency, accountability and public trust. Research limitations/implications To provide a comprehensive response, the research employs a multifaceted methodology that encompasses a thorough literature review, in-depth legal analysis, regulatory assessment, exploration of various liability models, consideration of challenges and ethical considerations and real-world case studies. This holistic approach aims to shed light on the intricate web of legal responsibility and accountability entwined with AI in the public sector. Practical implications Although as a tool, AI is different from the human agents who use it, and defining and attributing legal responsibility for such errors becomes a challenging task because of the classification of AI as either software or a tool, and the accountability of its human users. Social implications Consequently, the primary research question emerges: “‘Employing’ AI in the public sector: how can legal responsibility for errors be assigned and governed in ways that respond to the plural employment-aspects of AI?” Originality/value The significance of this research lies in its ability to address the emerging challenges associated with AI adoption in the public sector. As international governments increasingly rely on AI for critical selection and planning processes, establishing a clean prison system to educate and allocate responsibility when errors occur is paramount. What it has been found to have the potential to guide policy makers, criminologists and AI planners toward the challenges of implementing AI in the public sector. International Journal of Law and Management660The primary research question emerges: “‘Employing’ AI in the public sector: how can legal responsibility for errors be assigned and governed in ways that respond to the plural employment-aspects of AI?International Journal of Law and Management["Ahmed Oudah Mohammed Al-Dulaimi", "Mohammed Abd-Al Wahab Mohammed"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/04d2d2621cae0d1c583e37d9ae0352112ba179f9 097566c44c530925ef185ed11e042fd4334544d5Medicine has become increasingly receptive to the use of artificial intelligence (AI). This overview of systematic reviews (SRs) aims to categorise current evidence about it and identify the current methodological state of the art in the field proposing a classification of AI model (CLASMOD-AI) to improve future reporting. PubMed/MEDLINE, Scopus, Cochrane library, EMBASE and Epistemonikos databases were screened by four blinded reviewers and all SRs that investigated AI tools in clinical medicine were included. 1923 articles were found, and of these, 360 articles were examined via the full-text and 161 SRs met the inclusion criteria. The search strategy, methodological, medical and risk of bias information were extracted. The CLASMOD-AI was based on input, model, data training, and performance metric of AI tools. A considerable increase in the number of SRs was observed in the last five years. The most covered field was oncology accounting for 13.9% of the SRs, with diagnosis as the predominant objective in 44.4% of the cases). The risk of bias was assessed in 49.1% of included SRs, yet only 39.2% of these used tools with specific items to assess AI metrics. This overview highlights the need for improved reporting on AI metrics, particularly regarding the training of AI models and dataset quality, as both are essential for a comprehensive quality assessment and for mitigating the risk of bias using specialized evaluation tools.Frontiers in Digital Health560The need for improved reporting on AI metrics, particularly regarding the training of AI models and dataset quality, is highlighted, as both are essential for a comprehensive quality assessment and for mitigating the risk of bias using specialized evaluation tools.Frontiers in Digital Health["G. Morone", "Luigi De Angelis", "Alex Martino Cinnera", "Riccardo Carbonetti", "Alessio Bisirri", "I. Ciancarelli", "M. Iosa", "Stefano Negrini", "C. Kiekens", "Francesco Negrini"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/097566c44c530925ef185ed11e042fd4334544d5 ba11a2a73be6136a076d10de17a7ecca8652b203Smart Learning Environments530This research delves into the transformative potential of artificial intelligence (AI) interventions in advancing reading comprehension, sparking learner engagement, and empowering self-regulated learning and provides educators, policymakers, and curriculum developers insights into integrating AI into effective educational practices.Smart Learning Environments["Haniye Shafiee Rad"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/ba11a2a73be6136a076d10de17a7ecca8652b203 da783565fe67af1871079c8ad38545c662904d2dSmart farming leverages Artificial Intelligence (AI) to address modern agricultural sustainability challenges. This study investigates the application of machine learning (ML), deep learning (DL), and time series analysis in agriculture through a systematic literature review following the PRISMA methodology. The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management. ML algorithms facilitate tasks like crop selection and soil fertility classification, while DL techniques contribute to forecasting crop production and commodity prices. Additionally, time series analysis is employed for demand forecasting of crops, commodity price prediction, and forecasting crop yield production. The focus of this article is to provide a comprehensive overview of ML and DL techniques within the farming industry. Utilizing crop datasets, ML algorithms are instrumental in classifying soil fertility, crop selection, and various other aspects. DL algorithms, when applied to farming data, enable effective time series analysis and crop selection. By synthesizing the integration of these technologies, this review underscores their potential to enhance decision-making in agriculture and mitigate food scarcity challenges in the future.Sustainability710The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management, and underscores their potential to enhance decision-making in agriculture and mitigate food scarcity challenges in the future.Sustainability["Zulfiqar Ali", "Asif Muhammad", "Nangkyeong Lee", "Muhammad Waqar", "S. Lee"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/da783565fe67af1871079c8ad38545c662904d2d 583bb60a50c7d2132e1064884aaed28978eb9762Hydrology relates to many complex challenges due to climate variability, limited resources, and especially, increased demands on sustainable management of water and soil. Conventional approaches often cannot respond to the integrated complexity and continuous change inherent in the water system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing the most important facets of hydrological research, including soil and land surface modeling, streamflow, groundwater forecasting, water quality assessment, and remote sensing applications in water resources. In soil and land modeling, AI techniques could further enhance accuracy in soil texture analysis, moisture estimation, and erosion prediction for better land management. Advanced AI models could also be used as a tool to forecast streamflow and groundwater levels, therefore providing valuable lead times for flood preparedness and water resource planning in transboundary basins. In water quality, AI-driven methods improve contamination risk assessment, enable the detection of anomalies, and track pollutants to assist in water treatment processes and regulatory practices. AI techniques combined with remote sensing open new perspectives on monitoring water resources at a spatial scale, from flood forecasting to groundwater storage variations. This paper’s synthesis emphasizes AI’s immense potential in hydrology; it also covers the latest advances and future prospects of the field to ensure sustainable water and soil management.Sustainability1910This review paper revisits how artificial intelligence is dramatically changing the most important facets of hydrological research, including soil and land surface modeling, streamflow, groundwater forecasting, water quality assessment, and remote sensing applications in water resources.Sustainability["Seyed M. Biazar", "G. Golmohammadi", "Rohit R. Nedhunuri", "Saba Shaghaghi", "Kourosh Mohammadi"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/583bb60a50c7d2132e1064884aaed28978eb9762 b3033ac4c02d604ed0b485d27da2231b915c0c26European Radiology170An AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features, including nodules that have very subtle features.European radiology["Ward Hendrix", "N. Hendrix", "E. Scholten", "B. van Ginneken", "M. Prokop", "Matthieu Rutten", "C. Jacobs"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/b3033ac4c02d604ed0b485d27da2231b915c0c26 87b0154d7c70341ca2be1e5a17dba2448abdfe82BACKGROUND AND OBJECTIVES The recent rise of artificial intelligence (AI) chatbots has attracted many users worldwide. However, expert evaluation is essential before relying on them for transfusion medicine (TM)-related information. This study aims to evaluate the performance of AI chatbots for accuracy, correctness, completeness and safety. MATERIALS AND METHODS Six AI chatbots (ChatGPT 4, ChatGPT 4-o, Gemini Advanced, Copilot, Anthropic Claude 3.5 Sonnet, Meta AI) were tested using TM-related prompts at two time points, 30 days apart. Their responses were assessed by four TM experts. Evaluators' scores underwent inter-rater reliability testing. Responses from Day 30 were compared with those from Day 1 to evaluate consistency and potential evolution over time. RESULTS All six chatbots exhibited some level of inconsistency and varying degrees of evolution in their responses over 30 days. None provided entirely correct, complete or safe answers to all questions. Among the chatbots tested, ChatGPT 4-o and Anthropic Claude 3.5 Sonnet demonstrated the highest accuracy and consistency, while Microsoft Copilot and Google Gemini Advanced showed the greatest evolution in their responses. As a limitation, the 30-day period may be too short for a precise assessment of chatbot evolution. CONCLUSION At the time of the conduct of this study, none of the AI chatbots provided fully reliable, complete or safe responses to all TM-related prompts. However, ChatGPT 4-o and Anthropic Claude 3.5 Sonnet show the highest promise for future integration into TM practices. Given their variability and ongoing development, AI chatbots should not yet be relied upon as authoritative sources in TM without expert validation.Vox Sanguinis190None of the AI chatbots provided fully reliable, complete or safe responses to all TM-related prompts, however, ChatGPT 4-o and Anthropic Claude 3.5 Sonnet show the highest promise for future integration into TM practices.Vox sanguinis["Prateek Srivastava", "Ashish Tewari", "A. Al\u2010Riyami"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/87b0154d7c70341ca2be1e5a17dba2448abdfe82 7fac6edea5a36b12f25779eec00bf5a119254db1This paper uses existing studies to explore how Artificial Intelligence (AI) advancements enhance recruitment, retention, and the effective management of a diverse workforce in South Africa. The extensive literature review revealed key themes used to contextualize the study. This study uses a meta-narrative approach to literature to review, critique and express what the literature says about the role of AI in talent recruitment, retention and diversity mapping within South Africa. An unobtrusive research technique, documentary analysis, is used to analyze literature. The findings reveal that South Africa’s Human Resource Management (HRM) landscape, marked by a combination of approaches, provides an opportunity to cultivate alternative methods attuned to contextual conditions in the global South. Consequently, adopting AI in recruiting, retaining, and managing a diverse workforce demands a critical examination of the colonial/apartheid past, integrating contemporary realities to explore the potential infusion of contextually relevant AI innovations in managing South Africa’s workforce.Journal of Infrastructure Policy and Development650The findings reveal that South Africa’s Human Resource Management (HRM) landscape provides an opportunity to cultivate alternative methods attuned to contextual conditions in the global South, and adopting AI in recruiting, retaining, and managing a diverse workforce demands a critical examination of the colonial/apartheid past.Journal of Infrastructure, Policy and Development["Ashika Maharaj", "Grace O. Obalade"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/7fac6edea5a36b12f25779eec00bf5a119254db1 6ba9f0ec983805aeb0bc842b708ee08ea93946f0The application of Artificial Intelligence (AI) in Indonesia’s legal processes offers opportunities to enhance efficiency and transparency. However, existing regulations do not specifically address critical aspects such as ethics, accountability, and security. This study examines the current legal framework, prospects, and challenges of AI implementation within the criminal justice system in the digital era. The study employs a normative juridical method, analyzing relevant laws and regulations alongside a comparative study of practices in other countries. The data is analyzed qualitatively to identify regulatory gaps. The findings reveal that AI holds significant potential in improving the efficiency of legal document management. Nonetheless, major challenges persist, including algorithmic bias, data protection concerns, and institutional resistance to technological adoption. Specific regulations, strengthened digital infrastructure, and capacity-building programs for legal practitioners are urgently needed to ensure a fair and sustainable application of AI in the justice system.Jurnal Riset Multidisiplin Edukasi00The findings reveal that AI holds significant potential in improving the efficiency of legal document management, Nonetheless, major challenges persist, including algorithmic bias, data protection concerns, and institutional resistance to technological adoption.Jurnal Riset Multidisiplin Edukasi["Zahra Kamila"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/6ba9f0ec983805aeb0bc842b708ee08ea93946f0 3b0f1b4f56c089c74910b46e5c82b0e2dda93882Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries from healthcare to education, and now, the realm of governance and international diplomacy. This paper examines the multifaceted nature of AI, beginning with its core definitions and classifications—narrow, general, and superintelligent AI—and investigates its potential to automate processes, enhance decision-making, and generate predictive insights. In parallel, the study explores the discipline of diplomacy, emphasizing its role in resolving conflicts, fostering mutual understanding, and maintaining global stability. A significant portion of this paper is devoted to analyzing the challenges facing modern diplomacy, including misinformation, transparency deficits, voter apathy, bureaucratic inefficiencies, social inequality, election integrity issues, polarization, and corruption. For each challenge, a range of AI tools—such as Factmata, Full Fact, Voatz, Pol.is, and others—are discussed as potential solutions to promote democratic governance. The research employs a mixed-methods approach, integrating literature reviews, expert interviews, quantitative performance analyses, and case studies. Additionally, this paper identifies further AI-driven platforms that could bolster democracy, such as civic tech chatbots, automated legislative analysis systems, and predictive voter turnout models. The findings underscore the transformative potential of AI in democratizing governance, while also calling for careful consideration of ethical and regulatory frameworks.  Journal of Informatics Education and Research00The multifaceted nature of AI is examined, beginning with its core definitions and classifications—narrow, general, and superintelligent AI—and investigates its potential to automate processes, enhance decision-making, and generate predictive insights.Journal of Informatics Education and Research["Dr. Sumit Chauhan", "Dr. Nivedita Sharma", "Dr. Rahul Kumar"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/3b0f1b4f56c089c74910b46e5c82b0e2dda93882 de6bbc0929c4f0e88b933e5b804709ef9414a06cBMC Medical Informatics Decis. Mak.650A systematized review of the arguments supporting and opposing this purported necessity for explainability of artificial intelligence technologies in medical applications contests the claim that lack of explainability compromises clinician due diligence and undermines epistemological responsibility.BMC Medical Informatics and Decision Making["Justin Blackman", "Richard Veerapen"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/de6bbc0929c4f0e88b933e5b804709ef9414a06c 855287cd0f8e11c278b298ff41b8bfd4511e634dThis paper contributes to the ongoing discourse on integrating artificial intelligence (AI) technologies into educational settings while addressing the challenges within military institutions. Specifically, we investigate how the United States Military Academy (USMA) can integrate emerging technology into classrooms while upholding core military values. We conducted a comprehensive assessment of potential AI applications at USMA, culminating in the development of a use-case feasibility index for educational purposes. We developed an AI-powered platform that would enable cadets and faculty to create customizable chatbots aimed at enhancing learning experiences. We devised Portuguese AI chat and feedback bots aligned with Standard Portuguese (LP204) curriculum objectives, facilitating natural conversations, and delivering personalized feedback to users. Through a systematic test conducted across all LP204 sections, followed by a post-application survey, we examined the efficacy that AI can have on language learning programs. Findings reveal positive feedback, suggesting the potential utility of AI-driven educational tools.Industrial and Systems Engineering Review70An AI-powered platform that would enable cadets and faculty to create customizable chatbots aimed at enhancing learning experiences and a comprehensive assessment of potential AI applications at USMA, culminating in a use-case feasibility index for educational purposes.Industrial and Systems Engineering Review["Conner Leggett", "Maximus Marchi", "McKenzie Muse", "Samuel Wesley", "Jonathan Mellon"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/855287cd0f8e11c278b298ff41b8bfd4511e634d 85867a8e173c5440d8513c8e719ba8363cd0cc17Artificial Intelligence is the concept adopted by Financial institutions these days in order to safeguard and strengthen their cybersecurity practices and overcome cybercrimes that are  increasing worldwide. With the increase in the concept of Artificial Intelligence, the increase in cybersecurity issues has also risen. Artificial Intelligence has the capacity to detect the threat, predict the future and generate an automatic response. This paper discussed the role that AI has played in the improvement of cybersecurity among the various financial institutions and its capacity to detect, remove, and lessen the effects of a huge number of cyber threats that exist from smaller ones to the ones affecting a wider area.Journal of Informatics Education and Research200The role that AI has played in the improvement of cybersecurity among the various financial institutions and its capacity to detect, remove, and lessen the effects of a huge number of cyber threats that exist from smaller ones to the ones affecting a wider area is discussed.Journal of Informatics Education and Research["Simanpreet Kaur", "Anjali"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/85867a8e173c5440d8513c8e719ba8363cd0cc17 86cc05967111c457f21b512ec3524cdfa9bd8eacTraditional marketing still plays a vital role, especially for reaching demographics that are less active online. It can create a strong local presence and build brand recognition through consistent visibility. While digital marketing grows, many businesses find that a balanced mix of traditional and digital strategies yields the best results. Traditional marketing provides tangible, high-impact methods that complement the precision and reach of digital campaigns. However, Artificial Intelligence (AI) has had a transformative impact on traditional marketing, enhancing its effectiveness and efficiency. Researchers have explored the possible impact of (AI) on traditional marketing. Because (AI) has emerged as a powerful tool and played important role in data analysis, cost efficiency, improved targeting and personalization, automation which further help and excel the strategic decision making of marketers.Journal of Informatics Education and Research120AI has emerged as a powerful tool and played important role in data analysis, cost efficiency, improved targeting and personalization, automation which further help and excel the strategic decision making of marketers.Journal of Informatics Education and Research["Dr. Palwinder Kumar", "Ms. Tripti", "Dr. Sukhdeep Kaur", "Mr. Parteek Sood Senior Manager"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/86cc05967111c457f21b512ec3524cdfa9bd8eac 0fe6d4adbb48c500c26a9b9001211bc0ceeaceef Purpose This study aims to explore key questions within the context of Asian countries: How do artificial intelligence (AI) and blockchain adoption in accounting influence enterprise risk management and environmental, social and governance (ESG) performance? What role does enterprise risk management have as a mediator in this relationship? In addition, how does environmental uncertainty shape the interplay between AI and blockchain adoption in accounting, enterprise risk management and ESG performance? Design/methodology/approach The authors collected data from Thomson Reuters Eikon Datastream, initially targeting the 20 Asian countries with the highest gross domestic product (GDP) per capita. Using stringent selection criteria, the research sample included 22,212 firms from these countries: Bahrain, China, Hong Kong, Indonesia, Israel, Japan, Jordan, Kazakhstan, South Korea, Kuwait, Lebanon, Malaysia, Oman, Qatar, Saudi Arabia, Singapore, Sri Lanka, Thailand, the United Arab Emirates and Vietnam. After a rigorous screening process, the final sample comprised 1,742 firms, representing 17,420 firm-year observations over the 2014–2023 period. This paper applied maximum likelihood structural equation modeling to analyze the data. Findings The findings reveal that both AI and blockchain adoption in accounting, along with enterprise risk management, positively impact ESG performance in the Asian context. Enterprise risk management serves as a mediating factor between AI and blockchain adoption in accounting and ESG performance. In addition, environmental uncertainty significantly moderates the relationships between AI and blockchain adoption in accounting and enterprise risk management, as well as between enterprise risk management and ESG performance. Practical implications This study uncovers the interplay between internal factors – such as AI and blockchain adoption in accounting and enterprise risk management – and external factors, notably environmental uncertainty, in fostering sustainable value for Asian firms. Internal factors enable firms to integrate ESG considerations into their operations, facilitating risk mitigation and enhancing ESG performance. Meanwhile, heightened environmental uncertainty drives the adoption of sustainable practices. Consequently, Asian Governments should prioritize the development of regions characterized by high environmental uncertainty to advance national sustainable development goals and encourage responsible business practices. Originality/value This study contributes to the existing literature by uncovering the combined effects of internal and external factors on ESG performance, offering empirical evidence from Asian countries with high GDP per capita. Specifically, it underscores the efficacy of AI and blockchain adoption in accounting and enterprise risk management, as well as the moderating role of environmental uncertainty, within the Asian context. The International Journal of Organizational Analysis1110The findings reveal that both AI and blockchain adoption in accounting, along with enterprise risk management, positively impact ESG performance in the Asian context, as well as the moderating role of environmental uncertainty.International Journal of Organizational Analysis["N. Nguyen", "Malik Abu Afifa", "Vo Thi Truc Dao", "Duong Van Bui", "Hien Vo Van"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/0fe6d4adbb48c500c26a9b9001211bc0ceeaceef 739dd9a4be5b9a3d65eac23f6376757129e60d37The way traditional human resource management (HRM) is carried out in domestic and international organizations is changing because of artificial intelligence (AI). AI applications have become widely used in human resources management, controlling personnel, influencing recruitment, accounting allocation of resources, and the process by which decisions are made. In the past ten years, HRM has been affected by AI through the automation of functions such as hiring, performance appraisal, and workforce planning.  The research on AI in HRM is relatively fragmented and not systematic. In particular, there is a need for a thorough analysis of AI's role within multinational enterprises -for instance, enterprise-wide technology adoption varies by region. At present, AI plays a role in recruitment, because of the need for skilled employees to support economic growth, organizations constantly recruit, and recruitment functions are highly mobile. In particular, in the technology sector, companies use AI and machine learning (ML) tools to improve talent acquisition. Autonomous testing and self-learning algorithms figure into identifying, evaluating, and retaining candidates. The use of AI in HRM raises questions about efficiency, fairness, and decision-making. A sample of 219 people from learning organization were surveyed to know the factors that determines different Role of Artificial Intelligence in Talent Management in Learning Organisation and found that Talent acquisition and recruitment, Workforce Planning & Retention, Performance Management and Biasness are the factors showing role of AI in talent management.Journal of Informatics Education and Research130A sample of 219 people from learning organization were surveyed to know the factors that determines different Role of Artificial Intelligence in Talent Management in Learning Organisation and found that Talent acquisition and recruitment, Workforce Planning & Retention, Performance Management and Biasness are the factors showing role of AI in talent management.Journal of Informatics Education and Research["Dr. Simranjeet Kaur Bagga", "Ms.Chitra Jha", "Dr. Venkata Harshavardhan", "R. Dornadula"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/739dd9a4be5b9a3d65eac23f6376757129e60d37 3c40f492dbf31a91b7a2a603558fc1b3e6dd2872  This research aimed to plant a proposed conception for developing the administrative system in light of artificial intelligence applications in higher education in Yemen. The researcher explained the problem, objectives, and importance of the research, and referred to some relevant Arabic and foreign studies. The research tool also consisted of a questionnaire that was applied electronically, with a sample consisted of 137 individuals from higher education institutions in Yemen. The results demonstrated a high relative importance of using artificial intelligence in the administrative system. The research also mentioned the foundations of the proposed conception, the most important of which were: predicting the possibilities and imagining the desired future, taking into account that this future is variable due to artificial intelligence applications. Realism is achieved by monitoring the current reality and basing the goals that it seeks to achieve on available resources. Specific and prioritized goals are set based on statistics, data, and accurate information obtained through analysis of the internal and external environment. Flexibility and continuity are crucial, ensuring that the conception is an interconnected series of overlapping processes. Continuous follow-up and evaluation are necessary to track the success of the conception and assess the current situation, identifying its strengths and weaknesses. Several recommendations have been made, including: the development of higher education in light of artificial intelligence applications focuses on improving the technical infrastructure to enhance administrative performance efforts and to improve funding, spending, and work opportunities. The researcher also suggested providing highly qualified specialists to support the technical aspects by fixing network damage. Journal of Engineering and Technological Sciences260Several recommendations have been made, including: the development of higher education in light of artificial intelligence applications focuses on improving the technical infrastructure to enhance administrative performance efforts and to improve funding, spending, and work opportunities.Journal of Engineering and Technological Sciences - JOEATS["Taghreed Mahfooth Sultan Al-Zubairi"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/3c40f492dbf31a91b7a2a603558fc1b3e6dd2872 782a3f34ecd2da209d59617051a55bae69e2161cThis article explores how artificial intelligence (AI) is reshaping the path of developments in the accounting industry, emphasizing the importance of integrated applications, particularly in the role of a unified multi-source heterogeneous database and the use of deep learning to optimize data processing. It also highlights the critical value of human-computer interaction systems in enhancing the efficiency of accounting information retrieval and decision-making. Through the effective combination of support systems and operational rules, it ensures the orderly execution of accounting tasks. The article further analyzes the different application mechanisms of AI in routine and complex decision-making, providing scientific recommendations. Additionally, it critically points out the limitations of current AI accounting products, such as limited functionality and insufficient intelligence, while emphasizing the potential value of AI in financial information disclosure and supervision, as well as its importance in improving the accounting credit system.International Journal of Knowledge Management110International Journal of Knowledge Management["BaQun Li"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/782a3f34ecd2da209d59617051a55bae69e2161c 3295483893957bbadcac9b7ead988942a33b7ecaPurpose This paper aims to investigate the legal challenges arising from the increasing integration of artificial intelligence (AI) within the financial industry. It examines issues such as data privacy, cyber security, fraud and consumer protection, as well as ethical concerns like algorithmic bias, fairness and transparency. The study explores ways to create a regulatory environment that supports innovation while ensuring financial stability and consumer protection. It also examines the impact of FinTech innovations, such as mobile banking and block chain, on Tanzania’s legal landscape, providing recommendations for adapting laws to better manage AI and FinTech integration. Design/methodology/approach The study adopts a doctrinal legal approach and comparative analysis to assess Tanzanian laws, identifying gaps and proposing solutions. It draws on international legal frameworks and applies both deductive and inductive reasoning, alongside key rules of statutory interpretation, including the mischief rule, literal rule, golden rule and purposive approach. Key Tanzanian laws, such as the Cybercrime Act (2015), the Electronic Transactions Act (2015), the Personal Data Protection Act (2022) and the National Payments System Act (2015), are critically examined. Comparative analysis with international legal standards further highlights areas for improvement and opportunities for legal harmonization. Findings The study revealed that the existing legal instruments in Tanzania, such as the Cybercrime Act 2015, Electronic Transaction Act 2015 and others, are inadequate in addressing the specific legal challenges posed by AI and FinTech. There is a significant need for specialized legal frameworks that facilitate financial inclusion in the digital economy and protect consumers from potential financial risks associated with emerging technologies. The integration of AI and FinTech has democratized access to financial services but has also introduced unforeseen legal complexities and regulatory gaps. Originality/value This paper presents an in-depth analysis of the impact of AI on Tanzania’s financial sector. Its originality and value stem from its focused exploration of AI’s influence on FinTech, detailed legal analysis and practical legislative reform recommendations. The discussion provides insights into the local legal framework and its preparedness to handle the complexities introduced by AI and automated contracts. This localized analysis fills a significant gap in the literature, offering valuable information for policymakers and scholars interested in FinTech. The paper contributes to the discourse on digital economy regulations and the adaptation of legal frameworks to emerging technologies. International Journal of Law and Management420The existing legal instruments in Tanzania are inadequate in addressing the specific legal challenges posed by AI and FinTech, revealing a significant need for specialized legal frameworks that facilitate financial inclusion in the digital economy and protect consumers from potential financial risks associated with emerging technologies.International Journal of Law and Management["Abdallah Mrindoko Ally"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/3295483893957bbadcac9b7ead988942a33b7eca 5f861472c8dce59c2a860e6a0a1fc7e7a9806af3In the recent years, since the introduction into the market of generative artificial development (AI) tools, the discussions of copyright as related to AIgenerated works have aroused more questions than answers. The creative activity, the humans’ exclusive province before, has been now changing. The problems of AI-generated works have become the concern of the creative industries, education, library acquisitions, etc. At the same time, the conflict has blazed up around using copyrighted works for educating AI systems. During the recent two years, in many countries the legislators have been striving to regularize the challenging area and to find solutions. The authors review the latest initiatives, recommendations, active lawsuits though the lens of AI users and developers and the copyright owners. Within the range of AI-related issues, the authors focus on the dual problem: on one hand, on how to deal with copyrighted works used as the source to train AI tools, and, on the other hand, on who owns the rights to the contents generated with these tools. The authors’ arguments and recommendations may be useful for elaborating the approach to defining of the status of AI-generated works in various areas, including librarianship.Scientific and Technical Libraries10The authors focus on the dual problem: on one hand, on how to deal with copyrighted works used as the source to train AI tools, and, on the other hand, on who owns the rights to the contents generated with these tools.Scientific and Technical Libraries["Y. L. Shrayberg", "K. Y. Volkova"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/5f861472c8dce59c2a860e6a0a1fc7e7a9806af3 4482a92248cd1ef3ce5ab9504552d215008da57eInternational Journal of Biomedicine00International Journal of Biomedicine[]2025-03-05T00:00:00https://www.semanticscholar.org/paper/4482a92248cd1ef3ce5ab9504552d215008da57e 160dfa7a7c84fd2c102e9b9097841b0836799459International Journal of Human-Computer Interaction690International Journal of Human–Computer Interaction["Luis-Alfonso Maldonado-Canca", "Juan-Pedro Cabrera-S\u00e1nchez", "S. Molinillo"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/160dfa7a7c84fd2c102e9b9097841b0836799459 5efb2c6c93f71ee933ba88305def3e94ade79d1fComputers, Informatics, Nursing190Computers, informatics, nursing : CIN["Heather Carter-Templeton", "M. Oermann", "Jacqueline K Owens", "Gabriel M Peterson", "Joy Mbadiwe", "Mohammed Quazi", "Hannah E Bailey"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/5efb2c6c93f71ee933ba88305def3e94ade79d1f 0996a637ba344359e63c2bbb0c5bd98e6de35a0aWith the growing severity of global climate change, achieving carbon neutrality has become a central focus worldwide. The intersection of population studies and carbon neutrality introduces significant challenges in predicting and optimizing energy consumption, as demographic factors play a crucial role in shaping carbon emissions. This paper proposes a model based on a Region-based Convolutional Neural Network (RCNN) and Generative Adversarial Network (GAN), enhanced with a dual-stage attention mechanism for optimization. The model automatically extracts key features from complex demographic and carbon emission data, leveraging the attention mechanism to assign appropriate weights, thereby capturing the behavioral patterns and trends in energy consumption driven by population dynamics more effectively. By integrating multi-source data, including historical carbon emissions, population density, demographic trends, meteorological data, and economic indicators, experimental results demonstrate the model's outstanding performance across multiple datasets.Journal of Organizational and End User Computing280This paper proposes a model based on a Region-based Convolutional Neural Network and Generative Adversarial Network enhanced with a dual-stage attention mechanism for optimization, enhanced with a dual-stage attention mechanism for optimization, that automatically extracts key features from complex demographic and carbon emission data.Journal of Organizational and End User Computing["Sida Guo", "Ziqi Zhong"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/0996a637ba344359e63c2bbb0c5bd98e6de35a0a e6c2afd3bda47ed50413aab9e88c8dd9ce8661ff00["Luiz Vianna Sobrinho", "Leandro Modolo", "Maira Araujo de Santana", "Giselle Machado Magalh\u00e3es Moreno", "Fabiano Tonaco Borges", "Wellington Pinheiro dos Santos"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/e6c2afd3bda47ed50413aab9e88c8dd9ce8661ff 901ade4d08c190d63198b4029d51165de24b18e4npj Digit. Medicine360By emphasizing gender equity, fairness, and responsible design, LMICs can harness AI’s power to enhance healthcare outcomes and advance equitable care and should be central to AI deployment.NPJ Digital Medicine["Sushmita Adhikari", "Iftikhar Ahmed", "Deepak Bajracharya", "Bishesh Khanal", "Chandrasegarar Solomon", "K. Jayaratne", "Khondaker Abdullah Al Mamum", "Muhammad Shamim", "Hayder Talukder", "Sunila Shakya", "Suresh Manandhar", "Zahid Ali Memon", "Moinul Haque Chowdhury", "Ihtisham ul Islam", "Noor Sabah Rakhshani", "M. I. Khan"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/901ade4d08c190d63198b4029d51165de24b18e4 86620d982a9aac50c68e5a6a6c1fdc2fe9c8be36Indian Journal of Clinical Biochemistry100Indian Journal of Clinical Biochemistry["Manoj Khokhar", "Dharmveer Yadav", "Praveen Sharma"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/86620d982a9aac50c68e5a6a6c1fdc2fe9c8be36 ede07b2b72f540efd4153e0496a320d590162f72Journal of Real Estate Litterature380Journal of Real Estate Literature["Ashok Kandipati"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/ede07b2b72f540efd4153e0496a320d590162f72 6091d4ec76c97544f464f03fffeed2d9d826f88900["Leonie van Haeren", "Shaarini Ravitharan", "E. Murray", "Ephraim Barrera"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/6091d4ec76c97544f464f03fffeed2d9d826f889 c73de26bdf796e2f97eb94957c551b1f7c4a8ad8The purpose of this study was to understand the motivations for and impact of participating in program coursework and related workplace-based learning and provide insights into the emerging field of AI workforce training. This study drew on adult learning theory and the acknowledgement that learning takes place within the context of a complex and uncertain career landscape as described by the chaos theory of careers. A cross-sectional survey research study was conducted to determine participant motivations for attending the AI program as well as the impact of participation in the AI program on individuals in regard to personal benefits and career trajectories. Participants in the AI program were motivated by the opportunity for job growth, whether that was in current or future roles. Personal benefits, such as upskilling, career advancement potential, and growth in confidence were all reported outcomes of participation in the program. Though most respondents in this study indicated holding current roles in computer science areas, a majority were seeking to grow their AI content knowledge and skills in order to assume more responsibilities in this growing area or to move into new roles which emphasize AI specifically.Education sciences270Though most respondents in this study indicated holding current roles in computer science areas, a majority were seeking to grow their AI content knowledge and skills in order to assume more responsibilities in this growing area or to move into new roles which emphasize AI specifically.Education Sciences["Carla C. Johnson", "Sera Harold", "Jessica Chestnut", "Katherine Glover", "Janet B. Walton"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/c73de26bdf796e2f97eb94957c551b1f7c4a8ad8 1e34cbfbc19de60ecdfa3201b0e649703842f3cdInternational Journal of Research in Library Science00International Journal of Research in Library Science["Vikas Tukaramji Adlok"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/1e34cbfbc19de60ecdfa3201b0e649703842f3cd 901b09202b24f28484ee29289d6d404b436f5315Canadian Journal of African Studies / Revue canadienne des études africaines330Canadian Journal of African Studies / Revue canadienne des études africaines["Thompson Gyedu Kwarkye"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/901b09202b24f28484ee29289d6d404b436f5315 261155637bc9abdcdacd8c4a77542f5391c89916Anaesthesia40Anaesthesia["Cian Hurley"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/261155637bc9abdcdacd8c4a77542f5391c89916 2372e3f683ec9927daf4e89ef9555de08376f68eAI in Neuroscience910AI in Neuroscience["Minerva H. Zhou", "Emily Lin", "Allen Q. Ye", "Pratik Mukherjee", "Esther Yuh"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/2372e3f683ec9927daf4e89ef9555de08376f68e 4795dd8e4a9b27e59aa807cf9510188977b88800Cureus140Cureus["Kunmilayo Olayeye", "Christina Regine Owens-Charles", "Jashkumar Choudhari", "Esha Parikh", "Zhengrong Jerome Liang"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/4795dd8e4a9b27e59aa807cf9510188977b88800 2fb537493f099197069beed994e71b4c42915296International journal of latest research in engineering and technology00International Journal of Latest Research in Engineering and Technology (IJLRET)["Sergei Berezin"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/2fb537493f099197069beed994e71b4c42915296 674ae75f9ac52fde5b6236f57823506fa949c767With the significant progress of artificial intelligence (AI) and consciousness science, artificial consciousness (AC) has recently gained popularity. This work provides a broad overview of the main topics and current trends in AC. The first part traces the history of this interdisciplinary field to establish context and clarify key terminology, including the distinction between Weak and Strong AC. The second part examines major trends in AC implementations, emphasising the synergy between Global Workspace and Attention Schema, as well as the problem of evaluating the internal states of artificial systems. The third part analyses the ethical dimension of AC development, revealing both critical risks and transformative opportunities. The last part offers recommendations to guide AC research responsibly, and outlines the limitations of this study as well as avenues for future research. The main conclusion is that while AC appears both indispensable and inevitable for scientific progress, serious efforts are required to address the far-reaching impact of this innovative research path.00The main conclusion is that while AC appears both indispensable and inevitable for scientific progress, serious efforts are required to address the far-reaching impact of this innovative research path.["Aida Elamrani"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/674ae75f9ac52fde5b6236f57823506fa949c767 3490c4943e6e1f863410d7c5137caf03a4b9794fThis presentation places the topic of artificial intelligence in the context of the practice of medi­cine and, in particular, medical education. It defines the areas of clear benefit already observed, and of the eventual ones in the future, as well as the uncertainties and fears of their scope with potential loss of human control and potential loss of the humanistic component of the med­ical profession. Faced with its inexorable and non-programmable development, it raises the transcendental importance of regulating this development and application in good faith in all fields, especially that of medical education and the practice of medicineBoletín Academia Chilena de Medicina60Faced with its inexorable and non-programmable development, the topic of artificial intelligence is placed in the context of the practice of medi­cine and, in particular, medical education and the transcendental importance of regulating this development and application in good faith is raised.Boletín Academia Chilena de Medicina["Marcelo WOLFF REYES", "Gloria L\u00d3PEZ STEWART"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/3490c4943e6e1f863410d7c5137caf03a4b9794f edabe3f72382d772a5ec1bb67ac7cd7e38f57137This article explores the transformative impact of agentic workflows in healthcare settings, focusing on their implementation and effectiveness in addressing critical challenges in clinical operations. Agentic workflows, powered by advanced artificial intelligence technologies including domain-specific Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, represent a paradigm shift from traditional automation approaches. These intelligent systems demonstrate sophisticated capabilities in managing complex healthcare tasks, from clinical documentation to patient management. It examines the integration of these technologies across various healthcare domains, evaluating their performance through both technical metrics and clinical impact assessments. The article highlights significant improvements in operational efficiency, clinical decision support, and patient care delivery through the implementation of these advanced systems. Furthermore, it discusses future directions in healthcare AI, including enhanced subspecialty models, advanced natural language processing capabilities, and improved predictive analytics for population health management, providing a comprehensive overview of the evolving landscape of healthcare automation.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The article highlights significant improvements in operational efficiency, clinical decision support, and patient care delivery through the implementation of these advanced systems, and discusses future directions in healthcare AI, including enhanced subspecialty models, advanced natural language processing capabilities, and improved predictive analytics for population health management.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Manuel Joy"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/edabe3f72382d772a5ec1bb67ac7cd7e38f57137 92d646eca7f206211faad423c90287a349d4b3ea With the rapid development of technologies like big data, artificial intelligence (herein after AI), and blockchain, society is ushering into a new era of digital civilization. However, the same algorithms that assist in efficient decision-making for human society may also introduce issues of algorithmic discrimination. Therefore, this paper focuses on the judicial domain to deeply explore potential instances of algorithmic discrimination in AI. It identifies three key dimensions of algorithmic discrimination risks in the judicial AI domain: the ambiguity of algorithm usage boundaries, the diversity in discriminatory outcomes, and the injustice within the algorithmic environment. Building upon this, the author examines global AI governance landscapes to extract corresponding governance strategies and practical insights. Finally, a systematic regulatory approach for addressing algorithmic discrimination in judicial AI is proposed, unfolding across three levels and nine aspects: making algorithmic limitations explicit, diversifying algorithmic regulations, and justifying the algorithmic environment. This framework aims to contribute to a more reasonable, systematic, and just governance of algorithms in judicial AI.International Journal of Digital Law and Governance180A systematic regulatory approach for addressing algorithmic discrimination in judicial AI is proposed, unfolding across three levels and nine aspects: making algorithmic limitations explicit, diversifying algorithmic regulations, and justifying the algorithmic environment.International Journal of Digital Law and Governance["Landuo Dou", "Xiaodong Dou"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/92d646eca7f206211faad423c90287a349d4b3ea 9f5f77053a38f724989625f42666df19b6097646BACKGROUNDS Abdominal aortic aneurysms (AAA) carry significant risks, yet patient understanding is often limited, with online resources typically low quality. ChatGPT, an artificial intelligence (AI) chatbot, presents a new frontier in patient education, but concerns remain about misinformation. This study evaluates the quality of ChatGPT-generated patient information on AAA. METHODS Eight patient questions on AAA were sourced from a reputable online resource for patient information funded by the Australian Government's Healthdirect Australia (HDA) website and input into ChatGPT's free (ChatGPT-4o mini) and paid (ChatGPT-4) models. A vascular surgeon evaluated response appropriateness. Readability was assessed using the Flesch-Kincaid test. The Patient Education Materials Assessment Tool (PEMAT) measured understandability and actionability, with responses scoring ≥75% for both considered high-quality. RESULTS All responses were deemed clinically appropriate. Mean response length was longer for ChatGPT than HDA. Readability was at a college level for ChatGPT, while HDA was at a 10th to 12th-grade level. One response was high-quality (generated by paid ChatGPT) with a PEMAT actionability score of ≥75%. Actionability scores were otherwise low across all sources with ChatGPT responses more likely to contain identifiable actions, although these were often not clearly presented. ChatGPT responses were marginally more understandable than HDA. CONCLUSIONS ChatGPT-generated information on AAA was appropriate and understandable, outperforming HDA in both aspects. However, AI responses are at a more advanced reading level and lack actionable instructions. AI chatbots show promise as supplemental tools for AAA patient education, but further refinement is needed to enhance their effectiveness in supporting informed decision-making.ANZ journal of surgery260ChatGPT-generated information on AAA was appropriate and understandable, outperforming HDA in both aspects, however, AI responses are at a more advanced reading level and lack actionable instructions.ANZ journal of surgery["Harry Collin", "Chelsea Tong", "Abhishekh Srinivas", "Angus H Pegler", "Philip Allan", "Daniel Hagley"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/9f5f77053a38f724989625f42666df19b6097646 93dcb5522a1c5bdb0d7e14801ac574cd1ee47c2cBACKGROUND The increasing use of artificial intelligence (AI) in medical diagnosis and consultation promises benefits such as greater accuracy and efficiency. However, there is little evidence to systematically test whether the ideal technological promises translate into an improved evaluation of the medical consultation from the patient's perspective. This perspective is significant because AI as a technological solution does not necessarily improve patient confidence in diagnosis and adherence to treatment at the functional level, create meaningful interactions between the medical agent and the patient at the relational level, evoke positive emotions, or reduce the patient's pessimism at the emotional level. OBJECTIVE This study aims to investigate, from a patient-centered perspective, whether AI or human-involved AI can replace the role of human physicians in diagnosis at the functional, relational, and emotional levels as well as how some health-related differences between human-AI and human-human interactions affect patients' evaluations of the medical consultation. METHODS A 3 (consultation source: AI vs human-involved AI vs human) × 2 (health-related stigma: low vs high) × 2 (diagnosis explanation: without vs with explanation) factorial experiment was conducted with 249 participants. The main effects and interaction effects of the variables were examined on individuals' functional, relational, and emotional evaluations of the medical consultation. RESULTS Functionally, people trusted the diagnosis of the human physician (mean 4.78-4.85, SD 0.06-0.07) more than medical AI (mean 4.34-4.55, SD 0.06-0.07) or human-involved AI (mean 4.39-4.56, SD 0.06-0.07; P<.001), but at the relational and emotional levels, there was no significant difference between human-AI and human-human interactions (P>.05). Health-related stigma had no significant effect on how people evaluated the medical consultation or contributed to preferring AI-powered systems over humans (P>.05); however, providing explanations of the diagnosis significantly improved the functional (P<.001), relational (P<.05), and emotional (P<.05) evaluations of the consultation for all 3 medical agents. CONCLUSIONS The findings imply that at the current stage of AI development, people trust human expertise more than accurate AI, especially for decisions traditionally made by humans, such as medical diagnosis, supporting the algorithm aversion theory. Surprisingly, even for highly stigmatized diseases such as AIDS, where we assume anonymity and privacy are preferred in medical consultations, the dehumanization of AI does not contribute significantly to the preference for AI-powered medical agents over humans, suggesting that instrumental needs of diagnosis override patient privacy concerns. Furthermore, explaining the diagnosis effectively improves treatment adherence, strengthens the physician-patient relationship, and fosters positive emotions during the consultation. This provides insights for the design of AI medical agents, which have long been criticized for lacking transparency while making highly consequential decisions. This study concludes by outlining theoretical contributions to research on health communication and human-AI interaction and discusses the implications for the design and application of medical AI.Journal of Medical Internet Research420Journal of medical Internet research["Weiqi Guo", "Yang Chen"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/93dcb5522a1c5bdb0d7e14801ac574cd1ee47c2c 08a584c5f1c62f76de979b51bc84ecebc765f230The “One Belt, One Road” (OBOR) initiative, now widely referred to as the Belt and Road Initiative (BRI), represents one of the most ambitious infrastructure and economic development projects in modern history, encompassing over 140 participating countries. Despite its potential for fostering global connectivity and economic growth, OBOR projects face significant risks, including financial, operational, geopolitical, and environmental uncertainties. This study explores the potential of artificial intelligence (AI) to revolutionize risk management in OBOR infrastructure projects, addressing challenges such as cost overruns, project delays, and political instability. By leveraging AI technologies such as machine learning, natural language processing, predictive analytics, and risk assessment models, stakeholders can enhance their ability to identify, quantify, and mitigate risks in real-time. AI tools offer unparalleled capabilities in processing vast amounts of data from multiple sources, including financial reports, satellite imagery, and social media, to predict and analyse risks. For instance, AI-driven algorithms can monitor geopolitical developments to assess the likelihood of conflicts or trade disruptions affecting project timelines. Similarly, predictive models can forecast weather patterns and environmental hazards, enabling project planners to implement proactive strategies for mitigating potential disruptions. This study employs a mixed-methods approach, combining quantitative data analysis and qualitative case studies of OBOR infrastructure projects that have successfully implemented AI-driven risk management solutions. The findings demonstrate that AI significantly enhances decision-making accuracy, improves resource allocation, and reduces the probability of adverse events. Case studies from railway and port construction projects in Southeast Asia and Central Asia illustrate how AI tools have enabled project managers to optimize operations, minimize delays, and reduce costs. However, the study also identifies challenges associated with integrating AI into OBOR projects, including the high cost of technology adoption, the need for skilled professionals, and ethical concerns surrounding data privacy and algorithmic transparency. Moreover, disparities in digital infrastructure and AI readiness among OBOR partner countries pose additional barriers to widespread implementation. These challenges highlight the need for strategic investments in capacity-building and collaborative frameworks to ensure equitable access to AI technologies. The study concludes that AI has the potential to transform risk management practices in OBOR infrastructure projects, fostering greater efficiency, resilience, and sustainability. Policymakers and project stakeholders are encouraged to prioritize AI integration by establishing supportive regulatory environments, incentivizing innovation, and fostering partnerships between technology providers and infrastructure developers. Future research should focus on developing localized AI solutions tailored to the specific needs and contexts of OBOR partner countries, ensuring that the benefits of AI-driven risk management are accessible to all.International journal of research and innovation in social science00International Journal of Research and Innovation in Social Science["Lai Mun Keong"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/08a584c5f1c62f76de979b51bc84ecebc765f230 9fd5742cd8b6269f1e7300cfd5f037ee14695a94The increasing reliance on Artificial Intelligence (AI) for cyber defense has led to the development of advanced detection systems capable of identifying complex threats in real time. However, many of these systems function as “black boxes,” offering little insight into their decision-making processes. Explainable AI (XAI) seeks to address this limitation by providing transparent, interpretable outputs that empower security analysts to understand, validate, and trust AI-generated decisions. This paper reviews the state-of-the-art in XAI methodologies applied to cybersecurity, discusses key challenges such as balancing interpretability with model performance, and proposes a hybrid framework that integrates explainability into AI-based cyber defense systems. Through simulation-based benchmarking and case studies, we illustrate how XAI can enhance threat detection accuracy, streamline incident response, and ultimately foster greater trust in automated security solutions. Future research directions include the development of standardized XAI metrics for cybersecurity and the integration of real-time explanation engines into operational environments.International Journal of Advanced Research in Science, Communication and Technology30This paper reviews the state-of-the-art in XAI methodologies applied to cybersecurity, discusses key challenges such as balancing interpretability with model performance, and proposes a hybrid framework that integrates explainability into AI-based cyber defense systems.International Journal of Advanced Research in Science, Communication and Technology["Giriraj Agarwal"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/9fd5742cd8b6269f1e7300cfd5f037ee14695a94 12fb574e1fe895cccf79a6bd774c2adf811c89c1This article explores the critical role of advanced network security and artificial intelligence in protecting elderly populations from financial fraud and exploitation. As cybercriminals increasingly target vulnerable seniors, sophisticated technological countermeasures have emerged to create multi-layered defense systems. The article examines six key protective approaches: advanced threat detection systems that identify unusual patterns in financial transactions; behavioral analysis and anomaly detection that establish personalized usage baselines; enhanced authentication methods designed specifically for elderly users' unique needs; scam call and message filtering that operates at the telecommunications infrastructure level; predictive fraud prevention that anticipates emerging scam vectors before widespread impact; and collaborative protection networks that share intelligence across institutions. Through detailed case studies and technical analysis, the article demonstrates how these interconnected systems can effectively identify and neutralize fraudulent activities before seniors experience significant harm. The findings suggest that comprehensive digital protection frameworks can dramatically reduce elder fraud while preserving autonomy, highlighting the importance of age-inclusive design principles in cybersecurity solutions aimed at vulnerable populations.International Journal of Scientific Research in Computer Science Engineering and Information Technology00Six key protective approaches are examined, suggesting that comprehensive digital protection frameworks can dramatically reduce elder fraud while preserving autonomy, highlighting the importance of age-inclusive design principles in cybersecurity solutions aimed at vulnerable populations.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Sanchayan Chakraborty"]2025-03-05T00:00:00https://www.semanticscholar.org/paper/12fb574e1fe895cccf79a6bd774c2adf811c89c1 a991226302f16caa1e853b28f417fbbf45e4e34dThe integration of Cyber-Physical Systems (CPS) and Artificial Intelligence (AI) presents both opportunities and challenges. AI operates on the principle that “good things happen probabilistically,” while CPS adheres to the principle that “all bad things must not happen,” requiring uncertainty-awareness. Furthermore, the difference between AI’s resource accessibility assumption and CPS’s resource limitations highlights the need for resource-awareness. We introduce Cyber-Physical AI (CPAI), an interdisciplinary subfield of AI and CPS research, to address these constraints. To the best of our knowledge, CPAI is the first research domain on CPS-AI integration. We propose a three-dimensional classification schema of CPAI: Constraint (C),Purpose (P), and Approach (A). We also systematize the CPS-AI integration process into 3 phases and 9 steps. By analyzing 104 studies, we highlight 9 key challenges and insights from a CPAI perspective. CPAI aims to unify fragmented studies and provide guidance for reliable and resource-efficient integration of AI as a component of CPS.ACM Transactions on Cyber-Physical Systems740Cyber-Physical AI (CPAI) is introduced, an interdisciplinary subfield of AI and CPS research that aims to unify fragmented studies and provide guidance for reliable and resource-efficient integration of AI as a component of CPS.ACM Transactions on Cyber-Physical Systems["Sanghoon Lee", "Jiyeong Chae", "Haewon Jeon", "Taehyun Kim", "Yeong-Gi Hong", "Doosik Um", "Taewoo Kim", "Kyung-Joon Park"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/a991226302f16caa1e853b28f417fbbf45e4e34d 59e6f76680499eb87e53c37daed00f4baa166c28Artificial intelligence (AI) is transforming healthcare by enhancing clinical decision-making, particularly in nursing, where it supports tasks such as diagnostics, risk assessments, and care planning. However, the integration of non-explainable AI (NXAI) - which operates without fully transparent, interpretable mechanisms - presents ethical challenges related to accountability, autonomy, and trust. While explainable AI (XAI) aligns well with nursing's bioethical principles by fostering transparency and patient trust, NXAI's complexity offers distinct advantages in predictive accuracy and efficiency. This article explores the ethical tensions between XAI and NXAI in nursing, advocating a balanced approach that emphasises outcome validation, shared accountability, and clear communication with patients. By focusing on patient-centred, ethically sound frameworks, it is argued that nurses can integrate NXAI into practice, addressing challenges and preserving core nursing values in a rapidly evolving digital landscape.British Journal of Nursing60By focusing on patient-centred, ethically sound frameworks, it is argued that nurses can integrate NXAI into practice, addressing challenges and preserving core nursing values in a rapidly evolving digital landscape.British journal of nursing["M. Wynn"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/59e6f76680499eb87e53c37daed00f4baa166c28 55e5502e03aab530308a79bb31e3d7f47d602477This article examines the dynamic interplay between Artificial Intelligence, Platform Engineering, and Service Automation from an Enterprise Cloud Architect's perspective. It explores how cloud architects leverage AI technologies to transform cloud infrastructure design, management, and automation processes while navigating the technical complexities of modern enterprise environments. The discussion expands to cover the technical frameworks enabling human-AI collaboration in platform engineering, emphasizing the architectural patterns and implementation strategies that facilitate effective service automation. Through a detailed healthcare case study, the article demonstrates how an AI-augmented cloud platform revolutionizes patient diagnosis and treatment recommendation systems, highlighting the technical architecture, data integration challenges, and measurable clinical outcomes. It elaborates on governance structures, technical guardrails, and decision-making models essential for responsible AI implementation in enterprise cloud environments. Finally, this article identifies emerging technologies, architectural patterns for scaling human-AI synergy, and technical recommendations to guide enterprise cloud architects through the evolving landscape of AI-driven platform engineering and service automation.International Journal of Scientific Research in Computer Science Engineering and Information Technology00This article identifies emerging technologies, architectural patterns for scaling human-AI synergy, and technical recommendations to guide enterprise cloud architects through the evolving landscape of AI-driven platform engineering and service automation.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Baba Prasad Pendyala"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/55e5502e03aab530308a79bb31e3d7f47d602477 e9bfee16d153ecba30336ea7bbb6c1e8ec5b7000Nature Communications300Preliminary results show a significant improvement in CDRs without affecting RRs in a radiologist’s standard single-reading setting, and the preliminary results show a significant improvement in CDRs without affecting RRs in a radiologist’s standard single-reading setting.Nature Communications["Yun-Woo Chang", "Jung Kyu Ryu", "Jin Kyung An", "Nami Choi", "Young Mi Park", "Kyung Hee Ko", "Kyunghwa Han"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/e9bfee16d153ecba30336ea7bbb6c1e8ec5b7000 eedddf73faa1e3bb0612b0c8ccffe3ab2ca60872npj Digit. Medicine320A diagnostic meta-analysis to evaluate AI’s performance in classifying HER2 IHC scores indicates that AI holds promising potential in accurately identifying HER2-low patients and excels in distinguishing 2+ and 3+ scores.NPJ Digital Medicine["Daniel Arruda Navarro Albuquerque", "M. T. Vianna", "Luana Alencar Fernandes Sampaio", "A. Vasiliu", "Eduardo Henrique Cunha Neves Filho"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/eedddf73faa1e3bb0612b0c8ccffe3ab2ca60872 4e46cb483ef4835270b46ee4c9ffdb9eeab3bf58Artificial Intelligence (AI) is increasingly transforming various engineering disciplines, playing a pivotal role in design, manufacturing, maintenance, and optimization. This paper provides a comprehensive analysis of AI applications in advanced engineering, examining key trends, challenges, and future directions. The study systematically categorizes AI methodologies across different fields, including mechanical, civil, electrical, aerospace, and environmental engineering, as well as emerging areas such as biomedical engineering and material science. Through an extensive literature review and case study analysis, this work highlights the impact of AI-driven optimization in mechanical engineering, predictive maintenance in industrial applications, automation in manufacturing, and AI-enhanced smart infrastructure development.Methodologically, this research synthesizes findings from major scientific databases, including IEEE Xplore, PubMed, Scopus, and Web of Science, ensuring a robust and interdisciplinary perspective. The analysis identifies critical challenges in AI adoption, such as data privacy, scalability, and system integration, and explores strategies to address them. Furthermore, this paper discusses the ethical and societal implications of AI in engineering, emphasizing the need for transparent, explainable, and unbiased AI models.The findings suggest that AI has significantly improved engineering efficiency and innovation but also underline the necessity for interdisciplinary collaboration and standardized frameworks to maximize AI’s transformative potential. The study concludes by outlining future prospects, including the integration of AI with the Internet of Things (IoT) and blockchain, the evolution of AI-driven materials discovery, and the role of AI in personalized medicine and next-generation engineering solutions. Addressing these challenges and leveraging AI’s capabilities will be instrumental in shaping the future of engineering.Journal of Intelligent Communication00Journal of Intelligent Communication["Stefano Palazzo", "Federica Palazzo", "Giovanni Zambetta"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/4e46cb483ef4835270b46ee4c9ffdb9eeab3bf58 b3a9669adbacf6caf75af880040197ee288310f6Health Care Analysis300This research offers important insights into AI governance by investigating the impact of stakeholder engagement, ethical considerations, digital health disparities, governance structures, and health communication strategies on AI integration in healthcare, ultimately aiding in policy development and implementation.Health care analysis : HCA : journal of health philosophy and policy["Zhenwei You", "Yahui Wang", "Yineng Xiao"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/b3a9669adbacf6caf75af880040197ee288310f6 d71694851ab4bbda524f0243d502c471d122c0b3 Artificial Intelligence (AI) is transforming labour markets through automation, job displacement, and the creation of new employment opportunities. This study employs a descriptive and comparative research design to analyze AI's impact across various industries, using statistical trend analysis, comparative sector evaluations, and qualitative NVivo-style interview analysis. Findings indicate that industries such as manufacturing and retail experience high job displacement rates (45% and 35%), whereas healthcare and Education show higher AI-driven job creation (50% and 60%). A major challenge identified is the AI skills gap, where 84% of interview respondents highlighted difficulties in workforce adaptation due to the lack of AI-related training programs. The trend analysis reveals a 55% increase in AI job creation between 2015-2025, but many workers remain unprepared for these new roles. Comparative industry analysis suggests that countries and sectors investing in reskilling initiatives and AI governance policies experience lower AI-induced unemployment rates. Beyond economic concerns, this study highlights AI's psychological and social implications in the workplace, such as job insecurity, workplace surveillance, and mental health challenges. To address these issues, governments and corporations must implement AI workforce reskilling programs, fair labour policies, and ethical AI deployment strategies. The research concludes that proactive AI governance and workforce adaptation strategies are essential for ensuring an inclusive and sustainable labour market transition.International Journal of Engineering Science and Information Technology00Analysis of AI's impact across various industries concludes that proactive AI governance and workforce adaptation strategies are essential for ensuring an inclusive and sustainable labour market transition.International Journal of Engineering, Science and Information Technology["Siti Maria", "Purwinahyu Purwinahyu", "Fitriansyah Fitriansyah", "Arvita Rachmawaty", "Rohana Nur Aini"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/d71694851ab4bbda524f0243d502c471d122c0b3 6a37e371f476f3a4c2e1f82a611fef187f3cdc68PurposeThis study explores how artificial intelligence (AI) has been intertwined with rhetoric and the journey of institutionalization in selected case study firms. The mechanism of institutionalizing AI into organizational processes, future technology transformation and the driving forces behind the implementation of AI is being explored.Design/methodology/approachIt adopts the qualitative methodology and multiple case study approach, drawing evidence from ten leading retail sector organizations that have been practicing AI for over a decade. The main data collection method was face-to-face in-depth interviews, supplemented by focus group discussion and documentary reviews. From a theoretical stance, the paper draws on the notions of rhetoric institutionalism.FindingsEmpirical findings revealed that the rhetorical power of the word AI convinces the management of the firm to embrace AI. In contrast to the hype in the media, the real application of AI in the retail sector has not lived up. Therefore, the study delves into the noticeable discrepancy between the buzz surrounding AI and its actual use in retail sectors.Originality/valueThis study contributes to research by postulating that even though AI carries rhetorical power and prompt implementation, the real organizational application is far behind the rhetorical excitements. Foregrounding rhetoric institutionalism, it extends existing institutional theory-inspired management research. The paper also offers learning points to practitioners by illustrating the rise and fall of the AI implementation story. It further showcases how AI tools and techniques could be used by a business, how AI gets implicated in a firm’s business excellence journey and the ensuing management control ramifications.International Journal of Retail &amp; Distribution Management670Empirical findings revealed that the rhetorical power of the word AI convinces the management of the firm to embrace AI, and the real organizational application is far behind the rhetorical excitements.International Journal of Retail &amp; Distribution Management["Thisali Liyanage", "Ishini Gunasekara", "Sasuni Sipnara", "Rithmi Givindi", "Sanduni Ranathunga"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/6a37e371f476f3a4c2e1f82a611fef187f3cdc68 7c5d97e3f3c795e92230e156744e56d744dd39c8This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.European Journal of Educational Research250The findings suggest that AI can significantly improve educational experiences and provide tailored support and promotes ethical practices, and continued and expanded use of AI technologies in education while addressing potential implementation challenges is recommended.European Journal of Educational Research["Zouhaier Slimi", "Abdelghani Benayoune", "A. Alemu"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/7c5d97e3f3c795e92230e156744e56d744dd39c8 b63a1112592c552fa40386e393b5126b9cc9041eWith the continuous increase in the number of university graduates and the intensification of structural contradictions in the job market, employment guidance in local universities faces significant challenges. Based on the theory of digital transformation in education and the application framework of artificial intelligence (AI), this paper explores pathways for AI to enhance the quality of employment guidance in local universities, using employment data of university graduates in Sichuan Province and relevant policy orientations. Through case analysis, survey data, and theoretical research, the study finds that AI can optimize employment guidance processes and improve job-person matching efficiency through intelligent matching, personalized recommendations, and data-driven decision-making mechanisms, thereby facilitating the alignment between talent cultivation in universities and industry demands. The study proposes the construction of an “AI + Employment” ecosystem, the refinement of intelligent evaluation models, and the deepening of university-enterprise collaboration, providing theoretical foundations and practical references for the reform of local university employment guidance systems.Journal of Contemporary Educational Research20The study finds that AI can optimize employment guidance processes and improve job-person matching efficiency through intelligent matching, personalized recommendations, and data-driven decision-making mechanisms, thereby facilitating the alignment between talent cultivation in universities and industry demands.Journal of Contemporary Educational Research["Canhui Luo"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/b63a1112592c552fa40386e393b5126b9cc9041e 7fd363538b932b6b6b1d4f22cf5117195584c970This review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research.Sustainability880A research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration in Reconfigurable Manufacturing Systems (RMSs), and insight into the current DT, XR, and AI landscape in RMS is offered and suggests areas for future research.Sustainability["Anjela Mayer", "Lucas Greif", "Tim H\u00e4u\u00dfermann", "Simon Otto", "Kevin Kastner", "Sleiman El Bobbou", "J. Chardonnet", "Julian Reichwald", "J\u00fcrgen Fleischer", "J. Ovtcharova"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/7fd363538b932b6b6b1d4f22cf5117195584c970 492296c910ab8eafb33d110878b9d6d278dec135With the rapid development of artificial intelligence (AI),deployment it on cloud platforms has become a prevalent trend. But it also brings risks to cloud platforms. This study conducts an in-depth analysis of these risks,covering aspects such as the security of cloud platform architectures,data security,and supply chain security. It also puts forward coping strategies,such as improving policies,regulations,and industry standards,and promoting ecological construction. Future research can be carried out from aspects like cross-cloud platform security management and cross-cloud data securityAdvances in Computer and Materials Scienc Research00An in-depth analysis of risks to cloud platform architectures, data security, and supply chain security is conducted, putting forward coping strategies such as improving policies, regulations, and industry standards, and promoting ecological construction.Advances in Computer and Materials Scienc Research["Qiuyue Liu"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/492296c910ab8eafb33d110878b9d6d278dec135 a9b6aacf5fa8da00a29ea952449db80b8171251cThe current descriptive study explored teachers' perspectives on integrating artificial intelligence in higher education institutions. The study's population was comprised of all males and females (N = 220). Instructors are working in various departments of the faculty of social sciences at public universities in Multan, Punjab, Pakistan. A sample of one hundred and sixty teachers (N= 160), including forty-one males (M, 41) and one hundred and nineteen females (F, 119), were chosen through convenient sampling from the faculty of social sciences departments to accomplish this research. The questionnaire was utilized as a research tool to collect data. The data was analyzed using descriptive statistics, mean, standard deviation percentage, and frequency utilizing the Statistical Package for Social Sciences (SPSS) version 25. The results indicated a positive perception of teachers towards the integration of Artificial Intelligence in Higher Education Institutions. Based on the findings, the study recommended that higher educational institutions should focus on organizing AI-focused teacher training, ensuring access to essential AI resources, integrating AI into curricula, addressing ethical concerns, and launching pilot programs to facilitate effective AI adoption in education. These steps can support educators in leveraging AI tools to enhance teaching and learning experiences.The Critical Review of Social Sciences Studies00It is recommended that higher educational institutions should focus on organizing AI-focused teacher training, ensuring access to essential AI resources, integrating AI into curricula, addressing ethical concerns, and launching pilot programs to facilitate effective AI adoption in education.The Critical Review of Social Sciences Studies["Fazila Iqbal", "Dr. Samina Akhtar", "Dr. Farah Latif Naz"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/a9b6aacf5fa8da00a29ea952449db80b8171251c eb10a05d51756ce9f5c8ada0a20b4de4827841e9In present era, artificial intelligence (AI) continues to evolve at an unprecedented pace, profoundly impacting a wide array of industries and societal domains. Advancements in generative AI, such as large language models and multimodal systems, have led to more human-like interactions and creative outputs, enhancing fields from content creation to customer service. AI-driven automation is reshaping industries like healthcare, finance,manufacturing, Streamlining processes, improving efficiency, and enabling real-time decision-making. Ethical concerns surrounding AI, such as bias, privacy, and job displacement, are gaining increasing attention, prompting calls for regulation and transparent governance. Additionally, AI's role in addressing global challenges, such as climate change and healthcare inequality, is expanding, with researchers leveraging machine learning for predictive analytics, sustainable solutions, and medical diagnosis. As AI continues to advance, the balance between innovation, regulation, and societal impact remains a key focus for policymakers, technologists, and the global community. KEYWORDS: Artificial intelligence, Machine Learning, Large language models, Multimodal Models, Open AI, Convolution Neural networks.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00In present era, artificial intelligence (AI) continues to evolve at an unprecedented pace, profoundly impacting a wide array of industries and societal domains, with researchers leveraging machine learning for predictive analytics, sustainable solutions, and medical diagnosis.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Mrs Ambika Shabadkar"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/eb10a05d51756ce9f5c8ada0a20b4de4827841e9 1e07847c52032a0311bbd553efe8242646442cc4Amidst  the  increasingly  severe  global  environmental  crisis,  the  application  of artificial   intelligence   (AI)   in   the   fields   of  environmental   governance   and   sustainable development has become a hot topic in current scientific research and practice. The complexity and urgency of environmental issues have made the integration of AI technology particularly important  and  pressing.  To  comprehensively  understand  the  research  status,  hotspots,  and future trends in this field, this study employed Citespace and VOSviewer literature analysis tools to construct a knowledge map based on data from 2004 to 2024. The analysis results reveal that, in terms of research regions, Asia (especially China) has made the most significant contributions, while North America and Europe (particularly the United States and some EU countries) have closely collaborated, forming the core research regions. The top five authors in terms  of  publication  volume  are  Liu  J,  Vinuesa  R,  Nishant  R,  Bag  S,  and  Benzidia  S. Regarding research hotspots, the current themes in this field focus on four clusters: intelligent management  and  green  innovation  for  performance  lifecycle  assessment,  smart  cities  and sustainable development, and AI-enabled environmental management. These highlight the vast potential of AI in enhancing environmental governance efficiency and promoting sustainable development.  As  for  future  trends,  the  number  of publications  in  this  field  has  shown  a continuous upward trend in recent years, with predictions indicating that future research will continue to concentrate on keywords such as AI, life cycle, assessment, and the Internet of Things.  In  summary,  AI  is  forming  an  active  and  expanding  field  within  environmental governance, and future research will deepen understanding of the topic, explore the integration of  AI  with  environmental  science,  address  global  challenges,  and  drive  environmental governance towards a smart, efficient, and sustainable direction.Energy &amp; Environment Management170A knowledge map based on data from 2004 to 2024 reveals that, in terms of research regions, Asia has made the most significant contributions, while North America and Europe have closely collaborated, forming the core research regions.Energy &amp; Environment Management["Xuemei Jiang", "Tingyin Deng", "Wenying Zhang"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/1e07847c52032a0311bbd553efe8242646442cc4 a09ec60dee41fabe75f63d19f85c6f3048dddcf3BMC Proceedings220This sixth session of the of the WHO Pandemic and Epidemic Intelligence Innovation Forum examines the use of Artificial Intelligence in public health by collecting the experience of key global health organizations, such as the Boston Children's Hospital, the Global South AI for Pandemic & Epidemic Preparedness & Response network, Medicines Sans Frontières, and the University of Sydney.BMC Proceedings["Barbara Tornimbene", "Zoila Beatriz Leiva Rioja", "J. Brownstein", "Adam Dunn", "Sylvain Faye", "Jude Kong", "Nada Malou", "Clara Nordon", "Benjamin Rader", "Oliver Morgan"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/a09ec60dee41fabe75f63d19f85c6f3048dddcf3 9f42725014b202d8fc4f14b867f025b0fc24ab66Context In digital pathology, blurriness in whole slide images (WSI) is a common issue, with severe blurriness widely acknowledge as a critical factor that can degrade the performance of artificial intelligence (AI) models. However, the effects of the typical levels of blurriness observed in real-world pathological images on the robustness of AI predictions remains unclear and unexplored. Objective To evaluate the impact of WSI blurring on the robustness of AI prediction in real-world setting. Design A retrospective study was conducted using 8,000 WSIs and corresponding AI predictions from four AI models trained on data from two scanners and two organs. WSIs were categorized into concordant and discordant groups based on AI-prediction accuracy. Analyses included: 1) comparing blur metrics between groups, 2) determining the odds ratio between the proportions of blurry patch in WSIs and prediction concordance, and 3) assessing model performance across varying blur intensities. Results For each organ-scanner pair, the average wavelet score and Laplacian variance for WSIs between the two groups did not show a statistically significant difference model (p > 0.05 for both metrics), except for one, and their effect sizes were small (Cohen's D < 0.2 for both metrics). Additionally, no statistically significant association was observed between AI prediction concordance and the proportion of blurry images in WSIs (confidence intervals included 1, respectively). Model performance remained robust even at high blur level (radius=1) at which patch image had Laplacian variance of 162.88 and a wavelet score of 1880.07, corresponding to the top 1.22% and 2.16% of blurriness respective, in our dataset. Conclusions The findings empirically suggest that the typical levels of WSI blurriness encountered in real-world settings may not significantly compromise the robustness of AI predictions.medRxiv00The findings empirically suggest that the typical levels of WSI blurriness encountered in real-world settings may not significantly compromise the robustness of AI predictions.["MS Ho Heon Kim", "MD Ph.D Young Sin Ko", "MD Ph.D Kyungeun Kim"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/9f42725014b202d8fc4f14b867f025b0fc24ab66 5d311f29f585bc909855720c060486825d06b0fdThere is a growing concern about the sustainability of artificial intelligence, in terms of Environmental, Social and Governance (ESG) factors. We contribute to the debate measuring the impact of ESG factors on one of the most relevant applications of AI in finance: credit rating. There is not yet conclusive evidence on whether EGS factors impact on credit rating. In this paper, we propose several machine learning models to measure such impact, and a set of metrics that can improve their ability to do so. In this way, machine learning models and, more generally, decisions based on artificial intelligence, can become more sustainable.Frontiers in Artificial Intelligence370This paper proposes several machine learning models to measure the impact of ESG factors on one of the most relevant applications of AI in finance: credit rating, and a set of metrics that can improve their ability to do so.Frontiers in Artificial Intelligence["Paolo Giudici", "Lunshuai Wu"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/5d311f29f585bc909855720c060486825d06b0fd 84f898a1dcb370a5b901f6b5626daa4917300364A total of 12 papers were accepted for the special issue on the topic of ‘New Technologies of Artificial Intelligence in Convergence ICT’. Recently, as the convergence of artificial intelligence continues to occur in various fields, various technologies are emerging. In this paper, 12 papers introduce AI utilisation technologies in various fields such as smart city, security, medical, economy, healthcare and electricity.Expert systems110In this paper, 12 papers introduce AI utilisation technologies in various fields such as smart city, security, medical, economy, economy, healthcare and electricity.Expert Systems["Ji Su Park", "L. T. Yang", "Jong Hyuk Park"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/84f898a1dcb370a5b901f6b5626daa4917300364 c9f27b516ce7347b951bab9eedc0af46f3a8b9fbObjectives: This study aims to explore the role of artificial intelligence (AI) in alleviating consumer anxiety and enhancing customer experience across various industries. It seeks to analyze AI-driven tools and their effectiveness in mitigating consumer concerns while addressing the ethical and legal dimensions of their application.   Theoretical Framework: The research is grounded in theoretical principles related to AI, consumer behavior, and trust-building mechanisms in customer relationships. It examines key conceptual frameworks governing AI’s role in reducing consumer stress and fostering positive interactions.   Method: This study employs a theoretical investigation, drawing from existing literature on AI applications in customer service. It critically analyzes AI-powered tools such as chatbots, sentiment analysis, and predictive analytics, evaluating their impact on consumer anxiety and trust.   Results and Discussion: The findings highlight AI’s potential in addressing consumer anxiety through personalized interactions and predictive solutions. AI-driven tools enhance customer support efficiency and responsiveness, ultimately improving consumer confidence. However, the study also underscores ethical and legal challenges, including consumer rights protection, corporate accountability, and compliance with ethical guidelines.   Research Implications: This research provides valuable insights for businesses seeking to integrate AI into customer relations strategies. It offers a framework for developing AI-based solutions that foster trust, reduce stress, and ensure ethical compliance.   Originality/Value: By combining AI-driven consumer anxiety reduction with ethical and legal considerations, this study presents a comprehensive approach to responsible AI deployment in customer relations. It serves as a guide for businesses aiming to balance innovation with consumer protection.Journal of Lifestyle and SDGs Review1050This study critically analyzes AI-powered tools such as chatbots, sentiment analysis, and predictive analytics, evaluating their impact on consumer anxiety and trust and offers a framework for developing AI-based solutions that foster trust, reduce stress, and ensure ethical compliance.Journal of Lifestyle and SDGs Review["Baqer Khudair Al-Hadrawi", "Kais Khudhair Al-hadrawi", "Souad Ezzerouali", "S. Al-Hadraawy", "Hanan Khaled Aldhalmi", "Mohammad Abdallah Alshawabkeh"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/c9f27b516ce7347b951bab9eedc0af46f3a8b9fb fc37526c404395a4cb41553d10a695acaff93914algorithms have the potential to provide real-time feedback during AF endocardial catheter ablation operations. This may be an effective method evaluating voltage-dependent ablation techniques, substrate changes, and pulmonary vein isolation, regardless of the type of AF 7 . Furthermore, AFA-Recur, an ML-based probability score, demonstrated efficacy in predicting the one-year probability of recurrent atrial arrhythmia following AF ablation 8 . Another potential application of AI is an AI-based approach to determine the efficacy of electrical cardioversion for AF, based on patient characteristics and ECG data 9 . The perspective and multifaceted applications of AI in AF hold considerableNamık Kemal tıp dergisi170AI algorithms have the potential to provide real-time feedback during AF endocardial catheter ablation operations and an AI-based approach to determine the efficacy of electrical cardioversion for AF, based on patient characteristics and ECG data is proposed.Namık Kemal Tıp Dergisi["G\u00f6kay Taylan", "Servet Altay"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/fc37526c404395a4cb41553d10a695acaff93914 63b0dfb17089cdec7d5bcd5d7feee7306f712d94Cureus480Cureus["Almontasir Belah Alsadig Abdalwahab Abdallah", "Sally Ibrahim Hafez Sadaka", "Elryah I Ali", "Saadalnour Abusail Mustafa Bilal", "Mohammad Omar Abdelrahman", "Fatima Bashir Fakiali Mohammed", "Samah Dafallah Nimir Ahmed", "Nuha Elrayah Abdelrahim Saeed"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/63b0dfb17089cdec7d5bcd5d7feee7306f712d94 599ca9ef16fb81e3b170c7e727a9cbe214a2df07Neurology Today30Neurology Today["Dan Hurley"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/599ca9ef16fb81e3b170c7e727a9cbe214a2df07 2267fa9f109ccd53dd6769f197d0f3ddaca810fcJOM00JOM["Megan Enright"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/2267fa9f109ccd53dd6769f197d0f3ddaca810fc c8bf3ee937542c76f4ddf813c25ec723a48fbcb4Abstract: The rapid development of generative AI is transforming university information literacy education by reshaping how students access and process information. This study systematically reviews 49 research papers published between 2020 and 2024, using the PRISMA framework and thematic analysis to explore the applications, impacts, and pedagogical changes associated with generative AI in the field of information literacy education. Results show that generative AI has a wide range of applications in information literacy education, mainly in student learning support, learner-oriented personalized learning, academic research assistants, academic writing assistance, information literacy skills development, and curriculum design and teaching assistance. Generative AI has promoted students’ information retrieval, evaluation skills and critical thinking, but also brought the challenge that over-reliance on AI may weaken students’ critical thinking and information evaluation skills. Important changes in curriculum design and teaching methods are needed to introduce instruction in prompt engineering and computational thinking. The role of the teacher has shifted from knowledge transmitter to learning facilitator, emphasizing the importance of professional basic knowledge and ethical education. Through the results it is find that Generative AI can significantly enhance student learning outcomes and skills development in university information literacy education. However, its application requires caution and must fully consider potential challenges and risks. Through reasonable curriculum design, innovative teaching methods, and policy support, educators can leverage the advantages of Generative AI to cultivate high-quality talent with critical thinking, innovation, and a sense of moral responsibility. As AI technology continues to develop, information literacy education will usher in more innovations and opportunities, bringing new vitality and possibilities to higher education.International Journal of Latest Technology in Engineering Management &amp; Applied Science530Through the results it is find that Generative AI can significantly enhance student learning outcomes and skills development in university information literacy education, however, its application requires caution and must fully consider potential challenges and risks.International Journal of Latest Technology in Engineering Management &amp; Applied Science["He Li", "Elvira S. Balinas"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/c8bf3ee937542c76f4ddf813c25ec723a48fbcb4 f30947dbc041041994f4291737c81bb38e5fdef1No abstract.Ethiopian Medical Journal50Ethiopian Medical Journal["W. Dode"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/f30947dbc041041994f4291737c81bb38e5fdef1 f35bb32a7983d6052a622159ea18c9c3e1a8ba84Journal of Environmental Management760Journal of environmental management["Ana Beatriz Lopes de Sousa Jabbour", "Issam Laguir", "R\u00e9becca Stekelorum", "Shivam Gupta"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/f35bb32a7983d6052a622159ea18c9c3e1a8ba84 2d4e53ce9dde92c8b197c7cec104ab4977c030ceGenerative AI has the potential to transform higher education assessment. This study examines the opportunities and challenges of integrating AI into coursework assessments, highlighting the need to rethink traditional paradigms. A case study is presented that explores AI as an auxiliary learning tool in postgraduate coursework. Students found AI valuable for text generation, proofreading, idea generation, and research but noted limitations in accuracy, detail, and specificity. AI integration offers advantages such as enhancing assessment authenticity, promoting self-regulated learning, and developing critical thinking and problem-solving skills. A holistic approach is recommended, incorporating AI into feedback, adapting assessments to leverage AI’s capabilities, and promoting AI literacy among students and educators. Embracing AI while addressing its challenges can enable effective, equitable, and engaging assessment and teaching practices. Universities are encouraged to strategically integrate AI into teaching and learning, ultimately transforming the educational landscape to better prepare students for an AI-driven world.Intersection: A Journal at the Intersection of Assessment and Learning1110This study examines the opportunities and challenges of integrating AI into coursework assessments, highlighting the need to rethink traditional paradigms and incorporating AI into feedback, adapting assessments to leverage AI’s capabilities, and promoting AI literacy among students and educators.Intersection: A Journal at the Intersection of Assessment and Learning["Andrew Williams"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/2d4e53ce9dde92c8b197c7cec104ab4977c030ce 5cce9b9143ee7e2ea3733f2baeded89adfded4a2Basic and Applied Social Psychology630Basic and Applied Social Psychology["Heng Li"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/5cce9b9143ee7e2ea3733f2baeded89adfded4a2 f689bb15e7103f54c79aa1f19da52438375de4fb00["Osama Khattak", "Ahmed Shawkat Hashem", "M. Alqarni", "Raha Ahmed Shamikh Almufarrij", "A. Y. Siddiqui", "Rabia Anis", "Shahzad Ahmad", "Muhammad Amber Fareed", "O. S. Alothmani", "Lama Habis Samah Alkhershawy", "Wesam Waleed Zain Alabidin", "Rakhi Issrani", "Anshoo Agarwal"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/f689bb15e7103f54c79aa1f19da52438375de4fb 5b58d0f97ad013849b2be50c0b5398aaf82c4f1cFrontiers in Artificial Intelligence430Frontiers in Artificial Intelligence["Deborah M. Li", "Shruti Parikh", "Ana Costa"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/5b58d0f97ad013849b2be50c0b5398aaf82c4f1c 0113cdc50b5d3e143d17bd5b6c6752e287555c72Journal of Information Technology &amp; Politics300Journal of Information Technology &amp; Politics["Rafaa Chehoudi"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/0113cdc50b5d3e143d17bd5b6c6752e287555c72 edb37c66b5f1fb1eea8f5c7f288b419f63cd5a77Carbon Dioxide Enhanced Oil Recovery (CO2-EOR) is a well-established technology that has been deployed for over 2 decades, primarily to boost oil recovery rates. Recently, however, CO2-EOR has gained attention as a potential carbon mitigation strategy, given its ability to both enhance oil recovery without requiring extensive new drilling and store CO2 in subsurface formations. This dual function aligns with net-zero carbon goals, as CO2 is partly trapped in the reservoir through solubility and hysteresis effects on relative permeability. The performance of CO2-EOR, in terms of both oil recovery and CO2 storage potential, depends on numerous factors, including reservoir properties such as porosity, permeability, thickness, fluid composition, and operating conditions like bottom-hole pressure and injection rates. Traditional screening for CO2-EOR candidate reservoirs typically relies on experimental work, simulation studies, and field analogs, all of which require significant time and resources. However, a large dataset exists from prior CO2-EOR projects, which could enable more efficient screening.To leverage this data and capitalize on recent advancements in artificial intelligence, we developed an integrated methodology to predict CO2-EOR production profiles rapidly and accurately. Using Artificial Neural Networks (ANN), we trained a proxy model (PM) with over 2,000 simulation cases based on real-world CO2-EOR projects. The model’s novelty lies in its ability to generate dimensionless type curves and their derivatives, which can be matched with production data to estimate average reservoir characteristics at later project stages.Our results demonstrate that the proxy model achieves a high level of accuracy, with a maximum Mean Absolute Error (MAE) of 0.012 and a correlation coefficient of 0.99 between predicted and simulated results across three output variables. Additionally, a sensitivity analysis revealed the significant influence of parameters such as fluid composition, rock-fluid interaction, porosity, permeability, and initial reservoir pressure on CO2-EOR production profiles. This approach provides a rapid, cost-effective alternative to conventional methods, allowing for quicker and more informed decision-making in CO2-EOR projects.Frontiers in Energy Research270An integrated methodology to predict CO2-EOR production profiles rapidly and accurately, using Artificial Neural Networks (ANN), which provides a rapid, cost-effective alternative to conventional methods, allowing for quicker and more informed decision-making in CO2-EOR projects.Frontiers in Energy Research["Raghda Emera", "Amirmasoud Kalantari Dahaghi"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/edb37c66b5f1fb1eea8f5c7f288b419f63cd5a77 fb15428cd402ca3438977c070928afa3816884d5Studies in Conflict &amp; Terrorism00Studies in Conflict &amp; Terrorism["Christopher Wall"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/fb15428cd402ca3438977c070928afa3816884d5 4d40c116f91e477e463b9180408030d865de5ef6The study examines how artificial narrow intelligence innovation affect India’s retail and e-commerce industries with an emphasis on operational enhancement, moral issues and consumer satisfaction. Key parameters such as viability of chatbots interactions, suggestion precision, timeframe improvements for complaint settlement, frequency of consumer interactions, privacy related issue rate and permission level for utilizing information were examined via survey of 406 respondents. The real-world application and perks of artificial intelligence are demonstrated through Flipkart, Reliance Retail, Big Basket, Myntra and Tata Cliq. These investigations emphasize improvement in consumer experience, commitment and productivity and decrease in fraud and cost associated with inventory. The study proposes goals and theories to direct future research while identifying knowledge gaps in client responses, measuring client contentment, ethical considerations and regulatory effects. The result indicates that while ANI technologies greatly improve operational effectiveness and consumer happiness, more work needs to be done to deal with privacy and unethical practices. To promote safe and efficient implementation of ANI, important recommendations involve strengthening openness in ANI techniques, putting in place strong information securities safeguard and encouraging industry players and law enforcement agencies to collaborate.British journal of multidisciplinary and advanced studies190The result indicates that while ANI technologies greatly improve operational effectiveness and consumer happiness, more work needs to be done to deal with privacy and unethical practices.British Journal of Multidisciplinary and Advanced Studies["Prachi Malgaonkar"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/4d40c116f91e477e463b9180408030d865de5ef6 aacdf3f94bfb3aace878c75deb1a33759d3b8a22The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems. To address these challenges, the principles of the robustness, interpretability, controllability, and ethical alignment framework are essential. This paper explores the integration of metacognition—defined as “thinking about thinking”—into AI systems as a promising approach to meeting these requirements. Metacognition enables AI systems to monitor, control, and regulate the system’s cognitive processes, thereby enhancing their ability to self-assess, correct errors, and adapt to changing environments. By embedding metacognitive processes within AI, this paper proposes a framework that enhances the transparency, accountability, and adaptability of AI systems, fostering trust and mitigating risks associated with autonomous decision-making. Additionally, the paper examines the current state of AI safety and responsibility, discusses the applicability of metacognition to AI, and outlines a mathematical framework for incorporating metacognitive strategies into active learning processes. The findings aim to contribute to the development of safe, responsible, and ethically aligned AI systems.Technologies01This paper proposes a framework that enhances the transparency, accountability, and adaptability of AI systems, fostering trust and mitigating risks associated with autonomous decision-making, and outlines a mathematical framework for incorporating metacognitive strategies into active learning processes.Technologies["Peter B. Walker", "Jonathan J. Haase", "Melissa L. Mehalick", "Christopher T. Steele", "Dale W. Russell", "Ian N. Davidson"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/aacdf3f94bfb3aace878c75deb1a33759d3b8a22 bbe72bd3d8d47968c9c9b3afda066b7d0850c91dThe research study delved into the nuanced human aspect of artificial intelligence (AI) in health care, focusing on what is fundamentally important to patients in accepting this radical technology. With patients at the center of the research, it explored how social influence, individual backup choices, and trust influence the acceptance of AI healthcare services. The survey, which used 450 participants, tested Structural Equation Modeling (SEM) using AMOS and found the powerful role of such factors. Social influence (what do others think or say about AI) comes out strongly to shape patients’ perceptions. Personal backup desire (the need to know or feel secure in human support being always an option) is another crucial variable. Last, and most importantly, trust in the reliability and safety of AI systems is the bedrock of acceptance. This study did not just deal with numbers but speaks a human story where trust, reliability, and social connection can drive AI adoption. These insights are a guide for practical recommendations to healthcare providers and policymakers on not only how to nurture trust but also engage with patients in meaningful ways and balance this with the human touch. This is how health care is transformed by AI, not as a replacement but in a way that patients can embrace with confidence and satisfaction.International journal of innovative research and scientific studies520This study delved into the nuanced human aspect of artificial intelligence in health care, focusing on what is fundamentally important to patients in accepting this radical technology, and explored how social influence, individual backup choices, and trust influence the acceptance of AI healthcare services.International Journal of Innovative Research and Scientific Studies["A. E. E. Sobaih", "Asma Chaibi", "Zeineb Al Hadi Nagara", "I. Elshaer"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/bbe72bd3d8d47968c9c9b3afda066b7d0850c91d dfcaa53d9f3d5c16501ed7d8fe59d87653d2d971This paper takes the innovative trend of artificial intelligence (AI) technology as a starting point to explore its profound impact on the high-quality development of foreign trade. The article first clarifies the connotation and evaluation indicators of high-quality foreign trade development, and then reviews the global development dynamics of AI technology and its core application scenarios. Based on technology-driven theory, diffusion of innovations theory, and value chain reconfiguration theory, a theoretical framework for the integration of AI and foreign trade is constructed. The paper analyzes the empowering effects in key areas such as intelligent supply chain management, optimization of cross-border e-commerce platforms, and precision marketing, while also addressing potential risks such as data security and privacy protection. Building on theoretical review and conceptual exploration, the paper proposes a development model driven by AI to promote the transformation and upgrading of foreign trade, and provides strategic recommendations for governments and enterprises in policy formulation and implementation pathways. The aim is to offer both theoretical support and practical guidance for the high-quality development of foreign trade in the digital economy era.Journal of Economics and Management Sciences110A development model driven by AI is proposed to promote the transformation and upgrading of foreign trade, and strategic recommendations for governments and enterprises in policy formulation and implementation pathways are provided.Journal of Economics and Management Sciences["Huang Siyu"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/dfcaa53d9f3d5c16501ed7d8fe59d87653d2d971 b1153bdc3e0ea94973cbec85e606ba902ca53665BACKGROUND Wearable technology is used by consumers worldwide for continuous activity monitoring in daily life but more recently also for classifying or predicting mental health parameters like stress or depression levels. Previous studies identified, based on traditional approaches, that physical activity is a relevant factor in the prevention or management of mental health. However, upcoming artificial intelligence methods have not yet been fully established in the research field of physical activity and mental health. OBJECTIVE This systematic review aims to provide a comprehensive overview of studies that integrated passive monitoring of physical activity data measured via wearable technology in machine learning algorithms for the detection, prediction, or classification of mental health states and traits. METHODS We conducted a review of studies processing wearable data to gain insights into mental health parameters. Eligibility criteria were (1) the study uses wearables or smartphones to acquire physical behavior and optionally other sensor measurement data, (2) the study must use machine learning to process the acquired data, and (3) the study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in 5 electronic databases. RESULTS Of 11,057 unique search results, 49 published papers between 2016 and 2023 were included. Most studies examined the connection between wearable sensor data and stress (n=15, 31%) or depression (n=14, 29%). In total, 71% (n=35) of the studies had less than 100 participants, and 47% (n=23) had less than 14 days of data recording. More than half of the studies (n=27, 55%) used step count as movement measurement, and 44% (n=21) used raw accelerometer values. The quality of the studies was assessed, scoring between 0 and 18 points in 9 categories (maximum 2 points per category). On average, studies were rated 6.47 (SD 3.1) points. CONCLUSIONS The use of wearable technology for the detection, prediction, or classification of mental health states and traits is promising and offers a variety of applications across different settings and target groups. However, based on the current state of literature, the application of artificial intelligence cannot realize its full potential mostly due to a lack of methodological shortcomings and data availability. Future research endeavors may focus on the following suggestions to improve the quality of new applications in this context: first, by using raw data instead of already preprocessed data. Second, by using only relevant data based on empirical evidence. In particular, crafting optimal feature sets rather than using many individual detached features and consultation with in-field professionals. Third, by validating and replicating the existing approaches (ie, applying the model to unseen data). Fourth, depending on the research aim (ie, generalization vs personalization) maximizing the sample size or the duration over which data are collected.JMIR mHealth and uHealth1070The use of wearable technology for the detection, prediction, or classification of mental health states and traits is promising and offers a variety of applications across different settings and target groups but the application of artificial intelligence cannot realize its full potential.JMIR mHealth and uHealth["Simon Woll", "Dennis Birkenmaier", "Gergely Biri", "Rebecca Nissen", "Luisa Lutz", "Marc Schroth", "U. Ebner-Priemer", "M. Giurgiu"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/b1153bdc3e0ea94973cbec85e606ba902ca53665 f4a3739769266e98286fba19c6a927017d07a508This paper explores the groundbreaking use of artificial intelligence (AI) in Disney’s Frozen II, which significantly transformed the animation process across multiple domains. AI-driven tools, such as Disney’s in-house simulation system “Swoop,” were employed to create realistic natural elements, including snow, water, and ice, enhancing environmental immersion through precise simulations of their real-world physics. AI also played a pivotal role in the real-time rendering process, with the introduction of "Hyperion," a machine-learning-based lighting system that accelerated production by enabling immediate feedback and creative experimentation. Furthermore, AI facilitated enhanced character animation, refining facial expressions, lip-syncing, and dynamic movements like hair and clothing, contributing to a more lifelike portrayal of the characters. The implementation of AI-driven crowd simulation also added depth to the film’s complex scenes. By streamlining production and allowing for greater creative flexibility, Frozen II has set a new benchmark in the animation industry, demonstrating the potential of AI to not only improve efficiency but also to push the boundaries of visual storytelling. The success of these AI tools in Frozen II has inspired other studios to adopt similar technologies, signaling a shift towards more innovative and efficient animation practices.International Journal For Multidisciplinary Research00By streamlining production and allowing for greater creative flexibility, Frozen II has set a new benchmark in the animation industry, demonstrating the potential of AI to not only improve efficiency but also to push the boundaries of visual storytelling.International Journal For Multidisciplinary Research["Kunal Hossain", "Jyotirmay Deb"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/f4a3739769266e98286fba19c6a927017d07a508 d86e4cea3519c08a245a12f733df48c570b9dfcaAI &amp; SOCIETY390A three-dimensional classification system of possible artificial virtues is proposed, which can be classified according to the domain in which virtue is an excellence, norm that makes a virtue an excellence, and mode of how the virtue delivers the excellence.AI &amp; SOCIETY["Jakob Ohlhorst"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/d86e4cea3519c08a245a12f733df48c570b9dfca 6f366b42d61fc4983b57265340de0de698d6a4c8Enterprise architecture is experiencing a profound transformation through the integration of generative artificial intelligence into Enterprise Resource Planning (ERP) systems, fundamentally reshaping how organizations approach automation, decision-making, and strategic planning. This article examines the architectural implications of incorporating AI capabilities into ERP frameworks, focusing on three key dimensions: predictive analytics for enhanced forecasting and risk management, intelligent process automation for operational efficiency, and strategic decision support through natural language processing. Drawing from industry implementations and architectural patterns, this article explores the challenges and opportunities in designing resilient AI-enabled ERP systems that balance innovation with enterprise constraints. The discussion encompasses critical considerations for enterprise architects, including data privacy, integration complexity, and governance frameworks, while providing actionable insights for organizations transitioning to next-generation ERP architectures. This article suggests that successful AI integration in ERP systems requires a holistic architectural approach that aligns technological capabilities with organizational objectives, supported by robust governance mechanisms and clear implementation strategies. This article contributes to the growing body of knowledge on enterprise architecture evolution in the context of emerging AI technologies, offering practical guidance for architects and decision-makers navigating this transformative landscape.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The architectural implications of incorporating AI capabilities into ERP frameworks are examined, focusing on three key dimensions: predictive analytics for enhanced forecasting and risk management, intelligent process automation for operational efficiency, and strategic decision support through natural language processing.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Sanjiv Kumar Bhagat"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/6f366b42d61fc4983b57265340de0de698d6a4c8 ff74dd605fca4ab049b8ed084c2fe1806279f997Assessing the economic impacts of artificial intelligence requires integrating insights from both computer science and economics. We present the Growth and AI Transition Endogenous model (GATE), a dynamic integrated assessment model that simulates the economic effects of AI automation. GATE combines three key ingredients that have not been brought together in previous work: (1) a compute-based model of AI development, (2) an AI automation framework, and (3) a semi-endogenous growth model featuring endogenous investment and adjustment costs. The model allows users to simulate the economic effects of the transition to advanced AI across a range of potential scenarios. GATE captures the interactions between economic variables, including investment, automation, innovation, and growth, as well as AI-related inputs such as compute and algorithms. This paper explains the model's structure and functionality, emphasizing AI development for economists and economic modeling for the AI community. The model is implemented in an interactive sandbox, enabling users to explore the impact of AI under different parameter choices and policy interventions. The modeling sandbox is available at: www.epoch.ai/GATE.00The GATE model is explained, a dynamic integrated assessment model that simulates the economic effects of AI automation and captures the interactions between economic variables, including investment, automation, innovation, and growth, as well as AI-related inputs such as compute and algorithms.["Ege Erdil", "Andrei V. Potlogea", "T. Besiroglu", "Edu Roldan", "Anson Ho", "J. Sevilla", "Matthew Barnett", "Matej Vrzla", "Robert Sandler"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/ff74dd605fca4ab049b8ed084c2fe1806279f997 b5f4d6139ce740d3e37362b672a7958e6c54516dIntroduction The Canadian healthcare system is approaching a breaking point. With mental health being a leading cause of disability, innovative solutions are necessary to provide adequate care. Digital mental health programs, such as electronic cognitive behavioural therapy (eCBT), have proven effective in reducing the challenges of traditional psychotherapy such as long waitlists, stigma, geographic barriers, and reducing time constraints. Furthermore, artificial intelligence (AI) has shown potential utility within the healthcare system, particularly in treatment recommendations and improving patient engagement. Despite the benefits that AI and digital mental health programs provide, they are rarely implemented in real-world healthcare settings. Objective This study aims to explore patient experiences and perceptions of an AI-assisted triage system paired with a digital psychotherapy program. The objective is to highlight the potential modifiable barriers to implementing these digital systems in real-world healthcare settings. Methods 45 adult outpatient psychiatry patients (n=45) who used an AI-assisted triaging system and digital psychotherapy modules, were surveyed through Qualtrics. This survey examined their perceptions of AI within mental healthcare, its utility within triaging, and their experiences with the digital psychotherapy program. Free-text survey responses were independently coded and analyzed using thematic analysis. Results Thematic analysis revealed three major themes for client's perceptions of the AI-assisted triage system: (1) AI as a replacement, (2) the utility of AI, and (3) AI complexity recognition. For the digital psychotherapy program, the themes were: (1) interactions with technology, (2) online therapy program structure, and (3) differential user experience. Conclusion Participants highlighted the importance of human oversight to ensure accuracy and liked that the AI allowed them to access care faster. Suggestions for improving the digital psychotherapy program included enhancing user-friendliness, increasing human contact, and making it more accessible for neurodivergence.medRxiv260Patient experiences and perceptions of an AI-assisted triage system paired with a digital psychotherapy program and suggestions for improving the digital psychotherapy program included enhancing user-friendliness, increasing human contact, and making it more accessible for neurodivergence.["Oleksandr Knyahnytskyi", "J. Eadie", "Kimia Asadpour", "C. Stephenson", "Megan Yang", "T. Reshetukha", "Christina Moi", "Tricia Barrett", "Meghanne Hicks", "Gilmar Gutierrez", "Anchan Kumar", "J. Jagayat", "S. Sajid", "C. Patel", "Christina Holmes", "A. Shirazi", "V. Verter", "Claudio Soares", "M. Omrani", "N. Alavi", "Alina Marin", "Archana Patel", "Amanda Richer", "Behnia Haghiri", "Nicholas Axas", "Julia Lee"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/b5f4d6139ce740d3e37362b672a7958e6c54516d d07a000bc92af267dd80ef6e0ce16cbe69743244The purpose of this study is to depict the value added by human-AI collaboration in organizations to collaboration system design by virtue of the studies reached by the literature review on different databases are examined. Web of Science content and covering the title of “human-AI collaboration” has been selected in this study. Research using bibliometric analysis has been conducted and it has been determined that the terms “human-AI collaboration” and “generative artificial intelligence” should be searched for simultaneously in each and every article published in the journal between the years 1975 and 2024. Our study used a combination of bibliometric analysis and literature review, bibliometric analysis tools include HistCite and CiteSpace. The citation map shows three phases: human-machine collaboration into practice (before 2020), the intelligent and automated segment of AI (2020-2021), and the generative AI phase represented by ChatGPT (2022 to present). This article conducted a systematic overview study on human-AI collaboration in organizations, established a conceptual framework for the conceptual framework of human-machine collaboration guided by generative AI integration, and provided certain theoretical insights to guide the corresponding practical activities. Bibliometric analysis is a method that can be used to evaluate the performance of a research topic. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single theme. This circumstance is elaborately described as a limitation of this study. This article builds on data from WoS Core Collection, and some new but important articles may not be analyzed, since bibliometrics consider high citation as an indicator to select influential articles. while previous research focused on researching modes of collaboration between humans and cobats, such as virtual assistants, this study extends the literature on different types of AI. Our research addresses the emerging field of collaboration with software that is not just a mere tool designed for performing knowledge work but becomes a collaborative partner.Journal of Computer, Signal, and System Research540A systematic overview study on human-AI collaboration in organizations is conducted, a conceptual framework for the conceptual framework of human-machine collaboration guided by generative AI integration is established, and certain theoretical insights to guide the corresponding practical activities are provided.Journal of Computer, Signal, and System Research["Ying Liu", "Lei Shen"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/d07a000bc92af267dd80ef6e0ce16cbe69743244 92c682acaae6ea77af691e3547f98aac92b67bf2Health communication and promotion on the internet have evolved over time, driven by the development of new technologies, including generative artificial intelligence (GenAI). These technological tools offer new opportunities for both the public and professionals. However, these advancements also pose risks of exacerbating health disparities. Limited research has focused on combining these health communication mediums, particularly those enabled by new technologies like GenAI, and their applications for health promotion and health disparities. Therefore, this viewpoint, adopting a conceptual approach, provides an updated overview of health communication mediums and their role in understanding health promotion and disparities in the GenAI era. Additionally, health promotion and health disparities associated with GenAI are briefly discussed through the lens of the Technology Acceptance Model 2, the uses and gratifications theory, and the knowledge gap hypothesis. This viewpoint discusses the limitations and barriers of previous internet-based communication mediums regarding real-time responses, personalized advice, and follow-up inquiries, highlighting the potential of new technology for public health promotion. It also discusses the health disparities caused by the limitations of GenAI, such as individuals' inability to evaluate information, restricted access to services, and the lack of skill development. Overall, this study lays the groundwork for future research on how GenAI could be leveraged for public health promotion and how its challenges and barriers may exacerbate health inequities. It underscores the need for more empirical studies, as well as the importance of enhancing digital literacy and increasing access to technology for socially disadvantaged populations.Journal of Medical Internet Research520This study lays the groundwork for future research on how GenAI could be leveraged for public health promotion and how its challenges and barriers may exacerbate health inequities, as well as the importance of enhancing digital literacy and increasing access to technology for socially disadvantaged populations.Journal of medical Internet research["Jamal Uddin", "Cheng Feng", "Junfang Xu"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/92c682acaae6ea77af691e3547f98aac92b67bf2 e64f84c041483ddf0ebacedfd33d298f1cd80056This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.Journal of Infrastructure Policy and Development430A comprehensive conceptual framework is derived from several fields of human resources, operational excellence, and digital transformation that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives to address challenges in infrastructure governance, and sustainable development.Journal of Infrastructure, Policy and Development["Zoubida Benmamoun", "Hayet Benhamida", "J. M. Jizat"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/e64f84c041483ddf0ebacedfd33d298f1cd80056 aef58b977f4af66e7d2a75e204df619c20356cd7BMC Psychology550Examining AI anxiety’s effects on individuals’ willingness to pay for knowledge offers a new framework for understanding AI anxiety’s impact on consumer behavior and provides actionable insights for platforms and policymakers.BMC Psychology["Jinsong Chen", "Miao He", "Jinhua Sun"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/aef58b977f4af66e7d2a75e204df619c20356cd7 d6ee57d98373786a7a9e074cb28a2d2a97d1d9abThe rapid expansion of artificial intelligence (AI) in social sciences research introduces both transformative potential and critical ethical dilemmas. This study examines the role of researchers as either ethical gatekeepers or pioneers of AI-driven change. Through a bibliometric analysis of 464 records from the Web of Science Core Collection, we identify key themes in ethical AI discourse using VOSviewer Version 1.6.20. The findings highlight dominant ethical concerns, including governance, bias, transparency, and fairness, emphasizing the need for interdisciplinary collaborations and responsible AI frameworks. While AI offers efficiency and scalability in research, unresolved issues related to algorithmic bias, governance, and public trust persist. The overlay visualization underscores emerging trends such as generative AI, policy-driven governance, and ethical accountability frameworks. This study calls for a shift from passive oversight to proactive ethical stewardship in AI-driven social science research.Societies370This study examines the role of researchers as either ethical gatekeepers or pioneers of AI-driven change, and calls for a shift from passive oversight to proactive ethical stewardship in AI-driven social science research.Societies["Remus Runcan", "Vasile Ha\u021began", "O. Toderici", "Gabriel Croitoru", "Mihaela Gavrila-Ardelean", "L. Cuc", "Dana Rad", "Alina Costin", "T. Dughi"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/d6ee57d98373786a7a9e074cb28a2d2a97d1d9ab 39904f2ee216e81d5f916ae0c1c835dbc73ed763The integration of artificial intelligence into database performance tuning marks a pivotal evolution in data management practices. As traditional manual approaches by Database Administrators give way to predictive and autonomous systems, organizations are experiencing transformative benefits across multiple dimensions of database operations. AI technologies now enable workload prediction, automated indexing, anomaly detection, and resource optimization that far exceed human capabilities in both accuracy and efficiency. While challenges exist in implementing these systems—particularly regarding continuous learning requirements and legacy database integration—the trajectory toward fully autonomous database management continues to accelerate. This advancement fundamentally shifts the role of database professionals from routine maintenance to strategic data architecture and innovation, ultimately promising a future where databases self-optimize with minimal human oversight while delivering superior performance and reliability.International Journal of Scientific Research in Computer Science Engineering and Information Technology00A future where databases self-optimize with minimal human oversight while delivering superior performance and reliability is promised, ultimately promising a future where databases self-optimize with minimal human oversight while delivering superior performance and reliability.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ellavarasan Asokan"]2025-03-06T00:00:00https://www.semanticscholar.org/paper/39904f2ee216e81d5f916ae0c1c835dbc73ed763 7fd8fb8b5cbe2c6f80d3897da5fb80ba5cc90cb7This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing supply chain operations and financial forecasting in the USA. The research examines how AI-driven predictive analytics can foster business growth and stabilize markets. A diverse set of ML models is employed to address various challenges: Long Short-Term Memory (LSTM) networks are used for sequence forecasting in financial and economic domains, while Logistic Regression, Random Forest, and Boosting techniques support fraud detection. Additionally, autoencoders and Isolation Forest algorithms are applied to identify unusual financial transactions, and ARIMA models forecast demand spikes and seasonality. For logistics optimization, Reinforcement Learning ( Deep Q-Networks) is used to improve route planning, and Neural Networks predict optimal restocking periods based on demand patterns. XGBoost is used to assess customer price sensitivity and optimize pricing strategies. The performance of forecasting models is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). In contrast, fraud detection effectiveness is measured through Precision, Recall, F1-score, and the Area Under the Curve (AUC-ROC). Logistics models are assessed by Total Delivery Time, Cost Reduction, and Efficiency Gains while restocking predictions are validated via accuracy, Mean Squared Error (MSE), and inventory turnover rates. Pricing strategies are evaluated based on Revenue Impact, Elasticity Metrics, and Customer Retention Rates.International Journal of Applied Sciences and Radiation Research233The research examines how AI-driven predictive analytics can foster business growth and stabilize markets and a diverse set of ML models is employed to address various challenges.International Journal of Applied Sciences and Radiation Research["T. M. Olola", "Timilehin Isaiah Olatunde"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/7fd8fb8b5cbe2c6f80d3897da5fb80ba5cc90cb7 6d37e9ff708b15a1fc4b9aa57d8cd9630dfc5ba7The article considers the use of artificial intelligence (AI) in the educational process of higher education; carries out the analysis of concepts and reveals the main areas of AI application in education, including tutoring, adaptive curricula, automation of teachers’ routine tasks and development of personalized educational trajectories. The paper shows the dynamics of scientific publications growth on AI in education over the past ten years both in the world and in Russia and reveals a significant leap after 2020, due to emerging large language models (for example, GPT-3 and ChatGPT) available to a wide range of users. The work identifies the leading countries in the number of studies (China and Russia), analyzes the advantages and disadvantages of AI implementation for teachers and students, emphasizes the importance of critical thinking and preserving the human factor in the educational process. The authors consider ethical issues and problems of legal regulation, including the status of works created by AI and the possible increase in inequality in the access to technology. The paper concludes that introducing neural networks into higher education opens up significant prospects for the learning individualization and will become one of the priority tasks in the coming years. The work stresses the need for a conscious, responsible approach to integrating AI, which includes developing digital literacy, complying with ethical standards and improving the legislative framework. The conclusion outlines the prospects for further research in this area.Ergodesign00The paper concludes that introducing neural networks into higher education opens up significant prospects for the learning individualization and will become one of the priority tasks in the coming years.Ergodesign[]2025-03-07T00:00:00https://www.semanticscholar.org/paper/6d37e9ff708b15a1fc4b9aa57d8cd9630dfc5ba7 e0be91634e4efd9012646d27046fa75fecc208a2The article discusses the modern trends in the implementation of artificial intelligence (AI) technologies in the field of public administration, analyzes the global experiences of leading countries (China, USA), and outlines the prospects for their use in government structures. The potential of integrating intelligent systems into various aspects of government agencies' activities, such as public service provision, decision-making, and forecasting socio-economic trends, is explored. Special attention is given to studying Russian experiences within the framework of the "Digital Economy" national program, as well as analyzing the advantages and barriers to AI use in domestic public administration. Potential directions for further development of AI technologies in the public sector are proposed, including the creation of digital twins for modeling state programs, improving decision-making processes, and enhancing the efficiency of administrative functions. The expected economic benefits from the introduction of AI into public administration include increasing transparency in the work of government agencies, reducing corruption risks, improving the quality of public services, and reducing time and financial costs for fulfilling administrative tasks.Eurasian Science Review An International peer-reviewed multidisciplinary journal20Eurasian Science Review An International peer-reviewed multidisciplinary journal["Aisana Sanuar"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/e0be91634e4efd9012646d27046fa75fecc208a2 2ae97691ba7fd78e0e45c0b5ad9ddafadbe7f748With the rise in applications of artificial intelligence (AI) across various sectors, security concerns have become paramount. Traditional AI systems often lack robust security measures, making them vulnerable to adversarial attacks, data breaches, and privacy violations. Cryptography has emerged as a crucial component in enhancing AI security by ensuring data confidentiality, authentication, and integrity. This paper presents a comprehensive bibliometric review to understand the intersection between cryptography, AI, and security. A total of 495 journal articles and reviews were identified using Scopus as the primary database. The results indicate a sharp increase in research interest between 2020 and January 2025, with a significant rise in publications in 2023 and 2024. The key application areas include computer science, engineering, and materials science. Key cryptographic techniques such as homomorphic encryption, secure multiparty computation, and quantum cryptography have gained prominence in AI security. Blockchain has also emerged as an essential technology for securing AI-driven applications, particularly in data integrity and secure transactions. This paper highlights the crucial role of cryptography in safeguarding AI systems and provides future research directions to strengthen AI security through advanced cryptographic solutions.Cryptography470A comprehensive bibliometric review to understand the intersection between cryptography, AI, and security is presented and indicates a sharp increase in research interest between 2020 and January 2025, with a significant rise in publications in 2023 and 2024.Cryptography["Hamed Taherdoost", "Tuan-Vinh Le", "Khadija Slimani"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/2ae97691ba7fd78e0e45c0b5ad9ddafadbe7f748 3c725fb5ccd1636003c49742e54754775399357dAccentuating its role in many industries, Artificial Intelligence (AI) has transformed Human Resource Management (HRM), particularly Talent Acquisition (TA). This paper focuses on the ways in which AI changes TA in HRM by improving work processes, diversifying the applicant pool, and improving recruitment success. Using a quantitative research approach, the study invites 204 respondents. It applies complex statistical methods, regression analysis, Correlation, T-tests, and ANOVA to determine to what extent the AI can revolutionise the talent acquisition process. The study provided support for the hypothesised relationships, suggesting that there are benefits in implementing AI, and that implementation triggers change in talent acquisition processes. Finally, this study contributes to knowledge that can be applied to further strategic developments for HR departments as they continue to undergo change and seek ways toward implementing better and equal recruitment processes.Australasian Accounting, Business and Finance Journal00The study provided support for the hypothesised relationships, suggesting that there are benefits in implementing AI, and that implementation triggers change in talent acquisition processes.Australasian Accounting, Business and Finance Journal["Yashshri Choudhari", "Prajjwol Shrestha", "Gyanendra Singh", "Sunali Bindra"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/3c725fb5ccd1636003c49742e54754775399357d 4469f718f8e4b3ca8aed6a1ba0b76518acc9d164Purpose As artificial intelligence (AI) continues to make inroads into several industries, it has taken over tasks previously performed by humans. However, given that individuals frequently have their self-esteem, identity and feelings of self-worth wrapped up in financial matters, will there be a difference in their satisfaction when their credit applications are processed and approved through AI versus humans? Design/methodology/approach This work uses five studies, including a field study, three online experiments and one laboratory study, to underline the difference in customer satisfaction when credit application processing occurs via AI versus humans. Findings The authors find that customers are more satisfied when credit application processing is performed through an AI algorithm rather than by humans. This effect is explained by reduced embarrassment. Furthermore, the authors show that for emotionally intelligent individuals, credit application processing through humans will mitigate the impact of embarrassment, leading to higher customer satisfaction. Finally, the authors identify an individual’s relationship with the financial organisation as the boundary condition stating that for first-time customers (vs continuous customers), credit application processing through humans causes less embarrassment. Research limitations/implications This research makes significant contributions in the realm of consumer psychology and credit application processing. First, it advances the existing literature on AI versus human interactions by investigating their comparative impact on customer satisfaction within financial processes such as credit approval. In addition, it identifies credit application processing (whether by AI or humans) as an unexplored antecedent of embarrassment. Moreover, this study enhances the body of work on emotional intelligence by demonstrating its role as a coping mechanism for dealing with embarrassment. Finally, it uncovers a novel driver of embarrassment: the nature of individuals’ relationships with financial organisations, differentiating between continuous customers and first-time applicants. Practical implications This study suggests deploying AI for credit approval and adopting strategies to reduce customer embarrassment to boost consumer satisfaction. In addition, managers should consider customers’ emotional intelligence levels and use humans for first-time credit applications to minimise embarrassment. Originality/value Arguably, to the best of the authors’ knowledge, this study is the first to identify AI versus human processing as a novel factor influencing customer embarrassment in financial service satisfaction. It also provides a new aspect of emotional intelligence as a coping mechanism for embarrassment. Furthermore, it uncovers a unique driver of embarrassment: the nature of individuals’ relationships with financial organisations, distinguishing between continuous customers and first-time applicants. European Journal of Marketing770This study is the first to identify AI versus human processing as a novel factor influencing customer embarrassment in financial service satisfaction, and provides a new aspect of emotional intelligence as a coping mechanism for embarrassment.European Journal of Marketing["Parul Ahuja", "Mansi Gupta", "Abhirupa Roy", "Nazia Gera", "Gopal Das"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/4469f718f8e4b3ca8aed6a1ba0b76518acc9d164 6fa59dfda930ccb927aaca7e1465824f7e5a6f3bTraumatic brain injury (TBI) is a leading cause of disability and death globally, presenting significant challenges for diagnosis, prognosis, and treatment. As healthcare technology advances, artificial intelligence (AI) has emerged as a promising tool in enhancing TBI rehabilitation outcomes. This literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs. AI-driven algorithms have demonstrated high accuracy in predicting mortality, functional outcomes, and personalized rehabilitation strategies based on patient data. AI models have been developed to predict in-hospital mortality of TBI patients up to an accuracy of 95.6%. Furthermore, AI enhances neuroimaging by detecting subtle abnormalities that may be missed by human radiologists, expediting diagnosis and treatment decisions. Despite these advances, ethical considerations, including biases in AI algorithms and data generalizability, pose challenges that must be addressed to optimize AI’s implementation in clinical settings. This review highlights key clinical trials and future research directions, emphasizing AI’s transformative potential in improving patient care, rehabilitation, and long-term outcomes for TBI patients.Life1210A literature review explores the current and potential applications of AI in TBI management, focusing on AI’s role in diagnostic tools, neuroimaging, prognostic modeling, and rehabilitation programs, emphasizing AI’s transformative potential in improving patient care, rehabilitation, and long-term outcomes for TBI patients.Life["Seun Orenuga", "Philip Jordache", "Daniel Mirzai", "Tyler Monteros", "Ernesto Gonzalez", "Ahmed Madkoor", "Rahim Hirani", "Raj K Tiwari", "Mill Etienne"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/6fa59dfda930ccb927aaca7e1465824f7e5a6f3b d570c2316010ec64154b573ea544cfe256866807This study investigates the integration of Artificial Intelligence (AI) into financial statement preparation and its impact on accuracy, compliance, and corporate performance. The research aims to provide insights into how AI-driven financial reporting systems enhance efficiency, fraud detection, and regulatory adherence. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The study synthesizes empirical findings from indexed databases, analyzing AI applications such as Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) in financial reporting. The results indicate that AI-powered financial reporting significantly improves the accuracy and timeliness of financial disclosures, strengthens corporate governance, and enhances decision-making capabilities. AI-based fraud detection models outperform traditional auditing techniques, achieving higher accuracy and efficiency. The study also highlights key challenges, including concerns over algorithmic transparency, data privacy, and the cost of AI implementation, particularly for SMEs. AI has the potential to revolutionize financial statement preparation, improving regulatory compliance and corporate performance. However, challenges related to ethical considerations and cost barriers must be addressed to maximize AI’s benefits in financial reporting. The findings provide strategic insights for regulators, financial professionals, and policymakers to optimize AI adoption while ensuring compliance and accountability. Future research should focus on explainable AI models, long-term governance impacts, and regulatory frameworks for AI-driven financial reporting.International journal of innovative research and scientific studies410The research aims to provide insights into how AI-driven financial reporting systems enhance efficiency, fraud detection, and regulatory adherence, and highlights key challenges, including concerns over algorithmic transparency, data privacy, and the cost of AI implementation.International Journal of Innovative Research and Scientific Studies["Abdelrehim Awad", "Osama Akola", "Mohamed Amer", "Ezzat Kamal Abdallah Mousa"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/d570c2316010ec64154b573ea544cfe256866807 98513b98107c59f89f792555654096e81d00f94b Purpose The purpose of this study is to investigate and develop capabilities to make supply chains resilient using qualitative analysis of fast-moving consumer goods (FMCG) industry located in India. In particular, authors aim to propose a framework to make supply chains resilient by infusing artificial intelligence (AI). Design/methodology/approach The authors acquired supportive data by conducting semi-structured interviews with 25 FMCG supply chain professionals during 2023. Using open, axial and selective coding approaches, the authors mapped and discovered the themes that constitute the essential elements of AI-enabled supply chain resilience. Findings The research findings reveal that supply chain capabilities are useful for mitigating the disruptions impact when infused with AI. The authors’ analysis underscore four principal domains in which AI is poised to enhance the resilience of supply chains. This study delves into four key capabilities of interest, namely: Routing Optimization, Efficiency, Periodic Monitoring and Demand Forecasting. The result of this study is the proposed framework which shows the impact of different AI-powered capabilities on supply chain which builds resilient supply chains. Research limitations/implications Infusing AI to different supply chain capabilities appears to be a successful way for making FMCG supply chains resilient. Only the supply chain capabilities cannot overcome the impact of disruptions, but the use of AI helps professionals and policymakers to better respond to disruptions. Originality/value Few studies demonstrate the impact of advanced technology in building resilient supply chains. To the best of the authors’ knowledge, no earlier researcher has attempted to infuse AI into supply chain capabilities to make them resilient with empirical studies with the theoretical framework of Dynamic Capability View (DCV). Journal of Global Operations and Strategic Sourcing1010The research findings reveal that supply chain capabilities are useful for mitigating the disruptions impact when infused with AI, and a framework to make supply chains resilient by infusing artificial intelligence (AI) is proposed.Journal of Global Operations and Strategic Sourcing["Devnaad Singh", "Anupam Sharma", "Rohit Kumar Singh", "Prashant Singh Rana"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/98513b98107c59f89f792555654096e81d00f94b 3eae11c4925781e7858cde7265f6dae954ab0e42Abstract: The integration of Artificial intelligence (AI) into various sectors, including transportation, has a significant impact on human endeavors, in addition to eco-friendly advantages. One of the most promising areas of AI-powered systems is the manufacture of Autonomous Vehicles (AVs). These self-driving cars, also known as driverless, are intelligent vehicles that can operate without human aid or support. AVs are equipped with sophisticated AI-powered technologies such as sensors, radars, Global Positioning System (GPS), and advanced algorithms that can transmit information and navigate the environment using analyzed data. These driverless cars have the potential of revolutionizing the transport sector by improving efficiency, reducing road accidents, improving flexibility, and decreasing congestion. However, AI in AV applications poses some risks and challenges associated with securing systems from cybersecurity threats and attacks. This paper explores the dangers and difficulties of securing AI systems from cyber threats, highlighting various detection and prevention mechanisms. The ethical and legal implications, including strategies to address these challenges proactively, are also discussed. It is believed that the challenges in the automotive industry can be mitigated through collaboration among stakeholders, manufacturers, researchers, IT professionals, and policymakers by implementing robust security measures, conducting regular vulnerability assessments, and leveraging the expertise of software security specialists. Collaboration between industry and cybersecurity professionals is essential to safeguarding public safety.International Journal of Latest Technology in Engineering Management &amp; Applied Science120The dangers and difficulties of securing AI systems from cyber threats, highlighting various detection and prevention mechanisms are explored, including various detection and prevention mechanisms.International Journal of Latest Technology in Engineering Management &amp; Applied Science["Oluwatosin Ogunlade", "Abimbola Ogunlade", "Mobolaji Tenibiaje"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/3eae11c4925781e7858cde7265f6dae954ab0e42 a195ec61cd7992630902b279ca686d6017eef2aee integration of Artificial Intelligence (AI) in Human Resource Management (HRM) has revolutionized traditional recruitment and selection processes, particularly in the IT industry, where rapid technological advancements demand a skilled and dynamic workforce. AI-driven recruitment systems leverage machine learning, natural language processing (NLP), and predictive analytics to enhance efficiency, reduce hiring biases, and improve decision-making. This study explores the transformative role of AI in recruitment and selection within the IT sector, highlighting its benefits, challenges, and future implications. AI-powered tools have streamlined various stages of the hiring process, from resume screening to candidate assessment. Automated applicant tracking systems (ATS) equipped with AI algorithms can efficiently scan thousands of resumes, filtering out the most relevant candidates based on predefined criteria. Additionally, AI-driven chatbots and virtual assistants engage with applicants, provide real-time responses, schedule interviews, and enhance the candidate experience. These tools reduce the time-to-hire and improve the quality of recruitment by identifying the best-fit candidates based on skills, experience, and cultural alignment. Another critical advantage of AI in recruitment is its potential to reduce human biases. Traditional hiring processes are often influenced by unconscious biases related to gender, ethnicity, or educational background. AI-driven assessments focus on skills-based evaluation, utilizing predictive analytics to match candidates with job roles based on competencies rather than demographic factors. Furthermore, video interview analysis tools can assess verbal and non-verbal cues using facial recognition and speech analysis, helping recruiters make data-driven hiring decisions. Despite these benefits, AI in recruitment comes with challenges. One significant concern is data privacy and ethical considerations. AI algorithms rely on vast datasets, raising questions about data security, transparency, and fairness. If trained on biased historical data, AI systems may perpetuate discrimination rather than eliminate it. Ensuring algorithmic fairness and regulatory compliance, such as adhering to General Data Protection Regulation (GDPR) and other data protection laws, is crucial for ethical AI deployment in HR. Additionally, the human touch in recruitment remains essential. While AI can handle administrative tasks and initial screenings, final hiring decisions still require human judgment. Organizations must strike a balance between AI automation and human intuition to ensure a holistic approach to talent acquisition. HR professionals must also undergo upskilling to effectively leverage AI tools and interpret AI-driven insights. The future of AI in HRM will see advancements in predictive hiring, sentiment analysis, and skill gap analysis. AI-powered platforms will not only match candidates to current job roles but also predict future skill requirements and recommend personalized learning paths for employees. In the IT industry, where skills evolve rapidly, AI-driven workforce planning will play a crucial role in talent retention and upskilling initiatives. This paper analyzes the impact of AI on recruitment and selection in the IT industry, focusing on efficiency, bias reduction, candidate experience, and decision-making improvements. It examines AI-driven tools like ATS, chatbots, and predictive analytics, discusses ethical concerns, and explores the future role of AI in workforce planning and talent acquisition.Journal of Information Systems Engineering & Management100The transformative role of AI in recruitment and selection within the IT sector is explored, highlighting its benefits, challenges, and future implications, and the future role of AI in workforce planning and talent acquisition is explored.Journal of Information Systems Engineering and Management["Dr. G. Venkateshwaran", "Dr. N. Rajesh Kumar", "Luyang", "Vishnupriya S Devarajulu"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/a195ec61cd7992630902b279ca686d6017eef2ae ef20f393c0fca9d1deee5b763f53dfce92bf871aABSTRACT This integrative literature review examines the evolving role of artificial intelligence (AI) and machine learning (ML) based clinical decision support systems (CDSS) in mental health (MH) care, expanding on findings from a prior review (Higgins et al. 2023). Using and integrative review framework, a systematic search of six databases was conducted with a focus on primary research published between 2022 and 2024. Five studies met the inclusion criteria and were analysed for key themes, methodologies, and findings. The results reaffirm AI's potential to enhance MH care delivery by improving diagnostic accuracy, alleviating clinician workloads, and addressing missed care. New evidence highlights the importance of clinician trust, system transparency, and ethical concerns, including algorithmic bias and equity, particularly for vulnerable populations. Advancements in AI model complexity, such as multimodal learning systems, demonstrate improved predictive capacity but underscore the ongoing challenge of balancing interpretability with innovation. Workforce challenges, including clinician burnout and staffing shortages, persist as fundamental barriers that AI alone cannot resolve. The review not only confirms the findings from the first review but also adds new layers of complexity and understanding to the discourse on AI‐based CDSS in MH care. While AI‐driven CDSS holds significant promise for optimising MH care, sustainable improvements require the integration of AI solutions with systemic workforce enhancements. Future research should prioritise large‐scale, longitudinal studies to ensure equitable, transparent, and effective implementation of AI in diverse clinical contexts. A balanced approach addressing both technological and workforce challenges remain critical for advancing mental health care delivery.International Journal of Mental Health Nursing200The results reaffirm AI's potential to enhance MH care delivery by improving diagnostic accuracy, alleviating clinician workloads, and addressing missed care and highlight the importance of clinician trust, system transparency, and ethical concerns, including algorithmic bias and equity.International Journal of Mental Health Nursing["Oliver Higgins", "R. Wilson"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/ef20f393c0fca9d1deee5b763f53dfce92bf871a e954dcd7fbea349105d21de0257dad282d62c03bAdvances in digital technologies and artificial intelligence (AI) are reshaping healthcare delivery, with AI increasingly integrated into nursing practice. These innovations promise enhanced diagnostic precision, improved operational workflows, and more personalized patient care. However, the direct impact of AI on clinical outcomes, workflow efficiency, and nursing staff well-being requires further elucidation.This integrative review synthesized findings from 18 studies published through November 2024 across diverse healthcare settings. Using the PRISMA 2020 and SPIDER frameworks alongside rigorous quality appraisal tools (MMAT and ROBINS-I), the review examined the multifaceted effects of AI integration in nursing. Our analysis focused on three principal domains: clinical advancements and patient monitoring, operational efficiency and workload management, and ethical implications.The review demonstrates that AI integration in nursing has yielded substantial clinical and operational benefits. AI-powered monitoring systems, including wearable sensors and real-time alert platforms, have enabled nurses to detect subtle physiological changes—such as early fever onset or pain indicators—well before traditional methods, resulting in timely interventions that reduce complications, shorten hospital stays, and lower readmission rates. For example, several studies reported that early-warning algorithms facilitated faster clinical responses, thereby improving patient safety and outcomes. Operationally, AI-based automation of routine tasks (e.g., scheduling, administrative documentation, and predictive workload classification) has streamlined resource allocation. These efficiencies have led to a measurable reduction in nurse burnout and improved job satisfaction, as nurses can devote more time to direct patient care. However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology. These issues underscore the need for robust ethical frameworks and targeted AI literacy training within nursing curricula.This review demonstrates that AI integration holds transformative potential for nursing practice by enhancing both clinical outcomes and operational efficiency. However, to realize these benefits fully, it is imperative to develop robust ethical frameworks, incorporate comprehensive AI literacy training into nursing education, and foster interdisciplinary collaboration. Future longitudinal studies across varied clinical contexts are essential to validate these findings and support the sustainable, equitable implementation of AI technologies in nursing. Policymakers and healthcare leaders must prioritize investments in AI solutions that complement the expertise of nursing professionals while addressing ethical risks.Frontiers in Digital Health530It is demonstrated that AI integration holds transformative potential for nursing practice by enhancing both clinical outcomes and operational efficiency, and that policymakers and healthcare leaders must prioritize investments in AI solutions that complement the expertise of nursing professionals while addressing ethical risks.Frontiers in Digital Health["Salwa Hassanein", "Rabie Adel El Arab", "A. Abdrbo", "Mohammad S Abu-Mahfouz", "Mastoura Khames Gaballah", "M. Seweid", "Mohammed Almari", "Husam Alzghoul"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/e954dcd7fbea349105d21de0257dad282d62c03b 3e2d3fa66e19f156d142f15fffb00e23be65ca35BACKGROUND Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue. OBJECTIVE We aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities. METHODS Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 guidelines, we conducted a systematic review to synthesize current knowledge on this topic. Our search across 3 major databases (Scopus, PubMed, Association for Computing Machinery [ACM] Digital Library) yielded 1146 records, of which we included 152 (13.3%) studies in our analysis. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies version 2) was performed for the risk-of-bias assessment of all included studies. RESULTS Radiographic biomarkers (n=129, 84.9%) and deep learning (DL; n=122, 80.3%) approaches were predominantly used, with convolutional neural networks (CNNs) using Visual Geometry Group (VGG)-16 (n=37, 24.3%), ResNet-50 (n=33, 21.7%), and DenseNet-121 (n=19, 12.5%) architectures being the most common DL approach. The majority of studies focused on model development (n=143, 94.1%) and used a single modality approach (n=141, 92.8%). AI methods demonstrated good performance in all studies: mean accuracy=91.93% (SD 8.10%, 95% CI 90.52%-93.33%; median 93.59%, IQR 88.33%-98.32%), mean area under the curve (AUC)=93.48% (SD 7.51%, 95% CI 91.90%-95.06%; median 95.28%, IQR 91%-99%), mean sensitivity=92.77% (SD 7.48%, 95% CI 91.38%-94.15%; median 94.05% IQR 89%-98.87%), and mean specificity=92.39% (SD 9.4%, 95% CI 90.30%-94.49%; median 95.38%, IQR 89.42%-99.19%). AI performance across different biomarker types showed mean accuracies of 92.45% (SD 7.83%), 89.03% (SD 8.49%), and 84.21% (SD 0%); mean AUCs of 94.47% (SD 7.32%), 88.45% (SD 8.33%), and 88.61% (SD 5.9%); mean sensitivities of 93.8% (SD 6.27%), 88.41% (SD 10.24%), and 93% (SD 0%); and mean specificities of 94.2% (SD 6.63%), 85.89% (SD 14.66%), and 95% (SD 0%) for radiographic, molecular/biochemical, and physiological types, respectively. AI performance across various reference standards showed mean accuracies of 91.44% (SD 7.3%), 93.16% (SD 6.44%), and 88.98% (SD 9.77%); mean AUCs of 90.95% (SD 7.58%), 94.89% (SD 5.18%), and 92.61% (SD 6.01%); mean sensitivities of 91.76% (SD 7.02%), 93.73% (SD 6.67%), and 91.34% (SD 7.71%); and mean specificities of 86.56% (SD 12.8%), 93.69% (SD 8.45%), and 92.7% (SD 6.54%) for bacteriological, human reader, and combined reference standards, respectively. The transfer learning (TL) approach showed increasing popularity (n=89, 58.6%). Notably, only 1 (0.7%) study conducted domain-shift analysis for TB detection. CONCLUSIONS Findings from this review underscore the considerable promise of AI-based methods in the realm of TB detection. Future research endeavors should prioritize conducting domain-shift analyses to better simulate real-world scenarios in TB detection. TRIAL REGISTRATION PROSPERO CRD42023453611; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023453611.Journal of Medical Internet Research1820This work aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities and demonstrated good performance in all studies.Journal of medical Internet research["Seng Hansun", "A. Argha", "Ivan Bakhshayeshi", "Arya Wicaksana", "H. Alinejad-Rokny", "Greg J Fox", "S. Liaw", "Branko G. Celler", "G. Marks"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/3e2d3fa66e19f156d142f15fffb00e23be65ca35 72bf4243231f5521c1e7bc2ca1bf7fd57f37c351Artificial Intelligence (AI) technologies are changing many aspects of contemporary life, including the emergence of mental healthcare as a field for innovation. This interdisciplinary research investigates the complex interactions between Artificial Intelligence, Mental Health Care, Patient Rights, and Human Rights to assess the advantages and disadvantages of incorporating AI into Mental Health Services. Globally, millions of people suffer from mental disorders, placing significant strains on health systems and societies. Mental health care has always been restricted due to stigma, structural barriers and lack of resources, often leading to an inability to provide accessible and effective treatments despite increasing patient demands. AI technology has emerged as a possible solution for age-old issues like efficient early detection methods, treatment strategies, and personal follow-up. Conversely, AI application in this delicate area raises ethical and legal dilemmas about people’s freedoms, which must be assessed critically, and predictable steps must be taken to protect patient rights.Australasian Accounting, Business and Finance Journal00This interdisciplinary research investigates the complex interactions between Artificial Intelligence, Mental Health Care, Patient Rights, and Human Rights to assess the advantages and disadvantages of incorporating AI into Mental Health Services.Australasian Accounting, Business and Finance Journal["Sangramjeet Chavan", "Vojjala Sahiti", "Shashikala Gurpur", "Atmaram Shelke"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/72bf4243231f5521c1e7bc2ca1bf7fd57f37c351 18b63ea9bed8876c5cb2536b2df4a303d6010982The media environment is rapidly changing depending on the development of technology and technology. At the moment, the prospect of expanding the use of artificial intelligence to automate routine tasks, process data arrays or generate materials of various formats is already perceived as something inevitable. The emergence of artificial intelligence, on the one hand, helped to facilitate the creation of various content formats, on the other hand, gave rise to a large number of questions: about the authorship of content created by artificial intelligence, about the possibility of teaching intelligence ethical standards or corporate and professional values. In this context, the article aims to highlight the opportunities that the development of artificial intelligence brings, as well as the limitations that both creators and users may face when using artificial intelligence.Themed collection of papers from Foreign international scientific conference «Joint innovation - joint development». by HNRI «National development» in cooperation with PS of UA. December 202420The article aims to highlight the opportunities that the development of artificial intelligence brings, as well as the limitations that both creators and users may face when using artificial intelligence.Themed collection of papers from Foreign international scientific conference «Joint innovation - joint development». by HNRI «National development» in cooperation with PS of UA. December 2024["Sofia Alekseevna Novinskaya"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/18b63ea9bed8876c5cb2536b2df4a303d6010982 6c972f73d696fb2950d3880f8d49e8f7645d8358The question of being human is shaped by our contexts. The emergence of artificial intelligence (AI) technologies is drastically impacting our contexts and relationships, leaving us with questions about who we are and what our roles are in the experience of daily life. This article explores some of the concepts and conversations raised by Cornel W. du Toit and furthers these thoughts considering recent developments in the science of AI. This article offers some reflection on the discourse between science and religion.Intradisciplinary and/or interdisciplinary implications: This research contributes towards the science and religion discourse, focussing on the question of being human in the age of AI development.Verbum et Ecclesia00Some of the concepts and conversations raised by Cornel W. du Toit are explored and some reflection on the discourse between science and religion are offered.Verbum et Ecclesia["Wessel Bentley"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/6c972f73d696fb2950d3880f8d49e8f7645d8358 34b626c80bd5b8370bbb91bf21b44e8589826fedAs artificial intelligence (AI) systems become increasingly complex and autonomous, concerns over transparency and accountability have intensified. The"black box"problem in AI decision-making limits stakeholders' ability to understand, trust, and verify outcomes, particularly in high-stakes sectors such as healthcare, finance, and autonomous systems. Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a potential solution to enhance AI transparency and auditability. This paper explores the integration of blockchain with AI to improve decision traceability, data provenance, and model accountability. By leveraging blockchain as an immutable record-keeping system, AI decision-making can become more interpretable, fostering trust among users and regulatory compliance. However, challenges such as scalability, integration complexity, and computational overhead must be addressed to fully realize this synergy. This study discusses existing research, proposes a framework for blockchain-enhanced AI transparency, and highlights practical applications, benefits, and limitations. The findings suggest that blockchain could be a foundational technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.360The findings suggest that blockchain could be a foundational technology for ensuring AI systems remain accountable, ethical, and aligned with regulatory standards.["Afroja Akther", "Ayesha Arobee", "Abdullah Al Adnan", "Omum Auyon", "Asm Johirul Islam", "Farhad Akter"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/34b626c80bd5b8370bbb91bf21b44e8589826fed c0cd552b3e83912ab7a8f3d4648a925fa54eeed9Health data serves as a crucial foundation for artificial intelligence (AI) training in the healthcare sector. The pivotal procedure for acquiring numerous and effective health data lies in incentivizing participants to contribute their health data while adhering to privacy regulations like the General Data Protection Regulation. Federated learning achieves privacy protection by transmitting only parameters rather than data to the model. When integrated with blockchain smart contracts, this approach facilitates the automation of incentives according to health data quality, thereby mitigating human’s subjective intervention. Consequently, the synergy of these two methodologies offers new promise for the training of AI models in healthcare. However, this advantage encounters performance degradation due to the heterogeneity among diverse blockchains. This article posits the concept of Omnichain as a potential solution to this challenge by analyzing its operational mechanisms and future developmental trajectories and providing potential perspectives for its combination with hybrid federal learning solutions such as differential privacy and secure multi-party computation to promote its application in the sphere of AI in healthcare.Artificial Intelligence in Health00The concept of Omnichain is posits as a potential solution to this challenge by analyzing its operational mechanisms and future developmental trajectories and providing potential perspectives for its combination with hybrid federal learning solutions such as differential privacy and secure multi-party computation to promote its application in the sphere of AI in healthcare.Artificial Intelligence in Health["Dongfang Wu", "Yichen Wang"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/c0cd552b3e83912ab7a8f3d4648a925fa54eeed9 b0cd30703b9153e91b77dadf623be592389fc65aThe paper addresses the role of Artificial intelligence (A) in modern drug design and experimental work in biomedicine. It is shown how AI technologies can accelerate discovery and innovations and decrease the time of translational cycle. Advantages of AI and modern approaches are presented.Russian Journal for Personalized Medicine260It is shown how AI technologies can accelerate discovery and innovations and decrease the time of translational cycle.Russian Journal for Personalized Medicine["M. M. Galagudza", "Ya. G. Toropova", "A. Konradi"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/b0cd30703b9153e91b77dadf623be592389fc65a 0bd2e313d1b14dfa2e3c0faacb6840693a209c80The modern era is an era of development. Today is a time of rapid advancement in science and technology. Innovations are entering all spheres of society, and the standard of living has improved. The main feature of this era is the dominance of information technologies, which are fundamentally changing human life. This article is focused on the development of the healthcare sector through the use of artificial intelligence, in line with the demands of the modern age. With the advancement of digital literacy, mastering the direction of artificial intelligence and simplifying the work of doctors will undoubtedly bring many benefits. Artificial intelligence refers to the ability of computer systems to perform tasks typically associated with human intelligence. These systems execute capabilities such as logical thinking, learning, problem-solving, pattern recognition, and decision-making. Artificial intelligence technologies are widely used in many fields of science. In medicine, the use of artificial intelligence provides great opportunities to improve the healthcare sector. Through these opportunities, issues such as accelerating diagnostics, improving treatment effectiveness, easing patient monitoring, and registration can be solved.Eurasian Science Review An International peer-reviewed multidisciplinary journal00The use of artificial intelligence provides great opportunities to improve the healthcare sector through the development of the healthcare sector through the use of artificial intelligence, in line with the demands of the modern age.Eurasian Science Review An International peer-reviewed multidisciplinary journal["G\u00fcl\u00fcm Kaldanova"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/0bd2e313d1b14dfa2e3c0faacb6840693a209c80 32d40c92de395e60e30d6e1dd370791a0a3fb47eMachine learning (ML) models serve as powerful tools for threat detection and mitigation; however, they also introduce potential new risks. Adversarial input can exploit these models through standard interfaces, thus creating new attack pathways that threaten critical network operations. As ML advancements progress, adversarial strategies become more advanced, and conventional defenses such as adversarial training are costly in computational terms and often fail to provide real-time detection. These methods typically require a balance between robustness and model performance, which presents challenges for applications that demand instant response. To further investigate this vulnerability, we suggest a novel strategy for detecting and mitigating adversarial attacks using eXplainable Artificial Intelligence (XAI). This approach is evaluated in real time within intrusion detection systems (IDS), leading to the development of a zero-touch mitigation strategy. Additionally, we explore various scenarios in the Radio Resource Control (RRC) layer within the Open Radio Access Network (O-RAN) framework, emphasizing the critical need for enhanced mitigation techniques to strengthen IDS defenses against advanced threats and implement a zero-touch mitigation solution. Extensive testing across different scenarios in the RRC layer of the O-RAN infrastructure validates the ability of the framework to detect and counteract integrated RRC-layer attacks when paired with adversarial strategies, emphasizing the essential need for robust defensive mechanisms to strengthen IDS against complex threats.150Extensive testing across different scenarios in the RRC layer of the O-RAN infrastructure validates the ability of the framework to detect and counteract integrated RRC-layer attacks when paired with adversarial strategies, emphasizing the essential need for robust defensive mechanisms to strengthen IDS against complex threats.["Bet\u00fcl G\u00fcven\u00e7 Paltun", "Ramin Fuladi", "Rim El Malki"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/32d40c92de395e60e30d6e1dd370791a0a3fb47e ab4acf70459e710701922d62fd6950df4e6d45e0The future of artificial intelligence technologies in education looks promising. Every year we see more and more innovations that can make learning more accessible and effective. The successful integration of artificial intelligence into education can be the key to the formation of a functionally literate generation, ready for the challenges of the modern world.Themed collection of papers from Foreign international scientific conference «Joint innovation - joint development». by HNRI «National development» in cooperation with PS of UA. December 202400The successful integration of artificial intelligence into education can be the key to the formation of a functionally literate generation, ready for the challenges of the modern world.Themed collection of papers from Foreign international scientific conference «Joint innovation - joint development». by HNRI «National development» in cooperation with PS of UA. December 2024["Lidiya Chushievna He"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/ab4acf70459e710701922d62fd6950df4e6d45e0 cb6798f748f4e06562466adb36cadf009fc6c1baThis study explores the integration of artificial intelligence into enterprise financial management and strategic decision-making, identifying key combination points and application mechanisms. The research outlines artificial intelligence development trends and analyzes existing applications in human resource and accounting management to examine potential integration pathways in enterprise financial management. The study reveals that artificial intelligence enhances financial management through big data platforms that enable data collection, mining, and visualization. AI facilitates enterprise internal management innovation, particularly in human resource management, and provides quantitative support for strategic decision-making. Artificial intelligence transforms traditional financial management by providing intuitive data visualization, generating strategic insights, and reducing decision-making risks. The integration requires a paradigm shift in both technology and management mindset, with continued human oversight remaining essential. Enterprises should gradually introduce artificial intelligence into financial management and strategic processes, focusing on building AI teams and robust data governance frameworks while avoiding over-reliance on short-term benefits. Financial professionals need to develop new competencies to effectively collaborate with AI systems.Edelweiss Applied Science and Technology60Enterprises should gradually introduce artificial intelligence into financial management and strategic processes, focusing on building AI teams and robust data governance frameworks while avoiding over-reliance on short-term benefits.Edelweiss Applied Science and Technology["Miao Wang", "Jianhua Dai"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/cb6798f748f4e06562466adb36cadf009fc6c1ba 41d0e2278765c29be86f8d17ff41d91328bf0553This paper delves into the phenomenon of "kufal symbiosis,” which refers to the collaboration between artificial intelligence (AI) and terrorist organizations. It highlights AI’s potential to enhance the efficiency of terrorist operations. While AI can be used to recruit new members and plan more sophisticated attacks, security agencies face challenges in adapting this technology to counter terrorism effectively. This paper discussed the crucial need to strike a balance between individual privacy and national security, as well as the difficulties of managing large-scale data with limited resources. Additionally, the use of technology such as deepfakes and botnets by terrorist organizations might lead to confusion and intensify the impact of their attacks. The discussion also addresses cyberattacks on smart cities, exposing the vulnerabilities in infrastructure to cyber threats. In conclusion, while AI enhances the efficiency of terrorist operations, it also equips security agencies to prevent such threats despite the ongoing struggle to balance privacy and security.Security Intelligence Terrorism Journal (SITJ)240The crucial need to strike a balance between individual privacy and national security, as well as the difficulties of managing large-scale data with limited resources are discussed, despite the ongoing struggle to balance privacy and security.Security Intelligence Terrorism Journal (SITJ)["Wahyu Jati Arya Guna"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/41d0e2278765c29be86f8d17ff41d91328bf0553 7b888c1704596dec22802b3bf996ab901f06aa5aArtificial intelligence and other digital health technologies may optimize nurses' work. Therefore, we aimed to examine the roles of nurses in facilitating the adoption of digital health technologies and identify opportunities for these technologies to reduce burnout. We conducted a cross-sectional survey study focused on nurses' use of digital health and artificial intelligence technology with nursing informaticists. Data collection was guided by the implementation science framework, Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability. Participants were recruited electronically through professional nursing informatics organizations. Survey data were analyzed using basic descriptive statistics. Fifty-two participants from across the United States completed the survey. Telehealth (73%), patient portals (71%), and medical-grade devices (69%) were most frequently used, whereas artificial intelligence was frequently used by only 38%. Staffing shortages (88%), low staff retention (81%), and inadequate support when adopting new technologies (52%) were among the key drivers of nursing burnout. Participants endorsed most nursing tasks as being supported by digital health, especially patient assessment and evaluating outcomes, and especially artificial intelligence. Engaging nurses early in the process of developing and deploying digital health, especially artificial intelligence, may help address burnout by producing more nursing-centered technologies and providing technology-enabled nursing work alternatives to bedside care.Computers, Informatics, Nursing140Engaging nurses early in the process of developing and deploying digital health, especially artificial intelligence, may help address burnout by producing more nursing-centered technologies and providing technology-enabled nursing work alternatives to bedside care.Computers, informatics, nursing : CIN["Meghan Reading Turchioe", "Robin Austin", "Kay Lytle"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/7b888c1704596dec22802b3bf996ab901f06aa5a 69843e20f876238bf0fbd695a294acbb5dcc793fThis study aimed to "investigate the application of Artificial Intelligence (AI) in the area of Human Resources (HR). It offerings a view of the research that used AI in the area of HR, through the quantitative descriptive analysis of journals in the period between the years of 2017 and 2023. Ten research publications in google scholar have been recognized that address the application of AI in the HR area. As a result, it was concluded that AI is a powerful tool that can be used to enhance HR management performance by improving efficiency, improving employee experiences, and enabling informed strategic decision-making"Journal of Information Systems Engineering & Management150It was concluded that AI is a powerful tool that can be used to enhance HR management performance by improving efficiency, improving employee experiences, and enabling informed strategic decision-making.Journal of Information Systems Engineering and Management["Ahmad Albloush", "Motteh S Al-Shibly", "Mahmoud Alghizzawi"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/69843e20f876238bf0fbd695a294acbb5dcc793f f24a5d3b56f22fff5dfd382b488fa81dd6613263Endocrinology Research and Practice00Endocrinology Research and Practice["Ferhat Cetin", "Enver Sukru Goncuoglu", "S. Abal\u0131", "Ilknur Arslanoglu", "O. Deyneli", "Ozge Telci Caklili", "Hulya Yalin Turna", "Elif Sahiner", "Dila Guzel", "Mehmet Temel Yilmaz"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/f24a5d3b56f22fff5dfd382b488fa81dd6613263 5a3c3ea53543da11a294fa9f24851540fdca756bAn AI safety specialist confronts fears about the future of large language models.Science20Science["Michael Spezio"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/5a3c3ea53543da11a294fa9f24851540fdca756b eea4ec09f362dd51c0111972aeafd9928a55b153Current Psychology410Current Psychology["Mei-lan Yang"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/eea4ec09f362dd51c0111972aeafd9928a55b153 8fd99638b5bf9cb77b26b2d244282a38b7b8ab8bAsian Research Journal of Current Science00Asian Research Journal of Current Science["Oladayo Aghogho Perculiar", "O. Oladayo", "Tsetimi Jonathan"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/8fd99638b5bf9cb77b26b2d244282a38b7b8ab8b 574717efa75155817bf14f517489758bd490a2bdBMC Oral Health430Oral and maxillofacial surgeons say ongoing software developments and the increasing acceptance of chatbots among healthcare professionals hold promise that these tools can provide rapid solutions to the high demand for medical care, ease professionals’ workload, reduce costs, and save time.BMC Oral Health["Alanur \u00c7ift\u00e7i \u015ei\u015fman", "A. Acar"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/574717efa75155817bf14f517489758bd490a2bd 3cefd0912342062b6b57680817d5785f70bc893fThis study aims to develop a model that can detect and classify tyres, distinguishing between defective and functional ones to aid in quality inspection and support sustainable waste management practices. The study adopted a structured approach to developing an automated system for classifying tyre quality, using ResNet-50 and YOLO (You Only Look Once) for real-time detection. The precision peaks at 0.9552, indicating excellent performance of the model. The study's findings enforce the potential of deep learning models to increase efficiency and safety within the automotive sector, particularly in areas like preventive maintenance and tyre recycling. The quality control and enhancement in waste management practices of tyres within the automotive sector can be achieved by integrating real-time detection and the precise classification of tyres using this model.Australasian Accounting, Business and Finance Journal00The quality control and enhancement in waste management practices of tyres within the automotive sector can be achieved by integrating real-time detection and the precise classification of tyres using this model.Australasian Accounting, Business and Finance Journal["Atharv Phadnis", "Dashputre Amey Satish", "Viraja Dharane", "Manoj Hudnurka", "S. Ambekar"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/3cefd0912342062b6b57680817d5785f70bc893f 7cd50ecca27dc18ef57c869b2352c08636c6471cACS Sustainable Chemistry &amp; Engineering240ACS Sustainable Chemistry &amp; Engineering["M. Ghasemlou", "Hoang Chinh Nguyen", "Sachin Talekar", "Frederick M. Pfeffer", "Colin J. Barrow"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/7cd50ecca27dc18ef57c869b2352c08636c6471c d98e7ed3b34db9a79cd4f4e27c6c99bb7363b58aJAMA Health Forum20JAMA health forum["J. Ayanian", "Zirui Song"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/d98e7ed3b34db9a79cd4f4e27c6c99bb7363b58a 5e62cde90290ade1e832ca45abd892c257fc763c Internet of Things (H-IoT) technologies related to health are becoming increasingly important in managing patient health. These include preventing disease, monitoring patient functions in real-time via telemonitoring, testing treatments, tracking fitness and well-being, distributing medications, and gathering data for health research. H-IoT promises numerous advantages for healthcare. However, it also raises several ethical issues due to the dangers of using Internet-enabled devices, the delicate nature of data about health, and how these issues influence the healthcare system. Healthcare IoT is designed to work in both public and private domains. The sensors and equipment are carried by the person or placed in locations such as homes, workplaces, or hospital wards. These circumstances allow the third party a chance to gather and analyze information about a person's behavior or health. While remote monitoring and faster response healthcare is getting better these days, the technologies used in it also present chances for data or personal privacy breaches. It has been noted that malevolent attackers targeting mobile devices typically have specific objectives, such as obtaining user or patient data, causing harm to system resources, or even terminating vital programs. Concerns over data privacy and autonomy, data quality, intellectual property, algorithmic bias, unprotected consumer gadgets, hackable automobiles, and the responsibility of IoT systems are some ethical challenges surrounding the Internet of Things (IoT). Additionally, potential loss of trust, invasions of privacy, improper use of data, inconsistent copyright, digital divide, identity theft, difficulties with control and information access, and freedom of speech and expression are some more concerns. Methods like algorithmic social contracts, programming moral behavior, and rules and codes of ethics for IoT developers must all be used to address these ethical dilemmas. Current Artificial Intelligence00Concerns over data privacy and autonomy, data quality, intellectual property, algorithmic bias, unprotected consumer gadgets, hackable automobiles, and the responsibility of IoT systems are some ethical challenges surrounding the Internet of Things (IoT).Current Artificial Intelligence["Neelottama Kushwaha", "S. Kushwaha", "Shruti Khare"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/5e62cde90290ade1e832ca45abd892c257fc763c 98eecbfe51e12220e9c3cf6dad1469f472230ed7Agricultural education faces challenges in personalizing learning and adapting to the individual needs of students. In this context, artificial intelligence (AI) is positioned as a key tool to optimize educational models in the sector. This study aims to analyze the application of AI-based personalized learning models in agricultural education, identifying their benefits, limitations and implementation opportunities. For this purpose, a systematic literature review was conducted using indexed databases such as Scopus, Web of Science and ScienceDirect, covering studies published between 2019 and 2025. Inclusion criteria were applied that prioritized peer-reviewed research on AI in agricultural education, excluding papers with methodological shortcomings. Information was organized into key categories, including impact on teaching, technological barriers, and adoption strategies. The findings highlight that AI enhances personalization of learning through intelligent tutoring systems, predictive analytics, and simulation tools. However, its implementation faces challenges related to technological infrastructure, digital literacy, and resistance to change in traditional educational models. In addition, ethical concerns arise regarding data privacy and equity of access to these technologies. It is concluded that the integration of AI in agricultural education has the potential to transform teaching, optimize learning and strengthen the training of professionals in the sector. However, its success will depend on investment in infrastructure, equitable access policies and teacher training strategies.Multidisciplinary Latin American Journal (MLAJ)210It is concluded that the integration of AI in agricultural education has the potential to transform teaching, optimize learning and strengthen the training of professionals in the sector, however, its success will depend on investment in infrastructure, equitable access policies and teacher training strategies.Multidisciplinary Latin American Journal (MLAJ)["M. Espinosa-Aguilar", "Carlos Felipe Loayza-Romero", "Danny Xavier Romero-Herrera", "Darwin Alberto Gonz\u00e1lez-Romero"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/98eecbfe51e12220e9c3cf6dad1469f472230ed7 7299fee39028483042166a13b8f7487b15520c0dIn an increasingly digitized and competitive business environment, the ability to design products and services that meet customer needs and are secure and personalized has become a key differentiator for organizations. This study explores the intersection between Value Proposition Design and Artificial Intelligence, proposing a theoretical and practical framework that integrates these two disciplines to optimize value creation and ensure the delivery of products and services tailored to customer expectations and needs. Methodologically, value propositions are validated using Artificial Intelligence predictive models. This technique allows for the evaluation of the effectiveness of the proposals before their implementation, dynamically adjusting them according to success predictions and changing market conditions. The results reveal that Value Proposition Design focuses on deeply understanding customers and identifying their tasks, needs, and expected benefits in order to develop value propositions that effectively solve their problems. By incorporating Artificial Intelligence techniques, such as machine learning and predictive analytics, this process can be automated and improved, allowing organizations to identify customer behavior patterns, personalize offers in real time, and foresee potential problems before they materialize. It is concluded that case studies and applied examples demonstrate how this combination can transform how companies design and deliver value, increasing customer satisfaction and competitiveness in the marketplace.Gestión y Desarrollo Libre00This study explores the intersection between Value Proposition Design and Artificial Intelligence, proposing a theoretical and practical framework that integrates these two disciplines to optimize value creation and ensure the delivery of products and services tailored to customer expectations and needs.Gestión y Desarrollo Libre["Mario Gabriel Sari\u00e1n-Gonz\u00e1lez"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/7299fee39028483042166a13b8f7487b15520c0d e1dba8eea151a351bc02737b3acee53334350a52This article explores social network site (SNS) users’ understanding of the danger the integration of human intelligence and artificial intelligence (AI), termed “augmented intelligence,” presents.  Augmented intelligence, a subsection of artificial intelligence (AI), aims to enhance human intelligence with AI and is heralded as a significant step in problem-solving. A crucial concern is the profound threat to SNS users’ information security. A quantitative approach examined SNS understanding regarding the diffusion of augmented intelligence into SNS users’ spaces. An online survey was administered to 165 SNS users residing in the Gauteng province of South Africa.  Diffusion of Innovation (DOI) theory was used as the theoretical lens. Ethical clearance was obtained, and the data collected was anonymized and kept confidential. The article provides new insights that can help SNS users understand that a new threat to their information security in the form of augmented intelligence is emerging. Findings suggest that out of the five constructs drawn from DOI that explain the diffusion of augmented intelligence into sophisticated social engineering attacks, relative advantage, compatibility, and complexity were perceived by study participants as likely predictors of augmented intelligence adoption. Users, however, differed on exactly how the augmentation process was being achieved.International Journal of Business Ecosystem &amp; Strategy (2687-2293)630SNS users’ understanding of the danger the integration of human intelligence and artificial intelligence (AI), termed “augmented intelligence,” presents is explored, and relative advantage, compatibility, and complexity were perceived by study participants as likely predictors of augmented intelligence adoption.International Journal of Business Ecosystem &amp; Strategy (2687-2293)["K. Njenga", "Baswabile Matemane"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/e1dba8eea151a351bc02737b3acee53334350a52 f0af46be422bfb2fac508f5b7c12c649bb131bc2Multi-agent systems address issues of accessibility and scalability of artificial intelligence (AI) foundation models, which are often represented by large language models. We develop a framework - the"Society of HiveMind"(SOHM) - that orchestrates the interaction between multiple AI foundation models, imitating the observed behavior of animal swarms in nature by following modern evolutionary theories. On the one hand, we find that the SOHM provides a negligible benefit on tasks that mainly require real-world knowledge. On the other hand, we remark a significant improvement on tasks that require intensive logical reasoning, indicating that multi-agent systems are capable of increasing the reasoning capabilities of the collective compared to the individual agents. Our findings demonstrate the potential of combining a multitude of diverse AI foundation models to form an artificial swarm intelligence capable of self-improvement through interactions with a given environment.370A framework that orchestrates the interaction between multiple AI foundation models, imitating the observed behavior of animal swarms in nature by following modern evolutionary theories is developed, demonstrating the potential of combining a multitude of diverse AI foundation models to form an artificial swarm intelligence capable of self-improvement through interactions with a given environment.["Noah Mami\u00e9", "Susie Xi Rao"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/f0af46be422bfb2fac508f5b7c12c649bb131bc2 6b0d5e3eb445526a047868c7a341b4d02c85c14dnpj Climate and Atmospheric Science471The ability of the AI weather model to learn physically meaningful spatio-temporal links between atmospheric processes is demonstrated and should enable researchers to conduct initial condition studies in minutes, potentially at lead times into the non-linear regime.Npj Climate and Atmospheric Science["Jorge Ba\u00f1o-Medina", "Agniv Sengupta", "James D. Doyle", "Carolyn A. Reynolds", "Duncan Watson-Parris", "L. D. Monache"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/6b0d5e3eb445526a047868c7a341b4d02c85c14d ab5ba4bc412e60f8d78d5e96f9f6885b40c300e6The integration of wearable technology and artificial intelligence (AI) has transformed modern sports science by enhancing athlete monitoring, performance optimization, and injury prevention. Wearable sensors, including fitness trackers, GPS-based devices, and biomechanical motion trackers, provide real-time physiological and biomechanical data, enabling personalized training programs and workload management. AI-driven analytics, utilizing machine learning, deep learning, and computer vision, enhance performance assessment, injury prediction, and rehabilitation strategies by processing vast datasets to detect fatigue patterns, optimize recovery schedules, and refine tactical decision-making. Despite these advancements, challenges persist regarding data accuracy, privacy, and accessibility. Variability in sensor precision and standardization issues hinder reliable cross-comparisons, necessitating the development of validation protocols. Additionally, AI-driven wearables raise concerns over data security, ethical handling, and equitable access, as high costs limit their use in amateur sports. Future research should focus on refining AI-powered injury prevention models, improving biometric sensing capabilities, and advancing edge AI for real-time data processing. Addressing these challenges will ensure that wearable technology and AI continue to enhance sports performance, injury mitigation, and athlete well-being at all levels of competition.Quality in Sport180Challenges persist regarding data accuracy, privacy, and accessibility, and addressing these challenges will ensure that wearable technology and AI continue to enhance sports performance, injury mitigation, and athlete well-being at all levels of competition.Quality in Sport["Stanis\u0142aw Dudek", "Weronika Koziak", "Michalina Makie\u0142a", "Aleksandra B\u0119tkowska", "Agata Kornacka", "Wojciech Dudek", "Kamila Szostak", "Rafa\u0142 Tomaka", "Anna Byra"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/ab5ba4bc412e60f8d78d5e96f9f6885b40c300e6 c5e4aa7ab68541384b1ae034a3bb1f929722cba4This paper critically examined the impact of over-dependence on Artificial Intelligence (AI) by Information Technology (IT) Professionals, exploring the impact on cognitive functions such as critical thinking, decision-making, and problem-solving, and how these affect the autonomy of IT Professionals. Using quantitative research methodology, the study surveyed 180 IT Professionals to gauge their usage of and dependence on AI-powered tools, as well as their perceptions about AI technologies and the effects these might have on their cognitive abilities. The findings showed a very significant integration of AI by the respondents in both their personal and professional capacities, as well as a substantial dependence on AI-powered tools, particularly for decision-making purposes. Furthermore, there appeared to be a possible underestimation of the effects that AI usage has on cognitive abilities, as expressed through the paradoxical survey results. Moreover, several ethical issues were identified such as bias, privacy and the lagging behind of laws and regulations, further serving to complicate the effects of over-dependence on AI. Based on its findings this paper makes recommendations for the development of clear guidelines for the use of AI, continuous learning and development programs, focusing on AI advancements and risks, as well as the implementation of frequent impact and security assessments. These recommendations aim to assist organisations and IT Professionals to benefit from the advantages of AI while maintaining critical skills, human oversight and intuition.International Journal of Research In Business and Social Science460Recommendations are made for the development of clear guidelines for the use of AI, continuous learning and development programs, focusing on AI advancements and risks, as well as the implementation of frequent impact and security assessments to assist organisations and IT Professionals to benefit from the advantages of AI while maintaining critical skills, human oversight and intuition.International Journal of Research in Business and Social Science (2147- 4478)["Mark Frater", "Rodney Mushininga"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/c5e4aa7ab68541384b1ae034a3bb1f929722cba4 2cfc5a40f25c36076d8337840c6883240af023faAchieving sustainability in livestock farming requires advanced, non-invasive monitoring systems that enhance both productivity and animal welfare. Traditional methods for assessing dairy cow ingestive behavior, such as manual observation and sensor-based tracking, are often limited in scalability and accuracy. This study advances precision livestock farming by integrating multimodal artificial intelligence (AI) to decode bovine vocalizations in real time. Our approach leverages acoustic recordings, video analysis, and biometric sensor data to create a comprehensive system capable of detecting subtle patterns in feeding behavior and physiological well-being. By employing Generative AI and Large Language Models, our framework not only classifies ingestive behaviors but also interprets vocal signals linked to stress, health, and environmental conditions. The extracted features are transformed into spectrograms and fused with biometric indicators, enabling early detection of anomalies. This information is delivered through an intuitive dashboard, empowering farmers with real-time insights to optimize feeding strategies, reduce resource wastage, and mitigate welfare concerns. Unlike conventional deep learning approaches, which struggle with environmental variability, our system adapts dynamically across diverse farm settings, ensuring robustness and generalizability. This work directly contributes to global sustainability goals by improving resource efficiency, enhancing dairy herd management, and reducing the environmental footprint of livestock production. By integrating cutting-edge AI with practical farm applications, we pave the way for a more intelligent, responsive, and ethical approach to animal agriculture—where technology serves as a bridge between scientific advancements and on-farm decision-making.bioRxiv10This study advances precision livestock farming by integrating multimodal artificial intelligence (AI) to decode bovine vocalizations in real time and paving the way for a more intelligent, responsive, and ethical approach to animal agriculture.bioRxiv["Mayuri Kate", "S. Neethirajan"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/2cfc5a40f25c36076d8337840c6883240af023fa 998d7de2789121ef4bb165e3d166c8ff054c2ae4Artificial intelligence (AI) has transformed the quality control process with AI inspection technology, which reduces the need for costly physical resources and mitigates retail returns. Despite its revolutionizing impact on supply chain quality management, there is a notable gap in research on the implications of a manufacturer's adoption of AI inspection. This article addresses this gap by presenting a two‐stage model that explores the consequences of AI inspection adoption for a downstream manufacturer and an upstream supplier. Our results show that higher AI‐based inspection accuracy may not always benefit the manufacturer. This is because when the supplier's traditional inspection accuracy falls within an immediate range, the manufacturer's incentive to improve AI inspection accuracy diminishes, and the positive effect of AI inspection on retail returns cannot fully offset the technology expense. Moreover, our study explores the dynamics of technology‐sharing strategies between the manufacturer and supplier. Despite potential revenue gains, the manufacturer may hesitate to share technology due to the risk of increased defective products with lower AI inspection accuracy, leading to a paradox where profitability coexists with losses. Surprisingly, the successful collaborative technology‐sharing strategy may paradoxically lead to reduced technology investment. This occurs because technology‐sharing enables significant marginal cost savings in retail returns, rendering the manufacturer to achieve a comparable inspection level with lower investment. Overall, this research highlights that adopting AI inspection does not guarantee benefits for the supply chain members and can sometimes be detrimental. Our study offers strategic guidance for decision‐makers in supply chain quality management.Decision Sciences430Decision Sciences["Jun Pei", "Ruiqi Wang", "Ping Yan", "Yinliang (Ricky) Tan"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/998d7de2789121ef4bb165e3d166c8ff054c2ae4 f4cf6db59650363881cd8c9e06ac65e67a688171The integration of advanced technologies such as artificial intelligence (AI) and blockchain is transforming the agricultural insurance sector, providing more precise, accessible, and efficient solutions for producers. AI, with its ability to analyze large volumes of data, enables the customization of insurance policies, adjusting them in real time according to climatic and cultivation conditions. This provides a faster response to changes, offering greater security for farmers. Additionally, the implementation of blockchain-based smart contracts facilitates automated claims settlement, reducing costs and increasing the transparency of the process, which is crucial for farmers' trust in the insurance system. These innovations help producers manage financial and operational risks more efficiently, contributing to the sustainability and resilience of agricultural practices in the face of climate change and extreme weather events. Recent studies show that the adoption of AI and blockchain improves loss prediction, insurance pricing, and farmers' adaptation to climate challenges, significantly reducing uncertainty and increasing trust in the system. With the continued advancement of these technologies, the agricultural insurance sector is expected to become more efficient and play a critical role in the continuity and growth of global agricultural production in an increasingly unpredictable environment. Therefore, the future of agricultural insurance is closely tied to technological innovation, enabling producers to face risks more intelligently and effectively.Revista SISTEMAS130The future of agricultural insurance is closely tied to technological innovation, enabling producers to face risks more intelligently and effectively.Revista Sistemática["Rafael Elias Venturini"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/f4cf6db59650363881cd8c9e06ac65e67a688171 55132e171bd51ebb361686ccf2dab9b548fe9b25Benchmarks are essential for consistent evaluation and reproducibility. The integration of Artificial Intelligence into Software Engineering (AI4SE) has given rise to numerous benchmarks for tasks such as code generation and bug fixing. However, this surge presents challenges: (1) scattered benchmark knowledge across tasks, (2) difficulty in selecting relevant benchmarks, (3) the absence of a uniform standard for benchmark development, and (4) limitations of existing benchmarks. In this paper, we review 173 studies and identify 204 AI4SE benchmarks. We classify these benchmarks, analyze their limitations, and expose gaps in practices. Based on our review, we created BenchScout, a semantic search tool to find relevant benchmarks, using automated clustering of the contexts from associated studies. We conducted a user study with 22 participants to evaluate BenchScout's usability, effectiveness, and intuitiveness which resulted in average scores of 4.5, 4.0, and 4.1 out of 5. To advance benchmarking standards, we propose BenchFrame, a unified method to enhance benchmark quality. As a case study, we applied BenchFrame to the HumanEval benchmark and addressed its main limitations. This led to HumanEvalNext, featuring (1) corrected errors, (2) improved language conversion, (3) expanded test coverage, and (4) increased difficulty. We then evaluated ten state-of-the-art code language models on HumanEval, HumanEvalPlus, and HumanEvalNext. On HumanEvalNext, models showed a pass@1 score reduction of 31.22% and 19.94% compared to HumanEval and HumanEvalPlus, respectively.1690To advance benchmarking standards, this paper proposes BenchFrame, a unified method to enhance benchmark quality, and created BenchScout, a semantic search tool to find relevant benchmarks, using automated clustering of the contexts from associated studies.["Roham Koohestani", "Philippe de Bekker", "Maliheh Izadi"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/55132e171bd51ebb361686ccf2dab9b548fe9b25 9aa21181c54ec6ed4c43c29ef9bdbe6dd73d875f: The banking sector faces escalating cyber threats, necessitating robust cybersecurity solutions. This paper investigates AI-driven frameworks for unsupervised fraud detection, emphasizing their role in enhancing banking cybersecurity. By integrating artificial intelligence (AI) with unsupervised learning, these frameworks excel in identifying anomalous patterns indicative of fraud without relying on labeled datasets, making them adaptable to emerging threats. The study examines their IoT security and predictive analytics application, offering a proactive approach to real-time cyber attack prevention. A thorough literature review evaluates recent advancements, uncovering challenges such as model interpretability and adversarial robustness. The proposed methodology employs AI algorithms, including clustering and autoencoders, to detect subtle anomalies in transactional data, augmented by a hybrid approach combining natural language processing and graph theory for deeper insights. Results affirm the frameworks' effectiveness in bolstering fraud detection, highlighting their transformative potential for banking security. However, limitations like data dependency and the need for continuous updates are noted. The paper addresses these challenges and proposes future research directions, such as quantum machine learning and explainable AI, to counter evolving threats. This work underscores the critical need for innovative, adaptive cybersecurity strategies to safeguard the banking sector's sensitive data and financial assets.International Journal of Science and Engineering Applications280This paper investigates AI-driven frameworks for unsupervised fraud detection, emphasizing their role in enhancing banking cybersecurity, and proposes future research directions, such as quantum machine learning and explainable AI, to counter evolving threats.International Journal of Science and Engineering Applications["Raju Kumar", "Surya Kiran"]2025-03-07T00:00:00https://www.semanticscholar.org/paper/9aa21181c54ec6ed4c43c29ef9bdbe6dd73d875f cf58e699b55eb24a4c61a10e1d54ddfd746790c6Artificial intelligence promises to increase output and productivity by an amount that surpasses any innovation since the onset of the Industrial Revolution. The ability to automate routine tasks, perform clerical and research functions, operate robots, and enhance and optimize physical, human, and virtual networks can free labor into alternative uses. Its ability to simulate chemical, pharmaceutical, and physical reactions can speed research and our understanding of the physical and biological worlds around us. However, this technology depends on the fabrication of sophisticated microchips and the employment of large data processing centers, each of which is resource-intensive. This paper analyzes the energy and environmental aspects of this promising technology within the construct of the monopolistic competition model and finds that our economy may be ill-prepared for the resource risks from artificial intelligence if its success mirrors what many prognosticators expect.International Journal of Risk and Contingency Management30The energy and environmental aspects of this promising technology within the construct of the monopolistic competition model are analyzed and it is found that the economy may be ill-prepared for the resource risks from artificial intelligence if its success mirrors what many prognosticators expect.International Journal of Risk and Contingency Management["Colin L. Read"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/cf58e699b55eb24a4c61a10e1d54ddfd746790c6 9911d07bb9762b92af42de055280ab28beb3c0d9 病理大模型通过大模型微调实现特定下游任务，适应病理领域多样需求，为病理人工智能（artificial intelligence）高质量发展提供契机。本文结合病理人工智能的研发实践，梳理国外病理大模型的研究进展，剖析病理领域大模型构建的关键技术与核心算法，探讨大模型在医疗、教学及科研方向的潜在价值，归纳总结实际应用过程中所遭遇的挑战以及改进的方向。本文旨在帮助病理医师更好地理解病理大模型在病理领域的应用前景，推动人工智能技术安全且高效地演进，携手共同助力病理大模型的创新发展。. Zhonghua bing li xue za zhi = Chinese journal of pathology00Zhonghua bing li xue za zhi = Chinese journal of pathology["Q. Da", "S. H. Wang", "W. Wang", "C. X. Yang", "B. Wang", "M. Ruan", "Z. Fu", "Y. Xu", "Y. B. Zhou", "C. Wang", "D. R. Zhong", "D. G. Liu"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/9911d07bb9762b92af42de055280ab28beb3c0d9 85789b79dbe6ed9eeb85d1ccc6e766028dee77efBackground: Research indicates that surrogate decision-makers often struggle to accurately interpret and reflect the preferences of incapacitated patients they represent. This discrepancy raises important concerns about the reliability of such practice. Artificial intelligence (AI)-based Patient Preference Predictors (PPPs) are emerging tools proposed to guide healthcare decisions for patients who lack decision-making capacity. Objectives: This scoping review aims to provide a thorough analysis of the arguments, both for and against their use, presented in the academic literature. Methods: A search was conducted in PubMed, Web of Science, and Scopus to identify relevant publications. After screening titles and abstracts based on predefined inclusion and exclusion criteria, 16 publications were selected for full-text analysis. Results: The arguments in favor are fewer in number compared to those against. Proponents of AI-PPPs highlight their potential to improve the accuracy of predictions regarding patients’ preferences, reduce the emotional burden on surrogates and family members, and optimize healthcare resource allocation. Conversely, critics point to risks including reinforcing existing biases in medical data, undermining patient autonomy, raising critical concerns about privacy, data security, and explainability, and contributing to the depersonalization of decision-making processes. Conclusions: Further empirical studies are needed to assess the acceptability and feasibility of these tools among key stakeholders, such as patients, surrogates, and clinicians. Moreover, robust interdisciplinary research is needed to explore the legal and medico-legal implications associated with their implementation, ensuring that these tools align with ethical principles and support patient-centered and equitable healthcare practices.Healthcare280A thorough analysis of the arguments, both for and against their use, presented in the academic literature is provided, ensuring that these tools align with ethical principles and support patient-centered and equitable healthcare practices.Healthcare["P. Refolo", "Dario Sacchini", "C. Raimondi", "Simone S. Masilla", "Barbara Corsano", "Giulia Mercuri", "Antonio Oliva", "Antonio G. Spagnolo"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/85789b79dbe6ed9eeb85d1ccc6e766028dee77ef 17353aaa32a7181e446a532471585076cdbf37a4Penelitian ini bertujuan untuk menganalisis penggunaan aplikasi artificial intelligence (AI) sebagai alat bantu dalam penyelesaian skripsi oleh mahasiswa Fakultas Teknik Universitas Negeri Jakarta. Metode penelitian yang digunakan adalah deskriptif kuantitatif dengan teknik pengumpulan data melalui kuesioner. Data yang diperoleh dianalisis menggunakan explaratory factor analysis dan statistik deskriptif. Hasil penelitian menunjukkan bahwa aplikasi AI dapat diklasifikasikan ke dalam tujuh kategori, yaitu penulisan, riset dan analisis, manajemen dokumen dan referensi, chatbot, ringkasan teks, parafrase dan penerjemah, serta pemeriksaan tata bahasa. ChatGPT menjadi aplikasi yang paling sering digunakan mahasiswa dalam proses penyusunan skripsi. Secara umum, rata-rata tingkat pengetahuan mahasiswa tentang aplikasi AI sebesar 53% berada dalam kategori “cukup”, sementara tingkat pemahamannya sebesar 47,93. Mayoritas mahasiswa telah menggunakan aplikasi AI selama 6–12 bulan dengan frekuensi beberapa kali dalam seminggu dan durasi penggunaan per sesi sekitar 1–2 jam. Berdasarkan hasil tersebut, dapat disimpulkan bahwa aplikasi AI berperan dalam membantu mahasiswa menyelesaikan skripsi. Namun, masih diperlukan upaya untuk meningkatkan pemahaman mahasiswa dalam menggunakan aplikasi AI, dengan tetap mematuhi prinsip etika akademik.JIIP - Jurnal Ilmiah Ilmu Pendidikan00JIIP - Jurnal Ilmiah Ilmu Pendidikan["B. Nugroho", "T. Iriani", "R. E. Murtinugraha"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/17353aaa32a7181e446a532471585076cdbf37a4 776553516190ff0644dae0ff1f9a1788888ab552Future Business Journal320A dynamic analysis indicates that the average quantile-based total connectedness changes with time and strengthens during the COVID-19 outbreak, and a portfolio and risk analysis with tail risk measures confirms the importance of considering a dynamic approach to tail-risk minimization.Future Business Journal["Bechir Raggad", "Elie Bouri"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/776553516190ff0644dae0ff1f9a1788888ab552 071738270022627bd32317f497aeac68dd37af44The study aimed to assess the extent to which cloud artificial intelligence is used to improve academic guidance systems in virtual learning environments from the perspective of faculty members. It also sought to examine differences in the average scores of the study sample on the questionnaire regarding the use of cloud AI in enhancing academic guidance, based on factors such as gender, years of experience, and the number of AI-related courses taken. The study used the descriptive approach, with the researcher's tool as the study instrument. The sample consisted of 130 faculty members from Al-Baha University, aged between 25 and 40 years, with an average age of 31.76 years and a standard deviation of 3.521 years. The questionnaire was distributed electronically using a secure online data collection platform, making it easily accessible to faculty members across different institutions. This approach facilitated the collection of data from a broad and representative sample, enhancing external validity and the generalizability of the findings. Additionally, participant anonymity was maintained to encourage honest and unbiased responses, contributing to the credibility of the collected data. Results showed that the faculty members' evaluation level was high, with a weighted average of 3.4485 and an arithmetic mean of 137.93. The highest score was for "familiarity with cloud AI application basics" (4.056), followed by "solving academic guidance issues using applications" (4.025), and "employing applications in academic guidance tasks" (3.825). The lowest score was for "security and data protection in cloud AI usage" (1.888). No statistically significant differences were found between faculty scores based on gender. However, significant differences were found based on years of experience and the number of AI-related courses taken. The study recommended promoting cloud AI culture among students and faculty, as well as providing training courses on AI applications and their role in enhancing academic guidance.Journal of Educational and Human Sciences220Journal of Educational and Human Sciences["Ibraheem Abdullah", "Ali Al-zahrani"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/071738270022627bd32317f497aeac68dd37af44 294077fa06a92c320b05d0db31fa92b11af282ecAims: The aim of the paper was to examine the issues associated with AI in business decision-making, focusing on matters such as bias, competence, the absence of a comprehensible strategy as well as inadequate attention to strategic, legal and explainability factors. Study Design:  Qualitative research design. Place and Duration of Study: MFIs in Zimbabwe, between November 2024 to January 2025. Methodology: Purposive sampling was used to select four participants with specialized expertise in research area of this study. Each of the four interviewed participants had a diverse role in the field of artificial intelligence (1 partnership manager,1 concept manager for analytics and AI, 1 legal consultant and 1 software engineer). Results: The study highlights how AI is revolutionizing strategic decision-making, especially in relation to automation, predictive analysis, and organizational efficiency. Participants brought up a number of important topics, such as explainability difficulties, gaps in AI knowledge and biases in decision-making. 100 percent of the respondents agreed that AI systems and technologies are more effective as assisting tools for those who make decisions than as completely independent solutions. The report offers solutions to these issues, including developing diverse teams to contribute a range of viewpoints, putting explainable AI systems in place to guarantee transparency, and raising AI literacy throughout enterprises to reduce competence gaps. These contributions are noteworthy because they address the operational and ethical issues that come up when using AI in decision-making while also providing useful advice for companies wishing to use it. Conclusion: The findings offer valuable guidance for companies looking to adopt AI technologies into their decision-making frameworks, assisting them in overcoming existing obstacles in this area. However further research may be required on a larger scale to validate the findings.Asian Journal of Economics Business and Accounting00Examining issues associated with AI in business decision-making, focusing on matters such as bias, competence, the absence of a comprehensible strategy as well as inadequate attention to strategic, legal and explainability factors offers valuable guidance for companies looking to adopt AI technologies into their decision-making frameworks.Asian Journal of Economics, Business and Accounting["Noel Marimira", "Babandi Ibrahim Gumel"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/294077fa06a92c320b05d0db31fa92b11af282ec 82990461352c127d924d16f76e374e7b79b401f8Nurse Author &amp; Editor40Nurse Author &amp; Editor["Justin Fontenot"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/82990461352c127d924d16f76e374e7b79b401f8 2942918f0d403f2ca60770d42f7eb92d9a1b3939Journal of Trauma Intensive Care STIC70Journal of Trauma Intensive Care STIC["Sanjeev Kumar", "Sarita Kumari", "Manish Jaiswal", "Samir Kumar Madhukar"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/2942918f0d403f2ca60770d42f7eb92d9a1b3939 a58bdc2066a7cae907a6277f928f0996ad1418cfInternational Journal of Academic Research in Business and Social Sciences00International Journal of Academic Research in Business and Social Sciences["Muhammad Hamdi Che Hassan", "Nur Nabilah Kamarudin"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/a58bdc2066a7cae907a6277f928f0996ad1418cf 02d060cd608825e637a44f44a21b2f59a88ea243The aim of this research is to analyze the evolution and contribution of artificial intelligence (AI) in the Indonesian education system. This research aims to understand how AI technology has developed and been applied in various aspects of education, from administration, personalized learning, to evaluating learning outcomes. The research methods used are literature studies and secondary data analysis from various sources, including academic journals, government reports, and case studies of AI implementation in a number of educational institutions in Indonesia. The research results show that AI has made a significant contribution to improving administrative efficiency, facilitating more adaptive learning, and providing in-depth data analysis for educational decision making. However, challenges such as the digital divide, limited infrastructure, and the need to increase teacher competency in using AI technology still need to be overcome. This research concludes that with the right policy support and investment in infrastructure and training, AI has great potential to transform Indonesian education in a more inclusive and quality direction.Journal of International Multidisciplinary Research90It is concluded that with the right policy support and investment in infrastructure and training, AI has great potential to transform Indonesian education in a more inclusive and quality direction.Journal of International Multidisciplinary Research["Rahula Hananuraga", "Nasril", "A. Daga", "Opan Arifudin", "P. Pattiasina", "Institut Nalanda", "Institut Agama Islam", "Syekh Maulana", "Qori Bangko", "Unika Weerebula Sumba", "Stit Rakeyan", "Santang"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/02d060cd608825e637a44f44a21b2f59a88ea243 1f65fda3fdffe20bf24108fa996616371be5d69bSocial networks have become powerful media and communication tools that provide adequate support to state actors in cyberspace when planning and execution of influence operations. In this context, new patterns in planning and conducting covert offensive information operations will be presented, where artificial intelligence systems used by social networks play a crucial role. On a tactical level, these systems are utilized to exploit users' personal data on social networks regarding their political, ideological, and religious beliefs, as well as tendencies towards violent extremism, radicalism and terrorism, to create hybrid threats. The main hybrid threat presented here is automated and anonymous disinformation that adapts to these beliefs and tendencies. Hybrid intelligence is depicted as a key factor that has enabled the use of this category of user data for the creation of hybrid threats in cyberspace.The article aims to underscore that artificial intelligence systems used by social networks have enabled more effective exploitation of weaknesses in political and social systems based on personal data about the beliefs and tendencies of social media users who are not sufficiently aware of it. The application of hybrid intelligence has further complicated the counteraction and timely recognition, mitigation, and deterrence of the potential harmful consequences of hybrid threats.National security and the future00Artificial intelligence systems used by social networks have enabled more effective exploitation of weaknesses in political and social systems based on personal data about the beliefs and tendencies of social media users who are not sufficiently aware of it, to create hybrid threats in cyberspace.National security and the future["Nikola Mlinac"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/1f65fda3fdffe20bf24108fa996616371be5d69b 84cdde9e3f3ada678c0b29ab45127a153dc9db86Prediction of stock prices has always been a hot research topic. Internet of Things (IoT) devices can collect a large amount of data in real time, including market trends, consumer behavior, economic indicators, etc., providing valuable references for stock price prediction. The stock market is the barometer of the national economy, and the prediction of stock price fluctuation can not only provide a reference for investors and enterprises to set reasonable goals, make decisions and risk management, but also have great reference significance for the government to supervise and manage the market and formulate policies. In this paper, we use the quantum swarm intelligence algorithm to optimize the neural network method to predict the stock closing prices of two listed artificial intelligence companies, Dawning Digital Creation and Yunchuang Data and then analyze the data through IoT devices. In the stock price prediction results, the quantum particle swarm has the highest prediction efficiency and accuracy value among the four quantum optimization models.International Journal of High Speed Electronics and Systems00The quantum swarm intelligence algorithm is used to optimize the neural network method to predict the stock closing prices of two listed artificial intelligence companies and the quantum particle swarm has the highest prediction efficiency and accuracy value among the four quantum optimization models.International Journal of High Speed Electronics and Systems["Wenchao Pan", "Huizhen Jin", "Zhenyi Liang", "Xiaoqing Ou", "Simei Pan"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/84cdde9e3f3ada678c0b29ab45127a153dc9db86 7744cb9651e1ce22922981c16455e1beabf8be0dThe rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices, driven by the proliferation of the Internet of Things (IoT) and the need for real-time data processing. This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models. We define on-device AI models as those designed to perform local data processing and inference, emphasizing their characteristics such as real-time performance, resource constraints, and enhanced data privacy. The survey is structured around key themes, including the fundamental concepts of AI models, application scenarios across various domains, and the technical challenges faced in edge environments. We also discuss optimization and implementation strategies, such as data preprocessing, model compression, and hardware acceleration, which are essential for effective deployment. Furthermore, we examine the impact of emerging technologies, including edge computing and foundation models, on the evolution of on-device AI models. By providing a structured overview of the challenges, solutions, and future directions, this survey aims to facilitate further research and application of on-device AI, ultimately contributing to the advancement of intelligent systems in everyday life.2810This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models, defining on-device AI models as those designed to perform local data processing and inference, emphasizing their characteristics such as real-time performance, resource constraints, and enhanced data privacy.["Xubin Wang", "Zhiqing Tang", "Jianxiong Guo", "Tianhui Meng", "Chenhao Wang", "Tian Wang", "Weijia Jia"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/7744cb9651e1ce22922981c16455e1beabf8be0d c223744c92a6fb897a53347fddb6fada393f5691Journal of Intelligent Manufacturing310This review systematically examines AI-driven scheduling methods, highlighting how evolutionary heuristics, advanced machine learning, and RL-based algorithms each address the demands of modern manufacturing.Journal of Intelligent Manufacturing["Chinyere Ngwu", "Ying Liu", "Rui Wu"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/c223744c92a6fb897a53347fddb6fada393f5691 6d85003e053efb6b2f1a33b74f3c8451f1ee32d0This article explores the potential implications of domestic artificial intelligence (AI) systems in everyday households for chore distribution, family surveillance, and the (re)valuation of interpersonal communication.We differentiate between three types of domestic AI systems based on the social roles they are promised to fulfill: domestic AI as a home maintainer, a guardian, and a companion.We contrasted the findings from empirical studies with discourse on the development of these domestic AI systems to establish how scholarly research differ from the promises of developers when it comes to the social implications of domestic AI.We noticed that for each social role of domestic AI, scholarly research nuances the promises of developers. First, domestic AI as a home maintainer can lead to subtle shifts in the gender division of household chores and introduce new forms of control through digital housekeeping. Second, when domestic AI acts as a guardian, it may reshape intimate surveillance practices, blurring the line between care and control. Finally, as a companion, domestic AI might shape or be shaped by existing household dynamics.Our analysis shows that domestic AI systems should be interpreted in a larger vision for the future for the household, where implementations of domestic AI fit the norms and values embedded in our households. Therefore, we should reflect on (a) what roles we introduce or reconfigure by introducing domestic AI, (b) what “price” we want to pay to deploy domestic AI, and (c) to what extent automation through domestic AI aligns with household values and norms.We direct our focus toward researchers, urging them to look beyond deterministic views and effectively examine everyday negotiations, adoption, and the extent to which the domestic AI systems align with the norms and needs of family members. Moreover, we argue for policymakers and practice to shift from a technical perspective on domestic AI to a relational one. Regulation and services supporting families should focus on the social roles that domestic AI plays in the household.Family Relations230The potential implications of domestic artificial intelligence systems in everyday households for chore distribution, family surveillance, and the (re)valuation of interpersonal communication are explored and it is argued for policymakers and practice to shift from a technical perspective on domestic AI to a relational one.Family Relations["Marijn B. Martens", "M. V. Vanden Abeele", "Ralf De Wolf"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/6d85003e053efb6b2f1a33b74f3c8451f1ee32d0 864f461f0e1cf22512e1fcf0e33a7f7e38f4e2dcThis research was conducted to find out how the construction carried out by community participants of Artificial Intelligence (AI)-based Learning Media Training on the pintar.kemenag.go.id application or website facilitated by Badan Litbang dan Diklat Kementerian Agama RI as a virtual training medium. Using the netnography method, researchers try to reveal and analyze the way community participants present the "digital-self". Netnography describes how experience and learning are carried out virtually through the "repetition of experience" that occurs among members/participants in online communities. The AI-based Learning Media Training is constructed as a free and flexible community virtual training. This virtual training is designed to help the general public who are highly motivated to join the training. Training without physical contact and new media influence the construction of a virtual training. The construction can be seen through 3 aspects, namely: internalization, objectivation, and externalization. This research can be concluded to be very liked and considered very helpful in contemporary and useful training, analyzed from the number of comments and reviews of 4,503 and likes through the 4.9 stars icon with details of the number of 5 stars = 4,221; 4 stars = 244; 3 stars = 24; 2 stars = 2; and 1 star = 12 given by participants to the information or material published. The overall score for this training is 94% which is categorized as excellent.Journal of islamic education290ISLAMIC PEDAGOGY: Journal of Islamic Education["S. A. Pranajaya", "Ani Cahyadi", "Muhammad Nasir", "Agus Riwanda", "Dinar Pratama"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/864f461f0e1cf22512e1fcf0e33a7f7e38f4e2dc 6088bc29914809d48b7799002f6991656fa45e9a In the modern era, data-driven business world, firms are under more and more pressure to use cutting-edge technology to maintain their competitiveness and achieve long-term success.  The integration of Artificial Intelligence (AI) and Machine Learning (ML) into company operations and leadership initiatives is pivotal to this transition.  This research examines the influence of data-driven decision-making and strategic leadership on improving corporate performance using AI-powered solutions.  This research explores the synergies between AI technology and leadership techniques, demonstrating how businesses may leverage data to enhance decision-making, promote innovation, and maintain development in a competitive environment.  The initial portion of the study explores data-driven decision-making as a fundamental aspect of contemporary business practices.  In the era of Big Data, enterprises are overwhelmed with extensive information, and AI technologies—particularly machine learning algorithms—are essential for deriving meaningful insights.  These insights empower firms to make educated, real-time decisions that enhance efficiency and reveal new opportunities.  AI and data analytics are transforming resource management, workflow optimization, and overall operational efficiency through customer behavior analysis and predictive maintenance.  This paper's secondary focus is on strategic leadership on the adoption of AI and ML.  Contemporary leaders must traverse intricate technology environments and guide their enterprises through digital transformation.  Strategic leadership in the current era necessitates a profound comprehension of AI technology and the possible difficulties they entail.  Effective leaders must adopt AI technologies to enhance decision-making, while ensuring these tools are congruent with overarching business objectives.  Furthermore, leadership in the AI era transcends technology; it involves fostering a culture of perpetual learning, creativity, and adaptation, wherein AI serves as a vital facilitator of corporate success rather than a disruptive element.  This paper's primary finding is that AI-driven business processes substantially enhance competitive advantage.  AI technologies improve an organization’s capacity to promptly adapt to market fluctuations and customer requirements by automating mundane processes, streamlining supply chains, and delivering real-time information.  Machine learning models facilitate organizations in forecasting trends, customizing services, and implementing swift strategic modifications.  This proactive strategy is crucial for organizations aiming to maintain a competitive edge in a swiftly changing industry.  Moreover, AI-driven methods significantly influence sustainable growth.  AI solutions enhance resource allocation, minimize waste, and promote innovation, enabling organizations to develop sustainable models that are economically, socially, and ecologically responsible.  This study examines how AI facilitates long-term growth plans, enabling firms to not only endure in a competitive market but also prosper over time. Journal of Computer Science and Technology Studies290This paper's primary finding is that AI-driven business processes substantially enhance competitive advantage, enabling firms to not only endure in a competitive market but also prosper over time.Journal of Computer Science and Technology Studies["Shohoni Mahabub", "Russel Hossain", "Esrat Zahan Snigdha"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/6088bc29914809d48b7799002f6991656fa45e9a 9da64704cd39f7a9ff45ad2674c37691a70208c3Artificial Intelligence (AI) has made remarkable progress in the past few years with AI-enabled applications beginning to permeate every aspect of our society. Despite the widespread consensus on the need to regulate AI, there remains a lack of a unified approach to framing, developing, and assessing AI regulations. Many of the existing methods take a value-based approach, for example, accountability, fairness, free from bias, transparency, and trust. However, these methods often face challenges at the outset due to disagreements in academia over the subjective nature of these definitions. This paper aims to establish a unifying model for AI regulation from the perspective of core AI components. We first introduce the AI Pentad, which comprises the five essential components of AI: humans and organizations, algorithms, data, computing, and energy. We then review AI regulatory enablers, including AI registration and disclosure, AI monitoring, and AI enforcement mechanisms. Subsequently, we present the CHARME$^{2}$D Model to explore further the relationship between the AI Pentad and AI regulatory enablers. Finally, we apply the CHARME$^{2}$D model to assess AI regulatory efforts in the European Union (EU), China, the United Arab Emirates (UAE), the United Kingdom (UK), and the United States (US), highlighting their strengths, weaknesses, and gaps. This comparative evaluation offers insights for future legislative work in the AI domain.310This paper introduces the AI Pentad, which comprises the five essential components of AI: humans and organizations, algorithms, data, computing, and energy, and presents the CHARME$^{2}$D Model, a unifying model for AI regulation from the perspective of core AI components.["Di Kevin Gao", "Sudip Mittal", "Jiming Wu", "Hongwei Du", "Jingdao Chen", "Shahram Rahimi"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/9da64704cd39f7a9ff45ad2674c37691a70208c3 ec6a82e2c7d8ebdc0221580753048542db72ca27The integration of Artificial Intelligence (AI) into Integrated Development Environments (IDEs) is reshaping software development, fundamentally altering how developers interact with their tools. This shift marks the emergence of Human-AI Experience in Integrated Development Environment (in-IDE HAX), a field that explores the evolving dynamics of Human-Computer Interaction in AI-assisted coding environments. Despite rapid adoption, research on in-IDE HAX remains fragmented which highlights the need for a unified overview of current practices, challenges, and opportunities. To provide a structured overview of existing research, we conduct a systematic literature review of 89 studies, summarizing current findings and outlining areas for further investigation. Our findings reveal that AI-assisted coding enhances developer productivity but also introduces challenges, such as verification overhead, automation bias, and over-reliance, particularly among novice developers. Furthermore, concerns about code correctness, security, and maintainability highlight the urgent need for explainability, verification mechanisms, and adaptive user control. Although recent advances have driven the field forward, significant research gaps remain, including a lack of longitudinal studies, personalization strategies, and AI governance frameworks. This review provides a foundation for advancing in-IDE HAX research and offers guidance for responsibly integrating AI into software development.1070It is revealed that AI-assisted coding enhances developer productivity but also introduces challenges, such as verification overhead, automation bias, and over-reliance, particularly among novice developers, which highlights the urgent need for explainability, verification mechanisms, and adaptive user control.["Agnia Sergeyuk", "Ilya Zakharov", "Ekaterina Koshchenko", "Maliheh Izadi"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/ec6a82e2c7d8ebdc0221580753048542db72ca27 e0b0f63fcbce983b7cd11d99318d7a59cd4f9394. The article explores the impact of artificial intelligence on digital educational environments and analyzes its potential to improve the quality of learning. Artificial intelligence has significant potential to transform education,Věda a perspektivy00Věda a perspektivy["Tetiana Zavodnii", "Iryna Semenyshyna", "Ivan Bakhov", "Volodymyr Tereshchuk", "Valentyna Chaialo"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/e0b0f63fcbce983b7cd11d99318d7a59cd4f9394 6926575cacb16e8e21cc985269330185a4327d53The rapid advancement of artificial intelligence (AI) is revolutionizing various sectors, from healthcare to finance. AI-powered technologies, such as machine learning and deep learning, are enabling unprecedented breakthroughs in areas like disease diagnosis, drug discovery, and personalized medicine. This paper explores the influence of Artificial Intelligence (AI) features—such as personalized narratives, adaptive difficulty levels, and virtual companions—on user engagement within interactive and immersive entertainment experiences. Using the DMAIC (Define, Measure, Analyze, Improve, Control) framework, the study analyzes interaction data from 473 users, focusing on behavior patterns and sentiment toward these AI functionalities. Statistical analyses reveal that personalized narratives significantly enhance user sentiment, with an increase in positive sentiment from 45% to 60% after system improvements (t = 8.75, p = 0.0001). Adaptive difficulty levels contribute to sustained engagement, reflected in a notable growth in interaction frequency from 5.0 to 6.2 interactions per user (t = 4.23, p = 0.002). Virtual companions show mixed effectiveness, with their impact heavily influenced by implementation quality and user context. Correlation analysis highlights the importance of session length (r = +0.68, p < 0.001) and abandonment rates (r = -0.56, p < 0.001) as critical factors in shaping user sentiment. The paper includes visual representations of findings and provides actionable recommendations for developers and designers to optimize AI-driven interactive entertainment experiences.Journal of Intelligent Communication00Personalized narratives significantly enhance user sentiment, with an increase in positive sentiment from 45% to 60% after system improvements, and adaptive difficulty levels contribute to sustained engagement within interactive and immersive entertainment experiences.Journal of Intelligent Communication["Amaresh Jha", "Ananya Jha"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/6926575cacb16e8e21cc985269330185a4327d53 6ff78e0f0ae942125b7f77d9cd56810c0ac2aab8Artificial intelligence (AI) and machine learning (ML) have improved menstrual health tracking. AI-powered menstrual health tracking systems provide personalized predictions of menstrual cycles, detect irregularities, and offer tailored recommendations. This commentary discusses physiological and psychological correlates of menstrual health and machine learning algorithms for menstrual health tracking. We highlight future research directions, including integration with wearable devices and development of personalized models. AI-powered menstrual health tracking can enhance women's health.Greenfort International Journal of Applied Medical Science00This commentary discusses physiological and psychological correlates of menstrual health and machine learning algorithms for menstrual health tracking, and highlights future research directions, including integration with wearable devices and development of personalized models.Greenfort International Journal of Applied Medical Science["J. Deshpande", "Chanchal Kumar Singh"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/6ff78e0f0ae942125b7f77d9cd56810c0ac2aab8 12a33fe23c81927b879f12d3d94f04606bcf1a52Journal of Artificial Intelligence, Machine Learning and Data Science00Journal of Artificial Intelligence, Machine Learning and Data Science["Satyam Chauhan"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/12a33fe23c81927b879f12d3d94f04606bcf1a52 7e3506cc692a4495c91be2f9ca85fe762c163e71Journal of Artificial Intelligence, Machine Learning and Data Science00Journal of Artificial Intelligence, Machine Learning and Data Science["Sibin Thomas"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/7e3506cc692a4495c91be2f9ca85fe762c163e71 e2586ef31ab39ab015ad555b0ddbc59d60c48420Federated learning (FL) has become a crucial solution for distributed learning in edge intelligence, addressing communication constraints and privacy protection. However, challenges such as heterogeneous and asynchronous clients significantly impact model performance. This paper analyzes the harm of abnormal clients through parameter orthogonal decomposition innovatively and shows that the exit of abnormal clients can guarantee the effect of the model in most clients. To ensure the models' performance on exited abnormal clients and those who lack training resources, we also introduce a Federated Learning with Invariant Penalty for Generalization (FedIPG). With the assistance of the invariant penalty term, the model can achieve robust generalization capability. This approach indirectly mitigates the effects of data heterogeneity and asynchrony without additional communication overhead, making it ideal for edge intelligence systems. Our theoretical and empirical results demonstrate that FedIPG, combined with an exit strategy, enhances both in-distribution performance and out-of-distribution generalization capabilities while maintaining model convergence. This approach provides a robust framework for federated learning in resource-constrained environments while offering preliminary causal insights.380It is shown that the exit of abnormal clients can guarantee the effect of the model in most clients and that the FedIPG, combined with an exit strategy, enhances both in-distribution performance and out-of-distribution generalization capabilities while maintaining model convergence.["Ziruo Hao", "Zhenhua Cui", "Tao Yang", "Bo Hu", "Xiaofeng Wu", "Hui Feng"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/e2586ef31ab39ab015ad555b0ddbc59d60c48420 d0d05c98d64660b3e379af3e6db3438a329423d9Editorial Volumen 9, 2025Revista Caribeña de Psicología00Revista Caribeña de Psicología["Juan An\u00edbal Gonz\u00e1lez-Rivera"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/d0d05c98d64660b3e379af3e6db3438a329423d9 c73970dd99998d3b0cbd927bf71fe979f0edb989Carbon letters590Carbon Letters["Siqi Liu", "Yousef Zandi", "Alireza Sadighi Agdas", "Mohamed Amine Bouraoui", "Anas A. Salameh", "Amr Alalawi", "Majid Khorami"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/c73970dd99998d3b0cbd927bf71fe979f0edb989 4814eb9e02edd7e3ed8052957c1901657d80d075Durante el período comprendido entre 2020 y 2025, la automatización y la robótica han impactado significativamente la construcción, optimizando procesos y mejorando la seguridad laboral, aunque su implementación sigue siendo limitada en varias regiones, especialmente en América Latina y Ecuador. Esta investigación tiene como objetivo examinar, desde un enfoque textual discursivo, la manera en que la literatura científica aborda la automatización y la robótica en la planificación de la construcción, identificando y analizando su impacto en la reducción de costos, la mejora de la eficiencia operativa y el incremento de la seguridad laboral. Para ello, se adoptó una metodología cualitativa basada en el Análisis Textual Discursivo (ATD), aplicando criterios de selección rigurosos y triangulación de fuentes para garantizar la solidez del estudio. Los hallazgos indican que herramientas como la inteligencia artificial, la robótica colaborativa y el modelado BIM han optimizado la gestión de recursos y mejorado la seguridad en las obras, aunque su adopción enfrenta desafíos económicos, regulatorios y de capacitación. En conclusión, la automatización representa una oportunidad clave para modernizar la industria, pero su integración efectiva requiere inversiones estratégicas y políticas que impulsen su desarrollo sostenible.Reincisol.00Reincisol.["Carlos Alexander Camacho Crespo", "Erik Gabriel Villavicencio Cede\u00f1o", "V\u00edctor Alejandro Lino Calle", "Betty Geoconda Guaranda Mero"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/4814eb9e02edd7e3ed8052957c1901657d80d075 eef18480973f354770a626bf64b6fed5d8e8f4beWhile large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including disease progression, therapeutic response, and safe medication prescription - remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) through a new LLM-based agentic system optimised for clinical management and dialogue, incorporating reasoning over the evolution of disease and multiple patient visit encounters, response to therapy, and professional competence in medication prescription. To ground its reasoning in authoritative clinical knowledge, AMIE leverages Gemini's long-context capabilities, combining in-context retrieval with structured reasoning to align its output with relevant and up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multi-visit case scenarios designed to reflect UK NICE Guidance and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning as assessed by specialist physicians and scored better in both preciseness of treatments and investigations, and in its alignment with and grounding of management plans in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark derived from two national drug formularies (US, UK) and validated by board-certified pharmacists. While AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher difficulty questions. While further research would be needed before real-world translation, AMIE's strong performance across evaluations marks a significant step towards conversational AI as a tool in disease management.00AMIE is advanced through a new LLM-based agentic system optimised for clinical management and dialogue, incorporating reasoning over the evolution of disease and multiple patient visit encounters, response to therapy, and professional competence in medication prescription.["Anil Palepu", "Valentin Li'evin", "Wei-Hung Weng", "Khaled Saab", "David Stutz", "Yong Cheng", "Kavita Kulkarni", "S. Mahdavi", "Joelle Barral", "Dale R. Webster", "Katherine Chou", "Avinatan Hassidim", "Yossi Matias", "James Manyika", "Ryutaro Tanno", "Vivek Natarajan", "Adam Rodman", "Tao Tu", "A. Karthikesalingam", "M. Schaekermann"]2025-03-08T00:00:00https://www.semanticscholar.org/paper/eef18480973f354770a626bf64b6fed5d8e8f4be b28395c6e60c849d6a91c5982520631689ed03c1Explainable Artificial Intelligence (XAI) emerged when researchers attempted to identify methods that would interpret the models that are used to perform classification and predictions, in order to avoid having a black box just informing about the result. Methods of XAI are crucial to determine details of the model feature contribution towards the result. One of these methods is attributed to cooperative game theory and especially Shapley values. With this method the features are considered as players and the marginal contribution of the features are employed. In this paper, we take onboard the Potential Game paradigm to show the interconnection between them and the Shapley values. We show that the Shapley values are interlinked with the potential function. Moreover, we setup a game with the marginal contribution of the players as their utility functions and we prove that the game is a potential game. Finally, we show that the price of stability of this game is 1. We utilise the Simulated Annealing (SA) method to find the optimal solution.Mathematical Models in Engineering150This paper takes onboard the Potential Game paradigm and shows that the Shapley values are interlinked with the potential function and setup a game with the marginal contribution of the players as their utility functions and proves that the game is a potential game.Mathematical Models in Engineering["E. Spyrou", "V. Kappatos", "A. Anagnostopoulou", "E. Bekiaris"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/b28395c6e60c849d6a91c5982520631689ed03c1 51e2fda047b7e9d23acf5320e408b777dd05c0c2Background/Objectives: Esophageal cancer (EC) represents a major global contributor to cancer-related mortality. The advent of artificial intelligence (AI), including machine learning, deep learning, and radiomics, holds promise for enhancing treatment decisions and predicting outcomes. The aim of this review is to present an overview of the current landscape and future perspectives of AI in the management of EC. Methods: A literature search was performed on MEDLINE using the following keywords: “Artificial Intelligence”, “Esophageal cancer”, “Barrett’s esophagus”, “Esophageal Adenocarcinoma”, and “Esophageal Squamous cell carcinoma”. All titles and abstracts were screened; the results included 41 studies. Results: Over the past five years, the number of studies focusing on the application of AI to the treatment and prognosis of EC has surged, leveraging increasingly larger datasets with external validation. The simultaneous incorporation in AI models of clinical factors and features from several imaging modalities displays improved predictive performance, which may enhance patient outcomes, based on direct personalized therapeutic options. However, clinicians and researchers must address existing limitations, conduct randomized controlled trials, and consider the ethical and legal aspects that arise to establish AI as a standard decision-support tool. Conclusions: AI applications may result in substantial advances in EC management, heralding a new era. Considering the complexity of EC as a clinical entity, the evolving potential of AI is anticipated to ameliorate patients’ quality of life and survival rates.Journal of Clinical Medicine590An overview of the current landscape and future perspectives of AI in the management of Esophageal cancer is presented, indicating the evolving potential of AI is anticipated to ameliorate patients’ quality of life and survival rates.Journal of Clinical Medicine["Evgenia Mela", "D. Tsapralis", "Dimitri Papaconstantinou", "Panagiotis Sakarellos", "C. Vergadis", "M. Klontzas", "Ioannis Rouvelas", "A. Tzortzakakis", "Dimitrios Schizas"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/51e2fda047b7e9d23acf5320e408b777dd05c0c2 d92e338a851f365aa0a8c01ffd00085c4917fb93In the volatility, uncertainty, complexity, and ambiguity (VUCA) environment, the application of artificial intelligence (AI) technologies is a key engine for shaping supply chain resilience (SCR). This study employs the entropy method to develop an evaluation index system for SCR, incorporating two key dimensions: resistance and recovery capacity. Using a sample of Chinese-listed enterprises from 2009 to 2022, this study reveals that AI significantly enhances SCR, and CEOs’ sports experience can positively moderate the association between AI and SCR. Mechanism examination shows that AI promotes SCR through operational efficiency optimization, information, and knowledge spillover in the supply chain. Heterogeneity analysis reveals that the positive impact of AI is more significant in firms with a high-skilled labor force, firms with high heterogeneity of the executive team’s human capital, high-tech industries, and regions with strong digital infrastructure. Moreover, the AI application has a diffusion effect on the upstream and downstream enterprises of the supply chain, improving AI adoption levels. Our research not only augments the existing literature on the economic ramifications of AI adoption and the strategic value derived from CEOs’ extramural experience but also offers both theoretical frameworks and empirical insights for executive recruitment and fortifying SCR.Systems940It is revealed that AI significantly enhances SCR, and CEOs’ sports experience can positively moderate the association between AI and SCR, and an evaluation index system for SCR is developed, incorporating two key dimensions: resistance and recovery capacity.Systems["Yuxuan Xu", "Hua Yu", "Ran Qiu", "Liying Yu"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/d92e338a851f365aa0a8c01ffd00085c4917fb93 7ab86fd2c22f4682138a8a8bb5b028b894669b03Journal of Horticultural Science & Biotechnology360The Journal of Horticultural Science and Biotechnology["Simon Pearson"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/7ab86fd2c22f4682138a8a8bb5b028b894669b03 fb12c2dfcc5ee095f38ac4e67829a9efbe9b118fProfessional education in the modern world10Professional education in the modern world["S. I. Chernykh"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/fb12c2dfcc5ee095f38ac4e67829a9efbe9b118f 20f7f472335ea963c272a2fc1326898cd2e3b054Medical Science Monitor00Medical Science Monitor["Ezgi Ero\u011flu \u00c7akmako\u011flu", "Ay\u015fe G\u00fcnay"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/20f7f472335ea963c272a2fc1326898cd2e3b054 0351eaedae656763e0508dc169ac3322c753d443This comprehensive article explores the transformative impact of Artificial Intelligence (AI) and Business Intelligence (BI) integration in manufacturing environments within the Industry 4.0 framework. The article examines the implementation methodologies, architectural considerations, and performance metrics across various manufacturing domains, including predictive maintenance, quality control, and supply chain optimization. The article demonstrates significant improvements in operational efficiency, cost reduction, and product quality through AI-powered systems. Through detailed analysis of edge computing integration, advanced AI capabilities, and enterprise system architecture, the article provides insights into both current implementations and future technological considerations, offering a roadmap for manufacturing organizations pursuing digital transformation.International Journal of Scientific Research in Computer Science Engineering and Information Technology00Through detailed analysis of edge computing integration, advanced AI capabilities, and enterprise system architecture, the article provides insights into both current implementations and future technological considerations, offering a roadmap for manufacturing organizations pursuing digital transformation.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Indraneel Madabhushini"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/0351eaedae656763e0508dc169ac3322c753d443 f7bf1ecd84be48a16c9a93655b4c73d69da867e3The evolution of automotive infotainment systems through artificial intelligence integration represents a fundamental transformation in human-vehicle interaction. Modern vehicles have transcended their transportation role to become sophisticated technological ecosystems where AI technologies address longstanding challenges in user experience, cognitive load management, and driver safety. This technical perspective explores the multilayered architectural framework underpinning these systems, from dedicated hardware accelerators and virtualized operating systems to sophisticated middleware and continuously learning AI models. The implementation of natural language processing capabilities utilizing far-field microphone arrays and domain-specific language models has dramatically improved voice interaction in challenging acoustic environments. Predictive capabilities leveraging recurrent neural networks and contextual awareness enable personalized experiences while reducing manual interactions. Safety enhancements through driver monitoring systems, dynamic workload management, and multimodal feedback pathways significantly reduce distraction and improve reaction times. Despite substantial resource constraints in computational power, thermal management, and energy efficiency, innovative solutions including model compression, selective activation strategies, and privacy-preserving computation techniques have emerged to address these challenges while maintaining functional safety compliance and robust offline capabilities.International Journal of Scientific Research in Computer Science Engineering and Information Technology00This technical perspective explores the multilayered architectural framework underpinning automotive infotainment systems, from dedicated hardware accelerators and virtualized operating systems to sophisticated middleware and continuously learning AI models.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Ravinder Katla"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/f7bf1ecd84be48a16c9a93655b4c73d69da867e3 aa0d9df651a04f98e9063cde7de3eb9cbc8bf429Artificial intelligence generated content (AIGC), known as DeepFakes, has emerged as a growing concern because it is being utilized as a tool for spreading disinformation. While much research exists on identifying AI-generated text and images, research on detecting AI-generated videos is limited. Existing datasets for AI-generated videos detection exhibit limitations in terms of diversity, complexity, and realism. To address these issues, this paper focuses on AI-generated videos detection and constructs a diverse dataset named Chameleon. We generate videos through multiple generation tools and various real video sources. At the same time, we preserve the videos' real-world complexity, including scene switches and dynamic perspective changes, and expand beyond face-centered detection to include human actions and environment generation. Our work bridges the gap between AI-generated dataset construction and real-world forensic needs, offering a valuable benchmark to counteract the evolving threats of AI-generated content.450This work constructs a diverse dataset named Chameleon and bridges the gap between AI-generated dataset construction and real-world forensic needs, offering a valuable benchmark to counteract the evolving threats of AI-generated content.["Meiyu Zeng", "Xingming Liao", "Canyu Chen", "Nankai Lin", "Zhuowei Wang", "Chong Chen", "Aimin Yang"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/aa0d9df651a04f98e9063cde7de3eb9cbc8bf429 48fb7de2a43c73b50ce849be757cadf17cfd8771This article explores the implementation of artificial intelligence and machine learning techniques in payment processing systems to optimize transaction routing decisions. In the modern payments ecosystem, merchants face significant challenges with varying approval rates across different processing paths based on factors, including card type, issuing bank, transaction geography, and merchant category. Through analyzing several machine learning approaches—ranging from decision trees to advanced neural networks and reinforcement learning—the article demonstrates how intelligent routing systems can significantly enhance approval rates while simultaneously reducing processing costs. The article evaluates the effectiveness of various ML models in identifying optimal routing paths based on historical performance data and transaction attributes, highlighting strategies such as issuer-specific routing, prevention of futile authorization attempts, dynamic fee optimization, adaptive retry mechanisms, and cross-border transaction handling. Additionally, the article addresses critical implementation challenges, including latency requirements, data quality concerns, regulatory compliance, and concept drift, offering practical frameworks for deploying these systems in high-volume production environments.International Journal of Scientific Research in Computer Science Engineering and Information Technology00This article explores the implementation of artificial intelligence and machine learning techniques in payment processing systems to optimize transaction routing decisions and demonstrates how intelligent routing systems can significantly enhance approval rates while simultaneously reducing processing costs.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Krishna Chaitanya Saride"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/48fb7de2a43c73b50ce849be757cadf17cfd8771 f0bd172a157a311f509af404cee49a72a80ea6b0The integration of Artificial Intelligence (AI) with JD Edwards EnterpriseOne (JDE E1) represents a transformative evolution in Enterprise Resource Planning (ERP) systems, fundamentally changing how organizations manage their business processes. This technical article explores the comprehensive impact of AI integration across various functional domains of JDE E1, examining both the technological requirements and organizational implications. It investigates core AI technologies enhancing JDE E1, including predictive analytics and machine learning implementations while analyzing the architectural considerations necessary for successful integration. The article delves into practical applications across financial operations, supply chain optimization, and manufacturing intelligence, providing insights into how AI transforms these critical business functions. It also addresses implementation considerations, emphasizing technical prerequisites and integration strategies essential for successful AI adoption. Furthermore, the article examines future trends in natural language processing, advanced automation, and cognitive services, offering organizations a roadmap for leveraging AI capabilities within their JDE E1 environments to achieve improved operational efficiency and competitive advantage.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The article delves into practical applications across financial operations, supply chain optimization, and manufacturing intelligence, providing insights into how AI transforms these critical business functions.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Shyamlal Sama"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/f0bd172a157a311f509af404cee49a72a80ea6b0 37decead7ce84963b9a094b6dc8fea57af0d66b8This research examines the impact of marketing analytics and artificial intelligence applications on customer agility and marketing performance in businesses that adopt e-commerce. In this quantitative study, data were collected through a questionnaire. Data collected from 227 managers online were analyzed using the Smart PLS method. The study concluded that marketing analytics and AI-supported marketing capabilities affect customer agility and marketing performance. It is also concluded that customer agility has an impact on marketing performance. In addition, the results show that customer agility is a mediator of the effects of AI-supported marketing capabilities and analytics on marketing performance. It offers concrete suggestions for businesses, facilitating decision-making processes, and demonstrates how digital marketing strategies can be employed more effectively. The study also makes an academic contribution by analyzing the relationship between digital transformation and marketing capabilities, thus guiding future research.International Journal of Social Sciences and Education Research460The results show that customer agility is a mediator of the effects of AI-supported marketing capabilities and analytics on marketing performance, and makes an academic contribution by analyzing the relationship between digital transformation and marketing capabilities.International Journal of Social Sciences and Education Research["Fatma Demira\u011f"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/37decead7ce84963b9a094b6dc8fea57af0d66b8 d0813da775f2dc02eb8dc528392b89e43272bd50This comprehensive article examines the evolution and implementation of intelligent data governance frameworks in modern distributed systems, focusing on integrating artificial intelligence and metadata-driven approaches. The article explores how organizations address unprecedented challenges in managing sensitive data across complex infrastructures while maintaining regulatory compliance. The article investigates the transformation from traditional governance models to sophisticated, AI-enhanced systems that enable real-time monitoring, automated classification, and predictive analytics. The article provides insights into successful deployment strategies through a detailed examination of implementation considerations, including technical architecture requirements and organizational readiness factors. It demonstrates the significant improvements in operational efficiency, security controls, and compliance management achieved through intelligent governance frameworks.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The article investigates the transformation from traditional governance models to sophisticated, AI-enhanced systems that enable real-time monitoring, automated classification, and predictive analytics.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Amit Kumar"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/d0813da775f2dc02eb8dc528392b89e43272bd50 9cb78d9c048423f41f7082bba87a636aed70d45aWith the increasing integration of artificial intelligence into various applications across industries, numerous institutions are striving to establish requirements for AI systems to be considered trustworthy, such as fairness, privacy, robustness, or transparency. For the implementation of Trustworthy AI into real-world applications, these requirements need to be operationalized, which includes evaluating the extent to which these criteria are fulfilled. This survey contributes to the discourse by outlining the current understanding of trustworthiness and its evaluation. Initially, existing evaluation frameworks are analyzed, from which common dimensions of trustworthiness are derived. For each dimension, the literature is surveyed for evaluation strategies, specifically focusing on quantitative metrics. By mapping these strategies to the machine learning lifecycle, an evaluation framework is derived, which can serve as a foundation towards the operationalization of Trustworthy AI.ACM Computing Surveys610This survey contributes to the discourse by outlining the current understanding of trustworthiness and its evaluation, and derives an evaluation framework, which can serve as a foundation towards the operationalization of Trustworthy AI.ACM Computing Surveys["Nils Kemmerzell", "Annika Schreiner", "Haroon Khalid", "Michael Schalk", "Letizia Bordoli"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/9cb78d9c048423f41f7082bba87a636aed70d45a 0be075cb6328e50ca953d80e629236e6981db60aAs cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. However, many artificial intelligence (AI) models lack transparency and interpretability due to their opaque nature, limiting their trust and adoption in real-world medical applications, such as clinical decision support systems (CDSS). To address this issue, this paper enhances algorithm explainability underlying CDSS by integrating multiple Explainable Artificial Intelligence (XAI) methods and applying causal inference techniques to clarify the model' predictive decisions under various scenarios. By providing deeper interpretability of the XAI outputs using Large Language Models (LLMs), we provide users with more personalized and accessible insights to overcome the challenges posed by AI's"black box"nature. Our system dynamically adjusts feedback based on user queries and emotional states, combining text-based sentiment analysis with real-time facial emotion recognition to ensure responses are empathetic, context-adaptive, and user-centered. This approach bridges the gap between the learning demands of interpretability and the need for intuitive understanding, enabling non-technical users such as clinicians and clinical researchers to interact effectively with AI models.} Ultimately, this approach improves usability, enhances perceived trustworthiness, and increases the impact of CDSS in healthcare applications.640This approach improves usability, enhances perceived trustworthiness, and increases the impact of CDSS in healthcare applications, by integrating multiple Explainable Artificial Intelligence (XAI) methods and applying causal inference techniques to clarify the model' predictive decisions under various scenarios.["Tongze Zhang", "Tammy Chung", "Anind K. Dey", "Sang Won Bae"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/0be075cb6328e50ca953d80e629236e6981db60a 98141512f4c22f138230112fa27c78e30353cc12In this study, both application and implications for artificial intelligence are explored within the context of Kazakhstan, a nation that is increasingly adopting artificial intelligence technologies in criminal surveillance to enhance public security. Although AI-driven techniques like facial recognition, predictive policing, and smart city infrastructures present intriguing opportunities for crime prevention and surveillance, they also introduce complex legal and ethical dilemmas. This study seeks to assess the degree of AI integration into Kazakhstan’s law enforcement procedures, focusing on the alignment of these technologies with international human rights norms and state legislative safeguards. Following a critical analysis of Kazakhstan’s legal framework, significant gaps are uncovered in regulating artificial intelligence surveillance, particularly relating to privacy and transparency. This study utilizes global precedents and ethical frameworks to solve existing gaps and provides practical policy recommendations specific to Kazakhstan’s unique socio-political context. The findings of this study highlight the need for robust legal safeguards, such as independent oversight bodies and data protection laws, to balance security with civil liberties. By situating Kazakhstan’s approach within the broader discourse on AI ethics and surveillance, this study contributes valuable insights into developing AI policies that support both technological advancement and the protection of human rights. The practical significance of this work extends to policymakers, scholars, and human rights advocates aiming to navigate the delicate equilibrium between public safety and personal freedoms in the era of AI-enhanced policing.Eurasian Scientific Journal of Law60The degree of AI integration into Kazakhstan's law enforcement procedures is assessed, focusing on the alignment of these technologies with international human rights norms and state legislative safeguards, to highlight the need for robust legal safeguards to balance security with civil liberties.Eurasian Scientific Journal of Law["Sh. S. Daubassova", "G. T. Alaeva", "K. A. Dzhumabayeva"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/98141512f4c22f138230112fa27c78e30353cc12 00c37712dddabb4bd068a981800b95dd772fa4a3With cyberattacks increasing in volume and number, organizations are increasingly at risk of adverse financial and reputational impacts. Cyber attackers are quick to implement technologies like Generative Artificial Intelligence (GAI) to enhance attacks, while organizations have yet to fully benefit from GAI to improve cybersecurity defenses. This scoping literature review analyzes current research and identifies gaps in the literature about how Generative Artificial Intelligence (GAI) can be used to enhance cybersecurity resilience. The analysis includes an overview of GAI, ethical considerations and challenges, future directions and research opportunities, and a discussion of how this GAI research can be applied.International Journal of Applied Science930This scoping literature review analyzes current research and identifies gaps in the literature about how Generative Artificial Intelligence (GAI) can be used to enhance cybersecurity resilience.International Journal of Applied Science["Jessica Parker"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/00c37712dddabb4bd068a981800b95dd772fa4a3 0ae0ad8f8f6085f616eb49558fefa299739a1134Despite the rapid proliferation of artificial intelligence (AI) negotiation agents, there has been limited integration of computer science research and established negotiation theory to develop new theories of AI negotiation. To bridge this gap, we conducted an International AI Negotiations Competition in which participants iteratively designed and refined prompts for large language model (LLM) negotiation agents. We then facilitated over 120,000 negotiations between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that fundamental principles from established human-human negotiation theory remain crucial in AI-AI negotiations. Specifically, agents exhibiting high warmth fostered higher counterpart subjective value and reached deals more frequently, which enabled them to create and claim more value in integrative settings. However, conditional on reaching a deal, warm agents claimed less value while dominant agents claimed more value. These results align with classic negotiation theory emphasizing relationship-building, assertiveness, and preparation. Our analysis also revealed unique dynamics in AI-AI negotiations not fully explained by negotiation theory, particularly regarding the effectiveness of AI-specific strategies like chain-of-thought reasoning and prompt injection. The agent that won our competition implemented an approach that blended traditional negotiation preparation frameworks with AI-specific methods. Together, these results suggest the importance of establishing a new theory of AI negotiations that integrates established negotiation theory with AI-specific strategies to optimize agent performance. Our research suggests this new theory must account for the unique characteristics of autonomous agents and establish the conditions under which traditional negotiation theory applies in automated settings.440An International AI Negotiations Competition in which participants iteratively designed and refined prompts for large language model (LLM) negotiation agents revealed that fundamental principles from established human-human negotiation theory remain crucial in AI-AI negotiations.["Michelle Vaccaro", "Michael Caoson", "Harang Ju", "Sinan Aral", "Jared R. Curhan"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/0ae0ad8f8f6085f616eb49558fefa299739a1134 6a5eee564c03335393dfc0f2727af0c9343eed5dSmall business owners (SBOs) often lack the resources and design experience needed to produce high-quality advertisements. To address this, we developed ACAI (AI Co-Creation for Advertising and Inspiration), an GenAI-powered multimodal advertisement creation tool, and conducted a user study with 16 SBOs in London to explore their perceptions of and interactions with ACAI in advertisement creation. Our findings reveal that structured inputs enhance user agency and control while improving AI outputs by facilitating better brand alignment, enhancing AI transparency, and offering scaffolding that assists novice designers, such as SBOs, in formulating prompts. We also found that ACAI's multimodal interface bridges the design skill gap for SBOs with a clear advertisement vision, but who lack the design jargon necessary for effective prompting. Building on our findings, we propose three capabilities: contextual intelligence, adaptive interactions, and data management, with corresponding design recommendations to advance the co-creative attributes of AI-mediated design tools.660ACAI (AI Co-Creation for Advertising and Inspiration), an GenAI-powered multimodal advertisement creation tool, is developed and a user study is conducted with SBOs to explore their perceptions of and interactions with ACAI in advertisement creation.["Nimisha Karnatak", "Adrien Baranes", "Rob Marchant", "Triona Butler", "Kristen Olson"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/6a5eee564c03335393dfc0f2727af0c9343eed5d 5b8c118a8542360097fea5b7c696f2de4e4c826dThe high-quality development of specialized, refined, distinctive, and innovative enterprises (SRDIEs) is essential for advancing an innovation-driven strategy. This paper investigates the impact of financial technology (Fintech) on sustainable innovation within SRDIEs that face financing challenges, analyzing it from supply-side, demand-side, and environmental perspectives. We utilize fuzzy-set Qualitative Comparative Analysis (fSQCA) and Necessary Condition Analysis (NCA) to explore the configurational paths and complex causal effects of Fintech in facilitating the innovation of SRDIEs amid financing challenges. By employing a combination of NCA and fsQCA, this study identifies several effective pathways through which Fintech enhances the innovation efficiency of SRDIEs. We develop an integrative model to enhance innovation inputs, outputs, and sustainability. The key findings include the following: (1) Fintech significantly enhances innovation output, supported by business efficiency and digital intelligence; (2) two distinct pathways for achieving high-innovation inputs are identified, driven by Fintech intensity and effective credit allocation, with specialization and financial mismatches serving as auxiliary factors; (3) the core conditions of Fintech intensity and the financing environment, along with competitive banking, promote innovation motivation and sustainability in highly specialized enterprises. The conclusions of this study provide both theoretical and practical insights for SRDIEs to tackle innovation challenges characterized by an “inability to innovate”, a “lack of willingness to innovate”, and “ineffectiveness in innovation”, enabling their transition from merely being “able to innovate” and “daring to innovate” to becoming “proficient in sustainable innovation”. These findings offer differentiated sustainable innovation solutions for enterprises through three avenues: capacity building on the demand side, channel optimization on the supply side, and ecological cultivation on the environmental side.Sustainability400Sustainability["Fang Ji", "Junlin Wu", "Yiran Li"]2025-03-09T00:00:00https://www.semanticscholar.org/paper/5b8c118a8542360097fea5b7c696f2de4e4c826d 2a9d6c2fa0107a38986a853bd2c6c59694a06dadBreast reconstruction is a critical component of breast cancer treatment. With the rapid integration of Artificial Intelligence (AI) into healthcare, its potential to revolutionize breast reconstruction has become increasingly evident. This narrative review examines the latest AI developments across the preoperative, intraoperative, and postoperative phases of breast reconstruction. In preoperative consultations, AI and augmented reality (AR)-driven simulations help both the surgeons and the patients visualize reconstruction outcomes. Imaging analysis and predictive modeling enhance the precision and efficiency of autologous procedures such as deep inferior epigastric artery perforator flap-based reconstruction. Within the operating room, AI applications such as real-time perforator mapping and AR modeling offer plastic surgeons improved control and visualization, which helps to reduce postoperative complications. Furthermore, AI models help surgeons design and deliver more personalized and value-based postoperative care, thereby improving patient satisfaction and overall cost-effectiveness. While AI applications demonstrate promising utility, challenges such as high costs, reliability, and the need for extensive clinical validation remain. Ongoing research and large-scale clinical trials are crucial to fully harness AI’s potential in improving breast reconstruction outcomes.Artificial Intelligence Surgery450The latest AI developments across the preoperative, intraoperative, and postoperative phases of breast reconstruction are examined, demonstrating promising utility and improving patient satisfaction and overall cost-effectiveness.Artificial Intelligence Surgery["Yizhuo Shen", "Andrew J. Malek", "Renee Gao", "Justin M. Broyles"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/2a9d6c2fa0107a38986a853bd2c6c59694a06dad afce6a503347f33fac60979e23f8ee18183f10baDigital deliberation has expanded democratic participation, yet challenges remain. This includes processing information at scale, moderating discussions, fact-checking, or attracting people to participate. Recent advances in artificial intelligence (AI) offer potential solutions, but public perceptions of AI's role in deliberation remain underexplored. Beyond efficiency, democratic deliberation is about voice and recognition. If AI is integrated into deliberation, public trust, acceptance, and willingness to participate may be affected. We conducted a preregistered survey experiment with a representative sample in Germany (n=1850) to examine how information about AI-enabled deliberation influences willingness to participate and perceptions of deliberative quality. Respondents were randomly assigned to treatments that provided them information about deliberative tasks facilitated by either AI or humans. Our findings reveal a significant AI-penalty. Participants were less willing to engage in AI-facilitated deliberation and rated its quality lower than human-led formats. These effects were moderated by individual predispositions. Perceptions of AI's societal benefits and anthropomorphization of AI showed positive interaction effects on people's interest to participate in AI-enabled deliberative formats and positive quality assessments, while AI risk assessments showed negative interactions with information about AI-enabled deliberation. These results suggest AI-enabled deliberation faces substantial public skepticism, potentially even introducing a new deliberative divide. Unlike traditional participation gaps based on education or demographics, this divide is shaped by attitudes toward AI. As democratic engagement increasingly moves online, ensuring AI's role in deliberation does not discourage participation or deepen inequalities will be a key challenge for future research and policy.470AI-enabled deliberation faces substantial public skepticism, potentially even introducing a new deliberative divide, unlike traditional participation gaps based on education or demographics, which is shaped by attitudes toward AI.["Andreas Jungherr", "Adrian Rauchfleisch"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/afce6a503347f33fac60979e23f8ee18183f10ba b951893f4373b9106b7246f754e5a7a82eeacaf4PURPOSE Precision medicine, also known as personalized medicine, enables the provision of tailored health services to patients. In the prevention, early detection, and treatment of cancers, precision medicine is highly promising, given the increasing use of genomic profiling for diagnosis and adapting therapies in several tumor types. Artificial Intelligence (AI) can support this process by analyzing vast amounts of relevant data. However, high-quality data and financial investments in the health system are essential for the implementation of precision medicine and AI solutions in routine cancer care. DESIGN/METHODOLOGY/APPROACH Building on the quantitative outcomes of a foresight exercise published in another study, this article collects qualitative data to gain more detailed insights into the future of precision oncology in Belgium and discusses the role of AI in this field. It reports the results of a series of expert workshops, focusing on four hypothetical future scenarios that are centered around technological and economic issues that must be overcome for the widespread use of precision oncology in Belgium. FINDINGS The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine. ORIGINALITY/VALUE To the best of our knowledge, this is the first study to employ foresight methodology to illustrate possible future scenarios, scrutinize feasible approaches for implementing precision oncology in Belgium, and discuss the use of AI in this context.Journal of health organization and management280The study concludes that all four scenarios discussed in the workshops would require supportive policy measures in Belgium, which should go beyond mere technological and economic considerations, such as involving patient associations and the public in policy design or creating multi-disciplinary expert groups for precision medicine.Journal of health organization and management["T. Schmitt", "M. Delnord", "E. Cau\u00ebt", "E. Van Valckenborgh", "M. van den Bulcke"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/b951893f4373b9106b7246f754e5a7a82eeacaf4 95e7a3cd94e6ec6999a27dfe7a742898bd0970e8Background/Objectives: Rare diseases often present challenges in obtaining reliable and accurate information than common diseases owing to their low prevalence. Patients and families often rely on self-directed learning, but understanding complex medical information can be difficult, increasing the risk of misinformation. This study aimed to evaluate whether generative artificial intelligence (AI) provides accurate and non-harmful answers to rare disease-related questions and assesses its utility in supporting patients and families requiring genetic counseling. Methods: We evaluated four generative AI models available between 22 September and 4 October 2024: ChatGPT o1-Preview, Gemini advanced, Claude 3.5 sonnet, and Perplexity sonar huge. A total of 102 questions targeting four rare diseases, covering general information, diagnosis, treatment, prognosis, and counseling, were prepared. Four evaluators scored the responses for professionalism and accuracy using the Likert scale (1: poor, 5: excellent). Results: The average scores ranked the AI models as: ChatGPT (4.24 ± 0.73), Gemini (4.15 ± 0.74), Claude (4.13 ± 0.82), and Perplexity (3.35 ± 0.80; p < 0.001). Perplexity had the highest proportion of scores of 1 (very poor) and 2 (poor) (7.6%, 31/408), followed by Gemini (2.0%, 8/408), Claude (1.5%, 6/408), and ChatGPT (1.5%, 6/408). The accuracy of responses in the counseling part across all four diseases was significantly different (p < 0.001). Conclusions: The four generative AI models generally provided reliable information. However, occasional inaccuracies and ambiguous references may lead to confusion and anxiety among patients and their families. To ensure its effective use, recognizing the limitations of generative AI and providing guidance from experts regarding its proper utilization is essential.Diagnostics180The four generative AI models generally provided reliable information, however, occasional inaccuracies and ambiguous references may lead to confusion and anxiety among patients and their families.Diagnostics["Suok Jeon", "Su-A Lee", "Hae-Sun Chung", "Ji Young Yun", "Eun Ae Park", "M. So", "Jungwon Huh"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/95e7a3cd94e6ec6999a27dfe7a742898bd0970e8 3d760427f03b27aff9103cd8bb05fafd44bf5d39Artificial Intelligence (AI) has become one of the main technologies in various sectors, including the investment world. In an effort to improve financial literacy and understanding of high school students about the use of AI in optimizing investment portfolios, this community service activity was held. This training aims to provide a basic understanding of the stock market, investment risk, and how AI can be used to analyze data, predict stock price trends, and reduce investment risk. The methods used in this training include theory sessions, demonstrations of the use of AI tools, and investment portfolio simulations based on historical data. The results of this training show that students have increased their understanding of investment concepts and the role of AI in portfolio optimization. The simulations provided practical experience in using AI for stock analysis and risk management. Recommendations from this activity include the development of an AI learning module for investment, assistance in stock market simulations, and holding AI-based investment competitions to improve students' skills in making investment decisions. Thus, this training is expected to provide long-term benefits for high school students in facing the challenges of an increasingly technology-based financial world.Jurnal Pemberdayaan Komunitas MH Thamrin00This training aims to provide a basic understanding of the stock market, investment risk, and how AI can be used to analyze data, predict stock price trends, and reduce investment risk.Jurnal Pemberdayaan Komunitas MH Thamrin["Sondang Sibuea", "Fenty Tristanti Julfia", "Y. Widodo"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/3d760427f03b27aff9103cd8bb05fafd44bf5d39 86cfab2a09a86453954b566bcb2a333c916de863Objective: To explore the extent to which Jordanian television channels employ artificial intelligence technologies in the process of media monitoring and analysis, and to reveal the AI technologies adopted by Jordanian channels, as well as the main challenges faced. Methods: The research employed a descriptive approach, relying on a media survey method. A random sample of 50 individuals working in Jordanian channels was surveyed, utilizing a questionnaire as the data collection tool. Results: The research findings revealed a disparity among employees of Jordanian television channels regarding their knowledge of AI technologies used in the media domain. It was observed that 34% of the sample had some level of familiarity with these technologies, while 24% had no knowledge of AI technologies employed in media operations. Additionally, the research demonstrated a significant positive correlation between the adoption of modern technology by Jordanian television channels and the utilization of AI technologies in monitoring and analysis. Conclusions: The research concluded that Jordanian channels are still in the early stages of adopting AI technologies and employing them in the process of media monitoring and analysis. The research highlighted the need for Jordanian channels to focus on employing AI technologies in monitoring and analysis due to their effectiveness. It also recommended training television channel employees on using AI technologies in media monitoring and analysis, through practical training sessions and providing them with the necessary facilitative tools.Dirasat Human and Social Sciences300Jordanian channels are still in the early stages of adopting AI technologies and employing them in the process of media monitoring and analysis, and the research highlighted the need for Jordanian channels to focus on employing AI technologies in monitoring and analysis due to their effectiveness.Dirasat: Human and Social Sciences["Obaidah Ali Alrababah", "M. Alnawafah", "Tariq Mohammed Rababa'h"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/86cfab2a09a86453954b566bcb2a333c916de863 51e94502d1192f235bca360cac1049ae8ebf1a7dSmart Learning Environments540This systematic study intends to specify student learning outcomes in a chatbot-supported setting and explain how various factors such as rules, tools, and division of labor work together to enhance learning outcomes in this environment.Smart Learning Environments["Yan Li", "Xinyan Zhou", "Hong-biao Yin", "T. Chiu"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/51e94502d1192f235bca360cac1049ae8ebf1a7d 4bc88732e230e597c7cab0ff1244af0c3742869fArtificial Intelligence (AI) and Business Intelligence (BI) are critical tools for optimizing decision-making and enhancing business process efficiency in today's digital economy. This article explores the integration of AI and BI in business practices, focusing on their potential to overcome key challenges, such as technical complexity, limited financial and human resources, and insufficient managerial expertise. A comprehensive analysis is provided, highlighting modern solutions that enable automation, improve decision accuracy, and reduce costs. The study emphasizes the importance of combining AI's analytical capabilities with BI's visualization tools, creating a synergistic effect that facilitates strategic and operational decision-making. Presented models demonstrate how AI and BI integration can deliver significant benefits while maintaining cost efficiency, making these technologies accessible even to resource-constrained enterprises. Key barriers to adoption, such as the lack of infrastructure, technical expertise, and financial constraints, are addressed, alongside practical recommendations for their mitigation. Furthermore, the paper discusses the role of generative AI and large language models (LLMs) in transforming traditional business processes. These technologies enable businesses to automate routine tasks, analyze unstructured data, and generate actionable insights, significantly enhancing the ability to adapt to dynamic market environments. Special attention is given to the economic feasibility and scalability of AI and BI tools, offering a roadmap for successful implementation across various industries. The findings contribute to a deeper understanding of AI and BI integration, providing actionable recommendations for businesses seeking to leverage these technologies for sustainable growth. Future research directions include the development of industry-specific solutions and advanced tools for predictive analytics and resource optimization to further enhance the strategic impact of AI and BI on business practices.Economic scope00The study emphasizes the importance of combining AI's analytical capabilities with BI's visualization tools, creating a synergistic effect that facilitates strategic and operational decision-making, and contributes to a deeper understanding of AI and BI integration.Economic scope["Denis Solodkov", "N. Hryshko"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/4bc88732e230e597c7cab0ff1244af0c3742869f b869cffd9a6a51bd39ae6f5c46ac5bd0e029e8e9In the ever-evolving digital era, understanding and skills in artificial intelligence (AI) technology are becoming increasingly important, especially for students at the secondary level. This training aims to introduce students to the basic concepts of AI and its applications in academic contexts, as well as to improve their ability to use AI-based software to support the teaching and learning process. Research shows that the application of AI in education can provide strong technical support for personalized learning, allowing students to learn according to their own style and pace. In addition, AI also has the potential to increase student engagement through gamification methods, which can create a more engaging and effective learning experience. However, it is important to consider ethical and transparency issues in the use of AI, including the protection of student data and the adaptation of the role of teachers in learning contexts that are increasingly influenced by this technology. This training involves a variety of interactive and practical methods, including simulations and the use of relevant AI software, to ensure that students not only understand the theory but can also apply it in real situations. Thus, it is hoped that students can develop the skills needed to face challenges in the academic and professional world that are increasingly influenced by technology. The program also seeks to build awareness of the importance of AI education among students, which is in line with the national education policy that encourages the development of digital skills. Through this training, it is expected that students will not only gain knowledge about AI, but also develop a positive attitude towards this technology, so that they can utilize it optimally in their studies and in the future.Jurnal Pemberdayaan Komunitas MH Thamrin00This training aims to introduce students to the basic concepts of AI and its applications in academic contexts, as well as to improve their ability to use AI-based software to support the teaching and learning process.Jurnal Pemberdayaan Komunitas MH Thamrin["Y. Widodo", "Mohammad Narji", "Sondang Sibuea", "Agung Suryatno"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/b869cffd9a6a51bd39ae6f5c46ac5bd0e029e8e9 be2a3e7b0bfa2a59889e73e6c334e7130c7f0215Purpose This study aims to investigate the relevance, accuracy, specificity and justification of investment recommendations of generative artificial intelligence (GenAI) chatbots for different investment capitals and countries (UK and Bulgaria). Design/methodology/approach A two-stage mixed methods approach was used. Prompts were queried into OpenAI’s ChatGPT, Microsoft Bing and Google Bard (now Gemini). Finance and investment practitioners and finance and investment lecturers assessed the chatbots’ recommendations through an online questionnaire using a five-point Likert scale. The Chi-squared test, Wilcoxon-signed ranks test, Mann–Whitney U test and Friedman test were used for data analysis to compare GenAIs’ recommendations for the UK and Bulgaria across different amounts of investment capital and to assess the consistency of the chatbots. Findings GenAI chatbots’ responses were found to perform medium-to-high in terms of relevance, accuracy, specificity and justification. For the UK sample, the amount of investment had a marginal effect but prompt timing had an interesting impact. Unlike the British sample, the GenAI application, prompt timing and investment amount did not significantly influence the Bulgarian respondents’ evaluations. While the mean responses of the British sample were slightly higher, these differences were not statistically significant, indicating that ChatGPT, Bing and Bard performed similarly in both the UK and Bulgaria. Originality/value The study assesses the relevance, accuracy, specificity and justification of GenAI chatbots’ investment recommendations for two different periods, investment amounts and countries. Foresight540GenAI chatbots’ responses were found to perform medium-to-high in terms of relevance, accuracy, specificity and justification, indicating that ChatGPT, Bing and Bard performed similarly in both the UK and Bulgaria.foresight["Kumbirai Mabwe", "Nasir Aminu", "Stanislav Hristov Ivanov", "Diyan Dimov"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/be2a3e7b0bfa2a59889e73e6c334e7130c7f0215 665e9f923d1517f81ded26950fe921c9f1aa97bfObjective: This study examines the effectiveness of the Beneish M-Score model in detecting financial statement manipulation in companies charged with fraud by the U.S. Securities and Exchange Commission (SEC), aligning with Sustainable Development Goal (SDG) 16: Peace, Justice, and Strong Institutions, which promotes financial transparency and accountability.   Theoretical Framework: Earnings management is a major concern in financial reporting, as it affects the reliability of financial statements. The Beneish M-Score model is a mathematical tool developed to detect earnings manipulation. Additionally, artificial intelligence (AI) is emerging as a complementary method to enhance fraud detection.   Method: The research applies the Beneish M-Score model to three SEC-charged companies: DXC Technology Co., GTT Communications, Inc., and Luckin Coffee Inc. Financial data is collected from the SEC’s EDGAR database and company reports to compute the M-Score over multiple fiscal years.   Results and Discussion: The model successfully flagged Luckin Coffee as a likely manipulator during its peak fraudulent period. DXC Technology Co. showed signs of manipulation in 2018, aligning with the SEC’s findings of misleading non-GAAP disclosures. GTT Communications, Inc. had a more stable M-Score, with no strong indications of manipulation. AI techniques, such as machine learning and neural networks, have the potential to improve fraud detection by analyzing financial patterns.   Research Implications: This study supports the Beneish M-Score as a useful tool for detecting earnings manipulation and suggests integrating AI-driven methods to enhance accuracy in fraud detection.   Originality/Value: The study contributes to forensic accounting research by validating the Beneish M-Score in real-world fraud cases and highlighting AI’s role in financial fraud detection, supporting SDG 16’s objectives of promoting transparency and strong institutions.Journal of Lifestyle and SDGs Review480The study contributes to forensic accounting research by validating the Beneish M-Score in real-world fraud cases and highlighting AI’s role in financial fraud detection, supporting SDG 16’s objectives of promoting transparency and strong institutions.Journal of Lifestyle and SDGs Review["Ani Stoykova"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/665e9f923d1517f81ded26950fe921c9f1aa97bf df945d82c5e541abb53181c8e6baeb1526a3a721INTRODUCTION As artificial intelligence (AI) increasingly integrates into health workplaces, evidence suggests AI can exacerbate gender inequity. Health professional programmes have a role to play in ensuring graduates grasp the challenges facing working in an AI-mediated world. APPROACH Drawing from feminist scholars and empirical evidence, this conceptual paper synthesises current and future ways in which AI compounds gender inequities and, in response, proposes foci for an integrated approach to teaching about AI and equity. ANALYSIS We propose three concerns. Firstly, multiple literature reviews suggest that the gender divide is embedded within AI technologies from both process (AI development) and product (AI output) perspectives. Next, there is emerging evidence that AI is reinforcing already entrenched health workforce inequities, where certain types of roles are seen as being the domain of certain genders. Finally, AI may disassociate health professionals' interactions with an embodied, agentic patient by diverting attention to a gendered digital twin. IMPLICATIONS Responding to these concerns is not simply a matter of teaching about bias but needs to promote an understanding of AI as a sociotechnical phenomenon. Healthcare curricula could usefully provide clinically relevant educational experiences that illustrate how AI intersects with inequitable gendered knowledge practices. Students can be directed to: (1) explore doubts when working with AI-generated data or decisions; (2) refocus on caring through prioritising embodied connections; and (3) consider how to negotiate gendered workplaces in a time of AI. CONCLUSION The intersection of gender equity and AI provides an accessible, illustrative case about how changing knowledge practices have the potential to embed inequity and how health professional education programmes might respond.Medical Education370The intersection of gender equity and AI provides an accessible, illustrative case about how changing knowledge practices have the potential to embed inequity and how health professional education programmes might respond.Medical education["M. Bearman", "R. Ajjawi"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/df945d82c5e541abb53181c8e6baeb1526a3a721 5f3bc70e9318e679094b526b5c42c2885e72c0f5OBJECTIVES This article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs). BACKGROUND AND SIGNIFICANCE The rise of AI as a tool for the acceleration and falsification of science is impacting every aspect of the transformation of data to information, knowledge, and wisdom through the scientific processes. APPROACH This perspective discusses the philosophical foundations, threats, and opportunities of the AI revolution with a proposal for restructuring the mission of the National Library of Medicine (NLM), part of the National Institutes of Health, with a central role as the guardian of the integrity of scientific knowledge in an era of AI-driven science. RESULTS The NLM can rise to new challenges posed by AI by working from its foundations in theories of Information Science and embracing new roles. Three paths for the NLM are proposed: (1) Become an Authentication Authority For Data, Information, and Knowledge through Systems of Scientific Provenance; (2) Become An Observatory of the State of Human Health Science supporting living systematic reviews; and (3) Become A hub for Culturally Appropriate Bespoke Translation, Transformation, and Summarization for different users (patients, the public, as well as scientists and clinicians) using AI technologies. DISCUSSION Adapting the NLM to the challenges of the Internet revolution by developing worldwide-web-accessible resources allowed the NLM to rise to new heights. Bold moves are needed to adapt the Library to the AI revolution but offer similar prospects of more significant impacts on the advancement of science and human health.JAMIA Journal of the American Medical Informatics Association200The National Library of Medicine can rise to new challenges posed by AI by working from its foundations in theories of Information Science and embracing new roles, and three paths for the NLM are proposed.Journal of the American Medical Informatics Association : JAMIA["Leslie A Lenert"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/5f3bc70e9318e679094b526b5c42c2885e72c0f5 d9bcdc43c6561db8e96788921a67abaf0a9bd550Literature reviews are important components of any research undertaking, as they facilitate a broad overview of the existing knowledge as well as the identification of gaps for further studies. This paper examines how artificial intelligence can change the way literature review is conducted. Artificial intelligence (AI) -based technologies have transformed the conventional methodology by automatizing the processes of data extraction, organization, and critical analysis, making the overall process truly effective and more accurate. It might be said that researchers now can consider and contemplate larger and more diversified data sets in substantially less time. This article reviews current AI literature review tools available to explore strengths and limitations, and how AI is shaping the future of academic writing. KEYWORDS: Artificial Intelligence, Literature ReviewEPRA International Journal of Research &amp; Development (IJRD)00Current AI literature review tools available to explore strengths and limitations, and how AI is shaping the future of academic writing are reviewed.EPRA International Journal of Research &amp; Development (IJRD)["Shivangini R Desai"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/d9bcdc43c6561db8e96788921a67abaf0a9bd550 2d6517d405b412c0002e61bcba00cf665103a4daArtificial Intelligence (AI) is revolutionizing healthcare by transforming traditional diagnostic processes, enhancing treatment planning, and improving overall patient care. AI-driven technologies, including machine learning (ML), deep learning (DL), natural language processing (NLP), and fuzzy logic, are being increasingly integrated into clinical settings to assist healthcare professionals in making more accurate and timely decisions. AI has demonstrated its potential to surpass human expertise in medical imaging interpretation, predictive analytics, and personalized medicine by analyzing large datasets, identifying patterns, and offering data-driven insights. The integration of AI into medical diagnostics has led to significant advancements in disease detection, particularly in fields such as radiology, pathology, and genomics. AI-powered diagnostic tools can rapidly process and interpret imaging data, reducing diagnostic errors and enabling early detection of diseases such as cancer, cardiovascular disorders, and neurological conditions. Furthermore, AI is playing a critical role in decision support systems, allowing clinicians to develop tailored treatment plans based on individual patient characteristics, genetic information, and historical health records. By leveraging AI’s capabilities, healthcare providers can optimize medication dosages, predict patient responses to therapies, and improve overall treatment efficacy. Beyond individual patient care, AI is also making strides in population health management through predictive analytics. AI algorithms can analyze vast amounts of patient data to identify at-risk populations, detect emerging disease outbreaks, and allocate healthcare resources efficiently. By integrating AI into epidemiology and public health surveillance, healthcare systems can proactively address potential health crises and enhance disease prevention efforts. However, while AI presents numerous benefits, its implementation also brings forth several challenges, including concerns regarding data privacy, algorithmic bias, ethical considerations, and the need for regulatory oversight. This review explores the transformative impact of AI in medical diagnostics and healthcare, detailing its role in disease identification, treatment optimization, and population health management. It further examines the challenges and ethical implications associated with AI adoption in clinical practice. As AI technology continues to evolve, ongoing research and collaboration among healthcare professionals, AI developers, and policymakers will be essential to ensure the responsible and equitable integration of AI-driven solutions in healthcare. Future advancements must focus on refining AI algorithms, improving data security, and establishing standardized guidelines to maximize AI’s potential while safeguarding patient well-being.International Scientific Journal of Engineering and Management00This review explores the transformative impact of AI in medical diagnostics and healthcare, detailing its role in disease identification, treatment optimization, and population health management and the challenges and ethical implications associated with AI adoption in clinical practice.International Scientific Journal of Engineering and Management["Ms Reshma R"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/2d6517d405b412c0002e61bcba00cf665103a4da a5ab42e3bee4314231836ccc21ca7cb533536b8aThe article analyzes the problem of education development in the context of the formation of a society in which modern technologies, and in particular, artificial intelligence (AI) developments, play a key role. Since fast learning machines are being introduced into all spheres of life (production, services, entertainment) of society, and its development depends on the intensity of the introduction of AI technologies, virtual spaces are increasingly influencing the human life, everyday life and the labour market, the problem of the development of education, including university education, its purpose, tasks, content and nature is relevant. The point is that AI is actively involved in the learning process, and it is here that its use threatens to displace humans and interpersonal communication. Automation of assessment, generation of AI answers create a situation of a kind of iconic fetishism, and the educational process turns into a simulacrum. This prompts the search for new forms of human activity, which should be developed by the educational process aimed at stimulating thinking. Therefore, it is necessary to abandon the model of objective factual knowledge that can be automated and executed by machines. The development of AI will lead to the disappearance of linear thinking practices shaped by the culture of writing, cognitive functions will also be transformed, and humans will rapidly move beyond subject-centred vision. In the posthuman mindset, interaction with any non-human beings/entities creates prospects for new forms of human activity. In a world where there are more and more available resources, when each autonomous subject determines the trajectory of learning, the university as a centre of social development must be rethought. University education should be aimed at interpersonal communication, focusing on project-based activities, interpretive practices, problematisation, and creativity. The educational process should be shaped by a balance between AI technologies and the development of human intelligence, which increases the importance of interpersonal communication in the educational process.Filosofiya osvity. Philosophy of Education00Filosofiya osvity. Philosophy of Education["O. Perepelytsia", "Veronica Khrabrova"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/a5ab42e3bee4314231836ccc21ca7cb533536b8a 71968b0b77b18f3cb64bbd00b73e68c8fdf687a8Artificial Intelligence (AI) is steadily becoming the new normal in doing business through increasing operational performance, improving customer relations, and increasing predictive accuracy. This quantitative exploratory research employed a mixed-methods approach, integrating qualitative insights into organizational trends, best practices, and challenges with quantitative assessments of performance measures, cost savings, and business outcomes. Several surveys were administered to a diverse group of business professionals. The study, situated within the field of applied research, explores how AI facilitates business growth through change and proposes best practices for successful integration. It also studies what happens during transition periods when organizations emphasize artificial intelligence, NLP, and robotic process automation as top technologies since they contribute to completing work tasks, analyzing large data sets, and improving individual communication with clients. Besides potentially generated cost savings and a long-term increase in business value, there are several obstacles that organizations face if implementing AI, such as high initial costs and a market that requires professional knowledge on the topic. If implemented correctly, AI technologies hold huge potential for businesses going through transitions and taking advantage of AI’s strengths. For this reason, it is important to describe and analyze trends like AI technology integration accurately. This article suggests best practices for applying AI in business development during transformations. Keywords: Artificial intelligence, machine learning, business transitions, predictive analytics, robotic process automation, cost reduction, AI adoption strategiesWestcliff international journal of applied research170The study explores how AI facilitates business growth through change and proposes best practices for successful integration and what happens during transition periods when organizations emphasize artificial intelligence, NLP, and robotic process automation.Westcliff International Journal of Applied Research["Aysha Sidddiky Pinky"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/71968b0b77b18f3cb64bbd00b73e68c8fdf687a8 6ae326892460d6bb9e43852471195c32b4a11e94The rapid advancement of artificial intelligence has transformed many aspects of modern society, while heightening concerns about existential threats and unforeseen consequences. Researchers have systematically evaluated AI failures and hypothetical catastrophic predictions throughout this study, emphasizing superintelligence control problems combined with autonomous weapons, economic destruction of society, cyber warfare threats, and artificial intelligence-driven biotechnology dangers that affect the climate. This research investigates ethical, legal, and security concerns concerning AI autonomy through analyses of AI governance frameworks, empirical case studies, and policy reports. The research reveals that unrestricted AI deployment presents the risk of destructive situations, encompassing the combined effects of economic turmoil, personnel job loss, military operation supervision removal, elevated cyber hazards, environmental disturbances, and synthetic biology complications. The resolution of these challenges depends on strengthened lawmaking and ethical governance of AI and international partnerships. The research confirms the immediate need to protect against AI dangers before full AI potential can be attained for human development. KEYWORDS: Artificial Intelligence Risks, Superintelligence, AI Governance, Autonomous Weapons, AI Bias and Ethics, AI and Cyberwarfare, Deepfakes and Misinformation, Biotechnology and AI, AI and Climate Change, Regulatory Frameworks for AIInternational Journal of Asian Economic Light00The research reveals that unrestricted AI deployment presents the risk of destructive situations, encompassing the combined effects of economic turmoil, personnel job loss, military operation supervision removal, elevated cyber hazards, environmental disturbances, and synthetic biology complications.International Journal of Asian Economic Light["Dinesh Deckker", "Subhashini Sumanasekara"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/6ae326892460d6bb9e43852471195c32b4a11e94 3d1e3116f65a550adfebfa99f78b5ca753181cb5As artificial intelligence (AI) technology continues to advance and iterate, various industries have undergone intelligent reformation. China’s animal husbandry industry, given its importance for people’s livelihoods, is no exception to this transformation. Using AI technology in this field is becoming increasingly common since it not only improves production efficiency but also revolutionizes traditional business models. Animal science is a fundamental discipline that drives the progress of animal husbandry by studying the growth, breeding, nutritional needs, and feeding management of livestock and poultry. This discipline also explores advanced veterinary theories and technologies for epidemic prevention and control. The ultimate objective of this discipline is to ensure the production of high-quality and sufficient animal products to fulfill the demands of both production and daily life. It is predicted that the deep integration of AI technology into animal science will bring unprecedented opportunities to the animal husbandry industry. This study aims to explore the impact of artificial intelligence (AI) on students’ learning experiences and future educational directions. By situating the research within the context of current developments in educational technology, we hope to provide valuable insights for educators and policymakers and employ a questionnaire survey to explore the perceptions and attitudes of students majoring in animal science from various agricultural institutions in China toward this integration. The results of the study provide valuable and practical references for the cultivation and development of artificial intelligence talent in China’s livestock industry.Sustainability410This study aims to explore the impact of artificial intelligence (AI) on students’ learning experiences and future educational directions, and employs a questionnaire survey to explore the perceptions and attitudes of students majoring in animal science from various agricultural institutions in China toward this integration.Sustainability["Jun Shi", "Ye Feng", "Xiang Cao", "Rui Gao", "Zhi Chen"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/3d1e3116f65a550adfebfa99f78b5ca753181cb5 59bf665f4ed3938af7a6b99efdabed5da6ebc7cfDifferential equations are fundamental to the modeling of dynamical systems in various scientific fields, yet solving them analytically remains challenging in many cases. This paper explores the application of artificial intelligence (AI) methods, particularly machine learning algorithms, to solve complex differential equations. We propose a new approach using deep learning models such as neural networks to approximate solutions to both ordinary and partial differential equations without the need for closed-form analytical solutions. The models are trained using datasets generated from known solutions, providing flexibility and adaptability to changing boundary conditions and system dynamics. By comparing AI-generated solutions with traditional numerical methods (e.g., finite difference or finite element methods), we demonstrate the potential of AI to reduce computational time, increase accuracy, and handle traditionally intractable high-dimensional problems. This research also investigates the interpretability of AI-based solutions and provides insights into their robustness across a range of scientific and engineering applications. Our results indicate that AI offers a promising alternative to traditional methods for solving differential equations, especially in scenarios with complex, nonlinear dynamics or where data-driven models are preferred. Keywords: Artificial Intelligence, Machine Learning, Differential Equations, Neural Networks, Deep Learning, Numerical Methods, Ordinary Differential Equations (ODEs), Partial Differential Equations (PDEs), Computational Modeling, Data-Driven Solutions, Nonlinear Dynamics, Boundary Conditions, Scientific Computing, Approximation Methods, High-Dimensional Problems.International Scientific Journal of Engineering and Management00It is shown that AI offers a promising alternative to traditional methods for solving differential equations, especially in scenarios with complex, nonlinear dynamics or where data-driven models are preferred, and the interpretability of AI-based solutions is investigated.International Scientific Journal of Engineering and Management["Kirti Kumar Jain", "Sarla Raigar", "Harsha Tavse", "Manoj Sharma"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/59bf665f4ed3938af7a6b99efdabed5da6ebc7cf dbb1159b6b6ddc275a3e8dcdc8a7b841010dc40ePenerapan kecerdasan buatan (Artificial Intelligence/AI) dalam audit telah menghadirkan perubahan paradigmatik dalam proses audit. Kajian ini, kami mengeksplorasi penggunaan teknologi AI dalam audit dengan fokus pada perspektif Islam. AI, dengan kapasitas analisis data yang besar dan cepat, telah memungkinkan auditor untuk mengelola dan menganalisis volume data yang kompleks, memperbaiki efisiensi audit, dan meningkatkan kualitas hasil audit. Namun, tantangan etis muncul terkait bias algoritma dan ketidaktransparanan dalam pengambilan keputusan AI. Mengintegrasikan prinsip-prinsip etika Islam, seperti keadilan dan integritas, dalam praktik audit AI, kami menyoroti pentingnya mempertimbangkan nilai-nilai etika Islam untuk memperkuat integritas dan kepercayaan dalam audit berbasis AI. Kesimpulannya, kami menekankan pentingnya pendekatan yang holistik, yang menggabungkan prinsip-prinsip etika Islam dalam penggunaan teknologi AI dalam audit untuk mencapai tujuan audit yang adil dan transparan. Kata Kunci: Kecerdasan Buatan; Audit; Teknologi AI; Etika Islam.Journal of Equity00Jurnal EQUITY["Fandi Nur Ahmad Habibi", "Syal Sabillah Ayu Safitri", "Basuki Basuki"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/dbb1159b6b6ddc275a3e8dcdc8a7b841010dc40e 7b36709f78afccd02f8317bf2c360a921a997c94AIM We reviewed how artificial intelligence has been applied to inform care coordination by identifying and/or intervening in patients' unmet social needs. DESIGN Scoping review. DATA SOURCES PubMed, CINAHL, PsycInfo, and Scopus databases were searched for articles published by November 2023. METHODS Articles were excluded if they were reviews or protocols, did not explicitly mention artificial intelligence, or did not primarily focus on using it to identify and/or address unmet needs to inform care coordination. RESULTS Of 476 articles that underwent title and abstract screening, 102 were assessed for full-text eligibility, and eight were ultimately included. Five articles used both natural language processing and machine learning; two articles used natural language processing; and one article used machine learning. Half (n = 4) of the articles focused on using artificial intelligence to identify/predict social needs, and two each focused on artificial intelligence to examine social resource provision or to indirectly identify social needs or using artificial intelligence to facilitate addressing unmet needs through care coordination. CONCLUSIONS This review can inform an understanding of facilitators and barriers to the implementation of artificial intelligence in practice, to potentially improve patient care, health outcomes, and population health equity. IMPLICATIONS FOR PATIENTS AND THE PROFESSION Using artificial intelligence to promote care coordination can expand opportunities to identify and intervene on social needs across more patients, with implications for nurses and other health professionals. It can also potentially exacerbate inequities and harm patient trust. IMPACT The findings suggest a gap between the practice of incorporating artificial intelligence into integrated care platforms and the available scientific literature. This review can provide healthcare providers and organisations with insights into integrating artificial intelligence into clinical workflows, which may inform decisions about whether or how to implement these technologies in clinical settings. REPORTING METHOD We followed PRISMA-ScR guidelines. No Patient or Public Contribution.Journal of Advanced Nursing270How artificial intelligence has been applied to inform care coordination by identifying and/or intervening in patients' unmet social needs is reviewed to inform an understanding of facilitators and barriers to the implementation of artificial intelligence in practice.Journal of advanced nursing["Victoria H Davis", "A. Pinto", "Minal R Patel"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/7b36709f78afccd02f8317bf2c360a921a997c94 9e47321ad908fbceb9acc2f57388541ba0f37b24Artificial intelligence (AI) systems are permeating all domains of our lives. Aside from the many opportunities they raise, their deployment can also hamper individual and societal interests. To counter these risks, regulators across the globe are therefore adopting normative initiatives to govern the technology. While these mostly consist of the promulgation of ethics guidelines and non-binding recommendations, the European Union (EU) opted for binding rules instead. In Spring 2024, it adopted a landmark regulation titled the Artificial Intelligence Act. The Act entered into force on August 1, 2024, and most of its provisions become applicable after two years. Since it systematically and punctiliously regulates the use of AI across sectors rather than focusing on a specific application domain, the AI Act is heralded as the first legal instrument of its kind. Its trendsetter status in the global AI regulatory landscape, coupled with its extraterritorial scope, means the Act is likely to shape the course of AI's uptake in Europe and beyond.International Legal Materials00International Legal Materials["Nathalie A. Smuha"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/9e47321ad908fbceb9acc2f57388541ba0f37b24 879f363af87f57866c332207559c816f1034896bA bibliographic research, which analyzed Generation X workers in the financial sector and the obstacles faced by these workers, in the exercise of work with the arrival of the applicability of artificial intelligence (AI) in the financial sector.Observatorio de la Economía Latinoamericana00OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA["Kennya Rodrigues Nunes", "Rosana Barradas"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/879f363af87f57866c332207559c816f1034896b 8d9ac5d0c189254adf9cd0237fa3248c9685d592Humanities and Social Sciences Communications720By means of co-occurrence analysis of over 9399 peer-reviewed documents retrieved from Scopus discussing machine learning in business and management, fifteen clusters within the literature are identified and served as a starting point for firms looking to integrate ML into their routines across fifteen distinct topics.Humanities and Social Sciences Communications["Aglaya Batz", "David F. D\u2019Croz-Bar\u00f3n", "Carlos Jes\u00fas Vega P\u00e9rez", "Carlos A. Ojeda-Sanchez"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/8d9ac5d0c189254adf9cd0237fa3248c9685d592 5e247d2b66021a2d69d81d8f2c1c477dc32fa8cbArtificial Intelligence (AI) has significantly transformed multiple industries, enabling automation, data-driven decision-making, and advanced problem-solving capabilities. This research paper explores the evolution and advanced applications of AI, focusing on Prolog, a logic-based AI programming language used in expert systems, natural language processing (NLP), and automated reasoning. The study presents a comparative analysis of Prolog versus other AI programming languages, including Python and Lisp, and highlights its continued relevance in knowledge-based AI. Additionally, this paper discusses challenges and future research directions for Prolog-based AI models. Despite the rise of machine learning and deep learning, symbolic AI—represented by Prolog—remains crucial for explainable AI (XAI), rule-based automation, and high-level reasoning. Keywords: Artificial Intelligence, Prolog, AI Programming, Machine Learning, Expert Systems, Symbolic AIINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT00This research paper explores the evolution and advanced applications of AI, focusing on Prolog, a logic-based AI programming language used in expert systems, natural language processing (NLP), and automated reasoning.INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT["Prof. Priyadarshini Badgujar"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/5e247d2b66021a2d69d81d8f2c1c477dc32fa8cb a01c87edbcff25125e7fa787e1cf0998b3921eb5Turkish archives of pediatrics00Turkish Archives of Pediatrics["P. Solek", "Eka Nurfitri", "Indra Sahril", "Taufan Prasetya", "A. F. Rizqiamuti", "Burhan B.", "Irma Rachmawati", "U. Gamayani", "Kusnandi Rusmil", "L. Chandra", "I. Afriandi", "Kevin Gunawan"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/a01c87edbcff25125e7fa787e1cf0998b3921eb5 d2fdc9ab10cd371166fde756ec45df31ab178e60Interactive Learning Environments490Interactive Learning Environments["Xiaojiao Chen", "Zhebing Hu", "Yuanyuan Li", "Chengliang Wang"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/d2fdc9ab10cd371166fde756ec45df31ab178e60 3fe39d3606ce0db6e89eff0b9eb08a2cf0acce0dAIMS Current risk prediction models, such as the Norwegian NORRISK 2, explain only a modest proportion of cardiovascular disease (CVD) incidence. This study aimed to develop improved sex-specific models for predicting the 10-year CVD risk as well as sex- and age-specific thresholds for intervention. METHODS Data from 31,946 participants (40-79 years) without prior CVD were analyzed. Data were randomly split into a training set (for estimation) and a test set (for model evaluation). An extreme gradient boosting (XGBoost) model was used to identify the most important predictive variables. Next, prediction models were developed on the training set for each sex separately using XGBoost and logistic regression. The models were evaluated on the test set using receiver-operating characteristic (ROC) and precision recall (PR) curves. Finally, age- and sex-specific thresholds for intervention were explored. RESULTS All traditional risk factors included in NORRISK 2 and the European SCORE2 model were important predictors for males, but not for females. Potential new risk predictors were identified. The XGBoost model improved CVD risk prediction for males: 0.013- and 0.012-unit increase in ROC-AUC compared to NORRISK 2 and SCORE2 respectively, and 12% and 11% increase in PR-AUC respectively. For females, neither the XGBoost nor logistic regression model performed significantly better than NORRISK 2 and SCORE2. Age- and sex-specific thresholds showed an improvement in sensitivity compared with NORRISK 2-suggested thresholds. CONCLUSIONS By employing statistical learning and incorporating sex-specific risk factors, we propose improved risk prediction models for CVD in males. Introducing sex-specific thresholds for intervention could enhance CVD prevention for both sexes.European Journal of Preventive Cardiology00Improved sex-specific models for predicting the 10-year CVD risk as well as sex- and age-specific thresholds for intervention are proposed by employing statistical learning and incorporating sex-specific risk factors.European journal of preventive cardiology["V. De Martin Topranin", "A. Wiig-Fisketj\u00f8n", "Emma Botten", "H. Dalen", "M. Langaas", "A. Bye"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/3fe39d3606ce0db6e89eff0b9eb08a2cf0acce0d 337e2f1d18d8dcafc7e4ad5ff580e1dde6c18cb9The main purpose of this article is to explore the role of AI in the banking industry in regards to cybersecurity and fraud detection is its ability to enhance the detection and prevention of fraud. Real-time Detection: AI-based systems can analyse transactions online in real-time and track suspicious activities as they occur, allowing banks to respond to a potential threat quickly. Pattern Recognition: AI algorithms can recognize patterns and anomalies in large datasets that might indicate fraudulent behaviour. This helps in identifying fraud that might go unnoticed by traditional systems. Predictive Analytics: AI can predict potential risks by analysing historical data and trends, giving banks a proactive edge in preventing fraud. Scalability: AI systems can handle vast amounts of data and transactions, making them scalable and efficient for large banking institutions. Cost-Effectiveness: Over the years, the operational cost of fraud detection and cybersecurity would come down, and automation would take place with minimal human interference. Better Customer Experience: Reduced false positives and high accuracy in the identification of frauds are likely to make the customer experience smooth and secure. In essence, AI-driven cybersecurity and fraud detection systems help banks safeguard financial transactions, protect customer data, and maintain trust in the financial system.EPRA International Journal of Economic and Business Review00In essence, AI-driven cybersecurity and fraud detection systems help banks safeguard financial transactions, protect customer data, and maintain trust in the financial system.EPRA International Journal of Economic and Business Review["Mr. M. Pavan Sai Nagendra", "Dr. G. Ramesh"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/337e2f1d18d8dcafc7e4ad5ff580e1dde6c18cb9 de4ec1c0598e00075dcde5641940615fa1a7b317European Journal of Information Systems630European Journal of Information Systems["A. Hanelt", "Sebastian Firk", "Patryk Zapadka", "Jana Oehmichen"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/de4ec1c0598e00075dcde5641940615fa1a7b317 969b615c2e8527c80efc19d58eb498c1f65a6b82Livestream selling is an innovative form of online shopping that supports real-time interactions between streamers and consumers. However, a key challenge remains: Streamers have limited capacity to answer individual inquiries, whereas shoppers expect fast, personalized responses. This study investigates whether an AI-powered streaming assistant can address this tension by providing interactive, chat-based support to help consumers access and process information. Through a large-scale randomized field experiment on a leading livestream selling platform, we find that the AI assistant increases sales by 3.00% and reduces product return rates by 12.55%. Our analysis suggests that the AI assistant helps consumers feel more informed and confident in their purchases, thereby reducing uncertainty. At the same time, the AI assistant can occasionally disrupt the consumers’ livestream experience. Overall, the benefits of uncertainty reduction outweigh the negative influence of interruptions. For platform managers and policymakers, these findings evidence the potential of AI technology to enhance online commerce. The AI assistant is particularly effective for high-uncertainty products and for streamers with large audiences, offering implications for strategic deployment. Our research provides actionable insights for integrating AI into livestream selling and other digital commerce scenarios where real-time, AI-powered support can facilitate both consumer satisfaction and business growth.Information systems research460This study investigates whether an AI-powered streaming assistant can address this tension by providing interactive, chat-based support to help consumers access and process information, and finds that the benefits of uncertainty reduction outweigh the negative influence of interruptions.Information Systems Research["Lingli Wang", "Ni Huang", "Yumei He", "De Liu", "Xunhua Guo", "Yan Sun", "Guoqing Chen"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/969b615c2e8527c80efc19d58eb498c1f65a6b82 b0622dbc0c6fc83c0dd8685727ce318d86c142d9This paper includes monetary and non-monetary data to acquire a dependable image of organizations' presentations, and the yearly report is one of the principal hotspots for the dynamic course of financial backers in the monetary market. Assess how AI is enhancing or transforming trading strategies through automation and high-frequency trading. Examine how AI-driven predictive models are being used to forecast stock prices, market trends, and economic indicators more accurately than traditional methods. Explore how AI helps financial institutions manage risks, such as credit risk, market risk, and operational risk, by identifying patterns and anomalies. Design/Methodology/Approach The study employed used a Primary data, using a structured questionnaire administered through an online platform targeting a selection of forensic accounting investigators and forensic accountants. The reliability and validity of the instrument were confirmed with the use of Cronbach Alpha and descriptive statistics. The sample size is 300 from 10 various corporate financial companiesInternational Journal of Asian Economic Light00International Journal of Asian Economic Light["Mr. Dandla Rakesh", "Dr. R S Ch Murthy Chodisetty"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/b0622dbc0c6fc83c0dd8685727ce318d86c142d9 2c1fe2a8cf1360b6c7d45d1a917b41ef32896cebRevista Gaúcha de Enfermagem30Revista gaucha de enfermagem["Aline Franco da Rocha", "Mateus Perfeito Ribeiro", "Renata Perfeito Ribeiro"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/2c1fe2a8cf1360b6c7d45d1a917b41ef32896ceb ecc7b8c9336799b12ee9816b3cce63a2f55e9781Journal of Vascular and Interventional Radiology00Journal of vascular and interventional radiology : JVIR["James T. Anibal", "Hannah B. Huth", "T. Boeken", "Dania Daye", "J. Gichoya", "Fernando G\u00f3mez Mu\u00f1oz", "Julius Chapiro", "Bradford J. Wood", "Daniel Y Sze", "Klaus Hausegger"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/ecc7b8c9336799b12ee9816b3cce63a2f55e9781 70006a11c4fdbc56c6a49cb801eabb4c66d76488Evolutionary Behavioral Sciences00Evolutionary Behavioral Sciences["Slava Kalyuga"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/70006a11c4fdbc56c6a49cb801eabb4c66d76488 aa3572105b0dc818f909978d07a1899b3aa32948The article analyses foundational principles relevant to the creation of artificial general intelligence (AGI). Intelligence is understood as the ability to create novel skills that allow to achieve goals under previously unknown conditions. To this end, intelligence utilises reasoning methods such as deduction, induction and abduction as well as other methods such as abstraction and classification to develop a world model. The methods are applied to indirect and incomplete representations of the world, which are obtained through perception, for example, and which do not depict the world but only correspond to it. Due to these limitations and the uncertain and contingent nature of reasoning, the world model is constructivist. Its value is functionally determined by its viability, i.e., its potential to achieve the desired goals. In consequence, meaning is assigned to representations by attributing them a function that makes it possible to achieve a goal. This representational and functional conception of intelligence enables a naturalistic interpretation that does not presuppose mental features, such as intentionality and consciousness, which are regarded as independent of intelligence. Based on a phenomenological analysis, it is shown that AGI can gain a more fundamental access to the world than humans, although it is limited by the No Free Lunch theorems, which require assumptions to be made.830Based on a phenomenological analysis, it is shown that AGI can gain a more fundamental access to the world than humans, although it is limited by the No Free Lunch theorems, which require assumptions to be made.["Rolf Pfister"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/aa3572105b0dc818f909978d07a1899b3aa32948 c9694d2e5169a99e7513ca58ac5e795b54133bfeArtificial Intelligence (AI) has emerged as a transformative force in education, offering opportunities to personalize learning, enhance student engagement, and optimize administrative tasks. AI enables tailored learning experiences by adapting to individual student needs and providing educators with data-driven insights to monitor progress. Additionally, AI reduces educators’ workload through automated grading and feedback, allowing them to focus more on fostering meaningful interactions with students. Despite its potential, the implementation of AI in education faces significant challenges. Key issues include unequal access to technology, low levels of digital literacy among teachers and students, concerns over data privacy and security, and the high cost of adopting and maintaining AI systems. Furthermore, resistance to change from educators accustomed to traditional methods poses an additional barrier to AI integration. Addressing these challenges is essential to fully realize the benefits AI can bring to education. To overcome these obstacles, this study proposes practical recommendations, including strengthening technological infrastructure, enhancing teacher training, enforcing robust data protection policies, and fostering collaboration between educational institutions and AI developers. A phased approach to implementation is also emphasized to minimize resistance and ensure the effective adoption of AI tools. Curriculum alignment with AI technologies is highlighted as a critical step to maximize relevance and learning outcomes. This study contributes to the growing body of knowledge on AI in education by addressing implementation challenges and offering actionable solutions. By adopting these recommendations, stakeholders can create an inclusive, efficient, and adaptable educational environment. The findings aim to guide policymakers, educators, and institutions in leveraging AI to bridge educational gaps and prepare students for success in the digital era.International Journal of Social Science Research and Review01Practical recommendations are proposed, including strengthening technological infrastructure, enhancing teacher training, enforcing robust data protection policies, and fostering collaboration between educational institutions and AI developers in leveraging AI to bridge educational gaps and prepare students for success in the digital era.International Journal of Social Science Research and Review["Idha Novianti"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/c9694d2e5169a99e7513ca58ac5e795b54133bfe 6bcacab25f3fd0bd50c34c58d6b853add1273702IIC - International Review of Intellectual Property and Competition Law191The question whether reproductions of copyright-protected works are created inside the models during their training has seldom been discussed, and the widely propagated narrative that non-EU AI developers are not subject to EU copyright law is an illusion.IIC - International Review of Intellectual Property and Competition Law["Tim W. Dornis"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/6bcacab25f3fd0bd50c34c58d6b853add1273702 56cb6cfb9273c337a5397f9ae6e8820e107392afRecent advances in artificial intelligence (AI) have led to a diverse set of predictions about its long-term impact on humanity. A central focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from human extinction after a misaligned TAI takes over ("AI doom") to unprecedented economic growth and abundance ("post-scarcity"). However, the probabilities and implications of these scenarios remain highly uncertain. Here, we organize the various scenarios and evaluate their associated existential risks and economic outcomes in terms of aggregate welfare. Our analysis shows that even low-probability catastrophic outcomes justify large investments in AI safety and alignment research. We find that the optimizing representative individual would rationally allocate substantial resources to mitigate extinction risk; in some cases, she would prefer not to develop TAI at all. This result highlights that current global efforts in AI safety and alignment research are vastly insufficient relative to the scale and urgency of existential risks posed by TAI. Our findings therefore underscore the need for stronger safeguards to balance the potential economic benefits of TAI with the prevention of irreversible harm. Addressing these risks is crucial for steering technological progress toward sustainable human prosperity.610["Jakub Growiec", "Klaus Prettner"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/56cb6cfb9273c337a5397f9ae6e8820e107392af 87b5c106d906df1c6cae61c9da24bdd5ebcf4261This study aimed to explore the assessment trustworthiness of English as a Foreign Language (EFL) in the Artificial Intelligence (AI) age by identifying the main challenges and proposing viable solutions. Employing a qualitative case study approach, the research investigated the experiences and perceptions of EFL instructors regarding the challenges and solutions. To meet such an end, the study sought, through semi-structured interviews, to gain insights from the study sample which comprised nine experienced EFL instructors selected based on their expertise in the field of EFL teaching and AI technology. The findings revealed numerous significant challenges, including the disadvantageous effect of AI tools on academic integrity, classwork engagement, reliance on technology, students’ creativity, and current assessment metrics. Despite such challenges, the study portrayed some effective solutions, such as designing authentic assessment tools for assessing higher cognitive skills, adopting active learning strategies, developing training programs for EFL learners, implementing advanced AI content detectors, and updating traditional assessment methods. Based on the results, the study highlighted a dire need to reform conventional assessment practices to address the challenges to integrity posed by AI tools.World Journal of English Language00A dire need to reform conventional assessment practices to address the challenges to integrity posed by AI tools is highlighted and some effective solutions are portrayed, such as designing authentic assessment tools for assessing higher cognitive skills.World Journal of English Language["Iman El-Nabawi Abdel Wahed Shaalan", "Ayman Shaaban Khalifa Ahmad"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/87b5c106d906df1c6cae61c9da24bdd5ebcf4261 b5df64bc89ba663f43848a0b236e6bc7b33c6307In the United States, artificial intelligence (AI) has become a transformative force in the business analytics area related to financial risk assessment for banking and insurance industries. The aim of this research is to assess adoption, effectiveness and challenges of AI driven risk assessment models, by analyzing data collected through a survey, which was distributed to 200 financial professionals across the U.S. According to the findings, AI plays an important role in increasing the accuracy of fraud detection, reducing credit risk, predicting market risk, minimizing operational risk and other decisions and optimizing cost efficiency at the financial institutions. The adoption of AI technology in improving the efficiency of the pharmaceutical industry is hindered by some key barriers such as concerns about data privacy, compliance regulations, high implementation costs and shortage of AI specialists. According to the results, financial institutions need to expand governance frameworks to ensure the regulatory alignment and ethics in using AI in a transparent way while maintaining safe risk assessment model. The contribution of this study to the current debates on AI and finance risk management, as well as implications for both the policymakers and financial industry practitioners, might include practical advice and recommendations to financial institutions and researchers on better integrating AI in banking and insurance risk assessment systems.International journal of business and management sciences00According to the results, financial institutions need to expand governance frameworks to ensure the regulatory alignment and ethics in using AI in a transparent way while maintaining safe risk assessment model.International journal of business and management sciences["Sonia Akter", "Tanmoy Saha Turja", "Amjad Hossain", "Sanjida Alam Eshra", "Iftekhar Rasul"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/b5df64bc89ba663f43848a0b236e6bc7b33c6307 24326238d1004bf6f2d0d4a0021ebb92a6e06560Technology, Knowledge and Learning220This paper introduces AIRSim (AI Responses Simulator), an innovative AI tool designed to support students in practicing their questionnaire analysis skills within the café and restaurant discipline, and evaluated its capability in simulating participant responses to user-uploaded questionnaires.Technology, Knowledge and Learning["Kelvin Leong", "Anna Sung"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/24326238d1004bf6f2d0d4a0021ebb92a6e06560 c891eae565fcc02dc6af03476a4b08b0f78e9bdcFuture Business Journal800Investigating students’ mindsets regarding adopting AI chatbots for the effectiveness of online learning in higher education indicated that AI chatbot capability mediates the effect of PU, PEU, and TC on the adoption of AI chatbots; however, there is no mediating effect in the relationship between SN and AI chatbot capability.Future Business Journal["Muhammad Khalilur Rahman", "Noor Azizi Ismail", "Md Arafat Hossain", "Mohammad Shahadat Hossen"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/c891eae565fcc02dc6af03476a4b08b0f78e9bdc f686e16750d4e8c0e8ca758ad77f6594a7499237Kidney disease is a global health concern, impacting a substantial part of the overall population and contributing to high morbidity and mortality rates. The initially diagnosed phases of kidney disease are often present without noticeable indications, leading to delayed diagnosis and treatment. Therefore, early detection is crucial to reducing complications and improving the lives of those impacted. However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms’ “black-box” nature, which adversely affect their interpretability and clinical applicability. This paper aims to address these limitations by creating an effective machine-learning-based approach that integrates ant colony metaheuristic optimization algorithms for feature selection and explainable artificial intelligence techniques such as SHAP and LIME for model interpretation. The ant colony optimization method identified the most relevant feature subsets using a clinical dataset, reducing model complexity while preserving predictive accuracy. Performance evaluation shows that the extra trees classifier, when using optimized selected features, achieved the highest performance with an accuracy of 97.70% and an area under the curve of 99.55%, outperforming previous models trained on raw and complete processed feature sets. To enhance interpretability, the SHAP and LIME explainable techniques were employed, providing detailed insights into the contribution of key features such as TimeToEventMonths, HistoryDiabetes, and Age. This comprehensive framework, combining advanced feature selection with explainable models, improves clinical decision-making and fosters trust in machine learning applications for healthcare.Applied Sciences330This paper aims to address limitations of previous automated approaches to kidney disease detection by creating an effective machine-learning-based approach that integrates ant colony metaheuristic optimization algorithms for feature selection and explainable artificial intelligence techniques such as SHAP and LIME for model interpretation.Applied Sciences["Abbas Jafar", "Myungho Lee"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/f686e16750d4e8c0e8ca758ad77f6594a7499237 3088efc5d3b15293ff6e3a78fd96385ed2898887Purpose The aim of this study is to examine how organizations can integrate human-centric Artificial Intelligence (AI) into corporate learning programs to balance automation with empathy for better learning experiences. It explores strategies to successfully implement AI into corporate learning without compromising the human aspects. Design/methodology/approach The study employs a qualitative research method using semistructured interviews with 20 corporate learning practitioners. These interviews reveal insights into strategies, challenges, and opportunities for achieving this balance. Findings Key findings include strategies such as employing hybrid models that combine AI-driven processes with human interaction, using sentiment analysis to enhance AI’s empathetic integration, implementing role-based access to trainers, fostering feedback loops, and encouraging employee involvement in program design. Research limitations/implications A key limitation of the study is its reliance on qualitative data drawn from a small sample size, which may not fully capture broader industrial trends. Future research could incorporate larger, more diverse samples and quantitative analysis to validate and expand upon these findings. Practical implications This research offers practical implications for organizations seeking to integrate AI into corporate learning. It guides the design of human-centered AI systems that prioritize learner needs and engagement while balancing automation with human interaction. Originality/value This study contributes to the under-researched intersection of AI and human-centered learning in corporate systems. By offering a framework and strategies for balancing automation and empathy, it advances the development of responsible, inclusive, and effective AI-enhanced learning environments. Development and Learning in Organizations: an international journal50Key findings include strategies such as employing hybrid models that combine AI-driven processes with human interaction, using sentiment analysis to enhance AI’s empathetic integration, implementing role-based access to trainers, fostering feedback loops, and encouraging employee involvement in program design.Development and Learning in Organizations: An International Journal["Maryann Osadebamwen Asemota", "Gbolahan Owoeye"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/3088efc5d3b15293ff6e3a78fd96385ed2898887 623fbd817960794181211c3f4f3556afade9a5e4Incorporating Artificial Intelligence (AI) in healthcare has transformed disease diagnosis and treatment by offering unprecedented benefits. However, it has also revealed critical cybersecurity vulnerabilities in Deep Learning (DL) models, which raise significant risks to patient safety and their trust in AI-driven applications. Existing studies primarily focus on theoretical vulnerabilities or specific attack types, leaving a gap in understanding the practical implications of multiple attack scenarios on healthcare AI. In this paper, we provide a comprehensive analysis of key attack vectors, including adversarial attacks, such as the gradient-based Fast Gradient Sign Method (FGSM), evasion attacks (perturbation-based), and data poisoning, which threaten the reliability of DL models, with a specific focus on breast cancer detection. We propose the Healthcare AI Vulnerability Assessment Algorithm (HAVA) that systematically simulates these attacks, calculates the Post-Attack Vulnerability Index (PAVI), and quantitatively evaluates their impacts. Our findings revealed that the adversarial FGSM and evasion attacks significantly reduced model accuracy from 97.36% to 61.40% (PAVI: 0.385965) and 62.28% (PAVI: 0.377193), respectively, demonstrating their severe impact on performance, but data poisoning had a milder effect, retaining 89.47% accuracy (PAVI: 0.105263). The confusion matrices also revealed a higher rate of false positives in the adversarial FGSM and evasion attacks than more balanced misclassification patterns observed in data poisoning. By proposing a unified framework for quantifying and analyzing these post-attack vulnerabilities, this research contributes to formulating resilient AI models for critical domains where accuracy and reliability are important.Algorithms190This paper proposes the Healthcare AI Vulnerability Assessment Algorithm (HAVA) that systematically simulates these attacks, calculates the Post-Attack Vulnerability Index (PAVI), and quantitatively evaluates their impacts, and proposes a unified framework for quantifying and analyzing these post-attack vulnerabilities.Algorithms["S. Brohi", "Qurat-ul-ain Mastoi"]2025-03-10T00:00:00https://www.semanticscholar.org/paper/623fbd817960794181211c3f4f3556afade9a5e4 378c59359f151a0e68bd6b749abc1f14d886766dAI, Brain and Child90This launch editorial introduces AI, Brain and Child (ABC), an open-access journal dedicated to exploring the dynamic interplay between AI, neuroscience, and child education, aiming to examine the multifaceted roles of AI and neuroscience in shaping cognitive, social, and emotional growth among children.AI, Brain and Child["Philip Hui Li", "John Chi-Kin Lee"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/378c59359f151a0e68bd6b749abc1f14d886766d d962e9c4ad955607db14ffb3161018a7c8749262The advent of artificial intelligence (AI) heralds a new era in the field of robotic surgery. This article discusses recent trends in the integration of AI technology with robotic surgical procedures, highlighting the latest advancements in robotic breast surgery. The application of AI in robotic surgery ranges from preoperative planning to intraoperative assistance. Machine learning algorithms are now utilized to analyze medical imaging data, enabling surgeons to devise detailed surgical plans tailored to the unique characteristics of each patient’s tumor. This approach leads to more precise tumor excision and better preservation of healthy tissue. Robotic systems equipped with advanced visualization and sensor technologies can provide real-time feedback during surgery and training. Additionally, AI algorithms can predict the occurrence of postoperative complications, allowing for early intervention. With the ongoing development of AI and robotic technologies, significant progress has been made in robotic automation. The future of robotic breast surgery holds the promise of even greater accuracy, and the quality of life for breast cancer patients may be significantly improved.Plastic and Aesthetic Research1210Recent trends in the integration of AI technology with robotic surgical procedures are discussed, highlighting the latest advancements in robotic breast surgery.Plastic and Aesthetic Research["Tingting Li", "Chen Li"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/d962e9c4ad955607db14ffb3161018a7c8749262 e6d0be2b02e608855aab0a1e85f27848640e2a44PURPOSE OF REVIEW There has been an explosion of creative uses of artificial intelligence (AI) in healthcare, with AI being touted as a solution for many problems facing the healthcare system. This review focuses on tools currently available to pediatric urologists, previews up-and-coming technologies, and highlights the latest studies investigating benefits and limitations of AI in practice. RECENT FINDINGS Imaging-driven AI software and clinical prediction tools are two of the more exciting applications of AI for pediatric urologists. As nuanced pattern recognition improves in trained computer models, pediatric urologists will be able to better counsel and risk stratify patients with chronic diseases and surgical needs. AI is also being extensively used in product development for enuresis treatment. Large language models such as ChatGPT continue to be of strong interest as a patient-facing education tool, but it lacks the accuracy needed to serve as a suitable alternative to human response. SUMMARY AI is increasingly investigated for use across healthcare fields, including pediatric urology. Use of AI and machine learning (ML) is being explored for patient interface, imaging assessment, outcomes prediction, and product development. Though still in preclinical stages for most systems, ML presents as a promising new clinical tool with potential to shape healthcare systems and medical practice.Current Opinion in Urology280This review focuses on tools currently available to pediatric urologists, previews up-and-coming technologies, and highlights the latest studies investigating benefits and limitations of AI in practice.Current opinion in urology["Lauren M. McGee", "Elizabeth Soo", "Casey A. Seideman"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/e6d0be2b02e608855aab0a1e85f27848640e2a44 5a0add9f1b542e3a0d8484c39f73327ef383db50This is the first UK study to compare the number needed to biopsy (NNB) ratio for melanoma diagnosis for three different care models, with incorporation of artificial intelligence as a medical device (AIaMD) alongside teledermatology. We present encouraging results from our centre with NNB ratios that are comparable between all three models and lower than the average NNB ratio reported in a worldwide meta-analysis. We highlight the priority of AIaMD in correct identification of benign lesions and encourage further work to improve and validate performance to maintain patient safety. Finally, we give consideration to a hybrid model to streamline current pathways as a potential strategy to deal with rising demands on the urgent skin cancer service.Clincal and Experimental Dermatology00Clinical and experimental dermatology["Radhika Bali", "Jenny Ga-Yun Chung", "Lucy Thomas", "K. Hussain", "L. Fearfield"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/5a0add9f1b542e3a0d8484c39f73327ef383db50 a747b66710409e02ea5f1d878bbeadf5d2f14c86This research explores the strategic use of artificial intelligence (AI) to enhance sustainable cooperation in Northeast Asia among China, Japan, and South Korea towards UN SDGs. The research uses a mixed methodology approach to understand how AI can bring peace, economic resilience, and environmental sustainability to the region. This paper traces the evolution of cyber security, green technology, and financial partnerships among China, Japan, and South Korea by examining their past and current trilateral meetings. Using Porter’s Diamond Model, a comprehensive statistical analysis that describes each country’s role in global network security work within six dimensions will be applied to evaluate these key countries’ resilience and competitiveness in the digital economy. Cross-border data flow is one of the components of public-private partnerships within cyber defense technologies innovation. Moreover, a holistic perspective on AI adoption highlights its strategic positioning, competitive intelligence, and continuous improvement through unity. AI-powered initiatives could support regional harmony by promoting digital sustainability and facilitating economic resilience and environmental sustainability. Future studies should seek empirical evidence that gives more operational responses for practical AI cooperation among China, Japan, and the Republic of Korea. This article discusses how digital technologies can better address immediate sustainability needs while providing a platform for future generations seeking positive change based on academic insights translated into practical strategies. On a broader scale, AI can be used for peacebuilding, economic recovery, and environmental conservation to make societies more stable, prosperous, and equitable.American Journal of Business Science Philosophy (AJBSP)00How digital technologies can better address immediate sustainability needs while providing a platform for future generations seeking positive change based on academic insights translated into practical strategies is discussed.American Journal of Business Science Philosophy (AJBSP)["Xiuli Chen", "Joohan Ryoo"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/a747b66710409e02ea5f1d878bbeadf5d2f14c86 fc7d25e325a7491ec40dd16efec4a1d5cf626eacPurpose This study aims to explore the largely under-researched area of user perceptions of artificial intelligence (AI)-driven recommendations and how the visibility of AI-driven labels influences impulsive buying behavior on social media platforms. Addressing a gap in existing literature, it further examines the moderating effects of generational differences and the mediating role of consumer knowledge, providing new insights into how these factors shape consumer responses to AI-driven content in social media. Design/methodology/approach This study used a quantitative approach, using consumer surveys to explore the direct and indirect impacts of AI-driven recommendations and the visibility of AI-driven labels on impulsive buying behavior through social media. A conditional process analysis was conducted to examine how generational differences act as a moderator and how consumer knowledge of AI serves as a mediator in these relationships. This analysis integrated these factors into a single framework to provide a detailed understanding of how consumers respond to AI-driven personalization in social media. Findings This study confirms that AI-driven personalization effectively nudges impulsive buying on social media, with personalized recommendations impacting behavior more subtly than AI labels. Consumer knowledge does not mediate this effect, while generational differences emerge as a significant moderator; Millennials are found to be more responsive to both recommendations and labels in comparison to Gen Z, possibly due to less familiarity with AI technologies present in social media. Research limitations/implications This study identifies limitations such as its reliance on consumer perceptions from a questionnaire rather than direct interactions with AI-driven features. While insightful, future research should incorporate actual user data, like clickstreams, and include a wider range of generational cohorts to deepen the understanding of AI’s impact on impulsive buying behavior. Originality/value This study bridges gaps in the literature by examining the combined effects of AI-driven recommendations and visibility of AI-driven labels on impulsive buying behavior, offering new insights into the role of generational differences and consumer knowledge in AI-based social media marketing. Young Consumers660It is confirmed that AI-driven personalization effectively nudges impulsive buying on social media, with personalized recommendations impacting behavior more subtly than AI labels.Young Consumers["Afiqah Amin"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/fc7d25e325a7491ec40dd16efec4a1d5cf626eac 5569f08f37745287debb943831248ed50d298dfcThis study examines the impact of innovative technology, artificial intelligence (AI) driven accounting, and surrogate entrepreneurship on fostering a sustainable future, with a focus on digital start-ups. Surrogate entrepreneurship refers to managing ventures on behalf of other entities rather than for personal ownership. Digital start-ups can harness advanced technologies like blockchain and AI to improve transparency, accuracy, and efficiency in accounting practices. Data were gathered through a structured questionnaire using a five-point Likert scale from 354 respondents, including entrepreneurs, accountants, business managers, and auditors in Southern Rajasthan. Partial least squares structural equation modeling (PLS-SEM) was utilized to analyze complex relationships between latent and observable variables. The results indicate that innovative technology, AI-driven accounting, and surrogate entrepreneurship significantly influence sustainable futures, accounting for 64.4% of the variance in digital start-ups' contributions to sustainability. These findings provide valuable managerial insights into sustainable finance strategies and underscore the critical moderating roles of digital start-ups and surrogate entrepreneurship in driving sustainable development.American Journal of Business Science Philosophy (AJBSP)00The results indicate that innovative technology, AI-driven accounting, and surrogate entrepreneurship significantly influence sustainable futures, accounting for 64.4% of the variance in digital start-ups' contributions to sustainability.American Journal of Business Science Philosophy (AJBSP)["Asha Sharma"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/5569f08f37745287debb943831248ed50d298dfc 5aca8c94236c1b4fc7f53c48470453b7277b285cThis study examines the role of artificial intelligence (AI) in promoting Education for Sustainable Development (ESD) in lower-middle-income ASEAN economies, focusing on two critical areas: the challenges to AI adoption and strategies to optimize AI alignment with sustainable development goals. The research identifies key barriers to AI integration, including technological readiness, financial constraints, policy gaps, and socio-cultural factors, through document analysis and case studies. It highlights region-specific challenges, such as inadequate digital infrastructure and limited teacher training, that hinder AI adoption in education. Additionally, the study explores how AI can be optimized to support Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) and SDG 9 (Innovation and Infrastructure), by enhancing educational access, promoting gender equality, and enabling personalized learning. Using the Grounded Theory model, the findings suggest that AI can reduce inequalities and empower underserved communities by improving educational outcomes. However, successful AI integration requires a balanced approach that prioritizes ethical considerations and inclusivity. The study advocates for collaborative efforts between governments, educational institutions, and technology providers to establish an ecosystem that supports responsible AI deployment. By addressing these challenges and leveraging AI’s potential, stakeholders can unlock new opportunities to foster sustainable development and improve educational outcomes in ASEAN lower-middle-income economies.American Journal of Business Science Philosophy (AJBSP)00Using the Grounded Theory model, the findings suggest that AI can reduce inequalities and empower underserved communities by improving educational outcomes, however, successful AI integration requires a balanced approach that prioritizes ethical considerations and inclusivity.American Journal of Business Science Philosophy (AJBSP)["Masatoshi Hara"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/5aca8c94236c1b4fc7f53c48470453b7277b285c 63c99f5f87643c53f0d994f32e4d32bae79eaf08This study explores the potential of artificial intelligence (AI) in medical history taking (anamnesis) and assesses its acceptance using technology acceptance models. Through nine expert interviews with physicians from diverse medical backgrounds, the study aims to understand concerns and anticipated benefits of AI in the doctor–patient relationship. To demonstrate AI’s applications, digital anamnesis surveys were conducted with two actual patients, and the resulting data were interpreted by AI and reviewed by physicians. Findings indicate that physicians view AI as potentially beneficial, expecting that AI can facilitate improvements in care quality, efficiency, and time savings. Despite initial concerns about AI’s ability to address individual patient needs and its impact on the doctor–patient relationship, there is significant interest in integrating AI tools into daily practice. Key issues include patient constitution, the effort-to-benefit ratio, and potential risks to patient trust. The study identifies six areas for further research: Economic impact and cost-benefit analysis, patient acceptance and trust, stress reduction and job satisfaction, effects on doctor–patient relationships, development of verification mechanisms, and ethical and legal considerations. These findings underscore the complexities of AI integration in health care, emphasizing the need to address concerns about patient individuality, data privacy, and interpersonal relationships while harnessing AI’s potential.Design00Findings indicate that physicians view AI as potentially beneficial, expecting that AI can facilitate improvements in care quality, efficiency, and time savings, and underscore the complexities of AI integration in health care.Design+["Felix H\u00f6pfl", "Marina Schellhorn"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/63c99f5f87643c53f0d994f32e4d32bae79eaf08 738baf2c87c53477d773dfeb3489caa97f90692dPenelitian ini bertujuan untuk menganalisis pemanfaatan Artificial Intelligence (AI) dalam proses pembelajaran di kelas IX SMP Negeri 8 Palangka Raya. Penelitian ini menggunakan pendekatan kualitatif deskriptif dengan teknik pengumpulan data melalui survei menggunakan Google Form, wawancara dengan siswa dan guru, serta observasi langsung di sekolah. Hasil penelitian menunjukkan bahwa 87,40% siswa telah menggunakan AI untuk membantu menyelesaikan tugas, terutama pada mata pelajaran Matematika dan Bahasa Indonesia. AI memberikan manfaat berupa akses cepat ke materi, penjelasan interaktif, dan solusi untuk soal-soal kompleks. Namun, tantangan seperti ketergantungan pada teknologi, dan menurunnya kemampuan berpikir kritis juga ditemukan. Sebagai rekomendasi, sekolah perlu melatih guru untuk memanfaatkan AI secara efektif serta memberikan pembimbingan kepada siswa terkait penggunaan teknologi secara etis. Peneliti selanjutnya dapat mengeksplorasi pengembangan program literasi digital dan dampak jangka panjang AI pada pembelajaran di berbagai jenjang pendidikan.JIIP - Jurnal Ilmiah Ilmu Pendidikan150JIIP - Jurnal Ilmiah Ilmu Pendidikan["Ibnu Ikhsan", "Putu Artasoma", "Eli Karliani", "Ali Sunarno"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/738baf2c87c53477d773dfeb3489caa97f90692d 0b3890065b98d5890a111504b584e90c9f3c3b35 Rapid advances in artificial intelligence (AI), including large language models (LLMs) with abilities that rival those of human experts on a wide array of tasks, are reshaping how people make important decisions. At the same time, critics worry that LLMs may inadvertently discriminate against some groups. To address these concerns, recent regulations call for auditing the LLMs used in important decisions such as hiring. But neither current regulations nor the scientific literature offers clear guidance on how to conduct these audits. In this article, we propose and investigate one approach for auditing algorithms: correspondence experiments , a widely applied tool for detecting bias in human judgments. We applied this method to a range of LLMs instructed to rate job candidates using a novel data set of job applications for K-12 teaching positions in a large American public school district. By altering the application materials to imply that candidates are members of specific demographic groups, we measured the extent to which race and gender influenced the LLMs’ ratings of the candidates’ suitability. We found moderate race and gender disparities, with the models slightly favoring women and non-White candidates. This pattern persisted across several variations in our experiment. It is unclear what might be driving these disparities, but we hypothesize that they stem from posttraining efforts, which are part of the LLM training process and intended to correct biases in these models. We conclude by discussing the limitations of correspondence experiments for auditing algorithms. Behavioral Science &amp; Policy560This article proposes and investigates one approach for auditing algorithms: correspondence experiments, a widely applied tool for detecting bias in human judgments, and discusses the limitations of correspondence experiments for auditing algorithms.Behavioral Science &amp; Policy["Johann D. Gaebler", "Sharad Goel", "Aziz Huq", "Prasanna Tambe"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/0b3890065b98d5890a111504b584e90c9f3c3b35 5b7da2268d6c6ad88e714a26b11fdb0c8abc95a5PurposeSupply chain risk management (SCRM) is a multi-stage process that handles the adverse impact of disruptions in the supply chain network (SCN), and various SCRM techniques have been widely developed in the literature. As artificial intelligence (AI) techniques advance, they are increasingly applied in SCRM to enhance risk management’s capabilities.Design/methodology/approachIn the current, systematic literature review (SLR), which is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we analysed the existing literature on AI-based SCRM methods without any time limit to categorise the papers’ focus in four stages of the SCRM (identification, assessment, mitigation and monitoring). Three research questions (RQs) consider different aspects of an SCRM method: interconnectivity, external events exposure and explainability.FindingsFor the PRISMA process, 715 journal and conference papers were first found from Scopus and Web of Science (WoS); then, by automatic filtering and screening of the found papers, 72 papers were shortlisted and read thoroughly, our review revealed research gaps, leading to five key recommendations for future studies: (1) Attention to considering the ripple effect of risks, (2) developing methods to explain the AI-based models, (3) capturing the external events impact on the SCN, (4) considering all stages of SCRM holistically and (5) designing user-friendly dashboards.Originality/valueThe current SLR found research gaps in AI-based SCRM and proposed directions for future studies.Modern Supply Chain Research and Applications1280The current SLR found research gaps in AI-based SCRM and proposed directions for future studies, including attention to considering the ripple effect of risks and designing user-friendly dashboards.Modern Supply Chain Research and Applications["Amir Hossein Ordibazar", "O. Hussain", "R. Chakrabortty", "E. Irannezhad", "Morteza Saberi"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/5b7da2268d6c6ad88e714a26b11fdb0c8abc95a5 583652655cd4e9dd7a0cfe861585f9f6e1cf3d2cNigeria is the most populous country in Africa with the highest gross domestic product (GDP) as of 2022. However, Nigeria is burdened by significant health challenges including an extremely high maternal mortality ratio, inadequate human resources, poor healthcare infrastructure, and population-level poverty rates as high as 40%. Nigeria also has the highest reported prevalence of peripartum cardiomyopathy worldwide which contributes to maternal mortality. Unfortunately, the diagnosis of peripartum cardiomyopathy is often delayed and mortality rates following diagnosis are extremely high (approximately 50%). Thus, there is a huge unmet need for simple, effective, and accessible solutions for cardiomyopathy detection in this population. To address maternal mortality through screening and early diagnosis, we designed and conducted a randomized controlled clinical trial (NCT05438576) of an artificial intelligence (AI) technology in Nigeria. The objective of the study was to evaluate the impact of AI-guided screening on cardiomyopathy detection in obstetric patients. The study findings showed AI-guided screening doubled the detection of cardiomyopathy (defined as left ventricular ejection fraction <50%) when compared to usual care with a number needed to screen of 47. As we explore next steps in relation to deploying this technology for clinical use in Nigeria, we sought to gather contextual information and broadly share lessons learned from the recently completed trial. To that end, we convened a round table discussion with all study site investigators aimed at identifying site-specific contextual challenges related to the development and conduct of the study. The SPEC-AI Nigeria study is the first published randomized controlled clinical trial of a health AI intervention in Nigeria. Insights gained from this study can inform future AI intervention studies in clinical care, guide the development of implementation strategies to ensure effective interventions are successfully incorporated into clinical care, and provide a roadmap for key stakeholders to consider when evaluating AI-technologies for use in low-resource settings.Frontiers in Cardiovascular Medicine330The study findings showed AI-guided screening doubled the detection of cardiomyopathy when compared to usual care with a number needed to screen of 47.Frontiers in Cardiovascular Medicine["Demilade A. Adedinsewo", "Damilola Onietan", "A. C. Morales-Lara", "Serin Moideen Sheriff", "Bosede B Afolabi", "Oyewole A Kushimo", "A. Mbakwem", "Kehinde F Ibiyemi", "J. A. Ogunmodede", "H. Raji", "Sadiq H Ringim", "A. A. Habib", "Sabi\u2032uM Hamza", "O. Ogah", "G. Obajimi", "O. O. Saanu", "Solomon Aborisade", "O. Jagun", "Francisca O Inofomoh", "Temitope Adeolu", "K. Karaye", "S. Gaya", "Yahya Sa\u2019ad", "Isiaka Alfa", "Cynthia Yohanna", "P. Noseworthy", "Rickey E. Carter"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/583652655cd4e9dd7a0cfe861585f9f6e1cf3d2c 02d9ac4308c1d1d7c7649edc70baea15c66b4999Against the backdrop of addressing global climate change, whether the new generation of information technology, centered on artificial intelligence (AI), can promote comprehensive green transformation and achieve the “dual carbon” goal has become an important issue in China’s national development strategy. The research objective of this paper is to explore the causal relationship between AI and green innovation (GI). In this study, we conduct a quasi-natural experiment using the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (NAIPZ). On the basis of data from A-share-listed companies from 2013 to 2022, we use a staggered difference-in-difference model to study the impact and mechanism of AI on corporate GI. Research results show that AI can improve the GI of enterprises. Mechanism analysis results show that AI promotes GI in enterprises by improving internal governance and optimizing human capital, while industry competition can increase the promotion effect of AI on GI. Heterogeneity analysis results indicate that the promotion effect of AI on GI is particularly prominent in the eastern region, high-tech industries, and non-state-owned enterprises. This study addresses the important question of whether the NAIPZ can promote GI in enterprises, thereby providing empirical evidence and policy references for accelerating the integration and development of AI and GI in China.Sustainability460This study addresses the important question of whether the NAIPZ can promote GI in enterprises, thereby providing empirical evidence and policy references for accelerating the integration and development of AI and GI in China.Sustainability["Chunyan Zhao", "Linjing Wang"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/02d9ac4308c1d1d7c7649edc70baea15c66b4999 3b79550b09a3ac308444c20e646c626eaad71f00Pflügers Archiv: European Journal of Physiology100This special issue presents a collection of reviews on the recent advancements and applications of artificial intelligence (AI) in medicine and physiology, covering technical, applicative, and ethical viewpoints, and underlines the remarkable impact of AI on these fields.Pflugers Archiv : European journal of physiology["A. Westphal", "Ralf Mrowka"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/3b79550b09a3ac308444c20e646c626eaad71f00 bff197774a44f88838a0aa0c5505f5b53e803d28This study examines how artificial intelligence (AI) is transforming the food supply chain by improving forecasting, demand planning, and operational efficiency. AI technologies like predictive analytics and machine learning enhance accuracy and responsiveness but face challenges such as data quality, system integration, and high costs. By surveying 370 food industry professionals, the research explores the benefits and barriers of AI adoption, providing actionable insights to optimize supply chain processes. The findings aim to support businesses and policymakers in leveraging AI strategically for competitive advantage and supply chain resilience.Journal of Life Economics170The research explores the benefits and barriers of AI adoption, providing actionable insights to optimize supply chain processes and support businesses and policymakers in leveraging AI strategically for competitive advantage and supply chain resilience.JOURNAL OF LIFE ECONOMICS["Salman Alam Khan", "Aylin Erdo\u011fdu"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/bff197774a44f88838a0aa0c5505f5b53e803d28 b1f6cc3b7ba4c5262859fe5507172436b92da443Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup's findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence-focused research and applications in anesthesiology. Anesthesiology's progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup's recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence-based tools for perioperative care.Anesthesiology680An anesthesiologist artificial intelligence expert workgroup is charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress.Anesthesiology["Hannah Lonsdale", "Michael L Burns", "Richard H Epstein", "Ira S Hofer", "Patrick J Tighe", "Julia A G\u00e1lvez Delgado", "Daryl J Kor", "Emily J MacKay", "Parisa Rashidi", "J. Wanderer", "Patrick J McCormick"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/b1f6cc3b7ba4c5262859fe5507172436b92da443 9c1fc641656551e3a99eab77e5290ed90e2ff058AI &amp; SOCIETY210Collective intelligence then stands as an alternative ontological path for AI which puts intelligence at the service of humanity and the world rather than a technocratic elite.AI &amp; SOCIETY["Harry Halpin"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/9c1fc641656551e3a99eab77e5290ed90e2ff058 4f678a1c56dd083c83fdfd2e7b9e78c5a524e4ecIntroduction. Based on techniques of artificial intelligence (AI) robots equipped with algorithms are becoming more social and intelligent as they enter society. As a relatively unexplored topic of research the current study evaluated whether the perception of robot intelligence was influenced by different techniques of AI. Method. In an online study participants viewed two versions of a humanoid robot which varied by their surface colour and stated AI abilities. For each image participants rated the perceived intelligence of the robot. Results. Using an online survey, the results found no statistically significant effect for robot surface colour on judgments of robot intelligence but that robot voice enablement and the ability to detect a user’s face and emotions added significantly to the perception of robot intelligence. In addition, amongst the AI techniques evaluated, text used for human-robot communication was the least effective method for conveying the perception of intelligence for a humanoid robot. Conclusion. As tentative conclusions, the perception of robot intelligence can be based on the specific AI technique used to design the robot, and it appears that the more the human-like AI ability of the robot, the more likely that users will view the robot as intelligent.Information research. An international electronic journal00The perception of robot intelligence can be based on the specific AI technique used to design the robot, and it appears that the more the human-like AI ability of the robot, the more likely that users will view the robot as intelligent.Information Research an international electronic journal["Jessica K. Barfield"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/4f678a1c56dd083c83fdfd2e7b9e78c5a524e4ec 6add2f5dcbade9974fea4372a160899c37205fdaThe integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students’ development. This study investigates the impact of AI technologies on students’ learning processes and academic performance, with a focus on their perceptions and the challenges associated with AI adoption. Conducted at the National University of Science and Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience with AI-enhanced learning environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data were collected through a structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, and the effectiveness of AI tools; and four open-ended questions exploring experiences, expectations, and concerns. Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement. However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. The study underscores the necessity of a structured framework for AI integration, supported by ethical guidelines, to maximize benefits while mitigating risks. In conclusion, while AI holds immense potential to enhance learning efficiency and academic performance, its successful implementation requires addressing concerns related to accuracy, cognitive disengagement, and ethical implications. A balanced approach is essential to ensure equitable, effective, and responsible learning experiences in AI-enhanced educational environments.Education sciences120The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement, however, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were identified.Education Sciences["Aniella Mihaela Vieriu", "Gabriel Petrea"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/6add2f5dcbade9974fea4372a160899c37205fda 46c036bf3f3e6dbfc272c5fc70b61faf42c62db3Libraries, historically revered as custodians of knowledge, are undergoing a paradigm shift in the 21st century, driven by Artificial Intelligence (AI). This paper provides a comprehensive examination of how AI technologies such as machine learning, natural language processing (NLP), and predictive analytics are redefining library operations, user engagement, and ethical frameworks. Through a systematic review of secondary data from 2015–2023, including global surveys, institutional reports, and peer-reviewed studies, the study identifies transformative trends: 40% adoption of AI chatbots in academic libraries (ALA, 2022), a 50% surge in digital resource usage via personalized recommendations, and persistent challenges like algorithmic bias and data privacy concerns. The analysis underscores the necessity for libraries to harmonize technological innovation with ethical stewardship to maintain their societal relevance.International Scientific Journal of Engineering and Management00A systematic review of secondary data from 2015–2023 identifies transformative trends: 40% adoption of AI chatbots in academic libraries (ALA, 2022), a 50% surge in digital resource usage via personalized recommendations, and persistent challenges like algorithmic bias and data privacy concerns.International Scientific Journal of Engineering and Management["Anoop Kumar Ekka"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/46c036bf3f3e6dbfc272c5fc70b61faf42c62db3 c45f9cb092f6ceeaaa05b1e1378cd5780b814ba3Archives of Sexual Behavior370Initial insights are offered into the significance of parental communication with adolescents regarding sexting and the potential limitations of current language models in promoting improved discussions between parents and adolescents on sensitive topics.Archives of sexual behavior["T. Ricon", "Michal Dolev-Cohen"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/c45f9cb092f6ceeaaa05b1e1378cd5780b814ba3 cbbfb9e0f44bff94356bb91783739d4b55cda760Students with and without disabilities consistently fail to meet established writing benchmarks, highlighting the urgent need for intervention and innovation in this critical area. According to the National Assessment of Educational Progress (NAEP), key criteria for assessing writing include the development of ideas, organization of ideas, and language facility and conventions. One solution to improve writing outcomes is to leverage artificial intelligence (AI) tools to support struggling writers. The purpose of this article is to define AI, examine its current integration into tools used by teachers to support struggling writers, and provide a crosswalk between NAEP criteria and AI tools to enhance writing interventions.Journal of Special Education Technology270The purpose of this article is to define AI, examine its current integration into tools used by teachers to support struggling writers, and provide a crosswalk between NAEP criteria and AI tools to enhance writing interventions.Journal of Special Education Technology["Samantha R. Goldman", "Sean J. Smith", "Adam C. Carreon"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/cbbfb9e0f44bff94356bb91783739d4b55cda760 4399ac4cb8825e929ed2a92151425181a743266bThis study developed a new explainable artificial intelligence algorithm called PassAI, which classifies successful or failed passes in a soccer game and explains its rationale using both tracking and passer's seasonal stats information. This study aimed to address two primary challenges faced by artificial intelligence and machine learning algorithms in the sports domain: how to use different modality data for the analysis and how to explain the rationale of the outcome from multimodal perspectives. To address these challenges, PassAI has two processing streams for multimodal information: tracking image data and passer's stats and classifying pass success and failure. After completing the classification, it provides a rationale by either calculating the relative contribution between the different modality data or providing more detailed contribution factors within the modality. The results of the experiment with 6,349 passes of data obtained from professional soccer games revealed that PassAI showed higher classification performance than state-of-the-art algorithms by>5% and could visualize the rationale of the pass success/failure for both tracking and stats data. These results highlight the importance of using multimodality data in the sports domain to increase the performance of the artificial intelligence algorithm and explainability of the outcomes.00The results of the experiment revealed that PassAI showed higher classification performance than state-of-the-art algorithms by>5% and could visualize the rationale of the pass success/failure for both tracking and stats data.["Ryota Takamido", "Jun Ota", "Hiroki Nakamoto"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/4399ac4cb8825e929ed2a92151425181a743266b d25dafe6aee6f79c641ef84b9d0982f2870a8731npj Digital Medicine750The importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance, is highlighted.NPJ Digital Medicine["Fereshteh Hasanzadeh", "Colin B Josephson", "Gabriella Waters", "Demilade A. Adedinsewo", "Zahra Azizi", "James A White"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/d25dafe6aee6f79c641ef84b9d0982f2870a8731 e8ea2d290653e40731e2eef5b5b662e31b3c0f9aEvidence-based medicine (EBM) enhances clinical decision-making but faces implementation challenges, particularly in dentistry, where patient-specific complexities limit its effectiveness. This article examines EBM through the lens of Aristotelian logic, exploring its use of deductive and inductive reasoning and its limitations in addressing real-world variability. We then discuss how artificial intelligence (AI) can enhance EBM by synthesizing data, automating evidence appraisal, and generating personalized treatment insights. While AI offers a promising solution, it also presents challenges related to ethics, transparency, and reliability. Integrating AI into EBM requires careful consideration to ensure precise, adaptive, and patient-centered decision-making.Knowledge Transfer Statement:This commentary provides a critical discourse on the challenges of evidence-based medicine and how artificial intelligence could help address these shortcomings.JDR Clinical & Translational Research40This article examines EBM through the lens of Aristotelian logic, exploring its use of deductive and inductive reasoning and its limitations in addressing real-world variability, and discusses how artificial intelligence can enhance EBM by synthesizing data, automating evidence appraisal, and generating personalized treatment insights.JDR clinical and translational research["F. Tamimi", "K. Jasim"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/e8ea2d290653e40731e2eef5b5b662e31b3c0f9a fe13b5dfa605460a9b5e2adab4c08eb06694c2e5Since the structure of enzymes determines their function, elucidating the structure of enzymes lays a solid foundation for deciphering their catalytic mechanism and enabling rational design. The development of artificial intelligence (AI) has sparked a technological revolution, infusing new vitality into theoretical studies of enzymology and the advancement of enzyme engineering techniques. This Review outlines the development process and main methods of AI applied in the structural elucidation and functional prediction of enzymes. Furthermore, it emphasizes AI-based rational design of enzymes and provides a detailed exposition of representative AI algorithms and case studies. With the support of AI technology, the comprehension of enzyme structure and function and their relationship will become deeper and more efficient, thereby promoting the widespread application of enzyme engineering in various fields.Journal of Agricultural and Food Chemistry440The development process and main methods of AI applied in the structural elucidation and functional prediction of enzymes and their relationship will become deeper and more efficient, thereby promoting the widespread application of enzyme engineering in various fields.Journal of agricultural and food chemistry["Yuhang Wang", "Shuangxin Han", "Yi Wang", "Quanfeng Liang", "Wei Luo"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/fe13b5dfa605460a9b5e2adab4c08eb06694c2e5 1c16667e364532fd1ab3e09e248454db1fbae7f8IJARCCE00IJARCCE[]2025-03-11T00:00:00https://www.semanticscholar.org/paper/1c16667e364532fd1ab3e09e248454db1fbae7f8 2de6e7e896dde9848030e60591c6686b918ce7baEducation and Information Technologies : Official Journal of the IFIP technical committee on Education910It was determined that most participants had problems in modeling and drawing a model related to AI, and if the existing concerns were eliminated, AI could be easily integrated into the preschool period.Education and Information Technologies["Esra Bet\u00fcl K\u00f6lemen", "Bekir Y\u0131ld\u0131r\u0131m"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/2de6e7e896dde9848030e60591c6686b918ce7ba 84efa93f1745b316019671657e070e93f9f3bef2PurposeInnovation in AI technology has transformed the global economic landscape, thereby becoming a focal point for academic research. A review of extant literature reveals a preponderant focus on the application aspects of AI technology, underscoring the necessity for a more nuanced examination. However, the innovation of AI technology is led by managers who are likely influenced by cognitive biases.Design/methodology/approachThis study combines supervised machine learning models in the AI field and patent abstracts to accurately identify AI technology innovation. It also considers a vital type of cognitive bias, namely overconfidence, to provide novel insights into the literature on AI technology innovation.FindingsFind that overconfident CEOs are more likely to promote AI technology innovation than non-overconfident CEOs. Moreover, consistent with the BTOF prediction, the positive impact of CEO overconfidence on corporate AI technology innovation is strengthened by negative performance feedback, but the above relationship has been weakened by positive performance feedback.Research limitations/implicationsThis study does not posit that all cognitive biases invariably contribute positively to a firm’s AI technology innovation. Instead, it advocates for a nuanced understanding of the role of manager-specific cognitive biases in influencing such innovation, taking into account the particular characteristics of these biases. Future researchers could consider key decision-makers behavioral attributes and evaluate the influence of other cognitive biases such as escalation commitment, status quo bias and narcissism.Practical implicationsExecutives must comprehend how their inherent beliefs influence their interpretations and reactions to financial outcomes. Given an external environment with uncertainties and crises, organizations must revise the conventional perception of CEO overconfidence, recognizing its positive impact on risk mitigation, adversity response and AI technological innovation. The selection or replacement of overconfident managers should be contingent on the organization’s developmental stage, performance status and strategic requirements, complemented by suitable disciplinary and incentive systems.Originality/valueThis research indicates that when a CEO decides to adopt AI technology innovation in response to negative performance feedback, such a decision might be significantly influenced by personal beliefs rather than an objective assessment of the firm’s strategic predicament. Directors and investors find this perspective enlightening. This awareness can foster support for the firm’s activities in AI technology innovation, concentrate on emerging technologies and market opportunities and augment its strategic trajectory by enhancing the firm’s orientation toward technological innovation.European Journal of Innovation Management880It is found that overconfident CEOs are more likely to promote AI technology innovation than non-overconfident CEOs, and consistent with the BTOF prediction, the positive impact of CEO overconfidence on corporate AI technology innovation is strengthened by negative performance feedback, but the above relationship has been weakened by positive performance feedback.European Journal of Innovation Management["Bowen Zheng", "Ying-Yin Lin", "Veronica Hoi In Fong", "Xiaotong Huo"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/84efa93f1745b316019671657e070e93f9f3bef2 4fba5adaeaba4a64920d5cb05d44bf481a748d63International Journal of Human-Computer Interaction380International Journal of Human–Computer Interaction["D. Turgut", "Filiz Kunuroglu"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/4fba5adaeaba4a64920d5cb05d44bf481a748d63 a0ba2f8c096a06920404b38b4dd4d43f70f48d82PurposeThe need to optimize the triangle formed by “quality, cost and time” culminated in increasing the focus from product to process quality. By analyzing the evolution of quality and the impact of Industry 4.0 on it, this research seeks, through a technical point of view, to comprehend the state of the art of quality 4.0 and intelligent quality management (IQM) by defining concepts, technologies, challenges and applications.Design/methodology/approachThe review was conducted only in English, on IEEE Xplore, Scopus, Engineering Village and Web of Science databases with a backward citation analysis, having technology and quality as main concepts. In total, 109 papers were reduced to 24, and 11 characteristics were extracted.FindingsAlthough many authors point to the same 4.0 technologies and the importance of quality for Industry 4.0, they differ in the concept of quality 4.0 and the implementation frameworks to achieve it.Originality/valueThis paper is one of the few studies that have searched for the roots of quality 4.0 and IQM. The work also seeks to identify their differences and their relationship with Industry 4.0.Benchmarking : An International Journal480This research seeks to comprehend the state of the art of quality 4.0 and intelligent quality management (IQM) by defining concepts, technologies, challenges and applications by analyzing the evolution of quality and the impact of Industry 4.0.Benchmarking: An International Journal["Liandra Dos Santos Jesus", "E. V. C. Galdamez", "Syntia Lemos Cotrim", "G. C. Leal"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/a0ba2f8c096a06920404b38b4dd4d43f70f48d82 cd03a34a32a8a87c3d6ce088493885708e2836efCentral European Journal of Operations Research610Central European Journal of Operations Research["Mohammad Asl Zare", "S. B. Razavian"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/cd03a34a32a8a87c3d6ce088493885708e2836ef 0d44a925e5a3c9fd90914583231adc807477cbf300["Peng Wang", "Pete Smith"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/0d44a925e5a3c9fd90914583231adc807477cbf3 c9a136a79b1dd39412b90b16bf71035231d89dcdJournal of the Knowledge Economy300Journal of the Knowledge Economy["Shahid Hussain", "S. Rehman", "Abdul Rasheed", "Khalil Ur Rehman"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/c9a136a79b1dd39412b90b16bf71035231d89dcd 808c38dba4534652ff205b7739bbdf6788f276bcBackground: Previous research has demonstrated acceptable diagnostic accuracy of AI-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation for predicting paroxysmal or incident atrial fibrillation (AF). However, interethnic validations of these AI algorithms remain limited. We aimed to develop and comprehensively evaluate our AI model for predicting AF based on standard 12-lead SR ECG images in a Korean population, and to validate its performance in Brazilian patient cohorts. Methods: We developed a modified convolutional neural network model using a dataset comprising 811,542 ECGs from 121,600 patients at Seoul National University Bundang Hospital (2003~2020). Ninety percent of the patients were allocated to the training dataset, while the remaining 10% to the internal validation dataset. The model outputs a risk score (from 0 to 1) indicating the probability of concurrent paroxysmal or incident AF within 2 years, using standard-format 12-lead SR ECG images. External validation was performed using the CODE 15% dataset, an open ECG dataset from the Telehealth Network of Minas Gerais, Brazil, by applying a 1:4 (AF:Non-AF) random sampling strategy. Results: In the internal validation, our AI model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.907 (95% CI: 0.897-0.916), with a sensitivity of 80.6% and a specificity of 85.0% for AF prediction. Subgroup analyses showed an AUROC of 0.874 (95% CI: 0.856-0.891) for patients in routine health checkups or outpatient settings, and 0.852 (95% CI: 0.824-0.880) for patients with "Normal ECG" interpretations. In the external interethnic validation with the CODE 15% dataset, the AI model exhibited an AUROC of 0.884 (95% CI: 0.869-0.900), which increased to 0.906 (95% CI: 0.893-0.919) when adjusted for age and sex. In the subset of patients with "Normal ECG" interpretations, the AUROC was 0.826 (95% CI: 0.769-0.883), increasing to 0.861 (95% CI: 0.814-0.908) after applying the same adjustments. Conclusions: Our AI-powered SR ECG interpretation model demonstrated excellent performance in predicting paroxysmal or incident AF, with valid performance in the Brazilian population as well. This suggests that the model has potential for broad application across different ethnic groups. Keywords: electrocardiography, artificial intelligence, atrial fibrillation, diagnosismedRxiv160The authors' AI-powered SR ECG interpretation model demonstrated excellent performance in predicting paroxysmal or incident AF, with valid performance in the Brazilian population as well, which suggests that the model has potential for broad application across different ethnic groups.["Ji Hyun Lee", "Joonghee Kim", "Jina Choi", "Yun Young Choi", "M. Youngjin Cho", "I. Oh"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/808c38dba4534652ff205b7739bbdf6788f276bc e070f095ca4f7fe2b6f4242b4f2a7cba6cb0d2c5Journal of Education and Teaching420Journal of Education for Teaching["Y. Nissim", "Eitan Simon"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/e070f095ca4f7fe2b6f4242b4f2a7cba6cb0d2c5 fd87076a7fc14324d79ae6801834536ef539b4cbScientific Reports1360This study makes a unique contribution by systematically uncovering underexplored AGI themes, proposing a conceptual framework that connects AI advancements to practical applications, and addressing the multifaceted technical, ethical, and societal challenges of AGI development.Scientific Reports["R. Raman", "Robin Kowalski", "K. Achuthan", "Akshay Iyer", "Prema Nedungadi"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/fd87076a7fc14324d79ae6801834536ef539b4cb 4b4aa4a513d2439a282505a1d72a191ba3744bc7This article explores the transformative potential of integrating Artificial Intelligence into Business Intelligence (BI) systems to redefine how organizations manage and consume insights. Traditional BI relies heavily on users navigating reports, dashboards, spreadsheets, and databases to extract actionable information, often leading to inefficiencies, delayed decision-making, and overlooked critical insights. The proposed AI-enhanced BI framework addresses these challenges by automating exception management, alert generation, root cause analysis, insight discovery, quality assurance, and user-specific notifications. The system leverages AI to monitor BI platforms continuously, identifying anomalies, generating alerts for critical events, and performing automated root cause analyses to provide immediate context for exceptions. By understanding user roles, preferences, and interests, the AI delivers personalized notifications tailored to the user's specific domain. The article discusses implementation strategies, including natural language processing, machine learning, and predictive analytics, while addressing challenges like data privacy, scalability, and integration with existing BI tools. This AI-driven paradigm positions BI not just as an analytical tool but as an intelligent partner, ensuring organizations remain informed, agile, and competitive in an increasingly data-driven world.International Journal of Scientific Research in Computer Science Engineering and Information Technology00The proposed AI-enhanced BI framework addresses challenges by automating exception management, alert generation, root cause analysis, insight discovery, quality assurance, and user-specific notifications, while addressing challenges like data privacy, scalability, and integration with existing BI tools.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Dattatreya Raychowdhuri"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/4b4aa4a513d2439a282505a1d72a191ba3744bc7 5974016e36464bd9fc3c29ec4058bee41ba4a868Discover Artificial Intelligence160Discover Artificial Intelligence["Nilesh Kumar Sharma", "Rashmi Singh"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/5974016e36464bd9fc3c29ec4058bee41ba4a868 4d6d7ba65df0c468718c84e185bacb8c8f08bea8Purpose This paper explores the potential future of artificial intelligence (AI) in human resources (HR) management through a speculative narrative approach, examining how specialized AI systems might interact and manage each other within an HR context. Design/methodology/approach This study uses creative speculation grounded in current technological trends to present a narrative case study of a fictional AI-operated company’s HR department. This approach illustrates complex technological concepts and organizational challenges through accessible storytelling. Findings This narrative reveals potential challenges and opportunities in AI-driven HR management, including specialized AI roles for talent acquisition, performance management, professional development and conflict resolution. It highlights the importance of considering both technical and interpersonal aspects of AI system management within HR contexts. Originality/value This paper offers a unique perspective on AI in HR by examining potential interactions between specialized AI systems, moving beyond traditional human–AI interaction discussions. The creative narrative format makes complex technological concepts accessible to HR practitioners while encouraging strategic thinking about future challenges. Strategic HR Review01A narrative case study of a fictional AI-operated company’s HR department reveals potential challenges and opportunities in AI-driven HR management, including specialized AI roles for talent acquisition, performance management, professional development and conflict resolution.Strategic HR Review["Martin Sposato"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/4d6d7ba65df0c468718c84e185bacb8c8f08bea8 ba53c95792abcc00c0569a75a48d331a9138eb91Orientation: The integration of artificial intelligence (AI) into human resource management (HRM) is transforming decision-making processes and employee involvement.Research purpose: This study examines AI-driven decision-making in HRM, with a focus on employee involvement and ethical challenges.Motivation for the study: As AI adoption in HRM rapidly grows, it is crucial to understand its implications for organisational practices and employee experiences.Research approach/design and method: This study conducted a systematic review of 193 peer-reviewed articles (2019–2023), employing cluster analysis to identify four key themes in AI-driven HRM.Main findings: The study identifies four clusters: AI adoption, highlighting employee involvement in smooth transitions; AI Ethics, focussing on transparency and fairness; AI-driven human resource decision-making, showing enhanced recruitment and performance management; and AI performance, emphasising operational efficiency through AI systems.Practical/managerial implications: The findings highlight the role of employee involvement in successful AI transitions, emphasising its impact on acceptance and operational success.Contribution/value-add: This review also suggests future research directions, emphasising the need to explore AI’s long-term impacts on organisational culture and employee satisfaction.Sa Journal of Human Resource Management00This study examines AI-driven decision-making in HRM, with a focus on employee involvement and ethical challenges, and identifies four clusters: AI adoption, highlighting employee involvement in smooth transitions; AI Ethics, focussing on transparency and fairness; AI-driven human resource decision-making, showing enhanced recruitment and performance management; and AI performance, emphasising operational efficiency through AI systems.SA Journal of Human Resource Management["Wui San Taslim", "Titik Rosnani", "Rizky Fauzan"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/ba53c95792abcc00c0569a75a48d331a9138eb91 4a876a076bc85bc05331b46ed12edcb4bdf51d40Introduction. As the integration of artificial intelligence (AI) rapidly advances, academic libraries are increasingly pivotal in supporting AI literacy among students and faculty. Method. Through content analysis, the present study examines 70 newly developed generative AI LibGuides from academic libraries affiliated with the association of research libraries (ARL) and the Oberlin group, using the EDUCAUSE AI literacy framework. Analysis. Through a detailed examination, the present research reorganizes and improves the EDUCAUSE AI literacy framework, proposing a more comprehensive version tailored to higher education needs. The adapted framework fills the gaps in the original model and offers a nuanced approach to AI literacy, reflecting the unique challenges faced by academic libraries. Results. The findings reveal that most LibGuides emphasize foundational AI tools and responsible use, with less focus on advanced technical competencies related to AI creation. Significant differences were observed between ARL and Oberlin Group LibGuides, with ARL offering more comprehensive coverage. To address these differences, consistent training and knowledge sharing initiatives are recommended to ensure a common standard of AI literacy support across academic libraries. Conclusion. This study provides insights into the role of libraries in promoting generative AI literacy and identifies areas for future strategic partnerships and improvement.Information research. An international electronic journal00In insights into the role of libraries in promoting generative AI literacy, the EDUCAUSE AI literacy framework is reorganizes and improves and identifies areas for future strategic partnerships and improvement.Information Research an international electronic journal["Ko Chun Ru", "Rong Tang"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/4a876a076bc85bc05331b46ed12edcb4bdf51d40 87fd67b8820d496ece0def14acf3bcfe111f9195Introduction. Artificial intelligence (AI) tools garner more attention every day, questions have arisen regarding their possible negative impacts in future job markets. Some predict a potential for massive job losses, especially in high-skilled jobs. This study seeks to explore undergraduate students’ perceptions of how these tools might affect their future careers. Method. This study follows a case study design, employing phenomenological interviews as a research method. The data set was made up of interviews with 17 undergraduate students. Analysis. Data were analysed by employing constant comparative analysis, with various rounds of coding including the creation of open, axial, and structural codes. Results. Students saw AI tools as an inevitable part of their future work. Participants expressed their intention to learn how to optimize their use of various tools, which they see as having the potential to positively benefit them in their future careers. They do not perceive AI to be a viable substitute for their skills, especially in terms of identifying misinformation and emotions. Conclusions. Academic institutions must provide curricular spaces which allow students to harness the power of AI tools. While employers should also make efforts to train employees to make the most of AI tools.Information research. An international electronic journal00Students do not perceive AI to be a viable substitute for their skills, especially in terms of identifying misinformation and emotions, and academic institutions must provide curricular spaces which allow students to harness the power of AI tools.Information Research an international electronic journal["M\u00f3nica Col\u00f3n-Aguirre", "Kawanna Bright"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/87fd67b8820d496ece0def14acf3bcfe111f9195 58873c4791c9bde803a7ff8306a540a7a58356ddThe convergence of Identity and Access Management (IAM) and Artificial Intelligence (AI), mainly through Large Language Models (LLMs), represents a transformative shift in cybersecurity paradigms. This article explores how LLMs reshape identity security across multiple dimensions, enabling more sophisticated defense mechanisms against evolving threats. Traditional static IAM frameworks give way to dynamic, contextual systems capable of continuous evaluation and adaptive response. The integration of natural language processing enhances authentication through linguistic analysis and behavioral pattern recognition, while contextual access control architectures implement zero-trust principles with unprecedented granularity. LLM capabilities further enable autonomous policy generation and management, creating living security frameworks that evolve alongside threat landscapes. Predictive analytics capabilities shift organizational security postures from reactive to anticipatory, identifying attack precursors before exploitation. Despite significant implementation challenges, including computational requirements, potential vulnerabilities, and governance considerations, the strategic integration of LLMs with IAM systems promises to fundamentally transform cybersecurity from discrete tool collections into unified intelligent ecosystems. This technological convergence creates multidimensional security capabilities that adapt continuously to changing conditions, representing an incremental improvement and a fundamental rethinking of identity-centered security.International Journal of Scientific Research in Computer Science Engineering and Information Technology00This article explores how LLMs reshape identity security across multiple dimensions, enabling more sophisticated defense mechanisms against evolving threats.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Tuhin Banerjee"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/58873c4791c9bde803a7ff8306a540a7a58356dd 20d235c1276b13f3662784f892e492d92ad3c698As artificial intelligence transforms public sector operations, governments struggle to integrate technological innovations into coherent systems for effective service delivery. This paper introduces the Algorithmic State Architecture (ASA), a novel four-layer framework conceptualising how Digital Public Infrastructure, Data-for-Policy, Algorithmic Government/Governance, and GovTech interact as an integrated system in AI-enabled states. Unlike approaches that treat these as parallel developments, ASA positions them as interdependent layers with specific enabling relationships and feedback mechanisms. Through comparative analysis of implementations in Estonia, Singapore, India, and the UK, we demonstrate how foundational digital infrastructure enables systematic data collection, which powers algorithmic decision-making processes, ultimately manifesting in user-facing services. Our analysis reveals that successful implementations require balanced development across all layers, with particular attention to integration mechanisms between them. The framework contributes to both theory and practice by bridging previously disconnected domains of digital government research, identifying critical dependencies that influence implementation success, and providing a structured approach for analysing the maturity and development pathways of AI-enabled government systems.480This paper introduces the Algorithmic State Architecture (ASA), a novel four-layer framework conceptualising how Digital Public Infrastructure, Data-for-Policy, Algorithmic Government/Governance, and GovTech interact as an integrated system in AI-enabled states.["Zeynep Engin", "Jon Crowcroft", "David Hand", "Philip Treleaven"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/20d235c1276b13f3662784f892e492d92ad3c698 c1c87d122775d901261d7a88cfe7465972b65aa1Introduction. Review articles are essential in evolving scholarly information systems but have been underexplored in scientometrics. This paper aims to expand scientometric research on review articles, focusing on their role in understanding knowledge accumulation within specific domains. Method. This study collected 4,315 review articles on artificial intelligence (AI). Using keyword frequency analysis and the Task-Technology Fit (TTF) model, the articles were classified into three categories: task-oriented, technology-oriented, and application-oriented. Analysis. The temporal distribution of the review articles, the age distribution of their cited references, and the updating characteristics of references cited in review articles were analysed to provide preliminary insights into the evolution, dynamism, and updating patterns of knowledge in AI. Results. The results show a marked increase in the publication frequency of review articles, especially over the past five years, with the application domain exhibiting the highest growth rate. Over half of the references cited in review articles across all domains are from the past five years. Additionally, older references are cited more frequently in newer reviews than more recent ones. Conclusion. This study can be seen as an expansion of scientometric research based on review articles and highlights several intriguing research questions for exploration within this field.Information research. An international electronic journal00This study collected 4,315 review articles on artificial intelligence (AI) and used keyword frequency analysis and the Task-Technology Fit model to analyse their temporal distribution, age distribution, and updating characteristics to provide preliminary insights into the evolution, dynamism, and updating patterns of knowledge in AI.Information Research an international electronic journal["Siyuan Peng", "Lei Hu", "Jingrui Hou", "Youqing Xia", "Ping Wang"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/c1c87d122775d901261d7a88cfe7465972b65aa1 6217c784ae7742c77a71ad92a5b97451c3ca5b05The integration of artificial intelligence capabilities into enterprise operations has become a strategic imperative across industries, yet organizations face significant challenges related to infrastructure costs, operational complexity, and deployment efficiency. This article examines how Dell Technologies, VMware, and NVIDIA have collaborated to develop virtualized GPU solutions specifically designed for enterprise storage environments. The Dell Validated Design for Virtualizing GPUs for AI with VMware and NVIDIA enables organizations to leverage virtualized GPU capabilities within existing VMware infrastructure, creating a more flexible and cost-effective approach to AI implementation. The solution incorporates NVIDIA AI Enterprise, a comprehensive software suite optimized for virtualized environments, allowing organizations to virtualize and containerize AI workloads on NVIDIA-Certified Systems. Performance analyses demonstrate that virtualized environments can achieve performance comparable to bare-metal implementations while delivering substantial operational benefits including reduced deployment time, improved operational efficiency, enhanced resource utilization, and lower total cost of ownership. The unified infrastructure approach supports diverse AI requirements across enterprise departments including human resources, information technology, and customer service, enabling organization-wide AI integration while maintaining operational continuity.International Journal for Sciences and Technology00The Dell Validated Design for Virtualizing GPUs for AI with VMware and NVIDIA enables organizations to leverage virtualized GPU capabilities within existing VMware infrastructure, creating a more flexible and cost-effective approach to AI implementation.International Journal on Science and Technology["Venkatachala Nivas Chainuru"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/6217c784ae7742c77a71ad92a5b97451c3ca5b05 95da3077b8c789ca60258384b52a74474cb81d1fIn today's increasingly digital interactions, robust Identity Verification (IDV) is crucial for security and trust. Artificial Intelligence (AI) is transforming IDV, enhancing accuracy and fraud detection. This paper introduces ``Zero to One,'' a holistic conceptual framework for developing AI-powered IDV products. This paper outlines the foundational problem and research objectives that necessitate a new framework for IDV in the age of AI. It details the evolution of identity verification and the current regulatory landscape to contextualize the need for a robust conceptual model. The core of the paper is the presentation of the ``Zero to One'' framework itself, dissecting its four essential components: Document Verification, Biometric Verification, Risk Assessment, and Orchestration. The paper concludes by discussing the implications of this conceptual model and suggesting future research directions focused on the framework's further development and application. The framework addresses security, privacy, UX, and regulatory compliance, offering a structured approach to building effective IDV solutions. Successful IDV platforms require a balanced conceptual understanding of verification methods, risk management, and operational scalability, with AI as a key enabler. This paper presents the ``Zero to One'' framework as a refined conceptual model, detailing verification layers, and AI's transformative role in shaping next-generation IDV products.190The ``Zero to One'' framework is introduced, a holistic conceptual framework for developing AI-powered IDV products, detailing verification layers, and AI's transformative role in shaping next-generation IDV products.["Aniket Vaidya", "Anurag Awasthi"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/95da3077b8c789ca60258384b52a74474cb81d1f 38d9ef980882d04db234e662dcc1513efe56e52bThe increased focus on the ethical aspects of artificial intelligence (AI) follows the increased use in society of data-driven analyses of personal information collected in the use of digital applications for various purposes that the individual is often not aware of. The purpose of this study is to investigate how values and norms are transformed into design choices in a participatory design process of an AI-based digital coaching application for promoting health and to prevent cardiovascular diseases, where a variety of expertise and perspectives are represented.A participatory design process was conducted engaging domain professionals and potential users in co-design workshops, interviews and observations of prototype use. The design process and outcome was analyzed from a responsible design of AI systems perspective.The results include deepened understanding of the values and norms underlying health coaching applications and how an AI-based intervention could provide person-tailored support in managing conflicting norms. Further, the study contributes to increased awareness of the value of participatory design in achieving value-based design of AI systems aimed at promoting health through behavior change, and the inclusion of social norms as a design material in the process.It was concluded that the relationship between the anticipated future users and the organization(s) or enterprises developing and implementing the health-promoting application is directing which values are manifested in the application. Frontiers in Digital Health320This study investigated how values and norms are transformed into design choices in a participatory design process of an AI-based digital coaching application for promoting health and to prevent cardiovascular diseases, where a variety of expertise and perspectives are represented.Frontiers in Digital Health["Helena Lindgren", "Kristina Lindvall", "Linda Richter-Sundberg"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/38d9ef980882d04db234e662dcc1513efe56e52b 26bc4e7b732c40e01cc9b5622ee5a1f7e2dd6a06Introduction. Artificial intelligence has the potential to revolutionize the way that scholars produce work, including through the creation of literature reviews using free or low-cost systems. Despite the importance of the literature review, AI-powered tools to create literature reviews are understudied at present. Method. To address this gap, four specialized tools were assessed. The complex area of study chosen was e-reading in English and Spanish, a field where differences are evident in the two languages. Systems were prompted to write a literature review of 500-1000 words, and the resulting outputs were analysed based on the requirements for reviews of the literature. Results. Although none of the systems allowed for all the identified criteria to be addressed, Scite allowed many criteria to be met and was able to produce text in Spanish as well as in English. The remaining three systems investigated, Jenni.ai, OpenRead, and Wisio, performed less favourably. Conclusions. AI-powered systems show incredible promise, with one unstudied area being the creation of scholarly reviews of the literature. Although the kinds of research questions that might be examined in social sciences like LIS will necessarily be complex, until now little has been done to evaluate these systems.Information research. An international electronic journal00AI-powered systems show incredible promise, with one unstudied area being the creation of scholarly reviews of the literature, and Scite allowed many criteria to be met and was able to produce text in Spanish as well as in English.Information Research an international electronic journal["Heather Moulaison-Sandy", "Wilson Casta\u00f1o-Mu\u00f1oz", "Laura Ridenour", "Denice Adkins"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/26bc4e7b732c40e01cc9b5622ee5a1f7e2dd6a06 39c912aaaaf79b5f19d7861201657b86f97699eeThe increasing reliance on artificial intelligence (AI) in credit scoring has raised concerns about algorithmic bias and data privacy, necessitating robust cybersecurity risk assessment frameworks. This study investigates the role of cybersecurity risk assessment in mitigating these risks, utilizing multiple datasets, including the Home Mortgage Disclosure Act (HMDA) dataset, the Equifax Data Breach Report, the Financial Cybersecurity Incidents Database, and the MITRE ATT&CK Financial Sector Threat Intelligence Dataset. We employ statistical fairness metrics, Bayesian Probability Modeling, Markov Chain Analysis, and Monte Carlo Simulations to evaluate the extent of bias, privacy risks, and cybersecurity vulnerabilities. Findings reveal significant disparities in loan approvals, with Black applicants receiving approval rates 28% lower than White applicants (χ² = 59.83, p < 0.001), highlighting systemic bias in AI-driven credit scoring. Data privacy remains a pressing issue, as financial sector breaches affect an average of 5,069,760 individuals per incident. Insider threats pose the greatest risk, with a probability of 0.81 of leading to financial fraud. These findings underscore the urgency of integrating fairness-aware machine learning, enhancing regulatory compliance with AI governance policies, and deploying AI-driven cybersecurity tools to fortify financial AI applications against emerging threats. This research contributes to the broader discourse on ethical AI by providing a structured cybersecurity risk assessment approach to mitigate algorithmic bias and strengthen data privacy protections. Implementing these recommendations will enhance fairness, security, and transparency in AI-driven financial decision-making, ensuring compliance with evolving regulatory frameworks and fostering trust in automated credit scoring systems.Asian Journal of Research in Computer Science00Findings underscore the urgency of integrating fairness-aware machine learning, enhancing regulatory compliance with AI governance policies, and deploying AI-driven cybersecurity tools to fortify financial AI applications against emerging threats.Asian Journal of Research in Computer Science["Isaac Adinoyi Salami", "Temilade Oluwatoyin Adesokan-Imran", "Olufisayo Juliana Tiwo", "Olufunke Cynthia Metibemu", "Abayomi Titilola Olutimehin", "O. O. Olaniyi"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/39c912aaaaf79b5f19d7861201657b86f97699ee 660f5dff1e794f0743f06136af35485bc3ad5f11This study explores the hands-on experiences and perspectives of general practice staff regarding the feasibility of conducting artificial intelligence-assisted (AI-assisted) diabetic retinopathy screenings (DRS) in general practice settings.The screenings were tested in 12 general practices in the North Denmark Region and were conducted as part of daily care routines over ~4 weeks. Subsequently, 21 staff members involved in the DRS were interviewed.Thematic analysis generated four main themes: (1) Experiences with DRS in daily practice, (2) Effective DRS implementation in general practice in the future, (3) Trust and approval of AI-assisted DRS in general practice, and (4) Implications of DRS in general practice. The findings suggest that general practice staff recognise the potential for AI-assisted DRS to be integrated into their clinical workflows. However, they also emphasise the importance of addressing both practical and systemic factors to ensure successful implementation of DRS within the general practice setting.Focusing on the practical experiences and perspectives of general practice staff, this study lays the groundwork for future research aimed at optimising the implementation of AI-assisted DRS in general practice settings, while recognising that the insights gained may also inform broader primary care contexts.Frontiers in Medicine350The findings suggest that general practice staff recognise the potential for AI-assisted DRS to be integrated into their clinical workflows, however, they also emphasise the importance of addressing both practical and systemic factors to ensure successful implementation of DRS within the general practice setting.Frontiers in Medicine["Malene Krogh", "Malene Hentze", "Morten Sig Ager Jensen", "Martin Bach Jensen", "Marie Germund Nielsen", "Henrik Vorum", "Jette Kolding Kristensen"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/660f5dff1e794f0743f06136af35485bc3ad5f11 ffe0c7ab9691b91f672bb656f0dedabd9fc9d7ccThe study explores the application of artificial intelligence (AI) in addressing grammar challenges among children with learning disabilities, aiming to assess the efficacy of an AI-driven tool for personalized interventions. A sample of 100 children aged 8–12, diagnosed with learning disabilities, was recruited from special education programs. Participants were divided into an experimental group (n = 50), which used an AI-based grammar assessment tool with personalized feedback, and a control group (n = 50), which completed conventional paper-based grammar tests without feedback. The AI tool administered adaptive grammar tasks, including sentence correction and verb conjugation, and performance was evaluated over four weeks using pre-test and post-test measures. A quasi-experimental design and statistical analyses, including t-tests and repeated-measures ANOVA, revealed a significant improvement in grammar performance for the experimental group (M = 78.5, SD = 5.6) compared to the control group (M = 70.2, SD = 6.1; p < 0.001), with a large effect size (Cohen’s d = 0.84). Participants and educators reported high engagement and usability of the tool. The findings underscore AI’s potential to provide tailored learning experiences, addressing individual needs more effectively than conventional strategies. Further research should examine long-term outcomes and broader educational applications to maximize its impact.Education sciences00The findings underscore AI’s potential to provide tailored learning experiences, addressing individual needs more effectively than conventional strategies, and further research should examine long-term outcomes and broader educational applications to maximize its impact.Education Sciences["Dimitra V. Katsarou", "Evangelos Mantsos", "Soultana Papadopoulou", "M. Sofologi", "Efthymia Efthymiou", "Ilias Vasileiou", "Kalliopi Megari", "Maria Theodoratou", "G. Kougioumtzis"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/ffe0c7ab9691b91f672bb656f0dedabd9fc9d7cc b00d285c43b766e6715e102010cc16f86084cfdbTechnology-assisted emotion analysis opens new possibilities for the early identification of risk behaviors that may impact the well-being of university students, contributing to the creation of healthier, safer, and more proactive educational environments. This pilot study aimed to design and develop a technological prototype capable of analyzing students’ emotional states and anticipating potential risk situations. A mixed-methods approach was adopted, employing qualitative methods in the ideation, design, and prototyping phases and quantitative methods for laboratory validation to assess the system’s accuracy. Additionally, mapping and meta-analysis techniques were applied and integrated into the chatbot’s responses. As a result, an educational technological innovation was developed, featuring a chatbot structured with a rule-based dialogue tree, complemented by an ontology for knowledge organization and a pre-trained artificial intelligence (AI) model, enhancing the accuracy and contextualization of user interactions. This solution has the potential to benefit the educational community and is also relevant to legislative stakeholders interested in education and student well-being, institutional leaders, academic and well-being coordinators, school counselors, teachers, and students.Education sciences310An educational technological innovation was developed, featuring a chatbot structured with a rule-based dialogue tree, complemented by an ontology for knowledge organization and a pre-trained artificial intelligence (AI) model, enhancing the accuracy and contextualization of user interactions.Education Sciences["Alba Catherine Alves-Nore\u00f1a", "M. Rodr\u00edguez-Conde", "J. P. Hern\u00e1ndez-Ramos", "Jos\u00e9 William Castro-Salgado"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/b00d285c43b766e6715e102010cc16f86084cfdb a45eee704db5cf5406b49cfceccf78e4a0432516Introduction. Since its inception, the seemingly unlimited potential of artificial intelligence (AI) to alter human existence has evoked feelings of fear and amazement. Today, all sectors of industry, academia, and society are anticipating the potential changes new AI technologies are forecasted to bring and mitigate their harms. In this climate, there is a clear need to centre the complex interactions between discourse, power, and individual/institutional actors within sociotechnical systems and their material consequences. Theoretical framing. While scholars have previously made connections between discourses of fear and library and information science (LIS), there has not yet been an attempt to understand how discourses of fear may currently be shaping the field's response to AI. In this paper, we argue that focusing our critical gaze on the discourses of fear shaping the material interactions between LIS, technological artifacts, industry, and society better positions us to intervene in the predicted trajectory of AI innovation. Conclusion. We posit that cultivating discourses of refusal – which are committed to the belief that more just worlds must be possible – requires both individual and collective consideration of how fear has and continues to shape our own responses to new technologies.Information research. An international electronic journal00It is argued that focusing the critical gaze on the discourses of fear shaping the material interactions between LIS, technological artifacts, industry, and society better positions us to intervene in the predicted trajectory of AI innovation.Information Research an international electronic journal["Sarah Appedu", "Yigang Qin"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/a45eee704db5cf5406b49cfceccf78e4a0432516 9d4443a105811343bbd53d4bdc3b5e53fcfe9120This article examines PayPal's innovative implementation of artificial intelligence and behavioral analytics to revolutionize enterprise security and fraud prevention in digital payment systems. As a global payment platform processing millions of daily transactions across hundreds of markets, PayPal faced unique challenges balancing robust security with frictionless user experience. The article explores how PayPal transformed its security architecture from traditional rule-based systems to an intelligent, adaptive framework capable of evolving alongside emerging threats. The article details the multi-layered machine learning infrastructure, comprehensive data integration strategy, and sophisticated behavioral analytics that form the foundation of PayPal's approach. The article analyzes the real-time anomaly detection framework that evaluates user activities as they occur, the technical approaches to minimize false positives while maintaining strong security, and the quantifiable business outcomes achieved through this implementation. Finally, the article examines future technological directions and broader implications for enterprise security frameworks across the financial services industry, highlighting how PayPal's balanced approach has redefined what's possible in payment security.International Journal of Scientific Research in Computer Science Engineering and Information Technology00PayPal's innovative implementation of artificial intelligence and behavioral analytics to revolutionize enterprise security and fraud prevention in digital payment systems is examined, highlighting how PayPal's balanced approach has redefined what's possible in payment security.International Journal of Scientific Research in Computer Science, Engineering and Information Technology["Nagaraju Velur"]2025-03-11T00:00:00https://www.semanticscholar.org/paper/9d4443a105811343bbd53d4bdc3b5e53fcfe9120 0c99c5799419a51a488f9003bea25226f1b84e57The study "Impact of Artificial Intelligence on Customer Relationship Management" explores the transformative role of Artificial Intelligence (AI) in Customer Relationship Management (CRM) within Portugal’s banking sector. Employing a case study methodology, the research examines AI adoption across five leading banks through 50 semi-structured interviews with middle and senior management. The findings underscore AI's potential to revolutionize CRM by enhancing customer interaction, process automation, and data-driven decision-making. However, challenges persist, particularly in data agility, as many institutions lack the infrastructure necessary for efficient data utilization—an essential component for effective AI implementation. Trust in AI providers is another critical factor, with security, compliance, and data management capabilities heavily influencing vendor selection. This study proposes a diagnostic model to assess AI-CRM adoption stages, emphasizing the prerequisites for success: robust data management, organizational readiness, and employee training. Practical recommendations include fostering partnerships with trusted AI providers, ensuring data quality, and building internal capabilities. The research highlights the immediate need for banks to address these challenges to maximize the benefits of AI-driven CRM. The paper concludes with a call for future research to expand the scope of analysis, including diverse banking institutions and other sectors, to validate and enhance the applicability of the findings. By addressing these gaps, organizations can unlock AI’s full potential to deliver personalized, efficient, and secure customer relationship management.Proceedings of The International Conference on Modern Research in Management, Economics and Accounting231A diagnostic model is proposed to assess AI-CRM adoption stages, emphasizing the prerequisites for success: robust data management, organizational readiness, and employee training, and addressing gaps can unlock AI’s full potential to deliver personalized, efficient, and secure customer relationship management.Proceedings of The International Conference on Modern Research in Management, Economics and Accounting["Rui Murta", "Victor Santos"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/0c99c5799419a51a488f9003bea25226f1b84e57 eadbfad95f9308b28942ba205d5aa835b35b2097Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the manufacturing sector, such as the demand for customized products, market volatility and sustainability goals, and the integration of artificial intelligence and Big Data. This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, with no time limitation, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries. However, the adoption of modern technologies, particularly AI, can be challenging for them, due to the intrinsic structure of this type of enterprise, despite the positive effects obtained in large organizations.Applied Informatics510This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries.AI["F. Briatore", "Marco Tullio Mosca", "R. Mosca", "M. Braggio"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/eadbfad95f9308b28942ba205d5aa835b35b2097 2c9adfb0285e71fa9348e793fe37e40c2b483364Background Artificial intelligence (AI) has been deemed revolutionary in medicine; however, no AI tools have been implemented or validated in Danish general practice. General practice in Denmark has an excellent digitization system for developing and using AI. Nevertheless, there is a lack of involvement of general practitioners (GPs) in developing AI. The perspectives of GPs as end users are essential for facilitating the next stage of AI development in general practice. Objective This study aimed to identify the essential prerequisites that GPs perceive as necessary to realize the potential of AI in Danish general practice. Methods This study used semistructured interviews and vignettes among GPs to gain perspectives on the potential of AI in general practice. A total of 12 GPs interested in the potential of AI in general practice were interviewed in 2019 and 2021. The interviews were transcribed verbatim and thematic analysis was conducted to identify the dominant themes throughout the data. Results In the data analysis, four main themes were identified as essential prerequisites for GPs when considering the potential of AI in general practice: (1) AI must begin with the low-hanging fruit, (2) AI must be meaningful in the GP's work, (3) the GP-patient relationship must be maintained despite AI, and (4) AI must be a free, active, and integrated option in the electronic health record (EHR). These 4 themes suggest that the development of AI should initially focus on low-complexity tasks that do not influence patient interactions but facilitate GPs' work in a meaningful manner as an integrated part of the EHR. Examples of this include routine and administrative tasks. Conclusions The research findings outline the participating GPs' perceptions of the essential prerequisites to consider when exploring the potential applications of AI in primary care settings. We believe that these perceptions of potential prerequisites can support the initial stages of future development and assess the suitability of existing AI tools for general practice.JMIR Medical Informatics350Perceptions of potential prerequisites that GPs perceive as necessary to realize the potential of AI in Danish general practice can support the initial stages of future development and assess the suitability of existing AI tools for general practice.JMIR medical informatics["Natasha Lee J\u00f8rgensen", "C. Merrild", "M. B. Jensen", "T. Moeslund", "Kristian Kidholm", "J. Thomsen"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/2c9adfb0285e71fa9348e793fe37e40c2b483364 798f0e6f1df44418849564c70e0297e90e526671BACKGROUND Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown. OBJECTIVE This work stems from the Coordinating Health care with AI-supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program. METHODS Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that assessed patient health and provided self-management support and education. Interviews were recorded, transcribed, and thematically analyzed. RESULTS We conducted 30 interviews (mean age 65.4, SD 11.9 years; 21/30, 70% male). Four themes were identified: (1) interaction with a voice-based conversational AI program (human-like interactions, restriction to prespecified responses, trustworthiness of hospital-delivered conversational AI); (2) engagement is influenced by the personalization of content, delivery mode, and frequency (tailoring to own health context, interest in novel information regarding health, overwhelmed with large volumes of information, flexibility provided by multichannel delivery); (3) improving access to AF care and information (continuity in support, enhancing access to health-related information); (4) empowering patients to better self-manage their AF (encouraging healthy habits through frequent reminders, reassurance from rhythm-monitoring devices). CONCLUSIONS Although conversational AI was described as an engaging way to receive education and self-management support, improvements such as enhanced dialogue flexibility to allow for more naturally flowing conversations and tailoring to patient health context were also mentioned. TRIAL REGISTRATION Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://tinyurl.com/3nn7tk72. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/34470.Journal of Medical Internet Research270Improvements such as enhanced dialogue flexibility to allow for more naturally flowing conversations and tailoring to patient health context were also mentioned.Journal of medical Internet research["R. Trivedi", "Timothy Shaw", "Brodie Sheahen", "Clara K. Chow", "L. Laranjo"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/798f0e6f1df44418849564c70e0297e90e526671 2369e945a84148f9a419e4329ffcf5deed5f8d62Scientific Reports520Scientific Reports["Yuping Xiao", "Li Xiao"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/2369e945a84148f9a419e4329ffcf5deed5f8d62 2c7b10fce424155fc68229811b7c3d95856b4572This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce. This study highlights policy recommendations essential for workforce planning in the era of AI. We integrate several sources of detailed data on occupational requirements across the federal government from both centralized and decentralized human resource sources, including from the U.S. Office of Personnel Management (OPM) and various federal agencies. While our preliminary findings suggest some significant shifts in required competencies and potential vulnerability of certain roles to AI-driven changes, we provide nuanced insights that support arguments against abrupt or generic approaches to strategic human capital planning around the development of generative AI. The study aims to inform strategic workforce planning and policy development within federal agencies and demonstrates how this approach can be replicated across other large employment institutions and labor markets.00A multi-stage Retrieval-Augmented Generation system is developed to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulnerable occupations on a knowledge-by-skill-by-ability basis across the federal government workforce.["William G. Resh", "Yi Ming", "Xinyao Xia", "Michael Overton", "Gul Nisa Gurbuz", "Brandon De Breuhl"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/2c7b10fce424155fc68229811b7c3d95856b4572 2c77f69abf77787a39a59382a75b72816b6acb49AI &amp; SOCIETY560The hopes and fears within AI discourses are detailed, revealing that sentiment varies by actor group and contributing new insights about AI as an issue field shaped by the discursive work performed by heterogeneous actors.AI &amp; SOCIETY["K. Elmholdt", "Jeppe Agger Nielsen", "Christoffer Koch Florczak", "Roman Jurowetzki", "Daniel Hain"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/2c77f69abf77787a39a59382a75b72816b6acb49 849da6fad66ac4c9d9f5c829c89214fb4d7de4f3Synthese620It is argued that AI-MC poses a risk to epistemic trust being diminished in online communications on both normative and descriptive grounds and brings about the AI-MC dilemma, which creates a significant challenge for social media as an epistemic environment.Synthese["Siavosh Sahebi", "Paul Formosa"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/849da6fad66ac4c9d9f5c829c89214fb4d7de4f3 c8a3f3c5195365cd72383515eaa2595281f31ffaThe study is focused on the application of the electronic proof of concept Digital Twin (DT) model supporting Electroencephalogram (EEG) signal detection and interpretation. The EEG DT model integrates two open source tools: a first tool used for the circuit modeling and simulation of the electrodes, and a second one implementing an Artificial Intelligence (AI)-supervised algorithm to classify and adjust a noisy EEG signal. Specifically, the DT model adopts the Random Forest (RF) AI-supervised algorithm, replacing the signal filtering process and facilitating the time–domain peak and the wave shape morphology reading of a noisy detection. In order to prove the DT’s efficacy, the RF model is trained by considering the specific case of detections of EEG of patients under the effects of alcohol. The choice of the RF algorithm is justified by its good performance parameters. For the specific dataset, the RF exhibits a probabilistic error slightly lower than that of the ANN and a better cleaning action. The goal of the paper is to provide a methodology to use ‘intelligent’ electrodes supporting EEG data processing during data acquisition and to optimize the measurement’s interpretation through a data post-processing process. The proposed EEG DT could represent an alternative to the traditional denoising signal processing approaches.Electronics00The goal of the paper is to provide a methodology to use ‘intelligent’ electrodes supporting EEG data processing during data acquisition and to optimize the measurement’s interpretation through a data post-processing process.Electronics["A. Massaro"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/c8a3f3c5195365cd72383515eaa2595281f31ffa 051346a55e30ad4d67cdf191fe23bbbe9cd5a298PURPOSE OF REVIEW There is a growing interest in the applications of artificial intelligence in pediatric rheumatology. Although concerns with training datasets, ethical considerations, and the need for a major utilization of explainable artificial intelligence are still ongoing challenges, significant advancements have been made in recent years. In this review, we explore the most recent applications of artificial intelligence in pediatric rheumatology, with a special focus on machine learning models and their outcomes. RECENT FINDINGS Supervised and unsupervised machine learning models have been largely employed to identify key biomarkers, predict treatment responses, and stratify patients based on disease presentation and progression. In addition, innovative artificial intelligence driven imaging tools and noninvasive diagnostic methods have improved diagnostic accuracy and emerged as encouraging solutions for identifying inflammation and disease activity. Large language models have been utilized for patient-based questions with promising results. Nevertheless, critical examination and human oversight are still crucial in interpreting artificial intelligence's outputs. SUMMARY Artificial intelligence is revolutionizing pediatric rheumatology by improving diagnosis and disease classification, patient stratification and personalized treatment. However, we are only at the beginning, and the adventure has just begun.Current Opinion in Rheumatology380Artificial intelligence is revolutionizing pediatric rheumatology by improving diagnosis and disease classification, patient stratification and personalized treatment, with a special focus on machine learning models and their outcomes.Current opinion in rheumatology["Saverio La Bella", "Latika Gupta", "Vincenzo Venerito"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/051346a55e30ad4d67cdf191fe23bbbe9cd5a298 5029687924f27fda9129d1543fb4347632434b98Objectives and importance of the study Applications of artificial intelligence (AI) platforms and technologies to healthcare have been widely promoted as offering revolutionary improvements and efficiencies in clinical practice and health services organisation. Practical applications of AI in public health are now emerging and receiving similar attention. This paper provides an overview of the issues and examples of research that help separate the potential from the hype. Methods Selective review and analysis of cross-section of relevant literature. Results Great potential exists for the use of AI in public health practice and research. This includes immediate applications in improving health education and communication directly with the public, as well as great potential for the productive use of generative AI through chatbots and virtual assistants in health communication. AI also has applications in disease surveillance and public health science, for example in improving epidemic and pandemic early warning systems, in synthetic data generation, in sequential decision-making in uncertain conditions (reinforcement learning) and in disease risk prediction. Most published research examining these and other applications is at a fairly early stage, making it difficult to separate the probable benefits from the hype. This research is undoubtedly demonstrating great potential but also identifying challenges, for example in the quality and relevance of health information being produced by generative AI; in access, trust and use of the technology by different populations; and in the practical application of AI to support disease surveillance and public health science. There are real risks that current access and patterns of use may exacerbate existing inequities in health and that the orientation towards the personalisation of health advice may divert attention away from underlying social and economic determinants of health. Conclusions Realising the potential of AI not only requires further research and experimentation but also careful consideration of its ethical implications and thoughtful regulation. This will ensure that advances in these technologies serve the best interests of individuals and communities worldwide and don’t exacerbate existing health inequalities.Public Health Research & Practice270Realising the potential of AI not only requires further research and experimentation but also careful consideration of its ethical implications and thoughtful regulation to ensure that advances in these technologies serve the best interests of individuals and communities worldwide and don’t exacerbate existing health inequalities.Public Health Research and Practice["Don Nutbeam", "Andrew J. Milat"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/5029687924f27fda9129d1543fb4347632434b98 5b9c16b3f3698fd2a7fc7162c4589e83c9bcf63eWhile artificial intelligence's (AI's) potential role in enhancing diagnostic accuracy and personalising treatment is well-recognised, its application in evaluating physicians raises critical ethical concerns as well. The paper examines the impact of AI on the 'comparative abilities' exception to informed consent, which currently exempts physicians from disclosing information about the performance of other providers. With AI's ability to generate granular, accurate comparisons of physician metrics, this exception will be challenged, potentially empowering patients to make more informed decisions. However, AI's use in disclosing physician success rates may exacerbate healthcare disparities, as wealthier patients may have more access to higher-skilled providers. Policymakers and ethicists must proactively address these concerns to ensure equitable access to care as AI technologies advance.Journal of Medical Ethics150The paper examines the impact of AI on the 'comparative abilities' exception to informed consent, which currently exempts physicians from disclosing information about the performance of other providers, and its ability to generate granular, accurate comparisons of physician metrics.Journal of medical ethics["Jacob M Appel"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/5b9c16b3f3698fd2a7fc7162c4589e83c9bcf63e 9ceddcefbb16fb3946abde1009a8d7f7f95e1475Digital Society290Public policies are proposed in order to support the convergence of gender mainstreaming strategy and the forthcoming AI regulation.Digital Society["Maria Sideri", "S. Gritzalis"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/9ceddcefbb16fb3946abde1009a8d7f7f95e1475 cee7a6ad22e907fe4e971902267a49b7cb13ed87 This study aims to explore current approaches, challenges and practical lessons in auditing artificial intelligence (AI) systems for bias, focusing on legal compliance audits in the USA and the European Union (EU). This emphasizes the need for standardized methodologies to ensure trustworthy AI systems that align with ethical and regulatory expectations. A qualitative analysis compared bias audit practices, including US bias audit report summaries under New York City’s Local Law 144 and conformity assessments (CAs) required by the EU AI Act. Data was gathered from publicly available reports and compliance guidelines to identify key challenges and lessons. The findings revealed that AI systems are susceptible to various biases stemming from data, algorithms and human oversight. Although valuable, legal compliance audits lack standardization, leading to inconsistent reporting practices. The EU’s risk-based CA approach offers a comprehensive framework; however, its effectiveness depends on developing practical standards and consistent application. This study is limited by the early implementation stage of regulatory frameworks, particularly the EU AI Act, and restricted access to comprehensive audit reports. A geographic focus on US and EU jurisdictions may limit the generalizability of the findings. Data availability constraints and the lack of standardized reporting frameworks affect the comparative analysis. Future research should focus on longitudinal studies of audit effectiveness, the development of standardized methodologies for intersectional bias assessment and the investigation of automated audit tools that can adapt to emerging AI technologies while maintaining practical feasibility across different organizational contexts. This research underscores the necessity of adopting socio-technical perspectives and standardized methodologies in AI auditing. It provides actionable insights for firms, regulators and auditors into implementing robust governance and risk assessment practices to mitigate AI biases. Effective AI bias auditing practices ensure algorithmic fairness and prevent discriminatory outcomes in critical domains like employment, health care and financial services. The findings emphasize the need for enhanced stakeholder engagement and community representation in audit processes. Implementing robust auditing frameworks can help close socioeconomic gaps by identifying and mitigating biases disproportionately affecting marginalized groups. This research contributes to developing equitable AI systems that respect diversity and promote social justice while maintaining technological advancement. This study contributes to the discourse on AI governance by comparing two regulatory approaches, bias audits and CAs and offers practical lessons from current implementation. It highlights the critical role of standardization in advancing trustworthy and ethical AI systems in the finance and accounting contexts. Review of Accounting and Finance500The need for standardized methodologies to ensure trustworthy AI systems that align with ethical and regulatory expectations is emphasized, focusing on legal compliance audits in the USA and the European Union, and the critical role of standardization in advancing trustworthy and ethical AI systems in the finance and accounting contexts.Review of Accounting and Finance["Sabina Lacmanovi\u0107", "M. \u0160kare"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/cee7a6ad22e907fe4e971902267a49b7cb13ed87 1761d735fdd566dd3bef93de208f1eda1f7f80d7Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering significant potential to address workforce challenges and improve patient outcomes. This perspective article presents a framework for responsible AI innovation, emphasising ethical governance, responsible leadership and a commitment to human-centred AI. It provides guidance for healthcare organisations to position AI as a strategic enabler, augmenting the health and care workforce and fostering sustainable, patient-centred advancements in healthcare.International Journal of Health Planning and Management120A framework for responsible AI innovation is presented, emphasising ethical governance, responsible leadership and a commitment to human-centred AI for healthcare organisations to position AI as a strategic enabler, augmenting the health and care workforce and fostering sustainable, patient-centred advancements in healthcare.The International journal of health planning and management["A. Sriharan", "Ellen Kuhlmann", "Tiago Correia", "F. Tahzib", "K. Czabanowska", "Marius-Ionu\u021b Ungureanu", "B. Kumar"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/1761d735fdd566dd3bef93de208f1eda1f7f80d7 d61c1ee170082e187d8d5e3121b5c11b36ae6bf7BackgroundIn cases of surgical necrotizing enterocolitis (NEC), the choice between laparotomy (LAP) or comfort care (CC) presents a complex, ethical dilemma. A behavioral artificial intelligence technology (BAIT) decision aid was trained on expert knowledge, providing an output as "x percentage of experts advise laparotomy for this patient." This retrospective study aims to compare this output to clinical practice.DesignVariables required for the decision aid were collected of preterm patients with NEC for whom the decision of LAP or CC had been made. These data were used in 2 BAIT model versions: one center specific, built on the input of experts from the same center as the patients, and a nationwide version, incorporating the input of additional experts. The Mann-Whitney U test compared the model output for the 2 groups (LAP/CC). In addition, model output was classified as advice for LAP or CC, after which the chi-square test assessed correspondence with observed decisions.ResultsForty patients were included in the study (20 LAP). Model output (x percentage of experts advising LAP) was higher in the LAP group than in the CC group (median 95.1% v. 46.1% in the center-specific version and 97.3% v. 67.5% in the nationwide version, both P < 0.001). With an accuracy of 85.0% by the center-specific and 80.0% by the nationwide version, both showed significant correspondence with observed decisions (P < 0.001).LimitationsWe are merely examining a proof of concept of the decision aid using a small number of participants from 1 center.ConclusionsThis retrospective study demonstrates that treatment choices by artificial intelligence align with clinical practice in at least 80% of cases.ImplicationsFollowing prospective validation and ongoing refinements, the decision aid may offer valuable support to practitioners in future NEC cases.HighlightsThis study assesses the output of behavioral artificial intelligence technology in deciding between laparotomy and comfort care in surgical necrotizing enterocolitis.The model output aligns with clinical practice in at least 80% of patient cases.Following prospective validation, the decision aid may offer valuable support to physicians working at the neonatal intensive care unit.Medical decision making220This retrospective study demonstrates that treatment choices by artificial intelligence align with clinical practice in at least 80% of cases of surgical necrotizing enterocolitis.Medical decision making : an international journal of the Society for Medical Decision Making["Rosa Verhoeven", "Stella Mulia", "Elisabeth M W Kooi", "Jan B. F. Hulscher"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/d61c1ee170082e187d8d5e3121b5c11b36ae6bf7 6a4fa24aacd54bf455a40bbd76824cdf095c3ec9The advent of mobile health (mHealth) applications has fundamentally transformed the healthcare landscape, particularly within the field of ophthalmology, by providing unprecedented opportunities for remote diagnosis, monitoring, and treatment. Ocular surface diseases, including dry eye disease (DED), are the most common eye diseases that can be detected by mHealth applications. However, most remote artificial intelligence (AI) systems for ocular surface disease detection are predominantly based on self‐reported data collected through interviews, which lack the rigor of clinical evidence. These constraints underscore the need to develop robust, evidence‐based AI frameworks that incorporate objective health indicators to improve the reliability and clinical utility of remote health applications.Two novel deep learning (DL) models, YoloTR and YoloMBTR, were developed to detect key ocular surface indicators (OSIs), including tear meniscus height (TMH), non‐invasive Keratograph break‐up time (NIKBUT), ocular redness, lipid layer, and trichiasis. Additionally, back propagation neural networks (BPNN) and universal network for image segmentation (U‐Net) were employed for image classification and segmentation of meibomian gland images to predict Demodex mite infections. These models were trained on a large dataset from high‐resolution devices, including Keratograph 5M and various mobile platforms (Huawei, Apple, and Xiaomi).The proposed DL models of YoloMBTR and YoloTR outperformed baseline you only look once (YOLO) models (Yolov5n, Yolov6n, and Yolov8n) across multiple performance metrics, including test average precision (AP), validation AP, and overall accuracy. These two models also exhibit superior performance compared to machine plug‐in models in KG5M when benchmarked against the gold standard. Using Python's Matplotlib for visualization and SPSS for statistical analysis, this study introduces an innovative proof‐of‐concept framework leveraging quantitative AI analysis to address critical challenges in ophthalmology. By integrating advanced DL models, the framework offers a robust approach for detecting and quantifying OSIs with a high degree of precision. This methodological advancement bridges the gap between AI‐driven diagnostics and clinical ophthalmology by translating complex ocular data into actionable insights.Integrating AI with clinical laboratory data holds significant potential for advancing mobile eye health (MeHealth), particularly in detecting OSIs. This study aims to explore this integration, focusing on improving diagnostic accuracy and accessibility. This study demonstrates the potential of AI‐driven tools in ophthalmic diagnostics, paving the way for reliable, evidence‐based solutions in remote patient monitoring and continuous care. The results contribute to the foundation of AI‐powered health systems that can extend beyond ophthalmology, improving healthcare accessibility and patient outcomes across various domains.iLABMED170This study demonstrates the potential of AI‐driven tools in ophthalmic diagnostics, paving the way for reliable, evidence‐based solutions in remote patient monitoring and continuous care and introduces an innovative proof‐of‐concept framework leveraging quantitative AI analysis to address critical challenges in ophthalmology.iLABMED["M. Wang", "Yi Pan", "Xudong Jiang", "Zhiyuan Lin", "Haoyang Liu", "Yunxiao Liu", "Jiazheng Cui", "Jiaxiang Tan", "Chengqi Gong", "Guanghui Hou", "Xiaoxiao Fang", "Yang Yu", "Moawiya Haddad", "Marion Schindler", "Jos\u00e9 Lopes Camilo Da Costa Alves", "Junbin Fang", "Xiangrong Yu", "Kelvin Kam-Lung Chong"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/6a4fa24aacd54bf455a40bbd76824cdf095c3ec9 987ae7a6c3246ff2012e1fa376d31c454c6c39edPreterm neonates face significant neurological risks due to incomplete brain development at birth. The third trimester is critical for brain maturation, and premature birth disrupts essential developmental processes, leading to long-term cognitive, motor, and sensory impairments. Key vulnerabilities include cortical underdevelopment, white matter damage, and immature neurotransmission, contributing to neurodevelopmental disorders such as cerebral palsy, attention deficits, and learning difficulties. While advances in Neonatal Intensive Care Units (NICUs) have improved survival rates, early detection and continuous monitoring of complications remain challenging. The integration of Internet of Things (IoT) technology in neonatal care presents a transformative approach, enabling real-time physiological monitoring, predictive analytics, and automated alerts for timely interventions. IoT-driven neonatal monitoring systems enhance clinical decision-making, reduce caregiver burden, and improve patient outcomes. In parallel, Artificial Intelligence (AI) is revolutionizing neonatal healthcare by processing multimodal data, including clinical records, physiological signals, and imaging to provide real-time insights, predictive diagnostics, and risk assessments. Machine learning (ML) and deep learning (DL) techniques aid in disease prediction, anomaly detection, and precision diagnostics, significantly enhancing neonatal monitoring. However, challenges such as AI interpretability, data security, and integration into clinical workflows must be addressed to ensure adoption. Explainable-AI (XAI) tools such as SHAP, LIME, and Grad-CAM are crucial in making AI-driven decisions more transparent and actionable. The future of neonatal AI lies in developing multimodal frameworks that integrate physiological signals and facial, vocal, and motion data for comprehensive neonatal health monitoring. Addressing the technical and ethical challenges associated with AI and IoT adoption will be critical to fully realizing their potential in neonatal care and improving outcomes for preterm infants.Journal of Multiscale Neuroscience00Artificial Intelligence (AI) is revolutionizing neonatal healthcare by processing multimodal data, including clinical records, physiological signals, and imaging to provide real-time insights, predictive diagnostics, and risk assessments, and significantly enhancing neonatal monitoring.Journal of Multiscale Neuroscience["Syed Taimoor Hussain Shah", "Syed Adil Hussain Shah", "Konstantinos Panagiotopoulos", "Janet Pigueiras-del-Real", "Kainat Qayyum", "Syed Baqir Hussain Shah", "S. A. Qureshi", "Angelo di Terlizzi", "Giacomo Di Benedetto", "M. Deriu"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/987ae7a6c3246ff2012e1fa376d31c454c6c39ed 9c5093649486daa6f28cba2b1b59b1cd79f79f56Sports cardiology focuses on athletes' cardiovascular health, yet sudden cardiac death remains a significant concern despite preventative measures. Prolonged physical activity leads to notable cardiovascular adaptations, known as the athlete's heart, which can resemble certain pathological conditions, complicating accurate diagnoses and potentially leading to serious consequences such as unnecessary exclusion from sports or missed treatment opportunities. Wearable devices, including smartwatches and smart glasses, have become prevalent for monitoring health metrics, offering potential clinical applications for sports cardiologists. These gadgets are capable of spotting exercise-induced arrhythmias, uncovering hidden heart problems, and offering crucial information for training and recovery, to minimize exercise-related cardiac incidents and enhance heart health care. However, concerns about data accuracy and the actionable value of the obtained information persist. A major challenge lies in the integration of artificial intelligence with wearables, research gaps remain regarding their ability to provide real-time, reliable, and clinically relevant insights. Combining artificial intelligence with wearable devices can improve how data is managed and used in sports cardiology. Artificial intelligence, particularly machine learning, can classify, predict, and draw inferences from the data collected by wearables, revolutionizing patient data usage. Despite artificial intelligence's proven effectiveness in managing chronic conditions, the limited research on its application in sports cardiology, particularly regarding wearables, creates a critical gap that needs to be addressed. This review examines commercially available wearables and their applications in sports cardiology, exploring how artificial intelligence can be integrated into wearable technology to advance the field.ACS Applied Materials and Interfaces1410A review of commercially available wearables and their applications in sports cardiology explores how artificial intelligence can be integrated into wearable technology to advance the field, and identifies a critical gap that needs to be addressed.ACS applied materials & interfaces["Xiao Zheng", "Zheng Liu", "Jianyu Liu", "Caifeng Hu", "Yanxin Du", "Juncheng Li", "Zhongjin Pan", "Ke Ding"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/9c5093649486daa6f28cba2b1b59b1cd79f79f56 cd4bdc409f858c75b4d35409dd67e29830ff532aEpilepsy & Behavior140ChatGPT shows potential as a supplementary diagnostic tool but requires human oversight due to reduced specificity and limitations in nuanced clinical judgment, and further development with diverse datasets and targeted training is necessary to improve AI performance.Epilepsy & Behavior["F. Brigo", "S. Broggi", "Gionata Strigaro", "Sasha Olivo", "Valentina Tommasini", "Magdalena Massar", "G. Turcato", "A. Zaboli"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/cd4bdc409f858c75b4d35409dd67e29830ff532a 358e97e4b174eeb0f30d5b8d7a5fcaa417e8c405Aerosol Science and Engineering370Aerosol Science and Engineering["Jinyao Shi", "Hao Yuan", "Jie Guan", "Zhanchen Wang", "Liang Shang"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/358e97e4b174eeb0f30d5b8d7a5fcaa417e8c405 622d13260c0dd3aa530c9fba1f297a34ec60ee9dIn personalized technology and psychological research, precisely detecting demographic features and personality traits from digital interactions becomes ever more important. This work investigates implicit categorization, inferring personality and gender variables directly from linguistic patterns in Telegram conversation data, while conventional personality prediction techniques mostly depend on explicitly self-reported labels. We refine a Transformer-based language model (RoBERTa) to capture complex linguistic cues indicative of personality traits and gender differences using a dataset comprising 138,866 messages from 1,602 users annotated with MBTI types and 195,016 messages from 2,598 users annotated with gender. Confidence levels help to greatly raise model accuracy to 86.16\%, hence proving RoBERTa's capacity to consistently identify implicit personality types from conversational text data. Our results highlight the usefulness of Transformer topologies for implicit personality and gender classification, hence stressing their efficiency and stressing important trade-offs between accuracy and coverage in realistic conversational environments. With regard to gender classification, the model obtained an accuracy of 74.4\%, therefore capturing gender-specific language patterns. Personality dimension analysis showed that people with introverted and intuitive preferences are especially more active in text-based interactions. This study emphasizes practical issues in balancing accuracy and data coverage as Transformer-based models show their efficiency in implicit personality and gender prediction tasks from conversational texts.200A Transformer-based language model (RoBERTa) is refined to capture complex linguistic cues indicative of personality traits and gender differences using a dataset comprising 138,866 messages from 1,602 users annotated with MBTI types and 195,016 messages from 2,598 users annotated with gender, proving RoBERTa's capacity to consistently identify implicit personality types from conversational text data.["Kourosh Shahnazari", "Seyed Moein Ayyoubzadeh"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/622d13260c0dd3aa530c9fba1f297a34ec60ee9d 9485d37761dafeab1998271b96cfe5a2d412319dApplied Ergonomics590This study investigates issues through a survey of 60 emergency responders, identifying key HF/E concerns such as fatigue, cognitive overload, and emotional stress, and proposes innovative artificial intelligence and smart technology-driven solutions to address these challenges.Applied ergonomics["Junho Park", "Ava Rathenberg", "Jenn Panko", "Zach McGhie", "Changwon Son"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/9485d37761dafeab1998271b96cfe5a2d412319d ba22b7b84f7c348f3a7cb2464fee480aee5e1687Current Sleep Medicine Reports660Current Sleep Medicine Reports["Arjun N. Bhatt", "Sohawm Sengupta", "Ali Abolhassani", "David Brower", "Christy Forehand", "Kelli Keats", "Younghoon Kwon", "William J Healy"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/ba22b7b84f7c348f3a7cb2464fee480aee5e1687 c613df7fa51a1ba013db7b40c2a257b37cca5fbeEvidence-Based Nursing60Evidence-based nursing["Ahmad A. Abujaber", "A. Nashwan"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/c613df7fa51a1ba013db7b40c2a257b37cca5fbe 6882ecc1456de16a4567d7a073eafe1ea100d552JAMIA Journal of the American Medical Informatics Association40Journal of the American Medical Informatics Association : JAMIA["Ethan Layne", "Francesco Cei", "Giovanni E. Cacciamani"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/6882ecc1456de16a4567d7a073eafe1ea100d552 4cbba941ce5682233eccc7d6ff937c2d55184910Turkish Journal of Surgery00Turkish Journal of Surgery["Muhammer Ergen\u00e7"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/4cbba941ce5682233eccc7d6ff937c2d55184910 4f0eab63a0a9f7de3e7e74b6db3bb8cdd90ee967Computers in Biology and Medicine1140This paper provides a comprehensive review of foundation models in healthcare, highlighting their transformative potential in areas such as diagnostics, personalized treatment, and operational efficiency and the need for extensive computational resources.Computers in biology and medicine["Mohan Timilsina", "Samuele Buosi", "Muhammad Asif Razzaq", "Rafiqul Haque", "Conor Judge", "Edward Curry"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/4f0eab63a0a9f7de3e7e74b6db3bb8cdd90ee967 96cdc66e29cb50167e349f31780d6ca659ee1415In the context of global economic digital transformation and technological innovation, the application of AI Technology has a profound impact on corporate innovation and development. Existing research has primarily focused on the direct effect of AI Technology on Corporate Innovation Performance, while there is limited exploration of its interaction with organizational learning mechanisms. Based on the Dynamic Capabilities Theory, this study constructs a framework of “Technology—Individual Learning Capability—Team Learning Capability—Innovation Performance”, analyzing how AI Technology enhances learning capabilities to drive improvements in innovation performance and explores the moderating role of Organizational Learning Capability. Through empirical analysis of data from Specialized, Refined, Unique, and Innovative Enterprises in China, the study finds that AI Technology significantly enhances Corporate Innovation Performance, with Organizational Learning Capability playing a critical moderating role. Additionally, heterogeneity analysis indicates that factors such as production factors, industry characteristics, and firm size significantly influence the effectiveness of AI Technology in enhancing innovation performance. This research reveals the pathway through which AI Technology optimizes organizational learning mechanisms to improve innovation performance, offering both theoretical support and practical guidance for corporate strategic decision-making.Sustainability760The pathway through which AI Technology optimizes organizational learning mechanisms to improve innovation performance is revealed, offering both theoretical support and practical guidance for corporate strategic decision-making.Sustainability["Shumei Han", "Di Zhang", "Hongfeng Zhang", "Shuaijun Lin"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/96cdc66e29cb50167e349f31780d6ca659ee1415 0ac01f50fd2a9a8ad0d48436d7ec333c2482dcdcINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS00INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS["Yuvaraj J Patil"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/0ac01f50fd2a9a8ad0d48436d7ec333c2482dcdc a71454c9f795c7333eadb4146decfe9ce75688e4BackgroundInflammatory bowel disease (IBD) research is a dynamic field. However, the growing volume of electronic health records (EHRs) and research data presents significant challenges. Traditional methods for structuring unstructured medical records are labour-intensive and lack scalability. Large language models (LLMs) may present a solution, yet their usefulness in data standardisation in the context of IBD remains unknown. ObjectiveTo evaluate the use of LLMs in structuring free-text histology and radiology reports from IBD patients, compare their performance to manual clinician curation, and assess the usefulness of fine-tuning and retrieval-augmented generation (RAG). DesignWe developed an IBD-specialised LLM-based framework utilising structured prompt engineering and fine-tuning. Reports were manually curated and processed using various LLMs. Performance was assessed and RAG was used to enhance model responses with clinical guidelines from European Crohns and Colitis Organisation (ECCO) and the European Society for Paediatric Gastroenterology Hepatology and Nutrition (ESPGHAN). ResultsOverall, Llama 3.3 achieved the highest F1 for histology and imaging (1 {+/-} 0 and 0.85 {+/-} 0.29, respectively) in extracting findings and anatomical regions, surpassing other models in structured data generation. Fine-tuning improved the performance of the smaller Llama 3.1 8B model for imaging reports (0.7 {+/-} 0.46 vs 0.82 {+/-} 0.35), enabling better extraction with reduced computational requirements. ConclusionOur findings demonstrate the feasibility of LLM-based automated structuring of IBD-related medical records. Unstructured data from free text reports can be reliably converted to standardised ontologies with location, severity, and qualifiers. These advancements enable scalable, privacy-compliant AI-driven solutions for data standardisation. VISUAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/25323569v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@121708aorg.highwire.dtl.DTLVardef@639467org.highwire.dtl.DTLVardef@1a40cf5org.highwire.dtl.DTLVardef@14ec870_HPS_FORMAT_FIGEXP M_FIG C_FIG Key MessagesO_ST_ABSWhat is already known on this topicC_ST_ABSTraditional methods for structuring unstructured medical records for research are labour-intensive and lack scalability. IBD patients generate vast quantities of longitudinal medical data due to the chronicity of disease. Large language models (LLMs) are well-positioned for data extraction and standardisation purposes. What this study addsThis study demonstrates that Llama 3.3-70B and fine-tuned smaller models (Llama 3.1 8B) can accurately structure IBD-related histology and radiology reports. Additionally, retrieval-augmented generation (RAG) enhances clinical interpretability by incorporating guideline-based context. How this study might affect research, practice or policyThe use of LLMs in structuring EHR data can significantly accelerate IBD research, improve data standardisation, and facilitate privacy-compliant AI-driven solutions for clinical decision support and policy development.medRxiv360The findings demonstrate the feasibility of LLM-based automated structuring of IBD-related medical records, and enable scalable, privacy-compliant AI-driven solutions for data standardisation.["Alex Z Kadhim", "Zachary Green", "Iman Nazari", "Jonathan Baker", "Michael George", "Ashley I. Heinson", "Matt Stammers", "C. M. Kipps", "Mark Beattie", "James J. Ashton", "Sarah Ennis"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/a71454c9f795c7333eadb4146decfe9ce75688e4 802ff2f1a84d92e0fc61ac305fed2e0962847a35<jats:p>-</jats:p>PANACEA JOURNAL OF MEDICAL SCIENCES00Panacea Journal of Medical Sciences["Kunal"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/802ff2f1a84d92e0fc61ac305fed2e0962847a35 da9629c66f4dc1858aaeae6048bd250062cee0101st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024)2401st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024)["Talha Ahmed Khan", "Syed Mubashir Ali", "Kanwar Mansoor Ali", "Asif Aziz", "Sadique Ahmad", "Azeem Anwar", "Sharfuddin Ahmed Khan"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/da9629c66f4dc1858aaeae6048bd250062cee010 c261e63f3fe30afe12459171b620464508e95630British Journal of Occupational Therapy50British Journal of Occupational Therapy["Michael Rowe", "Gillian Ward"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/c261e63f3fe30afe12459171b620464508e95630 0a375921d05631878252f11a06decacad9edbb74The responsibilities of Finance Business Partners (FBPs) are shifting as a result of the revolution that has been brought by the implementation of Artificial Intelligence (AI) and Business Intelligence (BI) systems in the past few years. The role of the FBPs has been transformed in the process of moving from conventional qualitative analysis to a more strategic role that can facilitate the automation of many tasks, enhance the forecasting functions and offer real-time decision-making, allowing the FBPs to focus on value added work such as strategy, implementation and management, and integration with other areas of the organization. However, the integration of AI and BI is not without some challenges, which include resistance to change, data security risks, and a skills shortage. The importance of increasing the technical skills of the FBPs, the need for a strong partnership between the finance and operational teams and the need for strong ethical governance frameworks to guide the use of AI are also discussed. This paper also includes real world examples of how organizations are employing AI and BI to enhance their forecasting, improve the effectiveness of their financial processes and, most importantly, achieve their strategic objectives. Therefore, the results of this research support the concept that FBPs can be useful peers in relation to AI and BI if they adopt the technological tools and overcome the barriers to their usage. As a result of the findings, several practical recommendations are provided for FBPs to succeed in this evolving environment.Journal of Economics, Finance and Accounting Studies100The results of this research support the concept that FBPs can be useful peers in relation to AI and BI if they adopt the technological tools and overcome the barriers to their usage.Journal of Economics, Finance and Accounting Studies["Nadson Lucio", "Sousa Carvalho"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/0a375921d05631878252f11a06decacad9edbb74 34a16ff6425c52e2a9b3f4ce49a43bca531ee74fObjectives: This study examines how technology startups in emerging markets leverage artificial intelligence (AI) and business intelligence (BI) capabilities to enhance innovation performance in support of UN Sustainable Development Goal 9 (Industry, Innovation and Infrastructure).   Theoretical Framework: The research integrates Resource-Based View, Dynamic Capabilities, and Knowledge-Based View theories to understand how organizations build and maintain innovation capabilities through technological integration.   Method: Using structural equation modeling (PLS-SEM), we analyzed data from 357 Algerian technology startups, examining the mediating role of AI-enabled business intelligence in developing sustainable innovation capabilities.   Results and Discussion: AI and BI integration positively influences innovation outcomes (β = 0.227, p < 0.05), with BI serving as a crucial mediator. Success depends on three key factors: systematic integration of AI-BI technologies, robust knowledge management practices, and market-specific adaptation strategies.   Research Implications: The findings provide practical guidance for technology startups in emerging markets seeking to enhance their competitive advantage through AI-enabled innovation, while supporting SDG 9's target of upgrading technological capabilities.   Originality/Value: This research extends innovation capability theory by incorporating the role of emerging technologies in resource-constrained environments, offering novel insights into how startups can leverage AI and BI for sustainable innovation in developing economies.Journal of Lifestyle and SDGs Review260This research extends innovation capability theory by incorporating the role of emerging technologies in resource-constrained environments, offering novel insights into how startups can leverage AI and BI for sustainable innovation in developing economies.Journal of Lifestyle and SDGs Review["Khaled Mili", "Ismail Ben Gana", "Rahma Zighed", "Mekimah Sabri"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/34a16ff6425c52e2a9b3f4ce49a43bca531ee74f 52a925f0371960b8985c12156c5a37f0bd431ddcMuch research has focused on advancing techniques for explainable artificial intelligence (XAI) to improve the utility of AI recommendations. However, the metacognitive processes involved in interacting with AI explanations have not been fully explored. In this study, we examine the effects of AI explanations on human decisions from the perspective of cognitive mechanisms that evaluate the correctness of AI recommendations. To accomplish this, we conducted a large-scale, between-subject experiment (N=4,302) on Amazon Mechanical Turk, during which each participant was asked to classify a radiology report as describing a normal or abnormal finding. The participants were randomly assigned into three different groups: a) without accompanying AI input (control group,) b) with AI prediction only, and c) with AI prediction and AI explanation. Our results show that AI explanations improved the overall task performance. We hypothesize that explanations help decision-makers better evaluate their intuitions about their decisions--a process known as self-monitoring--and, as such, overcome their cognitive limitations and compensate for machine prediction errors. Additionally, our results show that explanations are more effective when AI prediction confidences are high or users self-confidence is low. We conclude this paper by discussing the theoretical and practical implications of our findings.medRxiv1120It is hypothesized that explanations help decision-makers better evaluate their intuitions about their decisions--a process known as self-monitoring--and, as such, overcome their cognitive limitations and compensate for machine prediction errors.["Y. Genc", "M. E. Ahsen", "Z. Zhang"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/52a925f0371960b8985c12156c5a37f0bd431ddc da9a0d7084b2b978dd84fbec5dc1a65929e4018fElectronic Markets740The authors provide researchers and practitioners with insights from interviews along with design knowledge for AI conformity assessments, which may prove particularly valuable in light of upcoming regulations such as the European Union AI Act.Electronic Markets["Moritz von Zahn", "Jan Zacharias", "Maximilian Lowin", "Johannes Chen", "Oliver Hinz"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/da9a0d7084b2b978dd84fbec5dc1a65929e4018f f8543d1ea23d976348d583bb82c49c523021d7c0Abstract: The software development landscape has undergone a significant transformation, driven by the rapid evolution of cloud computing and the increasing adoption of DevOps practices. In this context, the integration of Artificial Intelligence into cloud-based CI/CD (Continuous Integration/Continuous Deployment) pipelines has the potential to revolutionize the way software is developed, deployed, and maintained. This research paper explores the impact of AI-driven workflows on enhancing efficiency and productivity in the CI/CD process. The paper examines the challenges and opportunities presented by the intersection of AI and DevOps, drawing insights from real-world case studies and industry trends. It investigates how AI technologies, such as machine learning and natural language processing, can optimize various stages of the software development lifecycle, including requirements engineering, code generation, testing, and deployment. Furthermore, the paper discusses the ethical implications and potential risks associated with the integration of AI in software development, addressing concerns such as bias, transparency, and the need for human oversight. By analyzing the current state of AI-driven cloud workflows and their impact on CI/CD pipelines, this research paper aims to provide valuable insights for software development teams, DevOps practitioners, and decision-makers. The findings of this study suggest that the strategic integration of AI-powered technologies can enhance the efficiency, agility, and reliability of software delivery, ultimately enabling organizations to stay competitive in the rapidly evolving digital landscape. [1]International Journal of Latest Technology in Engineering Management &amp; Applied Science10The findings of this study suggest that the strategic integration of AI-powered technologies can enhance the efficiency, agility, and reliability of software delivery, ultimately enabling organizations to stay competitive in the rapidly evolving digital landscape.International Journal of Latest Technology in Engineering Management &amp; Applied Science["Dhruvitkumar V. Talati"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/f8543d1ea23d976348d583bb82c49c523021d7c0 35c639b75769f39f82752c86fcc368963f680548 Based on the technology affordance theory, this study aims to explore the relationship among artificial intelligence (AI) orientation, global value chain collaboration (collaboration breadth and collaboration depth) and the international performance of entrepreneurial firms while considering the contingency of board international experience. This study’s sample was selected using the Sci-Tech Innovation Board (STAR Market) of the Shanghai Stock Exchange in China from 2019 to 2023, from which 1,928 final usable observations from 570 entrepreneurial firms over five years were obtained. The empirical results indicate that AI orientation positively affects both collaboration breadth and collaboration depth of the global value chain. In addition, both collaboration breadth and collaboration depth mediate the relationship between AI orientation and the international performance of entrepreneurial firms, and board international experience enhances the positive effect of AI orientation on collaboration breadth. This study contributes to the literature on AI orientation, global value chain and board international experience by introducing the technology affordance theory into the international performance of entrepreneurial firms, and it provides managerial implications for entrepreneurial firms and government policymaking. Chinese Management Studies560The empirical results indicate that AI orientation positively affects both collaboration breadth and collaboration depth of the global value chain, and both collaboration breadth and collaboration depth mediate the relationship between AI orientation and the international performance of entrepreneurial firms.Chinese Management Studies["Yun Huang", "Xinru Sun", "Qihui Fan"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/35c639b75769f39f82752c86fcc368963f680548 33c5369e4a5d5834d32914840438f1729fce09d1Artificial intelligence integration in US insurance underwriting is revolutionizing the way risk is assessed, costs are made efficient and fraud is detected, such use raises many ethical and economic tradeoffs. A key problem of AI powered actuarial models is that speed and accuracy in the underwriting is enhanced, biases within the algorithms, transparency of the algorithms, trust of the consumer and regulatory oversight are issues that can still prevent the advancement of AI in underwriting. this research study uses a quantitative research approach in studying the impact of AI underwriting models through using survey data and data analysis as well as real life case studies in evaluating gains in efficiency, ethical risks and regulatory consideration. Findings indicate that AI can dramatically lower the cost of underwriting and enhance the rate of detecting fraud while consumers remain very skeptical about fully automated underwritten models, looking most positively upon hybrid AI and human models. Important factors that affect adoption of AI in underwriting are regulatory oversight and mitigation of bias. The study argues that the existence of explainable AI frameworks, the presence of the data governance and compliance measures are all necessary to strike a balance between efficiency and fairness. Overcoming these challenges, AI-powered underwriting can contribute to the country’s economic growth, improve consumer trust and be aligned with the country’s changing U.S. regulatory frameworks. These insights can benefit insurers, policymakers and regulatory bodies in responsible development of fair, efficient and transparent AI underwriting models for the U.S. insurance industry.Frontline Marketing, Management and Economics Journal00Findings indicate that AI can dramatically lower the cost of underwriting and enhance the rate of detecting fraud while consumers remain very skeptical about fully automated underwritten models, looking most positively upon hybrid AI and human models.Frontline Marketing, Management and Economics Journal["Araf Nishan", "Rokeya Begum Ankhi", "Muhammad Rafiuddin Haque", "Md Imran Hossain", "Siddikur Rahman"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/33c5369e4a5d5834d32914840438f1729fce09d1 12888182caf7975e748c3756be4039bf439242fbAbstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance human productivity, AI-driven sustainability practices for energy and resource efficiency, and predictive maintenance models that reduce downtime. Addressing ethical challenges, the Article highlights the importance of data privacy, unbiased algorithms, and the environmental responsibility of intelligent automation. Through case studies across manufacturing, healthcare, and energy sectors, readers gain insights into real-world applications of AI and ML, showcasing their impact on efficiency, quality, and safety. The Article concludes with future directions, emphasizing emerging technologies like quantum computing, human-machine synergy, and the sustainable vision for Industry 5.0, where intelligent automation not only drives innovation but also aligns with ethical and social values for a resilient industrial future. Keywords: Industry 5.0, intelligent automation, AI, machine learning, sustainability, human- machine collaboration, cobots, predictive maintenance, quality control, ethical AI, data privacy, Industry 4.0, computer vision, natural language processing, energy efficiency, adaptive logistics, environmental responsibility, industrial ecosystems, quantum computing.00["Murali Krishna Pasupuleti"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/12888182caf7975e748c3756be4039bf439242fb ca1f0ef08b99b667a5cddea9c3a8462b5687efb0Malnutrition is still a major worldwide health issue; hence creative methods of nutrition teaching are required. The transformational potential of artificial intelligence (AI)-powered platforms to provide individualized and interactive nutrition education in line with World Health Organization (WHO) guidelines is examined in this paper. It explores how AI improves engagement through gamification and virtual coaching, makes tailored dietary suggestions based on individual needs and tastes, and offers data-driven feedback for tracking success. The study looks at how well these platforms match WHO nutritional guidelines and considers the advantages—like higher engagement and better memory retention—as well as the drawbacks—like data privacy, algorithmic bias, and unequal access. Additionally, it investigates how AI improves user engagement through interactive features like gamification, chatbots that employ natural language processing to provide individualized virtual coaching, and dynamic feedback systems for behavior reinforcement and progress monitoring. To show how these AI-driven platforms can encourage adherence to evidence-based guidelines for balanced diets, appropriate nutrient intake, and the prevention of diet-related non-communicable diseases, the report explores the critical alignment of these platforms with specific WHO dietary recommendations. This study critically examines the associated challenges, including worries about data privacy and security, the possibility of algorithmic bias, the need for fairness and equity in AI-driven recommendations, and the crucial issue of ensuring equitable access to these technologies across diverse populations, addressing the digital divide, in addition to the advantages of increased user engagement and improved knowledge retentionABUAD Journal of Engineering Research and Development (AJERD)240This study looks at how well these AI-driven platforms match WHO nutritional guidelines and considers the advantages—like higher engagement and better memory retention—as well as the drawbacks—like data privacy, algorithmic bias, and unequal access.ABUAD Journal of Engineering Research and Development (AJERD)["Taiwo Folake Ojo", "O. Akpor", "Yetunde Justinah Talabi", "A. Afolalu"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/ca1f0ef08b99b667a5cddea9c3a8462b5687efb0 0a6f8acad1ab203dc2694eec73db2833cf15b381ObjectiveThis study investigates the effects of workload and task priority on multitasking performance and reliance on Level 1 Explainable Artificial Intelligence (XAI) systems in high-stakes decision environments.BackgroundOperators in critical settings manage multiple tasks under varying levels of workload and priority, potentially leading to performance degradation. XAI offers opportunities to support decision making by providing insights into AI's reasoning, yet its adoption and effectiveness in multitasking scenarios remain underexplored.MethodThirty participants engaged in a simulated multitasking environment, involving UAV command and control tasks, with the assistance of a Level 1 (i.e., basic perceptual information) XAI system on one of the tasks. The study utilized a within-subjects experimental design, manipulating workload (low, medium, and high) and AI-supported-task priority (low and high) across six conditions. Participants' accuracy, use of automatic rerouting, AI miss detection, false alert identification, and use of AI explanations were measured and analyzed across the different experimental conditions.ResultsWorkload significantly hindered performance on the AI-assisted task and increased reliance on the AI system especially when the AI-assisted task was given low priority. The use of AI explanations was significantly affected by task priority only.ConclusionAn increase in workload led to proper offloading by relying on the AI's alerts, but it also led to a lower rate of alert verification despite the alert feature's high false alert rate.ApplicationThe findings from the present work help inform AI system designers on how to design their systems for high-stakes environments such that reliance on AI is properly calibrated.Human Factors290An increase in workload led to proper offloading by relying on the AI's alerts, but it also led to a lower rate of alert verification despite the alert feature's high false alert rate.Human factors["J. Alami", "Mohamad El Iskandarani", "S. Riggs"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/0a6f8acad1ab203dc2694eec73db2833cf15b381 810644e9defe37fcd6bf089b908e2a3e5f4e3479ObjectivesDuring emergency surgery, patients with acute type A aortic dissection (ATAAD) experience unfavorable outcomes throughout their hospital stay. The combination of total aortic arch replacement (TAR) and frozen elephant trunk (FET) implantation has become a dependable choice for surgical treatment. The objective of this research was to utilize a machine learning technique based on artificial intelligence to detect the factors that increase the risk of mortality within 30 days after surgery in patients who undergo TAR in combination with FET. MethodsFrom January 2015 to December 2020, a total of 640 patients with ATAAD who underwent TAR and FET were included in this study. The subjects were divided into a test group and a validation group in a random manner, with a ratio of 7 to 3. The objective of our research was to create predictive models by employing different supervised machine learning techniques, such as XGBoost, logistic regression, support vector machine (SVM), and random forest (RF), to assess and compare their respective performances. Furthermore, we employed SHapley Additive exPlanation (SHAP) measures to allocate interpretive attributional values. ResultsAmong all the patients, 37 (5.78%) experienced perioperative mortality. Subsequently, a total 50 of 10 highly associated variables were selected for model construction. By implementing the new method, the AUC value significantly improved from 0.6981 using the XGBoost model to 0.8687 with the PSO-ELM-FLXGBoost model. ConclusionIn this study, machine learning methods were successfully established to predict ATAAD perioperative mortality, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.medRxiv70In this study, machine learning methods were successfully established to predict ATAAD perioperative mortality, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.["S. Zhang", "L. Li", "J. Wang", "Y. Li", "C. Yu", "X. Sun", "J. Sun", "X. Qian"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/810644e9defe37fcd6bf089b908e2a3e5f4e3479 c54d4e88fd8f2940f15d812050bc7d9ce37777ceIn the rapidly evolving field of artificial intelligence (AI), mapping innovation patterns and understanding effective technology transfer from research to applications are essential for economic growth. However, existing data infrastructures suffer from fragmentation, incomplete coverage, and insufficient evaluative capacity. Here, we present DeepInnovationAI, a comprehensive global dataset containing three structured files. DeepPatentAI.csv: Contains 2,356,204 patent records with 8 field-specific attributes. DeepDiveAI.csv: Encompasses 3,511,929 academic publications with 13 metadata fields. These two datasets leverage large language models, multilingual text analysis and dual-layer BERT classifiers to accurately identify AI-related content, while utilizing hypergraph analysis to create robust innovation metrics. Additionally, DeepCosineAI.csv: By applying semantic vector proximity analysis, this file presents approximately one hundred million calculated paper-patent similarity pairs to enhance understanding of how theoretical advancements translate into commercial technologies. DeepInnovationAI enables researchers, policymakers, and industry leaders to anticipate trends and identify collaboration opportunities. With extensive temporal and geographical scope, it supports detailed analysis of technological development patterns and international competition dynamics, establishing a foundation for modeling AI innovation and technology transfer processes.60A comprehensive global dataset containing three structured files, DeepInnovationAI supports detailed analysis of technological development patterns and international competition dynamics, establishing a foundation for modeling AI innovation and technology transfer processes.["Haixing Gong", "Hui Zou", "Xingzhou Liang", "Shiyuan Meng", "Pinlong Cai", "Xingcheng Xu", "Jingjing Qu"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/c54d4e88fd8f2940f15d812050bc7d9ce37777ce b6c2cae7b99e707d7c8571d3c0c05b482d406a1bThis research investigates how cognitive psychology principles, specifically Gestalt perception and attention processes, can be applied to artificial intelligence (AI) vision systems to enhance their accuracy and responsiveness in real-world applications. By utilizing a quantitative research methodology, data was collected from 120 AI industry experts in Punjab, Pakistan, through a structured questionnaire. The statistical analyses, including correlation (r = 0.602, p < 0.01) and regression (R² = 1.000), reveal a robust relationship between cognitive-inspired solutions and advancements in AI vision technologies. The results highlight that models based on Gestalt perception and biologically inspired attention mechanisms significantly contribute to AI’s ability to accurately recognize objects in complex visual scenes. Despite these advancements, current AI systems still face challenges with contextual reasoning and real-time adaptability, indicating a need for further development of hybrid AI models that combine cognitive approaches with deep learning techniques. The research concludes by suggesting that future studies should focus on integrating neuroscientific theories and exploring the ethical implications of AI to further improve the capabilities of AI vision systems.The Critical Review of Social Sciences Studies00Cognitive psychology principles, specifically Gestalt perception and attention processes, can be applied to artificial intelligence (AI) vision systems to enhance their accuracy and responsiveness in real-world applications and reveal a robust relationship between cognitive-inspired solutions and advancements in AI vision technologies.The Critical Review of Social Sciences Studies["Syeda Rida e Zehra", "Ali Majid", "Muhammad Saad Nizami", "Dr. Saleha Afridi"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/b6c2cae7b99e707d7c8571d3c0c05b482d406a1b f76ab2732e52bac63d423a4f69166946b5f016f5Background/Objectives: The purpose of this study is to investigate the role of the adoption of artificial intelligence technology in improving patient safety in hospitals working in gulf Cooperation Council (GCC) countries, with a focus on the mediating role of operational efficiency and moderating effect of digital competence. Methods: Applying a quantitative, cross-sectional, and explanatory research design, data were gathered from 300 healthcare professionals across five hospitals in the GCC region. Results: The results show that AI interventions improve patient safety by improving operational efficiency, while the digital competence of healthcare professionals further enhances the effectiveness of AI interventions. The findings exhibit that AI interventions enhance patient safety through high diagnostic accuracy at 95.2%, combined with 1.8% low medication errors and 92.4% efficient timely interventions. Based on previous research, the proposed approach achieves 5.7% better diagnostic accuracy and 1.4% fewer medication errors, together with 4.9% enhanced timely interventions. Conclusions and Implications: These findings highlight the importance of adopting AI technologies and enhancing digital competence among healthcare professionals to optimize operational efficiency and ensure safer healthcare delivery. This study offers actionable insights for healthcare managers and policymakers, emphasizing the need for AI-driven training programs and infrastructure investments.Healthcare440The findings exhibit that AI interventions enhance patient safety through high diagnostic accuracy at 95.2%, combined with 1.8% low medication errors and 92.4% efficient timely interventions.Healthcare["F. AlDhaen"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/f76ab2732e52bac63d423a4f69166946b5f016f5 9b1f7da106dde0fa5cd4c68a16b7621fc8cf4b73"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To test a commercial artificial intelligence (AI) system for breast cancer detection at the BC Cancer Breast Screening Program. Materials and Methods In this retrospective study of 136,700 women (age: µ = 58.8, σ = 9.4, M = 59.0, IQR = 14.0) who underwent digital mammography screening in British Columbia, Canada between February 2019 and January 2020, breast cancer detection performance of a commercial AI algorithm was stratified by demographic, clinical, and imaging features and evaluated using the receiver operating characteristic curve (AUC), and AI performance was compared with radiologists using sensitivity and specificity. Results At 1-year follow-up, the AUC of the AI algorithm was 0.93 (95% CI: 0.92-0.94) for breast cancer detection. Statistically significant differences were found for mammograms across radiologist-assigned BI-RADS breast densities-A: 0.96 (0.94-0.91); B: 0.94 (0.92-0.95); C: 0.93 (0.91-0.95) and D: 0.84 (0.76-0.91) (AAUC > DAUC, P = .002; BAUC > DAUC, P = .009; CAUC > DAUC, P = .02). The AI showed higher performance for mammograms with architectural distortion (0.96, 0.94-0.98) versus without (0.92, 0.90-0.93, P = .003) and lower performance for mammograms with calcification (0.87, 0.85-0.90) versus without (0.92, 0.91-0.94, P < .001). Sensitivity of radiologists (92.6 ± 1.0%) exceeded the AI algorithm (89.4 ± 1.1%; P =.01), but there was no evidence of difference at 2-year follow-up (83.5 ± 1.2% versus 84.3 ± 1.2%; P = .69). Conclusion The tested commercial AI algorithm is generalizable for a large external breast cancer screening cohort from Canada but showed different performance for some subgroups, including architectural distortion or calcification in the image. ©RSNA, 2025.Radiology: Artificial Intelligence00The tested commercial AI algorithm is generalizable for a large external breast cancer screening cohort from Canada but showed different performance for some subgroups, including architectural distortion or calcification in the image.Radiology. Artificial intelligence["John Brandon Graham-Knight", "Pengkun Liang", "Wenna Lin", "Quinn Wright", "Hua Shen", "Colin Mar", "J. Sam", "Rasika Rajapakshe"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/9b1f7da106dde0fa5cd4c68a16b7621fc8cf4b73 4bce72dc95f8a582d5a134099c6ecb671bf94811To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As a result, there is a gradual adoption of new generative artificial intelligence (AI) tools in the field of horticulture. These tools are expected to minimize bias in human perception when determining the quality of a watermelon based on its outer characteristics. This study aimed to compare the quality of watermelons selected by generative AI with a panel sensory evaluation test. The results of the two case studies indicate a significant difference in the quality of the generative AI-selected watermelons. As an average, watermelon evaluators favored the watermelons selected by ChatGPT as the best based on the Wilcoxon rank sum test and paired t-test (p < 0.05). In conclusion, watermelons can be selected by ChatGPT with minimal effort, promptly meeting consumer expectations.Horticulturae230Watermelons can be selected by ChatGPT with minimal effort, promptly meeting consumer expectations, and a significant difference in the quality of the generative AI-selected watermelons is indicated.Horticulturae["Serkan Ozdemir"]2025-03-12T00:00:00https://www.semanticscholar.org/paper/4bce72dc95f8a582d5a134099c6ecb671bf94811 7c32a34e8e9d5cde0d50c3c18959c8ed124a4f3b Background: Artificial intelligence (AI) helps to develop personalized medication therapy and regimens. It improves the patient care system. A cross-sectional study used and included pharmacy students, using validated survey questions. Objective: This study aimed to evaluate awareness, perception and opinion toward AI among pharmacy students. Design: This is a cross-sectional study (survey-based). Methods: A cross-sectional survey distribution among students in different levels of the college of pharmacy at National University (NU). The questions were classified to measure the variation of demographics, awareness, perceptions and opinions toward Artificial Intelligence (AI). Results: The results showed that more than 50% of pharmacy students are familiar with the uses of AI and know it’s important in scientific research, 46.4% have a basic understanding of AI technologies. However more than 75% don’t know the applications of AI used in pharmacy practice, 50.6 % don’t know AI can support therapeutic diagnosis and 57 % don’t know its importance in pharmacy education. A high perception was shown toward AI in facilitating pharmacy access to information (84.2%) and patients’ access to the service (80.8%). In addition, 92% suggested that AI training is needed and 86.1 % recommended using AI in scientific research. The conclusion of this study identified the needs for awareness toward AI, and the important role of AI for education in pharmacy and health communities. Hospital Pharmacy200The conclusion of this study identified the needs for awareness toward AI, and the important role of AI for education in pharmacy and health communities.Hospital Pharmacy["M. Al-Ghazali"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/7c32a34e8e9d5cde0d50c3c18959c8ed124a4f3b 4e6bbdbc1787234de193b6b97dfb319742a2d981Objective: This study aims to analyze the impact of artificial intelligence (AI) on business modernization, focusing on the challenges and opportunities associated with its adoption in developing countries, particularly Peru.   Theoretical Framework: The research is grounded in theories of technological diffusion, digital transformation, and innovation adoption, which provide a comprehensive understanding of AI’s role in business processes and its varying effects across different economic contexts.   Method: A qualitative methodology was employed, utilizing a bibliographic review of recent studies on AI adoption. The analysis included sources addressing international trends and national-specific challenges to identify key factors influencing AI implementation in business environments.   Results and Discussion: Findings reveal that AI has significantly enhanced the competitiveness of businesses in advanced economies. However, in Peru, adoption remains constrained by insufficient technological infrastructure, a shortage of specialized talent, and organizational resistance. Despite these barriers, the study highlights AI’s transformative potential if targeted strategies are implemented. The discussion underscores the necessity of bridging the digital gap through investment in infrastructure and capacity-building initiatives.   Research Implications: The study offers practical and theoretical insights into AI adoption in emerging markets. It emphasizes the importance of regulatory frameworks and policies that foster digital skill development, ultimately enabling Peruvian businesses to integrate AI effectively and enhance their global competitiveness.   Originality/Value: This research contributes to the literature by providing an in-depth analysis of AI adoption in a developing country, highlighting both obstacles and potential pathways for its successful implementation. The findings underscore the critical role of policy interventions and strategic investments in facilitating AI-driven business transformation.Journal of Lifestyle and SDGs Review250Analysis of the impact of artificial intelligence on business modernization in developing countries, particularly Peru, reveals that AI has significantly enhanced the competitiveness of businesses in advanced economies, however, adoption remains constrained by insufficient technological infrastructure, a shortage of specialized talent, and organizational resistance.Journal of Lifestyle and SDGs Review["Ricardo Edmundo Ruiz-Villavicencio", "Giovana Edith Ruiz-Villavicencio", "Danilo Hugo Carrero-Ramirez", "Judith Eliana Garavito-Baca", "Pablo Ramon Carrasco-Pintado"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/4e6bbdbc1787234de193b6b97dfb319742a2d981 c38ff6179c087763d3179d76efd05714da4cb749Standardised management of chronic sinusitis (CRS) is a challenging but vital area of research. Not only is accurate diagnosis and individualised treatment plans required, but post-treatment chronic disease management is also indispensable. With the development of artificial intelligence (AI), more “AI + medical” application models are emerging. Many AI-assisted systems have been applied to the diagnosis and treatment of CRS, providing valuable solutions for clinical practice.This study summarises the research progress of various AI-assisted systems applied to the clinical diagnosis and treatment of CRS, focusing on their role in imaging and pathological diagnosis and prognostic prediction and treatment.We used PubMed, Web of Science, and other Internet search engines with “artificial intelligence”、“machine learning” and “chronic sinusitis” as the keywords to conduct a literature search for studies from the last 7 years. We included literature eligible for AI application to CRS diagnosis and treatment in our study, excluded literature outside this scope, and categorized it according to its clinical application to CRS diagnosis, treatment, and prognosis prediction. We provide an overview and summary of current advances in AI to optimize the diagnosis and treatment of CRS, as well as difficulties and challenges in promoting standardization of clinical diagnosis and treatment in this area.Through applications in CRS imaging and pathology diagnosis, personalised medicine and prognosis prediction, AI can significantly reduce turnaround times, lower diagnostic costs and accurately predict disease outcomes. However, a number of challenges remain. These include a lack of AI product standards, standardised data, difficulties in collaboration between different healthcare providers, and the non-interpretability of AI systems. There may also be data privacy issues involved. Therefore, more research and improvements are needed to realise the full potential of AI in the diagnosis and treatment of CRS.Our findings inform the clinical diagnosis and treatment of CRS and the development of AI-assisted clinical diagnosis and treatment systems. We provide recommendations for AI to drive standardisation of CRS diagnosis and treatment.Frontiers in Physiology730This study summarises the research progress of various AI-assisted systems applied to the clinical diagnosis and treatment of CRS, focusing on their role in imaging and pathological diagnosis and prognostic prediction and treatment.Frontiers in Physiology["Yang-Yang Liu", "Shao-Peng Jiang", "Ying-Bin Wang"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/c38ff6179c087763d3179d76efd05714da4cb749 dcc96d14f895eed3629c27c441f5adc581329ff3With the remarkable developments in space technologies, artificial intelligence has gradually been used instead of humans in decision-making. Artificial intelligence has the ability to think logically, manage its own actions, and correct decisions in the event of changes in external conditions. New smart space technologies are being developed to perform various space activities such as processing space data and information, removing space debris, extracting natural space resources, and exploring without human intervention. However, the regulation of the activities of space actors, especially private actors, and the supervision of these activities by governments in the use of these types of technologies has become one of the new issues in the field of international space law. Since the obligations of States within the framework of international space law are explained on the basis of human behavior, in the face of monitoring the performance of intelligent space technologies and compensation for damage resulting from their performance, the question arises as to whether the existing international space regulations on the international responsibility of States for monitoring space activities and compensation for damage, which are based on human behavior, can also be applied to the use of these technologies, or should the regulations be A new space law should be drafted. With a broad interpretation of Articles 6 and 7 of the 1967 Outer Space Treaty regarding the responsibility of States for monitoring space activities and also the responsibility for compensation for damage, these provisions can still be considered applicable. Nevertheless, it seems that the development of new international space regulations could be an important step in better defining and recognizing the responsibility of states to monitor the use of intelligent space technologies by space actors and to compensate for damages resulting from it.Luminis Applied Science and Engineering200Luminis Applied Science and Engineering["Erdal Dursun"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/dcc96d14f895eed3629c27c441f5adc581329ff3 86b93d107d74ea4a546cf6cbda1b22c0348580f8PurposeWhile the literature on artificial intelligence (AI) capability is expanding, gaps remain in understanding how this capability is internally developed in technology-based startups (TBS) across different life cycle phases. This study, grounded in the resource orchestration theory (ROT), investigates the pathway through which TBS use organizational creativity to build AI capability and achieve performance.Design/methodology/approachA conceptual framework based on ROT emphasizes the role of organizational creativity in the structuring and bundling processes. Data were collected through a survey of 166 managers and employees of TBS operating in Brazil and international markets, using multiple linear regressions and the Sobel test for analysis. The study validated the AI capability scale in the TBS context.FindingsAI capability fully mediates the relationship between organizational creativity and performance, confirming that organizational creativity is a critical resource for AI capability development. These findings advance ROT by deepening the understanding of how AI capability is developed in TBS. The study offers a dynamic, process-based view of performance trajectories in TBS, demonstrating that the synchrony between creativity and AI capability creates a cyclical process, maximizing company performance.Originality/valueThis research identifies an alternative pathway for TBS to develop AI capability and achieve performance, highlighting the synchronization and co-evolution of resources and capabilities. It provides novel insights into AI capability’s mediating role and expands understanding of resource management in TBS across life cycle phases.Journal of Small Business and Enterprise Development480This research identifies an alternative pathway for TBS to develop AI capability and achieve performance, highlighting the synchronization and co-evolution of resources and capabilities.Journal of Small Business and Enterprise Development["Cristina Doritta Brand\u00e3o Majorana", "S\u00edlvio Lu\u00eds de Vasconcellos", "F. Borini"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/86b93d107d74ea4a546cf6cbda1b22c0348580f8 82ed509f87e277d983e0e58dce0e71d4a0cab99cPurpose This study aims to empirically examine the impact of intellectual capital on the adoption of artificial intelligence-based supply chain analytics in manufacturing companies. It also aims to examine the potential impact of artificial intelligence (AI)-based supply chain analytics on supply chain innovation and supply chain agility. Furthermore, this study explores the association supply chain innovation and supply chain agility. Design/methodology/approach Data were collected from 252 respondents who work in supply chain management of manufacturing companies in Jordan. AMOS software, which is based on the Structural Equation Modeling approach, was used to test hypotheses. Findings The findings reveal positive effects of the three components of intellectual capital, including human capital, structural capital, and social capital, on AI-based supply chain analytics. They also confirm a positive effect of AI-based supply chain analytics on both supply chain innovation and supply chain agility. Furthermore, the empirical results support a positive effect of supply chain agility on supply chain innovation. Originality/value This study provides valuable practical implications and enriches the literature on the determinants of supply chain analytics adoption and its role in developing the dynamic capabilities of manufacturing companies, such as supply chain innovation and supply chain agility. Supply Chain Management1220Positive effects of the three components of intellectual capital, including human capital, structural capital, and social capital, on AI-based supply chain analytics confirm a positive effect of AI-based supply chain analytics on both supply chain innovation and supply chain agility.Supply Chain Management: An International Journal["Al-Zoubi Lamees", "T. Ramayah"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/82ed509f87e277d983e0e58dce0e71d4a0cab99c cc105580aaee8b4902fd3a56a48ad745af7e6fcfAIM OF THE STUDY Artificial intelligence (AI) such as large language models (LLMs) tools are potential sources of information on hypothermic cardiac arrest (HCA). The aim of our study was to determine whether, for patients with HCA, LLMs provide information consistent with expert consensus on criteria that would usually contraindicate extracorporeal cardiopulmonary resuscitation (eCRP) in patients with normothermic cardiac arrest (NCA), but not HCA. METHODS Based on Extracorporeal Life Support Organization guidelines, selected factors were identified that may be contraindications to eCPR in NCA but not in HCA. Four questions were created and entered into AI software (GPT-3.5 turbo, GPT-4o, GPT-4o-mini, Claude 3.5 Sonnet, Claude 3 Haiku, Mistral Large, Mistral Small, Gemini Pro and Gemini Flash). The responses obtained and citations returned were assessed by an international panel of experts for consistency with current knowledge. RESULTS Complete agreement of responses with expert consensus was obtained for 5/10 AI tools. In total, all AI tools presented 101 items in the literature. No reference was rated as "correct"; 45 citations (45%) "existed but did not answer the question"; and 56 citations (55%) were considered "hallucinatory". CONCLUSION Use of artificial intelligence in decision-making for extracorporeal cardiopulmonary resuscitation in patients with hypothermic cardiac arrest risks unjustifiably withdrawing treatment from patients who have a chance of survival with a good neurological outcome. Large language models should not be used as the only tool for decision-making.Artificial Organs120Use of artificial intelligence in decision-making for extracorporeal cardiopulmonary resuscitation in patients with hypothermic cardiac arrest risks unjustifiably withdrawing treatment from patients who have a chance of survival with a good neurological outcome.Artificial organs["Micha\u0142 P Pluta", "Tomasz Darocha", "Micha\u0142 Pasternak", "Mathieu Pasquier", "K. Mendrala", "R. Gocol", "S. Kosi\u0144ski"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/cc105580aaee8b4902fd3a56a48ad745af7e6fcf 9109c3df869a4433a9b3c38407c2291b874caa2dFuture Business Journal590The study finds that following ethical concerns are the hinderance in the adaptation of AI in business (Privacy and data protection, bias and fairness, transparency and explainability, job displacement and workforce changes, algorithmic influence, and manipulation, accountability, and liability).Future Business Journal["Moinak Maiti", "Parthajit Kayal", "Aleksandra Vujko"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/9109c3df869a4433a9b3c38407c2291b874caa2d de2a4de365184ef7fafb217a6dc75d720a5d3bcfPersonalized learning (PL) has emerged as a promising approach to address diverse educational needs, with artificial intelligence (AI) playing an increasingly pivotal role in its implementation. This systematic literature review examines the landscape of PL across various educational contexts, focusing on the use of AI and associated challenges. Using the PRISMA guidelines, 68 empirical studies published between 2018 and 2024 were analyzed, revealing correlations between academic levels, learning modalities, technologies, and implementation barriers. Key findings include (a) predominant use of AI in higher education PL implementations, (b) preference for blended learning in secondary and elementary education, (c) shift from technological to pedagogical barriers across educational levels, and (d) persistent psychological barriers across all contexts. This review provides valuable insights for educators, policymakers, and researchers, offering a comprehensive understanding of the current state and future directions of AI-driven personalized learning.Applied Sciences780This systematic literature review examines the landscape of PL across various educational contexts, focusing on the use of AI and associated challenges, offering a comprehensive understanding of the current state and future directions of AI-driven personalized learning.Applied Sciences["Gina Paola Barrera Castro", "Andr\u00e9s Chiappe", "M. Ram\u00edrez-Montoya", "Carolina Alc\u00e1ntar Nieblas"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/de2a4de365184ef7fafb217a6dc75d720a5d3bcf 2b76abe5602f35f96482c0b4244083098051da6eThe integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI’s error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI’s diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.Bioengineering1210This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning and its subset, deep learning, in ultrasound diagnostics, highlighting their transformative potential to improve global healthcare outcomes.Bioengineering["Li Yan", "Qing Li", "Kang Fu", "Xiaodong Zhou", "Kai Zhang"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/2b76abe5602f35f96482c0b4244083098051da6e 5b36de88aaca01435dbb74f568318984a7a6d87a The construction industry in Ghana faces significant challenges in managing health and safety risks, leading to high rates of accidents and fatalities. Despite the potential of artificial intelligence (AI) technologies to improve health and safety management, their adoption in the Ghanaian construction industry remains limited. This paper aims to identify and evaluate key factors influencing the uptake of AI technologies in construction health and safety management within the Ghanaian industry. The study adopts a rigorous two-step qualitative approach to identify a set of 17 variables. First, an extensive analysis of scholarly publications was conducted to compile an initial variable list. Secondly, a pilot survey involving both academic and industry professionals assisted in refining the identified variables. Subsequently, a questionnaire survey involving 219 Ghanaian construction professionals then collects quantitative assessments of each variable using the purposive sampling technique. Statistical modelling using factor analysis and fuzzy synthetic evaluation (FSE) was applied to process the survey data and determine the criticality of the factor categories. The factor analysis yielded a three-factor solution underlying the 17 adoption variables: Extensive technological requirements and costs, resistance to change and AI adoption and uncertainty about AI outcomes and value. Subsequently, FSE confirmation showed the Extensive Technological Requirements category as the most critical, with specialized algorithmic demands, infrastructure limitations and expert support needs presenting major obstacles Ghanaian firms face in AI adoption. This research contributes robust empirical evidence and novel factor-based statistical analysis to augment the theoretical discourse surrounding construction safety technology integration and change dynamics. The developed fuzzy quantitative methodology offers a model for assessing complex innovation adoption decisions in the face of uncertainty. The research addresses a gap in existing literature by providing a comprehensive assessment of the technological, organizational and environmental factors shaping AI adoption decisions and offering practical strategies for overcoming adoption barriers. Journal of Engineering, Design and Technology720The research addresses a gap in existing literature by providing a comprehensive assessment of the technological, organizational and environmental factors shaping AI adoption decisions and offering practical strategies for overcoming adoption barriers.Journal of Engineering, Design and Technology["Alex Acheampong", "Elvis Konadu Adjei", "R. Asiedu", "David Wireko Atibila", "I. Abu"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/5b36de88aaca01435dbb74f568318984a7a6d87a d6f37f99bded747a5ab47f7d8a8744b980b309b3Much work has been published on artificial intelligence (AI) and oncology, with many focusing on an algorithm perspective. However, very few perspective articles have explicitly discussed the role of AI in oncology from the perspectives of the stakeholders-the clinicians and the patients. In this article, we delve into the opportunities of AI in oncology from the clinician's and patient's lens. From the clinician's perspective, we discuss reducing burnout, enhancing decision making, and leveraging vast data sets to provide evidence-based recommendations, eventually affecting diagnostic accuracy and treatment planning. From the patient's perspective, we discuss AI virtual concierge, which could improve the cancer care journey by facilitating patient education, mental health support, and personalized lifestyle wellness recommendations promoting a holistic approach to care. We aim to highlight the stakeholders' unmet needs and guide institutions to create innovative AI solutions in oncology. By addressing these perspectives, our article aims to bridge the gap between technological research advancements and their real-world AI-focused clinical applications in cancer care. Understanding and prioritizing the needs of the stakeholders will foster the development of impactful AI tools and intentional utilization of such technology, with an aim for clinical implementation and integration into workflows.JCO Oncology Practice760This article dives into the opportunities of AI in oncology from the clinician's and patient's lens, and discusses AI virtual concierge, which could improve the cancer care journey by facilitating patient education, mental health support, and personalized lifestyle wellness recommendations promoting a holistic approach to care.JCO oncology practice["Krunal Pandav", "Sahar Almahfouz Nasser", "Kristen H Kimball", "Kristin A. Higgins", "A. Madabhushi"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/d6f37f99bded747a5ab47f7d8a8744b980b309b3 2bdf191736e65c1c2328f82f16776d1fadb03a98 Generative Artificial Intelligence (GAI) is increasingly being integrated into marketing education and is reshaping the skillsets required in marketing careers. While research has highlighted the promise and perils of incorporating GAI into education, there remains a need for a comprehensive framework to guide its effective use. In this research, we conduct a multipronged analysis, including a review of marketing course syllabi, a survey of marketing educators, and follow-up qualitative interviews. Building on Role Theory and the Community of Inquiry (CoI) model, we propose that GAI can assume three roles in marketing education: tutor, teammate, and tool . Each role influences teaching, social, and cognitive presence differently, shaping the learning experience and preparing workplace-ready marketing graduates. For instance, as a tutor , GAI can aid students in grasping theoretical concepts, while as a teammate , it can foster collaboration by supporting brainstorming and problem-solving activities. However, ethical considerations such as data privacy, plagiarism, dependency on AI, and fairness in assessment must be addressed to ensure its responsible adoption in marketing education. We provide concrete examples for GAI’s careful integration in marketing courses, and its implications for marketing educators, learners, and policymakers. Journal of Public Policy &amp; Marketing00A multipronged analysis is conducted, including a review of marketing course syllabi, a survey of marketing educators, and follow-up qualitative interviews, that proposes that GAI can assume three roles in marketing education: tutor, teammate, and tool.Journal of Public Policy &amp; Marketing["Unnati Narang", "Vishal Sachdev", "Ruichun Liu"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/2bdf191736e65c1c2328f82f16776d1fadb03a98 2e4b47f9ace9e45707c383b3bd56e2b67e88454dBACKGROUND Physician autonomy has been found to play a role in physician acceptance and adoption of artificial intelligence (AI) in medicine. However, there is still no consensus in the literature on how to define and assess physician autonomy. Furthermore, there is a lack of research focusing specifically on the potential effects of AI on physician autonomy. OBJECTIVE This scoping review addresses the following research questions: (1) How do qualitative studies conceptualize and assess physician autonomy? (2) Which aspects of physician autonomy are addressed by these studies? (3) What are the potential benefits and harms of AI for physician autonomy identified by these studies? METHODS We performed a scoping review of qualitative studies on AI and physician autonomy published before November 6, 2023, by searching MEDLINE and Web of Science. To answer research question 1, we determined whether the included studies explicitly include physician autonomy as a research focus and whether their interview, survey, and focus group questions explicitly name or implicitly include aspects of physician autonomy. To answer research question 2, we extracted the qualitative results of the studies, categorizing them into the 7 components of physician autonomy introduced by Schulz and Harrison. We then inductively formed subcomponents based on the results of the included studies in each component. To answer research question 3, we summarized the potentially harmful and beneficial effects of AI on physician autonomy in each of the inductively formed subcomponents. RESULTS The search yielded 369 studies after duplicates were removed. Of these, 27 studies remained after titles and abstracts were screened. After full texts were screened, we included a total of 7 qualitative studies. Most studies did not explicitly name physician autonomy as a research focus or explicitly address physician autonomy in their interview, survey, and focus group questions. No studies addressed a complete set of components of physician autonomy; while 3 components were addressed by all included studies, 2 components were addressed by none. We identified a total of 11 subcomponents for the 5 components of physician autonomy that were addressed by at least 1 study. For most of these subcomponents, studies reported both potential harms and potential benefits of AI for physician autonomy. CONCLUSIONS Little research to date has explicitly addressed the potential effects of AI on physician autonomy and existing results on these potential effects are mixed. Further qualitative and quantitative research is needed that focuses explicitly on physician autonomy and addresses all relevant components of physician autonomy.JMIR AI250A scoping review of qualitative studies on AI and physician autonomy published before November 6, 2023 found little research to date has explicitly addressed the potential effects of AI on physician autonomy and existing results on these potential effects are mixed.JMIR AI["John Grosser", "J. D\u00fcvel", "L. Hasemann", "Emilia Schneider", "Wolfgang Greiner"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/2e4b47f9ace9e45707c383b3bd56e2b67e88454d 9f7bb45a82f361a878df70d5389ab44c109a2e95Artificial intelligence (AI) is changing companies and how they organize innovation management. In line with the rapid development of technology and the replacement of human organizations, AI may actually force management to rethink the entire innovation process of a company. In response, we explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we examine the implications for innovation management of AI technologies and AI systems based on machine learning. We outline a framework that shows to what extent AI can replace humans and explain what needs to be considered in transforming the digital innovation organization. We conclude our study by exploring future research directions.Acta Globalis Humanitatis et Linguarum780A framework is outlined that shows to what extent AI can replace humans and explain what needs to be considered in transforming the digital innovation organization is outlined.Acta Globalis Humanitatis et Linguarum["Abdulkadir Akman"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/9f7bb45a82f361a878df70d5389ab44c109a2e95 dcf633e21578db03dfbc7efbc8f0ecd05becd6baAmid rapid advancements in artificial intelligence (AI), personalized recommendation systems have become a key factor shaping consumer decision-making in functional food purchases. However, the influence of AI recommendation characteristics on purchase intention, particularly the underlying mediating mechanisms, remains underexplored. This study aims to investigate how AI recommendation features (personalization and transparency), along with functional food attributes (perceived health benefits and perceived naturalness), influence purchase intention through the mediating roles of perceived packaging and perceived value. Grounded in the Stimulus–Organism–Response framework, data were collected via a structured questionnaire survey, and structural equation modeling was employed for hypothesis testing and model validation. The results indicate that AI recommendation personalization significantly enhances purchase intention both directly and indirectly, while transparency influences purchase intention only through perceived value, emphasizing its role in fostering trust rather than directly driving purchasing behavior. Additionally, perceived health benefits positively influence purchase intention both directly and through mediation, whereas perceived naturalness affects purchase intention only indirectly via perceived value. These findings contribute to consumer behavior research by elucidating psychological mechanisms underlying AI-driven purchase decisions while also providing insights for functional food marketers on how to effectively integrate AI recommendation systems to enhance consumer engagement.Foods940Investigation of how AI recommendation features (personalization and transparency), along with functional food attributes (perceived health benefits and perceived naturalness), influence purchase intention through the mediating roles of perceived packaging and perceived value indicates that AI recommendation personalization significantly enhances purchase intention both directly and indirectly.Foods["Wenxin Wang", "Zhiguang Chen", "Jiwei Kuang"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/dcf633e21578db03dfbc7efbc8f0ecd05becd6ba facca73a63d2d072849c893ec6461eebf60ec37cObjectives: The research aims to analyze the methodology of inference in  artificial intelligence models and compare it with the curriculum of Islamic fundamentalist schools, and to study the possibility of integration between computer reasoning and jurisprudence. It also seeks to assess the ability of artificial intelligence algorithms to accommodate fundamental rules and legitimate purposes, Propose a methodological framework for employing artificial intelligence techniques in supporting jurisprudence, while maintaining Sharia and ethical standards.   Methodology:  The research relied on an inductive analytical methodology, as reasoning methodologies in Islamic fundamentalist schools and reasoning mechanisms in artificial intelligence were analyzed separately. The general rules and models extracted from legal texts and modern applications were extrapolated with the aim of providing an integrated understanding of the aspects of Similarities and differences between the two approaches, while proposing practical frameworks commensurate with the purposes of Sharia.   Results:  The research found a similarity between the methodology of inference in the principles of Islamic jurisprudence and artificial intelligence algorithms, especially in the use of measurement and induction. But it also showed fundamental differences, as the principles of jurisprudence rely on legal texts and human ijtihad, while adopting models Artificial intelligence on programming and text inputs, which limits its ability to understand legitimate intents. The research also showed the possibility of integration between artificial intelligence models and jurisprudence, with the need for human monitoring to ensure that Sharia purposes are observed.   Conclusion: The research provides an important scientific addition in the field of modern Islamic studies, as it combines the Islamic jurisprudential heritage with modern reasoning methodologies represented in artificial intelligence. AI can support jurisprudence by analyzing data and making recommendations, but it cannot replace ijtihad Human for detracting from the understanding of legitimate purposes and moral values. The employment of artificial intelligence in the fields of Sharia must be balanced, so as to enhance facilitation and remove embarrassment without prejudice to the principles and constants of Sharia.Journal of Lifestyle and SDGs Review30The research provides an important scientific addition in the field of modern Islamic studies, as it combines the Islamic jurisprudential heritage with modern reasoning methodologies represented in artificial intelligence.Journal of Lifestyle and SDGs Review["Amina A. Saleh", "Ziyad M. Said", "M. Al-Neama"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/facca73a63d2d072849c893ec6461eebf60ec37c b86e3af2fb3439d9d99f5fb60ea2df94c1986147Purpose: This review study examines the use of artificial intelligence (AI) technologies in physical education and movement development in children over the past 10 years. Various AI applications, such as educational robots, virtual reality scenes, and personalized education programs, are discussed. Method: Scientific studies published between 2014 and 2024 were reviewed using academic databases such as Google Scholar, PubMed, IEEE Xplore, SpringerLink, Web of Science, and Scopus. Keywords such as "artificial intelligence," "physical education," "movement development," "children," "AI in education," "virtual simulation," and "personalized learning programs" were used. Data were classified based on criteria such as student performance, feedback mechanisms, and the improvement of educational processe. Results: Various AI applications, including educational robots, virtual reality scenes, and personalized education programs, are effective in increasing children's physical activities and supporting their movement development. AI technologies offer significant advantages in monitoring student performance and providing real-time feedback. Conclusion: AI technologies have been found to make significant contributions to physical education and movement development in children, with great potential in monitoring student performance, providing feedback, and improving educational processes. It is also important to provide necessary training for teachers to effectively use AI technologies. Future research should focus on the integration of technologies such as augmented reality, virtual reality, and the Internet of Things.Spor eğitim dergisi170Artificial intelligence technologies have been found to make significant contributions to physical education and movement development in children, with great potential in monitoring student performance, providing feedback, and improving educational processes.Spor Eğitim Dergisi["Fatih Kaya"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/b86e3af2fb3439d9d99f5fb60ea2df94c1986147 90c722e4feb28b33dc7bf1182c3ffcb29eac5904In an era where technology facilitates both the generation of information and misinformation, it is crucial to equip students with critical thinking skills. This study aims to systematically review the role of artificial intelligence (AI) in fostering critical thinking, exploring its effectiveness, methodologies, and implications in educational contexts. A systematic literature review was conducted following PRISMA guidelines. Relevant peer-reviewed articles published in the last decade were sourced from databases such as Scopus, Web of Science, and IEEE Xplore. The inclusion criteria focused on studies that analyze AI-driven tools, techniques, and interventions designed to enhance critical thinking in students. The findings indicate that AI-based approaches, including machine learning algorithms, natural language processing, and intelligent tutoring systems, can support the development of critical thinking by providing personalized feedback, facilitating argument analysis, and detecting misinformation. However, challenges such as ethical concerns, biases in AI models, and accessibility issues remain significant barriers. The study provides insights for educators, policymakers, and AI developers on how to effectively integrate AI-driven tools into educational curricula. It also highlights the need for interdisciplinary collaboration to ensure that AI fosters rather than hinders critical thinking development. AI has the potential to enhance critical thinking skills in educational settings, but its implementation must be carefully designed to address ethical and technical challenges. Further research is needed to assess long-term impacts and to develop more inclusive and unbiased AI-based educational frameworks.Edelweiss Applied Science and Technology570The findings indicate that AI-based approaches can support the development of critical thinking by providing personalized feedback, facilitating argument analysis, and detecting misinformation, however, challenges such as ethical concerns, biases in AI models, and accessibility issues remain significant barriers.Edelweiss Applied Science and Technology["Teresa Chara-De los Rios", "B. Sol\u00eds-Trujillo", "J. P\u00e9rez-Ruiz", "M. Aquije-Mansilla"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/90c722e4feb28b33dc7bf1182c3ffcb29eac5904 fc429c145c6ab3242b69ae83d914a3ce2dab7ee9Subject. This article discusses the issues of climate change and energy consumption. Objectives. The article aims to assess the role of artificial intelligence in the development of alternative energy. Methods. For the study, I used the methods of systems, comparative, structural, statistical and logical analyses. Results. The article proposes certain recommendations for the development of energy-intensive artificial intelligence technologies while reducing the carbon footprint. Conclusions. Artificial intelligence technologies contribute to the preservation of the human environment, though at the same time they contribute to an increase in energy costs.National Interests Priorities and Security60The article proposes certain recommendations for the development of energy-intensive artificial intelligence technologies while reducing the carbon footprint.National Interests: Priorities and Security["Sergey Filin"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/fc429c145c6ab3242b69ae83d914a3ce2dab7ee9 f071eb539563efa6f0a48c3b88430238cf8c60a5Subject. The article explores organizational, managerial, and economic relations that arise when strategizing the development of artificial intelligence. Objectives. The purpose is to develop a methodology for strategic monitoring of the Russian artificial intelligence sector. Methods. The study rests on the theory of strategy and methodology of strategizing by V.L. Kvint, a foreign member of the Russian Academy of Sciences. Artificial intelligence is considered as a breakthrough technology, i.e. an innovation embodied in the form of a technology or product that can significantly improve existing technologies and create new ones. It changes existing markets and shapes new ones, affecting the economy and society both on the national and global scale. Results. I formulated a theoretical justification and designed three methods that determine the basis of methodology for strategic monitoring of artificial intelligence development. The paper describes these methods, gives examples of their application in other studies with my participation. Conclusions. The proposed methodology forms the theoretical basis for monitoring the implementation of strategic documents in the field of innovation, which, in turn, contributes to effective development of the innovative economy of Russia.Economic Analysis: Theory and Practice250The proposed methodology forms the theoretical basis for monitoring the implementation of strategic documents in the field of innovation, which, in turn, contributes to effective development of the innovative economy of Russia.Economic Analysis: Theory and Practice["A. O. AVER'YANOV"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/f071eb539563efa6f0a48c3b88430238cf8c60a5 2e937ea30058005130657d23ac8b5872e53dbf5cSubject. The article addresses the impact of artificial intelligence on the consciousness of individual and society as a whole. Objectives. The focus is on minimizing the risks of negative impact of artificial intelligence algorithms on civilizational values of the Russian society in the light of the current geopolitical situation. Methods. The study employs system tools, that open up opportunities for making adequate situational management decisions. To summarize the findings, we used well-known tests and standard methods for evaluating their results. Results. The paper presents the results of our experimental study on the issue of bias in assessments influenced by the manipulativeness of artificial intelligence algorithms. The results demonstrate that for the "individualism-collectivism" factor, there is no bias in assessments influenced by the manipulativeness of artificial intelligence algorithms, with a compression of variability that is indisputable for any accepted levels of significance. As for the "machiavellianism" factor, which characterizes the degree of manipulativeness, the bias in assessments generated by artificial intelligence for groups of conditional "personalities" significantly differs from the corresponding indicators of control groups of individuals. Conclusions. Further research on the manipulative impact of artificial intelligence algorithms on public consciousness regarding the factor of "Machiavellianism" is advisable.National Interests Priorities and Security110The results demonstrate that for the "individualism-collectivism" factor, there is no bias in assessments influenced by the manipulativeness of artificial intelligence algorithms, with a compression of variability that is indisputable for any accepted levels of significance.National Interests: Priorities and Security["I. D. Grachev", "Sergei N. Larin", "N. V. Noakk", "T.A. Kostina"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/2e937ea30058005130657d23ac8b5872e53dbf5c 5729fe287ab9f5648a5aa2faf16c9febce9d65c4With the expansion of the fifth generation Internet and the increased use of machine learning, one of the sub-branches of artificial intelligence, the legal system will face various legal questions. Considering the wide range of areas in which artificial intelligence is used and the impossibility of examining all of them in one article, this study addresses some of these questions by examining the challenges facing civil liability and artificial intelligence. This study focused on self-driving cars and the use of modern artificial intelligence by doctors, as two common examples today. The primary goal of this study was to provide lawyers with an introduction to artificial intelligence and machine learning and to draw the attention of lawyers and legislators to the challenges facing the legislative field in this area. In the final section, some suggestions are made on existing legal issues, including the legal personality of AI, strict liability, and compulsory insurance. At this stage, it is necessary for lawyers and policymakers to develop strategic ethical and legal principles for the use of AI.Acta Globalis Humanitatis et Linguarum220The primary goal of this study was to provide lawyers with an introduction to artificial intelligence and machine learning and to draw the attention of lawyers and legislators to the challenges facing the legislative field in this area.Acta Globalis Humanitatis et Linguarum["Mehmet U\u00e7ka\u00e7"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/5729fe287ab9f5648a5aa2faf16c9febce9d65c4 8df1c4be4c40016c2d22828a5f41d5233e21d593This study explores the transformative impact of artificial intelligence (AI) and digital technologies on the financial technology (FinTech) industry, highlighting their role in fostering business growth, operational efficiency, and enhanced customer engagement. AI-driven strategies have unlocked new avenues for streamlining workflows, boosting productivity, and expanding financial inclusion by reaching underrepresented populations. However, these advancements also pose challenges, including navigating complex regulatory frameworks and adapting to the rapidly evolving technological landscape. This paper delves into the macroeconomic effects of AI, examining its influence on labor markets, consumer behavior, and organizational success. Furthermore, the paper discusses blockchain applications and their potential to reshape consumer behaviors and financial systems. It also evaluates the implications of digital transformation on economic efficiency and the legal frameworks surrounding electronic payment systems. Ultimately, this study underscores the profound opportunities AI and digital technologies present for FinTech and offers insights relevant to both academic inquiry and policy-making.Journal of Economics, Finance and Accounting Studies40The macroeconomic effects of AI are delved into, examining its influence on labor markets, consumer behavior, and organizational success and the implications of digital transformation on economic efficiency and the legal frameworks surrounding electronic payment systems are evaluated.Journal of Economics, Finance and Accounting Studies["Soudeh Pazouki", "Mohamad (Behdad) Jamshidi", "Mirarmia Jalali", "Arya Tafreshi"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/8df1c4be4c40016c2d22828a5f41d5233e21d593 d0e969ee8a21a46a236fbce2a20a382b8b5f133cArtificial intelligence methods have been increasingly used in natural resource management as an alternative to classical methods. Three computational challenges in natural resource management are data management and communication, data analysis, and optimization and control. Artificial intelligence methods can be a solution to these problems due to their ability to manage dynamic activities in natural resources. There are several artificial intelligence algorithms that have found various applications in various fields. In this article, some artificial intelligence methods, including artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, collective intelligence, and hybrid systems, are introduced, and some of their applications in natural resource management are listed.Acta Globalis Humanitatis et Linguarum500Some artificial intelligence methods, including artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, collective intelligence, and hybrid systems, are introduced, and some of their applications in natural resource management are listed.Acta Globalis Humanitatis et Linguarum["Prof. Dr. Erdal Dursun"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/d0e969ee8a21a46a236fbce2a20a382b8b5f133c 48d3e9b2c6a8232f676094b9600df81cc3b21428Sport for Development (SFD) leverages sports as a tool to support broader sustainable development goals, particularly in underserved communities worldwide. As Artificial Intelligence (AI) technology advances, its application in SFD offers both promising opportunities and significant challenges in areas such as curriculum design, evaluation, and participant engagement. Through a qualitative survey of experts and practitioners analysed through Thematic Analysis (TA), this paper explores perspectives on the potential of AI to enhance the delivery and management of SFD initiatives, as well as potential risks and needs in the field. Key perceived benefits include compensating for deficient organisational capacities and supporting the performance of both administrative and conceptual tasks. Potential risks include the propagation of increasingly generic approaches to SFD programming, loss of critical thinking skills, and concerns around participant safeguarding. To mediate this, exchange, education, and SFD-specific policies are seen as crucial.The social science00From perspectives on the potential of AI to enhance the delivery and management of SFD initiatives, as well as potential risks and needs in the field, a qualitative survey of experts and practitioners is analysed through Thematic Analysis.Social Sciences["Louis Moustakas"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/48d3e9b2c6a8232f676094b9600df81cc3b21428 70ff374a5d62b48aaed5d226340348c50d0aec58Over the past decade, the development and improvement of artificial intelligence (AI) methods have contributed to its intensive application in many scientific disciplines. Thanks to its numerous advantages, AI has enabled the resolution of many problems in food process engineering and provided the opportunity to address various challenges faced by modern food production. In addition to AI methods, including artificial neural networks (ANNs), numerous chemometric methods (multivariate analysis, calibration and validation, experimental design, predictive modeling, signal processing, etc.) are also of great importance for this field. In some specific fields of food processing engineering, AI can be considered to be the flywheel of innovation, considering its contribution to the process optimization, product development and product design. The innovation, optimization and efficiency in food processing can be achieved through the synergy of artificial intelligence and experimental design. The present review focuses on contemporary and cutting-edge AI and experimental design approaches in food processing engineering and points out their main advantages and disadvantages. Recent applications and achievements in these fields are described and systematically discussed.Processes00The present review focuses on contemporary and cutting-edge AI and experimental design approaches in food processing engineering and points out their main advantages and disadvantages.Processes["Strahinja Z. Kova\u010devi\u0107", "Milica \u017d. Karad\u017ei\u0107 Banjac", "S. Podunavac-Kuzmanovic"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/70ff374a5d62b48aaed5d226340348c50d0aec58 1cf31e023d1c92e0e03f630ed4d2366637f6d1a4Today, artificial intelligence is effective in most aspects of human life. The reason for this can be attributed to the impressive speed and accuracy of artificial intelligence in processing a large volume of data in a short time and, consequently, the increase in speed and accuracy in performing various human tasks. One of these aspects is the use of artificial intelligence as a consultant in judicial proceedings. The speed and accuracy of artificial intelligence, while eliminating the delay of the trial, minimizes the damage caused by human error in the trial process. However, aside from these advantages, the special nature of artificial intelligence and the high volume of data have caused artificial intelligence to not enjoy sufficient transparency in its performance. Therefore, the entry of artificial intelligence into the judicial arena, despite its many desirable benefits, can lead to deterioration in transparency in trials, creating a fundamental challenge: how can the adverse effects of a lack of transparency be minimized while benefiting from the benefits of artificial intelligence in judicial proceedings? Taking all aspects into account, this article considers “supervision” at various stages of design, training, and use of artificial intelligence as the best solution in this regard; Surveillance that can be carried out from different dimensions, at different stages and by competent institutions.Acta Globalis Humanitatis et Linguarum230This article considers “supervision” at various stages of design, training, and use of artificial intelligence as the best solution in this regard; Surveillance that can be carried out from different dimensions, at different stages and by competent institutions.Acta Globalis Humanitatis et Linguarum["Asst. Prof. Dr. Bakhtiyar Najafov"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/1cf31e023d1c92e0e03f630ed4d2366637f6d1a4 7c3c3b19b8c4d32940981e33921cf75dfb8efd23Journal of International Business and Management00Journal of International Business and Management[]2025-03-13T00:00:00https://www.semanticscholar.org/paper/7c3c3b19b8c4d32940981e33921cf75dfb8efd23 21140e886e6fd0aa0ee605a59e62f4bbd176e953Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The requirements and expectations of learners are rapidly changing. AI is currently having a major impact on T&D processes offering learners personalized experiences and convenience. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format. Human Resource Management International Digest10The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.Human Resource Management International Digest[]2025-03-13T00:00:00https://www.semanticscholar.org/paper/21140e886e6fd0aa0ee605a59e62f4bbd176e953 1e543ade2f87ded593a18c6cce783cf91eb4fd51AI &amp; SOCIETY630AI &amp; SOCIETY["Gabriel Fernandez-Borsot"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/1e543ade2f87ded593a18c6cce783cf91eb4fd51 045db6b4769a8cbd8540c49a306144cd0a5dfc10Journal of Mechanics in Medicine and Biology00Journal of Mechanics in Medicine and Biology["Min Fan", "Mariusz Lipowski", "Xinghao Wang", "Taofeng Liu"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/045db6b4769a8cbd8540c49a306144cd0a5dfc10 24eb8812b63206414c9f749e280d6d2ba4c0a378JCO Oncology Practice40JCO oncology practice["Chirag Shah", "Stephen M Karlovits"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/24eb8812b63206414c9f749e280d6d2ba4c0a378 4c5b13159682f08002da45f9681d9581daa0b12eEvidence-Based Nursing150Evidence-based nursing["Andrew Paul Barker"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/4c5b13159682f08002da45f9681d9581daa0b12e 713a51534b87ae0c005805d288f5847fc1f6f67cManagement in Education70Management in Education["Ayman Hefnawi"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/713a51534b87ae0c005805d288f5847fc1f6f67c b4a82492650483067f0bfd48d1b1c091131ae152Studying the interaction between people and generative AI art has become a popular direction in interdisciplinary studies: this study explores the key mechanisms in people's cognitive responses when identifying generative AI paintings through natural language processing (NLP) techniques. By analyzing the textual feedback of 17 Chinese college students with art appreciation backgrounds, we adopt topic modeling and sentiment analysis to explain their perceptual criteria and emotional responses. This study reveals two key contributions: first, it finds the "paradoxical authenticity" of AI-generated artworks in the eyes of students. Second, it reveals that people tend to reinterpret human imperfections ,such as brushstroke tremors and color imbalances, as signs of artistic authenticity. These findings promote the discussion of creative coexistence between humans and AI and provide empirical insights into human-computer interaction and dynamic evaluation systems for generative art.Applied and Computational Engineering00This study finds the "paradoxical authenticity" of AI-generated artworks in the eyes of students and reveals that people tend to reinterpret human imperfections, such as brushstroke tremors and color imbalances, as signs of artistic authenticity.Applied and Computational Engineering["Jinhua Yang", "Tianyue Niu", "Huali Long"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/b4a82492650483067f0bfd48d1b1c091131ae152 67eea9d73ec920db777c3df5c6e4789ab6cfdd2bRisk Analysis140Risk analysis : an official publication of the Society for Risk Analysis["Vicki M. Bier", "Emanuele Borgonovo", "Tony Cox", "Cynthia Rudin"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/67eea9d73ec920db777c3df5c6e4789ab6cfdd2b 809ebe32409c47686dfb4aadbec6eee3086d758f00["Eirinaios Tsiartas", "Deepti Nayak"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/809ebe32409c47686dfb4aadbec6eee3086d758f d97898ad40a12bde215c78b4a1727dc16d1dfcc3Discover Water1140Discover Water["Adamu Muhammad Ibrahim", "O. Okesanya", "B. Ukoaka", "M. M. Ahmed", "Nimat Bola Idris", "Stephen Bamilosin", "J. Ogaya", "Don Lucero-Prisno Eliseo"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/d97898ad40a12bde215c78b4a1727dc16d1dfcc3 42849c5bf4e061272531feeb221ff8fe77b4aae8Anesthesia and Analgesia160Anesthesia and analgesia["R.D. Minehart", "Scott E Stefanski"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/42849c5bf4e061272531feeb221ff8fe77b4aae8 13daa3346e2ab838d081eac80e36087aff1babe900["Dinesh Sharma", "G. Tomar", "Anand Jha"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/13daa3346e2ab838d081eac80e36087aff1babe9 1f4e76a84c5f40e60af93563e69b39ae69fe4fadBritish medical journal00BMJ["Alexander Mafi", "Samantha Holmes"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/1f4e76a84c5f40e60af93563e69b39ae69fe4fad c2f15cb3540f36827dabe207f069855d5ef76589European Heart Journal260European heart journal["F. Crea"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/c2f15cb3540f36827dabe207f069855d5ef76589 51b2c01a90521e99c20a0efd6b59b8b59267c9dfEconomic Change and Restructuring590Economic Change and Restructuring["Yingji Liu", "Ju Guo", "Fangbing Shen", "Yuegang Song"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/51b2c01a90521e99c20a0efd6b59b8b59267c9df bf04b84adbc951d0f8e99a118ebc50e09691c559ACE Official Conference Proceedings00ACE Official Conference Proceedings["Jirarat Sitthiworachart", "Zhaodi Li", "Thanin Ratanaolarn"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/bf04b84adbc951d0f8e99a118ebc50e09691c559 3fd9beaa779fb0a66886333f70bbfb13eaea7c2dCureus470Cureus["Mohammad Omar Abdalrahman Mohammad Ali", "Selma Mohammed Abdelgadir Elhabeeb", "Nihal Eltayeb Abdalla Elsheikh", "Fatima Siddig Abdalla Mohammed", "Sulafa Hassan Mahmoud Ali", "Aya Abuelgasim Ibrahim Abdelhalim", "Dalia Saad Altom"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/3fd9beaa779fb0a66886333f70bbfb13eaea7c2d 892deb81a471e904f13b1d5f4829faeac732b854Pathological myopia (PM) is a serious visual impairment that may lead to irreversible visual damage or even blindness. Timely diagnosis and effective management of PM are of great significance. Given the increasing number of myopia cases worldwide, there is an urgent need to develop an automated, accurate, and highly interpretable PM diagnostic technology.We proposed a computational model called PMPred-AE based on EfficientNetV2-L with attention mechanism optimization. In addition, Gradient-weighted class activation mapping (Grad-CAM) technology was used to provide an intuitive and visual interpretation for the model’s decision-making process.The experimental results demonstrated that PMPred-AE achieved excellent performance in automatically detecting PM, with accuracies of 98.50, 98.25, and 97.25% in the training, validation, and test datasets, respectively. In addition, PMPred-AE can focus on specific areas of PM image when making detection decisions.The developed PMPred-AE model is capable of reliably providing accurate PM detection. In addition, the Grad-CAM technology was also used to provide an intuitive and visual interpretation for the decision-making process of the model. This approach provides healthcare professionals with an effective tool for interpretable AI decision-making process.Frontiers in Medicine510The developed PMPred-AE model is capable of reliably providing accurate PM detection and provides healthcare professionals with an effective tool for interpretable AI decision-making process.Frontiers in Medicine["Hong-Qi Zhang", "Muhammad Arif", "Maha A. Thafar", "Somayah Albaradei", "Peiling Cai", "Yang Zhang", "Hua Tang", "Hao Lin"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/892deb81a471e904f13b1d5f4829faeac732b854 6ae1c415899afe4d7756f72522a14b541529b6cdPurpose The purpose of this study is to examine the impact of adopting artificial intelligence (AI) on the quality of corporate sustainability reporting. The role of sustainability committees, including specialist environmental, social and governance (ESG) committees, in moderating this dynamic is also examined. Design/methodology/approach Regression analysis is used to analyze the quality of ESG/sustainability disclosures of listed Chinese companies from 2015 to 2022. Robustness is ensured through fixed effects analysis, while endogeneity concerns are addressed using one-year lagged measures and the three-stage least squares (3SLS) approach. Sustainability committees are categorized based on their ESG specific focus areas, and aligned with the corresponding ESG disclosure pillars. In addition, for the governance pillar, the analysis is extended by segmenting the sample based on state ownership status. Stakeholder theory and the dynamic capability view are used to frame the analysis. Findings The results reveal that AI adoption enhances overall sustainability reporting quality and pillar-specific ESG disclosure quality. This positive effect is amplified by the presence of sustainability committees. Examining the heterogeneous impact of these committees revealed stronger associations between sustainability committee specialization and relevant ESG pillar disclosure quality (except for governance), suggesting that use of specialist committees can improve disclosure outcomes. Notably, within non-state-owned enterprises, governance-focused committees positively moderate the AI−disclosure relationship, highlighting a nuanced effect based on ownership structure. Practical implications The findings offer empirical support for companies to leverage AI in sustainability reporting. This study finds evidence to support the creation of sustainability committees, as a key corporate governance mechanism to drive corporate sustainability reporting. The use of specialist sustainability committees can drive improvements in disclosure quality relating to specific ESG pillars. The research indicates that disclosure over governance remains poor and will require additional regulatory effort to encourage entities to provide higher quality governance-related disclosures. In terms of ownership structure, the study found that non-state-owned enterprises are more efficient in using specialist sustainability committees to improve disclosure quality. Social implications The findings highlight the potential of AI in supporting high-quality sustainability reporting and the strategic role of sustainability committees in this dynamic. The publication of high-quality sustainability reports is critical in meeting stakeholder demands for transparency and corporate accountability on sustainability. Originality/value The findings offer insights into AI’s role in supporting high-quality sustainability reporting. By examining the moderating effects of sustainability committees, the research goes beyond examining a general impact to exploring how corporate governance mechanisms impact this relationship. In addition, the unique data on Chinese companies highlights differences between state-owned and non-state-owned enterprises, with the latter exhibiting greater potential to leverage specialist sustainability committees for improving sustainability reporting. Meditari Accountancy Research781Meditari Accountancy Research["Khwaja Naveed", "Muhammad Bilal Farooq", "M. K. Zahir-ul-Hassan", "Fawad Rauf"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/6ae1c415899afe4d7756f72522a14b541529b6cd aeb4edda956670cd9db1e8160f9fb7fce3c139ebThe current article examines artificial intelligence's (AI) role in leadership training, education, and development across higher education and industry contexts. We analyze current implementations and explore how AI technologies reshape leadership preparation while investigating the essential balance between task‐oriented and relationship‐oriented approaches. Our analysis reveals that successful AI integration depends on human‐in‐the‐loop processes, pedagogical design that preserves relationship‐building, and comprehensive AI literacy development. The study introduces the concept of ‘taxonomical leapfrogging’ and demonstrates how AI can enhance traditional leadership development through sophisticated content sequencing, personalized learning pathways, and intelligent feedback systems. We provide a practical framework for implementing AI tools while identifying key challenges, including quality assurance at scale and ethical considerations. Our findings suggest that effective leadership development requires integrated approaches that leverage AI's capabilities while preserving essential human elements, with specific recommendations for both academic programs and industry initiatives.Journal of Leadership Studies351The study introduces the concept of ‘taxonomical leapfrogging’ and demonstrates how AI can enhance traditional leadership development through sophisticated content sequencing, personalized learning pathways, and intelligent feedback systems.Journal of Leadership Studies["Daniel Jenkins", "Gaurav Khanna"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/aeb4edda956670cd9db1e8160f9fb7fce3c139eb fcdc94d83521abe9748e884c640562a023e1741ePostpartum hemorrhage (PPH) is still a major cause of maternal death and morbidity globally, especially in areas with limited resources. This emphasizes the urgent need for strong training programs for healthcare professionals as well as efficient therapies. When first-line uterotonic medicines are ineffective, intrauterine balloon tamponade (IUBT) has become a useful, minimally invasive method for treating PPH. However, the ability of healthcare professionals, particularly midwives, to carry out IUBT accurately and effectively is crucial to its successful deployment. Conventional IUBT training approaches, such as lectures, demonstrations, and practical mannequin practice, frequently have drawbacks in terms of scalability, objectivity of evaluation, uniformity, and capacity to offer tailored feedback. These restrictions may make it more difficult to learn and retain critical skills, which may affect patient outcomes and safety. This study explores the revolutionary potential of artificial intelligence (AI)-driven evaluation methodologies to enhance midwives' competence with condom-catheter IUBT to address the shortcomings of conventional training methods.ABUAD Journal of Engineering Research and Development (AJERD)260This study explores the revolutionary potential of artificial intelligence (AI)-driven evaluation methodologies to enhance midwives' competence with condom-catheter IUBT to address the shortcomings of conventional training methods.ABUAD Journal of Engineering Research and Development (AJERD)["Oluwatoyin Olajumoke Akinyemi", "G. Ogbeye", "O. Adegbilero-Iwari", "A. Afolalu"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/fcdc94d83521abe9748e884c640562a023e1741e 696785f7cd03497e8216b5fc86c9ed276f4940c0Purpose This study aims to explore the application of ChatGPT to analyze hotel guest satisfaction from online reviews. As online feedback plays a critical role in consumer decision-making in the hospitality industry, the research evaluates the accuracy and reliability of ChatGPT’s ratings compared to those of human raters and classic supervised machine learning classification techniques. Design/methodology/approach Using TripAdvisor reviews of five-star hotels, the authors use a structured two-phase study to assess both inter- and intra-rater reliability. Findings The results highlight distinct differences in rating behavior between artificial intelligence (AI) and human judges, with ChatGPT showing a tendency toward more moderate ratings. In addition, the authors observe a slight tendency for guests to overrate their experiences, supporting the literature on the subjective nature of online reviews. Despite these variations, ChatGPT shows significant agreement with guest ratings, especially when minor discrepancies are accounted for, suggesting its utility as a feedback analysis tool in the hospitality industry. This paper highlights ChatGPT’s ability to process and evaluate textual data and discusses the implications of using AI to improve review analysis processes in hospitality management. The authors advocate the incorporation of AI tools into customer feedback systems to augment human analysis and suggest future research to refine AI models for practical applications. Originality/value This study advances the understanding of AI’s role in hospitality management by demonstrating the practical application of ChatGPT for analyzing guest satisfaction through online reviews and providing a methodological framework for assessing the reliability of AI-generated content. Journal of Hospitality and Tourism Technology590ChatGPT’s ability to process and evaluate textual data is highlighted and the implications of using AI to improve review analysis processes in hospitality management are discussed and the authors advocate the incorporation of AI tools into customer feedback systems to augment human analysis.Journal of Hospitality and Tourism Technology["Sandra Morini-Marrero", "Jos\u00e9 M. Ramos-Henr\u00edquez", "Anil Bilgihan"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/696785f7cd03497e8216b5fc86c9ed276f4940c0 72ca2beecf2e738d31145d921826577fd64e7dc7Scientific Reports220An Adaptive Gabor Filter (AGF) based on the Chaotic Map is created to improve filtering performance and the Grad Cam in the suggested technique assures the effective implementation of segmentation and classification performance.Scientific reports["Neeraj Sharma", "Praveen Lalwani"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/72ca2beecf2e738d31145d921826577fd64e7dc7 cdd2e24e78c22f52d0958ce2a9bfacd33b179839This study aims to explore the transformative role of Artificial Intelligence (AI) in food manufacturing by optimizing production, reducing waste, and enhancing sustainability. This review follows a literature review approach, synthesizing findings from peer-reviewed studies published between 2019 and 2024. A structured methodology was employed, including database searches and inclusion/exclusion criteria to assess AI applications in food manufacturing. By leveraging predictive analytics, real-time monitoring, and computer vision, AI streamlines workflows, minimizes environmental footprints, and ensures product consistency. The study examines AI-driven solutions for waste reduction through data-driven modeling and circular economy practices, aligning the industry with global sustainability goals. Additionally, it identifies key barriers to AI adoption—including infrastructure limitations, ethical concerns, and economic constraints—and proposes strategies for overcoming them. The findings highlight the necessity of cross-sector collaboration among industry stakeholders, policymakers, and technology developers to fully harness AI's potential in building a resilient and sustainable food manufacturing ecosystem.Frontiers in Nutrition950The study examines AI-driven solutions for waste reduction through data-driven modeling and circular economy practices, aligning the industry with global sustainability goals, and identifies key barriers to AI adoption and proposes strategies for overcoming them.Frontiers in Nutrition["Kushagra Agrawal", "Polat Goktas", "Maike Holtkemper", "Christian Beecks", "Navneet Kumar"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/cdd2e24e78c22f52d0958ce2a9bfacd33b179839 d04468d5289d97e1f95ff8b358cc96b066b2718bBACKGROUND The diagnosis of interstitial lung disease (ILD) can pose a challenge as the pulmonary function test (PFT) is only minimally affected at the onset. To improve early diagnosis, this study aims to explore the potential of artificial intelligence (AI) software in assisting pulmonologists with PFT interpretation for ILD diagnosis. The software provides an automated description of PFT and disease probabilities computed from an AI model. STUDY METHODS In study phase 1, a cohort of 60 patients, 30 of whom had ILD, were retrospectively diagnosed by 25 pulmonologists (8 junior physicians and 17 experienced pneumologists) by evaluating a PFT (body plethysmography and diffusion capacity) and a short medical history. The experts screened the cohort twice, without and with the aid of AI (ArtiQ.PFT, V.1.4.0, ArtiQ, BE) software and provided a primary diagnosis and up to three differential diagnoses for each case. In study phase 2, 19 pulmonologists repeated the protocol after using ArtiQ.PFT for 4-6 months. RESULTS Overall, AI increased the diagnostic accuracy for various lung diseases from 41.8% to 62.3% in study phase 1. Focusing on ILD, AI improved the detection of lung fibrosis as the primary diagnosis from 42.8% without AI to 72.1% with AI (p<0.0001). Phase 2 yielded a similar outcome: using AI increased ILD diagnosis based on primary diagnosis (53.2% to 75.1%; p<0.0001). ILD detections without AI support significantly increased between phase 1 and phase 2 (p=0.028) but not with AI (p=0.24). INTERPRETATION This study shows that AI-based decision support on PFT interpretation improves accurate and early ILD diagnosis.Thorax140This study shows that AI-based decision support on PFT interpretation improves accurate and early ILD diagnosis and increases the diagnostic accuracy for various lung diseases.Thorax["Daniela Gompelmann", "M. Gysan", "Paul Desbordes", "Julie Maes", "Karolien Van Orshoven", "Maarten De Vos", "Markus Steinwender", "E. Helfenstein", "Corina Marginean", "N. Henzi", "P. Cerkl", "Patrick Heeb", "Stephan Keusch", "Gianluca Calderari", "Paul von Boetticher", "Bernhard Baumgartner", "Daiana Stolz", "M. Simon", "H. Prosch", "Wim Janssens", "Marko Topalovic"]2025-03-13T00:00:00https://www.semanticscholar.org/paper/d04468d5289d97e1f95ff8b358cc96b066b2718b bf6350b9e83ace6ecd095e6e46a21782200a0253 This study aims to shed light on how artificial intelligence based on robust algorithms is used in providing public services and the public’s fears about dealing with these systems. The challenges facing governments that use these systems are accountability, transparency, integrity and addressing errors in advanced technologies. This study used the descriptive approach to describe and analyze public procurement and how public service systems are purchased. The analytical approach was also used to analyze the problems and issues that could result from using artificial intelligence in providing public services regarding concerns about its use and issues of transparency, access to information, accountability and responsibility. The government sector must uphold rights, freedoms, human rights and the rule of law, as well as a commitment to justice, responsibility, integrity, transparency, accountability and openness if this paper use private AI systems. These AI systems will still have the motivations and ideals of the organization and their creators. Accountability systems and governance processes are still needed. Therefore, developing these technologies in-house is not the solution to corporate adoption and interconnection. AI procurement requirements and documentation should apply to internal and external development scenarios. This study outlined the difficulties public bodies have when purchasing AI systems and the long-term effects that call for developing procurement policies and procedures tailored to the needs of AI. Future studies might analyze the advantages and disadvantages of openness, particularly regarding disclosures made to the public. In what ways are disclosures made to the public aid in AI system governance? What restrictions apply to disclosures? Is it possible to use new forms of emerging technology to help the public engage meaningfully in discussions about due process and fundamental rights? International Journal of Law and Management1332The difficulties public bodies have when purchasing AI systems and the long-term effects that call for developing procurement policies and procedures tailored to the needs of AI are outlined.International Journal of Law and Management["K. Aboelazm"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/bf6350b9e83ace6ecd095e6e46a21782200a0253 d07686e951fe41f184675f826dd1fafb9b042879This article explores the limits and risks of using artificial intelligence (AI) and large language models (LLMs) as tools to expedite and streamline empirical legal research and content analysis of cases. It emphasises the current risks and limits of AI for enabling socio-legal research and case-based analysis, including a lack of reproducibility, bias, hallucinations and inaccuracy. It concludes that, at present, legal researchers should be exceedingly cautious when utilising new tools and need to put in place stringent procedures to better evaluate the outputs of AI models.Alternative Law Journal00The limits and risks of using artificial intelligence (AI) and large language models (LLMs) as tools to expedite and streamline empirical legal research and content analysis of cases are explored.Alternative Law Journal["A. Blackham"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/d07686e951fe41f184675f826dd1fafb9b042879 50c20eb1460fa7237d61c344556f1e4f0137b72bThis research aims to explore the prospects and challenges of applying Artificial Intelligence (AI) in family law dispute resolution. The method used in this research is a qualitative approach with literature study and normative juridical analysis, as well as interviews with legal practitioners and academics. The results show that AI has great potential in improving legal efficiency and accessibility by accelerating the mediation process and providing initial legal consultation for the community. However, the research also identified several key challenges, including AI's limitations in understanding the emotional and psychological aspects of family disputes, the risk of algorithm bias, and the legal validity of the resulting decisions. The implications of this research confirm that AI can be a useful support tool in the legal process, but it cannot completely replace the role of humans. Therefore, strict regulations as well as human involvement in the verification process and final decision-making are needed to ensure that justice is maintained.Journal of Multidiscipline and Collaboration Research00The results show that AI has great potential in improving legal efficiency and accessibility by accelerating the mediation process and providing initial legal consultation for the community, but also identified several key challenges, including AI's limitations in understanding the emotional and psychological aspects of family disputes.Journal of Multidiscipline and Collaboration Research["Sri Yuliani", "Syarif Firmansyah", "Raynaldi Nugraha Prasetya"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/50c20eb1460fa7237d61c344556f1e4f0137b72b 55d17bdbb4cc3af780ac3ea57a1698fdcff43267Artificial Intelligence (AI) has brought new opportunities in medicine, with a great potential to improve care provided to patients. Given the technical complexity and continuously evolving field, it can be challenging for vascular specialists to anticipate and foresee how AI will shape their practice. The aim of this review is to provide an overview of the current landscape of applications of AI in clinical practice for the management of non-cardiac vascular diseases including aortic aneurysm, peripheral artery disease, carotid stenosis, and venous diseases. The review describes and highlights how AI has the potential to shape the three pillars in the management of vascular diseases including clinical practice, medical research and education. In the limelight of these results, we show how AI should be considered and developed within a responsible ecosystem favoring transdisciplinary collaboration, where multiple stake holders can work together to face current challenges and move forward future directions.Angiology860An overview of the current landscape of applications of AI in clinical practice for the management of non-cardiac vascular diseases including aortic aneurysm, peripheral artery disease, carotid stenosis, and venous diseases is provided.Angiology["Fabian Lareyre", "J. Raffort"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/55d17bdbb4cc3af780ac3ea57a1698fdcff43267 b88e6a1dc8d86729f1c652e330e2d5e4b8f797d0Innere Medizin120The aim of this work is to present the data protection aspects of using cloud-based LLMs in clinical research and patient care in Germany and the European Union and to develop guidelines for users.Innere Medizin["Eva Weicken", "M. Mittermaier", "Thomas Hoeren", "Juliana Kliesch", "Thomas Wiegand", "M. Witzenrath", "Miriam Ballhausen", "Christian Karagiannidis", "Leif Erik Sander", "Matthias I Gr\u00f6schel"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/b88e6a1dc8d86729f1c652e330e2d5e4b8f797d0 75697e303b20fffc972a2ca2e4e274188f670cfaDuring the past decade, technological advancements have transformed liver surgery. New tools are available to assist the surgeon during complex operations, such as a hepatectomy for liver cancer. Augmented reality (AR) is an innovative technology that utilizes computed tomography (CT) or magnetic resonance imaging (MRI) scans to create three-dimensional (3D) images of the area or the organ of interest. This is especially useful for minimally invasive liver resection (MILR), where the field of view and maneuverability during the operation is limited. A 3D image of vascular structures, hilar segments, and the tumor location is projected into the operating field, thus contributing to a more precise resection. Combining AR with the groundbreaking capabilities of artificial intelligence (AI) could further improve the surgical outcomes of MILR. Specialized AI programs are designed to analyze the surgical field, provide information and facilitate the operation plan, simplify intraoperative decision making and reduce human error. 3D printing of hepatocellular cancer liver models is another useful technology that allows for procedure simulation, proper preoperative planning, and effective intraoperative navigation. Even though the benefits could be outstanding, the large cost of those technologies is a major limiting factor. Future research should focus on making AI and 3D imaging tools more widely affordable to the healthcare industry as data show that they could improve diagnostic efficiency, increase surgical precision, minimize human error and optimize patient care.Mini-invasive Surgery480Artificial intelligence and 3D imaging tools could improve diagnostic efficiency, increase surgical precision, minimize human error and optimize patient care as data show that they could improve diagnostic efficiency, increase surgical precision, minimize human error and optimize patient care.Mini-invasive Surgery["P. Chatzikomnitsa", "Areti Danai Gkaitatzi", "M. Papakonstantinou", "E. Louri", "Dimitrios Giakoustidis", "Vasileios N. Papadopoulos", "A. Giakoustidis"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/75697e303b20fffc972a2ca2e4e274188f670cfa 569d78a4d82df31fc1f8c1f8b635114c6f8da269Given the increasing problem of infertility in the Russian Federation, assisted reproductive technologies (ART) have proven to be one of the most effective treatments for this condition. Notably, the introduction of ART methods, particularly in vitro fertilization (IVF), has led to markedly increased birth rates over the past two decades. Studies show that machine learning algorithms can process images of embryos to assess their quality, thus facilitating the selection of the most viable among them for transfer. There are ethical and technical barriers hindering the widespread adoption of artificial intelligence (AI) in clinical practice, including concerns over data privacy as well as a need to train specialists to deal with new technologies. AI can analyze vast amounts of data, including medical histories and research results, to more accurately predict pregnancy outcomes. This enables doctors to make more justified clinical decisions. In the future, AI algorithms will be able to analyze patient data more efficiently, helping to identify the causes of infertility at earlier stages.Obstetrics Gynecology and Reproduction1020Artificial intelligence algorithms will be able to analyze patient data more efficiently, helping to identify the causes of infertility at earlier stages, and enable doctors to make more justified clinical decisions.Obstetrics, Gynecology and Reproduction["V. A. Lebina", "O. K. Shikhalakhova", "A. A. Kokhan", "I. Y. Rashidov", "K. A. Tazhev", "A. V. Filippova", "E. P. Myshinskaya", "Yu. V. Symolkina", "Yu. I. Ibuev", "A. A. Mataeva", "A. N. Sirotenko", "T. T. Gabaraeva", "A. I. Askerova"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/569d78a4d82df31fc1f8c1f8b635114c6f8da269 0c9238e44b61f2cda21e1288a0de0cd4ffd0991fThe rapid development of artificial intelligence (AI) is fundamentally changing the methods and scale of cybercrime, and also poses significant challenges for legal regulation. This article highlights the fundamental aspects of the use of AI to commit cyberattacks (automated hacking tools, deep fakes, intellectual fraud, etc.) and considers ways to counter them by law enforcement agencies. The legal aspects related to the extended autonomy of AI systems are examined, leading to new liability issues, problems of assessing deep fakes evidence and the need for international cooperation in cybersecurity field. The rapid development of artificial intelligence (AI) is fundamentally changing the methods and scale of cybercrime, and also poses significant challenges for legal regulation. This article highlights the fundamental aspects of using AI to commit cyberattacks (automated hacking tools, deepfakes, intellectual fraud, etc.) and explores ways in which law enforcement agencies can counter them. It reveals legal aspects related to the extended autonomy of AI systems, which leads to new issues of liability, problems of assessing evidence of deepfakes, and the need for international cooperation in the field of cybersecurity. The aim of the study is to develop a holistic view of the threats posed by AI tools in the context of cybercrime and to formulate recommendations for improving national and transnational legislation. The article proposes specific mechanisms for ensuring the reliability of digital evidence (from the creation of algorithms for detecting manipulations to methods for analyzing the chain of their storage), highlights the current practice of criminal prosecution in Ukraine and abroad, and also provides proposals for the unification of procedures for forensic analysis of materials obtained or modified with the help of AI. The results of the work show that effective legal counteraction to AI-based cybercrime requires the simultaneous development of technical tools, enhanced protection of human rights, and international harmonization of legal norms. The development of specialized investigation methods, including big data analytics and machine learning technologies, must be balanced with increasing security and transparency standards. Particular attention is paid to the issue of further modernization of training programs for legal professionals and the involvement of experts in the field of cybersecurity, which will allow for a faster response to the dynamics of new threats.Contemporary Issues in Artificial Intelligence00The results of the work show that effective legal counteraction to AI-based cybercrime requires the simultaneous development of technical tools, enhanced protection of human rights, and international harmonization of legal norms.Contemporary Issues in Artificial Intelligence["Volodymyr Zverev", "Valeriy Bushkov", "Borys Khrushkov", "Volodymyr Sarychev", "Oleksiy Ostaltsev", "Yehor Prokopovych-Tkachenko"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/0c9238e44b61f2cda21e1288a0de0cd4ffd0991f 4ebd41ca6026c543b7f4c8ea24e2b244a69f1abfThis article discusses the manuscript recently published in the World Journal of Gastroenterology, which explores the application of deep learning models in decision-making processes via wireless capsule endoscopy. Integrating artificial intelligence (AI) into gastrointestinal disease diagnosis represents a transformative step toward precision medicine, enhancing real-time accuracy in detecting multi-category lesions at earlier stages, including small bowel lesions and precancerous polyps, ultimately improving patient outcomes. However, the use of AI in clinical settings raises ethical considerations that extend beyond technological potential. Issues of patient privacy, data security, and potential diagnostic biases require careful attention. AI models must prioritize diverse and representative datasets to mitigate inequities and ensure diagnostic accuracy across populations. Furthermore, balancing AI with clinical expertise is crucial, positioning AI as a supportive tool rather than a replacement for physician judgment. Addressing these ethical challenges will support the responsible deployment of AI, through equitable contribution to patient-centered care.World Journal of Gastroenterology300The manuscript recently published in the World Journal of Gastroenterology, which explores the application of deep learning models in decision-making processes via wireless capsule endoscopy, finds that balancing AI with clinical expertise is crucial, positioning AI as a supportive tool rather than a replacement for physician judgment.World Journal of Gastroenterology["Davide Ramoni", "Alessandro Scuricini", "F. Carbone", "L. Liberale", "Fabrizio Montecucco"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/4ebd41ca6026c543b7f4c8ea24e2b244a69f1abf d687d0c2d81fb94ad3b72255cca2e2f9b16536d2Advances in Manufacturing1650This study reviews the methods and applications of combining AI with CAE and discusses current research deficiencies as well as future research trends.Advances in Manufacturing["Xu-Wen Zhao", "Xiao-Meng Tong", "Fang-Wei Ning", "Mao-Lin Cai", "Fei Han", "Hong-Guang Li"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/d687d0c2d81fb94ad3b72255cca2e2f9b16536d2 b5064c5715b1f25fe3186c8f24c7a073b6292f9dIn recent years, there has been a growing interest in the application of artificial intelligence (AI) in education. AI is currently used in many areas of education, including administration in schools, learning adaptations, and expanding the accessibility of education. Although AI has a lot of potential benefits, there are obstacles that must be conquer before it can be put into practice. This study examined the relationship between AI and the teaching-learning process in education in Nigeria, specifically focusing on undergraduates at Delta State University, Abraka. A cross-sectional study approach was used in this study, which obtained data from 437 undergraduate students. A stratified sampling technique was used to select a sample of the respondents. To analyze the data for the study, the t-test, correlation, and linear regression analysis were employed. This study demonstrated the robust and beneficial relationship between artificial intelligence and the teaching-learning process in education in Nigerian. However, there are also difficulties with using AI in education. These difficulties include students' excessive reliance on technology, concerns about data security and privacy, unequal access, prejudice and discrimination, technological difficulties, and the decline of human values. The study concluded that in order to prepare their students for the challenges of the AI revolution, Nigerian educational institutions should educate and nurture their students. The use of AI has the potential to improve teaching- learning process, personalize learning to students' needs, and boost administrative effectiveness in school management. Ensuring fairness and inclusivity and the future of teaching in the AI age are crucial when creating AI-based educational systems.Journal Plus Education00The study concluded that in order to prepare their students for the challenges of the AI revolution, Nigerian educational institutions should educate and nurture their students.Journal Plus Education["U. C. Okolie", "Thomastina Nkechi Egbon"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/b5064c5715b1f25fe3186c8f24c7a073b6292f9d b0c5871ea0d431e3d473745e065e645f6a046ce6International Journal of Educational Technology in Higher Education290Trust and social reinforcement strongly influenced college professors’ GenAI adoption decisions and acted as significant mediators to better understand the relationship between TAM and SCT, emphasized the power of social dynamics in shaping professors’ self-efficacy, attitudes, and use of GenAI.International Journal of Educational Technology in Higher Education["Aya Shata", "Kendall Hartley"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/b0c5871ea0d431e3d473745e065e645f6a046ce6 09c9c69f4b9708139cabd53868cfc566eb6f8263Due to the rapidly growing adoption of artificial intelligence (AI) technology, there has been an increased focus in recent times on the opportunities and perils of AI use. The authors propose STRIFE, a novel socio-technical threat modeling framework which combines technical, ethical, and legal dimensions to proactively identify and address negative and unintended consequences of AI systems. A second contribution of this inquiry is to enable the use of the National Institute of Standards and Technology AI Risk Management Framework to perform threat modeling in conjunction with STRIFE. By addressing AI threats using socio-technical considerations throughout the AI lifecycle, organizations can better engage with their societal stakeholders in managing the risks associated with AI systems. For these reasons, this study is expected to benefit academics, practitioners, and industry as a whole.International Journal of Intelligent Information Technologies870By addressing AI threats using socio-technical considerations throughout the AI lifecycle, organizations can better engage with their societal stakeholders in managing the risks associated with AI systems.International Journal of Intelligent Information Technologies["R. Parthasarathy", "Anuradha Rangarajan", "Saran Ghatak", "P. Bingi"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/09c9c69f4b9708139cabd53868cfc566eb6f8263 cf45199ab8bc990dbf45636573e803cdcdf59bf9This work aims to improve intelligence decision-making using interval-valued picture fuzzy sets (IVPFS). In particular, it explores using Sugeno-Weber (SW) norms in IVPFS data recording, providing reliable estimates important for decision-making. This paper introduces a new class of aggregation operators such as the interval-valued picture fuzzy Sugeno-Weber power average (IVPFSWPA), interval-valued picture fuzzy Sugeno-Weber power geometric (IVPFSWPG), interval-valued picture fuzzy Sugeno-Weber power weighted average (IVPFSWPWA), and interval-valued picture fuzzy Sugeno-Weber power weighted geometric (IVPFSWPWG) operators. The real-life characteristics and specific situations of these operators are described as well as how they adapt to real-life situations. The new multi-attribute decision-making method suitable for many practical applications with different requirements or functions is proposed. An example of an intelligent selection process is given to demonstrate its effectiveness. In addition, a general comparative method is proposed to demonstrate the effectiveness and suitability of the collective strategy by comparing its results with existing methods. The study concludes by summarizing its findings and discussing its prospects, highlighting the potential contribution of the proposed studies to the advancement of cutting-edge technology in a dynamic and complex environment.Spectrum of Engineering and Management Sciences00This work explores using Sugeno-Weber (SW) norms in IVPFS data recording, providing reliable estimates important for decision-making, and introduces a new class of aggregation operators such as the interval-valued picture fuzzy Sugeno-Weber power average, interval-valued picture fuzzy Sugeno-Weber power geometric, and interval-valued picture fuzzy Sugeno-Weber power weighted geometric operators.Spectrum of Engineering and Management Sciences["Rizwan Gul", "Mehwish Sarfraz"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/cf45199ab8bc990dbf45636573e803cdcdf59bf9 025f1c1cb26cf0027a364dfaeb9ed6409f04e1c6European Journal of Communication30European Journal of Communication["Wenjing Xia", "Weixiao Li"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/025f1c1cb26cf0027a364dfaeb9ed6409f04e1c6 04427fa3048c6b621f0446a720dc41236f92f745Journal of Global Scholars of Marketing Science450Journal of Global Scholars of Marketing Science["Dong-Hwan Park", "Qi Jiang", "Kyung Hoon Kim"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/04427fa3048c6b621f0446a720dc41236f92f745 0750173534b28293521a6fd9006480c82c99acd700["P.V. Mohanan"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/0750173534b28293521a6fd9006480c82c99acd7 5e3d5be4cb49fe225f05b759cf06357441351a4c00["Ankita Sharma", "Nayancy", "Rajat Verma"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/5e3d5be4cb49fe225f05b759cf06357441351a4c 8c7e04d465b1b9e10e12ffd638de79fd7d9b6c3dIn this essay, we explore AI critically from the perspectives of the corpus and structure of knowledge, human control of knowledge-based processes (including business decision-making and public policymaking), and capitalist ideology. We present a preliminary test of our argument with a re-analysis of a corpus of citizen visions of desirable and sustainable futures across 30 countries in Europe, demonstrating how pre-trained AI provides findings that appear convincing but in effect lack situational and contextual understanding and depth. Indeed, AI relies on the data that has been used in its training. Our conclusions discuss how capitalism through AI aims for advanced forms of productivity and efficiency that pose new challenges to marketing as an embedded cultural practice. We offer some suggestions for ways macromarketing scholars can help overcome some of these challenges.Journal of Macromarketing270This essay explores AI critically from the perspectives of the corpus and structure of knowledge, human control of knowledge-based processes, and capitalist ideology to discuss how capitalism through AI aims for advanced forms of productivity and efficiency that pose new challenges to marketing as an embedded cultural practice.Journal of Macromarketing["N. Dholakia", "P. Repo", "A. Firat", "In\u00eas Campos", "P. Timonen"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/8c7e04d465b1b9e10e12ffd638de79fd7d9b6c3d 22c29263d637533ad9856c4f8c43c3fc97c8cdb3The rapid proliferation of artificial intelligence (AI) has intensified the need for ethical governance frameworks that address its societal impacts. This article investigates how UNESCO's ethical AI guidelines influence leadership practices across diverse cultural and organizational contexts, with particular attention to the role of human intelligence (HI) in complementing AI governance. Through comparative document analysis of artificial intelligence governance frameworks in the United States, European Union, and China, supplemented by a case study on India and insights from UNESCO contributors, the study reveals key patterns in the regional implementation of ethical principles, such as transparency, accountability, and inclusivity. The findings emphasize the critical role of leadership in navigating the intersection of global frameworks and local priorities. The research contributes to the growing discourse on ethical AI governance and leadership in transformative technologies by identifying essential leadership competencies and offering actionable recommendations.Journal of Leadership Studies201This article investigates how UNESCO's ethical AI guidelines influence leadership practices across diverse cultural and organizational contexts, with particular attention to the role of human intelligence in complementing AI governance.Journal of Leadership Studies["Erik Bean", "Cheryl Burleigh", "Christine Haskell", "Tashieka S Burris-Melville", "Jimmy Payne", "Bhavna Pathak"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/22c29263d637533ad9856c4f8c43c3fc97c8cdb3 ccfa97318bd7b5ba3685e3613b9e23799e5aaf00PurposeDespite increasing research on artificial intelligence (AI) in business, further studies are needed to understand how AI adoption shapes existing and develops new organisational capabilities. This paper aims to examine how AI adoption fosters ambidexterity, both directly and indirectly, through decision-making comprehensiveness (DMC), while also exploring the role of family involvement in this process.Design/methodology/approachWe gathered data in a 2-wave survey among 582 management-level participants from UK firms addressed through the Prolific platform. A moderated mediation model was tested in SPSS PROCESS.FindingsWe find evidence of partial mediation, as AI adoption directly and indirectly fosters ambidexterity through DMC. However, no moderating effect of family involvement is observed. Family firms leverage AI for ambidexterity as effectively as non-family firms, with their focus on long-term survival and adaptability complementing AI-driven decision-making comprehensiveness without compromising core values or socioemotional wealth.Practical implicationsManagers should consider adopting AI technologies as a strategic enabler to improve DMC and enhance ambidexterity. Our results suggest that family firms may benefit equally from AI despite potential hesitations. We provide suggestions for family firms on how to facilitate AI adoption while overcoming scepticism.Originality/valueOur study responds to calls for insights into organisational constructs that clarify the mechanisms behind AI integration and capability development. By examining the role of family involvement, we explore how family businesses can adopt AI to foster innovation capabilities while preserving their legacy. In doing so, we bridge AI research with the family business literature.Journal of Family Business Management700Examining how AI adoption fosters ambidexterity through decision-making comprehensiveness (DMC), while also exploring the role of family involvement, suggests that family firms may benefit equally from AI despite potential hesitations.Journal of Family Business Management["E. Daskalopoulos", "O. Machek"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/ccfa97318bd7b5ba3685e3613b9e23799e5aaf00 4aecc4217c318d8d265d1cc72dd9f6b05d48274bThe rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed logistics and supply chain management, particularly in the pursuit of sustainability and eco-efficiency. This study explores AI-based methodologies for optimizing logistics operations in the USA, focusing on reducing environmental impact, improving fuel efficiency, and minimizing costs. Key AI applications include predictive analytics for demand forecasting, route optimization through machine learning, and AI-powered fuel efficiency strategies. Various models, such as Linear Regression, XGBoost, Support Vector Machine, and Neural Networks, are applied to real-world logistics datasets to reduce carbon emissions based on logistics operations, optimize travel routes to minimize distance and travel time, and predict future deliveries to plan optimal routes. Other models such as K-Means and DBSCAN are also used to optimize travel routes to minimize distance and travel time for logistics operations. This study utilizes datasets from logistics companies' databases. The study also assesses model performance using metrics such as mean absolute error (MAE), mean squared error (MSE), and R² score. This study also explores how these models can be deployed to various platforms for real-time logistics and supply chain use. The models are also examined through a thorough case study, highlighting best practices and regulatory frameworks that promote sustainability. The findings demonstrate AI's potential to enhance logistics efficiency, reduce carbon footprints, and contribute to a more resilient and adaptive supply chain ecosystem.Journal of Ecohumanism00Journal of Ecohumanism["Reza E Rabbi Shawon", "MD Rokibul Hasan", "Md Anisur Rahman", "Md Abdullah Al Jobaer", "Md Raisul Islam", "Mohammed Kawsar", "Rubi Akter"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/4aecc4217c318d8d265d1cc72dd9f6b05d48274b 36cacb1534d47e590109fb30a3917d7baa1d35b0Price optimization is essential for the success of e-commerce, especially in highly competitive markets. Artificial intelligence (AI) and machine learning (ML) have revolutionized pricing strategies, enabling the analysis of large volumes of data and the implementation of dynamic pricing that adjusts in real-time based on factors such as competition, demand, and consumer behavior. Personalization is one of the key benefits of AI-based pricing, allowing for customer segmentation and tailored offers for different consumer profiles. Additionally, AI facilitates competitive analysis by continuously monitoring market prices for strategic adjustments. This is particularly useful in saturated sectors, where small price variations can significantly impact customer acquisition and retention. Recent studies demonstrate the practical applications of these technologies. Cheng and Zhang (2024) developed an intelligent pricing model for international e-commerce, while Nathalie et al. (2024) explored AI’s impact on operational efficiency and strategic decision-making. Other studies highlight how AI improves consumer engagement and personalizes interactions. Despite the benefits, challenges such as robust infrastructure, ethical concerns (price discrimination), and data privacy require attention. Businesses must ensure transparency and regulatory compliance to prevent negative impacts. AI-powered price optimization represents a significant competitive advantage, driving profitability and enhancing the consumer experience. However, its adoption must balance innovation and ethical responsibility to ensure a positive and sustainable impact on e-commerce.Revista SISTEMAS00Artificial intelligence-powered price optimization represents a significant competitive advantage, driving profitability and enhancing the consumer experience, however, its adoption must balance innovation and ethical responsibility to ensure a positive and sustainable impact on e-commerce.Revista Sistemática["Rafael Carvalho Turatti"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/36cacb1534d47e590109fb30a3917d7baa1d35b0 ea497824097ba65a93e8f85c201c9bd02a612297Scientific Reports160Analysis of how undergraduate students used AI in a large General Education course on sustainability and technology at a research university in the United States in 2023 provides insight into how students navigate AI use when it is explicitly permitted in coursework, with implications for effectively integrating AI into higher education to support student learning.Scientific Reports["Rebecca W. Black", "Bill Tomlinson"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/ea497824097ba65a93e8f85c201c9bd02a612297 b6c8af58458418f700066bd2ae6f40319ca24e09AI and Ethics100The research examines how varying levels of AI transparency directly influence emergency responders' decision-making during crises, exploring the delicate balance between operational openness and security considerations.AI and Ethics["Jaideep Visave"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/b6c8af58458418f700066bd2ae6f40319ca24e09 1128811013987e1cd1db33471356e1084c0a9cc0Background/Objectives: Mental health disorders among children and adolescents represent a significant global health challenge, with approximately 50% of conditions emerging before age 14. Despite substantial investment in services, persistent barriers such as provider shortages, stigma, and accessibility issues continue to limit effective care delivery. This narrative review examines the emerging application of conversational artificial intelligence (AI) in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental considerations required for implementation. Methods: We searched multiple electronic databases (PubMed/MEDLINE, PsycINFO, ACM Digital Library, IEEE Xplore, and Scopus) for literature published between January 2010 and February 2025 that addressed conversational AI applications relevant to pediatric mental health. We employed a narrative synthesis approach with thematic analysis to organize findings across technological approaches, therapeutic applications, developmental considerations, implementation contexts, and ethical frameworks. Results: The review identified promising applications for conversational AI in pediatric mental health, particularly for common conditions like anxiety and depression, psychoeducation, skills practice, and bridging to traditional care. However, most robust empirical research has focused on adult populations, with pediatric applications only beginning to receive dedicated investigation. Key therapeutic mechanisms identified include reduced barriers to self-disclosure, cognitive change, emotional validation, and behavioral activation. Developmental considerations emerged as fundamental challenges, necessitating age-appropriate adaptations across cognitive, emotional, linguistic, and ethical dimensions rather than simple modifications of adult-oriented systems. Conclusions: Conversational AI has potential to address significant unmet needs in pediatric mental health as a complement to, rather than replacement for, human-delivered care. Future research should prioritize developmental validation, longitudinal outcomes, implementation science, safety monitoring, and equity-focused design. Interdisciplinary collaboration involving children and families is essential to ensure these technologies effectively address the unique mental health needs of young people while mitigating potential risks.Children900A narrative review examines the emerging application of conversational artificial intelligence in pediatric mental health contexts, mapping the current evidence base, identifying therapeutic mechanisms, and exploring unique developmental considerations required for implementation.Children["Masab A Mansoor", "Ali Hamide", "Tyler Tran"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/1128811013987e1cd1db33471356e1084c0a9cc0 6317c2dc7ea710155cfe9b52b6012ecc990448ceToday, generative artificial intelligence is enabling industries to transform at an unprecedented pace. In this paper, illustrated case studies of the revolutionary role of GenAI in three sectors are explored: healthcare, finance, and education. GenAI accelerates the domain of healthcare by making its way into drug discovery, medical imaging and diagnostics, and virtual healthcare care assistance. In the financial sector, GenAI applies to crime detection, risk management, and prescriptive financial advisory services. GenAI helps educators in the education sector with personalized learning, automates teaching tasks, and creates an interactive learning environment. In this review, we check out exactly how these various kinds of GenAI devices have been made use of in the form of components such as GANs, VAEs, or transformer-based versions through these sector applications. These case studies are analyzed, pointing out realworld developments and their effect on outcomes. The paper ends by discussing the inevitable pitfalls, ethical concerns, and regulatory barriers to GenAI adoption, such as data privacy, algorithmic discrimination, and cybersecurity issues. In addition, it discusses future research directions and opportunities for responsible innovation, focusing on AI transparency, ethical frameworks, and the critical role of human oversightInternational Journal of Advanced Research in Science, Communication and Technology470This review checks out exactly how various kinds of GenAI devices have been made use of in the form of components such as GANs, VAEs, or transformer-based versions through these sector applications, and discusses future research directions and opportunities for responsible innovation.International Journal of Advanced Research in Science, Communication and Technology["Vivek Kumar Mishra", "Aayush Bharat Mandavia", "Gaston O. Adoyo", "Devdas Gupta", "Subhash Kumar Chand"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/6317c2dc7ea710155cfe9b52b6012ecc990448ce 4282305cd9e696f9891127534d25a85cfd3c98da The rapid rise of artificial intelligence has made profound impacts in teaching and learning. In this context, we have hence made an overview of AI Competency Framework for Teachers released by United Nations Educational, Scientific and Cultural Organization (UNESCO) in 2024. The framework defines the foundational principles, values, knowledge, and critical skills that teachers should develop to understand the role of AI in education and to utilize it to enhance teaching and learning practices in an ethical, effective, safe and responsible way. The framework outlines competencies for teachers across three developmental levels, emphasizing “human-centered” values that safeguard human agency, accountability, and determination.Global Medical Education00An overview of AI Competency Framework for Teachers released by United Nations Educational, Scientific and Cultural Organization (UNESCO) in 2024 defines the foundational principles, values, knowledge, and critical skills that teachers should develop to understand the role of AI in education.Global Medical Education["Jinyan Bai"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/4282305cd9e696f9891127534d25a85cfd3c98da 4da4db97b4e33590523811cd5c42feab89ff083aThe integration of Artificial Intelligence (AI) and blockchain technology is driving transformative advancements in Industry 4.0. This paper explores the synergistic potential of AI-powered blockchain systems in enhancing industrial efficiency, transparency and security. By combining AI’s predictive capabilities with blockchain’s decentralized and immutable nature, industries can improve processes in smart manufacturing, supply chain management, predictive maintenance, quality control and energy optimization. Advantages, scalability, interoperability, security, regulatory risk and shortage of skills present challenges to mainstream adoption. These challenges are expected to be resolved through innovative technologies, inter-sectoral collaboration and synchronization with sustainable development principles. The essential recommendations include encouragement of interdisciplinary in research, industry-academia collaborations, standardization, resolution of ethics issues and investment in workers' development. As block chain and AI keep developing, active participation from stakeholders is imperative to unleashing their maximum potential in Industry 4.0.International Journal of Advanced Research in Science, Communication and Technology150This paper explores the synergistic potential of AI-powered blockchain systems in enhancing industrial efficiency, transparency and security and the essential recommendations include encouragement of interdisciplinary in research, industry-academia collaborations, standardization, resolution of ethics issues and investment in workers' development.International Journal of Advanced Research in Science, Communication and Technology["Supriya S. Borhade"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/4da4db97b4e33590523811cd5c42feab89ff083a bf2e26109e25aa0ef254c4132f9d8a2b85ffdb65Integrating generative artificial intelligence (GenAI) into leadership practice represents a pivotal transformation in organizational dynamics, presenting unprecedented opportunities and complex challenges. The current article develops a comprehensive conceptual framework grounded in sociotechnical systems and complex adaptive leadership theories to guide future research and practice. By carefully examining leader‐follower relationships, decision‐making processes, and organizational learning patterns, we demonstrate how GenAI reshapes traditional leadership paradigms while raising critical ethical considerations. Our analysis reveals four key areas demanding attention: ethical decision‐making in AI implementation, trust dynamics between human and artificial agents, GenAI literacy development across organizational levels, and integrating AI systems with existing organizational structures and governance policies. The framework emphasizes the crucial balance between technological advancement and human‐centered leadership, particularly highlighting how the Human Interaction lens can guide responsible AI adoption. By identifying specific research questions in each domain, the article provides a roadmap for scholars and practitioners navigating the evolving landscape of AI‐enhanced leadership.Journal of Leadership Studies380A comprehensive conceptual framework grounded in sociotechnical systems and complex adaptive leadership theories to guide future research and practice is developed, demonstrating how GenAI reshapes traditional leadership paradigms while raising critical ethical considerations.Journal of Leadership Studies["Mary Tabata", "Cris Wildermuth", "Kevin Bottomley", "Daniel Jenkins"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/bf2e26109e25aa0ef254c4132f9d8a2b85ffdb65 8ccd3f3f3133d2baac00da10ac7e0222e5405039The integration of Artificial Intelligence (AI) in education presents transformative opportunities to personalize learning, enhance teaching methods, and improve student outcomes. AI offers adaptive tutoring systems, data-driven insights, and customized learning experiences, which can significantly improve the educational process. However, the rapid adoption of AI technologies also raises important ethical concerns that must be addressed to ensure responsible implementation. This paper provides an overview of AI’s potential in education, while highlighting key ethical issues such as data privacy, algorithmic bias, transparency, and equitable access to AI-powered tools. Through an analysis of existing frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible use of AI in educational contexts. A collaborative effort among educators, policymakers, and technology developers is essential to build ethical standards that balance innovation with fairness, accountability, and inclusivity. Ultimately, this paper offers insights and practical recommendations for fostering a responsible AI-driven educational environment that benefits all students while safeguarding their rights.F1000Research440An overview of AI’s potential in education is provided, while highlighting key ethical issues such as data privacy, algorithmic bias, transparency, and equitable access to AI-powered tools.F1000Research["\u00d6zlem Azman", "Song\u00fcl T\u00fcmkaya"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/8ccd3f3f3133d2baac00da10ac7e0222e5405039 56fbc9a302bf11f5d5902b23f720084e1c482c04Artificial intelligence (AI) has emerged as a transformative field in computational research with diverse applications in medicine, particularly in the field of liver transplantation (LT) given its ability to analyze and build upon complex and multidimensional data. This literature review investigates the application of AI in LT, focusing on its role in pre-implantation biopsy evaluation, development of recipient prognosis algorithms, imaging analysis, and decision-making support systems, with the findings revealing that AI can be applied across a variety of fields within LT, including diagnosis, organ allocation, and surgery planning. As a result, algorithms are being developed to assess steatosis in pre-implantation biopsies and predict liver graft function, with AI applications displaying great accuracy across various studies included in this review. Despite its relatively recent introduction to transplantation, AI demonstrates potential in delivering cost and time-efficient outcomes. However, these tools cannot replace the role of healthcare professionals, with their widespread adoption demanding thorough clinical testing and oversight.Livers850A literature review investigates the application of AI in liver transplantation, focusing on its role in pre-implantation biopsy evaluation, development of recipient prognosis algorithms, imaging analysis, and decision-making support systems, with the findings revealing that AI can be applied across a variety of fields within LT.Livers["Eleni Avramidou", "Dominik Todorov", "Georgios Katsanos", "Nikolaos Antoniadis", "Athanasios Kofinas", "S. Vasileiadou", "K. Karakasi", "Georgios Tsoulfas"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/56fbc9a302bf11f5d5902b23f720084e1c482c04 091ccd2f2d300d0fa314eeffe3207084746d6699Understanding healthcare professionals’ attitudes towards artificial intelligence (AI) in medicine is crucial for improving patient care and clinical practice. This study combines a systematic review and a survey targeting Polish physicians to explore these attitudes. While many healthcare professionals express enthusiasm and readiness for AI integration, others remain skeptical due to concerns about reliability, ethical implications, and legal accountability. The systematic review highlighted AI's potential benefits, such as improved diagnostic accuracy and workflow efficiency, alongside challenges like data privacy and the need for validation in atypical scenarios.This study combines insights from a systematic review and a targeted survey to assess healthcare professionals’ attitudes toward AI. The survey focused on Polish physicians, a group uniquely positioned to provide insights due to their healthcare system's specific challenges.The survey revealed optimism among Polish physicians (n86), with 68% ready to adopt AI tools, but underscored the necessity of tailored education and clear implementation guidelines.This study provides valuable insights into the dual narrative of optimism and skepticism surrounding AI in healthcare, emphasizing the importance of addressing barriers to maximize its benefits globally.Frontiers in Digital Health270Insight is provided into the dual narrative of optimism and skepticism surrounding AI in healthcare, emphasizing the importance of addressing barriers to maximize its benefits globally and the necessity of tailored education and clear implementation guidelines.Frontiers in Digital Health["E. Kowalewska"]2025-03-14T00:00:00https://www.semanticscholar.org/paper/091ccd2f2d300d0fa314eeffe3207084746d6699 b6cbb98ff017621d2f4b2894c20c8f1fed3d3752The integration of Artificial Intelligence (AI) into marketing has revolutionized the way businesses engage with consumers, design products, and optimize strategies. AI technologies, such as machine learning, data analytics, chatbots, and automation tools, are driving a paradigm shift in marketing. This paper explores the significance of marketing in the era of AI, examining the transformative impact of AI on customer experiences, business operations, and marketing strategies. It also discusses the challenges and ethical considerations involved in the application of AI in marketing, providing insights into future trends and the evolving role of marketing professionals.Journal of Management Research and Analysis50The significance of marketing in the era of AI is explored, examining the transformative impact of AI on customer experiences, business operations, and marketing strategies and the challenges and ethical considerations involved in the application of AI.Journal of Management Research and Analysis["Sanjeev Bansal", "Pankaj Kumar"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/b6cbb98ff017621d2f4b2894c20c8f1fed3d3752 e37e279f9b452a99b1692965f53df9c411691e98With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this article, we specifically consider Internet of Electric Vehicles (IoEV) and we categorize GenAI for IoEV into four different layers, namely, EV’s battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.IEEE Internet of Things Journal2100This survey categorizes GenAI for IoEV into four different layers, namely, EV’s battery layer, individual EV layer, smart grid layer, and security layer, and provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques.IEEE Internet of Things Journal["Hanwen Zhang", "Dusist Niyato", "Wei Zhang", "Changyuan Zhao", "Hongyang Du", "Abbas Jamalipour", "Sumei Sun", "Yiyang Pei"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/e37e279f9b452a99b1692965f53df9c411691e98 e35eb85aadd5723d0f7158379e05fe9b50eed17aThis study examines the impact of artificial intelligence (AI) and blockchain technology on crisis management, startup growth, and sustainability in the Indian business landscape. Using a quantitative, cross-sectional research design, data was collected from 123 research and development (R&D) employees across various enterprises. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze relationships between key constructs. The results indicate that AI plays a crucial role in enhancing crisis management and business resilience (β = 0.553, p = 0.000), while effective crisis management positively influences long-term sustainability (β = 0.272, p = 0.000). Additionally, blockchain technology is a key driver of startup growth (β = 0.684, p = 0.000), and growing startups significantly contribute to a sustainable future (β = 0.689, p = 0.000). The findings highlight the transformative potential of AI and blockchain in navigating business uncertainties, fostering innovation, and driving sustainable development. The study offers practical implications for business leaders, policymakers, and entrepreneurs in leveraging emerging technologies for long-term resilience and success.American Journal of Business Science Philosophy (AJBSP)00The results indicate that AI plays a crucial role in enhancing crisis management and business resilience, while effective crisis management positively influences long-term sustainability, and blockchain technology is a key driver of startup growth.American Journal of Business Science Philosophy (AJBSP)["Asha Sharma", "Aditya Mishra"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/e35eb85aadd5723d0f7158379e05fe9b50eed17a ea9627a757591ace552fa836b494d30a3c8179f8With AI increasingly transforming how various entities perform certain operations, it is necessary to explore the technology in detail to gain additional insight into its nature and effects. The research focuses on AI’s technical and social effects. In addition, it examines the impact of the technology on communication, and scrutinizes its ethical implications. This research draws data from secondary sources, particularly on works published in the past five years. Findings reveal that, technically, the technology enhances quality production and improves data processing and analysis. Socially, AI progresses healthcare, elevates education, and improves communication. With AI, the findings show, it is possible to incorporate personalization, augment language translation, advance sentiment analysis, upgrade chatbots and virtual assistants, boost content creation, and develop audience segmentation. Nevertheless, data from used sources shows the need to uphold ethical practices while interacting with the cutting-edge innovation. Specifically, users must consider the effects of bias, privacy violation, job loss, and the likelihood of creating autonomous weapons.Scholarly Review Journal00With AI, the findings show, it is possible to incorporate personalization, augment language translation, advance sentiment analysis, upgrade chatbots and virtual assistants, boost content creation, and develop audience segmentation, but the need to uphold ethical practices while interacting with the cutting-edge innovation is shown.Scholarly Review Journal["Jiayou Tang"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/ea9627a757591ace552fa836b494d30a3c8179f8 1d3895e73301c9e6fc1119c444b4945d3dd738abThis study investigated the integration of artificial intelligence (AI) tools into secondary school chemistry education in Zimbabwe, assessing their impact on student engagement and academic performance. Grounded in Vygotsky’s Sociocultural Theory and Cognitive Load Theory, the research employed a mixed-methods approach within a pragmatic framework. Quantitative data were collected through pre-test and post-test assessments and structured surveys, comparing an experimental group using AI tools with a control group employing traditional methods. Qualitative data from student and teacher interviews and classroom observations were analysed thematically. ANCOVA analysis revealed a statistically significant difference in post-test scores between the experimental and control groups, F (1, 117) = 188.86, p < .005, η² = 0.617, demonstrating a large effect size of AI integration on academic performance. Students in the experimental group exhibited a mean improvement of 20%, controlling for pre-test differences. Additionally, interaction effects between AI use and gender (F (1,115) = 0.17, p = .684) as well as prior chemistry knowledge (F (1,115) = 0.05, p = .829) were not statistically significant. Furthermore, 85% of the experimental group reported higher engagement levels, confirming AI’s role in fostering motivation and conceptual understanding. AI tools facilitated personalized learning paths, interactive simulations, and real-time feedback, optimizing cognitive efficiency and deep learning. Despite these advantages, significant challenges emerged, including limited internet access, insufficient technological resources, lack of teacher training, and curriculum integration difficulties. These barriers highlight the need for strategic investments in digital infrastructure, professional development for educators, and curriculum revisions to fully integrate AI into chemistry education. The findings underscore AI’s transformative potential in STEM education within developing nations. Addressing infrastructural and pedagogical challenges is critical to maximizing AI's impact, ensuring equitable access, and fostering long-term sustainability in educational innovation.European Journal of Mathematics and Science Education260The findings underscore AI’s transformative potential in STEM education within developing nations, highlighting the need for strategic investments in digital infrastructure, professional development for educators, and curriculum revisions to fully integrate AI into chemistry education.European Journal of Mathematics and Science Education["S. Mandina", "Richard Kusakara"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/1d3895e73301c9e6fc1119c444b4945d3dd738ab 295fdf04391ac64985da53ffefaf8be16a3d5f35Profound educational transformations occur due to Emotional Artificial Intelligence, which recognizes emotions in real time while developing personalized learning strategies. The paper systematically evaluates how Emotional AI systems foster student motivation while helping improve their retention levels. AI tools, including intelligent tutoring systems (ITS) and chatbots, utilize personalized learning methods while enhancing student engagement and detecting at-risk students through early intervention measures. Various privacy-related issues, algorithmic prejudice, and moral obstacles continue to impede progress. The lack of long-term study results limits research on AI’s lasting effects on education. The research findings indicate the use of privacy-conscious frameworks, bias reduction methods, and appropriate human oversight of AI systems in educational environments. Future studies need to be conducted in the form of long-term studies combined with ethical research on AI deployment. The research helps educational institutions establish ethically sound standards for implementing Emotional AI while maintaining its effectiveness. KEYWORDS: Emotional AI, Affective Computing, Student Motivation, Student Retention, AI in Education, Adaptive Learning, Ethical AI, Learning Analytics, Dropout Prevention.International Journal of Global Economic Light00The research systematically evaluates how Emotional AI systems foster student motivation while helping improve their retention levels, and helps educational institutions establish ethically sound standards for implementing Emotional AI while maintaining its effectiveness.International Journal of Global Economic Light["Dinesh Deckker", "Subhashini Sumanasekara"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/295fdf04391ac64985da53ffefaf8be16a3d5f35 b6df6bd4c16d3758b5da015c15911cd971768e09Artificial intelligence (AI) and blockchain are two rapidly evolving technologies that have found applications across various domains as abundant data is available through the Internet of Things (IoT) technology. As a digital ledger technology, blockchain establishes a distributed platform for transparent and reliable transactions. On the other hand, AI automates and enhances decision making in business activities and personal contexts. While each technology excels in its own right, their integration mutually benefits and forms a powerful alliance that provides substantial value. This article presents a comprehensive survey of current research on the integration of AI and blockchain. Specifically, it explores how blockchain enhances AI functionality, how AI supports blockchain, the advantages derived from their convergence in securing and improving various domains, including IoT applications and beyond, as well as the challenges, opportunities, and future research directions in exploring their convergence.IEEE Internet of Things Journal1490How blockchain enhances AI functionality, how AI supports blockchain, the advantages derived from their convergence in securing and improving various domains, including IoT applications and beyond, as well as the challenges, opportunities, and future research directions in exploring their convergence are explored.IEEE Internet of Things Journal["Yanjun Zuo"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/b6df6bd4c16d3758b5da015c15911cd971768e09 276e6b87383c153a26f6692491a30fd37e067699Integrating large language models (LLMs) with the Internet of Things (IoT) offer great potential for enhancing vehicle personalization and adaptability in autonomous driving (AD), particularly in open-world scenarios. However, the increasing scarcity of high-quality public data poses a challenge, which could hinder the progress of LLMs in AD. To address this, we propose a novel approach, federated instruction tuning (FIT), that leverages federated learning (FL) to enable collaborative training of a shared model across multiple data owners without sharing raw data, thereby preserving privacy and mitigating data scarcity. Complementing FIT, we introduce a feature diversity (FD) strategy that enriches visual and textual diversity and significantly expands AD data by generating new instruction-following data across key dimensions, such as time, weather, and occlusion. Extensive experiments using LLaMA-Adapter as the base model and four FL methods validate the effectiveness of the FIT framework and the FD strategy. Our analysis also compares LLMs ranging from 1.1 to 7B parameters, with results evaluated using GPT score, demonstrating the potential of FIT in AD. Our findings suggest that FIT and FD can support intelligent network operation and optimization in IoT, benefiting both the AD and artificial intelligence (AI) industries.IEEE Internet of Things Journal620This work proposes a novel approach, federated instruction tuning (FIT), that leverages federated learning (FL) to enable collaborative training of a shared model across multiple data owners without sharing raw data, thereby preserving privacy and mitigating data scarcity.IEEE Internet of Things Journal["Jiao Chen", "Jiayi He", "Fangfang Chen", "Zuohong Lv", "Jianhua Tang", "Yunjian Jia"]2025-03-15T00:00:00https://www.semanticscholar.org/paper/276e6b87383c153a26f6692491a30fd37e067699 db5d3562ddea8a7bf4ebaabd85cc246d582d8e04PurposeThis research aims to investigate the interaction between artificial intelligence (AI) capability, big data capabilities, sustainability design and organizational effectiveness in the context of the furniture industry. It aims to explore how investments in AI and big data technologies can spur sustainability-focused innovation and ultimately increase corporate performance.Design/methodology/approachBased on data collected from businesses operating in the furniture industry, this research uses a quantitative approach to analyze the relationships between independent variables (AI capability and big data features), mediating variable (sustainability design) and dependent variable (organizational effectiveness). The structural equation modeling (SEM) technique was used to test the proposed theoretical model and hypotheses. The SmartPLS program was used for analysis.FindingsAnalysis results show a significant positive relationship between AI capability, big data capabilities, sustainability design and organizational effectiveness in the furniture industry. Moreover, sustainability design demonstrates its important role in translating technological advances into tangible performance results by mediating the relationship between AI capability, big data capabilities and organizational effectiveness.Research limitations/implicationsAlthough this research contributes valuable insights, it also has limitations. It would not be appropriate to make a general assessment of the generalizability of the findings due to the focus on the furniture industry and the fact that the data of the research were collected from furniture-producing companies in Istanbul. Future research could explore additional industries and incorporate qualitative methods to provide a deeper understanding of the underlying mechanisms driving the observed relationships.Practical implicationsThe findings offer valuable insights to industry practitioners seeking to leverage the potential of AI and big data technologies to increase sustainable organizational effectiveness. Practical implications include strategic recommendations for integrating sustainability principles into organizational strategies, leveraging data-driven decision-making processes and encouraging innovation through technological investments.Originality/valueThe originality of this research lies in its comprehensive examination of the intertwined dynamics between AI capability, big data capabilities, sustainability design and organizational effectiveness, especially in the context of the furniture industry. By combining knowledge from multiple disciplines, this research offers a new perspective on the strategic implications of technological innovation for sustainable business practices.Management Decision730Management Decision["Zafer Adiguzel", "Fatma Sonmez Cakir", "Umran Altay Morgul"]2025-03-17T00:00:00https://www.semanticscholar.org/paper/db5d3562ddea8a7bf4ebaabd85cc246d582d8e04 dc36e4afdc4b357deadc03e46a2a4ae5be6c219cPurposeChat Generative Pretrained Transformer (ChatGPT), a chatbot with artificial intelligence (AI) technology, opens up new directions for innovation. However, the extent to which literature has not considered the trustworthiness and satisfaction of ChatGPT. Those are important elements leading to continuous use (CU). Particularly, this study investigates the use of the ChatGPT Translate function. Requirements for task-AI-technology fit, trust and satisfaction relevant to ChatGPT Translate are addressed in this study.Design/methodology/approachTask-technology fit (TTF) theory forms the theoretical lens to examine the influences of TTF, AI-tech trust and satisfaction on CU of AI technology. A questionnaire survey was used for data collection. Structural equation modeling was employed to test the research model.FindingsThe findings show task and technology characteristics have positive effects on task-AI-technology fit. Task-AI-technology fit has a positive effect on AI-tech trust, which in turn has a positive effect on the CU of AI technology. Finally, the level of CU of AI technology by users satisfied with its responses is higher than users dissatisfied with its responses.Originality/valueThe results have important theoretical and practical implications for academia and industry to devise strategies and policies on a free-to-use AI system.Aslib Journal of Information Management1200The findings show task and technology characteristics have positive effects on task-AI-technology fit, which has a positive effect on AI-tech trust, which in turn has a positive effect on the CU of AI technology.Aslib Journal of Information Management["Tri Lam"]2025-03-17T00:00:00https://www.semanticscholar.org/paper/dc36e4afdc4b357deadc03e46a2a4ae5be6c219c